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ywcui1990/nupic.research	projects/l2_pooling/notebooks/fault_tolerant_L2.ipynb	3	385532	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_format = \"svg\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Layer 2, when feedforward support is noisy\n", "\n", "Typically we measure noise tolerance in a layer by adding noise to its feedforward input. We find that the layer is very noise tolerant because of the proximal synapse algorithm. Cells grow synapses to subsets of SDRs in the feedforward input, and they recognize the SDR by checking whether enough of these synapses are active. You can directly control how much noise is required to cause false negatives by increasing this sample size or by lowering the threshold (being careful not to go too low and cause false positives). For most noise levels, the input may have noise, but the calculated list of feedforward supported cells does not. This list has been rescued by the proximal synapses.\n", "\n", "In this experiment, I assume that our proximal synapses have failed us. What happens when the list of feedforward supported cells is noisy? Does the rest of the algorithm respond gracefully?\n", "\n", "This experiment focuses on false negatives, randomly removing feedforward support from each cell." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from collections import defaultdict\n", "import itertools\n", "import math\n", "import random\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy\n", "\n", "from htmresearch.frameworks.layers.sensor_placement import greedySensorPositions\n", "from nupic.bindings.math import SparseMatrix, GetNTAReal, Random" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Today's L2 code\n", "\n", "Here's the current ColumnPooler, split into two functional components: the part that calculates feedforward support, and the part that consumes the feedforward support and selects the active cells. This is directly analogous to splitting sequence memory into a \"Spatial Pooler\" and \"Temporal Memory\".\n", "\n", "Splitting it this way allows us to inject noise in the middle." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class FeedforwardPooler(object):\n", " \n", " def __init__(self,\n", " inputWidth,\n", " cellCount=4096,\n", " sdrSize=40,\n", " synPermProximalInc=0.1,\n", " synPermProximalDec=0.001,\n", " initialProximalPermanence=0.6,\n", " sampleSizeProximal=20,\n", " minThresholdProximal=10,\n", " connectedPermanenceProximal=0.50,\n", " seed=42):\n", " \n", " self.inputWidth = inputWidth\n", " self.cellCount = cellCount\n", " self.sdrSize = sdrSize\n", " self.synPermProximalInc = synPermProximalInc\n", " self.synPermProximalDec = synPermProximalDec\n", " self.initialProximalPermanence = initialProximalPermanence\n", " self.connectedPermanenceProximal = connectedPermanenceProximal\n", " self.sampleSizeProximal = sampleSizeProximal\n", " self.minThresholdProximal = minThresholdProximal\n", "\n", " self._random = Random(seed)\n", "\n", " self.permanences = SparseMatrix(cellCount, inputWidth)\n", "\n", " \n", " def learn(self, learningCells, feedforwardInput):\n", " _learn(self.permanences, self._random,\n", " sorted(learningCells), sorted(feedforwardInput),\n", " self.sampleSizeProximal, self.initialProximalPermanence,\n", " self.synPermProximalInc, self.synPermProximalDec,\n", " self.connectedPermanenceProximal)\n", " \n", " \n", " def infer(self, feedforwardInput):\n", " overlaps = _rightVecSumAtNZGtThreshold_sparse(\n", " self.permanences, sorted(feedforwardInput),\n", " self.connectedPermanenceProximal)\n", " feedforwardSupportedCells = numpy.where(overlaps >= self.minThresholdProximal)[0]\n", " return set(feedforwardSupportedCells)\n", " \n", "\n", "\n", "class ActiveCellSelector(object):\n", " def __init__(self,\n", " lateralInputWidths=(),\n", " cellCount=4096,\n", " sdrSize=40,\n", "\n", " # Distal\n", " synPermDistalInc=0.1,\n", " synPermDistalDec=0.001,\n", " initialDistalPermanence=0.6,\n", " sampleSizeDistal=20,\n", " activationThresholdDistal=13,\n", " connectedPermanenceDistal=0.50,\n", " distalSegmentInhibitionFactor=0.6667,\n", "\n", " seed=42):\n", " self.cellCount = cellCount\n", " self.sdrSize = sdrSize\n", " self.synPermDistalInc = synPermDistalInc\n", " self.synPermDistalDec = synPermDistalDec\n", " self.initialDistalPermanence = initialDistalPermanence\n", " self.connectedPermanenceDistal = connectedPermanenceDistal\n", " self.sampleSizeDistal = sampleSizeDistal\n", " self.activationThresholdDistal = activationThresholdDistal\n", " self.distalSegmentInhibitionFactor = distalSegmentInhibitionFactor\n", "\n", " self.activeCells = ()\n", " self.supportedActiveCells = ()\n", " self._random = Random(seed)\n", "\n", " # These sparse matrices will hold the synapses for each segment.\n", " # Each row represents one segment on a cell, so each cell potentially has\n", " # 1+len(lateralInputWidths) distal segments.\n", " self.internalDistalPermanences = SparseMatrix(cellCount, cellCount)\n", " self.distalPermanences = tuple(SparseMatrix(cellCount, n)\n", " for n in lateralInputWidths)\n", " \n", " \n", " def getLateralOutput(self):\n", " \"\"\"\n", " This allows us to plug other algorithms into existing experiments.\n", " \n", " Some algorithms may not want the experiment to use the active cells\n", " as lateral output.\n", " \"\"\"\n", " return self.activeCells\n", "\n", "\n", " def declareObject(self, objectSDR):\n", " self.reset() # subclasses might need to do extra cleanup\n", " self.activeCells = sorted(objectSDR)\n", "\n", "\n", " def reset(self):\n", " self.activeCells = ()\n", " \n", " \n", " def learn(self, lateralInputs):\n", " # When learning, the current active cells are equal to the previous active cells\n", " prevActiveCells = self.activeCells\n", "\n", " # Internal distal learning\n", " _learn(self.internalDistalPermanences, self._random,\n", " self.activeCells, prevActiveCells,\n", " self.sampleSizeDistal, self.initialDistalPermanence,\n", " self.synPermDistalInc, self.synPermDistalDec,\n", " self.connectedPermanenceDistal)\n", "\n", " # External distal learning\n", " for i, lateralInput in enumerate(lateralInputs):\n", " _learn(self.distalPermanences[i], self._random,\n", " self.activeCells, sorted(lateralInput),\n", " self.sampleSizeDistal, self.initialDistalPermanence,\n", " self.synPermDistalInc, self.synPermDistalDec,\n", " self.connectedPermanenceDistal)\n", " \n", " \n", " def infer(self, feedforwardSupportedCells, lateralInputs):\n", " prevActiveCells = self.activeCells\n", " prevSupportedActiveCells = self.supportedActiveCells\n", "\n", " # Calculate lateral support\n", " numActiveSegmentsByCell = numpy.zeros(self.cellCount, dtype=\"int\")\n", " overlaps = _rightVecSumAtNZGtThreshold_sparse(\n", " self.internalDistalPermanences, prevActiveCells,\n", " self.connectedPermanenceDistal)\n", " numActiveSegmentsByCell[overlaps >= self.activationThresholdDistal] += 1\n", " for i, lateralInput in enumerate(lateralInputs):\n", " overlaps = _rightVecSumAtNZGtThreshold_sparse(\n", " self.distalPermanences[i], sorted(lateralInput),\n", " self.connectedPermanenceDistal)\n", " numActiveSegmentsByCell[overlaps >= self.activationThresholdDistal] += 1\n", "\n", " # Choose from the feedforward supported cells\n", " minNumActiveCells = self.sdrSize / 2\n", " chosenCells = self._chooseCells(feedforwardSupportedCells,\n", " minNumActiveCells, numActiveSegmentsByCell)\n", "\n", " # If necessary, choose from previously active cells\n", " if len(chosenCells) < minNumActiveCells:\n", " remainingCandidates = [cell for cell in prevActiveCells\n", " if cell not in feedforwardSupportedCells]\n", " chosenCells.extend(self._chooseCells(remainingCandidates,\n", " minNumActiveCells - len(chosenCells),\n", " numActiveSegmentsByCell))\n", "\n", " self.activeCells = sorted(chosenCells)\n", "\n", "\n", " def _chooseCells(self, candidates, n, numActiveSegmentsByCell):\n", " orderedCandidates = sorted(candidates,\n", " key=numActiveSegmentsByCell.__getitem__,\n", " reverse=True)\n", " activeSegmentCounts = sorted(set(numActiveSegmentsByCell[cell]\n", " for cell in candidates),\n", " reverse=True)\n", " \n", " chosenCells = []\n", " i = 0\n", "\n", " for activeSegmentCount in activeSegmentCounts:\n", " if len(chosenCells) >= n or i >= len(orderedCandidates):\n", " break\n", "\n", " if activeSegmentCount == 0:\n", " chosenCells.extend(orderedCandidates[i:])\n", " break\n", "\n", " # If one cell has 'distalSegmentInhibitionFactor' * the number of active\n", " # segments of another cell, the latter cell is inhibited.\n", " boundary = float(activeSegmentCount) / self.distalSegmentInhibitionFactor\n", "\n", " while (i < len(orderedCandidates) and\n", " numActiveSegmentsByCell[orderedCandidates[i]] > boundary):\n", " chosenCells.append(orderedCandidates[i])\n", " i += 1\n", "\n", " return chosenCells\n", "\n", " \n", "def _learn(# mutated args\n", " permanences, rng,\n", "\n", " # activity\n", " activeCells, activeInput,\n", "\n", " # configuration\n", " sampleSize, initialPermanence, permanenceIncrement,\n", " permanenceDecrement, connectedPermanence):\n", " permanences.incrementNonZerosOnOuter(\n", " activeCells, activeInput, permanenceIncrement)\n", " permanences.incrementNonZerosOnRowsExcludingCols(\n", " activeCells, activeInput, -permanenceDecrement)\n", " permanences.clipRowsBelowAndAbove(\n", " activeCells, 0.0, 1.0)\n", " if sampleSize == -1:\n", " permanences.setZerosOnOuter(\n", " activeCells, activeInput, initialPermanence)\n", " else:\n", " permanences.increaseRowNonZeroCountsOnOuterTo(\n", " activeCells, activeInput, sampleSize, initialPermanence, rng)\n", "\n", "#\n", "# Functionality that could be added to the C code or bindings\n", "#\n", "\n", "def _sampleRange(rng, start, end, step, k):\n", " \"\"\"\n", " Equivalent to:\n", "\n", " random.sample(xrange(start, end, step), k)\n", "\n", " except it uses our random number generator.\n", "\n", " This wouldn't need to create the arange if it were implemented in C.\n", " \"\"\"\n", " array = numpy.empty(k, dtype=\"uint32\")\n", " rng.sample(numpy.arange(start, end, step, dtype=\"uint32\"), array)\n", " return array\n", "\n", "\n", "def _rightVecSumAtNZGtThreshold_sparse(sparseMatrix,\n", " sparseBinaryArray,\n", " threshold):\n", " \"\"\"\n", " Like rightVecSumAtNZGtThreshold, but it supports sparse binary arrays.\n", "\n", " @param sparseBinaryArray (sorted sequence)\n", " A sorted list of indices.\n", "\n", " Note: this Python implementation doesn't require the list to be sorted, but\n", " an eventual C implementation would.\n", " \"\"\"\n", " denseArray = numpy.zeros(sparseMatrix.nCols(), dtype=GetNTAReal())\n", " denseArray[sparseBinaryArray] = 1\n", " return sparseMatrix.rightVecSumAtNZGtThreshold(denseArray, threshold)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Experiment code\n", "\n", "Touch every point on an object 7 times. In the 7th set of touches, measure how many cells were correctly active and how many are incorrectly active. A layer of cells is considered \"correct\" if it has at least 30 correctly active cells and no more than 10 incorrectly active cells." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def createRandomObjectDescriptions(numObjects,\n", " numLocationsPerObject,\n", " featurePool=(\"A\", \"B\", \"C\")):\n", " \"\"\"\n", " Returns {\"Object 1\": [(0, \"C\"), (1, \"B\"), (2, \"C\"), ...],\n", " \"Object 2\": [(0, \"C\"), (1, \"A\"), (2, \"B\"), ...]}\n", " \"\"\"\n", " return dict((\"Object %d\" % i,\n", " zip(xrange(numLocationsPerObject),\n", " [random.choice(featurePool)\n", " for _ in xrange(numLocationsPerObject)]))\n", " for i in xrange(1, numObjects + 1))\n", "\n", "\n", "def doExperiment(numColumns, sampleSizeDistal, objectDescriptions, faultProbability,\n", " numInitialTraversals, selectorConstructor=ActiveCellSelector):\n", "\n", " L4_CELL_COUNT = 81024\n", "\n", " # For each column, keep a mapping from feature-location names to their SDRs\n", " layer4sdr = lambda: set(random.sample(xrange(L4_CELL_COUNT), 40))\n", " featureLocationSDRs = [defaultdict(layer4sdr) for _ in xrange(numColumns)]\n", "\n", " feedforwardPoolers = [FeedforwardPooler(inputWidth=L4_CELL_COUNT)\n", " for _ in xrange(numColumns)]\n", " activeCellSelectors = [selectorConstructor(lateralInputWidths=[4096](numColumns-1),\n", " sampleSizeDistal=sampleSizeDistal)\n", " for _ in xrange(numColumns)]\n", "\n", " # Learn the objects\n", " objectL2Representations = {}\n", " for objectName, featureLocations in objectDescriptions.iteritems():\n", " objectL2Representations[objectName] = []\n", " for selector in activeCellSelectors:\n", " objectSDR = set(random.sample(xrange(selector.cellCount), selector.sdrSize))\n", " objectL2Representations[objectName].append(objectSDR)\n", " selector.declareObject(objectSDR)\n", "\n", " for featureLocationName in featureLocations:\n", " for _ in xrange(10):\n", " allLateralInputs = [s.getLateralOutput() for s in activeCellSelectors]\n", " for columnNumber in xrange(numColumns):\n", " activeCells = objectL2Representations[objectName][columnNumber]\n", " feedforwardInput = featureLocationSDRs[columnNumber][featureLocationName]\n", " feedforwardPoolers[columnNumber].learn(activeCells, feedforwardInput)\n", "\n", " lateralInputs = [lateralInput\n", " for i, lateralInput in enumerate(allLateralInputs)\n", " if i != columnNumber]\n", " activeCellSelectors[columnNumber].learn(lateralInputs)\n", "\n", " results = []\n", "\n", " # Try to infer the objects\n", " for objectName, featureLocations in objectDescriptions.iteritems():\n", " for selector in activeCellSelectors:\n", " selector.reset()\n", "\n", " sensorPositionsIterator = greedySensorPositions(numColumns, len(featureLocations))\n", "\n", " # Touch each location at least numInitialTouches times, and then touch it\n", " # once more, testing it.\n", " numTouchesPerTraversal = len(featureLocations) / float(numColumns)\n", " numInitialTouches = int(math.ceil(numInitialTraversals * numTouchesPerTraversal))\n", " numTestTouches = int(math.ceil(1 * numTouchesPerTraversal))\n", " for touch in xrange(numInitialTouches + numTestTouches):\n", " sensorPositions = next(sensorPositionsIterator)\n", " for _ in xrange(3):\n", " allLateralInputs = [selector.getLateralOutput() for selector in activeCellSelectors]\n", " for columnNumber in xrange(numColumns):\n", " position = sensorPositions[columnNumber]\n", " featureLocationName = featureLocations[position]\n", " feedforwardInput = featureLocationSDRs[columnNumber][featureLocationName]\n", "\n", " feedforwardSupportedCells = feedforwardPoolers[columnNumber].infer(feedforwardInput)\n", "\n", " feedforwardSupportedCells = set(cell for cell in feedforwardSupportedCells\n", " if random.random() > faultProbability)\n", "\n", " lateralInputs = [lateralInput\n", " for i, lateralInput in enumerate(allLateralInputs)\n", " if i != columnNumber]\n", " \n", " activeCellSelectors[columnNumber].infer(feedforwardSupportedCells, lateralInputs)\n", "\n", " if touch >= numInitialTouches:\n", " for columnNumber, selector in enumerate(activeCellSelectors):\n", " activeCells = set(selector.activeCells)\n", " inferredCells = objectL2Representations[objectName][columnNumber]\n", "\n", " results.append((len(activeCells & inferredCells),\n", " len(activeCells - inferredCells)))\n", "\n", " return results\n", "\n", "def varyColumns(selectorConstructor=ActiveCellSelector):\n", " #\n", " # Run the experiment\n", " #\n", " faultProbabilities = [x * 0.01 for x in xrange(0, 101, 5)]\n", " sampleSizeDistal = 20\n", " columnCounts = [1, 2, 3, 4]\n", "\n", " results = defaultdict(list)\n", "\n", " for trial in xrange(1):\n", " print \"trial\", trial\n", " objectDescriptions = createRandomObjectDescriptions(10, 10)\n", "\n", " for numColumns in columnCounts:\n", " print \"numColumns\", numColumns\n", " for faultProbability in faultProbabilities:\n", " r = doExperiment(numColumns, sampleSizeDistal, objectDescriptions,\n", " faultProbability, numInitialTraversals=6,\n", " selectorConstructor=selectorConstructor)\n", " results[(numColumns, faultProbability)].extend(r)\n", "\n", " #\n", " # Plot it\n", " #\n", " numCorrectActiveThreshold = 30\n", " numIncorrectActiveThreshold = 10\n", "\n", " plt.figure()\n", " colors = dict(zip(columnCounts,\n", " ('r', 'k', 'g', 'b')))\n", " markers = dict(zip(columnCounts,\n", " ('o', '', 'D', 'x')))\n", "\n", " for numColumns in columnCounts:\n", " y = []\n", " for faultProbability in faultProbabilities:\n", " trials = results[(numColumns, faultProbability)]\n", " numPassed = len([True for numCorrect, numIncorrect in trials\n", " if numCorrect >= numCorrectActiveThreshold\n", " and numIncorrect <= numIncorrectActiveThreshold])\n", " y.append(numPassed / float(len(trials)))\n", "\n", " plt.plot(faultProbabilities, y,\n", " color=colors[numColumns],\n", " marker=markers[numColumns])\n", "\n", " lgnd = plt.legend([\"%d columns\" % numColumns\n", " for numColumns in columnCounts],\n", " bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)\n", " plt.xlabel(\"Probability of feedforward false negative\")\n", " plt.xticks([0.01 n for n in xrange(0, 101, 10)])\n", " plt.ylabel(\"Success rate\")\n", " plt.yticks([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])\n", " plt.title(\"Inference with feedforward faults\")\n", "\n", " plt.show()\n", " \n", " \n", "def varySampleSize(selectorConstructor=ActiveCellSelector):\n", " #\n", " # Run the experiment\n", " #\n", " faultProbabilities = [x * 0.01 for x in xrange(0, 101, 5)]\n", " sampleSizes = [13, 20, 30, 40]\n", " numColumns = 3\n", "\n", " results = defaultdict(list)\n", "\n", " for trial in xrange(1):\n", " print \"trial\", trial\n", " objectDescriptions = createRandomObjectDescriptions(10, 10)\n", "\n", " for sampleSizeDistal in sampleSizes:\n", " print \"sampleSizeDistal\", sampleSizeDistal\n", " for faultProbability in faultProbabilities:\n", " r = doExperiment(numColumns, sampleSizeDistal, objectDescriptions,\n", " faultProbability, numInitialTraversals=6, \n", " selectorConstructor=selectorConstructor)\n", " results[(sampleSizeDistal, faultProbability)].extend(r)\n", "\n", " #\n", " # Plot it\n", " #\n", " numCorrectActiveThreshold = 30\n", " numIncorrectActiveThreshold = 10\n", "\n", " plt.figure()\n", " colorList = dict(zip(sampleSizes,\n", " ('r', 'k', 'g', 'b')))\n", " markerList = dict(zip(sampleSizes,\n", " ('o', '', 'D', 'x')))\n", "\n", " for sampleSizeDistal in sampleSizes:\n", " y = []\n", " for faultProbability in faultProbabilities:\n", " trials = results[(sampleSizeDistal, faultProbability)]\n", " numPassed = len([True for numCorrect, numIncorrect in trials\n", " if numCorrect >= numCorrectActiveThreshold\n", " and numIncorrect <= numIncorrectActiveThreshold])\n", " y.append(numPassed / float(len(trials)))\n", "\n", " plt.plot(faultProbabilities, y,\n", " color=colorList[sampleSizeDistal],\n", " marker=markerList[sampleSizeDistal])\n", "\n", " lgnd = plt.legend([\"Distal sample size %d\" % sampleSizeDistal\n", " for sampleSizeDistal in sampleSizes],\n", " bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)\n", " plt.xlabel(\"Probability of feedforward false negative\")\n", " plt.xticks([0.01 n for n in xrange(0, 101, 10)])\n", " plt.ylabel(\"Success rate\")\n", " plt.yticks([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])\n", " plt.title(\"Inference with feedforward faults\")\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How does the noise tolerance vary with column count? 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It contains three changes.\n", "\n", "### 1. Lateral support keeps cells active, even if the cell doesn't have feedforward support\n", "\n", "Previously this was possible, but now we're really embracing it. If Cell A has more lateral support than Cell B, and Cell A was previously active, it will inhibit Cell B -- even if it doesn't have feedforward support.\n", "\n", "Before, we prioritized the cells like this:\n", "\n", "1. Has feedforward support, 2 active segments\n", "2. Has feedforward support, 1 active segment\n", "3. Has feedforward support, 0 active segments\n", "4. Previously active, 2 active segments\n", "5. Previously active, 1 active segments\n", "\n", "With this change, we prioritize the cells like this:\n", "\n", "1. Has feedforward support, 2 active segments\n", "2. Previously active, 2 active segments\n", "3. Has feedforward support, 1 active segment\n", "4. Previously active, 1 active segments\n", "5. Has feedforward support, 0 active segments\n", "\n", "The principle: Layer 2 is noise tolerant because it tries to keep active cells active, even if their feedforward support drops out. If a cell was previously active and it has lateral support, it is prioritized over cells that have only feedforward support. So if you've inferred Object 1 -- if 40 Object 1 cells are active -- and you look at a feature-location that's on Object 1 and Object 2, you don't have to worry about noise causing some cells to drop out and be replaced by Object 2 cells.\n", "\n", "### 2. Cells without feedforward support are less active\n", "\n", "This is required by Change 1. Otherwise, the system can get stuck on the wrong object. If 40 Object 2 cells are active, and you look at a feature-location that's only on Object 1, nothing will happen. These previously active cells will stay active without feedforward support, and no Object 1 cells will ever become active. To solve this problem, cells that don't have feedforward support shouldn't provide lateral support. They should just quietly stay active. They'll still inhibit other cells from becoming active, but they won't contribute laterally to anybody's distal segments. So if you drop feedforward support for an entire SDR of cells, they will become inactive after one or two time steps (unless some other cortical column is supporting them).\n", "\n", "In other words, cells that don't have feedforward support have lower spike rates. In this implementation, these idly active cells provide no lateral support, but you could also build a system where they simply provide less support than other cells.\n", "\n", "### 3. Smoother inhibition\n", "\n", "Before, as we activated groups of cells, we would activate each entire group one by one until we reach a threshold (currently 20).\n", "\n", "With this change, cells are inhibited proportionally to the number of already-activated cells. The further we are from 40 active cells, the more of the next group that we'll activate. So if we have 10 active cells so far, there's a discrepancy of 30 cells, so we will activate 3/4 of the next group. (Though we'll make sure to activate at least 30, if possible.)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class FaultTolerantActiveCellSelector(ActiveCellSelector):\n", " def __init__(self, kwargs):\n", " super(FaultTolerantActiveCellSelector, self).__init__(kwargs)\n", " self.supportedActiveCells = ()\n", "\n", "\n", " def reset(self):\n", " super(FaultTolerantActiveCellSelector, self).reset()\n", " self.supportedActiveCells = ()\n", " \n", " \n", " def getLateralOutput(self):\n", " return self.supportedActiveCells\n", " \n", " \n", " def learn(self, lateralInputs):\n", " super(FaultTolerantActiveCellSelector, self).learn(lateralInputs)\n", " self.supportedActiveCells = self.activeCells\n", " \n", " \n", " def infer(self, feedforwardSupportedCells, lateralInputs):\n", " prevActiveCells = self.activeCells\n", " prevSupportedActiveCells = self.supportedActiveCells\n", "\n", " # Calculate lateral support\n", " numActiveSegmentsByCell = numpy.zeros(self.cellCount, dtype=\"int\")\n", " overlaps = _rightVecSumAtNZGtThreshold_sparse(\n", " self.internalDistalPermanences, prevSupportedActiveCells,\n", " self.connectedPermanenceDistal)\n", " numActiveSegmentsByCell[overlaps >= self.activationThresholdDistal] += 1\n", " for i, lateralInput in enumerate(lateralInputs):\n", " overlaps = _rightVecSumAtNZGtThreshold_sparse(\n", " self.distalPermanences[i], sorted(lateralInput),\n", " self.connectedPermanenceDistal)\n", " numActiveSegmentsByCell[overlaps >= self.activationThresholdDistal] += 1\n", "\n", " chosenSupportedCells = []\n", " chosenCells = []\n", " \n", " # Arrange the cells into inhibition groups.\n", " #\n", " # The first inhibition group is defined by the highest number of active\n", " # segments. It includes every cell with feedforward support that isn't\n", " # inhibited by this segment count.\n", " #\n", " # The second group is like the first group, but it's the cells that don't\n", " # have feedforward support.\n", " #\n", " # The third inhibition group is defined by the second highest number of\n", " # active segments. It includes every cell with feedforward support that wasn't\n", " # part of the first inhibition group and isn't inhibited by this segment count.\n", " #\n", " # The fourth inhibition group is like the third, but it's the cells that don't\n", " # have feedforward support.\n", " #\n", " # Etc.\n", " orderedSupportedCandidates = sorted(\n", " feedforwardSupportedCells,\n", " key=numActiveSegmentsByCell.__getitem__,\n", " reverse=True)\n", "\n", " orderedUnsupportedCandidates = sorted(\n", " (c for c in prevActiveCells\n", " if c not in feedforwardSupportedCells\n", " and numActiveSegmentsByCell[c] > 0),\n", " key=numActiveSegmentsByCell.__getitem__,\n", " reverse=True)\n", " \n", " activeSegmentCounts = sorted(set(numActiveSegmentsByCell[cell]\n", " for cell in orderedSupportedCandidates) \|\n", " set(numActiveSegmentsByCell[cell]\n", " for cell in orderedUnsupportedCandidates),\n", " reverse=True)\n", " \n", " chosenSupportedCells = []\n", " chosenCells = []\n", "\n", " i = 0 # index into orderedSupportedCandidates\n", " j = 0 # index into orderedUnsupportedCandidates\n", "\n", " for activeSegmentCount in sorted(set(numActiveSegmentsByCell), reverse=True):\n", " if len(chosenCells) >= self.sdrSize:\n", " break\n", " \n", " if (i >= len(orderedSupportedCandidates) and\n", " j >= len(orderedUnsupportedCandidates)):\n", " break\n", "\n", " if activeSegmentCount == 0:\n", " active = self._selectFromEquallyQualifiedCells(\n", " orderedSupportedCandidates[i:], self.sdrSize - len(chosenCells))\n", " chosenSupportedCells.extend(active)\n", " chosenCells.extend(active)\n", " break\n", "\n", " # If one cell has 'distalSegmentInhibitionFactor' * the number of active\n", " # segments of another cell, the latter cell is inhibited.\n", " boundary = float(activeSegmentCount) / self.distalSegmentInhibitionFactor\n", "\n", " begin = i\n", " while (i < len(orderedSupportedCandidates) and\n", " numActiveSegmentsByCell[orderedSupportedCandidates[i]] > boundary):\n", " i += 1\n", " \n", " active = self._selectFromEquallyQualifiedCells(\n", " orderedSupportedCandidates[begin:i], self.sdrSize - len(chosenCells))\n", " chosenSupportedCells.extend(active)\n", " chosenCells.extend(active)\n", " \n", " if len(chosenCells) >= self.sdrSize:\n", " break\n", " \n", " begin = j\n", " while (j < len(orderedUnsupportedCandidates) and\n", " numActiveSegmentsByCell[orderedUnsupportedCandidates[j]] > boundary):\n", " j += 1\n", " \n", " chosenCells.extend(orderedUnsupportedCandidates[begin:j])\n", "\n", "\n", " self.activeCells = sorted(chosenCells)\n", " self.supportedActiveCells = sorted(chosenSupportedCells)\n", "\n", "\n", " def _selectFromEquallyQualifiedCells(self, candidates, discrepancy):\n", " \"\"\"\n", " Select a subset of the candidates. The number to select is determined from two numbers:\n", " \n", " - The discrepancy between the current number of active cells and the 'sdrSize'\n", " - The size of the candidates list\n", " \n", " The larger the candidate list, the more cells will be activated.\n", " The larger the discrepancy, the more cells will be activated.\n", " \n", " @param discrepancy (int)\n", " How many more cells are needed to reach 'sdrSize'\n", " \"\"\"\n", " if len(candidates) > discrepancy:\n", " n = max(discrepancy,\n", " len(candidates)discrepancy / self.sdrSize)\n", " return random.sample(candidates, n)\n", " else:\n", " return candidates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How does the noise tolerance vary with column count? 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Some cells can be more active than others. In other words, this algorithm relies on spike rates." ] } ], "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 }	agpl-3.0
google-research/google-research	infinite_uncertainty/ess.ipynb	1	31035	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "4ro48hGpifFm" }, "source": [ "Copyright 2020 Google LLC\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", " https://www.apache.org/licenses/LICENSE-2.0\n", "\n", "Unless required by applicable law or agreed to in writing, software\n", "distributed under the License is distributed on an \"AS IS\" BASIS,\n", "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "See the License for the specific language governing permissions and\n", "limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "SCbRWUlKimvH" }, "source": [ "JAX implementation of Gaussian process classification (GPC) using parallelized elliptical slice sampling (ESS). The algorithm is taken from Iain Murray, Ryan Prescott Adams, and David JC MacKay. \"[Elliptical Slice Sampling.](http://proceedings.mlr.press/v9/murray10a.html)\" (2010). \n", "\n", "We leverage recent theoretical advances that characterize the function-space prior of an ensemble of infinitely-wide NNs as a Gaussian process, termed the neural network Gaussian process (NNGP). We use the NNGP with a softmax link function to build a probabilistic model for multi-class classification and marginalize over the latent Gaussian outputs to sample from the posterior using ESS. This gives us a better understanding of the implicit prior NNs place on function space and allows a direct comparison of the calibration of the NNGP and its finite-width analogue. See Adlam et al. \"[Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit.](https://arxiv.org/abs/2010.07355)\" (2020)." ] }, { "cell_type": "markdown", "metadata": { "id": "V5zZLyJdjKBj" }, "source": [ "##Imports" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "kR7ChISWh6aO" }, "outputs": [], "source": [ "import time\n", "import numpy as onp\n", "import tensorflow_datasets as tfds\n", "import jax\n", "from jax import device_put\n", "from jax import devices\n", "from jax import jit\n", "from jax import lax\n", "from jax import numpy as np\n", "from jax import pmap\n", "from jax import random\n", "from jax import vmap\n", "from jax.config import config as jax_config\n", "from jax.nn import softmax\n", "from jax.nn import log_softmax" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "executionInfo": { "elapsed": 311, "status": "ok", "timestamp": 1602775863746, "user": { "displayName": "Ben Adlam", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiwYcB0q-q5L_0TwtSgL_nnb0WSEspvc_2fCHej=s64", "userId": "10603308850729998596" }, "user_tz": 240 }, "id": "Ed6auXKyqCFt", "outputId": "daef333e-3605-4411-c535-ee14690fcfff" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/bin/sh: pip: command not found\n" ] } ], "source": [ "!pip install -q git+https://www.github.com/google/neural-tangents" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "WpZWwV6uqEFp" }, "outputs": [], "source": [ "import neural_tangents as nt\n", "from neural_tangents import stax" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "RHDBdSGeiDJx" }, "outputs": [], "source": [ "# Use float64 when possible to safeguard against numerical issues.\n", "jax_config.update('jax_enable_x64', True)\n", "\n", "# Hardcode the maximum number of ESS steps allowed.\n", "# To avoid getting stuck in the while loop due to numerical issues.\n", "MAX_STEPS = 1e4" ] }, { "cell_type": "markdown", "metadata": { "id": "zi1KDa6kjNOq" }, "source": [ "##Function Definitions" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xTMbweXFiFKH" }, "outputs": [], "source": [ "def make_sda(arrays, devices=None):\n", " \"\"\"Manually make a ShardedDeviceArray from a list of arrays.\"\"\"\n", " if devices is None:\n", " devices = jax.local_devices()\n", " buffers = []\n", " for arr, dev in zip(arrays, devices):\n", " buffers.append(jax.interpreters.xla.device_put(arr, device=dev))\n", " x_shape, x_dtype = arr.shape, arr.dtype\n", " aval = jax.ShapedArray((len(devices),) + x_shape, x_dtype)\n", " return jax.pxla.ShardedDeviceArray(aval, buffers)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "F58qUrGPiHFV" }, "outputs": [], "source": [ "def es_step(key, f_old, prior_sampler, log_l):\n", " \"\"\"Performs a single step of elliptical slice sampling.\n", "\n", " Args:\n", " key: A JAX PRNGKey.\n", " f_old: The current state of the Markov chain stored as a 1D DeviceArray of\n", " shape (dim,).\n", " prior_sampler: We assume the prior distribution is a mean zero multivariate\n", " Gaussian of dimension dim that can be parameterized by the Cholesky\n", " decomposition its covariance matrix, denoted l. Then prior_sampler is a\n", " function to sample from the prior that takes a PRNGKey and l as arguments\n", " and returns a 1D DeviceArray of shape (dim,).\n", " log_l: A function that returns the log-likelihood of the current state.\n", " Returns:\n", " A tuple (key, f_new, success, i), where key is a new PRNGKey, f_new is the\n", " new state of the Markov chain, success is a bool, and i is an int. The bool\n", " success indicates where the step of ESS was performed successfully. In some\n", " cases the step can fail due to numerical precision; for some samples of the\n", " randomness, the only acceptable transition for the chain is very close to\n", " its current state. The chain can take many loops to complete the step, and\n", " eventually the new state is equal to the current state (up to numerical\n", " precision). So success is an indicator for when this failure occurs. Note as\n", " long as it happens infrequently, the overall sampling is fine. Finally, i\n", " indicates the number of iterations taken in the while loop.\n", " \"\"\"\n", " key, subkey = random.split(key)\n", " nu = prior_sampler(subkey)\n", " key, subkey = random.split(key)\n", " log_y = log_l(f_old) + np.log(random.uniform(subkey))\n", " key, subkey = random.split(key)\n", " theta = 2 * np.pi * random.uniform(subkey)\n", " theta_min, theta_max = theta - 2 * np.pi, theta\n", "\n", " def _cond(vals):\n", " _, f_new, _, _, _, i = vals\n", " return np.logical_and(log_l(f_new) \u003c log_y, i \u003c= MAX_STEPS)\n", "\n", " def _body(vals):\n", " \"\"\"Body function for while loop to shrink the feasible region of theta.\"\"\"\n", " key, f_new, theta, theta_min, theta_max, i = vals\n", " i_new = i + 1\n", " # if theta \u003c 0, then theta_min = theta\n", " theta_min += (theta - theta_min) * (np.sign(-theta) + 1.) / 2.\n", " # else theta_max = theta\n", " theta_max += (theta - theta_max) * (np.sign(theta) + 1.) / 2.\n", " key, subkey = random.split(key)\n", " theta = theta_min + (theta_max - theta_min) * random.uniform(subkey)\n", " f_new = f_old * np.cos(theta) + nu * np.sin(theta)\n", "\n", " return key, f_new, theta, theta_min, theta_max, i_new\n", "\n", " f_new = f_old * np.cos(theta) + nu * np.sin(theta)\n", " key, f_new, theta, theta_min, theta_max, i = lax.while_loop(\n", " _cond, _body, (key, f_new, theta, theta_min, theta_max, 0))\n", "\n", " return (key, np.where(i \u003c= MAX_STEPS, f_new, f_old))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "A8n_zRZmh9e_" }, "outputs": [], "source": [ "def es_sample(key, dim, nc, prior_sampler, log_l, num_samples, burn_in,\n", " trace_tuple=None, eval_tuple=None, logging_fn=print,\n", " init_state=None):\n", " \"\"\"Sample from the posterior using MCMC given by elliptical slice sampling.\n", "\n", " Args:\n", " key: A JAX PRNGKey.\n", " dim: An int specifying the dimension of the state, since it is 1D.\n", " nc: An int specifying the number of classes in the classification.\n", " prior_sampler: We assume the prior distribution is a mean zero multivariate\n", " Gaussian of dimension dim that can be parameterized by the Cholesky\n", " decomposition its covariance matrix, denoted l. Then prior_sampler is a\n", " function to sample from the prior that takes a PRNGKey and l as arguments\n", " and returns a 1D DeviceArray of shape (dim,).\n", " log_l: A function that returns the log-likelihood of the current state.\n", " num_samples: An int for the number of samples from or steps of MCMC.\n", " burn_in: An int specifying the number of steps to throw out from the MCMC.\n", " trace_tuple: A tuple (fn, i), where fn is a function to apply to the state\n", " of the Markov chain and save as a DeviceArray, and i is an int specifying\n", " the interval at which to save the trace. Note saving lots of data\n", " frequently can cause OOMs.\n", " eval_tuple: A tuple (fn, i), where fn is a function to apply to the current\n", " posterior given by the Markov chain and log the result, and i is an int\n", " specifying the interval at which to evaluate.\n", " logging_fn: Defaults to logging.info, but if using the code in Colab print()\n", " can be used.\n", " init_state: A 1D DeviceArray of shape (dim,) that is the initial state for\n", " the ESS.\n", " Returns:\n", " A tuple (p, eval_traces, step_norms), where p is a DeviceArray for the\n", " posterior, eval_traces is a DeviceArray containing statisitics given by\n", " eval_fn from the trace, and step_norms contains the steps sizes of Markov\n", " chain's transitions.\n", " \"\"\"\n", " start_time = time.time()\n", " loop_time = start_time\n", " logging_fn('Starting MCMC sampling...\\n')\n", "\n", " p_num = jax.local_device_count()\n", " key = np.reshape(\n", " random.split(key, jax.device_count()),\n", " [jax.host_count(), jax.local_device_count(), 2])[jax.host_id()]\n", " total_samples = int(num_samples // p_num + burn_in)\n", " if init_state is None:\n", " sample = pmap(lambda x: np.zeros([dim]))(np.arange(p_num))\n", " else:\n", " sample = pmap(lambda x: init_state)(np.arange(p_num))\n", " p = pmap(lambda x: np.zeros([dim//nc, nc]))(np.arange(p_num))\n", " def accumulate_softmax(x, y, t):\n", " out = ((t-1.)/t) * x + (1./t) * softmax(np.reshape(y, [dim//nc, nc]))\n", " # Guess current state while still in burn in phase.\n", " out = np.where(t \u003e 0, out, softmax(np.reshape(y, [dim//nc, nc])))\n", " return out / np.sum(out, axis=-1, keepdims=True)\n", "\n", " # Set up function to evaluate the current p(y\|x).\n", " eval_print_steps = total_samples\n", " if eval_tuple is not None:\n", " eval_print, eval_print_steps = eval_tuple\n", "\n", " # Set up function to apply to current sample and save.\n", " eval_traces = []\n", " trace_fn_steps = total_samples\n", " if trace_tuple is not None:\n", " trace_fn, trace_fn_steps = trace_tuple\n", " trace_fn = pmap(trace_fn)\n", "\n", " epoch = int(min(eval_print_steps, trace_fn_steps))\n", "\n", " def body_fun(i, vals):\n", " key, sample, p = vals\n", " key, sample = es_step(key, sample, prior_sampler, log_l)\n", " p = accumulate_softmax(p, sample, i - burn_in + 1.)\n", " return key, sample, p\n", "\n", " @pmap\n", " def for_loop_fn(i, key, sample, p):\n", " return lax.fori_loop(i, i + epoch, body_fun, (key, sample, p))\n", "\n", " for i in range(0, total_samples, epoch):\n", " key, sample, p = for_loop_fn(i * np.ones([p_num]), key, sample, p)\n", "\n", " i += epoch\n", " if eval_tuple is not None:\n", " eval_print(p, i)\n", "\n", " if trace_tuple is not None:\n", " eval_traces.append(trace_fn(sample))\n", "\n", " logging_fn('Completed step {}/{} in {:.3f} mins at {}\\n'.format(\n", " i, total_samples, (time.time() - loop_time) / 60.,\n", " time.asctime(time.localtime())))\n", " loop_time = time.time()\n", "\n", " p = np.mean(p, axis=0)\n", "\n", " logging_fn('Sampling complete in {:.3f} mins.'.format(\n", " (time.time() - start_time) / 60.))\n", "\n", " return p, np.array(eval_traces)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ndM2Rx1Oh9hn" }, "outputs": [], "source": [ "def gpc_predict(\n", " k_fn, k_scale, x0, y0, x1, key, num_samples, burn_in, diag_reg=0.,\n", " ess_dtype=np.float32, trace_tuple=None, eval_tuple=None,\n", " logging_fn=print):\n", " \"\"\"Approximates test set posterior given a kernel and training data using ESS.\n", "\n", " Args:\n", " k_fn: A neural_tangents kernel function. Can be None if the kernel's\n", " Cholesky decomposition will be loaded from CNS.\n", " k_scale: A scalar to rescale the kernel matrix k. Note this is only used if\n", " the kernel is computed using the kernel_fn, not if it is loaded from CNS.\n", " x0: An array of training points.\n", " y0: An array of labels for the training data.\n", " x1: An array of test points.\n", " key: A PRNGKey.\n", " num_samples: A positive integer specifying the number of steps of MCMC.\n", " burn_in: A positive integer specifying to burn-in for the MCMC sampling.\n", " diag_reg: A nonnegative float to add to the diagonal and regularize the\n", " kernel matrix.\n", " ess_dtype: The dtype for the computed Cholesky and the state of the ess.\n", " trace_tuple: A tuple (fn, num), where fn is a function applied to the\n", " current state and num is an integer specifying how often the output is\n", " saved.\n", " eval_tuple: A tuple (fn, num), where fn is a function applied to the current\n", " probabilities to log their performance every num steps.\n", " logging_fn: Function to log progress.\n", "\n", " Returns:\n", " Two arrays containing the posterior on the training set and the test set.\n", " Consequently they have shapes (n0, nc) and (n1, nc), where each row is a\n", " distribution.\n", " \"\"\"\n", " start_time = time.time()\n", " logging_fn('========= jax.device_count(): %s' % jax.device_count())\n", " logging_fn('========= local_device_count: %s' % jax.local_device_count())\n", " logging_fn('========= devices: %s' % ',\"'.join(map(str, jax.devices())))\n", " logging_fn('========= host_id: %s' % jax.host_id())\n", "\n", " if k_fn is None and cns_l is None:\n", " raise ValueError('Either k_fn or cns_l must be specified!')\n", "\n", " n0, nc = y0.shape\n", " n1 = x1.shape[0]\n", "\n", " init_state = None\n", "\n", "\n", " logging_fn('Computing kernel matrices...')\n", " k = k_fn(np.vstack([x0, x1]), None, 'nngp')\n", " logging_fn('Computed kernel matrices in {:.3f} mins.'.format(\n", " (time.time() - start_time) / 60.))\n", "\n", " # Computing initial state.\n", " logging_fn('Computing initial state for ESS...')\n", " y1_hat = k[:n0, :n0] @ np.linalg.solve(\n", " k[:n0, :n0] + diag_reg * np.eye(n0), y0)\n", " init_state = np.reshape(np.vstack([y0, y1_hat]), [-1])\n", " logging_fn('Computing initial state for ESS in {:.3f} mins.'.format(\n", " (time.time() - start_time) / 60.))\n", "\n", " # Compute the Cholesky once, adding a diagonal regularizer for stability.\n", " start_time = time.time()\n", " logging_fn('Computing Cholesky decomposition...')\n", " l = onp.sqrt(k_scale) * onp.linalg.cholesky(\n", " k + diag_reg * np.trace(k) / (n0 + n1) * np.eye(n0 + n1))\n", " # ANY CASTING SHOULD HAPPEN HERE, AFTER THE CHOLESKY COMPUTATION.\n", " l = jax.device_put(l, jax.devices('cpu')[0])\n", " l = np.array(l, dtype=ess_dtype) # Cast Cholesky to desired precision.\n", " logging_fn('Computed Cholesky decomposition in {:.3f} mins.'.format(\n", " (time.time() - start_time) / 60.))\n", "\n", " def prior_sampler(key):\n", " normal_samples = random.normal(key, [n0+n1, nc])\n", " normal_samples = np.array(normal_samples, dtype=ess_dtype)\n", " # The Cholesky of the Kronecker is the Kronecker of the Choleskys, i.e.\n", " # np.kron(l, np.eye(nc)). Avoid instantiating large matrix.\n", " # NB. While this is correct, it seems buggy on TPU, i.e. there are different\n", " # answers for the code below and actually using the Kronecker product.\n", " return np.reshape(l @ normal_samples, [-1])\n", "\n", " def log_l(f):\n", " \"\"\"Log-likelihood: p(data\|f_train, f_test) == p(data\|f_train).\"\"\"\n", " f_ = np.reshape(f[:n0nc], [n0, nc])\n", " p_ = log_softmax(f_)\n", " # Assumes y0 contains one-hot labels.\n", " return np.sum(p_ y0)\n", "\n", " p, eval_trace = es_sample(\n", " key, nc(n0+n1), nc, prior_sampler, log_l, num_samples, burn_in,\n", " trace_tuple, eval_tuple, logging_fn=logging_fn, init_state=init_state)\n", " return p[:n0], p[n0:], eval_trace" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "B_CatUmomBV6" }, "outputs": [], "source": [ "DATASETS = {\n", " 'mnist': 10,\n", " 'cifar10': 10,\n", " 'cifar100': 100,\n", "}\n", "\n", "\n", "def to_one_hot(label, nc):\n", " return (np.arange(nc) == label).astype(int)\n", "\n", "\n", "def get_dataset(train_dataset, test_dataset, n0, n1, classes, valid_set=False,\n", " flatten=True):\n", " \"\"\"Function to load data and preprocess it.\n", " \n", " Note this implementation is slow due to filtering and preprocessing.\"\"\"\n", " if train_dataset not in DATASETS or test_dataset not in DATASETS:\n", " raise ValueError('Dataset \"{}\" or \"{}\" not recognized! Choose from: '\n", " '{}'.format(train_dataset, test_dataset, DATASETS))\n", " ds_train = tfds.load(name=train_dataset)\n", " ds_test = tfds.load(name=test_dataset)\n", "\n", " if classes is None:\n", " nc = DATASETS[train_dataset]\n", " filter_fn = lambda x: True\n", " elif isinstance(classes, int):\n", " if classes \u003e DATASETS[train_dataset]:\n", " raise ValueError('Requesting {} classes from {}, but it only has {}'\n", " 'classes!'.format(\n", " classes, train_dataset, DATASETS[train_dataset]))\n", " nc = classes\n", " classes = set(range(nc))\n", " filter_fn = lambda x: x['label'] in classes\n", " elif isinstance(classes, set) or isinstance(classes, list):\n", " classes = set(classes) # Remove any duplicate classes.\n", " nc = len(classes)\n", " filter_fn = lambda x: x['label'] in classes\n", " else:\n", " raise ValueError(\n", " '\"classes\" must be type None, int, or set! Given {}'.format(\n", " type(classes)))\n", "\n", " ds_train_np = tfds.as_numpy(ds_train)\n", " ds_test_np = tfds.as_numpy(ds_test)\n", " # Keep datapoints as regular NumPy arrays on CPU for Cholesky computation.\n", " # NB. Currently, requesting float64 does not work.\n", " x0_ = onp.array([np.array(x['image']).astype(onp.float64)\n", " for x in ds_train_np['train'] if filter_fn(x)])\n", " x1_ = onp.array([np.array(x['image']).astype(onp.float64)\n", " for x in ds_test_np['test'] if filter_fn(x)])\n", "\n", " ds_train_np = tfds.as_numpy(ds_train)\n", " ds_test_np = tfds.as_numpy(ds_test)\n", " y0_ = np.array([to_one_hot(x['label'], nc)\n", " for x in ds_train_np['train'] if filter_fn(x)])\n", " y1_ = np.array([to_one_hot(x['label'], nc)\n", " for x in ds_test_np['test'] if filter_fn(x)])\n", " y0 = y0_[:n0]\n", " y1 = y1_[:n1]\n", "\n", " if valid_set:\n", " if x0_.shape[0] \u003c n0 + n1:\n", " raise ValueError('Validation set is taken from end of training split. '\n", " 'So n0+n1 cannot exceed total training points in '\n", " 'requested dataset, but received {} and {}'.format(\n", " n0+n1, x0_.shape[0]))\n", " x1_ = x0_[-n1:]\n", " y1 = y0_[-n1:]\n", "\n", " mean = onp.mean(x0_[:n0])\n", " std = onp.std(x0_[:n0])\n", " if flatten:\n", " x0_ = onp.reshape(x0_[:n0], (n0, -1))\n", " x1_ = onp.reshape(x1_[:n1], (n1, -1))\n", " else:\n", " x0_ = x0_[:n0]\n", " x1_ = x1_[:n1]\n", " x0 = (x0_ - mean) / std\n", " x1 = (x1_ - mean) / std\n", " # NOTE: CURRENTLY THIS CASTS THE ARRAYS TO FLOAT32!\n", " x0 = device_put(x0, devices('cpu')[0])\n", " x1 = device_put(x1, devices('cpu')[0])\n", "\n", " return (x0, y0), (x1, y1)" ] }, { "cell_type": "markdown", "metadata": { "id": "jsWWSCRUm7Za" }, "source": [ "##Define Parameters" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "c0LeFdT_m-di" }, "outputs": [], "source": [ "n0 = 1000\n", "n1 = 1000\n", "nc = 10\n", "kernel_batch_size = None\n", "train_dataset = 'cifar10'\n", "test_dataset = 'cifar10'\n", "valid_set = False\n", "kernel_type = 'fc'\n", "activation = 'erf'\n", "W_std = 1.4142135624\n", "b_std = 0.\n", "k_scale = 1.\n", "k_depth = 5\n", "diag_reg = 1e-6\n", "ess_dtype = np.float32\n", "key = random.PRNGKey(0)\n", "mcmc_steps = 1e5\n", "eval_steps = 1e4\n", "burn_in = 1e4\n", "iterations = 1\n", "save_trace = False" ] }, { "cell_type": "markdown", "metadata": { "id": "fP1-7bNrjVNu" }, "source": [ "##Load Data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "HDeDppV5jWwA" }, "outputs": [], "source": [ "flatten = False if kernel_type == 'cnn' else True\n", "(x0, y0), (x1, y1) = get_dataset(train_dataset, test_dataset, n0, n1, nc,\n", " valid_set=valid_set, flatten=flatten)" ] }, { "cell_type": "markdown", "metadata": { "id": "Vid0VmiTje50" }, "source": [ "##Define Kernel" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "DTKO-oeYjhLu" }, "outputs": [], "source": [ "if activation == 'relu':\n", " act = stax.Relu()\n", "elif activation == 'erf':\n", " act = stax.Erf()\n", "\n", "if kernel_type == 'fc':\n", " collect_layers = [stax.Dense(512, W_std=W_std, b_std=b_std), act]k_depth\n", " collect_layers += [stax.Dense(1, W_std=W_std, b_std=b_std)]\n", " _, _, k_fn = stax.serial(collect_layers)\n", "\n", "elif kernel_type == 'cnn':\n", " conv = functools.partial(stax.Conv, W_std=W_std, b_std=b_std,\n", " padding='SAME', parameterization='ntk')\n", " collect_layers = [conv(512, (3, 3)), act]k_depth\n", " collect_layers += [stax.Flatten(),\n", " stax.Dense(1, W_std, b_std, parameterization='ntk')]\n", " _, _, k_fn = stax.serial(collect_layers)\n", "\n", "else:\n", " raise ValueError('Kernel type {} not recognized! Choose either fc or '\n", " 'cnn.'.format(kernel_type))\n", " \n", "if kernel_batch_size is not None:\n", " # Recommended batch size ~25 for pooling, ~800 for flattening\n", " if (n0+n1) % kernel_batch_size local_device_count() != 0:\n", " raise ValueError('Device count times batch size must divide the training '\n", " 'set plus test set size! Received {} and {}.'.format(\n", " kernel_batch_sizelocal_device_count(), n0+n1))\n", " k_fn = nt.batch(k_fn, batch_size=kernel_batch_size, store_on_device=False)\n", "\n", "else:\n", " k_fn = jit(k_fn, static_argnums=(1, 2))" ] }, { "cell_type": "markdown", "metadata": { "id": "qkMFByzcjRxg" }, "source": [ "##Run ESS" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Uz2DAfEFksSu" }, "outputs": [], "source": [ "@vmap\n", "def acc_(y, y_hat):\n", " return np.argmax(y) == np.argmax(y_hat)\n", "acc = lambda x, y: np.mean(acc_(x, y))\n", "\n", "def eval_print(p, t):\n", " p = onp.array(p, dtype=onp.float32)\n", " # Probabilities are 0 during burn in.\n", " if np.max(p) \u003c 1. / nc:\n", " return None\n", " p = p / onp.sum(p, axis=-1, keepdims=True)\n", "\n", " if p.ndim == 3:\n", " for i, p_ in enumerate(p):\n", " p0, p1 = p_[:n0], p_[n0:]\n", " print(\"Train acc for chain {}: {}\".format(i, acc(y0, p0)))\n", " print(\"Test acc for chain {}: {}\".format(i, acc(y1, p1)))\n", "\n", " p = np.mean(p, axis=0)\n", " p0, p1 = p[:n0], p[n0:]\n", " print(\"Train acc: {}\".format(acc(y0, p0)))\n", " print(\"Test acc: {}\".format(acc(y1, p1)))\n", "\n", "eval_tuple = (eval_print, eval_steps)\n", "\n", "trace_tuple = None\n", "if save_trace:\n", " # Save trace of subset of data.\n", " def trace_fn(sample):\n", " x = np.reshape(sample, (-1, nc))\n", " return np.vstack([x[:5], x[n0: n0+5]])\n", "\n", " trace_tuple = (trace_fn, 1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "uXwEXWBKivg4" }, "outputs": [], "source": [ "total_p0, total_p1 = 0., 0.\n", "for i in range(iterations):\n", " key, subkey = random.split(key)\n", " p0, p1, eval_trace = gpc_predict(\n", " k_fn, k_scale, x0, y0, x1, subkey, num_samples=mcmc_steps,\n", " burn_in=burn_in, diag_reg=diag_reg,\n", " ess_dtype=ess_dtype, trace_tuple=trace_tuple, eval_tuple=eval_tuple)\n", " total_p0 = (i total_p0 + p0) / (i+1)\n", " total_p1 = (i * total_p1 + p1) / (i+1)" ] } ], "metadata": { "colab": { "collapsed_sections": [ "V5zZLyJdjKBj", "zi1KDa6kjNOq", "jsWWSCRUm7Za", "fP1-7bNrjVNu", "Vid0VmiTje50", "qkMFByzcjRxg" ], "last_runtime": { "build_target": "//learning/deepmind/dm_python:dm_notebook3", "kind": "private" }, "name": "ESS for GPC with the NNGP.ipynb", "provenance": [ { "file_id": "1Fzx71NEiXmAwlCmvKyMvCkjpNl0x-rMk", "timestamp": 1603475071229 } ] }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
aleph314/K2	Classification/Tree_Methods-exercise.ipynb	1	3075805	null	gpl-3.0
autism-research-centre/Autism-Gradients	6b_networks-inside-gradients.ipynb	1	50371	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## 6b Calculate binned gradient-network overlap\n", "\n", "#### This file works out the average z-score inside a gradient percentile area\n", "\n", "##### written by Jan Freyberg for the Brainhack 2017 Project_\n", "\n", "This should reproduce [this analysis](https://github.com/NeuroanatomyAndConnectivity/gradient_analysis/blob/master/05_metaanalysis_neurosynth.ipynb)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "% matplotlib inline \n", "\n", "from __future__ import print_function\n", "\n", "import nibabel as nib\n", "from nilearn.image import resample_img\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "\n", "import os\n", "import os.path\n", "\n", "# The following are a progress bar, these are not strictly necessary:\n", "from ipywidgets import FloatProgress\n", "from IPython.display import display" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Define the variables for this analysis. \n", "1. how many percentiles the data is divided into\n", "2. where the Z-Maps (from neurosynth) lie\n", "3. where the binned gradient maps lie\n", "4. where a mask of the brain lies (not used at the moment)." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "percentiles = range(10)\n", "\n", "# unthresholded z-maps from neurosynth:\n", "zmaps = [os.path.join(os.getcwd(), 'ROIs_Mask', fname) for fname in os.listdir(os.path.join(os.getcwd(), 'ROIs_Mask'))\n", " if 'z.nii' in fname]\n", "\n", "# individual, binned gradient maps, in a list of lists:\n", "gradmaps = [[os.path.join(os.getcwd(), 'data', 'Outputs', 'Bins', str(percentile), fname)\n", " for fname in os.listdir(os.path.join(os.getcwd(), 'data', 'Outputs', 'Bins', str(percentile)))]\n", " for percentile in percentiles]\n", "\n", "# a brain mask file:\n", "brainmaskfile = os.path.join(os.getcwd(), 'ROIs_Mask', 'rbgmask.nii')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Next define a function to take the average of an image inside a mask and return it:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def zinsidemask(zmap, mask):\n", " # \n", " zaverage = zmap.dataobj[\n", " np.logical_and(np.not_equal(mask.dataobj, 0), brainmask.dataobj>0)\n", " ].mean()\n", " return zaverage" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "This next cell will step through each combination of gradient, subject and network file to calculate the average z-score inside the mask defined by the gradient percentile. This will take a long time to run!" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "34aeb942db6d4529aab3997a7af2a8f4" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "zaverages = np.zeros([len(zmaps), len(gradmaps), len(gradmaps[0])])\n", "\n", "# load first gradmap just for resampling\n", "gradmap = nib.load(gradmaps[0][0])\n", "\n", "# Load a brainmask\n", "brainmask = nib.load(brainmaskfile)\n", "brainmask = resample_img(brainmask, target_affine=gradmap.affine, target_shape=gradmap.shape)\n", "\n", "# Initialise a progress bar:\n", "progbar = FloatProgress(min=0, max=zaverages.size)\n", "display(progbar)\n", "\n", "# loop through the network files:\n", "for i1, zmapfile in enumerate(zmaps):\n", " # load the neurosynth activation file:\n", " zmap = nib.load(zmapfile)\n", " # make sure the images are in the same space:\n", " zmap = resample_img(zmap,\n", " target_affine=gradmap.affine,\n", " target_shape=gradmap.shape)\n", " # loop through the bins:\n", " for i2, percentile in enumerate(percentiles):\n", " # loop through the subjects:\n", " for i3, gradmapfile in enumerate(gradmaps[percentile]):\n", " gradmap = nib.load(gradmapfile) # load image\n", " zaverages[i1, i2, i3] = zinsidemask(zmap, gradmap) # calculate av. z-score\n", " progbar.value += 1 # update progressbar (only works in jupyter notebooks)\n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "To save time next time, we'll save the result of this to file:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# np.save(os.path.join(os.getcwd(), 'data', 'average-abs-z-scores'), zaverages)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "zaverages = np.load(os.path.join(os.getcwd(), 'data', 'average-z-scores.npy'))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Extract a list of which group contains which participants." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(160,)\n" ] } ], "source": [ "df_phen = pd.read_csv('data' + os.sep + 'SelectedSubjects.csv')\n", "diagnosis = df_phen.loc[:, 'DX_GROUP']\n", "fileids = df_phen.loc[:, 'FILE_ID']\n", "\n", "groupvec = np.zeros(len(gradmaps[0]))\n", "for filenum, filename in enumerate(gradmaps[0]):\n", " fileid = os.path.split(filename)[-1][5:-22]\n", " groupvec[filenum] = (diagnosis[fileids.str.contains(fileid)])\n", "\n", "print(groupvec.shape)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Make a plot of the z-scores inside each parcel for each gradient, split by group!" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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flF/8YrdxX//6pjzllG7jArz1rU159NHdx35X+xTHj360+9gHHNCUBx/cfexD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\namZmZmZmZtZUHkk1MzMzMzMrqXYcSXUn1czMzMzMrKTasZO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VhjupZmZmZmZm\nVhrupJqZmZmZmVlpuJNqZmZmZmZmpeFOqpmZmZmZmZWGO6lmZmZmZmZWGv8fMjfxwOPi1TAAAAAA\nSUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10fce79d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(15, 8))\n", "grouplabels = ['Control group', 'Autism group']\n", "for group in np.unique(groupvec):\n", " \n", " ylabels = [os.path.split(fname)[-1][0:-23].replace('_', ' ') for fname in zmaps]\n", " # remove duplicates!\n", " includenetworks = []\n", " seen = set()\n", " for string in ylabels:\n", " includenetworks.append(string not in seen)\n", " seen.add(string)\n", " \n", " ylabels = [string for index, string in enumerate(ylabels) if includenetworks[index]]\n", " \n", " tmp_zaverages = zaverages[includenetworks, :, :]\n", " tmp_zaverages = tmp_zaverages[:, :, groupvec==group]\n", " \n", " tmp_zaverages = tmp_zaverages[np.argsort(np.argmax(tmp_zaverages.mean(axis=2), axis=1)), :, :]\n", " \n", " # make the figure\n", " plt.subplot(1, 2, group)\n", " cax = plt.imshow(tmp_zaverages.mean(axis=2),\n", " cmap='bwr', interpolation='nearest',\n", " vmin=zaverages.mean(axis=2).min(),\n", " vmax=zaverages.mean(axis=2).max())\n", " \n", " ax = plt.gca()\n", " plt.title(grouplabels[int(group-1)])\n", "\n", " plt.xlabel('Percentile of principle gradient')\n", " ax.set_xticks(np.arange(0, len(percentiles), 3))\n", " ax.set_xticklabels(['100-90', '70-60', '40-30', '10-0'])\n", " \n", " ax.set_yticks(np.arange(0, len(seen), 1))\n", " ax.set_yticklabels(ylabels)\n", "\n", " ax.set_yticks(np.arange(-0.5, len(seen), 1), minor=True)\n", " ax.set_xticks(np.arange(-0.5, 10, 1), minor=True)\n", " ax.grid(which='minor', color='w', linewidth=2)\n", " \n", " fig.subplots_adjust(right=0.8)\n", " cbar_ax = fig.add_axes([0.85, 0.15, 0.01, 0.7])\n", " fig.colorbar(cax, cax=cbar_ax, label='Average Z-Score')\n", " #fig.colorbar(cax, cmap='bwr', orientation='horizontal')\n", "\n", "plt.savefig('./figures/z-scores-inside-gradient-bins.png')" ] }, { "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": 2 }	gpl-3.0
sdpython/ensae_teaching_cs	_doc/notebooks/expose/expose_einstein_riddle.ipynb	1	41850	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 2A.algo - L'\u00e9nigme d'Einstein et sa r\u00e9solution\n", "\n", "R\u00e9solution de l'\u00e9nigme [L'\u00e9nigme d'Einstein](http://fr.wikipedia.org/wiki/%C3%89nigme_d'Einstein). Impl\u00e9mentatin d'une solution \u00e0 base de r\u00e8gles." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from io import StringIO\n", "from pandas import read_csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[L'\u00e9nigme d'Einstein](http://fr.wikipedia.org/wiki/%C3%89nigme_d'Einstein) est une \u00e9nigme comme celle que r\u00e9soud [Hermionne](http://www.encyclopedie-hp.org/hogwarts/chamber_of_stone.php) dans le premier tome de Harry Potter. Je la reproduis ici :\n", "\n", "Il y a cinq maisons de cinq couleurs diff\u00e9rentes. Dans chacune de ces maisons, vit une personne de nationalit\u00e9 diff\u00e9rente. Chacune de ces personnes boit une boisson diff\u00e9rente, fume un cigare diff\u00e9rent et a un animal domestique diff\u00e9rent.\n", "\n", "1. L'Anglais vit dans la maison rouge.\n", "2. Le Su\u00e9dois a des chiens.\n", "3. Le Danois boit du th\u00e9.\n", "4. La maison verte est \u00e0 gauche de la maison blanche.\n", "5. Le propri\u00e9taire de la maison verte boit du caf\u00e9.\n", "6. La personne qui fume des Pall Mall a des oiseaux.\n", "7. Le propri\u00e9taire de la maison jaune fume des Dunhill.\n", "8. La personne qui vit dans la maison du centre boit du lait.\n", "9. Le Norv\u00e9gien habite dans la premi\u00e8re maison.\n", "10. L'homme qui fume des Blend vit \u00e0 c\u00f4t\u00e9 de celui qui a des chats.\n", "11. L'homme qui a un cheval est le voisin de celui qui fume des Dunhill.\n", "12. Le propri\u00e9taire qui fume des Blue Master boit de la bi\u00e8re.\n", "13. L'Allemand fume des prince.\n", "14. Le Norv\u00e9gien vit juste \u00e0 c\u00f4t\u00e9 de la maison bleue.\n", "15. L'homme qui fume des Blend a un voisin qui boit de l'eau.\n", "\n", "Question : Qui a le poisson ?\n", "\n", "Apr\u00e8s quelques essais, une bonne feuille de papier, on arrive \u00e0 reconstituer la solution apr\u00e8s de nombreuses d\u00e9ductions logiques et quelques essais. On peut voir aussi ce jeu comme un puzzle : chaque configuration est un pi\u00e8ce du puzzle dont la forme des bords est d\u00e9finie par toutes ces r\u00e8gles. Il faut trouver le seul embo\u00eetement possible sachant que parfois, une pi\u00e8ce peut s'embo\u00eeter avec plusieurs mais qu'il n'existe qu'une fa\u00e7on de les embo\u00eeter toutes ensemble. Ecrire un programme qui r\u00e9soud ce probl\u00e8me revient \u00e0 s'int\u00e9resser \u00e0 deux probl\u00e8mes :\n", "\n", "1. Comment d\u00e9finir une pi\u00e8ce du puzzle ?\n", "2. Comment parcourir toutes les combinaisons possibles ?\n", "\n", "Chaque r\u00e8gle ou pi\u00e8ce de puzzle peut \u00eatre exprimer comme une [clause](http://fr.wikipedia.org/wiki/Clause_de_Horn). Pour notre probl\u00e8me, chaque pi\u00e8ce du puzzle est simplement d\u00e9crite par un attribut (rouge, norv\u00e9gien) et un num\u00e9ro de maisons (1 \u00e0 5). Les r\u00e8gles d\u00e9finissent la compatibilit\u00e9 de deux pi\u00e8ces. On peut regrouper ces r\u00e8gles en cinq cat\u00e9gories :\n", "\n", "1. Un attribut est \u00e0 la position p (r\u00e8gle 9).\n", "2. Deux attributs sont \u00e9quivalents (r\u00e8gle 1).\n", "3. Deux attributs sont voisins (r\u00e8gle 11).\n", "4. Deux attributs sont ordonn\u00e9s par rapport aux positions (r\u00e8gle 4).\n", "5. Deux attributs font partie du m\u00eame ensemble et sont exclusives : on ne peut pas \u00eatre l'un et l'autre \u00e0 la fois (rouge et jaune par exemple).\n", "\n", "Une fois que chaque r\u00e8gle a \u00e9t\u00e9 exprim\u00e9e dans une de ces cinq cat\u00e9gories, il faut d\u00e9finir l'association de deux r\u00e8gles (ou clause) pour former une clause plus complexe. Trois cas possibles :\n", "\n", "1. Deux clauses sont compatibles : on peut avoir l'une et l'autre.\n", "2. Deux clauses sont incompatibles : on ne peut avoir l'une et l'autre.\n", "\n", "Dans le premier cas, la clause r\u00e9sultante est simplement qu'on peut la clause A et la clause B : $A \\, et\\, B$. Dans le second cas, il existe deux possibilit\u00e9s, on peut avoir l'une et l'oppos\u00e9 de l'autre ou l'inverse : $(A \\, et\\, non \\, B) \\, ou\\, (non \\, A \\, et\\, B)$.\n", "\n", "Avec cette description, il est plus facile d'exprimer le probl\u00e8me avec des objets informatiques ce que fait le programme suivant. Il explicite ensuite toutes les configurations compatibles avec une r\u00e8gle donn\u00e9e (mais pas toutes ensembles).\n", "\n", "On commence par la fonction [permutation](http://www.xavierdupre.fr/app/ensae_teaching_cs/helpsphinx/ensae_teaching_cs/special/einstein_prolog.html?highlight=permutation#ensae_teaching_cs.special.einstein_prolog.permutation) qui \u00e9num\u00e8re les permutations d'un ensemble :" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def permutation(nb):\n", " per = []\n", " p = [i for i in range(0, nb)]\n", " while p[0] < nb:\n", " cont = False\n", " for i in range(1, nb):\n", " if p[i] in p[0:i]:\n", " cont = True\n", " break\n", "\n", " if not cont:\n", " per.append(copy.copy(p))\n", "\n", " p[nb-1] += 1\n", " for j in range(nb-1, 0, -1):\n", " if p[j] >= nb:\n", " p[j] = 0\n", " p[j-1] += 1\n", "\n", " return per" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "position \t: \n", " ou [(2, ('lait', 2))]\n", "position \t: \n", " ou [(0, ('norvegien', 1))]\n", "equivalence \t: \n", " ou [(0, ('Pall Mall', 3)), (0, ('oiseaux', 4))]\n", " ou [(1, ('Pall Mall', 3)), (1, ('oiseaux', 4))]\n", " ou [(2, ('Pall Mall', 3)), (2, ('oiseaux', 4))]\n", " ou [(3, ('Pall Mall', 3)), (3, ('oiseaux', 4))]\n", " ou [(4, ('Pall Mall', 3)), (4, ('oiseaux', 4))]\n", "equivalence \t: \n", " ou [(0, ('anglais', 1)), (0, ('rouge', 0))]\n", " ou [(1, ('anglais', 1)), (1, ('rouge', 0))]\n", " ou [(2, ('anglais', 1)), (2, ('rouge', 0))]\n", " ou [(3, ('anglais', 1)), (3, ('rouge', 0))]\n", " ou [(4, ('anglais', 1)), (4, ('rouge', 0))]\n", "equivalence \t: \n", " ou [(0, ('suedois', 1)), (0, ('chiens', 4))]\n", " ou [(1, ('suedois', 1)), (1, ('chiens', 4))]\n", " ou [(2, ('suedois', 1)), (2, ('chiens', 4))]\n", " ou [(3, ('suedois', 1)), (3, ('chiens', 4))]\n", " ou [(4, ('suedois', 1)), (4, ('chiens', 4))]\n", "equivalence \t: \n", " ou [(0, ('danois', 1)), (0, ('the', 2))]\n", " ou [(1, ('danois', 1)), (1, ('the', 2))]\n", " ou [(2, ('danois', 1)), (2, ('the', 2))]\n", " ou [(3, ('danois', 1)), (3, ('the', 2))]\n", " ou [(4, ('danois', 1)), (4, ('the', 2))]\n", "equivalence \t: \n", " ou [(0, ('vert', 0)), (0, ('cafe', 2))]\n", " ou [(1, ('vert', 0)), (1, ('cafe', 2))]\n", " ou [(2, ('vert', 0)), (2, ('cafe', 2))]\n", " ou [(3, ('vert', 0)), (3, ('cafe', 2))]\n", " ou [(4, ('vert', 0)), (4, ('cafe', 2))]\n", "equivalence \t: \n", " ou [(0, ('jaune', 0)), (0, ('Dunhill', 3))]\n", " ou [(1, ('jaune', 0)), (1, ('Dunhill', 3))]\n", " ou [(2, ('jaune', 0)), (2, ('Dunhill', 3))]\n", " ou [(3, ('jaune', 0)), (3, ('Dunhill', 3))]\n", " ou [(4, ('jaune', 0)), (4, ('Dunhill', 3))]\n", "equivalence \t: \n", " ou [(0, ('biere', 2)), (0, ('Bluemaster', 3))]\n", " ou [(1, ('biere', 2)), (1, ('Bluemaster', 3))]\n", " ou [(2, ('biere', 2)), (2, ('Bluemaster', 3))]\n", " ou [(3, ('biere', 2)), (3, ('Bluemaster', 3))]\n", " ou [(4, ('biere', 2)), (4, ('Bluemaster', 3))]\n", "equivalence \t: \n", " ou [(0, ('allemand', 1)), (0, ('Prince', 3))]\n", " ou [(1, ('allemand', 1)), (1, ('Prince', 3))]\n", " ou [(2, ('allemand', 1)), (2, ('Prince', 3))]\n", " ou [(3, ('allemand', 1)), (3, ('Prince', 3))]\n", " ou [(4, ('allemand', 1)), (4, ('Prince', 3))]\n", "voisin \t: \n", " ou [(0, ('Dunhill', 3)), (1, ('cheval', 4))]\n", " ou [(1, ('Dunhill', 3)), (0, ('cheval', 4))]\n", " ou [(1, ('Dunhill', 3)), (2, ('cheval', 4))]\n", " ou [(2, ('Dunhill', 3)), (1, ('cheval', 4))]\n", " ou [(2, ('Dunhill', 3)), (3, ('cheval', 4))]\n", " ou [(3, ('Dunhill', 3)), (2, ('cheval', 4))]\n", " ou [(3, ('Dunhill', 3)), (4, ('cheval', 4))]\n", " ou [(4, ('Dunhill', 3)), (3, ('cheval', 4))]\n", "voisin \t: \n", " ou [(0, ('norvegien', 1)), (1, ('bleu', 0))]\n", " ou [(1, ('norvegien', 1)), (0, ('bleu', 0))]\n", " ou [(1, ('norvegien', 1)), (2, ('bleu', 0))]\n", " ou [(2, ('norvegien', 1)), (1, ('bleu', 0))]\n", " ou [(2, ('norvegien', 1)), (3, ('bleu', 0))]\n", " ou [(3, ('norvegien', 1)), (2, ('bleu', 0))]\n", " ou [(3, ('norvegien', 1)), (4, ('bleu', 0))]\n", " ou [(4, ('norvegien', 1)), (3, ('bleu', 0))]\n", "voisin \t: \n", " ou [(0, ('Blend', 3)), (1, ('eau', 2))]\n", " ou [(1, ('Blend', 3)), (0, ('eau', 2))]\n", " ou [(1, ('Blend', 3)), (2, ('eau', 2))]\n", " ou [(2, ('Blend', 3)), (1, ('eau', 2))]\n", " ou [(2, ('Blend', 3)), (3, ('eau', 2))]\n", " ou [(3, ('Blend', 3)), (2, ('eau', 2))]\n", " ou [(3, ('Blend', 3)), (4, ('eau', 2))]\n", " ou [(4, ('Blend', 3)), (3, ('eau', 2))]\n", "voisin \t: \n", " ou [(0, ('Blend', 3)), (1, ('chats', 4))]\n", " ou [(1, ('Blend', 3)), (0, ('chats', 4))]\n", " ou [(1, ('Blend', 3)), (2, ('chats', 4))]\n", " ou [(2, ('Blend', 3)), (1, ('chats', 4))]\n", " ou [(2, ('Blend', 3)), (3, ('chats', 4))]\n", " ou [(3, ('Blend', 3)), (2, ('chats', 4))]\n", " ou [(3, ('Blend', 3)), (4, ('chats', 4))]\n", " ou [(4, ('Blend', 3)), (3, ('chats', 4))]\n", "avant \t: \n", " ou [(0, ('vert', 0)), (1, ('blanc', 0))]\n", " ou [(0, ('vert', 0)), (2, ('blanc', 0))]\n", " ou [(1, ('vert', 0)), (2, ('blanc', 0))]\n", " ou [(0, ('vert', 0)), (3, ('blanc', 0))]\n", " ou [(1, ('vert', 0)), (3, ('blanc', 0))]\n", " ou [(2, ('vert', 0)), (3, ('blanc', 0))]\n", " ou [(0, ('vert', 0)), (4, ('blanc', 0))]\n", " ou [(1, ('vert', 0)), (4, ('blanc', 0))]\n", " ou [(2, ('vert', 0)), (4, ('blanc', 0))]\n", " ou [(3, ('vert', 0)), (4, ('blanc', 0))]\n", "ensemble \t: \n", " ou [(0, ('jaune', 0)), (1, ('bleu', 0)), (2, ('rouge', 0)), (3, ('blanc', 0)), (4, ('vert', 0))]\n", " ou [(0, ('jaune', 0)), (1, ('bleu', 0)), (2, ('rouge', 0)), (3, ('vert', 0)), (4, ('blanc', 0))]\n", " ou [(0, ('jaune', 0)), (1, ('bleu', 0)), (2, ('blanc', 0)), (3, ('rouge', 0)), (4, ('vert', 0))]\n", " ou [(0, ('jaune', 0)), (1, ('bleu', 0)), (2, ('blanc', 0)), (3, ('vert', 0)), (4, ('rouge', 0))]\n", " ou [(0, ('jaune', 0)), (1, ('bleu', 0)), (2, ('vert', 0)), (3, ('rouge', 0)), (4, ('blanc', 0))]\n", " ou [(0, ('jaune', 0)), (1, ('bleu', 0)), (2, ('vert', 0)), (3, ('blanc', 0)), (4, ('rouge', 0))]\n", " ou [(0, ('jaune', 0)), (1, ('rouge', 0)), (2, ('bleu', 0)), (3, ('blanc', 0)), (4, ('vert', 0))]\n", " ou [(0, ('jaune', 0)), (1, ('rouge', 0)), (2, ('bleu', 0)), (3, ('vert', 0)), (4, ('blanc', 0))]\n", " ou [(0, ('jaune', 0)), (1, ('rouge', 0)), (2, ('blanc', 0)), (3, ('bleu', 0)), (4, ('vert', 0))]\n", " ou [(0, ('jaune', 0)), (1, ('rouge', 0)), (2, ('blanc', 0)), (3, ('vert', 0)), (4, ('bleu', 0))]\n", "ensemble \t: \n", " ou [(0, ('danois', 1)), (1, ('norvegien', 1)), (2, ('anglais', 1)), (3, ('allemand', 1)), (4, ('suedois', 1))]\n", " ou [(0, ('danois', 1)), (1, ('norvegien', 1)), (2, ('anglais', 1)), (3, ('suedois', 1)), (4, ('allemand', 1))]\n", " ou [(0, ('danois', 1)), (1, ('norvegien', 1)), (2, ('allemand', 1)), (3, ('anglais', 1)), (4, ('suedois', 1))]\n", " ou [(0, ('danois', 1)), (1, ('norvegien', 1)), (2, ('allemand', 1)), (3, ('suedois', 1)), (4, ('anglais', 1))]\n", " ou [(0, ('danois', 1)), (1, ('norvegien', 1)), (2, ('suedois', 1)), (3, ('anglais', 1)), (4, ('allemand', 1))]\n", " ou [(0, ('danois', 1)), (1, ('norvegien', 1)), (2, ('suedois', 1)), (3, ('allemand', 1)), (4, ('anglais', 1))]\n", " ou [(0, ('danois', 1)), (1, ('anglais', 1)), (2, ('norvegien', 1)), (3, ('allemand', 1)), (4, ('suedois', 1))]\n", " ou [(0, ('danois', 1)), (1, ('anglais', 1)), (2, ('norvegien', 1)), (3, ('suedois', 1)), (4, ('allemand', 1))]\n", " ou [(0, ('danois', 1)), (1, ('anglais', 1)), (2, ('allemand', 1)), (3, ('norvegien', 1)), (4, ('suedois', 1))]\n", "ensemble \t: \n", " ou [(0, ('eau', 2)), (1, ('the', 2)), (2, ('lait', 2)), (3, ('cafe', 2)), (4, ('biere', 2))]\n", " ou [(0, ('eau', 2)), (1, ('the', 2)), (2, ('lait', 2)), (3, ('biere', 2)), (4, ('cafe', 2))]\n", " ou [(0, ('eau', 2)), (1, ('the', 2)), (2, ('cafe', 2)), (3, ('lait', 2)), (4, ('biere', 2))]\n", " ou [(0, ('eau', 2)), (1, ('the', 2)), (2, ('cafe', 2)), (3, ('biere', 2)), (4, ('lait', 2))]\n", " ou [(0, ('eau', 2)), (1, ('the', 2)), (2, ('biere', 2)), (3, ('lait', 2)), (4, ('cafe', 2))]\n", " ou [(0, ('eau', 2)), (1, ('the', 2)), (2, ('biere', 2)), (3, ('cafe', 2)), (4, ('lait', 2))]\n", " ou [(0, ('eau', 2)), (1, ('lait', 2)), (2, ('the', 2)), (3, ('cafe', 2)), (4, ('biere', 2))]\n", " ou [(0, ('eau', 2)), (1, ('lait', 2)), (2, ('the', 2)), (3, ('biere', 2)), (4, ('cafe', 2))]\n", " ou [(0, ('eau', 2)), (1, ('lait', 2)), (2, ('cafe', 2)), (3, ('the', 2)), (4, ('biere', 2))]\n", " ou [(0, ('eau', 2)), (1, ('lait', 2)), (2, ('cafe', 2)), (3, ('biere', 2)), (4, ('the', 2))]\n", "ensemble \t: \n", " ou [(0, ('Dunhill', 3)), (1, ('Blend', 3)), (2, ('Pall Mall', 3)), (3, ('Prince', 3)), (4, ('Bluemaster', 3))]\n", " ou [(0, ('Dunhill', 3)), (1, ('Blend', 3)), (2, ('Pall Mall', 3)), (3, ('Bluemaster', 3)), (4, ('Prince', 3))]\n", " ou [(0, ('Dunhill', 3)), (1, ('Blend', 3)), (2, ('Prince', 3)), (3, ('Pall Mall', 3)), (4, ('Bluemaster', 3))]\n", " ou [(0, ('Dunhill', 3)), (1, ('Blend', 3)), (2, ('Prince', 3)), (3, ('Bluemaster', 3)), (4, ('Pall Mall', 3))]\n", " ou [(0, ('Dunhill', 3)), (1, ('Blend', 3)), (2, ('Bluemaster', 3)), (3, ('Pall Mall', 3)), (4, ('Prince', 3))]\n", " ou [(0, ('Dunhill', 3)), (1, ('Blend', 3)), (2, ('Bluemaster', 3)), (3, ('Prince', 3)), (4, ('Pall Mall', 3))]\n", " ou [(0, ('Dunhill', 3)), (1, ('Pall Mall', 3)), (2, ('Blend', 3)), (3, ('Prince', 3)), (4, ('Bluemaster', 3))]\n", " ou [(0, ('Dunhill', 3)), (1, ('Pall Mall', 3)), (2, ('Blend', 3)), (3, ('Bluemaster', 3)), (4, ('Prince', 3))]\n", " ou [(0, ('Dunhill', 3)), (1, ('Pall Mall', 3)), (2, ('Prince', 3)), (3, ('Blend', 3)), (4, ('Bluemaster', 3))]\n", "ensemble \t: \n", " ou [(0, ('chats', 4)), (1, ('cheval', 4)), (2, ('oiseaux', 4)), (3, ('poisson', 4)), (4, ('chiens', 4))]\n", " ou [(0, ('chats', 4)), (1, ('cheval', 4)), (2, ('oiseaux', 4)), (3, ('chiens', 4)), (4, ('poisson', 4))]\n", " ou [(0, ('chats', 4)), (1, ('cheval', 4)), (2, ('poisson', 4)), (3, ('oiseaux', 4)), (4, ('chiens', 4))]\n", " ou [(0, ('chats', 4)), (1, ('cheval', 4)), (2, ('poisson', 4)), (3, ('chiens', 4)), (4, ('oiseaux', 4))]\n", " ou [(0, ('chats', 4)), (1, ('cheval', 4)), (2, ('chiens', 4)), (3, ('oiseaux', 4)), (4, ('poisson', 4))]\n", " ou [(0, ('chats', 4)), (1, ('cheval', 4)), (2, ('chiens', 4)), (3, ('poisson', 4)), (4, ('oiseaux', 4))]\n", " ou [(0, ('chats', 4)), (1, ('oiseaux', 4)), (2, ('cheval', 4)), (3, ('poisson', 4)), (4, ('chiens', 4))]\n", " ou [(0, ('chats', 4)), (1, ('oiseaux', 4)), (2, ('cheval', 4)), (3, ('chiens', 4)), (4, ('poisson', 4))]\n", " ou [(0, ('chats', 4)), (1, ('oiseaux', 4)), (2, ('poisson', 4)), (3, ('cheval', 4)), (4, ('chiens', 4))]\n" ] } ], "source": [ "import copy\n", "\n", "ttcouleur = [\"jaune\", \"bleu\", \"rouge\", \"blanc\", \"vert\"]\n", "ttnationalite = [\"danois\", \"norvegien\", \"anglais\", \"allemand\", \"suedois\"]\n", "ttboisson = [\"eau\", \"the\", \"lait\", \"cafe\", \"biere\"]\n", "ttcigare = [\"Dunhill\", \"Blend\", \"Pall Mall\", \"Prince\", \"Bluemaster\"]\n", "ttanimal = [\"chats\", \"cheval\", \"oiseaux\", \"poisson\", \"chiens\"]\n", "ensemble = [ttcouleur, ttnationalite, ttboisson, ttcigare, ttanimal]\n", "\n", "\n", "class Rule:\n", " \"\"\"\n", " This class defines a constraint of the problem\n", " or a clause (see `http://en.wikipedia.org/wiki/Clause_(logic)`)\n", "\n", " There are 5 different types of clauses described by Einstein's enigma\n", " each of them is described by a different class. There are defined by classes:\n", " @ref cl RulePosition, @ref cl RuleEquivalence, @ref cl RuleVoisin,\n", " @ref cl RuleAvant, @ref cl RuleEnsemble.\n", " \"\"\"\n", "\n", " def __init__(self):\n", " \"\"\"\n", " constructor\n", " \"\"\"\n", " #: name of the rule\n", " self.name = None\n", " #: set of clauses\n", " self.set = None\n", "\n", " def genere(self):\n", " \"\"\"\n", " Generates all possible clauses (list of lists)\n", " (`l[0][0]` et `l[0][1]`) ou (`l[1][0]` et `l[1][1]`),\n", " a clause is a triplet of\n", " `(person, (property, category))`.\n", " \"\"\"\n", " return None\n", "\n", " def __str__(self):\n", " \"\"\"\n", " display\n", " \"\"\"\n", " if self.name != None:\n", " if \"clauses\" not in self.__dict__:\n", " s = self.name + \" \\t: \"\n", " a = self.genere()\n", " for al in a:\n", " st = \"\\n ou \" + str(al)\n", " if len(st) > 260:\n", " st = st[:260] + \"...\"\n", " s += st\n", " if len(s) > 1000:\n", " break\n", " return s\n", " else:\n", " s = self.name + \" \\t: \" + str(self.set)\n", " for al in self.clauses:\n", " st = \"\\n ou \" + str(al)\n", " if len(st) > 260:\n", " st = st[:260] + \"...\"\n", " s += st\n", " if len(s) > 1000:\n", " break\n", " return s\n", " else:\n", " return \"None\"\n", "\n", " def combine(self, cl1, cl2):\n", " \"\"\"\n", " Combine two clauses, two cases:\n", " \n", " 1. nothing in common or everything in common --> concatenation of clauses\n", " 2. a position or a property in common --> null clause\n", "\n", " :param cl1: clause 1\n", " :param cl2: clause 2\n", " :return: the new clause\n", "\n", " A clause is a @ref cl Rule.\n", " \"\"\"\n", " # incompatibility\n", " for p1 in cl1:\n", " for p2 in cl2:\n", " if p1[1][0] == p2[1][0]: # same property\n", " if p1[0] != p2[0]: # but different positions\n", " return None\n", " if p1[0] == p2[0]: # same person\n", " if p1[1][1] == p2[1][1] and p1[1][0] != p2[1][0]:\n", " # same category but different properties\n", " return None\n", " # compatibility\n", " r = copy.deepcopy(cl1)\n", " for c in cl2:\n", " if c not in r:\n", " r.append(c)\n", " return r\n", "\n", " def combine_cross_sets(self, set1, set2):\n", " \"\"\"\n", " Combines two sets of clauses.\n", "\n", " :param set1: set of clauses 1\n", " :param set2: set of clauses 2\n", " :return: combination\n", " \"\"\"\n", " if len(set1) == 0:\n", " return copy.deepcopy(set2)\n", " if len(set2) == 0:\n", " return copy.deepcopy(set1)\n", " res = []\n", " for cl1 in set1:\n", " for cl2 in set2:\n", " r = self.combine(cl1, cl2)\n", " if r != None:\n", " res.append(r)\n", " return res\n", "\n", "\n", "class RulePosition (Rule):\n", " \"\"\"\n", " p1 at position\n", " \"\"\"\n", "\n", " def __init__(self, p1, pos):\n", " self.set = [p1]\n", " self.name = \"position\"\n", " self.position = pos\n", "\n", " def genere(self):\n", " \"\"\"\n", " overrides method ``genere``\n", " \"\"\"\n", " return [[(self.position, self.set[0])]]\n", "\n", "\n", "class RuleEquivalence (Rule):\n", " \"\"\"\n", " p1 equivalent to p2\n", " \"\"\"\n", "\n", " def __init__(self, p1, p2):\n", " self.set = [p1, p2]\n", " self.name = \"equivalence\"\n", "\n", " def genere(self):\n", " \"\"\"\n", " overrides method ``genere``\n", " \"\"\"\n", " l = []\n", " for i in range(0, 5):\n", " l.append([(i, self.set[0]), (i, self.set[1])])\n", " return l\n", "\n", "\n", "class RuleVoisin (Rule):\n", " \"\"\"\n", " p1 and p2 are neighbors\n", " \"\"\"\n", "\n", " def __init__(self, p1, p2):\n", " self.set = [p1, p2]\n", " self.name = \"voisin\"\n", "\n", " def genere(self):\n", " \"\"\"\n", " overrides method ``genere``\n", " \"\"\"\n", " l = []\n", " for i in range(0, 4):\n", " l.append([(i, self.set[0]), (i+1, self.set[1])])\n", " l.append([(i+1, self.set[0]), (i, self.set[1])])\n", " return l\n", "\n", "\n", "class RuleAvant (Rule):\n", " \"\"\"\n", " p1 before p2\n", " \"\"\"\n", "\n", " def __init__(self, p1, p2):\n", " self.set = [p1, p2]\n", " self.name = \"avant\"\n", "\n", " def genere(self):\n", " \"\"\"\n", " overrides method ``genere``\n", " \"\"\"\n", " l = []\n", " for j in range(1, 5):\n", " for i in range(0, j):\n", " l.append([(i, self.set[0]), (j, self.set[1])])\n", " return l\n", "\n", "\n", "class RuleEnsemble (Rule):\n", " \"\"\"\n", " permutation of the elements of a category\n", " \"\"\"\n", "\n", " def __init__(self, set, categorie):\n", " self.set = [(s, categorie) for s in set]\n", " self.name = \"ensemble\"\n", "\n", " def genere(self):\n", " \"\"\"\n", " overrides method ``genere``\n", " \"\"\"\n", " l = []\n", " per = permutation(5)\n", " for p in per:\n", " tl = []\n", " for i in range(0, len(p)):\n", " tl.append((i, self.set[p[i]]))\n", " l.append(tl)\n", " return l\n", "\n", "\n", "def find(p):\n", " for i in range(0, len(ensemble)):\n", " if p in ensemble[i]:\n", " return (p, i)\n", " return None\n", "\n", "\n", "regle = []\n", "\n", "regle.append(RulePosition(find(\"lait\"), 2))\n", "regle.append(RulePosition(find(\"norvegien\"), 0))\n", "\n", "regle.append(RuleEquivalence(find(\"Pall Mall\"), find(\"oiseaux\")))\n", "regle.append(RuleEquivalence(find(\"anglais\"), find(\"rouge\")))\n", "regle.append(RuleEquivalence(find(\"suedois\"), find(\"chiens\")))\n", "regle.append(RuleEquivalence(find(\"danois\"), find(\"the\")))\n", "regle.append(RuleEquivalence(find(\"vert\"), find(\"cafe\")))\n", "regle.append(RuleEquivalence(find(\"jaune\"), find(\"Dunhill\")))\n", "regle.append(RuleEquivalence(find(\"biere\"), find(\"Bluemaster\")))\n", "regle.append(RuleEquivalence(find(\"allemand\"), find(\"Prince\")))\n", "\n", "regle.append(RuleVoisin(find(\"Dunhill\"), find(\"cheval\")))\n", "regle.append(RuleVoisin(find(\"norvegien\"), find(\"bleu\")))\n", "regle.append(RuleVoisin(find(\"Blend\"), find(\"eau\")))\n", "regle.append(RuleVoisin(find(\"Blend\"), find(\"chats\")))\n", "\n", "regle.append(RuleAvant(find(\"vert\"), find(\"blanc\")))\n", "\n", "regle.append(RuleEnsemble(ttcouleur, 0))\n", "regle.append(RuleEnsemble(ttnationalite, 1))\n", "regle.append(RuleEnsemble(ttboisson, 2))\n", "regle.append(RuleEnsemble(ttcigare, 3))\n", "regle.append(RuleEnsemble(ttanimal, 4))\n", "\n", "\n", "for r in regle:\n", " print(r)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Parmi tous ces cas possibles, beaucoup sont incompatibles. L'objectif est d'\u00e9liminer tous ceux qui sont incompatibles pour ne garer que les 25 qui constituent la solution. L'algorithme est inspir\u00e9 de la [logique des pr\u00e9dicats](http://fr.wikipedia.org/wiki/Calcul_des_pr%C3%A9dicats). De mani\u00e8re r\u00e9cursive, la fonction ``solve`` combine les clauses jusqu'\u00e0 ce qu'il ne puisse plus continuer :\n", "\n", "1. Soit le m\u00eame attribut appara\u00eet \u00e0 deux positions diff\u00e9rentes : incompatibilit\u00e9.\n", "2. Soit deux attributs apparaissent \u00e0 la m\u00eame position : incompatibilit\u00e9.\n", "3. Soit il ne reste plus qu'une seule clause : c'est la solution." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "* 10 - properties in place : 14\n", "* 20 - properties in place : 12\n", "* 30 - properties in place : 21\n", "* 40 - properties in place : 19\n", "* 50 - properties in place : 22\n", "* 60 - properties in place : 21\n", "* 70 - properties in place : 22\n", "* 80 - properties in place : 12\n", "* 90 - properties in place : 14\n", "* 100 - properties in place : 24\n", "* 110 - properties in place : 22\n", "* 120 - properties in place : 16\n", "* 130 - properties in place : 12\n" ] }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>jaune</td>\n", " <td>norvegien</td>\n", " <td>eau</td>\n", " <td>Dunhill</td>\n", " <td>chats</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>bleu</td>\n", " <td>danois</td>\n", " <td>the</td>\n", " <td>Blend</td>\n", " <td>cheval</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>rouge</td>\n", " <td>anglais</td>\n", " <td>lait</td>\n", " <td>Pall Mall</td>\n", " <td>oiseaux</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>vert</td>\n", " <td>allemand</td>\n", " <td>cafe</td>\n", " <td>Prince</td>\n", " <td>poisson</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>blanc</td>\n", " <td>suedois</td>\n", " <td>biere</td>\n", " <td>Bluemaster</td>\n", " <td>chiens</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4\n", "0 jaune norvegien eau Dunhill chats\n", "1 bleu danois the Blend cheval\n", "2 rouge anglais lait Pall Mall oiseaux\n", "3 vert allemand cafe Prince poisson\n", "4 blanc suedois biere Bluemaster chiens" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class Enigma:\n", " \"\"\"\n", " This class solves the enigma.\n", " \"\"\"\n", "\n", " def __init__(self, display=True):\n", " \"\"\"\n", " We describe the enigma using the classes we defined above.\n", "\n", " :param display: if True, use print to print some information\n", " \"\"\"\n", " self.regle = []\n", "\n", " self.regle.append(RulePosition(self.find(\"lait\"), 2))\n", " self.regle.append(RulePosition(self.find(\"norvegien\"), 0))\n", "\n", " self.regle.append(RuleEquivalence(self.find(\"Pall Mall\"), self.find(\"oiseaux\")))\n", " self.regle.append(RuleEquivalence(self.find(\"anglais\"), self.find(\"rouge\")))\n", " self.regle.append(RuleEquivalence(self.find(\"suedois\"), self.find(\"chiens\")))\n", " self.regle.append(RuleEquivalence(self.find(\"danois\"), self.find(\"the\")))\n", " self.regle.append(RuleEquivalence(self.find(\"vert\"), self.find(\"cafe\")))\n", " self.regle.append(RuleEquivalence(self.find(\"jaune\"), self.find(\"Dunhill\")))\n", " self.regle.append(RuleEquivalence(self.find(\"biere\"), self.find(\"Bluemaster\")))\n", " self.regle.append(RuleEquivalence(self.find(\"allemand\"), self.find(\"Prince\")))\n", "\n", " self.regle.append(RuleVoisin(self.find(\"Dunhill\"), self.find(\"cheval\")))\n", " self.regle.append(RuleVoisin(self.find(\"norvegien\"), self.find(\"bleu\")))\n", " self.regle.append(RuleVoisin(self.find(\"Blend\"), self.find(\"eau\")))\n", " self.regle.append(RuleVoisin(self.find(\"Blend\"), self.find(\"chats\")))\n", "\n", " self.regle.append(RuleAvant(self.find(\"vert\"), self.find(\"blanc\")))\n", "\n", " self.regle.append(RuleEnsemble(ttcouleur, 0))\n", " self.regle.append(RuleEnsemble(ttnationalite, 1))\n", " self.regle.append(RuleEnsemble(ttboisson, 2))\n", " self.regle.append(RuleEnsemble(ttcigare, 3))\n", " self.regle.append(RuleEnsemble(ttanimal, 4))\n", "\n", " for r in self.regle:\n", " r.clauses = r.genere()\n", " r.utilise = False\n", "\n", " self.count = 0\n", "\n", " def find(self, p):\n", " \"\"\"\n", " Finds a clause in the different sets of clause (houses, colors, ...).\n", "\n", " :param p: clause\n", " :return: tuple (clause, position)\n", " \"\"\"\n", " for i in range(0, len(ensemble)):\n", " if p in ensemble[i]:\n", " return (p, i)\n", " return None\n", "\n", " def to_dataframe(self):\n", " sr = []\n", " matrix = [list(\" \" * 5) for _ in range(0, 5)]\n", " for row in self.solution:\n", " i = row[0]\n", " j = row[1][1]\n", " s = row[1][0]\n", " matrix[i][j] = s\n", " for row in matrix:\n", " sr.append(\", \".join(row))\n", " text = \"\\n\".join(sr)\n", " return read_csv(StringIO(text), header=None)\n", "\n", " def solve(self, solution=[], logf=print): # solution = [ ]) :\n", " \"\"\"\n", " Solves the enigma by eploring in deepness,\n", " the method is recursive\n", "\n", " :param solution: `[]` empty at the beginning, recursively used then\n", " :return: solution\n", " \"\"\"\n", "\n", " self.count += 1\n", " if self.count % 10 == 0:\n", " logf(\"*\", self.count, \" - properties in place : \", len(solution)-1)\n", "\n", " if len(solution) == 25:\n", " # we know the solution must contain 25 clauses,\n", " # if are here than the problem is solved unless some incompatibility\n", " for r in self.regle:\n", " cl = r.combine_cross_sets([solution], r.clauses)\n", " if cl == None or len(cl) == 0:\n", " # the solution is incompatible with a solution\n", " return None\n", " self.solution = solution\n", " return solution\n", "\n", " # we are looking for the rule which generates the least possible clauses\n", " # in order to reduce the number of possibilities as much as possible\n", " # the research could be represented as a tree, we avoid creating two many paths\n", " best = None\n", " rule = None\n", "\n", " for r in self.regle:\n", "\n", " cl = r.combine_cross_sets([solution], r.clauses)\n", "\n", " if cl == None:\n", " # the solution is incompatible with a solution\n", " return None\n", "\n", " # we check rule r is bringing back some results\n", " for c in cl:\n", " if len(c) > len(solution):\n", " break\n", " else:\n", " cl = None\n", "\n", " if cl != None and (best == None or len(best) > len(cl)):\n", " best = cl\n", " rule = r\n", "\n", " if best == None:\n", " # the solution is incompatible with a solution\n", " return None\n", "\n", " rule.utilise = True\n", "\n", " # we test all clauses\n", " for c in best:\n", " r = self.solve(c, logf=logf)\n", " if r != None:\n", " # we found\n", " return r\n", "\n", " rule.utilise = False # impossible\n", " return None\n", "\n", "\n", "en = Enigma()\n", "en.solve()\n", "en.to_dataframe()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.5" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
AlJohri/DAT-DC-12	notebooks/nlp_spacy.ipynb	1	22916	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Intro Spacy" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied (use --upgrade to upgrade): spacy in /Users/johria/anaconda3/lib/python3.5/site-packages\n", "Requirement already satisfied (use --upgrade to upgrade): nltk in /Users/johria/anaconda3/lib/python3.5/site-packages\n", "Requirement already satisfied (use --upgrade to upgrade): six in /Users/johria/anaconda3/lib/python3.5/site-packages (from spacy)\n", "Requirement already satisfied (use --upgrade to upgrade): thinc<5.1.0,>=5.0.0 in /Users/johria/anaconda3/lib/python3.5/site-packages (from spacy)\n", "Requirement already satisfied (use --upgrade to upgrade): cloudpickle in /Users/johria/anaconda3/lib/python3.5/site-packages (from spacy)\n", "Requirement already satisfied (use --upgrade to upgrade): sputnik<0.10.0,>=0.9.2 in /Users/johria/anaconda3/lib/python3.5/site-packages (from spacy)\n", "Requirement already satisfied (use --upgrade to upgrade): preshed<0.47,>=0.46.1 in /Users/johria/anaconda3/lib/python3.5/site-packages (from spacy)\n", "Requirement already satisfied (use --upgrade to upgrade): cymem<1.32,>=1.30 in /Users/johria/anaconda3/lib/python3.5/site-packages (from spacy)\n", "Requirement already satisfied (use --upgrade to upgrade): plac in /Users/johria/anaconda3/lib/python3.5/site-packages (from spacy)\n", "Requirement already satisfied (use --upgrade to upgrade): murmurhash<0.27,>=0.26 in /Users/johria/anaconda3/lib/python3.5/site-packages (from spacy)\n", "Requirement already satisfied (use --upgrade to upgrade): numpy>=1.7 in /Users/johria/anaconda3/lib/python3.5/site-packages (from spacy)\n", "Requirement already satisfied (use --upgrade to upgrade): semver in /Users/johria/anaconda3/lib/python3.5/site-packages (from sputnik<0.10.0,>=0.9.2->spacy)\n", "\u001b[33mYou are using pip version 8.1.1, however version 8.1.2 is available.\n", "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n" ] } ], "source": [ "!pip install spacy nltk" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## [Spacy Documentation](https://spacy.io/docs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Spacy is an NLP/Computational Linguistics package built from the ground up. It's written in Cython so it's fast!!\n", "\n", "Let's check it out. Here's some text from [Alice in Wonderland](https://www.gutenberg.org/files/11/11-h/11-h.htm) free on Gutenberg." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "text = \"\"\"'Please would you tell me,' said Alice, a little timidly, for she was not quite sure whether it was good manners for her to speak first, 'why your cat grins like that?'\n", "'It's a Cheshire cat,' said the Duchess, 'and that's why. Pig!'\n", "She said the last word with such sudden violence that Alice quite jumped; but she saw in another moment that it was addressed to the baby, and not to her, so she took courage, and went on again:—\n", "'I didn't know that Cheshire cats always grinned; in fact, I didn't know that cats could grin.'\n", "'They all can,' said the Duchess; 'and most of 'em do.'\n", "'I don't know of any that do,' Alice said very politely, feeling quite pleased to have got into a conversation.\n", "'You don't know much,' said the Duchess; 'and that's a fact.'\"\"\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Download and load the model. SpaCy has an excellent English NLP processor. It has the following features which we shall explore:\n", "- Entity recognition\n", "- Dependency Parsing\n", "- Part of Speech tagging\n", "- Word Vectorization\n", "- Tokenization\n", "- Lemmatization\n", "- Noun Chunks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Download the Model, it may take a while" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import spacy\n", "import spacy.en.download\n", "# spacy.en.download.main()\n", "processor = spacy.en.English()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'Please would you tell me,' said Alice, a little timidly, for she was not quite sure whether it was good manners for her to speak first, 'why your cat grins like that?'\n", "'It's a Cheshire cat,' said the Duchess, 'and that's why. Pig!'\n", "She said the last word with such sudden violence that Alice quite jumped; but she saw in another moment that it was addressed to the baby, and not to her, so she took courage, and went on again:—\n", "'I didn't know that Cheshire cats always grinned; in fact, I didn't know that cats could grin.'\n", "'They all can,' said the Duchess; 'and most of 'em do.'\n", "'I don't know of any that do,' Alice said very politely, feeling quite pleased to have got into a conversation.\n", "'You don't know much,' said the Duchess; 'and that's a fact.'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "processed_text = processor(text)\n", "processed_text" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like the same text? Let's dig a little deeper" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tokenization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sentences" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 'Please would you tell me,' said Alice, a little timidly, for she was not quite sure whether it was good manners for her to speak first, 'why your cat grins like that?'\n", "'It's a Cheshire cat\n", "1 ,' said the Duchess, 'and that's why.\n", "2 Pig!'\n", "\n", "3 She said the last word with such sudden violence that Alice quite jumped; but she saw in another moment that it was addressed to the baby, and not to her, so she took courage, and went on again:—\n", "'I didn't know that Cheshire cats always grinned; in fact, I didn't know that cats could grin.'\n", "'They all can,' said the Duchess; 'and most of 'em do.'\n", "'I don't know of any that do,'\n", "4 Alice said very politely, feeling quite pleased to have got into a conversation.\n", "'\n", "5 You don't know much,' said the Duchess; 'and that's a fact.'\n" ] } ], "source": [ "n = 0\n", "for sentence in processed_text.sents:\n", " print(n, sentence)\n", " n+=1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Words and Punctuation - Along with POS tagging" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 ' PUNCT '\n", "1 Please INTJ please\n", "2 would VERB would\n", "3 you PRON you\n", "4 tell VERB tell\n", "5 me PRON me\n", "6 , PUNCT ,\n", "7 ' PUNCT '\n", "8 said VERB say\n", "9 Alice PROPN alice\n", "10 , PUNCT ,\n", "11 a DET a\n", "12 little ADJ little\n", "13 timidly ADV timidly\n", "14 , PUNCT ,\n", "15 for ADP for\n", "16 she PRON she\n", "17 was VERB be\n", "18 not ADV not\n", "19 quite ADV quite\n", "20 sure ADJ sure\n", "21 whether ADP whether\n", "22 it PRON it\n", "23 was VERB be\n", "24 good ADJ good\n", "25 manners NOUN manner\n", "26 for ADP for\n", "27 her PRON her\n", "28 to PART to\n", "29 speak VERB speak\n", "30 first ADV first\n", "31 , PUNCT ,\n", "32 ' PUNCT '\n", "33 why ADV why\n", "34 your ADJ your\n", "35 cat NOUN cat\n", "36 grins VERB grin\n", "37 like ADP like\n", "38 that DET that\n", "39 ? PUNCT ?\n", "40 ' PUNCT '\n", "41 \n", " SPACE \n", "\n", "42 ' PUNCT '\n", "43 It PRON it\n", "44 's VERB '\n", "45 a DET a\n", "46 Cheshire PROPN cheshire\n", "47 cat NOUN cat\n", "48 , PUNCT ,\n", "49 ' PUNCT '\n", "50 said VERB say\n", "51 the DET the\n", "52 Duchess PROPN duchess\n", "53 , PUNCT ,\n", "54 ' PUNCT '\n", "55 and CONJ and\n", "56 that DET that\n", "57 's VERB '\n", "58 why ADV why\n", "59 . PUNCT .\n", "60 Pig PROPN pig\n", "61 ! PUNCT !\n", "62 ' PUNCT '\n", "63 \n", " SPACE \n", "\n", "64 She PRON she\n", "65 said VERB say\n", "66 the DET the\n", "67 last ADJ last\n", "68 word NOUN word\n", "69 with ADP with\n", "70 such ADJ such\n", "71 sudden ADJ sudden\n", "72 violence NOUN violence\n", "73 that ADJ that\n", "74 Alice PROPN alice\n", "75 quite ADV quite\n", "76 jumped VERB jump\n", "77 ; PUNCT ;\n", "78 but CONJ but\n", "79 she PRON she\n", "80 saw VERB saw\n", "81 in ADP in\n", "82 another DET another\n", "83 moment NOUN moment\n", "84 that ADJ that\n", "85 it PRON it\n", "86 was VERB be\n", "87 addressed VERB address\n", "88 to ADP to\n", "89 the DET the\n", "90 baby NOUN baby\n", "91 , PUNCT ,\n", "92 and CONJ and\n", "93 not ADV not\n", "94 to ADP to\n", "95 her PRON her\n", "96 , PUNCT ,\n", "97 so ADV so\n", "98 she PRON she\n", "99 took VERB take\n", "100 courage NOUN courage\n", "101 , PUNCT ,\n", "102 and CONJ and\n", "103 went VERB go\n", "104 on ADP on\n", "105 again:— PROPN again:—\n", "106 \n", " SPACE \n", "\n", "107 ' PUNCT '\n", "108 I PRON i\n", "109 did VERB do\n", "110 n't ADV not\n", "111 know VERB know\n", "112 that ADP that\n", "113 Cheshire PROPN cheshire\n", "114 cats NOUN cat\n", "115 always ADV always\n", "116 grinned VERB grin\n", "117 ; PUNCT ;\n", "118 in ADP in\n", "119 fact NOUN fact\n", "120 , PUNCT ,\n", "121 I PRON i\n", "122 did VERB do\n", "123 n't ADV not\n", "124 know VERB know\n", "125 that ADP that\n", "126 cats NOUN cat\n", "127 could VERB could\n", "128 grin VERB grin\n", "129 . PUNCT .\n", "130 ' PUNCT '\n", "131 \n", " SPACE \n", "\n", "132 ' PUNCT '\n", "133 They PRON they\n", "134 all DET all\n", "135 can VERB can\n", "136 , PUNCT ,\n", "137 ' PUNCT '\n", "138 said VERB say\n", "139 the DET the\n", "140 Duchess NOUN duchess\n", "141 ; PUNCT ;\n", "142 ' PUNCT '\n", "143 and CONJ and\n", "144 most ADJ most\n", "145 of ADP of\n", "146 'em PRON 'em\n", "147 do VERB do\n", "148 . PUNCT .\n", "149 ' PUNCT '\n", "150 \n", " SPACE \n", "\n", "151 ' PUNCT '\n", "152 I PRON i\n", "153 do VERB do\n", "154 n't ADV not\n", "155 know VERB know\n", "156 of ADP of\n", "157 any DET any\n", "158 that ADJ that\n", "159 do VERB do\n", "160 , PUNCT ,\n", "161 ' PUNCT '\n", "162 Alice PROPN alice\n", "163 said VERB say\n", "164 very ADV very\n", "165 politely ADV politely\n", "166 , PUNCT ,\n", "167 feeling VERB feel\n", "168 quite ADV quite\n", "169 pleased ADJ pleased\n", "170 to PART to\n", "171 have VERB have\n", "172 got VERB get\n", "173 into ADP into\n", "174 a DET a\n", "175 conversation NOUN conversation\n", "176 . PUNCT .\n", "177 \n", " SPACE \n", "\n", "178 ' PUNCT '\n", "179 You PRON you\n", "180 do VERB do\n", "181 n't ADV not\n", "182 know VERB know\n", "183 much ADJ much\n", "184 , PUNCT ,\n", "185 ' PUNCT '\n", "186 said VERB say\n", "187 the DET the\n", "188 Duchess NOUN duchess\n", "189 ; PUNCT ;\n", "190 ' PUNCT '\n", "191 and CONJ and\n", "192 that DET that\n", "193 's VERB '\n", "194 a DET a\n", "195 fact NOUN fact\n", "196 . PUNCT .\n", "197 ' PUNCT '\n" ] } ], "source": [ "n = 0\n", "for sentence in processed_text.sents:\n", " for token in sentence:\n", " print(n, token, token.pos_, token.lemma_)\n", " n+=1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Entities - [Explanation of Entity Types](https://spacy.io/docs#annotation-ner)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Alice PERSON\n", "first ORDINAL\n", "Cheshire GPE\n", "Alice PERSON\n", "Cheshire GPE\n", "Alice PERSON\n" ] } ], "source": [ "for entity in processed_text.ents:\n", " print(entity, entity.label_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Noun Chunks" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "you\n", "me\n", "Alice\n", "she\n", "it\n", "good manners\n", "her\n", "your cat\n", "It\n", "a Cheshire cat\n", "the Duchess\n", "She\n", "the last word\n", "such sudden violence\n", "Alice\n", "she\n", "another moment\n", "it\n", "the baby\n", "her\n", "she\n", "courage\n", "again:—\n", "I\n", "Cheshire cats\n", "fact\n", "I\n", "cats\n", "They\n", "the Duchess\n", "'em\n", "I\n", "Alice\n", "a conversation\n", "You\n", "the Duchess\n", "a fact\n" ] } ], "source": [ "for noun_chunk in processed_text.noun_chunks:\n", " print(noun_chunk)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Semi Holy Grail - Syntactic Depensy Parsing [See Demo for clarity](https://spacy.io/demos/displacy)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def pr_tree(word, level):\n", " if word.is_punct:\n", " return\n", " for child in word.lefts:\n", " pr_tree(child, level+1)\n", " print('\\t'* level + word.text + ' - ' + word.dep_)\n", " for child in word.rights:\n", " pr_tree(child, level+1)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\t\tPlease - intj\n", "\t\twould - aux\n", "\t\tyou - nsubj\n", "\ttell - ccomp\n", "\t\tme - dobj\n", "said - ROOT\n", "\tAlice - nsubj\n", "\t\t\ta - det\n", "\t\tlittle - npadvmod\n", "\ttimidly - advmod\n", "\t\tfor - mark\n", "\t\tshe - nsubj\n", "\twas - advcl\n", "\t\tnot - neg\n", "\t\t\tquite - advmod\n", "\t\tsure - acomp\n", "\t\t\t\twhether - mark\n", "\t\t\t\tit - nsubj\n", "\t\t\twas - ccomp\n", "\t\t\t\t\tgood - amod\n", "\t\t\t\tmanners - attr\n", "\t\t\t\t\t\tfor - mark\n", "\t\t\t\t\t\ther - nsubj\n", "\t\t\t\t\t\tto - aux\n", "\t\t\t\t\tspeak - relcl\n", "\t\t\t\t\t\tfirst - advmod\n", "\t\twhy - advmod\n", "\t\t\tyour - poss\n", "\t\tcat - nsubj\n", "\tgrins - ccomp\n", "\t\tlike - prep\n", "\t\t\tthat - pobj\n", "\t\tIt - nsubj\n", "\t's - ccomp\n", "\t\t\ta - det\n", "\t\t\tCheshire - compound\n", "\t\tcat - attr\n", "-------------------------------------------\n", "said - ROOT\n", "\t\tthe - det\n", "\tDuchess - nsubj\n", "\t\tand - cc\n", "\t\tthat - nsubj\n", "\t's - conj\n", "\t\twhy - ccomp\n", "-------------------------------------------\n", "Pig - ROOT\n", "-------------------------------------------\n", "\tShe - nsubj\n", "said - ROOT\n", "\t\tthe - det\n", "\t\tlast - amod\n", "\tword - dobj\n", "\t\twith - prep\n", "\t\t\t\tsuch - amod\n", "\t\t\t\tsudden - amod\n", "\t\t\tviolence - pobj\n", "\t\t\tthat - nsubj\n", "\t\t\tAlice - nsubj\n", "\t\t\tquite - advmod\n", "\t\tjumped - relcl\n", "\tbut - cc\n", "\t\tshe - nsubj\n", "\tsaw - conj\n", "\t\tin - prep\n", "\t\t\t\tanother - det\n", "\t\t\tmoment - pobj\n", "\t\t\tthat - mark\n", "\t\t\tit - nsubjpass\n", "\t\t\twas - auxpass\n", "\t\taddressed - ccomp\n", "\t\t\tto - prep\n", "\t\t\t\t\tthe - det\n", "\t\t\t\tbaby - pobj\n", "\t\t\tand - cc\n", "\t\t\t\tnot - neg\n", "\t\t\tto - conj\n", "\t\t\t\ther - pobj\n", "\t\tso - advmod\n", "\t\tshe - nsubj\n", "\ttook - conj\n", "\t\tcourage - dobj\n", "\t\tand - cc\n", "\t\twent - conj\n", "\t\t\ton - prep\n", "\t\t\t\tagain:— - pobj\n", "\t\t\t\t\t\n", " - \n", "\t\t\tI - nsubj\n", "\t\t\tdid - aux\n", "\t\t\tn't - neg\n", "\t\tknow - conj\n", "\t\t\t\tthat - mark\n", "\t\t\t\t\tCheshire - compound\n", "\t\t\t\tcats - nsubj\n", "\t\t\t\talways - advmod\n", "\t\t\tgrinned - ccomp\n", "\t\tin - prep\n", "\t\t\tfact - pobj\n", "\t\tI - nsubj\n", "\t\tdid - aux\n", "\t\tn't - neg\n", "\tknow - ccomp\n", "\t\t\tthat - mark\n", "\t\t\tcats - nsubj\n", "\t\t\tcould - aux\n", "\t\tgrin - ccomp\n", "\t\tThey - nsubj\n", "\t\t\tall - appos\n", "\tcan - ccomp\n", "\tsaid - conj\n", "\t\t\tthe - det\n", "\t\tDuchess - dobj\n", "\t\tand - cc\n", "\t\t\tmost - nsubj\n", "\t\t\t\tof - prep\n", "\t\t\t\t\t'em - pobj\n", "\t\tdo - conj\n", "\t\tI - nsubj\n", "\t\tdo - aux\n", "\t\tn't - neg\n", "\tknow - ccomp\n", "\t\tof - prep\n", "\t\t\tany - pobj\n", "\t\t\t\t\tthat - nsubj\n", "\t\t\t\tdo - relcl\n", "-------------------------------------------\n", "\tAlice - nsubj\n", "said - ROOT\n", "\t\tvery - advmod\n", "\tpolitely - advmod\n", "\tfeeling - advcl\n", "\t\t\tquite - advmod\n", "\t\tpleased - acomp\n", "\t\t\t\tto - aux\n", "\t\t\t\thave - aux\n", "\t\t\tgot - xcomp\n", "\t\t\t\tinto - prep\n", "\t\t\t\t\t\ta - det\n", "\t\t\t\t\tconversation - pobj\n", "-------------------------------------------\n", "\t\tYou - nsubj\n", "\t\tdo - aux\n", "\t\tn't - neg\n", "\tknow - ccomp\n", "\t\tmuch - dobj\n", "said - ROOT\n", "\t\tthe - det\n", "\tDuchess - nsubj\n", "\tand - cc\n", "\t\tthat - nsubj\n", "\t's - conj\n", "\t\t\ta - det\n", "\t\tfact - attr\n", "-------------------------------------------\n" ] } ], "source": [ "for sentence in processed_text.sents:\n", " pr_tree(sentence.root, 0)\n", " print('-------------------------------------------')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What is 'nsubj'? 'acomp'? See [The Universal Dependencies](http://universaldependencies.org/u/dep/)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Word Vectorization - [Word2Vec](http://deeplearning4j.org/word2vec)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.36491481428\n", "0.350866559366\n", "0.272271037182\n" ] } ], "source": [ "proc_fruits = processor('''I think green apples are delicious. \n", " While pears have a strange texture to them. \n", " The bowls they sit in are ugly.''')\n", "apples, pears, bowls = proc_fruits.sents\n", "fruit = processed_text.vocab['fruit']\n", "print(apples.similarity(fruit))\n", "print(pears.similarity(fruit))\n", "print(bowls.similarity(fruit))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Assingment - In Class\n", "Find your favorite news source and grab the article text.\n", "\n", "1. Show the most common words in the article.\n", "2. Show the most common words under a part of speech. (i.e. NOUN: {'Bob':12, 'Alice':4,})\n", "3. Find a subject/object relationship through the dependency parser in any sentence.\n", "4. Show the most common Entities and their types. \n", "5. Find Entites and their dependency (hint: entity.root.head)\n", "6. Find the most similar words in the article" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
MrFunBarn/ADRL	Basic-Notebook.ipynb	1	14408	{ "cells": [ { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# This is the setup and \n", "import re\n", "import subprocess\n", "\n", "from astropy.io import fits\n", "import pyfits\n", "\n", "# Allow cell output in markdown using printmd function.\n", "from IPython.display import Markdown, display\n", "def printmd(string):\n", " display(Markdown(string))\n", "printmd('bold')" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[0m\u001b[01;34m29-06-2014UT-ARCSAT\u001b[0m/ dataSet.py LICENSE masterlog.log\r\n", "Basic-Notebook.ipynb images.py log_handler.py README.md\r\n" ] } ], "source": [ "ls\n" ] }, { "cell_type": "code", "execution_count": 954, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def read_obs_log(logName):\n", " # The Latin-1 encoding is needed because of the degree symobl.\n", " log = open(logName,'r', encoding='Latin-1')\n", " log = log.read()\n", " return log\n", "\n", "def extract_log_metadata(log, filesMeta):\n", " \"\"\"\n", " Parse log string and return a list of filenames and a dictionary.\n", " \n", " Arguments:\n", " - ARCSAT log in the form of a sngle string.\n", " \n", " Returns:\n", " - List of filenames(strings) that apear in the log. The file names\n", " have no .fits extension.\n", " - Dictionary whith the filenames as keys. The value for each filename\n", " (key) is a dictionary with the time. \n", " \n", " Parses an arcsat log in the form of a single string; meant to handle\n", " a string comprised of all the logs for a given night. Finds all image\n", " file names referanced in the log and creates a list of those names (these\n", " file names don't have .fits extensions). Builds a dictionary with keys for\n", " each of these files. The value for each is itself a dictions with keys and\n", " vlues coresponding to information in the log. Find the time an image was\n", " finished and weither or not the image was plate solved. If plate solved\n", " the average FWHM is found.\n", " \"\"\"\n", " \n", " filenames = []\n", " # Find the files that have information in the logs.\n", " searches = re.findall(r'(Imaging to) (\\S+)', log)\n", " [filenames.append(x[1].strip('\\n')) for x in searches[:]]\n", " filenames = [x + '.fits' for x in filenames]\n", " # Find all the relevant information on each file in the logs.\n", " for name in filenames:\n", " nameInLog = name.replace('.fits','')\n", " # Build a string containing only information for a single file.\n", " searchstring = r''+nameInLog+'.?turning tracking off for safety\\)'\n", " singleImageMeta = str(re.findall(searchstring, log, re.DOTALL)[0])\n", " filesMeta[name] = {}\n", " # Find the time that a given file was finished.\n", " finishTime = re.search(r'(\$exposure complete and image downloaded\$)(.?)(\\S{8})',\n", " singleImageMeta, re.DOTALL)\n", " finishTime = finishTime.group(3)\n", " filesMeta[name]['finishTime'] = finishTime\n", " # Dtermine if an image was plate solved and if so, get the average FWHM.\n", " if 'Solved!' in singleImageMeta:\n", " filesMeta[name]['solved'] = True\n", " fwhm = re.search(r'(Image) (FWHM is) (\\d\\D\\d)', singleImageMeta)\n", " fwhm = float(fwhm.group(3))\n", " filesMeta[name]['fwhm'] = fwhm\n", " else:\n", " filesMeta[name]['solved'] = False\n", " return filesMeta\n", "\n", "def create_files_meta():\n", " filesMeta = {}\n", " # List all fits files in the current dir into a string.\n", " ls = subprocess.check_output(['ls -1 *.fits'],\n", " universal_newlines=True, shell=True).strip().split('\\n')\n", " # Determine the type of the image by file name, because french fries.\n", " for i in ls:\n", " filesMeta[i] = {}\n", " if 'Dark' in i:\n", " filesMeta[i]['imtype'] = 'dark'\n", " print(i)\n", " elif 'Bias' in i:\n", " filesMeta[i]['imtype'] = 'bias'\n", " elif 'skyflat' in i:\n", " filesMeta[i]['imtype'] = 'skyflat'\n", " elif 'domeflat' in i:\n", " filesMeta[i]['imtype'] = 'domeflat'\n", " else:\n", " filesMeta[i]['imtype'] = 'object'\n", " \n", " \n", " return filesMeta, ls" ] }, { "cell_type": "code", "execution_count": 875, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/brandon/Work/ADRL\n" ] } ], "source": [ "cd /home/brandon/Work/ADRL/" ] }, { "cell_type": "code", "execution_count": 876, "metadata": { "collapsed": false }, "outputs": [], "source": [ "log = read_arcsat_obs_log('masterlog.log')" ] }, { "cell_type": "code", "execution_count": 877, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/brandon/Work/ADRL/29-06-2014UT-ARCSAT\n" ] } ], "source": [ "cd 29-06-2014UT-ARCSAT/" ] }, { "cell_type": "code", "execution_count": 957, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dark_B2_20140629_120359.fits\n", "Dark_B2_20140629_120729.fits\n", "Dark_B2_20140629_121100.fits\n", "{'domeflat_johnson-cousins_V_003.fits': {'imtype': 'domeflat'}, 'skyflat_johnson-cousins_Rc_004.fits': {'imtype': 'skyflat'}, 'NAPG-A-I_johnson-cousins_Ic_20140629_090200.fits': {'fwhm': 2.5, 'solved': True, 'finishTime': '09:05:42'}, 'domeflat_johnson-cousins_V_006.fits': {'imtype': 'domeflat'}, 'skyflat_halpha_6563_50_003.fits': {'imtype': 'skyflat'}, 'NAPG-A-I_johnson-cousins_Ic_20140629_085010.fits': {'fwhm': 2.6, 'solved': True, 'finishTime': '08:53:52'}, 'Bias_B2_20140629_115958.fits': {'imtype': 'bias'}, 'domeflat_johnson-cousins_Ic_002.fits': {'imtype': 'domeflat'}, 'skyflat_johnson-cousins_V_004.fits': {'imtype': 'skyflat'}, 'NAPG-L-V_johnson-cousins_V_20140629_094549.fits': {'fwhm': 2.7, 'solved': True, 'finishTime': '09:49:21'}, 'Bias_B2_20140629_120040.fits': {'imtype': 'bias'}, 'domeflat_johnson-cousins_Rc_003.fits': {'imtype': 'domeflat'}, 'domeflat_johnson-cousins_V_008.fits': {'imtype': 'domeflat'}, 'NAPG-L-R_johnson-cousins_Rc_20140629_092013.fits': {'fwhm': 4.6, 'solved': True, 'finishTime': '09:23:51'}, 'skyflat_johnson-cousins_V_002.fits': {'imtype': 'skyflat'}, 'NAPG-A-R_johnson-cousins_Rc_20140629_082803.fits': {'imtype': 'object'}, 'domeflat_johnson-cousins_Rc_004.fits': {'imtype': 'domeflat'}, 'NAPG-L-V_johnson-cousins_V_20140629_095118.fits': {'fwhm': 2.7, 'solved': True, 'finishTime': '09:54:50'}, 'NAPG-L-I_johnson-cousins_Ic_20140629_090756.fits': {'fwhm': 3.0, 'solved': True, 'finishTime': '09:11:38'}, 'domeflat_johnson-cousins_Ic_001.fits': {'imtype': 'domeflat'}, 'Bias_B2_20140629_120009.fits': {'imtype': 'bias'}, 'skyflat_halpha_6563_50_002.fits': {'imtype': 'skyflat'}, 'NAPG-A-V_johnson-cousins_Rc_20140629_082015.fits': {'fwhm': 2.2, 'solved': True, 'finishTime': '08:23:54'}, 'skyflat_johnson-cousins_Ic_001.fits': {'imtype': 'skyflat'}, 'NAPG-A-V_johnson-cousins_V_20140629_070902.fits': {'fwhm': 2.2, 'solved': True, 'finishTime': '07:12:35'}, 'domeflat_johnson-cousins_Rc_005.fits': {'imtype': 'domeflat'}, 'domeflat_johnson-cousins_Ic_008.fits': {'imtype': 'domeflat'}, 'skyflat_johnson-cousins_Rc_001.fits': {'imtype': 'skyflat'}, 'M31-F4-R_johnson-cousins_Rc_20140629_103227.fits': {'fwhm': 2.5, 'solved': True, 'finishTime': '10:36:05'}, 'NAPG-L-V_johnson-cousins_V_20140629_093852.fits': {'fwhm': 2.5, 'solved': True, 'finishTime': '09:42:23'}, 'Dark_B2_20140629_120729.fits': {'imtype': 'dark'}, 'domeflat_johnson-cousins_Ic_011.fits': {'imtype': 'domeflat'}, 'domeflat_johnson-cousins_Rc_001.fits': {'imtype': 'domeflat'}, 'domeflat_johnson-cousins_Rc_008.fits': {'imtype': 'domeflat'}, 'domeflat_johnson-cousins_Rc_009.fits': {'imtype': 'domeflat'}, 'Bias_B2_20140629_120030.fits': {'imtype': 'bias'}, 'domeflat_johnson-cousins_Ic_004.fits': {'imtype': 'domeflat'}, 'NAPG-L-R_johnson-cousins_Rc_20140629_092534.fits': {'fwhm': 2.9, 'solved': True, 'finishTime': '09:29:13'}, 'NAPG-L-R_johnson-cousins_Rc_20140629_093059.fits': {'fwhm': 3.2, 'solved': True, 'finishTime': '09:34:38'}, 'NAPG-A-R_johnson-cousins_Rc_20140629_083632.fits': {'fwhm': 2.5, 'solved': True, 'finishTime': '08:40:11'}, 'NAPG-A-V_johnson-cousins_V_20140629_073059.fits': {'fwhm': 2.7, 'solved': True, 'finishTime': '07:37:51'}, 'M31-F4-I_johnson-cousins_Ic_20140629_104833.fits': {'fwhm': 2.2, 'solved': True, 'finishTime': '10:52:14'}, 'Bias_B2_20140629_120019.fits': {'imtype': 'bias'}, 'NAPG-A-I_johnson-cousins_Ic_20140629_084421.fits': {'fwhm': 2.5, 'solved': True, 'finishTime': '08:48:03'}, 'NAPG-A-V_johnson-cousins_V_20140629_065934.fits': {'fwhm': 2.2, 'solved': True, 'finishTime': '07:00:05'}, 'domeflat_johnson-cousins_Rc_002.fits': {'imtype': 'domeflat'}, 'NAPG-L-I_johnson-cousins_Ic_20140629_091458.fits': {'fwhm': 3.2, 'solved': True, 'finishTime': '09:18:40'}, 'domeflat_johnson-cousins_Ic_007.fits': {'imtype': 'domeflat'}, 'domeflat_johnson-cousins_Rc_011.fits': {'imtype': 'domeflat'}, 'skyflat_johnson-cousins_Ic_002.fits': {'imtype': 'skyflat'}, 'domeflat_johnson-cousins_Ic_006.fits': {'imtype': 'domeflat'}, 'NAPG-A-V_johnson-cousins_V_20140629_064108.fits': {'fwhm': 2.1, 'solved': True, 'finishTime': '06:41:40'}, 'domeflat_halpha_6563_50_001.fits': {'imtype': 'domeflat'}, 'M31-F4-R_johnson-cousins_Rc_20140629_102158.fits': {'fwhm': 2.9, 'solved': True, 'finishTime': '10:25:36'}, 'domeflat_johnson-cousins_V_005.fits': {'imtype': 'domeflat'}, 'domeflat_johnson-cousins_V_010.fits': {'imtype': 'domeflat'}, 'domeflat_johnson-cousins_Ic_010.fits': {'imtype': 'domeflat'}, 'domeflat_johnson-cousins_Rc_007.fits': {'imtype': 'domeflat'}, 'skyflat_johnson-cousins_Rc_003.fits': {'imtype': 'skyflat'}, 'domeflat_johnson-cousins_V_011.fits': {'imtype': 'domeflat'}, 'M31-F4-V_johnson-cousins_V_20140629_101145.fits': {'fwhm': 2.6, 'solved': True, 'finishTime': '10:15:16'}, 'Dark_B2_20140629_120359.fits': {'imtype': 'dark'}, 'domeflat_johnson-cousins_Ic_009.fits': {'imtype': 'domeflat'}, 'Bias_B2_20140629_115917.fits': {'imtype': 'bias'}, 'Bias_B2_20140629_120051.fits': {'imtype': 'bias'}, 'domeflat_johnson-cousins_Rc_006.fits': {'imtype': 'domeflat'}, 'NAPG-A-V_johnson-cousins_V_20140629_075006.fits': {'fwhm': 2.4, 'solved': True, 'finishTime': '07:56:57'}, 'skyflat_johnson-cousins_Ic_003.fits': {'imtype': 'skyflat'}, 'domeflat_johnson-cousins_V_001.fits': {'imtype': 'domeflat'}, 'domeflat_johnson-cousins_V_004.fits': {'imtype': 'domeflat'}, 'domeflat_johnson-cousins_Ic_005.fits': {'imtype': 'domeflat'}, 'skyflat_johnson-cousins_Rc_002.fits': {'imtype': 'skyflat'}, 'Bias_B2_20140629_115905.fits': {'imtype': 'bias'}, 'NAPG-A-V_johnson-cousins_Rc_20140629_082803.fits': {'fwhm': 2.4, 'solved': True, 'finishTime': '08:31:41'}, 'skyflat_johnson-cousins_V_001.fits': {'imtype': 'skyflat'}, 'Dark_B2_20140629_121100.fits': {'imtype': 'dark'}, 'NAPG-A-V_johnson-cousins_V_20140629_080555.fits': {'fwhm': 2.4, 'solved': True, 'finishTime': '08:09:27'}, 'M31-F4-V_johnson-cousins_V_20140629_101657.fits': {'fwhm': 2.6, 'solved': True, 'finishTime': '10:20:29'}, 'M31-F4-R_johnson-cousins_Rc_20140629_102712.fits': {'fwhm': 2.6, 'solved': True, 'finishTime': '10:30:50'}, 'domeflat_johnson-cousins_Ic_003.fits': {'imtype': 'domeflat'}, 'NAPG-A-R_johnson-cousins_Rc_20140629_082015.fits': {'imtype': 'object'}, 'Bias_B2_20140629_115927.fits': {'imtype': 'bias'}, 'NAPG-A-V_johnson-cousins_V_20140629_081442.fits': {'fwhm': 2.1, 'solved': True, 'finishTime': '08:18:14'}, 'domeflat_johnson-cousins_V_007.fits': {'imtype': 'domeflat'}, 'Bias_B2_20140629_115938.fits': {'imtype': 'bias'}, 'skyflat_halpha_6563_50_001.fits': {'imtype': 'skyflat'}, 'domeflat_johnson-cousins_V_002.fits': {'imtype': 'domeflat'}, 'NAPG-A-V_johnson-cousins_V_20140629_074110.fits': {'fwhm': 3.1, 'solved': True, 'finishTime': '07:48:02'}, 'skyflat_johnson-cousins_Ic_004.fits': {'imtype': 'skyflat'}, 'Bias_B2_20140629_115948.fits': {'imtype': 'bias'}, 'skyflat_halpha_6563_50_004.fits': {'imtype': 'skyflat'}, 'M31-F4-V_johnson-cousins_V_20140629_100403.fits': {'fwhm': 2.4, 'solved': True, 'finishTime': '10:07:35'}, 'skyflat_johnson-cousins_V_003.fits': {'imtype': 'skyflat'}, 'domeflat_johnson-cousins_Rc_010.fits': {'imtype': 'domeflat'}, 'domeflat_johnson-cousins_V_009.fits': {'imtype': 'domeflat'}, 'NAPG-A-I_johnson-cousins_Ic_20140629_085529.fits': {'fwhm': 3.1, 'solved': True, 'finishTime': '08:59:11'}}\n" ] } ], "source": [ "filesMeta, ls = create_files_meta()\n", "filesMeta = extract_log_metadata(log, filesMeta)\n", "print(filesMeta)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": 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for saving plots\n", "\n", "!wget https://raw.githubusercontent.com/d2l-ai/d2l-en/master/d2l/torch.py -q -O d2l.py\n", "import d2l" ], "execution_count": 1, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "VzjmKaBXlcY8" }, "source": [ "# Add dropout layer by hand to an MLP" ] }, { "cell_type": "code", "metadata": { "id": "CY8jl-m7k_mM" }, "source": [ "def dropout_layer(X, dropout):\n", " assert 0 <= dropout <= 1\n", " # In this case, all elements are dropped out\n", " if dropout == 1:\n", " return torch.zeros_like(X)\n", " # In this case, all elements are kept\n", " if dropout == 0:\n", " return X\n", " mask = (torch.Tensor(X.shape).uniform_(0, 1) > dropout).float()\n", " return mask * X / (1.0 - dropout)" ], "execution_count": 2, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "u62UJnwPlOj4", "outputId": "12132161-85ef-4982-9572-81dec6c2e4c1" }, "source": [ "# quick test\n", "torch.manual_seed(0)\n", "X = torch.arange(16, dtype=torch.float32).reshape((2, 8))\n", "print(X)\n", "print(dropout_layer(X, 0.0))\n", "print(dropout_layer(X, 0.5))\n", "print(dropout_layer(X, 1.0))" ], "execution_count": 3, "outputs": [ { "output_type": "stream", "text": [ "tensor([[ 0., 1., 2., 3., 4., 5., 6., 7.],\n", " [ 8., 9., 10., 11., 12., 13., 14., 15.]])\n", "tensor([[ 0., 1., 2., 3., 4., 5., 6., 7.],\n", " [ 8., 9., 10., 11., 12., 13., 14., 15.]])\n", "tensor([[ 0., 2., 0., 0., 0., 10., 0., 14.],\n", " [ 0., 18., 0., 0., 0., 0., 0., 30.]])\n", "tensor([[0., 0., 0., 0., 0., 0., 0., 0.],\n", " [0., 0., 0., 0., 0., 0., 0., 0.]])\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "RzNntL4slPud" }, "source": [ "# A common trend is to set a lower dropout probability closer to the input layer\n", "class Net(nn.Module):\n", " def __init__(\n", " self, num_inputs, num_outputs, num_hiddens1, num_hiddens2, is_training=True, dropout1=0.2, dropout2=0.5\n", " ):\n", " super(Net, self).__init__()\n", " self.dropout1 = dropout1\n", " self.dropout2 = dropout2\n", " self.num_inputs = num_inputs\n", " self.training = is_training\n", " self.lin1 = nn.Linear(num_inputs, num_hiddens1)\n", " self.lin2 = nn.Linear(num_hiddens1, num_hiddens2)\n", " self.lin3 = nn.Linear(num_hiddens2, num_outputs)\n", " self.relu = nn.ReLU()\n", "\n", " def forward(self, X):\n", " H1 = self.relu(self.lin1(X.reshape((-1, self.num_inputs))))\n", " # Use dropout only when training the model\n", " if self.training == True:\n", " # Add a dropout layer after the first fully connected layer\n", " H1 = dropout_layer(H1, self.dropout1)\n", " H2 = self.relu(self.lin2(H1))\n", " if self.training == True:\n", " # Add a dropout layer after the second fully connected layer\n", " H2 = dropout_layer(H2, self.dropout2)\n", " out = self.lin3(H2)\n", " return out" ], "execution_count": 7, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "9SV2d62Dlr28" }, "source": [ "## Fit to FashionMNIST\n", "\n", "Uses the [d2l.load_data_fashion_mnist](https://github.com/d2l-ai/d2l-en/blob/master/d2l/torch.py#L200) function." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 471, "referenced_widgets": [ "ef31731c8c3341e095f8481db32268e9", "ed68350fce8246c287f5aa93814aa14b", "67fccd492d944dd3a723116b238681c1", "7afc34c5e582451f94a03eee61b01582", "184401772a414e5eae5d6801624f4e98", "0a3298fe24de4f39a44aa88a621173b4", "e9ed770bc63a4a81b96766a8e9a016f8", "51498b301bf14fd3b5528f0eb58e19c8", "a51380f3658440818f3ad7c0135e2b90", "d074c256b9154cd48f36f7d156ac01dd", "e9b3a0832aaa406cbfb94dbaed15d9af", "ae79ba6852ba45deb38c4e753373c6f0", "92939089b55f418ba8c0c35fac364011", "a236377ba48f49a69425708288c1cd41", "ca4ffbd43667438d93d09520b1892d1c", "c071447004d3466db1d6c471e789acf0", "840cd3da067a4842b7ce3c5ef0b39520", "1356126b6b4c4bd6b68a8e2cbee9ca4a", "d945a599158847b3b89a3840eea62d6c", "bae79b1fec4e4aa1b114353f8bb821a8", "b0db1b443e504a62b1720ec317bde753", "fe7b59bc61bc401183ed143ae293d791", "3e7d7a5cfa0147f1853a0c6421f08257", "6a9336f89c6e42dca65603a202e9ec2b", "6184f2aa3cc84abe8d8bf4307037d9ba", "46bb91ac8ce44d328ea7de1f27c9e5d0", "c78c24865191425faea1923118f85e17", "271cb90726df4bb5b325ae606f3d53c9", "acf6d7de62754234b5edd131bae214db", "3363766204fe414dbe40f3f1f6b41da1", "abf7bbeecd9f4ac4b7738687ae6a8828", "3176f07b82a142b48139d65d32fc08e6" ] }, "id": "K2Igif6rl3ci", "outputId": "11eb8519-9d48-4c97-9bec-05be1040e089" }, "source": [ "train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size=256)" ], "execution_count": 5, "outputs": [ { "output_type": "stream", "text": [ "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ../data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ef31731c8c3341e095f8481db32268e9", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=26421880.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n", "Extracting ../data/FashionMNIST/raw/train-images-idx3-ubyte.gz to ../data/FashionMNIST/raw\n", "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ../data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a51380f3658440818f3ad7c0135e2b90", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=29515.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n", "Extracting ../data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to ../data/FashionMNIST/raw\n", "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ../data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "840cd3da067a4842b7ce3c5ef0b39520", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=4422102.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n", "Extracting ../data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to ../data/FashionMNIST/raw\n", "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ../data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6184f2aa3cc84abe8d8bf4307037d9ba", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=5148.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n", "Extracting ../data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to ../data/FashionMNIST/raw\n", "Processing...\n", "Done!\n" ], "name": "stdout" }, { "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/torchvision/datasets/mnist.py:479: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:143.)\n", " return torch.from_numpy(parsed.astype(m[2], copy=False)).view(s)\n", "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:477: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n", " cpuset_checked))\n" ], "name": "stderr" } ] }, { "cell_type": "markdown", "metadata": { "id": "gajH4V3gmLVv" }, "source": [ "Fit model using SGD.\n", "Uses the [d2l.train_ch3](https://github.com/d2l-ai/d2l-en/blob/master/d2l/torch.py#L326) function." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 262 }, "id": "MrRopaCFlrJB", "outputId": "d4391e67-8a76-4ec4-f0d2-eee52be0c973" }, "source": [ "torch.manual_seed(0)\n", "# We pick a wide model to cause overfitting without dropout\n", "num_inputs, num_outputs, num_hiddens1, num_hiddens2 = 784, 10, 256, 256\n", "net = Net(num_inputs, num_outputs, num_hiddens1, num_hiddens2, dropout1=0.5, dropout2=0.5)\n", "loss = nn.CrossEntropyLoss()\n", "lr = 0.5\n", "trainer = torch.optim.SGD(net.parameters(), lr=lr)\n", 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{ "id": "Vgi3EjMrrc28" }, "source": [ "When we turn dropout off, we notice a slightly larger gap between train and test accuracy." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 262 }, "id": "-wd8I2-pn69g", "outputId": "bee0b0aa-6bf6-4b19-9d8d-bfead734c96b" }, "source": [ "torch.manual_seed(0)\n", "net = Net(num_inputs, num_outputs, num_hiddens1, num_hiddens2, dropout1=0.0, dropout2=0.0)\n", "loss = nn.CrossEntropyLoss()\n", "trainer = torch.optim.SGD(net.parameters(), lr=lr)\n", "num_epochs = 10\n", "d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)" ], "execution_count": 10, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "<Figure size 252x180 with 1 Axes>" ], "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib 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nn.Dropout(dropout1),\n", " nn.Linear(num_hiddens2, num_hiddens1),\n", " nn.ReLU(),\n", " # Add a dropout layer after the second fully connected layer\n", " nn.Dropout(dropout2),\n", " nn.Linear(num_hiddens2, num_outputs),\n", ")\n", "\n", "\n", "def init_weights(m):\n", " if type(m) == nn.Linear:\n", " nn.init.normal_(m.weight, std=0.01)\n", "\n", "\n", "torch.manual_seed(0)\n", "net.apply(init_weights);" ], "execution_count": 13, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 262 }, "id": "YYha_QFlorJr", "outputId": "e95dc80b-80c1-4692-a489-fcec8f0fdc84" }, "source": [ "trainer = torch.optim.SGD(net.parameters(), lr=lr)\n", "d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)" ], "execution_count": 16, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "<Figure size 252x180 with 1 Axes>" ], "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE 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Extract first batch from iterator\n", " for X, y in test_iter:\n", " break\n", " # Get labels\n", " trues = d2l.get_fashion_mnist_labels(y)\n", " preds = d2l.get_fashion_mnist_labels(d2l.argmax(net(X), axis=1))\n", " # Plot\n", " titles = [true + \"\\n\" + pred for true, pred in zip(trues, preds)]\n", " d2l.show_images(d2l.reshape(X[0:n], (n, 28, 28)), 1, n, titles=titles[0:n])" ], "execution_count": 18, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 233 }, "id": "DE8DD-2Ys42Y", "outputId": "e0ef855c-bca5-4039-d63e-d18797a9c611" }, "source": [ "# d2l.predict_ch3(net, test_iter)\n", "display_predictions(net, test_iter)" ], "execution_count": 20, "outputs": [ { "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:477: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n", " cpuset_checked))\n" ], "name": "stderr" }, { "output_type": "display_data", "data": { "text/plain": [ "<Figure size 648x108 with 6 Axes>" ], "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"118.198357pt\" version=\"1.1\" viewBox=\"0 0 520.1 118.198357\" width=\"520.1pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;}\n </style>\n </defs>\n <g id=\"figure_1\">\n <g id=\"patch_1\">\n <path d=\"M 0 118.198357 \nL 520.1 118.198357 \nL 520.1 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n </g>\n <g 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Radcliffe/project-euler	Euler 003 - Largest prime factor.ipynb	1	1241	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Euler Problem 3\n", "===============\n", "\n", "The prime factors of 13195 are 5, 7, 13 and 29.\n", "\n", "What is the largest prime factor of the number 600851475143 ?" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6857\n" ] } ], "source": [ "N = 600851475143\n", "for k in range(2, N):\n", " while N > k and N % k == 0:\n", " N //= k\n", " if N < k*k:\n", " break\n", "print(N) " ] }, { "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": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
skkandrach/foundations-homework	data-databases/D&D_classwork_06-09.ipynb	2	7520	{ "cells": [ { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'document' 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-12-c5c4b97510ff>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mkittens_data\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----> 2\u001b[0;31m \u001b[0mkittens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdocument\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'div'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'class'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'kitten'\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 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkittens\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mh2_tag\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'h2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0ma_tags\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'document' is not defined" ] } ], "source": [ "kittens_data = []\n", "kittens = document.find_all('div', {'class': 'kitten'})\n", "for item in kittens:\n", " h2_tag = item.find('h2')\n", " a_tags = item.find_all('a')\n", " all_shows_str = []\n", " for a_tag_item in a_tags:\n", " tag_str = a_tag_item.string\n", " all_shows_str.append(tag_str)\n", " #(1) create a dictionary and add to it the relevant key/value pairs\n", " kitten_map = {}\n", " kitten_map['name'] = h2_tag.string\n", " kitten_map[\"tvshows\"] = all_shows_str\n", " #(2) append that dictionary to the kittens data list\n", " string_with_all_show_names = \", \".join(all_shows_str)\n", " #print(h2_tag.string + \":\", string_with_all_show_names)\n", " kittens_data.append(kitten_map)\n", "print(kittens_data) " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "len(kittens_data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# THIRD ASIDE: MAKING DICTIONARIES " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#declaring a dictionary \n", "x = {'a': 1, 'b': 2, 'c': 3}" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'x' 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-2-ccd0b25ef3a5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#get a value out of a dictionary:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'x' is not defined" ] } ], "source": [ "#get a value out of a dictionary:\n", "x['a']" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'x' 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-3-f915c109abb8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'x' is not defined" ] } ], "source": [ "x.keys()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for k in x.keys():\n", " print(k)\n", "#will come out in a random order...thats normal" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81, 10: 100}\n" ] } ], "source": [ "#target: {1: 1, 2: 4, 3: 9, 4: 16, 5: 25, ... 10: 100}\n", "squares = {}\n", "for n in range(1, 11):\n", " squares[n] = n* n\n", "print(squares)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "49" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "squares[7]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'Caroline': 8, 'Aaron': 5, 'Bob': 3, 'Daphne': 6}\n" ] } ], "source": [ "names = [\"Aaron\", \"Bob\", \"Caroline\", \"Daphne\"]\n", "#target: {\"Aaron\": 5, \"Bob\": 3, \"Caroline\": 8, \"Daphne\": 6}\n", "name_length_map = {}\n", "for item in names:\n", " name_length_map[item] = len(item)\n", "print(name_length_map)" ] }, { "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 }	mit
jokulhaup/directed_studies	.ipynb_checkpoints/Untitled0-checkpoint.ipynb	4	181	{ "metadata": { "name": "", "signature": "sha256:f3eef29b031d645116a14d47a0a6b4ee00001f2ef7387556f65b13e5399aeb51" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [] }	mit
ZoranPandovski/al-go-rithms	dynamic_programing/dynamic palindromic String.ipynb	1	2322	{ "cells": [ { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def longest_palindrome(string):\n", " length=len(string)#length of the parent string\n", " \n", " palindromes_length=[1]*length\n", " \n", " for fgap in range(1,length):#iterate on each substring\n", " \n", " pre=palindromes_length[fgap]\n", " for rgap in reversed(range(0,fgap)):\n", " tmp=palindromes_length[rgap]\n", " if string[fgap]==string[rgap]:\n", " if rgap+1>fgap-1:\n", " palindromes_length[rgap]=2\n", " else:\n", " palindromes_length[rgap]=2+pre\n", " else:\n", " palindromes_length[rgap]=max(\n", " palindromes_length[rgap+1],palindromes_length[rgap]\n", " )\n", " pre=tmp\n", " return palindromes_length[0]\n", "\n", "\n", "print(longest_palindrome(\"bbabcbcab\"))\n", "print(longest_palindrome(\"abbaab\"))\n", "print(longest_palindrome(\"opengenus\"))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<function __main__.main>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "main" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7\n", "4\n", "3\n" ] } ], "source": [ "__main__()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "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": "2.7.16" } }, "nbformat": 4, "nbformat_minor": 2 }	cc0-1.0
robcarver17/pysystemtrade	examples/introduction/asimpletradingrule.ipynb	1	74514	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Simple Trading Rule" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from sysdata.sim.csv_futures_sim_data import csvFuturesSimData\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Work up a minimum example of a trend following system" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's get some data\n", "\n", "We can get data from various places; however for now we're going to use\n", "prepackaged 'legacy' data stored in csv files" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No datapaths provided for .csv, will use defaults (may break in production, should be fine in sim)\n", "No datapaths provided for .csv, will use defaults (may break in production, should be fine in sim)\n", "No datapaths provided for .csv, will use defaults (may break in production, should be fine in sim)\n", "No datapaths provided for .csv, will use defaults (may break in production, should be fine in sim)\n" ] }, { "data": { "text/plain": [ "csvFuturesSimData object with 46 instruments" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = csvFuturesSimData()\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We get stuff out of data with methods" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['AEX', 'AUD', 'BITCOIN', 'BOBL', 'BTP', 'BUND', 'CAC', 'COPPER', 'CORN', 'CRUDE_W', 'CRUDE_W_mini', 'EDOLLAR', 'EUR', 'EUROSTX', 'GAS_US', 'GAS_US_mini', 'GBP', 'GOLD', 'GOLD_micro', 'JPY', 'KOSPI', 'KOSPI_mini', 'KR10', 'KR3', 'LEANHOG', 'LIVECOW', 'MXP', 'NASDAQ', 'NASDAQ_micro', 'NZD', 'OAT', 'PALLAD', 'PLAT', 'SHATZ', 'SMI', 'SOYBEAN', 'SP500', 'SP500_micro', 'US-REALESTATE', 'US10', 'US2', 'US20', 'US5', 'V2X', 'VIX', 'WHEAT']\n", "index\n", "2021-03-08 16:00:00 98.865\n", "2021-03-08 17:00:00 98.860\n", "2021-03-08 18:00:00 98.865\n", "2021-03-08 19:00:00 98.865\n", "2021-03-08 20:00:00 98.860\n", "Name: price, dtype: float64\n" ] } ], "source": [ "print(data.get_instrument_list())\n", "print(data.get_raw_price(\"EDOLLAR\").tail(5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "data can also behave in a dict like manner (though it's not a dict)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "index\n", "1997-12-12 23:00:00 1081.25\n", "1997-12-15 23:00:00 1089.25\n", "1997-12-16 23:00:00 1095.25\n", "1997-12-17 23:00:00 1089.25\n", "1997-12-18 23:00:00 1079.25\n", " ... \n", "2021-03-08 17:00:00 3869.50\n", "2021-03-08 18:00:00 3845.50\n", "2021-03-08 19:00:00 3849.75\n", "2021-03-08 20:00:00 3820.00\n", "2021-03-08 21:00:00 3822.50\n", "Name: price, Length: 16153, dtype: float64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['SP500']" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['AEX',\n", " 'AUD',\n", " 'BITCOIN',\n", " 'BOBL',\n", " 'BTP',\n", " 'BUND',\n", " 'CAC',\n", " 'COPPER',\n", " 'CORN',\n", " 'CRUDE_W',\n", " 'CRUDE_W_mini',\n", " 'EDOLLAR',\n", " 'EUR',\n", " 'EUROSTX',\n", " 'GAS_US',\n", " 'GAS_US_mini',\n", " 'GBP',\n", " 'GOLD',\n", " 'GOLD_micro',\n", " 'JPY',\n", " 'KOSPI',\n", " 'KOSPI_mini',\n", " 'KR10',\n", " 'KR3',\n", " 'LEANHOG',\n", " 'LIVECOW',\n", " 'MXP',\n", " 'NASDAQ',\n", " 'NASDAQ_micro',\n", " 'NZD',\n", " 'OAT',\n", " 'PALLAD',\n", " 'PLAT',\n", " 'SHATZ',\n", " 'SMI',\n", " 'SOYBEAN',\n", " 'SP500',\n", " 'SP500_micro',\n", " 'US-REALESTATE',\n", " 'US10',\n", " 'US2',\n", " 'US20',\n", " 'US5',\n", " 'V2X',\n", " 'VIX',\n", " 'WHEAT']" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "... however this will only access prices\n", "(note these prices have already been backadjusted for rolls)\n", "\n", "We have extra futures data here" ] }, { "cell_type": "code", "execution_count": 7, "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>PRICE</th>\n", " <th>CARRY</th>\n", " <th>PRICE_CONTRACT</th>\n", " <th>CARRY_CONTRACT</th>\n", " </tr>\n", " <tr>\n", " <th>index</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2021-03-08 15:00:00</th>\n", " <td>98.865</td>\n", " <td>99.015</td>\n", " <td>20231200</td>\n", " <td>20230900</td>\n", " </tr>\n", " <tr>\n", " <th>2021-03-08 16:00:00</th>\n", " <td>98.865</td>\n", " <td>99.020</td>\n", " <td>20231200</td>\n", " <td>20230900</td>\n", " </tr>\n", " <tr>\n", " <th>2021-03-08 17:00:00</th>\n", " <td>98.860</td>\n", " <td>99.015</td>\n", " <td>20231200</td>\n", " <td>20230900</td>\n", " </tr>\n", " <tr>\n", " <th>2021-03-08 18:00:00</th>\n", " <td>98.865</td>\n", " <td>99.020</td>\n", " <td>20231200</td>\n", " <td>20230900</td>\n", " </tr>\n", " <tr>\n", " <th>2021-03-08 19:00:00</th>\n", " <td>98.865</td>\n", " <td>99.015</td>\n", " <td>20231200</td>\n", " <td>20230900</td>\n", " </tr>\n", " <tr>\n", " <th>2021-03-08 20:00:00</th>\n", " <td>98.860</td>\n", " <td>99.010</td>\n", " <td>20231200</td>\n", " <td>20230900</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PRICE CARRY PRICE_CONTRACT CARRY_CONTRACT\n", "index \n", "2021-03-08 15:00:00 98.865 99.015 20231200 20230900\n", "2021-03-08 16:00:00 98.865 99.020 20231200 20230900\n", "2021-03-08 17:00:00 98.860 99.015 20231200 20230900\n", "2021-03-08 18:00:00 98.865 99.020 20231200 20230900\n", "2021-03-08 19:00:00 98.865 99.015 20231200 20230900\n", "2021-03-08 20:00:00 98.860 99.010 20231200 20230900" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.get_instrument_raw_carry_data(\"EDOLLAR\").tail(6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Technical note: csvFuturesSimData inherits from FuturesData which itself inherits from simData\n", "The chain is 'data specific' <- 'asset class specific' <- 'generic'\n", "\n", "Let's create a simple trading rule\n", "\n", "No capping or scaling" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from sysquant.estimators.vol import robust_vol_calc\n", "\n", "\n", "def calc_ewmac_forecast(price, Lfast, Lslow=None):\n", " \"\"\"\n", " Calculate the ewmac trading rule forecast, given a price and EWMA speeds\n", " Lfast, Lslow and vol_lookback\n", "\n", " \"\"\"\n", " # price: This is the stitched price series\n", " # We can't use the price of the contract we're trading, or the volatility\n", " # will be jumpy\n", " # And we'll miss out on the rolldown. See\n", " # https://qoppac.blogspot.com/2015/05/systems-building-futures-rolling.html\n", "\n", " price = price.resample(\"1B\").last()\n", "\n", " if Lslow is None:\n", " Lslow = 4 * Lfast\n", "\n", " # We don't need to calculate the decay parameter, just use the span\n", " # directly\n", " fast_ewma = price.ewm(span=Lfast).mean()\n", " slow_ewma = price.ewm(span=Lslow).mean()\n", " raw_ewmac = fast_ewma - slow_ewma\n", " vol = robust_vol_calc(price.diff())\n", " return raw_ewmac / vol" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "Try it out\n", "\n", "(this isn't properly scaled at this stage of course)\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "index\n", "2021-03-02 -1.961088\n", "2021-03-03 -2.065046\n", "2021-03-04 -2.260685\n", "2021-03-05 -2.515280\n", "2021-03-08 -2.784115\n", "Freq: B, Name: price, dtype: float64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "instrument_code = 'EDOLLAR'\n", "price = data.daily_prices(instrument_code)\n", "ewmac = calc_ewmac_forecast(price, 32, 128)\n", "ewmac.columns = ['forecast']\n", "ewmac.tail(5)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'Time')" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ewmac.plot();\n", "plt.title('Forecast')\n", "plt.ylabel('Position')\n", "plt.xlabel('Time')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "Did we make money?\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from systems.accounts.account_forecast import pandl_for_instrument_forecast\n", "account = pandl_for_instrument_forecast(forecast=ewmac, price = price)\n", "account.curve().plot();\n", "plt.title('Profit and Loss')\n", "plt.ylabel('PnL')\n", "plt.xlabel('Time');" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[('min', '-5.81'),\n", " ('max', '5.141'),\n", " ('median', '0'),\n", " ('mean', '0.01502'),\n", " ('std', '0.5036'),\n", " ('skew', '-0.1824'),\n", " ('ann_mean', '3.846'),\n", " ('ann_std', '8.057'),\n", " ('sharpe', '0.4773'),\n", " ('sortino', '0.5659'),\n", " ('avg_drawdown', '-11.37'),\n", " ('time_in_drawdown', '0.9738'),\n", " ('calmar', '0.1078'),\n", " ('avg_return_to_drawdown', '0.3382'),\n", " ('avg_loss', '-0.3198'),\n", " ('avg_gain', '0.33'),\n", " ('gaintolossratio', '1.032'),\n", " ('profitfactor', '1.107'),\n", " ('hitrate', '0.5175'),\n", " ('t_stat', '2.935'),\n", " ('p_value', '0.003342')],\n", " ('You can also plot / print:',\n", " ['rolling_ann_std', 'drawdown', 'curve', 'percent'])]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "account.percent.stats()" ] }, { "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": "3.8.6" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
dimarkov/pyBefit	examples/social_influence/fit_behavior_socinf_task.ipynb	1	1557988	null	mit
rparundekar/deep_learning_self	emnist/EMNIST-mnist-simple.ipynb	1	59739	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "%matplotlib inline \n", "import struct\n", "from struct import unpack\n", "from numpy import zeros, uint8, float32\n", "from pylab import imshow, show, cm\n", "import matplotlib\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import subprocess\n", "\n", "\n", "#Initialize for keras\n", "import keras\n", "from keras.datasets import mnist\n", "from keras.models import Sequential, Input,Model\n", "from keras.layers import Dense, Dropout, Flatten\n", "from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D\n", "from keras import backend as K" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Define functions for reading data.\n", "# Based on https://gist.github.com/tylerneylon/\n", "def read_idx(filename):\n", " \"\"\"\n", " Read from file and create numpy array\n", " \"\"\"\n", " with open(filename, 'rb') as f:\n", " zero, data_type, dims = struct.unpack('>HBB', f.read(4))\n", " shape = tuple(struct.unpack('>I', f.read(4))[0] for d in range(dims))\n", " return np.fromstring(f.read(), dtype=np.uint8).reshape(shape)\n", "\n", "def get_data(image_file, label_file, num_classes = 10):\n", " \"\"\"\n", " Read the image and label data\n", " \"\"\"\n", " # Read the files\n", " pre_images = read_idx(image_file)\n", " pre_labels = read_idx(label_file)\n", " \n", " images = np.zeros((len(pre_images), 28,28, 1), dtype=np.float32)\n", " labels = np.zeros((len(pre_labels),num_classes), dtype=np.int8)\n", " for i in range(len(pre_images)):\n", " pre_img=pre_images[i]\n", " pre_label=pre_labels[i]\n", " img = (pre_img.transpose() / 255.0)\n", " images[i] = img.reshape(28,28,1) \n", " labels[i] = keras.utils.to_categorical(pre_label, num_classes)\n", " \n", " return images, labels\n", "\n", "def file_len(fname):\n", " p = subprocess.Popen(['wc', '-l', fname], stdout=subprocess.PIPE, \n", " stderr=subprocess.PIPE)\n", " result, err = p.communicate()\n", " if p.returncode != 0:\n", " raise IOError(err)\n", " return int(result.strip().split()[0])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training Data\n", "Images Shape: (60000, 28, 28, 1)\n", "Labels Shape: (60000, 10)\n", "Training Data\n", "Images Shape: (10000, 28, 28, 1)\n", "Labels Shape: (10000, 10)\n" ] } ], "source": [ "train_mapping_file = '/home/carnd/data/emnist/emnist-mnist-mapping.txt'\n", "num_classes = file_len(train_mapping_file)\n", "train_image_file = '/home/carnd/data/emnist/emnist-mnist-train-images-idx3-ubyte'\n", "train_label_file = '/home/carnd/data/emnist/emnist-mnist-train-labels-idx1-ubyte'\n", "train_images, train_labels = get_data(train_image_file, train_label_file,num_classes)\n", "print ('Training Data')\n", "print ('Images Shape: {}'.format(train_images.shape))\n", "print ('Labels Shape: {}'.format(train_labels.shape))\n", "\n", "test_image_file = '/home/carnd/data/emnist/emnist-mnist-test-images-idx3-ubyte'\n", "test_label_file = '/home/carnd/data/emnist/emnist-mnist-test-labels-idx1-ubyte'\n", "test_images, test_labels = get_data(test_image_file, test_label_file,num_classes)\n", "print ('Training Data')\n", "print ('Images Shape: {}'.format(test_images.shape))\n", "print ('Labels Shape: {}'.format(test_labels.shape))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Label - 4 : [0 0 0 0 1 0 0 0 0 0] \n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7ffbc8eb5ac8>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Label - 1 : [0 1 0 0 0 0 0 0 0 0] \n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7ffbb16a7470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def view_image(image, label=\"\"):\n", " \"\"\"View a single image.\"\"\"\n", " print(\"Label - {} : {} \".format(np.argmax(label), label))\n", " plt.imshow((image.reshape(28,28)), cmap=\"gray\")\n", " plt.show()\n", "\n", "for i in range(2):\n", " view_image(train_images[i], train_labels[i])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Train, Test split\n", "from sklearn.model_selection import train_test_split\n", "X_val, X_test, y_val, y_test = train_test_split(test_images, test_labels, test_size=0.5, random_state=42)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Initialize the hyperparameters\n", "input_shape = (28,28, 1)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "input_1 (InputLayer) (None, 28, 28, 1) 0 \n", "_________________________________________________________________\n", "conv1 (Conv2D) (None, 28, 28, 32) 1600 \n", "_________________________________________________________________\n", "average_pooling2d_1 (Average (None, 9, 9, 32) 0 \n", "_________________________________________________________________\n", "conv2 (Conv2D) (None, 9, 9, 64) 18496 \n", "_________________________________________________________________\n", "average_pooling2d_2 (Average (None, 4, 4, 64) 0 \n", "_________________________________________________________________\n", "conv3 (Conv2D) (None, 4, 4, 128) 73856 \n", "_________________________________________________________________\n", "flatten_1 (Flatten) (None, 2048) 0 \n", "_________________________________________________________________\n", "dense2 (Dense) (None, 128) 262272 \n", "_________________________________________________________________\n", "dropout_1 (Dropout) (None, 128) 0 \n", "_________________________________________________________________\n", "output (Dense) (None, 10) 1290 \n", "=================================================================\n", "Total params: 357,514\n", "Trainable params: 357,514\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "# Build model\n", "inputs = Input(shape=input_shape)\n", "conv = Conv2D(32, (7, 7), strides=(1, 1), padding='same', name = \"conv1\", activation='relu')(inputs)\n", "conv = AveragePooling2D((3, 3))(conv)\n", "conv = Conv2D(64, (3, 3), strides=(1, 1), padding='same', name = \"conv2\", activation='relu')(conv)\n", "conv = AveragePooling2D((2, 2))(conv)\n", "conv = Conv2D(128, (3, 3), strides=(1, 1), padding='same', name = \"conv3\", activation='relu')(conv)\n", "flat = Flatten()(conv)\n", "dense = Dense(128, activation='relu', name = \"dense2\")(flat)\n", "dropout = Dropout(0.4)(dense)\n", "outputs = Dense(num_classes, activation='softmax', name = \"output\")(dropout)\n", "\n", "model = Model(inputs=inputs, outputs=outputs)\n", "\n", "model.summary()\n", "model.compile(loss=keras.losses.categorical_crossentropy,\n", " optimizer=keras.optimizers.Nadam(),\n", " metrics=['accuracy'])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 60000 samples, validate on 5000 samples\n", "Epoch 1/50\n", "7s - loss: 0.7582 - acc: 0.7600 - val_loss: 0.1943 - val_acc: 0.9414\n", "Epoch 2/50\n", "6s - loss: 0.1585 - acc: 0.9530 - val_loss: 0.0719 - val_acc: 0.9766\n", "Epoch 3/50\n", "6s - loss: 0.0875 - acc: 0.9744 - val_loss: 0.0515 - val_acc: 0.9822\n", "Epoch 4/50\n", "6s - loss: 0.0757 - acc: 0.9775 - val_loss: 0.0425 - val_acc: 0.9862\n", "Epoch 5/50\n", "6s - loss: 0.0563 - acc: 0.9836 - val_loss: 0.0392 - val_acc: 0.9878\n", "Epoch 6/50\n", "6s - loss: 0.0457 - acc: 0.9863 - val_loss: 0.0336 - val_acc: 0.9886\n", "Epoch 7/50\n", "6s - loss: 0.0402 - acc: 0.9876 - val_loss: 0.0359 - val_acc: 0.9874\n", "Epoch 8/50\n", "6s - loss: 0.0360 - acc: 0.9889 - val_loss: 0.0330 - val_acc: 0.9896\n", "Epoch 9/50\n", "6s - loss: 0.0316 - acc: 0.9902 - val_loss: 0.0275 - val_acc: 0.9912\n", "Epoch 10/50\n", "6s - loss: 0.0280 - acc: 0.9917 - val_loss: 0.0307 - val_acc: 0.9894\n", "Epoch 11/50\n", "6s - loss: 0.0249 - acc: 0.9923 - val_loss: 0.0232 - val_acc: 0.9922\n", "Epoch 12/50\n", "6s - loss: 0.0226 - acc: 0.9931 - val_loss: 0.0222 - val_acc: 0.9924\n", "Epoch 13/50\n", "6s - loss: 0.0213 - acc: 0.9934 - val_loss: 0.0211 - val_acc: 0.9932\n", "Epoch 14/50\n", "6s - loss: 0.0183 - acc: 0.9945 - val_loss: 0.0298 - val_acc: 0.9914\n", "Epoch 15/50\n", "6s - loss: 0.0176 - acc: 0.9944 - val_loss: 0.0240 - val_acc: 0.9930\n", "Epoch 16/50\n", "6s - loss: 0.0150 - acc: 0.9953 - val_loss: 0.0232 - val_acc: 0.9940\n", "Epoch 17/50\n", "6s - loss: 0.0140 - acc: 0.9955 - val_loss: 0.0251 - val_acc: 0.9918\n", "Epoch 18/50\n", "6s - loss: 0.0143 - acc: 0.9955 - val_loss: 0.0226 - val_acc: 0.9930\n", "Epoch 19/50\n", "6s - loss: 0.0125 - acc: 0.9961 - val_loss: 0.0282 - val_acc: 0.9914\n", "Epoch 20/50\n", "6s - loss: 0.0110 - acc: 0.9968 - val_loss: 0.0237 - val_acc: 0.9928\n", "Epoch 21/50\n", "6s - loss: 0.0107 - acc: 0.9964 - val_loss: 0.0256 - val_acc: 0.9928\n", "Epoch 22/50\n", "6s - loss: 0.0086 - acc: 0.9973 - val_loss: 0.0258 - val_acc: 0.9934\n", "Epoch 23/50\n", "6s - loss: 0.0090 - acc: 0.9972 - val_loss: 0.0228 - val_acc: 0.9936\n", "Epoch 24/50\n", "6s - loss: 0.0094 - acc: 0.9969 - val_loss: 0.0241 - val_acc: 0.9940\n", "Epoch 25/50\n", "6s - loss: 0.0086 - acc: 0.9973 - val_loss: 0.0238 - val_acc: 0.9938\n", "Epoch 26/50\n", "6s - loss: 0.0084 - acc: 0.9973 - val_loss: 0.0252 - val_acc: 0.9930\n", "Epoch 27/50\n", "6s - loss: 0.0082 - acc: 0.9974 - val_loss: 0.0218 - val_acc: 0.9940\n", "Epoch 28/50\n", "6s - loss: 0.0077 - acc: 0.9975 - val_loss: 0.0208 - val_acc: 0.9946\n", "Epoch 29/50\n", "6s - loss: 0.0076 - acc: 0.9976 - val_loss: 0.0224 - val_acc: 0.9944\n", "Epoch 30/50\n", "6s - loss: 0.0070 - acc: 0.9978 - val_loss: 0.0226 - val_acc: 0.9942\n", "Epoch 31/50\n", "6s - loss: 0.0065 - acc: 0.9980 - val_loss: 0.0287 - val_acc: 0.9932\n", "Epoch 32/50\n", "6s - loss: 0.0068 - acc: 0.9978 - val_loss: 0.0258 - val_acc: 0.9936\n", "Epoch 33/50\n", "6s - loss: 0.0056 - acc: 0.9982 - val_loss: 0.0225 - val_acc: 0.9940\n", "Epoch 34/50\n", "6s - loss: 0.0060 - acc: 0.9978 - val_loss: 0.0216 - val_acc: 0.9950\n", "Epoch 35/50\n", "6s - loss: 0.0058 - acc: 0.9983 - val_loss: 0.0269 - val_acc: 0.9946\n", "Epoch 36/50\n", "6s - loss: 0.0050 - acc: 0.9984 - val_loss: 0.0260 - val_acc: 0.9942\n", "Epoch 37/50\n", "6s - loss: 0.0054 - acc: 0.9983 - val_loss: 0.0212 - val_acc: 0.9954\n", "Epoch 38/50\n", "6s - loss: 0.0057 - acc: 0.9982 - val_loss: 0.0221 - val_acc: 0.9944\n", "Epoch 39/50\n", "6s - loss: 0.0049 - acc: 0.9984 - val_loss: 0.0271 - val_acc: 0.9934\n", "Epoch 40/50\n", "6s - loss: 0.0059 - acc: 0.9979 - val_loss: 0.0273 - val_acc: 0.9930\n", "Epoch 41/50\n", "6s - loss: 0.0063 - acc: 0.9980 - val_loss: 0.0216 - val_acc: 0.9946\n", "Epoch 42/50\n", "6s - loss: 0.0044 - acc: 0.9986 - val_loss: 0.0243 - val_acc: 0.9936\n", "Epoch 43/50\n", "6s - loss: 0.0041 - acc: 0.9987 - val_loss: 0.0264 - val_acc: 0.9950\n", "Epoch 44/50\n", "6s - loss: 0.0040 - acc: 0.9989 - val_loss: 0.0252 - val_acc: 0.9952\n", "Epoch 45/50\n", "6s - loss: 0.0040 - acc: 0.9989 - val_loss: 0.0291 - val_acc: 0.9940\n", "Epoch 46/50\n", "6s - loss: 0.0052 - acc: 0.9985 - val_loss: 0.0273 - val_acc: 0.9932\n", "Epoch 47/50\n", "6s - loss: 0.0038 - acc: 0.9989 - val_loss: 0.0222 - val_acc: 0.9954\n", "Epoch 48/50\n", "6s - loss: 0.0047 - acc: 0.9987 - val_loss: 0.0324 - val_acc: 0.9936\n", "Epoch 49/50\n", "6s - loss: 0.0046 - acc: 0.9986 - val_loss: 0.0284 - val_acc: 0.9940\n", "Epoch 50/50\n", "6s - loss: 0.0048 - acc: 0.9986 - val_loss: 0.0292 - val_acc: 0.9932\n" ] } ], "source": [ "batch_size = 1000\n", "epochs = 50\n", "history = model.fit(train_images, train_labels,\n", " batch_size=batch_size,\n", " epochs=epochs,\n", " verbose=2,\n", " validation_data=(X_val, y_val))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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/1szuNrPHzOxeM+uKbXuPmW2MHu/Zl889UFn1hhIRGaPWzP/LwJXARjP7vJm9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\ncj8hA1nM9D9vd/eB6GU6ejj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"text/plain": [ "<matplotlib.figure.Figure at 0x7ffb3c526cf8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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BtDk706eyftJb2Dn7SlKnX8KMyRM4paWBU1ryTGzMjOxMqK59sPZH8Nx9sPUp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\nbmb/v5mtNrNfmFlrtO4iM3v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"text/plain": [ "<matplotlib.figure.Figure at 0x7ffb384f6278>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# summarize history for accuracy\n", "plt.plot(history.history['acc'])\n", "plt.plot(history.history['val_acc'])\n", "plt.title('model accuracy')\n", "plt.ylabel('accuracy')\n", "plt.xlabel('epoch')\n", "plt.legend(['train', 'test'], loc='upper left')\n", "plt.show()\n", "# summarize history for loss\n", "plt.plot(history.history['loss'])\n", "plt.plot(history.history['val_loss'])\n", "plt.title('model loss')\n", "plt.ylabel('loss')\n", "plt.xlabel('epoch')\n", "plt.legend(['train', 'test'], loc='upper left')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5000/5000 [==============================] - 0s \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n", "Test loss: 0.0279825121164\n", "Test accuracy: 0.99279999733\n" ] } ], "source": [ "score = model.evaluate(X_test, y_test, verbose=1, batch_size=batch_size)\n", "print('Test loss:', score[0])\n", "print('Test accuracy:', score[1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": 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": 2 }	apache-2.0
jakehanson/Random-Walk-Simulation	Event Based Animation.ipynb	1	7100	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GEOMETRY PARAMS--------------------------------------------------\n", "\tBox Radius: 2\n", "\tAperture Size: 1\n", "\tNumber of Ants: 20\n", "\tEncounter Radius: 0.1\n", "\tVelocity: 0.1\n", "Collisions Conserve Momentum\n", "\tCollisions On.\n", "\tStart Ant 0 in Center.\n", "\tTrial Ends if Ant 0 Leaves\n", "SIMULATION PARAMS------------------------------------------------\n", "\tNumber of Events: 162\n", "\tTotal Time: 113.03\n", "\tMinimum Time Between Events: 0.003\n", "ANIMATION PARAMS-------------------------------------------------\n", "\tList of Colored Ants: [0]\n", "\tOutput Every: 1\n", "\tNumber of Frames: 162\n", "\tPlot Directory: /Users/jakehanson/Documents/Ants/plots/\n", "\tAnimation Output: /Users/jakehanson/Desktop/sim.gif\n", "RUNNING ANIMATION------------------------------------------------\n", "RUNNING CONVERSION-----------------------------------------------\n", "DONE!\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "%matplotlib inline\n", "\n", "# Get Geometric Info\n", "print 'GEOMETRY PARAMS--------------------------------------------------'\n", "params = pd.read_csv('params.txt',sep='\\t')\n", "R = params.R[0]\n", "a = params.a[0]\n", "velo = params.velocity[0]\n", "r_enc = params.r_enc[0]\n", "N_ants = params.num_ants[0]\n", "coll_flag = params.collision_flag[0]\n", "center_flag = params.center_flag[0]\n", "exit_flag = params.exit_flag[0]\n", "fixed_velo = params.fixed_velo[0]\n", "print '\\tBox Radius: ',R\n", "print '\\tAperture Size: ',a\n", "print '\\tNumber of Ants: ',N_ants\n", "print '\\tEncounter Radius: ',r_enc\n", "print '\\tVelocity: ',velo\n", "if coll_flag == 1:\n", " print '\\tCollisions On.'\n", "else:\n", " print '\\tCollisions Off.'\n", "if fixed_velo == 1:\n", " print '\\tFixed Velocity Collisions'\n", "else:\n", " print '\\tCollisions Conserve Momentum'\n", "if center_flag == 1:\n", " print '\\tStart Ant 0 in Center.'\n", "else:\n", " print '\\tStart Ant in 0 Center = False'\n", "if exit_flag == 1:\n", " print '\\tTrial Ends if Ant 0 Leaves'\n", "else:\n", " print '\\tTrial Ends if Ant 0 Leaves = False'\n", " \n", "## Get Simulation Info\n", "print 'SIMULATION PARAMS------------------------------------------------'\n", "data = pd.read_csv('output.txt',sep='\\t')\n", "uniq_time = np.unique(data.event_time)\n", "t_max = uniq_time[-1]\n", "t_min = uniq_time[1]-uniq_time[0]\n", "for i in range(np.size(uniq_time)-1):\n", " delta_t = uniq_time[i+1] - uniq_time[i]\n", " if delta_t < t_min:\n", " t_min = delta_t # Store the minimum time between events and use that as the resolution\n", "N_events = data.shape[0]/N_ants\n", "print '\\tNumber of Events: ',N_events\n", "print '\\tTotal Time: ',t_max\n", "print '\\tMinimum Time Between Events: ',t_min\n", "\n", "## Animation Parameters\n", "print 'ANIMATION PARAMS-------------------------------------------------'\n", "color1_ants = [0] # list of ants to be colored with a different color\n", "color1 = 'r' # choose a color for unique ants\n", "color2 = 'k' # choose a color for unique ants\n", "output_every = 1\n", "N_frames = N_events/output_every\n", "plot_dir = '/Users/jakehanson/Documents/Ants/plots/'\n", "output_path = '/Users/jakehanson/Desktop/sim.gif'\n", "print '\\tList of Colored Ants: ',color1_ants\n", "print '\\tOutput Every: ',output_every\n", "print '\\tNumber of Frames: ',N_frames\n", "print '\\tPlot Directory: ',plot_dir\n", "print '\\tAnimation Output: ',output_path\n", "\n", "## Generate Disk for Animation\n", "N_points = 100\n", "x_disk = []\n", "y_disk = []\n", "theta_crit = float(a)/R\n", "for i in range(N_points+1):\n", " x = Rnp.cos((np.pi+theta_crit)/2+(2np.pi-theta_crit)i/N_points)\n", " y = Rnp.sin((np.pi+theta_crit)/2+(2np.pi-theta_crit)i/N_points)\n", " x_disk.append(x)\n", " y_disk.append(y)\n", "\n", " \n", "## GENERATE ANIMATION\n", "print 'RUNNING ANIMATION------------------------------------------------'\n", "!rm $plot_dir/\n", "counter = 0\n", "x_positions = []\n", "y_positions = []\n", "colorz = [] # array to store ant colors\n", "\n", "for index in data.index:\n", " \n", " if data['in_nest'][index] == 1:\n", " if data['Name'][index] in color1_ants:\n", " colorz.append(color1)\n", " else:\n", " colorz.append(color2)\n", " x_positions.append(data.x[index])\n", " y_positions.append(data.y[index])\n", "\n", " if (index+1) % N_ants == 0:\n", " #print 'Counter {:d}/{:d}'.format(counter,N_frames)\n", " #print '\\tTime: {:7.6f}\\n\\tAnts in Nest: {:d}'.format(data.event_time[index],np.size(x_positions))\n", " \n", " plt.plot(x_disk,y_disk,color='k',linestyle='-',linewidth=2)\n", " plt.axis('off')\n", " plt.title('Time: {:5.4f}'.format(data.event_time[index]))\n", " #plt.title('R={:2.1f}, a={:2.1f}, r_encounter={:2.1f}'.format(R,a,r_enc))\n", " plt.scatter(x_positions,y_positions,s=100,color=colorz,alpha=0.7)\n", " plt.xlim((-R-r_enc,R+r_enc))\n", " plt.ylim((-R-r_enc,R+r_enc))\n", " plt.axes().set_aspect('equal', 'datalim')\n", " fname = ('plot' + '_%06d.png' % (counter)) # assign filename\n", " plt.savefig(plot_dir+fname)\n", " plt.clf()\n", " \n", " # Reset arrays\n", " counter = counter + 1\n", " x_positions = []\n", " y_positions = []\n", " colorz = []\n", "\n", "plt.close()\n", "print 'RUNNING CONVERSION-----------------------------------------------'\n", "!convert $plot_dir/ $output_path\n", "print 'DONE!'" ] }, { "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": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
robblack007/clase-cinematica-robot	Practicas/practica5/Problemas.ipynb	1	8664	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Problemas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Defina una función que obtenga la cinemática inversa de un pendulo doble." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": false, "nbgrader": { "checksum": "9c652e1e13046db6c793cee5954f8a9a", "grade": false, "grade_id": "cell-f163fd7318712300", "locked": false, "schema_version": 1, "solution": true } }, "outputs": [], "source": [ "def ci_pendulo_doble(x, y):\n", " # tome en cuenta que las longitudes de los eslabones son 2 y 2\n", " l1, l2 = 2, 2\n", " from numpy import arccos, arctan2, sqrt\n", " # YOUR CODE HERE\n", " raise NotImplementedError()\n", " return q1, q2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": false, "editable": false, "nbgrader": { "checksum": "1375c3944280b1ef38830a23b226b157", "grade": true, "grade_id": "cell-3d10c51b1dba5e80", "locked": true, "points": 2, "schema_version": 1, "solution": false } }, "outputs": [], "source": [ "from numpy.testing import assert_allclose\n", "assert_allclose(ci_pendulo_doble(4, 0), (0,0))\n", "assert_allclose(ci_pendulo_doble(0, 4), (1.57079632,0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Obtenga las posiciones en el espacio articular, $q_1$ y $q_2$, necesarias para que el punto final del pendulo doble llegue a las coordenadas $p_1 = (0,1)$, $p_2 = (1,3)$ y $p_3 = (3,2)$." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": false, "nbgrader": { "checksum": "d1a632175b4e1ce5b17415f5c3d05cce", "grade": false, "grade_id": "cell-382c0afd3a87e675", "locked": false, "schema_version": 1, "solution": true } }, "outputs": [], "source": [ "# YOUR CODE HERE\n", "raise NotImplementedError()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": false, "editable": false, "nbgrader": { "checksum": "6ab2924ae9f8bc975bf9f6a2ad03d7c2", "grade": true, "grade_id": "cell-8ea816fa511401d3", "locked": true, "points": 1, "schema_version": 1, "solution": false } }, "outputs": [], "source": [ "from numpy.testing import assert_allclose\n", "assert_allclose((q11, q21),(0.25268 , 2.636232), rtol=1e-05, atol=1e-05)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": false, "editable": false, "nbgrader": { "checksum": "7df55f8102c64c925f075624b02deae2", "grade": true, "grade_id": "cell-5cfff476c8df261c", "locked": true, "points": 1, "schema_version": 1, "solution": false } }, "outputs": [], "source": [ "from numpy.testing import assert_allclose\n", "assert_allclose((q12, q22),(0.589988, 1.318116), rtol=1e-05, atol=1e-05)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": false, "editable": false, "nbgrader": { "checksum": "b298f3fd943cfa81647498e5149b3d3d", "grade": true, "grade_id": "cell-22a0fbdc151777fd", "locked": true, "points": 1, "schema_version": 1, "solution": false } }, "outputs": [], "source": [ "from numpy.testing import assert_allclose\n", "assert_allclose((q13, q23),(0.14017 , 0.895665), rtol=1e-05, atol=1e-05)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Genere las trayectorias necesarias para que el pendulo doble se mueva del punto $p_1$ al punto $p_2$ en $2s$, del punto $p_2$ al punto $p_3$ en $2s$ y del punto $p_3$ al punto $p_1$ en $2s$.\n", "> Utiliza 100 puntos por segundo y asegurate de guardar las trayectorias generadas en las variables correctas para que ```q1s``` y ```q2s``` tengan las trayectorias completas." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": false, "nbgrader": { "checksum": "f996d8a751f3cc1e068ddd853414a0f1", "grade": false, "grade_id": "cell-07a4748edaf7fb89", "locked": false, "schema_version": 1, "solution": true } }, "outputs": [], "source": [ "from generacion_trayectorias import grafica_trayectoria\n", "# YOUR CODE HERE\n", "raise NotImplementedError()\n", "q1s = q1s1 + q1s2 + q1s3\n", "q2s = q2s1 + q2s2 + q2s3" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": false, "editable": false, "nbgrader": { "checksum": "201b2703295dacfffc71e6b52a45282b", "grade": true, "grade_id": "cell-93e4554052ececab", "locked": true, "points": 1, "schema_version": 1, "solution": false } }, "outputs": [], "source": [ "from numpy.testing import assert_allclose\n", "assert_allclose((q1s[0], q1s[-1]),(0.25268, 0.25268), rtol=1e-05, atol=1e-05)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": false, "editable": false, "nbgrader": { "checksum": "f29e537248bdb57f183e0246ce96cbe7", "grade": true, "grade_id": "cell-5a4e73c0366f29a6", "locked": true, "points": 1, "schema_version": 1, "solution": false } }, "outputs": [], "source": [ "from numpy.testing import assert_allclose\n", "assert_allclose((q2s[0], q2s[-1]),(2.636232, 2.636232), rtol=1e-05, atol=1e-05)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cree una animación con las trayectorias generadas y las funciones proporcionadas a continuación (algunas funciones estan marcadas con comentarios en donde hace falta agregar código)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": false, "nbgrader": { "checksum": "d1451b67b316857254052b706defcae2", "grade": false, "grade_id": "cell-a2efbe6a9e13f855", "locked": false, "schema_version": 1, "solution": true } }, "outputs": [], "source": [ "from matplotlib.pyplot import figure, style\n", "from matplotlib import animation, rc\n", "rc('animation', html='html5')\n", "from numpy import sin, cos, arange\n", "\n", "fig = figure(figsize=(8, 8))\n", "axi = fig.add_subplot(111, autoscale_on=False, xlim=(-0.6, 3.1), ylim=(-0.6, 3.1))\n", "linea, = axi.plot([], [], \"-o\", lw=2, color='gray')\n", "\n", "def cd_pendulo_doble(q1, q2):\n", " l1, l2 = 2, 2\n", " # YOUR CODE HERE\n", " raise NotImplementedError()\n", " return xs, ys\n", "\n", "def inicializacion():\n", " '''Esta funcion se ejecuta una sola vez y sirve para inicializar el sistema'''\n", " linea.set_data([], [])\n", " return linea\n", "\n", "def animacion(i):\n", " '''Esta funcion se ejecuta para cada cuadro del GIF'''\n", " # YOUR CODE HERE\n", " raise NotImplementedError()\n", " linea.set_data(xs, ys)\n", " \n", " return linea\n", "\n", "ani = animation.FuncAnimation(fig, animacion, arange(1, len(q1s)), interval=10, init_func=inicializacion)\n", "ani" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": false, "editable": false, "nbgrader": { "checksum": "d87ed6c04bdc796e1d7f237f69dab7a8", "grade": true, "grade_id": "cell-7ce827e6dd880efc", "locked": true, "points": 3, "schema_version": 1, "solution": false } }, "outputs": [], "source": [ "from numpy.testing import assert_allclose\n", "assert_allclose(cd_pendulo_doble(0, 0), ([0,2,4], [0,0,0]), rtol=1e-05, atol=1e-05)\n", "assert_allclose(cd_pendulo_doble(1.57079632,0), ([0, 0, 0],[0, 2, 4]), rtol=1e-05, atol=1e-05)" ] } ], "metadata": { "css": [ "" ], "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": 1 }	mit
minh5/cpsc	reports/api data.ipynb	1	48917	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook serves as a reporting tool for the CPSC. In this notebook, I laid out the questions CPSC is interested in learning from their SaferProduct API. The format will be that there are a few questions presented and each question will have a `findings` section where there is a quick summary of the findings while in Section 4, there will be further information on how on the findings were conducted." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given that the API was down during this time of the reporting, I obtained data from Ana Carolina Areias via Dropbox link. Here I cleaned up the pure JSON format and converted it into a dataframe (the cleaning code can be found in the `exploratory.ipynb` in the `/notebook` directory. After that I saved the data using pickle where I can easily load it up for analysis.\n", "\n", "The questions answered here are the result of a conversation between DKDC and the CPSC regarding their priorities and what information is available from the data.\n", "\n", "The main takeaways from this analysis is that:\n", "\n", " * From the self-reported statistics of people who reported their injury to the API, it appears that there is a skew against people who are older. The data shows that people who are reporting are 40-60 years old.\n", " * An overwhelming amount of reports did not involve bodily harm or require medical attention; much of the reports were just incident reports with a particular product\n", " * Out of the reports that resulted in some harm, the most reported product was in the footwear category regarding some harm and discomfort with walking with the Sketchers Tone-Ups shoes\n", " * Although not conclusive, but from the reports, there appears to be come indication that there are a lot of fire-related incidents from a cursory examination of the most popular words" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "ExecuteTime": { "end_time": "2016-10-03T23:26:17.894053", "start_time": "2016-10-03T23:26:17.890087" }, "collapsed": false }, "outputs": [], "source": [ "import pickle\n", "import operator\n", "\n", "import numpy as np\n", "import pandas as pd \n", "import gensim.models" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2016-09-29T15:11:29.240562", "start_time": "2016-09-29T15:11:27.713079" }, "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>AnswerExplanation</th>\n", " <th>CompanyCommentsExpanded</th>\n", " <th>IncidentDate</th>\n", " <th>IncidentDescription</th>\n", " <th>IncidentProductDescription</th>\n", " <th>IncidentReportDate</th>\n", " <th>IncidentReportId</th>\n", " <th>IncidentReportNumber</th>\n", " <th>IncidentReportPublicationDate</th>\n", " <th>LocaleId</th>\n", " <th>...</th>\n", " <th>__metadata</th>\n", " <th>LocaleDescription</th>\n", " <th>LocalePublicName</th>\n", " <th>GenderDescription</th>\n", " <th>GenderId</th>\n", " <th>GenderPublicName</th>\n", " <th>ProductCategoryDescription</th>\n", " <th>ProductCategoryPublicName</th>\n", " <th>SeverityTypeDescription</th>\n", " <th>SeverityTypePublicName</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>None</td>\n", " <td>Helen of Troy acknowledges receipt of the subm...</td>\n", " <td>/Date(1297036800000)/</td>\n", " <td>Using the Revlon rv050 curling iron and came h...</td>\n", " <td>rv050 curling iron</td>\n", " <td>/Date(1299801600000)/</td>\n", " <td>1170172</td>\n", " <td>20110311-B3E19-2147481666</td>\n", " <td>/Date(1301709487243)/</td>\n", " <td>-1</td>\n", " <td>...</td>\n", " <td>{u'type': u'CPSRMS_PUBModel.IncidentDetail', u...</td>\n", " <td>Unspecified</td>\n", " <td>Unspecified</td>\n", " <td>Missing</td>\n", " <td>Missing</td>\n", " <td>Missing</td>\n", " <td>Hair Curlers, Curling Irons, Clips & Hairpins</td>\n", " <td>Hair Curlers, Curling Irons, Clips & Hairpins</td>\n", " <td>Missing</td>\n", " <td>Missing</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>/Date(881884800000)/</td>\n", " <td>On December 12th 1997, I found my son, Tyler J...</td>\n", " <td>ChildCraft drop side crib, oak</td>\n", " <td>/Date(1299801600000)/</td>\n", " <td>1170340</td>\n", " <td>20110311-CFE0F-2147481661</td>\n", " <td>/Date(1301674156573)/</td>\n", " <td>1</td>\n", " <td>...</td>\n", " <td>{u'type': u'CPSRMS_PUBModel.IncidentDetail', u...</td>\n", " <td>Home/Apartment/Condominium</td>\n", " <td>Home/Apartment/Condominium</td>\n", " <td>Male</td>\n", " <td>2</td>\n", " <td>Male</td>\n", " <td>Cribs</td>\n", " <td>Cribs</td>\n", " <td>Death</td>\n", " <td>Death</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>I have contacted the manufacturer several time...</td>\n", " <td>None</td>\n", " <td>/Date(1294876800000)/</td>\n", " <td>I have a Frigidaire electric range that comes ...</td>\n", " <td>Electric Smoothtop Range</td>\n", " <td>/Date(1299801600000)/</td>\n", " <td>1170342</td>\n", " <td>20110311-CFAB7-2147481658</td>\n", " <td>/Date(1302051235430)/</td>\n", " <td>-1</td>\n", " <td>...</td>\n", " <td>{u'type': u'CPSRMS_PUBModel.IncidentDetail', u...</td>\n", " <td>Unspecified</td>\n", " <td>Unspecified</td>\n", " <td>Missing</td>\n", " <td>Missing</td>\n", " <td>Missing</td>\n", " <td>Electric Ranges or Ovens (Excl Counter-top Ovens)</td>\n", " <td>Electric Ranges or Ovens (Excl Counter-top Ovens)</td>\n", " <td>Missing</td>\n", " <td>Missing</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Sears wants $500 to fix this product. It is a...</td>\n", " <td>Sears Holdings takes product safety issues ver...</td>\n", " <td>/Date(1299801600000)/</td>\n", " <td>Kenmore Elite Model #795.77543600\\r\\n\\r\\nThe l...</td>\n", " <td>Kenmore Elite Trio 25Cubic Feet Bottom Freezer...</td>\n", " <td>/Date(1299801600000)/</td>\n", " <td>1170344</td>\n", " <td>20110311-EFD8B-2147481655</td>\n", " <td>/Date(1301709795607)/</td>\n", " <td>1</td>\n", " <td>...</td>\n", " <td>{u'type': u'CPSRMS_PUBModel.IncidentDetail', u...</td>\n", " <td>Home/Apartment/Condominium</td>\n", " <td>Home/Apartment/Condominium</td>\n", " <td>Male</td>\n", " <td>2</td>\n", " <td>Male</td>\n", " <td>Refrigerators</td>\n", " <td>Refrigerators</td>\n", " <td>Received care that did not involve medical per...</td>\n", " <td>First Aid Received by Non-Medical Professional</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>I will be writing a letter to the company foll...</td>\n", " <td>Thank you for sharing your experience with us ...</td>\n", " <td>/Date(1299456000000)/</td>\n", " <td>Since he was born two months ago, we have been...</td>\n", " <td>Pampers Swaddlers New Baby with Dry Max, Size 1-2</td>\n", " <td>/Date(1299801600000)/</td>\n", " <td>1170347</td>\n", " <td>20110311-DBB63-2147481650</td>\n", " <td>/Date(1301661238360)/</td>\n", " <td>1</td>\n", " <td>...</td>\n", " <td>{u'type': u'CPSRMS_PUBModel.IncidentDetail', u...</td>\n", " <td>Home/Apartment/Condominium</td>\n", " <td>Home/Apartment/Condominium</td>\n", " <td>Male</td>\n", " <td>2</td>\n", " <td>Male</td>\n", " <td>Diapers</td>\n", " <td>Diapers</td>\n", " <td>Received care that did not involve medical per...</td>\n", " <td>First Aid Received by Non-Medical Professional</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 42 columns</p>\n", "</div>" ], "text/plain": [ " AnswerExplanation \\\n", "0 None \n", "1 None \n", "2 I have contacted the manufacturer several time... \n", "3 Sears wants $500 to fix this product. It is a... \n", "4 I will be writing a letter to the company foll... \n", "\n", " CompanyCommentsExpanded IncidentDate \\\n", "0 Helen of Troy acknowledges receipt of the subm... /Date(1297036800000)/ \n", "1 None /Date(881884800000)/ \n", "2 None /Date(1294876800000)/ \n", "3 Sears Holdings takes product safety issues ver... /Date(1299801600000)/ \n", "4 Thank you for sharing your experience with us ... /Date(1299456000000)/ \n", "\n", " IncidentDescription \\\n", "0 Using the Revlon rv050 curling iron and came h... \n", "1 On December 12th 1997, I found my son, Tyler J... \n", "2 I have a Frigidaire electric range that comes ... \n", "3 Kenmore Elite Model #795.77543600\\r\\n\\r\\nThe l... \n", "4 Since he was born two months ago, we have been... \n", "\n", " IncidentProductDescription IncidentReportDate \\\n", "0 rv050 curling iron /Date(1299801600000)/ \n", "1 ChildCraft drop side crib, oak /Date(1299801600000)/ \n", "2 Electric Smoothtop Range /Date(1299801600000)/ \n", "3 Kenmore Elite Trio 25Cubic Feet Bottom Freezer... /Date(1299801600000)/ \n", "4 Pampers Swaddlers New Baby with Dry Max, Size 1-2 /Date(1299801600000)/ \n", "\n", " IncidentReportId IncidentReportNumber IncidentReportPublicationDate \\\n", "0 1170172 20110311-B3E19-2147481666 /Date(1301709487243)/ \n", "1 1170340 20110311-CFE0F-2147481661 /Date(1301674156573)/ \n", "2 1170342 20110311-CFAB7-2147481658 /Date(1302051235430)/ \n", "3 1170344 20110311-EFD8B-2147481655 /Date(1301709795607)/ \n", "4 1170347 20110311-DBB63-2147481650 /Date(1301661238360)/ \n", "\n", " LocaleId ... \\\n", "0 -1 ... \n", "1 1 ... \n", "2 -1 ... \n", "3 1 ... \n", "4 1 ... \n", "\n", " __metadata \\\n", "0 {u'type': u'CPSRMS_PUBModel.IncidentDetail', u... \n", "1 {u'type': u'CPSRMS_PUBModel.IncidentDetail', u... \n", "2 {u'type': u'CPSRMS_PUBModel.IncidentDetail', u... \n", "3 {u'type': u'CPSRMS_PUBModel.IncidentDetail', u... \n", "4 {u'type': u'CPSRMS_PUBModel.IncidentDetail', u... \n", "\n", " LocaleDescription LocalePublicName GenderDescription \\\n", "0 Unspecified Unspecified Missing \n", "1 Home/Apartment/Condominium Home/Apartment/Condominium Male \n", "2 Unspecified Unspecified Missing \n", "3 Home/Apartment/Condominium Home/Apartment/Condominium Male \n", "4 Home/Apartment/Condominium Home/Apartment/Condominium Male \n", "\n", " GenderId GenderPublicName \\\n", "0 Missing Missing \n", "1 2 Male \n", "2 Missing Missing \n", "3 2 Male \n", "4 2 Male \n", "\n", " ProductCategoryDescription \\\n", "0 Hair Curlers, Curling Irons, Clips & Hairpins \n", "1 Cribs \n", "2 Electric Ranges or Ovens (Excl Counter-top Ovens) \n", "3 Refrigerators \n", "4 Diapers \n", "\n", " ProductCategoryPublicName \\\n", "0 Hair Curlers, Curling Irons, Clips & Hairpins \n", "1 Cribs \n", "2 Electric Ranges or Ovens (Excl Counter-top Ovens) \n", "3 Refrigerators \n", "4 Diapers \n", "\n", " SeverityTypeDescription \\\n", "0 Missing \n", "1 Death \n", "2 Missing \n", "3 Received care that did not involve medical per... \n", "4 Received care that did not involve medical per... \n", "\n", " SeverityTypePublicName \n", "0 Missing \n", "1 Death \n", "2 Missing \n", "3 First Aid Received by Non-Medical Professional \n", "4 First Aid Received by Non-Medical Professional \n", "\n", "[5 rows x 42 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pickle.load(open('/home/datauser/cpsc/data/processed/cleaned_api_data', 'rb'))\n", "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Are there certain populations we're not getting reports from? \n", "\n", "We can create a basic cross tab between age and gender to see if there are any patterns that emerges." ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "ExecuteTime": { "end_time": "2016-09-29T16:24:31.125740", "start_time": "2016-09-29T16:24:31.075941" }, "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>age_range</th>\n", " <th>under 10</th>\n", " <th>10-20</th>\n", " <th>20-30</th>\n", " <th>30-40</th>\n", " <th>40-50</th>\n", " <th>50-60</th>\n", " <th>60-70</th>\n", " <th>70-80</th>\n", " <th>80-90</th>\n", " <th>90-100</th>\n", " <th>over 100</th>\n", " </tr>\n", " <tr>\n", " <th>GenderDescription</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></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>Female</th>\n", " <td>1339</td>\n", " <td>289</td>\n", " <td>613</td>\n", " <td>1179</td>\n", " <td>1523</td>\n", " <td>1485</td>\n", " <td>899</td>\n", " <td>240</td>\n", " <td>71</td>\n", " <td>7</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>Male</th>\n", " <td>1347</td>\n", " <td>324</td>\n", " <td>450</td>\n", " <td>902</td>\n", " <td>1213</td>\n", " <td>1328</td>\n", " <td>1030</td>\n", " <td>271</td>\n", " <td>49</td>\n", " <td>4</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>Missing</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>Unknown</th>\n", " <td>23</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>6</td>\n", " <td>9</td>\n", " <td>5</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>Unspecified</th>\n", " <td>50</td>\n", " <td>0</td>\n", " <td>10</td>\n", " <td>14</td>\n", " <td>25</td>\n", " <td>26</td>\n", " <td>21</td>\n", " <td>11</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "age_range under 10 10-20 20-30 30-40 40-50 50-60 60-70 70-80 \\\n", "GenderDescription \n", "Female 1339 289 613 1179 1523 1485 899 240 \n", "Male 1347 324 450 902 1213 1328 1030 271 \n", "Missing 0 0 0 0 0 0 0 0 \n", "Unknown 23 0 1 1 6 9 5 2 \n", "Unspecified 50 0 10 14 25 26 21 11 \n", "\n", "age_range 80-90 90-100 over 100 \n", "GenderDescription \n", "Female 71 7 1 \n", "Male 49 4 1 \n", "Missing 0 0 0 \n", "Unknown 1 0 0 \n", "Unspecified 1 1 0 " ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.crosstab(data['GenderDescription'], data['age_range'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the data, it seems that there's not much underrepresentation by gender. There are only around a thousand less males than females in a dataset of 28,000. Age seems to be a bigger issue. There appears to be a lack of representation of older people using the API. Given that older folks may be less likely to self report, or if they wanted to self report, they may not be tech-savvy enough to use with a web interface. My assumption that people over 70 are probably experience product harm at a higher rate and are not reporting this." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## If we wanted to raise awareness about a certain tool or item, where should we focus our efforts" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "To construct this, I removed any incidents that did not cause any bodily harm and taking the top ten categories. There were several levels of severity. We can remove complaints that does not involve any physical harm. After removing these complaint, it is really interesting to see that \"Footwear\" was the product category of harm." ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "ExecuteTime": { "end_time": "2016-09-30T17:25:17.249267", "start_time": "2016-09-30T17:25:17.213546" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Footwear 774\n", "Computers (Equipment and Electronic Games) 274\n", "Diapers 156\n", "Electric Ranges or Ovens (Excl Counter-top Ovens) 134\n", "Bicycles and Accessories, (Excl.mountain or All-terrain) 108\n", "Baby Strollers 108\n", "Electric Coffee Makers or Teapots 100\n", "Cribs 94\n", "Bassinets or Cradles 88\n", "Name: ProductCategoryPublicName, dtype: int64" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#removing minor harm incidents\n", "no_injuries = ['Incident, No Injury', 'Unspecified', 'Level of care not known',\n", " 'No Incident, No Injury', 'No First Aid or Medical Attention Received']\n", "damage = data.ix[~data['SeverityTypePublicName'].isin(no_injuries), :]\n", "damage.ProductCategoryPublicName.value_counts()[0:9]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is actually preplexing, so I decided to investigate further by analyzing the complaints filed for the \"Footwear\" category. To do this, I created a Word2Vec model that uses a convolution neural network for text analysis. This process maps a word and the linguistic context it is in to be able to calculate similarity between words. The purpose of this is to find words that related to each other. Rather than doing a simple cross tab of product categories, I can ingest the complaints and map out their relationship. For instance, using the complaints that resulted in bodily harm, I found that footwear was associated with pain and walking. It seems that there is injuries related to Sketcher sneakers specifically since it was the only brand that showed up enough to be included in the model's dictionary. In fact, there was a [lawsuit](https://www.ftc.gov/news-events/press-releases/2012/05/skechers-will-pay-40-million-settle-ftc-charges-it-deceived) regarding Sketchers and their toning shoes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Are there certain complaints that people are filing? Quality issues vs injuries?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Look below, we see that a vast majority are incidents with any bodily harm. Over 60% of all complaints were categorized as Incident, No Injury." ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "ExecuteTime": { "end_time": "2016-09-30T17:27:29.425960", "start_time": "2016-09-30T17:27:29.407867" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Incident, No Injury 17916\n", "No First Aid or Medical Attention Received 2672\n", "Received care that did not involve medical personnel (doctor, nurse, Emergency Medical Technician (EMT), etc.) 2177\n", "Treated by medical personnel (doctor, nurse, etc.) in any setting except a hospital emergency department. Includes both medical (doctor's office, clinic, etc.) and non-medical (school, accident scene, etc.) settings. 1470\n", "Unspecified 1330\n", "Treated and released from a hospital emergency department 1090\n", "Level of care not known 715\n", "Admitted for hospitalization 456\n", "No Incident, No Injury 224\n", "Missing 135\n", "Death 93\n", "Name: SeverityTypeDescription, dtype: int64" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.SeverityTypeDescription.value_counts()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ " Although, while it is label to have no injury, it does not necessarily mean that we can't take precaution. What I did was take the same approach as the previous model, I subsetted the data to only complaints that had \"no injury\" and ran a model to examine words used. From the analysis, we see that the word `to`, `was`, and `it` were the top three words. At first glance, it may seem that these words are meaningless, however if we examine words that are similar to it, we can start seeing a connection.\n", " \n", " For instance, the word most closely related to \"to\" was \"unable\" and \"trying\", which conveys a sense of urgency in attempting to turn something on or off. Examining the words \"unable,\" I was able to see it was related to words such as \"attempted\" and \"disconnect.\" Further investigation lead me to find it was dealing with a switch or a plug, possibly dealing with an electrical item.\n", " \n", " A similar picture is painted when trying to examine the word \"was.\" The words that felt out of place was \"emitting\", \"extinguish,\" and \"smelled.\" It is not surprise that after a few investigations of these words, that words like \"sparks\" and \"smoke\" started popping up more. This leads me to believe that these complaints have something to do with encounters closely related to fire. \n", " \n", "So while these complaints are maybe encounters with danger, it may be worthwile to review these complaints further with an eye out for fire related injuries or products that could cause fire.\n", " " ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "ExecuteTime": { "end_time": "2016-10-03T23:47:43.569648", "start_time": "2016-10-03T23:47:43.562096" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('came', 0.524124026298523),\n", " ('emitting', 0.5203149318695068),\n", " ('examined', 0.4794829785823822),\n", " ('smelled', 0.4679615795612335),\n", " ('immediately', 0.46619912981987),\n", " ('arrived', 0.4562903642654419),\n", " ('extinguish', 0.45025503635406494),\n", " ('sounded', 0.4436829388141632),\n", " ('next', 0.4382171630859375),\n", " ('mins', 0.43233776092529297)]" ] }, "execution_count": 121, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.most_similar('was')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Who are the people who are actually reporting to us?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This question is difficult to answer because of a lack of data on the reporter. From the cross tabulation in Section 3.1, we see that the majority of our the respondents are female and the largest age group are 40-60. That is probably the best guess of who are the people who are using the API." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Conclusion\n", "\n", "This is meant to serve as a starting point on examining the API data. The main findings were that:\n", "\n", " * From the self-reported statistics of people who reported their injury to the API, it appears that there is a skew against people who are older. The data shows that people who are reporting are 40-60 years old.\n", " * An overwhelming amount of reports did not involve bodily harm or require medical attention; much of the reports were just incident reports with a particular product\n", " * Out of the reports that resulted in some harm, the most reported product was in the footwear category regarding some harm and discomfort with walking with the Sketchers Tone-Ups shoes\n", " * Although not conclusive, but from the reports, there appears to be come indication that there are a lot of fire-related incidents from a cursory examination of the most popular words\n", " \n", "While text analysis is helpful, it is often not sufficient. What would really help the analysis process would be include more information from the user. The following information would be helpful to collect to make conduct more actionable insight.\n", "\n", "* Ethnicity/Race\n", "* Self Reported Income\n", "* Geographic information\n", " * Region (Mid Atlantic, New England, etc)\n", " * Closest Metropolitan Area\n", " * State\n", " * City\n", "* Geolocation of IP address \n", " * coordinates can be \"jittered\" to conserve anonymity\n", " \n", "A great next step would be a deeper text analysis on shoes. It may be possible to train a neural network to consider smaller batches of words so we can capture the context better. Other steps that I would do if I had more time would be to find a way to fix up unicode issues with some of the complaints (there were special characters that prevented some of the complaints to be converted into strings). I would also look further into the category that had the most overall complaints: \"Electric Ranges and Stoves\" and see what the complaints were.\n", "\n", "If we could implement these challenges, there is no doubt we could gain some valuable insights on products that are harming Americans. This report serves as the first step. I would like to thank CPSC for this data set and DKDC for the opportunity to conduct this analysis." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# References" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Question 2.1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data that we worked with had limited information regarding the victim's demographics beside age and gender. However, that was enough to draw some base inferences. Below we can grab a counts of gender, which a plurality is females." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Age is a bit tricky, we have the victim's birthday in months. I converted it into years and break them down into 10 year age ranges so we can better examine the data. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2016-09-29T15:32:10.209399", "start_time": "2016-09-29T15:32:10.196306" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Female 10629\n", "Male 9489\n", "Unspecified 6068\n", "Unknown 1957\n", "Missing 135\n", "Name: GenderDescription, dtype: int64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.GenderDescription.value_counts()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "ExecuteTime": { "end_time": "2016-09-29T16:06:09.186196", "start_time": "2016-09-29T16:06:09.178811" }, "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "data['age'] = map(lambda x: x/12, data['VictimAgeInMonths'])\n", "labels = ['under 10', '10-20', '20-30', '30-40', '40-50', '50-60',\n", " '60-70','70-80', '80-90', '90-100', 'over 100']\n", "data['age_range'] = pd.cut(data['age'], bins=np.arange(0,120,10), labels=labels)\n", "data['age_range'][data['age'] > 100] = 'over 100'" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "ExecuteTime": { "end_time": "2016-09-29T16:20:52.154116", "start_time": "2016-09-29T16:20:52.142218" }, "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:2: FutureWarning: sort is deprecated, use sort_values(inplace=True) for INPLACE sorting\n", " from ipykernel import kernelapp as app\n" ] }, { "data": { "text/plain": [ "over 100 2\n", "90-100 12\n", "80-90 122\n", "70-80 524\n", "10-20 613\n", "20-30 1074\n", "60-70 1955\n", "30-40 2096\n", "under 10 2759\n", "40-50 2767\n", "50-60 2848\n", "Name: age_range, dtype: int64" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "counts = data['age_range'].value_counts()\n", "counts.sort()\n", "counts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However after doing this, we still have around 13,000 people with an age of zero, whether it is that they did not fill in the age or that the incident involves infant is still unknown but looking at the distribution betweeen of the product that are affecting people with an age of 0 and the overall dataset, it appears that null values in the age range represents people who did not fill out an age when reporting" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "ExecuteTime": { "end_time": "2016-09-29T16:14:56.003454", "start_time": "2016-09-29T16:14:55.983817" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Electric Ranges or Ovens (Excl Counter-top Ovens) 1713\n", "Dishwashers 1089\n", "Microwave Ovens 691\n", "Refrigerators 519\n", "Gas Ranges or Ovens 493\n", "Electric Coffee Makers or Teapots 317\n", "Ranges or Ovens, Not Specified 279\n", "Light Bulbs 267\n", "Washing Machines, Other or Not Specified 249\n", "Name: ProductCategoryPublicName, dtype: int64" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Top products affect by people with 0 age\n", "data.ix[data['age_range'].isnull(), 'ProductCategoryPublicName'].value_counts()[0:9]" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "ExecuteTime": { "end_time": "2016-09-29T16:16:18.430988", "start_time": "2016-09-29T16:16:18.412546" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Electric Ranges or Ovens (Excl Counter-top Ovens) 2704\n", "Dishwashers 1605\n", "Microwave Ovens 1095\n", "Footwear 949\n", "Refrigerators 888\n", "Gas Ranges or Ovens 872\n", "Computers (Equipment and Electronic Games) 838\n", "Electric Coffee Makers or Teapots 748\n", "Nonmetal Cookware (Nonelectric) 530\n", "Name: ProductCategoryPublicName, dtype: int64" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#top products that affect people overall\n", "data.ProductCategoryPublicName.value_counts()[0:9]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Question 2.2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At first glance, we can look at the products that were reported, like below. And see that Eletric Ranges or Ovens is at top in terms of harm. However, there are levels of severity within the API that needs to be filtered before we can assess which products causes the most harm." ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "ExecuteTime": { "end_time": "2016-09-29T17:00:55.544015", "start_time": "2016-09-29T17:00:55.527485" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Electric Ranges or Ovens (Excl Counter-top Ovens) 2704\n", "Dishwashers 1605\n", "Microwave Ovens 1095\n", "Footwear 949\n", "Refrigerators 888\n", "Gas Ranges or Ovens 872\n", "Computers (Equipment and Electronic Games) 838\n", "Electric Coffee Makers or Teapots 748\n", "Nonmetal Cookware (Nonelectric) 530\n", "Name: ProductCategoryPublicName, dtype: int64" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#overall products listed\n", "data.ProductCategoryPublicName.value_counts()[0:9]" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "ExecuteTime": { "end_time": "2016-09-29T17:02:38.731655", "start_time": "2016-09-29T17:02:38.696895" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Footwear 774\n", "Computers (Equipment and Electronic Games) 274\n", "Diapers 156\n", "Electric Ranges or Ovens (Excl Counter-top Ovens) 134\n", "Bicycles and Accessories, (Excl.mountain or All-terrain) 108\n", "Baby Strollers 108\n", "Electric Coffee Makers or Teapots 100\n", "Cribs 94\n", "Bassinets or Cradles 88\n", "Name: ProductCategoryPublicName, dtype: int64" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#removing minor harm incidents\n", "no_injuries = ['Incident, No Injury', 'Unspecified', 'Level of care not known',\n", " 'No Incident, No Injury', 'No First Aid or Medical Attention Received']\n", "damage = data.ix[~data['SeverityTypePublicName'].isin(no_injuries), :]\n", "damage.ProductCategoryPublicName.value_counts()[0:9]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This shows that incidents where there are actually injuries and medical attention was given was that in footwear, which was weird. To explore this, I created a Word2Vec model that maps out how certain words relate to each other. To train the model, I used the comments that were made from the API. This will train a model and help us identify words that are similar. For instance, if you type in foot, you will get `left` and `right` as these words are most closely related to the word `foot`. However after some digging around, I found out that the word \"walking\" was associated with \"painful\". I have some reason to believe that there are orthopedic injuries associated with shoes and people have been experience pain while walking with Sketchers that were supposed to tone up their bodies and having some instability or balance issues." ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "ExecuteTime": { "end_time": "2016-10-03T23:44:22.499582", "start_time": "2016-10-03T23:44:22.485285" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('while', 0.9986404180526733),\n", " ('up', 0.9979861378669739),\n", " ('skecher', 0.9972473978996277),\n", " ('toning', 0.9970976114273071),\n", " ('suffered', 0.9960523843765259),\n", " ('sketchers', 0.9956487417221069),\n", " ('bought', 0.9945806264877319),\n", " ('instability', 0.9944456815719604),\n", " ('wore', 0.9942538142204285),\n", " ('fell', 0.9942276477813721)]" ] }, "execution_count": 115, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = gensim.models.Word2Vec.load('/home/datauser/cpsc/models/footwear')\n", "model.most_similar('walking')" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "ExecuteTime": { "end_time": "2016-09-30T17:39:21.797215", "start_time": "2016-09-30T17:39:21.788341" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('of', 0.9983473420143127),\n", " ('bottom', 0.9975317716598511),\n", " ('shoe', 0.9967656135559082),\n", " ('began', 0.9965482950210571),\n", " ('sneakers', 0.9964351654052734),\n", " ('stairs', 0.9962997436523438),\n", " ('balance', 0.9961462020874023),\n", " ('ago', 0.9960271716117859),\n", " ('last', 0.9959736466407776),\n", " ('new', 0.9958116412162781)]" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.most_similar('injury')" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "ExecuteTime": { "end_time": "2016-09-30T17:40:47.393719", "start_time": "2016-09-30T17:40:47.385329" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('due', 0.9992794990539551),\n", " ('suffered', 0.9977741241455078),\n", " ('while', 0.9953591823577881),\n", " ('up', 0.9946755170822144),\n", " ('walking', 0.9944456815719604),\n", " ('sketchers', 0.994174599647522),\n", " ('skecher', 0.9933757781982422),\n", " ('toning', 0.9927452802658081),\n", " ('sketcher', 0.9924877882003784),\n", " ('of', 0.9900130033493042)]" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.most_similar('instability')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Question 2.3" ] }, { "cell_type": "code", "execution_count": 122, "metadata": { "ExecuteTime": { "end_time": "2016-10-03T23:57:07.245166", "start_time": "2016-10-03T23:57:07.213633" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('to', 55216), ('was', 37765), ('it', 35055), ('is', 21825), ('not', 19165)]" ] }, "execution_count": 122, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = gensim.models.Word2Vec.load('/home/datauser/cpsc/models/severity')\n", "items_dict = {}\n", "for word, vocab_obj in model.vocab.items():\n", " items_dict[word] = vocab_obj.count\n", "sorted_dict = sorted(items_dict.items(), key=operator.itemgetter(1))\n", "sorted_dict.reverse()\n", "sorted_dict[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": "3.5.2" }, "nav_menu": {}, "toc": { "nav_menu": { "height": "171px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 6, "toc_cell": false, "toc_position": { "height": "674px", "left": "0px", "right": "1239px", "top": "106px", "width": "212px" }, "toc_section_display": "block", "toc_window_display": true }, "toc_position": { "height": "847px", "left": "0px", "right": "1376px", "top": "106px", "width": "220px" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
datacommonsorg/tools	import-validation-helper/ImportValidatorMaster.ipynb	1	24067	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "ImportValidatorMaster.ipynb", "provenance": [], "private_outputs": true, "collapsed_sections": [ "OtqIpPaUiMZF" ], "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "<a href=\"https://colab.research.google.com/github/IanCostello/tools/blob/ValidationTool/import-validation-helper/ImportValidatorMaster.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" ] }, { "cell_type": "markdown", "metadata": { "id": "0sTKUefKWTfr", "colab_type": "text" }, "source": [ "# Import Validation Helper\n", "This Colab notebook introduces a few tools to check your template MCF, StatVars, and CSV. \n", "\n", "A summary of features is as follows.\n", "\n", "* MCF format checking (no improperly defined nodes).\n", "* StatVar reference checking (makes sure that all references either exist locally or in the knowledge graph).\n", "* TMCF and CSV column valididation.\n", "* Description spell checking.\n", "* ASCII encoding checking.\n", "\n", "### Usage summary:\n", "1. Runtime -> Run All\n", "2. Authenticate with BigQuery in second cell\n", "3. Scroll to bottom, select three files to validate from your local computer.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "6CGTarNhjPuB", "colab_type": "text" }, "source": [ "# 1) At the top of the page, go to \"Runtime -> Run All\"." ] }, { "cell_type": "code", "metadata": { "id": "RHlcR9km48T7", "colab_type": "code", "colab": {} }, "source": [ "import re\n", "import pandas as pd\n", "!pip install --upgrade -q pyspellchecker\n", "!pip install --upgrade -q pygsheets\n", "\n", "from spellchecker import SpellChecker\n", "import subprocess\n", "\n", "from google.colab import auth\n", "from google.cloud import bigquery\n", "import gspread\n", "from oauth2client.client import GoogleCredentials\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "6f4ZjU27iEM9", "colab_type": "text" }, "source": [ "# 2) Authenticate BQ here.\n", "BigQuery is used to check your used references against the KG." ] }, { "cell_type": "code", "metadata": { "id": "KZx5kuFd_76D", "colab_type": "code", "cellView": "both", "colab": {} }, "source": [ "#@title Do you have BigQuery Access? (Internal Googler)\n", "bq_access = True\n", "\n", "if bq_access:\n", " auth.authenticate_user()" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "OtqIpPaUiMZF", "colab_type": "text" }, "source": [ "## Helper Functions" ] }, { "cell_type": "code", "metadata": { "id": "ZfqqK8zg-384", "colab_type": "code", "colab": {} }, "source": [ "# Setup BQ client\n", "client = None\n", "if bq_access:\n", " project_id = \"google.com:datcom-store-dev\"\n", " client = bigquery.Client(project=project_id)\n", "\n", "# Setup logging\n", "# gc = gspread.authorize(GoogleCredentials.get_application_default())\n", "\n", "# Enum definition\n", "from enum import Enum\n", "class PrecheckError(Enum):\n", " CRITICAL = \"Critical\"\n", " WARN = \"Warn\"\n", "\n", "# Helpers\n", "cache = {}\n", "\n", "def validateNodeStructure(mcf_contents):\n", " # See if node has been processed\n", " hash_of_contents = \"validateNodeStructure_\" + str(hash(mcf_contents))\n", " if hash_of_contents in cache:\n", " return cache[hash_of_contents]\n", "\n", " # Nodes in an MCF file are separated by a blank line \n", " mcf_nodes_text = mcf_contents.split(\"\\n\\n\")\n", "\n", " # Lines in an MCF file are separated as property: constraint\n", " mcf_line = re.compile(r\"^(\\w+): (.)$\")\n", "\n", " mcf_nodes = []\n", " errors = []\n", "\n", " for node in mcf_nodes_text:\n", " current_mcf_node = {}\n", "\n", " for line in node.split('\\n'):\n", " # Ignore blank lines if multiple spaces between lines\n", " if len(line) == 0:\n", " continue\n", "\n", " parsed_line = mcf_line.match(line)\n", "\n", " if parsed_line is None:\n", " errors.append((PrecheckError.CRITICAL, \"MalformedLine\", f\"Malformed MCF Line '{line}'\"))\n", " else:\n", " # Property = Constraint\n", " current_mcf_node[parsed_line.group(1)] = parsed_line.group(2)\n", " \n", " if len(current_mcf_node) > 0:\n", " mcf_nodes.append(current_mcf_node)\n", "\n", " # Add to cache\n", " cache[hash_of_contents] = (mcf_nodes, errors)\n", "\n", " return mcf_nodes, errors\n", " \n", "def get_nodes_with_property(mcf_contents, prop, constraint):\n", " mcf_nodes, errors = validateNodeStructure(mcf_contents)\n", "\n", " matching_nodes = []\n", " for node in mcf_nodes:\n", " if prop in node and node[prop] == constraint:\n", " matching_nodes.append(node)\n", "\n", " return matching_nodes\n", "\n", "def remove_prefix(s):\n", " \"\"\"Removes prefixes 'dcs:', 'dcid:' and 'schema:' to ease node comparison.\"\"\"\n", " s = s.strip()\n", " if s.startswith('dcs:'):\n", " return s[4:]\n", " if s.startswith('dcid:'):\n", " return s[5:]\n", " if s.startswith('schema:'):\n", " return s[7:]\n", " return s\n", "\n", "def cmp_nodes(n1, n2):\n", " \"\"\"Compares two nodes, ignoring prefixes such as in remove_prefix()\"\"\"\n", " return remove_prefix(n1) == remove_prefix(n2)\n", "\n", "def get_newly_defined_nodes(mcf_contents, typeOf = \"\"):\n", " mcf_nodes, errors = validateNodeStructure(mcf_contents)\n", "\n", " new_nodes = []\n", " for node in mcf_nodes:\n", " if \"Node\" in node and \"typeOf\" in node and \\\n", " (typeOf == \"\" or typeOf == remove_prefix(node['typeOf'])):\n", " new_nodes.append(node['Node'].replace(\"dcs:\",\"\").replace(\"dcid:\",\"\"))\n", "\n", " return new_nodes\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "FwyIS2U_iQUc", "colab_type": "text" }, "source": [ "## Definition of Tests" ] }, { "cell_type": "code", "metadata": { "id": "08YdJ9R14-Pu", "colab_type": "code", "colab": {} }, "source": [ "class TriplesChecks():\n", " \"\"\"Defines the various tests that run on the combined contents of TMCF,\n", " uploaded csv, and statistical variable files. \n", " \n", " To add a test: Make a new method with the following args.\n", " Args:\n", " df -> Dataframe of uploaded CSV\n", " tmcf_contents -> String of TMCF text content\n", " stat_vars -> String of Statistical Variables file\n", " Yields:\n", " Yields tuple of the precheck error level enum, error name, and an error message\n", " \"\"\"\n", "\n", " def ensure_ascii(_, tmcf_contents, stat_vars_content):\n", " \"\"\"Checks to ensure that files contents are solely ascii characters.\"\"\"\n", " ascii_character_match = re.compile(r\"^[\\x00-\\x7F]+$\")\n", "\n", " for file_name, contents in \\\n", " [(\"TMCF\", tmcf_contents), (\"Statistical Variables\", stat_vars_content)]:\n", "\n", " if ascii_character_match.match(contents) == None:\n", " yield (PrecheckError.CRITICAL, \"NonAsciiInFile\",\n", " f\"{file_name} file contains non-ascii characters.\")\n", "\n", " def tmcf_csv_column_checks(df, tmcf_contents, stat_vars_content):\n", " \"\"\"Handles column inconsistencies between tmcf and csv.\"\"\"\n", " column_matches = re.compile(r\"C:\\w+->(\\w+)\")\n", " tmcf_columns = column_matches.findall(tmcf_contents)\n", " csv_columns = df.columns\n", " for column in tmcf_columns:\n", " if column not in csv_columns:\n", " yield (PrecheckError.CRITICAL, \"ColInTMCFMissingFromCSV\",\n", " f\"Referenced column {column} in TMCF not found in CSV.\")\n", " for column in csv_columns:\n", " if column not in tmcf_columns:\n", " yield (PrecheckError.WARN, \"UnusedColumnPresent\",\n", " f\"Unused column {column} present in CSV.\")\n", " \n", " def ensure_mcf_not_malformed(_, tmcf_contents, stat_vars_content):\n", " \"\"\"Ensures lines of MCF files are property defined.\n", " Passes: Node: E:WorldBank->E0\n", " Fails: Node E:WorldBank->E0 \n", " \"\"\"\n", " # Grab error field of tuple\n", " for error in validateNodeStructure(tmcf_contents)[1]:\n", " yield error\n", "\n", " for error in validateNodeStructure(stat_vars_content)[1]:\n", " yield error\n", "\n", " def ensure_nodes_properly_referenced(_, tmcf_contents, stat_vars_content):\n", " \"\"\"Ensures that constraint field of mcf files are references or constants.\"\"\"\n", " tmcf_nodes, _ = validateNodeStructure(tmcf_contents)\n", " stat_var_nodes, _ = validateNodeStructure(stat_vars_content)\n", "\n", " # Ensure that each property is a string, integer, boolean, tmcf reference, or schema reference \n", " tmcf_match = re.compile(r\"^(\\\"[^\\\"]+\\\")\|(E:\\w+->E\\d+)\|(C:\\w+->\\w+)\|(\\d+)\|(True)\|(False)\|(((dcs)\|(dcid)\|(schema)):\\w+)$\")\n", "\n", " # Ensure that each property is a string, integer, boolean, tmcf reference, schema reference, or quantity range\n", " stvr_match = re.compile(r\"^(\\\"[^\\\"]+\\\")\|(\\d+)\|(True)\|(False)\|((((dcs)\|(dcid)\|(schema)):[A-Za-z0-9_\\-\\/]+,? ?)+)\|(\\[\\S+ ((\\d+)\|(\\d+ \\d+)\|(\\d+ \\+)\|(\\- \\d+))\\])$\")\n", " tmcf_node_match = re.compile(\"^E:\\S+->E\\d+$\")\n", " stvr_node_match = re.compile(\"^dcid:\\S+$\")\n", "\n", " for node_list, node_prop_regex, property_regex in \\\n", " [(tmcf_nodes, tmcf_node_match, tmcf_match),\n", " (stat_var_nodes, stvr_node_match, stvr_match)]:\n", " for node in node_list:\n", " for prop, constraint in node.items():\n", " if prop == \"Node\":\n", " if node_prop_regex.match(constraint) == None:\n", " yield (PrecheckError.CRITICAL, \"MalformedNode\",\n", " f\"Malformed Node Property '{prop}: {constraint}'\")\n", " \n", " # Validate properties of TMCF\n", " elif prop[0].islower():\n", " if property_regex.match(constraint) == None:\n", " yield (PrecheckError.WARN, \"MisformedReference\",\n", " f\"Misformed Reference: '{prop}: {constraint}'\")\n", "\n", " # All properties besides Node should be lower case\n", " else:\n", " yield (PrecheckError.WARN, \"LowerProperties\",\n", " f\"All MCF Properties besides Node should be lowercase. Triggered for '{prop}'.\")\n", " \n", " def spell_check_descriptions(_, tmcf_contents, stat_vars_content):\n", " \"\"\"Provides spell checking on all description fields.\"\"\"\n", " description_field_parser = re.compile(\"description: \\\"([^\\\"])\\\"\")\n", " spell = SpellChecker()\n", " sets_to_check = [(\"TMCF\", tmcf_contents), \n", " (\"Statistical Variables\", stat_vars_content)]\n", "\n", " for set_name, text in sets_to_check:\n", " potential_mispellings = set()\n", " for description in description_field_parser.findall(text):\n", " potential_mispellings = potential_mispellings.union(\n", " spell.unknown(spell.split_words(description))\n", " )\n", "\n", " if len(potential_mispellings) != 0:\n", " yield (PrecheckError.WARN, \"Misspelling\",\n", " f\"Potential Misspelling(s) in {set_name}: {list(potential_mispellings)})\")\n", "\n", " def ensure_all_references_exist(df, tmcf_contents, stat_vars_content):\n", " if not bq_access:\n", " return\n", " \n", " # Get locally defined instances.\n", " new_references = get_newly_defined_nodes(stat_vars_content)\n", "\n", " # Get all references instances in stat vars\n", " ref_finder = re.compile(r\"(?:(?:dcs)\|(?:dcid)):(\\S+)\")\n", " references = list(set(ref_finder.findall(stat_vars_content)))\n", "\n", " # Get all stat vars that are not locally defined\n", " global_references = []\n", " for ref in references:\n", " if len(ref) != 0 and ref not in new_references:\n", " global_references.append(ref)\n", "\n", " # Query database \n", " instance_query = \"\"\"\n", " SELECT distinct id\n", " FROM `google.com:datcom-store-dev.dc_v3_clustered.Instance` \n", " WHERE id IN ({str})\n", " \"\"\"\n", " obj_instances = client.query(instance_query.replace(\"{str}\",\n", " str(global_references).lstrip(\"[\").rstrip(\"]\"))).to_dataframe()['id'].values\n", "\n", " missing_references = []\n", " for ref in global_references:\n", " if ref not in obj_instances:\n", " missing_references.append(ref)\n", "\n", " if len(missing_references) != 0:\n", " yield (PrecheckError.WARN, \"UndefinedReference\",\n", " f\"Potential Undefined References: {missing_references}\")\n", " \n", " def ensure_all_statvars_defined(df, tmcf_contents, stat_vars_content):\n", " direct_ref = re.compile(r'variableMeasured:\\s(\\w+)$')\n", " indirect_ref = re.compile(r'variableMeasured:\\sC:\\w+->(\\w+)')\n", " defined_nodes = get_newly_defined_nodes(stat_vars_content)\n", " for ref in direct_ref.findall(tmcf_contents):\n", " if not any([cmp_nodes(ref, n) for n in defined_nodes]):\n", " yield (PrecheckError.CRITICAL, \"TMCFNodeRefNotInMCF\",\n", " f\"Node '{ref}' referenced in TMCF, undefined in MCF.\")\n", " \n", " for col_ref in indirect_ref.findall(tmcf_contents):\n", " if col_ref not in df.columns:\n", " yield (PrecheckError.CRITICAL, \"ColInTMCFNotInCSV\",\n", " f\"Column '{col_ref}' referenced in TMCF, not in CSV.\")\n", " else:\n", " for ref in df[col_ref].unique():\n", " if not any([cmp_nodes(ref, n) for n in defined_nodes]):\n", " yield (PrecheckError.CRITICAL, \"ReferencedFieldNotInMcf\",\n", " f\"Node '{ref}' referenced in TMCF through '{col_ref}' column in CSV, undefined in MCF.\")\n", " \n", " def ensure_dcid_not_too_long(_, __, stat_vars_content):\n", " for line in stat_vars_content.split(\"\\n\"):\n", " if 'Node:' in line and len(line) - len('Node: ') > 256:\n", " yield (PrecheckError.CRITICAL, \"MalformedNode\",\n", " f\"The following node is too long: '{line.strip()}'\\nMax dcid length is 256.\")\n", "\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "vbJptJFUBnKv", "colab_type": "code", "colab": {} }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "8G8pWgrhAVen", "colab_type": "code", "colab": {} }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "C7Tbwl3CrKN-", "colab_type": "code", "colab": {} }, "source": [ "from optparse import OptionParser\n", "import inspect\n", "\n", "def validate_prechecks(df, tmcf_contents, stat_vars_content, demo=False):\n", " \"\"\"Runs validation checks on provided triples.\n", "\n", " Args:\n", " df -> Dataframe of uploaded CSV\n", " tmcf_contents -> String of TMCF text content\n", " stat_vars_content -> String of Statistical Variables file\n", " \"\"\"\n", " # Log usage to find common errors\n", " # process = subprocess.Popen(\"gcloud config get-value account\", shell=True, stdout=subprocess.PIPE)\n", " # username = process.stdout.read().decode(\"utf-8\")\n", " # gc = gspread.authorize(GoogleCredentials.get_application_default())\n", " # workbook = gc.open_by_url('https://docs.google.com/spreadsheets/d/1l4YqvkhzRBKtab5lVCuoR0guGf2qIARK-A995DFZ6Nw/edit?usp=sharing')\n", " # sheet = workbook.worksheet('Usage')\n", " # if demo:\n", " # sheet.append_row([username, \"RanDemo\"])\n", "\n", " for function_name, function in inspect.getmembers(TriplesChecks, predicate=inspect.isfunction):\n", " errors = list(function(df, tmcf_contents, stat_vars_content))\n", " if len(errors) != 0:\n", " print(f\"Error In Test {function_name}\")\n", " for error in errors: \n", " # if not demo:\n", " # sheet.append_row([username, str(error[0].value), error[1], error[2]])\n", " print(f\"{error[0].value} - {error[2]}\")\n", " print(\"\")" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "6zqZfVVAo9bQ", "colab_type": "text" }, "source": [ "# Sample Upload Demonstrating Common Errors" ] }, { "cell_type": "code", "metadata": { "id": "unseBz2v7ziU", "colab_type": "code", "colab": {} }, "source": [ "# Sample Input\n", "stat_vars_content = \\\n", "\"\"\"\n", "Node: dcid:Tourism\n", "name: “Tourism“\n", "typeOf: dcid:TravelPurposeEnum\n", "description: \"Ptential mispeling in my description.\" \n", "\n", "Node: dcid:ThisIsAnExtremelyLongStatVarName__Count_MortalityEvent_From75To79Years_MalignantImmunoproliferativeDiseasesAndCertainOtherB-CellLymphomas_MultipleMyelomaAndMalignantPlasmaCellNeoplasms_OtherAndUnspecifiedMalignantNeoplasmsOfLymphoid_HematopoieticAndRelatedTissue_Female_AsFractionOf_Count_Person\n", "name: “Tourism“\n", "typeOf: dcid:TravelPurposeEnum\n", "description: \"Ptential mispeling in my description.\" \n", "\"\"\"\n", "\n", "tmcf_contents = \\\n", "\"\"\"\n", "Node E:WorldBank->E0\n", "typeOf: dcs:StatVarObservation\n", "variableMeasured: C:WorldBank->StatisticalVariable\n", "observationDate: C:WorldBank->Year\n", "observationPeriod: \"P1Y\"\n", "observationAbout: E:WorldBank->E1\n", "value: C:WorldBank->Value\n", "\n", "Node: E:WorldBank->E1\n", "typeOf: dcs:Country\n", "countryAlpha3Code: C:WorldBank->IsoCode\n", "BadProperty: someFieldThatShouldBeAReferenceButIsInterpretedAsAString\n", "\"\"\"\n", "\n", "df = pd.DataFrame.from_dict({\"Value\": [4], \"IsoCode\": [\"USA\"], \"Year\":[2018], \"Foo\": ['bar']})\n", "df" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "g9s8-wZNrXKE", "colab_type": "code", "colab": {} }, "source": [ "validate_prechecks(df, tmcf_contents, stat_vars_content, demo=True)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "2TYYTYGJoJEZ", "colab_type": "text" }, "source": [ "# Real Validation Code\n", "\n", "Upload three files.\n", "\n", "- StatisticalVariable -> Needs to end in .mcf\n", "- Template MCF -> Needs to end in .tmcf\n", "- CSV File -> Needs to end in .csv" ] }, { "cell_type": "code", "metadata": { "id": "OMTKha2y8PQ3", "colab_type": "code", "colab": {} }, "source": [ "from google.colab import files\n", "from io import StringIO\n", "\n", "LARGE_FILE_FLAG = False # Set this to true for large files\n", "selected_files = files.upload()\n", "\n", "# Parse out files\n", "df, tmcf_text, stat_var_text = None, None, None\n", "\n", "for file, contents in selected_files.items():\n", " if \".csv\" in file:\n", " if not LARGE_FILE_FLAG:\n", " df = pd.read_csv(StringIO(contents.decode()))\n", " else:\n", " df = pd.read_csv(StringIO(contents.decode()), nrows=100)\n", " elif \".tmcf\" in file:\n", " tmcf_text = contents.decode()\n", " elif \".mcf\" in file:\n", " stat_var_text = contents.decode()" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "e4_63BXZsxtx", "colab_type": "code", "colab": {} }, "source": [ "validate_prechecks(df, tmcf_text, stat_var_text)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "sfEfIQMNtiIK", "colab_type": "code", "colab": {} }, "source": [ "" ], "execution_count": null, "outputs": [] } ] }	apache-2.0
drvinceknight/gt	nbs/solutions/03-Rationalisation.ipynb	1	4837	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Rationalisation - Solutions\n", "\n", "1. Give the definition of a dominated strategy.\n", "\n", " Bookwork: https://vknight.org/gt/chapters/03/#Definition-of-a-strictly-dominated-strategy\n", "\n", "2. Give the definition of a weakly dominated strategy.\n", "\n", " Bookwork: https://vknight.org/gt/chapters/03/#Definition-of-a-weakly-dominated-strategy\n", "\n", "3. Give the defininition of common knowledge of rationality.\n", "\n", " Bookwork: https://vknight.org/gt/chapters/03/#Definition-of-a-weakly-dominated-strategy\n", "\n", "4. For the following games predict rational behaviour or explain why this cannot be done:\n", "\n", " 1. $\n", " A =\n", " \\begin{pmatrix}\n", " 2 & 1\\\\\n", " 1 & 1\\end{pmatrix}\n", " \\qquad\n", " B =\n", " \\begin{pmatrix}\n", " 1 & 1\\\\\n", " 1 & 3\\end{pmatrix}\n", " $ \n", " \n", " We see that \\\$r_1\\\$ weakly dominates \\\$r_2\\\$ so we have:\n", "\n", " $$A=(2,1)\\qquad B =(1,1)$$\n", "\n", " There are no further strategies that can be eliminated.\n", "\n", " We see however that \\\$c_2\\\$ weakly dominates \\\$c_1\\\$ which would give:\n", "\n", " $$\\begin{pmatrix}\n", " (1,1)\\\\\n", " (1,3)\\\\\n", " \\end{pmatrix}$$\n", "\n", " Again, there are no further strategies that can be eliminated.\n", " \n", " 2. $\n", " A =\n", " \\begin{pmatrix}\n", " 2 & 1 & 3 & 17\\\\\n", " 27 & 3 & 1 & 1\\\\\n", " 4 & 6 & 7 & 18\n", " \\end{pmatrix}\n", " \\qquad\n", " B =\n", " \\begin{pmatrix}\n", " 11 & 9 & 10 & 22\\\\\n", " 0 & 1 & 1 & 0\\\\\n", " 2 & 10 & 12 & 0\n", " \\end{pmatrix}\n", " $\n", " \n", " We see that \\\$c_2\\\$ is weakly dominated by \\\$c_3\\\$ so we have:\n", "\n", " $$\n", " A =\n", " \\begin{pmatrix}\n", " 2 & 3 & 17\\\\\n", " 27 & 1 & 1\\\\\n", " 4 & 7 & 18\n", " \\end{pmatrix}\n", " \\qquad\n", " B =\n", " \\begin{pmatrix}\n", " 11 & 10 & 22\\\\\n", " 0 & 1 & 0\\\\\n", " 2 & 12 & 0\n", " \\end{pmatrix}\n", " $$\n", "\n", " Now \\\$r_3\\\$ strictly dominates \\\$r_1\\\$ so we have:\n", "\n", " $$\n", " A =\n", " \\begin{pmatrix}\n", " 27 & 1 & 1\\\\\n", " 4 & 7 & 18\n", " \\end{pmatrix}\n", " \\qquad\n", " B =\n", " \\begin{pmatrix}\n", " 0 & 1 & 0\\\\\n", " 2 & 12 & 0\n", " \\end{pmatrix}\n", " $$\n", "\n", " Now \\\$c_3\\\$ stricly dominates \\\$c_1\\\$ and \\\$c_4\\\$ so we have:\n", "\n", "\n", " $$\n", " A =\n", " \\begin{pmatrix}\n", " 1\\\\\n", " 7\n", " \\end{pmatrix}\n", " \\qquad\n", " B =\n", " \\begin{pmatrix}\n", " 1 \\\\\n", " 12\n", " \\end{pmatrix}\n", " $$\n", "\n", " Thus the predicted rational behaviour is \\\$(r_3, c_3)\\\$.\n", " \n", " 3. $\n", " A =\n", " \\begin{pmatrix}\n", " 3 & 3 & 2 \\\\\n", " 2 & 1 & 3 \n", " \\end{pmatrix}\n", " \\qquad\n", " B =\n", " \\begin{pmatrix}\n", " 2 & 1 & 3 \\\\\n", " 2 & 3 & 2 \n", " \\end{pmatrix}\n", " $\n", " \n", " \\\$c_1\\\$ is weakly dominated by \\\$c_3\\\$:\n", "\n", " $$A =\n", " \\begin{pmatrix}\n", " 3 & 2 \\\\\n", " 1 & 3 \n", " \\end{pmatrix}\n", " \\qquad\n", " B =\n", " \\begin{pmatrix}\n", " 1 & 3 \\\\\n", " 3 & 2 \n", " \\end{pmatrix}$$\n", "\n", " There are no further dominated strategies.\n", " \n", " 4. $\n", " A =\n", " \\begin{pmatrix}\n", " 3 & -1\\\\\n", " 2 & 7\\end{pmatrix}\n", " \\qquad\n", " B =\n", " \\begin{pmatrix}\n", " -3 & 1\\\\\n", " 1 & -6\\end{pmatrix}\n", " $\n", " \n", " There are no dominated strategies." ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:gt]", "language": "python", "name": "conda-env-gt-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
opengeostat/pygslib	doc/source/Ipython_templates/histogram_html.ipynb	1	125666	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "\n", "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", "\n", " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" \|\| force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", "\n", " var JS_MIME_TYPE = 'application/javascript';\n", " var HTML_MIME_TYPE = 'text/html';\n", " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", " var CLASS_NAME = 'output_bokeh rendered_html';\n", "\n", " /*\n", " Render data to the DOM node\n", " /\n", " function render(props, node) {\n", " var script = document.createElement(\"script\");\n", " node.appendChild(script);\n", " }\n", "\n", " /\n", " Handle when an output is cleared or removed\n", " /\n", " function handleClearOutput(event, handle) {\n", " var cell = handle.cell;\n", "\n", " var id = cell.output_area._bokeh_element_id;\n", " var server_id = cell.output_area._bokeh_server_id;\n", " // Clean up Bokeh references\n", " if (id !== undefined) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", "\n", " if (server_id !== undefined) {\n", " // Clean up Bokeh references\n", " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", " cell.notebook.kernel.execute(cmd, {\n", " iopub: {\n", " output: function(msg) {\n", " var element_id = msg.content.text.trim();\n", " Bokeh.index[element_id].model.document.clear();\n", " delete Bokeh.index[element_id];\n", " }\n", " }\n", " });\n", " // Destroy server and session\n", " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", " cell.notebook.kernel.execute(cmd);\n", " }\n", " }\n", "\n", " /\n", " Handle when a new output is added\n", " /\n", " function handleAddOutput(event, handle) {\n", " var output_area = handle.output_area;\n", " var output = handle.output;\n", "\n", " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", " if ((output.output_type != \"display_data\") \|\| (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", " return\n", " }\n", "\n", " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", "\n", " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", " // store reference to embed id on output_area\n", " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", " }\n", " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", " var bk_div = document.createElement(\"div\");\n", " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", " var script_attrs = bk_div.children[0].attributes;\n", " for (var i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", " }\n", " }\n", "\n", " function register_renderer(events, OutputArea) {\n", "\n", " function append_mime(data, metadata, element) {\n", " // create a DOM node to render to\n", " var toinsert = this.create_output_subarea(\n", " metadata,\n", " CLASS_NAME,\n", " EXEC_MIME_TYPE\n", " );\n", " this.keyboard_manager.register_events(toinsert);\n", " // Render to node\n", " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", " render(props, toinsert[toinsert.length - 1]);\n", " element.append(toinsert);\n", " return toinsert\n", " }\n", "\n", " / Handle when an output is cleared or removed /\n", " events.on('clear_output.CodeCell', handleClearOutput);\n", " events.on('delete.Cell', handleClearOutput);\n", "\n", " / Handle when a new output is added /\n", " events.on('output_added.OutputArea', handleAddOutput);\n", "\n", " /\n", " Register the mime type and append_mime function with output_area\n", " /\n", " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", " / Is output safe? /\n", " safe: true,\n", " / Index of renderer in `output_area.display_order` */\n", " index: 0\n", " });\n", " }\n", "\n", " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", " if (root.Jupyter !== undefined) {\n", " var events = require('base/js/events');\n", " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", "\n", " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", " register_renderer(events, OutputArea);\n", " }\n", " }\n", "\n", " \n", " if (typeof (root._bokeh_timeout) === \"undefined\" \|\| force === true) {\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_failed_load = false;\n", " }\n", "\n", " var NB_LOAD_WARNING = {'data': {'text/html':\n", " \"<div style='background-color: #fdd'>\\n\"+\n", " \"<p>\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"</p>\\n\"+\n", " \"<ul>\\n\"+\n", " \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n", " \"<li>use INLINE resources instead, as so:</li>\\n\"+\n", " \"</ul>\\n\"+\n", " \"<code>\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"</code>\\n\"+\n", " \"</div>\"}};\n", "\n", " function display_loaded() {\n", " var el = document.getElementById(null);\n", " if (el != null) {\n", " el.textContent = \"BokehJS is loading...\";\n", " }\n", " if (root.Bokeh !== undefined) {\n", " if (el != null) {\n", " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", " }\n", " } else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(display_loaded, 100)\n", " }\n", " }\n", "\n", "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", " }\n", " finally {\n", " delete root._bokeh_onload_callbacks\n", " }\n", " console.info(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(js_urls, callback) {\n", " root._bokeh_onload_callbacks.push(callback);\n", " if (root._bokeh_is_loading > 0) {\n", " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null \|\| js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " root._bokeh_is_loading = js_urls.length;\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " var s = document.createElement('script');\n", " s.src = url;\n", " s.async = false;\n", " s.onreadystatechange = s.onload = function() {\n", " root._bokeh_is_loading--;\n", " if (root._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", " run_callbacks()\n", " }\n", " };\n", " s.onerror = function() {\n", " console.warn(\"failed to load library \" + url);\n", " };\n", " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", " }\n", " };\n", "\n", " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.15.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.15.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.15.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.15.min.js\"];\n", "\n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", " \n", " function(Bokeh) {\n", " \n", " },\n", " function(Bokeh) {\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.15.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.15.min.css\");\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.15.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.15.min.css\");\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.15.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.15.min.css\");\n", " }\n", " ];\n", "\n", " function run_inline_js() {\n", " \n", " if ((root.Bokeh !== undefined) \|\| (force === true)) {\n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i].call(root, root.Bokeh);\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", "\n", " }\n", "\n", " if (root._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(js_urls, function() {\n", " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(window));" ], "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" \|\| force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" \|\| force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"<div style='background-color: #fdd'>\\n\"+\n \"<p>\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n var el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) 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[\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.15.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.15.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.15.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.15.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.15.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.15.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.15.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.15.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.15.min.css\");\n 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\" />'\n", "</script>\n", "\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#general imports \n", "import pygslib" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#get the data in gslib format into a pandas Dataframe\n", "mydata= pygslib.gslib.read_gslib_file('../datasets/cluster.dat') " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "parameters = {\n", " 'hmin' : None, #in/output rank-0 array(float,'d')\n", " 'hmax' : None, #in/output rank-0 array(float,'d')\n", " 'ncl' : 30, #int, number of bins\n", " 'iwt' : 1, #int, 1 use declustering weight\n", " 'ilog' : 1, #int, 1 use logscale\n", " 'icum' : 0, #int, 1 use cumulative\n", " 'va' : mydata['Primary'], # array('d') with bounds (nd)\n", " 'wt' : None, # array('d') with bounds (nd), wight variable (obtained with declust?)\n", " 'figure' : None , # a bokeh figure object (Optional: new figure created if None). Set none or undefined if creating a new figure. \n", " 'title' : 'A test', # string. Figure title\n", " 'xlabel' : 'Grade', # string. X axis label \n", " 'ylabel' : 'f(%)', # string. Y axis label\n", " # visual parameter for the histogram\n", " 'color' : 'red', # string with valid CSS colour (https://www.w3schools.com/colors/colors_names.asp), or an RGB(A) hex value, or tuple of integers (r,g,b), or tuple of (r,g,b,a) \n", " 'legend': 'Non - Declustered', # string (Optional, default \"NA\")\n", " 'alpha' : 0.5, # float [0-1]. Transparency of the fill colour \n", " 'lwidth': 1, # float. Line width\n", " # legend \n", " 'legendloc': 'top_left'} \n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "res, fig = pygslib.plothtml.histgplt(parameters)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "<div class=\"bk-root\">\n", " <div class=\"bk-plotdiv\" id=\"ef246c82-8424-40c4-890c-eb8e9d128d8b\"></div>\n", "</div>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "(function(root) {\n", " function embed_document(root) {\n", " \n", " var docs_json = {\"c6e4f1ac-67f5-41b1-92e2-0ea8a8b29c58\":{\"roots\":{\"references\":[{\"attributes\":{},\"id\":\"d45572ee-6e19-4667-8ee7-80b85651b993\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"6b11f304-dfae-4568-a038-410730f782e3\",\"type\":\"LogScale\"},{\"attributes\":{\"label\":{\"value\":\"Non - 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\"WheelZoomTool\"},{\"id\":\"646007c3-9a0d-4e4d-8c55-1fcfb359aa7e\",\"type\":\"BoxZoomTool\"},{\"id\":\"f71e2d0a-baeb-4b9a-a390-0629d35f9f01\",\"type\":\"SaveTool\"},{\"id\":\"d45572ee-6e19-4667-8ee7-80b85651b993\",\"type\":\"ResetTool\"},{\"id\":\"529e058c-ba57-4b78-ace7-8232fece05bc\",\"type\":\"HelpTool\"}]},\"id\":\"78031736-cbd2-46f3-aa13-7a8eeb59f462\",\"type\":\"Toolbar\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x\",\"width\",\"top\"],\"data\":{\"top\":{\"__ndarray__\":\"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAd1EEd1EGNPx3UQR3UQX0/Fl/xFV/xpT8AAAAAAAAAAJqZmZmZmak/kiRJkiRJoj8WX/EVX/GlPx3UQR3UQZ0/kiRJkiRJoj/VQR3UQR20Px7UQR3UQa0/kyRJkiRJsj8XX/EVX/G1P5uZmZmZmbk/HtRBHdRBrT+TJEmSJEmyP5MkSZIkSbI/kyRJkiRJsj8WX/EVX/GVPxZf8RVf8aU/HdRBHdRBfT8AAAAAAAAAAAAAAAAAAAAAHdRBHdRBfT8AAAAAAAAAAAAAAAAAAAAA\",\"dtype\":\"float64\",\"shape\":[30]},\"width\":{\"__ndarray__\":\"AWVD8dYbAMB04t2AOQJ0v8C2R8TtMnu/BluHBY18gr8Q19knOSGJv/4nNoaFFJG/8O7brdA3l7/E5XRbw4+fv4YPQYmjc6W/8AAoJRgprb9ecCmh3NGzvxS7Kb8v8bq/5qUjBt5Pwr+07Sx7e+TIv36R4r8869C/mKOb8rH/1r/UsVrZeUPfv74GWMHJP+W/OH6jWJzi7L8CMCKn9KHzv3CetKEQsPq/rNLTB5sjAsB8U3OfUKgIwC7UOsNXwhDAuEAH3RrIFsCcv9C76PcewCCaWE1tDCXAPLOX6cqcLMC4GTt4gHIzwFAD0uuObzrA\",\"dtype\":\"float64\",\"shape\":[30]},\"x\":{\"__ndarray__\":\"/TV5HVLI77/gPM7Rv2uQPwfwcrpkUpY/oL1dtOVXnj8TBdufq5+kP/TJYyb0CKw/2Ce02QQOsz9uQt5a9+a5P4SRTlTsmsE/k7MbyoPuxz/kh4X7DkTQP1Ltj4dwHNY/Lg7+AI4O3j+KGakw0m3kP6BbB9Awxes/c3RTPvbf8j8gP9K3W6j5P4XLqbFecAFAJDzpdKu0B0D2G+MUvhwQQMT1/b3+5hVAPX4plOfFHUDko/0+cTwkQF7DukMRgitATKTl9VayMkBWpABpV2o5QGlx6Ok3RkFAFHvG8F57R0DqNwhsmOtPQJa/hSIOslVA\",\"dtype\":\"float64\",\"shape\":[30]}},\"selected\":null,\"selection_policy\":null},\"id\":\"f66c36ef-f122-4635-ac84-126c08350778\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"source\":{\"id\":\"f66c36ef-f122-4635-ac84-126c08350778\",\"type\":\"ColumnDataSource\"}},\"id\":\"6395fc5f-d8fe-471f-a71a-bd8964513fab\",\"type\":\"CDSView\"},{\"attributes\":{\"plot\":null,\"text\":\"A test\"},\"id\":\"a15ec902-129e-49c6-91cb-905b92502cb8\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"2106d6c2-b3c6-4a23-9ee9-2a74ba53db70\",\"type\":\"PanTool\"},{\"attributes\":{},\"id\":\"1f1e35ec-a23a-4d14-916a-97596243dd5f\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"cdc4c944-b407-4193-8dc1-4dc909feadf9\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"data_source\":{\"id\":\"f66c36ef-f122-4635-ac84-126c08350778\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"bc8bf347-fe81-4c38-86ea-e93e9633df14\",\"type\":\"VBar\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"a1cfeac2-698b-46ca-9a2b-7afc5389484e\",\"type\":\"VBar\"},\"selection_glyph\":null,\"view\":{\"id\":\"6395fc5f-d8fe-471f-a71a-bd8964513fab\",\"type\":\"CDSView\"}},\"id\":\"3346d60e-0553-4aa4-9b4d-3cad197fce46\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"overlay\":{\"id\":\"66d70840-5728-4e68-b792-43a3eb498537\",\"type\":\"BoxAnnotation\"}},\"id\":\"646007c3-9a0d-4e4d-8c55-1fcfb359aa7e\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"ticker\":null},\"id\":\"acd31bc8-3909-4bfa-ab19-7c3e8a9869f0\",\"type\":\"LogTickFormatter\"},{\"attributes\":{\"callback\":null},\"id\":\"57da6557-bec3-485b-a21c-8470f1ab190c\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"f71e2d0a-baeb-4b9a-a390-0629d35f9f01\",\"type\":\"SaveTool\"}],\"root_ids\":[\"eb4ee984-a6c9-4f21-80cf-705ae4ed111d\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.15\"}};\n", " var render_items = [{\"docid\":\"c6e4f1ac-67f5-41b1-92e2-0ea8a8b29c58\",\"elementid\":\"ef246c82-8424-40c4-890c-eb8e9d128d8b\",\"modelid\":\"eb4ee984-a6c9-4f21-80cf-705ae4ed111d\"}];\n", " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", "\n", " }\n", " if (root.Bokeh !== undefined) {\n", " embed_document(root);\n", " } else {\n", " var attempts = 0;\n", " var timer = setInterval(function(root) {\n", " if (root.Bokeh !== undefined) {\n", " embed_document(root);\n", " clearInterval(timer);\n", " }\n", " attempts++;\n", " if (attempts > 100) {\n", " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n", " clearInterval(timer);\n", " }\n", " }, 10, root)\n", " }\n", "})(window);" ], "application/vnd.bokehjs_exec.v0+json": "" }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { "id": "eb4ee984-a6c9-4f21-80cf-705ae4ed111d" } }, "output_type": "display_data" } ], "source": [ "# show the figure\n", "pygslib.plothtml.show(fig)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div style=\"display: table;\"><div style=\"display: table-row;\"><div style=\"display: table-cell;\"><b title=\"bokeh.plotting.figure.Figure\">Figure</b>(</div><div style=\"display: table-cell;\">id = 'eb4ee984-a6c9-4f21-80cf-705ae4ed111d', <span id=\"9202a5b3-4bb3-4fcb-a69f-d165ebc3e82b\" style=\"cursor: pointer;\">…)</span></div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">above = [],</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">aspect_scale = 1,</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">background_fill_alpha = {'value': 1.0},</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">background_fill_color = {'value': '#ffffff'},</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">below = [LogAxis(id='e53722cb-b74c-4424-a52e-84796f1a9028', ...)],</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">border_fill_alpha = {'value': 1.0},</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">border_fill_color = {'value': '#ffffff'},</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">css_classes = [],</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">disabled = False,</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">extra_x_ranges = {},</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">extra_y_ranges = {},</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">h_symmetry = True,</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">height = None,</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">hidpi = True,</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">js_event_callbacks = {},</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">js_property_callbacks = {},</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">left = [LinearAxis(id='6754f275-a602-4798-88bd-19519e61cdf3', ...)],</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">lod_factor = 10,</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">lod_interval = 300,</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">lod_threshold = 2000,</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">lod_timeout = 500,</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">match_aspect = False,</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">min_border = 5,</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">min_border_bottom = None,</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">min_border_left = None,</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">min_border_right = None,</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">min_border_top = None,</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">name = None,</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">outline_line_alpha = {'value': 1.0},</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">outline_line_cap = 'butt',</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">outline_line_color = {'value': '#e5e5e5'},</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">outline_line_dash = [],</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">outline_line_dash_offset = 0,</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">outline_line_join = 'miter',</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">outline_line_width = {'value': 1},</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">output_backend = 'canvas',</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">plot_height = 600,</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">plot_width = 600,</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">renderers = [LogAxis(id='e53722cb-b74c-4424-a52e-84796f1a9028', ...), Grid(id='77ed800d-04e9-49f7-a41a-b15647ed54ed', ...), LinearAxis(id='6754f275-a602-4798-88bd-19519e61cdf3', ...), Grid(id='cbf46d46-4f77-4381-86e6-88805e826319', ...), BoxAnnotation(id='66d70840-5728-4e68-b792-43a3eb498537', ...), Legend(id='3f6202d6-ff7e-4391-873a-54bcaf377c06', ...), GlyphRenderer(id='3346d60e-0553-4aa4-9b4d-3cad197fce46', ...)],</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">right = [],</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">sizing_mode = 'fixed',</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">subscribed_events = [],</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">tags = [],</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">title = Title(id='a15ec902-129e-49c6-91cb-905b92502cb8', ...),</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">title_location = 'above',</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">toolbar = Toolbar(id='78031736-cbd2-46f3-aa13-7a8eeb59f462', ...),</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">toolbar_location = 'above',</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">toolbar_sticky = True,</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">v_symmetry = False,</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">width = None,</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">x_range = Range1d(id='1dc6d76d-0f5f-46d8-ae86-d33d0b803aa5', ...),</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">x_scale = LogScale(id='6b11f304-dfae-4568-a038-410730f782e3', ...),</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">y_range = DataRange1d(id='57da6557-bec3-485b-a21c-8470f1ab190c', ...),</div></div><div class=\"ca4a631f-4a42-421c-bb1a-c86232065ecf\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">y_scale = LinearScale(id='53fa7f91-b940-4b74-b816-14bf3bd8565d', ...))</div></div></div>\n", "<script>\n", "(function() {\n", " var expanded = false;\n", " var ellipsis = document.getElementById(\"9202a5b3-4bb3-4fcb-a69f-d165ebc3e82b\");\n", " ellipsis.addEventListener(\"click\", function() {\n", " var rows = document.getElementsByClassName(\"ca4a631f-4a42-421c-bb1a-c86232065ecf\");\n", " for (var i = 0; i < rows.length; i++) {\n", " var el = rows[i];\n", " el.style.display = expanded ? \"none\" : \"table-row\";\n", " }\n", " ellipsis.innerHTML = expanded ? \"…)\" : \"&lsaquo;&lsaquo;&lsaquo;\";\n", " expanded = !expanded;\n", " });\n", "})();\n", "</script>\n" ], "text/plain": [ "Figure(id='eb4ee984-a6c9-4f21-80cf-705ae4ed111d', ...)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# note that parameters was updated\n", "parameters['figure']" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "<div class=\"bk-root\">\n", " <div class=\"bk-plotdiv\" id=\"f35d42b4-04bf-4e1f-821c-4ad72bf747ad\"></div>\n", "</div>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "(function(root) {\n", " function embed_document(root) {\n", " \n", " var docs_json = {\"007eea43-8c97-4c10-a325-4a22d47e5edf\":{\"roots\":{\"references\":[{\"attributes\":{},\"id\":\"d45572ee-6e19-4667-8ee7-80b85651b993\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"529e058c-ba57-4b78-ace7-8232fece05bc\",\"type\":\"HelpTool\"},{\"attributes\":{\"data_source\":{\"id\":\"275f8130-d242-4de6-854a-20ee18c0c9b8\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"2897046c-4cd9-4f28-8efa-12435e4a6145\",\"type\":\"VBar\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"f5a1514f-9aba-4513-8876-ea648206b6ae\",\"type\":\"VBar\"},\"selection_glyph\":null,\"view\":{\"id\":\"b52a3139-27ad-46ba-902b-923d56cbc8e8\",\"type\":\"CDSView\"}},\"id\":\"694725ea-ca93-4d4a-a4b5-f3030a186255\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x\",\"width\",\"top\"],\"data\":{\"top\":{\"__ndarray__\":\"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABKQC/l4q6XP35eIlyvtoQ/jEjCR7yYrT8AAAAAAAAAABYq05cD0a8/j4zf0C6fqz9SrhXFc8WwP4T5CVusj6E/Lw8nZcMnqj88YUQkQry8P/I49IL1d7Q/XPu5W4T1tj+ghPpR1a62P6SdKhLby7Y/TjmIn6aEpT+pCKwf/VapP0hagxc3pZo/3G9PD3m2oD9KQC/l4q6HP5QWLjkThY0/AS6qMS38aD8AAAAAAAAAAAAAAAAAAAAAkMp5hrNUaj8AAAAAAAAAAAAAAAAAAAAA\",\"dtype\":\"float64\",\"shape\":[30]},\"width\":{\"__ndarray__\":\"AWVD8dYbAMB04t2AOQJ0v8C2R8TtMnu/BluHBY18gr8Q19knOSGJv/4nNoaFFJG/8O7brdA3l7/E5XRbw4+fv4YPQYmjc6W/8AAoJRgprb9ecCmh3NGzvxS7Kb8v8bq/5qUjBt5Pwr+07Sx7e+TIv36R4r8869C/mKOb8rH/1r/UsVrZeUPfv74GWMHJP+W/OH6jWJzi7L8CMCKn9KHzv3CetKEQsPq/rNLTB5sjAsB8U3OfUKgIwC7UOsNXwhDAuEAH3RrIFsCcv9C76PcewCCaWE1tDCXAPLOX6cqcLMC4GTt4gHIzwFAD0uuObzrA\",\"dtype\":\"float64\",\"shape\":[30]},\"x\":{\"__ndarray__\":\"/TV5HVLI77/gPM7Rv2uQPwfwcrpkUpY/oL1dtOVXnj8TBdufq5+kP/TJYyb0CKw/2Ce02QQOsz9uQt5a9+a5P4SRTlTsmsE/k7MbyoPuxz/kh4X7DkTQP1Ltj4dwHNY/Lg7+AI4O3j+KGakw0m3kP6BbB9Awxes/c3RTPvbf8j8gP9K3W6j5P4XLqbFecAFAJDzpdKu0B0D2G+MUvhwQQMT1/b3+5hVAPX4plOfFHUDko/0+cTwkQF7DukMRgitATKTl9VayMkBWpABpV2o5QGlx6Ok3RkFAFHvG8F57R0DqNwhsmOtPQJa/hSIOslVA\",\"dtype\":\"float64\",\"shape\":[30]}},\"selected\":null,\"selection_policy\":null},\"id\":\"275f8130-d242-4de6-854a-20ee18c0c9b8\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"top\":{\"field\":\"top\"},\"width\":{\"field\":\"width\"},\"x\":{\"field\":\"x\"}},\"id\":\"a1cfeac2-698b-46ca-9a2b-7afc5389484e\",\"type\":\"VBar\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"red\"},\"line_alpha\":{\"value\":0.5},\"line_color\":{\"value\":\"red\"},\"top\":{\"field\":\"top\"},\"width\":{\"field\":\"width\"},\"x\":{\"field\":\"x\"}},\"id\":\"bc8bf347-fe81-4c38-86ea-e93e9633df14\",\"type\":\"VBar\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"top\":{\"field\":\"top\"},\"width\":{\"field\":\"width\"},\"x\":{\"field\":\"x\"}},\"id\":\"f5a1514f-9aba-4513-8876-ea648206b6ae\",\"type\":\"VBar\"},{\"attributes\":{\"data_source\":{\"id\":\"f66c36ef-f122-4635-ac84-126c08350778\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"bc8bf347-fe81-4c38-86ea-e93e9633df14\",\"type\":\"VBar\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"a1cfeac2-698b-46ca-9a2b-7afc5389484e\",\"type\":\"VBar\"},\"selection_glyph\":null,\"view\":{\"id\":\"6395fc5f-d8fe-471f-a71a-bd8964513fab\",\"type\":\"CDSView\"}},\"id\":\"3346d60e-0553-4aa4-9b4d-3cad197fce46\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"ticker\":null},\"id\":\"acd31bc8-3909-4bfa-ab19-7c3e8a9869f0\",\"type\":\"LogTickFormatter\"},{\"attributes\":{},\"id\":\"1f1e35ec-a23a-4d14-916a-97596243dd5f\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"label\":{\"value\":\"Declustered\"},\"renderers\":[{\"id\":\"694725ea-ca93-4d4a-a4b5-f3030a186255\",\"type\":\"GlyphRenderer\"}]},\"id\":\"74095df5-96f2-4fad-918e-041aa0edafaa\",\"type\":\"LegendItem\"},{\"attributes\":{\"source\":{\"id\":\"f66c36ef-f122-4635-ac84-126c08350778\",\"type\":\"ColumnDataSource\"}},\"id\":\"6395fc5f-d8fe-471f-a71a-bd8964513fab\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"6b11f304-dfae-4568-a038-410730f782e3\",\"type\":\"LogScale\"},{\"attributes\":{\"label\":{\"value\":\"Non 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you can do some edits\n", "fig.title.text = \"@@@@@@ new name @@@@@@\"\n", "pygslib.plothtml.show(fig)" ] }, { "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": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
raphael-group/machina	result/sims/simulations_3.ipynb	1	704605	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "#Load required modules\n", "import sys, os, argparse\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "from statsmodels.graphics.boxplots import violinplot\n", "import numpy as np\n", "import seaborn as sns\n", "import pandas as pd\n", "\n", "### Set up seaborn appearence\n", "mpl.rc('text', usetex = True)\n", "sns.set_context(\"notebook\", font_scale=1.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Downsampling experiments (decreasing coverage, purity, \\#samples)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [], "source": [ "res_m5_down = pd.read_csv(\"../sims/machina/results_MACHINA_downsampled_m5.txt\", sep=\",\")\n", "res_m5_down['samples'] = res_m5_down['samples'].fillna(0.0).astype(int)\n", "res_m5_down['coverage'] = res_m5_down['coverage'].fillna(0.0).astype(int)\n", "res_m5_down.rename(index=str, columns={'samples' : '\\#samples'}, inplace=True)" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>pattern</th>\n", " <th>seed</th>\n", " <th>\\#samples</th>\n", " <th>purity</th>\n", " <th>coverage</th>\n", " <th>mut_tree</th>\n", " <th>enforced</th>\n", " <th>inferred</th>\n", " <th>mu</th>\n", " <th>gamma</th>\n", " <th>...</th>\n", " <th>RF</th>\n", " <th>recallT</th>\n", " <th>precisionT</th>\n", " <th>FscoreT</th>\n", " <th>recallG</th>\n", " <th>precisionG</th>\n", " <th>FscoreG</th>\n", " <th>recallMultiG</th>\n", " <th>precisionMultiG</th>\n", " <th>FscoreMultiG</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>pM</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0.5</td>\n", " <td>10000</td>\n", " <td>0</td>\n", " <td>R</td>\n", " <td>pPS</td>\n", " <td>5</td>\n", " <td>4</td>\n", " <td>...</td>\n", " <td>11</td>\n", " <td>0.5</td>\n", " <td>1.00</td>\n", " <td>0.666667</td>\n", " <td>0.6</td>\n", " <td>0.75</td>\n", " <td>0.666667</td>\n", " <td>0.571429</td>\n", " <td>0.8</td>\n", " <td>0.666667</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>pM</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0.5</td>\n", " <td>1000</td>\n", " <td>0</td>\n", " <td>R</td>\n", " <td>pPS</td>\n", " <td>5</td>\n", " <td>4</td>\n", " <td>...</td>\n", " <td>10</td>\n", " <td>0.5</td>\n", " <td>1.00</td>\n", " <td>0.666667</td>\n", " <td>0.6</td>\n", " <td>0.75</td>\n", " <td>0.666667</td>\n", " <td>0.571429</td>\n", " <td>0.8</td>\n", " <td>0.666667</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>pM</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0.5</td>\n", " <td>1000</td>\n", " <td>1</td>\n", " <td>R</td>\n", " <td>pPS</td>\n", " <td>5</td>\n", " <td>4</td>\n", " <td>...</td>\n", " <td>10</td>\n", " <td>0.5</td>\n", " <td>1.00</td>\n", " <td>0.666667</td>\n", " <td>0.6</td>\n", " <td>0.75</td>\n", " <td>0.666667</td>\n", " <td>0.571429</td>\n", " <td>0.8</td>\n", " <td>0.666667</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>pM</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0.5</td>\n", " <td>200</td>\n", " <td>6</td>\n", " <td>R</td>\n", " <td>mM</td>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>...</td>\n", " <td>10</td>\n", " <td>0.5</td>\n", " <td>0.75</td>\n", " <td>0.600000</td>\n", " <td>0.8</td>\n", " <td>0.80</td>\n", " <td>0.800000</td>\n", " <td>0.571429</td>\n", " <td>0.8</td>\n", " <td>0.666667</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>pM</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0.5</td>\n", " <td>200</td>\n", " <td>1</td>\n", " <td>R</td>\n", " <td>mM</td>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>...</td>\n", " <td>10</td>\n", " <td>0.5</td>\n", " <td>0.75</td>\n", " <td>0.600000</td>\n", " <td>0.8</td>\n", " <td>0.80</td>\n", " <td>0.800000</td>\n", " <td>0.571429</td>\n", " <td>0.8</td>\n", " <td>0.666667</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 22 columns</p>\n", "</div>" ], "text/plain": [ " pattern seed \\#samples purity coverage mut_tree enforced inferred mu \\\n", "0 pM 0 1 0.5 10000 0 R pPS 5 \n", "1 pM 0 1 0.5 1000 0 R pPS 5 \n", "2 pM 0 1 0.5 1000 1 R pPS 5 \n", "3 pM 0 1 0.5 200 6 R mM 5 \n", "4 pM 0 1 0.5 200 1 R mM 5 \n", "\n", " gamma ... RF recallT precisionT FscoreT recallG precisionG \\\n", "0 4 ... 11 0.5 1.00 0.666667 0.6 0.75 \n", "1 4 ... 10 0.5 1.00 0.666667 0.6 0.75 \n", "2 4 ... 10 0.5 1.00 0.666667 0.6 0.75 \n", "3 5 ... 10 0.5 0.75 0.600000 0.8 0.80 \n", "4 5 ... 10 0.5 0.75 0.600000 0.8 0.80 \n", "\n", " FscoreG recallMultiG precisionMultiG FscoreMultiG \n", "0 0.666667 0.571429 0.8 0.666667 \n", "1 0.666667 0.571429 0.8 0.666667 \n", "2 0.666667 0.571429 0.8 0.666667 \n", "3 0.800000 0.571429 0.8 0.666667 \n", "4 0.800000 0.571429 0.8 0.666667 \n", "\n", "[5 rows x 22 columns]" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_m5_down.head()" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1224129d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "res_m5_down_RF = res_m5_down.groupby(['pattern', 'seed', '\\#samples', 'purity', 'coverage'])['RF'].mean().to_frame(\"RF\").reset_index(level=['\\#samples', 'purity', 'coverage', 'pattern'])\n", "ax = sns.factorplot(data=res_m5_down_RF, col=\"purity\", hue=\"\\#samples\", \n", " x=\"coverage\", order=[200,500,1000,10000],\n", " y=\"RF\", \n", " kind=\"box\")\n", "ax.axes[0,0].set_ylabel('clone tree distance')\n", "plt.savefig(\"downsampling_RF.pdf\")" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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isZju3Ck8qRoaGlIoFFJ/f7+lyT2ePcvq2bOt3W3W1p4vc2Ism13+Pdp9oK3m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\nywbXCgAAAACArfx+vyKRiE6cOKFIJKJQKKTl5WV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"text/plain": [ "<matplotlib.figure.Figure at 0x11fb42650>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "res_m5_down_mig = res_m5_down.groupby(['pattern', 'seed', '\\#samples', 'purity', 'coverage'])['FscoreT'].mean().to_frame(\"FscoreT\").reset_index(level=['\\#samples', 'purity', 'coverage', 'pattern'])\n", "ax = sns.factorplot(data=res_m5_down_mig, col=\"purity\", hue=\"\\#samples\", \n", " x=\"coverage\", order=[200,500,1000, 10000],\n", " y=\"FscoreT\", \n", " kind=\"box\")\n", "ax.axes[0,0].set_ylabel('migrating clone $F_1$ score')\n", "plt.savefig(\"downsampling_scoreT.pdf\")" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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lmqYWFhY0MTFRtO7JkyfV19enkZGRwrK33npLQ0NDmp6eLlo3kUjY7kUIAHAX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\nezv01Hv62KkBlh+u3tHDX21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"text/plain": [ "<matplotlib.figure.Figure at 0x11e631fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "res_m5_down_mig = res_m5_down.groupby(['pattern', 'seed', '\\#samples', 'purity', 'coverage'])['FscoreMultiG'].mean().to_frame(\"FscoreMultiG\").reset_index(level=['\\#samples', 'purity', 'coverage', 'pattern'])\n", "ax = sns.factorplot(data=res_m5_down_mig, col=\"purity\", hue=\"\\#samples\", \n", " x=\"coverage\", order=[200, 500, 1000, 10000],\n", " y=\"FscoreMultiG\", \n", " kind=\"box\")\n", "ax.axes[0,0].set_ylabel('migration graph $F_1$ score')\n", "plt.savefig(\"downsampling_scoreG.pdf\")" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false }, "outputs": [], "source": [ "res_m5_down_cluster = pd.read_csv(\"../sims/machina/results_CLUSTERING_m5_downsampled.txt\", sep=\"\\t\")\n", "res_m5_down_cluster['sample'] = res_m5_down_cluster['samples'].fillna(0.0).astype(int)\n", "res_m5_down_cluster['coverage'] = res_m5_down_cluster['coverage'].fillna(0.0).astype(int)\n", "res_m5_down_cluster.rename(index=str, columns={'samples' : '\\#samples'}, inplace=True)" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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lZaUSTRNpTii9fXvuw/Yt2Foq/9/m+sqyOOrPSldyeEuq+paWFnH27NsQBAEu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\nEWJA8kQS5du/j9LyzLcE52tlYUYc9bd+ogoiIqJ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"text/plain": [ "<matplotlib.figure.Figure at 0x11cb28690>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sns.factorplot(data=res_m5_down_cluster, col=\"purity\", hue=\"\\#samples\", \n", " x=\"coverage\", order=[200,500,1000, 10000],\n", " y=\"precision\", \n", " kind=\"box\")\n", "ax.axes[0,0].set_ylabel('clustering precision')\n", "plt.savefig(\"downsampling_precision.pdf\")" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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BZlYAKEKM//HV1tbp5Ze7E+63XNJmuX2zscohciufHgEG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\nTYr/huSM+A7n+wGKSOQ7w1dvmvfhvPPM88bjfdq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"text/plain": [ "<matplotlib.figure.Figure at 0x125837a90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sns.factorplot(data=res_m5_down_cluster, col=\"purity\", hue=\"\\#samples\", \n", " x=\"coverage\", order=[200,500,1000, 10000],\n", " y=\"width\", \n", " kind=\"box\")\n", "ax.axes[0,0].set_ylabel('95\\% CI width')\n", "plt.savefig(\"downsampling_width.pdf\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Different mutation rates" ] }, { "cell_type": "code", "execution_count": 223, "metadata": { "collapsed": true }, "outputs": [], "source": [ "res_m8_mut = pd.read_csv(\"../sims/machina/results_MACHINA_m8_mutrates.txt\", sep=\",\")\n", "res_m8_mut_cluster = pd.read_csv(\"../sims/machina/results_CLUSTERING_m8_mutrates.txt\", sep=\"\\t\")" ] }, { "cell_type": "code", "execution_count": 224, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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3b9ntUxTHgvwtFfyb0dHRgSNHjuDUqVNgjOGNN97Ali1WVsRrr72GV155BZ9+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xM7d/owPT2NW7e+XjrmwpYtW/WnAPPz83j33T/hk08+QjKZxPbtbejq+iFaWlpw8+Z/IhabzmrA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\nYGhoyOykFGWltGoik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FlEREREREREREQ6dhYREREREREREZGOnUVERERERERERKRjZxER\nEREREREREenYWURERERERERERDp2FhERERERERERke7/A7CZGPYP1IkCAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x124699e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xx=['RF', 'FscoreT', 'FscoreMultiG']\n", "xlabels=['clone tree distance', 'migrating clones $F_1$ score', 'migration graph $F_1$ score']\n", "for i in range(len(xx)):\n", " plt.subplot(1,3,i+1)\n", " res_m8_mut_grp = res_m8_mut.groupby(['pattern', 'seed', 'mutrate'])[xx[i]].mean().to_frame(xx[i]).reset_index(level=['mutrate', 'pattern'])\n", "\n", " sns.boxplot(data=res_m8_mut_grp, y=\"mutrate\", x=xx[i], orient=\"h\",showfliers=False)\n", " sns.stripplot(data=res_m8_mut_grp, jitter=0.15, x =xx[i], y = 'mutrate', color=\".3\", alpha=0.6, orient='h')\n", " if i == 0:\n", " plt.ylabel(\"mutation rate\")\n", " else: \n", " plt.ylabel(\"\")\n", " plt.yticks([],[])\n", " plt.xlabel(xlabels[i])\n", " \n", " if i == 0:\n", " plt.xlim((-1,max(res_m8_mut_grp['RF'])+1))\n", " plt.xticks(np.arange(0, max(res_m8_mut_grp['RF'])+2, 5.0))\n", " if i == 1 or i == 2:\n", " plt.xlim((-0.05,1.05))\n", " plt.xticks(np.arange(0, 1.05, 0.25))\n", "\n", "plt.gcf().set_size_inches(12, 3)\n", "plt.tight_layout()\n", "plt.savefig(\"mut_rates_MACHINA.pdf\")" ] }, { "cell_type": "code", "execution_count": 225, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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KIuPu3Tv4t3/7fwEAFs8miLYi4xprBpNs4HKyVpGaACR7cv1GcDkC2ddtnsGW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\ndqOpybhgk2Ubtm17EMeOHUa6mpq5i2gSQu4+crETos9mdGRkxriTnoa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5WwQC1vMRzjkm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\nN1evXsWpU6dKvRtEO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Tg4uOW1Y6cTFEVRSr0TRKFQCHNzc7qCGolE0r7JS0eWZczPz2tv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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x12aacae50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xx=['n', 'clusters', 'width', 'precision']\n", "xlabels=['\\#SNVs', '\\#clusters', '95\\% CI width', 'clustering precision']\n", "for i in range(len(xx)):\n", " plt.subplot(1,4,i+1)\n", " ax1 = sns.boxplot(data=res_m8_mut_cluster, y=\"mutrate\", x=xx[i], orient=\"h\",showfliers=False)\n", " if i == 0:\n", " ax1.set_xscale('log')\n", " ax2 = sns.stripplot(data=res_m8_mut_cluster, jitter=0.15, x =xx[i], y = 'mutrate', color=\".3\", alpha=0.6, orient='h')\n", " if i == 0:\n", " ax2.set_xscale('log')\n", " ax2.set_xticks([10, 100, 1000, 10000])\n", " ax2.get_xaxis().set_major_formatter(mpl.ticker.ScalarFormatter())\n", "\n", " if i == 0:\n", " plt.ylabel(\"mutation rate\")\n", " else:\n", " plt.ylabel(\"\")\n", " plt.yticks([],[])\n", " plt.xlabel(xlabels[i])\n", " \n", " if i == 1:\n", " plt.xlim((0,35))\n", " plt.xticks(np.arange(0, 40, 5.0))\n", " if i == 2:\n", " plt.xlim((0,0.08))\n", " plt.xticks(np.arange(0, 0.1, 0.02))\n", " if i == 3:\n", " plt.xlim((-0.05,1.05))\n", " plt.xticks(np.arange(0, 1.05, 0.25))\n", "\n", "plt.gcf().set_size_inches(12, 3)\n", "plt.tight_layout()\n", "plt.savefig(\"mut_rates_clustering.pdf\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Downsampling SNVs" ] }, { "cell_type": "code", "execution_count": 237, "metadata": { "collapsed": true }, "outputs": [], "source": [ "res_m8_down = pd.read_csv(\"../sims/machina/results_MACHINA_m8_downsampled.txt\", sep=\",\")\n", "res_m8_down_cluster = pd.read_csv(\"../sims/machina/results_CLUSTERING_m8_downsampled.txt\", sep=\"\\t\")" ] }, { "cell_type": "code", "execution_count": 238, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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P2LXrm1i7dj3u3r2T1g5xu11IJBLqjGiDwQC/X4AsyxgJh2DSarF9SS1q8wvTzjcSDuFY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\nTUokEgiFgupaGQBw7lyjGmhIGhwcwO3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tLVvW0jpCCLlfpR/oL178NKc9LbQcuZB0mwzk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\n5Fc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h7h+3sq0ozGSyLAuX239dx/H6q+3UI6XO2+128/jjT/LYY1uL3vtaSZsqxKyb+X2o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rQhGAxCqdzc1yRbAQUb8oRCoYxsQSOEEEJI/qEJ7NxHfyNCCCFbiVarg06ng89H9bFI5kiM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ASAQ4lHIRaGiuXnC98t2792DLlu2x+v1NTc149NG9aG7esqD3u99JkoQnnjiUdH62WKqx\nf/+hJeu1RBYu0++1xZJ5PuVyOaFUKrFr1+5YybHIpkqR3buM/0+5Vg2YdeBFBnCjTqzgLzGKUqeq\nu6A5NCDKvG2pEUEZrQoo0IE3WpKDNDoVYCkCKszxAIpcBj63ATZj4lo2egxXKcR1y0oEGgCRDZdh\nX7wkYcHI2LQIDs8xNDSYEoTM1+CgLWMWrMvlxODgQOx8mk5Hx7W0j7e3f7ag8cxHzmBDutJIAPDm\nm29iYGBgyQe01IxGI3bv3o19+/bh1VdfRWtrK06ePIljx46hubkZzz//fNrtOjo64HK50NLSkvS4\nxWJJClzkyo5YrNHRkazPr+UGPIQQQhZPLpdDpVJCoVBAJpNBLpdDqVRBkiQolZknnIwxUS9TJsXL\nJunUAAPY8DTg8SeXA4hu5w8CYw6xemRLjQguFBnAKwvBt9WK1e/LiA1NpZ2oscGp7A2vSTK1ImOp\nK65fO9kI97qSkjKUlVWguroGZWVlKC+vwLp1tTCbzSgpKcu6rV5vwJ49X4BcHr/AUas1eOyxfTmz\nE/r7+2KrVRMNDQ1ifDx1tTFZXhZLNZqamrF+fR2qqiyoqanB+vV12LWrZcXS2QlZac3NW7B37/5Y\nM9vGxmZIkoTBwfT3EIaGBmP9Ax54YGfKHEet1mDbttx1pr1eDywWS3LvARkTGQtzG4/OJUminKAy\n4Rgrk0TQgQEsXVmQaTcwlWezWLVCNICNlkORxW9Yco1SrNpNnAONOwCPX5Q5NOvAlXKxbTgixpml\nJFQuGo0We/Y8GruJ+uijj2Hv3v1rIlB/t9JotNi6dUfsd37btu15lDghK6GysgqPP74PZnMhADHH\nevjhFmzf/lDGbQoLi5O+55yvTFZBeYGYw6sUgFEjruFkElBasGQlS1eEWgleVw6+vRa8ySoCBLno\n1GLRm0kLzrk4J0SPewmBVb6SGe+MieNtYma/xMBrS5P63DF/hj4VQNqsmnxEe/iGw6nnrui9YJ8v\nc9NwpzP94vVsAYqlsuC/lFdffRXHjx/H008/vZTjWRavv/46Tp48iVOnTuHUqVOwWq146aWXMgYa\nANGrIbF8UtQzzzyDZ599Fq2trXA4HDmzIxarvDx7qmc4HKZ0eEIIuYfV1NRicnIyZVVUSUlp1gtC\nzrlIs5+78IYxgGe+ac/lMrBo+qdcBlQWYkXD2t70pRMQjohm13NXu5D0JAm8qhCsbzz5cbmMSlKt\nIKu1GqWl5RgbG4FOFw/UbdrUGKsNnM369XWwWq0YGRmGJIkSF/ncCLLbJ7M8N4WSEirTuZIYY3j8\n8f3o6bmN4WEbjEYdKiqqUVqao6QLIXc5SUo9XmW6dpXL5bG5TmFhEZ566qu4caMLTqcTZnMh6us3\n5RWcKyoqhkqlRnl5ZTywYdKJm4T5BNuNWlGew+kRGQtGLTDuAOtMEySRSaKpqcMtAgV54HXlgD8I\ndntEBEECHjFni67UTRgjcyXcRFIqAKViSedkiT04oyWsCLlXWa01sFprEsrzCBZLNQYG+pNeW1RU\nDKu1eqWHKKiV4M1WUVLW4RELwMoKRBbAPY5XFYFNziQ14OYapciCmM1w4FVFImi7knRq8AfWi/8f\nofDsOSX5/Mb1GtHcew5JkmJBrvmqqrJAkiRotdqUgIVOp4dKpc763kVFRWkXsBcWLv/vUs671I88\n8gjOnDmDF198EXp9PBJ1t62of/7557MGFxK1tbXBarWmzVhoaWnB0aNHcezYMQDA0aNH0dzcnPK6\npVJVZYUkyRCJpEay9HoDBRoIIeQe9+CDu2C3T2F4eAjBYBAymYTCwhI89NBOKBSZb7xzzkXZpLlZ\nAholMLt6jo2mSenUqlLTV1eSRiku8OeSSckrDUluVUXgSjnYyLQoq2TUgFuKVy7tmECSJOzbdwDd\n3V2w2fohk0lYv37DvJo7KxRKWK0189qv0Zj5IsxkWr46wyQzSZKwYUM9Gho2rWhTTELWmg0bNuLK\nlYspj9fV1SfdANTpdNixI/Oq40xKS8tQVWVJLVth1AImbfqN5pJYciZnqQkYmgIcCeUrGQOf7a/A\n57PamTGg0QrOGDBsB5+efU8G8NoyIKHcGlfJU9aMxNwtjWIJWWPmLuD6whf2orPzGnp6boNzjurq\nGmzevC2pPpG+EwAAIABJREFU9OGK02tENsD9RqsC32wFukRwl3MOvrECbEOFyExTyBdcOm7RGMue\n4V9qAkbsKQvnmpo2LziLVaPRYOfOR3Du3AdwOh2xvkVmcxHUajUefHBn1kVIW7fuwNmzrXM+BsPW\nrTsWNJ75yHlW/O53v4u9e/di586dKQ3MXn31Vbz66qtZt2eMLWsD5eWwefPmlPJJiV5++WW8+OKL\nALDsTd3kcjn27t2Hs2fPJD0uk8nw5S9/dVn3TQghZPU1NjZjZGQIZnMhgsHgbENRE3bv/nzW7Tjn\nYmX7QEL5IbUS3KAGry4Bio2I9I2DucSNfa6QiQmUXr3yq0USx11hBht3pPQb4BXmpFUuJE8lJtGA\njawauVyO5uYtK1oHu6amFp9+ejUlTbq4uBRlZeUrNg5CCJmrsbEZDsc0bt26EXvMYrHigQd2Ltk+\nHntsH0KhMC5fviAeKDUBjZaF11mXJPDttaI5q8srAgJqhfivxESPq3m9HwNvtIi5V++IWGldXwFW\nPOd9SguAIXtqGUmTLtb/gRCyODKZDFu2bMeWLdtXeygEEIGWmhJErt4GAEgFOnGsVa3x60CZBL65\nBuiygfeKbIKtW3cs+ty2aVMjyssrcevWDQwO9gNgKCoqxsaNDTkzlSsqKvHEE0/i978XPds459i1\na/eKNK3PGWwwGAy4dOkSTp48ifb29li/AqfTCYvFklcK+N0mnwDCcgcZEj399B/DZutHV1cHAECv\n1+MP//Ar2Lv3iRUbAyGEkOVVWVkJrVYX+zpKLpdj//5DGB4ewuTkBHQ6Haqr1+VVSoVbixHZWCVK\n6QSCgFYtbtrPruzjGysR6R6AJElgBVpRMqm6OGOt/xWhVYE3V4P1j4sLeqVc1OWsoPR+QvIll8tx\n4MAf4PLlC7DZ+iBJEmpr65IueDIdcwghZKmkO85IkoSWls9j69btsNvtMBqNS55xJZPJUFdXH+sB\nIZWZwBa7YEGSwLevB7sxCLjF6lIo5ODry0Rj5/liDDBpY2Nk6TIV1ArwJgtY76jYJ2NAkR68dmmC\nxpWVldDpdGCM5dV8m+RGP1NC7mMKGXh5Qey4vlQ39U0mEx58cCcefHD+gYuysnJ86Utfwbvvnp3N\nati2JGPKJe98v7kliBoaGvDCCy/cFT0b7nZyuRy7drXg2rVPAQB/9EeH8YUv7FvlURFCCFlKWq0O\nP/jB38W+nquiohIVFQu4IWjQgG/OUHNUYuCcIxwOQ2qygpWukfIqBg148yrVSSXkHqHT6fCFL+zN\n+HyuYw4hhCxWtuOMXm+AXn+XLVzUKMG31YqmzeGIaGa63CU9jFqxz0BI7GsJF4RotTq8/vrfo6BA\nh0AAVNptCdDPlBCy1qzGcWnBxY+PHz+O3bt3L+VYSJ5WtXYcIYSQZUM3/AghK4mOOYSQ5XZPHmdW\no4TRMvWt0mp10Ol0CATcuV9M8kI/U0LIWrPSx6UFn7EOHz68lOMghBBCCCGEEEIIIYQQQshdipbI\nE0IIIYQQQgghhBBCCCFkUSjYQAghhBBCCCGEEEIIIYSQRaFgAyGEEEIIIYQQQgghhBBCFmV5ugwR\nQgghZNXxqZkcz7vSfk3ISsv1u0oIIYQs1Fo9xyzFPGytfjZC7kX097Yy7uZrVPodESjYQAghhNyj\n+Pvt4Hm/tiPv1xJCCCGE3C3mMx9aLTQPI2TtuxuOJfcaOjbenaiM0l1i166HodPpoNfr8fDDu1d7\nOIQQQgghhBBCCCGEEEJITM7Mhq6uLjQ2Nq7EWEgWWq0Or7/+9ygo0CEQAEKhyGoPiRBCyBpksVjw\n7W//97xf7/P5IJczlJSY4XJ5EQqtnbUjcjmDwaBZU+Nai2MC1ua4Fjomi8WyjKMihBByP7BYLPjr\nv/5/19y5cS6fzwcAUKvVi34vOn8SsvTme221lNbi/H6l5HtsXMs/o/v5mJwz2PCVr3wFNTU1OHLk\nCJ544on7+oe12rRaHXQ6HQIB92oPhRBCyBqlVmuwYUP9vLaRyyWYzTrY7e41Fcxei+Nai2MC1ua4\n1uKYCCGE3B/EfGgjnYcIIYuykGurpUJz6dzoZ7Q25Syj9NxzzyESieB73/se9u/fj6997Wt48803\nMTNDTS8IIYQQQgghhBBCCCGEEJJHsOHll1/GO++8g3/5l3/B008/jf7+frzyyivYuXMnnnvuObz5\n5psrMU5CCCGEEEIIIYQQQgghhKxReTeIbm5uxvHjx3Hx4kX89Kc/xdNPP42PPvoIr7zyChobG/HN\nb34T77zzznKOlRBCCCGEEEIIIYQQQggha1DOng3ptLS0oKWlBcePH0dbWxveeOMNnD59GqdPnwZj\nDEeOHMGBAwewe/fupR4vmeXxiL4NWq1ulUdCCCFkJfh8XgwMDCxi+8xNtlaisZbFYoFarVmW9yaE\nEEIIWU6LnYflv5/cTVFpTkXIvc/n82JkZHDNNj9eKxZzHZtvE+rldK8ezxcUbEgUDTwAQFtbG1pb\nW/HWW2/h1KlTMBqNOHLkCP7yL/9y0QMlcR6PG9/85l8AAH7wg7+jgAMhhNwHBgYG8Fd/9d9WexgL\n9u1v//dVa65GCCGEELIYa2keRnMqQu59a+mYQ5bPvXo8z7uMUj6i2Q4XL17Ec889B4fDgZMnTy7l\nLgiACxfOweNxw+Nx4+LFC6s9HEIIIYQQQgghhBBCCCH3uUVnNiR6++238dZbb+HcuXNwOp0AREoI\nIYQQQpaO/PP7wcxFeb8+Yp9E+APRV0n2+f2Q5rHtYnD7JEIfUD8nQgghhNw75jsPy1e2+RrNqQi5\nfy3XMed+tlrXx8D9cTxfdLDh3LlzOHXqFM6cOQMA4JzDaDTiueeew5NPPommpqZFD5IQQgghccxc\nBKm0Yl7bhGf/Ky1g24WKrMheCCGEEEJWzkLmYfnKNF+jORUh96/lPObcz1bj+hi4P47nCwo2nDt3\nDq2trWhtbYXT6QTnognH4cOHcejQIWoMTQghhBBCCCGEEEIIIYTcR/IONnR1deGNN95ICTAcOHAA\nTz75JA4cOLBsgySEEEIIIYQQQgghhBBCyNqVM9jw/e9/H7/4xS+SAgwtLS04ePAgDh06BIPBsOyD\nJIQQQgghhBBCCCGEEELI2pUz2HDy5EkAQFNTE5588kkcOXKEAgyEEEIIIYQQQgghhBBCCInJGWw4\nevQojhw5AqvVuhLjIXnw+XyrPQRCCCEL5PG4oVSu9ijufR6PGwCg1epWeSSErK5IRLShkyRplUdC\nCCHp0Tl79dC8lCSiv0VC7k0rfazPedXx0ksv3TeBhlOnTmHnzp3YuXMn2traMr7utddey/r8cuGc\n480338T//b//CIVCAYVCgffeO4tgMLjiYyGEEDJ/Ho8bX//6n+NP//RPY5P51cYjEfCpCfDxEXC/\nH3xqHHzgjvg+EgbnHNzvAw+HV3uoefN43PjmN/8C3/zmX6yZnzMhK83lcuF3v3sH//RPP8PPf/7/\n4YMP3oXX613tYRFCSBI6Z68eh2Ma/+W/vIg/+ZM/QXd3Vyw4Te5P9LdIyL1pNe5B3DNLnJ599lk4\nnc6Mz7/66qvYuXMnNm3ahK9//espr+3o6MCrr76Kw4cP49ChQxnfz2az4dy5c2hpaVnyz5BLW9sH\nOHXq1Jzx9OP110+s+FgIIYTM39DQENxuN2ZmZjA0NJjyfCQSQSgUSnmcc47x8VEwxsAYA+ZcDPJA\nAHx4AHywD9wzk/d4+IwT+Pgc0H0NuNkB/PbXwNXzgK0XuNUFnHsXuPyReM2Vj8B7b4LfBReiQ0ND\n8Hjc8HjcGBoaWu3hELLiQqEQ3n77PzAw0A/OOSKRCHp7b+N3v3s71oONEELWgsWes71eD27duoHe\n3tsYHBxAd3cXRkaGlu1YxxgD/NkrDXCXA7y7HfyTi+A3OsBnXMsylsVwOh34xS9+jkAgAL/fj9bW\n/8Dp079BMBhY7aGRVULz57VjYmIcFy+eg0wmg0wmA8aGaf52n4lEIvj004/x5ps/x//5Pz/FO++0\nYnJyYkHvlesexHLIq0H0Yn3rW99a9Huk43Q60d7ejhMnTqCjoyPj67761a/CZrPh8OHDqK6uxsmT\nJ/HVr34VZ8+ejb3m1KlTsFqtePnll2Pf//CHP4x9H/Xaa6/hpZdeWpbPk8tvfvMrBALJJ3/O+eyE\nahjl5RWrMi5CCCGL4/f7cenSedy504NIJIKKiko89NAjMJvNCIVCOHv2DG7e7I6XQbnZCW4yg2m0\n4FMTwM1OIDKbedDfA15ZDVZTl3WfnHOguwOIXlS6Z4BAQPxTKAHOAfsUoFYDJjMQDgMjA+K1tfUi\n6DBwBxgbFs8VFALV68E02uX5IRFC8nbnTi/c7tSVS5OTExgdHUZ5eeUqjIoQQpbW9euduHTpAsLh\nEIaGBuDz+VBRUQWdToeCgkI0NGyC2WxAQUEpFArVgvfj9/tx4cJH4qYfANzqAvf7gLoGEXxIwKen\ngOufiXkUAHjdgH0CvGk7mMGU1/4YY/H52TK5dOl8SnnmyckJXLv2GR544KFl3TchJDOXy4m3334L\nAwM2MMbENdvIIKDTA7UbV3t4ZIVcvHgOly5dgMvlBMBht09hfHwUTz311buij3LeDaIXInriXY5g\ng81mw759+wAARqMx4+taW1vR0dGBX/7yl2hubgYAtLS0YN++fTh58iSef/55AEB7e3tSuajm5mbY\nbLak9+ro6IDL5VqVrAbOOcbGRtM+Fw6HceNGFwUbCCHkLvW7372Nrq5OOJ0OcM4xMjKMsbFRfO1r\nz6C7+zrGxkaSNwgGgN6b4A2bRQbCjAPwegFwQKkCBu6AFxZnv6h1TgOB2YvMgB9w2EXQgEmA2y3e\nCzx19d7YMHj1eqCnG5gYFRfTPq/YfmwIfNcXwFQLv6AnhCyey+XI+JzT6aRgAyHkrjc9bcfFi+cA\niJvkHo8HADA8PIiSkjLcvNmNGzc6UV1tRSgUwa5de7BhQ/2C9nXp0nlMTU3FH+AcGB8BtHqgck7J\n6YE78UBDVCQC2HrBy6rE3EtvTJmj8XAY6LsdD2jc6AD3+4Ha+pSAxmKFQiEMDg4kPRYOi8xVm62P\ngg2ErKLOzg7cudOL6Wk7gNn7qk47YLsDbqkFUyhWeYT3Nh7wA0P9gMshFuCVVYGZixb+fl43YJ8E\nJAkoLAXLo3GCz+fDhx++F/sdAACPxwO3243u7i489NCuvPcvjve22HkkvEKlkXMGG375y1/O+00d\nDgfeeOMNnDlzZslPjFFWqxVnz56F1WrFa6+9hh//+MdpX/fWW2+hubk5FmiIbnvgwAGcOnUqFmxw\nOp0wmZJP+C5XcrrjiRMncPz48SX+JPlhjGX9peB8eX7OhBBCltfExDg+/fQqZhJS7J1OB7xeD27c\n6EZ//x0ASK2j67QDUxOAfQJwu4Bo+SWfTwQPxkeAbMEGPvt+fh8wPRtoiEQARAC3E1CpZ1845/wS\nCYv9TY4B4ZCYPEXPT54Z4NL74A8/lnMiPDk5gQsX2uD1etHQ0ITm5i0IBgMYGLAhHA6jqsoCg0Gf\n9T0IuR95PG6Mj49Dp9OhuLgk7WsKC4szbl9YuPALJkLIygsGg7Dbp6DRaGAwZF5kd7+5c6c39rVY\n+SlEb6zI5fJYWeRwOIJz5z5ARUUldLr5Nb6NRCK4c6cn/ZMTo6nBhpk0pZ3DYaD3pliYMYsXlgD1\nTWDRrNW+W+LmVvyDiIxSrQ4or5rXmHOJ3qOZnByPfT04OIBgMASzuXBJ90XuHqtZpsfhmMbNmzfg\n83lRXl6B2tq6eOBtld2+fROdne1wuVwoLi7Btm07UFZWvmz76+m5lVpXn3Nx/PB5AQo2LBseCADX\nPo4vyAMA+yR47UawOcdhHvADwwPi+lelAcqrwOY0Vue2XhGAjuq7Db6hKec4BgdtSYGGKLd7BgMD\n/XkHG7xeD/7X//of6Om5Dblc3P5/441/wje/+V+XvQl8zmBDU1PuH0TUzMwMfvjDH+IXv/gFHA4H\njEYjDh8+vKgBZpNP4+pz587h0KFDKY9v2bIFZ86ciX3f1NSEzs7O2PcdHR04evRo7Pu2tjYYDIZV\na5adq1nT/dLEmxBC7jVDQ4NJgYaoYDCInp5bkMvlmJqaxPDwcDyA73YBGq3IcHA5gGAwvmE4DERC\ngDPzymYAgKEAkCtE0AAckMnivSAkGTC7wg1qdfJ2KrW4AOYccDnjgYYo9www1AfUbMi468uXL+LX\nv/6X2Lnt6tXLqKiogslUgMhsOSjGJOzevRu7d+/M/jkIuY9cvnwRXV3tsRsCRUXF2Lt3PzRzypdZ\nrdUwm4vQ23sLMzMzYIzBYDCisbE5FqDw+324ffsWnE4HzOZCrF+/AQq6gCVkTenq6sDVq1cQConz\nfGVlFT7/+cegUqlzbHnvy3R9HAoF0y545Jyjr68XTU2b572ftPsKBICQHXxqHDAXx/ep0oiAg9ct\n5ktyhVgEMndIU+PA2BBQbhHH9PFRIBKJv4/TDvi9YuxOOzA0AJlMBs45HI7peX2GuaLvE80GETjs\n9kko81h1S+49o6MjeP/938Vu8H/44bsoKipakeBTf/8dvPfe78FnF0L19NzCzZvd2L//UOwG6Wrp\n7u7ChQttse9HRoYwOjqCgwf/ACUlpcuyT7/fn/6JSDheNneJcM7FwrGZ2YVmRaVgq/wzXy7c5wUG\n+0R2v0IpqgHMNTKQHGiIGugFL62IBYe5zwu0f5xc7m58BLxxK5ixQLxmxpkcaADEtfbt60B1bdax\ner3eeAmtOXLdG0506tQ/oqfnVixzDRD3Hn7+8/+No0f/PO/3WYglaRA9MDCAb3zjG9i5cydOnjwJ\no9GI48eP4+LFi6vW3yDK6XSmvREffSza6+GZZ56BzWZDa2trrAnzk08+GXv9iRMn8J3vfGcFRpxe\nrkYgGo1mhUZCCCFkIWZmZtDV1QFJkiBJEqanxYWiTJb5VMwYg1arxcTEeGwCDkBctEYiQJjHMxoS\nhSM5a/0ymQyoqYsHC+QKEXCQy0Wap8REoEGfsIqSMcBaK8oGRMcxl1wOTE1m3G8gEMDp079Omihx\nznH9eidGRoYTHovg/Pk22O2pqzoIuR/19t5GZ+e1pAuPyckJnDv3YcprGWNQKpUIBoOz/wIIBgOx\n+aLD4cCvfvVLXL58ATduXMeFC234zW/+NXUlHSFk1QwNDeDSpfOxQIN4bBAfffTBKo5q7aiurol9\nrdcn1q+WIJcrkh73+30YHh7CuXMf4oMP3sXExHjK+2Va1S2Xy1FYWITpaXs8EOByiMxSnwfobgc+\nvSh6OABAgVlknnrcYp7kmZm9kZfmxtbk7Dh4RNxEnJsVEQqJm1UjQ4BjCowxSJKE3/zmX/Huu7/N\nuxyGx+PB+PhY7CZmZDaoIZPJwBgDYwx+fwAajRahdPNKck/z+3347W/fTgo+uVwu/Pa3by97yZVI\nJIILF84lX+cAGB8fw61bN5Z137lwznHt2qdpHo+go+OzZdtvVZUFkpQmq0OrS7lBzu0T4NeugF94\nH/yzy+ATY3nvh4fDQOcnQPc1cRO+pxv45AK4Z2axH2HN4X6fCA6MDYvsEJcDGLiTGphOl5kGiIV9\nPm/8+8G+1GvtSBjoT8iCm8pwDzccAtIsNExkNBpRUGBOeVySZKiv35R120QdHe1pH+/qytzzeKks\nKmR17tw5/PjHP0ZbWxs452hqasKLL76IAwcOLNX4FiWaNplOtM+DwyFWfra0tODIkSM4duwYAOCl\nl16KlV46deoUdu/enbU3xHLLlUJGKfGEELJ2uVxOvPXWbzA2Nhqb1LS1fYji4jJUVFTBaDTBOScT\nQalUoa6uHgMD/dDrDZicTLgw5lysiPO4Z+sCcyQtmYsGDnIpKQfKKsWEh3MgWnolFBJNoZt3iPTQ\nGQegVIv0UJOY+PCScmBsZHbfs2QyQK0VwYoMbty4nrJiR9xIESvqSkvjq4Q4B27duoUNeaSbEnKv\ncjodsNn68cknVxAKhVJW+Q0M2ODz+aBOyEIaHh7C6OgwSkpKk1be3bp1A83NW3HlygX4Ei+aAMzM\nuPDZZ5/gkUf2LO8HIoTk5caN7rSPDwz0w+v13PelBouLS7B58za0t3+KoqJieL0e+P1+VFVVYXx8\nHAqFAiUlpfB4POjr6wPnERQVFaO39zb6+nqxf/8hlJWVo7OzHV1d7XC73SgqKsb27Q+iqsoS28/k\n5ATGx8fhdosbcIwxEUDQGeOLL7weUSapYYvooaU3iEzPSFjMiRQKIJTmpu1sgINJMnC1RtyEiopE\ngEhALPSYngQSgk5jY6M4e/YMJicn8OUv/1HG1d+hUAjnz3+E3t7b4JxDJpOhqWkzNm/eBo/Hg1Ao\nFAuyMAYEAn7MzNx7NxpJdr29PUlBzSiPx43BQRuqq9ct277tdju8Xk/a54aGBtDQsHrXAIFAIOMi\njOiiseXQ2NiMvr4eDAwk9JDV6ETvgIRMVm6fFMHOaKDU7QJudoCDgxWX5d7RyIBY5Z8oGAB6bgCb\nH1iCT7Jw3OeNZVtk7UGYr5FB8dkiEZGZ7/MAAR9kMplYAOewA6UV6bMdANHTUJGQ9eXIsBjO5QCP\nROLl8RbIYqlGTU0t5HI5HI5phMNhaLVaVFRUoaGhMe/3CWZYfLgSQeUFBRvefvttnDhxAjabDZxz\ntLS04KWXXppXyaWVEA0k5BskOH78eCwTI3GbkydPLqh3xVIyGrP/gblcLkqnJYSQNeratU/hn9No\n2eVy4uzZM/jc574Aq7UGvb234Xa7wTmHRqNBSUkpZDIZhoaGZlcmJ0wKQiFAHhYXrzKZuDiVy8VE\nSJJEsKFI3GDkoSAwPgopOulJGAdjDLyqJjXFU6EEajeCqTVAbYZminUNIu12ZFCsyFOqxMW1JAFp\nJrjRCfPo6AgCgeSJTygUQjgcQTAYjJ27AfFW/f39AOQIhfKvI5s0OSfkLnbt2qe4evUyAMBm64/V\nMp5bt33uRUNKU/kEo6PDKY1Bo2y2fgo2ELJGzD1X5vvc3SzT+TsUCsFm68PExDiUSiUslmoUFRXD\naDRh8+ZtGB0dQWWlBXK5HIFAAFVVFgwPD8HjmcHk5AQCAR9MJjO8Xi+8XhFoPXv2DEpKStHd3RXb\nj8PhQG9vDx5+uCVWPubixXOYmXHBaCzAyMhsSUuZAlAqxR16r0fMw/w+8A2N4uadVif+RSJiMmOf\nTJ8NWpTQd6esUgQsMBvQiN4kkslFPy4W77UQiUTg9bpx5colRCIR7NiRvqFzV1dHSr+JDz98Hw6H\nA6OjI3C5nLH39Hg8CIcjKYFocu/z+VLLxkSDa7dv30IgkBqIWCy5nMFg0GB0dDJp7p9Ip9Pj1q2b\nS77vuUKhIG7cuI6hoUFwzlFWVo6NGxuhUqng9/vT/nxUKvWyjq2qqhqTk5PxYKDJDGxsTn7RYF9q\nM/ro4/kEGzKtvHc5wIMBMMXiSqqJMkJ9IotApRLBkrLK7NtwDvTeEBkIs5+N6w3Apq15NVbOyD2b\nSTA9JY6tfh8QDsWyxWDrAddoRX+cidHkn2skApjNSFpgp1AmXVPHyBXivACIa/HBvvSvSagckOm8\nV1+/CT6fN5blYjQasXnzNvT1pXnPDHQ6Pex2e0rppcLC5S+PNq9gw09+8hP86Ec/gtPpBOcchw8f\nxvPPP79m+wVEGz6ny3CIPja3KfTcwMTJkydx5MiR2OOtra145ZVXAAB//dd/jYMHDy75uNOJrkTI\nnMZGDaIJIWStGhsbBQCEE1asdXV1oqurE7/9regfJElS7IKPc44bN67jo4/ej5Vdiqa5izcKidVy\nKrVYVeedXXWjUIib/sYCoLwKPBgQKaP2yfi2t6+DG0xgBbOTDMs6MaEaHRRBDKUasK4Dy9JgFpgN\nVOx4GLh+LbmhYXEZUCFWBPKE1RQ/+cnfx75OtwKPMQaPxx37WUWdP38u6zhy8fvpopncnex2eyzQ\nAABqtQZ2+yRu3rwBs7kQJlMBzGYzCguLoNcnr3BWqzOX19RqtZDJ5GlXMVLPBkLWjoqKCoyMDKU8\nrtPpcy5Eu5sknqcT5wqJEudIUZFIJGdD29iNJACjo6NZX5vo0qXzsZszaSsMSJK40TQ5ltC7igEd\nV8Xij+j8J7rQw2gSq2kTmYuAsoSGoyXlgE4fX2ksycT2oRDAg0k3vwIBf2wRy9DQAH796/QLIzNV\nR7h8+ULKzzQSicDv9+Us30zuPRUVlfjss6tJ1ynXr4t+phcv5j8Pz1RjPpd0f99i32H827/987zf\nb6n2Hw6Hk44hc5/793//t2UfW/RnymrqIM2d22XICMn4+FyZVt8zFrthzj1uccN8xiWuOyssYObc\nVVW4xy1KNEWPj6Eg0NMNHg6BVVZn3nBsGBidc96bcYkAxKbUfjvc5wMmR8V+zEWZsyDUGhFECAbE\nMTUcjvUqZIyJXocDfeKYXN8E3OgUmf3BYDyAcOWcyOyvrZ+tDJCmkk5pRex3ien04DV1orRS9O9C\nJgM2NCaVqsp03kvn7NkzuV80x9y5vSTJ8NRTX5n3+8xXzmBDtOnzj3/8Y3DOYTQa8dxzz+HFF1+E\nwWDItfmqigYIsqU4Zct6cDqdOH36dCyrwWaz4dixYzh69ChcLheOHTuGs2fPrkiwRaFQZK2X98kn\nV7Bv39ooX0UIISSZRqNJKZM0V+IkNxpYiKe2z5kAh8PiH+eAqVBM4AJ+kdmgUgMbm8FUavC+28n1\nJQExseq7DUSDDe6Z2AQNepOYJOWZ+snkCmDzA+DRdFSdAUyry7ldulIw6ZpdzacBFiH3mr6+3tjX\nopHnzGwWUBhu90ysD8PBg3+Ysm1tbR0++eRKyupnvd6AykoL6urq0d3dmbJdXV3mxu6EkJW1aVMT\nent7MD0dL9fAGMPOnQ+nvTF2r0pabJFAkqSc9eQ55+JG3RL+vDjnYplfMCgCC1EqlSivpNam1vJm\nkrjVD9NNAAAgAElEQVRRpjeK7QzGWBPR2EsUSvDESgXRjLVIQjAjg/ne5E33M41uT8GG+09ZWTlq\namoxNafnWr7z8HQ35OfT6yESiaQNfq2UTMeH6N9VdHwAYt/ner/otgsJviSKbZ9ujFpdahkkQJRc\nykdxWfrtC4rA5AoRMGj/OF7ezecBnHbwDY25yzQN2xICsQkG+8HLLZmvNScyBIXtE+ChUFLzaj4x\nBtzqjN/IH+wDL68Cq92Yun15VbyfQrTRduL/G79PlKubccbLBEsyIDAjrrPVakDBgLEhQC4Dq9kA\nHvADQzbx85EkETC21oJHIuJcIJODVVaDF5aK95YkoLBYXD+vYF+MYDAIuVwe+53cv/8AHnhg57Lv\nN2ew4aGHHgJjDFarFc8//zyefvrpZR/UUrPZUtNSrl27BiA1syHRiRMn8MILL8S+jza/fvnllwGI\nXg5vvPFG7PvllOuA1tNzCwAFGwghZC1qaGjC6OgIZLL4abexsQnNzdtQX78JZ878B/r778ym9nMo\nFEqUlVXgwIEn8atf/TPGx8cQDocRDM6uRI6m13MeTwvVGwGDSayYGLgDXlgcryc59xzimRFZB1MT\nYqVIdLI1Pgo47OAbm+d1Yc4MRsCQGrxPTL997rk/h8USD86LlOVu+P0+rFtXB7PZDI/Hg5GRIUQi\nEZSVlcNsNsFg0MDl8uZdRslm68OVK5fQ3i4aunV0tKO+vgHKxaTeErIKEv8EZ2Zm4Pf7odMZEAwG\nYDAYodfrYTKZ0mYjqFQq7Nt3EG1tH8RuVJaUlGLPnkchSRIeeOAhuN2upNTt9es3oLl567J/LkJI\nfpRKJQ4degq3b9/AyMgINBotNm7cFCvvc69QqeKrdefOFQCxqG54ODXDAwAefrgla+9CuZxhcnIU\n589fSJkKNTY2o7f39pwSKRxerw96vR5NTZtRUVGJW7duoqfnFtzumdhqb9GDISE7TCaPz4MYgAqr\nWJ0bDgIuFyCTRNlJ5QRQU5cSaIiRZPH5VySCpLIdCV8rlSoUFBSgoqISMpkMjz76RWi1Wsx1/vyH\nsNtTa4uXl1fg979/B4FAEJFI/IYg5zw+1yT3lUcffRyMMVy9egUA8OUvfw1bt27Pud3U1CQuXGhL\nedxoNGHPnkczbhcto5Q4x3c6HQgEAjCZClYs09Jm60N7u2j2HAgEwDmHUqkEYwx1dfXYuLEh7/ey\n26dw6dL5pECLWq3GI498DiMjQ+jpuYVAIAClUol169ajri5DqdpZAwO22Kr3tCWNqqpFdvncgEZV\nTX4DLq0Q248nlN7U6oH1szfrh/qT+8gAYl+2XvCi0uzXihl6XYgFcgFx8z6dTPc9ORdle6PfhsOi\nofXczz4yKMY2N5ir1YNv2gp8/FF8IV7i+JkkytVNTYjAABB/XSQiMh+iZe/GhsGr68As68ArrOJ1\nShWYQgE+Ogx0fCyqDjAJvKgU2L4LrDwhiw3Zr5EzmZlx4ZNPLsPlEoEKhUKBxsbNST2G0m1z/vxH\nmJ62x85fIyNDsNunln0uMa8ySidPnsTJkyfntQPGGM6cmX+qx1I5cOAAOjtTV251dnaiubk5Y2aD\nzWZDe3s7jh8/Hnusvb09KYuhubk57Xsvh1wR0VwNpAkhhKyemppa7Nr1CN577/exx5qbt+DJJ5/C\nwIAN/f134PF4Yhd8nPsxPDwIt9sFzqPHeJZ8ARjhYsLm84jJksEoVthFIsDkOHDtilgV53IACTV5\n4XIAGq3Yvu9W6iRtahywTwCFJbNj4WK1h0IBJkudNnDPbGaEzpA1I8JisWLDhuRJdbqmb1u3bot9\nLZdLMJt1sNvdCIVyr3AaGRmGzdYHlSre3Gt0dARtbR/gsce+mHN7QtaSmppafPrpVQCIlctgTNQJ\ntlqtsRquU1OTaW+2FReX4Etf+upsTW4pqdSSQqHA3r1PwG63w+l0oLCwMKUPBCFk9SkUCjQ0NKOh\noTn3i+9Cvb238f77v49dy3IeSZkrTE1NZGzSumlTA0ymDDfuIeYRO3fugF5fgE8++QR+vw9KpQrN\nzVuwZcs2dHd3xW6URiIRDA7a4PP5YDYXoL//DsbHx7Bv3wEUFBTEbsICAGo2iAUdwYBY5KFSx29c\nSTKwdRvALTVA52fxGIHXI+Zg01PgOx4BKyqJz7FkcjCFYk75kznzMyYBPBzr7WW11sBgMGD9+g1J\nc6dERqMRZ8+2JvX10Wi02L//ED766L2U7DfOOdSZbgKSexpjDJWVltgi161bt6f8LabT1jaScQFv\nUVExzGZz2ufmO8dfLkajEbdudWN4eCj29yCXK1BeXo6mpmasW7c+53v4/X5cv96Bjz56D16vFwUF\nBdDp4nOunp6bcDimodFooNGI4Oro6DBqatYtqgE2KygC37RFlDnyuAGNBqisAUvsBZNte8ZESZ/K\n6lgzZhgL4teMM670G/q84hozW0BIo01fZkiuAJRZtjMXpd/OYEoOuDjsqYGQqKlxUVJ4DlZRBV7f\nBNzsApx2cQ0cDQzJJJGd5piKb5AY+AgFxf5kcvHZIxFAJgOTyUT5O0Bk+l/6IDkQPdQvsjIqLPG+\nhuXJwQGLxYqqqip8/PFl2Gz9kCQJtbV12L79ASgUCni9Xly+fAHvvvtb+P0+6HR6lJSUQqlUor+/\nF1u3bs/4d/buu7+FRqOBK6GMXyAQxNWrV7B37/70P78lklewgXM+26Dx7vPkk0/izJkzaG1tjfVX\nsNlsaGtrw9GjRzNu99prr8WaRSdKPJBmy4pYarmCCY89tm+FRkIIIWQhGhqaIUkyfPjhewCAzZu3\nQJIk3LnTA7d7JimDLRwOIxKJoKurEwUFZjgcdoTDCRMeSSYCC5IkalBq9WLyFgiIxlc8MltmKSLS\nY6WEc0goKCZI0SBBOtNTQGEJ+PiISDkN+AFJBl5SBqyrB5MkcJ8XuNkRn4gqFODr6nOn1S6jGzeu\np33cZuuD1+uNTfAJuRsUFJjx0EMP48qVi5DLxYUZYwzl5RWxQAMgSiNlky2IYDabM16gEELIcurv\nv4MPPng31ogWANrbP4PFYkV9/abYY/X1m9Dd3ZWy+K6srDxroCFRc/MW1Nc3wufzQqVSx66tN21q\nhFKpRFdXB3p7b0OSZLBaq6GaLWfk9Xpw5cpF7Nt3ECZTQSwwIVVaRQmTybHUnUXnQZEI4HEB4KJB\ndMINf3zcBt60Q9Qnn100wguLM988A5KqKKnVapSWlqGhoTFrRlppaRmeeuor6O7ugsvlQmFhETZu\nFNmeKpUaHo83Nv9kjEEmk+U8pxCSKFsmTLreUGtNUVEx7PbppMBbKBTExMQESkpKc24fDAbQ2vrv\nsNunMDExDkA01y4pKY2tHL9+vRMVFamNkbu6OhYVbAAg+ifk0UMh63tEG9rPpVLH+wImkiuSS8il\nU2ERx8e5mQoVFjApy73NCqs4XiYGHOQKYN2cwJeUJauCZSkHvKFJZDD0hgCvB5yL/+9MqRYlhlXq\neONnpRLwJh6TZ/epN4ogw1w915MDDZyLa2i/VwRflCpx3eyeSQqGRCJhnDnzVlLJ5a6udtjtU9i/\n/yB+97u3MTBgiy08crtnZisDrIckSbh9+wYeeujhtB93YMAGm60fTqcjFkQaHR2GMVN23RLKGWy4\nfj39hfta0NYmTvbRMkltbW0wGo2wWq2xDISDBw+iubkZr7zyChwOB5xOJ370ox/BaDTixRdfTPu+\nHR0dcLlcaGlpSXrcYrEkZTK0t7fj0KFDy/HR5m0luokTQghZHCnN5Mrv96ctlcc5h8/nxfbtD2By\nchwejwc+n1fUClapgEorsP0R4NOL8Y1c0/EUU7lcTJaYlJR2CoVqtvFXloHK5ODTU8Dt6/HMh0hY\nlARgDKjdCNxoF5OlqGAQuNUFrtXn1bdhOSSXQojjnMPv91Gwgdx1mpo2o7p6He7c6UFb2/tQKJRJ\nC1CKiopRVla+iiMkhJCF6ei4lvbx9vbPkoINZnMhHn30cVy8eB7e2ZX/lZVV2LPnC/PanyRJ0KaZ\nn9TW1qG2tg6/+c2/wm6fSnl+aGgQwWAQ6rnNWWvrxU2kxNW/RaXiJhsg5kWci7lSaE4Qwe8HPrsI\nFMzeJORcZKXm0diVc45HH30chw49lfO1gAg4z70RFQwGYTYXwufzweUSN8cUCgX0ekPeARxCALEq\nO7HHVJRarUFRUfEqjGh+BgZsKC0thUwmwel0AuDQ6w0oKipGf38fGhuzZ5XdvHkDDsf0bJ8GCXz2\nmmtycgImUwEkScpYEj0x0LomVVrFSv+5WfAVlpzldpnOAN64DbD1Ai6nyBooq4ofHzNtJ5OBN+8Q\n5YxmnOIGfUlZahkpo1k8F/DPeQMWD/ime39JEuMKBgHHlOhvCNHMGeUWUSkg2sdCpxfH6khYBB5k\nMrGAr6Yu/ZvPzQSJxJtQIxgQ4wVEX4qE/jzDw8NpezuOjAyhq6sDk5MTSeXuANH/0OVywWQypWSo\nJXI47PDN6d8YCAQwPZ16rltq8yqjtNY8++yzSd8fO3YMgCid9Prrr8ce/9nPfoYTJ07gxIkTcDqd\nOHDgAL7zne9kLKF04sSJpPJJUc888wyeffZZtLa2xgIXR44cWcJPlF3iwWuu6Wn7PVe/kxBC7gdm\nsxlyuTwpxR0QF8Umkxl79jyKoaEBdHV1xptE6o3A1p1gWh24uViUPQqF4hezMpnIePB5RYorY+Cz\nEyCmN4iJmFYvVlnMvbBlTDS4svWmTi4BYGwEvLAkOdAQxbmo+5lpErbMysrKMTKSWtdZo9HCaFy5\nbERClpJer8fmzVthtdbg4sVzGB4eBGMSqqtrsGvX7vuqUSwh5N4hbuylSiz3EFVTUwurtQYOxzSU\nShV0uqVf1JCtSWy655hCCWx5CNw5LeZbemPyYguNFlAoU2+GAWK+5PeLG1GJJSg50je0ZgyQ5MBs\n5mt9ff515NORy+Worl6HmRlX7CaXXm9AYWER1q2rXdR7k/tLbW0d7tzpweDgQOwxxiQ88khLStPo\ntSgQ8EOSJJSUlKZkMvj9af525xgbEw2NGWMwGk1wzPbLi0QiswudtFi3rjZtBkg+mROriZnMouyQ\nrVdcLyqUotFynj0hmLEAaN4x//1KElBcKv5leQ3f2Ax0t4sb+YA4llbXicBBtvdXKMA37wA6PwX6\nbousuQoLsG6DaKLscogFdjI5EM04MxeLQERZVeZFdSZzcv+LaKCBMUCZUJ6O86Trb7c7Q7kqiBLB\nAKDRaFLuBwdnP3dlZVXabcWu0/8NZnp8KS15sGFmZiapJuxy6u7uzut1RqMRx48fTxtAmKutrS0p\nMyJRS0sLjh49GgtqHD16FM3NK1c/U6vVpo1+MsaSasIRQgi5e1itNTCbCzE9bUckEgHnItCg0aix\ndet2qFQqPPPMn+DDD9/HP/zDD8E5h3zrQ2D62YB5dBI4MjBb0F0N6AxiwqVSiQnY3AmF3gimVIFv\n3Azc6IinyMoVYqKl1YEH0mcJIBKOp5ems4op0w0NTejtvQ2HI746hDHgoYd23RUXPIRkYzKZsH//\nQYRCoVi5C0IIuVsVFhambfycaQGdJEnLuriutrYOU1OTKY9brdWQZykZwowF6euDSxJ4TR0wNucz\nyhViUUgknNygFIjVQOeciz5Z0RtLMjmgViHkdefs5ZgPxhi2bNmGyckJDAyIKhEVFRUoKirJWpaJ\nkLkkScLjj++HzdaP4eFBqFQq1NXV3zWLfMrLU8sbRaUrfTRXYmP2kpISRCLhWMBUqVRh69btqKqy\n4u2330pqHC1JErZvf3ARI18ZrKgUKCoFD4eSG9ivAcxgAn/gEcA+JY6nBYXpG2mn21alBq+qRviK\nqJYjS2h4zWo3gpdbRI8dpRIwFeb3uesaxDV59Do5uo1Gm9rfQhnvL6jTGWIluOayWqsxMNAPSZKh\npKQkFtwCRA+3ysoqVFevyzikwsIieDxujI/HS/7p9QYUFy9/1lHOYMPAwABcLhcaGxszvqarqwsn\nTpyIlTUyGo04dOgQXnrppRULPCyVzZs3p5RPSvTyyy/Hyi9lyoxYLlu3bsWFC8nd7QHxC7TWo6KE\nEELSKysrx86dj6Cj4xqmpiYRiURgNJqwbl0tGhrEuVcul8NiscbTcOXxCQuTyYB1G4B1G8DbPxYT\noyjtbPpnYmNnmUyk/kPU6OTbdoq0z3BINN+K3sA0mNI3BlNrxCqPO7L0PR9Mq5dlp1KpcOjQU3j3\n3d/i8uULAIBHHvkcamtXJ9OCkOWQ7aYXIYTcLbZu3YGRkZGkxxgDtm9/YFXG09jYjPHxUfT398Ue\nM5kKsGvX7gW/JyspFyU7bnSIVa4KpaiN7vOIRSFzb2BptOBTIpjACovFnC0YFOU86hrB3/63BY9l\nrs2bt2JoaBBXrohynAUFZnzuc4/BYklddElINpIkoaZmHWpq1q32UObNYDCguXkrOjo+S3q8trYu\nrzKV9fUN6O6+Ds4jkCQJFRWVKCkpQWFhMQ4degoqlbipfOjQU+jsFHX4CwrMaGrafFeUmYpisrU5\n92SSDMizIfa83lejFUGCeW7DWx4XGRMOO8A0AEdqMNpgSuqRUVFRAbt9MqWUUnl5JerrN2Fw0Ib+\n/j4UFJihUqnhdDqg1Wpx8OAfoLa2LuuCunXrahEI+CGXKzA6Ks63hYWFqKlZ/gy2nL8xr776Ks6d\nO4ef/vSn2L079UR75swZfOMb3wDnHFarFU1NTbDZbHjjjTdw+vRp/PKXv0RVVea0jrUmnwDCSgcZ\nog4f/mP09NxOmpQplSr85//87JqKMBJCCJmf/fsPQqfTYWhoEIBY1ffwwy3QzHOSgw2NwPVr8UwF\nSQI27wB8PvChfnDOIdU3xbMiMFs2IF0D2QorMDEWT00VLwas68HkCvDqOuDOzeRSS6bCZZnwzUd0\nRVU0MFNQQM1vCSGEkLWmrKwcTzxxCL///VkAYjX/gw8+DKs1vxIdS02SJDz22D5MTIxjcnICer0B\nlZVVi7/OXlcv5k8jg/E5U7lV9HuYU0sblhpEbL3i5pFaI2526QzAps3i5tUSYoyhrq4+tpDxscf2\n0uIMcl968MGdKC+vQG/vbUQiEVRX5x84MZvNeOyxL+LSpfOYmXGBMYb16+uxe/fnYoEGQCwQ/tzn\n5tdnhtx9mKkQ2P04eDAg+jv4feJ62WEX1+WFJeKcYJ+IbSNJMjzxxJO4evUybLY+MCZh/fq6WObL\n5z//ODo729HbextGowk7dz6MzZu3QanMncWxY8eDGB8fS8r61+sN2LHjoaX/8HNkDTbYbDa0tbXh\nyJEjaQMNNpsNx44dA2MMR48exUsvvRR7rq2tDX/2Z3+GY8eO4Z//+Z+XfuT3ocLCIhw/fhwvvPDC\nbKkNjr1792Pr1u2rPTRCCCF5qKyshE6nA2Msqb6iVqvDvn0H4fV6EA6HodcbFvT+TK0RmQouBxAI\nAEaTKJc0Npw2KyLre6nU4FseBIYH4g26yqtEuQAArLwKXKcXtSnDInUVCSmoq6mysjLWBLKyMncK\nNCGEEEJWXllZOb70pa/EAg7btq3+dW1xcQmKi5du4QRjDFhXD15ZDXhmAJUaTKMDDwVFAMJhFxmo\nZRVAMAjOOcLhMKTq9WLeNbtAZPHFk1IlzkurqrI3biX3vvt5/lxVZVnw34DVWg2LxYqZGReUSiVU\nCc1/yf0pVs5JqwOatoOHwwCbzcRA6vFcq9Viz55H076XTCbDli3bsGXLtnmPQ6VS4w/+4Mu4desG\nrl37BIwxHDnyx1CrNfN+r/nKGmwYGBgAYwzPPPNM2udPnToFQPQySAw0RB/71re+hb/5m79BV1dX\n1jJMJH8VFRX4T//p/8HPfvYTAEBJSeZO64QQQtYWrVaH11//exQU6BAIAKFQJOn5eWcypMEYS1s7\neEHvpVKLEk2ZnjeYRCroGqPV6vCDH/xd7GtCCCGErE33yzmbKVVJdbqZXAFY1ol/s/jYcHwDY0FS\nJupyyDUvJfeX++VvcTkwxmBIlylOCBAvU7wKJEnCxo0N+J//84creqzP2i2xo6MDANI2SwZECSXG\nGA4ePJj2+T179oBzjvb29kUOkyTKJ12GEELI2qTV6qDT0QR+uWm1OrpQIoQQQu4CdM5ePTQvJYno\nb5GQe9NKH+uzBhuiQQabzZbynM1miz1+6NChZRgaIYQQQgghhBBCCCGEEELuBlmDDU1NTeCcx8ol\nJTp58mTsNXq9Pu32bW1tYIzBYqH6f4QQQgghhBBCCCGEEELIvSprzwar1YonnngCp06dQnV1NQ4f\nPgxA9Gr4xS9+AcZYSq+GRG1tbQCALVu2LOGQCSGEEEIIIYQQQgghhBCylmQNNgDAd7/7XbhcLnzv\ne9/Da6+9lvTcX/3VX2H37t1pt+vs7ERbWxsOHjyYMfOBEEIIIfPH7ZOYT1uniH0y7dfLja/gvggh\nhBBCVsJ852H5yjZfozkVIfev5Trm3M9W6/oYuD+O5zmDDQaDAf/wD/+A1tZWtLW1YWBgABaLBc88\n8wyampoybvfDH/4QRqMxa+YDIYQQQuYv9ME7C942/ME7CC/hWAghhBBC7ieLmYfli+ZrhJColTjm\n3M/oeLv0cgYbog4ePIiDBw/m/cZ/+7d/u6ABkdwefng3/vEf/zcAYNeuh1d5NIQQQggh5P9n786D\n27ruPNF/74IdIAnuC8BNlGiRkizZkW3Ji7wolu1OOmOn23a/qn5Vnra731TPxC/97LdMV5xqJ6+n\neqyqlPOqpydR5SXzZnoStpeetBNLlmXZlmTtsiiJq7gTILhg35d7ce/7A+QlIQDcSZDU7+Mql3Av\n7sUBCNx7zvmd8zuEEEIIIYQQcrdbdLCBbBx6vQE/+cnfK/8mhBCy9VksFvzwh3+77ONjsRgAQKvV\nZuzjeQYmkw7BYBSiKC/7NeZjsVjW5LyEEEIIIWttpfWwxZqvvja3LISQrc1iseBHP/oPa95G2+xW\n0o5dzPV2rW3V6zkFGzYpCjIQQsjdRavVoalp+5qcm+dZmM0GeL1hiCJlBCWEEEIImWst62GEEHKn\n1DVnB7XRFkDt2I2JzXcBCCGEEEIIIYQQQgghhBCyuVGwgRBCCCGEEEIIIYQQQgghK0LBBkIIIYQQ\nQgghhBBCCCGErAgFGwghhBBCCCGEEEIIIYQQsiK0QDTZEmKxKOx2O0QxDqNRi8pKK3hek+9iEULI\nos1cx+Z/TgwAoNVqV/W1eZ6ByaRDMBiFKMqreu6VWE65LBYLtFrdGpeMEEIIIXejxdTXUs9b3Tob\n1W8IubvEYlFMTIxtyDbaRrIe7Vi6ni8dBRvIlmC32/E3f/Pvlcd//dc/xD337MpjiQghZGnuvI6R\n5fnhD/8WTU3b810MQgghhGxB+aqvUf2GkLsLtQ23rrvhek5plMiW5HRO5bsIhBBCCCGEEEIIIYQQ\nctegmQ2EEELIBvPAY3+GwmJL2ja/x47LZ36Rc//dbO5nQwghhBCyHnLVx1arzkb1G0IIQG2/fKLr\n+fJQsIEQQgjZYAqLLSgtb1r2fkIIIYQQsrYWUx+jOhshZKXoOrIx0N9h8SiNEiGEEEIIIYQQQggh\nhBBCVoSCDYQQQgghhBBCCCGEEEIIWREKNhBCCCGEEEIIIYQQQgghZEUo2EAIIYQQQgghhBBCCCGE\nkBWhBaLJlhCJRJR/MwyD0dERuFxTKC0tz2OpCCEkUyQShlqd71KQrYa+V4QQQraaSCQMANDrDXku\nycYiSRIYhgHDMCs+F9UfVh99pltbKBSCLEswmQryXRSSZ5KUhJCIgFdpwXGqfBdnXut9Xdr0wYZA\nIJB1e0HB8n74bW1tOHr0KADg3XffxcGDB7M+75133sHDDz+ccz9ZH07nFE6fPomTJ49DpZr9cZ8+\nfQqnT5/C97//v2PXrnvzWEJCCEmZmprE1auX8M///B4YhsEbb/wf2LGjNd/Fuus5nVMYHh4CwzCo\nr29AaWlZvou0ZJFIGH/1V38JhmHw7rv/CWq1Lt9FIoQQQlYkEgnj+9//SwDAT37y96sacBBFEUND\nA/B43DCZCrBtWxM0Gu2SzzM2fA1jw1dhLKiAteFBqDX61PmFmBIMcE/1o7CoBqpVuDd7PG5cu3YZ\n4+MO8DyPbdu247779qe1g5eC6g+rb7N+ppIkYXh4EKOjw2AYFo2N22C11uW7WBuCJElwOOyYnJzA\n0NCAMtC1qMiMgwcf3ZRth/WSiIcQDrqg0hhgNG2tz2nK0YWJsVsQhThYjkdZxQ5U190Hhpk/gRDD\nMOjsvAWNRgOLxboqQeOF5OO6tKmDDSdOnMDrr7+edd8bb7yB1157LW3bW2+9hePHjyMQCODIkSP4\n8Y9/nBaU6OzsxFtvvYVXX30VwWAQr7zyCq5cuZIRuLDZbLhw4QLefPPN1X9TZNECAT+OH/8durpu\nKaM7ZsiyDAA4ceL3aGzcDr1en69iEkIIXC4nTp78GF6vF4IgAAA+/fQTABx27LhnzV8/EY8gEnZD\nrTFAbyhe89fbLL7++io6Om4oj7u6bmHfvvuxe/fePJZq6RwOB8Lh8PS/x1Bf35TnEhFCCCEr43A4\nlJkNDocDTU3bV+W80WgUJ09+DL/fp2y7desGnn76OZjNZkiSBJttBFNTk9Dp9Ni2rQk6XWZbkud5\nOEaugePVmBzrgG3wEvY/+ipYjsNw3zmwbKrDyT3Zh3g0gB27noFWt/CAyEjYg/HRdgQDExCEKBiG\ngSzLiMdjOHnyOBKJOIBUwKS3txvhcBhPPvnNZX0WVH9YfZv1Mz179guMjAwpj0dGhrBzZyv2738o\nj6XKv0gkjE8/PQGfz4vh4UEIggCDwYiqqhr4fF6cOnUCzz//IjQaTb6LuuHYh65gaqIHmO6b0xtL\nsO2eJ6BSb/6+OY9rCPbhq8pjKSli0tEFhuNRbc1sR8ZjQXidQ+A4DgAwOjoMv9+LuroGPPbYEzNF\ncG8AACAASURBVBkBB4/HjdHRETAMg4aGRhQUFK6ovPm4Lm3qYMOMd999NyMgsGvXrrTHL7zwAmw2\nG1588UXU1tbi2LFjeOGFF3Dq1CnlOW1tbbBarUoQoa2tDT/72c8yggrvvPMO3njjjTV6N2Sxenu7\n4fN5IIpizmigwzEGm20Ezc0717l0hBAyq6PjJvx+P2y2EeV6FQj4cetWO7Zvb0YiEceNG18rFRDb\n4GUYDCXQGcwAgGBgErbBy+A4DrIsw++xo7R84UqCLMsYG76aVtEzFpSjsfkJ8Kq7u1Ls9XrTAg0z\nrl+/hoaGbTAaTXkoFdmskskkRkeHEQoFUVJSiqqqmnUZqUQIIWRpOjpupAUaACAej+Hq1Yt44olv\n4rPPPsHk5ISy7+bNdjz11NOoqKhUts3U1+ZKxEPo+PoD6I2lSArxtH2iEMO47QYadjw6b9nisSBu\nd5yAlBQBAEI8ApZlIcsyzpw5jVAohIKCQuh0s6NS7fZR+P1+FBaurDOKrA5BSCj3f1EU8lyaxZmc\nnEgLNMzo7u5Ec/POFXd0AkAoFIQkSatyrvV0+fIF+P0+RCJhZcBYOByC1+tBSUkJEokEhoYGcM89\nLXku6cbicQ5iarw7bVsk5MbowEVs2/nkgsfLsgxRiILj1WDZ5Xdb+9wjcE7chihEYSyoQEVNK9Qa\n47LPN8M53pN1+5SjCwxYBP0OcJwaOkMxfO4RRMNeTE10K8HjGSMjQ7Db02cRtbdfw82b7crjGze+\nxoMPHtx0fZpbIthw8ODBedMmnThxAp2dnfjwww/R2tqqHHP48GEcO3ZMmQHR0dEBq9WqHNfa2gqb\nzZZ2rs7OTgSDQUqftAEEAgGIoohYLJbzOcFgAOFwaB1LRQghmUZHR9Dd3YlEIq40QEZGRsDzagiC\ngM8/PwWHY0x5fjTsRl/XSbTs/VeIx4Lo7/oU4aATQGrq5eTYLWh1JiSTAiJBF9QaI8qq7oGpsDLt\ndT3OgYyKXigwBdvQRTTsOLRq708QonBO9CIccEKt0aO0ohkGU+mqnX8tjI3Zcu6z223UaCCLFgwG\n8emnxxEKBZVtFRWVeOqpI+D5LVHVJoSQLcNun73/z3T6MAyD8XEHenq6MDk5gUQigWg0Ap7nodcb\ncPHiV/jOd76rHHdnMFmWJSQSYUw5uqDRFUBIRJXnREJu8LwWPs8oRvrPw+cZBQAUlzagunYfOH42\nifbUeLcSaAAAGTIYhgHLsuju7gIgQ6VSo7a2Dmbz7EzVYDBAwYYNYHh4EJ9/fkqZ1XLq1KdQq3Wo\nra3Pb8EWMD7uyLlvYmJ8RQECv9+Pr746A5drCsBs6qHKyopln3O9iKIIm21U+fdcoVAAJSUlAIBo\nNJJx7N1ClmVEQm7IkGEwligphNxTAwAAIRGBIMTAsjw0WiP8XjtEIT7voDePcxCO0etIxMNgOR6l\nFTtQs4j0RHeaGu+GfeiK8jga8cHnGcU9e/5g3tkV4ZAL46PtCAWnoFJpodZmDkATEhFIUhLxaACi\nGAPHqaDWFiDoHkFSTIBhWEhJEf3dn4FhWCSTAhKx0Jx7x2zAwW63KcEGj8edFmiYceXKRdTW1qcF\nmje6u6IF9PHHH6O1tVUJNACA1WrFkSNH0NbWpgQbAoHMm3QwGEx7fPToUbz99ttrX2iyoOLiYmi1\nOghCIudzJEnChQtfgWEY7Nv3jXUsHSGEzBoY6IMgJDBnIMP0NP1R+HxeTE1NZhwjCnF4nAMIBqYQ\n9E8gHJhSKihB/zi6b3wEc2kDGDCIhD3weUbRsOMxmEvrlXPMVPTu5HWPolYUwPErX8hKSETRe+tj\nJOLh2dd1DqJxx2MoKtl4uV4lScLYmA2TkxMQBCFrnmN+FT4Xcve4cuVCWqABSI0S7Oy8iXvvvS9P\npSKEEJINz/OQJAlOpxOBgB+yLEGv16Oiogp2e6p+MHfmg0qlRk2NgGAwkHNB2EQiAlmSwLIcJCkJ\nMRFRRrAKiQh8nhHwKi1kKakcMzZ6HfaRaygt3w5TURXKKpoRi/ggS0nEon4IQhSxaECp+zEMIEky\nEok4RkdHUFhYCJblwDBMWuBhKSKRsLK2RH9/H6qqLMtau4KkOpzPnfsSyeTs3ziZFHH27Bf4oz96\neUN/rlpt7rLNt28hkiTh1KkTaYM/Z1IPvfjinwBY/4XfRVFEMBiEXq9fVOqjmYDknanU5rbp5s56\nWi1utwtXrlxUZlG5p/pRUta45A73tRQOujDcdwbxWOrvq9YYUNf0MEyFlUgmBQR8Y2ntw0iIR4G5\nBtKc6+CdAr5xDPedUx5LSRFTji4AgKV+8f15kiRi3JY5g11IRDE13oOauuz181jUj77Ok0rQNxSd\ngn/0upJdIOBzoLS8CVp9ISbGbqUFh0OBSfC8VvkbRaM+JOJByLI8HdyYDW7HYnHMdD3PbXfOBLfm\nisfjCAT8+OST32Pv3vtRW1unBDQXY3JyAlevXlLeg9vtWpc0Shvnm7qGLly4kJFWCQB2796dNnOh\npaUl7XFnZydaWmZHNp4/fx4mkylt9gPJn+bmFmg06owo81wMw0ynKrkBp3NqHUtHCCGzwuEwZFmG\nLEvKNlmWkUyKcLmmIMsygsGg0uCLRXyp0RLxELzOQUTDXojJ2WnZ8VgQ8WggrdEKAI7R62lTM6Vk\njunbsgxZzl3RW4qp8a60iuTM+e0j19LKshF4PG58+OE/4fPPT2FkZAjDw4NwuZxpz+F5FerqNl6Q\nhGxMoiimjZKda2RkeH0LQwghW5QgJNDffxu9vd3KOg7LtW3bdkxMOOD3e5V6WSQSgd/vg9vtykix\nJAgJTE5OgOM4ZVa9JM2pz0lJpT6m0RVi7pyHmXpbMilAFGZn40cjPgS8dgR9DnicA3CMfI3bnSfA\nqbTweWwIh1xIxMOIRwPKMSqVWulgSiTiyqDI7dubYTAsvdPW6ZzCuXNfgmVZMAyD3t5u/O53/2PF\nn+/damRkKO17MSOVZnEkDyVavIaGxqwDbfR6A2pqlt/3NTZmy5plIpFIYHAw+4CotdTRcRPvv/9r\nfPTRh3j//V/j0qULWf9mM3ieR02NBQCgVqtRWFik7DOZUqPda2osqKqqWdVyBoNBfPLJx2ltFPdk\nX9oo/XyTkiIGej5TAg0AkIiHMdBzGqIYVx6nHSOJiMeCUGtyzypwTnRn3e6avA1Jyt3vd6d4LIik\nmH1QciTkyv364z2zaewSUfi9Y0gKcaWNPmG7AddkHxiwkO/47iSTAuQ5N4BELKS0hWVZBuYEiuLx\n2fvBtm2zHf93pugLBgMYGRmC1+uB3W7DmTOn8fnnn877vZ1rYsKB9977Na5du6zMkjt16lM4HPZF\nHb8SWyLY0NbWhsOHD2P//v343ve+h0AgkLY/EAhkDRDMbOvs7AQAvPzyy7DZbDhx4gTa2toAAM89\n95zy/KNHj+LHP/7xWr0NskR6vX7eFEpAqoLn8/kQj8ep0U8IyRuOY6eDDbOd76l/MygpKcPk5AR8\nPo+yLx4Lwu+1Q6szI54IIRb1QYinpugyDANRiCEpCqlhbnOkKlazeYILzJas5dEbS8CrVmeEVSiQ\nOSsDSFWwMoIQSyQI0XlHvyzVmTOfw2YbweBgP4aGBsBxHNxul7Jglkajwf79D2B4eAg22+iiK3Lk\n7pZrbQZas4EQQlaOYRh8/vkpnD9/FpcunccHH7Shp6dz2eerrq4By6Z36Oj1BpjNxfB6PTmOSqWx\naGv7b/j8809nr+/MbDIMlVqPouI6MAwLZk6O8WRSAMdrlNGusiSldXaJ0x1i0bAXkaDrjg612Xoj\nwzAwGIxQqVSQZYDjeOzb9w1UVFSiv79vyalcrly5hGg0qnSieb1e+Hw+3LqVORqYLCyZzF1nnDvb\nYSPSaLR46qmn09YrM5uL8dRTT2ddn2SxotHoPPvWN/XQ4GA/vv76ChKJ1O8tmUyit7cL7e1fI5GI\n46OP/hn/8T/+GH/3dz/C++//Rpmx+sADB2EwpHL8l5dXoLKyGpWV1di5cxcefPAgHn/88KrX93p6\nurKu9+Ga6oN4x3ow+eLzjGYti5QU4XUNA5Az2poMw0CtMUBI5P5e5Go7SkkxZ/AgG5VKn/PvMt+a\nDdHobLA5GvFg7jV4xoT9JqIRL4pKaqHVFYJXaaHRmmAwlaeCzzPp+ebMPmBZDjyvmfOYBc/zeOih\nh1FcXKJsr69vUMotSbKS/YBlWRiNqaDy2Jgdw8ODC30EAIBPP/0Et293IxyencU2OjqMkyePL+r4\nldgSaZSOHj2Kl156Ca2trTh27BieeuopfPbZZygoKMgIPMw1s86D3+8HkFrH4aWXXsLrr78OAHjj\njTeU1EttbW04cODAvGtDkPXX25t9YZYZsixDFAUEAgFq9BNC8kavN2SMoJdlGRzHKtOT587SSiYF\nyLIMhkktDpgUE3cEKiQkJTFV/2FS05QT8RAYloEoJpTKXXn1Tvi9NkTDXuVYjlPB2vDAqr23XEEL\nhmXTKlVL4fPY4Bi5hlg0kMrVWb4d1XX3ZXQOLIXH40ZfX2/aiEVRFMGyHMrKKvDQQwdhs43iwoWv\nlP0GgxGHDx9JG8lEyFw8z8Nqrc06arG+viEPJSIblSAk0NnZgfFxO0wmPaqr69DQ0ET1U0IWwLJs\nWmetLMu4fPkiqqosy1qnIBwOo7KyCiUlJYjF4lCpVEpdTKVSZQQd1GoNWJZFf/9tqFSz6yvIsoy6\nbQ9DqyuEc6IXvEqb+j0zrDJ7VJZlsCwPWRLBcKljRTGeNtOVn7NmQyg4iUKzBeGQG6IQAafSQJie\nESHLEpJJEaIoguNS6Zp+97t/Rnl5JVQqFRiGxf79Dy5qzSlBEDAyMpS2EHYwGIAgCDAYjHjwQVqf\ncqms1lpcu3Y5YzvDMGmLv25UFRWVeP75P4bX6wHLsigqMq/4nOXluddlqKysWvH5l6KnJ/uI+du3\ne3DlyoW09DXt7ddgs43g3/27/w0mkwnf+c53MTo6jFAohOLiEtTUWNb03h0I+LJulyUJ8Xhw3vUO\n1os4T8d/UkyAZXkUmq1IxIMQElGwnApaXcGCiz0bjGVp7VYFw8DvHYPeWAK9YeG0cbxKg+KybXBP\n9d9xGgZllc05j9PpihDypzr4c73HRDwMra4QHKeGsWD2O55MJiAIs4EUjbYA4ZALsiQhKSYgSaIy\n+HD//ofw+ONPpt1TAMBoNOHAgUdx6dJXiESCSCaTYFkWVVXVae3gsTE7GhsXToXU2Xkza6aB7u7l\nB+wXa0sEG959910888wzAGYXfv7Zz36GN998UwkkLDZI8Pbbb+ONN97IOObYsWP48MMPV7nkZCVi\nsRgEIUeKkGkcxyGRiEMUBdTV1a9PwQgh5A7JpACWZTNGykuShGvXriCRiEMQBKXiKiOV99c9NYig\nfzytUZrCQJaT0wsSxhH0OSDLEnQGM7raf4tq615UWnaD5zVovOcJDN8+h4DPDo22EHXbD8JgKlu1\n91ZW0Qy/J3MqZnFpw7LWhAgHnRjs/UJJhiolRUyNd0OWJVgbH1zw+FwpbaamJnKm05ucHMfQ0CDa\n26+lbff7/fjtbz/EwYOPKtt4noHJpEMwGIUobow0UbneM1kf+/cfgN/vTwtk1dRY0NKyO4+lIhtJ\nMpnEyZPH4Xa7wDAMgkEeQ0OjcDqd1KlHSA6xWExJG5RtxsGFC+fQ1LRj3nNku2fHYlEEAgGlAyYe\njyMeT43QNZuLkUgkUFJShkQiDo7joFKp4HQ6EYlEAUTT0sJIUhKN9zyO4rJGDN3+MjVQBEhP6M6k\n/qdSpwIaczuMVCpd2kKlapUeYBgUmlNpWaIRLxzhdjAMg0RCgChGwXEsiooKYbfbIEkSQqGw0nF7\n6tQJxGKxtBHq2ciyBIfDnrH2YTKZnHd2B8mtoKAQ+/Z9A1988ZmyLRQKY8eOlnkXYN6oXK7cqWaW\noqCgCDZb+oCM8vIKhMMh9PT0rFt9enx8DJFI5myKUCgIj8dz52RxjI878PHHH6GlZTYdu15vQCwW\nw8BAP9ZSLBaD3+/PSEHFsBw0WRYrzoeCotzBooKianC8GqHAJDTaAmi0s/2qxoIKqNS5FzmuqGmF\nzzOizJqQpSQCfge02kKMDlwAABSaLWhofmzBwIW18UGwHA/3VD+kpAitvhA1dfdDbyzJeUxZ1U64\nnQOQkiJ4To3EdEriuR32ao0BpZU7MtJacZwatY0PIeifQCgwBTAMVGodJFGAJCXT2vMqlRojI7nT\nq+3btx8jI0Pw+33QanUQxaTStw0Abrcb/f19WY+VZQkTE+PKukSSJKdlCpBlWbnnraVNHWx45pln\ncOXKlbSggNVqhdVqxT/90z/hzTffVEY7ZJvhMLPtzhERdwYmjh07hpdeeknZfuLECfzgBz8AAPzo\nRz9SAh1kfYVCQWg0aiQSuX8okiRBkiTs3NmC0tLV61wjhJClSCQEaDQaCIKgBEllWUYkEsEvf/lz\n8Hz67ZhleURCLvR3f4pEIts0YxkMWPC8Gj7PKAAZOr0ZemMpIMtwjF6HqagKarUefR2fIBEPg+PU\nEIUoBns+R2Pz4yjMkWJpqQrMNbA2PohxW3uqYsgwKC6th7Vh4cBANs6JnvRG+jTXVD+qa/eB49UZ\n+4Q5eZB/8Yt/yHnuOz/nGZOTE7h8+ULOUUqffrr2U01Xy0LpBcnqMxgM+Pa3n8fYmB3BYAClpWXz\njuZbTS6XE93dnQgE/CguLkFLy+5Fj/QVhFTO5EDAD7O5GPX1jTl/I2RlRkaG4HZndtz09najtXX3\ngh2DhNxt7HYbTp8+qaR9GBoawNDQQFqHz5UrF5e9NtTMOgV3mhlFmm2f3Z65cGcs6kcs6kdBUTV2\n7v1DuKcGEApOIpEIQxBiYBgGQiICjcmIQrMVJWWN8HlGodEVAtMdTx7XIBiGg05fiKaWp2Efmh0d\nPzMKVpZlBAJ+ZdHpmXW+ZoyNzQ46uHx5cZ/Lndd7SUodo1Zn1rPI4uzefS8ikRCuXLkIILVGQEfH\nzTyXKv9mfsfA7Hf6d7/7H+tahly/69Tso+zZ5T/44Dd47738pFS9M32VJCVRXtm87Fnjq02rK0R5\n9U5MOdJnjJRWbIfeWAKdwYygfwI+92yHulpjQO22A/OeV6M1oXn3c5h0dCIcdCIccEJnKIF6TlDW\n77Vjwt6B6tq9856LZTlYGx5ATd39kJLColIIa3UF2LHrGThGr0NMJpD0CVCp9QgGZgesVdTsQmnF\nDsSjATgnbyvt1kKzBaKYAMty0BtLIApxxCI+qLUmQJZSj6Op9FzztVfT30P27+3lyxfwwQe/WfCY\nbN9tQRBgNi88O2SlNn2LItuMBavVqiz0PLPf58s+FSnXOWYEAgEcP35cmdVgs9nw+uuv49VXX0Uw\nGMTrr7+OU6dO0aLReVBYWKjkvpyPRqPFE098cx1KRAgh2ZWWliEcDoHjuLQZWalUSZkViEQ8DIZh\nU1PywWTJFsmA43jUbjsIURSmKxWz10NJSsI2lFoIKh4Lpb2GLEkYG762asEGACirbEZJeRPisQB4\nlRYqVe4RKwvJlatTlpIQhGjWYMNi5fq8c23fLGYacTMBLLL+WJaF1Vq7rq/pcIzhs89OKiOl3G4X\nhoYG8eyz34bZPH/6g2AwgBMnfp+WM7mj4yaOHPkD6HTL//2S7JxOZ859breLgg2EzCFJEs6fP7vg\nmk3LDTTMvMbMvXPm/jkz+3Rm39zXyZW3ftLRAY9rECqVDtaGB9DUchgjA+fBMpxSPp7XIpkUkIiH\nUbvtAGq3HUAs4sOVs79ANJJKF8KyAMNwSMRDqN12AI7Rr2dH9k6XLVeH6HLdWfdhGAZmc8mKFgQm\nqTQoK/lubkV3rluXD5IkZf0d57tcudwZ+Cyp2IHquvvzXKp0lvr9MBVWTa/RABSV1CntS4Zh0dh8\nCOGgC+HgFFQaAwrNlkWlxNVoTahtfAgA0H7pv4NNZnZbe1yDCwYbZrAst6RUvHpDMZp2PgUACPgc\n6O8+DefEbciyjArLbiUNU0FRNQI+B4L+CegMZjAsi3AwFZTgODViET8YhoUsJVFc2oho1IeAP5W6\nbua+s5CZa//ca/V8awrODewB2du4HMehsrJ6kZ/G8m36YMNizQQf5rp16xaAzJkNcx09ehR//ud/\nrjw+duwYCgoK8OabbwJIreXwm9/8RnlM1g/H8dBo5o/szkSqHQ4HmptpvQ1CSH488sjjcLmmEArN\ndqQbjUbs3r0Xe/bsw8cf/wscjjEkk6l1G6SkAJZTgWVYsByPpJje4GZYFhpdIZjpoMNc0YgP4ZAT\nkbAbQiIKSRJRUFidNmU1FvVDSETnnca6GNGIDx7nIKSkgIKiahSYV57DVG8sTU09vQOv0uRc0Es1\nZ6TKn/3Zv4HFkr2h3Nl5C0NDA4hEwpAkCRqNFgUFBXj44cfgcjmzLopoNBrx6KNPKI+TyQT6+rox\nOmpDMimjqMiMlpZdeVnXQZYlfP31VQwNDaCnpwsMw+DSpfMoK6vYFPmB7zaRSCqIuFqd+devX81I\nsSaKAm7evI5Dh56c99irVy9nLM4YCPhx8+Z1SuuzBgwGwzz7ci9USEguiUQCbrcPHFcGYPnrGW1E\nTucUYrGoUr+RZRnFxSXgeR56vQGlpaVoampeMIUSsDqpDyUpie7uTvT390GtVk+nf+kCy7IQhThE\nIYY4p8JAz2lwvBqyJEEQokp9SBSiqWu1PFuX83lsMJjKoNWbIctJcJwaDMPAOdGDXfd/F8VlDYhG\nfAj4x2EbSqV4/Pa3X0BHRzucTidkWYIoimAYFkajEbW19Uow4sCBRxaVb7+3txu3brWjp6cLAGCx\nWFFUZMY99+xc1udEUjSa2Xv8n//5v0FVFQVvsslHWtJoNIqRkSEEAn7odHrU1zfAaDTi17/+rwgE\n/Mr6eTzPQ6fT4eWX/xQazcKj4deCKAr4+uur+OST3wMAjKbSjDaW32PD5HgXEvEwDMYSVNTsXtR6\nBqup0GyZdwCbwVQKg6l02efPTCU8vX2BYPRqKSiqRu22h9B76xMAUN5rwDeOgZ7PAaRmbCTFBGyD\nl8CrdNDpU23CmYGAspy6J8wNeDz//IvYvfveRZfD6/VAFAUUFRVDpcqdprij40ba+iN2+ygCgQAS\nibgS3DAajTSzYSGBQCDrrISOjo60mQZHjhxBV1dXxvO6urrQ2tqac2aDzWZDR0cH3n777Zznbm1t\nzXpusvZYlkV1tSVnDu4Zoijgq6++RH19w4LBCUIIWQsPPPAQvF43Ll48r+TfbGnZhT/903+NZFLE\nP/7jLzOOkZIiZMjgOBVkKQlJml1AWqMxoaikFgXFNeB4NZLTC1iJYlwZUaHRmJAUE0iKcQT9DphL\nG5VKKsNy4Lilr6cwl8c5iOH+r5Spo86JXhSV1KJhx6EVBRzKq3bC4xyEKKSnA6qy7l3UqBSLxYqm\npu1Z9zU2bkNXVwdu3+5BIhFHVVUN9u69H4WFhZAkCbIsp+WVVanUOHz4CMrKypVtJ058hEgkApOp\ncHqkloS+vh585zt/tO4jwgcH+xGPx9I6KyVJwoULX6GmxrrqoyDJ8rhcTly8+BU8HjcAoLq6BgcO\nPDpvB/RCJEnKmpYHwIL1IlmWs6YDAQCbbZSCDWtg27bt6Oi4gUQiPT96aWk5pfkkS3bzZjtu3boB\nSUpCq1WhoqIaBw48mrHQ5GaVbfRxbW0dGIZBdbUFTz/93KLTxfE8C7PZAK83DFFcejqUoaEBXLt2\nCfF4HDqdDqIooLS0NC2QAAAQYhDiYQz3nZuuX6XXgxiGgTBnsdFoxDP9XnnM7ZKRJQmxiB+mwkoY\njKWIRmazM5SVlcHv94FlGaQCTAySSRGCkEBRUREYhkFr6x7cf//+Rb23xsZtCIWC6O1NpUGJRMJ4\n5JHHUV/fuLQPieRktdaivn7hRVzvRiv9bS7X7t17MrZ961t/iPfea0MikYAsy9BoNHjyyW+itTU/\na28JQgIff/yRss4TANgGL0OnN6O0ItXGcU8NYKT/K+WYRCwEv3cMzbufhU6/8sW9N4pCswU+d2ad\ntah4fWcT32nS0ZGxjWU5RMMecLwKQjystBMZhgXLcmlt+dbW3Tnbqyvh83kRCMyu7TA+nrqfchyn\nBNOSyWTGgKO1sGlboYFAAE899VTGjIUTJ04gEAjgpZdeUrY999xzsNlsOHHihLLNZrPh/PnzOHAg\nd86wd955R1kseq65lZvFVnTI2lioMpWangT4/T4MDQ2sU6kIISQdy7J49tlv4/nn/xjJZBKiKOKZ\nZ56DVqvFjRvXIUnydONxRmoKZFJMgFdpU7McWF6ZhmwqqoKl/hvgeQ3qmh4GM92pHI+l8kDq9Gao\n1Dpodal7lCQlIcxZ+6GkrBEst/zxBlJShG3ocsbaCj73KPzezMWil0KtMaB593Moq2yGTl+EgqJq\nbLvnCWXK6kqwLItdu/bghRdexMsv/ykOHXpSuY+zLIsnnjiMb37zWezZsxcPPHAAL7zwYlqgwemc\nypoOJZFIYHBwbReLy2buyJW5YrFozo5osr7i8RhOnTqhBBqAVPqj06dPrmjqPsuyOUfb6fXzBzEY\nhsmZEiTXdrIyOp0Ohw8/g9LS1PWEYRjU19fjiScO57lkZLMZHh5Ee/s1ZSakLMsYGRnB5csX81yy\n1VNSUgqTKX0wIMfxMJuL8eijj69b+9vr9eLcuS+VhTSNRiOKiszgeXWOlIwS/B4bZFmGSqVV6mwq\ntT61CPScEbpqTe7UabkWgO3u7oJeb4BOpwfPq6DValFYWASNRoOSkjJ861v/atGBBiC1XpXL5VTK\naTAY0dfXi2AwuOhzELLZRaNRdHV1YufOFtx77z7s3bsPra27YbONwuv15qVMvb098PvvTAMvwzH6\n9fRCwzLGbe0Zx0lJEZNjmZ3gm1lN3Teg1qTXa7W6QlRaM4NG6ykezVwTWK0tQDwWgN9jw8M+MgAA\nIABJREFURzTiQyzqhyQlwTBc2noRMymy1sK2bdvT7k+JRGI6JWH6tszv1+rbtDMbCgoKcODAARw+\nfBgvvfQSDh48CJvNhqNHj6K1tRWvvfaa8txnnnkGra2t+MEPfgC/349AIICf//znKCgowF/8xV9k\nPX9nZyeCwSAOHkwf3WWxWNJmMnR0dODZZ59dmzdJFrRnz15wHA9RFHI8g4Fer0coFFyX6B0hhMwn\nWx7XYDAAhkk1pGfWc2CYVG5GjtfCYCwFy3IQxQSEYKoDuayyGdW1+wAARcVW7Lrvu/C6hzHl6IJG\nYwKvSs3i0mhNkCQRkZA7NQ11evFmS/3iG6PZhIJTymyKO/m9NhQVr2zKuEZrhLVxeQtMr1RVVTWq\nqrLnsZyZlbLUfWtlvmm0tNDvxjAw0J8xmh1ITYeenJxAZWXVss+9c2cr2tuvZdnesuCxjY1NuH27\nJ+t2sjZKS8vw3HPfhigmUFJiQjgsrOtoTrI19PX1Zt0+NDSABx44MO99YbNgGAaHDj2JDz5oS9t2\n7737VnTNXKqBgdtZg8LBYGYn04ykJEBvLEEkPBtgZhgWDMvBYJxNJVJWuQPOiR5ISTHteHNpQ0bH\n2gxBSIBhGKjV6oxFnGtqalBcXLKo9zXj66+vpOX+ZlkG8XgMt2614+DBR5d0LkI2q7Exu/I7uPP6\nabONLLgG1lqYnJzIul0U4ohGvNBoTDnXuIuEPGtZtHWn0Rqxc+8fwusaRjwWgE5vRlFJ3ZLWYFgL\nOr05428wswg1y3JK+ieNxoii4lo0Nj+OgH8cIwOX17RcZrMZDz/8GC5fvoBEIgFRFMDzKsiyrPSZ\nqtXqdVnjb1O3RH/605/i2LFjaGtrQ1tbG6xWK9544420QMOMX/3qVzh69CiOHj2KQCCAI0eO4Mc/\n/nHOFEpHjx5NS5804+WXX8Yrr7yCEydOKIGLubMoyPoKhUIwmUzwerNfVDmOA8dxYBgGFRXrVzkl\nhJDF2r79HrAsh2RyToOP4yDLgN5QhMJiKxqbH4dKa8Lpj/5vAEBZ1c60BaFVah3Kq3ZCZyhGX8cn\naefX6c3QG0rQ1HIYWl3hitdpAACWzV194NjN39GRS0lJGXJliJoZsbyeGhub0N9/O2O72Vy8Lrk4\nycIikeyNwYX2Lcbu3fdCFEX09HRBFAVoNFrs2bMXDQ3bFjz2/vv3IxAIYGLCoWyrra3Hrl35HSl2\nN9BqtdN533MNlCEkt1gslnW7JEkQRWFLBBsAoLi4BIcOPYmzZ78AABw69FTW9CdraWZGw1IwYFFa\n0YR4LICgfxIMw0CjK4CpsBol5bPBXLXGiO2tT2Ns5BpC/klwnAolFU3KQJJsrNY6TE6OZ2znOB7N\nzUtbZ0EQhJwzIHN1dBKyFc03wnyl69At13xpWVUqHTheBV6lURaRnyvXzKjNjONUSvqojaLCshsB\n31haQDqRCMNUWA2N1ghRiE3PaEgNAOR4NfTGpQWEl6uxsQm1tfVwOqcwOjqCWCyKeDyhDL5Wq9Xr\nUlfY1MEGAHjttdeyBhfuVFBQgLfffjtrAOFO58+fh9VqTVubYcbBgwfx6quv4vXXXwcAvPrqq2ht\nbV16wcmq0Ol0KC+vzBlsmBnZ2dDQmHOkKiGE5FNtbR22b29WFugDUo3VQnM1Hjz0vygVE9fUwml6\nTAUVKKtshnNizshHhkHttgMwFVauWpkNpjJodAVZp5AWl23dXL8mkwnNzTsxNNSXtr2kpBR1dfXr\nXp7Kyircd99+fPnlaWWbwWDEY4/NvzgwWT9lZRUAsk9pT+1bPoZhcN9938CePXsRi0Wh1xsWPS1b\npVLj6aefhcvlRDAYQFFRcV5G7xFClqaqqiZru6eoyAydTp+HEq2d1OjQVEfOeq+JBKTW1xkY6MvY\nznGpcqV3RDIAGBiMpaiuvR/OydRxsixDqyuE3liMKuvetPMYjKXY0XoEspSaebpQx2ZLyy5MTIzB\nbk9PI33o0BMLps/L9h5yrfGRj8+akHyxWq3geV7JZz9XvtYv2b69OetgokKzRZn5VFa1E+OjmamU\nyqsXnt1KVs5oKsP21qcxYb+FSNgNjdaUmtUWSs1qU6nT78fMOs/E4HkeVVXV2LVrD65du5w2WI5h\nmCUHqJdVhjV/hU1o165dGemT5nrzzTeV9Eu5ZkaQ9VFUZIbFYkVPT2fWCprJVIDdu+/Fd7/7ct4i\n04QQMh+e5/H883+MDz/8J1y7lppaWVzWiO2tTy9rBIS18UGYyxrh99jAshyKyxpXfZQLwzBobD6E\nwZ7PEY+l0gexLIea+m+s26iNfHnooYOor7fg+vVbEAQBFosVLS278pbrfteuPWBZFpcunYcsyzh0\n6AlaT2oDsVprUV5eiamp9JGizc07YTKtzu+S53kYjcs7V2lpGS1QTMgmksolPpKWyofjOOzf/1Ae\nS7U11dU1oL//NsbHHWnbm5t34uLFr8Dz/HTay1SQl+VU2L3/JWi0RtQ3PYLBnjMAUiNgG7Y/knNW\nKLPIIDHLsnjttb/E5csX0NvbDY1GiwceOIBt25ae/o5lWWzf3gyXK3Mdqh071r4TipCNQqVS49FH\nn8DZs58rAQeWZfHAAwdWrZ62VGVl5XjkkUM4dWp2trqhoBx1TQ8rjytrdoMBg6nxbohCDFp9Iaqt\ne1d1cBmZn7GgAk0tswOHQkEnbt86nvE8jdYEg7EUsag/Y99ae+SRQ/B43LDZRuF0TgFIDVp45JFD\na/7aFGzIYjEBBAoybBzPPvstnD598o6ck6mK37/9t/8rampWljucEEJWS3V1NQwGAxiGQXV1jbK9\npsaCw4eP4MqV1AKPTa3fRKVl97Jfx2gqg9G0th2IOr0ZLfueRygwiWRSgKmgAhyffZTcVsIwDFpa\nWlBVVbdh8q3X1zdAr9eDYRjU1FjyXRwyB8uyOHz4CHp6umC3j4LjODQ2NtHaCISQZdHpdHjuuT9E\nX18vvF4XystLYLE0wGDYem3T6upqZcR+dfX6z1BnWRZPPvk0hocHMTZmh1qtxrZt2+H3+6fzX4so\nq2qEKERhMJahefcfoKwyleqD5XhlVkah2TJv+sml4DgOBw48ggMHHlnxufbtux/hcAg3b14HABQW\nmrF37/1obFw4FR/JLVddn2xcVmst/uiP/gR2+ygkSUJNjTXvM3waG5vw+ONJJZVcTd39SkoeINUe\nqbTsRkXNLshSEixHXbv5ZjSVoab+fjhGr6dmrAFQawxoaD6Ut4HPpaVleP75F9Hefg12+ygYhsHL\nL/9PKC9f2ezqxaBvJNn0iotL8P3v/5/4u79LpchiGAYvvfQneOyxp5Y8pZQQQtaSXm/AT3/6Dygq\nMiCRQFpntUqlVhqmd0693KgYhqERNBvAfN8rkn88z2PXrj20HgIhZFVoNBrs2rUHPM/CbDbA6w1v\nyeu+Xm/AT37y98q/84HjOGzbth3bts3mC/f7U6NTZVnGvQ+8jNLyzRk85jgOhw49ifvuuw8qFaDV\nFgBY3CwLkhvVyTYntVq94QaCLCY1JsMwYCjQsGFUVLeiuKxRWYvHVFSVts5iPpjNZjzxxGE8/PDD\n63pdorsJ2RK0Wq3yb1mWYTAYKdBACNmQ9HoDDAa6PpHVRd8rQgghW41eb6A23Rozm4uVvPVkdVCd\njJC7l0qlg7m0HgXmmrwHGuZa7+vSxnnnhBBCCCGEEEIIIYQQQgjZlCjYQAghhBBCCCGEEEIIIYSQ\nFaFgAyGEEEIIIYQQQgghhBBCVoSCDYQQQgghhBBCCCGEEEIIWRFaBYgQQgjZYPwe+7zbsu2/m9Hn\nQQghhJD1lqv+sVp1NqrfEEIAuhbkE13Pl4eCDYQQQsgGc/nML1a0nxBCCCGErK3F1MeozkYIWSm6\njmwM9HdYPEqjRLaksrLyfBeBEEIIIYQQQgghhBBC7ho0s4FsCRaLBT/84d9CFOMwGrWorLTmu0iE\nELIkM9ex+cRiMQCAVqtd1dfmeQYmkw7BYBSiKK/quVdiOeWyWCxrXCpCCCGE3K0WU18DVr/ORvUb\nQu4uFosFP/rRf9iQbbSNZD3asXQ9XzoKNpAtQavVoalpO3iehdlsgNcbhihK+S4WIYQs2sx1LB82\n6rVzo5aLEEIIIXenfNbXCCF3j9S1Zge1hRZA7cWNidIoEUIIIYQQQgghhBBCCCFkRSjYQAghhBBC\nCCGEEEIIIYSQFaFgAyGEEEIIIYQQQgghhBBCVoSCDYQQQgghhBBCCCGEEEIIWRFaIJoQQsiSxGJR\n2O12iGIcRqMWlZVW8Lwm38Xa9GY+15WfJwYA0Gq1iz6G5xmYTDoEg1GIorziMuRisVig1erW7PyE\nEEIIubvMV39aSf1mOfWpxaC6ECFksWKxKHp6bGveRtvM1qsdC6zdfQHYevcGCjYQQghZErvdjr/5\nm3+vPP7rv/4h7rlnVx5LtDXc+bluRT/84d+iqWl7votBCCGEkC1is9WfqC5ECFksu92GH/zg/8p3\nMcg62Gr3BkqjRAghZEWczql8F4EQQgghhBBCCCGE5BnNbCCEEEI2mFd2/ikshpolHzcWcuD/7fn/\nAAD/+p7/GTXG6tUu2pLZw2P4Zfd/zXcxCCGEELLFLbf+dKfVrk9RXYgQslKrdX0jy7MW7eytfG+g\nYAMhhBCywVgMNdhW2LCic9QYq1d8DkIIIYSQzWI16k93ovoUIWQjWIvrG1keui8sjNIoEUIIIYQQ\nQgghhBBCCCFkRSjYQAghhBBCCCGEEEIIIYSQFaFgAyGEEEIIIYQQQgghhBBCVoTWbCCEEELWUSQS\nhlqd71KQ5aK/HyGEkM0iEgkDAPR6Q55LQjYqqteQueiaQcjWtN7Xego2EEIIWZJYLAYAYBgGABCJ\nRPJZnE0lEgnjr/7qL8EwDN599z9BrdYt+lhfwg93zA0jb0S5rkz5/AFAlmW4Ym64Yx5lW0gI4brr\nBkRZRI2+GlX6yrRjVoMgCRgN2ZBICqgxVKFAXZCx35fwr+pr5tNK/n5rRRRFDAz0YWpqHGZzAWpq\nGmA2l+S7WIQQQvIsEgnj+9//SwDAT37y95u+81CSJCSTSUxMjMPr9aCwsBBWa12+i7Uo/oRfqYN5\nvZ4Fnp1JlmWEQkHwvAo63erVPTZivYbkz1a7ZiyFJEmYmpqEJEmoqKgEx3Fr9lqyLKO//zYGBvqR\nTIqwWKxoadkFlYqifmRt5ONaT8GGOdra2nD06FEAwLvvvouDBw9mfd4777yDhx9+OOd+QgjZyr74\n4jPwPK80mv7lX/4Z5eXV2LfvvjyXbONzOBwIh8PT/x5DfX2Tsk+WZQCpII4oicp2SZZwfvIShoIj\nyrZiTRGerD4EHa+DL+7HmYlz8CeCCCQC4DgOsizjq8mLMKlNAIAeXx8aTfU4WPHgqgUcJiNT+GL8\nLBKSAAC46rqOVvM9uK90LwCg29uLq67rmIhMKmUSRWFVXjtf5vv75YMoijh58jhcrikwDIPxcR7t\n7Tfx4IMPY/v25ryWjRBCSH45HA5llLLD4UBT0/Y8l2h54vEYrly5hIGBPthsI+A4HmVl5VCr1Sgo\nKERT08L3O0mWcNvfh+GgDZKcRJ2pFvcU7QDHrE2HoiglMRoahT8RgCvuRq+3DyybymB98eJX4DgW\n9923f1HnGhoawFdfnUE8HodKpYLFYsWBA4+uStBho9VrSH5tlWvGUjmdU/jyy9PKe9doNDh48DFY\nrbUAgOHhIXR13UIwGERpaSn27NmHsrLyZb/epUvncft2j/LY7XbBbrfhmWe+taZBDnL3yse1fsOv\n2fDKK68gEAjk3P/WW29h//79aG5uxve+972cz13oeZ2dnXjrrbfw4osv4tlnn835ujabDRcuXKBA\nAyHkrtTe/jXa26+mdVjHYjH8l//yc6WCRpbO43HjzJnT4DgOLMvii4mzGAoOAwBu+/vTAg0A4In7\ncNl5DbIs44vxs/Angmn7WZaFIKV37A8GhzEemch4bUmWYA87MBwcQSwZX1R5JVnCuckLSqBhRqe3\nB5ORKdjDDhy3n0Sfvx/uuAcMw4BlWbS3X1vU+dfS5OQEzp37Ep999gm6ujogCJs3ADIw0AeXaypt\nmywD165dgSiKOY4ihBBCNo/PPjuJwcF+OJ1TiMViCIdDsNtHIUkSAgE/enu7FjzH2Ynz+GzsS5yb\nOI8vxs/hNwPv47/1tUGSJQBAPJmAO+ZBPJkAAITFiFLXnRkMslgRMYJ/Gfk9PradxKmxL/CZ/QuM\nhR1pz+nouAmfzzvveWRZxpkzn+OXv/w5enq6MDQ0gLExO9rbr+E//+f/BydPHsfgYP+SykbIckQi\nYdy82Y4LF86hr693S9UxRVHE6dOfprVj4/E4zpw5jUgkgr6+Xpw5cxoulxPxeAxjY3Z88snv4XI5\nl/V6wWAwLdAww+12YWRkeLlvg6wDSZbAMAwYhlHuFSS3DTmzIRAIoKOjA0ePHkVnZ2fO573wwguw\n2Wx48cUXUVtbi2PHjuGFF17AqVOnlvy8trY2WK1WvPnmm8rjn/3sZ8rjGe+88w7eeOONVXy3hBCy\nebz//m+yNrpCoRC++OIUnnvuO3ko1eYmSdJ0JXc2HZUoiTg/cQklmpLpoIOMoBBCLBmHiuVRoDLB\nHhqDIzyOoBDKet6wGEExitO22cNjqDZUKY/dMQ++GD+LiBgFAHAMi/tK9+Keoh3zltkZdSnH3Gk4\nNApbyA5fPDN9Und3J+LxODQazbznX4lIJIzOzluYnJyATqdDc3MLLBYrAKC3txvnz59FIOCHJEno\n77+NoaEBHDnyB+D5DVklmtfExHjW7YlEHB6PG+XlFetcIkIIIWT1TE5OKJ16odDswApRFBEMBlBY\nWJTzXjjDFXPjuusGhkOjSh02noyj09uFz+yfo1hXgl7fbSRlCRzDgmU4uKNuZSbCucnzqDCUw6Qy\nLqrM5ycvo8PbBVESERbCiCZjkGVZCV5IUirAYbfbUFRkznmenp4uXLlySXm+LMuYmBgHz/PQ6XQY\nGRnCxIQDXq8X99+/uFkShCyV0zmFTz89ocxO7uvrRU9PF55++rk1rc+v1NTUJBwOO1QqNRoaGnOm\nhBobsyEej2VsTyaTGBoaQE9PZjBTkiR0dNzE448/teRyud25gxQu1xQaG7ct+ZxbgSzLuO3vx0Bg\nEIIkoEpfhd3FLdDxGyO1myfuxZmJr5T7wpcTZ8EwwE5z5sw6WZYREsNQMalMEB2eLtjDDnAshwZT\nHXYWNYNl1m/cv8MxhsuXLyiZBqamptZlZsOGa1nbbDYcPnwYAFBQUJDzeSdOnEBnZyc+/PBDtLa2\nAgAOHjyIw4cP49ixY3jttdeW9LyOjg5YrVbl/K2trbDZbGmv2dnZiWAwSLMaCCF3pVgsBqdzMuu+\nVO7JvnUu0eZy+3YP2tr+UenUvnGjHfX1TZiYcGSdFSJBxlBwGIIkwhayp8068MS8qDFUIy7lnokg\nQcrYxrOzt31JlvDl+Lm0oEFSlnDV+TXKtKUo0RZnHD9Dxnyj/GRMRLN/T6LRKAKBAMrKyuY5fvmi\n0Qg+/vijtM9zbMyOBx88iG3btuPcuS8xPDwEeXoko9sN+Hw+NDY2YefO1jUp01qar5G3kRuAhBBC\nFi+RSCAcDsNoNEKlUuW7OOsqGJybaWB2poEsy0gkUiNLF0oPOR6ZhCMynjFYJp5M4MLUZdSZasFO\np1PyJfyYiExCx812cIXFCC5MXsLTlsV1LN5w34QoiRAkAQlJgDz9H8MwkGUZXq8HZrN5wUEOqRHk\ns7MvBSEBSUoikZCg0+kgigI0Gg26ujrQ0tIKnU6/qPIRshSXLp3PSIPq9XrQ3d2JvXs3XgpdWZZx\n4cI59PffVrZdv34Vhw49paRFmmvmOpJNNBpFOJx9UNdCM5NyMRhyBy3n27fVXXNdR7dv9m8W8Pdh\nLOLAH1iPQM3ldy0LWZbx5fg5xJKzQSlJlnDVdR1lulKUamfXynOEx3HF+TUCQhCADF/cD6PKqLTB\nvXEfvHEfHqk8sOBr9vffRn//bQiCgOpqC3bt2gOtVruksjscdpw69QlcLheA1P3yypVLqK62wGLJ\n/D2spg2XRslqteLUqVPo7e3Fiy++mPN5H3/8MVpbW5UAwsyxR44cQVtb25Kfly1lUjCYnpbi6NGj\nePvtt5f1vgghZLPjeX7eabPRaPaR7gTo6enGP/zDTzE+7lCmX548eQLHj/9OSeMztyIfnw4sCJII\nSZYy0hsl5SRiyRhqDDXgpkdGyLKMyJxp/5DSp/4zABpNDcrjqagTYTFzcW8ZyEjbNMMX96PP3w9R\nEqHjsld26oy1MPDZG7wzo/HWSnd3V9bATXv7NTidk7DZRpVAw4xgMIDu7o41K9NaamrKPgOlvLwS\nhYVF61waQgghq0mSJFy5chHvvfff8dFHH+K9936NGzeur3s5EokEBgf70d9/e93resXFs504JpMJ\n8XgcwWAQwWAALpcTHo8blZXV854jKkaQlJNZ9wWFEERZnPM41f4Pi+l1icmoE5EsdaZswkLq2IQk\ngGUYMEgPhkSjqfPU1TVkHAuk6m52+yhGRoaRTIrKzAZRnHkPqcCFVqubfr4Et9u9qLLlwjAMYrHM\n0d3k7haNRuHxZP9ujY3Zsm7Pt7ExW1qgAUhdS8+fP4tkMgm/34/h4UFlxlR1tSVnwLKurj5nu2W+\ngdHzKSsrR0lJacZ2tVqNxsa7c82UqBhFrz9z0GJICGMgMJSHEqWbijkREjLbl7Is46rzGm64b2Ew\nMARvzIcvxs9OBxqAYCIEZ8yVMQhvKDgCfyL3UgEAcPXqZVy4cA5O5xR8Pi+6um7hk09+v+T0vzdu\nXMfAQB8GBm6DZVmwLAubbRTt7V8v6TzLseGCDQDSZhjkcuHCBezatStj++7du9NmJCz2eS0tLWmP\nOzs70dLSojw+f/48TCbTospGCCFbUSw2fwOT5++u0XZL8dvfvp9lcWQZX3xxChUVlQgGg2mBiMmo\nE1PRKdQYqsAyDLR3dOxzDKdsu790HxikKkLumEd5TkyKYTKayufPMxweLP8GijSFyv5cDe9s+2RZ\nxvnJi/ho9DguTl3F5+NnkZDiwB0zHFqKmlGpr8C+0nuhYlWQZTntXDU1VhiNazdqx+mcyro9Ho/D\n5XLmXKB6vrWhNrLS0jI8/PBjabMYKisr8dhjj+evUIQQQlbFrVvt6O7uRDKZuo+KooAbN75GX1/v\nupXBZhvF++//GufOfYnz58/igw9+kzXf+FopLi5BbW0dgFRnnCAIkGUJLMuBZTkEg0Ho9fMPYqjS\nV0LNZs72YxkGPMtDxczWX2cGaSTlzNmh2bZlM1PXSp2LAc+mL/gaj8fQ0rI7aydmMpnEZ599gtOn\nP0U0GkE4HEYkEkEkEkUikYAoimAYFsXFJWkLyRoM2VPEzEcQBFy7dllZL+z06U9x+fKFJa9RQbYu\nnudydsRv1HafzTaadXssFsXx47/Db3/7Ps6c+Rwff/wv+OST34Pnedx7b+YMjR077kFZWTlaW/dk\n7GMYBi0tu5ddxieffBq1tXXKZ1taWo7Dh59JuyZEoxFcvXoJZ89+CZZlF5zBtZl54z5IOa47nvjy\nZpCsJinLtT+15uEYOr09uOnpxFeTl/CbwffTBgjGpjMQRMWoMpBwhifuQS6RSAQ9PZnLCfj9viWv\n03P9+jV4PG7M/XiDwcC6BBs2XBqlxQoEAlk7/me2dXZ2orW1ddHPe/nll/HKK6/gxIkT8PtTeaaf\ne+455flHjx7Fr371qzV4J4QQsjncvHlj3v3shgxfbwwulxOyPJunF5ieiRAJIxgMIJFIjdSbqUiG\nhBAMKj0YmYGaVcNiqIEv4UNICEPDaVCqLQHPcPj/27v34KaufE/0Xz0tS5Zsg99YxrxkgpykEyCJ\nTZJOB3fbpPv0PXjuiVNTc6vwDISaP8D/wH/BU0W67h/gqjlk6tTc2MyQundOXZxTl64zZw62O3S6\nkzQiCXl0guWEJARi8cYvSX7rse8fYm9L1mvLepvvp4oqvLW3tPbaS2sv7bXWbykVCmw01eM75w+4\nMmGXFoUWBAHV+iqolWr8bM3jaCjZEjYFtaKwHFqlJmyRZwAwG9aF/P2D6xquuW6EbPMJAip05dho\nqofH70WNoRol2sAP7MbSbfjiwVf43vUDPH6PFDrg6adTE1PY6/Xi5s1RLCwsoLq6BiZT4HOj/dhW\nKJQoK6uAXq8PWRvj4atxR0VGIj78ybZNm7agvn4jnM4JVFSUAtDC65X3QISIiHLX1auRH+p/++0I\ntmwJjxOdjMBoegfGxx+gqMiI+vqNEAQ/PvroTyGzWv1+Pz7++CKqq9fBaDSmNA3RvPDCLx6O6jyP\n0tI10Gg0KCwshE6ng15vwM2bsUdY1xiq0VC8BfbJEanNo1aooFPrsNm0CYqg2NkGjQGz3jnoVKGd\nE6XaYtlrNmwvewp/vvMXzPsWAm0FQQGlQgmvP5CPer0B33xjR1VVtbSmlOiHH77D7du3AABr15Zh\nZmYGgICFhTmo1Rr4fF4UFGiluOEAUFlZjdLS6KEvo/nss09C1ruYnp7GN98Mo7i4BA0NjyX8frT6\naDRa1NXV46efwkeXb968JQspik8Z5Qfp5OQk5ubmoNcvzb6+d+8uPv/8UzQ3v4Cqqmpcv/4jBMEP\ns3k91q2rBQBs29YIpVKJkZFhTE+7sXZtGZ588mlUVVVH/Bw5CgsL8dJLLfB4FuH1+sI6Hufn53H+\n/L9gZmYaLteUNBhtZPJbbCqOPCMqnxXFqFvl1rvpVK4L/GYO5va4Me9bwBrd0ro7054ZuD1uVOsD\nZUPzMHTSot+Du7P3UKAqgElrhF6tj3legc6ByJ0vY2MPEqqfp6Ym4PP5Q2ZE+P3+FYcBS0RedjbE\nGoEoTmdyOp2y9wMC6zh0dHSgq6sLAHDkyBEp9FJ/fz+amppWPFWKiCjf+f1+XLv6no74AAAgAElE\nQVT2Xcx9NJrsxlPMZVptwcNFiZcaDh6PBwqFEsPDV6Sp8iI//JiYn8RV5w+oL1qP4clv4Ho43XLe\nN48F3wKerdgBjVKD9299gOvun1CgKnj4gznQaeEX/NCr9VAqlRFjXWqUGjxbsRMX716CP2iGwibT\nBlTrq0L2jRZW6cH8GH5e80LYj/K7c/egUalRV2TGxMIk3AuB6aTDw1+jvLxCdr6p1QoYjYVwu+fg\n9QbSODU1ic8//zQoVjOwYcMmNDRsg05XCJfLFdZAq601Y3p6GmVllbh58ycpVIBSqUJpaSmKi4vj\nrjmysDCPDz54XxpJ+Mc//gF+P1L+wGclVCoVKioqUVpqwORk+DRfIiLKL4IgRJ1RGm+maSSxHsh7\nvV589tnHmJxcevig0+lQV1cfNTyPzfYRNm1a+cPGubk5jI5eh8vlgl5vwPr19Sgqit55UVhogNFo\nQkHB0kxPr9cHl8v18IH8komFSYxMfgvnohPF2mJsK92KPeZfQq1U4dbMHcz55lCgKkBzxTN4Yu3j\n+MPNP0qdEMVaE+a981AHPVhSK9V4tiL+YAmP34PLD77AdfdPUCmU0KsLoVIoA+GUBCUWfYF2S0GB\nDlNTk3jvvQG88MIvQt7jyy8/l55PiPsCCqhUapSWroFOVwi324mbNx3w+fyoq1sPs3l9wuum+f1+\nfPLJJTgcP0kDXcbHx7CwsICSkjXsbCCpzigvr8TNm6OYmAiMxFYoFFi/fgMEAUmt1xepjZ8KSqUq\n5DskmpqawNq1ZXA6QwdZffnl5ygvr4BCoZTCG83NzYWcm1qtwRNPPCUt9D4/P5/WtQrFTkev14Pb\nt29K39FL9z9BaUEJdlU9t6pmOpi0RpgNNXDM3A7ZrlVqsLl4Y8i2We8crrtvYM47jyp9Bdbpa9Ke\nF2qlCs9V7MR5xx+kbXPeeZi0ppDQwYVqHR7MjcHr92DONw8VVJjzzsPjX4RaocaifxFujxsbTRtQ\npgsPpSV+56an3RHLMBB4Fp5I2fN4PFhcXAj5bRyYIZj+GWx52dkgZny8h/9y9xMdP34cR44cCTum\nr68P586dW0lSiYhWBZfLCUEQoFKpoo7oNpvXZzhV+aOubj3Gxu4jOOyQz+fD7OwMzpzpDVskUBAE\nLPoX8b3rB1QUlmF5uCJAgAIKTC04cWv2DtQKFZab8c6ipKAkLARTsHpjHdbq1kgLUdfqa1CpD+8M\niDa1VQDC1kAAgO+d1wAoUKQpgse31Ki/ePEDfPjh+1HTI0dw2ADRZ599KpVLhUIRMqpJEAR8+ukl\nnDv3bsTjb9++ieHhr+N+7vIpzHNz87h06S8oKSlNqAOFiIgoHoVCgYqKKty/fzfstYqKqghHhFtY\nWOqU+G//7b/G/KxIo4EvXvww6jGXL3+c1MOKSPfyeDMGo4USCU7H3dl7+OTBZSnk0cTCFEanHWhZ\n9wu8tunvcGf2DnyCD9X6KhQ8HCjx67o2XJ36Ds5FF0xaE/au/xt88eArXBkbhiAIeKFyF8oLwx8M\nTS5M4pupq3AuulCiLcb4/CQmF6cAAOsM67Dgm4fX74PX78XY/BimF6chCAKuX7+G69evAQDef/+9\nmOcY/H+Xyxmy7cGD+/jqqy/wz//8/8XMt2iWtz39fgELCwu4ffvmit6P8t/8/NLs30h1hjhT+ZNP\nbJlMVsKi1Wk3bkSO///xxxdDvnuCIITMRs80MS3L6zuf4MM3zquoMVRjo6k+O4lLk11VTfhi7K+4\n7v4JHr8XlYXl2F72FPRBD/Pvzt7Dn+98BM/DWWLfTF1Fjb4KL9W8AFWE38KptN5Yh+crm/Hp3c8A\nADX6ahRpQ2cnGDVG3Ju7jxvuUQgQ4BW88AgeGNSGwPo9CgWMmiIoocSsdw56dSHmvUtr5QR/56Ld\n7z799FJC6Var1RHfJxN9VXnZ2VBcHAiXEGnmgrituLhY9n7BlndM9PX1oaOjQ9o+ODiIY8eOAQDe\nfPNNtLW1JXMqRER5oaBAF7hBGk1Rp91NTU1lOFX5o7KyCiUlazA+/kDaJgiCNEImEgGBBZ9/mh5F\nZWEl1hSUYt63ALVCjUJ1Ie7M3pXiPRo0BqjmVQCWHux7/V7oVFrUFdXGTJtRU4Qn1oSvbRSsrqgW\nD+bHwraX69aiUB0ec3h5XMpMUCqV8Pv9gXUiYjyw8Pl8IT8m5Ip2nb7//io7G4iIKOWefnoH3ntv\nIOSeptVq8eSTT8k63u12hTxwi/bwLNJDLSD2PTKZjoZoYU7E+3g0fr8/YidF8DHX3TcgPDwVvxB4\n0K9WqvHVxBX8ct3LMEdoExVpDNhevpSnnz/4El9PXpHy5bOxL7DeaIZBs/TQ6/7cA1y49Sd4/T74\n4cet6Tu4OXsT6wzrpEEeBSodClQAIMAv+OGYvhUrWwAgrF24/O/l10l8qJrKB6Ncs+HRdPv2LXz0\n0Qch37Hl7el8KRvib4Hg9n60DghBECJ28sUaYJctCiigVqhxY/qnVdfZIM6431m+HX5BCFvrRhAE\nfHz/stTRILo9exc/uq5jS3H8xbVj/e5ebt47j7GFcRSqCrFWtwaCIGBiYUI6vkBdAL/ggzKok2PR\nt4BCVSE8vkVMe2fhFXxQQQWVUgkFAI/Pg1nFHKYWp3Bv9h42xLmGwWn1+/0pq+eVSmVGIlLkZWeD\n+OA/1oMtk8kke79oXC4XBgYGpFkNDocDXV1d2L9/P9xuN7q6unDhwgUuGk1Eq15hYSHWr9+Ay5c/\nibrP3bu3o772qAuE/BFQUFCAubnASMOioiJUVdVg48bN+PDD9+H1epdG5yMQT7i0oATehwssa5Ra\naJRLDQM/BJg0JigAKBVKrDPUYHR6aVG0Yq0JL9e8hHnfAr4c+woP5sdhUOvxWElDxNkLsViKt+DW\n7G3cnV1agFmnKsCzFTsi7l+lr8KD+UC8yalFZ9ACaOXYtq0RO3c+B50u+oKOguDH/fv34XJNorTU\nhNLScqhUWkxOTuDDD/+EsbH7IT8AiotL8NhjVjzxhLwHMImamprEpUt/wczMNL79dgRAYNE8ILD4\nNBERUapVVFTi17/+3/DttyNwuVwoLV2Dxx7bFjPckOjOndv4/PPPpPvv1q3bUFJSiqam52EwhI7G\n/Jd/+T3u3Al/EK7X6/Hiiy9jZGQ45CHjli0N2LzZsuLz+tOf3pPCGS7X1vbrkDUUlnO5nLh+/Rpc\nLhcMBgM2bNiEmZkZaUTojHcGeo0eY/PjcC464RcEKBVKuD3T+OW6l+Om7ZP7l/GHm+9jxjsr5Z1r\n0QXb/Y9Djv9q4grG5ycwvjARmMGpCIw6npifQI0hdB2oUm0JXItu6e+tW7fBYChCXV09rNbwRWa/\n+caOn376EYIQCHcxNvYA5eXlD+O3BwZMarVaVFYuxYxvbn4BxcUlcc9P5Pf78K//+s+4edOBiYlA\nqCylUgGNRovq6sTXsaL8Njc3hz/96ULI+ixbt27DmjVr8fOf747YyZesdIVRimZxcRGffGLD9PTS\nd7GgoABbtjREneH8xBNPSWs3ZJLb7cKHH76PW7duwuv1SCHu9Go91Ep13nT6JGrOO/dwZoMHNfrq\nkBllzkUX3J7pkP2Fh2GH/3V0EOsMNVhnqMGTa54I6RgGALdnGl+M/RU3Z25BpVBhg3E9nlr7ZMQw\nwwDw9fgwhidHpBlyZbo1KNaaMDw5It0XZj2z8MGPEq1J6nDwCF4s+hfhhx+F6kIs+BYw453FzNwM\nNEoNlAolZn1zmPfN47r7BjaY6qFTL0Ug+A//4T+isrISH3zwPjweD/x+PyYmxjA7OweNRo0NGzbh\nscesCa01+D/+x3+H0+nE7OyMtG6DwRAIS5huednZIHI4wmNPXrlyBUDojAW5+y3X09OD119/Xfq7\nr68PJpMJR48eBRBYy+Hs2bPS30REq9mzzzajv/8fo76+ksXpHhVutwszM9Pw+ZZGJCwsLODBg3so\nL6+AWq2G3++XHqArFSroVIXYsfZpLAgLmFwI7zSvLCxHWeFa1BvXS2s2VBZW4tZ0YLG/X63bDa1K\ni0HHHzD/ME7wxMIkbs7cwvNVTag3xg975Vp0wz75DR7Mj0GvKkRj6WNQKBTQq/XYYFwPzbLFskSP\nlVgwOj2Kn9wOzPqWwjiUl1dCpVLh/v17ePnlX0Y81uv14r33BvDdd99ifn4OOl0BSkrW4pVXfosN\nGzbiwoUBqFSqkB8+c3Oz2LBhY9oWq/P5fLh27Xvcvx/+Gn+UExFRupSUlOK553YlfNwXX1wOGQVp\nMBRBr9fD5XKGzYwwmUwhMy9FhYV6vPjiL/DMM8/hxo3r8PsDawSUlJSG7ZuIq1dHMD4+Bq/Xh8XF\nBWi1GqjVGmi1BbLWQXr66dCBDsHxq3UqHSYWJkPaTX7Bj6kFJ75z/gBLjNGv4/MT+OD2X+Dxe+AP\nChE5vjCBu7P3MeudlUJ6fDf1A0anb8LjXwwEuhQE+OCHAgqUF5Y9bB8FHkptKd6MQpUeXzz4KwCg\nqMiIp57ajmefbQ4LZQQEFt51u924f/8uCgsLsbi4iE8//Rg3bvwIrVYLna4Q1dU10GiW2mCVlVVh\ni03Hc+/eXSgUCoyPj0GhUKC0dA2qqqrR0LAtofeh/Hfjxo8ha8cBgTpDp9NBpyvA+vWpX5RYrVZK\na415vZkJWWSxNODGjeuYmBhDUZERmzZtwU8/XYfD8RM8nkW43W4IgoCiIiMKCgpgMhmzthB2RUUl\n/vEf3wmpm0sLAh2K8Wat56NbM7fxwZ2/SA/4v56wY7NpA56reCYw0yRCJ/Td2fuY9kyjSFOEed8i\nrrlu4N7cffymbo/0G9Xj9+C9hx3IAOAXvPjOeQ2TC1PYbNqEO7N3oFVpsenhOgo3Z27hq4nhsM/5\nenw4ZCFrhUKBNdpSNBRvQUlBMYyaInx0x4Y7s3fh8Xvg9XvhF/zw+j2BsMMPQyILgoAF7wLuzN6D\nxx+6fkhtrRler0daxPzWrZvw+XzQajVYWJjHjRs/4tatm3jxxZewa9eLIWsYRWO1PoEPP3wfKpVa\n6mxYXFzExo2b5FyWpORtZ0NraytGRkbCto+MjMBqtUozFuTut5zD4cDw8DCOHz8ubRseHg6ZxWC1\nWiO+NxHRauR2u+D1eqK+vnbt2gymJr9MTEyEhffx+/3weLxQq9UoLi7B/aAn2SqlEpWFFdhcvBEC\nBNyauYOJhaCFG1Va7Cx/GgDQXPksSrTF+NF9HYu+RSnOqFqpxsjkN1JHg0gA8OX411hfVBdzKqnb\nM43Bm+9h4eHxTrhwZ+4emiufwSbTxqjHAUCBqgBttb/E/3vtnzD5MN2CIKCwMDCb4dYtBzweT8gP\nZZHdfgWXL38sLQA9OzuDsbFxDA39K9rafoPS0jWYm5sLeYhiMhVjdnY27L1SRaVSYefOZ3H+/P8M\n2b52bVlSozuJiIhSzefzYXw8PPQhgIhrQJSXV+DBg/thI37LyyukB2+NjU+kLH1btmzFN9+cg9M5\nKY3QNRpNaGlpTfq91xfV4ad7F8O2lxQUx+1suOr8Hn74Meedx7xvXmojTSxMoMZbBZ9/qQ13f+4B\nFv1B7SsF4PP74Fx04YZ7FBqlGmW6MtToA7HV1Qq11Ab8xS9+Cas1dvhKo9EIo3FpBktdXT2+/PIz\nfPnl59BqQ0fjqlQqlJeXx3y/SJ555jn89a9fSOc5MzON0tK12LqVnQ2PmsXF6LN0V9MMXrVa/bDz\nYKkDobR0DZzOKdy7dw/iGnnj42NYu7YMpaXZ+227bl0t/v2/P4h33/3Hh2kLzGQ3G2ri/g7LNz7B\nh0v3PpU6GkQ/uK7DXGRGraEGRq0R5bq1eDAfmIm16F/E9MOZDkbNUl057ZnFj64baCgJXOMb7lGp\no0EkCAK+HP8ajumb0D0MB/y98xqerdiB27Ph90hxhkKk0MFewSvdVwpUBZj1zsL78F7hE3xSR4P4\ni1ulUEKn1sHtcYelCwDm5wOD9DweD2ZmAuc3OzsDr9cLhUIFhUKBzz+/jOnpafzmN38bNSyhqKjI\niJKSUikMduCeXiR7XeNkxE5ZDnvllVfgcDgwODgobXM4HLDZbGhqakp4v+VOnjwpLRYdLHgmRKxZ\nEUREq83ExHjI9NrlRkaGo772qJudnX4YH3Gpjz8QY9cn5ak4ikEQBFToKlCmW4O7c/egVWnRZm7B\n81XP4bESC3aUPYXfrv81SgsCIwuVCiUa12zDb9f/Gi9VvxDyEF5skC037ZnBvC9yCAPRyOS3UkdD\nsK/Gr4SM+ItGq9KiTFeGtbq1YdN9Yy289uWXn0sdDcH7X7v2PVwuJ/T6QNiEiopKrF1bhrq69aiq\nqk77Qm4bN25GU9ML0lobjY1PoLX11xFHJRIREWWLUqmEVlsQ8bXCQkPYtvXr61FTsw51dfWoqKjC\nunVm1NXVY/36+oiDApLl9/ugUi3FSA/ER1fC708+NIi5qBal2hJpsVCVQoUy3VoUa4sx552Leeys\ndxaAMrQTAYD/YUhIo3bpgZZPCI3l7hV8gdjvCiV0Kh0UUGLBt4BnKraHzQItKIh8bWJRKpX42c+2\nRwzp8uSTT8sa4brcjz8GFqkW2zVr1pTB6ZzC/fv3En4vym81NdFHytfUrMtgSjKvqMiI6elpiB0N\noulpd0YeyMZSVlaOX/2qVYrXv7NsO16qeRHKGKHm8tHY3DjmovwudUwvRanZVfkcTA87FhZ9iwAU\nKC0oRZEm9L7mXHRK/3d53FjO7XFjzjsXMrNAAPDF2FcRfx+LdXik8FVF6qXP1qkKoMCydXWggBJK\nGNQGFGkMKNIUQaVQQalUwaDWL387VFRUAYA008jr9UrPCsTfnF6vF1NTkxgdvRF2/HKTkxPYvNmC\nurp6aW1Ds3m9FJornXLyF7LNFljdXgx/ZLPZYDKZYDabpZkFbW1tsFqtOHbsGJxOJ1wuF3p7e2Ey\nmXDw4EHpveTuF8xut8PtdqO5uTlke21tbchMhuHhYezZsyel505ElKtu3469sF2goUaRGAxFGBt7\nACxrgCiVKiwsBBo1Wq1W+sFXWlAClVKNe3P3YS6qfRhfsh4bjPWJfa5aHzEEk0apjhqnUjQepaNi\nxjuHOe98WDzMSOqKanHD/VPY9srK6qg/tqenXRG3ezweFBbqUVhYiLm5ubAwDnV18cNCJau4uETq\n1Fi/vp4dDURElHMUCgW2bn0MDx6Ex/7buvWxsG3btj2OO3fu4P79u9DpAg+tDQYDnnkm+sC8ZHz/\n/dWHo4bXwONZhFqtgUqlwrVr32HnzmeTfv+Gki24O3sfPsEH1cORoABQURh79H+FrhyAH2qlCotB\nYS8VCgVMWlNIGKUiTRE8fg8W/IvwC4EHOGqlGjqVLmQR6p+mHajWVyV9TkBgBsMvf7kHP/xwFbdu\n3YRGo8WmTVtWFFNeEISwOPVabeCBmt3+NaqqqiMdRqtUeXkFNm+24PPPL4dsf/zxJ2WtEZPPbt26\nierqGoyPj2N62gVBEGAwGFFWVgaHYxRr1mR35r5arZEecq/Vrc6QxbFm2gcvwGzUGvHb9a/g7tw9\njM2N4/LYl9Aow3+LmbRLnUSl2vC1bGYfdjxrVaG/RRf9HhjUBgChMwO1Ki3WFJRCveyzNEp1yCwT\nrUqLInURFv2L8Ape+AUBC1iAgEBHtNgJrlQo8fTaJyOGI66urkFtbR1GR29AqVTB7w/MLNJqtVCp\nAp1MYqSAyckJ1NfHnuViMBjgdHpC1kpUKBC2dlM65OSv5M7OzpC/u7q6AARCIr311lvS9nfeeQc9\nPT3o6emBy+VCa2srfve734X1QMrdT9TT0xMSPkn02muvobOzE4ODg1LHRUdHR7KnS0SUF5bH8lyO\nMV6jq6/fiPHxsZAOGZVKhaKiopAH1stHTBSoEh/9FmxriQW3Zm5j+TiMLaZNUoMnGoPGgPGF8FEP\nGqVadrq2llhwZdwesk2nK8Szz0Z/gFFVVY27d++EbdfrDSgtXYOmphfwwQd/DAlJVV+/EXV19bLS\nREREtNo98cRTcDgc+PzzTwEAGo0GTz+9Exs3hocR0mg0aG19Bbdu3cT4+AMUFRmxfv2GtHWoi2FZ\nlEplyIj8xcVF+P3+uGEh4nlq7ZO4MP8nKISlB1hapQZPrglfjDmYpWQLLtz+EwxqAxQILOIpCAKq\nC6ugV+tDwig1lGzBX8e+RoFKC4/fi9mHC0qXaEMjH8SbTZEotVqNrVut2LrVmtT7eL1ezM1FDj/p\ndDojbqfVranpeQDAZ599AiAQZuupp3bEOmRVUCqVUCqVKC8vDwtHlo6FsSlcua4MRRoDpj0zYa/V\nG+tC/lYoFKjWV6FaX4XxhXE4Zm6HvG5QF2Jj0OC89UYz7JMjmFpcGsymVChRpDFE/D3bULwF057p\nkOgAKoUSe+v/Bl+MfYUvEFh7p0RbjJZ1L4UMvjNpjDAXrcPYwgRmPbNQKpQwaY2Y9y1AryqEV/BC\no1RjV+Vz2F72VNhni+f30ku7ce3a99Bqtfj++++gUCig0QQGCWq1WhQXBzpQTKb4kXa2bWvEpUt/\nCdv+2GPJ3UPkyMnOhqtXr8raz2Qy4fjx4xE7BlayHxCYRRE8gyJYc3Mz9u/fL3V+7N+/H1Zr+i8S\nEVEueOKJn+H3v/8nzM1F+uGkSEms3dWqsfFxTE5O4Natm1JMZIPBgLVr10KvN0Cr1YYsHg0EGjYb\njcktyFatr0Jz5bP4auIKpj2z0CjV2GzahKfKnox77NYSCxzTN8M7Koo3Q62U1/jWKDV4pnwH3nO8\nDyAwQmrXrhdjhmV47rnncePGdSm2JABotQXYvv0Z6HQ61NaasXfv3+HHH69hcXEBNTW1HIFHREQU\nRKlU4rHHrCHrBDQ0bI26v0KhQG2tOeFFhleiuroG1659H7a9srI66Y4GACgvLMOeul/h6tR3cHnc\nKNWWoKHEAqMm9khOnaoAu2tewvu3P4ACCkwtTEEQBJi0RpRoTSFhlH5e/TymFqbwYH4MC75F+AQf\nClQFqNJXhrxnvNkU2aJWq1FUZIzYsbBmzeocPU2xKRQKVFRUSTN4167NzbKbamazGWq1JmxdQoVC\nkZaFsSmcQqHAC1XN+PPtj6RwSkoo8PgaKyoLK6Ie93zVLnw1cQU/uq7D6/ei1lCDn5U9GTJ7X6VQ\n4ZfrXsbXE3bcnLkJlUKFjcZ6/Oi+HvZ+Zbo1KC8swy9rX8ZPbgfuzz9AoUqHjaYNMGqK4PX78M8/\n/i8AwHMVz6BMVxZy/Kbijfh26jvU6EN/lxrUemwp3gS1Qo31RrM0Qy4apVKJLVsasGVLA27fvoXf\n//6fMDU1Cb1ej5KS0ocDFo2yyueWLQ3weDz46KM/S9u2bt2GhobwWY6plpOdDdnU2NgYFj4p2NGj\nR6XwS9mO4UZElEnl5ZV45pkmfPTRB2GzHF555W8yEsYmXzU0bMPk5CQWFuZx48aPAIDGxiewZUsD\nFhYW0NCwDd98Y8edO4FQVYWqQrxU/YKsUEXxbDRtwAZjPeZ8c9AqC2R3FFQWVuDF6l34cuxruDxu\naJUabCnehJ+tTWyRSIVCIc3YqK2tixv/ubbWjFde+S0+/fRjzMy4UVhYgPr6Tdi16yVpH73ekNLF\nKomIiFazXBqh++STT+H27VshI+vVag22b9+Zss8o0Rbj2YrE369xzTbcn3+A76Z+wM3pQJtMrVCH\nvdeaglL87xv34rup7+FcdGHWO4v782MhYTFMGiM2mzYldyJpolAo8MQTT+HWrZsh25VKJRob4w9I\nIVotNBotXnzxJXz44Z+lDgelUolnn20OWaSd0qtMtxZ/W/83uD17G4s+D6r1VXF/B6uVKmwv+xm2\nl/0s5n46tQ7PVGzHM9gubavWV+KzsS+l9QnLdWvxQlXgObBKocJGUz02muoTOocSbTGer27GZw++\nkEI1VRaWY1dl04p/09fUrMP+/f8RX3xxGdev/wi/34e6unps3/6M7NmH27Y1Qq3W4C9/+QAAsGlT\n+AzHdGBnwzJyOhDYyUBEj6p/9+864fcL+POfLzxc4NiPl19uwb/5NwwpF4tCoUBT0/PYtGkzrlz5\nCgqFAv/23/4f0GqX4ieWlZXj/ff/AAB4sWoXagypG62vUCjijqKIpK7IDLOhFov+RWiUmowtSLZt\nWyO2bLHA5ZpCdXUZ/H41vN70LgAtR01NDQwGAxQKxapfMI+IiPJbTU0N9HqD9P9cUVRkxG9+87f4\n4YerGB8fh8lUDItlK4qK0h9DOh6VQoWWml+gSFWEz+59AQB4oWpXxBkKRk0RtpcvhcK4PXMH37uu\nYcG3gMrCCmwtsaAgzvpY2bR58xb86ldt+PrrLx/+bcFTT+1EWdmjMaKdwuVqnZFutbV1+Lu/ew03\nbzrg8/lRW2uW1q+hzFErVagrSv/sOiAwGK+uqA4TCxPQqrRhIfBWan2RGWbDOkwuTEGr1ITMiFup\ngoICNDU9L4U6W4na2tqM/4ZlZwMREcmmVCrx/PM/xx//OCRNzd+wIfbCRLSkoqIK/+W//F8oKTFg\ncRFRH6DHWigr0xQKRdJrR6yERqNFZWUViosNmJwMj+GZDXq9AW+99V/jXj8iIqJs0+sN+M//+R+k\n/+eSwsJCPP547NGo2aJQKFBRWC7NypTbYVBjqE7pQJFMaGjYhn/4h162awhAbtcZ6abRaLFhQ27O\nRKL0UCtVaQl1p1Qoc24x72z8hs3MEEUiIiICELjZGwyPVgN+NeH1IyKifKHXGx65h4aUGLZrKBjr\nDKLVKdN1PTsbiIiIiIiIiIiIiIgoKexsICIiIiIiIiIiIiKipLCzgYiIiIiIiIiIiIiIksLOBiIi\nIiIiIiIiIiIiSoo62wkgIiKiUDdnbq3ouFvTtyP+P5tWei5EREREiUhVmyPV7Sm2hYgoWaxHsisd\nv7NX8zVlZwMREVGOOfPN/5P0e/z3b//vFKSEiIiIKD+kov20HNtTRJQL0sZKpQ4AACAASURBVFG/\n0crwvhAfwygREVFSyssrsp0EIiIiIiIiIiLKMs5sICKihNTW1uI//af/E17vAoqKdKiqMmc7SauC\nmK/Jmp+fBwDodDrZx6jVChiNhXC75+D1CkmnIZra2tq0vTcRERE9emK1n5Jp36ykPSUH20JEJFdt\nrRl///d/n/bfaPksU79jgfTdF4DVd29gZwMRESVEpyvE5s1boFYrUVpqwOTkDLxef7aTlffEfM0G\nXksiIiLKR7HaT2zfEFE+0+kKUV29lXVYDKzncxPDKBERERERERERERERUVIUgiBwLg4RERERERER\nEREREa0YZzYQEREREREREREREVFS2NlARERERERERERERERJYWcDERERERERERERERElhZ0NRERE\nRERERERERESUFHY2EBERERERERERERFRUtjZQERERERERERERERESWFnAxERERERERERERERJYWd\nDURERERERERERERElBR1thNARETZ19nZiTNnziR0jMvlwttvvw0AqKurw+joKF577TWYzeYV7UeP\ntlhlMFtlKFaaOjs70dbWhubmZpjNZtjtdrz99ts4evQoyzbltcHBQTgcDhw4cCBkO+tyIqLcxvY8\nEdHqli/1PDsbMizZi/coNAaSTbvD4cDZs2cBACMjIzAajREf/uTzg6Jk80juuedzOQKSS//g4CBs\nNhva2toi7l9cXAyTyQQgv8uSw+FAd3c3bDZbwse2t7fj1KlTsFqtAAL53d7ejnPnzkl5k8h+q12u\nfZ9ypdzKKYOZLkNy0jQ8PBzyuslkwptvvpm2vJN7b8tkOcvl+63dbsf58+dRUlKCqakpOBwOHDx4\nUCpDokzml9w0ZfO76XK5cOzYMbz++uthr7EuX8L2fHxsz8vDNn18bM/Hx/Z86rGej4/1vDys5+Nj\nPR9f3tXzAmXU7t27heHhYelvp9Mp7N69W3A6nSk9PtnPyaZk0j46OiqcOHEiZNuJEycEi8UijI6O\nhmzfsWOHYLFYpH87duwQBgYGUnMSaZbs9ZV77vlcjgQhufT39vaG5NHyf/v27ZP2zcey5HQ6hUOH\nDgnHjh0TDh06JFgsloSOP3v2rLB3796w7ceOHQv5Dsrd71GQa9+nbJdbuWUwk2Uoke/FoUOHhLNn\nzwq9vb3CwMBAWq9jIve2TJWzXL7fRkrbwMCAYLFYQvJGELKbX9HSlM3vZm9vr7Bjxw6ht7c3ZDvr\n8lBsz8fH9rw8bNPHx/Z8dGzPpw/r+fhYz8vDej4+1vPR5Ws9z5kNGdTf3w+TyRQygs1kMqG5uVnq\nTUvF8cl+TjYlm/a+vj4cP348ZNvRo0fx7rvvoqurC+fOnZO2NzU1YdeuXXC5XDCbzWhubs6LURmp\nuL5yzj2fyxGQfPodDgeOHz+O4uLiiO996tQp6e98LEsmkwlvvfUWgMD3ZmhoKKHjBwcH0djYGLbd\nbDajv79fyl+5+612ufh9yna5lVsGM1mGEvlelJSUoKOjI2WfHYvce1smy1ku32/Pnj2Ld999FwcP\nHpQ+p7m5GQDw9ttvS9c4k/klN01A9r6bdrs9bJaFiHX5Erbn42N7Xh626eNjez42tufTg/V8fKzn\n5WE9Hx/r+djytZ7nAtEZFOviySkwco9P9nOyKdm0DwwMoLu7O2x7U1MT7HZ7yDbxQdGBAwfQ1taW\n85WMKBXXV86553M5ApJPv9lsRkdHB9ra2kL+AcCBAwdC8ixfy1IybDZbxKmGZrMZDocDLpcrof1W\nu1z8PuVLuWUZkn9vy2Q5y+X77a5du6JOhS4pKZH+n8n8kpsm8e9sfDdtNpvUARLptUf9eyhiez4+\ntuflYZs+Prbn04vt+chYz8fHel4e1vPxsZ5Pr2zV8+xsyKBkL96j0BhINu3ifqtZpq5vPpcjIPn0\nRxqx7HA44HA4oj6MIUg37HjfQ7n7rRb5/n3KRblQhvr7+6V/3d3daUuL3HtbJstZLt9vm5ubw2KL\nivFNg+v2TOaX3DRlS39//4rSkQvfw0xjez4+tuflYZs+Prbns+NRb8+zno+P9bw8rOfjYz2fHemu\n5xlGKQcEX7xo09dTcXyyn5NNctMePN0u2MjISMQKrL+/X/q/3W7HgQMHcn5hmGgSvb4rPfd8LkdA\n4t+XYJGme4pWU1mKR7zRxBoJ4HQ6Ze/3KMv29ynXy20ul6GpqSns2bMn5BqKC2ilOg8Tvbctl45y\nlk/3W5fLhZ6eHhw5ckTW+WfiexkvTZnMr3jfs1z+HuYStufjY3teHrbp42N7PnlszyeO9Xx8rOfl\nYT0fH+v55GWznmdnQ4Yke/EehcZAutJus9ngcDhCYrUBmX1QlCqpyqN4557P5QhIT1kaHByUpuMt\nl49lKRUijTJIZttqlavfp3wqt7lYhoJj7AOB0TeNjY04efJk2GvpsPzelgvlLNfut3a7HTabDQMD\nA9KUaVG28itWmkSZzq+zZ8/KisOai9/DTGN7Pj625+Vhmz4+tuczg+35UKzn42M9Lw/r+fhYz2dG\nNup5hlHKsGQv3qPQGEh12ru7u3HkyJGwCuett94KqdSCHxTlumTzSO6553M5AlKb/t7e3qjT8PK5\nLK2EuPjS1NRU2GtiY6C4uFj2fo+KXPs+5UO5zbcylMm4qNHubdksZ7l2v7VarThw4ADOnTuHqakp\ntLS0hE0BznR+yUlTJvNrcHAQr732Wsx98u17mAlsz8fH9rw8bNPHx/Z8erA9Hxvr+fhYz8vDej4+\n1vPpkc16np0NGZLsxXsUGgPpSPvhw4ejjhyMJNcX0Enn9Q0+93wuR0Dq09/f35/w+eZ6WUqGeIN2\nu91hr4mNArPZLHu/1S6fvk+5Vm5ztQydPHkSfX19UV9Pd8M+0r0t2+Us1++3R48ehdPpRFdXF4Ds\n51ekNMWSjvxyuVxwOBxxv0O5+j3MBrbn42N7Xh626eNjez692J6PjPV8fKzn5WE9Hx/r+fTKZj3P\nzoYMSfbiPQqNgVSnva+vD2azOeINK9sPilYqFXkk59zzuRwBqS9Lg4ODUffP17KUrObm5oiNAiC0\nLpK732qWi9+nfCq3uViG3n333YiLZE1NTcFkMqU1TdHubdksZ7l2v7Xb7RHft7GxEXa7HQ6HI+P5\nJSdNQGbzSwxX0N3dHfLP5XJhYGAA3d3dGBwcBJCb38NsYHs+Prbn5WGbPj6259OP7flwrOfjYz0v\nD+v5+FjPp1+26nl2NmRQshfvUWgMpCrtg4ODmJqaColBbLfbpf9n80FRspLNI7nnns/lCEht+m02\nG4xGY8TX8rksJaOtrQ0jIyNh2202G1pbWxPeb7XLte9TPpXbXCxDr776asRFxy5duoQ9e/ak7XPj\n3duyUc5y7X7rcrnQ3t6Offv2xdwHyFx+JZKmTOZXW1sbjh8/HvYPAPbs2YPjx49LoQxy8XuYLWzP\nx8f2vDxs08fH9nx6sT0fGev5+FjPy8N6Pj7W8+mVrXqenQ0ZlOzFexQaA6lIuzhSb/lih+fPn5f+\nn60HRamQbB7JPfd8LkdA6tIv3pDq6uoivp7PZUmulpaWsDiGHR0dAAL5KXI4HBgeHsbBgwcT3m+1\ny7XvUz6V21wsQ6+99lrYyJi+vj4UFxdHzNdUkHNvy3Q5y8X7rclkgtlsxuuvvx72mjijwWq1Ashc\nfiWSplz5bi7/0ZeL38NsYXs+Prbn5WGbPj6251OH7Xn5WM/Hx3peHtbz8bGeT52cqucFyqjdu3cL\nFy9elP4eHR0VduzYITidzrD9Tpw4kdTxcvbLRcnk0ejoqLB3716ht7c37N++fftC9uvt7Q05tre3\nV9i9e3cazij1ks0jueeez+VIEJL/vgmCIFy8eFGwWCzC2bNnI76ez2XpxIkTwqFDh4QdO3YIFotF\n2Lt3r3Ds2LGQPBOE6PnjdDqFEydOCGfPnhXOnj0rHDt2TBgdHV3xfqtdLn2fcqXcyi2DmSxDctM0\nOjoqnDhxQjhx4oRw7NixqHVIKsi9twlC5spZLt9vz549KwwMDIRsGxgYECwWS9j2TOWX3DRl87t5\n4sQJYd++fYLFYhF27NgRVu5Zly9hez4+tuflYZs+PrbnY2N7Pj1Yz8fHel4e1vPxsZ6PLR/reXXi\n3ROUjHPnzuHtt9+Wet3sdjvOnTsne8qO3OOT/ZxsSibtnZ2dcDgcIVPvRMG9omazGW1tbVKvn9vt\nhtFoxIULF1J0FumVTB4lcu75XI6A1KRfXGCosbEx4uv5XJaWjyKJJtq5mEwmWe8hd7/VLpe+T7lS\nbuWWi0yWIbmfYzabM5Ymufc2IHPlLJfvtx0dHbDZbOju7pa2ORwOnDt3TppBIMpUfslNUza/m/HK\nM+vyJWzPx8f2vDxs08fH9nxsbM+nB+v5+FjPy8N6Pj7W87HlYz2vEARBSMk7ERERERERERERERHR\nI4lrNhARERERERERERERUVLY2UBERERERERERERERElhZwMRERERERERERERESWFnQ1ERERERERE\nRERERJQUdjYQERERERFRTIODg2hoaEB3d3e2kxJXPqVVZLfb0dDQgL6+vmwnhYgecflUh+ZTWnOB\nzWbjvYbSjp0NRERERERERERERESUFHW2E0BERERERES5ra2tDZcvX4bJZMp2UuLKp7Q+KlwuF7q6\nuuBwOOBwOGAymdDY2Biyj9PphMPhgMvlgtVqxblz57KUWqJHWz7VofmU1tWK9Tstx5kN9MhbTVOW\nOYVQnkjXfDWVAyIiIqJ0yKWHOYcPH8bOnTujvp5LaaXA9Thz5gwOHDgAADhy5AjOnDkT8u/cuXO4\nfPkyWltbUVtbm+UUEz3acqkOZX2f21i/03LsbCAiIqK8wE7B7HC5XGhoaMDhw4elbezcJiKilbh4\n8SIAoLm5Oeo+Bw8ehNlszlSSiIgoBVi/k4hhlIhWEU4hJCIiIiKiXHXp0iWYzeaYD5tMJhPq6uoy\nmCoiIkoW63cScWYDUYbEm/qXKuxoICKidBM7t48fP57tpBBRHDabDQ0NDRgcHITNZkN7ezsaGhrQ\n3t6O/v5+AIGZY52dnWhoaEBLS0vEGWSxZpd1d3dj586d2LlzJ06ePInBwUGp7SvOihKPHxwchMvl\nko6x2+3S+wwODqK9vV16r87OTjgcjrDPamhowNDQkDTzSvwXK63L80E83/b2dgwODkbMOznnJVd/\nfz9aWlrQ0NAgnZvNZpN9bPB1i3QNVnJ+fX19Upra29vD0hN8ncR0Hz58OOSaJUKM1x1p1KvL5Qr5\nf7IjXxNJu9xrE+86yCnjQPx8J1op1veh0lV3xpPJPFqeB+L13blzJ1paWqTrHsnyMrLS8wVYv4tY\nvwdwZgMRERFRGh0+fBiXLl3C5cuXs52UlFpp5/ZqzQ+iXNff34/h4WG8+uqraGxsRH9/vxQKraen\nB3v27EFHRwcGBgbQ09MDk8mEjo6OuO8r/nDfv38/3G43Tp8+DZPJhNdffx2PP/542EMFh8OB9vZ2\nOBwOWK1WqS6x2Wzo6upCc3MzmpqaUFJSgt7eXrS0tITM3O3o6IDVakVfXx+cTieOHDmScD44HA60\ntrbCbDajv78fXV1dOHfuHKxW64rPK5bu7m709/fDarVK7zc8PIzOzk5cvXo15rGHDx/G0NAQmpub\nceTIEVy5cgU9PT2w2Ww4c+ZM0ufX0dEBs9mMgYEBdHZ2huwnXqf9+/ejpKQEDocDNpsN58+fD3kv\nucSHLssfRi3fvpL3Xk5u2uVem0SuQ7QyDsjLd6Jksb5PX92Za3kUrLe3F729vdizZw+MRiOGhoak\n6778+g4MDKC3tzekjKzkfEWs31m/hxCIHnHDw8OCxWIRent70/o5hw4dEnbs2JHWzyB5Il3zTJUD\nIlq5fP2e5nv973Q6BYvFIhw6dCgl75fv+UGUby5evChYLBbBYrEIo6Oj0vaBgQFp+8WLF6Xto6Oj\ngsViEfbt2xfyPpHqYPG9BwYGpG29vb2CxWIRhoeHIx5vsViEvXv3hqRFEAJ1TbRjjh07FnZe+/bt\ni1qXxErr8nw4e/asYLFYhBMnTqzovOIR8znSOQTne6Q0i2k7e/Zs2HHR0izn/MRtwZ8vCIE8Fa+7\nWA4i3XOdTqfc0w9x6NAhwWKxhBzvdDqFvXv3ruj9opGbdrnXRu51iFfG5eQ7UTJY3wekq+6MJ1t5\nJG7fu3dvSB0npic471J5vsFYv7N+D8YwSrTqrWTKsjiVbvnUqUiLZMabwiVn6p8o3pSreFO3cmnK\neKqnmcm5jnKnGRIRERE9apqbm0NGVIqjDE0mU8hIRLPZDJPJJKsNJS4G2dbWJm0T/z88PBz1uFOn\nToWN7jSZTGEj/8RRg7HeK1HL82HPnj0AALfbLW1b6XlFIo4ajhR2LtYimuKxVqs1bERqc3MzWltb\ncfr06ZDwFOJr8c6vp6cHra2tYZ9/4MAB2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"text/plain": [ "<matplotlib.figure.Figure at 0x121815a90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xx=['n', 'clusters', 'width', 'precision', 'recall']\n", "xlabels=['\\#SNVs', '\\#clusters', '95\\% CI width', 'clustering precision', 'clustering recall']\n", "for i in range(len(xx)):\n", " plt.subplot(2,4,i+1)\n", " ax1 = sns.boxplot(data=res_m8_down_cluster, y=\"frac\", x=xx[i], orient=\"h\",showfliers=False)\n", " if i == 0:\n", " ax1.set_xscale('log')\n", " ax2 = sns.stripplot(data=res_m8_down_cluster, jitter=0.15, x =xx[i], y = 'frac', color=\".3\", alpha=0.6, orient='h')\n", " if i == 0:\n", " ax2.set_xscale('log')\n", " ax2.set_xticks([10, 100, 1000, 10000])\n", " ax2.get_xaxis().set_major_formatter(mpl.ticker.ScalarFormatter())\n", "\n", " if i == 0 or i == 4:\n", " plt.ylabel(\"SNV fraction\")\n", " ax1.set_yticklabels(['0.1\\%', '0.5\\%', '1\\%', '5\\%', '10\\%', '50\\%', '100\\%'])\n", " else:\n", " plt.ylabel(\"\")\n", " plt.yticks([],[])\n", " plt.xlabel(xlabels[i])\n", " \n", " if i == 1:\n", " plt.xlim((0,35))\n", " plt.xticks(np.arange(0, 40, 5.0))\n", " if i == 2:\n", " plt.xlim((0,0.08))\n", " plt.xticks(np.arange(0, 0.1, 0.02))\n", " if i == 3 or i == 4:\n", " plt.xlim((-0.05,1.05))\n", " plt.xticks(np.arange(0, 1.05, 0.25))\n", "\n", "xx=['RF', 'FscoreTT', 'FscoreMultiG']\n", "xlabels=['clone tree distance', 'migrating clones $F_1$ score', 'migration graph $F_1$ score']\n", "for i in range(len(xx)):\n", " plt.subplot(2,4,i+6)\n", " res_m8_mut_grp = res_m8_down.groupby(['pattern', 'seed', 'frac'])[xx[i]].mean().to_frame(xx[i]).reset_index(level=['frac', 'pattern'])\n", "\n", " sns.boxplot(data=res_m8_mut_grp, y=\"frac\", x=xx[i], orient=\"h\",showfliers=False)\n", " sns.stripplot(data=res_m8_mut_grp, jitter=0.15, x =xx[i], y = 'frac', color=\".3\", alpha=0.6, orient='h')\n", "# if i == 1:\n", "# plt.ylabel(\"SNV fraction\")\n", "# else: \n", " plt.ylabel(\"\")\n", " plt.yticks([],[])\n", " plt.xlabel(xlabels[i])\n", " \n", " if i == 0:\n", " plt.xlim((-1,max(res_m8_mut_grp['RF'])+1))\n", " plt.xticks(np.arange(0, max(res_m8_mut_grp['RF'])+2, 5.0))\n", " if i == 1 or i == 2:\n", " plt.xlim((-0.05,1.05))\n", " plt.xticks(np.arange(0, 1.05, 0.25))\n", " \n", "plt.gcf().set_size_inches(16, 8)\n", "plt.tight_layout()\n", "plt.savefig(\"SNV_frac_MACHINA.pdf\")" ] }, { "cell_type": "code", "execution_count": 206, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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6eEeUmmxtvRJ0GK+lW15eQX19PeFwOcPDQ/h8Ex8NpRQjIyNUVFTmbtp0Xaem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3YOHjy4qvcIh5efTSnc3WZn5+uSRyLT9iyK8+e7GBsbJZfLYRgGqppFVXPkcrmrWhxaVVUkSWJm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"text/plain": [ "<matplotlib.figure.Figure at 0x1239086d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "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 }	bsd-3-clause
okartal/popgen-systemsX	exercises.ipynb	1	30453	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Population Genetics\n", "\n", "Önder Kartal, University of Zurich \n", "\n", "This is a collection of elementary exercises that introduces you to the most fundamental concepts of population genetics. We use Python to explore these topics and solve problems.\n", "\n", "The exercises have been chosen for a one day workshop on modeling with 2.5 hours exercises preceded by approx. 3 hours of lectures (a primer on population genetics and probability theory). Evidently, it is not possible to cover a lot of material in this time; but upon finishing this workshop, you should feel comfortable picking up a textbook on population genetics and exploring the many software packages that are available for population genetics.\n", "\n", "__Note__: You can skip the exercises marked by an asterisk and tackle them if time permits.\n", "\n", "## Preliminaries\n", "\n", "All exercises can in principle be solved by only using the Python standard library and a plotting library. However, if you like and it feels more comfortable to you, you can use as well the libraries numpy and pandas. Note, that you have a link to the documentation of Python and standard scientific libraries in the \"Help\" menu of the Jupyter/IPython notebook.\n", "\n", "IPython has so-called [magic commands](http://ipython.readthedocs.org/en/stable/interactive/magics.html) (starting with %) to facilitate certain tasks. In our case, we want to import libraries for efficient handling of numeric data (numpy) and for plotting data (matplotlib). Evaluate the following two commands by pressing shift+enter in the cell; they import the necessary libraries and enable inline display of figures (it make take a few seconds)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us define two vector variables (a regular sequence and a random one) and print them." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 1 2 3 4 5 6 7 8 9]\n", "[ 0.43191072 0.17692517 0.42616389 0.73142861 0.52226624 0.54028909\n", " 0.19863538 0.08375168 0.03776252 0.73497815]\n" ] } ], "source": [ "x, y = np.arange(10), np.random.rand(10)\n", "print(x, y, sep='\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following command plots $y$ as a function of $x$ and labels the axes using $\\LaTeX$." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0xad64d92c>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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7IjXHH/8I5eXhj2z77XMSnkhebLstnHdeOFZroegoKWyNWRh0e+UVGD++6df5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\nTm3veJOZrYWwL0VqK9h7zWy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"text/plain": [ "<matplotlib.figure.Figure at 0xad61faec>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x, y, linestyle='--', color='r', linewidth=2)\n", "plt.xlabel('time, $t$')\n", "plt.ylabel('frequency, $f$')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the [tutorial](http://matplotlib.org/users/pyplot_tutorial.html): \"matplotlib.pyplot is a collection of command style functions that make matplotlib work like MATLAB.\"\n", "\n", "__Comment__: The tutorial is a good starting point to learn about the most basic functionalities of matplotlib, especially if you are familiar with MATLAB. Matplotlib is a powerful library but sometimes too complicated for making statistical plots à la R. However, there are other libraries that, in part, are built on matplotlib and provide more convenient functionality for statistical use cases, especially in conjunction with the data structures that the library pandas provides (see [pandas](http://pandas.pydata.org/pandas-docs/stable/visualization.html), [seaborn](http://stanford.edu/~mwaskom/software/seaborn/), [ggplot](http://ggplot.yhathq.com/) and many more)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Hardy-Weinberg Equilibrium\n", "\n", "These exercises should make you comfortable with the fundamental notions of population genetics: allele and genotype frequencies, homo- and heterozygosity, and inbreeding.\n", "\n", "We will use data from a classical paper on enzyme polymorphisms at the alkaline phosphatase (ALP) locus in humans ([Harris 1966](http://www.jstor.org/stable/75451)). In this case, the alleles have been defined in terms of protein properties. Harris could electrophoretically distinguish three proteins by their migration speed and called them S (slow), F (fast), and I (intermediate).\n", "\n", "We use a Python [dictionary](https://docs.python.org/3.4/library/stdtypes.html#mapping-types-dict) to store the observed numbers of genotypes at the ALP locus in a sample from the English people." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "alp_genotype = {'obs_number':\n", " {'SS': 141, 'SF': 111, 'FF': 28, 'SI': 32, 'FI': 15, 'II': 5}\n", " }" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Calculate the observed genotype frequencies at the ALP locus." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Calculate the observed allele frequencies at the ALP locus." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Calculate the expected genotype frequencies if the ALP locus were in Hardy-Weinberg equilibrium." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [$\\ast$] 4. Calculate the estimate of the inbreeding coefficient $F$ for the ALP locus.\n", "\n", "The inbreeding coefficient is defined as\n", "$$F = 1 - \\frac{h_{\\mathrm{obs}}}{h_{\\mathrm{exp}}},$$\n", "where $h$ denotes the (observed and expected) frequency of heterozygotes. Can you interpret the result in simple terms?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Genetic Drift\n", "\n", "Not all gametes that are produced by an organism pass over to the next generation. Due to numerous possible influences there is only a finite sample that contributes to the next generation. Therefore, not all alleles of a gene are guaranteed to appear in the next generation in proportions equal to those in the present generation. As long as we cannot specify a process that leads to a specific selection of alleles and we have no reason to believe that the allele itself has a bearing upon its selection, sampling is an undirected (i.e. random) cause of allele frequency changes in a population. We call such an undirected carry-over of genes genetic drift.\n", "\n", "Genetic drift does not introduce any new assumptions compared to the Hardy-Weinberg case; it just drops the assumption of infinite population size." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5. Write a function that runs the Wright-Fisher model of genetic drift.\n", "\n", "The function must at least take the following __arguments__:\n", "\n", "* number of generations\n", "* size of the population (i.e. number of diploid individuals)\n", "* initial allele frequency (we have only two alleles, so considering a single allele is enough)\n", "\n", "The function should __return__ a list (or an array if you like) that represents the trajectory of the allele over the generations." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6. Plot several trajectories (i.e. replicate populations) of the Wright-Fisher model and study genetic drift with different parameter values.\n", "\n", "* What is the long-term behaviour of the locus?\n", "* What is the effect of small/large population sizes on the trajectories?\n", "* Do the trajectories of the replicate populations differ?\n", "* Do rare alleles become extinct more often than abundant alleles?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [$\\ast$] 7. Plot the distribution of allele frequency under genetic drift. \n", "\n", "There is another way to look at the dynamics of a locus under genetic drift. If we have a large collection of replicate populations, we can take, at each time point, the allele frequencies of all these populations and plot a histogram. Thus, instead of looking at individual trajectories, we can observe how the distribution of this allele changes due to genetic drift across all replicate populations. This viewpoint of looking at a time-dependent probability density, is central for understanding the diffusion approximation to genetic drift ([Kimura 1955](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC528040/)).\n", "\n", "Write a function that takes the output of the Wright-Fisher model, a list of generation times and plots a series of histograms of allele frequencies. What can you observe?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 8. Model genetic drift as a Markov chain.\n", "\n", "The temporal evolution of the probability distribution is actually governed by a deterministic equation, the Markov chain. To simulate it, we have to only know the transition probabilities and the initial frequencies of all possible states. Since we are looking at a population, the possible states are given by the number of reference alleles $A$; for a population of $2N$ alleles, we have the states $X(A)=0, 1, 2, \\ldots, 2N$. The transition probability from $X(A)=i$ to $X(A)=j$ is given by the binomial distribution:\n", "\n", "$$T_{ij} = \\binom{2N}{j} \\left(\\frac{i}{2N}\\right)^j \\left(1-\\frac{i}{2N}\\right)^{2N-j}$$\n", "\n", "* Write a function that gives the transition matrix for a given population size $N$. Tip: For an efficient implementation, you can use the binom() function from [scipy.stats](http://docs.scipy.org/doc/scipy-0.16.0/reference/generated/scipy.stats.binom.html). How can you test if your matrix is consistent?\n", "* For $N=4$, calculate the probability that a population with 4 copies of allele A transitions into a state with 3, 4, 5 copies. Why should these values be symmetric around 4 copy numbers? \n", "* Use the function [matrix_power](http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.matrix_power.html) from numpy.linalg to compute the distribution for 19 generations with the parameters $N=16$ and initial population frequency of the reference allele of $\\frac{1}{2}$. The state probability vector after $t$ transitions is given by\n", "$$p(t) = p(0)T^t$$" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mutation\n", "\n", "You have seen yourself that genetic drift removes variation from the population. Since we can observe standing variation, it is evident that genetic drift cannot be the only evolutionary force. There must be something that causes variation. To a certain extent, new variants can arise in a population due to migration, that is an influx of new alleles. However, the ultimate cause of allelic variation is mutation.\n", "\n", "To study the interplay of drift and mutation, we will focus on the decay of heterozygosity or the dynamics of the inbreeding coefficient. With mutation, inbreeding changes according to the formula\n", "\n", "$$ F_t = \\left[ \\frac{1}{2N} + \\left( 1 - \\frac{1}{2N} \\right) F_{t-1} \\right] \\left( 1 - u \\right)^2 $$\n", "\n", "where $(1-u)^2$ is the probability that no mutation occured in either of the two alleles and $u$ is the mutation probability (also called mutation rate).\n", "\n", "### 9. Simulate the dynamics of the inbreeding coefficient with and without mutation and observe the stationary state. Pick a population size not too small. Play with the number of generations." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "Gillespie (2004) Population Genetics: A Concise Guide The Johns Hopkins University Press\n", "\n", "Hartl & Clark (2007) Principles of Population Genetics Sinauer Associates, Inc." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Other Resources\n", "\n", "[Genetic Simulation Resources](http://popmodels.cancercontrol.cancer.gov/gsr/)" ] } ], "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": 0 }	cc0-1.0
ShinJJang/analyze_kakao_talk	analyze_kakao_analog.ipynb	1	460662	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Kakao talk 대화 분석" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이 프로젝트는 오픈윙스님의 [데이터로 풀어보는 나] 이메일로 분석해 보는 나의 3년( http://goodvc.tistory.com/14 )로 영감을 받아, 단톡방에서 혼자 외롭게 소리없는 아우성을 지르는 저를 정성적으로 나타내어 보고 친구들에게 매마른 감정을 돌아보게 하는 목표를 가지고 있습니다.\n", "\n", "일부 코드는 그대로 옮겨왔으므로, 이 지면을 빌려 양해를 구합니다." ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [], "source": [ "text_file = open('KakaoTalk_20150823_2347_04_589_group.txt', 'r', encoding=\"utf8\")\n", "\n", "lines = text_file.readlines() " ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# coding: utf-8\n", "import re\n", "import datetime\n", "from datetime import date\n", "import time\n", "import numpy as np\n", "from pandas import *\n", "import matplotlib.pyplot as plt\n", "from matplotlib import rcParams\n", "import seaborn as sns\n", "\n", "%matplotlib inline\n", "sns.set_style(\"whitegrid\")\n", "rcParams['font.family'] = 'NanumGothic'\n", "rcParams.update({'font.size': 12})" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [], "source": [ "date_pattern = re.compile(\"-{15} (\\d+)년 (\\d+)월 (\\d+)일 ([월, 화, 수, 목, 금, 토, 일]+)요일 -{15}\")\n", "message_pattern = re.compile(\"\\[([\\w+\|\\s+]+)\\] \\[([오후, 오전]+ \\d+:\\d+)\\] (.+)\")\n", "messages = []\n", "date_now = date.today()\n", "for idx, line in enumerate(lines): \n", " date_match = date_pattern.match(line)\n", " message_match = message_pattern.match(line)\n", " if date_match:\n", " date_now = date(int(date_match.group(1)), int(date_match.group(2)), int(date_match.group(3))) \n", " elif message_match: \n", " name = message_match.group(1)\n", " send_time = message_match.group(2)\n", " content = message_match.group(3)\n", " \n", " send_time = datetime.strptime(send_time.replace('오후','pm').replace('오전','am'), '%p %I:%M').time() \n", " send_datetime = datetime.combine(date_now, send_time)\n", " message_item = {'name':name, 'date':date_now, 'timestamp':send_datetime, 'content':content} # type 추가 예정\n", " messages.append(message_item) \n", " \n", " " ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = DataFrame(messages)\n", "df['hour'] = df['timestamp'].apply( lambda x : x.hour+ x.minute/60)" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Python34\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": 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DpEsMghwnZVsHjwfPLzkZoF07fI/GDB1GjQLYsIH/X7gQWgPgPVywwPl4N38+9udQKCzk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CKHdpKbsxZGdziWVCaytOyo2NXA1r0CD8my7pksj1c8/h5PLFF0gWL7rIGOVTtY8qAlnyowFw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0EDW96Ax5UrwvMUEmI/97VW/eqCx2NOjnhOf/oTzR8WgioqiCry9GlR5MTLi8bCyy+r52RV\nXf+RkWKuymORheX27UXss2zh1OOudoWxxKhQFb/33HNC0JU/HzCAnp8zdhOtpV9mCOJjznHQ4x6W\nLZh+fo6NEzJrkhbe3qSA8VznMDJNzQEvuR/1DAAnTgjlkr0cMnNFixZk7NB6yhxBVswjIognPjOT\n1sD77ydhX/ZeNGsmfnPtmkh4rgI8PT3h6empqgboqCCJp6fnXS9YUhXUnZaYqFuQtbzaiGdmyFX6\ngJrboLTIyRGCzLVrwD/+QcdGApz2faMsf4A2fxaaOXnICI7iSo04fPXgKiMFC4yy0Pyf/5DwFBoq\nFshmzei+3nlHbKzvv0/WLV7I5WfTsaNw186ZQ89Pjis9dow2Gdnq5+2tzpp3Bfw9OWnu97+3t4rL\n9GW8WLOrV4/mjZ8/QJbkb7/VV6AAUdhFj8VCD/K1+vUTlGRajB8vwi64fK4e5NLOcoKoNolJdrWz\nlUu2OO/aJcILnAnO2nEq0zDyvNRTEGVLuSPPxfr1YoyfPaumjpN5t6sSUiILghzXzGEO7LE5ccIu\nHMv3P/8RCtmAAYL2T/aKaNciVz1Azr7XsydZC0+dEkw3AweS0JqRIZRU7gd/f6J+bN6cnomjEJEH\nH6S/8viT+0ir4MrnkEuJO1rTjPjaqwumpnzuORHrrkdHJscRs0euRQshZMpcvvfcow4ta9WKfi9X\nMzx8WL0PaufWp5+KBDwZen3HioBm7F6JikIAxztPmmR/rtxcsXbxuixTAvJxz55ibZPXAy3k8tij\nRlF7WrcW3hKGNk8nKorG3smTxmxWDrBixQoAQFZWFjxdqEw4tZKT/caNG2hQRe+Jp6enS0mIVYEp\nNJswRnWFZatVaKB3YmXWFnKQBeeahtUqqHSs1pq1jgcHUyUzPjaCkeCtV4nOEVxlpHjnHZHs2aGD\neD8/3z7r/uxZak///sCGDSQgFhfTq3VrIThziWre4AF7ijcZcvJmy5bO7+1OMWcOWer4eONGEhqa\nNVNbzGTLcVERCS6DBwvBimPm5swBmBIpOpr6p08fQcN04QJ5MYYMEWNZrpwoWwa1gqg8zvWEMo7T\njokRygo/z7Q0NV94QACNvbZt6T0eh1rGk1u3yG0tFw3RIi9PWL5zc9WCs2wlNUoE1G5ispDKxRSW\nLxfKVMOGJATKMZhVEZZZcHzxRfobF6fPH8zzXsOGEixb7iZMUI8NR+FEVVX85P+ZV5s/i4wUApDV\nSgqJu7sQmh9+mMbY/v2iQEv79uJZaBXRBx8UytuDD9orbtqCUnIY1fr1wOzZdHzunH0xEC8vofAY\n8YMD9Ay56I5MJcisDlarmoGCqRCPHSOhnQtt6IUBXbok4ohZcJ80SayLLJSyEN6ypViHxo8n5V0W\nguUwiLQ0cV+7dtGzMSoOxH0ury0sNIeEqNpdMGKEuI7emi1b93fvpu/I3kAWKE+cEKFg3H96Y/HU\nKRGe54zCVBac//Y3x8WSXER4eDjCWWl1AT4+Pvgn04q6iBfk3Icagik0m6h55OSIjbB9+59GeEZ1\ny4XLFgej4hxWqxCsqtoXchnjyZOFZp+aaly+W15Y/vlPY6H5yBFxrFdQQ4a8kW3bprbs5uZSWy5e\nVCdtsUWOx8LOnfbWq9BQtfuXr6EHRwLT4MFikx8wwLhvWGAGqH3MBqANNZEt4F5e5M4NChJt8/Oj\nv40bixjAkhLqCzmBpV8/2phu3CBBzdOT7pevx/eupzDphVMwZFrHwEBB+8f3vXeviPfl4iZyOVyZ\n61Yr9OzfLyzba9fa92OHDsIr06GDusCPlpfY1WQ4bbJtZKS4Z547ISFCGeOkQBks3MpxlkwPefSo\niFeeONGYHlHb1iFD4CHHTcs0XcyYcafQFiBJThbPlnm/te1iXnoWekaMoJyMI0fEvemxXLiKL7/U\nP9bCz4/aIvdFWZnop169RGEkLWTh29+fntvrr6vDhXj8v/029cULL4gwlOJi8lrohWl06iRKm7PH\nIzzc3sMAUPufflq0JTycrhsSQnPIYlFX/UxKEvM3KYnGKofz6SEvT4QROcvZcOQZlAsb8fHmzUKB\n57jvtWtF+ElcHN2LnveuZ0+hRDuySOvhLvIg13WYQrOJugt5galOghjHjrHV1Jl1qirCMieE8eam\nLSsrQ04y/OQT43Y4q4rXvDklf3FM4e7d9qEgALneORxETjbRIiJC9I1s/WQEBYkNSY4dtVrt45f/\n+leybvz73yLemj+XN00m/pfb7Eq/Owp/AWhTOHSIBKPmzUm40hOshgwR7crLo81w5Ej6391dbDax\nsdR3SUnUR2Vl1KedOpHywsLK7t0kJH/8MVHLvfeeOh6arZIeHtSuxo1pw2H3alUrrAEkMMvu8YQE\nYSkcN46UE63nhKFltFi1ivpVjvcsKhLhKQMGCCGUBWCZmkpLUyXzEl+8aO8irwq0ScA5OSL2VGvF\n5kREBo9XruwoJ/gVFwuh35GFrdLa6guQFa9dO+prFsaGDKlSDL4KrADGx7tegESrgMj98c475OWR\nlYHu3YVFVdvGr7/WD89gDBok2jVokPozLUvR0KH2nOI8npx5vOTnAqgV+X/9S93er78WSYk3b4rf\n6sUTy8mJ8jhhJTU/X61YDx8u8gEWLaL1JD1djGVA7CePPSbe4/U1MFBN9SZjzhzxnFavVidNGin3\nerBaxbrOzzMoSFh9d+6kvpANIEVFxmFjVqtgoXE2hvWqcjoLvalucbQ6DlNoNlHzuBNGBy1YWHbV\nYsXo108sMD16EM+knrBVHXASFmfxs3Bi1MapU0nY5WOZI1QLpqfjTWDECFFIYsQIkQyjpRqS0b+/\nCAXo31//O6+/rg77CA1Vl7IF1Bswu/8Zcvzyjh1kmTx+XCRMyW5VDi2Q41I9PUmojYsTiysnWFWH\nY3vfPtogysuBTZtImOM4PRlyQt2VK2Ql9fcXFli2KrJgIAti771H15BppT7/nNhWZKue7Io9eVIk\noMmhOSxksBXV1TLiMk3gyy+TwpKUJBQBPd7aoUMpuScvz95SGxJiX0Ld21ts/ixkyImHe/YIAYJd\n5DK4/ceOCeWgJmJbXa2uV1goFKxf/YoSmm7dovFpsdD44ARYRxY2efxHRAghfs0aStT6v/+jl1EM\nvpHQID8HOUmOldahQ+25oHNyhLD+ySf21zt3TrS3aVMaa5xYaASjWHpALbzrCfKy4Gy1Gpd/ZzC9\nmhbasTdkiFDk9dh4mjWjPv/wQ8EaY2Tl1fY7JwTn59P69OmnooiLlirOaqUxy16Ul18WzBaAiOPm\nEEYWXrXjPCpKHdqyf799wqOr+5I87+RS6g0aqEPq2HrMuHRJn6I0OVlY12WPhhZ5eeKcJ09SWzt3\nVveH3m+qen8/EZhCs4m7g5oMyZAtCcnJdSfcIzCQXKtt2tAmZcQYIsdP6sVSMiorQ9kwdCgJg1wG\ned8+es+ZgCWHxOiVmuWQD21sqZbmqKREUFDpxZ7xd5OTaSPnKoweHhRjyZuojw9ZAuUNpbycNqyi\nIqrOd+AA8Nln4nPtBuvsnrm0MwsqFRVkIeNYawZXcgRow27SxLhqpdWqViQaNyaLkbe3sFQ2bSqs\nsoMGUR/K1jXAvigCx7cDZOlhyiTtfXFsKh/HxKhpAlu1onZHRgohS1bItJvgxYv29G16KCwEtm61\nP4dcPl1PWK4JOFKOHVXXk4W4p58WG3anTiSwxceTkHjrlroq55NPGlMVvv020KcPim7cgP/SpSIE\n6aWXaPw+9BCNXT3IFVVXrVLHr168KATwadMEOwGHAOTlCVf700/T7+LjBcNIfLwIc2JrsewlatQI\n6Nq1auskh5nJY9XVEtDM4esIekqTHhWnXDJ78WI1s03r1vT5iy/S/GNBzlUGFUBQGXJIEhdx0cOL\nLwqhXisgavN9OnWidmgLamhzQ3r3Fsey0pKXRywtziALy3zdJ5+kBF5e+7TGDyO89ZZQjt96y1ho\ntlrF3LdaafzK60zDhuK+q1LM5icKU2g2cXdQk64ZOTNZtmA62hRkrdzV8AxXoWdJlzk6Za5ZoGph\nJnrUZXrvOboXZ5R4DDc3EvpCQ6n9OTn2blYWmmWhUivYXL0qBObhw8kq16YNWVhKS6n/jcr5nj5N\n7ZA5jI3g6J4jI8mKLwuselRYDRsKi1zDhtQuI4FZz5Ly6KP0euMNCudYtkyEdzBdm1Zo7tGD/h46\nROeVXbhGHLGAPR0fQAl6nCzHyXo5OUKgchYuoLXscQU/Wahp0UJ/A2VviTPIAth994ljZ9BjPNDC\n0RiQBWdZwQoNVVOKycwzehz0MvPMvn34fu9ePMSeiHfeEb8/dYriRa1W+3hquaLqsGGkRK5YQW1J\nTxfr2ZkzJHwDoo/i40UYU3y8ELoZV6+SsiUrMXK/5ObSKyTENcE3LY3mbVkZhQsFBLjOYJOcTJ6P\nyn68DcBd73t6c1GvqmZOjhDCZ89WV8Ts358EWW111apWrA0OFv3laFxqhU9+TlrBFRDhIMOHCy8G\nU+Px5z17ivV/3z7xWUoK8NlnaH7lChkcgoON+15bCZOv27696CuLRb1nDBigv++4Grcurw3yMQvO\neXlk+QfUITF6lTzrA5SfAA4fPlzbTVAUpe60o87jwgVFefVVel24oCjKHfRddLSidOjA9fcUZfhw\nRWnenF7Z2TXY6CoiO9v++tnZipKYqCiBgfRKTXV6GlW/pKYqir8/vfi3qamK4u5OL/l8etd3BO0z\nWbaMXlqIWofqV9u2dP3sbEXp1YteW7fS/w89pP4e94Onp/H5AEVp0EBR5swR/3fooCgREaK9lWPH\nBk2b7fpOe73oaP1+4P7Unl+Gu7t9e3/xC/qNfL/h4dQPW7eK306eLD5v2FDdN4pC3+X35N9pMXy4\netzL93DhAt1zaiqdo1Ejehmc7/Dhw/SbSZPopXfvLVrQSw/yfTRsaNzm1FRFsVjoxePFlXEaHa0o\nAwZUae5UCQMGqJ8jH2vbtmyZori50atyrOX8/e9izGvHRFQUfY/HVGKiOA9/x81NUTw8xLNp0UJ8\n1qKFfR/Jc2LOHHUfRUfTNbTjUx6T/PLycq1vEhOpfYCi3H8/3Se3KTtbfy4y5LEOKBWAogQF6c93\n7XkiIsRnPO9TUxUlOJhe8phr0ID6T352jua5DHl+8j0lJuo/K34piv17vO84G5t8Dfk5NmmiKB07\niufMz9Ddndrv66vcslgU5YEHRP/r3YevL72087xtW/01B7DfOxh698z9Ie8N8twZMMD+PHrj9cIF\nGhuTJzteZ2sAd0suMzqvaWmuLdREFa66DFcsh86gpZwDKHaUM7VropS0K9BazY2Kt4SEEDuDzFpQ\n1cIwWnq25cuFdXD5cjpfdYvHyO5AvfjCnByyJmqtzQBZXfr3F6Wdi4vJferrqy6n/f339NxiY8lK\nbTQOfvELcjHLdF7Hj9NryhTh9uZ4OGc0elyNDiAL8vDhNDb0GBWGDaO/+/ZRG/WekZ+ffdGTuDhq\ni5Zb1FEioxyXyZarPn2EF4StxXowCuvh6m9yCfT776djvXAc+XdG4Q2A44qK8n0YVX4EyBLIVq4X\nXxRJSI48T9p57uZmnKDnKHxD71nzb0aNIgsvIEoyA8D8+eqYUx1UBAWJWGot9V9KCo1ZnqMff0zX\n1xajqagQLnC5IMXZs/b3oseRn5cnvA2FheJ6jRrRWH33XTq/nFRYVuZaLkhgIFk4PTzomXEeBFez\nKy+n62zbZv/8evcmS69s2ezf396zFBzseowrxxHLXpjSUrKuz59Pz1EOLXNEaSmXXM/NJc/A1auU\n9OflRSwU2j3GCM648Rnc1zLt5pUrREuXnk7Pad8+8QyvXBGia2mp2oMgw1ElTNkqrp2ft29TTg97\noxj+/mKNY/52vXVWZtjRW1+2bVMfx8WRB4vjxPVyS37CMIXm2oBchQuof4LzxYvC3eModrKqsFjI\n3cvMAcyFawStK6s6cDWhgZN7ZFogLoXrKvRKKXMilva4qti3TxSu2LfPnt5Ndo1nZ9P72qpuFRXA\na68BH3ygLgKxbBnw/PO0yfn7Ez/q/v32lcDkAgJffUUCNlO3ySguFhX02M0uZ9UfOSJiADkUQKY9\na9xYCMycyPfFF7SZBwXRBlBaStn53t76zCe//a2aWeT++wV1VWamiEvWKeGqqqClRXIytYEVGD2O\nWcbw4SLMRqa8AtSxzYGB+uE4eqjuXJSZVDhkRw9y8hwfy+wxK1aIJCpWeLWxnB4e+mOd6eP4PHL5\n+vXrhSIJqBk/+P2mTSm+Xg4V0CavycpY5XFFkyZC2ZBLlgOkQD38sFiTODmWwzlkMFOKK30p7wl5\nefR8OXZeFvKLi6m/OBRDK8D+6lf0d/du47HRvr3gae/fX1QlBEiR4nnMoSIyeG399FMgKQkKQPNc\ny8bDIQsy/vIXwUTBwv4bbxiHLJWW0ty3WNTf0ZaHliELmnpcynq0gRyGwQl7AD0nFtRdLREeHS3W\nkMaNaa35+GM1/zVj0CBc/fZbBBcVGRf9efppoRjKcdxTptiHZGihp1jI4S+cEKjHBb1hg3hvwwb7\nMSDPIT6+dEkYTGq6wE0twxSaTdwdyNac6kLeVL/9lo7//GfK3AUcKxsca8e409LAMvRimi9epLjJ\n69dJELRYjJkrHEErvMkJF3xcHXaSnByxeXz6qVj42FKtVwxA5j5mcAxsSIha8G7fnn7Hm4Sfn/3i\nL8fTFheTEC5bbS0WKtuqLTPNSWmMkBDgr39Fs8OHSfh2d1dvCkxLxbh1i6xJSUm0kV29Kqx/ejGt\ngOjrI0doI8jJUcdOcrKSDI6FdVTKetQo2uS0VG16CA4WlFbyRv3gg7RBdutG//fpUzOKqZGlFqA+\nZ0HPEXPF3LmiSAR70/btE4Le+fNqpoKNG2lOsyID0L3JljTmax41ip737dskND/wgGAB2btXjK+9\ne+1ZMTIzxbOWq8HpVXnU88JwX2vh5kYKDSvw/Nv//Ef9vfBwuteoKFoXODfjyhXH1mBW2rOy7OPR\nGYWForKkr6/aQ79k7jMAABiLSURBVMIKycyZ9hR+7M25ft1e6eI1ZvVqYYnVU3ABWluHDgW6d8e3\n33+Pdr//Pa2Fcp5Haqr4n8eq1UrsM/J1HTFwuLnRfGCKtagoEpj1xitDLrk+YYLYB3iNk0uqAzQv\nma2oXz+xngUFCWvuW2+5FisuW7DDw0lZZAYUGVFRQJ8+uLRzJ4L52nrYt0/wzHNS+JQp9lX5srNp\n/JeWijV4yRL1d5KTiQ2IwR4UeezwcUiIYMFxNPdlBAcL6k1XlYyfCGpNaE5JScHHH38MDw8PdO3a\nFaPZyvVzQF0pUX23cLcp51w55/79YpPcv7/6QrMRa4O2DSEhVFXq6FFhAfz005ph+tCjqKvqeSMi\nhMW8e3dh6WSqNL1iAHJxDcbjj+tb3tmyxh6UY8fEcXQ0CdU7d6qTeG7cEIsxQMJSq1bqpK38fFJ+\n5OIgvXvbJ64EB4tNjZPPeDNlIQ5Qb2Q3b9IrK0s/FOBvfxPsBIBwPcqhEe+8Q7+Vi9C0aaOuZsfW\ndTc32sSsVnuPggwWNocOtbe4ypXcmP7p2DHxeXWEZ7ZkyuWIZUEkLU2t3Di6RmCgKGzBbZetfXpK\nSnq62kLt5iY27CeeEELN558LwfGzz0gAZ9evzDnepo06fOnNN8k9zYmdFmnbY7e0A7R54gkhZAUH\nq4+9vGiMsndhwgRqj9ZFfuSIOrl0+HAaX7IlPCSE2Fz0Qs4eeID+Nmigb6nk8aYNKWJoC9j4+oo2\n+vrqhwRYrdQW9lA526tiYnAtM1MkIsthXpcuCYs1hxcB9uO/VStjwblpU3rOERH6v9WDtuS6EfNO\nWRk9T1kxkENMXGWkMEKzZtTexo3VCb7t2tHaGBqKG927C3YNq9U+pElOuNaGZ8j4wx/E9zw97Q1Y\nzNKk5eUH1POTjw8epHbysbZd4eHC08IsS67SQ/4EUStCc3FxMVJSUrCxcnGYMWMGTp8+jZY/Rhnd\nHxOO4vjqo7Aso6ZdMlUVEjnWjo/vBK4IIhcvkhXg6FHx3nvvVY9z+G4gMlJs7FySVw6xePNNis8E\nxKYtx7h26EBu6JUrhdDM0Fo7Vq0iGiTGkSN0zthYcl9rs99l/P3v6kV4+HDRrqlTacOMjAT69MGF\nnBzcwxbbU6fEeeXqeTExaqFZD44qp8n8sx060JyWy8/yBnjtmrDqyIqAHAfp40MbtCPLsDZ0y5Hy\n6e5OlrH8/OpzorIl88MPhUD6xRdCaE5Lo3LdcgjFY49VLfSpZ08h9K1bJ0IceJzJVRrDwkhpY6VE\nFgLlTb6khNy/vM7I35Nj7AERbsB9OGiQ6GNHcdyVUFUELCoSLCJ87TfesC8uoweZjUX7nRMn1PHS\n3Dey0s7QE5rlohN6TBVauBqj3quXOJ+WktIR9MYrh2gsWmRcFGrUKCHgL1smwgcSEwU1JAvcetDz\nljhj3dm6leY0K7JGYMVRzzuhBz0v4Z49Qpno0sWeslAuu62d03KhlVdeoXvk9fujj4RgL4+zW7eA\nLVtonsqWf21RGVZateOCfyMXSOHQkJQUalefPmK9lnM05CJP9Qi1IjQfOXIEvSVBJjIyEocOHapf\nQnM9Jvd2Ct5oAceV8u4mqlsWu7q4dIni4+SQhLqmYcvPQbup9esnBMBmzShpRFYAOnQQC78rRThk\nQUg+PnDAVmkNgDq+k+HtLcIf+vcXVuWhQ0W7Q0NRce6ciC2WBc2//129acrlx1u1Ugu1fL+uoLSU\n4jqLi0kA8PERm8SECUK4lzcnOc6wpIReU6cKPlPt3GBObj7Wq+TG7mlWgjp1ckwZ5Qrk8BZtfxQX\n0zMZMoQEF6vVnk+cIZfwXbtWPDN2BbP1UsbAgcIDMGcOCdk8ttatE3HCfn5CgLNYaFOWXb8cg9+8\nuWif1Upj+9o1delzhgvKeP7QofBjq+PYsfa/kV3uHHcsJ1m5u9O4iY8XY93bm+7FaiWlc+pUcQ45\nLwIQfcH3Pny4faJdgwb0989/FkqiVvmUEREhwmTYcqsHWcF1pOzqYetWUeJZbsumTeI7/B7Pg/bt\nhfVfjq/du5fmtKO9RVsV0lHYhgzmOudQKH6+0dFiXMrrRny84wJVMrTfkylTIyJonN6pN3LVKhFe\nowcWmNmjMWUKhSjJgjB7AWR+/Vat7BPOR48WnsA5c2gu6xXCcbVY008QtSI0FxYWwl9yi/n7++P0\n6dO10RQT9Rk/hrDMyM8n4YJLMVsswPTpP971qwM5jEYv/rpJE/1j7SKot2jqJYQxdu4UxRl27lS7\n4AGymLCFPi8P+M1v6NiREsKCpPYYUAtlXbvaC80cdqEHWXE4elQkEy1cqE5EO3ZMbDxyOW6ABBq5\nDy5cMN745bLneiXQ584VwtjevaLd1d2geHPr2lUoN7IQdeqUuF5EBAkirhSz0MKRYBAZSUpUfj4J\n/6dPi2IgVquwbs6cKcYJu395TOgk8AFQVwb18xNJXixkuoCrI0eiJY8ZOYSF0aKFGFMcL83ucYDW\nAm4TW0659PyvfkVzRrb8actWA2ojzMqV9FcWnNkK37OnCCkaO5YURsC+6ESXLqJf9Nhy5Laz4mep\norjQp49aWGcBuWNHGrdZWUKZ5XmwerUYb/I8dcUAUVgo2moUpmIEucoqz2tZueMQFUCfp7kqkJU7\no3mhJ3Ru3Sqs9do8D0feA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"text/plain": [ "<matplotlib.figure.Figure at 0x940a690>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,6))\n", "plt.scatter(df.date.tolist(), df.hour.tolist(), s=5, alpha=0.5, color='r', label='메세지')\n", "plt.ylim(0,24)\n", "plt.ylabel('Hour(0-24)')\n", "plt.title(\"주고 받은 카톡 산점도\",fontsize=14)\n", "plt.legend(scatterpoints=3,\n", " loc='lower right',\n", " ncol=3,\n", " fontsize=14, markerscale = 3) \n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Uh8smo6OjA0I8ikRSwuHDR2A2mzLWPXtJ+f4B3xg6OjowOqQM8k6f7YbHNJZz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"text/plain": [ "<matplotlib.figure.Figure at 0x940ac30>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.groupby('date').count()['timestamp'].plot(kind='line', figsize=(12,3))\n", "plt.title('일일 카톡 건수')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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W5ixVoFK0qbw627HkmWGoVCr++vlJMnLlbFq0L3q9nvV7EgCYPTVQ4WhMz9Ag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\nAQnahBBCCCHMgARtQgghhBBm4P8AYB0LGbTNNC4AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0xab78610>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "count_df = df.groupby('name').count()['content']\n", "count_df.plot(kind='line', figsize=(10,3))\n", "plt.title('카톡 보낸 순위')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "df['delta'] = (df['timestamp'].shift(-1)-df['timestamp']).fillna(0)\n", "df['is_indifferent'] = ((df['name'] != df['name'].shift(-1)) & (df['delta'].dt.seconds//60 >= 10))" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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dqpUiFcJwrN2VSp1KzbQwX5TK9vuxFjFcymoYivb7v1iIFlZdq+KfS49QVFrN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\nYUQkORNCCCGEMCL/Awb5aFIJ99tWAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x9ce2650>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#print(df[['is_indifferent', 'name', 'timestamp', 'delta', 'content']])\n", "cold_df = df[df['is_indifferent']].groupby('name').count()['content']\n", "cold_df.plot(kind='line', figsize=(10,3))\n", "plt.title('누가 대화를 얼어붙게 했는가')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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xrQPvv9IdS3Mjvlgfw9o/T6HRyMK4QgghRG1pXNnWfcrKzuP/VhcPDnxjhD8W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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x9808ed0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[df['is_indifferent']].groupby('name').count()['content'].div(count_df).plot(kind='line', figsize=(10,3))\n", "plt.title('누가 대화를 얼어붙게 했는가 ver.전체 메세지 비율')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "시간대 별 카톡 통계를 해보겠다." ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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5sToUn2vD7denDzqgzB8+k7PBYOhT81Wr1airqxv0eFNTEyoqKvDiiy9CJBJh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\nVFSEXbt2QSAQoKCgAOvXr/fr+ObNm8G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"text/plain": [ "<matplotlib.figure.Figure at 0xab850f0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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ZEELIsBesRwkWsykI0fhHDxgIIYQQjhnWNWe324O6ZgsqG01oaLFAIRUiXCOF\nVi1BuEYCiWhYXz4hhJAhalhmp/pWO/aV1aBWZ4bL3f7gXijgwWhxoq7Z4j1OoxAhJz0SkaGywQqV\nEEIIucCwSs4sy6Kk3oWyxjYAgFImREKkEgkRCoRrJHC4PNC12aDTW9HUZkOdzoyNu6uQkRyKCSPD\nwKdhBIQQQjhg2CRnt8eDXUcacLrRDbmYj8smxiFUJQbDMN5jxEI+YrVyxGrbe+w1tlqx43Adjpa3\noFZnxozx0QhRigfrEgghpEdYloXD5UGb0Q6rwwWPB+DzGPD5DPg8BgI+D3KpgCoeQ9CwSM52pxvb\n9tegodUKtYzB9PRQhKklfs+LCJHimmlJ2HuiESXVeqzfWYHsMVqMTggZgKgJISRwNofrl5a/9ta/\nNpMDVrsLLAsA+m7PYwAoZEKo5SKo5CJoFGJEhkqhlIkGKnTSC0M+OZssTmzZVw292YGESAVGax0Q\niwL/lSgU8HDJ2CjERyiw80g9io41gsdjEBvC78eoCSHENw/LoqnVisoGE6qbTDBanJ32yyUCqGV8\nSIQ8qJRySMV88BgGbg8Lt8cDl5uFy+2B0eKEwexAdZMZaDJ7z1dIhYgKkyE6TAaNtP8n1SA94zc5\nFxQUYMOGDeDz+ZgwYQIWLVoU0P7KykqsXLkSAMDn8/Hggw8iIiIiqMG73R5s3d+emNOTQjBptBZN\ndRW9KisuQoErp8Tju6Iq/HykAVPHUO2ZEDKwWJZFXbMFFfVGVDWaYHO4AbRXImLC5QhXS6DVSBCm\nlkIi4kPXWAcej4/QcP/frXaHGwazA80GG+qaLahvsaC0Wo/Saj0YACW1FkwfF4uskVrIJEO+3jbk\n+fwbMJlMKCgowKpVqwAAS5cuRUVFBRITE33uT0hIwEsvvYTnnnsOanX/TQe3/5QObSYHRsWrMTmt\n74lfrRAjd1Isvt9ThaITrZg4yohJw3Q6O0IId1hsLpTWtCdKk7W9hiwR8TEyTo2ESCWiwmTg8xg/\npfgmFvGhFUmhDZEiLTEEHg+Lll8S9Zk6PU5UGnCi0gAB/wTGjQjDtLFRyEwNh4A//J5X2xwuGMxO\nmK1OmG1OmKwu2J1uKKRCaBQiqBViqOUiCAWDd+0+k3NxcTGmT5/ufZ2bm4uioiJvcu5uv16vR3R0\nNF5++WWYzWbk5ORg/vz5QQ28psmM4xWtUMtFQUnMHcI1UlyWFYst+6qx6ttShIeokRhFU+MRQoKL\nZVnUt1j1qzVOAAAgAElEQVRwsrINVY0msCwg4DNIjVMjJUYFbYgUPKZvCdkXHo9BuEaKcI0UKVEi\njE4IwbEqK/Ycb0BxiQ7FJTqoZEJMGxuNmZnRiA7r+9SXg4VlWejNTpTVN6Oq0QSd3hbQeSFKMTJT\nwxAfoejUuXgg+EzOer2+U81XrVajoqLC7/6amhqUlJTg9ddfh0gkwrJly5CcnIzJkyf7DEap8N+J\nCwAsNid2HqkHj2Fw5dREhKjPjlO2KMSQyiUBl9WV0QoJnE47fjzYhH9/dgj/eHAmosOH7gfTF62W\nfngMNLrnA49L95zHd6FR78Sxw1XQtVkBAOEaCTKSwzAqIQQiYeD9XaxmEXg8YZ++7wCABwfGpGgx\ndaIa9+SPQ0WdAZt2V2Lr3ip8t7sS3+2uRHpyKK6cmoTpmTEQBxAjF+55nc6MTbsr8MP+KjS2tidk\nhgFitXJoQ2RQykRQyoRQykSQiAUwmOxoMdjQYrSjRW9DbZMJ24prERkqwyVjoxEboQjqPQ8P7/4e\n+UzOGo0GpaWl3tdtbW3QaDR+90ulUkybNg0iUXtvwDlz5uDo0aN+k7PR5P/XDMuyKNxfA6vdhUmj\ntZAIeZ3OM5nscMMGhh/YL6PuaFVCzJsZj89/qsJf3y7Ck7dPGnbNO1qtEk1NwVv0nfhH93zgceWe\n2xwu/HCgFht3V6DN5AQDIDFKifSkEISrJWAYBna7E3a7029ZHcxmB3g8N8TSvn3fWcx26HRGOBzt\n33EyAYP8aYm4eko8ikua8OPBWhwrb8Gx8ha8+eUhXDI2CpdNiEVMN5WWwbznTpcH+0414scDtThR\n2T7nhVjIQ1y4BEkxGsSGKyAWXfjjgnW7oZQKoJQqkBipAAC0mew4UKJDZYMJ634sQ0y4DKkRDFSy\n4N3z7h79+kzOmZmZePfdd3HXXXcBAAoLC3H//ff73a9QKPDll196jzt06BCys7P7dCEdTla2obrJ\njOgwGdKT+rfT1sxxEahvdWLHkXp8tb0cN8xK6df3I4QMPxabE5v3VWPTniqYbS6IBDykxsgxLjWC\nM8OZfK0LnRYrQVrsCOj0dvx8XIei4zps3luNzXurkRwlx5S0cExICYFUfDbhhQ1CE3hNkwk/HqzD\nrqP13uf2o+M1uHRCDEZGi7H7eEOPF77QKMS4LCsWOr0Vxad0qNVZ0NQKTBnJQ2h4f1zFWT6Ts1Kp\nRH5+PpYsWQI+n4/09HQkJycHtH/GjBl49NFHIZVKERcXh5ycnD4HazA7sO9kE8RCPqaPix6QZwAL\n547Cqeo2fLurAmOTQ2kMNCEkIAaLA5v2VGHr/mpY7W7IJQJcPyMZ2aNUKC5pgowjiRkIfF1ojZyH\nuRO1qG2x4XSdBeX1ZpTXm/HZj5WICZMgKVIKpciFheFKDMS6SjaHC7uPN+Kng7Uoq23/caGUCfGr\nnARcmhmDqF+mZjYYuh8HHohwtRRzs+NRWq3HziP1+PmUEXKVxVt+f2BYlu3xALfFixdj+fLl4AVx\n1pn1W/fB4vb9a2tbcQ0qG0y4NDMaSdFdr6XZWHsGUoUGSpWmy/2BspiNmDEuGiqVGmU1erzw/n6o\nFSI895spkEuEfSqbK7jS3HcxoXs+8Ab6nhssDmwsqsSW/dVwOD1QyYS4MicBl02IhVQsgMGgx/bD\ndUFZvrAnQ6n6oxyz1YnTtQaU1ui947BFAh5yMiKQmaJFRnJoQM+ne8Jic+FQmQ77TjXh8OlmOJwe\nMADGjgjDpZnRXfYwD+Y9P3yyEgfOWMFjGFyWFYNYraJ31/FLjklJietyf68Gs61YsaJXwfSFrq19\nML5WIxnw3tMpsWpcNyMJ634qxzvfncTv8jMGvOceIYTbzk/KGoUIN8xKxKzMmB518hpK5FIhxqWE\nYeyIUDS12XC6Vo/KeiN+OliPnw7WQyjgYUxiCEbGqZEUrUJylBKyHlZubA4XKhtMOFNvxJHyZhw/\n0wq3p71OGRkiRU56JGaOjwloVshgiA4RQijgY1+ZGYX7azAzM6ZfctKQGGnOsiz2n9IBACaO0g5K\nYsy7JBFHyluw90QjdqaEYfq46AGPgRDCPUaLA9+dl5TnX5aESzOjIRQMz6R8PoZhEBEiRUSIFOMS\nZRg3Khp7jjejuESHQ2XNOFTW7D02KlSGmHA5VDIhFB29paVCuNwsrHYXrHYXLHYXDGYHKhqMqG+2\n4Nzm3YRIBSaN0mLiKC1iwuWDkg8i1EJcPjkOW/fV4McDtZg9KRZxvaxBd2dIJOeO2Wxiw+WDtrwj\nn8fDfdek45m3d+P9TacwOkGDcLV0UGIhhAw+k9WJ7/dUYtPeatgd7osyKXeFYRikxqmQEKHBDbNS\n0Gq0o7zOgPI6A07XGnCm3oD6Fov/ggBIxXyMTtAgMUqJxCglUmPVnPnejQyV4fLsOGwsqsKOQ/W4\ndnpSUGdW43xybq81NwEAskb1c/c4P8I1UtySOwqrvz2OtZtL8OAN4wc1HkKIbx6PB3q9HgZDcJ45\nKxRK2J0efL+nCt/vqYTV7oZKLsK8S0fgsgkxF3VS7k6IUowQZXtNF2ifM9xkdcJoccJkccDwy38F\nfB6kYgGkEgFkYgHkUiHC1ZJ+nYilr7QaKSalabHneCO2H67D3MlxQavJcz45n6k3osVgR3K0EqGq\ngXmm4Mv0cVHYcbgOxSU6HCjRYcLIwf3BQAjpnslkxPc/V8HD9v2rzmQyQSxVYUtxA0xWJxRSIRbM\nTsbsibFB7/Q0nPEYBiqZCCqZCMDQn9wpLUGDOp0Z1U1mHC1vwdgRvnu8B4rTydnjYXGgRAeGAWeS\nIMMwuO3K0Vi2ejc+2HQKYxJDuhzQTgjhBplMDg96P2zJ42FRWqPHwRITrA4DpGIBfn3pCMydHAeJ\niNNfoWQAMAyDaeOi8PWOMygu0SEyVAatpu9N75ye8qqkur17/qh4DWcG6wNAbLgcV05JQLPBhq93\nnhnscAgh/YBlWVQ2GFGwvRw/H22A3eXBnKxI/P3+S3DttCRKzMRLIhJgxvhosCzw08E6OJzuPpfJ\n2eTs9nhwqEwHAZ/B+JTgNBME07XTkxCmkmDj7krU6Mz+TyCEDBnNehu+312FbcW1MFqdGBWvxlWT\nI3HdJXFQSIfHPAckuKLD5Bg3IhQmqxNFxxr6XB5nk3N5rRFWuxuj4jWQirn3C1Us5GPh3JFwe1i8\nv/EkejGXCyGEYyw2J3YcqsP6XRVoaLUiTivHtdOTMDUjqtP0lIR0JTM1HOFqCcrrjKjtY6WNe1kP\n7c1Jx860gGGAMYncnS4za6QWE1LDcaBUh11H6zFtLI19JmQo8nhYnKhsxYESHVxuFiFKMSanaYf0\nMomDpaOHvNMZnLqfQqEM6myU/YnHY5CTHon1uyqw90Qjrpme1Ove5pxMznXNFrSZHEiKUkLO8Sak\nhXNH4lhFCz4pLEPWSC0na/mEkO41tVnx89EGtBrtEAl5uCQtAilx6gu+VH0tDtETRqMBrGf4trTZ\nrBZs3FUGkbjvk3JYLWbMzUmFStX1yk1cFKaWICVWhbIaA0qr9RgV37uppDmZSY6faQWAfl91KhjC\n1VJcPTUR634qx4aiCsy7lFauImQocDjd2H+qCaeq2hdFSIlVtS9D201Hr0AXh/CnRdcAmVwFubLr\n9QGGA6lUDrF08NdzHixZI7WoqDfiQIkOydEqCAU9r/lzLjm3meyo0Zmh1UgRHoTu6APhyikJv6zT\nWoVZmbEDNscrIaR36pst2HG4DmabC2qFCFPTIwOafVAilfV58QSL2dSn8wn3ySQCZCSH4mBpMw6f\nbvZOwNITnEvOXKk197QJ66rsaHy49Qw+2nwCt89NvmD/UHpuQshw5XZ7sP+UDscrWsEwwPiUMIxL\nCQOfx91ZqMjQlJEcipIqPY6dacWoeE2Pe/lzKjnbHC6crjVAIRUiPjK4k4j3OJYeNmGxLAuNQoh9\nJS1Qy3kIVZ4dlz0Un5sQMtw0G2zYfqgOepMDKpkQM8ZHD5nWOTL0CPg8ZI0Kx47D9dh/qgmXZsb0\n7Px+iqtXTlXp4fawGJMYwon5VHvahDUlXYDvd1fh8BkzfpUTSstKEtJLHo8HJlPf58M2Gg3weDw4\nWd2GPccb4GGB0QkaTBqtvWDNX0KCbUSMCicq2nCmzogxidYezRzGmeTsdrM4UdEKoYCH1LihWcOM\nCpUhIVKBygYTKhtMA77uNCHDhclkxKaiUkhlfRvK1NRYj0q9GOX1VoiFfMwYH41YLQ2PGiqGeg95\nhmEweYwWG4uqsP9kE67MSQj4XM4k55JaC2wON9KTQnrVs40rJo7SorrRhH0nmxAXIQefnjMT0itS\nmbxPna9MFieKKwGD1YowlRizsmJpdq8hZjj0kI8MaV+/ulZnRkOrBZEhgS17zJnkfLTCBAZAGocn\nHQmESi7C6IQQHK9oxYmKNmQkhw52SIRcdOqazfjhQC0cTg+So6SYNi4OfGrGHpKGQw/5cSmhqNWZ\ncaSsBZGTA0vOnPm0NumdiI1QDItftuNTwyAS8HD4dDPsQZgAnRASuLIaPTbvrYbL5cHYBAmyR6kp\nMZNBFRkiQ2SIFDU6M5oNtoDO4dQndlT80HzWfD6xkI+xKWFwOD04crplsMMh5KLAsiwOlzVjx+F6\nCPk8zM2OR0K4iDpmEk7oWOf5SFlzQMdzJjkrpHzEhA+fjhppCRrIJAKcqGiFxU61Z0L6k4dlUXSs\nEcUlOsglAvxqakJAk4oQMlBiwmUIU4lR0WBCm8nu93jOJOcx8XJODJ8KFgGfhwmp4XB7WByr6PuQ\nEEJI11xuD34orsWpqjaEKMW4amoiNArxYIdFSCcMw3hrz0cDaFHlTHIeHT98as0dRsSqoFGIcKbB\ngroW62CHQ8iw43R5sHVfDaoaTYgKk+HKnHjIJJzp50pIJwmRCqgVIpyuM8Bsc/k8ljPJWSEZfmul\n8hgGWb/Mqbr+55pBjoaQ4aU9MVejvsWChEgFcifFQSQYft8jZPhgGAbjRoSCZYFT1b57kHMmOQ9X\ncVo5wlUiHDmjx6mqtsEOh5BhwenyYMu+ajS0WpEYqcClmTE0PzYZEpKiVFBIhSivt0BvdnZ7HCXn\nfsYwDMYltw98/3RbKVh2+K7jSshAcLjc2Ly3Co2tViRFKTEzMwY8SsxkiODxGGQkh8LDAtsPN3Z/\n3ADGdNEKU4kwPlmDshoDikt0gx0OIUOWw+XG5j3VaGqzITlaiRnjoykxkyEnJVYFkYCHnceauj2G\nkvMAuTonBgwDfP5DGdwez2CHQ8iQ43J7ULivBjq9DSNiVJhOiZkMUQI+DyOiZTDbuh9m67dbY0FB\nATZs2AA+n48JEyZg0aJFAe93uVx4/PHHIZfL8dxzz/XhUoa+qFApZo6Pxo8H67DzcD1m9nD5MEIu\nZh4Pix8P1KKh1YqESAWmjY0aVkMvycUnJVqOUzXddwrzWXM2mUwoKCjA66+/jldffRWnTp1CRUVF\nwPtXrlyJefPmwUM1RQBA/owREAp4WLe9HA6a1pOQgHhYFtsP16G6yYzoMBlmZlKNmQx9UjEfE1K6\nX0vCZ3IuLi7G9OnTva9zc3NRVFQU0P6vv/4a48aNQ1JSUm9jH3ZClGJcPikOrUY7tuyvHuxwCOE8\nlmWx+1gDztQZodVIcFlWLK30RoaNWeMju93ns1lbr9dDrT4737Vare5UM+5u//Hjx9Hc3Ixrr70W\n1dWBJyGlQhLwsd2xKMSQyiV9LstqFoHHEwYlJh4cCA9XQq1W4o5rMvDjoTps+LkS83JHD/pCH1ot\nrTk90Oie+ycSeaCQt+BQeRtOVekRrpEg/9JUiEU9G8dsNYsABOe7JVjfCcH8buFqTADdc394cGBS\nRlS3+30mZ41Gg9LSUu/rtrY2aDSabvfr9XpoNBqsX78eBoMBzzzzDMxmM44dO4a1a9filltu8Rms\n0RTYah2+mEx2uGEDw+9bWWazAzyeG2Jp32OymO3Q6YxwONp/8V+dk4BPt5XhvW+O4sbLUvpcfm9p\ntUo0NdHUogOJ7nlgDAYjDpS04ECZHkqZELOzYuFwOOFwdD8utCtmswNKpTAo3y3B+k4I5ncLV2Oi\ne+5fR144t4J7Lp/tQ5mZmdi5c6f3dWFhIbKzs7vdv3XrVmRnZ+Oxxx7Dc889h2effRaPPPIIJk6c\n6DcxX0xyJ8UhRCnGpr1VaDX6nwCdkIvNwbJWHCjTQyLi4/LJcZCKaUpOcnHx+YlXKpXIz8/HkiVL\nwOfzkZ6ejuTk5ID3AwCfzwefT1PqnUsk5CN/RjLWbDiBr7aX466r0gY7JEI441RVG97bXA4Bn0Hu\npDgoZaLBDomQAef352heXh7y8vI6bVu8eDGWL18OHo/X5f5zRUVF4dlnn+17pMPM9HFR2Li7Ej8d\nqsUV2fHDarlMQnqrRmfGis8OwcOymJYeijB1358REjIU9arb44oVK8CjHpN9wufxcONlKWBZ4LNt\nZYMdDiGDrtVoxyufHIDF7sIts5MQFUKJmVy8KMMOogmp4RgVp8aBUh1OVrYOdjiEDBqr3YXlnx5E\ni8GOG2aNQPbosMEOiZBBRcl5EDEMg/lzUgEAnxSW0aIY5KLkcnvw2rojqGo0YXZWLK6emjjYIREy\n6Cg5D7KUGDUmp0WgvM6AvSe7nwSdkOGIZVm8u/Ekjpa3IDMlDAvnjgRD03ISQsmZC26YNQJ8HoPP\nt5XB5aapTsnF4+udZ7D9UB0So5S4P38szf5FyC/oXwIHRIbIcFlWLBrbrNhWXDPY4RAyIHYcrsO6\nn8oRrpbg4RvH93j2L0KGM0rOHHHt9CRIRHwU7DgDi8012OEQ0q+OnmnBmg0nIBML8PD8TKgV4sEO\niRBOoeTMESqZCFdPTYTJ6sS3P1f4P4GQIaqywYj/fnEYDAM8eMM4GuNPSBdoTrwB4PF4YDQa/B43\ndbQaW/cJ8f2eSkxKVSJM1XVtQqFQ0jhzMiQ1621Y/ulB2Bxu3J+fgdEJ3S+ZR8jFjJLzALBZLfhh\nfys0of7Hbo6Kk2P3yTas/q4Ul4wJvWC/1WLG3JxUqFRdT5ZOCFeZbU68/MkBtJkcuGlOKqaM6X65\nPEIudpScB4hEKoNM7n+pwNFJCpyut6FGZ4PRzkdkqGwAoiOkfzldbvzn88Ooa7Zg7uR4XDklYbBD\nIoTTKDlzDMMwyB4TgQ0/V2LPiUZcfUkieDTukwwBHo8HJtOFy2F6PCze3VSOU1VtyEzR4KpsLQwG\nvc+yjEYDWA9NykMuXpScOUirkWJEjAqnaw0oqzFgZBw1YRPuM5mM2FRUCqnsbAcvlmVRXKrH6XoL\nwlUijIiSYueRer9ltegaIJOrIFeq+jNkQjiLkjNHZY0KR2WDEcWnmpAYpYBIQGNACfdJZfJOj2+K\nTzXhdL0FIUoxLs+Oh0gY2OfYYjb1V4iEDAnU5Zej5BIhMpJDYXO4caSsZbDDIaTHjp1pweHTLVDK\nhLh8clzAiZkQQsmZ0zKSQyGTCHDsTCuMFsdgh0NIwMpq9Nh7oglSsQCXT46DVEyNdIT0BCVnDhPw\neZg0WgsPy6LoWCOtWkWGhMoGI3YeqYdIyMPlk+OglIkGOyRChhxKzhyXFKVEdJgMtTozKhvoORzh\nttpmK344UAs+j0HuxDiEKGlaTkJ6g5IzxzEMg5z0SPAYBntONNKqVYSzjpxpw67jre2JeVIctCHS\nwQ6JkCGLkvMQoJKLMHZEKCw2F45VXDiOlJDBdqBUh7e/Ow0e056YafIcQvqGkvMQMXZEKBRSIUpq\nzKhttg52OIR4HSzV4bUvD4PPYzAjI5QSMyFBQMl5iBDwechJjwAL4NMfKuChzmGEA/aeaMR/vzwM\nHsPg3rxUaDX0jJmQYKDkPITEahWIDZOgvN6MnYf9z7JESH8qLK7B6+uOgM/n4aH5mRgZ63/ueEJI\nYCg5DzGZKWqIBDx8UlgKvZnGPpOBx7Isvtpejvc2noRCJsTjC7MwJpGWfiQkmCg5DzEyMR95U2Nh\nsjrx/saTNPaZDCiPh8X7m07hq+3lCFdL8KfbJiEpiua/JiTYKDkPQTPHaTEqTo19p5qw50TjYIdD\nLhJ2hxuvf3UEhftrEKdV4E+3T6LOX4T0E0rOQxCPYXB33hiIBDy8//0pGKh5m/SzxjYr/vreXuw7\n2YTR8Rr8361Z0Cio8xch/YWS8xAVGSLDDbNSYLI68d731LxN+s/RMy14fs0eVDeZMWdiLB69eQJk\nEuFgh0XIsEbJeQjLnRzX3rx9kpq3SfCxLIuNuyvx8scHYHe6cddVabjtitEQ8Olrg5D+Rv/KhjAe\nw+Duq6l5mwSf0eLAa18ewcdbS6GSi7B04URcmhkz2GERctEIaB23goICbNiwAXw+HxMmTMCiRYsC\n2r9s2TLweDzo9XrMmjUL1113XfCv4CIXGSrDvFkp+GhLCd7+9jgW3zgeDMMMdlhkCDtU1oy3vz0O\nvdmBUfEa/Pa6DFrAgpAB5jc5m0wmFBQ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"text/plain": [ "<matplotlib.figure.Figure at 0xa22a9d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## day \n", "sns.set_style(\"darkgrid\")\n", "rcParams['font.family'] = 'NanumGothic'\n", "rcParams.update({'font.size': 12})\n", "\n", "def distplot(ds, col, title=''):\n", " plt.figure(figsize=(8,4))\n", " sns.distplot (ds[col], bins=24 )\n", " plt.xlim(0,24)\n", " skew = ds[(ds[col]>8) & (ds[col]<19 )][col].skew()\n", " skew = \"skew = {0}\".format(round(skew, 4))\n", " plt.text(1,0.1,skew, fontsize=14)\n", " plt.title(title, fontdict = {'fontsize': 18} )\n", " plt.show()\n", " \n", "distplot(df, 'hour', '카톡 시간대별 분포')\n", "distplot(df[df['name'] == '신영민'], 'hour', '내가 보낸 카톡 시간대별 분포')" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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7iL2nMm1djrgOEs6EaEGKorBi81mMJoUHJnTFRae95rJ3jOzETT2DSEov4LNN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\nwKFDhwz6Mb2nHXS2WHLm5+f30MeOHTvGwYMHqaq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PZ+XKlZSXl7No0SKDbZrSXyfvweLB\n9u3bja57KAiC0JbECQGCIPxrlJWV4efnh0wmIzs7+4kvNuzl5cXOnTul22q1uqV3URAE4R+J5EwQ\nhA5Jv7YMdGXLzp07Ex4eTmhoKMHBwZiZmWFpaQnoZs2aJmpNn2tmZibNlEVERHDmzBkCAwMJDQ1l\n9+7dbRSNIAjC/xMnBAiCIAiCIJgQMXMmCIIgCIJgQkRyJgiCIAiCYEJEciYIgiAIgmBCRHImCIIg\nCIJgQkRyJgiCIAiCYEJEciYIgiAIgmBCRHImCIIgCIJgQkRyJgiCIAiCYEL+D6l7TZZs8te0AAAA\nAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x9819850>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[df['content'].str.contains(\"ㅋ\")].groupby('name').count()['content'].plot(kind='line', figsize=(10,3))\n", "plt.title('가장 많이 웃은 사람(ㅋ를 포함한 카톡 갯수)')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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hhOiBP5MLIatvxsQRzjA27NIwnz1iVkjLwOx05eNdlHwRQggh/ZxcocSpC7kQ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\n1iCXy432k0qlwtLSUnrcvU5ra2vj+PHj0jmxa9c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GhsGl3ObE2dmZFStWGCUcS5cuxc/P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\nAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0xa9f6910>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[df['content'].str.contains(\"ㅋ\")].groupby('name').count()['content'].div(count_df).plot(kind='line', figsize=(10,3))\n", "plt.title('가장 많이 웃은 사람 ver.전체 메세지 비율(ㅋ를 포함한 카톡 갯수)')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Python34\\lib\\site-packages\\pandas\\core\\strings.py:207: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.\n", " \" groups, use str.extract.\", UserWarning)\n" ] }, { "data": { "image/png": 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wEJ9//jkAoHv37oiPj0fv3r3h7u4u3LFn6FEzx7DNZ599hldffVVYb+j5rHiMJyYmYs6c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\nn376CaNGjcKoUaNw4sQJe4d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"text/plain": [ "<matplotlib.figure.Figure at 0xa596970>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[df['content'].str.contains('(emoticon)\|(이모티콘)')].groupby('name').count()['content'].div(count_df).plot(kind='bar', figsize=(10,3))\n", "plt.title('누가 이모티콘을 많이 사용하는가 ver.전체 메세지 비율')\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": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
CompPhysics/ComputationalPhysicsMSU	doc/Projects/2018/Project4/FinancialEngineering/ipynb/FinancialEngineering.ipynb	1	15163	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- dom:TITLE: Project 4, deadline May 2 -->\n", "# Project 4, deadline May 2\n", "<!-- dom:AUTHOR: Stock Market model -->\n", "<!-- Author: --> \n", "Stock Market model\n", "\n", "Date: Spring semester 2018\n", "\n", "## Theoretical background and description of the system\n", "\n", "The aim of this project is to simulate financial transactions among financial agents\n", "using Monte Carlo methods. The final goal is to extract a distribution of income as function\n", "of the income $m$. From Pareto's work ([V. Pareto, 1897](http://www.institutcoppet.org/2012/05/08/cours-deconomie-politique-1896-de-vilfredo-pareto)) it is known from empirical studies\n", "that the higher end of the distribution of money follows a distribution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "w_m\\propto m^{-1-\\alpha},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with $\\alpha\\in [1,2]$. We will here follow the analysis made by [Patriarca and collaborators](http://www.sciencedirect.com/science/article/pii/S0378437104004327). \n", "\n", "Here we will study numerically the relation between the micro-dynamic relations among financial \n", "agents and the resulting macroscopic money distribution.\n", "\n", "We assume we have $N$ agents that exchange money in pairs $(i,j)$. We assume also that all agents\n", "start with the same amount of money $m_0 > 0$. At a given 'time step', we choose randomly a pair\n", "of agents $(i,j)$ and let a transaction take place. This means that agent $i$'s money $m_i$ changes\n", "to $m_i'$ and similarly we have $m_j\\rightarrow m_j'$. \n", "Money is conserved during a transaction, meaning that" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"eq:conserve\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " m_i+m_j=m_i'+m_j'.\n", "\\label{eq:conserve} \\tag{1}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The change is done via a random reassignement (a random number) $\\epsilon$, meaning that" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "m_i' = \\epsilon(m_i+m_j),\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "leading to" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "m_j'= (1-\\epsilon)(m_i+m_j).\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The number $\\epsilon$ is extracted from a uniform distribution.\n", "In this simple model, no agents are left with a debt, that is $m\\ge 0$.\n", "Due to the conservation law above, one can show that the system relaxes toward an equilibrium\n", "state given by a Gibbs distribution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "w_m=\\beta \\exp{(-\\beta m)},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\beta = \\frac{1}{\\langle m\\rangle},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and $\\langle m\\rangle=\\sum_i m_i/N=m_0$, the average money.\n", "It means that after equilibrium has been reached that the majority of agents is left with a small\n", "number of money, while the number of richest agents, those with $m$ larger than a specific value $m'$,\n", "exponentially decreases with $m'$.\n", "\n", "We assume that we have $N=500$ agents. In each simulation, we need a sufficiently large number of transactions, say $10^7$. Our aim is find the final equilibrium distribution $w_m$. In order to do that we would need\n", "several runs of the above simulations, at least $10^3-10^4$ runs (experiments).\n", "\n", "\n", "\n", "\n", "### Project 4a): Simulation of Transactions\n", "\n", "Your task is to first set up an algorithm which simulates the above transactions with an initial\n", " amount $m_0$.\n", " The challenge here is to figure out a Monte Carlo simulation based on the\n", " above equations.\n", " You will in particular need to make an algorithm which sets up a histogram as function of $m$.\n", " This histogram contains the number of times a value $m$ is registered and represents\n", " $w_m\\Delta m$. You will need to set up a value for the interval $\\Delta m$ (typically $0.01-0.05$).\n", " That means you need to account for the number of times you register an income in the interval\n", " $m,m+\\Delta m$. The number of times you register this income, represents the value that enters the histogram.\n", " You will also need to find a criterion for when the equilibrium situation has been reached.\n", "\n", "### Project 4b): Recognizing the distribution\n", "\n", "Make thereafter a plot of $\\log{(w_m)}$ as function of $m$\n", " and see if you get a straight line.\n", " Comment the result.\n", "\n", "### Project 4c): Transactions and savings\n", "\n", "We can then change our model to allow for a saving criterion, meaning that the agents save\n", " a fraction $\\lambda$ of the money they have before the transaction is made. The final distribution will then no longer be given by Gibbs distribution. It could also include a taxation on financial transactions.\n", "\n", " The conservation law of Eq. ([eq:conserve](#eq:conserve)) holds, but the money to be shared in a transaction between\n", " agent $i$ and agent $j$ is now $(1-\\lambda)(m_i+m_j)$. This means that we have" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "m_i' = \\lambda m_i+\\epsilon(1-\\lambda)(m_i+m_j),\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "m_j' = \\lambda m_j+(1-\\epsilon)(1-\\lambda)(m_i+m_j),\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which can be written as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "m_i'=m_i+\\delta m\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "m_j'=m_j-\\delta m,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\delta m=(1-\\lambda)(\\epsilon m_j-(1-\\epsilon)m_i),\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "showing how money is conserved during a transaction.\n", " Select values of $\\lambda =0.25,0.5$ and $\\lambda=0.9$ and try to extract the corresponding\n", " equilibrium distributions and compare these with the Gibbs distribution. Comment your results.\n", "Extract a parametrization of the above curves, see for example [Patriarca and collaborators](http://www.sciencedirect.com/science/article/pii/S0378437104004327) and see if you can parametrize the high-end tails of the distributions in terms of power laws. Comment your results.\n", "\n", "### Project 4d): Nearest neighbor interactions\n", "\n", "In the rest of this project we will follow the work of [Goswami and Sen](http://www.sciencedirect.com/science/article/pii/S0378437114006967). \n", "In the studies above the agents were selected randomly, irrespective of whether we allowed for\n", "saving or not during a transaction. What is often observed is that various agents tend to make preferences for for whom to interact with. We will now study the evolution of the distribution of wealth $w_m$ by assuming that there is a likelihood" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "p_{ij} \\propto \\vert m_i-m_j\\vert^{-\\alpha},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "for an interaction between agents $i$ and $j$ with respective wealths $m_i$ and $m_j$. The parameter $\\alpha > 0$. For $\\alpha=0$ we recover our model from part 5a). \n", "Perform the same analysis as previously with $N=500$ as well as with $N=1000$ agents and study the distribution of wealth for $\\alpha =0.5$, $\\alpha =1.0$, $\\alpha =1.5$ and $\\alpha =2.0$. \n", "You should try to reproduce Figure 1 of [Goswami and Sen](http://www.sciencedirect.com/science/article/pii/S0378437114006967). \n", "Extract the tail of the distribution and see if it follows a Pareto distribution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "w_m\\propto m^{-1-\\alpha}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What happens if $\\alpha \\gg 1$?\n", "\n", "Perform the analysis with and without a saving $\\lambda$ on each transaction and comment your results. \n", "### Project 4e): Nearest neighbors and former transactions\n", "\n", "We add to the previous probability the possibility that two agents who interact have performed similar transactions earlier. That is, in addition to being financially close, we assume that the likelihood for interacting increases if two agents have interacted earlier. \n", "We add this feature by modifying the previous likelihood to" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "p_{ij} \\propto \\vert m_i-m_j\\vert^{-\\alpha}\\left(c_{ij}+1\\right)^{\\gamma},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $c_{ij}$ represents the number of previous interactions that have taken place between $i$ and $j$. The factor $1$ is added in order to ensure that if they have not interacted earlier they can still interact. Perform similar studies as above with $N=1000$, $\\alpha=1.0$ and $\\alpha=2.0$ using $\\gamma = 0.0, 1.0, 2.0, 3.0$ and $4.0$. Plot the wealth distributions for these cases and try to extract eventual power law tails with and without a saving $\\lambda$ in each transaction. Comment your results and compare them with figures 5 and 6 of [Goswami and Sen](http://www.sciencedirect.com/science/article/pii/S0378437114006967). \n", "\n", "Finally, (this part is optional) if you have time, which features would you add to these models in order to make them even more realistic? \n", "\n", "## Background literature\n", "\n", " * [V. Pareto, Cours d'economie politique, Lausanne, 1897](http://www.institutcoppet.org/2012/05/08/cours-deconomie-politique-1896-de-vilfredo-pareto).\n", "\n", " * [M. Patriarca, A. Chakraborti, K. Kaski, Physica A 340, 334 (2004)](http://www.sciencedirect.com/science/article/pii/S0378437104004327).\n", "\n", " * [S. Goswami and P. Sen, Physica A 415, 514 (2014)](http://www.sciencedirect.com/science/article/pii/S0378437114006967).\n", "\n", "## Introduction to numerical projects\n", "\n", "Here follows a brief recipe and recommendation on how to write a report for each\n", "project.\n", "\n", " * Give a short description of the nature of the problem and the eventual numerical methods you have used.\n", "\n", " * Describe the algorithm you have used and/or developed. Here you may find it convenient to use pseudocoding. In many cases you can describe the algorithm in the program itself.\n", "\n", " * Include the source code of your program. Comment your program properly.\n", "\n", " * If possible, try to find analytic solutions, or known limits in order to test your program when developing the code.\n", "\n", " * Include your results either in figure form or in a table. Remember to label your results. All tables and figures should have relevant captions and labels on the axes.\n", "\n", " * Try to evaluate the reliabilty and numerical stability/precision of your results. If possible, include a qualitative and/or quantitative discussion of the numerical stability, eventual loss of precision etc.\n", "\n", " * Try to give an interpretation of you results in your answers to the problems.\n", "\n", " * Critique: if possible include your comments and reflections about the exercise, whether you felt you learnt something, ideas for improvements and other thoughts you've made when solving the exercise. We wish to keep this course at the interactive level and your comments can help us improve it.\n", "\n", " * Try to establish a practice where you log your work at the computerlab. You may find such a logbook very handy at later stages in your work, especially when you don't properly remember what a previous test version of your program did. Here you could also record the time spent on solving the exercise, various algorithms you may have tested or other topics which you feel worthy of mentioning.\n", "\n", "## Format for electronic delivery of report and programs\n", "\n", "The preferred format for the report is a PDF file. You can also use DOC or postscript formats or as an ipython notebook file. As programming language we prefer that you choose between C/C++, Fortran2008 or Python. The following prescription should be followed when preparing the report:\n", "\n", " * Use your github repository to upload your report. Indicate where the report is by creating for example a Report folder. Please send us as soon as possible your github username.\n", "\n", " * Place your programs in a folder called for example Programs or src, in order to indicate where your programs are. You can use a README file to tell us how your github folders are organized. \n", "\n", " * In your git repository, please include a folder which contains selected results. These can be in the form of output from your code for a selected set of runs and input parameters.\n", "\n", " * In this and all later projects, you should include tests (for example unit tests) of your code(s).\n", "\n", " * Comments from us on your projects, with score and detailed feedback will be emailed to you. \n", "\n", "Finally, \n", "we encourage you to work two and two together. Optimal working groups consist of \n", "2-3 students. You can then hand in a common report." ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 2 }	cc0-1.0
DRVV/jupyter-notebooks	decision-tree/MLMastery/.ipynb_checkpoints/simple_tree-checkpoint.ipynb	1	2372	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy\n", "import pandas" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def gini_index(groups, classes):\n", "\t# count all samples at split point\n", "\tn_instances = float(sum([len(group) for group in groups]))\n", "\t# sum weighted Gini index for each group\n", "\tgini = 0.0\n", "\tfor group in groups:\n", "\t\tsize = float(len(group))\n", "\t\t# avoid divide by zero\n", "\t\tif size == 0:\n", "\t\t\tcontinue\n", "\t\tscore = 0.0\n", "\t\t# score the group based on the score for each class\n", "\t\tfor class_val in classes:\n", "\t\t\tp = [row[-1] for row in group].count(class_val) / size\n", "\t\t\tscore += p * p\n", "\t\t# weight the group score by its relative size\n", "\t\tgini += (1.0 - score) * (size / n_instances)\n", "\treturn gini" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def test_split(index, value, dataset):\n", " left, right = [], []\n", " for row in dataset:\n", " if row[index] < value:\n", " left.append(row)\n", " else:\n", " right.append(row)\n", " return left, right" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'C:\\\\Users\\\\gsim\\\\repos\\\\jupyter-notebooks\\\\decision-tree'" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pwd" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pandas.read_csv('data')" ] } ], "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
mfouesneau/ezdata	examples/examples.ipynb	1	4933532	null	mit
I2Cvb/retinopathy	notebook/midlevel_features.ipynb	1	4913	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#Mid-Level Feature Coding\n", "\n", "This work is a study to explore different types of Coodebooks and coding stregies for representing SD-OCT information applied to retinopathy. This work is an extension to explore some of the elementes encountered during the development of [this study](https://github.com/I2Cvb/oct-Report/blob/master/master.pdf).\n", "\n", "Some of the ideas here explored can be found in [Kouniusz et al., 2013](http://www3.ee.surrey.ac.uk/CVSSP/Publications/papers/Koniusz-CVIU-2012.pdf) and in this [seminar](https://speakerdeck.com/debaylo/bag-of-visual-words) given by Desire Sidibe.\n", "\n", "## Ideas\n", "\n", "###Useless words supression\n", "BoW comes from text analysis. Roughly speaking, the idea is to find keywords and then analyse them. Words like and, the, for, etc. don't a\n", "pport much therefore during TF-IDF, these words are eliminated and the keywords reminding are used for the analysis. At the original application of BoW to image retraival, image keypoints are extracted, these keypoints are described using some descriptor and those are used as keywords to build the visual dictionary. Therefore,TF-IDF study is not necessary since all the words belong to keypoints and thus they qualify as keywords. However, later applications take advantage of dense description of the images. In those cases we belive that the TF-IDF study to suppress visualwords present in all the classes shall be detected an eliminated of the dictionary, and yet (up to our knowledge) this sutdy is never carried out.\n", "\n", "###Dictionary class, clustering convergence, etc..\n", "Having the right size matters, no matter what they might say. \n", "\n", "##BoW formalization\n", "BoW can be defined as X~DA, where X is the raw representation of the image. Understanding raw as the image representation of the original space. D is the dictionary representation in this original space and A is the image representation using the dictionary D. In this manner feature to describe an image can be seen as f(A).\n", "\n", "The BoW formulation can be seen as a 4-steps process.\n", " 1. Sampling strategy to compute X. The feature space of X can be generated either only by keywords or in a dense extraction manner. Look at TF-IDF.\n", " 2. Quantification to hard-quantify the space of X. \n", " 3. Coding to determine A.\n", " 4. Pooling to determine the final descriptor f.\n", " \n", " ##Experimental Set-up (or stuff to try, compare and then try to make sense out of it)\n", " \n", " Results combining all those configurations might be explored.\n", " \n", " 1. Sampling\n", " 1.1. Keypoints\n", " 1.2. No-Kp\n", " 1.3. No-Kp + TF-IDF\n", " \n", " 2. Quantification\n", " 2.1. to take into account\n", " 2.1.1. Dictionary Size (num clusters, bandwidth, PCA info, etc)\n", " 2.1.2. K vs d\n", " 2.1.2.1. K << d\n", " 2.1.2.1. K < d\n", " 2.1.2.1. K ~ d \n", " 2.1.2.1. K > d \n", " 2.1.2.1. K >> d \n", " 2.2. Dictionary building strategy\n", " 2.2.1. Random\n", " 2.2.2. Clustering\n", " 2.2.2.1 K-means\n", " 2.2.2.2 heretical\n", " 2.2.2.3 mean-shift\n", " 2.2.3. PCA\n", " 2.2.4. ICA\n", " 2.2.5. NMF\n", " 2.2.6. sparse\n", " 3. Coding\n", " 3.1. Hard coding (one sample is associated to one word)\n", " 3.2. Soft coding (i.e. distance to all words in the dictionary)\n", " 3.3. Soft coding bounded (only give a weight to the n closests words in the dictionary)\n", " 3.4. Sparse codign of A\n", " 4. Pooling\n", " 4.1. Histogram\n", " 4.2. Piramide\n", " 4.3. other...\n", " \n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
google-research/google-research	graph_sampler/molecule_sampling_demo.ipynb	1	165710	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Molecule sampling demo", "provenance": [], "collapsed_sections": [], "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "<a href=\"https://colab.research.google.com/github/google-research/google-research/blob/master/graph_sampler/molecule_sampling_demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" ] }, { "metadata": { "id": "DhWmZgAVwDGu", "colab_type": "text" }, "cell_type": "markdown", "source": [ "Copyright 2022 Google LLC\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", "https://www.apache.org/licenses/LICENSE-2.0\n", "\n", "Unless required by applicable law or agreed to in writing, software\n", "distributed under the License is distributed on an \"AS IS\" BASIS,\n", "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "See the License for the specific language governing permissions and\n", "limitations under the License." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NtzgAVZFf189" }, "outputs": [], "source": [ "# Install graph_sampler\n", "!git clone https://github.com/google-research/google-research.git\n", "!pip install google-research/graph_sampler" ] }, { "cell_type": "code", "source": [ "from rdkit import Chem\n", "import rdkit.Chem.Draw\n", "from graph_sampler import molecule_sampler\n", "from graph_sampler import stoichiometry\n", "import numpy as np" ], "metadata": { "id": "_TgiDMUlgNH3" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Generating samples from a single stoichiometry" ], "metadata": { "id": "MmZ9-Rm2gRfx" } }, { "cell_type": "code", "source": [ "stoich = stoichiometry.Stoichiometry({'C': 10, 'O': 2, 'N': 3, 'H': 16, 'F': 2, 'O-': 1, 'N+': 1})\n", "assert stoichiometry.is_valid(stoich), 'Cannot form a connected graph with this stoichiometry.'\n", "print('Number of heavy atoms:', sum(stoich.counts.values()) - stoich.counts['H'])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "52YK9MpUgXK1", "outputId": "3305185d-5efb-473a-a721-cd66f2338492" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Number of heavy atoms: 19\n" ] } ] }, { "cell_type": "code", "source": [ "%%time\n", "sampler = molecule_sampler.MoleculeSampler(stoich,\n", " relative_precision=0.03,\n", " rng_seed=2044365744)\n", "weighted_samples = [graph for graph in sampler]\n", "stats = sampler.stats()\n", "rejector = molecule_sampler.RejectToUniform(weighted_samples,\n", " max_importance=stats['max_final_importance'],\n", " rng_seed=265580748)\n", "uniform_samples = [graph for graph in rejector]\n", "print(f'generated {len(weighted_samples)}, kept {len(uniform_samples)}, '\n", " f'estimated total: {stats[\"estimated_num_graphs\"]:.2E} ± '\n", " f'{stats[\"num_graphs_std_err\"]:.2E}')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "tfIGyjY0gaSb", "outputId": "bb38e50c-b09d-49b4-aa32-7c37d2cc4bf2" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "generated 3646, kept 115, estimated total: 7.06E+15 ± 2.11E+14\n", "CPU times: user 17.4 s, sys: 1.44 s, total: 18.9 s\n", "Wall time: 18.1 s\n" ] } ] }, { "cell_type": "code", "source": [ "#@title Draw some examples\n", "mols = [molecule_sampler.to_mol(g) for g in uniform_samples]\n", "Chem.Draw.MolsToGridImage(mols[:8], molsPerRow=4, subImgSize=(200, 140))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 297 }, "id": "xO7SDND0g8jc", "outputId": "acb74304-f154-4381-c3bd-89796923415e" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "image/png": 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\n", "text/plain": [ "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=800x280 at 0x7F0B2D4E8B10>" ] }, "metadata": {}, "execution_count": 5 } ] }, { "cell_type": "markdown", "source": [ "# Combining samples from multiple stoichiometries\n", "Here we'll generate random molecules with 5 heavy atoms selected from C, N, and O. These small numbers are chosen just to illustrate the code. In this small an example, you could just enumerate all molecules. For large numbers of heavy atoms selected from a large set, you'd want to parallelize a lot of this." ], "metadata": { "id": "j2GpWm59hAgk" } }, { "cell_type": "code", "source": [ "#@title Enumerate valid stoichiometries subject to the given constraint\n", "heavy_elements = ['C', 'N', 'O']\n", "num_heavy = 5\n", "\n", "# We'll dump stoichiometries, samples, and statistics into a big dictionary.\n", "all_data = {}\n", "for stoich in stoichiometry.enumerate_stoichiometries(num_heavy, heavy_elements):\n", " key = ''.join(stoich.to_element_list())\n", " all_data[key] = {'stoich': stoich} \n", "\n", "max_key_size = max(len(k) for k in all_data.keys())\n", "print(f'{len(all_data)} stoichiometries')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "25GrPsRRhE_4", "outputId": "4ec1a8cf-d1c9-4cd1-b4e5-73ddb4a8220b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "90 stoichiometries\n" ] } ] }, { "cell_type": "code", "source": [ "#@title For each stoichiometry, generate samples and estimate the number of molecules\n", "for key, data in all_data.items():\n", " sampler = molecule_sampler.MoleculeSampler(data['stoich'], relative_precision=0.2)\n", " data['weighted_samples'] = [graph for graph in sampler]\n", " stats = sampler.stats()\n", " data['stats'] = stats\n", " rejector = molecule_sampler.RejectToUniform(data['weighted_samples'],\n", " max_importance=stats['max_final_importance'])\n", " data['uniform_samples'] = [graph for graph in rejector]\n", " print(f'{key:>{max_key_size}}:\\tgenerated {len(data[\"weighted_samples\"])},\\t'\n", " f'kept {len(data[\"uniform_samples\"])},\\t'\n", " f'estimated total {int(stats[\"estimated_num_graphs\"])} ± {int(stats[\"num_graphs_std_err\"])}')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "x-Gjd5W8wKkz", "outputId": "6ca83d9f-105d-444d-d28a-1b848e74910b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " OOOOOHH:\tgenerated 500,\tkept 500,\testimated total 1 ± 0\n", " OOOOO:\tgenerated 366,\tkept 366,\testimated total 1 ± 0\n", " NOOOOHHH:\tgenerated 500,\tkept 336,\testimated total 3 ± 0\n", " NOOOOH:\tgenerated 438,\tkept 378,\testimated total 4 ± 0\n", " NNOOOHHHH:\tgenerated 500,\tkept 94,\testimated total 8 ± 0\n", " NNOOOHH:\tgenerated 460,\tkept 197,\testimated total 15 ± 0\n", " NNOOO:\tgenerated 455,\tkept 121,\testimated total 4 ± 0\n", " NNNOOHHHHH:\tgenerated 500,\tkept 126,\testimated total 10 ± 0\n", " NNNOOHHH:\tgenerated 472,\tkept 205,\testimated total 23 ± 0\n", " NNNOOH:\tgenerated 469,\tkept 138,\testimated total 14 ± 0\n", " NNNNOHHHHHH:\tgenerated 500,\tkept 93,\testimated total 6 ± 0\n", " NNNNOHHHH:\tgenerated 468,\tkept 91,\testimated total 18 ± 0\n", " NNNNOHH:\tgenerated 477,\tkept 182,\testimated total 17 ± 0\n", " NNNNO:\tgenerated 489,\tkept 363,\testimated total 4 ± 0\n", " NNNNNHHHHHHH:\tgenerated 500,\tkept 245,\testimated total 2 ± 0\n", " NNNNNHHHHH:\tgenerated 461,\tkept 187,\testimated total 6 ± 0\n", " NNNNNHHH:\tgenerated 474,\tkept 127,\testimated total 8 ± 0\n", " NNNNNH:\tgenerated 491,\tkept 334,\testimated total 4 ± 0\n", " COOOOHHHH:\tgenerated 500,\tkept 65,\testimated total 5 ± 0\n", " COOOOHH:\tgenerated 463,\tkept 185,\testimated total 5 ± 0\n", " COOOO:\tgenerated 455,\tkept 216,\testimated total 2 ± 0\n", " CNOOOHHHHH:\tgenerated 500,\tkept 105,\testimated total 16 ± 0\n", " CNOOOHHH:\tgenerated 475,\tkept 67,\testimated total 31 ± 1\n", " CNOOOH:\tgenerated 470,\tkept 148,\testimated total 18 ± 0\n", " CNNOOHHHHHH:\tgenerated 500,\tkept 109,\testimated total 26 ± 0\n", " CNNOOHHHH:\tgenerated 479,\tkept 86,\testimated total 70 ± 2\n", " CNNOOHH:\tgenerated 478,\tkept 52,\testimated total 63 ± 2\n", " CNNOO:\tgenerated 494,\tkept 153,\testimated total 11 ± 0\n", " CNNNOHHHHHHH:\tgenerated 500,\tkept 63,\testimated total 21 ± 0\n", " CNNNOHHHHH:\tgenerated 470,\tkept 108,\testimated total 68 ± 2\n", " CNNNOHHH:\tgenerated 476,\tkept 57,\testimated total 87 ± 3\n", " CNNNOH:\tgenerated 487,\tkept 130,\testimated total 33 ± 0\n", " CNNNNHHHHHHHH:\tgenerated 500,\tkept 23,\testimated total 7 ± 0\n", " CNNNNHHHHHH:\tgenerated 473,\tkept 113,\testimated total 29 ± 0\n", " CNNNNHHHH:\tgenerated 475,\tkept 82,\testimated total 48 ± 1\n", " CNNNNHH:\tgenerated 487,\tkept 132,\testimated total 30 ± 1\n", " CNNNN:\tgenerated 490,\tkept 123,\testimated total 5 ± 0\n", " CCOOOHHHHHH:\tgenerated 500,\tkept 34,\testimated total 10 ± 0\n", " CCOOOHHHH:\tgenerated 483,\tkept 75,\testimated total 20 ± 0\n", " CCOOOHH:\tgenerated 483,\tkept 89,\testimated total 19 ± 0\n", " CCOOO:\tgenerated 470,\tkept 191,\testimated total 5 ± 0\n", " CCNOOHHHHHHH:\tgenerated 500,\tkept 47,\testimated total 30 ± 1\n", " CCNOOHHHHH:\tgenerated 474,\tkept 57,\testimated total 84 ± 3\n", " CCNOOHHH:\tgenerated 490,\tkept 52,\testimated total 104 ± 4\n", " CCNOOH:\tgenerated 490,\tkept 134,\testimated total 40 ± 1\n", " CCNNOHHHHHHHH:\tgenerated 500,\tkept 41,\testimated total 29 ± 1\n", " CCNNOHHHHHH:\tgenerated 479,\tkept 126,\testimated total 111 ± 4\n", " CCNNOHHHH:\tgenerated 472,\tkept 54,\testimated total 175 ± 7\n", " CCNNOHH:\tgenerated 488,\tkept 121,\testimated total 117 ± 3\n", " CCNNO:\tgenerated 498,\tkept 34,\testimated total 19 ± 0\n", " CCNNNHHHHHHHHH:\tgenerated 500,\tkept 68,\testimated total 13 ± 0\n", " CCNNNHHHHHHH:\tgenerated 470,\tkept 45,\testimated total 54 ± 2\n", " CCNNNHHHHH:\tgenerated 475,\tkept 68,\testimated total 109 ± 4\n", " CCNNNHHH:\tgenerated 484,\tkept 155,\testimated total 96 ± 2\n", " CCNNNH:\tgenerated 497,\tkept 37,\testimated total 34 ± 1\n", " CCCOOHHHHHHHH:\tgenerated 500,\tkept 65,\testimated total 11 ± 0\n", " CCCOOHHHHHH:\tgenerated 480,\tkept 29,\testimated total 36 ± 2\n", " CCCOOHHHH:\tgenerated 491,\tkept 72,\testimated total 53 ± 2\n", " CCCOOHH:\tgenerated 490,\tkept 109,\testimated total 34 ± 1\n", " CCCOO:\tgenerated 494,\tkept 190,\testimated total 7 ± 0\n", " CCCNOHHHHHHHHH:\tgenerated 500,\tkept 40,\testimated total 21 ± 0\n", " CCCNOHHHHHHH:\tgenerated 486,\tkept 77,\testimated total 82 ± 3\n", " CCCNOHHHHH:\tgenerated 490,\tkept 77,\testimated total 162 ± 6\n", " CCCNOHHH:\tgenerated 496,\tkept 104,\testimated total 132 ± 4\n", " CCCNOH:\tgenerated 493,\tkept 67,\testimated total 43 ± 1\n", " CCCNNHHHHHHHHHH:\tgenerated 500,\tkept 46,\testimated total 13 ± 0\n", " CCCNNHHHHHHHH:\tgenerated 481,\tkept 43,\testimated total 60 ± 2\n", " CCCNNHHHHHH:\tgenerated 488,\tkept 31,\testimated total 131 ± 7\n", " CCCNNHHHH:\tgenerated 493,\tkept 74,\testimated total 164 ± 6\n", " CCCNNHH:\tgenerated 499,\tkept 72,\testimated total 82 ± 3\n", " CCCNN:\tgenerated 499,\tkept 70,\testimated total 13 ± 0\n", " CCCCOHHHHHHHHHH:\tgenerated 500,\tkept 29,\testimated total 6 ± 0\n", " CCCCOHHHHHHHH:\tgenerated 485,\tkept 94,\testimated total 25 ± 1\n", " CCCCOHHHHHH:\tgenerated 485,\tkept 51,\testimated total 53 ± 2\n", " CCCCOHHHH:\tgenerated 489,\tkept 82,\testimated total 62 ± 2\n", " CCCCOHH:\tgenerated 494,\tkept 37,\testimated total 36 ± 1\n", " CCCCO:\tgenerated 497,\tkept 133,\testimated total 7 ± 0\n", " CCCCNHHHHHHHHHHH:\tgenerated 500,\tkept 50,\testimated total 8 ± 0\n", " CCCCNHHHHHHHHH:\tgenerated 475,\tkept 33,\testimated total 33 ± 1\n", " CCCCNHHHHHHH:\tgenerated 480,\tkept 40,\testimated total 87 ± 4\n", " CCCCNHHHHH:\tgenerated 490,\tkept 78,\testimated total 111 ± 4\n", " CCCCNHHH:\tgenerated 498,\tkept 53,\testimated total 82 ± 3\n", " CCCCNH:\tgenerated 497,\tkept 55,\testimated total 26 ± 1\n", "CCCCCHHHHHHHHHHHH:\tgenerated 500,\tkept 10,\testimated total 4 ± 0\n", " CCCCCHHHHHHHHHH:\tgenerated 475,\tkept 99,\testimated total 10 ± 0\n", " CCCCCHHHHHHHH:\tgenerated 483,\tkept 91,\testimated total 27 ± 1\n", " CCCCCHHHHHH:\tgenerated 487,\tkept 22,\testimated total 42 ± 2\n", " CCCCCHHHH:\tgenerated 496,\tkept 50,\testimated total 40 ± 2\n", " CCCCCHH:\tgenerated 497,\tkept 73,\testimated total 21 ± 0\n", " CCCCC:\tgenerated 498,\tkept 74,\testimated total 5 ± 0\n" ] } ] }, { "cell_type": "code", "source": [ "#@title Combine into one big uniform sampling of the whole space\n", "bucket_sizes = [data['stats']['estimated_num_graphs'] for data in all_data.values()]\n", "sample_sizes = [len(data['uniform_samples']) for data in all_data.values()]\n", "base_iters = [data['uniform_samples'] for data in all_data.values()]\n", "\n", "aggregator = molecule_sampler.AggregateUniformSamples(bucket_sizes, sample_sizes, base_iters)\n", "merged_uniform_samples = [graph for graph in aggregator]\n", "\n", "total_estimate = sum(data['stats']['estimated_num_graphs'] for data in all_data.values())\n", "total_variance = sum(data['stats']['num_graphs_std_err']**2 for data in all_data.values())\n", "total_std = np.sqrt(total_variance)\n", "\n", "print(f'{len(merged_uniform_samples)} samples after merging, of an estimated '\n", " f'{total_estimate:.1f} ± {total_std:.1f}')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "TfgR7VZqwgia", "outputId": "abf4939f-cf9c-4d60-b0d5-c82c76089f42" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "842 samples after merging, of an estimated 3563.4 ± 22.0\n" ] } ] }, { "cell_type": "code", "source": [ "#@title Draw some examples\n", "mols = [molecule_sampler.to_mol(g) for g in merged_uniform_samples]\n", "Chem.Draw.MolsToGridImage(np.random.choice(mols, size=16), molsPerRow=4, subImgSize=(200, 140))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 577 }, "id": "KiHlfH8VwnOW", "outputId": "2a24825f-de0a-46b7-dbd4-486895b80283" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "image/png": 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\n", "text/plain": [ "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=800x560 at 0x7F0B2812DF10>" ] }, "metadata": {}, "execution_count": 9 } ] } ] }	apache-2.0
aidiary/notebooks	keras/180105-cnn-lstm.ipynb	1	113561	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Moving Square Video Prediction Problem" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from numpy import zeros\n", "from random import randint, random\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "# generate the next frame in the sequence\n", "def next_frame(last_step, last_frame, column):\n", " lower = max(0, last_step - 1)\n", " upper = min(last_frame.shape[0] - 1, last_step + 1)\n", " step = randint(lower, upper)\n", " frame = last_frame.copy()\n", " frame[step, column] = 1\n", " return frame, step\n", "\n", "# generate a sequence of frames of a dot moving across an image\n", "def build_frames(size):\n", " frames = list()\n", " frame = zeros((size, size))\n", " step = randint(0, size - 1)\n", " # 左に行くか、右に行くかを決める\n", " right = 1 if random() < 0.5 else 0\n", " col = 0 if right else size - 1\n", " frame[step, col] = 1\n", " frames.append(frame)\n", " # create all remaining frames\n", " for i in range(1, size):\n", " col = i if right else size - 1 - i\n", " frame, step = next_frame(step, frame, col)\n", " frames.append(frame)\n", " return frames, right" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAABPCAYAAADcB79hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11736f438>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# generate sequence of frames\n", "size = 5\n", "frames, right = build_frames(size)\n", "plt.figure()\n", "for i in range(size):\n", " plt.subplot(1, size, i + 1)\n", " plt.imshow(frames[i], cmap='Greys')\n", " ax = plt.gca()\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "# generate multiple sequences of frames and reshape for network input\n", "def generate_examples(size, n_patterns):\n", " X, y = list(), list()\n", " for _ in range(n_patterns):\n", " frames, right = build_frames(size)\n", " X.append(frames)\n", " y.append(right)\n", " # (samples, timesteps, width, height, channels)\n", " X = np.array(X).reshape(n_patterns, size, size, size, 1)\n", " y = np.array(y).reshape(n_patterns, 1)\n", " return X, y" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "size = 50" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "time_distributed_6 (TimeDist (None, None, 49, 49, 2) 10 \n", "_________________________________________________________________\n", "time_distributed_7 (TimeDist (None, None, 24, 24, 2) 0 \n", "_________________________________________________________________\n", "time_distributed_8 (TimeDist (None, None, 1152) 0 \n", "_________________________________________________________________\n", "lstm_2 (LSTM) (None, 50) 240600 \n", "_________________________________________________________________\n", "dense_1 (Dense) (None, 1) 51 \n", "=================================================================\n", "Total params: 240,661\n", "Trainable params: 240,661\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "None\n" ] } ], "source": [ "from keras.models import Sequential\n", "from keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, LSTM, TimeDistributed\n", "# define the model\n", "model = Sequential()\n", "model.add(TimeDistributed(Conv2D(2, (2, 2), activation='relu'),\n", " input_shape=(None, size, size, 1))) # (timesteps, width, height, channels)\n", "model.add(TimeDistributed(MaxPooling2D(pool_size=(2, 2))))\n", "model.add(TimeDistributed(Flatten()))\n", "model.add(LSTM(50))\n", "model.add(Dense(1, activation='sigmoid'))\n", "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['acc'])\n", "print(model.summary())" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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0+kz9TZ/U456nr8UxsK/48qBbY6Dt4jOarF/5flAPwYJJfBSKcnpGzz//fI+r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\nMc1XBERABERABERABERABESg6gj8L1bAxMeXfbdvAAAAAElFTkSuQmCC\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from keras.utils.vis_utils import plot_model\n", "from IPython.display import Image\n", "plot_model(model, to_file='model.png', show_shapes=True, show_layer_names=True)\n", "Image('model.png')" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/1\n", "5000/5000 [==============================] - 101s - loss: 0.1404 - acc: 0.9356 \n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x3247eab38>" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# fit model\n", "X, y = generate_examples(size, 5000)\n", "model.fit(X, y, batch_size=32, epochs=1)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loss: 0.002858, acc: 100.000000\n" ] } ], "source": [ "# evaluate the model\n", "X, y = generate_examples(size, 100)\n", "loss, acc = model.evaluate(X, y, verbose=0)\n", "print('loss: %f, acc: %f' % (loss, acc * 100))" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Expected: Left, Predicted: Left\n" ] } ], "source": [ "# prediction on new data\n", "X, y = generate_examples(size, 1)\n", "yhat = model.predict_classes(X, verbose=0)\n", "expected = 'Right' if y[0] == 1 else 'Left'\n", "predicted = 'Right' if yhat[0] == 1 else 'Left'\n", "print('Expected: %s, Predicted: %s' % (expected, predicted))" ] }, { "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
sampathweb/movie-sentiment-analysis	00-build-dataset-kaggle.ipynb	1	6692	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Create the Movie Reviews Dataset to Host Competition\n", "\n", "\n", "* Competition: https://www.kaggle.com/c/movie-sentiment-analysis\n", "\n", "I built this tutorial and kaggle InClass competition to help participants learn and also practice building a sentiment classifier.\n", "\n", "\n", "* SF Project Night: https://www.meetup.com/sfpython/events/234956048/ \n", "\n", "* Date: Oct'18th 2017" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dataset\n", "\n", "This dataset of Movie Reviews is from Stanford AI group ( http://ai.stanford.edu/~amaas/data/sentiment/)\n", "\n", "This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import os.path\n", "from glob import glob\n", "\n", "import numpy as np\n", "import pandas as pd\n", "from sklearn.utils import shuffle" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def extract_reviews_data(file_pattern):\n", " \"\"\"Returns the extracted review texts from all files that match the pattern\"\"\"\n", " data = []\n", " for filename in glob(file_pattern):\n", " with open(filename, \"rb\") as f:\n", " review = f.read().decode(\"utf-8\")\n", " doc_id = filename.split(\"/\")[-1].split(\".\")[0]\n", " data.append({\n", " \"review\": review\n", " })\n", " return data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Get the Train Data of Positive and Negative Reviews\n", "train_pos_dir = os.path.expanduser(\"~/datasets/movie-sentiment-analysis/aclImdb/train/pos/.txt\")\n", "train_neg_dir = os.path.expanduser(\"~/datasets/movie-sentiment-analysis/aclImdb/train/neg/.txt\")\n", "\n", "train_pos_data = extract_reviews_data(train_pos_dir)\n", "train_neg_data = extract_reviews_data(train_neg_dir)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Get the Test Data of Positive and Negative Reviews\n", "test_pos_dir = os.path.expanduser(\"~/datasets/movie-sentiment-analysis/aclImdb/test/pos/.txt\")\n", "test_neg_dir = os.path.expanduser(\"~/datasets/movie-sentiment-analysis/aclImdb/test/neg/.txt\")\n", "\n", "test_pos_data = extract_reviews_data(test_pos_dir)\n", "test_neg_data = extract_reviews_data(test_neg_dir)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Build the train.tsv file. Positive Reviews are marked as 1 and Negative as 0\n", "# Data is shuffled before saving to file\n", "\n", "train_pos_df = pd.DataFrame(train_pos_data)\n", "train_pos_df[\"sentiment\"] = 1\n", "train_neg_df = pd.DataFrame(train_neg_data)\n", "train_neg_df[\"sentiment\"] = 0\n", "\n", "train_df = pd.concat([train_pos_df, train_neg_df], axis=0)\n", "train_df = shuffle(train_df)\n", "train_df[\"document_id\"] = np.arange(len(train_df))\n", "\n", "train_df[[\"document_id\", \"sentiment\", \"review\"]].to_csv(\"data/train.tsv\", sep=\"\\t\", index=False)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Build the test.tsv file. Positive Reviews are marked as 1 and Negative as 0\n", "# Data is shuffled before saving to file\n", "\n", "test_pos_df = pd.DataFrame(test_pos_data)\n", "test_pos_df[\"sentiment\"] = 1\n", "test_neg_df = pd.DataFrame(test_neg_data)\n", "test_neg_df[\"sentiment\"] = 0\n", "\n", "test_df = pd.concat([test_pos_df, test_neg_df], axis=0)\n", "\n", "test_df = shuffle(test_df)\n", "test_df[\"document_id\"] = np.arange(len(test_df))\n", "\n", "test_df[[\"document_id\", \"review\"]].to_csv(\"data/test.tsv\", sep=\"\\t\", index=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Build the Solutions File to upload to Kaggle Competition\n", "test_df[[\"document_id\", \"sentiment\"]].to_csv(\"data/solutions_submission.csv\", index=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create a Sample Submission File based on Random Guess (All Even reviews are Positive)\n", "#\n", "sample_submission_df = pd.DataFrame({\n", " \"document_id\": np.arange(len(test_df))\n", "})\n", "\n", "sample_submission_df[\"sentiment\"] = sample_submission_df.index % 2\n", "sample_submission_df[[\"document_id\", \"sentiment\"]].to_csv(\"data/sample_submission.csv\", index=False)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "document_id,sentiment\r\n", "0,0\r\n", "1,1\r\n", "2,0\r\n", "3,1\r\n", "4,0\r\n", "5,1\r\n", "6,0\r\n", "7,1\r\n", "8,0\r\n" ] } ], "source": [ "!head data/sample_submission.csv" ] } ], "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
Gelvazio/CODIGOS_ABERTOS	PROJETOS DE USUARIOS MEU GITHUB/SITE PYTHON/Capitulo35/Capitulo35_Processamento_de_imagem.ipynb	2	105907	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[Python para Desenvolvedores](http://ricardoduarte.github.io/python-para-desenvolvedores/#conteudo)\n", "===================================\n", "2ª edi\u00e7\u00e3o, revisada e ampliada\n", "-----------------------------------\n", "\n", "Cap\u00edtulo 35: Processamento de Imagem\n", "=============================\n", "_____________________________\n", "[Python Imaging Library](http://www.pythonware.com/products/pil/) (PIL) \u00e9 uma biblioteca de processamento de imagens matriciais para Python.\n", "\n", "PIL possui m\u00f3dulos que implementam:\n", "\n", "+ Ferramentas para cortar, redimensionar e mesclar imagens.\n", "+ Algoritmos de convers\u00e3o, que suportam diversos formatos.\n", "+ Filtros, tais como suavizar, borrar e detectar bordas.\n", "+ Ajustes, incluindo brilho e contraste.\n", "+ Opera\u00e7\u00f5es com paletas de cores.\n", "+ Desenhos simples em 2D.\n", "+ Rotinas para tratamento de imagens: equaliza\u00e7\u00e3o, auto-contraste, deformar, inverter e outras.\n", "\n", "Exemplo de tratamento de imagem:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "\"\"\"\n", "Cria miniaturas suavizadas para cada\n", "JPEG na pasta corrente\n", "\"\"\"\n", "\n", "import glob\n", "\n", "# M\u00f3dulo principal do PIL\n", "from PIL import Image\n", "\n", "# M\u00f3dulo de filtros\n", "from PIL import ImageFilter\n", "\n", "# Para cada arquivo JPEG\n", "for fn in glob.glob(\".jpg\"):\n", "\n", " # Retorna o nome do arquivo sem extens\u00e3o\n", " f = glob.os.path.splitext(fn)[0]\n", "\n", " print 'Processando:', fn\n", " imagem = Image.open(fn)\n", "\n", " # Cria thumbnail (miniatura) da imagem\n", " # de tamanho 256x256 usando antialiasing\n", " imagem.thumbnail((256, 256), Image.ANTIALIAS)\n", "\n", " # Filtro suaviza a imagem\n", " imagem = imagem.filter(ImageFilter.SMOOTH)\n", "\n", " # Salva como arquivo PNG\n", " imagem.save(f + '.png', 'PNG')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exemplo de desenho:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "\"\"\"\n", "Cria uma imagem com v\u00e1rios gradientes de cores\n", "\"\"\"\n", "\n", "from PIL import Image\n", "\n", "# M\u00f3dulo de desenho\n", "from PIL import ImageDraw\n", "\n", "# Largura e altura\n", "l, a = 512, 512\n", "\n", "# Cria uma imagem nova com fundo branco\n", "imagem = Image.new('RGBA', (l, a), 'white')\n", "\n", "# O objeto desenho age sobre o objeto imagem\n", "desenho = ImageDraw.Draw(imagem)\n", "\n", "# Calcula a largura da faixa de cor\n", "faixa = l / 256\n", "\n", "# Desenha um gradiente de cor\n", "for i in xrange(0, l):\n", "\n", " # Calcula a cor da linha\n", " rgb = (0.25 i / faixa, 0.5 * i / faixa, i / faixa)\n", " cor = '#%02x%02x%02x' % rgb\n", "\n", " # Desenha uma linha colorida\n", " # Primeiro argumento \u00e9 uma tupla com\n", " # as coordenadas de inicio e fim da linha\n", " desenho.line((0, i, l, i), fill=cor)\n", "\n", "# Copia e cola recortes invertidos do gradiente\n", "for i in xrange(l, l / 2, -l / 10):\n", "\n", " # Tamanho do recorte\n", " area = (l - i, a - i, i, i)\n", " # Copia e inverte\n", " flip = Image.FLIP_TOP_BOTTOM\n", " recorte = imagem.crop(area).transpose(flip)\n", "\n", " # Cola de volta na imagem original\n", " imagem.paste(recorte, area)\n", "\n", "# Salva como arquivo PNG\n", "imagem.save('desenho.png', 'PNG')\n", "\n", "# Para exibir o gr\u00e1fico no IPython Notebook\n", "from IPython.display import Image as Img\n", "img = Img(filename='desenho.png')\n", "img" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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kDQAADCqcAQAAPkUDAACD6vYAAACfpgEAgEFmAABgkAYAAAa9bfv/jQGAT9MAAMAgMwAA\nMEgDAACD7AEAgEEaAAAYZAYAAAZpAABgkD0AADBIAwAAg8wAAMAgDQAADLIHAAAGaQAAYJAZAAAY\npAEAgEH2AADAIA0AAAwyAwAAgzQAADDIHgAAGKQBAIBBZgAAYJAGAAAG2QMAAIM0AAAwyAwAAAzS\nAADAIHsAAGCQBgAABpkBAIBBGgAAGGQPAAAM0gAAwCAzAAAwSAMAAIPsAQCAQRoAABhkBgAABmkA\nAGDQy+PxogIAgDEaAAAY9PIwBAAAczQAADDov0bAOhzeU5zKAAAAAElFTkSuQmCC\n", "prompt_number": 1, "text": [ "<IPython.core.display.Image at 0x2b72e50>" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\u00c9 poss\u00edvel calcular os dados da imagem com o NumPy e usar o PIL para gerar a imagem real.\n", "\n", "Exemplo com modula\u00e7\u00e3o de amplitude de onda:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "\"\"\"\n", "Criando uma imagem usando NumPy\n", "\"\"\"\n", "\n", "import numpy\n", "from PIL import Image\n", "\n", "def coords(xy, tam):\n", " \"\"\"\n", " coords(xy, tam) => x, y\n", " Transforma as coordenadas normalizadas\n", " para o centro da imagem de tamanho \"tam\"\n", " \"\"\"\n", " X, Y = tam\n", "\n", " x = int((1. + xy[0]) * (X - 1.) / 2.)\n", " y = int((1. + xy[1]) * (Y - 1.) / 2.)\n", " return x, y\n", "\n", "if __name__ == '__main__':\n", "\n", " # Dimens\u00f5es\n", " tam = 900, 600\n", "\n", " # Cria um arranjo apenas com zeros\n", " # com as dimens\u00f5es transpostas\n", " # \"tam[::-1]\" \u00e9 o reverso de \"tam\" e \n", " # \"(3,)\" \u00e9 uma tupla para representar \"(R, G, B)\"\n", " imag = numpy.zeros(tam[::-1] + (3,), numpy.uint8)\n", "\n", " # Preenche de branco\n", " imag.fill(255)\n", "\n", " # Dados do eixo X\n", " xs = numpy.arange(-1., 1., 0.00005)\n", "\n", " # Onda moduladora\n", " # Valor m\u00e9dio, amplitude e freq\u00fc\u00eancia\n", " vmed = 0.6\n", " amp = 0.4\n", " fm = 2.\n", " mod = vmed + amp * numpy.cos(fm * numpy.pi * xs)\n", "\n", " # Frequ\u00eancia da portadora\n", " fc = 8.\n", " # N\u00famero de curvas internas\n", " ci = 32.\n", " # Contador\n", " i = 0\n", "\n", " # Gera um conjunto de curvas\n", " for delta_y in numpy.arange(1. / ci, 1. + 1. / ci,\n", " 1. / ci):\n", "\n", " # Dados do eixo Y\n", " ys = mod * delta_y * numpy.sin(fc * numpy.pi * xs)\n", "\n", " # Pares x, y\n", " xys = zip(xs, ys)\n", "\n", " # Desenha a portadora e as curvas internas\n", " # Para cada ponto na lista\n", " for xy in xys:\n", "\n", " # Coordenadas invertidas\n", " x, y = coords(xy, tam)[::-1]\n", "\n", " # Aplica cor a xy\n", " imag[x, y] = (250 - 100 * delta_y,\n", " 150 - 100 * delta_y,\n", " 50 + 100 * delta_y)\n", " i += 1\n", "\n", " for x, y in zip(xs, mod):\n", " # Desenha as envolt\u00f3rias\n", " imag[coords((x, y), tam)[::-1]] = (0, 0, 0)\n", " imag[coords((x, -y), tam)[::-1]] = (0, 0, 0)\n", "\n", " # Bordas superior e inferior\n", " imag[coords((x, 1.), tam)[::-1]] = (0, 0, 0)\n", " imag[coords((x, -1.), tam)[::-1]] = (0, 0, 0)\n", " i += 4\n", "\n", " for y in xs:\n", "\n", " # Bordas laterais\n", " imag[coords((1., y), tam)[::-1]] = (0, 0, 0)\n", " imag[coords((-1., y), tam)[::-1]] = (0, 0, 0)\n", " i += 2\n", "\n", " print i, 'pontos calculados'\n", "\n", " # Cria a imagem a partir do arranjo\n", " imagem = Image.fromarray(imag, 'RGB')\n", " imagem.save('curvas.png', 'PNG')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1520000 pontos calculados\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "# Para exibir o gr\u00e1fico no IPython Notebook\n", "from IPython.display import Image as Img\n", "img = Img(filename='curvas.png')\n", "img" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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xasEDn5ZGKcKkM4IzqWOe6DONirJMu/YYw1KdQKcPNBeP31xzn8SXWp9Nz+hz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XDlIKFQZNHEBS2FUjyyVP9rZhz9tODGhVnjw6FfVdS1ypymUGRRodBz9r9i/TzVCXxhpdAGYsqP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\nXaw4hULVsCgHl/jkuaXd+9/L4egUyrb00/IaL9r8/GWPBpd5Ek8vazXPB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"prompt_number": 4, "text": [ "<IPython.core.display.Image at 0x2b56ad0>" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Observa\u00e7\u00f5es:\n", "\n", "+ A biblioteca trabalha com o conceito de bandas, que s\u00e3o camadas que comp\u00f5em a imagem. Cada imagem pode ter v\u00e1rias bandas, mas todas devem ter as mesmas dimens\u00f5es e profundidade.\n", "+ A origem do sistema de coordenadas \u00e9 no canto superior esquerdo.\n", "\n", "Al\u00e9m do PIL, tamb\u00e9m \u00e9 poss\u00edvel usar o [ImageMagick](http://www.imagemagick.org/) com Python. Com uma proposta diferente, ImageMagick \u00e9 um conjunto de utilit\u00e1rios para processar imagens raster, feito basicamente para uso atrav\u00e9s de linha de comando ou atrav\u00e9s de linguagens de programa\u00e7\u00e3o." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<style>\n", " @font-face {\n", " font-family: \"Computer Modern\";\n", " src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n", " }\n", " div.cell{\n", " width:800px;\n", " margin-left:16% !important;\n", " margin-right:auto;\n", " }\n", " h1 {\n", " font-family: Helvetica, serif;\n", " }\n", " h4{\n", " margin-top:12px;\n", " margin-bottom: 3px;\n", " }\n", " div.text_cell_render{\n", " font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n", " line-height: 145%;\n", " font-size: 130%;\n", " width:800px;\n", " margin-left:auto;\n", " margin-right:auto;\n", " }\n", " .CodeMirror{\n", " font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n", " }\n", " .note{\n", " border-bottom: 1px black dotted;\n", " }\n", " .prompt{\n", " display: None;\n", " }\n", " .text_cell_render h5 {\n", " font-weight: 300;\n", " font-size: 16pt;\n", " color: #4057A1;\n", " font-style: italic;\n", " margin-bottom: .5em;\n", " margin-top: 0.5em;\n", " display: block;\n", " }\n", " \n", " .warning{\n", " color: rgb( 240, 20, 20 )\n", " } \n", "</style>\n", "<script>\n", " MathJax.Hub.Config({\n", " TeX: {\n", " extensions: [\"AMSmath.js\"]\n", " },\n", " tex2jax: {\n", " inlineMath: [ ['$','$'], [\"\\\$\",\"\\\$\"] ],\n", " displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n", " },\n", " displayAlign: 'center', // Change this to 'center' to center equations.\n", " \"HTML-CSS\": {\n", " styles: {'.MathJax_Display': {\"margin\": 4}}\n", " }\n", " });\n", "</script>" ], "output_type": "pyout", "prompt_number": 1, "text": [ "<IPython.core.display.HTML at 0x50f8f98>" ] } ], "prompt_number": 1 } ], "metadata": {} } ] }	gpl-2.0
pioneers/topgear	quickstart-winston.ipynb	1	31707	{ "metadata": { "colabVersion": "0.1", "name": "", "signature": "sha256:c0bfd009d6b92f92c31db6f6b0a822a5513d5317206ba368c8595ed756f25453" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"http://mirageforum.com/forum/attachment.php?attachmentid=159&d=1370617142\">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "back to [Index](index.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Important Information" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The API:\n", "\n", "r.drive(left, right) makes the motors move. The values range from -1 to 1." ] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Quickstart-Robot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's make a robot drive forward:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-success\">\n", "Click on the cell below, and press `SHIFT` + `ENTER` to run\n", "</div>" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import lbot, time, IPython, sys, time\n", "r = lbot.Robot()\n", "r.drive(.1, .1)" ], "language": "python", "metadata": { "cellView": null, "executionInfo": null }, "outputs": [], "prompt_number": 85 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The robot should now be driving forward!\n", "\n", "Stop the robot by running the cell below:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "r.drive(0,0)" ], "language": "python", "metadata": { "cellView": null, "executionInfo": null }, "outputs": [], "prompt_number": 86 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Robot Commands" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the empty cell below, try making the robot spin to the right:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-success\">\n", "Click on the cell below (\"Your code goes here!~\") and start typing code!\n", "</div>" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import lbot, time, IPython, sys, time\n", "r = lbot.Robot()\n", "r.drive(.1, .05)" ], "language": "python", "metadata": { "cellView": null, "executionInfo": null }, "outputs": [], "prompt_number": 87 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, in the empty cell below, try to make the robot stop." ] }, { "cell_type": "code", "collapsed": false, "input": [ "r.drive(0,0)" ], "language": "python", "metadata": { "cellView": null, "executionInfo": null }, "outputs": [], "prompt_number": 92 }, { "cell_type": "markdown", "metadata": {}, "source": [ "now, try making the robot drive backwards:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "r.drive(-.1, -.1)" ], "language": "python", "metadata": { "cellView": null, "executionInfo": null }, "outputs": [], "prompt_number": 89 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "How to use this interface" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These code blocks are called cells." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The menu and toolbar:\n", "<img src=\"ipython-in-depth/examples/Notebook/images/menubar_toolbar.png\">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Try clicking on `Insert` ==> `Insert Cell Below`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "r.drive(.1, .05)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 91 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Fancy Robot Commands" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By making new cells below this one, try commanding the robot to drive in different radius circles." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make a cell below here that spins the robot in ~5 inch diameter circles. (Don't spend too much time tuning)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "r.drive(.05, .15)" ], "language": "python", "metadata": { "cellView": null, "executionInfo": null }, "outputs": [], "prompt_number": 98 }, { "cell_type": "code", "collapsed": false, "input": [ "r.drive(0, 0)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 99 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make a cell below here that spins the robot in ~10 inch diameter circles. (Don't spend too much time tuning)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Your code goes here!~" ], "language": "python", "metadata": { "cellView": null, "executionInfo": null }, "outputs": [], "prompt_number": 77 }, { "cell_type": "code", "collapsed": false, "input": [ "# Your code goes here!~" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 89 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Robot Trajectories" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Commands can be strung together to make a robot follow paths." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compare the behavior of the two cells, and try to record an explaination:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "r.drive(0,.2)\n", "r.drive(0,0)" ], "language": "python", "metadata": { "cellView": null, "executionInfo": null }, "outputs": [], "prompt_number": 100 }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-success\">\n", "Double click on the cell below (\"What happened: Why:\") to record your thoughts. Press `SHIFT` + `ENTER` to render the text.\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What happened: The robot moved a little then stopped\n", "\n", "Why: The lines got executed one after another" ] }, { "cell_type": "code", "collapsed": false, "input": [ "r.drive(.15,.15)\n", "time.sleep(.5)\n", "r.drive(0,0)" ], "language": "python", "metadata": { "cellView": null, "executionInfo": null }, "outputs": [], "prompt_number": 102 }, { "cell_type": "markdown", "metadata": {}, "source": [ "What happened: THe robot moved quite a bit then stopped\n", "\n", "Why: the time.sleep line allowed the code above to last for a longer amount of time so it continued driving lonver" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ " Using Robot Trajectories with Sleep" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Try making the robot turn 90 degrees using what you've learned (hint: time.sleep might help!)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "r.drive(.3,-.3)\n", "time.sleep(.5)\n", "r.drive(0,0)\n" ], "language": "python", "metadata": { "cellView": null, "executionInfo": null }, "outputs": [], "prompt_number": 105 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Try making the robot drive 5 inches." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Your code goes here!~" ], "language": "python", "metadata": { "cellView": null, "executionInfo": null }, "outputs": [], "prompt_number": 84 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, use these together to make the robot drive forwards, turn 180 degrees, drive back, and then turn back to the original angle." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Your code goes here!~" ], "language": "python", "metadata": { "cellView": null, "executionInfo": null }, "outputs": [], "prompt_number": 87 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ " Variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can pass variables to functions:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "left = .1\n", "right = .1\n", "\n", "r.drive(left, right)" ], "language": "python", "metadata": { "cellView": null, "executionInfo": null }, "outputs": [], "prompt_number": 88 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also assign variables:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "left = .1\n", "right = .1\n", "\n", "print left\n", "print right\n", "\n", "left = left * 2\n", "\n", "print left\n", "print right\n", "\n", "r.drive(left, right)\n" ], "language": "python", "metadata": { "cellView": null, "executionInfo": null }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.1\n", "0.1\n", "0.2\n", "0.1\n" ] } ], "prompt_number": 106 }, { "cell_type": "code", "collapsed": false, "input": [ "r.drive(0,0)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 159 }, { "cell_type": "code", "collapsed": false, "input": [ "for i in range(10):\n", " value = i5\n", " print i\n", " print value" ], "language": "python", "metadata": { "cellView": null, "executionInfo": null }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0\n", "0\n", "1\n", "5\n", "2\n", "10\n", "3\n", "15\n", "4\n", "20\n", "5\n", "25\n", "6\n", "30\n", "7\n", "35\n", "8\n", "40\n", "9\n", "45\n" ] } ], "prompt_number": 109 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ " Using Robot Trajectories With loops\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, using the for loop, make a robot drive in a square:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "for i in range(4):\n", " r.drive(-.3,.3)\n", " time.sleep(.5)\n", " r.drive(0,0)\n", " r.drive(.2,.2)\n", " time.sleep(.7)\n", " r.drive(0,0)\n" ], "language": "python", "metadata": { "cellView": null, "executionInfo": null }, "outputs": [], "prompt_number": 111 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ " Sensors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the cell below a couple times under various lighting conditions.\n", "For example, try:\n", " facing up\n", "* on retro-reflective tape\n", "* on carpet\n", "* on the table surface" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-success\">\n", "Press `CTRL` + `ENTER` to run a cell repeatedly while the cell is selected\n", "</div>" ] }, { "cell_type": "code", "collapsed": false, "input": [ "reflectances = r.read_sensors()\n", "print reflectances\n", "print len(reflectances)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(0.6039072275161743, 0.18119658529758453, 0.06275946646928787, 0.11672772467136383, 0.07399267703294754, 0.3672771751880646)\n", "6\n" ] } ], "prompt_number": 121 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The output of `read_sensors` is a tuple: a group of values." ] }, { "cell_type": "code", "collapsed": false, "input": [ "tups = (12, -1)\n", "print tups\n", "print len(tups)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(12, -1)\n", "2\n" ] } ], "prompt_number": 38 }, { "cell_type": "code", "collapsed": false, "input": [ "print tups[0]\n", "print tups[1]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "12\n", "-1\n" ] } ], "prompt_number": 27 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the examples above to print out the value of the third reflectance sensor." ] }, { "cell_type": "code", "collapsed": false, "input": [ "print reflectances[2]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.0627594664693\n" ] } ], "prompt_number": 122 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use your knowlege of loops and tuples to print all the values of read_sensors." ] }, { "cell_type": "code", "collapsed": false, "input": [ "for i in range(6):\n", " print reflectances[i]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.603907227516\n", "0.181196585298\n", "0.0627594664693\n", "0.116727724671\n", "0.0739926770329\n", "0.367277175188\n" ] } ], "prompt_number": 124 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Streaming data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "for i in range(5):\n", " print i\n", " time.sleep(1)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0\n", "1" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "3" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "4" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 125 }, { "cell_type": "code", "collapsed": false, "input": [ "for i in range(5):\n", " IPython.display.clear_output()\n", " print i\n", " sys.stdout.flush()\n", " time.sleep(1)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "4\n" ] } ], "prompt_number": 126 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Streaming the reflectance values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Try this:\n", "\n", "(To stop it remember that there is a stop button in the toolbar ar the top)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "while True:\n", " values = r.read_sensors()\n", " IPython.display.clear_output()\n", " for val in values:\n", " print '='int(30val) + int(30(1.-val))' ' + str(val)\n", " sys.stdout.flush()\n", " time.sleep(.01)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "=========== 0.391941428185\n", "= 0.0393162406981\n", "= 0.0415140427649\n", "= 0.0468864515424\n", "== 0.0727716758847\n", "=========== 0.378510415554\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-183-1682ec94e1c0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m'='\u001b[0m\u001b[1;33m\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m30\u001b[0m\u001b[1;33m\u001b[0m\u001b[0mval\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m 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}, { "cell_type": "code", "collapsed": false, "input": [ "5 < 4" ], "language": "python", "metadata": { "cellView": null, "executionInfo": null }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 130, "text": [ "False" ] } ], "prompt_number": 130 }, { "cell_type": "code", "collapsed": false, "input": [ "5 > 4" ], "language": "python", "metadata": { "cellView": null, "executionInfo": null }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 131, "text": [ "True" ] } ], "prompt_number": 131 }, { "cell_type": "code", "collapsed": false, "input": [ "5 >= 4" ], "language": "python", "metadata": { "cellView": null, "executionInfo": null }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 132, "text": [ "True" ] } ], "prompt_number": 132 }, { "cell_type": "code", "collapsed": false, "input": [ "4 >= 4" ], "language": "python", "metadata": { "cellView": null, "executionInfo": null }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 133, "text": [ "True" ] } ], "prompt_number": 133 }, { "cell_type": "code", "collapsed": false, "input": [ "4 <= 3" ], "language": "python", "metadata": { "cellView": null, "executionInfo": null }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 134, "text": [ "False" ] } ], "prompt_number": 134 }, { "cell_type": "code", "collapsed": false, "input": [ "5 == 4" ], "language": "python", "metadata": { "cellView": null, "executionInfo": null }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 135, "text": [ "False" ] } ], "prompt_number": 135 }, { "cell_type": "code", "collapsed": false, "input": [ "5 == 5.0" ], "language": "python", "metadata": { "cellView": null, "executionInfo": null }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 136, "text": [ "True" ] } ], "prompt_number": 136 }, { "cell_type": "code", "collapsed": false, "input": [ "5 != 4" ], "language": "python", "metadata": { "cellView": null, "executionInfo": null }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 137, "text": [ "True" ] } ], "prompt_number": 137 }, { "cell_type": "code", "collapsed": false, "input": [ "2 + 2 == 4" ], "language": "python", "metadata": { "cellView": null, "executionInfo": null }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 138, "text": [ "True" ] } ], "prompt_number": 138 }, { "cell_type": "code", "collapsed": false, "input": [ "2 >= 3" ], "language": "python", "metadata": { "cellView": null, "executionInfo": null }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 139, "text": [ "False" ] } ], "prompt_number": 139 }, { "cell_type": "markdown", "metadata": {}, "source": [ "What do the following comparison operators do?\n", "\n", "1. \">\":\n", "\n", "2. \"<\":\n", "\n", "3. \">=\":\n", "\n", "4. \"<=\":\n", "\n", "5. \"!=\":" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Playing with logicals" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See if the value read in by the first sensor is greater than or equal to .3\n", "\n", "Store the result in variable boo" ] }, { "cell_type": "code", "collapsed": false, "input": [ "reflectances = r.read_sensors()\n", "boo = (reflectances[1] >= .3)\n", "print reflectances[1]\n", "print boo" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.0556776598096\n", "False\n" ] } ], "prompt_number": 154 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Now let's use sensors while driving the robot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Try to do the following:\n", "\n", "* Set the robot to be perpendicular to some retro reflective tape a few inches away\n", "\n", "* Let the robot drive until the robot reaches the tape\n", "\n", "* Run the cell multiple times for varrying distance from the reflective tape.\n", "\n", "Think about your tests under different lighting conditions and logical operators." ] }, { "cell_type": "code", "collapsed": false, "input": [ "beforeline = True\n", "while beforeline:\n", " reflectances = r.read_sensors()\n", " boo = 0\n", " r.drive(.2,.2)\n", " for i in range (6):\n", " if reflectances[i] < .5:\n", " boo = boo + 1\n", " if boo > 5:\n", " r.drive(0,0)\n", " beforeline = False\n", " \n", " " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 187 }, { "cell_type": "code", "collapsed": false, "input": [ "r.drive(0,0)# Your code goes here!~" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 186 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now put the robot on reflective tape parallel to it and have it drive until it is off the reflective tape." ] }, { "cell_type": "code", "collapsed": false, "input": [ "online = True\n", "while online:\n", " reflectances = r.read_sensors()\n", " boo = 0\n", " r.drive(.1,.1)\n", " for i in range (6):\n", " if reflectances[i] > .05:\n", " boo = boo + 1\n", " if boo > 3:\n", " r.drive(0,0)\n", " beforeline = False" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-180-c42c82c2429f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0monline\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[0monline\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mreflectances\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_sensors\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0mboo\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrive\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m.1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m.1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/ajc/topgear/odroid/topgear/lbot.pyc\u001b[0m in \u001b[0;36mread_sensors\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[0mlc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubscribe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'robot0/state'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m \u001b[0mlc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 24\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "prompt_number": 180 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Congratulations, you are on your way to follow a line!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Conditionals" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The final step to make to follow lines is learning about conditionals" ] }, { "cell_type": "code", "collapsed": false, "input": [ "temp = 0\n", "\n", "if temp > 8:\n", " print \"hello\"\n", "else:\n", " if temp > 4:\n", " print \"bye\"\n", " else:\n", " print \"Your number is too small\"\n", " " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Your number is too small\n" ] } ], "prompt_number": 59 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	apache-2.0
steve-federowicz/om	examples/crp_heatmap_anabolic.ipynb	1	46548	{ "metadata": { "name": "", "signature": "sha256:1e637bd210730028f07d5e03eb0b3e4ed8758c7e4e66d7505e04b74cd2b0cf7c" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from om import base, settings\n", "from om.components import \n", "from om.data import \n", "from om.util import \n", "from scipy.spatial.distance import pdist, squareform\n", "from scipy.cluster.hierarchy import linkage, dendrogram\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import math,cobra\n", "\n", "\n", "ome = base.Session()\n", "model = cobra.io.load_matlab_model(settings.data_directory+'/models/iJO1366.mat')\n", "\n", "ged = GeneExpressionData\n", "dged = DifferentialGeneExpressionData\n", "cpge = ChIPPeakGeneExpression" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "ome.query(GeneGroup).all()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "[Gene Group (#1, Glycolysis / Gluconeogenesis) 40 genes,\n", " Gene Group (#2, Citrate cycle (TCA cycle)) 28 genes,\n", " Gene Group (#3, Pentose phosphate pathway) 30 genes,\n", " Gene Group (#4, Pentose and glucuronate interconversions) 27 genes,\n", " Gene Group (#5, Fructose and mannose metabolism) 38 genes,\n", " Gene Group (#6, Galactose metabolism) 33 genes,\n", " Gene Group (#7, Ascorbate and aldarate metabolism) 14 genes,\n", " Gene Group (#8, Fatty acid biosynthesis) 12 genes,\n", " Gene Group (#9, Fatty acid metabolism) 16 genes,\n", " Gene Group (#10, Ubiquinone and other terpenoid-quinone biosynthesis) 17 genes,\n", " Gene Group (#11, Oxidative phosphorylation) 41 genes,\n", " Gene Group (#12, Purine metabolism) 87 genes,\n", " Gene Group (#13, Pyrimidine metabolism) 53 genes,\n", " Gene Group (#14, Alanine, aspartate and glutamate metabolism) 29 genes,\n", " Gene Group (#15, Glycine, serine and threonine metabolism) 32 genes,\n", " Gene Group (#16, Cysteine and methionine metabolism) 28 genes,\n", " Gene Group (#17, Valine, leucine and isoleucine degradation) 12 genes,\n", " Gene Group (#18, Geraniol degradation) 6 genes,\n", " Gene Group (#19, Valine, leucine and isoleucine biosynthesis) 21 genes,\n", " Gene Group (#20, Lysine biosynthesis) 16 genes,\n", " Gene Group (#21, Lysine degradation) 11 genes,\n", " Gene Group (#22, Arginine and proline metabolism) 43 genes,\n", " Gene Group (#23, Histidine metabolism) 9 genes,\n", " Gene Group (#24, Tyrosine metabolism) 9 genes,\n", " Gene Group (#25, Phenylalanine metabolism) 18 genes,\n", " Gene Group (#26, gamma-Hexachlorocyclohexane degradation) 5 genes,\n", " Gene Group (#27, Benzoate degradation via hydroxylation) 5 genes,\n", " Gene Group (#28, Fluorobenzoate degradation) 1 genes,\n", " Gene Group (#29, Tryptophan metabolism) 11 genes,\n", " Gene Group (#30, Phenylalanine, tyrosine and tryptophan biosynthesis) 21 genes,\n", " Gene Group (#31, Novobiocin biosynthesis) 4 genes,\n", " Gene Group (#32, beta-Alanine metabolism) 15 genes,\n", " Gene Group (#33, Taurine and hypotaurine metabolism) 6 genes,\n", " Gene Group (#34, Phosphonate and phosphinate metabolism) 2 genes,\n", " Gene Group (#35, Selenoamino acid metabolism) 14 genes,\n", " Gene Group (#36, Cyanoamino acid metabolism) 6 genes,\n", " Gene Group (#37, D-Glutamine and D-glutamate metabolism) 5 genes,\n", " Gene Group (#38, D-Alanine metabolism) 4 genes,\n", " Gene Group (#39, Glutathione metabolism) 17 genes,\n", " Gene Group (#40, Starch and sucrose metabolism) 32 genes,\n", " Gene Group (#41, Other glycan degradation) 4 genes,\n", " Gene Group (#42, Amino sugar and nucleotide sugar metabolism) 44 genes,\n", " Gene Group (#43, Streptomycin biosynthesis) 9 genes,\n", " Gene Group (#44, Polyketide sugar unit biosynthesis) 6 genes,\n", " Gene Group (#45, Lipopolysaccharide biosynthesis) 27 genes,\n", " Gene Group (#46, Peptidoglycan biosynthesis) 23 genes,\n", " Gene Group (#47, Glycerolipid metabolism) 13 genes,\n", " Gene Group (#48, Inositol phosphate metabolism) 3 genes,\n", " Gene Group (#49, Glycerophospholipid metabolism) 28 genes,\n", " Gene Group (#50, Arachidonic acid metabolism) 2 genes,\n", " Gene Group (#51, alpha-Linolenic acid metabolism) 3 genes,\n", " Gene Group (#52, Sphingolipid metabolism) 3 genes,\n", " Gene Group (#53, Pyruvate metabolism) 43 genes,\n", " Gene Group (#54, Biphenyl degradation) 2 genes,\n", " Gene Group (#55, Toluene and xylene degradation) 2 genes,\n", " Gene Group (#56, 1- and 2-Methylnaphthalene degradation) 4 genes,\n", " Gene Group (#57, Naphthalene and anthracene degradation) 1 genes,\n", " Gene Group (#58, 1,4-Dichlorobenzene degradation) 3 genes,\n", " Gene Group (#59, Fluorene degradation) 3 genes,\n", " Gene Group (#60, Carbazole degradation) 2 genes,\n", " Gene Group (#61, Glyoxylate and dicarboxylate metabolism) 36 genes,\n", " Gene Group (#62, Benzoate degradation via CoA ligation) 16 genes,\n", " Gene Group (#63, Trinitrotoluene degradation) 7 genes,\n", " Gene Group (#64, Propanoate metabolism) 30 genes,\n", " Gene Group (#65, 3-Chloroacrylic acid degradation) 4 genes,\n", " Gene Group (#66, Ethylbenzene degradation) 4 genes,\n", " Gene Group (#67, Styrene degradation) 3 genes,\n", " Gene Group (#68, Butanoate metabolism) 35 genes,\n", " Gene Group (#69, C5-Branched dibasic acid metabolism) 9 genes,\n", " Gene Group (#70, One carbon pool by folate) 13 genes,\n", " Gene Group (#71, Methane metabolism) 15 genes,\n", " Gene Group (#72, Thiamine metabolism) 14 genes,\n", " Gene Group (#73, Riboflavin metabolism) 11 genes,\n", " Gene Group (#74, Vitamin B6 metabolism) 9 genes,\n", " Gene Group (#75, Nicotinate and nicotinamide metabolism) 17 genes,\n", " Gene Group (#76, Pantothenate and CoA biosynthesis) 21 genes,\n", " Gene Group (#77, Biotin metabolism) 7 genes,\n", " Gene Group (#78, Lipoic acid metabolism) 3 genes,\n", " Gene Group (#79, Folate biosynthesis) 12 genes,\n", " Gene Group (#80, Porphyrin and chlorophyll metabolism) 25 genes,\n", " Gene Group (#81, Terpenoid backbone biosynthesis) 13 genes,\n", " Gene Group (#82, Limonene and pinene degradation) 5 genes,\n", " Gene Group (#83, Nitrogen metabolism) 35 genes,\n", " Gene Group (#84, Sulfur metabolism) 13 genes,\n", " Gene Group (#85, Caprolactam degradation) 4 genes,\n", " Gene Group (#86, Aminoacyl-tRNA biosynthesis) 26 genes,\n", " Gene Group (#87, Biosynthesis of unsaturated fatty acids) 6 genes,\n", " Gene Group (#88, Biosynthesis of siderophore group nonribosomal peptides) 6 genes,\n", " Gene Group (#89, ABC transporters) 182 genes,\n", " Gene Group (#90, Two-component system) 130 genes,\n", " Gene Group (#91, Bacterial chemotaxis) 21 genes,\n", " Gene Group (#92, Flagellar assembly) 38 genes,\n", " Gene Group (#93, Phosphotransferase system (PTS)) 45 genes,\n", " Gene Group (#94, Ribosome) 79 genes,\n", " Gene Group (#95, RNA degradation) 15 genes,\n", " Gene Group (#96, RNA polymerase) 4 genes,\n", " Gene Group (#97, DNA replication) 17 genes,\n", " Gene Group (#98, Protein export) 19 genes,\n", " Gene Group (#99, Bacterial secretion system) 32 genes,\n", " Gene Group (#100, Base excision repair) 14 genes,\n", " Gene Group (#101, Nucleotide excision repair) 8 genes,\n", " Gene Group (#102, Mismatch repair) 22 genes,\n", " Gene Group (#103, Homologous recombination) 27 genes]" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "catabolic_genes = ome.query(GeneGroup).filter(GeneGroup.name.ilike('Seleno%')).first().genes\n", "\n", "heatmap_args = gene_heatmap(catabolic_genes, analysis_type=GeneExpressionData,\n", " dataset_type='rnaseq_experiment',\n", " strain1=ome.query(Strain).filter(Strain.name.in_(['delta-crp','delAr1','delAr2','delAr1delAr2'])).all())\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "catabolic_heatmap_diagram = HeatmapWidget(heatmap_args)\n", "display(catabolic_heatmap_diagram)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "%%javascript\n", "\n", "require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n", "\n", "require([\"widgets/js/widget\", \"d3\"], function(WidgetManager, d3){\n", " var HeatmapView = IPython.DOMWidgetView.extend({\n", "\n", " render: function(){\n", " \n", " this.$el.append(this.model.get(\"html_style\"));\n", "\n", " this.update();\n", " },\n", " update: function(){\n", " var margin = { top: 175, right: 10, bottom: 150, left: 400 },\n", " cellSize=18,\n", " // - margin.top - margin.bottom,\n", " //gridSize = Math.floor(width / 24),\n", " legendElementWidth = cellSize2.5,\n", " colorBuckets = 17;\n", " //colors = ['#005824','#1A693B','#347B53','#4F8D6B','#699F83','#83B09B','#9EC2B3','#B8D4CB','#D2E6E3','#EDF8FB','#FFFFFF','#F1EEF6','#E6D3E1','#DBB9CD','#D19EB9','#C684A4','#BB6990','#B14F7C','#A63467','#9B1A53','#91003F'];\n", " var colors = [\"#081d58\",\"#162876\",\"#253494\",\"#23499E\",\"#225ea8\",\"#1F77B4\",\"#1d91c0\",\"#2FA3C2\",\"#41b6c4\",\"#60C1BF\",\"#7fcdbb\",\"#A3DBB7\",\"#c7e9b4\",\"#DAF0B2\",\"#edf8b1\",\"#F6FBC5\",\"#ffffd9\"];\n", " var rowLabel = this.model.get(\"row_labels\");\n", " var colLabel = this.model.get(\"col_labels\");\n", " var data = this.model.get(\"heatmap_data\");\n", " var hcrow = this.model.get(\"hcrow\");\n", " var hccol = this.model.get(\"hccol\");\n", " \n", " var col_number = colLabel.length,\n", " row_number = rowLabel.length;\n", " \n", " var width = cellSizecol_number, // - margin.left - margin.right,\n", " height = cellSizerow_number;\n", " \n", " var maxval = JSON.parse(this.model.get(\"maxval\"));\n", " var minval = JSON.parse(this.model.get(\"minval\"));\n", " \n", " var colorScale = d3.scale.quantile()\n", " .domain([ minval , 0, maxval])\n", " .range(colors);\n", " \n", " this.svg = d3.select(this.el).append(\"svg\")\n", " .attr(\"width\", width + margin.left + margin.right)\n", " .attr(\"height\", height + margin.top + margin.bottom)\n", " .append(\"g\")\n", " .attr(\"transform\", \"translate(\" + margin.left + \",\" + margin.top + \")\");\n", " \n", " var svg = this.svg;\n", "\n", " var rowSortOrder=false;\n", " var colSortOrder=false;\n", " var rowLabels = svg.append(\"g\")\n", " .selectAll(\".rowLabelg\")\n", " .data(rowLabel)\n", " .enter()\n", " .append(\"text\")\n", " .text(function (d) { return d; })\n", " .attr(\"x\", 0)\n", " .attr(\"y\", function (d, i) { return hcrow.indexOf(i+1) * cellSize; })\n", " .style(\"text-anchor\", \"end\")\n", " .attr(\"transform\", \"translate(-25,\" + cellSize / 1.5 + \")\")\n", " .attr(\"class\", function (d,i) { return \"rowLabel mono r\"+i;} )\n", " .on(\"mouseover\", function(d) {d3.select(this).classed(\"text-hover\",true);})\n", " .on(\"mouseout\" , function(d) {d3.select(this).classed(\"text-hover\",false);})\n", " .on(\"click\", function(d,i) {rowSortOrder=!rowSortOrder; sortbylabel(\"r\",i,rowSortOrder); this.$('#order').property(\"selectedIndex\", 4).node().focus();;});\n", "\n", " var colLabels = svg.append(\"g\")\n", " .selectAll(\".colLabelg\")\n", " .data(colLabel)\n", " .enter()\n", " .append(\"text\")\n", " .text(function (d) { return d; })\n", " .attr(\"x\", 0)\n", " .attr(\"y\", function (d, i) { return hccol.indexOf(i+1) * cellSize; })\n", " .style(\"text-anchor\", \"left\")\n", " .attr(\"transform\", \"translate(\"+cellSize/2 + \",-25) rotate (-90)\")\n", " .attr(\"class\", function (d,i) { return \"colLabel mono c\"+i;} )\n", " .on(\"mouseover\", function(d) {d3.select(this).classed(\"text-hover\",true);})\n", " .on(\"mouseout\" , function(d) {d3.select(this).classed(\"text-hover\",false);})\n", " .on(\"click\", function(d,i) {colSortOrder=!colSortOrder; sortbylabel(\"c\",i,colSortOrder); this.$('#order').property(\"selectedIndex\", 4).node().focus();;});\n", "\n", " var heatMap = svg.append(\"g\").attr(\"class\",\"g3\")\n", " .selectAll(\".cellg\")\n", " .data(data,function(d){return d.row+\":\"+d.col;})\n", " .enter()\n", " .append(\"rect\")\n", " .attr(\"x\", function(d) { return hccol.indexOf(d.col) * cellSize; })\n", " .attr(\"y\", function(d) { return hcrow.indexOf(d.row) * cellSize; })\n", " .attr(\"class\", function(d){return \"cell cell-border cr\"+(d.row-1)+\" cc\"+(d.col-1);})\n", " .attr(\"width\", cellSize)\n", " .attr(\"height\", cellSize)\n", " .style(\"fill\", function(d) { return colorScale(d.value); })\n", " /* .on(\"click\", function(d) {\n", " var rowtext=d3.select(\".r\"+(d.row-1));\n", " if(rowtext.classed(\"text-selected\")==false){\n", " rowtext.classed(\"text-selected\",true);\n", " }else{\n", " rowtext.classed(\"text-selected\",false);\n", " }\n", " })/\n", " .on(\"mouseover\", function(d){\n", " //highlight text\n", " d3.select(this).classed(\"cell-hover\",true);\n", " d3.selectAll(\".rowLabel\").classed(\"text-highlight\",function(r,ri){ return ri==(d.row-1);});\n", " d3.selectAll(\".colLabel\").classed(\"text-highlight\",function(c,ci){ return ci==(d.col-1);});\n", " \n", " //Update the tooltip position and value\n", " d3.select(\"#tooltip\")\n", " .style(\"left\", (d3.event.pageX+10) + \"px\")\n", " .style(\"top\", (d3.event.pageY-10) + \"px\")\n", " .select(\"#value\")\n", " .text(rowLabel[d.row-1]+\",\"+colLabel[d.col-1]+\"\\ndata:\"+d.value); \n", " //Show the tooltip\n", " d3.select(\"#tooltip\").classed(\"hidden\", false);\n", " })\n", " .on(\"mouseout\", function(){\n", " d3.select(this).classed(\"cell-hover\",false);\n", " d3.selectAll(\".rowLabel\").classed(\"text-highlight\",false);\n", " d3.selectAll(\".colLabel\").classed(\"text-highlight\",false);\n", " d3.select(\"#tooltip\").classed(\"hidden\", true);\n", " })\n", " ;\n", "\n", " var legend = svg.selectAll(\".legend\")\n", " .data(d3.range(Math.floor(minval), Math.ceil(maxval)))\n", " .enter().append(\"g\")\n", " .attr(\"class\", \"legend\");\n", " \n", " var color_factor = Math.floor(colors.length/(maxval-minval));\n", " console.log(height+(cellSize2));\n", " legend.append(\"rect\")\n", " .attr(\"x\", function(d, i) { return legendElementWidth * i; })\n", " .attr(\"y\", height+(cellSize2))\n", " .attr(\"width\", legendElementWidth)\n", " .attr(\"height\", cellSize)\n", " .style(\"fill\", function(d, i) { return colors[icolor_factor]; });\n", " \n", " legend.append(\"text\")\n", " .attr(\"class\", \"mono\")\n", " .text(function(d) { return d; })\n", " .attr(\"width\", legendElementWidth)\n", " .attr(\"x\", function(d, i) { return legendElementWidth * i; })\n", " .attr(\"y\", height + (cellSize4));\n", "\n", "// Change ordering of cells\n", "\n", " function sortbylabel(rORc,i,sortOrder){\n", " var t = svg.transition().duration(3000);\n", " var log2r=[];\n", " var sorted; // sorted is zero-based index\n", " d3.selectAll(\".c\"+rORc+i) \n", " .filter(function(ce){\n", " log2r.push(ce.value);\n", " })\n", " ;\n", " if(rORc==\"r\"){ // sort log2ratio of a gene\n", " sorted=d3.range(col_number).sort(function(a,b){ if(sortOrder){ return log2r[b]-log2r[a];}else{ return log2r[a]-log2r[b];}});\n", " t.selectAll(\".cell\")\n", " .attr(\"x\", function(d) { return sorted.indexOf(d.col-1) cellSize; })\n", " ;\n", " t.selectAll(\".colLabel\")\n", " .attr(\"y\", function (d, i) { return sorted.indexOf(i) * cellSize; })\n", " ;\n", " }else{ // sort log2ratio of a contrast\n", " sorted=d3.range(row_number).sort(function(a,b){if(sortOrder){ return log2r[b]-log2r[a];}else{ return log2r[a]-log2r[b];}});\n", " t.selectAll(\".cell\")\n", " .attr(\"y\", function(d) { return sorted.indexOf(d.row-1) * cellSize; })\n", " ;\n", " t.selectAll(\".rowLabel\")\n", " .attr(\"y\", function (d, i) { return sorted.indexOf(i) * cellSize; })\n", " ;\n", " }\n", " }\n", "\n", " //d3.select(\"#order\").on(\"change\",function(){\n", " this.$('#order').on(\"change\",function(){\n", " order(this.value);\n", " });\n", " \n", " function order(value){\n", " if(value==\"hclust\"){\n", " var t = svg.transition().duration(3000);\n", " t.selectAll(\".cell\")\n", " .attr(\"x\", function(d) { return hccol.indexOf(d.col) * cellSize; })\n", " .attr(\"y\", function(d) { return hcrow.indexOf(d.row) * cellSize; })\n", " ;\n", "\n", " t.selectAll(\".rowLabel\")\n", " .attr(\"y\", function (d, i) { return hcrow.indexOf(i+1) * cellSize; })\n", " ;\n", "\n", " t.selectAll(\".colLabel\")\n", " .attr(\"y\", function (d, i) { return hccol.indexOf(i+1) * cellSize; })\n", " ;\n", "\n", " }else if (value==\"probecontrast\"){\n", " var t = svg.transition().duration(3000);\n", " t.selectAll(\".cell\")\n", " .attr(\"x\", function(d) { return (d.col - 1) * cellSize; })\n", " .attr(\"y\", function(d) { return (d.row - 1) * cellSize; })\n", " ;\n", "\n", " t.selectAll(\".rowLabel\")\n", " .attr(\"y\", function (d, i) { return i * cellSize; })\n", " ;\n", "\n", " t.selectAll(\".colLabel\")\n", " .attr(\"y\", function (d, i) { return i * cellSize; })\n", " ;\n", "\n", " }else if (value==\"probe\"){\n", " var t = svg.transition().duration(3000);\n", " t.selectAll(\".cell\")\n", " .attr(\"y\", function(d) { return (d.row - 1) * cellSize; })\n", " ;\n", "\n", " t.selectAll(\".rowLabel\")\n", " .attr(\"y\", function (d, i) { return i * cellSize; })\n", " ;\n", " }else if (value==\"contrast\"){\n", " var t = svg.transition().duration(3000);\n", " t.selectAll(\".cell\")\n", " .attr(\"x\", function(d) { return (d.col - 1) * cellSize; })\n", " ;\n", " t.selectAll(\".colLabel\")\n", " .attr(\"y\", function (d, i) { return i * cellSize; })\n", " ;\n", " }\n", " }\n", " // \n", " var sa=d3.select(\".g3\")\n", " .on(\"mousedown\", function() {\n", " if( !d3.event.altKey) {\n", " d3.selectAll(\".cell-selected\").classed(\"cell-selected\",false);\n", " d3.selectAll(\".rowLabel\").classed(\"text-selected\",false);\n", " d3.selectAll(\".colLabel\").classed(\"text-selected\",false);\n", " }\n", " var p = d3.mouse(this);\n", " sa.append(\"rect\")\n", " .attr({\n", " rx : 0,\n", " ry : 0,\n", " class : \"selection\",\n", " x : p[0],\n", " y : p[1],\n", " width : 1,\n", " height : 1\n", " })\n", " })\n", " .on(\"mousemove\", function() {\n", " var s = sa.select(\"rect.selection\");\n", " \n", " if(!s.empty()) {\n", " var p = d3.mouse(this),\n", " d = {\n", " x : parseInt(s.attr(\"x\"), 10),\n", " y : parseInt(s.attr(\"y\"), 10),\n", " width : parseInt(s.attr(\"width\"), 10),\n", " height : parseInt(s.attr(\"height\"), 10)\n", " },\n", " move = {\n", " x : p[0] - d.x,\n", " y : p[1] - d.y\n", " }\n", " ;\n", " \n", " if(move.x < 1 \|\| (move.x2<d.width)) {\n", " d.x = p[0];\n", " d.width -= move.x;\n", " } else {\n", " d.width = move.x; \n", " }\n", " \n", " if(move.y < 1 \|\| (move.y2<d.height)) {\n", " d.y = p[1];\n", " d.height -= move.y;\n", " } else {\n", " d.height = move.y; \n", " }\n", " s.attr(d);\n", " \n", " // deselect all temporary selected state objects\n", " d3.selectAll('.cell-selection.cell-selected').classed(\"cell-selected\", false);\n", " d3.selectAll(\".text-selection.text-selected\").classed(\"text-selected\",false);\n", "\n", " d3.selectAll('.cell').filter(function(cell_d, i) {\n", " if(\n", " !d3.select(this).classed(\"cell-selected\") && \n", " // inner circle inside selection frame\n", " (this.x.baseVal.value)+cellSize >= d.x && (this.x.baseVal.value)<=d.x+d.width && \n", " (this.y.baseVal.value)+cellSize >= d.y && (this.y.baseVal.value)<=d.y+d.height\n", " ) {\n", " \n", " d3.select(this)\n", " .classed(\"cell-selection\", true)\n", " .classed(\"cell-selected\", true);\n", "\n", " d3.select(\".r\"+(cell_d.row-1))\n", " .classed(\"text-selection\",true)\n", " .classed(\"text-selected\",true);\n", "\n", " d3.select(\".c\"+(cell_d.col-1))\n", " .classed(\"text-selection\",true)\n", " .classed(\"text-selected\",true);\n", " }\n", " });\n", " }\n", " })\n", " .on(\"mouseup\", function() {\n", " // remove selection frame\n", " sa.selectAll(\"rect.selection\").remove();\n", " \n", " // remove temporary selection marker class\n", " d3.selectAll('.cell-selection').classed(\"cell-selection\", false);\n", " d3.selectAll(\".text-selection\").classed(\"text-selection\",false);\n", " })\n", " .on(\"mouseout\", function() {\n", " if(d3.event.relatedTarget.tagName=='html') {\n", " // remove selection frame\n", " sa.selectAll(\"rect.selection\").remove();\n", " // remove temporary selection marker class\n", " d3.selectAll('.cell-selection').classed(\"cell-selection\", false);\n", " d3.selectAll(\".rowLabel\").classed(\"text-selected\",false);\n", " d3.selectAll(\".colLabel\").classed(\"text-selected\",false);\n", " }\n", " });\n", "\n", " \n", " }\n", " });\n", " WidgetManager.register_widget_view(\"HeatmapView\", HeatmapView);\n", "});" ], "language": "python", "metadata": {}, "outputs": [ { "javascript": [ "\n", "require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n", "\n", "require([\"widgets/js/widget\", \"d3\"], function(WidgetManager, d3){\n", " var HeatmapView = IPython.DOMWidgetView.extend({\n", "\n", " render: function(){\n", " \n", " this.$el.append(this.model.get(\"html_style\"));\n", "\n", " this.update();\n", " },\n", " update: function(){\n", " var margin = { top: 175, right: 10, bottom: 150, left: 400 },\n", " cellSize=18,\n", " // - margin.top - margin.bottom,\n", " //gridSize = Math.floor(width / 24),\n", " legendElementWidth = cellSize2.5,\n", " colorBuckets = 17;\n", " //colors = ['#005824','#1A693B','#347B53','#4F8D6B','#699F83','#83B09B','#9EC2B3','#B8D4CB','#D2E6E3','#EDF8FB','#FFFFFF','#F1EEF6','#E6D3E1','#DBB9CD','#D19EB9','#C684A4','#BB6990','#B14F7C','#A63467','#9B1A53','#91003F'];\n", " var colors = [\"#081d58\",\"#162876\",\"#253494\",\"#23499E\",\"#225ea8\",\"#1F77B4\",\"#1d91c0\",\"#2FA3C2\",\"#41b6c4\",\"#60C1BF\",\"#7fcdbb\",\"#A3DBB7\",\"#c7e9b4\",\"#DAF0B2\",\"#edf8b1\",\"#F6FBC5\",\"#ffffd9\"];\n", " var rowLabel = this.model.get(\"row_labels\");\n", " var colLabel = this.model.get(\"col_labels\");\n", " var data = this.model.get(\"heatmap_data\");\n", " var hcrow = this.model.get(\"hcrow\");\n", " var hccol = this.model.get(\"hccol\");\n", " \n", " var col_number = colLabel.length,\n", " row_number = rowLabel.length;\n", " \n", " var width = cellSizecol_number, // - margin.left - margin.right,\n", " height = cellSizerow_number;\n", " \n", " var maxval = JSON.parse(this.model.get(\"maxval\"));\n", " var minval = JSON.parse(this.model.get(\"minval\"));\n", " \n", " var colorScale = d3.scale.quantile()\n", " .domain([ minval , 0, maxval])\n", " .range(colors);\n", " \n", " this.svg = d3.select(this.el).append(\"svg\")\n", " .attr(\"width\", width + margin.left + margin.right)\n", " .attr(\"height\", height + margin.top + margin.bottom)\n", " .append(\"g\")\n", " .attr(\"transform\", \"translate(\" + margin.left + \",\" + margin.top + \")\");\n", " \n", " var svg = this.svg;\n", "\n", " var rowSortOrder=false;\n", " var colSortOrder=false;\n", " var rowLabels = svg.append(\"g\")\n", " .selectAll(\".rowLabelg\")\n", " .data(rowLabel)\n", " .enter()\n", " .append(\"text\")\n", " .text(function (d) { return d; })\n", " .attr(\"x\", 0)\n", " .attr(\"y\", function (d, i) { return hcrow.indexOf(i+1) cellSize; })\n", " .style(\"text-anchor\", \"end\")\n", " .attr(\"transform\", \"translate(-25,\" + cellSize / 1.5 + \")\")\n", " .attr(\"class\", function (d,i) { return \"rowLabel mono r\"+i;} )\n", " .on(\"mouseover\", function(d) {d3.select(this).classed(\"text-hover\",true);})\n", " .on(\"mouseout\" , function(d) {d3.select(this).classed(\"text-hover\",false);})\n", " .on(\"click\", function(d,i) {rowSortOrder=!rowSortOrder; sortbylabel(\"r\",i,rowSortOrder); this.$('#order').property(\"selectedIndex\", 4).node().focus();;});\n", "\n", " var colLabels = svg.append(\"g\")\n", " .selectAll(\".colLabelg\")\n", " .data(colLabel)\n", " .enter()\n", " .append(\"text\")\n", " .text(function (d) { return d; })\n", " .attr(\"x\", 0)\n", " .attr(\"y\", function (d, i) { return hccol.indexOf(i+1) * cellSize; })\n", " .style(\"text-anchor\", \"left\")\n", " .attr(\"transform\", \"translate(\"+cellSize/2 + \",-25) rotate (-90)\")\n", " .attr(\"class\", function (d,i) { return \"colLabel mono c\"+i;} )\n", " .on(\"mouseover\", function(d) {d3.select(this).classed(\"text-hover\",true);})\n", " .on(\"mouseout\" , function(d) {d3.select(this).classed(\"text-hover\",false);})\n", " .on(\"click\", function(d,i) {colSortOrder=!colSortOrder; sortbylabel(\"c\",i,colSortOrder); this.$('#order').property(\"selectedIndex\", 4).node().focus();;});\n", "\n", " var heatMap = svg.append(\"g\").attr(\"class\",\"g3\")\n", " .selectAll(\".cellg\")\n", " .data(data,function(d){return d.row+\":\"+d.col;})\n", " .enter()\n", " .append(\"rect\")\n", " .attr(\"x\", function(d) { return hccol.indexOf(d.col) * cellSize; })\n", " .attr(\"y\", function(d) { return hcrow.indexOf(d.row) * cellSize; })\n", " .attr(\"class\", function(d){return \"cell cell-border cr\"+(d.row-1)+\" cc\"+(d.col-1);})\n", " .attr(\"width\", cellSize)\n", " .attr(\"height\", cellSize)\n", " .style(\"fill\", function(d) { return colorScale(d.value); })\n", " /* .on(\"click\", function(d) {\n", " var rowtext=d3.select(\".r\"+(d.row-1));\n", " if(rowtext.classed(\"text-selected\")==false){\n", " rowtext.classed(\"text-selected\",true);\n", " }else{\n", " rowtext.classed(\"text-selected\",false);\n", " }\n", " })/\n", " .on(\"mouseover\", function(d){\n", " //highlight text\n", " d3.select(this).classed(\"cell-hover\",true);\n", " d3.selectAll(\".rowLabel\").classed(\"text-highlight\",function(r,ri){ return ri==(d.row-1);});\n", " d3.selectAll(\".colLabel\").classed(\"text-highlight\",function(c,ci){ return ci==(d.col-1);});\n", " \n", " //Update the tooltip position and value\n", " d3.select(\"#tooltip\")\n", " .style(\"left\", (d3.event.pageX+10) + \"px\")\n", " .style(\"top\", (d3.event.pageY-10) + \"px\")\n", " .select(\"#value\")\n", " .text(rowLabel[d.row-1]+\",\"+colLabel[d.col-1]+\"\\ndata:\"+d.value); \n", " //Show the tooltip\n", " d3.select(\"#tooltip\").classed(\"hidden\", false);\n", " })\n", " .on(\"mouseout\", function(){\n", " d3.select(this).classed(\"cell-hover\",false);\n", " d3.selectAll(\".rowLabel\").classed(\"text-highlight\",false);\n", " d3.selectAll(\".colLabel\").classed(\"text-highlight\",false);\n", " d3.select(\"#tooltip\").classed(\"hidden\", true);\n", " })\n", " ;\n", "\n", " var legend = svg.selectAll(\".legend\")\n", " .data(d3.range(Math.floor(minval), Math.ceil(maxval)))\n", " .enter().append(\"g\")\n", " .attr(\"class\", \"legend\");\n", " \n", " var color_factor = Math.floor(colors.length/(maxval-minval));\n", " console.log(height+(cellSize2));\n", " legend.append(\"rect\")\n", " .attr(\"x\", function(d, i) { return legendElementWidth * i; })\n", " .attr(\"y\", height+(cellSize2))\n", " .attr(\"width\", legendElementWidth)\n", " .attr(\"height\", cellSize)\n", " .style(\"fill\", function(d, i) { return colors[icolor_factor]; });\n", " \n", " legend.append(\"text\")\n", " .attr(\"class\", \"mono\")\n", " .text(function(d) { return d; })\n", " .attr(\"width\", legendElementWidth)\n", " .attr(\"x\", function(d, i) { return legendElementWidth * i; })\n", " .attr(\"y\", height + (cellSize4));\n", "\n", "// Change ordering of cells\n", "\n", " function sortbylabel(rORc,i,sortOrder){\n", " var t = svg.transition().duration(3000);\n", " var log2r=[];\n", " var sorted; // sorted is zero-based index\n", " d3.selectAll(\".c\"+rORc+i) \n", " .filter(function(ce){\n", " log2r.push(ce.value);\n", " })\n", " ;\n", " if(rORc==\"r\"){ // sort log2ratio of a gene\n", " sorted=d3.range(col_number).sort(function(a,b){ if(sortOrder){ return log2r[b]-log2r[a];}else{ return log2r[a]-log2r[b];}});\n", " t.selectAll(\".cell\")\n", " .attr(\"x\", function(d) { return sorted.indexOf(d.col-1) cellSize; })\n", " ;\n", " t.selectAll(\".colLabel\")\n", " .attr(\"y\", function (d, i) { return sorted.indexOf(i) * cellSize; })\n", " ;\n", " }else{ // sort log2ratio of a contrast\n", " sorted=d3.range(row_number).sort(function(a,b){if(sortOrder){ return log2r[b]-log2r[a];}else{ return log2r[a]-log2r[b];}});\n", " t.selectAll(\".cell\")\n", " .attr(\"y\", function(d) { return sorted.indexOf(d.row-1) * cellSize; })\n", " ;\n", " t.selectAll(\".rowLabel\")\n", " .attr(\"y\", function (d, i) { return sorted.indexOf(i) * cellSize; })\n", " ;\n", " }\n", " }\n", "\n", " //d3.select(\"#order\").on(\"change\",function(){\n", " this.$('#order').on(\"change\",function(){\n", " order(this.value);\n", " });\n", " \n", " function order(value){\n", " if(value==\"hclust\"){\n", " var t = svg.transition().duration(3000);\n", " t.selectAll(\".cell\")\n", " .attr(\"x\", function(d) { return hccol.indexOf(d.col) * cellSize; })\n", " .attr(\"y\", function(d) { return hcrow.indexOf(d.row) * cellSize; })\n", " ;\n", "\n", " t.selectAll(\".rowLabel\")\n", " .attr(\"y\", function (d, i) { return hcrow.indexOf(i+1) * cellSize; })\n", " ;\n", "\n", " t.selectAll(\".colLabel\")\n", " .attr(\"y\", function (d, i) { return hccol.indexOf(i+1) * cellSize; })\n", " ;\n", "\n", " }else if (value==\"probecontrast\"){\n", " var t = svg.transition().duration(3000);\n", " t.selectAll(\".cell\")\n", " .attr(\"x\", function(d) { return (d.col - 1) * cellSize; })\n", " .attr(\"y\", function(d) { return (d.row - 1) * cellSize; })\n", " ;\n", "\n", " t.selectAll(\".rowLabel\")\n", " .attr(\"y\", function (d, i) { return i * cellSize; })\n", " ;\n", "\n", " t.selectAll(\".colLabel\")\n", " .attr(\"y\", function (d, i) { return i * cellSize; })\n", " ;\n", "\n", " }else if (value==\"probe\"){\n", " var t = svg.transition().duration(3000);\n", " t.selectAll(\".cell\")\n", " .attr(\"y\", function(d) { return (d.row - 1) * cellSize; })\n", " ;\n", "\n", " t.selectAll(\".rowLabel\")\n", " .attr(\"y\", function (d, i) { return i * cellSize; })\n", " ;\n", " }else if (value==\"contrast\"){\n", " var t = svg.transition().duration(3000);\n", " t.selectAll(\".cell\")\n", " .attr(\"x\", function(d) { return (d.col - 1) * cellSize; })\n", " ;\n", " t.selectAll(\".colLabel\")\n", " .attr(\"y\", function (d, i) { return i * cellSize; })\n", " ;\n", " }\n", " }\n", " // \n", " var sa=d3.select(\".g3\")\n", " .on(\"mousedown\", function() {\n", " if( !d3.event.altKey) {\n", " d3.selectAll(\".cell-selected\").classed(\"cell-selected\",false);\n", " d3.selectAll(\".rowLabel\").classed(\"text-selected\",false);\n", " d3.selectAll(\".colLabel\").classed(\"text-selected\",false);\n", " }\n", " var p = d3.mouse(this);\n", " sa.append(\"rect\")\n", " .attr({\n", " rx : 0,\n", " ry : 0,\n", " class : \"selection\",\n", " x : p[0],\n", " y : p[1],\n", " width : 1,\n", " height : 1\n", " })\n", " })\n", " .on(\"mousemove\", function() {\n", " var s = sa.select(\"rect.selection\");\n", " \n", " if(!s.empty()) {\n", " var p = d3.mouse(this),\n", " d = {\n", " x : parseInt(s.attr(\"x\"), 10),\n", " y : parseInt(s.attr(\"y\"), 10),\n", " width : parseInt(s.attr(\"width\"), 10),\n", " height : parseInt(s.attr(\"height\"), 10)\n", " },\n", " move = {\n", " x : p[0] - d.x,\n", " y : p[1] - d.y\n", " }\n", " ;\n", " \n", " if(move.x < 1 \|\| (move.x2<d.width)) {\n", " d.x = p[0];\n", " d.width -= move.x;\n", " } else {\n", " d.width = move.x; \n", " }\n", " \n", " if(move.y < 1 \|\| (move.y2<d.height)) {\n", " d.y = p[1];\n", " d.height -= move.y;\n", " } else {\n", " d.height = move.y; \n", " }\n", " s.attr(d);\n", " \n", " // deselect all temporary selected state objects\n", " d3.selectAll('.cell-selection.cell-selected').classed(\"cell-selected\", false);\n", " d3.selectAll(\".text-selection.text-selected\").classed(\"text-selected\",false);\n", "\n", " d3.selectAll('.cell').filter(function(cell_d, i) {\n", " if(\n", " !d3.select(this).classed(\"cell-selected\") && \n", " // inner circle inside selection frame\n", " (this.x.baseVal.value)+cellSize >= d.x && (this.x.baseVal.value)<=d.x+d.width && \n", " (this.y.baseVal.value)+cellSize >= d.y && (this.y.baseVal.value)<=d.y+d.height\n", " ) {\n", " \n", " d3.select(this)\n", " .classed(\"cell-selection\", true)\n", " .classed(\"cell-selected\", true);\n", "\n", " d3.select(\".r\"+(cell_d.row-1))\n", " .classed(\"text-selection\",true)\n", " .classed(\"text-selected\",true);\n", "\n", " d3.select(\".c\"+(cell_d.col-1))\n", " .classed(\"text-selection\",true)\n", " .classed(\"text-selected\",true);\n", " }\n", " });\n", " }\n", " })\n", " .on(\"mouseup\", function() {\n", " // remove selection frame\n", " sa.selectAll(\"rect.selection\").remove();\n", " \n", " // remove temporary selection marker class\n", " d3.selectAll('.cell-selection').classed(\"cell-selection\", false);\n", " d3.selectAll(\".text-selection\").classed(\"text-selection\",false);\n", " })\n", " .on(\"mouseout\", function() {\n", " if(d3.event.relatedTarget.tagName=='html') {\n", " // remove selection frame\n", " sa.selectAll(\"rect.selection\").remove();\n", " // remove temporary selection marker class\n", " d3.selectAll('.cell-selection').classed(\"cell-selection\", false);\n", " d3.selectAll(\".rowLabel\").classed(\"text-selected\",false);\n", " d3.selectAll(\".colLabel\").classed(\"text-selected\",false);\n", " }\n", " });\n", "\n", " \n", " }\n", " });\n", " WidgetManager.register_widget_view(\"HeatmapView\", HeatmapView);\n", "});" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Javascript at 0x107e50150>" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
wcmckee/wcmckee.com	posts/artcgallery.ipynb	1	13903	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "<h3>artcontrol gallery</h3>\n", "\n", "Create gallery for artcontrol artwork. \n", "\n", "Uses Year / Month / Day format.\n", "\n", "Create blog post for each day there is a post.\n", "\n", "It will need to list the files for that day and create a markdown file in posts that contains the artwork. Name of art then followed by each pience of artwork -line, bw, color. \n", "\n", "write a message about each piece of artwork. \n", "\n", "need to read a config file which lists the blog to update. \n", "\n" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [], "source": [ "import os\n", "import arrow\n", "import getpass\n", "import configparser\n", "import time\n", "import subprocess\n", "#import boto" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "myusr = getpass.getuser()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "raw = arrow.now()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "yraw = raw.strftime(\"%Y\")\n", "mntaw = raw.strftime(\"%m\")\n", "dytaw = raw.strftime(\"%d\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#dytaw = str(28)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "fulda = yraw + '/' + mntaw + '/' + dytaw" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "fultim = fulda + ' ' + raw.strftime('%H:%M:%S')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "arnow = arrow.now()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "curyr = arnow.strftime('%Y')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "curmon = arnow.strftime('%m')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "curday = arnow.strftime('%d')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#curday = '28'" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "#config = configparser.ConfigParser()" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": true }, "outputs": [], "source": [ "config = configparser.RawConfigParser()" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": true }, "outputs": [], "source": [ "artpath = '/home/{}/.config/artcgallery.ini'.format(myusr)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/home/wcm/.config/artcgallery.ini'" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "artpath" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#config.add_section('test')\n", "##config.set('test', 'defaultpath', '15')\n", "#config.set('test', 'postname', 'true')\n", "#config.set('test', 'imgorigin', 'true') \n", " # Writing our configuration file to 'example.cfg'\n", "#with open(artpath, 'w') as configfile:\n", "# config.write(configfile)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "try:\n", " config.add_section('test')\n", " config.set('test', 'defaultpath', '/home/wcm/artctrl/')\n", " config.set('test', 'postname', 'willtest')\n", " config.set('test', 'imgorigin', '/home/wcm/aug17/') \n", " # Writing our configuration file to 'example.cfg'\n", " with open(artpath, 'w') as configfile:\n", " config.write(configfile)\n", "except configparser.DuplicateSectionError:\n", " print('config exists')" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['/home/wcm/.config/artcgallery.ini']" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "config.read(artpath)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "config = configparser.RawConfigParser()\n", "config.read('/home/{}/.config/artcgallery.ini'.format(myusr))\n", "\n", "# getfloat() raises an exception if the value is not a float\n", "# getint() and getboolean() also do this for their respective types\n", "defpath = (config.get('test', 'defaultpath'))\n", "nameofblogpost = (config.get('test', 'postname'))\n", "imgorigin = config.get('test', 'imgorigin')" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#artctrlpath = '/home/{}/git/artctrl-stage'.format(myusr)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "galerdir = ('{}/galleries/'.format(defpath))" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "pathdir = ('{}/posts/'.format(defpath))" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [], "source": [ "oslispa = os.listdir(pathdir)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [], "source": [ "galdir = os.listdir(galerdir)" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "if curyr in galdir:\n", " pass\n", "else:\n", " os.mkdir(galerdir + curyr)" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "mondir = os.listdir(galerdir + curyr)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "if curmon in mondir:\n", " pass\n", "else:\n", " os.mkdir(galerdir + curyr + '/' + curmon)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "daydir = os.listdir(galerdir + curyr + '/' + curmon )" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fridpath = ('{}{}/{}/{}').format(galerdir, curyr, curmon, curday)" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fulldaypath = (galerdir + curyr + '/' + curmon + '/' + curday)" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [], "source": [ "if curday in daydir:\n", " pass\n", "else:\n", " os.mkdir(galerdir + curyr + '/' + curmon + '/' + curday)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [], "source": [ "daypost = ('{}/posts/{}.md'.format(defpath, nameofblogpost))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": true }, "outputs": [], "source": [ "daymetapost = ('{}/posts/{}.meta'.format(defpath, nameofblogpost))" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": true }, "outputs": [], "source": [ "daymdpost = ('{}/posts/{}.md'.format(defpath, nameofblogpost))" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "subprocess.call('scp -r {}/.png {}'.format(imgorigin, fridpath), shell=True)" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": true }, "outputs": [], "source": [ "todayart = os.listdir(fulldaypath)" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": true }, "outputs": [], "source": [ "todayart.sort()" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [], "source": [ "gallerpath = ('/galleries/{}/{}/{}/'.format(curyr, curmon, curday))" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": true }, "outputs": [], "source": [ "urlpatz = 'http://artctrl.me'" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": true }, "outputs": [], "source": [ "patchurl = urlpatz + gallerpath" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "post tags: gimp, art\n", "thoughts on: aug17-acct101.png \n", "thoughts on: aug17-armsquid.png \n", "thoughts on: aug17-centerportal.png \n", "thoughts on: aug17-claws.png \n", "thoughts on: aug17-feelstars.png \n", "thoughts on: aug17-lovefeet.png \n", "thoughts on: aug17-pigteeth.png \n", "thoughts on: aug17-tentsliverl.png \n", "thoughts on: august17-milfscape.png \n", "thoughts on: august17-monsterchars.png \n", "thoughts on: august17-portraitboom.png \n", "thoughts on: august17-teethscape.png \n" ] } ], "source": [ "if nameofblogpost not in oslispa:\n", " tagblog = input('post tags: ')\n", " \n", " with open(daymetapost, 'w') as daympo: \n", " daympo.write('.. title: {}\\n.. slug: {}\\n.. date: {}\\n.. tags: {}\\n.. link:\\n.. description:\\n.. type: text'.format(nameofblogpost, nameofblogpost, fultim, tagblog))\n", " \n", " with open(daymdpost, 'w') as daymark:\n", " for toar in todayart:\n", " thoughon = input('thoughts on: {} '.format(toar))\n", " daymark.write('![{}]({}{})\\n\\n{}\\n\\n'.format(toar.replace('.png', ''), gallerpath, toar, thoughon))\n", " \n", " #api.update_with_media('{}{}/{}'.format(gifpat, namofgifsea, ranlocgif), status='Started typing script {} {}'.format(blognam, jointag))\n", " \n", " \n", "else:\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "check to see if that blog post name already excist, if so error and ask for something more unique! \n", "\n", "input art piece writers. Shows the art then asks for input, appending the input below the artwork. Give a name for the art that is appended above. " ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [], "source": [ "os.chdir(defpath)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "subprocess.call('nikola build', shell=True)" ] }, { "cell_type": "code", "execution_count": 139, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "256" ] }, "execution_count": 139, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#os.system('aws s3 sync {} s3://artctrl-staging'.format(defpath))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#subprocess.call('scp {} s3://artctrl-staging'.format(daypost), shell=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#os.system('scp /media/pi/UNTITED/artcontrolme/posts/' + nameofblogpost + '. [email protected]:/home/wcmckee/artcontrolme/posts/')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#os.system('scp /media/pi/UNTITED/artcontrolme/galleries/' + fridpath + ' ' + nameofblogpost + '.* [email protected]:/home/wcmckee/artcontrolme/posts/')" ] } ], "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 }	mit
LucaCanali/Miscellaneous	Oracle_Jupyter/Oracle_Jupyter_oracledb_pandas.ipynb	1	30244	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Oracle and Python with oracledb\n", "This is an example of how to query Oracle from Python\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup and prerequisites" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is how you can setup an Oracle instance for testing using a docker image for oracle-xe \n", "\n", "1. run oracle xe on a container from gvenzl dockerhub repo https://github.com/gvenzl/oci-oracle-xe\n", "\n", "`docker run -d --name mydb1 -e ORACLE_PASSWORD=oracle -p 1521:1521 gvenzl/oracle-xe:latest # or use :slim`\n", "\n", "wait till the DB is started, check logs at:\n", "`docker logs -f mydb1`\n", "\n", "2. Install the scott/tiger schema with the emp table in PDB xepdb1:\n", "```\n", "docker exec -it mydb1 /bin/bash\n", "sed -e s=SCOTT/tiger=SCOTT/tiger@xepdb1= -e s/OFF/ON/ /opt/oracle/product/21c/dbhomeXE/rdbms/admin/utlsampl.sql > script.sql\n", "sqlplus system/oracle@xepdb1 <<EOF\n", "@script.sql\n", "EOF\n", "exit\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "oracledb library: This uses oracledb to connect to oracle, so no need to install the Oracle client. \n", "Note: oracledb can also work with the oracle client as cx_Oracle did,\n", "see documentation for details." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Query Oracle from Python using the oracledb library" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# connect to Oracle using oracledb\n", "# !pip install oracledb\n", "\n", "import oracledb\n", "\n", "db_user = 'scott'\n", "db_connect_string = 'localhost:1521/XEPDB1'\n", "db_pass = 'tiger'\n", "\n", "# To avoid storig connection passwords use getpas or db_config\n", "# db_connect_string = 'dbserver:1521/orcl.mydomain.com'\n", "# import getpass\n", "# db_pass = getpass.getpass()\n", "\n", "ora_conn = oracledb.connect(user=db_user, password=db_pass, dsn=db_connect_string)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[('SMITH', 800.0), ('ALLEN', 1600.0), ('WARD', 1250.0), ('JONES', 2975.0), ('MARTIN', 1250.0), ('BLAKE', 2850.0), ('CLARK', 2450.0), ('SCOTT', 3000.0), ('KING', 5000.0), ('TURNER', 1500.0), ('ADAMS', 1100.0), ('JAMES', 950.0), ('FORD', 3000.0), ('MILLER', 1300.0)]\n" ] } ], "source": [ "# open a cursor, run a query and fetch the results\n", "\n", "cursor = ora_conn.cursor()\n", "cursor.execute('select ename, sal from emp')\n", "res = cursor.fetchall()\n", "cursor.close()\n", "\n", "print(res)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## oracledb integration with Pandas" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "# query Oracle using ora_conn and put the result into a pandas Dataframe\n", "df_ora = pd.read_sql('select * from emp', con=ora_conn) \n", "df_ora" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Use of bind variables" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df_ora = pd.read_sql('select * from emp where empno=:myempno', params={\"myempno\":7839}, \n", " con=ora_conn) \n", "df_ora" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic visualization" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt \n", "plt.style.use('seaborn-darkgrid')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df_ora = pd.read_sql('select ename \"Name\", sal \"Salary\" from emp', con=ora_conn) \n", "\n", "ora_conn.close()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x432 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_ora.plot(x='Name', y='Salary', title='Salary details, from Oracle demo table', \n", " figsize=(10, 6), kind='bar', color='blue');" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 1 }	apache-2.0
dotsdl/seaborn	examples/timeseries_plots.ipynb	1	1041361	{ "metadata": { "name": "", "signature": "sha256:70fb0a281cfd361668b53ed3350722c25b2fa3ed97091ca6c1d8e6d00c61c390" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Plotting statistical timeseries data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook focuses on the `tsplot` function, which can be used to plot statistical timeseries data. Timeseries data consists of values for some dependent variable that are observed at several timepoints. In general, these data are thought of as realizations from some continuous process, so the `tsplot` function makess the visuzalization of that process simple (though not strictly enforced). Importantly, the function is concerned with plotting statistical timecourses: the expected dataset is additionally structured into sets of observations for several sampling units, such as subjects or neurons. In the presentation of these data, the unit dimension is frequently collapsed across to plot some measure of central tendency along with a representation of the variability introduced by sampling and measurement error. `tsplot` is capable of representing that variabilty with several sophisticated methods that are appropriate in different circumstances." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "np.random.seed(9221999)\n", "import pandas as pd\n", "from scipy import stats, optimize\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "sns.set(palette=\"Set2\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Specifying input data with multidimensional arrays" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll use several sets of fake data to demonstrate the `tsplot` functionality. The simplest dataset will be noisy observations from an underlying sine wave model." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def sine_wave(n_x, obs_err_sd=1.5, tp_err_sd=.3):\n", " x = np.linspace(0, (n_x - 1) / 2, n_x)\n", " y = np.sin(x) + np.random.normal(0, obs_err_sd) + np.random.normal(0, tp_err_sd, n_x)\n", " return y" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "sines = np.array([sine_wave(31) for _ in range(20)])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "`tsplot` can accept input data from one of two structures. In the first, as is the case here, a rectangular array-type object with timepoints in the columns and sampling units in the rows can be passed." ] }, { "cell_type": "code", "collapsed": false, "input": [ "sns.tsplot(sines);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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G3t9jLMqui6pBzWbEobSD4ZwS150mvrfzEHlFx1eWrsc27hY1A2/NnMPHq2ex7jZxux0F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\n2Rbg2g5R26VM41PZru3wQPYQf3bhOM9OnSHrpRj0mqsA14y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"text": [ "<matplotlib.figure.Figure at 0x10775d490>" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's create the second example dataset. Here we'll add an additional dimension: `condition`. This dataset will consist of three random walks with different probabilities of increasing position at each timepoint." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def random_walk(n, start=0, p_inc=.2):\n", " return start + np.cumsum(np.random.uniform(size=n) < p_inc)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "starts = np.random.choice(range(4), 10)\n", "probs = [.1, .3, .5]\n", "walks = np.dstack([[random_walk(15, s, p) for s in starts] for p in probs])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the input data are a three dimensional array, the third dimension is assumed to correspond with condition and the traces are separated out and separately colored." ] }, { "cell_type": "code", "collapsed": false, "input": [ "sns.tsplot(walks);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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+b2oQK3UBerGlE8aIMNbVZ5RYvn18wJNXbQBWlyrcvd2YitBzNjuOVKsWtC3l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\neCNL+O3BOjeTzWHh1bzt8+HGItdmL1F3TlcB0eg4QAd7eD+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"text": [ "<matplotlib.figure.Figure at 0x1094f3f50>" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although using arrays as input objects allows for a very compact specification of a relatively complex plot, they lack semantic information about what variables are represented on each dimension. You can, however, pass that information as seen below. If you use `Series` objects, the names will be used to label the axes and legend. However, any sequence will work." ] }, { "cell_type": "code", "collapsed": false, "input": [ "step = pd.Series(range(1, 16), name=\"step\")\n", "speed = pd.Series([\"slow\", \"average\", \"fast\"], name=\"speed\")\n", "sns.tsplot(walks, time=step, condition=speed, value=\"position\");" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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I2gvILqWTpnU1gL5SqWQAfYAT8vMLIYSIkNaapuPRtH08X2GaJmDQ6fVkrudzeqrOiRUF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\nCDEBJGALIYQQE0ACthBCCDEB/j8rFgB9Oyzh5gAAAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x1097c8950>" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "For single-condition data, you can set the color of the plot with any valid matplotlib color spec." ] }, { "cell_type": "code", "collapsed": false, "input": [ "sns.tsplot(sines, color=\"indianred\");" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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DTZJ1/iygFNInT3etJcDjca/z4fgmmBs3Awx9r7BpeZTCMrDGWpb1WwbaxQnI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\nhBGLVReemcaRoO15HlbfYkmbu8BJsBZCiBCZWnhmxGK4TgWrt1fyfQv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"text": [ "<matplotlib.figure.Figure at 0x1097bef50>" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "For multi-condition data, you can additionally use any valid seaborn palette spec." ] }, { "cell_type": "code", "collapsed": false, "input": [ "sns.tsplot(walks, color=\"husl\");" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Fb553rLZgLAVfOhTw4bn4V52d89J7CzqecBCEtkU5KtGIqjRcgwC6HatG7Zbz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\ng6TngfiPn1nlVH6Z7IZEqXcxBupNVKv1wADcijV1c51Vc4V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"text": [ "<matplotlib.figure.Figure at 0x10a666610>" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you are providing condition information, you can further use a dictionary that maps condition names to colors." ] }, { "cell_type": "code", "collapsed": false, "input": [ "color_map = dict(slow=\"indianred\", average=\"darkseagreen\", fast=\"steelblue\")\n", "sns.tsplot(walks, condition=speed, color=color_map);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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8bc+kFBSdZ23VG4uYJKMRupIRElG5UhAWrTUlr4Dt2xiYGKz//87VfPeGgEng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\nJFeRqE1UabkAXamgwsKBFiznLCiVVedj4+gHCJ+o12s86N/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"text": [ "<matplotlib.figure.Figure at 0x10a737650>" ] } ], "prompt_number": 12 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Specifying input data with long-form DataFrames" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is also a second, substantially different way to pass data into `tsplot`. If you use a `DataFrame`, it is expected to be in \"long-form\" (\"tidy\") organization with a single column containing all observations of the dependent variable and other columns containing information about the time, sampling unit, and (optionally) condition of each observation.\n", "\n", "Let's make a third dataset with two gamma PDF traces in this format." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def gamma_pdf(x, shape, coef, obs_err_sd=.1, tp_err_sd=.001): \n", " y = stats.gamma(shape).pdf(x) * coef\n", " y += np.random.normal(0, obs_err_sd, 1)\n", " y += np.random.normal(0, tp_err_sd, len(x))\n", " return y" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "gammas = []\n", "n_units = 20\n", "params = [(5, 1), (8, -.5)]\n", "x = np.linspace(0, 15, 31)\n", "for s in range(n_units):\n", " for p, (shape, coef) in enumerate(params):\n", " y = gamma_pdf(x, shape, coef)\n", " gammas.append(pd.DataFrame(dict(condition=[[\"pos\", \"neg\"][p]] * len(x),\n", " subj=[\"subj%d\" % s] * len(x),\n", " time=x * 2,\n", " BOLD=y), dtype=np.float))\n", "gammas = pd.concat(gammas)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "When using a DataFrame, you must provide the names for each of the relevant columns as arguments to `time=`, `unit=`, etc." ] }, { "cell_type": "code", "collapsed": false, "input": [ "sns.tsplot(gammas, time=\"time\", unit=\"subj\", condition=\"condition\", value=\"BOLD\");" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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cULJt+0kqk/l+0bbt99m2/aH1HLMB7dz2as3+LwZlLhbn2JnsrWsnvyDUde8O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"text": [ "<matplotlib.figure.Figure at 0x10a6e66d0>" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although this is somewhat more verbose, it produces a plot that has rich semantic information with no additional effort. Everthing else you've learned so far works with this style, so you can specify the colors in any way you please." ] }, { "cell_type": "code", "collapsed": false, "input": [ "color_map = dict(pos=\"indianred\", neg=\"steelblue\")\n", "ax = sns.tsplot(gammas, time=\"time\", unit=\"subj\", condition=\"condition\", value=\"BOLD\", color=color_map)\n", "ax.set_xlabel(\"time (seconds)\");" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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D4Isuzf8r+d2rj7xP9ZP3qvWWIgB4CrgFuMM0zbcC9dRBbeQYRkdlLLR8eBSc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\n/5pebtjczXWbOhe1Dq5UPbVaWS3JgDLAwtphYUM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fCOTmHVQ270BMjAZC4K03CD3xMN4L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"text": [ "<matplotlib.figure.Figure at 0x10aa47ad0>" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Becaues the confidence intervals are generated with a bootstrapping procedure, you can pass in any arbitrary estimator to collapse over the unit dimension. For instance, you may want to use the median instead of the mean." ] }, { "cell_type": "code", "collapsed": false, "input": [ "sns.tsplot(sines, estimator=np.median, color=\"#F08080\");" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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yGf9qkuUS1JWPwZEo9PMvtj5Oa2DaY5mWXyIRYGHRl8Hmk6fASkHdux0oRHS0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\ngbUnwOa6KTkJ6150nI7NC7xAD++btoFnnm67+uB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ud3gQsvDox/BBmFTmamFD5JLmZgmO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\nRK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x10aa83690>" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, the 68% confidence interval is plotted, which corresponds to the standard error of the estimator. However, it's easy to change this." ] }, { "cell_type": "code", "collapsed": false, "input": [ "pal = sns.dark_palette(\"cornflowerblue\", 3)\n", "sns.tsplot(walks, ci=95, color=pal);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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0iFXHC2PDRhmE3ijIWto0fHDL7oFnkvDOvMvL57Ut5XEwxoap50c4jiHpGFKu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\nkY05C254PrNzZaaawXd5pd7+uxP5TDvrLeRSezo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"text": [ "<matplotlib.figure.Figure at 0x109435350>" ] } ], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to change other aesthetics of the plot, you can either pass additional keyword arguments (which get passed to the main call to `plt.plot()` that draws the central tendency, or you can provide a dictionary to the `err_kws` parameter, and those arguments will be used for the error plot." ] }, { "cell_type": "code", "collapsed": false, "input": [ "sns.tsplot(gammas, time=\"time\", unit=\"subj\", condition=\"condition\", value=\"BOLD\",\n", " ci=95, color=\"muted\", linewidth=2.5, err_kws={\"alpha\": .3});" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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/WBqGYqA7zkB3OLZeqflMFzyKlYBSNdwRcSULI62W63dnGM26HBmuMjxV4weH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\n", "text": [ "<matplotlib.figure.Figure at 0x10aa37690>" ] } ], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The entire plot is constrained within a single axis, and you can provide an existing axis to plot into." ] }, { "cell_type": "code", "collapsed": false, "input": [ "f, (ax1, ax2) = plt.subplots(2, 1, sharex=True)\n", "c1, c2 = sns.color_palette(\"Dark2\", 2)\n", "sns.tsplot(sines, color=c1, ax=ax1)\n", "sns.tsplot(-sines, color=c2, ax=ax2);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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FWkU9d3c8y+cOPszb1p7D29ads6i/sZJXfF3Xf65p2vo5DvmB0Ky/R4DT9ng8lnylNqwZ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"text": [ "<matplotlib.figure.Figure at 0x10a6b5590>" ] } ], "prompt_number": 20 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Different approaches to representing estimator variability" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because of measurement and sampling error, the mean (or other aggregate value) at each time point is only an estimate of the true value. It is important to communicate this variability in a way that accurately represents the precision of your estimate and facilitates comparisons, for instance, between different conditions or against a baseline value. `tsplot` can visualize the uncertainty in a variety of ways. Each has advantages and disadvantages, so choose the approach (or set of approaches) that is best suited to what you want to communicate with each plot." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Visualizing uncertainty at each observation with error bars" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default `tsplot` draws confidence bands, as they help emphaisze the underyling trend in the data. However, a somewhat more common approach is to draw an error bar with the width of some confidence interval at the point of each observation:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "sns.tsplot(sines, err_style=\"ci_bars\");" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Dio1bqaZz5aViztOFvgEMoC4YImCYNXGOZ6/jYpSarOPA3DM6N1ED/Klt23EA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\nRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x10a4c2a50>" ] } ], "prompt_number": 21 }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's also not actually necessary to plot the linear interpolation between the central tendency estimates. This is arguably a more pure approach, although it sacrifices some visual immediacy." ] }, { "cell_type": "code", "collapsed": false, "input": [ "ax = sns.tsplot(sines, err_style=\"ci_bars\", interpolate=False);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x10b0c9510>" ] } ], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perhaps the optimal approach is to fit a statistical model to the data and then plot that on top of point-estimates and confidence bars." ] }, { "cell_type": "code", "collapsed": false, "input": [ "ax = sns.tsplot(sines, err_style=\"ci_bars\", interpolate=False)\n", "xmin, xmax = ax.get_xlim()\n", "x = np.linspace(xmin, xmax, sines.shape[1])\n", "out, _ = optimize.leastsq(lambda p: sines.mean(0) - (np.sin(x / p[1]) + p[0]), (0, 2))\n", "a, b = out\n", "xx = np.linspace(xmin, xmax, 100)\n", "plt.plot(xx, np.sin(xx / b) + a, c=\"#444444\");" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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zsWfPTgIDA8nLW6R1OG7nuPkYrpw97hLz9crjAD5Xxh7MaJ5rCO9RVLQfq9XK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\nVCIGsyRJJfI/aln7fJiMl1EAAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x10b460b10>" ] } ], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "A problem with the error bars style is that it can become confusing when you have multiple traces on the same plot, as it is difficult to visualize the extent of the overlap." ] }, { "cell_type": "code", "collapsed": false, "input": [ "sns.tsplot(walks, err_style=\"ci_bars\", ci=95);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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M4zg/CDwD/JrjOJ91XffOx70gk2nsPpeNeP4RO7hAGta533v82zOr/MHrEyws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\nRK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x10b0c9410>" ] } ], "prompt_number": 24 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Drawing comparisons with overlapping error bands" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This kind of comparison lends itself well to the tranlucent error bands, where it is easy to see the overlap in as the bands get darker." ] }, { "cell_type": "code", "collapsed": false, "input": [ "sns.tsplot(walks, err_style=\"ci_band\", ci=95);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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iTcMqOMkc3zza4/HLDgDLC1Xu3GpSr+oq67x47wkCQ7EQUC4WKRWDt77A81mC\nOXiFSXoYOPGdX3V2TNyh/M0fUnx6D4Ds8k/ztpTV2SE/spwGsZpIUWLpRhlmQlfB3guPnh/w7eM9\nrBMa1Xw84VJTxxOetaNVbzAI30IhfPM3QZZAlmB8islSsCWCNB7adqcCnKW0+S8pff//YFyGm13O\n21IurA77kb1Gg1hNFBHhoJeRpm5iK6Lz8YS7dCNLIQy4uzbPjcsz2pbyDPl+E4xSIaBYKFAuhUcv\n6PrtJcnSPHRtCs7mq95jK95J78420kQovPyO8v3fJ4j28aUqyZ1fkq3+7GStQC+IBrGaGM552v2t\n6EkM4U4v4+vNFtutvC3ljcsNfnpjXscTnoHDVW8hzLecK6UwX/V6B2mE6Wb5HV+X5p2ujHn9G7pu\nOQ+P93nxW9LFJF2CpEvh2X0Ku48RE5Cu/Sskn/0VKI5u9zgNYjURkjSfGTyJW9GZ9Xz7eI9Hzw8Q\ngcW5fDzhbF3HE34K6b9gK4SGcjGkHILJIozLoJPloesdxoSvby9r6F4Mm2GSDkHSzc/Zkw4m6fV/\n3en/v24ewm9796U14i/+JtJYuPCHflIaxGrsdXopUeomriBLRHj8ssM3j/ZI++MJv7jVZGVBxxOe\nxvGz3gKWskkpGZd/w49TjPi8h/Pgc2sg0G+RZ0oEk0aYtJtXkfdXsObYj8GvXfb+DxWWkEodV28i\nlQZSqiHlOr7SwNeb+PkrF/SkPp3+K1Njy4uw30lxzk9cCO/sx9x7uMtBNyMMDOs357l1dZZwArfc\nz5N3nsBnFCWjZByVwBLENg/d460LTTCSZ4fjyPT28a++ptRqvR6scX/1Kv6d7yuAlGr42jxSrufB\n2v/vm78e9pWjs6RBrMZSmuVb0TBZW9G92HJ/s8WLnR4A15brrN+Yp6JtKT/Me7ApkqUEYimb/o9C\ncLSdLPQHJ2jonimXUXj5HcUnv6aw8xgPHD+RlSDMQ3Ru5Z3BKuU6Uqqd2YzfcaJf3WrsHB/WMCms\n8zx40ubBszbeC/MzJe6uLTA/M7oFJkPlXX5VyFnwWf+/DgHqpZBa+fAF2uSsmkaOCEF7i+KTX1N8\ndr8/Ixls8xqlz39BxxwFLYWyXuN6Dw1iNTZEhP1uSmb9xFzVERGebXfZeLRHnDrKpZAvbs5zdUnb\nUg44BzbOw9ZZjM/yID52niteKBdCGhUzcccUIyeNKD67T/HJrwkPtgHw5TrpjV+QrX6J1JtUmjVc\nqzfkBzrstMVrAAAgAElEQVQ+NIjVWLDWsd/Nt6In5Rvt3kHCvYct9g4SAgOfr85xe3V20CJxKrns\n2ErX5gU77ymiEhEKBho1Q3GaP2/nTYTw1SOKT7+i8OI7jDjEBGQrn5Otfom7dGsqt5TPigaxGnlx\nYun0u2RNgji1bDza4+lWPp7w8mI+nrBWmaIvRxFwKWQZZrC1bPNz3o8oohIRjIGZsqFS1AA4L6a3\nT/HpVxSffEUQHwDg6gtk17/EXr1zoaMCJ9kUfeWrcTNpXbKcFzaftfnu8T7OCzP1InfXFlicm/Dx\nhOIhS/NrQpJhbH9rGV6/k2sC+IhVrXihWjTUy5NVqDcynB0UXoU7P2AACYukq1+SrX6ZXwvSz/uZ\n0iBWI2mSumSJCC93I+5vtujFllIh4M5ak+srjckMEpdhkggf9Aj2D/rnuZ/eiUq8UCoYZmrBxBxP\njJJgf4vik1/9qPAqW/0Se/mnE3VdaNRoEKuRM0ldsg66KfcettjZjzEGbl2d4SfX5ydrPKF4SGOM\nTfK+y95BEGJsf6X/iZ2oDs+B6zVDSc+Bz1YaUXx+n+LjdxdeqfOnQaxGyqR0yUozx7/41Qu+edQC\nYKlZ4c6tBRq1CVlVOItJe/kZr0v6Z7n9v7MzagF5eA7cKBuqeg58dkQId37I7/y+/A7jDwuvPiO7\n9iVuaU0Lry6YBrEaCZPSJct74YcXB3z7eJ/MeurVAnduLbC8MObjCftj/kwWvbbqBc6l97KeA589\nE7UpPvmK4tOvCKI20C+8Wv0Se00Lr4ZJg1gN3aR0ydrei/j6QYtOlFEIDX/h7jLLc5XxvfPsHSTR\n0Zaz4eic95wGH3gvlAuGRsUQ6jb0pxsUXn1FuPNIC69GlAaxGqpJ6JLVjfLxhFu7+XjC6yv5eMLL\nKzO0xqmpwWDVm5/3Iv2mGXD+W5UiBAZm9Rz404kQHGz3O159jckOC6+uHiu80sldo0SDWA2FiNDu\npqRj3CUrs57vn+zz8FkbEViYLXN3bYHZxhh9kzuct5sl+di/QS9mjkL4vIlQKxtqeg58ei4j3HlM\nYfshhe1NgmgfAF+qka79xbzwagzGAU6EfKjFuydbvIUGsbpw494lS0R4stVl41GLNPNUyyFf3Gpy\nebE2HlvrWZKPorMp+Oxo1J8JeOtg13MiIlQKhkY5GI/P24gx3b1+8D4k3H2M6d/NlkKZ7PJPya7e\nwS3d0vnJF0UECQx+5hLF/+iftE7yrhrE6kKNe5es3XbMvQct2t2UMDD89MYca1dnR/s887VVb79l\n5KDQ6uK/BXgvlML8HHiq23melLOEraeD8A26R9/r3cwl7NIabmkNN39Fw/eCiTikMofU50915q5B\nrC7EoEtW5sYyhKMkH0/4/FV+5nt1qc76zXmqoziesN/JytgEk/VXvYfXi4y5uC3nHz2u/DrSfMVQ\n0m3oj2KiNoXtTcLthxR2fshfSJEXXGXLn+GW1rBLa0h1ZsiPdDqJeKRURRqLn/TiZwS/i6hJ81qX\nrDELYec8D562+f5pPp5wrpGPJ2zOjsh4wneMA3ytk9UorI5EqBUNtbIG8Ht5R9h6lgfv9kPCzs7g\nTa6+MAhe17z6ek9udbG8RwpFfH0JSp/eolb/JtW5SjLHQXf8umSJCM9f9bi/2crHExZD1j+b59ow\nxxP+aBxgf1DCa5OJGI3g7dNz4A8zcYdwezPfct55lJ/dAxKE2H7w2qVbSG1+yI9UIYIY8I0mVGfP\n7MNqEKtzISK0Owntbjp2BVn7nXw8Yaudjye8fW2Wz1fnKFxkW8ofjQO0/XGAwRuhO5pfwuKFQgDN\nqp4D/4h4gr0XR4VW7a3Bm3x1juza3XzVu7AK4YR0YpsAIh6pzPTPgc/23/RofhWrsSUi9GJLnFrm\ng/Fqzp+kjo1HezzZ6gCwslDli1tN6tVz/Gb42jhAmw9MeOs4wCGe7Z6A90IxhHrVsDhToGU1hAFM\n0iN81V/1vnqEyWIAxATYxRuDla/Um9pgY9R4j5TK+PriuQ2+0CBWZ0JE6EYZceoBGautaO+Fzef5\neELrhEatyN21Jpfmz7gt5eE4wH7YGpeBs/nbTjEOcJSIF4qhoaYNOXIiyM4zSt9/nVc4778Y3Azz\nlQbZ5Z/n572LN7S5xqgSjwQF/NwlKJ1vi9pTB/H6+voy8CfAv7axsfHN2T0kNU68CL0oI07zO4x5\n+I5HAIsIW62Irx/m4wmLhYCf3W5y/XLj01fy4vFxBL32scH3DjCvd6kaofPc0/AilENDfZquIrkM\nk3QxSY8g6Qx+bpIOQdLDJF2CqI3LYsqAGINbWB0UWvnGoq56R5yIx9fmoTZ3IX/eqYJ4fX29CPwj\noHu2D0eNC+8PV8COIBif1e+hTi/j3sNdXu3FGODmlRl+cn2OUvETgzFLMGm3f22oQtBvLwiMfege\nJ/2e0LXShASwCGQxQdLtB2v+462/7hdTvfNDhQWk3CC4vk5v9jr20g0ofnplrTp/Ih4p1/MuZGd8\nDvw+p10R/0PgvwF++wwfixoDznm6cUaS5q0px609ZWYd3/6wz6PnBwhwab7CnbUmM7VP2B70DhN3\nMVn0WkHVuL04+Rje51XQ9XEZyuBdf7X6nmA9/CHv70roi1V8dRYp1ZFKHV+uD34upRq+0sgnGIVF\nMIZms4Ydp17j00w8vlDOA3gIRwUnDuL19fV/B9je2Nj4Z+vr67/NuOxDqk9iraMbW9JsPAPYi/D4\nRYdvftgjs55apcCdtSbLzerpAzPp9VtFJker3Qt8FX2RxAuVoqFeG+ECPO8J954dNb/o7RP0i6Le\nRUyIlGv42eU8WI/9eP3XtYna0VB9Iogx+MYlqAxvDKQRkRO9w/r6+u+Rt4YX4DeADeAfbGxsvHzH\nu5zsD1AjJbP5PeAkcQThiH4D/oAXr7r8yVcv2TtIKBQCfv6TS6zfap5qRedtBnEHkrjfKWo8Pycf\n4/B7Q61kaFTDkQxgiTrI8++RZ98jzx/AYfAGITTmMZUGVPMfRz+vYyoz+c9LlYn+O1TvJt5Do4lp\nzJ/Hv4ETfcATB/Fx6+vrvwv8ex8o1pLt7YNT/xnjbmlphnF8/ql19CJLZh3BKUfgNZu1oY4B7MUZ\nXz9s8bI/nnB1uc76zSbl0glXNuL7q98Y49OPvkY0O1OhffD+FdkoEhEMUC3mZ8Cn/SZ1Ln//4gn2\nXx7dwd0/ev3vq7ODa0Bu4fq5XTX5WMP+9z9Mo/zcRVx+pNBYOLddjqWlmRN90ej1JfWaNHV04wzr\nhcCYU4fwMFnr+f7pPg+ftvECzZkyd283mWucsC3l8cIrGJu7vKcl/V7QtU8M4DOXRhRebfZ7Lm8S\nZPkLq/wO7vXBsANfX9BqZPVuh20pG8tQHJEWtX2fFMQbGxt/66weiBquOHVEcYZzggnGdzzh0+0u\nG5t7JJmjUsrHE165dILxhIPCqxjE/biT1SQ6DOCSoVYagRdeIgTtraMpQ3svMP0TLl+uk65+eXQH\nd8S+oaoRNBhPuACV0RyOoSviKRcnll5icU7ya0hjVoR1qHWQcO/BLvudlCAwfH59js+unWA84RQV\nXh0SEQIDtbKhOuxpSFlC4dWjfLv51SZBkt+MFGNwzatHd3BnLk3+CyN1ZvLxhLMj37FMg3hKRUlG\nL7Z4ob8FPbr/SN8nTiz3H+3xbDv/xn3lUo0vbjU/bjyhs5i4k4fv4bWjKaiMFRHCfgBXhhXAIgSd\nnTx4tx8Stp5i+vUqvlQb9Fu2l27qHVx1ct7jS9X8HHgMplSN/iNUZ+Z4H2iRvAvWmOYvznkePjvg\n+yf7OC/M1kvcXWuyMPeBb9oikHR/XHg14atfOBrEUCsbysMIYJtS2PlhMOIviPOe3gL4uct58C6v\n4WdXRnr1ooZIBLxDDECQd6kLQqT/X0yImDB/8VYan2MLDeIp8LYAHtfvcyLCi518PGGUOErFgLu3\nm6wuN95/DpwlmLSHOex0NeGFV8d5L5RCqFUNpQucICUimM7uUYXz7lOM5K1QpVghu7KeF1pdupXf\n01XTS3xeTGVM/rUZhEgQDnapxIR56IaFvGFKUHi9VeyY0yCeYG8fxDDsR3V67W7KvQe77LYTTH88\n4WercxTfFS7e51vP01R4dczhIIZ6zVC8qC5YLiPcfUJh+yFuZ5NGZ+/oTbPLg+tFfv7yVOxCTDWR\nPGDpN5MIjodqHqxHAVvsB2wwlf8uNIgnUGodcWxJrT8WvuMbQEnm+ObRHo9f5luZy80qd9beM57Q\nZZj4IF/9Hn5RT9EX90UPYjC9vaNV785jjM9XvRTLZJd/crTqrTTO/bGoIetPLJLaLC4t9wO2MBW1\nF59Cg3hCeBGi/vaz94zlIIY3eS88en7At4/38vGE1SJ31posNd8xksylmOggb8pvpu+V9YUNYnCW\nsPX0qNCq2zp6U2NxcK935vbnHOwn7/lAamJ4hw+LeXVypUEwMwPx+DUyGhYN4jGXpI4osWTW9yuf\nzUQcneTjCXfpRpZCGHB3bZ4bl2feXt2dpfkK2E1nAB8OYmhUg3OrfjfRwSB4Czs/5LOUAQmLZMuf\nDa4XSfXonqbRVdDkE98P4IWh9moedxrEY8g6T5RY0syN/fWjN3V6GV9vtthu5d2Tblxu8NMb828f\nT5jFmKiTh8IUni2J9CchnccgBu8I954PKpzDg1eDN7l6cxC8rnltLK6HqDPmPb5YQmpzUJruQjsR\nIfGOxGdk3pF5x3/xB//L/D/6G//23offO6dfQWNCRIhTS5w4rPP91pPje/3oTZn1fPd4j83nB4jA\n4lyZO2sLzNbfMpIsiTBJB+Ntv6pyegL4sDd8tWCol4MzPX4wcYfw1WZ+3vvqh/x+NSBBiF261b/X\nu4bU58/sz1TjRcQhhQoy0xyr60FnxYsQ+4zUWaw4MvHY/k2AoL8QkLw07UTbQRrEI+544RX07/5O\nUPCICI9fdvjm0R6p9VTL+XjClYW3jCdMevndU2/7dwYn5/PwIYeDGM60D7R4gr0XR4VW7a3Bm3x1\njuzqF/mqd/F6XnSjppaIR4qV/EVYYToC+M3QTcVjvev3Xzj6+gvO4PuQBvEIOiy8SlKH8zIRhVdv\ns7Mfc+/hLgfdjDAwrN+c59bVWcI3l/lJlyDuHl1BmqKzx7MexGDSiHD7cNW7mV/t4nCAwo3B9aJR\nbwmoLoZ4h5RrSK059GlW58mJJ3L51rL1jgyP9Z7A8NrXXHhOiyAN4hGSpI44taSZH5z5TsrZ73G9\n2HJ/s8WLnXxM2rXlOus35qkcb0vZ74AVJF36XUimagU8GMRQNtQ+pQuWCEH7JYWtPHiDveeDi2y+\n0iC7/POjAQqFtxwDqKkk4pFSLX9BNmE1AE48kU1JxWG9J8PhvCcw5o3QvbjvvZP1GR5D1nnixJJM\nYOHVm6zzPHjS5sGzNt4L8zMl7q4tMD9zbKtLJK+ATqNjATyZn4+3OZNBDFl8NEBhe5MgzV/w5AMU\nruXBu7yGb+gABfU6EY+U63kAT8DOk/UuX+mKI/OeTBwih/0Vzn+l+7E0iIdAREhSSzShhVdvEhGe\nbXfZeLRHnDrKpZAvbs5zdal+9MUgPr8DnObV0tMYwKcexCBCcPDq6F7v3rM3Bij8rF9odUMHKEwQ\nEcEhOMm3UfNfewTJm1qd7IPhyjVsfT7feeq/eDst2/Xsxt1P+hifQhAyyRc3x1e2+beV0dtZ0yC+\nQJl1/WtHk1l49TZ7Bwn3HrbYO0gIDHy+Osft1dmjhhPeH62Apyx8Ib8DXDzNIIbjAxS2HhIkxwYo\nzF85aiU5uzx1n9Nx50XwCLa/dXoYsACu/zYvPg9aAQynu77Wf7FmKw1sdbb/70QA98nPIfWO7Aw+\nzqcwxhCOyT99DeJz5n3e73nSC6/e1Isz7v/pMx482Qfg8mKNL27NU6v0Cz68y1fAWS8fvjAFn5Pj\nTjyIQYSgu9u/17tJuPsE0+/j64uVowrnS7eQ0js6j6mhcuKxIjjvBmHq+6tXh+Dx+MOlrMmb0r7r\ne4UxhlN3rpX8D7DVGWxlZuq+9kaRBvE5EREOuikWQ5z275lN6t5zn3OeF7sRT1522NnPq3Fn6kXu\nri2weDie0Nl+H+i43wVr/M+hTuJEgxhcRrjzeHC9KIjaR2+aXRnc7R21AQrWO3ouxcXCQRYN++EM\njY08e2l0ooANjDm/tvAieXV8dRZXqWsAjxAN4nOQWUe7k8IEF14dt99JePyyw7PtHtblq7TmbJkv\nbi8yXyvk33QOBzGk8dTdAYaPH8RguscGKOweDVCQQpns8k/7fZxvIeXRaifoRei5lNhn+bWPwFDy\njlSGuz05TFY8vr+lfK4B+yFekPAwgHXwxijSID5jUZLRiezZtxwcMWnmeLbd5clWh3Y37ztcLobc\nuDzL6kqDRrVIs1mjtb2XB7BN8tXvBFRinsQHBzEcG6BQ2H5IcHyAwsylwQAFN39lJD93iXdELiH2\ndrDSm4YXn2PBCxKG2PpMvgJWI0uD+IyICAe9lDT1E/uNSER4tR/z5GWHlzs9fP920cpCldWVBkvN\nav4CxHtIuvi9HsHBfr8P9OiFyHnyXigXYLHx4z7QJmoPrhYVdh5hnAX6AxRW+gMULr0+QGGUOPH0\nbErsLY78/uWkv/AcGyIggi+UsPUZvNYLjAUN4jNgnafdSfEimAkM4V5sebLV4clWhzjJtxrr1QLX\nVxpcW2pQLoVgU0zUzich+QxMgClWp6oPNLw+iGG+XqCVpvkAhdazo+tFnZ3B73f1hWMDFK6OdPOE\nyGdENiP1Nn+xaSAY4znXE8N7MAGuWMYXK7hyTc9/x8zoftWPiTixdKMMzGRVQzsvvNzp8fhY4VUY\nGFaXG1xfaTDfKGCyGJO1MVGSfzM43DodwS3U83TYB7pybBCDiTv477+lsrmRr3ptmv/eoDC4WmSX\n1vLpNSPMeke3f/YLuvU8EvqrXgmLuGIFV64i2hVtrGkQf4JOLyVK3URty+13Up5sdXi23SWzR4VX\nq8sNrjQLFG0C7gDTToGg/8rbTF34wht9oItCuP+Cwg9HAxQ8UKQ/QOHa3XzVu7A68gMUjgqvLNa7\nqblyN9K8gMmvqrliub/qna7dpkmmQXwK3gt7nQTvZSJCOLOOp9tdnrzs0u7mK7dyMeT2tRlWmwVm\nChZju9B1R4E7ZWe+xx0GcF0iZvceUXi1SeHVo2MDFELs4k1KN39Ku7GK1ObHYqtQC69GiEhebFUo\n5sFbqiLF6Zh6NI00iE8ozRwH3Sy/DzgG31zfRUTY2Y95fLzwClhpVrjeDFmqCaGPQQKw/ec5have\n13hPufuS2f1HVHc2CfZfHBugMHN0vWjxOhRKVJo1pPVprQLPW94APyPymRZeDZvvtyUtlvur3vrU\n1VhMKw3iE+hGGVGcYcb4iyM6VngVHRZeVUKuLxRYnYFK6ODw6U178AJBFlNpPaK+u0mttUnQb1Ah\nJsAtrA6uF/nG4liseg9FPiN2GYm3gzuuWng1BN4hYRFfLGOLVaSkvcCnkQbxRxAR9rsp1vqxDOHD\nwqsnWx1e7R0WXsFqs8CNOWjWgmPV3lMeviKUuttUdzep7m5SaT/H9Nvn+3KddPVn/bGBN2HMtgqt\neHo2ITpeeDVGLx4mghdA3lj1TvnXnNIg/pDjXbLGbSu63U37Ha+OFV7VDNfnAq7OFyiMS0f0c2Zs\nQnXvMbXdh1R3Nymk+dQYwbwxQGFprFa9kL+I7Ll861kLr4bDeIcP8lWvK1XxxfLY/TtS50uD+D3G\nrUuWc57ddsJWK2J7N6KX5I0iygW4vRhwY6FAozx+K/ozJ0Kxt0t1d5Pa7kMq7WeDAQquWCW5cge/\nvIa9dBPGsCGCE08qjsRl01t45f1w/3xj8GERWwmxuupVH6BB/BZ5l6yMNHUj/82rF2dst2K2WhE7\n+zG+X/BRCODybMDqfMjyzI+7O00b4zIqe4+p7W5S3X1IMTkYvC1urJAs3oLLawTN0Rqg8CFWPKmz\nWHE4ETIc3kt/ouSUbT17j4QFXKmKrc4M/e+xOl/H7g1vJq8aHxrEb7DO0+6m+TezEQxh74Xddsx2\nK2a7FdGJssHbGmXDciNgeSZkoRaM/IuI81aIWoPgrew9JegPIHCFMp2ln9Jr3sJeukm1UX/vIIZR\nkXlH6l0+p1byebWHV6mObzdP1d97v7mFK1exlQZSGK9ze6VAg/g1SWLzYBux8+AosWy3IrZbEa/2\nYlx/1RsGsDwTslIXlmaL1Eqj85iHwXhLZe9Jf8t5k2K8N3hbUl8iWrhFb+EWceMy5WJIvQTVEQxg\nESEVT+bta6ELrw+AfzOAp4p3+EIJV67nBU/T+nlQE0GDuG+UumR5EfYOz3pbEQe9o1VvrRKy3AhZ\nqXsWagHhCAbJRSrE+4Pgrew9JvD5ubgPS3QXP6O3sEa0cAtXbiBeqBQNi+XR2bL1IqTisM5heXfo\njsrjHar+lBFbruIqM8gI9+VW6iSm/l+y98J+N8G54XbJSlLH9l5eZLW9Fw/m+gYGLs1VWJ4JWK56\nGkXXv+Q/pQHsHZX2s0GhVam3O3hTWlsYBG88e3VQICMiVEKo14Z7Vu7Ek4kjcw6HJxOP8z6/w6uh\n+25e8MUStt7QaUJqIk11EA+zS5aIsNdJ+8Ebsd9JB2+rlkOuLjVYmilwqZxRlBTozxycwnu+YdIZ\nBG917wcCl+8Q+KBAb2GN3sItooVb2MrRAAXvPdZZKkWolfKjhiy/wnlhOllKO4sGRVTOC8E0n+ee\nxIgVXikF/U50LqPrUnqHP+yxn7uUnss+/IHeMLVBPIwuWWnm2N6LB+e9h3d7jYHFuQpLzSrLcyUa\nJiawCfjDwQpTFr7iKbefDwqtyt1Xgzdl1XkOFm4RNW8Rz68iwdE/4cRanFh84AkCT7EIqYHUDuNJ\ngE+FWI7+8FBD9/208EoNgYiQST5lLHIpXZuH6evhmg4GobxPgKEWnnwS1tQF8WGXrMx6gnMOYRFh\ndz/i+8d7bLVi9g6SwdvKpZDrKw2WmlUWZ8sUfYxJYkzWOTZOcHpWAUHao9bKu1lVW48Ibf658iak\n17w5KLSy1ebgfbz3JFmKDzxiHMViPgs4N/zQm9pCqpPSwit1DrwIsc/o2fQoZI+tWo8HrJX33zsv\nBQVqYZGFUp16WKL22o8itbBEPSxR6i8M/ttHf3iixzpVQXy8S9Z5nsOJCE+3umz8sEeSusH/b86W\n81Vvs8pMrYjxGSbuYrrt/DeYKRonKJ7ywct8y7m1Sfng5eBNWXmG7tJ6XuE8fx05NjYws5ZULASC\nCRylyvHtXf0GPjZEAC28Oo3D7dE3V2t52GR0bT4/+gJPYX4kfGYGtzuGIf8cpe/9HBigGpaYL9Ze\nC9PaW4K2cILvyyInf95T868/SizdKDv3VUqcWH79/S5brYgwMKytzjFXK7LUrFAshCAe4h6mvY/x\nNl/1TskKIMgiqq1H+ZZz6xHhsQEK0fx1es38rDerLQw+J+I9sc3wxiHGUSgI1aIG79g6LLyqaOHV\nm1Jvf7wlal8P2p7LiP37zyADDJWwONQhHl5OF0hnpWACVsqzP1qxVg/DtlCiEhRHpjBy4oP4eJes\n82zQISI82+5y72GLzHoW5yr84vNFrl6ZpdXqQRZjOvsYG5Of+5rJ33oWodTZGhRaldsvBgMUbKnO\nweUv80Kr+euvnQdmzpF5iwQeAkepfPx8dTS+cNQJTHHhlYgQ+exYqGZvWcXm//3g9qgJqYUlFoo1\naoWjgDn+ox6WKAeFoR+LNOfrtLSr2Eeb6CB2zrN/AV2yktTx6+93eLmbr4J/dnuBG5cbGPH4zh7B\nfgu8y7edJ7zwytiEausHarub1PceESQdIB+gkMxe6Vc4r5HWL7226k1shidf9YahUKlo8I4jK55v\nsy730wOSnmA9EASICaAH7H3oI0wGESF5aulmyUdsjxaZL1bfsiWah+3hSq44LcdWU2gigzhOHXFi\nyVw+6Pw8Xx0+f9Xl19/vklnPwmyZX/xkkVoRTK+FSWPMXD3/jZP6RSRCsbdz1Eqy/XwwQMGXahws\n3yFauEXUvIkvHs1atd6ROosYQQJLuQwlXfWOJfGOlzblK9fhftYh7f/9lyTMj4KHPH9hWOrFUn97\n9Mcr18PVayUcne1RNTwTE8TWeaLEkmYOL3lThPP8B55mjq++3+X5To8gMNxda3JzqUyQtDFxkq98\nJzR8jUup7j0edLQq9AcoCJDMXO5XOK9RuXKDdueoUjx2GU4cGE8YeiplDd6x059qJEGBXmD4Outw\nL26xY/Pz/npY4svGCl80Vri1dGmqtyd1e1Z9rLEOYhEhTi1x4rDu8DqS4byva77Y6fHr73dIM09z\npszPb80wY2JMp9svvpqwABahEO1R232Yt5Lcf4p5bYBCXuEcNW/iS7XBuxUQujbBBIIYS6mkq96x\n4j0G8GERHxaRQoEsLPLYRtzvvORhbwePEGC4XVvki8ZlrlebusJT6oTGMohT64hjSzpoiGHO/U4w\n5Kvgew9bPNvuEhj44nqD23OewO3nq98JKr4yzlLZf3xsgML+4G1JY4momXe0SmbzsYFefD4ZKItx\nHkzoCE1KtXp4AV6/OY8y4xxiAqRQ7AdvAV8s51fHjKGd5eF7v/OSrsu7wDWLNe40LvPTxhLVUzQx\nUErlxiaIvQhRbIlTi/f53dGLrAzc2u3xq+92STLHXK3An7tWYLbU73w1IVvQhWifaiuvcM4HKOSr\nXh+W6F76PO/j3LxJWqqR9YcUeJvicFgnFEIoFw3VQl6HVS0WyOKTt3tT50gEIx4xYX+VW8SHIb5Y\n/dFdXusdD7rb3O+84Gn/hVjRhNxtXOaLmcsslxpDr85VahKMfBAnqSNKbL8TlgHMhS48M+v5+uEu\nT7a6GANfrBS4vWD62TvmK2Bvqew/GxRalaLW4E1pbZHewi06zZu060u4wOBEcOKQtNcveM6/CRcL\nUG3TungAABoiSURBVC8bCpPxemRyiM8LpcJwsMqVsIgrVd754lFEeJV2+Lrzkm87W6T9I4gr5Vnu\nzFzmdu2SVu8qdcZGMojfWng1hD69262IX323Q5w6ZiuG37gWMlsdyU/ZRwvjg6NWkm8MUOgsrNGe\nv0G7uUpUauSFVUBg/GBYgjH5TeAwhFII5eI7/iB18UTAC1Io4oolfLGCL5Y/6t5u7DK+6W5x/+Al\nO1leYFQLS/yscZUvGivMF7X5hlLnZWRS5f9v795DLNvyg45/19qv86x3Vfe9fet298y9ve8dRhmN\noEZNJqiDEwhKEJRExYgvJsJIhGgmMIhEEILjKyohGk1EHJgwioMoAzpEHURFfESc2d137u376Nvd\n9eiqOlXnvfda/rH2OXWqu95d5+yqOr8PNF3dfapq7a6q8zu/tX779yuq8OowaWr4zsMtPny6hwLu\nLXu8seJfzSIUk1FqPM63nB8SjgxQ6JZm2V15ne25VXbqN7DaG7lGc+B63Qsil/2WAgr5uohD5G0E\nTRCRBZHr1XzKLSNjLY8623xn98mBwqu7lUXelsIrISam8EBcVOHVUTa2W/zGg03aPUM9UnzmtYDZ\n8tXagvZ6TZfxDlpJ5sU1Rnk05lbZnn2NnfnX6Jfnhu+jeLGcKh+GQ+BDJYBAdiQvB5NhvQATRKRB\nGRuWTn6fEY1+h+/uPSXZe8pe5m4vmw/KvFW7yb3ayrmmxwghzq+QQDwovOr28hmtYyi86mZ9dtPj\nu9qMyrKMR+83ePrMAJbVBVhdclN9tl+i3qjXSWn1eyc/8GVYQ2VvnZmtD5nd/oDKaNYb1dhceoPG\n/Cq7M6+eqrm+MfnWc579SlJUMOMGKR/Mes/2qig1hvdaG3xn7ymPOq69VaA83q7d5K3aDW5EdSm8\nEqIgEw3ELxZeXfxg9NRk7KYdejZDneqWGUNrY5d3H6V0+opyaHnzpqGeH4ldRNtyO4Y5KF6/w8z2\nR8xuf8jM9of4w7GBmsbsqzTmVmnMrdIpz50qkg76swc+RBFSeFUwZTKMdllvFpbzs96z/6ysd/f4\n7t4T7jfX6eWzVF+JZnirdpNPVqXwSojLYOyBOE0zdlu9sRdeGWvZSzu0Mzdh6cQgbA26uctHT3p8\nvO22nm8tGF5ftJfzdmBrKTc3XODd+pDq3trwCnthlY2Vu+zMr7I7+yrmDFuLxrqgK4VXBbMGUBg/\nwAQl0jNkvcZaWs/NW22mPR62N9nojRRezb6WF15VTviIQohJOnMgjuM4AH4ZuA1EwM8lSfKNox6/\n/qxNr++eZMZV4NNKu+zl56Anba8pk+F19mg1utx/6tHpe5QCy5uvGGYuWWGol3ap7zxidstlvcFg\nbCCKvfpNGvMu622PjA08DSm8uhyUMRjtjWS9pQNfx77JXpjQMxxqPjIer33EWLxB4dVbtRu8Xl6Q\nwishLqnzZMQ/DqwnSfLH4zieB/4XcGQg1t4Y+z1nKY20g8EF+uOoLMXr7KF6XT7Y8nj0zGUbr8wb\nbi9ZvMuQBVtLqbXF7PYHzGx9SG336XBsYD8os7l8j52519ide41sZGzgKT/0sPCq7LvzXzFh1mKN\noen57HmK3TCkaTNaaZNWd2tkPJ4bldfPbx87SpCPxZsLysMZq6NDBRbDqhReCXEFnOfp+GvAr+Vv\nayA95rFjkZmMRtqlZ9I8Az46CKusj99potMuuz3Ng8c+7Z6iFFjeuGmYLXiXTmd96juP8kKrDwnz\nrUQLNGsrNOZX2ZlbpT0yNvAsrHE7nNNSeGWtZcv0Wc+67J4QyMZNmQZb7TZNDHsYmiajbVLMCTUD\nZR0wG5T2h5h7h8+elfNdIa6HMwfiJEmaAHEc13FB+WdPep/5+YuJdtZaGr0O7X5GuRRSJnSRxlpU\n1ocscyP4jPulbAY2xYaKd5+FvPfUlU2tLinefNXDH2O2PqpaGclerSVsbVPdfEh1430q2x8PxwZm\nfkTjxj32lm7TWnidLHR75RqonuLzWOuuT6HwtKt8LofgF5juz9THt9+fWcNav8uTfofHvTZP+h2e\n9Dv0TxiwPjGd/Td9pakFEXN+lVoQUQsiqn40fLvmh9SCiIofok/RgOOqmJ87zXfu9TXN1z+t126t\nhffP9j7n2qCM43gV+Drw95Mk+epJj9/aap39k5jM/cpSsIZO2qXV74DJ0Nj9gItFWbCaQzsI7XXg\nwRNNq6uIfJcFz1Wh24XuC4++eNVKRGu3Sb3x8TDrjfKxgQCt6hI7c6s05ldp1pb3ryEF0qNXONhq\nVgoXdPNfwWD2hAWbQmvi+xX7ZuplGrvtC/lYPWtYz7qsZz3Wsi7rWZcN0zsw6lYBCzpkxQ9Z8SJm\ndTC+URN5mblVGuv5WO1hPR/j7W87zNermLah4oeEyju6fsEAPej3Mna4mP+vy2DaxwBO8/VP87Vb\ne/a7ZM5TrHUD+CbwhSRJvnWmd7Y2z1bT/JdxAdWOZrAmv2/Snfum1rJn+hib4T3/RJb3nj7sstMM\nPt5SfLSpsChuzBruLNuJ3Zbj99vMb7zL/O4jKs8+Qg/GBnoBWwuuwrkxt0oanrxbkHcuHAZbrcDX\n4PvXs9CqbTLWTDcPuC7wbj1XkOShWPEilr2IFe0C75IX4o8jmzwwDtDHej5ZEGH9428pmq9X2cqm\n88lICHF658mIvwTMAl+O4/jL+d99PkmSzmEPNttr6J1mnsK5DNZNLDrqCVOBVhgLraxHN0tP3fDD\nWthpw9qOYnNXYawi9C1v3MyYn8QuiTXMbH/E0lrC7Nb7qPyVUbuyMMx692o3jm1BmPduGAZdpV3Q\nDbzrd75rrWXPZqxl3WGWu5b12LUH0/gIzWteaT/weiELOhxLFfDBcYB+Pg6wNBwHKIQQF+08Z8Rf\nBL542serLC+YUQrUKdJRa2lnfdqml7e7PPnJr9uHtYZibUfR6bvHlwLLjVnDzbnxZ8FRe4fF9fss\nrN0n7Ltt+FZlgc2VmN6te+yYwytXTb6vOsx084Dr6ev3nG+tZdv0Wcsz3DXjAm/7ufPcivK441dY\n8cJh4J1V/sV3fTowDtBNJTK+f+g4QCGEGKdL9YzTNSnttIvBnvjEayw824O1Hc1WE9x9ypaVGcPK\nrGWmPN5gprM+c5vvsbiWUN99AkDqhazf+BQbK/eGVc7VUgStLplx28ijQdfX17eD1bbp871mhw/a\ne8Nst//cIcKM8rnll1nJs9xlL6Kmx/AtOdzb9/IsNzhxHKAQQkzKpQjEad64oG8z9AldsZpdeLqj\nWG8o0sw9rlZy2e9SfczZr7VU9tZZWkuY3/weXj5CsDHzKpsrMdsLd4bZlM2bZoQ+ZMFIEdU1lVrL\no6zNw36L99LWgTNdV0QVsOxF3Miz3GUvpHSaHZLTGFSu2fyTac9tL2vPdaryAkxYOtU4QCGuEmut\nux3OAkrhKYVGo0/Z4HdcQu0RMJ0vcq2ycOCeiZMVGoittTSz7vAc+KgzvzSDjV3F0x3FXicfRu9Z\nXp033Ji1VM7W2+LM/H6bhfUHLK7dp9zeAlxbybWbn2Zz5R690szINblMvBy6lpG1ssYUWLk8Trsm\n5b20xcN+kw/S9jDj9VF8wq8Q12aZ7WuWvJDgPEHQGJS1WKVAKazysFqPBFo9DLjWD92/SbAV14Cx\nrljEWuXujFAuuHq450mttAu6ShMqD09rvEv0vb9creO3Ls96Ju0Xf8+PnalKs7BA3E57x54DWwuN\ntst+B4VXYJmvuux3vjbmiuG88GpxLWEuL7wySrO1eJeNlZjd2VsvPOkbA1EI5Wta15NZy+Oskwff\nFhtmf6rUvA6441e461e45ZfxlWKmdsjtS8OiPXe/8zCQ5kHVBVnlslnPBy9wf38d/0PF1MmsgfzF\nukIPM1hPgR4EW6XxtAuwWmlpTToFJh6IeyallfawmEPPgY8rvFqesWMfTHBY4VW7ssDGSsyzpTfI\nghdnv1rjbiWqlbgcrTIv0J5JeT9t8V6/xQdpm25+566HGgbeO36FOS//wliLMiYPrB7GC4dvW61A\nu0pkq/3rvVc/xax1NR6eUnhM79c41B6R8oeZq4fC1z6BzreOJcCK3MQCcWaNOwc+pC3lUYVXyzNu\n63nchVcqS5l/9i6La/epNx4Dg8Krt9lciWkd0V7SWhdLKmV3BnwdGGt5knV5mLZ4r99kbSTrnVE+\nbwU17voVXvPLBCj3KkR5wwKowXms1R7luSo9T+6jnRaZsZS0TzUIqfoRy7U6UftSlKEUYtq3Z8Xp\njf2nxFjYS7v0TD6ecCSgtfLCq7UCC68W1xMWNvYLr3ZnXmFj5a0DhVdHvPvwHPiqa5uMh2lr+KuT\nbx1rYNUrczeocNcrMa/yreI84Pb9wO0QSGY7tYy1eCgqXkg9Kl2qc0ohroqxB+Jn3T36Nh0G4MMK\nr/y88Gpl1lKdSOHVOyyuJScWXh3GWlcJXQ6v7rGltZanedb7MG3xONtvpVlTHr8pqHPHK7Ma1Qn8\nyN3y40d0zzmcXlwvg57mZR1QDSLK3jV4NSpEgSayb3Rc4dXKrGFhIoVXj1jMO15pa1zh1cJdNldi\nGnMvFl49z+TnwJXADVO4ajo24/1+exh8W3nLTQXc8krcCWrcjmZZiKquqYUfYJWid/yHFVPEGEOg\nPSp+RM0vSRGREBdk7IH43SeGjzb0gcKrlVnDyrgLr6wl6jRYWL/P4vqD4XjBdnmejRsxz5bePLTw\n6pAPg1JQu2LnwKk1bJk+7/Vd4P046wzbaVSUx9vRHLdLc9yqLhGG+xOSih0cKC4bYy1aKco6oBZE\nhNJ1TIgLN/afqnceG7RivIVXedAtNzeoNDcpNzepNDcIUndPdeYFJxZeHfFhiQK3DX0ZWGvpWkPT\nZjRtStNk7m2T0sp/d3/OhtXNAzeDKq9XFni9sshSWJOKTXGs5wuvhBDjM/ZA/PaqZibqX1zhlTGU\n21vDYFtublJpbQ6LrQa6UY3tmTtsL9xha+HumfoHm/wcuDKhc2BjLS2bsTcIqM8F1aZNaeVBNzth\nqHxJeVT9kOV8+/BWaY7V8ryc44kTGWvw0FJ4JcSEjT0Qry5pmucYRwzutqJya5NKczPPdDcot7aG\nIwUBLIpOeZZ2dYlWdZFWdYl2dZHMjzCD9m8wbCJxHGNd/+dS5HpAp+4TnJsBnqVdnqRtmiajZQ8G\n18Hvzw8+eJ4GqspnWYdUtEdV+1S8kIofUQ7KlMIKVb9E2QvkyVOciRReCVG8S3Pg46Xd4dbyIOiW\n2jsHpg0bpWlXFkaC7iLtyuKBbNdYyyPb4n66xQe2efY4mgH9Ex91eo2j/ylEU9Ueizqkqn2qynOB\nVvlU0dSUR9kvEfoRBAGZX8L617Rtl5goYyyB1lJ4JcQlMPlAbC1BvzXcWh4E3ai7d+BhmRewV79B\neyTLbZfnj7xndcf2eGB2ecfs0spLjuYIqKnjX+EPmnKMoyOWAmbDkDBzGW1Ve1SUNwy6gdL7naiG\nM3CD/Rm4vlv7NW1VLSZMCq+EuJzG/pMYtHaY2/x4GHArzU2C/sH+w32/xM7sa3nQXaRdXaJbmjkx\n8+tbw0O7xwOzyxPrCrNCNG/pGe6pOosqOrIoyRgIfKhE4711aqY+0m950Gc570SVDYbPh2WsjOMT\nY2KMJcoLrypeKIV6QlwyYw/En/gv/+zAn7tRje3527SqS7Rqi7QrS/TDyhkqmS3rtssDu8u7Znc4\n8ecVVeKenuG2quIfc046yIDr5THPAs5n4FoLxgtdC0g/IAsimYE7ZsZarB3MRCxOZjMy8xJFBi/J\nV9LxSoirYOyBuHHjHo3SvMt2K4ununf3MG2b8T2zy33TYDs/xK3i8Sk9w5u6zswptqAVY25Ladwn\nMUFEFkRkYZXyQp3etvRbflmjc1eVcqPgPFwj/cGAgcH0mkB7+NpDFxyIl+t1qp3dwj6/nPsKcTWM\nPRA//vTnaLa6Jz/wEIPCqwdmlw9sE4OrIL6jqtzTM7yqyqd6srEWwmBM4wmNwXoBWRCShWXsOV9o\nTKv97JX9Oau4wKrykXBaKTztEaAv3dzV42h19IxtIYQYuJTVGg3b54Fp8GCk8GqekHu6zid1nZI6\n3dbu4By4HF5gMVa+1WiCkCwokUVVGXpwhMyaYYGQzrPXQXCSuatCCOFcmkCcWsND2+S+aQwLr4K8\n8OpNVWfpmMKrAWvdL89z57+hd0HnwCZzE4eCiDQoY0PJeo/ipvFoql7I67V56l3pyiSEEMcpNBBb\na9mwXe7bXd41e/Tztow388KrOycUXoFLUDUu+Aae64j10omVNWBHznqjqhRYHWPQFKKiA6pBiVJ+\nW4wv/2dCCHGiQgJxZ1h4tctWPt+ngsen9Bxv6pljC6/yYmR8z3XBCv2LyXqVMRjtuy3nsIwJStI4\n4wSZMYTap+a7fsRyW4wQQpzdxAKxsZaPbZv7pvFC4dWbus4tVTnyjDAvRsa/6KwXhfEDjO+y3rP0\no55Wg8KqqhdSC0sEkvUKIcRLGXvk2TE9/nf2jHdMg+ZIx6t7eoZP6jrlQwqvrLtLBa32A+/FZb0e\nJogwQYksHMcoqOspM5ay51MJIqreJRlHJYQQ18DYA/Gv7r4DuMKrOO94dVjh1SDrHT3rfemOV3n1\nlvFDd94bVoZtI8XJRguvalF0ZW4bEkKIq2TsgfiWV+GT1A4tvDLG3fkzuuX80owB7ZH5oct6o9N3\n7RJHF14JIYQYj7E/y/5o7c6woUd+vOhuL9Kuw9VFZb2D9pFZWJWs9xyMMQTap+oHVGUajxBCTMzY\nA7HJo+8g6w28C0hQjQGlyYIQE5Ql6z2nQeFVxQupBxGBZL9CCDFxY3/mna9pwovKer08640qWF8K\nhs5LCq+EEOLyGHsg9s6793xggMLgrFeKhc5rUHhV8QKZxiOEEJfI5dqLPDBAoYINpD3iyxgUXrlB\n8FJ4JYQQl1Gxz8wyQOHcXJC1WKtQCjelCDUyWEETKE3Fj6TwSgghLrHJB2IZoHCswVhApUCh3Zxd\nNJ5iOG9XK4WvPQLlDccECiGEuJrGH4itAWOmeoDCSUPtB3/Wan+o/eDfhBBCXG9jD8TpzBId30z0\n9iJjLL7SRJ6PorhgpoDZsIQN7ZUbai+EEGIyxh6IbVSBdnO8nyO/HzbSPpH2qV6idoxzUYW+lxW9\nDCGEEJfUlS2jzYzBVx4lz6fsBZTlflghhBBX0JUJxIMOXZHyiTxfhhAIIYS4Fi51IDbG4ClNpH3K\nQUhZB1LAJIQQ4lq5VIF4P+v1iHRAJQxl8LwQQohrrfBAnBmLpxQl7VMKQiqS9QohhJgiEw/Eg7aL\ngfIoaZ9qEMrUHyGEEFNrIhHQWIsCSjqg5PnSdlEIIYTIjT0Q14IQL6wTSdYrhBBCvGDs9/8slmoS\nhIUQQogjyI24QgghRIEkEAshhBAFkkAshBBCFEgCsRBCCFEgCcRCCCFEgSQQCyGEEAWSQCyEEEIU\n6Mw3+MZxrIF/APxmoAv86SRJvnfRCxNCCCGmwXky4j8EhEmSfD/wV4C/ebFLEkIIIabHeQLx7wL+\nHUCSJP8V+G0XuiIhhBBiipwnEM8AjZE/Z/l2tRBCCCHO6DxNoBtAfeTPOkkSc8zj1fJy/Zh/vv6m\n+fqn+dpBrl+uf3qvf5qv/azOk8l+G/hhgDiOfwfwfy50RUIIIcQUOU9G/C+B3x/H8bfzP//EBa5H\nCCGEmCrKWlv0GoQQQoipJUVWQgghRIEkEAshhBAFkkAshBBCFEgCsRBCCFGg81RNn2ja+1HHcRwA\nvwzcBiLg55Ik+Uaxq5q8OI5XgP8B/N4kSe4XvZ5JiuP4Z4AfAQLgF5Ik+ZWClzQR+c/+PwLuAQb4\nM0mSJMWuajLiOP7twN9IkuSH4jh+A/inuP+D/wv8ZJIk17oy9rnr/wzwd4EMFwP+RJIka4UucIxG\nr33k734M+At5O+hjjSsjnvZ+1D8OrCdJ8gPAHwB+oeD1TFz+YuQXgWbRa5m0OI4/C/zO/Pv/s8An\nCl3QZH0OqCZJ8ruBvwb89YLXMxFxHP808Eu4F94AXwG+lD8HKOAPFrW2STjk+v82Lgj9EPB14C8X\ntbZxO+TaieP4twB/6rQfY1yBeNr7UX8N+HL+tgbSAtdSlJ8H/iHwuOiFFOBzwG/EcfyvgG8A/7rg\n9UxSG5iN41gBs0Cv4PVMyjvAj+KCLsBvTZLkP+Zv/1vg9xWyqsl5/vr/aJIkg2ZPAe774ro6cO1x\nHC/iXoD+Rfb/P441rkA81f2okyRpJkmyF8dxHReUf7boNU1SHMd/Ercj8M38r071zXiNLAPfB/xh\n4M8D/7zY5UzUt4ES8F3cjsjfK3Y5k5Ekydc5+IJ79Ht+D/ei5Np6/vqTJHkCEMfx9wM/CfytgpY2\ndqPXnse5fwz8FO7rfirjCo5n7Ud97cRxvAr8B+BXkyT5atHrmbCfwHVf+xbwGeBX4ji+UfCaJmkD\n+GaSJGl+Nt6J43ip6EVNyE8D306SJGb/ax8WvKYijD7f1YHtohZSlDiO/whuV+yHkyTZLHo9E/J9\nwBu46/4XwKfiOP7KSe80lmIt3KviHwG+No39qPOg803gC0mSfKvo9UxakiQ/OHg7D8Z/LkmSpwUu\nadL+M/BF4CtxHL8KVIFpeSKqsr8btoXblvSKW05h/mccxz+YJMmvA58H/n3RC5qkOI7/GPBngc8m\nSbJV9HomJUmS/w58GiCO49vAV5Mk+amT3m9cgXja+1F/CbcV9eU4jgdnxZ9PkqRT4JrEhCRJ8m/i\nOP6BOI7/G27X6QvXvWJ2xM8D/ySO4/+EC8I/kyTJdT4ffN7g6/yXgF/KdwP+H/BrxS1pomy+Pft3\ngPeBr8dxDPDrSZL81SIXNgHP/4yrQ/7uUNJrWgghhCjQ1BRQCSGEEJeRBGIhhBCiQBKIhRBCiAJJ\nIBZCCCEKJIFYCCGEKJAEYiGEEKJAEoiFEEKIAv1/IiXR0+RONJ0AAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x10a4f9410>" ] } ], "prompt_number": 25 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Representing a distribution with multiple confidence intervals" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This starts to give the impression that there is some region around our central estimate that we consider to be reliable.\n", "\n", "But, it still binarizes \"trustworthy\" and \"untrustworthy\", when in reality we have a continuous distribution giving the likelihood of observing the central value with repeated samples.\n", "\n", "To get a better feel for the shape of this distribution, we can use the fact that the error bands are translucent and stack several on top of each other by supplying a list of confidence intervals." ] }, { "cell_type": "code", "collapsed": false, "input": [ "cis = np.linspace(95, 10, 4)\n", "ax = sns.tsplot(sines, err_style=\"ci_band\", ci=cis)\n", "ax.set_ylim(-2, 2);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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mIVQJLrNeasE4ftR7PhesqmqpqiI9nNro2x4GwoUDDkYEIsIr9whdYeMq8fE2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\n52mfE8tbCoE7JYdRr4FGRn4wffZA690VFk7WTJL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dsNwKm0xPhBmLwbYQQjALH/co1zz0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\nSLWylGJIc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"text": [ "<matplotlib.figure.Figure at 0x10aecb710>" ] } ], "prompt_number": 26 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This can make for a very informative plot, but it can get cluttered when you have multiple overlapping traces." ] }, { "cell_type": "code", "collapsed": false, "input": [ "sns.tsplot(walks, ci=cis);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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j83UOqS0CAXw6zXA2S9w8MIDEJ2F1pTZeRQGSno6z+4SFmGGYV4HS7igae67Ie00cwnjV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"text": [ "<matplotlib.figure.Figure at 0x10aa22450>" ] } ], "prompt_number": 27 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Visualizing the data for each sampling unit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above methods compress the information in the data to visually present a statisical inference about the central tendency. It is often the case, though, that you will want to visualize the data for each sampling unit at some point in your analysis. Although this does not present the most informative production graphics, it can be very important in the early stages as you being to understand the structure of the data." ] }, { "cell_type": "code", "collapsed": false, "input": [ "sns.tsplot(sines, err_style=\"unit_traces\");" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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chXv9BWYsMzTeovQamZO0SBFHiQ8Bc1ujdLHGgT8KnDH2+Cse/gVv/2yDx9RU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\nedeljystIa3zhNbW+h4KC9PKj7j+iEpLbDHnpQ64zJBgHAdZD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"text": [ "<matplotlib.figure.Figure at 0x10b074c10>" ] } ], "prompt_number": 28 }, { "cell_type": "markdown", "metadata": {}, "source": [ "You may want to make the trace for each unit a different color, to make the structure more interpretable." ] }, { "cell_type": "code", "collapsed": false, "input": [ "sns.tsplot(sines, err_style=\"unit_traces\", err_palette=sns.dark_palette(\"crimson\", len(sines)), color=\"k\");" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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LhFy9qHRaTBtW35rQebvbkDQafHfakZOpXIcjLLPErI+J354iOjSaSXhffow2357rsF5J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\nyN+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Q+cc3pamyHEdHK5aGujkTnxKagWSqQFxQAVUt6Ds//v7pn4MKijr78/zAKIrHj9Te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\nWc/Ny5cYHRggNTOT7QcPLWpRTiQS5sGD+zx4cJ9oNEJ6egb19dt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"text": [ "<matplotlib.figure.Figure at 0x10b567310>" ] } ], "prompt_number": 29 }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you pass a list of error styles to `tsplot`, it will compose them" ] }, { "cell_type": "code", "collapsed": false, "input": [ "sns.tsplot(gammas[gammas.condition == \"pos\"], time=\"time\", unit=\"subj\", value=\"BOLD\",\n", " err_style=[\"unit_traces\", \"ci_band\"]);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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spdPOsmgW1ExMEdL6MmWYZA9ev253zs9NxdQUHOkFQ5exdc2Smjb9frrim193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\nW/GPRF4LMaK/J85EHvbSwbXibL3jXTV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1omJHzzataM465loHI3l+h/+gnJebTHBTA5Hw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\n+KcBJAD+BIBfQXhPLbHitfp1AP8LgL9vH/bev6/SN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"text": [ "<matplotlib.figure.Figure at 0x10bbaa350>" ] } ], "prompt_number": 30 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, it's also possible to plot each individual observation with a point, rather than joining them. This is more useful for the gestalt it presents than as a quantitative visualization but you may prefer it." ] }, { "cell_type": "code", "collapsed": false, "input": [ "sns.tsplot(sines, err_style=\"unit_points\", color=\"mediumpurple\");" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Mz+mzy2Qh7pDRwBO5tyLPrcizF+/2VyK/99r5zIYttU4vZOUlBAAdLmbRYuuc6k3kRZTL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\n+/8BXf7ZHacenoQAAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x10b567050>" ] } ], "prompt_number": 31 }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] } ], "metadata": {} } ] }	bsd-3-clause
Qumulo/python-notebooks	notebooks/File and Data Management.ipynb	1	8416	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# File and Data Management with Qumulo API python bindings\n", "\n", "This jupyter notebook walks through some of the basics of file and data management with Qumulo API python bindings.\n", "\n" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import os\n", "import re\n", "import sys\n", "import StringIO\n", "from qumulo.lib.request import RequestError\n", "from qumulo.rest_client import RestClient" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# set your environment variables or fill in the variables below\n", "API_HOSTNAME = os.environ['API_HOSTNAME'] if 'API_HOSTNAME' in os.environ else '{your-cluster-hostname}'\n", "API_USER = os.environ['API_USER'] if 'API_USER' in os.environ else '{api-cluster-user}'\n", "API_PASSWORD = os.environ['API_PASSWORD'] if 'API_PASSWORD' in os.environ else '{api-cluster-password}'" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "logged in as: admin\n" ] } ], "source": [ "rc = RestClient(API_HOSTNAME, 8000)\n", "rc.login(API_USER, API_PASSWORD)\n", "print(\"logged in as: %(name)s\" % rc.auth.who_am_i())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# A few Qumulo API file and direcotory python bindings\n", "\n", "### fs.create_directory\n", "\n", "arguments:\n", "- name: Name of directory to be created\n", "- dir_path\\: Destination path for the parent of created directory\n", "- dir_id\\: Destination inode id for the parent of the created directory\n", "\n", "\\Either dir_path* or dir_id is required\n", "\n", "_________________________________________________________________________________\n", "\n", "\n", "### fs.create_file\n", "\n", "arguments:\n", "- name: Name of file to be created\n", "- dir_path: Destination path for the directory of created file\n", "- dir_id: Destination inode id for the directory of the created file\n", "\n", "_________________________________________________________________________________\n", "\n", "\n", "### fs.write_file\n", "\n", "arguments:\n", "- data_file: A python object of the local file's content\n", "- path: Destination file path on Qumulo \n", "- id_: Destination inode file id on Qumulo\n", "- if_match:\n", "\n", "_________________________________________________________________________________\n", "\n", "\n", "### fs.get_attr\n", "arguments:\n", "- path:\n", "- id_:\n", "- snapshot:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create a working directory for this exercise" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Exception: fs_entry_exists_error - Details: Error 409: fs_entry_exists_error: { ino=2203379931 }\n", "\n", " id - 2203379931\n", " size - 1024\n", " path - /test-qumulo-fs-data/\n", " change_time - 2017-05-02T19:24:26.493575055Z\n" ] } ], "source": [ "base_path = '/'\n", "dir_name = 'test-qumulo-fs-data'\n", "\n", "try:\n", " the_dir_meta = rc.fs.create_directory(dir_path=base_path, name=dir_name)\n", " print(\"Successfully created %s%s.\" % (base_path, dir_name))\n", "except RequestError as e:\n", " print(\" Exception: %s - Details: %s\\n\" % (e.error_class,e))\n", " if e.error_class == 'fs_entry_exists_error':\n", " the_dir_meta = rc.fs.get_attr(base_path + dir_name)\n", "\n", "for k, v in the_dir_meta.iteritems():\n", " if re.search('(id\|size\|path\|change_time)', k):\n", " print(\"%19s - %s\" % (k, v))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create a file in an existing path" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Exception: fs_entry_exists_error - Details: Error 409: fs_entry_exists_error: { ino=2206379876 }\n", "\n", "We've got a file. Its id is: 2206379876\n" ] } ], "source": [ "file_name = 'first-file.txt'\n", "\n", "# relies on the base path and direcotry name created in the code above.\n", "try:\n", " the_file_meta = rc.fs.create_file(name=file_name, dir_path=base_path + dir_name)\n", "except RequestError as e:\n", " print(\"** Exception: %s - Details: %s\\n\" % (e.error_class,e))\n", " if e.error_class == 'fs_entry_exists_error':\n", " the_file_meta = rc.fs.get_attr(base_path + dir_name + '/' + file_name)\n", "print(\"We've got a file. Its id is: %s\" % the_file_meta['id'])" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "name: /test-qumulo-fs-data/first-file.txt\n", "bytes: 5300\n", "mod time: 2017-05-02T19:45:00.372716888Z\n" ] } ], "source": [ "# writing a local file from /tmp/ to the qumulo cluster\n", "fw = open(\"/tmp/local-file-from-temp.txt\", \"w\")\n", "fw.write(\"Let's write 100 sentences on this virtual chalkboard\\n\" * 100)\n", "fw.close()\n", "\n", "write_file_meta = rc.fs.write_file(data_file=open(\"/tmp/local-file-from-temp.txt\"), \n", " path=base_path + dir_name + '/' + file_name)\n", "\n", "print(\"\"\"name: %(path)s\n", "bytes: %(size)s\n", "mod time: %(modification_time)s\"\"\" % write_file_meta)" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Exception: fs_entry_exists_error - Details: Error 409: fs_entry_exists_error: { ino=2142379950 }\n", "\n", "name: /test-qumulo-fs-data/write-from-string-io.txt\n", "bytes: 10600\n", "mod time: 2017-05-02T19:45:32.137036906Z\n" ] } ], "source": [ "string_io_file_name = 'write-from-string-io.txt'\n", "\n", "try:\n", " rc.fs.create_file(name=string_io_file_name, dir_path=base_path + dir_name)\n", "except RequestError as e:\n", " print(\"Exception: %s - Details: %s\\n\" % (e.error_class,e))\n", "\n", "fw = StringIO.StringIO()\n", "fw.write(\"Let's write 200 sentences on this virtual chalkboard\\n\" * 200)\n", "write_file_meta = rc.fs.write_file(data_file=fw, \n", " path=base_path + dir_name + '/' + string_io_file_name)\n", "fw.close()\n", "print(\"\"\"name: %(path)s\n", "bytes: %(size)s\n", "mod time: %(modification_time)s\"\"\" % write_file_meta)" ] }, { "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": 2 }	gpl-3.0
thephilross/rosalind	rosalind/string-algo/Finding a Motif in DNA.ipynb	1	2460	{ "metadata": { "name": "", "signature": "sha256:2eb9c485a5825e1f7b2753d9e02fe403c577986d8799f75f567fb301b678a304" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import re" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "motif = re.compile('(?=AAGCCGAAA)')\n", "seq = 'CTGCCATCGCCACTGTAAGCCGACGAAGCCGAAAAGCCGATAAGCCGATTGAAGCCGAGAAGCCGAAAGCCGACGAAGCCGAAAGCCGAGCAAGCCGAAAGCCGAGACGTACGTAAGCCGAGTTAAGCCGAAAAGCCGAAAGCCGAGGGAACAAGCCGAAAGCCGAAAGCCGATTATAAGCCGACAAGCCGAAGTACAAAAGCCGAAAGCCGACCAAGGGAAGCCGAACTCAAAAGGCAAGCCGAGACTTGAAGCCGAGAAGCCGAAGTTCCTCAAGCCGACGTCAAAGCCGAGAAGCCGAAAGCCGATAAGCCGAGCAGGCACGATAGAAGCCGAAAGCCGAACAACGAAAGCCGAAAGCCGAAACAAAGCCGACGGGAAGCCGAGATAAGCCGATGCCCTAAGCCGAGAAGCCGAACTTTTCTAAGCCGAAAAGCCGAAAGCCGATTTAAGCCGAGAAGCCGACGATGGCTAGGCCACCAAAGCCGATTAAGCCGATGGCTAAGCCGAACGAAGCCGAAAGCCGAGTTAAGCCGAAAGCCGACAAGCCGACACGTCACAAAGCCGATTGTAAGCCGAAAGCCGATGAAGCCGATAAAGCCGATTGCAAGCCGAGAAGCCGAGGAAGCCGAAAGCCGAAAGCCGACCCATCTATACAAGCCGACAAAGCCGACGAAGCCGAAAGCCGACGGAAGCCGATAAAGCCGAAAGCCGAAAGCCGATTAGAAGCCGAAAGCCGATTCAAGCCGAGGTAAGCCGACTAAGCCGAGCAAGCCGAAGTGAAGCCGACCTGGAAGCCGAAAGCCGAATAAGCCGAGAAAGCCGAAAGCCGATTGGAATGACACGAAGCCGAAAAAGCCGACCAAGCCGATCCAGGTAAGCCGACTCAAGCCGAAAAAGCCGACAGCGTAAGCCGAAAGCCGAGCGAAGCCGAACTAAGCCGA'\n", "a = [m.start() + 1 for m in re.finditer(motif, seq)]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "\" \".join(str(a))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 25, "text": [ "'[ 2 6 , 6 0 , 7 6 , 9 2 , 1 2 5 , 1 3 3 , 1 5 3 , 1 6 0 , 2 0 0 , 2 9 5 , 3 3 0 , 3 5 1 , 3 5 8 , 4 2 6 , 4 3 4 , 5 1 4 , 5 3 1 , 5 7 3 , 6 2 6 , 6 3 3 , 6 7 6 , 7 0 2 , 7 0 9 , 7 2 7 , 7 9 5 , 8 2 0 , 8 4 7 , 8 8 9 , 9 1 1 ]'" ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
tommytwoeyes/continuity	07_Integration_Techniques/Integration_by_Parts.ipynb	2	3009	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Integration by Parts\n", "\n", "Integration by Parts is a method of integration used for integrands which consist of a product of two or more functions.\n", "\n", "## Formula\n", "$$\\int u dv = uv - \\int v du$$\n", "\n", "## Choosing $u$ and $dv$\n", "The choice of which functions to assign to the $u$ and $dv$ variables is the most critical decision you'll make while using the Integration by Parts method.\n", "\n", "In general:\n", "1. Choose $dv$ to be something that's easy to integrate.\n", "2. Choose $u$ to be something that becomes more simple, or \"smaller,\" when you take the derivative.\n", "\n", "### Professor Teague's Shortcut for Choosing $u$\n", "Professor Teague taught us an acronym which is extraordinarily useful in helping you to easily determine which function should be $u$. Unless you're integrating a product of 3 or more functions, then once you've chosen the $u$ function, all you're left with is $dv$.\n", "\n", "The shortcut is based on an acronym:\n", "\n", "> __LIATE__\n", ">\n", "> __L__ = _Natural Logarithm_<br/>\n", "> __I__ = _Inverse Trigonometric Functions_<br/>\n", "> __A__ = _Algebraic Functions_<br/>\n", "> __T__ = _Trigonometric Functions_<br/>\n", "> __E__ = _Exponential Functions_\n", "\n", "To use this method of choosing $u$ and $dv$, you examine the integrand, while reading the acronym from __left to right.__ In that order, choose the $u$ function.\n", "\n", "For instance, if you were asked to integrate:\n", "$$\\int x^2e^x dx$$\n", "\n", "$x^2$ is an _algebraic function,_ while $e^x$ is an _exponential function._ Because algebraic functions come before exponential functions in the acronym—reading it left-to-right—the choice of function for $u$ is obvious: it must be $x^2$.\n", "\n", "Similarly, in the following integrand,\n", "$$\\int x\\arctan(x) dx$$\n", "\n", "you can see that $\\arctan(x)$ (or $\\tan^{-1}(x)$—the alternate notation) is an inverse trigonometric function, which appears before the algebraic functions in the <abbr title='Natural Log \| Inverse Trig Functions \| Algebraic Functions \| Trigonometric Functions \| Exponential Functions'>LIATE</abbr>, you should assign the function $x$ to $u$." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
nayutaya/tensorflow-rnn-sin	ex2/lstm_seq60/output.ipynb	1	92147	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import yaml\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'forget_bias': 1.0,\n", " 'learning_rate': 0.1,\n", " 'length_of_sequences': 60,\n", " 'num_of_hidden_nodes': 2,\n", " 'num_of_input_nodes': 1,\n", " 'num_of_output_nodes': 1,\n", " 'num_of_prediction_epochs': 100,\n", " 'num_of_training_epochs': 2000,\n", " 'optimizer': 'GradientDescentOptimizer',\n", " 'seed': 0,\n", " 'size_of_mini_batch': 100,\n", " 'train_data_path': '../train_data/normal.npy'}" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with open(\"param.yaml\", \"r\") as file:\n", " param = yaml.load(file.read())\n", "param" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.00000000e+00, 1.25333234e-01],\n", " [ 1.25333234e-01, 2.48689887e-01],\n", " [ 2.48689887e-01, 3.68124553e-01],\n", " ..., \n", " [ -3.68124553e-01, -2.48689887e-01],\n", " [ -2.48689887e-01, -1.25333234e-01],\n", " [ -1.25333234e-01, 3.92877345e-15]])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train = np.load(param[\"train_data_path\"])\n", "train" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.00000000e+00, 1.25333234e-01, 2.48689887e-01,\n", " 3.68124553e-01, 4.81753674e-01, 5.87785252e-01,\n", " 6.84547106e-01, 7.70513243e-01, 8.44327926e-01,\n", " 9.04827052e-01, 9.51056516e-01, 9.82287251e-01,\n", " 9.98026728e-01, 9.98026728e-01, 9.82287251e-01,\n", " 9.51056516e-01, 9.04827052e-01, 8.44327926e-01,\n", " 7.70513243e-01, 6.84547106e-01, 5.87785252e-01,\n", " 4.81753674e-01, 3.68124553e-01, 2.48689887e-01,\n", " 1.25333234e-01, -3.21624530e-16, -1.25333234e-01,\n", " -2.48689887e-01, -3.68124553e-01, -4.81753674e-01,\n", " -5.87785252e-01, -6.84547106e-01, -7.70513243e-01,\n", " -8.44327926e-01, -9.04827052e-01, -9.51056516e-01,\n", " -9.82287251e-01, -9.98026728e-01, -9.98026728e-01,\n", " -9.82287251e-01, -9.51056516e-01, -9.04827052e-01,\n", " -8.44327926e-01, -7.70513243e-01, -6.84547106e-01,\n", " -5.87785252e-01, -4.81753674e-01, -3.68124553e-01,\n", " -2.48689887e-01, -1.25333234e-01, 6.43249060e-16,\n", " 1.25333234e-01, 2.48689887e-01, 3.68124553e-01,\n", " 4.81753674e-01, 5.87785252e-01, 6.84547106e-01,\n", " 7.70513243e-01, 8.44327926e-01, 9.04827052e-01])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "initial = np.load(\"initial.npy\")\n", "initial" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.97054219, 0.99542677, 0.99664259, 0.97980571, 0.94903433,\n", " 0.90724421, 0.85622942, 0.79683995, 0.72917295, 0.65274036,\n", " 0.56663024, 0.46971533, 0.360993 , 0.24014762, 0.1083529 ,\n", " -0.0309262 , -0.17179006, -0.3071579 , -0.43118346, -0.5411582 ,\n", " -0.63743806, -0.72183192, -0.79586411, -0.85972786, -0.9121083 ,\n", " -0.95087218, -0.97426164, -0.98175704, -0.974038 , -0.95231724,\n", " -0.91766751, -0.87065327, -0.8112452 , -0.73892903, -0.65298265,\n", " -0.55295956, -0.43940848, -0.3146871 , -0.18339175, -0.05171674,\n", " 0.07443893, 0.19164547, 0.29994646, 0.40220353, 0.50240505,\n", " 0.60358119, 0.70556152, 0.80333221, 0.88798857, 0.95103145,\n", " 0.98856533, 1.00176418, 0.99455345, 0.97127593, 0.93548083,\n", " 0.88959348, 0.83499062, 0.7721864 , 0.70101511, 0.6207912 ,\n", " 0.53047967, 0.42894408, 0.31536409, 0.18989888, 0.05454114,\n", " -0.08623961, -0.22583987, -0.35735172, -0.47602576, -0.58048177,\n", " -0.67187834, -0.75209439, -0.82221299, -0.88180852, -0.92912066,\n", " -0.96201837, -0.97917163, -0.98058474, -0.96723819, -0.94035602,\n", " -0.90083849, -0.84901559, -0.78464675, -0.707093 , -0.61566848,\n", " -0.51021785, -0.39190477, -0.26395342, -0.13173604, -0.00160569,\n", " 0.12127978, 0.23482224, 0.34031877, 0.44130209, 0.54164779,\n", " 0.64340353, 0.74461508, 0.8384285 , 0.91547823, 0.96872914])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "output = np.load(\"output.npy\")\n", "output" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1.00000000e+01, 5.06403089e-01],\n", " [ 2.00000000e+01, 4.65100288e-01],\n", " [ 3.00000000e+01, 4.53504324e-01],\n", " [ 4.00000000e+01, 4.30691004e-01],\n", " [ 5.00000000e+01, 4.28720653e-01],\n", " [ 6.00000000e+01, 2.52964824e-01],\n", " [ 7.00000000e+01, 1.28045768e-01],\n", " [ 8.00000000e+01, 9.34872925e-02],\n", " [ 9.00000000e+01, 6.41007647e-02],\n", " [ 1.00000000e+02, 5.14015183e-02],\n", " [ 1.10000000e+02, 4.26473096e-02],\n", " [ 1.20000000e+02, 3.38089503e-02],\n", " [ 1.30000000e+02, 2.28780098e-02],\n", " [ 1.40000000e+02, 1.59108117e-02],\n", " [ 1.50000000e+02, 1.17567368e-02],\n", " [ 1.60000000e+02, 7.27781793e-03],\n", " [ 1.70000000e+02, 5.79333678e-03],\n", " [ 1.80000000e+02, 4.57283249e-03],\n", " [ 1.90000000e+02, 4.75384993e-03],\n", " [ 2.00000000e+02, 3.71356611e-03],\n", " [ 2.10000000e+02, 3.90847493e-03],\n", " [ 2.20000000e+02, 2.94273742e-03],\n", " [ 2.30000000e+02, 3.27187241e-03],\n", " [ 2.40000000e+02, 3.23887938e-03],\n", " [ 2.50000000e+02, 2.81045493e-03],\n", " [ 2.60000000e+02, 2.46776524e-03],\n", " [ 2.70000000e+02, 2.72319233e-03],\n", " [ 2.80000000e+02, 2.48362566e-03],\n", " [ 2.90000000e+02, 2.16595759e-03],\n", " [ 3.00000000e+02, 2.25789077e-03],\n", " [ 3.10000000e+02, 2.25824770e-03],\n", " [ 3.20000000e+02, 2.17676419e-03],\n", " [ 3.30000000e+02, 1.99272204e-03],\n", " [ 3.40000000e+02, 1.86030532e-03],\n", " [ 3.50000000e+02, 1.56256172e-03],\n", " [ 3.60000000e+02, 1.55303883e-03],\n", " [ 3.70000000e+02, 1.69490988e-03],\n", " [ 3.80000000e+02, 1.58757810e-03],\n", " [ 3.90000000e+02, 1.67618145e-03],\n", " [ 4.00000000e+02, 1.47449540e-03],\n", " [ 4.10000000e+02, 1.27437117e-03],\n", " [ 4.20000000e+02, 1.17170962e-03],\n", " [ 4.30000000e+02, 1.23217446e-03],\n", " [ 4.40000000e+02, 1.24156335e-03],\n", " [ 4.50000000e+02, 1.32552418e-03],\n", " [ 4.60000000e+02, 1.12539809e-03],\n", " [ 4.70000000e+02, 1.28757383e-03],\n", " [ 4.80000000e+02, 1.25204073e-03],\n", " [ 4.90000000e+02, 1.16147427e-03],\n", " [ 5.00000000e+02, 9.60597245e-04],\n", " [ 5.10000000e+02, 7.29211257e-04],\n", " [ 5.20000000e+02, 1.06216129e-03],\n", " [ 5.30000000e+02, 9.32767813e-04],\n", " [ 5.40000000e+02, 9.26595356e-04],\n", " [ 5.50000000e+02, 9.56396339e-04],\n", " [ 5.60000000e+02, 7.88826321e-04],\n", " [ 5.70000000e+02, 1.00343931e-03],\n", " [ 5.80000000e+02, 1.00611302e-03],\n", " [ 5.90000000e+02, 9.82254161e-04],\n", " [ 6.00000000e+02, 6.86758547e-04],\n", " [ 6.10000000e+02, 7.12899957e-04],\n", " [ 6.20000000e+02, 7.27548904e-04],\n", " [ 6.30000000e+02, 7.64576369e-04],\n", " [ 6.40000000e+02, 7.58014037e-04],\n", " [ 6.50000000e+02, 7.47552665e-04],\n", " [ 6.60000000e+02, 8.14763305e-04],\n", " [ 6.70000000e+02, 7.39449926e-04],\n", " [ 6.80000000e+02, 8.02603085e-04],\n", " [ 6.90000000e+02, 7.19150470e-04],\n", " [ 7.00000000e+02, 7.02608202e-04],\n", " [ 7.10000000e+02, 6.08150032e-04],\n", " [ 7.20000000e+02, 7.03473983e-04],\n", " [ 7.30000000e+02, 7.08433217e-04],\n", " [ 7.40000000e+02, 7.24861457e-04],\n", " [ 7.50000000e+02, 5.37842978e-04],\n", " [ 7.60000000e+02, 6.41418563e-04],\n", " [ 7.70000000e+02, 5.75507409e-04],\n", " [ 7.80000000e+02, 6.03403838e-04],\n", " [ 7.90000000e+02, 6.54002884e-04],\n", " [ 8.00000000e+02, 5.54654165e-04],\n", " [ 8.10000000e+02, 4.99101589e-04],\n", " [ 8.20000000e+02, 6.56683056e-04],\n", " [ 8.30000000e+02, 5.56540850e-04],\n", " [ 8.40000000e+02, 6.25622924e-04],\n", " [ 8.50000000e+02, 4.45001177e-04],\n", " [ 8.60000000e+02, 5.40971174e-04],\n", " [ 8.70000000e+02, 5.67872601e-04],\n", " [ 8.80000000e+02, 4.75532404e-04],\n", " [ 8.90000000e+02, 4.92830703e-04],\n", " [ 9.00000000e+02, 5.90068870e-04],\n", " [ 9.10000000e+02, 5.11613616e-04],\n", " [ 9.20000000e+02, 4.64697456e-04],\n", " [ 9.30000000e+02, 4.30622429e-04],\n", " [ 9.40000000e+02, 5.59505192e-04],\n", " [ 9.50000000e+02, 5.31189842e-04],\n", " [ 9.60000000e+02, 5.05464501e-04],\n", " [ 9.70000000e+02, 3.56924895e-04],\n", " [ 9.80000000e+02, 4.99868009e-04],\n", " [ 9.90000000e+02, 3.87771142e-04],\n", " [ 1.00000000e+03, 4.41522920e-04],\n", " [ 1.01000000e+03, 4.31795575e-04],\n", " [ 1.02000000e+03, 5.34437015e-04],\n", " [ 1.03000000e+03, 4.28901461e-04],\n", " [ 1.04000000e+03, 3.87969078e-04],\n", " [ 1.05000000e+03, 3.35424847e-04],\n", " [ 1.06000000e+03, 4.73805732e-04],\n", " [ 1.07000000e+03, 4.10100678e-04],\n", " [ 1.08000000e+03, 3.57456418e-04],\n", " [ 1.09000000e+03, 3.90132511e-04],\n", " [ 1.10000000e+03, 4.39921481e-04],\n", " [ 1.11000000e+03, 3.61111946e-04],\n", " [ 1.12000000e+03, 3.77521355e-04],\n", " [ 1.13000000e+03, 4.22277371e-04],\n", " [ 1.14000000e+03, 4.74101602e-04],\n", " [ 1.15000000e+03, 3.80392885e-04],\n", " [ 1.16000000e+03, 3.88564164e-04],\n", " [ 1.17000000e+03, 3.65295477e-04],\n", " [ 1.18000000e+03, 3.40796338e-04],\n", " [ 1.19000000e+03, 3.56410659e-04],\n", " [ 1.20000000e+03, 4.02213162e-04],\n", " [ 1.21000000e+03, 3.43823718e-04],\n", " [ 1.22000000e+03, 3.85664112e-04],\n", " [ 1.23000000e+03, 3.88184446e-04],\n", " [ 1.24000000e+03, 4.04180726e-04],\n", " [ 1.25000000e+03, 3.86747182e-04],\n", " [ 1.26000000e+03, 3.41747858e-04],\n", " [ 1.27000000e+03, 3.48885718e-04],\n", " [ 1.28000000e+03, 3.13018973e-04],\n", " [ 1.29000000e+03, 3.07582028e-04],\n", " [ 1.30000000e+03, 3.73672869e-04],\n", " [ 1.31000000e+03, 3.87388834e-04],\n", " [ 1.32000000e+03, 4.19116666e-04],\n", " [ 1.33000000e+03, 3.27760878e-04],\n", " [ 1.34000000e+03, 3.08957970e-04],\n", " [ 1.35000000e+03, 3.30988230e-04],\n", " [ 1.36000000e+03, 3.46238434e-04],\n", " [ 1.37000000e+03, 3.44896660e-04],\n", " [ 1.38000000e+03, 3.70746973e-04],\n", " [ 1.39000000e+03, 3.50284274e-04],\n", " [ 1.40000000e+03, 3.14876030e-04],\n", " [ 1.41000000e+03, 3.73970484e-04],\n", " [ 1.42000000e+03, 3.45496956e-04],\n", " [ 1.43000000e+03, 3.40876257e-04],\n", " [ 1.44000000e+03, 3.22644337e-04],\n", " [ 1.45000000e+03, 3.35026300e-04],\n", " [ 1.46000000e+03, 2.88841547e-04],\n", " [ 1.47000000e+03, 3.12957622e-04],\n", " [ 1.48000000e+03, 3.08143673e-04],\n", " [ 1.49000000e+03, 2.95434991e-04],\n", " [ 1.50000000e+03, 2.98283383e-04],\n", " [ 1.51000000e+03, 3.24022199e-04],\n", " [ 1.52000000e+03, 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2.87918781e-04],\n", " [ 1.77000000e+03, 2.88327225e-04],\n", " [ 1.78000000e+03, 2.44195980e-04],\n", " [ 1.79000000e+03, 2.75243481e-04],\n", " [ 1.80000000e+03, 2.20340080e-04],\n", " [ 1.81000000e+03, 2.81056040e-04],\n", " [ 1.82000000e+03, 2.27521130e-04],\n", " [ 1.83000000e+03, 2.58146698e-04],\n", " [ 1.84000000e+03, 2.18406873e-04],\n", " [ 1.85000000e+03, 2.33469735e-04],\n", " [ 1.86000000e+03, 2.25574753e-04],\n", " [ 1.87000000e+03, 2.32736464e-04],\n", " [ 1.88000000e+03, 2.42906332e-04],\n", " [ 1.89000000e+03, 2.63917493e-04],\n", " [ 1.90000000e+03, 2.74311577e-04],\n", " [ 1.91000000e+03, 2.39641056e-04],\n", " [ 1.92000000e+03, 1.75235909e-04],\n", " [ 1.93000000e+03, 3.02739878e-04],\n", " [ 1.94000000e+03, 2.05147531e-04],\n", " [ 1.95000000e+03, 2.08231912e-04],\n", " [ 1.96000000e+03, 2.31014754e-04],\n", " [ 1.97000000e+03, 2.28317673e-04],\n", " [ 1.98000000e+03, 2.48514290e-04],\n", " [ 1.99000000e+03, 2.36065680e-04],\n", " [ 2.00000000e+03, 2.02242474e-04]])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "losses = np.load(\"losses.npy\")\n", "losses" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x112980940>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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U4nLoZQAGDRrEl19+yf37922eQQjxpv6/9CdL0ix0L9Y9xq9dL1c9fOr5UHdFXfaEvDls\nu2HDhjRs2JBdu3bFeDYhHNXw7cMpkq4IfT/v+9rj71tlVpo3K7h69SG1atWmadOv8PNrpzoOLi6w\ndCnEjx9E4cJ9sFiseydTxkQbn9TIGCwWaNsW4scfyfz5819bNc7WNdI0jbGVx+JV1Iuq31Xl9pPb\nAPTo0YOCBQva9Nr2RF5LxmeWGq36fRWbzm9icd3FaJrG8ePHiemRRjWz12R5w+U09G/Ijks7Xvte\ntWrVWLFiBU2aNOHChQtWva5ZauTopE4x6/iN4/wQ+AOTq02OXGQMYOPGjdSvX/+dPyfNWzRZLDrF\ni3uSI0dlvv++q+o4kZydwc8vE1eu7KRJk5mq4wjhkLy94fZtmDw5zict920NfT7vQ/N8zfli2Rc8\nfPFQSQYhHN3FPy/itcEL/8b+uMZ15eeff6Z27dqEhobGeJaq2aqysslKmq1q9tpecACVK1dm+PDh\nNGrUiKdPn8Z4NiEchUW30G1DN8ZVGkeK+CkiH3/8+DEdO3Zk0HsWzpA5b9FUs+YEdu1azfXru0ic\nOI7qOG/YufMilSp9zqxZa/DyKq06jhAOY8uWV3fdDh2CDBnUZtF1nZ6bevLb7d/Y1HIT8WLFUxtI\nCAdTd0VdSmUsxeAygwkODqZkyZIEBARQrlw5ZZl+Df6VJiubRC5L/jdd12nVqhV58uRh6NChyvIJ\nYc/mH5mP3yk/drfb/do+jADXr18nXbp075zzJs1bNGzfDg0bLufnn8tRooRxJ/+PGrWJb77pxPHj\nRyhQII3qOELYvZAQKF4cVqyAihVVp3nFoltotaYVT8KfsLrpalyc/lnt8v79+yRLlkxhOiHs145L\nO+jwYwfOdD+D/lKnTJkytGjRgr59+374h23s7w2BD3Q4QNpEaSMff/r0KS4uLsSOHVthOiHs0+0n\nt8k3Jx9b22ylQOoC73yeLFhiZVevQsuWsHp1S0M3bgAjRtSgbNlOlCvXnLCw6DfGMiba+KRG6jx8\n+IJixXrSvfuj9zZuMV0jJ80J3/q+hEWE0eHHDlj0V5OhAwMDKViwILdu3YrRPGYhryXjM3KNLLqF\nfr/049vK3xLHJQ49e/Yka9as9OnTR3U0AFrkb0Gnwp2os6IOT8L+WeEsfvz4Vm3cjFwj8Q+pU8wY\nuGUgrQu0fm/j9j7SvH2CFy+gcWP48kuoXFl1mo+zdetwcucey5AhbzTwQggrKlnyS1xcrjN0aELV\nUd4Q2zk2q5uu5vz98wzd9mo4VN68efH09KR58+a8fPlScUIh7MuyU8uI4xKHpnmb8uDBA/78808W\nL1782uIEqg0tO5QCqQvgsdqDCEuE6jhC2LVdl3ex7dI2vq7w9SefQ4ZNfoKePeHKFfjf/8BA778f\ndP8+FCkCEye+aj6FENbVubMfS5eO48KFQ2TIkFh1nHe6+/QuRRcUZWLViTTJ24SIiAhq1qyJu7s7\n3t7equMJYReehj8l56yc+Df2p1TGUqrjvFd4RDg1ltcgT8o8zKgxQ3UcIexSWEQYheYXYlSFUTTK\n0wh4NW3h2LFjVKlS5Y3ny7BJK9m6NYK1a8HX11yNG0CyZODvDz16wJ07qtMIYV+OHbvOokX9WL48\nwNCNG0CK+ClY02wNXhu9+O32bzg7O7N8+XL8/Pw4ePCg6nhC2IWp+6fyeYbPDd+4AcRyjsWqpqvY\ndmkbMw6+2bzdvn2brVu3KkgmhP2YfWg2mZJkomHuhpGP9evXjx9//DFK55HmLQru339GrVpFGDHi\nEq6uqtN8muLFoUULiM48aRkTbXxSo5hXp04PSpfuSuPGHzeGXXWNCqctzJRqU2jg34DQ56GkSJGC\nKVOmMGbMGKW5jEZ1ncSHGbFGNx/fZOqBqXxb+VvVUT6aa1xXNrTYwNjdY9l3Zd9r37tz5w4eHh5c\nu3btk85txBqJN0mdbOdJ2BO893ozocqEyGHTW7duZfv27YwdOzZK55LmLQpq1x5NqlQ56NQpi+oo\n0TJqFOzZA+vXP1cdRQi7sHq1hZcvq7B+vbmW1W5dsDU13GrQck1LLLqF5s2bExAQoDqWEKY3csdI\n2hZsS7Zk2VRHiZLMrplZVGcRzVc1597Te5GP582bl+7du9O9e/cY31hcCHsw5/Acyn5Wlvyp8wOv\nVnTt0qULc+fOJVGiRFE6l8x5+0gBASdp3rwqJ06csovl9pcsOU+XLjW5du04qVIlUB1HCNMKDYV8\n+eD770Hhlk2fLDwinMp+lamYuSLfVPxGdRwhTC/wdiAVl1bkj+5/sGjWIrp3706CBOb6/9kBvwzg\nzN0z/OjxY+QeVC9evKBQoUKMHj2aRo0aKU4ohHk8DntMthnZ2NZmW+SeigMHDuTq1at8//337/w5\n2ectGsLCIkiatCRNmnTF17eD6jhWkyVLK5InT82RI5NVRxHCtLp2ffW/8+apzREdtx7foujCosyq\nMYt6ueqpjiOEqdX7oR4VM1ck8e+JmTdvHvv378fZ2Vl1rCgJjwinnG85GuRqwMDSAyMf37t3L02b\nNuW3334jadKkChMKYR4T9k7g6I2j+Df2B+DZs2eULVuWjRs3kipVqnf+nCxYEg1Nm84gduxELFnS\nXnUUq9q4cSrHji1j6dLDUfo5GRNtfFKjmLF7N/z0E3zKAo1GqlHqhKlZ1WQVndZ34sqDK6rjGIqR\n6iTezkg1OnnzJIevHaZ+hvoMHjyYhQsXmq5xg1cLmPg39mfy/snsDdkb+Xjp0qVp2LAhW7ZsidL5\njFQj8W5SJ+t7HPaYyfsnM6LciMjH4sWLx+HDh9/buL2PNG8fEBwMv/5ai1WrFuHkZLLlJT8gd+6U\ndOkyma5dO/H0abjqOEKYyvPn0KkTzJwJSZKoThN9JTKUoFeJXrRb1y5yA+9nz55x//59xcmEMI9x\ne8bR7/N+DOo3iI4dO1KwYEHVkT5ZpiSZWFRnER6rPbj79G7k4zNmzKBp06YKkwlhHrMOzaJi5ork\nTZX3tcejs9ejDJt8D12HGjWgfHkYMkR1GtuwWHRSpapBsWKV2bRpgOo4QphGtWpTeP68OLt2lVEd\nxWpeWl5SzqccTfM2pXfJ3kyaNIn9+/ezevVq1dGEMLw/7v5BOZ9yzMo2i2GDh3Hy5EnixYunOla0\nDfhlAL/f/Z2fPH4y1ObiQhjdoxePyDYjGzs9d5InZZ4o/7zMefsE69bB0KFw/DjEiqU6je3s23eV\nWrWcCQxMS7p0qtMIYXy7dl2iQoWiHDlymsKF7etFc/7+eUouKsmvnr+SLXE28uXLx9y5c6latarq\naEIYmudaT9ySuaH/qlOqVCkqV66sOpJVhEeEU2pJKToV7kTnIp1VxxHCNL7d/S2nbp9iRaMVn/Tz\nMuctil6+hEGDYOJE+27cAEqVykDnzmkZOfLjni9joo1PamRbbdoMpUKFL6PVuBm1Rm7J3Pi28re0\n+l8rnGI5MX78eAYOHIjFYlEdTQmj1kn8wwg1uvTnJdafXU+P4j0YPny43TRu8Gr+m089H4ZuH0rI\ng5BPOocRaiQ+TOpkPQ9fPGTqgamRc91CQkJ4/tw6W3RJ8/YOixZB+vTwxReqk8SMIUNe3WkMDFSd\nRAhj8/M7wpUrv/L999HY6d7gOhbuSMbEGRm5YySNGjUibty4713OWAhHN2HvBLoW6YprXFfVUWwi\nX6p89C7Rm07rO72xz9vly5cVpRLCuGYdmkXVbFXJnTI3uq7TtGlT1q1bZ5Vzy7DJt7h+/RFubr3Z\nuXM+xYu7KM0Sk6ZNg61bX62eJ4R4k8Wikzx5JerUaYGfXyfVcWzq1uNbuM93Z2WTlRACLVu25OzZ\ns8SJE0d1NCEM5fqj6+Sbk4+gHkGkTJBSdRybCY8Ip8SiEvQo3oP2hV6tvv3o0SOyZs3K/v37cXNz\nU5xQCGN4EvaELNOz8Kvnr+ROmZuVK1fy7bffcuTIEZycPv6+mQybjAIPj8mkTv3CoRo3gG7d4Pff\nYccO1UmEMCZ///tERKRkwYJ2qqPYXOqEqZlfez5t/tcG9+LurF69mtixY6uOJYThTNo3CU93T7tu\n3ODV8Enf+r4M2jqIqw+vApAoUSL69OnDEHtd1U2IT7Dk+BJKZypN7pS5CQsLY8iQIUycODFKjdv7\nSPP2HydO3GD37pl8991Y1VFiXJw4MGTIPZo18+Lly3fPb5Ex0cYnNbK+ly9hzJjkLF8eQNy40f9g\nxww1qpuzLqUzlWbUr6MoWrSoQ640Z4Y6OTqVNbrz5A6+J3xpkraJsgwxqUDqAvQs3pMuP3WJHD7Z\nu3dvDhw4wIEDB975c/I6MgepU/SFR4Qzef9kBpUeBMC8efPInj27VefBSvP2Hx4eX1OkSHvKlPlM\ndRQl2rdPxtOnR+jd2191FCEMxccHUqaE2rVVJ4lZk6pOwveEL6dvnVYdRQjDmXZgGpXjVqbRF42s\nthiB0Q0pM4Trj67jd9IPgPjx4zN69Gj69+//xnw4IRyNf6A/mV0zUzJDSR49esTYsWOZMGGCVa8h\nc97+5ccff6d+/QpcuBBElixJlWQwgunTf6V/f0/u3fuDxIllfosQT55Ajhywdi0UK6Y6Tcybf2Q+\nfqf82N1uN06afOYnBMCD5w/IOj0ruTblolWzVnTr1k11pBhz4uYJqn1XjZNdT5I2UVoiIiIoVKgQ\no0aNon79+qrjCaGErusUmFeAiVUn8oXbF+i6zsmTJ3F3d/+k88mct48wcuRx6tcf6dCNG8CXX5Yn\nefL8tGw5S3UUIQxh8mQoV84xGzeATkU6YdEtLDm+RHUUIQxj/tH5FHhUgHs379GxY0fVcWKUexp3\nOhTqwMCtAwFwdnbG39+fihUrKk4mhDobz23EWXOmerbqwKvm61Mbt/eR5u0vu3ZBaGhLVqzorjqK\nISxYMJ4NG8Zz+XLoG9+TMdHGJzWynnPnQpk27SFjrTwN1kw1ctKcmFdrHl9t+4o7T+4wZcoUjhw5\nojpWjDBTnRyVihq9ePmCqfumcm3NNcaPH08se98Q9i2GlhvKzuCd7A3ZC0Du3LlJkiTJW58rryNz\nkDpFj/debwaVHmTz+eHSvP3lm29gxIhXi3YIqFs3D7lzt2H0aNn4TTi21q3HkzHjSLJmVZ1ErYJp\nCtK6QGsGbBlA7NixGTVqlOpIQijz3anvSHM1DUkSJKFevXqq4yiRMHZCJlSZQM9NPYmwRKiOI4RS\n+67s4+rDqzTJa/vFi2TOG7BvH7RsCWfPggN+ePZOQUFQpgxcvAiJEqlOI0TMO3fuHjlz5mDPnuOU\nKpVJdRzlHr14RN45eVlYYyHtKrVj48aNNhkSIoSRRVgiyDMnD5PKTsItthu5c+dWHUkZXdcp71ue\nVgVa0blIZ9VxhFCm3g/1qJ6tOl7FvKx2Tpnz9h6jR8PgwdK4/VfOnFClCsyZozqJEGp06DCd7Nkb\nSOP2l0RxEjH9i+n03tab3n17M2bMGNWRhIhx64LW4RrXldoFajt04wavfrmcUWMGw3cM5/6z+6rj\nCKFE4O1ADl49SDv3doSEhDB+/HibXs/hm7ddu55w+rSOp6fqJMY0dChMnfpqtb2/yZho45MaRV9I\nyAP27JnD3Lm22XzWrDWqn6s+WZNm5WWhl+zevZvAQPseWm3WOjmSmKyRrusxNq/FLNzTuNModyNG\n7BgR+diePXvYtGlT5LG8jsxB6vRpJu6bSM/iPYkXKx7e3t7cv2/bDzIcvnlr1qwlVar4y1y3d8iX\nD0qXhgULVCcRIma1bz+LLFlqUqlSNtVRDEXTNCZVncTUY1Pp2rMrP//8s+pIQsSYXZd3Efo8lHo5\nHXOe27uMrjiagMAATt06BcCLFy/o06cPEREyF07Yt5AHIfwY9CNexby4fv06K1asoF+/fja9pkPP\neVu58hTNm1fnzp2LJEsWL0auaUbHj0PNmhbOnbOQMKGL6jhC2NyjR5Ap0wH8/VNSrZo0b2/TZX0X\nEsZOyOTqk1VHESLG1Fxek4a5G9KxsGNtDfAx5h6eyw+BP7Cz7U4ASpUqRe/evWnWrJnaYELYUJ/N\nfXB2cmZStUn07dsXi8XCtGnTrHJumfP2Fn37jqVGjX7SuH1AoUIQJ04vOnaU22/CMcybB9WqlZTG\n7T2+rvAV6Gg9AAAgAElEQVQ1vid9CQ4NVh1FiBhx6tYpDh0+RMb7GVVHMaTORTrz4PkDAgID0DSN\n4cOHM2bMGCwWi+poQtjEvaf3WHpyKX1K9uH27dv4+voyYMAAm1/XYZu3n346w7VrO1m0qKvqKKYw\nfHhrVq3y5vHjMBkTbQJSo0/39ClMmQLDhtn2OmavUdpEaelZvCdDtw9VHcWmzF4nRxBTNZqwdwJJ\n9yTl+pXrMXI9s3F2cmZK9SkM3T6U8IhwatSoQZw4cVi3bp28jkxC6hQ1sw/PpkGuBqRPnJ4NGzbg\n4eFB+vTpbX5dh23eevYcR5UqX5ImTULVUUyhQ4cSJEmSk+7d/VRHEcKmFi6EkiUhf37VSYyv3+f9\n2H5pO0evH1UdRQibCg4N5sdtP/LizgtatWqlOo5hVcpSicyumfE54YOmaQwbNowlS5aojiWE1T0J\ne8KsQ7MYUPrVnbZ27doxY8aMGLm2Q855O3cOChWaxZkzrcmYMYlNr2VPZs3aTZ8+njx6FETcuDL3\nTdif58/BzQ3WrYMiRVSnMYd5R+YREBjAtjbbAGQFPmGXem3qxYaRGxjQfgBdu8qInfc5ePUgjVc2\n5lzPc8R2ik14eDhxZFU4YWdmHpzJjuAdrGm2xmbXeNecN4ds3jp1gvTp4euvbXoZu+TqWgEPj47M\nnSufPAr7M3jwAQ4fzsS2belURzGN8Ihw8s/NT0NLQ8KvhDNx4kTVkYSwqrtP75J1cFYSrk7IpYuX\npBH5CPV/qE/5z8rT5/M+qqMIYXXhEeG4zXQjoHEAJTKUsNl1ZMGSv9y6BatWQY8eqpOY05Ah37B2\nbTAG6/nFf8i49ah7+dLC1Kme1KlzNkauZy81iuUcC+8q3qx5sobFixcTGhqqOpJV2Uud7JmtazTr\n0CzSn0tPn959pHH7SKMrjmb83vE8evEIkNeRWUidPo5/oD9ZXLPYtHF7H4dr3ubOhWbNIEUK1UnM\nacCA8sSKVYZff1WdRAjrGjNmM87O8enVq7zqKKZTN2ddUqZJSc7Pc7Jw4ULVcYSwmidhT5hzeA4r\nF6+ke/fuquOYRv7U+amStQrTDlhnyXQhjELXdbz3ejO4zGBlGRxq2OTz5/DZZ/Drr5Arl00u4RDm\nz4cNG+DHH1UnEcJ6kiWrQrNmnjIk+BPtCdlDsxnNcPJ34uLFi8SKFUt1JCGibebBmey8vJPVTVer\njmI65++fp+SikpzteZZk8ZKpjiOEVWw4u4Gh24dyvMtx1q9fT1BQkM22B5Bhk8CMGZcpUiRCGrdo\nat0aDhyAszEzukwImwsIOMmDB2eYOLGp6iimVSZTGXLkz0GC1AlYtWqV6jhCRFt4RDiT909mYKmB\nqqOYklsyNxrlbsSEvRMA2LdvH5MnT1acSojoGb93PINKD0LTNCZMmECWLFliPIPDNG8Wi86IETWp\nXHmX6iimd+jQTrp0genTVScR7yLj1qPmq6+mUrVqTxImjB1j17THGg0rO4xHxR4RfDlYdRSrscc6\n2Rtb1SggMIDMrpmVzWuxB8PLD2fB0QWs3riaDBkyMG7cOB4+fKg6lngHeb97v70he7n28BpN8jbh\n4MGDXLt2jfr168d4Dqs0b5qmfaFp2h+app3VNG3QW75fXtO0UE3Tjv31ZePtb9/07be/oGnO9OlT\nIaYvbZe6d4fly19w9epT1VGEiJYbN+DOna+YO1eW/46uSlkq8VnRz8hSO+Y/iRTCmnRdZ8K+CQwq\n/cavNCIKMiTOgKe7J8tOLSNTpkxUq1aNxYsXq44lxCfx3utN/1L9cXFyYerUqfTq1QsXl5jfOiva\nc940TXMCzgKVgevAYaC5rut//Os55YF+uq7X/Yjz2WTOW4oU1alf34NFizytfm5HlTt3Dz77LAOb\nN6ubtClEdA0bBqGhMGuW6iT2YdO5TQzYMoBT3U7hpDnM4A5hZzad20SP2T2YUnEK9erVUx3H1G4/\nuU3OWTk50/0MIb+H0LRpU86fP6/kl14hPlXg7UAq+1Xm0peXuHPjDoUKFeLSpUskTpzYZte05Zy3\n4sA5Xdcv67oeDvwAvO2dTtnOrf/732/8+edppkzxUBXBLo0a1YktW2by+HGY6ihCfJKnT18twPPl\nl6qT2I8v3L4grktc1v2xTnUUIT6Z915vYu+JzbNnz1RHMb1UCVLhkc+DGQdnULx4cTJmzMj//vc/\n1bGEiJIJ+ybQs3hP4sWKx759++jcubNNG7f3sUbzlh648q/jq3899l+fa5p2QtO0DZqm5bHCdT/a\noEFTqVSpO4kTy/4s1vD3mOgmTQri6pqb/v0D1AYSb5Bx6x/Hzw9Kl4bs2WP+2vZaI03TGFZuGKN3\njcZoqxl/Cnutkz2xdo0OXj1I0Okgntx+QqNGjax6bkdVxlKGBUcX8PDFQ/r06cNPP/2kOpJ4C3m/\ne7uQByGsD1qPVzEvAJo3b863336rLE9M3bM+CmTSdf2ppmk1gLVAjnc92dPTk8yZMwPg6uqKu7s7\nFSpUAP75h/Wxx2vW7CQkJAPr13f5pJ+X4zePT5w4EXlct24VfHy+Yc6cljg5aYbIJ8f/MEoeIx5b\nLDB27E769wdQn8eejuuWr8vwHcPxXu5NifQlqFixoqHyReX43+93Rsgjx7Z/v5txawYZAjNQtFZR\n9u7dq/zPZw/H6RKlo8CzAgxaOIjZXrNp0KCBofLJsbzfve946v6pVHWqysmDJ23+9x8aGgpAcHAw\n72KNOW8lga91Xf/ir+PBgK7ruvd7fuYSUETX9ftv+Z5V57yNHAm3bsG8eVY7pfiXly8tJEiQl/Hj\n58piMMJUpk07xoIFcQkMzIOmbFC3/fL/zZ+B3wyke7HuDBwoS60Lcwi6G0SpqaXQ5+pcvHgRV1dX\n1ZHsxvEbx6mzog4Xel0gjouMhBLmcO/pPbLPzM7pbqdJn/htAwttx5Zz3g4DbpqmfaZpWmygOfDa\n9s2apqX+138X51XT+EbjZm0vXryaz9K7t62v5LhcXJzo3HkSa9bIG7Ewl9GjB1G+/DFp3GykcZ7G\naFk1Jk2bxMuXL1XHEeKjTNw3kcJ3C9OqVStp3KysUNpC5EmZh+Wnl6uOIsRHm314Ng1yNYjxxu19\not286boeAfQAfgECgR90XT+jaVoXTdM6//W0xpqm/aZp2nFgGtAsutf9GKtWQYECyKbcVvb3rd6/\njR9fi99//5yQEDV5xJv+WyPxuk2bgggNPY23dxNlGey9Rs5OzoxqMYoXCV6Yen6LvdfJHlirRtce\nXmPNmTWsmLKC8ePHW+Wc4pW/azSo9CAm7puIRbeoDSTeSt7vXvck7AmzDs1iQOkBqqO8xhp33tB1\nfbOu6zl1Xc+u6/r4vx6br+v6gr/+e7au6/l0XS+k63opXdcPWuO6HzJnDnh5xcSVHFuCBNC69au7\nnEKYwVdfzaVkyQ6yiJGNeeTzwKWEC95T3zmKXgjDmHZgGm0KtiFFghTEjx9fdRy7VClLJRLESsD6\noPWqowjxQUuOL6FMpjLkSpGLoKAg2rdvrzoSYIU5b9ZmrTlv27ffpnXrBFy+nADZSsT2goKgfHm4\nfBniyO/DwsBu335CmjSZ2L37GKVLf6Y6jt0bvW004xqN4+Shk+TIkUN1HCHe6s9nf+I2043jXY6T\nKUkm1XHs2srAlUw9MJW97ffy3Xff4erqSt26H9wGWIgYFR4RjttMNwIaB1AiQwl69+5N/PjxGTdu\nXIxlsOWcN0Pq3n04efLMkMYthuTMCfnywerVqpMI8X79+39P6tRlpHGLIV6fe0EpOHz6sOooQrzT\n3CNzqZ2jtjRuMaBh7obcfnKbPSF7SJgwIRMmTFAdSYg3+Af6kzVpVkpkKMGTJ0/47rvv6NKli+pY\ngJ02b5cvhxIUFMDkye1UR7FL7xoT7eUFM2Y8itkw4q1k3Prb6TocO1aPceMmq47iMDVKHj85rbq2\n4mKKi6qjfBJHqZOZRbdGz8KfMePgDAaWklVRbeXfNXJ2cqZ/qf547/Wmbt26BAcHc/LkSXXhRCR5\nv3tF13W893ozqPQgAFasWEGZMmX47DNjfOhrl81b375+ZMz4BQUKpFEdxaHUqaNz9GhRAgLkTVgY\n04ED8OJFKtq2dVMdxaH0KtGLuUfmEhYRpjqKEG9YenIp7q7ubPDdoDqKw/B09+Tw9cOcDz1P586d\nmTt3rupIQkTaeG4jzpoz1bNVR9d1Zs+ejZeBFtGwuzlvFotOvHi5mTx5IT16lLViMvExKlcezfXr\nVzlzRlYvEcbTujUUKgR9+6pO4ngq+1WmvXt7WhZoqTqKEJFeWl6Sc1ZO6vxZh1t/3GLFihWqIzmM\nr7Z9xdPwpwwqOIg8efIQHBxMkiRJVMcSgrI+ZfEq6oVHfg+uXr1Kw4YNOXDgAE5OMXvPy2HmvE2Z\nsgNNi4WXVxnVURzS5MkdCQoKICTkgeooQrzmzh346Sfw9FSdxDF9WeJLph+cjtE+MBSObfXvq0mT\nIA2bV2w21CfrjqBLkS58d+o7EidPTLVq1diyZYvqSEKwN2Qv1x5eo0neV1sJZciQgYMHD8Z44/Y+\nxkliJRs3utKx40ScnGTnXVt535hod/e0ZMhQjb59/WIukHiDjFt/05Il0KABJEumOskrjlajWtlr\nce/ZPQ5cPaA6SpQ4Wp3M6FNr9Pe8lhrONYgVKxZlysiHvrbythp95voZZTKV4fvT37Ns2TIaN24c\n88HEa+T9Drz3etO/VH9cnP5Z8VDTjNVT2FXzdvUqnDhRmG+//UJ1FIfWr58X69fPwWKRT9iFMYSF\nRTBlyla6dZN/k6o4OznTo2gP6teoz507d1THEYKtF7cSFhHGsfXH8PLyMtwvaI7Aq6gXc47MwUWW\nBhcGEHg7kEPXDtHO3dgLHtrVnLcRI+D+fZg1y8qhRJRYLDqpUg3Ex2c4deokVh1HCEaM+IkpU0bz\n+PFB1VEc2sMXD0lZOiX96vRj3MiY2ytHiLep7FeZBhkbMKLRCC5fvkyiRIlUR3I4Ft1Czlk5WVp/\nKaUyllIdRzi4tmvbkiNZDoaWG6o6CuAAc97CwmDhQujWTXUS4eSkMWrURHx9pXETxjBv3hw8PGQ+\ni2qJ4ySmQesGzJk7h4iICNVxhAM7cv0I5+6do0u5Lvz+++/SuCnipDnRrWg35hyeozqKcHAhD0JY\nH7Qer2LG/13Bbpq3H3+E7Nkhb17VSezfx4yJbtUKtm+HGzdsn0e8Scat/2PPnmDu3j2Et3dT1VFe\n46g1GtN6DE9cnrB+43rVUT6Ko9bJTD6lRt57ven3eT9iOcciTRrZVsjW3lcjT3dPNpzbwO0nt2Mu\nkHgrR36/m7J/Cu0LtSdpvKQADB48mPPnzytO9XZ207zNnfuQzp1VpxB/S5wYGjUCX1/VSYSjGzJk\nMQUKtCRZsniqowjALZkbOarlYOy0saqjCAd19t5Zfg3+lY6FO6qOIoBk8ZLRMFdDlhxfwosXLxg4\ncKDcmRcx6t7Te/id9KNPyT4AXL16lQULFpA2bVrFyd7OLua87dkTTLlypXjwIIREiWTSq1EcPAgt\nWsC5c2CgFVaFA3n+/CUJEmRm1arNNGiQT3Uc8ZfvDn1Hd4/u3D1zl9ixY6uOIxxM5/WdSZswLd9U\n/EZ1FPGXo9eP0iigERd6XaBE8RKMHTuW6tWrq44lHMQ3O78h5EEIi+stBmDUqFHcvHmTOXPUDue1\n6zlvQ4YsIX/+xtK4GUzx4pAgAWzbZlEdRTiojRstuLnNlMbNYJoVaUa8bvG4/Oiy6ijCwdx4dINV\nv6+iZ4meqqOIfymSrghpEqZh47mNdOrUiQULFqiOJBzEs/BnzDkyhwGlBwAQERHB4sWL6dSpk+Jk\n72b65u3585fs27eEESOM+5dsbz52TLSmQcmS6+nUqY1tA4k3OPK49X/z9Y3NoEENVMd4K0euUWzn\n2LQp0IYlx5eojvJBjlwns4hKjaYdmEarAq04tucYly/Lhwcx5WNq5FXs1bYBHh4ebN++nVu3btk+\nmHiNI77ffXfqO4qnL06uFLkA2LJlCylTpqRQoUKKk72b6Zu3sWM3ET9+Rho1yq86iniLIUPKcPny\nTwQF3VUdRTiYa9dgzx5o1kx1EvE2HQp3wPekL+ER4aqjCAfx4PkDFh1fxJfFv6RTp07cv39fdSTx\nL03zNuXo9aPcfnmbhg0b4iuT5oWNWXQLUw9MpW/JvpGPrVmzxtB33cAO5rylSVOXGjXq4+PT3oap\nRHRky9aGfPncWbeu74efLISVjB4N16/D3Lmqk4h3KetTlv6f96dernqqowgH4L3Hm9/u/EbLeC0Z\nNmwYR44cUR1J/Ee/n/sR1yUudRLUoX379gQGBsrm6cJmNpzdwPAdwzna+Wjkv7OIiAgiIiIMMR/b\nLue8Xb2q8+efmRk/Xj5aN7I+fTqxefNCLBZjfVAg7JfFAosXg8E/PHN4HQt1ZNHxRapjCAfw4uUL\nph+czsBSA1m4cKHhP1l3VJ7unvid8qNosaLs3LlTGjdhU1MOTKHv531f+3fm7OxsiMbtfUzdvPn6\narRrN4PUqROojuJQojom2surDADz5u21QRrxNo44bv3ffvjhBkmTPqdwYdVJ3s3RawTQOE9jdmze\nwfT501VHeSepk/F9TI38A/3Jnzo/qUjF9u3b8fDwsH0wEeljX0f5U+cndYLUbA/eTqpUqWwbSrzB\nkd7vTtw8QdDdIJrmNdYesB/DtM2bfLJuHk5OGnXr9mHFCpkcLmLGwIE9yZPHT3UM8QEJYiegUu5K\njP12LEYbwi/sh67rTDswjS9LfImvry8NGzYkceLEqmOJd/B098T3hK/qGMLOTdk/hZ7FexLb2dh3\n2d7GtHPefvkFBg+GY8diIJSItjt3IHt2CA4GV1fVaYQ9++23WxQokIuQkMtkyCC/oBnd4WuHKV24\nNNtWb6NsmbKq4wg7tPvybjqu78iZ7me4dvUaERERZM6cWXUs8Q73nt4j24xsBPcOxjWu/MIgrO/a\nw2vkn5ufC70ukDReUtVx3snu5rwtXCh33cwkZUqoXh2WL1edRNi7gQOX4ubWQBo3kyiarigpyqRg\nzNQxqqMIOzX94HR6Fe+Fk+ZExowZpXEzuOTxk1M1W1UCAgNURxF2atahWbQq0Cqycbt37x4zZ85U\nnOrjmbJ5u3lTZ8sWaNFCdRLH9Kljojt1ggULwGA3e+2SI41b/zeLRWfr1oX072/8T3YctUb/pWka\nPTv3ZMfmHYSGhqqO8wapk/G9r0bBocHsDN5JW/e2MRdIvCGqryPPgp74nPABYN++fQQGBtoglfgv\nR3i/exz2mIXHFvJliS8jH/Pz8+PQoUMKU0WNKZu3Tp2m4ObmTZIkqpOIqKhUCR4/BlmdWdjKzJm7\ncHKKQ8eOJVVHEVHQtVxXyAYr161UHUXYmdmHZuPp7knC2AlVRxFRUN2tOsGhwfxx9w/27dvHpEmT\nVEcSdmLpiaWUz1yebMmyAa/mxC5atMhUK9Cabs6bxaITL15epk5dELmKoTCP0aPhxg2YM0d1EmGP\natU6TOrU11myRPYNM5vmPzSnVJZS9CrRS3UUYScehz0m87TMHOl8hMyumVXHEVE04JcBuDi58GW+\nL8mdOzdXrlwhYUJpwsWni7BEkGt2Lnzr+VI6U2kADh48SKtWrTh79qzhtqawmzlvS5YcRNdf0rVr\nadVRxCfw8Ahj0aKahIY+Vx1F2JlHj2Dv3mKMHy+Nmxl5FvVk+WmZFCusx++kH+Uzlyeza2aOHDki\nK5qazN97vqVMlZKyZcuycqXcmRfR8/OFn3GN60qpjKUiH/Px8cHT09Nwjdv7mK55mzx5CZUqtcfJ\nyTx/yfYmOmOi3dxikyhROCNGrLVeIPEGRxi3/l8BAVChAphlayBHrNH7VMlahcuhlzl375zqKK+R\nOhnf22pk0S1MPzid3iV6c+rUKRo0aIDFYon5cAL4tNdR3lR5SZ8oPVsubqF9+/b4+PhYP5h4jb2/\n380+PJvuxbpHNmrPnj0jICCANm3aKE4WNaZq3m7ffkJQ0CrGjjXXX7J4XfPm7fj+e3kTFtbl4wPt\n2qlOIT6Vi5MLzfI2k7tvwip+Pv8zCWIloEymMvj4+NC2bVucnZ1VxxJR9Peeb7Vq1SIoKIjz58+r\njiRM6uKfFzl07RDN8jaLfCxu3Ljs37+fjBkzKkwWdaaa8zZq1DEWLpzFlStLYjiVsKb795+RIkUG\n9u8/QYkS5nrBCGM6exbKloWrVyFWLNVpxKc6fO0wLda04GwP4809EOZSfVl1WuZvSfPczcmQIQP7\n9u3Dzc1NdSwRRfef3SfL9CwEfxnM2VNnyZs3r8x7E59k4JaBWHQLk6qZZ/Ebu5jztn17YaZNk8bN\n7JIli0fu3E0ZNsxPdRRhJ5YsiaB1a2nczK5ouqJYHlsYPnm46ijCxM7cOcOpW6dolrcZGzZsIFeu\nXNK4mVSyeMmonq06P/z2AyVKlJDGTXySZ+HP8DnhQ7ei3VRHsQrTNG/nz8Pvv0OdOqqTCGuMie7f\nvx17926VPd9sxN7Hrf9bWFgEkyblplat66qjRIkj1ehjaZpGc/fmTPp6Erdv31YdB5A6mcF/azTv\nyDw6FOpAHJc4+Pj40E7GUysXnddRm4JtZDh1DLHX97uVv6+kaLqikdsDmJ1pmjdfX2jZEmLHVp1E\nWEPbtsXInHkre/aoTiLMbsKELcSN60rFiulURxFW0K5EO8gFS/2Wqo4iTOhZ+DOWn15Op8Kv9myq\nWbMmTZo0UZxKREe1bNX44+4fXA69rDqKMKm/FyqxF6aY8xYRAZ99Bps2Qf78ioIJq5s0CQIDXy00\nIcSnypixGWXKVGDFCvsYDiEg78C8PFn7hEtBl2Tum4iSpSeW4h/oz8aWG1VHEVbUeX1nsiXNxqAy\ng1RHESZz5PoRGgc05kKvCzg7vVq06MKFC2iaRtasWRWnez9Tz3nbuhXSppXGzd60agVr18Ljx6qT\nCLO6cOE+V6/+zLhxzVVHEVbUrVE37j26x5EjR1RHESYz/+h8uhTpojqGsLIW+Vuw4rcVADx//pwL\nFy4oTiTMYu7huXQt2jWycQMYPXo0a9ead8sqUzRvffoMo3r106pjiL9Ya0x0mjSvVgiUfTetz17H\nrf/XkCEryJSpBlmyJFUdJcocpUafolm+ZoTnD2f+ovmqo0idTODvGp2+dZqQByHUylFLbSDxhui+\njspmKsudp3c4c+cMv/76K82bywd2tmBv73f3n91nzR9r6FCoQ+Rjjx49Yt26dbRq1UphsugxfPN2\n7tw9zpyZRYcOGVRHETbQrp0MmxSfbs+eu3Tv3kl1DGFlKROkpFyjcuRtkFd1FGEi84/Op0OhDrg4\nuaiOIqzM2cmZZnmbseK3FVSpUoWbN29y+rR8qC/ez/eEL7Vz1CZlgpSRj61cuZLy5cuTKlUqhcmi\nx/Bz3ho3nsmRI/sJDv5eYSphK2FhkDz5Qtatq0ylSsYeeyyM5dQpqFULgoNB9t61PytOr2DpyaVs\nbrVZdRRhAk/CnpBxakZOdj1JmvhpAIgle4fYlcPXDuOx2oNzPc8xbNgwnj17xpQpU1THEgZl0S3k\nnJUTv/p+fJ7x88jHy5YtS//+/alXr57CdB/HtHPeNm5cQteussyvvYodG7JnP8OIEbJ/n4gaHx9o\n21YaN3tVL1c9Dlw9wM3HN1VHESbgH+hP6UylyZgkIytXrqR169aqIwkrK5quKJqmceT6ETw9PVm+\nfDnh4eGqYwmD2npxKwljJ6RkhpKRj509e5Zz585Rs2ZNhcmiz9DNm7//CcLC7tG3byXVUcS/WHtM\n9NCh7ThwwJewsAirnteR2du49f8KC4Ply8HTU3WST2fvNYqu+LHiUzdnXX747QelOaROxrdz587X\nFirx8fGhQYMGilOJf7PG60jTNDzyebDitxVkz56dHDlysGHDhuiHE5Hs6f1u8fHFdC7c+bUVixMl\nSsSSJUtMf1fe0M3btGnr+PzztsSOLR+t27NGjfITJ04aJk3aqjqKMIkNGyB3bnBzU51E2FLL/C0j\nV5gT4l3O3z/PjUc3qOFWg5CQEI4fP26KIVEi6jzyeeAf6E+EJYKRI0eSMmXKD/+QcDj3nt7j5/M/\n45Hf47XH06ZNa/q7bmDgOW9hYZA+vc7OnS/Imzeu6ljCxpo2nc3Bg7u5fFntp+zCHOrWhYYNzX3n\nTXxYeEQ4aSenZX7B+TSs1lD2fBNv1e2nbqRNlJYR5UcwevRobt68yezZs1XHEjbiPs+dqdWnUjFL\nRdVRhEHNPDiTg9cOsqzhMtVRosV0c942boRcuTRp3BzEt996EBKyieDgUNVRhMGdOnWTzZtb0Lix\n6iTC1mI5x6JhroZ4dfVi9+7dquMIA3oc9hj/QH86FOqAxWLBx8eHdu1knrw9+3vopBBvo+s6i48v\npn2h9qqj2Ixhmzdf31fLyAvjscWY6GzZklGz5nE2bkxi9XM7Insat/5fX331HZkzxyVhQtVJosee\na2RNzfM3x6WwCz6K9hSROhnbitMryPMkD+kTp+f+/ftUrlyZIkWKqI4l/sOar6Pm+Zqz5swawiLC\nrHZO8Yo9vN8dv3mcBy8eUCFzBdVRbMaQzdvt27BzJzRpojqJiEk9e2bFx0eGRYl3s1h0tmzxoVcv\n+WTHUZT/rDzh+cJZ8781PH78WHUcYTA+J3yo6fZqDkuKFClYuHChDK+1c5+5fkauFLn45cIvqqMI\nA1pyfAnt3NvhpP3T4vz5558YbZpYdBiyeVu+/NWclkSJVCcRb1OhQgWbnLdqVbhxA377zSandyi2\nqpFqPj6H0PVwvLzKqI4SbfZaI2tzdnKmWYlmpM2blpUrV8b49aVOxhV0N4iLf15kQMsBqqOID7D2\n68gjnwffn/5n/1/ZMsA6zP5+9yz8GSt+W4Gnu+drj1epUsWuht4bsnmbNGkOLVs+Vx1DxDBnZ2jT\n5hoKtaQAACAASURBVNX+XUK8zeTJPpQv74mTk3yy7kia5WvG07xPlQ2dFMbkd9KPVgVaEcvZ3Mt+\ni6hrkrcJG85t4Gn4U0JDQ8maNSvPn8vvjY5u7R9rKZquKJmSZIp87NSpU9y+fZvSpUsrTGZdhmze\nbt+eSOXKsVXHEO9gyzHRnp6wbBnIh2jRYw/j1v/ryRMLQUHbGDOmjeooVmGPNbKVUhlLEeEWQfna\n5WN86IvUyZgiLBH4nfKjbcG2UiMTsHaNUiVIRbF0xdh0bhOurq7kzp2btWvXWvUajsjsr6UlJ5bQ\n3v31hUp8fHxo27Ytzs72s+2YVZo3TdO+0DTtD03TzmqaNugdz5mhado5TdNOaJrm/r7zlS7dFhcX\nQ/aVwsZy5ICMGS8xb16g6ijCYNatc6JSpTOUKJFRdRQRw5w0J5oVbIZWTJP5TAKA7Ze2kypBKvKn\nzq86ilCkad6mBPweAEC7du3kzryDCw4N5viN49TL9c8ej2FhYSxfvhxPO9tXKNodkqZpTsAsoDqQ\nF/DQNC3Xf55TA8im63p2oAsw733n/OabttGNJWzI1mOi8+ffwbhxQ216DXtn9nHrb+PjAx06uKiO\nYTX2WCNbap6vOf6B/jF+503qZEy+J33xLOgJwL59+wgICFAbSLyXLV5HDXM3ZPP5zTwJe0L9+vU5\ncuQIV65csfp1HImZ3+98T/jikc+DuC7/bDG2YcMGcuXKhZubm8Jk1meN21vFgXO6rl/WdT0c+AGo\n95/n1AP8AHRdPwgk0TQt9btOWL58FivEEmY1dmwTbt36lcDA26qjCIMICYHjx6F+fdVJhCrF0hUj\nPCKcEzdPqI4iFHvw/AEbzm7AI78HERERzJ49mzx58qiOJWJYivgpKJmhJBvPbSRevHg0bdoUPz8/\n1bGEAhbdgs8JHzoU7vDG9/r166cgkW1Zo3lLD/z7o46rfz32vudce8tzhEnYekx0unSJyJq1Hl99\ntcym17FnZh+3/l9Ll0KzZhA37oefaxb2ViNb0zSNpnmb4h/oH6PXlToZz8rfV1IpSyVSxE/BL7/8\nQqJEiciXL5/qWOI9bPU6aprnn6GTHTp04NGjRza5jqMw6/vdtovbSB4vOe5pXp+V1aBBA+rV++/9\nJPMz5BgkT09PMmfODICrqyvu7u6Rt3L//oclx+qOT5w4YfPr9ezZjkGDerB9eyGcnDRD/fnNcPw3\no+SJzrHFAr6+FfD3N0YeOVZ3nO1BNobvGM7o8qOJiIjgwIEDNr9+TLzfyXHUjn0v+jKw9EB27tyJ\nt7c3NWrUMFQ+OY654xTPU/DLhV94HPaYx48f88UXX/A3I+Qz27FZ3+98T/pSJqIMO3fuNESeTz0+\nceIEoaGhAAQHB/MuWnTnD2iaVhL4Wtf1L/46Hgzouq57/+s584Aduq77/3X8B1Be1/Vbbzmfbk8b\n6YlPY7HoxInjxuLF/rRpU1R1HKHQggVHGTv2KsHB9ZC1KhybruvknJWTPPvzUK1MNby8vFRHEjHs\n/P3zlF5Smqt9rvLowSOyZs3KpUuXSJo0qepoQpEay2vQtmBbmudrrjqKUODRi0dknJrx/+3dd1gU\n1xrH8e9BUEAs2LtiL9iNPYq99y4WMNHEHnvU2BKjiSbXEltsiF2jYu8aW2LU2LtYsNdYsQNz/0CJ\nRlGRhTO7+36ehyfuMDvzY987e/fsnEJg50CSx0+uO45FKaUwDOONTz4OFjj2HiCrUiqjUiou0BRY\n/p99lgOtXgQpDtx9W8NNiJccHBQtWkxh06ZUuqMIzUaMGEuBAmek4SZQStHUsylx8sSRmeXslP8B\nf5p7NscpjhM7d+6kTp060nCzc41zN2bhUZmwxl4tPbGUTzN+anMNt3eJduPNMIxQoBOwHjgKzDcM\n47hS6gulVLsX+6wGzimlTgO/AvJ1qRV7eas3pg0ZUp6VK9Mh625GXWzVKKZdufKAM2eWMWxYC91R\nLM5WahTbmuRpwl/x/uLatWscOXIkxs8ndTKPMCOMmYdm4lPAB4AaNWowY8YMqZEViMka1c1Zl41n\nN/LgqYx3iy5rvJZmH55Ni7y29xnhXSxx5w3DMNYahpHDMIxshmH88GLbr4ZhTH5ln06GYWQ1DCO/\nYRj7LHFeYdsyZIBChUDW3bRf/fotJFUqL/LkSaE7ijCJPCny4O7qTrk65eTum53ZErSFJC5JyJ8q\nf8Q2WfdPuLu482nGT1lxaoXuKCKWXX1wld2Xd1MrR62IbY8fP6Zw4cI8fvxYY7KYZZHGm7AvLwdX\nxoY2bcLX9xJRE5s1iklLlvjRpk0b3TFihK3USIcmeZpg5DeYPXs2z58/j9FzSZ3MY8aBf9d2e5XU\nyPxiukZN8jR5retkmzZtCAwMjNFz2iJru5bmH5lP3Zx1cXVyjdgWEBBAsmTJcHFx0ZgsZknjTZha\n3brw998g627an/XrA3n48DT9+1fTHUWYTBPPJmy8v5GmzZpy/boMn7YHwc+CWX5yOc3yNtMdRZhQ\n7Ry1+T3od+4/vQ+Ez1Q+Y8YMvaFEjHtbl0k/Pz98fX01JYod0ngTURabfaJdXKBRI4OJE2/G2jlt\ngTX2W/+vzZsz4+OzA1dXJ91RYoQt1EiX7Emzk9otNXW71iVdunQxei6pkzksO7GMUhlKkSL+m12o\npUbmF9M1SuycmLIZy7LsxDIAfH198ff3JzQ0NEbPa2us6Vo6dvMY14Kv4ZXJK2LbhQsX2LdvH3Xr\n1tUXLBZI402YXunSh/jppxKEhckSEvYiNBRmz45D9+5ZdUcRJtUkTxPmH5mvO4aIJa9+wz5ixAgu\nXbqkOZEwm8Z5/l2wO2/evKRKlYqNGzdqTiViypxDc2ju2Zw4DnEitvn7+9O0aVOcnZ01Jot50V7n\nzdJknTfxX2FhBq6unowcOZHOncvojiNiwdq1MHAg7N6tO4kwq3N3zlF0alGudL+CUxzbvDsrwl0P\nvk7O8Tm53P0y9/+5T65cubh48SJubm66owkTuf/0Pun+l44L3S6Q2Dkx48ePZ/v27cyfL1/y2Jow\nIwyPMR4sb7r8tQmMmjVrRo8ePShSxDbWB47Jdd6EiFEODoqKFX0ZO1ZmLrEXfn5g413WRTR5uHuQ\n2T0zm89t1h1FxLAFRxdQK3stXJ1cmT17NvXq1ZOGm3hDwngJKe9RPqLrZLNmzdi9ezfPnj3TnExY\n2o4LO0gYLyH5UuZ7bfu8efNspuH2LtJ4E1Gmo0/0sGEtOHNmKdeuBcf6ua2RNfVb/6/bt2HdOmja\nVHeSmGXNNTKLpnmasuDoghg9h9RJv9mHZtMiXwsMw2D69OlvTEYgNTK/2KpRkzxNIrpOJkmShMDA\nQOLGjRsr57YF1nItzT4U3o3aXpcKkcabsAr58qUiRYpP6dfvN91RRAz78cf9lC9/E3d33UmE2TXK\n04hlJ5cRsCyAPn366I4jYsDJWye5eP8i5T3Ks3v3bp4/f07p0qV1xxImVTN7TXZc2MGdx3cAiBMn\nznueIazNk5AnLDq2iOZ5m+uOoo003kSU6VoHpEOHrvz5p+2u22FJ1rZWy6t++eVzihY9oDtGjLPm\nGplFuoTpyJ08N3cS3WHKlCkxsiir1EmvOYfn0MyzGY4OjsyaNQsfH583vm2XGplfbNUoQbwEVMxc\nkaUnlsbK+WyNNVxLqwNXkz9VftInSq87ijbSeBNWo2/fCty505TTp3UnETFl0aJDPHt2k+7dy+uO\nIqxEkzxN2Hx7M0WKFGHpUvnAZksMw4joMgnwww8/0KlTJ82phNk1zt04xrtTC31edpm0Z9J4E1Gm\nq0+0kxN4e4Osu/l+1tJv/b+GDfOjRInWxI1r+11drLVGZtMwd0NWnlqJd0tv/PwsP6mR1EmfnZd2\n4uzoTMFUBQFwc3MjUaJEb+wnNTK/2KxRjew12HlpJ/88+ifWzmkrzH4t3X58m03nNtEgd4OIbYGB\ngXz77bcaU8U+abwJq+LrC/7+4euACdsSHPyMAwfmMGSIj+4owoqkcktF4TSFiesZl71793LhwgXd\nkYSFzD40G++83nY7KYH4OG5x3aicpTIBJwIitq1evZpt27ZpTCUsYdGxRVTOUpnEzokjtvn5+XH/\n/n2NqWKfrPMmrE6RIjBsGFSurDuJsKQ+fZbw669juXt3i+4owspM3juZTec2kWp7KkqVKkXjxo11\nRxLR9Cz0GWn/l5bdn+/Gw91DdxxhZX47+htT9k1hfcv1AEyfPp1ly5axbNkyzclEdJTxK0PPkj2p\nnaM2AKGhoWTMmJF169aRJ08ezeksT9Z5EzbD1xemT5cGvq3ZvTsnnToN1x1DWKH6ueqz9vRavh/x\nvTTcbMS60+vImSynNNzER6mRvQa7Lu/i5sObADRq1IitW7dy/fp1zcnExwq6G8Sxm8eomrVqxLYN\nGzaQJk0am2y4vYs03kSU6e4T3ahRCL/9VpBz5+5ozWFmumsUVdeuwYEDufn66xK6o8Qaa6uRmSVz\nTUaJdCVYGbjS4seWOukx+3D4pARhYWEsXryYsLCwSPeVGplfbNfI1cmValmrRXSdTJAgAXXr1mX2\n7NmxmsPamPlamnt4Lo3zNCZunH/X7fPz83tj3Ud7II03YXVSpHAkbdoc9O07T3cUYSGzZkH9+uDm\npjuJsFZN8jSRGeZsxL0n91h7ei2N8jRi27ZtDB48WMa9iShrnKcxC48ujHjs6+uLn58fMjTH+hiG\nwaxDsyJmngUIDg5m48aNNGvWTGMyPWTMm7BK33+/lmHDBvDw4R7dUUQ0GQbkyQOTJ4OsvSs+1t0n\nd8k4OiMXu10kYbyEuuOIaPDb78fyU8sJaBJA69atyZ8/P927d9cdS1iZx88fk/rn1JzsdJKUbikx\nDINs2bIxf/58ihQpojueiIJ9V/fRcGFDznQ589oXOffu3XvrDLS2Qsa8CZvSq1clnj69SkDAEd1R\nRDTt3g0hIVCqlO4kwpoldk5M2YxlWXZCJiSwdrMPh88y+eDBA5YtW0aLFva9ppP4OC5OLlTPVp0l\nx5cA4R+EN27cSMGCBTUnE1H1cr3H/96Bt+WG27tI401EmRn6RMeNG4dixVrz/feWX9fJFpihRh9q\n0qQ7+PiAvfWKsqYaWYuXXSdv3LjBoEGDLHJMqVPsunT/Evuv7qdm9posXLgQLy8vUqRI8c7nSI3M\nT1eNGudpzMJj/3adzJQpE3Hi2P46oh/LjNdSSFgI847Mwzuvt+4opiGNN2G1Bg/24fDh0zx/rjuJ\n+Fj//PMIf/+sVK9+Q3cUYQNq56jN9gvbMZwNJk2aRGBgoO5IIormHZ5H/Vz1cXZ0Zvr06XY5GYGw\nnKpZq3Lg2gGuBV/THUV8pE1nN5E+YXpyJMuhO4ppyJg3YdU+/RR69oQ6dXQnER+jY8c5LFgwi1u3\n1uqOImxEg4UNqJ61OkdnH8XFxYXvv/9edyQRBQUmFWB01dF4ZfJi+fLlVKtWDScnJ92xhBVrGdCS\nommK0rlYZ91RxEdoGdCST9J8QpdiXXRHiXUy5k3YJF9f8JOek1Zr/vwZeHvLN+vCcprmacqCowvw\n9fXF39+f0NBQ3ZHEBzp8/TD/PP6HMhnLAFC7dm1puIlo887rzZzDc3THEB8h+FkwK06uoEmeJhHb\n9uzZw969ezWm0k8abyLKzNQnulEj2LoVZN3N15mpRpHZufMCd+7sZ8gQ+7xtag01skY1stdg9+Xd\npMqcitSpU7Nhw4ZoHU/qFHvmHJ5Dc8/mOKiofTSRGpmfzhpVzFyRoLtBBP7zbzfqf/75h7/++ktb\nJrMy27W07MQySqYvSUq3lBHbvvvuOw4fPqwxlX7SeBNWLUECqFcPZs7UnUREVd++0/H0bEbixM66\nowgb4urkSrVs1Vh8fDFt2rRh1qxZuiOJDxBmhDHn8JzX1nESwhIcHRxp6tn0tbtv586do3nz5u9c\n/F3oN/vw7NfeEy5fvsz27dtp2LChxlT6yZg3YfV27oTWreHkSfubsdBahYZC0qSjmDatEg0aeOqO\nI2zM0hNLGf3XaFY3Xk1YWBhusvq76W0J2kLXtV05+OVB3VGEDfr7yt80XdSUwM6BL8cRUahQIUaM\nGEGlSpV0xxNvcT34OjnG5eBy98vEjxsfgKFDh3Lp0iUmTZqkOV3skDFvwmYVLw737/+PceP+0B1F\nfKC1ayFHjm7ScBMxomrWqhy8fpC7oXel4WYlZh+aTYu8Lbh79y537tzRHUfYmMKpCxPHIQ67Lu8C\nwj8Ut23blilTpmhOJiIz9/Bc6uasG9FwCwsLY9q0abRt21ZzMv2k8SaizGx9opWCkiXj8PPPE3RH\nMQ2z1ei/pkwBe3//NXuNrJmzozO1c9Rm0bFF0T6W1CnmPQl5wpLjS2iWtxnjx4+nX79+UXq+1Mj8\ndNdIKUWLvC2Yc+jfrpPe3t6sX7+emzdvakxmLrrr9Cr/g/60yt8q4vHGjRtxd3encOHCGlOZgzTe\nhE0YObIl58+v4syZ27qjiPe4ejV8kpmmTXUnEbbs5YLdwvxWnlpJwdQFSeOWhmnTpvHZZ5/pjiRs\nkHc+bxYcXcDz0PDFYRMlSkTdunXx9/fXnEz818FrB7n9+DZembwithUtWpSZMsEBIGPehA3x8PCm\nYMGiLFnSVXcU8Q7DhsH58/Drr7qTCFv2LPQZaX5Ow74v9pEhUQbdccQ71FtQj1rZa5H+Vnp69+7N\nvn37UDKAWcSAUtNL0a90P2pkrwHAyZMnefz4MQUKFNCcTLyq5/qexIsTj+8r2Pc6nTLmTdi8rl3b\nsnr1FMLCpPFvVqGhBlOmGHbfZVLEvLhx4lIvZz3mH5kPwKJFi7h9W+7Mm83tx7fZfG4zDXI1YMqU\nKbRt21YabiLGtMjbgtmHZ0c8zpEjhzTcTCYkLIQ5h+fQMn9L3VFMSxpvIsrM1Cf6VV26lMUwQlmw\n4ITuKNqZtUY//bSRe/daIF3WzVsjW9K6QGv8DvhhGAYBAQEftWyA1ClmLTiygCpZqvDswTPWr1+P\nt7d3lI8hNTI/s9SocZ7GrAlcw4OnD3RHMSUz1GnDmQ1kSJSBnMly6o5iWtJ4EzbDwUExaNB+NmzI\npTuKiMS4cVOoXLmULOkgYkWp9KUIDQtl1+VdETPLSbd8c/E74IdvAV8eP37MyJEjSZQoke5IwoYl\ndU1KmYxlCDgRoDuKiMTMQzNpnb+17himJmPehE25cQNy5ICgIJDPAOZy/PhNcufOxvnz58mQQYoj\nYsfw7cMJuhvEpJqTyJEjB/7+/pQoUUJ3LAEcvXGUyrMrc+GrC8RxiKM7jrATC48uZOq+qaxvuV53\nFPEf957cI8PoDJztcpakrkkB+Pvvv8mfPz9OTk6a08U+GfMm7EKKFFCxIsydqzuJ+K9evfzJkqWu\nNNxErGqVvxW/HfuNxyGP+fzzz2VdJxPxO+BHy3wtpeEmYlWt7LXYc2UPVx5ceW17UFCQ3JnX7Ldj\nv1HBo0JEw+327dtUrFiR+/fva05mLtJ4E1Fmhj7R79K2bfg6YvbMbDUKCzPYsGEqPXrITCUvma1G\ntiptwrQUS1eMJceX0Lp1awICAqL0QUDqFDOehz5n9qHZ+BbwjfaxpEbmZ6YauTi5vDaZEYBhGFSp\nUoVdu3ZpTKaf7jrNPPh6l8lZs2ZRo0YNkiZNqjGV+UjjTdicihXhzh3Yu1d3EvHSsmXXiBs3B198\nUVJ3FGGH2hRog98BP1KmTMnatWtxcXHRHcnurT29lszumcmRLIfuKMIOtcrfKmIyIwjvntamTRsm\nT56sOZn9OnvnLMdvHadatmpAeIP65Qy04nUy5k3YpJ49g9i9ezPbtrXRHUUA3t7wySfw1Ve6kwh7\n9DTkKWn/l5Y9bffg4e6hO44A6i+oT7Ws1WiZpyXOzs664wg7YxgGOcblwL+uPyXSh4+BvX79Ojly\n5CAoKIjEiRNrTmh/hmwZwq1Ht/il+i8A/PHHH/j6+nLy5Em7XT5ExrwJu9KqlTM7dvTg/Pm7uqPY\nvRs3YPVqaC2TRwlN4jnGo5lnM/wP+uuOIoCbD2+y+dxmGuVuRKFChTh27JjuSMLOKKVoW6gtk/f9\ne6ctZcqUVKtWDX9/eZ+IbYZhhM8yWeDfDwoTJkygQ4cOdttwexdpvIko090n+kPky5eK9Omr0q2b\nfb4Jm6lG06ZBgwbg7q47ibmYqUb2oE3BNsw4MIMwIyxKz5M6Wd7cw3OplaMWf//xN05OTuTKFb3l\nXaRG5mfGGrUu0JqA4wHcffLvl7wdOnRgwoQJdjtxia46/XHxD+LFiUfh1P8uAluvXj1ay7e+byWN\nN2GzevfuwMqVEwgLs883YTMIDYVJk6BDB91JhL0rmLogiZ0TsyVoi+4ods/vgB8++X3km3WhVYr4\nKaiStQpzD/87PXXp0qVp27YtT5480ZjM/kzbPw3fAr6vvRc0bNgQd/nW962sZsxbpkyZOH/+vIZE\ntiVjxowEBQXpjhErwsIMXF3z8d13o+nVq4LuOHZp+XIYPhx27tSdRAgY89cY9lzZw+z6s7l37x6n\nT5+mcOHC73+isJj9V/dTb0E9ttbfSqGChTh//jxubm66Ywk7tensJrqv786BLw7Ilwia3H1yF48x\nHpzqdIrk8ZPrjmMqVj/m7fz58xiGIT/R/LGnBrCDg6J+/Y6MGSOzR+nSocNn1Kp1WHcMIQDwzufN\nylMrufckvOHWoEEDQkNDdceyK34H/GidvzVTp0zF29tbGm5Cq3Ie5Qh+FsyeK3t0R7Fbcw7NoUqW\nKtJwiwKrabwJ8zBj3/XIjBrViuDgSVy6pDtJ7DJDjTZsCOTKlRV06JBNdxRTMkON7E0y12RUzFyR\nBUcXULhwYVKmTMmaNWve+Rypk+U8DXnKvCPz8Cngg7OzM+3bt7fIcaVG5mfWGjkoh/CJS/bKl7wQ\n+3UyDINf9/5Ku8LtYvW81k4ab8KmpUzpSsuW7sjSLbGvb99JFC3qS+LEMg24MI/PCn7Gr3t/xTAM\nOnbsyPjx43VHshsrT63EM4UnHu4e9O/fP9oTlQhhCT4FfFh8fDH3n97XHcXu7Lq8i8chj/HK5AXA\nP//8Q3BwsN5QVsBqxry96PepIZFtscfX8dgxqFABzp+HuHF1p7EPt249IkWKDGzZsocyZWRdLWEe\nYUZYxPpOhZIXIn369Pz1119kyZJFdzSbV3V2VbzzetMyf0vdUYR4TYOFDaicuTJfFPkiYpthGNy/\nf59EiRJpTGbb2ixrQ85kOeldqjcAPXv2JG7cuAwbNkxzMnOw+jFvZubp6cm2bduivV/16tWZNWvW\nB53Tw8ODzZs3f3BGe5Y7N+TMCQEBupPYj969F5A8eXFpuAnTcVAOdPqkE2N3jcXZ2RlfX18mTZqk\nO5bNO337NPuu7qNRnka6owjxhnaF2r225hvA2rVrqV27tqZEtu/ek3ssOb4EnwI+ADx69IgZM2bQ\ntm1bvcGsQLQab0opd6XUeqXUSaXUOqXUW7+eUEoFKaUOKqX2K6V2R+ecZnTkyBHKlCkTpf2GDBlC\nq1atXvv96tWradnS/N9ImrXv+rt07AgTJuhOEXt01sgwYPnynXTsKOsDvIs1Xke2wqeAD+vPrOfy\n/ct06dKF+vXrR7qv1MkyJu6ZiG8BX5wdLd+NWmpkfmavUaUslfjn0T/svbI3YlvFihUJDAzkyJEj\nGpPFrtis05zDc6icpTIp4qcAYMGCBRQvXhwPD/nS932ie+fta2CjYRg5gM1A30j2CwO8DMMoaBhG\n0WieU4goq1MHjhzZwKpVgbqj2Lw9eyBhwsn0719NdxQh3iqRcyK883oz6e9JpEuXjhIlSuiOZNMe\nPX+E/0F/2hWSSQmEOb1t4hInJyfatWvHxIkTNSazTS8nKvmi8L/dVF+u+yjeL1pj3pRSJ4CyhmFc\nV0qlArYYhpHzLfudA4oYhvHPBxzT6sa8eXh4MG3aNLZv386xY8dwdnYmICCAjBkz4u/vT6FChV7b\n7/nz5xG34uPGjUvWrFnZv38/5cqVo2XLlrRp04azZ8/Stm1bDh48iIODA5UrV2bChAkkTJjwtWOV\nL18+SlnN/DrGNC+vwdy6dYMjR+zoFpwGrVuDpyf06qU7iRCRO3nrJGVmlOH8V+dj5G6Q+Nf0/dNZ\ncnwJVW9X5cqVKzKeRZjSlQdX8JzgyZkuZ3B3CV8c+vLly+TNm5egoKCIz18i+nZf3k3zxc051fkU\nDsqB3bt307RpUwIDA4kTJ47ueKYRU2PeUhiGcR3AMIxrQIpI9jOADUqpPUopm+7MumLFCpo3b869\ne/eoVasWHTt2fGOfKlWq0K9fP5o0acKDBw/Yv3//G/sYhkG/fv24du0ax48f59KlSwwePDgW/gLb\nNXr0Fxw7Np9z5+7ojmKzrl0LX5i7TRvdSYR4txzJclAwVUEWHFmgO4pNMwyD8XvG82XhLxk7dixV\nqlTRHUmIt0qTIA01s9d87e5b2rRpqVSpEn5+fhqT2Z7JeyfTtlBbHFR4M8TFxYVRo0ZJw+0Dvbfx\nppTaoJQ69MrP4Rf/fdsozshu6ZQyDKMQUB3oqJQqHZ3QZla6dGmqVKmCUoqWLVty6NChjzpOlixZ\nqFChAo6OjiRNmpRu3bqxdetWC6f9OGbvux6ZAgVS4+FRiw4dbH/dAF01mjABmjWDpEm1nN6qWOt1\nZEu6FOvCmF1j3tkbQeoUPXuu7OHO4zuEngwlYcKEHzQ+PKqkRuZnLTXqUaIHY3eP5Vnos4htvXv3\nxt3dXWOq2BMbdbr/9D6Ljy+OmKgEIG/evNSpUyfGz20rHN+3g2EYlSL7nVLqulIq5SvdJm9Ecoyr\nL/57UykVABQFdkR2XB8fHzJlygRA4sSJKVCgwPtiot64qfhxotujMFWqVBH/dnV15cmTJ4SFheHg\nELWbnDdu3KBr165s376d4OBgQkNDSZIkSfTCvbBlyxa8vLwi/g1E6fGBAwei9Xydj729P2Xoawvx\nZAAAIABJREFU0L4EB3fDzS2u9jwx9fil2Dz/48cwduwWfvkFQO/fL4/l8Yc8dr7kzPWj1/nz4p+U\nylCKhQsX4ujoGDGByRYrf78zw+PhO4bTvlx7Rn8zmqpVq7J161aLn+8lM/y98tj6H+dOnpv5R+aT\n4U6GiN8XLlzYNPli8nFsvN+dcDtBxcwVOf73cY5z3FR/v+7HBw4c4O7duwAEBQURmeiOefsRuG0Y\nxo9KqT6Au2EYX/9nH1fAwTCMYKVUfGA9MMQwjPWRHNOqx7ydOXOGmTNnAnD+/HkyZ87M8+fPcXBw\neG2c2rfffsvp06cj9gVeG/P2+eef8/jxYyZMmECiRIlYtmwZnTt35sKFC6+dU8a8RZ27e3maN2/L\n+PHNdEexKW3azGLfvuccONBGdxQhPtjYXWP54+IfLGi4gD59+vD06VNGjx6tO5ZNuPXoFtl+ycaS\nT5fQqkkrzp49i5OTk+5YQrzT2tNr6b2hNwe/PIiy1J0BAYR3o/ac6MnYqmOpkLmC7jimF1Nj3n4E\nKimlTgIVgB9enCy1Umrli31SAjuUUvuBv4AVkTXcbFFkDaWUKVMSFBQU6e8fPHiAm5sbCRIk4PLl\ny4wcOTImY9qVUaP8+fPP+tG+yyr+FRISxpw5w2jdWqb4FdbFp4APG85s4NL9S3Tu3JmZM2dGfPMp\nomf6/unUyVGHR3ceMXDgQGm4CatQJUsVDAw2nN2gO4rNWXdmHU4OTpT3iNqNB/G6aDXeDMO4bRhG\nRcMwchiGUdkwjLsvtl81DKPmi3+fMwyjwItlAvIahvGDJYKbybu+mXn1d6/+u1GjRhiGQdKkSSlS\npMgbvx80aBB79+4lceLE1KpViwYNGnzwOWPay1u91qpVq/Q8fhyPD1hX3WrFdo2GDVtHnDjOdO3q\nFavntWbWfh3ZioTxEtIiXwsm7plIunTpqFatGlOnTo34vdTp44SGhTLp70l0+KQDNWrUiNGFd6VG\n5mdNNVJK0aNED37e+bPuKLEupuv0886f6V6iO0opQkNDuXr1aoyez1a9d8ybeL+zZ88CvNGFMWPG\njISGhr6xH0CSJEnYvn37a/tv3rw54t+5c+fm77//fu333bp1e+uxRNQ4OMBXX8GoUVC2rO40tmHM\nmP/RqlV3HByki4mwPp2KdqL09NL0+7Qf3bp1o379+nTt2lXuFEXDujPrSOqalKJpZWlXYX2aeTaj\n36Z+HLp+iHwp80VsDw0N5fHjx7i5uWlMZ50OXT/EsZvHaOrZFIDly5czatQottnyN+kxJFpj3mKC\nNY55sybyOoZ79AgyZYI//oBs2XSnsW6LFh2iSZNq3Lt3Dje3uLrjCPFRGi5sSMn0Jeleojtly5al\nffv2NG3aVHcsq1Vjbg0a5mqIb0Ff3VGE+CjDtw/n5D8nmVF3RsS2b7/9lnv37vHzz/Z3Vy66fJb6\nkCNpDvp+2heATz/9lC5dutCoUSPNycwrsjFv0nizM/I6/uubb+DuXRg3TncS61a8+Hjc3R+yZk1v\n3VGE+GgHrh2g+pzqnOlyhhNHTpAgQQKyZs2qO5ZVOnLjCJVmVeJsl7O4OLnojiPER7n9+DZZx2bl\nSIcjpEmQBgifiK5QoUKcO3dOFu2OgqsPrpJnQh5OdzlNEpck7N69m8aNG3P69GkcHaUTYGRiasIS\nYYesqe/6u3TsCH5+Mzhz5rbuKBYXWzW6ehVOnuzI7Nm9YuV8tsRWriNbUSBVAT5J+wlT902lYMGC\nEQ03qVPU/fjHj3T+pDPx4sSLlfNJjczPGmuUxCUJLfK14Jddv0Rsy5gxI5UqVWL69Okak8WcmKrT\nuN3jaJ63OUlcwpe8GjVqFF26dJGG20eSxpuwW6lTQ+rUv9Oxo+0v2h1TJkyA5s0haVIZ6yas34Ay\nAxjx5wiehjzVHcVqnbtzjtWBq/G44UGTJk10xxEiWr4q/hVT9k3hwdMHEdu6d+/OmDFjCAkJ0ZjM\nejx89pDJ+ybzVfGvALhw4QLr1q3js88+05zMekm3STsjr+PrFi48SLNm1blz5ywJE8bOt8S24uW4\nwR07IHt23WmEsIzqc6pTJ0cdvijyhe4oVqnT6k4kiJuAP7/7ky+//JJmzWQ9TWHdmi9uTp7keehf\npn/EttKlS9O1a1cZr/UBJuyZwIazGwhoEgDAxYsX2blzJ40bN9aczPxkzJsA5HV8m+TJq1GlSj1m\nz26nO4pVGTUqvOG2eLHuJEJYzs6LO2m2uBmBnQNxiiOzTUbF9eDr5BqfixkFZ/DVl19x8uRJmbFT\nWL1T/5yi5LSSBHYOxN3FHQjvXvjs2TMqV66sOZ25hYaFknN8Tvzq+FE6Q2ndcayOjHkTFmONfdff\n5dtvv2HBguE8evRcdxSLiekaPXkCP/0UPumL+Di2dh3ZihLpS5AtaTZmHZoFwLRp07h8+bLmVNZh\nzK4xNPVsyoT/TaBv376x1nCTa8n8rLlG2ZNmp3aO2vxv5/8itnl5edlkw83SdVp5aiXuzu6USl/K\nose1d9J4E3avfftSuLl50Lv3Mt1RrIav7yQSJx5FwYK6kwhheQPKDGDY9mGEhIWwYcMGhg0bpjuS\n6d17co/JeydTOV5ljh07RuvWrXVHEsJiBpYdyIS/J3Dz4U3dUazKTzt/okeJHigl4+ItSbpN2hl5\nHd9u6dJ/6N3bnePHHYgTR3cacwsOfkbixFmZMmURvr6yAK+wTV4zvPis4GdUTlWZXLlyceTIEdKk\nSaM7lmn9uONHDt84zOdJPuf69esyWYmwOR1XdcTVyZWRlUfqjmIVNp/bzBcrv+B4x+M4Osiskh9D\nxryZlIODA6dPnyZz5syxcjxbfR2jyzDg00/Dlw+Q8fXv1rr1VFat+o1bt9bpjiJEjNl0dhMdV3fk\naIej9OzREwif3lq86fHzx2Qem5n1LdaTN2Ve3XGEiBFXHlzBc4InRzscJXWC1LrjmJphGJSaXopO\nRTvRPG9zHjx4wPnz5/H09NQdzarImDeTsvSt5Ni4NW3NfdcjoxQMGADffw9hYbrTRF9M1ejJkxDm\nzh3Ot98OiJHj2xNbvI5sSXmP8iRzTUb/6f3p1asX/v7+3LhxQ3csU/I/6E+RNEW0NdzkWjI/W6hR\nmgRp8C3gy7Dtr3ejfvjwIefPn9eUyrIsVac1p9dw/+l9muQJvwM/YcIEhg4dapFjC2m8WcyJEyco\nV64c7u7u5M2blxUrVgBQrly51xZz9Pf359NPPwWgbNmyGIZBvnz5SJgwIb/99htbt24lffr0DB8+\nnOTJk5M5c2bmzp0b8fyoHk98uMqVwcUFli7VncS8unadS/z4GejQQWaNErZNKcXISiOZtn8aiZMn\nplmzZowePVp3LNMJCQth5J8j6Vu6r+4oQsS4PqX7MPfIXM7f/bextnjxYtq0aaMxlbkYhsE3m7/h\n23LfEschDg8fPmTUqFH079///U8WH0QabxYQEhJCrVq1qFq1Kjdv3mTs2LG0aNGCU6dOvXX/l3fH\ntm7dCsDhw4e5f/9+xHoh165d4/bt21y5coUZM2bQrl07AgMDIz3/+45naV5eXjFyXN1e3n0bOjS8\nG6U1i4kahYbCkiXXGDBgoMWPbY9s9TqyJSXSl6B8ufL8/OfPDBo0iG7duumOZDrT9k0jU+JMlExf\nUlsGuZbMz1ZqlCJ+Cr4s/CVDt/17F6lZs2acPXuWP//8U2Myy7BEnQJOhK/nVi9nPQCmTJlCyZIl\nyZtXulRbijTeLOCvv/7i4cOH9OnTB0dHR8qVK0fNmjWZN2/eBz3/v2PQlFJ89913ODk5UaZMGWrU\nqMHChQs/OI+Maft4tWrBrVsz+O679bqjmM6iRZAtW2+6dy+nO4oQseaHCj8wetdoQl1CSZ48ue44\nphL8LJghW4fQr1A/Hj16pDuOELGiZ8meLD25lMB/wr9Ud3Jy4uuvv5ZugYSv6zbw94EMLT8UpRRP\nnjxh5MiRfCPrClmUzTTe1BBlkZ+PceXKFdKnT//atgwZMnD58uWPGoPm7u6Os7NzxOOMGTNy5cqV\nj8oWE2yh73pklIJGjeIzYsQAwsKstxFs6RqFhYXfkRwwIPw1EtFny9eRLTl/8DxtCrRh4O9yx/m/\nfvrzJ8p5lGP2z7MZM2aMthxyLZmfLdXI3cWd7sW702djn4htPj4+HD58mL///ltjsuiLbp0WHF1A\nwngJqZa1GgDTp0+nYMGCFCpUyALpxEs2M3enMUjfB+00adJw8eLF17ZduHCBHDlycPXq1de+kbx2\n7dp7j3fnzh0eP36Mi4tLxLFe3m6OHz9+lI8noubHHxswfvxghg5dy8CB1XTHMYWAAIgXD6pW1Z1E\niNjXv0x/cozLQZfrXWQ2xReuPrjKL7t/YUmlJTRY0eCdXfuFsDXdSnQjz4Q8bDizgUpZKhEvXjx6\n9+7NkCFDIuY8sDchYSEM2jKIX2v+GnHjokGDBlSpUkVzMttjM3fedCpWrBiurq6MGDGCkJAQtmzZ\nwsqVK2natCn58+dnyZIlPH78mNOnTzNt2rTXnpsqVSrOnj372jbDMBg0aBDPnz9n+/btrFq1isaN\nGwNQoECBKB/P0myl73pkHB0d6Nbte4YP78OzZ6G643wUS9bo+XPo2xeGD5e7bpZk69eRrfDy8iKx\nc2IGlBlAj/U9pFv6C4O3DMa3gC/jh4+ne/fuuLu7a8si15L52VqNnB2dGVVlFF3XduV56HMA2rVr\nR48ePTQni57o1GnmwZmkT5ie8h7lI7alTJmSLFmyWCCZeJU03izAycmJFStWsHr1apIlS0anTp2Y\nNWsW2bNnp1u3bjg5OZEqVSp8fX1p0aLFa88dPHgwrVq1IkmSJCxatAgIb4C5u7uTJk0aWrZsya+/\n/kq2bNkAPup4Iuq+/74OceMmon37mbqjaPfzzzfInBkqVdKdRAh9vij8BRfuXWDt6bWcOnXKrtd8\nO3bzGEtOLKFyvMrs3LmTr776SnckIWJdrey1yJg4I7/s/gWAePHi2Vwj9UM9DXnKt1u/ZWh5GfcX\nKwzDMNVPeKQ3Rbbd1mzZssVInz59jB3fEq/j77//Hv0gVmDKlJ2Gq6uP8fCh7iRRZ6kaXbx4z3Bw\nSGmsXHnGIscT/7KX68javVqnZSeWGbnH5zau3bhmJEuWzDh+/Li+YBrVmlvLGLljpFG6dGlj+vTp\nuuPItWQFbLVGJ26eMJKNSGZcfXBVdxSL+Ng6Dds2zKg9r7Zlw4iXn9nfaCvJnTchIvH558WpXt0P\njePwtWvS5Ec8PKpSo0Zm3VGE0K5W9lqkiJ+CxUGL6dWrF3372t/aZluDtnL4xmE6F+vM999/T6tW\nrXRHEkKbHMly4FvAl76b7O+94KWgu0H8vPNnRleRdTBjizJM1n9fKWW8LZNSyi7GGmzdupWWLVty\n4cKFGDm+vbyOlnL6NBQvDsePg73NEr5nzyWKFcvPzp0HKFYs/fufIIQdOHLjCOX8y7HLZxflipRj\nzpw5lC5tH4vWhxlhFJtajG7Fu9E8b3PdcYQwhftP75NrfC6WNF5CsXTFdMeJdXXm16FomqL0LxO+\nCPfQoUPx9vbGw8NDczLr9+Iz+xuzDcidN5MpW7ZsjDXcRNRlzQrNm8N33+lOEvu8vQdSrFg7abgJ\n8QrPFJ58WfhLev3ei6FDh9KrVy+7+UJszqE5GIZBU8+muqMIYRoJ4yXkhwo/0HlNZ8KMMCB8SNLA\ngQO5c+eO5nQxa8XJFZy4dYKeJXsCsGfPHiZOnEiKFCk0J7Nt0ngTUWZL67V8iAEDYO5csKaZsKNb\no8WLD3P69CoWLPjaMoHEG+ztOrJWb6tT/zL9OXrjKPELxydRokQEBQXFeq7YduPhDXpu6MnEGhNx\nUOb66CDXkvnZeo2883nj6OCI334/IPyOydWrVxk+fLjmZFETlTo9ev6ILmu7ML76eOI5xsMwDHr1\n6sXgwYOJHz9+zIUU0ngT4n2SJ4cePaBHj390R4k1v/6aia5dl5IhQyLdUYQwHWdHZybXmkzXdV1Z\nsHSBXXQP6rKmC63yteKTtJ/ojiKE6TgoB8ZXH0+/zf248uAKAEOGDGHatGmcP39ec7qYMWz7MIqn\nK07FzBUBWLVqFTdv3sTX11dzMtsnY97sjLyOH+fhwzASJ87J+PH+tGtXQnecGLVpE3zxBRw7BnHj\n6k4jhHm1W9EORwdHJtSYoDtKjFp2Yhk9N/RkbZ21zPabzaBBg3RHEsKUBv4+kP3X9rO86XKUUgwc\nOJBz584xa9Ys3dEs6uStk5SaXopD7Q+RJkEaQkJCyJcvHyNGjKBmzZq649kMGfMmRDTEj+/AZ58N\n4KuvOvLkSYjuODHm6VPo3BlGjJCGmxDvM6LSCJadXMaOCzt0R4kxd5/cpePqjkyrPY1v+nzDkydP\ndEcSwrS+KfMNF+9dZObB8DVie/Xqxe+//862bds0J7McwzDouLoj35T5hjQJ0gBw8uRJPD09qVGj\nhuZ09kEabyLKbL3vemQmTGiBi0tSGjQw/+K8H1uj4cMhe3aoV8+yecSb7PU6sjbvqlNi58SMqTqG\ntiva8jTkaeyFikU91vWgdo7aPDjygD179jBgwADdkd4g15L52UuN4saJi39df3pt6MWl+5dIkCAB\n48aNY926dbqjfZAPqdOcw3O4+egmnYp2itiWJ08eFi5ciFJv3CQSMUAabybQvn17vv/+e90xxHs4\nOCgWL/6VNWt+ZPPmM7rjWNzRozB+fPiPvP8K8WEa5GpA9qTZGbZ9GCEhIZw+fVp3JIvZcGYDG89t\npH+x/nTo0IHJkyfj6uqqO5YQppY/VX46F+3M58s/xzAM6tatazOf8c7eOUv3dd3xq+OHo4Oj7jh2\nS8a8WYCHhwfTpk2jfPnyuqO8l5lfR2tRs+ZI/vprKzdvrrSZRk5ISBipU7fiq6+G07+/LA0gRFRc\nvn+ZwpMLMyTrEIZ2HsrRo0dJmDCh7ljREvwsmLwT8zKxxkRWj11NcHAw06dP1x1LCKvwPPQ5xacV\np32R9nxe6HPdcSziWegzSk8vTYt8LehSrIvuOHZBxrxpEhoaqjuCsLBFi7qRKtUY/P11J7Gc5s0n\n8vRpEH36pNUdRQirkzZhWqbWnsqwoGGUq1iOvn376o4Ubb3W96JMxjJUzlyZZ8+e8dNPP+mOJITV\ncIrjhH9df/pu6sv5u7Yx22T/Tf1J5ZaKzkU7645i96TxFk2tWrXiwoUL1KxZk4QJEzJy5EgcHByY\nPn06GTNmpEKFCgA0btyY1KlT4+7ujpeXF8eOHYs4hq+vLwMHDgRg69atpE+fnv/973+kTJmStGnT\nMmPGDB1/WqTspe96ZJydHZk1Kwt9+sD167rTvF1UarRr10UWLRrE3LlTcHSUt4TYYu/XkbX40DrV\nzF6TBrkacLPUTQICAvjjjz9iNlgM8j/gz6ZzmxhTdQwODg5MmjSJJEmS6I4VKbmWzM8ea+SZwpMe\nJXrQZnkbQsOs44v8yOq09vRa5h+dz/Q60yPGta1bt05uUGgin9SiaebMmWTIkIFVq1Zx//59Gjdu\nDMC2bds4ceJExCDV6tWrc+bMGW7cuEGhQoXw9vaO9JjXrl3jwYMHXLlyhalTp9KxY0fu3bsXK3+P\n+DAFC4KPD3z1le4k0RMWZlC7dge8vLpQs2Yu3XGEsGo/VPyBW8YtqnauStu2bXn61PomMdl7ZS89\nN/RkadOlJHZOrDuOEFatZ8mehIaFMmTrkIhthw4d4sKFCxpTRc3VB1fxXebL7HqzSeaaDIA1a9bQ\nvn17q3yPswXSeLOQV8eRKaUYMmQILi4uxIsXDwAfHx9cXV1xcnJi4MCBHDx4kAcPHrz1WHHjxmXA\ngAHEiROHatWq4ebmxsmTJ2Pl7/gQXl5euiOYwqBBsGcPrFqlO8mbPrRGPXr8xr17Z1m+/OuYDSTe\nINeRdYhKneLGicv8BvNZEWcFKTKksLq1nW4+vEn9hfWZVGMSuZPn1h3ng8m1ZH72WiNHB0cWNFyA\n3wE/lp1YBsCKFSto3769Kecf+G+dwowwWga05IvCX1A2U1kAgoODad++Pb/++qtMYKSJzTTeBg8e\njFLqjZ/Bgwd/8P6R7fsx0qVLF/HvsLAwvv76a7JmzUrixInx8PBAKcWtW7fe+tykSZPi4PBvaVxd\nXQkODrZYNmEZrq4weTK0a2dw9uxD3XGi7OJF8Pd3Y9y46bi5yaJuQlhCliRZGFd9HBcrXKSRdyPd\ncT5YSFgITRc3pblncxrkbqA7jhA2I6VbShY1WkTbFW05eeskvXr14tKlS0yePFl3tPcatn0Yz0Kf\n8U2ZbyK2de3aFS8vLypVqqQxmX2zqcabYRhv/Lyr8fah+77P29a1eHXb3LlzWbFiBZs3b+bu3bsE\nBQVFnNMa2WPf9ciULw8lSiyiUKEaplq8+301evoUGjWCPn2q8/nnxWInlHiNXEfW4WPq1MSzCRVy\nVqD9anN+u/42X2/8GkcHR4aWH8qPP/5oVd265FoyP3uvUbF0xfi+/PfUW1CPp8ZTFi5cyDfffMOe\nPXt0R3vNq3WaeXAmU/ZNYV6DeRHLAkydOpWdO3cybtw4TQkF2FDjTadUqVJx9uxZgLc2yh48eEC8\nePFwd3fn4cOH9O3bVxYytCFz59bH0TEeZcpYzwxz3btDmjTQu7fuJELYptFVR3Pi1gm+2/ad7ijv\nNe/wPJYcX8K8BvOYNXMWfn5+JE4s492EsKS2hdtSKn0pfJf5kj17dn799VcaNmwYaS8snVYHrqb3\nht6s9V5L2oThs1AbhsHKlStZsmQJbm5umhPaN2m8WcDXX3/Nd999R5IkSVi8ePEbDbNWrVqRIUMG\n0qZNi6enJyVLlozS8c3W0LPXvuuRiRs3Djt3zmXfvt/o1Wux7jjAu2s0cyZs2AB+frIYt05yHVmH\nj62Tq5Mrq71X43/Qnyl7p1g2lAWtP7Oermu7EtAkgPMnztOrVy+WLFliVevUybVkflKjcOOqj+Pi\n/YuM+GME9evXx9fXl4MHD+qOFcHLy4udF3fis9SHpU2Xkiv5vxOZKaVYunQpOXPm1JhQgCzSbXfk\ndYw5M2f+jY9PNVau3E716uZ8c9uz5ynVq8fj99/B01N3GiFsX+A/gZSZUYahhYfiEeZB+fLldUeK\nsP7MelosacGSJkvI7ZabIkWK8MMPP0TMmiyEsLxL9y9RdEpRfqn2i+nGlx67eYzy/uXxq+NHtWzV\ndMexe7JIt7AYe++7HplWrYrQsuUPNGs2GN3zy7ytRufO3aFUqfz07XtKGm4mINeRdYhunbIlzcaK\nZivouaQnDRo34NSpU5YJFk3rTq+jxZIWBDQJoGS6krRs2ZK6detaZcNNriXzkxr9K13CdKxqvoqO\nqzuy5PgS3XEiXLx3Ea/BXvxU+SdpuJmcNN6EsCB//8+oX38mn30GZrrB+exZKMWLtyJ37ip0755d\ndxwh7EqRNEWY32U+oV6h1KhdQ/vswWtPr6VlQEsCmgRQKkMplFL4+Pjw448/as0lhL0omLoga7zX\n0GFVBwKOB+iOQ9DdICrNqkTD3A1pka+F7jjiPaTbpJ2R1zHmPXkCFSpA3rwwYQI4aP6K5NmzUHLl\n8uXOnctcuLBGlgUQQpNZB2fRvkN7chu52bRuEwkSJIj1DGtPr6VVQCuWNl1KyfRRG38thLCs/Vf3\nU21ONSbWmEi9XPUAePr0acQawbHhjwt/0PC3hnxd6mu6Fu8asX3jxo34+/tb3XqVtkS6TQoRS5yd\nYe1aOHoU2rWDsDB9WZ48CSF79pbcvXuVU6dWSMNNCI1a5m/JiFEjOBJ2hBJeJXjy5EmsndswDKbu\nmyoNNyFMpGDqgqz2Xs2Xq74k4HgAFy5cIHfu3AQGBsbK+WcdnEW9BfWYXnv6aw23tWvX0rx5c9q1\naxcrOUTUSONNRJn0XX+/BAlgzRo4cwZat37Os2ehsXr+LVu28Pw51Kt3jqdPDQIDl5MsmWusZhDv\nJteRdbB0nToU7cDS2Uu5lP8S0w9Nt+ixI3PvyT2aLW7G2F1j2eKzxeYabnItmZ/UKHKFUhdijfca\nvlz1JVvubKFv376UK1eOEydOxNg5w4ww+m3qx+Ctg/m99e8RY9y2bNnCypUradWqFUuXLuXTTz+N\nsQzi40njTYgY4uYGq1bBzp0/kT17q1hdxPv5c2jSBOLEyca5c/NIksQl1s4thHi3ylkr8/dPfzNu\nzzg6rOrA89DnMXauPZf3UGhyIZK4JGHX57vIlSwXP/30E8ePH4+xcwohoqZQ6kJsaLmBH3b8wMbE\nG+k7qC/ly5fn6NGjFj/Xncd3aLiwIdsvbOevz/4iT4o8Eb/bsWMHn332GStXrozyslYi9siYNzsj\nr2Psu337Mdmz18PFJSEnT87B1dUpRs/39Ck0ahQ+1m7hQogrPSWFMKX7T+/TfHFzHj5/yMKGC0ke\nP7nFjh1mhDFq5yh+/ONHJtaYSIPcDTAMgwEDBrB06VI2bdpEypQpLXY+IUT0PX7+mD4b+xBwIoCW\nqiV+P/qxbt068uXLF+1jh4SFMHnvZIZsHUKj3I34ufLPxHP8d2ydYRi0adOGTp06Ubhw4WifT0Rf\nZGPerKbxlilTJs6fP68hkW3JmDEjQUFBumPYnbt3n5A7dzPu3bvIzJl+NGiQN0bOs28f+PpC7tzh\ni3E7xWw7UQgRTaFhoQz4fQCT907GJ4cP/Sr3I4lLko8+XkhYCIuOLWLEHyNwdnRmboO5ZEqcicuX\nL/PFF19w9epV1q5dS/LklmsoCiEsa/2Z9bRZ1ob8/+Sne6XuVPCqEO3jdVvXjZTxUzKqyijyp8pv\noaQiJsXIhCVKqYZKqSNKqVClVKF37FdVKXVCKXVKKdXnY84VFBSEYRjyE80fSzTcpO961CVO7Myl\nS0to1Kg9jRqVp1WrdTy3YE+p+/efUqpUf8qWnUCvXtCu3RZpuJmcXEfWIabrFMchDsMWkOL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"text/plain": [ "<matplotlib.figure.Figure at 0x112952d68>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_df = pd.DataFrame(train[:len(initial) + len(output), 0], columns=[\"train\"])\n", "initial_df = pd.DataFrame(initial, columns=[\"initial\"])\n", "output_df = pd.DataFrame(output, columns=[\"output\"], index=range(len(initial), len(initial) + len(output)))\n", "merged = pd.concat([train_df, initial_df, output_df])\n", "merged.plot(figsize=(15, 5), grid=True, style=[\"-\", \"-\", \"k--\"])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1129525f8>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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FUnZ22KMB8qVSejOQrphHQDCYS0BqIciLwyGHSNdcI51/vqVwAgAAAECqoiYvTps3S+3b\nSzfeaM1YAAAAACAoGVmTl2rdNQurVEkaMcJW9NatC3s0AAAAADIB3TVTwNlnS3vvLQ0ZEvZIUN7F\nYjFFIpGwhwGkNeYREAzmEpC4jFzJSxd33WUdN7/+OuyRAAAAAMC2WMkrhXvvlSZMkCZOtG0WAAAA\nACARrOSF7LLLpJ9/ll5+OeyRAAAAAMDWCPJKYaedpOHDpZtuYksFhCeVGxUB6YJ5BASDuQSkFoK8\nUurSxbZV+OyzsEcCAAAAAPmoyUtAVpa0Zo3V6AEAAABAaQVZk0eQl4AFC6TOnaUlS6SKFcMeDQAA\nAIB0lZGNV1J9M/SiNG8uNWggTZoU9khQHqXbfAFSEfMICAZzCSi9ZGyGnlJBXjpuonnWWdLYsWGP\nAgAAAEA6ikQigQd5pGsm6OefpVatpKVLpapVwx4NAAAAgHSUkema6apePengg6U33gh7JAAAAABA\nkBcIUjYRBuofgMQxj4BgMJeA1EKQF4CePaUpU6Tffgt7JAAAAADKO2ryAnLGGbZB+kUXhT0SAAAA\nAOmGmrwURMomAAAAgFRAkBeQ44+X5s2Tvv027JGgvKD+AUgc8wgIBnMJSC0EeQGpXNlW8556KuyR\nAAAAACjPqMkL0OzZUo8e0nffSRUrhj0aAAAAAOmCmrwUdcAB0p57SpMmhT0SAAAAAOVVygR50Wg0\nI/K5zz9feuKJsEeB8iAT5gsQNuYREAzmElB6sVhM0Wg00GeSrhmwlSulpk2tAUvt2mGPBpksFosp\nEomEPQwgrTGPgGAwl4DEBZmuSZCXBH36SEccIV16adgjAQAAAJAOqMlLcaRsAgAAAAgLQV4SdOki\nLV8uzZoV9kiQyah/ABLHPAKCwVwCUgtBXhJUrCj16yc9+WTYIwEAAABQ3lCTlyTffSd17CgtWSJV\nqRL2aAAAAACkMmry0kDTptKBB0rPPx/2SAAAAACUJwR5SXTNNdLdd0sZtkiJFEH9A5A45hEQDOYS\nkFoI8pLo2GOtPu/tt8MeCQAAAIDygpq8JBs7Vho1Spo0KeyRAAAAAEhV1OSlkdNPlxYtkmbODHsk\nAAAAAMoDgrwk22knacAAq80DgkT9A5A45hEQDOYSkFoI8srAhRdK778vfftt2CMBAAAAkOmoySsj\nN90krV4tjRgR9kgAAAAApJoga/II8srIzz9L++0nLVgg7bFH2KMBAAAAkErSqvGKc+7vzrlHnXPP\nOueOTfbrpap69aRTTpFGjw57JMgU1D8AiWMeAcFgLgGpJelBnvf+Ne99f0mXSDo92a+Xynr2lN56\nK+xRAAAAAMhkcadrOudGSTpJ0jLvfdsC54+XNFwWMI7y3g8p5v6hkp7x3s8q4nsZn64pSevX24re\njz9Ku+0W9mgAAAAApIqw0jWflNSt0EAqSBqRe761pD7OuZa53zvbOXePc25v59ydkiYUFeCVJ7vu\nKnXqJL33XtgjAQAAAJCp4g7yvPdTJa0sdLqjpIXe+8Xe+z8lPSfp77nXj/HeXyWpl6Sukv7hnOsf\nzLDT1wknSBMmhD0KZALqH4DEMY+AYDCXgNRSKcH760v6ocDxElng9xfv/QOSHtjRg/r166cmTZpI\nkmrWrKl27dopEolIyv8fRyYcn3CCNGhQTGedJR19dPjj4Th9j/Okyng45jgdj2fNmpVS4+GYY445\n5rj8HM+aNUurVq2SJGVnZytIJdpCwTnXWNIbeTV5zrlekrrlNlaRc66vpI7e+8tLNIhyUpOXp3lz\n6YUXpHbtwh4JAAAAgFSQSlso/CipUYHjBrnnsB2kbAIAAABIlpIGeS73K88MSfs45xo75ypL6i3p\n9dIMJBqN/rWMmekI8hCE8jJfgGRiHgHBYC4BpReLxRSNRgN9ZtxBnnNunKSPJTV3zn3vnDvPe58j\n6d+SJkr6StJz3vt5pRlINBr9K0c10x11lDRnjrRiRdgjAQAAABCmSCQSeJBXopq8ZClvNXmSdPLJ\nUp8+9gUAAACgfEulmjyUEimbAAAAAJIhZYK88lSTJ0ndu0tvvy3l5IQ9EqSr8jRfgGRhHgHBYC4B\npRdqTV6ylaeaPElq3FiqW1eaOTPskQAAAAAICzV5Gea666SddpJuvz3skQAAAAAIU5A1eQR5IZo/\nX+rUyTpt7r132KMBAAAAEJaMbLxS3mryJKlFC6l/f+maa8IeCdJReZsvQDIwj4BgMJeA0ktGTR4r\neSHbsEHabz9p1Cipa9ewR4N0EovFylUdK5AMzCMgGMwlIHGka2aY11+3+rzZs6UqVcIeDQAAAICy\nlpHpmuXZySdL++4rDRsW9kgAAAAApDuCvBRx//0W5GVnhz0SpAvqH4DEMY+AYDCXgNSSMkFeeWy8\nUlDTptJVV0lXXBH2SAAAAACUFRqvZLiNG6X997dVve7dwx4NAAAAgLJCTV6GqlJFGj5cuvJKadOm\nsEcDAAAAIB0R5KWYE0+U9tlHuu++sEeCVFee05uBoDCPgGAwl4DUQpCXgoYPl4YMkX76KeyRAAAA\nAEg3KVOTl5WVpUgkwkaauQYOtCBv9OiwRwIAAAAgWWKxmGKxmAYNGsRm6Jlu7VqpVSvphRekww+X\nfv1VmjhRmjJFuuEG68YJAAAAIDPQeKUcqF7dUjbPOUc6+GCr0xs/3oK/a64Je3RIBdQ/AIljHgHB\nYC4BqaVS2ANA8c48U1qzRtpvP+mww6TKlaU//pBatpQ++EA66qiwRwgAAAAg1ZCumYaee04aOlSa\nPl2qwFosAAAAkPZI1yznzjhD2mknaezYsEcCAAAAINUQ5KUh56Rhw6Qbb5Q2bAh7NAgL9Q9A4phH\nQDCYS0BqSZkgLxqN8j+IEjj8cPsaNiz/3JdfStdea/vsAQAAAEh9sVhM0Wg00GdSk5fGvvtO6tBB\nuu466fnnbZuF3r2lUaOkuXOlevXCHiEAAACAeARZk0eQl+aGDZPmzLGtFiIRqWJF6fLLrRPn0KFh\njw4AAABAPAjysF1Llkht20rz50t16oQ9GiRLLBZTJBIJexhAWmMeAcFgLgGJo7smtqtBA+vAee+9\nYY8EAAAAQFljJS9DLV4sHXSQtHChVLt2/nnvpbfflrZskXbf3b722EOqVSu8sQIAAADlHemaiMsF\nF9iq3qBBdrxqlXTeedI339j5336TVqyQfv5ZuuYaKeCmPgAAAADiRLom4nLDDdLIkdLq1dL//ie1\nby81bCh99pn03/9K06ZZwPftt9L48dLgwWGPGCXBliNA4phHQDCYS0BqqRT2AJA8++wjnXCCdNpp\n0uefSyNGWK1eYXvuKb3/vnXnrFzZtmQAAAAAkJ5SJl0zKytLkUiEzkwBW7BAuuwy6f77pZYtt3/t\njz9aoPevf0kDBpTJ8AAAAIByLRaLKRaLadCgQdTkITl++EHq3Fn6978J9AAAAICyQk0ekqZhQykW\nkx56yBq2FBV7v/WWpYDOmVPmw0MB1D8AiWMeAcFgLgGphSAP22jUSPrwQ+mVV2w1b8sWO79hg3Tp\npfbVtq3Utat0223Sn3+GO14AAAAA+UjXRLFWrZJOPFFq3twCu7PPtg6dI0dKNWpYamf//rYFw5NP\nSu3ahT1iAAAAID2xTx7KzPr10qmn2nYLDz4onXnm1t/3Xho9Wrr2Wunhh6VevcIZJwAAAJDOqMlD\nmdl1V+nNN6VFi7YN8CTJOalfP2niRFvtGz++zIdYblH/ACSOeQQEg7kEpBb2ycMO7bSTtPvu27/m\nwAOld96RunWTcnKk3r23/v7vv0vLlm19rmZN+wIAAAAQHNI1EagvvrBA7+67bc+9N9+U3nhD+uAD\nqVYtW/nL89tv0r77SsccY1+dOkm77BLa0AEAAIDQUJOHlDZ3rgVtGzdKxx8vnXSS/Vqr1tbXbdok\nTZ8uvfee9P770rx51q3zooukCiQSAwAAoBwhyEPK27BBqlxZqlSChOCvvrJunc5Jjz4q7bdf8saX\nCWKxmCKRSNjDANIa8wgIBnMJSByNV5DydtmlZAGeJLVubfvznXmm1LmzlJVl3T0BAAAAxC9lVvKy\nsrIUiUT4VyBIkpYska6+WpoyRbrqKulf/5KqVQt7VAAAAECwYrGYYrGYBg0aRLomyocvv5QGD5Ym\nT5auvNK+qlYNe1QAAABAsEjXRLmx//7Sc89ZkPfBB9J559kG7GBPIiAIzCMgGMwlILUQ5CEt7Lef\n9Mortin7sGFhjwYAAABIXaRrIq388IPUsaM0Zoxt07A9a9dKEyZI3btLu+1W9DWbN5e8QQwAAAAQ\nNNI1UW41bCg9+6zUt6/03XfFX/fqq7b6d9990t/+Jt18s/TLL/Y976VPP5UuvFCqWVM6/XTb0w8A\nAADIBAR5SDuRiDRwoHTqqbYfX0FLlkg9e0rXX2+rfR9/LE2bJi1fLrVsKfXrJ7VpI519trTvvtbY\nZfNmqUcPad26MN5N6VH/ACSOeQQEg7kEpBaCPKSlK66QDjhA2mMPqW5dW61r00Zq105q21aaPduC\nQUlq1kx6+GHbbL11a2nECGnBAgsEmzSRXnhBatRI6tpVWrEizHcFAAAAJI6aPKQt763ubv36/K/d\nd5caNCjdswYOlN54Q5o4sXTPAAAAAEoryJo8gjyggCFDpFGjbLuGevXCHg0AAADKCxqvAEly/fXS\nOedIxx6b+qmb1D8AiWMeAcFgLgGphSAPKOSmm6QTTpC6dZNWrw57NAAAAEDJkK4JFMF76bLLpDlz\npLfflnbdtXTPeOst6c47pdq1pZdfLn5PvvXrpV12kVwgC/QAAABIN6RrAknmnPTAA9aZs1s3af78\n+O/dvFkaO9a6fN58swWLGzdKAwYUff24cRYE7rmnbdx+883Sa6+xdx8AAABKhyAPKEaFCtaE5bTT\npCOOkG680Vbc8qxebd8//njbvqFxY6lWLWnnnaVHHpHuukv6/HOpd2/bpmHSJNu+oaAnn5Suvdau\nmzVLuuQSe93hw21fv7FjpS1bih4f9Q9A4phHQDCYS0BqKSZ5DIAkVaxoe/Kdfrp0zTXSfvvZr1On\nWhpn167SBRdILVpIu+1mX9WrSzvttPVzatSQ3nxTOvxwWx3s3t0CwdtvlyZPlpo3t+vq15dOPtl+\nP2WKNYK5+27r+nnccaRzAgAAYMeSWpPnnGsp6QpJtSVN9N6PKuY6avKQFiZPlh56SOrSxQK/2rVL\ndv/HH0unnCKde6704ovS++/bRu7F8d5q+QYOlM48Uxo0KLHxAwAAIDWl3T55zjkn6Tnv/RnFfJ8g\nD+XGs89aM5bXX7cUz3gsWyYdeKD0zDMWYAIAACCzlHnjFefcKOfcMufcnELnj3fOfe2cW+Ccu76Y\ne3tIekvSc4kPF0h/ffpIs2fHH+BJUt260lNP2R5+v/5q53ZU/7Bwoa0EAigedURAMJhLQGqJt/HK\nk5K6FTzhnKsgaUTu+daS+uSmZ8o5d7Zz7h7n3F7e+ze89ydI6hfcsIHy57jjpLPOks47b8fB2+LF\nVid4zTUEegAAAOVN3OmazrnGkt7w3rfNPT5UUpb3vnvu8UBJ3ns/pMA9nSWdKmlnSfO898OLeTbp\nmkAc/vzTOn326SNdeWXx1w0eLM2dK333ndS6tTV5qVgxsdf23jqGLlli4/jzT9suokED6y66//4W\nWBZuOgMAAIAdCzJdM5HumvUl/VDgeImkjgUv8N5PkTQlnof169dPTZo0kSTVrFlT7dq1UyQSkZSf\nAsAxx+X9eKedpCuuiOmSS6Qjj4yoffttr588OaaHH5ZefDGi/feXOneOqUsXaeLEiKpUKf75Rxxh\nx1OnFv39n36K6JlnpEgkpqpVpQ4dIqpYUZoyJaapU6VlyyJavFj6179i6tEjNT4vjjnmmGOOOeaY\n41Q9njVrllatWiVJys7OVpASWcnrJamb975/7nFfSR2995eXeBCs5AEl8vTT0h13xDRvXmSbbRU+\n+UTq10/6+mvbcmHjRlv5W79e+te/pGrVbJuHXXeVvv/eOn5+9JE0Y4btzTd1qlSlytbP3LAhf9++\nI48sflwff2yvPX8+2z0gPcRisb9+4AIoPeYSkLgyb7xSjB8lNSpw3CD3HIAk69vXgrcJE7b93tNP\nW4OWvCBlAJDIAAAgAElEQVSrShXbjP3gg6UnnrC9+S67TPrHPyz90nvbkP377y318qabtn3mXXfZ\nHn/bC/Ak6bDDLF3zww8Tf48AAAAonZKs5DWRreS1yT2uKGm+pK6SfpI0XVIf7/28Eg/COZ+VlaVI\nJMK/AgFxevFF2yR9+vT8gO6PP2xD9c8/lxo12v79RVmxQmrXTnr8calbbqul77+37RvifeY990iz\nZlmwCQAAgO2LxWKKxWIaNGhQ2e6T55wbJykiaXdJy2QNV550znWXNFy2IjjKe39nqQZBuiZQYlu2\nWPA1eLDUo4edGz/eNmufNKn0z5082bp4zpol7bmnpXo2bx7/Ruy//irtu691+KxRo/TjAAAAKE/K\nPF3Te3+m935v730V730j7/2Tuef/671v4b3ft7QBHoDS+eCDmLKypGg0f5uEp5+Wzj03secefbRt\n09Cvn6VdTp0qXXdd/PfXqSMde6xt+l4WtmyRAq5VRjmSVwgPIDHMJSC1JFKTByBkp5wi5eRIr78u\nLVtmQdmppyb+3GhU+u03WyG86y5r0lISF1xgKZ/J9uWX0lFHWVOYiy6y5jKFffedBa2jRiV/PAAA\nAKkgZYK8aDTKvwIBJRCJRFShggVk0ag0bpx08snWOTNRO+1kzzv/fKl375Lff+yx0i+/SLNnl+y+\nLVus0+c770jTplmXzmXLbE++gtavl66/3lYd+/aVfvrJ6hHbtbP7JOsIeuutUocOVqc4bJjUv781\nrAHyUAcOBIO5BJReLBZTNBoN9JlxN15JJmrygNLzXmrfXvrmG+nll6Vjjgl7RCYrS1q5Urr//h1f\n+8UXtj3DuHFSzZpSvXrSqlXS6tX2jJUrpVq1pL33lvbaS5o3T+rUyQK3evXyn/Pii9Kll0q9elnn\n0UMOkYYOlRo2lNautRW9H36QXnrJOokCAACkiiBr8gjygDRVcE+it96SrrjCVr4qVgx3XHmys20V\nbckSaeed7dzGjdalc/58acECaeFCC/DWr5fOPNMavrRps+2zcnKsocvSpbZqV7u2bddQlKVLpTvv\nlHr2tJW+gry3jqT33y/dcot0wAFS69alaxCzZYt1Hp07N/+ra1d7D0gf7O0FBIO5BCSOIA/ANj9Q\nN22SKlcObzxFOe44qUsXG9e771oq5j77SPvtZx04mzeXWrSwNMsKZZg8PmmSNan56itbFaxZUzrx\nRGnECEtVLWz1amtoM3++tGaNrQquX2+riq1b2/vZay/p3nutBjAvqEXq4y+mQDCYS0DiMjLIY588\nIPO88YZ0zTW2onbssRbw1aoV9qi2lrcid+mltqI3ZszWq6Hr19uegW3aSP/+t9U8Vq8uVasmVaq0\n9bNOOsma1Vx0Udm+BwAAkL5C2ycv2VjJAxC233+3IK1xY+sMWqGCNXPp0cPq90aN2vFq44cfWrOa\nr79OPG3We6uxrFDBUk/jFY1Kp51mK4wAACB9lPk+eQBSD91og1W1qvTaa1YrePnllv562mlW/5cX\n9O3IEUfYPoEvv7zt92bOtAYw48fv+Dlz5tiq56BB0sUXx7+5/SefSMOHW7D6yy9FX7N+vbR8eXzP\nKw+YR0AwmEtAaiHIA4Bc1apZE5tp02wlzDnpmWfiX5VzzrZ2GDIkf4N6yYKqXr2kSy6xjeWvuUba\nvHnb+3/5xa459ljp9NOl//1Peu45qU8fCz53JCvL9jU86yzbL7HwdhHZ2dKhhwazlyIAAEhdBHlA\nmqJ+NTlq1LB9+vr2lV54oehGLNvTo4ft0Ze3+rZ5s+012KePdOONtqL31Ve21cWyZdbU5emnpRNO\nsGY0lSpZM5hLLrHfH320dPvttjr322/Fv+7UqdattF8/6f/+zxrB/POf+cHmxx9Lhx9u20h8842N\nIVnWr7ftLNIhC595BASDuQSklpSpyaPxCoBM8eST0rPPShMnSgMHSp99Jv33v/mNWnJypNtuk0aO\ntLTQo4+WzjjDAsRq1Yp+5jXX2Mre228X3UW1a1fbhuKCC+x4wwbpqKNsBbFRI2nAAGn0aKl7d9s+\nYvXq+PYwzMmRZs2yxjPxdG/13lYSX3lFuvZaCzgBAEDxaLwC4C+0q05dmzZJf/ubrZqNGWOrd3vs\nse11c+faBu81a+74mTk51oClRg1rAlMw4JoyJb/hS8GVxx9/tPTMSpWs0+n++9v577+XDjzQft11\n121fy3tp+nQLVF94wVYjTz7ZahN3ZORI6dFHpddft9XJM8+Ubrppx/cV9vbb0n/+Y6ugZ5xhtZHJ\nwDwCgsFcAhJH4xUASGGVK9vK2d13Sy+9VHSAJ9n+evEEeJLVBY4bJ61bJx15pLR4cf73srJsda5w\namn9+rY34cyZ+QGeZCt7nTpZvV9hy5fbqt3ZZ9vYJk+Wvv3Wmro89tj2xzhtmjWLeekl61L63nuW\nijp0aHzvMU9OjnTVVVabOGWK1LSp1RG++qoF0GVpyhTpp5/K9jUBAEgUK3kAkASbNlmzlILBVRC8\nl+65xxqsPPGEtMsuUv/+VsdXeN++7ZkwwYLDGTO2fnbPnrYKOWyYNZLJM3++BZdvvSUdfPC2z1u+\nXGrfXrrvPumUU/LP//ij1LmzdSy9/PL4xvbUU/bepkyxMaxaZTV+Y8ZYLeEZZ1gQesghW48xaNnZ\n9ud35JH2eSXztQAAyMjN0FNhHACQLj76yFIZN2+2gO/ss0t2f06O1KyZBU8dOti5xx6THnxQ+vRT\nqUqVbe955RXpyiutxrDg6mROjqVmtmtnnUULW7zYagaPPtoC1OrVix/Xxo1S8+a2atmp07bf/+47\naexYWyHcaSfbm7C4VM6FCy1wbd68+NcrjvfSccdZgPr885Zy2rt3yZ8DAEC8MjJdMxqNsscKUALM\nl/KtUydrxHLZZda5s6QqVpQuukh6+GE7XrDAun+OG1d0gCfZKl+fPvb1ww8WbF14obTPPhbo3X57\n0fc1bix9/rkFTu3aWSfQ4jz8sHTAAUUHeJKlbt58s60stm1rzWSKc+mltuq3ZUvx1xQ3j5580rqZ\nDhxowe+AAdvvbgqUd/xMAkovFospGo0G+kxW8oA0RZE7ErVsmdSypQV43btbZ85LLtn+PZs323YO\n06dLkYht2t6li9SqVXzpjK+9Zhu8n3uuFI1KO++c/721a20biXfftbrAHfnwQ0tVnTt329deuNAC\nxSZNLE20b9+in1HUPFq61ILRd9+1gFOyYHrjxh3XJRbmvW1rcfHF0mGHlezeID3/vK3Ejh0b/76P\nQEnwMwlIHOmaAIBAnHGG9MUXlrr5+uvxBWre21eFUuaC/PKL9K9/WUOY22+3lcEKFaxpy6JFlooZ\nD++tec2jj1rdXEFXX201iiedZAHe/PlbB5Tbe+app1ot3m235Z9fs0Zq3dqCpKOOiv+9fvKJ1Sh6\nb9totG8f/71B2LLFmvKMHWsptv36WcAKAEg9BHkAgEB8+KHtazdzprTnnmX72h98YHvpbd5sqaL9\n+9s4mjaN/xn33mtpq2PG5J/7/XfrIDptmjWROeUU6YgjbK/BgrKzLbDce29biWzVyhq73HmnpZcW\nTlt95RXphhuk2bOLT2ktrF8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v1cu6sp54ojR1qq3ujBkjjR5tNV5vv514nVbPntL++9tr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\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x112980518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "losses_df = pd.DataFrame(losses, columns=[\"epoch\", \"loss\"])\n", "losses_df.plot(figsize=(15, 5), grid=True, logy=True, x=\"epoch\")" ] }, { "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 }	mit
Juan-Mateos/coll_int_ai_case	notebooks/jaklinger/microsoft_academic_knowledge/get_citations_from_title.ipynb	1	34122	{ "cells": [ { "cell_type": "code", "execution_count": 239, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import requests\n", "import json\n", "from alphabet_detector import AlphabetDetector\n", "import pandas as pd\n", "import numpy as np\n", "from fuzzywuzzy import process as fuzzy_proc\n", "from fuzzywuzzy import fuzz\n", "import ast" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ad = AlphabetDetector()\n", "def title_processor(title):\n", " result = \"\".join([x if len(ad.detect_alphabet(x)) > 0 or x.isnumeric()\n", " else \" \" for x in title.lower()])\n", " while \" \" in result:\n", " result = result.replace(\" \",\" \")\n", " return result" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": false }, "outputs": [], "source": [ "headers = {\n", " 'Ocp-Apim-Subscription-Key': 'a9a9efa851b44d5bbd6c841215a99e00',\n", " 'Content-Type': 'application/x-www-form-urlencoded'\n", "}\n", "\n", "def process_titles(raw_titles):\n", "\n", " titles = [(pid,title_processor(t)) for pid,t in raw_titles]\n", "\n", " title_count = 800\n", " title_offset = 0\n", " query_count = 1000\n", "\n", " calls = 0\n", " \n", " data = []\n", " while title_offset < len(titles):\n", "\n", " calls += 1\n", " if calls > 10:\n", " break\n", " \n", " last_title = title_offset+title_count\n", " if last_title > len(titles):\n", " last_title = None\n", "\n", " titles_subset = titles[title_offset:last_title]\n", " expr = [\"Ti='\"+t+\"'\" for _,t in titles_subset]\n", " expr = ','.join(expr)\n", " expr = \"expr=OR(\"+expr+\")\"\n", " title_offset += title_count\n", "\n", " query = expr+\"&count=\"+str(query_count)+\"&attributes=Id,Ti,D,AA.AuN,AA.AuId,F.FId,J.JId,AA.AfId,CC,ECC,AA.AfN,J.JN\" \n", " #print(query)\n", " \n", " r = requests.post('https://westus.api.cognitive.microsoft.com/academic/v1.0/evaluate', \n", " data=query.encode(\"utf-8\"), headers=headers)\n", " js = r.json()\n", "\n", " print(len(js[\"entities\"]),len(titles))\n", " \n", " for pid,t in titles_subset:\n", " matched = False\n", " for row in js[\"entities\"]:\n", " if t != row[\"Ti\"]:\n", " continue\n", " insts = list(set(author[\"AfN\"] for author in row[\"AA\"] if \"AfN\" in author))\n", " data.append(dict(pid=pid,title=t,institutes=insts,citations=row[\"CC\"],date=row[\"D\"],matched=True))\n", " matched = True\n", " break\n", " if not matched:\n", " data.append(dict(pid=pid,title=t,matched=False))\n", "\n", " print(\"Made\",calls,\"calls\")\n", " return data" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "629 7017\n", "758 7017\n", "776 7017\n", "786 7017\n", "796 7017\n", "797 7017\n", "809 7017\n", "790 7017\n", "660 7017\n", "Made 9 calls\n" ] } ], "source": [ "#raw_titles = [(1,\"Search for invisible decays of a Higgs boson using vector-boson fusion in pp collisions at s√=8 TeV with the ATLAS detector\"),\n", "# (2,\"Muon-induced background to proton decay in the p→K+ν decay channel with large underground liquid argon TPC detectors\"),\n", "# (3,\"personalizing search via automated analysis of interests and activities\")]\n", "\n", "df = pd.read_csv(\"/Users/hep/Downloads/ai_id_title.csv\")\n", "raw_titles = df[[\"id\",\"title\"]].values\n", "data = process_titles(raw_titles)" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7017 6408 4459 5544 4117\n" ] } ], "source": [ "ncite = 0\n", "ninst = 0\n", "nmatch = 0 \n", "nboth = 0\n", "for row in data:\n", " if not row[\"matched\"]:\n", " continue\n", " nmatch += 1\n", " if row[\"citations\"] > 0:\n", " ncite += 1\n", " if len(row[\"institutes\"]) > 0:\n", " ninst += 1\n", " if row[\"citations\"] > 0 and len(row[\"institutes\"]) > 0:\n", " nboth += 1\n", "print(len(data),nmatch,ncite,ninst,nboth)" ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with open('/Users/hep/Downloads/ai_id_title_MAK-matched.json', 'w') as fp:\n", " json.dump(data, fp)" ] }, { "cell_type": "code", "execution_count": 264, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mak_df = pd.read_json('/Users/hep/Nesta/coll_int_ai_case/notebooks/MAK_disambiguate/modules/CS_STATS_id_title_tag_MAK-matched.json')" ] }, { "cell_type": "code", "execution_count": 265, "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>citations</th>\n", " <th>date</th>\n", " <th>institutes</th>\n", " <th>matched</th>\n", " <th>pid</th>\n", " <th>title</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>12.0</td>\n", " <td>2008-11-13</td>\n", " <td>[max planck society, heidelberg institute for ...</td>\n", " <td>True</td>\n", " <td>http://arxiv.org/abs/0811.2055v2</td>\n", " <td>gpu based interactive visualization of billion...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>3.0</td>\n", " <td>2007-01-10</td>\n", " <td>[bielefeld university, washington university i...</td>\n", " <td>True</td>\n", " <td>http://arxiv.org/abs/0707.0808v1</td>\n", " <td>the cyborg astrobiologist porting from a weara...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>12.0</td>\n", " <td>2008-09-20</td>\n", " <td>[university of california berkeley, university...</td>\n", " <td>True</td>\n", " <td>http://arxiv.org/abs/0706.4108v1</td>\n", " <td>event weighted tests for detecting periodicity...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2.0</td>\n", " <td>2008-11-01</td>\n", " <td>[massachusetts institute of technology]</td>\n", " <td>True</td>\n", " <td>http://arxiv.org/abs/0706.4048v1</td>\n", " <td>getting more from your multicore exploiting op...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.0</td>\n", " <td>2007-01-06</td>\n", " <td>[harvard university]</td>\n", " <td>True</td>\n", " <td>http://arxiv.org/abs/cs/0701035v1</td>\n", " <td>finding astronomical communities through co re...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " citations date institutes \\\n", "0 12.0 2008-11-13 [max planck society, heidelberg institute for ... \n", "1 3.0 2007-01-10 [bielefeld university, washington university i... \n", "2 12.0 2008-09-20 [university of california berkeley, university... \n", "3 2.0 2008-11-01 [massachusetts institute of technology] \n", "4 0.0 2007-01-06 [harvard university] \n", "\n", " matched pid \\\n", "0 True http://arxiv.org/abs/0811.2055v2 \n", "1 True http://arxiv.org/abs/0707.0808v1 \n", "2 True http://arxiv.org/abs/0706.4108v1 \n", "3 True http://arxiv.org/abs/0706.4048v1 \n", "4 True http://arxiv.org/abs/cs/0701035v1 \n", "\n", " title \n", "0 gpu based interactive visualization of billion... \n", "1 the cyborg astrobiologist porting from a weara... \n", "2 event weighted tests for detecting periodicity... \n", "3 getting more from your multicore exploiting op... \n", "4 finding astronomical communities through co re... " ] }, "execution_count": 265, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mak_df.head()" ] }, { "cell_type": "code", "execution_count": 266, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "count 122198.000000\n", "mean 10.942757\n", "std 82.726236\n", "min 0.000000\n", "25% 0.000000\n", "50% 1.000000\n", "75% 7.000000\n", "max 15810.000000\n", "Name: citations, dtype: float64" ] }, "execution_count": 266, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mak_df.loc[~pd.isnull(mak_df[\"citations\"]),\"citations\"].describe()" ] }, { "cell_type": "code", "execution_count": 267, "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>Name</th>\n", " <th>lat</th>\n", " <th>lng</th>\n", " <th>ID</th>\n", " <th>alias</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Australian National University</td>\n", " <td>-35.277800</td>\n", " <td>149.120500</td>\n", " <td>grid.1001.0</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Monash University</td>\n", " <td>-37.908300</td>\n", " <td>145.138000</td>\n", " <td>grid.1002.3</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>University of Queensland</td>\n", " <td>-27.495964</td>\n", " <td>153.009627</td>\n", " <td>grid.1003.2</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Macquarie University</td>\n", " <td>-33.775259</td>\n", " <td>151.112915</td>\n", " <td>grid.1004.5</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>UNSW Australia</td>\n", " <td>-33.917731</td>\n", " <td>151.230964</td>\n", " <td>grid.1005.4</td>\n", " <td>University of New South Wales</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Name lat lng ID \\\n", "0 Australian National University -35.277800 149.120500 grid.1001.0 \n", "1 Monash University -37.908300 145.138000 grid.1002.3 \n", "2 University of Queensland -27.495964 153.009627 grid.1003.2 \n", "3 Macquarie University -33.775259 151.112915 grid.1004.5 \n", "4 UNSW Australia -33.917731 151.230964 grid.1005.4 \n", "\n", " alias \n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 University of New South Wales " ] }, "execution_count": 267, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid_full = pd.read_csv(\"/Users/hep/Downloads/grid20170810/grid.csv\",low_memory=False)\n", "grid_address = pd.read_csv(\"/Users/hep/Downloads/grid20170810/full_tables/addresses.csv\",low_memory=False)\n", "grid_alias = pd.read_csv(\"/Users/hep/Downloads/grid20170810/full_tables/aliases.csv\",low_memory=False)\n", "\n", "grid_df = grid_full.join(grid_address.set_index(keys=[\"grid_id\"]),on=\"ID\")\n", "grid_df = grid_df.join(grid_alias.set_index(keys=[\"grid_id\"]),on=\"ID\")\n", "grid_df = grid_df[[\"Name\",\"lat\",\"lng\",\"ID\",\"alias\"]]\n", "grid_df.head()" ] }, { "cell_type": "code", "execution_count": 268, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#_______________________\n", "class ComboFuzzer:\n", " def __init__(self,fuzzers):\n", " self.fuzzers = fuzzers\n", " # Define the normalisation variable in advance\n", " # NB: defined as inverse for speed\n", " self.norm = 1/np.sqrt(len(fuzzers))\n", " \n", " def combo_fuzz(self,target,candidate):\n", " _score = 0\n", " for _fuzz in self.fuzzers:\n", " _raw_score = (_fuzz(target,candidate)/100)\n", " _score += _raw_score*2\n", " return np.sqrt(_score)self.norm" ] }, { "cell_type": "code", "execution_count": 273, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Already got 41 matches\n" ] } ], "source": [ "#_______________________\n", "class LatLonGetter:\n", " def __init__(self,grid_df,scorer):\n", " self.scorer = scorer\n", " # Find the null aliases\n", " self.df = grid_df\n", " null_alias = pd.isnull(self.df.alias)\n", " not_null = self.df.loc[~null_alias]\n", " # Now generate the list of names + not null aliases\n", " alias_names = list(not_null.alias.values)\n", " std_names = list(grid_df.Name.values)\n", " self.all_possible_values = std_names + alias_names\n", " self.lower_possible_values = [x.lower() for x in \n", " self.all_possible_values]\n", " with open(\"fuzzy_scores.pydict\") as f:\n", " self.fuzzy_matches = ast.literal_eval(f.read())\n", "# self.fuzzy_matches = {\"ibm\" : (\"IBM (United States)\",1.),\n", "# \"microsoft\" : (\"Microsoft (United States)\",1.),\n", "# \"xerox\" : (\"Xerox (United States)\", 1.),\n", "# \"pricewaterhousecoopers\" : (\"PricewaterhouseCoopers (United States)\",1.),\n", "# \"university of california berkeley\": (\"University of California, Berkeley\",1.),\n", "# \"university of california santa cruz\": (\"University of California, Santa Cruz\",1.),\n", "# \"linkoping university\": (\"Linköping University\",1.),\n", "# \"nec\" : (\"NEC (United States)\",1.),\n", "# \"university of michigan\" : (\"Michigan State University\",1.),\n", "# \"google\" : (\"Google (United States)\",1.),\n", "# \"yahoo\" : (\"Yahoo (United States)\",1.),\n", "# \"at t\" : (\"AT&T (United States)\",1.),\n", "# \"at t labs\" : (\"AT&T (United States\",1.)}\n", " \n", " def get_latlon(self,mak_name):\n", "\n", " # Super-fast check to see if there is an exact match\n", " try:\n", " idx = self.lower_possible_values.index(mak_name)\n", " match = self.all_possible_values[idx]\n", " score = 1.\n", " # Otherwise, fuzzy match\n", " except ValueError:\n", " # If already done a fuzzy match for this\n", " if mak_name in self.fuzzy_matches:\n", " match,score = self.fuzzy_matches[mak_name]\n", " # Otherwise, do the fuzzy match\n", " else:\n", " match,score = fuzzy_proc.extractOne(query=mak_name,\n", " choices=self.all_possible_values,\n", " scorer=self.scorer)\n", " self.fuzzy_matches[mak_name] = (match,score)\n", " \n", " # Check whether the match was a Name or alias\n", " condition = grid_df.Name == match\n", " if condition.sum() == 0:\n", " condition = grid_df.alias == match\n", " _df = grid_df.loc[condition]\n", "\n", " # Get the lat/lon\n", " lat = _df[\"lat\"].values[0]\n", " lon = _df[\"lng\"].values[0]\n", " return (lat,lon,score)\n", "\n", "\n", " def process_latlons(self,mak_institutes):\n", " isnull = pd.isnull(mak_institutes)\n", " if type(isnull) is bool:\n", " if isnull:\n", " return []\n", " elif all(isnull): \n", " return []\n", " return [self.get_latlon(mak_name) \n", " for mak_name in mak_institutes]\n", " \n", "#_______________________\n", "# Fuzzy combination of partial ratio and token sort ratio\n", "cf = ComboFuzzer([fuzz.token_sort_ratio,fuzz.partial_ratio])\n", "llg = LatLonGetter(grid_df=grid_df,scorer=cf.combo_fuzz)\n", "\n", "print(\"Already got\",len(llg.fuzzy_matches),\"matches\")" ] }, { "cell_type": "code", "execution_count": 275, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "writing 2028\n" ] } ], "source": [ "mak_df[\"lat_lon_score\"] = [llg.process_latlons(insts) for insts in mak_df[\"institutes\"]]\n", "with open(\"fuzzy_scores.pydict\",\"w\") as f:\n", " print(\"writing\",len(llg.fuzzy_matches))\n", " f.write(str(llg.fuzzy_matches))" ] }, { "cell_type": "code", "execution_count": 277, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mak_df.to_json('/Users/hep/Downloads/CS_STATS_id_title_tag_MAK-matched_GRID-matched.json')" ] }, { "cell_type": "code", "execution_count": 278, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['citations', 'date', 'institutes', 'matched', 'pid', 'title',\n", " 'lat_lon_score'],\n", " dtype='object')" ] }, "execution_count": 278, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mak_df.columns" ] }, { "cell_type": "code", "execution_count": 279, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['citations', 'date', 'institutes', 'matched', 'pid', 'title'], dtype='object')" ] }, "execution_count": 279, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_json('/Users/hep/Nesta/coll_int_ai_case/notebooks/MAK_disambiguate/modules/CS_STATS_id_title_tag_MAK-matched.json').columns" ] }, { "cell_type": "code", "execution_count": 282, "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>citations</th>\n", " <th>date</th>\n", " <th>institutes</th>\n", " <th>matched</th>\n", " <th>pid</th>\n", " <th>title</th>\n", " <th>lat_lon_score</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>12.0</td>\n", " <td>2008-11-13</td>\n", " <td>[max planck society, heidelberg institute for ...</td>\n", " <td>True</td>\n", " <td>http://arxiv.org/abs/0811.2055v2</td>\n", " <td>gpu based interactive visualization of billion...</td>\n", " <td>[(48.141292, 11.581925, 1.0), (49.415617, 8.73...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>3.0</td>\n", " <td>2007-01-10</td>\n", " <td>[bielefeld university, washington university i...</td>\n", " <td>True</td>\n", " <td>http://arxiv.org/abs/0707.0808v1</td>\n", " <td>the cyborg astrobiologist porting from a weara...</td>\n", " <td>[(52.037778, 8.493056, 1.0), (38.649033, -90.3...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>12.0</td>\n", " <td>2008-09-20</td>\n", " <td>[university of california berkeley, university...</td>\n", " <td>True</td>\n", " <td>http://arxiv.org/abs/0706.4108v1</td>\n", " <td>event weighted tests for detecting periodicity...</td>\n", " <td>[(37.872162, -122.258572, 1.0), (52.355792, 4....</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2.0</td>\n", " <td>2008-11-01</td>\n", " <td>[massachusetts institute of technology]</td>\n", " <td>True</td>\n", " <td>http://arxiv.org/abs/0706.4048v1</td>\n", " <td>getting more from your multicore exploiting op...</td>\n", " <td>[(42.35982, -71.09211, 1.0)]</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.0</td>\n", " <td>2007-01-06</td>\n", " <td>[harvard university]</td>\n", " <td>True</td>\n", " <td>http://arxiv.org/abs/cs/0701035v1</td>\n", " <td>finding astronomical communities through co re...</td>\n", " <td>[(42.377053, -71.116657, 1.0)]</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " citations date institutes \\\n", "0 12.0 2008-11-13 [max planck society, heidelberg institute for ... \n", "1 3.0 2007-01-10 [bielefeld university, washington university i... \n", "2 12.0 2008-09-20 [university of california berkeley, university... \n", "3 2.0 2008-11-01 [massachusetts institute of technology] \n", "4 0.0 2007-01-06 [harvard university] \n", "\n", " matched pid \\\n", "0 True http://arxiv.org/abs/0811.2055v2 \n", "1 True http://arxiv.org/abs/0707.0808v1 \n", "2 True http://arxiv.org/abs/0706.4108v1 \n", "3 True http://arxiv.org/abs/0706.4048v1 \n", "4 True http://arxiv.org/abs/cs/0701035v1 \n", "\n", " title \\\n", "0 gpu based interactive visualization of billion... \n", "1 the cyborg astrobiologist porting from a weara... \n", "2 event weighted tests for detecting periodicity... \n", "3 getting more from your multicore exploiting op... \n", "4 finding astronomical communities through co re... \n", "\n", " lat_lon_score \n", "0 [(48.141292, 11.581925, 1.0), (49.415617, 8.73... \n", "1 [(52.037778, 8.493056, 1.0), (38.649033, -90.3... \n", "2 [(37.872162, -122.258572, 1.0), (52.355792, 4.... \n", "3 [(42.35982, -71.09211, 1.0)] \n", "4 [(42.377053, -71.116657, 1.0)] " ] }, "execution_count": 282, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mak_df.loc[mak_df.matched == True].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Output from MAK and GRID matching\n", "\n", "## Method\n", "\n", "### Matching to MAK\n", "\n", "MAK can be queried by concatenating OR-statements together. The number of results from a MAK query can be no larger than 1000, so we nominally query with 600 sub-queries. We use the paper title from arXiv for the matching, which are prepared by the following procedure:\n", "\n", "1. Identify any foreign characters as non-symbolic.\n", "2. Replace all symbolic characters with spaces.\n", "3. Ensure no more than one space separates characters.\n", "\n", "This procedure returns a 90% match rate, which may be missing paper where the title is different from that presented on arXiv, or where the paper has not been published in a journal. It may be possible to recuperate some of these missing 10% of papers in the future, for example by matching paper credentials, although this is currently not a limiting factor in our analysis.\n", "\n", "### Matching to GRID\n", "\n", "The GRID dataset contains institute names, and aliases (where applicable), and a corresponding geospatial coordinate (latitude and longitude). Each institute name from MAK is matched to the comprehensive list from GRID in the following manner:\n", "\n", "1. If there is an exact match amongst the institute names or aliases, then extract the coordinates of this match. Assign a \"score\" of 1 to this match (see step 3. for the definition of \"score\").\n", "2. Otherwise, check whether a match has previously been found. If so, extract the coordinates and score of this match.\n", "3. Otherwise, calculate a matching score of the MAK by convoluting the matching scores of various fuzzy-matching algorithms in the following manner:\n", "$$ \\frac{1}{\\sqrt{N}} \\sqrt{ \\sum_{n=0}^{N} F_{n}(w_{MAK},W_{GRID})^{2} } $$\n", "\n", "where $N$ is the number of fuzzy-matching algorithms to use, $F_{n}()$ returns a fuzzy-matching score (in the range $0 \\rightarrow 1$) from the $n^{\\text{th}}$ algorithm, $w_{MAK}$ is the name from MAK to be matched and $W_{GRID}$ is the comprensive list of institutes in the GRID data. \n", "\n", "I currently use the `token_sort_ratio` and `partial_ratio` algorithms implemented in the `fuzzywuzzy` module.\n", "\n", "## Fields\n", "\n", "\| field \| source \| description \|\n", "\|---\|---\|---\|\n", "\| citations \| MAK \| number of citations \|\n", "\| date \| MAK \| date of publication \|\n", "\| matched \| joel \| flag indicating a successful match between arXiv and MAK \|\n", "\| pid \| arXiv \| arXiv publication ID, for matching back to arXiv data \|\n", "\| title \| joel \| the normalised publication title, used for matching to MAK \|\n", "\| institutes \| MAK \| list of institutes from successful matches between arXiv and MAK \|\n", "\| lat_lon_score \| GRID / joel \| A list of triplets, with a one-to-one correspondence with institutes. The first two fields are, respectively, latitude and longitude. The third field is the best fuzzy-matching score between GRID and MAK institutes. \|\n", "\n", "It is generally recommended to only use institutes with scores of 1 of used, which is sufficient for 80% of individual institute-paper matches. Therefore the above method yields an approximate efficiency of 72%, although there are known issues with the GRID matching procedure which leads to a very small number of false matches." ] }, { "cell_type": "code", "execution_count": 289, "metadata": { "collapsed": false }, "outputs": [], "source": [ "condition = mak_df.lat_lon_score.apply(lambda x: all(_x[2] == 1.0 for _x in x) and len(x) > 0)" ] }, { "cell_type": "code", "execution_count": 290, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "74752" ] }, "execution_count": 290, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(condition & (mak_df.matched == True)).sum()" ] }, { "cell_type": "code", "execution_count": 291, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "122198" ] }, "execution_count": 291, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(mak_df.matched == True).sum()" ] }, { "cell_type": "code", "execution_count": 292, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.6117285061948641" ] }, "execution_count": 292, "metadata": {}, "output_type": "execute_result" } ], "source": [ "74752/122198" ] }, { "cell_type": "code", "execution_count": 299, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ValueError", "evalue": "not enough values to unpack (expected 3, got 2)", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-299-46108cea6e97>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mlat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlon\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mscore\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmak_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlat_lon_score\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mscore\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[0m", "\u001b[0;32m<ipython-input-299-46108cea6e97>\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mlat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlon\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mscore\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmak_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlat_lon_score\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mscore\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[0m", "\u001b[0;31mValueError\u001b[0m: not enough values to unpack (expected 3, got 2)" ] } ], "source": [ "sum(1 for lat,lon,score in list(mak_df.lat_lon_score.values) if score == 1. )" ] }, { "cell_type": "code", "execution_count": 305, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.8080228249255462\n" ] } ], "source": [ "n_good = 0\n", "n_total = 0\n", "for row in mak_df.lat_lon_score.values:\n", " for lat,log,score in row:\n", " n_total += 1\n", " if score == 1.0:\n", " n_good += 1\n", "print(n_good/n_total)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:py35]", "language": "python", "name": "conda-env-py35-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
InsightLab/data-science-cookbook	2019/09-clustering/cl_AlefCarneiro.ipynb	1	243077	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Notebook_KMeans_Assignment.ipynb", "version": "0.3.2", "provenance": [] }, "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" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" } }, "cells": [ { "metadata": { "id": "rdcg6yEKB-Z3", "colab_type": "text" }, "cell_type": "markdown", "source": [ "# <p style=\"text-align: center;\">Clusterização e algoritmo K-means</p> \n", "\n", "Organizar dados em agrupamentos é um dos modos mais fundamentais de compreensão e aprendizado. Como por exemplo, os organismos em um sistema biologico são classificados em domínio, reino, filo, classe, etc. A análise de agrupamento é o estudo formal de métodos e algoritmos para agrupar objetos de acordo com medidas ou características semelhantes. A análise de cluster, em sua essência, não utiliza rótulos de categoria que marcam objetos com identificadores anteriores, ou seja, rótulos de classe. A ausência de informação de categoria distingue o agrupamento de dados (aprendizagem não supervisionada) da classificação ou análise discriminante (aprendizagem supervisionada). O objetivo da clusterização é encontrar estruturas em dados e, portanto, é de natureza exploratória. \n", "\n", "A técnica de Clustering tem uma longa e rica história em uma variedade de campos científicos. Um dos algoritmos de clusterização mais populares e simples, o K-means, foi publicado pela primeira vez em 1955. Apesar do K-means ter sido proposto há mais de 50 anos e milhares de algoritmos de clustering terem sido publicados desde então, o K-means é ainda amplamente utilizado.\n", "\n", "Fonte: Anil K. Jain, Data clustering: 50 years beyond K-means, Pattern Recognition Letters, Volume 31, Issue 8, 2010" ] }, { "metadata": { "id": "GI821AaiB-Z8", "colab_type": "text" }, "cell_type": "markdown", "source": [ "# Objetivo\n", "\n", "- Implementar as funções do algoritmo KMeans passo-a-passo\n", "- Comparar a implementação com o algoritmo do Scikit-Learn\n", "- Entender e codificar o Método do Cotovelo\n", "- Utilizar o K-means em um dataset real " ] }, { "metadata": { "id": "FPuDOT_0B-Z_", "colab_type": "text" }, "cell_type": "markdown", "source": [ "# Carregando os dados de teste" ] }, { "metadata": { "id": "L7hne9ZKB-aB", "colab_type": "text" }, "cell_type": "markdown", "source": [ "Carregue os dados disponibilizados, e identifique visualmente em quantos grupos os dados parecem estar distribuídos." ] }, { "metadata": { "id": "_YjaMYUNB-aF", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "# import libraries\n", "\n", "# linear algebra\n", "import numpy as np \n", "# data processing\n", "import pandas as pd \n", "# data visualization\n", "from matplotlib import pyplot as plt \n", "# sys - to get maximum float value\n", "import sys" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "lRFP_ljfB-aN", "colab_type": "code", "outputId": "5b857435-df1c-47a8-faa6-7ff56a7c8d6a", "colab": { "base_uri": "https://localhost:8080/", "height": 269 } }, "cell_type": "code", "source": [ "# load the data with pandas\n", "url = 'https://raw.githubusercontent.com/InsightLab/data-science-cookbook/master/2019/09-clustering/dataset.csv'\n", "dataset = pd.read_csv(url, header=None)\n", "dataset = np.array(dataset)\n", "\n", "plt.scatter(dataset[:,0], dataset[:,1], s=10)\n", "plt.show()" ], "execution_count": 117, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [] } } ] }, { "metadata": { "id": "nV2Up-j6B-aZ", "colab_type": "text" }, "cell_type": "markdown", "source": [ "# 1. Implementar o algoritmo K-means" ] }, { "metadata": { "id": "iR9-r9ZFB-aa", "colab_type": "text" }, "cell_type": "markdown", "source": [ "Nesta etapa você irá implementar as funções que compõe o algoritmo do KMeans uma a uma. É importante entender e ler a documentação de cada função, principalmente as dimensões dos dados esperados na saída." ] }, { "metadata": { "id": "7Ic921PdB-ac", "colab_type": "text" }, "cell_type": "markdown", "source": [ "## 1.1 Inicializar os centróides" ] }, { "metadata": { "id": "3n5196okB-ae", "colab_type": "text" }, "cell_type": "markdown", "source": [ "A primeira etapa do algoritmo consiste em inicializar os centróides de maneira aleatória. Essa etapa é uma das mais importantes do algoritmo e uma boa inicialização pode diminuir bastante o tempo de convergência.\n", "\n", "Para inicializar os centróides você pode considerar o conhecimento prévio sobre os dados, mesmo sem saber a quantidade de grupos ou sua distribuição. \n", "\n", "> Dica: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.uniform.html " ] }, { "metadata": { "id": "zTb8cKvoB-ah", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "def calculate_initial_centers(dataset, k):\n", " \"\"\"\n", " Inicializa os centróides iniciais de maneira arbitrária \n", " \n", " Argumentos:\n", " dataset -- Conjunto de dados - [m,n]\n", " k -- Número de centróides desejados\n", " \n", " Retornos:\n", " centroids -- Lista com os centróides calculados - [k,n]\n", " \"\"\"\n", " \n", " #### CODE HERE ####\n", " m = dataset.shape[0]\n", " \n", " centroids = list(dataset[np.random.randint(0, m - 1, 1)])\n", " \n", " for it1 in range(k - 1):\n", " max_dist = -1\n", "\n", " for it2 in range(m):\n", " nrst_cent_dist = sys.float_info.max\n", "\n", " for it3 in range(len(centroids)):\n", " dist = np.linalg.norm(dataset[it2] - centroids[it3])\n", " # Get the distance to the nearest centroid\n", " if (dist < nrst_cent_dist):\n", " nrst_cent_dist = dist\n", " nrst_cent = dataset[it2]\n", "\n", " if (nrst_cent_dist > max_dist):\n", " max_dist = nrst_cent_dist\n", " new_cent = nrst_cent\n", "\n", " centroids.append(new_cent)\n", "\n", " centroids = np.array(centroids)\n", " ### END OF CODE ###\n", " \n", " return centroids" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "YOa0AtAjB-an", "colab_type": "text" }, "cell_type": "markdown", "source": [ "Teste a função criada e visualize os centróides que foram calculados." ] }, { "metadata": { "id": "wtKBpmZPB-ao", "colab_type": "code", "outputId": "8c2292d0-5498-4607-bc84-f4405d1333ab", "colab": { "base_uri": "https://localhost:8080/", "height": 269 } }, "cell_type": "code", "source": [ "k = 3\n", "centroids = calculate_initial_centers(dataset, k)\n", "\n", "plt.scatter(dataset[:,0], dataset[:,1], s=10)\n", "plt.scatter(centroids[:,0], centroids[:,1], marker='^', c='red',s=100)\n", "plt.show()" ], "execution_count": 119, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [] } } ] }, { "metadata": { "id": "ECocKf-XB-aw", "colab_type": "text" }, "cell_type": "markdown", "source": [ "## 1.2 Definir os clusters" ] }, { "metadata": { "id": "_cnHHwdaB-ay", "colab_type": "text" }, "cell_type": "markdown", "source": [ "Na segunda etapa do algoritmo serão definidos o grupo de cada dado, de acordo com os centróides calculados." ] }, { "metadata": { "id": "0WSBS72NB-az", "colab_type": "text" }, "cell_type": "markdown", "source": [ "### 1.2.1 Função de distância " ] }, { "metadata": { "id": "l5Ql0nNZB-a2", "colab_type": "text" }, "cell_type": "markdown", "source": [ "Codifique a função de distância euclidiana entre dois pontos __(a, b)__.\n", "\n", "Definido pela equação:\n", "\n", "$$ dist(a, b) = \\sqrt{(a_1-b_1)^{2}+(a_2-b_2)^{2}+ ... + (a_n-b_n)^{2}} $$\n", "\n", "$$ dist(a, b) = \\sqrt{\\sum_{i=1}^{n}(a_i-b_i)^{2}} $$" ] }, { "metadata": { "id": "HkFp8WtJB-a4", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "def euclidean_distance(a, b):\n", " \"\"\"\n", " Calcula a distância euclidiana entre os pontos a e b\n", " \n", " Argumentos:\n", " a -- Um ponto no espaço - [1,n]\n", " b -- Um ponto no espaço - [1,n]\n", " \n", " Retornos:\n", " distance -- Distância euclidiana entre os pontos\n", " \"\"\"\n", " \n", " #### CODE HERE ####\n", " n = len(a)\n", " \n", " distance = 0\n", " for i in range(n):\n", " distance = distance + (a[i] - b[i])2\n", " \n", " distance = distance0.5\n", " ### END OF CODE ###\n", " \n", " return distance" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "UoandUrjB-a_", "colab_type": "text" }, "cell_type": "markdown", "source": [ "Teste a função criada." ] }, { "metadata": { "id": "zBaldVW4B-bC", "colab_type": "code", "outputId": "00fbc4ff-e6c0-4bd8-8e72-327bf3c70b78", "colab": { "base_uri": "https://localhost:8080/", "height": 34 } }, "cell_type": "code", "source": [ "a = np.array([1, 5, 9])\n", "b = np.array([3, 7, 8])\n", "\n", "if (euclidean_distance(a,b) == 3):\n", " print(\"Distância calculada corretamente!\")\n", "else:\n", " print(\"Função de distância incorreta\")" ], "execution_count": 121, "outputs": [ { "output_type": "stream", "text": [ "Distância calculada corretamente!\n" ], "name": "stdout" } ] }, { "metadata": { "id": "uQJJ3Ww0B-bM", "colab_type": "text" }, "cell_type": "markdown", "source": [ "### 1.2.2 Calcular o centroide mais próximo" ] }, { "metadata": { "id": "Pvb46PMyB-bO", "colab_type": "text" }, "cell_type": "markdown", "source": [ "Utilizando a função de distância codificada anteriormente, complete a função abaixo para calcular o centroid mais próximo de um ponto qualquer. \n", "\n", "> Dica: https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmin.html" ] }, { "metadata": { "id": "Yl9ZWW52B-bP", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "def nearest_centroid(a, centroids):\n", " \"\"\"\n", " Calcula o índice do centroid mais próximo ao ponto a\n", " \n", " Argumentos:\n", " a -- Um ponto no espaço - [1,n]\n", " centroids -- Lista com os centróides - [k,n]\n", " \n", " Retornos:\n", " nearest_index -- Índice do centróide mais próximo\n", " \"\"\"\n", " \n", " #### CODE HERE ####\n", " # Check if centroids has two dimensions and, if not, convert to\n", " if len(centroids.shape) == 1:\n", " centroids = np.array([centroids])\n", " nrst_cent_dist = sys.float_info.max\n", " \n", " for j in range(len(centroids)):\n", " dist = euclidean_distance(a, centroids[j])\n", " if (dist < nrst_cent_dist):\n", " nrst_cent_dist = dist\n", " nearest_index = j\n", " ### END OF CODE ###\n", " \n", " return nearest_index" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "X2agS6uSB-bU", "colab_type": "text" }, "cell_type": "markdown", "source": [ "Teste a função criada" ] }, { "metadata": { "id": "ncTlMZG8B-bW", "colab_type": "code", "outputId": "a9c5a607-5c04-4d6a-f6a1-d948762658c0", "colab": { "base_uri": "https://localhost:8080/", "height": 269 } }, "cell_type": "code", "source": [ "# Seleciona um ponto aleatório no dataset\n", "index = np.random.randint(dataset.shape[0])\n", "a = dataset[index,:]\n", "\n", "# Usa a função para descobrir o centroid mais próximo\n", "idx_nearest_centroid = nearest_centroid(a, centroids)\n", "\n", "\n", "# Plota os dados ------------------------------------------------\n", "plt.scatter(dataset[:,0], dataset[:,1], s=10)\n", "# Plota o ponto aleatório escolhido em uma cor diferente\n", "plt.scatter(a[0], a[1], c='magenta', s=30)\n", "\n", "# Plota os centroids\n", "plt.scatter(centroids[:,0], centroids[:,1], marker='^', c='red', s=100)\n", "# Plota o centroid mais próximo com uma cor diferente\n", "plt.scatter(centroids[idx_nearest_centroid,0], \n", " centroids[idx_nearest_centroid,1],\n", " marker='^', c='springgreen', s=100)\n", "\n", "# Cria uma linha do ponto escolhido para o centroid selecionado\n", "plt.plot([a[0], centroids[idx_nearest_centroid,0]], \n", " [a[1], centroids[idx_nearest_centroid,1]],c='orange')\n", "plt.annotate('CENTROID', (centroids[idx_nearest_centroid,0], \n", " centroids[idx_nearest_centroid,1],))\n", "plt.show()" ], "execution_count": 123, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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V7LmbOnsGLTn2wA0XF2TKT66Y4y3FcGLMZz/hPpbASeckPe4CYcyi17UfyPhc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\nSMigcBNCSMigcBNCSMigcBNCSMj4/0T5DCZcaepyAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [] } } ] }, { "metadata": { "id": "RdC__sVsB-be", "colab_type": "text" }, "cell_type": "markdown", "source": [ "### 1.2.3 Calcular centroid mais próximo de cada dado do dataset" ] }, { "metadata": { "id": "Hlw01Ej7B-bf", "colab_type": "text" }, "cell_type": "markdown", "source": [ "Utilizando a função anterior que retorna o índice do centroid mais próximo, calcule o centroid mais próximo de cada dado do dataset. " ] }, { "metadata": { "id": "rXbj8LClB-bh", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "def all_nearest_centroids(dataset, centroids):\n", " \"\"\"\n", " Calcula o índice do centroid mais próximo para cada \n", " ponto do dataset\n", " \n", " Argumentos:\n", " dataset -- Conjunto de dados - [m,n]\n", " centroids -- Lista com os centróides - [k,n]\n", " \n", " Retornos:\n", " nearest_indexes -- Índices do centróides mais próximos - [m,1]\n", " \"\"\"\n", " \n", " #### CODE HERE ####\n", " # Check if centroids has two dimensions and, if not, convert to\n", " if len(centroids.shape) == 1:\n", " centroids = np.array([centroids])\n", "\n", " nearest_indexes = np.zeros(len(dataset))\n", " \n", " for i in range(len(dataset)):\n", " nearest_indexes[i] = nearest_centroid(dataset[i], centroids)\n", " ### END OF CODE ###\n", " \n", " return nearest_indexes" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "DD7ceo14B-bm", "colab_type": "text" }, "cell_type": "markdown", "source": [ "Teste a função criada visualizando os cluster formados." ] }, { "metadata": { "id": "6C0WynHUB-bn", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "nearest_indexes = all_nearest_centroids(dataset, centroids)" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "Cx-rJMyqB-bu", "colab_type": "code", "outputId": "ff3d2869-83c6-4653-9079-3dc88fbf8bc5", "colab": { "base_uri": "https://localhost:8080/", "height": 269 } }, "cell_type": "code", "source": [ "plt.scatter(dataset[:,0], dataset[:,1], c=nearest_indexes)\n", "plt.scatter(centroids[:,0], centroids[:,1], marker='^', c='red', s=100)\n", "plt.show()" ], "execution_count": 126, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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1MOiQmP2dZrlZ4tPb8DttxVKFLUbeBUjwfJzFQy9KKeyyc3BtjqD84B0DsUnb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\nYTFEXolh96/7+emTDR6zZRp3qZ+hsf7be5b/dZyJ3WbHbLLgk89IuZp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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [] } } ] }, { "metadata": { "id": "VGxo4S5tB-b7", "colab_type": "text" }, "cell_type": "markdown", "source": [ "## 1.3 Métrica de avaliação " ] }, { "metadata": { "id": "Ce8R4GadB-b-", "colab_type": "text" }, "cell_type": "markdown", "source": [ "Após formar os clusters, como sabemos se o resultado gerado é bom? Para isso, precisamos definir uma métrica de avaliação.\n", "\n", "O algoritmo K-means tem como objetivo escolher centróides que minimizem a soma quadrática das distância entre os dados de um cluster e seu centróide. Essa métrica é conhecida como __inertia__.\n", "\n", "$$\\sum_{i=0}^{n}\\min_{c_j \\in C}(\|\|x_i - c_j\|\|^2)$$\n", "\n", "A __inertia__, ou o critério de soma dos quadrados dentro do cluster, pode ser reconhecido como uma medida de o quão internamente coerentes são os clusters, porém ela sofre de alguns inconvenientes:\n", "\n", "- A inertia pressupõe que os clusters são convexos e isotrópicos, o que nem sempre é o caso. Desta forma, pode não representar bem em aglomerados alongados ou variedades com formas irregulares.\n", "- A inertia não é uma métrica normalizada: sabemos apenas que valores mais baixos são melhores e zero é o valor ótimo. Mas em espaços de dimensões muito altas, as distâncias euclidianas tendem a se tornar infladas (este é um exemplo da chamada “maldição da dimensionalidade”). A execução de um algoritmo de redução de dimensionalidade, como o PCA, pode aliviar esse problema e acelerar os cálculos.\n", "\n", "Fonte: https://scikit-learn.org/stable/modules/clustering.html" ] }, { "metadata": { "id": "uJks8qvdB-b_", "colab_type": "text" }, "cell_type": "markdown", "source": [ "Para podermos avaliar os nosso clusters, codifique a métrica da inertia abaixo, para isso você pode utilizar a função de distância euclidiana construída anteriormente.\n", "\n", "$$inertia = \\sum_{i=0}^{n}\\min_{c_j \\in C} (dist(x_i, c_j))^2$$" ] }, { "metadata": { "id": "xJVrHCGzB-cB", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "def inertia(dataset, centroids, nearest_indexes):\n", " \"\"\"\n", " Soma das distâncias quadradas das amostras para o \n", " centro do cluster mais próximo.\n", " \n", " Argumentos:\n", " dataset -- Conjunto de dados - [m,n]\n", " centroids -- Lista com os centróides - [k,n]\n", " nearest_indexes -- Índices do centróides mais próximos - [m,1]\n", " \n", " Retornos:\n", " inertia -- Soma total do quadrado da distância entre \n", " os dados de um cluster e seu centróide\n", " \"\"\"\n", " \n", " #### CODE HERE ####\n", " # Check if centroids has two dimensions and, if not, convert to\n", " if len(centroids.shape) == 1:\n", " centroids = np.array([centroids])\n", "\n", " inertia = 0\n", " \n", " for i in range(len(dataset)):\n", " inertia = inertia + euclidean_distance(dataset[i], centroids[int(nearest_indexes[i])])**2\n", " ### END OF CODE ###\n", " \n", " return inertia" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "S8B1dY-SB-cJ", "colab_type": "text" }, "cell_type": "markdown", "source": [ "Teste a função codificada executando o código abaixo." ] }, { "metadata": { "id": "HkO6K3rlB-cK", "colab_type": "code", "outputId": "6e1fdb49-c95d-4c57-fa56-3e9c04d6e2da", "colab": { "base_uri": "https://localhost:8080/", "height": 34 } }, "cell_type": "code", "source": [ "tmp_data = np.array([[1,2,3],[3,6,5],[4,5,6]])\n", "tmp_centroide = np.array([[2,3,4]])\n", "\n", "tmp_nearest_indexes = all_nearest_centroids(tmp_data, tmp_centroide)\n", "if inertia(tmp_data, tmp_centroide, tmp_nearest_indexes) == 26:\n", " print(\"Inertia calculada corretamente!\")\n", "else:\n", " print(\"Função de inertia incorreta!\")" ], "execution_count": 128, "outputs": [ { "output_type": "stream", "text": [ "Inertia calculada corretamente!\n" ], "name": "stdout" } ] }, { "metadata": { "id": "kASf0mL1B-cR", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "outputId": "a9bf52f8-1dda-489a-c958-b38ac8d24c6c" }, "cell_type": "code", "source": [ "# Use a função para verificar a inertia dos seus clusters\n", "inertia(dataset, centroids, nearest_indexes)" ], "execution_count": 129, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "2402.4442703793466" ] }, "metadata": { "tags": [] }, "execution_count": 129 } ] }, { "metadata": { "id": "ISC1UVsDB-cX", "colab_type": "text" }, "cell_type": "markdown", "source": [ "## 1.4 Atualizar os clusters" ] }, { "metadata": { "id": "rxdrnokGB-cZ", "colab_type": "text" }, "cell_type": "markdown", "source": [ "Nessa etapa, os centróides são recomputados. O novo valor de cada centróide será a media de todos os dados atribuídos ao cluster." ] }, { "metadata": { "id": "mLrgSPcSB-cb", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "def update_centroids(dataset, centroids, nearest_indexes):\n", " \"\"\"\n", " Atualiza os centroids\n", " \n", " Argumentos:\n", " dataset -- Conjunto de dados - [m,n]\n", " centroids -- Lista com os centróides - [k,n]\n", " nearest_indexes -- Índices do centróides mais próximos - [m,1]\n", " \n", " Retornos:\n", " centroids -- Lista com centróides atualizados - [k,n]\n", " \"\"\"\n", " \n", " #### CODE HERE ####\n", " # Check if centroids has two dimensions and, if not, convert to\n", " if len(centroids.shape) == 1:\n", " centroids = np.array([centroids])\n", " \n", " sum_data_inCentroids = np.zeros((len(centroids), len(centroids[0])))\n", " num_data_inCentroids = np.zeros(len(centroids))\n", " \n", " for i in range(len(dataset)):\n", " cent_idx = int(nearest_indexes[i])\n", " sum_data_inCentroids[cent_idx] += dataset[i]\n", " num_data_inCentroids[cent_idx] += 1\n", " \n", " for i in range(len(centroids)):\n", " centroids[i] = sum_data_inCentroids[i]/num_data_inCentroids[i]\n", " ### END OF CODE ###\n", " \n", " return centroids" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "MQh95x0hB-cf", "colab_type": "text" }, "cell_type": "markdown", "source": [ "Visualize os clusters formados" ] }, { "metadata": { "id": "0U4Q_vVKB-ci", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 269 }, "outputId": "b8ad4056-d561-40c0-805d-d7cbc418bd9c" }, "cell_type": "code", "source": [ "nearest_indexes = all_nearest_centroids(dataset, centroids)\n", "\n", "# Plota os os cluster ------------------------------------------------\n", "plt.scatter(dataset[:,0], dataset[:,1], c=nearest_indexes)\n", "\n", "# Plota os centroids\n", "plt.scatter(centroids[:,0], centroids[:,1], marker='^', c='red', s=100)\n", "for index, centroid in enumerate(centroids):\n", " dataframe = dataset[nearest_indexes == index,:]\n", " for data in dataframe:\n", " plt.plot([centroid[0], data[0]], [centroid[1], data[1]], \n", " c='lightgray', alpha=0.3)\n", "plt.show()" ], "execution_count": 131, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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xygGpjTLqzvO8qB5N06LCcoX/D9Q8cGrhXgHsvxi3Gt6wSe8HDKf/F6rZaAjB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/73Cfrbh0jF0qjJDLmcgeJRWLdsIz848RrO/O7J1E+voXtrL96gFwMwcgayU6Z3ez8zD3+vBksu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\ns4ZtrcX62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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [] } } ] }, { "metadata": { "id": "H7ar2hKLB-cm", "colab_type": "text" }, "cell_type": "markdown", "source": [ "Execute a função de atualização e visualize novamente os cluster formados" ] }, { "metadata": { "id": "Zddno6sUB-ct", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "centroids = update_centroids(dataset, centroids, nearest_indexes)" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "8aP_gDkQB-cy", "colab_type": "text" }, "cell_type": "markdown", "source": [ "# 2. K-means" ] }, { "metadata": { "id": "Pz-JCfAhB-c1", "colab_type": "text" }, "cell_type": "markdown", "source": [ "## 2.1 Algoritmo completo" ] }, { "metadata": { "id": "kzPFCZ3XB-c3", "colab_type": "text" }, "cell_type": "markdown", "source": [ "Utilizando as funções codificadas anteriormente, complete a classe do algoritmo K-means!" ] }, { "metadata": { "id": "Txqr9Nd1B-c4", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "class KMeans():\n", " \n", " def __init__(self, n_clusters=8, max_iter=300):\n", " self.n_clusters = n_clusters\n", " self.max_iter = max_iter\n", " \n", " def fit(self,X):\n", " \n", " # Inicializa os centróides\n", " self.cluster_centers_ = calculate_initial_centers(X, self.n_clusters)\n", " \n", " # Computa o cluster de cada amostra\n", " self.labels_ = all_nearest_centroids(X, self.cluster_centers_)\n", " \n", " # Calcula a inércia inicial\n", " old_inertia = inertia(X, self.cluster_centers_, self.labels_)\n", " self.inertia_ = old_inertia\n", " \n", " for index in range(self.max_iter):\n", " \n", " #### CODE HERE ####\n", " self.cluster_centers_ = update_centroids(X, self.cluster_centers_, self.labels_)\n", " self.labels_ = all_nearest_centroids(X, self.cluster_centers_)\n", " self.inertia_ = inertia(X, self.cluster_centers_, self.labels_)\n", " \n", " if (self.inertia_ == old_inertia):\n", " break\n", " else:\n", " old_inertia = self.inertia_\n", " ### END OF CODE ###\n", " \n", " return self\n", " \n", " def predict(self, X):\n", " \n", " return all_nearest_centroids(X, self.cluster_centers_)" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "IJFVlbCRB-dA", "colab_type": "text" }, "cell_type": "markdown", "source": [ "Verifique o resultado do algoritmo abaixo!" ] }, { "metadata": { "id": "PxvOSFa2B-dB", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 286 }, "outputId": "26267682-b4fc-4af2-92e3-04c47b89549a" }, "cell_type": "code", "source": [ "kmeans = KMeans(n_clusters=3)\n", "kmeans.fit(dataset)\n", "\n", "print(\"Inércia = \", kmeans.inertia_)\n", "\n", "plt.scatter(dataset[:,0], dataset[:,1], c=kmeans.labels_)\n", "plt.scatter(kmeans.cluster_centers_[:,0], \n", " kmeans.cluster_centers_[:,1], marker='^', c='red', s=100)\n", "plt.show()" ], "execution_count": 134, "outputs": [ { "output_type": "stream", "text": [ "Inércia = 608.6035508327781\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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pyv9OP4sOwZ10wzNwjNNZr8Ge214IOkJMyjcQOk905MYAPHuCbHHyz1OwwdoI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\nPQbWqvPQFAOoPHqcORjBoDrvKPZGuRONVlC3XU2M/ga2L96DJSn1joK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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [] } } ] }, { "metadata": { "id": "CVvC4LXiB-dF", "colab_type": "text" }, "cell_type": "markdown", "source": [ "## 2.2 Comparar com algoritmo do Scikit-Learn" ] }, { "metadata": { "id": "udzmME5nB-dH", "colab_type": "text" }, "cell_type": "markdown", "source": [ "Use a implementação do algoritmo do scikit-learn do K-means para o mesmo conjunto de dados. Mostre o valor da inércia e os conjuntos gerados pelo modelo. Você pode usar a mesma estrutura da célula de código anterior.\n", "> Dica: https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans" ] }, { "metadata": { "id": "XypBC9zsB-dI", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 51 }, "outputId": "5f58951e-8d11-4f21-fa08-d9fe303d9a31" }, "cell_type": "code", "source": [ "#### CODE HERE ####\n", "from sklearn.cluster import KMeans as sk_KMeans\n", "\n", "skkmeans = sk_KMeans(n_clusters=3).fit(dataset)\n", "\n", "print(\"Scikit-Learn KMeans' inertia: \", skkmeans.inertia_)\n", "print(\"My KMeans inertia: \", kmeans.inertia_)" ], "execution_count": 135, "outputs": [ { "output_type": "stream", "text": [ "Scikit-Learn KMeans' inertia: 608.6035508327782\n", "My KMeans inertia: 608.6035508327781\n" ], "name": "stdout" } ] }, { "metadata": { "id": "YaW8Sax8B-dM", "colab_type": "text" }, "cell_type": "markdown", "source": [ "# 3. Método do cotovelo" ] }, { "metadata": { "id": "Rw9zqMZeB-dM", "colab_type": "text" }, "cell_type": "markdown", "source": [ "Implemete o método do cotovelo e mostre o melhor K para o conjunto de dados." ] }, { "metadata": { "id": "YCVms_q7B-dN", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 320 }, "outputId": "8dbc9483-9055-49c7-b3b2-aca842a524b8" }, "cell_type": "code", "source": [ "#### CODE HERE ####\n", "\n", "# Initialize array of Ks\n", "ks = np.array(range(1, 11))\n", "# Create array to receive the inertias for each K\n", "inertias = np.zeros(len(ks))\n", "\n", "for i in range(len(ks)):\n", " # Compute inertia for K\n", " kmeans = KMeans(ks[i]).fit(dataset)\n", " inertias[i] = kmeans.inertia_\n", " \n", " # Best K is the last one to improve the inertia in 30%\n", " if (i > 0 and (inertias[i - 1] - inertias[i])/inertias[i] > 0.3):\n", " best_k_idx = i\n", "\n", "print(\"Best K: {}\\n\".format(ks[best_k_idx]))\n", "plt.plot(ks, inertias, marker='o')\n", "plt.plot(ks[best_k_idx], inertias[best_k_idx], 'ro')" ], "execution_count": 169, "outputs": [ { "output_type": "stream", "text": [ "Best K: 3\n", "\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f9fe0e2be48>]" ] }, "metadata": { "tags": [] }, "execution_count": 169 }, { "output_type": "display_data", "data": { "image/png": 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m9raZ7TazqqBrCpKZ/Zn/d/KWmf3czCbmh4nThJn92MzqzOythLFSM9tgZnv9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\n/5SUbO6GQdU7AAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [] } } ] }, { "metadata": { "id": "LlsLROFPB-dS", "colab_type": "text" }, "cell_type": "markdown", "source": [ "# 4. Dataset Real" ] }, { "metadata": { "id": "gEyugVF5B-dT", "colab_type": "text" }, "cell_type": "markdown", "source": [ "Exercícios\n", "\n", "1 - Aplique o algoritmo do K-means desenvolvido por você no datatse iris [1]. Mostre os resultados obtidos utilizando pelo menos duas métricas de avaliação de clusteres [2].\n", "\n", "- [1] http://archive.ics.uci.edu/ml/datasets/iris\n", "- [2] http://scikit-learn.org/stable/modules/clustering.html#clustering-evaluation\n", "\n", "> Dica: você pode utilizar as métricas completeness e homogeneity.\n", "\n", "2 - Tente melhorar o resultado obtido na questão anterior utilizando uma técnica de mineração de dados. Explique a diferença obtida. \n", "\n", "> Dica: você pode tentar normalizar os dados [3].\n", "> - [3] https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.normalize.html\n", "\n", "\n", "3 - Qual o número de clusteres (K) você escolheu na questão anterior? Desenvolva o Método do Cotovelo sem usar biblioteca e descubra o valor de K mais adequado. Após descobrir, utilize o valor obtido no algoritmo do K-means.\n", "\n", "4 - Utilizando os resultados da questão anterior, refaça o cálculo das métricas e comente os resultados obtidos. Houve uma melhoria? Explique." ] }, { "metadata": { "id": "9qkU5g7vB-dT", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "#### CODE HERE ####" ], "execution_count": 0, "outputs": [] } ] }	mit
nkmk/python-snippets	notebook/pillow_save_lq_images.ipynb	1	965	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from PIL import Image" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "img = Image.open('data/src/lena.jpg')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "img.save('data/src/lena_q95.jpg', quality=95)\n", "img.save('data/src/lena_q50.jpg', quality=50)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
sophie63/FlyLFM	Notebooks/Utils/.ipynb_checkpoints/972Large_FlyLFMpaperOdorLight-checkpoint.ipynb	2	3768380	null	bsd-2-clause
johntanz/ROP	Old Code/.ipynb_checkpoints/Masimo160127-checkpoint.ipynb	1	35440	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Masimo Analysis\n", "\n", "For Pulse Ox. Analysis, make sure the data file is the right .csv format:\n", " \n", " a) Headings on Row 1\n", " b) Open the csv file through Notepad or TextEdit and delete extra \n", " row commas (non-printable characters)\n", " c) There are always Dates in Column A and Time in Column B. \n", " d) There might be a row that says \"Time Gap Present\". Delete this row from Notepad \n", " or TextEdit" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#the usual beginning\n", "import pandas as pd\n", "import numpy as np\n", "from pandas import Series, DataFrame\n", "from datetime import datetime, timedelta\n", "from pandas import concat\n", "\n", "#define any string with 'C' as NaN\n", "def readD(val):\n", " if 'C' in val:\n", " return np.nan\n", " return val" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Import File into Python\n", "Change File Name!" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = pd.read_csv('/Users/John/Dropbox/LLU/ROP/Pulse Ox/ROP018PO.csv',\n", " parse_dates={'timestamp': ['Date','Time']},\n", " index_col='timestamp',\n", " usecols=['Date', 'Time', 'SpO2', 'PR', 'PI', 'Exceptions'],\n", " na_values=['0'],\n", " converters={'Exceptions': readD}\n", " )\n", "\n", "#parse_dates tells the read_csv function to combine the date and time column \n", "#into one timestamp column and parse it as a timestamp.\n", "# pandas is smart enough to know how to parse a date in various formats\n", "\n", "#index_col sets the timestamp column to be the index.\n", "\n", "#usecols tells the read_csv function to select only the subset of the columns.\n", "#na_values is used to turn 0 into NaN\n", "\n", "#converters: readD is the dict that means any string with 'C' with be NaN (for PI)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#dfclean = df[27:33][df[27:33].loc[:, ['SpO2', 'PR', 'PI', 'Exceptions']].apply(pd.notnull).all(1)]\n", "#clean the dataframe to get rid of rows that have NaN for PI purposes\n", "df_clean = df[df.loc[:, ['PI', 'Exceptions']].apply(pd.notnull).all(1)]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"Pulse ox date/time is 1 mins and 32 seconds faster than phone. Have to correct for it.\"\"\"\n", "\n", "TC = timedelta(minutes=1, seconds=32)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Set Date and Time of ROP Exam and Eye Drops" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_first = df.first_valid_index() #get the first number from index\n", "\n", "Y = pd.to_datetime(df_first) #convert index to datetime\n", "# Y = TIME DATA COLLECTION BEGAN / First data point on CSV\n", "\n", "# SYNTAX: \n", "# datetime(year, month, day[, hour[, minute[, second[, microsecond[,tzinfo]]]]])\n", "\n", "W = datetime(2016, 1, 20, 7, 30)+TC\n", "# W = first eye drop dtarts\n", "X = datetime(2016, 1, 20, 8, 42)+TC\n", "# X = ROP Exam Started\n", "Z = datetime(2016, 1, 20, 8, 46)+TC\n", "# Z = ROP Exam Ended\n", "\n", "df_last = df.last_valid_index() #get the last number from index\n", "\n", "Q = pd.to_datetime(df_last) \n", "\n", "# Q = TIME DATA COLLECTION ENDED / Last Data point on CSV" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Baseline Averages" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Baseline Averages\n", "PI :\t1.35605010241 \n", "SpO2 :\t99.2296877312 \n", "PR :\t166.723203769\n" ] } ], "source": [ "avg0PI = df_clean.PI[Y:W].mean()\n", "avg0O2 = df.SpO2[Y:W].mean()\n", "avg0PR = df.PR[Y:W].mean()\n", "\n", "print 'Baseline Averages\\n', 'PI :\\t',avg0PI, '\\nSpO2 :\\t',avg0O2,'\\nPR :\\t',avg0PR,\n", "#df.std() for standard deviation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Average q 5 Min for 1 hour after 1st Eye Drops" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " PI DurEyeD SpO2 DurEyeD PR DurEyeD\n", "2016-01-20 07:31:32 1.700662 99.768212 170.284768\n", "2016-01-20 07:36:32 1.671034 99.344371 163.066225\n", "2016-01-20 07:41:32 1.566434 99.046358 158.715232\n" ] } ], "source": [ "# Every 5 min Average from start of eye drops to start of exam\n", "\n", "def perdeltadrop(start, end, delta):\n", " rdrop = []\n", " curr = start\n", " while curr < end:\n", " rdrop.append(curr)\n", " curr += delta\n", " return rdrop\n", " \n", "dfdropPI = df_clean.PI[W:W+timedelta(hours=1)]\n", "dfdropO2 = df.SpO2[W:W+timedelta(hours=1)]\n", "dfdropPR = df.PR[W:W+timedelta(hours=1)]\n", "windrop = timedelta(minutes=5)#make the range\n", "rdrop = perdeltadrop(W, W+timedelta(minutes=15), windrop)\n", "\n", "avgdropPI = Series(index = rdrop, name = 'PI DurEyeD')\n", "avgdropO2 = Series(index = rdrop, name = 'SpO2 DurEyeD')\n", "avgdropPR = Series(index = rdrop, name = 'PR DurEyeD')\n", "\n", "for i in rdrop:\n", " avgdropPI[i] = dfdropPI[i:(i+windrop)].mean()\n", " avgdropO2[i] = dfdropO2[i:(i+windrop)].mean()\n", " avgdropPR[i] = dfdropPR[i:(i+windrop)].mean()\n", " \n", "resultdrops = concat([avgdropPI, avgdropO2, avgdropPR], axis=1, join='inner')\n", "print resultdrops\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Average Every 10 Sec During ROP Exam for first 4 minutes" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " PI DurEx SpO2 DurEx PR DurEX\n", "2016-01-20 08:43:32 1.066667 99.500000 160.000000\n", "2016-01-20 08:43:42 0.916667 99.500000 161.166667\n", "2016-01-20 08:43:52 0.866667 99.833333 154.666667\n", "2016-01-20 08:44:02 NaN 99.833333 148.666667\n", "2016-01-20 08:44:12 0.966667 100.000000 146.000000\n", "2016-01-20 08:44:22 1.000000 100.000000 144.166667\n", "2016-01-20 08:44:32 0.950000 100.000000 144.166667\n", "2016-01-20 08:44:42 0.933333 100.000000 142.666667\n", "2016-01-20 08:44:52 0.900000 100.000000 142.500000\n", "2016-01-20 08:45:02 0.883333 100.000000 145.166667\n", "2016-01-20 08:45:12 0.800000 100.000000 145.333333\n", "2016-01-20 08:45:22 0.866667 100.000000 142.333333\n", "2016-01-20 08:45:32 1.033333 100.000000 143.500000\n", "2016-01-20 08:45:42 1.083333 100.000000 143.333333\n", "2016-01-20 08:45:52 0.966667 100.000000 145.500000\n", "2016-01-20 08:46:02 0.966667 100.000000 144.333333\n", "2016-01-20 08:46:12 1.033333 100.000000 143.000000\n", "2016-01-20 08:46:22 1.216667 100.000000 145.166667\n", "2016-01-20 08:46:32 1.083333 100.000000 145.166667\n", "2016-01-20 08:46:42 1.050000 100.000000 142.666667\n", "2016-01-20 08:46:52 1.033333 100.000000 143.166667\n", "2016-01-20 08:47:02 1.100000 100.000000 145.333333\n", "2016-01-20 08:47:12 1.150000 100.000000 148.500000\n", "2016-01-20 08:47:22 1.083333 100.000000 147.333333\n" ] } ], "source": [ "#AVERAGE DURING ROP EXAM FOR FIRST FOUR MINUTES\n", "def perdelta1(start, end, delta):\n", " r1 = []\n", " curr = start\n", " while curr < end:\n", " r1.append(curr)\n", " curr += delta\n", " return r1\n", "\n", "df1PI = df_clean.PI[X:X+timedelta(minutes=4)]\n", "df1O2 = df.SpO2[X:X+timedelta(minutes=4)]\n", "df1PR = df.PR[X:X+timedelta(minutes=4)]\n", "win1 = timedelta(seconds=10) #any unit of time & make the range\n", "\n", "r1 = perdelta1(X, X+timedelta(minutes=4), win1)\n", "\n", "#make the series to store\n", "avg1PI = Series(index = r1, name = 'PI DurEx')\n", "avg1O2 = Series(index = r1, name = 'SpO2 DurEx')\n", "avg1PR = Series(index = r1, name = 'PR DurEX')\n", "#average!\n", "for i1 in r1:\n", " avg1PI[i1] = df1PI[i1:(i1+win1)].mean()\n", " avg1O2[i1] = df1O2[i1:(i1+win1)].mean()\n", " avg1PR[i1] = df1PR[i1:(i1+win1)].mean()\n", "\n", "result1 = concat([avg1PI, avg1O2, avg1PR], axis=1, join='inner')\n", "print result1\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Average Every 5 Mins Hour 1-2 After ROP Exam" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " PI q5MinHr1 O2 q5MinHr1 PR q5MinHr1\n", "2016-01-20 08:47:32 0.912500 100.000000 145.841060\n", "2016-01-20 08:52:32 0.931852 99.907285 160.304636\n", "2016-01-20 08:57:32 1.201626 99.125828 184.920530\n", "2016-01-20 09:02:32 1.088350 98.907285 177.973510\n", "2016-01-20 09:07:32 0.982781 99.854305 161.543046\n", "2016-01-20 09:12:32 0.974648 99.337748 159.417219\n", "2016-01-20 09:17:32 0.920530 99.953642 154.516556\n", "2016-01-20 09:22:32 0.704636 100.000000 150.807947\n", "2016-01-20 09:27:32 0.833775 100.000000 154.304636\n", "2016-01-20 09:32:32 0.890728 100.000000 153.470199\n", "2016-01-20 09:37:32 0.865517 99.847682 157.529801\n", "2016-01-20 09:42:32 0.930464 99.198675 162.814570\n" ] } ], "source": [ "#AVERAGE EVERY 5 MINUTES ONE HOUR AFTER ROP EXAM\n", "\n", "def perdelta2(start, end, delta):\n", " r2 = []\n", " curr = start\n", " while curr < end:\n", " r2.append(curr)\n", " curr += delta\n", " return r2\n", "\n", "# datetime(year, month, day, hour, etc.)\n", "\n", "df2PI = df_clean.PI[Z:(Z+timedelta(hours=1))]\n", "df2O2 = df.SpO2[Z:(Z+timedelta(hours=1))]\n", "df2PR = df.PR[Z:(Z+timedelta(hours=1))]\n", "win2 = timedelta(minutes=5) #any unit of time, make the range\n", "\n", "r2 = perdelta2(Z, (Z+timedelta(hours=1)), win2) #define the average using function\n", "\n", "#make the series to store\n", "avg2PI = Series(index = r2, name = 'PI q5MinHr1')\n", "avg2O2 = Series(index = r2, name = 'O2 q5MinHr1')\n", "avg2PR = Series(index = r2, name = 'PR q5MinHr1')\n", "\n", "#average!\n", "for i2 in r2:\n", " avg2PI[i2] = df2PI[i2:(i2+win2)].mean()\n", " avg2O2[i2] = df2O2[i2:(i2+win2)].mean()\n", " avg2PR[i2] = df2PR[i2:(i2+win2)].mean()\n", "\n", "result2 = concat([avg2PI, avg2O2, avg2PR], axis=1, join='inner')\n", "print result2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Average Every 15 Mins Hour 2-3 After ROP Exam" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " PI q15MinHr2 O2 q15MinHr2 PR q15MinHr2\n", "2016-01-20 09:47:32 0.979405 98.707317 157.152993\n", "2016-01-20 10:02:32 0.761419 99.995565 149.669623\n", "2016-01-20 10:17:32 1.014856 99.101996 154.711752\n", "2016-01-20 10:32:32 1.071788 99.082040 168.013304\n" ] } ], "source": [ "#AVERAGE EVERY 15 MINUTES TWO HOURS AFTER ROP EXAM\n", "\n", "def perdelta3(start, end, delta):\n", " r3 = []\n", " curr = start\n", " while curr < end:\n", " r3.append(curr)\n", " curr += delta\n", " return r3\n", "\n", "# datetime(year, month, day, hour, etc.)\n", "\n", "df3PI = df_clean.PI[(Z+timedelta(hours=1)):(Z+timedelta(hours=2))]\n", "df3O2 = df.SpO2[(Z+timedelta(hours=1)):(Z+timedelta(hours=2))]\n", "df3PR = df.PR[(Z+timedelta(hours=1)):(Z+timedelta(hours=2))]\n", "win3 = timedelta(minutes=15) #any unit of time, make the range\n", "\n", "r3 = perdelta3((Z+timedelta(hours=1)), (Z+timedelta(hours=2)), win3)\n", "\n", "#make the series to store\n", "avg3PI = Series(index = r3, name = 'PI q15MinHr2')\n", "avg3O2 = Series(index = r3, name = 'O2 q15MinHr2')\n", "avg3PR = Series(index = r3, name = 'PR q15MinHr2')\n", "\n", "#average!\n", "for i3 in r3:\n", " avg3PI[i3] = df3PI[i3:(i3+win3)].mean()\n", " avg3O2[i3] = df3O2[i3:(i3+win3)].mean()\n", " avg3PR[i3] = df3PR[i3:(i3+win3)].mean()\n", " \n", "result3 = concat([avg3PI, avg3O2, avg3PR], axis=1, join='inner')\n", "print result3\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Average Every 30 Mins Hour 3-4 After ROP Exam" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " PI q30MinHr3 O2 q30MinHr3 PR q30MinHr3\n", "2016-01-20 10:47:32 1.386217 99.134259 171.837958\n", "2016-01-20 11:17:32 1.026341 99.591565 154.558269\n" ] } ], "source": [ "#AVERAGE EVERY 30 MINUTES THREE HOURS AFTER ROP EXAM\n", "\n", "def perdelta4(start, end, delta):\n", " r4 = []\n", " curr = start\n", " while curr < end:\n", " r4.append(curr)\n", " curr += delta\n", " return r4\n", "\n", "# datetime(year, month, day, hour, etc.)\n", "\n", "df4PI = df_clean.PI[(Z+timedelta(hours=2)):(Z+timedelta(hours=3))]\n", "df4O2 = df.SpO2[(Z+timedelta(hours=2)):(Z+timedelta(hours=3))]\n", "df4PR = df.PR[(Z+timedelta(hours=2)):(Z+timedelta(hours=3))]\n", "win4 = timedelta(minutes=30) #any unit of time, make the range\n", "\n", "r4 = perdelta4((Z+timedelta(hours=2)), (Z+timedelta(hours=3)), win4)\n", "\n", "#make the series to store\n", "avg4PI = Series(index = r4, name = 'PI q30MinHr3')\n", "avg4O2 = Series(index = r4, name = 'O2 q30MinHr3')\n", "avg4PR = Series(index = r4, name = 'PR q30MinHr3')\n", "\n", "#average!\n", "for i4 in r4:\n", " avg4PI[i4] = df4PI[i4:(i4+win4)].mean()\n", " avg4O2[i4] = df4O2[i4:(i4+win4)].mean()\n", " avg4PR[i4] = df4PR[i4:(i4+win4)].mean()\n", " \n", "result4 = concat([avg4PI, avg4O2, avg4PR], axis=1, join='inner')\n", "print result4\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Average Every Hour 4-24 Hours Post ROP Exam" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " PI q60MinHr4+ O2 q60MinHr4+ PR q60MinHr4+\n", "2016-01-20 11:47:32 1.237150 98.755136 159.428096\n", "2016-01-20 12:47:32 1.112176 99.624098 158.508051\n", "2016-01-20 13:47:32 0.893443 98.670011 169.287618\n", "2016-01-20 14:47:32 0.833333 99.376458 161.461966\n", "2016-01-20 15:47:32 0.980292 99.831205 161.027207\n", "2016-01-20 16:47:32 0.969146 99.183661 172.601333\n", "2016-01-20 17:47:32 0.739141 99.748318 161.591893\n", "2016-01-20 18:47:32 0.869034 99.560414 167.052748\n", "2016-01-20 19:47:32 1.115094 97.426471 151.538937\n", "2016-01-20 20:47:32 1.054682 99.752915 154.097723\n", "2016-01-20 21:47:32 1.372682 99.913381 144.239867\n", "2016-01-20 22:47:32 1.048323 99.640200 167.298168\n", "2016-01-20 23:47:32 0.997668 99.843420 161.482510\n", "2016-01-21 00:47:32 1.389339 99.641866 156.231538\n", "2016-01-21 01:47:32 1.238602 99.194892 161.962243\n", "2016-01-21 02:47:32 0.914874 99.347029 160.974459\n", "2016-01-21 03:47:32 1.070903 99.767351 160.925597\n", "2016-01-21 04:47:32 1.106503 99.515677 171.529675\n", "2016-01-21 05:47:32 1.040858 99.350916 159.776791\n", "2016-01-21 06:47:32 1.701805 99.360957 177.924276\n", "2016-01-21 07:47:32 0.864127 99.222357 185.818291\n" ] } ], "source": [ "#AVERAGE EVERY 60 MINUTES 4-24 HOURS AFTER ROP EXAM\n", "\n", "def perdelta5(start, end, delta):\n", " r5 = []\n", " curr = start\n", " while curr < end:\n", " r5.append(curr)\n", " curr += delta\n", " return r5\n", "\n", "# datetime(year, month, day, hour, etc.)\n", "\n", "df5PI = df_clean.PI[(Z+timedelta(hours=3)):(Z+timedelta(hours=24))]\n", "df5O2 = df.SpO2[(Z+timedelta(hours=3)):(Z+timedelta(hours=24))]\n", "df5PR = df.PR[(Z+timedelta(hours=3)):(Z+timedelta(hours=24))]\n", "win5 = timedelta(minutes=60) #any unit of time, make the range\n", "\n", "r5 = perdelta5((Z+timedelta(hours=3)), (Z+timedelta(hours=24)), win5)\n", "\n", "#make the series to store\n", "avg5PI = Series(index = r5, name = 'PI q60MinHr4+')\n", "avg5O2 = Series(index = r5, name = 'O2 q60MinHr4+')\n", "avg5PR = Series(index = r5, name = 'PR q60MinHr4+')\n", "\n", "#average!\n", "for i5 in r5:\n", " avg5PI[i5] = df5PI[i5:(i5+win5)].mean()\n", " avg5O2[i5] = df5O2[i5:(i5+win5)].mean()\n", " avg5PR[i5] = df5PR[i5:(i5+win5)].mean()\n", "\n", "result5 = concat([avg5PI, avg5O2, avg5PR], axis=1, join='inner')\n", "print result5\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Mild, Moderate, and Severe Desaturation Events" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_O2_pre = df[Y:W]\n", "\n", "\n", "#Find count of these ranges\n", "below = 0 # v <=80\n", "middle = 0 #v >= 81 and v<=84\n", "above = 0 #v >=85 and v<=89\n", "ls = []\n", "\n", "b_dict = {}\n", "m_dict = {}\n", "a_dict = {}\n", "\n", "for i, v in df_O2_pre['SpO2'].iteritems():\n", " \n", " if v <= 80: #below block\n", " \n", " if not ls: \n", " ls.append(v)\n", " else:\n", " if ls[0] >= 81: #if the range before was not below 80\n", "\n", " if len(ls) >= 5: #if the range was greater than 10 seconds, set to 5 because data points are every 2\n", "\n", " if ls[0] <= 84: #was it in the middle range?\n", " m_dict[middle] = ls\n", " middle += 1\n", " ls = [v]\n", " elif ls[0] >= 85 and ls[0] <=89: #was it in the above range?\n", " a_dict[above] = ls\n", " above += 1\n", " ls = [v]\n", "\n", " else: #old list wasn't long enough to count\n", " ls = [v]\n", " else: #if in the same range\n", " ls.append(v)\n", " \n", " elif v >= 81 and v<= 84: #middle block\n", " \n", " if not ls:\n", " ls.append(v)\n", " else:\n", " if ls[0] <= 80 or (ls[0]>=85 and ls[0]<= 89): #if not in the middle range\n", " if len(ls) >= 5: #if range was greater than 10 seconds\n", "\n", " if ls[0] <= 80: #was it in the below range?\n", " b_dict[below] = ls\n", " below += 1\n", " ls = [v]\n", " elif ls[0] >= 85 and ls[0] <=89: #was it in the above range?\n", " a_dict[above] = ls\n", " above += 1\n", " ls = [v]\n", " else: #old list wasn't long enough to count\n", " ls = [v]\n", "\n", " else:\n", " ls.append(v)\n", " \n", " elif v >= 85 and v <=89: #above block\n", " \n", " if not ls:\n", " ls.append(v)\n", " else:\n", " if ls[0] <=84 : #if not in the above range\n", "\n", " if len(ls) >= 5: #if range was greater than \n", " if ls[0] <= 80: #was it in the below range?\n", " b_dict[below] = ls\n", " below += 1\n", " ls = [v]\n", " elif ls[0] >= 81 and ls[0] <=84: #was it in the middle range?\n", " m_dict[middle] = ls\n", " middle += 1\n", " ls = [v]\n", " else: #old list wasn't long enough to count\n", " ls = [v]\n", " else:\n", " ls.append(v)\n", " \n", " else: #v>90 or something else weird. start the list over\n", " ls = []\n", "#final list check\n", "if len(ls) >= 5:\n", " if ls[0] <= 80: #was it in the below range?\n", " b_dict[below] = ls\n", " below += 1\n", " ls = [v]\n", " elif ls[0] >= 81 and ls[0] <=84: #was it in the middle range?\n", " m_dict[middle] = ls\n", " middle += 1\n", " ls = [v]\n", " elif ls[0] >= 85 and ls[0] <=89: #was it in the above range?\n", " a_dict[above] = ls\n", " above += 1\n", " \n", "b_len = 0.0\n", "for key, val in b_dict.iteritems():\n", " b_len += len(val)\n", "\n", "m_len = 0.0\n", "for key, val in m_dict.iteritems():\n", " m_len += len(val)\n", " \n", "a_len = 0.0\n", "for key, val in a_dict.iteritems():\n", " a_len += len(val)\n", " \n", "\n", " " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ " #post exam duraiton length analysis\n", "df_O2_post = df[Z:Q]\n", "\n", "\n", "#Find count of these ranges\n", "below2 = 0 # v <=80\n", "middle2= 0 #v >= 81 and v<=84\n", "above2 = 0 #v >=85 and v<=89\n", "ls2 = []\n", "\n", "b_dict2 = {}\n", "m_dict2 = {}\n", "a_dict2 = {}\n", "\n", "for i2, v2 in df_O2_post['SpO2'].iteritems():\n", " \n", " if v2 <= 80: #below block\n", " \n", " if not ls2: \n", " ls2.append(v2)\n", " else:\n", " if ls2[0] >= 81: #if the range before was not below 80\n", "\n", " if len(ls2) >= 5: #if the range was greater than 10 seconds, set to 5 because data points are every 2\n", "\n", " if ls2[0] <= 84: #was it in the middle range?\n", " m_dict2[middle2] = ls2\n", " middle2 += 1\n", " ls2 = [v2]\n", " elif ls2[0] >= 85 and ls2[0] <=89: #was it in the above range?\n", " a_dict2[above2] = ls2\n", " above2 += 1\n", " ls2 = [v2]\n", "\n", " else: #old list wasn't long enough to count\n", " ls2 = [v2]\n", " else: #if in the same range\n", " ls2.append(v2)\n", " \n", " elif v2 >= 81 and v2<= 84: #middle block\n", " \n", " if not ls2:\n", " ls2.append(v2)\n", " else:\n", " if ls2[0] <= 80 or (ls2[0]>=85 and ls2[0]<= 89): #if not in the middle range\n", " if len(ls2) >= 5: #if range was greater than 10 seconds\n", "\n", " if ls2[0] <= 80: #was it in the below range?\n", " b_dict2[below2] = ls2\n", " below2 += 1\n", " ls2 = [v2]\n", " elif ls2[0] >= 85 and ls2[0] <=89: #was it in the above range?\n", " a_dict2[above2] = ls2\n", " above2 += 1\n", " ls2 = [v2]\n", " else: #old list wasn't long enough to count\n", " ls2 = [v2]\n", "\n", " else:\n", " ls2.append(v2)\n", " \n", " elif v2 >= 85 and v2 <=89: #above block\n", " \n", " if not ls2:\n", " ls2.append(v2)\n", " else:\n", " if ls2[0] <=84 : #if not in the above range\n", "\n", " if len(ls2) >= 5: #if range was greater than \n", " if ls2[0] <= 80: #was it in the below range?\n", " b_dict2[below2] = ls2\n", " below2 += 1\n", " ls2 = [v2]\n", " elif ls2[0] >= 81 and ls2[0] <=84: #was it in the middle range?\n", " m_dict2[middle2] = ls2\n", " middle2 += 1\n", " ls2 = [v2]\n", " else: #old list wasn't long enough to count\n", " ls2 = [v2]\n", " else:\n", " ls2.append(v2)\n", " \n", " else: #v2>90 or something else weird. start the list over\n", " ls2 = []\n", "#final list check\n", "if len(ls2) >= 5:\n", " if ls2[0] <= 80: #was it in the below range?\n", " b_dict2[below2] = ls2\n", " below2 += 1\n", " ls2= [v2]\n", " elif ls2[0] >= 81 and ls2[0] <=84: #was it in the middle range?\n", " m_dict2[middle2] = ls2\n", " middle2 += 1\n", " ls2 = [v2]\n", " elif ls2[0] >= 85 and ls2[0] <=89: #was it in the above range?\n", " a_dict2[above2] = ls2\n", " above2 += 1\n", " \n", "b_len2 = 0.0\n", "for key, val2 in b_dict2.iteritems():\n", " b_len2 += len(val2)\n", "\n", "m_len2 = 0.0\n", "for key, val2 in m_dict2.iteritems():\n", " m_len2 += len(val2)\n", " \n", "a_len2 = 0.0\n", "for key, val2 in a_dict2.iteritems():\n", " a_len2 += len(val2)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Desat Counts for X mins\n", "\n", "Pre Mild Desat (85-89) Count: 0\tfor 0.0 min\n", "Pre Mod Desat (81-84) Count: 0\tfor 0.0 min\n", "Pre Sev Desat (=< 80) Count: 1\tfor 0.6 min\n", "\n", "Post Mild Desat (85-89) Count: 3\tfor 0.633333333333 min\n", "Post Mod Desat (81-84) Count: 4\tfor 0.9 min\n", "Post Sev Desat (=< 80) Count: 8\tfor 2.6 min\n", "\n", "Data Recording Time!\n", "*********\n", "Pre-Exam Data Recording Length\t0 days 16:19:32\n", "Post-Exam Data Recording Length\t0 days 23:27:40\n", "Total Data Recording Length\t1 days 15:51:12\n" ] } ], "source": [ "#print results from count and min\n", "\n", "print \"Desat Counts for X mins\\n\" \n", "print \"Pre Mild Desat (85-89) Count: %s\\t\" %above, \"for %s min\" %((a_len2)/60.)\n", "print \"Pre Mod Desat (81-84) Count: %s\\t\" %middle, \"for %s min\" %((m_len2)/60.) \n", "print \"Pre Sev Desat (=< 80) Count: %s\\t\" %below, \"for %s min\\n\" %((b_len2)/60.)\n", "\n", "print \"Post Mild Desat (85-89) Count: %s\\t\" %above2, \"for %s min\" %((a_len22)/60.) \n", "print \"Post Mod Desat (81-84) Count: %s\\t\" %middle2, \"for %s min\" %((m_len22)/60.) \n", "print \"Post Sev Desat (=< 80) Count: %s\\t\" %below2, \"for %s min\\n\" %((b_len22)/60.) \n", "\n", "\n", "\n", "print \"Data Recording Time!\"\n", "print '' * 10\n", "print \"Pre-Exam Data Recording Length\\t\", X - Y # start of exam - first data point\n", "print \"Post-Exam Data Recording Length\\t\", Q - Z #last data point - end of exam\n", "print \"Total Data Recording Length\\t\", Q - Y #last data point - first data point\n", "\n", "Pre = ['Pre',(X-Y)]\n", "Post = ['Post',(Q-Z)]\n", "Total = ['Total',(Q-Y)]\n", "RTL = [Pre, Post, Total]\n", "\n", "PreMild = ['Pre Mild Desats \\t',(above), 'for', (a_len2)/60., 'mins']\n", "PreMod = ['Pre Mod Desats \\t',(middle), 'for', (m_len2)/60., 'mins']\n", "PreSev = ['Pre Sev Desats \\t',(below), 'for', (b_len2)/60., 'mins']\n", "PreDesats = [PreMild, PreMod, PreSev]\n", "\n", "PostMild = ['Post Mild Desats \\t',(above2), 'for', (a_len22)/60., 'mins']\n", "PostMod = ['Post Mod Desats \\t',(middle2), 'for', (m_len22)/60., 'mins']\n", "PostSev = ['Post Sev Desats \\t',(below2), 'for', (b_len22)/60., 'mins']\n", "PostDesats = [PostMild, PostMod, PostSev]\n", "\n", "#creating a list for recording time length" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'\\nprint \"Mild check\"\\nfor key, val in b_dict.iteritems():\\n print all(i <=80 for i in val)\\n\\nprint \"Moderate check\"\\nfor key, val in m_dict.iteritems():\\n print all(i >= 81 and i<=84 for i in val)\\n \\nprint \"Severe check\"\\nfor key, val in a_dict.iteritems():\\n print all(i >= 85 and i<=89 for i in val)\\n'" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#did it count check sort correctly? get rid of the ''' if you want to check your values\n", "'''\n", "print \"Mild check\"\n", "for key, val in b_dict.iteritems():\n", " print all(i <=80 for i in val)\n", "\n", "print \"Moderate check\"\n", "for key, val in m_dict.iteritems():\n", " print all(i >= 81 and i<=84 for i in val)\n", " \n", "print \"Severe check\"\n", "for key, val in a_dict.iteritems():\n", " print all(i >= 85 and i<=89 for i in val)\n", "'''" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Export to CSV" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import csv\n", "class excel_tab(csv.excel):\n", " delimiter = '\\t'\n", "csv.register_dialect(\"excel_tab\", excel_tab)\n", "\n", "with open('ROP018_PO.csv', 'w') as f: #CHANGE CSV FILE NAME, saves in same directory\n", " writer = csv.writer(f, dialect=excel_tab)\n", " #writer.writerow(['PI, O2, PR']) accidently found this out but using commas = gives me columns YAY! fix this\n", " #to make code look nice ok nice\n", " writer.writerow([avg0PI, ',PI Start'])\n", " for i in rdrop:\n", " writer.writerow([avgdropPI[i]]) #NEEDS BRACKETS TO MAKE IT SEQUENCE\n", " for i in r1:\n", " writer.writerow([avg1PI[i]])\n", " for i in r2:\n", " writer.writerow([avg2PI[i]])\n", " for i in r3:\n", " writer.writerow([avg3PI[i]])\n", " for i in r4:\n", " writer.writerow([avg4PI[i]])\n", " for i in r5:\n", " writer.writerow([avg5PI[i]])\n", " writer.writerow([avg0O2, ',SpO2 Start'])\n", " for i in rdrop:\n", " writer.writerow([avgdropO2[i]])\n", " for i in r1:\n", " writer.writerow([avg1O2[i]])\n", " for i in r2:\n", " writer.writerow([avg2O2[i]])\n", " for i in r3:\n", " writer.writerow([avg3O2[i]])\n", " for i in r4:\n", " writer.writerow([avg4O2[i]])\n", " for i in r5:\n", " writer.writerow([avg5O2[i]])\n", " writer.writerow([avg0PR, ',PR Start'])\n", " for i in rdrop:\n", " writer.writerow([avgdropPR[i]])\n", " for i in r1:\n", " writer.writerow([avg1PR[i]])\n", " for i in r2:\n", " writer.writerow([avg2PR[i]])\n", " for i in r3:\n", " writer.writerow([avg3PR[i]])\n", " for i in r4:\n", " writer.writerow([avg4PR[i]])\n", " for i in r5:\n", " writer.writerow([avg5PR[i]])\n", " writer.writerow(['Data Recording Time Length'])\n", " writer.writerows(RTL)\n", " writer.writerow(['Pre Desat Counts for X Minutes'])\n", " writer.writerows(PreDesats)\n", " writer.writerow(['Post Dest Counts for X Minutes'])\n", " writer.writerows(PostDesats)\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-2.0
liulixiang1988/documents	Python数据科学101.ipynb	2	667190	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python数据科学101" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. 配置系统\n", "\n", "- Python\n", "- JDK\n", "- 创建`C:\\Hadoop\\bin`\n", "- 在这里下载windows版的hadoop https://github.com/steveloughran/winutils 拷贝winutils到`C:\\Hadoop\\bin`下面\n", "- 创建`HADOOP_HOME`环境变量，指向`C:\\Hadoop`\n", "- 创建`C:\\temp\\hive`文件夹\n", "- 运行`c:\\hadoop\\bin\\winutils chmod 777 \\temp\\hive`\n", "- 下载Spark: https://spark.apache.org/downloads.html\n", "- 解压下载的Spark的文件到`C:\\SPARK`目录下，其它操作系统的放到`home`目录\n", "- 创建`SPARK_HOME`，指向`C:\\SPARK`\n", "- 运行`c:\\spark\\bin\\spark-shell`看看是否安装成功\n", "\n", "## 2. 使用Python \n", "\n", "- 安装Anaconda\n", "- 检查conda: `conda --version`\n", "- 检查安装的包: `conda list`\n", "- 升级: `conda update conda`\n", "\n", "## 3. 实验环境\n", "\n", "输入`jupyter notebook`" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'Sine wave')" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ZA3dBzV6/UiqJW0nkGJADAB3PR8OxkjnilkWx907+PoZmkjHko5K4bxFHtFs2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"text/plain": [ "<matplotlib.figure.Figure at 0x259af035eb8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "import matplotlib\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "x = np.linspace(0, 3np.pi, 500)\n", "plt.plot(x, np.sin(x2))\n", "plt.title('Sine wave')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Pandas简介\n", "\n", "最重要的是`DataFrame`和`Series`" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.1 Series\n", "\n", "创建一个series，包含空值NaN" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6.0" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = pd.Series([1, 3, 5, np.nan, 6, 8])\n", "s[4] # 6.0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.2 Dataframes" ] }, { "cell_type": "code", "execution_count": 4, "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>data</th>\n", " <th>qty</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2016-01-01</td>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2016-01-02</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2016-01-03</td>\n", " <td>40</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " data qty\n", "0 2016-01-01 20\n", "1 2016-01-02 30\n", "2 2016-01-03 40" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame({'data': ['2016-01-01', '2016-01-02', '2016-01-03'], 'qty': [20, 30, 40]})\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "更大的数据应当从文件里获取" ] }, { "cell_type": "code", "execution_count": 5, "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>City</th>\n", " <th>Month</th>\n", " <th>Rainfall</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>San Francisco</td>\n", " <td>Jan</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Seattle</td>\n", " <td>Jan</td>\n", " <td>30.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Los Angeles</td>\n", " <td>Jan</td>\n", " <td>2.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Seattle</td>\n", " <td>Feb</td>\n", " <td>20.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>San Francisco</td>\n", " <td>Feb</td>\n", " <td>4.0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Los Angeles</td>\n", " <td>Feb</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Seattle</td>\n", " <td>Mar</td>\n", " <td>22.0</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>San Francisco</td>\n", " <td>Mar</td>\n", " <td>4.0</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Los Angeles</td>\n", " <td>Mar</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>Seattle</td>\n", " <td>Apr</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>San Francisco</td>\n", " <td>Apr</td>\n", " <td>5.0</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>Los Angeles</td>\n", " <td>Apr</td>\n", " <td>4.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " City Month Rainfall\n", "0 San Francisco Jan 10.0\n", "1 Seattle Jan 30.0\n", "2 Los Angeles Jan 2.0\n", "3 Seattle Feb 20.0\n", "4 San Francisco Feb 4.0\n", "5 Los Angeles Feb 0.0\n", "6 Seattle Mar 22.0\n", "7 San Francisco Mar 4.0\n", "8 Los Angeles Mar NaN\n", "9 Seattle Apr NaN\n", "10 San Francisco Apr 5.0\n", "11 Los Angeles Apr 4.0" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rain = pd.read_csv('data/rainfall/rainfall.csv')\n", "rain" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 San Francisco\n", "1 Seattle\n", "2 Los Angeles\n", "3 Seattle\n", "4 San Francisco\n", "5 Los Angeles\n", "6 Seattle\n", "7 San Francisco\n", "8 Los Angeles\n", "9 Seattle\n", "10 San Francisco\n", "11 Los Angeles\n", "Name: City, dtype: object" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 加载一列\n", "rain['City']" ] }, { "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>City</th>\n", " <th>Month</th>\n", " <th>Rainfall</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>Seattle</td>\n", " <td>Jan</td>\n", " <td>30.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " City Month Rainfall\n", "1 Seattle Jan 30.0" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 加载一行(第二行)\n", "rain.loc[[1]]" ] }, { "cell_type": "code", "execution_count": 8, "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>City</th>\n", " <th>Month</th>\n", " <th>Rainfall</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>San Francisco</td>\n", " <td>Jan</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Seattle</td>\n", " <td>Jan</td>\n", " <td>30.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " City Month Rainfall\n", "0 San Francisco Jan 10.0\n", "1 Seattle Jan 30.0" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 第一行和第二行\n", "rain.loc[0:1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.3 过滤" ] }, { "cell_type": "code", "execution_count": 10, "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>City</th>\n", " <th>Month</th>\n", " <th>Rainfall</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2</th>\n", " <td>Los Angeles</td>\n", " <td>Jan</td>\n", " <td>2.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>San Francisco</td>\n", " <td>Feb</td>\n", " <td>4.0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Los Angeles</td>\n", " <td>Feb</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>San Francisco</td>\n", " <td>Mar</td>\n", " <td>4.0</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>San Francisco</td>\n", " <td>Apr</td>\n", " <td>5.0</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>Los Angeles</td>\n", " <td>Apr</td>\n", " <td>4.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " City Month Rainfall\n", "2 Los Angeles Jan 2.0\n", "4 San Francisco Feb 4.0\n", "5 Los Angeles Feb 0.0\n", "7 San Francisco Mar 4.0\n", "10 San Francisco Apr 5.0\n", "11 Los Angeles Apr 4.0" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 查找所有降雨量小于10的数据\n", "rain[rain['Rainfall'] < 10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "查找4月份的降雨" ] }, { "cell_type": "code", "execution_count": 11, "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>City</th>\n", " <th>Month</th>\n", " <th>Rainfall</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>9</th>\n", " <td>Seattle</td>\n", " <td>Apr</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>San Francisco</td>\n", " <td>Apr</td>\n", " <td>5.0</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>Los Angeles</td>\n", " <td>Apr</td>\n", " <td>4.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " City Month Rainfall\n", "9 Seattle Apr NaN\n", "10 San Francisco Apr 5.0\n", "11 Los Angeles Apr 4.0" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rain[rain['Month'] == 'Apr']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "查找Los Angeles的数据" ] }, { "cell_type": "code", "execution_count": 12, "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>City</th>\n", " <th>Month</th>\n", " <th>Rainfall</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2</th>\n", " <td>Los Angeles</td>\n", " <td>Jan</td>\n", " <td>2.0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Los Angeles</td>\n", " <td>Feb</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Los Angeles</td>\n", " <td>Mar</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>Los Angeles</td>\n", " <td>Apr</td>\n", " <td>4.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " City Month Rainfall\n", "2 Los Angeles Jan 2.0\n", "5 Los Angeles Feb 0.0\n", "8 Los Angeles Mar NaN\n", "11 Los Angeles Apr 4.0" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rain[rain['City'] == 'Los Angeles']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.4 给行起名(Naming Rows)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rain = rain.set_index(rain['City'] + rain['Month'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "注意，当我们修改dataframe时，其实是在创建一个副本，因此要把这个值再赋值给原有的dataframe" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "City San Francisco\n", "Month Apr\n", "Rainfall 5\n", "Name: San FranciscoApr, dtype: object" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rain.loc['San FranciscoApr']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Pandas 例子" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = pd.read_csv('data/nycflights13/flights.csv.gz')" ] }, { "cell_type": "code", "execution_count": 17, "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>year</th>\n", " <th>month</th>\n", " <th>day</th>\n", " <th>dep_time</th>\n", " <th>sched_dep_time</th>\n", " <th>dep_delay</th>\n", " <th>arr_time</th>\n", " <th>sched_arr_time</th>\n", " <th>arr_delay</th>\n", " <th>carrier</th>\n", " <th>flight</th>\n", " <th>tailnum</th>\n", " <th>origin</th>\n", " <th>dest</th>\n", " <th>air_time</th>\n", " <th>distance</th>\n", " <th>hour</th>\n", " <th>minute</th>\n", " <th>time_hour</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>517.0</td>\n", " <td>515</td>\n", " <td>2.0</td>\n", " <td>830.0</td>\n", " <td>819</td>\n", " <td>11.0</td>\n", " <td>UA</td>\n", " <td>1545</td>\n", " <td>N14228</td>\n", " <td>EWR</td>\n", " <td>IAH</td>\n", " <td>227.0</td>\n", " <td>1400</td>\n", " <td>5</td>\n", " <td>15</td>\n", " <td>2013-01-01 05:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>533.0</td>\n", " <td>529</td>\n", " <td>4.0</td>\n", " <td>850.0</td>\n", " <td>830</td>\n", " <td>20.0</td>\n", " <td>UA</td>\n", " <td>1714</td>\n", " <td>N24211</td>\n", " <td>LGA</td>\n", " <td>IAH</td>\n", " <td>227.0</td>\n", " <td>1416</td>\n", " <td>5</td>\n", " <td>29</td>\n", " <td>2013-01-01 05:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>542.0</td>\n", " <td>540</td>\n", " <td>2.0</td>\n", " <td>923.0</td>\n", " <td>850</td>\n", " <td>33.0</td>\n", " <td>AA</td>\n", " <td>1141</td>\n", " <td>N619AA</td>\n", " <td>JFK</td>\n", " <td>MIA</td>\n", " <td>160.0</td>\n", " <td>1089</td>\n", " <td>5</td>\n", " <td>40</td>\n", " <td>2013-01-01 05:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>544.0</td>\n", " <td>545</td>\n", " <td>-1.0</td>\n", " <td>1004.0</td>\n", " <td>1022</td>\n", " <td>-18.0</td>\n", " <td>B6</td>\n", " <td>725</td>\n", " <td>N804JB</td>\n", " <td>JFK</td>\n", " <td>BQN</td>\n", " <td>183.0</td>\n", " <td>1576</td>\n", " <td>5</td>\n", " <td>45</td>\n", " <td>2013-01-01 05:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>554.0</td>\n", " <td>600</td>\n", " <td>-6.0</td>\n", " <td>812.0</td>\n", " <td>837</td>\n", " <td>-25.0</td>\n", " <td>DL</td>\n", " <td>461</td>\n", " <td>N668DN</td>\n", " <td>LGA</td>\n", " <td>ATL</td>\n", " <td>116.0</td>\n", " <td>762</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>2013-01-01 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>554.0</td>\n", " <td>558</td>\n", " <td>-4.0</td>\n", " <td>740.0</td>\n", " <td>728</td>\n", " <td>12.0</td>\n", " <td>UA</td>\n", " <td>1696</td>\n", " <td>N39463</td>\n", " <td>EWR</td>\n", " <td>ORD</td>\n", " <td>150.0</td>\n", " <td>719</td>\n", " <td>5</td>\n", " <td>58</td>\n", " <td>2013-01-01 05:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>555.0</td>\n", " <td>600</td>\n", " <td>-5.0</td>\n", " <td>913.0</td>\n", " <td>854</td>\n", " <td>19.0</td>\n", " <td>B6</td>\n", " <td>507</td>\n", " <td>N516JB</td>\n", " <td>EWR</td>\n", " <td>FLL</td>\n", " <td>158.0</td>\n", " <td>1065</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>2013-01-01 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>557.0</td>\n", " <td>600</td>\n", " <td>-3.0</td>\n", " <td>709.0</td>\n", " <td>723</td>\n", " <td>-14.0</td>\n", " <td>EV</td>\n", " <td>5708</td>\n", " <td>N829AS</td>\n", " <td>LGA</td>\n", " <td>IAD</td>\n", " <td>53.0</td>\n", " <td>229</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>2013-01-01 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>557.0</td>\n", " <td>600</td>\n", " <td>-3.0</td>\n", " <td>838.0</td>\n", " <td>846</td>\n", " <td>-8.0</td>\n", " <td>B6</td>\n", " <td>79</td>\n", " <td>N593JB</td>\n", " <td>JFK</td>\n", " <td>MCO</td>\n", " <td>140.0</td>\n", " <td>944</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>2013-01-01 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>753.0</td>\n", " <td>745</td>\n", " <td>8.0</td>\n", " <td>AA</td>\n", " <td>301</td>\n", " <td>N3ALAA</td>\n", " <td>LGA</td>\n", " <td>ORD</td>\n", " <td>138.0</td>\n", " <td>733</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>2013-01-01 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>849.0</td>\n", " <td>851</td>\n", " <td>-2.0</td>\n", " <td>B6</td>\n", " <td>49</td>\n", " <td>N793JB</td>\n", " <td>JFK</td>\n", " <td>PBI</td>\n", " <td>149.0</td>\n", " <td>1028</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>2013-01-01 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>853.0</td>\n", " <td>856</td>\n", " <td>-3.0</td>\n", " <td>B6</td>\n", " <td>71</td>\n", " <td>N657JB</td>\n", " <td>JFK</td>\n", " <td>TPA</td>\n", " <td>158.0</td>\n", " <td>1005</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>2013-01-01 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>924.0</td>\n", " <td>917</td>\n", " <td>7.0</td>\n", " <td>UA</td>\n", " <td>194</td>\n", " <td>N29129</td>\n", " <td>JFK</td>\n", " <td>LAX</td>\n", " <td>345.0</td>\n", " <td>2475</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>2013-01-01 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>923.0</td>\n", " <td>937</td>\n", " <td>-14.0</td>\n", " <td>UA</td>\n", " <td>1124</td>\n", " <td>N53441</td>\n", " <td>EWR</td>\n", " <td>SFO</td>\n", " <td>361.0</td>\n", " <td>2565</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>2013-01-01 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>559.0</td>\n", " <td>600</td>\n", " <td>-1.0</td>\n", " <td>941.0</td>\n", " <td>910</td>\n", " <td>31.0</td>\n", " <td>AA</td>\n", " <td>707</td>\n", " <td>N3DUAA</td>\n", " <td>LGA</td>\n", " <td>DFW</td>\n", " <td>257.0</td>\n", " <td>1389</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>2013-01-01 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>559.0</td>\n", " <td>559</td>\n", " <td>0.0</td>\n", " <td>702.0</td>\n", " <td>706</td>\n", " <td>-4.0</td>\n", " <td>B6</td>\n", " <td>1806</td>\n", " <td>N708JB</td>\n", " <td>JFK</td>\n", " <td>BOS</td>\n", " <td>44.0</td>\n", " <td>187</td>\n", " <td>5</td>\n", " <td>59</td>\n", " <td>2013-01-01 05:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>559.0</td>\n", " <td>600</td>\n", " <td>-1.0</td>\n", " <td>854.0</td>\n", " <td>902</td>\n", " <td>-8.0</td>\n", " <td>UA</td>\n", " <td>1187</td>\n", " <td>N76515</td>\n", " <td>EWR</td>\n", " <td>LAS</td>\n", " <td>337.0</td>\n", " <td>2227</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>2013-01-01 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>600.0</td>\n", " <td>600</td>\n", " <td>0.0</td>\n", " <td>851.0</td>\n", " <td>858</td>\n", " <td>-7.0</td>\n", " <td>B6</td>\n", " <td>371</td>\n", " <td>N595JB</td>\n", " <td>LGA</td>\n", " <td>FLL</td>\n", " <td>152.0</td>\n", " <td>1076</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>2013-01-01 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>600.0</td>\n", " <td>600</td>\n", " <td>0.0</td>\n", " <td>837.0</td>\n", " <td>825</td>\n", " <td>12.0</td>\n", " <td>MQ</td>\n", " <td>4650</td>\n", " <td>N542MQ</td>\n", " <td>LGA</td>\n", " <td>ATL</td>\n", " <td>134.0</td>\n", " <td>762</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>2013-01-01 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>601.0</td>\n", " <td>600</td>\n", " <td>1.0</td>\n", " <td>844.0</td>\n", " <td>850</td>\n", " <td>-6.0</td>\n", " <td>B6</td>\n", " <td>343</td>\n", " <td>N644JB</td>\n", " <td>EWR</td>\n", " <td>PBI</td>\n", " <td>147.0</td>\n", " <td>1023</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>2013-01-01 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>602.0</td>\n", " <td>610</td>\n", " <td>-8.0</td>\n", " <td>812.0</td>\n", " <td>820</td>\n", " <td>-8.0</td>\n", " <td>DL</td>\n", " <td>1919</td>\n", " <td>N971DL</td>\n", " <td>LGA</td>\n", " <td>MSP</td>\n", " <td>170.0</td>\n", " <td>1020</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>2013-01-01 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>602.0</td>\n", " <td>605</td>\n", " <td>-3.0</td>\n", " <td>821.0</td>\n", " <td>805</td>\n", " <td>16.0</td>\n", " <td>MQ</td>\n", " <td>4401</td>\n", " <td>N730MQ</td>\n", " <td>LGA</td>\n", " <td>DTW</td>\n", " <td>105.0</td>\n", " <td>502</td>\n", " <td>6</td>\n", " <td>5</td>\n", " <td>2013-01-01 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>606.0</td>\n", " <td>610</td>\n", " <td>-4.0</td>\n", " <td>858.0</td>\n", " <td>910</td>\n", " <td>-12.0</td>\n", " <td>AA</td>\n", " <td>1895</td>\n", " <td>N633AA</td>\n", " <td>EWR</td>\n", " <td>MIA</td>\n", " <td>152.0</td>\n", " <td>1085</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>2013-01-01 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>606.0</td>\n", " <td>610</td>\n", " <td>-4.0</td>\n", " <td>837.0</td>\n", " <td>845</td>\n", " <td>-8.0</td>\n", " <td>DL</td>\n", " <td>1743</td>\n", " <td>N3739P</td>\n", " <td>JFK</td>\n", " <td>ATL</td>\n", " <td>128.0</td>\n", " <td>760</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>2013-01-01 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>607.0</td>\n", " <td>607</td>\n", " <td>0.0</td>\n", " <td>858.0</td>\n", " <td>915</td>\n", " <td>-17.0</td>\n", " <td>UA</td>\n", " <td>1077</td>\n", " <td>N53442</td>\n", " <td>EWR</td>\n", " <td>MIA</td>\n", " <td>157.0</td>\n", " <td>1085</td>\n", " <td>6</td>\n", " <td>7</td>\n", " <td>2013-01-01 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>608.0</td>\n", " <td>600</td>\n", " <td>8.0</td>\n", " <td>807.0</td>\n", " <td>735</td>\n", " <td>32.0</td>\n", " <td>MQ</td>\n", " <td>3768</td>\n", " <td>N9EAMQ</td>\n", " <td>EWR</td>\n", " <td>ORD</td>\n", " <td>139.0</td>\n", " <td>719</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>2013-01-01 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>611.0</td>\n", " <td>600</td>\n", " <td>11.0</td>\n", " <td>945.0</td>\n", " <td>931</td>\n", " <td>14.0</td>\n", " <td>UA</td>\n", " <td>303</td>\n", " <td>N532UA</td>\n", " <td>JFK</td>\n", " <td>SFO</td>\n", " <td>366.0</td>\n", " <td>2586</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>2013-01-01 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>613.0</td>\n", " <td>610</td>\n", " <td>3.0</td>\n", " <td>925.0</td>\n", " <td>921</td>\n", " <td>4.0</td>\n", " <td>B6</td>\n", " <td>135</td>\n", " <td>N635JB</td>\n", " <td>JFK</td>\n", " <td>RSW</td>\n", " <td>175.0</td>\n", " <td>1074</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>2013-01-01 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>615.0</td>\n", " <td>615</td>\n", " <td>0.0</td>\n", " <td>1039.0</td>\n", " <td>1100</td>\n", " <td>-21.0</td>\n", " <td>B6</td>\n", " <td>709</td>\n", " <td>N794JB</td>\n", " <td>JFK</td>\n", " <td>SJU</td>\n", " <td>182.0</td>\n", " <td>1598</td>\n", " <td>6</td>\n", " <td>15</td>\n", " <td>2013-01-01 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>615.0</td>\n", " <td>615</td>\n", " <td>0.0</td>\n", " <td>833.0</td>\n", " <td>842</td>\n", " <td>-9.0</td>\n", " <td>DL</td>\n", " <td>575</td>\n", " <td>N326NB</td>\n", " <td>EWR</td>\n", " <td>ATL</td>\n", " <td>120.0</td>\n", " <td>746</td>\n", " <td>6</td>\n", " <td>15</td>\n", " <td>2013-01-01 06:00:00</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", " <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>336746</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2123.0</td>\n", " <td>2125</td>\n", " <td>-2.0</td>\n", " <td>2223.0</td>\n", " <td>2247</td>\n", " <td>-24.0</td>\n", " <td>EV</td>\n", " <td>5489</td>\n", " <td>N712EV</td>\n", " <td>LGA</td>\n", " <td>CHO</td>\n", " <td>45.0</td>\n", " <td>305</td>\n", " <td>21</td>\n", " <td>25</td>\n", " <td>2013-09-30 21:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336747</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2127.0</td>\n", " <td>2129</td>\n", " <td>-2.0</td>\n", " <td>2314.0</td>\n", " <td>2323</td>\n", " <td>-9.0</td>\n", " <td>EV</td>\n", " <td>3833</td>\n", " <td>N16546</td>\n", " <td>EWR</td>\n", " <td>CLT</td>\n", " <td>72.0</td>\n", " <td>529</td>\n", " <td>21</td>\n", " <td>29</td>\n", " <td>2013-09-30 21:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336748</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2128.0</td>\n", " <td>2130</td>\n", " <td>-2.0</td>\n", " <td>2328.0</td>\n", " <td>2359</td>\n", " <td>-31.0</td>\n", " <td>B6</td>\n", " <td>97</td>\n", " <td>N807JB</td>\n", " <td>JFK</td>\n", " <td>DEN</td>\n", " <td>213.0</td>\n", " <td>1626</td>\n", " <td>21</td>\n", " <td>30</td>\n", " <td>2013-09-30 21:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336749</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2129.0</td>\n", " <td>2059</td>\n", " <td>30.0</td>\n", " <td>2230.0</td>\n", " <td>2232</td>\n", " <td>-2.0</td>\n", " <td>EV</td>\n", " <td>5048</td>\n", " <td>N751EV</td>\n", " <td>LGA</td>\n", " <td>RIC</td>\n", " <td>45.0</td>\n", " <td>292</td>\n", " <td>20</td>\n", " <td>59</td>\n", " <td>2013-09-30 20:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336750</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2131.0</td>\n", " <td>2140</td>\n", " <td>-9.0</td>\n", " <td>2225.0</td>\n", " <td>2255</td>\n", " <td>-30.0</td>\n", " <td>MQ</td>\n", " <td>3621</td>\n", " <td>N807MQ</td>\n", " <td>JFK</td>\n", " <td>DCA</td>\n", " <td>36.0</td>\n", " <td>213</td>\n", " <td>21</td>\n", " <td>40</td>\n", " <td>2013-09-30 21:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336751</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2140.0</td>\n", " <td>2140</td>\n", " <td>0.0</td>\n", " <td>10.0</td>\n", " <td>40</td>\n", " <td>-30.0</td>\n", " <td>AA</td>\n", " <td>185</td>\n", " <td>N335AA</td>\n", " <td>JFK</td>\n", " <td>LAX</td>\n", " <td>298.0</td>\n", " <td>2475</td>\n", " <td>21</td>\n", " <td>40</td>\n", " <td>2013-09-30 21:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336752</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2142.0</td>\n", " <td>2129</td>\n", " <td>13.0</td>\n", " <td>2250.0</td>\n", " <td>2239</td>\n", " <td>11.0</td>\n", " <td>EV</td>\n", " <td>4509</td>\n", " <td>N12957</td>\n", " <td>EWR</td>\n", " <td>PWM</td>\n", " <td>47.0</td>\n", " <td>284</td>\n", " <td>21</td>\n", " <td>29</td>\n", " <td>2013-09-30 21:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336753</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2145.0</td>\n", " <td>2145</td>\n", " <td>0.0</td>\n", " <td>115.0</td>\n", " <td>140</td>\n", " <td>-25.0</td>\n", " <td>B6</td>\n", " <td>1103</td>\n", " <td>N633JB</td>\n", " <td>JFK</td>\n", " <td>SJU</td>\n", " <td>192.0</td>\n", " <td>1598</td>\n", " <td>21</td>\n", " <td>45</td>\n", " <td>2013-09-30 21:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336754</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2147.0</td>\n", " <td>2137</td>\n", " <td>10.0</td>\n", " <td>30.0</td>\n", " <td>27</td>\n", " <td>3.0</td>\n", " <td>B6</td>\n", " <td>1371</td>\n", " <td>N627JB</td>\n", " <td>LGA</td>\n", " <td>FLL</td>\n", " <td>139.0</td>\n", " <td>1076</td>\n", " <td>21</td>\n", " <td>37</td>\n", " <td>2013-09-30 21:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336755</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2149.0</td>\n", " <td>2156</td>\n", " <td>-7.0</td>\n", " <td>2245.0</td>\n", " <td>2308</td>\n", " <td>-23.0</td>\n", " <td>UA</td>\n", " <td>523</td>\n", " <td>N813UA</td>\n", " <td>EWR</td>\n", " <td>BOS</td>\n", " <td>37.0</td>\n", " <td>200</td>\n", " <td>21</td>\n", " <td>56</td>\n", " <td>2013-09-30 21:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336756</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2150.0</td>\n", " <td>2159</td>\n", " <td>-9.0</td>\n", " <td>2250.0</td>\n", " <td>2306</td>\n", " <td>-16.0</td>\n", " <td>EV</td>\n", " <td>3842</td>\n", " <td>N10575</td>\n", " <td>EWR</td>\n", " <td>MHT</td>\n", " <td>39.0</td>\n", " <td>209</td>\n", " <td>21</td>\n", " <td>59</td>\n", " <td>2013-09-30 21:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336757</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2159.0</td>\n", " <td>1845</td>\n", " <td>194.0</td>\n", " <td>2344.0</td>\n", " <td>2030</td>\n", " <td>194.0</td>\n", " <td>9E</td>\n", " <td>3320</td>\n", " <td>N906XJ</td>\n", " <td>JFK</td>\n", " <td>BUF</td>\n", " <td>50.0</td>\n", " <td>301</td>\n", " <td>18</td>\n", " <td>45</td>\n", " <td>2013-09-30 18:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336758</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2203.0</td>\n", " <td>2205</td>\n", " <td>-2.0</td>\n", " <td>2339.0</td>\n", " <td>2331</td>\n", " <td>8.0</td>\n", " <td>EV</td>\n", " <td>5311</td>\n", " <td>N722EV</td>\n", " <td>LGA</td>\n", " <td>BGR</td>\n", " <td>61.0</td>\n", " <td>378</td>\n", " <td>22</td>\n", " <td>5</td>\n", " <td>2013-09-30 22:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336759</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2207.0</td>\n", " <td>2140</td>\n", " <td>27.0</td>\n", " <td>2257.0</td>\n", " <td>2250</td>\n", " <td>7.0</td>\n", " <td>MQ</td>\n", " <td>3660</td>\n", " <td>N532MQ</td>\n", " <td>LGA</td>\n", " <td>BNA</td>\n", " <td>97.0</td>\n", " <td>764</td>\n", " <td>21</td>\n", " <td>40</td>\n", " <td>2013-09-30 21:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336760</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2211.0</td>\n", " <td>2059</td>\n", " <td>72.0</td>\n", " <td>2339.0</td>\n", " <td>2242</td>\n", " <td>57.0</td>\n", " <td>EV</td>\n", " <td>4672</td>\n", " <td>N12145</td>\n", " <td>EWR</td>\n", " <td>STL</td>\n", " <td>120.0</td>\n", " <td>872</td>\n", " <td>20</td>\n", " <td>59</td>\n", " <td>2013-09-30 20:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336761</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2231.0</td>\n", " <td>2245</td>\n", " <td>-14.0</td>\n", " <td>2335.0</td>\n", " <td>2356</td>\n", " <td>-21.0</td>\n", " <td>B6</td>\n", " <td>108</td>\n", " <td>N193JB</td>\n", " <td>JFK</td>\n", " <td>PWM</td>\n", " <td>48.0</td>\n", " <td>273</td>\n", " <td>22</td>\n", " <td>45</td>\n", " <td>2013-09-30 22:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336762</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2233.0</td>\n", " <td>2113</td>\n", " <td>80.0</td>\n", " <td>112.0</td>\n", " <td>30</td>\n", " <td>42.0</td>\n", " <td>UA</td>\n", " <td>471</td>\n", " <td>N578UA</td>\n", " <td>EWR</td>\n", " <td>SFO</td>\n", " <td>318.0</td>\n", " <td>2565</td>\n", " <td>21</td>\n", " <td>13</td>\n", " <td>2013-09-30 21:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336763</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2235.0</td>\n", " <td>2001</td>\n", " <td>154.0</td>\n", " <td>59.0</td>\n", " <td>2249</td>\n", " <td>130.0</td>\n", " <td>B6</td>\n", " <td>1083</td>\n", " <td>N804JB</td>\n", " <td>JFK</td>\n", " <td>MCO</td>\n", " <td>123.0</td>\n", " <td>944</td>\n", " <td>20</td>\n", " <td>1</td>\n", " <td>2013-09-30 20:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336764</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2237.0</td>\n", " <td>2245</td>\n", " <td>-8.0</td>\n", " <td>2345.0</td>\n", " <td>2353</td>\n", " <td>-8.0</td>\n", " <td>B6</td>\n", " <td>234</td>\n", " <td>N318JB</td>\n", " <td>JFK</td>\n", " <td>BTV</td>\n", " <td>43.0</td>\n", " <td>266</td>\n", " <td>22</td>\n", " <td>45</td>\n", " <td>2013-09-30 22:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336765</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2240.0</td>\n", " <td>2245</td>\n", " <td>-5.0</td>\n", " <td>2334.0</td>\n", " <td>2351</td>\n", " <td>-17.0</td>\n", " <td>B6</td>\n", " <td>1816</td>\n", " <td>N354JB</td>\n", " <td>JFK</td>\n", " <td>SYR</td>\n", " <td>41.0</td>\n", " <td>209</td>\n", " <td>22</td>\n", " <td>45</td>\n", " <td>2013-09-30 22:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336766</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2240.0</td>\n", " <td>2250</td>\n", " <td>-10.0</td>\n", " <td>2347.0</td>\n", " <td>7</td>\n", " <td>-20.0</td>\n", " <td>B6</td>\n", " <td>2002</td>\n", " <td>N281JB</td>\n", " <td>JFK</td>\n", " <td>BUF</td>\n", " <td>52.0</td>\n", " <td>301</td>\n", " <td>22</td>\n", " <td>50</td>\n", " <td>2013-09-30 22:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336767</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2241.0</td>\n", " <td>2246</td>\n", " <td>-5.0</td>\n", " <td>2345.0</td>\n", " <td>1</td>\n", " <td>-16.0</td>\n", " <td>B6</td>\n", " <td>486</td>\n", " <td>N346JB</td>\n", " <td>JFK</td>\n", " <td>ROC</td>\n", " <td>47.0</td>\n", " <td>264</td>\n", " <td>22</td>\n", " <td>46</td>\n", " <td>2013-09-30 22:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336768</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2307.0</td>\n", " <td>2255</td>\n", " <td>12.0</td>\n", " <td>2359.0</td>\n", " <td>2358</td>\n", " <td>1.0</td>\n", " <td>B6</td>\n", " <td>718</td>\n", " <td>N565JB</td>\n", " <td>JFK</td>\n", " <td>BOS</td>\n", " <td>33.0</td>\n", " <td>187</td>\n", " <td>22</td>\n", " <td>55</td>\n", " <td>2013-09-30 22:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336769</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2349.0</td>\n", " <td>2359</td>\n", " <td>-10.0</td>\n", " <td>325.0</td>\n", " <td>350</td>\n", " <td>-25.0</td>\n", " <td>B6</td>\n", " <td>745</td>\n", " <td>N516JB</td>\n", " <td>JFK</td>\n", " <td>PSE</td>\n", " <td>196.0</td>\n", " <td>1617</td>\n", " <td>23</td>\n", " <td>59</td>\n", " <td>2013-09-30 23:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336770</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1842</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2019</td>\n", " <td>NaN</td>\n", " <td>EV</td>\n", " <td>5274</td>\n", " <td>N740EV</td>\n", " <td>LGA</td>\n", " <td>BNA</td>\n", " <td>NaN</td>\n", " <td>764</td>\n", " <td>18</td>\n", " <td>42</td>\n", " <td>2013-09-30 18:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336771</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1455</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1634</td>\n", " <td>NaN</td>\n", " <td>9E</td>\n", " <td>3393</td>\n", " <td>NaN</td>\n", " <td>JFK</td>\n", " <td>DCA</td>\n", " <td>NaN</td>\n", " <td>213</td>\n", " <td>14</td>\n", " <td>55</td>\n", " <td>2013-09-30 14:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336772</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>2200</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2312</td>\n", " <td>NaN</td>\n", " <td>9E</td>\n", " <td>3525</td>\n", " <td>NaN</td>\n", " <td>LGA</td>\n", " <td>SYR</td>\n", " <td>NaN</td>\n", " <td>198</td>\n", " <td>22</td>\n", " <td>0</td>\n", " <td>2013-09-30 22:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336773</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1210</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1330</td>\n", " <td>NaN</td>\n", " <td>MQ</td>\n", " <td>3461</td>\n", " <td>N535MQ</td>\n", " <td>LGA</td>\n", " <td>BNA</td>\n", " <td>NaN</td>\n", " <td>764</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>2013-09-30 12:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336774</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1159</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1344</td>\n", " <td>NaN</td>\n", " <td>MQ</td>\n", " <td>3572</td>\n", " <td>N511MQ</td>\n", " <td>LGA</td>\n", " <td>CLE</td>\n", " <td>NaN</td>\n", " <td>419</td>\n", " <td>11</td>\n", " <td>59</td>\n", " <td>2013-09-30 11:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>336775</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>840</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1020</td>\n", " <td>NaN</td>\n", " <td>MQ</td>\n", " <td>3531</td>\n", " <td>N839MQ</td>\n", " <td>LGA</td>\n", " <td>RDU</td>\n", " <td>NaN</td>\n", " <td>431</td>\n", " <td>8</td>\n", " <td>40</td>\n", " <td>2013-09-30 08:00:00</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>336776 rows × 19 columns</p>\n", "</div>" ], "text/plain": [ " year month day dep_time sched_dep_time dep_delay arr_time \\\n", "0 2013 1 1 517.0 515 2.0 830.0 \n", "1 2013 1 1 533.0 529 4.0 850.0 \n", "2 2013 1 1 542.0 540 2.0 923.0 \n", "3 2013 1 1 544.0 545 -1.0 1004.0 \n", "4 2013 1 1 554.0 600 -6.0 812.0 \n", "5 2013 1 1 554.0 558 -4.0 740.0 \n", "6 2013 1 1 555.0 600 -5.0 913.0 \n", "7 2013 1 1 557.0 600 -3.0 709.0 \n", "8 2013 1 1 557.0 600 -3.0 838.0 \n", "9 2013 1 1 558.0 600 -2.0 753.0 \n", "10 2013 1 1 558.0 600 -2.0 849.0 \n", "11 2013 1 1 558.0 600 -2.0 853.0 \n", "12 2013 1 1 558.0 600 -2.0 924.0 \n", "13 2013 1 1 558.0 600 -2.0 923.0 \n", "14 2013 1 1 559.0 600 -1.0 941.0 \n", "15 2013 1 1 559.0 559 0.0 702.0 \n", "16 2013 1 1 559.0 600 -1.0 854.0 \n", "17 2013 1 1 600.0 600 0.0 851.0 \n", "18 2013 1 1 600.0 600 0.0 837.0 \n", "19 2013 1 1 601.0 600 1.0 844.0 \n", "20 2013 1 1 602.0 610 -8.0 812.0 \n", "21 2013 1 1 602.0 605 -3.0 821.0 \n", "22 2013 1 1 606.0 610 -4.0 858.0 \n", "23 2013 1 1 606.0 610 -4.0 837.0 \n", "24 2013 1 1 607.0 607 0.0 858.0 \n", "25 2013 1 1 608.0 600 8.0 807.0 \n", "26 2013 1 1 611.0 600 11.0 945.0 \n", "27 2013 1 1 613.0 610 3.0 925.0 \n", "28 2013 1 1 615.0 615 0.0 1039.0 \n", "29 2013 1 1 615.0 615 0.0 833.0 \n", "... ... ... ... ... ... ... ... \n", "336746 2013 9 30 2123.0 2125 -2.0 2223.0 \n", "336747 2013 9 30 2127.0 2129 -2.0 2314.0 \n", "336748 2013 9 30 2128.0 2130 -2.0 2328.0 \n", "336749 2013 9 30 2129.0 2059 30.0 2230.0 \n", "336750 2013 9 30 2131.0 2140 -9.0 2225.0 \n", "336751 2013 9 30 2140.0 2140 0.0 10.0 \n", "336752 2013 9 30 2142.0 2129 13.0 2250.0 \n", "336753 2013 9 30 2145.0 2145 0.0 115.0 \n", "336754 2013 9 30 2147.0 2137 10.0 30.0 \n", "336755 2013 9 30 2149.0 2156 -7.0 2245.0 \n", "336756 2013 9 30 2150.0 2159 -9.0 2250.0 \n", "336757 2013 9 30 2159.0 1845 194.0 2344.0 \n", "336758 2013 9 30 2203.0 2205 -2.0 2339.0 \n", "336759 2013 9 30 2207.0 2140 27.0 2257.0 \n", "336760 2013 9 30 2211.0 2059 72.0 2339.0 \n", "336761 2013 9 30 2231.0 2245 -14.0 2335.0 \n", "336762 2013 9 30 2233.0 2113 80.0 112.0 \n", "336763 2013 9 30 2235.0 2001 154.0 59.0 \n", "336764 2013 9 30 2237.0 2245 -8.0 2345.0 \n", "336765 2013 9 30 2240.0 2245 -5.0 2334.0 \n", "336766 2013 9 30 2240.0 2250 -10.0 2347.0 \n", "336767 2013 9 30 2241.0 2246 -5.0 2345.0 \n", "336768 2013 9 30 2307.0 2255 12.0 2359.0 \n", "336769 2013 9 30 2349.0 2359 -10.0 325.0 \n", "336770 2013 9 30 NaN 1842 NaN NaN \n", "336771 2013 9 30 NaN 1455 NaN NaN \n", "336772 2013 9 30 NaN 2200 NaN NaN \n", "336773 2013 9 30 NaN 1210 NaN NaN \n", "336774 2013 9 30 NaN 1159 NaN NaN \n", "336775 2013 9 30 NaN 840 NaN NaN \n", "\n", " sched_arr_time arr_delay carrier flight tailnum origin dest \\\n", "0 819 11.0 UA 1545 N14228 EWR IAH \n", "1 830 20.0 UA 1714 N24211 LGA IAH \n", "2 850 33.0 AA 1141 N619AA JFK MIA \n", "3 1022 -18.0 B6 725 N804JB JFK BQN \n", "4 837 -25.0 DL 461 N668DN LGA ATL \n", "5 728 12.0 UA 1696 N39463 EWR ORD \n", "6 854 19.0 B6 507 N516JB EWR FLL \n", "7 723 -14.0 EV 5708 N829AS LGA IAD \n", "8 846 -8.0 B6 79 N593JB JFK MCO \n", "9 745 8.0 AA 301 N3ALAA LGA ORD \n", "10 851 -2.0 B6 49 N793JB JFK PBI \n", "11 856 -3.0 B6 71 N657JB JFK TPA \n", "12 917 7.0 UA 194 N29129 JFK LAX \n", "13 937 -14.0 UA 1124 N53441 EWR SFO \n", "14 910 31.0 AA 707 N3DUAA LGA DFW \n", "15 706 -4.0 B6 1806 N708JB JFK BOS \n", "16 902 -8.0 UA 1187 N76515 EWR LAS \n", "17 858 -7.0 B6 371 N595JB LGA FLL \n", "18 825 12.0 MQ 4650 N542MQ LGA ATL \n", "19 850 -6.0 B6 343 N644JB EWR PBI \n", "20 820 -8.0 DL 1919 N971DL LGA MSP \n", "21 805 16.0 MQ 4401 N730MQ LGA DTW \n", "22 910 -12.0 AA 1895 N633AA EWR MIA \n", "23 845 -8.0 DL 1743 N3739P JFK ATL \n", "24 915 -17.0 UA 1077 N53442 EWR MIA \n", "25 735 32.0 MQ 3768 N9EAMQ EWR ORD \n", "26 931 14.0 UA 303 N532UA JFK SFO \n", "27 921 4.0 B6 135 N635JB JFK RSW \n", "28 1100 -21.0 B6 709 N794JB JFK SJU \n", "29 842 -9.0 DL 575 N326NB EWR ATL \n", "... ... ... ... ... ... ... ... \n", "336746 2247 -24.0 EV 5489 N712EV LGA CHO \n", "336747 2323 -9.0 EV 3833 N16546 EWR CLT \n", "336748 2359 -31.0 B6 97 N807JB JFK DEN \n", "336749 2232 -2.0 EV 5048 N751EV LGA RIC \n", "336750 2255 -30.0 MQ 3621 N807MQ JFK DCA \n", "336751 40 -30.0 AA 185 N335AA JFK LAX \n", "336752 2239 11.0 EV 4509 N12957 EWR PWM \n", "336753 140 -25.0 B6 1103 N633JB JFK SJU \n", "336754 27 3.0 B6 1371 N627JB LGA FLL \n", "336755 2308 -23.0 UA 523 N813UA EWR BOS \n", "336756 2306 -16.0 EV 3842 N10575 EWR MHT \n", "336757 2030 194.0 9E 3320 N906XJ JFK BUF \n", "336758 2331 8.0 EV 5311 N722EV LGA BGR \n", "336759 2250 7.0 MQ 3660 N532MQ LGA BNA \n", "336760 2242 57.0 EV 4672 N12145 EWR STL \n", "336761 2356 -21.0 B6 108 N193JB JFK PWM \n", "336762 30 42.0 UA 471 N578UA EWR SFO \n", "336763 2249 130.0 B6 1083 N804JB JFK MCO \n", "336764 2353 -8.0 B6 234 N318JB JFK BTV \n", "336765 2351 -17.0 B6 1816 N354JB JFK SYR \n", "336766 7 -20.0 B6 2002 N281JB JFK BUF \n", "336767 1 -16.0 B6 486 N346JB JFK ROC \n", "336768 2358 1.0 B6 718 N565JB JFK BOS \n", "336769 350 -25.0 B6 745 N516JB JFK PSE \n", "336770 2019 NaN EV 5274 N740EV LGA BNA \n", "336771 1634 NaN 9E 3393 NaN JFK DCA \n", "336772 2312 NaN 9E 3525 NaN LGA SYR \n", "336773 1330 NaN MQ 3461 N535MQ LGA BNA \n", "336774 1344 NaN MQ 3572 N511MQ LGA CLE \n", "336775 1020 NaN MQ 3531 N839MQ LGA RDU \n", "\n", " air_time distance hour minute time_hour \n", "0 227.0 1400 5 15 2013-01-01 05:00:00 \n", "1 227.0 1416 5 29 2013-01-01 05:00:00 \n", "2 160.0 1089 5 40 2013-01-01 05:00:00 \n", "3 183.0 1576 5 45 2013-01-01 05:00:00 \n", "4 116.0 762 6 0 2013-01-01 06:00:00 \n", "5 150.0 719 5 58 2013-01-01 05:00:00 \n", "6 158.0 1065 6 0 2013-01-01 06:00:00 \n", "7 53.0 229 6 0 2013-01-01 06:00:00 \n", "8 140.0 944 6 0 2013-01-01 06:00:00 \n", "9 138.0 733 6 0 2013-01-01 06:00:00 \n", "10 149.0 1028 6 0 2013-01-01 06:00:00 \n", "11 158.0 1005 6 0 2013-01-01 06:00:00 \n", "12 345.0 2475 6 0 2013-01-01 06:00:00 \n", "13 361.0 2565 6 0 2013-01-01 06:00:00 \n", "14 257.0 1389 6 0 2013-01-01 06:00:00 \n", "15 44.0 187 5 59 2013-01-01 05:00:00 \n", "16 337.0 2227 6 0 2013-01-01 06:00:00 \n", "17 152.0 1076 6 0 2013-01-01 06:00:00 \n", "18 134.0 762 6 0 2013-01-01 06:00:00 \n", "19 147.0 1023 6 0 2013-01-01 06:00:00 \n", "20 170.0 1020 6 10 2013-01-01 06:00:00 \n", "21 105.0 502 6 5 2013-01-01 06:00:00 \n", "22 152.0 1085 6 10 2013-01-01 06:00:00 \n", "23 128.0 760 6 10 2013-01-01 06:00:00 \n", "24 157.0 1085 6 7 2013-01-01 06:00:00 \n", "25 139.0 719 6 0 2013-01-01 06:00:00 \n", "26 366.0 2586 6 0 2013-01-01 06:00:00 \n", "27 175.0 1074 6 10 2013-01-01 06:00:00 \n", "28 182.0 1598 6 15 2013-01-01 06:00:00 \n", "29 120.0 746 6 15 2013-01-01 06:00:00 \n", "... ... ... ... ... ... \n", "336746 45.0 305 21 25 2013-09-30 21:00:00 \n", "336747 72.0 529 21 29 2013-09-30 21:00:00 \n", "336748 213.0 1626 21 30 2013-09-30 21:00:00 \n", "336749 45.0 292 20 59 2013-09-30 20:00:00 \n", "336750 36.0 213 21 40 2013-09-30 21:00:00 \n", "336751 298.0 2475 21 40 2013-09-30 21:00:00 \n", "336752 47.0 284 21 29 2013-09-30 21:00:00 \n", "336753 192.0 1598 21 45 2013-09-30 21:00:00 \n", "336754 139.0 1076 21 37 2013-09-30 21:00:00 \n", "336755 37.0 200 21 56 2013-09-30 21:00:00 \n", "336756 39.0 209 21 59 2013-09-30 21:00:00 \n", "336757 50.0 301 18 45 2013-09-30 18:00:00 \n", "336758 61.0 378 22 5 2013-09-30 22:00:00 \n", "336759 97.0 764 21 40 2013-09-30 21:00:00 \n", "336760 120.0 872 20 59 2013-09-30 20:00:00 \n", "336761 48.0 273 22 45 2013-09-30 22:00:00 \n", "336762 318.0 2565 21 13 2013-09-30 21:00:00 \n", "336763 123.0 944 20 1 2013-09-30 20:00:00 \n", "336764 43.0 266 22 45 2013-09-30 22:00:00 \n", "336765 41.0 209 22 45 2013-09-30 22:00:00 \n", "336766 52.0 301 22 50 2013-09-30 22:00:00 \n", "336767 47.0 264 22 46 2013-09-30 22:00:00 \n", "336768 33.0 187 22 55 2013-09-30 22:00:00 \n", "336769 196.0 1617 23 59 2013-09-30 23:00:00 \n", "336770 NaN 764 18 42 2013-09-30 18:00:00 \n", "336771 NaN 213 14 55 2013-09-30 14:00:00 \n", "336772 NaN 198 22 0 2013-09-30 22:00:00 \n", "336773 NaN 764 12 10 2013-09-30 12:00:00 \n", "336774 NaN 419 11 59 2013-09-30 11:00:00 \n", "336775 NaN 431 8 40 2013-09-30 08:00:00 \n", "\n", "[336776 rows x 19 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "这里我们主要关注统计数据和可视化。我们来看一下按月统计的晚点时间的均值。" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "month\n", "1 6.129972\n", "2 5.613019\n", "3 5.807577\n", "4 11.176063\n", "5 3.521509\n", "6 16.481330\n", "7 16.711307\n", "8 6.040652\n", "9 -4.018364\n", "10 -0.167063\n", "11 0.461347\n", "12 14.870355\n", "Name: arr_delay, dtype: float64" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mean_delay_by_month = df.groupby(['month'])['arr_delay'].mean()\n", "mean_delay_by_month" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x259b18d6ac8>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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dkhpjsI9I8tQkL0iy99jy46ZQ66gkR/b3D0vyH5fyImpJzlnCWs/tX9+xU2r/2Uke1d/f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\nkhpjsEtSYwx2SWqMwS5JjTHYJakx/x/CZy0LZPYJegAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x259b1908e80>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mean_month_plt = mean_delay_by_month.plot(kind='bar', title='Mean Delay By Month')\n", "mean_month_plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "注意，这里9、10月均值会有负值。" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Flights to Chicago (ORD)\n", "month\n", "1 7.287694\n", "2 3.680794\n", "3 -2.702473\n", "4 19.179352\n", "5 7.938280\n", "6 13.299376\n", "7 8.405514\n", "8 4.256851\n", "9 -4.745370\n", "10 -1.597090\n", "11 2.071058\n", "12 16.462817\n", "Name: arr_delay, dtype: float64\n" ] }, { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x259b19e7f98>" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x259b19e7e48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mean_delay_by_month_ord = df[(df.dest == 'ORD')].groupby(['month'])['arr_delay'].mean()\n", "print(\"Flights to Chicago (ORD)\")\n", "print(mean_delay_by_month_ord)\n", "\n", "mean_month_plt_ord = mean_delay_by_month_ord.plot(kind='bar', title=\"Mean Delay By Month (Chicago)\")\n", "mean_month_plt_ord" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Flights to Chicago (LAX)\n", "month\n", "1 -4.160312\n", "2 -7.601824\n", "3 -5.280928\n", "4 3.085153\n", "5 -7.150657\n", "6 13.007027\n", "7 8.191432\n", "8 1.028667\n", "9 -8.719044\n", "10 -1.205694\n", "11 -0.103290\n", "12 10.724460\n", "Name: arr_delay, dtype: float64\n" ] }, { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x259b19d8550>" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x259b19ea940>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 再看看Los Angeles进行比较一下\n", "mean_delay_by_month_lax = df[(df.dest == 'LAX')].groupby(['month'])['arr_delay'].mean()\n", "print(\"Flights to Chicago (LAX)\")\n", "print(mean_delay_by_month_lax)\n", "\n", "mean_month_plt_lax = mean_delay_by_month_lax.plot(kind='bar', title=\"Mean Delay By Month (Los Angeles)\")\n", "mean_month_plt_lax" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "从上面的图表中我们可以直观的看到一些特征。现在我们再来看看每个航空公司晚点的情况，并进行一些可视化。" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'Average Departure Delay by Carrier in 2008, All airports')" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Aqnr7hFQkSZIkDbixerLnTFoVkiRJ0gpk1JBdVSf3Xk+yWlXdM/ElSZIkSYNt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\nDzUeN5SJ2uv/k+RzPdePbb+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"text/plain": [ "<matplotlib.figure.Figure at 0x259b1cdfbe0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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PY8abUFUPVdV3q+oAYCvgOuCHSf6+T/f9N8A54909cG6Si5Mc3Kf7kyRJkgZm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\ndcCgLUmSJHXAoC1JkiR1wKA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JktSBQVuSJEnqwKAtSZIkdWDQliRJkjowaEvSlEuyJMnx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\nCRUb9QAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x259b1d60e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 看看是否不同的航空公司对晚点会有不同的影响\n", "df[['carrier', 'arr_delay']].groupby('carrier').mean().plot(kind='bar', figsize=(12, 8))\n", "plt.xticks(rotation=0)\n", "plt.xlabel('Carrier')\n", "plt.ylabel('Average Delay in Min')\n", "plt.title('Average Arrival Delay by Carrier in 2008, All airports')\n", "\n", "df[['carrier', 'dep_delay']].groupby('carrier').mean().plot(kind='bar', figsize=(12, 8))\n", "plt.xticks(rotation=0)\n", "plt.xlabel('Carrier')\n", "plt.ylabel('Average Delay in Min')\n", "plt.title('Average Departure Delay by Carrier in 2008, All airports')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "从上面的图表里我们可以看到F9(Front Airlines)几乎是最经常晚点的，而夏威夷(HA)在这方面表现最好。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5.3 Joins" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "我们有多个数据集，天气、机场的。现在我们来看一下如何把两个表连接在一起" ] }, { "cell_type": "code", "execution_count": 25, "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>origin</th>\n", " <th>year</th>\n", " <th>month</th>\n", " <th>day</th>\n", " <th>hour</th>\n", " <th>temp</th>\n", " <th>dewp</th>\n", " <th>humid</th>\n", " <th>wind_dir</th>\n", " <th>wind_speed</th>\n", " <th>wind_gust</th>\n", " <th>precip</th>\n", " <th>pressure</th>\n", " <th>visib</th>\n", " <th>time_hour</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>37.04</td>\n", " <td>21.92</td>\n", " <td>53.97</td>\n", " <td>230.0</td>\n", " <td>10.35702</td>\n", " <td>11.918651</td>\n", " <td>0.00</td>\n", " <td>1013.9</td>\n", " <td>10.00</td>\n", " <td>2012-12-31 19:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>37.04</td>\n", " <td>21.92</td>\n", " <td>53.97</td>\n", " <td>230.0</td>\n", " <td>13.80936</td>\n", " <td>15.891535</td>\n", " <td>0.00</td>\n", " <td>1013.0</td>\n", " <td>10.00</td>\n", " <td>2012-12-31 20:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>37.94</td>\n", " <td>21.92</td>\n", " <td>52.09</td>\n", " <td>230.0</td>\n", " <td>12.65858</td>\n", " <td>14.567241</td>\n", " <td>0.00</td>\n", " <td>1012.6</td>\n", " <td>10.00</td>\n", " <td>2012-12-31 21:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>37.94</td>\n", " <td>23.00</td>\n", " <td>54.51</td>\n", " <td>230.0</td>\n", " <td>13.80936</td>\n", " <td>15.891535</td>\n", " <td>0.00</td>\n", " <td>1012.7</td>\n", " <td>10.00</td>\n", " <td>2012-12-31 22:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>37.94</td>\n", " <td>24.08</td>\n", " <td>57.04</td>\n", " <td>240.0</td>\n", " <td>14.96014</td>\n", " <td>17.215830</td>\n", " <td>0.00</td>\n", " <td>1012.8</td>\n", " <td>10.00</td>\n", " <td>2012-12-31 23:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>6</td>\n", " <td>39.02</td>\n", " <td>26.06</td>\n", " <td>59.37</td>\n", " <td>270.0</td>\n", " <td>10.35702</td>\n", " <td>11.918651</td>\n", " <td>0.00</td>\n", " <td>1012.0</td>\n", " <td>10.00</td>\n", " <td>2013-01-01 01:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>7</td>\n", " <td>39.02</td>\n", " <td>26.96</td>\n", " <td>61.63</td>\n", " <td>250.0</td>\n", " <td>8.05546</td>\n", " <td>9.270062</td>\n", " <td>0.00</td>\n", " <td>1012.3</td>\n", " <td>10.00</td>\n", " <td>2013-01-01 02:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>8</td>\n", " <td>39.02</td>\n", " <td>28.04</td>\n", " <td>64.43</td>\n", " <td>240.0</td>\n", " <td>11.50780</td>\n", " <td>13.242946</td>\n", " <td>0.00</td>\n", " <td>1012.5</td>\n", " <td>10.00</td>\n", " <td>2013-01-01 03:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>39.92</td>\n", " <td>28.04</td>\n", " <td>62.21</td>\n", " <td>250.0</td>\n", " <td>12.65858</td>\n", " <td>14.567241</td>\n", " <td>0.00</td>\n", " <td>1012.2</td>\n", " <td>10.00</td>\n", " <td>2013-01-01 04:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>10</td>\n", " <td>39.02</td>\n", " <td>28.04</td>\n", " <td>64.43</td>\n", " <td>260.0</td>\n", " <td>12.65858</td>\n", " <td>14.567241</td>\n", " <td>0.00</td>\n", " <td>1011.9</td>\n", " <td>10.00</td>\n", " <td>2013-01-01 05:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>11</td>\n", " <td>37.94</td>\n", " <td>28.04</td>\n", " <td>67.21</td>\n", " <td>240.0</td>\n", " <td>11.50780</td>\n", " <td>13.242946</td>\n", " <td>0.00</td>\n", " <td>1012.4</td>\n", " <td>10.00</td>\n", " <td>2013-01-01 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>12</td>\n", " <td>39.02</td>\n", " <td>28.04</td>\n", " <td>64.43</td>\n", " <td>240.0</td>\n", " <td>14.96014</td>\n", " <td>17.215830</td>\n", " <td>0.00</td>\n", " <td>1012.2</td>\n", " <td>10.00</td>\n", " <td>2013-01-01 07:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>13</td>\n", " <td>39.92</td>\n", " <td>28.04</td>\n", " <td>62.21</td>\n", " <td>250.0</td>\n", " <td>10.35702</td>\n", " <td>11.918651</td>\n", " <td>0.00</td>\n", " <td>1012.2</td>\n", " <td>10.00</td>\n", " <td>2013-01-01 08:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>14</td>\n", " <td>39.92</td>\n", " <td>28.04</td>\n", " <td>62.21</td>\n", " <td>260.0</td>\n", " <td>14.96014</td>\n", " <td>17.215830</td>\n", " <td>0.00</td>\n", " <td>1012.7</td>\n", " <td>10.00</td>\n", " <td>2013-01-01 09:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>15</td>\n", " <td>41.00</td>\n", " <td>28.04</td>\n", " <td>59.65</td>\n", " <td>260.0</td>\n", " <td>13.80936</td>\n", " <td>15.891535</td>\n", " <td>0.00</td>\n", " <td>1012.4</td>\n", " <td>10.00</td>\n", " <td>2013-01-01 10:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>16</td>\n", " <td>41.00</td>\n", " <td>26.96</td>\n", " <td>57.06</td>\n", " <td>260.0</td>\n", " <td>14.96014</td>\n", " <td>17.215830</td>\n", " <td>0.00</td>\n", " <td>1011.4</td>\n", " <td>10.00</td>\n", " <td>2013-01-01 11:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>17</td>\n", " <td>39.20</td>\n", " <td>28.40</td>\n", " <td>64.93</td>\n", " <td>270.0</td>\n", " <td>16.11092</td>\n", " <td>18.540125</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>10.00</td>\n", " <td>2013-01-01 12:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>18</td>\n", " <td>39.20</td>\n", " <td>28.40</td>\n", " <td>64.93</td>\n", " <td>330.0</td>\n", " <td>14.96014</td>\n", " <td>17.215830</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>10.00</td>\n", " <td>2013-01-01 13:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>19</td>\n", " <td>39.02</td>\n", " <td>24.08</td>\n", " <td>54.68</td>\n", " <td>280.0</td>\n", " <td>13.80936</td>\n", " <td>15.891535</td>\n", " <td>0.00</td>\n", " <td>1010.8</td>\n", " <td>10.00</td>\n", " <td>2013-01-01 14:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>20</td>\n", " <td>37.94</td>\n", " <td>24.08</td>\n", " <td>57.04</td>\n", " <td>290.0</td>\n", " <td>9.20624</td>\n", " <td>10.594357</td>\n", " <td>0.00</td>\n", " <td>1011.9</td>\n", " <td>10.00</td>\n", " <td>2013-01-01 15:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>21</td>\n", " <td>37.04</td>\n", " <td>19.94</td>\n", " <td>49.62</td>\n", " <td>300.0</td>\n", " <td>13.80936</td>\n", " <td>15.891535</td>\n", " <td>0.00</td>\n", " <td>1012.1</td>\n", " <td>10.00</td>\n", " <td>2013-01-01 16:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>22</td>\n", " <td>35.96</td>\n", " <td>19.04</td>\n", " <td>49.83</td>\n", " <td>330.0</td>\n", " <td>11.50780</td>\n", " <td>13.242946</td>\n", " <td>0.00</td>\n", " <td>1013.2</td>\n", " <td>10.00</td>\n", " <td>2013-01-01 17:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>23</td>\n", " <td>33.98</td>\n", " <td>15.08</td>\n", " <td>45.43</td>\n", " <td>310.0</td>\n", " <td>12.65858</td>\n", " <td>14.567241</td>\n", " <td>0.00</td>\n", " <td>1014.1</td>\n", " <td>10.00</td>\n", " <td>2013-01-01 18:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>33.08</td>\n", " <td>12.92</td>\n", " <td>42.84</td>\n", " <td>320.0</td>\n", " <td>10.35702</td>\n", " <td>11.918651</td>\n", " <td>0.00</td>\n", " <td>1014.4</td>\n", " <td>10.00</td>\n", " <td>2013-01-01 19:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>32.00</td>\n", " <td>15.08</td>\n", " <td>49.19</td>\n", " <td>310.0</td>\n", " <td>14.96014</td>\n", " <td>17.215830</td>\n", " <td>0.00</td>\n", " <td>1015.2</td>\n", " <td>10.00</td>\n", " <td>2013-01-01 20:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>30.02</td>\n", " <td>12.92</td>\n", " <td>48.48</td>\n", " <td>320.0</td>\n", " <td>18.41248</td>\n", " <td>21.188714</td>\n", " <td>0.00</td>\n", " <td>1016.0</td>\n", " <td>10.00</td>\n", " <td>2013-01-01 21:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>28.94</td>\n", " <td>12.02</td>\n", " <td>48.69</td>\n", " <td>320.0</td>\n", " <td>18.41248</td>\n", " <td>21.188714</td>\n", " <td>0.00</td>\n", " <td>1016.5</td>\n", " <td>10.00</td>\n", " <td>2013-01-01 22:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>4</td>\n", " <td>28.04</td>\n", " <td>10.94</td>\n", " <td>48.15</td>\n", " <td>310.0</td>\n", " <td>16.11092</td>\n", " <td>18.540125</td>\n", " <td>0.00</td>\n", " <td>1016.4</td>\n", " <td>10.00</td>\n", " <td>2013-01-01 23:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>26.96</td>\n", " <td>10.94</td>\n", " <td>50.34</td>\n", " <td>310.0</td>\n", " <td>14.96014</td>\n", " <td>17.215830</td>\n", " <td>0.00</td>\n", " <td>1016.3</td>\n", " <td>10.00</td>\n", " <td>2013-01-02 00:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>EWR</td>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>6</td>\n", " <td>26.06</td>\n", " <td>10.94</td>\n", " <td>52.25</td>\n", " <td>330.0</td>\n", " <td>12.65858</td>\n", " <td>14.567241</td>\n", " <td>0.00</td>\n", " <td>1016.3</td>\n", " <td>10.00</td>\n", " <td>2013-01-02 01:00:00</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", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>26100</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>29</td>\n", " <td>18</td>\n", " <td>42.80</td>\n", " <td>37.40</td>\n", " <td>81.07</td>\n", " <td>70.0</td>\n", " <td>9.20624</td>\n", " <td>10.594357</td>\n", " <td>0.06</td>\n", " <td>NaN</td>\n", " <td>2.50</td>\n", " <td>2013-12-29 13:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26101</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>29</td>\n", " <td>19</td>\n", " <td>39.20</td>\n", " <td>37.40</td>\n", " <td>93.19</td>\n", " <td>40.0</td>\n", " <td>16.11092</td>\n", " <td>18.540125</td>\n", " <td>0.06</td>\n", " <td>NaN</td>\n", " <td>1.75</td>\n", " <td>2013-12-29 14:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26102</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>29</td>\n", " <td>20</td>\n", " <td>41.00</td>\n", " <td>39.02</td>\n", " <td>92.59</td>\n", " <td>40.0</td>\n", " <td>13.80936</td>\n", " <td>15.891535</td>\n", " <td>0.37</td>\n", " <td>999.9</td>\n", " <td>1.50</td>\n", " <td>2013-12-29 15:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26103</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>29</td>\n", " <td>21</td>\n", " <td>41.00</td>\n", " <td>37.94</td>\n", " <td>88.76</td>\n", " <td>350.0</td>\n", " <td>8.05546</td>\n", " <td>9.270062</td>\n", " <td>0.28</td>\n", " <td>998.7</td>\n", " <td>1.50</td>\n", " <td>2013-12-29 16:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26104</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>29</td>\n", " <td>22</td>\n", " <td>41.00</td>\n", " <td>39.20</td>\n", " <td>93.24</td>\n", " <td>50.0</td>\n", " <td>20.71404</td>\n", " <td>23.837303</td>\n", " <td>0.03</td>\n", " <td>NaN</td>\n", " <td>2.00</td>\n", " <td>2013-12-29 17:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26105</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>29</td>\n", " <td>23</td>\n", " <td>42.08</td>\n", " <td>39.02</td>\n", " <td>88.81</td>\n", " <td>330.0</td>\n", " <td>14.96014</td>\n", " <td>17.215830</td>\n", " <td>0.00</td>\n", " <td>997.2</td>\n", " <td>3.00</td>\n", " <td>2013-12-29 18:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26106</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>42.80</td>\n", " <td>37.40</td>\n", " <td>81.07</td>\n", " <td>320.0</td>\n", " <td>17.26170</td>\n", " <td>19.864419</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>8.00</td>\n", " <td>2013-12-29 19:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26107</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>30</td>\n", " <td>1</td>\n", " <td>41.00</td>\n", " <td>37.40</td>\n", " <td>86.89</td>\n", " <td>310.0</td>\n", " <td>19.56326</td>\n", " <td>22.513008</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>6.00</td>\n", " <td>2013-12-29 20:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26108</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>30</td>\n", " <td>2</td>\n", " <td>42.80</td>\n", " <td>37.40</td>\n", " <td>81.07</td>\n", " <td>320.0</td>\n", " <td>16.11092</td>\n", " <td>18.540125</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>10.00</td>\n", " <td>2013-12-29 21:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26109</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>30</td>\n", " <td>3</td>\n", " <td>42.98</td>\n", " <td>37.04</td>\n", " <td>79.38</td>\n", " <td>300.0</td>\n", " <td>9.20624</td>\n", " <td>10.594357</td>\n", " <td>0.00</td>\n", " <td>1003.8</td>\n", " <td>10.00</td>\n", " <td>2013-12-29 22:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26110</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>30</td>\n", " <td>4</td>\n", " <td>42.98</td>\n", " <td>35.06</td>\n", " <td>73.39</td>\n", " <td>310.0</td>\n", " <td>17.26170</td>\n", " <td>19.864419</td>\n", " <td>0.00</td>\n", " <td>1005.1</td>\n", " <td>10.00</td>\n", " <td>2013-12-29 23:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26111</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>30</td>\n", " <td>5</td>\n", " <td>42.08</td>\n", " <td>33.98</td>\n", " <td>72.78</td>\n", " <td>320.0</td>\n", " <td>11.50780</td>\n", " <td>13.242946</td>\n", " <td>0.00</td>\n", " <td>1005.9</td>\n", " <td>10.00</td>\n", " <td>2013-12-30 00:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26112</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>30</td>\n", " <td>6</td>\n", " <td>42.08</td>\n", " <td>33.98</td>\n", " <td>72.78</td>\n", " <td>250.0</td>\n", " <td>9.20624</td>\n", " <td>10.594357</td>\n", " <td>0.00</td>\n", " <td>1007.6</td>\n", " <td>10.00</td>\n", " <td>2013-12-30 01:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26113</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>30</td>\n", " <td>7</td>\n", " <td>41.00</td>\n", " <td>33.98</td>\n", " <td>75.88</td>\n", " <td>240.0</td>\n", " <td>8.05546</td>\n", " <td>9.270062</td>\n", " <td>0.00</td>\n", " <td>1008.3</td>\n", " <td>10.00</td>\n", " <td>2013-12-30 02:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26114</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>30</td>\n", " <td>8</td>\n", " <td>42.98</td>\n", " <td>33.98</td>\n", " <td>70.30</td>\n", " <td>270.0</td>\n", " <td>9.20624</td>\n", " <td>10.594357</td>\n", " <td>0.00</td>\n", " <td>1008.2</td>\n", " <td>10.00</td>\n", " <td>2013-12-30 03:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26115</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>30</td>\n", " <td>9</td>\n", " <td>41.00</td>\n", " <td>33.08</td>\n", " <td>73.19</td>\n", " <td>0.0</td>\n", " <td>0.00000</td>\n", " <td>0.000000</td>\n", " <td>0.00</td>\n", " <td>1008.9</td>\n", " <td>10.00</td>\n", " <td>2013-12-30 04:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26116</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>30</td>\n", " <td>10</td>\n", " <td>42.98</td>\n", " <td>33.08</td>\n", " <td>67.81</td>\n", " <td>250.0</td>\n", " <td>10.35702</td>\n", " <td>11.918651</td>\n", " <td>0.00</td>\n", " <td>1009.2</td>\n", " <td>10.00</td>\n", " <td>2013-12-30 05:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26117</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>30</td>\n", " <td>11</td>\n", " <td>42.98</td>\n", " <td>33.98</td>\n", " <td>70.30</td>\n", " <td>230.0</td>\n", " <td>6.90468</td>\n", " <td>7.945768</td>\n", " <td>0.00</td>\n", " <td>1010.8</td>\n", " <td>10.00</td>\n", " <td>2013-12-30 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26118</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>30</td>\n", " <td>12</td>\n", " <td>44.06</td>\n", " <td>35.06</td>\n", " <td>70.42</td>\n", " <td>240.0</td>\n", " <td>11.50780</td>\n", " <td>13.242946</td>\n", " <td>0.00</td>\n", " <td>1011.9</td>\n", " <td>10.00</td>\n", " <td>2013-12-30 07:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26119</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>30</td>\n", " <td>13</td>\n", " <td>44.06</td>\n", " <td>33.98</td>\n", " <td>67.45</td>\n", " <td>260.0</td>\n", " <td>11.50780</td>\n", " <td>13.242946</td>\n", " <td>0.00</td>\n", " <td>1012.9</td>\n", " <td>10.00</td>\n", " <td>2013-12-30 08:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26120</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>30</td>\n", " <td>14</td>\n", " <td>44.06</td>\n", " <td>33.08</td>\n", " <td>65.07</td>\n", " <td>260.0</td>\n", " <td>13.80936</td>\n", " <td>15.891535</td>\n", " <td>0.00</td>\n", " <td>1013.7</td>\n", " <td>10.00</td>\n", " <td>2013-12-30 09:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26121</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>30</td>\n", " <td>15</td>\n", " <td>42.80</td>\n", " <td>33.80</td>\n", " <td>70.28</td>\n", " <td>330.0</td>\n", " <td>16.11092</td>\n", " <td>18.540125</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>10.00</td>\n", " <td>2013-12-30 10:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26122</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>30</td>\n", " <td>16</td>\n", " <td>41.00</td>\n", " <td>28.40</td>\n", " <td>60.54</td>\n", " <td>340.0</td>\n", " <td>13.80936</td>\n", " <td>15.891535</td>\n", " <td>0.00</td>\n", " <td>NaN</td>\n", " <td>10.00</td>\n", " <td>2013-12-30 11:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26123</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>30</td>\n", " <td>17</td>\n", " <td>37.94</td>\n", " <td>23.00</td>\n", " <td>54.51</td>\n", " <td>330.0</td>\n", " <td>21.86482</td>\n", " <td>25.161598</td>\n", " <td>0.00</td>\n", " <td>1015.7</td>\n", " <td>10.00</td>\n", " <td>2013-12-30 12:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26124</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>30</td>\n", " <td>18</td>\n", " <td>37.04</td>\n", " <td>21.92</td>\n", " <td>53.97</td>\n", " <td>340.0</td>\n", " <td>17.26170</td>\n", " <td>19.864419</td>\n", " <td>0.00</td>\n", " <td>1016.5</td>\n", " <td>10.00</td>\n", " <td>2013-12-30 13:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26125</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>30</td>\n", " <td>19</td>\n", " <td>35.96</td>\n", " <td>19.94</td>\n", " <td>51.78</td>\n", " <td>340.0</td>\n", " <td>13.80936</td>\n", " <td>15.891535</td>\n", " <td>0.00</td>\n", " <td>1017.1</td>\n", " <td>10.00</td>\n", " <td>2013-12-30 14:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26126</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>30</td>\n", " <td>20</td>\n", " <td>33.98</td>\n", " <td>17.06</td>\n", " <td>49.51</td>\n", " <td>330.0</td>\n", " <td>17.26170</td>\n", " <td>19.864419</td>\n", " <td>0.00</td>\n", " <td>1018.8</td>\n", " <td>10.00</td>\n", " <td>2013-12-30 15:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26127</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>30</td>\n", " <td>21</td>\n", " <td>32.00</td>\n", " <td>15.08</td>\n", " <td>49.19</td>\n", " <td>340.0</td>\n", " <td>14.96014</td>\n", " <td>17.215830</td>\n", " <td>0.00</td>\n", " <td>1019.5</td>\n", " <td>10.00</td>\n", " <td>2013-12-30 16:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26128</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>30</td>\n", " <td>22</td>\n", " <td>30.92</td>\n", " <td>12.92</td>\n", " <td>46.74</td>\n", " <td>320.0</td>\n", " <td>17.26170</td>\n", " <td>19.864419</td>\n", " <td>0.00</td>\n", " <td>1019.9</td>\n", " <td>10.00</td>\n", " <td>2013-12-30 17:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26129</th>\n", " <td>LGA</td>\n", " <td>2013</td>\n", " <td>12</td>\n", " <td>30</td>\n", " <td>23</td>\n", " <td>28.94</td>\n", " <td>10.94</td>\n", " <td>46.41</td>\n", " <td>330.0</td>\n", " <td>18.41248</td>\n", " <td>21.188714</td>\n", " <td>0.00</td>\n", " <td>1020.9</td>\n", " <td>10.00</td>\n", " <td>2013-12-30 18:00:00</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>26130 rows × 15 columns</p>\n", "</div>" ], "text/plain": [ " origin year month day hour temp dewp humid wind_dir \\\n", "0 EWR 2013 1 1 0 37.04 21.92 53.97 230.0 \n", "1 EWR 2013 1 1 1 37.04 21.92 53.97 230.0 \n", "2 EWR 2013 1 1 2 37.94 21.92 52.09 230.0 \n", "3 EWR 2013 1 1 3 37.94 23.00 54.51 230.0 \n", "4 EWR 2013 1 1 4 37.94 24.08 57.04 240.0 \n", "5 EWR 2013 1 1 6 39.02 26.06 59.37 270.0 \n", "6 EWR 2013 1 1 7 39.02 26.96 61.63 250.0 \n", "7 EWR 2013 1 1 8 39.02 28.04 64.43 240.0 \n", "8 EWR 2013 1 1 9 39.92 28.04 62.21 250.0 \n", "9 EWR 2013 1 1 10 39.02 28.04 64.43 260.0 \n", "10 EWR 2013 1 1 11 37.94 28.04 67.21 240.0 \n", "11 EWR 2013 1 1 12 39.02 28.04 64.43 240.0 \n", "12 EWR 2013 1 1 13 39.92 28.04 62.21 250.0 \n", "13 EWR 2013 1 1 14 39.92 28.04 62.21 260.0 \n", "14 EWR 2013 1 1 15 41.00 28.04 59.65 260.0 \n", "15 EWR 2013 1 1 16 41.00 26.96 57.06 260.0 \n", "16 EWR 2013 1 1 17 39.20 28.40 64.93 270.0 \n", "17 EWR 2013 1 1 18 39.20 28.40 64.93 330.0 \n", "18 EWR 2013 1 1 19 39.02 24.08 54.68 280.0 \n", "19 EWR 2013 1 1 20 37.94 24.08 57.04 290.0 \n", "20 EWR 2013 1 1 21 37.04 19.94 49.62 300.0 \n", "21 EWR 2013 1 1 22 35.96 19.04 49.83 330.0 \n", "22 EWR 2013 1 1 23 33.98 15.08 45.43 310.0 \n", "23 EWR 2013 1 2 0 33.08 12.92 42.84 320.0 \n", "24 EWR 2013 1 2 1 32.00 15.08 49.19 310.0 \n", "25 EWR 2013 1 2 2 30.02 12.92 48.48 320.0 \n", "26 EWR 2013 1 2 3 28.94 12.02 48.69 320.0 \n", "27 EWR 2013 1 2 4 28.04 10.94 48.15 310.0 \n", "28 EWR 2013 1 2 5 26.96 10.94 50.34 310.0 \n", "29 EWR 2013 1 2 6 26.06 10.94 52.25 330.0 \n", "... ... ... ... ... ... ... ... ... ... \n", "26100 LGA 2013 12 29 18 42.80 37.40 81.07 70.0 \n", "26101 LGA 2013 12 29 19 39.20 37.40 93.19 40.0 \n", "26102 LGA 2013 12 29 20 41.00 39.02 92.59 40.0 \n", "26103 LGA 2013 12 29 21 41.00 37.94 88.76 350.0 \n", "26104 LGA 2013 12 29 22 41.00 39.20 93.24 50.0 \n", "26105 LGA 2013 12 29 23 42.08 39.02 88.81 330.0 \n", "26106 LGA 2013 12 30 0 42.80 37.40 81.07 320.0 \n", "26107 LGA 2013 12 30 1 41.00 37.40 86.89 310.0 \n", "26108 LGA 2013 12 30 2 42.80 37.40 81.07 320.0 \n", "26109 LGA 2013 12 30 3 42.98 37.04 79.38 300.0 \n", "26110 LGA 2013 12 30 4 42.98 35.06 73.39 310.0 \n", "26111 LGA 2013 12 30 5 42.08 33.98 72.78 320.0 \n", "26112 LGA 2013 12 30 6 42.08 33.98 72.78 250.0 \n", "26113 LGA 2013 12 30 7 41.00 33.98 75.88 240.0 \n", "26114 LGA 2013 12 30 8 42.98 33.98 70.30 270.0 \n", "26115 LGA 2013 12 30 9 41.00 33.08 73.19 0.0 \n", "26116 LGA 2013 12 30 10 42.98 33.08 67.81 250.0 \n", "26117 LGA 2013 12 30 11 42.98 33.98 70.30 230.0 \n", "26118 LGA 2013 12 30 12 44.06 35.06 70.42 240.0 \n", "26119 LGA 2013 12 30 13 44.06 33.98 67.45 260.0 \n", "26120 LGA 2013 12 30 14 44.06 33.08 65.07 260.0 \n", "26121 LGA 2013 12 30 15 42.80 33.80 70.28 330.0 \n", "26122 LGA 2013 12 30 16 41.00 28.40 60.54 340.0 \n", "26123 LGA 2013 12 30 17 37.94 23.00 54.51 330.0 \n", "26124 LGA 2013 12 30 18 37.04 21.92 53.97 340.0 \n", "26125 LGA 2013 12 30 19 35.96 19.94 51.78 340.0 \n", "26126 LGA 2013 12 30 20 33.98 17.06 49.51 330.0 \n", "26127 LGA 2013 12 30 21 32.00 15.08 49.19 340.0 \n", "26128 LGA 2013 12 30 22 30.92 12.92 46.74 320.0 \n", "26129 LGA 2013 12 30 23 28.94 10.94 46.41 330.0 \n", "\n", " wind_speed wind_gust precip pressure visib time_hour \n", "0 10.35702 11.918651 0.00 1013.9 10.00 2012-12-31 19:00:00 \n", "1 13.80936 15.891535 0.00 1013.0 10.00 2012-12-31 20:00:00 \n", "2 12.65858 14.567241 0.00 1012.6 10.00 2012-12-31 21:00:00 \n", "3 13.80936 15.891535 0.00 1012.7 10.00 2012-12-31 22:00:00 \n", "4 14.96014 17.215830 0.00 1012.8 10.00 2012-12-31 23:00:00 \n", "5 10.35702 11.918651 0.00 1012.0 10.00 2013-01-01 01:00:00 \n", "6 8.05546 9.270062 0.00 1012.3 10.00 2013-01-01 02:00:00 \n", "7 11.50780 13.242946 0.00 1012.5 10.00 2013-01-01 03:00:00 \n", "8 12.65858 14.567241 0.00 1012.2 10.00 2013-01-01 04:00:00 \n", "9 12.65858 14.567241 0.00 1011.9 10.00 2013-01-01 05:00:00 \n", "10 11.50780 13.242946 0.00 1012.4 10.00 2013-01-01 06:00:00 \n", "11 14.96014 17.215830 0.00 1012.2 10.00 2013-01-01 07:00:00 \n", "12 10.35702 11.918651 0.00 1012.2 10.00 2013-01-01 08:00:00 \n", "13 14.96014 17.215830 0.00 1012.7 10.00 2013-01-01 09:00:00 \n", "14 13.80936 15.891535 0.00 1012.4 10.00 2013-01-01 10:00:00 \n", "15 14.96014 17.215830 0.00 1011.4 10.00 2013-01-01 11:00:00 \n", "16 16.11092 18.540125 0.00 NaN 10.00 2013-01-01 12:00:00 \n", "17 14.96014 17.215830 0.00 NaN 10.00 2013-01-01 13:00:00 \n", "18 13.80936 15.891535 0.00 1010.8 10.00 2013-01-01 14:00:00 \n", "19 9.20624 10.594357 0.00 1011.9 10.00 2013-01-01 15:00:00 \n", "20 13.80936 15.891535 0.00 1012.1 10.00 2013-01-01 16:00:00 \n", "21 11.50780 13.242946 0.00 1013.2 10.00 2013-01-01 17:00:00 \n", "22 12.65858 14.567241 0.00 1014.1 10.00 2013-01-01 18:00:00 \n", "23 10.35702 11.918651 0.00 1014.4 10.00 2013-01-01 19:00:00 \n", "24 14.96014 17.215830 0.00 1015.2 10.00 2013-01-01 20:00:00 \n", "25 18.41248 21.188714 0.00 1016.0 10.00 2013-01-01 21:00:00 \n", "26 18.41248 21.188714 0.00 1016.5 10.00 2013-01-01 22:00:00 \n", "27 16.11092 18.540125 0.00 1016.4 10.00 2013-01-01 23:00:00 \n", "28 14.96014 17.215830 0.00 1016.3 10.00 2013-01-02 00:00:00 \n", "29 12.65858 14.567241 0.00 1016.3 10.00 2013-01-02 01:00:00 \n", "... ... ... ... ... ... ... \n", "26100 9.20624 10.594357 0.06 NaN 2.50 2013-12-29 13:00:00 \n", "26101 16.11092 18.540125 0.06 NaN 1.75 2013-12-29 14:00:00 \n", "26102 13.80936 15.891535 0.37 999.9 1.50 2013-12-29 15:00:00 \n", "26103 8.05546 9.270062 0.28 998.7 1.50 2013-12-29 16:00:00 \n", "26104 20.71404 23.837303 0.03 NaN 2.00 2013-12-29 17:00:00 \n", "26105 14.96014 17.215830 0.00 997.2 3.00 2013-12-29 18:00:00 \n", "26106 17.26170 19.864419 0.00 NaN 8.00 2013-12-29 19:00:00 \n", "26107 19.56326 22.513008 0.00 NaN 6.00 2013-12-29 20:00:00 \n", "26108 16.11092 18.540125 0.00 NaN 10.00 2013-12-29 21:00:00 \n", "26109 9.20624 10.594357 0.00 1003.8 10.00 2013-12-29 22:00:00 \n", "26110 17.26170 19.864419 0.00 1005.1 10.00 2013-12-29 23:00:00 \n", "26111 11.50780 13.242946 0.00 1005.9 10.00 2013-12-30 00:00:00 \n", "26112 9.20624 10.594357 0.00 1007.6 10.00 2013-12-30 01:00:00 \n", "26113 8.05546 9.270062 0.00 1008.3 10.00 2013-12-30 02:00:00 \n", "26114 9.20624 10.594357 0.00 1008.2 10.00 2013-12-30 03:00:00 \n", "26115 0.00000 0.000000 0.00 1008.9 10.00 2013-12-30 04:00:00 \n", "26116 10.35702 11.918651 0.00 1009.2 10.00 2013-12-30 05:00:00 \n", "26117 6.90468 7.945768 0.00 1010.8 10.00 2013-12-30 06:00:00 \n", "26118 11.50780 13.242946 0.00 1011.9 10.00 2013-12-30 07:00:00 \n", "26119 11.50780 13.242946 0.00 1012.9 10.00 2013-12-30 08:00:00 \n", "26120 13.80936 15.891535 0.00 1013.7 10.00 2013-12-30 09:00:00 \n", "26121 16.11092 18.540125 0.00 NaN 10.00 2013-12-30 10:00:00 \n", "26122 13.80936 15.891535 0.00 NaN 10.00 2013-12-30 11:00:00 \n", "26123 21.86482 25.161598 0.00 1015.7 10.00 2013-12-30 12:00:00 \n", "26124 17.26170 19.864419 0.00 1016.5 10.00 2013-12-30 13:00:00 \n", "26125 13.80936 15.891535 0.00 1017.1 10.00 2013-12-30 14:00:00 \n", "26126 17.26170 19.864419 0.00 1018.8 10.00 2013-12-30 15:00:00 \n", "26127 14.96014 17.215830 0.00 1019.5 10.00 2013-12-30 16:00:00 \n", "26128 17.26170 19.864419 0.00 1019.9 10.00 2013-12-30 17:00:00 \n", "26129 18.41248 21.188714 0.00 1020.9 10.00 2013-12-30 18:00:00 \n", "\n", "[26130 rows x 15 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "weather = pd.read_csv('data/nycflights13/weather.csv.gz')\n", "weather" ] }, { "cell_type": "code", "execution_count": 26, "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>year</th>\n", " <th>month</th>\n", " <th>day</th>\n", " <th>dep_time</th>\n", " <th>sched_dep_time</th>\n", " <th>dep_delay</th>\n", " <th>arr_time</th>\n", " <th>sched_arr_time</th>\n", " <th>arr_delay</th>\n", " <th>carrier</th>\n", " <th>...</th>\n", " <th>temp</th>\n", " <th>dewp</th>\n", " <th>humid</th>\n", " <th>wind_dir</th>\n", " <th>wind_speed</th>\n", " <th>wind_gust</th>\n", " <th>precip</th>\n", " <th>pressure</th>\n", " <th>visib</th>\n", " <th>time_hour_y</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>517.0</td>\n", " <td>515</td>\n", " <td>2.0</td>\n", " <td>830.0</td>\n", " <td>819</td>\n", " <td>11.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " 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<td>12.65858</td>\n", " <td>14.567241</td>\n", " <td>0.0</td>\n", " <td>1012.6</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>849.0</td>\n", " <td>851</td>\n", " <td>-2.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>39.92</td>\n", " <td>26.06</td>\n", " <td>57.33</td>\n", " <td>260.0</td>\n", " <td>13.80936</td>\n", " <td>15.891535</td>\n", " <td>0.0</td>\n", " <td>1011.9</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>853.0</td>\n", " <td>856</td>\n", " <td>-3.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>39.02</td>\n", " <td>26.06</td>\n", " <td>59.37</td>\n", " <td>270.0</td>\n", " <td>10.35702</td>\n", " <td>11.918651</td>\n", " <td>0.0</td>\n", " <td>1012.0</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>853.0</td>\n", " <td>856</td>\n", " <td>-3.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>39.02</td>\n", " <td>26.06</td>\n", " <td>59.37</td>\n", " <td>260.0</td>\n", " <td>12.65858</td>\n", " <td>14.567241</td>\n", " <td>0.0</td>\n", " <td>1012.6</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>853.0</td>\n", " <td>856</td>\n", " <td>-3.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>39.92</td>\n", " <td>26.06</td>\n", " <td>57.33</td>\n", " <td>260.0</td>\n", " <td>13.80936</td>\n", " <td>15.891535</td>\n", " <td>0.0</td>\n", " <td>1011.9</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>924.0</td>\n", " <td>917</td>\n", " <td>7.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>39.02</td>\n", " <td>26.06</td>\n", " <td>59.37</td>\n", " <td>270.0</td>\n", " <td>10.35702</td>\n", " <td>11.918651</td>\n", " <td>0.0</td>\n", " <td>1012.0</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>924.0</td>\n", " <td>917</td>\n", " <td>7.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>39.02</td>\n", " <td>26.06</td>\n", " <td>59.37</td>\n", " <td>260.0</td>\n", " <td>12.65858</td>\n", " <td>14.567241</td>\n", " <td>0.0</td>\n", " <td>1012.6</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>924.0</td>\n", " <td>917</td>\n", " <td>7.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>39.92</td>\n", " <td>26.06</td>\n", " <td>57.33</td>\n", " <td>260.0</td>\n", " <td>13.80936</td>\n", " <td>15.891535</td>\n", " <td>0.0</td>\n", " <td>1011.9</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>923.0</td>\n", " <td>937</td>\n", " <td>-14.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>39.02</td>\n", " <td>26.06</td>\n", " <td>59.37</td>\n", " <td>270.0</td>\n", " <td>10.35702</td>\n", " <td>11.918651</td>\n", " <td>0.0</td>\n", " <td>1012.0</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</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", " <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>1007561</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2240.0</td>\n", " <td>2250</td>\n", " <td>-10.0</td>\n", " <td>2347.0</td>\n", " <td>7</td>\n", " <td>-20.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>68.00</td>\n", " <td>53.06</td>\n", " <td>58.80</td>\n", " <td>150.0</td>\n", " <td>5.75390</td>\n", " <td>6.621473</td>\n", " <td>0.0</td>\n", " <td>1015.4</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007562</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2240.0</td>\n", " <td>2250</td>\n", " <td>-10.0</td>\n", " <td>2347.0</td>\n", " <td>7</td>\n", " <td>-20.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>64.04</td>\n", " <td>55.94</td>\n", " <td>74.94</td>\n", " <td>180.0</td>\n", " <td>6.90468</td>\n", " <td>7.945768</td>\n", " <td>0.0</td>\n", " <td>1016.0</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007563</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2240.0</td>\n", " <td>2250</td>\n", " <td>-10.0</td>\n", " <td>2347.0</td>\n", " <td>7</td>\n", " <td>-20.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>66.92</td>\n", " <td>51.98</td>\n", " <td>58.65</td>\n", " <td>160.0</td>\n", " <td>9.20624</td>\n", " <td>10.594357</td>\n", " <td>0.0</td>\n", " <td>1015.4</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007564</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2241.0</td>\n", " <td>2246</td>\n", " <td>-5.0</td>\n", " <td>2345.0</td>\n", " <td>1</td>\n", " <td>-16.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>68.00</td>\n", " <td>53.06</td>\n", " <td>58.80</td>\n", " <td>150.0</td>\n", " <td>5.75390</td>\n", " <td>6.621473</td>\n", " <td>0.0</td>\n", " <td>1015.4</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007565</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2241.0</td>\n", " <td>2246</td>\n", " <td>-5.0</td>\n", " <td>2345.0</td>\n", " <td>1</td>\n", " <td>-16.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>64.04</td>\n", " <td>55.94</td>\n", " <td>74.94</td>\n", " <td>180.0</td>\n", " <td>6.90468</td>\n", " <td>7.945768</td>\n", " <td>0.0</td>\n", " <td>1016.0</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007566</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2241.0</td>\n", " <td>2246</td>\n", " <td>-5.0</td>\n", " <td>2345.0</td>\n", " <td>1</td>\n", " <td>-16.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>66.92</td>\n", " <td>51.98</td>\n", " <td>58.65</td>\n", " <td>160.0</td>\n", " <td>9.20624</td>\n", " <td>10.594357</td>\n", " <td>0.0</td>\n", " <td>1015.4</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007567</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2307.0</td>\n", " <td>2255</td>\n", " <td>12.0</td>\n", " <td>2359.0</td>\n", " <td>2358</td>\n", " <td>1.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>68.00</td>\n", " <td>53.06</td>\n", " <td>58.80</td>\n", " <td>150.0</td>\n", " <td>5.75390</td>\n", " <td>6.621473</td>\n", " <td>0.0</td>\n", " <td>1015.4</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007568</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2307.0</td>\n", " <td>2255</td>\n", " <td>12.0</td>\n", " <td>2359.0</td>\n", " <td>2358</td>\n", " <td>1.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>64.04</td>\n", " <td>55.94</td>\n", " <td>74.94</td>\n", " <td>180.0</td>\n", " <td>6.90468</td>\n", " <td>7.945768</td>\n", " <td>0.0</td>\n", " <td>1016.0</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007569</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2307.0</td>\n", " <td>2255</td>\n", " <td>12.0</td>\n", " <td>2359.0</td>\n", " <td>2358</td>\n", " <td>1.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>66.92</td>\n", " <td>51.98</td>\n", " <td>58.65</td>\n", " <td>160.0</td>\n", " <td>9.20624</td>\n", " <td>10.594357</td>\n", " <td>0.0</td>\n", " <td>1015.4</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007570</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2349.0</td>\n", " <td>2359</td>\n", " <td>-10.0</td>\n", " <td>325.0</td>\n", " <td>350</td>\n", " <td>-25.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>66.02</td>\n", " <td>53.96</td>\n", " <td>65.07</td>\n", " <td>160.0</td>\n", " <td>4.60312</td>\n", " <td>5.297178</td>\n", " <td>0.0</td>\n", " <td>1015.6</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 18:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007571</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2349.0</td>\n", " <td>2359</td>\n", " <td>-10.0</td>\n", " <td>325.0</td>\n", " <td>350</td>\n", " <td>-25.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>62.06</td>\n", " <td>55.94</td>\n", " <td>80.34</td>\n", " <td>190.0</td>\n", " <td>5.75390</td>\n", " <td>6.621473</td>\n", " <td>0.0</td>\n", " <td>1016.0</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 18:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007572</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2349.0</td>\n", " <td>2359</td>\n", " <td>-10.0</td>\n", " <td>325.0</td>\n", " <td>350</td>\n", " <td>-25.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>64.94</td>\n", " <td>53.96</td>\n", " <td>67.57</td>\n", " <td>200.0</td>\n", " <td>9.20624</td>\n", " <td>10.594357</td>\n", " <td>0.0</td>\n", " <td>1015.5</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 18:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007573</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1842</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2019</td>\n", " <td>NaN</td>\n", " <td>EV</td>\n", " <td>...</td>\n", " <td>71.96</td>\n", " <td>46.94</td>\n", " <td>40.90</td>\n", " <td>90.0</td>\n", " <td>5.75390</td>\n", " <td>6.621473</td>\n", " <td>0.0</td>\n", " <td>1015.7</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 13:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007574</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1842</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2019</td>\n", " <td>NaN</td>\n", " <td>EV</td>\n", " <td>...</td>\n", " <td>68.00</td>\n", " <td>55.04</td>\n", " <td>63.21</td>\n", " <td>190.0</td>\n", " <td>11.50780</td>\n", " <td>13.242946</td>\n", " <td>0.0</td>\n", " <td>1016.6</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 13:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007575</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1842</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2019</td>\n", " <td>NaN</td>\n", " <td>EV</td>\n", " <td>...</td>\n", " <td>71.96</td>\n", " <td>46.94</td>\n", " <td>40.90</td>\n", " <td>NaN</td>\n", " <td>3.45234</td>\n", " <td>3.972884</td>\n", " <td>0.0</td>\n", " <td>1015.8</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 13:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007576</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1455</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1634</td>\n", " <td>NaN</td>\n", " <td>9E</td>\n", " <td>...</td>\n", " <td>66.92</td>\n", " <td>50.00</td>\n", " <td>54.51</td>\n", " <td>50.0</td>\n", " <td>3.45234</td>\n", " <td>3.972884</td>\n", " <td>0.0</td>\n", " <td>1018.3</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 09:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007577</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1455</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1634</td>\n", " <td>NaN</td>\n", " <td>9E</td>\n", " <td>...</td>\n", " <td>68.00</td>\n", " <td>48.92</td>\n", " <td>50.44</td>\n", " <td>20.0</td>\n", " <td>5.75390</td>\n", " <td>6.621473</td>\n", " <td>0.0</td>\n", " <td>1018.4</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 09:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007578</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1455</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1634</td>\n", " <td>NaN</td>\n", " <td>9E</td>\n", " <td>...</td>\n", " <td>64.94</td>\n", " <td>50.00</td>\n", " <td>58.39</td>\n", " <td>NaN</td>\n", " <td>3.45234</td>\n", " <td>3.972884</td>\n", " <td>0.0</td>\n", " <td>1018.2</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 09:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007579</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>2200</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2312</td>\n", " <td>NaN</td>\n", " <td>9E</td>\n", " <td>...</td>\n", " <td>68.00</td>\n", " <td>53.06</td>\n", " <td>58.80</td>\n", " <td>150.0</td>\n", " <td>5.75390</td>\n", " <td>6.621473</td>\n", " <td>0.0</td>\n", " <td>1015.4</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007580</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>2200</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2312</td>\n", " <td>NaN</td>\n", " <td>9E</td>\n", " <td>...</td>\n", " <td>64.04</td>\n", " <td>55.94</td>\n", " <td>74.94</td>\n", " <td>180.0</td>\n", " <td>6.90468</td>\n", " <td>7.945768</td>\n", " <td>0.0</td>\n", " <td>1016.0</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007581</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>2200</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2312</td>\n", " <td>NaN</td>\n", " <td>9E</td>\n", " <td>...</td>\n", " <td>66.92</td>\n", " <td>51.98</td>\n", " <td>58.65</td>\n", " <td>160.0</td>\n", " <td>9.20624</td>\n", " <td>10.594357</td>\n", " <td>0.0</td>\n", " <td>1015.4</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007582</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1210</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1330</td>\n", " <td>NaN</td>\n", " <td>MQ</td>\n", " <td>...</td>\n", " <td>59.00</td>\n", " <td>53.96</td>\n", " <td>83.34</td>\n", " <td>10.0</td>\n", " <td>6.90468</td>\n", " <td>7.945768</td>\n", " <td>0.0</td>\n", " <td>1018.8</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 07:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007583</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1210</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1330</td>\n", " <td>NaN</td>\n", " <td>MQ</td>\n", " <td>...</td>\n", " <td>62.06</td>\n", " <td>53.96</td>\n", " <td>74.75</td>\n", " <td>40.0</td>\n", " <td>5.75390</td>\n", " <td>6.621473</td>\n", " <td>0.0</td>\n", " <td>1018.8</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 07:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007584</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1210</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1330</td>\n", " <td>NaN</td>\n", " <td>MQ</td>\n", " <td>...</td>\n", " <td>60.98</td>\n", " <td>51.08</td>\n", " <td>69.86</td>\n", " <td>NaN</td>\n", " <td>5.75390</td>\n", " <td>6.621473</td>\n", " <td>0.0</td>\n", " <td>1018.6</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 07:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007585</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1159</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1344</td>\n", " <td>NaN</td>\n", " <td>MQ</td>\n", " <td>...</td>\n", " <td>55.94</td>\n", " <td>53.96</td>\n", " <td>93.05</td>\n", " <td>20.0</td>\n", " <td>5.75390</td>\n", " <td>6.621473</td>\n", " <td>0.0</td>\n", " <td>1018.7</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007586</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1159</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1344</td>\n", " <td>NaN</td>\n", " <td>MQ</td>\n", " <td>...</td>\n", " <td>57.92</td>\n", " <td>53.96</td>\n", " <td>86.63</td>\n", " <td>360.0</td>\n", " <td>6.90468</td>\n", " <td>7.945768</td>\n", " <td>0.0</td>\n", " <td>1018.9</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007587</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1159</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1344</td>\n", " <td>NaN</td>\n", " <td>MQ</td>\n", " <td>...</td>\n", " <td>60.08</td>\n", " <td>51.98</td>\n", " <td>74.56</td>\n", " <td>360.0</td>\n", " <td>4.60312</td>\n", " <td>5.297178</td>\n", " <td>0.0</td>\n", " <td>1018.5</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 06:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007588</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>840</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1020</td>\n", " <td>NaN</td>\n", " <td>MQ</td>\n", " <td>...</td>\n", " <td>53.96</td>\n", " <td>51.08</td>\n", " <td>89.95</td>\n", " <td>350.0</td>\n", " <td>4.60312</td>\n", " <td>5.297178</td>\n", " <td>0.0</td>\n", " <td>1018.1</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 03:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007589</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>840</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1020</td>\n", " <td>NaN</td>\n", " <td>MQ</td>\n", " <td>...</td>\n", " <td>53.06</td>\n", " <td>50.00</td>\n", " <td>89.31</td>\n", " <td>30.0</td>\n", " <td>3.45234</td>\n", " <td>3.972884</td>\n", " <td>0.0</td>\n", " <td>1018.1</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 03:00:00</td>\n", " </tr>\n", " <tr>\n", " <th>1007590</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>840</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1020</td>\n", " <td>NaN</td>\n", " <td>MQ</td>\n", " <td>...</td>\n", " <td>59.00</td>\n", " <td>53.06</td>\n", " <td>80.64</td>\n", " <td>10.0</td>\n", " <td>3.45234</td>\n", " <td>3.972884</td>\n", " <td>0.0</td>\n", " <td>1017.8</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 03:00:00</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>1007591 rows × 30 columns</p>\n", "</div>" ], "text/plain": [ " year month day dep_time sched_dep_time dep_delay arr_time \\\n", "0 2013 1 1 517.0 515 2.0 830.0 \n", "1 2013 1 1 533.0 529 4.0 850.0 \n", "2 2013 1 1 542.0 540 2.0 923.0 \n", "3 2013 1 1 544.0 545 -1.0 1004.0 \n", "4 2013 1 1 554.0 600 -6.0 812.0 \n", "5 2013 1 1 554.0 600 -6.0 812.0 \n", "6 2013 1 1 554.0 600 -6.0 812.0 \n", "7 2013 1 1 554.0 558 -4.0 740.0 \n", "8 2013 1 1 555.0 600 -5.0 913.0 \n", "9 2013 1 1 555.0 600 -5.0 913.0 \n", "10 2013 1 1 555.0 600 -5.0 913.0 \n", "11 2013 1 1 557.0 600 -3.0 709.0 \n", "12 2013 1 1 557.0 600 -3.0 709.0 \n", "13 2013 1 1 557.0 600 -3.0 709.0 \n", "14 2013 1 1 557.0 600 -3.0 838.0 \n", "15 2013 1 1 557.0 600 -3.0 838.0 \n", "16 2013 1 1 557.0 600 -3.0 838.0 \n", "17 2013 1 1 558.0 600 -2.0 753.0 \n", "18 2013 1 1 558.0 600 -2.0 753.0 \n", "19 2013 1 1 558.0 600 -2.0 753.0 \n", "20 2013 1 1 558.0 600 -2.0 849.0 \n", "21 2013 1 1 558.0 600 -2.0 849.0 \n", "22 2013 1 1 558.0 600 -2.0 849.0 \n", "23 2013 1 1 558.0 600 -2.0 853.0 \n", "24 2013 1 1 558.0 600 -2.0 853.0 \n", "25 2013 1 1 558.0 600 -2.0 853.0 \n", "26 2013 1 1 558.0 600 -2.0 924.0 \n", "27 2013 1 1 558.0 600 -2.0 924.0 \n", "28 2013 1 1 558.0 600 -2.0 924.0 \n", "29 2013 1 1 558.0 600 -2.0 923.0 \n", "... ... ... ... ... ... ... ... \n", "1007561 2013 9 30 2240.0 2250 -10.0 2347.0 \n", "1007562 2013 9 30 2240.0 2250 -10.0 2347.0 \n", "1007563 2013 9 30 2240.0 2250 -10.0 2347.0 \n", "1007564 2013 9 30 2241.0 2246 -5.0 2345.0 \n", "1007565 2013 9 30 2241.0 2246 -5.0 2345.0 \n", "1007566 2013 9 30 2241.0 2246 -5.0 2345.0 \n", "1007567 2013 9 30 2307.0 2255 12.0 2359.0 \n", "1007568 2013 9 30 2307.0 2255 12.0 2359.0 \n", "1007569 2013 9 30 2307.0 2255 12.0 2359.0 \n", "1007570 2013 9 30 2349.0 2359 -10.0 325.0 \n", "1007571 2013 9 30 2349.0 2359 -10.0 325.0 \n", "1007572 2013 9 30 2349.0 2359 -10.0 325.0 \n", "1007573 2013 9 30 NaN 1842 NaN NaN \n", "1007574 2013 9 30 NaN 1842 NaN NaN \n", "1007575 2013 9 30 NaN 1842 NaN NaN \n", "1007576 2013 9 30 NaN 1455 NaN NaN \n", "1007577 2013 9 30 NaN 1455 NaN NaN \n", "1007578 2013 9 30 NaN 1455 NaN NaN \n", "1007579 2013 9 30 NaN 2200 NaN NaN \n", "1007580 2013 9 30 NaN 2200 NaN NaN \n", "1007581 2013 9 30 NaN 2200 NaN NaN \n", "1007582 2013 9 30 NaN 1210 NaN NaN \n", "1007583 2013 9 30 NaN 1210 NaN NaN \n", "1007584 2013 9 30 NaN 1210 NaN NaN \n", "1007585 2013 9 30 NaN 1159 NaN NaN \n", "1007586 2013 9 30 NaN 1159 NaN NaN \n", "1007587 2013 9 30 NaN 1159 NaN NaN \n", "1007588 2013 9 30 NaN 840 NaN NaN \n", "1007589 2013 9 30 NaN 840 NaN NaN \n", "1007590 2013 9 30 NaN 840 NaN NaN \n", "\n", " sched_arr_time arr_delay carrier ... temp dewp \\\n", "0 819 11.0 UA ... NaN NaN \n", "1 830 20.0 UA ... NaN NaN \n", "2 850 33.0 AA ... NaN NaN \n", "3 1022 -18.0 B6 ... NaN NaN \n", "4 837 -25.0 DL ... 39.02 26.06 \n", "5 837 -25.0 DL ... 39.02 26.06 \n", "6 837 -25.0 DL ... 39.92 26.06 \n", "7 728 12.0 UA ... NaN NaN \n", "8 854 19.0 B6 ... 39.02 26.06 \n", "9 854 19.0 B6 ... 39.02 26.06 \n", "10 854 19.0 B6 ... 39.92 26.06 \n", "11 723 -14.0 EV ... 39.02 26.06 \n", "12 723 -14.0 EV ... 39.02 26.06 \n", "13 723 -14.0 EV ... 39.92 26.06 \n", "14 846 -8.0 B6 ... 39.02 26.06 \n", "15 846 -8.0 B6 ... 39.02 26.06 \n", "16 846 -8.0 B6 ... 39.92 26.06 \n", "17 745 8.0 AA ... 39.02 26.06 \n", "18 745 8.0 AA ... 39.02 26.06 \n", "19 745 8.0 AA ... 39.92 26.06 \n", "20 851 -2.0 B6 ... 39.02 26.06 \n", "21 851 -2.0 B6 ... 39.02 26.06 \n", "22 851 -2.0 B6 ... 39.92 26.06 \n", "23 856 -3.0 B6 ... 39.02 26.06 \n", "24 856 -3.0 B6 ... 39.02 26.06 \n", "25 856 -3.0 B6 ... 39.92 26.06 \n", "26 917 7.0 UA ... 39.02 26.06 \n", "27 917 7.0 UA ... 39.02 26.06 \n", "28 917 7.0 UA ... 39.92 26.06 \n", "29 937 -14.0 UA ... 39.02 26.06 \n", "... ... ... ... ... ... ... \n", "1007561 7 -20.0 B6 ... 68.00 53.06 \n", "1007562 7 -20.0 B6 ... 64.04 55.94 \n", "1007563 7 -20.0 B6 ... 66.92 51.98 \n", "1007564 1 -16.0 B6 ... 68.00 53.06 \n", "1007565 1 -16.0 B6 ... 64.04 55.94 \n", "1007566 1 -16.0 B6 ... 66.92 51.98 \n", "1007567 2358 1.0 B6 ... 68.00 53.06 \n", "1007568 2358 1.0 B6 ... 64.04 55.94 \n", "1007569 2358 1.0 B6 ... 66.92 51.98 \n", "1007570 350 -25.0 B6 ... 66.02 53.96 \n", "1007571 350 -25.0 B6 ... 62.06 55.94 \n", "1007572 350 -25.0 B6 ... 64.94 53.96 \n", "1007573 2019 NaN EV ... 71.96 46.94 \n", "1007574 2019 NaN EV ... 68.00 55.04 \n", "1007575 2019 NaN EV ... 71.96 46.94 \n", "1007576 1634 NaN 9E ... 66.92 50.00 \n", "1007577 1634 NaN 9E ... 68.00 48.92 \n", "1007578 1634 NaN 9E ... 64.94 50.00 \n", "1007579 2312 NaN 9E ... 68.00 53.06 \n", "1007580 2312 NaN 9E ... 64.04 55.94 \n", "1007581 2312 NaN 9E ... 66.92 51.98 \n", "1007582 1330 NaN MQ ... 59.00 53.96 \n", "1007583 1330 NaN MQ ... 62.06 53.96 \n", "1007584 1330 NaN MQ ... 60.98 51.08 \n", "1007585 1344 NaN MQ ... 55.94 53.96 \n", "1007586 1344 NaN MQ ... 57.92 53.96 \n", "1007587 1344 NaN MQ ... 60.08 51.98 \n", "1007588 1020 NaN MQ ... 53.96 51.08 \n", "1007589 1020 NaN MQ ... 53.06 50.00 \n", "1007590 1020 NaN MQ ... 59.00 53.06 \n", "\n", " humid wind_dir wind_speed wind_gust precip pressure visib \\\n", "0 NaN NaN NaN NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN NaN NaN NaN \n", "3 NaN NaN NaN NaN NaN NaN NaN \n", "4 59.37 270.0 10.35702 11.918651 0.0 1012.0 10.0 \n", "5 59.37 260.0 12.65858 14.567241 0.0 1012.6 10.0 \n", "6 57.33 260.0 13.80936 15.891535 0.0 1011.9 10.0 \n", "7 NaN NaN NaN NaN NaN NaN NaN \n", "8 59.37 270.0 10.35702 11.918651 0.0 1012.0 10.0 \n", "9 59.37 260.0 12.65858 14.567241 0.0 1012.6 10.0 \n", "10 57.33 260.0 13.80936 15.891535 0.0 1011.9 10.0 \n", "11 59.37 270.0 10.35702 11.918651 0.0 1012.0 10.0 \n", "12 59.37 260.0 12.65858 14.567241 0.0 1012.6 10.0 \n", "13 57.33 260.0 13.80936 15.891535 0.0 1011.9 10.0 \n", "14 59.37 270.0 10.35702 11.918651 0.0 1012.0 10.0 \n", "15 59.37 260.0 12.65858 14.567241 0.0 1012.6 10.0 \n", "16 57.33 260.0 13.80936 15.891535 0.0 1011.9 10.0 \n", "17 59.37 270.0 10.35702 11.918651 0.0 1012.0 10.0 \n", "18 59.37 260.0 12.65858 14.567241 0.0 1012.6 10.0 \n", "19 57.33 260.0 13.80936 15.891535 0.0 1011.9 10.0 \n", "20 59.37 270.0 10.35702 11.918651 0.0 1012.0 10.0 \n", "21 59.37 260.0 12.65858 14.567241 0.0 1012.6 10.0 \n", "22 57.33 260.0 13.80936 15.891535 0.0 1011.9 10.0 \n", "23 59.37 270.0 10.35702 11.918651 0.0 1012.0 10.0 \n", "24 59.37 260.0 12.65858 14.567241 0.0 1012.6 10.0 \n", "25 57.33 260.0 13.80936 15.891535 0.0 1011.9 10.0 \n", "26 59.37 270.0 10.35702 11.918651 0.0 1012.0 10.0 \n", "27 59.37 260.0 12.65858 14.567241 0.0 1012.6 10.0 \n", "28 57.33 260.0 13.80936 15.891535 0.0 1011.9 10.0 \n", "29 59.37 270.0 10.35702 11.918651 0.0 1012.0 10.0 \n", "... ... ... ... ... ... ... ... \n", "1007561 58.80 150.0 5.75390 6.621473 0.0 1015.4 10.0 \n", "1007562 74.94 180.0 6.90468 7.945768 0.0 1016.0 10.0 \n", "1007563 58.65 160.0 9.20624 10.594357 0.0 1015.4 10.0 \n", "1007564 58.80 150.0 5.75390 6.621473 0.0 1015.4 10.0 \n", "1007565 74.94 180.0 6.90468 7.945768 0.0 1016.0 10.0 \n", "1007566 58.65 160.0 9.20624 10.594357 0.0 1015.4 10.0 \n", "1007567 58.80 150.0 5.75390 6.621473 0.0 1015.4 10.0 \n", "1007568 74.94 180.0 6.90468 7.945768 0.0 1016.0 10.0 \n", "1007569 58.65 160.0 9.20624 10.594357 0.0 1015.4 10.0 \n", "1007570 65.07 160.0 4.60312 5.297178 0.0 1015.6 10.0 \n", "1007571 80.34 190.0 5.75390 6.621473 0.0 1016.0 10.0 \n", "1007572 67.57 200.0 9.20624 10.594357 0.0 1015.5 10.0 \n", "1007573 40.90 90.0 5.75390 6.621473 0.0 1015.7 10.0 \n", "1007574 63.21 190.0 11.50780 13.242946 0.0 1016.6 10.0 \n", "1007575 40.90 NaN 3.45234 3.972884 0.0 1015.8 10.0 \n", "1007576 54.51 50.0 3.45234 3.972884 0.0 1018.3 10.0 \n", "1007577 50.44 20.0 5.75390 6.621473 0.0 1018.4 10.0 \n", "1007578 58.39 NaN 3.45234 3.972884 0.0 1018.2 10.0 \n", "1007579 58.80 150.0 5.75390 6.621473 0.0 1015.4 10.0 \n", "1007580 74.94 180.0 6.90468 7.945768 0.0 1016.0 10.0 \n", "1007581 58.65 160.0 9.20624 10.594357 0.0 1015.4 10.0 \n", "1007582 83.34 10.0 6.90468 7.945768 0.0 1018.8 10.0 \n", "1007583 74.75 40.0 5.75390 6.621473 0.0 1018.8 10.0 \n", "1007584 69.86 NaN 5.75390 6.621473 0.0 1018.6 10.0 \n", "1007585 93.05 20.0 5.75390 6.621473 0.0 1018.7 10.0 \n", "1007586 86.63 360.0 6.90468 7.945768 0.0 1018.9 10.0 \n", "1007587 74.56 360.0 4.60312 5.297178 0.0 1018.5 10.0 \n", "1007588 89.95 350.0 4.60312 5.297178 0.0 1018.1 10.0 \n", "1007589 89.31 30.0 3.45234 3.972884 0.0 1018.1 10.0 \n", "1007590 80.64 10.0 3.45234 3.972884 0.0 1017.8 10.0 \n", "\n", " time_hour_y \n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 2013-01-01 01:00:00 \n", "5 2013-01-01 01:00:00 \n", "6 2013-01-01 01:00:00 \n", "7 NaN \n", "8 2013-01-01 01:00:00 \n", "9 2013-01-01 01:00:00 \n", "10 2013-01-01 01:00:00 \n", "11 2013-01-01 01:00:00 \n", "12 2013-01-01 01:00:00 \n", "13 2013-01-01 01:00:00 \n", "14 2013-01-01 01:00:00 \n", "15 2013-01-01 01:00:00 \n", "16 2013-01-01 01:00:00 \n", "17 2013-01-01 01:00:00 \n", "18 2013-01-01 01:00:00 \n", "19 2013-01-01 01:00:00 \n", "20 2013-01-01 01:00:00 \n", "21 2013-01-01 01:00:00 \n", "22 2013-01-01 01:00:00 \n", "23 2013-01-01 01:00:00 \n", "24 2013-01-01 01:00:00 \n", "25 2013-01-01 01:00:00 \n", "26 2013-01-01 01:00:00 \n", "27 2013-01-01 01:00:00 \n", "28 2013-01-01 01:00:00 \n", "29 2013-01-01 01:00:00 \n", "... ... \n", "1007561 2013-09-30 17:00:00 \n", "1007562 2013-09-30 17:00:00 \n", "1007563 2013-09-30 17:00:00 \n", "1007564 2013-09-30 17:00:00 \n", "1007565 2013-09-30 17:00:00 \n", "1007566 2013-09-30 17:00:00 \n", "1007567 2013-09-30 17:00:00 \n", "1007568 2013-09-30 17:00:00 \n", "1007569 2013-09-30 17:00:00 \n", "1007570 2013-09-30 18:00:00 \n", "1007571 2013-09-30 18:00:00 \n", "1007572 2013-09-30 18:00:00 \n", "1007573 2013-09-30 13:00:00 \n", "1007574 2013-09-30 13:00:00 \n", "1007575 2013-09-30 13:00:00 \n", "1007576 2013-09-30 09:00:00 \n", "1007577 2013-09-30 09:00:00 \n", "1007578 2013-09-30 09:00:00 \n", "1007579 2013-09-30 17:00:00 \n", "1007580 2013-09-30 17:00:00 \n", "1007581 2013-09-30 17:00:00 \n", "1007582 2013-09-30 07:00:00 \n", "1007583 2013-09-30 07:00:00 \n", "1007584 2013-09-30 07:00:00 \n", "1007585 2013-09-30 06:00:00 \n", "1007586 2013-09-30 06:00:00 \n", "1007587 2013-09-30 06:00:00 \n", "1007588 2013-09-30 03:00:00 \n", "1007589 2013-09-30 03:00:00 \n", "1007590 2013-09-30 03:00:00 \n", "\n", "[1007591 rows x 30 columns]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_withweather = pd.merge(df, weather, how='left', on=['year', 'month', 'day', 'hour'])\n", "df_withweather" ] }, { "cell_type": "code", "execution_count": 27, "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>faa</th>\n", " <th>name</th>\n", " <th>lat</th>\n", " <th>lon</th>\n", " <th>alt</th>\n", " <th>tz</th>\n", " <th>dst</th>\n", " <th>tzone</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>04G</td>\n", " <td>Lansdowne Airport</td>\n", " <td>41.130472</td>\n", " <td>-80.619583</td>\n", " <td>1044</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>06A</td>\n", " <td>Moton Field Municipal Airport</td>\n", " <td>32.460572</td>\n", " <td>-85.680028</td>\n", " <td>264</td>\n", " <td>-6</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>06C</td>\n", " <td>Schaumburg Regional</td>\n", " <td>41.989341</td>\n", " <td>-88.101243</td>\n", " <td>801</td>\n", " <td>-6</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>06N</td>\n", " <td>Randall Airport</td>\n", " <td>41.431912</td>\n", " <td>-74.391561</td>\n", " <td>523</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>09J</td>\n", " <td>Jekyll Island Airport</td>\n", " <td>31.074472</td>\n", " <td>-81.427778</td>\n", " <td>11</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>0A9</td>\n", " <td>Elizabethton Municipal Airport</td>\n", " <td>36.371222</td>\n", " <td>-82.173417</td>\n", " <td>1593</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>0G6</td>\n", " <td>Williams County Airport</td>\n", " <td>41.467306</td>\n", " <td>-84.506778</td>\n", " <td>730</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>0G7</td>\n", " <td>Finger Lakes Regional Airport</td>\n", " <td>42.883565</td>\n", " <td>-76.781232</td>\n", " <td>492</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>0P2</td>\n", " <td>Shoestring Aviation Airfield</td>\n", " <td>39.794824</td>\n", " <td>-76.647191</td>\n", " <td>1000</td>\n", " <td>-5</td>\n", " <td>U</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>0S9</td>\n", " <td>Jefferson County Intl</td>\n", " <td>48.053809</td>\n", " <td>-122.810644</td>\n", " <td>108</td>\n", " <td>-8</td>\n", " <td>A</td>\n", " <td>America/Los_Angeles</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>0W3</td>\n", " <td>Harford County Airport</td>\n", " <td>39.566838</td>\n", " <td>-76.202403</td>\n", " <td>409</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>10C</td>\n", " <td>Galt Field Airport</td>\n", " <td>42.402889</td>\n", " <td>-88.375111</td>\n", " <td>875</td>\n", " <td>-6</td>\n", " <td>U</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>17G</td>\n", " <td>Port Bucyrus-Crawford County Airport</td>\n", " <td>40.781556</td>\n", " <td>-82.974806</td>\n", " <td>1003</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>19A</td>\n", " <td>Jackson County Airport</td>\n", " <td>34.175864</td>\n", " <td>-83.561597</td>\n", " <td>951</td>\n", " <td>-5</td>\n", " <td>U</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>1A3</td>\n", " <td>Martin Campbell Field Airport</td>\n", " <td>35.015806</td>\n", " <td>-84.346833</td>\n", " <td>1789</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>1B9</td>\n", " <td>Mansfield Municipal</td>\n", " <td>42.000133</td>\n", " <td>-71.196771</td>\n", " <td>122</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>1C9</td>\n", " <td>Frazier Lake Airpark</td>\n", " <td>54.013333</td>\n", " <td>-124.768333</td>\n", " <td>152</td>\n", " <td>-8</td>\n", " <td>A</td>\n", " <td>America/Vancouver</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>1CS</td>\n", " <td>Clow International Airport</td>\n", " <td>41.695974</td>\n", " <td>-88.129231</td>\n", " <td>670</td>\n", " <td>-6</td>\n", " <td>U</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>1G3</td>\n", " <td>Kent State Airport</td>\n", " <td>41.151389</td>\n", " <td>-81.415111</td>\n", " <td>1134</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>1G4</td>\n", " <td>Grand Canyon West Airport</td>\n", " <td>35.899904</td>\n", " <td>-113.815674</td>\n", " <td>4813</td>\n", " <td>-7</td>\n", " <td>A</td>\n", " <td>America/Phoenix</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>1H2</td>\n", " <td>Effingham Memorial Airport</td>\n", " <td>39.070000</td>\n", " <td>-88.534000</td>\n", " <td>585</td>\n", " <td>-6</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>1OH</td>\n", " <td>Fortman Airport</td>\n", " <td>40.555325</td>\n", " <td>-84.386619</td>\n", " <td>885</td>\n", " <td>-5</td>\n", " <td>U</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>1RL</td>\n", " <td>Point Roberts Airpark</td>\n", " <td>48.979722</td>\n", " <td>-123.078889</td>\n", " <td>10</td>\n", " <td>-8</td>\n", " <td>A</td>\n", " <td>America/Los_Angeles</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>23M</td>\n", " <td>Clarke CO</td>\n", " <td>32.051700</td>\n", " <td>-88.443400</td>\n", " <td>320</td>\n", " <td>-6</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>24C</td>\n", " <td>Lowell City Airport</td>\n", " <td>42.953920</td>\n", " <td>-85.343906</td>\n", " <td>681</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>24J</td>\n", " <td>Suwannee County Airport</td>\n", " <td>30.300125</td>\n", " <td>-83.024694</td>\n", " <td>104</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>25D</td>\n", " <td>Forest Lake Airport</td>\n", " <td>45.247746</td>\n", " <td>-92.994385</td>\n", " <td>925</td>\n", " <td>-6</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>29D</td>\n", " <td>Grove City Airport</td>\n", " <td>41.146028</td>\n", " <td>-80.167750</td>\n", " <td>1371</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>2A0</td>\n", " <td>Mark Anton Airport</td>\n", " <td>35.486250</td>\n", " <td>-84.931083</td>\n", " <td>718</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>2B2</td>\n", " <td>Plum Island Airport</td>\n", " <td>42.795361</td>\n", " <td>-70.839444</td>\n", " <td>11</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</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", " </tr>\n", " <tr>\n", " <th>1428</th>\n", " <td>X59</td>\n", " <td>Valkaria Municipal</td>\n", " <td>27.960861</td>\n", " <td>-80.558333</td>\n", " <td>26</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1429</th>\n", " <td>XFL</td>\n", " <td>Flagler County Airport</td>\n", " <td>29.282100</td>\n", " <td>-81.121200</td>\n", " <td>33</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1430</th>\n", " <td>XNA</td>\n", " <td>NW Arkansas Regional</td>\n", " <td>36.281869</td>\n", " <td>-94.306811</td>\n", " <td>1287</td>\n", " <td>-6</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>1431</th>\n", " <td>XZK</td>\n", " <td>Amherst Amtrak Station AMM</td>\n", " <td>42.375000</td>\n", " <td>-72.511389</td>\n", " <td>258</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1432</th>\n", " <td>Y51</td>\n", " <td>Municipal Airport</td>\n", " <td>43.579360</td>\n", " <td>-90.896474</td>\n", " <td>1292</td>\n", " <td>-6</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>1433</th>\n", " <td>Y72</td>\n", " <td>Bloyer Field</td>\n", " <td>43.976222</td>\n", " <td>-90.480611</td>\n", " <td>966</td>\n", " <td>-6</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>1434</th>\n", " <td>YAK</td>\n", " <td>Yakutat</td>\n", " <td>59.301200</td>\n", " <td>-139.393700</td>\n", " <td>33</td>\n", " <td>-9</td>\n", " <td>A</td>\n", " <td>\\N</td>\n", " </tr>\n", " <tr>\n", " <th>1435</th>\n", " <td>YIP</td>\n", " <td>Willow Run</td>\n", " <td>42.237928</td>\n", " <td>-83.530408</td>\n", " <td>716</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1436</th>\n", " <td>YKM</td>\n", " <td>Yakima Air Terminal McAllister Field</td>\n", " <td>46.568200</td>\n", " <td>-120.544000</td>\n", " <td>1095</td>\n", " <td>-8</td>\n", " <td>A</td>\n", " <td>America/Los_Angeles</td>\n", " </tr>\n", " <tr>\n", " <th>1437</th>\n", " <td>YKN</td>\n", " <td>Chan Gurney</td>\n", " <td>42.871100</td>\n", " <td>-97.396900</td>\n", " <td>1200</td>\n", " <td>-6</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>1438</th>\n", " <td>YNG</td>\n", " <td>Youngstown Warren Rgnl</td>\n", " <td>41.260736</td>\n", " <td>-80.679097</td>\n", " <td>1196</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1439</th>\n", " <td>YUM</td>\n", " <td>Yuma Mcas Yuma Intl</td>\n", " <td>32.656578</td>\n", " <td>-114.605980</td>\n", " <td>216</td>\n", " <td>-7</td>\n", " <td>N</td>\n", " <td>America/Phoenix</td>\n", " </tr>\n", " <tr>\n", " <th>1440</th>\n", " <td>Z84</td>\n", " <td>Clear</td>\n", " <td>64.301203</td>\n", " <td>-149.120144</td>\n", " <td>552</td>\n", " <td>-9</td>\n", " <td>A</td>\n", " <td>America/Anchorage</td>\n", " </tr>\n", " <tr>\n", " <th>1441</th>\n", " <td>ZBP</td>\n", " <td>Penn Station</td>\n", " <td>39.307222</td>\n", " <td>-76.615556</td>\n", " <td>66</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1442</th>\n", " <td>ZFV</td>\n", " <td>Philadelphia 30th St Station</td>\n", " <td>39.955700</td>\n", " <td>-75.182000</td>\n", " <td>0</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1443</th>\n", " <td>ZPH</td>\n", " <td>Municipal Airport</td>\n", " <td>28.228056</td>\n", " <td>-82.155833</td>\n", " <td>90</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1444</th>\n", " <td>ZRA</td>\n", " <td>Atlantic City Rail Terminal</td>\n", " <td>39.366500</td>\n", " <td>-74.442000</td>\n", " <td>8</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1445</th>\n", " <td>ZRD</td>\n", " <td>Train Station</td>\n", " <td>37.534300</td>\n", " <td>-77.429450</td>\n", " <td>26</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1446</th>\n", " <td>ZRP</td>\n", " <td>Newark Penn Station</td>\n", " <td>40.734722</td>\n", " <td>-74.164167</td>\n", " <td>0</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1447</th>\n", " <td>ZRT</td>\n", " <td>Hartford Union Station</td>\n", " <td>41.768880</td>\n", " <td>-72.681500</td>\n", " <td>0</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1448</th>\n", " <td>ZRZ</td>\n", " <td>New Carrollton Rail Station</td>\n", " <td>38.948000</td>\n", " <td>-76.871900</td>\n", " <td>39</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1449</th>\n", " <td>ZSF</td>\n", " <td>Springfield Amtrak Station</td>\n", " <td>42.106000</td>\n", " <td>-72.593054</td>\n", " <td>65</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1450</th>\n", " <td>ZSY</td>\n", " <td>Scottsdale Airport</td>\n", " <td>33.622889</td>\n", " <td>-111.910528</td>\n", " <td>1519</td>\n", " <td>-7</td>\n", " <td>A</td>\n", " <td>America/Phoenix</td>\n", " </tr>\n", " <tr>\n", " <th>1451</th>\n", " <td>ZTF</td>\n", " <td>Stamford Amtrak Station</td>\n", " <td>41.046937</td>\n", " <td>-73.541493</td>\n", " <td>0</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1452</th>\n", " <td>ZTY</td>\n", " <td>Boston Back Bay Station</td>\n", " <td>42.347800</td>\n", " <td>-71.075000</td>\n", " <td>20</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1453</th>\n", " <td>ZUN</td>\n", " <td>Black Rock</td>\n", " <td>35.083228</td>\n", " <td>-108.791778</td>\n", " <td>6454</td>\n", " <td>-7</td>\n", " <td>A</td>\n", " <td>America/Denver</td>\n", " </tr>\n", " <tr>\n", " <th>1454</th>\n", " <td>ZVE</td>\n", " <td>New Haven Rail Station</td>\n", " <td>41.298669</td>\n", " <td>-72.925992</td>\n", " <td>7</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1455</th>\n", " <td>ZWI</td>\n", " <td>Wilmington Amtrak Station</td>\n", " <td>39.736667</td>\n", " <td>-75.551667</td>\n", " <td>0</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1456</th>\n", " <td>ZWU</td>\n", " <td>Washington Union Station</td>\n", " <td>38.897460</td>\n", " <td>-77.006430</td>\n", " <td>76</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1457</th>\n", " <td>ZYP</td>\n", " <td>Penn Station</td>\n", " <td>40.750500</td>\n", " <td>-73.993500</td>\n", " <td>35</td>\n", " <td>-5</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>1458 rows × 8 columns</p>\n", "</div>" ], "text/plain": [ " faa name lat lon alt \\\n", "0 04G Lansdowne Airport 41.130472 -80.619583 1044 \n", "1 06A Moton Field Municipal Airport 32.460572 -85.680028 264 \n", "2 06C Schaumburg Regional 41.989341 -88.101243 801 \n", "3 06N Randall Airport 41.431912 -74.391561 523 \n", "4 09J Jekyll Island Airport 31.074472 -81.427778 11 \n", "5 0A9 Elizabethton Municipal Airport 36.371222 -82.173417 1593 \n", "6 0G6 Williams County Airport 41.467306 -84.506778 730 \n", "7 0G7 Finger Lakes Regional Airport 42.883565 -76.781232 492 \n", "8 0P2 Shoestring Aviation Airfield 39.794824 -76.647191 1000 \n", "9 0S9 Jefferson County Intl 48.053809 -122.810644 108 \n", "10 0W3 Harford County Airport 39.566838 -76.202403 409 \n", "11 10C Galt Field Airport 42.402889 -88.375111 875 \n", "12 17G Port Bucyrus-Crawford County Airport 40.781556 -82.974806 1003 \n", "13 19A Jackson County Airport 34.175864 -83.561597 951 \n", "14 1A3 Martin Campbell Field Airport 35.015806 -84.346833 1789 \n", "15 1B9 Mansfield Municipal 42.000133 -71.196771 122 \n", "16 1C9 Frazier Lake Airpark 54.013333 -124.768333 152 \n", "17 1CS Clow International Airport 41.695974 -88.129231 670 \n", "18 1G3 Kent State Airport 41.151389 -81.415111 1134 \n", "19 1G4 Grand Canyon West Airport 35.899904 -113.815674 4813 \n", "20 1H2 Effingham Memorial Airport 39.070000 -88.534000 585 \n", "21 1OH Fortman Airport 40.555325 -84.386619 885 \n", "22 1RL Point Roberts Airpark 48.979722 -123.078889 10 \n", "23 23M Clarke CO 32.051700 -88.443400 320 \n", "24 24C Lowell City Airport 42.953920 -85.343906 681 \n", "25 24J Suwannee County Airport 30.300125 -83.024694 104 \n", "26 25D Forest Lake Airport 45.247746 -92.994385 925 \n", "27 29D Grove City Airport 41.146028 -80.167750 1371 \n", "28 2A0 Mark Anton Airport 35.486250 -84.931083 718 \n", "29 2B2 Plum Island Airport 42.795361 -70.839444 11 \n", "... ... ... ... ... ... \n", "1428 X59 Valkaria Municipal 27.960861 -80.558333 26 \n", "1429 XFL Flagler County Airport 29.282100 -81.121200 33 \n", "1430 XNA NW Arkansas Regional 36.281869 -94.306811 1287 \n", "1431 XZK Amherst Amtrak Station AMM 42.375000 -72.511389 258 \n", "1432 Y51 Municipal Airport 43.579360 -90.896474 1292 \n", "1433 Y72 Bloyer Field 43.976222 -90.480611 966 \n", "1434 YAK Yakutat 59.301200 -139.393700 33 \n", "1435 YIP Willow Run 42.237928 -83.530408 716 \n", "1436 YKM Yakima Air Terminal McAllister Field 46.568200 -120.544000 1095 \n", "1437 YKN Chan Gurney 42.871100 -97.396900 1200 \n", "1438 YNG Youngstown Warren Rgnl 41.260736 -80.679097 1196 \n", "1439 YUM Yuma Mcas Yuma Intl 32.656578 -114.605980 216 \n", "1440 Z84 Clear 64.301203 -149.120144 552 \n", "1441 ZBP Penn Station 39.307222 -76.615556 66 \n", "1442 ZFV Philadelphia 30th St Station 39.955700 -75.182000 0 \n", "1443 ZPH Municipal Airport 28.228056 -82.155833 90 \n", "1444 ZRA Atlantic City Rail Terminal 39.366500 -74.442000 8 \n", "1445 ZRD Train Station 37.534300 -77.429450 26 \n", "1446 ZRP Newark Penn Station 40.734722 -74.164167 0 \n", "1447 ZRT Hartford Union Station 41.768880 -72.681500 0 \n", "1448 ZRZ New Carrollton Rail Station 38.948000 -76.871900 39 \n", "1449 ZSF Springfield Amtrak Station 42.106000 -72.593054 65 \n", "1450 ZSY Scottsdale Airport 33.622889 -111.910528 1519 \n", "1451 ZTF Stamford Amtrak Station 41.046937 -73.541493 0 \n", "1452 ZTY Boston Back Bay Station 42.347800 -71.075000 20 \n", "1453 ZUN Black Rock 35.083228 -108.791778 6454 \n", "1454 ZVE New Haven Rail Station 41.298669 -72.925992 7 \n", "1455 ZWI Wilmington Amtrak Station 39.736667 -75.551667 0 \n", "1456 ZWU Washington Union Station 38.897460 -77.006430 76 \n", "1457 ZYP Penn Station 40.750500 -73.993500 35 \n", "\n", " tz dst tzone \n", "0 -5 A America/New_York \n", "1 -6 A America/Chicago \n", "2 -6 A America/Chicago \n", "3 -5 A America/New_York \n", "4 -5 A America/New_York \n", "5 -5 A America/New_York \n", "6 -5 A America/New_York \n", "7 -5 A America/New_York \n", "8 -5 U America/New_York \n", "9 -8 A America/Los_Angeles \n", "10 -5 A America/New_York \n", "11 -6 U America/Chicago \n", "12 -5 A America/New_York \n", "13 -5 U America/New_York \n", "14 -5 A America/New_York \n", "15 -5 A America/New_York \n", "16 -8 A America/Vancouver \n", "17 -6 U America/Chicago \n", "18 -5 A America/New_York \n", "19 -7 A America/Phoenix \n", "20 -6 A America/Chicago \n", "21 -5 U America/New_York \n", "22 -8 A America/Los_Angeles \n", "23 -6 A America/Chicago \n", "24 -5 A America/New_York \n", "25 -5 A America/New_York \n", "26 -6 A America/Chicago \n", "27 -5 A America/New_York \n", "28 -5 A America/New_York \n", "29 -5 A America/New_York \n", "... .. .. ... \n", "1428 -5 A America/New_York \n", "1429 -5 A America/New_York \n", "1430 -6 A America/Chicago \n", "1431 -5 A America/New_York \n", "1432 -6 A America/Chicago \n", "1433 -6 A America/Chicago \n", "1434 -9 A \\N \n", "1435 -5 A America/New_York \n", "1436 -8 A America/Los_Angeles \n", "1437 -6 A America/Chicago \n", "1438 -5 A America/New_York \n", "1439 -7 N America/Phoenix \n", "1440 -9 A America/Anchorage \n", "1441 -5 A America/New_York \n", "1442 -5 A America/New_York \n", "1443 -5 A America/New_York \n", "1444 -5 A America/New_York \n", "1445 -5 A America/New_York \n", "1446 -5 A America/New_York \n", "1447 -5 A America/New_York \n", "1448 -5 A America/New_York \n", "1449 -5 A America/New_York \n", "1450 -7 A America/Phoenix \n", "1451 -5 A America/New_York \n", "1452 -5 A America/New_York \n", "1453 -7 A America/Denver \n", "1454 -5 A America/New_York \n", "1455 -5 A America/New_York \n", "1456 -5 A America/New_York \n", "1457 -5 A America/New_York \n", "\n", "[1458 rows x 8 columns]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "airports = pd.read_csv('data/nycflights13/airports.csv.gz')\n", "airports" ] }, { "cell_type": "code", "execution_count": 28, "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>year</th>\n", " <th>month</th>\n", " <th>day</th>\n", " <th>dep_time</th>\n", " <th>sched_dep_time</th>\n", " <th>dep_delay</th>\n", " <th>arr_time</th>\n", " <th>sched_arr_time</th>\n", " <th>arr_delay</th>\n", " <th>carrier</th>\n", " <th>...</th>\n", " <th>visib</th>\n", " <th>time_hour_y</th>\n", " <th>faa</th>\n", " <th>name</th>\n", " <th>lat</th>\n", " <th>lon</th>\n", " <th>alt</th>\n", " <th>tz</th>\n", " <th>dst</th>\n", " <th>tzone</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>517.0</td>\n", " <td>515</td>\n", " <td>2.0</td>\n", " <td>830.0</td>\n", " <td>819</td>\n", " <td>11.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>IAH</td>\n", " <td>George Bush Intercontinental</td>\n", " <td>29.984433</td>\n", " <td>-95.341442</td>\n", " <td>97.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>533.0</td>\n", " <td>529</td>\n", " <td>4.0</td>\n", " <td>850.0</td>\n", " <td>830</td>\n", " <td>20.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>IAH</td>\n", " <td>George Bush Intercontinental</td>\n", " <td>29.984433</td>\n", " <td>-95.341442</td>\n", " <td>97.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>542.0</td>\n", " <td>540</td>\n", " <td>2.0</td>\n", " <td>923.0</td>\n", " <td>850</td>\n", " <td>33.0</td>\n", " <td>AA</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>MIA</td>\n", " <td>Miami Intl</td>\n", " <td>25.793250</td>\n", " <td>-80.290556</td>\n", " <td>8.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>544.0</td>\n", " <td>545</td>\n", " <td>-1.0</td>\n", " <td>1004.0</td>\n", " <td>1022</td>\n", " <td>-18.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>554.0</td>\n", " <td>600</td>\n", " <td>-6.0</td>\n", " <td>812.0</td>\n", " <td>837</td>\n", " <td>-25.0</td>\n", " <td>DL</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>ATL</td>\n", " <td>Hartsfield Jackson Atlanta Intl</td>\n", " <td>33.636719</td>\n", " <td>-84.428067</td>\n", " <td>1026.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>554.0</td>\n", " <td>600</td>\n", " <td>-6.0</td>\n", " <td>812.0</td>\n", " <td>837</td>\n", " <td>-25.0</td>\n", " <td>DL</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>ATL</td>\n", " <td>Hartsfield Jackson Atlanta Intl</td>\n", " <td>33.636719</td>\n", " <td>-84.428067</td>\n", " <td>1026.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>554.0</td>\n", " <td>600</td>\n", " <td>-6.0</td>\n", " <td>812.0</td>\n", " <td>837</td>\n", " <td>-25.0</td>\n", " <td>DL</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>ATL</td>\n", " <td>Hartsfield Jackson Atlanta Intl</td>\n", " <td>33.636719</td>\n", " <td>-84.428067</td>\n", " <td>1026.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>554.0</td>\n", " <td>558</td>\n", " <td>-4.0</td>\n", " <td>740.0</td>\n", " <td>728</td>\n", " <td>12.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>ORD</td>\n", " <td>Chicago Ohare Intl</td>\n", " <td>41.978603</td>\n", " <td>-87.904842</td>\n", " <td>668.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>555.0</td>\n", " <td>600</td>\n", " <td>-5.0</td>\n", " <td>913.0</td>\n", " <td>854</td>\n", " <td>19.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>FLL</td>\n", " <td>Fort Lauderdale Hollywood Intl</td>\n", " <td>26.072583</td>\n", " <td>-80.152750</td>\n", " <td>9.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>555.0</td>\n", " <td>600</td>\n", " <td>-5.0</td>\n", " <td>913.0</td>\n", " <td>854</td>\n", " <td>19.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>FLL</td>\n", " <td>Fort Lauderdale Hollywood Intl</td>\n", " <td>26.072583</td>\n", " <td>-80.152750</td>\n", " <td>9.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>555.0</td>\n", " <td>600</td>\n", " <td>-5.0</td>\n", " <td>913.0</td>\n", " <td>854</td>\n", " <td>19.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>FLL</td>\n", " <td>Fort Lauderdale Hollywood Intl</td>\n", " <td>26.072583</td>\n", " <td>-80.152750</td>\n", " <td>9.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>557.0</td>\n", " <td>600</td>\n", " <td>-3.0</td>\n", " <td>709.0</td>\n", " <td>723</td>\n", " <td>-14.0</td>\n", " <td>EV</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>IAD</td>\n", " <td>Washington Dulles Intl</td>\n", " <td>38.944533</td>\n", " <td>-77.455811</td>\n", " <td>313.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>557.0</td>\n", " <td>600</td>\n", " <td>-3.0</td>\n", " <td>709.0</td>\n", " <td>723</td>\n", " <td>-14.0</td>\n", " <td>EV</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>IAD</td>\n", " <td>Washington Dulles Intl</td>\n", " <td>38.944533</td>\n", " <td>-77.455811</td>\n", " <td>313.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>557.0</td>\n", " <td>600</td>\n", " <td>-3.0</td>\n", " <td>709.0</td>\n", " <td>723</td>\n", " <td>-14.0</td>\n", " <td>EV</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>IAD</td>\n", " <td>Washington Dulles Intl</td>\n", " <td>38.944533</td>\n", " <td>-77.455811</td>\n", " <td>313.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>557.0</td>\n", " <td>600</td>\n", " <td>-3.0</td>\n", " <td>838.0</td>\n", " <td>846</td>\n", " <td>-8.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>MCO</td>\n", " <td>Orlando Intl</td>\n", " <td>28.429394</td>\n", " <td>-81.308994</td>\n", " <td>96.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>557.0</td>\n", " <td>600</td>\n", " <td>-3.0</td>\n", " <td>838.0</td>\n", " <td>846</td>\n", " <td>-8.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>MCO</td>\n", " <td>Orlando Intl</td>\n", " <td>28.429394</td>\n", " <td>-81.308994</td>\n", " <td>96.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>557.0</td>\n", " <td>600</td>\n", " <td>-3.0</td>\n", " <td>838.0</td>\n", " <td>846</td>\n", " <td>-8.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>MCO</td>\n", " <td>Orlando Intl</td>\n", " <td>28.429394</td>\n", " <td>-81.308994</td>\n", " <td>96.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>753.0</td>\n", " <td>745</td>\n", " <td>8.0</td>\n", " <td>AA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>ORD</td>\n", " <td>Chicago Ohare Intl</td>\n", " <td>41.978603</td>\n", " <td>-87.904842</td>\n", " <td>668.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>753.0</td>\n", " <td>745</td>\n", " <td>8.0</td>\n", " <td>AA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>ORD</td>\n", " <td>Chicago Ohare Intl</td>\n", " <td>41.978603</td>\n", " <td>-87.904842</td>\n", " <td>668.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>753.0</td>\n", " <td>745</td>\n", " <td>8.0</td>\n", " <td>AA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>ORD</td>\n", " <td>Chicago Ohare Intl</td>\n", " <td>41.978603</td>\n", " <td>-87.904842</td>\n", " <td>668.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>849.0</td>\n", " <td>851</td>\n", " <td>-2.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>PBI</td>\n", " <td>Palm Beach Intl</td>\n", " <td>26.683161</td>\n", " <td>-80.095589</td>\n", " <td>19.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>849.0</td>\n", " <td>851</td>\n", " <td>-2.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>PBI</td>\n", " <td>Palm Beach Intl</td>\n", " <td>26.683161</td>\n", " <td>-80.095589</td>\n", " <td>19.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>849.0</td>\n", " <td>851</td>\n", " <td>-2.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>PBI</td>\n", " <td>Palm Beach Intl</td>\n", " <td>26.683161</td>\n", " <td>-80.095589</td>\n", " <td>19.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>853.0</td>\n", " <td>856</td>\n", " <td>-3.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>TPA</td>\n", " <td>Tampa Intl</td>\n", " <td>27.975472</td>\n", " <td>-82.533250</td>\n", " <td>26.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>853.0</td>\n", " <td>856</td>\n", " <td>-3.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>TPA</td>\n", " <td>Tampa Intl</td>\n", " <td>27.975472</td>\n", " <td>-82.533250</td>\n", " <td>26.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>853.0</td>\n", " <td>856</td>\n", " <td>-3.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>TPA</td>\n", " <td>Tampa Intl</td>\n", " <td>27.975472</td>\n", " <td>-82.533250</td>\n", " <td>26.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>924.0</td>\n", " <td>917</td>\n", " <td>7.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>LAX</td>\n", " <td>Los Angeles Intl</td>\n", " <td>33.942536</td>\n", " <td>-118.408075</td>\n", " <td>126.0</td>\n", " <td>-8.0</td>\n", " <td>A</td>\n", " <td>America/Los_Angeles</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>924.0</td>\n", " <td>917</td>\n", " <td>7.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>LAX</td>\n", " <td>Los Angeles Intl</td>\n", " <td>33.942536</td>\n", " <td>-118.408075</td>\n", " <td>126.0</td>\n", " <td>-8.0</td>\n", " <td>A</td>\n", " <td>America/Los_Angeles</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>924.0</td>\n", " <td>917</td>\n", " <td>7.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>LAX</td>\n", " <td>Los Angeles Intl</td>\n", " <td>33.942536</td>\n", " <td>-118.408075</td>\n", " <td>126.0</td>\n", " <td>-8.0</td>\n", " <td>A</td>\n", " <td>America/Los_Angeles</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>923.0</td>\n", " <td>937</td>\n", " <td>-14.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>SFO</td>\n", " <td>San Francisco Intl</td>\n", " <td>37.618972</td>\n", " <td>-122.374889</td>\n", " <td>13.0</td>\n", " <td>-8.0</td>\n", " <td>A</td>\n", " <td>America/Los_Angeles</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", " <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>1007561</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2240.0</td>\n", " <td>2250</td>\n", " <td>-10.0</td>\n", " <td>2347.0</td>\n", " <td>7</td>\n", " <td>-20.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>BUF</td>\n", " <td>Buffalo Niagara Intl</td>\n", " <td>42.940525</td>\n", " <td>-78.732167</td>\n", " <td>724.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007562</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2240.0</td>\n", " <td>2250</td>\n", " <td>-10.0</td>\n", " <td>2347.0</td>\n", " <td>7</td>\n", " <td>-20.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>BUF</td>\n", " <td>Buffalo Niagara Intl</td>\n", " <td>42.940525</td>\n", " <td>-78.732167</td>\n", " <td>724.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007563</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2240.0</td>\n", " <td>2250</td>\n", " <td>-10.0</td>\n", " <td>2347.0</td>\n", " <td>7</td>\n", " <td>-20.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>BUF</td>\n", " <td>Buffalo Niagara Intl</td>\n", " <td>42.940525</td>\n", " <td>-78.732167</td>\n", " <td>724.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007564</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2241.0</td>\n", " <td>2246</td>\n", " <td>-5.0</td>\n", " <td>2345.0</td>\n", " <td>1</td>\n", " <td>-16.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>ROC</td>\n", " <td>Greater Rochester Intl</td>\n", " <td>43.118866</td>\n", " <td>-77.672389</td>\n", " <td>559.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007565</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2241.0</td>\n", " <td>2246</td>\n", " <td>-5.0</td>\n", " <td>2345.0</td>\n", " <td>1</td>\n", " <td>-16.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>ROC</td>\n", " <td>Greater Rochester Intl</td>\n", " <td>43.118866</td>\n", " <td>-77.672389</td>\n", " <td>559.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007566</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2241.0</td>\n", " <td>2246</td>\n", " <td>-5.0</td>\n", " <td>2345.0</td>\n", " <td>1</td>\n", " <td>-16.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>ROC</td>\n", " <td>Greater Rochester Intl</td>\n", " <td>43.118866</td>\n", " <td>-77.672389</td>\n", " <td>559.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007567</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2307.0</td>\n", " <td>2255</td>\n", " <td>12.0</td>\n", " <td>2359.0</td>\n", " <td>2358</td>\n", " <td>1.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>BOS</td>\n", " <td>General Edward Lawrence Logan Intl</td>\n", " <td>42.364347</td>\n", " <td>-71.005181</td>\n", " <td>19.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007568</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2307.0</td>\n", " <td>2255</td>\n", " <td>12.0</td>\n", " <td>2359.0</td>\n", " <td>2358</td>\n", " <td>1.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>BOS</td>\n", " <td>General Edward Lawrence Logan Intl</td>\n", " <td>42.364347</td>\n", " <td>-71.005181</td>\n", " <td>19.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007569</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2307.0</td>\n", " <td>2255</td>\n", " <td>12.0</td>\n", " <td>2359.0</td>\n", " <td>2358</td>\n", " <td>1.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>BOS</td>\n", " <td>General Edward Lawrence Logan Intl</td>\n", " <td>42.364347</td>\n", " <td>-71.005181</td>\n", " <td>19.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007570</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2349.0</td>\n", " <td>2359</td>\n", " <td>-10.0</td>\n", " <td>325.0</td>\n", " <td>350</td>\n", " <td>-25.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 18:00:00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1007571</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2349.0</td>\n", " <td>2359</td>\n", " <td>-10.0</td>\n", " <td>325.0</td>\n", " <td>350</td>\n", " <td>-25.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 18:00:00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1007572</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2349.0</td>\n", " <td>2359</td>\n", " <td>-10.0</td>\n", " <td>325.0</td>\n", " <td>350</td>\n", " <td>-25.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 18:00:00</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1007573</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1842</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2019</td>\n", " <td>NaN</td>\n", " <td>EV</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 13:00:00</td>\n", " <td>BNA</td>\n", " <td>Nashville Intl</td>\n", " <td>36.124472</td>\n", " <td>-86.678194</td>\n", " <td>599.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>1007574</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1842</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2019</td>\n", " <td>NaN</td>\n", " <td>EV</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 13:00:00</td>\n", " <td>BNA</td>\n", " <td>Nashville Intl</td>\n", " <td>36.124472</td>\n", " <td>-86.678194</td>\n", " <td>599.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>1007575</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1842</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2019</td>\n", " <td>NaN</td>\n", " <td>EV</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 13:00:00</td>\n", " <td>BNA</td>\n", " <td>Nashville Intl</td>\n", " <td>36.124472</td>\n", " <td>-86.678194</td>\n", " <td>599.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>1007576</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1455</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1634</td>\n", " <td>NaN</td>\n", " <td>9E</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 09:00:00</td>\n", " <td>DCA</td>\n", " <td>Ronald Reagan Washington Natl</td>\n", " <td>38.852083</td>\n", " <td>-77.037722</td>\n", " <td>15.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007577</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1455</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1634</td>\n", " <td>NaN</td>\n", " <td>9E</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 09:00:00</td>\n", " <td>DCA</td>\n", " <td>Ronald Reagan Washington Natl</td>\n", " <td>38.852083</td>\n", " <td>-77.037722</td>\n", " <td>15.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007578</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1455</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1634</td>\n", " <td>NaN</td>\n", " <td>9E</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 09:00:00</td>\n", " <td>DCA</td>\n", " <td>Ronald Reagan Washington Natl</td>\n", " <td>38.852083</td>\n", " <td>-77.037722</td>\n", " <td>15.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007579</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>2200</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2312</td>\n", " <td>NaN</td>\n", " <td>9E</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>SYR</td>\n", " <td>Syracuse Hancock Intl</td>\n", " <td>43.111187</td>\n", " <td>-76.106311</td>\n", " <td>421.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007580</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>2200</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2312</td>\n", " <td>NaN</td>\n", " <td>9E</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>SYR</td>\n", " <td>Syracuse Hancock Intl</td>\n", " <td>43.111187</td>\n", " <td>-76.106311</td>\n", " <td>421.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007581</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>2200</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2312</td>\n", " <td>NaN</td>\n", " <td>9E</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>SYR</td>\n", " <td>Syracuse Hancock Intl</td>\n", " <td>43.111187</td>\n", " <td>-76.106311</td>\n", " <td>421.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007582</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1210</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1330</td>\n", " <td>NaN</td>\n", " <td>MQ</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 07:00:00</td>\n", " <td>BNA</td>\n", " <td>Nashville Intl</td>\n", " <td>36.124472</td>\n", " <td>-86.678194</td>\n", " <td>599.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>1007583</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1210</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1330</td>\n", " <td>NaN</td>\n", " <td>MQ</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 07:00:00</td>\n", " <td>BNA</td>\n", " <td>Nashville Intl</td>\n", " <td>36.124472</td>\n", " <td>-86.678194</td>\n", " <td>599.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>1007584</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1210</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1330</td>\n", " <td>NaN</td>\n", " <td>MQ</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 07:00:00</td>\n", " <td>BNA</td>\n", " <td>Nashville Intl</td>\n", " <td>36.124472</td>\n", " <td>-86.678194</td>\n", " <td>599.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>1007585</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1159</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1344</td>\n", " <td>NaN</td>\n", " <td>MQ</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 06:00:00</td>\n", " <td>CLE</td>\n", " <td>Cleveland Hopkins Intl</td>\n", " <td>41.411689</td>\n", " <td>-81.849794</td>\n", " <td>791.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007586</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1159</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1344</td>\n", " <td>NaN</td>\n", " <td>MQ</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 06:00:00</td>\n", " <td>CLE</td>\n", " <td>Cleveland Hopkins Intl</td>\n", " <td>41.411689</td>\n", " <td>-81.849794</td>\n", " <td>791.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007587</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>1159</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1344</td>\n", " <td>NaN</td>\n", " <td>MQ</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 06:00:00</td>\n", " <td>CLE</td>\n", " <td>Cleveland Hopkins Intl</td>\n", " <td>41.411689</td>\n", " <td>-81.849794</td>\n", " <td>791.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007588</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>840</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1020</td>\n", " <td>NaN</td>\n", " <td>MQ</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 03:00:00</td>\n", " <td>RDU</td>\n", " <td>Raleigh Durham Intl</td>\n", " <td>35.877639</td>\n", " <td>-78.787472</td>\n", " <td>435.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007589</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>840</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1020</td>\n", " <td>NaN</td>\n", " <td>MQ</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 03:00:00</td>\n", " <td>RDU</td>\n", " <td>Raleigh Durham Intl</td>\n", " <td>35.877639</td>\n", " <td>-78.787472</td>\n", " <td>435.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007590</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>840</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1020</td>\n", " <td>NaN</td>\n", " <td>MQ</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 03:00:00</td>\n", " <td>RDU</td>\n", " <td>Raleigh Durham Intl</td>\n", " <td>35.877639</td>\n", " <td>-78.787472</td>\n", " <td>435.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>1007591 rows × 38 columns</p>\n", "</div>" ], "text/plain": [ " year month day dep_time sched_dep_time dep_delay arr_time \\\n", "0 2013 1 1 517.0 515 2.0 830.0 \n", "1 2013 1 1 533.0 529 4.0 850.0 \n", "2 2013 1 1 542.0 540 2.0 923.0 \n", "3 2013 1 1 544.0 545 -1.0 1004.0 \n", "4 2013 1 1 554.0 600 -6.0 812.0 \n", "5 2013 1 1 554.0 600 -6.0 812.0 \n", "6 2013 1 1 554.0 600 -6.0 812.0 \n", "7 2013 1 1 554.0 558 -4.0 740.0 \n", "8 2013 1 1 555.0 600 -5.0 913.0 \n", "9 2013 1 1 555.0 600 -5.0 913.0 \n", "10 2013 1 1 555.0 600 -5.0 913.0 \n", "11 2013 1 1 557.0 600 -3.0 709.0 \n", "12 2013 1 1 557.0 600 -3.0 709.0 \n", "13 2013 1 1 557.0 600 -3.0 709.0 \n", "14 2013 1 1 557.0 600 -3.0 838.0 \n", "15 2013 1 1 557.0 600 -3.0 838.0 \n", "16 2013 1 1 557.0 600 -3.0 838.0 \n", "17 2013 1 1 558.0 600 -2.0 753.0 \n", "18 2013 1 1 558.0 600 -2.0 753.0 \n", "19 2013 1 1 558.0 600 -2.0 753.0 \n", "20 2013 1 1 558.0 600 -2.0 849.0 \n", "21 2013 1 1 558.0 600 -2.0 849.0 \n", "22 2013 1 1 558.0 600 -2.0 849.0 \n", "23 2013 1 1 558.0 600 -2.0 853.0 \n", "24 2013 1 1 558.0 600 -2.0 853.0 \n", "25 2013 1 1 558.0 600 -2.0 853.0 \n", "26 2013 1 1 558.0 600 -2.0 924.0 \n", "27 2013 1 1 558.0 600 -2.0 924.0 \n", "28 2013 1 1 558.0 600 -2.0 924.0 \n", "29 2013 1 1 558.0 600 -2.0 923.0 \n", "... ... ... ... ... ... ... ... \n", "1007561 2013 9 30 2240.0 2250 -10.0 2347.0 \n", "1007562 2013 9 30 2240.0 2250 -10.0 2347.0 \n", "1007563 2013 9 30 2240.0 2250 -10.0 2347.0 \n", "1007564 2013 9 30 2241.0 2246 -5.0 2345.0 \n", "1007565 2013 9 30 2241.0 2246 -5.0 2345.0 \n", "1007566 2013 9 30 2241.0 2246 -5.0 2345.0 \n", "1007567 2013 9 30 2307.0 2255 12.0 2359.0 \n", "1007568 2013 9 30 2307.0 2255 12.0 2359.0 \n", "1007569 2013 9 30 2307.0 2255 12.0 2359.0 \n", "1007570 2013 9 30 2349.0 2359 -10.0 325.0 \n", "1007571 2013 9 30 2349.0 2359 -10.0 325.0 \n", "1007572 2013 9 30 2349.0 2359 -10.0 325.0 \n", "1007573 2013 9 30 NaN 1842 NaN NaN \n", "1007574 2013 9 30 NaN 1842 NaN NaN \n", "1007575 2013 9 30 NaN 1842 NaN NaN \n", "1007576 2013 9 30 NaN 1455 NaN NaN \n", "1007577 2013 9 30 NaN 1455 NaN NaN \n", "1007578 2013 9 30 NaN 1455 NaN NaN \n", "1007579 2013 9 30 NaN 2200 NaN NaN \n", "1007580 2013 9 30 NaN 2200 NaN NaN \n", "1007581 2013 9 30 NaN 2200 NaN NaN \n", "1007582 2013 9 30 NaN 1210 NaN NaN \n", "1007583 2013 9 30 NaN 1210 NaN NaN \n", "1007584 2013 9 30 NaN 1210 NaN NaN \n", "1007585 2013 9 30 NaN 1159 NaN NaN \n", "1007586 2013 9 30 NaN 1159 NaN NaN \n", "1007587 2013 9 30 NaN 1159 NaN NaN \n", "1007588 2013 9 30 NaN 840 NaN NaN \n", "1007589 2013 9 30 NaN 840 NaN NaN \n", "1007590 2013 9 30 NaN 840 NaN NaN \n", "\n", " sched_arr_time arr_delay carrier ... visib \\\n", "0 819 11.0 UA ... NaN \n", "1 830 20.0 UA ... NaN \n", "2 850 33.0 AA ... NaN \n", "3 1022 -18.0 B6 ... NaN \n", "4 837 -25.0 DL ... 10.0 \n", "5 837 -25.0 DL ... 10.0 \n", "6 837 -25.0 DL ... 10.0 \n", "7 728 12.0 UA ... NaN \n", "8 854 19.0 B6 ... 10.0 \n", "9 854 19.0 B6 ... 10.0 \n", "10 854 19.0 B6 ... 10.0 \n", "11 723 -14.0 EV ... 10.0 \n", "12 723 -14.0 EV ... 10.0 \n", "13 723 -14.0 EV ... 10.0 \n", "14 846 -8.0 B6 ... 10.0 \n", "15 846 -8.0 B6 ... 10.0 \n", "16 846 -8.0 B6 ... 10.0 \n", "17 745 8.0 AA ... 10.0 \n", "18 745 8.0 AA ... 10.0 \n", "19 745 8.0 AA ... 10.0 \n", "20 851 -2.0 B6 ... 10.0 \n", "21 851 -2.0 B6 ... 10.0 \n", "22 851 -2.0 B6 ... 10.0 \n", "23 856 -3.0 B6 ... 10.0 \n", "24 856 -3.0 B6 ... 10.0 \n", "25 856 -3.0 B6 ... 10.0 \n", "26 917 7.0 UA ... 10.0 \n", "27 917 7.0 UA ... 10.0 \n", "28 917 7.0 UA ... 10.0 \n", "29 937 -14.0 UA ... 10.0 \n", "... ... ... ... ... ... \n", "1007561 7 -20.0 B6 ... 10.0 \n", "1007562 7 -20.0 B6 ... 10.0 \n", "1007563 7 -20.0 B6 ... 10.0 \n", "1007564 1 -16.0 B6 ... 10.0 \n", "1007565 1 -16.0 B6 ... 10.0 \n", "1007566 1 -16.0 B6 ... 10.0 \n", "1007567 2358 1.0 B6 ... 10.0 \n", "1007568 2358 1.0 B6 ... 10.0 \n", "1007569 2358 1.0 B6 ... 10.0 \n", "1007570 350 -25.0 B6 ... 10.0 \n", "1007571 350 -25.0 B6 ... 10.0 \n", "1007572 350 -25.0 B6 ... 10.0 \n", "1007573 2019 NaN EV ... 10.0 \n", "1007574 2019 NaN EV ... 10.0 \n", "1007575 2019 NaN EV ... 10.0 \n", "1007576 1634 NaN 9E ... 10.0 \n", "1007577 1634 NaN 9E ... 10.0 \n", "1007578 1634 NaN 9E ... 10.0 \n", "1007579 2312 NaN 9E ... 10.0 \n", "1007580 2312 NaN 9E ... 10.0 \n", "1007581 2312 NaN 9E ... 10.0 \n", "1007582 1330 NaN MQ ... 10.0 \n", "1007583 1330 NaN MQ ... 10.0 \n", "1007584 1330 NaN MQ ... 10.0 \n", "1007585 1344 NaN MQ ... 10.0 \n", "1007586 1344 NaN MQ ... 10.0 \n", "1007587 1344 NaN MQ ... 10.0 \n", "1007588 1020 NaN MQ ... 10.0 \n", "1007589 1020 NaN MQ ... 10.0 \n", "1007590 1020 NaN MQ ... 10.0 \n", "\n", " time_hour_y faa name \\\n", "0 NaN IAH George Bush Intercontinental \n", "1 NaN IAH George Bush Intercontinental \n", "2 NaN MIA Miami Intl \n", "3 NaN NaN NaN \n", "4 2013-01-01 01:00:00 ATL Hartsfield Jackson Atlanta Intl \n", "5 2013-01-01 01:00:00 ATL Hartsfield Jackson Atlanta Intl \n", "6 2013-01-01 01:00:00 ATL Hartsfield Jackson Atlanta Intl \n", "7 NaN ORD Chicago Ohare Intl \n", "8 2013-01-01 01:00:00 FLL Fort Lauderdale Hollywood Intl \n", "9 2013-01-01 01:00:00 FLL Fort Lauderdale Hollywood Intl \n", "10 2013-01-01 01:00:00 FLL Fort Lauderdale Hollywood Intl \n", "11 2013-01-01 01:00:00 IAD Washington Dulles Intl \n", "12 2013-01-01 01:00:00 IAD Washington Dulles Intl \n", "13 2013-01-01 01:00:00 IAD Washington Dulles Intl \n", "14 2013-01-01 01:00:00 MCO Orlando Intl \n", "15 2013-01-01 01:00:00 MCO Orlando Intl \n", "16 2013-01-01 01:00:00 MCO Orlando Intl \n", "17 2013-01-01 01:00:00 ORD Chicago Ohare Intl \n", "18 2013-01-01 01:00:00 ORD Chicago Ohare Intl \n", "19 2013-01-01 01:00:00 ORD Chicago Ohare Intl \n", "20 2013-01-01 01:00:00 PBI Palm Beach Intl \n", "21 2013-01-01 01:00:00 PBI Palm Beach Intl \n", "22 2013-01-01 01:00:00 PBI Palm Beach Intl \n", "23 2013-01-01 01:00:00 TPA Tampa Intl \n", "24 2013-01-01 01:00:00 TPA Tampa Intl \n", "25 2013-01-01 01:00:00 TPA Tampa Intl \n", "26 2013-01-01 01:00:00 LAX Los Angeles Intl \n", "27 2013-01-01 01:00:00 LAX Los Angeles Intl \n", "28 2013-01-01 01:00:00 LAX Los Angeles Intl \n", "29 2013-01-01 01:00:00 SFO San Francisco Intl \n", "... ... ... ... \n", "1007561 2013-09-30 17:00:00 BUF Buffalo Niagara Intl \n", "1007562 2013-09-30 17:00:00 BUF Buffalo Niagara Intl \n", "1007563 2013-09-30 17:00:00 BUF Buffalo Niagara Intl \n", "1007564 2013-09-30 17:00:00 ROC Greater Rochester Intl \n", "1007565 2013-09-30 17:00:00 ROC Greater Rochester Intl \n", "1007566 2013-09-30 17:00:00 ROC Greater Rochester Intl \n", "1007567 2013-09-30 17:00:00 BOS General Edward Lawrence Logan Intl \n", "1007568 2013-09-30 17:00:00 BOS General Edward Lawrence Logan Intl \n", "1007569 2013-09-30 17:00:00 BOS General Edward Lawrence Logan Intl \n", "1007570 2013-09-30 18:00:00 NaN NaN \n", "1007571 2013-09-30 18:00:00 NaN NaN \n", "1007572 2013-09-30 18:00:00 NaN NaN \n", "1007573 2013-09-30 13:00:00 BNA Nashville Intl \n", "1007574 2013-09-30 13:00:00 BNA Nashville Intl \n", "1007575 2013-09-30 13:00:00 BNA Nashville Intl \n", "1007576 2013-09-30 09:00:00 DCA Ronald Reagan Washington Natl \n", "1007577 2013-09-30 09:00:00 DCA Ronald Reagan Washington Natl \n", "1007578 2013-09-30 09:00:00 DCA Ronald Reagan Washington Natl \n", "1007579 2013-09-30 17:00:00 SYR Syracuse Hancock Intl \n", "1007580 2013-09-30 17:00:00 SYR Syracuse Hancock Intl \n", "1007581 2013-09-30 17:00:00 SYR Syracuse Hancock Intl \n", "1007582 2013-09-30 07:00:00 BNA Nashville Intl \n", "1007583 2013-09-30 07:00:00 BNA Nashville Intl \n", "1007584 2013-09-30 07:00:00 BNA Nashville Intl \n", "1007585 2013-09-30 06:00:00 CLE Cleveland Hopkins Intl \n", "1007586 2013-09-30 06:00:00 CLE Cleveland Hopkins Intl \n", "1007587 2013-09-30 06:00:00 CLE Cleveland Hopkins Intl \n", "1007588 2013-09-30 03:00:00 RDU Raleigh Durham Intl \n", "1007589 2013-09-30 03:00:00 RDU Raleigh Durham Intl \n", "1007590 2013-09-30 03:00:00 RDU Raleigh Durham Intl \n", "\n", " lat lon alt tz dst tzone \n", "0 29.984433 -95.341442 97.0 -6.0 A America/Chicago \n", "1 29.984433 -95.341442 97.0 -6.0 A America/Chicago \n", "2 25.793250 -80.290556 8.0 -5.0 A America/New_York \n", "3 NaN NaN NaN NaN NaN NaN \n", "4 33.636719 -84.428067 1026.0 -5.0 A America/New_York \n", "5 33.636719 -84.428067 1026.0 -5.0 A America/New_York \n", "6 33.636719 -84.428067 1026.0 -5.0 A America/New_York \n", "7 41.978603 -87.904842 668.0 -6.0 A America/Chicago \n", "8 26.072583 -80.152750 9.0 -5.0 A America/New_York \n", "9 26.072583 -80.152750 9.0 -5.0 A America/New_York \n", "10 26.072583 -80.152750 9.0 -5.0 A America/New_York \n", "11 38.944533 -77.455811 313.0 -5.0 A America/New_York \n", "12 38.944533 -77.455811 313.0 -5.0 A America/New_York \n", "13 38.944533 -77.455811 313.0 -5.0 A America/New_York \n", "14 28.429394 -81.308994 96.0 -5.0 A America/New_York \n", "15 28.429394 -81.308994 96.0 -5.0 A America/New_York \n", "16 28.429394 -81.308994 96.0 -5.0 A America/New_York \n", "17 41.978603 -87.904842 668.0 -6.0 A America/Chicago \n", "18 41.978603 -87.904842 668.0 -6.0 A America/Chicago \n", "19 41.978603 -87.904842 668.0 -6.0 A America/Chicago \n", "20 26.683161 -80.095589 19.0 -5.0 A America/New_York \n", "21 26.683161 -80.095589 19.0 -5.0 A America/New_York \n", "22 26.683161 -80.095589 19.0 -5.0 A America/New_York \n", "23 27.975472 -82.533250 26.0 -5.0 A America/New_York \n", "24 27.975472 -82.533250 26.0 -5.0 A America/New_York \n", "25 27.975472 -82.533250 26.0 -5.0 A America/New_York \n", "26 33.942536 -118.408075 126.0 -8.0 A America/Los_Angeles \n", "27 33.942536 -118.408075 126.0 -8.0 A America/Los_Angeles \n", "28 33.942536 -118.408075 126.0 -8.0 A America/Los_Angeles \n", "29 37.618972 -122.374889 13.0 -8.0 A America/Los_Angeles \n", "... ... ... ... ... ... ... \n", "1007561 42.940525 -78.732167 724.0 -5.0 A America/New_York \n", "1007562 42.940525 -78.732167 724.0 -5.0 A America/New_York \n", "1007563 42.940525 -78.732167 724.0 -5.0 A America/New_York \n", "1007564 43.118866 -77.672389 559.0 -5.0 A America/New_York \n", "1007565 43.118866 -77.672389 559.0 -5.0 A America/New_York \n", "1007566 43.118866 -77.672389 559.0 -5.0 A America/New_York \n", "1007567 42.364347 -71.005181 19.0 -5.0 A America/New_York \n", "1007568 42.364347 -71.005181 19.0 -5.0 A America/New_York \n", "1007569 42.364347 -71.005181 19.0 -5.0 A America/New_York \n", "1007570 NaN NaN NaN NaN NaN NaN \n", "1007571 NaN NaN NaN NaN NaN NaN \n", "1007572 NaN NaN NaN NaN NaN NaN \n", "1007573 36.124472 -86.678194 599.0 -6.0 A America/Chicago \n", "1007574 36.124472 -86.678194 599.0 -6.0 A America/Chicago \n", "1007575 36.124472 -86.678194 599.0 -6.0 A America/Chicago \n", "1007576 38.852083 -77.037722 15.0 -5.0 A America/New_York \n", "1007577 38.852083 -77.037722 15.0 -5.0 A America/New_York \n", "1007578 38.852083 -77.037722 15.0 -5.0 A America/New_York \n", "1007579 43.111187 -76.106311 421.0 -5.0 A America/New_York \n", "1007580 43.111187 -76.106311 421.0 -5.0 A America/New_York \n", "1007581 43.111187 -76.106311 421.0 -5.0 A America/New_York \n", "1007582 36.124472 -86.678194 599.0 -6.0 A America/Chicago \n", "1007583 36.124472 -86.678194 599.0 -6.0 A America/Chicago \n", "1007584 36.124472 -86.678194 599.0 -6.0 A America/Chicago \n", "1007585 41.411689 -81.849794 791.0 -5.0 A America/New_York \n", "1007586 41.411689 -81.849794 791.0 -5.0 A America/New_York \n", "1007587 41.411689 -81.849794 791.0 -5.0 A America/New_York \n", "1007588 35.877639 -78.787472 435.0 -5.0 A America/New_York \n", "1007589 35.877639 -78.787472 435.0 -5.0 A America/New_York \n", "1007590 35.877639 -78.787472 435.0 -5.0 A America/New_York \n", "\n", "[1007591 rows x 38 columns]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_withairport = pd.merge(df_withweather, airports, how='left', left_on='dest', right_on='faa')\n", "df_withairport" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6 Numpy和SciPy\n", "Numpy和SciPy是Python数据科学的CP。早期Python的list比较慢，并且对于处理矩阵和向量运算不太好，因此有了Numpy来解决这个问题。它引入了array-type的数据类型。\n", "\n", "创建数组：" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 2, 3])" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "a = np.array([1, 2, 3])\n", "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "注意这里我们传的是列表，而不是`np.array(1, 2, 3)`。\n", "\n", "现在我们创建一个arange" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.arange(10)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0. , 3.14159265, 6.28318531, 9.42477796,\n", " 12.56637061, 15.70796327, 18.84955592, 21.99114858,\n", " 25.13274123, 28.27433388])" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 给序列乘以一个系数\n", "np.arange(10) np.pi" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "我们也可以使用`shape`方法从一维数组创建多维数组" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1, 2, 3],\n", " [4, 5, 6]])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = np.array([1, 2, 3, 4, 5, 6])\n", "a.shape = (2, 3)\n", "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.1 矩阵Matrix" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "matrix([[1, 2],\n", " [3, 4]])" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.matrix('1 2; 3 4')" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "matrix([[13, 18],\n", " [29, 40]])" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#矩阵乘\n", "a1 = np.matrix('1 2; 3 4')\n", "a2 = np.matrix('3 4; 5 7')\n", "a1 * a2" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "matrix([[1, 2],\n", " [3, 4]])" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#array转换为矩阵\n", "mat_a = np.mat(a1)\n", "mat_a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.2 稀疏矩阵(Sparse Matrices)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<1x100000 sparse matrix of type '<class 'numpy.float64'>'\n", "\twith 50077 stored elements in Dictionary Of Keys format>" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy, scipy.sparse\n", "n = 100000\n", "x = (numpy.random.rand(n) * 2).astype(int).astype(float) #50%稀疏矩阵\n", "x_csr = scipy.sparse.csr_matrix(x)\n", "x_dok = scipy.sparse.dok_matrix(x.reshape(x_csr.shape))\n", "x_dok" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.3 从CSV文件中加载数据" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[2.0, 3.0, 4.0, 5.0], [3.0, 4.0, 5.0, 6.0], [7.0, 9.0, 9.0, 10.0]]" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import csv\n", "with open('data/array/array.csv', 'r') as csvfile:\n", " csvreader = csv.reader(csvfile)\n", " data = []\n", " for row in csvreader:\n", " row = [float(x) for x in row]\n", " data.append(row)\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.4 求解矩阵方程(Solving a matrix)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 2., -2., 9.])" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import scipy as sp\n", "a = np.array([[3, 2, 0], [1, -1, 0], [0, 5, 1]])\n", "b = np.array([2, 4, -1])\n", "x = np.linalg.solve(a, b)\n", "x" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ True, True, True], dtype=bool)" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#检查结果是否正确\n", "np.dot(a, x) == b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 7 Scikit-learn 简介\n", "前面我们介绍了pandas和numpy、scipy。现在我们来介绍python机器库Scikit。首先需要先知道机器学习的两种：\n", "\n", "- 监督学习(Supervised Learning): 从训练集建立模型进行预测\n", "- 非监督学习(Unsupervised Learning)：从数据中推测模型，比如从文本中找出主题\n", "\n", "Scikit-learn有一下特性：\n", "- 预处理(Preprocessing)：为机器学习reshape数据\n", "- 降维处理(Dimensionality reduction)：减少变量的重复\n", "- 分类(Classification): 预测分类\n", "- 回归(regression)：预测连续变量\n", "- 聚类(Clustering)：从数据中发现自然的模式\n", "- 模型选取(Model Selection)：为数据找到最优模型\n", "\n", "这里我们还是看nycflights13的数据集。" ] }, { "cell_type": "code", "execution_count": 47, "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>year</th>\n", " <th>month</th>\n", " <th>day</th>\n", " <th>dep_time</th>\n", " <th>sched_dep_time</th>\n", " <th>dep_delay</th>\n", " <th>arr_time</th>\n", " <th>sched_arr_time</th>\n", " <th>arr_delay</th>\n", " <th>carrier</th>\n", " <th>...</th>\n", " <th>visib</th>\n", " <th>time_hour_y</th>\n", " <th>faa</th>\n", " <th>name</th>\n", " <th>lat</th>\n", " <th>lon</th>\n", " <th>alt</th>\n", " <th>tz</th>\n", " <th>dst</th>\n", " <th>tzone</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>4</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>554.0</td>\n", " <td>600</td>\n", " <td>-6.0</td>\n", " <td>812.0</td>\n", " <td>837</td>\n", " <td>-25.0</td>\n", " <td>DL</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>ATL</td>\n", " <td>Hartsfield Jackson Atlanta Intl</td>\n", " <td>33.636719</td>\n", " <td>-84.428067</td>\n", " <td>1026.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>554.0</td>\n", " <td>600</td>\n", " <td>-6.0</td>\n", " <td>812.0</td>\n", " <td>837</td>\n", " <td>-25.0</td>\n", " <td>DL</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>ATL</td>\n", " <td>Hartsfield Jackson Atlanta Intl</td>\n", " <td>33.636719</td>\n", " <td>-84.428067</td>\n", " <td>1026.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>554.0</td>\n", " <td>600</td>\n", " <td>-6.0</td>\n", " <td>812.0</td>\n", " <td>837</td>\n", " <td>-25.0</td>\n", " <td>DL</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>ATL</td>\n", " <td>Hartsfield Jackson Atlanta Intl</td>\n", " <td>33.636719</td>\n", " <td>-84.428067</td>\n", " <td>1026.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>555.0</td>\n", " <td>600</td>\n", " <td>-5.0</td>\n", " <td>913.0</td>\n", " <td>854</td>\n", " <td>19.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>FLL</td>\n", " <td>Fort Lauderdale Hollywood Intl</td>\n", " <td>26.072583</td>\n", " <td>-80.152750</td>\n", " <td>9.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>555.0</td>\n", " <td>600</td>\n", " <td>-5.0</td>\n", " <td>913.0</td>\n", " <td>854</td>\n", " <td>19.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>FLL</td>\n", " <td>Fort Lauderdale Hollywood Intl</td>\n", " <td>26.072583</td>\n", " <td>-80.152750</td>\n", " <td>9.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>555.0</td>\n", " <td>600</td>\n", " <td>-5.0</td>\n", " <td>913.0</td>\n", " <td>854</td>\n", " <td>19.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>FLL</td>\n", " <td>Fort Lauderdale Hollywood Intl</td>\n", " <td>26.072583</td>\n", " <td>-80.152750</td>\n", " <td>9.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>557.0</td>\n", " <td>600</td>\n", " <td>-3.0</td>\n", " <td>709.0</td>\n", " <td>723</td>\n", " <td>-14.0</td>\n", " <td>EV</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>IAD</td>\n", " <td>Washington Dulles Intl</td>\n", " <td>38.944533</td>\n", " <td>-77.455811</td>\n", " <td>313.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>557.0</td>\n", " <td>600</td>\n", " <td>-3.0</td>\n", " <td>709.0</td>\n", " <td>723</td>\n", " <td>-14.0</td>\n", " <td>EV</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>IAD</td>\n", " <td>Washington Dulles Intl</td>\n", " <td>38.944533</td>\n", " <td>-77.455811</td>\n", " <td>313.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>557.0</td>\n", " <td>600</td>\n", " <td>-3.0</td>\n", " <td>709.0</td>\n", " <td>723</td>\n", " <td>-14.0</td>\n", " <td>EV</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>IAD</td>\n", " <td>Washington Dulles Intl</td>\n", " <td>38.944533</td>\n", " <td>-77.455811</td>\n", " <td>313.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>557.0</td>\n", " <td>600</td>\n", " <td>-3.0</td>\n", " <td>838.0</td>\n", " <td>846</td>\n", " <td>-8.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>MCO</td>\n", " <td>Orlando Intl</td>\n", " <td>28.429394</td>\n", " <td>-81.308994</td>\n", " <td>96.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>557.0</td>\n", " <td>600</td>\n", " <td>-3.0</td>\n", " <td>838.0</td>\n", " <td>846</td>\n", " <td>-8.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>MCO</td>\n", " <td>Orlando Intl</td>\n", " <td>28.429394</td>\n", " <td>-81.308994</td>\n", " <td>96.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>557.0</td>\n", " <td>600</td>\n", " <td>-3.0</td>\n", " <td>838.0</td>\n", " <td>846</td>\n", " <td>-8.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>MCO</td>\n", " <td>Orlando Intl</td>\n", " <td>28.429394</td>\n", " <td>-81.308994</td>\n", " <td>96.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>753.0</td>\n", " <td>745</td>\n", " <td>8.0</td>\n", " <td>AA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>ORD</td>\n", " <td>Chicago Ohare Intl</td>\n", " <td>41.978603</td>\n", " <td>-87.904842</td>\n", " <td>668.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>753.0</td>\n", " <td>745</td>\n", " <td>8.0</td>\n", " <td>AA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>ORD</td>\n", " <td>Chicago Ohare Intl</td>\n", " <td>41.978603</td>\n", " <td>-87.904842</td>\n", " <td>668.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>753.0</td>\n", " <td>745</td>\n", " <td>8.0</td>\n", " <td>AA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>ORD</td>\n", " <td>Chicago Ohare Intl</td>\n", " <td>41.978603</td>\n", " <td>-87.904842</td>\n", " <td>668.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>849.0</td>\n", " <td>851</td>\n", " <td>-2.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>PBI</td>\n", " <td>Palm Beach Intl</td>\n", " <td>26.683161</td>\n", " <td>-80.095589</td>\n", " <td>19.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>849.0</td>\n", " <td>851</td>\n", " <td>-2.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>PBI</td>\n", " <td>Palm Beach Intl</td>\n", " <td>26.683161</td>\n", " <td>-80.095589</td>\n", " <td>19.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>849.0</td>\n", " <td>851</td>\n", " <td>-2.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>PBI</td>\n", " <td>Palm Beach Intl</td>\n", " <td>26.683161</td>\n", " <td>-80.095589</td>\n", " <td>19.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>853.0</td>\n", " <td>856</td>\n", " <td>-3.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>TPA</td>\n", " <td>Tampa Intl</td>\n", " <td>27.975472</td>\n", " <td>-82.533250</td>\n", " <td>26.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>853.0</td>\n", " <td>856</td>\n", " <td>-3.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>TPA</td>\n", " <td>Tampa Intl</td>\n", " <td>27.975472</td>\n", " <td>-82.533250</td>\n", " <td>26.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>853.0</td>\n", " <td>856</td>\n", " <td>-3.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>TPA</td>\n", " <td>Tampa Intl</td>\n", " <td>27.975472</td>\n", " <td>-82.533250</td>\n", " <td>26.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>924.0</td>\n", " <td>917</td>\n", " <td>7.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>LAX</td>\n", " <td>Los Angeles Intl</td>\n", " <td>33.942536</td>\n", " <td>-118.408075</td>\n", " <td>126.0</td>\n", " <td>-8.0</td>\n", " <td>A</td>\n", " <td>America/Los_Angeles</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>924.0</td>\n", " <td>917</td>\n", " <td>7.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>LAX</td>\n", " <td>Los Angeles Intl</td>\n", " <td>33.942536</td>\n", " <td>-118.408075</td>\n", " <td>126.0</td>\n", " <td>-8.0</td>\n", " <td>A</td>\n", " <td>America/Los_Angeles</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>924.0</td>\n", " <td>917</td>\n", " <td>7.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>LAX</td>\n", " <td>Los Angeles Intl</td>\n", " <td>33.942536</td>\n", " <td>-118.408075</td>\n", " <td>126.0</td>\n", " <td>-8.0</td>\n", " <td>A</td>\n", " <td>America/Los_Angeles</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>923.0</td>\n", " <td>937</td>\n", " <td>-14.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>SFO</td>\n", " <td>San Francisco Intl</td>\n", " <td>37.618972</td>\n", " <td>-122.374889</td>\n", " <td>13.0</td>\n", " <td>-8.0</td>\n", " <td>A</td>\n", " <td>America/Los_Angeles</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>923.0</td>\n", " <td>937</td>\n", " <td>-14.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>SFO</td>\n", " <td>San Francisco Intl</td>\n", " <td>37.618972</td>\n", " <td>-122.374889</td>\n", " <td>13.0</td>\n", " <td>-8.0</td>\n", " <td>A</td>\n", " <td>America/Los_Angeles</td>\n", " </tr>\n", " <tr>\n", " <th>31</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>923.0</td>\n", " <td>937</td>\n", " <td>-14.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>SFO</td>\n", " <td>San Francisco Intl</td>\n", " <td>37.618972</td>\n", " <td>-122.374889</td>\n", " <td>13.0</td>\n", " <td>-8.0</td>\n", " <td>A</td>\n", " <td>America/Los_Angeles</td>\n", " </tr>\n", " <tr>\n", " <th>32</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>559.0</td>\n", " <td>600</td>\n", " <td>-1.0</td>\n", " <td>941.0</td>\n", " <td>910</td>\n", " <td>31.0</td>\n", " <td>AA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>DFW</td>\n", " <td>Dallas Fort Worth Intl</td>\n", " <td>32.896828</td>\n", " <td>-97.037997</td>\n", " <td>607.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>33</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>559.0</td>\n", " <td>600</td>\n", " <td>-1.0</td>\n", " <td>941.0</td>\n", " <td>910</td>\n", " <td>31.0</td>\n", " <td>AA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>DFW</td>\n", " <td>Dallas Fort Worth Intl</td>\n", " <td>32.896828</td>\n", " <td>-97.037997</td>\n", " <td>607.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>34</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>559.0</td>\n", " <td>600</td>\n", " <td>-1.0</td>\n", " <td>941.0</td>\n", " <td>910</td>\n", " <td>31.0</td>\n", " <td>AA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>DFW</td>\n", " <td>Dallas Fort Worth Intl</td>\n", " <td>32.896828</td>\n", " <td>-97.037997</td>\n", " <td>607.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</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", " <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>1007540</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2207.0</td>\n", " <td>2140</td>\n", " <td>27.0</td>\n", " <td>2257.0</td>\n", " <td>2250</td>\n", " <td>7.0</td>\n", " <td>MQ</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 16:00:00</td>\n", " <td>BNA</td>\n", " <td>Nashville Intl</td>\n", " <td>36.124472</td>\n", " <td>-86.678194</td>\n", " <td>599.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>1007541</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2207.0</td>\n", " <td>2140</td>\n", " <td>27.0</td>\n", " <td>2257.0</td>\n", " <td>2250</td>\n", " <td>7.0</td>\n", " <td>MQ</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 16:00:00</td>\n", " <td>BNA</td>\n", " <td>Nashville Intl</td>\n", " <td>36.124472</td>\n", " <td>-86.678194</td>\n", " <td>599.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>1007542</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2207.0</td>\n", " <td>2140</td>\n", " <td>27.0</td>\n", " <td>2257.0</td>\n", " <td>2250</td>\n", " <td>7.0</td>\n", " <td>MQ</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 16:00:00</td>\n", " <td>BNA</td>\n", " <td>Nashville Intl</td>\n", " <td>36.124472</td>\n", " <td>-86.678194</td>\n", " <td>599.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>1007543</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2211.0</td>\n", " <td>2059</td>\n", " <td>72.0</td>\n", " <td>2339.0</td>\n", " <td>2242</td>\n", " <td>57.0</td>\n", " <td>EV</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 15:00:00</td>\n", " <td>STL</td>\n", " <td>Lambert St Louis Intl</td>\n", " <td>38.748697</td>\n", " <td>-90.370028</td>\n", " <td>618.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>1007544</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2211.0</td>\n", " <td>2059</td>\n", " <td>72.0</td>\n", " <td>2339.0</td>\n", " <td>2242</td>\n", " <td>57.0</td>\n", " <td>EV</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 15:00:00</td>\n", " <td>STL</td>\n", " <td>Lambert St Louis Intl</td>\n", " <td>38.748697</td>\n", " <td>-90.370028</td>\n", " <td>618.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>1007545</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2211.0</td>\n", " <td>2059</td>\n", " <td>72.0</td>\n", " <td>2339.0</td>\n", " <td>2242</td>\n", " <td>57.0</td>\n", " <td>EV</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 15:00:00</td>\n", " <td>STL</td>\n", " <td>Lambert St Louis Intl</td>\n", " <td>38.748697</td>\n", " <td>-90.370028</td>\n", " <td>618.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>1007546</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2231.0</td>\n", " <td>2245</td>\n", " <td>-14.0</td>\n", " <td>2335.0</td>\n", " <td>2356</td>\n", " <td>-21.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>PWM</td>\n", " <td>Portland Intl Jetport</td>\n", " <td>43.646161</td>\n", " <td>-70.309281</td>\n", " <td>77.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007547</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2231.0</td>\n", " <td>2245</td>\n", " <td>-14.0</td>\n", " <td>2335.0</td>\n", " <td>2356</td>\n", " <td>-21.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>PWM</td>\n", " <td>Portland Intl Jetport</td>\n", " <td>43.646161</td>\n", " <td>-70.309281</td>\n", " <td>77.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007548</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2231.0</td>\n", " <td>2245</td>\n", " <td>-14.0</td>\n", " <td>2335.0</td>\n", " <td>2356</td>\n", " <td>-21.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>PWM</td>\n", " <td>Portland Intl Jetport</td>\n", " <td>43.646161</td>\n", " <td>-70.309281</td>\n", " <td>77.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007549</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2233.0</td>\n", " <td>2113</td>\n", " <td>80.0</td>\n", " <td>112.0</td>\n", " <td>30</td>\n", " <td>42.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 16:00:00</td>\n", " <td>SFO</td>\n", " <td>San Francisco Intl</td>\n", " <td>37.618972</td>\n", " <td>-122.374889</td>\n", " <td>13.0</td>\n", " <td>-8.0</td>\n", " <td>A</td>\n", " <td>America/Los_Angeles</td>\n", " </tr>\n", " <tr>\n", " <th>1007550</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2233.0</td>\n", " <td>2113</td>\n", " <td>80.0</td>\n", " <td>112.0</td>\n", " <td>30</td>\n", " <td>42.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 16:00:00</td>\n", " <td>SFO</td>\n", " <td>San Francisco Intl</td>\n", " <td>37.618972</td>\n", " <td>-122.374889</td>\n", " <td>13.0</td>\n", " <td>-8.0</td>\n", " <td>A</td>\n", " <td>America/Los_Angeles</td>\n", " </tr>\n", " <tr>\n", " <th>1007551</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2233.0</td>\n", " <td>2113</td>\n", " <td>80.0</td>\n", " <td>112.0</td>\n", " <td>30</td>\n", " <td>42.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 16:00:00</td>\n", " <td>SFO</td>\n", " <td>San Francisco Intl</td>\n", " <td>37.618972</td>\n", " <td>-122.374889</td>\n", " <td>13.0</td>\n", " <td>-8.0</td>\n", " <td>A</td>\n", " <td>America/Los_Angeles</td>\n", " </tr>\n", " <tr>\n", " <th>1007552</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2235.0</td>\n", " <td>2001</td>\n", " <td>154.0</td>\n", " <td>59.0</td>\n", " <td>2249</td>\n", " <td>130.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 15:00:00</td>\n", " <td>MCO</td>\n", " <td>Orlando Intl</td>\n", " <td>28.429394</td>\n", " <td>-81.308994</td>\n", " <td>96.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007553</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2235.0</td>\n", " <td>2001</td>\n", " <td>154.0</td>\n", " <td>59.0</td>\n", " <td>2249</td>\n", " <td>130.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 15:00:00</td>\n", " <td>MCO</td>\n", " <td>Orlando Intl</td>\n", " <td>28.429394</td>\n", " <td>-81.308994</td>\n", " <td>96.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007554</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2235.0</td>\n", " <td>2001</td>\n", " <td>154.0</td>\n", " <td>59.0</td>\n", " <td>2249</td>\n", " <td>130.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 15:00:00</td>\n", " <td>MCO</td>\n", " <td>Orlando Intl</td>\n", " <td>28.429394</td>\n", " <td>-81.308994</td>\n", " <td>96.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007555</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2237.0</td>\n", " <td>2245</td>\n", " <td>-8.0</td>\n", " <td>2345.0</td>\n", " <td>2353</td>\n", " <td>-8.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>BTV</td>\n", " <td>Burlington Intl</td>\n", " <td>44.471861</td>\n", " <td>-73.153278</td>\n", " <td>335.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007556</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2237.0</td>\n", " <td>2245</td>\n", " <td>-8.0</td>\n", " <td>2345.0</td>\n", " <td>2353</td>\n", " <td>-8.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>BTV</td>\n", " <td>Burlington Intl</td>\n", " <td>44.471861</td>\n", " <td>-73.153278</td>\n", " <td>335.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007557</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2237.0</td>\n", " <td>2245</td>\n", " <td>-8.0</td>\n", " <td>2345.0</td>\n", " <td>2353</td>\n", " <td>-8.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>BTV</td>\n", " <td>Burlington Intl</td>\n", " <td>44.471861</td>\n", " <td>-73.153278</td>\n", " <td>335.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007558</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2240.0</td>\n", " <td>2245</td>\n", " <td>-5.0</td>\n", " <td>2334.0</td>\n", " <td>2351</td>\n", " <td>-17.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>SYR</td>\n", " <td>Syracuse Hancock Intl</td>\n", " <td>43.111187</td>\n", " <td>-76.106311</td>\n", " <td>421.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007559</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2240.0</td>\n", " <td>2245</td>\n", " <td>-5.0</td>\n", " <td>2334.0</td>\n", " <td>2351</td>\n", " <td>-17.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>SYR</td>\n", " <td>Syracuse Hancock Intl</td>\n", " <td>43.111187</td>\n", " <td>-76.106311</td>\n", " <td>421.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007560</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2240.0</td>\n", " <td>2245</td>\n", " <td>-5.0</td>\n", " <td>2334.0</td>\n", " <td>2351</td>\n", " <td>-17.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>SYR</td>\n", " <td>Syracuse Hancock Intl</td>\n", " <td>43.111187</td>\n", " <td>-76.106311</td>\n", " <td>421.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007561</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2240.0</td>\n", " <td>2250</td>\n", " <td>-10.0</td>\n", " <td>2347.0</td>\n", " <td>7</td>\n", " <td>-20.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>BUF</td>\n", " <td>Buffalo Niagara Intl</td>\n", " <td>42.940525</td>\n", " <td>-78.732167</td>\n", " <td>724.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007562</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2240.0</td>\n", " <td>2250</td>\n", " <td>-10.0</td>\n", " <td>2347.0</td>\n", " <td>7</td>\n", " <td>-20.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>BUF</td>\n", " <td>Buffalo Niagara Intl</td>\n", " <td>42.940525</td>\n", " <td>-78.732167</td>\n", " <td>724.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007563</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2240.0</td>\n", " <td>2250</td>\n", " <td>-10.0</td>\n", " <td>2347.0</td>\n", " <td>7</td>\n", " <td>-20.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>BUF</td>\n", " <td>Buffalo Niagara Intl</td>\n", " <td>42.940525</td>\n", " <td>-78.732167</td>\n", " <td>724.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007564</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2241.0</td>\n", " <td>2246</td>\n", " <td>-5.0</td>\n", " <td>2345.0</td>\n", " <td>1</td>\n", " <td>-16.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>ROC</td>\n", " <td>Greater Rochester Intl</td>\n", " <td>43.118866</td>\n", " <td>-77.672389</td>\n", " <td>559.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007565</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2241.0</td>\n", " <td>2246</td>\n", " <td>-5.0</td>\n", " <td>2345.0</td>\n", " <td>1</td>\n", " <td>-16.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>ROC</td>\n", " <td>Greater Rochester Intl</td>\n", " <td>43.118866</td>\n", " <td>-77.672389</td>\n", " <td>559.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007566</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2241.0</td>\n", " <td>2246</td>\n", " <td>-5.0</td>\n", " <td>2345.0</td>\n", " <td>1</td>\n", " <td>-16.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>ROC</td>\n", " <td>Greater Rochester Intl</td>\n", " <td>43.118866</td>\n", " <td>-77.672389</td>\n", " <td>559.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007567</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2307.0</td>\n", " <td>2255</td>\n", " <td>12.0</td>\n", " <td>2359.0</td>\n", " <td>2358</td>\n", " <td>1.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>BOS</td>\n", " <td>General Edward Lawrence Logan Intl</td>\n", " <td>42.364347</td>\n", " <td>-71.005181</td>\n", " <td>19.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007568</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2307.0</td>\n", " <td>2255</td>\n", " <td>12.0</td>\n", " <td>2359.0</td>\n", " <td>2358</td>\n", " <td>1.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>BOS</td>\n", " <td>General Edward Lawrence Logan Intl</td>\n", " <td>42.364347</td>\n", " <td>-71.005181</td>\n", " <td>19.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007569</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2307.0</td>\n", " <td>2255</td>\n", " <td>12.0</td>\n", " <td>2359.0</td>\n", " <td>2358</td>\n", " <td>1.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>BOS</td>\n", " <td>General Edward Lawrence Logan Intl</td>\n", " <td>42.364347</td>\n", " <td>-71.005181</td>\n", " <td>19.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>838182 rows × 38 columns</p>\n", "</div>" ], "text/plain": [ " year month day dep_time sched_dep_time dep_delay arr_time \\\n", "4 2013 1 1 554.0 600 -6.0 812.0 \n", "5 2013 1 1 554.0 600 -6.0 812.0 \n", "6 2013 1 1 554.0 600 -6.0 812.0 \n", "8 2013 1 1 555.0 600 -5.0 913.0 \n", "9 2013 1 1 555.0 600 -5.0 913.0 \n", "10 2013 1 1 555.0 600 -5.0 913.0 \n", "11 2013 1 1 557.0 600 -3.0 709.0 \n", "12 2013 1 1 557.0 600 -3.0 709.0 \n", "13 2013 1 1 557.0 600 -3.0 709.0 \n", "14 2013 1 1 557.0 600 -3.0 838.0 \n", "15 2013 1 1 557.0 600 -3.0 838.0 \n", "16 2013 1 1 557.0 600 -3.0 838.0 \n", "17 2013 1 1 558.0 600 -2.0 753.0 \n", "18 2013 1 1 558.0 600 -2.0 753.0 \n", "19 2013 1 1 558.0 600 -2.0 753.0 \n", "20 2013 1 1 558.0 600 -2.0 849.0 \n", "21 2013 1 1 558.0 600 -2.0 849.0 \n", "22 2013 1 1 558.0 600 -2.0 849.0 \n", "23 2013 1 1 558.0 600 -2.0 853.0 \n", "24 2013 1 1 558.0 600 -2.0 853.0 \n", "25 2013 1 1 558.0 600 -2.0 853.0 \n", "26 2013 1 1 558.0 600 -2.0 924.0 \n", "27 2013 1 1 558.0 600 -2.0 924.0 \n", "28 2013 1 1 558.0 600 -2.0 924.0 \n", "29 2013 1 1 558.0 600 -2.0 923.0 \n", "30 2013 1 1 558.0 600 -2.0 923.0 \n", "31 2013 1 1 558.0 600 -2.0 923.0 \n", "32 2013 1 1 559.0 600 -1.0 941.0 \n", "33 2013 1 1 559.0 600 -1.0 941.0 \n", "34 2013 1 1 559.0 600 -1.0 941.0 \n", "... ... ... ... ... ... ... ... \n", "1007540 2013 9 30 2207.0 2140 27.0 2257.0 \n", "1007541 2013 9 30 2207.0 2140 27.0 2257.0 \n", "1007542 2013 9 30 2207.0 2140 27.0 2257.0 \n", "1007543 2013 9 30 2211.0 2059 72.0 2339.0 \n", "1007544 2013 9 30 2211.0 2059 72.0 2339.0 \n", "1007545 2013 9 30 2211.0 2059 72.0 2339.0 \n", "1007546 2013 9 30 2231.0 2245 -14.0 2335.0 \n", "1007547 2013 9 30 2231.0 2245 -14.0 2335.0 \n", "1007548 2013 9 30 2231.0 2245 -14.0 2335.0 \n", "1007549 2013 9 30 2233.0 2113 80.0 112.0 \n", "1007550 2013 9 30 2233.0 2113 80.0 112.0 \n", "1007551 2013 9 30 2233.0 2113 80.0 112.0 \n", "1007552 2013 9 30 2235.0 2001 154.0 59.0 \n", "1007553 2013 9 30 2235.0 2001 154.0 59.0 \n", "1007554 2013 9 30 2235.0 2001 154.0 59.0 \n", "1007555 2013 9 30 2237.0 2245 -8.0 2345.0 \n", "1007556 2013 9 30 2237.0 2245 -8.0 2345.0 \n", "1007557 2013 9 30 2237.0 2245 -8.0 2345.0 \n", "1007558 2013 9 30 2240.0 2245 -5.0 2334.0 \n", "1007559 2013 9 30 2240.0 2245 -5.0 2334.0 \n", "1007560 2013 9 30 2240.0 2245 -5.0 2334.0 \n", "1007561 2013 9 30 2240.0 2250 -10.0 2347.0 \n", "1007562 2013 9 30 2240.0 2250 -10.0 2347.0 \n", "1007563 2013 9 30 2240.0 2250 -10.0 2347.0 \n", "1007564 2013 9 30 2241.0 2246 -5.0 2345.0 \n", "1007565 2013 9 30 2241.0 2246 -5.0 2345.0 \n", "1007566 2013 9 30 2241.0 2246 -5.0 2345.0 \n", "1007567 2013 9 30 2307.0 2255 12.0 2359.0 \n", "1007568 2013 9 30 2307.0 2255 12.0 2359.0 \n", "1007569 2013 9 30 2307.0 2255 12.0 2359.0 \n", "\n", " sched_arr_time arr_delay carrier ... visib \\\n", "4 837 -25.0 DL ... 10.0 \n", "5 837 -25.0 DL ... 10.0 \n", "6 837 -25.0 DL ... 10.0 \n", "8 854 19.0 B6 ... 10.0 \n", "9 854 19.0 B6 ... 10.0 \n", "10 854 19.0 B6 ... 10.0 \n", "11 723 -14.0 EV ... 10.0 \n", "12 723 -14.0 EV ... 10.0 \n", "13 723 -14.0 EV ... 10.0 \n", "14 846 -8.0 B6 ... 10.0 \n", "15 846 -8.0 B6 ... 10.0 \n", "16 846 -8.0 B6 ... 10.0 \n", "17 745 8.0 AA ... 10.0 \n", "18 745 8.0 AA ... 10.0 \n", "19 745 8.0 AA ... 10.0 \n", "20 851 -2.0 B6 ... 10.0 \n", "21 851 -2.0 B6 ... 10.0 \n", "22 851 -2.0 B6 ... 10.0 \n", "23 856 -3.0 B6 ... 10.0 \n", "24 856 -3.0 B6 ... 10.0 \n", "25 856 -3.0 B6 ... 10.0 \n", "26 917 7.0 UA ... 10.0 \n", "27 917 7.0 UA ... 10.0 \n", "28 917 7.0 UA ... 10.0 \n", "29 937 -14.0 UA ... 10.0 \n", "30 937 -14.0 UA ... 10.0 \n", "31 937 -14.0 UA ... 10.0 \n", "32 910 31.0 AA ... 10.0 \n", "33 910 31.0 AA ... 10.0 \n", "34 910 31.0 AA ... 10.0 \n", "... ... ... ... ... ... \n", "1007540 2250 7.0 MQ ... 10.0 \n", "1007541 2250 7.0 MQ ... 10.0 \n", "1007542 2250 7.0 MQ ... 10.0 \n", "1007543 2242 57.0 EV ... 10.0 \n", "1007544 2242 57.0 EV ... 10.0 \n", "1007545 2242 57.0 EV ... 10.0 \n", "1007546 2356 -21.0 B6 ... 10.0 \n", "1007547 2356 -21.0 B6 ... 10.0 \n", "1007548 2356 -21.0 B6 ... 10.0 \n", "1007549 30 42.0 UA ... 10.0 \n", "1007550 30 42.0 UA ... 10.0 \n", "1007551 30 42.0 UA ... 10.0 \n", "1007552 2249 130.0 B6 ... 10.0 \n", "1007553 2249 130.0 B6 ... 10.0 \n", "1007554 2249 130.0 B6 ... 10.0 \n", "1007555 2353 -8.0 B6 ... 10.0 \n", "1007556 2353 -8.0 B6 ... 10.0 \n", "1007557 2353 -8.0 B6 ... 10.0 \n", "1007558 2351 -17.0 B6 ... 10.0 \n", "1007559 2351 -17.0 B6 ... 10.0 \n", "1007560 2351 -17.0 B6 ... 10.0 \n", "1007561 7 -20.0 B6 ... 10.0 \n", "1007562 7 -20.0 B6 ... 10.0 \n", "1007563 7 -20.0 B6 ... 10.0 \n", "1007564 1 -16.0 B6 ... 10.0 \n", "1007565 1 -16.0 B6 ... 10.0 \n", "1007566 1 -16.0 B6 ... 10.0 \n", "1007567 2358 1.0 B6 ... 10.0 \n", "1007568 2358 1.0 B6 ... 10.0 \n", "1007569 2358 1.0 B6 ... 10.0 \n", "\n", " time_hour_y faa name \\\n", "4 2013-01-01 01:00:00 ATL Hartsfield Jackson Atlanta Intl \n", "5 2013-01-01 01:00:00 ATL Hartsfield Jackson Atlanta Intl \n", "6 2013-01-01 01:00:00 ATL Hartsfield Jackson Atlanta Intl \n", "8 2013-01-01 01:00:00 FLL Fort Lauderdale Hollywood Intl \n", "9 2013-01-01 01:00:00 FLL Fort Lauderdale Hollywood Intl \n", "10 2013-01-01 01:00:00 FLL Fort Lauderdale Hollywood Intl \n", "11 2013-01-01 01:00:00 IAD Washington Dulles Intl \n", "12 2013-01-01 01:00:00 IAD Washington Dulles Intl \n", "13 2013-01-01 01:00:00 IAD Washington Dulles Intl \n", "14 2013-01-01 01:00:00 MCO Orlando Intl \n", "15 2013-01-01 01:00:00 MCO Orlando Intl \n", "16 2013-01-01 01:00:00 MCO Orlando Intl \n", "17 2013-01-01 01:00:00 ORD Chicago Ohare Intl \n", "18 2013-01-01 01:00:00 ORD Chicago Ohare Intl \n", "19 2013-01-01 01:00:00 ORD Chicago Ohare Intl \n", "20 2013-01-01 01:00:00 PBI Palm Beach Intl \n", "21 2013-01-01 01:00:00 PBI Palm Beach Intl \n", "22 2013-01-01 01:00:00 PBI Palm Beach Intl \n", "23 2013-01-01 01:00:00 TPA Tampa Intl \n", "24 2013-01-01 01:00:00 TPA Tampa Intl \n", "25 2013-01-01 01:00:00 TPA Tampa Intl \n", "26 2013-01-01 01:00:00 LAX Los Angeles Intl \n", "27 2013-01-01 01:00:00 LAX Los Angeles Intl \n", "28 2013-01-01 01:00:00 LAX Los Angeles Intl \n", "29 2013-01-01 01:00:00 SFO San Francisco Intl \n", "30 2013-01-01 01:00:00 SFO San Francisco Intl \n", "31 2013-01-01 01:00:00 SFO San Francisco Intl \n", "32 2013-01-01 01:00:00 DFW Dallas Fort Worth Intl \n", "33 2013-01-01 01:00:00 DFW Dallas Fort Worth Intl \n", "34 2013-01-01 01:00:00 DFW Dallas Fort Worth Intl \n", "... ... ... ... \n", "1007540 2013-09-30 16:00:00 BNA Nashville Intl \n", "1007541 2013-09-30 16:00:00 BNA Nashville Intl \n", "1007542 2013-09-30 16:00:00 BNA Nashville Intl \n", "1007543 2013-09-30 15:00:00 STL Lambert St Louis Intl \n", "1007544 2013-09-30 15:00:00 STL Lambert St Louis Intl \n", "1007545 2013-09-30 15:00:00 STL Lambert St Louis Intl \n", "1007546 2013-09-30 17:00:00 PWM Portland Intl Jetport \n", "1007547 2013-09-30 17:00:00 PWM Portland Intl Jetport \n", "1007548 2013-09-30 17:00:00 PWM Portland Intl Jetport \n", "1007549 2013-09-30 16:00:00 SFO San Francisco Intl \n", "1007550 2013-09-30 16:00:00 SFO San Francisco Intl \n", "1007551 2013-09-30 16:00:00 SFO San Francisco Intl \n", "1007552 2013-09-30 15:00:00 MCO Orlando Intl \n", "1007553 2013-09-30 15:00:00 MCO Orlando Intl \n", "1007554 2013-09-30 15:00:00 MCO Orlando Intl \n", "1007555 2013-09-30 17:00:00 BTV Burlington Intl \n", "1007556 2013-09-30 17:00:00 BTV Burlington Intl \n", "1007557 2013-09-30 17:00:00 BTV Burlington Intl \n", "1007558 2013-09-30 17:00:00 SYR Syracuse Hancock Intl \n", "1007559 2013-09-30 17:00:00 SYR Syracuse Hancock Intl \n", "1007560 2013-09-30 17:00:00 SYR Syracuse Hancock Intl \n", "1007561 2013-09-30 17:00:00 BUF Buffalo Niagara Intl \n", "1007562 2013-09-30 17:00:00 BUF Buffalo Niagara Intl \n", "1007563 2013-09-30 17:00:00 BUF Buffalo Niagara Intl \n", "1007564 2013-09-30 17:00:00 ROC Greater Rochester Intl \n", "1007565 2013-09-30 17:00:00 ROC Greater Rochester Intl \n", "1007566 2013-09-30 17:00:00 ROC Greater Rochester Intl \n", "1007567 2013-09-30 17:00:00 BOS General Edward Lawrence Logan Intl \n", "1007568 2013-09-30 17:00:00 BOS General Edward Lawrence Logan Intl \n", "1007569 2013-09-30 17:00:00 BOS General Edward Lawrence Logan Intl \n", "\n", " lat lon alt tz dst tzone \n", "4 33.636719 -84.428067 1026.0 -5.0 A America/New_York \n", "5 33.636719 -84.428067 1026.0 -5.0 A America/New_York \n", "6 33.636719 -84.428067 1026.0 -5.0 A America/New_York \n", "8 26.072583 -80.152750 9.0 -5.0 A America/New_York \n", "9 26.072583 -80.152750 9.0 -5.0 A America/New_York \n", "10 26.072583 -80.152750 9.0 -5.0 A America/New_York \n", "11 38.944533 -77.455811 313.0 -5.0 A America/New_York \n", "12 38.944533 -77.455811 313.0 -5.0 A America/New_York \n", "13 38.944533 -77.455811 313.0 -5.0 A America/New_York \n", "14 28.429394 -81.308994 96.0 -5.0 A America/New_York \n", "15 28.429394 -81.308994 96.0 -5.0 A America/New_York \n", "16 28.429394 -81.308994 96.0 -5.0 A America/New_York \n", "17 41.978603 -87.904842 668.0 -6.0 A America/Chicago \n", "18 41.978603 -87.904842 668.0 -6.0 A America/Chicago \n", "19 41.978603 -87.904842 668.0 -6.0 A America/Chicago \n", "20 26.683161 -80.095589 19.0 -5.0 A America/New_York \n", "21 26.683161 -80.095589 19.0 -5.0 A America/New_York \n", "22 26.683161 -80.095589 19.0 -5.0 A America/New_York \n", "23 27.975472 -82.533250 26.0 -5.0 A America/New_York \n", "24 27.975472 -82.533250 26.0 -5.0 A America/New_York \n", "25 27.975472 -82.533250 26.0 -5.0 A America/New_York \n", "26 33.942536 -118.408075 126.0 -8.0 A America/Los_Angeles \n", "27 33.942536 -118.408075 126.0 -8.0 A America/Los_Angeles \n", "28 33.942536 -118.408075 126.0 -8.0 A America/Los_Angeles \n", "29 37.618972 -122.374889 13.0 -8.0 A America/Los_Angeles \n", "30 37.618972 -122.374889 13.0 -8.0 A America/Los_Angeles \n", "31 37.618972 -122.374889 13.0 -8.0 A America/Los_Angeles \n", "32 32.896828 -97.037997 607.0 -6.0 A America/Chicago \n", "33 32.896828 -97.037997 607.0 -6.0 A America/Chicago \n", "34 32.896828 -97.037997 607.0 -6.0 A America/Chicago \n", "... ... ... ... ... .. ... \n", "1007540 36.124472 -86.678194 599.0 -6.0 A America/Chicago \n", "1007541 36.124472 -86.678194 599.0 -6.0 A America/Chicago \n", "1007542 36.124472 -86.678194 599.0 -6.0 A America/Chicago \n", "1007543 38.748697 -90.370028 618.0 -6.0 A America/Chicago \n", "1007544 38.748697 -90.370028 618.0 -6.0 A America/Chicago \n", "1007545 38.748697 -90.370028 618.0 -6.0 A America/Chicago \n", "1007546 43.646161 -70.309281 77.0 -5.0 A America/New_York \n", "1007547 43.646161 -70.309281 77.0 -5.0 A America/New_York \n", "1007548 43.646161 -70.309281 77.0 -5.0 A America/New_York \n", "1007549 37.618972 -122.374889 13.0 -8.0 A America/Los_Angeles \n", "1007550 37.618972 -122.374889 13.0 -8.0 A America/Los_Angeles \n", "1007551 37.618972 -122.374889 13.0 -8.0 A America/Los_Angeles \n", "1007552 28.429394 -81.308994 96.0 -5.0 A America/New_York \n", "1007553 28.429394 -81.308994 96.0 -5.0 A America/New_York \n", "1007554 28.429394 -81.308994 96.0 -5.0 A America/New_York \n", "1007555 44.471861 -73.153278 335.0 -5.0 A America/New_York \n", "1007556 44.471861 -73.153278 335.0 -5.0 A America/New_York \n", "1007557 44.471861 -73.153278 335.0 -5.0 A America/New_York \n", "1007558 43.111187 -76.106311 421.0 -5.0 A America/New_York \n", "1007559 43.111187 -76.106311 421.0 -5.0 A America/New_York \n", "1007560 43.111187 -76.106311 421.0 -5.0 A America/New_York \n", "1007561 42.940525 -78.732167 724.0 -5.0 A America/New_York \n", "1007562 42.940525 -78.732167 724.0 -5.0 A America/New_York \n", "1007563 42.940525 -78.732167 724.0 -5.0 A America/New_York \n", "1007564 43.118866 -77.672389 559.0 -5.0 A America/New_York \n", "1007565 43.118866 -77.672389 559.0 -5.0 A America/New_York \n", "1007566 43.118866 -77.672389 559.0 -5.0 A America/New_York \n", "1007567 42.364347 -71.005181 19.0 -5.0 A America/New_York \n", "1007568 42.364347 -71.005181 19.0 -5.0 A America/New_York \n", "1007569 42.364347 -71.005181 19.0 -5.0 A America/New_York \n", "\n", "[838182 rows x 38 columns]" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%matplotlib inline\n", "\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", "\n", "flights = pd.read_csv('data/nycflights13/flights.csv.gz')\n", "weather = pd.read_csv('data/nycflights13/weather.csv.gz')\n", "airports = pd.read_csv('data/nycflights13/airports.csv.gz')\n", "\n", "df_withweather = pd.merge(flights, weather, how='left', on=['year', 'month', 'day', 'hour'])\n", "df = pd.merge(df_withweather, airports, how='left', left_on='dest', right_on='faa')\n", "\n", "df = df.dropna()\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7.1 特征向量" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pred = 'dep_delay'\n", "features = ['month', 'day', 'dep_time', 'arr_time', 'carrier', 'dest', 'air_time',\n", " 'distance', 'lat', 'lon', 'alt', 'dewp', 'humid', 'wind_speed', 'wind_gust',\n", " 'precip', 'pressure', 'visib']\n", "features_v = df[features]\n", "pred_v = df[pred]\n", "\n", "pd.options.mode.chained_assignment = None #default='warn'\n", "\n", "# 因为航空公司不是一个数字，我们把它转化为数字哑变量\n", "features_v['carrier'] = pd.factorize(features_v['carrier'])[0]\n", "\n", "# dest也不是一个数字，我们也把它转为数字\n", "features_v['dest'] = pd.factorize(features_v['dest'])[0]" ] }, { "cell_type": "code", "execution_count": 49, "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>month</th>\n", " <th>day</th>\n", " <th>dep_time</th>\n", " <th>arr_time</th>\n", " <th>carrier</th>\n", " <th>dest</th>\n", " <th>air_time</th>\n", " <th>distance</th>\n", " <th>lat</th>\n", " <th>lon</th>\n", " <th>alt</th>\n", " <th>dewp</th>\n", " <th>humid</th>\n", " <th>wind_speed</th>\n", " <th>wind_gust</th>\n", " <th>precip</th>\n", " <th>pressure</th>\n", " <th>visib</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>554.0</td>\n", " <td>812.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>116.0</td>\n", " <td>762</td>\n", " 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<td>913.0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>158.0</td>\n", " <td>1065</td>\n", " <td>26.072583</td>\n", " <td>-80.152750</td>\n", " <td>9.0</td>\n", " <td>26.06</td>\n", " <td>57.33</td>\n", " <td>13.80936</td>\n", " <td>15.891535</td>\n", " <td>0.0</td>\n", " <td>1011.9</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>557.0</td>\n", " <td>709.0</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>53.0</td>\n", " <td>229</td>\n", " <td>38.944533</td>\n", " <td>-77.455811</td>\n", " <td>313.0</td>\n", " <td>26.06</td>\n", " <td>59.37</td>\n", " <td>10.35702</td>\n", " <td>11.918651</td>\n", " <td>0.0</td>\n", " <td>1012.0</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>557.0</td>\n", " <td>709.0</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>53.0</td>\n", " <td>229</td>\n", " <td>38.944533</td>\n", " <td>-77.455811</td>\n", " <td>313.0</td>\n", " <td>26.06</td>\n", " <td>59.37</td>\n", " <td>12.65858</td>\n", " <td>14.567241</td>\n", " <td>0.0</td>\n", " <td>1012.6</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>557.0</td>\n", " <td>709.0</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>53.0</td>\n", " <td>229</td>\n", " <td>38.944533</td>\n", " <td>-77.455811</td>\n", " <td>313.0</td>\n", " <td>26.06</td>\n", " <td>57.33</td>\n", " <td>13.80936</td>\n", " <td>15.891535</td>\n", " <td>0.0</td>\n", " <td>1011.9</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>557.0</td>\n", " <td>838.0</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>140.0</td>\n", " <td>944</td>\n", " <td>28.429394</td>\n", " <td>-81.308994</td>\n", " <td>96.0</td>\n", " <td>26.06</td>\n", " <td>59.37</td>\n", " <td>10.35702</td>\n", " <td>11.918651</td>\n", " <td>0.0</td>\n", " <td>1012.0</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>557.0</td>\n", " <td>838.0</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>140.0</td>\n", " <td>944</td>\n", " <td>28.429394</td>\n", " <td>-81.308994</td>\n", " <td>96.0</td>\n", " <td>26.06</td>\n", " <td>59.37</td>\n", " <td>12.65858</td>\n", " <td>14.567241</td>\n", " <td>0.0</td>\n", " <td>1012.6</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>557.0</td>\n", " <td>838.0</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>140.0</td>\n", " <td>944</td>\n", " <td>28.429394</td>\n", " <td>-81.308994</td>\n", " <td>96.0</td>\n", " <td>26.06</td>\n", " <td>57.33</td>\n", " <td>13.80936</td>\n", " <td>15.891535</td>\n", " <td>0.0</td>\n", " <td>1011.9</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>753.0</td>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>138.0</td>\n", " <td>733</td>\n", " <td>41.978603</td>\n", " <td>-87.904842</td>\n", " <td>668.0</td>\n", " <td>26.06</td>\n", " <td>59.37</td>\n", " <td>10.35702</td>\n", " <td>11.918651</td>\n", " <td>0.0</td>\n", " <td>1012.0</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>753.0</td>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>138.0</td>\n", " <td>733</td>\n", " <td>41.978603</td>\n", " <td>-87.904842</td>\n", " <td>668.0</td>\n", " <td>26.06</td>\n", " <td>59.37</td>\n", " <td>12.65858</td>\n", " <td>14.567241</td>\n", " <td>0.0</td>\n", " <td>1012.6</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>753.0</td>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>138.0</td>\n", " <td>733</td>\n", " <td>41.978603</td>\n", " <td>-87.904842</td>\n", " <td>668.0</td>\n", " <td>26.06</td>\n", " <td>57.33</td>\n", " <td>13.80936</td>\n", " <td>15.891535</td>\n", " <td>0.0</td>\n", " <td>1011.9</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>849.0</td>\n", " <td>1</td>\n", " <td>5</td>\n", " <td>149.0</td>\n", " <td>1028</td>\n", " <td>26.683161</td>\n", " <td>-80.095589</td>\n", " <td>19.0</td>\n", " <td>26.06</td>\n", " <td>59.37</td>\n", " <td>10.35702</td>\n", " <td>11.918651</td>\n", " <td>0.0</td>\n", " <td>1012.0</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>849.0</td>\n", " <td>1</td>\n", " <td>5</td>\n", " <td>149.0</td>\n", " <td>1028</td>\n", " <td>26.683161</td>\n", " <td>-80.095589</td>\n", " <td>19.0</td>\n", " <td>26.06</td>\n", " <td>59.37</td>\n", " <td>12.65858</td>\n", " <td>14.567241</td>\n", " <td>0.0</td>\n", " <td>1012.6</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>849.0</td>\n", " <td>1</td>\n", " <td>5</td>\n", " <td>149.0</td>\n", " <td>1028</td>\n", " <td>26.683161</td>\n", " <td>-80.095589</td>\n", " <td>19.0</td>\n", " <td>26.06</td>\n", " <td>57.33</td>\n", " <td>13.80936</td>\n", " <td>15.891535</td>\n", " <td>0.0</td>\n", " <td>1011.9</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>853.0</td>\n", " <td>1</td>\n", " <td>6</td>\n", " <td>158.0</td>\n", " <td>1005</td>\n", " <td>27.975472</td>\n", " <td>-82.533250</td>\n", " <td>26.0</td>\n", " <td>26.06</td>\n", " <td>59.37</td>\n", " <td>10.35702</td>\n", " <td>11.918651</td>\n", " <td>0.0</td>\n", " <td>1012.0</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>853.0</td>\n", " <td>1</td>\n", " <td>6</td>\n", " <td>158.0</td>\n", " <td>1005</td>\n", " <td>27.975472</td>\n", " <td>-82.533250</td>\n", " <td>26.0</td>\n", " <td>26.06</td>\n", " <td>59.37</td>\n", " <td>12.65858</td>\n", " <td>14.567241</td>\n", " <td>0.0</td>\n", " <td>1012.6</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>853.0</td>\n", " <td>1</td>\n", " <td>6</td>\n", " <td>158.0</td>\n", " <td>1005</td>\n", " <td>27.975472</td>\n", " <td>-82.533250</td>\n", " <td>26.0</td>\n", " <td>26.06</td>\n", " <td>57.33</td>\n", " <td>13.80936</td>\n", " <td>15.891535</td>\n", " <td>0.0</td>\n", " <td>1011.9</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>924.0</td>\n", " <td>4</td>\n", " <td>7</td>\n", " <td>345.0</td>\n", " <td>2475</td>\n", " <td>33.942536</td>\n", " <td>-118.408075</td>\n", " <td>126.0</td>\n", " <td>26.06</td>\n", " <td>59.37</td>\n", " <td>10.35702</td>\n", " <td>11.918651</td>\n", " <td>0.0</td>\n", " <td>1012.0</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>924.0</td>\n", " <td>4</td>\n", " <td>7</td>\n", " <td>345.0</td>\n", " <td>2475</td>\n", " <td>33.942536</td>\n", " <td>-118.408075</td>\n", " <td>126.0</td>\n", " <td>26.06</td>\n", " <td>59.37</td>\n", " <td>12.65858</td>\n", " <td>14.567241</td>\n", " <td>0.0</td>\n", " <td>1012.6</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>924.0</td>\n", " <td>4</td>\n", " <td>7</td>\n", " <td>345.0</td>\n", " <td>2475</td>\n", " <td>33.942536</td>\n", " <td>-118.408075</td>\n", " <td>126.0</td>\n", " <td>26.06</td>\n", " <td>57.33</td>\n", " <td>13.80936</td>\n", " <td>15.891535</td>\n", " <td>0.0</td>\n", " <td>1011.9</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>923.0</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>361.0</td>\n", " <td>2565</td>\n", " <td>37.618972</td>\n", " <td>-122.374889</td>\n", " <td>13.0</td>\n", " <td>26.06</td>\n", " <td>59.37</td>\n", " <td>10.35702</td>\n", " <td>11.918651</td>\n", " <td>0.0</td>\n", " <td>1012.0</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>923.0</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>361.0</td>\n", " <td>2565</td>\n", " <td>37.618972</td>\n", " <td>-122.374889</td>\n", " <td>13.0</td>\n", " <td>26.06</td>\n", " <td>59.37</td>\n", " <td>12.65858</td>\n", " <td>14.567241</td>\n", " <td>0.0</td>\n", " <td>1012.6</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>31</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>923.0</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>361.0</td>\n", " <td>2565</td>\n", " <td>37.618972</td>\n", " <td>-122.374889</td>\n", " <td>13.0</td>\n", " <td>26.06</td>\n", " <td>57.33</td>\n", " <td>13.80936</td>\n", " <td>15.891535</td>\n", " <td>0.0</td>\n", " <td>1011.9</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>32</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>559.0</td>\n", " <td>941.0</td>\n", " <td>3</td>\n", " <td>9</td>\n", " <td>257.0</td>\n", " <td>1389</td>\n", " <td>32.896828</td>\n", " <td>-97.037997</td>\n", " <td>607.0</td>\n", " <td>26.06</td>\n", " <td>59.37</td>\n", " <td>10.35702</td>\n", " <td>11.918651</td>\n", " <td>0.0</td>\n", " <td>1012.0</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>33</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>559.0</td>\n", " <td>941.0</td>\n", " <td>3</td>\n", " <td>9</td>\n", " <td>257.0</td>\n", " <td>1389</td>\n", " <td>32.896828</td>\n", " <td>-97.037997</td>\n", " <td>607.0</td>\n", " <td>26.06</td>\n", " <td>59.37</td>\n", " <td>12.65858</td>\n", " <td>14.567241</td>\n", " <td>0.0</td>\n", " <td>1012.6</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>34</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>559.0</td>\n", " <td>941.0</td>\n", " <td>3</td>\n", " <td>9</td>\n", " <td>257.0</td>\n", " <td>1389</td>\n", " <td>32.896828</td>\n", " <td>-97.037997</td>\n", " <td>607.0</td>\n", " <td>26.06</td>\n", " <td>57.33</td>\n", " <td>13.80936</td>\n", " <td>15.891535</td>\n", " <td>0.0</td>\n", " <td>1011.9</td>\n", " <td>10.0</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", " <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>1007540</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2207.0</td>\n", " <td>2257.0</td>\n", " <td>5</td>\n", " <td>45</td>\n", " <td>97.0</td>\n", " <td>764</td>\n", " <td>36.124472</td>\n", " <td>-86.678194</td>\n", " <td>599.0</td>\n", " <td>53.06</td>\n", " <td>54.94</td>\n", " <td>5.75390</td>\n", " <td>6.621473</td>\n", " <td>0.0</td>\n", " <td>1015.0</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007541</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2207.0</td>\n", " <td>2257.0</td>\n", " <td>5</td>\n", " <td>45</td>\n", " <td>97.0</td>\n", " <td>764</td>\n", " <td>36.124472</td>\n", " <td>-86.678194</td>\n", " <td>599.0</td>\n", " <td>55.04</td>\n", " <td>67.69</td>\n", " <td>6.90468</td>\n", " <td>7.945768</td>\n", " <td>0.0</td>\n", " <td>1015.6</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007542</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2207.0</td>\n", " <td>2257.0</td>\n", " <td>5</td>\n", " <td>45</td>\n", " <td>97.0</td>\n", " <td>764</td>\n", " <td>36.124472</td>\n", " <td>-86.678194</td>\n", " <td>599.0</td>\n", " <td>51.08</td>\n", " <td>52.67</td>\n", " <td>6.90468</td>\n", " <td>7.945768</td>\n", " <td>0.0</td>\n", " <td>1015.0</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007543</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2211.0</td>\n", " <td>2339.0</td>\n", " <td>2</td>\n", " <td>40</td>\n", " <td>120.0</td>\n", " <td>872</td>\n", " <td>38.748697</td>\n", " <td>-90.370028</td>\n", " <td>618.0</td>\n", " <td>51.98</td>\n", " <td>49.36</td>\n", " <td>6.90468</td>\n", " <td>7.945768</td>\n", " <td>0.0</td>\n", " <td>1015.2</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007544</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2211.0</td>\n", " <td>2339.0</td>\n", " <td>2</td>\n", " <td>40</td>\n", " <td>120.0</td>\n", " <td>872</td>\n", " <td>38.748697</td>\n", " <td>-90.370028</td>\n", " <td>618.0</td>\n", " <td>53.06</td>\n", " <td>58.80</td>\n", " <td>8.05546</td>\n", " <td>9.270062</td>\n", " <td>0.0</td>\n", " <td>1015.8</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007545</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2211.0</td>\n", " <td>2339.0</td>\n", " <td>2</td>\n", " <td>40</td>\n", " <td>120.0</td>\n", " <td>872</td>\n", " <td>38.748697</td>\n", " <td>-90.370028</td>\n", " <td>618.0</td>\n", " <td>51.08</td>\n", " <td>46.04</td>\n", " <td>6.90468</td>\n", " <td>7.945768</td>\n", " <td>0.0</td>\n", " <td>1015.1</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007546</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2231.0</td>\n", " <td>2335.0</td>\n", " <td>1</td>\n", " <td>51</td>\n", " <td>48.0</td>\n", " <td>273</td>\n", " <td>43.646161</td>\n", " <td>-70.309281</td>\n", " <td>77.0</td>\n", " <td>53.06</td>\n", " <td>58.80</td>\n", " <td>5.75390</td>\n", " <td>6.621473</td>\n", " <td>0.0</td>\n", " <td>1015.4</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007547</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2231.0</td>\n", " <td>2335.0</td>\n", " <td>1</td>\n", " <td>51</td>\n", " <td>48.0</td>\n", " <td>273</td>\n", " <td>43.646161</td>\n", " <td>-70.309281</td>\n", " <td>77.0</td>\n", " <td>55.94</td>\n", " <td>74.94</td>\n", " <td>6.90468</td>\n", " <td>7.945768</td>\n", " <td>0.0</td>\n", " <td>1016.0</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007548</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2231.0</td>\n", " <td>2335.0</td>\n", " <td>1</td>\n", " <td>51</td>\n", " <td>48.0</td>\n", " <td>273</td>\n", " <td>43.646161</td>\n", " <td>-70.309281</td>\n", " <td>77.0</td>\n", " <td>51.98</td>\n", " <td>58.65</td>\n", " <td>9.20624</td>\n", " <td>10.594357</td>\n", " <td>0.0</td>\n", " <td>1015.4</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007549</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2233.0</td>\n", " <td>112.0</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>318.0</td>\n", " <td>2565</td>\n", " <td>37.618972</td>\n", " <td>-122.374889</td>\n", " <td>13.0</td>\n", " <td>53.06</td>\n", " <td>54.94</td>\n", " <td>5.75390</td>\n", " <td>6.621473</td>\n", " <td>0.0</td>\n", " <td>1015.0</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007550</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2233.0</td>\n", " <td>112.0</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>318.0</td>\n", " <td>2565</td>\n", " <td>37.618972</td>\n", " <td>-122.374889</td>\n", " <td>13.0</td>\n", " <td>55.04</td>\n", " <td>67.69</td>\n", " <td>6.90468</td>\n", " <td>7.945768</td>\n", " <td>0.0</td>\n", " <td>1015.6</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007551</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2233.0</td>\n", " <td>112.0</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>318.0</td>\n", " <td>2565</td>\n", " <td>37.618972</td>\n", " <td>-122.374889</td>\n", " <td>13.0</td>\n", " <td>51.08</td>\n", " <td>52.67</td>\n", " <td>6.90468</td>\n", " <td>7.945768</td>\n", " <td>0.0</td>\n", " <td>1015.0</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007552</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2235.0</td>\n", " <td>59.0</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>123.0</td>\n", " <td>944</td>\n", " <td>28.429394</td>\n", " <td>-81.308994</td>\n", " <td>96.0</td>\n", " <td>51.98</td>\n", " <td>49.36</td>\n", " <td>6.90468</td>\n", " <td>7.945768</td>\n", " <td>0.0</td>\n", " <td>1015.2</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007553</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2235.0</td>\n", " <td>59.0</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>123.0</td>\n", " <td>944</td>\n", " <td>28.429394</td>\n", " <td>-81.308994</td>\n", " <td>96.0</td>\n", " <td>53.06</td>\n", " <td>58.80</td>\n", " <td>8.05546</td>\n", " <td>9.270062</td>\n", " <td>0.0</td>\n", " <td>1015.8</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007554</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2235.0</td>\n", " <td>59.0</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>123.0</td>\n", " <td>944</td>\n", " <td>28.429394</td>\n", " <td>-81.308994</td>\n", " <td>96.0</td>\n", " <td>51.08</td>\n", " <td>46.04</td>\n", " <td>6.90468</td>\n", " <td>7.945768</td>\n", " <td>0.0</td>\n", " <td>1015.1</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007555</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2237.0</td>\n", " <td>2345.0</td>\n", " <td>1</td>\n", " <td>47</td>\n", " <td>43.0</td>\n", " <td>266</td>\n", " <td>44.471861</td>\n", " <td>-73.153278</td>\n", " <td>335.0</td>\n", " <td>53.06</td>\n", " <td>58.80</td>\n", " <td>5.75390</td>\n", " <td>6.621473</td>\n", " <td>0.0</td>\n", " <td>1015.4</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007556</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2237.0</td>\n", " <td>2345.0</td>\n", " <td>1</td>\n", " <td>47</td>\n", " <td>43.0</td>\n", " <td>266</td>\n", " <td>44.471861</td>\n", " <td>-73.153278</td>\n", " <td>335.0</td>\n", " <td>55.94</td>\n", " <td>74.94</td>\n", " <td>6.90468</td>\n", " <td>7.945768</td>\n", " <td>0.0</td>\n", " <td>1016.0</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007557</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2237.0</td>\n", " <td>2345.0</td>\n", " <td>1</td>\n", " <td>47</td>\n", " <td>43.0</td>\n", " <td>266</td>\n", " <td>44.471861</td>\n", " <td>-73.153278</td>\n", " <td>335.0</td>\n", " <td>51.98</td>\n", " <td>58.65</td>\n", " <td>9.20624</td>\n", " <td>10.594357</td>\n", " <td>0.0</td>\n", " <td>1015.4</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007558</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2240.0</td>\n", " <td>2334.0</td>\n", " <td>1</td>\n", " <td>29</td>\n", " <td>41.0</td>\n", " <td>209</td>\n", " <td>43.111187</td>\n", " <td>-76.106311</td>\n", " <td>421.0</td>\n", " <td>53.06</td>\n", " <td>58.80</td>\n", " <td>5.75390</td>\n", " <td>6.621473</td>\n", " <td>0.0</td>\n", " <td>1015.4</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007559</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2240.0</td>\n", " <td>2334.0</td>\n", " <td>1</td>\n", " <td>29</td>\n", " <td>41.0</td>\n", " <td>209</td>\n", " <td>43.111187</td>\n", " <td>-76.106311</td>\n", " <td>421.0</td>\n", " <td>55.94</td>\n", " <td>74.94</td>\n", " <td>6.90468</td>\n", " <td>7.945768</td>\n", " <td>0.0</td>\n", " <td>1016.0</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007560</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2240.0</td>\n", " <td>2334.0</td>\n", " <td>1</td>\n", " <td>29</td>\n", " <td>41.0</td>\n", " <td>209</td>\n", " <td>43.111187</td>\n", " <td>-76.106311</td>\n", " <td>421.0</td>\n", " <td>51.98</td>\n", " <td>58.65</td>\n", " <td>9.20624</td>\n", " <td>10.594357</td>\n", " <td>0.0</td>\n", " <td>1015.4</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007561</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2240.0</td>\n", " <td>2347.0</td>\n", " <td>1</td>\n", " <td>20</td>\n", " <td>52.0</td>\n", " <td>301</td>\n", " <td>42.940525</td>\n", " <td>-78.732167</td>\n", " <td>724.0</td>\n", " <td>53.06</td>\n", " <td>58.80</td>\n", " <td>5.75390</td>\n", " <td>6.621473</td>\n", " <td>0.0</td>\n", " <td>1015.4</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007562</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2240.0</td>\n", " <td>2347.0</td>\n", " <td>1</td>\n", " <td>20</td>\n", " <td>52.0</td>\n", " <td>301</td>\n", " <td>42.940525</td>\n", " <td>-78.732167</td>\n", " <td>724.0</td>\n", " <td>55.94</td>\n", " <td>74.94</td>\n", " <td>6.90468</td>\n", " <td>7.945768</td>\n", " <td>0.0</td>\n", " <td>1016.0</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007563</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2240.0</td>\n", " <td>2347.0</td>\n", " <td>1</td>\n", " <td>20</td>\n", " <td>52.0</td>\n", " <td>301</td>\n", " <td>42.940525</td>\n", " <td>-78.732167</td>\n", " <td>724.0</td>\n", " <td>51.98</td>\n", " <td>58.65</td>\n", " <td>9.20624</td>\n", " <td>10.594357</td>\n", " <td>0.0</td>\n", " <td>1015.4</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007564</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2241.0</td>\n", " <td>2345.0</td>\n", " <td>1</td>\n", " <td>28</td>\n", " <td>47.0</td>\n", " <td>264</td>\n", " <td>43.118866</td>\n", " <td>-77.672389</td>\n", " <td>559.0</td>\n", " <td>53.06</td>\n", " <td>58.80</td>\n", " <td>5.75390</td>\n", " <td>6.621473</td>\n", " <td>0.0</td>\n", " <td>1015.4</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007565</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2241.0</td>\n", " <td>2345.0</td>\n", " <td>1</td>\n", " <td>28</td>\n", " <td>47.0</td>\n", " <td>264</td>\n", " <td>43.118866</td>\n", " <td>-77.672389</td>\n", " <td>559.0</td>\n", " <td>55.94</td>\n", " <td>74.94</td>\n", " <td>6.90468</td>\n", " <td>7.945768</td>\n", " <td>0.0</td>\n", " <td>1016.0</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007566</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2241.0</td>\n", " <td>2345.0</td>\n", " <td>1</td>\n", " <td>28</td>\n", " <td>47.0</td>\n", " <td>264</td>\n", " <td>43.118866</td>\n", " <td>-77.672389</td>\n", " <td>559.0</td>\n", " <td>51.98</td>\n", " <td>58.65</td>\n", " <td>9.20624</td>\n", " <td>10.594357</td>\n", " <td>0.0</td>\n", " <td>1015.4</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007567</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2307.0</td>\n", " <td>2359.0</td>\n", " <td>1</td>\n", " <td>19</td>\n", " <td>33.0</td>\n", " <td>187</td>\n", " <td>42.364347</td>\n", " <td>-71.005181</td>\n", " <td>19.0</td>\n", " <td>53.06</td>\n", " <td>58.80</td>\n", " <td>5.75390</td>\n", " <td>6.621473</td>\n", " <td>0.0</td>\n", " <td>1015.4</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007568</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2307.0</td>\n", " <td>2359.0</td>\n", " <td>1</td>\n", " <td>19</td>\n", " <td>33.0</td>\n", " <td>187</td>\n", " <td>42.364347</td>\n", " <td>-71.005181</td>\n", " <td>19.0</td>\n", " <td>55.94</td>\n", " <td>74.94</td>\n", " <td>6.90468</td>\n", " <td>7.945768</td>\n", " <td>0.0</td>\n", " <td>1016.0</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>1007569</th>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2307.0</td>\n", " <td>2359.0</td>\n", " <td>1</td>\n", " <td>19</td>\n", " <td>33.0</td>\n", " <td>187</td>\n", " <td>42.364347</td>\n", " <td>-71.005181</td>\n", " <td>19.0</td>\n", " <td>51.98</td>\n", " <td>58.65</td>\n", " <td>9.20624</td>\n", " <td>10.594357</td>\n", " <td>0.0</td>\n", " <td>1015.4</td>\n", " <td>10.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>838182 rows × 18 columns</p>\n", "</div>" ], "text/plain": [ " month day dep_time arr_time carrier dest air_time distance \\\n", "4 1 1 554.0 812.0 0 0 116.0 762 \n", "5 1 1 554.0 812.0 0 0 116.0 762 \n", "6 1 1 554.0 812.0 0 0 116.0 762 \n", "8 1 1 555.0 913.0 1 1 158.0 1065 \n", "9 1 1 555.0 913.0 1 1 158.0 1065 \n", "10 1 1 555.0 913.0 1 1 158.0 1065 \n", "11 1 1 557.0 709.0 2 2 53.0 229 \n", "12 1 1 557.0 709.0 2 2 53.0 229 \n", "13 1 1 557.0 709.0 2 2 53.0 229 \n", "14 1 1 557.0 838.0 1 3 140.0 944 \n", "15 1 1 557.0 838.0 1 3 140.0 944 \n", "16 1 1 557.0 838.0 1 3 140.0 944 \n", "17 1 1 558.0 753.0 3 4 138.0 733 \n", "18 1 1 558.0 753.0 3 4 138.0 733 \n", "19 1 1 558.0 753.0 3 4 138.0 733 \n", "20 1 1 558.0 849.0 1 5 149.0 1028 \n", "21 1 1 558.0 849.0 1 5 149.0 1028 \n", "22 1 1 558.0 849.0 1 5 149.0 1028 \n", "23 1 1 558.0 853.0 1 6 158.0 1005 \n", "24 1 1 558.0 853.0 1 6 158.0 1005 \n", "25 1 1 558.0 853.0 1 6 158.0 1005 \n", "26 1 1 558.0 924.0 4 7 345.0 2475 \n", "27 1 1 558.0 924.0 4 7 345.0 2475 \n", "28 1 1 558.0 924.0 4 7 345.0 2475 \n", "29 1 1 558.0 923.0 4 8 361.0 2565 \n", "30 1 1 558.0 923.0 4 8 361.0 2565 \n", "31 1 1 558.0 923.0 4 8 361.0 2565 \n", "32 1 1 559.0 941.0 3 9 257.0 1389 \n", "33 1 1 559.0 941.0 3 9 257.0 1389 \n", "34 1 1 559.0 941.0 3 9 257.0 1389 \n", "... ... ... ... ... ... ... ... ... \n", "1007540 9 30 2207.0 2257.0 5 45 97.0 764 \n", "1007541 9 30 2207.0 2257.0 5 45 97.0 764 \n", "1007542 9 30 2207.0 2257.0 5 45 97.0 764 \n", "1007543 9 30 2211.0 2339.0 2 40 120.0 872 \n", "1007544 9 30 2211.0 2339.0 2 40 120.0 872 \n", "1007545 9 30 2211.0 2339.0 2 40 120.0 872 \n", "1007546 9 30 2231.0 2335.0 1 51 48.0 273 \n", "1007547 9 30 2231.0 2335.0 1 51 48.0 273 \n", "1007548 9 30 2231.0 2335.0 1 51 48.0 273 \n", "1007549 9 30 2233.0 112.0 4 8 318.0 2565 \n", "1007550 9 30 2233.0 112.0 4 8 318.0 2565 \n", "1007551 9 30 2233.0 112.0 4 8 318.0 2565 \n", "1007552 9 30 2235.0 59.0 1 3 123.0 944 \n", "1007553 9 30 2235.0 59.0 1 3 123.0 944 \n", "1007554 9 30 2235.0 59.0 1 3 123.0 944 \n", "1007555 9 30 2237.0 2345.0 1 47 43.0 266 \n", "1007556 9 30 2237.0 2345.0 1 47 43.0 266 \n", "1007557 9 30 2237.0 2345.0 1 47 43.0 266 \n", "1007558 9 30 2240.0 2334.0 1 29 41.0 209 \n", "1007559 9 30 2240.0 2334.0 1 29 41.0 209 \n", "1007560 9 30 2240.0 2334.0 1 29 41.0 209 \n", "1007561 9 30 2240.0 2347.0 1 20 52.0 301 \n", "1007562 9 30 2240.0 2347.0 1 20 52.0 301 \n", "1007563 9 30 2240.0 2347.0 1 20 52.0 301 \n", "1007564 9 30 2241.0 2345.0 1 28 47.0 264 \n", "1007565 9 30 2241.0 2345.0 1 28 47.0 264 \n", "1007566 9 30 2241.0 2345.0 1 28 47.0 264 \n", "1007567 9 30 2307.0 2359.0 1 19 33.0 187 \n", "1007568 9 30 2307.0 2359.0 1 19 33.0 187 \n", "1007569 9 30 2307.0 2359.0 1 19 33.0 187 \n", "\n", " lat lon alt dewp humid wind_speed wind_gust \\\n", "4 33.636719 -84.428067 1026.0 26.06 59.37 10.35702 11.918651 \n", "5 33.636719 -84.428067 1026.0 26.06 59.37 12.65858 14.567241 \n", "6 33.636719 -84.428067 1026.0 26.06 57.33 13.80936 15.891535 \n", "8 26.072583 -80.152750 9.0 26.06 59.37 10.35702 11.918651 \n", "9 26.072583 -80.152750 9.0 26.06 59.37 12.65858 14.567241 \n", "10 26.072583 -80.152750 9.0 26.06 57.33 13.80936 15.891535 \n", "11 38.944533 -77.455811 313.0 26.06 59.37 10.35702 11.918651 \n", "12 38.944533 -77.455811 313.0 26.06 59.37 12.65858 14.567241 \n", "13 38.944533 -77.455811 313.0 26.06 57.33 13.80936 15.891535 \n", "14 28.429394 -81.308994 96.0 26.06 59.37 10.35702 11.918651 \n", "15 28.429394 -81.308994 96.0 26.06 59.37 12.65858 14.567241 \n", "16 28.429394 -81.308994 96.0 26.06 57.33 13.80936 15.891535 \n", "17 41.978603 -87.904842 668.0 26.06 59.37 10.35702 11.918651 \n", "18 41.978603 -87.904842 668.0 26.06 59.37 12.65858 14.567241 \n", "19 41.978603 -87.904842 668.0 26.06 57.33 13.80936 15.891535 \n", "20 26.683161 -80.095589 19.0 26.06 59.37 10.35702 11.918651 \n", "21 26.683161 -80.095589 19.0 26.06 59.37 12.65858 14.567241 \n", "22 26.683161 -80.095589 19.0 26.06 57.33 13.80936 15.891535 \n", "23 27.975472 -82.533250 26.0 26.06 59.37 10.35702 11.918651 \n", "24 27.975472 -82.533250 26.0 26.06 59.37 12.65858 14.567241 \n", "25 27.975472 -82.533250 26.0 26.06 57.33 13.80936 15.891535 \n", "26 33.942536 -118.408075 126.0 26.06 59.37 10.35702 11.918651 \n", "27 33.942536 -118.408075 126.0 26.06 59.37 12.65858 14.567241 \n", "28 33.942536 -118.408075 126.0 26.06 57.33 13.80936 15.891535 \n", "29 37.618972 -122.374889 13.0 26.06 59.37 10.35702 11.918651 \n", "30 37.618972 -122.374889 13.0 26.06 59.37 12.65858 14.567241 \n", "31 37.618972 -122.374889 13.0 26.06 57.33 13.80936 15.891535 \n", "32 32.896828 -97.037997 607.0 26.06 59.37 10.35702 11.918651 \n", "33 32.896828 -97.037997 607.0 26.06 59.37 12.65858 14.567241 \n", "34 32.896828 -97.037997 607.0 26.06 57.33 13.80936 15.891535 \n", "... ... ... ... ... ... ... ... \n", "1007540 36.124472 -86.678194 599.0 53.06 54.94 5.75390 6.621473 \n", "1007541 36.124472 -86.678194 599.0 55.04 67.69 6.90468 7.945768 \n", "1007542 36.124472 -86.678194 599.0 51.08 52.67 6.90468 7.945768 \n", "1007543 38.748697 -90.370028 618.0 51.98 49.36 6.90468 7.945768 \n", "1007544 38.748697 -90.370028 618.0 53.06 58.80 8.05546 9.270062 \n", "1007545 38.748697 -90.370028 618.0 51.08 46.04 6.90468 7.945768 \n", "1007546 43.646161 -70.309281 77.0 53.06 58.80 5.75390 6.621473 \n", "1007547 43.646161 -70.309281 77.0 55.94 74.94 6.90468 7.945768 \n", "1007548 43.646161 -70.309281 77.0 51.98 58.65 9.20624 10.594357 \n", "1007549 37.618972 -122.374889 13.0 53.06 54.94 5.75390 6.621473 \n", "1007550 37.618972 -122.374889 13.0 55.04 67.69 6.90468 7.945768 \n", "1007551 37.618972 -122.374889 13.0 51.08 52.67 6.90468 7.945768 \n", "1007552 28.429394 -81.308994 96.0 51.98 49.36 6.90468 7.945768 \n", "1007553 28.429394 -81.308994 96.0 53.06 58.80 8.05546 9.270062 \n", "1007554 28.429394 -81.308994 96.0 51.08 46.04 6.90468 7.945768 \n", "1007555 44.471861 -73.153278 335.0 53.06 58.80 5.75390 6.621473 \n", "1007556 44.471861 -73.153278 335.0 55.94 74.94 6.90468 7.945768 \n", "1007557 44.471861 -73.153278 335.0 51.98 58.65 9.20624 10.594357 \n", "1007558 43.111187 -76.106311 421.0 53.06 58.80 5.75390 6.621473 \n", "1007559 43.111187 -76.106311 421.0 55.94 74.94 6.90468 7.945768 \n", "1007560 43.111187 -76.106311 421.0 51.98 58.65 9.20624 10.594357 \n", "1007561 42.940525 -78.732167 724.0 53.06 58.80 5.75390 6.621473 \n", "1007562 42.940525 -78.732167 724.0 55.94 74.94 6.90468 7.945768 \n", "1007563 42.940525 -78.732167 724.0 51.98 58.65 9.20624 10.594357 \n", "1007564 43.118866 -77.672389 559.0 53.06 58.80 5.75390 6.621473 \n", "1007565 43.118866 -77.672389 559.0 55.94 74.94 6.90468 7.945768 \n", "1007566 43.118866 -77.672389 559.0 51.98 58.65 9.20624 10.594357 \n", "1007567 42.364347 -71.005181 19.0 53.06 58.80 5.75390 6.621473 \n", "1007568 42.364347 -71.005181 19.0 55.94 74.94 6.90468 7.945768 \n", "1007569 42.364347 -71.005181 19.0 51.98 58.65 9.20624 10.594357 \n", "\n", " precip pressure visib \n", "4 0.0 1012.0 10.0 \n", "5 0.0 1012.6 10.0 \n", "6 0.0 1011.9 10.0 \n", "8 0.0 1012.0 10.0 \n", "9 0.0 1012.6 10.0 \n", "10 0.0 1011.9 10.0 \n", "11 0.0 1012.0 10.0 \n", "12 0.0 1012.6 10.0 \n", "13 0.0 1011.9 10.0 \n", "14 0.0 1012.0 10.0 \n", "15 0.0 1012.6 10.0 \n", "16 0.0 1011.9 10.0 \n", "17 0.0 1012.0 10.0 \n", "18 0.0 1012.6 10.0 \n", "19 0.0 1011.9 10.0 \n", "20 0.0 1012.0 10.0 \n", "21 0.0 1012.6 10.0 \n", "22 0.0 1011.9 10.0 \n", "23 0.0 1012.0 10.0 \n", "24 0.0 1012.6 10.0 \n", "25 0.0 1011.9 10.0 \n", "26 0.0 1012.0 10.0 \n", "27 0.0 1012.6 10.0 \n", "28 0.0 1011.9 10.0 \n", "29 0.0 1012.0 10.0 \n", "30 0.0 1012.6 10.0 \n", "31 0.0 1011.9 10.0 \n", "32 0.0 1012.0 10.0 \n", "33 0.0 1012.6 10.0 \n", "34 0.0 1011.9 10.0 \n", "... ... ... ... \n", "1007540 0.0 1015.0 10.0 \n", "1007541 0.0 1015.6 10.0 \n", "1007542 0.0 1015.0 10.0 \n", "1007543 0.0 1015.2 10.0 \n", "1007544 0.0 1015.8 10.0 \n", "1007545 0.0 1015.1 10.0 \n", "1007546 0.0 1015.4 10.0 \n", "1007547 0.0 1016.0 10.0 \n", "1007548 0.0 1015.4 10.0 \n", "1007549 0.0 1015.0 10.0 \n", "1007550 0.0 1015.6 10.0 \n", "1007551 0.0 1015.0 10.0 \n", "1007552 0.0 1015.2 10.0 \n", "1007553 0.0 1015.8 10.0 \n", "1007554 0.0 1015.1 10.0 \n", "1007555 0.0 1015.4 10.0 \n", "1007556 0.0 1016.0 10.0 \n", "1007557 0.0 1015.4 10.0 \n", "1007558 0.0 1015.4 10.0 \n", "1007559 0.0 1016.0 10.0 \n", "1007560 0.0 1015.4 10.0 \n", "1007561 0.0 1015.4 10.0 \n", "1007562 0.0 1016.0 10.0 \n", "1007563 0.0 1015.4 10.0 \n", "1007564 0.0 1015.4 10.0 \n", "1007565 0.0 1016.0 10.0 \n", "1007566 0.0 1015.4 10.0 \n", "1007567 0.0 1015.4 10.0 \n", "1007568 0.0 1016.0 10.0 \n", "1007569 0.0 1015.4 10.0 \n", "\n", "[838182 rows x 18 columns]" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "features_v" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7.2 对特征向量进行标准化(Scaling the feature vector)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 因为各个特征的维度各不相同，我们需要做标准化\n", "scaler = StandardScaler()\n", "scaled_features = scaler.fit_transform(features_v)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-1.62498171, -1.69693092, -1.62908082, ..., -0.11420569,\n", " -0.79438586, 0.29213712],\n", " [-1.62498171, -1.69693092, -1.62908082, ..., -0.11420569,\n", " -0.71389571, 0.29213712],\n", " [-1.62498171, -1.69693092, -1.62908082, ..., -0.11420569,\n", " -0.80780089, 0.29213712],\n", " ..., \n", " [ 0.71764151, 1.6218685 , 1.98106144, ..., -0.11420569,\n", " -0.33827498, 0.29213712],\n", " [ 0.71764151, 1.6218685 , 1.98106144, ..., -0.11420569,\n", " -0.25778483, 0.29213712],\n", " [ 0.71764151, 1.6218685 , 1.98106144, ..., -0.11420569,\n", " -0.33827498, 0.29213712]])" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scaled_features" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7.3 特征降维(Reducing Dimensions)\n", "我们使用PCA(Principle Component Analysis主成分析)把特征降维为2个" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.decomposition import PCA\n", "\n", "pca = PCA(n_components=2)\n", "X_r = pca.fit(scaled_features).transform(scaled_features)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-0.32039321, -0.88647489],\n", " [-0.32303154, -0.72462068],\n", " [-0.32447915, -0.59863037],\n", " ..., \n", " [-2.11022832, 0.78923842],\n", " [-2.10108117, 0.41150146],\n", " [-2.11397223, 1.04517555]])" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_r" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7.4 画图(Plotting)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "explained variance ratio (first two components): [ 0.16961823 0.14269873]\n" ] }, { "data": { "text/plain": [ "Text(0.5,1,'PCA of flights dataset')" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x259b8ac11d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "print('explained variance ratio (first two components): %s' \n", " % str(pca.explained_variance_ratio_))\n", "\n", "plt.figure()\n", "lw = 2\n", "\n", "plt.scatter(X_r[:,0], X_r[:,1], alpha=.8, lw=lw)\n", "plt.title('PCA of flights dataset')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 8 构建分类器(Build a classifier)\n", "我们来预测一个航班是否会晚点" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import sklearn\n", "from sklearn import linear_model, cross_validation, metrics, svm, ensemble\n", "from sklearn.metrics import classification_report, confusion_matrix, precision_recall_fscore_support, accuracy_score\n", "from sklearn.cross_validation import train_test_split, cross_val_score, ShuffleSplit\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.preprocessing import StandardScaler, OneHotEncoder" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": true }, "outputs": [], "source": [ "flights = pd.read_csv('data/nycflights13/flights.csv.gz')\n", "weather = pd.read_csv('data/nycflights13/weather.csv.gz')\n", "airports = pd.read_csv('data/nycflights13/airports.csv.gz')\n", "\n", "df_withweather = pd.merge(flights, weather, how='left', on=['year', 'month', 'day', 'hour'])\n", "df = pd.merge(df_withweather, airports, how='left', left_on='dest', right_on='faa')\n", "\n", "df = df.dropna()" ] }, { "cell_type": "code", "execution_count": 63, "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>year</th>\n", " <th>month</th>\n", " <th>day</th>\n", " <th>dep_time</th>\n", " <th>sched_dep_time</th>\n", " <th>dep_delay</th>\n", " <th>arr_time</th>\n", " <th>sched_arr_time</th>\n", " <th>arr_delay</th>\n", " <th>carrier</th>\n", " <th>...</th>\n", " <th>visib</th>\n", " <th>time_hour_y</th>\n", " <th>faa</th>\n", " <th>name</th>\n", " <th>lat</th>\n", " <th>lon</th>\n", " <th>alt</th>\n", " <th>tz</th>\n", " <th>dst</th>\n", " <th>tzone</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>4</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>554.0</td>\n", " <td>600</td>\n", " <td>-6.0</td>\n", " <td>812.0</td>\n", " <td>837</td>\n", " <td>-25.0</td>\n", " <td>DL</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", 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<td>745</td>\n", " <td>8.0</td>\n", " <td>AA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>ORD</td>\n", " <td>Chicago Ohare Intl</td>\n", " <td>41.978603</td>\n", " <td>-87.904842</td>\n", " <td>668.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>753.0</td>\n", " <td>745</td>\n", " <td>8.0</td>\n", " <td>AA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>ORD</td>\n", " <td>Chicago Ohare Intl</td>\n", " <td>41.978603</td>\n", " <td>-87.904842</td>\n", " <td>668.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>753.0</td>\n", " 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<td>851</td>\n", " <td>-2.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>PBI</td>\n", " <td>Palm Beach Intl</td>\n", " <td>26.683161</td>\n", " <td>-80.095589</td>\n", " <td>19.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>849.0</td>\n", " <td>851</td>\n", " <td>-2.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>PBI</td>\n", " <td>Palm Beach Intl</td>\n", " <td>26.683161</td>\n", " <td>-80.095589</td>\n", " <td>19.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>853.0</td>\n", " <td>856</td>\n", " <td>-3.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>TPA</td>\n", " <td>Tampa Intl</td>\n", " <td>27.975472</td>\n", " <td>-82.533250</td>\n", " <td>26.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>853.0</td>\n", " <td>856</td>\n", " <td>-3.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>TPA</td>\n", " <td>Tampa Intl</td>\n", " <td>27.975472</td>\n", " <td>-82.533250</td>\n", " <td>26.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>853.0</td>\n", " <td>856</td>\n", " <td>-3.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>TPA</td>\n", " <td>Tampa Intl</td>\n", " <td>27.975472</td>\n", " <td>-82.533250</td>\n", " <td>26.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>924.0</td>\n", " <td>917</td>\n", " <td>7.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>LAX</td>\n", " <td>Los Angeles Intl</td>\n", " <td>33.942536</td>\n", " <td>-118.408075</td>\n", " <td>126.0</td>\n", " <td>-8.0</td>\n", " <td>A</td>\n", " <td>America/Los_Angeles</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>924.0</td>\n", " <td>917</td>\n", " <td>7.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>LAX</td>\n", " <td>Los Angeles Intl</td>\n", " <td>33.942536</td>\n", " <td>-118.408075</td>\n", " <td>126.0</td>\n", " <td>-8.0</td>\n", " <td>A</td>\n", " <td>America/Los_Angeles</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>924.0</td>\n", " <td>917</td>\n", " <td>7.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>LAX</td>\n", " <td>Los Angeles Intl</td>\n", " <td>33.942536</td>\n", " <td>-118.408075</td>\n", " <td>126.0</td>\n", " <td>-8.0</td>\n", " <td>A</td>\n", " <td>America/Los_Angeles</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>923.0</td>\n", " <td>937</td>\n", " <td>-14.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>SFO</td>\n", " <td>San Francisco Intl</td>\n", " <td>37.618972</td>\n", " <td>-122.374889</td>\n", " <td>13.0</td>\n", " <td>-8.0</td>\n", " <td>A</td>\n", " <td>America/Los_Angeles</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>923.0</td>\n", " <td>937</td>\n", " <td>-14.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>SFO</td>\n", " <td>San Francisco Intl</td>\n", " <td>37.618972</td>\n", " <td>-122.374889</td>\n", " <td>13.0</td>\n", " <td>-8.0</td>\n", " <td>A</td>\n", " <td>America/Los_Angeles</td>\n", " </tr>\n", " <tr>\n", " <th>31</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>558.0</td>\n", " <td>600</td>\n", " <td>-2.0</td>\n", " <td>923.0</td>\n", " <td>937</td>\n", " <td>-14.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>SFO</td>\n", " <td>San Francisco Intl</td>\n", " <td>37.618972</td>\n", " <td>-122.374889</td>\n", " <td>13.0</td>\n", " <td>-8.0</td>\n", " <td>A</td>\n", " <td>America/Los_Angeles</td>\n", " </tr>\n", " <tr>\n", " <th>32</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>559.0</td>\n", " <td>600</td>\n", " <td>-1.0</td>\n", " <td>941.0</td>\n", " <td>910</td>\n", " <td>31.0</td>\n", " <td>AA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>DFW</td>\n", " <td>Dallas Fort Worth Intl</td>\n", " <td>32.896828</td>\n", " <td>-97.037997</td>\n", " <td>607.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>33</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>559.0</td>\n", " <td>600</td>\n", " <td>-1.0</td>\n", " <td>941.0</td>\n", " <td>910</td>\n", " <td>31.0</td>\n", " <td>AA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>DFW</td>\n", " <td>Dallas Fort Worth Intl</td>\n", " <td>32.896828</td>\n", " <td>-97.037997</td>\n", " <td>607.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>34</th>\n", " <td>2013</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>559.0</td>\n", " <td>600</td>\n", " <td>-1.0</td>\n", " <td>941.0</td>\n", " <td>910</td>\n", " <td>31.0</td>\n", " <td>AA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-01-01 01:00:00</td>\n", " <td>DFW</td>\n", " <td>Dallas Fort Worth Intl</td>\n", " <td>32.896828</td>\n", " <td>-97.037997</td>\n", " <td>607.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</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", " <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>1007540</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2207.0</td>\n", " <td>2140</td>\n", " <td>27.0</td>\n", " <td>2257.0</td>\n", " <td>2250</td>\n", " <td>7.0</td>\n", " <td>MQ</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 16:00:00</td>\n", " <td>BNA</td>\n", " <td>Nashville Intl</td>\n", " <td>36.124472</td>\n", " <td>-86.678194</td>\n", " <td>599.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>1007541</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2207.0</td>\n", " <td>2140</td>\n", " <td>27.0</td>\n", " <td>2257.0</td>\n", " <td>2250</td>\n", " <td>7.0</td>\n", " <td>MQ</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 16:00:00</td>\n", " <td>BNA</td>\n", " <td>Nashville Intl</td>\n", " <td>36.124472</td>\n", " <td>-86.678194</td>\n", " <td>599.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>1007542</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2207.0</td>\n", " <td>2140</td>\n", " <td>27.0</td>\n", " <td>2257.0</td>\n", " <td>2250</td>\n", " <td>7.0</td>\n", " <td>MQ</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 16:00:00</td>\n", " <td>BNA</td>\n", " <td>Nashville Intl</td>\n", " <td>36.124472</td>\n", " <td>-86.678194</td>\n", " <td>599.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>1007543</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2211.0</td>\n", " <td>2059</td>\n", " <td>72.0</td>\n", " <td>2339.0</td>\n", " <td>2242</td>\n", " <td>57.0</td>\n", " <td>EV</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 15:00:00</td>\n", " <td>STL</td>\n", " <td>Lambert St Louis Intl</td>\n", " <td>38.748697</td>\n", " <td>-90.370028</td>\n", " <td>618.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>1007544</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2211.0</td>\n", " <td>2059</td>\n", " <td>72.0</td>\n", " <td>2339.0</td>\n", " <td>2242</td>\n", " <td>57.0</td>\n", " <td>EV</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 15:00:00</td>\n", " <td>STL</td>\n", " <td>Lambert St Louis Intl</td>\n", " <td>38.748697</td>\n", " <td>-90.370028</td>\n", " <td>618.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>1007545</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2211.0</td>\n", " <td>2059</td>\n", " <td>72.0</td>\n", " <td>2339.0</td>\n", " <td>2242</td>\n", " <td>57.0</td>\n", " <td>EV</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 15:00:00</td>\n", " <td>STL</td>\n", " <td>Lambert St Louis Intl</td>\n", " <td>38.748697</td>\n", " <td>-90.370028</td>\n", " <td>618.0</td>\n", " <td>-6.0</td>\n", " <td>A</td>\n", " <td>America/Chicago</td>\n", " </tr>\n", " <tr>\n", " <th>1007546</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2231.0</td>\n", " <td>2245</td>\n", " <td>-14.0</td>\n", " <td>2335.0</td>\n", " <td>2356</td>\n", " <td>-21.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>PWM</td>\n", " <td>Portland Intl Jetport</td>\n", " <td>43.646161</td>\n", " <td>-70.309281</td>\n", " <td>77.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007547</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2231.0</td>\n", " <td>2245</td>\n", " <td>-14.0</td>\n", " <td>2335.0</td>\n", " <td>2356</td>\n", " <td>-21.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>PWM</td>\n", " <td>Portland Intl Jetport</td>\n", " <td>43.646161</td>\n", " <td>-70.309281</td>\n", " <td>77.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007548</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2231.0</td>\n", " <td>2245</td>\n", " <td>-14.0</td>\n", " <td>2335.0</td>\n", " <td>2356</td>\n", " <td>-21.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>PWM</td>\n", " <td>Portland Intl Jetport</td>\n", " <td>43.646161</td>\n", " <td>-70.309281</td>\n", " <td>77.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007549</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2233.0</td>\n", " <td>2113</td>\n", " <td>80.0</td>\n", " <td>112.0</td>\n", " <td>30</td>\n", " <td>42.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 16:00:00</td>\n", " <td>SFO</td>\n", " <td>San Francisco Intl</td>\n", " <td>37.618972</td>\n", " <td>-122.374889</td>\n", " <td>13.0</td>\n", " <td>-8.0</td>\n", " <td>A</td>\n", " <td>America/Los_Angeles</td>\n", " </tr>\n", " <tr>\n", " <th>1007550</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2233.0</td>\n", " <td>2113</td>\n", " <td>80.0</td>\n", " <td>112.0</td>\n", " <td>30</td>\n", " <td>42.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 16:00:00</td>\n", " <td>SFO</td>\n", " <td>San Francisco Intl</td>\n", " <td>37.618972</td>\n", " <td>-122.374889</td>\n", " <td>13.0</td>\n", " <td>-8.0</td>\n", " <td>A</td>\n", " <td>America/Los_Angeles</td>\n", " </tr>\n", " <tr>\n", " <th>1007551</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2233.0</td>\n", " <td>2113</td>\n", " <td>80.0</td>\n", " <td>112.0</td>\n", " <td>30</td>\n", " <td>42.0</td>\n", " <td>UA</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 16:00:00</td>\n", " <td>SFO</td>\n", " <td>San Francisco Intl</td>\n", " <td>37.618972</td>\n", " <td>-122.374889</td>\n", " <td>13.0</td>\n", " <td>-8.0</td>\n", " <td>A</td>\n", " <td>America/Los_Angeles</td>\n", " </tr>\n", " <tr>\n", " <th>1007552</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2235.0</td>\n", " <td>2001</td>\n", " <td>154.0</td>\n", " <td>59.0</td>\n", " <td>2249</td>\n", " <td>130.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 15:00:00</td>\n", " <td>MCO</td>\n", " <td>Orlando Intl</td>\n", " <td>28.429394</td>\n", " <td>-81.308994</td>\n", " <td>96.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007553</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2235.0</td>\n", " <td>2001</td>\n", " <td>154.0</td>\n", " <td>59.0</td>\n", " <td>2249</td>\n", " <td>130.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 15:00:00</td>\n", " <td>MCO</td>\n", " <td>Orlando Intl</td>\n", " <td>28.429394</td>\n", " <td>-81.308994</td>\n", " <td>96.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007554</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2235.0</td>\n", " <td>2001</td>\n", " <td>154.0</td>\n", " <td>59.0</td>\n", " <td>2249</td>\n", " <td>130.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 15:00:00</td>\n", " <td>MCO</td>\n", " <td>Orlando Intl</td>\n", " <td>28.429394</td>\n", " <td>-81.308994</td>\n", " <td>96.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007555</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2237.0</td>\n", " <td>2245</td>\n", " <td>-8.0</td>\n", " <td>2345.0</td>\n", " <td>2353</td>\n", " <td>-8.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>BTV</td>\n", " <td>Burlington Intl</td>\n", " <td>44.471861</td>\n", " <td>-73.153278</td>\n", " <td>335.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007556</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2237.0</td>\n", " <td>2245</td>\n", " <td>-8.0</td>\n", " <td>2345.0</td>\n", " <td>2353</td>\n", " <td>-8.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>BTV</td>\n", " <td>Burlington Intl</td>\n", " <td>44.471861</td>\n", " <td>-73.153278</td>\n", " <td>335.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007557</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2237.0</td>\n", " <td>2245</td>\n", " <td>-8.0</td>\n", " <td>2345.0</td>\n", " <td>2353</td>\n", " <td>-8.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>BTV</td>\n", " <td>Burlington Intl</td>\n", " <td>44.471861</td>\n", " <td>-73.153278</td>\n", " <td>335.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007558</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2240.0</td>\n", " <td>2245</td>\n", " <td>-5.0</td>\n", " <td>2334.0</td>\n", " <td>2351</td>\n", " <td>-17.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>SYR</td>\n", " <td>Syracuse Hancock Intl</td>\n", " <td>43.111187</td>\n", " <td>-76.106311</td>\n", " <td>421.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007559</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2240.0</td>\n", " <td>2245</td>\n", " <td>-5.0</td>\n", " <td>2334.0</td>\n", " <td>2351</td>\n", " <td>-17.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>SYR</td>\n", " <td>Syracuse Hancock Intl</td>\n", " <td>43.111187</td>\n", " <td>-76.106311</td>\n", " <td>421.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007560</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2240.0</td>\n", " <td>2245</td>\n", " <td>-5.0</td>\n", " <td>2334.0</td>\n", " <td>2351</td>\n", " <td>-17.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>SYR</td>\n", " <td>Syracuse Hancock Intl</td>\n", " <td>43.111187</td>\n", " <td>-76.106311</td>\n", " <td>421.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007561</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2240.0</td>\n", " <td>2250</td>\n", " <td>-10.0</td>\n", " <td>2347.0</td>\n", " <td>7</td>\n", " <td>-20.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>BUF</td>\n", " <td>Buffalo Niagara Intl</td>\n", " <td>42.940525</td>\n", " <td>-78.732167</td>\n", " <td>724.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007562</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2240.0</td>\n", " <td>2250</td>\n", " <td>-10.0</td>\n", " <td>2347.0</td>\n", " <td>7</td>\n", " <td>-20.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>BUF</td>\n", " <td>Buffalo Niagara Intl</td>\n", " <td>42.940525</td>\n", " <td>-78.732167</td>\n", " <td>724.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007563</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2240.0</td>\n", " <td>2250</td>\n", " <td>-10.0</td>\n", " <td>2347.0</td>\n", " <td>7</td>\n", " <td>-20.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>BUF</td>\n", " <td>Buffalo Niagara Intl</td>\n", " <td>42.940525</td>\n", " <td>-78.732167</td>\n", " <td>724.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007564</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2241.0</td>\n", " <td>2246</td>\n", " <td>-5.0</td>\n", " <td>2345.0</td>\n", " <td>1</td>\n", " <td>-16.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>ROC</td>\n", " <td>Greater Rochester Intl</td>\n", " <td>43.118866</td>\n", " <td>-77.672389</td>\n", " <td>559.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007565</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2241.0</td>\n", " <td>2246</td>\n", " <td>-5.0</td>\n", " <td>2345.0</td>\n", " <td>1</td>\n", " <td>-16.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>ROC</td>\n", " <td>Greater Rochester Intl</td>\n", " <td>43.118866</td>\n", " <td>-77.672389</td>\n", " <td>559.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007566</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2241.0</td>\n", " <td>2246</td>\n", " <td>-5.0</td>\n", " <td>2345.0</td>\n", " <td>1</td>\n", " <td>-16.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>ROC</td>\n", " <td>Greater Rochester Intl</td>\n", " <td>43.118866</td>\n", " <td>-77.672389</td>\n", " <td>559.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007567</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2307.0</td>\n", " <td>2255</td>\n", " <td>12.0</td>\n", " <td>2359.0</td>\n", " <td>2358</td>\n", " <td>1.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>BOS</td>\n", " <td>General Edward Lawrence Logan Intl</td>\n", " <td>42.364347</td>\n", " <td>-71.005181</td>\n", " <td>19.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007568</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2307.0</td>\n", " <td>2255</td>\n", " <td>12.0</td>\n", " <td>2359.0</td>\n", " <td>2358</td>\n", " <td>1.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>BOS</td>\n", " <td>General Edward Lawrence Logan Intl</td>\n", " <td>42.364347</td>\n", " <td>-71.005181</td>\n", " <td>19.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " <tr>\n", " <th>1007569</th>\n", " <td>2013</td>\n", " <td>9</td>\n", " <td>30</td>\n", " <td>2307.0</td>\n", " <td>2255</td>\n", " <td>12.0</td>\n", " <td>2359.0</td>\n", " <td>2358</td>\n", " <td>1.0</td>\n", " <td>B6</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>2013-09-30 17:00:00</td>\n", " <td>BOS</td>\n", " <td>General Edward Lawrence Logan Intl</td>\n", " <td>42.364347</td>\n", " <td>-71.005181</td>\n", " <td>19.0</td>\n", " <td>-5.0</td>\n", " <td>A</td>\n", " <td>America/New_York</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>838182 rows × 38 columns</p>\n", "</div>" ], "text/plain": [ " year month day dep_time sched_dep_time dep_delay arr_time \\\n", "4 2013 1 1 554.0 600 -6.0 812.0 \n", "5 2013 1 1 554.0 600 -6.0 812.0 \n", "6 2013 1 1 554.0 600 -6.0 812.0 \n", "8 2013 1 1 555.0 600 -5.0 913.0 \n", "9 2013 1 1 555.0 600 -5.0 913.0 \n", "10 2013 1 1 555.0 600 -5.0 913.0 \n", "11 2013 1 1 557.0 600 -3.0 709.0 \n", "12 2013 1 1 557.0 600 -3.0 709.0 \n", "13 2013 1 1 557.0 600 -3.0 709.0 \n", "14 2013 1 1 557.0 600 -3.0 838.0 \n", "15 2013 1 1 557.0 600 -3.0 838.0 \n", "16 2013 1 1 557.0 600 -3.0 838.0 \n", "17 2013 1 1 558.0 600 -2.0 753.0 \n", "18 2013 1 1 558.0 600 -2.0 753.0 \n", "19 2013 1 1 558.0 600 -2.0 753.0 \n", "20 2013 1 1 558.0 600 -2.0 849.0 \n", "21 2013 1 1 558.0 600 -2.0 849.0 \n", "22 2013 1 1 558.0 600 -2.0 849.0 \n", "23 2013 1 1 558.0 600 -2.0 853.0 \n", "24 2013 1 1 558.0 600 -2.0 853.0 \n", "25 2013 1 1 558.0 600 -2.0 853.0 \n", "26 2013 1 1 558.0 600 -2.0 924.0 \n", "27 2013 1 1 558.0 600 -2.0 924.0 \n", "28 2013 1 1 558.0 600 -2.0 924.0 \n", "29 2013 1 1 558.0 600 -2.0 923.0 \n", "30 2013 1 1 558.0 600 -2.0 923.0 \n", "31 2013 1 1 558.0 600 -2.0 923.0 \n", "32 2013 1 1 559.0 600 -1.0 941.0 \n", "33 2013 1 1 559.0 600 -1.0 941.0 \n", "34 2013 1 1 559.0 600 -1.0 941.0 \n", "... ... ... ... ... ... ... ... \n", "1007540 2013 9 30 2207.0 2140 27.0 2257.0 \n", "1007541 2013 9 30 2207.0 2140 27.0 2257.0 \n", "1007542 2013 9 30 2207.0 2140 27.0 2257.0 \n", "1007543 2013 9 30 2211.0 2059 72.0 2339.0 \n", "1007544 2013 9 30 2211.0 2059 72.0 2339.0 \n", "1007545 2013 9 30 2211.0 2059 72.0 2339.0 \n", "1007546 2013 9 30 2231.0 2245 -14.0 2335.0 \n", "1007547 2013 9 30 2231.0 2245 -14.0 2335.0 \n", "1007548 2013 9 30 2231.0 2245 -14.0 2335.0 \n", "1007549 2013 9 30 2233.0 2113 80.0 112.0 \n", "1007550 2013 9 30 2233.0 2113 80.0 112.0 \n", "1007551 2013 9 30 2233.0 2113 80.0 112.0 \n", "1007552 2013 9 30 2235.0 2001 154.0 59.0 \n", "1007553 2013 9 30 2235.0 2001 154.0 59.0 \n", "1007554 2013 9 30 2235.0 2001 154.0 59.0 \n", "1007555 2013 9 30 2237.0 2245 -8.0 2345.0 \n", "1007556 2013 9 30 2237.0 2245 -8.0 2345.0 \n", "1007557 2013 9 30 2237.0 2245 -8.0 2345.0 \n", "1007558 2013 9 30 2240.0 2245 -5.0 2334.0 \n", "1007559 2013 9 30 2240.0 2245 -5.0 2334.0 \n", "1007560 2013 9 30 2240.0 2245 -5.0 2334.0 \n", "1007561 2013 9 30 2240.0 2250 -10.0 2347.0 \n", "1007562 2013 9 30 2240.0 2250 -10.0 2347.0 \n", "1007563 2013 9 30 2240.0 2250 -10.0 2347.0 \n", "1007564 2013 9 30 2241.0 2246 -5.0 2345.0 \n", "1007565 2013 9 30 2241.0 2246 -5.0 2345.0 \n", "1007566 2013 9 30 2241.0 2246 -5.0 2345.0 \n", "1007567 2013 9 30 2307.0 2255 12.0 2359.0 \n", "1007568 2013 9 30 2307.0 2255 12.0 2359.0 \n", "1007569 2013 9 30 2307.0 2255 12.0 2359.0 \n", "\n", " sched_arr_time arr_delay carrier ... visib \\\n", "4 837 -25.0 DL ... 10.0 \n", "5 837 -25.0 DL ... 10.0 \n", "6 837 -25.0 DL ... 10.0 \n", "8 854 19.0 B6 ... 10.0 \n", "9 854 19.0 B6 ... 10.0 \n", "10 854 19.0 B6 ... 10.0 \n", "11 723 -14.0 EV ... 10.0 \n", "12 723 -14.0 EV ... 10.0 \n", "13 723 -14.0 EV ... 10.0 \n", "14 846 -8.0 B6 ... 10.0 \n", "15 846 -8.0 B6 ... 10.0 \n", "16 846 -8.0 B6 ... 10.0 \n", "17 745 8.0 AA ... 10.0 \n", "18 745 8.0 AA ... 10.0 \n", "19 745 8.0 AA ... 10.0 \n", "20 851 -2.0 B6 ... 10.0 \n", "21 851 -2.0 B6 ... 10.0 \n", "22 851 -2.0 B6 ... 10.0 \n", "23 856 -3.0 B6 ... 10.0 \n", "24 856 -3.0 B6 ... 10.0 \n", "25 856 -3.0 B6 ... 10.0 \n", "26 917 7.0 UA ... 10.0 \n", "27 917 7.0 UA ... 10.0 \n", "28 917 7.0 UA ... 10.0 \n", "29 937 -14.0 UA ... 10.0 \n", "30 937 -14.0 UA ... 10.0 \n", "31 937 -14.0 UA ... 10.0 \n", "32 910 31.0 AA ... 10.0 \n", "33 910 31.0 AA ... 10.0 \n", "34 910 31.0 AA ... 10.0 \n", "... ... ... ... ... ... \n", "1007540 2250 7.0 MQ ... 10.0 \n", "1007541 2250 7.0 MQ ... 10.0 \n", "1007542 2250 7.0 MQ ... 10.0 \n", "1007543 2242 57.0 EV ... 10.0 \n", "1007544 2242 57.0 EV ... 10.0 \n", "1007545 2242 57.0 EV ... 10.0 \n", "1007546 2356 -21.0 B6 ... 10.0 \n", "1007547 2356 -21.0 B6 ... 10.0 \n", "1007548 2356 -21.0 B6 ... 10.0 \n", "1007549 30 42.0 UA ... 10.0 \n", "1007550 30 42.0 UA ... 10.0 \n", "1007551 30 42.0 UA ... 10.0 \n", "1007552 2249 130.0 B6 ... 10.0 \n", "1007553 2249 130.0 B6 ... 10.0 \n", "1007554 2249 130.0 B6 ... 10.0 \n", "1007555 2353 -8.0 B6 ... 10.0 \n", "1007556 2353 -8.0 B6 ... 10.0 \n", "1007557 2353 -8.0 B6 ... 10.0 \n", "1007558 2351 -17.0 B6 ... 10.0 \n", "1007559 2351 -17.0 B6 ... 10.0 \n", "1007560 2351 -17.0 B6 ... 10.0 \n", "1007561 7 -20.0 B6 ... 10.0 \n", "1007562 7 -20.0 B6 ... 10.0 \n", "1007563 7 -20.0 B6 ... 10.0 \n", "1007564 1 -16.0 B6 ... 10.0 \n", "1007565 1 -16.0 B6 ... 10.0 \n", "1007566 1 -16.0 B6 ... 10.0 \n", "1007567 2358 1.0 B6 ... 10.0 \n", "1007568 2358 1.0 B6 ... 10.0 \n", "1007569 2358 1.0 B6 ... 10.0 \n", "\n", " time_hour_y faa name \\\n", "4 2013-01-01 01:00:00 ATL Hartsfield Jackson Atlanta Intl \n", "5 2013-01-01 01:00:00 ATL Hartsfield Jackson Atlanta Intl \n", "6 2013-01-01 01:00:00 ATL Hartsfield Jackson Atlanta Intl \n", "8 2013-01-01 01:00:00 FLL Fort Lauderdale Hollywood Intl \n", "9 2013-01-01 01:00:00 FLL Fort Lauderdale Hollywood Intl \n", "10 2013-01-01 01:00:00 FLL Fort Lauderdale Hollywood Intl \n", "11 2013-01-01 01:00:00 IAD Washington Dulles Intl \n", "12 2013-01-01 01:00:00 IAD Washington Dulles Intl \n", "13 2013-01-01 01:00:00 IAD Washington Dulles Intl \n", "14 2013-01-01 01:00:00 MCO Orlando Intl \n", "15 2013-01-01 01:00:00 MCO Orlando Intl \n", "16 2013-01-01 01:00:00 MCO Orlando Intl \n", "17 2013-01-01 01:00:00 ORD Chicago Ohare Intl \n", "18 2013-01-01 01:00:00 ORD Chicago Ohare Intl \n", "19 2013-01-01 01:00:00 ORD Chicago Ohare Intl \n", "20 2013-01-01 01:00:00 PBI Palm Beach Intl \n", "21 2013-01-01 01:00:00 PBI Palm Beach Intl \n", "22 2013-01-01 01:00:00 PBI Palm Beach Intl \n", "23 2013-01-01 01:00:00 TPA Tampa Intl \n", "24 2013-01-01 01:00:00 TPA Tampa Intl \n", "25 2013-01-01 01:00:00 TPA Tampa Intl \n", "26 2013-01-01 01:00:00 LAX Los Angeles Intl \n", "27 2013-01-01 01:00:00 LAX Los Angeles Intl \n", "28 2013-01-01 01:00:00 LAX Los Angeles Intl \n", "29 2013-01-01 01:00:00 SFO San Francisco Intl \n", "30 2013-01-01 01:00:00 SFO San Francisco Intl \n", "31 2013-01-01 01:00:00 SFO San Francisco Intl \n", "32 2013-01-01 01:00:00 DFW Dallas Fort Worth Intl \n", "33 2013-01-01 01:00:00 DFW Dallas Fort Worth Intl \n", "34 2013-01-01 01:00:00 DFW Dallas Fort Worth Intl \n", "... ... ... ... \n", "1007540 2013-09-30 16:00:00 BNA Nashville Intl \n", "1007541 2013-09-30 16:00:00 BNA Nashville Intl \n", "1007542 2013-09-30 16:00:00 BNA Nashville Intl \n", "1007543 2013-09-30 15:00:00 STL Lambert St Louis Intl \n", "1007544 2013-09-30 15:00:00 STL Lambert St Louis Intl \n", "1007545 2013-09-30 15:00:00 STL Lambert St Louis Intl \n", "1007546 2013-09-30 17:00:00 PWM Portland Intl Jetport \n", "1007547 2013-09-30 17:00:00 PWM Portland Intl Jetport \n", "1007548 2013-09-30 17:00:00 PWM Portland Intl Jetport \n", "1007549 2013-09-30 16:00:00 SFO San Francisco Intl \n", "1007550 2013-09-30 16:00:00 SFO San Francisco Intl \n", "1007551 2013-09-30 16:00:00 SFO San Francisco Intl \n", "1007552 2013-09-30 15:00:00 MCO Orlando Intl \n", "1007553 2013-09-30 15:00:00 MCO Orlando Intl \n", "1007554 2013-09-30 15:00:00 MCO Orlando Intl \n", "1007555 2013-09-30 17:00:00 BTV Burlington Intl \n", "1007556 2013-09-30 17:00:00 BTV Burlington Intl \n", "1007557 2013-09-30 17:00:00 BTV Burlington Intl \n", "1007558 2013-09-30 17:00:00 SYR Syracuse Hancock Intl \n", "1007559 2013-09-30 17:00:00 SYR Syracuse Hancock Intl \n", "1007560 2013-09-30 17:00:00 SYR Syracuse Hancock Intl \n", "1007561 2013-09-30 17:00:00 BUF Buffalo Niagara Intl \n", "1007562 2013-09-30 17:00:00 BUF Buffalo Niagara Intl \n", "1007563 2013-09-30 17:00:00 BUF Buffalo Niagara Intl \n", "1007564 2013-09-30 17:00:00 ROC Greater Rochester Intl \n", "1007565 2013-09-30 17:00:00 ROC Greater Rochester Intl \n", "1007566 2013-09-30 17:00:00 ROC Greater Rochester Intl \n", "1007567 2013-09-30 17:00:00 BOS General Edward Lawrence Logan Intl \n", "1007568 2013-09-30 17:00:00 BOS General Edward Lawrence Logan Intl \n", "1007569 2013-09-30 17:00:00 BOS General Edward Lawrence Logan Intl \n", "\n", " lat lon alt tz dst tzone \n", "4 33.636719 -84.428067 1026.0 -5.0 A America/New_York \n", "5 33.636719 -84.428067 1026.0 -5.0 A America/New_York \n", "6 33.636719 -84.428067 1026.0 -5.0 A America/New_York \n", "8 26.072583 -80.152750 9.0 -5.0 A America/New_York \n", "9 26.072583 -80.152750 9.0 -5.0 A America/New_York \n", "10 26.072583 -80.152750 9.0 -5.0 A America/New_York \n", "11 38.944533 -77.455811 313.0 -5.0 A America/New_York \n", "12 38.944533 -77.455811 313.0 -5.0 A America/New_York \n", "13 38.944533 -77.455811 313.0 -5.0 A America/New_York \n", "14 28.429394 -81.308994 96.0 -5.0 A America/New_York \n", "15 28.429394 -81.308994 96.0 -5.0 A America/New_York \n", "16 28.429394 -81.308994 96.0 -5.0 A America/New_York \n", "17 41.978603 -87.904842 668.0 -6.0 A America/Chicago \n", "18 41.978603 -87.904842 668.0 -6.0 A America/Chicago \n", "19 41.978603 -87.904842 668.0 -6.0 A America/Chicago \n", "20 26.683161 -80.095589 19.0 -5.0 A America/New_York \n", "21 26.683161 -80.095589 19.0 -5.0 A America/New_York \n", "22 26.683161 -80.095589 19.0 -5.0 A America/New_York \n", "23 27.975472 -82.533250 26.0 -5.0 A America/New_York \n", "24 27.975472 -82.533250 26.0 -5.0 A America/New_York \n", "25 27.975472 -82.533250 26.0 -5.0 A America/New_York \n", "26 33.942536 -118.408075 126.0 -8.0 A America/Los_Angeles \n", "27 33.942536 -118.408075 126.0 -8.0 A America/Los_Angeles \n", "28 33.942536 -118.408075 126.0 -8.0 A America/Los_Angeles \n", "29 37.618972 -122.374889 13.0 -8.0 A America/Los_Angeles \n", "30 37.618972 -122.374889 13.0 -8.0 A America/Los_Angeles \n", "31 37.618972 -122.374889 13.0 -8.0 A America/Los_Angeles \n", "32 32.896828 -97.037997 607.0 -6.0 A America/Chicago \n", "33 32.896828 -97.037997 607.0 -6.0 A America/Chicago \n", "34 32.896828 -97.037997 607.0 -6.0 A America/Chicago \n", "... ... ... ... ... .. ... \n", "1007540 36.124472 -86.678194 599.0 -6.0 A America/Chicago \n", "1007541 36.124472 -86.678194 599.0 -6.0 A America/Chicago \n", "1007542 36.124472 -86.678194 599.0 -6.0 A America/Chicago \n", "1007543 38.748697 -90.370028 618.0 -6.0 A America/Chicago \n", "1007544 38.748697 -90.370028 618.0 -6.0 A America/Chicago \n", "1007545 38.748697 -90.370028 618.0 -6.0 A America/Chicago \n", "1007546 43.646161 -70.309281 77.0 -5.0 A America/New_York \n", "1007547 43.646161 -70.309281 77.0 -5.0 A America/New_York \n", "1007548 43.646161 -70.309281 77.0 -5.0 A America/New_York \n", "1007549 37.618972 -122.374889 13.0 -8.0 A America/Los_Angeles \n", "1007550 37.618972 -122.374889 13.0 -8.0 A America/Los_Angeles \n", "1007551 37.618972 -122.374889 13.0 -8.0 A America/Los_Angeles \n", "1007552 28.429394 -81.308994 96.0 -5.0 A America/New_York \n", "1007553 28.429394 -81.308994 96.0 -5.0 A America/New_York \n", "1007554 28.429394 -81.308994 96.0 -5.0 A America/New_York \n", "1007555 44.471861 -73.153278 335.0 -5.0 A America/New_York \n", "1007556 44.471861 -73.153278 335.0 -5.0 A America/New_York \n", "1007557 44.471861 -73.153278 335.0 -5.0 A America/New_York \n", "1007558 43.111187 -76.106311 421.0 -5.0 A America/New_York \n", "1007559 43.111187 -76.106311 421.0 -5.0 A America/New_York \n", "1007560 43.111187 -76.106311 421.0 -5.0 A America/New_York \n", "1007561 42.940525 -78.732167 724.0 -5.0 A America/New_York \n", "1007562 42.940525 -78.732167 724.0 -5.0 A America/New_York \n", "1007563 42.940525 -78.732167 724.0 -5.0 A America/New_York \n", "1007564 43.118866 -77.672389 559.0 -5.0 A America/New_York \n", "1007565 43.118866 -77.672389 559.0 -5.0 A America/New_York \n", "1007566 43.118866 -77.672389 559.0 -5.0 A America/New_York \n", "1007567 42.364347 -71.005181 19.0 -5.0 A America/New_York \n", "1007568 42.364347 -71.005181 19.0 -5.0 A America/New_York \n", "1007569 42.364347 -71.005181 19.0 -5.0 A America/New_York \n", "\n", "[838182 rows x 38 columns]" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pred = 'dep_delay'\n", "features = ['month', 'day', 'dep_time', 'arr_time', 'carrier', 'dest', 'air_time',\n", " 'distance', 'lat', 'lon', 'alt', 'dewp', 'humid', 'wind_speed', 'wind_gust',\n", " 'precip', 'pressure', 'visib']\n", "features_v = df[features]\n", "pred_v = df[pred]\n", "\n", "how_late_is_late = 15.0\n", "\n", "pd.options.mode.chained_assignment = None #default='warn'\n", "\n", "\n", "# 因为航空公司不是一个数字，我们把它转化为数字哑变量\n", "features_v['carrier'] = pd.factorize(features_v['carrier'])[0]\n", "\n", "# dest也不是一个数字，我们也把它转为数字\n", "features_v['dest'] = pd.factorize(features_v['dest'])[0]\n", "\n", "scaler = StandardScaler()\n", "scaled_features_v = scaler.fit_transform(features_v)\n", "\n", "features_train, features_test, pred_train, pred_test = train_test_split(\n", " scaled_features_v, pred_v, test_size=0.30, random_state=0)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 使用logistic回归来执行分类\n", "\n", "clf_lr = sklearn.linear_model.LogisticRegression(penalty='l2',\n", " class_weight='balanced')\n", "logistic_fit = clf_lr.fit(features_train, np.where(pred_train >= how_late_is_late, 1, 0))\n", "\n", "predictions = clf_lr.predict(features_test)" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Confusion Matrix\n", " 0 1\n", "0 135181 64481\n", "1 17910 33883\n", "\n", "precision = 0.34, recall = 0.65, F1 = 0.45, accuracy = 0.67\n" ] } ], "source": [ "# summary Report\n", "\n", "# Confusion Matrix\n", "cm_lr = confusion_matrix(np.where(pred_test >= how_late_is_late, 1, 0),\n", " predictions)\n", "print(\"Confusion Matrix\")\n", "print(pd.DataFrame(cm_lr))\n", "\n", "# 获取精确值\n", "report_lr = precision_recall_fscore_support(\n", " list(np.where(pred_test >= how_late_is_late, 1, 0)),\n", " list(predictions), average='binary')\n", "\n", "#打印精度值\n", "print(\"\\nprecision = %0.2f, recall = %0.2f, F1 = %0.2f, accuracy = %0.2f\"\n", " % (report_lr[0], report_lr[1], report_lr[2],\n", " accuracy_score(list(np.where(pred_test >= how_late_is_late, 1, 0)),\n", " list(predictions))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 9 聚合数据(Cluster data)\n", "最简单的聚类方法是K-Means" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import sklearn\n", "from sklearn.cluster import KMeans\n", "from sklearn import linear_model, cross_validation, cluster\n", "from sklearn.metrics import classification_report, confusion_matrix, precision_recall_fscore_support, accuracy_score\n", "from sklearn.cross_validation import train_test_split, cross_val_score, ShuffleSplit\n", "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", "\n", "flights = pd.read_csv('data/nycflights13/flights.csv.gz')\n", "weather = pd.read_csv('data/nycflights13/weather.csv.gz')\n", "airports = pd.read_csv('data/nycflights13/airports.csv.gz')\n", "\n", "df_withweather = pd.merge(flights, weather, how='left', on=['year', 'month', 'day', 'hour'])\n", "df = pd.merge(df_withweather, airports, how='left', left_on='dest', right_on='faa')\n", "\n", "df = df.dropna()" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pred = 'dep_delay'\n", "features = ['month', 'day', 'dep_time', 'arr_time', 'carrier', 'dest', 'air_time',\n", " 'distance', 'lat', 'lon', 'alt', 'dewp', 'humid', 'wind_speed', 'wind_gust',\n", " 'precip', 'pressure', 'visib']\n", "features_v = df[features]\n", "pred_v = df[pred]\n", "\n", "how_late_is_late = 15.0\n", "\n", "pd.options.mode.chained_assignment = None #default='warn'\n", "\n", "# 因为航空公司不是一个数字，我们把它转化为数字哑变量\n", "features_v['carrier'] = pd.factorize(features_v['carrier'])[0]\n", "\n", "# dest也不是一个数字，我们也把它转为数字\n", "features_v['dest'] = pd.factorize(features_v['dest'])[0]\n", "\n", "scaler = StandardScaler()\n", "scaled_features_v = scaler.fit_transform(features_v)\n", "\n", "features_train, features_test, pred_train, pred_test = train_test_split(\n", " scaled_features_v, pred_v, test_size=0.30, random_state=0)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n", " n_clusters=8, n_init=10, n_jobs=1, precompute_distances='auto',\n", " random_state=None, tol=0.0001, verbose=0)" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cluster = sklearn.cluster.KMeans(n_clusters=8, init='k-means++', n_init=10, max_iter=300, tol=0.0001, precompute_distances='auto', random_state=None, verbose=0)\n", "cluster.fit(features_train)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 预测测试数据\n", "result = cluster.predict(features_test)" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([7, 0, 7, ..., 7, 5, 0])" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "scrolled": true }, "outputs": [ { "ename": "AttributeError", "evalue": "Unknown property extend", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-78-605c50db6054>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 20\u001b[0m plt.imshow(z, interpolation='nearest',\n\u001b[0;32m 21\u001b[0m \u001b[0mextend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\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;32m---> 22\u001b[1;33m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPaired\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 23\u001b[0m \u001b[1;31m#aspect='auto'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[1;31m# origin='lower'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32md:\\app\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mimshow\u001b[1;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, hold, data, kwargs)\u001b[0m\n\u001b[0;32m 3078\u001b[0m \u001b[0mfilternorm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfilternorm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfilterrad\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3079\u001b[0m \u001b[0mimlim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mimlim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresample\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mresample\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\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[1;32m-> 3080\u001b[1;33m kwargs)\n\u001b[0m\u001b[0;32m 3081\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3082\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32md:\\app\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, args, kwargs)\u001b[0m\n\u001b[0;32m 1708\u001b[0m warnings.warn(msg % (label_namer, func.__name__),\n\u001b[0;32m 1709\u001b[0m RuntimeWarning, stacklevel=2)\n\u001b[1;32m-> 1710\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1711\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1712\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32md:\\app\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mimshow\u001b[1;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, kwargs)\u001b[0m\n\u001b[0;32m 5190\u001b[0m im = mimage.AxesImage(self, cmap, norm, interpolation, origin, extent,\n\u001b[0;32m 5191\u001b[0m \u001b[0mfilternorm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfilternorm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfilterrad\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5192\u001b[1;33m resample=resample, kwargs)\n\u001b[0m\u001b[0;32m 5193\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5194\u001b[0m \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32md:\\app\\Anaconda3\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, ax, cmap, norm, interpolation, origin, extent, filternorm, filterrad, resample, kwargs)\u001b[0m\n\u001b[0;32m 755\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfilterrad\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 756\u001b[0m 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\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 236\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 237\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__getstate__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32md:\\app\\Anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, props)\u001b[0m\n\u001b[0;32m 845\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 846\u001b[0m ret = [_update_property(self, k, v)\n\u001b[1;32m--> 847\u001b[1;33m for k, v in props.items()]\n\u001b[0m\u001b[0;32m 848\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 849\u001b[0m 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\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAttributeError\u001b[0m: Unknown property extend" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x25980d35c88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "from sklearn.decomposition import PCA\n", "\n", "reduced_data = PCA(n_components=2).fit_transform(features_train)\n", "kmeans = KMeans(init='k-means++', n_clusters=8, n_init=10)\n", "kmeans.fit(reduced_data)\n", "\n", "# mesh的步长\n", "h = .02\n", "\n", "x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1\n", "y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1\n", "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n", "\n", "z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])\n", "\n", "z = z.reshape(xx.shape)\n", "plt.figure(1)\n", "plt.clf()\n", "plt.imshow(z, interpolation='nearest',\n", " extend=(xx.min(), xx.max(), yy.min(), yy.max()),\n", " cmap=plt.cm.Paired\n", " #aspect='auto' \n", " # origin='lower'\n", " )\n", "\n", "plt.plot(reduced_data[:, 0], reduced_data[:, 1], 'k.', markersize=2)\n", "\n", "centroids = kmeans.cluster_centers_\n", "plt.scatter(centroids[:, 0], centroids[:, 1],\n", " marker='x', s=169, linewidths=3,\n", " color='w', zorder=10)\n", "plt.title('K-Means clustering on the dataset (PCA-reduced data)\\n'\n", " 'Centroids are marked with white cross')\n", "plt.xlim(x_min, x_max)\n", "plt.ylim(y_min, y_max)\n", "plt.xticks(())\n", "plt.yticks(())\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## 10 PySpark简介\n", "\n", "扩展我们的算法：有时我们需要处理大量数据，并且采样已经无效，这个时候可以通过把数据分到多个机器来处理。\n", "\n", "Spark是一个用来并行进行大数据处理的API。它将数据切割到集群来处理。在开发阶段，我们可以只在本地运行。\n", "\n", "我们使用PySpark Shell来连接到集群。\n", "\n", "运行下面路径的pyspark，会启动PySpark Shell\n", "\n", "```\n", "~/spark/bin/pyspark (Max/Linux)\n", "C:\\spark\\bin\\pyspark (Windows)\n", "```\n", "\n", "此时，可以在Shell中运行文件加载：\n", "\n", "```\n", "lines = sc.textFile(\"README.md\")\n", "lines.first() # 加载第一行\n", "```\n", "\n", "可以在`http://localhost:4040`查看PySpark运行的Job\n", "\n", "大多数情况下，我们希望能够在Jupyter Notebook中运行PySpark，为此，我们需要设置环境变量：\n", "\n", "```\n", "PYSPARK_PYTHON=python3\n", "PYSPARK_DRIVER_PYTHON=\"jupyter\"\n", "PYSPARK_DRIVER_PYTHON_OPTS=\"notebook\"\n", "```\n", "\n", "然后运行`~/spark/bin/pyspark`，最后一个命令会启动一个jupyter server,样子跟我们用的一样。" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'sc' 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-1-2f627e434df0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'README.md'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'sc' is not defined" ] } ], "source": [ "lines = sc.text('README.md')\n", "lines.take(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "我们看看http://localhost:4040 可以查看运行的Job" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'lines' 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-2-a9d79c569bce>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlinesWithSpark\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'spark'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mlinesWithSpark\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'lines' is not defined" ] } ], "source": [ "linesWithSpark = lines.filter(lambda line: 'spark' in line)\n", "linesWithSpark.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Spark的基本类型是RDD（resilient distributed dataset），它是基本分布式数据类型。RDD有两类操作，第一个是变换(transformation)，返回值仍然是RDD，另外一种是动作(action)，用来计算结果。Spark的操作是Lazy的，也就是说只有在执行action时才会真正的开始处理。" ] }, { "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
wcmckee/wcmckee.com	posts/taxscale.ipynb	1	112529	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Tax Scale\n", "\n", "estimates the change in tax revenue when you modify the personal income tax system. The tool allows you to modify the tax brackets, the tax rates, and the indirect and company tax offset, and outputs an estimated change in revenue along with plots of the marginal tax rates and the average tax rates" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "lowtax = dict({'lownum' : 0, 'highnum' : 14000, 'percent' : 10.5})\n", "midtax = dict({'percent' : 17.5, 'lownum' : 14000, 'highnum' : 48000})\n", "hightax = dict({'percent' : 30, 'lownum' : 48000, 'highnum' : 70000})\n", "supertax = dict({'percent' : 33, 'lownum' : 70000, 'highnum' : float(\"inf\")})" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "{'highnum': 14000, 'lownum': 0, 'percent': 10.5}" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lowtax" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "{'highnum': 48000, 'lownum': 14000, 'percent': 17.5}" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "midtax" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "{'highnum': 70000, 'lownum': 48000, 'percent': 30}" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hightax" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "{'highnum': inf, 'lownum': 70000, 'percent': 33}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "supertax" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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ZgOuoN95/eGKkX4FpzwRarWfHAHOAa8ocX5SaFAvouPXqRx+5u8XddktmXfmk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\nhDuvoogI1tqtYhBiZBQwsdI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"text/plain": [ "<matplotlib.figure.Figure at 0x6fdde1f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib.ticker as ticker\n", "\n", "# Fixing random state for reproducibility\n", "np.random.seed(19680801)\n", "\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "ax.plot(100np.random.rand(20))\n", "\n", "formatter = ticker.FormatStrFormatter('$%1.2f')\n", "ax.yaxis.set_major_formatter(formatter)\n", "\n", "for tick in ax.yaxis.get_major_ticks():\n", " tick.label1On = False\n", " tick.label2On = True\n", " tick.label2.set_color('green')\n", "\n", "plt.show() " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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oFkYRKQpcDLQDPg1DbIVmTVMFS02FwYOhTx+vI0ksO3a49vqzz/Y6kuh1xhnw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\nXx3Ql3zp0qWpXr06jRo14qa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"text/plain": [ "<matplotlib.figure.Figure at 0x6fd07a10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "t = np.arange(0.0, 2.0, 0.01)\n", "s = np.sin(2np.pit)\n", "\n", "plt.plot(t,s)\n", "plt.title(r'$\\alpha_i > \\beta_i$', fontsize=20)\n", "plt.text(1, -0.6, r'$\\sum_{i=0}^\\infty x_i$', fontsize=20)\n", "plt.text(0.6, 0.6, r'$\\mathcal{A}\\mathrm{sin}(2 \\omega t)$',\n", " fontsize=20)\n", "plt.xlabel('time (s)')\n", "plt.ylabel('volts (mV)')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x6fb34bf0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from mpl_toolkits.axes_grid.axislines import SubplotZero\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "if 1:\n", " fig = plt.figure(1)\n", " ax = SubplotZero(fig, 111)\n", " fig.add_subplot(ax)\n", "\n", " for direction in [\"xzero\", \"yzero\"]:\n", " ax.axis[direction].set_axisline_style(\"-\|>\")\n", " ax.axis[direction].set_visible(True)\n", "\n", " for direction in [\"left\", \"right\", \"bottom\", \"top\"]:\n", " ax.axis[direction].set_visible(False)\n", "\n", " x = np.linspace(-0.5, 1., 100)\n", " ax.plot(x, np.sin(xnp.pi))\n", "\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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lULVqDNE8/7y7dnkrRDrh3JOTmaG7bBln7m6U5b35Zu4TtWlDGWq0T7rSo527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\nVvr3B2bMAP75x//v7WzYkTs31Rh58jA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"text/plain": [ "<matplotlib.figure.Figure at 0x6fa6de50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\"\"\"\n", "=======================\n", "Artist within an artist\n", "=======================\n", "\n", "Show how to override basic methods so an artist can contain another\n", "artist. In this case, the line contains a Text instance to label it.\n", "\"\"\"\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib.lines as lines\n", "import matplotlib.transforms as mtransforms\n", "import matplotlib.text as mtext\n", "\n", "\n", "class MyLine(lines.Line2D):\n", " def __init__(self, args, kwargs):\n", " # we'll update the position when the line data is set\n", " self.text = mtext.Text(0, 0, '')\n", " lines.Line2D.__init__(self, args, *kwargs)\n", "\n", " # we can't access the label attr until after* the line is\n", " # inited\n", " self.text.set_text(self.get_label())\n", "\n", " def set_figure(self, figure):\n", " self.text.set_figure(figure)\n", " lines.Line2D.set_figure(self, figure)\n", "\n", " def set_axes(self, axes):\n", " self.text.set_axes(axes)\n", " lines.Line2D.set_axes(self, axes)\n", "\n", " def set_transform(self, transform):\n", " # 2 pixel offset\n", " texttrans = transform + mtransforms.Affine2D().translate(2, 2)\n", " self.text.set_transform(texttrans)\n", " lines.Line2D.set_transform(self, transform)\n", "\n", " def set_data(self, x, y):\n", " if len(x):\n", " self.text.set_position((x[-1], y[-1]))\n", "\n", " lines.Line2D.set_data(self, x, y)\n", "\n", " def draw(self, renderer):\n", " # draw my label at the end of the line with 2 pixel offset\n", " lines.Line2D.draw(self, renderer)\n", " self.text.draw(renderer)\n", "\n", "\n", "fig, ax = plt.subplots()\n", "x, y = np.random.rand(2, 20)\n", "line = MyLine(x, y, mfc='red', ms=12, label='line label')\n", "#line.text.set_text('line label')\n", "line.text.set_color('red')\n", "line.text.set_fontsize(16)\n", "\n", "\n", "ax.add_line(line)\n", "\n", "\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x6fab0b10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "for i in range(0,29):\n", " plt.plot([i])\n", "plt.ylabel('percent tax')\n", "for i in range(0, 100):\n", " plt.plot([i])\n", "plt.xlabel('income')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (<ipython-input-12-3a8d1c5316ff>, line 9)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-12-3a8d1c5316ff>\"\u001b[0;36m, line \u001b[0;32m9\u001b[0m\n\u001b[0;31m plt.\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "for i in range(0,29):\n", " plt.plot([i])\n", "#plt.plot([1,2,3,4], [1,4,9,16], 'ro')\n", "#plt.axis([0, 6, 0, 20])\n", "plt.ylabel('percent tax')\n", "plt.xlabel('income')\n", "plt.\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x6fcf4690>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams['savefig.facecolor'] = \"0.8\"\n", "\n", "def example_plot(ax, fontsize=8):\n", " ax.plot([1, 2])\n", " ax.locator_params(nbins=6)\n", " ax.set_xlabel('percent tax', fontsize=fontsize)\n", " ax.set_ylabel('income', fontsize=fontsize)\n", " ax.set_title('Tax Scale', fontsize=fontsize)\n", "\n", "plt.close('all')\n", "fig, ax = plt.subplots()\n", "example_plot(ax, fontsize=12)\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 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": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
GD-park/HR_Analytics	EDA & Modeling.ipynb	1	1586319	null	mit
whitead/numerical_stats	unit_6/hw_2019/Homework_6.ipynb	1	6065	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Homework 6\n", "#### CHE 116: Numerical Methods and Statistics\n", "\n", "\n", "2/21/2019\n", "\n", "----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Plotting Distributions (20 Points)\n", "\n", "For the following problems, choose the distribution that best fits the process and plot the distribution. State whether the sample space is discrete, the range of the sample space, and then use that to justify your choice of distribution. Make sure your axes labels are descriptive and not just the variable letters.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.1\n", "\n", "About 3 deer cross through my yard each day in winter. What is the distribution that could explain the random variable of number of deer? Remember to read the instructions above!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.2\n", "\n", "You sleep about 8 hours per night with standard deviation of 2. What distribution could explain the random variable of number of hours slept?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.3\n", "\n", "You are going out to read your poetry. You can go to only one of three different poetry readings. At each poetry reading, they randomly select a poet to read for a given number of timeslots. You can read multiple times, since you have many poems to read. At CLUB POET, there will be 11 poem reading timeslots and your probability of reading at particular timeslot is 0.4. At THE DEAD HEDGEHOG, there will be 5 timeslots and your probability of reading will be 0.5. At THE DANCING CLOUD, there are 7 timeslots and your probability of reading will be 0.7. Plot each of the distributions representing number of poems read for the different poetry readings. You should plot three distributions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.4\n", "\n", "You recently got a boyfriend. Your boyfriend texts you about every 20 minutes. What distribution could represent the time between text messages?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Loops (20 Points)\n", "For the following problems, use loops to solve them. You cannot use `numpy` arrays. Justify your choice of distribution, state what quantity is being asked (probability of sample, interval, or prediction interval), and answer the prompt in words. The only formula you may use for expected value is $E[x] = \\sum P(x) x$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.1 (Warmup)\n", "\n", "Show that the binomial distribution sums to one over the whole sample space. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.2\n", "\n", "You ask your students questions during lecture and offer candy bars if they get problems correct. You ask 12 questions per lecture and the students get the questions right with probability 0.7. How many candy bars should you bring to have enough for 90% of the lectures?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3\n", "\n", "About 25 per 100,000 people die per year in a car accident. How many years can you drive before there is a 10% chance of dying? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.4\n", "\n", "In a computer program, each line of code can cause a security vulnerability. A particular company finds that after 1 year, there are 25 security vulnerabilities per 10,000 lines of code. After inspecting a new program which contains 23,000 lines of code, they find 11 secutity vulnerabilities. What is the probability that they found all vulnerabilities?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. Normal Distribution (18 Points)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.1 (2 Points)\n", "\n", "Take $\\mu = 3$ and $\\sigma = 4$. What is $P(-4 < x < 0)$?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.2 (2 Points)\n", "\n", "You spend 111 minutes per day thinking about what to eat, with a standard deviation of 30. What is the probability you spend 43 minutes thinking about food?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.3 (2 Points)\n", "\n", "Using the numbers from 3.2, what is the probability that you spend more than 2 hours thinking about food in a day?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.4 (4 Points)\n", "\n", "What is unphysical about the sample space implicitly defined in problem 3.2? Compute a quantity to prove that the unphysical part of the sample space is negligible. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.5 (2 Points)\n", "\n", "Take $\\mu = -2$, $\\sigma = 4$. Convert the following interval to z-scores: $(-4, 4)$. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.6 (4 Points)\n", "Can $\\sigma$ be negative? Why or why not?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.7 (2 Points)\n", "\n", "How much probability is covered between Z-scores $-3$ to $3$?" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
JasonNK/udacity-dlnd	autoencoder/Simple_Autoencoder_Solution.ipynb	1	40583	{ "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", "![Autoencoder](assets/autoencoder_1.png)\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": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n", "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n", "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n", "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\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', validation_size=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below I'm plotting an example image from the MNIST dataset. These are 28x28 grayscale images of handwritten digits." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x11abae4a8>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11aa66da0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "img = mnist.train.images[2]\n", "plt.imshow(img.reshape((28, 28)), cmap='Greys_r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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 basically the simplest autoencoder with a single ReLU hidden layer. This layer will be used as the compressed representation. Then, the encoder is the input layer and the hidden layer. The decoder is the hidden layer and the output layer. Since the images are normalized between 0 and 1, we need to use a sigmoid activation on the output layer to get values matching the input.\n", "\n", "![Autoencoder architecture](assets/simple_autoencoder.png)\n", "\n", "\n", "> Exercise: Build the graph for the autoencoder in the cell below. The input images will be flattened into 784 length vectors. The targets are the same as the inputs. And there should be one hidden layer with a ReLU activation and an output layer with a sigmoid activation. Feel free to use TensorFlow's higher level API, `tf.layers`. For instance, you would use [`tf.layers.dense(inputs, units, activation=tf.nn.relu)`](https://www.tensorflow.org/api_docs/python/tf/layers/dense) to create a fully connected layer with a ReLU activation. The loss should be calculated with the cross-entropy loss, there is a convenient TensorFlow function for this `tf.nn.sigmoid_cross_entropy_with_logits` ([documentation](https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits)). You should note that `tf.nn.sigmoid_cross_entropy_with_logits` takes the logits, but to get the reconstructed images you'll need to pass the logits through the sigmoid function." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Size of the encoding layer (the hidden layer)\n", "encoding_dim = 32\n", "\n", "image_size = mnist.train.images.shape[1]\n", "\n", "inputs_ = tf.placeholder(tf.float32, (None, image_size), name='inputs')\n", "targets_ = tf.placeholder(tf.float32, (None, image_size), name='targets')\n", "\n", "# Output of hidden layer\n", "encoded = tf.layers.dense(inputs_, encoding_dim, activation=tf.nn.relu)\n", "\n", "# Output layer logits\n", "logits = tf.layers.dense(encoded, image_size, activation=None)\n", "# Sigmoid output from\n", "decoded = tf.nn.sigmoid(logits, name='output')\n", "\n", "loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=targets_, logits=logits)\n", "cost = tf.reduce_mean(loss)\n", "opt = tf.train.AdamOptimizer(0.001).minimize(cost)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create the session\n", "sess = tf.Session()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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", "Calling `mnist.train.next_batch(batch_size)` will return a tuple of `(images, labels)`. We're not concerned with the labels here, we just need the images. Otherwise this is pretty straightfoward training with TensorFlow. We initialize the variables with `sess.run(tf.global_variables_initializer())`. Then, run the optimizer and get the loss with `batch_cost, _ = sess.run([cost, opt], feed_dict=feed)`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "epochs = 20\n", "batch_size = 200\n", "sess.run(tf.global_variables_initializer())\n", "for e in range(epochs):\n", " for ii in range(mnist.train.num_examples//batch_size):\n", " batch = mnist.train.next_batch(batch_size)\n", " feed = {inputs_: batch[0], targets_: batch[0]}\n", " batch_cost, _ = sess.run([cost, opt], feed_dict=feed)\n", "\n", " print(\"Epoch: {}/{}...\".format(e+1, epochs),\n", " \"Training loss: {:.4f}\".format(batch_cost))" ] }, { "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": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x125d34908>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(20,4))\n", "in_imgs = mnist.test.images[:10]\n", "reconstructed, compressed = sess.run([decoded, encoded], feed_dict={inputs_: in_imgs})\n", "\n", "for images, row in zip([in_imgs, reconstructed], axes):\n", " for img, ax in zip(images, row):\n", " ax.imshow(img.reshape((28, 28)), cmap='Greys_r')\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "\n", "fig.tight_layout(pad=0.1)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sess.close()" ] }, { "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.\n", "\n", "In practice, autoencoders aren't actually better at compression compared to typical methods like JPEGs and MP3s. But, they are being used for noise reduction, which you'll also build." ] } ], "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
masve/saav-deliveries	assignment1/Assignment1.ipynb	1	20347188	null	mit
Gurupradeep/CIFAR-10-Object-Recognition-in-Images	Models/VGG16.ipynb	1	23020	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "from keras.applications.vgg16 import VGG16\n", "from keras.applications.vgg16 import preprocess_input\n", "from keras.layers import Input, Flatten, Dense,Dropout\n", "from keras.models import Model\n", "from keras.utils import np_utils\n", "from keras.preprocessing.image import ImageDataGenerator\n", "from keras import regularizers,optimizers\n", "import numpy as np\n", "import keras" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from keras.datasets import cifar10\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from keras.callbacks import ReduceLROnPlateau, CSVLogger,EarlyStopping\n", "from scipy.misc import toimage, imresize" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Defining the shape of input image\n", "no_of_channels = 3\n", "height = 64\n", "width = 64" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Resizing the dataset\n", "def Resize(images) :\n", " X = np.zeros((images.shape[0],height,width,3))\n", " for i in range(images.shape[0]):\n", " X[i]= imresize(images[i], (height,width,no_of_channels), interp='bilinear', mode=None)\n", " return X" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n", "170491904/170498071 [============================>.] - ETA: 0s" ] } ], "source": [ "(x_train, y_train), (x_test, y_test) = cifar10.load_data()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.save(\"x_train_out.npy\",x_train)\n", "np.save(\"y_train_out.npy\",y_train)\n", "np.save(\"x_test_out.npy\",x_test)\n", "np.save(\"y_test_out.npy\",y_test)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Loading testing and training data from numpy arrays\n", "x_train = np.load(\"x_train_out.npy\")\n", "y_train = np.load(\"y_train_out.npy\")\n", "x_test = np.load(\"x_test_out.npy\")\n", "y_test = np.load(\"y_test_out.npy\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x_train = x_train.astype('float32')\n", "x_test = x_test.astype('float32')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(50000, 32, 32, 3)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x_train.shape" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "#Changing the shape of CIFAR10 dataset from 32323 to 64643\n", "x_train = Resize(x_train)\n", "x_test = Resize(x_test)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#z-score\n", "mean = np.mean(x_train,axis=(0,1,2,3))\n", "std = np.std(x_train,axis=(0,1,2,3))\n", "x_train = (x_train-mean)/(std+1e-7)\n", "x_test = (x_test-mean)/(std+1e-7)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(50000, 1)\n" ] } ], "source": [ "print(y_train.shape)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[6]\n" ] } ], "source": [ "print(y_train[0])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#one hot encoding of target labels\n", "num_classes = 10\n", "y_train = np_utils.to_categorical(y_train,num_classes)\n", "y_test = np_utils.to_categorical(y_test,num_classes)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(50000, 10)\n" ] } ], "source": [ "print(y_train.shape)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n" ] } ], "source": [ "print(y_train[0])" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def PretrainedModel(num_of_classes) :\n", " \n", " #load vgg model from keras\n", " vgg_16_model = VGG16(weights=None,include_top= False,input_shape=(height,width,no_of_channels))\n", " vgg_16_model.summary()\n", " \n", " #get weights from pretrained model on imagenet\n", " vgg_16_model.load_weights('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5')\n", " \n", " print(len(vgg_16_model.layers))\n", " \n", " #Freeze all layers\n", " #for i in range(19):\n", " # vgg_16_model.layers[i].trainable = False\n", " \n", " #change model input layer according to cifar-10 dataset\n", " inputs = Input(shape = (height,width,no_of_channels), name = \"image_input\")\n", " \n", " #create dummy layer\n", " output_vgg16_model = vgg_16_model(inputs)\n", " \n", " #Add the fully-connected layers \n", " #Adding one fully connected layer instead of 2 to decrease overfitting\n", " x = Flatten(name='flatten')(output_vgg16_model)\n", " x = Dense(2048, activation='relu', name='fc1')(x)\n", " x = Dropout(0.5)(x)\n", " x = Dense(num_of_classes, activation='softmax', name='predictions')(x)\n", "\n", " #Create custom model\n", " cifar10_vgg = Model(inputs=inputs, outputs=x)\n", "\n", " #In the summary, weights and layers from VGG part will be hidden, but they will be fit during the training\n", " cifar10_vgg.summary()\n", " \n", " print(len(cifar10_vgg.layers))\n", " \n", " return cifar10_vgg\n", "\n", " " ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "input_1 (InputLayer) (None, 64, 64, 3) 0 \n", "_________________________________________________________________\n", "block1_conv1 (Conv2D) (None, 64, 64, 64) 1792 \n", "_________________________________________________________________\n", "block1_conv2 (Conv2D) (None, 64, 64, 64) 36928 \n", "_________________________________________________________________\n", "block1_pool (MaxPooling2D) (None, 32, 32, 64) 0 \n", "_________________________________________________________________\n", "block2_conv1 (Conv2D) (None, 32, 32, 128) 73856 \n", "_________________________________________________________________\n", "block2_conv2 (Conv2D) (None, 32, 32, 128) 147584 \n", "_________________________________________________________________\n", "block2_pool (MaxPooling2D) (None, 16, 16, 128) 0 \n", "_________________________________________________________________\n", "block3_conv1 (Conv2D) (None, 16, 16, 256) 295168 \n", "_________________________________________________________________\n", "block3_conv2 (Conv2D) (None, 16, 16, 256) 590080 \n", "_________________________________________________________________\n", "block3_conv3 (Conv2D) (None, 16, 16, 256) 590080 \n", "_________________________________________________________________\n", "block3_pool (MaxPooling2D) (None, 8, 8, 256) 0 \n", "_________________________________________________________________\n", "block4_conv1 (Conv2D) (None, 8, 8, 512) 1180160 \n", "_________________________________________________________________\n", "block4_conv2 (Conv2D) (None, 8, 8, 512) 2359808 \n", "_________________________________________________________________\n", "block4_conv3 (Conv2D) (None, 8, 8, 512) 2359808 \n", "_________________________________________________________________\n", "block4_pool (MaxPooling2D) (None, 4, 4, 512) 0 \n", "_________________________________________________________________\n", "block5_conv1 (Conv2D) (None, 4, 4, 512) 2359808 \n", "_________________________________________________________________\n", "block5_conv2 (Conv2D) (None, 4, 4, 512) 2359808 \n", "_________________________________________________________________\n", "block5_conv3 (Conv2D) (None, 4, 4, 512) 2359808 \n", "_________________________________________________________________\n", "block5_pool (MaxPooling2D) (None, 2, 2, 512) 0 \n", "=================================================================\n", "Total params: 14,714,688.0\n", "Trainable params: 14,714,688.0\n", "Non-trainable params: 0.0\n", "_________________________________________________________________\n", "19\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "image_input (InputLayer) (None, 64, 64, 3) 0 \n", "_________________________________________________________________\n", "vgg16 (Model) (None, 2, 2, 512) 14714688 \n", "_________________________________________________________________\n", "flatten (Flatten) (None, 2048) 0 \n", "_________________________________________________________________\n", "fc1 (Dense) (None, 2048) 4196352 \n", "_________________________________________________________________\n", "dropout_1 (Dropout) (None, 2048) 0 \n", "_________________________________________________________________\n", "predictions (Dense) (None, 10) 20490 \n", "=================================================================\n", "Total params: 18,931,530.0\n", "Trainable params: 18,931,530.0\n", "Non-trainable params: 0.0\n", "_________________________________________________________________\n", "6\n" ] } ], "source": [ "model = PretrainedModel(10)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_augmentation = True" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "batch_size = 64\n", "epochs = 100" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Defining Callback functions which will be called by model during runtime when specified condition satisfies\n", "lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1), cooldown=0, patience=2, min_lr=0.5e-6)\n", "csv_logger = CSVLogger('./vgg16imagenetpretrained_upsampleimage_cifar10_data_argumentation.csv')\n", "early_stopper = EarlyStopping(min_delta=0.001, patience=10)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "opt = optimizers.rmsprop(lr=0.0001, decay=1e-6)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model.compile(loss = 'categorical_crossentropy',optimizer='adam',metrics = ['accuracy'])" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-------------Using Data augmentation------------\n", "Epoch 1/100\n", "781/781 [==============================] - 2070s - loss: 1.6934 - acc: 0.3396 - val_loss: 1.5510 - val_acc: 0.3817\n", "Epoch 2/100\n", "781/781 [==============================] - 2058s - loss: 1.4432 - acc: 0.4343 - val_loss: 1.4019 - val_acc: 0.4665\n", "Epoch 3/100\n", "781/781 [==============================] - 2055s - loss: 1.2544 - acc: 0.5241 - val_loss: 1.1731 - val_acc: 0.5672\n", "Epoch 4/100\n", "781/781 [==============================] - 2048s - loss: 1.0923 - acc: 0.6033 - val_loss: 1.0339 - val_acc: 0.6367\n", "Epoch 5/100\n", "781/781 [==============================] - 2047s - loss: 0.9946 - acc: 0.6427 - val_loss: 0.9119 - val_acc: 0.6775\n", "Epoch 6/100\n", "781/781 [==============================] - 2047s - loss: 0.9315 - acc: 0.6714 - val_loss: 0.9422 - val_acc: 0.6732\n", "Epoch 7/100\n", "781/781 [==============================] - 2046s - loss: 0.8698 - acc: 0.6937 - val_loss: 0.8044 - val_acc: 0.7209\n", "Epoch 8/100\n", "781/781 [==============================] - 2046s - loss: 0.8387 - acc: 0.7078 - val_loss: 0.8348 - val_acc: 0.7155\n", "Epoch 9/100\n", "781/781 [==============================] - 2046s - loss: 0.8045 - acc: 0.7207 - val_loss: 0.7550 - val_acc: 0.7378\n", "Epoch 10/100\n", "781/781 [==============================] - 2046s - loss: 0.7844 - acc: 0.7311 - val_loss: 0.7678 - val_acc: 0.7368\n", "Epoch 11/100\n", "781/781 [==============================] - 2046s - loss: 0.7538 - acc: 0.7434 - val_loss: 0.7736 - val_acc: 0.7359\n", "Epoch 12/100\n", "781/781 [==============================] - 2046s - loss: 0.7440 - acc: 0.7497 - val_loss: 0.7325 - val_acc: 0.7583\n", "Epoch 13/100\n", "781/781 [==============================] - 2046s - loss: 0.7169 - acc: 0.7568 - val_loss: 0.7336 - val_acc: 0.7509\n", "Epoch 14/100\n", "781/781 [==============================] - 2046s - loss: 0.6984 - acc: 0.7628 - val_loss: 0.7597 - val_acc: 0.7529\n", "Epoch 15/100\n", "781/781 [==============================] - 2046s - loss: 0.6926 - acc: 0.7670 - val_loss: 0.7584 - val_acc: 0.7469\n", "Epoch 16/100\n", "781/781 [==============================] - 2046s - loss: 0.5484 - acc: 0.8154 - val_loss: 0.5872 - val_acc: 0.8052\n", "Epoch 17/100\n", "781/781 [==============================] - 2046s - loss: 0.5043 - acc: 0.8295 - val_loss: 0.5592 - val_acc: 0.8164\n", "Epoch 18/100\n", "781/781 [==============================] - 2046s - loss: 0.4825 - acc: 0.8356 - val_loss: 0.5542 - val_acc: 0.8149\n", "Epoch 19/100\n", "781/781 [==============================] - 2046s - loss: 0.4689 - acc: 0.8414 - val_loss: 0.5751 - val_acc: 0.8116\n", "Epoch 20/100\n", "781/781 [==============================] - 2046s - loss: 0.4499 - acc: 0.8485 - val_loss: 0.5652 - val_acc: 0.8141\n", "Epoch 21/100\n", "781/781 [==============================] - 2046s - loss: 0.4415 - acc: 0.8503 - val_loss: 0.5494 - val_acc: 0.8214\n", "Epoch 22/100\n", "781/781 [==============================] - 2046s - loss: 0.4254 - acc: 0.8546 - val_loss: 0.5139 - val_acc: 0.8300\n", "Epoch 23/100\n", "781/781 [==============================] - 2046s - loss: 0.4152 - acc: 0.8596 - val_loss: 0.5352 - val_acc: 0.8281\n", "Epoch 24/100\n", "781/781 [==============================] - 2046s - loss: 0.4061 - acc: 0.8621 - val_loss: 0.5294 - val_acc: 0.8280\n", "Epoch 25/100\n", "781/781 [==============================] - 2046s - loss: 0.3943 - acc: 0.8653 - val_loss: 0.5326 - val_acc: 0.8268\n", "Epoch 26/100\n", "781/781 [==============================] - 2046s - loss: 0.3457 - acc: 0.8806 - val_loss: 0.4960 - val_acc: 0.8412\n", "Epoch 27/100\n", "781/781 [==============================] - 2046s - loss: 0.3349 - acc: 0.8863 - val_loss: 0.5007 - val_acc: 0.8410\n", "Epoch 28/100\n", "781/781 [==============================] - 2046s - loss: 0.3184 - acc: 0.8898 - val_loss: 0.4962 - val_acc: 0.8408\n", "Epoch 29/100\n", "781/781 [==============================] - 2046s - loss: 0.3105 - acc: 0.8931 - val_loss: 0.4995 - val_acc: 0.8454\n", "Epoch 30/100\n", "781/781 [==============================] - 2046s - loss: 0.2989 - acc: 0.8985 - val_loss: 0.4980 - val_acc: 0.8442\n", "Epoch 31/100\n", "781/781 [==============================] - 2046s - loss: 0.2895 - acc: 0.9006 - val_loss: 0.5010 - val_acc: 0.8452\n", "Epoch 32/100\n", "781/781 [==============================] - 2046s - loss: 0.2848 - acc: 0.9018 - val_loss: 0.4979 - val_acc: 0.8439\n", "Epoch 33/100\n", "781/781 [==============================] - 2046s - loss: 0.2880 - acc: 0.9010 - val_loss: 0.4945 - val_acc: 0.8456\n", "Epoch 34/100\n", "781/781 [==============================] - 2046s - loss: 0.2806 - acc: 0.9031 - val_loss: 0.4963 - val_acc: 0.8445\n", "Epoch 35/100\n", "781/781 [==============================] - 2046s - loss: 0.2814 - acc: 0.9035 - val_loss: 0.4980 - val_acc: 0.8464\n", "Epoch 36/100\n", "781/781 [==============================] - 2046s - loss: 0.2823 - acc: 0.9020 - val_loss: 0.4994 - val_acc: 0.8452\n", "Epoch 37/100\n", "781/781 [==============================] - 2046s - loss: 0.2808 - acc: 0.9036 - val_loss: 0.4988 - val_acc: 0.8454\n", "Epoch 38/100\n", "781/781 [==============================] - 2046s - loss: 0.2769 - acc: 0.9043 - val_loss: 0.4981 - val_acc: 0.8451\n", "Epoch 39/100\n", "781/781 [==============================] - 2046s - loss: 0.2771 - acc: 0.9048 - val_loss: 0.4978 - val_acc: 0.8446\n", "Epoch 40/100\n", "781/781 [==============================] - 2046s - loss: 0.2777 - acc: 0.9036 - val_loss: 0.4977 - val_acc: 0.8454\n", "Epoch 41/100\n", "781/781 [==============================] - 2046s - loss: 0.2782 - acc: 0.9042 - val_loss: 0.4976 - val_acc: 0.8455\n", "Epoch 42/100\n", "781/781 [==============================] - 2046s - loss: 0.2782 - acc: 0.9042 - val_loss: 0.4976 - val_acc: 0.8455\n", "Epoch 43/100\n", "781/781 [==============================] - 2046s - loss: 0.2790 - acc: 0.9047 - val_loss: 0.4977 - val_acc: 0.8450\n", "Epoch 44/100\n", "781/781 [==============================] - 2046s - loss: 0.2793 - acc: 0.9046 - val_loss: 0.4978 - val_acc: 0.8452\n" ] } ], "source": [ "if data_augmentation :\n", " print(\"-------------Using Data augmentation------------\")\n", " # This will do preprocessing and realtime data augmentation:\n", " datagen = ImageDataGenerator(\n", " featurewise_center=False, # set input mean to 0 over the dataset\n", " samplewise_center=False, # set each sample mean to 0\n", " featurewise_std_normalization=False, # divide inputs by std of the dataset\n", " samplewise_std_normalization=False, # divide each input by its std\n", " zca_whitening=False, # apply ZCA whitening\n", " rotation_range=0, # randomly rotate images in the range (degrees, 0 to 180)\n", " width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)\n", " height_shift_range=0.1, # randomly shift images vertically (fraction of total height)\n", " horizontal_flip=True, # randomly flip images\n", " vertical_flip=False) # randomly flip images\n", " \n", " datagen.fit(x_train)\n", " model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),\n", " steps_per_epoch=x_train.shape[0] // batch_size,\n", " epochs=epochs,verbose=1,validation_data=(x_test,y_test),callbacks=[lr_reducer, early_stopper, csv_logger])\n", " \n", "else :\n", " print(\"-----Not Using Data augmentation---------------\")\n", " model.fit(x_train, y_train,\n", " batch_size=batch_size*4,\n", " epochs=epochs,\n", " validation_data=(x_test, y_test),\n", " shuffle=True,callbacks=[lr_reducer, early_stopper, csv_logger])\n", " \n", " \n", " " ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model.save_weights(\"cifar_adam_vgg_lr_reducer.h5\")" ] }, { "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": 2 }	mit
daniestevez/jupyter_notebooks	Falcon-9/Falcon-9 frames.ipynb	1	100053	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "import collections\n", "\n", "plt.rcParams['figure.figsize'] = (12,6)\n", "plt.rcParams['figure.facecolor'] = 'w'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook shows an analysis of the Falcon-9 upper stage S-band telemetry frames. It is based on [r00t.cz's analysis](https://www.r00t.cz/Sats/Falcon9).\n", "\n", "The frames are CCSDS Reed-Solomon frames with an interleaving depth of 5, a (255,239) code, and an (uncoded) frame size of 1195 bytes." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "x = np.fromfile('falcon9_frames_20210324_084608.u8', dtype = 'uint8')\n", "x = x.reshape((-1, 1195))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first byte of all the frames is `0xe0`. Here we see that one of the frames has an error in this byte." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Counter({224: 11132, 124: 1})" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "collections.Counter(x[:,0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next three bytes form a header composed of a 13 bit frame counter and an 11 bit field that indicates where the first packet inside the payload starts (akin to a first header pointer in CCSDS protocols)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "header = np.unpackbits(x[:,1:4], axis = 1)\n", "counter = header[:,:13]\n", "counter = np.concatenate((np.zeros((x.shape[0], 3), dtype = 'uint8'), counter), axis = 1)\n", "counter = np.packbits(counter, axis = 1)\n", "counter = counter.ravel().view('uint16').byteswap()\n", "start_offset = header[:,-11:]\n", "start_offset = np.concatenate((np.zeros((x.shape[0], 5), dtype = 'uint8'), start_offset), axis = 1)\n", "start_offset = np.packbits(start_offset, axis = 1)\n", "start_offset = start_offset.ravel().view('uint16').byteswap()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x432 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(counter, '.')\n", "plt.title('Falcon-9 frame counter')\n", "plt.ylabel('13-bit frame counter')\n", "plt.xlabel('Decoded frame');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Valid packets contain a 2 byte header where the 4 MSBs are set to 1 and the remaining 12 bits indicate the size of the packet payload in bytes (so the total packet size is this value plus 2). Using this header, the packets can be defragmented in the same way as CCSDS Space Packets transmitted using the M_PDU protocol." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def packet_len(packet):\n", " packet = np.frombuffer(packet[:2], dtype = 'uint8')\n", " return (packet.view('uint16').byteswap()[0] & 0xfff) + 2\n", "\n", "def valid_packet(packet):\n", " return packet[0] >> 4 == 0xf\n", "\n", "def defrag(x, counter, start_offset):\n", " packet = bytearray()\n", " frame_count = None\n", " \n", " for frame, count, first in zip(x, counter, start_offset):\n", " frame = frame[4:]\n", " if frame_count is not None \\\n", " and count != ((frame_count + 1) % 2*13):\n", " # broken stream\n", " packet = bytearray()\n", " frame_count = count\n", "\n", " if first == 0x7fe:\n", " # only idle\n", " continue\n", " elif first == 0x7ff:\n", " # no packet starts\n", " if packet:\n", " packet.extend(frame)\n", " continue\n", " \n", " if packet:\n", " packet.extend(frame[:first])\n", " packet = bytes(packet)\n", " yield packet, frame_count\n", "\n", " while True:\n", " packet = bytearray(frame[first:][:2])\n", " if len(packet) < 2:\n", " # not full header inside frame\n", " break\n", " first += 2\n", " if not valid_packet(packet):\n", " # padding found\n", " packet = bytearray()\n", " break\n", " length = packet_len(packet) - 2\n", " packet.extend(frame[first:][:length])\n", " first += length\n", " if first > len(frame):\n", " # packet does not end in this frame\n", " break\n", " packet = bytes(packet)\n", " yield packet, frame_count\n", " packet = bytearray()\n", " if first == len(frame):\n", " # packet just ends in this frame\n", " break\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "packets = list(defrag(x, counter, start_offset))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Only ~76% of the frames payload contains packets. The rest is padding." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.7581248567526004" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum([len(p[0]) for p in packets])/x[:,4:].size" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After the 2 byte header, the next 8 bytes of the packet can be used to identify its source or type." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Counter({'0012420100110C09': 2260,\n", " '0112520107DC0504': 357,\n", " '0012620100640C08': 1603,\n", " '0012420100640C08': 1600,\n", " '00122201000D0C09': 5668,\n", " '00124201000D0C09': 5656,\n", " '00126201000D0C09': 5673,\n", " '0012620100110C09': 2246,\n", " '01123201042E1403': 2996,\n", " '00126201006A0C08': 800,\n", " '0112520107DB0504': 364,\n", " '0012620100690C08': 328,\n", " '0012220100110C09': 2272,\n", " '0112320107D80504': 1111,\n", " '0112520107D50504': 2612,\n", " '00124D0100650108': 310,\n", " '0012220100150C09': 215,\n", " '0012620100150C09': 213,\n", " '0112320107DD0504': 370,\n", " '0112520107E00504': 183,\n", " '0112520107E10504': 184,\n", " '0012420100150C09': 206,\n", " '0112520107E40504': 125,\n", " '0017FE08D0440108': 148,\n", " '0017FE08D0480108': 162,\n", " '0017FE08D0420108': 163,\n", " '0012620100650C08': 31,\n", " '0012620103EE0C08': 31,\n", " '0012420100650C08': 31,\n", " '0117FE0800320303': 64,\n", " '00124201001D0C09': 20,\n", " '00126201001D0C09': 19,\n", " '00122201001D0C09': 21,\n", " '0017FE08D0470108': 1,\n", " '00127201010D0C08': 6,\n", " '0017FE08D0490108': 18})" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "source_ids = [p[0][2:10].hex().upper() for p in packets]\n", "collections.Counter(source_ids)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some packets have 64-bit timestamps starting 3 bytes after the packet source ID. These give nanoseconds since the GPS epoch." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "timestamps = np.datetime64('1980-01-06') + \\\n", " np.array([np.frombuffer(p[0][13:][:8], dtype = 'uint64').byteswap()[0] for p in packets]) \\\n", " np.timedelta64(1, 'ns')\n", "timestamps_valid = (timestamps >= np.datetime64('2021-01-01')) & (timestamps <= np.datetime64('2022-01-01'))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x432 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(timestamps[timestamps_valid],\n", " np.array([p[1] for p in packets])[timestamps_valid], '.')\n", "plt.title('Falcon-9 packet timestamps')\n", "plt.xlabel('Timestamp (GPS time)')\n", "plt.ylabel('Frame counter');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Video packets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Video packets are stored in a particular source ID. If we remove the first 25 and last 2 bytes of these packets, we obtain 5 188-byte transport stream packets." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "video_source = '01123201042E1403'\n", "video_packets = [p for p,s in zip(packets, source_ids)\n", " if s == video_source]\n", "video_ts = bytes().join([p[0][25:-2] for p in video_packets])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Only around 28% of the transmitted data is the transport stream video." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.2801592434275875" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(video_ts)/sum([len(p[0]) for p in packets])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "with open('/tmp/falcon9.ts', 'wb') as f:\n", " f.write(video_ts)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "ts = np.frombuffer(video_ts, dtype = 'uint8').reshape((-1,188))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([71], dtype=uint8)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# sync byte 71 = 0x47\n", "np.unique(ts[:,0])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0], dtype=uint8)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# TEI = 0\n", "np.unique(ts[:,1] >> 7)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0], dtype=uint8)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pusi = (ts[:,1] >> 6) & 1\n", "# priority = 0\n", "np.unique((ts[:,1] >> 5) & 1)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0, 32, 511, 2748, 4112, 8191], dtype=uint16)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pid = ts[:,1:3].ravel().view('uint16').byteswap() & 0x1fff\n", "np.unique(pid)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PID 0 ratio 0.4%\n", "PID 32 ratio 0.4%\n", "PID 511 ratio 6.5%\n", "PID 2748 ratio 11.8%\n", "PID 4112 ratio 79.4%\n", "PID 8191 ratio 1.4%\n" ] } ], "source": [ "for p in np.unique(pid):\n", " print(f'PID {p} ratio {np.average(pid == p) * 100:.1f}%')" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0], dtype=uint8)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# TSC = 0\n", "np.unique(ts[:,3] >> 6)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 2, 3], dtype=uint8)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "adaptation = (ts[:,3] >> 4) & 0x3\n", "np.unique(adaptation)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "continuity = ts[:,3] & 0xf" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PID 0 PUSI values [1] adaptation field values [1]\n", "PID 32 PUSI values [1] adaptation field values [1]\n", "PID 511 PUSI values [0] adaptation field values [2]\n", "PID 2748 PUSI values [1] adaptation field values [3]\n", "PID 4112 PUSI values [0 1] adaptation field values [1 3]\n", "PID 8191 PUSI values [0] adaptation field values [1]\n" ] } ], "source": [ "for p in np.unique(pid):\n", " print('PID', p, 'PUSI values', np.unique(pusi[pid == p]),\n", " 'adaptation field values', np.unique(adaptation[pid == p]))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "pcr_pid = ts[pid == 511]\n", "pcr = np.concatenate((np.zeros((pcr_pid.shape[0], 2), dtype = 'uint8'), pcr_pid[:,6:12]), axis = 1)\n", "pcr = pcr.view('uint64').byteswap().ravel()\n", "pcr_base = pcr >> 15\n", "pcr_extension = pcr & 0x1ff\n", "pcr_value = (pcr_base * 300 + pcr_extension) / 27e6" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "video_timestamps = timestamps[[s == video_source for s in source_ids]]\n", "ts_timestamps = np.repeat(video_timestamps, 5)\n", "pcr_pid_timestamps = ts_timestamps[pid == 511]" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x432 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(pcr_pid_timestamps, pcr_value, '.')\n", "plt.title('Falcon-9 PCR timestamps')\n", "plt.ylabel('PID 511 PCR (s)')\n", "plt.xlabel('Packet timestamp (GPS time)');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### GPS log" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "gps_source = '0117FE0800320303'\n", "gps_packets = [p for p,s in zip(packets, source_ids)\n", " if s == gps_source]" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1300610994400436905> 23 PLL stress\n", "1300611017022690372> Track: [21s 10s 32e 23s 25e 01c 20s . . . . . . . . . \| 01s 28s 19s .611017204400061> Running hot start for sid:5 TOW_ms 291017118!\n", "1300611017204597275> Tracking! PRN:5\n", "1300611026057717790> 1 PLL stress\n", "1300611031677221262> 23 PLL stress\n", "1300611032023066595> Track: [21c 10s 32e 23s 25e 01c 20s . . . . . . . . . \| 01s 28s 19s . 17s . 23c 13s 24s 12s 05e 15s 21s . 14s . ]\n", "1300611032023330905> dB: [34 42 41 40 38 33 38 . . . . . . . . . \| 38 44 48 . 48 . 26 48 45 39 36 46 35 . 41 . ]\n", "1300611032215555222> solution x/y/z: 4134817.9 257063.8 5103019.7 PDOP=1.7 n_sats=12\n", "1300611032215837521> solution tow:291031.8257235776, wn:2150\n", "1300611034525282910> 23 PLL stress\n", "1300611039022514132> 23 low CN0 too long, dropping (@ 103712228202000000.000)\n", "1300611040045135624> Tracking! PRN:6\n", "1300611040049727411> Found 6 over 120.0 kHz in 351 ms, CN0:37.2 dB\n", "1300611042023334560> Track: [21c 10s 32e 23s 25e 01c 20s 06f . . . . . . . . \| 01s 28s 19s . 17s . . 13s 24s 12s 05e 15s 21s . 14s . ]\n", "1300611042023788264> dB: [37 42 42 38 39 32 611042415694256> solution x/y/z: 4148881.9 330812.5 5088143.2 PDOP=1.7 n_sats=13\n", "13006110424158396681300611047022176101> SPI Usage: 31%\n", "1300611047023339673> Track: [21c 10s 32e 23s 25e 01c 20s . . . . . . . . . \| 01s 28s 19s . 17s . . 13s 24s 12s 05e 15s 21s . 14s . ]\n", "1300611047023633682> dB: [37 40 41 39 42 31 37 . . . . . . . . . \| 38 43 46 . 49 . . 51 46 36 39 48 38 . 41 . ]\n", "1300611048072040182> Tracking! PRN:16\n", "1300611048075843614> Found 16 over 120.0 kHz in 349 ms, CN0:37.0 dB\n", "1300611049047563556> 1 PLL stress\n", "1300611049267786224> New ephemeris(valid: 1) for GPS L1CA 24, fi:14400\n", "1300611049269743486> New ephemeris(valid: 1) for GPS L1CA 15, fi:14400\n", "1300611049274042188> New ephemeris(valid: 1) for GPS L1CA 13, fi:14400\n", "1300611049275736844> New ephemeris(valid: 1) for GPS L1CA 23, fi:14400\n", "1300611049276025096> New ephemeris(valid: 1) for GPS L1CA 19, fi:14400\n", "1300611049276089286> New ephemeris(valid: 1) 1300611055288333542> GPS L1CA 21 Nav phase flip - half cycle slip detected, but not corrected\n", "1300611056047443389> Tracking! PRN:30\n", "1300611056051622393> Found 30 over 120.0 kHz in 351 ms, CN0:37.4 dB\n", "1300611057023867233> Track: [21s 10s 32e 23s 25e 01c 20s . . . . . . . . . \| 01s 28s 19s 30f 17s . . 13s 24s 12s 05e 15s 21c . 14s . ]\n", "1300611057024141399> dB: [36 41 44 41 41 21 38 . . . . . . . . . \| 40 43 48 41 51 . . 47 46 38 39 46 31 . 42 . ]\n", "1300611057025518321> 1 PLL stress\n", "1300611057587603019> 30 synced @ 1536 ms, 37.9 dBHz, 34804 Hz, -0.10 code err\n", "1300611062023354576> Track: [21s 10s 32e 23s 25e 01c 20s . . . . . . . . . \| 01s 28s 19s 30e 17s . . 13s 24s 12s 05e 15s 21c . 14s . ]\n", "1300611062023648177> dB: [38 39 41 38 36 24 36 . . . . . . . . . \| 38 46 47 43 46 . . 50 43 38 37 43 37 . 44 . ]\n", "1300611062815688897> solution x/y/z: 4175538.8 478094.6 5056175.4 PDOP=1.7 n_sats=12\n", "1300611062815772957> solution tow:291062.4256663217, wn:2150\n", "1300611072022586022> Track: [21c 10s 32e 23s 25e 01c 20s . . . . . . . . . \| 01s 28s 19s 30e 17s . . 13s 24s 12s 05e 15s 21s . 14s . ]\n", "1300611072022789733> dB: [33 38 41 40 37 21 40 . . . . . . . . . \| 40 44 47 42 47 . . 50 41 41 35 45 34 . 46 . ]\n", "1300611073015721896> solution x/y/z: 4188124.5 551606.8 5039080.0 PDOP=1.7 n_sats=12\n", "1300611073015803844> solution tow:291072.6256922208, wn:2150\n", "1300611073287751158> GPS L1CA 21 subframe parity mismatch (word 5)\n", "1300611073821442473> 1 PLL stress\n", "meris(valid: 1) for GPS L1CA 15, fi:14400\n", "1300611079273785243> New ephemeris(valid: 1) for GPS L1CA 13, fi:14400\n", "1300611079274795263> New ephemeris(valid: 1) for GPS L1CA 19, fi:14400\n", "1300611079274864959> New ephemeris(valid: 1) for GPS L1CA 17, fi:14400\n", "1300611079276795298> New ephemeris(valid: 1) for GPS L1CA 23, fi:14400\n", "1300611079276872325> New ephemeris(valid: 1) for GPS L1CA 12, fi:14400\n", "1300611079277785924> New ephemeris(valid: 1) for GPS L1CA 20, fi:14400\n", "1300611079277853753> New ephemeris(valid: 1) for GPS L1CA 28, fi:14400\n", "1300611079278801714> New ephemeris(valid: 1) for GPS L1CA 14, fi:14400\n", "1300611079279789312> New ephemeris(valid: 1) for GPS L1CA 10, fi:14400\n", "1300611079283793374> New ephemeris(valid: 1) for GPS L1CA 1, fi:14400\n", "1300611079287783689> New ephemeris(valid: 1) for GPS L1CA 30, fi:14400\n", "1300611079289747977> New ephemeris(valid: 1) for GPS L1CA 32, fi:14400\n", "1300611079289848356> New ephemeris(valid: 1) for GPS L1CA 25, fi:14400\n", "1300611079290749987> New ephemeris(valid: 1) for GPS L1CA 5, fi:14400\n", "1300611080459645540> Tracking! PRN:4\n", "1300611080463966765> Found 4 over 120.0 kHz in 386 ms, CN0:37.1 dB\n", "1300611082024723680> Track: [21c 10s 32e 23s 25e 01c 20s 04f . . . . . . . . \| 01s 28s 19s 30e 17s . . 13s 24s 12s 05e 15s 21s . 14s . ]\n", "1300611082024928640> dB: [33 37 41 44 40 291300611107022157462> SPI Usage: 31%\n", "1300611107023191559> Track: [21c 10s 32e 23s 25e 01c 20s . . . . . . . . . \| 01s 28s 19s 30\n" ] } ], "source": [ "gps_log = ''.join([str(g[0][25:-2], encoding = 'ascii') for g in gps_packets])\n", "with open('/tmp/gps.txt', 'w') as f:\n", " f.write(gps_log)\n", "print(gps_log)" ] } ], "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": 4 }	gpl-3.0
ellisonbg/ipython-d3	demo Barabasi Albert.ipynb	1	16799	{ "metadata": { "name": "", "signature": "sha256:c6b927c7494cec797aca9145c463396ae64de30d58595f7e140b8edda7b29bf1" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from IPython.html import widgets\n", "from IPython.display import display\n", "from eventful_graph import EventfulGraph, empty_eventfulgraph_hook\n", "from widget_forcedirectedgraph import ForceDirectedGraphWidget, publish_js\n", "publish_js()" ], "language": "python", "metadata": {}, "outputs": [ { "javascript": [ "require([\"//cdnjs.cloudflare.com/ajax/libs/d3/3.4.1/d3.min.js\", \"notebook/js/widgetmanager\"], function(d3, WidgetManager){\n", "\n", " // Define the D3ForceDirectedGraphView\n", " var D3ForceDirectedGraphView = IPython.DOMWidgetView.extend({\n", " \n", " render: function(){\n", " this.guid = 'd3force' + IPython.utils.uuid();\n", " this.setElement($('<div />', {id: this.guid}));\n", " \n", " this.model.on('msg:custom', this.on_msg, this);\n", " this.has_drawn = false;\n", " \n", " // Wait for element to be added to the DOM\n", " var that = this;\n", " setTimeout(function() {\n", " that.update();\n", " }, 0);\n", " },\n", " \n", " try_add_node: function(id){\n", " var index = this.find_node(id);\n", " if (index == -1) {\n", " var node = {id: id};\n", " this.nodes.push(node);\n", " return node;\n", " } else {\n", " return this.nodes[index];\n", " }\n", " },\n", " \n", " update_node: function(node, attributes) {\n", " if (node !== null) {\n", " for (var key in attributes) {\n", " node[key] = attributes[key];\n", " }\n", " this._update_node(d3.select('#' + this.guid + node.id));\n", " }\n", " },\n", " \n", " remove_node: function(id){\n", " this.remove_links_to(id);\n", " \n", " var found_index = this.find_node(id);\n", " if (found_index>=0) {\n", " this.nodes.splice(found_index, 1);\n", " }\n", " },\n", " \n", " find_node: function(id){\n", " var found_index = -1;\n", " for (var index in this.nodes) {\n", " if (this.nodes[index].id == id) {\n", " found_index = index;\n", " break;\n", " }\n", " }\n", " return found_index;\n", " },\n", " \n", " find_link: function(source_id, target_id){\n", " for (var index in this.links) {\n", " if (this.links[index].source.id == source_id && this.links[index].target.id == target_id) {\n", " return index;\n", " }\n", " }\n", " return -1;\n", " },\n", " \n", " try_add_link: function(source_id, target_id){\n", " var index = this.find_link(source_id, target_id);\n", " if (index == -1) {\n", " var source_node = this.try_add_node(source_id);\n", " var target_node = this.try_add_node(target_id);\n", " var new_link = {source: source_node, target: target_node};\n", " this.links.push(new_link);\n", " return new_link;\n", " } else {\n", " return this.links[index]\n", " }\n", " },\n", " \n", " update_link: function(link, attributes){\n", " if (link != null) {\n", " for (var key in attributes) {\n", " link[key] = attributes[key];\n", " }\n", " this._update_edge(d3.select('#' + this.guid + link.source.id + \"-\" + link.target.id));\n", " }\n", " },\n", " \n", " remove_links: function(source_id){\n", " var found_indicies = [];\n", " for (var index in this.links) {\n", " if (this.links[index].source.id == source_id) {\n", " found_indicies.push(index);\n", " }\n", " }\n", " found_indicies.reverse();\n", " \n", " for (var index in found_indicies) {\n", " this.links.splice(index, 1);\n", " };\n", " },\n", " \n", " remove_links_to: function(id){\n", " var found_indicies = [];\n", " for (var index in this.links) {\n", " if (this.links[index].source.id == id \|\| this.links[index].target.id == id) {\n", " found_indicies.push(index);\n", " }\n", " }\n", " found_indicies.reverse();\n", " \n", " for (var index in found_indicies) {\n", " this.links.splice(index, 1);\n", " };\n", " },\n", " \n", " on_msg: function(content){\n", " this.update();\n", " \n", " var dict = content.dict;\n", " var action = content.action;\n", " var key = content.key;\n", " \n", " if (dict=='node') {\n", " if (action=='add' \|\| action=='set') {\n", " this.update_node(this.try_add_node(key), content.value)\n", " } else if (action=='del') {\n", " this.remove_node(key);\n", " }\n", " \n", " } else if (dict=='adj') {\n", " if (action=='add' \|\| action=='set') {\n", " var links = content.value;\n", " for (var target_id in links) {\n", " this.update_link(this.try_add_link(key, target_id), links[target_id]);\n", " }\n", " } else if (action=='del') {\n", " this.remove_links(key);\n", " }\n", " }\n", " this.start();\n", " },\n", " \n", " start: function() {\n", " var node = this.svg.selectAll(\".node\"),\n", " link = this.svg.selectAll(\".link\");\n", " \n", " var link = link.data(this.force.links(), function(d) { return d.source.id + \"-\" + d.target.id; });\n", " this._update_edge(link.enter().insert(\"line\", \".node\"))\n", " link.exit().remove();\n", " \n", " var node = node.data(this.force.nodes(), function(d) { return d.id;});\n", " var that = this;\n", " this._update_node(node.enter().append(\"circle\"));\n", " node.exit().remove();\n", " \n", " this.force.start();\n", " },\n", " \n", " _update_node: function(node) {\n", " var that = this;\n", " node\n", " .attr(\"id\", function(d) { return that.guid + d.id; })\n", " .attr(\"class\", function(d) { return \"node \" + d.id; })\n", " .attr(\"r\", function(d) {\n", " if (d.r == undefined) {\n", " return 8; \n", " } else {\n", " return d.r;\n", " }\n", " \n", " })\n", " .style(\"fill\", function(d) {\n", " if (d.fill == undefined) {\n", " return that.color(d.group); \n", " } else {\n", " return d.fill;\n", " }\n", " \n", " })\n", " .style(\"stroke\", function(d) {\n", " if (d.stroke == undefined) {\n", " return \"#FFF\"; \n", " } else {\n", " return d.stroke;\n", " }\n", " \n", " })\n", " .style(\"stroke-width\", function(d) {\n", " if (d.strokewidth == undefined) {\n", " return \"#FFF\"; \n", " } else {\n", " return d.strokewidth;\n", " }\n", " \n", " })\n", " .call(this.force.drag);\n", " },\n", " \n", " _update_edge: function(edge) {\n", " var that = this;\n", " edge\n", " .attr(\"id\", function(d) { return that.guid + d.source.id + \"-\" + d.target.id; })\n", " .attr(\"class\", \"link\")\n", " .style(\"stroke-width\", function(d) {\n", " if (d.strokewidth == undefined) {\n", " return \"1.5px\"; \n", " } else {\n", " return d.strokewidth;\n", " }\n", " \n", " })\n", " .style('stroke', function(d) {\n", " if (d.stroke == undefined) {\n", " return \"#999\"; \n", " } else {\n", " return d.stroke;\n", " }\n", " \n", " });\n", " },\n", " \n", " tick: function() {\n", " var node = this.svg.selectAll(\".node\"),\n", " link = this.svg.selectAll(\".link\");\n", " \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", " update: function(){\n", " if (!this.has_drawn) {\n", " this.has_drawn = true;\n", " var width = this.model.get('width'),\n", " height = this.model.get('height');\n", " \n", " this.color = d3.scale.category20();\n", " \n", " this.nodes = [];\n", " this.links = [];\n", " \n", " this.force = d3.layout.force()\n", " .nodes(this.nodes)\n", " .links(this.links)\n", " .charge(function (d) {\n", " if (d.charge === undefined) {\n", " return -80;\n", " } else {\n", " return d.charge;\n", " }\n", " })\n", " .linkDistance(function (d) {\n", " if (d.distance === undefined) {\n", " return 90;\n", " } else {\n", " return d.distance;\n", " }\n", " })\n", " .linkStrength(function (d) {\n", " if (d.strength === undefined) {\n", " return 0.5;\n", " } else {\n", " return d.strength;\n", " }\n", " })\n", " .size([width, height])\n", " .on(\"tick\", $.proxy(this.tick, this));\n", " \n", " this.svg = d3.select(\"#\" + this.guid).append(\"svg\")\n", " .attr(\"width\", width)\n", " .attr(\"height\", height);\n", " \n", " var that = this;\n", " setTimeout(function() {\n", " that.start();\n", " }, 0);\n", " }\n", " \n", " return D3ForceDirectedGraphView.__super__.update.apply(this);\n", " },\n", " \n", " });\n", " \n", " // Register the D3ForceDirectedGraphView with the widget manager.\n", " WidgetManager.register_widget_view('D3ForceDirectedGraphView', D3ForceDirectedGraphView);\n", "});" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Javascript at 0x4db3390>" ] } ], "prompt_number": 9 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Barabasi Albert Graph" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import networkx.generators.random_graphs as random_graphs\n", "\n", "# Add a listener to the eventful graph's construction method.\n", "# If an eventful graph is created, build and show a widget\n", "# for the graph.\n", "def handle_graph(graph):\n", " print(graph.graph._sleep)\n", " popup = widgets.PopupWidget()\n", " popup.description = \"NetworkX Barabasi Albert Graph\"\n", " popup.button_text = \"Render Window\"\n", " popup.set_css({\n", " 'width': '420px',\n", " 'height': '350px'}, selector='modal')\n", " graph_widget = ForceDirectedGraphWidget(graph)\n", " popup.children = [graph_widget]\n", " display(popup)\n", "EventfulGraph.on_constructed(handle_graph)\n", "\n", "# Replace the empty graph of the networkx classic module with\n", "# the eventful graph type.\n", "random_graphs.empty_graph = empty_eventfulgraph_hook(sleep=0.5)\n", "\n", "# Graph!\n", "random_graphs.barabasi_albert_graph(15, 2)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.5\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "<eventful_graph.EventfulGraph at 0x4d90690>" ] } ], "prompt_number": 15 } ], "metadata": {} } ] }	mit
seanabu/seanabu.github.io	Seasonal_ARIMA_model_Portland_transit.ipynb	1	1047248	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import datetime\n", "from dateutil.relativedelta import relativedelta\n", "import seaborn as sns\n", "import statsmodels.api as sm \n", "from statsmodels.tsa.stattools import acf \n", "from statsmodels.tsa.stattools import pacf\n", "from statsmodels.tsa.seasonal import seasonal_decompose" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = pd.read_csv('portland-oregon-average-monthly-.csv', index_col=0)\n", "df.index.name=None\n", "df.reset_index(inplace=True)\n", "df.drop(df.index[114], inplace=True)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "start = datetime.datetime.strptime(\"1973-01-01\", \"%Y-%m-%d\")\n", "date_list = [start + relativedelta(months=x) for x in range(0,114)]\n", "df['index'] =date_list\n", "df.set_index(['index'], inplace=True)\n", "df.index.name=None" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df.columns= ['riders']\n", "df['riders'] = df.riders.apply(lambda x: int(x)*100)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Yhi+ilAyE75FnviVVl87G0fp9eOy1aogASnLTcFfVImSnawEEg//d687Dgy/u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YWFGDx4MMaPH89tmzdvHvbu3QulUgkfHx8MGfLv5fJCQkJQVFQEAFAqlQgNDeXabG1t4ePjg6Ki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\nz59HeXk5k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"text/plain": [ "<matplotlib.figure.Figure at 0x10e30d3d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.riders.plot(figsize=(12,8), title= 'Monthly Ridership', fontsize=14)\n", "plt.savefig('month_ridership.png', bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x11132f350>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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keIgbw4Nd8XHTXDZv3WQy8+XxIrYm5tJuNDE+youFU0Oxt73yx1WNrYoIf+eb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zAAAg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\nI8Nh53Ml8dfrZMm8EEIIcYUJC+ro9X7hZfM7soppbmljSlykVJM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"text/plain": [ "<matplotlib.figure.Figure at 0x10e30d390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "decomposition = seasonal_decompose(df.riders, freq=12) \n", "fig = plt.figure() \n", "fig = decomposition.plot() \n", "fig.set_size_inches(15, 8)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from statsmodels.tsa.stattools import adfuller\n", "def test_stationarity(timeseries):\n", " \n", " #Determing rolling statistics\n", " rolmean = pd.rolling_mean(timeseries, window=12)\n", " rolstd = pd.rolling_std(timeseries, window=12)\n", "\n", " #Plot rolling statistics:\n", " fig = plt.figure(figsize=(12, 8))\n", " orig = plt.plot(timeseries, color='blue',label='Original')\n", " mean = plt.plot(rolmean, color='red', label='Rolling Mean')\n", " std = plt.plot(rolstd, color='black', label = 'Rolling Std')\n", " plt.legend(loc='best')\n", " plt.title('Rolling Mean & Standard Deviation')\n", " plt.show()\n", " \n", " #Perform Dickey-Fuller test:\n", " print 'Results of Dickey-Fuller Test:'\n", " dftest = adfuller(timeseries, autolag='AIC')\n", " dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])\n", " for key,value in dftest[4].items():\n", " dfoutput['Critical Value (%s)'%key] = value\n", " print dfoutput" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ZFChQMKGNPHny8NNPIbi4uFC+fEUmTJiCTqfj228XExkZyYwZQfTrN5Dx40cT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YVDh8wMGuS65LIiIiIi8uekoP0b5cpBnz659OmTy65dZhISrCxYYGXWrEBmzQrEZDJo1szLzTd7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\n976bzp27U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"text/plain": [ "<matplotlib.figure.Figure at 0x1112909d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Results of Dickey-Fuller Test:\n", "Test Statistic -1.536597\n", "p-value 0.515336\n", "#Lags Used 12.000000\n", "Number of Observations Used 101.000000\n", "Critical Value (5%) -2.890611\n", "Critical Value (1%) -3.496818\n", "Critical Value (10%) -2.582277\n", "dtype: float64\n" ] } ], "source": [ "test_stationarity(df.riders)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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+/IC++mpnF65huHJdQyefHNMDD4Q1ZMjO3pK1a01NmZKs4mJTRUURPfBAeI/z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\nQgghhBC9Q1HTry7rI42Nvr5exDnn4MH97Nmzm0m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"text/plain": [ "<matplotlib.figure.Figure at 0x1112ed7d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Results of Dickey-Fuller Test:\n", "Test Statistic -1.677830\n", "p-value 0.442570\n", "#Lags Used 12.000000\n", "Number of Observations Used 101.000000\n", "Critical Value (5%) -2.890611\n", "Critical Value (1%) -3.496818\n", "Critical Value (10%) -2.582277\n", "dtype: float64\n" ] } ], "source": [ "df.riders_log= df.riders.apply(lambda x: np.log(x)) \n", "test_stationarity(df.riders_log)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Wf74yUm4uhBBCCCECmdvdSI4dO8awYcN4+OGHufvuu3n11Vft3ysoKCAhIYG4uDiXiRtM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\nSDRNykjMEiTFMd+Wv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iq2uOFUKg/en+rttyby7NOdb7OqkpqN7HFIdyPRz1VWr6Li5OOo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"text/plain": [ "<matplotlib.figure.Figure at 0x1048fcf90>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Results of Dickey-Fuller Test:\n", "Test Statistic -1.938696\n", "p-value 0.314082\n", "#Lags Used 11.000000\n", "Number of Observations Used 101.000000\n", "Critical Value (5%) -2.890611\n", "Critical Value (1%) -3.496818\n", "Critical Value (10%) -2.582277\n", "dtype: float64\n" ] } ], "source": [ "df['first_difference'] = df.riders - df.riders.shift(1) \n", "test_stationarity(df.first_difference.dropna(inplace=False))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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hw57C2rXfu029FywSE2O9/q28tkTFMGuHXs/j+nWKTIcCBQUcata0oqyMItP+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\niRw5Uvz7H38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"text/plain": [ "<matplotlib.figure.Figure at 0x103dd6d50>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Results of Dickey-Fuller Test:\n", "Test Statistic -2.047539\n", "p-value 0.266126\n", "#Lags Used 11.000000\n", "Number of Observations Used 101.000000\n", "Critical Value (5%) -2.890611\n", "Critical Value (1%) -3.496818\n", "Critical Value (10%) -2.582277\n", "dtype: float64\n" ] } ], "source": [ "df['log_first_difference'] = df.riders_log - df.riders_log.shift(1) \n", "test_stationarity(df.log_first_difference.dropna(inplace=False))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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xbNgwunTpQkFBAaNGjeLIkSPExMQwZcoUkpKS2LhxI5MnT8ZqtXL55ZczfPjw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\ndfpAG03D99VRmLZuJrFNW2Le/0j6aYv7I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"text/plain": [ "<matplotlib.figure.Figure at 0x1137443d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Results of Dickey-Fuller Test:\n", "Test Statistic -2.469741\n", "p-value 0.123011\n", "#Lags Used 3.000000\n", "Number of Observations Used 98.000000\n", "Critical Value (5%) -2.891516\n", "Critical Value (1%) -3.498910\n", "Critical Value (10%) -2.582760\n", "dtype: float64\n" ] } ], "source": [ "df['seasonal_difference'] = df.riders - df.riders.shift(12) \n", "test_stationarity(df.seasonal_difference.dropna(inplace=False))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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pKWzdupXQ0FAmTZpE69atPdd/+umnvP766xiGweDBgxk5cuRJt5GG4ciRisvi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\n2hMIKUIQtfQBqYo1lEU2FRFQS92OqAlkYSbJZDIz9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"text/plain": [ "<matplotlib.figure.Figure at 0x1112a47d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Results of Dickey-Fuller Test:\n", "Test Statistic -1.919681\n", "p-value 0.322860\n", "#Lags Used 0.000000\n", "Number of Observations Used 101.000000\n", "Critical Value (5%) -2.890611\n", "Critical Value (1%) -3.496818\n", "Critical Value (10%) -2.582277\n", "dtype: float64\n" ] } ], "source": [ "df['log_seasonal_difference'] = df.riders_log - df.riders_log.shift(12) \n", "test_stationarity(df.log_seasonal_difference.dropna(inplace=False))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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ZPvNRKyJerMTB9jvvvMNzzz1HZmYmAJMmTWLYsGHMnj0bh8PBDz/8QEJCArNm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\nb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aFDg//et2/4tQXA9Omwbi1ccgkFA/oYOmYsHHGEOFeXSzF0fp2EKwmHw9j9J6+hLl2i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"text/plain": [ "<matplotlib.figure.Figure at 0x1145e10d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Results of Dickey-Fuller Test:\n", "Test Statistic -9.258520e+00\n", "p-value 1.427874e-15\n", "#Lags Used 0.000000e+00\n", "Number of Observations Used 1.000000e+02\n", "Critical Value (5%) -2.890906e+00\n", "Critical Value (1%) -3.497501e+00\n", "Critical Value (10%) -2.582435e+00\n", "dtype: float64\n" ] } ], "source": [ "df['seasonal_first_difference'] = df.first_difference - df.first_difference.shift(12) \n", "test_stationarity(df.seasonal_first_difference.dropna(inplace=False))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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0sIqP13bicXGSDRFCiIIkJWmrHyr/7jJdnZlOS3NqU0KIEiLBtLDSMyo3bkgw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\n/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BDBdEGBcGawm1W/tSEHZMl0pIKc/TuVgGhGf5g8OOTCTOc/+RiulctpuPBS4n372W+o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\n2pdrzHiq9WAVvvpa4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H7i7dq3KBduBrLqbGtmOvNz6ffnzkxDdt308uUKkYiSs8QDdpBg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\neRidf3Mw/eH7gD2JB8CoUVEef7yB4483pw+WLMKsWaK7Ki117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PrYPyDy8ouF3zRNxB58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"text/plain": [ "<matplotlib.figure.Figure at 0x115164f10>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Results of Dickey-Fuller Test:\n", "Test Statistic -8.882112e+00\n", "p-value 1.309452e-14\n", "#Lags Used 0.000000e+00\n", "Number of Observations Used 1.000000e+02\n", "Critical Value (5%) -2.890906e+00\n", "Critical Value (1%) -3.497501e+00\n", "Critical Value (10%) -2.582435e+00\n", "dtype: float64\n" ] } ], "source": [ "df['log_seasonal_first_difference'] = df.log_first_difference - df.log_first_difference.shift(12) \n", "test_stationarity(df.log_seasonal_first_difference.dropna(inplace=False))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/seanwilson/anaconda/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": 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Qa2GF4vLycvl8PpWWlmrmzJkqKSk5ZJ7S0lJt2bJFhmFEXCQAAAAQS2GF4srKShUUFEiS\n8vLyVFVVdcj09957T5dddpls2468SgAAACCGwgrF9fX18nq9wfsul0uWZUmSdu3apYULF6q4uJhA\nDAAAgITgDmchr9erhoaG4H3LsmSabfn6L3/5i2pra3XDDTdo9+7dam5u1nHHHacpU6Z0u75hw9Lk\ndrvCKSVsptk2rCM7O6NftwvnoY0hlmhfiCXaF2JpoLWvsEJxfn6+KioqNHnyZG3YsEGjR48OTisq\nKlJRUZEk6YUXXtC//vWvHgOxJNXWNoZTRkQsy5ZpGqqpqev3bcM5srMzaGOIGdoXYon2hViKZ/vq\nLoyHFYonTpyotWvXqrCwUJI0b948lZWVqbGxUdOnTw+ZlwPtAAAAMNCFFYoNw9CcOXNCHhs5cuQh\n802dOjW8qgAAAIB+xMU7AAAA4HiEYgAAADgeoRgAAACORygGAACA4xGKAQAA4HiEYgAAADgeoRgA\nAACORygGAACA4xGKAQAA4HiEYgAAADgeoRgAAACORygGAACA4xGKAQAA4HiEYgAAADieO94FAAAA\nYHCzbVuWZcmyLAUCAVlWerxLOgShGAAAOIZlWcGAZtt2yG3LskLmtWWH3rdC71t26Pzh19S23rYa\nJLt9vbZ1oIKOW3b7A3awlAM1hVZ3gN3dhOB0O3R5u9NfB03rvK6O18+2JSvk79DHOm4bpinZhiQp\nKVlymSk9F9bPCMUAgEHpwBf2gcARzt8dQalzQOocjjoHI+ug0HRgXZ3DzIH5O6+n20Aj+5BQc3Ag\nsnVo8LHt9ortA+GmY77OdXXMYB+0zM49aaqtbeymqt6zDy4spPJDb3cqrOf19jDB6vy6d5rZsm0Z\nhiEZhmRLhmG23ZZkdIwoNUJXZxhGn+6Hz5BhGO3r63x7gDMU8pr1Zlxud20i3gjFABBjBweznoJa\nX26npZlqaGgIqx6pPWxZtizbCgalzj1VnXupgvOH9FKFhqw+B7BO83UOZe13e5x+YH0Kzi879HHb\nsGXIkK224GLYhmy1hSLb7hRmDEN2R1hSp8ftjukHttM5pHR3u/MCXc9j9LBsPzMO+rsT25UmuSOv\nrbs1JEDcg8MQigEMSrZty+/3q6WlRS2trfL5AgpYlvyWrUDAlhWwFLBs+W37kPDX1boOv732v4P/\nO/C4bbQnuvaeH8M2JEOhwUwKhrO2m6GhqWP9RqcosbvBr717w+/J6+iJOjistf0xox/WeghgfZgF\nAGKCUAw4nG3bam1tVVNzk5qbfQpY1iE/xQZ7AaUDPYFtE7r92bbvdfR5kRCWbSsQsBSw20OvJckw\n5XK5ZbrccrsP+rgz2/64IttsXHk8KfJ4ojOmEQCcjlAMDGKWZam1tVWNTU1qbvHJH7DkD7SFR3/A\nki9gKRCQDJdbLpdbSUnJ8Ss20q5BQzLMtg81PtgAAH0V1neHZVmaPXu2Nm/erKSkJM2dO1c5OTnB\n6WVlZXr66aflcrk0atQozZ49OzEGiwMJwufzqaWlRU3NrfL5/QpYHUHXDg4R8PstWbZkmG653cly\nu5MOrMBsC5DJSd1vAwAAJwkrFJeXl8vn86m0tFQbN25USUmJHnnkEUlSc3OzHnroIZWVlcnj8eiW\nW25RRUWFxo8fH9XCgYGs49Q+Pp9Pra2tIaf7sdoPYPIHAu2PtY1vbTuw6MCBTAHLlj9gKdAp6AY6\nenZNsz3sJsnl6pRsDUkuyeWSXAReAAB6LaxQXFlZqYKCAklSXl6eqqqqgtM8Ho+effZZeTweSZLf\n71dKysA6Dx0Sy8HnkezqvJIdYdOy2kOm1fWR812vv+ftWx3B1W473ZLdfmCWJbUfud/xp2O6ZMmW\nYZjK3JWuffuaggdYyTZkmKEHMhmGIdPs4a1I0AUAIObCCsX19fXyer3B+y6XS5ZlyTTbvuSzsrIk\nSYsXL1ZTU5O+9a1vRadaHFYgEJDf72/rofT5gkfc27atQHuwC+ohDXZ7vsyDDr469PEDR2EFT6nU\nPsPBp2eyunj84CP4g0fuy2j7r/38jbYks+Mk4Ibag6WpzkfPm2YMD6EyD/zdnlm7PGArNS1NLa1c\nTR0AgIEurFDs9XpDzo3ZEYg737/vvvtUXV2thx9++LDrGzYsTW53/x4DbpptY5yzszPCWr7mi1rV\n1zdJ6nwqpkNPwn7wUfiHXomm9xqa/fr7lj2SbJ163DClJrvaf1q3FWjvzWzrfTRlmC65XMlyJbtk\nto/nTuSj7BNZZmZavEvAIEb7QizRvhALHdko3AwWK2GF4vz8fFVUVGjy5MnasGGDRo8eHTK9uLhY\nHo9HCxcu7NUBdtG4Yk5fWZYt0zRUU1PX52X9fr+q/vmpUlKHxKCyrjW1+PXkyx+prtEnSXrl3Z26\n5rwTlOrp7p/QktTab/Wha5mZaRGdRxboCe0LsUT7QqzYtq0RQ9LCymDR0F0YDysUT5w4UWvXrlVh\nYaEkad68eSorK1NjY6NOOeUULV++XGPHjtVVV10lSbr66qs1YcKEMEsfeLbt2NWvgViSPqyuDQZi\nSapr9OnD6lrlj8ru1zoAAAAGo7BCsWEYmjNnTshjI0eODN7+8MMPI6tqAGtublZdU0Aejh0EAAAY\nNDgCqI+2f7ZbnhTv4WeMshNzhykj7cCpBzLSknRi7rB+rwMAAGAwIhT3QUNDoxp98XnJUj1uXXPe\nCap+5zlVv/PcYcYTAwAAoC9IVX2w/fPdSvbE70jJVI9bNVvWBG8DAAAgOugp7qV9+/er1UqOdxkA\nAACIAUJxL326s1ZJyZ54lwEAAIAYIBT3whd79sgyOd0EAADAYMXA1F74bHed3J7+PS/xYNLU4teH\n1bWS2s6iwXhoIDHxXk4c/FsBfce75DA+31kjw81lLsN18JX41n14uCvxHbo8H+xA/PFeThyR/lsB\nTsXwiR5YlqVdexvlcvFBEq7ursTXGx0f7OXvblf5u9v15MsfqanFH6tSAUdoavGrcnONKjfX9On9\nxHs5cUTybwU4GaG4Bzs+3yV3cv9fqANt+GAHoite4ZT3MoBEQCjuRiAQ0Bf7W2SavESR4Ep8wMAR\nSTjlvZw4+LcCwkPi68a2HTuVnBK/C3UMFpFciY8PdmDgSOT3crhDRuIt3Lq5AioQHt4lXWhpadH+\nhoA8qUaX0zlgpG/CvRJfxwf7zF/eIUm6/9d381oDETgxd5jWfbgz2Fvc13CaiO/lRD3oLNK6uQIq\n0He8U7rwyY4aeVK7HkucqB+wiYoPdiB64hlO4/Ve7m7ISP6o7F4tH0knSCTLRlp3vNBphERGaz1I\nQ2OjGnyGPN1cvC5RP6gAQGJHsy8i6QRxYgeKE58zBhfGFB/k08+/kMfDeYkBYDCIZDxzJAcmRnrG\njXiPww4HZxlBoiMUd1JXV6dmf897tIn4QQUATpWoB50lat3oP4l6AOlARijuZPvOPUr2pPQ4T6J+\nUPHmAeBUHUNGaras6dPndSSdINHoQAm37niJ9DnzPdV7XBAnNgjF7fbU7pVf3QwkPkiifVDx5gGA\nvoukEyRRO1AiEclz5nuqbxiqEhuD+x3aBztq9inJMyTeZcQEBwcCQHgiOTDRiQc1hvuc+Z7CQEBP\nsaSdu76Q4U6NdxkAAMRdxzCGN9//jN7aAYrjm2IjrFBsWZaKi4tVWFiooqIibdu2LWT66tWrNW3a\nNBUWFmrZsmVRKTRWbNvWrtp6uVxJh585QfHmAQYXQgtipfMwhpf+71/9NoyB76m+ceLwnP4QVigu\nLy+Xz+dTaWmpZs6cqZKSkuA0n8+nkpISLVq0SIsXL9azzz6rL774ImoFR9uOz2vkSu76Qh2DBW8e\nYPCIV2iBM8RrrGoif0/F6wDBRDu+qUPH67W68jPVN/kOv0A/CutVrKysVEFBgSQpLy9PVVVVwWlb\nt25VTk6OMjIyJEljxozR+vXrdd5550Wh3Ojbva9JntT+G0scr6v9OHFsGzAYMfYSg1Uifk9xwZK+\nOfj1+svbOzT72tPlTR0Yv9Ybtm3bfV3o9ttv16RJk3TWWWdJks455xy98sorMk1T77zzjpYsWaIH\nH3xQkvTb3/5WRx11lC699NJu15eTY4VZfvh27PhUkpQ94qiwlt+16zNJ0og+LO9KbtVJ570uT3qz\nJKmlIUWbXi5QoDU5ptuNxrKRStS6I2Wahiyrz28xoFvZx/9buWM/CHms+p2TVbPlq71eR7zej078\nDIr0Off3tp38PRWOaLwfIzEYXq/tlafoi60j+7WObdu6HigR1q6M1+tVQ0ND8L5lWTLNtg1kZGSE\nTGtoaNDQoUN7XJ9pGpKMcEoJ29FHHyO/3y87zO1+6Utf7vMyR3x1R/CDRpI86c064qs7VPPP3jeG\ncLYbjWV3fr5DknRkmOtIxLojfc7x2nYiLutKbpU7c5Mkyb/3pD59AUe67URrI3s/+YqOOnGrkts/\nS1obUrT3k6+0f472Trzej5EsK0X2eifqc+7vbdt+jz7661nKymnrONqz7Suy/cky+zDYMlHbSDjL\ndvW2Mw316f0Yr3Yd6baj9nqZRjBDxltYoTg/P18VFRWaPHmyNmzYoNGjRwenHXvssaqurta+ffuU\nmpqq9evX6/rrr+9xfevX14dTRsSGDUtVxZsfKqWfhk9Ubt6n8ndDH7v2+/uUP+rTftl+JKZf+G1J\nUumf1sa5kr6JpO5In/P0C78t0zRU+qf/69dtJ9qyB/+clpHW958fE+05R7p8U8vx+rC6Vqmpyfrq\niHSlfq8mrO0nmkT9HEpMycrMTNPevYnVtvr7/dzUYurJl5M6fX4laf5cU6me3n+vx7Ndx/v1yhri\n0W8XZcqbWtfn7Ucmo8tHwwrFEydO1Nq1a1VYWChJmjdvnsrKytTY2Kjp06dr1qxZuv7662VZlqZN\nm6YRI0aEX3cMud1uZWV41OCzZRix76k+MXeY1n24M+TNw9G1cLp4jpFtavEr+/izgrcTZRxgqset\n/FHZ7aGlMd7lAI7VcYBgPI4VSkQdr9emf+/RiKxkffPkowfMeGIpzFBsGIbmzJkT8tjIkQeGAJxz\nzjk655xzIqusnxx91AhVbf5Eyf3QW8ybp38lauBJRIn4Wnf0UOeOnS5JevLljzhABkCfdeykonc6\nXq9ROWlymQMnEEtcvEOmaeqIzFQFAoF+2V5HY8gflc2Xbwx1Djy5Y6dz2qoYivS1jtf5SblMKgCg\nM1KZpKOOzNYXm7fJ5Rqcl3l2Ik5b1X8ifa35BQVAvCXir12IPv7V1TYc5MjhGarZ7xvUV7YDBqp4\n/PwY7zH+fAkDAwNDqdDB8cMnOow4IksKNMW7DEQJlwztP4n6Wnf0UE8Yc7QmjDm6X78EGd4DDBwM\npUIHdoM6+cqITH1S06SkJE+8SxlQErFHi5/k+08iv9bxOkCG4T0AMPAkxjdXP8kcOlSf7d4niVDc\nIZF/VuKI4P7Daw0gUcV7KFW8JGKHV6zxChzk6COz9PFn+5WcnBrvUgYEerScgw/I/uPUL+FI0D4R\nK4n8a1e4ErnDK5ac/ey7kOH1KtVdq/45QRswMPAB2XeRhDQnfglHgvaJWIvXr13x2tmjw6trHGjX\nha986Qi1NjfEu4wBIVEPokLfcKBJ30TjQDnOWd57tE8MRhxwO/DwSdyFtNRUeT1Sa7wLGQDo0QpP\nx96/aRj81DsI0csCIFLx/BxhCFfX+KbuxtFfHqEPP/5cnpSMeJcSdxxE1TeJ+FMvH5AYyGifQHTR\n4dU1XoFuJCcnKzM9SY1+W4ZhxLscJJBE7EXkA7JvCGn9i/aJ3ki0gzHj/TlCh9ehBnaLibOjjxqh\nqi3b5Unl8s8Y/PiA7D1CWv+jfaInifgLHZ8jAw+vfg9cLpeOGJqqfc2WTJNjEtE78d77R/8gpAED\nRyL+QifxOTLQEIoP48tfytaezdtkptBbjN7pvPefmpqsr45IZ+8fAIABju7PwzAMQyOGeRUI+A4/\nM9CuY+//m187ikAMADHG6UMRDXxb98KRI4Zr975qyZV0+JkBAEC/YnwuooEW00tHHTFUn37RrKQk\nT7xLQT9ItKOYAcDpGJ/bfwbrdyTDJ3opa1im3GqJdxnoB1xlCACArg3m70hCcR8cfWSWWlua410G\nYoxLygLYuFZMAAAgAElEQVQA0LXB/B1JKO6DjIwMpbgHx94QAAAADuhzKG5ubtbNN9+sK664Qt//\n/ve1Z8+eQ+Z58sknNX36dE2fPl0LFiyISqEDxdFHHaGWlsZ4l4EY4ihmAAC6Npi/I/s8Mnrp0qUa\nPXq0brrpJq1atUqPPvqofvWrXwWnf/LJJ3rppZf0/PPPyzAMzZgxQxMnTtTo0aOjWni8pKWmKj3J\nFv3FgxdHMQMA0LXB/B3Z557iyspKnXVW2xGHBQUFevPNN0OmH3XUUfr9738vwzAkSX6/XykpKVEo\ndeA45svZammqj3cZiKGOo5jzR2UPmjc7AADRMFi/I3t8JsuWLdPTTz8d8tjw4cOVnp4uSUpPT1dd\nXV3oCt1uZWZmyrZtzZ8/XyeddJJyc3OjXHZ8eTwefWl4mhqaDhx0Z9t2p9vBW8G/7NBHOs/UZ7Yk\ny7IVsG1Zli3LkizZMg2XDNMls/2Py+UKexsAAABO0mMovvTSS3XppZeGPHbzzTeroaFBktTQ0KAh\nQw69/HFLS4t++ctfyuv1avbs2YctYtiwNLnd8Qlw2dkZ/bpcrNi2Lb/fL7/fr5ZWn1pbW+XzBWTZ\ntgKWLStgK2BbsqwullUPAd3u+u7BOwF2px0AHTRPx6x2+86BbR+YZtl2+2P2gfkkmYYp2UbbbdNo\nX60hwzBkGmbbLxGGIdNsu93xZyDKzEzr8zKmacRl2UTlxOfcwYnPGf2H9oVY6MgAAy1L9bnPOz8/\nX2vWrNGpp56qNWvWaOzYsSHTbdvWj370I5155pm64YYberXO2tr4HLiWnZ2hmpq6w8+YcAwZ8ig5\nQS/AZ9u2LMtqD8qhtwOBgGzZsi1bASsgy7JkBSTbtmRZ7SFcdnvw7rh38Pr7WIvd1jMfrMeWbMuW\nZXf86Zje1mNvyJRhmDJMU5mZ6dq3r6k9tLcF+I4g35OO57J3b9/fG5Esm6ic+JyltsDitOeM/kP7\nQqzYtq0RQ9LilsG6C+N9DsUzZszQL37xC11++eVKTk7WAw88IKntjBM5OTmyLEvr16+Xz+fTmjVr\nJEm33HKLvv71r0dQPpzEMIyEHfrREeIDgYACgYCystK0q8Zsf8xqn27LCgTaesgPCvCWLTU2+zWi\n/UpBrc318iQZ8lu2AoH2nn7DlGG65XYnJezrFE2D9cpKAID+Zdh2X/rNYiOeewqDs6cYA0Vf21h9\nk0+zF72tPfvbrp6YNcSj2deeLm/qgW5/n8+nlpYWNTW3yuf3K9AemP0BWwHL0rQpEyRJTz37skwz\nSW5325/BqOPKSh0nks9IS9I1553gmGBMTx5iifaFWLFtW6Ny0uQy43Mihqj1FAOInXWbdgYDsSTt\n2d+idZt26v+NOTr4WFJSkpKSkuT1dr2O5KS2t/VpJ+SqtbVVjU1NamnxyRew5A9YCvitttuWrUDA\nli1TLneSkpKSB+y47O50d2Wl/FHZcawKAJCICMXAIGUYhjwejzweT4/ztba2qqWlRQ2NzfJbVntQ\nbmfbnQ6u7Pj74DOtHHqWlc66/Smqix+p+vyzldVyyEN+X7MaG/fJNNwyTBfDTAAAvUIoBgaQM046\nUv+7rjpk+MQZJx3Z6+Xrm3waflxB8HbnYRfdSU5OVnJysjIyBtZRwL3xlaOOVOXW/SGv18XnnKj0\nFLd8vrazsLS0tqrV52sfZtJxNpb2nnLLarvfOY2HMaDMtiXbaN8r6Dgbim1IRtu0jrOlSO237fYe\n+Y6/OvXQd3c7OHOnxzvGrofD6FRPov1CAACxwJhixhQjhsJpY/VNPq3btFNSW0juTbDtWO5w45EH\no3Bfr2g7cApC+5A/3T1u23bI+VHsTuncsg+cP9GyOvfOH7g9fLhXu3eHdyGhtgM8rZBTJEoHDv7s\nmOfAo6G/FnT8SGC3z3jw6RbtTjPYXSwTnE+h6zhkeuezPYac8rG9KqPtfDsyDLXdNdr3H9oes237\nQPDvZmfk4L87z9TVtO53XAYXxhQjVhhTDKBXvKlJIWOIe6s345EHo3Bfr2jrOljFVvYRGZKd3G/b\nG2i62uHo6f7B04Kx3O4I7R3TDl535x0CSx3pvPNOTHe9SyE7Pd3MFPr4oes8eLkDtR+Y6eALQx28\nE3NgUqcdlY5p3eyoBFotBVobunlmh9fN0wp5/HD9cuF02wWfZvsvOIYMdZznXuo4/33bufBlqNNp\nMo1u38eHu4/BgVAMAEhI8dgRcZLB8GtqV+e7Dzn3vRVoP+9822kzJR1ykavOO0JS6M6QpG53iMKq\nt9MvNW3bPrCFLnd0uiigx3p6OJYjdLsHPcfOe2GddsRsKXjeflvtF+Sy28/d3z6TZbddcVeGIcM0\ndWDnY+BdGIZQDAwSkY5HBoDBpmPojGma8S7FsSzLCv7pOA4iYAWUfUSW9uwZWMNzCMXAIOFNTdLs\na08fEONrAQCQ2oandLVTMhDPCkQoBgaRgTK+FgCARMPvCQAAAHA8QjEAAAAcj1AMAAAAxyMUAwAA\nwPEIxQAAAHA8QjEAAAAcz7APd41FAAAAYJCjpxgAAACORygGAACA4xGKAQAA4HiEYgAAADgeoRgA\nAACORygGAACA4xGKAQAA4HjueBcQD5Zlafbs2dq8ebOSkpI0d+5c5eTkxLssDAIbN27U/fffr8WL\nF6u6ulqzZs2SaZo6/vjjdeedd8owjHiXiATl8/n0y1/+Ujt27FBra6tuvPFGHXfccbQxREUgENDt\nt9+uf//73zIMQ3PmzFFycjLtC1H1xRdf6OKLL9aTTz4p0zQHXPtyZE9xeXm5fD6fSktLNXPmTJWU\nlMS7JAwCjz/+uG6//Xb5fD5J0rx58/Tzn/9cS5YskW3beuWVV+JcIRLZSy+9pKysLC1ZskRPPPGE\n7rrrLpWUlNDGEBUVFRUyTVNLly7Vz372M/3mN7+hfSGqfD6fiouLlZqaKtu2B+R3pCNDcWVlpQoK\nCiRJeXl5qqqqinNFGAxyc3O1YMECdVwkctOmTfrGN74hSTrrrLP0xhtvxLM8JLjzzjtPP/nJTyS1\n/drldrtpY4iaCRMm6K677pIkffrppxo6dKg++OAD2heiZv78+ZoxY4ays7MlDczvSEeG4vr6enm9\n3uB9l8sly7LiWBEGg0mTJsnlcgXvd76Celpamurq6uJRFgaJtLQ0paenq76+Xj/96U/1s5/9LORz\nizaGSLlcLv3iF7/Q3LlzdcEFF/AZhqhZsWKFsrKyNG7cOElt348DsX05ckyx1+tVQ0ND8L5lWTJN\nR+4fIIY6t6mGhgYNGTIkjtVgMPjss89000036YorrtD555+v++67LziNNoZouPfee7V7925deuml\nam1tDT5O+0IkVqxYIcMw9MYbb+ijjz7SrFmzVFtbG5w+UNqXI5Ngfn6+1qxZI0nasGGDRo8eHeeK\nMBideOKJevvttyVJa9as0dixY+NcERLZ7t27dd111+nWW2/VxRdfLIk2huh58cUX9bvf/U6SlJKS\nItM0dcopp9C+EBV//OMftXjxYi1evFgnnHCC7r33Xo0bN27AtS9H9hRPnDhRa9euVWFhoaS2A6KA\naOk4enbWrFm644475PP5dNxxx+m8886Lc2VIZI899pjq6uq0cOFCLVy4UJL0q1/9SnPnzqWNIWKT\nJk3SbbfdpiuvvFJ+v1+/+tWvdOyxx/IZhpgwDGNAfkcadudBHQAAAIADOXL4BAAAANAZoRgAAACO\nRygGAACA4xGKAQAA4HiEYgAAADgeoRgAAACORygGAACA4xGKAQAA4HiEYgAAADgeoRgAAACORygG\nAACA4xGKAQAA4HiEYgAAADgeoRjAoLN9+3adeOKJmjJlSvDPRRddpOXLl/d5Xa+++qp++9vfSpJW\nr16te+6557DbPu2007qdvnjxYp1wwgnauHFjr2tYsGCBXnnllV7P3x9mzZqlP/zhDz3OU1dXp6uu\nuip4f8qUKaqvr491aQAQFne8CwCAWEhJSdGf/vSn4P2dO3fqggsu0CmnnKLRo0f3ej3vv/++9u3b\nJ0kaP368xo8fH1FdpaWluvDCC/XUU0/pN7/5Ta+WWbdunY4//viIthtthmHIMIwe59m3b5/ef//9\n4P3O/x4AMNAQigE4wpFHHqnc3FxVV1frmGOO0ezZs1VdXa29e/cqPT1dDzzwgEaOHKmioiJlZmbq\nX//6lyZPnqxnn31WgUBAXq9Xubm5+utf/6rHHntMGzZs0P3336/W1lbV1NToW9/6lubOndtjDevW\nrdO+ffs0c+ZMTZw4UZ9//rm+9KUvSZKKiop05ZVX6txzzw25v3v3blVVVWn+/PlyuVw644wzNGfO\nHP3jH/+QJJ111ln6+c9/LpfLpY0bN+qee+5Rc3OzkpKS9N///d8688wz9c477+i+++5TU1OTkpKS\n9LOf/UwFBQVasWKFnn/+eTU3N8vr9Wrq1KlatmyZmpublZGRoaeeekrLli3T0qVLZdu2MjMzdccd\nd+jYY48NeV7PP/+8nnvuOfl8Pu3bt0833HCDZsyYodtuu00tLS2aOnWqli9frpNOOklvvfWWMjMz\ntXDhQq1atUoul0tf/epXVVxcrCOOOEJFRUU67bTTVFlZqR07dmjs2LG69957DxvAASBShGIAjvD3\nv/9d27ZtU15enl5//XUNHTpUzz77rCTpzjvv1JIlS3T77bdLkoYOHao///nPwWX37t2r//qv/9KK\nFSuCjy1evFg//elP9Y1vfEMNDQ2aMGGCNm3apCFDhnRbw9KlS3XhhRdqxIgROvPMM/XHP/5RM2fO\nDE4/OPgZhqErrrhCL7/8soqKijRhwgT94he/UFZWll566SW1trbqxhtv1O9//3tde+21+vGPf6y5\nc+fq7LPP1gcffKDbbrtNTz/9tH7605/q0Ucf1amnnqp//vOfuvLKK/X8889LkrZu3arVq1crPT1d\nK1asCLn/9ttv68UXX9QzzzyjlJQU/d///Z9uvvnmkNemsbFRzz//vB5//HENHTpUGzZs0HXXXacZ\nM2aopKRE559/vl544YWQ57V8+XK9/vrrWr58uVJSUrRgwQLNmjVLTzzxhCTpk08+0R//+Ec1NDRo\n8uTJevvtt3XGGWf06d8bAPqKUAxgUGppadGUKVMkSYFAQJmZmbr//vt15JFH6txzz9XRRx+txYsX\nq7q6Wm+//XbIOOCxY8cGb9u2Ldu2D1l/SUmJXnvtNf3P//yPtm7dqubmZjU2NnYbimtqalReXh4M\n1hdddJHmzJmjm266SSkpKb1+Xq+//rpKS0slScnJyZoxY4aeeuopjRs3Ti6XS2effbYk6eSTT9bK\nlSv12muvKScnR6eeeqok6T/+4z+Un5+vt99+W4ZhaNSoUUpPTw+uv/P9V199VdXV1SosLAxO37dv\nX3A4iW3bSktL02OPPaaKigpVV1frww8/VFNTU3D6wWzb1po1a3TJJZcEn3dRUZEee+wx+Xw+SdI5\n55wjSUpPT1dubq7279/f69cHAMJFKAYwKHk8nm7HsD7zzDNatmyZrrzySl144YXKzMzUp59+Gpye\nlpYWvN3d2NnLL79cJ554os466yxNnjxZ7733XpchsMOyZctkGIZ++MMfSmoLh/X19VqxYoUuv/xy\nGYYhy7KC83cExINZlhWynUAgIJ/PJ5fLdUidmzdv7rImy7IUCATkdrtDArGkkPu2beuiiy4K9mbb\ntq1du3Zp6NChwdfm888/12WXXabCwkKNHTtW5557rl599dVuX4eO9Rxcj9/vDz5+8E5CT68rAEQL\nZ58A4Dhr167V1KlTdckll+irX/2qVq9eHRJIO4cwl8ul1tbWkOX379+vDz74QDNnztSECRP0+eef\na9u2bQoEAl1uLxAI6LnnntNdd92l1atXa/Xq1aqoqNAPfvADPf3005KkrKwsVVVVSZK2bdsWHDMs\nSW63OxiSx40bpyVLlkiSWltb9dxzz2ncuHEaOXKkDMPQG2+8IUn64IMPdM011+jUU0/Vxx9/rPfe\ne0+StGXLFr3zzjs6/fTTDxs2v/3tb+vPf/6zampqJLXtTFx99dXB18i2bVVVVWn48OG68cYb9e1v\nf1sVFRXB6W63O+R1ldqCdEFBgZYvXx7sUV68eLG+8Y1vKDk5+ZDXHwD6Cz3FAAalng7Muu6661Rc\nXKwVK1bINE2dcsop2rx5c5fLfvOb39RNN92k5ORknXzyyZKkIUOG6Pvf/76mTp2qzMxMDRs2TGPG\njNG2bdt0zDHHHLLtjqB4wQUXhDx+zTXX6Omnn9Zrr72mG2+8UbNmzdJrr72mkSNH6vTTTw/Od845\n5+jee++Vz+fT7bffrrvvvlsXXHCBWltbddZZZ+mHP/yh3G63Hn74Yf3617/W/PnzlZSUpAULFigr\nK0sPPfSQ7rnnHjU1Nck0TZWUlCg3N1eVlZU9vmbjxo3T9773PV133XUyDEMZGRlauHBhcF7DMDRu\n3DgtX75c5557rtLS0vS1r31Nw4cPV3V1tXJycnTSSSfpP//zP/XMM88E1z9t2jR99tlnuvTSS2VZ\nlnJzc3X//ff36t8OAGLFsNklBwAAgMMxfAIAAACORygGAACA4xGKAQAA4HgD4kC7mpq6uGx32LA0\n1dY2xmXbcAbaGGKJ9oVYon0hluLZvrKzM7p83NE9xW63K94lYJCjjSGWaF+IJdoXYmkgti9Hh2IA\nAABAIhQDAAAAhGIAAAAgolC8ceNGFRUVHfL46tWrNW3aNBUWFmrZsmWRbAIAAACIubDPPvH4449r\n5cqVSk9PD3nc5/OppKREy5cvV0pKimbMmKHx48dr+PDhERcbLfVNPq3btFNer0cn52TKm5oU75IA\nAAAQR2H3FOfm5mrBggU6+CrRW7duVU5OjjIyMpSUlKQxY8Zo/fr1ERcaLfVNPs1e9LaW/G2z/ueF\n9zV70duqb/LFuywAAADEUdiheNKkSXK5Dj2dRn19vTIyDpz/LT09XXV18TkPcVfWbdqpPftbgvf3\n7G/Ruk0741gRAAAA4i3qF+/IyMhQQ0ND8H5DQ4OGDh3a4zLDhqX12/nqvF5Pl491dyJnIFK0LcQS\n7QuxRPtCLA209hX1UHzsscequrpa+/btU2pqqtavX6/rr7++x2X684omJ+dkKmuIJ9hbnDWkbVxx\nvK6qh8EtOzuDtoWYoX0hlmhfiKV4tq/uwnjEodgwDElSWVmZGhsbNX36dM2aNUvXX3+9LMvStGnT\nNGLEiEg3EzXe1CTNvvZ0XXz1T2Sahn676CEOtAMAAHA4wz74SLk4iMeewpgxp8g0Da1f/36/bxvO\nQU8LYon2hViifSGWBmJPMRfvAAAAgOMRigEAAOB4hGIAAAA4HqEYAAAAjkcoBgAAgOMRigEAAOB4\nhGIAAAA4HqEYAAAAjkcoBgAAgOMRigEAAOB4hGIAAAA4HqEYAAAAjkcoBgAAgOMRigEAAOB4hGIA\nAAA4HqEYAAAAjkcoBgAAgOMRigEAAOB4hGIAAAA4HqEYAAAAjkcoBgAAgOMRigEAAOB4hGIAAAA4\nXlih2LIsFRcXq7CwUEVFRdq2bVvI9JUrV+riiy/WtGnTtHTp0qgUCgAAAMSKO5yFysvL5fP5VFpa\nqo0bN6qkpESPPPJIcPr8+fO1atUqpaam6rvf/a7OP/98ZWRkRK1oAAAAIJrCCsWVlZUqKCiQJOXl\n5amqqipk+ujRo7V//36ZpinbtmUYRuSVAgAAADESViiur6+X1+sN3ne5XLIsS6bZNhrj+OOP1yWX\nXKLU1FRNmjQpZN6uDBuWJrfbFU4pYTPNtqCenU0PNmKLNoZYon0hlmhfiKWB1r7CCsVer1cNDQ3B\n+50D8UcffaTXXntNq1evVmpqqm699Va9/PLLOu+887pdX21tYzhlRMSybJmmoZqaun7fNpwjOzuD\nNoaYoX0hlmhfiKV4tq/uwnhYB9rl5+drzZo1kqQNGzZo9OjRwWkZGRlKSUlRcnKyTNNUVlaW6up4\nUwEAAGDgCquneOLEiVq7dq0KCwslSfPmzVNZWZkaGxs1ffp0XXbZZbr88suVlJSk3NxcTZ06NapF\nAwAAANFk2LZtx7uIeHSfjxlzikzT0Pr17/f7tuEc/PyIWKJ9IZZoX4ilQTN8AgAAABhMCMUAAABw\nPEIxAAAAHI9QDAAAAMcjFAMAAMDxCMUAAABwPEIxAAAAHI9QDAAAAMcjFAMAAMDxCMUAAABwPEIx\nAAAAHI9QDAAAAMcjFAMAAMDxCMUAAABwPEIxAAAAHI9QDAAAAMcjFAMAAMDxCMUAAABwPEIxAAAA\nHI9QDAAAAMcjFAMAAMDxCMUAAABwPHc4C1mWpdmzZ2vz5s1KSkrS3LlzlZOTE5z+3nvv6d5775Vt\n2zriiCN0//33Kzk5OWpFAwAAANEUVk9xeXm5fD6fSktLNXPmTJWUlASn2bat4uJilZSU6JlnnlFB\nQYE+/fTTqBUMAAAARFtYPcWVlZUqKCi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"text/plain": [ "<matplotlib.figure.Figure at 0x1151bc210>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12,8))\n", "ax1 = fig.add_subplot(211)\n", "fig = sm.graphics.tsa.plot_acf(df.seasonal_first_difference.iloc[13:], lags=40, ax=ax1)\n", "ax2 = fig.add_subplot(212)\n", "fig = sm.graphics.tsa.plot_pacf(df.seasonal_first_difference.iloc[13:], lags=40, ax=ax2)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Statespace Model Results \n", "==========================================================================================\n", "Dep. Variable: riders No. Observations: 114\n", "Model: SARIMAX(0, 1, 0)x(0, 1, 1, 12) Log Likelihood -976.135\n", "Date: Wed, 23 Mar 2016 AIC 1956.271\n", "Time: 13:16:18 BIC 1961.743\n", "Sample: 01-01-1973 HQIC 1958.492\n", " - 06-01-1982 \n", "Covariance Type: opg \n", "==============================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "ma.S.L12 -0.1377 0.050 -2.757 0.006 -0.236 -0.040\n", "sigma2 1.424e+07 2.62e-10 5.44e+16 0.000 1.42e+07 1.42e+07\n", "===================================================================================\n", "Ljung-Box (Q): 44.28 Jarque-Bera (JB): 4.18\n", "Prob(Q): 0.30 Prob(JB): 0.12\n", "Heteroskedasticity (H): 1.44 Skew: 0.20\n", "Prob(H) (two-sided): 0.29 Kurtosis: 3.91\n", "===================================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using the outer product of gradients.\n" ] } ], "source": [ "mod = sm.tsa.statespace.SARIMAX(df.riders, trend='n', order=(0,1,0), seasonal_order=(0,1,1,12))\n", "results = mod.fit()\n", "print results.summary()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Statespace Model Results \n", "==========================================================================================\n", "Dep. Variable: riders No. Observations: 126\n", "Model: SARIMAX(0, 1, 0)x(1, 1, 1, 12) Log Likelihood -970.257\n", "Date: Wed, 23 Mar 2016 AIC 1946.514\n", "Time: 13:20:09 BIC 1955.023\n", "Sample: 01-01-1973 HQIC 1949.971\n", " - 06-01-1983 \n", "Covariance Type: opg \n", "==============================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "ar.S.L12 0.5591 0.004 142.852 0.000 0.551 0.567\n", "ma.S.L12 -0.9986 0.009 -116.113 0.000 -1.015 -0.982\n", "sigma2 1.143e+07 7.45e-10 1.53e+16 0.000 1.14e+07 1.14e+07\n", "===================================================================================\n", "Ljung-Box (Q): nan Jarque-Bera (JB): 8.68\n", "Prob(Q): nan Prob(JB): 0.01\n", "Heteroskedasticity (H): 0.73 Skew: 0.31\n", "Prob(H) (two-sided): 0.40 Kurtosis: 4.29\n", "===================================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using the outer product of gradients.\n", "[2] Covariance matrix is singular or near-singular, with condition number 8.33e+30. Standard errors may be unstable.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/seanwilson/anaconda/lib/python2.7/site-packages/statsmodels-0.8.0-py2.7-macosx-10.5-x86_64.egg/statsmodels/tsa/statespace/sarimax.py:989: VisibleDeprecationWarning: boolean index did not match indexed array along dimension 0; dimension is 113 but corresponding boolean dimension is 101\n", " trend_data = trend_data[~np.isnan(endog)]\n" ] } ], "source": [ "mod = sm.tsa.statespace.SARIMAX(df.riders, trend='n', order=(0,1,0), seasonal_order=(1,1,1,12))\n", "results = mod.fit()\n", "print results.summary()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/seanwilson/anaconda/lib/python2.7/site-packages/statsmodels-0.8.0-py2.7-macosx-10.5-x86_64.egg/statsmodels/base/data.py:551: FutureWarning: TimeSeries is deprecated. Please use Series\n", " return TimeSeries(squeezed, index=self.predict_dates)\n" ] }, { "data": { "image/png": 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X+vr68Pv9uN3uEdf29fWlfA4RERGZPcme6QVcmYahiR4TafVITpSY5DHrS0s8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7c9Igv1cz8eHh5oa2vDwMAAmpub0dLSgqamJrx48QKurq7msxqjo6OQy+V2Tm9blnTzt7G0m9ev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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1151e2e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df['forecast'] = results.predict(start = 102, end= 114, dynamic= True) \n", "df[['riders', 'forecast']].plot(figsize=(12, 8)) \n", "plt.savefig('ts_df_predict.png', bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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9Mzh06Q8ACvsXoWO5SDqW70yJnCXtcp47sVcdd/65nZjtn7LwyAKsNivFAovz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\nFfYiIiIiIqXA/wN0H44WC6NeEQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1157c41d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "npredict =df.riders['1982'].shape[0]\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "npre = 12\n", "ax.set(title='Ridership', xlabel='Date', ylabel='Riders')\n", "ax.plot(df.index[-npredict-npre+1:], df.ix[-npredict-npre+1:, 'riders'], 'o', label='Observed')\n", "ax.plot(df.index[-npredict-npre+1:], df.ix[-npredict-npre+1:, 'forecast'], 'g', label='Dynamic forecast')\n", "legend = ax.legend(loc='lower right')\n", "legend.get_frame().set_facecolor('w')\n", "plt.savefig('ts_predict_compare.png', bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "start = datetime.datetime.strptime(\"1982-07-01\", \"%Y-%m-%d\")\n", "date_list = [start + relativedelta(months=x) for x in range(0,12)]\n", "future = pd.DataFrame(index=date_list, columns= df.columns)\n", "df = pd.concat([df, future])" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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TiU86t+JnW1fjkKkB09Im495Z30N6XKrUYZ7ShfkLkavNxrbuHWgwNUkdjl8w\nmSbys2PNh/5f2HI6WrUSV80rhNXhxtvbI3cw/qmYBofwu5fr8Oc3D+Ddz9qx5YtuqUMioiDS2QxI\niklETACXj4SqXG027qtZiQW5cwEAlxZ+BXdO+xrUiliJIzs9uUyOZZU3QoCA9fWvwuV1Sx3ShDGZ\nJvKzQC9sOZ2LZ+UhOT4GH+zshNF89rNIw40oithc142H/vIZ6pr6MLkwGUqFDG9ubWXtOFGUGPI4\n0T80EDUlHqeikquwtOIGPL7o57i29ArIhNBP7YoSCrAo7zzobAa83/qR1OFMWOj/iROFmZGFLcnB\nTaZVSjmuW1gMl9uLDZ+2BPXewWY0O/Dbl+rw13cOQhRFfO3yCtx7ywxcWJ0Lo3kIm2q7pA6RiIIg\n2uqlR6OUB3Z6lL9dU3IZkmIS8X7bx+ix6qUOZ0KYTBP5WY/RhkSNCuqY4I8hmj81G7npGmzZ141O\ngyXo9w80URTxaW0XfvbMZ/iiuQ/nFKfg5yvOxfkzciEIAq6YW4gYpRxvbW3FkCsy19YS0THRNMkj\n0qgVsVgy6Vq4RQ/W179yxrnZoYzJNJEfudxe9JkdQW0+PJ5MJuCm80shisDLGyOjscPHaHZg7Yu1\n+Nu7hwAAX7+iEvcsmY7UxGO1gQlxKlwyOw8DVic+3n1EqlCJKEiisfkwkkxPn4rpaeegsb8F27t3\nSh3OuDGZJvIjQ78doghkBbH58GRVpamoyE9CXVMf6ttNksXhL6IoYlNtFx76y2fY12LE1JLh0+hF\n03NOOcf7sjkFUMco8M72NtiHwr+xhYhOLxoWtkS6xZOuRaw8Bq81vg2zc1DqcMaFyTSRH0lVL308\nQRCw+CvDs0Vf/LgxrNe29g048PgLe/H3dw9BEIA7rqzEjxZPR0rC6TvVNbFKXD4nHxa7Cx/s7Ahi\ntEQUbHqbAQqZAimxSVKHQuOUHJuEa0ouh81txysNb0odzrgwmSbyIylmTJ9KSU4Caioz0NI9iB2H\nwq+xQxRFbNx7BD975jPsbzWhqjQVP19xLhZWnfo0+mQX1+RDq1bivc87YI3CudtE0UAURehsBmSo\n08JiggWd3qK8eShMyMdO3V7s76uXOpyzxu8+Ij8aOZmWOJkGgBvPL4FcJuDVT5rDapFJ74Ada17Y\ni3++Vw9BELDiqsn4wU1Vo55Gn0wdo8AVcwtgH3LjP5/zdJooEg04zRjyOFkvHQFkggzLKm6ETJDh\nhfrX4PRsm/hiAAAgAElEQVQ4pQ7prDCZJvKjHqMdAoCMJOlqpn0yk+NwwYxc6Pvt+HBXZ8iXe4ii\niI/3HMHPnvkcB46eRv/im+di/rTsMZ1Gn+zC6jwkaFT4784OmG3h9cZMRGemt/UCADLi0iSOhPwh\nLz4HF+YvRJ/DiHdaPpA6nLPCZJrIj3RGG1ITY6FUhMZfrWvmFyFGJccLHzXip09vw/oPGnCwzQSP\nN7ROqnv77fjN83vx7H/qIT/uNDo5Pmbc14xRynH1vEIMOT14b3u7H6MlolDA5sPIc2XxJUiNTcaH\nHZvQORg++wJC4198oghgH3JjwOoM+ubD0SRoVLhnyXTUVGZg0ObCf3d24LH1e/DDJzbjT2/ux45D\nekknXnhFER/v7sTPnvkcB9tMmF6aip9P4DT6ZOfPyEVKQgw+3N2JfsuQHyImolDBhS2RJ0auws0V\nN8ArevHvMJo9HfytEkQRShcizYcnK89LQnleElxuL+rbTdjT2Iu9Db3Yvl+H7ft1UMgFVBYkY2Z5\nGqaXpZ1VbfJEGPrt+Ns7B3GovR+aWAVuv2wK5p6T6Zck2kepkOGa84rwj/fq8fbWNtx66SS/XZuI\npMWT6ch0TmoFZmVMxy59LTYd2YYL8uZLHdIZMZkm8pOeo82HoXQyfTylQoapJamYWpKK2y6ZhHad\nBXsaDNjb0It9LUbsazHi2fcPozArHjPL0jCjPA35GVq/JreA7zT6CF7e2IQhlwczytJw++UVSNKO\nv6RjNPOnZeOd7W34pPYILj+34IQlL0QUvnQ2A7RKDeKUofmeS+N306Sv4oDxMN5seg8z0qciKSZR\n6pBGxWSayE90RjsAIFPChS1jJQgCCrPiUZgVj+sWlqBvwIG9jb3Y22DAofZ+tPUMYsPmFqQmxGJG\n+XBiXZGfBIV8YpVhepMNf3vnEOo7jp5GXz4Fc6f49zT6ZAq5DF+dX4xn3j6IN7e24utXVAbsXkQU\nHC6vG312I4oTC6UOhQIgQRWP68uuxL8PvYIXD7+OO6fdLnVIo2IyTeQnvrF4WRIubBmv1MRYXDQr\nDxfNyoPN4ca+lj7saehFXVMfPtzViQ93dUIdo8C0khTMLE/HtJIUxMUqx3x9ryjio12dePmTJjhd\nXswsT8Ptl1UgMUCn0Sebd04W3tnehs113bhiboGkS3WIaOL67H0QIbLEI4LNy56Nz7p3o9awD7WG\n/Ziefo7UIZ0Wk2kiP+kx2qCQy4JWcxwocbEKzJmciTmTM+H2eHG4ox97G3qxp6EXnx/U4/ODeshl\nAioKkjDjaDlIWuLpT+N1R0+jDx89jf76FZU4d3JgT6NPJpMJuHZBMf74+n68sbkV37pmStDuTUT+\nx3rpyCcTZFhWeQN+9flv8eLhDahILkWsIjT/fWUyTeQHoihCZ7IhM1kNmSx4SWKgKeQyTClKwZSi\nFCy9uBydButInfWBVhMOtJrw7w8akJ+hxcyj5SCFmfEQBAFeUcSHOzvxyidNcLq9qJ6UjuWXVSBR\no5LktdRUZiBvaxu27+/BVfMKkZOmkSQOIpo4XzLNhS2RLUuTiUsLv4J3Wz/Am83/weJJ10od0ikx\nmSbyA7PNBfuQB5mFkVs+IAgC8jO0yM/Q4qvzi2E0O1Db1Ic9DQYcajOhQ2/BG1takRwfgxllaeg0\nWNDQOQCtWolvXDUZsyszgnoafTKZIOD6hcX4/atf4PXNLbjruqmSxUJEE8OT6ehxWeFXsEu/F590\nbsWcrGoUJuRLHdKXMJkm8oNja8RDv/nQX1ISYvGVmbn4ysxc2Ifc2N9iPFpn3YuP9xwBAMyalI7b\nJDyNPtmM8jQUZcVjxyE9rtINoiAzXuqQiGgc9DYDZIIMaeoUqUOhAFPKlVhacSN+t+dp/PvQK/if\nmpWQy+RSh3UCJtNEftATxs2H/qCOUaCmMgM1lRnweL1o7ByAIAgoz0uU9DT6ZIIg4IZFJXj8xVps\n+LQF37+pSuqQiGgcdDYDUmOToZAxjYkGk5JLMTe7Btu7d+Ljzs24uOB8qUM6Ab8L6QReUcSew71w\nOIO7FU8mEzC9NPWsJkSEkmMn09GZTB9PLpOhoiBZ6jBO65ziFJTnJWJvYy+au8woyUmQOiQiOgsW\nlxVWlw3FCQVSh0JBdH3ZVdjXexBvN7+PmenTkBpCn0owmaYT1Db24g+vfSHJvS+uycOyi8NzQ53O\nNDxjOlQXttAxvtPpX/97D177tBk/vnmG1CER0VnQ23oBsPkw2miVGtxYfg3+ceB5vN3yX9w+5Wap\nQxrBZJpO0NAxAAC4+rwipCcFaQSNCDz7/mEc7ugPzv0CQGe0QR2jQHxceJ6sR5uKgmRMLkzG/hYj\nDnf0Y1J+ktQhEdEYsfkwes3OnIm3W/6L3fo6LJ50LdQhMiqPyTSdoLlrAIIAXDm3ALGq4H17bKrr\nQkvXIIacHsSoQqux4Ey8XhE6kx156ZqQqg+m0V2/qAQHn92FVzc1475lM/n/HVGY0DOZjlqCIGBu\nVg3eavkPdutrMT/nXKlDAgBMbDcwRRSP14tW3SBy0zRBTaQBoDQnEV5RRGuPOaj39Qej2QG3x8sS\njzBTlpuIqtJUHO7ox4E2k9ThENEYHZsxnSFxJCSFudmzIEDAtq6dUocygsk0jThisMLp8krSkFWW\nmwgAaDwyEPR7T1SPic2H4er6hSUAgNc2NUMURYmjIaKx0NkMiJXHIEGllToUkkBybBIqU8rRYm5D\nj1UndTgAmEzTcZq7hk+FS3ISg37v0qPJdNOR8DuZ1hmHmw+jacZ0pCjMisesinQ0d5lR29QndThE\ndAZe0YteWy8y4tJZmhXF5mXXAAC2d++SOJJhTKZpxEgynR38k+nk+BikJMSgqWsg7E4IR2ZM82Q6\nLF23oBgCgA2bmuENs+89omhjdJjgFj2sl45yVWnnIE6hxmc9u+DxeqQOh8k0HdPcbUaMSo6cNI0k\n9y/JScSgzQVDv12S+4/XyIzpKF3YEu5y07U4d0om2vUW7K43SB0OEY2CkzwIGN6KWJM5E2bnIA4Y\n66UOh8k0DbMPudHda0VxVjxkMmk+Ois7WqsdbqUePUYbEjUqqGM4HCdcfXVBMWSCgA2bW+D18nSa\nKFQdaz5kMh3t5uUMl3ps65a+EZHJNAEAWrrNEAEUS7gNzlc33dgVPk2ILrcXfQMONh+GuayUOJw3\nLQtdvVZ8djA0GlqI6MuYTJNPvjYXudpsfNF7AINOi6SxMJkmAMfXSwe/+dCnIDMeCrmApjCa6KHv\nt0MEkMXmw7D31fOKIJcJeH1zC9wer9ThENEpHNt+mCZxJCQ1QRAwL3s2vKIXO3p2SxoLk2kCcPwk\nD+lOppUKGQqz4tGpt2LIKX1DwViM1EvzZDrspSWpsWhGDvQmO7bu65E6HCI6Bb3NgOSYJMTIVVKH\nQiFgduZMyAU5tnbvkHR4AZNpgiiKaO42Izk+BsnxMZLG4lve0tIdHnXTvmQ6i82HEeHqeUVQyGV4\nc0sLXG6eThOFEod7CP1DA2w+pBFalQbT0qag26pD+2CnZHEwmSb0mR0wW52Snkr7+Ja3NIVJ3XQP\nT6YjSnJ8DC6szkWfeQif1nVJHQ4RHUdvZ700fZlv5rSUjYhMpikkSjx8wm15i85ogyAA6UmsmY4U\nV84thEopw5tbW+F0hUe5EVE00Fs5Fo++bHLKJCSqErBTtwdOj0uSGJhMk6TLWk4Wbstbekx2pCXG\nQqngX6VIkaBR4ZKafAxYnPh4zxGpwyHyG6fHhZaBdmzq3IrnDr6Ejzs2Sx3SWTk2yYPNh3SMXCbH\nudmzYHc7UGfYJ0kMHIxLaO4yQyYIKMqSPpkGhuumdxzSw9BvR0YI1yLbh9wwW52YWpIidSjkZ5fN\nKcBHuzvx9rY2nD8jB7EqvlVSeHF6XDhi6UbHYCfaB4+gfbAT3VYdvOKxXgCZIMPc7BqoFbESRjp2\nevvwJA+eTNPJ5mbX4P22j7GteydqsmYG/f78FyLKuT1etOkGkZuuQYxKLnU4AIZLPXYc0qPpiDmk\nk2mdic2HkUqrVuLS2QV4fXMLPtzViavmFUkdEtFpjSVxVsoUKIzPQ358HgoS8tAy0IotXZ+jwdSE\nqvRzJIx+7HQ2A5QyBZJjk6QOhUJMZlw6ShKLUG9qRJ/dhFR1clDvz2Q6ynUaLHC5vSFRL+1Tmjsc\nS2PXAOZNzZI4mtNj82Fku6QmHx/s7MC729vxlZm5iItVSh0S0VklzgUJR5Pn+FxkxWVALjt2YJKu\nTsWWrs9x0Hg4LJJpURShtxmQrk6DTGBZHX3ZvOzZaB5oxWc9O3Fl8SVBvTeT6SgXSvXSPgUZ4bG8\nRWe0AxjenkeRJy5WgSvmFuLljU14f0cHrltYInVIFGWOT5zbBjvRMXjkFImzEoXx+ShIyD1t4nwq\nxQkFiJXH4KDxcKBfhl8MOM0Y8jhZ4kGnVZ0xDS81vI7t3TtxedFFQf2hi8l0lAulSR4+vuUtLV2D\nGHJ6Qqb85GTHFrZwkkekuqg6D+9/3o73d3Tgoll5iI/joggKjPEkzoXxeciMSz9j4nwqcpkck5LL\nUNe7H732PqSpU/35cvxOx0kedAaxilhUp1dhe89ONPY3Y1JyWdDuzWQ6yjV3maGOkSM7TSN1KCco\nzUlE0xEzWrrNqCwMbu3TWPUYbVDIZUhJCI/mHTp7MSo5rppXhPUfNuC9z9qx+CvBe3Om6PHXfc9h\nj+GL0ybOBfF5KJhA4nw6k1PKUde7HweNDViYG+LJtI0zpunM5uXMxvaendjatTOoyfQZz8Bra2ux\nfPnyEx578803ccstt4z8+sUXX8SNN96Im2++GRs3bgQAOBwOrFy5ErfeeivuvPNOGI1GAMDevXux\nZMkSLF26FOvWrRu5xrp167B48WLccsstqKur88drozOwOlzoMdpQlJUAmSBIHc4JQn15iyiK0Jls\nyExWh9yfHfnXBTNzkBwfgw93dWLAMiR1OBRhTI5+7NLXIkEVj/PzzsPyyUvw4Jx7sGbRo7i35ntY\nMuk6zM2uQY42y6+JNABUpkwCABwKg1IPLmyhsShNLEK6OhV7DXWwu+1Bu++oyfSf//xnPPTQQ3C5\njg3BPnDgAF555ZWRXxsMBjz77LN4/vnn8cwzz2DNmjVwOp1Yv349Kioq8Nxzz+G6667DU089BQBY\ntWoV1qxZg/Xr16Ourg4HDx7E/v37sWPHDrz00ktYu3YtHn300QC9XDqeb2V3KJV4+IT68hazzQX7\nkIfNh1FAqZDj6vOK4HR78fb2NqnDoQhzyNQIALi44PyAJs6nkq5ORWpsCupNjfB4Q3tBke9kmmUe\nNBpBEDA3ezZcXjd26WqDdt9Rk+nCwkKsW7duZHmGyWTC2rVr8cADD4w8VldXh+rqaiiVSmi1WhQW\nFqK+vh67d+/GokWLAAALFy7Etm3bYLFY4HK5kJ+fDwBYsGABtm7dit27d2P+/PkAgOzsbHg8HphM\npoC9aBoWivXSPr7lLY1HQnN5C+ulo8vCqmykJcZi454jMJodUodDEaTeOJxMVwTxI2kfQRAwOaUc\ndrcDbYOdQb//2dBbDYhXahGn5Hsuje7crGoIEIK6XnzUZPrSSy+FXD7807HX68WDDz6In/70p4iL\nO3YaZ7FYEB8fP/JrjUYDi8UCi8UCjUYz8tjg4CCsViu0Wu0Jzx0cHDztNSiwjiXTiRJHcmqlOYmw\n2F3Q9wfvo5qx8o3F44zp6KCQy/DV+cVwe0S8tbVV6nAoQoiiiMOmBsSrtMjWZEoSw+SjpR6hPNXD\n5XWjz2FiiQeNSXJsEianTEKruR3dVl1Q7jnmBsR9+/ahvb0dDz/8MJxOJxobG7F69Wqce+65sFqt\nI8+zWq2Ij4+HVqsdedxqtSIhIQEajeaE51osFiQkJECpVJ7yGqNJTo6DQhGaUx7CgSiKaO0ZREay\nGmVFodl4Mr0iY3gT4qATUydJ8w/N6Qw63ACAipI0pKeP/r16KuP5PSStr16gwX92tOPTum7ceuUU\nZKWGVtMuhZ/OgW4MOAexoGA2MjKk+YTwvMQZ+Mv+f6HJ3IT09BskieFMOga6IEJEYWrOuN87+Z4b\nXS6rXIgDW+tR21+LqqIbA36/MSfTVVVVeOuttwAAR44cwT333IP7778fBoMBa9euhdPpxNDQEJqa\nmjBp0iRUV1dj06ZNqKqqwqZNm1BTUwOtVgulUomOjg7k5eVhy5YtuPvuuyGXy/HYY49hxYoV6O7u\nhtfrRVLS6BuOTEe3z9H46PvtMFudqMjPgMEwKHU4p5SZGAMA2HNIh6kFobXxqrmzHwAQK8NZ//ml\np8eH7J85je7qeUV4+o39+Psb+7Di6ilSh0NhblvHcE1nYVyRpO8JRfEFaDC2oq1LH5JlFIf0rQCA\nBCFxXH9OfM+NPoWqEmgUcdjYvB2XZF/klx6E0X4gG1MyLZw0rUAUxZHH0tPTcfvtt2PZsmXwer24\n5557oFKpsHTpUtx3331YtmwZVCoV1qxZAwB45JFHcO+998Lj8WDBggWoqqoCANTU1ODmm2+G1+vF\nqlWrxvVCaeyaj07JCMV6aZ/CzHgo5LKQXN6iM9mhjlEgPo5b8aLJ7MkZeGtbK7bu78GV8wqRzdNp\nmoB6k3T10sebnFKOFnMbDvc3YUb6VEljORW9rRcAmw9p7JQyBWqyZuKTzi3Y33co4Fs+z5hM5+Xl\n4fnnnx/1scWLF2Px4sUnPCc2Nha/+93vvnS96dOn44UXXvjS43fffTfuvvvuMQdOExPKzYc+CrkM\nRVnxaO4yh9TyFq9XhN5kQ36G9ks/aFJkkwkCrltQgj+89gVe39yC71wbeokHhQeP14OG/iakqVOR\nqpZ2lv7k1Aq80/oBDhoPh2QyzUkeNB7zsmfjk84t2Na9M+DJNBfcR6mWLjPkMgGFmaFdR1aamwCv\nKI6M8QsFfWYH3B6RY/GiVPWkNBRmxuPzg3p06NkoTePTPngEdrdD8lNpACiMz4NaEYtDfaHZhKiz\nGSATZCG/pZFCS358DvK0OdjXdxBmZ2DLfJhMRyG3x4s2nQV56VqolKFx2ns6pTmht7xFx0keUU0Q\nBFy/qAQAsOHTZomjoXDlK/GoTCmXOJLh1eIVyWXodRhhsPVJHc6X6G0GpKlTgjJ7myLLvOzZ8Ipe\nfN6zO6D3YTIdhTr0Frg93pAu8fAJxeUtPSMzpplMR6tpJSkoy03EnobekPrUhMKHL5melFQqcSTD\nKkN0RJ7FaYXVbUOGmiUedPZqsmZAIcixrXtnQHdWMJmOQuFQL+0TistbdMbhuddZTKajliAIuH5h\nMQBgw6ctEkdD4cbpcaF5oBV52hxoVaHRxDo5RFeLs16aJkKr1GBa+jnoserQNtgRsPswmY5C4TDJ\n43ihtrxFd3QsY0Zy6I2QouCZXJSCyoIkfNHch7Yejt2isWseaIXb6w6JemmfNHUK0tWpqDc1hdRq\ncT2TaZqgedmzAQDbunYE7B5MpqNQc5cZ6hhF2JQpHCv1CI266R6jDYlaFdQxYx7TThHqwuo8AMCe\nBoPEkVA4GRmJFwL10sebnDIJDo8DrebAneCdLd/JNLcf0nhNTilHUkwidupq4fQ4A3IPJtNRxmJ3\nQWeyoyQ7HrIwGetWmjt8gh4KddMutxd9Aw42HxIAYEpRCuQyAXVNode0RaGr3tgIuSBHWVKx1KGc\n4FjddL3EkRwzcjKtYTJN4yMTZDg3axYcHgf2GvYF5h4BuSqFLF+zVPHRKRnhYGR5SwhM9ND32yGC\nzYc0LC5WgfK8RLT2DGLAGpgTD4osNpcN7YOdKEooQIxcJXU4J5iUXAqZIMNBY4PUoYzQ2QxQK2IR\nr9RKHQqFsbnZNQCAbd07A3J9JtNRJpyaD318y1s69VYMOaWt5RsZi8dkmo6qKk0DAHzB02kag8P9\nzRAhoiIldOqlfdSKWBQnFKDN3AGryyZ1OPB4PTDY+5ChTueCLJqQjLg0lCYW47CpEb12o9+vz2Q6\nyoRjMg2EzvIWXzKdyeZDOqqqdHiRRF1Tr8SRUDioNx6dL50cWvXSPpNTJkGEOFLXLSWjox8e0cN6\nafKLeTnDjYifBeB0msl0FBFFEc1dA0hLjEVCXGh9vHgmobK8hTOm6WTZqXFIS4zF/lYj3B6v1OFQ\niKs3NSJGrkJRQr7UoZxSZQiNyNPZ9AA4yYP8Y2b6NKjkKmzv2QWv6N/3aibTUURvssPqcIfdqTQQ\nOstbdEYbBAFIT+LJNA0TBAHTS9NgH/KgsVP6un4KXf1DA9DZ9ChLKgnZbX6FCXlQK9Q4aGyQfLY/\nmw/Jn2IVMZiVMR1GhwmHTU1+vTaT6ShyrMQjfJoPfZLjY5AaAstbekx2pCXGQqngXx06pqrMV+rB\numk6PV+JRyjNlz6ZTJChMrkMRocJeru0pUtc2EL+NjJzutu/M6eZEUSRcK2X9imReHmLzeGG2epk\niQd9SUV+ElQKGWpZN02j8NUhV4bYfOmTTQ6R1eK+ZDpdnSZpHBQ5ShILkRGXhlrDPthc/sslmExH\nkebuAchlAgozw3PEkNTLW3ybDzljmk6mUsoxuTAZ3X02GEJkUyeFFlEcburTKjXI1mRKHc6ofMm+\n1HXTepsByTFJUMmVksZBkUMQBMzLmg2X141d+r1+uy6T6SjhcnvQrrMgP0MLpSI0a/XOROrlLTo2\nH9IoqsqGT89Y6kGnorcZ0D80gIrkMsiE0P6nN1Wdgoy4NBw2NcHtdUsSg8PtwIBzkCUe5Hdzsqsh\nQMC2Lv9N9Qjtv9HkN+06CzxecWQqRjgaWd4i0cl0D2dM0yiqSlg3Tad3yBT69dLHm5xSgSGPEy0D\n7ZLcX28bLpli8yH5W1JMIqakVqBtsANdlh6/XJPJdJQI93pp4Njylg6DBQ5n8E9LdKbhj+8zUzjJ\ng74sNTEWeekaHGo3Ycgl7XIhCj2+eumKEK+X9pkscamHr16aM6YpEPzdiMhkOko0d4d/Mg0Ml3qI\nItDSPRj0e/cYbVDIZUhJiA36vSk8TCtNhcvtxcE2k9ShUAjxil4cNjUhNTYFaeoUqcMZk/KkUsgF\nuWSrxTnJgwJpWtpkaJRx+LxnNzzeiR9+MJmOEs1dA9DEKpAR5pv7fGUqzUFe3iKKInRGGzJT1JBx\nrS2dxnSuFqdT6Bg8ArvbHjYlHsDwTN6SxEK0D3bC4rIG/f6+GdMZaibT5H8KmQJzMqthcVmxr+/g\nhK/HZDoKmG1OGPodKM5JgBDmiaBUy1vMViccTg8nedCoSnMToIlVoK6pV/KFFxQ6RuZLp4RPMg0M\nb0MUIY7EH0w6mwFKmRLJseHb50OhbW52DQD/lHowmY4CLb566ezwLvEApFvewjXiNBZymQznFKeg\nzzyEI73BP82j0FQfZs2HPlLVTYuiCL3NgIy4tJCffELhKy8+B/nxudjfV4+BoYmVjvK7NAqE8+bD\nUynNDf7yFjYf0lhVlXKqBx3j8rjQNNCCXG024lXhNeM/Pz4XGmVc0FeL9w8NwOl1sfmQAm5e9mx4\nRS8+79k1oeswmY4CkdJ86OOrmw7miDwdx+LRGE0tSYUAJtM0rMXcBpfXHXan0oBvtXg5TEP9Iw2B\nwcDmQwqWmswZUAhybOveOaEfGJlMRzivKKKly4yMZDW06sjYIiVF3TTLPGisEuJUKMlJQGPnAKwO\nl9ThkMQOGcOzxMOnUoLV4nom0xQkGmUcpqdPhc6mR6t5/DPVmUxHOJ3RBtuQO2JOpQGgIFMb9OUt\nOpMdcTEKxEfIDyQUWFWlqfCKIva3GKUOhSRWb2qETJChLKlY6lDGxVc3Hcxk+tiM6bSg3ZOilz9m\nTjOZjnDNEdR86BPs5S1erwi9yYbMlLiwn4ZCwVF1dERebSNLPaKZ3W1Hm7kDRQkFiFWE53z65Ngk\nZMVloMHUBFeQVouzzIOCqSKlDEkxidilq8WQxzmuazCZjnDH6qUjo/nQJ5jLW/rMDrg9IrLYfEhj\nVJCpRaJWhS+a++DliLyo1WBqhggxbEs8fCanTILT60LLQGtQ7qe39SJepYVawfdcCjyZIMPc7Bo4\nPEPYq/9ifNfwc0wUYpq7zFDIBeRnhFcX+ZkEswnR13yYyRnTNEaCIKCqJBUWuwst3cGdiU6h41CY\njsQ7WeVIqUfgtyG6PC4YHSaeSlNQzc2a2MxpJtMRzOnyoFNvQUFmPJSKyPq/+lgTYuCTaTYf0nj4\nSj3qWOoRtepNjVDJlChOLJA6lAkpT/atFg983bTB3gcRIpNpCqr0uFSUJ5Wgob8Zvfazf8+OrAyL\nTtCus8DjFSOqXtrHt7ylqcsc8PmnOuPwjGmOxaOzMaUoGXKZwBF5UWpgyIweqw5lSSVQyBRShzMh\nMXIVShOL0DF4BINOS0Dvdaz5kMk0BZevEXF7986z/r1MpiNYc9fwqW0kTfI4XrCWt/SYhk+mM5JZ\nv0djp45RYFJ+Etp0g+i3DEkdDgXZyNbDMFshfjqTj47Iqw9wqQebD0kqMzKmIVYeg+3du+AVvWf1\ne5lMR7BIW9ZysmDVTeuMNiRqVVDHhPfpEgXf9KPbEL/g6XTUqQ/z+dInq0wNTt20nifTJJEYuQrV\nGdNhGuof+WF4rJhMR7DmLjO0aiXSkyLzRDUYy1tcbg/6BhzIYvMhjUNV2dG6aSbTUUUURdSbGqFR\nxiFXmy11OH6Rp82BVqnBQePhgJbW6W0GyAQZ0mJTAnYPotOZl3O0EbHr7BoRmUxHqAGrE70DDpTk\nJETsbOSCTC2UisAub9Gb7BDB5kMan8xkNTKS1NjfaoTbc3YfG1L4Mth7YRrqx6TkMsiEyPhnVibI\nUJlSjgGnGd1WXUDuIYoidDYD0tWpkMvkAbkH0WiKEwqRGZeO2t79sLlsY/59kfG3nL5kpF46ApsP\nfRRyGQoDvLylh82HNAGCIKCqNBUOpweHO/qlDoeCpD5CRuKdzFc3fShAUz0sLitsbjtLPEgygiBg\nXrbVWX4AACAASURBVPZsuL1u7NTtHfPvYzIdoUY2H0ZovbRPWU5iQJe36Ey+sXiRWSpDgVdVNlw3\nzVKP6HEowuqlfQI9b5prxCkUzMmqhkyQndXMaSbTEcqXTBdHeDJdmjv8+gJV6uGbMc2TaRqvivxk\nqJQyJtNRwit60WBqQnJMEtLVqVKH41dJMYnI0WShob8ZLo/L79fXc5IHhYDEmARMSalA++ARHLF0\nj+n3MJmOQF5RRGuPGZkpcdDEKqUOJ6BKAjzRQ2e0QRAQsU2cFHhKhQxTClPQY7RBbxp7DR6Fp05L\nF6xuGypSyiKyX6UypRwurwtNAVgtrrf1AgAy4zL8fm2iszEvZ3jm9FhPp5lMR6DuPhvsQ56Irpf2\nCfTyFp3JjvRENRRy/lWh8fOVetTydDri+UbiVSaXSxxJYByrm/Z/qQdnTFOomJpaCa1Sgx09e+D2\nnrknixlCBPI1H/pKICLdyPIWk3+Xt9gcbpitTk7yoAmrKuG86Wjhaz6cFGH10j5lScVQyBQBWS2u\nsxmgVqihVWr8fm2is6GQKTAnqxoWlxX7eg+e8flMpiNQS5Q0H/r4lrc0+rnUg82H5C8pCbHIz9Di\nUHs/hpweqcOhAHF53Wjsb0G2JhOJMfFShxMQKrkKZYnF6LR0wez0X+O3x+tBr70PmXHpEVkeQ+Fn\nbvbRmdNjKPVgMh2BmrvMUCpkyEvXSh1KUIwsb+ny7/IWHZsPyY+qSlPh9nhxoM0odSgUIK0DbXB5\nXRE3xeNkvqke/iz16HMY4RE9nORBISNXm42C+Dzs76tH/9Doh3VMpiPMkMuDToMVhZnxUVPn61ve\n0uznk2nfJA+WeZA/VJVyRF6k85V4+JLNSOWrm/ZnqQfrpSkUzcueDREiPu/ZPerzoiPbiiJtPYPw\nimLUlHgAgVveojtag81V4uQPpTmJ0MQqUNfUF9B1zCSdQ8ZGyAQZypJKpA4loHK0WYhXaf26Wtw3\nyYMLWyiU1GROh0KmOGOpB5PpCBMty1pOFojlLT1GG5QKGZITYvx2TYpeMpmAaSWpMA0OodNglToc\n8jO724G2wQ4UxudBrYiVOpyAkgkyVCZPwqDTgi5rj1+uyZNpCkVxyjjMSJ868sPe6TCZjjDRsEb8\nVPy9vEUUReiMNmQkqyFjMwz5ybFSj9HfmCn8NPY3wyt6I75e2mfyyDZE/5R66G0GCBCQrmbNNIUW\nXyPiaJhMR5jmbjMS4pRITYzsk5GTjTQh+imZNludcDg9LPEgv5pakgpB4LzpSOSrl66I8Hppn5HV\n4n3+SaZ1NgNSYv9/e3ca3XZ95n//I8mSF0nencVLTGJIwnRqIJhCS0I7tE1p/1AohSQmNQOTmXQ5\nyelpoQOcwuSGw6Gd0jSdU88NJR3umbppaNMN/i1toSxNBzI0JUMMZKFOgm3Jji1vsSQvkiXdD2w5\nC4njRdJPy/v1DMfLpZD88vHX1/e6CmWzpPeiMaSeZUUX6uryK6d8H8J0GhnwjapvcFRLygsybrRQ\noSNbJfk5MVvewuVDxIMj16qa8gIdcZ+Qbzj265hhnMN9LbKarVpcUG10KQlRkJ2vCsdCtZw4psAc\nV4sPj41oMOClXxpJyWwy67bln536fRJUCxIg2i+9OMP6paNqKvJjtrwlevmQGdOItdqaEkUi0lvH\nOJ1OF4MBrzr8x1VTcIGs5iyjy0mY5cUXaSw8piMDx+b0ebon+qUJ00hVhOk0kqmXD6NiubzlODOm\nESeMyEs/7/RFWzwyo186KlYj8rh8iFRHmE4jRztOyCRp8YIMDdMxXN7SRZsH4qRqnkNFzmy9dbRP\n4TAj8tLB5Hzposzol466sGCxrDFYLd5NmEaKI0yniXA4omPHvVpQkqe8nMz5MeOpostbYnEJ8Xjf\nkPKys+TM5TIMYstkGh+R5xsO6mhnbLd2IvEikYgO9bcoLytXlc5yo8tJKKvFqgsLl6jDf1wnRmf/\nZ5mTaaQ6wnSa6Oj1azQQytgWD+nk8haXx6fh0dkvbwmHI+ruH9b84ryMu8iJxLiEEXlpo2e4T30j\n/VpaVCOzKfP+SY22esxltXjXkEc2s1UF2Zn77xdSW+b9zU9TJ/ulCwyuxFjR5S3vzuHEr2dwRKFw\nRAu4fIg4ufiCImVZTGpuoW861R3uHw+RmTJf+kxz7ZsOR8LqHupRWV5pRn4zgvTAn9w0MRmmM2xZ\ny5kml7fMoW+afmnEW44tS8uqCtXW7VO/d9TocjAHmTZf+kwL7fNVYHPqUN9fFY6EZ/zxA6MnFAwH\nafFASiNMp4mjHYOyZZlVOc9udCmGisXyli4meSABamvGN729eZTT6VQVjoR1uL9FhdkFmpehm/tM\nJpOWFy+VN+iT2zfz1eLRNc2EaaQywnQaGAmMyd3jU/UCpyzmzP5fGovlLV19EzOm2X6IOKq9cLxv\nen8LfdOpyu07Ln9wSMuKLszo+xUn+6Zn3urRxYxppIHMTl5povW4V5FI5s6XPtNcl7cc74+2edAz\njfiZX5Sn+cV5OvBuv4JjM//xOIyX6f3SUZOrxecQpjmZRiojTKeBI1w+PE201WO2y1u6+oZU6LAp\nx5aZIwaROLVLSjQaDOmd9gGjS8EsnOyXzuww7bQ5VOUo15GBYxoNBWb0sWw/RDogTKcBLh+e7sI5\nLG8JjoXUe2KEfmkkRLTVg22IqWcsPKaW/qNakDdPhdkcZCwvXqqxSEgtA0dn9HFdQx7l25zKzcqJ\nU2VA/BGm08DRjhMqsNtUnJ9tdClJoWre7Je3dPcPKyImeSAxllUVKttmYd50Cnp3sF2BcDDjT6Wj\nZjMiLxAKqn9kgBYPpDzCdIrrGxzRgC+gJeX5GX0B5lRzWd5ynMuHSKAsi1nvu6BYXf3Dk1NkkBoO\n99EvfaolhRfIarbq4AyWt3iGexRRhBYPpDzCdIo7uayFFo9TzXZ5S1c/Y/GQWLUT2xD30+qRUg73\nt8gkky4qrDG6lKRgNWfpoqIlOu7vUv/I9O4AcPkQ6YIwneKOdnL58Gyiy1taZtg3fbyPSR5IrPcv\nYbV4qhkZG9WxwTYtyq9UnpVnRdRMV4t3E6aRJgjTKe5ox6BMki5Y4DS6lKQy2+UtXX1DMpmkskL+\ngURiFDmztWi+Q4fbBjQSmFlbEozRMnBU4UiYFo8zzLRv+uSM6cxceIP0cd4wvX//fjU0NEiSWlpa\nVF9fr/r6et13330KhUKSpIcfflg333yzGhoa1NDQIJ/Pp5GREW3evFnr16/Xxo0b1dfXJ0l64403\ntGbNGtXX16uxsXHy6zQ2NurWW2/VunXr1NzcHI/XmnZC4bDePT6o8jK7crMZ43aq6PKWozNc3tLV\nN6SyglxlWfg+E4lTW1OqUDiiA+/2G10KpmFyJB5h+jTRySaH+qe3WrxryCOLyaKSnOIEVAfEz5SJ\nYfv27br//vsVDAYlSdu2bdNdd92lnTt3SpJeeuklSdKBAwf05JNPqqmpSU1NTXI4HNq5c6eWLVum\nHTt26KabbtJjjz0mSdqyZYu2bt2qnTt3qrm5WQcPHtTbb7+tvXv3ateuXdq2bZseeuiheL7mtOH2\n+BUIhhmJdw4zXd4yNBLU4FCQSR5IuGjfNK0eqeFwf4uyzFlaUnCB0aUklfHV4hfJHxySy9sx5ftG\nIhF1D/WoNLdEFrMlQRUC8TFlmK6urlZjY+Pkyd73vvc91dXVKRAIyOPxyOl0KhwOq7W1VQ888IDq\n6+v185//XJK0b98+XXPNNZKkVatWac+ePfL5fAoGg6qqqpIkrVy5Uq+++qr27dunq6++WpK0cOFC\nhUIh9fdzQnM+J/ulCdNnM9PlLV0ToZt+aSTakoX5cuRa1Xykd0Y/SUHieQM+uX2dqim4QDaL1ehy\nks7fTLPVwxf0a3hsmH5ppIUpw/Tq1atlsZz8jtFsNsvtduuGG27QwMCAli1bpuHhYTU0NOjb3/62\nfvCDH+jHP/6xDh8+LJ/PJ4fDIUmy2+3yer3y+/2Tbzv17T6fT06n87S3+3y+WL/WtHOUzYdTmuny\nlujlQyZ5INHMZpPev6RYA76A2rt59iWzd2jxmNKyootkkum8YZpJHkgnM260raio0O9//3vt2rVL\n3/zmN/XII4+ooaFB2dnZys7O1lVXXaVDhw7J4XBMBmK/36/8/HzZ7Xb5/f7Jz+Xz+ZSfny+r1Xra\n2/1+/2nh+myKivKUlZXZPxpq6/Ypx2bRJRcvkMXMjOkzFRbZZcsyq7XLq7Ky81/Q9I64JUnLFpdM\n6/3nIt6fH6ln5WWV2vN2l1qOe3X535YbXQ7OofXdNknSVUsuUVkJf4/PVCanlhQt0tETrXIWWpVj\nPftmw+bB8UOOmvlVCXke8sxFPM0oTH/xi1/Uvffeq+rqatntdpnNZh07dkxf+cpX9Ktf/UqhUEiv\nv/66br75ZvX19Wn37t2qra3V7t27VVdXJ4fDIavVqvb2dlVWVuqVV17Rpk2bZLFY9Oijj2rDhg3q\n7OxUOBxWYWHhlLX092f2goPh0TG1H/dqaVWh+no5yTqX6gVOtbhPqM3Vf95Lmkdd461FOWaTPB5v\n3GoqK3PG9fMjNS0qzZPJJO1p7tC1lxCmk9X+jgPKzcqRM1TE3+NzuDC/Rkf6W7WnpVl/W3rxWd+n\npbtdkpQbcsT995FnLmJhqm/IphWmo5v1Nm7cqHvvvVdWq1V5eXl6+OGHVVpaqhtvvFFr1qxRVlaW\nPvOZz6impkYVFRW65557dNttt8lms2nr1q2SpAcffFB33323QqGQVq5cqdraWklSXV2d1q5dq3A4\nrC1btsz1Nae9dzsHFRH90udTU1Ggv7pO6N3OQV18wdQ3xrv6hmXNMquItewwgD3HqgsrCtTiOiHv\nUEDOPJvRJeEMPcN96hnp0yWl75PZxMSfc7m4+CL9vvVFHex755xhmhnTSCfnDdOVlZV66qmnJEmX\nXXbZ5CSPU23YsEEbNmw47W05OTn6t3/7t/e87yWXXKKf/OQn73n7pk2btGnTpmkXnum4fDg9NRP9\n5C0dU4fpSCSi4/1Dml+UKzNr2WGQ2poS/dV1Qm8d69MH37fA6HJwhsP948tIlhbTLz2VxQXVslls\nU64W7x7qUV5WrhxWewIrA+KDb61TFJcPpye6CfF8y1sG/QGNBkKMxYOhLqkZX17RzGrxpHS4b/zy\n4XIuH04py5ylpYU16hrqVt/IeydzhcIheYZ7NT+vbPIn30AqI0ynoEgkoqMdgypyZqvISUvCVKa7\nvIVJHkgGFWV2Fedn662jvQqFz7/0AokTjoR1uL9FBTan5ufNM7qcpDfVavGekT6FI2HNo8UDaYIw\nnYL6Bkd1wh9gWcs0RZe3dE2xvGVyxnQRYRrGMZlMql1SIv/ImI64pzfSEYnR6e+SL+jX0qKLOE2d\nhouLL5IkHTjLiLzuyTXihGmkB8J0CqJfemaiy1umavXgZBrJonai1ePNo7R6JJPDEyesy+mXnpZ5\neWUqyi7U4b73rhZnxjTSDWE6BR3tGA+FhOnpmc7ylq6JMM32Qxjt4uoiZVnM2t9CmE4mh1nWMiMm\nk0kXFy/V0Niw2ryu036ty0+YRnohTKegox2DMpnGZyjj/KrmOWTNMp/3ZNqekyVHLuuBYaxsm0XL\nqwvl8vjUNzhidDnQ+IW5vw4c1by8UhXlTL0DASddXDKxWrz39L7p7mGPTDKpLLfEiLKAmCNMp5ix\nUFitx72qKHUoxzbjBZYZKcti1gULnHJ5fBoeHXvPr4fDEXX3D2t+cR69kEgKtUvGQwZTPZLDu4Pt\nGg0FtKzoIqNLSSnLii4862rxriGPinOKZLVweIH0QJhOMW6PX4GxMC0eM1RTUaBIZHzZzZl6BkcU\nCke4fIikUXshI/KSSXS+NCPxZsZuzdOi/EodG2zV8Nj4T1mGx4blDfho8UBaIUynGC4fzs6py1vO\n1DV5+ZB+aSSHeYW5WliSpwOtfQqOhYwuJ+Md7m+RSSZdVFRjdCkp5+LipQpHwvpr/xFJJy8fzssr\nNbIsIKYI0ynmqJvLh7Nx4RTLW45PXj7kZBrJo7amRIFgWIfbBowuJaONhgI6dqJNVc5y2a08I2Yq\nOm86ug2Ry4dIR4TpFHO0c1DZNovKS1jBOhMFjmyVFpx9ecvkJA/aPJBEon3T+2n1MFTLwDGFIiH6\npWdpcf4i5ViydWiib5oZ00hHhOkUMjQSVGfvkBYvcMps5qLcTNVUFJx1eQtj8ZCMLqoqVI7NouYj\nPVNu70R8RfullzFfelYsZouWFl2o7uEe9Qz3qWu4RxIn00gvhOkUcqzTK0laMtH/i5mJtsac2epx\nvG9YhQ4b01GQVLIsZr1vcbE8AyOTrUhIvHf6WpRlsqim4AKjS0lZ0W2Ih/reUfeQRzaLTYXZ/DuG\n9EGYTiEsa5mbC8+yCTEQDKlvcITNh0hKtTWMyDOSL+CXy9epxQXVsllsRpeTspZP9E0f6HtH3UM9\nmp9byhhSpBXCdAo52sEkj7mILm9pcZ+c6NE9MKyIuHyI5MS8aWO9M3BEEUXol56jstwSleQU662e\ngwqGg/RLI+0QplNEJBLR0c5BleRnq9CRbXQ5KSm6vMXdc3J5C5cPkcwKHNmqXuDUO+0DZ104hPg6\nPDGBYjn90nMyvlr8IoUi42MeCdNIN4TpFNFzYkTeoaAW0y89J2cubzk+OWOaMI3kdElNiULhiA68\n22d0KRnncH+Lciw5WuSsNLqUlBcdkSdx+RDphzCdIiZbPBbS4jEXZy5viU72YJIHklVtzfhyC0bk\nJVbvcL88w726qGixLGaL0eWkvKUTq8UlwjTSD2E6RdAvHRtnLm/p6huS2WRSWSFhGsnpgoVOOfOs\nevNIr8KMyEuYw/0tkkS/dIzkWXO1pKBaWSYL2w+RdpgFliKOdp6Q2WRS9QKn0aWktDOXt3T1Dam0\nMEdZFr6vRHIym0x6/5ISvfrWcbV3+XgGJMjkfOki+qVj5fa/WaeB0RPKycoxuhQgpkgQKWAsFFbr\ncZ8q59mVbeXHjXMVXd7y7nGvBoeC9Esj6UVH5O0/0mNwJZkhEononf4jyrc5tdA+3+hy0kZpbrEu\nLFxsdBlAzBGmU0B7t09joTDLWmKkZqJV5pU3OyUxyQPJ728XF8tsMjEiL0E6/V0aDHi1tKiGecgA\nzoswnQK4fBhbNRPLW1470CVJWsDlQyS5vByrLqws0LGOQQ0OBYwuJ+3RLw1gJgjTKYDLh7FVNc8h\nW5ZZ/pHxub0sbEEquKSmRBFJb3I6HXf0SwOYCcJ0CjjaOajc7CwtKCH0xUJ0eUsUPdNIBdG+6TeP\nEqbjKRQO6a/9x8a39uUWGV0OgBRAmE5y/pGguvqGtHihU2Z692JmyUSrhy3LrEInGyWR/MpL7SrJ\nz9FbR/sUCoeNLidttXldGgmNcCoNYNoI00nuGC0ecRFd3jKvKI9vUpASTCaTai8s0dDomFpcJ4wu\nJ20d6pvoly6mXxrA9BCmk9zJy4dM8oiliyoLlGUxq3q+w+hSgGmrXTLe6sFUj/iJ9ksvLawxuBIA\nqYIwneSOdnIyHQ/5dpv+nzuv0LqPcfqE1LG8ukjWLLOa6ZuOi0AoqGMnWlXlKJfDZje6HAApgjCd\nxCKRiI52DKq0IEf5dpvR5aSd8lK77DlWo8sApi3batHF1UVye/zqPTFidDlpx+Xr0FgkpCUsFgEw\nA4TpJOYZGJZvOMipNIBJK5aWSZK6+ocMriT9tA62S5KqnZUGVwIglWQZXQDOjWUtAM60snahquY5\nThvviNho87okSdX5VQZXAiCVEKaTWFu3T5JUzT+aACaYTSYt5hvsuGgddCnHkq15eaVGlwIghdDm\nkcTcHr8kqaKMiRMAEE/DYyPqHvKoylkhs4l/GgFMH0+MJOby+FTosMmRyyU5AIindq9LEUVo8QAw\nY4TpJOUfCarfO6pKTqUBIO5aB+mXBjA7hOkkFW3xIEwDQPy1Tlw+XMQkDwAzRJhOUm7P+OXDijIW\nBwBAvLUNtstuzVNJTpHRpQBIMYTpJOXiZBoAEsIb8Kl3pF/VziqZTCajywGQYgjTScrl8clkkhaW\n5BldCgCktTavW5JUnU+LB4CZI0wnoUgkIrfHr/lFebJZLUaXAwBprW1i8yH90gBmgzCdhPq9oxoa\nHaNfGgASoNU7sUacSR4AZoEwnYTolwaAxIhEImoddKkwu0AF2WyWBDBzhOkkFJ3kUcnJNADE1YnA\noAYDXlXT4gFglgjTSYiTaQBIjNZovzSXDwHMEmE6Cbk9PtmyzCorzDW6FABIa5ObD530SwOYHcJ0\nkgmFw+roHdLCUrvMZuadAkA8cTINYK4I00mmu39YY6Ew/dIAEGeRSERtXpdKc0tktzLTH8DsEKaT\nDP3SAJAYPcN9Ghob5vIhgDkhTCcZV/f4JA9mTANAfEXnS9PiAWAuCNNJxjU5Fo+TaQCIp2i/NJcP\nAcwFYTrJuHv8cuRaVWC3GV0KAKS11kGXTDKpyllhdCkAUhhhOomMBkLy9A+rotQuk4lJHgAQL+FI\nWO0+txbY5yknK9vocgCkMMJ0Euno9SsiWjwAIN6O+7sVCAW0iMuHAOaIMJ1Eov3SFfO4fAgA8dTq\nnVjWkk+/NIC5IUwnETdj8QAgIdqilw+Z5AFgjgjTSWTyZLqUk2kAiKdWr0sWk0UVjnKjSwGQ4gjT\nScTl8askP0e52VlGlwIAaWssPCa3t0PljgWymnneApgbwnSSGBwKaNAfYI04AMRZh++4xiIhNh8C\niAnCdJKI9ktX0C8NAHEV3XzI5UMAsUCYThInNx9yMg0A8dQ2yCQPALFDmE4STPIAgMRo9bpkNVu1\nIG+e0aUASAOE6STh9vhkMZu0oCTP6FIAIG0FQgF1+rtU5SyXxWwxuhwAaYAwnQTCkYhcPX4tKM5T\nloX/JQAQL+3eDoUjYVU7afEAEBsktyTQe2JEo4GQKuiXBoC4apvYfLiIZS0AYoQwnQTolwaAxGiN\nbj5kLB6AGCFMJ4HJzYecTANAXLV625VjyVFZXqnRpQBIE+cN0/v371dDQ4MkqaWlRfX19aqvr9d9\n992nUCgkSfrpT3+qz372s1q7dq1efvllSdLIyIg2b96s9evXa+PGjerr65MkvfHGG1qzZo3q6+vV\n2Ng4+XUaGxt16623at26dWpubo7160xqJ8ficTINAPEyFBxW91CPFuVXymziLAlAbEz5NNm+fbvu\nv/9+BYNBSdK2bdt01113aefOnZKkl156SR6PR01NTXrqqaf0H//xH9q6dasCgYB27typZcuWaceO\nHbrpppv02GOPSZK2bNmirVu3aufOnWpubtbBgwf19ttva+/evdq1a5e2bdumhx56KM4vO7m4PX5l\n2ywqKcgxuhQASFvtXrckWjwAxNaUYbq6ulqNjY2KRCKSpO9973uqq6tTIBCQx+OR0+lUc3OzVqxY\nIavVKofDoerqah0+fFj79u3TNddcI0latWqV9uzZI5/Pp2AwqKqq8VvUK1eu1Kuvvqp9+/bp6quv\nliQtXLhQoVBI/f398XzdSWMsFNbxviFVltplNpmMLgcA0habDwHEw5RhevXq1bJYTs7hNJvNcrvd\nuv766zUwMKBly5bJ7/fL6XROvo/dbpfP55PP55Pdbp98m9frld/vl8PhOO19vV6vfD7fWT9HJjje\nO6RQOEK/NADEWevE5sNFnEwDiKGsmX5ARUWFnnvuOe3atUvf/OY3tXr1avn9/slfj4Zrh8Mx+Xa/\n36/8/HzZ7fbT3tfn8yk/P19Wq/Wsn2MqRUV5yspK/YH7b7efkCQtW1yisrKpXzPSB/+vgcRz+d3K\nz3ZoWVWVTPwkMKPwzEU8zShMf/GLX9S9996r6upq2e12mc1m1dbWatu2bQoEAhodHdWRI0e0dOlS\nrVixQrt371Ztba12796turo6ORwOWa1Wtbe3q7KyUq+88oo2bdoki8WiRx99VBs2bFBnZ6fC4bAK\nCwunrKW/f2hOLzxZHDraI0kqzLXK4/EaXA0SoazMyf9rIMG8AZ96hvr0vpLl6unJjJ98YhzPXMTC\nVN+QTStMR7+D37hxo+69915ZrVbl5eXp4YcfVmlpqW6//XbddtttCofD+upXvyqbzab6+nrdc889\nuu2222Sz2bR161ZJ0oMPPqi7775boVBIK1euVG1trSSprq5Oa9euVTgc1pYtW+b6mlOGq5uxeAAQ\nb8yXBhAvpkj0dmGKSZfvMr/2/76qYCis725eaXQpSBBOSYDE+82x5/Xssef1hdo79P7SvzG6HCQQ\nz1zEwlQn0wzaNNDw6Jh6B0dUyak0AMRV2yCTPADEB2HaQO4e1ogDQLxFIhG1el0qyi5Uvo2LaABi\nizBtoMk14qWcTANAvAyMnpA34FN1Pv3SAGKPMG0gd/fEyfQ8TqYBIF6ilw+ZLw0gHgjTBnL3+GSS\nVM7JNADETat3fFkL/dIA4oEwbZBIJCKXx6+yolxlW1N/+QwAJKs2Nh8CiCPCtEFO+APyDQfplwaA\nOIpePpyXW6o8a67R5QBIQ4Rpg0QvHzLJAwDixzPco+GxYS3i8iGAOCFMG8Tt4fIhAMRb60SLB5sP\nAcQLYdogjMUDgPhr9U5M8uDyIYA4IUwbxOXxK8ti1vxievgAIF7aBl0yyaQqZ4XRpQBIU4RpA4TD\nEXX2+FVekieLmf8FABAPoXBI7V63FtrnK9tiM7ocAGmKJGcAz8CwAmNhVXD5EADi5vhQtwLhIJcP\nAcQVYdoAJyd50C8NAPFy8vIh/dIA4ocwbQDXxCQPTqYBIH7aJjcfcjINIH4I0wZwczINAHHXOtiu\nLJNF5Y6FRpcCII0Rpg3g8viVm52lIme20aUAQFoKhsfk9nWq3LFQVnOW0eUASGOE6QQLBEPq6h9S\nZZldJpPJ6HIAIC11+DoVioRUzXxpAHFGmE6wzt4hRSKsEQeAeGLzIYBEIUwnGJM8ACD+opsPXt03\nOwAAHaVJREFUOZkGEG+E6QRzM8kDAOKubdAlm9mq+XllRpcCIM0RphMsejJdwck0AMTFaCigTn+X\nqpwVspgtRpcDIM0RphPM3eNXkTNb9hyr0aUAQFpq97oVUYQWDwAJQZhOIP9IUP3eUU6lASCO2gYn\n+qW5fAggAQjTCeTqjl4+pF8aAOKldWLz4SI2HwJIAMJ0AkXXiDPJAwDip3WwXblZuSrLLTW6FAAZ\ngDCdQO6eaJjmZBoA4mEoOCTPcK+qnZUsxgKQEITpBHJ5fDKbTFpYkmd0KQCQltq8bkm0eABIHMJ0\ngkQiEbk9fs0vzpU1i1FNABAPrVw+BJBghOkE6feOanh0jGUtABBH0cuHjMUDkCiE6QSZXCNeyuVD\nAIiXtkGXnDaHCrMLjC4FQIYgTCeIizXiABBXgwGv+kcHVO2s4vIhgIQhTCfI5Mn0PE6mASAeov3S\nXD4EkEiE6QRxe/yyZZlVVphrdCkAkJZaByf6pbl8CCCBCNMJMBYKq7PXr/JSu8z86BEA4qKNy4cA\nDECYToCu/mGNhSIsawGAOIlEImodbFdxTpGcNp61ABKHMJ0A7mi/NGvEASAu+kYG5Av6afEAkHCE\n6QSYnOQxj9MSAIiHVi+XDwEYgzCdAG5mTANAXLVNXj6kXxpAYhGmE8Dl8cmRa1W+3WZ0KQAwK2Ph\nMaNLmFJ08+Gi/AqDKwGQaQjTcTYaCMkzMKLKMjtLBACkpK4hj77yx/v1SsdrRpdyVuFIWG2DLs3P\nK1NuFuNHASQWYTrO3D1sPgSQ2hxWu2xmm5458jsNj40YXc57eIZ6NBIa0SIuHwIwAGE6zlxM8gCQ\n4uzWPH1s0YflC/r1Qttuo8t5j1bmSwMwEGE6zk6GaU6mAaSuaxetktPm0AvtuzUY8BpdzmkmLx8y\nyQOAAQjTceaeGItXziQPACks22LTpy74uAKhgH577AWjyzlNq7ddZpNZlY5yo0sBkIEI03Hm9vhU\nWpCj3Owso0sBgDm5uvwDKsst0X93/I88Q71GlyNJCoVDavd2aKF9vmwWJiYBSDzCdBwN+gMaHArS\n4gEgLVjMFt2w5BMKR8L69bHfG12OJKnT36VgOMjmQwCGIUzHUXRZSwWXDwGkicvm1arKWaG/dL2h\ndq/b6HLUNjlfmsuHAIxBmI6jyTXihGkAacJsMuvGmk9Kkp4+8luDq5F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"text/plain": [ "<matplotlib.figure.Figure at 0x115a6be50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df['forecast'] = results.predict(start = 114, end = 125, dynamic= True) \n", "df[['riders', 'forecast']].ix[-24:].plot(figsize=(12, 8)) \n", "plt.savefig('ts_predict_future.png', bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/seanwilson/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:31: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n" ] } ], "source": [ "## A little extra I was doing to try and include an exogenous variable for number of weekdays in each month.\n", "## Thinking that people take public transportation more during the week, a count of weekdays in each month could help explain some of the variance. \n", "\n", "start = datetime.datetime.strptime(\"1973-01-01\", \"%Y-%m-%d\")\n", "moving = start\n", "d = {}\n", "year =0\n", "month =0\n", "while moving < datetime.datetime(1982,7,1):\n", "# print moving\n", " if moving.year == year:\n", " if moving.month == month:\n", " if moving.weekday() < 5:\n", " d[str(moving.year)+\"-\"+ str(moving.month)] += 1\n", " else:\n", " d[str(moving.year)+\"-\"+ str(moving.month)]=0\n", " if moving.weekday() < 5:\n", " d[str(moving.year)+\"-\"+ str(moving.month)] += 1\n", " else:\n", "# d[moving.year] = {}\n", " d[str(moving.year)+\"-\"+ str(moving.month)]=0\n", " if moving.weekday() < 5:\n", " d[str(moving.year)+\"-\"+ str(moving.month)] += 1\n", "\n", "\n", " year = moving.year\n", " month = moving.month\n", " moving += datetime.timedelta(days=1)\n", "df_dow = pd.DataFrame(d.items(), columns=['Month', 'DateValue'])\n", "df_dow.Month = pd.to_datetime(df_dow.Month)\n", "df_dow.sort('Month', inplace=True)\n", "\n", "def holiday_adj(x):\n", " if x['Month'].month==1:\n", " x['DateValue'] -=1\n", " return x['DateValue'] \n", " elif x['Month'].month==2:\n", " x['DateValue'] -=1\n", " return x['DateValue']\n", " elif x['Month'].month==5:\n", " x['DateValue'] -=1\n", " return x['DateValue']\n", " elif x['Month'].month==7:\n", " x['DateValue'] -=1\n", " return x['DateValue']\n", " elif x['Month'].month==9:\n", " x['DateValue'] -=1\n", " return x['DateValue']\n", " elif x['Month'].month==10:\n", " x['DateValue'] -=1\n", " return x['DateValue'] \n", " elif x['Month'].month==11:\n", " x['DateValue'] -=3 \n", " return x['DateValue']\n", " elif x['Month'].month==12:\n", " x['DateValue'] -=2\n", " return x['DateValue']\n", "\n", " else:\n", " return x['DateValue']\n", " \n", "df_dow['days'] = df_dow.apply(holiday_adj, axis=1)\n", "df_dow.set_index('Month', inplace=True)\n", "df_dow.index.name = None" ] } ], "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 }	apache-2.0
google-research/google-research	scrna_benchmark/Generate Tabula Muris.ipynb	1	1061512	null	apache-2.0
JShadowMan/package	python/course/ch02-syntax-and-container/常用容器.ipynb	1	17760	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Python中的常用容器\n", "\n", "Python中的容器类型包括`序列类型`, `集合类型`, `映射类型`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 序列类型\n", "\n", "Python中有以下三种基本序列类型, 所谓序列(`Sequence`)即表示容器内的元素是有顺序的.\n", " * `tuple`(元组): 元组是不可变序列，通常用于储存异构数据的多项集, 当然也可以被用于储存同构数据.\n", " * `list`(列表): 列表是可变序列，通常用于存放同类项目的集合.\n", " * `range`: range对象表示不可变的数字序列，通常用于在 for 循环中循环指定的次数" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### `tuple`元组\n", "\n", "元组是不可变序列，通常用于储存异构数据的多项集(例如映射类型`dict`的`items()`方法的返回值)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 空元祖可以直接使用 `()` 进行声明, 也可以使用tuple()进行声明\n", "(), type(()), tuple(), type(tuple())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 单个元素的元组使用`()`的方式声明必须在元素之后增加`,`\n", "print( (1), (1,) )" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "t1 = (1, 2.33, (\"tuple\",), [\"hello\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "元组中为不可变序列, 具体体现为元组元素__指向__的内存地址不能变, 但是如果元素为可变类型, 那元素的内容可变." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "t1[0] = 2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "t1[1] = 6.66" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "t1[2] = (\"variadic\",)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "t1[3].append(\"world\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(t1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### list列表\n", "\n", "列表是可变序列，通常用于存放同类项目的集合, 通常用于储存相同类型的数据.当然也可以储存不同类型的数据." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 与tuple相同, 可以直接使用 [] 或者 list() 来声明一个空数组\n", "print( [], type([]), list(), type(list()) )" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 与tuple不同的是, 在声明单个元素的时候不需要在元素之后多写一个 ," ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "[1], type([1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "list1 = [1, 2.33, (\"tuple\",), [\"hello\"]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "另一个与`tuple`不同的是, `list`中元素指向的内存地址是可变的. 这就意味着我们能修改元素的指向" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "list1[0] = 2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 这里同时修改了类型\n", "list1[1] = \"6.66\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 思考是否可以这样修改, 为什么?\n", "list1[2][0] = \"variadic\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 思考是否可以这样修改, 为什么?\n", "list1[2] = \"variadic\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "list1[3].append(\"world\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(list1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "比较常用的是, 我们可以使用`sort`方法来排序列表中的字段" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "list2 = [1, 2, 0, -1, 9, 7, 6, 5]\n", "list2.sort()\n", "list2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### range对象\n", "\n", "对于range对象, 我们之前有简单的介绍过, 这里我们复习一下.\n", "\n", "range类型表示不可变的数字序列，一般用于在 for 循环中循环指定的次数。\n", "\n", "range 类型相比常规 list 或 tuple 的优势在于一个 range 对象总是占用固定数量的（较小）内存.\n", "不论其所表示的范围有多大（因为它只保存了 start, stop 和 step 值，并会根据需要计算具体单项或子范围的值）" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 只传递一个参数的话, 表示从`0`开始到`X`的序列\n", "list(range(10))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 如果传递2个参数, 表示从`i`到`j`之间的序列\n", "list(range(2, 8))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 如果传递了3个参数, 表示从`i`到`j`步长为`k`的序列\n", "list(range(0, 10, 2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> 扩展阅读, 其实range的简单实现其实看做是一个生成器, 当然实际的range不是一个生成器这么简单" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def my_range(stop: int):\n", " start = 0\n", " while start != stop:\n", " yield start\n", " start += 1\n", "list(my_range(10))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 容器中的基本操作" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 判断元素是否存在于一个容器中\n", "\n", "使用`in`或者`not in`来判断一个元素是否存在于一个容器中" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "1 in [1, 2, 3], 2 not in [1, 2, 3]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 容器拼接\n", "\n", "在Python中我们可以非常简单的使用`+`进行容器的拼接." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "[1, 2, 3] + [4, 5, 6]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(1, 2, 3) + (4, 5, 6)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "[1, 2, 3] + list(range(4, 10))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 重复容器内元素\n", "\n", "在Python中可以将一个容器 `` 一个整数, 可以得到这个容器重复 X 次的结果." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "[[]] 3, [[1, [2, ]]] * 3" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "list(range(5)) * 3" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### 其余操作\n", "\n", "* 使用`len()`获取容器的长度\n", "* 使用`min()`获取容器中的最小项\n", "* 使用`max()`获取容器中的最大项\n", "* 使用`s.count(x)`获取容器`s`中`x`出现的次数" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "len([]), len([()]), len(([], [], [])), len(range(10))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "min([1, 2, 3, 4, -1]), max([1, 2, 3, 4, -1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import random\n", "\n", "list3 = []\n", "for _ in range(1000):\n", " list3.append(random.randint(0, 10))\n", "print(list3.count(6))" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## 容器切片(重点)\n", "\n", "\n", "在Python中, 切片是非常好用且非常常用的一个特性.\n", "\n", "* 使用`[i]`获取第`i`项数据(从0开始)\n", "* 使用`[i:j]`获取从`i`到`j`的切片(左闭右开)\n", "* 使用`[i:j:k]`获取`i`到`j`步长为`k`的切片" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "list4 = list(range(10)); print(list4)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# python中支持使用负数作为索引, 表示从尾部开始取, 注意: 负数的起始值为 -1\n", "list4[-1], list4[-2], list4[-3]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 获取一个切片, 从第2个元素到底6个元素\n", "list4[2:6]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 如果不写 i 表示从头部开始取\n", "list4[:5]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 如果不写 j 表示取到尾部为止\n", "list4[5:]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 思考: 如果 i 和 j 都不写, 那会打印什么?\n", "list4[:]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 第三个参数为步长参数, 表示每次隔几个元素取一次值\n", "list4[1:8:2]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 思考1: 如何反转一个容器?\n", "\n", "# 思考2: 如下打印什么\n", "list4[::]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 集合类型\n", "\n", "Python中的集合类型有`set`和`frozenset`(较少使用, 表示创建之后不可修改, 可以简单看做是`tuple`版本的`set`).\n", "\n", "`set`对象和`frozenset`对象是由具有唯一性的 hashable 对象所组成的__无序__多项集.\n", "\n", "常见的用途包括成员检测、从序列中去除重复项以及数学中的集合类计算，例如交集、并集、差集与对称差集等等。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "set1 = set([1, 2, 3, 1, 4, 5, 5, 2])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "set1.update([6, 7, 2, 3]); print(set1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fset1 = frozenset([1, 2, 3, 1, 4, 5, 5, 2])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# 可以使用 dir 来打印一个对象所包含的所有内容\n", "print(dir(fset1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 映射类型\n", "\n", "在Python中仅有一种映射类型, 即`dict`. 即javascript中的对象, php中的命名数组. 映射属于无序可变对象.\n", "\n", "字典的键几乎可以是任何值。非 hashable 的值，即包含列表、字典或其他可变类型的值（此类对象基于值而非对象标识进行比较）不可用作键。数字类型用作键时遵循数字比较的一般规则：如果两个数值相等 (例如 1 和 1.0) 则两者可以被用来索引同一字典条目。（但是请注意，由于计算机对于浮点数存储的只是近似值，因此将其用作字典键是不明智的。）" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dict1 = {\n", " \"key1\": \"value1\",\n", " 123: 456,\n", " 123.0: 789,\n", " (\"k\", \"e\", \"y\"): (\"k\", \"e\", \"y\")\n", "}\n", "print(dict1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 如果获取字典中不存在的键, 则会发生KeyError错误\n", "dict1[\"key2\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 为了避免这个问题我们可以使用get方法获取字典中的值, 并自定义获取不到的时候返回的默认值\n", "dict1.get(\"key2\", \"default value\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 这里我们可以更近一步, 使用setdefault方法来当获取不到指定键的值的时候自动新增一个默认值, 并且返回默认值\n", "print(dict1.setdefault(\"key2\", \"default value and set\"))\n", "print(dict1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 但是可以给字典中不存在的键赋值\n", "dict1[\"key3\"] = \"value3\"\n", "print(dict1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 在字典上, 我们也可以使用`in`和`not in`判断一个键是否存在于一个字典中\n", "\"key1\" in dict1, \"key2\" not in dict1" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## 常用的原生扩展容器\n", "\n", "Python中的常用原生扩展容易位于包`collections`中\n", "\n", "* `namedtuple`命名元组: 简易的纯属性类声明方式, 实际上是一个元组\n", "* `deque`双向链表: 用于解决需要频繁插入删除的业务下原生`list`效率太差的问题\n", "* `OrderedDict`有序字典: 用于解决原生`dict`无序的问题" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### namedtuple命名元组" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from collections import namedtuple\n", "\n", "Pointer = namedtuple('Pointer', 'x y')\n", "Coordinate = namedtuple('Coordinate', 'x, y, z')\n", "\n", "start, end = Pointer(0, 0), Pointer(9, 9)\n", "coord1, coord2 =Coordinate(0, 0, 0), Coordinate(9, 9, 9)\n", "\n", "print(start, end, coord1, coord2)\n", "print(start.x, start.y)\n", "print(coord2.x, coord2.y, coord2.z)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### deque双向链表" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from collections import deque\n", "\n", "print(dir(deque()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### OrderedDict有序字典" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from collections import OrderedDict\n", "\n", "od = OrderedDict()\n", "od[\"k1\"] = 123\n", "od[\"k2\"] = 123\n", "od[\"k3\"] = 123\n", "\n", "for k, v in od.items():\n", " print(k, v)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
ledeprogram/algorithms	class5/donow/Kromrei_Georgia_05_donow.ipynb	1	15081	{ "cells": [ { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import pg8000\n", "conn = pg8000.connect(user='dot_student', host='training.c1erymiua9dx.us-east-1.rds.amazonaws.com', port=5432, database='training', password='qgis')\n", "import dateutil.parser" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "conn.rollback()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pg8000.core.Cursor'>\n" ] } ], "source": [ "cursor = conn.cursor()\n", "print(type(cursor))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cursor.execute(\"select * from noise_311\")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = pd.read_sql(\"select * from noise_311\", conn)" ] }, { "cell_type": "code", "execution_count": 18, "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>b'unique_key'</th>\n", " <th>b'created_date'</th>\n", " <th>b'closed_date'</th>\n", " <th>b'agency'</th>\n", " <th>b'agency_name'</th>\n", " <th>b'complaint_type'</th>\n", " <th>b'descriptor'</th>\n", " <th>b'location_type'</th>\n", " <th>b'incident_zip'</th>\n", " <th>b'incident_address'</th>\n", " <th>...</th>\n", " <th>b'bridge_highway_name'</th>\n", " <th>b'bridge_highway_direction'</th>\n", " <th>b'road_ramp'</th>\n", " <th>b'bridge_highway_segment'</th>\n", " <th>b'garage_lot_name'</th>\n", " <th>b'ferry_direction'</th>\n", " <th>b'ferry_terminal_name'</th>\n", " <th>b'latitude'</th>\n", " <th>b'longitude'</th>\n", " <th>b'location'</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>28792167</td>\n", " <td>2014-08-31 23:59:00</td>\n", " <td>2014-09-01 03:52:00</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Noise - Street/Sidewalk</td>\n", " <td>Loud Music/Party</td>\n", " <td>Street/Sidewalk</td>\n", " <td>11222</td>\n", " <td>200 KINGSLAND AVENUE</td>\n", " <td>...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>40.723888</td>\n", " <td>-73.941349</td>\n", " <td>(40.723888303549415, -73.94134888943505)</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>28789088</td>\n", " <td>2014-08-31 23:56:00</td>\n", " <td>2014-09-01 06:17:00</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Noise - Vehicle</td>\n", " <td>Car/Truck Music</td>\n", " <td>Street/Sidewalk</td>\n", " <td>11234</td>\n", " <td>FLATLANDS AVENUE</td>\n", " <td>...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>40.619489</td>\n", " <td>-73.938051</td>\n", " <td>(40.61948901090983, -73.93805104516916)</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>28791854</td>\n", " <td>2014-08-31 23:54:00</td>\n", " <td>2014-09-01 01:29:00</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Noise - Commercial</td>\n", " <td>Loud Music/Party</td>\n", " <td>Club/Bar/Restaurant</td>\n", " <td>10002</td>\n", " <td>161 LUDLOW STREET</td>\n", " <td>...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>40.721410</td>\n", " <td>-73.987694</td>\n", " <td>(40.72141034382407, -73.98769444021134)</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>28789936</td>\n", " <td>2014-08-31 23:52:00</td>\n", " <td>2014-09-01 02:53:00</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Noise - Street/Sidewalk</td>\n", " <td>Loud Music/Party</td>\n", " <td>Street/Sidewalk</td>\n", " <td>10033</td>\n", " <td>624 WEST 182 STREET</td>\n", " <td>...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>40.850167</td>\n", " <td>-73.933972</td>\n", " <td>(40.85016671877659, -73.93397220795968)</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>28789931</td>\n", " <td>2014-08-31 23:47:00</td>\n", " <td>2014-09-01 01:06:00</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Noise - Street/Sidewalk</td>\n", " <td>Loud Music/Party</td>\n", " <td>Street/Sidewalk</td>\n", " <td>11217</td>\n", " <td>525 DEAN STREET</td>\n", " <td>...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>40.681208</td>\n", " <td>-73.972775</td>\n", " <td>(40.68120794066068, -73.97277535440028)</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 53 columns</p>\n", "</div>" ], "text/plain": [ " b'unique_key' b'created_date' b'closed_date' b'agency' \\\n", "0 28792167 2014-08-31 23:59:00 2014-09-01 03:52:00 NYPD \n", "1 28789088 2014-08-31 23:56:00 2014-09-01 06:17:00 NYPD \n", "2 28791854 2014-08-31 23:54:00 2014-09-01 01:29:00 NYPD \n", "3 28789936 2014-08-31 23:52:00 2014-09-01 02:53:00 NYPD \n", "4 28789931 2014-08-31 23:47:00 2014-09-01 01:06:00 NYPD \n", "\n", " b'agency_name' b'complaint_type' b'descriptor' \\\n", "0 New York City Police Department Noise - Street/Sidewalk Loud Music/Party \n", "1 New York City Police Department Noise - Vehicle Car/Truck Music \n", "2 New York City Police Department Noise - Commercial Loud Music/Party \n", "3 New York City Police Department Noise - Street/Sidewalk Loud Music/Party \n", "4 New York City Police Department Noise - Street/Sidewalk Loud Music/Party \n", "\n", " b'location_type' b'incident_zip' b'incident_address' \\\n", "0 Street/Sidewalk 11222 200 KINGSLAND AVENUE \n", "1 Street/Sidewalk 11234 FLATLANDS AVENUE \n", "2 Club/Bar/Restaurant 10002 161 LUDLOW STREET \n", "3 Street/Sidewalk 10033 624 WEST 182 STREET \n", "4 Street/Sidewalk 11217 525 DEAN STREET \n", "\n", " ... b'bridge_highway_name' \\\n", "0 ... None \n", "1 ... None \n", "2 ... None \n", "3 ... None \n", "4 ... None \n", "\n", " b'bridge_highway_direction' b'road_ramp' b'bridge_highway_segment' \\\n", "0 None None None \n", "1 None None None \n", "2 None None None \n", "3 None None None \n", "4 None None None \n", "\n", " b'garage_lot_name' b'ferry_direction' b'ferry_terminal_name' b'latitude' \\\n", "0 None None None 40.723888 \n", "1 None None None 40.619489 \n", "2 None None None 40.721410 \n", "3 None None None 40.850167 \n", "4 None None None 40.681208 \n", "\n", " b'longitude' b'location' \n", "0 -73.941349 (40.723888303549415, -73.94134888943505) \n", "1 -73.938051 (40.61948901090983, -73.93805104516916) \n", "2 -73.987694 (40.72141034382407, -73.98769444021134) \n", "3 -73.933972 (40.85016671877659, -73.93397220795968) \n", "4 -73.972775 (40.68120794066068, -73.97277535440028) \n", "\n", "[5 rows x 53 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df.columns =[ b'unique_key', 'opened',\n", " 'closed', b'agency',\n", " b'agency_name', b'complaint_type',\n", " b'descriptor', b'location_type',\n", " b'incident_zip', b'incident_address',\n", " b'street_name', b'cross_street_1',\n", " b'cross_street_2', b'intersection_street_1',\n", " b'intersection_street_2', b'address_type',\n", " b'city', b'landmark',\n", " b'facility_type', b'status',\n", " b'due_date', b'resolution_description',\n", " b'resolution_action_updated_date', b'community_board',\n", " b'borough', b'x_coordinate',\n", " b'y_coordinate', b'park_facility_name',\n", " b'park_borough', b'school_name',\n", " b'school_number', b'school_region',\n", " b'school_code', b'school_phone_number',\n", " b'school_address', b'school_city',\n", " b'school_state', b'school_zip',\n", " b'school_not_found', b'school_or_citywide_complaint',\n", " b'vehicle_type', b'taxi_company_borough',\n", " b'taxi_pick_up_location', b'bridge_highway_name',\n", " b'bridge_highway_direction', b'road_ramp',\n", " b'bridge_highway_segment', b'garage_lot_name',\n", " b'ferry_direction', b'ferry_terminal_name',\n", " b'latitude', b'longitude',\n", " b'location']" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df['open_time'] = (df['closed']-df['opened']).astype('timedelta64[h]')" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "count 37615.000000\n", "mean 7.261146\n", "std 36.100546\n", "min 0.000000\n", "25% 0.000000\n", "50% 2.000000\n", "75% 4.000000\n", "max 1157.000000\n", "Name: open_time, dtype: float64" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['open_time'].describe()\n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#The data is cleaner and less standard deviation. No NaN values, no NaT values." ] }, { "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
birdsarah/bokeh-miscellany	old/world map/world map geojson.ipynb	1	4453637	null	gpl-2.0
ITAM-DS/analisis-numerico-computo-cientifico	libro_optimizacion/temas/4.optimizacion_en_redes_y_prog_lineal/4.1/Programacion_lineal_y_metodo_simplex.ipynb	1	428989	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "(PROGLINEAL)=" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4.1 Programación lineal (PL) y método símplex" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Notas para contenedor de docker:\n", "\n", "Comando de docker para ejecución de la nota de forma local:\n", "\n", "nota: cambiar `<ruta a mi directorio>` por la ruta de directorio que se desea mapear a `/datos` dentro del contenedor de docker y `<versión imagen de docker>` por la versión más actualizada que se presenta en la documentación.\n", "\n", "`docker run --rm -v <ruta a mi directorio>:/datos --name jupyterlab_optimizacion -p 8888:8888 -d palmoreck/jupyterlab_optimizacion:<versión imagen de docker>`\n", "\n", "password para jupyterlab: `qwerty`\n", "\n", "Detener el contenedor de docker:\n", "\n", "`docker stop jupyterlab_optimizacion`\n", "\n", "Documentación de la imagen de docker `palmoreck/jupyterlab_optimizacion:<versión imagen de docker>` en [liga](https://github.com/palmoreck/dockerfiles/tree/master/jupyterlab/optimizacion).\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Al final de esta nota la comunidad lectora:\n", ":class: tip\n", "\n", "* Conocerá el modelo de programación lineal, su interpretación y diferentes formas del mismo.\n", "\n", "* Comprenderá el método gráfico y aspectos esenciales del método de símplex para resolver programas lineales.\n", "\n", "* Aprenderá las definiciones de programación entera, mixta y binaria.\n", "\n", "* Tendrá una lista de métodos heurísticos y meta heurísticas que ayudan a resolver problemas de optimización, en particular de optimización combinatoria.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{sidebar} Un poco de historia ...\n", "\n", "El desarrollo de la programación lineal (PL) ha sido clasificado como uno de los avances científicos más importantes de mediados del siglo XX. Es quizás el modelo prototipo de la optimización con restricciones. El efecto que ha tenido en la práctica y en áreas del conocimiento desde 1950 es en verdad grande. El tipo más común de aplicación abarca el problema general de asignar de la mejor manera posible, esto es, de forma óptima, recursos limitados a actividades que compiten entre sí por ellos. Con más precisión, se desea elegir el nivel de ciertas actividades que compiten por recursos escasos necesarios para realizarlas y se puedan asignar recursos a tales actividades. El desarrollo por Dantzig del método símplex para resolver programas lineales en los $40$'s marcó el inicio de la era moderna en optimización. \n", "\n", "La PL utiliza un modelo matemático para describir el problema. El adjetivo lineal significa que todas las funciones del modelo deben ser funciones lineales. En este caso, la palabra programación no se refiere a términos computacionales; en esencia es sinónimo de planeación. Por lo tanto, la PL involucra la planeación de actividades para obtener un resultado óptimo; esto es, el resultado que mejor alcance la meta establecida, de acuerdo con el modelo matemático, entre todas las alternativas factibles.\n", "\n", "Aunque la asignación de recursos a las actividades es la aplicación más frecuente en PL, cualquier problema cuyo modelo se ajuste al formato general del modelo de PL, es un problema de PL.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(FORMAESTPL)=" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Forma estándar de un PL" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un programa lineal (PL) en su forma estándar es un problema de optimización con una función lineal objetivo, un conjunto de restricciones lineales de igualdad y un conjunto de restricciones no negativas impuestas a las variables. Es un modelo de optimización de la forma:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\displaystyle \\min_{x \\in \\mathbb{R}^n} c^Tx$$\n", "\n", "$$\\text{sujeto a:}$$\n", "\n", "$$Ax=b$$\n", "\n", "$$x \\geq 0$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "donde: $c \\in \\mathbb{R}^n$ es un vector de costos, $A \\in \\mathbb{R}^{m \\times n}$, se asume $m < n$ y tiene rank completo por renglones y la última desigualdad se refiere a que todas las componentes del vector $x \\in \\mathbb{R}^n$ son mayores o iguales a cero (son mayores o iguales a cero de una forma pointwise). La función objetivo es $f_o(x) = c^Tx$ y se busca minimizar el costo. El modelo anterior realiza suposiciones como son: proporcionalidad y aditividad para la función objetivo y restricciones. Tales supuestos se deben mantener respecto a las variables en $x$. La proporcionalidad se resume en que los exponentes de cada componente de $x$ deben ser igual a uno y la aditividad en cuanto a que las contribuciones individuales de las componentes de $x$ es su suma en la función objetivo y restricciones." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Comentario\n", "\n", "El PL es un problema convexo pues una función lineal es convexa y cóncava al mismo tiempo. Obsérvese que la forma estándar de un problema convexo pide que el problema se escriba con desigualdades del tipo $\\leq$. Tal forma se puede obtener si se definen $h: \\mathbb{R}^n \\rightarrow \\mathbb{R}^m, f:\\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, $h(x) = Ax-b$, $f(x) = -x$ con $x \\in \\mathbb{R}^n$, ver {ref}`problemas de optimización convexa en su forma estándar o canónica <PROBOPTCONVEST>`.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(EJFLUJOENREDESYPL)=" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ejemplo de flujo en redes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Considérese el problema de satisfacer el flujo neto de todos los nodos con etiquetas \"A, B, C, D\" y \"E\" de la siguiente red de acuerdo a las capacidades de cada uno de ellos al menor costo posible:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pprint\n", "from scipy.optimize import linprog\n", "import numpy as np\n", "import networkx as nx\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 648x648 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nodes_pos = [[0.18181818181818182, 0.7272727272727273],\n", " [0.18181818181818182, 0.2727272727272727],\n", " [0.5454545454545454, 0.2727272727272727],\n", " [0.5454545454545454, 0.7272727272727273],\n", " [0.36363636363636365, 0.5454545454545454]]\n", "\n", "nodes = ['A', 'B', 'E', 'D', 'C']\n", "\n", "nodes_and_pos = dict(zip(nodes, nodes_pos))\n", "\n", "G_min_cost_flow = nx.DiGraph()\n", "\n", "G_min_cost_flow.add_node('A', netflow = 50, node_and_netflow=\"A [50]\")\n", "G_min_cost_flow.add_node('B', netflow = 40, node_and_netflow=\"B [40]\")\n", "G_min_cost_flow.add_node('C', netflow = 0, node_and_netflow=\"C [0]\")\n", "G_min_cost_flow.add_node('D', netflow = -30, node_and_netflow=\"D [-30]\")\n", "G_min_cost_flow.add_node('E', netflow = -60, node_and_netflow=\"E [-60]\")\n", "\n", "edge_labels_min_cost_flow = {('A', 'B'): {\"weight\": 2},\n", " ('A', 'C'): {\"weight\": 4},\n", " ('A', 'D'): {\"weight\": 9},\n", " ('B', 'C'): {\"weight\": 3},\n", " ('C', 'E'): {\"weight\": 1},\n", " ('E', 'D'): {\"weight\": 2},\n", " ('D', 'E'): {\"weight\": 3}\n", " }\n", "\n", "\n", "G_min_cost_flow.add_edges_from(edge_labels_min_cost_flow)\n", "for e in G_min_cost_flow.edges():\n", " G_min_cost_flow[e[0]][e[1]][\"weight\"] = edge_labels_min_cost_flow[e][\"weight\"]\n", " \n", "plt.figure(figsize=(9, 9))\n", "nx.draw_networkx_edges(G_min_cost_flow, pos=nodes_and_pos, \n", " alpha=0.3,\n", " min_target_margin=25, connectionstyle=\"arc3, rad = 0.1\")\n", "nx.draw_networkx_edge_labels(G_min_cost_flow, pos=nodes_and_pos, \n", " edge_labels=edge_labels_min_cost_flow, label_pos=0.4,\n", " font_size=10)\n", "nodes_pos_modified = {}\n", "\n", "y_off = 0.03\n", "\n", "nodes_and_pos_modified = nodes_and_pos.copy()\n", "\n", "for node in G_min_cost_flow.nodes():\n", " if node == 'B' or node == 'E':\n", " nodes_and_pos_modified[node] = [nodes_and_pos_modified[node][0], \n", " nodes_and_pos_modified[node][1] - y_off]\n", " else:\n", " nodes_and_pos_modified[node] = [nodes_and_pos_modified[node][0], \n", " nodes_and_pos_modified[node][1] + y_off]\n", " \n", " \n", "labels = nx.get_node_attributes(G_min_cost_flow, \"node_and_netflow\")\n", "\n", "nx.draw_networkx_labels(G_min_cost_flow, pos=nodes_and_pos_modified, \n", " labels=labels)\n", "nx.draw_networkx_nodes(G_min_cost_flow, pos=nodes_and_pos, \n", " node_size=1000, alpha=0.6)\n", "plt.axis(\"off\")\n", "plt.show() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En la red anterior el arco $(D, E)$ tiene costo igual a $3$ y el arco $(E, D)$ tiene costo igual a $2$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "Obsérvese que es ligeramente distinta la nomenclatura de este problema en cuanto a los términos de flujo neto y demanda que tiene un nodo de acuerdo a lo que se describe en el {ref}`ejemplo de flujo de costo mínimo <EJREDFLUJOCOSTOMIN>`\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Al lado de cada nodo en corchetes se presenta el flujo neto generado por el nodo. Los nodos origen tienen un flujo neto positivo y en la red son los nodos \"A\" y \"B\" (por ejemplo fábricas). Los nodos destino tienen un flujo neto negativo que en la red son los nodos \"D\" y \"E\" (por ejemplo clientes). El único nodo de transbordo es el nodo \"C\" que tiene flujo neto igual a cero (centro de distribución por ejemplo). Los valores de los costos se muestran en los arcos. Es una red sin capacidades." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Entonces el modelo de PL que minimiza el costo de transferencia de flujo de modo que el flujo neto satisfaga lo representado en la red, considerando el flujo neto como el flujo total que sale del nodo menos el flujo total que entra al nodo es:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\displaystyle \\min_{x \\in \\mathbb{R}^7} 2 x_{AB} + 4 x_{AC} + 9 x_{AD} + 3 x_{BC} + x_{CE} + 3 x_{DE} + 2x_{ED}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\text{sujeto a: }$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{eqnarray}\n", "&x_{AB}& + &x_{AC}& + &x_{AD}& && && && && &=& 50 \\nonumber \\\\\n", "&-x_{AB}& && && + &x_{BC}& && && && &=& 40 \\nonumber \\\\\n", "&& - &x_{AC}& && - &x_{BC}& + &x_{CE}& && && &=& 0 \\nonumber \\\\\n", "&& && - &x_{AD}& && && + &x_{DE}& - &x_{ED}& &=& -30 \\nonumber \\\\\n", "&& && && && - &x_{CE}& - &x_{DE}& + &x_{ED}& &=& -60 \\nonumber\n", "\\end{eqnarray}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$x_{ij} \\geq 0 \\forall i,j$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La primer restricción de igualdad representa el flujo neto para el nodo $A$ y la última el flujo neto para el nodo $E$. A tales ecuaciones de las restricciones de igualdad se les conoce con el nombre de ecuaciones de conservación de flujo." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Observación\n", ":class: tip\n", "\n", "Obsérvese que la matriz que representa a las restricciones de igualdad es la matriz de incidencia nodo-arco. Ver {ref}`Representación de redes: matriz de incidencia nodo-arco <MATINCIDNODOARCO>`\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "Multiplicamos por $-1$ pues el resultado de la función `incidence_matrix` está volteado respecto a la definición de la matriz de incidencia nodo-arco. \n", "```" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1. 1. 1. 0. 0. 0. 0.]\n", " [-1. 0. 0. 1. 0. 0. 0.]\n", " [ 0. -1. 0. -1. 1. 0. 0.]\n", " [ 0. 0. -1. 0. 0. 1. -1.]\n", " [ 0. 0. 0. 0. -1. -1. 1.]]\n" ] } ], "source": [ "print(-1nx.incidence_matrix(G_min_cost_flow, oriented=True).todense())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El problema anterior lo podemos resolver directamente con [scipy-optimize-linprog](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linprog.html#scipy-optimize-linprog) que es una función que resuelve PL's:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "c = np.array([2, 4, 9, 3, 1, 3, 2])\n", "\n", "A_eq = -1nx.incidence_matrix(G_min_cost_flow, oriented=True).todense()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1. 1. 1. 0. 0. 0. 0.]\n", " [-1. 0. 0. 1. 0. 0. 0.]\n", " [ 0. -1. 0. -1. 1. 0. 0.]\n", " [ 0. 0. -1. 0. 0. 1. -1.]\n", " [ 0. 0. 0. 0. -1. -1. 1.]]\n" ] } ], "source": [ "print(A_eq)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "b = list(nx.get_node_attributes(G_min_cost_flow, \n", " \"netflow\").values())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "Cada tupla hace referencia a las cotas inferiores y superiores que tiene cada variable.\n", "\n", "```" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "bounds = [(0, None), (0,None), (0,None), (0,None), (0,None), (0, None), (0, None)]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " con: array([ 1.23415534e-06, 1.05034484e-06, 2.62587889e-08, -7.61500058e-07,\n", " -1.54925893e-06])\n", " fun: 469.9999898969424\n", " message: 'Optimization terminated successfully.'\n", " nit: 5\n", " slack: array([], dtype=float64)\n", " status: 0\n", " success: True\n", " x: array([3.62017054e-07, 4.99999977e+01, 7.19274437e-07, 3.99999993e+01,\n", " 8.99999970e+01, 3.55827520e-09, 2.99999985e+01])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: OptimizeWarning: A_eq does not appear to be of full row rank. To improve performance, check the problem formulation for redundant equality constraints.\n", " \"\"\"Entry point for launching an IPython kernel.\n" ] } ], "source": [ "print(linprog(c=c, A_eq=A_eq, b_eq=b,bounds=bounds))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "Los solvers son paquetes de software para resolver modelos de programación lineal y modelos relacionados que se encuentran en los lenguajes de modelado.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "Se instala [cvxopt](http://cvxopt.org/userguide/intro.html), un paquete de Python para resolver problemas de optimización convexa que ya trae el solver GLPK, ver [cvxpy: install-with-cvxopt-and-glpk-support](https://www.cvxpy.org/install/#install-with-cvxopt-and-glpk-support).\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "También con `cvxpy` podemos resolver el PL anterior. Para mostrar la flexibilidad que tienen los lenguajes de modelado como `cvxpy` se define $x$ como variable entera. `cvxpy` puede resolver este tipo de problemas si se instala el solver [GLPK](https://www.gnu.org/software/glpk/glpk.html) :" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "tags": [ "margin" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[33mWARNING: You are using pip version 20.3.3; however, version 21.0.1 is available.\n", "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n" ] } ], "source": [ "!pip install --quiet cvxopt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "Ver [cvxpy: linear_program](https://www.cvxpy.org/examples/basic/linear_program.html)\n", "\n", "```" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "ename": "RuntimeError", "evalue": "module compiled against API version 0xe but this version of numpy is 0xd", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", "\u001b[0;31mRuntimeError\u001b[0m: module compiled against API version 0xe but this version of numpy is 0xd" ] } ], "source": [ "import cvxpy as cp" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "n = 7 #number of variables\n", "x = cp.Variable(n, integer=True) #x as integer optimization variable\n", "fo_cvxpy = c.T@x #objective function\n", "\n", "constraints = [A_eq@x == b,\n", " x >=0\n", " ]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "opt_objective = cp.Minimize(fo_cvxpy)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "470.0\n" ] } ], "source": [ "prob = cp.Problem(opt_objective, constraints)\n", "print(prob.solve())" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "The optimal value is 470.0\n", "A solution x is\n", "[ 0. 50. 0. 40. 90. 0. 30.]\n" ] } ], "source": [ "# Print result.\n", "print(\"\\nThe optimal value is\", prob.value)\n", "print(\"A solution x is\")\n", "print(x.value)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(EJPROTOTIPO)=" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ejemplo prototipo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Supóngase que una compañía tiene tres plantas en las que se producen dos productos. La compañía nos entrega los siguientes datos relacionados con:\n", "\n", "1. Número de horas de producción disponibles por semana en cada planta para fabricar estos productos.\n", "\n", "2. Número de horas de fabricación para producir cada lote de los productos.\n", "\n", "3. La ganancia por lote de cada producto.\n", "\n", "Lo anterior se resume en la siguiente tabla:\n", "\n", "\| \|Tiempo de producción por lote en horas \|\|\|\n", "\|:---:\|:---:\|:---:\|:---:\|\n", "\| Planta \|Producto 1\|Producto 2\| Tiempo de producción disponible a la semana en horas\|\n", "\|1\|1\|0\|4\|\n", "\|2\|0\|2\|12\|\n", "\|3\|3\|2\|18\|\n", "\|Ganancia por lote\| 3000\| 5000\|\|" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La tabla anterior indica en su primer renglón que cada lote del producto 1 que se produce por semana emplea una hora de producción en la planta 1 y sólo se dispone de 4 horas semanales (recursos disponibles). Como se lee en la tabla, cada producto se fabrica en lotes de modo que la tasa de producción está definida como el número de lotes que se producen a la semana. \n", "\n", "Obsérvese que el producto 1 requiere parte de la capacidad de producción en las plantas 1, 3 y nada en la planta 2. El producto 2 necesita trabajo en las plantas 2 y 3. Por lo anterior no está claro cuál mezcla de productos sería la más rentable.\n", "\n", "Se permite cualquier combinación de tasas de producción que satisfaga estas restricciones, incluso no fabricar uno de los productos y elaborar todo lo que sea posible del otro. La tasa de producción está definida como el número de lotes que se producen a la semana.\n", " \n", "Se desea determinar cuáles tasas de producción (no negativas) deben tener los dos productos con el fin de maximizar las ganancias totales sujetas a las restricciones impuestas por las capacidades de producción limitadas disponibles en las tres plantas.\n", "\n", "Se asume que las plantas únicamente destinan su producción a estos dos productos y la ganancia incremental de cada lote adicional producido es constante sin importar el número total de lotes producidos. La ganancia total de cada producto es aproximadamente la ganancia por lote que se produce multiplicada por el número de lotes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Se modela el problema anterior como un PL con las siguientes variables:\n", "\n", "$x_1$: número de lotes del producto 1 que se fabrican por semana.\n", "\n", "$x_2$: número de lotes del producto 2 que se fabrican por semana.\n", "\n", "$f_o(x_1, x_2)$: ganancia semanal total (en miles de pesos) que generan estos dos productos." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Se debe resolver el PL siguiente:\n", "\n", "$$\\displaystyle \\max_{x \\in \\mathbb{R}^2} 3x_1 + 5x_2$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\text{sujeto a: }$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$x_1 \\leq 4$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$2x_2 \\leq 12$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$3x_1 + 2x_2 \\leq 18$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$x_1 \\geq 0, x_2 \\geq 0$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El término $3x_1$ representa la ganancia generada (en miles de pesos) cuando se fabrica el producto 1 a una tasa de $x_1$ lotes por semana. Se tienen contribuciones individuales de cada producto a la ganancia." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Modelo de PL" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Algunas características generales de los problemas de PL se presentan a continuación" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Terminología en PL" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\|Ejemplo prototipo \| Problema general\|\n", "\|:---:\|:---:\|\n", "\|Capacidad producción de las plantas \| Recursos\|\n", "\|3 plantas \| m recursos \|\n", "\|Fabricación de productos \| Actividades \|\n", "\|2 productos \| n actividades\|\n", "\|Tasa de producción del producto \| Nivel de la actividad\|\n", "\|Ganancia \| Medida global de desempeño\|" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y en la terminología del problema general se desea determinar la asignación de recursos a ciertas actividades. Lo anterior implica elegir los niveles de las actividades (puntos óptimos) que lograrán el mejor valor posible (valor óptimo) de la medida global de desempeño." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En el PL:\n", "\n", "$f_o$: valor de la medida global de desempeño (función objetivo).\n", "\n", "$x_j$: nivel de la actividad $j$ con $j=1, 2, \\dots, n$. También se les conoce con el nombre de variables de decisión (variables de optimización).\n", "\n", "$c_j$: incremento en $f_o$ que se obtiene al aumentar una unidad en el nivel de la actividad j.\n", "\n", "$b_i$: cantidad de recurso $i$ disponible para asignarse a las actividades con $i=1, 2, \\dots, m$.\n", "\n", "$a_{ij}$: cantidad del recurso $i$ consumido por cada unidad de la actividad $j$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Observación\n", ":class: tip\n", "\n", "Los valores de $c_j, b_i, a_{ij}$ son las constantes o parámetros del modelo.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Formas de un PL" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Es posible que se encuentren con PL en diferentes formas por ejemplo:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1.Minimizar en lugar de maximizar la función objetivo.\n", "\n", "2.Restricciones con desigualdad en sentido mayor, menor o igual que.\n", "\n", "3.Restricciones en forma de igualdad.\n", "\n", "4.Variables de decisión sin la restricción de no negatividad (variables libres)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pero siempre que se cumpla con que la función objetivo y las restricciones son funciones lineales entonces tal problema se clasifica como un PL." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Observación\n", ":class: tip\n", "\n", "\n", "Si se utiliza un PL con otras formas diferentes a la del ejemplo prototipo (por ejemplo variables libres en lugar de no negativas) es posible que la interpretación de \"asignación de recursos limitados entre actividades que compiten\" puede ya no aplicarse muy bien; pero sin importar cuál sea la interpretación o el contexto, lo único necesario es que la formulación matemática del problema se ajuste a las formas permitidas.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(EJMETGRAFICOPL)=" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ejemplo: método gráfico" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A continuación se muestra un procedimiento gráfico para resolver el PL del ejemplo prototipo. Esto es posible realizar pues tenemos sólo dos variables. Se tomará $x_1$ como el eje horizontal y $x_2$ el eje vertical. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recordando las variables del ejemplo prototipo:\n", "\n", "$x_1$: número de lotes del producto 1 que se fabrican por semana.\n", "\n", "$x_2$: número de lotes del producto 2 que se fabrican por semana.\n", "\n", "$f_o(x_1, x_2)$: ganancia semanal total (en miles de pesos) que generan estos dos productos." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y el PL es:\n", "\n", "$$\\displaystyle \\max_{x \\in \\mathbb{R}^2} 3x_1 + 5x_2$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\text{sujeto a: }$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$x_1 \\leq 4$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$2x_2 \\leq 12$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$3x_1 + 2x_2 \\leq 18$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$x_1 \\geq 0, x_2 \\geq 0$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Entonces se tiene la siguiente región definida por las desigualdades del PL:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#x_1 ≤ 4\n", "\n", "point1_x_1 = (4,0)\n", "\n", "point2_x_1 = (4, 10)\n", "\n", "point1_point2_x_1 = np.row_stack((point1_x_1, point2_x_1))\n", "\n", "#x_1 ≥ 0\n", "point3_x_1 = (0,0)\n", "\n", "point4_x_1 = (0, 10)\n", "\n", "point3_point4_x_1 = np.row_stack((point3_x_1, point4_x_1))\n", "\n", "#2x_2 ≤ 12 or x_2 ≤ 6\n", "\n", "point1_x_2 = (0, 6)\n", "\n", "point2_x_2 = (8, 6)\n", "\n", "point1_point2_x_2 = np.row_stack((point1_x_2, point2_x_2))\n", "\n", "#x_2 ≥ 0\n", "\n", "point3_x_2 = (0, 0)\n", "\n", "point4_x_2 = (8, 0)\n", "\n", "point3_point4_x_2 = np.row_stack((point3_x_2, point4_x_2))\n", "\n", "#3x_1 + 2x_2 ≤ 18\n", "\n", "x_1_region_1 = np.linspace(0,4, 100)\n", "\n", "x_2_region_1 = 1/2(18 - 3x_1_region_1)\n", "\n", "\n", "x_1 = np.linspace(0,6, 100)\n", "\n", "x_2 = 1/2(18 - 3x_1)\n", "\n", "plt.plot(point1_point2_x_1[:,0], point1_point2_x_1[:,1],\n", " point3_point4_x_1[:,0], point3_point4_x_1[:,1],\n", " point1_point2_x_2[:,0], point1_point2_x_2[:,1],\n", " point3_point4_x_2[:,0], point3_point4_x_2[:,1],\n", " x_1, x_2)\n", "\n", "plt.legend([\"$x_1 = 4$\", \"$x_1 = 0$\", \n", " \"$2x_2 = 12$\", \"$x_2 = 0$\",\n", " \"$3x_1+2x_2 = 18$\"], bbox_to_anchor=(1, 1))\n", "\n", "plt.fill_between(x_1_region_1, 0, x_2_region_1, where=x_2_region_1<=6, color=\"plum\")\n", "x_1_region_2 = np.linspace(0,2, 100)\n", "plt.fill_between(x_1_region_2, 0, 6, color=\"plum\")\n", "plt.title(\"Región factible del PL\")\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La región sombreada es la región factible. Cualquier punto que se elija en la región factible satisface las desigualdades definidas en el PL. Ahora tenemos que seleccionar dentro de la región factible el punto que maximiza el valor de la función objetivo $f_o$.\n", "\n", "El procedimiento gráfico consiste en dar a $f_o$ algún valor arbitrario, dibujar la recta definida por tal valor y \"mover tal recta de forma paralela\" en la dirección que $f_o$ crece (si se desea maximizar y en la dirección en la que $f_o$ decrece si se desea minimizar) hasta que se mantenga en la región factible.\n", "\n", "Para la función objetivo del PL anterior queda como sigue:\n", "\n", "$$y = f_o(x) = 3x_1 + 5x_2$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "y vamos dando valores arbitrarios a $y$:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "Todas las rectas tienen la misma pendiente por lo que son paralelas. Cada una de las rectas son las curvas de nivel de $f_o$\n", "\n", "```" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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t27YN3rBhQ1rPnj2zShv3ypUrXRYuXOj1888/n77ReVu2bDlas2bNwtLOU1Ky8iOEEEJYidq1axdc3MXcw8PDXL9+/ZyzZ8/abt682al27dp5jRs3zre3t9eGDBmSvGTJEvfSzGEwGHBzczMD5Ofnq8LCQqWUAqBdu3aNli1b5gowceLEWqNGjQq0zCe7vvKY0ypXfmbNmgXAtGnTdI5EiNKRe1gI6/bB2F8DzxxKsGi30tpNfbInfTEwqqTnR0ZG2h45csSxa9eumb/88oubv79//sVjAQEB+bt27XK+/PzWrVsHZ2VlGa8c5+233466++67My7/WmFhIU2bNm189uxZu1GjRsX36NEjC+DVV1+NeeWVV/xjY2NNBw8edFy/fv3xW/+k19ezZ8+GSinGjBmTMG3atMTymtMqk5+VK1cC8o1DWC+5h4UQZZGWlmYYMmRI/bfffjvK09PTXJJr9uzZE1nS8U0mExEREUcSExON/fr1q//333/bt23bNjcsLCzz5Zdf5uOPP66xbdu2SJPJxJEjR2xfffXVmunp6ca1a9eevHycZs2aheTn5xuys7MNaWlpppCQkMYAb775ZvTQoUPTLz9327ZtEXXr1i04d+6cqUePHo2aNGmSGxYWlnmtOb/++mv3VatWuWVkZBgffvjhxCFDhvxrrJt+vls5WQghhBBwKys0lpaXl6f69etXf/jw4cmjRo1KBQgMDMw/d+6c7cVzoqOjbS9fCYJbW/m5yNvbu6hz584ZK1ascGvbtm3uX3/95ZCQkGDj7u5e6OHhYQZo3Lhx/uLFi8/06dOn3pXXHzx4MAJKVvNTt27dAgB/f//Cfv36pe7YscMpLCws81pzjhw5MnXkyJGpCQkJxvHjxwfcavIjNT9CCCGElTCbzdx33321GzVqlPvqq6/GXfx6165ds06fPm0fERFhm5ubq5YuXeo5dOjQ1Muv3bNnT2RERMSRK39dmfjExMSYEhMTjQCZmZlq06ZNrqGhoblnzpyxGTFiRN2lS5ced3R0LFqyZImrpT5Xenq6ISUlxXDx95s2bXJt1qxZzs3mfOGFF2pOnDgx4Vbnk+RHCCGEsBK///678/Lly722bdvmEhIS0jgkJKTxjz/+6Fb8FtjZPn36NGrYsGGTu+++O7lNmza3/KYXQFRUlE3nzp2DGzVq1Lhly5aNu3fvnt6/f/+MQYMG1X/nnXeiW7VqlTt9+vTzM2bMsNgr8NHR0ab27duHBAcHN27VqlXoXXfdldq7d+/M681pNpt5/PHH/fv165d2sQD8VihN0ywV+021adNG2717d5nHCQsLA2DNmjVlHksIPcg9LMQFSqk9mqa10TuOkjhw4MDp5s2bJ+odR2UTGxtrnDJliv/WrVtdR4wYkThz5szY8p5zxowZvt9//71X8+bNs1q0aJHzzDPPXHP158CBA97Nmzevc+XXrTL5EUIIUTVI8iPK0/WSH3nsJYQQQohqxSqTnzfeeIM33nhD7zCEKDW5h4UQQj9Wmfxs2LCBDRs26B2GEKUm97AQQujHKpMfIYQQQojSkuRHCCGEENWKJD9CCCGEqFascnsLLy8vvUMQokzkHhZCCP1YZfLz888/6x2CEGUi97AQQuhHHnsJIYQQolqxypWfMWPGkJeXx3fffad3KEKUyvPPPw/AzJkzdY5ECGFNsrOzVbt27ULy8/NVUVGRGjBgQMr7778fA5CYmGgcMWJE7cjISAelFPPmzTt95513ZllyjvLk7+9/m5OTU5HBYMBkMmmHDh0KL6+5rC750TSNpUuXkpWVxXPPPUezZs30DkmIW7Zjxw69QxBCWCF7e3tt27ZtkW5ubua8vDzVtm3b4A0bNqT17Nkz69FHHw2866670teuXXsyNzdXZWZmlurpzo3mKG3cK1eudFm4cKHXzz//fPpG523ZsuVozZo1C0s7T0lZXfKjlCI0NJR9+/YRFhbGjh07CAoK0jssIYQQ1cjYM1GBh3JzHS05ZlN7++wvagdG3egcg8GAm5ubGSA/P18VFhYqpRRJSUnGXbt2uSxZsuQ0XEhg7O3ti0oTx/XmAGjXrl2j5557Lnbw4MHpEydOrJWWlmb88ssvbxhzWZXHnFaX/ADY29tz2223cezYMcLCwti2bRseHh56hyWEEEKUu8LCQpo2bdr47NmzdqNGjYrv0aNH1p9//ung6elZOHz48DpHjhxxbNasWdbnn38e5erqar54XevWrYOzsrKMV4739ttvR919990ZN5sD4NVXX4155ZVX/GNjY00HDx50XL9+/XFLfraePXs2VEoxZsyYhGnTpiWW15xWuat7t27dAHjllVfo3bs306ZN46233irzuEJUlIv38ObNm3WNQwi9ya7upZeYmGjs169f/Y8//vhsTk6OoUePHqHr1q2L6NGjR9aYMWMCXV1diz788MMy1epcPkfbtm1zAdq2bRucnZ1t2LZtW6SHh4f5yJEjtq+++mrN9PR049q1a09efn2zZs1C8vPzDdnZ2Ya0tDRTzZo18wHefPPN6KFDh6Zffu6pU6ds6tatW3Du3DlTjx49Gn3wwQdnw8LCMq8159dff+2+atUqt4yMDOPDDz+cOGTIkH+NddH1dnW3ypWfgIAAALp3786WLVto08Yq/t0IccnFe1gIIUrL29u7qHPnzhkrVqxwGzduXFKNGjXyL67Q3HvvvSlvv/223+Xn38rKz7XmaNu2be5ff/3lkJCQYOPu7l7o4eFhBmjcuHH+4sWLz/Tp06feldcfPHgwAkpW81O3bt0CAH9//8J+/fql7tixwyksLCzzWnOOHDkydeTIkakJCQnG8ePHB1wv+bkeq3zV/ZtvvuGbb74BoEOHDtjY2BAXF8ecOXN0jkyIkrn8HhZCiJKKiYkxJSYmGgEyMzPVpk2bXENDQ3ODgoIK/fz88g8cOGAHsG7dOtfg4ODcy6/ds2dPZERExJErf12Z+FxvjjNnztiMGDGi7tKlS487OjoWLVmyxNVSnys9Pd2QkpJiuPj7TZs2uTZr1iznZnO+8MILNSdOnJhwq/NZ5crPtXz++edMnz6doqIiJk2apHc4QgghhMVFRUXZjB49um5RURGapqlBgwYl33///WkAc+bMOfvggw/Wy8/PV0FBQXnff//9aUvN0b9//4zOnTs3euedd6JbtWqVO3369PPPP/98wLBhw25pxeV6oqOjTYMHD24AUFRUpIYOHZrUu3fvzOvNaTabGT9+vH+/fv3SOnXqlH2r81llzc/F5OaDDz649LWioiLuvfdeli5dyo8//sjw4cPLPI8Q5eVa97AQ1ZHU/Fi/2NhY45QpU/y3bt3qOmLEiMSZM2fGlvecM2bM8P3++++9mjdvntWiRYucZ5555pqrP1Wq5mf//v1Xfc1oNPLNN9/Qq1cvRowYga+vL127dq344IQogWvdw0IIYY38/PyKvvvuu7MVOedLL70U/9JLL8WX9nqrrPm5Hnt7e3755Rfq16/PM888Q0WuagkhhBDCOljlys+NeHp6snbtWhwdHbnYlEkIIYQQ4qIqtfJzUVBQEN7e3uTn5zN9+nTS0tL0DkkIIYQQlYRVrvw0atSoROft37+ft99+m+3bt7NmzRrs7OzKOTIhSqak97AQQgjLs8rkZ968eSU67/bbb2fhwoWMHDmSMWPG8M0332AwVMnFLmFlSnoPCyGEsDyrTH5uxYgRI4iOjub5558nICCAd999V++QhBBCCKEjq1wGefTRR3n00UdLfP6zzz7L+PHj+eyzz4iOji7HyIQomVu9h4UQQliOVa78HD169JbOV0rx4YcfMnnyZNlTSVQKt3oPCyGEsByrTH5Kw2g0Ur9+fTRN4/3336dt27Z07txZ77CEEEJYqXff9W6ek5Nkse+jDg5ehc88k3igLGM88sgjAX/88Ydr+/btM7788suoy49lZmaq7t27N9qxY0ekyXTtsHNzc1WnTp0a7dixI9LGxqYsoVRqVvnYqyyys7P57LPPGDRoEOHh4XqHI4QQwkpZMvEp6XgNGjRo4uPj0ywoKKjpxV9OTk4tJ0yY4H/48GG7v/76yzkyMvLIlYkPwJw5c7wHDhyYcr3EB8De3l7r2rVr+vz58z3L+HEqtWqX/Dg5ObF27VpsbW3p06cPMTExeockhBBClMjIkSMThg4dmnz27NlDZ8+ePXT69OlD3t7eBePGjUvs2bNncExMjG1oaGjj9PT0q76/L1682Ouee+5Jvfjndu3aNVq2bJkrwMSJE2uNGjUqEGDYsGGpP/zwQ5VOfqzysVeLFi3KdH3dunVZvXo1Xbt2pW/fvvzxxx+4urpaJjghSqCs97AQonp67LHHklq1atV4zpw50TY2NqxatcrF398/r3nz5nn33HNPYp06dfKnTJly1earubm5Kioqyi44ODj/4tdeffXVmFdeecU/NjbWdPDgQcf169cfB2jbtm3OwYMHnSryc1U0q1z5+eCDD8q8G3arVq1YsmQJ4eHhbNy40TKBCVFClriHhRDVj5+fX1GrVq2yfvjhB3eA+fPne48ZMyYR4PDhww6tW7fOvtZ1sbGxJhcXl8LLvxYWFpapaRoff/xxjWXLlp28+DjMZDJhY2OjpaSkWGWOUBJWufJjKb179+bEiRPyBpgQQgir8cgjjyR8+OGHNfr27Zvx119/Of/444+nAY4dO+bQunXrHICMjAzDE088EWBnZ2d2dHQ0T58+PS4/P/9fycxff/3lkJCQYOPu7l7o4eFhvvxYQUGBcnR0rLK7g1tlVjdixAhGjBhhkbEuJj6//fYbr7/+ukXGFOJmLHkPCyGqlwEDBmScPHnS/o033qjRv3//FHt7ey0lJcVgMpk0Z2dnDeDdd9/1GTduXOL8+fOjjx49au/j41NUVFSksrOzFcCZM2dsRowYUXfp0qXHHR0di5YsWXKp9iM2Ntbo7u5eaGdnJ8lPZRIdHW3xZoUrVqzglVde4dNPP7XouEJcS3ncw0KIiuXg4FV487MsP57BYOD+++9PnDNnTs3HH388EWDPnj0OwcHBORfPOXLkiEOnTp2yc3NzlYODgxmgS5cuaevWrXPOyMgwDBo0qP4777wT3apVq9zp06efnzFjRq2L165Zs8b1zjvvrNI7gitNq7jErk2bNtru3bvLPE63bt0A2Lx5c5nHuqiwsJAhQ4awatUqli5dyqBBgyw2thBXKo97WAhrpJTao2laG73jKIkDBw6cbt68+VXFxHpIT083REZG2rZt2zb3Wsd/+OEHt19//dUdYMKECfEdOnTI2bZtm+OsWbNqLF++/NSNxr7rrrvqz5o1K7pZs2Z55RB6hTpw4IB38+bN61z59Wpd83M5k8nEDz/8QI8ePbjvvvvYsGEDHTt21DssIYQQ4iqurq7m6yU+APfdd1/afffd96/Vm06dOmXv3r07vbCwkBs1ORw4cGBqVUh8bsQqH3uVF0dHR1asWEFAQABLlizROxwhhBDCoiZNmpR0syaHTz75ZFIFhqQLq1z56dChQ7mN7ePjw86dO/H0rNL9nYTOyvMeFkIIcWNWmfzMnDmzXMf38vICLmw++cILL7Bw4UJcXFzKdU5RvZT3PSyEEOL6yvTYSyk1WSl1WCl1SCn1vVLK3lKBVQYnTpxg+fLlDB8+nIKCAr3DEUIIoS+z2WxWegchSqb4v5X5WsdKnfwopfyBiUAbTdOaAkbgvtKOdyuGDh3K0KFDy32esLAwPvvsM3777TceeeQRKvLNOFG1VdQ9LISwqEMJCQlukgBVfmazWSUkJLgBh651vKyPvUyAg1KqAHAEKmSX0KSkiqvFevjhh4mKiuK1114jMDCQN954o8LmFlVXRd7DQgjLKCwsHBcbGzs/Nja2KfLCUGVnBg4VFhaOu9bBUic/mqadU0rNAs4COcA6TdPWXXmeUupR4FGAoKCg0k6nq1deeYXo6Gg2btzI9OnTsbW11TskIYQQFax169bxwEC94xBlV5bHXh7AIKAuUAtwUkpd1a9f07R5mqa10TStjY+PT+kj1ZFSik8//ZT169dL4iOEEEJYubIs290JnNI0LUHTtAJgKVBluwLa2Njg4OBAWloaAwYMYOfOnXqHJIQQQohSKEvNz1mgvVLKkQuPvXoCZd+7ogR69uxZEdNcU15eHkeOHGHAgAH8+eefNGzYULdYhPXS8x4WQojqrkx7eymlXgPuBQqBfcA4TdOu2xLbUnt76e3YsWN07NgRV1dXduzYga+vr94hCSGEVbKmvb1E1VGmanVN017RNC1E07SmmqaNvFHiU5U0bNiQlStXcv78efr160dWVpbeIQkhhBCihKzyVb2wsDDCwsJ0jaFdu3b8+OOPJCQkEBNTIW/4iyqkMtzDQghRXVnl9hY5OTl6hwDAgAED6NWrF/b29pcaIColva/EzVWWe1gIIaojq1z5qUzs7e0xm8089dRTvP7663qHI4QQQoibkOTHApRSpKen8+qrr/LFF1/oHY4QQgghbsAqH3tVNkopPv/8c86fP8+jjz6Kn58fffv21TssIYQQQlyDVSY//fv31zuEq9jY2LBkyRK6du3K8OHD2bx5M23bttU7LFFJVcZ7WAghqgurTH6mTZumdwjX5OLiwurVq+nRowexsbF6hyMqscp6DwshRHVglclPZebn58fBgwcxmS781ZrNZgwGKa0SQgghKgur/K7crVs3unXrpncY13Ux8Vm0aBFdu3YlOztb54hEZVPZ72EhhKjKrDL5sRZubm5s376d+++/n8LCQr3DEUIIIQSS/JSrwYMH8/HHH/Prr78yYcIEyrKPmhBCCCEsQ2p+ytkTTzxBVFQUb7/9NgEBAbz44ot6hySEEEJUa5L8VIC33nqLc+fOUVRUpHcoQgghRLVnlcnPPffco3cIt0QpxaJFiy699VVQUICNjY3OUQk9Wds9LIQQVYmqyDqUNm3aaLt3766w+Sqjv//+m+HDh7N06VJatWqldzhCCKErpdQeTdPa6B2HqF6ssuA5Ozvbal8f9/f3R9M0+vbty6lTp/QOR+jEmu9hIYSwdlaZ/PTt29dq986qVasWa9asIS8vj7CwMJKSkvQOSejAmu9hIYSwdlaZ/Fi7xo0b8+uvv3L69GkGDBhATk6O3iEJIYQQ1YYkPzrp3Lkz33zzDf7+/nqHIoQQQlQrVpn85Lm56R2CRQwbNozFixfj4OBAdna2NEEUQgghKoDVJT9/Z2Wz8+XXODx6LH9mZukdTpkppUhOTqZdu3a8++67eocjhBBCVHlW1+cnyNaG/gnxbGrSlDuOnaCDkyNTfX24280Vo1J6h1cq7u7uNG3alOeee46AgAAefPBBvUMS5Wz06NF6hyCEENWW1fb5ySoyszA5mffjEzmZn099W1sm+XozxtMTJ6PVLWhdevtr27ZtrF69mjvvvFPvkIQQotxJnx+hB+vLEoDExERyUpJ50sebo42DWVK3Nj4mExOiYwg8HM4LMec5X1Cgd5i3xM7OjqVLlxIcHMyQIUM4cOCA3iGJcpSYmEhiYqLeYQghRLVklcnPsGHDGDZsGABGpRjq7saO4AZsb1ifbs5OvB2XQO3DEYw5E8WhnFydoy05d3d31qxZQ6dOnfDy8tI7HFGOLr+HhRBCVCyrq/m5kY7OTix1duJ4Xh4fxCeyMDmZRckp9HZxZpqvDz1dnFGVvC4oICCA1atXA1BUVERWVhaurq46RyWEEEJUHVa58nMzDezs+DjQn7NNQplRswb7c3LpdeIULSOP8VVSCvlms94hlsjIkSPp378/ubnWs3olhBBCVHZVMvm5yMtk4kW/GpxpEsKCoAAKNI1RZ6OoeySCd+LiSS0s0jvEG7r77rvZunUrI0eOxGwlCZsQQghR2VXp5OciO4OBsV6eHAppxJr6dQm1s+e5mFgCD4czKTqG03n5eod4Tffccw/vvfceS5YsYcqUKdIEUQghhLAAq6z5efzxx0t1nVKKPq4u9HF1YX92DrPjE/gkIZE5CYkMc3djqq8Ptzs5Wjjaspk8eTJRUVG8//771K5dm8mTJ+sdkrCA0t7DQgghys5q+/xYSnR+PnMSkvgsKYm0IjOdnZyY6uvNADdXDJWkONpsNjN+/HgeeughOnTooHc4QghhMdLnR+jBKpOfqKgoAAIDA8s81kUZRUUsSLrQNPFsQQGN7GyZ7OvDKE8PHAyV6+lgXFwcNWrU0DsMUQblcQ8LYY0k+RF6qFzf1Uto5MiRjBw50qJjuhiNTPL14USTEH6oE4Sr0cjjUecIOhzOK+djiS8otOh8pfXpp58SHBzMP//8o3coogzK4x4WQghRMlaZ/JQnk1Lc6+HOX40asKVhPTo4OvJ6bDxBh8N59Gw0ETq/dt6vXz+cnJwICwu7tHoghBBCiJKT5Oc6lFJ0cXbm1/p1CQ9txGhPD75OTiE0/CgDTpxic0amLm9fBQUFsXr1atLT0wkLCyM1NbXCYxBCCCGsmSQ/JRBib8//ggI42ySUV/1qsDM7m+7HT9Im8jjfJadQUMFJUPPmzVm2bBlHjx5l8ODB0gNICCGEuAWS/NwCHxsTr9SswdkmoXwW6E+muYgHz0RR/3AEs+MSSC+quKaJPXv2ZNGiRYwaNQpDJSvIFkIIISozq3zba8WKFQAMGDCgzGOVhVnTWJWewez4BLZkZuFqMPCItydP+XgTaGtbobGcPn2aOnXqVOicovQqyz0shN7kbS+hB6tMfiqj3dnZzI5L4KfUNBRwj4c7U329aeVY/k0T9+3bR8eOHXn77bd56qmnyn0+IYSwFEl+hB6s8nlJZGQkkZGReofxL20cHfm+bm1ONAlhoo83K9LSaR15nB7HTrA6LR1zOSaZzZo1o2/fvkyePJklS5aU2zzCcirjPSyEENWFVa78dOvWDYDNmzeXeazyklZUxOeJyXyYkEh0QQGh9nZM8fFmhKcH9uVQo5OTk0OvXr3YvXs369ato0uXLhafQ1iONdzDQlQEWfkRerDKlR9r4GY0Mq2GDyebhPBN7UDslOKRqHPUPhzBG7FxJBZatmmig4MDv/zyC3Xr1mXQoEGcPn3aouMLIYQQVYUkP+XMRike9PRgb3BD1jeoS2tHB14+H0fQoXCeiDrHsdw8i83l5eXFmjVrmDBhgmybIIQQQlyHJD8VRClFTxcXVtevy6GQRtzv4c6CpGSCwyO5++RptmVmWaRpYp06dXj99dcxGo1ERUWRlpZmgeiFEEKIqkOSHx00cbBnQe1AzjQJ4cUavmzNzKLzsRO0P3qcn1JSKbRAEpSXl0eXLl0YMmQI+fn5FohaCCGEqBqssuB5/fr1ANx5551lHqsyyCoy82VyMu8nJHI8L586tjZM9vFhrJcHzkZjqcf9+uuveeihh3jggQf4+uuvpRliJVLV7mEhSksKnoUerDL5qaqKNI1f09KZHZ/A9qxs3I1G/uPtyQQfb2rZ2JRqzJkzZ/LCCy/wzDPP8M4771g4YiGEKBtJfoQerHIpYP/+/ezfv1/vMCzOqBSD3d3Y1qgBOxrV504XZ96NS6DO4QhGnYniYE7OLY/53HPP8fjjj/Puu+/yzTfflEPUojSq6j0shBDWwCpXfqpTj5STeXl8kJDIgqRkss0avVycmebrQy8XZ5RSJRqjqKiIt956i4kTJ+Lm5lbOEYuSqE73sBA3Iis/Qg9WufJTndSzs+OjAH+imoTyVk0/DuXk0vvEKZpHHGNRUjJ5JdjR3Wg0Mn36dNzc3MjOzubgwYMVELkQQghROUnyYyU8TSae9/PlVJMQFgYFoAFjzkZT93AEM2PjSSlh08THHnuMbt26ER4eXr4BCyGEEJWUJD9Wxs5gYLSXJwdDGvJb/bo0dbDnhfOxBB6OYGL0OU7m3bhp4muvvYaNjQ19+vTh/PnzFRS1EEIIUXlI8mOllFLc5erCugb1OBDSkGHubvwvMZmGRyIZfuoMu7Kyr3ldvXr1WL16NUlJSfTt25f09PQKjlwIIYTQl1UWPP/5558AdOzYscxjVSXn8guYk5DIZ0nJpBYVcYeTI1N9fRjo5orxiuLoNWvWMGDAAIYPH87333+vU8TVl9zDQlwgBc9CD1aZ/Igbyywq4oukFN5PSOB0fgEN7GyZ4uPDKC8PHC9rdLh48WJatmxJw4YNycyEb7+FefPgyBEwm6FePXj4YRgzBry8dPxAQogqS5IfoQerfOz1559/XvrJWVzN2Whkoq83xxqH8GOdIDyNRp6IPkfQoXCmx8QSV1AAwD333EPDhg3ZtUujdu31/PYbvP02JCVBaip88QX88w8EB8OqVfp+pqpG7mEhhNCPVa78SI+UW6NpGtuzspkVn8CvaenYKsUIT3em+PhgPmFPx45fkZExik8//ZT//Oc/V12/cycMGgTffQc9e+rwAaoguYeFuEBWfoQeTHoHIMqfUopOzk50cnbiaG4e7ycksCgphQVJKXiFuzDqk0GcWtyf8ePHU7NmTQYNGvSv69u3h0WL4PHHISICZIswIYQQ1qxM38aUUu5KqSVKqQilVLhSqoOlAhPlo5G9HZ8GBhDVNJQJNjVI9svh46ZniH7nv9R5Yjz3jRjBzp07r7quTx9wcIANG3QIWgghhLCgsv4M/yGwVtO0EKA5IJ3zrIS3yUTNFTV4fGUInwf6k68UJ8eOo2DpL9z19becz8j41/lKwejRsGSJPvEKIYQQllLqx15KKTegCzAaQNO0fCDfMmHd3P79+y/VTVx0zz338MQTT5CdnU3fvn2vumb06NGMHj2axMREhg0bdtXxxx9/nHvvvZeoqChGjhx51fGpU6cyYMAAIiMjeeyxx646/tJLL3HnnXeyf/9+Jk2adNXxt956i44dO/Lnn3/ywgsvXHX8gw8+oEWLFqxfv54ZM2Zcdfyzzz4jODiYFStWMHv27KuOf/311wQGBvLjjz/y6aefXnV8yZIleHt7s2jRIhYtWsSJE2BrC4f3go9SzPhxMf81Gtg57hECD4Xjt3MHAX9sxj4lBYDx4zezfTvMmjWLlStX/mtsBwcH1qxZA8Abb7zBhiuWiLy8vPj5558BeP7559mxY8e/jgcEBFzaeHXSpElXbfrZqFEj5s2bB8Cjjz7K0aNH/3W8RYsWfPDBBwCMGDGC6Ojofx3v0KEDM2fOBGDo0KEkJSX963jPnj2ZPn06AGFhYeRcsYls//79mTZtGsBV9x3c+r138fNdHKu63XtXWr16NY6OjsydO5fFixdfdfxibZTce924kp7/vyc1a8JalaXmpy6QACxUSjUH9gBPaZqWdflJSqlHgUcBgoKCyjDd//vggw8YOWQkubG5//p64s5ETjqdJCcv56pjAPHb4jmpTpKckXzN43Fb4jiZe5KYpJhrHo/dEMvJ5JNEnY+65vHz685z8txJos9EX/P4udXnOHnsJOeOnbvm8ehfo3E94Mr5w+eveTxqWRQ2NW2I3Rd7zeNnfjpDgVcBcbvirnn89A+nSXdJJ35bPLmxuWg5JnJzINfmwtYYwcvj+dbOkXf/WcGSOo6c69yFc5274HNgP4GbN5KeDk5OVw0rSqFBgwZ6hyCEENVWqd/2Ukq1AXYCd2iatksp9SGQrmna9OtdY8k+Pye/PGmRcaqzXRH2vPyVN2vfjObKDeJjkmLoPvt+ak1+mpRePcgwm/E86cRI5cN7g10wlHBHeSGEuBF520vooSw1P9FAtKZpu4r/vARoVfaQbm79+vVsP7y9Iqaq0m4PzkUBfx5xuOpYLa9avN77Cc4+/RRh777Ps7Z+pDrn82Hd0zQOP8q8xCRySrCjvLi29evXs379er3DEEKIaqnUyY+mabFAlFIquPhLPYEjFonqJmbMmMEnv35SEVNVaUrBlKHJPLPAh7PxVz8BvbfbvUwYNIHF333BF90+5r+RIXxXOxAng4HHos5R+3A4r52PI6GgZDvKi/83Y8aMa9bWCCGEKH9lfdtrAvCtUuog0AJ4q8wRiQp1V+tsxg9I4d63arFwnSvpWf9/S+TkKWp5voST/WgSEt+gVaM/uN/Tg93BDdjUoB63OzryamwcQYfD+c/ZaCJzr64zEkIIISqbMjU51DRtPyDPaq3cA90zaByUz5e/u/LRcg9q1yjAoOBMnA2tGuTy4eOvkZvfhjqptSnMKsTkZKKbizPdXJwJz83lvfhEFiWnMC8pmQFurkzz9aGTkyNK6oKEEEJUQtLhWQDQon4eLeonkJJp4Gy8DUVm8PcupIZ7UfEZYZjzzWz43wbc2rvR/o72AITa2/N5UAAzatbgk8Qk5iYk8WtaOm0dHZjm68MQdzdMkgQJIYSoRGSjAvEvHs5mmtfLo1WDPGp4FP3rmGbWmDpnKv379edY5LF/HathY8PrNf042zSUTwP9SS0q4t7TZ2lwJIIP4hPIKPr3WEIIIYRerHJj08jISKKWRVGvZj0LRCWuSwFX3B6nYk8xfMZw3Fzd2LVvF76+vte8tEjTWJGWzuz4BLZlZeNmNPCYlxcTfLwIsLUt/9grucjISACCg4NvcqYQVZu86i70YJXJD0ifnwpxjeQHYN/xfYx4dwSNGzTmj7/+wOkmnQ93ZWUzOz6Bn1PTMAD3ebgz1deHFo5Xv2IvhKheJPkRerDKx14rVqxgwz7ZYVMvLRu05MPHP2R/+H7eePqNm57fzsmRxXVrc7xxCON9vFmWlk7LyGPceewka9LSqcgEvLJYsWIFK1as0DsMIYSolqxy5adbt27kxuby3fPfWSAqcV3XWfm5aPvh7dze+Hb8u/njXMe5xMOmFhYxLymJDxMSiSkopIm9HVN9fXjAwx07g1Xm47fs4h5NsjeSqO5k5UfooXp8pxHl4o4md2CjbIhYHcFnH3xW4uvcTUaeqeHLqcYhfFU7ECOKsWejqXM4gjdj40gqlKaJQgghyo8kP6LMFqxewH8m/4d5c+bd0nW2BgMjPT3YH9KQ3+vXpbmDPS+dv9A08cmoc5zIyyuniIUQQlRnkvyIMntq8FN0atKJJyY9wcplK2/5eqUUd7q6sLZBPQ6GNOQed3fmJSXT8EgkQ0+eZkdWVjlELYQQorqS5EeUma3Jlk+e/ISQwBDufeBe/t75d6nHus3BgYW1AzndJITnaviwKTOLjkdP0PHocX5OTaOoGhZHCyGEsCyrLHiOiorizE9nqOVVywJRieu6ScHzleJT4xk2Yxi1fGux88BODKay59ZZRWYWJifzXnwip/LzqWdry2Rfb8Z4euJktN7cPSoqCoDAwECdIxFCX1LwLPRglckPSJ+fCnGLyQ9caILo6uxKYONAanSrYbH9vYo0jeVp6cyKS2BndjYeRiOPe3vxpI8XNW1sLDKHEKLiSfIj9GCVPzr/+OOPrNx167UlovzV9auLl7MX6WfTeWn8S2RnZ1tkXKNSDHV3Y0dwA7Y3rE83ZydmxsVT53AEY89EcTjHunaU//HHH/nxxx/1DkMIIaolq0x+Pv30U77bKD1+KrO9kXuZ+b+ZDOs/jEILv7re0dmJpfXqcLRxMOO8PPkhJZWmEUcJO36K9ekZVtE08dNPP+XTTz/VOwwhhKiWrDL5EZXf7cG388qDr7Bm0xr+M/o/5ZKQNLCz45NAf6KahjKjZg325eTQ68QpWkYe4+vkFPLNZovPKYQQwvpJ8iPKzcg7R/JY38dY8O0C3njx5ttglJaXycSLfjU43SSEBUEBFGgaD52Jou6RCN6Niye1UHaUF0II8f8k+RHlatqwaQzqMIj/fvBfoo5Gletc9gYDY708ORTSiDX16xJiZ8+zMbEEHg5nUnQMp/Pyy3V+IYQQ1sGkdwCiajMYDLz98NucjT9L0b4iCgMKMTmW722nlKKPqwt9XF3Yn53D7PgEPklIZE5CIsPc3Zjq68PtTo7lGoMQQojKyypfdU9MTOT0D6fxdPG0QFTiukrxqvvNxvvmj2/o92g/Wt/e2oID31x0fj4fJSTxWWIS6WYznZ2cmFbDm/6urhgs9Dr+rUhMTATA29u7wucWojKRV92FHqzysZe3t7ckPlYoIzuD/y37H3379OXUiVMVOneArS3v+tckumko7/vX5Ex+PoNOniE0PJLPEpPIqeDiaG9vb0l8hBBCJ1aZ/CxatIglW5foHYa4RS4OLiycupDcvFzu6nEXSUlJ5TJPYWEuv//+LN9/P+jqGIxGJvn6cKJJCN/XCcLVaOQ/UecIOhzOq+djiS+omB3lFy1axKJFiypkLiGEEP9mtcnP0m1L9Q5DlEJD/4Z89tRnnI05S7+e/cjJybHo+GfPbmPOnEbs2DGLoqLrNz40KcV9Hu781agBmxvUo4OjI6/FxhN0OJzHzkYTmVu+TRMl+RFCCP1YZfIjrNvtwbcz+9HZ7P5nN78s+sUiY+blpfPLLw/z9dd3kZ4ehcnkQJMm9930OqUUXV2c+bV+XcJDGzHK04Mvk1MICT/KwBOn2JKRaRVNE4UQQpScvO0ldNH39r40rt2Yui51yTqbhVOQU6nHioxcyS+/jCY/P+vSao/ZXEDDhn1vaZwQe3s+CwrgjZp+zE1M5JPEJLodP0lrBwem+nozzMMdGx2Ko4UQQliWrPwI3dSpUQetSGPp3KXMmjHrlq/Pykrghx/u5uef7yUnJ+lfj7nc3evh7FyjVHH52ph4taYfZ5uE8r9AfzLMRTxwJooGhyN4Pz6B9CJpmiiEENZMkh+hu6V/LOXp6U/z1fyvbum6H34YyNGjKygo+PfmqQaDDU2b3lvmuBwMBh7z9iI8NJhf6tWmjp0tU86dJ/BQOE+fiyEqX5omCiGENbLKPj/Z2dmc+uYUDnYOFohKXJel+/xcR15BHmNmj2Hvsb2s/HUld/W9q0TXJSSEs3z5KOLjD1FY+P+F07a2zowe/Qc1a7a0eKx/Z2XzXnwCP6WmoYB7PdyZ6utDS8dbuxcv7nbv6CjNFkX1Jn1+hB6scuXH0dFREp8qxM7Gjv9N+B91a9Zl2PBh7Nu9r0TXKWUgIeEIhYU5GAwmlDJgMjlgMJjw82tRLrG2dXLk+7q1OdEkhAk+3vySlk6ryGP0OHaC1WnpmEv4w4Sjo6MkPkIIoROrTH7mzp3LNxu+0TsMYUGuTq4smLwAJzsnPpv5GZr5xklEXl46X3/d69IjLzs7NyZMOE6vXu/Sr9+nqHIuTK5ta8t7AbWIahLKu7X8OJqXT7+Tp2kacZT5iUnk3qRp4ty5c5k7d265xiiEEOLarPKxV7du3ciNzeW757+zQFTiuirosdfl4lPj8fX0xSnICd8uvtdMYjTNzDffhHHmzBaKivIwmRwZPXoz/v5tKzbYy+SbzSxOTWNWfAIHcnLxNZl40seLx7298DZd/VJlt27dANi8eXPFBipEJSOPvYQerHLlR1Rdvu6+YIbw3eE8OOhBcq/RbHDz5teIitpGUVEeNjaO9O07R9fEB8DWYGCEpwf7ghuyvkFdWjk48PL5OIIOhfNE1DmO5ebpGp8QQoj/J8mPqJQOnTzE9yu+5/7B92O+7BFSZORK/vzzvxQUZGMyOdC06f20bDlWx0j/TSlFTxcX1jSoy6GQRtzv4c6CpGSCwyMZfPI02zOzpGmiEELoTJIfUSn1vb0vz9/7PMvXLmfioxMBSEo6ys8/309hYQ5KmfD2DqVfv091jvT6mjjYs6B2IGeahPBCDV/+yMyi07ETdDh6goTmLdAM8s9PCCH0IB2eRaX1cJ+HOZ98nk8WfEJNf1+cPL6goCALAHt7Vx58cDVGo43OUd6cn40NM2r58XwNX75MTua9+EROjB6LfVIiH8UnMtbLA2ejUe8whRCi2rDKgmeAk1+etMg44gZ0KHi+ktlsZsLcCRyN385DY/OAfEwmB0aN2kRAQDt9gyulIk3j17R0ZscnsD0rG3ejkf94ezLBx5taNpU/mRPCkqTgWehB1t1FpWYwGBh9Tz0eHFnAhcTHkbCwD6028QEwKsVgdze2NWrAn43q09PFmXfjEqhzOIJRZ6I4aOGd7oUQQvybVSY/s2bN4vM1n+sdhqgA0dmbOJbzBUabXAry7Ph1iScmZb2Jz0WzZs1i1qxZdHByYknd2hxtHMxjXp4sSU2lecQxeh8/ybr0DCmOFkKIcmCVyc/KlSvZtH+T3mGIcpZecJqtCU9RpOWiMGFTGMjp03n0uasPUWej9A6vTFauXMnKlSsv/bm+nR1zAv2JahLKWzX9+Ccnl94nTtE84hhfJiWTf5OmiUIIIUrOKpMfUfUVmDNZHzuKQu1CB2cbgzPDgr/hiykLycjM4K5ud5GSkqJzlJbnaTLxvJ8vp5qEsDAoAA0YfTaaOocjeDs2npTCQr1DFEIIqyfJj6h0NE3jj4SJ5BQlABpGZU8P3/k4mHwIDQpl7sS5nDh7goF3DSQvr2o2D7QzGBjt5cnBkIasrV+Xpg72PH8+lsDDETwVfY5TebKjvBBClJYkP6LSOZj6MXG5f2EmD6NyoI3HS/jY//8O7Xc0voO3H36bYyeOceSPIzpGWv6UUvR2dWFdg3rsD27IUHc3Pk1MpsGRCIafOsOurGy9QxRCCKtjlcmPg4MDdrZ2eochysG57M0cTv8fRVoORuyp7RhGI9f7rzrv7o538/vM33GPcyc72voSAAcHBxwcHG7pmuaODnxZO5BTjUN42teH3zMyaH/0OJ2OHmdZahpFUhwthBAlIn1+xPVVcJ+fjIIzrIwZQKGWhcKIu01Dwmotw6hsr3uN2Wxm5uKZNO7UmKkvTq24YCuBjKIivkhK4YOEBE7nF9DAzpbJPt6M9vLEUbpHCyshfX6EHuT/IUWlUGDOYn3cvwuce9RYeMPEB0BDIzohmqenP80PX/9QEaFWGi5GI0/5enOscQg/1gnCw2hkfHQMQYfCmR4TS1xBgd4hCiFEpWSVyc8bb7zBnF/m6B2GsBBN09ia8BTZhXFcXuDsaPK96bVGg5H3H3ufVg1aMerhUWxev7nc47WEN954gzfeeMMiY5mU4h4Pd3Y1asAfDevTydmJN+PiqX04gnFnoziSk2uReYQQoqqwyuRnw4YN7DiyQ+8whIX8k/YJsbk7MZOPUTnQ2uMFfOxblfh6e1t7PnvqMwK8Axh09yAOHTxUjtFaxoYNG9iwYYNFx1RK0dnZieX16hARGswYLw++TU6lScRR+p04xcaMTGmaKIQQWGnyI6qOc9lbOJT2aXGBsx21HfsQ7PrgLY/j4ezBwikLcXVwZfey3Wjm6v1NvpG9HZ8GBnC2SSiv+dXg7+xseh4/SevIY3ybnEKBJEFCiGpMkh+hm4yCM/yRMKG4g7MRF5s6tPd+s9TjBfgEsG7mOrrW70rCjgTM0hUZHxsTL9eswdkmoXwe6E+upjHiTBT1DkcwKy6BtKIivUMUQogKJ8mP0EWBObu4wPnCJp42Bid61liIUZWthYGdjR1akcY3X39D7y69yc+XZoAA9gYD47y9OBTSiJX16tDQzpanY84TeCicqdExnJW/JyFENWKVyY+Xlxfuzu56hyFKSdM0tiU8RXZhPGDGqOzp7vs5jqYaFpujqKiI9dvXM3L4yEq5AuTl5YWXl1eFz2tQin5urmxsWJ89wQ3o7+bKhwmJ1DscwQOnz7In2/p6JgkhxK2SPj/i+sqpz88/qXP5J23uhTof5UArj2cJcR1p8Xk++fUT3lv6HlOemMLsT2ZbfPyq4mx+Ph8lJDIvMZkMs5luzk5M9fWhr6sLBqX0Dk9UcdLnR+jBKld+hPWKyd7KP2mfXCpwDnK8i2CXEeUy1xMDnuD+bvfz3tz3+OCdD8pljqogyNaWWf61iGoayqxaNTmRl8+Ak6dpEn6UeYlJ5FTClTMhhCgLq0x+nn/+ef7703/1DkPcooyCKP5IePKyAufadPCeiSqn1QWlFK+OfJU7W97Jqb9OUZBZeZr+Pf/88zz//PN6h/EvbkYjU2v4cKJJCN/WDsTRYOCxqHPUPhzOa+fjSCiQHeWFEFWDSe8ASmPHjh3kxkrjNmtyocD5IQoudnBWlilwvhmT0cTcCXMxGo3ErImhZt+a2DrduGt0Rdixo/L2qbJRigc8Pbjfw53NmVnMjk/g1dg43o6LZ5SnB1N8fWhkL3vrCSGsl1Wu/AjrcqHAeVJxB+fiAucan+No8quQ+Y0GI2iw78g+Ggc3JvxweIXMa+2UUnR3cWZl/bocCW3ECE8PFiWnEBIeyd0nT7M1M0uaJgohrJIkP6LcHU7/jPO5f2Imr7jA+Rl87Su+vtHd0Z3UjFR69+zN+ZjzFT6/NQu1t+fzoADONAnhJT9ftmVm0eXYCdofPc7ilFQKJQkSQlgRSX5EuYrJ3sbB1DnFb3bZE+h4J8EuD+kSS5BvEPMnzycpNYne3XuTnp6uSxzWrIaNDa/X9ONs01DmBviTUlTEvafP0vBIBB/EJ5AhTROFEFagzMmPUsqolNqnlFppiYBKIiAgAD/PinlkIkovsyCaPxLGXypwdjYF0tHrnXIrcC6JZnWbMWf8HI4cP8LgsMEU6LTzeUBAAAEBAbrMbQmOBgOP+3gRHhrMsrq18bexYfK58wQeDufZc+eJlqaJQohKrMx9fpRSU4A2gKumaf1vdK70+bEyZejzU2jOYWVMfzILz6Jhxka5MMB/DU6mmhYNsbR++uMnNuzfwDdffYNvk5vvHi9ubldWNrPjE/g5NQ0DcL+HO1N9fWju6KB3aKISkz4/Qg9lWvlRSgUA/YD5lgnn5jJTc3nnvp85ERkvxZaV1IUC5ylkF51Hu1TgPK/SJD4Aw7sM59MJn5K1L4u0M2l6h1MltHNyZHHd2hxvHMITPl4sTUunReQxeh0/ydr0DPn3KoSoNMr62OsD4Bngul3QlFKPKqV2K6V2JyQklHE6OHs4ge2/HOLDmb/z3hu/sfevMxQVSRO2yuRI+ufE5G6lSLtQ4NzCfRo17G/XO6yrKKVISk2iQ5cOfPzexxU696RJk5g0aVKFzllR6trZ8mGAP1FNQni7lh9HcnMJO3GKZhHHWJiUTJ40TRRC6KzUyY9Sqj8Qr2nanhudp2naPE3T2mia1sbHx6e0013S+I5A0ttsIdP3H7Kz8lk0dxtvPPsrm9dFkJtTeZrYVVfnc/7kQOqHlwqcAxx6EOo6Wu+wrsvV0RU/Dz+emvYUy35cVmHz7t+/n/3791fYfHrwMJl4toYvpxqH8GVQIAZg7Nlo6hyO4K3YeJILpWmiEEIfZVn5uQMYqJQ6DfwA9FBKfWORqG7GaCbX4ywvzuzPuAldcPdwZOl3e3hlyjJ+XbyP1BTZnFEPmYXn2BL/+KUCZyeTP3d4/1fXAuebMRlNfPTERzSt05QHHnqA7X9s1zukKsfWYOAhLw/2hzRkXf26NHew58XzsQQeDmdC1DlO5OXpHaIQopqxyMamSqluwLSKKnju1q0bubG5fPf8d5e+dvpEIhvXhHNgTxQGg6J1+9p07xOKf6BHmeertm6h4LnQnFtc4HzmsgLn1TiZapVriJaSmJ7I8BnDyczNZPvO7YSEhpTrfN26dQNg8+bN5TpPZfVPTg7vxSfybXGPoMFurkyr4UMHJye9QxMVTAqehR6qTJ+fOvW9GftkZ15+dyCdejRk/+4o3pm+mk/+u4Hwf2Kk2LIcaZrG9sQpZBfFXFbg/JnVJD4A3q7eLJy6kEb+jcjclyn3Szm7zcGBhbUDOd0khOdq+LApM4uOR0/Q8ehxlqamUSR//0KIcmSRlZ+SstTKz6OPPkr60XTeGvPWdc/Jzspj++bj/PF7JGmpOdQMcKNH71Bata+DjY2xzDFUCyVc+TmcNp8DqR8U1/k40MJ9Co3dxpZ7eOVFGRVO9ZxwaemCg0P5vKb96KOPAjBv3rxyGd/aZBYVsTA5hffjEzmVn099W1sm+3oz2tMTJ2OV+RlNXIOs/Ag9WGXyAyXv81NYWMTeXWfYuDacmKhUXN3s6dIrmDu6NcTJWTZnvKESJD+xOTvYGD+OIi0Xo7LD36EHXXzmVOo6n5vRNI1Jn00i15TLmk1rMJmscv9fq1SkaSxLTWN2fCI7s7PxNBp53NuLJ3288LOx0Ts8UQ4k+RF6qPLJz0WaphF5OJaNa8OJOHQeW1sj7TvXp1vvELx9XSwSU5Vzk+QnqzCGFefCKNAyURhwMdWhX60VmAz2FRZiefl+8/e8tOglHrrnIRb9sMiqkzlrtb14R/nlaenYKMUID3em+PrQxMH67y/x/yT5EXqwyh9pS/LY60pKKUKa1iSkaU3ORaWwaW0E2zcfZ+vGYzRrHUCPPo2p28C7HKOuWgrNuayPHUWhlgOASTlyp9+XVSLxAbi/2/2cTzrPJ4s/ITAokBn/nWHR8eWx183d4ezEHc5OHMvN44OERBYmJfNFcgp9XFyYVsObHs7OkpQKIUrFKpOfo0ePkhubW+rr/QM9GPFIBwYMa86W9ZFs33ScA7ujqNvAmx59QrmtVQAGg9QZXM+FAudpZBWdQ6MIo7Knm691FTiXxOQhk4lNieXNWW8SGBjIYxMfs9jYR48etdhYVV1Dezs+CfTntZo1+F9iEnMSkrjz+CmaO9gz1deHe93dsJV/r0KIW2CVyY+luHk4MnB4S3oPaMrOrSfZvC6CBR9vxdvXmW53hdCuc33s7Kr1X9E1hacv5FzOZoq0PEzKgWbuk/BzaK93WBanlOLN0W+SX5iPZ7InhVmFmJzkftCLt8nES341mObrw7cpqcyOT+ChM1E8HxPLRB8vHvXywt0kLzMIIW7OKmt+rtXnxxLMZjMH9kSzaW04p08k4uhkS6ceDenSMxhX92q4OeM1an7icnexIW7sZQXO3eni83HVf/ygwORkwuEOB3z8yt6pvLr3+bEEs6axNj2D2fGJbMzMxNlgYJyXJ0/5eFPHzlbv8EQJSc2P0IOsFV/GYDDQsm0QU6b3ZtKLvWgQUoPfVx7mlWnL+W7BTs6fS9U7RF1lFcawKe6x4g7OBhyNtbjDe3bVT3wANFj460Iahzbm+NHjekcjAINS9HVzZUPDeuwNbsjdbq58nJBI/SMR3HfqDH9nSad3IcS1WeUafosWLUg7Ur47cddr6Eu9hr7Ex6azeV0Eu7adZOfWEzRuVovufUJpFFqjenzTL1ZozmV93GgKtQvfUKpagXNJ3BF6Bx8u/ZC7etzFzr078fX1LfVYLVq0sFxggpaODnxdJ4iZtfz4KCGJzxKT+DE1jS7OTkz19aa/qyuGavTvVQhxY1b52Atu/VX3ssrMyGX7pmP8sf4oGem5+Ad50KNPKK1ur43RVEUX0Iofe2maxtaEiUTnbCx+3GVPd9/PqenQUe8IK9ze43sZ8c4ImjRswh9//YGTbMdQKaUXFbEgKZkP4hM5W1BAIztbpvj68JCnBw5SHF2pyGMvoQdJfm5RQX4Ru3ecYuNv4cTFpOPm4UC3XiF06NoAR6cqVmdQnPwcSVvI/tTZlzo4N3efSBO3R/WOTje/7/2dJ+Y8wV1d72L1xtXVagXQ2hRqGktS05gVl8CenBy8TUbGe3vxhLc3vjZWufBd5UjyI/RglcnPiBEjyDyZyXuPvWeBqErHbNYI/yeGTWvDORoeh529iQ5dGtDtrmA8vZ11i8uiFMTl/MWGuDGXCpxrOXSlq8/cav8N/7uN32GyMfHkS0/iXOfW/3uPGDECgG+++cbSoYlr0DSNP4qbJq5Iz8BOKUZ5ejDF15tg++rz6LYykuRH6MEqf/SJjo4mN7n0fX4swWBQNGnuT5Pm/kSdSWbT2nD+2BDJH+sjadE2iB59Qgmq66VrjGWVVXieTXGPXlbgXJNO3u9V+8QH4IEeDwCQsC2B+PR46jWrd0vXR0dHl0dY4jqUUnR1caarizMRubm8H5/Il8kpzEtKZoCrC1N9feji7CT3thDVhDz8toDA2p489NgdvPLuILrdFcKRgzHMem0tH838nX/2RWM2W98O1UXmPDbEXlHgXONLTIZq+Mr/DRw4foCmtzdl/tz5eociSijE3p7PggI42ySUV/x82ZGdTbfjJ2kbeZwfUlIplB3lhajyJPmxIA8vJ+6+rxWvvTeYu+9rRVJCJp9/uIW3XljJ9k3HyM8v1DvEEtE0jT+TniGzIOpSB+cuPnNxtgnQO7RKJyQwhFYNWvH4xMdZ/ctqvcMRt8DXxsSrNf042ySU/wX6k2Eu4v7TZ6l/OIL34xNILyrSO0QhRDmR5KccODjY0KNPKC+/O4hR/7kDWzsTP375F69MXc7qZQfJSNf3kd3NRGZ8RVT2BorIw6gcaOY2gVqOd+gdVqVka7Llkyc/oZF/I4bfN5zduyxT0C8qjoPBwGPeXoSHBvNLvdrUsbNlyrnzBB4K5+lzMUTl5+sdohDCwqyy5qdDhw6k/pOqdxg3ZTQZaN2+Dq3a1eZ4ZDwb14Sz9pd/WL/qMLd3qkf33qHUqOmqd5j/Ep+7m70p71Kk5WJQdtSy70QTN8vtaVUVuTi4sGDKAobNGEbfPn3Ze2AvAUE3XiXr0KFDBUUnSsqgFAPd3Bjo5sbu7GxmxyXwfnwiH8Qncq+HO1N9fWjpKI99hagKrPJtL9DvVfeyiotJY9O6CP7adpLCQjNNW/jTvU8oDYJ9dS+2zC6MZcW5MPK1dMCAiymQ/rVWSZ1PCR2POc7P23/m5UkvU6tnLd3/e4qyO5Ofz4fxiXyelEym2UwPZ2em+nrTx9VFmiZaiLztJfQgyY9OMtJz2brhKFs3HiUrI4+gup706BNK8zZBGI0V/zSySMtjdczdpBWcQKMIG+VMP/+VuJgCKzwWa6eMiiyvLOp1rYejo6Pe4QgLSC0sYl5SEh8lJHGuoIBQezum+vrwoIc79tI0sUwk+RF6sMrkZ+jQoWSdyWLuhLkWiEpf+fmF/L39FBvXhpMQl4GnlxNd7wqmQ5cG2DvYVEgMmqaxLXEKUVnrKOJCB+duPv+jllPnqzY2FTeXk5dDnxf70Lx5c3757ReMxqt3Gh86dCgAP//8c0WHJ8og32xmcWoas+ITOJCTi6/JxAQfLx739sLLZJVVBLqT5EfowSp/ZElKSiI1M1XvMCzC1tbEHd0b8uLMAYyb2AUPL0eWfb+XV6Ys45cf95KSXP6bM0ZmfENU9u/FiY8Dt7mNp5Zj53Kft6pysHNgTO8xrNqwisfHPM61fsBISkoiKSlJh+hEWdgaDIzw9GBfcEPWN6hLa0cHpp+PI/BQOOOjznE8L0/vEIUQJSA/qlQSBoOiWatAmrUK5MzJJDatDWfTbxFsWhdBq9tr071PKIG1PS0+74UC57cvFDhjR037DjR1e9zi81Q3o3uN5nzSeT7/+nMCgwKZPmO63iEJC1JK0dPFhZ4uLhzOyeW9+ATmJyXzaWISd7u5MtXXhzucZd83ISorSX4qodr1vBj9RCeSEjLZ8nskO/44zu4dp2kUWoMeYaGE3maZYtrswjg2xT9CkZYLGHA0+dLJ50Mp1LWQZ+95lrjUOF5+82VCQkIYPmK43iGJctDEwZ4FtQN5s5YfHyck8WliEsvS0mnv6MhUX28Gu7thlH9TQlQqkvxUYl4+zgx5oDV9Bt3Gn1uOsWVdJP97bzM1/d3o1juUNh3qYGNzdT1JSRRpeWyIG0OBOQsAk3LgzhpfYWOQAl1LMRgMvPPwO9T0rElwfjCF2YWYHOWfXFXlZ2PDjFp+PF/Dl0XJybwfn8jw02epZ2vLJF9vxnh64HyN+i8hRMWzyoLnN954g+R9yUwYNMECUVmPwsIi9u46w6a1EZyLSsHVzZ7OPYPp1KMhTs52tzTW9oRpnMlac6nAuavPp/g7dvn3ScW7ugsLUFBkW4S5mZngxsG88cYbAEyfLo/DqqoiTeOXtHRmxSewIysbD6OR/3h7MsHHm5o2FfMygzWQgmehB6tMfsD6X3UvC03TOHoklo1rwwn/5zy2tsZLTRN9arjc9PrI9G/Zk/JW8U7tDtzm9ji3uY+/+kRJfizqqf89xd9H/2bn7p3UqVtH73BEBdqRlcXs+ESWpqZhUooHPdyZ4uvNbQ7SQ0uSH6EHSX6sXEx0Kpt+C2f3jtOYi8zc1iqQHn1CqNfQ95rnJ+Tu5fe4kcUFzrbUdLiD7r6fX7vOR5Ifizp67ij3vHkPft5+7Ni7Ay8vL71DEhXsRF4e78cnsjA5mWyzxl0uzkz19aGXi3O1rbWT5EfowSqTn7CwMLLPZbNw6kILRFU1pKXmsHV9JNs2HSM7K5869b3p3ieU5q0DMBQ3YcsujGNFTBj55jRA4WwKoH+tVdgYrvNWiiQ/FrcrYhejZ43GwcGB29vfzrp16/QOSeggubCQ/yUmMychkdjCQm6zt2eqrzf3e7hjW82aJkryI/RglclPt27dyI3N5bvnv7NAVFVLXl4hu7aeYNNvESQlZOLl40y3u0Jo2ymQDcnDSSs4hkYRJuVE/1orcLGpff3BJPkpF6v+WsXEuROp4V2D2IRYvcMROsozm/k+JZXZ8Ykcys2llo2JCd7ePObtiUc1aZooyY/QgyQ/VZTZbOafvdFsWBvO6eOJ2DoU4t1mFz63b8fBtZCuPp/g79jtxoNI8lNu7nz2Ttw83NgXvk/vUEQloGka6zIymRWfwPqMTJwMBh728mCSjw917Wz1Dq9cSfIj9FA9frSohgwGA83bBNG8TRBb9n/Lpt+OE7O1A+f/bEeD1kWogS1A3mrXja+7L0Z7I5qmsXPnTtnlvZpTStHb1YXeri4cyM7hvYRE5iYk8XFCEkPd3Zjq60M7J/kHK4SlVK+Hy9VQQu4+znm8Rf17v6P5xE+p0+48Z/Y78vZLq5g7ayORh89fc/sFUTG++OILOnbsyHffySqmuKC5owNf1g7kdJNQpvn6sC4jg/ZHj9P56HGWp6ZRJP9ehSgzq1z56d+/P0m7ZV+km8kpTGBj/LjiDs4KDx8bHho3gfwHTGzfdIw/1kfyyX83UivQnR59QmnVrjYmkzRhqwjdW3THztOOESNG8PXXXzN69Gj8/Pzo0aOH3qGJSsLf1oZ3/Gvykp8vXySl8EFCAoNPnaGhnS2TfXwY5eWBYzUrjhbCUqyy5gfkVfebKdLyWRMzhNSCY2gUYlJO9Kv1K642dS6dU1BQxJ6dp9m0Npzz59Jwc3egS69g7ujWAEcnO6n5KWfO9Zzx7exLSkoKnTt3Jioqiq1bt9KsWTO9QxOVUKGmsbR4R/m/s3PwMhp5wseL8d5e1LDipolS8yP0IMlPFfVnwrOczl5Z3MjQni4+HxPg2P2a52qaRvg/59n0WziRh2OxtTPRoUt9uvUOwcvbuYIjrz4uJj8AUVFRtG/fHhsbGyIjI7Gzu7WO3aL60DSNbVnZzIpPYEVaOrZKMdLTgym+3oTa2+sd3i2T5EfowSqTH3nb68aOpn/P7pQ3KdJyMCoHmriOo7nHpBJdG30mmU2/RbBn12k0MzRvE0iPPqHUqe9dvkFXMw/MfACjg5Ht+7Zf+to///xDVFQUffv21TEyYU2O5ubxfkICi5JSyNU0+rm6MM3Xh67OTlbTNFGSH6EHq6z5EdeXkLef3SkzLnVwrmHXlmbuT5X4+oDanox8tCP9h7Xgj/WR/LnpGPv/Pku9Rj706BNK0xYBGAzW8X+q1ua2227jtttuA2Dz5s20b98eeyv8SV5UnEb2dnwaGMDrfn58mpjEx4mJdD9+klYODkz19Wa4hzs2VpIECVGRpFquCskpTGBj3MOXCpwdjD508Z1Tqp8APTwdGXRvS157bzCD729NalI28z/6gzefX8G2jUfJzyu0/AcQAJw4cYI777yThx56CLPZrHc4wgr42Jh4uWYNzjQJZV6gP1lmMw+eiaL+4QhmxSWQVlSkd4hCVCqS/FQRRVo+G+LHUmDOAMCkHLjT70tsDGWr2bF3sKF77xCmvzuQ0U90wsHRlsVf/c0rU5ezaukB0tNyLBG+uEz9+vWZOXMmP/30E9OmTdM7HGFFHAwGHvH24khoI1bUq0N9O1uejjlP4KFwpkbHcDY/X+8QhagU5LFXFfFX0sukF5xEowijsqeT94e42tS12PhGo4FWt9emZdsgThyNZ+PaCNatOMSG1Udo27Eu3fuE4lfLzWLzVXfTpk0jKiqK999/n8DAQCZPnqx3SMKKGJSiv5sr/d1c2ZOdzXvxiXyYcOHXPR7uTPX1prWjNE0U1ZdVJj/33HMPiTsT9Q6j0jiWsZhTWRff7HKgses4Ap3Kp1+MUooGwTVoEFyD+Nh0Nv0WwV/bTrLjjxM0aV6L7n1CaRhSw2qKLfXS9/a+2Hlf/40upRTvv/8+MTExTJkyhS5dutC6desKjFBUFa0dHfm2ThAza/nxUUIi8xKT+T4llW7OTkz19aGvqwsG+fcqqhmrfNsL5FX3ixLzDrIu9v7/L3C2v52eNRailAWeaJawz09Gei7bNh5l64ajZGbkEVDbkx59QmjZtjZGkzxZvZ7LX3W/ntzcXH766SdGjBghCaWwiLSiIuYnJvNBQiLRBQWE2NkxxdebkZ4e2OvQNFHe9hJ6sMrkJzs7m1PfnMLBzsECUVmvnKJEVpzrQ545BVA4Gv0Y4L8GW4OLZSa4xSaH+fmF7P7zNBvXhhMfm46HpyNde4XQoWt9HByr9uaMtyonLwenuk7U6VWnxNccOnQIpRRNmjQpv8BEtVGgafyUksqs+AT25eTiazIx3tuLJ3y88K7AHeUl+RF6sMrkR/r8gFkrYM35YaTkRxR3cHakb61fcLOpZ7lJStnh2WzWOHLwHBvXhHM8Mh47exN3dGtI117BeHg5WS4+K3atPj83UlRUxG233UZGRgY7d+7E39+/nCMU1YWmaWzOzGJWfAKr0zNwUIpRXh5M9vGhkX35N9uU5EfowSprfgTsSnqFtILjaBQWFzi/b9nEpwwMBkXTFgE0bRHA2VNJbPotnM3rIti8LoKWt9emR59QAut46h2mVTEajXz33Xd06dKFsLAwtm7dipubFJiLslNK0d3Fme4uzhzJyeX9hES+SErhs8RkBrq5MtXXh05OjvLYVVQpUpBhhY5n/MSprF8vFTiHuo4l0OlOvcO6pqC6Xoz6Tyde/u8guvYK5tD+aP776hrmvLOew/vPYTbL5mEl1aJFC37++WfCw8MZMmQI+fLasrCwxg72fB4UwNkmIbzk58vWzCy6HDtB+6PHWZySSqHsKC+qCEl+rExS3kH+Sn6VIi0HA7b42LWihXvlfw3a08uJwfe35vX3BjPo3pYkxGXw2QebmfnSSv7cfJyCfGnCVhK9evXiiy++YOPGjbz33nt6hyOqqBo2Nrxe04+opqHMDfAnuaiIe0+fpeGRCD6MTyRDmiYKKyePvaxITlEiG+LGXurgbG/0oqvvJ5Z5s6uCODja0jOsMd16hbD3rzNs+i2cHxbtYtXSA3Tu2YhOPRri7CJbOtzIyJEjcXNzo3fv3nqHIqo4R4OBx328eNTbkxVp6cyOT2DSuRheiY3lMS8vJvp4429rvTvKi+rLKpOf0aNHE78tXu8wKpRZK2Bj3DjyL+/gXONLy73ZVcGMJgNtO9alTYc6HAuPY+PacFYvO8jvqw7TrlM9ut0Vgq+fq95hlpshnYZg71v6JG/gwIEAJCUlsXHjRoYPH26p0IS4ilEp7nZ34253N3ZlZTM7PoFZ8QnUsrHhKV/Z9FhYH6t82wuqX5+fnYkvcTJrWXGdjz2dvD8gyKlX+U5ayre9Suv8uTQ2rQ3n7x2nMBeZadoigB59QqnXyKdKFluWpM/PzUyaNImPPvqIn376iaFDh1ooMiFu7mReHr4mE85GY5nGkbe9hB6sMvlJTEzk9A+n8XSpHm8MHc/4mb+SX75U4BziMppWnhWw51MFJz8XpafmsHXDUbZuPEp2Vj6163nRvU8ozVsHYjRazyO+G0nOSMapthOhA0LLNE5OTg49e/Zk7969rF+/nk6dOlkoQiEqhiQ/Qg9WmfxUpz4/SXn/8FvsfZc6OPvat+bOGl9VTJ2PTsnPRfl5hezafpLNv0WQEJeBp7cT3e4KoX3n+tg7WHedwa32+bmRpKQkOnbsSEJCAtu3byc0tGwJlRAVSZIfoYeq8WN0FZVblHRFgbMnXX0/taoC57KwtTPRuUcjXpzZn3ETuuDu6cjS7/bwytTl/Lp4H6kp2XqHWCl4eXmxdu1abG1teeKJJ/QORwghKj2rLHiuDsxaIRvjHiHfnA5Yf4FzWRgMBpq1DqRZ60BOHU9k09pwNqwJZ9NvEbRuX5vufULxD/TQO0xd1a1bl3Xr1uHrW7YaIiGEqA4k+amk/k56ndSCyEsdnO/wno2bbQO9w9Jd3Qbe1H2yM4nxGWxeF8nOrSf4a/spgpv40SOsMSFN/KpkcXRJNGvWDIDCwkI+/vhjnnjiCWxtZU81IYS4kiQ/ldCJjKWcyPr5sgLnhwhyukvvsCoVb18Xho1oQ9/Bt7F903G2rI/k01kbqRXgTvfeIbRqXwcbm7K9hWKtNmzYwOTJk9m7dy9ffvlltU0GhRDieqwy+Xn88ceJ2xKndxjlIjnvMLuSp18qcPa2bUZLjwp4s8tKOTrZ0at/E7r3CWHPzjNsWhvOtwt2suLnA3S5sxF3dGuIk3P5b854qx7o8UCZ+vzcSO/evXn99dd5+eWXCQwM5M033yyXeYQQwlpZ5dteUDX7/OQWJfPrud7kmZMBcDDWYGCttdgadWr2p/PbXqWhaRoRh2PZtDaciEPnsbU10r5LA7rdFYy3b+Wql7JEn5/r0TSN//znP8ybN4+5c+fy+OOPl8s8QpSVvO0l9GCVyU9UVBRnfjpDLa9aFoiqcjBrhfx2/l6S8g+jUYBJORBWcxnutg31C8oKk5/LnYtKYdPaCPbsPI3ZrNG8dSDd+4RSt4H+HWljkmJwCnKi5bCW5TZHYWEhQ4YMYcuWLZw8eRIvL69ym0uI0pLkR+ih1MmPUioQ+AqowYVvkfM0TfvwRtdIn5/r+yvpVY5n/nSpg/Md3rOp7dRH36CsPPm5KC0lmy3rI9m+6Tg52fnUbeBNj7DG3NbSH4NBn7YBluzzcyNZWVmcOHHiUjG0EJWNJD9CD2X5f/5CYKqmaY2B9sB4pVRjy4R1Yzn2ORUxTYU5mbn8ssTHgWCXkfonPlWIm4cjA4e35PX37mbog61JS81hwZw/ePP5lWzdcJT8vEK9Qyw3Tk5OlxKfefPmERkZqXNEQgihv1IXPGuadh44X/z7DKVUOOAPHLFQbNe0I2oHf3X4C7fabmzP2U5H+45W/TZLct4Rdia9WFzgbIOX7W209Hha77CqJDt7G7r2CqFTj0Yc3BPFxrXh/PT136xaeoBOPRrSpWcwru4OeodZLpKTk5k+fTqOjo7s2LEDPz8/vUMSQgjdWGTNXylVB2gJ7LrGsUeVUruVUrsTEhLKPFcjr0bUOVmHbI9sHop7iIHnB7I8czkFWkGZx65oeUUpbIgbXdzBGeyMHnT3/QyDqp6vaFcUo9FAy9trM2V6bya92IsGwb78vvIwr0xbzncLdnL+XKreIVqcp6cnK1euJD4+nn79+pGRkaF3SEIIoZsyFzwrpZyBLcCbmqYtvdG5lqz5yY7PZugTQ1mQvoBjBcfwM/rxkOtD3O98P656vR11Cy4WOCfnH8ZMAUblQFjNn/GwDdY7tP9XRWp+SiI+Np3N6yLYte0kBflFNG5Wi+59QmkUWqNcVhYrqubnSqtXr2bgwIHceeedrFixAhsb694jTVg/qfkReihT8qOUsgFWAr9pmvbezc63VPKzYsUKYjfE0rNlT8yamT9y/mB++nx25O7ASTlxj8s9jHYZTYBNQJnnKi9/Jb3G8czF/1/g7PVfajv31Tusf6tGyc9FmRm5bNt4jK0bjpKRnot/kAc9+oTS6vbaGE2WK47esG8D9n72jHxupMXGLKkFCxYwbtw4Vq9eTVhYWIXPL8TlJPkReijL214K+BJI1jRtUkmuKe8+P4fzDrMgfQGrslahodHHsQ/j3MbRzK5yvelyKvNXdiQ9f6nAuZHLA7TxfEHvsK5WDZOfiwryi9i94xQbfwsnLiYdNw8HuvUKoWO3Bjg4WmbLiPLs83Mzhw4domnTprrMLcTlJPkReihL8tMJ2Ar8A5iLv/yCpmmrr3eNpZKfyMhIopZFUa9mvWsejymM4av0r/g+43sytUxut7udcW7j6O7QHYPOO6In54ez9vywSwXO3nYt6OX3beWs86nGyc9FZrNG+D8xbFobztHwOOzsTXTo2oBuvYLx9HYu9bgnz5/EMdCRjg92tGC0t27Tpk2cPn2aMWPG6BqHqL4k+RF6sMomhyXt85NhzuCnjJ9YmL6QmKIY6pnqMdZtLIOdBmNvKJ+tBW4kryiFX8/1IdecCICD0YcBtX7DzuhW4bGUiCQ//xJ1JplNa8PZ+9cZ0KBF2yB69AklqO6tNw/Uq+bnSoMHD+bXX39l+fLlDBgwQNdYRPUkyY/Qg77LIOXMxeDCWLexbAzYyPve7+NocOSlpJfoHN2ZD1M+JKkoqcJiMWtFbIp/lHxzGgBG5UDPGl9W3sRHXCWwticPPXYHr7w7iG53hXDkYAyzXlvLRzN/55990ZjN1pcpfv3117Rq1Yp7772XXbuuellTCCGqpCqd/Fxko2wY6DyQ5TWX853fd7Swa8FHaR/RObozLya+yIn8E+Uew57kt0jODy9+s8uejl7vVK43u0SJeXg5cfd9rXjtvcEMvr8VSYmZfP7hFt56YSXbNx8jP996miY6OzuzatUqatasSf/+/Tl27JjeIQkhRLmrFsnPRUop2tm34/Man7Ou1joGOw1maeZS7oq5i0fjHmVX7i7K4zHg6cyVHMv8gSItB6NyoKHL/dRx7mfxeUTFcnCwoXvvUF5+ZxCj/nMHdvYmflz0F69OXc6a5QfJSM/VO8QS8fX1Ze3atQB8/vnnOkcjhBDlr9Qdnq1dfdv6vOn9JpM9JvNt+rd8k/END8Q+wG22t/Gw68OEOYVhUmX/60nJj+DPpOcuFTh72jamtcfzFvgEorIwmgy0bl+HVu1qczwino1rw1mz/B/WrzpC2zvq0r13KDVqVu7eUw0bNuTvv/8mKChI71CEEKLcWWXB8/r16zm/7jx3NLnDAlFdkGvOZWnWUr5I+4JThaeoZazFGNcx3ONyD86G0r3Vk1eUWlzgfKGztb3Bm4H+v2FndLdY3OVKCp5LLTYmjc2/RfDX9pMUFppp2sKfHmGh1G/ki1KK7Ye3Y1/TnuGTh+sd6lVOnjzJe++9xwcffIDJVG1/PhIVRAqehR6sMvmBa/f5sQSzZmZjzkbmp83n77y/cVbO3OdyH6NcR1HLVOsWxiliXewDJOUduKyD8xI8bEPKJe5yIclPmWWk57J1w1G2bjhKVmYeQXU96dEnlOZtgnBr6Kpbn58bWbhwIWPHjmXcuHHMmzfPqvfOE5WfJD9CD1aZ/Ozfv5/oX6NpXLt8N5E/mHeQ+WnzWZO9BgMG+jn1Y5zrOBrb3Xze3clvcjTj+0t1Ph28ZlLX2cpeJZbkx2Ly8wr5a/tJNv0WQUJcBs7utrTuXYvxn9+Do4ud3uFd5aWXXuLNN9/ktdde4+WXX9Y7HFGFSfIj9GCVyU9J+/xYSnRBNIsyFrE4YzFZWhYd7DswznUcXR26XvOn4tOZq/gz6ZlLHZwbOt9DWy8r/AYiyY/Fmc0ah/ZH8+nny7DJ8cLJzY7ej7Zi4MTb8Q6oPHVBmqYxZswYvvzySxYsWMDYsWP1DklUUZL8CD1Uq7e9SivAJoCXPF9iW8A2nvF4hlMFp3g4/mHCYsJYnLGYPHPepXNT8iP5M+nZywqcQ2nt+aKO0YvKxGBQNGsVSFrQTtIb7qBV7/osn72Th+vOYfbI5ZzcH6t3iMCFNyM///xzevfuzeeff05RUZHeIQkhhMVI8nMLXI2uPOb2GJsCNjHbezZGjDyf9DxdorvwcerHxOWfYUPcKIq0HABsDW50951XObeuELorckrj2R+HMu/4ePqNb8OOZRFMbPk5L975DbvXHC+Xtgu3wsbGhp9++onff/8do1HuYSFE1SHJTynYKlvudr6blbVW8lWNr2hs15j3U9+na8yd/FyURBJgVPb09FuEndFD73BFJedX14NHP+jNoqinGP12D6LCE3m17/eMv+0z1n2xn4I8/Zomuri44OzsTGZmJg899BAnTpR/Q1AhhChvkvyUgVKKOxzuYGGNhcx2GsptKPZg5mNgrU1DTpmz9Q5RWBFnDweGPXsHC05NYMpXgzAYFR89vIKxdebw45tbSU/S7346f/48q1atIiwsjISEBN3iEEIIS7DKguc///yTc6vP0bphawtEVXZnMtewPWkaRVou2dhx1LYRmwrPkmZOo6VdSx52fZi7HO/CaG2Pv6TgudzsObYHx1qO9Hvi+p2+NU3jwIZTLJ21k72/ncDO0YY7xzTn7sntqFnfswKjveDPP/+kZ8+eNG/enI0bN+Lo6FjhMYiqRwqehR6sMvmB8uvzc6tS84+y+vwQirQcFDZ42Tahd80fydXy+TnzZ75I/4KzhWcJMgUx2nU0w5yH4WRw0jvskpHkp1w513MucZ+f04fiWf7eTjZ/8w9FhWY6DA5h8NT2hHYMLOco/23ZsmUMHTqUAQMG8PPPP0sTRFFmkvwIPVhl8lNZVn7yi9L5NaY3OUXxANgbvBjgvxZ74///VF6kFbE+ez3z0+ezN28vbgY3HnB5gIdcHsLXVPka3P2LJD/lpiQrP9eSfD6DlR//zepP95CZkktIhwAGT21P+7uDMRor5in2J598whtvvMGOHTuoW7duhcwpqi5JfoQerDL5qeg+P9di1opYHzuShLx9mMnHqOzp4/cTnjdogLg3dy8L0hewLnsdRowMcBrAOLdxBFfW3d0l+Sk3D8x8AKODke37tpfq+tysfH5feIBf3t9F7MkU/Op5MGhyO3qNaY69k62Fo71aSkoKHh5SzC/KTpIfoQcpeC6lfSmzSMw/eCnxae/15g0TH4BW9q34xPcT1vuv5z6X+1iTvYa+MX0ZHTuabTnbdH+1WVgPeydbBjzZls+OPsELPw/DvYYTn01Yy+jAD/nqxY0kn88o1/k9PDwwm808++yzfPnll+U6lxBCWJokP6VwJmstkRlfFW9dYU99p2HUc767xNfXtqnNq16vsi1gG1PdpxJREMGouFH0j+nP0syl5Gv55Re8qFKMRgMdh4Qy688x/Hf7aJp1r8NPM7czts4cPhjzK6cPxZfb3IWFhezZs4dx48bx22+/lds8QghhaZL83KLU/GP8mXjhzS6FDR42IbT1ml6qsdyN7jzh/gRbArbwttfbFFHE04lP0y26G/9L/R9pRWkWjl5UZaEdA3nh5+F8dnQ8vR9pydbFR3jyts94uc937F9/0uIri7a2tixdupTGjRszbNgw9u7da9HxhRCivEjycwvyzRlsiBtF4aUOzi50rzEPgyrbGy92yo7hLsNZU2sNX/h+QQObBvw39b90iu7E60mvE1UQZYnwRTVRq4Enj38cxsKzE3noze6c3B/LS72+ZWLLz9nw1QEK8i23VYWrqytr1qzBw8ODfv36cfr0aYuNLYQQ5cUqC54ralf3y2mamd/jRpKQuwczBRiVPb39fsTLrmm5zBeeH878tPmszFqJGTN9HPvwsNvDtLBrUS7zXZMUPJebI2eO4BjgSI+xPcp9roK8QjZ/d4hls3dy9nACnrVcGDixLX0ea42zu71F5jhy5Ag9evRg3rx5DBw40CJjiupBCp6FHqwy+YGK7/OzN/ldIi6r82nn+Qb1XYaU+7znC8/zVfpXfJ/xPRlaBm3s2vCw68P0dOxZ/k0TJfkpV7fS58cSNE1j728nWDprJwc2nMLeyYZeD7dk0KTb8atb9je3srKycHJyujSXUqrMY4qqT5IfoQerTH7Wr1/P+XXnuaPJHRaI6ubOZq1jW+JkirRcjMqeek5DaO/9RoXMfVGmOZOfMn9iUfoiogujqWOqw1jXsQxxHoKDwaF8JpXkp9xsP7wd+5r2DJ88XJf5T+6PZdnsnfzxw2E0s8Ydw0K5e2p7gm/3L/PY3377LStWrODbb7+VDVHFTUnyI/RglclPRfb5Scs/zurzd1Oo5aAw4WnbmD41F2NQNuU+97UUaoX8lv0b89PmczD/IB4GD0a4jGCE6wi8jd6WnUySn3JT1j4/lpIYnc6KOX+x9rO9ZKXl0bhTIEOmdeD2AY0wGEq3cvPhhx8yadIkxo8fz5w5c2QFSNyQJD9CD1LwfAP55gzWx42iUMsFLhQ496gxX7fEB8CkTPRz6sfSmkv53u97Wtu15uO0j+kc1ZnnE5/neP5x3WIT1sc7wJUx79zJwqineOT9u0iMSmfG3Yv5T8hcVv9vD7nZBbc85lNPPcXUqVP55JNPePfdd8shaiGEKBtJfq5D08xsiX+C3KJkQMOo7OlZYyH2Ri+9QwMu7Ch/u/3tfFbjM9b5r2Oo81B+yfqF3jG9GRc3jh05O6RpoigxRxc7Bk1qx+fHn+SZH4bg5G7P3MdXM7b2R3z7ymZS47Nuabx3332X++67j+eee45vv/22nKIWQojSkeTnOvanvv+vrStu93wNL7vb9A7rmurZ1GOG9wy2BmxlkvskDuQdYETcCAadH8Svmb9SoN36T++iejKaDHS5twnv7RrL21seIqSDP9+/vpUxQR/y0SMriQpPKNE4BoOBRYsW0b17dyIjI8s5aiGEuDWyJfM1nM36nfD0Ly4rcB5MA5dheod1U15GLya4T+AR10dYnrWcBWkLmJw4mXdT3mWU6yjuc7kPF4OL3mEKK6CUommX2jTtUpvoyESWv7+LjV8eZN38fbTt15DB09pzW9faN6znsbOzY+3atdjaXthrTN4AE0JUFlZZ8BwZGUnUsijq1axngaj+LS3/BKvPD7pU4OxhG0pYzZ90rfMpLbNmZnPOZuanz2dX7i6clTP3utzLaNfR1DLVuvkAUvBcbk6eP4ljoCMdH+yodygllpaQxaq5u1n1yW7SErKp38qPwVPb02l4Y0w2N36ra+/evYwbN45ly5ZRu3btCopYWAMpeBZ6sMrkB8qnz0++OYMV58LILooFNOwMHgzwX4uDpd+i0sE/ef+wIH0Bq7NWA9DPqR8Puz5M0xs1aZTkp1xVdJ8fS8nLKWDT1/+w/L2dREcm4RPoyoCnbqfPI61wdLW75jWHDh2iU6dO1KpVi23btuHp6VnBUYvKSpIfoQerTH5WrFhB7IZYerbsaYGoLtA0M+vjRhOf+/elOp/eft/jZdfMYnNUBjGFMSxKX8SPGT+SqWXSzr4dj7g+QleHrhjUFSVgkvyUmw37NmDvZ8/I50bqHUqpmc0au1cfY+msnRzacgZHVzt6P9KSgU/djk+g21Xnb9myhbvuuot27dqxbt067O0t011aWDdJfoQerDL5KY8+P/tT3uNI+hfFHZwdaOv5Cg1d9GlAVxEyzBn8kPEDi9IXEVsUSwObBox1HcvdTndjZyj+6V2Sn3JTWfr8WMqx3TEsm72TbT8dQSlFp3saM3hqexq0qvmv83788Ufuu+8+hg0bxo8//ojBIO9cVHeS/Ag9yP/zAFFZGziSvuDS1hV1nQZW6cQHwMXgwiNuj7A5YDPve7+PnbLjhaQX6BzdmTmpc0guStY7RGFFGrapxTPfD2H+iSfpP6Etf/16lEmt5/NCj6/5e9UxzOYLWfS9997L7NmzycnJIS8vT+eohRDVVbVf+UkvOMWqmIEUatnFBc7BhNX82SoLnMtC0zR25u5kfvp8Nudsxl7ZM8R5CGNdx1LXpq7e4VU5VW3l50pZabmsnbeXXz/8i6RzGQSGenP3lPZ0H3EbtvYmioqKMBqNFBYWYjLJS6fVmaz8CD1U65WffHMG62MfolDLAcDW4EyPGl9Uu8QHLrza3MGhAwtqLGBtrbUMcBrAkowl9DrXi8fiHmN37m5pmihKzMnNnqFPd2TBqQlM/eZubOyMzHlkJWNrf8T3r/9BZkoeiYmJdOjQgR9++EHvcIUQ1Uy1TX40zcwf8U+SU5TIxQ7OPWp8USXe7CqrhrYNedv7bbYGbeUJtyfYnbebe2PvZej5oazKWkWhVqh3iMJKmGyMdH/wNj7c+whvbhhBgzY1+faVLYwN+pDvX9yBk/Ji1KhRbNq0Se9QhRDViFU+9oqKiuLMT2eo5VWCXjXXsT/lA46kz7+swHk6DV3uLXNsVUpxwXOOOYelmUtZkL6AM4VnCDAFMNp1NPc434OTwUnvKK1STFIMTkFOtBzWUu9QKtzZIwksf28nG7/+h6KCIjKdozlh3sbKP7+nWbOq9XaluDl57CX0YJXJD5Stz09U1ka2Jk641MG5jtMAOnq/bZG4qpQr3vYq0orYkL2B+enz2ZO3B1eDK/c7389Drg/hZ/LTLUxrZa19fiwlJTaTlZ/8zcqP/yYrNY8smzie+vBeBjxyB0ZTtV2UrnYk+RF6sMrk58cffyRuSxz92/W/5WuvLHB2t21EWM2fMSrbMsdV5dzgVff9efuZnzaf37J/w4iR/k79Gec2jhDbkAoN0Vqt3LUSe197Hn31Ub1D0V1uVj5fvbWGZbN3YMxzokYddwZNbkevsS1wcJZ/l1WdJD9CD1aZ/JT2ba8CcyYrzvUlqygG0LA1uDOw1locTD5ljqlKKkGfn7MFZ1mUvoifMn8iW8umk30nHnZ7mM72nWUfpxuo6m97lUZhYRF/rTjGstk7CN8ejZO7PWGPtaL/hLZ4+7vqHZ4oJ5L8CD1Um7VlTdP4I2HCFQXOCyTxKaMgmyBe9nqZbQHbeNr9aY4WHGVM3Bj6xfRjScYS8jTp5SJKxmQy0uHuYM4F/Y7DnSdp3rMOS/+7g3F15/DeqF84dTBO7xCFEFVEtUl+DqbOIS73b8zkYVQOtPF4CR+7FnqHVWW4Gd34j/t/2BKwhXe93wXg2aRn6RbdjU9TPyW1KFXfAIVVUErRokULlqxfRHKdXcw7Np6wx9vw58/hTGg+j+l3fcvedSek7YIQokyqRfITnb2Zw+mfXXizC3tqO/alkev9eodVJdkqW4Y6D2VVrVUsqrGIYJtgZqXOolN0J15Leo2zBWf1DlFUck8//TRPPvkks2fP5odfv+SxD3uzKOopRs3swZlD8bzc+zsmNJ/Hhi8PUJBfpHe4QggrVOWTn/SC02xNuPBml8KEq00d2nvP0DusKk8pRWeHzizyW8SqWqsIcwzj+4zv6XmuJ+Pjx7M3d6/eIYpKSinFBx98wODBg5kyZQqLFy/G2cOB4c/dwYJTE5i0cCCapvH+6F95uM5HLJ65jcyUHL3DFkJYEasseE5MTOT0D6fxdPG84XkF5qziAudzXChwdmNArbU4mqrv68W3xMIbm8YVxvFVxld8l/Ed6eZ0Wtm1YpzrOO50vBOjMlpuIiuQnJGMU20nQgeE6h1KpZWTk8M999zDtGnT6Nq167+OaZrG3nUnWT57J/t+P4m9kw29xrZg0KR2+NXz0CliURpS8Cz0YJXJD9y8z4+maWyKH8f5nD8xk49R2dOrxtf42LeyyPzVQjnt6p5lzuKnzJ9YmL6Q6MJogkxBjHUdy1DnoTgaHC0/YSVV3fv83KrMzEycnZ2v+vqpg3Esf28nW747hLlIo/3gYIZM60BI+wAdohS3SpIfoQerfOy1aNEilmxdcsNz/kn9mNjcXcWJjwNtPF6UxKeScDI4Mdp1NBv8NzDHZw6eBk9eTX6VztGdmZ0ym4TCBL1DLHdLti7hhzWyp1VJffLJJzRt2pRz585ddaxusxpMXjSIBacnMuSZDhzYcJppHRby9B0L+XNZBEVFZh0iFkJUZla58nOzPj/nsjezJWH8hQ7O2FPbKYw7fGaVed5qp5xWfq6kaRp78vawIH0Bv2f/jg02DHIexFjXsTSybVT+AehA+vzcmn379tGlSxfq1avHH3/8gZub23XPzcnM5/cv9vPL+7uIO51KzQae3D25HT1HN8fesfptWlzZycqP0INVrvzcSEbBGf5ImFhc4GzExaY27b3f1DsscQNKKdrYt+FT30/53f93hrsMZ0XWCsJiwhgTN4btOdvl1eZqrmXLlixdupQjR44wdOhQ8vPzr3uug7MtAyfezrxj43nup6G4ejnw6fg1jA36kK+nbyIlLrMCIxdCVEZVKvkpMGexPm4UhVo2ADYGZ3rWWIhR2ekcmSipujZ1ed3rdbYGbGWy+2QO5x3mobiHGHh+IMszl1OgFegdotBJr169WLBgARs2bGDs2LE3TYiNJgOdhjVm1o4xvLttNI07B7H4zW2Mrf0RH41bwdkjVf/xqhDi2kx6B2ApmqaxNWES2YVxXOzg3N33cxxNNfQOTZSCp9GTJ92f5BHXR/gl6xcWpC9gauJU/pvyX0a5juJ+l/txMbjoHaaoYA899BAxMTG4ubmVePsUpRSN7wik8R2BnDuWxC/v72LDogOsW7Cf1mENGDKtPc2615HtWISoRqpMzc8/qXP5J23uhUaGyoHWHs8S7DqyzHNVaxVU81MSZs3MlpwtLEhfwI7cHTgpJ+5xuYcxrmPwN/nrHd4tk5ofy0hKSsLLy+uWr0tLzGb1p7tZ9fFuUuOzqNeiBoOndaDzPY0x2VSvtgt6k5ofoQerTH6ys7M59c0pHOwcADiX/QdbEh6/VOAc5NSbO7xny09yZVWJkp/LHco7xBfpX7AqaxUaGn0c+zDObRzN7JrpHVqJ5eTl4FTXiTq96ugditXatWsXvXr1YtGiRQwZMqRUY+TnFrL5239YNnsnUeGJeAe4MvCp2+n9SEuc3OwtHLG4Fkl+hB6sMvmB/+/zk1FwlpUx/SnUslAYcbOpT99ay6XOxxIqafJzUUxhDF+lf8X3Gd+TqWVyu93tjHMbR3eH7hhU5S9nkz4/ZZOdnU3Pnj3Zt28f69evp1OnTqUey2zW2LP2OMtm7+TgxtM4uNjS+5FWDHzqdnyDrv9mmSg7SX6EHqwy+Zk7dy6JOxO5t/sQVsb0I7MwGjBja3BlQK01OJr8yh6sqPTJz0UZ5gwWZyxmYfpCzhedp56pHmPdxjLYaTD2hsr50/s3G77BztuOZ997Vu9QrFpiYiIdO3YkMTGRP//8k5CQkDKPeXzveZbN3snWHw8D0Gl4YwZPbU/DNrXKPLa4miQ/Qg9Wmfx069aNnNgcHh3rQkzOtuKd2u25s8aX+NrLvyGLsZLk56ICrYA1WWuYnz6fw/mH8TR4MsJlBA+6Poi30Vvv8P5Fan4s5+TJk3To0AEHBwf+/vtvfHx8LDJu/Nk0Vnz0F2vn7SUnI5+mXWszeGp72vZriMEgj9QtRZIfoYfK/2zgOrILYzif+2dx4uNAK49nJfGp5myUDQOdB/JLzV/4tsa3tLBrwUdpH9ElugsvJr7IyYIbb4kirFO9evVYvXo1gwYNwsPDcvt6+Qa58fCsXnwZPYmxs+4k7mQKbwz8kScaf8raeXvJy5G2C0JYqzKt/Cil+gAfAkZgvqZpb9/ofEut/LRr15z4+H8YPVrDiB2BTr3p5P2eFDhbmpWt/FzL8fzjfJH+Bcsyl5FPPj0devKw28Pcbne7rveLrPyUn/j4eDw8PLCxsWw358KCIrYvCWfprB2c2BuLm48j/ca3od8TbXDzcbLoXNWJrPwIPZR65UcpZQQ+AcKAxsD9SqnGlgrselJSTpGQcBhN01AYcbYJoqPX25L4iGtqYNuAt7zfYmvgVia6TWRf3j4eiH2AwecHszJrJYVaod4hCgvKyMigXbt2jBs3zuJdwU02Rrre35QPdo/jrU0jadTOn+9e/YMxQR/x8X9WER2ZaNH5hBDlp9QrP0qpDsCrmqb1Lv7z8wCaps283jWWWPn58ssevPLKJmJjoVZNI162t116s6vv7X0Z0XMEOXk5PPzew1ddO6TTEIZ1HkZyRjJPfvzkVccf6PEA/dv1JyYphmnzpl11/OE+D9OzZU9Onj/JS4teuur4+IHjuaPJHRw5c4QZ38246vjUYVNp3bA1e47tYfaS2Vcdf+mBl2hcuzHbD2/nk18/uer4jNEzqFezHhv2bWDB2gVXHZ/16CxqedVi5a6VfLfx6n3PPn7yYzxdPFmydQlLty296viCKQtwsHPgmw3fsPqv1Vet/Fzsq/T5ms/ZtH/Tv661s7Vj4dSFAMz5ZQ47juz413F3Z3fmTpgLwH9/+i/7ju/713E/Tz/ee+w9AN749g3Cz4b/63gdvzq8NeYtAF5Y+AKnY0//63hoUCjTH5wOwJTPphCbHPuv4y0btOTp4U+Ta85l+MrhnKxzklzXXGwzbfE76kd/m/5MHTAVgDGzx5CXn/ev67u36M4jYY8AF1ZtrnSr996Rs0dQBkXLNi0BePzxx7n33nuJ+r/27j04qvIO4/j3lw1oEhQVEJUkilBpUWugDhB0HGy8AMVboY6XqlArjBY1NEynII5jRet0jIJjZWQwoGAVGrwXUfEyXrgqRASReCFyEcELKAaYkPDrH7u2QHRJTMK7x30+M5nJ5s2efWazu3n2Pe85u24dV1xR//xUJSUlnHfeeaxevZoRI0bUGx83bhxnnXUWFRUVFBcX1xu/44476Nu3L/Pnz2fs2LH1xidMmEBBQQHz5s1j/Pj6j90HHniAbt268cwzz1BaWv+xO336dPLy8pg5cyaTJk2qN15eXk779u2ZNm0a06ZNqzc+Z84csrOzuf/++5k1a1a98VdffRWAu+66i2effXavsaysLJ577jkAbrvtNsrKyqiqqqJLly7k5ubSrl07Zs+eDcCYMWNYsGDvx2Zubi4zZswAoLi4mIqKir3GTzjhBCZPngzA8OHDqays/N+YVWdz9M4e7Pggh9qaOmKdtvFNu0p2t90af/4AhYWF/P3v8ZfFwYMH8+WXX+61/aKiIm6+Of7YHTBgADt27NhrfNCgQYweHX896tevX7375uKLL+a6665j+/btDBw4sN740KFDGTp0KF988QVDhgypN96Ux953f5em0MyPhNCUMzx3AtbtcXk90HvfXzKz4cBwgPz8/CbcXFy/frcyblw1t49fzGGtTtAh7S3lu+Kzx9ygtYq/mlvM6s0ZmlnScTL+f30ySD4eqz9uGXtsP+N7th9r2PazyCJvXR5tVrVhyzFb+KzbZ6ztuZYpdVOo/bqWq9peFZ9F3Pf2Y7b39veVuH3b/T3Z9ri+tYqPdz+2O7GDdTK9lpCfn08sFuOoo1r+qE/P2c6Rp1Vzy7wx/OefbzHzH6+Qvf5X1B3yDbtyP6G2gz5CQyQVNWXmZwjQ393/mLh8BdDb3etPqSQ053l+6upqiMVaN8u25PvVfF2D10Z80U8DLNm0hInLJvL4R4+TYRkM6TqEUT1GcUqHU1r0djPbZBI7SAXop2Tn9l28/PBynrx7IZ9+8BVHHtuWC4p7c/bVBWQfojdq30czPxJC5HZ7ibSUNVvWMHHRRKYsnUL1rmqKOhdRUlhC/679taZMGmX3bmfxM5U8UbqQla+vJaftQfQf0ZPzru9F+9xDQ8dLKSo/EkJTyk8mUAkUARuAJcBl7r7yh66j8iNRsHXnVia/PZl7F93Lhm0b6N6hOyWFJVx+8uUclKl379I4qxdv4MnShbxZvgrLMM645EQuKunD8QU6GSuo/EgYTT3UfSAwgfgqjTJ3vz3Z76v8SJTU1NUwc8VMSheU8s6md+iY05GRvUZy7anX0i678R+mKeltU9VWnpqwiBemLGNn9S5OKerMb0f3oee5XdJ6ZlHlR0KI5BmeRQ4kd+elNS9RuqCUuR/OJSszi2EFwxhVOIquR3QNHU8i5tutO5k7eSlPT1zMV59uI//EDlxU0od+l51Eq4OacgxKNKn8SAgqPyKNsGLzCkoXlPLI8keo3V3LRb+4iJLCEvrm9Q0dTSJmV00drz22gidKF1K1fDOHH9WGvz1/GZ1/2TF0tANK5UdCUPkR+RE2btvIfYvvY9Jbk9iycwt9cvswunA0F/78QmIZOoJLGs7dqZi3hhfLKhg17fy0m/1R+ZEQVH5EmqC6ppqpFVO5Z+E9fLzlY44//HiKexczrMcw2rRuEzqeSMpT+ZEQIvvBpiKpIKd1DiN7jaRyZCXlvyunY05Hbph7A/n35DP2pbFs3LYxdEQREdmHyo9IM4hlxBjcfTDzr57Pm394kzM7n8mdb9zJcROPY9hTw1ixeUXoiCIikqDyI9LM+ub1ZfbFs/ng+g+4puc1zFo5i5Mnncy5M87lxY9ebPYP3BQRkcZR+RFpIV2O6MJ9A+9j3ah1jD9zPMs3LeecGedQ8EAB09+ZTk1dTeiIIiJpSeVHpIUdkXUEN51xE1U3VlF2fhl1u+u48skrmbpsauhoIiJpSUd7iRxg7s7zHz3P6fmn64gwSXs62ktCSK8TSoikADOjf9f+oWOIiKQt7fYSERGRtKLyIyIiImlF5UdERETSisqPiIiIpBWVHxEREUkrKj8iIiKSVlR+REREJK2o/IiIiEhaUfkRERGRtKLyIyIiImlF5UdERETSisqPiIiIpBWVHxEREUkrKj8iIiKSVlR+REREJK2Yux+4GzP7HPikmTbXHviimbZ1IChvy4paXoheZuVtWVHLC82T+Vh379AcYUQa6oCWn+ZkZm+5+6mhczSU8rasqOWF6GVW3pYVtbwQzcwioN1eIiIikmZUfkRERCStRLn8TA4doJGUt2VFLS9EL7Pytqyo5YVoZhaJ7pofERERkR8jyjM/IiIiIo2m8iMiIiJpJXLlx8z6m9lqM/vQzP4aOs/+mFmZmW02sxWhszSEmeWZ2Stm9p6ZrTSzG0NnSsbMDjazxWb2TiLvraEzNYSZxcxsmZk9GzrL/phZlZm9a2YVZvZW6DwNYWaHmVm5mb1vZqvMrDB0ph9iZt0S9+13X9+YWXHoXMmY2ajE822FmT1qZgeHziTSGJFa82NmMaASOBtYDywBLnX394IGS8LMzgC+BR5295NC59kfMzsaONrdl5rZIcDbwIWpeh+bmQE57v6tmbUC3gBudPeFgaMlZWZ/Bk4FDnX3QaHzJGNmVcCp7h6ZE/CZ2UPA6+4+xcxaA9nuvjVwrP1KvMZtAHq7e3OdELZZmVkn4s+z7u6+w8xmAXPcfVrYZCINF7WZn17Ah+7+sbvXAI8BFwTOlJS7vwZ8FTpHQ7n7Rndfmvh+G7AK6BQ21Q/zuG8TF1slvlK60ZtZLvAbYEroLD9FZtYWOAN4EMDda6JQfBKKgI9StfjsIRPIMrNMIBv4NHAekUaJWvnpBKzb4/J6Uvgfc9SZ2XFAD2BR4ChJJXYhVQCbgRfdPaXzAhOAvwC7A+doKAdeMLO3zWx46DAN0Bn4HJia2LU4xcxyQodqoEuAR0OHSMbdNwB3AWuBjcDX7v5C2FQijRO18iMHiJm1AWYDxe7+Teg8ybh7nbsXALlALzNL2d2LZjYI2Ozub4fO0ginu3tPYADwp8Su3FSWCfQEJrl7D6AaiML6wNbA+cC/Q2dJxswOJz7j3hk4Bsgxs9+HTSXSOFErPxuAvD0u5yZ+Js0osXZmNvCIuz8eOk9DJXZtvAL0DxwlmdOA8xPraB4Dfm1mM8JGSi7xTh933ww8QXz3cypbD6zfYwawnHgZSnUDgKXuvil0kP04C1jj7p+7+y7gcaBv4EwijRK18rME+JmZdU68S7oEeDpwpp+UxALiB4FV7n536Dz7Y2YdzOywxPdZxBfDvx80VBLuPsbdc939OOKP35fdPWXfNZtZTmLhO4ldR+cAKX3kort/Bqwzs26JHxUBKblgfx+XkuK7vBLWAn3MLDvxelFEfG2gSGRkhg7QGO5ea2YjgeeBGFDm7isDx0rKzB4F+gHtzWw9cIu7Pxg2VVKnAVcA7ybW0QCMdfc54SIldTTwUOIomQxglrun/OHjEdIReCL+P45M4F/uPjdspAa5Hngk8SbpY2BY4DxJJYrl2cCI0Fn2x90XmVk5sBSoBZahj7mQiInUoe4iIiIiTRW13V4iIiIiTaLyIyIiImlF5UdERETSisqPiIiIpBWVHxEREUkrKj8iIiKSVlR+REREJK38F0o93dUct8DBAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 504x504 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(7,7))\n", "plt.plot(point1_point2_x_1[:,0], point1_point2_x_1[:,1], \"--\", color=\"black\", label = \"_nolegend_\")\n", "plt.plot(point3_point4_x_1[:,0], point3_point4_x_1[:,1], \"--\", color=\"black\", label = \"_nolegend_\")\n", "plt.plot(point1_point2_x_2[:,0], point1_point2_x_2[:,1], \"--\", color=\"black\", label = \"_nolegend_\")\n", "plt.plot(point3_point4_x_2[:,0], point3_point4_x_2[:,1], \"--\", color=\"black\", label = \"_nolegend_\")\n", "plt.plot(x_1, x_2, \"--\", color=\"black\", label=\"_nolegend_\")\n", "\n", "plt.fill_between(x_1_region_1, 0, x_2_region_1, where=x_2_region_1<=6, color=\"plum\")\n", "plt.fill_between(x_1_region_2, 0, 6, color=\"plum\")\n", "plt.title(\"Región factible del PL\")\n", "\n", "x_1_line_1 = np.linspace(0, 4, 100)\n", "\n", "x_2_line_1 = 1/5(-3x_1_line_1 + 10)\n", "\n", "x_1_line_2 = np.linspace(0, 7, 100)\n", "\n", "x_2_line_2 = 1/5(-3x_1_line_2 + 20)\n", "\n", "x_1_line_3 = np.linspace(0, 8, 100)\n", "\n", "x_2_line_3 = 1/5(-3x_1_line_3 + 36)\n", "\n", "plt.plot(x_1_line_1, x_2_line_1, \"green\",\n", " x_1_line_2, x_2_line_2, \"indigo\",\n", " x_1_line_3, x_2_line_3, \"darkturquoise\"\n", " )\n", "\n", "\n", "optimal_point = (2, 6)\n", "\n", "plt.scatter(optimal_point[0], optimal_point[1], marker='o', s=150,\n", " facecolors='none', edgecolors='b')\n", "\n", "point_origin = (0, 0)\n", "\n", "point_gradient_fo = (3, 5)\n", "\n", "\n", "points_for_gradient_fo = np.row_stack((point_origin,\n", " point_gradient_fo))\n", "\n", "\n", "plt.arrow(point_origin[0], point_origin[1],\n", " point_gradient_fo[0], point_gradient_fo[1],\n", " width=.05, color=\"olive\")\n", "\n", "plt.legend([\"$10 = 3x_1 + 5x_2$\",\n", " \"$20 = 3x_1 + 5x_2$\",\n", " \"$36 = 3x_1 + 5x_2$\",\n", " \"$\\\\nabla f_o(x)$\"], bbox_to_anchor=(1.4, 1))\n", "\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si realizamos este proceso para valores de $y$ iguales a $36, 20, 10$ observamos que la recta que da el mayor valor de la $f_o$ y que se mantiene en la región factible es aquella con valor $y_1= f_o(x) = 36$. Corresponde a la pareja $(x_1, x_2) = (2, 6)$ y es la solución óptima. Entonces produciendo los productos $1$ y $2$ a una tasa de $2$ y $6$ lotes por semana se maximiza la ganancia siendo de 36 mil pesos. No existen otras tasas de producción que sean tan redituables como la anterior de acuerdo con el modelo." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Comentarios\n", "\n", "* El método gráfico anterior sólo funciona para dos o tres dimensiones.\n", "\n", "* El gradiente de la función objetivo nos indica la dirección de máximo crecimiento de $f_o$. En el ejemplo prototipo $\\nabla f_o(x) = \\left [ \\begin{array}{c} 3 \\\\ 5 \\end{array} \\right ]$ y tal vector apunta hacia la derecha y hacia arriba. Entonces en esa dirección es hacia donde desplazamos las rectas paralelas.\n", "\n", "* La región factible que resultó en el ejemplo prototipo se le conoce con el nombre de poliedro y es un conjunto convexo (en dos dimensiones se le nombra polígono). Es una intersección finita entre hiperplanos y semi espacios, también puede pensarse como el conjunto solución de un número finito de ecuaciones y desigualdades lineales.\n", "\n", "```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Ejercicio\n", ":class: tip\n", "\n", "Resuelve con el método gráfico el siguiente PL:\n", "\n", "$$\\displaystyle \\max_{x \\in \\mathbb{R}^2} 2x_1 + x_2$$\n", "\n", "$$\\text{sujeto a: }$$\n", "\n", "$$x_2 \\leq 10$$\n", "\n", "$$2x_1 + 5x_2 \\leq 60$$\n", "\n", "$$x_1 + x_2 \\leq 18$$\n", "\n", "$$3x_1 + x_2 \\leq 44$$\n", "\n", "$$x_1 \\geq 0, x_2 \\geq 0$$\n", "\n", "Marca al gradiente de la función objetivo en la gráfica.\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tipo de soluciones en un PL" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Los puntos factibles que resultan de la intersección entre las rectas del ejemplo prototipo que corresponden a las desigualdades se les nombra soluciones factibles en un vértice (FEV) (se encuentran en una esquina). Las soluciones FEV no son una combinación convexa estricta entre puntos distintos del poliedro formado en la región factible (no caen en algún segmento de línea formado por dos puntos distintos)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Observación\n", ":class: tip\n", "\n", "También a las soluciones FEV se les conoce como puntos extremos pero resulta más sencillo recordar FEV.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Comentario\n", "\n", "El método gráfico en la región anterior ilustra una propiedad importante de los PL con soluciones factibles y una región acotada: siempre tiene soluciones FEV y al menos una solución óptima, aún más, la mejor solución en un FEV debe ser una solución óptima.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ¿A qué le llamamos solución en un PL?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cualquier conjunto de valores de las variables de decisión ($x_1, x_2, \\dots, x_n$) se le nombra una solución y se identifican dos tipos:\n", "\n", "* Una solución factible es aquella para la cual todas las restricciones se satisfacen.\n", "\n", "* Una solución no factible es aquella para la cual al menos una restricción no se satisface." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En el ejemplo prototipo los puntos $(2,3)$ y $(4,1)$ son soluciones factibles y $(-1, 3), (4,4)$ son soluciones no factibles." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(point1_point2_x_1[:,0], point1_point2_x_1[:,1], \"black\", label = \"_nolegend_\")\n", "plt.plot(point3_point4_x_1[:,0], point3_point4_x_1[:,1], \"black\", label = \"_nolegend_\")\n", "plt.plot(point1_point2_x_2[:,0], point1_point2_x_2[:,1], \"black\", label = \"_nolegend_\")\n", "plt.plot(point3_point4_x_2[:,0], point3_point4_x_2[:,1], \"black\", label = \"_nolegend_\")\n", "plt.plot(x_1, x_2, \"black\", label = \"_nolegend_\")\n", "\n", "\n", "plt.fill_between(x_1_region_1, 0, x_2_region_1, where=x_2_region_1<=6, color=\"plum\", label = \"_nolegend_\")\n", "plt.fill_between(x_1_region_2, 0, 6, color=\"plum\", label = \"_nolegend_\")\n", "\n", "plt.scatter(2, 3, marker='o', s=150)\n", "plt.scatter(4, 1, marker='', s=150)\n", "plt.scatter(-1, 3, marker='v', s=150)\n", "plt.scatter(4, 4, marker='^', s=150)\n", "\n", "plt.legend([\"Solución factible\", \"Solución factible\",\n", " \"Solución no factible\", \"Solución no factible\"])\n", "\n", "plt.title(\"Tipos de soluciones en un PL\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "\"Valor más favorable de la función objetivo\" depende si se tiene un problema de maximización o minimización.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "De las soluciones factibles se busca aquella solución óptima (puede haber más de una) que nos dé el valor \"más favorable\" (valor óptimo) de la función objetivo." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ejemplo: más de una solución óptima" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Es posible tener más de una solución óptima, por ejemplo si la función objetivo hubiera sido $f_o(x) = 3x_1 + 2x_2$ entonces:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(6,6))\n", "point4 = (2, 6)\n", "point5 = (4, 3)\n", "\n", "point4_point5 = np.row_stack((point4, point5))\n", "\n", "plt.plot(point1_point2_x_1[:,0], point1_point2_x_1[:,1],\n", " point3_point4_x_1[:,0], point3_point4_x_1[:,1],\n", " point1_point2_x_2[:,0], point1_point2_x_2[:,1],\n", " point3_point4_x_2[:,0], point3_point4_x_2[:,1],\n", " x_1, x_2)\n", "\n", "plt.fill_between(x_1_region_1, 0, x_2_region_1, where=x_2_region_1<=6, color=\"plum\")\n", "plt.fill_between(x_1_region_2, 0, 6, color=\"plum\")\n", "\n", "plt.plot(point4_point5[:,0], point4_point5[:,1], \n", " linewidth=2, color = \"darkred\", linestyle='dashed')\n", "\n", "plt.legend([\"$x_1 = 4$\", \"$x_1 = 0$\", \n", " \"$2x_2 = 12$\", \"$x_2 = 0$\",\n", " \"$3x_1+2x_2 = 18$\",\n", " \"$18 = f_o(x) = 3x_1 + 2x_2$\"], bbox_to_anchor=(1, 1))\n", "plt.title(\"Región factible del PL\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El segmento de recta que va de $(2,6)$ a $(4,3)$ (en línea punteada) son soluciones óptimas. Tal segmento es la curva de nivel de $f_o(x)$ con el valor $18$. Cualquier PL que tenga soluciones óptimas múltiples tendrá un número infinito de ellas, todas con el mismo valor óptimo." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Comentario\n", "\n", "Si un PL tiene exactamente una solución óptima, ésta debe ser una solución FEV. Si tiene múltiples soluciones óptimas, al menos dos deben ser soluciones FEV. Por esto para resolver problemas de PL sólo tenemos que considerar un número finito de soluciones.\n", "\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ejemplo: PL's sin solución" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Es posible que el PL no tenga soluciones óptimas lo cual ocurre sólo si:\n", "\n", "1. No tiene soluciones factibles y se le nombra PL no factible.\n", "\n", "2. Las restricciones no impiden que el valor de la función objetivo mejore indefinidamente en la dirección favorable. En este caso se tiene un PL con función objetivo no acotada y se le nombra PL no acotado.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un ejemplo de un PL no factible pues su región factible es vacía se obtiene al añadir la restricción $3x_1+ 5x_2 \\geq 50$ a las restricciones anteriores:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#3x_1 + 5x_2 ≥ 50\n", "\n", "x_1_b = np.linspace(0,8, 100)\n", "\n", "x_2_b = 1/5(50 - 3x_1_b)\n", "\n", "plt.plot(point1_point2_x_1[:,0], point1_point2_x_1[:,1],\n", " point3_point4_x_1[:,0], point3_point4_x_1[:,1],\n", " point1_point2_x_2[:,0], point1_point2_x_2[:,1],\n", " point3_point4_x_2[:,0], point3_point4_x_2[:,1],\n", " x_1, x_2,\n", " x_1_b, x_2_b)\n", "\n", "plt.legend([\"$x_1 = 4$\", \"$x_1 = 0$\", \n", " \"$2x_2 = 12$\", \"$x_2 = 0$\",\n", " \"$3x_1+2x_2 = 18$\",\n", " \"$3x_1 + 5x_2 = 50$\"], bbox_to_anchor=(1, 1))\n", "\n", "plt.fill_between(x_1_b, x_2_b, 10, color=\"plum\")\n", "plt.fill_between(x_1_region_1, 0, x_2_region_1, where=x_2_region_1<=6, color=\"plum\")\n", "plt.fill_between(x_1_region_2, 0, 6, color=\"plum\")\n", "plt.title(\"No existe solución factible\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La intersección entre las dos regiones sombreadas es vacía." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un ejemplo de un PL no acotado resulta de sólo considerar las restricciones $x_1 \\leq 4, x_1 \\geq 0, x_2 \\geq 0$:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "points = np.column_stack((4np.ones(11), np.arange(11)))\n", "plt.plot(point1_point2_x_1[:,0], point1_point2_x_1[:,1],\n", " point3_point4_x_1[:,0], point3_point4_x_1[:,1],\n", " point3_point4_x_2[:,0], point3_point4_x_2[:,1])\n", "\n", "plt.plot(points[:,0], points[:,1], 'o', markersize=5)\n", "\n", "plt.legend([\"$x_1 = 4$\", \"$x_1 = 0$\", \n", " \"$x_2 = 0$\"], bbox_to_anchor=(1, 1))\n", "\n", "\n", "x_1_region = np.linspace(0,4, 100)\n", "plt.fill_between(x_1_region, 0, 10, color=\"plum\")\n", "plt.title(\"Región factible no acotada\")\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Se observa en la gráfica anterior que se tiene una región factible no acotada y como el objetivo es maximizar podemos elegir el valor $x_1 = 4$ y arbitrariamente un valor cada vez más grande de $x_2$ y obtendremos una mejor solución dentro de la región factible." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{sidebar} Un poco de historia ...\n", "\n", "El [método símplex](https://en.wikipedia.org/wiki/Simplex_algorithm) pertenece a una clase general de algoritmos de optimización con restricciones conocida como [métodos de conjuntos activos](https://en.wikipedia.org/wiki/Active-set_method) en los que la tarea fundamental es determinar cuáles restricciones son activas y cuáles inactivas en la solución. Mantiene estimaciones de conjuntos de índices de restricciones activas e inactivas que son actualizadas y realiza cambios modestos a tales conjuntos en cada paso del algoritmo.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(METODOSIMPLEX)=" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Método símplex" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para comprender sus conceptos fundamentales se considera un PL en una forma no estándar y se utiliza el mismo PL del ejemplo prototipo:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\displaystyle \\max_{x \\in \\mathbb{R}^2} 3x_1 + 5x_2$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\text{sujeto a: }$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$x_1 \\leq 4$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$2x_2 \\leq 12$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$3x_1 + 2x_2 \\leq 18$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$x_1 \\geq 0, x_2 \\geq 0$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(SOLFEVNFEV)=" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Soluciones FEV y NFEV" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "ax.plot(point1_point2_x_1[:,0], point1_point2_x_1[:,1], label = \"_nolegend_\")\n", "ax.plot(point3_point4_x_1[:,0], point3_point4_x_1[:,1], label = \"_nolegend_\")\n", "ax.plot(point1_point2_x_2[:,0], point1_point2_x_2[:,1], label = \"_nolegend_\")\n", "ax.plot(point3_point4_x_2[:,0], point3_point4_x_2[:,1], label = \"_nolegend_\")\n", "ax.plot(x_1, x_2, label = \"_nolegend_\")\n", "\n", "\n", "ax.fill_between(x_1_region_1, 0, x_2_region_1, where=x_2_region_1<=6, color=\"plum\", label = \"_nolegend_\")\n", "x_1_region_2 = np.linspace(0,2, 100)\n", "ax.fill_between(x_1_region_2, 0, 6, color=\"plum\", label = \"_nolegend_\")\n", "\n", "\n", "point_FEV_1 = (0,0)\n", "point_FEV_2 = (0,6) \n", "point_FEV_3 = (2,6) \n", "point_FEV_4 = (4,3) \n", "point_FEV_5 = (4,0)\n", "\n", "\n", "array_FEV = np.row_stack((point_FEV_1,\n", " point_FEV_2,\n", " point_FEV_3,\n", " point_FEV_4,\n", " point_FEV_5))\n", "\n", "point_NFEV_1 = (0, 9)\n", "point_NFEV_2 = (4, 6)\n", "point_NFEV_3 = (6, 0)\n", "\n", "array_NFEV = np.row_stack((point_NFEV_1,\n", " point_NFEV_2,\n", " point_NFEV_3))\n", "\n", "\n", "ax.plot(array_FEV[:,0], array_FEV[:,1], 'o', color=\"orangered\", markersize=10, label=\"FEV\")\n", "\n", "ax.plot(array_NFEV[:,0], array_NFEV[:,1], '', color=\"darkmagenta\", markersize=10, label=\"NFEV\")\n", "\n", "ax.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Los puntos en la gráfica con etiqueta \"FEV\" son soluciones factibles en un vértice:\n", "\n", " $(0, 0), (0, 6), (2, 6), (4, 3), (4, 0)$\n", "\n", "\n", "y están definidos por las restricciones de desigualdad tomando sólo la igualdad, esto es, por las rectas: $x_1 = 4, 2x_2 = 12, 3x_1 + 2 x_2 = 18, x_1 = 0, x_2 = 0$. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Definiciones\n", "\n", "* A las rectas que se forman a partir de una desigualdad tomando únicamente la igualdad se les nombra ecuaciones de frontera de restricción o sólo ecuaciones de frontera.\n", "\n", "* Las ecuaciones de frontera que definen a las FEV se les nombra ecuaciones de definición.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Análogamente los puntos con etiqueta \"NFEV\" son soluciones no factibles en un vértice:\n", "\n", "\n", "* $(0, 9), (4, 6), (6,0)$\n", "\n", "y también están definidos por las ecuaciones de frontera." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Observación\n", ":class: tip\n", "\n", "Aunque las soluciones en un vértice también pueden ser no factibles (NFEV) el método símplex no las revisa.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "En más de dos dimensiones cada ecuación de definición genera un hiperplano en un espacio $n$ dimensional. Y la intersección de las $n$ ecuaciones de frontera es una solución simultánea de un sistema de $n$ ecuaciones lineales de definición.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Comentarios\n", "\n", "* En general para un PL con $n$ variables de decisión se cumple que cada solución FEV se define por la intersección de $n$ ecuaciones de frontera. Podría ser que se tengan más de $n$ fronteras de restricción que pasen por el vértice pero $n$ de ellas definen a la solución FEV y éstas son las ecuaciones de definición.\n", "\n", "* Cada solución FEV es la solución simultánea de $n$ ecuaciones elegidas entre $m + n$ restricciones. El número de combinaciones de las $m + n$ ecuaciones tomadas $n$ a la vez es la cota superior del número de soluciones FEV. Para el ejemplo prototipo $m = 3, n=2$ por lo que $C^{m+n}_n = C^5_2 = 10$ y sólo $5$ conducen a soluciones FEV.\n", "\n", "```" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "tags": [ "hide-input", "margin" ] }, "outputs": [ { "data": { "image/png": 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59hf9OevSTFZ8tJUZjy5k7+6DXscSEY+Ed4EX5sOTP4Wpb8PLc2BUSvDjwnyvk3nG5/dxzvVncPltvflq+36yJ+awZfXu4CcPf79GpcBlPn2/ash/oJwz/rqK0uJSr6OInJbwLfAFc+G2vjDnWSgvD24r2Rv8+La+wc9HsS5ntWLs3VkkpsTy+hOLWfvXf+AOf79K9oJz+n7VUNMlu2m2ZDf5r+s/OoksdSpwM2tqZtPMbLWZrTKzoSFJVZgP94+B0hKoOGZ0rqIsuP3+MVF/ZNmsdRKjfzOQPt1L6TjzJ5i+X7XS8rMdACx7bpnHSUROT6COX/8E8KZzboyZxQKJIcgE0x+F8lPMPJeXwfTH4OdPhWSXkSo2PsB5vtdxVg4ne62Pvl/Vsi/OZvO8zdUfJ/sNgK2fbmWSTarenjkik3HvjmvwfCI1VesCN7NU4ALgBwDOuUPAoZCkmvfi8UeSx6ooo+StZ7i96/6Q7DKS/eXtl0ioLD/5jfT9qpZ4SSIdPumArzT4C6ivIvg/X+Whb6Z6KuMq+fDSD5n7ppaepO56NO/BXYPvCvn91uUIvBNQBDxvZv2APOAO59xRDWFmtwK3AmRmZtbsng/sq9HN4g+VcXCHpjDiDtXsFZrxNbxdY1fSr4RN922iw++/KfEjVcZVsul/NlHSt8SDdCI1Z87V7hwbZpYF/Bs41zk338yeAPY45353oq/Jyspyubm5p77zUSnBJ+BOoTImmY3jltQ8dCPV8ZV++Mpr8J9eYgrMLK7/QBEi/418Zo2dRcXBb07d64/3c+3Ua+lydRcPk4kczczynHNZx26vy5OYBUCBc25+1cfTgAF1uL9vjLgJ/DEnvYmzAHs7jQrJ7iLd3k4jcXbyX6acLwZG3NxAiSJD6del+AI+Kg0qYnyYz/AFfJR+rXFCiQy1LnDn3HZgi5l1r9o0AlgZklSjx0PgFAXui6G4xy0h2V2kK+75w2BBn0S587Os2Rhq+xtXY7Rs8jLKSso40C6JdT/pSVq/NMpKyjSNIhGjrnPgPwdeMrOlQH9gYp0TAWR0gd9Ng7jE447EnQWo9Cew4/ynKG/SISS7i3TlTTqw4/ynqPQnHHckfvj7Nf+MiXz0SSWz/7SEQwdO8YRnlIhNjeXCRy5k5YR+7OnVlJtybuLChy8kNiXW62giNVLrNfDaqPEa+GGF+cHRtzefxpWV42KS2dtpFMU9blF5f4vA3k2krn6OJhtmYmX7cTFJ1d+vsuRMNm7bwxebviI5NY6rftGPFm2TvY4cFr7z9OcAvHJbaF7GIBJqJ1oDr+sceP3K6BKcW07ewMEdB9jW5lmvE4W18iYd2DXoPnYNug8A8xuuakTOgE4ZqaQkxbF47U6mTszhopt70m1Iaw8Ti0hdhO9L6aVetEiN59y+GTRJjOWdf6zkwxdWU1GhsxqKRCIVeBSKjw1wdq/WdGjdhOWfFjJjYi77dSInkYijAo9SPp/Rq1ML+nVtya5t+5ny+/lsXf2V17FE5DSowKNcRstkhvZpg9+M155YxMI5GzVqKBIhVOBCk8RYhvZuQ6tmiXw+az1zn1rKoYMaNRQJdypwASAm4OOsbml0z2zGhhW7eOW+BezeVrNz0oiIN1TgUs3M6Nw2lcG90jm47xBTH8hl7fztXscSkRNQgctxWqQmcE6fDJITYnj7+ZV89NIXVGrUUCTsqMDlWyXEBUcNM9ObsOzjrcx4ME+jhiJhRgUuJ+TzGWd2bkHfM1pSVLiPKffOp3CtRg1FwoUKXE6pbVoyQ3u3wedg5h8XsejNTRo1FAkDKnCpkZSkWM7p04a0pgl8NjOfN/+8lLLSilN/oYjUGxW41FhMwM+A7q3oltmU9ct38cp98/lqu66xKeIVFbicFjOjS9umDOqZTsmeQ2T/bw7rcnd4HUskKqnApVZaNk3g3L4ZJMUHeOvZFXzy8hqNGoo0MBW41FpCXICzz2xD+/RklnxYwKsP5VGy55DXsUSihgpc6sTvM3p3bkmfLi3YWRAcNdyW/7XXsUSiggpcQqJdqyYM7d0aKhyvTlrIknc2a9RQpJ6pwCVkUpLiOLdvG1o2TeCT6et462/LKTukUUOR+qICl5CKCfgZ2L0VXds3JX9JEdn3zefrHRo1FKkPKnAJOTPjjHZNyeqZzv7i4Khh/sKdXscSaXRU4FJv0pomcE6fNiTEBnjzmeV8+soaKiu1Li4SKipwqVeJ8TEM6d2adq2SWfx+ATMfzuPAPo0aioSCClzqnd/no0+XlvTu3IIdm/Yy5ffz2b6+2OtYIhFPBS4Npn16E4b0bo0rD44aLpu3RaOGInWgApcGlZocxzl92tA8JY6Ppq7lnWeWU65RQ5FaUYFLg4uN8ZPVI50z2qWydlER2fcv4OudJV7HEok4KnDxhJnRtX0zBvZoxd7dpWTfv4ANi4u8jiUSUVTg4qlWzRI5t29w1HDO35bx+dS1GjUUqSEVuHguMT6GIWe2pm1aMgvnbeG1SQs5uK/M61giYa/OBW5mfjNbZGZvhCKQRCe/30efLi04s3MLtm8o5uV7/82OjXu8jiUS1kJxBH4HsCoE9yNRzszITG/C2b3bUFlWyYyH81j+foHXsUTCVp0K3MzaAVcBz4Ymjgg0TY7j3D4ZNEuJ48NX1vDO35dTXqZRQ5Fj1fUI/HHgN8AJr6VlZreaWa6Z5RYVacpAaiY2xs+gHul0aZvKmrydTL0/h+IvNWoocqRaF7iZXQ3sdM7lnex2zrlnnHNZzrmstLS02u5OopCZ0S2zGQO6t2LProNk/08OG5d+6XUskbBRlyPwc4FrzWwjMAW4yMxeDEkqkSOkN0/knD5tiAv4mf2XpcyfkY/TqKFI7QvcOTfBOdfOOdcRuAF4zzl3U8iSiRwhKSGGob1bk5GWRO7bm5j1x0Uc3K9RQ4lumgOXiOH3++jbpSW9OjVna/7XTLl3PkWbNGoo0SskBe6c+8A5d3Uo7kvkZMyMDq1TOPvM1lSUVjDtoTxWfrTV61gintARuESkZk3iOadvBs2axPH+v75g3uQVVJSdcBhKpFFSgUvEiovxk9Uznc4ZqazO2cHU/13A3t0HvY4l0mBU4BLRfGZ079CMs7qlUfzlAabcN5/NKzRqKNFBBS6NQusWSZzTJ4NYv4/Xn1zKgtc0aiiNnwpcGo3gqGEb2rRMImfuJl5/fDGlJRo1lMZLBS6NSsDvo98ZLenZsTkFa78Kjhpu2et1LJF6oQKXRsfM6NgmhbN7tabsYAXT/5DLqk8KvY4lEnIqcGm0mqXEc26fNqQkxfLei6t5//mVVJRr1FAaDxW4NGpxsQEG92xNpzYprJy/nWkP5LDvK40aSuOgApdGz+czenRsTv9uaXy1s4Qp986nYNVuKMyHJ3/K869dzMvTz4VRKfDkT4PbRSKAClyiRpsWSZzTuw0Bn49FDz5NxY/64OY8S2J5CT4clOyFOc/CbX1hwVyv44qckgpcokpyYiwXdjzEFTv/B3/5AazimDHDijIoLYH7x+hIXMKeClyiTvM1z+N35Se/UXkZTH+sYQKJ1JIKXKJOkw2vYacq8IoymPdCwwQSqSUVuEQdK99fsxse2Fe/QUTqSAUuUccFkmp2w4Tk+g0iUkcqcIk6ezuNxFngpLepIMC+rLENlEikdlTgEnWKe/4Q54s56W2cL8DMTcNY9M5mnNNZDSU8qcAl6pQ36cCO85+i0p9w/JG4PwbiEnH/PZUWA/vx2fR1vPX3FRw6eIonPUU8oAKXqHSg7TAKrprNnq43sN+fSCWGS0iBK2+Fp5cSc97VXH5bb4Ze34X1i3Yy7Q+57N5Wwyc/RRrIyRcCRRqx8iYd2DXoPu5sdQsYvHLbUOJbxVd/3swYcGkHWnVI4e1nlzP1D7lcdHMPumale5ha5Bs6Ahc5hXbdmzHunsG0bJvE28+u4JNpa6mo0FkNxXsqcJEaSG4Wx6j/GkCfYe1Y8u4WZj2+mP3FpV7HkiinAhepIX/AxwU3dOPi/+zFzo17yJ6YQ+G6r72OJVFMBS5ymrqf3Zoxd2cRE+vntT8uYsm8LRo1FE+owEVqoUXbZMZOyKJDnxZ8MnUtb0/WqKE0PBW4SC3FJcZwxW19GDKqM/l5O5n2UB5fbdeooTQcFbhIHZjPGHh5R665oz8H9h5i6h9yyV+00+tYEiVU4CIh0L5Hc8bdM4hmrZN48+nlfDZjHZUaNZR6pgIXCZEmzeO5fvwAel/QlkVvb2bWE4sp2XPI61jSiNW6wM2svZm9b2YrzWyFmd0RymAikcgf4+PC/+jOiO/3ZPuGPWQ/sIDt64u9jiWNVF2OwMuB8c65XsAQ4HYz6xWaWCKRrcfQNoz+zUD8MT5efXQhS98v0KihhFytC9w5t805t7Dqz3uBVUDbUAUTiXRp7ZswdsIgMns15+NX1vDu8yspK63wOpY0IiFZAzezjsBZwPxv+dytZpZrZrlFRUWh2J1IxIhPiuHKn/Tl7Gs7sSZnB9MeyuXrHSVex5JGos4FbmbJwHTgl865Pcd+3jn3jHMuyzmXlZaWVtfdiUQc8xlZV3bimp/1Y39xKVMfzGH9Yh3MSN3VqcDNLIZgeb/knJsRmkgijVPmmS0Yd88gmqYnMvdvy/h8Zj6VlVoXl9qryxSKAZOBVc65P4YukkjjldIiget+NYBe52Ww8M1NvP6nxRzYq1FDqZ26HIGfC9wMXGRmi6vergxRLpFGKxDjZ/hNPRh+cw+2rSsme2IOOzYct/oockq1viKPc+4TwEKYRSSq9Do3g7T2TZj79DJmTMrj/O9048zzMwj+cityanolpoiH0jKbMO6eQbTr0ZwP//UF8/65irJDGjWUmlGBi3gsPimGq2/vy6CrO/HF/O1MfziP4iKNGsqpqcBFwoD5jMFXd+Lq2/uxb/dBsifmsnHpl17HkjCnAhcJIx16B0cNU1rGM/svS5k/a71GDeWEVOAiYSalZQKjfz2QHue0IXfORmY/tYSD+8q8jiVhSAUuEoYCsX4uurkHw77bnYI1X5E9MYedmzRqKEdTgYuEKTPjzPPbcv2vBuJwzHhkISs/KfQ6loQRFbhImEvvmMK4ewaR0a0p77+4mvdeWEV5mUYNRQUuEhESkmO5+mf9yLqyI6s+3caMRxay58sDXscSj6nARSKEz2ecfW1nrvppX4qLDpD9YA6bVuzyOpZ4SAUuEmE69m3J2AlZJDeN542nlpAzewNOo4ZRSQUuEoGatkpk9F0D6TY4nQWvb2D2X5ZycL9GDaONClwkQsXE+rn4B7244IZubFm1m6kP5lC0ea/XsaQBqcBFIpiZ0WdYO64bP4CKcsf0R/JY9dk2r2NJA1GBizQCrTunMu6eQbTunMp7/7eK919aTUVZpdexpJ6pwEUaicSUWK79RT8GXNaBlR8XMmNSHnt3H/Q6ltQjFbhII+Lz+xh6XReu+HEfvt5RQvYDOWxZtdvrWFJPVOAijVDn/mmMnTCIxNRYZv1pMblzN2rUsBFSgYs0Uk3TExlzVxZds9KZ/9p65vxtGaUlGjVsTFTgIo1YTJyfS27pxfnf6crm5bvIfjCXLws0athYqMBFGjkzo+/w9owaP4CKQxVMfyiPL/6tUcPGQAUuEiXadEll3G8H06pjCu/+YxUfvvwFFeUaNYxkKnCRKJKYEsvIX/an/yWZLP9wK68+upB9X2nUMFKpwEWijM/v49zRZ3DZ/+vN7sL9ZE/MoWC1Rg0jkQpcJEqdMbAVYydkEZ8Uw6wnFrPwrU04p1HDSKICF4lizVonMebuLLoMaMXnr+Yz92/LKD1Q7nUsqSEVuEiUi40PcOmPzuS8sV3ZuGwXUx/MYdfWfV7HkhpQgYsIZka/Ee0ZdedZlB2sYNpDuazJ2e51LDkFFbiIVMvo2pRxvx1EWmYT3pm8ko9eWaNRwzAW8DrASRXmw/RHYe7bxJeX0zHQj72dRlLc84eUN+ngdTqJcIG9m0hdNZmX179KfMUBLCcZLrkJRo+HjC5ex/NMUmocI+88i89n5LNk3haKNu3l8lt7k9Q0DoDS4lLm/mAuV/zjCuJS4zxOG93C9wh8wVy4rS/MeRbKyzHAV76PlHWv0G72VSRs/cDrhBLBErZ+QLvZV5Gy7hUSK0rw4bCDe4OPt9v6Bh9/Uczv93He2K5c+qMz+XLrPl6ZmMPWNV8BsG7WOtbNXEf+6/kep5Q6FbiZXW5mX5jZOjO7O1ShKMyH+8dAaQlUHH3yHXPl+CoOkP7xzwjs3RSyXUr0COzdRPrHP8NXcQBzx0xcVJQFH3f3jwk+DqNc16x0xt6VRVxCgNceX8yidzaz7LllANXvxTu1XkIxMz/wZ+ASoADIMbNZzrmVdU41/VEoP/lZ06yyjNTVz7Fr0H113p1El9RVk7HKU5yVr7wMpj8GP3+qYUKFsXe/N5tt8zYDMO+veZjfANj66VYm2aTq22WOyGTcu+M8yRit6rIGPhhY55xbD2BmU4CRQN0LfN6Lxx15H8tcOclrprBtTWmddyfRJZlXMU4x61xRRuUbz7BlwZ6GCRXGOlkSW/2dqKgI/sLuKoIv9qk89M2Tm35/JZ3sfTbd/IYnGcNdXM8etL7nnpDfb12WUNoCW474uKBq21HM7FYzyzWz3KKioprd84GazaD6TvWPMJoZmC/4Xm9Hv9X0cWNO584GaNV6P+cN34Df/+3TKH5/JecN30Cr1vsbOJnU+xSKc+4Z4BmArKysmr1ONyEZSk59zmJLTKHHzOw65ZMoNCqlxo+vDi/8XwMECn8dgOZv5DNr7CwqDlZUb/fH+7l26nV0uTp6p3a8VJcj8K1A+yM+ble1re5G3AT+mJPfxh8DI24Oye4kyujxVSulX5fiC/gwnxFICGA+wxfwUfq1ljG9UpcCzwG6mlknM4sFbgBmhSTV6PEQOMU/sEAMjL4zJLuTKKPHV60sm7yMspIy0vqlMeq1UaT1S6OspEzTKB6qdYE758qBnwFvAauAbOfcipCkyugCv5sGcYnHHyn5Y4Lbfzctql9sIXWgx1etxKbGcuEjF3Jz7s10vKQjN+XcxIUPX0hsSqzX0aKWNeTpI7Oyslxubm7Nv6AwPzjKNe+F4BObCcnBX2tH36l/XFJ3enxJhDCzPOdc1nHbw7rARUTkhAUevi+lFxGRk1KBi4hEKBW4iEiEatA1cDMrAmp7BqqWwJchjBMqynV6lOv0KNfpaay5Ojjn0o7d2KAFXhdmlvtti/heU67To1ynR7lOT7Tl0hKKiEiEUoGLiESoSCrwZ7wOcALKdXqU6/Qo1+mJqlwRswYuIiJHi6QjcBEROYIKXEQkQkVEgdfbxZPrwMyeM7OdZrbc6yxHMrP2Zva+ma00sxVmdofXmQDMLN7MFpjZkqpcYXUxUzPzm9kiMwuba4KZ2UYzW2Zmi80sbE4iZGZNzWyama02s1VmNjQMMnWv+j4dfttjZr/0OheAmd1Z9ZhfbmYvm1l8yO473NfAqy6evIYjLp4M3BiSiyfXLdcFwD7g/5xzvb3MciQzawO0cc4tNLMmQB4wKgy+XwYkOef2mVkM8Alwh3Pu317mOszM/gvIAlKcc1d7nQeCBQ5kOefC6oUpZvZP4GPn3LNV1wJIdM597XGsalWdsRU42zlX2xcOhipLW4KP9V7OuQNmlg3Mcc79IxT3HwlH4NUXT3bOHQIOXzzZU865j4DdXuc4lnNum3NuYdWf9xI8V/tx1yptaC7o8MVOY6rewuLowczaAVcBz3qdJdyZWSpwATAZwDl3KJzKu8oIIN/r8j5CAEgwswCQCBSG6o4jocBrdPFkOZ6ZdQTOAuZ7HAWoXqZYDOwE3nHOhUUu4HHgN8C3X7XXOw5428zyzOxWr8NU6QQUAc9XLTk9a2ZJXoc6xg3Ay16HAHDObQUmAZuBbUCxc+7tUN1/JBS41IKZJQPTgV865/Z4nQfAOVfhnOtP8Pqpg83M86UnM7sa2Omcy/M6y7c4zzk3ALgCuL1q2c5rAWAA8Ffn3FnAfiAsnpcCqFrSuRaY6nUWADNrRnDFoBOQASSZ2U2huv9IKPD6u3hyI1W1xjwdeMk5N8PrPMeq+pX7feByj6MAnAtcW7XePAW4yMxe9DZSUNXRG865ncCrBJcTvVYAFBzx29M0goUeLq4AFjrndngdpMrFwAbnXJFzrgyYAZwTqjuPhAKvv4snN0JVTxZOBlY55/7odZ7DzCzNzJpW/TmB4JPSqz0NBTjnJjjn2jnnOhJ8bL3nnAvZEVJtmVlS1ZPQVC1RXAp4PvHknNsObDGz7lWbRgCePkF+jBsJk+WTKpuBIWaWWPVvcwTB56VCIhCqO6ovzrlyMzt88WQ/8FzILp5cB2b2MjAMaGlmBcDvnXOTvU0FBI8obwaWVa03A9zjnJvjXSQA2gD/rJoQ8BG8CHbYjOyFoXTg1eC/eQLAv5xzb3obqdrPgZeqDqjWA//pcR6g+j+6S4DbvM5ymHNuvplNAxYC5cAiQviy+rAfIxQRkW8XCUsoIiLyLVTgIiIRSgUuIhKhVOAiIhFKBS4iEqFU4CIiEUoFLiISof4/4eLH++RubhEAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "ax.plot(point1_point2_x_1[:,0], point1_point2_x_1[:,1], label = \"_nolegend_\")\n", "ax.plot(point3_point4_x_1[:,0], point3_point4_x_1[:,1], label = \"_nolegend_\")\n", "ax.plot(point1_point2_x_2[:,0], point1_point2_x_2[:,1], label = \"_nolegend_\")\n", "ax.plot(point3_point4_x_2[:,0], point3_point4_x_2[:,1], label = \"_nolegend_\")\n", "ax.plot(x_1, x_2, label = \"_nolegend_\")\n", "\n", "ax.fill_between(x_1_region_1, 0, x_2_region_1, where=x_2_region_1<=6, color=\"plum\", label = \"_nolegend_\")\n", "x_1_region_2 = np.linspace(0,2, 100)\n", "ax.fill_between(x_1_region_2, 0, 6, color=\"plum\", label = \"_nolegend_\")\n", "\n", "ax.plot(array_FEV[:,0], array_FEV[:,1], 'o', color=\"orangered\", markersize=10, label=\"FEV\")\n", "\n", "ax.plot(array_NFEV[:,0], array_NFEV[:,1], '', color=\"darkmagenta\", markersize=10, label=\"NFEV\")\n", "\n", "ax.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\|Solución FEV\| Ecuaciones de definición\|\n", "\|:---:\|:---:\|\n", "\|(0,0)\| $x_1 = 0, x_2 = 0$\|\n", "\|(0,6)\| $x_1 = 0, 2x_2 = 12$\|\n", "\|(2,6)\| $2x_2 = 12, 3x_1 + 2x_2 = 18$\|\n", "\|(4,3)\| $3x_1 + 2x_2 = 18, x_1 = 4$\|\n", "\|(4,0)\| $x_1 = 4, x_2 = 0$\|" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### FEV adyacentes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Definición\n", "\n", "En un PL con $n$ variables de decisión nombramos soluciones FEV adyacentes a dos soluciones FEV que comparten $n-1$ fronteras de restricción. Las soluciones FEV adyacentes están conectadas por una arista (segmento de recta)\n", "```" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "tags": [ "hide-input", "margin" ] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "ax.plot(point1_point2_x_1[:,0], point1_point2_x_1[:,1], label = \"_nolegend_\")\n", "ax.plot(point3_point4_x_1[:,0], point3_point4_x_1[:,1], label = \"_nolegend_\")\n", "ax.plot(point1_point2_x_2[:,0], point1_point2_x_2[:,1], label = \"_nolegend_\")\n", "ax.plot(point3_point4_x_2[:,0], point3_point4_x_2[:,1], label = \"_nolegend_\")\n", "ax.plot(x_1, x_2, label = \"_nolegend_\")\n", "\n", "ax.fill_between(x_1_region_1, 0, x_2_region_1, where=x_2_region_1<=6, color=\"plum\", label = \"_nolegend_\")\n", "x_1_region_2 = np.linspace(0,2, 100)\n", "ax.fill_between(x_1_region_2, 0, 6, color=\"plum\", label = \"_nolegend_\")\n", "\n", "ax.plot(array_FEV[:,0], array_FEV[:,1], 'o', color=\"orangered\", markersize=10, label=\"FEV\")\n", "\n", "ax.plot(array_NFEV[:,0], array_NFEV[:,1], '', color=\"darkmagenta\", markersize=10, label=\"NFEV\")\n", "\n", "ax.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En el ejemplo prototipo $(0,0)$ y $(0,6)$ son adyacentes pues comparten una arista formada por la ecuación de frontera $x_1=0$ y de cada solución FEV salen dos aristas, esto es tienen dos soluciones FEV adyacentes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Comentario\n", "\n", "Una razón para analizar las soluciones FEV adyacentes es la siguiente propiedad: \n", "\n", "si un PL tiene al menos una solución óptima y una solución FEV no tiene soluciones FEV adyacentes que sean mejores entonces ésa debe ser una solución óptima.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Observación\n", ":class: tip\n", "\n", "En el ejemplo prototipo $(2, 6)$ es un punto óptimo pues sus soluciones FEV adyacentes, $(0, 6)$, $(4,3)$ tienen un valor de la función objetivo menor (recuérdese es un problema de maximización).\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pasos que sigue el método símplex" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para el ejemplo prototipo el método símplex a grandes rasgos realiza lo siguiente:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Paso inicial: se elige $(0,0)$ como la solución FEV inicial para examinarla (esto siempre se puede hacer para problemas con restricciones de no negatividad)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "En el ejemplo numérico se entenderá la frase \"solución FEV adyacente que es mejor\"\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Prueba de optimalidad: revisar condición de optimalidad para $(0,0)$. Concluir que $(0,0)$ no es una solución óptima (existe una solución FEV adyacente que es mejor)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Iteración 1: moverse a una solución FEV adyacente mejor, para esto se realizan los pasos:\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1.Entre las dos aristas de la región factible que salen de $(0,0)$ se elige desplazarse a lo largo de la arista que aumenta el valor de $x_2$ (con una función objetivo $f_o(x) = 3x_1 + 5x_2$ si $x_2$ aumenta entonces el valor de $f_o$ crece más que con $x_1$)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2.Detenerse al llegar a la primera ecuación de frontera en esa dirección: $2x_2 = 12$ para mantener factibilidad.\n", "\n", "3.Obtener la intersección del nuevo conjunto de ecuaciones de frontera: $(0,6)$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "En el ejemplo numérico se entenderá la frase \"solución FEV adyacente que es mejor\"\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Prueba de optimalidad: revisar condición de optimalidad para $(0,6)$. Concluir que $(0,6)$ no es una solución óptima (existe una solución FEV adyacente que es mejor)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Iteración 2: moverse a una solución FEV adyacente mejor:\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1.De las dos aristas que salen de $(0,6)$ moverse a lo largo de la arista que aumenta el valor de $x_1$ (para que la $f_o$ continue mejorando no podemos ir hacia abajo pues esto implicaría disminuir el valor de $x_2$ y por tanto $f_o$)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2.Detenerse al llegar a la primera ecuación de frontera en esa dirección: $3x_1+2x_2 = 12$ para manterner factibilidad.\n", "\n", "3.Obtener la intersección del nuevo conjunto de ecuaciones de frontera: $(2,6)$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "En el ejemplo numérico se entenderá la frase \"ninguna solución FEV adyacente es mejor\"\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Prueba de optimalidad: revisar condición de optimalidad para $(2,6)$. Concluir que $(2,6)$ es una solución óptima (ninguna solución FEV adyacente es mejor)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(FORMAAUMENTADAPL)=" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Forma aumentada de un PL" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El método símplex inicia con un sistema de ecuaciones lineales con lado derecho igual a $b$ (que es el lado derecho de las restricciones funcionales) y una matriz del sistema con menos renglones que columnas. Asume que las entradas de $b$ son no negativas y que el rank de $A$ es completo." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para revisar los pasos del método símplex descritos anteriormente en esta sección continuaremos con el ejemplo prototipo de PL:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\displaystyle \\max_{x \\in \\mathbb{R}^2} 3x_1 + 5x_2$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\text{sujeto a: }$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$x_1 \\leq 4$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$2x_2 \\leq 12$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$3x_1 + 2x_2 \\leq 18$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$x_1 \\geq 0, x_2 \\geq 0$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y vamos a nombrar a las desigualdades $x_1 \\leq4, 2x_2 \\leq 12, 3x_1 + 2x_2 \\leq 18$ restricciones funcionales y a las desigualdades $x_1 \\geq 0, x_2 \\geq 0$ restricciones de no negatividad." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Observación\n", ":class: tip\n", "\n", "Aunque hay diversas formas de PL en las que podríamos tener lados derechos negativos o desigualdades del tipo $\\geq$, es sencillo transformar de forma algebraica tales PL a una forma similar descrita en esta sección. \n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En el ejemplo prototipo tenemos desigualdades por lo que se introducen *variables de holgura, slack variables,* no negativas para obtener la forma aumentada:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\displaystyle \\max_{x \\in \\mathbb{R}^5} 3x_1 + 5x_2$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\text{sujeto a: }$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$x_1 + x_3 = 4$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$2x_2 + x_4 = 12$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$3x_1 + 2x_2 + x_5 = 18$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$x_1 \\geq 0, x_2 \\geq 0, x_3 \\geq 0, x_4 \\geq 0, x_5 \\geq 0$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "Forma estándar de un PL:\n", "\n", "$$\n", "\\displaystyle \\min_{x \\in \\mathbb{R}^n} c^Tx\\\\\n", "\\text{sujeto a:} \\\\\n", "Ax=b\\\\\n", "x \\geq 0\n", "$$\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Comentarios\n", "\n", "* La forma aumentada que se obtuvo para el ejemplo prototipo no es la forma estándar de un PL salvo porque en la estándar se usa una minimización, ver {ref}`forma estándar de un PL <FORMAESTPL>`. Sin embargo, la forma estándar del PL se puede obtener considerando que maximizar $3x_1 + 5x_2$ sujeto a las restricciones dadas tiene mismo conjunto óptimo al problema minimizar $-3x_1 - 5 x_2$ sujeto a las mismas restricciones (los valores óptimos entre el problema de maximización y minimización son iguales salvo una multiplicación por $-1$).\n", "\n", "* Las variables de holgura al iniciar el método tienen un coeficiente de $0$ en la función objetivo $f_o(x) = 3x_1 + 5x_2 = 3x_1 + 5x_2 + 0x_3 + 0x_4 + 0x_5$\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y en notación matricial el sistema de ecuaciones lineales es:\n", "\n", "$$\n", "Ax = \n", "\\left [\n", "\\begin{array}{ccccc}\n", "1 & 0 & 1 & 0 & 0 \\\\\n", "0 & 2 & 0 & 1 & 0 \\\\\n", "3 & 2 & 0 & 0 & 1 \\\\\n", "\\end{array}\n", "\\right ]\n", "\\left [\n", "\\begin{array}{c}\n", "x_1 \\\\\n", "x_2 \\\\\n", "x_3 \\\\\n", "x_4 \\\\\n", "x_5\n", "\\end{array}\n", "\\right ]\n", "=\n", "\\left[\n", "\\begin{array}{c}\n", "4 \\\\\n", "12 \\\\\n", "18\n", "\\end{array}\n", "\\right ]\n", "=\n", "b\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Observación\n", ":class: tip\n", "\n", "Obsérvese que en la matriz de la forma aumentada se tiene una matriz identidad.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Comentario\n", "\n", "Interpretación de algunos valores de las variables en la forma aumentada:\n", "\n", "Si una variable de holgura es igual a $0$ en la solución actual, entonces esta solución se encuentra sobre la ecuación de frontera de la restricción funcional correspondiente. Un valor mayor que $0$ significa que la solución está en el lado factible de la ecuación de frontera, mientras que un valor menor que $0$ señala que está en el lado no factible de esta ecuación de frontera.\n", "\n", "```" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "tags": [ "hide-input", "margin" ] }, "outputs": [ { "data": { "image/png": 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59hf9OevSTFZ8tJUZjy5k7+6DXscSEY+Ed4EX5sOTP4Wpb8PLc2BUSvDjwnyvk3nG5/dxzvVncPltvflq+36yJ+awZfXu4CcPf79GpcBlPn2/ash/oJwz/rqK0uJSr6OInJbwLfAFc+G2vjDnWSgvD24r2Rv8+La+wc9HsS5ntWLs3VkkpsTy+hOLWfvXf+AOf79K9oJz+n7VUNMlu2m2ZDf5r+s/OoksdSpwM2tqZtPMbLWZrTKzoSFJVZgP94+B0hKoOGZ0rqIsuP3+MVF/ZNmsdRKjfzOQPt1L6TjzJ5i+X7XS8rMdACx7bpnHSUROT6COX/8E8KZzboyZxQKJIcgE0x+F8lPMPJeXwfTH4OdPhWSXkSo2PsB5vtdxVg4ne62Pvl/Vsi/OZvO8zdUfJ/sNgK2fbmWSTarenjkik3HvjmvwfCI1VesCN7NU4ALgBwDOuUPAoZCkmvfi8UeSx6ooo+StZ7i96/6Q7DKS/eXtl0ioLD/5jfT9qpZ4SSIdPumArzT4C6ivIvg/X+Whb6Z6KuMq+fDSD5n7ppaepO56NO/BXYPvCvn91uUIvBNQBDxvZv2APOAO59xRDWFmtwK3AmRmZtbsng/sq9HN4g+VcXCHpjDiDtXsFZrxNbxdY1fSr4RN922iw++/KfEjVcZVsul/NlHSt8SDdCI1Z87V7hwbZpYF/Bs41zk338yeAPY45353oq/Jyspyubm5p77zUSnBJ+BOoTImmY3jltQ8dCPV8ZV++Mpr8J9eYgrMLK7/QBEi/418Zo2dRcXBb07d64/3c+3Ua+lydRcPk4kczczynHNZx26vy5OYBUCBc25+1cfTgAF1uL9vjLgJ/DEnvYmzAHs7jQrJ7iLd3k4jcXbyX6acLwZG3NxAiSJD6del+AI+Kg0qYnyYz/AFfJR+rXFCiQy1LnDn3HZgi5l1r9o0AlgZklSjx0PgFAXui6G4xy0h2V2kK+75w2BBn0S587Os2Rhq+xtXY7Rs8jLKSso40C6JdT/pSVq/NMpKyjSNIhGjrnPgPwdeMrOlQH9gYp0TAWR0gd9Ng7jE447EnQWo9Cew4/ynKG/SISS7i3TlTTqw4/ynqPQnHHckfvj7Nf+MiXz0SSWz/7SEQwdO8YRnlIhNjeXCRy5k5YR+7OnVlJtybuLChy8kNiXW62giNVLrNfDaqPEa+GGF+cHRtzefxpWV42KS2dtpFMU9blF5f4vA3k2krn6OJhtmYmX7cTFJ1d+vsuRMNm7bwxebviI5NY6rftGPFm2TvY4cFr7z9OcAvHJbaF7GIBJqJ1oDr+sceP3K6BKcW07ewMEdB9jW5lmvE4W18iYd2DXoPnYNug8A8xuuakTOgE4ZqaQkxbF47U6mTszhopt70m1Iaw8Ti0hdhO9L6aVetEiN59y+GTRJjOWdf6zkwxdWU1GhsxqKRCIVeBSKjw1wdq/WdGjdhOWfFjJjYi77dSInkYijAo9SPp/Rq1ML+nVtya5t+5ny+/lsXf2V17FE5DSowKNcRstkhvZpg9+M155YxMI5GzVqKBIhVOBCk8RYhvZuQ6tmiXw+az1zn1rKoYMaNRQJdypwASAm4OOsbml0z2zGhhW7eOW+BezeVrNz0oiIN1TgUs3M6Nw2lcG90jm47xBTH8hl7fztXscSkRNQgctxWqQmcE6fDJITYnj7+ZV89NIXVGrUUCTsqMDlWyXEBUcNM9ObsOzjrcx4ME+jhiJhRgUuJ+TzGWd2bkHfM1pSVLiPKffOp3CtRg1FwoUKXE6pbVoyQ3u3wedg5h8XsejNTRo1FAkDKnCpkZSkWM7p04a0pgl8NjOfN/+8lLLSilN/oYjUGxW41FhMwM+A7q3oltmU9ct38cp98/lqu66xKeIVFbicFjOjS9umDOqZTsmeQ2T/bw7rcnd4HUskKqnApVZaNk3g3L4ZJMUHeOvZFXzy8hqNGoo0MBW41FpCXICzz2xD+/RklnxYwKsP5VGy55DXsUSihgpc6sTvM3p3bkmfLi3YWRAcNdyW/7XXsUSiggpcQqJdqyYM7d0aKhyvTlrIknc2a9RQpJ6pwCVkUpLiOLdvG1o2TeCT6et462/LKTukUUOR+qICl5CKCfgZ2L0VXds3JX9JEdn3zefrHRo1FKkPKnAJOTPjjHZNyeqZzv7i4Khh/sKdXscSaXRU4FJv0pomcE6fNiTEBnjzmeV8+soaKiu1Li4SKipwqVeJ8TEM6d2adq2SWfx+ATMfzuPAPo0aioSCClzqnd/no0+XlvTu3IIdm/Yy5ffz2b6+2OtYIhFPBS4Npn16E4b0bo0rD44aLpu3RaOGInWgApcGlZocxzl92tA8JY6Ppq7lnWeWU65RQ5FaUYFLg4uN8ZPVI50z2qWydlER2fcv4OudJV7HEok4KnDxhJnRtX0zBvZoxd7dpWTfv4ANi4u8jiUSUVTg4qlWzRI5t29w1HDO35bx+dS1GjUUqSEVuHguMT6GIWe2pm1aMgvnbeG1SQs5uK/M61giYa/OBW5mfjNbZGZvhCKQRCe/30efLi04s3MLtm8o5uV7/82OjXu8jiUS1kJxBH4HsCoE9yNRzszITG/C2b3bUFlWyYyH81j+foHXsUTCVp0K3MzaAVcBz4Ymjgg0TY7j3D4ZNEuJ48NX1vDO35dTXqZRQ5Fj1fUI/HHgN8AJr6VlZreaWa6Z5RYVacpAaiY2xs+gHul0aZvKmrydTL0/h+IvNWoocqRaF7iZXQ3sdM7lnex2zrlnnHNZzrmstLS02u5OopCZ0S2zGQO6t2LProNk/08OG5d+6XUskbBRlyPwc4FrzWwjMAW4yMxeDEkqkSOkN0/knD5tiAv4mf2XpcyfkY/TqKFI7QvcOTfBOdfOOdcRuAF4zzl3U8iSiRwhKSGGob1bk5GWRO7bm5j1x0Uc3K9RQ4lumgOXiOH3++jbpSW9OjVna/7XTLl3PkWbNGoo0SskBe6c+8A5d3Uo7kvkZMyMDq1TOPvM1lSUVjDtoTxWfrTV61gintARuESkZk3iOadvBs2axPH+v75g3uQVVJSdcBhKpFFSgUvEiovxk9Uznc4ZqazO2cHU/13A3t0HvY4l0mBU4BLRfGZ079CMs7qlUfzlAabcN5/NKzRqKNFBBS6NQusWSZzTJ4NYv4/Xn1zKgtc0aiiNnwpcGo3gqGEb2rRMImfuJl5/fDGlJRo1lMZLBS6NSsDvo98ZLenZsTkFa78Kjhpu2et1LJF6oQKXRsfM6NgmhbN7tabsYAXT/5DLqk8KvY4lEnIqcGm0mqXEc26fNqQkxfLei6t5//mVVJRr1FAaDxW4NGpxsQEG92xNpzYprJy/nWkP5LDvK40aSuOgApdGz+czenRsTv9uaXy1s4Qp986nYNVuKMyHJ3/K869dzMvTz4VRKfDkT4PbRSKAClyiRpsWSZzTuw0Bn49FDz5NxY/64OY8S2J5CT4clOyFOc/CbX1hwVyv44qckgpcokpyYiwXdjzEFTv/B3/5AazimDHDijIoLYH7x+hIXMKeClyiTvM1z+N35Se/UXkZTH+sYQKJ1JIKXKJOkw2vYacq8IoymPdCwwQSqSUVuEQdK99fsxse2Fe/QUTqSAUuUccFkmp2w4Tk+g0iUkcqcIk6ezuNxFngpLepIMC+rLENlEikdlTgEnWKe/4Q54s56W2cL8DMTcNY9M5mnNNZDSU8qcAl6pQ36cCO85+i0p9w/JG4PwbiEnH/PZUWA/vx2fR1vPX3FRw6eIonPUU8oAKXqHSg7TAKrprNnq43sN+fSCWGS0iBK2+Fp5cSc97VXH5bb4Ze34X1i3Yy7Q+57N5Wwyc/RRrIyRcCRRqx8iYd2DXoPu5sdQsYvHLbUOJbxVd/3swYcGkHWnVI4e1nlzP1D7lcdHMPumale5ha5Bs6Ahc5hXbdmzHunsG0bJvE28+u4JNpa6mo0FkNxXsqcJEaSG4Wx6j/GkCfYe1Y8u4WZj2+mP3FpV7HkiinAhepIX/AxwU3dOPi/+zFzo17yJ6YQ+G6r72OJVFMBS5ymrqf3Zoxd2cRE+vntT8uYsm8LRo1FE+owEVqoUXbZMZOyKJDnxZ8MnUtb0/WqKE0PBW4SC3FJcZwxW19GDKqM/l5O5n2UB5fbdeooTQcFbhIHZjPGHh5R665oz8H9h5i6h9yyV+00+tYEiVU4CIh0L5Hc8bdM4hmrZN48+nlfDZjHZUaNZR6pgIXCZEmzeO5fvwAel/QlkVvb2bWE4sp2XPI61jSiNW6wM2svZm9b2YrzWyFmd0RymAikcgf4+PC/+jOiO/3ZPuGPWQ/sIDt64u9jiWNVF2OwMuB8c65XsAQ4HYz6xWaWCKRrcfQNoz+zUD8MT5efXQhS98v0KihhFytC9w5t805t7Dqz3uBVUDbUAUTiXRp7ZswdsIgMns15+NX1vDu8yspK63wOpY0IiFZAzezjsBZwPxv+dytZpZrZrlFRUWh2J1IxIhPiuHKn/Tl7Gs7sSZnB9MeyuXrHSVex5JGos4FbmbJwHTgl865Pcd+3jn3jHMuyzmXlZaWVtfdiUQc8xlZV3bimp/1Y39xKVMfzGH9Yh3MSN3VqcDNLIZgeb/knJsRmkgijVPmmS0Yd88gmqYnMvdvy/h8Zj6VlVoXl9qryxSKAZOBVc65P4YukkjjldIiget+NYBe52Ww8M1NvP6nxRzYq1FDqZ26HIGfC9wMXGRmi6vergxRLpFGKxDjZ/hNPRh+cw+2rSsme2IOOzYct/oockq1viKPc+4TwEKYRSSq9Do3g7T2TZj79DJmTMrj/O9048zzMwj+cityanolpoiH0jKbMO6eQbTr0ZwP//UF8/65irJDGjWUmlGBi3gsPimGq2/vy6CrO/HF/O1MfziP4iKNGsqpqcBFwoD5jMFXd+Lq2/uxb/dBsifmsnHpl17HkjCnAhcJIx16B0cNU1rGM/svS5k/a71GDeWEVOAiYSalZQKjfz2QHue0IXfORmY/tYSD+8q8jiVhSAUuEoYCsX4uurkHw77bnYI1X5E9MYedmzRqKEdTgYuEKTPjzPPbcv2vBuJwzHhkISs/KfQ6loQRFbhImEvvmMK4ewaR0a0p77+4mvdeWEV5mUYNRQUuEhESkmO5+mf9yLqyI6s+3caMRxay58sDXscSj6nARSKEz2ecfW1nrvppX4qLDpD9YA6bVuzyOpZ4SAUuEmE69m3J2AlZJDeN542nlpAzewNOo4ZRSQUuEoGatkpk9F0D6TY4nQWvb2D2X5ZycL9GDaONClwkQsXE+rn4B7244IZubFm1m6kP5lC0ea/XsaQBqcBFIpiZ0WdYO64bP4CKcsf0R/JY9dk2r2NJA1GBizQCrTunMu6eQbTunMp7/7eK919aTUVZpdexpJ6pwEUaicSUWK79RT8GXNaBlR8XMmNSHnt3H/Q6ltQjFbhII+Lz+xh6XReu+HEfvt5RQvYDOWxZtdvrWFJPVOAijVDn/mmMnTCIxNRYZv1pMblzN2rUsBFSgYs0Uk3TExlzVxZds9KZ/9p65vxtGaUlGjVsTFTgIo1YTJyfS27pxfnf6crm5bvIfjCXLws0athYqMBFGjkzo+/w9owaP4CKQxVMfyiPL/6tUcPGQAUuEiXadEll3G8H06pjCu/+YxUfvvwFFeUaNYxkKnCRKJKYEsvIX/an/yWZLP9wK68+upB9X2nUMFKpwEWijM/v49zRZ3DZ/+vN7sL9ZE/MoWC1Rg0jkQpcJEqdMbAVYydkEZ8Uw6wnFrPwrU04p1HDSKICF4lizVonMebuLLoMaMXnr+Yz92/LKD1Q7nUsqSEVuEiUi40PcOmPzuS8sV3ZuGwXUx/MYdfWfV7HkhpQgYsIZka/Ee0ZdedZlB2sYNpDuazJ2e51LDkFFbiIVMvo2pRxvx1EWmYT3pm8ko9eWaNRwzAW8DrASRXmw/RHYe7bxJeX0zHQj72dRlLc84eUN+ngdTqJcIG9m0hdNZmX179KfMUBLCcZLrkJRo+HjC5ex/NMUmocI+88i89n5LNk3haKNu3l8lt7k9Q0DoDS4lLm/mAuV/zjCuJS4zxOG93C9wh8wVy4rS/MeRbKyzHAV76PlHWv0G72VSRs/cDrhBLBErZ+QLvZV5Gy7hUSK0rw4bCDe4OPt9v6Bh9/Uczv93He2K5c+qMz+XLrPl6ZmMPWNV8BsG7WOtbNXEf+6/kep5Q6FbiZXW5mX5jZOjO7O1ShKMyH+8dAaQlUHH3yHXPl+CoOkP7xzwjs3RSyXUr0COzdRPrHP8NXcQBzx0xcVJQFH3f3jwk+DqNc16x0xt6VRVxCgNceX8yidzaz7LllANXvxTu1XkIxMz/wZ+ASoADIMbNZzrmVdU41/VEoP/lZ06yyjNTVz7Fr0H113p1El9RVk7HKU5yVr7wMpj8GP3+qYUKFsXe/N5tt8zYDMO+veZjfANj66VYm2aTq22WOyGTcu+M8yRit6rIGPhhY55xbD2BmU4CRQN0LfN6Lxx15H8tcOclrprBtTWmddyfRJZlXMU4x61xRRuUbz7BlwZ6GCRXGOlkSW/2dqKgI/sLuKoIv9qk89M2Tm35/JZ3sfTbd/IYnGcNdXM8etL7nnpDfb12WUNoCW474uKBq21HM7FYzyzWz3KKioprd84GazaD6TvWPMJoZmC/4Xm9Hv9X0cWNO584GaNV6P+cN34Df/+3TKH5/JecN30Cr1vsbOJnU+xSKc+4Z4BmArKysmr1ONyEZSk59zmJLTKHHzOw65ZMoNCqlxo+vDi/8XwMECn8dgOZv5DNr7CwqDlZUb/fH+7l26nV0uTp6p3a8VJcj8K1A+yM+ble1re5G3AT+mJPfxh8DI24Oye4kyujxVSulX5fiC/gwnxFICGA+wxfwUfq1ljG9UpcCzwG6mlknM4sFbgBmhSTV6PEQOMU/sEAMjL4zJLuTKKPHV60sm7yMspIy0vqlMeq1UaT1S6OspEzTKB6qdYE758qBnwFvAauAbOfcipCkyugCv5sGcYnHHyn5Y4Lbfzctql9sIXWgx1etxKbGcuEjF3Jz7s10vKQjN+XcxIUPX0hsSqzX0aKWNeTpI7Oyslxubm7Nv6AwPzjKNe+F4BObCcnBX2tH36l/XFJ3enxJhDCzPOdc1nHbw7rARUTkhAUevi+lFxGRk1KBi4hEKBW4iEiEatA1cDMrAmp7BqqWwJchjBMqynV6lOv0KNfpaay5Ojjn0o7d2KAFXhdmlvtti/heU67To1ynR7lOT7Tl0hKKiEiEUoGLiESoSCrwZ7wOcALKdXqU6/Qo1+mJqlwRswYuIiJHi6QjcBEROYIKXEQkQkVEgdfbxZPrwMyeM7OdZrbc6yxHMrP2Zva+ma00sxVmdofXmQDMLN7MFpjZkqpcYXUxUzPzm9kiMwuba4KZ2UYzW2Zmi80sbE4iZGZNzWyama02s1VmNjQMMnWv+j4dfttjZr/0OheAmd1Z9ZhfbmYvm1l8yO473NfAqy6evIYjLp4M3BiSiyfXLdcFwD7g/5xzvb3MciQzawO0cc4tNLMmQB4wKgy+XwYkOef2mVkM8Alwh3Pu317mOszM/gvIAlKcc1d7nQeCBQ5kOefC6oUpZvZP4GPn3LNV1wJIdM597XGsalWdsRU42zlX2xcOhipLW4KP9V7OuQNmlg3Mcc79IxT3HwlH4NUXT3bOHQIOXzzZU865j4DdXuc4lnNum3NuYdWf9xI8V/tx1yptaC7o8MVOY6rewuLowczaAVcBz3qdJdyZWSpwATAZwDl3KJzKu8oIIN/r8j5CAEgwswCQCBSG6o4jocBrdPFkOZ6ZdQTOAuZ7HAWoXqZYDOwE3nHOhUUu4HHgN8C3X7XXOw5428zyzOxWr8NU6QQUAc9XLTk9a2ZJXoc6xg3Ay16HAHDObQUmAZuBbUCxc+7tUN1/JBS41IKZJQPTgV865/Z4nQfAOVfhnOtP8Pqpg83M86UnM7sa2Omcy/M6y7c4zzk3ALgCuL1q2c5rAWAA8Ffn3FnAfiAsnpcCqFrSuRaY6nUWADNrRnDFoBOQASSZ2U2huv9IKPD6u3hyI1W1xjwdeMk5N8PrPMeq+pX7feByj6MAnAtcW7XePAW4yMxe9DZSUNXRG865ncCrBJcTvVYAFBzx29M0goUeLq4AFjrndngdpMrFwAbnXJFzrgyYAZwTqjuPhAKvv4snN0JVTxZOBlY55/7odZ7DzCzNzJpW/TmB4JPSqz0NBTjnJjjn2jnnOhJ8bL3nnAvZEVJtmVlS1ZPQVC1RXAp4PvHknNsObDGz7lWbRgCePkF+jBsJk+WTKpuBIWaWWPVvcwTB56VCIhCqO6ovzrlyMzt88WQ/8FzILp5cB2b2MjAMaGlmBcDvnXOTvU0FBI8obwaWVa03A9zjnJvjXSQA2gD/rJoQ8BG8CHbYjOyFoXTg1eC/eQLAv5xzb3obqdrPgZeqDqjWA//pcR6g+j+6S4DbvM5ymHNuvplNAxYC5cAiQviy+rAfIxQRkW8XCUsoIiLyLVTgIiIRSgUuIhKhVOAiIhFKBS4iEqFU4CIiEUoFLiISof4/4eLH++RubhEAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "ax.plot(point1_point2_x_1[:,0], point1_point2_x_1[:,1], label = \"_nolegend_\")\n", "ax.plot(point3_point4_x_1[:,0], point3_point4_x_1[:,1], label = \"_nolegend_\")\n", "ax.plot(point1_point2_x_2[:,0], point1_point2_x_2[:,1], label = \"_nolegend_\")\n", "ax.plot(point3_point4_x_2[:,0], point3_point4_x_2[:,1], label = \"_nolegend_\")\n", "ax.plot(x_1, x_2, label = \"_nolegend_\")\n", "\n", "ax.fill_between(x_1_region_1, 0, x_2_region_1, where=x_2_region_1<=6, color=\"plum\", label = \"_nolegend_\")\n", "x_1_region_2 = np.linspace(0,2, 100)\n", "ax.fill_between(x_1_region_2, 0, 6, color=\"plum\", label = \"_nolegend_\")\n", "\n", "ax.plot(array_FEV[:,0], array_FEV[:,1], 'o', color=\"orangered\", markersize=10, label=\"FEV\")\n", "\n", "ax.plot(array_NFEV[:,0], array_NFEV[:,1], '', color=\"darkmagenta\", markersize=10, label=\"NFEV\")\n", "\n", "ax.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Definiciones\n", "\n", "Una solución aumentada* es una solución de las variables originales que se aumentó con los valores correspondientes de las variables de holgura.\n", "\n", "Una solución básica es una solución FEV o NFEV aumentada.\n", "\n", "Una solución básica factible (BF) es una solución FEV aumentada.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "$$\n", "\\left [\n", "\\begin{array}{ccccc}\n", "1 & 0 & 1 & 0 & 0 \\\\\n", "0 & 2 & 0 & 1 & 0 \\\\\n", "3 & 2 & 0 & 0 & 1 \\\\\n", "\\end{array}\n", "\\right ]\n", "\\left [\n", "\\begin{array}{c}\n", "x_1 \\\\\n", "x_2 \\\\\n", "x_3 \\\\\n", "x_4 \\\\\n", "x_5\n", "\\end{array}\n", "\\right ]\n", "=\n", "\\left[\n", "\\begin{array}{c}\n", "4 \\\\\n", "12 \\\\\n", "18\n", "\\end{array}\n", "\\right ]\n", "$$\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En el ejemplo prototipo:\n", "\n", "* $\\left [ \\begin{array}{c} x_1 \\\\ x_2 \\end{array} \\right ] = \\left [ \\begin{array}{c} 3 \\\\ 2 \\end{array} \\right ]$ es solución (de hecho factible) y $\\left [ \\begin{array}{c} x_1 \\\\ x_2 \\\\ x_3 \\\\ x_4 \\\\ x_5 \\end{array} \\right ] = \\left [ \\begin{array}{c} 3 \\\\ 2 \\\\ 1 \\\\ 8 \\\\ 5 \\end{array} \\right ]$ es solución aumentada (factible).\n", "\n", "* $\\left [ \\begin{array}{c} x_1 \\\\ x_2 \\end{array} \\right ] = \\left [ \\begin{array}{c} 4 \\\\ 6 \\end{array} \\right ]$ es solución NFEV y $\\left [ \\begin{array}{c} x_1 \\\\ x_2 \\\\ x_3 \\\\ x_4 \\\\ x_5 \\end{array} \\right ] = \\left [ \\begin{array}{c} 4 \\\\ 6 \\\\ 0 \\\\ 0 \\\\ -6 \\end{array} \\right ]$ es solución básica.\n", "\n", "* $\\left [ \\begin{array}{c} x_1 \\\\ x_2 \\end{array} \\right ] = \\left [ \\begin{array}{c} 0 \\\\ 6 \\end{array} \\right ]$ es solución FEV y $\\left [ \\begin{array}{c} x_1 \\\\ x_2 \\\\ x_3 \\\\ x_4 \\\\ x_5 \\end{array} \\right ] = \\left [ \\begin{array}{c} 0 \\\\ 6 \\\\ 4 \\\\ 0 \\\\ 6 \\end{array} \\right ]$ es solución BF." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Soluciones BF adyacentes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Definición\n", "\n", "Dos soluciones BF son adyacentes si sus correspondientes soluciones FEV lo son. \n", "\n", "```" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "tags": [ "hide-input", "margin" ] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "ax.plot(point1_point2_x_1[:,0], point1_point2_x_1[:,1], label = \"_nolegend_\")\n", "ax.plot(point3_point4_x_1[:,0], point3_point4_x_1[:,1], label = \"_nolegend_\")\n", "ax.plot(point1_point2_x_2[:,0], point1_point2_x_2[:,1], label = \"_nolegend_\")\n", "ax.plot(point3_point4_x_2[:,0], point3_point4_x_2[:,1], label = \"_nolegend_\")\n", "ax.plot(x_1, x_2, label = \"_nolegend_\")\n", "\n", "ax.fill_between(x_1_region_1, 0, x_2_region_1, where=x_2_region_1<=6, color=\"plum\", label = \"_nolegend_\")\n", "x_1_region_2 = np.linspace(0,2, 100)\n", "ax.fill_between(x_1_region_2, 0, 6, color=\"plum\", label = \"_nolegend_\")\n", "\n", "ax.plot(array_FEV[:,0], array_FEV[:,1], 'o', color=\"orangered\", markersize=10, label=\"FEV\")\n", "\n", "ax.plot(array_NFEV[:,0], array_NFEV[:,1], '', color=\"darkmagenta\", markersize=10, label=\"NFEV\")\n", "\n", "ax.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "$$\n", "\\left [\n", "\\begin{array}{ccccc}\n", "1 & 0 & 1 & 0 & 0 \\\\\n", "0 & 2 & 0 & 1 & 0 \\\\\n", "3 & 2 & 0 & 0 & 1 \\\\\n", "\\end{array}\n", "\\right ]\n", "\\left [\n", "\\begin{array}{c}\n", "x_1 \\\\\n", "x_2 \\\\\n", "x_3 \\\\\n", "x_4 \\\\\n", "x_5\n", "\\end{array}\n", "\\right ]\n", "=\n", "\\left[\n", "\\begin{array}{c}\n", "4 \\\\\n", "12 \\\\\n", "18\n", "\\end{array}\n", "\\right ]\n", "$$\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En el ejemplo prototipo $\\left [ \\begin{array}{c} 0 \\\\ 0 \\\\ 4 \\\\ 12 \\\\ 18 \\end{array} \\right ]$ y $\\left [ \\begin{array}{c} 0 \\\\ 6 \\\\ 4 \\\\ 0 \\\\ 6 \\end{array} \\right ]$ son soluciones BF adyacentes. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(VARBASICASNOBASICAS)=" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Variables básicas y no básicas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Definición\n", "\n", "Dada la matriz $A \\in \\mathbb{R}^{m \\times n}$ de la forma aumentada aquellas variables de decisión que corresponden a columnas linealmente independientes se les nombra variables básicas. Las restantes son variables no básicas.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Al inicio del método símplex la matriz de la forma aumentada es:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\left [\n", "\\begin{array}{ccccc}\n", "1 & 0 & 1 & 0 & 0 \\\\\n", "0 & 2 & 0 & 1 & 0 \\\\\n", "3 & 2 & 0 & 0 & 1 \\\\\n", "\\end{array}\n", "\\right ]\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Por lo que las variables básicas son $x_3, x_4, x_5$ y las no básicas son $x_1, x_2$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Definición\n", "\n", "La matriz que se forma a partir de las columnas de $A$ que corresponden a las variables básicas se denota como $B \\in \\mathbb{R}^{m \\times m}$ es no singular y se nombra basis matrix. La matriz que se forma con las columnas de las variables no básicas se denota con $N$ y su nombre es nonbasis matrix.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "$$\n", "\\left [\n", "\\begin{array}{ccccc}\n", "1 & 0 & 1 & 0 & 0 \\\\\n", "0 & 2 & 0 & 1 & 0 \\\\\n", "3 & 2 & 0 & 0 & 1 \\\\\n", "\\end{array}\n", "\\right ]\n", "\\left [\n", "\\begin{array}{c}\n", "x_1 \\\\\n", "x_2 \\\\\n", "x_3 \\\\\n", "x_4 \\\\\n", "x_5\n", "\\end{array}\n", "\\right ]\n", "=\n", "\\left[\n", "\\begin{array}{c}\n", "4 \\\\\n", "12 \\\\\n", "18\n", "\\end{array}\n", "\\right ]\n", "$$\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En el ejemplo prototipo la basis matrix* y la nonbasis matrix al inicio del método son:\n", "\n", "$$B\n", "=\\left [\n", "\\begin{array}{ccc}\n", "1 & 0 & 0 \\\\\n", "0 & 1 & 0 \\\\\n", "0 & 0 & 1 \\\\\n", "\\end{array}\n", "\\right ]\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$N\n", "=\n", "\\left [\n", "\\begin{array}{ccccc}\n", "1 & 0 \\\\\n", "0 & 2 \\\\\n", "3 & 2 \\\\\n", "\\end{array}\n", "\\right ]\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "La forma aumentada recuérdese es:\n", "\n", "$$\\displaystyle \\max_{x \\in \\mathbb{R}^5} 3x_1 + 5x_2 \\\\\n", "\\text{sujeto a: }\\\\\n", "x_1 + x_3 = 4 \\\\\n", "2x_2 + x_4 = 12 \\\\\n", "3x_1 + 2x_2 + x_5 = 18 \\\\\n", "x_1 \\geq 0, x_2 \\geq 0, x_3 \\geq 0, x_4 \\geq 0, x_5 \\geq 0\n", "$$\n", "\n", "```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Comentarios\n", "\n", "* Obsérvese en el ejemplo prototipo que al tener 5 variables y tres ecuaciones si se le asigna un valor arbitrario a $x_1, x_2$ entonces quedan determinadas las variables $x_3, x_4, x_5$. En el método símplex las variables no básicas se igualan a $0$ por lo que se tiene: $\\left [ \\begin{array}{c} x_1 \\\\ x_2 \\\\ x_3 \\\\ x_4 \\\\ x_5\\end{array} \\right ] = \\left [ \\begin{array}{c} 0 \\\\ 0 \\\\ 4 \\\\ 12 \\\\ 18 \\end{array} \\right ]$\n", "\n", "* Una forma de distinguir si dos soluciones BF son adyacentes es comparar qué variables no básicas (análogamente sus básicas) tienen. Si difieren en sólo una entonces son soluciones BF adyacentes. Por ejemplo: $\\left [ \\begin{array}{c} 0 \\\\ 0 \\\\ 4 \\\\ 12 \\\\ 18 \\end{array} \\right]$ y $\\left [ \\begin{array}{c} 0 \\\\ 6 \\\\ 4 \\\\ 0 \\\\ 6 \\end{array} \\right ]$ son BF adyacentes pues tienen como variables no básicas $x_1, x_2$ y $x_1, x_4$ respectivamente. Esto también se puede describir como: $x_2$ \"pasa de ser no básica a básica\" (análogamente $x_4$ pasa de básica a no básica). Lo anterior ayuda a identificar soluciones BF adyacentes en PL's con más de dos variables en los que resulta más complicado graficar.\n", "\n", "* El método símplex al considerar variables no básicas con valor de $0$ indica que la restricción no negativa $x_j \\geq 0$ es activa para $j$ en los índices de las variables no básicas.\n", "\n", "* En el método símplex se puede verificar que una solución es BF si las variables básicas son no negativas (recuérdese que las no básicas en el método son igualadas a cero).\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Variables básicas no degeneradas y degeneradas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Considerando un problema con $n$ variables al que se le añadieron $m$ variables de holgura denotemos a $\\mathcal{B}$ como el conjunto de índices en el conjunto $\\{1, 2, \\dots, m+n\\}$ que representan a las variables básicas y $\\mathcal{N}$ al conjunto de índices de las no básicas. \n", "\n", "El ejemplo prototipo en su forma aumentada $\\mathcal{B} = \\{3, 4, 5\\}$, $\\mathcal{N} = \\{1, 2\\}$ con $m=3, n=2, m+n=5$. El método símplex en sus iteraciones elige algún índice de $\\mathcal{N}$ y lo sustituye por un índice del conjunto $\\mathcal{B}$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "En el ejemplo numérico se entenderá la frase \"mejoren la función objetivo $f_o$\".\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Comentarios\n", "\n", "* El quitar y añadir variables a los conjuntos de índices $\\mathcal{N}, \\mathcal{B}$ y realizar los ajustes necesarios (recalcular valores de las variables básicas) en los valores de todas las variables básicas y no básicas se le conoce con el nombre de pivoteo.\n", "\n", "* La interpretación geométrica de quitar, añadir variables de las matrices $N, B$ y realizar los ajustes necesarios (recalcular valores de las variables básicas) en una solución BF es equivalente en dos dimensiones a moverse por una arista y detenerse hasta encontrar una solución FEV.\n", "\n", "* La elección de cuál variable no básica sustituir por una variable básica depende de la existencia de soluciones BF que mejoren la función objetivo $f_o$ y para ello se utiliza un criterio de optimalidad.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En el método símplex al recalcular los valores de las variables básicas algunas pueden tener valor igual a cero lo que da lugar a la siguiente definición." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Definición\n", "\n", "Una solución BF para un PL con restricciones de no negatividad en la que todas sus variables básicas son positivas se nombra no degenerada y degenerada si existe al menos una con valor igual a cero.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(EJMETSIMPLEXAPLICADOEJPROTOTIPO)=" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ejemplo del método símplex aplicado al ejemplo prototipo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "La forma aumentada recuérdese es:\n", "\n", "$$\\displaystyle \\max_{x \\in \\mathbb{R}^5} 3x_1 + 5x_2 \\\\\n", "\\text{sujeto a: }\\\\\n", "x_1 + x_3 = 4 \\\\\n", "2x_2 + x_4 = 12 \\\\\n", "3x_1 + 2x_2 + x_5 = 18 \\\\\n", "x_1 \\geq 0, x_2 \\geq 0, x_3 \\geq 0, x_4 \\geq 0, x_5 \\geq 0\n", "$$\n", "\n", "```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Continuemos con el ejemplo prototipo en su forma aumentada. En notación matricial el sistema de ecuaciones lineales es:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "Ax = \n", "\\left [\n", "\\begin{array}{ccccc}\n", "1 & 0 & 1 & 0 & 0 \\\\\n", "0 & 2 & 0 & 1 & 0 \\\\\n", "3 & 2 & 0 & 0 & 1 \\\\\n", "\\end{array}\n", "\\right ]\n", "\\left [\n", "\\begin{array}{c}\n", "x_1 \\\\\n", "x_2 \\\\\n", "x_3 \\\\\n", "x_4 \\\\\n", "x_5\n", "\\end{array}\n", "\\right ]\n", "=\n", "\\left[\n", "\\begin{array}{c}\n", "4 \\\\\n", "12 \\\\\n", "18\n", "\\end{array}\n", "\\right ]\n", "=\n", "b\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Defínanse al vector $x$ que contiene las variables \"originales\" y a las de holgura: $x = \\left [ \\begin{array}{c} x_1 \\\\ x_2 \\\\ x_3 \\\\ x_4 \\\\ x_5 \\end{array} \\right ]$ y $c = \\left [ \\begin{array}{c} -3 \\\\ -5 \\\\ 0 \\\\ 0 \\\\ 0 \\end{array}\\right]$ al vector de costos unitarios o equivalentemente $-c$ el vector de ganancias unitarias. Así, la función objetivo es: $f_o(x) = (-c)^Tx$ y se busca maximizar la ganancia total. También defínanse a los vectores de variables básicas y no básicas como: $x_B = [x_j]_{j \\in \\mathcal{B}}$, $x_N = [x_j]_{j \\in \\mathcal{N}}$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "Siendo rigurosos la forma estándar de un PL es:\n", "\n", "$$\n", "\\displaystyle \\min_{x \\in \\mathbb{R}^n} c^Tx\\\\\n", "\\text{sujeto a:} \\\\\n", "Ax=b\\\\\n", "x \\geq 0\n", "$$\n", "\n", "por lo que aunque maximizar $(-c)^Tx$ sujeto a las restricciones dadas tiene el mismo conjunto óptimo que el problema de minimizar $c^Tx$ sujeto a las mismas restricciones (los valores óptimos entre el problema de maximización y minimización son iguales salvo una multiplicación por $-1$), el problema debe escribirse explícitamente como minimización para considerarse en forma estándar.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Entonces el PL con esta notación que se debe resolver es:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\displaystyle \\max_{x \\in \\mathbb{R}^5} (-c)^Tx$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\text{sujeto a: }$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$Ax = b$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$x \\geq 0$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "con $x=\\left [ \\begin{array}{c} x_B \\\\ x_N\\end{array} \\right ] \\in \\mathbb{R}^5$, $x_B \\in \\mathbb{R}^{3}, x_N \\in \\mathbb{R}^2$, $A \\in \\mathbb{R}^{3 \\times 5}$.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Paso inicial del ejemplo prototipo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "La forma aumentada recuérdese es:\n", "\n", "$$\\displaystyle \\max_{x \\in \\mathbb{R}^5} 3x_1 + 5x_2 \\\\\n", "\\text{sujeto a: }\\\\\n", "x_1 + x_3 = 4 \\\\\n", "2x_2 + x_4 = 12 \\\\\n", "3x_1 + 2x_2 + x_5 = 18 \\\\\n", "x_1 \\geq 0, x_2 \\geq 0, x_3 \\geq 0, x_4 \\geq 0, x_5 \\geq 0\n", "$$\n", "\n", "```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Se tiene la siguiente situación: \n", "\n", "$$(-c)^Tx= 3x_1 + 5x_2 + 0x_3 + 0x_4 + 0x_5$$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "A = \n", "\\left [\n", "\\begin{array}{ccccc}\n", "1 & 0 & 1 & 0 & 0 \\\\\n", "0 & 2 & 0 & 1 & 0 \\\\\n", "3 & 2 & 0 & 0 & 1 \\\\\n", "\\end{array}\n", "\\right ]\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Como $A = [ N \\quad B ]$, $x=\\left [ \\begin{array}{c} x_N \\\\ x_B\\end{array} \\right ]$ y $Ax = b$ entonces $Ax = B x_B + N x_N = b$. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Se designa $x_N$ como un vector de ceros:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$x_N = \\left [ \\begin{array}{c} x_1 \\\\ x_2 \\end{array} \\right ] = \\left [ \\begin{array}{c} 0 \\\\ 0 \\end{array} \\right ]$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Por tanto:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$Ax = Bx_B + N x_N = B x_B = b$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "y se tiene:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$x_B = B^{-1}b.$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En este paso inicial para el ejemplo prototipo $x_B = b$ pues $B$ es la identidad:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\therefore x_B = \\left [ \\begin{array}{c} x_3 \\\\ x_4 \\\\ x_5\\end{array}\\right ] = B^{-1}b = \\left [ \\begin{array}{ccc} 1 & 0 & 0 \\\\ 0 & 1 & 0 \\\\ 0 & 0 & 1 \\end{array} \\right ]^{-1} \\left [ \\begin{array}{c} 4 \\\\ 12 \\\\ 18 \\end{array}\\right ]=\\left [ \\begin{array}{c} 4 \\\\ 12 \\\\ 18 \\end{array}\\right ]$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El vector de costos $c$ lo dividimos en $c = \\left [ \\begin{array}{c} c_N\\\\ c_B \\end{array} \\right ]$, con $c_B = \\left [ \\begin{array}{c} c_{B_3} \\\\ c_{B_4} \\\\ c_{B_5} \\end{array} \\right ] = \\left [ \\begin{array}{c} 0 \\\\ 0 \\\\ 0 \\end{array} \\right ]$ contiene los costos de las variables básicas. El vector $c_N=\\left [ \\begin{array}{c} c_{N_1} \\\\ c_{N_2} \\end{array} \\right ]=\\left [ \\begin{array}{c}-3 \\\\ -5 \\end{array} \\right ]$ contiene los costos de las variables no básicas." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Las variables básicas son $x_3, x_4, x_5$ y las no básicas son $x_1, x_2$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "La forma aumentada recuérdese es:\n", "\n", "$$\\displaystyle \\max_{x \\in \\mathbb{R}^5} 3x_1 + 5x_2 \\\\\n", "\\text{sujeto a: }\\\\\n", "x_1 + x_3 = 4 \\\\\n", "2x_2 + x_4 = 12 \\\\\n", "3x_1 + 2x_2 + x_5 = 18 \\\\\n", "x_1 \\geq 0, x_2 \\geq 0, x_3 \\geq 0, x_4 \\geq 0, x_5 \\geq 0\n", "$$\n", "\n", "```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Comentario\n", "\n", "La solución BF en el paso inicial $x = \\left [ \\begin{array}{c} x_1 \\\\ x_2 \\\\ x_3 \\\\ x_4 \\\\ x_5 \\end{array} \\right ] = \\left [ \\begin{array}{c} 0 \\\\ 0 \\\\ 4 \\\\ 12 \\\\ 18 \\end{array} \\right ]$ tiene como variables no básicas $x_1, x_2$ e indican que las restricciones $x_1 \\geq 0, x_2 \\geq 0$ son restricciones activas.\n", "\n", "```" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "B = np.eye(3)\n", "b = np.array([4, 12, 18])\n", "x_B = b\n", "A = np.array([[1, 0, 1, 0, 0],\n", " [0, 2, 0, 1, 0],\n", " [3, 2, 0, 0, 1]])\n", "c_B = np.array([0,0,0])\n", "c_N = np.array([-3, -5])\n", "\n", "#list of indexes of nonbasic variables correspond to x1, x2\n", "N_list_idx = [0, 1]\n", "#list of indexes of basic variables correspond to x3, x4, x5\n", "B_list_idx = [2, 3, 4] \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prueba de optimalidad" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para revisar tanto en el paso inicial como en las iteraciones posteriores la condición de optimalidad para encontrar soluciones FEV adyacentes mejores, realicemos algunas reescrituras de la función objetivo." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1.Obsérvese que la función objetivo se puede escribir como:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "Recuérdese que la función objetivo es: $(-c)^Tx= 3x_1 + 5x_2 + 0x_3 + 0x_4 + 0x_5$.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$f_o(x) = (-c)^Tx = [-c_B \\quad -c_N] ^T \\left [ \\begin{array}{c} x_B \\\\ x_N\\end{array} \\right ] = -c_B^Tx_B - c_N^T x_N = -c_B^T B^{-1}b = [0 \\quad 0 \\quad 0]^T \\left [ \\begin{array}{c} 4 \\\\ 12 \\\\ 18 \\end{array}\\right ]=0$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2.Obsérvese que las restricciones en su forma igualadas a cero pueden ser restadas de la función objetivo sin modificar su valor. Por ejemplo si tomamos la primer restricción con lado derecho igual a cero: $x_1 + x_3 - 4 = 0$ entonces:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$f_o(x) = f_o(x) - 0 = f_o(x) - (x_1 + x_3 - 4) = f_o(x) -x_1 -x_3 + 4$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "La forma aumentada recuérdese es:\n", "\n", "$$\\displaystyle \\max_{x \\in \\mathbb{R}^5} 3x_1 + 5x_2 \\\\\n", "\\text{sujeto a: }\\\\\n", "x_1 + x_3 = 4 \\\\\n", "2x_2 + x_4 = 12 \\\\\n", "3x_1 + 2x_2 + x_5 = 18 \\\\\n", "x_1 \\geq 0, x_2 \\geq 0, x_3 \\geq 0, x_4 \\geq 0, x_5 \\geq 0\n", "$$\n", "\n", "```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y esto podemos hacer para todas las restricciones con lado derecho igual a cero:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{eqnarray}\n", "f_o(x) &=& f_o(x) - (x_1 + x_3 - 4) - (2x_2 + x_4 - 12) - (3x_1 + 2x_2 + x_5 - 18) \\nonumber \\\\\n", "&=& f_o(x) + (-4x_1 - 4x_2) + (-x_3 - x_4 - x_5) + (4 + 12 + 18) \\nonumber \\\\\n", "&=& f_o(x) - 4 \\displaystyle \\sum_{j \\in \\mathcal{B}} x_{B_j} - \\sum_{j \\in \\mathcal{N}}x_{N_j} + \\sum_{i = 1}^3 b(i)\n", "\\end{eqnarray}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "con $x_{B_j}$ $j$-ésima componente del vector $x_B$, $x_{N_j}$ $j$-ésima componente del vector $x_N$ y $b(i)$ $i$-ésima componente de $b$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "No es coincidencia que se elijan las cantidades $\\lambda, \\nu$ para representar esta igualdad, ver {ref}`la función Lagrangiana <FUNLAGRANGIANA>`.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En el método símplex no solamente se restan de la $f_o$ las restricciones con lado derecho igual a cero sino se multiplican por una cantidad $\\nu_i$ y se suman a $f_o$. También si $\\lambda$ es un vector tal que $\\lambda ^T x = 0$ entonces:\n", "\n", "$$f_o(x) = f_o(x) + \\lambda^Tx + \\sum_{i = 1}^3 \\nu_i h_i(x) = f_o(x) + \\displaystyle \\sum_{j \\in \\mathcal{B}} \\lambda_{B_j} x_{B_j} + \\sum_{j \\in \\mathcal{N}}\\lambda_{N_j}x_{N_j} + \\sum_{i = 1}^3 \\nu_i h_i(x)$$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "con $\\lambda_{B_j}$, $\\lambda_{N_j}$ coeficientes asociados a $x_{B_j}$ y $x_{N_j}$ respectivamente y $h_i(x)$ $i$-ésima restricción de igualdad con lado derecho igual a cero." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Los coeficientes $\\lambda_{B_j}, \\lambda_{N_j}$ de la expresión anterior en el método de símplex se escriben como:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\lambda_{B_j} = -c_{B_j} + \\nu^Ta_j \\quad j \\in \\mathcal{B}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\lambda_{N_j} = -c_{N_j} + \\nu^Ta_j \\quad j \\in \\mathcal{N}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "con $a_j$ $j$-ésima columna de $A \\in \\mathbb{R}^{3 \\times 5}$ y $\\nu \\in \\mathbb{R}^{3}$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En el método símplex se mantiene en cada iteración $\\lambda_{B_j} = 0 \\forall j \\in \\mathcal{B}$ y se busca que $\\lambda_{N_j} \\forall j \\in \\mathcal{N}$ sea no negativo para problemas de minimización o no positivo para problemas de maximización. Si la búsqueda anterior no se logra, se continúa iterando hasta llegar a una solución o mostrar un mensaje si no fue posible encontrar una solución. Por lo anterior el vector $\\nu$ se obtiene resolviendo la ecuación: $\\nu ^T B = c_B^T$ y por tanto $\\nu = B^{-T} c_B $." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Comentarios\n", "\n", "* La justificación del por qué $\\lambda = -c + A^T \\nu$ se realizará más adelante, por lo pronto considérese que esto se obtiene de las {ref}`condiciones KKT de optimalidad <PRIMERAFORMULACIONCONDKKT>`.\n", "\n", "* Por la definición de $\\nu$ se cumple: $f_o(x) = (-c)^Tx = -c_B^Tx_B - c_N^T x_N = -c_B^T B^{-1}b = - \\nu^Tb = b^T(-\\nu)$.\n", "\n", "* No se recomienda aprenderse fórmulas o expresiones pues este problema se planteó como maximizar $(-c)^Tx$, si se hubiera elegido maximizar $c^Tx$ (sin signo negativo) se modificarían un poco las expresiones anteriores para $\\lambda, \\nu, f_o(x)$.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La prueba de optimalidad consiste en revisar los $\\lambda_{N_j}$, $j \\in \\mathcal{N}$. Se selecciona(n) aquella(s) variable(s) no básica(s) que tenga(n) la tasa más alta de mejoramiento (esto depende si es un problema de maximización o minimización) del valor en la función objetivo.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "$c_B = \\left [ \\begin{array}{c} c_{B_3} \\\\ c_{B_4} \\\\ c_{B_5} \\end{array} \\right ] = \\left [ \\begin{array}{c} 0 \\\\ 0 \\\\ 0 \\end{array} \\right ]$\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El vector $\\nu$ es:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\nu = B^{-T}c_B = \\left [ \\begin{array}{ccc} 1 & 0 & 0 \\\\ 0 & 1 & 0 \\\\ 0 & 0 & 1 \\end{array} \\right ] ^{-T} \\left [ \\begin{array}{c} 0 \\\\ 0 \\\\ 0 \\end{array}\\right ] = \\left [ \\begin{array}{c} 0 \\\\ 0 \\\\ 0 \\end{array}\\right ]$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "Resolviendo un sólo sistema de ecuaciones lineales nos ayuda a evitar calcular la inversa de una matriz que implica resolver un sistema de ecuaciones lineales más grande.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para el cálculo de $\\nu$ resolvemos el sistema de ecuaciones lineales para el vector de incógnitas $\\nu$: " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$B^T \\nu = c_B$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Como $B = \\left [ \\begin{array}{ccc} 1 & 0 & 0 \\\\ 0 & 1 & 0 \\\\ 0 & 0 & 1 \\end{array} \\right ]$ entonces directamente $\\nu = c_B$. Por tanto:\n", "\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "nu = np.array([0, 0, 0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Valor de la función objetivo en la solución BF actual: $f_o(x) = (-c)^Tx = b^T(-\\nu) = 0$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "$\n", "\\begin{eqnarray}\n", "f_o(x) &=& (-c)^Tx \\nonumber\\\\\n", "&=& -c_B^Tx_B - c_N^T x_N \\nonumber\\\\\n", "&=& -c_B^T x_B \\quad \\text{pues } x_N=0\\\\\n", "\\end{eqnarray}$ \n", "\n", "```" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n" ] } ], "source": [ "print(np.dot(-c_B, x_B))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "$f_o(x) = b^T(-\\nu)$.\n", "\n", "```" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n" ] } ], "source": [ "print(np.dot(b, -nu))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "$c_N= \\left [ \\begin{array}{c}-3 \\\\ -5 \\end{array} \\right ]$\n", "\n", "```" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "lambda_N_1 = -c_N[0] + np.dot(nu, A[:, N_list_idx[0]])" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "lambda_N_2 = -c_N[1] + np.dot(nu, A[:, N_list_idx[1]])" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n" ] } ], "source": [ "print(lambda_N_1)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n" ] } ], "source": [ "print(lambda_N_2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\lambda_{N_1} = -c_{N_1} + \\nu^Ta_1 = 3 + [0 \\quad 0 \\quad 0] \\left [ \\begin{array}{c} 1 \\\\ 0 \\\\ 3 \\end{array}\\right ] = 3$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\lambda_{N_2} = -c_{N_2} + \\nu^Ta_2 = 5 + [0 \\quad 0 \\quad 0] \\left [ \\begin{array}{c} 0 \\\\ 2 \\\\ 2 \\end{array}\\right ] = 5$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "\"tasa más alta de mejoramiento\" se refiere a mejorar $f_o$ por un incremento de una unidad en la variable $x_2$.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "Incrementar una variable no básica equivale geométricamente a moverse por una arista desde una solución FEV.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Como tenemos un problema de maximización la tasa más alta de mejoramiento de $f_o$ la da la variable $x_2$ por lo que es la variable no básica que sustituye a una variable básica. ¿Cuál variable básica se debe elegir?" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "#index for nonbasic variables, in this case value 1 correspond to x2\n", "\n", "idx_x_N = 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prueba del cociente mínimo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El objetivo de esta prueba es determinar qué variable(s) básica(s) llega(n) a cero cuando crece la variable entrante. Tal variable(s) básica(s) en la siguiente iteración será no básica y la que aumenta pasa de ser no básica a básica." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En el paso inicial las variables básicas son $x_3$, $x_4$ y $x_5$, por lo que hay que determinar de éstas cuál(es) es(son) la(s) que sale(n) al incrementar la variable no básica $x_2$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Esta prueba del cociente mínimo primero se explicará de forma detallada para posteriormente representarla de forma matricial." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Las ecuaciones de $Ax = b$ son:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "La forma aumentada recuérdese es:\n", "\n", "$$\\displaystyle \\max_{x \\in \\mathbb{R}^5} 3x_1 + 5x_2 \\\\\n", "\\text{sujeto a: }\\\\\n", "x_1 + x_3 = 4 \\\\\n", "2x_2 + x_4 = 12 \\\\\n", "3x_1 + 2x_2 + x_5 = 18 \\\\\n", "x_1 \\geq 0, x_2 \\geq 0, x_3 \\geq 0, x_4 \\geq 0, x_5 \\geq 0\n", "$$\n", "\n", "```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\begin{eqnarray}\n", "x_1 + x_3 &=& 4 \\nonumber \\\\\n", "2x_2 + x_4 &=& 12 \\nonumber \\\\\n", "3x_1 + 2x_2 + x_5 &=& 18\n", "\\end{eqnarray}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Despejamos cada variable básica de las ecuaciones anteriores:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\begin{eqnarray}\n", "x_3 &=& 4 - x_1 \\nonumber \\\\\n", "x_4 &=& 12 - 2x_2 \\nonumber \\\\\n", "x_5 &=& 18 - 3x_1 - 2x_2\n", "\\end{eqnarray}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y se debe cumplir por las restricciones de no negatividad que al aumentar $x_2$:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\begin{eqnarray}\n", "x_3 &=& 4 - x_1 \\geq 0 \\nonumber \\\\\n", "x_4 &=& 12 - 2x_2 \\geq 0 \\nonumber \\\\\n", "x_5 &=& 18 - 3x_1 - 2x_2 \\geq 0\n", "\\end{eqnarray}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En la primera ecuación $x_3 = 4 - x_1$ no tenemos contribución alguna de $x_2$ por lo que no la tomamos en cuenta. La segunda y tercer ecuación sí aparece $x_2$ y como $x_1$ es variable no básica con valor $0$ se debe cumplir:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\begin{eqnarray}\n", "x_2 \\leq \\frac{12}{2} = 6 \\nonumber \\\\\n", "x_2 \\leq \\frac{18}{2} = 9\n", "\\end{eqnarray}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Entonces se toma el mínimo de las cantidades anteriores y como es igual a $6$ y esa desigualdad la obtuvimos de despejar $x_4$ entonces se elige $x_4$ como variable básica que se vuelve no básica. Tomamos el mínimo pues el valor de $6$ es lo máximo que podemos incrementar $x_2$ sin que $x_4$ se haga no negativa." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Comentario\n", "\n", "Es importante en la prueba del cociente mínimo que el lado derecho, el vector $b$, tenga entradas no negativas.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prueba del cociente mínimo: forma general y con notación matricial y vectorial" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El procedimiento de la prueba del cociente mínimo entonces consiste en: \n", "\n", "1.Elegir la columna de $A$ correspondiente a la variable no básica que sustituye a la variable básica, que por lo anterior es $x_2$ y corresponde a la segunda columna de $A$, $a_2$:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "A = \n", "\\left [\n", "\\begin{array}{ccccc}\n", "1 & 0 & 1 & 0 & 0 \\\\\n", "0 & 2 & 0 & 1 & 0 \\\\\n", "3 & 2 & 0 & 0 & 1 \\\\\n", "\\end{array}\n", "\\right ]\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2.Hacer la multiplicación $d = B^{-1}a_2$ (ver siguiente paso para el cálculo de $d$). \n", "\n", "Esto en general se realiza, en el paso inicial para el ejemplo prototipo $B^{-1}$ es la identidad por lo que en este paso inicial no tuvo efecto hacer la multiplicación. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3.Para las entradas estrictamente positivas de tal multiplicación anterior se divide el lado derecho entre tales entradas y se toma el mínimo. Como el lado derecho en cada iteración es $x_B = B^{-1}b$ entonces se dividen los valores de las variables básicas entre las entradas estrictamente positivas. Esto es, si se denota como $x_2^{+}$ al mínimo:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$x_2^{+} = \\min \\{\\frac{x_{B_i}}{d_i} : d_i > 0, i = 1, 2, \\dots, m \\}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "con $d_i$ $i$-ésima componente del vector $d$ que es solución del sistema de ecuaciones: $Bd = a_2$ y $x_{B_i}$ $i$-ésima entrada del vector $x_B$ de la iteración actual. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "4.El índice donde ocurre el mínimo es el de la variable básica que será sustituida. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En el ejemplo prototipo se tienen las siguientes asignaciones en el paso inicial:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "$\n", "B = \n", "\\left [\n", "\\begin{array}{ccccc}\n", "1 & 0 & 0 \\\\\n", "0 & 1 & 0 \\\\\n", "0 & 0 & 1 \\\\\n", "\\end{array}\n", "\\right ]\n", "$\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "La segunda columna de $A$ se elige pues $x_2$ es la variable no básica a la que se le aumentará su valor y sustituirá a una variable básica.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Se resuelve la ecuación: $Bd = a_2$ para $d$ vector de incógnitas y $a_2$ segunda columna de $A$. " ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "d = np.linalg.solve(B, A[:,idx_x_N])" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0. 2. 2.]\n" ] } ], "source": [ "print(d)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En esta iteración: $x_B = \\left [ \\begin{array}{c} x_3 \\\\ x_4 \\\\ x_5\\end{array}\\right ] = \\left [ \\begin{array}{c} 4 \\\\ 12 \\\\ 18 \\end{array}\\right ]$ pues $B$ es la matriz identidad por lo que $x_B = b$." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 4 12 18]\n" ] } ], "source": [ "print(x_B)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "Se hace la división únicamente entre las entradas estrictamente positivas\n", "\n", "```" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "idx_positive = d >0" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[6. 9.]\n" ] } ], "source": [ "print(x_B[idx_positive]/d[idx_positive])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "Hacer cero una variable básica y convertirla en no básica equivale geométricamente a detener el movimiento por una arista hasta encontrar una solución FEV.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Entonces el mínimo ocurre en la segunda posición de $x_B$ que corresponde a la variable básica $x_4$. Se elige $x_4$ como variable básica que se vuelve no básica. $x_4$ será sustituida por $x_2$ en la próxima iteración." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "#index for basic variables, in this case value 1 correspond to x4\n", "\n", "idx_x_B = 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Actualización del vector $x_B$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La actualización de las variables básicas después del paso inicial se realiza con la expresión computacional:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$x_B = x_B - dx_{nb}^{+}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "donde: $nb$ es el índice de la variable no básica que se volverá básica en la iteración actual. El superíndice $+$ se utiliza para señalar que se actualizará tal variable no básica (variable que entra). Después de incrementarla, la variable básica con índice $ba$ pasa a estar en $x_N$ con valor de $0$ (variable que sale).\n", "\n", "Posterior a la actualización de $x_B$ se intercambian las columnas de $B$ correspondientes a las variables $x_{nb}$ y $x_{ba}$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La justificación de la expresión anterior es la siguiente:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Como $Ax = b$ y $Bx_B + Nx_N = b$ pero será incrementada la variable $x_{nb}$ y disminuida $x_{ba}$ a cero entonces si $x^+$ denota el nuevo valor de $x$ se tiene:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$b = Ax ^+ = Bx_B^+ + a_{nb}x_{nb}^+ = B x_B = Ax = b$$\n", "\n", "recordando que $Nx_N = 0$ pues $x_N=0$ donde: $a_{nb}$ es la ${nb}$-ésima columna de $A$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Por tanto:\n", "\n", "$$Bx_B^+ = Bx_B - a_{nb}x_{nb}^+$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y premultiplicando por la inversa:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$x_B^+ = x_B - B^{-1}a_{nb}x_{nb}^+ = x_B - d x_{nb}^+.$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La interpretación geométrica de esta actualización es: un movimiento a lo largo de una arista del poliedro que mantiene factibilidad y mejora la función objetivo. Nos movemos a lo largo de esta arista hasta encontrar una solución FEV. En esta nueva solución FEV una nueva restricción no negativa se vuelve activa, la que corresponde a la variable $x_{ba}$. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Después de la actualización se remueve el índice $ba$ del conjunto $\\mathcal{B}$ y se sustituye por el índice $nb$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Iteración 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La matriz $B$ del paso inicial era:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$B = \\left [ \\begin{array}{ccc} 1 & 0 & 0 \\\\ 0 & 1 & 0 \\\\ 0 & 0 & 1 \\end{array} \\right ]$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "y correspondía cada columna a las variables $x_3, x_4, x_5$ en ese orden." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Se realiza la actualización descrita para $x_B$:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$x_B = x_B - dx_2^{+}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "con $x_2$ es la variable no básica que se volverá básica en la iteración actual." ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "x_2_plus = np.min(x_B[idx_positive]/d[idx_positive])" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6.0\n" ] } ], "source": [ "print(x_2_plus)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "x_B = x_B - dx_2_plus" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[4. 0. 6.]\n" ] } ], "source": [ "print(x_B)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Aquí el valor de la variable $x_4$ se hace cero y tenemos que intercambiar tal entrada con la de $x_2^+$ para el vector $x_B$:" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[4. 6. 6.]\n" ] } ], "source": [ "x_B[idx_x_B] = x_2_plus\n", "print(x_B)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "Antes de hacer el intercambio de columnas: $B = \\left [ \\begin{array}{ccc} 1 & 0 & 0 \\\\ 0 & 1 & 0 \\\\ 0 & 0 & 1 \\end{array} \\right ]$ y la matriz original $A = \n", "\\left [\n", "\\begin{array}{ccccc}\n", "1 & 0 & 1 & 0 & 0 \\\\\n", "0 & 2 & 0 & 1 & 0 \\\\\n", "3 & 2 & 0 & 0 & 1 \\\\\n", "\\end{array}\n", "\\right ]\n", "$\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Como $x_4$ se intercambia por $x_2$ entonces se intercambia la columna $2$ de $A$, $a_2$, por la $2$ de $B$, $b_2$ por lo que al final de la iteración 1:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$B = \\left [ \\begin{array}{ccc} 1 & 0 & 0 \\\\ 0 & 2 & 0 \\\\ 0 & 2 & 1 \\end{array} \\right ]$$" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "B[:,idx_x_B] = A[:,idx_x_N]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$x_B = \\left [ \\begin{array}{c} x_3 \\\\ x_2 \\\\ x_5 \\end{array}\\right ] = \\left [ \\begin{array}{c} 4 \\\\ 6 \\\\ 6 \\end{array}\\right ]$, $x_N = \\left [ \\begin{array}{c} x_1 \\\\ x_4\\end{array}\\right ] = \\left [ \\begin{array}{c} 0 \\\\ 0\\end{array}\\right ]$." ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "aux = B_list_idx[idx_x_B]\n", "B_list_idx[idx_x_B] = N_list_idx[idx_x_N]\n", "N_list_idx[idx_x_N] = aux" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Observación\n", ":class: tip\n", "\n", "La actualización de $x_B$ anterior se puede verificar que es equivalente a:\n", "\n", "$$x_B = \\left [ \\begin{array}{c} x_3 \\\\ x_2 \\\\ x_5\\end{array}\\right ] = B^{-1}b = \\left [ \\begin{array}{ccc} 1 & 0 & 0 \\\\ 0 & 2 & 0 \\\\ 0 & 2 & 1 \\end{array} \\right ]^{-1} \\left [ \\begin{array}{c} 4 \\\\ 12 \\\\ 18 \\end{array}\\right ] = \\left [ \\begin{array}{ccc} 1 & 0 & 0 \\\\ 0 & \\frac{1}{2} & 0 \\\\ 0 & -1 & 1 \\end{array} \\right ]\\left [ \\begin{array}{c} 4 \\\\ 12 \\\\ 18 \\end{array}\\right ] = \\left [ \\begin{array}{c} 4 \\\\ 6 \\\\ 6 \\end{array}\\right ] $$\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "La forma aumentada recuérdese es:\n", "\n", "$$\\displaystyle \\max_{x \\in \\mathbb{R}^5} 3x_1 + 5x_2 \\\\\n", "\\text{sujeto a: }\\\\\n", "x_1 + x_3 = 4 \\\\\n", "2x_2 + x_4 = 12 \\\\\n", "3x_1 + 2x_2 + x_5 = 18 \\\\\n", "x_1 \\geq 0, x_2 \\geq 0, x_3 \\geq 0, x_4 \\geq 0, x_5 \\geq 0\n", "$$\n", "\n", "```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Comentario\n", "\n", "En este punto, la solución BF obtenida en esta iteración $x = \\left [ \\begin{array}{c} x_1 \\\\ x_2 \\\\ x_3 \\\\ x_4 \\\\ x_5 \\end{array} \\right ] = \\left [ \\begin{array}{c} 0 \\\\ 6 \\\\ 4 \\\\ 0 \\\\ 6 \\end{array} \\right ]$ tiene como variables no básicas $x_1, x_4$ e indican que las restricciones $x_1 \\geq 0, x_4 \\geq 0$ son restricciones activas. Además como $x_4$ es variable de holgura la restricción funcional asociada $2x_2 + x_4 \\leq 12$ indica que la solución BF se encuentra sobre la ecuación de frontera.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prueba de optimalidad" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Se recalcula el vector $\\nu$ considerando que $c_B = \\left [ \\begin{array}{c} c_{B_3} \\\\ c_{B_2} \\\\ c_{B_5} \\end{array}\\right ]$ tomando ahora $x_3, x_2, x_5$ como variables básicas." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "$B = \\left [ \\begin{array}{ccc} 1 & 0 & 0 \\\\ 0 & 2 & 0 \\\\ 0 & 2 & 1 \\end{array} \\right ]$\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "$c_B = \\left [ \\begin{array}{c} c_{B_3} \\\\ c_{B_2} \\\\ c_{B_5} \\end{array} \\right ] = \\left [ \\begin{array}{c} 0 \\\\ -5 \\\\ 0 \\end{array} \\right ]$\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El vector $\\nu$ es:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\nu = B^{-T}c_B = \\left [ \\begin{array}{ccc} 1 & 0 & 0 \\\\ 0 & \\frac{1}{2} & 0 \\\\ 0 & -1 & 1 \\end{array} \\right ] ^T \\left [ \\begin{array}{c} 0 \\\\ -5 \\\\ 0 \\end{array}\\right ] = \\left [ \\begin{array}{c} 0 \\\\ -\\frac{5}{2} \\\\ 0 \\end{array}\\right ]$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "Resolviendo un sólo sistema de ecuaciones lineales nos ayuda a evitar calcular la inversa de una matriz que implica resolver un sistema de ecuaciones lineales más grande.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para el cálculo de $\\nu$ resolvemos el sistema de ecuaciones lineales para el vector de incógnitas $\\nu$: " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$B^T \\nu = c_B$$" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "aux = c_B[idx_x_B]\n", "c_B[idx_x_B] = c_N[idx_x_N]\n", "c_N[idx_x_N] = aux" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "nu = np.linalg.solve(B.T, c_B)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. -2.5 0. ]\n" ] } ], "source": [ "print(nu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Por tanto:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "$c_N= \\left [ \\begin{array}{c}-3 \\\\ 0 \\end{array} \\right ]$\n", "```" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "lambda_N_1 = -c_N[0] + np.dot(nu, A[:,N_list_idx[0]])" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "lambda_N_4 = -c_N[1] + np.dot(nu, A[:,N_list_idx[1]])" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.0\n" ] } ], "source": [ "print(lambda_N_1)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-2.5\n" ] } ], "source": [ "print(lambda_N_4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\lambda_{N_1} = -c_{N_1} + \\nu^Ta_1 = 3 + [0 \\quad -\\frac{5}{2} \\quad 0] \\left [ \\begin{array}{c} 1 \\\\ 0 \\\\ 3 \\end{array}\\right ] = 3$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\lambda_{N_4} = -c_{N_4} + \\nu^Ta_4 = 0 + [0 \\quad -\\frac{5}{2} \\quad 0]\\left [ \\begin{array}{c} 0 \\\\ 1 \\\\ 0 \\end{array}\\right ] = -2.5$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Como tenemos un problema de maximización la tasa más alta de mejoramiento de $f_o$ la da la variable $x_1$ por lo que es la variable no básica que sustituye a una variable básica." ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "#index for nonbasic variables, in this case value 0 correspond to x1\n", "\n", "idx_x_N = 0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Valor de la función objetivo en la solución BF actual: $f_o(x) = (-c)^Tx = b^T(-\\nu) = 30$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "$\n", "\\begin{eqnarray}\n", "f_o(x) &=& (-c)^Tx \\nonumber\\\\\n", "&=& -c_B^Tx_B - c_N^T x_N \\nonumber\\\\\n", "&=& -c_B^T x_B \\quad \\text{pues } x_N=0\\\\\n", "\\end{eqnarray}$ \n", "\n", "```" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "30.0\n" ] } ], "source": [ "print(np.dot(-c_B, x_B ))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "$f_o(x) = b^T(-\\nu)$.\n", "\n", "```" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "30.0\n" ] } ], "source": [ "print(np.dot(b, -nu))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prueba del cociente mínimo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "$B = \\left [ \\begin{array}{ccc} 1 & 0 & 0 \\\\ 0 & 2 & 0 \\\\ 0 & 2 & 1 \\end{array} \\right ]$\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "La primer columna de $A$ se elige pues $x_1$ es la variable no básica a la que se le aumentará su valor y sustituirá a una variable básica.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Se resuelve la ecuación: $Bd = a_1$ para $d$ vector de incógnitas y $a_1$ primera columna de $A$. " ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "d = np.linalg.solve(B, A[:, idx_x_N])" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1. 0. 3.]\n" ] } ], "source": [ "print(d)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En esta iteración: $x_B = \\left [ \\begin{array}{c} x_3 \\\\ x_2 \\\\ x_5\\end{array}\\right ] = \\left [ \\begin{array}{c} 4 \\\\ 6 \\\\ 6 \\end{array}\\right ]$." ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[4. 6. 6.]\n" ] } ], "source": [ "print(x_B)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "Se hace la división únicamente entre las entradas estrictamente positivas\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$x_1^{+} = \\min \\{\\frac{x_{B_i}}{d_i} : d_i > 0, i = 1, 2, \\dots, m \\}$$" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "idx_positive = d >0" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[4. 2.]\n" ] } ], "source": [ "print(x_B[idx_positive]/d[idx_positive])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Entonces el mínimo ocurre en la tercera posición de $x_B$ que corresponde a la variable básica $x_5$. Se elige $x_5$ como variable básica que se vuelve no básica. $x_5$ será sustituida por $x_1$ en la próxima iteración." ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "#index for basic variables, in this case value 2 correspond to x5\n", "\n", "idx_x_B = 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Iteración 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La matriz $B$ de la iteración anterior era:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$B = \\left [ \\begin{array}{ccc} 1 & 0 & 0 \\\\ 0 & 2 & 0 \\\\ 0 & 2 & 1 \\end{array} \\right ]$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "y correspondía cada columna a las variables $x_3, x_2, x_5$ en ese orden." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Se realiza la actualización descrita para $x_B$:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$x_B = x_B - dx_1^{+}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "con $x_1$ es la variable no básica que se volverá básica en la iteración actual." ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [], "source": [ "x_1_plus = np.min(x_B[idx_positive]/d[idx_positive])" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.0\n" ] } ], "source": [ "print(x_1_plus)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "x_B = x_B - dx_1_plus" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2. 6. 0.]\n" ] } ], "source": [ "print(x_B)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Aquí el valor de la variable $x_5$ se hace cero y tenemos que intercambiar tal entrada con la de $x_1^+$ para el vector $x_B$:" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2. 6. 2.]\n" ] } ], "source": [ "x_B[idx_x_B] = x_1_plus\n", "print(x_B)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Observación\n", ":class: tip\n", "\n", "La actualización de $x_B$ anterior se puede verificar que es equivalente a:\n", "\n", "$$x_B = \\left [ \\begin{array}{c} x_3 \\\\ x_2 \\\\ x_1\\end{array}\\right ] = B^{-1}b = \\left [ \\begin{array}{ccc} 1 & 0 & 1 \\\\ 0 & 2 & 0 \\\\ 0 & 2 & 3 \\end{array} \\right ]^{-1} \\left [ \\begin{array}{c} 4 \\\\ 12 \\\\ 18 \\end{array}\\right ] = \\left [ \\begin{array}{ccc} 1 & \\frac{1}{3} & -\\frac{1}{3} \\\\ 0 & \\frac{1}{2} & 0 \\\\ 0 & -\\frac{1}{3} & \\frac{1}{3} \\end{array} \\right ]\\left [ \\begin{array}{c} 4 \\\\ 12 \\\\ 18 \\end{array}\\right ] = \\left [ \\begin{array}{c} 2 \\\\ 6 \\\\ 2 \\end{array}\\right ]$$\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "Antes de hacer el intercambio de columnas: $B = \\left [ \\begin{array}{ccc} 1 & 0 & 0 \\\\ 0 & 2 & 0 \\\\ 0 & 2 & 1 \\end{array} \\right ]$ y la matriz original $ A = \n", "\\left [\n", "\\begin{array}{ccccc}\n", "1 & 0 & 1 & 0 & 0 \\\\\n", "0 & 1 & 0 & 1 & 0 \\\\\n", "3 & 0 & 0 & 0 & 1 \\\\\n", "\\end{array}\n", "\\right ]\n", "$\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Como $x_5$ se intercambia por $x_1$ entonces se intercambia la columna $1$ de $A$, $a_1$, por la $3$ de $B$, $b_3$ por lo que al final de la iteración $2$:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$B = \\left [ \\begin{array}{ccc} 1 & 0 & 1 \\\\ 0 & 2 & 0 \\\\ 0 & 2 & 3 \\end{array} \\right ]$$" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "B[:,idx_x_B] = A[:,idx_x_N]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$x_B = \\left [ \\begin{array}{c} x_3 \\\\ x_2 \\\\ x_1 \\end{array}\\right ] = \\left [ \\begin{array}{c} 2 \\\\ 6 \\\\ 2 \\end{array}\\right ]$, $x_N = \\left [ \\begin{array}{c} x_5 \\\\ x_4\\end{array}\\right ] = \\left [ \\begin{array}{c} 0 \\\\ 0\\end{array}\\right ]$." ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "aux = B_list_idx[idx_x_B]\n", "B_list_idx[idx_x_B] = N_list_idx[idx_x_N]\n", "N_list_idx[idx_x_N] = aux" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "La forma aumentada recuérdese es:\n", "\n", "$$\\displaystyle \\max_{x \\in \\mathbb{R}^5} 3x_1 + 5x_2 \\\\\n", "\\text{sujeto a: }\\\\\n", "x_1 + x_3 = 4 \\\\\n", "2x_2 + x_4 = 12 \\\\\n", "3x_1 + 2x_2 + x_5 = 18 \\\\\n", "x_1 \\geq 0, x_2 \\geq 0, x_3 \\geq 0, x_4 \\geq 0, x_5 \\geq 0\n", "$$\n", "\n", "```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Comentario\n", "\n", "En este punto, la solución BF obtenida en esta iteración $x = \\left [ \\begin{array}{c} x_1 \\\\ x_2 \\\\ x_3 \\\\ x_4 \\\\ x_5 \\end{array} \\right ] = \\left [ \\begin{array}{c} 2 \\\\ 6 \\\\ 2 \\\\ 0 \\\\ 0 \\end{array} \\right ]$ tiene como variables no básicas $x_4, x_5$ e indican que las restricciones $x_4 \\geq 0, x_5 \\geq 0$ son restricciones activas. Además como $x_4$ y $x_5$ son variables de holgura las restricciones funcionales asociadas $2x_2 + x_4 \\leq 12$, $3x_1 + 2x_2 + x_5 \\leq 18$ indican que la solución BF se encuentra sobre sus ecuaciones de frontera respectivas.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prueba de optimalidad" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Se recalcula el vector $\\nu$ considerando que $c_B = \\left [ \\begin{array}{c} c_{B_3} \\\\ c_{B_2} \\\\ c_{B_1} \\end{array}\\right ]$ tomando ahora $x_1$ como básica." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "$B = \\left [ \\begin{array}{ccc} 1 & 0 & 1 \\\\ 0 & 2 & 0 \\\\ 0 & 2 & 3 \\end{array} \\right ]$\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "$c_B = \\left [ \\begin{array}{c} c_{B_3} \\\\ c_{B_2} \\\\ c_{B_1} \\end{array} \\right ] = \\left [ \\begin{array}{c} 0 \\\\ -5 \\\\ -3 \\end{array} \\right ]$\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El vector $\\nu$ es:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\nu = B^{-T}c_B = \\left [ \\begin{array}{ccc} 1 & \\frac{1}{3} & -\\frac{1}{3} \\\\ 0 & \\frac{1}{2} & 0 \\\\ 0 & -\\frac{1}{3}& \\frac{1}{3} \\end{array} \\right ] ^T \\left [ \\begin{array}{c} 0 \\\\ -5 \\\\ -3 \\end{array}\\right ] = \\left [ \\begin{array}{c} 0 \\\\ -\\frac{3}{2} \\\\ -1 \\end{array}\\right ]$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "Resolviendo un sólo sistema de ecuaciones lineales nos ayuda a evitar calcular la inversa de una matriz que implica resolver un sistema de ecuaciones lineales más grande.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para el cálculo de $\\nu$ resolvemos el sistema de ecuaciones lineales para el vector de incógnitas $\\nu$: " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$B^T \\nu = c_B$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "$B = \\left [ \\begin{array}{ccc} 1 & 0 & 1 \\\\ 0 & 2 & 0 \\\\ 0 & 2 & 3 \\end{array} \\right ]$\n", "\n", "```" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "aux = c_B[idx_x_B]\n", "c_B[idx_x_B] = c_N[idx_x_N]\n", "c_N[idx_x_N] = aux" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "nu = np.linalg.solve(B.T, c_B)" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. -1.5 -1. ]\n" ] } ], "source": [ "print(nu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Por tanto:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "$c_N= \\left [ \\begin{array}{c}0 \\\\ 0 \\end{array} \\right ]$\n", "\n", "```" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "lambda_N_5 = -c_N[0] + np.dot(nu, A[:,N_list_idx[0]])" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [], "source": [ "lambda_N_4 = -c_N[1] + np.dot(nu, A[:,N_list_idx[1]])" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-1.0\n" ] } ], "source": [ "print(lambda_N_5)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-1.5\n" ] } ], "source": [ "print(lambda_N_4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\lambda_{N_5} = -c_{N_5} + \\nu^Ta_5 = 0 + [0 \\quad -\\frac{3}{2} \\quad -1]\\left [ \\begin{array}{c} 0 \\\\ 0 \\\\ 1 \\end{array}\\right ] = -1$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\lambda_{N_4} = -c_{N_4} + \\nu^Ta_4 = 0 + [0 \\quad -\\frac{3}{2} \\quad -1] \\left [ \\begin{array}{c} 0 \\\\ 1 \\\\ 0 \\end{array}\\right ] = -1.5$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Índices de las variables básicas:" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2, 1, 0]\n" ] } ], "source": [ "print(B_list_idx)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Índices de las variables no básicas:" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[4, 3]\n" ] } ], "source": [ "print(N_list_idx)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Valores de $\\nu$:" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. -1.5 -1. ]\n" ] } ], "source": [ "print(nu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "Recuérdese que en el método símplex se mantiene en cada iteración $\\lambda_{B_j} = 0 \\forall j \\in \\mathcal{B}$ y se busca que $\\lambda_{N_j} \\forall j \\in \\mathcal{N}$ sea no negativo para problemas de minimización o no positivo para problemas de maximización.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Comentario\n", "\n", "Entonces al finalizar el método símplex aplicado al ejemplo prototipo:\n", "\n", "* $\\lambda_{B_1} = \\lambda_{B_2} = \\lambda_{B_3} = 0$ para los índices de las variables básicas $x_1, x_2, x_3$, $\\mathcal{B} = \\{1, 2, 3\\}$.\n", "\n", "* $\\lambda_{N_4} = -1.5$, $\\lambda_{N_5} = -1$ para los índices de las variables no básicas $x_4, x_5$, $\\mathcal{N} = \\{4, 5\\}$.\n", "\n", "* $\\nu_{1} = 0, \\nu_{2} = -1.5, \\nu_{3} = -1$.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Como tenemos un problema de maximización la tasa más alta de mejoramiento de $f_o$ no la da ninguna de las variables no básicas por lo que la solución BF actual $x = \\left [ \\begin{array}{c} x_1\\\\ x_2\\\\ x_3\\\\ x_4 \\\\ x_5 \\end{array} \\right ]= \\left [ \\begin{array}{c} 2\\\\ 6\\\\ 2\\\\ 0 \\\\ 0 \\end{array}\\right ]$ es la solución óptima." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Valor de la función objetivo en la solución BF actual: $f_o(x) = (-c)^Tx = b^T(-\\nu) = 36$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "$\n", "\\begin{eqnarray}\n", "f_o(x) &=& (-c)^Tx \\nonumber\\\\\n", "&=& -c_B^Tx_B - c_N^T x_N \\nonumber\\\\\n", "&=& -c_B^T x_B \\quad \\text{pues } x_N=0\\\\\n", "\\end{eqnarray}$ \n", "\n", "```" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "36.0\n" ] } ], "source": [ "print(np.dot(-c_B, x_B))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{margin}\n", "\n", "$f_o(x) = b^T(-\\nu)$.\n", "\n", "```" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "36.0\n" ] } ], "source": [ "print(np.dot(b, -nu))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Algoritmo para un paso del método símplex\n", "\n", "Para un problema de la forma:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\displaystyle \\min_{x \\in \\mathbb{R}^n} c^Tx$$\n", "\n", "$$\\text{sujeto a:}$$\n", "\n", "$$Ax=b$$\n", "\n", "$$x \\geq 0$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ">Dados $\\mathcal{B}, \\mathcal{N}, x_B = B^{-1}b \\geq 0, x_N=0$\n", ">\n", ">>Resolver $B^T \\nu = c_B$ para $\\nu$\n", ">>\n", ">>Calcular $\\lambda_N = c_N - N^T\\nu$\n", ">>\n", ">>Si $\\lambda \\geq 0$ se encontró un punto óptimo, si no:\n", ">>>\n", ">>>Seleccionar $nb \\in \\mathcal{N}$ con $\\lambda_{nb} < 0$ como el índice que entra\n", ">>>\n", ">>>Resolver $Bd = A_{nb}$ para $d$.\n", ">>>\n", ">>>Si $d \\leq 0$ detenerse, el problema es no acotado.\n", ">>>\n", ">>>Calcular $x_{nb}^+ = \\min\\{\\frac{x_{B_i}}{d_i} : d_i >0\\}$ y sea $ba$ el índice que minimiza.\n", ">>>\n", ">>>Actualizar $x_B = x_B - dx^+_{nb}$, $x_N^+ = (0, \\dots, 0, x_{nb}^+, 0, \\dots, 0)^T$\n", ">>>\n", ">>>Cambiar $\\mathcal{B}$ al añadir $nb$ y remover la variable básica correspondiente a la columna $ba$ de B.\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Ejercicio\n", ":class: tip\n", "\n", "Resuelve con el método de símplex y corrobora con algún software tu respuesta:\n", "\n", "$$\\displaystyle \\min_{x \\in \\mathbb{R}^3}x_1 + x_2 - 4x_3$$\n", "\n", "$$\\text{sujeto a:}$$\n", "\n", "$$x_1 + x_2 + 2x_3 \\leq 9$$\n", "\n", "$$x_1 + x_2 - x_3 \\leq 2$$\n", "\n", "$$-x_1 + x_2 + x_3 \\leq 4$$\n", "\n", "$$x_1 \\geq 0, x_2 \\geq 0 , x_3 \\geq 0$$\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Consideraciones sobre el método símplex" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* El método símplex termina con una solución BF si el PL no tiene variables básicas degeneradas y tiene una región acotada." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Al método símplex anteriormente descrito se le tienen que añadir funcionalidades para realizar una implementación que maneje los siguientes puntos:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-)Sobre empates en la variable básica, no básica.\n", "\n", "-)Sobre el álgebra matricial numérica.\n", "\n", "-)Sobre variables básicas degeneradas.\n", "\n", "-)Sobre variables de decisión con cotas inferiores distintas de cero, con cotas superiores." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "No se profundizará sobre los puntos anteriores y se sugiere ir a las referencias de esta nota para su consulta." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## PL's large scale" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Los PL's que modelan aplicaciones reales tienden a encontrarse en la clasificación large scale. Tal término aunque es ambiguo pues depende la máquina en la que se realice el cómputo e involucra el número de variables o parámetros y cantidad de almacenamiento para datos, lo asociamos con problemas de optimización con restricciones que tienen un número de variables y restricciones mayor o igual a $10^5$ (ambas)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Enunciado de un ejemplo para un problema medium scale" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Supóngase que al igual que en el {ref}`ejemplo prototipo <EJPROTOTIPO>` una compañía desea resolver un problema de mezcla de productos. Tal compañía tiene $10$ plantas en varias partes del mundo. Cada una elabora los mismos $10$ productos y después los vende en su región. Se conoce la demanda (ventas potenciales) de cada producto en cada planta en cada uno de los próximos $10$ meses. Aunque la cantidad de producto vendido en un mes dado no puede exceder la demanda, la cantidad producida puede ser mayor, y la cantidad en exceso se debería almacenar en inventario (con un costo unitario por mes) para su venta posterior. Cada unidad de cada producto ocupa el mismo espacio en almacén y cada planta tiene un límite superior para el número total de unidades que se puede guardar (la capacidad del inventario).\n", "\n", "Cada planta realiza los $10$ procesos de producción con máquinas y tales máquinas se pueden usar para producir cualquiera de los $10$ productos. Tanto el costo de producción por unidad como la tasa de producción de un producto (número de unidades producidas por día dedicado a ese producto) dependen de la combinación de plantas y máquinas involucradas y no del mes que se realizará la producción. El número de días hábiles (días de producción disponibles) varía un poco de un mes a otro.\n", "\n", "Como algunas plantas y máquinas pueden producir un producto dado ya sea a menor costo o a una tasa más rápida que otras plantas y máquinas, en ocasiones vale la pena enviar algunas unidades del producto de una planta a otra para que esta última las venda. Existe cierto costo asociado con cada unidad enviada de cualquier producto de cada combinación de una planta que envía (planta origen) y una planta que recibe (planta destino), donde este costo unitario es el mismo para todos los productos.\n", "\n", "La administración necesita determinar cuántas unidades de cada producto debe producir en cada máquina de cada planta cada mes, al igual que cuántas unidades de cada producto debe vender cada planta cada mes y cuántas unidades de cada producto debe enviar cada planta cada mes a cada una de las otras plantas.\n", "\n", "El objetivo es encontrar el plan factible que maximice la ganancia total: ingreso por ventas totales menos la suma de los costos totales de producción, inventario y envío.\n", "\n", "Debido a los costos de inventario y a que las capacidades de almacenamiento son limitadas, es necesario mantener un registro de la cantidad de cada producto que se guarda en cada planta durante cada mes. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Variables de decisión" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El modelo PL tiene cuatro tipo de variables de decisión: cantidades de producción, cantidades de inventario, cantidades de venta y cantidades enviadas. Con $10$ plantas, $10$ máquinas, $10$ productos y $10$ meses da un total de $21, 000$ variables de decisión." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* $10,000$ variables de producción: una por cada combinación de planta, máquina, producto y mes.\n", "\n", "* $1,000$ variables de inventario: una por cada combinación de planta, producto y mes.\n", "\n", "* $1,000$ variables de ventas: una por cada combinación de planta, producto y mes.\n", "\n", "* $9,000$ variables de envío: una por cada combinación de producto, mes, planta (planta origen) y otra planta (la planta destino). (Para las combinaciones de las plantas origen-destino, realícense combinaciones de $10$ en $2$ y multiplíquese por $2$ por la designación de planta origen-destino)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Función objetivo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Al multiplicar cada variable de decisión por el costo unitario o ingreso unitario correspondiente y sumar según cada tipo, se tiene: \"Maximizar ganancia=ingresos totales por ventas - costo total\" donde: \"costo total = costo total de producción + costo total de inventario + costo total de envío\"." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Restricciones funcionales" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Las $21,000$ variables de decisión deben satisfacer las restricciones de no negatividad al igual que los cuatro tipos de restricciones funcionales: de capacidad de producción, de balanceo de plantas (restricciones de igualdad que proporcionan valores adecuados para las variables de inventario), de inventario máximo y de ventas máximas. En total se tienen $3,100$ restricciones funcionales.\n", "\n", "* $1,000$ restricciones de capacidad de producción, una por cada combinación de planta, máquina y mes:\n", "\n", "\"Días de producción usados $\\leq$ días de producción disponibles,\n", "\n", "donde: el lado izquierdo es la suma de $10$ fracciones, una por cada producto. Cada fracción es la cantidad de ese producto (una variable de decisión) dividida entre la tasa de producción del producto (una constante dada).\n", "\n", "* $1,000$ restricciones de balance de plantas, una por cada combinación de planta, producto y mes:\n", "\n", "\"Cantidad producida + inventario del mes pasado + cantidad recibida = ventas + inventario actual + cantidad enviada\",\n", "\n", "donde: la \"cantidad producida\" es la suma de las variables de decisión que representan las cantidades de producción de las máquinas, la \"cantidad recibida\" es la suma de las variables de decisión que representan las cantidades enviadas desde otras plantas y la \"cantidad enviada\" es la suma de las variables de decisión correspondientes a las cantidades que se mandan a las otras plantas.\n", "\n", "* $100$ restricciones de inventario máximo, una por cada combinación de planta y mes:\n", "\n", "\"Inventario total $\\leq$ capacidad del inventario\",\n", "\n", "donde: el lado izquierdo es la suma de las variables de decisión que representan las cantidades de inventario de los productos individuales.\n", "\n", "* $1,000$ restricciones de ventas, una por cada combinación de planta, producto y mes:\n", "\n", "\"Ventas $\\leq$ demanda\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "¿Cómo escribir el problema anterior y resolverlo en un lenguaje de programación?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Podemos utilizar lenguajes de modelado, [modeling languages](https://en.wikipedia.org/wiki/Modeling_language#Algebraic), que permiten el uso de solvers como:\n", "\n", "* [cvxpy](https://www.cvxpy.org/), [cvx](http://cvxr.com/cvx/), [cvxr](https://cvxr.rbind.io/), [Convex](https://github.com/jump-dev/Convex.jl), [cvxopt](https://cvxopt.org/)\n", "\n", "* [or-tools](https://github.com/google/or-tools)\n", "\n", "* [JuMP](https://github.com/jump-dev/JuMP.jl)\n", "\n", "* [AMPL](https://ampl.com/), [AMPL Python API](https://ampl.com/api/nightly/python/), [AMPL R API](https://ampl.com/api/nightly/R/), [AMPL MATLAB API](https://ampl.com/api/nightly/matlab/)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Comentario\n", "\n", "El problema anterior puede resolverse eficientemente con el método símplex. El método símplex resuelve en la práctica problemas con restricciones del orden de $10^5$ de manera eficiente. No tiene problemas manejando un número grande de variables, por ejemplo mayor a $10^5$ pero sí afecta su desempeño computacional aumentar el número de restricciones, por ejemplo con un número mayor o igual de $10^6$ restricciones.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Unas palabras sobre PL entera y métodos" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Uno de los supuestos de la PL es la de divisibilidad que requiere que las variables de decisión puedan tomar valores no enteros. En el {ref}`ejemplo de flujo en redes <EJFLUJOENREDESYPL>` se vio que con `cvxpy` se puede resolver tal problema imponiendo la restricción que las variables de decisión sean enteras. Esto ocurre en muchos problemas prácticos por ejemplo si consideramos a personas, cajas que contengan materiales o vehículos. Un PL al tener la restricción anterior se le nombra problema de programación entera (PE)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Observación\n", ":class: tip\n", "\n", "Siendo rigurosos el nombre sería programación lineal entera pero es más común omitir \"lineal\" a menos que se encuentre en un contexto de programación no lineal entera.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Comentarios\n", "\n", "* Si un PL admite variables de decisión que sean enteras y otras cumplan con el supuesto de divisibilidad entonces se nombra al problema de optimización programación entera mixta (PEM).\n", "\n", "* Si las variables de decisión únicamente toman valores binarios (por ejemplo decisiones \"sí\", \"no\") se le nombra al problema de optimización programación entera binaria (PEB).\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ejemplo prototipo de PEB" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Suponga que una compañía analiza la posibilidad de llevar a cabo una expansión mediante la construcción de fábricas ya sea en Monterrey o en Torreón. También piensa en construir, a lo sumo, un nuevo almacén, pero la decisión sobre el lugar en donde lo instalará está restringida a la ciudad donde se construyan las fábricas. Se presenta la siguiente tabla con datos del valor presente neto (VPN, rendimiento total que toma en cuenta el valor del dinero en el tiempo), el capital requerido y el disponible para llevar a cabo tal obra:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\|Número de decisión\| Pregunta\| Variable de decisión\| VPN \| Capital requerido\|\n", "\|:---:\|:---:\|:---:\|:---:\|:---:\|\n", "\|1\|¿Construir la fábrica en Monterrey?\| $x_1$\| 9 millones\| 6 millones\|\n", "\|2\|¿Construir la fábrica en Torreón?\|$x_2$\|5 millones\| 3 millones\|\n", "\|3\|¿Construir el almacén en Monterrey?\|$x_3$\|6 millones\|5 millones\|\n", "\|4\|¿Construir el almacén en Torreón?\|$x_4$\|4 millones\|2 millones\|\n", "\|-\|-\|-\|Capital disponible:\|10 millones\|" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En la tabla anterior se muestra el VPN de cada alternativa. En la última columna se proporciona el capital que se requiere (incluido el VPN) para las inversiones, donde el capital total disponible es de 10 millones de pesos. El objetivo es encontrar la combinación factible de alternativas que maximice el VPN." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### El modelo PEB" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sean las variables de decisión:\n", "\n", "$$x_j = \\begin{cases} 1 & \\text{ si la decisión } j \\text{ es sí }\\\\\n", "0 & \\text{si la decisión } j \\text{ es no }\n", "\\end{cases}, \\quad j=1, 2, 3, 4\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "y la función objetivo: $f_o(x) = 9x_1 + 5x_2 + 6x_3 + 4x_4$ que represente el VPN de estas decisiones." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dados los datos de la tabla anterior una restricción del modelo es:\n", "\n", "$$6x_1 + 3x_2 + 5x_3 + 2x_4 \\leq 10$$\n", "\n", "por el capital disponible.\n", "\n", "Las siguientes restricciones tienen que ver con \n", "\n", "* alternativas mutuamente excluyentes dado que la compañía quiere construir cuando mucho un almacén nuevo.\n", "\n", "* decisiones condicionales dado que la compañía consideraría la construcción de un almacén en determinada ciudad sólo si la nueva fábrica va a estar ahí.\n", "\n", "Tales restricciones se modelan como:\n", "\n", "$$x_3 + x_4 \\leq 1$$\n", "\n", "para las alternativas mutuamente excluyentes y\n", "\n", "$$x_3 \\leq x_1$$\n", "\n", "$$x_4 \\leq x_2$$\n", "\n", "para las decisiones condicionales. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Entonces el PEB es:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\displaystyle \\max_{x \\in \\mathbb{R}^4} 9x_1 + 5x_2 + 6x_3 + 4x_4$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\text{sujeto a: }$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$6x_1 + 3x_2 + 5x_3 + 2x_4 \\leq 10$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$x_3 + x_4 \\leq 1$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$-x_1 + x_3 \\leq 0$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$-x_2 + x_4 \\leq 0$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$x_j \\in \\{0,1\\}, \\quad j=1, 2, 3, 4$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Observaciones\n", ":class: tip\n", "\n", "* La última restricción es equivalente a $0 \\leq x_j \\leq 1$ y $x_j$ entera.\n", "\n", "* Si en el problema se especifica que se quiere construir exactamente una fábrica en Monterrey o Torreón no importando la ciudad, entonces se añadiría la restricción $x_1 + x_2 = 1$.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Consideraciones sobre los modelos de PE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Podría pensarse que los PE son más sencillos de resolver que los PL, lo cual no es correcto en general principalmente por algunas razones que se enlistan a continuación:\n", "\n", "* Un número finito de soluciones factibles no asegura que un problema se pueda resolver. Por ejemplo, en el PEB si se tienen $n$ variables, existen $2^n$ soluciones que considerar aproximadamente (quizás algunas se puedan eliminar). Con $n=10$ se tienen mil soluciones y $n=30$ más de mil millones. Por lo que enlistar las soluciones factibles no es un buen método para resolver problemas de PEB (o PE).\n", "\n", "* Resolver un PL relajado que resulta del PE eliminando la restricción de variables enteras no siempre resuelve el PE original. Esto sólo ocurre en algunos casos especiales como es el problema del flujo de costo mínimo con parámetros enteros. Ver {ref}`ejemplo de flujo en redes <EJFLUJOENREDESYPL>` redondeando la solución de `scipy` que se obtuvo. La clave de la eficiencia del método símplex es la continuidad de las variables de decisión.\n", "\n", "* Los tres factores determinantes de la dificultad computacional de un problema de PE son:\n", "\n", "1)El número de variables enteras\n", "\n", "2)Variables enteras ¿binarias o generales?\n", "\n", "3)Estructura especial del problema (si por ejemplo pueden eliminarse variables o restricciones dadas las características del problema).\n", "\n", "Contrástese esto con la situación que la presencia de restricciones en un PL es más importante que el número de variables. En la PE el número de restricciones es secundario a los factores anteriores." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Observación\n", ":class: tip\n", "\n", "Existen casos en los que aumentar el número de restricciones disminuye el número de soluciones factibles y por tanto el tiempo de cálculo.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Comentarios\n", "\n", "* Aunque resulta tentador resolver los PE como si fueran PL (trabajarlos como relajados) y redondear el resultado se corren riesgos:\n", "\n", " * No necesariamente una solución óptima del PL será factible después de redondearla.\n", "\n", " * No existe garantía de que la solución redondeada sea la solución óptima del PE.\n", "\n", "Aún así hay algoritmos de PE que utilizan en sus pasos intermedios resolver PL relajados.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sobre algoritmos de PE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Los ejemplos anteriores muestran la cantidad de variables y restricciones que pueden surgir en un problema real debido al número de combinaciones posibles que resultan. En particular el área de optimización que se relaciona con el número de combinaciones que resultan de enumerar el conjunto solución en el que las soluciones factibles son discretas es la área de [optimización combinatoria](https://en.wikipedia.org/wiki/Combinatorial_optimization)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si bien el método símplex y métodos de puntos interiores (ver {ref}`introducción a los métodos de puntos interiores <INTMETPIN>`) han probado ser métodos para abordar una amplia variedad de problemas prácticos de PL no siempre funcionan en problemas que surgen en la optimización combinatoria. Por ejemplo, en un problema en el que el número de restricciones funcionales sea mayor al del número de variables, el método símplex deberá realizar un esfuerzo computacional grande. Por lo que se han desarrollado algoritmos específicos para la PE. Dentro de los algoritmos más populares en los PE, PEM se encuentra el de [ramificación y acotamiento](https://en.wikipedia.org/wiki/Branch_and_bound) (aplica la idea de [divide y vencerás](https://en.wikipedia.org/wiki/Divide-and-conquer_algorithm)) y [cortes de Gomory](https://en.wikipedia.org/wiki/Cutting-plane_method)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Comentario\n", "\n", "Podemos utilizar el método símplex para resolver PE's si la matriz de la restricciones del problema cumple con la propiedad de [total unimodularity](https://en.wikipedia.org/wiki/Integer_programming#Using_total_unimodularity).\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sobre métodos heurísticos en optimización combinatoria" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Los métodos [heurísticos](https://en.wikipedia.org/wiki/Heuristic_(computer_science)) y [meta heurísticas](https://en.wikipedia.org/wiki/Metaheuristic) (estrategias para mejorar o diseñar métodos heurísticos) encuentran una buena solución factible que al menos está razonablemente cerca de ser óptima, también pueden reportar que no se encontró tales soluciones. Sin embargo son métodos que no dan una garantía acerca de la calidad de la solución que se obtiene.\n", "\n", "Los métodos heurísticos se desarrollaron principalmente para el manejo de problemas large scale sean PL's o de optimización combinatoria. Típicamente se han ajustado a problemas específicos en lugar de aplicarse a una variedad de aplicaciones. \n", "\n", "Como se mencionó anteriormente no existe garantía de que la mejor solución que se encuentre con un método heurístico sea una solución óptima o incluso que esté cerca de serlo. Por tanto, siempre que sea posible resolver un problema mediante un algoritmo que pueda garantizar optimalidad, debe usarse éste en lugar de uno heurístico. El papel de los métodos heurísticos es abordar problemas que son muy grandes y complicados como para resolverlos por medio de algoritmos exactos.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ejemplos: [Vehicle routing problem](https://en.wikipedia.org/wiki/Vehicle_routing_problem),aka VRP, y [Travelling Salesman Problem](https://en.wikipedia.org/wiki/Travelling_salesman_problem), aka TSP" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Problema del VRP: encontrar las rutas óptimas para vehículos de modo que satisfagan las demandas de clientes. Este problema generaliza al del TSP: dada una lista de ciudades y las distancias entre cada par de ellas, ¿cuál es la ruta más corta posible que visita cada ciudad exactamente una vez y al finalizar regresa a la ciudad origen?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Observación\n", ":class: tip\n", "\n", "Un TSP con $10$ ciudades requiere un poco menos de $200,000$ soluciones factibles que deben ser consideradas, un problema con $20$ ciudades tiene alrededor de $10^{16}$ y uno con $50$ ciudades tiene alrededor de $10^{62}$.\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ejemplos de algoritmos heurísticos o meta heurísticas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Una lista de algoritmos que se clasifican como heurísticos o meta heurísticos utilizados en optimización en general (no sólo combinatoria) son:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* [Greedy algorithm](https://en.wikipedia.org/wiki/Greedy_algorithm)\n", "\n", "* [Alpha-beta pruning](https://en.wikipedia.org/wiki/Alpha-beta_pruning)\n", "\n", "* [Best-first search](https://en.wikipedia.org/wiki/Best-first_search)\n", "\n", "* [Simulated annealing](https://en.wikipedia.org/wiki/Simulated_annealing)\n", "\n", "* [Tabu search](https://en.wikipedia.org/wiki/Tabu_search)\n", "\n", "* [Hill climbing](https://en.wikipedia.org/wiki/Hill_climbing)\n", "\n", "* [Genetic algorithm](https://en.wikipedia.org/wiki/Genetic_algorithm)\n", "\n", "* [Particle swarm optimization](https://en.wikipedia.org/wiki/Particle_swarm_optimization)\n", "\n", "* [Ant colony optimization](https://en.wikipedia.org/wiki/Ant_colony_optimization)\n", "\n", "* [Guided local search](http://en.wikipedia.org/wiki/Guided_Local_Search)\n", "\n", "* [Christofides algorithm](https://en.wikipedia.org/wiki/Christofides_algorithm)\n", "\n", "* [Nelder–Mead method](https://en.wikipedia.org/wiki/Nelder%E2%80%93Mead_method)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El paquete [or-tools](https://github.com/google/or-tools) es un paquete de Python que tiene métodos como los anteriores y también para resolver PL's y de optimización de flujo en redes. Ver [About OR-Tools](https://developers.google.com/optimization/introduction/overview), [routing_options](https://developers.google.com/optimization/routing/routing_options), [routing](https://developers.google.com/optimization/routing).\n", "\n", "La librería [concorde](http://www.math.uwaterloo.ca/tsp/concorde.html) escrita en C resuelve tipo de problemas TSP y de optimización de flujo en redes con cómputo en paralelo. Ver [TSPLIB](http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/) para instancias de problemas TSP." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```{admonition} Ejercicios\n", ":class: tip\n", "\n", "1.Resuelve los ejercicios y preguntas de la nota.\n", "```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Preguntas de comprehensión\n", "\n", "1)Escribe el modelo de programación lineal en forma estándar con nomenclatura matemática y asócialo con la forma estándar de un problema de optimización convexa. ¿Cómo se escribe el problema de optimización convexa en el caso de programación lineal?. \n", "\n", "2)Describe con palabras coloquiales lo que se desea realizar en un programa lineal y algunos de sus supuestos.\n", "\n", "3)Describe al problema de mezcla de productos.\n", "\n", "4)¿Qué es un poliedro y cómo se obtienen poliedros en un PL?\n", "\n", "5)¿Qué es una solución factible en un vértice? \n", "\n", "6)¿Qué es una solución básica factible? \n", "\n", "7)¿Qué es la basis y nonbasis matrix?\n", "\n", "8)¿Qué es una variable básica degenerada? e investiga qué mensaje se obtiene en un programa de computadora que tenga implementado el método símplex en un ejemplo en el que se tenga tales variables básicas.\n", "\n", "9)Si se conocen los valores numéricos de las variables básicas y no básicas que se obtienen en una solución no degenerada con el método símplex ¿cómo puede distinguirse si tal solución es una solución BF? \n", "\n", "\n", "10)¿Qué es una variable de holgura y para qué fueron utilizadas en la nota?\n", "\n", "11)Describe el proceso de pivoteo en el método símplex.\n", "\n", "12)Investiga qué es lo que puede concluirse a partir del método símplex si se elige una variable no básica que puede entrar al conjunto de variables básicas y en la prueba del cociente mínimo todos los denominadores son negativos.\n", "\n", "13)¿En qué casos podemos usar el método símplex para resolver programas lineales enteros?\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Referencias:\n", "\n", "1. F. Hillier, G. Lieberman, Introduction to Operations Research, Mc Graw Hill, 2014.\n", "\n", "2. R. K. Ahuja, T. L. Magnanti, J. B. Orlin, Network Flows, Theory, Algorithms and Applications, Prentice Hall, 1993.\n", "\n", "3. M. S. Bazaraa, J. J. Jarvis, H. D. Sherali, Linear Programming and Network Flows, Wiley, 2010.\n", "\n", "4. J. Nocedal, S. J. Wright, Numerical Optimization, Springer, 2006.\n", "\n", "\n", "\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 4 }	apache-2.0
jmschrei/pomegranate	tutorials/C_Feature_Tutorial_2_Out_Of_Core_Learning.ipynb	1	13198	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Out-of-Core Learning\n", "\n", "author: Jacob Schreiber <br>\n", "contact: [email protected] <br>\n", "\n", "Out-of-core learning refers to the process of training a model on an amount of data that cannot fit in memory. There are several approaches that can be described as out-of-core, but here we refer to the ability to derive exact updates to a model from a massive data set, despite not being able to fit the entire thing in memory.\n", "\n", "This out-of-core learning approach is implemented for all of pomegranate's models using two methods. The first is a `summarize` method that will take in a batch of data and reduce it down to additive sufficient statistics. Because these summaries are additive, after the first call, these summaries are added to the previously stored summaries. Once the entire data set has been seen, the stored sufficient statistics will be identical to those that would have been derived if the entire data set had been seen at once. The second method is the `from_summaries` method, which uses the stored sufficient statistics to derive parameter updates for the model.\n", "\n", "A common solution to having too much data is to randomly select an amount of data that does fit in memory to use in the place of the full data set. While simple to implement, this approach is likely to yield lower performance models because it is exposed to less data. However, by using out-of-core learning, on can train their models on a massive amount of data without being limited by the amount of memory their computer has." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tue Nov 27 2018 \n", "\n", "numpy 1.15.1\n", "scipy 1.1.0\n", "pomegranate 0.10.0\n", "\n", "compiler : GCC 7.2.0\n", "system : Linux\n", "release : 4.15.0-39-generic\n", "machine : x86_64\n", "processor : x86_64\n", "CPU cores : 4\n", "interpreter: 64bit\n" ] } ], "source": [ "%matplotlib inline\n", "import time\n", "import pandas\n", "import random\n", "import numpy\n", "import matplotlib.pyplot as plt\n", "import seaborn; seaborn.set_style('whitegrid')\n", "import itertools\n", "\n", "from pomegranate import \n", "\n", "random.seed(0)\n", "numpy.random.seed(0)\n", "numpy.set_printoptions(suppress=True)\n", "\n", "%load_ext watermark\n", "%watermark -m -n -p numpy,scipy,pomegranate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Training a Probability Distribution\n", "\n", "Let's start off simple with training a multivariate Gaussian distribution in an out-of-core manner. First, we'll generate some random data." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = numpy.random.normal([5, 7], [1.5, 0.4], size=(1000, 2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we can make a blank distribution with 2 dimensions. This is equivalent to filling in the mean and standard deviation with dummy values that will be overwritten, and don't effect the calculation." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " [\n", " 0.0,\n", " 0.0\n", " ],\n", " [\n", " [\n", " 1.0,\n", " 0.0\n", " ],\n", " [\n", " 0.0,\n", " 1.0\n", " ]\n", " ]\n", " ],\n", " \"name\" :\"MultivariateGaussianDistribution\"\n", "}" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d1 = MultivariateGaussianDistribution.blank(2)\n", "d1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's summarize through a few batches of data." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "d1.summarize(X[:250])\n", "d1.summarize(X[250:500])\n", "d1.summarize(X[500:750])\n", "d1.summarize(X[750:])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d1.summaries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we've seen the entire data set let's use the `from_summaries` method to update the parameters." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " [\n", " 4.967395273422254,\n", " 6.996038686884455\n", " ],\n", " [\n", " [\n", " 2.1362097536595757,\n", " -0.007992485878115985\n", " ],\n", " [\n", " -0.007992485878115985,\n", " 0.15420875108571636\n", " ]\n", " ]\n", " ],\n", " \"name\" :\"MultivariateGaussianDistribution\"\n", "}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d1.from_summaries()\n", "d1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And what do we get if we learn directly from the data?" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{\n", " \"frozen\" :false,\n", " \"class\" :\"Distribution\",\n", " \"parameters\" :[\n", " [\n", " 4.967395273422257,\n", " 6.996038686884458\n", " ],\n", " [\n", " [\n", " 2.136209753659561,\n", " -0.007992485878137813\n", " ],\n", " [\n", " -0.007992485878137813,\n", " 0.1542087510856727\n", " ]\n", " ]\n", " ],\n", " \"name\" :\"MultivariateGaussianDistribution\"\n", "}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "MultivariateGaussianDistribution.from_samples(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The exact same model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Training a Mixture Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This summarization option enables a variety of different training strategies that can be written by hand. This notebook focuses on out-of-core learning, so let's make a data set and \"read it in\" one batch at a time to train a mixture model with a custom training function. We'll make another data set here, but one could easily have a function that read through some number of lines in a CSV, or loaded up a chunk from a numpy memory map, or whatever other massive data store you had." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = numpy.concatenate([numpy.random.normal(0, 1, size=(5000, 10)), numpy.random.normal(1, 1, size=(7500, 10))])\n", "n = X.shape[0]\n", "\n", "idx = numpy.arange(n)\n", "numpy.random.shuffle(idx)\n", "\n", "X = X[idx]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we have to initialize our model. We can do that either by hand to some value we think is good, or by fitting to the first chunk of data, anticipating that it will be a decent representation of the remainder. We can also calculate the log probability of the data set now to see how much we improved. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# First we initialize our model on some small chunk of data.\n", "model = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X[:200], max_iterations=1, init='first-k')\n", "\n", "# The base performance on the data set.\n", "base_logp = model.log_probability(X).sum()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2b14bbabfb784fb2958f59567f60c26c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=5), HTML(value=u'')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "from tqdm import tqdm_notebook as tqdm\n", "\n", "# Now we write our own iterator. This outer loop will be the number of times we iterate---hard coded to 5 in this case.\n", "for iteration in tqdm(range(5)):\n", "\n", " # This internal loop goes over chunks from the data set. We're just loading chunks of a fixed size iteratively\n", " # until we've seen the entire data set.\n", " for i in range(10):\n", " model.summarize(X[i (n // 10):(i+1) * (n //10)])\n", " \n", " # Now we've seen the entire data set and summarized it. We can update the parameters now.\n", " model.from_summaries() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How we does did our model do on the data originally, and how well does it do now?" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-188806.57618011418, -184074.12225611554)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "base_logp, model.log_probability(X).sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like a decent improvement.\n", "\n", "Now, let's compare to having fit our model to the entire loaded data set for five epochs." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-188806.57618011418, -184074.12225611557)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, 2, X[:200], max_iterations=1, init='first-k')\n", "base_logp = model.log_probability(X).sum()\n", "\n", "model.fit(X, max_iterations=5)\n", "base_logp, model.log_probability(X).sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like the exact same values.\n", "\n", "You may ask why we bothered to write a summarization function for data that did fit in memory. The purpose here was entirely illustrative. Our function that use the summarize method would scale to any amount of data that could be loaded in batches, whereas the fit function can only scale to the amount of data that can fit in memory. However, they yield identical answers at the end, suggesting that if one wanted to scale to massive data sets but still get the same performance, this summarize function is the way to go." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.14" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
ThyrixYang/LearningNotes	MOOC/stanford_cnn_cs231n/assignment2/FullyConnectedNets.ipynb	1	325770	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Fully-Connected Neural Nets\n", "In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would become impractical as we move to bigger models. Ideally we want to build networks using a more modular design so that we can implement different layer types in isolation and then snap them together into models with different architectures.\n", "\n", "In this exercise we will implement fully-connected networks using a more modular approach. For each layer we will implement a `forward` and a `backward` function. The `forward` function will receive inputs, weights, and other parameters and will return both an output and a `cache` object storing data needed for the backward pass, like this:\n", "\n", "```python\n", "def layer_forward(x, w):\n", " \"\"\" Receive inputs x and weights w \"\"\"\n", " # Do some computations ...\n", " z = # ... some intermediate value\n", " # Do some more computations ...\n", " out = # the output\n", " \n", " cache = (x, w, z, out) # Values we need to compute gradients\n", " \n", " return out, cache\n", "```\n", "\n", "The backward pass will receive upstream derivatives and the `cache` object, and will return gradients with respect to the inputs and weights, like this:\n", "\n", "```python\n", "def layer_backward(dout, cache):\n", " \"\"\"\n", " Receive derivative of loss with respect to outputs and cache,\n", " and compute derivative with respect to inputs.\n", " \"\"\"\n", " # Unpack cache values\n", " x, w, z, out = cache\n", " \n", " # Use values in cache to compute derivatives\n", " dx = # Derivative of loss with respect to x\n", " dw = # Derivative of loss with respect to w\n", " \n", " return dx, dw\n", "```\n", "\n", "After implementing a bunch of layers this way, we will be able to easily combine them to build classifiers with different architectures.\n", "\n", "In addition to implementing fully-connected networks of arbitrary depth, we will also explore different update rules for optimization, and introduce Dropout as a regularizer and Batch Normalization as a tool to more efficiently optimize deep networks.\n", " " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "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 time\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from cs231n.classifiers.fc_net import \n", "from cs231n.data_utils import get_CIFAR10_data\n", "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\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": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('y_test: ', (1000,))\n", "('X_val: ', (1000, 3, 32, 32))\n", "('y_val: ', (1000,))\n", "('X_train: ', (49000, 3, 32, 32))\n", "('y_train: ', (49000,))\n", "('X_test: ', (1000, 3, 32, 32))\n" ] } ], "source": [ "# Load the (preprocessed) CIFAR10 data.\n", "\n", "data = get_CIFAR10_data()\n", "for k, v in list(data.items()):\n", " print(('%s: ' % k, v.shape))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Affine layer: foward\n", "Open the file `cs231n/layers.py` and implement the `affine_forward` function.\n", "\n", "Once you are done you can test your implementaion by running the following:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Testing affine_forward function:\n", "difference: 9.76985004799e-10\n" ] } ], "source": [ "# Test the affine_forward function\n", "\n", "num_inputs = 2\n", "input_shape = (4, 5, 6)\n", "output_dim = 3\n", "\n", "input_size = num_inputs np.prod(input_shape)\n", "weight_size = output_dim * np.prod(input_shape)\n", "\n", "x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, input_shape)\n", "w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim)\n", "b = np.linspace(-0.3, 0.1, num=output_dim)\n", "\n", "out, _ = affine_forward(x, w, b)\n", "correct_out = np.array([[ 1.49834967, 1.70660132, 1.91485297],\n", " [ 3.25553199, 3.5141327, 3.77273342]])\n", "\n", "# Compare your output with ours. The error should be around 1e-9.\n", "print('Testing affine_forward function:')\n", "print('difference: ', rel_error(out, correct_out))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Affine layer: backward\n", "Now implement the `affine_backward` function and test your implementation using numeric gradient checking." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Testing affine_backward function:\n", "dx error: 1.09081995087e-10\n", "dw error: 2.17526355046e-10\n", "db error: 7.73697883449e-12\n" ] } ], "source": [ "# Test the affine_backward function\n", "np.random.seed(231)\n", "x = np.random.randn(10, 2, 3)\n", "w = np.random.randn(6, 5)\n", "b = np.random.randn(5)\n", "dout = np.random.randn(10, 5)\n", "\n", "dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)\n", "dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)\n", "db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)\n", "\n", "_, cache = affine_forward(x, w, b)\n", "dx, dw, db = affine_backward(dout, cache)\n", "\n", "# The error should be around 1e-10\n", "print('Testing affine_backward function:')\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": {}, "source": [ "# ReLU layer: forward\n", "Implement the forward pass for the ReLU activation function in the `relu_forward` function and test your implementation using the following:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Testing relu_forward function:\n", "difference: 4.99999979802e-08\n" ] } ], "source": [ "# Test the relu_forward function\n", "\n", "x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)\n", "\n", "out, _ = relu_forward(x)\n", "correct_out = np.array([[ 0., 0., 0., 0., ],\n", " [ 0., 0., 0.04545455, 0.13636364,],\n", " [ 0.22727273, 0.31818182, 0.40909091, 0.5, ]])\n", "\n", "# Compare your output with ours. The error should be around 5e-8\n", "print('Testing relu_forward function:')\n", "print('difference: ', rel_error(out, correct_out))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# ReLU layer: backward\n", "Now implement the backward pass for the ReLU activation function in the `relu_backward` function and test your implementation using numeric gradient checking:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Testing relu_backward function:\n", "dx error: 3.27563491363e-12\n" ] } ], "source": [ "np.random.seed(231)\n", "x = np.random.randn(10, 10)\n", "dout = np.random.randn(x.shape)\n", "\n", "dx_num = eval_numerical_gradient_array(lambda x: relu_forward(x)[0], x, dout)\n", "\n", "_, cache = relu_forward(x)\n", "dx = relu_backward(dout, cache)\n", "\n", "# The error should be around 3e-12\n", "print('Testing relu_backward function:')\n", "print('dx error: ', rel_error(dx_num, dx))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# \"Sandwich\" layers\n", "There are some common patterns of layers that are frequently used in neural nets. For example, affine layers are frequently followed by a ReLU nonlinearity. To make these common patterns easy, we define several convenience layers in the file `cs231n/layer_utils.py`.\n", "\n", "For now take a look at the `affine_relu_forward` and `affine_relu_backward` functions, and run the following to numerically gradient check the backward pass:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Testing affine_relu_forward:\n", "dx error: 6.39553504205e-11\n", "dw error: 8.16201110576e-11\n", "db error: 7.82672402146e-12\n" ] } ], "source": [ "from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n", "np.random.seed(231)\n", "x = np.random.randn(2, 3, 4)\n", "w = np.random.randn(12, 10)\n", "b = np.random.randn(10)\n", "dout = np.random.randn(2, 10)\n", "\n", "out, cache = affine_relu_forward(x, w, b)\n", "dx, dw, db = affine_relu_backward(dout, cache)\n", "\n", "dx_num = eval_numerical_gradient_array(lambda x: affine_relu_forward(x, w, b)[0], x, dout)\n", "dw_num = eval_numerical_gradient_array(lambda w: affine_relu_forward(x, w, b)[0], w, dout)\n", "db_num = eval_numerical_gradient_array(lambda b: affine_relu_forward(x, w, b)[0], b, dout)\n", "\n", "print('Testing affine_relu_forward:')\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": {}, "source": [ "# Loss layers: Softmax and SVM\n", "You implemented these loss functions in the last assignment, so we'll give them to you for free here. You should still make sure you understand how they work by looking at the implementations in `cs231n/layers.py`.\n", "\n", "You can make sure that the implementations are correct by running the following:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Testing svm_loss:\n", "loss: 8.9996027491\n", "dx error: 1.40215660067e-09\n", "\n", "Testing softmax_loss:\n", "loss: 2.3025458445\n", "dx error: 9.38467316199e-09\n" ] } ], "source": [ "np.random.seed(231)\n", "num_classes, num_inputs = 10, 50\n", "x = 0.001 * np.random.randn(num_inputs, num_classes)\n", "y = np.random.randint(num_classes, size=num_inputs)\n", "\n", "dx_num = eval_numerical_gradient(lambda x: svm_loss(x, y)[0], x, verbose=False)\n", "loss, dx = svm_loss(x, y)\n", "\n", "# Test svm_loss function. Loss should be around 9 and dx error should be 1e-9\n", "print('Testing svm_loss:')\n", "print('loss: ', loss)\n", "print('dx error: ', rel_error(dx_num, dx))\n", "\n", "dx_num = eval_numerical_gradient(lambda x: softmax_loss(x, y)[0], x, verbose=False)\n", "loss, dx = softmax_loss(x, y)\n", "\n", "# Test softmax_loss function. Loss should be 2.3 and dx error should be 1e-8\n", "print('\\nTesting softmax_loss:')\n", "print('loss: ', loss)\n", "print('dx error: ', rel_error(dx_num, dx))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Two-layer network\n", "In the previous assignment you implemented a two-layer neural network in a single monolithic class. Now that you have implemented modular versions of the necessary layers, you will reimplement the two layer network using these modular implementations.\n", "\n", "Open the file `cs231n/classifiers/fc_net.py` and complete the implementation of the `TwoLayerNet` class. This class will serve as a model for the other networks you will implement in this assignment, so read through it to make sure you understand the API. You can run the cell below to test your implementation." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Testing initialization ... \n", "Testing test-time forward pass ... \n", "Testing training loss (no regularization)\n", "Running numeric gradient check with reg = 0.0\n", "W1 relative error: 1.22e-08\n", "W2 relative error: 3.17e-10\n", "b1 relative error: 6.19e-09\n", "b2 relative error: 4.33e-10\n", "Running numeric gradient check with reg = 0.7\n", "W1 relative error: 2.53e-07\n", "W2 relative error: 1.37e-07\n", "b1 relative error: 1.56e-08\n", "b2 relative error: 9.09e-10\n" ] } ], "source": [ "np.random.seed(231)\n", "N, D, H, C = 3, 5, 50, 7\n", "X = np.random.randn(N, D)\n", "y = np.random.randint(C, size=N)\n", "\n", "std = 1e-3\n", "model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std)\n", "\n", "print('Testing initialization ... ')\n", "W1_std = abs(model.params['W1'].std() - std)\n", "b1 = model.params['b1']\n", "W2_std = abs(model.params['W2'].std() - std)\n", "b2 = model.params['b2']\n", "assert W1_std < std / 10, 'First layer weights do not seem right'\n", "assert np.all(b1 == 0), 'First layer biases do not seem right'\n", "assert W2_std < std / 10, 'Second layer weights do not seem right'\n", "assert np.all(b2 == 0), 'Second layer biases do not seem right'\n", "\n", "print('Testing test-time forward pass ... ')\n", "model.params['W1'] = np.linspace(-0.7, 0.3, num=DH).reshape(D, H)\n", "model.params['b1'] = np.linspace(-0.1, 0.9, num=H)\n", "model.params['W2'] = np.linspace(-0.3, 0.4, num=HC).reshape(H, C)\n", "model.params['b2'] = np.linspace(-0.9, 0.1, num=C)\n", "X = np.linspace(-5.5, 4.5, num=ND).reshape(D, N).T\n", "scores = model.loss(X)\n", "correct_scores = np.asarray(\n", " [[11.53165108, 12.2917344, 13.05181771, 13.81190102, 14.57198434, 15.33206765, 16.09215096],\n", " [12.05769098, 12.74614105, 13.43459113, 14.1230412, 14.81149128, 15.49994135, 16.18839143],\n", " [12.58373087, 13.20054771, 13.81736455, 14.43418138, 15.05099822, 15.66781506, 16.2846319 ]])\n", "scores_diff = np.abs(scores - correct_scores).sum()\n", "assert scores_diff < 1e-6, 'Problem with test-time forward pass'\n", "\n", "print('Testing training loss (no regularization)')\n", "y = np.asarray([0, 5, 1])\n", "loss, grads = model.loss(X, y)\n", "correct_loss = 3.4702243556\n", "assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss'\n", "\n", "model.reg = 1.0\n", "loss, grads = model.loss(X, y)\n", "correct_loss = 26.5948426952\n", "assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss'\n", "\n", "for reg in [0.0, 0.7]:\n", " print('Running numeric gradient check with reg = ', reg)\n", " model.reg = reg\n", " loss, grads = model.loss(X, y)\n", "\n", " for name in sorted(grads):\n", " f = lambda _: model.loss(X, y)[0]\n", " grad_num = eval_numerical_gradient(f, model.params[name], verbose=False)\n", " print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Solver\n", "In the previous assignment, the logic for training models was coupled to the models themselves. Following a more modular design, for this assignment we have split the logic for training models into a separate class.\n", "\n", "Open the file `cs231n/solver.py` and read through it to familiarize yourself with the API. After doing so, use a `Solver` instance to train a `TwoLayerNet` that achieves at least `50%` accuracy on the validation set." ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(Iteration 1 / 1470) loss: 2.310443\n", "(Epoch 0 / 15) train acc: 0.135300; val_acc: 0.127000\n", "(Epoch 1 / 15) train acc: 0.383000; val_acc: 0.372000\n", "(Epoch 2 / 15) train acc: 0.428350; val_acc: 0.432000\n", "(Epoch 3 / 15) train acc: 0.461100; val_acc: 0.464000\n", "(Iteration 301 / 1470) loss: 1.491986\n", "(Epoch 4 / 15) train acc: 0.486350; val_acc: 0.473000\n", "(Epoch 5 / 15) train acc: 0.504450; val_acc: 0.477000\n", "(Epoch 6 / 15) train acc: 0.513950; val_acc: 0.498000\n", "(Iteration 601 / 1470) loss: 1.431557\n", "(Epoch 7 / 15) train acc: 0.525300; val_acc: 0.517000\n", "(Epoch 8 / 15) train acc: 0.531550; val_acc: 0.502000\n", "(Epoch 9 / 15) train acc: 0.543600; val_acc: 0.520000\n", "(Iteration 901 / 1470) loss: 1.318077\n", "(Epoch 10 / 15) train acc: 0.558450; val_acc: 0.521000\n", "(Epoch 11 / 15) train acc: 0.557400; val_acc: 0.518000\n", "(Epoch 12 / 15) train acc: 0.562650; val_acc: 0.531000\n", "(Iteration 1201 / 1470) loss: 1.295301\n", "(Epoch 13 / 15) train acc: 0.574500; val_acc: 0.512000\n", "(Epoch 14 / 15) train acc: 0.575700; val_acc: 0.528000\n", "(Epoch 15 / 15) train acc: 0.593050; val_acc: 0.513000\n" ] } ], "source": [ "model = TwoLayerNet(reg=1e-2, hidden_dim=200)\n", "optim_config = {\n", " 'learning_rate': 1e-3\n", "}\n", "solver = Solver(model, data, \n", " num_train_samples=20000,\n", " num_epochs=15, \n", " batch_size=500, \n", " num_val_samples=1000,\n", " optim_config=optim_config,\n", " print_every=30000,\n", " lr_decay=0.95)\n", "solver.train()\n", "##############################################################################\n", "# TODO: Use a Solver instance to train a TwoLayerNet that achieves at least #\n", "# 50% accuracy on the validation set. #\n", "##############################################################################\n", "pass\n", "##############################################################################\n", "# END OF YOUR CODE #\n", "##############################################################################" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'solver' 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-10-1de3c2c05c0d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Training loss'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msolver\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloss_history\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'o'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 6\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Iteration'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'solver' is not defined" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fcc0088a128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Run this cell to visualize training loss and train / val accuracy\n", "\n", "plt.subplot(2, 1, 1)\n", "plt.title('Training loss')\n", "plt.plot(solver.loss_history, 'o')\n", "plt.xlabel('Iteration')\n", "\n", "plt.subplot(2, 1, 2)\n", "plt.title('Accuracy')\n", "plt.plot(solver.train_acc_history, '-o', label='train')\n", "plt.plot(solver.val_acc_history, '-o', label='val')\n", "plt.plot([0.5] len(solver.val_acc_history), 'k--')\n", "plt.xlabel('Epoch')\n", "plt.legend(loc='lower right')\n", "plt.gcf().set_size_inches(15, 12)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Multilayer network\n", "Next you will implement a fully-connected network with an arbitrary number of hidden layers.\n", "\n", "Read through the `FullyConnectedNet` class in the file `cs231n/classifiers/fc_net.py`.\n", "\n", "Implement the initialization, the forward pass, and the backward pass. For the moment don't worry about implementing dropout or batch normalization; we will add those features soon." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initial loss and gradient check" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. Do the initial losses seem reasonable?\n", "\n", "For gradient checking, you should expect to see errors around 1e-6 or less." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running check with reg = 0\n", "Initial loss: 2.30047908977\n", "W1 relative error: 1.48e-07\n", "W2 relative error: 2.21e-05\n", "W3 relative error: 3.53e-07\n", "b1 relative error: 5.38e-09\n", "b2 relative error: 2.09e-09\n", "b3 relative error: 5.80e-11\n", "Running check with reg = 3.14\n", "Initial loss: 7.05211477653\n", "W1 relative error: 3.90e-09\n", "W2 relative error: 6.87e-08\n", "W3 relative error: 2.13e-08\n", "b1 relative error: 1.48e-08\n", "b2 relative error: 1.72e-09\n", "b3 relative error: 1.57e-10\n" ] } ], "source": [ "np.random.seed(231)\n", "N, D, H1, H2, C = 2, 15, 20, 30, 10\n", "X = np.random.randn(N, D)\n", "y = np.random.randint(C, size=(N,))\n", "\n", "for reg in [0, 3.14]:\n", " print('Running check with reg = ', reg)\n", " model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n", " reg=reg, weight_scale=5e-2, dtype=np.float64)\n", "\n", " loss, grads = model.loss(X, y)\n", " print('Initial loss: ', loss)\n", "\n", " for name in sorted(grads):\n", " f = lambda _: model.loss(X, y)[0]\n", " grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n", " print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As another sanity check, make sure you can overfit a small dataset of 50 images. First we will try a three-layer network with 100 units in each hidden layer. You will need to tweak the learning rate and initialization scale, but you should be able to overfit and achieve 100% training accuracy within 20 epochs." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(Iteration 1 / 40) loss: 270.150074\n", "(Epoch 0 / 20) train acc: 0.320000; val_acc: 0.149000\n", "(Epoch 1 / 20) train acc: 0.360000; val_acc: 0.102000\n", "(Epoch 2 / 20) train acc: 0.420000; val_acc: 0.139000\n", "(Epoch 3 / 20) train acc: 0.700000; val_acc: 0.122000\n", "(Epoch 4 / 20) train acc: 0.760000; val_acc: 0.143000\n", "(Epoch 5 / 20) train acc: 0.740000; val_acc: 0.162000\n", "(Epoch 6 / 20) train acc: 0.960000; val_acc: 0.153000\n", "(Epoch 7 / 20) train acc: 0.960000; val_acc: 0.160000\n", "(Epoch 8 / 20) train acc: 0.960000; val_acc: 0.160000\n", "(Epoch 9 / 20) train acc: 0.940000; val_acc: 0.154000\n", "(Epoch 10 / 20) train acc: 0.980000; val_acc: 0.156000\n", "(Epoch 11 / 20) train acc: 0.960000; val_acc: 0.169000\n", "(Epoch 12 / 20) train acc: 0.960000; val_acc: 0.169000\n", "(Epoch 13 / 20) train acc: 0.980000; val_acc: 0.165000\n", "(Epoch 14 / 20) train acc: 0.980000; val_acc: 0.155000\n", "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.157000\n", "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.157000\n", "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.157000\n", "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.157000\n", "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.157000\n", "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.157000\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fcbfe5cdba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# TODO: Use a three-layer Net to overfit 50 training examples.\n", "\n", "num_train = 50\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", "weight_scale = 1e-1\n", "learning_rate = 1e-3\n", "model = FullyConnectedNet([100, 100],\n", " weight_scale=weight_scale, dtype=np.float64)\n", "solver = Solver(model, small_data,\n", " print_every=1000, num_epochs=20, batch_size=25,\n", " update_rule='sgd',\n", " optim_config={\n", " 'learning_rate': learning_rate,\n", " }\n", " )\n", "solver.train()\n", "\n", "plt.plot(solver.loss_history, 'o')\n", "plt.title('Training loss history')\n", "plt.xlabel('Iteration')\n", "plt.ylabel('Training loss')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now try to use a five-layer network with 100 units on each layer to overfit 50 training examples. Again you will have to adjust the learning rate and weight initialization, but you should be able to achieve 100% training accuracy within 20 epochs." ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(Iteration 1 / 40) loss: 150.367686\n", "(Epoch 0 / 20) train acc: 0.240000; val_acc: 0.125000\n", "(Epoch 1 / 20) train acc: 0.180000; val_acc: 0.116000\n", "(Epoch 2 / 20) train acc: 0.360000; val_acc: 0.131000\n", "(Epoch 3 / 20) train acc: 0.540000; val_acc: 0.127000\n", "(Epoch 4 / 20) train acc: 0.680000; val_acc: 0.115000\n", "(Epoch 5 / 20) train acc: 0.780000; val_acc: 0.121000\n", "(Epoch 6 / 20) train acc: 0.900000; val_acc: 0.123000\n", "(Epoch 7 / 20) train acc: 0.900000; val_acc: 0.123000\n", "(Epoch 8 / 20) train acc: 0.960000; val_acc: 0.123000\n", "(Epoch 9 / 20) train acc: 1.000000; val_acc: 0.123000\n", "(Epoch 10 / 20) train acc: 1.000000; val_acc: 0.126000\n", "(Epoch 11 / 20) train acc: 1.000000; val_acc: 0.126000\n", "(Epoch 12 / 20) train acc: 1.000000; val_acc: 0.125000\n", "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.125000\n", "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.125000\n", "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.125000\n", "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.125000\n", "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.125000\n", "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.124000\n", "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.124000\n", "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.125000\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fcbfe5afef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# TODO: Use a five-layer Net to overfit 50 training examples.\n", "\n", "num_train = 50\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", "learning_rate = 1e-3\n", "weight_scale = 1e-1\n", "model = FullyConnectedNet([100, 100, 100, 100],\n", " weight_scale=weight_scale, dtype=np.float64)\n", "solver = Solver(model, small_data,\n", " print_every=10000, num_epochs=20, batch_size=25,\n", " update_rule='sgd',\n", " optim_config={\n", " 'learning_rate': learning_rate,\n", " }\n", " )\n", "solver.train()\n", "\n", "plt.plot(solver.loss_history, 'o')\n", "plt.title('Training loss history')\n", "plt.xlabel('Iteration')\n", "plt.ylabel('Training loss')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Inline question: \n", "Did you notice anything about the comparative difficulty of training the three-layer net vs training the five layer net?\n", "\n", "# Answer:\n", "5 layer net is far more sensitive....\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Update rules\n", "So far we have used vanilla stochastic gradient descent (SGD) as our update rule. More sophisticated update rules can make it easier to train deep networks. We will implement a few of the most commonly used update rules and compare them to vanilla SGD." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# SGD+Momentum\n", "Stochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochstic gradient descent.\n", "\n", "Open the file `cs231n/optim.py` and read the documentation at the top of the file to make sure you understand the API. Implement the SGD+momentum update rule in the function `sgd_momentum` and run the following to check your implementation. You should see errors less than 1e-8." ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "next_w error: 8.88234703351e-09\n", "velocity error: 4.26928774328e-09\n" ] } ], "source": [ "from cs231n.optim import sgd_momentum\n", "\n", "N, D = 4, 5\n", "w = np.linspace(-0.4, 0.6, num=ND).reshape(N, D)\n", "dw = np.linspace(-0.6, 0.4, num=ND).reshape(N, D)\n", "v = np.linspace(0.6, 0.9, num=ND).reshape(N, D)\n", "\n", "config = {'learning_rate': 1e-3, 'velocity': v}\n", "next_w, _ = sgd_momentum(w, dw, config=config)\n", "\n", "expected_next_w = np.asarray([\n", " [ 0.1406, 0.20738947, 0.27417895, 0.34096842, 0.40775789],\n", " [ 0.47454737, 0.54133684, 0.60812632, 0.67491579, 0.74170526],\n", " [ 0.80849474, 0.87528421, 0.94207368, 1.00886316, 1.07565263],\n", " [ 1.14244211, 1.20923158, 1.27602105, 1.34281053, 1.4096 ]])\n", "expected_velocity = np.asarray([\n", " [ 0.5406, 0.55475789, 0.56891579, 0.58307368, 0.59723158],\n", " [ 0.61138947, 0.62554737, 0.63970526, 0.65386316, 0.66802105],\n", " [ 0.68217895, 0.69633684, 0.71049474, 0.72465263, 0.73881053],\n", " [ 0.75296842, 0.76712632, 0.78128421, 0.79544211, 0.8096 ]])\n", "\n", "print('next_w error: ', rel_error(next_w, expected_next_w))\n", "print('velocity error: ', rel_error(expected_velocity, config['velocity']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once you have done so, run the following to train a six-layer network with both SGD and SGD+momentum. You should see the SGD+momentum update rule converge faster." ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "running with sgd\n", "(Iteration 1 / 200) loss: 2.793674\n", "(Epoch 0 / 5) train acc: 0.116000; val_acc: 0.111000\n", "(Iteration 11 / 200) loss: 2.169697\n", "(Iteration 21 / 200) loss: 2.141660\n", "(Iteration 31 / 200) loss: 2.133201\n", "(Epoch 1 / 5) train acc: 0.263000; val_acc: 0.206000\n", "(Iteration 41 / 200) loss: 2.244651\n", "(Iteration 51 / 200) loss: 2.055617\n", "(Iteration 61 / 200) loss: 1.957956\n", "(Iteration 71 / 200) loss: 1.853537\n", "(Epoch 2 / 5) train acc: 0.324000; val_acc: 0.244000\n", "(Iteration 81 / 200) loss: 1.891388\n", "(Iteration 91 / 200) loss: 1.727903\n", "(Iteration 101 / 200) loss: 1.769105\n", "(Iteration 111 / 200) loss: 1.740365\n", "(Epoch 3 / 5) train acc: 0.333000; val_acc: 0.300000\n", "(Iteration 121 / 200) loss: 1.676502\n", "(Iteration 131 / 200) loss: 1.845456\n", "(Iteration 141 / 200) loss: 1.767788\n", "(Iteration 151 / 200) loss: 1.826301\n", "(Epoch 4 / 5) train acc: 0.400000; val_acc: 0.327000\n", "(Iteration 161 / 200) loss: 1.604524\n", "(Iteration 171 / 200) loss: 1.696647\n", "(Iteration 181 / 200) loss: 1.484001\n", "(Iteration 191 / 200) loss: 1.650075\n", "(Epoch 5 / 5) train acc: 0.426000; val_acc: 0.345000\n", "\n", "running with sgd_momentum\n", "(Iteration 1 / 200) loss: 2.870557\n", "(Epoch 0 / 5) train acc: 0.127000; val_acc: 0.106000\n", "(Iteration 11 / 200) loss: 2.075757\n", "(Iteration 21 / 200) loss: 1.989978\n", "(Iteration 31 / 200) loss: 1.804864\n", "(Epoch 1 / 5) train acc: 0.304000; val_acc: 0.264000\n", "(Iteration 41 / 200) loss: 1.872969\n", "(Iteration 51 / 200) loss: 1.855292\n", "(Iteration 61 / 200) loss: 1.734940\n", "(Iteration 71 / 200) loss: 1.752338\n", "(Epoch 2 / 5) train acc: 0.387000; val_acc: 0.315000\n", "(Iteration 81 / 200) loss: 1.722690\n", "(Iteration 91 / 200) loss: 1.609142\n", "(Iteration 101 / 200) loss: 1.688580\n", "(Iteration 111 / 200) loss: 1.551952\n", "(Epoch 3 / 5) train acc: 0.426000; val_acc: 0.285000\n", "(Iteration 121 / 200) loss: 1.781280\n", "(Iteration 131 / 200) loss: 1.645617\n", "(Iteration 141 / 200) loss: 1.438367\n", "(Iteration 151 / 200) loss: 1.457875\n", "(Epoch 4 / 5) train acc: 0.487000; val_acc: 0.331000\n", "(Iteration 161 / 200) loss: 1.579531\n", "(Iteration 171 / 200) loss: 1.442287\n", "(Iteration 181 / 200) loss: 1.561984\n", "(Iteration 191 / 200) loss: 1.434399\n", "(Epoch 5 / 5) train acc: 0.525000; val_acc: 0.348000\n", "\n" ] }, { "data": { "image/png": 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2Ekzmn5OK3HRLh32FxHV9ZFQBhO56x3LEUUwiIiJKOAZ0pUJ3ZKxYPgNH83TD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EROUV1LF998chePb6+hOCuJ//ux1Vy0KDvSZU283i44/GrInm/l0HDAP2iQswQWXzXQOpl7plon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\nQc/2uTwyZRzd2ubWzQuXlUD+f6cag++GvOvhvFttDC5JkiR9CAOdDnpq3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"text/plain": [ "<matplotlib.figure.Figure at 0x7fcbfe5254a8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "num_train = 4000\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", "solvers = {}\n", "\n", "for update_rule in ['sgd', 'sgd_momentum']:\n", " print('running with ', update_rule)\n", " model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n", "\n", " solver = Solver(model, small_data,\n", " num_epochs=5, batch_size=100,\n", " update_rule=update_rule,\n", " optim_config={\n", " 'learning_rate': 1e-2,\n", " },\n", " verbose=True)\n", " solvers[update_rule] = solver\n", " solver.train()\n", " print()\n", "\n", "plt.subplot(3, 1, 1)\n", "plt.title('Training loss')\n", "plt.xlabel('Iteration')\n", "\n", "plt.subplot(3, 1, 2)\n", "plt.title('Training accuracy')\n", "plt.xlabel('Epoch')\n", "\n", "plt.subplot(3, 1, 3)\n", "plt.title('Validation accuracy')\n", "plt.xlabel('Epoch')\n", "\n", "for update_rule, solver in list(solvers.items()):\n", " plt.subplot(3, 1, 1)\n", " plt.plot(solver.loss_history, 'o', label=update_rule)\n", " \n", " plt.subplot(3, 1, 2)\n", " plt.plot(solver.train_acc_history, '-o', label=update_rule)\n", "\n", " plt.subplot(3, 1, 3)\n", " plt.plot(solver.val_acc_history, '-o', label=update_rule)\n", " \n", "for i in [1, 2, 3]:\n", " plt.subplot(3, 1, i)\n", " plt.legend(loc='upper center', ncol=4)\n", "plt.gcf().set_size_inches(15, 15)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# RMSProp and Adam\n", "RMSProp [1] and Adam [2] are update rules that set per-parameter learning rates by using a running average of the second moments of gradients.\n", "\n", "In the file `cs231n/optim.py`, implement the RMSProp update rule in the `rmsprop` function and implement the Adam update rule in the `adam` function, and check your implementations using the tests below.\n", "\n", "[1] Tijmen Tieleman and Geoffrey Hinton. \"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.\" COURSERA: Neural Networks for Machine Learning 4 (2012).\n", "\n", "[2] Diederik Kingma and Jimmy Ba, \"Adam: A Method for Stochastic Optimization\", ICLR 2015." ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "next_w error: 9.52468751104e-08\n", "cache error: 2.64779558072e-09\n" ] } ], "source": [ "# Test RMSProp implementation; you should see errors less than 1e-7\n", "from cs231n.optim import rmsprop\n", "\n", "N, D = 4, 5\n", "w = np.linspace(-0.4, 0.6, num=ND).reshape(N, D)\n", "dw = np.linspace(-0.6, 0.4, num=ND).reshape(N, D)\n", "cache = np.linspace(0.6, 0.9, num=ND).reshape(N, D)\n", "\n", "config = {'learning_rate': 1e-2, 'cache': cache}\n", "next_w, _ = rmsprop(w, dw, config=config)\n", "\n", "expected_next_w = np.asarray([\n", " [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],\n", " [-0.132737, -0.08078555, -0.02881884, 0.02316247, 0.07515774],\n", " [ 0.12716641, 0.17918792, 0.23122175, 0.28326742, 0.33532447],\n", " [ 0.38739248, 0.43947102, 0.49155973, 0.54365823, 0.59576619]])\n", "expected_cache = np.asarray([\n", " [ 0.5976, 0.6126277, 0.6277108, 0.64284931, 0.65804321],\n", " [ 0.67329252, 0.68859723, 0.70395734, 0.71937285, 0.73484377],\n", " [ 0.75037008, 0.7659518, 0.78158892, 0.79728144, 0.81302936],\n", " [ 0.82883269, 0.84469141, 0.86060554, 0.87657507, 0.8926 ]])\n", "\n", "print('next_w error: ', rel_error(expected_next_w, next_w))\n", "print('cache error: ', rel_error(expected_cache, config['cache']))" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "next_w error: 0.00152184517579\n", "v error: 4.20831403811e-09\n", "m error: 4.21496319311e-09\n" ] } ], "source": [ "# Test Adam implementation; you should see errors around 1e-7 or less\n", "from cs231n.optim import adam\n", "\n", "N, D = 4, 5\n", "w = np.linspace(-0.4, 0.6, num=ND).reshape(N, D)\n", "dw = np.linspace(-0.6, 0.4, num=ND).reshape(N, D)\n", "m = np.linspace(0.6, 0.9, num=ND).reshape(N, D)\n", "v = np.linspace(0.7, 0.5, num=ND).reshape(N, D)\n", "\n", "config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}\n", "next_w, _ = adam(w, dw, config=config)\n", "\n", "expected_next_w = np.asarray([\n", " [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],\n", " [-0.1380274, -0.08544591, -0.03286534, 0.01971428, 0.0722929],\n", " [ 0.1248705, 0.17744702, 0.23002243, 0.28259667, 0.33516969],\n", " [ 0.38774145, 0.44031188, 0.49288093, 0.54544852, 0.59801459]])\n", "expected_v = np.asarray([\n", " [ 0.69966, 0.68908382, 0.67851319, 0.66794809, 0.65738853,],\n", " [ 0.64683452, 0.63628604, 0.6257431, 0.61520571, 0.60467385,],\n", " [ 0.59414753, 0.58362676, 0.57311152, 0.56260183, 0.55209767,],\n", " [ 0.54159906, 0.53110598, 0.52061845, 0.51013645, 0.49966, ]])\n", "expected_m = np.asarray([\n", " [ 0.48, 0.49947368, 0.51894737, 0.53842105, 0.55789474],\n", " [ 0.57736842, 0.59684211, 0.61631579, 0.63578947, 0.65526316],\n", " [ 0.67473684, 0.69421053, 0.71368421, 0.73315789, 0.75263158],\n", " [ 0.77210526, 0.79157895, 0.81105263, 0.83052632, 0.85 ]])\n", "\n", "print('next_w error: ', rel_error(expected_next_w, next_w))\n", "print('v error: ', rel_error(expected_v, config['v']))\n", "print('m error: ', rel_error(expected_m, config['m']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once you have debugged your RMSProp and Adam implementations, run the following to train a pair of deep networks using these new update rules:" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "running with adam\n", "(Iteration 1 / 200) loss: 3.113808\n", "(Epoch 0 / 5) train acc: 0.113000; val_acc: 0.104000\n", "(Iteration 11 / 200) loss: 2.205393\n", "(Iteration 21 / 200) loss: 1.903206\n", "(Iteration 31 / 200) loss: 1.694077\n", "(Epoch 1 / 5) train acc: 0.349000; val_acc: 0.297000\n", "(Iteration 41 / 200) loss: 1.685195\n", "(Iteration 51 / 200) loss: 1.710109\n", "(Iteration 61 / 200) loss: 1.611100\n", "(Iteration 71 / 200) loss: 1.589632\n", "(Epoch 2 / 5) train acc: 0.426000; val_acc: 0.326000\n", "(Iteration 81 / 200) loss: 1.592429\n", "(Iteration 91 / 200) loss: 1.462363\n", "(Iteration 101 / 200) loss: 1.675086\n", "(Iteration 111 / 200) loss: 1.620851\n", "(Epoch 3 / 5) train acc: 0.496000; val_acc: 0.378000\n", "(Iteration 121 / 200) loss: 1.322093\n", "(Iteration 131 / 200) loss: 1.473820\n", "(Iteration 141 / 200) loss: 1.273066\n", "(Iteration 151 / 200) loss: 1.578286\n", "(Epoch 4 / 5) train acc: 0.548000; val_acc: 0.376000\n", "(Iteration 161 / 200) loss: 1.269984\n", "(Iteration 171 / 200) loss: 1.101511\n", "(Iteration 181 / 200) loss: 1.283082\n", "(Iteration 191 / 200) loss: 1.095372\n", "(Epoch 5 / 5) train acc: 0.617000; val_acc: 0.378000\n", "\n", "running with rmsprop\n", "(Iteration 1 / 200) loss: 2.767889\n", "(Epoch 0 / 5) train acc: 0.118000; val_acc: 0.120000\n", "(Iteration 11 / 200) loss: 2.055002\n", "(Iteration 21 / 200) loss: 1.745417\n", "(Iteration 31 / 200) loss: 1.849283\n", "(Epoch 1 / 5) train acc: 0.402000; val_acc: 0.307000\n", "(Iteration 41 / 200) loss: 1.722857\n", "(Iteration 51 / 200) loss: 1.791896\n", "(Iteration 61 / 200) loss: 1.728939\n", "(Iteration 71 / 200) loss: 1.845318\n", "(Epoch 2 / 5) train acc: 0.433000; val_acc: 0.336000\n", "(Iteration 81 / 200) loss: 1.684801\n", "(Iteration 91 / 200) loss: 1.563837\n", "(Iteration 101 / 200) loss: 1.408093\n", "(Iteration 111 / 200) loss: 1.553074\n", "(Epoch 3 / 5) train acc: 0.488000; val_acc: 0.358000\n", "(Iteration 121 / 200) loss: 1.476579\n", "(Iteration 131 / 200) loss: 1.539102\n", "(Iteration 141 / 200) loss: 1.572919\n", "(Iteration 151 / 200) loss: 1.452254\n", "(Epoch 4 / 5) train acc: 0.522000; val_acc: 0.352000\n", "(Iteration 161 / 200) loss: 1.464924\n", "(Iteration 171 / 200) loss: 1.433092\n", "(Iteration 181 / 200) loss: 1.531765\n", "(Iteration 191 / 200) loss: 1.615817\n", "(Epoch 5 / 5) train acc: 0.544000; val_acc: 0.364000\n", "\n" ] }, { "data": { "image/png": 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Se6yNhvs7snspWip0lSpi9iwFzCIiIiKSDQV0WahtbMloe6SqjVW0BqIv5FsD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\nc/aTY6A0Zz9PtP4vn3n/Imq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T2HN2CuYizzP22JGf/4Y32uI/o4EcGt5I/BymqzszC1kX\nmcpCOln33izQkkhfF44Z6PoyC5xMfx2X9A9aQyfdKtV6scg1d0POWILDkjjHNXHpiF3PF9nDLqdw\nLG99+EZufns8tY0tjCzaRuCk5bTZo47jTCXq28+8NkaXHcY7Lvqird0aPnz0F8kf67e3pl5rlqQB\nOhAMnBY8Ggx+Y9auVRfkUzW8kHq3i6IhJQkL1ADM+6/J1LniX5Biv5/V79UFs27j58HO1QkbumNc\nYNvBW4rv0CQaVtXgP2JJuEbOGHZVPxj3nrlwu4uvvp6HZ5+v25qWhx8yds1BBuuGEq7fCXG7cQ0Z\nQsAXP+5M1qYlykJEjjvdb+BTrftxWpcRex6Ra/0Snr8xibNooexqbLYKwu9f3z/yqLv9duyxzgDC\nDPJQfM89aZ1zus9vaDyZvKccnyMnHeu4Yv99GFLcypG6XPzNLoynHQI52PbOz0SidTBdXkuZZsGg\nSNvPmJjwtok7tqe8f9Jj9+G6t+6WVka0j9fUaa1fdvrrWrX+Oi7pOZmsoVNAJ92uelc1VRurqG+q\nj6tyGVtwxSmgKy4oZvWVq9N6rMgeeDnGEOh4P1+as5Zlnp9EtT1osYO4te2rrGyfTcGHl5EzKH56\naCaPHbJn6Ycpzdkfv719JLOPPQpA/vC/cNLYlznUti/6OaksJHpCa4gJruWLlarYSJIL5lTFDcqe\nmOxcdMZaanYnCOASCE6xLExrGuUN33A5ViTtymsByS++4y7kM3y+MgkaANoHDab0nu8lLHQSK/KP\nc7ZTptItDJPpxV+y/VMGoknevzt/PQT/fl/8cUd6Gb/2jZTnmzLYjhhnVy5q072Qd3oOgp/xzKcU\np3ptujNQ2jl7ZlbPf9JjZ/ge6+7pgj01/bC/Bqr9dVwDSX+dstpfx5XKQB13X8skoNMaOul2Ti0S\nQuadu5df/WNF3FTLkMg1cU4iAzhvnoemY37aAsE/XIGIP2C3uJdHBXMAeeYYt7iXs/LY7JRr/TLx\nk0HXckvbY1GP12wH8YC/MyBoPngWw+1MapbE9IpItEbO5ASDvdiMUcIiKe917n/W5zqyaB0ZPYAV\nXwtmoZJkn4raoc6hfV+iYjTJBKdYJr+ADU0Fq//gdsfbu/JaQIbrkBymw9HWAr/59+BzFvP8O1+s\nJ5Zz7Cj/uP8hyiKal0eOKzLzZbxecoDaW26l4eFHGL3o5qiL3XCBjzT+IMYFjxGfjdigNtN1GcnW\nMKV87n178O3Oo6FmKP5mF+78QEd2ew/+/c7ThpyCDCfprP3LZq1VZAP6RN+Uj150c4I1nKkL/zg9\n311dS9mVKVijb73dMUM6+lbnz2dGx85g3Vt3twboyVYD3fn8d6f+Oq6BJPLz3p/013Elo3YfvUNr\n6KRXrfvgqYTBXHFBMZd95DKqNlY5rmuLbWLe2NIWDuYiuYyhxMRnzABKzAEg2CTdScI+dUlMLf8a\nS+3X2NM+knZr2NM+kiUdmcCQS3PW8mzzv9F+h5f6yo/w1sofBW9wWCNnAWyA0Nq0lb/7v8x7enbw\nOTm5NLqReuw9fe/BX54OHveKx8HfAi0f4LjOLcYlQ+fFN1Nvb6fioHPwm4y/2bmxOxC37iXpGsMu\nSrZWKEqiADni+Y98zqLW76TJvb8h4biK77iD8WtepuSB+6G1lUBjo+Oal0zXxCQqDBPKiEQ+H5mu\ny0i1hinZc+9rKKHuLS/+ZjdggoU33vLiayjBne93HkeC7bEc1ze53bgKC9OujppuFcZkz0FXCxQ4\nPd9dXUvZlaDVu2ABxffcE31OEdNds5HJurfuruTXk5UBu/P5707ZjkvN06U7qTpn71CGTnpVoqyL\nwVBxdkXUWqrYqpdORU+ctFtLTuFYx8xXrR0BwNF98x375SXLDkaKnVY6a84XuPrNH8dN/YT46Z9F\n7MO74Tu8BZxz6fXBnTrWcLUbQ47tDKqqC/L53klDaW0LZinqXIbKkcFzKG9qdh5cW0vweKGfnW5z\nyNI9+/dLONXu48CojexzG4r8ASoONiZ+nA7WQoAc3KZz3O78QMdFezSnKVaxrztk9lpAdOa2pDCP\nR87cyTl//0/HdXFRUz8Kihk9+WDc+scoMc9Z6BtS36pVcRkNJw15hZQvqaakMI/F8yc4VlxNVR0y\n0+qRmWTdulI1sKvfEjfUDMMGojNuNpBDQ80wRs88RN0fA1GFOYyrndEz0yvQkk2T87S+QY5Za+md\nuxSvw3TBjKqEdjAuy+hPz4zbnuq1yficU6wX7clv/9M9dlfbUyR6DnqyMmDs8++UZe+LDERPfxZi\nn+9E/TRlYOmpaZGqztk7tIZOetW85+YlXC8FJF1LFVv0JJExhXms+9T+uLVRx6yLI+RRyBFq7Uj+\nb/4sto3ajXUdJCcwnE+f+m/ccdEXwvu/tfJHjN34IKPtPhrMKN47ezHnXHp9ysIvse0T1g66yXGN\nXTfdKbYAACAASURBVD2jKKr8W9S29spCciLOcl5pSVTrgfBzErCs/ucenNffQecUrxTr8yIu8Pa0\nj+AB/1XhzKLTOsRYzXYQS9q+ChC1r293HnvWF5Lj75xq1j7YQ+ndzt/4J1t3mUrs831pzlru9/yE\nPBNfOMb3j7z4C2SXpficxuRBndOaxo6+XQ2bOotd2EAORBS7aHV5qJp6Ja+MnQ4EW344tdFItv5r\n4jV1bH8mwVSpBGti+nrNUiJJ1/Y8Wxn1fLrzAwwZ4+fIwQ/hP3AoOK5Pz8R77Lfd3vg65fOVYq1l\npu0SMO24PJbAsZzOaadnjewshBSh216bLNbXdos0iw915b3blcIx3V0g5HgpWNGlIkoxBuJ5n+h6\n8v2rIj1dp6Io0m8lC4Zue+02rEMAEqp6OWvZGvY2Rl90u4dtChdYsW2F2A8+yb3zrgteMMdUhwy0\nHsYV0cMuFIyEApjIi+23Vv6IyRu+ExUUtNhBbJ1+N7cf+3XKIh6RGaO/D/4cOQ5LaNqtIefO6CAh\ntsBK2bixzoVKOp6T5vvPIL8lfizNecXkD3I7ZinrGcVHW6u4bsibfMf+EHfEaxH7nFyas5ZbPcsp\n5gANZiRNp8zlw43raG/cQ62NDwBvcS+nNOcA1aNKeekfOVz5ShsjDsGBYfDcRYOZ/7V70g7U0hX7\nvkgUQOMdy85VH3L+wzIExl9SF1y7aB2ywJFVR0McCnz4dudRV1NIoNmwL6+QJ878ZDiYC3EZQ7u1\nURm7hH/w8v2Mv7SBnStHO2Y8P/C6+Po3cuKC4P56cZlW4NTxmfU1lFC31h29pis2+O6mgCRlEYkk\nxVx8H7kvoyqhiariJiyElKXI4jjufH/8Yzu9t7ubQzDpe28YDe+M6QzWOwLVTN+7XQlAeuKzcLxc\ntKb6LGRSrXcgnfeJrjsr5sbqr3+PBgIVRZF+K3TB6ZSNqdpY5RgohdZSxTYxdw/bFDVt0gxqxFO8\nAo/3LGBM8CIvdKH38GRcLR9EHTc/okgKdPasWzhtDGM3Phid4SFYVGXsxgepP9V5DVvkdNKF08aE\nszD1laMoYl/c/g1mJLGrxGILrMQ2B499Th5ou5pbrENBlrarqfzkpLiLqBY7iHvbPoMFvnrs57hz\nor9ljX1OVrbPZuXRzrWAebtc3HfF0nDT+Ugr22dT3fYx2q1laO792EkH+cOkyLEH2LGxKvweyCYr\nF6k2ZhyJ1k/i24O/zrkXnL+p42I6URZj7lLH48Xyjmth6LhWPtz6i4S509B03L2NLdy2YgsAFzpN\nrYvobTi67HBEY/agox546oJ2LCZuenI2U656UsrpnRGf2YaL5mKPRV9g2IChoWZoZ0CSZApxJlIW\nkUhYjGiP83pFvx+Tn8/EN/4Uf5+HJ4OvIX67tzTDUacWN32uY80iBN+rwQI1R/E/3g1NsZNl4GKK\nDwWr4OaHp986TetL972bajpXtp+FdDOkx8u0slSfhXTPZ6Cd94ku4evVUc03m0Im/fXv0fFGRVGk\n15WfVs7qK1dTc10Nq69cHb6Irzi7glxX9ELuyLVUsU3M8z+0Oq7ASps9Gt2YPCTBBVmoSEpIKDgY\nbeMDsOD2/WkV8YhsWn7Nh0fzm/xhUfu22EG8d/biuGPEFli59oMAOe3RBUYin5Mnj5zLkravxhVk\nefLIucGLqQWPBr+Bx1DPqHDbhuC576e6IJ95pSWUjRvLvNISqgvyKck5gCGYSYoVCnoXz59Anie+\n8EnABnOs7a6Djs9R3ZE6Tl1SzTmPPMDS175DXVMdFhsMSNZ+N+3m7pFKCqOLytTakc47ektTFwCJ\nec7wjk2cAUpwAZ7jLeXdZeWMKczj0py1rB10E7sGf461g27i0py1UfuGns+4ohH5forP8YUDF++4\nForP8QULhBjDB14XP/ykYd2kztegNdAa9d5PWhgmSXPvnpRJcYyEF8ixBXcSBVsZSFRQxTY3BwtD\n/E8xvt158Xf0lmZ+Ie9QCMn33jB2/npItxehcFx7GcihoWZoR1DVUaAm2+bToS9CfO/hWIAp5jUK\nVsGNaWIeUSQh7aJGJKncmJMTfj6BxIV6khQAyaQY0fHS9DnVZ4Gc9C4bQ+fdmwVWjpdiLn1xHum8\nT7MpZJLJZ1q6RgGd9Bvlp5VTeX4lxQXFGAzFBcVxjb4XThvDuiUX8e6ycnBn0HogwcV3qEhKSCg4\naDCjHPdvMCNTBp6haaWhYOVA+2Hu/tBIflFQRLsNBlZbp9/dWRAlwsJpYxg65ww+dcoYyk4dy0Mj\nihg7+EKMfzjWgvEP55KSm8LPSUlhHivbZzP72KOcdvQXzD72KCvbZ3cGOWVXBadTVTby0daqqMqb\nPy/4EJUjT6LO48YaQ53HTeXIk3hxVDAgaU8wHXtvYwuLnt3MYHcOw/M9jsFfoiqi7W2FWMAOeY5j\nRFcvbLVtVL1xn+P9kokNLh/wX0WLHRS9U0eWLa3qbxHPGYu2Js7+OFyYR2bzHjlzJ/d7fkJpzn5y\nDJTm7GeZ5ydxQV3oS4SoP3hfGBw3Jc87roXxXxjMxO1v8/Vv5EQFcyFptXpIdfHdzWIvTiDxxXWk\nhBfI+TFTYrshsxUbaJrCQowxnVVHj0DdW4XRQV3Ha53xhXzMlwa+hjHUvTU82J4h28AqRrKgOFVQ\nlZFE7T9CxZliXqNEVXC7ktVxDEAgmFlI8XymCtgyqc7XlcqS3Xnh3l3HSvVZcOq/GCt03plW581G\nbz5WT+qr80j4OYqhzGv/pYBO+pVE2TsnGZW7d7j4bonpFefJMTQf83Pqkmqq+CzNMUFBKKuWKvCs\n2lgVtUYQ4Bh+njz1FHLubKSo8m+OwRwEg8H/qX0U6z4Y7AXtPsjuo3+k+f15HNmxjEM7b+Xnvx/B\ntLuCRWKajvoZXLiJgg8vY8gZSyj48DIKin+LOfmeuNYPsZmsR08qpDXm29bWnBweHDbMcX8ITnMt\n+PAyCs5YQlvJ9zg6eD0PXz01Lvg7um8+tj26OqFt93B03/zg8+F2rpxZf6yRWcvW8PymvY63O4nN\n3G4Y9gm2Tr/bMcuWSYYoJads3lmfC17EVhZyzqbb4qbthqa0RnJ6nh2DxRwPHGuCykKKAs5TR9Nq\n9ZDq4rsbZXNx4niB7LLhaahA4umwXRAZULvy8+OKmtiAoWHrcGLfU07jtB43g07+Z3ybkpCILw0a\ndhTHVUrtrpLeyYJipzWZEH3BlnaQkGRKKhD3fo4LylOMN5nYzzSu+GAx0fOZKmDLJPua6b8t3Xnh\nns6xUmUiE33p4vRZAILPc8d5Fn72Gsfz7s1y9cdLafy+Oo90Pkcw8DLOJxKtoZNuF1tCPlGp9mxl\nVO4+ci1HxxqPrR++kQ1vj8dENCk/2Bz8w/XL1pm0uv9/9t48vqrq3P//rDORiZwTMpCEoKDlWgfS\nguD1Cg7Va6hNRUREa0upX7X6a6vR3gviyJFWQWxrY2+9baWt1GoVcUJTL7FSB6xWQGyc61wgCUkg\nA0lOkjOs3x/77JM9rD2dIScJz/v16styhr33WXvtnfXs53k+nyhWeB5BOZcEQfacuCIRiJmZp7cK\n+gDNXlciCgaZK4wJpVsR6ZkFAAjHeOI4e72vI2eyuo8QvlfRHf/7q+ytWrHgy6oexAFPSGh3fCB6\nKP55655FXrYZt7/oQWVgjqqnLtIzCwOQymLh6UJ0yI/B9gWJ32DYGxiJ4p+K3jK780bZsyhxJgBx\n0PxP3oypp30eVy8dwj95M+ba2ksco14hbf8dj6IhPw/1RQG0etwJG4hzeofLfHO9bqxYcIx+H9r5\nmlsEDPXGPQWBugMHECwpxoBCbce21YPV4tshZr2QTu0WlAj7LjQql92+89B27a8RaQmOjMx2n164\nRHucMX8Byr64D6XTegEY2JTY2Zf8uma+dfvOQ9tjr9nqRSm77lrsvekWuIYGE6/FfBNQdtv6hFCK\nFmWpnFa+vvmGG7H/9jsSYi+JffurDERj4pk5zXwuO9mrF7xJk+n7e8ceJ/zMSBi3O7F9SOXacLot\nMysCAKY2BYYZmVhMqLCrZCT7CsdLD2M2f4dy/hoJmVhlnEeiT26k9jPWoICOSCtaCfl9SSzO7WIm\nsCJEKZICYC6AVxZK/3/eum3oCqmfQj4RmYfXC/4Tr6w6E+UAXtu9D9eu22YZqJZHOVrc+lCpXGGC\nbrQINvTp84rLSyeUbjU0apeRe6tkBU452ObhgBQAaojFyyXl3yZ/XrQv5gqjP/9p3HT6N1XnHQC8\noTlYM/tSLJo1RVKi7BkO+L51MIpflLpUGcKcWAzfOhjFrVAL1AixKYGu/eygtxBfGuqDj0UAZr3Y\nBjTnyluI0w624KWJPrQWVUlB2l9WoBbQZb4a8vMQLJmU+I1ySWsXImCDsH7YoRH1gULUR/YG/FnR\nJOz3uOCKFuHrU69QzX3DBytWi28HaFVrteIsqS5OxAvk2wHY9I5LklQW8q3BL6AUvar3ZUElCOaY\n6b40Dwm6/9GBlh2PgUel+4vVb/5r1Wz8+ctLcMlbDSgNdaE9N4CHZtbia1WzxUI8igWbkdhLtKtL\nv++zbhWrWP6lQC24ElfT9AOAg0WZkwWc1blTbgsul7CEUGnc7nRRa5d0LtyttmWV9TELBp1eC9rP\nJPtdp4zkvjLJSP4Os+vKqZBJJu/H2djPWIRsC4i0IrIWAOLecKvOzMIR2cPI444B+HSd3lsOMPYU\na7irEsGSIl2wEuzoRO2KZqF1A2JeDLQsRu7kRnCPXlAkNhRA38erAKitGgCpOsIK2eZAydyfr0fI\n/7AqSOMxL3K7L8aOa1fqtjHz/pkQpvQ48NZ33jLNzIq84r4S+CP+d1JBInt11cFevND1rUSfnzz2\ngD6o+kHLv7Dw0LBBdcSdA895v0gEQMOfb5GCroOdpgbpSk9A5e8oKX8H0UmbEObDGQ6pmVGRGYvF\nEOxnqG1X+wIaegh6/Wi8ZLvudVOCAdW2ZWKc4ajBBwGo56NovK/3bkIlOwAmZ/uiep8+p2qRZr6S\njUsabUm5J/u0NZMy8anIbMdW+23blFju66MbVMG3kX2F0W+2uh+bjb2ZN6IKWda8uBBl1T3wlzWL\nLSeSlCl3ei7MPg9A954W7bYzlQ2wY7dgd79W2zKzIgBgalOQyrWQqly9kzEYL9L4I/U70r2fkbLt\nGC/2IHZxYltAPXREWtFKyFu9nm6U6pLK/jErhH1Mitfv2vqBKpgDhrNIWmo9kxDsOIiKcASMc1SE\nIwh2HEStZxIAcVklXGH4Sreif3+Nae+ZXPbo8nWBMXvBHCDurbrp9G8i1rYEsaEAOJeCxljbEtx0\n+jcTn1GOJ2Pi24XfVwZALVjzyqozVYGuqM+t6os3o/GQG29+the/+9eQKphb6NqOV3PqgGAADb88\nAcHttwwrYoa78aNJE9GQP2wf4YkOoP/ZWxPHPCxKA7R43FhVWoyZCiVPLWVcsjqQA6F9XSFwAP35\nT6uDOUA36AMuF+onRHUZrlaPuAehNdwjfN0UG6I+yvmonK+yQfwU1gEGLmX6OAdyJ8FSydMCo4yy\n/LqVUEQqfUSZLE1KpdfSTFDJ8b40ZbBOxUSs7sdmynO2MwKy+EhHN1q2e9B94gMp9wU+uXsf5q3b\nhumrGtAUXOuop8hsPIVZR0DVD6Y9z5lS5zO7NpxeF1bXmZlwj5WoTyrXQirfdToGae2PziIj9TvS\n3as3UqWi46W0NhNQySWRVioDucInwkYBUzqxKv8yQ9svBqj7mxwFqmfditqnr0HtXsVTJG8usEAK\nOMzKKrW9Z+X55Zg3aRka909BM0LIndwIWJRYajHqrZICruW4a+vJwqyaPpOof4rrZRNww8k/tHUc\nRn1uW+RsUmw4ALnTuwG5kDJI9ROiGODq3zzgcqG+KKDKuuWEpHEVBszxIEwuewSg+q7sCagN3I1K\nXbW0etxS2aei7MywTzAakzJuVqWiSgQlbf0aUR9AKnHWZptXejapfAoBALEw4MsHrv/U1u8zojy/\n3NQ70qpsJ5U+olRLk6x6fZ30QynZM3sF/LtuVgnihLgPe05cofOdTOzryBD85+4HupsBvws4Mn6e\nNeWxRmImRr/Z8n5sUrosKje0gg+F0Xbn7YgcED+0MFt0ydmYcHMLivICmHHsV7Fv6omY1Ce2QDHb\nltG5S6UfLN2YXRsfnnmW8LpoXnUDmlde77g8zuhcRpqbJRVLr1clfKItK032Wkjlu8ncG6z2ZZbx\ny2ZfVvcvb0Lb7x9HpJfDU8BQdunijGeb0h0YjVSpqJ39aM9lwemnoffFl8Z9zx0FdERasQqMMolo\nIS/3j9UeVWsq3qDtF9Mu8CoDudgf+1ui1JGHAxhsX4DJrlP0ByIQYFEulAq9pegO642FZan/SM8s\nHOqZlSg3BIDV8WrV6o03GBpWMzCU55dj6oQTsaPtFcTcncLeKiX6IGsYYWAEwMVc4JzrxlA7vvMm\nLUPj61MS4/nz4z7E3I9/oRsT7djf6Hs0EcwBJpkuzevNsWJUwVq6XxsMKhfb2gDdqM9QS7kvoDvv\ndYNuBH1eVTDqiXGEEEP1tHj/XeMPUfvs9UCo0zzA02y7FSW4I3yhyoZioWs7Vno2oZJ1oJmXYH1k\nqWRhYWK0nip2hIkMgxWktqhIpb8pk72+cxdeiR0Apr5xF8p4h05QSYdWTEe2kQB0gbxkMB9I9NAB\n5r/Z9H5stt+4Iiz+9VpiockmMCDqBo+Yy9ZHOrrhKfFLNgwaPMWFgm+oy78YgLL+TtS9uRkA0J4b\nwOSQ/hpMZqE40n1WVkGC48DTxOTZLJhRBXzNzdIDrniZJe/qAjweuAMBvdiNAekUPjMao3QHHKkI\nw6TyO6w+y/JzgP5+8BgDwCSLlHsfk/b9/duT+q12SPe1kMl+U9V4+f2mDyBE57nrTw8nPjuee+6o\nh45IOyOlcqmlemM1uCDcYWBYe+pa4cJT63NnxG3bHsCjn9+t6ze78MjrsPrMZY6OU9i7Fj9sZaAo\n6jm06lkS9foxSLm1KQ7Phdl4avvxRH2BPN4XGOmZNZx1Yzb6tjT9Yoa9aOEIGuNZ0EHuRojlIYBe\n1BxRJRSlUf0GzvHmp3ulxfbs4cW2tufIU7gb+RWbEXMZL2JzmBfB+T8SziNlkFsYi6GfAWFt/13H\nweFsoc1eNlGP3DrvBlUmrp/7sCp8OVZ6NqHKJQjq/FMl6fwUMXtQogsaNL8x1X6IZJ+q2+r1dSK8\nkwp3n2AgUhM/PymoXAIm92M7+xUJnXwwJZ6BiwFcf5158iKSiuWLUZXPHXPHUHG6G/579Zkwo3mw\nPzeA+487B3VvbkZO1KQfz+a5Gsk+q1T2ZTQeWjryi/Dts29y9Hc21WvOST+5FWZjZKjCmmSvlNnv\nBpDyfcjuuRZ9VoSnAJix8z3LfSdLJq6FTGQ5hePl8cBdUCB8AGH32kn0/o7yjJ2THjoK6Ihxg1mw\nA8A0EEpl23a+r2T6qga4TYRNzAJFUeCkDEyNFqoyS3x/w5r8x5AXarVcpDr5zUaflcVctvuusR9U\naBaaWrVIAHDF3Piv9n58q68NnTwfE9mApFyZ+Lxa0l+3W28Ztl+i/0NtR7zltIEhvDSpAq3hHmtl\nVQU1G461DEwBYG+sBBfl3We5QFMu1F82GN+9MSlTpw32QtxnaG6fViyChmwJGViJIKFpExr+sgL1\nhXnDlhM9/aj9z7vSH9QZCN4AeosEO9h+oGa1X6tz973jdEGbtD0GT14EBRUD6G3JQaTfDU9eFGXV\nh+CfNjD8mxRB2HsPi7MCMQC1i36CM/bswmXv/x9K+rv0CzCLhwZaRqq0LpXAye6iXx4fwH5QZSaQ\nYqfsNJ3CZ2ZjZJTxSfbekIowjBVOzrXtgAMcx77/fkbn61iQ/3d6HdkWc1IwmsVznAR0VHJJjBvM\nyr9uePkG4XesyvOsPmf3+0qkvpZZiPTMQv7R6+DSlPQxVxivHHwAgD6gs7JqMBOfWejajjVsA/JC\n8YW9psRKixOfPyu7BaOyv1j3Xhy9qkG98NSUmdX29QNuH+rLpyaCqHmTluHX+6dg9WAIr+bUwaeQ\niZezXfXFxWhxMWmJqQmYB9sWCI9HVP5Z3t+D8/s1PUHhXMfZLbulo5XsgK0SQGW5bCz4TeFnKl0H\nsKvwbKzqQbwc8wCaeTHWR5ai4dWpiP2twd7TfbMMiOa9hlnno77j79L8nBhFXSRPrzAaL/d0Ko2d\nruy/VW9Zw8trECwqUFtOFBUAL69BreBauW3bA3js0/sSZc4XTL/CfuY+RRsJ5ZjIfprhuEWK6Twy\n2G8rSvAfqxrwcc5esWqafO6uCgKD/4W23TkKsZa4nUK/B92f5aFibjf80xTj7J8q/VcThHnyIsLe\nwPZcqQz970edhEX/fRlOFZ1rjV0IAOnfz68R3tdS6QdzQiolg9rrwsheQR4fwIbVS5xUS+2s+smd\nBAlmY+T03mCF1e9OZUycnGu7JaOeApZxif6RuhaSITGPDIJfo3E0Os9mJOv/ONqggI4YN5gFO/Vv\n1JuKN1hhJf7gBGVfi5HohlmgaGZqbrRQBQzEMUwWPk58/ozGR+4LbOYl+EdBv85k+0uH8sChXXgu\nxY7POlU9SGVfWIFGTTZJ7itE8JtCA+/Gf+3F9IEHVdlQuaS1r+d44RhJ+1f0FRoESsn0n5X7AmgJ\n6/uKyjU9SbJypXaBpg1mVD2Jip4YJQO55WjuDGEf5mPL0HzNuzYW/YB5rxWgeq8hcgDBT59IZEeN\nRGiUwYrdRUU6+96sen3rJ0Qx4FL/eZTUTCPQzv5EObYnLJU3ezrx6Oc/wZ//+CuEooess7gCwRt4\nc6XXLdCOidZLEzBZ6Av2G+I+3BG+EBzxnlRhVn3YLNxfB/ifX4MPHxjUBWQ86kJb08ThgE75mzRB\nmNQb6Fdl+wbdXmw87hzrUnGja9HmNWpaMpwCqQZOVibPQ8yFnOgQGp78b7THS1NfxImW202118ns\nYYjTAMRojDryApi+qgGVgXys+Okf0tKyYfW7UxkTJ+faTsDB3Bxll16QVvP5sYSdDLXRdZSMmBMw\nPlQyybaAGFfUHlWLxiWNaFrehMYljYk/zHWz65DjVss6G2WbRKT6fSVKCX854NGSTKAISAvVXK84\nE5SMOIbReGoRjY/SbuGHuacgWDIJLV4POGOJhf4Pc4dFZeSF55O79+HbO47EyQP1OGrwQZw8UI9H\ndv4L/Xd+USoTu/sEKciI01BaJdz2g/mTwSGJzPR9vAq9769D38erEOmZZV911ShLEn/diU1G3ck3\nIIepLSlyYjHUdQ4H9VrlSvmpt9ZO4cSe53DCrpvjGRYOF4/piuci7hzknbPG1m81suAAYJwBeeIq\n4PErVO/VFwV0pa6yCE0Cm8GKFifWIVZobTSmBHJV5Wp2s6kA8Nin96n6YQGAuWLoj/ZINhtxtV3D\nuVG9VCoP9E+FUxuJu7Z+gLOjL2K77xp8MuESbPddg4UuvcehMKui2W8rSnF9+PKE0M76yFL0c5/6\nO9pzV70UuO5tRELqeS0jBXmC36S55/inhVAxtxuevEhCqn36utvx+wdv1Vmg6LC4RgFI94u7T9Dd\nP9QWJ9K52vqbm9B02n/gvWOPw4dnnmXLQkOElZWAE7RS9j2+PDAw+If64QIwOdSFujc34/wDbzne\nllNZfNHfGPlhiB0p/O6nn8aHZ56F9449DtH+fjCveu4MuL347Re/qnrQ9+TufbaOzQyz321nTJTH\nrZ0XTs616LNwu+CeAAAcngKg4nsXwP/920edRL/ZGKRzW4b2InHMriPRuQx84+LEv+EW39vHmgG9\nCMrQEeMWbUbj6yddg1cOPpDUk1gn2So7yFmghk9Ctssa7W4XkBZ6+7pCCUEUQMqSVYmCOpulXWaI\nxkdpt/B+2efgLvXzowGXC++UfAYoklbNXSHdwt2qVLS+KIABTeZrwOXC3UV+4ID+WB2prppkT5za\nZAjnUMm/o7brCcQQQnOsOKFMKWPkg7jSs0ktMIN4sRtzAzwG+KvgiZdFrojqRQxEGJbrGgX8XL89\n80CIORYXUWZPosV+eGILEOmZlXjfU7gbXcVbUb3xWsfXpJnCq2E21ad/ABNzd8LKDlKptiukemlS\nvXlzep7DWkV/ZBXrwDrvBiAM4Twy2+9/aPoKt8TmA2HERXUOmJ47wwxFZSUQFAg7CMo9/dNC8H+p\nxLlQj1WG0yTDXP/PDap777x3orj0zxF4I5L3ZColbukuGVRm7Jrmnw5vh7qMOScaxnfeexbAKutt\nmSjPWmGmCP2eRQCizbxoFTY78gL47Re/ihemDmca7ZaS2sFKCdSuabl2XujOdXEhyqp74N+1DPhI\nfd04mRfJZHkz1ReXzvJPq22ZBaxyf6XZPp2cSyB9apzZhkRRiHFJOpW4Mk2mSn4AdVC7vOB13Mx/\nBY/SisCmqmKqGClmcg70vr8u8e8pgVw0x7NQMlaCKna3LW/fcd+VQf9YOoVynty9D9ufuBfX4uGE\n7cBPYxfhBd8Z6OoP637dJxMugVbzJVF26vXq5pFyHrgYQ1Rw3zcUNTASxxBgqEhqc0yUx1lS/g6i\nkzapjN2Vyqmewt3IqXhclR1zolxrRsMnDQhuv0VlOWGkZlr929PAPWKvNCUiddhUaQ1+AeVo172+\nN1aC+UP3ALB/30tF7MKxuI1DIRNLFUuz903mb/W0qeCKBttf/jKCUoGFXrLKik6xuxhPSdjE6dg7\nQAo09ZY84ZIyVG9/0VLgwlKsKEs4EuZo2oTu+uHeUk9eFAVTIujtnIzIgR5HQZbT6yqp69Cmkm+q\n6qhOtpXOfYkYC2IwMiSKQhz2mJVnjbaAzqwnLlXUWYhaoOn4kZFi12DVYwcMZ87u2vqByvPv/0V8\nqOs0Ftaws21AWhQ4VWEDYJg9SadQziL3K/i6d0Mi2K5iHbid3YdVAxxboO1902db1Uqg8TK/YZ6z\nUAAAIABJREFU7bcAz16P2va9WOSvwqKvSefa6GGHYdZSlAExoK6zS6cwajfjrD2u/vyn4VIEc4Ak\nGDShdCsiPbOk+aEpdRyIDuCGv96JH/xGykp95Yul+Ov77Y5FVJxk5C+YfoXO0kREsmXUWlQPgKq8\nwmuj0nUADHD0m1PxEHWcjbLw6lQhyrA9+T1A699olNkzKSkvj0RVDyCKxX7oCJtkDNL1QM5JBiSl\n/jyHIjJOuP/Yc7Dsbw+pLCYG3F48cOw5+BmsxUOsxIqyhZPSx+5fBdHyWl6iHzTS70HXh27IpShO\nMltOrytHPXcWPpR2fqvZ62ZYbSuTnnbA6BaDSQUK6IhxiZUS12FLkqVdqSJSzPSyCXD3nYt+qBee\n/+jchkc/H868iIQ1GvLzUF9cjNaN1Sj0FcLr8iIcU3sEyv17MkaLAqcLMjmLFC326xRKgSQX7s+v\nUWdOAeSxIaz0bBKImUj9TUpfP6l3TVPSysO4MR+4IT9uYv6XFaiFJDrzj85tOlVGw0W/dvHNXMJy\nSwCo9RQD0xUql3YWuPGnxAu792IOK8Z6l1R2aiQY5PJ2gcX/K4K7OxP9N3987V+J122JvygCjNqz\nbkWtjazi6jOXAduQGE8Wy4PbM4QojyQ+k0oZtRJdma+B6IzLX4VPg86CCrNSOjs4XiTZvReJApBY\nGAgdlP6/aCGqPJcm81V6ADFsiXKgEMIM3YE8ca+z07JrM5wsxssuOBkt9z6mNph3c5RdcLL1jlIU\nkTHjieKZOPjlJfjOu8+iNNQ1LNZSPBM/g3UgavVQIZ2ZFSfbchJAt70WBo9ql9bqcgonwiZ/rZqN\nu2puGr4mq47BIoPPOgq6HAb26TQiDxeXCjO5nDG8d+xx8FRUwH/+IvS++NKYyKKNFiigI8Ylo/VJ\n3+GKk4zHKwcf0Gde4sIatX39Gp85ju6hbniYB4EJAXQPdqPQW4qDe85CpOdLie8rFwUqs29fIfoj\n/Ylg0GpBpswieWILhCV/SS3cDRZTlUzfBMgA7Co8G28fNy2hcmnUuxZjCrXJuOw+CvLxTPM94J6B\nhCrjM3vvxpxf3onadimY2XH01bj23RmKhf08LJIzIBYlW7VNm1C7+4l4YBQD/q3P+HcrtuUCUOUa\n7gF7PhwAEwTMFQUVaFxXi5rfXS80kC+LxHDIYHeGWXqHT6u1rC6ZgNX/OCi2bkhjGXX9G/WqhyKA\n+toAkLToDGDeV5g17AQayoWo9lwaBHOAwuIkXqr84PwCXLU1hByFRcCA24vfffGrOFXwfeH5MOmX\n7P7lTWj7/eOI9HJ4ChjKLl0M//dvB+BsMe4fegqY24W2polqn7+hpwDcbvh7pS+nZpNhRmUgFy/g\nRFUPHCCV7QLAv85fjkm/+okug3fw/OWYAfOHCsn0cBkFbU635SRjJLLfEGEns+VU2ddR0OUwsE9n\n1kyUyeUAWCwGQDof3U88aVusZyyVUGYS6qEjxiVjqYeOUGPUE8c4R9Nne1FzRJVwIa/s0zLyKxMZ\ns4twMRc457rFuLbPyBO3RHB5u1BRUIHTqk7DS3tfsr2QTwSXvc0JuwVlpkXZCwUY9zMZ9fPp4Bwu\nlxsxHtO9pTQ4H+Ju9CIXAfSimZfg57gY88//XuLa2bHl1ypbiT2zV0gm5U77cwz6m/bGSnBGzqWm\nPXINd1UiWFKkykzmxGK4tb0TVx+8z3AIhP04FibapqShJ8nMX0/5AEJ0XQDD18ZIllFbHXfasN3D\naWGIztzGwV38PM9btw0z3tquyjA1HX80ln7hNZSjQze+hvcqQb9k9y9vEmbVZEVDR31DqZjRZ7CH\nzurvrmh87z/uHHw4c75lObzTviqzfjIjfzOzHi27QcOH809GpEMvqORkXzJO+1od9dAlcc/TjkHB\n6acllUWbvqoBp+/ZlZgHnDG4BbGInTFy3Dc4xqAeOuKwJ9XyIcKaTIm5GHr+FVQCwbfRurEaosWM\nsnfNKNMgeqIuQg54WvpacMv21QhueQcdrcfrrQF6JIN4BqDuu3BUfqUKLhV2C4CUOQhpLAzM+plE\nJa1CGBMGc4BaofK5ggmoLypEq6cI5ZEorjr4B7zZ4MGiWbdJi7YdRyIUrh8+th1urJ26D4te0Jfx\nNPgY6neuQevuH+vniUlmMtIzCzG3C5OmPo+ecLvuu7WeSUDHAb23YW+e6RAIs/Tde4VehrUm2SE5\nmHmk/wZUuZLvSTJ7Cu/1v2nrvMrXxkiSTl9AU+z2cMoZJkNV1hiw+D5TRcwVC47BDX1DiQzTQtd2\nqbQZYoVdJ/6kbb9/XBXMAQCPMrT9/nH4v3+7swxIKlk2G/2Lyd7bF82agil7nok/7GlHGyuVHvbM\n+ioAqeVh31R9Bo8pghajwMkwg9ncnCjTUwYUZiWsyfSD2S0pLrv+JrTcdBP4kHFPrd3MltPWETs9\nd8PjG4UnbzLKZvaIvSINtm/kjeikN1CbyW148r+Fn5PPx/ADRMWcivvSHq5efSIooCPGLaOyfGic\nkM7eES2i4ERZypiKyXsygiVhPojB/KfBYWxGzgHc8Nc7wT32y69My+c8xXj76Kux690ZYDYeSGhL\nWhkAvTOdObLBuVpgRSrXXFtaiKvb/wTgNnPBoQH1Ynp4W1J5rG6eGCxMm3mxpEhasxyLZt0oPuCz\nbkXt09egdu/wk/YQ9+H6iHEQZRQUN5RWIZjHVb85WDIJyGM6I3FAHcxUTnDu76hEZNOxkm1C5ZMd\nOHtqFQa85naxlmW+DpTsnJCq8JTtoEEbgOQWAUO9QFRh26FciJoFOxbBjPZB4I2+R4eDOZlwCK2P\n34j/eCgfJeX/Ca9GidXofER6ObS9VPLrson2msuuwxFPbLTOeKRgRg/AtH9RdG9XPtQyvRc1bcLc\nt1YDCAEMKEc7yt9aDUwrAqqXWrZCmAUJnuJC48wX59Jnb7oJePZ6+MuaEWkW93bJY5uufjAtoqAq\n2UyWndYRUQBslmVUjW+fGy07i6Tj/lIJMKNGujYe/66lmmzbMxXgmudMZoGUMpvvz/XC62YIR6W/\nUe25AUwO6TPLnooK7Njya5yw62apXzw+p/y7bsYOAHMXXmkrODerJBhP5ZoU0BEE4RinvSNOsOq3\nswr4zDAKBq0wEuhQYuRH5lQNs9XrBa5rwlwAryy0f4xKtVSR7L4ZSoNzocCKy4Wflvmw/v6Z4MUB\nnR8cEH9qPFm9mBZuS6FEubzgAtzI/hc+xYI44s5B1eK1eKXaQpFUsDjXBsF2VS6NvAzri/zCgE42\n9F7p22TsQWezJ0n5tH2hazvWKXzl2j3GDncMTBwIKQM4bfDjsDfQbCGUivCU5QMhURCqLAPTvL/j\n6Ktx7Z9L0PxQA5YXXICb3QJ7FjnYsRBjUT4I5MFvCj9TxjvAAbS3Ho+8wcWGWWQlngKGSK9+WywP\nCRGfZ/s/wJoF+5AXajX3h3OiEuoQ0b1d+VDLNBNrIbRhJXpilm0pq+5By4uxhHqkCD4URttrEfgX\ncnjyIsJ+NnnRnmo/mNm1kS4VRTsiMZZZMqsgLAK0/et4+H94pXkfsaZU1/ABhSDA0mbzu0JheF0M\nRXledPWHsWXOebjs7w/DNTT8d0A+HxN3XafzXM1lQ5j6xl3Awistg3OzSoKv7H0jbd56owEK6AiC\ncEw6JftFaK0cnty9D/PWbUvZJF4UDPKYCzyWA+buB8DAmMDTLixWudN+RiTiYZQ5TCXTaIUyKDYK\nYBN9gt5C1LXuSfTumQmsMADM14WciscxAKiCusqAtGiOPHV1YjFtuK24EuX9vSfhoEtS86xkB9DM\ni/Hz2MWYH52HRbDu0XoyOg93Dd6D5oEQKnNysWLqMXhlofOsfGtYrFdv9LrW0FuHg2yJ8in8Ss8m\n1Ta1svoyZeEYntuzF22sH3t8e4Gj4m9o+6NkJUglNstBrUoqUxGeMn0g1NunW1hGnroaP97yDjb2\nnhQP1I/FX+Pn3c+86Pt7BOGo9Pn7e09Cry+CNfmPxQOj5IOd/SgR+vw18+LE/+/v/BKK+MlosugB\nK7t0sbCHrvE4qT1moWs71rANyAvZDL41gemTu/fhLsU9MtkWA6N7uPKhlmEm1kJow6oVwizb4j+t\nGZibkxCCiR+V/rPx98qqD6Flh18VAMpBQqqm7yNVbmw1XpblhjaDsKHmZrQ+fiPKYVI6rgnWPXlR\nw4BZiyibH45x5Pk82H1rDYBadD99vPB8TNz5LdEho4xLlRFWwblZJcHxjeOrXJMCOoIgHJPJYESL\n6I/nw38txdrFv3f8x1Ob/YsO+THYPpxtEplViywQRAbog+3OVC/nTVqGRw/drdvXvEnLhJ932tci\nB8UiIRidAbfiKW55lKPFJDMEqP3ggOGnxk9Gj8H28OVxg/QDKI1wtHn121IGyFti83XWDK9u/QAA\nTBdN6VxU2ZnPyuBy+4RNwmCOA2D+qY4CCOVT+EqmLt/UyuoDUjb1h50H4YqXH736/o+xonsjDsZ6\nUR6Noc7HUGuVmLVRDmpVUpmKb53pAyFBlscTHcDlsT/ifpyks6PoCul/7OahU/Bq3ll4JSgFWckG\nO2uHLtQF7v2a3lZAkZU0KW+V1SxllUuWBzQeNwc/q/oGAH0wD0Cv3mmwbafXgtmDEru+nsJMrI3e\nPrNWCNNsi98F/7Q9iX6vD7eUiQOKPGkM5M+1NU1EJOTVBW2Os2iK8T8ZJTg7eqHKIzRTPrdm4xVu\nbhFWCCR8E20GYe25ARzL3xUGTvK9gnfvVb1tFjAD6lLG23P8uP+4c3S9k8o5ZHQ+2lip8KFKGytB\nOaz7Bs0qCdLprTcaMC/OJwiCEFA3uw457hzVa+ny2tJitrBMhtqjatG4pBFNy5sQOHCbKssU6ZmF\ngZbFYJEiMDD4vWWItS1RfUZetGozEdrvVuRXqIMmDY2vT8FAy2LEhgLgHIgNBTDQshiNr+v/eMtB\nWUtfC7iiF63hkwZbvzd4ShAV+RWGx/VkdB7mDd6D6QMPovPQcvhsPOtjcT+4QK4XOV4XrnvkTfzX\npn9g89ApmD90D44afBCft30DPOZVfU8UIMt4Cncj/+h16C6vw61vfAPhXLXisfK8pzwvmjZJSm/B\nAOr2NyOHqY9TOZ/lBfO+eBBfAYO+OTCpNNBBNmjRrClYu3im9JCAl6jeq+3rR7DjIMrCMXAuZeaC\nHQdVfox3lBbiQOyQNC/cDMGSSWjINxeGkRfYcuZ7+qoGzFu3DU/u3pf4iGghtNC1HY/0X4HYaj9O\nfup03Db9HUwJ5IJBeshhV0W40Ftq/LoDCw8z5OPXnjs52FH+ViN2Fp6NVeHLsTdWghhn2Bsrwarw\n5dgSUz+AqAzkDmdDuvcA4MMZtqZNic/5v387Zux8D8e+/z4uW3pvIpiTfp9JL6bFtp1cC1bjIbq3\n2/b1POtWKTutxEG2uuy6a8Fy1PtOBAmabZdVHwJzq6spmDuGsuph0xL/tBBmLJuAY997FzO2PQ//\nkaHENY+7T1CdG1M041+OdqzzbsBC13bVx0ba5/ZAvrhyJOGbqLmWpDFTi2INuL24/7hzdPeeBPF7\nxX6o3/dPC6FibjdceRxgDJ7KyoSypFwKGmluBjjH5FAX6t7cjDP27FJtw042f8/sFQhxn+q1EPdh\nz+wVw8dy7rmYse354fOsCAyN9lEZyDXsmUxHL2U2oAwdQRCOceIrlyqZNIkXZRm8oTlYM/vSxMLU\n7Gm21XfNkDJ8s/S9aNqyF6Tes6gtYVXy5O59uLFxI1jxs8gv70J/OAB0zUVR6cfoCbeDcwYwvTKm\nK1qEuy/6smoMohrp6UjPLAwAmFC6FczbBR4OqDKiSrTZUe7pRH7FZvyX5w/4Vt9+NPMSrI8sxdNd\n0mI6pXmhKUWqbd8DDAZQXz4VreEe3XzWLpibeQmqBAtwlqSXl/wUfseWlSiWBQDifKU3gq2dC/Fx\nbD6aJlwCl+IxuVG/o8qXTkMIE7Cq/Vy8eFsj+oYiCWECq5JKbX9fOdpx7ufrMP3EHycU5+wy2LYA\n3P+wPhPetgDw7zcUynGCvJCzFG8xyXxJ94chXQZZSSIr+bxAidOkvFV77zGaU/BXWfamObkWrMZD\ne2+38vVUkWJvn61SyPi2/V8qAeaeh7bHXpM+W1yIsmP2wT/VQCgmFa9JwfjnMalUXDk3jIIH7d8Q\nu729Vvzui1/FNW9u1vn6yb6J/bnlyAsNZ5vkrOX+twII97GEbcQLU0/E+khIdX0DUI2fKFvtPTKK\neyvPR/0da1XHJSoFzYmG8Z13n01k6exm8+cuvBI7ALVNzokrbN9zzCoJyo5Pn7feaIACOoIgksIs\nSEgnmTSJt2NvYVTykqo1hpPflcmexdtffBCuss2JxTXzdYF7dmKw7WI0XbsSt217AI9+ri8NXTL9\nCuHiUIts7WDFhNKtOkP5mCuKP05y49v9QBWTTMcneX0AalObF4IFWm1PF2rZRKEHk3ZhvD6y1HTx\nkyzaxUsLinFneGkiI6Rd9Bv1KKped3mBCRPBQ51o5ortCUoVzUoqRSWBSnECJ3S0Hg93/2JdoN/X\nczxwiV7BUVTmaIZysWga7Fgs8kXXuOFi/ClnRs3abW/wfQs3cwMxl8e/a7ptJ9eCneBP1MNs+z5n\nITpjhWkppGbbfgD+7yveN1N0tQiKTbGRNTYKUETlsMqS4VRKxf85cz7qAaGvHwCsD1+ElfxeXRD2\n0BcuwnOe01VzZktsPhAGbvQ9KvRc3Fl4Nlb1QNXvvD6yFLsKz9Ydl1HJYllIqupw+rdy7sIrE/eY\n8vj/7GL6d1rRi3jYq1wyxn4H4OsA2jjnJwjePwPAUwA+jb/0OOd8TSr7JAji8CKVXh07pGJvkcp3\ntb/LU7gbOWVb0ePtRs1mdYYokz2L/flPw6UJpJgrjP78pwGsxOozlwHbgMc+vQ8xdydc0SIsmX4F\nVp+5DPc3Wpd8GiH/YZcXyN0GSqLK4CSPDWGl9xEAtwnnxRLf37CGPQYEJTGMhlnno77j7/ossoV4\ngxbtgtlq8ZMKysXLjt378NzjbwEx6TdqA0kj0ZTyGAAw1XHNNzAp1iIv7HULIYOSQFmcwAnSeOoD\n/SmBXKA6HkjEF+b9ueW4te8CbImdYrg9r4uhIMeDrv6wbrFoGuw8f73lIt/qGpfLVh+JFaPKZZBh\nM0C97Vqg6XhxQPL8GtPeNCf3yGQehNgZg2x4vgr7ig1MsZ1e8yoMegPbWIllgGLnoVey/Xda30Qg\nbuQeP+8bBaJT6yNL8fTgSbj7Iv2cec59Os487wfC4xBlq5X7UmLUC+mtrMSn6zL/EFiL2fxNlyLp\naCDVDN39AP4HwB9MPvMy5/zrKe6HIIjDlPFqEq/8XW2xvyGn4nEgHlhpZdxTsWqwwmUQSClfX33m\nMqyGXqzFaHHoZgwxzuFiTFeGCUgL91c0qoA1myvEQWtEvRjKC0lZSe28WF7wOm7mG+AJSWPUEDmA\n4KePD3vL9bUguP0WAECtQ2Nm0YLZbPGTLrS/cVfh2XimrASn/et/UcY7sLwzhp+VeTCESOI7Oe4c\n1J0aBDTZc7slysqFvXIh1Bo0Fydwgp1gXA5m8gDM370PryZZsmYa7DjMqmlRZl/Wu/RZ2xD34e2j\nr8ZcW1uDcXbLwnfOyT1SWGbuYugfisT98JzdXy0FWbRZsxk1wIeN9koyTTJujr1QUzFjNxj/8nPv\nwKfV5gGK3esumRYCq/NeGcjFli696NSUQK7jv6uiz//8uA8x94X/lq4jxfkpu+5anbk683nHbCnj\nWIFxwR9bRxtgbBqAZ0wydP/tJKCbM2cO37lzp/UHCYIgxgk1m2uEwUxFfgUalzQCcK5yaZf5D52F\n7nCb7nW/twzbLxEb1MpoF3NA/KltXBzD6n0lQjXOmFoERDqwqcKySNx9gmrBVlNVKcxeVXj9aDzh\nGvEC+dx7DBeX2ixEzUn7krLOSDd258U8Gxk6o3MDQG3wGyfEfXg73kPnNEuj/LwUjAvKDU3Ohxna\nMZk3aRkaX5+iPzbNnElgNMc0zFu3DSf2PBfPgHSgkxeAMSCAPlVJmvbhRTLs2PJrdR/RbPt9RFq0\nJs/KXkrAfB5oMZpXUwK5eOVrHfrrTIvRedaWw2o+a3TPZJEiHPrwev0ctNieJZrgstun6N8zKdWz\nc90B4odctrBQP/3z3b/HJW81JEoyH5pZi69dZ6/P23K/RuMJoLv+v9C2OweRfjc8eVGUzRqAv+6n\naaliOJxgjO3inM+x9dkRCOgeA7AXQDOk4O4dwee+C+C7AHDEEUec+Pnnn6d0TARBEGOJ6o3V4NDf\nixkYmpY3ZXTfDZ804JbtqxFWmHt72QT8aP5ttgIUS684Bwt91UJc9sfrUWQQzRZgwQCgGMPqaVPB\nmV6Hm3GOpu+8LTanfneG7eO0tIIYZYiCa7NSRRFGQYWTwF2IRWBlFbQq3y/0FaI/0o9wbDg74GEe\nFPgK0D3Yrf5+Eov8hhduQf0nT6DVBZRFYrj6YA/O6x9WVuznPpUSJgOSKzNTzE+57HTz0HDZqaPx\nNcE0ILMRYExf1SC4c8V/9+TrxedViyiAtpgTRvdMzoHe99cBEIyRZkzXhy9KeBs6uW9pDb0BSUxD\nVnlUIro2tCR9LkXzN94zi1Anutsqse9lN1h4OIMf801A1e0/slVmaHrvNjs/QEoPSohhRlNAVwgg\nxjnvZYx9DUA953yG2fYoQ0cQxOGGnQxdJslU9i/lfZuJHGixm6ELR9B4+Xuq15wGJKmer0z2HJmN\nZ6b2KwoKPIW7MaFsK5inC65oES6I910K0QTjwzA0fPtB0+BZFFxbEvNioGUxylynoK5sd6KE1Srz\n1fDCLQh++gQGFFKjoizy3lgJ5g/dg4Wu7cn1WQoW6tpAETAPuuyea1FAttC1HSs9m1DlOmB53KYB\n4cBiiM+rFgYENaXfBnMiBoajBx7ExBl3gns69e8PBdD38Sr1caxS+BFu/QD7ukJgmq07qSz48Myz\nxH55lZWYsU1f1ZAplUvDoCqOoVefwXFqj9n0nmhyzUoYvKc9z4QpoyagE3z2MwBzODfuoqaAjiCI\nww3RotQwqzCOSGumS7MIbsjPE5pyB/sZar+vzvogEkD//hqhSIdowZxKRjXlbJYJTsczXYG8NijQ\nWlAAkjKq68CFONRR7ehpf83UStPg2Si4tkK78JcxDeR/dwJa3Pqsb0U4gsa9wwv8GGe4Nvz/4U7v\nBlWJqu0SP4PxkANFGaPsn5NsrDYg09pTAEDEnQPPeb8QHrfpfH5hQdozdPIYGM2xgZbFqutYHiOr\nTJky+G5FCe4YulDnNyjfD9479jgpFaiFMRz73rvWv9cJZg+1DIMqifceroDQLdzGcVpmbilDNyI4\nCegyaizOGCtnTKp5YYydFN+fM3dQgiCIcY7W/Nvv84Mxhq7BLsdG4mMJM389EQ2fNKBmcw2qN1aj\nZnONejyql6Jh3hWoOWIqqqdNRf2kSTivN4SKcASMc1SEIwh29qL21Ft1Ru3c04mcisfhKdyt2p9S\nqEC5byYo5QTsqY6KVO/CuTtx6xvfEP8uBzgZz1TM6rVoFRJFFhTMFUbU/2exubeJIbWVZUey1h3M\nQAzIzJy+1WDFpLWPaObFuNH3qDqYA9DgYzjr9dtwwv0zUf3b03DPoz8UG13bNFc3UqYUzrEYR2d/\nWDf+KxYcg1zv8PGL7Ck80QH0Pyu25Fg0awrWLp4pNJjfcfTVOlPoZ/LyUFNVieppU1FTVYktE/0I\n9l2gN7cXzAmlfUWkZxYGWhaDRYrAwMAiRbpgTjlGZmqTchArif5YG4cbGlLnc+em5SKaNsXnhV+y\nrFAYykeeuhrBH6/G9FUNaIWBGbh8PHni32vHONvS3sLMRD5Fg3kiOVIK6BhjfwLwKoBjGGN7GWOX\nMcauYoxdFf/IEgBvM8b+AeAeABfzVFOCBEEQ45Dao2rRuKQRTcubkOfNU/UAAeaBzljFib+eVQDS\n8EkDgnv/Dy1uBs4YWjwuPOUPoG7QjabP9uKqPuA2/2Sc8MaPsOqlG3WBD3OFMaF0q+o1eTGo3XeM\n643W7aqOahdKcqaBezoTv2vVy6swc+NMx8Gdk/F0GkyboQ0KjIIl5euqwKl6qZS58k+FZLcwNZHJ\nMgqS5ddTse7IP3qdLogHjBez5frTLr2uVGL15qJqyVqpzFKBnDFu87rAGMA9nXigdysaIgcgL9bx\n9DXSYt5AeVFprm5m3WJHMVEpla8MyIzsKXJCxoHzollT8MqqM/Hpulq8surMRHbz2ndn4Prw5dgb\nK0GMM/whbzJuKS1Fi9cjXaNeD26ZNAl/dHn1gb5mTuyNlehKTiM9s3Dow+vRtLwJa2b/Cd6QOpGx\nxPc3PMe+BwQDeKT/Cl2AJiMKYmXjcCXy/aDsumvBcnJU7zE3R9kJnVAGXkkFdXKlQSLDpV4ye6ID\nuHzoj+AA7hi6UBcwKymrPgTmVk9au8bZRg8LEq8LrtkdM2/DvD+XYPpD+QjyK9GfWwHt9az6ncqH\nGc8YPNzIMLLtiO6BwhgkJdsCzvk3LN7/H0i2BgRBEIRNMmkkPppw4q9nFoDUHlUrfp+HUT+5EjuP\nvT5ujh6KFyCJV+bKgEO5YBZtGwBczAXOuaNyxcpALvbH/pYw1QYYGBM/57SUYtfgZDzTOce0kuY8\nEhAGdTwcUP1bFXgYSPaLLDs8zINQJITqjdUo9BXC6/IaiqCIRFIAgDGA+bqQU/E4BgBVZsdoMVt3\n1PnCHrq6vgi0vn9a77j6ooCq/BcABlwu1BcFEv13DT6G+p1r0DKJoXxiJa7t7Eq81899+KXrElvG\nzEZ2IlqUnoPytvbeqjavT3w2VoxEmGnTiqC5K4R9GJbNz69aB5dLPS9irigmlG5NjL/Kk00xJy4y\nKAGUz5WVjUmVqwPrvBuAMHSllEZBrDIjqrZ2yMeay67DEU9sRKSlBe68GCbP7IJ/2vCB6wg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okpgVwwSOV7yZZE505u1PfAucLInRwP6o2yovHXV5+5DBceeR1YpEgSQ4kU4cIjr5OM6S2oPaoW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\nTY0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y+eWXA7Bw4UIaGxupr68nObmXmkh0N5L24ER9mmVHrpFw1eqTftl//vOfZGdns3q1\n/hydXZy2u/vuu5k/f3744vTpp5/u8rk/++wzXn75ZZ555hlmzpzJCy+8wPr163nttde49957efXV\nV7nrrruYNm0ar776Km+//Tbf+ta3+OCDD4Duw8GyZcv44Q9/yN/+9jfS09P585//zH/913/xzDPP\nAOD3+9m8eTOvv/46d999N//+97+555572LJlC//7v/8LwMqVK0/p/HvTj2f9uMv9x/tkOs2Wxq0z\nbwXAZrLhMDtwWpzYTLZOL37bA0t0l8vXfv8a31v1PQAOHjzIc889x6JFi0hPTwf098iePXsAfY3K\nyy67jOrqarxeb8z6aV/4whfC78lAIBDzfm1/j/aWzJ/8pMv9e89dgr+qKm67KTubvOf+cNKvO2nS\nJH70ox/x4x//mC9+8YskJiZSWFgY/j1cfvnlPPHEEwCsXbuWV155BYBly5aRkpJy0q97urUHJyOh\nsHuSs89vnnFzpxfcN8+4mdEpo3vhTMWZrH16+VBpuiMfTwghBoxblo7FZo79r7/NbOSWpWNP+bkd\nDkf4ttlsDk/3MBgMJCQkhG/7/X4ArrjiCl577TVsNhsXXHABb7/9NhDfrCD6fvRrdKWr5zjtlqwA\nsy12m9mmbz8FkyZN4s033+THP/4x69at48CBA3EXp+3Wrl3LlVdeCfTs4rSgoIBJkyZhMBiYMGEC\nS5YsQdO0mIv79evX881v6lMCzz33XOrq6mhs1Kf3dhcOdu/ezYcffsh5553H1KlT+elPf0pFRUX4\n9b/85S8DcM4555xUmOjJ+feVQDDAtZOuJcGYELM9wZjANROvYUTiCMYOG0thciHDHcNxmB1djmQk\nJyQzZtgYJqRNoGpnFevfXc+mTZvYsWMH06ZN4+yzzz7uY3/4wx/ygx/8gF27dvHb3/42Zg216Pdk\nx/dr+3u0r2TceAOa1RqzTbNaybjxhlN63jFjxrBt2zYmTZrEHXfcwWuvvXZKzzfULCtcxsq5K8ly\nZKGhkeXIYuXclYP2glsMPMsKl/HGV99g57d38sZX3xjUf1syQieEGDDaG58MhC6X+/fvp7CwkOuv\nv57y8nJ27txJYWEh5eXlbNq0iaKiIl544QXmz58f91iXy0VKSgrr1q1jwYIFPPfcc+HROoA///nP\nLF68mPXr1+NyuXC5XH33g7U3PunlLpftF6evv/46d9xxB0uWLOmFk9W1X9zD8QN4Tx5/vHCglGLC\nhAls2rSpy8cbjcbjvp7JZCIYjEwN7iycnOz5n4qgCuLxe2j2NdPsa6bV18q41HFcO+laXtz9IrWt\ntQy3D2f59OV8cdQXT+m1GhoaSElJwW638+mnn1JSUkJraytr1qyhrq6OpKQkXn75ZaZMmRI+PidH\nf28/++xx6jcHANeFFwJw5MGH8FdXY8rKIuPGG8LbT1ZVVRXDhg3jyiuvJDk5mUceeYT9+/dTWlpK\nfn4+f/7zn8PHLly4kBdeeIE77riDf/zjHxw7duyUXnuwWFa4bFBfZAvRVyTQCSEGlC9NyxkQyxS8\n9NJLPPfcc5jNZjIzM/nJT35CY2MjY8eO5dFHH+Xqq69m/PjxfO973+v08c8++2y4KUphYSG/+93v\nwvusVivTpk3D5/OFp/X1qcmX9npHy/6+OF2wYAHPP/88d955J++++y5paWkkJSX16LFjx46lpqYm\nHNR9Ph979uxhwoTjTxFOTEykqakpfD8/Pz9cM7dt2zYOHDhwaj/QSVJK4Q16afaGulH63OGOeDaT\njXR7Og6zQw91U67t1dc+//zzefzxxxk3bhxjx45lzpw5ZGVlsXLlSoqKikhOTmbq1Knh41euXMnX\nvvY1UlJSOPfcc/vtd9YTrgsvPOUA19GuXbu45ZZbwh80PPbYY1RXV3P++efjcDiYOXNm+Ni77rqL\nyy+/nAkTJjB37lxyc3N79VyEEIObBDohxJCXn58f03ikubk5fLtj7VP7vttuu43bbrstZl9jYyMm\nk4k//vGPca/Rcerc1KlTKSkp6fR8rrzyynCDlKGivy9OV65cydVXX83kyZOx2+0nNOJjsVj4y1/+\nwvXXX09DQwN+v58bbrihy0C3ePFi7rvvPqZOncrtt9/OV77yFf7whz8wYcIEZs+ezZgxY075Z+op\nf9AfDm/NvmZ8AR8AZqMZV4ILp9mJ3WzHZDi9/8lPSEjgH//4R9z2RYsWcdVVV8Vtv/jii7n44ovj\nth/vPdnZvsFs6dKlLF26NGZbc3Mzn376KUopvv/97zNjxgxAb7j0xhtv9MdpCiEGAe20rNlyCmbM\nmKG2bNnS36chhOgln3zyCePGjevv0+gVpaWlfPGLXzylrpSLFi3il7/8ZfhCbShrbm7G6XSGL05H\njx7NjTfe2N+nNegFVZBWf6u+JpzXTatfbyRk0Ax6IxOzE4fFgcVg6ffW4OLEPPjggzz77LN4vV6m\nTZvGk08+id1u7/Hjh9K/t0Kcbq9urxwQJR7Ho2naVqVUjy4WJNAJIU4rucA4c53qxanQKaViF/WO\nnkZptunrkZn1bpQS4M5s8u+tED3z6vZKbn9lF62+yFqaNrORn3950oAJdScS6GTKpRBCiNPixhtv\n7PGIXF1dXaeNVN566y1SU1N7+9QGvPZplO2jcL6gPo3SYrSEp1E6zA6MhpPsCS+EEEOUUgq3N0Bj\nq49Gj4/GVn/4dkOrfv+JdftiwhxAqy/AA//aPWAC3YmQQCeEOO2UUjJyILqUmpoaXjfuTBSeRunV\nu1F6/HqXTINmwGl2kmZJCy/qLURnBtqMKyFOllKKFm8gEsY8vkgga/HR6PHHhjWPL+7Y4Em+Hao6\nWQt3MJBAJ4Q4raxWK3V1daSmpkqoEyIkehpls6+ZFl9LeBql3Wwn3Z4u0yhFjymlqKurw9phvTwh\n+oNSilZfIC6MNbb6QyNkXYexRo+fQDeJzG4xkmQ1k2QzkWQ1k5Fo5ax0E0k2My6bOWZfUof7iVYT\nxQ+8S2Un4S072dbJqw18EuiEEKfViBEjqKiooKampr9PRYh+FVRB2gJt+pe/jYDSp/uYDCYSjAkk\nGBOwGC14NA8ePNRR189n3L0Wr5/GVv3iy2jQSLKZsFvk0qI/WK1WRowY0d+nIYYApRQeXzAujLXf\nb2g9zihZ1HZ/N4HMZjbGBK40p4XCdEe3YSzJpgcys9FwSj/jLUvHdlpDd8vSsaf0vP1F/tUVQpxW\nZrOZgoKC/j4NIfqcN+Bl+5HtbKzayKaqTXxy9BMAkixJzM6azdzsuRRlF5HjHHz1GjA4mgoIcSZS\nStHmD0bVjcUHrq7CWKPHhy/QdSCzmg1RgcvEMIeFgjRHt2EsyWoi0WrGYjq1QHaq2v+NGshdLk+E\ndLkUQggheoFSis/qP2NT1SY2Vm9k66GteAIeTJqJKRlT9ACXVcT41PGDqplJMKiobW6jsr6V6gYP\nVfWtVNV7eGFzGR5fMO54o0FjdIaTBJOBBJORBLMhcttkCN0P3TYZSDAbY78f53HWmMfp+y1GAwaD\nTEkVQ4/H11kNWXxzj+h9TVEhzRuIf29GSzAZwgGrJ9MUo49NtJpIMA2ef8MGK+lyKYQQQvSB2tZa\nSqpL2FS1iU1Vm6hp1acWF7gK+PLoLzM3ey4zMmfgMDv6+Uw7p5Si0eOnqr6V6oZWKus9VIeCW2Vo\n26EGT9yn9XaLsdMwBxAIKvJS7bT5g7T5grjb/Bx1B/X7/gBtvsjt4z3HibAYDXFB0dKDgNh+vPV4\nAbOHgVRqHEVn2vwda8j88bVkx9nX6PHh9Xf93rAYQ4EsFLiSbWZyh9nDoet4Yay9hsxqlkA2lEig\nE0IIIXrI4/ew7cg2SqpK2Fi1kd3HdgOQnJDMnKw54WmUmY7Mfj5TnccXoLpBD2lVodG16OBWVd+K\n2xvbuttk0Mh0Wcl22Ziem0J2so1sl5XsZBtZLhs5yTaSbCbm3/9Op00FcpJt/PabPfpQGaUUvoDC\nEw56gXAQDN/2B2nzRd3uEAq7Pj5Ac5ufumbvcZ/vVFlMXQe+7kceT+JxodsWowTK08XrD3YZuCLN\nPaL3Re5397dlNmpRo2L614gUW7dhrH27BDIRTQKdEEIIcRxKKfYc26OPwFVvYuvhrbQF2jAZTEzP\nmM7y6cspyi5i3LBxGLS+rQkJBBVHmjxU1XuobmgNT4WsipoaWef2xj0uzZlAdrKVwnQH80enke2y\n6WEt2UpOso00ZwLGHkxj7I2mApqmYTFpej1NPzRobK81OvGg2PPj2y/uOzu+u1GYnuj5yGLn+8OB\nsieBtMNzmI3aaQ2Ur26vPOkaJ68/SJPn+M07GrqpIetu9Nhs1Eiy6lMVE0OhKzvZ1mUYc0Vtl9Fd\n0Zt6VEOnadr5wMOAEXhKKXVfh/3XAd8HAkAzcK1S6mNN0/KBT4DdoUNLlFLXdfVaUkMnhBCiP9W0\n1FBSXRJuZlLn0btNjnKNoii7iKLsImYMn4HdbD9t56CUoqHVp097rPdQ1RAd1vTbhxs9cZ3knAkm\nspOtZIVCWnhkLRTWhidZe/WT/VO54BZ6faI30DEI6lNReztYeqOO94T2d9f4ojuaRtdhsNspq+1T\nXqPDov59S9lRntlQGhN6LUaNS6blMCrD2WkNWXRw67hodEcmgxZVO9b9NMWOtWVWswQycXqdSA1d\nt4FO0zQjsAc4D6gA3gcuV0p9HHVMklKqMXT7IuA/lVLnhwLd35VSE3t68hLohBBC9KVWfyvbDm/T\nA1z1JvYe2wvAMOuwcDfKOVlzenUaZas3QFVDKKzVt4YCW1TtWr0n7oLUYjToUyGT9emQWcnWUGiL\njLAlWc29do5i6AsEVSjodRYET8+IpSfqcd21tu+K0aCddBhLspmwmY0SyMSA1ttNUWYBnyml9oee\n/EXgYiAc6NrDXIgDGFitM4UQQoiQoAqy++huNlVvYmPVRrYf3o436MVsMDN9+HRuPOdGirKKGDts\n7ElNo/QHghxuaqO6vjWuM2T7CNuxFl/MYzQN0p0JZCXbODszkcVjM+JG2NIcCdLRUfQqo0HDZjFi\ns/RPPZY/EIwaoYwNghf97/pOLyY14MO7l2K3SCATp2jnS/DWPdBQAa4RsGQFTL60v8/qpPQk0OUA\nB6PuVwCzOx6kadr3gZsAC3Bu1K4CTdO2A43AHUqpdZ089lrgWoDc3Nwen7wQQgjRE4fdh9lUrXei\nLKku4ajnKABnJZ/F18/+OnOz5zJ9+HRsJluXz6OU4qjbGzWSFmk20j7CdrjRQ8eBh/b6muxkG9Ny\nk0O3reHRteFJ1n5fl0mIvmYyGjAZDdgt8fuyk22dNt3JTrbhSJAWEOIU7XwJVl0PvtDfWMNB/T4M\nylDXa+8IpdSjwKOapl0B3AF8G6gGcpVSdZqmnQO8qmnahA4jeiilngCeAH3KZW+dkxBCiDNTi6+F\nrYe3huvg9jXsAyDVmhruRDknaw4Z9oyYx7nb/HFdIKsaPOG6tar61rjudRaTITySNndUGjnJVrKS\nIzVsWck2nHIBKsQJ6Y2mO+IM5m0B9xFortG/u2sit5uPwO5/0LDPwJGdGfhbjJjsATImN+F6654h\nG+gqgZFR90eEth3Pi8BjAEqpNqAtdHurpmn7gDGAFMkJIYToNUEV5JOjn4TXg9t+ZDu+oI8EYwLT\nM6bzpbO+xMzMOSRqI/U2/g0eXi5vpLrhMNX1nvDUyIbW2KmQBg0yEvW6tfHZSZw3fjhZrujaNSvD\nHBaZ+iVEL2tvriNNdwQASoGnQQ9m7ho9lIW/HwF3beR2cw343J0/T4ILnOk07DNQ/b4LFdBnRvhb\nTFS/7wJqcfXdT9VretIUxYTeFGUJepB7H7hCKfVR1DGjlVJ7Q7cvBO5SSs3QNC0dOKqUCmiaVgis\nAyYppY4e7/WkKYoQQoieOOQ+xKYqvQ6upLqE+rZ6ALJshWRbpuAMjsffks+hhgDVDa0caWqj43/y\nku3mcDBrX2et/XZ2so2MxATMRpkKKYQQvS4YgJajoZDWxWiau1a/HYhfhgU0sKeCMwMcaeDICN1O\n179Ct5UjnWDAir+hCX9NLZX/8W0CnvhnMzlh9JZPTvuP3hO92hRFKeXXNO0HwL/Qly14Rin1kaZp\n9wBblFKvAT/QNO1zgA84hj7dEmAhcI+maT4gCFzXVZgTQgghOtPk8bGv9ijrKkrYdmQzexu30hDQ\nJ4togST8zaPxNp9FwH0WewKJ7AGsZgPZyR6yXTaKx6SHF8XOCoc3K3aLTIUUQohe4/dCS22HEbSa\nzkfTWmpBdbLen8EcCmRpeiDLGA/OUEBzZIRu60EtaHQSOFaPv7ZW/6qpxf9ZLf6aGvy1+/HX1hCo\n0fcpb2eBsMPpH2dgb6Dr0Tp0fUlG6IQQ4szS5g9wuKEtNO0xUrdWWe+mvGkvtYGd+BN2Y7SXo2kB\nVNBMsKUAe3A8WebJ5CeNIjslFNZckWYjyXazTIUUQohT1V09WvsIWvMR8NR3/hwmWySIdTKCFr1N\nJbgI1NdHAlptDYH22zU1kfBWW0uwsbHTlzMOG4YpLU3/Sk/DmJaGKT0dU1o6prQ0qm65BX9NTfxp\nZmcz+u23evO3d9J6e9kCIYQQ4qQEg4qa5rZwB8iO7fsr6z3UNreFj9dMxzA592JP2ge2zwi69I9L\nsyyFjEv+CnOy5rAwdyYjkl0YpYW/EEKcuF6uR8ORARnjoGBh3Aha+21lcRB0txCojQpkVe2jalvx\nh7YHamrxHz0KgfiF4Q12O8b0NExp6SSMHo2jqEgPael6cDOm6ftMw1LQzF2vyZlx6y1U37kC5YnM\nu9SsVjJuvOGUfrX9RQKdEEKIk6KUotHjjwln0Z0hq+pbOdzowReInQnisBjDXSDPyjSB9TDH1IeU\nt+zgsEdfJSfNnkFR1ueYmz2X2VmzSbWl9sePKIQQg8PpqEfLmdHpCFr4tikB5fXiP3o0PJLmr6kl\n8Ekt/pptoW2RL9UavwwFJhOm1FRMaWmY0zOwjh8fGlULjaaFwpopNRWDw9Frvy7XhRcCcOTBh/BX\nV2PKyiLjxhvC2wcbmXIphBBD2KvbK0+6S5zHF9A7QnZcIDv0vbq+Fbc39lNUk0EjM9wFsn1RbJve\nyt9lIyPRzMGW3ZRUl7CpahM7a3biV35sJhte/Qv5AAAgAElEQVQzhs8ILylQ6CqU6ZJCiDNbb9aj\nxdWgxdejYU8FowmlFIH6en2aY3RtWni6Y2QKZKC+8ymWRpcrPJoWCWhpsaNp6ekYXS40gzSe6syJ\nTLmUQCeEEEPUq9srO13H6edfnsSFU7I50uSJmf7YfruqoZXqeg917vhPcNOcCeFw1r44dnRnyDRn\nQtxUyIqminAnypLqEpq8TWhojE8dT1F2EXOz5zIlfQoWYyerCwshxFDSG/VoZnuXNWj67dAomy0F\nQh+OBVtbY+rS9NtR9Wnt4a2uDny+uJfVEhJig1l6emiaY1rMaJoxNRWDRf49P1US6IQQ4gynlKLo\n529zqDG+L7NR00CDQDD23//EBFNoRC0ywhYd3DJdVhJMxm5fu8nbxOZDm8NrwpU3lQOQ6cgMj8DN\nzpxNijWld35YIYToL6ejHq2zEbTotvwJzsjL+/34jx6NjKa1B7Po0bTQtqC7k9c2GDCmDouMpHUx\nmmZwOGTmRB+SpihCCHGGcLf5OVDrZl9NM/tr3OyvdbO/ppkDtW5avPFF5QABpfjBorMi4c2lf0+y\ndl1Efjz+oJ8Paz8Mrwm3q3YXARXAbrIzM3MmV4y7grnZc8lPypeLASHEwNfr9WjpPapHa6eUItjU\nFAlo+2sI1H6ih7QOdWmBo0eJW2ATMCQmhgOadcL4qNG0DmEtJQXN2P0HdWJgk0AnhBADXCCoqDjW\nEhPY9NvNHG6M6hCpwYgUG4VpTmYVDOOVbZU0tMZPm8lJtnHz0rGndE4HGw+ysWojG6s2svnQZpp9\nzWhoTEybyDWTrqEoq4gp6VMwG08uJAohBDtfgrfugYYKcI2AJStg8qUn91y9Xo8WvT5ahxE0Z4Ye\n5gyxQSnY1hYZSTtYi79mL/6ajVEBres10zSzOVyXZs7JwTZ1akw400fT0jGlpWKwWk/u9yQGJQl0\nQggxQBxze9lf28y+GjcHooJbWV0L3kDk4sJlM1OY7mD+WekUpjsYle6gMN1J7jA7VnPkAmLKiORO\na+huOYkw19DWEJ5GubFqI5XN+qLe2Y5sluYvDXejdCW4TuE3IIQQITtfglXXgy/UGbHhoH4fIqGu\nt+vRknMh55zO69Gc6WBNDtejtVPBIIFjx/RAVl2Lv/YzArUlnY6mdbpmmqZhTEkJB7KE/ILIumkd\nRtMMSUkyy0F0SmrohBCiD7X5A5TXtbAvNMK2Pyq8HWuJjKaZjRp5qQ4K0hx6aEtzUhgKbsMcPS82\nP9kul76gj101u9hYtZFN1Zv4sPZDgiqIw+xgVuascDOT3MRcucAQQvS+ByfqIa4jowWSsruuR7O6\nOu/i2E09WjulVPyaaR26PJ7ImmnhmrSOdWk9XDNNnJmkKYoQQvQjpRRHmtoidW1R4a3iWAvRvUgy\nEhPCQa0wFN4K05yMSLFhMvZdK2elFGWNZWyq1kfg3j/0Pm6fG4NmYGLaROZmz2Vu9lwmpk3EbJCL\nDyHEaVBfDmUb9a9tz9JQauPIzkT8LUZM9gAZk5tw5bfCpK913tGxk3q0aHFrptXWhjo8xo+mdbdm\nmiktDVNG510ee3vNNHFmkqYoQgjRB9obknSsaztQ445Zn81mNlKQ5mDyCBdfmpYTDm4FaQ4ST7IR\nSU+t3r+ah7c9zCH3ITIdmSyfvpxlhcsAfRpl+3pwm6o2UeWuAmCEcwTLCpZRlF3ErKxZJFmSTus5\nCiHOQEpB7R4o2wBlm/QQ11ih77O6aDiYRPX7dlRA/2DL32Ki+n0X2Ifh+spTUU8TtWZa+UH8tdtP\nac20cF2arJkmBhEZoRNCiC4EgorKY63sCwW19pG2/TXumCUBNE1vNtI+0jYq3UFBaJpkZpIVg6Hv\npyWu3r+alRtX4glEztNisDAvex41rTV8VPcRCkWiOZFZWbP0JQWyihiZNLLPz1UIMcQF/HB4Vyi8\nbYDyTdBSp+9zDoe8uZA3D3KLIGM8exfOxV/bEPc0WoIZR9G87tdMs1rjWvDLmmliMJEpl0IIcYLq\nW7x6XVtNM/tr3eHwVlrXgtcfaUiSZDXpoS3dwajwNEkneamxDUn6my/oY+lfllLTWtPp/mkZ0yjK\nKqIou4iJaRMxGWTChhCiF/nboHJbaARuIxzcDN4mfV9Kfji8BVIm4W3U8JYfxFtehq+8HG9ZOa0f\nfHDcp04YNy4UyI5TmyZrpokhQKZcCiFEJ7z+IOVH3aHgFhXeat0cdUdaRJsMGnmpdgrSnCwemxFT\n4zbMYRkwFwlKKeo8dZQ2lFLWWEZpYymlDaWUNpZS0VSBX/k7fZyGxh++8Ic+PlshxJDW1qSHtrKN\n+uhbxRYItKEUBBLPxpt0Hj5y8HrseA8ew7uhHF/ZowQaokbhNA1TViaW3Dw0ux3V0hL3MqbsbAr/\n75U+/MGEGPgk0AkhhpT2hiT7Y6ZH6sHt4NHYhiTpiQkUpjlYOmE4hVFdJEf2cUOS7nj8npjAFn27\nydcUPs5isJCblMtZyWfxubzP8fKel2loi5+ylOnI7MvTF0IMRe46PbiVb0KVridw4CO8TRreZjNe\ncvB5p+BtBO/heoLNjUCJ/jiDAXN2NpbcXKwXfAFLbh6WvFwsubmYR47EkKA3NGlYtYrqO1egPFFT\n261WMm68oR9+WCEGNgl0QohBqcXrDy+0faDDEgDNbZGRKavZQEGak4k5Li6ekk1BqItkQbqDpNPc\nkOREBFWQw+7DHGg8EB5lK2sso7ShlGp3NYpIEh1uH06+K58LCi8gPymffFc++Un5ZDmyMEYtZHtW\n8llxNXRWo5Xl05f36c8mhBj81LGD+He8gXfHWny7d+KtrsHbbMLbbMbnNhP0pkcONrZiHjEMS24e\nrjm5WPJyMefm6uFtRA5aD2rWXBdeCMCRBx/CX12NKSuLjBtvCG8XQkRIDZ0QYsAKBBVV9a3h9v96\nR0n9dnVDbEOSbJctUteW3r5+m5OsfmpIcjxN3ibKGss40HAgZopkeWN5TPCym+zhoJbvyqcgqYC8\npDzykvKwm+09fr2uulwKIUQ0FQziP3QIb1kZ3o+34v1kC74D+/BW1+JtCIY7TgJgNGDJysBSOBpz\nfn5kpC0vD3NWlqytJsQpkqYoQohBpaHFx77o6ZGh8Hagzh3TkCQx1JBkVPt6belOCtL08DbQGpJU\nNlWGp0ZGh7c6T134OINmIMeZEzPKVuDSg1u6LX3A1OoJIYYOFQjgq67GWxZpQOItK8N7YC++ymqU\nL7LkimZQmJMUluHDsOQXYBk3DfPEOVjyCzFnZaIZB86/u0IMNdIURQgx4OgNSVrC9WyRddviG5Lk\nptopTHNQPDY93EWyMN1B6gBrSHLUczSmrq19umTHhiQpCSnkJeWxYMSCmBG3kYkjMRvlU2whRO9S\nPh++qiq87YGtvAxfWbl+v6Iips2/ZtKwJAZIcLThHBXAkp6EZcwELFPmY5q6FC19jD4NQggxYEmg\nE0L0GqUUNU1t7GufHhkV3g4eayUQ1ZEkzZlAYbqDz48fro+2hZqSjBxmxzyAGpK0BdrCtWwdw1uT\nN9KQxGwwk5eUx1nJZ7Ekd0nMiJsrwdV/P8DOl+Cte6ChAlwjYMkKmHxp/52PEKJXKK8Xb0VlTKt/\nb3kovFVWgT/yoZLBbsecOYyEFCOJwx1YqMRsb8WS6Mc04iy0/Kg14JJlHUohBhsJdEKIE9bqDcQs\nsH2gNrJ2W1NUQ5IEk4GCNAcTsl18cXJ2zDRJl23gjEwFVZAjLUfi6trKGsuoaq6KaUiSYc+gIKmA\nCwouIC8pLzzilu3IjmlIMiDsfAlWXQ++Vv1+w0H9PkioE2IQCLa14Tt4MH6krawMX3U1BCNT0g1O\nJ5a8PGwTJpB03mIs9jYs2iEsbZ9gbNiFpj4DzQCZkyDvG3p4yy0CZ3oXZyCEGAykhk4I0algUFFZ\n3xozPbJ91K0qqiEJQE6yLTTKFmlGUpjuINtlG1ANSZq9zTFTI9vDW3lTOa3+1vBxdpNdD2uhqZH5\nrvxweDuRhiT9Sin4n3HQVB2/zzkclu8As63vz0sIESPY2hq3qLa3rAxveTn+Q4f093KI0eXCnJeH\nJVdv8x9uQpJqw1j/IVr5Jn0duCMfAwqMFsg5B/LmQu5cGDkLrEn998MKIXpMauiEED3W0OKLjLZF\nj7p1bEiSYKIw3cHswtRwXVt7QxKbZeCMTPmDfiqbKyOBLWrErba1NnycQTOQ7cgm35XPzMyZFLgK\nyE/Sg1uGPWPA1OqdkOYaOLAG9r+rf3UW5gCaD8PPsvSpVWljIG0spI0O3R4DjjSpmRGiFwWa3fgO\ndpgWGQpu/iNHYo41DhuGJTcXx6yZkVb/+XlYRo7EmJysB7xjpXpwK3sD1m6Eo/v1B5sdkDsbJlyi\nh7icc8Bs7fsfWAjRpyTQCXEG8AWClNXpDUn0UbZIeKuLakhiNGjkDtMbkiwck6aPtIXCW5pzYDUk\nOdZ2rNPQdrDpIP5gZNpnckIyeUl5zMueFzPiNjJxJBZj92shDWjeFijfqIe3fe/C4V369gQXFCwA\nbzO0Hot/nD0VZv0H1O6G2j1QugGiRiixJuvBLn1MJOSljYHkPDDKfzaE6EygqQlvWTm+8rLIFMnQ\nSFugtjbmWGN6GpbcPBzz5kUW1Q6NvBkTE2OfOBiEmk9hz8v6Qt5lGyMf1tiG6dMmZ1yjB7jMyfIe\nFeIMJFMuhRgilFLUNLfF1rWFukiWH23p0JDEoi+uHdX+vzDdQe4AbEhS3lgeE9jabzd6G8PHmQ1m\nchNzY6ZGto+4JVuT+/En6GXBAFR9APvf0UPcwfcg4NWnVY2cDYWLoHAxZE8FgzG+hg70aZYX/jq2\nhi4YhMYKPdzV7tW/1+zRv7ujRg8MZkgdFRvy0kbrXwkdLkKFGIIC9fWx9Wzl5XhLQ6HtWOyHJ6bh\nw0NBLTTKlpsbHmkzOBxdvIgPqnfqH9aUbdRDXPsHM4nZenBr/0obC4aB82+2EKL3yDp0Qgwir26v\n5IF/7aaqvpXsZBu3LB3Ll6blHPf4Vm8gZoHt6FG3zhqShBfZDnWRLExz4rIPnIYkSikOtxyODW2h\n73ENSWwZ4e6R0TVuWc4sTIYh+Km0UvpUqvYAd2AteBr0fcMnwahFeojLLQLLcS4QT7XLZesxqP0s\nFPaivo4eABVZr4rEbD3YpY+NCnpjIDFLpm+KQUMpReDYsdg12sojI23BhobIwZqGKSszEtba69ly\nc/XQZuthjaqvFSq3hqZQboCD74PPre8bNio2wCXnyftJiDOEBDohBolXt1dy+yu7aI1ayNVmNnLv\nlyYyo2BYh9b/nTckyXZZwyNs4YYkaQ5ykgdWQxK3z93pFMmyxrKYhiQ2k03vHBm12HaeSx91c5i7\n+FR7qOhYB9dwUN/uGhkagVsEBcX935nO74VjB6JC3l6o2a1/j1rOAYszFO461OkNKwTTIJ/yKgYl\npRSB2tqYkTZvWWSdtmBzc+RggwFzdnbsSFteHpa8XMwjRmBISDjxE/A0wMHNengr26SHuaAP0GD4\nxFB4K9KbmCQO77WfWwgxuEigE2KQmHff21TWt3Z7nDPUkCS6GUl7gLNbBs7IlD/op6q5itLGUg40\nHNDXbwuFt5rWmvBxGhrZzuxITVsovOUl5THcPnzA1Or1ie7q4EYt1qdRDiscHJ/MKwVNh2KDXvvt\nxsrIcZoRUvI7qdUbDbaUfjt9MTSoYBB/TU3sSFtolM1bXo5qaYkcbDRiHpHT+UhbTg6a5RQ/eGiu\niUyfLNsIhz8EFQSDCbKn6+Etb54+bdo2hKaICyFOiXS5FGKQqOoizN17yaRwiEtPTBhQIeeY51g4\nqB1oPEBZgx7cypvKYxqSJFmSyHflU5RdFK5py0/KZ2TSSBKMJ/HJ9lDQXR3cuXfG1sENNpoGSVn6\nV2Fx7L62Jqj7LDbk1eyBfW/pv4N2jvQOdXqhoOcaKfVCIkwFg/gPHep8pO3gQZQnajaD2YxlxAgs\nubnYZ82MHWnLykIz9+I09PrySHgr2wh1e/XtJhuMnAnFP9anSY+YCZZBsgyKEGJAk0AnRD9RSuFI\nMNLcFojbl5Ns44rZuf1wVhFtgTYONh4MT5FsX3S7rLGMhrZIHYnJYCI3MZe8pDyKRxaHu0jmJ+WT\nYpWRlm7r4Gb/R/d1cENFQiJkT9O/ogX8UF8WG/Rq98LHr8Z26TTZIPWsqFq90BTO1LNkTb0hSvn9\n+A4dioy0lUZG2XwHD6K8kQ8CNIsFc+7ISPfIfH3EzZybhzkrE814Gj4gUUr/e40OcI0V+j6rS39f\nT/+mPn0ya4pMMxZCnBYS6IToB0opVr72Ec1tAYwGLaYDpc1s5JalY/vsPA63HNanRoZq2toX3a52\nVxNUkXXo0m3p5Lvy+Xze58lLyguPuGU7s4dmQ5JT0VUd3LiLBk4d3EBhNOndM1NHwdjzI9uVgpa6\n+OmblVvho/+DcMMcLWpNvQ5fsqbegKd8PnxVVR1a/YdG2iorwecLH6tZrVhyc0koLMC5qFgfZcvV\nR9pMw4ejne4R3IBfnxZdtkmvgSsvgZbQkgTO4aEFvJfr3zPGy4iyEKJP9KiGTtO084GHASPwlFLq\nvg77rwO+DwSAZuBapdTHoX23A9eE9l2vlPpXV68lNXRiqAsGFXf87UNeeK+c7y4oYHxWEr98Y0+P\nu1yeDLfPHZ4iGR3eShtL4xqStLf9bx9la+8o6bQ4e/WchpTj1cFZXVCwMLKcwGCpgxsMfK1Qty++\nTq92b+dr6nWs1ZM19XpFw6pVHHnwIfzV1Ziyssi48QZcF14Yd5zyevFWVMa1+veWl+OrrIRAZKaC\nwW4Pr8kWbvUfGmkzZaT37fRzfxtUbguFt01Q/l6k6U9Kvl77llukBzh5fwshelGvNkXRNM0I7AHO\nAyqA94HL2wNb6JgkpVRj6PZFwH8qpc7XNG088CdgFpAN/BsYo5SKn2MWIoFODGWBoOK2v+7k5a0V\n/OeiUdyydGyvXZwEggGqmqvCI2zt0yNLG0o50hpZSyzckCQ6tIW+Z9gzMGjyiXK3TnQ9ONF3OltT\nr/178+HIceE19do7cMqaeieqYdUqqu9cEVOrpiUkkHz51zFnDI+Et7JyfNXV+v83IQanM1LDlpuL\nJS8/vMC2MTW1/2qG25pCHShD679VbIFAm74vY3wkvOXNhaTs/jlHIcQZobeboswCPlNK7Q89+YvA\nxUA40LWHuRAHkXkwFwMvKqXagAOapn0Wer5NPTk5IYaSQFBxy8s7eGV7JdcvGc2NnxuNpmms3r+a\nh7c9zCH3ITIdmSyfvpxlhcuO+zz1nvqYmrb2UbfypnJ8wcjUpERLIgVJBczJnhMT2kYmjsRqsvbF\njzx0dFUHl3mG1cENdAYDJOfqX2d9LnZf3Jp6e+HIp/Dp652vqZc2JrZWT9bUi3Hkl7+KbTwCqLY2\njv3+WQCMLhfmvDxs06bh+tKXwoHNnJeHMTl5YDR6cteFRt5CUyird+p/C5pRr3mb9d3QNMoisA/r\n77MVQohO9STQ5QAHo+5XALM7HqRp2veBmwALcG7UY0s6PDZuLpmmadcC1wLk5vZvIwghTgd/IMiN\nL+1g1Y4qfnTeGH64ZDQAq/evZuXGlXgC+kVRtbualRtX4g/6mZg2MdxFMnqKZMeGJCMTR5KflM/C\nEQtjRtxSElIGxgXTYCV1cEOPLUXvMjhyZuz2ztbUq90DO148zpp6Her0zpA19VQggGfXLprWrKF5\nzRr8hw93fqCmMWbTRozJA7AFf0NlJLyVbYSaT/XtxgS96+SCH+nLCIyYBQkyzVwIMTj0WgGBUupR\n4FFN064A7gC+fQKPfQJ4AvQpl711TkIMBL5AkOv/tJ1/fHiI275wNtcVjwrve3jbw+Ew184T8HDH\nhjtitqXZ0shPyue8vPNiFt3OceZIQ5Le0l0d3PwbpA5uqDJZ9JG49A7NiI63pl7pBtj558hx0Wvq\nhTtwDo019QINDTSvX4977Vqa164jcOwYGI3Ypk3FkJhIsKkp7jGmrKyBEeaU0usso9eAqy/T91kS\nIXcOTL5Ur4PLngamM3QpFSHEoNeTK8FKYGTU/RGhbcfzIvDYST5WiCGlzR/gBy9s582PD3PnF8dz\nzfyCmP2H3IeO+9h7599LgauAvKQ8Ei1S09Prhvp6cOLUdbmmXrO+vljHhizHXVOvQ63eAF1TTylF\n2969NIdG4Vq3fwCBAMbkZBwLF+AsLsY5fz5Gl6vzGjqrlYwbb+ifkw8G4MjHsUsIuEP1w/Y0feRt\nzvf0KZTDJ8r7WggxZPQk0L0PjNY0rQA9jH0duCL6AE3TRiulQitnsgxov/0a8IKmaf+D3hRlNLC5\nN05ciIHO4wvwvT9u5Z3dNdxz8QS+VZQfs18phd1sx+1zxz02y5HFhaPiO8WJUyB1cKI3JTg7X1Mv\nGIBjpZ2sqfe3AbumXtDjwV1SQvOaNbjXrMVXVQVAwrhxpH73OziLi7FNnhy3jlt7N8uedLk8Lfxe\nqP4gNH1yk76EQPuUdFcujFocqn+bq/9+ZWRdCDFEdRvolFJ+TdN+APwLfdmCZ5RSH2madg+wRSn1\nGvADTdM+B/iAY4SmW4aOewm9gYof+H5XHS6FGCpavQGufW4L6/bWcu8lk+IWCVdK8eC2B3H73Bg1\nI4Got4XVaGX59OV9fcpDk9TBib5mMHa+ph7oDThqd8dO36za1i9r6vkqK8O1cC0l76Ha2tDsdhxF\nRaRe9x84i4sxDx/e7fO4Lryw7wKc1w0V70fWgKvYElmiIm0MTLxED295RXpTHCGEOEP0aB26viTL\nFojBrsXr55rfb6HkQB33f2Uyl84YGbNfKcXD2x7m6Q+f5rKxlzE1fSq/3v7rHne5FF2Q9eDEYORr\n1UePa3bHjuzVfQa+lshxp7CmnvL7ad2+PTyVsm3vZwCYc3P1aZTFxdhnzcRgGUDNXVqP6eu+tTcw\nqf4Agn7QDPqoevsacLlF8qGMEGLI6dV16PqaBDoxmDW3+bn6d++zpewov7p0CpdMGxGzXynFI9sf\n4cldT/LVMV/lzjl3yrpvp0LWgxNDWTAIjZVRUzdPbE09vyEN9+ZQiFu/gWBjI5hM2GfMCIc4S0H+\nwOmG23QoUvtWvgkOfwQo/f2cc05oDbh5MHIWWJP6+2yFEOK0kkAnRD9o9Pj4f89sZkdFAw9dNpUL\np8QvOvvoB4/y+I7H+cror7CiaIWEuRPVXR1c4SKpgxNnhtZ6fQSvJjKFU9XsxrPvIO4qM81VVlrr\nzICG0WHEOSEL5+xpOIoXY8yd0v9r6iml1xqWbYx0oTy6X99nduihLW+eXgOXM73P6wqFEKK/9fbC\n4kKIbjS0+PjW7zbzUWUDj14xjfMnZsUd89gHj/H4jse55KxLJMydCKmDEyKeLRlGzCCQPA537Uaa\nPwrgXlOBvyYVNA3rWSNIm5eOMxesliq0uk+hZjP85bf64/t6Tb1gUF/zrWxDaB24jdBUHfpZUvTa\ntxnX6PVvmVO6nUIqhBAiQv7FFOIUHXN7+eYz77H7UBOPXXkO542PbyTw2I7H+M2O33DxqItZOXel\nhLmuyHpwQnTJW1oaroVreX8LyufD4HTimD9fn0q5cAGm1NTYBymlT9Os3RNbq3eqa+rtfAneugca\nKsA1Apas0Nd2C/igemdk9K18U6TLZ2J2aPQtNIUybeyAXMJBCCEGC5lyKcQpqGtu4xtPvcf+Wje/\nvfIcFp+dEXfMEzuf4JHtj3DRqIu4Z+49GKWWK5bUwQnRJeX10rJlix7i3l2Dt0xfHNsyalSkocn0\naWhm88m9QFuzPn2zdm9sF866z7pYU28MNB2Gzb8Ff2QdOgwmSB0N9eXQviTLsFH61Mn2r+Q8+TBG\nCCG6IVMuhegDNU1tfOOpEsrqWnj62zNYMDp+yt9Tu57ike2P8MXCL0qYa9fj9eDmgsXejycqRP/x\nHTmCe+1afW24DRsJtrSgWSzYZ88m5ZvfxLmoGMuIEd0/UU8kOPUPTLKnxm4PBqC+TA93UbV6cWvq\nxTzGrwfBGVdF1oBL7H75AyGEECdPAp0QJ+Fwo4crniyhqt7D766aydxRaXHHPL3raR7e9jAXFFzA\nT+f99MwOcz2pgytcpK+zJcQZSAWDeHbtCo/CeT7+GABTZiZJF16Is7gYx5zZGOx9+CGHwahPbR5W\nCGOWxu5z18EDo4isnxcl6IcLHuiTUxRCCCGBTogTVlXfyhVPllDT1MazV89iVsGwuGN+/+HveWjb\nQ3wh/wv8bP7PzrwwJ3VwQnQr0NiIe8MGmt9dQ/O6dQSOHgWDAdvUqaTfeCPORcUkjBkzcJYViOZI\n1Wvm2j+ciebqpZFDIYQQPSKBTogTcPBoC1c8VUK928cfrpnNOXnxTQKe/ehZfrX1VyzNX8q9C+7F\nZDgD3mbd1cGde6fUwYkznlIK77594VG4lm3bIBDA6HLhWLBAH4WbPw9TSifNRwaiJStg1fX6wujt\nzDZ9uxBCiD5zBlxpCtE7yutauPzJEpo8Pv74ndlMGZkcd8xzHz/HL7f8ks/nfZ77Ftw3dMOc1MEJ\n0SNBj4eW994LdaVci6+yEoCEs88m9TvfwVlcjG3KZDTjIPygY/Kl+vfOulwKIYToM0P0alOI3nWg\n1s3lT5Tg8Qd44btzmJjjijvm+U+e5xfv/4Lz8s7jvoVDMMxJHZwQPeKrqgqPwrnfew/l8aDZbDiK\niki99lqcxQsxZ2b292n2jsmXSoATQoh+NsSuOIXofZ8daeaKJ0vwBxV/+u4cxmUlxR3zp0//xH2b\n72NJ7hLuX3g/ZsNJtg8fSKQOTogeUX4/rR98EA5xbXv3AmAeOZLkr35VX1Zg1kwMCQn9fKZCCCGG\nIgl0QnRh96EmvvFUCaDx4rVzGDM8Me6YFz99kXvfu5fFIxfzwMIHBm+Ykzo4IXrMf+wY7nXr9IYm\nGzYQbGgAkwn7OeeQceut+rICBQUDs0DHTdAAACAASURBVKGJEEKIIUUCnRDH8XFVI1c+/R5mo8YL\n353DqHRn3DEv7X6Jn733MxaNWMSvin+F2TiIwpzUwQnRY0op2j79NDwK17pjByiFMTWVxHPP1Rua\nzJuLMTH+Qx8hhBDidJJAJ0QndlU0cOXT7+GwGHnhu3PIT3PEHfOXPX/hv0v+m4UjFvKrRYMkzEkd\nnBA9FnS7cZeU6KNwa9fiP3wYAOvEiaT953/iXFSMdcIENIOhn89UCCHEmUwCnRAdbC8/xree2UyS\n1cyL185h5LD40alX9r7C3ZvuZkHOAh5c9CAWo6UfzrQHpA5OiBPiLSuLLCvw/vsonw+Dw4Fj3jyc\nxcU4Fy7AlJ7e36cphBBChEmgEyLK1rKjfPuZ9xnmsPDCd2czIiU+zP3f3v9j5caVzMuZx4OLB1iY\nkzo4IU6I8npp2bpVH4VbswZvaSkAloICUr7xDZyLirFPn45mGUDvcyGEECKKBDohQt7bX8fVv3+f\njCQrL3x3NlkuW9wxf/vsb9y18S6Ksot4ePHDJBj7uWud1MEJccL8NTU0r12rLyuwcSNBtxvNbMY+\ne7Ye4ooXYsnN7e/TFEIIIXpEAp0QwMbParnm2S1kJ1v503fnkJFkjTtm1b5V3LnhTmZnze7fMCd1\ncEKcEBUM4vnww/AonOejjwAwDR9O0rJlOBcV45gzB4NdPvQQQggx+EigE2e8tXtq+O4ftpCf6uCP\n35lNemJ8UFu1bxX/tf6/mJU5i1+f+2uspvjAd9pIHZwQJyzQ1IR7wwY9xK1bR6CuDgwGbFOmkH7D\nDTgXFZMwdqwsKyCEEGLQk0Anzmhvf3qY657bxqgMJ89/ZzbDHPF1Mqv3r+aODXcwM3Mmjyx5BJsp\nfirmKdn5Erx1DzRUgGsEnHsHpI7uug5u1GLIkjo4IdoppfDu3x8ehWvZtg38fgwuF8758/VRuPnz\nMaWk9PepCiGEEL1KAp04Y73x0SG+/8I2zs5M4rlrZpFsjw9z/zzwT36y/idMz5jOI+eepjC36nrw\nter3Gw7C//1HZH+4Dm4x5BZJHZwQUYJtbbRs3hwOcb6KCgASxowh9aqrcC4qxjZlCppJ/lMnhBBi\n6JL/yokz0uu7qrn+T9uZmOPi2atn4bLFryH3z9J/ctu625iaPpVHlzyK3XwawtRb90TCXDR7Knx/\ns9TBCdGBr7paX1ZgzVrcJSWo1lY0qxXHnDmkfucanAsXYs7O7u/TFEIIIfqMBDpxxnltRxU3/vkD\npo5M5vdXzSTRGh/m3ih9g9vW3saU9Ck89rnHTk+YC/giDU06ajkqYU4IQPn9tO7YER6Fa9uzBwBz\nTg7Jl1yiLyswaxYGax/WtQohhBADiAQ6cUZ5ZVsFN7+8gxn5w/jd/5uJIyH+LfDvsn/z47U/ZlLa\nJH7zud+cnjDnroOXvnX8/a4Rvf+aQgwS/mPHcK9fry8rsH49gYYGMBqxT59Oxi034ywuxjJqlDQ0\nEUIIIZBAJ84gL71/kB+/spO5o1J58lszsFvi//zfKn+LW9bcwoS0CTz2ucdwmB29fyJHPoEXLoOm\nQzDjGtjxQuy0S7MNlqzo/dcVYoBSStG2e3d4FK51xw4IBjEOG4Zz8WK9ocncuRiTkvr7VIUQQogB\nRwKdOCM8/14Z//V/H7JwTDpPfPMcrOb47pDvlL/Dze/ezPjU8Tz+ucdxWpy9fyK7/wl/vQYsDrjq\ndRgxA3LnxHa5XLICJl/a+68txAASbGnBXVKih7i1a/EfOgSAdcIE0q67DueiYqwTJ6IZDP18pkII\nIcTAJoFODHm/33CAlas+5tyzM/jNN6Z3GubWHFzDTWtuYlzqOB4/7zSEOaVgw8Pw75WQNRm+/idw\n5ej7Jl8qAU6cEbwHD0aWFdi8GeX1YrDbccybh/OHP8CxYAHmjIz+Pk0hhBBiUJFAJ4a0p9bt56er\nP+Hz44fzv1dMx2KK/7R/bcVabnz3RsamjOXx8x4n0ZLYuyfh88Cq5bDzRZhwCVz8G1l+QJwRlM9H\ny9Ztoa6Ua/Du3w+AJT+flMsv1xuanHMOmiV+yRAhhBBC9IwEOjFkPfbuPu7/56csm5TFQ1+fitkY\nH+bWVazjhndu4Kzks/jteb8lydLLNTpNh+DFb0DlFlh8Byy8GaSRgxjC/LW1NK9dR/OaNbg3bCDY\n3IxmNmOfOZOUr1+mNzTJy+vv0xRCCCGGDAl0Ykj69Vt7+Z8393Dx1Gx+9bUpmDoJcxsqN4TD3JOf\nfxJXgqt3T6JqO/zpCvDUw6XPwfiLevf5hRgAVDCI56OPwlMpPR9+CIApI4OkL5yPs7gYR1ERBsdp\naDAkhBBCiJ4FOk3TzgceBozAU0qp+zrsvwn4DuAHaoCrlVJloX0BYFfo0HKllFzVitNGKcX/vLmH\nR97+jC9Pz+GBr07BaIgfEdtYuZHr376ewuTC0xPmPnwFXv1PfS25q/+l180JMUQEmptxr9+gT6Vc\nt45AbS1oGrbJk0lffj3O4mISxo2TZQWEEEKIPtBtoNM0zQg8CpwHVADva5r2mlLq46jDtgMzlFIt\nmqZ9D/gFcFloX6tSamovn7cQcZRS3P/P3Ty+Zh+XzRjJz788CUMnYW5T1Sauf+d68l35PHleL4e5\nYBDe/Tms/QWMnAOX/RGc6b33/EL0A6UU3gMHIg1Ntm4Fvx9DUhLO+fP0UbgFCzANG9bfpyqEEEKc\ncXoyQjcL+EwptR9A07QXgYuBcKBTSr0TdXwJcGVvnqQQ3VFK8dPVn/D0/2/vvuOqLvs/jr8uNjhQ\nwIErR47AcoSjLG2qlWZ2V85MUUzL0vplO9vd7azUUsF2mg0r74baMisX5ijcknsCgot1zrl+fxxS\nLJYyDuD7+XjwgPM91/d7fb51um/efK/xy18M6tSIJ/u0zjPMLd2zlDt+uING1RsR2z2WGgE1Sq6I\nrKMw51ZYNxfaDoZeL4OPf8ldX6QMuTIzObZs+fEFTbJ37ADAv3lzQocNpWq3bgS2bYvx0ch9ERER\nTyrK/xPXB3bker0T6FRA++HAN7leBxhj4nEPx3zWWvv5P08wxowERgI0atSoCCWJnGCt5bEvE3hn\n8TaGXtiYR3tH5DnUa/ne5Yz5fgwNqzUktnssNQNqllwRqTtg5gDYnwA9noHOt2nxE6lwsvfu5cjC\nn90LmixejE1Px/j7U6VzZ0Kjh1G1a1d869f3dJkiIiKSS4n+adUYMxiIArrlOnyWtXaXMaYp8IMx\n5g9r7Zbc51lrpwHTAKKiomxJ1iSVm8tlefiLP/lw6XZiLm7Cg1fnPW9n+d7l3P797dSvWp/Y7rGE\nBJTg0LDtS+GjQeDIhIEfQ/MrSu7aIqXIOp2kr15z/Clc5vr1APjWq0eNvtdRtVs3gjp1wisgwMOV\nioiISH6KEuh2AQ1zvW6Qc+wkxpgrgIeAbtbazL+PW2t35XxPNMb8BLQDtvzzfJFT5XRZ7v90DR+v\n2MltlzRjfI+WeYa5FftWcPv3txNeJZzYHrGEBoaWXBErP4D/jYPgBjD0K6jVsuSuLVIC0ubOZf8r\nE3Hs2YNPeDhhI0fiVbWq+yncokU4U1PB25ugdu2ofc//ubcVOPtsLWgiIiJSQRQl0C0HmhtjmuAO\ncv2BgbkbGGPaAVOBntba/bmO1wSOWWszjTFhQBfcC6aIFIvD6WL8J2uYs3IXYy9vzrgrmuf5C+jv\n+35n9HejqVulLnE94ggLDCuZAlxOWDABFk+CJt3gxrchSAtCSPmSNncuex6ZgM3IAMCxezd7H3sM\nAO+aNanarat7QZMuXfAOLuGVXkVERKRMFBrorLUOY8wYYB7ubQtmWGsTjDFPAPHW2i+BF4CqwMc5\nv1T/vT3BOcBUY4wL8MI9h25tnh2JFFG208Xds1czd/Vu7unegjGXNc+z3ar9qxj93WjqBNUhrnsJ\nhrmMNPhkOGxeAB1HuufMefuWzLVFisGVlUXmho1krF1LRkICqXPmQHb2v9p5h4XRfOFPGG9vD1Qp\nIiIiJalIc+istV8DX//j2IRcP+c5acha+xtwbnEKFMkty+Fi7KyVfPPnXu6/qhWjujXLs93qA6sZ\n9d0oagXVIq5HHLWCSmjrgOQtMLM/pCRCr1cgKrpkrityilyZmWRu3EhGQgIZCQmkJySQuWnz8QDn\nVb16nmEOwJmcrDAnIiJSSWi9aakwMh1Oxny4kgVr9/FIrwiGX9Qkz3ZrDqxh1IJRhAaEEtc9jtpB\ntUumgMSfYPYtYLzg5s+hycUlc12RQrgyMsjcsIH0nPCWsXYdmZs2gcMBgHdwMAGRkVQdegsBkZEE\nREbi26ABmy+/Asfu3f+6nk94eFnfgoiIiJQSBTqpEDKynYx+fwU/bjjAE30iGXJB4zzb/XHgD25d\ncCs1A2oS1yOOOlXqFL9za2F5LHxzH4S1gAEzISTvMClSXK70dDLWr88ZNukeOpm5eTM4nQB416jh\nDm/R0SfCW/16ec4hrX3XuJPm0AGYgABq3zWuzO5HRERESpcCnZR76VlORr4Xzy+bk/jv9ecyoGPe\nexUmJCVw64JbCfYPZkaPGdStUrf4nTuz4Zt7IX4GtLgKrp8GAdWLf10RwHXsGBnrNxwfNpmRkEBm\nYuKJ8BYS4g5vl15CQGQkgRER+NTLO7zlJbh3b4CTVrmsfde448dFRESk4lOgk3LtWJaD4W/Hs+Sv\nZJ7/z3ncGNUwz3Zrk9cSsyCG6v7VSy7MHUuB2UNg6yK46C647BHw0rwjOT2uo0fdT95yzXnLSvwL\nXC7AvVBJQGQEVa+4nMCcJ28+desWe/uA4N69FeBEREQqMQU6KbeOZDqIfms58dtSePmmNvRt1yDP\nduuS1xEzP4ZqvtWI6xFHvar1it/5/nXuxU8O7YG+06BNv+JfU84YziNHyVy3loy1a3Pmva0lKzHR\nPXwX8K4VRmBEJNW79yCgdU54q11be7+JiIjIKVOgk3LpUEY2Q2csY/XONF7t347ebfIOaetT1hOz\nIIYqvlWI6xFH/ar1i9/5hm/h0xHgFwTDvoYGUcW/plRaziNHTprvlpGQQNbWrcfDm0/t2gRERlL9\nqqsIiIxwz3mrXUIL9YiIiMgZT4FOyp20Y9kMeWsZCbvSmDywHT1b570i34aUDcTMjyHAO4C4HnE0\nqJb3E7wisxZ+fRW+ewzCz4P+MyG4BAKiVBrOw4dPCm4ZCQlkbdt2/H2funXd4a13LwIiIgiMjMSn\nVgltmSEiIiKSBwU6KVcOHs3i5hlL2bj3CG8OPp8rIvJepXLjwY3EzI/Bz9uPt3q8RcNqec+tK7Ls\nDJg7FtbMgsi+0GeK+wmdnLGcaWnHN+j+e+hk9rbtx9/3CQ8nIDKC4Ov6HF9t0ic01IMVi4iIyJlI\ngU7KjeQjmQyKXUpi0lGmDjmfS1vmPSxt08FNjJg3Al8vX3eYq17MMHd4L8waBLvi4dKHoes9oLlM\nZxRnaupJ890yEhLI3rHj+Pu+9eoREBlJjb7X54S3CHxCQjxYsYiIiIibAp2UC/sPZzBo+lK2pxwj\n7pYoLm6e9zC1LalbGDF/BD5ePsT1iKNR9by3MCiy3Sth5kDISIWb3oOIa4t3PSn3HAcP/mvYZPau\nXcff923QwB3ebryRgIgId3irWdODFYuIiIjkT4FOPG7foQwGTF/CntQM3hrWgQubheXZLjE1keHz\nhuNlvIjrEUfj4MbF6zhhDswZDVXCIHqee96cVCqOlJRcwS3nydvu3cff923UiIBzz6VG/37urQIi\nIvCuUcODFYuIiIicGgU68ajdqekMnL6EA4czeSe6Ix2b5D2MLTEtkeh50QDE9YijSXCT0+/U5YKF\nz8LC56BhZ+j3PlTVwhUVnSMp6fict7+HTjr27Dn+vu9ZjQhs24aagwa6h02ecw7ewcEerFhERESk\n+BToxGN2pBxjYOwSUo9m8+7wTpx/Vt7D2rambWXEvBFYLDN6zKBpcNPT7zTrKMwZBeu+hLaDodfL\n4ON/+tcTj3AcOJAT2k48eXPs23f8fb/GjQlq3/74YiUB57TCu3p1D1YsIiIiUjoU6MQjticfY8D0\nJRzOyOb9EZ1o0zDvYW7bDm1j+LzhOK2TuO5xNKvR7PQ7Td0BswbAvgTo/jRccLsWP6kAsvftPzFs\nMucJnGP/fvebxuDXpAlBHTsen+8WEBGBd9Wqni1aREREpIwo0EmZ+yvpKAOmLSHD4eTDmM60rp/3\nsLfth7YTPS+abFc2cT3iOLvm2aff6fal8NEgcGTCwNnQ/MrTv5aUCmstjn373KHtT3eAS1+bgPNA\nkruBMfg1bUpQ507u+W6Rkfi3OgfvqlU8W7iIiIiIBynQSZnavP8wA6cvxeGyzIzpzDnheQ+D23Fo\nB9HzoslyZhHbPZbmNZuffqcrP4D/jYPgBjD0K6jV8vSvJSXCWotj795c893cQyedycnuBl5e+Ddr\nStULu7iHTLaOJKBlS7yqKLyJiIiI5KZAJ2Vmw97DDIpdAhhmjexMizrV8my34/AOoudHk+HMIK57\nHC1DTjOAuZywYAIsngRNusGNb0OQ9g4ra9ZaHLt3n9jjLWfYpDMlxd3A2xv/Zs2o2rWrO7xFRBDQ\nqiVeQdrYXURERKQwCnRSJtbuPsTguKX4ehs+jOlMs1p5z3HadWQXw+cN51j2MeJ6FCPMZaTBJ8Nh\n8wLoOBJ6PAPevsW4AykKay3Zu3b9a583Z2qqu4G3N/7Nm1P1kksIiIwgMDIS/5Yt8QoM9GzhIiIi\nIhWUAp2Uuj92pjE4bilV/Lz5MKYzjcPyHja3+8huor+N5kj2EWK7x9IqpNXpdZi8BWb2h5RE6PUK\nREUXo3rJj7WW7J07TwpuGQlrcaaluRv4+LjD2xWXn5jz1rIlXv5aVVRERESkpCjQSalauf0gQ2Ys\no3qAL7NGdqZhSN7D6PYc2UP0vGgOZx9mevfpRIRGnF6HiQth9hAwXnDz59Dk4mJUL3+z1pK9fftJ\ne7xlrF2L69AhdwNfXwKaN6da9+45WwVE4N+ihcKbiIiISClToJNSE781haFvLSekih8zR3amfo28\nh9XtPbqX6HnRHMo8xLTu04gMjTy9DpdNh2/ug7AWMGAmhBRj8/EzmHW5yNq2LWeu29rj2wW4Dh8G\nwPj64t+yJdV79jy+z5t/i+Z4+fl5uHIRERGRM48CnZSKpYnJDHt7OXWqBzAzpjN1gwPybPd3mEvN\nTGXaldNoHdb61DtzZsM390L8DGjRE66fDgHaRLoorMtF1tZtJw+bXLcO15EjABg/P3d4u+ZqAiIj\n3XPezj4bo/AmIiIiUi4o0EmJ+21zEtHvLKd+jUBmxnSmdvW8w9y+o/sYPm84KRkpTL1yKufWOvfU\nOzuW4h5iuXURdBkHl08AL+9i3kHlZJ1OsrZuPR7c0hMSyFy7DtexYwAYf3/8W7Uk+NreJ568NWuG\n8dViMiIiIiLllQKdlKifNx4g5t14GodW4f0RnahVLe85VPuP7WfE/BEkpScx9cqptKnV5tQ727/O\nvfjJoT3Qdxq06VfM6isP63SSlZhIxtq1J+a8rVuH/Tu8BQQQ0KoVwX37urcJaB2Jf9OmCm8iIiIi\nFYwCnZSYH9bvY9R7v3N27aq8P6ITIVXyHpZ34NgBhs8bzr5j+5h65VTa1m576p1t+BY+HQF+QTDs\na2gQVczqKy7rcJCZmHjyVgHr12PT0wEwgYEEtGpFjeuvP7FgSdOmGB/95y8iIiJS0ek3OikR8xP2\ncvuHv9OqbnXeG96RGkF5h7mk9CSGz3eHuTeueIN2tdudWkfWwq+vwnePQfh50H8mBNcv/g1UENbh\nIHPLFjL+zDXnbcMGbEYGACYoiIBzzqHGjTcc3yrAr0kTjLeGoYqIiIhURgp0Umxf/7GHO2eupHX9\nYN6J7khwYN7D9pLSkxgxbwR7j+5lyuVTOL/O+afWUXYGzB0La2ZBZF/oM8X9hK6CS5s7l/2vTMSx\nZw8+4eHUvmscwb17Y7Ozydy8+fgqk+kJCWSu34DNzATAKyiIgIgIavbrR0DrSAIiIvBr3FjhTURE\nROQMYqy1nq7hJFFRUTY+Pt7TZUgRfbFqF3fPXk27hjV4a1gHqgXkHeaS05MZMX8EOw/vZMoVU+hQ\nt8OpdXR4H3w0CHYuh0sfhq73gDElcAeelTZ3LnsemXD8CRsA3t74hIfj3L8fm5UFgFeVKu65bjlP\n3QIiI/FrfBbGy8tDlYuIiIhIaTHGrLDWFmlOkZ7QyWn77Ped3PPxaqIah/DW0A5U8c/745SSkXI8\nzE26fNKph7ndq2DWQEg/CDe9BxHXlkD15cP+l146OcwBOJ049++n5uDBBERGEBgZiW+jRgpvIiIi\nIvIvCnRyWmYv38F9n63hwmahTB8SRZBf3h+lgxkHiZkfw47DO5h0+SQ6hXc6tY4S5sCc0RAUCtHz\n3PPmKgHHwYOkzHgLx959eb5vs7Opc+/4Mq5KRERERCqaIv3J3xjT0xizwRiz2Rhzfx7v322MWWuM\nWWOM+d4Yc1au924xxmzK+bqlJIsXz/hg6Tbu/XQNFzevRdwtHfINc6kZqcTMj2Fr2lZeu+w1Ood3\nLnonLhf8+Ax8PNQd4kb+WCnCnCMlhf0vvcTmy68gOTYWE5D3Hn0+4eFlXJmIiIiIVESFPqEzxngD\nk4ErgZ3AcmPMl9batbmarQSirLXHjDGjgeeBfsaYEOBRIAqwwIqccw+W9I1I2Xj71794bO5aLm9V\nm8mD2hPgm/cCHGmZacQsiOGvtL947bLXuLDehUXvJOsozBkF676EtoOh18vgk/d+dhWFIzmZ5Bkz\nODhzFjY9nepXX03Y6FFkrFv3rzl0JiCA2neN82C1IiIiIlJRFGXIZUdgs7U2EcAYMwvoAxwPdNba\nH3O1XwIMzvm5B7DAWpuSc+4CoCcws/ilS1mLXZTIU1+to3tEHSYNbI+fT94PeNMy04iZH8OW1C28\ndtlrdKnfpeidpO6AWQNgXwJ0fxouuL1CL37iSEoiOW4GB2fNwmZmUv2aawgbPQr/pk0B8D/7bIA8\nV7kUERERESlMUQJdfWBHrtc7gYImQg0Hving3DNn07BKZMpPm3n+2w1cc244E/u3xdc77zB3KOsQ\nIxeMZHPqZiZeOpGL6l9U9E62L3WvZOnIhIGzofmVJVR92XMcOHAiyGVlUb3XNYSNGo1/0yb/ahvc\nu7cCnIiIiIiclhJdFMUYMxj38Mpup3jeSGAkQKNGjUqyJCkBr363iVe+20iftvV46cY2+BQQ5m6d\nfysbD25k4iUT6dqga9E7WfkB/G8cBDeAoV9BrZYlVH3Zyt6/n5S4OA7O+gibnU1w796EjroV/yb/\nDnIiIiIiIsVVlEC3C2iY63WDnGMnMcZcATwEdLPWZuY695J/nPvTP8+11k4DpoF7H7oi1CRlwFrL\nyws28voPm7m+fX1euKEN3l55D388nHWYUQtGsf7gel655BW6NSxipnc5YcEEWDwJmnSDG9+GoJCS\nu4kykr1vP8mxsaTOno11OAi+9lrCRt2K31lnFX6yiIiIiMhpKkqgWw40N8Y0wR3Q+gMDczcwxrQD\npgI9rbX7c701D3jGGFMz53V34IFiVy2lzlrLc99u4M2FW+jfoSHP9D0Xr3zC3JGsI4z6bhTrktfx\n0iUvcUnDS4rWSUYafDoCNs2HjiOhxzPgnffG5OVV9r59JE/PCXJOJ8HX9SHs1lvx05NmERERESkD\nhQY6a63DGDMGdzjzBmZYaxOMMU8A8dbaL4EXgKrAx8a9gMV2a+211toUY8yTuEMhwBN/L5Ai5Ze1\nlqe+WkfcL38xuHMjnri2db5h7mj2UUZ9N4q1SWt5sduLXNbosqJ1krwFZg6AlC3Q6xWIii7BOyh9\n2Xv3kjxtOqkff4y19kSQa9iw8JNFREREREqIsbZ8jXCMioqy8fHxni7jjOVyWR6bm8C7i7cx9MLG\nPNo7ApPPKpNHs48y+rvRrDmwhhe6vcCVZxVxEZPEhTB7iHv1ypvegyYXl+AdlK7sPXtImjaNtE8+\nxVpLjb59Cb11JH4NGni6NBERERGpJIwxK6y1UUVpW6KLokjF5nJZHvr8T2Yu207MxU148Opz8g1z\nx7KPcdt3t7HmwBqe6/pc0cPcsunwzX0Q1gIGzISQirFYSPbu3SRNm0bqp58BUOP66wkbGYNvfS3a\nKiIiIiKeo0AnADhdlvs/XcPHK3Zy2yXNGN+jZcFh7vvbWHVgFc9d/Bw9GvcoQgfZ8M29ED8DWvSE\n66dDQPUSvouSl71rF0lTp5E6Zw4ANf5zPWEjR+Jbr56HKxMRERERUaATwOF0Mf6TNcxZuYuxlzdn\n3BXN8w1z6Y50xvwwhpX7V/Lfi/5LzyY9C+/gWIp7iOXWRdBlHFw+Aby8S/guSlbWzp0kT51K6pzP\nMcZQ88YbCI2JwTc83NOliYiIiIgcp0B3hst2urh79mrmrt7NPd1bMOay5vm2TXekc8f3d7Bi3wqe\nuegZrm56deEd7F8HM/vDoT3Qdxq06VeC1Ze8rB07SJo6lbTPv3AHuZtuInRkDL5163q6NBERERGR\nf1GgO4NlOVyMnbWSb/7cywNXteLWbs3ybZvhyOCOH+5g2d5lPH3R01zT9JrCO9g4Dz4ZDn5BMOxr\naFCkeZ0ekbV9O0lvTiXtiy8w3t7U7N+f0JgR+Nap4+nSRERERETypUB3hsp0OLn9g5V8t24fj/SK\nYPhF+S9OkuHI4M4f7mTZnmU82eVJejfrXfDFrYXfXoMFj0L4edB/JgSXz8VDsrZtI+mNN0mbOxfj\n40PNQQMJHT4C3zq1PV2aiIiIiEihFOjOQBnZTka/v4IfNxzgiT6RDLmgcb5tM52ZjPtxHEv2LOGJ\nLk/Q5+w+BV88OwPmjoU1syCyL/SZ4n5CV85kbd16Isj5+hIyeBAhw4fjW1tBTkREREQqDgW6M0x6\nlpOR78Xzy+Yk/nv9uQzo2CjflWyegwAAHCxJREFUtpnOTMb+OJZfd//KExc+wXVnX1fwxQ/vg48G\nwc7lcOlD0HW8e6+5ciQz8S+S3nyDQ//7CuPnR8iQIYQOj8anVi1PlyYiIiIicsoU6M4gx7IcDH87\nniV/JfP8f87jxqiG+bbNcmZx14938euuX3n0gkfp27xvwRffvQpmDYT0g3DTuxBRyJO8MpaZmEjS\nlDc49PXXGH9/QoYOJTR6GD5hYZ4uTURERETktCnQnSGOZDqIfms58dtSeOWmtlzXLv85bVnOLO7+\n6W4W7VrEhAsmcEOLGwq+eMIcmDMagkIhep573lw5kblly4kgFxBAyLChhEZH4xMa6unSRERERESK\nTYHuDHAoI5uhM5axemcarw1oR6/z8t8UO9uZzf8t/D8W7lzIw50e5sYWN+Z/YZcLFj4LC5+Dhp2g\n3/tQtXzMQcvctImkN97g0DffYgIDCR0xnJBhw/AJCfF0aSIiIiIiJUaBrpJLO5bNkBlLSdh9iMkD\n29Gzdf4bY/8d5n7a8RMPdnqQfq0K2DMu6yjMGQXrvoS2g6HXy+DjXwp3cGoyNm4kacobHJ43D6/A\nQEJjYggZNhSfmjU9XZqIiIiISIlToKvEDh7NYnDcUjbtO8Kbg8/nioj891TLdmUz/ufx/LjjR+7v\neD8DWg3I/8KpO2DWANiXAN2fhgtu9/jiJxkbNpA0eQqH58/Hq0oVQm8dScgttyjIiYiIiEilpkBX\nSSUfyWRQ7FISk44ydcj5XNoy/6GQ2a5s7vv5Pr7f/j33dbiPQecMyv/C25e6V7J0ZMLA2dD8ylKo\nvugy1q93B7kFC/CqWpXQ0aMIveUWvGvU8GhdIiIiIiJlQYGuEtp/OINB05ey4+AxZtzSgYua57+S\no8Pl4P6f72fBtgWMjxrP4IjB+V941YfuPeaCG8DQr6BWy1Kovmgy1q7lwJQpHPnue7yqViXsttsI\nuWUI3sHBHqtJRERERKSsKdBVMvsOZTBg+hL2pmXw1tCOXNAs/9UcHS4HDyx6gPnb5nNP1D0MiRyS\nd0OXExZMgMWToEk3uPFtCPLM4iLpCQkkTZ7CkR9+wKtaNcJuv52QITcryImIiIjIGUmBrhLZnZrO\nwOlLOHA4k3eiO9Khcf6hy+Fy8OAvD/Lt1m+5+/y7uSXylrwbZqTBpyNg03zoEAM9/wvevqV0B/lL\n/zOBpMmTOfLjj3hVr07YHWMIuflmvKtXL/NaRERERETKCwW6SmJHyjEGxi4h9Wg27w7vxPln5b8Y\niNPl5OFfH+abv75hbPuxDGs9LO+GyVtg5gBI2QK9XoGo6FKqPn/pf/xB0qTJHFm4EK/gYGqNvZOa\ngwfjXa1amdciIiIiIlLeKNBVAtuSjzJw+lIOZ2Tz/ohOtGmY/4IgTpeTR359hK8Sv+LOdncy4twR\neTdMXAizh7hXr7z5c2hycSlVn7f01as5MHkyR39ehHdwMLXGjXUHuapVy7QOEREREZHyTIGugvsr\n6SgDpi0hw+Hkw5jOtK6f/1wyp8vJhN8mMDdxLmPajiHmvJi8Gy6bDt/cB2EtYMBMCGlSStX/W/qq\nVRyYPIWjixbhXaMGte66i5qDBuFdtUqZ1SAiIiIiUlEo0FVgm/cfZuD0pThdlpkxnTknPP/5ZC7r\n4rHFj/Hlli+5rc1t3Nrm1n83cma7g1x8HLToCddPh4CymaN27PeVJE2ezNFff8W7Zk1q/d/d1Bww\nUEFORERERKQACnQV1Ia9hxkUuwQwzBrZmeZ18p9T5rIuHl/8OJ9v/pxRbUYxuu3ofzc6luIeYrl1\nEXQZB5dPAC/v0ruBv7tdscId5H5bjHdICLXH30PN/v3xqqIgJyIiIiJSGAW6CihhdxqDY5fi5+PF\nhzGdaVYr/3llLuviicVP8Nmmzxh53khua3PbvxvtXw8z+8GhPdB3GrTpV4rVux2Lj+fA5MkcW7wE\n79BQao8fT80B/fEKCir1vkVEREREKgsFugrmj51pDI5bShU/bz6M6UzjsPyfZLmsi6eWPMWnmz5l\nxLkjGNN2DMaYkxttnAefDAffQPdm4Q07lGr9R5ctI2nyFI4tXYp3WBi177uPmv374RUYWKr9ioiI\niIhURgp0FcjK7QcZMmMZ1QN8mTWyMw1D8n+aZa3lmaXP8PHGj4luHc2d7e48OcxZC7+9BgsehfDz\noP+HENyg1Go/unQZSZMmcWz5crxrhVHngfupcdNNCnIiIiIiIsWgQFdBxG9NYehbywmp4sfMkZ2p\nXyP/IPR3mPtow0cMixzGuPbjTg5z2RkwdyysmQWRfaHPFPAr+aGO1lqOLV1K0qTJHIuPx6dWLeo8\n+IA7yAUElHh/IiIiIiJnGgW6CmBpYjLD3l5O3eoBfBjTmbrB+Ychay3PLX+OWRtmMSRiCHedf9fJ\nYe7wPvhoEOxcDpc+BF3Hu/eaK0HWWo4tXsyByVNIX7ECn9q1qfPQQ9S48QYFORERERGREqRAV879\nujmJ4e8sp0HNID4c0Yna1QsOc88vf54P1n3A4HMGc0/UPSeHud2rYNZASD8IN70LEX1KtFZrLUd/\n+42kSZNJX7kSnzp1qPPIw9S44Qa8/P1LtC8REREREVGgK9cWbjzAyHfjaRxahQ9iOhFWNf9QZK3l\nxfgXeX/d+ww6ZxD3drj35DCXMAfmjIagUIie5543V0KstRz95VeSJk8mfdUqfOrWpc6ER9xBzs+v\nxPoREREREZGTKdCVUz+s38eo937n7NpVeX9EJ0Kq5B+MrLW8vOJl3l37LgNaDeC+DvedCHMuFyx8\nDhY+Cw07Qb/3oWrtEqnRWsvRRYs4MHkyGavX4BMeTt3HHiX4+usV5EREREREyoACXTk0P2Evt3/4\nO63qVue94R2pEVRwmHvl91d4O+Ft+rXsxwMdHzgR5rKOwpxRsO5LaDsYer0MPsUf+mit5ejPP3Ng\n8hQy1qzBp144dR9/nBp9r8MoyImIiIiIlJkiBTpjTE/gVcAbiLXWPvuP97sCE4HzgP7W2k9yvecE\n/sh5ud1ae21JFF5Zff3HHu6cuZLW9YN5J7ojwYG++ba11vLaytd468+3uKnFTTzY6cETYS51B8wa\nAPsSoPvTcMHtxV78xFrLkZ9+ImnyFDL+/BPf+vWp+8Tj1LhOQU5ERERExBMKDXTGGG9gMnAlsBNY\nboz50lq7Nlez7cBQ4J48LpFurW1bArVWel+s2sXds1fTrmEN3hrWgWoBBYe511e+TuwfsdzQ4gYe\n6vwQXsbL/eb2pe6VLB2ZMHA2NL+yWHVZazny44/uIJeQgG+DBoQ/9STBffpgfPOvUURERERESldR\nntB1BDZbaxMBjDGzgD7A8UBnrd2a856rFGo8I3y6YifjP1lNh8YhzBjagSr+Bf+rmbJ6CtP/mM5/\nmv+HRzo/ciLMrfrQvcdc9fow9Cuo1fK0a7LWcuT77zkwZQqZa9fh27Ah4U8/TfC1vRXkRERERETK\ngaIEuvrAjlyvdwKdTqGPAGNMPOAAnrXWfv7PBsaYkcBIgEaNGp3CpSuHj5Zv5/7P/uDCZqHEDulA\noJ93ge3fWPUGb65+k+vOvo4JF0xwhzmXExZMgMWToEk3uPFtCAo5rXqsy8Xh778nafIUMtevx7dR\nI8KfeYbg3r0U5EREREREypGyWBTlLGvtLmNMU+AHY8wf1totuRtYa6cB0wCioqJsGdRUbry/ZBsP\nf/4nXVvUYtrN5xPgW3CYe3P1m0xZPYU+zfrw+IWPu8NcRhp8OgI2zYcOMdDzv+B96sHLulwcXvAd\nSVOmkLlhA35nnUX4s/8luFcvjI/WzxERERERKW+K8lv6LqBhrtcNco4VibV2V873RGPMT0A7YEuB\nJ50h3v71Lx6bu5bLW9Vm8qD2hYa5aWumMXnVZHo37X0izCVvgZkDIGUL9HoFoqJPuQ7rcnF4/nyS\nprxB5saN+DVuTL3nn6P61VcryImIiIiIlGNF+W19OdDcGNMEd5DrDwwsysWNMTWBY9baTGNMGNAF\neP50i61MYhcl8tRX6+gRWYfXB7THz8er4PZ/xPL6yte5puk1PNnlSby9vCFxIXx8i7vBzZ9Dk4tP\nqQbrcnF43jz3E7lNm/Fr0oR6L7xA9auvwngXHC5FRERERMTzCg101lqHMWYMMA/3tgUzrLUJxpgn\ngHhr7ZfGmA7AHKAm0NsY87i1NhI4B5ias1iKF+45dGvz6eqMMeWnzTz/7QauOTecif3b4utdcJib\n8ecMXv39Va5ucjVPd3naHeaWTYdv7oOwFjBgJoQ0KXL/1unk0LffkvTGG2Rt3oJfs2bUe/FFql/V\nU0FORERERKQCMdaWrylrUVFRNj4+3tNllJpXv9vEK99tpE/berx0Yxt8Cglzb//5Ni+teImrGl/F\nMxc/g4+17iAXHwctesL10yGgepH6tk4nh77+xh3kEhPxO7sZtW67jWo9eijIiYiIiIiUE8aYFdba\nqKK01QSpMmKt5eUFG3n9h81c374+L9zQBm+vgjf6fifhHV5a8RLdz+ruDnMZh2D2ENi6CLqMg8sn\ngFfhQcwd5L4macobZP31F/7Nz6b+xFeo1r07xqvgQCkiIiIiIuWXAl0ZsNby7Lfrmbowkf4dGvJM\n33PxKiTMvbf2PV6Mf5Erz7qSZ7s+i0/SZpjZDw7thr5ToU3/wvt1ODj01VckvfEmWVu34t+iBfUn\nTqRa9ysV5EREREREKgEFulJmreWpr9YR98tfDO7ciCeubV1omPtg3Qc8v/x5rmh0Bc91fQ7fzT/A\nJ8PBNxCGfg0NOxTcp8NB2tz/kfTmG2Rv245/q1bUf+1Vql1xhYKciIiIiEglokBXilwuy2NzE3h3\n8TaGdWnMhF4RGFNwmJu5fibPLnuWyxpexvMXP4fv4imw4FEIPw/6fwjBDfI91zocpH05l6Q33yR7\n+3b8zzmHBpNep+pllynIiYiIiIhUQgp0pcTlsjz0+Z/MXLadkV2b8sBVrQoNcx+t/4hnlj7DJQ0v\n4cULn8J37p2weiZE9oU+U8AvKM/zbHY2aV9+SdKbU8nesQP/iHNoMHmSO8gV0qeIiIi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"text/plain": [ "<matplotlib.figure.Figure at 0x7fcbfe69f6d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}\n", "for update_rule in ['adam', 'rmsprop']:\n", " print('running with ', update_rule)\n", " model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n", "\n", " solver = Solver(model, small_data,\n", " num_epochs=5, batch_size=100,\n", " update_rule=update_rule,\n", " optim_config={\n", " 'learning_rate': learning_rates[update_rule]\n", " },\n", " verbose=True)\n", " solvers[update_rule] = solver\n", " solver.train()\n", " print()\n", "\n", "plt.subplot(3, 1, 1)\n", "plt.title('Training loss')\n", "plt.xlabel('Iteration')\n", "\n", "plt.subplot(3, 1, 2)\n", "plt.title('Training accuracy')\n", "plt.xlabel('Epoch')\n", "\n", "plt.subplot(3, 1, 3)\n", "plt.title('Validation accuracy')\n", "plt.xlabel('Epoch')\n", "\n", "for update_rule, solver in list(solvers.items()):\n", " plt.subplot(3, 1, 1)\n", " plt.plot(solver.loss_history, 'o', label=update_rule)\n", " \n", " plt.subplot(3, 1, 2)\n", " plt.plot(solver.train_acc_history, '-o', label=update_rule)\n", "\n", " plt.subplot(3, 1, 3)\n", " plt.plot(solver.val_acc_history, '-o', label=update_rule)\n", " \n", "for i in [1, 2, 3]:\n", " plt.subplot(3, 1, i)\n", " plt.legend(loc='upper center', ncol=4)\n", "plt.gcf().set_size_inches(15, 15)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Train a good model!\n", "Train the best fully-connected model that you can on CIFAR-10, storing your best model in the `best_model` variable. We require you to get at least 50% accuracy on the validation set using a fully-connected net.\n", "\n", "If you are careful it should be possible to get accuracies above 55%, but we don't require it for this part and won't assign extra credit for doing so. Later in the assignment we will ask you to train the best convolutional network that you can on CIFAR-10, and we would prefer that you spend your effort working on convolutional nets rather than fully-connected nets.\n", "\n", "You might find it useful to complete the `BatchNormalization.ipynb` and `Dropout.ipynb` notebooks before completing this part, since those techniques can help you train powerful models." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(Iteration 1 / 1630) loss: 3.384998\n", "(Epoch 0 / 10) train acc: 0.140000; val_acc: 0.163000\n", "(Iteration 11 / 1630) loss: 2.050596\n", "(Iteration 21 / 1630) loss: 1.964341\n", "(Iteration 31 / 1630) loss: 1.917916\n", "(Iteration 41 / 1630) loss: 1.769217\n", "(Iteration 51 / 1630) loss: 1.712934\n", "(Iteration 61 / 1630) loss: 1.733420\n", "(Iteration 71 / 1630) loss: 1.747500\n", "(Iteration 81 / 1630) loss: 1.640847\n", "(Iteration 91 / 1630) loss: 1.637538\n", "(Iteration 101 / 1630) loss: 1.725998\n", "(Iteration 111 / 1630) loss: 1.668272\n", "(Iteration 121 / 1630) loss: 1.665384\n", "(Iteration 131 / 1630) loss: 1.613706\n", "(Iteration 141 / 1630) loss: 1.538027\n", "(Iteration 151 / 1630) loss: 1.438601\n", "(Iteration 161 / 1630) loss: 1.513318\n", "(Epoch 1 / 10) train acc: 0.428000; val_acc: 0.445000\n", "(Iteration 171 / 1630) loss: 1.620378\n", "(Iteration 181 / 1630) loss: 1.521286\n", "(Iteration 191 / 1630) loss: 1.639234\n", "(Iteration 201 / 1630) loss: 1.524642\n", "(Iteration 211 / 1630) loss: 1.468406\n", "(Iteration 221 / 1630) loss: 1.510748\n", "(Iteration 231 / 1630) loss: 1.576592\n", "(Iteration 241 / 1630) loss: 1.415114\n", "(Iteration 251 / 1630) loss: 1.448097\n", "(Iteration 261 / 1630) loss: 1.449249\n", "(Iteration 271 / 1630) loss: 1.550051\n", "(Iteration 281 / 1630) loss: 1.386890\n", "(Iteration 291 / 1630) loss: 1.364643\n", "(Iteration 301 / 1630) loss: 1.380491\n", "(Iteration 311 / 1630) loss: 1.525156\n", "(Iteration 321 / 1630) loss: 1.449422\n", "(Epoch 2 / 10) train acc: 0.488000; val_acc: 0.484000\n", "(Iteration 331 / 1630) loss: 1.506308\n", "(Iteration 341 / 1630) loss: 1.382195\n", "(Iteration 351 / 1630) loss: 1.317769\n", "(Iteration 361 / 1630) loss: 1.347456\n", "(Iteration 371 / 1630) loss: 1.264789\n", "(Iteration 381 / 1630) loss: 1.422957\n", "(Iteration 391 / 1630) loss: 1.324047\n", "(Iteration 401 / 1630) loss: 1.362608\n", "(Iteration 411 / 1630) loss: 1.295100\n", "(Iteration 421 / 1630) loss: 1.433657\n", "(Iteration 431 / 1630) loss: 1.358002\n", "(Iteration 441 / 1630) loss: 1.322887\n", "(Iteration 451 / 1630) loss: 1.302578\n", "(Iteration 461 / 1630) loss: 1.304497\n", "(Iteration 471 / 1630) loss: 1.304353\n", "(Iteration 481 / 1630) loss: 1.403411\n", "(Epoch 3 / 10) train acc: 0.528000; val_acc: 0.482000\n", "(Iteration 491 / 1630) loss: 1.387320\n", "(Iteration 501 / 1630) loss: 1.285971\n", "(Iteration 511 / 1630) loss: 1.252958\n", "(Iteration 521 / 1630) loss: 1.361409\n", "(Iteration 531 / 1630) loss: 1.409354\n", "(Iteration 541 / 1630) loss: 1.254851\n", "(Iteration 551 / 1630) loss: 1.294496\n", "(Iteration 561 / 1630) loss: 1.218601\n", "(Iteration 571 / 1630) loss: 1.164685\n", "(Iteration 581 / 1630) loss: 1.241353\n", "(Iteration 591 / 1630) loss: 1.206849\n", "(Iteration 601 / 1630) loss: 1.244032\n", "(Iteration 611 / 1630) loss: 1.416000\n", "(Iteration 621 / 1630) loss: 1.214717\n", "(Iteration 631 / 1630) loss: 1.210489\n", "(Iteration 641 / 1630) loss: 1.215908\n", "(Iteration 651 / 1630) loss: 1.314133\n", "(Epoch 4 / 10) train acc: 0.576000; val_acc: 0.484000\n", "(Iteration 661 / 1630) loss: 1.209332\n", "(Iteration 671 / 1630) loss: 1.224548\n", "(Iteration 681 / 1630) loss: 1.275705\n", "(Iteration 691 / 1630) loss: 1.311225\n", "(Iteration 701 / 1630) loss: 1.335745\n", "(Iteration 711 / 1630) loss: 1.212197\n", "(Iteration 721 / 1630) loss: 1.192594\n", "(Iteration 731 / 1630) loss: 1.101223\n", "(Iteration 741 / 1630) loss: 1.214173\n", "(Iteration 751 / 1630) loss: 1.231760\n", "(Iteration 761 / 1630) loss: 1.195017\n", "(Iteration 771 / 1630) loss: 1.163030\n", "(Iteration 781 / 1630) loss: 1.410662\n", "(Iteration 791 / 1630) loss: 1.215337\n", "(Iteration 801 / 1630) loss: 1.207495\n", "(Iteration 811 / 1630) loss: 1.188255\n", "(Epoch 5 / 10) train acc: 0.584000; val_acc: 0.509000\n", "(Iteration 821 / 1630) loss: 1.183808\n", "(Iteration 831 / 1630) loss: 1.178757\n", "(Iteration 841 / 1630) loss: 1.128746\n", "(Iteration 851 / 1630) loss: 1.067505" ] } ], "source": [ "best_model = None\n", "################################################################################\n", "# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might #\n", "# batch normalization and dropout useful. Store your best model in the #\n", "# best_model variable. #\n", "################################################################################\n", "update_rule = 'adam'\n", "model = FullyConnectedNet([200, 200, 100, 100, 100], weight_scale=5e-2)\n", "solver = Solver(model, data,\n", " num_epochs=10, batch_size=300,\n", " update_rule=update_rule,\n", " optim_config={\n", " 'learning_rate': learning_rates[update_rule]\n", " },\n", " verbose=True)\n", "solver.train()\n", "best_model = solver\n", "################################################################################\n", "# END OF YOUR CODE #\n", "################################################################################" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Test you model\n", "Run your best model on the validation and test sets. You should achieve above 50% accuracy on the validation set." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y_test_pred = np.argmax(best_model.loss(data['X_test']), axis=1)\n", "y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)\n", "print('Validation set accuracy: ', (y_val_pred == data['y_val']).mean())\n", "print('Test set accuracy: ', (y_test_pred == data['y_test']).mean())" ] } ], "metadata": { "kernelspec": { "display_name": "cs231n", "language": "python", "name": "cs231n" }, "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 }	gpl-3.0
arbalet-project/arbadoc	notebooks/en/3.create_an_arbapp.ipynb	1	12103	{ "metadata": { "name": "", "signature": "sha256:e2602fda9dbbd3b2d9f2df737aae5ad4b74167755e166f90099d6f9345809a54" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Create an Arbalet application" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we know the bare interface to send models to our table, we learn now how to package a more complex application for Arbalet by inheriting from a class literrally named `Application`. This tutorial assumes that you already know the basics of [Object-Oriented Programming](http://en.wikipedia.org/Object-oriented programming) and especially the notions of class, its constructor, the attributes/methods of a class, and inheritance." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The mother class `Application` already contains all what we need to create an application packaged into a class. We don't even need to import `Arbalet` nor `Mdel` since they are already included in this magic `Application` class. Let's start by importing some modules:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from arbalet.core import Application, Rate\n", "import argparse # For argument parsing" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create a stub of application" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we create a stub for our custom application `WormApp`, displaying a worm by inheriting from `Application` and creating an empty constructor." ] }, { "cell_type": "code", "collapsed": false, "input": [ "class WormApp(Application):\n", " def __init__(self):\n", " Application.__init__(self) # Basic inheritance calls the super constructor" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The previous cell contains almost no code so it does not show anything, this is just the basic template we are going to improve. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This `Application` class is just a way of packaging/organizing an application, but the most important point to remember is just that it contains an `Arbalet` controller class accessible via `WormApp.arbalet` and that all the code of the application must be in the `run()` method.\n", "\n", "This way of packaging the app in the `Application` class might looks silly at first sight but allows powerful features such as application inheritence and method overloading, automatic closure and more.\n", "\n", "To illustrate this, we can just print the size of the connected table in `run()`:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "class WormApp(Application):\n", " def __init__(self):\n", " Application.__init__(self)\n", " \n", " def run(self):\n", " print(\"My height:\", self.arbalet.height, \"pixels\") # Contains the number of pixels in height (15 for the default table)\n", " print(\"My width:\", self.arbalet.width, \"pixels\") # Contains the number of pixels in width (10 for the default table)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Execution of the previous cell did not show anything, because this is only the declaration of the application `WormApp`, running it requires a call to `start()`:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "WormApp().start()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This time the code in `run()` has been executed and the size has been printed, but the simulator opened and closed instantly. This is because this app has a very short execution time, and because the `Application` class always release resources when there is no more code to run in `run()`, thus using `Application` there is no more need for calling `close()`, it is automatic." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Improve the worm app with actual code dealing with pixels" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the previous tutorial we have drawn a flag with no harcoded digit and used only fields width and height. In practice your application __must__ use these fields to adapt its content to any size. In some occasions you will need some constraints to be respected, e.g. some ratio between height and width, an odd or even number of columns or rows, or, more rarely, a specific size. In that case you should check at starting whether the constraints are respected and, if not, exit cleanly with a meaningful error message." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This class also encapsulates for you command-line argument parsing, which are then stored in a [namespace](https://docs.python.org/2.7/library/argparse.html#argparse.Namespace) at `self.args`. Some of arguments are reserved to the super class and are common to all applications. For instance the `--no-gui` argument that you have already heard about in first [software tutorials](https://github.com/arbalet-project/arbadoc/wiki/Software-tutorials#arguments) can be accessed through the field `self.args.no_gui`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Applications needing their own arguments can declare their own `argparse.ArgumentParser`, fill it with its accepted arguments (make sure there is no name collision with the common ones) and pass the object to `Application`. Do not call `parser.parse_args()` yourself, `Application` will do this for us!\n", "\n", "Let's add a `--color` argument with short name `-col`, a string corresponding to the color of the worm, red by default:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "class WormApp(Application):\n", " def __init__(self):\n", " parser = argparse.ArgumentParser(description='This trivial application shows a worm')\n", " parser.add_argument('-col', '--color',\n", " type=str,\n", " default='red',\n", " help='Color of the worm (string of a HTML color)')\n", " Application.__init__(self, parser) # The argparse object is passed to class Application here" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The constructor should just be used to initialize the workspace, declare arguments, and eventually initialize the model with some color, not more. The heart of the application must overload the method `Application.run()`. This `run()` also comes with a `start()` whose call ensures that all the resources are closed after a successful or failed run.\n", "\n", "As our first code in `run()`, let's display a red pixel browsing the whole table. For this we will simply cleanup the model (setting all pixels black with `set_all()`) and then attributing the red color to a single pixel with `set_pixel()`.\n", "\n", "There are 3 important elements in the code here after:\n", "\n", "## The `Rate` class\n", "The `Rate` class is a controlled loop, it allows you to loop at a specific frame rate in hertz whatever the time your calculations take (assuming that the CPU is not overloaded). For instance `Rate(5)` will loop at ~5Hz, resulting each pixel to stay lit 200ms.\n", "\n", "## Model locking\n", "The `with self.arbalet.user_model` statement allows to lock the model. In general this prevents the hardware and simulator from reading an unstable frame. For instance, to draw the next pixel we decided to set all pixels to black. If the frame is sent to the table right after `set_all` the table will actually show a black screen which we do not want. Therefore, locking the model while painting on it allows to display only stable models.\n", "\n", "## Text display\n", "The model allows to render pixelated text on the table thanks to a single call `user_model.write(\"Some text\", \"color\")`" ] }, { "cell_type": "code", "collapsed": false, "input": [ "class WormApp(Application):\n", " def __init__(self):\n", " parser = argparse.ArgumentParser(description='This trivial application shows a worm')\n", " parser.add_argument('-col', '--color',\n", " type=str,\n", " default='red',\n", " help='Color of the worm (string of a HTML color)')\n", " Application.__init__(self, parser) # The argparse object is passed to class Application here\n", "\n", " def run(self):\n", " # We start by displaying some text\n", " self.arbalet.user_model.write('My color: ' + self.args.color.upper(), self.args.color)\n", "\n", " # Then we display the actual worm\n", " rate = Rate(5)\n", " for h in range(self.arbalet.height):\n", " for w in range(self.arbalet.width):\n", " with self.arbalet.user_model:\n", " self.arbalet.user_model.set_all('black')\n", " # The model is now all black (i.e. light off), in an unstable state\n", " self.arbalet.user_model.set_pixel(h, w, self.args.color)\n", " # The next pixel has been lit, the model is now stable we can release the lock\n", " rate.sleep()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can execute this new app by calling `start()`:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "WormApp().start()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: Python notebook like this page are not very suitable for Arbalet applications, they must be saved into a new subfolder of the [arbapp](https://github.com/arbalet-project/arbapps). To be able to change the color thanks to the argument `--color` that we specified you must convert this notebook into python script and execute it directly. For this you can clic on File > Download as > Python (.py). Then run `python 3.create_an_arbapp.py --color blue` to execute the app with a blue worm." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Congratulations! You are now ready to develop your own app, make sure you create its own folder in the [`arbapps/`](https://github.com/arbalet-project/arbapps) repository.\n", "\n", "If you need extra features for games like reading a keyboard/joystick, playing music, you can use [pygame](http://www.pygame.org/docs/), for any other feature you should find a python module satisfying your needs. The last tutorial illustrates the touch feature." ] } ], "metadata": {} } ] }	gpl-3.0
JuliaPackageMirrors/JFVM.jl	examples/jfvm-a-finite-volume-tool-for-julia.ipynb	1	117508	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "During the Christmass holydays, I spent some time to convert my Matlab [FVTool](https://github.com/simulkade/FVTool) to a [Julia package](http://pkg.julialang.org/). The result, which I call [JFVM](https://github.com/simulkade/JFVM.jl) is a bit Matlabesque, but the package works. In this post I'm going to show how it works.\n", "\n", "## Installation\n", "To install the package, you can use `Pkg.add(\"JFVM\")` in Julia, or clone the most recent version (recommended). You need to have Julia installed. JFVM relies on `PyPlot`, `PyCall`, and `Mayavi` for visualization. Here's the procedure.\n", "\n", "<!-- TEASER_END -->\n", "\n", "### Linux\n", "You need to first install [Python](https://www.python.org/), [Matplotlib](http://matplotlib.org/), and [Mayavi](http://code.enthought.com/projects/mayavi/). To do it on an Ubuntu-based system, try\n", "```\n", "sudo apt-get install python2.7 python-matplotlib mayavi2\n", "```\n", "Then go to your `.julia/v0.4` or `.julia/v0.3` (recommended) folder and type\n", "```\n", "git clone https://github.com/simulkade/JFVM.jl.git\n", "```\n", "\n", "### Windows\n", "There used to be a few issues with 3D visualization in windows. The current version however should work fine. This is the workflow if you want to give it a try:\n", "\n", " - Download and install Anaconda\n", " - Run anaconda command prompt (as administrator) and install mayavi and wxpython:\n", " * `conda install mayavi`\n", " * `conda install wxpython` (Not necessary if you clone the last version of JFVM)\n", " - Install github for windows\n", " - open github shell, go to `.julia/v0.4` or `.julia/v0.3` and type:\n", " * `git clone https://github.com/simulkade/JFVM.git` \n", "\n", "Please let me know if it does not work on your windows machines.\n", "\n", "## What does it do?\n", "It solves a transient convection-diffusion equation, i.e., $$\\alpha\\frac{\\partial\\phi}{\\partial t}+\\nabla.\\left(\\mathbf{u}\\phi\\right)+\\nabla.\\left(-D\\nabla\\phi\\right)+\\beta\\phi=\\gamma$$ with the boundary condition: $$a\\nabla\\phi.\\mathbf{n}+b\\phi=c.$$ Each term, i.e., transient, convection, diffusion, linear source, and constant source terms can be called via a separate function, which makes it possible to use the code for solving various coupled and even non-linear convection-diffusion equations. The main difference between this code and the previous Matlab version is that in this one, nonuniform grids can be defined. The supported coordinates are \n", " - 1D axisymmetric (radial)\n", " - 2D radial (r, theta)\n", " - 2D Cartesian\n", " - 3D Cartesian\n", " - 2D axisymmetric (cylindrical, r, z)\n", " - 3D cylindrical (r, theta, z)\n", "One other thing that I always had in mind is the possibility to use hetrogeneous transfer coefficients, e.g., a diffusion coefficient that is different on each cell. In addition, I wanted to be able to define different boundary conditions on the faces of the boundary cell. The last feature that I constantly miss in other PDE solvers is the Robin boundary condition, which can be defined quite easily in JFVM. \n", "One last feature that I added in later stages was the periodic boundary condition.\n", "\n", "## A simple example\n", "This is perhaps the simplest example: a diffusion equation on a 1D domain, with a Dirichlet boundary on each side. The equation read $$\\nabla (-D \\nabla \\phi) = 0$$ where $\\phi$ is zero on the left and one on the right side of the domain. The formulation in `JFVM` always follows this sequence:\n", " - Create a domain and mesh\n", " - Create a boundary for the domain (default: Neumann)\n", " - Change the boundary condition (optional)\n", " - Define the initial condition (optional, only if you have transient terms)\n", " - Define the transfer coefficients\n", " - Calculate the matrix of coefficients and RHS vector for each transfer term in the PDE\n", " - Call the linear solver\n", " - Call the visualization tool\n", "There can be other steps in between depending on the problems." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "using JFVM, PyPlot" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ 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" ], "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x7fa9bcbbd5d0>)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject <matplotlib.text.Text object at 0x7fa9bb423310>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L= 1.0 # length of the domain\n", "Nx= 20 # number of cells\n", "m= createMesh1D(Nx, L) # create the domain and mesh\n", "BC= createBC(m) # create the boundary condition\n", "BC.left.a[:]= 0.0 # left side Neumann term equal to zero\n", "BC.left.b[:]= 1.0 # left side Dirichlet term coefficient equal to one\n", "BC.left.c[:]= 0.0 # left side Dirichlet term value equal to zero\n", "BC.right.a[:]= 0.0\n", "BC.right.b[:]= 1.0\n", "BC.right.c[:]= 1.0\n", "D_val= 1.0 # value of the transfer coefficient\n", "D= createCellVariable(m, D_val) # assign D_val to all the cells\n", "# harmonic average of the transfer coefficient on the cell faces:\n", "D_face= harmonicMean(D)\n", "(Mbc, RHSbc)= boundaryConditionTerm(BC)\n", "Mdiff= diffusionTerm(D_face)\n", "M= Mdiff+Mbc # matrix of coefficients\n", "RHS= RHSbc # off course!\n", "phi= solveLinearPDE(m, M, RHS) # solve the linear PDE\n", "figure(figsize=(6.0,5.0)) # unit is [cm] for me\n", "visualizeCells(phi) # visualize the results\n", "# decorate the plot\n", "xlabel(\"x\", fontsize=16)\n", "ylabel(L\"\\phi\", fontsize=16)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A colorful a bit playful case\n", "The other night, my daughter wanted to see what I do with my computer. So I decided to solve an equation with fancy colorful results. So I created a diffusion process on a radial coordinate, Dirichlet boundary condition near the center of the circle (with alternationg zero-one values on the left boundary). I reduced the value of the diffusion coefficient in the direction of increasing $\\theta$ to increase the contrast in the result. Let me recreate it here." ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ 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" ], "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x7fa9ba2f0b90>)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject <matplotlib.collections.PolyCollection object at 0x7fa9b9744350>" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L= 1.0 # length of the domain\n", "Nx= 30 # number of cells\n", "Ntheta= 520\n", "m= createMeshRadial2D(Nx, Ntheta, L, 2π) # create the domain and mesh\n", "m.cellcenters.x+=0.1\n", "m.facecenters.x+=0.1\n", "BC= createBC(m) # create the boundary condition\n", "BC.left.a[:]= 0.0 # left side Neumann term equal to zero\n", "BC.left.b[:]= 1.0 # left side Dirichlet term coefficient equal to one\n", "BC.left.c[:]= 0.0 # left side Dirichlet term value equal to zero\n", "BC.left.c[1:5:Ntheta]= 1.0\n", "BC.right.a[:]= 0.0\n", "BC.right.b[:]= 1.0\n", "BC.right.c[:]= 0.0\n", "BC.bottom.periodic=true # periodic boundary condition\n", "D_val= 1.0 # value of the transfer coefficient\n", "D= createCellVariable(m, D_val) # assign D_val to all the cells\n", "# harmonic average of the transfer coefficient on the cell faces:\n", "D_face= harmonicMean(D)\n", "D_face.yvalue[:]=0.003D_val\n", "(Mbc, RHSbc)= boundaryConditionTerm(BC)\n", "Mdiff= diffusionTerm(D_face)\n", "M= -Mdiff+Mbc # matrix of coefficients\n", "RHS= RHSbc # off course!\n", "phi= solveLinearPDE(m, M, RHS) # solve the linear PDE\n", "visualizeCells(phi) # visualize the results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Not really bad!\n", "That's it for now. I will come back with more real life examples." ] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.3.6", "language": "julia", "name": "julia 0.3" }, "language_info": { "name": "julia", "version": "0.3.7" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
SunnyMarkLiu/Kaggle-House-Prices	Data_Cleaning_and_Feature_Engineering_Final_Version.ipynb	1	588937	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# pandas\n", "import pandas as pd\n", "from pandas import Series,DataFrame\n", "\n", "# numpy, matplotlib, seaborn\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "sns.set_style('whitegrid')\n", "%matplotlib inline\n", "\n", "from IPython.display import display\n", "\n", "# remove warnings\n", "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Load and pick Datas" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1460, 81)\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Id</th>\n", " <th>MSSubClass</th>\n", " <th>MSZoning</th>\n", " <th>LotFrontage</th>\n", " <th>LotArea</th>\n", " <th>Street</th>\n", " <th>Alley</th>\n", " <th>LotShape</th>\n", " <th>LandContour</th>\n", " <th>Utilities</th>\n", " <th>...</th>\n", " <th>PoolArea</th>\n", " <th>PoolQC</th>\n", " <th>Fence</th>\n", " <th>MiscFeature</th>\n", " <th>MiscVal</th>\n", " <th>MoSold</th>\n", " <th>YrSold</th>\n", " <th>SaleType</th>\n", " <th>SaleCondition</th>\n", " <th>SalePrice</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>60</td>\n", " <td>RL</td>\n", " <td>65.0</td>\n", " <td>8450</td>\n", " <td>Pave</td>\n", " <td>NaN</td>\n", " <td>Reg</td>\n", " <td>Lvl</td>\n", " <td>AllPub</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>2008</td>\n", " <td>WD</td>\n", " <td>Normal</td>\n", " <td>208500</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>1 rows × 81 columns</p>\n", "</div>" ], "text/plain": [ " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", "0 1 60 RL 65.0 8450 Pave NaN Reg \n", "\n", " LandContour Utilities ... PoolArea PoolQC Fence MiscFeature MiscVal \\\n", "0 Lvl AllPub ... 0 NaN NaN NaN 0 \n", "\n", " MoSold YrSold SaleType SaleCondition SalePrice \n", "0 2 2008 WD Normal 208500 \n", "\n", "[1 rows x 81 columns]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "(1459, 80)\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Id</th>\n", " <th>MSSubClass</th>\n", " <th>MSZoning</th>\n", " <th>LotFrontage</th>\n", " <th>LotArea</th>\n", " <th>Street</th>\n", " <th>Alley</th>\n", " <th>LotShape</th>\n", " <th>LandContour</th>\n", " <th>Utilities</th>\n", " <th>...</th>\n", " <th>ScreenPorch</th>\n", " <th>PoolArea</th>\n", " <th>PoolQC</th>\n", " <th>Fence</th>\n", " <th>MiscFeature</th>\n", " <th>MiscVal</th>\n", " <th>MoSold</th>\n", " <th>YrSold</th>\n", " <th>SaleType</th>\n", " <th>SaleCondition</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1461</td>\n", " <td>20</td>\n", " <td>RH</td>\n", " <td>80.0</td>\n", " <td>11622</td>\n", " <td>Pave</td>\n", " <td>NaN</td>\n", " <td>Reg</td>\n", " <td>Lvl</td>\n", " <td>AllPub</td>\n", " <td>...</td>\n", " <td>120</td>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>MnPrv</td>\n", " <td>NaN</td>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>2010</td>\n", " <td>WD</td>\n", " <td>Normal</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>1 rows × 80 columns</p>\n", "</div>" ], "text/plain": [ " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", "0 1461 20 RH 80.0 11622 Pave NaN Reg \n", "\n", " LandContour Utilities ... ScreenPorch PoolArea PoolQC Fence \\\n", "0 Lvl AllPub ... 120 0 NaN MnPrv \n", "\n", " MiscFeature MiscVal MoSold YrSold SaleType SaleCondition \n", "0 NaN 0 6 2010 WD Normal \n", "\n", "[1 rows x 80 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_data = pd.read_csv('data/train.csv')\n", "test_data = pd.read_csv('data/test.csv')\n", "\n", "print train_data.shape\n", "display(train_data.head(1))\n", "# display(train_data.info())\n", "\n", "print test_data.shape\n", "display(test_data.head(1))\n", "# display(test_data.info())" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold']\n" ] } ], "source": [ "previous_num_columns = train_data.select_dtypes(exclude=['object']).columns.values.tolist()\n", "previous_num_columns.remove('Id')\n", "previous_num_columns.remove('SalePrice')\n", "print previous_num_columns" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "train: (1,)\n", "test: (0,)\n" ] }, { "data": { "image/png": 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uCTLTWpv7bbQIeAV3Bcw6YBd7Ltk9jD2Hc2Q/0bnUtmv4qIyF87uc5lbEiIiI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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f4184991590>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "test_column = 'MasVnrArea'\n", "sns.kdeplot(train_data[test_column])\n", "sns.kdeplot(test_data[test_column])\n", "\n", "print 'train:', train_data[test_column][train_data[test_column] > 1500].shape\n", "print 'test:', test_data[test_column][test_data[test_column] > 1500].shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "测试集中不存在一定范围的数据，而训练集中存在少量数据，将其从训练集中删除。\n", "\n", "- LotFrontage: 训练集中存在2条大于200的记录，测试集中没有\n", "- LotArea : 训练集中存在5条大于70000的记录，测试集中没有\n", "- MasVnrArea : 训练集中存在1条大于1500的记录，测试集中没有" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1460, 81)\n", "(1452, 81)\n" ] } ], "source": [ "print train_data.shape\n", "train_data.drop(train_data[train_data[\"LotFrontage\"] > 200].index, inplace=True)\n", "train_data.drop(train_data[train_data[\"LotArea\"] > 70000].index, inplace=True)\n", "train_data.drop(train_data[train_data[\"MasVnrArea\"] > 1500].index, inplace=True)\n", "print train_data.shape\n", "\n", "train_length = train_data.shape[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "测试集和训练集合并" ] }, { "cell_type": "code", "execution_count": 6, "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>Id</th>\n", " <th>MSSubClass</th>\n", " <th>MSZoning</th>\n", " <th>LotFrontage</th>\n", " <th>LotArea</th>\n", " <th>Street</th>\n", " <th>Alley</th>\n", " <th>LotShape</th>\n", " <th>LandContour</th>\n", " <th>Utilities</th>\n", " <th>...</th>\n", " <th>ScreenPorch</th>\n", " <th>PoolArea</th>\n", " <th>PoolQC</th>\n", " <th>Fence</th>\n", " <th>MiscFeature</th>\n", " <th>MiscVal</th>\n", " <th>MoSold</th>\n", " <th>YrSold</th>\n", " <th>SaleType</th>\n", " <th>SaleCondition</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>60</td>\n", " <td>RL</td>\n", " <td>65.0</td>\n", " <td>8450</td>\n", " <td>Pave</td>\n", " <td>NaN</td>\n", " <td>Reg</td>\n", " <td>Lvl</td>\n", " <td>AllPub</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>2008</td>\n", " <td>WD</td>\n", " <td>Normal</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>1 rows × 80 columns</p>\n", "</div>" ], "text/plain": [ " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", "0 1 60 RL 65.0 8450 Pave NaN Reg \n", "\n", " LandContour Utilities ... ScreenPorch PoolArea PoolQC Fence \\\n", "0 Lvl AllPub ... 0 0 NaN NaN \n", "\n", " MiscFeature MiscVal MoSold YrSold SaleType SaleCondition \n", "0 NaN 0 2 2008 WD Normal \n", "\n", "[1 rows x 80 columns]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "(2911, 80)\n" ] } ], "source": [ "conbined_data = pd.concat([train_data.loc[:, : 'SalePrice'], test_data])\n", "conbined_data = conbined_data[test_data.columns]\n", "display(conbined_data.head(1))\n", "print conbined_data.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Filling up missing values" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "how many data missed each column of train/test/conbine datas\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>MSZoning</th>\n", " <th>LotFrontage</th>\n", " <th>Alley</th>\n", " <th>Utilities</th>\n", " <th>Exterior1st</th>\n", " <th>Exterior2nd</th>\n", " <th>MasVnrType</th>\n", " <th>MasVnrArea</th>\n", " <th>BsmtQual</th>\n", " <th>BsmtCond</th>\n", " <th>...</th>\n", " <th>GarageYrBlt</th>\n", " <th>GarageFinish</th>\n", " <th>GarageCars</th>\n", " <th>GarageArea</th>\n", " <th>GarageQual</th>\n", " <th>GarageCond</th>\n", " <th>PoolQC</th>\n", " <th>Fence</th>\n", " <th>MiscFeature</th>\n", " <th>SaleType</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>train</th>\n", " <td>0</td>\n", " <td>256</td>\n", " <td>1362</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>37</td>\n", " <td>37</td>\n", " <td>...</td>\n", " <td>81</td>\n", " <td>81</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>81</td>\n", " <td>81</td>\n", " <td>1446</td>\n", " <td>1171</td>\n", " <td>1400</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>test</th>\n", " <td>4</td>\n", " <td>227</td>\n", " <td>1352</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>16</td>\n", " <td>15</td>\n", " <td>44</td>\n", " <td>45</td>\n", " <td>...</td>\n", " <td>78</td>\n", " <td>78</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>78</td>\n", " <td>78</td>\n", " <td>1456</td>\n", " <td>1169</td>\n", " <td>1408</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>conbine</th>\n", " <td>4</td>\n", " <td>483</td>\n", " <td>2714</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>24</td>\n", " <td>23</td>\n", " <td>81</td>\n", " <td>82</td>\n", " <td>...</td>\n", " <td>159</td>\n", " <td>159</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>159</td>\n", " <td>159</td>\n", " <td>2902</td>\n", " <td>2340</td>\n", " <td>2808</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>3 rows × 34 columns</p>\n", "</div>" ], "text/plain": [ " MSZoning LotFrontage Alley Utilities Exterior1st Exterior2nd \\\n", "train 0 256 1362 0 0 0 \n", "test 4 227 1352 2 1 1 \n", "conbine 4 483 2714 2 1 1 \n", "\n", " MasVnrType MasVnrArea BsmtQual BsmtCond ... GarageYrBlt \\\n", "train 8 8 37 37 ... 81 \n", "test 16 15 44 45 ... 78 \n", "conbine 24 23 81 82 ... 159 \n", "\n", " GarageFinish GarageCars GarageArea GarageQual GarageCond PoolQC \\\n", "train 81 0 0 81 81 1446 \n", "test 78 1 1 78 78 1456 \n", "conbine 159 1 1 159 159 2902 \n", "\n", " Fence MiscFeature SaleType \n", "train 1171 1400 0 \n", "test 1169 1408 1 \n", "conbine 2340 2808 1 \n", "\n", "[3 rows x 34 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# miss data columns\n", "has_null_columns = conbined_data.columns[conbined_data.isnull().any()].tolist()\n", "# how many data missed each column of train/test/conbine datas\n", "train_null = train_data[has_null_columns].isnull().sum()\n", "test_null = test_data[has_null_columns].isnull().sum()\n", "conbined_null = conbined_data[has_null_columns].isnull().sum()\n", "\n", "print 'how many data missed each column of train/test/conbine datas'\n", "missed_data = pd.DataFrame(data=[train_null, test_null, conbined_null],\n", " index=['train', 'test', 'conbine'], \n", " columns=has_null_columns)\n", "missed_data" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def fill_missing_conbined_data(column, value):\n", " conbined_data.loc[conbined_data[column].isnull(),column] = value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " LotFrontage/LotArea \n", "\n", "对于缺失的 LotFrontage（Linear feet of street connected to property），一般采用平均值进行填充，但考虑到可能与不同的 Neighborhood 有关系。" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes.AxesSubplot at 0x7f411fef5590>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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d1DQP3U0t9dURVHbVMuCcmtPEBFOfHvvQeyEz6eIRsrFMsPFMQpryPFotA7jc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\nQRAEQRAEQRAEQRAEQRAEQRAEQSjk/wODwT8GEX5e+AAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f411fef5550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "conbined_data['LotFrontage'].groupby(conbined_data[\"Neighborhood\"]).median().plot()\n", "conbined_data['LotFrontage'].groupby(conbined_data[\"Neighborhood\"]).mean().plot()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "lf_neighbor_map = conbined_data['LotFrontage'].groupby(conbined_data[\"Neighborhood\"]).median()\n", " \n", "rows = conbined_data['LotFrontage'].isnull()\n", "conbined_data['LotFrontage'][rows] = conbined_data['Neighborhood'][rows].map(lambda neighbor : lf_neighbor_map[neighbor])" ] }, { "cell_type": "code", "execution_count": 11, "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>Id</th>\n", " <th>MSSubClass</th>\n", " <th>MSZoning</th>\n", " <th>LotFrontage</th>\n", " <th>LotArea</th>\n", " <th>Street</th>\n", " <th>Alley</th>\n", " <th>LotShape</th>\n", " <th>LandContour</th>\n", " <th>Utilities</th>\n", " <th>...</th>\n", " <th>ScreenPorch</th>\n", " <th>PoolArea</th>\n", " <th>PoolQC</th>\n", " <th>Fence</th>\n", " <th>MiscFeature</th>\n", " <th>MiscVal</th>\n", " <th>MoSold</th>\n", " <th>YrSold</th>\n", " <th>SaleType</th>\n", " <th>SaleCondition</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "<p>0 rows × 80 columns</p>\n", "</div>" ], "text/plain": [ "Empty DataFrame\n", "Columns: [Id, MSSubClass, MSZoning, LotFrontage, LotArea, Street, Alley, LotShape, LandContour, Utilities, LotConfig, LandSlope, Neighborhood, Condition1, Condition2, BldgType, HouseStyle, OverallQual, OverallCond, YearBuilt, YearRemodAdd, RoofStyle, RoofMatl, Exterior1st, Exterior2nd, MasVnrType, MasVnrArea, ExterQual, ExterCond, Foundation, BsmtQual, BsmtCond, BsmtExposure, BsmtFinType1, BsmtFinSF1, BsmtFinType2, BsmtFinSF2, BsmtUnfSF, TotalBsmtSF, Heating, HeatingQC, CentralAir, Electrical, 1stFlrSF, 2ndFlrSF, LowQualFinSF, GrLivArea, BsmtFullBath, BsmtHalfBath, FullBath, HalfBath, BedroomAbvGr, KitchenAbvGr, KitchenQual, TotRmsAbvGrd, Functional, Fireplaces, FireplaceQu, GarageType, GarageYrBlt, GarageFinish, GarageCars, GarageArea, GarageQual, GarageCond, PavedDrive, WoodDeckSF, OpenPorchSF, EnclosedPorch, 3SsnPorch, ScreenPorch, PoolArea, PoolQC, Fence, MiscFeature, MiscVal, MoSold, YrSold, SaleType, SaleCondition]\n", "Index: []\n", "\n", "[0 rows x 80 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "conbined_data[conbined_data['LotFrontage'].isnull()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Alley " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "((2911, 80), (2714, 80))" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "conbined_data.shape, conbined_data[conbined_data['Alley'].isnull()].shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2919 条数据缺失 2721 条，缺失数据过多(93.2%),将缺失数据填充为 NA（NA->No alley access）。" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fill_missing_conbined_data('Alley', 'NA')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " MasVnrType / MasVnrArea \n", "\n", "方形单板砌体类型/面积，将缺失数据填充为出现次数最多的类型" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "conbined_data['MasVnrType'].fillna('None', inplace=True)\n", "conbined_data['MasVnrArea'].fillna(0, inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " BsmtQual / BsmtCond / BsmtExposure / BsmtFinType1 / BsmtFinType2 \n", "\n", "缺失 37 / 38 条数据。" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "basement_cols=['BsmtQual','BsmtCond','BsmtExposure','BsmtFinType1','BsmtFinType2','BsmtFinSF1','BsmtFinSF2']" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 对于缺失的字符串类型的数据填充为 NA，表示 No Basement\n", "for column in basement_cols:\n", " if 'FinSF'not in column:\n", " # NA\tNo Basement\n", " fill_missing_conbined_data(column, 'NA')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 对于数值型的 BsmtFinSF1 和 BsmtFinSF2\n", "fill_missing_conbined_data('BsmtFinSF1', 0)\n", "fill_missing_conbined_data('BsmtFinSF2', 0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Electrical \n", "\n", "缺失一条数据,填充为出现次数最多的。" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes.AxesSubplot at 0x7f411fea71d0>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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CH9Vn8Bc0hJD6RPP5VCeg0xr/jyLih8CPM/Mf6nWPogp2r8zM323i9o+JyX7b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\nSZIkSZIkqZT/DxVywB+HW2QyAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f411fe212d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(conbined_data['Electrical'])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fill_missing_conbined_data('Electrical', 'SBrkr')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " FireplaceQu \n", "\n", "缺失 1420 条数据" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 对于 Fireplaces 为 0 的，FireplaceQu 设置为 NA，表示 No Fireplace，此即缺失的 1420 条数据的情况\n", "fill_missing_conbined_data('FireplaceQu', 'NA')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " PoolQC \n", "\n", "PoolQC 缺失 2909 条数据，是否与 PoolArea 有关。" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fill_missing_conbined_data('PoolQC', 'NA')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " MiscFeature " ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fill_missing_conbined_data('MiscFeature', 'NA')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Fence " ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fill_missing_conbined_data('Fence', 'NA')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Garages " ] }, { "cell_type": "code", "execution_count": 24, "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>GarageType</th>\n", " <th>GarageQual</th>\n", " <th>GarageCond</th>\n", " <th>GarageYrBlt</th>\n", " <th>GarageFinish</th>\n", " <th>GarageCars</th>\n", " <th>GarageArea</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>39</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " GarageType GarageQual GarageCond GarageYrBlt GarageFinish GarageCars \\\n", "39 NaN NaN NaN NaN NaN 0.0 \n", "\n", " GarageArea \n", "39 0.0 " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "garage_cols=['GarageType','GarageQual','GarageCond','GarageYrBlt','GarageFinish','GarageCars','GarageArea']\n", "conbined_data[garage_cols][conbined_data['GarageType'].isnull()==True].head(1)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 对于缺失的字符串类型的数据填充为 NA，表示 No Garage\n", "for column in garage_cols:\n", " if column != 'GarageCars' and column != 'GarageArea':\n", " # NA\tNo Basement\n", " fill_missing_conbined_data(column, 'NA')\n", " else:\n", " fill_missing_conbined_data(column, 0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " MSZoning " ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes.AxesSubplot at 0x7f411fe8a1d0>" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f411fe8a110>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(conbined_data['MSZoning'])" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fill_missing_conbined_data('MSZoning', 'RL')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Utilities \n", "\n", "Definitely ignoring Utilities : all records are \"AllPub\", except for one \"NoSeWa\" in the train set and 2 NA in the test set." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes.AxesSubplot at 0x7f411fd9ac10>" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f411fdb6890>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(conbined_data['Utilities'])" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fill_missing_conbined_data('Utilities', 'AllPub')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Exterior1st / Exterior2nd" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes.AxesSubplot at 0x7f411fa4c290>" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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6urpYHe/p7+rqsuVEkiTG1nKyF8sHJ33AlcAhq1yiqt4qOh44NCJ+DtxNeTz4VOAU4KMR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\nm75V9+e2DWvfVve3lXEjSvD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"text/plain": [ "<matplotlib.figure.Figure at 0x7f411fc44850>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(conbined_data['Exterior1st'])" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fill_missing_conbined_data('Exterior1st', 'VinylSd')" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes.AxesSubplot at 0x7f411fa33f10>" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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T7+WZYc/Q/HK54UXTDwdeTH8vaWX8o53JwPjx46mrq6N+Xm1nZuuUCRMmMGnSpG5LX0RE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\nXEJE6ROB7xM73VeJ6PumtAP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"text/plain": [ "<matplotlib.figure.Figure at 0x7f411f940e90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(conbined_data['Exterior2nd'])" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fill_missing_conbined_data('Exterior2nd', 'VinylSd')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " BsmtUnfSF / TotalBsmtSF " ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 可以发现，对于缺失的 TotalBsmtSF 和 BsmtUnfSF 填充为0\n", "fill_missing_conbined_data('BsmtUnfSF', 0)\n", "fill_missing_conbined_data('TotalBsmtSF', 0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " BsmtFullBath / BsmtHalfBath " ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fill_missing_conbined_data('BsmtFullBath', 0)\n", "fill_missing_conbined_data('BsmtHalfBath', 0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " KitchenQual " ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes.AxesSubplot at 0x7f411f94be50>" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f411f823390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(conbined_data['KitchenQual'])" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fill_missing_conbined_data('KitchenQual', 'TA')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " SaleType / Functional " ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes.AxesSubplot at 0x7f411f717b50>" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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yZtR/RMRsqqu7vjsz/6NvgYj4PlXaXU6Vgj9d/z0HuILqG8HIfzu5rA3ANcD7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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f411f8230d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(conbined_data['Functional'])" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fill_missing_conbined_data('SaleType', 'WD')\n", "fill_missing_conbined_data('Functional', 'Typ')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "完成了缺失数据的填充。" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Series([], dtype: float64)" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 存在缺失数据的列名\n", "has_null_columns = conbined_data.columns[conbined_data.isnull().any()].tolist()\n", "# 每列包含多少缺失数据\n", "conbined_data[has_null_columns].isnull().sum()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Feature Engineering" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " 时间属性（YearBuilt、GarageYrBlt）" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "built_year_data = conbined_data[['YearBuilt', 'GarageYrBlt']][conbined_data['GarageYrBlt'] != 'NA']" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.8346885266143993" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "built_year_data['GarageYrBlt'] = built_year_data['GarageYrBlt'].map(lambda g : int(g))\n", "built_year_data['GarageYrBlt'].corr(built_year_data['YearBuilt'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "可以发现 YearBuilt、GarageYrBlt 之间存在很高的相关性。" ] }, { "cell_type": "code", "execution_count": 43, "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>YearBuilt</th>\n", " <th>GarageYrBlt</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2003</td>\n", " <td>2003</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " YearBuilt GarageYrBlt\n", "0 2003 2003" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "built_year_data.head(1)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f416ccad390>]" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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j3a6veyNJoUdYa99KY9diYBQSpGCMyQJ2Be4FvkGaTEYBS8PrhwK54f1SUlRURLdu3dK4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\n8P/g+FvR1s5aYgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f411f1a3150>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.linear_model import LinearRegression\n", "regr = LinearRegression()\n", "garage_year = built_year_data.loc[:,'GarageYrBlt'].values\n", "built_year = built_year_data.loc[:,'YearBuilt'].values\n", "\n", "length = garage_year.shape[0]\n", "garage_year = garage_year.reshape(length, 1)\n", "built_year = built_year.reshape(length, 1)\n", "\n", "# Train the model using the training sets\n", "regr.fit(built_year, garage_year)\n", "plt.scatter(built_year, garage_year, color='blue')\n", "plt.plot(built_year, regr.predict(built_year), color='red',\n", " linewidth=3)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 对于 NA 的 GarageYrBlt，进行填充\n", "conbined_data['GarageYrBlt'] = conbined_data.apply(lambda row : int(regr.predict(row['YearBuilt']))\n", " if row['GarageYrBlt'] == 'NA' else int(row['GarageYrBlt']),\n", " axis=1)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 2003\n", "1 1976\n", "2 2001\n", "3 1998\n", "4 2000\n", "Name: GarageYrBlt, dtype: int64" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "conbined_data['GarageYrBlt'].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "YearBuilt建造时间，YearRemodAdd修建时间，确定是否翻新改造过" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# How many years has remoded from built\n", "conbined_data['RemodYears'] = conbined_data['YearRemodAdd'] - conbined_data['YearBuilt']\n", "# Did a remodeling happened from built?\n", "conbined_data[\"HasRemodeled\"] = (conbined_data[\"YearRemodAdd\"] != conbined_data[\"YearBuilt\"]) * 1\n", "# Did a remodeling happen in the year the house was sold?\n", "conbined_data[\"HasRecentRemodel\"] = (conbined_data[\"YearRemodAdd\"] == conbined_data[\"YrSold\"]) * 1" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": true }, "outputs": [], "source": [ "conbined_data['GarageBltYears'] = conbined_data['GarageYrBlt'] - conbined_data['YearBuilt']" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 0\n", "1 0\n", "2 0\n", "3 83\n", "4 0\n", "Name: GarageBltYears, dtype: int64" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "conbined_data['GarageBltYears'].head()" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# How many years has build now?\n", "conbined_data['Now_YearBuilt'] = 2017 - conbined_data['YearBuilt']\n", "conbined_data['Now_YearRemodAdd'] = 2017 - conbined_data['YearRemodAdd']\n", "conbined_data['Now_GarageYrBlt'] = 2017 - conbined_data['GarageYrBlt']" ] }, { "cell_type": "code", "execution_count": 51, "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>Now_YearBuilt</th>\n", " <th>Now_YearRemodAdd</th>\n", " <th>Now_GarageYrBlt</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>14</td>\n", " <td>14</td>\n", " <td>14</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>41</td>\n", " <td>41</td>\n", " <td>41</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>16</td>\n", " <td>15</td>\n", " <td>16</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Now_YearBuilt Now_YearRemodAdd Now_GarageYrBlt\n", "0 14 14 14\n", "1 41 41 41\n", "2 16 15 16" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "conbined_data[['Now_YearBuilt','Now_YearRemodAdd','Now_GarageYrBlt']].head(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 房子卖的月份存在旺季\n", "- 房子卖的月份为数值类型，将其转为字符串类型" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes.AxesSubplot at 0x7f411f6c4e90>" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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5Ue7N8CQlelnkvnFH+01yqFraOik6eBZwqtsmxMC0TE9fuHkpGenJADz2271R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\nA74DHIw7HwMmuXuVmQ0C7gM+CgwClgPXuPvOqPw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"text/plain": [ "<matplotlib.figure.Figure at 0x7f411cd2ba50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_data['SalePrice'].groupby(train_data['MoSold']).mean().plot()" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes.AxesSubplot at 0x7f411f71f610>" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f411fb9f510>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(conbined_data['MoSold'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "可以看出每月卖出房屋的数量和价格基本成反比。" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sale_price_month = train_data['SalePrice'].groupby(train_data['MoSold']).mean().to_dict()\n", "# 该月卖的平均价格\n", "conbined_data[\"MonthSaledMeanPrice\"] = conbined_data[\"MoSold\"].replace(sale_price_month)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 统计每月卖的数量\n", "sale_month = {\"1\": 0, \"2\": 0, \"3\": 0, \"4\": 0, \"5\": 0, \"6\": 0, \"7\": 0, \"8\": 0, \"9\": 0, \"10\": 0, \"11\": 0, \"12\": 0}\n", "for m in conbined_data['MoSold'].values:\n", " sale_month[str(m)] = sale_month[str(m)] + 1" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 月份为数值类型，将其转为字符串类型\n", "conbined_data['MoSold'] = conbined_data['MoSold'].map(lambda m : str(m))" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "# 该月卖的数量\n", "conbined_data[\"MonthSaledCount\"] = conbined_data[\"MoSold\"].replace(sale_month)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "对于 MSSubClass 的数值仅仅代表 the type of dwelling，所以将其编码。" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes.AxesSubplot at 0x7f411fb85ad0>" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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nj7y0CbtdesLEyCTBhgiZ8lpjcmh2RiLxsT5vQDwsMdFRTCwwNt7zd8/G+p3V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\nMCmTmRP6zs7dXenZqMT1LNyP630IdglQpKqPe1+/KiLLgKuBVcC1wKO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"text/plain": [ "<matplotlib.figure.Figure at 0x7f411f8cd790>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_data['SalePrice'].groupby(train_data['MSSubClass']).mean().plot()" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes.AxesSubplot at 0x7f411cbdaa50>" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f411f64ba90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(conbined_data['MSSubClass'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "可以看出价格和销量都与 MSSubClass 存在一定的联系。查看数据描述是因为房屋是否 NEWER！" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [], "source": [ "conbined_data[\"NewerDwelling\"] = conbined_data[\"MSSubClass\"].replace(\n", " {20: 1, 30: 0, 40: 0, 45: 0,50: 0, 60: 1, 70: 0, 75: 0, 80: 0, 85: 0,\n", " 90: 0, 120: 1, 150: 0, 160: 0, 180: 0, 190: 0})" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 新增每种类别的平均价格\n", "sale_price_mssc = train_data['SalePrice'].groupby(train_data['MSSubClass']).mean().to_dict()\n", "# 该月卖的平均价格\n", "conbined_data[\"MSSubClassMeanPrice\"] = conbined_data[\"MSSubClass\"].replace(sale_price_mssc)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mssubclass_dict = {\n", " 20: 'SC20',\n", " 30: 'SC30',\n", " 40: 'SC40',\n", " 45: 'SC45',\n", " 50: 'SC50',\n", " 60: 'SC60',\n", " 70: 'SC70',\n", " 75: 'SC75',\n", " 80: 'SC80',\n", " 85: 'SC85',\n", " 90: 'SC90',\n", " 120: 'SC120',\n", " 150: 'SC150',\n", " 160: 'SC160',\n", " 180: 'SC180',\n", " 190: 'SC190',\n", "}\n", "conbined_data['MSSubClass'] = conbined_data['MSSubClass'].replace(mssubclass_dict)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "对于那些存在大小特质的属性进行编码。" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['KitchenQual', 'BsmtCond', 'HeatingQC', 'GarageQual', 'ExterCond', 'CentralAir', 'ExterQual', 'Utilities', 'Alley', 'Functional', 'Street', 'FireplaceQu', 'Fence', 'BsmtFinType2', 'BsmtQual', 'PoolQC', 'BsmtExposure', 'BsmtFinType1', 'GarageCond']\n", "(2911, 110)\n" ] } ], "source": [ "good_level_map = {'Street': {'Grvl': 0, 'Pave': 1},\n", " 'Alley': {'NA':0, 'Grvl': 1, 'Pave': 2},\n", " 'Utilities': {'AllPub':3, 'NoSeWa': 1, 'NoSewr': 2, 'ELO': 0},\n", " 'ExterQual': {'Ex': 4, 'Gd': 3, 'TA': 2, 'Fa': 1,'Po': 0},\n", " 'ExterCond': {'Ex': 4, 'Gd': 3, 'TA': 2, 'Fa': 1,'Po': 0},\n", " 'BsmtExposure': {'Gd': 4, 'Av': 3, 'Mn': 2, 'No': 1,'NA': 0},\n", " 'BsmtQual': {'Ex': 5, 'Gd': 4, 'TA': 3, 'Fa': 2,'Po': 1,'NA': 0},\n", " 'BsmtCond': {'Ex': 5, 'Gd': 4, 'TA': 3, 'Fa': 2,'Po': 1,'NA': 0},\n", " 'BsmtFinType1': {'NA':0, 'Unf':1, 'LwQ':2, 'Rec':3, 'BLQ':4, 'ALQ':5, 'GLQ':6},\n", " 'BsmtFinType2': {'NA':0, 'Unf':1, 'LwQ':2, 'Rec':3, 'BLQ':4, 'ALQ':5, 'GLQ':6},\n", " 'HeatingQC': {'Ex': 4, 'Gd': 3, 'TA': 2, 'Fa': 1,'Po': 0},\n", " 'CentralAir': {'N':0, 'Y':1},\n", " 'KitchenQual': {'Ex': 4, 'Gd': 3, 'TA': 2, 'Fa': 1, 'Po': 0},\n", " 'Functional': {'Typ':0,'Min1':1,'Min2':1,'Mod':2,'Maj1':3,'Maj2':4,'Sev':5,'Sal': 6},\n", " 'FireplaceQu': {'Ex': 5, 'Gd': 4, 'TA': 3, 'Fa': 2, 'Po': 1, 'NA': 0},\n", " 'GarageQual': {'Ex': 5, 'Gd': 4, 'TA': 3, 'Fa': 2, 'Po': 1, 'NA': 0},\n", " 'GarageCond': {'Ex': 5, 'Gd': 4, 'TA': 3, 'Fa': 2, 'Po': 1, 'NA': 0},\n", " 'PoolQC': {'Ex': 4, 'Gd': 3, 'TA': 2, 'Fa': 1, 'NA': 0},\n", " 'Fence': {'GdPrv': 2, 'GdWo': 2, 'MnPrv': 1, 'MnWw': 1, 'NA': 0}\n", " }\n", "\n", "print good_level_map.keys()\n", "good_level_data = conbined_data[good_level_map.keys()].replace(good_level_map)\n", "\n", "good_level_data.columns = good_level_data.columns.map(lambda m : m + '_')\n", "\n", "conbined_data[good_level_data.columns] = good_level_data[good_level_data.columns]\n", "print conbined_data.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Neighborhood 属性表示的是附近的地名，可将其转为经纬度。" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 0.054604\n", "1 0.026794\n", "2 0.054604\n", "3 0.012657\n", "4 0.031042\n", "Name: NeighborDistance, dtype: float64" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 纬度\n", "conbined_data[\"latitude\"] = conbined_data.Neighborhood.replace(\n", " {'Blmngtn' : 42.062806,\n", " 'Blueste' : 42.009408,\n", " 'BrDale' : 42.052500,\n", " 'BrkSide': 42.033590,\n", " 'ClearCr': 42.025425,\n", " 'CollgCr': 42.021051,\n", " 'Crawfor': 42.025949,\n", " 'Edwards': 42.022800,\n", " 'Gilbert': 42.027885,\n", " 'GrnHill': 42.000854,\n", " 'IDOTRR' : 42.019208,\n", " 'Landmrk': 42.044777,\n", " 'MeadowV': 41.991866,\n", " 'Mitchel': 42.031307,\n", " 'NAmes' : 42.042966,\n", " 'NoRidge': 42.050307,\n", " 'NPkVill': 42.050207,\n", " 'NridgHt': 42.060356,\n", " 'NWAmes' : 42.051321,\n", " 'OldTown': 42.028863,\n", " 'SWISU' : 42.017578,\n", " 'Sawyer' : 42.033611,\n", " 'SawyerW': 42.035540,\n", " 'Somerst': 42.052191,\n", " 'StoneBr': 42.060752,\n", " 'Timber' : 41.998132,\n", " 'Veenker': 42.040106})\n", "# 经度\n", "conbined_data[\"longitude\"] = conbined_data.Neighborhood.replace(\n", " {'Blmngtn' : -93.639963,\n", " 'Blueste' : -93.645543,\n", " 'BrDale' : -93.628821,\n", " 'BrkSide': -93.627552,\n", " 'ClearCr': -93.675741,\n", " 'CollgCr': -93.685643,\n", " 'Crawfor': -93.620215,\n", " 'Edwards': -93.663040,\n", " 'Gilbert': -93.615692,\n", " 'GrnHill': -93.643377,\n", " 'IDOTRR' : -93.623401,\n", " 'Landmrk': -93.646239,\n", " 'MeadowV': -93.602441,\n", " 'Mitchel': -93.626967,\n", " 'NAmes' : -93.613556,\n", " 'NoRidge': -93.656045,\n", " 'NPkVill': -93.625827,\n", " 'NridgHt': -93.657107,\n", " 'NWAmes' : -93.633798,\n", " 'OldTown': -93.615497,\n", " 'SWISU' : -93.651283,\n", " 'Sawyer' : -93.669348,\n", " 'SawyerW': -93.685131,\n", " 'Somerst': -93.643479,\n", " 'StoneBr': -93.628955,\n", " 'Timber' : -93.648335,\n", " 'Veenker': -93.657032})\n", "\n", "# Ames city 的经纬度：经度-93.63191310000002，纬度42.0307812，计算距离\n", "conbined_data[\"NeighborDistance\"] = np.sqrt(np.power((conbined_data[\"longitude\"] - (-93.63191310000002)),2) + \\\n", " np.power((conbined_data[\"latitude\"] - 42.0307812),2))\n", "\n", "display(conbined_data[\"NeighborDistance\"].head())\n", "\n", "conbined_data.drop(['longitude', 'latitude'], axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Bin by neighborhood (a little arbitrarily). Values were computed by: \n", "neighbor_price_map = train_data[\"SalePrice\"].groupby(train_data[\"Neighborhood\"]).median().sort_values().to_dict()" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": true }, "outputs": [], "source": [ "neighbor_bin_map = {\n", " \"MeadowV\" : 0, # 88000\n", " \"IDOTRR\" : 1, # 103000\n", " \"BrDale\" : 1, # 106000\n", " \"OldTown\" : 1, # 119000\n", " \"Edwards\" : 1, # 119500\n", " \"BrkSide\" : 1, # 124300\n", " \"Sawyer\" : 1, # 135000\n", " \"Blueste\" : 1, # 137500\n", " \"SWISU\" : 2, # 139500\n", " \"NAmes\" : 2, # 140000\n", " \"NPkVill\" : 2, # 146000\n", " \"Mitchel\" : 2, # 153500\n", " \"SawyerW\" : 2, # 179900\n", " \"Gilbert\" : 2, # 181000\n", " \"NWAmes\" : 2, # 182900\n", " \"Blmngtn\" : 2, # 191000\n", " \"CollgCr\" : 2, # 197200\n", " \"ClearCr\" : 3, # 200250\n", " \"Crawfor\" : 3, # 200624\n", " \"Veenker\" : 3, # 218000\n", " \"Somerst\" : 3, # 225500\n", " \"Timber\" : 3, # 228475\n", " \"StoneBr\" : 4, # 278000\n", " \"NoRidge\" : 4, # 290000\n", " \"NridgHt\" : 4, # 315000\n", "}\n", "\n", "conbined_data[\"NeighborPrice\"] = conbined_data[\"Neighborhood\"].map(neighbor_price_map)\n", "conbined_data[\"NeighborBin\"] = conbined_data[\"Neighborhood\"].map(neighbor_bin_map)" ] }, { "cell_type": "code", "execution_count": 67, "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>Id</th>\n", " <th>MSSubClass</th>\n", " <th>MSZoning</th>\n", " <th>LotFrontage</th>\n", " <th>LotArea</th>\n", " <th>Street</th>\n", " <th>Alley</th>\n", " <th>LotShape</th>\n", " <th>LandContour</th>\n", " <th>Utilities</th>\n", " <th>...</th>\n", " <th>Fence_</th>\n", " <th>BsmtFinType2_</th>\n", " <th>BsmtQual_</th>\n", " <th>PoolQC_</th>\n", " <th>BsmtExposure_</th>\n", " <th>BsmtFinType1_</th>\n", " <th>GarageCond_</th>\n", " <th>NeighborDistance</th>\n", " <th>NeighborPrice</th>\n", " <th>NeighborBin</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>SC60</td>\n", " <td>RL</td>\n", " <td>65.0</td>\n", " <td>8450</td>\n", " <td>Pave</td>\n", " <td>NA</td>\n", " <td>Reg</td>\n", " <td>Lvl</td>\n", " <td>AllPub</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>6</td>\n", " <td>3</td>\n", " <td>0.054604</td>\n", " <td>197200</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>SC20</td>\n", " <td>RL</td>\n", " <td>80.0</td>\n", " <td>9600</td>\n", " <td>Pave</td>\n", " <td>NA</td>\n", " <td>Reg</td>\n", " <td>Lvl</td>\n", " <td>AllPub</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>3</td>\n", " <td>0.026794</td>\n", " <td>218000</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>SC60</td>\n", " <td>RL</td>\n", " <td>68.0</td>\n", " <td>11250</td>\n", " <td>Pave</td>\n", " <td>NA</td>\n", " <td>IR1</td>\n", " <td>Lvl</td>\n", " <td>AllPub</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>6</td>\n", " <td>3</td>\n", " <td>0.054604</td>\n", " <td>197200</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>SC70</td>\n", " <td>RL</td>\n", " <td>60.0</td>\n", " <td>9550</td>\n", " <td>Pave</td>\n", " <td>NA</td>\n", " <td>IR1</td>\n", " <td>Lvl</td>\n", " <td>AllPub</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>5</td>\n", " <td>3</td>\n", " <td>0.012657</td>\n", " <td>200624</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>SC60</td>\n", " <td>RL</td>\n", " <td>84.0</td>\n", " <td>14260</td>\n", " <td>Pave</td>\n", " <td>NA</td>\n", " <td>IR1</td>\n", " <td>Lvl</td>\n", " <td>AllPub</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>3</td>\n", " <td>0.031042</td>\n", " <td>301500</td>\n", " <td>4</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 113 columns</p>\n", "</div>" ], "text/plain": [ " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", "0 1 SC60 RL 65.0 8450 Pave NA Reg \n", "1 2 SC20 RL 80.0 9600 Pave NA Reg \n", "2 3 SC60 RL 68.0 11250 Pave NA IR1 \n", "3 4 SC70 RL 60.0 9550 Pave NA IR1 \n", "4 5 SC60 RL 84.0 14260 Pave NA IR1 \n", "\n", " LandContour Utilities ... Fence_ BsmtFinType2_ BsmtQual_ PoolQC_ \\\n", "0 Lvl AllPub ... 0 1 4 0 \n", "1 Lvl AllPub ... 0 1 4 0 \n", "2 Lvl AllPub ... 0 1 4 0 \n", "3 Lvl AllPub ... 0 1 3 0 \n", "4 Lvl AllPub ... 0 1 4 0 \n", "\n", " BsmtExposure_ BsmtFinType1_ GarageCond_ NeighborDistance NeighborPrice \\\n", "0 1 6 3 0.054604 197200 \n", "1 4 5 3 0.026794 218000 \n", "2 2 6 3 0.054604 197200 \n", "3 1 5 3 0.012657 200624 \n", "4 3 6 3 0.031042 301500 \n", "\n", " NeighborBin \n", "0 2 \n", "1 3 \n", "2 2 \n", "3 3 \n", "4 4 \n", "\n", "[5 rows x 113 columns]" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "conbined_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Create new features" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ref: [juliencs : A study on Regression applied to the Ames dataset\n", "](https://www.kaggle.com/juliencs/house-prices-advanced-regression-techniques/a-study-on-regression-applied-to-the-ames-dataset)\n", "- Create some boolean features\n", "- Simplifications of existing features - Ref\n", "- Combinations of existing features - Ref\n", "- Polynomials on the top 10 existing features - Ref" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [], "source": [ "str_columns = conbined_data.select_dtypes(include=['object']).columns.values\n", "num_columns = conbined_data.select_dtypes(exclude=['object']).columns.values" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(['MSSubClass', 'MSZoning', 'Street', 'Alley', 'LotShape',\n", " 'LandContour', 'Utilities', 'LotConfig', 'LandSlope',\n", " 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n", " 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd',\n", " 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n", " 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2',\n", " 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual',\n", " 'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish',\n", " 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence',\n", " 'MiscFeature', 'MoSold', 'SaleType', 'SaleCondition'], dtype=object)" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "str_columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1.Create some boolean features" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Normal' 'Partial' 'Abnorml' 'Family' 'Alloca' 'AdjLand']\n" ] }, { "data": { "text/plain": [ "<matplotlib.axes.AxesSubplot at 0x7f411cc79c50>" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f411cce5a90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# test str column\n", "column = \"SaleCondition\"\n", "count_duct = {}\n", "for key in set(conbined_data[column]):\n", " count_duct[key] = 0\n", " \n", "for m in conbined_data[column].values:\n", " count_duct[str(m)] = count_duct[str(m)] + 1\n", "\n", "count_duct= sorted(count_duct.items(), key=lambda d:d[1], reverse = True)\n", "print np.array(count_duct)[:,0]\n", "sns.countplot(conbined_data[column])" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# IR2 and IR3 don't appear that often, so just make a distinction \n", "# between regular and irregular.\n", "conbined_data[\"IsRegularLotShape\"] = (conbined_data[\"LotShape\"] == \"Reg\") * 1\n", "\n", "# Bnk, Low, HLS don't appear that often, so just make a distinction\n", "conbined_data[\"IsLandContourLvl\"] = (conbined_data[\"LandContour\"] == \"Lvl\") * 1\n", "conbined_data[\"IsLotConfigInside\"] = (conbined_data[\"LotConfig\"] == \"Inside\") * 1\n", "conbined_data[\"IsLandSlopeGentle\"] = (conbined_data[\"LandSlope\"] == \"Gtl\") * 1\n", "conbined_data[\"IsCondition1Norm\"] = (conbined_data[\"Condition1\"] == \"Norm\") * 1\n", "conbined_data[\"IsCondition2Norm\"] = (conbined_data[\"Condition2\"] == \"Norm\") * 1\n", "conbined_data[\"IsBldgType1Fam\"] = (conbined_data[\"BldgType\"] == \"1Fam\") * 1\n", "conbined_data[\"IsRoofStyleGable\"] = (conbined_data[\"RoofStyle\"] == \"Gable\") * 1\n", "conbined_data[\"IsRoofMatlCompShg\"] = (conbined_data[\"RoofMatl\"] == \"CompShg\") * 1\n", "conbined_data[\"IsGasAHeating\"] = (conbined_data[\"Heating\"] == \"GasA\") * 1\n", "conbined_data[\"IsGarageFinished\"] = (conbined_data[\"GarageFinish\"] == \"Fin\") * 1\n", "conbined_data[\"IsPavedDrive\"] = (conbined_data[\"PavedDrive\"] == \"Y\") * 1\n", "conbined_data[\"IsSaleTypeWD\"] = (conbined_data[\"SaleType\"] == \"WD\") * 1\n", "conbined_data[\"IsSaleConditionNormal\"] = (conbined_data[\"SaleCondition\"] == \"Normal\") * 1" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# The only interesting \"misc. feature\" is the presence of a shed.\n", "conbined_data[\"HasShed\"] = (conbined_data[\"MiscFeature\"] == \"Shed\") * 1. \n", "\n", "# Was this house sold in the year it was built?\n", "conbined_data[\"IsVeryNewHouse\"] = (conbined_data[\"YearBuilt\"] == conbined_data[\"YrSold\"]) * 1\n", "\n", "conbined_data[\"Has2ndFloor\"] = (conbined_data[\"2ndFlrSF\"] == 0) * 1\n", "conbined_data[\"HasMasVnr\"] = (conbined_data[\"MasVnrArea\"] == 0) * 1\n", "conbined_data[\"HasWoodDeck\"] = (conbined_data[\"WoodDeckSF\"] == 0) * 1\n", "conbined_data[\"HasOpenPorch\"] = (conbined_data[\"OpenPorchSF\"] == 0) * 1\n", "conbined_data[\"HasEnclosedPorch\"] = (conbined_data[\"EnclosedPorch\"] == 0) * 1\n", "conbined_data[\"Has3SsnPorch\"] = (conbined_data[\"3SsnPorch\"] == 0) * 1\n", "conbined_data[\"HasScreenPorch\"] = (conbined_data[\"ScreenPorch\"] == 0) * 1" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 2.Simplifications of existing features\n", "conbined_data[\"SimplOverallQual\"] = conbined_data.OverallQual.replace(\n", " {1 : 1, 2 : 1, 3 : 1, # bad\n", " 4 : 2, 5 : 2, 6 : 2, # average\n", " 7 : 3, 8 : 3, 9 : 3, 10 : 3 # good\n", " })\n", "conbined_data[\"SimplOverallCond\"] = conbined_data.OverallCond.replace(\n", " {1 : 1, 2 : 1, 3 : 1, # bad\n", " 4 : 2, 5 : 2, 6 : 2, # average\n", " 7 : 3, 8 : 3, 9 : 3, 10 : 3 # good\n", " })" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 3.Combinations of existing features\n", "\n", "# Overall quality of the house\n", "conbined_data[\"OverallGrade\"] = conbined_data[\"OverallQual\"] * conbined_data[\"OverallCond\"]\n", "# Overall quality of the garage\n", "conbined_data[\"GarageGrade\"] = conbined_data[\"GarageQual_\"] * conbined_data[\"GarageCond\"]\n", "# Overall quality of the exterior\n", "conbined_data[\"ExterGrade\"] = conbined_data[\"ExterQual_\"] * conbined_data[\"ExterCond\"]\n", "# Overall kitchen score\n", "conbined_data[\"KitchenScore\"] = conbined_data[\"KitchenAbvGr\"] * conbined_data[\"KitchenQual_\"]\n", "# Overall fireplace score\n", "conbined_data[\"FireplaceScore\"] = conbined_data[\"Fireplaces\"] * conbined_data[\"FireplaceQu_\"]\n", "# Overall garage score\n", "conbined_data[\"GarageScore\"] = conbined_data[\"GarageArea\"] * conbined_data[\"GarageQual_\"]\n", "# Overall pool score\n", "conbined_data[\"PoolScore\"] = conbined_data[\"PoolArea\"] * conbined_data[\"PoolQC_\"]\n", "\n", "# Total number of bathrooms\n", "conbined_data[\"TotalBath\"] = conbined_data[\"BsmtFullBath\"] + (0.5 * conbined_data[\"BsmtHalfBath\"]) + \\\n", "conbined_data[\"FullBath\"] + (0.5 * conbined_data[\"HalfBath\"])\n", "\n", "# Total yard area in square feet\n", "conbined_data[\"TotalPorchSF\"] = conbined_data[\"OpenPorchSF\"] + conbined_data[\"EnclosedPorch\"] +\\\n", " conbined_data[\"3SsnPorch\"] + conbined_data[\"ScreenPorch\"]\n", "# Total SF for house (living, basement, porch, pool)\n", "conbined_data[\"AllSF\"] = conbined_data[\"GrLivArea\"] + conbined_data[\"TotalBsmtSF\"] + \\\n", " conbined_data[\"TotalPorchSF\"] + conbined_data[\"WoodDeckSF\"] + \\\n", " conbined_data[\"PoolArea\"]\n", "\n", "# House completed before sale or not\n", "conbined_data[\"BoughtOffPlan\"] = conbined_data.SaleCondition.replace(\n", " {\"Abnorml\" : 0, \"Alloca\" : 0, \"AdjLand\" : 0, \"Family\" : 0, \"Normal\" : 0, \"Partial\" : 1})" ] }, { "cell_type": "code", "execution_count": 75, "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>Id</th>\n", " <th>MSSubClass</th>\n", " <th>MSZoning</th>\n", " <th>LotFrontage</th>\n", " <th>LotArea</th>\n", " <th>Street</th>\n", " <th>Alley</th>\n", " <th>LotShape</th>\n", " <th>LandContour</th>\n", " <th>Utilities</th>\n", " <th>...</th>\n", " <th>GarageGrade</th>\n", " <th>ExterGrade</th>\n", " <th>KitchenScore</th>\n", " <th>FireplaceScore</th>\n", " <th>GarageScore</th>\n", " <th>PoolScore</th>\n", " <th>TotalBath</th>\n", " <th>TotalPorchSF</th>\n", " <th>AllSF</th>\n", " <th>BoughtOffPlan</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>SC60</td>\n", " <td>RL</td>\n", " <td>65.0</td>\n", " <td>8450</td>\n", " <td>Pave</td>\n", " <td>NA</td>\n", " <td>Reg</td>\n", " <td>Lvl</td>\n", " <td>AllPub</td>\n", " <td>...</td>\n", " <td>TATATA</td>\n", " <td>TATATA</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>1644.0</td>\n", " <td>0</td>\n", " <td>3.5</td>\n", " <td>61</td>\n", " <td>2627.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>SC20</td>\n", " <td>RL</td>\n", " <td>80.0</td>\n", " <td>9600</td>\n", " <td>Pave</td>\n", " <td>NA</td>\n", " <td>Reg</td>\n", " <td>Lvl</td>\n", " <td>AllPub</td>\n", " <td>...</td>\n", " <td>TATATA</td>\n", " <td>TATA</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>1380.0</td>\n", " <td>0</td>\n", " <td>2.5</td>\n", " <td>0</td>\n", " <td>2822.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>SC60</td>\n", " <td>RL</td>\n", " <td>68.0</td>\n", " <td>11250</td>\n", " <td>Pave</td>\n", " <td>NA</td>\n", " <td>IR1</td>\n", " <td>Lvl</td>\n", " <td>AllPub</td>\n", " <td>...</td>\n", " <td>TATATA</td>\n", " <td>TATATA</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>1824.0</td>\n", " <td>0</td>\n", " <td>3.5</td>\n", " <td>42</td>\n", " <td>2748.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>SC70</td>\n", " <td>RL</td>\n", " <td>60.0</td>\n", " <td>9550</td>\n", " <td>Pave</td>\n", " <td>NA</td>\n", " <td>IR1</td>\n", " <td>Lvl</td>\n", " <td>AllPub</td>\n", " <td>...</td>\n", " <td>TATATA</td>\n", " <td>TATA</td>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>1926.0</td>\n", " <td>0</td>\n", " <td>2.0</td>\n", " <td>307</td>\n", " <td>2780.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>SC60</td>\n", " <td>RL</td>\n", " <td>84.0</td>\n", " <td>14260</td>\n", " <td>Pave</td>\n", " <td>NA</td>\n", " <td>IR1</td>\n", " <td>Lvl</td>\n", " <td>AllPub</td>\n", " <td>...</td>\n", " <td>TATATA</td>\n", " <td>TATATA</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>2508.0</td>\n", " <td>0</td>\n", " <td>3.5</td>\n", " <td>84</td>\n", " <td>3619.0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 149 columns</p>\n", "</div>" ], "text/plain": [ " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", "0 1 SC60 RL 65.0 8450 Pave NA Reg \n", "1 2 SC20 RL 80.0 9600 Pave NA Reg \n", "2 3 SC60 RL 68.0 11250 Pave NA IR1 \n", "3 4 SC70 RL 60.0 9550 Pave NA IR1 \n", "4 5 SC60 RL 84.0 14260 Pave NA IR1 \n", "\n", " LandContour Utilities ... GarageGrade ExterGrade KitchenScore \\\n", "0 Lvl AllPub ... TATATA TATATA 3 \n", "1 Lvl AllPub ... TATATA TATA 2 \n", "2 Lvl AllPub ... TATATA TATATA 3 \n", "3 Lvl AllPub ... TATATA TATA 3 \n", "4 Lvl AllPub ... TATATA TATATA 3 \n", "\n", " FireplaceScore GarageScore PoolScore TotalBath TotalPorchSF AllSF \\\n", "0 0 1644.0 0 3.5 61 2627.0 \n", "1 3 1380.0 0 2.5 0 2822.0 \n", "2 3 1824.0 0 3.5 42 2748.0 \n", "3 4 1926.0 0 2.0 307 2780.0 \n", "4 3 2508.0 0 3.5 84 3619.0 \n", "\n", " BoughtOffPlan \n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 0 \n", "\n", "[5 rows x 149 columns]" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 4.Polynomials on the top n existing features\n", "train_data_new = conbined_data.iloc[:train_length,:]\n", "# 添加价格\n", "train_data_new.head()" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f411c916650>" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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UHYg0A2PMbIS7z0tpk4GZFVoxmoFJwNUAZjaQGKB6GfE+Gsi8n3S1zEAiqKlZY2MjTU1N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pX9nK/TUIE08j38X3fE8Ccgof2QZQL0QtVKjBOB1zLA6GWUyvxpSMW2irVGyH0Ty9g3KHJtib/j\nbjM7J9OHYkWr8u+KY66LQFn2LIzVCCg8bGZ/c+D3GApaW0kCfl/gmToZ99kBSIvfHtH+AuBGU5n7\nHxDoriDV5OmDtO1BiGa7ojibz/x5eVBYQfWViu9AZui3cvdEYD0z7rXqWlBAdzPkCupofuaM/1as\nJk0FAkazqVrTJx4Y+CPiER+a2V0+pi5mBaepXoNcEPV8HDUQ2Nkd0VB8XylSGroi68bozDxk13c5\nMpW3DFXjvGogxWkicgtUZuVleEM7pIhsAvQ3xTPEa+ogq1exKsTxmq1Jgcv5d9+P6Hkg4m+1Mv1v\nhZSuDtmbTAG1m+BHySNLxK0IgG6SAyIfITDwvitiM5Hgb4ssIwf4O49DgHkKoscKxFNfQwrRG8hl\nXeLXz0TCvhda167mJym7+28YAp590fw/jwAASGHaBwGeNxAvfCZLq847dkGCOxbeKyGVqj/S+7kc\nuCOuiVuCt0SyoBYCOP/2fubPLRrg470FrfHViM/PRqCuI9qzE7xbGyPleK6lcvWH+XM6IZfh5ii4\n9RX/fR6i74IkipDJVOS/aP8DIkWaC8ampniAddCmOhSZYctIh4n9G2nQ57vWmtVMQRtmFDJXn4rM\n0o+g+IsZVG/mK0E+5UMyaPpFJNBjNsALyLT2ufdnH+TuORRpO3khDFUZfBVBmfELtkWbJQrdjREw\neJFUUbDKOR/5FhQI9ldkOYm+8PqIWdVwF8dMxHhuQADkXQR8bgPu9c12LNKqZ5IAxQ4ITP0LbcRn\nkcbzFjpPJ6upxkycNbX8kffxnJmOPlcTUQXJ6L6IkeTZ52aFxlxkOXnG+zMkKMvoaWRe/aeZXR1C\nGGdm3VxjOtXMbgqKP7kRMfZsa0VVqw4URq+Dn+CKrHY3I4AVY1deRwzvAdKJvh2QxrQ7YmTnIEB4\nD7Ke/Oj74mnEjOshUBJTZQ/2/pb47y8gV9xGyPJ2JaKjT9AJr3McuMe2NdK+RiCNrJRkmaqFLBcv\nFBk3IYQTUGbSdu6KKwYAIxhejPbfPB/LQl+XFigguQOy7FX42Lv7XMVnlvm8LkXgKltI8Hmfhz3N\nY8m8f6OR0O5OCu7bEAncBd6vBZlnZfu/FAHi04MyOQajNYrWtNgqzDNrQgjv++8N0RrU9HGdi4D9\ntkHxUa+hgwgjsG9ISmHOgrE8fcc2xeejGUnzLsZ3sq3Y2gzP/FYDWQh/QMpXrEz9iJnNduB1JQlY\nnMv/sXeW4VpWWdz/HbqlBAkl3WAHiu3YgY3doow4jjqOXWOPPSa2jt0tjh2Y2IKi4JISAYPu5rwf\n/mufvZ/7PAdx5n0/vezr4uI8z3Pf+973jrX+qwVEWiD6cgQ6b1MR2NoiO6/7ozVdhuhMB6R9PQud\njweR03RT85T+IeWOutfMjq9B+MnnZp9y+zQoOmwyOmsHI7p9EqVMP+a06UcqMvgCAiLvIZrZw+dk\nCHCppcKH1yKh8xF0jnqidT+IlAU1+l+18zF19H5H+1xVkqUcMEVFfoDA6g9+z2QE4kuS6eXanuK7\nr0hbCUTKtBDCx8D9ZnZH4fs/o1wVuTr5NZRNtGEZybQt2hAfkQhhA7TZfkUHILYOSJLZHTGKVazU\nG3s6qVhcXX/etohJbo4O3gBk5+6PCF4L5Mj3J+8m2mMHISk1OsStjZjFfkjSfQ5phHJV/Vjv69vC\ndK2LDlYMJ90BmV8a+vM6IvPCef4eFyBQdidwvilj55PInDAdMYpV0KE6Ex2CmENie6QSfNPfozsK\n5/3Ef5+KQFojJPHv598fiqSDZmbW2k0FpyBpvjIoHfcZSCsUHSmb+7w1RKCxQ/bORe1DJSJWjZBW\noSGJoDdDDGsKIhaX+rqsQyoSF/vogfZKM7QX9iDVB4kEMBZZG1wYTzHc9VhS7pTD/POmpjwLv5Eq\nkrZGRG86YgJbICC4KdJA/Yb8TRo5sTmMlNAtH8MmCHBP9ffZGoHw2Uh7tqf3NwSBsMeQZFYH7b/D\n/Z12cc1YTBbXH+3xyUjj8hCljpwN0H6ZicybLUlS+PeIwK/r3y0mFTdc6s9eRioo+B1iJDGh3hy0\nTp+ZWRsHPJsi82w0u7RDe2uir00/f9aXiPD/hhjJLsB/zOx4v+9nlNzvx6BIqLEIMG6ABJjmPk9/\nQ+fk+6Dot0YIaB9FilwDMY5LvO81EWjfguqtnOZsis9FzJRZH53/oq9JLbRuvwEXm6IqRvt4niUJ\nGi8g4WAuAne7+vcHe98P+u9dEFCv0nh464zo5ll+TT8EwBuSQvvLgYD8/SqQCeddM+saL3Lzx4dm\ntpt/fhAJRr8i2mXISTVqsm9DQt7WZjYpyKG0yDe/QgLLjui89TNP0+59VCDauZ/PUax8/AESbGb7\ndeXy+VQi+rM5ohtf+/dzUR6XekHlEAahPbMEnY9tEejf15RUci3kDHwGOu8Po2CIdogfLS7MXSXK\nRt0LaYE/RGa1C5EW926kOXs9A/Ht47z+0bYSiJRpbid9Ey1QPCQxi2AtklNYbC8U1eVZX10sFXE6\nDjkAdkKHeyQiFr8hKfI/jrqHIclnDTO73u/9FTknXYKY4vmuCm6AnDFb+CELKBvo9dkY1kQMYEeU\n4CYyyfj7hyTiFtMOxwRFRY97/LcFPhf1/LcZJBPANJJT3g1+bfStmYASGA0PyVu8sb/X8UgK+sLn\n6CnkIBx9W5r6mqyPiOSLlGaNHICY208k34oIfO5Bh+gIxCDfQOt2QVCEygaI2D3gfe3m7/GFyV5/\nEPK9mYWI4WVIc4D3WxsxyuEIZP3DzPZwRjXcx7QaYlZxXmtq+XxHU0dPRJBm4WHe8eJQxoPdJZfB\niIhM8z7uN/lsPIiYYw8EDnoi5jkQEZM+Qf4Jz/h8ro8AYA90DqqqJGdj6IuI2z9dy3OOmV3l5pAb\nEdBohpjJMSQNYyUCDLshLWTMUnwjUlnfhLRClyPt4xIEbn7xd14F7YMohe6IgM/jUZAIIUxDhPQZ\nZ57HoHN4NGK46yOTzLWUMrljkZZoe1PiuZhJuSSqxvflCDPr6H/f7P21QIwtIKByA5Lsf3XBoqUD\n4dkAbmpoAUzNNJProlDKLR0YrY4Y50gzaxKSH0neGvn8HoyYxsco827R4bYWYkqHIYAwCZlep/nZ\nGYKYapV/igOo3ZFAtpmPvaW5edKvmYqKIi4OCj3vYArXbQKMcVA3B+27R81s2+ILBPnpnYcA2vdI\nqGuLztBlZnZpmXtKzJZBYd0HFL4rJqcbiwSSgf65FtKSxcSLHZHZLxbsfMPMdi48tz8ylfRCdOV4\nJDCOJWmjVkM040oE2i/x71dHIPr2rMv6SHj5BJn8qjRfQQ7i2yMtxmxfp08R+DvT732dVNV4gCkt\nexVYQMD5cEQvP6Z6baPcZ3C17PtyJtOpCPyPIAPxf7StBCI1tIIKDspEmGRtYU1AxPtaHXjHzLq7\npHcsQscdkcT1HtrAN+IJZ5DkUQsRL9DGaYs2z8HIN2UrBGTOQlLEOP/tDdc0rEKqJ3Cm//Yd2oi9\nSCCiE2JwU9DGbI3Uhc8is8qhyEdmMSIOtRHzXYgO1HmmOh8/o1CxQZZKrZ/p/e9kZj1dvb+KE+C8\nMFwDZKuMYbFvmdkVGVjpiaSKDohgdqE0qVLOvJch1e5XiCEMRkBpPwTuJgflfBhsyr46BwGRbczs\nfh/PXJSGOTLGd5DX+zco02jrIL+fPsCZGej4yscWsxdeh8wL8xEjbomI1JlUP38LEeNaigjAL1CV\n/fQdZFKYSQr//RxpovYlqcaXZM961N95LSsNgWyMnFYvRuBiPHJuvgwx9KhxaIq0CTuT/A4eN7Pc\nsZkQQvSXiI7MY1C2yGbZNVshx8zjKNMcDI9DhHoMAkhHoL13lMlhOvh3pyNgVVVkMOvnF7R37zCz\nI/27nxHT+9rMOoUUkbYHkhy3RdLwe4VhtUBg5x6kifkIMe0xoLTz3n935KhXopYOIQQzMz//ff3f\nZmg/dkHg7hx0ZjqSiux9hYDiOYgG9EVAYle/diMEkPuE8hWbFyImeCcSAnZE9GIWAiMXu3RfB0nW\nx5OZHpHze0MHGW3iGfB3OhNpoc4xs8Yh84vKrimGnn9Mik5ZD4HC3ugcNLNkKmiOtEHR/6QpOu/R\nCfM2FAjwC9KwrY6A6rFIg3oYKVN1B39GgwIQ+R6FpjYIIWzgc72OqajfG8jc87qZdc7uaYZAwY1I\nCxWTI8YW87pAeW3p6aRimHWRVu0+RLPrIdDxLQKsC5Af4Kpo/12EaNlOfl0rn5uY66o9oodfI6Gh\nLzL7PY9MgRWUAQtB0TDPIy3KL2bWI3vfsqVH8hZUOTgKEiWRiv9NWwlE/kBzgvMDqUorJG1AbiuO\nDlhNSYyoocme1wpJ3Z8gRrC8NYiFlKJ6bg1ELK+i1HmuiFSHoQPzL1JoYSWpQNUIdBCuQwzpX0hq\nPxgRuokI8AxHEvYqIdVPmI4O+wned21U8v5BBxlrAMPNrKPP2fMIMLXyd1kbEZIpiFBshg7Oekh9\n/TRSH7ZEDLUBOqTRa/1JUrbKfv4elWZ2blD+lWI7DDHkSuCQGoDPSGR+qNIUhRB+RMAkJi+ah4hD\nc8R0OoYQvkDMsw8CiWsjKaSC6s6NVdlIQwhti8w8bz62aVbIM+NEbHsEFl5HjGkpYi7NkPp2JAJo\nbX1ev6NMmW6X9D41RVXcjxj+CErzXERC2hvt17i/uiOi1wQRz5joKydEL5vZntTQgsLBj0TS8j4O\nMgb5mPNnG7JJNwghHIXMhr84E6yNmNBmaP98iwD7MJQAroU/K4LhY1yLkDOs6HTXzt9pJzP7ye8r\nMvmiI/SpCKgdhrRO0XeqDQKS9VzLUWUKRCB/H7R3N0dn6jvEcKLJ6Bu/bg5iRrWQo2R7BBxfJyVI\nq2oZMDoLCRvl8rtU+FhHkRyZJ/pvbRGAWQPtp8EIEF6N1qEOYl5b+pi+8nG2Q3RhgV+7KgKMtyAa\n8iap4nGlz8POCEj0Q0JYNBmvi8BQsaBfTPYYzRDT/f67SGUWaiOmG0sNjEMA9NOsn44IpJzlz56K\ntGOb+HhHIJCaO7Z3RwLZW+is/0hpq+Pv/hiiR/uShYBbSoK4JQIHk9C+W4B8wuK6XYJSvtfkk7MQ\n7YEnEbjM6f4L/j6TMhp3BAJqx5HMpkegc7caWpsO3t9PJPPvQBSKe1con8Svtr9LJyT4lPjO/Tdt\nJRCpoTlhLLY10KH5vQmPNQ8eQAt9D6rEWeEMYBA66EPRpn2HRHj/idDvi6iMeDEChRBCG8SMDyaZ\nCkCM4TRKS3BXIqe0Y5D6Lda+2J5Un+NBJBU2QoewAzqw1+Cqen/upohIvExKJLatJR+NjxHBPDVK\nhyGEi9Hh2wQRjS7IDPWbP6eCZAa6CknwVyDUfz7a5C8gSbmDKR15Phc7AcvM7G3/vANiAK/65yHI\n2fLBIFt8tD3vhgjvNwhEtsad23w8/0J+Dtf4NU/43LRAgOoE//1x/y7XyPxI0jJFiecbUjRRE5+H\nhojZ1PW1WYwIRIxYmI4YRp4sqJ0/52C0b8aTqsi+iswCwxEguRMx6g2Qz8x22Zx09/5GIaAx3MdQ\n6WaAyOR7IYl5qD8jSqlX+/c/IEba35+zC/JrmEh1Rhj7/jPKpvokyoFS38/Fx0i6fMX/f8fMzvJ1\ni1lfo5lqZ6Qt7IsAyI6Iqe/o6/Ghr9MSBBTaI8I/HEmks3yOlvqYmiMTUGefNxB4ju0xf5+DEABs\ni85YO8Q0n/b/t0TmxaEo+22doJT/b6B9fDkpu+zuPp72CBy8i/bTo8jpsDcCBlcg1X0M84bqmtlK\n5DT+REhFM5+0ghkJqkx5I9A61qe6FA/VhZv881R0HiYjMJb/njvQYqo3NBUBxG+AUU5zZqC9VM7R\nO9LP6Cd0JKJDzUMIn6N53hWdjxkI0G3mY7oYrcsuSNjZjZSNF//tPLRvRyLtRg9EN/dA++VnSs/c\nfOQ0f0MI4QUz24cyzeny6ggodrLSqKyHkMDwtskv6Begm2VOn0EmxHOQlnNNH5Ia0tEAACAASURB\nVOtsJCS8iAtOfu1+SJDcD+3vvohm3Yzo6wakCtGjkcZzEwQu70Q0PEZ2FflZZ2TC71KDxi1fs4no\nLIwhA/F/tK0EIjW0GhYAyptmdih8rjSla56GNvkqqDBchast10cI/nHEdBZk9/Xzg1u1kf1AzgEa\nWSo61dZka66LCFnuH/A82hgPIDNCbmP8Fak4N0Gaiu4IaZ+BTA9rI4I+DhHZ40iM9WzEqNdBBLQL\nkiLGIKlgC8QgZ5BSjvdEqvK+5g5crqo+xPsbghhoBSK425nZl35dD6Ti7+LzdraZDcve5WRkTmiK\nmPI9iIDcjtTFtzoweR4xq8iAoknnMX/Pc0gOjnkrEuFliBnl6/8tqZjeEBRe/G6Ql/yf/doNfK6j\nHfhKRCSXIDV0VMW+jpjYvxFBHU+q39IdAeEOiNEOQ4zqSERcWiMwEPOftEAAqIHP9SaISK2NmHxH\n5HA2HUmEt/h4Ks3skhDCLYhZDkHE7ilkEpiDqv6+46r7Zm5mW+Bai498LHWoXtAx9j0aVer8oGCe\n2w4xk9uQVuEwtDfbUF0bEdenMuu7dpCN/3l//6gxaI8YT4xEOB8Bqfx9Y3QXlEq8tRDQ7eJgZXuU\ndvtrH/NsxCSjw2E5H6haSBK9jRTuPsf/Lfb3aIlAwc9kWS1jczC2VvZVubouI/x5U5FJpZgmvx0C\nBEciGnQPAky5A3oDtAY5s22B9mJ9JHGPo4bmtOrbgqq/ms8I2o8NSeZD0H75CyoqOTgkP6HrEBBo\nEGQy/dTf/xiSg3IttJ4NzGxhkK/N14hJ5qaUCsqYV1z6fx1Vmi4n/NXEDxagfbUE0ZclyFdsdwTi\nB5C0TDv7u09DZ/Uwy6p+hxDGk0zrTVzw+wQvNpjtp5be/32Idk1E9OEwRGuXoHWsRfVzE4XQkmZm\nVWbJEMJC8yzFfibzdiMCNnejSMDBfi6uAtrVBNJ+r60EIjW0UD4hTyfE8LoiBPosWtifs2saIknx\nJrRBuqHN950DkTkIvX/satuNEdNZkyQJN/F+ZiPtyGJ0GD8DnjAzC6VpqqN/wAKkEuyDUO8vPobX\nzGyWv9dTCGCsg3wMJiEGtSVKbhQTID2L1N15m0oq2d7AP8/1f/W8rw8Rc1sDMe4xPkd9EMHe0CWH\nExD4ae7zGe3a9XAGQamzLNnfs5EDWw+kQq3rzzrIf38ZORhG81AEPt2QlH8e8G9LKb+3Y/ltGGJY\nM4LCmtv7WH516aaFmU0PciRb1+dmAkorPskl0CnmPhNuwloVMYETHLjEYoLrmyIkDkQagiFuBlrm\n73wHSg09N6j+y3QUPv6b970akqajv0YdqkvObwJ/M3dCDIoq6o0coOf5d3lUR6Xv3VpI6hrhhHs8\niiYY7xLeloj4dwHGmlle5LGq+RloapmfUAYYm5IigGJiunsRILsYScKRKQxCzsrz/Z1a50zIzFav\n4fklZjf/bhrak9dn++YQxACbBplZI2Orj0D/GkFRc8OBM/x9qvlA+dzELMs3U5pJdSDar3WQ9P4S\nkswvKQz73AywtUHpvEtyfWTPux4VXXwoKNKpLwIf2yCw+xDSSOWmx6Nca9gBNz369y2R5mmYKay8\nEyp7sK//fiM6U1OQBuacIB+v+y0VNqypXMGWiJn2MIXm7orO6TWIFoxHAP5HFKHRMCgR2BHorDRH\nNOxDpDH4EIUjDwzy/3gXCUXlImxKvjMv/xBCaO3z35nkNzPIx1qurYIEqE4IGN1NMt3ngkzOa2Pm\n6FUQbXkb0bw/IbD/pM/pdETr9jCzLxyMnoA0ywsQnfmO5MAc6xb9ivLVvORjj2OILQdatdC+HJi9\n7z0oxP6t7LoIZEeR+c5lvzXGfedqmKfltpVAJGshhAEs3+zSBoVfxix+bZA00bFwXTHSpJKUfGY0\nQvPLzGyvIP+EH5FWIC7ubVRv+Vp9QXJ0muj3HYw279l+bSyw15nSyqut0UE/FCH4lxBomIuY4iPZ\nfGyCEPkWSIJsg7Qs32d9D0CMqW+5CQuFcF2E5F9EUkF7M6sbUsbOBiY1/UFInfoMUpkeiAhXN0Sc\nliATRiMEzpaZsoTGFOLHIQD3CcksFiv0fgXsbma/hDJlq4N8eJohprAE+TpMpEwLCol7wOdxGckv\npBKZcT5FQOk4ZJ6KKf23RABtGzOr633VQ5Jg/Bwd965EhKkNAi9tEMD4HAGZXX1epvtzq5zS0L7o\n58/viPxXjvB+zyeBlAj8YnbVSpLnfKU/s3GRyYcQrkJ7rjuSMtf3az9Hauey5QmCkqWdZQr9i8zz\nC0Tk1jOztf262khLt72Z7RgKvjUhhPcR82mPgNe9iHGt4vMS81PECqUg80j0T/jI3/cmn5uxiIEf\n5/2PBE7xcZ6EwMECZH7sb3Kg3cj7Gel9H4fW/WuklasVlAOnO2Io5aoWf+If62djjX4NkZZsiNb5\nPsons9obaSIahhBuJmWfbYZo1U+Iziw0s8NcA7QNkmR/QJqvfsikMxSd683RWW0GLPaz+S7av7uj\n/fICYmxLEACrFVR1+RBEJ4Yj+rMpAgYtkSAWQeoSAFMY6kxSvZ783cne9xIk+cfEWecjwWKp/2vl\na9QQGGhmp7KCLQPDc0n+Pmv6e25nZsNruG8WorsH+7tFzVV9RKuXIg3M+/6u4xzU34LO4lBk5qqP\n6ERzSluchyXIfDrSr3saaUMrkJZjF6QhuQ6t4zJ/XrXyFoX3bUwyRdUm0bE8aVmlj2syBRDvfZUA\n2D/aVgKRrIUQxlEeiNRGByimFC86IhbvmYVCAxsghtrZ74lZNCcjQvER8pVoj6dnNjm5di4zhvxA\nzUAbbqD3GdXLhtSbl1oK/6uNgMDJZlalyi2ojdsCk80zwmbXdEBJamoFOWtuhaJZWvrvh6KDtB1C\n6fv5O9+LvOoXFVXViDhd5+8+KhvnISiUr1bwCBUz+zgb5zwUXtzPUoTKOIT8LzU3P7lG4HKkLfkJ\naWIaI3+DXYJ8Sk5BzOQERDwaI0KyN9KuLEJarnbogO7o83wPUrdGM048P6MQYTzI+66N1Py3IaK9\nDDHMcX59I0TImyAgE1WmRyFfjmNdilzP16aYERbvezvE+LohsFbNgz1UBW6EHZFEGhBj+QaBrXuR\nNLcj0uJciwjeuaQMsyehhE4DnfEORowFBPxe8HeK/kkNENN+i0LyNR/MQcj8NAiZf27zZyxDoedP\nh9JkcUWtGL4uCxDxXoCYR/TpqI0I9tP+ebfs/pHeR1e0vhGMzfc52SHTClUVvQyqyro18vsYnmmJ\nXkKgtxZiOs3Q3puP1vdFkil2HzMbnM9HqB5yOt/nqZg9dz7y9ZqD1ma8pWRWVyIzzY5+Vu73245G\nmsaiqSHWDGlBdT8NKJ3rSnRWe3nfM5Hp4VSSj9slJFV9hT+/M9IOvpT1Gfs7DWntmiIQ8br3vTuK\nMKlAJtd/oZwbjyCtbv2gEODXEWCL6zwaCTr3ov29ERJYfqJ69tcqp95iczD8hJldk31XAoZruG8S\nMjV9jQB71Fyt7+MfYGbFxITx3gFISNzB+xmE1uVNUuqIrRCwboaA0XNoL84vdNcJgc0YODHT37fL\n8t6X0mSIFSgiaT9SiQTQut2B6Py2SAsUTZ1rozmqAvF/tK0EIivQgrKa1keH6jrk2XwlImoXkQ52\ndDK9Fpk+zjSzfwfZ+mKG0odJ2RmhjJ278Ox2ZpabfuL31TLcBdmrO6ANeGV2eS3kB3A9cv66FplF\nnqa62hC04XuTbK85EfnSzDbJNB0PIGLyNdqo5yHAdpeZ/aOoqkbMphli5vMpRd0xZHU1kjTe1sHJ\n58jh6jtTxERDv2YMYtizfKxNkTf/k0ha+hQRrdbIPv5w4X3z949M80xEWO9E0s1UxLDGIcb8FDqo\nzyLzyQGoTlDMd7IHcKSZHRJkhuiRa1WC/Cg6+Xs+Q1r//f3zkUhjtJ6/byRsDX1dtkIAJyayG0ky\nE45GhGgh2o9n+nxMQ3u4G/KNWQNphlbxvlsjrdyLZnapa8Me97mL2oVoe47zlrfoo1GTLb3qGn9e\nb8Qo47nYDjmu/sd/j1qxdxCz+zNiNA0Q8Imq7BiB8BqSuC8BbjSzIdl857l8upCijd5CmopPURXV\nrbzf8WgvbIxU30sRU12C9ld7tN4VDpA3QVFRcZ2iKfBKZIoZjbRyxaJ8NQGR2pT3a1iKJN2FaG9G\nE0ADtDcbUhq9t62P+ffaBH/PnSk9G68ggPoZSikf/U/WQGdvJgJ/TXzMC8yswt8jhkg3p+Az4r9P\nQ+dicij1E2qONDQ9kRYvlriI2uduiHHGBHWx5ee4sY8r0q68VaOx2ZjmIVq1uPB9fRSp1aKG+x72\nOdiWlEE3akohJUN8HaVLH+P37Yy0zechxn4OAl2H+vtdg3KmxBpChBAeQ8JKnjCtOdLExKKWW6Cz\nuyoSevMzWWme4O133jdfy1rozDyL1vMKBFLiXpuO6MMZVsY5ekVanf/mpv8P29bI3j83hHC1mV0T\nFBGzK1LZnQQQQngauNLMtnV17LVBIYcDzOxxvyaGNMZDsypSDU8CNgoh3Ecp44/ptHMNREskpdwe\nQhgDXOIS8FuIkU2nNKtiS5J3+RRkM/4AOdmORMw8J0C9/PrvEXHeEx2OXYCzXSr5CwI3+/o9R3pf\nbyPmEbOtfuu/RcfFMf78Hoh4Rlv4tj6f5yNfgN+QdPBbkJ37UcT0Zwf5U5zmz7sAEad44Mb4fL2E\nCFE3xAzGe78x/PofuKoUSXY7IGY9FrjNzBaEEE706zshQruzyeGu0sxedmJ6O9JMzXY1eSfkt7C/\n9z0BEfPccXMD5EfxCwIxeTvQ/1WQ1MM4EFsfmeLeQYTmJqRZ2AOtGSQtzt6IYdZGzLYeIm4xamQ+\n0pDgGqi4584OymBb6XP2or/DWT5nDyOfgUhw9kLr2dY/9yDZymtspuq/eQXgfwC3hRAGIknrAsSE\n9kIpzl8IijzYHWnb9gghrOF9rIsIfx1kkro0hBALHQIMC/IZ6I0YeIzouQ45AjYOMhcNLQxzLqny\n7ZtIUh2AzmBUZX+H9nesfnwWkqqvDSH8k1Ss7oBQNhCvbIjtJCRhFr/riASIyFgWZL9HepF/V06w\nedXMdgsysXVA+7QzEkq2RnliRvu1E1Eyr8XZ2D9AoC9qhici7dLhlDK8Y/z/Z4AJDs4bmlnUUkWH\n3XxsTZEzZGuUpLGeA9bn0X6ahkxJPZEZKW83Iy1kBQKALyMN1f2seJuINCrF7NFdKEQDFdop6EzG\n6Lnv/P/nUJmOSa65ilFte4ZU3XsEokd7+tj3QALeJKTtPSiE8BfzqEBEnx4Kinz7Hq3DKWg+Xwsy\nlw4gVQDP9wOUArPlvW9lULqKY5GmtrmPrx/a0+uh81FpWTDEf9tWakRWoAU5/3Qxs3nOfLqjw3sp\nIoxRhVZ0Mq2PGGZf5CC2OlrAKcgMcW0mHX+KmPrj3lckLPtaaYbQNqT05i8hpraElFp8GWKwn/vf\nPRGYuNPMznPtTkTqsxBx+8avjeGVMxExaItMMxUh1WrJ/V/KRi+QzEy5E2KMWvkUAaJKkmNrScbO\nUN5ZtlwYYX6o/uTztx8isLWRWeZNk8d5fVLK6cv83TqTCEZHM5sfVe5Q5S8yDjG46cC6phopk33e\nL/G57YhAYEBE+Qfk61ARQjgcqTNvIpXifh0BuQ7ULLFuisDRo0gDcxQyiYzzuV0UZK8fhsxY0Qfm\nZ0QUGyNNyPEmB+SOaJ82syys0O+5GCVyGoMAza1Iyu+GJLQ2iFA2RARpmT+/XLr+GKo6mKRxG13Q\nCLVEQGNPUsTXT4gwtkHnYRUEDB5DmS+XhhAeQUDoaQcPMey7plZO2/cjybm8B2KOy0tGuBViMG+g\ntX4e7YUOyLy3BQL6N6A9cLPP2/dIm/YTpaGgxcJ8rRDjiK0qh0OZsTyM1iBqIPdHdOB4NHdDUej0\naHR+5yE61JhUbTmaePb2ufnW72uJtGzNEEBeiNa6CWLCzZBQtBVprxyD1vBwv64lEkD6+fdDkWb4\nAQQQmyMmPRL5p8xDe3RvpxVPo3NswMb+XVO0vyrM7NCgel6LUObkPDw216rMRNleS6KGfq85GO7v\na5CbHf7qY3q6zG2N0Jk5ENGe6Qi09jCziQV60hA5ObcMMlnvYSpUWjTzzUVm0UokWHREwKInooux\nRfrXHK13jFarjZyLN/ov3ncDxLNa+mWfIk3kRLTff/D+W6Nw3c+W94wVbSuByAq0oEiT6E39AkKr\n2yK02R75F0B5J1Oo2RRwMmKMXczst3zTZs+OPhITkK37M0g25BDC12iDfo+k3Lz4VP7sSmTXzSWJ\nH0m2xi9IviYx+qMRIqQdEJOt4++/B2JWLyCm+g4pJOxItJnXKaOq7oaIzxSk/Wjs45qOCFRRrdcE\nEe7bKUSoeL9HoEOUq6NLnNuKc+lz9SQJjMU5moDUmxOdKHRG4KEWIg4PIyLcG4HKRshx8CS0B7ZF\nh7QWklq6uE17hr9nMXolAqkTqe5HcVd2X1FirvBrKoJCGS8DrrbkEzIHgawfETF7FEUp9EAg9+jC\nsx4MIdyFHCbvpjrAKwc4oxmmXKhqJ7QvNij08zZwoCm66BUEPO9A4Kia43OQ/Xo0Ms+MRYCtN9KS\n1UJMYSek6fnc5+k+pHka6n1/htbsZrRPTiT5wIAAwumm/BQVJJ+hziQz1/1m9rD/vrP/1s7/zUeM\nv6ZWgQSEj5Dq+ikfa03zC8nOX2LKcODXEjH1vMREJQJrJyOmcaKZveEMfBACKr8AL5nZxSGEheic\nzUQCS2dTteXoND8FgavoJ7ETAigtKfhjuLm1AjGuY0nnuxHSbJzrn6PTcEtkgnvWr9mJ5Jg5B531\nT5Cv1fekHCdFASi2WGpiNtrzPzvofgiZL97gDzR/l5OQ31Vnks/VE6TK0sW2CNHH3czsA+9nEgIw\nINr2mI+7Iakq+gFoP1Qg2lgHaRxjhfa8jtAqCNi1QPQ4T/dQEs0TQuiFBJ9V0f5+FK3VmDLvWwuB\nrOhnVs/HMQsXONBZWoQAZRNSltx2QHPz4oD/a1tpmlmx9hd0MJcgc8RLiGDMIYs0CXIwjK0SEcHL\nEDFqBwRzr/8gG/xr6PA3o3r11GJrRnUVGog4Tzez9bzf5dnoB2d/LzaPFS/T/oNUsFEteh+SCNsi\nQDA4KIfH8yQA8ncypzxkXwbAlOTm2hBCa0vFwnZBBAQEnjZAEuSIbBwV6LB96ve0B0aEEPqZqv1e\njQ7sOggcfJXdO5Ly7VZ0mI8mmT62R0Rhgj9nPiKCo5E0sKv33QZFnQxBJoP7kYbieDOb4FqjRpb8\nYUBamjpoz8QkcFHrVUkCsauQbO13IVPY8QiELUIAb29kKom5QqYhqbQJ0jCAmHZ/n7teyFnuRlKm\nyqvzyQghPO7POg2BgpdIRcrqIqIZpefXkXT8jauAVwUOcwIeacmtiHj3JEn/PZED8U1Is/MnoLuZ\nTcrGEdON3xZCuBCBp0MpNV1VoDP4EVqf2mgtYiK2xxzALEYax3uBr0MIU8zsqRDCCWZ2QdZflApB\n2qr+iNG/ggDGfOBGN0XuiZjILESgtyFVbB1vZlMpNN8DXXz+jkBhr/UK15Q7r50omFhQfZRpwF5B\n4btd/T0/IDkVrga8GxSB1QVpHm9C5pxYk6Y20nL1Qwxosn/fHp3BZSjU/A7/PtbrqUB7dbCZfezf\nHY6AXXTK3dgUWTUHaekqQwiY2TF+fT1gRva5gqR5m4/ozPaumQQx6H8iejTAPx+HhIgISHdA0npL\npCkCCTX3BZUNGEsC+pGexOtKmoP5WygFAbFdU/wiyAF8J5/fPm5+qkTn8mMSfan08fVCtGchAoJd\nEOCaiTTfF7vmcoCZXZw9px/SOn2JwPDPSCP2dZl3+CIoMnMtBCz+Dpzl4OhzPGrKr10G3BJkgl6M\nwP3DSGi4CK33ez6+jZBvSATx9SgNoPif2kogsgLNmWc87MOD6pQ8hepZvAEQ5JC6yFVt7ZAqrzEi\n3K8hjcrsrM/PQwh7Iwb/mjMEQgjH1zCM6GtRz6+LzngdgKXZ5x+Q89oDSIuxLpJ8piKisTaSECtC\nCENNeT2qUlD7YTwZ+QQs8L8f83unIYKKmb0dFIFyvz93R4TA5/v19d1MUIEYUkNgiasnT0aH8AN0\nKON71Dez7YPCYqsiVEJp3Z9lsX/vdwCSRr50wra7yX8jOjzuicDCo37/dkh7sQxpLQx43TUgPyCi\nsQwdzDre/yTkSLYUgS0Q8ZyNND9RBTwKEYrX46I5aDsGMeex3n8HK62sXJK4yb/+FDma7odAxq6I\nSA8PSlEOkix3Agb4/qlERPt6UuTBaYih30kKy83bAr+2Kl24eWRHkEf/Qn/PA/zvbxED2Mzv3ReB\n6BhJ9icEfNaw5AT3jc/Bd/75Z6qn8H4CSfrLSBlExyImUxeBhMFIUl1shQy7WVuIJPHe2ZkghHAe\n2pNHZdd2JGkQj0W5E77wufwIaUi6IDDWHoGTsxH4uAyZWnsBC0MIl6J9EjUpH6F5PR7twXVIjpd5\nKyZDLNeqJHHXiuyImHIs+BhBSwWlAOYjtG9PAd4IKtNQGzHxHdA5iG0Q0o7OwQueBfmRnGVm56I5\nPRvoH1SU80oEHO5D5+vviDH2QOfgUlJFboIcKC/HzVQuje+PGF0Myf4CuCPItwYkAJ6FwPabZvYC\n8EIIYXNKi0p+hTLk3u/37epjaIuEh9iK2qcVakG+SG+aWdHJZxqiQbUo9cmbjoBAD3/eoWhfP46i\nF6e7IPY0AohDgMeD6uB0AWYG+SwdgYDg+gicPBZK6xadEhSZ8wia+74otLkzEipGobNWz8e3GeI3\nOO1uZWZPIOD7BdJGHQDMcVD0N+AGU56gs8uA+L//0bmsqa00zfxOC2Wymvr3HdEhGo0O/+5IFTwS\nodfaiAhMRUjyR0TEzzazyVk/vyKQEkuRR8YcW120QVZBG7s2qahbW8TgH0fEYF8EPCb75w0p9f/Y\nIsiufyICK40Kdv0XEPFY3arHifdEAKYrIhA9ETF+zJ9Rk7d0f6RSfAERq3uQPXUxAh9PI7Q9DzHL\n4UiK+8Xn63TEoAb6u2+OKng2dBXsHFTUbTsnYIdZSk1c3N9F35Y8iqMlkk56Ub1NQMy4WQFAlNir\nQ/mw1F5Itb2UZGZZgPbOvian2GlIu/QVcE1mG78VEaMlfk8XBDBjmGZ9pOH4W/ZO0xFIvsiUeTem\n0t+E0hpJedscMY75CFTMQMSsddZvnLfPgMNNhbOK/j//QWs1D9n9B2dz1QJVjG3rkuMRCHhFh7tJ\nSBJ7gFRJFyuNGHgHOTO/5Hb2cnl/ziX5R8RMthuTnAErSWH00ffhRLTn/o7OypUosdzETFPzEVJX\nD/Txjfbvj0GMoBUCTfX876Ukf6CnUKXnktoweQueUKzM942Q1vX6EMLRpBozPyHasC7ag5f4PJ6P\npNq78RIRQSUcdiPVY6mDJ29D5+xaRF/OQiaTYehMb+fXvoEAdju0T9ZBjLaJz9X1fs2+pgi3E9De\n/wox0Xe9r1oIMNT1tbuN5Wczjus1GdjUGeJj3s8BiHlWIPpRgST+kjBZqyGHxh9pQU6bZoVst9nv\nN5vZKWW+j5qrRcgfrjGiOdGM2tiSb9dOaP57ITr7D1JtrY2B58oAgVWR9vlfaB//igTDf5pn+s2u\nbYEif+oH+T29hSqiXxdUHPVwpG2KJtW/I+C4PqIf76Pq8Ps4iOwLPGS/UxxvRdtKILICLchJ7j0z\nuzP7Lncy3QQRvi/RnK4BbGTJR6I/Qqotgc/NrH+h/y0Rg16L6hkV89YMMZ27SfbaqrDAoHCsLc1s\nqH+ejiT2XAU+AW26F1FNljjGriSJsxrT9WuGIHV8N3RIRlDwfPdWZbsMhTA9UvTOTGRSqggh7IOI\nddTQRQI0H2lJ6ri693AkXezjjPhcZONsg4jhX5GJZiaSGisQ6FkNaQ+2JBUe+xcCc+ORhuZSUmGu\nkndB63IaAk/3ouqkFkL4xvtu4e+12PtoiAj+y37vXKTViKrayxEBuRNpD8b43LdDicDqBkVlreFz\nBbLV3ocI+kEon8CuiMF08e9j9teNSJl6dyOZhkBM6nDzrJexuWS5Aylr7MkIAL/qa9MYmRgORpqA\nE/3WVf3Z7Xw+t/T3PZtSh7/LkKR1QAhhCdVz8ZSTVisQMM/T71cg5nmJz2PeZqMz+RoC/DEN+2gf\n750+pz+i9WmFmPoCH/8c72O17Fz8GYGUK1C0ThP/finaQ5shxt3PlIF1IQJU9VAI5B6hfM2UpoiJ\nn4kAS7XCmd56ouKEDYKiWK5AUV2Rma2G1uQMU5K5TZCk+1rsIKRqy2ujKKGD/bv3EZNvi3xJaiMA\nErMh90X7ZhE6R8+a2UFBpqppCBTtjsycHyGQER0zD0dA+mOkaXnR12yopWyzLyGNQDQbxbYu2rOX\nI6l+DtJYLUGgb11Eg+aiPR59cZYgH5ecOeYaMADM7PXid8trKwBEbkQVyGP+GScPoRPSmu/uPOQz\nZD5qh4IHinwg97lahup4fRCySuGF6xugM3khotvfkKrxXovAY1cz29XBw3lmdnlQxeSPzeyyMu+y\nIVrXDdC5ex/xtwZon/4VAfJzkTboxPz+/xb4rTTNrFhrBlwSlNVvAtr0vRHwiMWvdrHSUut5spln\ngSvMrE1QSWmy6yrQwq6P1GdLkDr3S+BWS7VlzkJM5K9mdkIN45yKJMzYXkUq2dtJ9WBaIRvom8jm\nHdskYFVThMnNwIUhhBLPdIS410VMsTkCAJV44pysVYYQrvC/6wNnOvMBbejxuFe2S/WRIJ/p45uB\n0PmxwA5BviFTkYSVM9BdSZEWmyECtBYiTD2QVHEkImjNTRkN+yPQ9xylOVJGo3mPoG0vv78JKQkW\neH4HJ6SR4M1GduKSOTCzB4KcTnuaWVW+lBDCNojpP440GDORRPkgKQJr2nGcKgAAIABJREFUL6RF\nqvB3Ox9pH0YihhHNSLUQYOtuZp+FEG5DGqsokYPWfX//7i/AJkGZQpdGwGjKVZNn1b0YMZgpZnYP\nUhdHZ+wKypgZXBpu4fMbndrwcb5GIlo7Fe9F63UEkp6nIIIXNUWXIG3fI2j/LEJAJ0p90fbfJShz\nbr98vlHdldeAzg7Wu5r8FzojTcUGIYStkfbiV7R/ByGAF5343gFO9L3Y2J85yMd5qI89PmumM/pI\nD65DprdpSELG+1wbncPNqW5WydtH/v8qiIHlgK29z2eHEMLY+GWQj0WlmXU1pR24zU0C17om630z\n2ziE8JAzqrmoxs1vWR8zULKtJa7te9l/WoqY1LtII/QhKb9MNIkOQunxd4tChM95vKwJArtTzUsN\n+L210Jmo733fhcDY/iSg+i0CN+f7c/ujfDtNvY/c76acOawsoPgf2sbAtyGEOxBd7BJCeAOZvkY4\nLeyNgF19BPDXCSrvsZAkuOV+gAvNnV9x2gxV87MTEsr2Q3SxI4okO9RBbxQeHiGBvPbACUG5pjYl\nhVeXNDMb6hrLYUhDczep7EZdUlj5PLSGxTDzlUDk/2H7wv/lbTVk25uKpK8fC79Hhz9MIZ9N/e+i\nevZykjlhAtqI9REo6URicAcjhl0ryHb3pFVPdHYWcEUIIdYM6Y8O618prbQZfV5yYn0G6RDs6e93\nvhOjxaQEQTPMw8JCCMciKXMqpU6mXUg20/no0Izyz8ORSrYDkkb/jCTswSRtUEwcdzQi4Pch/4KP\nkBTUwqX++5Gm4S4zO8k1FPf5Mx9FdtNXfWxTQ6KC5yKT0P2IyX+AwEJVKG1QvYx/oQM9j+r5PqZk\nf++NJO2c8e7gElFd4KQQwiIzu9R/j5WXGyONxR2IKJ+MfHe+RlqW7b3vT0w+OeugddsEEf8nEZjp\nh/ZGJFAPkLK4lmuT/blPeJ/vFH5fimjDWMQUgCqgsSZah0EUNKpBOXBA6/oYmr9KVMlzRtbPYL++\nEyk6YS2kTdnKL4sakk1RBNTLaN3zZHFrIOLe3fs7BTkV3uXgOzoq/uqajXfRnt+WVIekm4/pg6Bk\nZ9FJuBvah+v62IYgP5bPEAGeaQo1n4oEhChlz/FnbQM0DCH82/v8FAkblf68qUG+Pl8hCXkC1ROK\ngfbyl/73IL/mlez3R5HGYBipanBHpIG92gWd54C9QwgLEOM7HfmVTQeahBAu8vEVnea/BG4NKuCY\nR6zc7p/fIPlF1SYBhYHe10LXdo6m4DuFzt+lZPs0qL7SHWjvl2ubk0XPoSy4URio0oLUpLmoqYXy\npe7zVnd5P5ryRh2GtLIz0dnZHNG6WYgW1kFr/BWiZdPR3hoNzAjyDZwM3BtUxTxvOW2ehMxnzyAa\nuRaeeygDeRHYdsvGOCEoh9XTKPtrlXtAmTYNJTp72MFVD0R3/m6pJk90PO6yvLlZ0bYSiKxAs8yD\nObYgR8wTEbFaGEI4nVJn1LuyazshZl2u9UNEC6SmX4IAwxzg5CB7HqS0443Qxos5SDZCxCoSgWZ+\n3xRKVd1zzKxZSHb9V5Ej6AuURruAGEix7U+qKxHbT4gB7ktiZh2RPbdz9u4vIgZTy8e7NSLsZyL1\nbW0E9Ab6c+7yvkfgviyIIc1D6LyeXz8L+SW8EuT89aTfG/1qnvNrn/K5KEbSvOZ9vg4cEUL4yKwq\n/XMT4PLMPHVdmTkhKDvifkjyGOvz0xhJvmPRuh+M7NexjUfMZDyyCR+ETBenIKJzL2IkX5IAHD5P\nGyOgsKuZLfAxxIy2IIn+RFtOhsNMYjzc/xXbsWiNdgE+DKod8gRi+EciUHoQAiS/IKIaAUtMs15J\nckhtE2QvjyneO1E+hPpb5GMSM+b28n5/ck3Ezz4Hffy8XY3Wb4L/2xOB90r/O293IVByMfBiCOE9\nRMQ/9DmpZ4pouzEon8WraC2mAge4pvAMtK67Zf2/iJtGQgjrIpAbNY0N0T6qh85lX5JWAZ+fZqbQ\n/dUtq6NTQxtL9WRW0Wn6IRIYPgKFbN4ZQjjAxzsdaQ/XQ+d/M3ROpqI9NhkY4gD8QwRYBpMSKl6B\nQMnZiDE94mbAK0iau8qgGjbfof3zNvJ3aA38Jyhyo14I4TMk6HRE++tKtGanIEA1EpkW8jbUSqPn\nqtV9CSFcYmYXFb472OSQubxWkyZqhZuZPRrkiH83otV9zOz9mq4PKieyAAlkHZDfTQOkqTgMqFOg\nzVHTfSbwjCmnVRdELw/036J/YX0EVCYjoSW275BmZGwIYWPzKufZmOqTEpb9FEKIWtSr/PvcOfUw\nli/s/KG20kdkBVtQVtNo493QN1IzhIBbowWbTqYmzu59DJhvZseW6XdOZnf+DEnxRyFNyQUofC22\n+chpyYJsw3sj6aYbniWzTFsPEZ1WiJCPRkS2Kynuv8YU1GXGe5kpdXueGXBDBAyCP+NxSqXsjkhd\nf6y/w2jzAlJBTrDnIamxNdKKHIQO0obIpl3lkR6UOnpV72d5kROz0KGbhg74XiiSKcbTl9v7lYi5\nVfr1w1EV2fP82TsjIBCdPrdF/hEVuCMeYna/IK/+B4Kcmp/3+/LCdPF5l5vZRUEp1kf5fbdkAG49\nkg28LpJYW1FqoqpENVpivY4YJTMRaQhyQDqXVHAsjvVdUuhzCzP7qqDersl/I7bFyMH517ACKd6D\nEjctoNRvZhwCXtPN7KgQwt1Uz9sSEwUuQMy9Mano3UtmdkYI4TgkmZ6GHPLqoDO1C0rLPs6v6Y0Y\n++1IAJhlZiU5QYKiGAaZ2Rn+uRViGPX8nSsRAIq5GJr5vMxAGqF+viZvoT3zIArhbRhkurmO5Gi9\nMdWrcOfz1jWEMLjMnK6Fzlw8CxUIBNQ3JYF7GAHltdF+WGyFSsqIHnzpz41h0T9m/UWz1xYoKmq0\nmT2VzdNNSOqfRqIn95iqSFcgwPkXBJBqk6p4/0ypELUNWs9lVj16rj6le/BLslxFPo6SdPk1fVds\n4ferb+Nz8G4N96+HANdqSJt2ARK4BqGoo6l+3cnI3DvJykcrEhQV0w/t6SrajPIblX0PB8iXWfJr\nqlZU0r8/BmnNH0VAek/zdAr++51oHZsgAe8vyKn+T+h8roG02Rugs3mopUy5/1NbCURWoIXlZ5B8\nAanCP0cbbyBiKHXQ4T8dEb1NyphvCDInXEsyJ3RDwGE8Agddy9yzHiIuMSzwLTPbu8x1JyM0O4hE\n8PdGxO4wUy6Omt45Mt1LkDQEAhTHmFIvl8sMeBCyS+ZJuCrQgXoIHbqa2tbIRALSBr2DGMUViNgH\ngNyenI21jylkN0qIlyDiGsFQL7RGrUlS48NQVWwujrOSJB2t4e87A5l7tkCmgynIL+hzJG2MQQzk\nbqRiPwIRopiW/hFX4W+Ip072e77za+pQaubJW1SHz0cq2SGIQZyDgGodBO7aIZtyw6CIhWtIYKN4\nxiMoaoC0RmugPRRTyNf19W3lc3YMyq5brghbE+TrMsD//xTN+2tUL8gFIuY/BtmpO5rZzKy/vZAm\nYX8fU12kkbgKEfgH0R4Zg/aHAe3MbGsHe0PMbHXv63Dk1H0OOrcbmodehhBaWZbzI4RwGtqXfaOE\nGEJoiwDKRgg0nuxjO4hS81tsb5OKDo5FWT0r/T1bIx+H13A7P9L8dEUS6z5m9nUoX4U7n7c7KdNC\nafTeRB/DIcgvbQnSMNVHIKkxAnMNLKukjPbQTghYb4hqw2wSyid5G4VS7j/sYKaa43NhfM3RXm2H\n1wYys/VruPZ+dF5iiOlgUn2nmEixF9prB1LYl/8tEPHrfvddlnPvXATqLjclnnsPaeZGIabdhpRH\n6EuUobmEh5hHxERtj3lh0d97DweHv/k9DV0rdwcyz3yBznoJePCxvIwA4hNI21IH5Q4BOagOA9Y2\nsz5BET0v+PclQPOPzlVNbSUQWYEWymeQrEDI9WIzWyMoP8GV6MDFFu2op2Qq/2LfeyENQgNKpePx\npBwK0a9jMlKLdqBMWGBINUNi2wKp2aIkPgFJHZ0Qwz2PMr4mBW3HxsguHnw8azqjmmVmzfz6SrSR\nv0ESzF8Rom+FfCHWpLpEXa5FiTmGxd6KmMR+pNLaXyK1528+TzcD+5vC0rZEKv8Y+VEEGcW/8/Dd\nZsgzfWP/PDgfl8/l1+ggbuPjnIUY+zMIdDRzKbQDAlV3IiLeAh34WxFRCGjN70DMOzoRL0XS/BFI\namyIgFhtZAbsgtazN3I4PoSUV+VU5EgbQym/8TGdihjSrf6+I4OcND/weYpzAiJc61gKH74H7bXN\nzKyuz0tHk725WKytITLt3eRfvW1mh2a/V82vq+jXKwCCmaRMstUil3y+55hZkyC/pe8R07rZz+Jc\nM2vkmqSnKDX7VCIg14LqQKszcJplkRRBeUTaoXU9CTmJDkcMfZGv10Skuezp79wSaZV297Edj9Y4\nVi3eDAGWy3CtIKoou9ifOQs5ixfD96takEPtQQhsL0N7YRcEdj7FHR8RE4qZMachrdfHPsYD0L6c\njkyqvX28b/l3/zI5tW6BNEU9ESgbjZjZBsi34VxET55FTO158zweQb47+/q/bdD+Wor25d0kH5cL\nES36yT9v7Ne/hADn330MlQhUxlT46/k7bkBpccCBVj1h3AoBEb92KnLYLRcJWO76g83siRDCulHD\nm/12GNKuxrT2GyEN2SMO4jdG52sTpGHLfXBGmdmaWV/roPNcDFLYCPnY3YU04qcjIegntF57IzD0\nAwXwEOTUegwyF0YBaU2k2XsIAZFbzOxuB2mzzStR/79oK31EVqyVy2p6ObKvrxpkq7sDHfZ5iCjc\njEziNZkPQBcMCorjboTsgicgxjsPEcKoybgdEZZHUI6I8WW6exeBgNEIAGyNVPFro83VBhHjG9AB\nzn1NngCeclXdCcAWmbajd1DI3m24YyDwQ1BGvjvQJt4XOZ0+iTQEmOyYT6HDUh8RoOgQGg/d/Yjw\n7AFVTlB7Ia3D+XhVTwTCKpD0fpWru29EknHM/XEz0l68i4jeukgrsgAd1pYI2ERmSZCTWG1kdotg\nJ46rh8/1Igdevf2eua59+QHti+sQcNwXrdkopLr/HkmYIJXoWf53JVLR/gNJIdsjwr4lAg6jfA7W\nIyVuehUxuqhtOhUxjtloHSd5Hw38mRua2YUhhINROGrO4AaiPbotYri/+n0Ts/GuhxwMV8GjDBzA\nDA9KjFVsWyGitiracx1CaXK+7qS05W8CjzrgjSG+ZyEiOB/tn5dJCc5iG+uAvz4i5NE23pvkeH0r\nWosN0Vl4DvncbIkkuj/5dYuQ+a4HpRmHQea29R3svonWeACa/46IcQw35ehYC50zEFj5Cq3nMAQ6\nbnBg3RAxyqvKzB2khGJvlfsxKGJpgL/DSLQmvZEW61bzwpt+bQzXbY7oRkt0vscimn8mOoe1EHP6\nc1AOnsPN7AKnZ2+R8rvUQTTwWLReFYjhNUR+QmsDO4YQ1kbMdSQ6G6+Q6rRsg/bHzWhP/YrWfbbP\nKUjDtsTnoY0/ZytSrZ4Lsr87oXXOozb+V352Nkqo9oj3nfvDUUaYvB8VOIxm5t/MLPpCPergPKZl\nWIwEg9huR3N0PXIgXYAAS0d0dg5DgPp8BMIqqB6h0g7RgN3Ru1+I1mw7U16jXZHJpm/xRU2O+ff4\nP3z8n5ACJNYEnnfhNkY4vl3oJgq3L9vv++Est63UiKxAC3IOu91UlyNqRCYhiWdbxEh3KqcmXk6f\n7RBz7IHARUwAtBBthvkoJHhIkKPf1Uht3ZSU5OdJxKhmeJ93oQiLe/3zMMTsVkWS7mlBtQj+A0w2\ns/VC8jU5Ati8Jm2HS/qxnkOtkDIDdkDSewViGgu9/62R6SjWa9kKqZBjq48fOpa/D5chySg6Aq+C\ngNQMxLBHZfcfgBjY5kBrl6JnI7X69Yj49SHZv2NrhQ7yLWZ2tr93LyQ5LkIaoa0RQ4sVPS/wMT3j\n63EmYvDjESCrRAT4A8R4p/q1f0ZS6mqIMLVAtvJYG2IuSpU9zAnZFFOBt6ZIBbvEr+tumX03tqCI\njfao/syCoIR5J1mpTX820ubdbWYtXBsxCTHraSYz26dofWMIc/SL2R9JXusgsBj3zva+JquWmV/Q\nfr7bzG4IKcR3X6qH+B7j73cGheJmmfawEdI09A/VfWtmI03RrZYcpqchpv0+CuOONvPmvhaNzB1/\n/fsZSDux2DU5M31OJiGANh+dn8YhhBFob11uZm0LfTTyOanr71TC2OJ7WypKWDSxRCm5O14p18zy\nMgb4Oq2BtDqP8jsthPAdYladEON7llS1exszGx0Uur0TAjnroT28AOhvZu8GRbc8QSo5EFvRl6hc\n9d/oM/IOMru0NbPW2e+tUL2hNqG8n9Bd6HzvbwWztWsa8lws0V+nJD+LZUnyCvf/rn9T8XlWxjQU\nSn1GuiLt2D0ItPVFzHsR0mSdjQDrzmb2ZlBU2Nl+7yIkIJyINN9Fs9NcZAZc6O8+CAHGYWgdngHe\nseSD2BbRx7JmPh9rPzTPz5vZkSGEaxBt+wFp9Oaj9etOEm73Ruf0irK9rkBbqRFZsXY+Qod/IUWa\nrIYkuH2QHTsSgYmkyIHlteuQRHET0qxshcJQL3U17aXAZUFOsp8g5HsqUsnvjADMicjTfQsHI4ci\ne3ZsFyFm8hawnR+0tRADOStU9zWJMffltB3PkCWWMjlCtTPlKFiKJPhTkR30rwidv4RMFxuipEzb\nO4O/myR5432OQI65E7Pv5yN1flXthxDCZeiA9iQ5YkYCGJ1Ur0bl7Lsi4r4IOQVuGkJ40Qr+NEHV\ndLtYab2QDkijUgtJ33v7HCxDksrjSOOx1J/RFgGdVZE25kJTuvB7zOwZf05Dc78cBxaVppwTI5Am\n55/+vB9c0j4blVDfDRH+hSgs/O1yIMTbScjccIEz4PrAk0F5XJb4mtT3/v7pGqimSJ2bE7p1kZZh\nMNofeWRBBA/Rx6PS36VtufktNtcS7h+UzTZGuUxBoGcs2mffI4nvH0FhppF5zEJzvnEI4UzE3PuY\nWcy1MRcBwpwpR4BXDOuMWYoPQj4osX2BzseV+Bnz79/0fn8DvnDNQ2cEzt53FXbw552OzupZLL9F\nxv0kKVquQfZ9BTqzhxdBiLd70T652kFTzCi8FdIYLcmure3PGO/v0NHvPQHN9Y1B+S/2837rIGm7\nFXIgjc6arUi5ZCIYqIU0RxsixtsH5W2Zg87244g5N0Znty6ioXlV5npoD8R9eDKa7wuQ8/GDSGgb\n588otvqUj4CpVrenzDVk71KurYhpObaPcZ8RBGIfcJr+BDpTVSYYUtG/mP7gDWRqbYTo2NrIFFdO\nWFtKWt9/IHN6U9I6n0ZKqX+Mj6kx1TUreZvn/6KWrzkCIsPNrConS5DD9zpmdrTT9KdJvoR/uK3U\niKxgC9UryB6MNtoU3MnUlIJ4M2pwMi309zOSfH92VehoFLEwMyiO/GGElB9EUsMhLu0OMLNNvI8K\nJJGMNLMzQwjjkbniBkte2OsjDcJWSFW6OjpwsZR5OV+TctoO889N0CGJLUod6yM03gwxr6gqjBE7\n/zSzZ4Occ2ehg3cOOkgbI01BTwR6Yl6JDX2OYwbAmB+hkRVyBQRlC/wGqY+7Iyawvo+7DyJGUTuU\nE5UnkcZjZxQXb97fcCTpXo8iEGKkQ/TPuA2BwSFo/Zch4LQAEZhnkLSQ27n/RqoPEpAGpiKE0A1J\npjFCJo7vWySx9/T/+yCG/GUufRfm4ZgyX3fyvlsgBr+lj/UUtA6bIVVxd3/+bETQvkAS3A+kvDhr\n+btPRwD5F792JtKERCZVicwksXX2/8eVGzcium3R3P4beew3QLlk6iFG1w7N9zsIhNZDAKMOAnJN\n0P6YhYDjUB/HKz4HXyJfr4YhVVdui4j92UhjsySEsAHSznzg/exlZq+5JucnUtVo/F0/RkzzflLV\n1ZFo31TlcijXghKKHenSbRfLEopl18xCtGFpmd/G+Z8xYyxIld6YlA/mK/+9NaIFP2RdVJrCfA2t\n504IIHTye95Ec78QmfPaIhr4pJkd7WPoifbXtYgexvDs9/3eTUlVvxujPbcIjxpD4LMWEs62QJL9\noYhWrYr2xFAzK8niWWYutlve79n7lo1+WU6/tZBAtG3h+6JGZJFrlKt8RkJp1MrqaE8+hehxa0RP\nxqF5rtL2IEfshkE5XrZBmaIn+nVv+b9XkZbq0xBCzI1zspkN9OddhEDHe8gX5RwrTStRy7KyJf5d\nMySEtENa91cRUL0talb8uvpIO97S52dW/vsfbSuByAq0oKymT1hp5tSanEyr1MS/0+c8M2vkf1eg\n8N4G/jlmeWyAGMZ76FB2Q979r2T9dEUFmbq6Wu8ZJFX9hJh8R7ThT0YEfiEiOLehw1XO14TgdRBq\nUFcWVbD553EoNfEE7+dOBDTeNrOzgzJb7oyI1lAzOzFT1fellMBPRYwtd5a9EtU4KNm7mVr0KATi\nmiBGeAgixpMQ8etceJfcSWwZcgCrhZjLdz5X/ZDZamiQk1eUzqcjhtUemeiiF3lXpB2JDKH4PNDa\n1EYMN7YW/n4PIt+UH8vMf7mojWpq4+W15ZhGPkbM9Fek2doJSVE3o3k8CklKE0lMHuRf8yUCTseU\nedeaxl18r2iOWFQwn/yCQMFzSHvYFs3RWGSO2wyt99uIcO5Q5t3mklLvx1ozHyHgsgti0LXR/q2D\n9sl84G+WiqlFkG5orSqRP8nFiAFP9zH2QIx0uMk5tzYCyL3wopXeOgC93Tz2DvBnM8vzxsRn1ghE\n/Pd6KENpzCz6M9qvP2bnohbatydGZuTj2gKBzANJPjxfm7LNtkYC0pv+vlugfXNdpIUhRcotRgAx\nattuQBF2T/se/g4JKc+Z2d+CauhcgfZUXkr+dqRZXYqXmvDfR5j7X/xeCytQt6eG+5oi4SiuU9yT\n7ZBJr13h+iIQqczpUlCdp6Y+/8+gebzBFDHTAE9A6e/5OQJs45BWc1Ch766Ijm+NgMnmiE4tQMBt\nPyT4NDKZFLujddkUaVj+XgS5OQ/yz3sjoapR9u4VaN/cmwPBoFpBl7oW9EQEiDYuN68r0lYCkRVo\nQaXFN0QSweN4pIkzpRiCeRACDp9mauLl9VnWvph9bovARAVSNY9B2o8Oxf4LoCavGVIHbdxYuCgS\n5Lokm3+Jr0lQFeHFlurVTCCpMr9FG391ZI76GRGv2xEDuhMRodrIbAViWn2BN/wAfoKIzxSSLfgx\nJI03RRqD95GZ4nDEZB7zMfciRf48Rtq/nf23/lSPkAGBmSkmH5/OxbVA63aWjzOm4K9Eh3IgYtCn\nmHwQ+iAfmDfQXrgCgbwHM+bZHwHGPiGEYWa2gX8/jpoBXGx1fQyv+PMfRIAgIHX0RojJjslvMuXH\n2BKZCisRcP4qpAq0dZF3vGXP7EUKHV5CqWMoPo6m/v08xBD+grQPedseadQa1DC/kBwSJ9Tw+xdI\nU7MQ7YuGQSHkWyPN2rV4gUMEipqjffQmAiQ/m1lVRs6C2WesaxrzkvOjzezb7PrmCJB09Xt+QGtc\nwnxqaNvjzuloHscgQHSxmXUIIdyCANMQpGF5Fq3jHOTb8Y4T9pgg8SdK1+IcpAl9o/hgF2IuRVWn\nNwmKdnmHlCxwJvKXin4EMxDY3Rk57jZE+/tVf9+R1EDPgpyUJ5jZ59nzv/f3fgyt04Fm9k0I4U+o\nzspaIYS1zGxEkCn4Sf+uwhTiXIGbfhDoCUiTNCGk6LkHgS/MLAcs1ZqDrbqsQN2eGu5/GNHKN9GZ\nvgGdkebAUfl+8esXkaJ2KpApe0B2yUAA15IsT7MxhlTVO/Y1rPBdHSTYPODv1gydlV8RSJyLaPzD\nyA9kC8SzNrPqviX7o/14IKL9kU72QcLUMkR3G/v7XYm0jOMoL9xORkn/qu3PFW0rgcgKNiewfZFJ\nZlPEkN9CTnVvoU1RC22W3cxsSPmeqvorbuJbKA1Fi9/FWiXTHOV+n0u/LrWMMLNVKbQgT/vtkQp6\nivcXawa8SIoMORFJiCcgBnh21OgEpYUegA7RngjAbBoltxDCdUjaW4XSwmTFcNmPTM68xyCJ53mf\nu8MRQx+NDrIhqXI/BHwmoMMHIox9EPiLeUAqkPoeqkc/xNaQFEIbvcRXozTFfWzz0EE8DPnYzEQM\n/AwULniwj6OzS5z90b7Y3se1O5JuhiNQENPbx7TYJc0UkZMnbop+OPnZjO/5KZI8vin2E5RB8xGk\nPauPJKY9EXPfEDHZKWguuyENzkhEBBchYvKZv38lUuWejgjgVQgILI9ejEPMOG/V8tSYzJf1zaN4\nnBHthADd13j9EQREz0fnahXKh5C/g1TbM5CW5O8o9PEV73usP7YROq9Ly7xDpS3HjFqDRrBcq1iO\nlq4mDcW/0Nm9KzOxFPdIBZ5SHlW3LVn7oBTyRyOA+AY6M4v8XR9A+zKGEC9Ae+MnpMl7DZkzryM5\nze9Jlooerf8Iqhe3rDSzLUNmUgohzDUP8XSJfxoCGWcgptcdncXP0VlqbB6+7PfcjUxbG6B9+wxa\n16Zo/arKBPjz22efCcoJ83v79CMz27rcD0HZqHua2ZSCSeVMoKGlEg3x+nGUrlcnSh21OwE/Wmly\ny/kIAG6LzDObI1q0PE1hbK8jTc1GCHx/gkD11kg4bIWEl71R4ryLiwKuj6ELMv9chRxO4zMOR3tg\nHnCcmdX367dDZqO7/dpfgdfN0z6ErIrwf9tWApE/2IJCu1ZHDHIPdEgujpvUD0MfMytX2CvvZxy/\nLx2DDu3nZnZ1KFMFMiji4S0z6+sEL29tEFFf6uN8yeRrEoBXzO3XIfma9ABetayktR+c5ogInIpM\nOvnG/hwRj/akehlvkIotnYGQ+mWIIQ/N3hdKnU2LcxF9Dw42s2eCIgu+Q7kgcu3RaYiY/oh8Wx7J\nNDpb4mm8s+flz8j/LjFxhBCORNLYaCQRjEZanzcsVRltiRj6AUgJpjuiAAAgAElEQVTFfRgiLDGK\nKJ+r4vrG/BiDSYmbnkIgrJf3dxma+2uQlPMdAmGPWmlV5c+Ryvxx/3wsArZTKNiHXSJ6zMf3jY+1\nMwJYRyBCdyFa6/NdU9YGrW9VGQNvMYV5uVbtfZHEdbGZPeljeQmBy9kk34vczPQRsJXJlyaGkB+c\nEcm6/n7vkUI7xyPhYJh/PgUB0VZ4PR///nL/P75TUZMGYsSViHGUa9+gtfq6KGlnoGM6hSRnliUU\ns9+PsKtAzqNHI21HTEC1FhIQHjGzI0KKdjkV7aO6/u5PIEbeBHjYzI7M+n4EMfvnkIYp5jCKfhRb\noFDOaOpYDdG9e5AmZiqiD1MQyOmAwOP5pDT47REj+xGt3yjkhzDZ3yeCvdqIqa6Wvf48ZJZ7irS+\noLPzAIWW7dMa6/bk4Kdw71QU6r7Y6WoHUxhsE2DM75mGfk/LHb9DGqiZ/r6boTmfiATZpmgNoxll\npL/PC8gEcyQ6E7cgc93CIHP3yUiQa4zO7+NIA1Rj1FAI4QwzqypdEUIYhaKRhgU58O9kKXpvspX6\niJT1m/lv20og8gebL9B9yDa8LmI0zc2zRLokUKIm/h+ftxE6iLehzfieKXy2K5JY+yP129BQ3VHx\nVuQguQRJCpubwvPqAb/lqk7vz5DTbe4LsxgdkHLajvh5uCkUuC2Sxr5HWqNKJBVdTqktOGYrHWhm\nY4OikKajtMS/Bdnhn0HEcWMEkGojMPAKQvPltEeXIwljUx9HTPo1CCH9oci8cRwCUAciCXIXUuXi\n3HSwBrBlGUl3MvIY/80/5yHd2/iY70US/gXI4e5WpO4uaWY22IleayeACyz5ClUVePPPrZC0EzUw\nnyBQ8jQerWUpQVZMif44BftwkGPiQORAHP0KaqM91s/7NZ+LPHy2rWX1UII85zdFmrUore6MvPef\nQ1qgWgio/tV//wXZ6aeEELZHoHVbZLb4J1q/u5DKt7cpiWAl0tZVIpX0tZSqwCt87O8gqfFoJNnG\nZHW1ECPb2zwxm3+fJ4ha1ft8zt89jnsPlOTrBr9nJ2SeGOmfb0SSbx+kncnz5MRstY8i09NNaB9G\nDcUGwLtm1tzBxu6m4n64ieUQxLRvNdW62Q4B3m4kUDfAkln2YwRQY5K3heictEV7+ZhsHToiCbgV\nAjS3o71VgdIGxOymjVB68Rb++ROUiO1C18L9mxRm2gCZa5shU8HzSIu1l5nND6pTswcSeq5CtGEC\nOiexzfP5Gokidab53mxn7nfm47jMzP5BmVbcpyvagnL1TEE09S0Ebq9D2pn7LAszruH+GoFICOEq\nBLK2QoDofrTXPkagowk6N+uhjLGn+3310b7aFtGWXZB2sBOiiaP890/R2SnXavQnC3JOXQuByAPR\nOTsBgZpfkBm6DwJIedRWWb+Z/7atBCIr0II8kvdFqvauiLE9hYDBbzVJQv8Xn78z2hhdSOXC6yOC\nOcDKeIEHmWyeRAT+YhRG1sglyAuBPxXRbCg4W/l3UYUfpYy3SVVfj0Fqxg5OaFqgA5yH5i5AhCUS\nxLP9HaYgE1A913T8Bx3CvB7LCDTnnRBB+BypoEdRXoWdf1cfEbyYBOoRFL0yCjli/oKAzUWIKD7r\n7/eC318LzfdmSBOy2OdgEDrwE83svKBMnh+aWceQMn8+hmzBl1sh8ydlmgOYdZ3oTgY2MLNJDhin\nmOd0KdzTGElHVyAwXBclMRueXTMfqbmL7SvEFI80s9WDKure7Ne2cFD1DZJMWyCQsRhJ4TE8cAJJ\nMxi1SV39vv2Rc3L0C8HnaTTKXDnRv7sJ7ZURSAuE998UEdZ7kRR8ByLW8/zZHZF03ZBUHyWmrH8L\nSYxDLKud5FrAkVZDZVZnQheZ2SeF77dGodi7BJVMuAylZX83m+OyPgfeci3LMkTso4YiJjk7NSih\n2GGmmi5dEED+mKyOTg3j/hZFHM1H5/Inn7P6iFksKXPbBP8t5t04CKnd90Hz97GZbeNA6UWkJYga\nqAVINb8nOsszkH/V7uicVyImexpyYD4Q+dvEudvd5BNzOgKNT5rZWt73vOIZcdq3JfKTudK/7uhz\nVTZKI6xA3Z4a7uuGQPAeiLm/hADCMuBcMysW4ivevzxz+0Ak7KyG5u0WM5vv90VBZiI607fGOSnz\njPGIpvdCvOgS/k975x0nZ1W98e8mJCH0JkQgdA4gQmgiwfCjqYgNpSmdICJNQEEEAaUjoSO9Bwhd\nVBAERHpREBBpcmgBQUJCM4QkkLK/P557933nnXdmZ3cmu9l4n88nn+zMvL3ce8pzniNn509o/Loe\nOW6Gxqp2stQ2UNH9Ok9OzWMKStlMQE7bIDQ+7k4nvJnuIumINIa7ELnoGKT10aGTELy1lsBEWKu1\nvVFootgbPRAvxvRDbv350YS9M5qY1g7H/TOgzcyeQpN67EGRX3cxYKaZLZ73nt39gfD7UPRST0De\n+PeQJ7RzfKHIjJCoB3EZigRshLzYr6FJZhH07EWjZy7k5e2DvKnpYVvPIGNrLmTErIrIrG9SKQ7V\n7u4bFi9YiCZtH65bLHddFuW710PluqeGe3gxMi52D+vGvj8voWjD7Sh98Svkee9nkrJeHLjfJAw3\n3tRwKk7+ZwaPuR3oZ9V8g+id3AY8ZGbro+jX5SZxug0o9KEJHurWKAW0GTLOrkaD3sNmVlHlQXXH\nYdB1PyCcw0losLwIpZ2iLHtZB+aTwvV4hsxbvJxMJRN0feehMiUFMjD7RyMkYGN0L4ajiWsQCvkf\nhQyjS5GxOAY9bxPQJHS9u+8QrueLyFA5yd1reYSE467neI2g0uOLeBx5saA04wivlPPeEhmsJ1Pe\nS6mipbuLVLwGejfHesYlG0nWhXs3lELYLBjpj6LuvmshYcXhwbj9HfJmR6PJ8jY0ec6LJv0Z6N5W\n8I1c5boTUUpgEnKyxrj7Aya9mUiSPA89E/nIw0R0X84j0zeZbGZ3IuPqxLD8rmi8uAk9E5OBK9z9\n3rCdeN9XMLPzCY3tQsSlDRmtq6Oo1NSwrW+S8YSKcud5XIMM1Yso6dtTayV3fwU5EAAPBON5VeCN\nwnNbC1HduOzzW2SN+zZHOjUvkynlgt6vXwDLh2joNPQerUYmILgoMu4+CecYNX62pA551MwW9mqV\n718jXtX16B1eFN2HA1F1z4sm7axVQgRzZ3c/NGzvZ8jhSIZIT8CUc7wWvaxfRxP6RDQB3AL0t0op\n6zZqC+Z0hsPpXDjnda8tp3sOWY52EBnp6wo0uPwbTaT3eHU/hdg74iCylvJ5LIXOd27kBY1DL8op\nZjYqeBmG8vc3moiEx7r7iabeK0+HdR2lUN4jiE6FwXmPcGzLo0jJP6HDAPoRmnw3RxPg84Vj67hm\nYV87I89iCIqADEF6IGeT8VSWQBP+msiz+CsSD4v3chuUKpiJDLnx4fregIy7A9HkE8s4dyArP439\nX64O9+BEFKa+jCzkvwsyLkEDyKFoQPlxWLdCuMnMvomMj2+jgeZKpBnwSvj9DDRZXxaMqDj5xJLM\ng3PHNgANZheEzw8jo2dP4JYQBeuoyskZo2cjkm4kmq6OjMergVfN7AB0X/8G3GuS/J4LTUQHAJ+Y\n2RB3HxcMt8+H67q5q6oicpoORLyT34ZzHG9ml5OVN8aOn5uikPU30CT2GDJgH0JRqjy5cSXq4xXg\neDM73jM9nQXCfYnXYlH0/HQgpNYeR5GNyM/p0GcIaYV1UZXOe8GAGIqM+LyexwLxXiKDvUwg8VxE\nMAU9nxujFPH66P3YFRneMxDP4C9eo1keetZidGsr4NLgCPVHoo17heO+isrx7A4UvVozbGNeUzr6\nTLKKHNAzCno2nkEpuCdy24kT9FxoMp2H7N0AveMrIuPqLUSezLeaKHKV8lgSRRVr9u2JCJGyengP\nnaO5l/cLi/Cg5NvJ/v6LjIb+yOD8GdmzGcnebSiaEf+O2A1FKKaTVUZG8ujr6Fm4EI2Rl5pSn68i\ng/XbJjLudzyruhzq7heH4wI9gw+aOE0PmLSZ5kNjxfnAp2Y2X5g7zg/briDwdhcpNVMHZvYZFCL+\nAD0k/0I3NfZeaEMTclV9v+eY0j0Fk5LmKujB+Q0iRC2IJt74gp+DGNVR5yNyTUainPRVlHcRvgVN\nDr9Gln4e0cuagCaqj02kvCXd/aPgxX8QtrtlOKYoRvUo2XO4AbqeY3PbXt+zXH9HWWzhvPdHntqu\nyPC6A03UtyNj6aeosieWbs5EBkREW/j+XbJ7ORQNgDPQIH9O2HZcfiYaXI8hM0AceXqDc5PZo8CZ\nRePR1CXz0nDOQ72Gnktu+f+iCfhKxBNqL/weoy21SM/xGr8QzvWjsO/8sgPQ9RpH1ikWshDvhmjw\nGefuO5rK1/dAE9NSyPidgAavlcmiItNRSm8imrzOJtNrGJLLox+PJrgtkQFyQfj+JTKn6VF337Hk\n+lyIBuYH0WC9IuJDRDL0gejd2Ke4rqtq5Yvo+sb+HfEdj9UqD5hUR2PqKU+wXChcu3HI2P4MInz2\nRxGzQcg52B4Z1V8P12ImklV/zkq6cHtBINFyeiJmdlO4ftMIOhFhm/nqvbljSqXkel2JUqGfC8u/\nGa7TouG430HRp3eBqXE8M6UEz0NjxdPh/3eQcf5F5ATcGc7lq4iYnO9nspJnFXc/BzZz9y3C54PR\nBHe5u3/PKltNfEzWVHIJ4CHPNYYrnNsY4DJ3L+3bU1i20aqoLmn1lOxnfhQBugzxaAwZ4fcgGfZo\nxG4LHBKje2a2ome8viXRM/OW5zRlTFouV6NU2MuI5D4NjXEnoPF9KxQF/q67bxTWexk9y2+jMf1c\nl8bLYEIEC93DIcjgH0UXeTONIkVE6uNI1Lvl+7nvPgsdTPYOVdPeOLgSzAAmuft5ZnYaCr2uh9IZ\nw8LvP0Vh3k/RQx25Jlu6+khsjCbdvFZJnGiHxZxgiDwcigb3BcwsXqOXwwPeBkwKxtzJYd/3odTN\ncmRe5p1h2aXQQHYdmXQ44fgjngZOM4mWRSyNXrZxyADZCZHg2snIW48Dj0fPPsKkK/EfxPOZXvht\nJBo4PhNSYCNMCpLHo3TAqcVQp4lY9oPcBLoN8uI3NLPbClGoV1BEoA14wcwW8NqCVf0RYbKe97FZ\nje9XRF7qosi7WwAZCa+iMtjY+RQzu8jdYwTmCiormmL58JbAxNzv7ejav+3uexSOe1H0fI13KZYu\nje7RY+FYtg2fMbMTkbGwL3peB+Q2FaNi/YETzWw7D71zwqC5Prrnu6FowruI63M0mS5LP6pD5xEX\nufvfTCX666FnalBY/m+51OOPUK58KDIY+oXlpqOJ4CmU/38Iebnxfq4drvmp4X6MICPnHosmg19Q\nKZD4hildeidZamQqML+Jp/FNFDW4FEXXBqNnMl+9V4/TsA+5xnju/mowMm5GJeL/NrOdkWHwVzM7\nPVz/VZDnPRnxNF4N+1sjbPe08K8fGn8uCmPR5PD74sG42hQZ8fmS7wHIUJ47RGQ+CEbFA8jIyrea\nKFUWDvg58ujL+va0F57TugrYLcQHyNh7A41/3/YCt8+Umj2fyrYA71tJ+j9EMIpVf19w9w2CwToE\njYWnkaXdHkFR54hz0dg4Gj3r25iI3WshovBIE2/mbvReHo5Sfz8n8Ga6dymqkSIidWDSIfhyLmRa\n/L1D1bRnj6wcZnYLSr/8DCkt5qWs+6HQ/R7h96EonVDFNQnbWoKsPXSssujwMszsYRTZuBt5nqeh\nUO/W6CWYiQaPBdGktwLKc34/7PcqNAhvjYyk/RALOz8BYWb/F16iPVEqoT9Zb5LF0Msdy3zLIgH9\n0CQ8yd2HhG2eivL+qyPyVUf+17MS19fDNWpDk1x/MhGhUj2D4JWv6xKW+jyKRMX+NQ8jbzGmKg5G\n/W++aJlwU0eFShEmItta7j6h7PfcchWqkmZ2PzLgjkXkstODgRDL0ItpvoPcfZHCNm9zdQB+JBx7\nvuX8Mug5GexSCB2IPKcnqOaJgLg4SyAS7nTLiHoboet8LCKDdjhJVll9kBeLi1UpbyHeTux8+2UU\nlQE9F79DZcjF8vbiteuPvM6q486H5U3coxXRM34wEiX7W/gtEpZHoAn0Apd6cKxiedjdNw3LfgZ4\n3oMGkHUikJiLYsxAk8Wy6Pm6Ar3bxeq9KV4gnzcCC7Lz4e91UaRxxbDf19A1PYLK/jxRwTifpr4Y\nTXI7offwXTIy8kfIsPuVu68a7v994RpNICt9XxyNafMgo+lFZCj+yd13oAQ1nlPIDJFa69UqQwdq\nN8trBLnIRhsyOLdBY+fLyLBaDRmo8TmLyq6fQdfhQzRuL0umXvw0MkRvDNsZ5e5XmBRdDRnIE5GT\neacprfWWu3eU85rSsFeg5+fbyKh/Danixuq95VAUdhlTQUJXeDMNIUVE6mOJWkYIQPAihtT6vRfw\nYxSl+S/irkxEA/LLiFT0Chrc7kOTXi2uCa7yt3ypZtHL+CKaxBZEYk6HhUX3M5HqjkBlrJchL/Uh\nFw/gGDTp/jwsf2k4rsOBY6wGWTb8PpLg3bsUWZch9IPxGmq2ZnZ9OOcDw+ezwnX6lKyd+TtowAJ5\ncBuhQe8Z5IFNRoNo9KryyBs/W6MoB8hLvSvsdwxKX+SNDEeDEci7HQIcYeraWiTiLhnO+1qTVPQb\nVHdyvRsNaBea2XW572NEbDnEVTkdGY2bIuNqy9yybZQrUsZoyzBy/VDM7EhEsDwf3W+Ql7UnSrtV\nGU3B0MsLyRU5Vpsh0nT+u3xI/Gay5lrnI77Ta7nffxMMithTaLxnfZfaiimtiOD9n0OmrJtHh7ZJ\n2Pa74fxABsEksw4OwUQzW5KMHHttWG5hNBl3pAvcfYKZzWdmt1LehbsfsIiZRYHEfBRjdXd/K0Qx\nFg7Lfz/nPUedm6rmcCEVFWXn1yFTWYYgO59b9gly3A6TSuxe6PnbFd2PddE79jMPROkwYY0i6y77\nBWTMgKLKuyNjdNXwHB2Joo1H5+9ROL92RIq+iGqeUBkqntMuoKxhXkRR36arGB8clVhI8DXkkG2P\nDIonyFLEf0TRs4OQo/QPFEF5y5Tq3QIZco+6+x9CBDoqPF+Booz3onsyHbjbMh5PXlOJ4OQtgZRr\nH4/fm9mbwTB+Dhkei4XlPyBrAtkyJEOkPiaZ2SKeKwPMI4ROm1KUayVc+h/DzOwQVMq1OrrHryMC\n4UnogStNAXSCYnfQj8h6dxSP4x9mthN68L+OuDX7B29gaTTYjQOecveN43rBAKhFll08bOsuNOC2\nhfD1ISgMX1amCroOQz1T/tsFecyPoQn9IBQe3s3d/2wi7J2Fwt7Lo/A+aPK5niCFX2Nfg8iEiLZE\nJXovmdkG6LptFJb5j7uPza1XVqESEQflGMKulYI5hExVstgpNx5vNNa2R8bP5e4+PL8RUzlqLTyG\nJup4jnsjFeFnzSzes23QYHuSVypKvpb/n8yoa6OyOV58XvNh347KIVeJ8/zh42XA3mb2PWRotaPn\n5EfufnXhvBZCxl8tUaqT0aB/I1kaoQI1jJU2pKI7E70L1yMie5xAXwrHewMyLO7NbS8ey2BKunCH\nZX6KJvIvh2f4hPD9/uF6fAxsYap2KZLd26mRikLvZi3Z+a3DPpZFaYLVyMpg10MG+gBktO6L3pE9\n4nUzlR//HkUiozEaJ9j9XBUYd6Ou0PeH/R/goVlb2MYXkLHzftjW58kMpEc9cCpqoPicNoriu9Uf\nGU+7ouejGcRCgq8hVdK7TRVCg5Ej9xMTSXRpF8fuZM9S7Oej+7g/ug6vo+cu9rE6K2x/3vBcPIUi\nIqshJ21GMESGAnua5ObzTlUbcgKn5T63h4jlm4hzWG9caBrJEKmP+1FX2FoP4eGIHNeryA3uEcuh\nB20SepFXROS3Xzexm2GoaiUaZTejCMWFwHZm9iSV0YI4GX6IvP0bUejxBWRhH+jVCofHA38PkY4i\nWRZkiKyJctSx2+SHZJ5WGaajATEaIp+ge7Y8MMPVf+Z9skFxE7KJ+yfIiOpHJoX/qJkNr2GMvAAc\nacrhr4wGUFDYejxZ9YOZKcnr7nd5ZbltBczsuLBcqf5FYdmrqVaV3B9ds8uBP5kqXSYjb/XOsu3U\nwXXAb01qqP8O57OhSb02GqSfIMPwksK6+XeonnjYfIigXDqJhMkxpruORdGX0cijXA2lii4KRsOT\nuVVXoNLzL2I+xM2oZ6SXGStroSjG4iay9lxogp6CiIijwu/Lo2uW9yYPQPdpF1fvqttRdHC73DLn\nETgihSjGQFN6FBQh6U9lG/Y2ZAjXIs1vS6Xs/PaWyc6vjAyma1FK5E6yiWhtRF5eDL0P41F08Ufo\n/T0/rBvL5OM40B9xPL4T3jdQqufbKOp4jJk94e6PhnTQ/chg2oFMvfloSnhCJSg+pxWEVM91oC18\nf1/J138xkZSvIatk6Q6+hcpgJ4RXHxeRf1+y6qnYkRhgikm1eSCqjvoWepf/idLa/YAnTJVdj6Br\ntA56H9pROvx0DwrGOYP1KmTwTkCR8cloTN8YPXtQSXh/BUWtp5vZjVTq0rR7CXG8O0iGSH2ciPQd\nFqN2pUmVfkUvYGTh8wrIc1kXGRBzAdPMbDsUyn6PruMxKgmsEVugB3ctKmXbi8TKdpc65NnoZTmF\nQnohRA9qkWWfRRGe/6JJ9RYzewBNPhXhxgIuA+4yaRW8hgbVW8nUJHH3P5rEfW5Bk9d4ryQoAzxi\nImjegtIQZQTlg1DefCFkaE0wyW5HD+SOknX6AZiEm4ph8qVRyW6HjkPwFIe6+83h89zuPjWcx3gz\nG+ru75iE65ZExvI+yLM9IWzm47DfPCmuEUTvOg4+bWGbH5ONJacjI7VCX8ADgTcc8x1ISrpMPOwG\nakfFQFVbd4W/9wC+6e5PmHgbsdHi88hQjdeyDQ24e9Y5t1uQR1yvcVeZsTI2pFYuJSuH/RiR/+ZG\nRuguaCK/2bOy3kjOJcddGYs6D3eQtd19avBmoTKK0Q+924PQRPsO1dGPejn8uVFEEDTJDHLJhR+L\nJruLkAE71HOkbDPbPKz7ExTh+zN6l4eE8welXs6mWpdlXuS959+DZVCE8hY06V+IJsW/oojbycgA\nmexB0j0YTCORQViG4nMaESfYUkOkDsZR2YCuO5hBdb8e0PMS7+8DwG0mDZEnkNE7DV2PBU0S9G1k\nc047mdPkZBVY0WDd1My+lttXTLtti6KWX0GpwgtRRPVKFCWdAR08o7XJjNFi9L9lGlqJrNoJrFLV\nNM+Ur6lqOjsgPESHoyqZu5GFPAINXs8jjYGGq31CyuJA5OW9TWUDsWORFxXR7jmZ+MJ2yhQ78+tF\n4mcFWdbdPzCz5SMfwCQvHolV5+cH78L++iHPcyQy0OJLHz2BvLc0DwrdH0OOx5D3oKyLBGUTcewk\n1KviDzWWORJNvC8gg+4J6BBuOsndrzY1xruZIO3s7oNMJLLHUHrkybCthdHz+h2yypPolcZ0Gshw\nGEjGfYkYQrV8erEhY+QZbIaiPs+h3P1taFIZjKoEXqby+sZGaZNQ75UK8p+p6ugD9EyUlZAfjO75\nesGTnxi2Mz23jd+gSfJ9L1GkrQUzOxwRph9Gz1RFObRLRfcq1GW5rAvuE2TXEzROxG0MDOfU7hlR\nNZJz/+4N9CfxBpvndeF8HwznehRBxdarZeefQkTHcbn11kMRh2HICSlTiX0BleW+XdjnZ1Fa4W0q\nn4toIAxG0bI29Kzu5O5/LF6XECn4l3fS+6WrsHJByXlRGnNRd1+req2Gt123kMDdvxUc3lHIADOU\nyoqRpRPR+BCjHRPISub/g1LBMUW5MBrn/okMiapuz+GYFkLRt50IgnIo4vEaGal+ErBPWfTJzLb0\n0GCyWaSISCcIvIGVkbcaUwCllSazC0ztus9AYfoR7v50+H4V9MDujwb1rpQdRy8jKrIuhgbdNjQx\njg37mB9Z1t8tbiCgHh+iA14gy4bvohGyqLtfirzQzrYzE3mSZ4Z1j879fBDlUvHFCFPHAO+dEJSt\nsncDZLyRMWSVHEXsDQx3NZia4uXCTecir/EoQnmzSwjuJBSF2CQsdx6KhOyO0orLo+jShsjI+Cic\n41zIQCqSsU+l2rPOl70ORgZ5VK28EaVoLgmf70ccgrKccj7cW0s87BUUQSiLiv0ZNcCLRu7zKMrR\nEW1x9x+HHPgkkwhae9jmdV6HeI4m1ZdRRU9+gsuHqZ9HInh5Y2VRxItZEJU8rlHDQCyKnE1Cxm6e\nrNtGfYHERqIYjeInyKA4Lvy7PjxLgwnt69H4cFEumhgr4b6O2jrcbNK9OAXxKP5mZr9HaczXw7t2\nC+Im/AoZq1HQbSpK4ezm7h0coGCgPYCaNP4xd7xbxD+8kidUBWugb0+NVcsEJaei1MlPau2vQdQq\nJHiEELkJ12GPcMxRuydGPfatsd3N0X3aDqU6x6D7MRpxfWbWSLsR0ssXAxeb0uF7oPu4JCLI/hxF\nH+cylVwXo7UXUXtM6xJSRGQOglX2DDkUhUC/jEJwX0EPz3Ph+zu8AcGfkn2siDQSbkF5S9CEFicq\nQxU5Leu1E/ZbIV/v6lGzKHrhds8PZiXrDkeT21LuvlV4Kbd29yrmfYhgrOr1CcoveCi5LPxW1rsh\nlm1e5u6lg4k1INwUogiLhokn7x0OQNUHC4bPHyIOzmlkXVV3Qh75MA+NDq3BLtGF4+xWP5SS7XQq\nHhaWq4qKFbYzAg2UryFvERQxGYq4PbESYVWU1vuKu3eZ02Vma7j7M2Z2X+7rOGGthYy7V1HzyXhf\nfoLKkDcJ6YzRrn5EA1EkYl30XEykkhxbKkgXrnmnUYyunlvuHFchk53/a/iuGLUoYixZz5oP0XM/\nkKzyKq9BQ8l5tSHD9drCb6cjz/wX5DgJnpXWL0vo71TjXFrynM4KhIhSFFZ8xQu9WkxaOzui5/gc\nlBIZUNwO5d3LYzfvD5Cg3XrovOfxTro9mypkvoWM6i3Ru5bOcoAAACAASURBVBNl44+nvB/N6Fpj\nWleRIiJzAEzKpUeR9Qz5F0rJjEYDXeyTc6fnOlh2cR9t6MGcG6ViBqCXJA4gMe87lcC9yK072t13\nC3/nB5224t9em/xUwToP330azu83ZD1Sisf9QzSw3QB8LZzHz4GjTfnoichTGu2qtGiGoFzWu+FK\n9IJ/yVRWW1HVEM73JRNp7QLk8ZYJN72HBpeiFsYK4TpETEMT42bAOl5JgvwwXJN70fX+kpndQ+V9\naEeRtNu9urx7JDDOVEK4NJokBqPc8Y/NLPZkiT0xYgPD/PXFGxMPK42K5eHuD4WJZgdksMxE6ccD\nUcqjI6JiEsE7hdodSuNyS1Dt+d2NBvNNSpafhLzM81BJe8Q5ZBVXp5IJSe0YtrkcmmwOd/d16x1T\nDo1EMerCqontVSXppuaXK1Ap9rVTYb1foef9TGQwtKPn43F0vv0RR2RNNB6tj961PBZEE+VyVFf7\nQHXENkZ88jyhMoykk749ZStZpVBiETNRqvRxr6H1U2Obxetd/L0dwKWc+200Tj2EItnbht8nIV7a\nXYjvtScaH05D1/QZZBBH7tKzyDk8Onz+IXo+liHj8cT9DydrDfBfNMZ8DkW7/4re0WkoojSayn40\nZzV6HTpDMkTmDLyIPJFDUfj4QlSue2n4PAOgu0ZIwPFkXsZTaGK7l8a8jE8Kf+et+PwgWI/81Ajr\nvAyHoTDtQ2a2I1mlxRiU+z0RecxnhnRIMwTlit4NrmZgk1BnzA2pJu/F8z0cRQiuQgP7dWYWhZtu\nC8vcCtxoqqJpM5EzhyH9hetNJNV/o1DvucCCuRz9JDSQPhY+34/4EP0R12kKCpuvFI5hceACkwhT\nvhJjAbLJYE/knUfC6ZrIMN0U5fn/QSZ41XF9PUdaRcZHPsoxNFy3uj098gjGypnxc0hr3IEiJPnS\n8muRwVoKU+XPDeh5rvtcmtn4HD8hGohFAzpvIK4cUomg5/gqV+n5vxHxuiG4+9/J+pLcbBLNq4hi\nNICigX028nqrztcrS8xPyP2Nmf3S1dbhOHRtp6Ix4pchZRhD9jORcT0w/I2HdgYmzsgr7v75wrZX\nRlUgD5PjCZk4SR08oTrn2EjfnjIciSI884bzmYqI5x+Hf4sgtdfveNassDMU07wR7egZOYFsHj4R\n2MHdf2dZGX07kme/AjmXb6L0ZD7d/hqK9sV3Kxqs/w6/nZE3WMNYFvtxzYfu3+boWTzB1fl7dzT+\nfIzSvIcDuPtkIPajuZwWFWskQ2TOwAw0meSrIP4v/MujVilfIyh6GY+gdM86qIKlIj+dn0zcfS8z\ne9XdV/DQ3RbAgmJng/tvhHVehiWorKrZAxkgz6Duu9FwuA5NEKuaODYXAAeHASFPUN7Sa/OD3jGz\nYWGQeDf8vbtJhnxC/tzzcPe7zOyzrhK7C00CRUXhpp8hItsNaFB/Ak2CFyLv+AMUPt0PEUgHm1j2\n7WgAhYxwuiQy0M5x944upiYC8OruvpuphPImKktCX0MpiCnhum7oWT+UXd396BCB2gT1tLg4t+3r\nkJFzgTUoHtZNvE5lBVfEOtSJrqAJ+XLEeXksLL8uyr1/pbBs3qu8NazTD2gvGohhmShyNgX17oi9\nkhamWpiuCl2MYtRFwRDEzM7wQmM8E3/hwtzn09z94MKmogppFM+agZ7Bu03qt+eGZcaG5R4Px91u\nZusgg2pf1DSzo/GcC/Wq54o8oTK8FqIbU9A93D58vz6VYnpFHIGia79APMB2M1sJvQNXhH0fjiKs\nw2ttJA8vKQk2FRI8iQyRm8jey+VR6XseZ6EI7lBkMEwCFguplFvJuDPnhXTgM+HzVBSZ2hq9sx0G\na0i5jUcO0oPo3q2JuG3TzGw3NI5cFdZ/CaXc382Nb6/QfCVRB5IhMgfAG+j62AIUvQwn613SRmW7\n+bLJ5LMl26wlzlWGR4FTTe2nAbCMdX5fnfVeRhNJDOXOj5QMd6ayud5TaIJuhqB8LvC4Sbr7t8Af\nTeS9TYHJgWMwE73EV7o6XX4BmBa3bYUGb8A+ZnYxcKOLiHkQijhM8VylUEg5xajXeiZxojgRvosM\nj82CV7lruAbFe3Q1Sv38NFyPIg+mrB/KNmG9e8zsl8jj3BsN6Efm1u24vjQgHtYEzkLE2TaTuFM7\nCjXvRKVRVcQqqFdHe4jKPA88b1ISrmcsRwPxS2FfRQMRdB8eRwP+v8J9nx9dt3ophoiGoxgtQpGT\nsC+KRJSh2LNmhomj8QaqVnsNRWXzKdh8ZHDT3N8d44Zn/Z3q8oRqoJG+PWUYBaztOY0gd3/ZVE3z\naHBSTqJGaqcRWFZI0A9FDbcCNjEJvH2Ixqo7w7InIYPgzygFsylyqsaEf1MIVXQoTRMVmkFp0/Eo\nZZjffzsiA4NSYsvlfm5DFXMzkeHyUzQePI4iI/kxbS0yo6dpJEMkoVEUvYzl0Av1L5R7L0ZfWgJT\n06uDaIB1XgMnojD2reh5/5BMNTXPKxlJ1uchVtv8PfxrCO5+hqmMcyKaoD5CNfsrIe8jvrirAn82\nle0eizyeaORUNHhD1RBnI+nyMcAlDRhEoDB0Xr49X/kyV9hvUWdidzIPfW9y1yOc361mtiChH4qZ\n/SHs41VknH4mbPsbYf088te3EfGwbiGkC9qRIZAXd9rLg7hTDXyArv0bwIdmtoKrodvjBK2XGvub\ngvgxeyKjdVt0737vWRv6g9FztBCZ5PsnYV+H0QkaiWL0Fjyn9lrAakiTYpeQCuju9uvyhGqsU3xO\n90ZGyde9RiuIgMVQOqIoVrgY4leAxr0i16VTWKGQIPKlQmRjHcRtWhu4PaQ95kaRkmfR+zQFRWxe\nQdof5wLfC+nYWHr8KlnrDCg3WBd095PDOst4ruu3mZ2BokczkOr1g2b2j/Dd62Rj2vqIAFzPsO8S\nkiGS0CgqvAxkHJyPJpejCvnkVmJpZOz8zN2HhQhCnnX+bL2V3f0GMxuL0kl/QXnSDVE05PumrsGr\nhm1u3ezBetYbZxpwVEhDbOnud+eXM7OvojTLJe6e5y60e6Vw09YokvAdZCj8PQwOl6CumrG8d6Cp\n2VccdMoavEUdj28g76bdxPeZjq7zYmhSHYjIbtuG4/gs4oYU+6EMQZPEZ4HprrLjryCj68Tg/UH1\n9W1EPKwmrFzvIY/Fw7l2RffhGnRtV0Ie6W9NSrXrl+2r5JpA1mStHyJD/57M2Iv35ZBcJuIDlE6t\nJd4226BwzdvCd3uFv7+EJuoBiK/2BuLoxP4kY8Pyi1OSRs1Phk0eY63ntKxvTxlGo8jeGBTJmY4i\nDjsBd4T34gG6wOux6kKC7XPvLOj6GUpjxc68A8K+B6L0x/soFfrL3Hr9UFprLiSV0A4M8krhwDNR\nReHw3HeTgyH7KRpX89UwtQzmp1E59TTqR5S6jWSIJDSEopeBcpNH07mX0ex+NzazXdBLtysSkavX\nZ6JsG4+RETVjZUS+0uJ+4JZO8s6lsKwKqKy8sR1N8Hua2R5eWRF0D0pr1dNVuRk40aWceh0isS6F\nSGYHAqeZZJcvCedxZ43jyB8P7n6bqdpkM2TktCGD4i4PBFczW9Kz/jynUtIPBTjE3Zey0HLezPqH\ntNYwlC57i/LrW6bHATnxsDrnAOV6D0VU3EtT2fkL7l6rw+rh4bgmIS/yXFRtMBaYYQWtD8TDWQAN\n3LsiDtFFXtkj5iiySNdsgRKDoqhbAuUcnVp9bBZB5M5P0NjwFjJQl0DNC18P3y9CFlkqbqcZTlAe\npc+pl/TtqbH+gSji8HWyNgmRDH2eu39qZoeilFqjKBYSrB0M0R+hTspLo4n/QVQNtGPBUCEc+xSv\n7N00BaXsLkKpv0HIGdkRpTyPCN+NLWzqBeBlUzfvQZZ1Kq4wMoHDTbyuAQSDORiS8R7+rZVRuaQj\nklAXdbyMwQQRJzRJxoqc0snEzD6lUp2zpmJnjeNYEIWAR6IBp9hFsirXbqqE2Aq9ZNe7+1NmNjAM\nKEsgsuZ2ZrZwg7nnsuO6gtqGCKiXxlOobfbI3HrfAG71Qg8ZM/u/XFSlQlGyZN8boEHyC0gavKXa\nLbn9vE1WCrw8GqwXRsbUpchDnIRCuLeiiMTznmmjVFxfK9fjgOzZyfMGWnUOKyGOQac9e0rWHUv1\ncS6N0l0zkGG+NLBQ5O2YCIlvu/vCTR562fHUfCYaWHcsJde8sNiyZJGLNpSqWLCwzodeW+311bDc\n44jIOgoZps8gg6UiRebueX5Zt1HrOe2Je1LnmMaGP8uu8XTEvZiJlFLbkIrpPSXbmUpGDm1DUYo3\n0Jj4aPjuGUKbAHT/hrr7oMJ2lkQE7IXIOh6PLDm+fug9/g+ZiN6GKHLYH73rG3muY28zSIZIQl2E\nMGVeGGsSqnOPRNOlkbfzdPhcOpmUDIAdy+a/8NpNugih0dFIyrgCJRP6tshwegB5BhugQfFfyDA4\nDREHv2RmzwO3eRck7xtFSE/cgNIQUXRrNVQ98REST6vX4K1KuCkYWLuj1Ml7qJ/OL+tNTmb2Wry2\nYcCuhQ6Z/dy6k919nvB3GyLKzh3SSzchfZf30IT8fvj/T+6+Q1in4etrQTyss+W6iu4aIrUiKbWu\nSWGZbhsMhe0UoxhVBjzUNuK7sb9ayqPF/fULfIZFXCTfj1A6ZgLSVvm7uw81s3eQmOD0ettrFq24\nJ2a2A4o4Lunua4Ux5wDgNHdvGSHY1M5gfcSt2wiVI09G0aQHge08J6pY456UGZEzUaTkOOCDTs71\nOHcvTbWY2bkopbqVB90UqxRSPB5Y1t13aeB0O0VKzSR0hjJhrO1y4c7oZdT1Yr3Jyh4z+zIKl3+K\nxH46SwcdhuSjrwvrX4dyqQuggeaw3MC9LcoB/9Pdr+rm8e0OzOvu5xZ+2g2RvJZHpNV2ZAwdi6oR\nGmrwZhJj2jVsbxmUttnGs74RnUUR8vnlstbwEWWDbYfDEiacmOLpKDsOA/94FBZ+hMqGZKXX1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"text/plain": [ "<matplotlib.figure.Figure at 0x7f411c8f6390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from xgboost import XGBRegressor\n", "\n", "train_Y = train_data['SalePrice']\n", "train_X = train_data_new.select_dtypes(exclude=['object']).drop(['Id'], axis=1)\n", "\n", "xgb_regressor = XGBRegressor(seed=10)\n", "xgb_regressor.fit(train_X, train_Y)\n", "\n", "feature_importances = pd.Series(xgb_regressor.feature_importances_, train_X.columns.values)\n", "feature_importances = feature_importances.sort_values(ascending=False)\n", "# feature_importances= feature_importances.head(40)\n", "feature_importances.plot(kind='bar', title='Feature Importances')\n", "plt.ylabel('Feature Importance Score')" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [], "source": [ "top_n = 15\n", "poly_columns = feature_importances.index.values[:top_n]\n", "for column in poly_columns:\n", " conbined_data[column+'-s2'] = conbined_data[column] 2\n", " conbined_data[column+'-s3'] = conbined_data[column] 3\n", " conbined_data[column+'-sq'] = np.sqrt(conbined_data[column])" ] }, { "cell_type": "code", "execution_count": 78, "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>Id</th>\n", " <th>MSSubClass</th>\n", " <th>MSZoning</th>\n", " <th>LotFrontage</th>\n", " <th>LotArea</th>\n", " <th>Street</th>\n", " <th>Alley</th>\n", " <th>LotShape</th>\n", " <th>LandContour</th>\n", " <th>Utilities</th>\n", " <th>...</th>\n", " <th>NeighborPrice-sq</th>\n", " <th>WoodDeckSF-s2</th>\n", " <th>WoodDeckSF-s3</th>\n", " <th>WoodDeckSF-sq</th>\n", " <th>OverallCond-s2</th>\n", " <th>OverallCond-s3</th>\n", " <th>OverallCond-sq</th>\n", " <th>1stFlrSF-s2</th>\n", " <th>1stFlrSF-s3</th>\n", " <th>1stFlrSF-sq</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>SC60</td>\n", " <td>RL</td>\n", " <td>65.0</td>\n", " <td>8450</td>\n", " <td>Pave</td>\n", " <td>NA</td>\n", " <td>Reg</td>\n", " <td>Lvl</td>\n", " <td>AllPub</td>\n", " <td>...</td>\n", " <td>444.072066</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.000000</td>\n", " <td>25</td>\n", " <td>125</td>\n", " <td>2.236068</td>\n", " <td>732736</td>\n", " <td>627222016</td>\n", " <td>29.257478</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>SC20</td>\n", " <td>RL</td>\n", " <td>80.0</td>\n", " <td>9600</td>\n", " <td>Pave</td>\n", " <td>NA</td>\n", " <td>Reg</td>\n", " <td>Lvl</td>\n", " <td>AllPub</td>\n", " <td>...</td>\n", " <td>466.904701</td>\n", " <td>88804</td>\n", " <td>26463592</td>\n", " <td>17.262677</td>\n", " <td>64</td>\n", " <td>512</td>\n", " <td>2.828427</td>\n", " <td>1592644</td>\n", " <td>2009916728</td>\n", " <td>35.524639</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>SC60</td>\n", " <td>RL</td>\n", " <td>68.0</td>\n", " <td>11250</td>\n", " <td>Pave</td>\n", " <td>NA</td>\n", " <td>IR1</td>\n", " <td>Lvl</td>\n", " <td>AllPub</td>\n", " <td>...</td>\n", " <td>444.072066</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.000000</td>\n", " <td>25</td>\n", " <td>125</td>\n", " <td>2.236068</td>\n", " <td>846400</td>\n", " <td>778688000</td>\n", " <td>30.331502</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>SC70</td>\n", " <td>RL</td>\n", " <td>60.0</td>\n", " <td>9550</td>\n", " <td>Pave</td>\n", " <td>NA</td>\n", " <td>IR1</td>\n", " <td>Lvl</td>\n", " <td>AllPub</td>\n", " <td>...</td>\n", " <td>447.910705</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.000000</td>\n", " <td>25</td>\n", " <td>125</td>\n", " <td>2.236068</td>\n", " <td>923521</td>\n", " <td>887503681</td>\n", " <td>31.000000</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>SC60</td>\n", " <td>RL</td>\n", " <td>84.0</td>\n", " <td>14260</td>\n", " <td>Pave</td>\n", " <td>NA</td>\n", " <td>IR1</td>\n", " <td>Lvl</td>\n", " <td>AllPub</td>\n", " <td>...</td>\n", " <td>549.090157</td>\n", " <td>36864</td>\n", " <td>7077888</td>\n", " <td>13.856406</td>\n", " <td>25</td>\n", " <td>125</td>\n", " <td>2.236068</td>\n", " <td>1311025</td>\n", " <td>1501123625</td>\n", " <td>33.837849</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 194 columns</p>\n", "</div>" ], "text/plain": [ " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", "0 1 SC60 RL 65.0 8450 Pave NA Reg \n", "1 2 SC20 RL 80.0 9600 Pave NA Reg \n", "2 3 SC60 RL 68.0 11250 Pave NA IR1 \n", "3 4 SC70 RL 60.0 9550 Pave NA IR1 \n", "4 5 SC60 RL 84.0 14260 Pave NA IR1 \n", "\n", " LandContour Utilities ... NeighborPrice-sq WoodDeckSF-s2 \\\n", "0 Lvl AllPub ... 444.072066 0 \n", "1 Lvl AllPub ... 466.904701 88804 \n", "2 Lvl AllPub ... 444.072066 0 \n", "3 Lvl AllPub ... 447.910705 0 \n", "4 Lvl AllPub ... 549.090157 36864 \n", "\n", " WoodDeckSF-s3 WoodDeckSF-sq OverallCond-s2 OverallCond-s3 OverallCond-sq \\\n", "0 0 0.000000 25 125 2.236068 \n", "1 26463592 17.262677 64 512 2.828427 \n", "2 0 0.000000 25 125 2.236068 \n", "3 0 0.000000 25 125 2.236068 \n", "4 7077888 13.856406 25 125 2.236068 \n", "\n", " 1stFlrSF-s2 1stFlrSF-s3 1stFlrSF-sq \n", "0 732736 627222016 29.257478 \n", "1 1592644 2009916728 35.524639 \n", "2 846400 778688000 30.331502 \n", "3 923521 887503681 31.000000 \n", "4 1311025 1501123625 33.837849 \n", "\n", "[5 rows x 194 columns]" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "conbined_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Feature Scale / Skew \n", "\n", "Ref: [Lasso model for regression problem](https://www.kaggle.com/klyusba/house-prices-advanced-regression-techniques/lasso-model-for-regression-problem/notebook)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](http://itknowledgeexchange.techtarget.com/writing-for-business/files/2012/12/skewness-300x247.png)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": true }, "outputs": [], "source": [ "str_columns = conbined_data.select_dtypes(include=['object']).columns.values\n", "num_columns = conbined_data.select_dtypes(exclude=['object']).columns.values[1:]" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(['LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt',\n", " 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2',\n", " 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF',\n", " 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath',\n", " 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces',\n", " 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF',\n", " 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch',\n", " 'PoolArea', 'MiscVal', 'YrSold', 'RemodYears', 'HasRemodeled',\n", " 'HasRecentRemodel', 'GarageBltYears', 'Now_YearBuilt',\n", " 'Now_YearRemodAdd', 'Now_GarageYrBlt', 'MonthSaledMeanPrice',\n", " 'MonthSaledCount', 'NewerDwelling', 'MSSubClassMeanPrice',\n", " 'KitchenQual_', 'BsmtCond_', 'HeatingQC_', 'GarageQual_',\n", " 'ExterCond_', 'CentralAir_', 'ExterQual_', 'Utilities_', 'Alley_',\n", " 'Functional_', 'Street_', 'FireplaceQu_', 'Fence_', 'BsmtFinType2_',\n", " 'BsmtQual_', 'PoolQC_', 'BsmtExposure_', 'BsmtFinType1_',\n", " 'GarageCond_', 'NeighborDistance', 'NeighborPrice', 'NeighborBin',\n", " 'IsRegularLotShape', 'IsLandContourLvl', 'IsLotConfigInside',\n", " 'IsLandSlopeGentle', 'IsCondition1Norm', 'IsCondition2Norm',\n", " 'IsBldgType1Fam', 'IsRoofStyleGable', 'IsRoofMatlCompShg',\n", " 'IsGasAHeating', 'IsGarageFinished', 'IsPavedDrive', 'IsSaleTypeWD',\n", " 'IsSaleConditionNormal', 'HasShed', 'IsVeryNewHouse', 'Has2ndFloor',\n", " 'HasMasVnr', 'HasWoodDeck', 'HasOpenPorch', 'HasEnclosedPorch',\n", " 'Has3SsnPorch', 'HasScreenPorch', 'SimplOverallQual',\n", " 'SimplOverallCond', 'OverallGrade', 'KitchenScore',\n", " 'FireplaceScore', 'GarageScore', 'PoolScore', 'TotalBath',\n", " 'TotalPorchSF', 'AllSF', 'BoughtOffPlan', 'AllSF-s2', 'AllSF-s3',\n", " 'AllSF-sq', 'LotArea-s2', 'LotArea-s3', 'LotArea-sq',\n", " 'OverallGrade-s2', 'OverallGrade-s3', 'OverallGrade-sq',\n", " 'BsmtFinSF1-s2', 'BsmtFinSF1-s3', 'BsmtFinSF1-sq', 'GrLivArea-s2',\n", " 'GrLivArea-s3', 'GrLivArea-sq', 'YearBuilt-s2', 'YearBuilt-s3',\n", " 'YearBuilt-sq', '2ndFlrSF-s2', '2ndFlrSF-s3', '2ndFlrSF-sq',\n", " 'BsmtUnfSF-s2', 'BsmtUnfSF-s3', 'BsmtUnfSF-sq', 'LotFrontage-s2',\n", " 'LotFrontage-s3', 'LotFrontage-sq', 'OverallQual-s2',\n", " 'OverallQual-s3', 'OverallQual-sq', 'TotalBath-s2', 'TotalBath-s3',\n", " 'TotalBath-sq', 'NeighborPrice-s2', 'NeighborPrice-s3',\n", " 'NeighborPrice-sq', 'WoodDeckSF-s2', 'WoodDeckSF-s3',\n", " 'WoodDeckSF-sq', 'OverallCond-s2', 'OverallCond-s3',\n", " 'OverallCond-sq', '1stFlrSF-s2', '1stFlrSF-s3', '1stFlrSF-sq'], dtype=object)" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "num_columns" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "146\n", "117\n" ] } ], "source": [ "scater_skew_num_columns = num_columns.tolist()\n", "print len(scater_skew_num_columns)\n", "for column in num_columns:\n", " # for boolean features, do not scatter and skewed\n", " if set(conbined_data[column]) == {0, 1}:\n", " scater_skew_num_columns.remove(column)\n", "\n", "print len(scater_skew_num_columns)" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [], "source": [ "t = conbined_data[scater_skew_num_columns].quantile(.95)\n", "use_max_scater = t[t == 0].index\n", "use_95_scater = t[t != 0].index\n", "conbined_data[use_max_scater] = conbined_data[use_max_scater] / conbined_data[use_max_scater].max()\n", "conbined_data[use_95_scater] = conbined_data[use_95_scater] / conbined_data[use_95_scater].quantile(.95)" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "skewed features 61 from total 194 features\n" ] } ], "source": [ "# Transform the skewed numeric features by taking log(feature + 1).\n", "# This will make the features more normal.\n", "from scipy.stats import skew\n", "\n", "skewed = conbined_data[scater_skew_num_columns].apply(lambda x: skew(x.astype(float)))\n", "skewed = skewed[skewed > 0.75]\n", "skewed = skewed.index\n", "skewed = skewed.drop(['NeighborPrice','NeighborPrice-s2','NeighborPrice-s3'])\n", "print 'skewed features', skewed.shape[0],' from total ',conbined_data.shape[1],' features'\n", "conbined_data[skewed] = np.log1p(conbined_data[skewed])" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": true }, "outputs": [], "source": [ "price_feature = [\"MonthSaledMeanPrice\",\"MSSubClassMeanPrice\",\"NeighborPrice\",\"NeighborPrice-s2\",\"NeighborPrice-s3\",\"NeighborPrice-sq\"]\n", "conbined_data[price_feature] = np.log1p(conbined_data[price_feature])" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(2911, 194)" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "conbined_data.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Dummy Encoding " ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(['MSSubClass', 'MSZoning', 'Street', 'Alley', 'LotShape',\n", " 'LandContour', 'Utilities', 'LotConfig', 'LandSlope',\n", " 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n", " 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd',\n", " 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n", " 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2',\n", " 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual',\n", " 'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish',\n", " 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence',\n", " 'MiscFeature', 'MoSold', 'SaleType', 'SaleCondition', 'GarageGrade',\n", " 'ExterGrade'], dtype=object)" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "str_columns" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dummies_data = pd.get_dummies(conbined_data[str_columns])\n", "conbined_data[dummies_data.columns] = dummies_data[dummies_data.columns]\n", "conbined_data.drop(str_columns, axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(2911, 471)" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "conbined_data.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Delete some features to prevent overfitting." ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# These onehot columns are missing in the test data, so drop them from the\n", "# training data or we might overfit on them.\n", "drop_cols = [\n", " \"Exterior1st_ImStucc\", \"Exterior1st_Stone\",\n", " \"Exterior2nd_Other\",\"HouseStyle_2.5Fin\", \n", " \n", " \"RoofMatl_Membran\", \"RoofMatl_Metal\", \"RoofMatl_Roll\",\n", " \"Condition2_RRAe\", \"Condition2_RRAn\", \"Condition2_RRNn\",\n", " \"Heating_Floor\", \"Heating_OthW\",\n", "\n", " \"Electrical_Mix\", \n", " \"MiscFeature_TenC\",\n", " \"GarageQual_Ex\", \"PoolQC_Fa\"\n", " ]\n", "\n", "conbined_data.drop(drop_cols, axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(2911, 455)" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "conbined_data.shape" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "train_X : (1452, 454)\n", "test_X : (1459, 454)\n", "train_Y : (1452,)\n" ] } ], "source": [ "train_X = conbined_data.iloc[:train_length, 1:]\n", "train_Y = train_data['SalePrice']\n", "train_Id = conbined_data.iloc[:train_length, 0]\n", "\n", "test_X = conbined_data.iloc[train_length:, 1:]\n", "test_Id = conbined_data.iloc[train_length:, 0]\n", "\n", "print(\"train_X : \" + str(train_X.shape))\n", "print(\"test_X : \" + str(test_X.shape))\n", "print(\"train_Y : \" + str(train_Y.shape))" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "(array([ 5., 12., 54., 184., 469., 400., 215., 88., 19., 6.]),\n", " array([ 10.46027076, 10.76769112, 11.07511148, 11.38253184,\n", " 11.6899522 , 11.99737256, 12.30479292, 12.61221328,\n", " 12.91963363, 13.22705399, 13.53447435]),\n", " <a list of 10 Patch objects>)" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Kz/Wv8baO8bZWr8TbK9P/JElqhobucwT8GfD3wP3AvwH/CnwoM3dQLMt9PsW0u2uAszLz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\nJEkCz0uSesD+RZ5aZeoCKEt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TCIgCoscAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f411805e110>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 价格分布\n", "fig, (axis1,axis2) = plt.subplots(1,2,figsize=(10,5))\n", "axis1.hist(train_Y)\n", "# Transform skewed numeric features using log(p+1) transformation making them more normal\n", "train_Y = np.log1p(train_Y)\n", "axis2.hist(train_Y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Save New Data" ] }, { "cell_type": "code", "execution_count": 93, "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>LotFrontage</th>\n", " <th>LotArea</th>\n", " <th>OverallQual</th>\n", " <th>OverallCond</th>\n", " <th>YearBuilt</th>\n", " <th>YearRemodAdd</th>\n", " <th>MasVnrArea</th>\n", " <th>BsmtFinSF1</th>\n", " <th>BsmtFinSF2</th>\n", " <th>BsmtUnfSF</th>\n", " <th>...</th>\n", " <th>ExterGrade_GdGdGd</th>\n", " <th>ExterGrade_GdGdGdGd</th>\n", " <th>ExterGrade_Po</th>\n", " <th>ExterGrade_PoPo</th>\n", " <th>ExterGrade_TA</th>\n", " <th>ExterGrade_TATA</th>\n", " <th>ExterGrade_TATATA</th>\n", " <th>ExterGrade_TATATATA</th>\n", " <th>Id</th>\n", " <th>SalePrice</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.625000</td>\n", " <td>0.405761</td>\n", " <td>0.875</td>\n", " <td>0.625</td>\n", " <td>0.998007</td>\n", " <td>0.998007</td>\n", " <td>0.353963</td>\n", " <td>0.440935</td>\n", " <td>0.0</td>\n", " <td>0.096725</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1</td>\n", " <td>12.247699</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.769231</td>\n", " <td>0.450153</td>\n", " <td>0.750</td>\n", " <td>1.000</td>\n", " <td>0.984554</td>\n", " <td>0.984554</td>\n", " <td>0.000000</td>\n", " <td>0.569657</td>\n", " <td>0.0</td>\n", " <td>0.175869</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>2</td>\n", " <td>12.109016</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.653846</td>\n", " <td>0.510589</td>\n", " <td>0.875</td>\n", " <td>0.625</td>\n", " <td>0.997010</td>\n", " <td>0.997509</td>\n", " <td>0.300867</td>\n", " <td>0.323152</td>\n", " <td>0.0</td>\n", " <td>0.257614</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>3</td>\n", " <td>12.317171</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.576923</td>\n", " <td>0.448263</td>\n", " <td>0.875</td>\n", " <td>0.625</td>\n", " <td>0.954160</td>\n", " <td>0.981565</td>\n", " <td>0.000000</td>\n", " <td>0.156615</td>\n", " <td>0.0</td>\n", " <td>0.311598</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>4</td>\n", " <td>11.849405</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.807692</td>\n", " <td>0.612228</td>\n", " <td>1.000</td>\n", " <td>0.625</td>\n", " <td>0.996512</td>\n", " <td>0.996512</td>\n", " <td>0.564402</td>\n", " <td>0.414840</td>\n", " <td>0.0</td>\n", " <td>0.286497</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>5</td>\n", " <td>12.429220</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 456 columns</p>\n", "</div>" ], "text/plain": [ " LotFrontage LotArea OverallQual OverallCond YearBuilt YearRemodAdd \\\n", "0 0.625000 0.405761 0.875 0.625 0.998007 0.998007 \n", "1 0.769231 0.450153 0.750 1.000 0.984554 0.984554 \n", "2 0.653846 0.510589 0.875 0.625 0.997010 0.997509 \n", "3 0.576923 0.448263 0.875 0.625 0.954160 0.981565 \n", "4 0.807692 0.612228 1.000 0.625 0.996512 0.996512 \n", "\n", " MasVnrArea BsmtFinSF1 BsmtFinSF2 BsmtUnfSF ... \\\n", "0 0.353963 0.440935 0.0 0.096725 ... \n", "1 0.000000 0.569657 0.0 0.175869 ... \n", "2 0.300867 0.323152 0.0 0.257614 ... \n", "3 0.000000 0.156615 0.0 0.311598 ... \n", "4 0.564402 0.414840 0.0 0.286497 ... \n", "\n", " ExterGrade_GdGdGd ExterGrade_GdGdGdGd ExterGrade_Po ExterGrade_PoPo \\\n", "0 0.0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 \n", "\n", " ExterGrade_TA ExterGrade_TATA ExterGrade_TATATA ExterGrade_TATATATA Id \\\n", "0 0.0 0.0 1.0 0.0 1 \n", "1 0.0 1.0 0.0 0.0 2 \n", "2 0.0 0.0 1.0 0.0 3 \n", "3 0.0 1.0 0.0 0.0 4 \n", "4 0.0 0.0 1.0 0.0 5 \n", "\n", " SalePrice \n", "0 12.247699 \n", "1 12.109016 \n", "2 12.317171 \n", "3 11.849405 \n", "4 12.429220 \n", "\n", "[5 rows x 456 columns]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>LotFrontage</th>\n", " <th>LotArea</th>\n", " <th>OverallQual</th>\n", " <th>OverallCond</th>\n", " <th>YearBuilt</th>\n", " <th>YearRemodAdd</th>\n", " <th>MasVnrArea</th>\n", " <th>BsmtFinSF1</th>\n", " <th>BsmtFinSF2</th>\n", " <th>BsmtUnfSF</th>\n", " <th>...</th>\n", " <th>ExterGrade_GdGd</th>\n", " <th>ExterGrade_GdGdGd</th>\n", " <th>ExterGrade_GdGdGdGd</th>\n", " <th>ExterGrade_Po</th>\n", " <th>ExterGrade_PoPo</th>\n", " <th>ExterGrade_TA</th>\n", " <th>ExterGrade_TATA</th>\n", " <th>ExterGrade_TATATA</th>\n", " <th>ExterGrade_TATATATA</th>\n", " <th>Id</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.769231</td>\n", " <td>0.523724</td>\n", " <td>0.625</td>\n", " <td>0.750</td>\n", " <td>0.977080</td>\n", " <td>0.977080</td>\n", " <td>0.000000</td>\n", " <td>0.312872</td>\n", " <td>0.286817</td>\n", " <td>0.167887</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1461</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.778846</td>\n", " <td>0.612453</td>\n", " <td>0.750</td>\n", " <td>0.750</td>\n", " <td>0.975585</td>\n", " <td>0.975585</td>\n", " <td>0.210277</td>\n", " <td>0.544931</td>\n", " <td>0.000000</td>\n", " <td>0.242853</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1462</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.711538</td>\n", " <td>0.598325</td>\n", " <td>0.625</td>\n", " <td>0.625</td>\n", " <td>0.995017</td>\n", " <td>0.995516</td>\n", " <td>0.000000</td>\n", " <td>0.482969</td>\n", " <td>0.000000</td>\n", " <td>0.088703</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1463</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.750000</td>\n", " <td>0.464324</td>\n", " <td>0.750</td>\n", " <td>0.750</td>\n", " <td>0.995516</td>\n", " <td>0.995516</td>\n", " <td>0.042424</td>\n", " <td>0.386980</td>\n", " <td>0.000000</td>\n", " <td>0.198329</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1464</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.413462</td>\n", " <td>0.259604</td>\n", " <td>1.000</td>\n", " <td>0.625</td>\n", " <td>0.992526</td>\n", " <td>0.992526</td>\n", " <td>0.000000</td>\n", " <td>0.187671</td>\n", " <td>0.000000</td>\n", " <td>0.523875</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1465</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 455 columns</p>\n", "</div>" ], "text/plain": [ " LotFrontage LotArea OverallQual OverallCond YearBuilt YearRemodAdd \\\n", "0 0.769231 0.523724 0.625 0.750 0.977080 0.977080 \n", "1 0.778846 0.612453 0.750 0.750 0.975585 0.975585 \n", "2 0.711538 0.598325 0.625 0.625 0.995017 0.995516 \n", "3 0.750000 0.464324 0.750 0.750 0.995516 0.995516 \n", "4 0.413462 0.259604 1.000 0.625 0.992526 0.992526 \n", "\n", " MasVnrArea BsmtFinSF1 BsmtFinSF2 BsmtUnfSF ... ExterGrade_GdGd \\\n", "0 0.000000 0.312872 0.286817 0.167887 ... 0.0 \n", "1 0.210277 0.544931 0.000000 0.242853 ... 0.0 \n", "2 0.000000 0.482969 0.000000 0.088703 ... 0.0 \n", "3 0.042424 0.386980 0.000000 0.198329 ... 0.0 \n", "4 0.000000 0.187671 0.000000 0.523875 ... 0.0 \n", "\n", " ExterGrade_GdGdGd ExterGrade_GdGdGdGd ExterGrade_Po ExterGrade_PoPo \\\n", "0 0.0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 \n", "\n", " ExterGrade_TA ExterGrade_TATA ExterGrade_TATATA ExterGrade_TATATATA \\\n", "0 0.0 1.0 0.0 0.0 \n", "1 0.0 1.0 0.0 0.0 \n", "2 0.0 1.0 0.0 0.0 \n", "3 0.0 1.0 0.0 0.0 \n", "4 0.0 0.0 1.0 0.0 \n", "\n", " Id \n", "0 1461 \n", "1 1462 \n", "2 1463 \n", "3 1464 \n", "4 1465 \n", "\n", "[5 rows x 455 columns]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Done.\n" ] } ], "source": [ "save_train = train_X.copy()\n", "save_test = test_X.copy()\n", "save_train['Id'] = train_Id\n", "save_train['SalePrice'] = train_Y\n", "save_test['Id'] = test_Id\n", "\n", "display(save_train.head())\n", "display(save_test.head())\n", "\n", "save_train.to_csv(\"data/new_train.csv\", index=False)\n", "save_test.to_csv(\"data/new_test.csv\", index=False)\n", "\n", "print 'Done.'" ] }, { "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": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
CINPLA/exdir	tests/benchmarks/benchmarks.ipynb	1	16213	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Benchmarks for Exdir #\n", "\n", "This notebook contains a number of benchmarks for Exdir.\n", "They compare the performance of Exdir with h5py." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Warning: Please make sure the files are not created in a folder managed by Syncthing, Dropbox or any other file synchronization system. \n", "We will be making a large number of changes to the files and a file synchronization system will reduce performance and possibly become out of sync in the process.\n", "\n", "Note: You may experience unreliable results on some systems, where the numbers vary greatly between each run. \n", "This can be caused by the large number of I/O operations performed by the benchmarks. \n", "We have tried to improve the reliability by adding a call to `time.sleep` between setting up the benchmark and running the benchmark.\n", "This should allow the system to completely flush to disk the changes made while setting up and have the benchmark run unaffected.\n", "However, if you still experience unreliable results, you may want to try to set up a RAM disk and change the below paths to read `/tmp/ramdis/test.exdir` and `/tmp/ramdisk/test.h5`:\n", "\n", " mkdir /tmp/ramdisk/\n", " sudo mount -t tmpfs -o size=2048M tmpfs /tmp/ramdisk/\n", " \n", "## Helper functions ##\n", "\n", "The following functions are used to set up an exdir or hdf5 file for benchmarking:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import exdir\n", "import os\n", "import shutil\n", "import h5py\n", "\n", "def setup_exdir():\n", " testpath = \"test.exdir\"\n", " if os.path.exists(testpath):\n", " shutil.rmtree(testpath)\n", " f = exdir.File(testpath)\n", " return f, testpath\n", "\n", "def setup_exdir_no_validation():\n", " testpath = \"test.exdir\"\n", " if os.path.exists(testpath):\n", " shutil.rmtree(testpath)\n", " f = exdir.File(testpath, name_validation=exdir.validation.minimal)\n", " return f, testpath\n", "\n", "def teardown_exdir(f, testpath):\n", " f.close()\n", " shutil.rmtree(testpath)\n", "\n", "def setup_h5py():\n", " testpath = \"test.h5\"\n", " if os.path.exists(testpath):\n", " os.remove(testpath)\n", " f = h5py.File(testpath)\n", " return f, testpath\n", "\n", " \n", "def teardown_h5py(f, testpath):\n", " f.close()\n", " os.remove(testpath)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following function is used to run the different benchmarks.\n", "It takes a target function to test, a setup function to create the file and the number of iterations the function should be run to get a decent average:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import time\n", "\n", "def benchmark(target, setup=None, teardown=None, iterations=10):\n", " total_time = 0\n", " setup_teardown_start = time.time()\n", " for i in range(iterations):\n", " data = tuple()\n", " if setup is not None:\n", " data = setup()\n", " time.sleep(1) # allow changes to be flushed to disk\n", " start_time = time.time()\n", " target(data)\n", " end_time = time.time()\n", " total_time += end_time - start_time\n", " if teardown is not None:\n", " teardown(data)\n", " setup_teardown_end = time.time()\n", " total_setup_teardown = setup_teardown_end - setup_teardown_start\n", " \n", " mean = total_time / iterations\n", " \n", " return mean" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following functions are used as wrappers to make it easy to run a benchmark of Exdir or h5py:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "all_results = []\n", "\n", "def benchmark_both(function, iterations=10, name_validation=True):\n", " if name_validation:\n", " setup_exdir_ = setup_exdir\n", " name = function.__name__\n", " else:\n", " setup_exdir_ = setup_exdir_no_validation\n", " name = function.__name__ + \" (minimal name validation)\"\n", " \n", " exdir_mean = benchmark(\n", " target=lambda f, path: function(f),\n", " setup=setup_exdir_,\n", " teardown=teardown_exdir,\n", " iterations=iterations\n", " )\n", " hdf5_mean = benchmark(\n", " target=lambda f, path: function(f),\n", " setup=setup_h5py,\n", " teardown=teardown_h5py,\n", " iterations=iterations\n", " )\n", " \n", " result = pd.DataFrame(\n", " [(name, hdf5_mean, exdir_mean, hdf5_mean/exdir_mean)],\n", " columns=[\"Test\", \"h5py\", \"Exdir\", \"Ratio\"]\n", " )\n", " all_results.append(result)\n", " return result\n", "\n", "def benchmark_exdir(function, iterations=10):\n", " exdir_mean = benchmark(\n", " target=lambda f, path: function(f),\n", " setup=setup_exdir,\n", " teardown=teardown_exdir,\n", " iterations=iterations\n", " )\n", " result = pd.DataFrame(\n", " [(function.__name__, np.nan, exdir_mean, np.nan)],\n", " columns=[\"Test\", \"h5py\", \"Exdir\", \"Ratio\"]\n", " )\n", " all_results.append(result)\n", " return result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are now ready to start running the different benchmarks.\n", "\n", "## Benchmark functions ##\n", "\n", "The following benchmark creates a small number of attributes.\n", "This should be very fast with both h5py and Exdir:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def add_few_attributes(obj):\n", " for i in range(5):\n", " obj.attrs[\"hello\" + str(i)] = \"world\"\n", "\n", "benchmark_both(add_few_attributes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following benchmark adds a larger number of attributes one-by-one.\n", "Because Exdir needs to read back and rewrite the entire file in case someone changed it between each write, this is significantly slower with Exdir than h5py:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def add_many_attributes(obj):\n", " for i in range(200):\n", " obj.attrs[\"hello\" + str(i)] = \"world\"\n", "\n", "benchmark_both(add_many_attributes, 10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, Exdir is capable of writing all attributes in one operation.\n", "This makes writing the same attributes about as fast (or even faster than h5py).\n", "Writing a large number of attributes in a single operation is not possible with h5py.\n", "We therefore need to run this only with Exdir:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def add_many_attributes_single_operation(obj):\n", " attributes = {}\n", " for i in range(200):\n", " attributes[\"hello\" + str(i)] = \"world\"\n", " obj.attrs = attributes\n", " \n", "benchmark_exdir(add_many_attributes_single_operation)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exdir also supports adding nested attributes, such as Python dictionaries, which is not supported by h5py:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def add_attribute_tree(obj):\n", " tree = {}\n", " for i in range(100):\n", " tree[\"hello\" + str(i)] = \"world\"\n", " tree[\"intermediate\"] = {}\n", " intermediate = tree[\"intermediate\"]\n", " for level in range(10):\n", " level_str = \"level\" + str(level)\n", " intermediate[level_str] = {}\n", " intermediate = intermediate[level_str]\n", " intermediate = 42\n", " obj.attrs[\"test\"] = tree\n", " \n", "benchmark_exdir(add_attribute_tree)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following benchmarks create a small, a medium, and a large dataset:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def add_small_dataset(obj):\n", " data = np.zeros((100, 100, 100))\n", " obj.create_dataset(\"foo\", data=data)\n", " obj.close()\n", " \n", "benchmark_both(add_small_dataset)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def add_medium_dataset(obj):\n", " data = np.zeros((1000, 100, 100))\n", " obj.create_dataset(\"foo\", data=data)\n", " obj.close()\n", " \n", "benchmark_both(add_medium_dataset, 10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def add_large_dataset(obj):\n", " data = np.zeros((1000, 1000, 100))\n", " obj.create_dataset(\"foo\", data=data)\n", " obj.close()\n", " \n", "benchmark_both(add_large_dataset, 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is some overhead in creating the objects themselves.\n", "This is rather small in h5py, but can be high in Exdir with name validation enabled.\n", "This is because the name of every created object must be checked against all the existing objects in the same group:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def create_many_objects(obj):\n", " for i in range(5000):\n", " group = obj.create_group(\"group{}\".format(i))\n", "\n", "benchmark_both(create_many_objects, 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Without minimal validation, this is almost as fast in Exdir as it is in h5py.\n", "Minimal name validation only checks if file with the exact same name exist in the folder:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "benchmark_both(create_many_objects, 3, name_validation=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Not only the number of created objects matter.\n", "Creating them in a tree structure can also incur a performance penalty.\n", "The following test creates an object tree:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def create_large_tree(obj, level=0):\n", " if level > 4:\n", " return\n", " for i in range(3):\n", " group = obj.create_group(\"group_{}_{}\".format(i, level))\n", " data = np.zeros((10, 10, 10))\n", " group.create_dataset(\"dataset_{}_{}\".format(i, level), data=data)\n", " create_large_tree(group, level + 1)\n", " \n", "benchmark_both(create_large_tree)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The final benchmark tests writing a \"slice\" of a dataset, which means only a part of the entire dataset is modified.\n", "This is typically fast in both h5py and in Exdir thanks to [memory mapping](https://docs.scipy.org/doc/numpy/reference/generated/numpy.memmap.html)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def write_slice(dataset):\n", " dataset[320:420, 0:300, 0:100] = np.ones((100, 300, 100))\n", "\n", "def create_setup_dataset(setup_function):\n", " def setup():\n", " f, path = setup_function()\n", " data = np.zeros((1000, 500, 100))\n", " dataset = f.create_dataset(\"foo\", data=data)\n", " time.sleep(1) # allow changes to get flushed to disk\n", " return dataset, f, path\n", " return setup\n", "\n", "exdir_mean = benchmark(\n", " target=lambda dataset, f, path: write_slice(dataset),\n", " setup=create_setup_dataset(setup_exdir),\n", " teardown=lambda dataset, f, path: teardown_exdir(f, path),\n", " iterations=3\n", ")\n", "\n", "hdf5_mean = benchmark(\n", " target=lambda dataset, f, path: write_slice(dataset),\n", " setup=create_setup_dataset(setup_h5py),\n", " teardown=lambda dataset, f, path: teardown_h5py(f, path),\n", " iterations=3\n", ")\n", "result = pd.DataFrame(\n", " [(\"write_slice\", hdf5_mean, exdir_mean, hdf5_mean/exdir_mean)],\n", " columns=[\"Test\", \"h5py\", \"Exdir\", \"Ratio\"]\n", ")\n", "all_results.append(result)\n", "\n", "result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Benchmark summary ##\n", "\n", "The results are summarized in the following table:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pd.concat(all_results)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Profiling the largest differences #\n", "\n", "While the performance of Exdir in many cases is close to h5py, there are a few cases that can be worth investigating further.\n", "\n", "For instance, it might be interesting to know what takes most time in create_large_tree, which is about 2-3 times slower in Exdir than h5py:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import cProfile\n", "\n", "f, path = setup_exdir()\n", "cProfile.run('create_large_tree(f)', sort=\"cumtime\")\n", "teardown_exdir(f, path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we see that `create_dataset` and `create_group` take up about 2/3 and 1/3 of the total run time, respectively.\n", "Some of the time in both of these are spent on building paths using pathlib and name validation.\n", "The remaining time is mostly spent on writing the array header of the NumPy files.\n", "Only a small amount of time is spent on actually writing files.\n", "Increasing performance in this case will likely mean that we need to outperform pathlib in building paths and numpy in writing files.\n", "While it might be possible, it is also beneficial to stick with the existing, well-tested implementations of both of these libraries." ] }, { "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": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
Santana9937/Classification_ML_Specialization	Week_1_Predicting_Sentiment_from_Reviews/week_1_lin_classifier_assign.ipynb	1	63452	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Predicting sentiment from product reviews\n", "\n", "In this notebook, you will use product review data from Amazon.com to predict whether the sentiments about a product (from its reviews) are positive or negative.\n", "\n", "* Use DataFrames to do some feature engineering\n", "* Train a logistic regression model to predict the sentiment of product reviews.\n", "* Inspect the weights (coefficients) of a trained logistic regression model.\n", "* Make a prediction (both class and probability) of sentiment for a new product review.\n", "* Given the logistic regression weights, predictors and ground truth labels, write a function to compute the accuracy of the model.\n", "* Inspect the coefficients of the logistic regression model and interpret their meanings.\n", "* Compare multiple logistic regression models." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Importing Libraries" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "import zipfile\n", "import string\n", "import numpy as np\n", "import pandas as pd\n", "from sklearn import linear_model\n", "from sklearn.feature_extraction.text import CountVectorizer\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "sns.set_style('darkgrid')\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Unzipping files with Amazon Baby Products Reviews" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The dataset consists of baby product reviews from Amazon.com." ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Put files in current direction into a list\n", "files_list = [f for f in os.listdir('.') if os.path.isfile(f)]" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Filename of unzipped file\n", "unzipped_file = 'amazon_baby.csv'" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# If upzipped file not in files_list, unzip the file\n", "if unzipped_file not in files_list:\n", " zip_file = unzipped_file + '.zip'\n", " unzipping = zipfile.ZipFile(zip_file)\n", " unzipping.extractall()\n", " unzipping.close" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading the products data \n", "\n", "The dataset is loaded into a Pandas DataFrame called products." ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [], "source": [ "products = pd.read_csv(\"amazon_baby.csv\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let us see a preview of what the dataset looks like." ] }, { "cell_type": "code", "execution_count": 74, "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>name</th>\n", " <th>review</th>\n", " <th>rating</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Planetwise Flannel Wipes</td>\n", " <td>These flannel wipes are OK, but in my opinion ...</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Planetwise Wipe Pouch</td>\n", " <td>it came early and was not disappointed. i love...</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Annas Dream Full Quilt with 2 Shams</td>\n", " <td>Very soft and comfortable and warmer than it l...</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Stop Pacifier Sucking without tears with Thumb...</td>\n", " <td>This is a product well worth the purchase. I ...</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Stop Pacifier Sucking without tears with Thumb...</td>\n", " <td>All of my kids have cried non-stop when I trie...</td>\n", " <td>5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name \\\n", "0 Planetwise Flannel Wipes \n", "1 Planetwise Wipe Pouch \n", "2 Annas Dream Full Quilt with 2 Shams \n", "3 Stop Pacifier Sucking without tears with Thumb... \n", "4 Stop Pacifier Sucking without tears with Thumb... \n", "\n", " review rating \n", "0 These flannel wipes are OK, but in my opinion ... 3 \n", "1 it came early and was not disappointed. i love... 5 \n", "2 Very soft and comfortable and warmer than it l... 5 \n", "3 This is a product well worth the purchase. I ... 5 \n", "4 All of my kids have cried non-stop when I trie... 5 " ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "products.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Performing text cleaning" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us explore a specific example of a baby product.\n" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "name Planetwise Wipe Pouch\n", "review it came early and was not disappointed. i love...\n", "rating 5\n", "Name: 1, dtype: object" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "products.ix[1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we will perform 2 simple data transformations:\n", "\n", "1. Remove punctuation using [Python's built-in](https://docs.python.org/2/library/string.html) string functionality.\n", "2. Transform the reviews into word-counts.\n", "\n", "Aside. In this notebook, we remove all punctuations for the sake of simplicity. A smarter approach to punctuations would preserve phrases such as \"I'd\", \"would've\", \"hadn't\" and so forth. See [this page](https://www.cis.upenn.edu/~treebank/tokenization.html) for an example of smart handling of punctuations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before removing the punctuation from the strings in the review column, we will fall all NA values with empty string." ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [], "source": [ "products[\"review\"] = products[\"review\"].fillna(\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below, we are removing all the punctuation from the strings in the review column and saving the result into a new column in the dataframe." ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [], "source": [ "products[\"review_clean\"] = products[\"review\"].str.translate(None, string.punctuation) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extract sentiments\n", "\n", "We will ignore all reviews with rating = 3, since they tend to have a neutral sentiment." ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "166752" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "products = products[products['rating'] != 3]\n", "len(products)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we will assign reviews with a rating of 4 or higher to be positive reviews, while the ones with rating of 2 or lower are negative. For the sentiment column, we use +1 for the positive class label and -1 for the negative class label." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below, we are create a function we will applyi to the \"ratings\" column of the dataframe to determine if the review is positive or negative." ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sent_func(x):\n", " # If rating is >=4, return a positive sentiment (+1)\n", " if x>=4:\n", " return 1\n", " # Else, return a negative sentiment (-1)\n", " else:\n", " return -1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creating a \"sentiment\" column by applying the sent_func to the \"rating\" column in the dataframe." ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": true }, "outputs": [], "source": [ "products['sentiment'] = products['rating'].apply(sent_func)" ] }, { "cell_type": "code", "execution_count": 81, "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>name</th>\n", " <th>review</th>\n", " <th>rating</th>\n", " <th>review_clean</th>\n", " <th>sentiment</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>20</th>\n", " <td>Nature's Lullabies Second Year Sticker Calendar</td>\n", " <td>I had a hard time finding a second year calend...</td>\n", " <td>5</td>\n", " <td>I had a hard time finding a second year calend...</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>Nature's Lullabies Second Year Sticker Calendar</td>\n", " <td>I only purchased a second-year calendar for my...</td>\n", " <td>2</td>\n", " <td>I only purchased a secondyear calendar for my ...</td>\n", " <td>-1</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>Nature's Lullabies Second Year Sticker Calendar</td>\n", " <td>I LOVE this calendar for recording events of m...</td>\n", " <td>5</td>\n", " <td>I LOVE this calendar for recording events of m...</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name \\\n", "20 Nature's Lullabies Second Year Sticker Calendar \n", "21 Nature's Lullabies Second Year Sticker Calendar \n", "22 Nature's Lullabies Second Year Sticker Calendar \n", "\n", " review rating \\\n", "20 I had a hard time finding a second year calend... 5 \n", "21 I only purchased a second-year calendar for my... 2 \n", "22 I LOVE this calendar for recording events of m... 5 \n", "\n", " review_clean sentiment \n", "20 I had a hard time finding a second year calend... 1 \n", "21 I only purchased a secondyear calendar for my ... -1 \n", "22 I LOVE this calendar for recording events of m... 1 " ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "products.ix[20:22]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we can see that the dataset contains an extra column called sentiment which is either positive (+1) or negative (-1)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Split data into training and test sets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's perform a train/test split with 80% of the data in the training set and 20% of the data in the test set." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Loading the indicies for the train and test data and putting them in a list" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open('module-2-assignment-train-idx.txt', 'r') as train_file:\n", " ind_list_train = map(int,train_file.read().split(',')) " ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with open('module-2-assignment-test-idx.txt', 'r') as test_file:\n", " ind_list_test = map(int,test_file.read().split(','))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the indicies of the train and test data to create the train and test datasets." ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": true }, "outputs": [], "source": [ "train_data = products.iloc[ind_list_train,:]\n", "test_data = products.iloc[ind_list_test,:]" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "133416\n", "33336\n" ] } ], "source": [ "print len(train_data)\n", "print len(test_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Build the word count vector for each review" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now compute the word count for each word that appears in the reviews. A vector consisting of word counts is often referred to as bag-of-word features. Since most words occur in only a few reviews, word count vectors are sparse. For this reason, scikit-learn and many other tools use sparse matrices to store a collection of word count vectors. Refer to appropriate manuals to produce sparse word count vectors. General steps for extracting word count vectors are as follows:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Learn a vocabulary (set of all words) from the training data. Only the words that show up in the training data will be considered for feature extraction.\n", "- Compute the occurrences of the words in each review and collect them into a row vector.\n", "- Build a sparse matrix where each row is the word count vector for the corresponding review. Call this matrix train_matrix.\n", "- Using the same mapping between words and columns, convert the test data into a sparse matrix test_matrix." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following cell uses CountVectorizer in scikit-learn. Notice the token_pattern argument in the constructor." ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Use this token pattern to keep single-letter words\n", "vectorizer = CountVectorizer(token_pattern=r'\\b\\w+\\b')\n", "# First, learn vocabulary from the training data and assign columns to words\n", "# Then convert the training data into a sparse matrix\n", "train_matrix = vectorizer.fit_transform(train_data['review_clean'])\n", "# Second, convert the test data into a sparse matrix, using the same word-column mapping\n", "test_matrix = vectorizer.transform(test_data['review_clean'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Train a sentiment classifier with logistic regression\n", "\n", "We will now use logistic regression to create a sentiment classifier on the training data. This model will use the column word_count as a feature and the column sentiment as the target.\n", "\n", "Note: This line may take 1-2 minutes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creating an instance of the LogisticRegression class" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false }, "outputs": [], "source": [ "logreg = linear_model.LogisticRegression()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the fit method to train the classifier. This model should use the sparse word count matrix (train_matrix) as features and the column sentiment of train_data as the target. Use the default values for other parameters. Call this model sentiment_model." ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sentiment_model = logreg.fit(train_matrix, train_data[\"sentiment\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Putting all the weights from the model into a numpy array." ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "121713\n" ] } ], "source": [ "weights_list = list(sentiment_model.intercept_) + list(sentiment_model.coef_.flatten())\n", "weights_sent_model = np.array(weights_list, dtype = np.double)\n", "print len(weights_sent_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are a total of `121713` coefficients in the model. Recall from the lecture that positive weights $w_j$ correspond to weights that cause positive sentiment, while negative weights correspond to negative sentiment. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Quiz question: How many weights are >= 0?" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of positive weights: 85915\n", "Number of negative weights: 35798\n" ] } ], "source": [ "num_positive_weights = len(weights_sent_model[weights_sent_model >= 0.0])\n", "num_negative_weights = len(weights_sent_model[weights_sent_model < 0.0])\n", "\n", "print \"Number of positive weights: %i\" % num_positive_weights\n", "print \"Number of negative weights: %i\" % num_negative_weights" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Making predictions with logistic regression\n", "\n", "Now that a model is trained, we can make predictions on the test data. In this section, we will explore this in the context of 3 examples in the test dataset. We refer to this set of 3 examples as the sample_test_data." ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "59 5\n", "71 2\n", "91 1\n", "Name: rating, dtype: int64\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>review</th>\n", " <th>rating</th>\n", " <th>review_clean</th>\n", " <th>sentiment</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>59</th>\n", " <td>Our Baby Girl Memory Book</td>\n", " <td>Absolutely love it and all of the Scripture in...</td>\n", " <td>5</td>\n", " <td>Absolutely love it and all of the Scripture in...</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>71</th>\n", " <td>Wall Decor Removable Decal Sticker - Colorful ...</td>\n", " <td>Would not purchase again or recommend. The dec...</td>\n", " <td>2</td>\n", " <td>Would not purchase again or recommend The deca...</td>\n", " <td>-1</td>\n", " </tr>\n", " <tr>\n", " <th>91</th>\n", " <td>New Style Trailing Cherry Blossom Tree Decal R...</td>\n", " <td>Was so excited to get this product for my baby...</td>\n", " <td>1</td>\n", " <td>Was so excited to get this product for my baby...</td>\n", " <td>-1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name \\\n", "59 Our Baby Girl Memory Book \n", "71 Wall Decor Removable Decal Sticker - Colorful ... \n", "91 New Style Trailing Cherry Blossom Tree Decal R... \n", "\n", " review rating \\\n", "59 Absolutely love it and all of the Scripture in... 5 \n", "71 Would not purchase again or recommend. The dec... 2 \n", "91 Was so excited to get this product for my baby... 1 \n", "\n", " review_clean sentiment \n", "59 Absolutely love it and all of the Scripture in... 1 \n", "71 Would not purchase again or recommend The deca... -1 \n", "91 Was so excited to get this product for my baby... -1 " ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_test_data = test_data.ix[[59,71,91]]\n", "print sample_test_data['rating']\n", "sample_test_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's dig deeper into the first row of the sample_test_data. Here's the full review:" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'Absolutely love it and all of the Scripture in it. I purchased the Baby Boy version for my grandson when he was born and my daughter-in-law was thrilled to receive the same book again.'" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_test_data['review'].ix[59]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That review seems pretty positive.\n", "\n", "Now, let's see what the next row of the sample_test_data looks like. As we could guess from the sentiment (-1), the review is quite negative." ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'Would not purchase again or recommend. The decals were thick almost plastic like and were coming off the wall as I was applying them! The would NOT stick! Literally stayed stuck for about 5 minutes then started peeling off.'" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_test_data['review'].ix[71]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now make a class prediction for the sample_test_data. The `sentiment_model` should predict +1 if the sentiment is positive and -1 if the sentiment is negative. Recall from the lecture that the score (sometimes called margin) for the logistic regression model is defined as:\n", "\n", "$$\n", "\\mbox{score}_i = \\mathbf{w}^T h(\\mathbf{x}_i)\n", "$$ \n", "\n", "where $h(\\mathbf{x}_i)$ represents the features for example $i$. We will write some code to obtain the scores . For each row, the score (or margin) is a number in the range [-inf, inf]." ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 5.61129645 -3.14942405 -10.42641065]\n" ] } ], "source": [ "sample_test_matrix = vectorizer.transform(sample_test_data['review_clean'])\n", "scores = sentiment_model.decision_function(sample_test_matrix)\n", "print scores" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Predicting sentiment\n", "\n", "These scores can be used to make class predictions as follows:\n", "\n", "$$\n", "\\hat{y} = \n", "\\left\\{\n", "\\begin{array}{ll}\n", " +1 & \\mathbf{w}^T h(\\mathbf{x}_i) > 0 \\\\\n", " -1 & \\mathbf{w}^T h(\\mathbf{x}_i) \\leq 0 \\\\\n", "\\end{array} \n", "\\right.\n", "$$\n", "\n", "Using scores, write code to calculate $\\hat{y}$, the class predictions:" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, -1, -1]\n" ] } ], "source": [ "pred_sent_test_data = []\n", "for val in scores:\n", " if val>0:\n", " pred_sent_test_data.append(1)\n", " else:\n", " pred_sent_test_data.append(-1)\n", "print pred_sent_test_data " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Checkpoint: Run the following code to verify that the class predictions obtained by your calculations are the same as that obtained from Scikit-Learn." ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Class predictions according to Scikit-Learn:\n", "[ 1 -1 -1]\n" ] } ], "source": [ "print \"Class predictions according to Scikit-Learn:\" \n", "print sentiment_model.predict(sample_test_matrix)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Probability predictions\n", "\n", "Recall from the lectures that we can also calculate the probability predictions from the scores using:\n", "$$\n", "P(y_i = +1 \| \\mathbf{x}_i,\\mathbf{w}) = \\frac{1}{1 + \\exp(-\\mathbf{w}^T h(\\mathbf{x}_i))}.\n", "$$\n", "\n", "Using the variable scores calculated previously, write code to calculate the probability that a sentiment is positive using the above formula. For each row, the probabilities should be a number in the range [0, 1]." ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 9.96356994e-01, 4.11139780e-02, 2.96383837e-05])" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prob_pos_score = 1.0/(1.0 + np.exp(-scores))\n", "prob_pos_score" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Checkpoint: Make sure your probability predictions match the ones obtained from Scikit-Learn." ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Class predictions according to Scikit-Learn:\n", "[ 9.96356994e-01 4.11139780e-02 2.96383837e-05]\n" ] } ], "source": [ "print \"Class predictions according to Scikit-Learn:\" \n", "print sentiment_model.predict_proba(sample_test_matrix)[:,1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Quiz Question: Of the three data points in sample_test_data, which one (first, second, or third) has the lowest probability of being classified as a positive review?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " The 3rd data point has the lowest probability of being positive " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Find the most positive (and negative) review" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now turn to examining the full test dataset, test_data.\n", "\n", "Using the `sentiment_model`, find the 40 reviews in the entire test_data with the highest probability of being classified as a positive review. We refer to these as the \"most positive reviews.\"\n", "\n", "To calculate these top-40 reviews, use the following steps:\n", "1. Make probability predictions on test_data using the `sentiment_model`.\n", "2. Sort the data according to those predictions and pick the top 40. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Computing the scores with the sentiment_model decision function and then calculating the probability that y = +1" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false }, "outputs": [], "source": [ "scores_test_data = sentiment_model.decision_function(test_matrix)\n", "prob_test_data = 1.0/(1.0 + np.exp(-scores_test_data))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To find the 40 most positive and the 40 most negative values, we will create a list of tuples with the entries (probability, index). We will then sort the list and will be able to extract the indicies corresponding to each entry." ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# List of indicies in the test data\n", "ind_vals_test_data = test_data.index.values\n", "# Empty list that will be filled with the tuples (probability, index)\n", "score_label_lst_test = len(scores_test_data)[-1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Filling the list of tuples with the (probability, index) values" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for i in range(len(scores_test_data)):\n", " score_label_lst_test[i] = (prob_test_data[i],ind_vals_test_data[i])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sorting the list with the entries (probability, index)" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": false }, "outputs": [], "source": [ "score_label_lst_test.sort()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Extracting the top 40 positive reviews and the top 40 negative reviews" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": false }, "outputs": [], "source": [ "top_40_pos_test_rev = score_label_lst_test[-40:]\n", "top_40_neg_test_rev = score_label_lst_test[0:40]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Getting the indicies of the top 40 positive reviews." ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ind_top_40_pos_test = 40[-1]\n", "for i,val in enumerate(top_40_pos_test_rev):\n", " ind_top_40_pos_test[i] = val[1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Getting the indicies of the top 40 negative reviews." ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ind_top_40_neg_test = 40[-1]\n", "for i,val in enumerate(top_40_neg_test_rev):\n", " ind_top_40_neg_test[i] = val[1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Quiz Question: Which of the following products are represented in the 40 most positive reviews? [multiple choice]" ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "8841 Peg Perego Primo Viaggio Car Seat / Infant Car...\n", "172085 Stokke Scoot Stroller - Light Green\n", "49749 Peg Perego Aria Light Weight One Hand Fold Str...\n", "154622 Rainy Day Indoor Playground toddler swing to b...\n", "42907 Prince Lionheart bebePOD Plus, Watermelon\n", "178750 Joovy Groove Ultralight Umbrella Stroller, Cha...\n", "119618 Quinny 2012 Buzz Stroller, Rebel Red\n", "70808 Skip Hop Studio Diaper Bag, Black Dot\n", "2985 BABYBJORN Potty Chair - Red\n", "51417 The First Years True Fit Convertible Car Seat,...\n", "26833 Lilly Gold Sit 'n' Stroll 5 in 1 Car Seat and ...\n", "115731 Emily Green 6" Bowl, Sunshine Safari\n", "99692 Dr. Brown's Bottle Warmer\n", "13168 Roundabout Convertible Car Seat - Grey Wicker\n", "166827 Britax Boulevard 70-G3 Convertible Car Seat Se...\n", "14008 Stork Craft Beatrice Combo Tower Chest, White\n", "59276 Britax Frontier Booster Car Seat\n", "121668 Door Monkey, Childproof Door Lock & Pinch ...\n", "80881 Delta Universal 6 Drawer Dresser, Black Cherry\n", "172351 Phil & Teds Navigator Buggy Golden Kiwi Fr...\n", "147996 Baby Jogger City Mini GT Double Stroller, Shad...\n", "182089 Summer Infant Wide View Digital Color Video Mo...\n", "22586 Britax Decathlon Convertible Car Seat, Tiffany\n", "165593 Ikea 36 Pcs Kalas Kids Plastic BPA Free Flatwa...\n", "50315 P'Kolino Silly Soft Seating in Tias, Green\n", "52631 Evenflo X Sport Plus Convenience Stroller - Ch...\n", "66059 Evenflo 6 Pack Classic Glass Bottle, 4-Ounce\n", "80155 Simple Wishes Hands-Free Breastpump Bra, Pink,...\n", "87017 Baby Einstein Around The World Discovery Center\n", "97325 Freemie Hands-Free Concealable Breast Pump Col...\n", "100166 Infantino Wrap and Tie Baby Carrier, Black Blu...\n", "114796 Fisher-Price Cradle 'N Swing, My Little Snuga...\n", "119182 Roan Rocco Classic Pram Stroller 2-in-1 with B...\n", "133651 Britax 2012 B-Agile Stroller, Red\n", "137034 Graco Pack 'n Play Element Playard - Flint\n", "140816 Diono RadianRXT Convertible Car Seat, Plum\n", "147949 Baby Jogger City Mini GT Single Stroller, Shad...\n", "168081 Buttons Cloth Diaper Cover - One Size - 8 Colo...\n", "168697 Graco FastAction Fold Jogger Click Connect Str...\n", "180646 Mamas & Papas 2014 Urbo2 Stroller - Black\n", "Name: name, dtype: object" ] }, "execution_count": 106, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_data.ix[ind_top_40_pos_test][\"name\"]" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Quiz Question: Which of the following products are represented in the 20 most negative reviews? [multiple choice]" ] }, { "cell_type": "code", "execution_count": 107, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "16042 Fisher-Price Ocean Wonders Aquarium Bouncer\n", "120209 Levana Safe N'See Digital Video Baby Monitor w...\n", "77072 Safety 1st Exchangeable Tip 3 in 1 Thermometer\n", "48694 Adiri BPA Free Natural Nurser Ultimate Bottle ...\n", "155287 VTech Communications Safe & Sounds Full Co...\n", "94560 The First Years True Choice P400 Premium Digit...\n", "53207 Safety 1st High-Def Digital Monitor\n", "81332 Cloth Diaper Sprayer--styles may vary\n", "113995 Motorola Digital Video Baby Monitor with Room ...\n", "10677 Philips AVENT Newborn Starter Set\n", "9915 Cosco Alpha Omega Elite Convertible Car Seat\n", "59546 Ellaroo Mei Tai Baby Carrier - Hershey\n", "75994 Peg-Perego Tatamia High Chair, White Latte\n", "172090 Belkin WeMo Wi-Fi Baby Monitor for Apple iPhon...\n", "40079 Chicco Cortina KeyFit 30 Travel System in Adve...\n", "149987 NUK Cook-n-Blend Baby Food Maker\n", "154878 VTech Communications Safe & Sound Digital ...\n", "1116 Safety 1st Deluxe 4-in-1 Bath Station\n", "83234 Thirsties Hemp Inserts 2 Pack, Small 6-18 Lbs\n", "31741 Regalo My Cot Portable Bed, Royal Blue\n", "176046 Baby Trend Inertia Infant Car Seat - Horizon\n", "105055 Angelcare Baby Sound Monitor, White\n", "95420 One Step Ahead Hide-Away Extra Long Bed Rail\n", "116391 Keekaroo Height Right High Chair, Infant Inser...\n", "153488 Kidz Delight Smooth Touch Tablet, Fun N Play\n", "83571 Nuby Natural Touch Silicone Travel Infa Feeder...\n", "96572 Baby Jogger Summit XC Double Stroller, Red/Black\n", "139654 Fisher-Price Discover 'n Grow Take-Along Play ...\n", "99594 Valco Baby Tri-mode Twin Stroller EX- Hot Choc...\n", "66354 Levana BABYVIEW20 Interference Free Digital Wi...\n", "17089 Playtex Nurser With Drop-Ins Liner, 4 Ounce, C...\n", "140418 Jolly Jumper Arctic Sneak A Peek Infant Car Se...\n", "82324 Cloud b Gentle Giraffe On The Go Travel Sound ...\n", "172823 Ergobaby Performance Collection Charcoal Grey ...\n", "54267 Pearhead Babyprints Keepsake, Year-Round\n", "76000 Peg-Perego Tatamia High Chair, White Latte\n", "148431 Munchkin Nursery Projector and Sound System, W...\n", "1020 Safety 1st Deluxe 4-in-1 Bath Station\n", "122105 Baby Trend Encore Travel System-Columbia\n", "26194 Sunshine Kids Travel - Bag\n", "Name: name, dtype: object" ] }, "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_data.ix[ind_top_40_neg_test][\"name\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compute accuracy of the classifier\n", "\n", "We will now evaluate the accuracy of the trained classifer. Recall that the accuracy is given by\n", "\n", "\n", "$$\n", "\\mbox{accuracy} = \\frac{\\mbox{# correctly classified examples}}{\\mbox{# total examples}}\n", "$$\n", "\n", "This can be computed as follows:\n", "\n", " Step 1: Use the trained model to compute class predictions\n", "* Step 2: Count the number of data points when the predicted class labels match the ground truth labels (called `true_labels` below).\n", "* Step 3: Divide the total number of correct predictions by the total number of data points in the dataset.\n", "\n", "Complete the function below to compute the classification accuracy:" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "def get_classification_accuracy(model, data, true_labels):\n", " \n", " # Constructing the wordcount vector\n", " data_matrix = vectorizer.transform(data['review_clean'])\n", " \n", " # Getting the predictions\n", " preds_data = model.predict(data_matrix)\n", " \n", " # Computing the number of correctly classified examples and the total examples\n", " n_correct = float(np.sum(preds_data == true_labels.values))\n", " n_total = float(len(preds_data))\n", "\n", " # Computing the accuracy by dividing number of \n", " #correctly classified examples by total number of examples\n", " accuracy = n_correct/n_total\n", " \n", " return accuracy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's compute the classification accuracy of the sentiment_model on the test_data." ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.932145428366\n" ] } ], "source": [ "acc_sent_mod_test = get_classification_accuracy(sentiment_model, test_data, test_data['sentiment'])\n", "print acc_sent_mod_test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Quiz Question: What is the accuracy of the sentiment_model on the test_data? Round your answer to 2 decimal places (e.g. 0.76)." ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy on Test Data: 0.93\n" ] } ], "source": [ "print \"Accuracy on Test Data: %.2f\" %(acc_sent_mod_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Quiz Question: Does a higher accuracy value on the training_data always imply that the classifier is better?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " No, you may be overfitting. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, computing the accuracy of the sentiment model on the training data for a future quiz question." ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.96789740361\n" ] } ], "source": [ "acc_sent_mod_train = get_classification_accuracy(sentiment_model, train_data, train_data['sentiment'])\n", "print acc_sent_mod_train" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Finding the weights of significant words for the sentiment_model." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "In this section, we will find the weights of significant words for the sentiment_model." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Creating a vocab list. The vocab list constains all the words used for the sentiment_model" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "121712\n" ] } ], "source": [ "vocab = vectorizer.get_feature_names()\n", "print len(vocab)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creating a list of the significant words in the utf-8 format" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": true }, "outputs": [], "source": [ "un_sig_words = [u'love', u'great', u'easy', u'old', u'little', u'perfect', u'loves', \n", " u'well', u'able', u'car', u'broke', u'less', u'even', u'waste', u'disappointed', \n", " u'work', u'product', u'money', u'would', u'return']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creating a list that will store all the indicies where the significant words appear in the vocab list." ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ind_vocab_sig_words = []" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finding the index where each significant word appears. " ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for word in un_sig_words:\n", " ind_vocab_sig_words.append(vocab.index(word))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Creating an empty list that will store the weights of the significant words. Then, using the index to find the weight for each signigicant word." ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ws_sent_mod_sig_words = []\n", "for ind in ind_vocab_sig_words:\n", " ws_sent_mod_sig_words.append(sentiment_model.coef_.flatten()[ind])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creating a series that will store the weights of the significant words and displaying this Series." ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "love 1.574856\n", "great 1.228180\n", "easy 1.358323\n", "old 0.052743\n", "little 0.637940\n", "perfect 1.862897\n", "loves 1.518146\n", "well 0.540171\n", "able 0.390223\n", "car 0.124075\n", "broke -1.392661\n", "less -0.277272\n", "even -0.464528\n", "waste -1.992954\n", "disappointed -2.195897\n", "work -0.460691\n", "product -0.191460\n", "money -0.784845\n", "would -0.288619\n", "return -1.654747\n", "dtype: float64" ] }, "execution_count": 117, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ws_sent_mod_ser = pd.Series(data=ws_sent_mod_sig_words, index=un_sig_words)\n", "ws_sent_mod_ser" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Learn another classifier with fewer words\n", "\n", "There were a lot of words in the model we trained above. We will now train a simpler logistic regression model using only a subet of words that occur in the reviews. For this assignment, we selected a 20 words to work with. These are:" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": true }, "outputs": [], "source": [ "significant_words = ['love', 'great', 'easy', 'old', 'little', 'perfect', 'loves', \n", " 'well', 'able', 'car', 'broke', 'less', 'even', 'waste', 'disappointed', \n", " 'work', 'product', 'money', 'would', 'return']" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "20" ] }, "execution_count": 119, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(significant_words)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute a new set of word count vectors using only these words. The CountVectorizer class has a parameter that lets you limit the choice of words when building word count vectors:" ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "collapsed": false }, "outputs": [], "source": [ "vectorizer_word_subset = CountVectorizer(vocabulary=significant_words) # limit to 20 words\n", "train_matrix_word_subset = vectorizer_word_subset.fit_transform(train_data['review_clean'])\n", "test_matrix_word_subset = vectorizer_word_subset.transform(test_data['review_clean'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train a logistic regression model on a subset of data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now build a classifier with word_count_subset as the feature and sentiment as the target. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creating an instance of the LogisticRegression class. Using the fit method to train the classifier. This model should use the sparse word count matrix (train_matrix) as features and the column sentiment of train_data as the target. Use the default values for other parameters. Call this model simple_model." ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "collapsed": false }, "outputs": [], "source": [ "log_reg = linear_model.LogisticRegression()\n", "simple_model = logreg.fit(train_matrix_word_subset, train_data[\"sentiment\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Getting the weights for the 20 significant words from the simple_model" ] }, { "cell_type": "code", "execution_count": 122, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ws_simp_model = list(simple_model.coef_.flatten())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Putting the weights in a Series with the words corresponding to the weights as the index." ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "love 1.363690\n", "great 0.944000\n", "easy 1.192538\n", "old 0.085513\n", "little 0.520186\n", "perfect 1.509812\n", "loves 1.673074\n", "well 0.503760\n", "able 0.190909\n", "car 0.058855\n", "broke -1.651576\n", "less -0.209563\n", "even -0.511380\n", "waste -2.033699\n", "disappointed -2.348298\n", "work -0.621169\n", "product -0.320556\n", "money -0.898031\n", "would -0.362167\n", "return -2.109331\n", "dtype: float64" ] }, "execution_count": 123, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ws_simp_mod_ser = pd.Series(data=ws_simp_model, index=significant_words)\n", "ws_simp_mod_ser" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Quiz Question: Consider the coefficients of simple_model. How many of the 20 coefficients (corresponding to the 20 significant_words and excluding the intercept term) are positive for the `simple_model`?" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "print len(simple_model.coef_[simple_model.coef_>0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Quiz Question: Are the positive words in the simple_model (let us call them `positive_significant_words`) also positive words in the sentiment_model?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Yes, see weights below for the significant words for the sentiment model " ] }, { "cell_type": "code", "execution_count": 125, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "love 1.574856\n", "great 1.228180\n", "easy 1.358323\n", "old 0.052743\n", "little 0.637940\n", "perfect 1.862897\n", "loves 1.518146\n", "well 0.540171\n", "able 0.390223\n", "car 0.124075\n", "broke -1.392661\n", "less -0.277272\n", "even -0.464528\n", "waste -1.992954\n", "disappointed -2.195897\n", "work -0.460691\n", "product -0.191460\n", "money -0.784845\n", "would -0.288619\n", "return -1.654747\n", "dtype: float64" ] }, "execution_count": 125, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ws_sent_mod_ser" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparing models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now compare the accuracy of the sentiment_model and the simple_model using the `get_classification_accuracy` method you implemented above.\n", "\n", "First, compute the classification accuracy of the sentiment_model on the train_data:" ] }, { "cell_type": "code", "execution_count": 126, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.967897403609762" ] }, "execution_count": 126, "metadata": {}, "output_type": "execute_result" } ], "source": [ "acc_sent_mod_train" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, compute the classification accuracy of the simple_model on the train_data:" ] }, { "cell_type": "code", "execution_count": 127, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.866822570007\n" ] } ], "source": [ "preds_simp_mod_train = simple_model.predict(train_matrix_word_subset)\n", "n_cor_preds_simp_mod_train = float(np.sum(preds_simp_mod_train == train_data['sentiment'].values))\n", "n_tol_preds_simp_mod_train = float(len(preds_simp_mod_train))\n", "acc_simp_mod_train = n_cor_preds_simp_mod_train/n_tol_preds_simp_mod_train\n", "print acc_simp_mod_train" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Quiz Question: Which model (sentiment_model or simple_model) has higher accuracy on the TRAINING set?" ] }, { "cell_type": "code", "execution_count": 128, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sentiment_model\n" ] } ], "source": [ "if acc_sent_mod_train>acc_simp_mod_train:\n", " print \"sentiment_model\"\n", "else:\n", " print \"simple_model\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we will repeat this excercise on the test_data. Start by computing the classification accuracy of the sentiment_model on the test_data:" ] }, { "cell_type": "code", "execution_count": 129, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.9321454283657308" ] }, "execution_count": 129, "metadata": {}, "output_type": "execute_result" } ], "source": [ "acc_sent_mod_test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we will compute the classification accuracy of the simple_model on the test_data:" ] }, { "cell_type": "code", "execution_count": 130, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.869360451164\n" ] } ], "source": [ "preds_simp_mod_test = simple_model.predict(test_matrix_word_subset)\n", "n_cor_preds_simp_mod_test = float(np.sum(preds_simp_mod_test == test_data['sentiment'].values))\n", "n_tol_preds_simp_mod_test = float(len(preds_simp_mod_test))\n", "acc_simp_mod_test = n_cor_preds_simp_mod_test/n_tol_preds_simp_mod_test\n", "print acc_simp_mod_test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Quiz Question: Which model (sentiment_model or simple_model) has higher accuracy on the TEST set?" ] }, { "cell_type": "code", "execution_count": 131, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sentiment_model\n" ] } ], "source": [ "if acc_sent_mod_test>acc_simp_mod_test:\n", " print \"sentiment_model\"\n", "else:\n", " print \"simple_model\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Baseline: Majority class prediction\n", "\n", "It is quite common to use the majority class classifier as the a baseline (or reference) model for comparison with your classifier model. The majority classifier model predicts the majority class for all data points. At the very least, you should healthily beat the majority class classifier, otherwise, the model is (usually) pointless.\n", "\n", "What is the majority class in the train_data?" ] }, { "cell_type": "code", "execution_count": 132, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Positive Sentiment is Majority Classifier for Training Data\n" ] } ], "source": [ "num_positive = (train_data['sentiment'] == +1).sum()\n", "num_negative = (train_data['sentiment'] == -1).sum()\n", "acc_pos_train = float(num_positive)/float(len(train_data['sentiment']))\n", "acc_neg_train = float(num_negative)/float(len(train_data['sentiment']))\n", "if acc_pos_train>acc_neg_train:\n", " print \"Positive Sentiment is Majority Classifier for Training Data\"\n", "else:\n", " print \"Negative Sentiment is Majority Classifier for Training Data\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now compute the accuracy of the majority class classifier on test_data.\n", "\n", "Quiz Question: Enter the accuracy of the majority class classifier model on the test_data. Round your answer to two decimal places (e.g. 0.76)." ] }, { "cell_type": "code", "execution_count": 133, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy of Majority Class Classifier on Test Data: 0.84\n" ] } ], "source": [ "num_pos_test = (test_data['sentiment'] == +1).sum()\n", "acc_pos_test = float(num_pos_test)/float(len(test_data['sentiment']))\n", "print \"Accuracy of Majority Class Classifier on Test Data: %.2f\" %(acc_pos_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Quiz Question: Is the sentiment_model definitely better than the majority class classifier (the baseline)?" ] }, { "cell_type": "code", "execution_count": 134, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Yes, the sentiment_model is better than majority class classifier\n" ] } ], "source": [ "if acc_sent_mod_test>acc_pos_test:\n", " print \"Yes, the sentiment_model is better than majority class classifier\"\n", "else:\n", " print \"No, the majority class classifier is better than sentiment_model\"" ] }, { "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
oroszgy/oroszgy.github.io	content/handouts/concurrency-exercise.ipynb	1	3706	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Benchmarking your code" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def fun():\n", " max(range(1000))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using magic functions of Jupyter and `timeit`\n", "\n", "* https://docs.python.org/3.5/library/timeit.html\n", "* https://ipython.org/ipython-doc/3/interactive/magics.html#magic-time" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10000 loops, best of 3: 27.8 µs per loop\n" ] } ], "source": [ "%%timeit\n", "fun()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 44 µs, sys: 1 µs, total: 45 µs\n", "Wall time: 47 µs\n" ] } ], "source": [ "%%time\n", "fun()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. What is the fastest way to download 100 pages from index.hu?\n", "2. How to calculate the factors of 1000 random integers effectively using `factorize_naive` function below?" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<Response [200]>" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import requests\n", "def get_page(url): \n", " response = requests.request(url=url, method=\"GET\")\n", " return response\n", "get_page(\"http://index.hu\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def factorize_naive(n):\n", " \"\"\" A naive factorization method. Take integer 'n', return list of\n", " factors.\n", " \"\"\"\n", " if n < 2:\n", " return []\n", " factors = []\n", " p = 2\n", "\n", " while True:\n", " if n == 1:\n", " return factors\n", "\n", " r = n % p\n", " if r == 0:\n", " factors.append(p)\n", " n = n // p\n", " elif p * p >= n:\n", " factors.append(n)\n", " return factors\n", " elif p > 2:\n", " # Advance in steps of 2 over odd numbers\n", " p += 2\n", " else:\n", " # If p == 2, get to 3\n", " p += 1\n", " assert False, \"unreachable\"" ] }, { "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 }	mit
aricsanders/html-examples	HPBASIC_HTML/.ipynb_checkpoints/HTML_ASCII_Conversion_20160218_001-checkpoint.ipynb	1	3252	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Write HTML versions of HP Basic Text Files\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['CALREP71_COMMA.txt', 'CAP.txt', 'DMULTI1_04d.txt', 'DMULTI1_04E.TXT', 'evaluate.txt', 'FINDLEN.TXT', 'LINEPAR.txt', 'lrmcal.txt', 'MEASLPX.txt', 'Multical.txt', 'verify.txt']\n" ] } ], "source": [ "import os\n", "os.chdir(r'C:\\Share\\HPBASIC_ASCII')\n", "files=os.listdir(os.getcwd())\n", "print files" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "HTML_PREFIX=\"\"\"<!DOCTYPE html>\n", "<html lang=\"en\">\n", "<head>\n", " <meta charset=\"UTF-8\">\n", " <title>%s</title>\n", " <link href=\"prism.css\" rel=\"stylesheet\" />\n", "</head>\n", "<body>\n", "<h1> A highlighted Plain Text version of %s Basic Program</h1>\n", "<pre><code class=\"language-basic\"><script>\"\"\"\n", "HTML_POSTFIX=\"\"\"</script>\n", "\n", "</code></pre>\n", "\n", "<script src=\"prism.js\" data-default-language=\"markup\"></script>\n", "</body>\n", "</html>\"\"\"\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "\n", "for file_name in files:\n", " in_lines=[]\n", " in_file=open(file_name,'r')\n", " for line in in_file:\n", " in_lines.append(line)\n", " out_file=open(os.path.join(r'c:\\Share',file_name.replace('.txt','.html').replace('.TXT','.html')),'w')\n", " out_file.write(HTML_PREFIX%(file_name,file_name))\n", " for line in in_lines:\n", " out_file.write(line)\n", " out_file.write(HTML_POSTFIX)\n", " out_file.close()\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<!DOCTYPE html>\n", "<html lang=\"en\">\n", "<head>\n", " <meta charset=\"UTF-8\">\n", " <title>CALREP 7.1</title>\n", " <link href=\"prism.css\" rel=\"stylesheet\" />\n", "</head>\n", "<body>\n", "<h1> A highlighted Plain Text version of CALREP HP Basic Program</h1>\n", "<pre><code class=\"language-basic\"><script>\n" ] } ], "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 }	gpl-3.0
necromuralist/student_intervention	student_intervention/student_intervention.ipynb	1	1916454	null	mit
jimregan/tesseract-gle-uncial	Update_gle_uncial_traineddata_for_Tesseract_4.ipynb	1	36400	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Update gle_uncial.traineddata for Tesseract 4.ipynb", "provenance": [], "authorship_tag": "ABX9TyMXESPRb8Yj9yimGI5YEXCF", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "<a href=\"https://colab.research.google.com/github/jimregan/tesseract-gle-uncial/blob/master/Update_gle_uncial_traineddata_for_Tesseract_4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" ] }, { "cell_type": "markdown", "metadata": { "id": "iHUxzWctjFyV", "colab_type": "text" }, "source": [ "Grab this for later" ] }, { "cell_type": "code", "metadata": { "id": "a236_7rmMEFw", "colab_type": "code", "colab": {} }, "source": [ "!wget https://github.com/jimregan/tesseract-gle-uncial/releases/download/v0.1beta2/gle_uncial.traineddata" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "NQIJlHDIjK6-", "colab_type": "text" }, "source": [ "Install dependencies" ] }, { "cell_type": "code", "metadata": { "id": "4eiDnu7MOk7m", "colab_type": "code", "colab": {} }, "source": [ "!apt-get install libicu-dev libpango1.0-dev libcairo2-dev libleptonica-dev\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "2aJLdU60jN7F", "colab_type": "text" }, "source": [ "Clone, compile and set up Tesseract" ] }, { "cell_type": "code", "metadata": { "id": "Ay9TBwa2OyS2", "colab_type": "code", "outputId": "c1a53437-3bd2-4799-d067-e16be4b50931", "colab": { "base_uri": "https://localhost:8080/", "height": 136 } }, "source": [ "!git clone https://github.com/tesseract-ocr/tesseract" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Cloning into 'tesseract'...\n", "remote: Enumerating objects: 5, done.\u001b[K\n", "remote: Counting objects: 100% (5/5), done.\u001b[K\n", "remote: Compressing objects: 100% (4/4), done.\u001b[K\n", "remote: Total 34087 (delta 0), reused 3 (delta 0), pack-reused 34082\u001b[K\n", "Receiving objects: 100% (34087/34087), 44.79 MiB \| 9.99 MiB/s, done.\n", "Resolving deltas: 100% (26460/26460), done.\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "wKWjma0dO2o6", "colab_type": "code", "colab": {} }, "source": [ "import os\n", "os.chdir('tesseract')" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "hPw7D97vO6L9", "colab_type": "code", "colab": {} }, "source": [ "!sh autogen.sh" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "YOvwWqHPPH0e", "colab_type": "code", "colab": {} }, "source": [ "!./configure --disable-graphics\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "yIBy1uMpPbFz", "colab_type": "code", "colab": {} }, "source": [ "!make -j 8\n", "!make install\n", "!ldconfig\n", "!make training\n", "!make training-install" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "0LxiYKLWjVjU", "colab_type": "text" }, "source": [ "Grab some things to scrape the RIA corpus" ] }, { "cell_type": "code", "metadata": { "id": "jxMzngnITmb0", "colab_type": "code", "outputId": "1647b04e-e732-4343-d92f-e0acbbceb0c5", "colab": { "base_uri": "https://localhost:8080/", "height": 153 } }, "source": [ "import os\n", "os.chdir('/content')\n", "!git clone https://github.com/jimregan/tesseract-gle-uncial/" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Cloning into 'tesseract-gle-uncial'...\n", "remote: Enumerating objects: 29, done.\u001b[K\n", "remote: Counting objects: 100% (29/29), done.\u001b[K\n", "remote: Compressing objects: 100% (23/23), done.\u001b[K\n", "remote: Total 1402 (delta 5), reused 0 (delta 0), pack-reused 1373\u001b[K\n", "Receiving objects: 100% (1402/1402), 200.19 MiB \| 13.09 MiB/s, done.\n", "Resolving deltas: 100% (634/634), done.\n", "Checking out files: 100% (630/630), done.\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "tVB4UgGzhMpC", "colab_type": "code", "colab": {} }, "source": [ "!apt-get install lynx" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "B-f-SM11jcZX", "colab_type": "text" }, "source": [ "Scrape the RIA corpus" ] }, { "cell_type": "code", "metadata": { "id": "mjstRngogwZ9", "colab_type": "code", "colab": {} }, "source": [ "! for i in A B C D E F G H I J K L M N O P Q R S T U V W X Y Z;do lynx -dump \"http://corpas.ria.ie/index.php?fsg_function=1&fsg_page=$i\" \|grep http://corpas.ria.ie\|awk '{print $NF}' >> list;done" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "kOlwWCHthbf2", "colab_type": "code", "colab": {} }, "source": [ "!grep 'function=3' list \|sort\|uniq\|grep corpas.ria\|sed -e 's/function=3/function=5/' > input" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "y9awrS5UhuQk", "colab_type": "code", "colab": {} }, "source": [ "!wget -x -c -i input" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "sbhsu7ulh39e", "colab_type": "code", "colab": {} }, "source": [ "!mkdir text\n", "!for i in corpas.ria.ie/;do id=$(echo $i\|awk -F'=' '{print $NF}');cat $i \| perl /content/tesseract-gle-uncial/scripts/extract-ria.pl > text/$id.txt;done" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "r6pGaPM2peVa", "colab_type": "text" }, "source": [ "Get the raw corpus in a single text file" ] }, { "cell_type": "code", "metadata": { "id": "wfKt8NGjo6dB", "colab_type": "code", "colab": {} }, "source": [ "!cat text/.txt\|grep -v '^$' > ria-raw.txt\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "tYjMsLPvpj_0", "colab_type": "text" }, "source": [ "Compress the raw text; this can be downloaded through the file browser on the left, so the scraping steps can be skipped in future" ] }, { "cell_type": "code", "metadata": { "id": "GibldvZ9psaY", "colab_type": "code", "colab": {} }, "source": [ "!gzip ria-raw.txt" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "b6EcoZpNp6jV", "colab_type": "text" }, "source": [ "...and can be re-added using the upload feature in the file browser" ] }, { "cell_type": "code", "metadata": { "id": "IrNYEOk5qB5t", "colab_type": "code", "colab": {} }, "source": [ "!gzip -d ria-raw.txt.gz" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "NFM4Uo3VqRZV", "colab_type": "text" }, "source": [ "This next part is so I can update the langdata files" ] }, { "cell_type": "code", "metadata": { "id": "3oaQWq5foukg", "colab_type": "code", "outputId": "fe427d93-d82f-41b5-aeef-486dcbface65", "colab": { "base_uri": "https://localhost:8080/", "height": 153 } }, "source": [ "import os\n", "os.chdir('/content')\n", "!git clone https://github.com/tesseract-ocr/langdata" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Cloning into 'langdata'...\n", "remote: Enumerating objects: 35, done.\u001b[K\n", "remote: Counting objects: 100% (35/35), done.\u001b[K\n", "remote: Compressing objects: 100% (33/33), done.\u001b[K\n", "remote: Total 2164 (delta 9), reused 12 (delta 2), pack-reused 2129\u001b[K\n", "Receiving objects: 100% (2164/2164), 400.19 MiB \| 13.53 MiB/s, done.\n", "Resolving deltas: 100% (366/366), done.\n", "Checking out files: 100% (1022/1022), done.\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "4sN2ZqGWvy25", "colab_type": "code", "colab": {} }, "source": [ "!cat ria-raw.txt \| perl /content/tesseract-gle-uncial/scripts/toponc.pl > ria-ponc.txt" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "sk5irOI9v_oz", "colab_type": "code", "colab": {} }, "source": [ "!mkdir genwlout" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "Ba8tk80tsTQb", "colab_type": "code", "colab": {} }, "source": [ "!perl /content/tesseract-gle-uncial/scripts/genlangdata.pl -i ria-ponc.txt -d genwlout -p gle_uncial" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "KjXJZn8gt1J_", "colab_type": "code", "colab": {} }, "source": [ "import os\n", "os.chdir('/content/genwlout')\n", "#!for i in gle_uncial.word.bigrams gle_uncial.wordlist gle_uncial.numbers gle_uncial.punc; do cat $i.unsorted \| awk -F'\\t' '{print $1}' \| sort \| uniq > $i.sorted;done\n", "!for i in gle_uncial.word.bigrams gle_uncial.wordlist gle_uncial.numbers gle_uncial.punc; do cat $i.sorted /content/langdata/gle_uncial/$i \| sort \| uniq > $i;done" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "yFnmk5pnysoc", "colab_type": "code", "colab": {} }, "source": [ "!for i in gle_uncial.word.bigrams gle_uncial.wordlist gle_uncial.numbers gle_uncial.punc; do cp $i /content/langdata/gle_uncial/;done" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "5Bngw4sYqpaU", "colab_type": "code", "colab": {} }, "source": [ "Grab the fonts" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "TSECxvfFiCev", "colab_type": "code", "colab": {} }, "source": [ "import os\n", "os.chdir('/content')\n", "!mkdir fonts\n", "os.chdir('fonts')\n", "!wget -i /content/tesseract-gle-uncial/fonts.txt" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "VC_DFGpTqyqV", "colab_type": "code", "colab": {} }, "source": [ "!for i in *.zip; do unzip $i;done" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "M81Z0RiM2v9A", "colab_type": "text" }, "source": [ "Generate" ] }, { "cell_type": "code", "metadata": { "id": "r8GW1jnI2yno", "colab_type": "code", "outputId": "4bc05f5b-b385-42b7-aeb3-1b554a795771", "colab": { "base_uri": "https://localhost:8080/", "height": 289 } }, "source": [ "os.chdir('/content')\n", "!mkdir unpack\n", "!combine_tessdata -u /content/gle_uncial.traineddata unpack/gle_uncial." ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Extracting tessdata components from /content/gle_uncial.traineddata\n", "Wrote unpack/gle_uncial.lstm\n", "Wrote unpack/gle_uncial.lstm-punc-dawg\n", "Wrote unpack/gle_uncial.lstm-word-dawg\n", "Wrote unpack/gle_uncial.lstm-number-dawg\n", "Wrote unpack/gle_uncial.lstm-unicharset\n", "Wrote unpack/gle_uncial.lstm-recoder\n", "Wrote unpack/gle_uncial.version\n", "Version string:4.00.00alpha\n", "17:lstm:size=4291340, offset=192\n", "18:lstm-punc-dawg:size=890, offset=4291532\n", "19:lstm-word-dawg:size=5760002, offset=4292422\n", "20:lstm-number-dawg:size=226, offset=10052424\n", "21:lstm-unicharset:size=9938, offset=10052650\n", "22:lstm-recoder:size=1147, offset=10062588\n", "23:version:size=12, offset=10063735\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "ArIW_eg9B6JA", "colab_type": "code", "colab": {} }, "source": [ "os.chdir('unpack')\n", "!for i in gle_uncial.word.bigrams gle_uncial.wordlist gle_uncial.numbers gle_uncial.punc; do cp /content/genwlout/$i .;done" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "rgCTjJdBCVcm", "colab_type": "code", "outputId": "092087b1-d325-4547-aadf-38d7e3090fe1", "colab": { "base_uri": "https://localhost:8080/", "height": 221 } }, "source": [ "!wordlist2dawg gle_uncial.numbers gle_uncial.lstm-number-dawg gle_uncial.lstm-unicharset\n", "!wordlist2dawg gle_uncial.punc gle_uncial.lstm-punc-dawg gle_uncial.lstm-unicharset\n", "!wordlist2dawg gle_uncial.wordlist gle_uncial.lstm-word-dawg gle_uncial.lstm-unicharset" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Loading unicharset from 'gle_uncial.lstm-unicharset'\n", "Reading word list from 'gle_uncial.numbers'\n", "Reducing Trie to SquishedDawg\n", "Writing squished DAWG to 'gle_uncial.lstm-number-dawg'\n", "Loading unicharset from 'gle_uncial.lstm-unicharset'\n", "Reading word list from 'gle_uncial.punc'\n", "Reducing Trie to SquishedDawg\n", "Writing squished DAWG to 'gle_uncial.lstm-punc-dawg'\n", "Loading unicharset from 'gle_uncial.lstm-unicharset'\n", "Reading word list from 'gle_uncial.wordlist'\n", "Reducing Trie to SquishedDawg\n", "Writing squished DAWG to 'gle_uncial.lstm-word-dawg'\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "wXTpu7WyEVmJ", "colab_type": "code", "colab": {} }, "source": [ "!rm gle_uncial.numbers gle_uncial.word.bigrams gle_uncial.punc gle_uncial.wordlist" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "4TGsyBAfEzc7", "colab_type": "code", "outputId": "6820aa58-996c-4d08-fa23-a12dfd270597", "colab": { "base_uri": "https://localhost:8080/", "height": 187 } }, "source": [ "os.chdir('/content')\n", "!mv gle_uncial.traineddata gle_uncial.traineddata.orig\n", "!combine_tessdata unpack/gle_uncial." ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Combining tessdata files\n", "Output unpack/gle_uncial.traineddata created successfully.\n", "Version string:4.00.00alpha\n", "17:lstm:size=4291340, offset=192\n", "18:lstm-punc-dawg:size=24834, offset=4291532\n", "19:lstm-word-dawg:size=11350106, offset=4316366\n", "20:lstm-number-dawg:size=61530, offset=15666472\n", "21:lstm-unicharset:size=9938, offset=15728002\n", "22:lstm-recoder:size=1147, offset=15737940\n", "23:version:size=12, offset=15739087\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "ZdrcgsR1KxyY", "colab_type": "text" }, "source": [ "" ] }, { "cell_type": "code", "metadata": { "id": "bfTU7NnPMLPo", "colab_type": "code", "outputId": "a2af3842-ea33-45f6-f60d-ea62c1db8f90", "colab": { "base_uri": "https://localhost:8080/", "height": 561 } }, "source": [ "os.chdir('/content')\n", "!bash /content/tesseract/src/training/tesstrain.sh" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "USAGE: tesstrain.sh\n", " --exposures EXPOSURES # A list of exposure levels to use (e.g. \"-1 0 1\").\n", " --fontlist FONTS # A list of fontnames to train on.\n", " --fonts_dir FONTS_PATH # Path to font files.\n", " --lang LANG_CODE # ISO 639 code.\n", " --langdata_dir DATADIR # Path to tesseract/training/langdata directory.\n", " --linedata_only # Only generate training data for lstmtraining.\n", " --output_dir OUTPUTDIR # Location of output traineddata file.\n", " --overwrite # Safe to overwrite files in output_dir.\n", " --run_shape_clustering # Run shape clustering (use for Indic langs).\n", " --maxpages # Specify maximum pages to output (default:0=all)\n", " --save_box_tiff # Save box/tiff pairs along with lstmf files.\n", " --xsize # Specify width of output image (default:3600)\n", "\n", " OPTIONAL flag for specifying directory with user specified box/tiff pairs.\n", " Files should be named similar to ${LANG_CODE}.${fontname}.exp${EXPOSURE}.box/tif\n", " --my_boxtiff_dir MY_BOXTIFF_DIR # Location of user specified box/tiff files.\n", "\n", " OPTIONAL flags for input data. If unspecified we will look for them in\n", " the langdata_dir directory.\n", " --training_text TEXTFILE # Text to render and use for training.\n", " --wordlist WORDFILE # Word list for the language ordered by\n", " # decreasing frequency.\n", " OPTIONAL flag to specify location of existing traineddata files, required\n", " during feature extraction. If unspecified will use TESSDATA_PREFIX defined in\n", " the current environment.\n", " --tessdata_dir TESSDATADIR # Path to tesseract/tessdata directory.\n", " NOTE:\n", " The font names specified in --fontlist need to be recognizable by Pango using\n", " fontconfig. An easy way to list the canonical names of all fonts available on\n", " your system is to run text2image with --list_available_fonts and the\n", " appropriate --fonts_dir path.\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "62p1uKWwMsUC", "colab_type": "code", "outputId": "24cb3959-b04e-461f-d34a-dfbecc72b827", "colab": { "base_uri": "https://localhost:8080/", "height": 408 } }, "source": [ "!text2image --fonts_dir fonts --list_available_fonts" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ " 0: Bunchlo Arsa Dubh GC\n", " 1: Bunchlo Arsa GC\n", " 2: Bunchlo Arsa GC Bold\n", " 3: Bunchlo Dubh GC\n", " 4: Bunchlo GC\n", " 5: Bunchlo GC Bold\n", " 6: Bunchlo Nua GC Bold\n", " 7: Bunchló na Nod GC\n", " 8: Gadelica\n", " 9: Glanchlo Dubh GC\n", " 10: Glanchlo GC\n", " 11: Glanchlo GC Bold\n", " 12: Seanchló Dubh GC\n", " 13: Seanchló GC\n", " 14: Seanchló GC Bold\n", " 15: Seanchló na Nod GC\n", " 16: Seanchló Ársa Dubh GC\n", " 17: Seanchló Ársa GC\n", " 18: Seanchló Ársa GC Bold\n", " 19: Tromchlo Beag GC\n", " 20: Tromchlo Mor GC\n", " 21: Urchlo GC\n", " 22: Urchlo GC Bold\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "Hff0WNbTUnQQ", "colab_type": "code", "colab": {} }, "source": [ "!cat genwlout/gle_uncial.wordlist.unsorted\|awk -F'\\t' '{print $2 \"\\t\" $1'}\|sort -nr > freqlist" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "OdHNE5mDVLvf", "colab_type": "code", "colab": {} }, "source": [ "!cat freqlist\|awk -F'\\t' '{print $2}'\|grep -v '^$' > wordlist" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "oFkOliGOVTzt", "colab_type": "code", "colab": {} }, "source": [ "!cat ria-ponc.txt\|sort\|uniq\|head -n 400000 > gle_uncial.training_text" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "YztuEZRnXFbA", "colab_type": "code", "colab": {} }, "source": [ "!cp unpack/gle_uncial.traineddata /usr/share/tesseract-ocr/4.00/tessdata" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "yfbMAyDTWoe_", "colab_type": "code", "colab": {} }, "source": [ "!cp gle_uncial.trainingtext langdata/gle_uncial/" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "wMJZlczSXOYk", "colab_type": "code", "colab": {} }, "source": [ "!mkdir output" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "LqWdROBKWCrW", "colab_type": "code", "outputId": "80858c05-b292-4133-b88d-eaf63ad649dd", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "source": [ "!bash tesseract/src/training/tesstrain.sh --fonts_dir fonts --lang gle_uncial --linedata_only --noextract_font_properties --langdata_dir langdata --tessdata_dir /usr/share/tesseract-ocr/4.00/tessdata --output_dir output" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Rendered 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appleby/fastai-courses	deeplearning1/nbs/lesson2.ipynb	1	1745057	null	apache-2.0
ncfausti/machine_learning	Week 1.ipynb	1	3468216	null	gpl-3.0
atulsingh0/MachineLearning	Sklearn_MLPython/CH01.ipynb	1	35442	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Machine Learning – A Gentle Introduction" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# import\n", "from sklearn.datasets import load_iris\n", "from sklearn.linear_model import SGDClassifier\n", "from sklearn.cross_validation import train_test_split, KFold, cross_val_score\n", "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n", "from sklearn import preprocessing, pipeline\n", "\n", "import numpy as np\n", "import scipy as sp\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "%matplotlib inline\n", "sns.set()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n", "['setosa' 'versicolor' 'virginica']\n" ] } ], "source": [ "#Loading the IRIS dataset\n", "iris_data = load_iris()\n", "\n", "X = iris_data['data']\n", "y = iris_data['target']\n", "\n", "print(iris_data['feature_names'])\n", "print(iris_data['target_names'])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 5. 2.3 3.3 1. ]\n", " [ 4.9 3.1 1.5 0.1]]\n", "X_train shape (112, 4)\n", "X_test shape (38, 4)\n", "[[-0.91090798 -1.59761476 -0.15438202 -0.14641523]\n", " [-1.0271058 0.09442168 -1.15513491 -1.35614105]]\n" ] } ], "source": [ "# splitting and Pre-Processing the data\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=33)\n", "print(X_train[:2])\n", "print(\"X_train shape\", X_train.shape)\n", "print(\"X_test shape\", X_test.shape)\n", "\n", "# Preprocessing and Standardize the features\n", "scaler = preprocessing.StandardScaler().fit(X_train)\n", "\n", "X_train = scaler.transform(X_train)\n", "X_test = scaler.transform(X_test)\n", "print(X_train[:2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " SGDClassifier \n", "\n", " SGD stands for Stochastic Gradient Descent, a very popular numerical procedure \n", " to find the local minimum of a function (in this case, the loss function, which \n", " measures how far every instance is from our boundary). The algorithm will learn the \n", " coefficients of the hyperplane by minimizing the loss function." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "coefficient [[ -9.18360001 12.3553701 -20.48666511 -19.67619653]\n", " [ -7.45335653 -10.85334472 17.47448214 -25.00324191]\n", " [ 0.14640522 1.04172728 31.69797189 35.7343131 ]]\n", "intercept: [-19.53199304 -3.39921378 -46.95465167]\n", "[0]\n", "Model Accuracy on Train data: 0.910714285714\n", "Model Accuracy on Test data: 0.894736842105\n" ] } ], "source": [ "# instantiate\n", "sgd = SGDClassifier()\n", "\n", "# fitting\n", "sgd.fit(X_train, y_train)\n", "\n", "# coefficient\n", "print(\"coefficient\", sgd.coef_)\n", "\n", "# intercept\n", "print(\"intercept: \", sgd.intercept_)\n", "\n", "# predicting for one\n", "y_pred = sgd.predict(scaler.transform([[4.9,3.1,1.5,0.1]]))\n", "print(y_pred)\n", "\n", "# predicting for X_test\n", "y_pred = sgd.predict(X_test)\n", "\n", "# checking accuracy score\n", "print(\"Model Accuracy on Train data: \", accuracy_score(y_train, sgd.predict(X_train)))\n", "print(\"Model Accuracy on Test data: \", accuracy_score(y_test, y_pred))\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x13360c6fc18>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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2cFiu6qtMqggAgOQjdAIms7W3xzUOAEA6InQCJou6XHGNAwCQjgidgMnaK5cq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\n6o5fY/oXImDPknqa4fXXX9dXvvIVPfL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"text/plain": [ "<matplotlib.figure.Figure at 0x13360ac4b38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# let's plot the data\n", "plt.figure(figsize=(8,6))\n", "\n", "plt.scatter(X_train[:,0][y_train==0],X_train[:,1][y_train==0],color='red', label='setosa')\n", "plt.scatter(X_train[:,0][y_train==1],X_train[:,1][y_train==1],color='blue', label='verginica')\n", "plt.scatter(X_train[:,0][y_train==2],X_train[:,1][y_train==2],color='green', label='versicolour')\n", "\n", "plt.legend(loc='best')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Classification Report \n", "\n", " Accuracy = (TP+TN)/m \n", " Precision = TP/(TP+FP) \n", " Recall = TP/(TP+FN) \n", " F1-score = 2 * Precision * Recall / (Precision + Recall) \n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 1.00 1.00 1.00 8\n", " 1 0.82 0.82 0.82 11\n", " 2 0.89 0.89 0.89 19\n", "\n", "avg / total 0.89 0.89 0.89 38\n", "\n" ] } ], "source": [ "# predicting \n", "print(classification_report(y_pred=y_pred, y_true=y_test))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 8, 0, 0],\n", " [ 0, 9, 2],\n", " [ 0, 2, 17]])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "confusion_matrix(y_pred=y_pred, y_true=y_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Using a pipeline mechanism to build and test our model " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.93333333 0.9 0.83333333 0.86666667 0.86666667]\n" ] } ], "source": [ "# create a composite estimator made by a pipeline of the standarization and the linear model\n", "clf = pipeline.Pipeline([\n", " ('scaler', preprocessing.StandardScaler()),\n", " ('linear_model', SGDClassifier())\n", "])\n", "\n", "# create a k-fold cross validation iterator of k=5 folds\n", "cv = KFold(X.shape[0], 5, shuffle=True, random_state=33)\n", "\n", "# by default the score used is the one returned by score method of the estimator (accuracy)\n", "scores = cross_val_score(clf, X, y, cv=cv)\n", "\n", "print(scores)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.88 0.016996731712\n" ] } ], "source": [ "# mean accuracy \n", "print(np.mean(scores), sp.stats.sem(scores))" ] }, { "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": 0 }	gpl-3.0
kimkipyo/dss_git_kkp	통계, 머신러닝 복습/160524화_7일차_기초 확률론 3 - 확률 모형 Probability Models(단변수 분포)/8.감마 분포.ipynb	1	32636	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 감마 분포" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* 정규분포로 답을 하면 된다. 평균을.\n", "* 그러면 분산은? 카이제곱 같은 걸로 하면 된다? \n", "* 감마 분포 첫 번째 공식 외워라." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "감마 분포(Gamma distribution)도 베타 분포(Beta distribution)처럼 모수의 베이지안 추정에 사용된다. 다만 베타 분포가 0부터 1사이의 값을 가지는 모수를 베이지안 방법으로 추정하는데 사용되는 것과 달리 감마 분포는 0부터 무한대의 값을 가지는 양수 값을 추정하는데 사용된다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "감마 분포의 확률 밀도 함수는 a와 b라는 두 개의 모수(parameter)를 가지며 수학적으로 다음과 같이 정의된다.\n", "\n", "$$ \\text{Gam}(x;a,b) = \\frac{1}{\\Gamma(a)} b^a x^{a-1}e^{-bx} $$\n", "\n", "이 식에서\n", "\n", "$$ \\Gamma(a) = \\int_0^\\infty x^{a-1} e^{-x}\\, dx $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "감마 분포의 확률 밀도 함수는 모수 $a$, $b$의 값에 따라 다음과 같은 형상을 가진다.\n", "\n", "SciPy의 stats 서브패키지에서 제공하는 `gamma` 클래스는 모수 $b=1$로 고정되어 $a$ 값만 설정할 수 있다. $b$를 바꾸려면 $x$값 스케일과 계수를 수동으로 설정하여야 한다." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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FG3QES0NWFgwdlMEdV42mqOQkS9cfZPmGQ2d2pU6Ii2bKqL5MH5vD1FF9u7RH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\nA74LfA1YB7wghKjCNFxPJsLOtfSTSKbYdH9ZJps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"text/plain": [ "<matplotlib.figure.Figure at 0xa9cc5f8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xx = np.linspace(0, 10, 100)\n", "plt.subplot(221)\n", "plt.plot(xx, sp.stats.gamma(9).pdf(xx))\n", "plt.ylim(0, 0.4)\n", "plt.title(\"(A) a=9, b=1, mode=8\")\n", "plt.subplot(222)\n", "plt.plot(xx, sp.stats.gamma(6).pdf(xx))\n", "plt.ylim(0, 0.4)\n", "plt.title(\"(B) a=6, b=1, mode=5\")\n", "plt.subplot(223)\n", "plt.plot(xx, sp.stats.gamma(3).pdf(xx))\n", "plt.ylim(0, 0.4)\n", "plt.title(\"(C) a=3, b=1, mode=2\")\n", "plt.subplot(224)\n", "plt.plot(xx, sp.stats.gamma(2).pdf(xx))\n", "plt.ylim(0, 0.4)\n", "plt.title(\"(D) a=2, b=1, mode=1\")\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "위 그림이 베이지안 추정 결과라면 각각은 모수에 대해 다음과 같이 추정한 것과 같다.\n", "\n", "* (A): 모수값이 8일 가능성이 가장 크다. (정확도 아주 낮음)\n", "* (B): 모수값이 5일 가능성이 가장 크다. (정확도 낮음)\n", "* (C): 모수값이 2일 가능성이 가장 크다. (정확도 높음)\n", "* (D): 모수값이 1일 가능성이 가장 크다. (정확도 아주 높음)\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "감마 분포의 기댓값, 최빈값, 분산은 각각 다음과 같다.\n", "\n", "\n", "\n", "* 기댓값\n", "$$ \\text{E}[X] = \\dfrac{a}{b}$$\n", "\n", "* 최빈값\n", "$$ \\dfrac{a-1}{b}$$\n", "\n", "\n", "* 분산\n", "$$\\text{Var}[X] = \\dfrac{a}{b^2}$$\n" ] } ], "metadata": { "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": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
rnowling/notebooks	notebooks/Differential Equations and Markov Models.ipynb	1	16999	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%load_ext autoreload\n", "%autoreload 2\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import random" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The solutions of Ordinary Differential Equations (ODEs) and Stochastic Differential Equations (SDEs) can be represented as Markov Models.\n", "\n", "To examine these ideas, we'll consider a toy system (harmonic oscillator) simulated in the NVE and NVT ensembles and analyze the resulting Markov Models.\n", "\n", "The potential of the harmonic oscillator is given as:\n", "\n", "$$\n", "U(x) = \\frac{1}{2} k x^2\n", "$$\n", "\n", "where $k$ is the force constant, given by $ k = m \\omega^2$, $\\omega$ is the frequency of oscillation, and $m$ is the mass.\n", "\n", "To simulate the harmonic oscillator, we compute the force as the derivative of the potential:\n", "\n", "$$\n", "F(x) = - \\nabla U = -kx\n", "$$\n", "\n", "In the NVE ensemble, we use Newton's equations of motion:\n", "\n", "$$\n", "m \\ddot{x} = -kx\n", "$$\n", "\n", "Although an analytic solution exists, we'll use numerical integration since it's more general. The leapfrog method is a good second-order method for numerical integration:\n", "\n", "$$\n", "x_i = x_{i-1} + v_{i-1/2} \\Delta t\n", "$$\n", "\n", "$$ \n", "v_{i+1/2} = v_{i-1/2} + F(x_i) \\Delta t\n", "$$\n", "\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def simulate_spring(k, x0, v0, deltaT, numsteps):\n", " xs = []\n", " vs = []\n", " xi = x0\n", " v_half = v0\n", " for i in xrange(numsteps):\n", " xi = xi + v_half * deltaT\n", " fi = -1.0 * k * xi\n", " v_half = v_half + fi * deltaT\n", " xs.append(xi)\n", " vs.append(v_half)\n", " return xs, vs" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 45 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The number of steps comes from 2.0 * $\\pi$ / 0.1" ] }, { "cell_type": "code", "collapsed": false, "input": [ "timestep= 0.1\n", "numsteps = (int(2.0 * np.pi / timestep) + 2) * 100\n", "print numsteps\n", "nve_xs, nve_vs = simulate_spring(1, 0, 10, timestep, numsteps)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "6400\n" ] } ], "prompt_number": 53 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.plot(nve_xs, nve_vs)\n", "plt.xlabel(\"Position\", fontsize=16)\n", "plt.ylabel(\"Velocity\", fontsize=16)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 54, "text": [ "<matplotlib.text.Text at 0x11841ecd0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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dlZWVObQOA0FYWBgAoG/fvpg2bRry8/Mxbtw4latSlsFgQEVFBfr374/y8nL0\n69dP7ZIUZT89cpY7v2hpyCVaHBRYX18PAK0eFOhP7KcvNTUV7777Lurq6lBSUoKioiK/33ulvLzc\ndjsnJ8dhDw9/NGrUKBQVFaG0tBR1dXXYunUrUlNT1S5LMVevXrW13q9cuYL9+/f7/XfmSmpqKt5+\n+20AwNtvv42pU6eqXJGyPF7uFO6c97kdO3YIo9EoOnfuLAwGg5g4caIQQojt27eL6OhoMWLECBEf\nHy/27NmjcqVt4276hBDixRdfFBEREWLIkCEiNzdXxSqV8eijj4qYmBgxfPhw8cADD4iKigq1S2q3\nDz74QERFRYmIiAixYsUKtctR1Ndffy1iY2NFbGysiI6ODojpmzFjhggLCxMhISHCaDSKjRs3igsX\nLoh7771XDB48WCQnJ4uLFy+qXWabtZy+DRs2eLzc8eA+IiKSLaA2TxERkXcxNIiISDaGBhERycbQ\nICIi2RgaREQkG0ODiIhkY2iQrmzatAlBQUG2vx49emDEiBFYs2aN7WBQJWRkZCAoqHnx+uGHH5CR\nkYGCggKnx1osFiQmJir23kTe5BenESFS2vbt22E0GnHp0iVkZ2fjqaeewvnz55GZmanI6z/++OOY\nNGmSbfjixYtYtmwZfv7znyMuLs7hsWvXrlXkPYl8gaFBujRixAjbaWWSkpJw5swZvPbaa4qFRnh4\nuMtzgbk6ltZsNivynkS+wM1TRABGjhyJS5cu4fvvv0dubi7GjBmDW265BT179sS0adNw+vRph8d/\n+OGHuOuuu9CzZ090794dZrMZy5cvt91vv3mqtLTUFlCPP/64bdNYVlYWANebpwoLCzFt2jSEhobi\nlltuwZgxY/Dhhx86PKbpPYqLi5GSkoLu3bvDZDJh+fLlAXWNC9IWhgYRGk9qGRwcjKNHjyIlJQU9\nevRAdnY23nzzTfznP//B3Xffje+++8722NTUVERERCA7Oxu7d+/GwoULcfXqVYfXbDqF9oABA7Bj\nxw4AwO9//3scOXIER44cQUpKitNjgcZrpdx99904efIk1qxZg+zsbPTs2RMpKSkuL8U5bdo0JCUl\nYdeuXZg6dSqWLl1qO8EekeJ8cI4sIs3429/+JiRJEoWFheL69euiurparF27VnTo0EFMmzZNjBw5\nUkRFRTlcOrekpESEhISIhQsXCiGE2LZtm5AkSVy+fNnt+yxdulRIkuTwGpIkiQ0bNjg9NiEhQSQm\nJtqGFy3C96eSAAAC/UlEQVRaJIKDg8WZM2ds4+rr68WQIUNEfHy803ts2rTJ4fViYmLEhAkTPPhU\niORjS4N0yWw2o2PHjujduzd++9vf4pFHHsFf//pXFBQUYPr06Q57PplMJowdOxZ5eXkAGvtDQkJC\nMH36dLz33ns4f/68orUdPHgQY8aMcTiVf1BQEGbMmIHjx4+jtrbW4fH2LRYAiI6OxjfffKNoTURN\nGBqkSzt37sTRo0dRWFiIq1evYtOmTWhoaIAQwnZhIXsGgwEXL14EAERGRuLDDz9EQ0MDHn30UYSF\nhWHMmDE4ePCgIrVVV1e7rKF///4QQtjqaNKrVy+H4U6dOuHHH39UpBailhgapEvDhg1DfHw8Bg8e\nbLvCY2hoKCRJQkVFhdPjKyoqHH6cLRYL9u3bhx9++AEfffQRgoODkZKSgurq6nbX1rt3b4cL49jX\nIEkSQkND2/0eRG3F0CD6r65du2LkyJHIzs5GQ0ODbfzZs2fx2WefwWKxOD0nJCQEiYmJePbZZ3Hl\nyhWUlJS4fO1OnToBAK5du3bTOhISEnDkyBGcPXvWNq6+vh5bt25FfHw8unXrdtPXCLTrWJN28DgN\nIjvLly9HSkoKJk+ejPnz56O2thZLly5FaGgoFi1aBKDxYLxPP/0UkyZNgtFoRFVVFVauXInw8HAM\nGzbM5esaDAb07t0bW7ZsQUxMDG655RbcdtttttaLsNtF9plnnsGmTZuQnJyMzMxMdO/eHW+88QaK\ni4uxd+9eWdMhuMsteQlbGqQ7ra2F33fffdi7dy9qamowffp0zJ8/H9HR0Th06BD69+8PoLEj/MqV\nK3jhhRdw33334amnnkJERAT+/ve/21oUkiQ5vE9QUBDWr1+PixcvIikpCaNHj8aePXtcPjYsLAyH\nDh1CdHQ05s+fjwcffBA1NTXYu3cvJkyY4DAdrqbF3XgiJfByr0REJBtbGkREJBtDg4iIZGNoEBGR\nbAwNIiKSjaFBRESyMTSIiEg2hgYREcnG0CAiItkYGkREJNv/Awaf6HIZKFcbAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x118380250>" ] } ], "prompt_number": 54 }, { "cell_type": "markdown", "metadata": {}, "source": [ "ODE solutions are deterministic. Each point in phase space $(x_1, x_2, \\dots, x_n, v_1, v_2, \\dots, v_n)$ maps to one and only one other point in phase space. The reverse is also true. As such, choice of state at each timestep $t_i$ is determined by the the choice of state at timestep $t_{i-1}$, also known as the Markov property. \n", "\n", "From the solution to the ODE, we can build a Markov Model. Let's bin the phase space so that we can reduce the number of states needed." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	apache-2.0
miller-lover/jingwei-sea	Python_for_Data_Analysis/numpy_learn.ipynb	1	46812	{ "cells": [ { "cell_type": "code", "execution_count": 234, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 235, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### Creating ndarrays" ] }, { "cell_type": "code", "execution_count": 236, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = {i : np.random.randn() for i in range(6)}" ] }, { "cell_type": "code", "execution_count": 237, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{0: -0.08730089878649104,\n", " 1: -0.24144139630183212,\n", " 2: 1.0494958953335303,\n", " 3: -0.9562884089145647,\n", " 4: -0.49534433469939876,\n", " 5: 0.4534923168092112}" ] }, "execution_count": 237, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "code", "execution_count": 238, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data2 = [[1,2,3,4],[1,4,9,16]]" ] }, { "cell_type": "code", "execution_count": 239, "metadata": { "collapsed": false }, "outputs": [], "source": [ "arr2 = np.array(data2)" ] }, { "cell_type": "code", "execution_count": 240, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1, 2, 3, 4],\n", " [ 1, 4, 9, 16]])" ] }, "execution_count": 240, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr2" ] }, { "cell_type": "code", "execution_count": 241, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1 2 3 4]\n", " [ 1 4 9 16]]\n" ] } ], "source": [ "print(arr2)" ] }, { "cell_type": "code", "execution_count": 242, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 242, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr2.ndim" ] }, { "cell_type": "code", "execution_count": 243, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(2, 4)" ] }, "execution_count": 243, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr2.shape" ] }, { "cell_type": "code", "execution_count": 244, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "dtype('int32')" ] }, "execution_count": 244, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr2.dtype" ] }, { "cell_type": "code", "execution_count": 245, "metadata": { "collapsed": true }, "outputs": [], "source": [ "a = np.zeros(10)" ] }, { "cell_type": "code", "execution_count": 246, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])" ] }, "execution_count": 246, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a" ] }, { "cell_type": "code", "execution_count": 247, "metadata": { "collapsed": true }, "outputs": [], "source": [ "b = np.ones(10)" ] }, { "cell_type": "code", "execution_count": 248, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])" ] }, "execution_count": 248, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b" ] }, { "cell_type": "code", "execution_count": 249, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a1 = np.zeros((3, 6))" ] }, { "cell_type": "code", "execution_count": 250, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 0., 0.]])" ] }, "execution_count": 250, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a1" ] }, { "cell_type": "code", "execution_count": 251, "metadata": { "collapsed": true }, "outputs": [], "source": [ "c = np.empty((2, 3, 2))" ] }, { "cell_type": "code", "execution_count": 252, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[[ 0., 0.],\n", " [ 0., 0.],\n", " [ 0., 0.]],\n", "\n", " [[ 0., 0.],\n", " [ 0., 0.],\n", " [ 0., 0.]]])" ] }, "execution_count": 252, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c" ] }, { "cell_type": "code", "execution_count": 253, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "dtype('float64')" ] }, "execution_count": 253, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c.dtype" ] }, { "cell_type": "code", "execution_count": 254, "metadata": { "collapsed": true }, "outputs": [], "source": [ "d = np.arange(15)" ] }, { "cell_type": "code", "execution_count": 255, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])" ] }, "execution_count": 255, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d" ] }, { "cell_type": "code", "execution_count": 256, "metadata": { "collapsed": false }, "outputs": [], "source": [ "e = np.ones_like(c)" ] }, { "cell_type": "code", "execution_count": 257, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[[ 1., 1.],\n", " [ 1., 1.],\n", " [ 1., 1.]],\n", "\n", " [[ 1., 1.],\n", " [ 1., 1.],\n", " [ 1., 1.]]])" ] }, "execution_count": 257, "metadata": {}, "output_type": "execute_result" } ], "source": [ "e" ] }, { "cell_type": "code", "execution_count": 258, "metadata": { "collapsed": false }, "outputs": [], "source": [ "f = np.eye(10)" ] }, { "cell_type": "code", "execution_count": 259, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [ 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [ 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n", " [ 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n", " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]])" ] }, "execution_count": 259, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f" ] }, { "cell_type": "code", "execution_count": 260, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### Data Types for ndarrays" ] }, { "cell_type": "code", "execution_count": 261, "metadata": { "collapsed": false }, "outputs": [], "source": [ "array1 = np.array([1, 2, 3, 4], dtype = np.float64)" ] }, { "cell_type": "code", "execution_count": 262, "metadata": { "collapsed": true }, "outputs": [], "source": [ "array2 = np.array([1, 2, 3, 4], dtype = np.int32)" ] }, { "cell_type": "code", "execution_count": 263, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "dtype('float64')" ] }, "execution_count": 263, "metadata": {}, "output_type": "execute_result" } ], "source": [ "array1.dtype" ] }, { "cell_type": "code", "execution_count": 264, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "dtype('int32')" ] }, "execution_count": 264, "metadata": {}, "output_type": "execute_result" } ], "source": [ "array2.dtype" ] }, { "cell_type": "code", "execution_count": 265, "metadata": { "collapsed": false }, "outputs": [], "source": [ "arr_string = np.array(['1.32','6.87','5.0'], dtype = np.string_)" ] }, { "cell_type": "code", "execution_count": 266, "metadata": { "collapsed": true }, "outputs": [], "source": [ "arr_float = arr_string.astype(np.float64)" ] }, { "cell_type": "code", "execution_count": 267, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 1.32, 6.87, 5. ])" ] }, "execution_count": 267, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr_float" ] }, { "cell_type": "code", "execution_count": 268, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### Operations between Arrays and Scalars" ] }, { "cell_type": "code", "execution_count": 269, "metadata": { "collapsed": false }, "outputs": [], "source": [ "arr = np.array([[1., 2. ,3.], [4., 5., 6.]], dtype=float)" ] }, { "cell_type": "code", "execution_count": 270, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1., 2., 3.],\n", " [ 4., 5., 6.]])" ] }, "execution_count": 270, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr" ] }, { "cell_type": "code", "execution_count": 271, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1., 4., 9.],\n", " [ 16., 25., 36.]])" ] }, "execution_count": 271, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arrarr" ] }, { "cell_type": "code", "execution_count": 272, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 0., 0.],\n", " [ 0., 0., 0.]])" ] }, "execution_count": 272, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr-arr" ] }, { "cell_type": "code", "execution_count": 273, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1. , 0.5 , 0.33333333],\n", " [ 0.25 , 0.2 , 0.16666667]])" ] }, "execution_count": 273, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1/arr" ] }, { "cell_type": "code", "execution_count": 274, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1. , 1.41421356, 1.73205081],\n", " [ 2. , 2.23606798, 2.44948974]])" ] }, "execution_count": 274, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr0.5" ] }, { "cell_type": "code", "execution_count": 275, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### Basic Indexing and Slicing" ] }, { "cell_type": "code", "execution_count": 276, "metadata": { "collapsed": true }, "outputs": [], "source": [ "arr = np.arange(15)" ] }, { "cell_type": "code", "execution_count": 277, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([3, 4, 5, 6, 7])" ] }, "execution_count": 277, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr[3:8]" ] }, { "cell_type": "code", "execution_count": 278, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'\\nAn important first distinction from lists is that array slices are views on the original array. This means that\\nthe data is not copied, and any modifications to the view will be reflected in the source array\\n'" ] }, "execution_count": 278, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'''\n", "An important first distinction from lists is that array slices are views on the original array. This means that\n", "the data is not copied, and any modifications to the view will be reflected in the source array\n", "'''" ] }, { "cell_type": "code", "execution_count": 279, "metadata": { "collapsed": true }, "outputs": [], "source": [ "slice = arr[3:8]" ] }, { "cell_type": "code", "execution_count": 280, "metadata": { "collapsed": true }, "outputs": [], "source": [ "slice[2] = 112" ] }, { "cell_type": "code", "execution_count": 281, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0, 1, 2, 3, 4, 112, 6, 7, 8, 9, 10, 11, 12,\n", " 13, 14])" ] }, "execution_count": 281, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr" ] }, { "cell_type": "code", "execution_count": 282, "metadata": { "collapsed": true }, "outputs": [], "source": [ "slice[3:6] = 12" ] }, { "cell_type": "code", "execution_count": 283, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0, 1, 2, 3, 4, 112, 12, 12, 8, 9, 10, 11, 12,\n", " 13, 14])" ] }, "execution_count": 283, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr" ] }, { "cell_type": "code", "execution_count": 284, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### If you want a copy of a slice of an ndarray instead of a view, you will need to explicitly copy the array" ] }, { "cell_type": "code", "execution_count": 285, "metadata": { "collapsed": true }, "outputs": [], "source": [ "array = np.arange(15)" ] }, { "cell_type": "code", "execution_count": 286, "metadata": { "collapsed": true }, "outputs": [], "source": [ "slice = array[3:8].copy()" ] }, { "cell_type": "code", "execution_count": 287, "metadata": { "collapsed": true }, "outputs": [], "source": [ "slice[:] = 12" ] }, { "cell_type": "code", "execution_count": 288, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([12, 12, 12, 12, 12])" ] }, "execution_count": 288, "metadata": {}, "output_type": "execute_result" } ], "source": [ "slice" ] }, { "cell_type": "code", "execution_count": 289, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])" ] }, "execution_count": 289, "metadata": {}, "output_type": "execute_result" } ], "source": [ "array" ] }, { "cell_type": "code", "execution_count": 290, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### higher dimensional arrays" ] }, { "cell_type": "code", "execution_count": 291, "metadata": { "collapsed": true }, "outputs": [], "source": [ "arr2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])" ] }, { "cell_type": "code", "execution_count": 292, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "8" ] }, "execution_count": 292, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr2d[2][1]" ] }, { "cell_type": "code", "execution_count": 293, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "8" ] }, "execution_count": 293, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr2d[2,1]" ] }, { "cell_type": "code", "execution_count": 294, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([4, 5])" ] }, "execution_count": 294, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr2d[1, :2]" ] }, { "cell_type": "code", "execution_count": 295, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(2,)" ] }, "execution_count": 295, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr2d[1, :2].shape" ] }, { "cell_type": "code", "execution_count": 296, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[4, 5]])" ] }, "execution_count": 296, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr2d[1:2, :2]" ] }, { "cell_type": "code", "execution_count": 297, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(1, 2)" ] }, "execution_count": 297, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr2d[1:2, :2].shape" ] }, { "cell_type": "code", "execution_count": 298, "metadata": { "collapsed": true }, "outputs": [], "source": [ "arr3d = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])" ] }, { "cell_type": "code", "execution_count": 299, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[1, 2, 3],\n", " [4, 5, 6]])" ] }, "execution_count": 299, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr3d[0]" ] }, { "cell_type": "code", "execution_count": 300, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([4, 5, 6])" ] }, "execution_count": 300, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr3d[0,1]" ] }, { "cell_type": "code", "execution_count": 301, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 301, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr3d[0,1,0]" ] }, { "cell_type": "code", "execution_count": 302, "metadata": { "collapsed": false }, "outputs": [], "source": [ "old_values = arr3d[0].copy()" ] }, { "cell_type": "code", "execution_count": 303, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[1, 2, 3],\n", " [4, 5, 6]])" ] }, "execution_count": 303, "metadata": {}, "output_type": "execute_result" } ], "source": [ "old_values" ] }, { "cell_type": "code", "execution_count": 304, "metadata": { "collapsed": false }, "outputs": [], "source": [ "arr3d[0] = 42" ] }, { "cell_type": "code", "execution_count": 305, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[[42, 42, 42],\n", " [42, 42, 42]],\n", "\n", " [[ 7, 8, 9],\n", " [10, 11, 12]]])" ] }, "execution_count": 305, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr3d" ] }, { "cell_type": "code", "execution_count": 306, "metadata": { "collapsed": true }, "outputs": [], "source": [ "arr3d[0] = old_values" ] }, { "cell_type": "code", "execution_count": 307, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[[ 1, 2, 3],\n", " [ 4, 5, 6]],\n", "\n", " [[ 7, 8, 9],\n", " [10, 11, 12]]])" ] }, "execution_count": 307, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr3d" ] }, { "cell_type": "code", "execution_count": 308, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[1, 2, 3],\n", " [4, 5, 6],\n", " [7, 8, 9]])" ] }, "execution_count": 308, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr2d" ] }, { "cell_type": "code", "execution_count": 309, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[1, 2, 3],\n", " [4, 5, 6]])" ] }, "execution_count": 309, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr2d[:2]" ] }, { "cell_type": "code", "execution_count": 310, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[1, 2],\n", " [4, 5]])" ] }, "execution_count": 310, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr2d[:2,:2]" ] }, { "cell_type": "code", "execution_count": 311, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### Boolean Indexing" ] }, { "cell_type": "code", "execution_count": 312, "metadata": { "collapsed": false }, "outputs": [], "source": [ "names = np.array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'])" ] }, { "cell_type": "code", "execution_count": 313, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = np.random.randn(7,4)" ] }, { "cell_type": "code", "execution_count": 314, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.10289297, 2.90246559, 2.27300433, -0.87664228],\n", " [ 0.28073289, -0.36370018, 0.59231769, -0.67343579],\n", " [-0.79142045, 0.89950742, 0.60077744, -0.77022984],\n", " [ 0.03157877, -1.28593835, 0.13791617, -0.81559219],\n", " [ 0.6814858 , 1.55010797, 0.6058013 , -1.34349395],\n", " [-1.66586023, -1.56269374, 0.94419005, 0.58805994],\n", " [ 0.63490795, 1.43822816, 2.55016348, 0.34348943]])" ] }, "execution_count": 314, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "code", "execution_count": 315, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ True, False, False, True, False, False, False], dtype=bool)" ] }, "execution_count": 315, "metadata": {}, "output_type": "execute_result" } ], "source": [ "names == 'Bob'" ] }, { "cell_type": "code", "execution_count": 316, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.10289297, 2.90246559, 2.27300433, -0.87664228],\n", " [ 0.03157877, -1.28593835, 0.13791617, -0.81559219]])" ] }, "execution_count": 316, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[names == 'Bob']" ] }, { "cell_type": "code", "execution_count": 317, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 2.27300433, -0.87664228],\n", " [ 0.13791617, -0.81559219]])" ] }, "execution_count": 317, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[names == 'Bob', 2:]" ] }, { "cell_type": "code", "execution_count": 318, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 2.27300433, 0.13791617])" ] }, "execution_count": 318, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[names == 'Bob', 2]" ] }, { "cell_type": "code", "execution_count": 319, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.28073289, -0.36370018, 0.59231769, -0.67343579],\n", " [-0.79142045, 0.89950742, 0.60077744, -0.77022984],\n", " [ 0.6814858 , 1.55010797, 0.6058013 , -1.34349395],\n", " [-1.66586023, -1.56269374, 0.94419005, 0.58805994],\n", " [ 0.63490795, 1.43822816, 2.55016348, 0.34348943]])" ] }, "execution_count": 319, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[names != 'Bob']" ] }, { "cell_type": "code", "execution_count": 320, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.28073289, -0.36370018, 0.59231769, -0.67343579],\n", " [-0.79142045, 0.89950742, 0.60077744, -0.77022984],\n", " [ 0.6814858 , 1.55010797, 0.6058013 , -1.34349395],\n", " [-1.66586023, -1.56269374, 0.94419005, 0.58805994],\n", " [ 0.63490795, 1.43822816, 2.55016348, 0.34348943]])" ] }, "execution_count": 320, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[~(names == 'Bob')]" ] }, { "cell_type": "code", "execution_count": 321, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.10289297, 2.90246559, 2.27300433, -0.87664228],\n", " [-0.79142045, 0.89950742, 0.60077744, -0.77022984],\n", " [ 0.03157877, -1.28593835, 0.13791617, -0.81559219],\n", " [ 0.6814858 , 1.55010797, 0.6058013 , -1.34349395]])" ] }, "execution_count": 321, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[(names == 'Bob') \| (names == 'Will')]" ] }, { "cell_type": "code", "execution_count": 322, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data[data < 0] = 0" ] }, { "cell_type": "code", "execution_count": 323, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.10289297, 2.90246559, 2.27300433, 0. ],\n", " [ 0.28073289, 0. , 0.59231769, 0. ],\n", " [ 0. , 0.89950742, 0.60077744, 0. ],\n", " [ 0.03157877, 0. , 0.13791617, 0. ],\n", " [ 0.6814858 , 1.55010797, 0.6058013 , 0. ],\n", " [ 0. , 0. , 0.94419005, 0.58805994],\n", " [ 0.63490795, 1.43822816, 2.55016348, 0.34348943]])" ] }, "execution_count": 323, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "code", "execution_count": 324, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data[names != 'Joe'] = 7" ] }, { "cell_type": "code", "execution_count": 325, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 7. , 7. , 7. , 7. ],\n", " [ 0.28073289, 0. , 0.59231769, 0. ],\n", " [ 7. , 7. , 7. , 7. ],\n", " [ 7. , 7. , 7. , 7. ],\n", " [ 7. , 7. , 7. , 7. ],\n", " [ 0. , 0. , 0.94419005, 0.58805994],\n", " [ 0.63490795, 1.43822816, 2.55016348, 0.34348943]])" ] }, "execution_count": 325, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "code", "execution_count": 326, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### Fancy Indexing" ] }, { "cell_type": "code", "execution_count": 327, "metadata": { "collapsed": false }, "outputs": [], "source": [ "arr = np.empty((8, 4))" ] }, { "cell_type": "code", "execution_count": 328, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0.]])" ] }, "execution_count": 328, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr" ] }, { "cell_type": "code", "execution_count": 329, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for i in range(8):\n", " arr[i] = i" ] }, { "cell_type": "code", "execution_count": 330, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 0., 0., 0.],\n", " [ 1., 1., 1., 1.],\n", " [ 2., 2., 2., 2.],\n", " [ 3., 3., 3., 3.],\n", " [ 4., 4., 4., 4.],\n", " [ 5., 5., 5., 5.],\n", " [ 6., 6., 6., 6.],\n", " [ 7., 7., 7., 7.]])" ] }, "execution_count": 330, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr" ] }, { "cell_type": "code", "execution_count": 331, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 4., 4., 4., 4.],\n", " [ 3., 3., 3., 3.],\n", " [ 2., 2., 2., 2.],\n", " [ 5., 5., 5., 5.]])" ] }, "execution_count": 331, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr[[4, 3, 2, 5]]" ] }, { "cell_type": "code", "execution_count": 332, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 5., 5., 5., 5.],\n", " [ 3., 3., 3., 3.],\n", " [ 1., 1., 1., 1.]])" ] }, "execution_count": 332, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr[[-3, -5, -7]]" ] }, { "cell_type": "code", "execution_count": 333, "metadata": { "collapsed": true }, "outputs": [], "source": [ "arr = np.arange(32).reshape(8, 4)" ] }, { "cell_type": "code", "execution_count": 334, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0, 1, 2, 3],\n", " [ 4, 5, 6, 7],\n", " [ 8, 9, 10, 11],\n", " [12, 13, 14, 15],\n", " [16, 17, 18, 19],\n", " [20, 21, 22, 23],\n", " [24, 25, 26, 27],\n", " [28, 29, 30, 31]])" ] }, "execution_count": 334, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr" ] }, { "cell_type": "code", "execution_count": 335, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 4, 23, 29, 10])" ] }, "execution_count": 335, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr[[1, 5, 7, 2], [0, 3, 1, 2]]" ] }, { "cell_type": "code", "execution_count": 336, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 4, 7, 5, 6],\n", " [20, 23, 21, 22],\n", " [28, 31, 29, 30],\n", " [ 8, 11, 9, 10]])" ] }, "execution_count": 336, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr[[1, 5, 7, 2]][:, [0, 3, 1, 2]]" ] }, { "cell_type": "code", "execution_count": 337, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 4, 7, 5, 6],\n", " [20, 23, 21, 22],\n", " [28, 31, 29, 30],\n", " [ 8, 11, 9, 10]])" ] }, "execution_count": 337, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr[np.ix_([1, 5, 7, 2], [0, 3, 1, 2])]" ] }, { "cell_type": "code", "execution_count": 338, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([[1],\n", " [5],\n", " [7],\n", " [2]]), array([[0, 3, 1, 2]]))" ] }, "execution_count": 338, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.ix_([1, 5, 7, 2], [0, 3, 1, 2])" ] }, { "cell_type": "code", "execution_count": 339, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### Transposing Arrays and Swapping Axes" ] }, { "cell_type": "code", "execution_count": 340, "metadata": { "collapsed": true }, "outputs": [], "source": [ "arr = np.arange(15).reshape((3,5))" ] }, { "cell_type": "code", "execution_count": 341, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0, 1, 2, 3, 4],\n", " [ 5, 6, 7, 8, 9],\n", " [10, 11, 12, 13, 14]])" ] }, "execution_count": 341, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr" ] }, { "cell_type": "code", "execution_count": 342, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0, 5, 10],\n", " [ 1, 6, 11],\n", " [ 2, 7, 12],\n", " [ 3, 8, 13],\n", " [ 4, 9, 14]])" ] }, "execution_count": 342, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr.T" ] }, { "cell_type": "code", "execution_count": 343, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[125, 140, 155, 170, 185],\n", " [140, 158, 176, 194, 212],\n", " [155, 176, 197, 218, 239],\n", " [170, 194, 218, 242, 266],\n", " [185, 212, 239, 266, 293]])" ] }, "execution_count": 343, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.dot(arr.T, arr)" ] }, { "cell_type": "code", "execution_count": 344, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 30, 80, 130],\n", " [ 80, 255, 430],\n", " [130, 430, 730]])" ] }, "execution_count": 344, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.dot(arr, arr.T)" ] }, { "cell_type": "code", "execution_count": 345, "metadata": { "collapsed": true }, "outputs": [], "source": [ "arr = np.arange(16).reshape(2, 2, 4)" ] }, { "cell_type": "code", "execution_count": 346, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[[ 0, 1, 2, 3],\n", " [ 4, 5, 6, 7]],\n", "\n", " [[ 8, 9, 10, 11],\n", " [12, 13, 14, 15]]])" ] }, "execution_count": 346, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr" ] }, { "cell_type": "code", "execution_count": 347, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[[ 0, 1, 2, 3],\n", " [ 8, 9, 10, 11]],\n", "\n", " [[ 4, 5, 6, 7],\n", " [12, 13, 14, 15]]])" ] }, "execution_count": 347, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr.transpose(1, 0, 2)" ] }, { "cell_type": "code", "execution_count": 348, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[[ 0, 4],\n", " [ 1, 5],\n", " [ 2, 6],\n", " [ 3, 7]],\n", "\n", " [[ 8, 12],\n", " [ 9, 13],\n", " [10, 14],\n", " [11, 15]]])" ] }, "execution_count": 348, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr.swapaxes(1, 2)" ] }, { "cell_type": "code", "execution_count": 349, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[[ 0, 1, 2, 3],\n", " [ 4, 5, 6, 7]],\n", "\n", " [[ 8, 9, 10, 11],\n", " [12, 13, 14, 15]]])" ] }, "execution_count": 349, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr" ] }, { "cell_type": "code", "execution_count": 350, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### Universal Functions: Fast Element-wise Array Functions" ] }, { "cell_type": "code", "execution_count": 351, "metadata": { "collapsed": true }, "outputs": [], "source": [ "arr = np.arange(10)" ] }, { "cell_type": "code", "execution_count": 352, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0. , 1. , 1.41421356, 1.73205081, 2. ,\n", " 2.23606798, 2.44948974, 2.64575131, 2.82842712, 3. ])" ] }, "execution_count": 352, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sqrt(arr)" ] }, { "cell_type": "code", "execution_count": 353, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 1.00000000e+00, 2.71828183e+00, 7.38905610e+00,\n", " 2.00855369e+01, 5.45981500e+01, 1.48413159e+02,\n", " 4.03428793e+02, 1.09663316e+03, 2.98095799e+03,\n", " 8.10308393e+03])" ] }, "execution_count": 353, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.exp(arr)" ] }, { "cell_type": "code", "execution_count": 360, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x, y = np.random.randn(8), np.random.randn(8)" ] }, { "cell_type": "code", "execution_count": 361, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 1.56990835, 2.27947146, 0.30208168, 1.16897605, 1.65010192,\n", " 0.84616608, 0.52930149, 1.29454268])" ] }, "execution_count": 361, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.maximum(x, y)" ] }, { "cell_type": "code", "execution_count": 364, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 7.22846789, -8.46133524, 3.5496447 , -8.61704727, -4.40502151,\n", " -3.77209693, 4.00360582])" ] }, "execution_count": 364, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr = np.random.randn(7)5\n", "arr" ] }, { "cell_type": "code", "execution_count": 365, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([ 0.22846789, -0.46133524, 0.5496447 , -0.61704727, -0.40502151,\n", " -0.77209693, 0.00360582]), array([ 7., -8., 3., -8., -4., -3., 4.]))" ] }, "execution_count": 365, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.modf(arr)" ] }, { "cell_type": "code", "execution_count": 367, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 7., -9., 3., -9., -5., -4., 4.])" ] }, "execution_count": 367, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.floor(arr)" ] }, { "cell_type": "code", "execution_count": 368, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 8., -8., 4., -8., -4., -3., 5.])" ] }, "execution_count": 368, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.ceil(arr)" ] }, { "cell_type": "code", "execution_count": 369, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 7., -8., 4., -9., -4., -4., 4.])" ] }, "execution_count": 369, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.rint(arr)" ] }, { "cell_type": "code", "execution_count": 370, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([False, False, False, False, False, False, False], dtype=bool)" ] }, "execution_count": 370, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.isnan(arr)" ] }, { "cell_type": "code", "execution_count": 372, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n", " array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19]))" ] }, "execution_count": 372, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a, b = np.arange(10), np.arange(10,20)\n", "a, b" ] }, { "cell_type": "code", "execution_count": 373, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0, 11, 24, 39, 56, 75, 96, 119, 144, 171])" ] }, "execution_count": 373, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.multiply(a, b)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-2.0
gchrupala/reimaginet	examples/audioviz/attention.ipynb	1	266671	{ "metadata": { "name": "", "signature": "sha256:6372d4f9794da0c020983f574d403839d6d4aefc1c624a593594b45a8367a3b6" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import numpy\n", "import imaginet.task as task\n", "import imaginet.defn.audiovis_rhn as audiovis" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "Using gpu device 2: GeForce GTX 980 Ti (CNMeM is disabled, cuDNN 5005)\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "import imaginet.vendrov_provider as dp\n", "prov = dp.getDataProvider(dataset='coco', root='/home/gchrupala/reimaginet/')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "sent = list(prov.iterSentences(split='val'))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "model = task.load(\"/home/gchrupala/reimaginet/run-rhn-coco-9-resume/model.r.e9.zip\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "import imaginet.tts as tts\n", "\n", "def synthesize(text):\n", " return tts.decodemp3(tts.speak(text))\n", "\n", "def speak(data):\n", " voc = set()\n", " for (w1,w2,_) in data:\n", " voc.add(w1)\n", " voc.add(w2)\n", " voc = list(voc)\n", " speech = [ synthesize(word) for word in voc ]\n", " return (voc, speech)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "import pydub" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "import funktional.context as context\n", "import theano" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "from funktional.layer import softmax_time\n", "def _make_attention(model):\n", " with context.context(training=False):\n", " task = model.task\n", " rep = task.Encode(task.inputs)\n", " alpha = softmax_time(task.Attn.Regress2(task.Attn.activation(task.Attn.Regress1(rep))))\n", " return theano.function(task.inputs, alpha)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "attention = _make_attention(model)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "s = 'A baby sits on a bed laughing with a laptop computer open'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "sound = tts.from_mp3(tts.speak(s))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "alpha = attention([tts.extract_mfcc(synthesize(s))])[0,:,0]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_attention(text, bounds=None, marks=None, rotation=None):\n", " alpha = attention([tts.extract_mfcc(synthesize(text))])[0,:,0]\n", " if bounds is not None and marks is not None:\n", " xticks(numpy.array(bounds)1000, marks, rotation=rotation)\n", " plot(range(0, len(alpha)30, 30), alpha)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 36 }, { "cell_type": "code", "collapsed": false, "input": [ "mfcc = tts.extract_mfcc(synthesize(s))\n", "mfcc.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "(433, 13)" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "sound[50:100]" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", " <audio controls>\n", " <source src=\"data:audio/mpeg;base64,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\" type=\"audio/mpeg\"/>\n", " Your browser does not support the audio element.\n", " </audio>\n", " " ], "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "<pydub.audio_segment.AudioSegment at 0x7f98d088bdd0>" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "alpha.shape[0]30" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "4380" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "mfcc.shape[0]10" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": [ "4330" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "plot(range(0,len(alpha)30, 30), alpha)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ "[<matplotlib.lines.Line2D at 0x7f98d01c0b50>]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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J+VkJg373Z54Jr70W/lh++QePKPIQjzyijUGy5iC6d9d84ptvqnso7lBXT0wg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\nquHaJMc1S4uDsBBTg5D2H2V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"text": [ "<matplotlib.figure.Figure at 0x7f98d088b690>" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "sound.export('baby.wav', format='wav')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 20, "text": [ "<open file 'baby.wav', mode 'wb+' at 0x7f98d00d8270>" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "alpha.repeat(30)[1380-40:1380+100]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 21, "text": [ "array([ 0.00952864, 0.00952864, 0.00952864, 0.00952864, 0.00952864,\n", " 0.00952864, 0.00952864, 0.00952864, 0.00952864, 0.00952864,\n", " 0.02235831, 0.02235831, 0.02235831, 0.02235831, 0.02235831,\n", " 0.02235831, 0.02235831, 0.02235831, 0.02235831, 0.02235831,\n", " 0.02235831, 0.02235831, 0.02235831, 0.02235831, 0.02235831,\n", " 0.02235831, 0.02235831, 0.02235831, 0.02235831, 0.02235831,\n", " 0.02235831, 0.02235831, 0.02235831, 0.02235831, 0.02235831,\n", " 0.02235831, 0.02235831, 0.02235831, 0.02235831, 0.02235831,\n", " 0.0246903 , 0.0246903 , 0.0246903 , 0.0246903 , 0.0246903 ,\n", " 0.0246903 , 0.0246903 , 0.0246903 , 0.0246903 , 0.0246903 ,\n", " 0.0246903 , 0.0246903 , 0.0246903 , 0.0246903 , 0.0246903 ,\n", " 0.0246903 , 0.0246903 , 0.0246903 , 0.0246903 , 0.0246903 ,\n", " 0.0246903 , 0.0246903 , 0.0246903 , 0.0246903 , 0.0246903 ,\n", " 0.0246903 , 0.0246903 , 0.0246903 , 0.0246903 , 0.0246903 ,\n", " 0.02096539, 0.02096539, 0.02096539, 0.02096539, 0.02096539,\n", " 0.02096539, 0.02096539, 0.02096539, 0.02096539, 0.02096539,\n", " 0.02096539, 0.02096539, 0.02096539, 0.02096539, 0.02096539,\n", " 0.02096539, 0.02096539, 0.02096539, 0.02096539, 0.02096539,\n", " 0.02096539, 0.02096539, 0.02096539, 0.02096539, 0.02096539,\n", " 0.02096539, 0.02096539, 0.02096539, 0.02096539, 0.02096539,\n", " 0.0199234 , 0.0199234 , 0.0199234 , 0.0199234 , 0.0199234 ,\n", " 0.0199234 , 0.0199234 , 0.0199234 , 0.0199234 , 0.0199234 ,\n", " 0.0199234 , 0.0199234 , 0.0199234 , 0.0199234 , 0.0199234 ,\n", " 0.0199234 , 0.0199234 , 0.0199234 , 0.0199234 , 0.0199234 ,\n", " 0.0199234 , 0.0199234 , 0.0199234 , 0.0199234 , 0.0199234 ,\n", " 0.0199234 , 0.0199234 , 0.0199234 , 0.0199234 , 0.0199234 ,\n", " 0.0190598 , 0.0190598 , 0.0190598 , 0.0190598 , 0.0190598 ,\n", " 0.0190598 , 0.0190598 , 0.0190598 , 0.0190598 , 0.0190598 ], dtype=float32)" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "alpha.repeat(30).argmax()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 22, "text": [ "1380" ] } ], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "import textgrid" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "grid = textgrid.TextGrid.fromFile(\"baby.TextGrid\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "tier = grid.tiers[0]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "bounds = [ iv.minTime for iv in tier ]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "marks = [ iv.mark for iv in tier ]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "def from_grid(path):\n", " grid = textgrid.TextGrid.fromFile(path)\n", " tier = grid.tiers[0]\n", " min_bounds = numpy.array([ iv.minTime for iv in tier ])\n", " max_bounds = numpy.array([ iv.maxTime for iv in tier ])\n", " marks = [ iv.mark for iv in tier ]\n", " return min_bounds, max_bounds, marks" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 33 }, { "cell_type": "code", "collapsed": false, "input": [ "x=range(0,len(alpha)30, 30)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 46 }, { "cell_type": "code", "collapsed": false, "input": [ "figure(figsize=(15,1))\n", "plot(x,alpha)\n", "yticks([],[])\n", "xticks(numpy.array(bounds)1000, marks)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 47, "text": [ "([<matplotlib.axis.XTick at 0x7f24d5261090>,\n", " <matplotlib.axis.XTick at 0x7f24d52f6d90>,\n", " <matplotlib.axis.XTick at 0x7f24d50e0290>,\n", " <matplotlib.axis.XTick at 0x7f24d50e08d0>,\n", " <matplotlib.axis.XTick at 0x7f24d50eb050>,\n", " <matplotlib.axis.XTick at 0x7f24d50eb790>,\n", " <matplotlib.axis.XTick at 0x7f24d50ebed0>,\n", " <matplotlib.axis.XTick at 0x7f24d50f6650>,\n", " <matplotlib.axis.XTick at 0x7f24d50f6d90>,\n", " <matplotlib.axis.XTick at 0x7f24d507f510>,\n", " <matplotlib.axis.XTick at 0x7f24d507fc50>,\n", " <matplotlib.axis.XTick at 0x7f24d50893d0>,\n", " <matplotlib.axis.XTick at 0x7f24d5089b10>],\n", " <a list of 13 Text xticklabel objects>)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7f24d50eb150>" ] } ], "prompt_number": 48 }, { "cell_type": "code", "collapsed": false, "input": [ "plot_attention(\"a frame on the wall\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7f24d535bc90>" ] } ], "prompt_number": 49 }, { "cell_type": "code", "collapsed": false, "input": [ "plot_attention(\"a dog is nice\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7f24d50b3810>" ] } ], "prompt_number": 51 }, { "cell_type": "code", "collapsed": false, "input": [ "plot_attention(\"a dog is brown\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7f24d4e0a250>" ] } ], "prompt_number": 52 }, { "cell_type": "code", "collapsed": false, "input": [ "plot_attention(\"a cat sat on the floor\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7f24d4ea2e50>" ] } ], "prompt_number": 53 }, { "cell_type": "code", "collapsed": false, "input": [ "sound = tts.from_mp3(tts.speak(\"a cat sat on the floor\"))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [ "sound = tts.from_mp3(tts.speak('while in the immediate foreground juts a gnarled tree branch the majority of the view consists of a an expanse of short grass dotted with a few longer tufts and a number of scattered grazing sheep'))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [ "sound" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", " <audio controls>\n", " <source 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\" type=\"audio/mpeg\"/>\n", " Your browser does not support the audio element.\n", " </audio>\n", " " ], "metadata": {}, "output_type": "pyout", "prompt_number": 29, "text": [ "<pydub.audio_segment.AudioSegment at 0x7f98d01c0dd0>" ] } ], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "sound.export(\"sheep.wav\", format=\"wav\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 30, "text": [ "<open file 'sheep.wav', mode 'wb+' at 0x7f98d00d89c0>" ] } ], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "bounds_min, bounds_max, marks = from_grid(\"sheep.TextGrid\")\n", "bounds = ((bounds_max - bounds_min) / 2.0) + bounds_min" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 46 }, { "cell_type": "code", "collapsed": false, "input": [ "?savefig" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 58 }, { "cell_type": "code", "collapsed": false, "input": [ "sheep_text ='while in the immediate foreground juts a gnarled tree branch the majority of the view consists of a an expanse of short grass dotted with a few longer tufts and a number of scattered grazing sheep'\n", "figure(figsize=(20,2), dpi=300)\n", "plot_attention(sheep_text, bounds, marks, rotation=45)\n", "yticks([])\n", "matplotlib.rcParams.update({'font.size': 18})\n", "savefig(\"sheep.pdf\", pad_inches=20)\n", "#savefig(\"sheep.png\", width=10, height=2)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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IIYQQQoiiyc9nDpk+fWL/Ta1a9JjJz/evXA5eLU0eD/LMESJAVq2iqJAIEnMSE3MGDmS8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\niyyPXyLutT1jjC8U2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saketkc/notebooks	python/PDF.ipynb	1	9395	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "CDF:\n", "$$\n", "F(x) = \\begin{cases}\n", "0 & x <0,\\\\\n", "x & 0 \\leq x < 0.5,\\\\\n", "\\frac{x}{2} + 0.5 & 0.5 \\leq x \\leq 1\\\\\n", "1 & x>2\n", "\\end{cases}\n", "$$\n", "\n", "PDF:\n", "\n", "$$\n", "f(x) = \\begin{cases}\n", "0 & x <0,\\\\\n", "1 & 0 \\leq x < 0.5,\\\\\n", "\\frac{1}{2} & 0.5 \\leq x \\leq 1\\\\\n", "0 & x>2\n", "\\end{cases}\n", "$$\n", "\n", "$\\int f(t) dt = 0.75$??\n", "\n", "What's missing?\n", "\n", "p[X=0.5] = 0.25. But defining such a probability should be $0$ for a PDF.[It's technically a 'PMF'. The mass at 0.5 being 1/4]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/saket/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:19: DeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future\n" ] }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f95e581c410>]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f95e5ab1050>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from __future__ import division\n", "def F(x):\n", " if x<0:\n", " return 0\n", " if x<0.5:\n", " return x\n", " if x>=0.5 and x<=1:\n", " return x/2+0.5\n", " return 1\n", " \n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "x = np.linspace(-2,2, num=100)\n", "fx =[F(i) for i in x]\n", "pos = np.where((x<=0.51) & (x>=0.49))[0]\n", "\n", "x[pos] = np.nan\n", "fx[pos] = np.nan\n", "plt.plot(x,fx)\n", "\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": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-2-clause
huilyu2/DataVisualization	project-spring2017/part3/Part3_2.0/Part3_version2 (1).ipynb	1	3136489	null	mit
mjbrodzik/ipython_notebooks	charis/pdd_melt_model/.ipynb_checkpoints/Playing_with_ConfigObj-checkpoint.ipynb	1	4029	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from configobj import ConfigObj" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "config = ConfigObj()\n", "config.indent_type = ' '\n", "config.initial_comment = [\n", " \"Configuration file for CHARIS melt modeling\",\n", "]\n", "input_section = {\n", " 'forcing': {\n", " 'temperature': {\n", " 'path': '/path/to/temperature/data',\n", " 'pattern': 'filename%pattern%',\n", " 'type': 'type_of_file(binary, etc)'\n", " }\n", " }\n", "}\n", "config['input'] = input_section\n", "config['output'] = {}\n", "config['validation'] = {}\n", "config.filename = 'test_config.ini'\n", "config.write()\n", "\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Untitled.ipynb Untitled.txt modis_tiles_config.ini~\r\n", "Untitled.py modis_tiles_config.ini test_config.ini\r\n" ] } ], "source": [ "!ls\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2400\n", "/projects/CHARIS/basins/major_basins_from_GRDC/MODIStiles\n", "%DRAINAGEID%.basin_mask.%TILEID%.tif\n", "1\n", "%DRAINAGEID%.%TILEID%.tif\n" ] } ], "source": [ "config = ConfigObj('modis_tiles_config.ini')\n", "print config['modis_tile']['rows']\n", "print config['input']['fixed']['basin_mask']['dir']\n", "print config['input']['fixed']['basin_mask']['pattern']\n", "print config['input']['fixed']['basin_mask']['type']\n", "print config['input']['fixed']['country_mask']['pattern']\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['# Data processing configuration for MODIS-tile data', '# used by CHARIS melt modeling', '#', '# First section contains general information', '# about MODIS tiles (dimensions/resolutions)', '#', '# Each section thereafter contains information for reading', '# a type of MODIS tile data', '# directory, filename pattern and expected data type (byte, float)', '# Use IDL SIZE() type values until I figure out the python equivalents,', '# e.g.:', '# 1 = byte', '# 2 = int', '# 4 = float', '']\n" ] } ], "source": [ "print config.initial_comment\n" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'forcing': ['', ' # Model forcing data that varies by time (annual, daily)'], 'fixed': ['']}\n" ] } ], "source": [ "print config['input'].comments" ] }, { "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": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
lneuhaus/pyrpl	docs/example-notebooks/asg-synchronization-example.ipynb	1	3162	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#define hostname\n", "HOSTNAME = '192.168.1.100'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:pyrpl.redpitaya:Successfully connected to Redpitaya with hostname 192.168.178.26.\n" ] } ], "source": [ "import pyrpl\n", "p = pyrpl.Pyrpl(config=\"\", # do not use a config file \n", " hostname=HOSTNAME)\n", "rp = p.redpitaya # shortcut for the the redpitaya handler" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "# setup both asgs\n", "for asg in [rp.asg0, rp.asg1]:\n", " asg.setup(waveform='square', \n", " amplitude=0.5, \n", " offset=0, \n", " frequency=1e6, \n", " output_direct='off',\n", " trigger_source='immediately',\n", " )\n", "\n", "# make the assertion that signals are not synhronized\n", "ch1, ch2 = rp.scope.curve()\n", "assert ((ch1-ch2)!=0).any(), 'asg channel outputs are identical'\n", "\n", "# manually check on the scope that signals are not synchronized\n", "rp.scope.setup(input1='asg0',\n", " input2='asg1',\n", " duration=1e-6,\n", " average=False,\n", " trigger_source='asg0',\n", " rolling_mode=False,\n", " running_state='running_continuous'\n", " )" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "# setup the trigger pin\n", "rp.hk.expansion_P0_output = True\n", "rp.hk.expansion_P0 = False\n", "\n", "# setup asg trigger\n", "for asg in [rp.asg0, rp.asg1]:\n", " asg.trigger_source = \"ext_positive_edge\"\n", " \n", "# launch the trigger by creating a 0-to-1 transition on trigger pin\n", "rp.hk.expansion_P0 = True\n", "rp.hk.expansion_P0 = False\n", "\n", "# make the assertion that signals are synhronized\n", "ch1, ch2 = rp.scope.curve()\n", "assert ((ch1-ch2)==0).all(), 'asg channel outputs are not identical'\n", "\n", "# now check that the asg signals are synchronized on the scope...\n", "rp.scope.setup(running_state='running_continuous')" ] }, { "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": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
sertansenturk/tomato	demos/audio_analysis_demo.ipynb	1	166235	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import copy\n", "from matplotlib import pyplot as plt\n", "\n", "from tomato.audio.audioanalyzer import AudioAnalyzer\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# instantiate\n", "audio_filename = os.path.join('..',\n", " 'sample-data',\n", " 'ussak--sazsemaisi--aksaksemai----neyzen_aziz_dede',\n", " 'f970f1e0-0be9-4914-8302-709a0eac088e',\n", " 'f970f1e0-0be9-4914-8302-709a0eac088e.mp3')\n", "\n", "audioAnalyzer = AudioAnalyzer(verbose=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can use the single line call \"analyze,\" which does all the available analysis simultaneously" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "- Getting relevant metadata of ../sample-data/ussak--sazsemaisi--aksaksemai----neyzen_aziz_dede/f970f1e0-0be9-4914-8302-709a0eac088e/f970f1e0-0be9-4914-8302-709a0eac088e.mp3\n", " The call took 1.42 seconds to execute.\n", "- Extracting predominant melody of ../sample-data/ussak--sazsemaisi--aksaksemai----neyzen_aziz_dede/f970f1e0-0be9-4914-8302-709a0eac088e/f970f1e0-0be9-4914-8302-709a0eac088e.mp3\n", " The call took 45.65 seconds to execute.\n", "- Filtering predominant melody of ../sample-data/ussak--sazsemaisi--aksaksemai----neyzen_aziz_dede/f970f1e0-0be9-4914-8302-709a0eac088e/f970f1e0-0be9-4914-8302-709a0eac088e.mp3\n", " The call took 11.49 seconds to execute.\n", "- Computing pitch distribution of ../sample-data/ussak--sazsemaisi--aksaksemai----neyzen_aziz_dede/f970f1e0-0be9-4914-8302-709a0eac088e/f970f1e0-0be9-4914-8302-709a0eac088e.mp3\n", " The call took 0.07 seconds to execute.\n", "- Computing pitch class distribution of ../sample-data/ussak--sazsemaisi--aksaksemai----neyzen_aziz_dede/f970f1e0-0be9-4914-8302-709a0eac088e/f970f1e0-0be9-4914-8302-709a0eac088e.mp3\n", "- Computing pitch distribution of ../sample-data/ussak--sazsemaisi--aksaksemai----neyzen_aziz_dede/f970f1e0-0be9-4914-8302-709a0eac088e/f970f1e0-0be9-4914-8302-709a0eac088e.mp3\n", " The call took 0.05 seconds to execute.\n", " The call took 0.05 seconds to execute.\n", "- Identifying tonic from the predominant melody of ../sample-data/ussak--sazsemaisi--aksaksemai----neyzen_aziz_dede/f970f1e0-0be9-4914-8302-709a0eac088e/f970f1e0-0be9-4914-8302-709a0eac088e.mp3\n", " The call took 0.46 seconds to execute.\n", "- Identifying the transposition of ../sample-data/ussak--sazsemaisi--aksaksemai----neyzen_aziz_dede/f970f1e0-0be9-4914-8302-709a0eac088e/f970f1e0-0be9-4914-8302-709a0eac088e.mp3\n", " The call took 0.00 seconds to execute.\n", "- Computing the note models for ../sample-data/ussak--sazsemaisi--aksaksemai----neyzen_aziz_dede/f970f1e0-0be9-4914-8302-709a0eac088e/f970f1e0-0be9-4914-8302-709a0eac088e.mp3\n", " The call took 0.04 seconds to execute.\n", "- Computing the melodic progression model of ../sample-data/ussak--sazsemaisi--aksaksemai----neyzen_aziz_dede/f970f1e0-0be9-4914-8302-709a0eac088e/f970f1e0-0be9-4914-8302-709a0eac088e.mp3\n", " The call took 3.25 seconds to execute.\n" ] }, { "data": { "image/png": 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0SHcMRPnAt2d2Xq0ec7r0mEsBAKcceYrmSEgXDtpARPnu7fVvAwB21OzQHEk75zlEQYB9khERkX49e/ZkJRlRBvg2Sebk1UqyMf3GAAAG9BygORIiIqLsaAub/RAXFxRrjiQ2VpIREZEblJaWFhmG0V93HERexzM7eLfjfiIiony3t34vAPfe0GKSjIiI3MBqYXKR7jiIvM63Z3b50CcZERFRvqtqqAIAhMIhzZHE1tjaqDsEIiIiNDY2tgH4iu44iLzOv0kyeDNJ1tTWhIONB3WHQZS2DdUbsGTXEt1hEJHHhJQ7k2TD+wzXHQIRERG2bdtWA+ASwzB8e41PlAncgDzmrn/chevfuF53GERpu+Mfd+C+effpDoOIPMZNN7ScsfQs6qkxEiLqrjX71mBf/T7dYRB128aNGw8DGAiAo7oRdYNvk2TO5pZeGuly6+GtAIAPN32Il9a+pDcYcgUvrb9EROlyU3NLNyXs3Kwt3Ia6ljrdYRB16e6Zd+PWd27VHQZRt33++ec1ABTY5JKoW/ybJHMkFrzYcf/jix/H+5ve1x0GaSYQ3SEQEWXNwp0LI4/ddEOgZyGrx5Lx8CcP49pXr9UdhifM3jIbC7Yv0B2Gb7WEWnSHkLIDjQc8GTdlT11dXRuARQD+RXcsRF7m2ySZk5tOvImIiMh0//z7I49zXb3V3Nac0/nlowU7zKQPK+8S++3C3+LBjx/UHQZ5yLff+DZ+MfcXusMg95kN4EuGYRTpDoTIq3ybJPNi9RgREZFf5TLR8tHmj/CNl7+BXTW7Yr7Om2upaQu36Q6BKC+tqFyhOwRyn1IArQC44yVKk2+TZFOOnIKRfUcCYMKMiIjI7XLZJ5ndzHNnzc6czTOf8TyLiChnLgcwOxgMcsdLlKaESTIRCYjIZBH5qoicLyKDchFYtt085WZcdsxlAHhHmIgSW7tvLeZunas7DKKM21mzE/Ut9brDSEhXk70PN32I2Vtmd3iOSR8iInKbYcOG9QQwGsArumMh8rLCeC+IyFgAPwZwIYANAPYDKAFwjIg0APgTgGeV8m5HEyLe6/S8b4++ONx8WHcYRL7zk5k/AQCcO+pczZEQZdat79yKMUeMwWP/8pjuULo0a8ssXDDmgpzP94NNH6BnYU+cN/q8nM+biIgoWccdd1xf6+E7WgMh8riuKsl+CeDvAMYqpS5WSn1LKfUNpdQEAF8D0BfAdbkIMlvskQG9dEd44uCJukMgl/HS+ktE7rT50GbdISR0zshzcjYv5341IIFOFeesQCcinXjuR7GMGzfuCABLg8HgHt2xEHlZ3EoypVTcMbuVUpUAHs1KRBp46WQ3IL7tRo6IiHzsknGXaJlvgRRwdEYichUvXbtQ7gwePLgXgPd1x0Hkdcn0SbZJRG6Jeu7t7IWUO15sbunFmImIiLxKRDolyZxVHKzoIKJcY+KeYgkEAgCwUnccRF6XTFlSK4DzRORpESm2nhuWxZhyxovNLVlJRkRElDsBCXROkrGKg6hbFu5cGBlFllLnpWsXyrklugMg8rpkMi4NSqmrAXwGYL6IHAXk19mhl052nUmyrx37NY2REBERZdfA0oG6Q4iZJCOi7nnz8zfx5udv6g7Ds7x07UK509DQ0AZgu+44iLwubp9kDgIASqmHRGQ5gH8A6J/VqHLEbrropbsxdvUbwKoyIiLKb2XFZdjfsF9rDEySEWVeWIVRGEjmMiS7vHQN4MR9EsVy8ODBpgcffNCbKzWRiySTZfm5/UAp9RGAiwH8PmsR5ZAz4eRFXo+fMoN3E4koXW6/QEz2QvCLqi9wz8x70BZuy3gMMZtbuny5EbmdguLN3m7gPohiYd/VRJkR9+gkIieLyMkAdtmPrb8HAMiLjvttXk0y8OSCeDAkonyWbJLssUWPYfW+1dhVsyvjMbBPMqLMC6uwK85h0qnIamxtxGUvXKa1uSgrySiWvn379tAdA1E+6CrL8rDj3xwAv3H8/ZusR5YDXmxu6eSGkwsiIvIut19oJRtfZCCeLCSv2NySKPPCKuyKm73p7DPqW+sBAK99/lqmw0kaE/UUS69evYoMwxisOw4ir4t7dFJKnWf/A7BJKXW+47nzcxhj1ni9uaLX46fMWlW5yrMJXyLSw00XWm3hNhxsPNjhuWTjy/RNI+d8mSQjyjyl3NHcsjvnTaFwKIORpIbne9SFHxmGwYtEom5I9uiUl3viSCWZR7+eG04uyB2W7FqCe2fdi+dXP687FCLyEDclfx5d+Ciuf+P6SEwtoZZIxUYikUqyDFw47q/fj6W7lwIACgOFCfskm/HFjG7Pk/ytua1Zdwg5F1ZhV9zsTecawN4f6Nx/umnf7XavffYa9tXv0x1GTqxatWo/gDsAvGsYxrG64yHyKl9nWTJ5Uq0Dm1uSrbqxGgAwfe10zZEQ6dUSasFjCx/rVJGkyyOfPoK/rfyb7jDi+suyv+gOIeLjHR8DAFpDrQCAr7/0dRxqOpTSZ2Tiptf33/t+5HPiJskc81m1b1XKcRI5XfPqNbpDyDm39EmWzjWAXUGmM357H+SGRKOb1TbX4ukVT+M7M77ji8TiG2+8sR3ADwGcCeBzwzDmGIbxH4ZhlGkOjchTuuq4/3ci8riIPA5guP3Y8ZznsZKM0uW2xOpHmz+KPP77qr9rjKRrbltuyXp04aO6Q6Akfbz9Y3y05SP89Z9/1R0KAGDW1ll4ad1LusOI6/1N7+sOoZN4x+SDjQfx/sb3I1VeTslcrD708UMd9pXxOKvXCgIFSY1ueec/7kz4uUTxZGNUVrdTUCiQAt1hpHUN8Nb6twDoTVDZ+yQ3JBrdzLl8dA60kCvhcBjBYPAxAMcA+BmAYQCeBlBtGMZ8wzDuMwyjwjCMEq2BErlcYRevOc9Cl2U7EJ28euG+8cBG3SH41h+W/kF3CADMiosDjQew4cCGyHMvrn0R35rwLY1RxfeLeb/QHUJaZm6ZGXmslOJJqYvZFw5uu4nwyY5PcOaIM3WH4Wp2oqCmuQYlhZ3P369/43oAwOnDTseUI6d0eC2ZyvD52+dj/vb5uHDMhUnHVCCxk2TRKusrk/5MIr9bs28Nth3ehuF9husOBcv3LE/5PXaSTCe7ms1txzq3cR4TqhqqNEaSW8FgsBLALw3DuB9mVdllAM4HcC/M5FmLYRgrYV7j2//WBoPBFk0hE7lK3CSZUurZXAaiQzZHw8oFNu/Q572N7+kOAQCws3an7hBSsmT3Et0hdJuCYvMGF9OdJGtua8Z1r1+HRy5+BANKB0SeX7hzIZNkSfrOjO90+Xqs39ZOXMdLZrWE2s/7DzcdRt+SvknFEq+5JSXmpwtSSs3dM+8G4I6mgg8seCDt9+pMUNn7tB4FPbTF4DVevd7rjmAwqAB8bP2DYRh9AUy1/k0B8O8AbrEmbzEMYxU6Js7WJJ04EzkPZtXaDVBqdueX5XcALldKjbT+vgLAHwDstSZ5TSl1X9ezkCcBfAVAiVJqiOP52wH8B8xWclUArlZK7bNe+xaAewAIgJ8ppV6xnv8azIRhkRXDNUqprPTVISK9AXxTKfWnbHw+ZV7cJJmI/AXAY0qpNTFe6wXgagDNSqnn4ry/BMA8AD2s+byilAqKyPkAfgOgGObG9x2lVJu1ov4CQBhAG4AfKqUWxPjc+wFcD6CfUqp3St+282d15+3afVH9he4QiHzHLcPWU2w1zTUA9F28/GHpH9DY1ohb3rkFY/uNjTzvhmZF+axnYU8AQFNbU8zX7X7OAKChtSHpJFljWyM+3PwhAOBX83+FQb0G4aaTb/LlxVYyapprUFZcBhHBrz/+te5wyOW8fizVuR9oDpmDPfQoZJKsK84bHG5IyuoWDAYPA3jL+gfDMAIAxgI4xfHvGgA3W29pMQxjNYA1ANZa/9YA2GEl4ExmguxtAKUA3obIpc5EmYiMAnAegGIRKVNK1QI4EcB9SqknU/gKT1vzuT3q+TUATlVKtYjI72H2y3aPiJxhTXs6gN4AFgN4RUT6AvgTgElKqb0i8huYycL0s+ZdOwVmJR+TZB7RVXPLJwD8XETGw1zx9gMoAXA0gD4A/hdAzASZpRnA+UqpOhEpArBARD4A8CyAC5RS60XkPgDfBvBXADMBzFBKKRGZAOAlAMfF+Ny3APwewIYYr6XFq80t+/boi8PNh3WHQeQrXt1f+EFYhfHMymcA6EtKOZM0mw5uijwuKijSEY7nTRg0Ad+c8E00tjZi2txpANoHKnHqVdwLAOKOhum8mLUvLpNx76x7I48/3fkpAGDl3pXYenhr0p/hFw2tDfjma98EALx17VtYV7Uu8hqTihSL129W6xyV9I9L/wiAlWSp8Pr6lg3BYDAM85p6A4DpQCRxNgbtSbOTAVwM85rdVmsYxjoAiy56//0dpwOGmAkyIHaizADwSwDfhZkcWwjgBADPpBKvUmqJiNwGYHXU8x86/myAWR0GmCN93quUqgFQIyIhERkAs6qsBUCzVdhzvP39naxk2iMAJsFMst2llHpTRK4GcBvMnMgSmEU/SkR2wqyO+1drOVwIoKf12W0isgLA/4OZmLzNirNGKXV2KsuBsq+r5pYrAFxllQdOATAUQCOAz5RSCUuYlHklWWf9WWT9CwFoUUqtt57/EMDdAP6qlKpzvL0XEPuMSim1EMjMjs7rzS1/ef4v8f33vq87DHKZ48uP1x0CkRbOzq91JaVOG3ZaZJRGp6IAk2TpGNx7ME4YeEKH52JVUfcqMpNki3ctxqYDm/DNCd/s8LqzmiBetZltVN9RHZJghYHCDusWE2SxOZfR7trdGiMhr3BWeHqFsxlxon1JNn1W9RkAoLigWFsMXuDc93u9cjFXrMTZRuvfi/bzhmH0h5ngsv+dNHrz5ltOWbasOMZVeSRRJsA+ACfBbA55tvV4IczE1EMi8giAp5RSjyQZ4ngAn8R6QUQmwmxxNtVq+XY+gGsdk/SGmRxrADDb+o4lMFu5xRr5bDqAt5RSN4pIIYC+IlIB4F8AnGdVrr1ozW8jgIEAZiul7heRxwBcpJR6VkTeBPC2UuptESkD8GeYVWwtInJEkt+bcqirSjIAgJW8mpPOh4tIAcwmleNgVqYtBlAoIlOUUksBfAPACMf0V8AscxwE4KvpzNPxWf8J4D8BYPDgwZgzp/NXWFezDtVV1Vi4cCG299we83O2N2xHn8I+OKLYHevvhl0b0HC4ATePuRkrFq9AdVU1Vq5ciaIdvADLpeqq9kqGWOuWjjgiz7VUa42pK25Zbqlyxj137lwUBhLuOkmD1nBr5LfaW7gXc2rn5DyGJQeWxNwuN8pGLfEkkqltctOOTdjduLtbnxFruW1VWzGncU6H18uKyjrNZ8OODag+VI3pVeaN4GEHhnV4va6tLvL+BQsXYG/vvYinuaoZ1YfbYxnXexw21iUeKOfVD17FgB4DEk6XrxraGiLL+IE3H8DRgaOxsHohAGDevHkoDvBiPh6vHhvTZX/fL5q/wJzQHFfEIiJJLXv72gEwm3nr+r3sGAK1AV+sM+mqaa2JLKtnP3kWYw6P0RxR1h0rInOinntJKfWkiJQCeDfGe55RSj0jIuUAXonx+h+UUi9OmzatF4BfFBQUSHl5ecnAgQN7ztqwIVDcGjfZXQqzeeQ/AfzcqrT6DGaCDQAuVUrtEpFhAP4pIu8rpT5L4juehBjNFkVkCsyWaFdbLdYmA9iolGqzXj8SQJ1SqlZE/ghgjVLq2yJyLoC7VVRTEevz+tnNQa3PqRaRuwCMBrDYKtopg1mZNh7Au0opO4FXBMDuQHwCgF9Zj0Mwq8seFpFnrZwIuUxWr/SUUiEAk6wM6eswN4prADwiIj0A/APmimJP/zqA10VkKsz+yZIfgqrzvP8MM0uLKVOmqIqKik7T9NzZE+/Vv4fTTjsNY/uP7fQ6AFz2wmUAzKYDbrB60Woc3nMYV11yFfbX78eA6gGYOHEiKsZW6A7NV06oOSEymlmsdStXBuzpfEE2ethoVEytyH0wSXDGq3O5pcoZ99Rzp/LOrUu1hFowoNL8rS447QKcddRZOY+haWMT5jfP7/T8+BPGo2JCRc7jSWRy/WRsr9mOEwee2K1tcsnHS4CD3duuY+3e0Q7uAAAgAElEQVTPxh83HhWTKzq9Hj2fbSu2YcdnO+K+Xt1QjQH7zfePnzQepw0/LW4cqxetxp7NeyJ/P/FvT+DfX/v3hPH3GtsLFeMqEk6Xr2qbayPL+IrTr8CWg1uw4QuzZ4ypU6fGHLGUTOV7yiOtGrx0bEyXvS0fM/wYVJxT4YpYgOSWvX3tAABnjzgbFWcnfk822HGfOKh7++58d6DxAAZUefPcU6eysrKi8vLykn79+pUcd9xxlxqGUXHPPfec2NTUdGrv3r1LAgGzKu/dLVtw7XPPobitLdbHNNwLPATgYQCTReQJmFVbqwFAKbXL/r+IbIfZdLFLYmaljgewLur582F2BXWlUsoeJawH2lu1AWYTyOetIp5rAfSz5j9XRN4VkQIrd2GbBLPiLdoEAKOipoWI3Bk1/QSYiTABMFwptd2aX4OInARzxNE/i8hTKfbLRjmQk3IIpdQhEZkN4BKl1G8AnAMAInIRgGNiTD9PRMaISLlSKuvDI3m1uSXpM7zPcFTWV+KoPkdpjePec+7FJzs+weytnQaRcb0X17yIq0+6WncYSbnsmMtcMeQ7dc15E1BX3yPxOgZ2e3PLfiX9dIcQU2s4ueZYiX7vVJpbKigM6Dkg0vdZWY8yXHnClXh53cudpr3mxGswfW2nbkx8r3/P/thxeEfiCQmA2fm6zqZ7urjhhlNxQTFaQi04bVj8xLmT85pB5/WD3Sx8eJ/h2mLwAue+/xvHf0NjJDnzhVKqItYLSqkGABUAYBhGAcx+sSYCGGsYxh+mTZs2CmZrrpFo718MMPsOqy4uLt5UXFz8GoBN1r+NW0eP3lQYCh2P9k77bQ0ALv0V8FMAlymlPgIAERkMs2pssFKq0nruKphVV0usv+8A8HSckSZHAdhnfRdY0/8rzH7DLo0acHAjgLFWn2P9YfYBdrFSKiQijTD7Wf9CRL4JYG100gvmiJdXikhAKRUWkUHWiJl7YTa3fNtKuB2nlFoLs5LseSsmsWLdDGAAHMk6ETlaKbUBwHQROQFm4pBcJmGSTETGK6VWJ5ouxvsGAmi1EmQ9AXwZwIP2CmZVkv0YwP3W9OMAbLJKMU+Gmf3t3PYig+yTanbETamy1xndze5OH346Th9+OqYcOQUvr30Ze+vjNyFym7+v/rtnkmTsx8Ib3HDD44gSs2l+WXEZTj3yVMzaOgsAXFtF44ZlZispLEkqUTBh0IROzyUatSylJJlSnZJu10+8HtdPvB6Hmg7hutevAwDcfMrNqG2ujUzzzvp3cMm4SxLG7xe6j49e4tfRb93Q6XyfHn1Q1VAV6dcwkQmD2/c/zv0KuZ89wIsfGYZxDMz+uSbCrJCagI5JrSoA22BWaL1nPd5q/X9bMBg8hHiCwb0QuRTtibIGAJcKUACg2E6QAYBSqtLq7/wvInI0zIH+1gP4ipWIGgKzY/vHo2cjIqsBDIbZL9hOmK3TFsLsN6wawN+tY/enSqlblVJVVvXaCms+tymltlkfdyOAV0WkDWbS61p09i6ArwNYJyJNAP5o/fsBgCdF5FcA2gD8Guaon+OteQFmgmyHldeoBrBdRNYB+DmAS61RN+ut93037rIlbZI5g3nSSmg9A+A5pVSywykOBfCslWENwGwP/baI/FrMDSkAs43zLGv6rwO4XkRaYQ4QcLXdNlhEViilJlmPHwLw7wBKrQ3kKaXUtCRj6oBDAVO63HRhCQBTR07F1JFT8YP3fqA7lLzERDolq6xHGQDgrjPvwuShk3Hy0JPxm09/gx6F+i8GY8nkut3dzxrSawiG9B6CnkU9I9WxBxs730iOdaGTqJIs5LhBnEwlWbzzAzsJCgAXj70Yr332WuTvS4+5tMvP9ZuCgD8TP+lwrr+xkrT5youJ1NKiUrx17Vu47b3bmCTzAD+fvxmGUQSz/++bAZxrPX0IwEoAf4GZ0FkJYH0wGIw9NHSylJptJcqeBnADlJptLfmPOk+qumpWeTOAG+1+xKLeNz7Oe+LehVRK/RpmEiv6+XcRu3825zRhADfEeP4TmInG6OdPdjzeAuAM67EC4LyDFqvfN3KZZDruP8fK9N4IYJmILIZZAvlhgvetAjA5xvN3AbgrxvMPAngwzmdNcjz+EYAfJYo7FW5LeJD7+fmg2x0FUtDhYtUruI+gZNn7Bvsid/zgeOd07uCmdVvBTA7cccYdqKyrxLqqdVi8e3Gn6cb1H9fpueiL1ehEg/P15lBz13Go+Ekyp8JAYYd5+LlKgbrHeU7x0tqXPFNl3V2V9ZWoba6N3FzwkgIpcEWSjOejXXPTMS6XDMM4BcBTMJM5mwHcDbNT+y3BYDA7C0Wp2TArqLrxEcrITDBE3ZNUGyKr3exPYTaPPBfA4yLyuYj8WzaDyzY2t6R0+fWg210nDz058UREaXLuy+dtm6d1385K5dQ5k1PD+pijU35n8ncirw8sHYhRfUfhyhOuTPmznRezoXDXiXo7WZeIiODUI09NORa/4LlV8pw3j+Zum6sxktw4a4Q5qMqyPcuwdLc3B3YLSCDhvoT08+N+yDCMrwH4FMAQAFcBODoYDP5PMBjcnLUEGVGeSaZPsgkwSw2/CuBDmJ3vLbeGUf0UwGtdvd/NeBFD6fLjQTcTvLrcvBq33ziT1x/v+Bj/3PvPnCdmvZZAd+u6ff3E6yEQXDimfZBrBYWx/cfGTGCVFZuVKOccdQ7mb+88uqjzYrYtHHMUrvb5KJV0P4Sj+43Gk195Ev/17n+5dlnq4oYqG6/oV9IPe+rMEVX9sNwKpCBSWe61faZt/YH1ukOgJHh1/UpX//79iwG8DGA5gK8Eg8EDmkMi8qRkzgJ/B3NDm6iU+p5SajkAKKV2w6wu8zwv7UC9FCtRNK+uvx1Gs+KFsGtF/zZ1LXVxpsw+r/QpZK/bC3YswJJdSxJMnZtYALPvr++f9v1Oo9/Fu7llv7e8tDzm6x0qyRI0+U43SeHV/Rvp16dHexc9fkiSKbQnov3wfbOB+5vkOM8Lnl/9PKoaqjRGk32TJk3qB3OkyGuYICNKXzJJsq8CeF4p1QgAIhIQkVIAUEr9LZvBZZtXm1uyAk4/+6Ru86HNWFW5SnM03uHVk2Gv7SOIkuVct+3O8nXqbnIx3vHRue9JVEkGpDairVcSornm1f29bn5owues1uTxlbLJmUxsDbfiwQUxu7/OG/379y8BsD8YDG7VHQuRlyVzFvgRgJ6Ov0sRY6QKL/J6sokn5u5w76x7dYfgGTwZplzy+j4+F9xUjZBMh/nxjnvRAyZEc1aPJUpC2Mmdi8ZchP49+3c5LdC+nnH/1pGb1i0vCcMfyUU7SeaWwXy4vuan6P1yY1ujpkhyY9iwYb0BeLOjPyIXSSZJVqKUirRZsR6XZi+k3IlUkvHASCly8zrj5gs1r1YWuPn3pnbRvxNvJCTmpv1FJrazTFaSJbv+8FwiNjetW27nXFZePU6myi2VZLrnT9kVvV9OpUrYi/r161cCYJbuOIi8Lpk9Rb2IRHo+FpFTAORVGp4HSEoV15n0ePXk3/l780LYvaK3S1aSpcYN+7WuElNdxZdou0y1T7JUqsNYSRYb95XJ82O/l27rk2z21tn4ePvHSU9fMbICQ3oNyWJEXfPLetJdPj0v2K87ACKvSyZJ9kMAL4vIfBFZAOBFAP8vu2HlRuTElidyeWHZ7mVoCbXkZF5cZ9LD5Ua5pKOSzGsXLm7aJpNpbpkuZxPL5rbmruOAgohAICktHzcty67sq9+HOVvnZH0+XtsW3MItzQ+zya0d9++r35f0tKxU9obo/XJBoEBTJLnR3NwcAnC+7jiIvC5hkkwptQTAcQBuBXALgOOVUsuyHVgu8ACXP7Yd2oZpc6fhj0v/mJP5ufXkvzXcisW7F2PF3hW6Q4nJTSfDqfDKxS91pLNZhVfuVrtp3U4US3dide575m2f1/V8EiTrjAoDPz7rx5G/vTYI0I8/+jEe/vThrHcQ76Z1i9xFKRVJVug+L3Cup/+74n/R1NaU1nvJnfxWSbZz585aAKfrjoPI65K9gjgVwAQAJwO4VkSuz15IucMmEvnDPqnZemhrTubn1hOjyvpKAMhZsjBVXt3WvBq333Tqk0zDybBb9w1ekejmVbId+0f/DqlciNuVZEDsbf/koSfj7KPOTjomtznQeABA9iuWuN9Mj9fWp3S5sZIMABpaG5Ke1g3rOI85XfNbX6WhUEgh+et7IoqjMNEEIvI3AGMBrABgn1EpAP+Xxbhygp3t5g+3dACrm/393doxqdtOhpPFfYQ3dLpjrPFk2Csn4s5tUvd63p3mlpHRLeO8P5WEkB1HvnbcH5AAwiqM1lAriguKM/a50d/fK8vDDfx47hKwruM3H9ysOZKOkj1P0Z3M5PaVnOhty63nx5lSUFAgAFp1x0HkdcnsKaYAOEsp9V9Kqe9b/27LdmC55MeTk3xjX6TkKgnj1mSPfSHo1pMAntT5l1Iq5/ta3RcxlLy2cBsONh1MXEmWZvIx3UqyVHjlXMI+PrSGs3sd5dbjpBv57dio0N7ccs62Ofjnnn9qjqhdMqPfUmbk4rzAb6Nb7t27twHA8YZhHKM7FiIvS2ZPsQaAvuFbsogd9+ePSCWZz39L+6IklT41csmrF01eufh1s8unX47/WfA/WZ2H306GM6HDyK0a1/Nff/xrNIe67lC/K8mObjmk1xCM6juq689SZqfiyXbc77VkbCRJFmKxQSJzts5JqTN3Sp5z/7z98HaNkXS0Zt+apKbzSrWwm10+/XI8uvDRnM4z388LFi1atB9APYD7dcdC5GXJ7CnKAawTkQ9EZIb9L9uB5QIPcO6V6t2lxtbGyPtywe1JE7tvMrdx+3KLx3mh7NXv4Aaf7Pwkp/Pz6snwqspVuGnGTQlHYcwEt9xYSGbd6Grb2127G0D847rdSX1xQXHCppepLhOvNbe0b6JkO95sDwyQbUopPPzpw7jzH3fmZF42P5yb2oloW3cS5JnWr6Rf0tN6ZZt3s1lbZ2X186Nvznrtpkaqampq2gA8BOAbhmFcqDseIq9K5gpiGoB/BfArAA87/nkeO+53r8unX45fzPtF0tP/ZOZPAOSuUoknRunxaiUZeYMb+iTLxPHk3ln3orK+MifVFYeaDmV9HqlIlFiJd4Ezc8vMDu+P/h3sfXZRQVHC5lRhFU6rn0svnEvsOLwj8jjbSSyv7+/tdeZg08GczjffL+IBq7mlFET+tm90ukGqTbN1W75nOaoaqnSH4Vp+65PM8hsAnwGYbhjGKL2hEHlTwj2FUmougK0AiqzHSwAsz3JcOeWGgxx1tmT3kqSm69BcKEe/pdsvhgoDCcfk0MKrF01u/73J5IbRLbs7b2f1WK63F53HwmS6P+jqtbNHmKNNlhSWxHx96sipmHHNDBzV96iEyaGUO+73UNcNzoRPtuP16v7eZsef6/2IHyrJgI7fM9v946Ui2UE+3JLM3N+wHze8eQP21+/XHUpKos+rXv/sdVz2wmVZ7xPOD0myYDDYALPApRDAq4ZhuGNlJfKQhHsKEfkugFcA/Ml6ahiAN7IZVK50NcQ7ecc3Xv5G5DEryUynDTtNdwgxuX25xdOhuaVHv0MmVdZV4vOqz3WHkZCbLjaT7Zi6rqUu8jjnSTIXHAvTjWFo2dAOlSmxiAgKA4VJNbcUkaQvgt20niVSFCiKPM727+31JFlkxNQc/L5+P67oWFcaWxvx0tqXcKDxQIfn3bAfTMfeur26Q0hJQ2tDh79fXPsigMxXFXZqbumh/XV3BIPB9QDuAHAygLM1h0PkOcmk078H4CwANQCglNoAYFA2g8qVVO4CefWg6QctoZbIY7+Pbmlz650yty83t3tu1XO47IXLsLpytdY4bnrrJtz14V2uOynv1NxSw53+eBe7n+z8JKn133kXPdmKhnTFa5LoRc4mkl0pkIKElQrpVid74TzBuQ5me3/s9f29Hb9bj6deZldr2nSsK08tfwp/W/W3Ts8nu837JdmSLd9793sd/s7V8vTZ9vwigDoA9xmGMVB3MEReksyeolkpFclCiEgh4OEz6RiSOSB6/WTPL3J1keL29cGtJwFeuIiM1hJqweyts3WHAQCYvnY6AODVz17VHInpbys7X2DEsmjnoixHEpvOi5hY865trk34vg5Jsiz3GeW1pFhX+4+wCqMg0HUlGYCkKslCKoTCQCFEBHUtdQn3W15qbun87kySdc3+PXORbHeuY9GVTfmmLdyGxbsXd6gk0nFu4KzadfJqP4ReS9pVN1bnZD5+HvU6GAzWA7gTZrHLF4ZhPGUYxr8ahtFbc2hErpfMnmKuiNwDoKeIfBnAywDeym5YuZFKc0uvn+x5ya6aXSlNf8rQUyKPc/U7uX3ULrfG54WLyGjvbXhPdwidbDiwQXcIAJI/2fzl/F9mORKTm/oki6W2JbUkWbb7ZtlXvy+rn58K+7dLtI/oavRK5/oY73MKAgUJ949t4TYUSAG2HtoKAJjxRdcDenvp4tQ5GAT7JOtapE8yD/2+XvDD938IANhb316JrGNdiW7uZ0upelTjOU30tUui5uZul62bDW6oMNcpGAz+CWaTy38A+AaA1wFUG4bxsWEYjxiGcY1hGGMy3W+ZiFwsIvNFZKmIrBaRZ0Sk3HrtdhHZLiIrrH83JfF5v7I+Z5WIXOp4vkJElljP/0kcO2wRuVNEPheRNSJyTia/X4z4LhWRY7M5D8qtZK5wfgJgP4DVAG4G8C6AnyZ6k4iUiMhiEVkpImtFxIh6/XERqYt67ioRWWdN/3yMzyxzbFArRKRKRB5N4jvEjjGFHaXXT/a85I3PU+vyLtd3hepb6rGnbk9O55mqbF9cp2PboW2uX26xRFenuOHOcU1zje4QALj/4tFtA1j0KOiRcBpnpU+2t+Npc6Zl9fOzaX/9/g6VeXZzy0TrZIEUJK4kC5uVZKv3mc2an1v9XJfT2+cS66vXJxO6Vn9a9qfI49c/ez2r83KeN83ZOier88oGO4mS7Yvq+pZ6VNZXZnUebrLt8LZOz+lINq2oXNGt97st2eKl65RthzqvA7ZMn2P5uZLMFgwG1wSDwWsADARwHoDHAIRhXte/AGATgCrDMOYYhvE7wzD+0zCMMwzDKOvyg0XOg8hWiJzX8Wm5EsBDAL6tlJoCYBKADQDskXVOBHCLUmqS9e+prmcjNwMYDmAigC8D+IOIBERkKIAnAfybUmqC9f0utd5zlTXteAD/AeDhhAuqe/4byeVVyCMSXkEopcIA/mL9S0UzgPOVUnUiUgRggYi8p5RaKCJTAPRzTiwiRwO4G8BZSqmDItKp3zOlVC3MDc1+zzIAr6UYVyfJHJy9WAHjVV8a9iW8v+l9/OjMHyU1vfOAt68h+5URd8+82/XrQ7b7MkrHT2b+RHcIaelV1Et3CK6VzEVCvLv12aCrWWcssZZNMhcxH23+KPLY2d9iNuyq7Vi1qysBbFdsAYkrie3leuOMGwEAr131GvbW7cWnOz/tMDJoPIWBwoTJx7ZwW4fkeGNbch1Jz9wyEz88/YdJTesGs7bOwu1n3J6Vz167by0W7FgQ+fuJJU/gknGXZGVe2XLDmzcAyP7NgDs+uCPpdSxfuSnB44YbYenQfXP0QOMBrN23FueMTFywM2vLrE7PZWs7e2H1CzmZj4scKyJzop57SSn15LRp04oATLOeCxUUFCwZNmxYrylTpqyZMGFCSygUmhwOh28tKiqKHADvvPPOpra2trX9+vX7oLKycveMGTP+Y+/evQ2XhkJ9pwPjS8zE0NsQuRRKzRaRXgB+B+ACpdRmAFBKhQDc74jnBJhJtGRdB+A2KydRKSK9YfaP/lUAHymldljTbYeZgHsLwF0ArlNKtYrIcgDHx/pgEfkqgJ8DKAWwXyl1vogMBPAogHEwE3u3KqU+EZHbYCbqhlrf4U9KqQdE5CGYTVpfEJE1AH4I4GkARwIoA3CvUurlFL4vuUDCJJmIbEGMPsiUUmO6ep8yjzJ2pViR9U+JSAGAXwP4dwBXON7yXQBPKKUOWu/vMtshIsfA3EDmJ/oOXXyGHWvCab160PQieyjw4X2GJzV9ru8KbTm0JafzS0f/nv11h9BJvP4/3K53sfu6buhX0i/xRDmQTDI21slwtvxx2R9zNq94ujpWJLO83lrf3pvBQ588lNQFR6bUt9bnbF5OlXXtVTRbD2+NOc2Hmz7E4ebDnZ7/0Yc/wsaDG5OeV0GgAGEVNjsOj3OxtPXw1k7b/aGmQzii5IiY0/vgoitli3a5J2HdXU1tTVn9/N11u7P6+W5S1VAV83k3nWOn0nG/m+LWnSS7d+a92Fm7E6cceQpKi0q7nDbWeXu2mltGVwwm0zeoX4RCIbV9+/a67du3z3n11VefEZFyEXmlvLy85Mgjj+w1ePDgXgMHDuw9fPjwwQB+PHjw4ILvfve7GL5hQ/hb06cHeoQi5zSlsBNlQDmAlUqptV3M+ngAz1tFNP+jlHqhi2kBs/DmRADLReR2AEcAqLeeP9bKLYyDmVu4zyq86a2Usodh7wWg011HETkLZvLuIqXUPhEZYr30VwD3KaWWisgJAJ6AWYE3HmaRz78C6A1gGYAHAPwWZmHQFOtzHwTwqlLqGREJWNOSxyTTFmWK43EJgCsBJHUFbq20y2CuuE8opRaJyA8AzFBK7Yk6sTzGes/HAAoATFNKvd/Fx18D4EXVjSMUm1u6k32gT7apVLZPYKMN6TWkQ18abjSw1H2D2IzqO6rDBfCgUm8Mkuum0vy+PfricPNhXHHcFYknzoFkRrdkJV47tx9HBvXSs032Kk68jjy++HEAnZPtqSTIgPZ+e0IqhELpfIyxKx/X7F/T4fmbZtyEV656JaV5+Zkbby54xYWjL9QdQtY8ueTJmM+7vTo/Ft3NLaOXme4WBDtrdwIA/rr8r/j+ad/vclqdlZO6k4k58IVSqiLWC0qpBgAxX7Ner4r3umEYPQAce868eddMnTv3x4WhTutbKYC3TwFeXgZEDqAi8jiA8wHUKaVOt5JGxyul9orISQAWisg7Sqmu+hH5EYAnRORWAJ8C2KSUqhWRlwBcBDPXsAzAAQArAJwEYJXj/ePhiMnhbgA/sQtzrJhOBXA2gKesPIUA2OH4nJuVUi3WQIaHrOcnRM1vF4D/tr7rKwm+G7lUMs0to4cfedRq5vjzJN4bAjBJRI4A8LqITIWZZKuIE8vR1mvDAcwTkfFKqUMxpgXMJNl18eYtIv8J4D8BYPDgwZgzZ06naXY37kZ1VTWWLV+Gxo2xd9jVVebXnzd/HnoW9Iw3u5zZsHMD9tbtxZw5c3C49TCqq6qxcuVKFO0o0h1axqw4tALVVdVYtHARNvXYlHD6lsqWyO8EIOZvnUk9anqg+mD7/IoDxVmfZ7Ls5bB23VrMqZqjN5goJYdLOvxOgZKAa5ZbVz6v+bxD3Dr3BXsr96KhrQFr1q5Bv0p91WT28vik6hPMKZ7T5bTra9d3WH6zZ8/OWuWNcz4AsGjxIuwpzW0/eBtqN6C6qhqLFy9GZa9K1LTWROL6+JOPMbhkcJfvj/4O2dxGoue1Q+3AnOb05rdpxybsbtydVrxb6rck3Ifbr79e9TpObT21U+y2tWvXorqqGnPnzo3ZkfXSPUtRXVWNf8z6B0oLO1c7NIYa4352vO92qOVQ5D1u36dlc/2qb6uPfH51azWqD7fP67ajb3P9solmf5dBJYNyuh1uCG3AnKbszU+ndZvWobqh8/al4zvH286X/3M5wlsS39BYv2s99tbs1bZe7969G9XN7d9h8dLFaNiQu+4NotnLc3bdbIxvHN/ltGt2rulwHv3RrI9QWVmJ+rZ6zJ8/H2VFXXeFlU5ckb9bqj23L8olqwP/wQBGAxgV/f+Tly0bFyNBZiu9FLh8mdnUEACglLpNRC4BcJP1dxjAXuvxGhFpgplgi5tIUkotA3A6AIjIvQBesZ5vhpULEJGjACyAmUS7Eu2t2QCz8qtTX+cwu3BaGPXcRJiFPT9zPmkNCDASZh/tgJkYW+N4HEmSKaUet5q8XgtgtYiMU0q1xvt+5E7JNLc82fFnAGZlWUq9ISulDonIbJiliuMAbLQukkpFZKNSahyAnQAWWSvRFhFZDzNptiRGTBMBFFobTbx5/hnAnwFgypQpqqKiotM0Ww5uwQuHXsCkyZNw5ogzY37OgD0DAABnn302ynpkbqedrlWLVqF2Ty0qKipQ1VCFAVUDMHHiRFSMrdAdWuZsBWY2zsSZZ56JI8uOTDj5jpU7sNaq6hUIYv3WmbRy4UrUV9bj+gnX47cLf4tjBxyb9Xkmy15fTzjhBFScVKE3mCjbVmzD5599Hvn7yLIjXbPculK6qxTv1L8T+XvqOVOTqnzJhj7H9kFwThAnnXgSKo6v0BID0L6eAUj4G/bd0xdv170d+XvquVM7DYaQjbgA4LQvnYZjy3M72FCfPX0wo24GvvSlL+H4gcejuqEaA6rMuM444wwc1feoLt9/SsMpHSous7mNXBC+oENTlHFjx6HiS+nNb8nHS6AOqLTi7V/ZH6/VmN2L3n767agY3fkzotc5++/Lj7kcM9abo09OGjwJJw4+EctDy3HuuefGrEZ++IWHMaB8AGqG1OArJ32l0+u1zbUYsG9Ap+dH9R0V97s1tzXjr9V/RYEUuH6fFr2NZDLemuYaDNhvfv74seOxe1N7M8KzzjgLI/qOyNi8csFeVmP7j83q7zq4cnCH6pZxo8eh4vTszU+nGc0zUH+gc7PukUeNRMVZFTmN5fjDx8fsx3bypMk4d9S5Cd+/dvFaHNh1QNs2P71uOlRtezXZxEkTc9o8P5q9vZw6/FRUnFPR5bTFO4qxbUF75/1nTz0bzx5+FoeaDuGss8/KaJch0fu8r076qtbzJzcwDKMnzCaPYxEjEYb2DvZt+wFsAbBs3knV110AACAASURBVLnnLvnq229/vSAcLo7x0Q3FZtXXT0XkYaXUbiu59GUAywFARAYrpSqtx3cCWGxVcBUAuBfAL61EGqxpAgD6KaWqrev/bwGYar02UCm1X0R6AvgjgHuUUiER+RztlVzHw0ySnRIj3r0wm3F+LCJlAFoB7AFwg4j8wqoYG2N9/3IAWx2xORNjowDMs2LqD0CUUqtEZBfMoh73dRRNCSWT7HKOBtEGYCuAqxK9yer0rtVKkPWEuYE8qJQa4pimzkqQAcAbMDOuT4s5ROwxADbH+fhrYY7G0S2pVDO4vZlMPkm3Be1JA0/Cry74VYaj6cwucT9v9HmYvXV2TjsmT5ab+smwhVUYxQXFkc7IvdK8wl6W540yf2+dji+P2e+oq/l13xnp8zLF9XzkESMjSbKKkRUZjqqj8tJylJeWR/oJ0rVNOteReE2YRvQZgR01Ozo972zWV1wQ65y9ox4FPdAcao67j7RjGXPEGBxsOoiDTQcBAI9eEn8g7R6FPfDWtW/Ffd2Pord7L/fblsx61R0XjL4AH2z6IKvzcIt4I/zG6+8vmypGVeCldS/h9atfxxUvtndhkMp+0E3nWrqbEdrnSCcOOjHhtNHrwd66vdqbr+YrwzBKYBapTIaZ2JkI8xrb2ZfIQZjX9+sAvGM93mL9f2swGOyY2TZHs3wbZgWYrQHApfcoNftekUYA74tICGbiaSmAv1kJs7etzv3bACyCeU0PmEmsi5VS90V9hXIAc0WkDWZS69+UUvut1x4QkTOsefxeKfV3AFBKrbC6b1oLoBbANdbgf9HugDlSprLiPx/AewAuA7BWROphJs2+BrOppbODu4lor057F8DvrVZsPwfwZxEJA2gC8F/OpB95RzLNLc9LNE0cQwE8a2WGAzBH1ni7i+k/AHCRiKyDmXG9y27qKSIrlFKTHNNeBaDzLeAU2TvkZC7ivHJBn0+SPWAqKBRIAR648IEsR9QuYB1b3HpQd+P6qqBc1b9Xsuxlacd+zavX+P6C2E5YnDk8dgWukxvXxWyKvmhKNUnofH+2EwsKqsM+TFdC0zkqZbzvXBSI3aWAM+Zk9i8/nfpT/Gz2zzC0bGiXn3fxuItx/ujzsXjXYkweMjlr1Y/5Knpdyua+/3DTYZT1KMvaPJKpaM+UQaWDXJV4ybQzRpzRqb8/XRQUCgOFnSpOk13+bkv86j7W2sstneNIa6g1pcHUukP3csoVwzBOBPB9AFfD7OgeMJNeKwG8DLPZ4HqYSbDOo+J0RanZVif9dqKsAcClUGq2+bJ6DsBzcd59apznbwTwnc6zUvsQZ2RKpdRN8UNUCYeaVkrNg5k4jHZLjOdmWP/s937b8fhdAM4BDU9KNG9yv2SaW97R1etKqd/GeX4VzKx1V+/t7XisYGZ0O80vKkGWcGTNZKVyQuXXaggv6GqUsmzwwrrgxpPssAq74oI8FbO3zMZvF5q7ODckRN1yUm4nyQaUdm6WFs3+ncuKy1Dbkt1RpQoDhRheNjxSiaXzZDgyUleK26Iz5mxvI0q5I0k2f3vag1R3WF7JHNOH9jaTY6Fw7NYP9jIISAAlhSWYOnJq2rH5jXNd71RJlqX9Z21zLb71+rdwxXFX4MbJN2ZlHtluXm8vtztOvwN/X/X3rM6L2kXv/1LlhnMCN+nOwAHlpeUZjKSjwb0G4+j+R2PBjgVZm4fbGIYxCWbzv0IArwL4O4CFKSfDutKeKHsawA12giz9j1OxElNE2iSTJZoC4FYAw6x/twA4GUCZ9c/zkqokc2HSgfRxJiu4biQnrMKeqySbtWVW5LFbElRuksy6b08zflDXHflmghu3xe5Ukl04Jruj3EUnEXUtvw7Vc3EuPO3leNUJV3VYpvEqyeJ9F3s7jve72M/H6vSfkperSjI78b5wZ3Tfy91XVpy7U9z+PfvjvNFmw418rnSJ3i6f+zez2ERHgj6swjGP6yk1t3TRb6X7+BfvxkNXLhh9Qafn3LRMPexRmB3XHxMMBq8LBoMfZDRBZlNqNpQa1d0EGZEbJdMn2XAAJ9tteUVkGoB3lFLfymZg2dIaasXmg5txbPmxKV30cqdNNqXamw26NXHixvU1OknmhUoy5+/rpgSfW37fZOKwf+dcbCteWKcSCaswhvYeiie+8gSKCrI/arEbtskOfZLFWU9CKoQzh5+J6yZeh9ZQ+yBRzvf2LOqZsLoj0evRzavzzbCyYdhVuyvr84m+YM/W9q87MZAJzv2oW88pMiV6H9OnRx+UFZdp+R3jdQGRD+uUDt2pJItu+p9Jfvs9e/XqVQDgXADTgsHgTt3xEHlVMmeBgwG0OP5usZ7zpL8s/wvu/PBO7K7dnVJzmHy4+MpX2Ty4xuLmdeHC0WbliRtPCpzJRftvt3PGm68Xzd2RVCWZdQGYq23ULQlEWzr7i+KC4pwkyKIrKdy8b3PuP5wXY3bMg0oH4TuTO3VnkrK6lrrEE3lYnx59cjKfXPZJBmR3/5KLY5Udv0A8cWxMV6x9jK5ja3QXEOnQ+Vu5bT1J5/jhXP7Z7JMs35PPTr1797YLYKq1BkLkcckcmf4PwGIRmWZVkS0C8GxWo8qijQc2AjBPhBM1u3By88VDvkn1IjfXfZIBHQ/sbroo/8HpP3BtMif6gtxNyy2eZXuWRR47f/ODjQd1hOM6qVSS2etlNn93tzQf7BCDI6Zkb8jkLKEYFY8bRrfsappIkszRrMf+DtdPvD6pvqOc+6Ca5hpsqN7Q4fUfffgjAMCiXYsSB+5BuTqXyXWSLBsa2xpzNi97vRQRTxwb0xXvu+lqbtmd9dJPiZdM62odz8b63+Gc3QXnBdm0b9++ZgCrAPzWMIy7rNEtiShFCY8OSqn7AdwAc3jYgwBuUEr9KtuBZYu9cxTrv1TfR7mT7AlIrk8oFXKflEuFwJ0n2dEnpG6M0cnZpAsAdta0V60/tfypXIfTgVv2R6lU4WZ7dEA7Ft3LpruJulzvX9zQ3DIZzv1HW7itw/NA6hesCgrffO2buOMfHccKag6ZI23Wt9THepvn5TpJducZd+KE8hPQt0ffnMw3U/bU7omsZ9k+VuneZ+WSc/177JLHAOhLNsW7wZrs7+22jvvdfk4Vi3P5u215epW1P6kA8B6AhwB8YRjGfxuGkXikJSKKSPYWSimAGqXUYwB2isjoLMaUVRsPmpVkIsJKsjySy4Nrd0dEyjYRdzbXiO7/w40xOjW1NXX4e9W+VZHH87bPy3U4ANxzEmmfjCdzUu68MZELpxx5Sk7mk0gqxxenXO5fomPT1nF/khWJsZpb2tWeH276MKXPXLZ7WZevFwaS6bLVe5RSkZHksjmYhr38Jw+djAe//GDWk+SZTrTsrt2d0c9LhduPjd1h73POG3UexvQzB6oPSEBLgideJVkqy9+LiSm3csv5TT4IBoMHg8Hg1wBcCGA7gN8A2GkYxjOGYXzJMAwubKIEEibJRCQI4McA7raeKoI5lKyn7azZmVolGQ+EOZPoBKUt3JbWKDqZ4vZKMjdqbG3EwcaDrm2mGkt3OqH1i1QqybK9zdjrU2lRKe6ruC+r8+oyjm42YYzXmXS2OLfJ6KRZS6gFBxoPZD2GDh33xzku72vYFxnJ0NlE0u6EflCvQeb7rfXsQOOBDtWf0T7Z+UmXMXmxeWAywiqMMUeMgUBw4sATszofIPsXvtk6jmQ7qefUoeP+PE8U2PvH20+/vcPzOm5Ed7c/W7fdkHRTLN2RL9/DDYLB4MxgMHgOgIkAngHwdZjdJi01DOMmwzAS91FA5FPJnAVeAeByAPUAoJTaDSB342JnycYDG9s7iUyhGoJyJ97Jy9WvXI3vvfu9yN+57pPMuS7k+wltptzyzi1YumcpAhLAjZNuBGAux//+4L/xyrpXNEcXW3Qitndxb02RxNbY2oj3N76vt+PgNPoky5ZcXZQny46jR0GPlN4X3XdfKpramvDgggeTTmxFJ/yjL1R/Pvvn+PYb304rlnR1NWDBkt1LAAC/nP/LTq8dPeDoDn9/963v4tZ3bu00Xaz1w96G/j975x0eR3H//9eoF8uyJcu9F9wb7mBs0SFACDXUUJIQSgpfCOEHMYhLIE5ICBBCTWix6dUU021hG+OOJfciW7asYklnW71rfn/s7Wl3b/du73R3Otl6P889dzc7OzM7MzvlPZ9SUV/hV1kjDTWNNby6+VWdOqoR2v4VSmIiXO99qBBuG0Zuw/3HuU0ys0OTjhqzjc6E3OGdpP4lksHdB3PbdM9xriPglwRehK9bjjdkZWXlZmVl3QYMAO4A4oD/AEs7tGBd6EIEw87qpVEqo5kEEEIcN6xzl3fLzonGlkadC/uOmPAieeEfiTbJ1E17lIjikrGXcN6I82iVrew+sptXcyLTD4jxnc9IyuigknhCInlm/TM8vf5pdpbvDH/+ftj/UvtiuN4ZrSp9JKBfSj/mDZ7n1z2BbhpXHljJqoJVtoktVbVzyVVLGN5juMe4sa1sW0Dl8Bcpccq52+Q+k5k1YFZAaZwx7Axb8czmcvW5X9/yujsskvqQXSzOXcy7O97l2/xvLeNIJNEiOuTPpx4yhPq9D9VmO5xOZowq1sZnamxpZN/RfSEtQ7hgpuIohOgww/2mNsk60aH4sJ7DmNF/RkcXI2CYHlpE2Pr1eEJWVlYl8Dzwd1fQnC7Vyy50wRx2Vi9vCyGeB3oIIX4JfI3CPndqaKWPbEmSRdCg3RkX76FAeW05BysOknckz8N+VChh9D4XaQuqSFMB0EJdHAshqGiIbKkNo7rlfXPv4zczf9NBpVGgfffLa8sBOFhxkI93fdwh5Ykkm2Thtn3mD04ZdIrtuO2RJNNuPr1JE6lQJcmiRBTRUdEddhh0y7RbuHXarfz59D9bPvtt02/j0bMeNR3brplwDXHRcQA0NDd4zctMjbq6sRqwp/YZyVDb/L0d71nGUftXqO1A2SHHVx5YSWVDZVDyC2Z7ldaUhtUmWYtscat3mj3Ho989yu8+/x3H6o+FrUyhgpk6eYdJkmnKMrXvVM4efrY73A464xgRqdDtySJ0/Xo8wOFwXA5sA14FNgMXZWVldVV4F7pgAp+WaaWU/xBCnA1UAqOBB6WUX/m4LeKRFJvknuC6DPdHJnxtFG9aclOYSqKHzn6ISRkr6itIiU/pUGmzSCJ1tXCrlHSCxaXxne+V1ItzRpzD+sL1lFSXhDTvf37/T2KjYvnNLGtSTi3fv9f/G4BzR57rJgrCiYMVB+md3JuEGHMv4yopEXKJElefD6cESCgQTsP92ryiRBQbizey5tAaZg+c7RkvhIczibGJXHDSBV7j/GjUj4A2r5OnDT6NlQdXAnDJ2Evc8ZbsWuI1HTN7lrmHc5k7eC6T+07m87zPgc55GKW+YwWVBdQ11ZEYm+gRRyvJ489mdOmepVQ3VnPl+Cttxfdli9BZ6+TR1Y8yIWMCC89aaLsc4cDPP/p5UNKRUuKsc7odJVjBKF31XcF33PDhDbz44xeJiYphbeFaQKmzHgk9glK2joLxkBFc0u8dQIxoy/Kn0//EkbojfLXPv+1NoHPMhqINTOg9wXLe9AeRMlYFUheRoHZ7IsDhcEQDLwA3A1tRbJN9mJWV1bW57UIXLOB11yKEiBZCLJdSfiWlvEdK+fvjgSADGNNrjF8TS9fJRhe0sOo7lQ2VXPfBdby6ueNUCMO5wfZXgk8rSRbpUDd5ybGKhrnq7S4cNmOW5y/ny31f2iqfinCOUerzN7c2c8fSO3hkxSPWcWV41C0jZYz21jfsSi1r34+Xf3iZpXv8NxsipSSnJIe1h9baykttn0dWerZlJDmxUKWlhvYYCsApA0/RbTT7devn9X6zAy/13db2oR+P/nF7ixp2aN8xqzFWJUYFgtqmWttpP7vhWRblLmJ1wWru/+Z+N1lpBe17f6z+GHuce7jri7vYWroVUFQIAcpqy2yXwRtCOacEOrZ8kfcFNy25yasDCVCIW+3cKJEcqTvCrvJdunjHw2GtlbqlnbHxcPVhbv/09qA5E2nvgYQQgrqmOrdUd2lNKYWVhT7uguKqYhzfOnhyzZMB522GzngopEVnWBd2YvwGhSD7KzAlKyvr/S6CrAtd8A6vuxYpZQvQKoRIDVN5Qo5B3QcBihHuLkmyyESkT/Qe9kM05VVVR9ST345AONQtnbVOXvrhJa545wq/pKrcG4FOcGKoSpzcMeMO3rniHfcCLlLKbhyTOoLIUPtZbmmudZww2SRzS5JFSPvoTsg1v3c7d3t9P43GpL8/9D3by7b7nadEsmD5AlMj96CoJVY1VHlIdw5NHeoRN5LmP3W8WXtoLR9f/TH3nXaf7npdc53X+83ek77d+gL6sTwtMa29RQ077LxjKknR1NrE0r1LWV2wGiml7TmjvLacLaVbfM7TWgnS2z+9nbu+vIs9R/bw73X/tpVPJOGTPZ/4rdJeUFHAsv3LAIUU8YZW2Uq0UNQtnXVOd/hHuz7yiNfZYSaVqpUkyzuSZ6kq/snuTyioLPBqc8+vslh4Em6VrSzZucTWIaBEurUafv7Rz7n101t93qOOUasKVvkkm30hUuY7LfxZf3bZJAsbVBHUecACh8NxgcPh6N2RBepCFyIdPtUtgWpgixDiK1weLgGklL8NWanCBL8kyboG7bDj8e8f596594ZcvWBT8Sam9J1iexNvlPSINNQ31/NDyQ8hzePGJTe6fxdUFLg3mb6QX5EPdI4TQ3VDEhMV46ESEQlSS8YxqaPUVXzl7Uvtyg7+sfofFFUV8c9z/2kZJ1wSa+3BzvKdPL3+aa6fdL2p2lp9cz0l1SX0Tm5bu4ZK/fL/vvg/CioLGNFzBNBGauRX5FPTWENyXJuPnubW5g5R5TVDRrLiQGPu4Lmm15tam7ze3zOhJwBXT7iaN7a+AZhv1NIT09tTzA6Btu+bqZWCpyTPusJ1LFy1kMvHXs4NU3w7fLBr+08br6qxyiM8WOR5uNZmL2x6gYtGX2Q7/u1Lb3f/9kVutcg2STKtdJ9xzDweSDJvkmTFVcXc+cWdXDDqAm6d7ptssoODFQcZ1H2Q6fxjtP+o9ul1hevYWLyRwqpCbp9xu8d97YX23Xlw+YM8du5jQUn3WP0xLnrjIu455R7mDZnH5pLNvLjpRR4/73G3tGykQHW+pc472vf4tdzXuOfUe4KWV2ltKSNbRwYtvU4KB3AY+DWQBUondDgch4B8oAA45PrWfsq6bJZ14USFnVHzfdfnuIB2M+ePd8tI2BSfaNhatpXXt7wekkWKihc3vciHuz7kqvFXce2ka33G31m+k/VF63VheUfz+OM3f+ShzIcipp8cqDgQcjtCKlS1GX8QiaefRnizpRWujdn2su2MyxjnEW7mEbQjNlBeVQulpLSm1F0utR6P1h11Ex128e0B35IDZmWpaqjyCGtsaSQ2KjZk74a3MeDp9U8DsLFooylJdu9X91JaW0qfbn3a0guQlPc1FhVUFgBt76JWemPhqoU8fEabBNpu526m9J3idxlCgbTENN654h3io+NNr/tyWJCakOq+XyXJVKh99bkLnjO15+UNy/Yv4/E1jwOKCqhRwi0c0I5VZu9Dc2szLbJF15+aWhRS8YOdH9giyeyS3qq9J2M81UB9RzraKKspo665jsGpg8OSny8p36KqIpJikzzCjZJMxwNJdrjmsIearypJpjpLWHVwlSlJ5u+8m1OSw4LlC/j1jF9z7shzPdMzSO2qfXVj8UZAsS/rDdq+6886SCdZfGS37ft8lWOPcw8Af1/9d+YNmcdTa5+itLYUZ61TN6dosbV0K3lH8rh4zMXtLoc/ePGHFwH4et/X7jD1OVYcXMHvZv8uKAczBysOArD60Op2p9WZ4SK6ngWedTgcKcBUYAYwBRgEzEKxU2as9GaHw3EEcNr8HAUqgNqwkWtCnA68DNyElMvbgsWvgIdQyEGArVLK64QQ/wf8H6Dqbf9bSvlf6+TFTwHthH4S8DcppUMIkYniKTQe+B64VUophRA9gYNAnuueg1LKkNlwEEJcCOyRUu7yGbkLtmFJkgkhBkspD0opAzKuJIRIAFagdJwY4F0pZZYQYhjwJpAObASul1I2CiGGAC8BGSgd9zoppYchByHE1cD9gASKXPHK7ZbLzOh6l7plZMG4uVtdsJo9zj22FvD+4sNdHwKKDSg7JNk9X+lPt3aU7wAUdbP8Y/k0tCie1QqrCmlsaexQ6Yt9R/cxIm1Eh+XvDcaNU3FVMbd8cguvX/o6KfEpHVQqPYzkjopwSsGtL1yvI8m8bSg7QtrVGxHz9b6v+de6f7n/f7DzAwBu/uhmPr7anupSRX0F3eK62Yqbfywf0LfPwysf1uWl9rMbJ9/IZeMus5VuoNC2VU5Jju6aVkrLTRYIwb5j+zzutSNJVt9cz+LcxfRP6d92n83+sPfoXo+wXc5d3LzkZvf/B5Y/YLvNwgFvxq5n9J/hNr7dJ9l8Y+ghGeqqK21b+AstIdsRGzIppc5jsNma5ZK3FAcH3+z/xh2mkor+rnF8SYCZSQwBbrXCYEiYQmAHmDd/pPTtUPRpKaWbAFDhrW73OPe4pWqMUMkaFeFWqZdS8v2h75k9cHZQJHQLKgpMTVGokmTfFXwHYOn5Wi2Dus7yBbVe9x7Zy7l4kmQrDq7Ql8Mwzvoi3Gua2lQlL3s7sPmkPfVq7PsqAa1eM3NmY8R93yh7/3CTZOePPJ/P9n7G1ROu5vWtr/Pg8gcpqm7zKLvbuZsJvSd43FdWU8Znez/j+knX2xo7zOzXvZrzKpeOvZQoEcXb295mUe4iusd35x9n/4N+Kd5tWh4PyMrKqkLZn+teAIfDEQX0QiHNBgEDgf4o+3X1MxyFXEtH2d9bocXhcFSgEGbqp9Lw3/jRknEVtmymKQTZJ0AS8AlCXKghyiYCC6SULxruGo9CZtky9iqlfAt4S8lOnAR8CzwhhOgHPAOcK6UsEEK8D1wIfOzK4zMppT1PN+3H3UDoJEpOUHiTJPsQOBlACPGelNLfGaABOENKWS2EiAVWCSE+A+4CHpdSvimEeA74OQq7/Q/gf1LKV4UQZwALgeu1CQohYoAngXFSynIhxKMooqMP+Vk2JT3XhFjZUMlFb1zEQ/MfYlr/aaZxu9QtOwYCwcJViuerUJBkKuxuxI2obqx2/26VrTp7Kw+veJg/nf6ndpctUPz+q9/zwU8/CHk+/mxy7jlFIRm3lW7Thd/yyS0AXPP+NWQkZfDCRS90uHqAqq6kXXhCeD1xDeg+wHbcjiDyvdmjW3VwVbvSfmrtU3y570tTG1lmuPfrewHFZpLReHt5bTkvbnqRVQVKmb7Z/03ISLJnNjzjEaaeZqvQ9p8n1jzBsvxlXDa2rTzazZcdSbKv8r7y6dXRF7QbxPrmer+dckQKbp9xO+eNPI+/rPxLwO9pINJNu5xtB7inDDwloHwDRXNrM45sB5sPb3aH2X12VT3VWx/TpuUmt3zUkVGVTYVKCqgERLAkySJFOvm2T2/zIL28tYUvo/5ahHuM/3zv5zyz4RnOGnYWZww7g4l9JrYrPa0KqhbOOieFVYVM6j3J8t78Y/nug5bXtrzGVROu8pmf24uryfr9WP0xn/f7qm9/PWGq0PbVqydcHVAa7rQ075hKMoIyhqvP3dDcwFd5X3HW8LPCcsi3oWgDk/tOJjU+lfQkT7X1xpZGtwRr9/juQJtkswqVTDdCJbgn9p7I1H5TfZbFiuh86YeX+MXJv2BR7iJA2Qcu27/M1mH58QoXKVXq+mz0FtfhcAgUYird8OkBpFp8Bhv+e2OIW31IsBXc8PLLjUMVCTJVDNdIlE1CuW7EOOBRb8/nBX8FFkopK4QQVwBfSynVznsQhRz72JWH5wmkAUKIC4AHXWUvk1KeIYTIAJ4ARgIJwG1SytVCiN8Ck4F+rvSfl1IudHEhpwJvCCG2Ane6nrs/kAL8UUr5ToDPe0LD2y5UO5IO9zdhqawKVAYh1vWRwBnANa7wV1EIrmdRGvwuV/hyFJLOrEwCSBZCOIHu2OiEhnK1JeaaLPYeUZL4YOcHliRZlyRZx0BryySUGJ0+2mecw9WHSUtMs/Ss1CpbdYvjH0p+YHPJ5g5TU/J1CtoedIvr5iYIrRYzZlBPBs2kV1SU1ZaxfP9yzh5xdvsK2U5YSZJBaElz7Vjjj1HfjhijVMknM3gjObcc3uJzw6V691Tt2AG23qclu5YwMk1vf+Sdbe+4CTIIrb0ps/HBuDHR9p9l+Ypx7/d2vOcOyzuaZxrXCmYSq/4SRMHyMtjRiImK4aT0k5jad6qONDLDpN6TdE4n2iPdtPLgSvfvcEsQL8pZ5PGs6rM0tTTRIlssy6SqiXkbP1SHNIAt6RSwHhuio6LZ7dzN3V/ebSsdX/B3LNYS1lUNVX5JLu8o28HYjLFe45hJhXkroy8bejvLd7p/W9mZCxVUwv/r/V/z9f6vQyZNqhLy6riXGu/pK+w3n/3G73TVudusbz+97mmf92sPQYMJbZ+fN2ReSPKoa65zzwEvbHyBzYc30y+ln6l0VrCRW5rL7z7/HVEiiiVXeR7e3P3F3e553Wqd4OuQ9PE1j/O/S/7nsyxGKW4VZgd8ncFWrk2MFkJkG8LellI+I4RIAswkqF6RUr4ihOgFvGty/Vkp5VtCiEHAIpPrj7mujwaeN7n+Zynl10KIKSgEEPHx8dFJSUnRiYmJMRMnTvzvnDlzCvfu3TursLDwmqSkpNjExMSYhISEtMTExD4ZGRmlcXFxSa2trRlRUVHxQ/fvp39RkUk2GqJMIaxeFkK0AuVSyrNcccYCr7sEeP4qpXzDLCEjhBCnABOAn7qCGlDqOhqF0LoGUCUjxgEXCiF+AqwG7pBSNhjSOxV4BDhHSlkqhFCNO78I/ElKuUEIMQ54GjgdRTKuJ/AToBsKkbkQ+CeKx05PxAAAIABJREFUUNJ0V7p/A95ztWeUK24XAoC3UUha/LYNV8fZiNJ5nkbRzT0mpVR374cAVVQiB7gURVLsEiBFCJEupXS7+pFSNgkhbgO2oDgR2APcYZH3LcAtAH369CE7OxuAwuJCjjQcYe26tWTEZ+Asd7K/YT/OKie7a3e746lwlivZr1u3jvJutrU6Q4Y9h/ZQUl1CdnY2FU0VOMud5OTkEFsQ29FFCxpyjuS46/3D8jauVG0b9ZoRxrazAzWt5qRmn/cv2LrAvdj6f2P+H9nZ2bqyrFm3xqNs73z7Dsf6+D6xDCZGM5rV5Yq6TyB1YgdlpWXUNit2RTZt2kTdXu8e5Xo19GJX1S5y1yob0iPOI1438Rs3b+zwPr2veh/OciebNmziWLe2Ntx3cB/F9cUhq9vXDr6Gs0LpR7lbc0ktadswNLc2W/b/latWkhobHkfE+w7sw1mpL4exPmSZ1JV1as+p/HBUcSjxq7d/xV8m/sVrHlFVUZQ16ImbDRs26NpCCzWv1LhU1m1a5/6fnZ3Nmvw1OKvaypIanxqy9lPzXbt2LQWJygFjdUk1ziNt+ec35Nsez0qKS+hW1Y3seuvy7ji2wyOdb1d8q6sDq3Kq1/cVWhOeVmlokVeQR2FdYcjq1V/sPbSX4mrv72m/6n4sL1/OmjVryE/IZ/PRzTjLnaxevZq0OP+8W16RegXP5T0HwP6W/WQ3WucbbKwvWI/zmL79V6xaQWpsKq8dfI3ShlL6JfTziAOwrHyZ+7dVXR2qPeTuL1u2bsFZ7mTlipUeByTVzdUe/dA4TybVJnHj7hvd/2OqY9rVZ0rqS3CWO4mujraVTlFdkbs8X2Z/SXq8J2Fu9U4uWraIc/qc4zX9zIRM3jv0ni5s46aNtO43JyE3H1H63Mk9TyY7O5tudd04UHPAff3mN9rUntdvWE9V9/AcHEopTduyPTCOOcZwES9wNjgZ0nOI7rqxLMkxybbKknskF2e5k90tu8mu08f/dMunHmWpaa7R5fNd+Xe83fo2vRPMnQA2VjRS1eTZHq9/9jr9E/ub3KGgtL7Unc/3q7837YN2UFxcTGxFLGtr1nq21cpsikuKqWqqIrcqF2eDkzXr1njsY7zNEf4iPz9fN89apbsxr01IaePmjabv24Z1GyhM9CSc1bhOnLbK/OIWo6adgqXlS5nbOleX944dO8h2+k6zC8FBQ0NDS0NDQ0tFRUVTcnJy4Zw5c/LKy8sHHjlypK6+vr45MTExJikpqbmhoaG5R48eB+Pi4pJbW1uTo6Ki4i/+8EPimiwPGJL2w/+AEimlTjzVRRqNlVKWCCEmAGuEEJ9KKStNU9LjH8B9Uko147eBc1B4jo0o6qLqadWjKHbPElDUQW8CnjOkdx/w/6SUpQCuMs0A5gL/dZG2AsWBAigk2a9cJqpiAHUxPAnQupgvBO52Peu7Np+tCybwRpJNFkJUojRQous3rv9SStndV+JSyhZgihCiB/ABMMZL9N8D/xZC3IiiJ10I6I7NXKzvbSgGB/cBT6F0Mg8f91LKF4AXAKZPny4zMzMBeK3yNUSNYNbMWQxKHcQzpc8wpO8QnCVO6qhDjacivViZvKbPmM6kPtai4OFC7tpcqkuqyczMpLy2nPTydCZPnkzmiMyOLlrQ0JTXxLcNnoa61bZR28Tquj9Q05o6daqltzQVPYsVr2g9E3ry47MV+4vnifPchvynTptKeqW+bOPGjiNzkv/lag/6lfdj11eK6k8gdWIHT5c9TWKzYth6xvQZTO8/3Wv89d+tJ/5ovLs8acXeN6ATJk4g86TMYBQ1YPQo6UF6VTqzZs5ifO/x7vB1361DHBUhq9vn3n2O9FilH40dN5bMcW35NLc2k37YvP+feuqp9ErqFZIyGbHy25WUFekJLGN9xB6MZf13bU4unvzpk1z+9uVuuzq+6m9p01Jay/Uby1kzZllKoE2omkBxdTEf/PQDPtn9CekNSj3NnTeX6GHRPLq6Tbp+ypQpZA73nn+gUMeUaTOmMbqXIqE6rWEa17x/jTvO0H5DPcazi066iI93t0lqqNdfPvYyo/qNInOWdXmTC5P5pu4bXdhpp51Gemm6Li2zcp434jwyZ2bqxtVoEa2zf5QQk+CzvTas3kCrszVk74W/2LpuK5VFlV7LE1cQxxe1XzB79myG9hhK875msuuzmXvqXJ2HUbt45w1Fo2H08NFe2yvYaNjbQMF6vbqSOh5sXL0RyqGwppD0XkobW0lEW9XVBzs+IL1CuXf02NFsatlE5vxMD1X0ivoK0sva+lF8tDLmvz/9fX758S8BGNprKLXlbYbbB6YObFefOXDsAK8dfY0B3QfYTqf+h3pFc2DWNIb2GOoOb2xppK6pznKNceaMM32OG5lkkv1Gti5s8pTJZA4zv692Ty0rG1by8CUP0yOhB0XpRR4OJVRMOXkKswfO9pp/sFDVUEV6ib4eJs+aTM/Enn6nVVBRwKs5r7r7H+j7mlrfSbFJpDelM3bEWDJntl3PP5avK0tiTKKttm7Z18K3Dd8yYugIMufo42vbWE2rsqFS138BMkZnWLbd2Olj2V62nX+u0XtdfuPYG3x8vrXUXUFFAYuOKsI4p5xySsB2sBZXLmZkxkh+NOdHfFj3IcXVxe5rM2bN4GXny6STTiutpKekM3PGTI99jFoPwRi3ly9fjrNET3gZ061qqNLV/dDRQ0lv8nzf5p4yl0GpgzzCzdrNG6zeZfV+7fWJ4yeSOd53mp0Au6SUmWYXpJS1gOk11/VyH9cLrK47HA7x0EMPlQA3o6heptGmhjnX4XD8+KGHHkoH6g3XU4F5ALNne4xvzSjk0wCgOCYm5n1g3w9TpybO//bb30dJaWagtPZhRTDnZJPytwIlrt9bhRD1KNJnXokkIcTlyi3SfQLikgy73nV9MLAKxXg/UkpVzK1OCPGD6xmNmAKsMYRNBp6WUj5gyF8AQ1CEhEAhxrZqfrtJMinlv1yShFcDW4QQIzXEXhf8gCVJJqW0r0PlA1LKY0KI5cAcoIcQIsYlTTYQhQxTO9SlAEKIbsBlUkqjyMAUV9w8V7y3gf8XUJloM4ZsR1y/S93y+IY/7asVydaqBZipQnSE+LaZl6xgQ1tfdgzP1jTVUNfcJm02sudIryqXkfC+qWUIt02yu+bcxZ9X/BnwT2U2EupMC3VcvW36bcRGxRITFUNqQipH6o7wq2m/8nm/2bN7U8GIiYph7qC5xETF6DyNFVcVe7RhOOpK299T4lOY2X8m64rWWeZvNn5sLtnMkbojHK4+7HFNi/gYb/ZzzXHKwFM4VHmIO2YqwthXjb+KN7e9CUBsdCwtzW3lOWPoGX6n39EQCL/bOVgeF4Nh5NwfjEof5RGmPntsdCyNrXrPe2PSx/jlXGBbWZsNSZXAsTO3qZ6p+3br6w4zvovtVV0P5P4BKYoCw382/odHznzEHb5g2QJ2lO9gVNoo9hzZ43FfoLZLy2qsVZlV20zq2JYYY+1VNZzqlmbG88trywMiyZ5Z/wxby7ZaXleNuKu2GGOj9VLkhZV6iaK0RHtSnmofNesjU/tO5YeSH3Rh/piOAOjTrQ99uvXxIMlAsclpdfBq5lGzvfjT6X9yE9FgrsYbG9XxGidGW3ArDqwwjWccJ1R0j++uU//2B8Z7j9Yd1V0P97jdWeFwOGKBmcAoFE+PozQfbxuQY7QZ5y8HdqE31u80+V9l6iUzKwuEWEab0X4VtcCFLyneOrcZbxNC9JFSHnb9/j2wziXBFQ38EXjYRaRp74kF/gL8zBCeIaUsE0IkokiJ3S+lbHHZFCt3ebkch+I19HST+ihBUQn9TgiRAjQBxcBNQog/uyTGhgNlKA4V8jVl0xJjQ3E5YRBCpAFCSpkrhCgErsIgcNQF+wiZZWxXJ2lyEWSJwNnA31DsjV2O4uHyBmCJK34v4IirA9yH4unSiEJgnNoxXWnu8KdcgXq3DJeh7i60b9GcdySPpNgkv0/mGlsa2VyymfTEdNPTKy20E6l2MWe2qe8IY8I9EnoEJZ3CykJ6JPTQeeIzQ05JDtWN1V5taxi9dP35jD9z05Kb3LZItAQCRMb7pm5Iwm2TbEb/GR5lsINwkmQSSUJMglfj7g3NivmFCb0nMDh1MND2PHacMqibRy28LWJbWlvc17U2x7aVbSMlTm93KJT2+lTUNtXq/l8z8Rp3Hzfr32bt96dvFfMW3my/WcGXjTGJ1NXnleOvdJNkx8NmIUpE+T2O2LW3ZSfvcGJ4T0+zsWp/iouOo6mlSXcwYTZ+eSuzluRSEejcZiQigma43482U8mY3NJcPt71MRecdAFRIsrtrdpqLH1/x/tM7TfVp805gdDVsbdxUpXYVMfEs4afxUubleVvj4QeOlIhnGN8Rb0nSaZ6laxsqCQxJtGDzLKCtm/1Te7Lg/Mf1F2/bOxlfLb3M/d/4/xglJAurCpUvP76aHO1ry3PX85NU27SEXzq/HL3nLvdYclxyfxs0s/4X26bratAx4K8I3k+tRMgeP3f2DfM5s9QO0Oysy5Sy3DqoFP53azf8fCKh03t+FnVi9HzM9hvo5d+/BKXv3O5+79xjo4EEjHS4XA4koANKHa9QJH02g/sRtnfH8Kc9DqalZUV3IWXlMtdtsdUoqwWuNAV/gsMts1d0lifCCGSXeVeiyJtBTANxUulmbe124DNUkqj1NdCIcQcFHLr31LKxa7wnwM/F0LUoHjtvFZKmW+S7l3As0II6Sr7GcBnwEXANtf9xcDFKKqWWsOjk4HXXb+Xomji3YLiBOAFlx22euB2I+nXBfsI5YjZD3jVxc5GoRgN/EQIsR14UwjxMPADioE6UMQ3F7o6ywo0tsaEEJullFOklEVCCAewQgjRBBwAbvSnUDrD/aokmY2FdKRJaZwI0BqHt4s7v7gT8N+1+7/W/ss9wfu6VztJXz7ucvfizsw9e0dIkmkNEZfVlJGRnBFQOrd+eisDUgbw3IVGNXo93t/5PuCfAdpucd0Y1mOYe1PSEZI+vmBluD/UbSqE4Ilzn+DOL+7kzW1v2va2FG5iMSk2yevm74m1TwBtZBm0LaLtnNibnYR7W+Q3tza7+9EZw85w51/VUOUhARIOiYz+KXqbNFr1PTOD8uoGVAu1DuKj/ZcU+3Cnme+bNhjfsdjoWGb0n8H6ovUeG5QxvbxZSohMCOG/JJldz42+YCUFEUrccvItvLDpBfd/dTyIi46jurFa5wTH7P3zl9izMw5qJa1T41OpaKjwqJv8inwaWxrD6uxA+/wvbHqBhJgEnaMYreMMLbaXb+fFTS9y24zbvKZvJAu89QeVsFfLpJ2/Txt8mk4FO5zzojr2PHrWo/zh6z8AsObQGu775j53HLvrLO243Supl8dBpHFcN5IVZuRLWW2Zh0r01tKtRItot3MFbZ/+2Yc/05W3oaWBaf2mkTk0U5fG+aPO15Fkgc6rR+uPWl7TvjvBWk8kxOi1zkwlyWySmqGE2t9PHXQqibHWRKsV4aZ9B97a9havbXmNd69415Y0dXxMPOeNOI/P8z4HIDFWL7UZqNrrCYZptBFka4FXUGyKb8vKygq/7as2ouxl4CaXV0uklB4LZ5dDwRnGcBduRiG3TLKQ/wL+ZRL+C4v4f0Xxgumj6HIFikSYEbeahH3k+qj33qD5vRS9g8XQe+c4QRAykkxKmYtiO8wYvg9FTNMY/i7mHjWQUk7R/H4OT+N3AcGbKLZHGUIoOdIFPdRFydUTruY/m/7jM/6wHsPavXj0p321cbWLtEggdoyobqwOmCQD5cS2pbXF1qZvU/EmsrKzuG/ufZwy6BSf8YuqTD3TAOGR9PEFlfQ0k3wINiF1qPIQX+z9gpum3kSUiGJE2gi/0wh3//OmFqSFti3VerOzITc7CffWD1tpdbeVEIL46HgaWhrYUb7DY/Ebyv6VGp/K5D6TdbaOwPOZ39n2DleMv8L9f3n+cssyBkIg2FFJMZZJJQe04WmJaZw+zExTILIRJaICnrcD3bjePOVmXtr8UodI4hk9urrVLaNiPerh9GGn67y9gnfi2jje2SURtRtgtU7M6mZT8aZ229ryh9g0jiPeyH4jdjt3247rC+sL17MoV7FNZUfKx8wjXzgQJaJola0s2eXpqdAOtPVt9pweJJkNMudQ5SG+zf9WN4aqBJ5Khnl7D5tamkzHVWM/CvRdVlV6zRAKDYO0xDT+dtbfaGxp5IHlD7D/6H6POP6qkwYb+cfy3V5K1T5hdWBVXFXscdAE+nXOa1teAxTTBlYk2dDUoToP2RecdIGbJEtLTGPhmQvd/UYrxd8Fc2RlZa10OByno3hyvBx4Vr3mcDgOoag45gNFKFJQRZrfZVlZWcE/oVSIsaGm14Q4HQOBpgSLXwEPAVpbFrGuaznoHRaeoxrXN89CvIaiNgmKof6RwElSyn1CiPdRTE0dlFLO0tzzD+BMIN5Vhp96y6O9EELcLaV8LFTpH68IpbrlIBTvEn1QOtsLUsonhRCTUUiubigv0rVSykohxFAU1cldriTWSCnN2FQ1/btRPE1kuAwN2oJO3dIPSbJIUP860WBcwFiJ1//rfIXgb2ltQSA4Y5h9+zm+VMbMUF5r3t38VQ8LB7aXbee3n/+WGybfwOXjLvd9gwme2/Cc226RCiklw3oMY/+xtoVYVnYWAAtXLbR1wmxm80RFJBCO4ZQke3b9s+SW5nL6sNM9VKeKq4rdJI+3egl3nRlPrh9Y9gB/Ol2RVF+wbIE7XDvmqr9tkWQmJ+HeFvktrS26zVZKfAoNtQ2sL1rvIS0QSpKsVbbqpEFUGDfm/8v9n26DZ4RWAi8QkswXidkqWz36sirBEQnvXzDgt7plO22SWRHrHQGtTTIjZg6YybzB81hxsM0ekD/Sb3bjat9Hb3XTHtIgkLXZrAGzeH7j8+7/VqTMjZNv5JWcV3RhgaisWREB/1rXJpxgNq8Yn23xlsX8dMJP/c4/EGjzjhbR7RoTtG1uNpb5lCQzaWN1vTGt/zSG9xxOTWONZ74m/VRKSYtsoaGlwVS9LtyS4+1ZIxrrZVzGOPY4FVt6WqlSd3wvhwZf5n3JOSO8e271tzxGvLPtHfdvtc3NNDAAHvr2IZ46/ymPwyaz+N7q0NdByaDubVKNHaH50RmRlZWVDWQ7HI5bgWEoBJH6GYdiMN/sdL7F4XCUAKW02SXTqmaWm/yuNrVJZgcKQaaqYn6CEBdqiLKJwAIp5Yv6W0Q0kCal9G53RwOt1JpL262/iyCLBR5Bkb4zMrCPSCl/77pnMQrh+Ixfz2cTQoghwJVAF0nmJ0KpbtkM3C2l3OQySLdRCPEV8F/g91LKb4UQNwP3AKoXhzyt1JgVXATcOcBBfwtlZpOssxvuP16l3Ix1/vCKh3lg/gMWseEnb/0EwKeR61DBbFOfU5LDleOv7IDSKHhuoyJ0+WrOqwGTZMvyl3mQZGC9sRmdPtrvPIxtbbVwCiesSLKymjJKakq44cMbuGv2XUzuO7ndeZXWKAdI721/j3tOvUd37fmNz/NQ5kOAsnmYM3AO3x/63iMNZ53Tpz29YEIgdCTz5sObaWptIqckh9zSNm/U2rZV1aftkmR9kvtwuKbtfbbanK88sJKj9Ud16abEpbgJ7fd3vK+LH0qSzGjrS4W/myGt+mUgJJn2HdpZvpPaplpO7ney+//6ovUem9NxGeM4XHNYN5Z11gMifyTJ1Gdsr00yqzEjHDDmWVhVyKDUQZZ9Z3jP4XqSzA9iz+77oyUhVMnGYLwb7YXRzuYXe7/g3BHnesS7bNxlHiRZe99FLXyRg5GytouOijZd34Ai0fXHZX/kPxf9x7JutOO22aGkkaT0h4j83ee/48nznuT1La97XDOr3yfXPsk3+xVPwBN6e2olBYso8dZ22jz87ftlNWV8tvczrp90vZKW4Rm9SeF5G8ufWvdUu0kyX9C2qzreDO0xlC2lW0zj/+az33gcuJrtxazeo5rGGp2auVncjj7I7szIyspqBfJcn4+01xwORxyKkEx/16ef5ncGiiH6ISjeLXuC5WDY5HA4rAg04/8CoCgrK6vVQJCBJ1E2CUXCzIgRrnT8hhCiH4qqpOpksAmF+7iWNq+UuK4ddd2T5srzUQwQQsSjkGxnAMnAk1LKZ4QQZwBZKB4zC4ArpZR1QohNwHvABUBfFFtme4BvgUQhxGYUBwRVgANFii0amOnyetoFA0KpblmMIl6JlLJKCLEDxYXrSbi8MABfAV/QRpLZxePAH3AZ/fcHWikgdbC0Y6wxkkmy4w3q4sIoaq0avU6OTXYb3jWDNy9K7YFRbNsIM3JOSxZ0NqjG9Cf38Y8E0noWBHuqLB4kWRi9eFlB3QgaF+xq/zpSd4T/bvovT/3oqXbndaxBMc684uAKD5JsSOoQ928hBPefdj9rD63l4ZUP6+I9sPwBv23xtRdaaSeAuiZrtQct7EiiNLU0EZeg33RZLWgfXe2xvtBt2Iw2hkJFkj234TmqG6spqPBcY1mVXVVlUvHQ/IdIjE1ke9l2d1ggkknfFXzn/n3PV0qfUvuH+t9YD+pcqH2HI2Wj7i/seLc0bpjaK0nWkSSZEY+sfISPr/7Ycn1jJAPaI41hBbP3rKm1ibTENI7UHbGVty8EUjZjfnuP7mXfUb1zjKsnXI0ZApnTreazpNgkr7arOpKg1tart/a57VPFPtsXe7/gotEXmcbRjl9mtheNc6y3/My8G+49spe1hWs94mrTGddrHICbIAPztb/x3TeuZ4IBbR7+9v2/ffc3djl3WToF8DbmdcRYnnckj97JvUmJT9E9q7oGuHnqzRRVFXk4d7KC2btk9cxXvXeVR5ixviNhrD4ekZWV1YhC4PgknBwORzQKUZbu+vTy8nuM5r8Zh9H42nXXHf5pdPSAmJYWY+O2EWWK1NvLLgP3AM9IKV9AkYQbKYTYguKE4HYppafussWjAM+6+A8tJgK6xbkQYjrwKook3vUuE1VGPAFUokiiAWQIIUYD/wdcIKWsFkI8ClzpkkYbC+ySUj4ihPg/4FIppUMI8RxQLaX8tyvvPcA0lxZfjy6CzBqhdXXigkuVciqKkb9tKOzmh8AVgFb0YZgQ4geUTrFASrnSJK2LgUIpZY63Ex+Xl4dbAPr06UN2djZNrU04y50ArFu3jqLEIpzlTvbX78dZ7WRQ0iCys7N16ajxN27aSEOe5+Qebuw5tIeS6hKys7OpaKrAWe4kJyeHuILwGb0NNXKP5OIsd3Jwy0F3/avIzs7mWPkxRnUbxcajG91hALNiZ7G0eCmxUbEe7WiF0tJS3Qlpr/helvfGHIvB6XTq8oS2PrJ3116P8hrjhgsTxAS+Lfu2XeWY1DSJz8o/IzE+0ePesrIyRJXAWef5vFFVUbr4T+55Eme9Z711r+vO/hpl7tnXsA9nVVta7657l0FHwycVZURzazOP7X6MiqYK1q1ZR2psmwFqbRvHVtvva94wsGkga51rOb336e70Lux2Ia/mv0rB3gKyK/R5lNaXdmhf25+/n+rmasrr9KrHS5cvpVk268q2bt06yrsp8dTw9RvX07zPO1FVXFoMCej62Perv6dnXE+PuGq6n1d9ztgaxaZsaWEpzmrPOgLYumMr2UezfTyl//jfFsXg8zfl33Bm1Jm6a1JKjzb76KuPiK2OpaiuzT5f1e4qqqji/i33u8MKagu8tm1edZ5H2r8c/kv+s09v01FNQxtXm+7+ov3uMU5FfUy9rX6VV5BHUV1Rh4x3ZthVvIvSI6Vey7OtYhvOcidr1qzhQOIBcp3K3LNq1SqSY7x79TVDQ00DznInzYeaQ9K/vOFQ7SHT+TKnPMc0PLElURfeGNtoWVe7inZ59AuzuNXN1bo016xbw+Fk5fBIDd9dvxtng5OKpjZ1+00bN1G1Wy/xYRcry1fiLHcSVR1lu++1yBaPOsnPyafiSIWb2CvaU0S2MzugcdZ4T21iLdlVnveMaR5DbnmuR5ruupK7bdV7KLC7ajfOcifr16+noNh8n5ud3VY/O7btIKXYU80cYF/BPpzHlHj7qveZPoO2zrZs2aJL60DNAff18RnjWVG+Qnfv2h/W0lzR7O5Tavo7K3e67zvccJjs7GxqjtZQ36Ic3O1jH9k1+rIY+8aDnz5IzETvWyWzPvLG6jfoXdbbJDYcbTzqvmfVqlV0i+lmGs8M+QfzcdY5+X7N9xSXFJNQlUB2Q9sz1LXUmZYHYM2aNeQn5FuWvb1968CBAx5z7vWvXU/32O7cO/pe3tr6ljt8w4YNHOumHA6KMmFZZo+1Z3mZB3mcvTJbt0ZTYTbuaddOxn1hpMxdJxpcdsrKXR9bcDgcAuiOQpb1RyG3pgGzLvjkk8kxLZYH7Un7FVNQZVJKM6P5q4CBUspGIcQjwELAk201QAgxDjgfhcQzYgKekmQbgPFCiLNQ1CzfM6TXB/gRMFK2dfhSIUQWChm2ysWBJLnSHgXsdNl3B8W+muoaeRJ6W+5VKN4wX5JSZvt6thMZISfJhBDdUBr/ThdreTPwLyHEAyjimeoxTTEwWErpFEJMAz4UQoyXUlZq0koC7kdRtfQKFyP8AsD06dNlZmYmz294nvRe6QDMnDmT0emjSS9JZ0CvAVSUVzAofRCZmZm6dNKLlfhTpk6x5c451Mhdm0t1STWZmZmU15aTXp7O5MmTyRyR2dFFo7apluc2PMcvT/6lqU0eu6jfW8/KhpXMnTuXU+Wp7HTuBGByn8lkZmbyyrFXGNVvFIOaB1FSXeJus/lyPnXL6rhk7CVM7z/dVl4ZpRm6k81RaaM8+oCKHet3sFfsBdDFUfvIsJOGcV6f8yirKdNJnFmlF0pkkslFb7Sd6l457koyJ/tXDm/9q9dhxUNV41HPU9bkuGTdMz9W/Bjp3ZQ60obvSdlD5W7l9R7SdwjOkrYF4/9XAAAgAElEQVQFzYTeEzqk3kAhM55Y8wQxqTGkk868ufN0ruPV9gYY1MNzzAgEeZvy2LtrL9MnTSdzrJLePDmPT978hJPGnkTmBH0eTS1NLDq6yCOdcNXZt9nfUtFQQf0RvZTg6CmjSYpN4u2Kt91h15x3jdt+mVp3EyZN8DpmSSlJK05jaPpQGpxt7+dZ888yHVvUdFtpddfBobRDHN1mLqUx6qRRZJ5snX+g0PYNs7ZIL0nX/Z9/2nwKexSydO9Sj/u0aQ3uNdhr26YdTuPDqjZvlgLBTRfcxPtv6NVM1TSuiL+CZfnLGNxdn27+5nz27Niju2dKnym2+tWG1RtocbZ02HtrxHtL36M73Rk3Y5yHFzwV8QXxfF77ObNnz2ZYz2FU7apideNq5p02L+A57NrWawOyW9Ve7Hbu5q2Kt3RhmZmZVO+uZm3TWo9wgPPPPJ9fL/01ByoOkJGUYdl2uzfuZvduvcF6s7gV9RWkl7X12xkzZrhV2tT+fOHEC/ky70sS6hPchNSMGTOY0tenpQ1TPPbGY6T3Ste9+3bwRMkTuv9nnn4m30d/75ZImjBpApkjM3XvIcCk3pN85mO8Z+a0mcwZNMcjXvXuatLrPOdG9f5RI0e51xwqwvV+pRSl8HH1x8ycOZN3Kt4xjTN62mh3Wc+YfYbluitnTQ6H9h8CoIkm87FRU2cTJk4gc3RbnB1lO3i38l2un3Q9Zw47k21LtunuHX7ScFIGprilxNT0U4pS+LTmUyVO/+Fkzs9kR/IO93i7k538PfPvHmUx9g1fdX5377s91HKbaba8r6ymjJecLwFwVuZZHvY9veGjho9oONLAzJkzWda4jJG9R5I5R5/PM6Xmpo1mz57tYePr8vjLWZ6/nKGpQ9vdt75p/Yajh83n3DmnzdHNf+Mmj3M7eDq64ygbmjeY3qctU1NLE2nFaR5x3qp8i9cv81S31fap5y98nv4p/ckpySH9qP6dmzp7Kt3ju4fVw26IMVoIkW0Ie9ulqpcELDW55xUp5StCiF6YO9F7Vkr5lsvckefiEx6TUn7sknR63uT6w1LKr4UQU1AkpBBCkJCQEJ2QkBA9ZsyYf5177rl5GzdunH3w4MFb4uPjY+Lj46Ndn5iRI0eu6tmzZ0NFRcWIioqKifHx8dGJiYlxCQkJsXFxcTpR+yU/+QlXv/46cU2mKuK1D8PTwCyziwYb59+j2Fezg0eBP0kpdWpOrvo0pqtFDmC2QJkI/OBS2dRiMnCulFKnHiGEuBxYownSqpNOBLSSarOB84AHhBA/klL+wfKpTnCEdCXnMlr3HvCalPJ9ACnlTlwklxDiJBTdWaSUDUCD6/dGIUQeimqmduQcgSKaqEqRDQQ2CSFmSil9uv3JP5ZvLB/QZi/Cq7pBJ7XLEk6sOLCC5fnLSY1P5ecnm3rS9Rv7jrWpQDx8hqJepoqM/+FU/XsthOCRMx/xK32j+LkdcXSjOP7Dpz/MguULqG1SJFb9MYAcLoTTIKnRBoSK+Gi9Gt6kPpP4aPdHxEfH0z2+u+5an259QlY+X1hxYAXL8pe5/3vb8AarXtV0tGoDUSKKmKgYD5VGiAxX7lrcMPkGXs15lc0lm5kzUL8ZNNsA2FVfH5w6mD7JfVhxcAXnDD/HJ3GRHNsm/WPsb1q015NeoPjtzN/qjHWD9XjhS73bCj8a+SNum3Gb1ziqTaYnz39SF37Z2Mt4b8d7zB001+398LyR5/ldhkiAWncbijbwo1E/8hpXHfeDoY7UEQQZKA4+zGD0emnEgYoDgG9vqFozBxedZK5W5w2LLlnE9R9cT0xUDBJJ5pBMvt7/NRCc9ZVWLd2f8gCcP/J8QFF/VDE+Q3FWdv/c+9l/bD/by7aTcziH8b3HeyZmgeE9hrPv2D7LfmU2tgO8cvEr3LjkxohYdwoE4zPGs61sm8c1rRS+N4JhZNpId1u/+OMXLeNZQa2/k9JPIj0pnfvm3sfCVQvd12uaakiJU+YGrU1Us7mnR0IPn/kZ1YF94bJxlzGw+0DiY+J5YHmb9Zjy2nJ6JfXyiK9dN3ibp8ygrj/VOdRsDbLgtAUe5hjA/D1T8x+XMc6vcvgLo2dWrSq21RxoXGvvcu4yjWe15tRCNd+i1pvaXwDTNuqCOWJiYoiNjY1OTk6OTUxMjE5ISIiJj4+PHjly5FyHw9HnjjvuGJ6XlzckLi4uOi4uLsb1HZ2RkfEPh8PR/Mc//rFXU1NTv9jY2JjY2FjtpnsOwLRp05g2bZouz9bWVoAfAxUJCQktDQ0NMY2Njc0VFRWVdXV1TTU1NY3Dhw9/uW/fvluWL19+0s6dOy/7VIik/8G4BNDmUQtc+JJCkO00PpsQojvQKKWsd9kKuxd4ynXtZGCIlPIDk/tOR/Gu+ZJJlU1EI0UmhEhAkVTb63IS8CBgdgpRAowSQsS5pNp6u7xfFqNIrKmqk5NcqpoTgc2a+yfRRoz1Bipc8UcA+6WUH7kIvMBOp04QhNK7pQBeBHZIKf+pCe8tpSwVQkQBC3CJAAohMoAjUsoWIcRwFNFBnZEIKeUWNIyrECIfmG7Xu6XWeGpOSQ5jeo3R2S3xZoeks9plCSfUSWjGgOC5Ub50zKW8ue1Nj/BQuNC2g8SYRBZfulgXNrnvZNIS09y2EgSCc0ecyxd5X3REEd144cIXWLJrCUv3mB0ahQ5j0s2kjeHvZ+tPbGcNnMXg7oNJS0yjW1w3okU0fzj1DyxctZC+3fqGo6imMJLpRkLqrtl38c81ypAWLHsW6mLeKE0RHx1vasMFFHfl64vWByV/f6GOh29d/hYFFQXUNdcByhhgx+mCrzFCTb93cm8m9ZnEioMrOG3IaX6V0WzT9uC8B4M6PlnBkekwDT97xNkeJJkWF4++2P17Wv9pbqLHn/Hu/FHnu3+bkXIqkmOTPQidlPgU4qLj3P0xLTHNVAKmMyAlLoWqxir+l/M/3ySZarhf2ve+GmkwbpbVjZ9RcsQKvt5bbfpGe6FGXDHuChpbGnUbby0BZfRUvbZwLVP7TbVVTiuM6WU+71ihR0IP/nH2P4iPiXfXkeoR9r8X/dd9UDNn0BzmDJqDlJIfv/ljv/Lw1Y/mDp7LKzmv8MgZ+sM9fySLwgFt22kRiK1EK6nOly9+mZuW3AT4Jk2n9ZvmtpkKikMY9aBNe6+2j7m92bvmlt/O/K2ldsjLF7/MxW9ebHrNCrMGegqm2BlH/D1os+NwbNbAWVw1/irTtbMVQrnHmT9kvofNXq2t225x5uqmRoLfqh/6wssXt9lnz0hWnC5eOvbSgNLqJNglpcw0u+CyP+W+5nA4YlDMHg13OBw3PvTQQ2nASiANxU6Y+v1nh8Px9IIFC3qgGHs3YiJARkYGGRkZAPUoan3qpxKoio2N3RsbG1tluKaLY/xERUXV2vVwmZWVBaoRfL3x/lpAMdovxC+A+UIIdbEkgdNQPFA+K4SoA1qAf0kpVbWIXwA/GPNzcR1/B/4gZdsk6nJYuN2Vd4IQ4hAwF2gFPnARZM0ottnvMaYrpdwqhFgCbBVC1KJo3j2Iwpu8IoT4lauMr6KQYRNxSQgKIeKAVCllmSu5T4BdQoh/ozhTuEIIUQUUAjfbqdcTFaE88jwVuB7Y4vKoAIqq5CghhOoq733axAHnAX8SQjShdKJbpZRHAIQQ/wWec+nwBgyt9MKb2950u9J2kxteJqsuw/2+EYoNxjUTr2Fn+U4uPOlCXXioJKN8Lc5io2NNN9+qJzX1/usnXd/hJFm/lH7cOv3WkJFkVht3Y/ufNewsvt7/NcN6DvOImxyX7F6cJcUmudU1OnKTajQ2bdwIaImsYJVTJSuG9NBLQ8THxFsaDj5/5PkdRpKpSIpNYnSv0ew9oqgEpSWmud+B++feb+n5My3RU2WiPYiLjqOxpZG75tzlDtMSranxqVQ0VDCw+8Cg5mvE6PTRxEbFur1ImuGxcx7j7i/vBto2JsmxySy+dLGOtLpu0nU0tTTx0e6PbI93C89cqCNF0pPSrSNbQCDc5bp87OWdkjACuH3G7fztu795Lf8PJcqaNys7i8WXLmZDkbLE6IzzvXGc6p2kkBEJMQnERMVYOqt494p3ufydy5k3eJ7PPF79yavc8OENPuP1SurllZhU+9e4XuPYXr6dT/d8yq3Tb/WZrjcEQiyN7uXpiTk1PtVUktlNTgRRuqtPtz5ena2o9TQwZSCHqg4FLV9/Yce5lTfYIWB6JfWy3b/iY+J5YP4DbpMS5408j9UFq73mZWy/s0ecbZl+lIjiynFX8vb2ty3jtAftOeRVxzNf/dDMgU44D/ufv/B5fvXJrwDISMrQSYEnxiSSmtBmQ2z+kPk8vuZxn2mqJLZW0hng1EGnmsafkDEBIYROUmxg94G89OOXTkjpMYfDMRjFU+IsFM2s4SgeJo1cgASOaj5HgP2u76Oa7wrMya3qrKwsc3e44YRCiF2IwjPc5PJqiZTyWos7vkHRYNPBJWHWH7jDeM1lL8xDz1xKWYXe5roWE+0VX96Pwptow3bhkrozhF+q+d2I0q7qf6Nq1x/t5N+F0Hq3XIW1S9cnjQFSyvcwGK7TXPuFRfhQf8qkXfiqC0YhRJe6ZZCgTsCBnC7q0jGcBP75jD9bXg82Al1E6CQShdAtACIBwa4zf9KLjor2SopIKT3VXjvwfTNukI3SNkbV0FAiLirOUiXHiJPSPOb2DoFbnSE+xePk9+zhZ9t+Hn8QJaK4ZMwlzBww0x02c8BMTh10Kt8VfEe3uG5UNFSEXO1YSunTrslJ6W3tJKV0S9UY+1lMVAxT+03lo90fsbPcQzPAFrxtxqzqIpyq2aGE2he00nlGVNQrhr4rGpRvVfWwM9bBnEFzuHbitYzoOcJjw+ltPI2PiXcTalZQ71fn9mBIcgsEswfOZnv5dt+RvWBS70nkluZaSij5g2CRCP8+/9/8+rNft6Ub4Hym3nf1xKvZVLxJ55kxnNC+DxlJGZTVlnmJ7Qn1Oc4dca7XeFaqh77qLyMpw3Zcu5gxYEZAJNmZw850t5NVWYIxvrTKVq/91d96CPaaq39KfxZdsogbP7xRFx4bFcv9p+n2/URHRXPHjDt4ev3TXsukPq9Run9Gf3PpcIk0HatUabITAQ6HYwxwE3AZCjEGikH3PSjmjN4G8lA0tw4CTqAyKyur850UmUEhxoa2LwlZCfwkKOXpQqdCxxjP6CCYuQ6OElE0tSiEt7eFX2c8WQ437Ejk+QOr9rCa+AKBxyQc4EJBIDzInlkDZlFW499iMhQI1YbPKl1/NhpuFQhXvRtVIjoCRrUj43OG0+acNwkQLQamDIwYYtYbWf7bWb+1l0YQFvhCCO6cfSffFXwX1M2913IgA3rfrMo1IGUAAD8e7Z+aV3txPBwKqdIvO8p3WMcxbLYuGHUBi7csdkssdCZEiSiumqA44RrWY5hu3LCjAu1rzDX20ZbWFj7d8ynnjzzfbxuJKjE8td9U2KzUe6AI56GFXQzpMURH3AQKtU2iRBTpientPoAMJG9oa/uLR1/MjVNu5JK3LvGIYwc3TPYtJeYtXW9rQrfKtOZe3YGrZm1hZx7wV31XxZ2z72RcxjieWveUz/qZkDHB7/TtrpG0KtG3nHwLL2x6IeTjurFMPRJ6eMyHD85/MGAnHcZ1op34nfHAIxhwOBwpwLPAtShqeV+g2NdaBmw7bkiwLnQhhDihSDIrFFUXAeCsM3dBDF02yexAJRJDrZ4TiRs4IYRHH9FKl53I8NZeOpshQkTEgkbbZo+e9ahpnNHpo9nl3BW09vV24hypY4/VQjWYZLntxbCPOlKvh6N/2Slzt7huVDdW+yx3v5R+fHSVfXVLj7JYEdne3kkit8/5A/XZNxZvtIyTGq8nlu1IlXcWaNvw2onX8tqW1yzjBtK/vtr3Ff/Z9B9qm2rd5Jw/ZRMIt/Fsu3bTQg1fawt/SHZtXH/fp0iYB1VoyzI6fbRO4tXuWszu8/t78NY7qTeltaW6A0pfZfJn/ZgQk8B5I/x3XGI8/LPC/KHz/U7b7th0yqBT3LZxO9JrY3sOpqwca0XS+xHB+DtwNfAw8HRWVpZP53Zd6EIX9DjhSTLthFNea23/PxKJmUiD9tQzGOl4Q7AmyWBtBqNElEcfUe2UHY/w9lx9kv3zTGl2Yt2R5KKVlzgtxmeMDypJpsJsQWln7ImkRWOwxgF/4dXxSpjG71bZaqstbph8g4dqiRVCJglqUV+R1JdCDeP729LaQpSI6vR1YCz/VROu4qoJV/HRro90HmDtwmy8V705q99W8UzTc0l4BKOegz3H+irT8TqnG6EdM1Vv5WbG6bXxr3v/OuKj43nxYnMPlnbb266aoplXaMs0tesMm+UI9MDAjnH9QGH3mYUQDO85XBfmVT2zE/RrvyXJOsEzhRDTgOVZWVkP+IzZhS50wRQnFEnma8BUXX6boUsiyDeCLUnWERuVoNgkc03gQpyYkmSJsXpVJW91qlVTFUSGJNnmw21elK3K87PJP+P9ne+TdzQvKHn6q17S0fBGOk3uM5lFlyyy9FhlK/12bEy8pRdydcvj4DBFfSePJ6jkV0NLg87Au3F8bpWtYVVpCzes1HYDeS8W5y4O+F6jylsw+lu4+mw4341IeA8FiuHze+fe63HNOE6rtv084tl8Dn/7UnpiOodrDuskpXTqliaql/6Y6xAisLHQriRZIAgk7VA4nTBDuNQ5jXO91zVmBKwpOwhFwEifsbrQhS5YovPrFPgB7QA+sbfiXCJKRLnFx73Z9DnBTyRsIWzqlkG0SeaRdqA2yVxqcUYbGJGwyI0E2F6UhnBxGQisyh0qu2RmJ+W+vHV1FKzyj42OpUdCDw9D9GbIKcmhoKLAax52+oKl5IHR5l2EqFuqMHNaEQ7YVU89XlDbVMvi3MVc9a5eLdBDkky2hNXmYCTB3zG3qVWx5RrIfB9JNiiNCMTZRbDiG8tgJPcjqZ6MsCLItPA1NvpbX/efdj93zb6L9KT0NhLMoh9r1TFDLUlmzDMUkNifO+zMSZFIoFnZDI7Uw8NIgat/TwUOdHBRutCFTo0TiiSrb653/9YajnTb0Oky3N8uhIsk60hY9RGtuqW6ADuu1S29LHiCshg6TuvNXxzvROuC5Qu4fenttuJ6Vaf0QZyHqz/ZNdwfrk1vIJsJLTHZ0URse3HnrDsBhSR7e/vbHgbsjfP6F3lf6NYJJwp8qhgaxqCg2BuMEBuU4cDxNobPGjCLYT2G6Z7rlc2vWMb3d5yzGz81IZXTh51u695wtoG6Bg6JJJnxIK2TEUbBsFHmzx6js9VPMNC3b98EYBDwbkeXpQtd6Mw4ftkME1Q3Vbt/q4Os9qTIbMEWaZItkQyjumF7YenJKIRt0R51S+PJ3vGubumXEWOLzZBWUkq7aerI900rARXuTZypTTI/7PyEC52FxDT2p0hRt4xkcuB4MdwPkBSbBMCW0i3uMG0bjUgboYuvta/VBT2s+mxAkmSqen0Q1lfBHPfsePisaqwKuZ1Ij3ErAscLoxSsse7qmurcdn79fY5AvI77Mtzvvh4GdUsV28u2c6jyUMD3txfG5wyXTTI7UuRG+CPt1l7bdsc76uvrW1A8Ws7s6LJ0oQudGScWSdbYRpLNHKCMHdoFntliTw07XsmOZfuXsblks++IPiCl5Ik1TwDhkSQLxqKxubWZ5tZmXZh2Ui2vLeemJTex6uAqn2lZGu6PgEm6vcSAXSO6/t4PekmpSDnx0/aJcJWpvXUcKXUXqXAT+BGibhmu9grEu6Wd650F6mbtybVPml4/f+T5jEkfQ1piWjiLFXIEaicsXDCqvEUSKettjGiRLXy29zPe3/G+X2kG+nwdWS++bIn6wu+//D03LbnJr/vac11rc8wbwqFuqa6Bn1j7BLd9epvf94cK4bIZZiTJgtWPw2VbtLPj6NGjTcAi4JcOh6N7R5enC13orDihSDIVA1IGMDh1sEe42cAbSi81kYDH1zzOA8vb7/yksqGyXTZKtPC5gfPjJLCgooBj9cdMry1YtsA0bRWtspXy2nJb6jeq1JjRJllnJlfDuRAxerjsqPfNH1WrK8ddye3T7akKdsE/+LuY92fjE0rYVbd0x+9AMsqbdOfxArPxVxsmhGBIjyHA8UMMhhuBzvdaRy2RUvd2y7G2cG2ISxI5sDseGL3DH6w86P7tTVvDDEEjVaR+Pedv2gEb7j+OxlB/oB4wap2ftKcu2muP9Xjdt/lCXFxcFHAWsAWo6eDidKELnRYnJEmmXSRrF3hmA28obQvYQUV9BW9ve9s9+UTKYtKIhpYG9+9ARK39hd1J8valt3P9B9e77c5psa1sm0fYT8f/tC0PP1RBdGq7tHm3PFEnaS3sGAnXkhztVXFoD7TSpmpZrHD95Os5f9T5QcnXm3dLX0bp4fhdlPvj6t30kMMV1kpwVcEtyyH9cypidPZhBUe2g4veuIjVBaupa6prTxF94nhSt1QlxrWwejbVXtl1E68LaZkiFb76YTDfnVbZqqjXByHNjuirdojBoDxbB673vEqAmzgqMmJgykBbaRnTNS2LH21sqW5pOMC0VZ4IHAv9cUYT7gMbNY3u8aERXgrEpMvxujbyhu7du8cA/YBG4DKHw5HUwUXqQhc6JULPZkQg7Gw6jWEdIRF0uPowv/j4FwAsyl3Ex1d/HLS0K+orPLx4/fGbP1LXXMd9c+8jIznDr/S0tlziY+KDUkYr2J3MtWpzj33/GH849Q9e40/pM4X5Q+e7//sjRWhmfywSF1jBgr/PZbWoEUJwsOIgA1IGmNoJDDfMVGZ94buD3xElopgzaE678zdb0EViH7KzEYlEhHrBbNvbmIkXU2/YULwBgIWrFjJv8DzuOfUee/kEuFGP9Ha0C9P3yeLZ1IOUcBzyRBr8IaONCNQmma90OwLB9BbYHkSSN0tv62Jv5RuXMY7aZr2Nv/bWm7dxUmtzzAyJsYnKdT+ljgMZCyPNeVW4NGJS4lMAePiMh0OSvhUxeLzMV8FCeXl5I/AL4C/AW0CTw+HYAmxyfbYC+4GirKyszqvu0oUuhBgnzGrQTOwafNsk60h1y1s+ucV2XCklP5T8wNS+UxFC8N9N/yU7P5vFly42jX/dB56n5bmluQA8lP0QT1/wtEf6r295nXlD5jEodZDHvVoJnPjo9pFk7a3r2qZaNhRt4O+r/+4OW3lwpU+SzDjxqv3BaLfMKs/GlkbSE9N1ElGdWd3SF3wteFtlq884uYdzkUi+3v813eK6Kel2oCRZIO311+/+CuCVxG5pbfEgpbWwet5W2UpVQ5XfZQoHdJJsQd40ejMIvTh3MW9te4v75t5H7+TejEwb6dOmW7CdinhDqDdIZbVlQUnHG3F9PGFAygAKqwrd/31Jknl7TzsTKuor2OPcw6j0Ue1Oy6t9Kj/7i9YGZTDULYM9V9gZI/x9xwMtY6TZ69TC2zNd9MZFDE0dSpSIory2nFdyXgla2r7u0d6r7bf9U/r7nWYkj4V+qY26+k/u4VzG9BrT7vS8YUTPEfRO7h2UtKz6gdX7l1OSQ1FVkU7CPxLfnXAgKyvrFYfDsQiYD5wLTAUuRSHPVDQ4HI4DKITZfqAYKAfKNN9lgDMrK8v3RqgLXTjOcMKQZFqiQ1W9sYOONNxvzNPbwuGT3Z/wwqYXuPfUe5k7eC5Ldi0BFPJKJR/soqrRc1P+h6/+wE7nTj7e/TFvXv6mx3UdSWYiSdbc2kxBRQHDeg6zXQ7jAqWxpZHvDn5HTVMN+47us7zvyTVPsvrQatv5qGhqadLn75pcn93wLPOHzCfvaJ57I2VEcXUxoNSdKpEQJaJoaG6gsaWR2KjYiF5whQIXv3kxpww8xX2CawbtwkztQ82tzby7411umHJDyMuoxdvb3mZR7iJdmD8LrLKaMlMJzMqGSq59/1p+NulnXDH+Cr/KlF+RbztuJEgdBBvad0b9/da2twBFqgrayMlIeL/szhPt8eqntfeiwq7jh6zlWfz85J/7LNvx1Je0BBn4liQzq99QorKhklbZSmxULMlxyUFLt6i6iLu+vMuWBLq/787RuqNt9/qhugawqXiTW5qns25gm1ub+WLvF5wz4pyQ2PZT711xcEXAaYQavtQt8yvyEQjyj+W7w3zVSSjVb6WUHKk7woe7PiQuOs5WWu013K/NO1jzk87hhc3549WcVwFFI+XK8VcGpRxWsPRKH4Q5xdv8WlZTxoLlip1hlSQ70SXMsrKyWoBlrg8Oh0MAg4ExwDBguOt7GDCD/8/eecdJUd5//P3s7RU4+h0d5ER6R0A6HiIaiQjGhklMURONUWMSE2PLsYkltuRnLFGUYMdYKRYEgaNIld45yh1wcG2v9/b8/pidvZndmd2Z3b1iwofXvZidedo889TP8y1g6sHG5XIVoBBmxSh2zso9//teB3pWrvm/CkUdtOq8NFsjQIjpwCLg50i5tuG2uAOYD2QDAjgK/FFKedLz/LfAb4F8T5QXpZSvB85K7AFdB79CSpkjhEgE/g30QyFhb5RSlgkhkoG/o5j8auXJf2l4LxywfFcDaVLKI3bjNhpJJoToDbwFdEWpvAVSyuc9z+4Bfo3iovZzKeUfhRAJwEcoHfUNKeXdAdL2ix+sPKpRefA33BvwPTwDflVtVcBwjYG2MW11hFUgiabssmwA3OVu3X0jW1y+8N0UaOtKxWH3YQDKaoxtQJ4uOm2a/q5zu/hz6p8BeGPOGyS0TghaJiNc98F13msje2IqQiHIAPbn7tf91raNfTn7OOIO3r+0TgIKKgooqynzlvvlWS8bSuEFw5LDS9h6ZitPXv6k7bgqmtRzmSavTWc2MePCGSEtfpcfWc7sgbMjWTRTpBem+xFkEFx1ePbihvLduuxWww1pWbXSZ97a+5ZtkkybzwtXvUBSh6SQ4rc0vLjtRb46/uyisSkAACAASURBVJWtOIWVhbr6VpFZnBnUmUd+hTLfq1KKHxz4gBl9Z5DYOtFewS3AzmZIVae3g0BSLMH62c6snVRvrw44DgkhvPOGOudU11XjEI7/ClVEI0kxKWWzSZL96JMfea8jaVLBLuzMEWvTvWtu3tn3DnMGzSHOGWcprjrPam3rhTM/RXJuO5J3BHeFO2i4g3kHOZh3kFhnLMlJyUHDh1vG5nRwYDSe2TGcHooEVyS0CTae2khxVbEuzZe3vwwo45kVREqqfW36Wi678LKw0wkVBZUNpHYwqfbGgJSSbZnbgMgcZhnNc7cuu9V7XVFTQXRUtK381EPSn478KdcPuT7sMrZEpKSkSCDD8+cHl8sVDSQAnYFEn//Vv7ZAa891EhDv+R2PQnjYhsvlqqOBNKvWXJv9H2qYGs+f2XWwZzXfCUJPIcg+Q/kunyHE1RqibDjwZynla0pQcSfwiRDiYqkMdkOBO6WUX1jLSkQBnaSURovKt4D3pZRvCSFeBH4KvAzsAsZKKeuFEJejqAU3GkkG/B4IyctaY654a4HfSyl3CiHaAjuEEKtQSLM5wEgpZZUQQpXLrQQeBYZ5/gwhlI9vFD9wYTQEk3bSCyY2rz5//8D7/GjEjwKGjTSuH3I9i3YvYs7AOSw9sjTgwiGciXxk15G6377Gy62kPbzrcAA6xHXwe/aPLf/wXltZnLSU0x/tRPz4hscDhu0Q18HPi+a2s9t0v+/64q6QNkELdy20HUeLxpKwUYlTX3x1/Ct+MPgH3t+rT64OKf0FOxeERZKVVZdxIPeAofFuX9zz5T2G9+OjzSU7nt30rN+9ZUeWcc3Aa7y/S6pKdAv1ellvOuYEIzju+fIe/jr9r4zqNgpAl25OWQ4ZRRlN6uXRKJ/HNjTYIjFr62eKz/gRZHnleaaE1ZLDSwB4f7+/BCvA8YLjlsoLSh0fzjvMO/ve4Z1979CnfR9uGHKDzhZhqKipqyGzJJOLOl4UvBw2vpHv4YgqtRoqymrKSC9Mp6iqyPB5fkU+2yqUsWvJ4SXMHTSX6z64jk6tOvHm3DfDyru5MaDTAD+iTyAoqS7hlk9vAZpWkizQXFdaXcp7+95jbI+xXNz94pDzmL14NsvmLQvY5qrrqsmvyOejgx/xedrnvHr1qzppG99yqodyKm748Abbc9uK4yv4+eifA8oc171Nd8b3Gm8rjUghsziTOz+/03a8syVnTZ9V1VXhrFXamrruSXOn8buVvwOUucVIKt8MdbIOieSGD2/gwxs+tF1Wuwi23jxdfJqK2uBORLT9LdgcZ2q438KaUC3vudJz3PTRTYZhrJjOMEvXDnzf8x9b/sElPS/x0+rILcv1HvwsuWmJLfLKSp1oJT5V3PPlPbz8/Zf97p8pPkN1XbVlKTu7Zfr08KeAtYN70/RV51g+7cR3PXHjRzfSLrYdXeO7Bkzv1W9f5bO0z/i/K//Pe0j65p43/2tJsmBISUmpAbI8f7bhcrkcKMSMSprF+1zHa57HAjGev1iT/33vtQY6BgkTegO29o512CfXjH5X+fxVWrgXNEzK/PlTaSDIwJ8oGwG8q76PlPIVIUQK0Bs4BQwBnrZRJRcBflIyQoihKOTZW55bO4CLPXkWecIIYDLwtVHCQojvA3/2vEOulPIyIURn4P9QpNPigF9JKTcJIe4FRqI4rBgCvCqlfFII8bQnj8VCiP3AfSgSdj1QCN+HpZSmE2qjkWRSynMo+s1IKUuEEIeAnsAvgL9JKas8z3I8/5cBG4UQ/YIk/Suj+MGgVaULJLK789xOVh5fyQOTH1DUAZpRhSfaoZyENMaiPT463lQqzBdaz5VmUCfH3034nd+z4V2Ge9UG7Bj1911o/Hj4j3lnn2Jj7cEpD1pOB/SellTM7DuTVSdWeX/7LvLtfPsx3ceETAYFwlfH7EnbNAWsLM7CWQhFAnX1dcz7eB4AC69ZGLKNjECqousy1vnde23nazqS7Ief/FD3fEPGBkNCxupC/NG1j3rb6eL9i7331c1KbnluxOyBhIva+lpDyaOHVj/kd+/nS39uuslWF/qni42lVT88oMxvRhs23zGkuq7ae6INkFGUwbObnyU6KppJvSeZvIk1qFJh60+tt2xY3wqe2PCE7ne4NskySzItS1Jo54j8inwvCVtbX8vJgpMRsXfVlDBTl9duns8Unwkrj9r6Wtalr2P50eXcMuIWxvQYYxrWVxVUi8fXP87+3P0sP7o8bAmza96/JmAaueW55JbneiW0Mwoz/L5tY6yFtP3zsQ2PNZsk3YcHQyOdFu9fzI1DbzQc5/Ir8r0SrC9se4Gq2ioW7FzgfV5WU0Z5TTmto4M7nhMIVhxbAUBlbSXFVcWN5kHQClRp/Xf2Gtu81SKYB3kjvL33bS7vezmdWum1v8JVx7TrGTi/Ip9VJ1ZxzyX32Gr/Rgdhi3Yt4p7x+sO4Dw5+4L3+2dKf8fa1/tLsvrBTByoxpYXZPHrEfYTrPriOD2/40LJUqGH5DOppa+ZW77XvYbKKPdl7/O75OfjwrD1969dIy6O4qphOcZ1M93sfHviQz9I+A/y1Z/LK83h5+8sUVhby4JQHcTqcdGzV0TCdFoqBQohUn3sfSClfFkK0BoykhN6QUr7hUZH7yOD5v6SU//FoiRk11OeklMuFED2BVw2ePyal/EwIMQqF5PDFQx6yYxKKZJEv7pNS7vZIHj1i8PwOKeURh8Mx2+Fw3O90Oh3R0dHC6XQ6oqKixKRJkx4YM2aMe/ny5d/Pz8+/KSoqSkRFRTk8/4vk5ORnEhISKr/99tsZeXl5yer9qKgoh8PhEOPHj38rJiZGHDt2bHxRUdFQ7TOHwxE9ePDgDUB0dnb24MrKygs999U8ZOfOnU8C0WVlZd3q6+vjHQ6Hw/PMERUVJRwOR9gTbNLJk1RHRxNT46cN1kCUKUJIvqpYFSjk4ylgMPCeECIahWtZTGAMAfoJIfYBZ4C7PKqbPwS0A1AbFHIQACHEF8AEIBW4Dh8IISYDj9OgutnN82gh8Bcp5bdCiCHAS8B0FAm5jsBcT147gCdR1Dovk1KO9aT7FPCxp607PGFN0SS6E0KIJBSjgVuBZ4CpQojHURjQ+6WU220kN8BKfCHEL4FfAnTt2pV1G9fhzlPE6Mud5aSmpgKQk5VDUY1yor6iZAVvb1H6/qiqUcRFxZGTnUNZrbJRUOM0Ffbl7cOd5+bwkcO489ysW7eOtLNpZJVmkZqaSlFNEe48N3v27CG7Kht3npt9+/fRPqu99103bNxAG6d/G8jKyfJulNKr0klNTfXGcTqcunctrS31PhufMN6wHtLL0nHnudmxYwdFbfQSCjJfeuNv3LiRdtGBF3fqe2/YsIHWzoYF5NG8o7jz3Nx+4e1UH68m9bh/OQBvXloMZrBfudPOpFFTWsMFrS9gf9F+v+clNSWGaRm9f3V+tTdsh5gOpKam0r6yPSdKFdtpibGJtItuZ7sNufa5AuZrBXm5eRySh0gtsBff27727iH2TAO5KaU0rBct1n2zzi+MUfkvb3U5/zmt2Jia1nmarh2axbGCze7Nuj6QEBtYxde3rPf2v5f86nw2bTBX3e1Y2ZFjpcf87mvL7Jvu7t27ken+hNih7EO43W6/9w3U/jqVdWJ/3n6cDqd3k79502baR7c3LXOkkJ6RTk19jbcsmRWZfmXduH6jYdxjZ/zrDPT1Vl2v9KcDBw4QVx8XsL2pzxa5F9GvWH/GUi/rdXH3bdtHZp5/Wd9b9x7VvawRR2ZIO53mvQ7WbvcU7NGV4c6L7jSNs2LfCr97a9eu1W1GjpUew53nZtv2beTGN5Boq3NWB6w7ozy14eOd8axdu9Z779XlrzKo3SAe2qcQnXf0vYM+8X0AOH76OGcrzjb5PBkM2vdx57lJjUvVPf/44MdU1lV6fy/MW8hFxcGlAc3w4rEXOVuhSBhtS9vGE8ON1vsKcqtyTce7dYfWGd63goyMDNylwcdfFb5tZNf2XWTGNRB4RzOPkl2cza7Nu7iv2308uv9RvzRSU1O9a4W9e/fSOlNP/tTW1/rls2HDhrDH+5MZJ3EXu3lq1VNkHs1kSLsh9tPIPIk7P3g5jPrS80ufZ0xHfyLUN+yTK/3NJaxKXWU4XvvW1aotq8jKzqK6Xhmj1q1fR9votsYvEyEcKDqAO8/Nli1bONXqlO6ZWrb1ecFtpm3ZtKUh/Lr1AbU36mSdN+zVr1/t7Ttm45sWRzOP6r6hLw4fPszGvIY5yUpbU8vy1CdPMSFhQtDwKozG3cV5ixleoWhcFNcU+z134z//GyE9Ix13iZtvd3zLuaxzpJWmkVphHG9s1FiWZC3xu6/Nx7feLvvXZQHHrIBlS0+ntLZUl35ubi7n6hukn3ft3oXI8OcD9p/c7zdmOUv1exF1n3H48OGga1Bo+H6XCX9V13/sa9BwWbdZv1596fOX+CpLOaCec3QOgOU6OVtxluNlx5maONVS+POILKSU1NXVybq6urqqqgYBj+XLl58cM2ZM5sGDB0fV19eXO51OERUV5SXR8vLyjiYkJJRlZmYOLiwsLHM6nQ4PySWioqJEbW3t4ZiYGOl2u/sUFBRc6HQ6vSSYh+A6CEQXFRV1Kikpaa8lv5xOZ33nzp0PADElJSWOqqqqrloSzel01iUmJp4G2lRVVXWLioqKczqdtj1AzVmyxIggU9H6pKL+WCql9KqheMiw7sAJD2k0WEqZJYQYBmwRQnyuDW+AjUAvKWW1h5d5EpiHQsa9ogk3HNis/pBSzhJCtAO2ABMB383Wg8CfNIJUWUKIccAU4HXP+lfQIMU2HIUorRZCOAGVjR8B7NWkmwn83vOuHwV5t8YnyYQQbYCPUVjgYk/hO6EwiOOAD4QQfaV1HTtL8aWUC4AFAGPHjpWXTLiEBLeyUY6Pjic5ORmAN4vexFmuVEM99SS0VcJMnTqV+Jh4FhYs9J58qHGaCiVHSthcs5lBAwdxUB7k0ksv5eD2g5Rll5GcnExeeR4JeQmMHDmSjMIMjnCE4cOGkzwomYRznveYMpX2cf6LsM65namsVTYGfbr1ITm5Ic5lSZeRPDHZG7awspCEXOXZhX0vJHl8sm9y7M/Zz8fFHzNu7DhGdtOrb1Yeq2RD1QYAJk+eHNQmmfreU6dO9bqUBig4VMC2mm388Hs/DHjSpb6HFoMGDSJ5tL7ce7fupTSrlAVzFviFB0XdZUGe/tmbc9/0O90EmFQ3iYszLiYmKoapF0xFCEFycrJXlL5P+z60i21nuw3dEHsDa9LXAKG3v8TsRAYPGkzySHvx3eVuXs97nZEjRpLcryGulJKErAQcwkG9rGdQwiBKqkvILMnE6XBy09CbGNJ5CAlFCTrDt0blTyaZrxcrkrZ/m6d4idR+v1Df2X3QTUK1ks6kSZPo3rZ7wPC+bWbe9+YFz6OL29Brl7bMvulOHT/VUHXq9J7T7Kvf5/e+Rm1ZDeM+6ObAngPce8m9/HPbPwGYOGlik0iSfV3/NdV11d6yHM8/zvuFDapD8y+dbypB84v2v+CTw5/43de+e1VtFQnZCQwbOoyubbqy95u9fuGNYNRexlWO40ThCe/zTtmd2LzGO1fz5IwnGZQ4KGx7W5OqJ3HEfYS/X/H3oBJW8qRkTaXSrx+d9mhAleDuOd39JL8mT5usU4vpmNWRpSVLuWTcJQztMtR7f1DpIHYv322atlF9jSgd4ZVw+tnIn3Gu7hwJiUo7HDpyKBd2vNDbLm+ZdYt30/vtpm+pc9cZprk+Yz3PbHqG/1z/H0uSM5HEvFbzdBLDvuV77txzxNOgVn3vJfeSfJE+jB1sEBuoOtuwOA80hqW500jINx7vvie+x/az24OmYYTV9aspyG5Qtbp73N26MdwXvuNM8tRk3ThyYNsB8jPzmT59OgBr5Vo/yY/k5GTvWmHEiBEk99fnV1NXQ0K2Pp9p06bp7oUy3m9Yt4Gcs4pCQbukdiSPsJ9Gx6yOnFjb4AzIrBxG4/HsabMZkDDAUlhfjBk/hl7t/KXcfeuqTa82tK1pWAtNnTrV0LSFFRzKPURMVAwXdQpMBMeejmVF+QomTJhA3459dc+svJuKSZMnedePl00PbJerrr6OhCz/9tAhqwNLS5Yy/pLxDOlsTILu3bqXkydOmqY9cOBADnHIL+1AUN8zv12+rbY5rnocuz/2H3fVNPIr8knI09fhnWPuJHlA8DxSU1PJP5fP2DFj2VS3iQHdBxiuyQEulZey4f0NpuUApW+fOH7C9LkdpKamUlxVrIvf/px+/zF0+FDDsWijYyMFmXr10B7teujSWnp4KQnFCST0TiBBWm+Dvu+TX5Gva8PDRw0nobTh94hhI/i29tuAaZhBXfPfN/E+3f6liXFESpls9EBKWQ4YPvM8zwvy/LTZc5fLJebPn38axYtmrOYvBoh1uVyT58+fHws8RYN6pPo32uVyjfc8X+sbF/ity+WKnT9/fgyK8X+tumU08JHL5YqeP39+tOe3el/9SwccDzzwgNmrbQGYM2eO2fP3AcaPNzUJ8BjAgAED6vBXr6xD4SxqunXrVoNixN5QHTM2Ntaquqbf712jR/e/dN26PzikNFLZKn9Mkbqa5nP/58AaKaVqAD0LQEq5XwhRiSKFZkokedqLis14VCpRvk8peIm4y4A/+sQtFkKcBIw2LaPwfBMNRgIvSSl1p3Qetc0+wD7PrRHAfs21dwMhpfynR8ryZmCfEKKflNKUWWxUksxTMR8D70op1V3RGeATD6m1TQhRj2IY0KoOSUjxzdQtHRiTtaXVpRH1NBUKGtPYuhEnGe2INjTar62v9rGBpVSMRK3NXHLbhVXPcXHOOC8BqP4Oxc6Z1n7EgE4DOJp/1KsC64uYqBgu73u5aVp2XcarCEfsvbGh1umobqMorCwksySTX178S67qfxVSSgYmDCRKRHGm5AxTek+xnf6sfrNCLptqtBXstzmrZIlRumO7j9X97tSqk1flBgK3YbvqTHMGzaF9XHtmXDiDFcdWcDT/aEg2VyKNYCpmdkmSKRdMoUfbHmQUZvD3LX83DedrW1HF81c9z4mCE16iSfsNrh10LcO6mJrAtIUOcR3o26GvbRXEYGPTRR0v4lDeId09q3ZjurXpxvKblxs6PDDDkzOe5CdLfkJCqwR+MPgHOsPyraNbczxfsQE3sddEy+OaqhKbXZpty8NxJHDXuLvYlbWLvPK8oGFHdR3FzItmhpXfDUNv8Nqj7Nza39utFqopg/6d+pNRpLel3LdjX7af3c6Y7uZ9yQzqWBKqHTmjcUqnGnnZYzy36TlSM1IBaOW0b695etJ0XZrfu+h7ttPwRTAbRGZQ62vOwDk6dXkrsGoOY2KviWw+s5mZfWcysddE/rL+L5TXlAcsj4rv9/++zpxDOGqHf/xa2adYVW01yksrvRwMHeM6MuPCGVzYIXi/1773D4f9MEDIwPjFxb9g9oDZXPO+/lu2crayZEdNxTUDrmHZ0WX0ad/HVv6h7B3srhGtrGuEELx41Yukpqfy0SFFg65jXPOqDWrX5lpY6Uev71Kc7O3NsXZoZgbfQ6cJvfRSgpHYe7UEb9tW4XK5nCiG+Lug2A1PQFFD8/2Lt/A7Ui9ei7Htrmqf6yJsEEmN9Fu9rm1Ww/4pKSDEGvQ2yUDxJnr1v2E8DeQRQogrUCS2Znl+d5VSZnuu7we2eSS4ooCHUVRm6zXx2wHVUspKIUQn4AHgBc/jQygahBs8eayQUhZ6bJUdlFJKIcQ0FOKrwRNQA7JQnAh847FrX4NiwuvnQoi/eiTG+qJwP4lAuqZsWmIsCVjvKW8nQEgp9wohMlEk3gLaBmpM75YCRXf0kJRSu7NZgqI/ulYIMQCF7Q2+eg0zvs5wv4UB8KHVD7FwTngG0yMFK4Ot3UHdKLyVNMw2+epGrzFduqvlC7aYeOryp9iQsYGe7Xpy1H2UL499GdD2ixXYsaUWSXxxrMF0QFMaZbcC9Xs4HU5uGHoDGUUZTLlAIcOEEMRExSCl4iY+VJKwKaFKxlklIPZk6SUperfr7Rc3xqH/bZXotQKnw+klZq8dfC1PffNUk5FkgYidYN/aaJy5oN0FAcP07diXvh37Mv3C6fzw4x/qvP6quH/S/aZ5aiUhGstenp1vq+3HgQhFUCTNtLbt+nboGyC0Me655B5e2PZC8IBAx1YduXvc3UzoNQEhBDcPu1lnS0k9SLn9YvueOZsDToeTRXMWWRo/5w6aG3Z+2rYWbE6NjYplRJcRxDpj/UiySGBo56HBA4WIW0ffyqYzm5g7cG7AAyIz3DfhvoiMV5HY0Krj2YReE2xL4pqNd/Mvnc/8dfO9vx+a2mCLUZ07rNgG/OOkP9I/oT8xUTHe8I3lZfZQ7iEGJQ5SvDoGqNefjvypn0Ohi7tdzM6snYBihD4tP41BiYMA5VtbgXb9aGTE3ur68oqLrtD19ZioGCQNHmyvG+xnAscQMy+aybKjyxjdfbSl8HZx/eDrmXzBZH771W9tx7V68NunQx9+MvInXpIs2DzVGB6ftTAjKdWxIKl9EulF6X7Pte87Z+Ac3txjTP6/94P3+OOqP3KmRLEteeuoW/3CaNO6ffTtfn24X6d+dG7d2bb9T60wRmPuhUKBy+XqjkJaXIxiR6obCiHWBYUUC1TgKhSpoFIUKS712u3zW33ua2Del+wKSn6lpKQ0r2Hj7yqkXOuxPaYSZeXA1Z77twPJQogZKN/7EPA9KeURD2fzmRAiHoWg3IoibQUwBrhSSvkXn9zGAf8SQlSgkE3/lFKqhhafAz4UQvzKk9avPPd/C1wmhCgEcoBZqiF/H/zOk7b0vMNlwJfAbOCAEKIMhTSbg6JqqRXdHQm857n+AnjRY4Lrz8ACj3BVJYr9tIADYmNKkk0GbkERZ1ML/xDwb+DfHi8D1cBPVVVJIUQ60A6IEULMRTHYdlAI8TrwipTy20DxA0G7GNNOEmaL5pxyf38AzUVS2BlsrZbPjmSVNqyZ4WMzrzPaZ1bzNVucWSXi1A01wOV9L+fLY1+y/ex20txpOikPO3UQ7qI01HYzJHEIB/MOhpV3pKF+n74d+nKi8AT9OvWjS3wXnp6pd4giENTJOuplfUjvHylJSivfuZWzFSO6jmBr5lbLZIevAVyjdun73uESNLMHGEsEqaewTSlJZtYPrRKiia0TySvPC3iCbNRufNO/edjNLN6/OKAnUi0iSVT6wuq7a+su2NiiVdl4+vKnQ5LEuuKiKzhRcILP0z733ntt9mum4a/sd6X3WqteLpHe+vsuEN9amI1B6jj24JQHgxKWlvKxMV/3T+jP4zMe543dbxgarobwvD1bLcvobqPZlbXL9LnRWNyxVUc+vvFjfTgbZXUIh67vh9ovI+EN27t+CVJfy+Yt85NOMmtX6oHJyK4j+cMkvSMPq/NhlIhiah/FtpF2A94YfW9b5jb+uv6v/Grsr5jVv0GK26isPdv21P3u3a43f5ryJ2786Eal3I4oL0FmB0IIPr3pU679z7W242rbQWyUcqj57MxnWZexzqturc6PVqX/1PYQStt859p3eGTNI4aEj4qubbqS0Mq62mCoEEIwpfcUNp42thOqYljnYY22x7l73N3szd7L1QOuNnyu7i205Kh23FG/wRV9r/Be3zDkBj+nG7HOWHq16+UlyYwOPrTktHoAriXn4qPj6d6mO13iu1BRU+E11xAMWo/RjakJZBUulysGxVne7wHtwiEdOAscQZGyyfb85Xj+zwNK8JBeKSkpza+icB7W0UCULQJ+7vFqiZTyR+ZRpEQhvYxwK3CbQZzVKHbijdI7g2JrzPe+pdNVKeV6FIkwXxi5oV7m+VPj/lRz/QWgPVm2pTrSmN4tN2LOTP/YJE6Syf3bNdfVZvEDQatGqFO3tLHYcFe4G/2URYtILP5M0zaSJDPJTxv2cN5h4zAWCSw7E4fvZB2IiAuEtjFtKaku4Xcrfxey96xwSbJQF7VPzHiC9/a9p/OCFArCaUtm32xi74k8Mu0ROsebqxRJz79Q3r8x278v6mW9V5XW6oJ4etJ0lh5ZaqiirMK3P5iRzEZhjWBWj2r7bG6vomBhDPB8V5WkeeabZ2x5FFTr4O5xd1Mn6yipUqTKrI4LjUWS2SGDQ92IDO48OKR4gJ8NI6tjmm84tf5a2kl5qLjt4tt4eM3DIW3qjaDto2GNu004/s3sOzMgSQb22qzVtmG2IbYDK963rSLYO9qqA0/YG4feaGgX1gqSOiR5r1tFt/KqZzbGBjynTDkYPlV0KkhI/zH0ttG30Sq6Fb8Z/xudWrOUivRWuOsnu4erat0PTBzIwMSBfH3ia125jaTUjBAOGdk+rj2Pz3hcp67enFDfvaiqiNmLZzeaJ1mzvt82tm1Ar8/q2iVYnXeJ7+J9l2hHNC9c9QL3fHlPwDi+6NqmK73b9dYddD5zxTMsP7Kct/a+pQub1CGJ0upSS+lq119NOX4bweVyCRRD7TehGEV/HtgJ7ElJSQlorPw8/gugEGNJkUlKGhFT/xP4bh0FhwHt4GVnk6Qd6MzsUX0XYTSAB5PgAmNXy9q4jSlp1xQqnWZoLjsOUY4or32tpj6ZsvItAxFk6mKnXtbb+mZ/v0LRzm7K95VInR0zK5g7aC7/uPIfAcP41qGpunKY76puQlqCTbJgi9z2ce1Jap9ElIjylvtU8SkqavzVMAJJ543tMZZZ/WdZVsNWEYioDBeNNTa9cNULvDzr5YimabW+xvUc57W1J+V3V5LMDCO6jmD5zcsNHbKEgihHFIvmLGJ60nRb/drKIZVdNDZpGwk8M/MZBCJk8jpUA/ZahLO2MPtu4UghOR1OHpv+GH+d/lfvveeueI5u8d1sp2UVan+2srlXJfLbxSqeytX2c3nfy5k3rMHpzaLdi7j2P9c2++GNEEL3XlZJdXf9HwAAIABJREFUO/W9Qm2bWpuycwaaGgS3Bd82arXvzh44my6tG9epTzhmGNR52Sycdhy8ZuA1XHnRlcwdNJekDklMT5pumq5R/cQ543AluxjeZThTL5jqvXdB+wv8wtoZG7XtpAVIks1GIcgeBaakpKQ8n5KSsuE8QXYe52Ed/x2rXAsw2zzaWeg35uaqqaEdzHdl7aKsusw0rJXB3rIkmZUTwSCbBbsL+khsALQnuqEgEpvn5j6ZUmGnHPWy3raacv+E/nRq1SloPv/a/i/u/CwyBxzVddW267d9XHv6dGgw6Gv0jn6SZAE2C+G0U/VkPJBUW6RhVt5gY+qs/rN4YdYL3viqJMgTG6y5WFcXta2iFWPhdiWbWoIkmV0kdUiid/veEU3T6tznEA5+OLzBJpraT3zjSyk5V3ou4Fzyv4LE1olem4xWEKjthjLuq+lFirRtzLlnUOIgOrfuHHIeDuEI+wDTO4ZY7L99O/Tl3kvuDRgm3LFgZLeROlXrXu168f0B3w8rzUDwJfUCfQ9VtX1wYmDJVlW9O9x5KVzCwbcf2FW3DLVtxkTFsGjOIoAmUasMhGFdhjG+l6lnvojBrN0HG4vUtVGwbyOEoHV0a+6+5G7vGkD9Pr3b9dbNS4Hy7BzfmSdmPGHoZMG3vYXS/lrAel1lDp9OSUlp9sKcx3l8F/E/SZL5eqCzCtXOwX8DfAf9LWe2eAd17bO92XttibpH0rul7wQXaUmypjzpCWfB7F2oNf/JlA7BvoP3ZBpp+5sJAhsOBsWpQbgOGQDS3GkAXk9tkZQGsixJFuaCyo4UQGPDbltXx+a0/DTvvUDvcevoW3nvB+95vWRKqbSv5la3VMthBS1BVTEkFegANslUD5wvbzeXeGtJjkcaG1bGsEYvQxPW99mSs9z1xV2mzwPVRZQjKiybZOp7RppMMcLCaxbyt8v/pvN+bYTBiYP59KZPTb3utjRoJb/tIph9ykiNuYHac7C5T9s27EqShdOugrUTuwinTM057wSbb1QpLi0xrNs7BPi+E3srpo8enPKg7ts29YF6Y9o7DQFrPf//KmCo8ziP8zDF/xxJ9ui0R3lgygPe+3Y22aG4dY4EvJOihQ1wqJPgazv9jThvPbOVh9c8zPKjDbYLzIxjByKw7BruN4Pd014VgeqkqRYN4eTT0jaWVhdnQigqNPWy3vaG3Fc9ImB5bCyOjeBrbyKSG1s7NskimY8v6mU9ty+7nbs+N9/E2kGgOrLtyl6aE+xm6WsX0nYluI66j9oqn1VIrEtMtoQ+HapDGDOSTLWXdLbkbARK99+BpiCtI6HKFgnV2br6uoC2ewJtIK2oW+7P2c/GU4ENj4cKO2N+l/guXgmWQHGFEDgdzhbR163AjNCyIuUYTKq4udUtfWHVJpn67kWVRs7XIo8zxWdszU92xxcrbbEx7NdayfsXY37BkzOepHc7Y4npQM41JvWexJKblvhJW4czrun2LRbHh1DiNCKWA58DT7pcrv7BAp/HeZyHP/7nSLLe7Xp7vQ61VOzP2c9Xx77y/lYnhUhIzZihrKbMb1DPLstW8i1uyHdI5yGG8ZvEJlkIEkmRzDscRKJeWoKUkBZBjRwjqKqtora+lhXHV5iGS2qfZHg/UJ3vOtdgZLqsJjz1rsZss76kWHNtFrZlbiO7LNvPI2c40PZFbR3aXZiGawjermOIRtto25Akaw74li1UZxpmhxWq2uyxgmMhlvC/C5EaVwKNg5tOb2Luf+Zyusi4X4fcp3zmGivzXzDpHK0a1aAEvZMEIYJL3a08vpI3dr8RtByhwEyFOBC+K+RXqLBCeAT7ZpFyNBBs7XMg54Atct6uJNnru17XrckbAxLJrz7/Fb9f+Xtb8WwddmjC+hLaAxMG2srXLoKVMyYqhmFdhgVtK2b9Tkt8RqpvCs8/q7Aq+dYU8KhY3gFUAl+5XK4pzVqg8ziP7yD+Z0iyv29RjIGH62WnKfDg6gd5cfuL3t/qpuR3X/2uScthR8JDXQwFSgfCWyzZtW3VGAg1/7AkySKgbllUVURVbeS8gFmBEIKCygLAf0GmxXNXPsf7172vu+cIMjT9OfXPlsvRXIuV2vpaP2I7mDRFYyG3LNd7fSTP2PlGpGD3PYwM9to1eG5rIdtIJ7yhenGNVN7B4Dt2WZWmAP03NXOU0BKcRrQkhKtuqcYNNH5tObMFgGP5xsSkZclGC/0nWJhg7Sk6KpoFVy/g4xs/5pkrntE9cwgHG05toLqu2jCulJK6+jrT/hXu2NkCpD5sIVCbqK2v5WDuQdvzntlBZ0s64DP7zn9a/ScOuxXP60ZmUcpqynQaEVZtkmnbm3ZNHkk05ZpWm9fNH9/svW4f255nZj5jFCVisDrfaNvKmZIzrDhmfsAaDE09H7cww/2kpKRkAlcCTmCDy+V62+VydW/mYp3HeXxn8D9DkqlobpIlFKiSMk3tOMCyiLGUPLNJmWC3Z2733k8vTOfdve/6hQ0131AlyVrCN7dyUh4obrhYdWIVf1z1x7DTsQtVuiQQYqJiDFWZG8t+V1PBiJR8ZccrpKan+t0P1jZen/16WGXJLW8gye5fdX9YaUF4Xqx8EchbopW2b1fdMtT2U15TzuoTqyOSbmMRonbqIdLqlufhj+/KWBWJb2klje5tuxtK8qsSrkaSYlJKrv3Ptaw/tV6Xx/0r7+epjU9FhJz1Gg23QRw3B6z02a9PfM0DXz/A/pz9ttIORaLXqp3YUGw1bTmzhcrayqDhThWd0v3+6MaPgsaxLEnWhJLB2oMsKwjHoYcvHMLR6GSo1br0XQu9tP2loGmbIRLjmi3THy2AGPNFSkrKdmAw8BhwI3DU5XI97nK5/F15nsd5nIcOLV+sKgLQDnDN7WHGDtQBV6vuaBq2ERbjVtPcnbXbe902ti3bM7cztsdY7vnyHgBuGXFLSPn7TtqhSpIVVhaa5/EdsEkWqbxPFJ5o0nyraqtMJQOCIdB39iWLI932w0mvTtax7Mgyrup3lenGYOXxlSQnJfvdD/TOXdt0pZWzlelzFWaLtE8Pfxo0bqQQsrql5v2tfoN/bv0nq06sIs4ZZzm/UBeyL29/mXUZ6wCY0XeG3/P9ufvpENfBVpqhjAtSSupkXdhS0ZE23K9Fdmk2JwtPMr7n+LAOCL7LaAp1y2B5W3YkEYGyRkJKv6CiwO9edV21obTpEfcRjrgVqVg7/d8IKtFmx0tmS1Wt3pe9D4D8ivyQ4vtJkgWySWbRzEYobTgtP40bPryB5TcvDxjuwdUP2k7bsk2yCB6wBquDjw4FJ/fAWKrXKqy8T2VtJbMXzyapfRJ3jL2DYV2G2cvDpL1Yrcur+l3FhlMb6BjXUe/MJ4BNMjM0tWkd7ZqvJRnxT0lJKQMedblcbwJPAX8CHnK5XIeB9cAmYBdwKCUlpelcpJ/HebRw/E+QZCXVJd7rliBVZBdH8xvH0HQwGC1MjVBRW+G9Vh0AvHPtO957h/MOe6/D2Sx9nvZ5s6n0vLnnzWbJV4vmkkp4aftLTL1gqm3HFXtz9oacp0M4DN9XSskTG54IOV0rCLWNZhRlkFGUwdbMrZRWlzKr/yzDcIbOLaw45QgwdqnPdp3bxahuo3TPQt0shQq7Y6wqxWFUL8EWxKq0nhWJAxXauraz4FaNN//f1v9jZLeRJLZO9AsTiJDXIpx5aPXJ1Ty/9XkuS7qM30z4Tcin5WHbJAtQd7cvvx2A9697XzduuMvdfHLoE+6+5O4Wbxs0XKjqlkWVRcQ6Y4MSOb7jjp3xPpDxeCvw/ZZ55Xl0b6vXygmWllUVtkBQPdypkFJy/8oGyddI5GEEdV0RCtH38cGPWX9qPUvnLbXdp17b8RoTe09k3rB5tvM1w/pT6wH77xKK93CrbbQxCQOtnc+hnYdaitMSJcmaAsEca0GD1Hl6UTp7svbYJsnMYLVvdG/bnUVzFvHY+scMn1sZ0ypqlD2JHdJbi3Xp63S/7WjVtGSkpKQcA65zuVx9gJuAaZ7/f+kJUuVyufYDh4Cjmr90oNBj5+w8zuN/Bv8TJJkVla+WjDYxbWxtAiOFcAxqv7z9Ze9159advdeW1C1NwnSN70p6UbrtskQCqkRUc5B04dokq6kP/2BowY4F/Hbib5VyNMFC4FzpOc6VnuP3k/RGbL8+8TXbz243jPPw6ofZm7OXZfOW2cqrMRbC5TXlpm0lHPIwGD45/Anjeo7TLWzv+OyORsvPCJGQJLOK6KjQFsFGqK2vpbCy0JD8Av17FVQUmIazg1DeeUPGBgDWpK/hgvYXcN2Q66zlFYbhfjXuxlMb6dWuV9D4k3tP5pvT3xAdFc0TG57w2ub7965/c6r4FGN7jGVan2mW8/8uQghBaXUpP/70x/Rs25NXrn4lYPja+lr2ZO1hZLeRuvtNMd76tsOH1jwUVILHF1rpnFBJkRXHV3Dz8AZbSZtOb9LN+aU15rYttViXvo4TBSf4+eifWwqvHvRpvVZahUpK1dbXWiZ+tdLdJwpPRIwkK64q9l5riaCy6jK2nNliKAGrwg5Jq5Z/Z9ZO3W8ttOTVXZ/fxXvXvReytGGgtY/2mVVSpE1MG0vhWuKhulWnCYHiWr0fSZV6u+uscIjV3dmKdou7wm0rnlrGJUeWMLzLcN29QJBSsun0Jjq26qi711KRkpKSATwNPO1yuaKAAcBoz98oFPLsxz7Ryl0uVyag/csC8gG353/1ryCiEmlCTAcWAT9HyrX6R+IG4H4gFogGPpBSuoQQo4HVgKqPvUVKeWfgbMSbwBhPOuuAO6SUUghxAfAa0BXIBX4opcz13F8A9ABaAc9IKRdE5J2NyzcM6CmlbFwvIufhRaORZEKI3sBbKI1KAguklM8LIf4KzAHqgRzgZ1LKs0KIQSid4GLgYSnlsybpbgDaen52AbZJKecGKos6WNlVhQlVVSxSUMutnSxOFDSdytzbe98GQpsoR3UbxaYzm4DQJzvt5JTmTiO9KJ1OrTqFlJYRQpnEIkWSvb7zdaZcMIVBiYP8nkkpKa8p90pgtISFmhHR1hynrJ8c+sT0mUo++Rqxbg5VrygRRU2d9TVCJL1/Ld63mMdnPA4o9dVYBLtZu7Sbn1diNQQTmeGqeK0/td67kf7Zkp9RVFXEsnnLDN9NOw6Gm6/ad+z2obLqMh3J2ja2bYDQevi2sVDGlfWn1jNv6Lyg8bvGdyUmKoYDOQfYfGaz9746jjSWRFBLgqr2BtY9Uz+y9hHb5JQR1HnKsrplkHBW5krtN9VKl9uBr9Srr72pvPI8w3jegyRPOZ/drCwfrZJkZdWK3dfW0a2tF9YHTaletTd7LxN6TUAIQVl1GV+kfcEPBv/AK0ED+jHqJ0t+QnVdtZ+UsRFCWe8Zvfuak2u81xW1FZwrOUfv9r1tp62F0ZijdRpl9dBkQMIAS+EibXexuZ1PWRkPiqqKvNdWJaN1eZi83+G8wwztYk3SD/zbVCiSjpFCfkU+2zO3M67nOMPn+3L28bdv/qaTZPyumBhISUmpQ5EcOwS8p953uVytgYuAgcAFQC+gp+dvMgoxZHoq4HK5itETZ+qfL6GWD+QBZ4ESP2k1hSD7DGgNfIYQV6tEmRDip8A9wFwp5RkhRGvgNk/MocDrUkpLxpiFEHHAW1LKnwohooAdwExgJbAYcEkpVwohfoOirvp7FDLtPinlYSFEd+C4EOJdKWWZlTxDwHUovMl5NBEaU5KsFvi9lHKnEKItsEMIsQqFaX0UQAhxL/Bn4E6UjnIvEIzwmqpeCyE+BpZaLZDdjY3ZgqypoT2RO1F4IiISDMGQXZrtvQ7kudIMZieqoU4c6mQdrurYjrM7GNNjTMjxI2HTrqy6jKVHlrL0yFLDDdEXaV/wyo5XeGTqI4zvNd5vA9Ac0PadUL7h4MTBYZehvKacMyVn/O77lkf1qGkVKtEwvMtw9uXsCxLaGhzCYdvRRjiLP+07a/veot2LAsb7+sTXnCs5xy0jQ7MbqEK7oFXVEq1C3dBX1VVRVl1mS61XtREWKrRjvLo52Ju9l17tetGpVSfdgl8rKeO7GQu1b9rdMJ0pPqMj6u2QTeGMH76G+wNtHp0OpzeMr5MKdS77LniZDhd2pJ4DSbuHMt6qJg6+Of0N94y/J2h4o+9pd0Ov7R8q6RQuVp5YaSmctpwHcw/azmfViVVAeDaMjLwqmiHUdZAqKfbExif4w6Q/MK3PNBbuWsiqE6u4qNNFukNE7RgV7Yimuq46YL5+pISNMp4qOuUnAZlemK77Hco6EpR2aHXsCoXUCYRIEjIrj69k0e5FvPeD94IHtgi7Y3qoUt6RwIReE8LKu7lIJ3Vs+cv6v5geYKjtzq7zhUbEQCFEqs+9D6SUL3uIpC8M4rwhpXxDCJEIGBnI+5eU8j8eAZi3hRAZrVu3dsbHxzvj4+OjBwwYsHTixInHsrOzB508efKWuLi46FatWrWKi4tLiouL69exY8fimJiYOCllJyFMG2LZI488kp+bm9uutLS0qt/x4zwoROc4KdWO6CXKhEJi/R0YJ6U8AyClLAde8IQdAhi7fTaAlLISRfIMKWWdEKIaiBZC9APaSinVySgDuMoT7rgmiXJAAH6LMo8Q0PMoQkPRnviZgAuYAXQEnpdS/ksIMQJ4GTgCTALSUASLVIm5M0KIX3qePQEkA3HA51LK8L1ynYcOjbZSlVKeA855rkuEEIdQxAS1K5h4FCkzpJQ5QI4Q4vtW0hdCtAMuA4IeFaqDq93T65ZiM8XKRKW+44KdC8I+rQO92L4qugzGk7J2IXH76NtpG9vW1BVyqBs1X1sloWL+uvlhndRH4hRwW+a2gM/3ZiuSIo9teExX1qZeJGi/oVZlVoWdunh65tMhlUF7epdVmmUYxrdNlVbrVXLyK/IDth9VRWNm35nsy9kXETKyXtZHbKNoBaGW+fmtzwMwvOtwRnUbRW19LRU1FbYklEBf55N6TwopblFVEfM+nmerf7aLaRdxSblH1j4CwLQLpvGHyX/wKyf4b57UMsy40FydSQt1s2L3u/mRczbGhHDGD62n1pr6GsPNltPhpLa+ljhnnDevNelrdGFqZei2n75riImKsSyNvvqkudfUUKCSbqpn7GCIxLymXV/5jsFWoZ1nSqpKQjqoNPIeHAjnShTV/nAQHx1vqw6P5B0JKR/twcvpIsUjqCo5nVWapWtv6veQUlpqB15JHV/D/Qbv5Xtv5kUz/cL4ShMOTBwYtAzgP7Zq39mItBraeSgHcg8A6Ay9B4JVRw+RlPpSpUmzSrNIaB05B2KRLKNvWnZNdQSaY3q262krLa1wAJi3z0gi2Bz5RdoXhrZmVa0B7UFBS1a3jASklJSVldWWlZXVApUnT57ctWLFiuVCiIEo6pq+eExK+XVMTMxop9P5zzZt2kTHx8dHt27d2tmmTZvoESNGrO7Vq1dNRUXFCCHE2Kk1NR1v3bkzOsa/HlsDn82Gfy6HrVJKM/WqIcAcIcR9wHLgT9LiRxFC3I6iPrkKhdjqJoToBNQBv8NHmksojfJF4G0pZbHPsw4oknDzpJTfCiE6AmXAw8AZKeVEjxTbPiHE68BwoDtwnZQyWwixB7hASvmBEGI+MN7DqVwFdJRSjtbkcx4RRpOsVIUQSSi6zls9vx8HfgIUAdNDTHYusNq3QWry/CUeY4QdunXAnedmcsxkUlNTdeHceeY66+s3ric7O5s2zjZc3+t6v7iNBbVM+w/sx53npiyqjMq6ho1gbXQtqampFNUU4c5zs2fPHs5VnsPtVuItWr0Id75yvWHjBto4/e0vqHlc0+MaEmMTef/0+3SL60ZBdQHHjh3j28JvDesmvSrdrx4OFB3whm17ri0O4WBn/k7vvaP1R73leffrdxmfMD4gYbk/T3nvDRs2EONQiMp6Wa98w0T/bxgMvu+hxj925hjnys4FTM8srt28T1Wc8sZ/4tATlNWWmaaXezbX+y1TU1M5kKfU77r162gVZd9mivYd7JTfXeX2xi1KLyK1QIlbU1+DO8/NgQMHSMwxl2qsLaqlqKbIdr7aMj/95dP8ut+vAXgz/U3cJf5tUm3jXaq7cKj4EI989gizus/ypnHXh3fxxHBzY//Zldm489zs27OPsoIyvtfte/bqyaCfvLbxNV4Xr5sulHzTP3zuMHn5eabjU2pqKjk5ORypOUJqsX/ZdhXs8obV9lGz8c33ecqyFH474LcsPrWYfUX7eHzY4wEXoxmnMhAIbzqHig81jFvf7ud41HHTuL5IP5vuV7aKugrlm+zfR/us9qZxZ8bO5J+n/ql7p2BoU9ZG5+nVrK4+zfuUcTUN6hU5mTm4i5UwmzZvonNsw4ZeHYtrYmssleNY6THdt7UKta2q2LtnL85TTm9627ZtIyfeWBr/RN6JkMcCbXkPHDqAu8DtF/+2hNv46MxHnCo4xaGqQ2S6/VUMK5wVlNeWs2vnLkqOlvg9/2/C3Z3v5uH9D3t/B6rv0+dO+4U7lKX0qYyKDNO4x04fw13oZtfuXTgyGojLuLI4Misyg+ar4njpcb/2vyZ1jXeeTjuTRlZpVtC01DS+KvmKoeXWVavu6nIXTx1+iujSaG8eZuO9UX9V10h79u7x9kVt2EDIrcoNqS/uKdzjjVfhrLAVNy0vzRt3To85luOuObkGd2lDP0zNT+X4qeMUVBfw2Aq9ofNt27eRG59LVmWWN6+NGzeavuu+vH2489wclUdJLU31vt/mzZt1Yx0oxJW2/rds3OJX1rycvJDGG9+016au9bbPrVu3ktlaP66sP7Re99soH2167WPaWy5LeW25Lu5nX39muJ4OhOr6al0am7dupkO0sifZu3cvtem1ttaZJzNO4i52s2PHDrKzszlacZTUMmvvczjnsN83SctMI6tE6dsZGRm4yxqeH649TGqltbQBMjIyqKnXz4Gh9C2AtQcbTE9p1wT79++nQ1ZgPiDUeW5F1gpv3IyKDADKY8u9+5fHVjxGqzOt/NZGm/I24c5z4yh14K70rF2XPc3sHrMt5x1hHJFSJhs98EhbGT7zPM8L8vx0kOdHfJ+7XK44oKPL5Rry8MMPtwL+hiI9pf51ADoB3dq1a5fQrl27innvv98+psaUpG2dBHcBL5kFAH4tpcz0kEebgLXAigDhAS9Bdh9wpZSyGjgthHgWxUZZBlAA7NaEj0KxS9YKuNUgyduBj6SU3wJIKQuEEE7gbuCsEEK1kxYLOFBIsn9KKVV1LidQJIRoBTillOrCKQ+4XAjxEPCOlFJvm+A8IoJGJ8mEEG2Aj1H0dosBpJQPAw8LIR5EaSgpISR9M/C62UOP8bwFAANHDJQJiQncMvMWv9OMhHP6E50x3ccwPWk6z25+lvETxvNJ6SdM6j2JH437UQhFDA1qmYYNHca2mm20craioraC2QNms/zoctrEtCE5OZm88jwS8hIYOXIkbQvakpamnKIN7j+YE2nKJnDqlKm0j/PfZKp53Pa92+gc35k1H69hap+pfHv2W/p17sfYAWNJKPQ/7UrqnkRycrL+ZjoklCthL5t+GQBVx6pYV6WoQvXr24+TJ04CsLlmM/dOvjeg4dSCQwVsq9nGtGnTdCd+66atw+lw2j5J8v3Gavl3bt5JZU6l//sYxJ1/6XwySzJJHmgeNlD8CzpfgBCC5ORknjv3HHEo71Xdu5orLrpCFyfnQA5pe9O8ZS08XMjWmq1MmzrNtodJgAe7P8iCnQsY33M8ydPslf+N/DcAGDV6FFMumKKUua6ahOwEhg4dSvIQ8/QmTZ3EljNbKKkqIXmAvXzVehvYdaD3+1Qeq+Sl7f5zotrGd2zaQU6GQhAMHzacrTVbvWECfeNTRad4p+AdLr74Yu6de6+tcgK8MfwNMoszKa8p55UdgY1zA0xPmk7yRH15ju88zsnjJ/3KqdZDcnIyL+W+xOHaw9w88GYu7n6xLlzN8RrWVCpSO3179CX50mRdfF+o+ajPx/UcR/K0ZJ5b/BwJiQlMnDox4Gn7itoVRIkobzoxp2P4okyR4L/ysittqXUY9c/S6lISchIYMXxEwD5XWFnIuwXv6t4pGCbXTSa3PJd/bP4HVw+4mkuTLjUsR2LrRF2aNcdr+Oc2hZCbOHGi14A9wG++/I1Sb2MnevtJIHTN7crSkqW2yg2KuuU7BQ2eg0ePGk3yhcl0yOrA0pKlXHLJJQzpPMQw7tT6qYw4NoJXd7xqO99ued285e17UV8y0jMM45/dcZbKk5X0T+rP0TR/r8zx0fGU1ZQx4ZIJEfOa1pKRkNXQpgLVd5esLl6JGTVc+u50DsgD9E7sbRp35+adnE4/zaiRo0ju2xBm4pSJLD2ylLmD5lqSiB9ZMZIlS5Z4pQEBLr30Uq/E3+4tuynPLg/aZl4b8hp/Wv0nerbtaat9AeyO2c3p4tPeeM8tfo6EWP/xS32eeC7RK/mhtquRI0bidDjZu3WvLmwgFFcV81b+W5bDqzi64ygJFUr5OrXqZCvupLpJbP5AsdU3auQokvtZi/tZ9WcUuBXV+oEDB5I8SllPJOBfT+PGKQ5cjrqPklCgPJ80eRIJ7oY5RYviI8VsrtnMoAGDSB6TjCPDwdcVXzNp4iS/dXNtfa23bb/3g/cMJY8n101m1we7vL+t1k9dfZ2u30ydNpUO2R34vOxzxo8fT/+E/rrwj/d5nL9v+TsA3dt0N8zHbA0YDEWVRSTkNsQdNnYYSR2SLMVVUVdfxwvZL3h/Txg/gcTWiSTkJTBixAgm9JqgyyNY+b5Z/w05mTmMvng0m+s2M6D3AJLHmYfXImt/FrvrGjRDkpOT2b9tP4VnC0lOTmZl3UqKcxrkDi7odQHJU62lDfB1/ddU11Xryq9dw9iB9pslJydTVl1GQk4Cw4cNJ3lQ4LR841rFsZ3HOCAVqcQLOivaB93bdufEiYZDtYsnXqzbU+XS47upAAAgAElEQVSV5/HcUmXdlNQhifpCRQPjKEdtv3NLh8vlikYhszoBCZ6/YNedUAikQChFMbmUDZwGvt0wdWrsrC++uCmqvt5oAivfDJ+AuSFbKWWm5/9CIcRBwPy01QMhxB+AecB0KaVXd1ZK+TTwtEdN9ADwoCd8LIodt2LgJikNbayMwl99NQmFyJzqG9hjnP9TTfqtPe8wFvBq4kkptwshLkGxU7ZJCHG1lHK3b3rnER4alSQTQkSjEGTvSimNLG6/i6IbbYsk8+hMXwJcaymCR5jDysbNIRxekdnm8GRohA5xHagoraB7G8UlezBVBjuqLEIISqpKKKku0YnH27FFYGQDxI7tC6txI+nJToVVwm1MjzGMIXRbZmZYdXyVH0nm+/3CtYsxe+BsPjj4AR3jOgYP7INnZz7L/avuD8k2RUxUTMge7Pp17MexgmPsyd7D7MWzWTZvmZ/4vS+ayyDugIQBDEgYwIpj+kOqpPZJpBelc3X/q/ks7TPvfauqHqDUv6rKp/7/t41/44MbPtCF06qkaMe5nm17mhoN10q5+aqj2lUVUMfKKb2nRMTYsdX8QzG0HR0VTY+2PXjmimcChmsTbU7k+5ZPlUyz+m0v7HihpXC+8B0L7NqMunrA1V6SzA4GJQ5iXI9xbD+73VTdUi2fRJqOF14nDRE2iP1dh5H9wo8PfRxyerHOWG4ceqPl8B1bdWT5zct5btNzpGakAqGpC6neIY0O5oJBtWVnFR1bdTS0UWrXFmS72Ha88v3ghxu+0NaP3fYcExXDwmsW8t6+95jax2+fZIpfXPwL7l9lz+yM2bz5713/5tbRRoIPweF0OHnl+6/QOb6zKQkbHRXNW3Pf4idLfhJSHiqCzfvTL5zuJcmevcLQ31fIiI+J987jYM/unIooRxRDEodwME9ZJ4dr5yucdY6ZyQozBKt7X0RSxfDJGU/y4OoHvXN8KOYC7NoQVp27/W7C71h5XDFB5euAyVcFddPpTd5r38P/ytpKW+u95oDL5RI0GOTv4fPXHUikgexqFyCpWhqM8ruBkyh2w9wo0ldmf4UpKSnGm20hFtFgtF9FOXD1t4rK4mIhxD88aomxwE+klK8JIbqqklhCiGkoDgFv9fy+FVgrpTypz0r8DcUhwWVSyiLN/UTA7VHVfBT4Rkp50CMAtASFNLsvgCpnFooTgSUeqbP2KB4yBwshenqk3doDHaSUGShqoqpx5KE0EGNJKM4N1HINkFIeFUK8DPwAA1to5xE+GtO7pQAWAoeklH/X3O8vpVQNB8wBDoeQ/PXAZx5De5ah1Rc3g0M4vCSF3cWWGY66j9KpVaeQDe6nXJrC4bzDdG3T1fC51l4M2LO95hAO72J89cnVdIvvBtgjCL889iWgSFup0Nkk8xk7mtILVHPj1atf5UTBCT4/+rn33qiuo7x23ozsNGjrXvttm8NwqXpC3NTfzHch+MmhT0z7YyQ9Q4YDX8+zgxIHUVhVyB1j7+COsXcwe7G56L3ZO2jtuKgEhJENLu1CztfI+oBOA0jLT/PLQ/u7Z1t9OwxKZEuJlq9R22ykiexgBHFMVAwDEwYye0Dk1Br+MOkPHMk7wsoTK20bL7dqxzJSC+dQyKbnv/e87c0PwOhuoxWSrC4ASSYEUgYgyULI978Bwdpx25i2lFSXGLaLQONTpD29acfYUMbVCztcyI+H/5jL+15uO26UI8pW+2gT3cZLkql9VCKpr7c/V9m1lwRw3ZDrvIcfoXhr7RLfhfsm3GcrTjC7XlpSSm03Zmu5Tw9/qiPJfL0HBpsXrdRZx1YdWXD1grDm6DpZFzT+y7NeJs4ZR7vYQHt4BXeNvcty3k6HkxdmvcCXaV/y8rcvhzy/afcezbn+TXNbs9mmIhS7zJEak4Z1GUaPNj287SwUm2TPf+95W3nOHTSXxNaJXNrn0gaSzIcU03qQBUWKVYUvSVZWXdbiSDKXyxWFYgR+Oopq3zDAVxS0DoXcOYdif+swCtml/uUbXPt7pgwXUq5FiKtpIMrKgauRcq0EPDa6vvJIeEUDqpj9I0KIK1GItCzg+1LKYg+R9iiwTJuNEGIK8ABwFFjnaWMvSCkXovAUfxRC1ABfAb/2RLsPpQ4TgV2eOD+VUu7xeYu/o5B5N6EQiXdKKbd5NOnWCiEqPOW8w2NrvdKjEgswElDdmW8FXEKIvcCPgIVCiHigGlgspdxhr3LPwwoaU5JsMnALijE6VQTwIeA2j2G/ehT93jsBhBDdgG9RmOp6j7G9IZ6G/QVwu5RSZVHnoeg0W4I6wVqWJPMseH6z4je0j7V/IuqL36/8PYBtg/FquRNaJzCj7wyOuv3VV0Ax1j+rX4MxSVuSZAjDSdsqQaj1ZKedvLQLrP05+3VxQjmNCwcO4Qh5YfLU5U+RUxa6x90ebXvQo20PFu1aRE55DlJK2sS0oXe73rSJaaPzIqpCu6h9dO2jXvWtsLzThbhwUftMINKzMeDbV08WnqRz685EiSi/tulbHu2ixQ7CXdyN6zHOSxgDrDiulyzrGt+V7LJsVp1YRUlVCQ9MecBW/up4oHUzrkK3udXUR119HT3b9SSrLEvnjAMCL9btbmoGJiibt0v7XGorXiQQaemBoZ2HUlRZRGVtJcVVxZakYgYmDOSI+wjDuwy3nM/9E+1LaPoZ1Q6hzfbt2Nd2HGhw5rL+1Hraxpg7dggkSaaObf/tRo19EUzi8bkrnuPuL+8OaIagKaAlqf709Z94ZuYz3g2+lbYmhOCmYTeFlLevR2DVzMTgxMEcyjvkF95ojHpp+0u6tVBjIrF1ItcMuIZlR5e1CEcUY7uPpWOrBmlxtX6064lA/U4NH2mJ7O5tu4cVX9smzcpm1VlVqE6bVHIs1HWkdj3jm4a2X3Vp3YXq+sDOPvykiW3MAd3adONUcXDTRYMTB9O5dWd+OeaXltMOhFDXZPEx8QHrKxjsSrQ6HU6Sk5J193wlyU4WntS1Ny0x67s2fXbTszx5+ZO2ytAE+AYYj0Ju7QPeRJGGOo0iqXQWyEtJSWkZJ1oNRNki4OdIubbhkXwbeNs/ijRz6fxDYL7H7po2/EYwblgeomyhwf3HgMf8Y/iFOwv4LYyllK8BrxlEGaIJs0hzfRpFskzFJcHyPo/w0ZjeLc0anZHrWaSUWUAvk2ezfH4nh1ImKyTZ5jObuarfVaEk3+iweqpjtGDbl72PWGcsAxIG6O47hINTRQ2TZnV9NcVVxZZPdB/f8LguLRXaiS2rTC/irapkmCHSG6hFcxbx0OqHTNXOAmFI5yGmNn7sIKdcIdq2nNlCnawjSkTRNb6rTlVVSklNfY2u7g/lHWJsj7Fh5x8qAnnhi7QUgxaX9LyEI+4GD2C5Zbl0atWJKEcUb17zJgdyD/DkRv3iQy2j3YVspCT0xvUcx0c3fMT1H15v+PylWS8x7+N51NbXsvH0Rh7gAcNwwWDkPXJ8z/Es3OU3j1NPvY741yJQH7fbB3u26xmW19iWgKT2SXRt05WE1gl0a6NI1GaVZtE+rj1/WPkHjhc0OCPwbTP1sp6x3cfa2mCqttDsoHub7szsO5NVJ1YBTatirFXzN5tLHcIRUJJMrbfmkIptDnxy4ycs3LUwqMfF7m27c2mfS9l5bqffs6asK+0mLy0/jeKqYh3x0pjwPQAZ3mU4eeV5PD3zaRbtWsQnh40sdijQzkXqYWJTqPSqBGJLUB82K4OOJAvQlnzH/JbSR90V5s61mgpq+wqVJNPOv4HSiImKCUiS5Vfks+nMJtPnwWD1m/Zp34dfX/Lr4AEN4DsnPX7Z4/Ro2yO0tBpxjWkVqiRZ59adyS3P5ZlNz+jMiGi/p++aan+uXkCghUBtBK8Cb6WkpISizdW0UIixpPCTaSCdzuM8rKD5Z/YmgB1JMrCmlmkVobr6NkLvdvrTMu0Jh1bKTEuSqe/+0JqHvBJtWjiEgx3nGqQ08yvy2XFuhyXXz1JK3QlvoNOyUBCpCbJTq05c2MHYBlBTT8K7snZRV1+HQzjo2qYrueW53kXs0iNLue6D68gtz9XFeXuvclDSHItWQ0myJijHDUNu0P0+VnCMellPlIiifVx7HVEUSI3QDiJBOMQ6zaUkY52xAU9UreZvNI51b9vdqyq9JVNxlgDKd3MIh2GcQH00VLuB33Wo44GWJAM47D6sGxOllLpNZU1dTZNIkwghdH2jKSWyrB7SSGTQcv2vSJJFR0UTJaL8+kthZSHv7n1XVw+qOnVzwrffe0nNJvhevuqW6tgFcP0Q44MHFSXVDZ5S1XdoSuIqFHXLSMP3fXPLcrn7i7vZcqbB86SV7xiO3cNIwLcPxEfHh93+Fl6zkNdmGwltWINat+sz1gcJaQyr6pbqHFJWXcbsxbN5cduLuufr0td5ryPRJ81UcbVz2ZniM5wsOBnUFrIZRnQdEbKpGSEaNF2aa2xU91mTe08GYEii/tBcW4ctxY51EFyLYvT+T8Ahl8t1xuVyLXO5XPNdLtd1LpdrmMcb5Xmcx/88ml9GvClgw3A/2FNXDAa7RlYDwZe8q6prsFc1IGEAxwqOAfYN9xvhr+v/GjTu2ZKzut/axVVzL/Z90VxG3X3Rv1N/cstyiXJE4XQ4kUiyS7Pp2a6ndwFmZly1OTaW6jc1Os1tzDr1TXtM9zG6TZMWGYUZ3LbsNq/KX01djaFUVUtAUVWDerLW3pWdb2tW71qCbsOpDczqP8tLLPpu4m5behudWnUyzcNK/43U9+8Q14HCysKIpBUpdI7vDEBueS5/Xec/Ft795d1EiSiWzFsCKKfNTaVypVNrb8JxVtu+tO1YC4Gguq6aNelrAqbV0uaHxoRqp02LWz69BYCR3UZ6vXyamQVoynF/Vv9ZbD+73fvbV9WoMRElonTvrx3vtW1+9uLZ/OLiX5im05Qk2bbMbUDk7NfagW8f8n3fqroqMooyLEvx+KpbNheRHSWiGNN9DPHR8aw/pSelQj3U7BLfJawyqXX79t63bTnE8I0PgSW41TX+Y+v/n70zj4+qutv490wmC9mADIQdwm6AACoCsg4oFRHccEXr0vpqXaq+amtblzC2te1rtRWtUkrFpVXEHXFFJYDs+07CvmQlEwjZSDKZ8/5x507uzNzZklkC5MlnPrn33HPPOffcc8/ynN+iaHF9c+AbHhr5kPO6+5jb3H70lXWvuNhZU+tXLceO4h387offAfDLkb/0cDLljlD366pkMjTNJlkooBJfQ9KH8Gnup04HDDa7zUNFXLU13JKRnZ1dBMy0WCxdUQiz0SiG7a+iUXDGbrFYDgMHgaNuvwIUw/Nl2dnZUTUwLYS4D5iN4hUzGdgJ3CSlrNPEmQB8B8ySUn4ohIhDsZXeVye9Z4Hbgb5AqpSyyk/+7YD3gRHAd1LKmzXXXkapU5Vd/p2U8kuH8f4/A1c7rt0upfS0JxAiCCFuB5aqjgxaERzCabi/B/A20AmFpponpXxZCHEjSqPOBEZKKTc64o8E5qm3o+gNf+Ij/TnAz6SUfo14qB13oLt94doVrKqrIinOt27+oZONDjf8TVJiDY2DW9+0xu89mMWaQRi4sPOFbCna4tX2hwp3SQL3CX1VfWN/0pwJVjgWUCuPrgx5mk1B99TuTnVLdWduXf46rk+93mn7zFodmHrB/M3ziREx3H3h3WErrzq5+++O/5KVnsXgdE97WJGAQNBgb9CV8nw251kAdp1QXHdHY8HSFKgElopAFwDeFn/PTHiGez6/B4DXN77uJMm0HntVlFSXOFWAAQ6cPMBlXOZStkhh3vR5PPDlA5RWN5qJiDaJotpNPFlzkvUF63XjaNtZWU2Zhyp7JNAcey3BIhBJspayGdFS8fLal1mbv1b3mhCCU2dOUVpd2mTJi+ZiRNcR3HPhPczfMh+I7HcYY3BVt9SSZO593r82e5cKiiRJNrXfVP695d9eSeNIwv15VTImUClwd8P9KiItbS+EYLZ5NssOLWPF0RW8tuE1pvabGtEyuENrl7a+oT5oA/6BqlvGGmKxS3tAanpN+Tbd5+Vrjq/R9UCuriEOnWpcjwS6Lgple7FLO1uLt/Lxno+5rPdlIU8/EKgS5O7v/Lr3rwMUT5hnI7KzswuAfzh+WCyWJGAAcAEw0PHLAKaieLl0r3i7xWKxohj2P+H4FdNo8F/9XwicaJaNMyEmoWOTDMXxwO+klAscxvvzgKEo9s0RQnRHMbB/HFAZzEwUG2x6+BzFY+VH/ggyB+zA4yh20mvcrg0GrpZS7nYLfxzFU+gFwEzAAgTPvAeO2YBXLqUVvhHOrW8b8LiUcrMQIgXYJIRYisL0Xo+iD63FTmCElNImhOgCbBNCfC6l9JBfFUKMAAI2lOEkyXQW2Ddk3sCHez50CQu1hzYVt3x0i0+7Pe9se4dFuxd5hGsHhdemveYcsLXehbRGuYNRFzUIA4+MeoTPcj/jruF38fGej3lr21u6cbWkHHh6fdEaHQ6JumWYF1zR2Ck1CIOT7JkxYAaf5X7mrDd1oq0lL7T4Yt8XzMqa5TzPs+aF3QmClnBdnLs4aiSZKmXREmy/hAoNsoEYh9fmYCa83upAz/utWmeFlYU+0/w873NmZs50nkfy22gT28aFINMi0qSLmp/6f3HeYl/RAVh6YCk1thqWHV7GY5eGf8Jc19BosyaSZOa2InenTZ4IdAFzvqhbqlC/7+8Ofec1jur04+7P7mbBNY2mUyJNGKvSUdD4niJRhrqGOqrrq1m0axE3DLoBu7Q7v8Ng+n3VMHkkFtMZ7TIAPJyiRAIf7fmIw6cOO8/d62jeZmXPeV3+OmfYfUvu85peuAz3Nxdbi7c67eJFq2z7yhq9QjalDFqti1NnTnnt/+Ji4ny2pVC3aW8bHyohps0vGs4p1LFuwdYFjSRZhNuAKk3rvv5REU1vpT4wUAiR4xa2SEr5mhAiEX3b4G9KKd8UQhwD1I6iFthnNBr39+3b98Nbb711Z3Fx8aA9e/Y8mpiYGJuYmBiXmJjYqU2bNt3T0tIq4+Pjk4B27gnb7XaeeeaZMqPReKS6urrq8OHD/aqqquoqKyvrTp8+XXv69Om6du3aPT9jxowvZs+efSHwvHrvNdBuIWQlKFJuSxBiuoYoGwrMdxz3QyHy8gAcnixfQyGlNgCqUdnhQIUQ4jvHPXOklC8BSCm3CCGuRnFo4BdSytPATiHEIOBNt8sXoEjiOSGEMKI4K7xYSimFEJuAbL20hRB3Aw8B8cBmKeUdQojewN+BbigE3U+llLlCiJcczz4Uhej8nZTyHSHE+ygE5yohxLcogkj/ROFPklAEjVYF8qznK8JpuF9lkJFSVggh9gDdpJRLQUdsuNHlKUAC6M/KHKKKL6B4qbgusMIo//R2QvR2/0MpSTaowyCneK476hrqKD9T7lTt0SPI3NGjbQ8Xzypq+m9ve9sZFpTqFgJTosnpCnxm5kwXkuyRUY/w8jrFjXKDbGBj4UanmphW/75tfFun2gjgdJ88NH0o20u201IR6QE3xhDjJC5MiSYEghNVJ/zfCLy38z0Xksxmt/n1mtZcaAljd4OxkdzRU8XaW4LtF394eerLPPL1I37j2ey2sLtYD4ZY1PYhgUz8wvn+o0GiNIUIeOr7p5pslLipCNRbXajhrl7fHERbUjCSUL+TGe/N8Ljm7dvUsx+qm3YYxi/teL2laIvT3mm4x0qVJHxn+zt0T+3uImnblH4/Em2sfULjXu0Z2xkSjJE15aO1J6tujv7f5f/Hr7/7ddBpeZMkixa8eXOPBq7oewVrjq8BmkYWHS4/7Dyes34Ob17zJuBph9CXTdNQwP2bcBcIOHhKWdOrz6g1sxHIPCLU41GgTifcMWvIrCY56gKorq/m5g8VrblhnYY5pVuNBiOX977cQ6WyqbbazibYbDaZm5tbkp2dvUwIsR+4USfai1LKzzt16pRlt9v/1bZt27iUlJS41NTUuOTk5Li+ffvuMplM0mg09unVq1damzZt4gwGlzb1AcCzzz5bWV5ebqisrKztlZtr//Xq1WnxdrvaKSXiSpQNBt4WQsSiEEfTHcQVwBzgRRSprV2ysXEOAzqgqEKmAHuFEK9JKc84rmcRIEmmgcs9DjXMdOBHIYQEfiul/A6YBORKKU86oiYDHp46hBA3o6h9TpRSVgohOjuecT5wr5TygBBiGopdubsd+e8BLkchAV9C8fr5NyBGSnmDI933gd9LKXMcaqfnh8mtZiAiFSSEyAAuBNb5iTcKeAPohcKQ6llBfAhYLKUsDHTSpnaueoObXscfyh2TTsmdvJJkf/7xz2wo2MDiWxZ7TEADnWw9Oe5J7vz0TpcBJJidDffn15ZjWr9pjO0x1kmSnbEpfcia42sY02OMc4dlWKdhPDPhGed9tbZaCiqVBdWx08cCLks48a8Z/+J/PvduxySSaJANxBviMRqMpMancvLMSf836aXjcAAQTmi/BaPBSP7pfH619FdhzdNbOcIlSRbqiV2f9n2cKswvT33Za7xAPci6I5g6UOvso5s+4sPdH/Lezve8xtXakYo0iZGVnsWOEs95ScRVfTT5jesxjh0lO3yqUm0v2e408q8a9g03tE5I7NKOlJKqukA0A5qHQBZwAY/J55kkmTd4+5bLasqcx5Guqwk9JzhtQb2+8XUAJmVMCnu+0/pN48v9jQIOEumsn9iYWO4fcT/DOg3jF1/8IuxlCRSmRJPz+L4l9/HWtfpS+JFAc8fGlkZca/uSpo6VocKwzsNCmp43j4jepJX04O48pilwJ59VwkdtSx/t+ch5LRpG6bXrh2Ce9dasW5uc559WNnpNr6mvcVHfTo5LdjpFUqGqprcw5EopzXoXHAIputcc10v9XD/m63pxcfEOFDtnLrBYLPFASlxcXEpcXFwK0BbojSL5NADoDwwwGAzJ7du358JTpxJvXbeOOLvHejYRWLJIkbQqkVIOBRBC3AE8A0wRQtwP5Ekplwsh7gW0YvDDgf+VUtYCtUKIEkdZVJJsCPARAUII0QboCBzWBNcCnaSUJ4UQPwH+A3R2pK2VGslC0aJzxzPADVLKSgApZZHDVNVg4CNH32gEVDtCg4CbHdJpsYBq5HeoW375wB8c5qoWuwkntUIHYSfJhBDJKA3uUQ3Dqwsp5TpgsBAiE3hLCPGVht1FCNEVhcE2B5DvvcC9AG07t6XMWsby5cs94u09vRdrqasNqHVr1znDrFjpUNOBnKocf1nqIu9YHtZTjenn5DSm8/WOrwH4IecHYkSMSzniY+LZuWsn1lIry1csJ87gXeLEvfzbdmxzhv34448kG5Od52r+6vmKFSs8BsrkmmSOVB3hIAdZV7XOI/2tW7ZSd6CO/ZX7sZZa6ZvalzU/rnFer7RVOu+Z2G0iH5cqrtuv73Y9CTEJLnWgh10ndinPvXx5UJMGf+hr78uu07vIycmhpqGGncd2cqLuhN/yhAJqfaxbt47DBYdJjEkkJyeHg/kHOZh/kCHVQzzqWQ/ash47foyquCpyZI7X+CqKiovIq84jp9p/XHcMFoNZcWIFV3S+ghvfatxA2rlrJ+2LA9Z6Dhra+qiMraSwppCS6hLn+wukvkAh2Hy948KaQqylVrZs3kLdAe/u14PBZDGZyV0mc3TbUY5y1BmuLfPylctJNiqqtrkFuZw45bstqvdu27aNmKP6khXa9HNyciguKSavIY/Vp1fTWXbGWmqlc0Jnis7oO4dQsfLHlZjiTV6vHz16lDhDXMi+HVEqnGVftmwZXxZ9ibXUyvYd20kq8G3HMVQ4nn+c2vhachpyADhw9AAHyw/6vgl4r1QhHjM7ZEakLwGYnDCZD45/wNZtW1m5aSXfFStqfOvXr6c4KTz2WTNtmWwtbdxF13vWvcWe46keNmzcQHlK9O04RQK5hbmUlJXoeozesH4DRYnKt9juTDsOVB7wiBNfFe+1Xe0/vh/rSStbt2312icEixFyBEvKlriUd79tP4XVhWFt3wlVCc62s2fbHg4XHybWEOvMM5FEdh/bHbJ+P1S4gAtYVboKK1Z+WPZD2DeuvD3/gYYD5JzJ4Wj10YDrSFs/u0qUedfevXvJOZXDlpNbsJZaWb16tc+xIFzYW7AXq1V5jh1ndmCttLJ27VqOtjnq587wwH0OHQweTH+Q53Y/5zxf8eMKrKVWtm3fRtXBKmfaRfYirGWu706b387Snc64W7ZsobikmH21+wJenxw+fBhrhSb9CthXsY+iiiJycnKcaW/duZVOpZ1c2tGaTWsQR3xvghw9etSjzM2BNv+VP67EWmplx/YdJOaHT4Pi+13fO483lG0gxZjCybqTbFi/gfyqfAqKC/jm+2+a9I2dbbBYLAJog0IitXP82rr9T0WRyPL3C3QxVwdUXPfxx23j6uu9cRSJsfAK8KMmbBuKaiXAz4EOQohfokiS1QkhNkkp/41CKO0Ap8RXGxS7aiqyUOx4BYrBKI4AnCyulLKGRhtla4BUoTBb8TQa8ge4FnDxMuZQyewmpdzrls8w4CnHM2jjdwDKpZTqztpQGom3oShOC9RyPSaEuBS4E3gCGBnEc56XCCtJ5mA0PwL+K6X8OND7pJR7hBCVKKzrRs2lC1F0iPc7mNREIcR+KWU/nTTm4XAE0HtQb5neMR2z2eyRV0pBCl9UfeESNm7sOBZYG22CpHdPxzze815fqLXVEmOIYf2a9eQfbRT71ZbBVKhMPMZPGE9cTJzzHCAxNpHBgwaz0bYR80SzT7Ws0bWjXWwmDB48mHX1itDeuHHjaBvf1pm2mr96Psk8yWNS923Dt1SWVDI4czDm4WaXcgFcP/l6Oid3JrkgGVOFiTGjxjCww0DndWu1FdMJEw9d8hCju49m+ScKOfnwNQ+7PEdpdSm5pblc1OUi2sS2aQzfXcpG20YmTpzYJHU0b9i7cS8lR0owm82K6ks8dDV11Q+0+pYAACAASURBVG0XoYZahyNHjmTDhg2ktUnDPLGxbidOnMiCIQuc3lAnZ0zm8j6XOz0LqZhfNp+3r3sbgzCwqGoRPdv2xDzOf/kXnFpA/679MY/0H9cdYxrGMHPRTNp0bYPJ1tgWhgwegjkz+PQCxd/6/Y0/rFS8PA3PGk5CaQINZQ2YzWZsdhuvlbzmEj8lLoV3Z77rotY00DQQKaXPd3zo5CHePfUuF110EZf2uDQsz6JC+y2NHjPaaaA7d2MuBUcKfJZTvfeiCy/CnKEfT5u+2WzGVGxi0MBBmIcr8cfbxmM0GKkvaVwA66nnjLp0FN1Tu3styxd1X5BgTAjZtzOwYiDblygbXr2G92Lv13sxdTAxNGso5gGhycMfPqj6gO6p3Z19/T8/+iem2MAXh+bxZtomtA1T6VwxtGYoP3z6A1lDs/gi7wtMDY39S2bHzLDkOaxmGN9++q3zXO/dH9t2jB12V4nAKX2msPTgUpewiy++mIu7XhyWcrY0HNxykIP7DpIYm+ghNXzp6Evpl6ZMX8ZPGM+171/rcX/39t29fmdb127lyKEjDB82HHMf/ThNQXy/eP70Y6NERb+MftSfqA/rWNm3vC8fn1amiWNGjWHPlj0kxia65FnXUIepWP+b7J7SneMVx53ncTFxERnbzZidY86Y8WPCrnLpPh9TcUHfCzCPNJNbmssH5R8ElJa2fop3FrO5YTPxneNpd0E7htcMZ9mZZYwdO9YpLRtJ7Fq/i31CmdeOGzCOk3knGT16NH3a94l4WQD+3PvP2KXd69jrC3Zp5x8l/3CeXzrmUkxlJoYNVbzbmqwm0hPTGdh1IAf3N27MpCe6rltO555mbb3i+OPCCy9kbcNaBvQagHlEYGX6/ofvsRY3kjs9UnvQv2N/ThWcUuYLjrb13I3PIYRwaWt3X3E3XVK6+Ez/G9s3gP7Y0BRo8x83bhymUhPDhg3D3C806fvLMzU+lYSYBAzVBi4dfSlJxUlssG0gfVA6ppLA5gaR6IOChcViiQF6okhwdUdxsOf+64hChPkjtxqACs2v0vG/2C08oF92drayUz179iRgCYrkmDuq/w8+w0FuOQioO3EQQlLKEWpEIcQy4Bkp5Y8Oh4KdUKTWclFsn72mElwOFcSewH7N/WnAnVLKv3l5fndVSwF0lFKWOAiv36PYe5MOs1N3OuJNcpTDZXLksMteK4ToJaU8IoToCJSimK+6QgixQEppF0KoUmhZuErKDaVRwiwDxRupKmR0Wkq5xiE912rMPwCE07ulQGFI96hG8fzE7w0cczSQXihG7w5r40gpv0ARWVTvqdQjyNwhkV5tWugZuXcnjYKxxdJgb2Dl0ZW8uOZFBpoG0jGxo9e4qj0C1TZR+4T2zkl0MKLF7uqV7mLzPxz6AW/QU2dSw7ypnZ6sOckzPzzDBR0uADztGqg6/DGGGJc03PPafWI3L6x+gdemveZiZy1c6iWq8fcXV78YlvQDRYO90bbWZb0v4/tD32Oz20iJT3HG6dG2h4tHpYs6X8Tmos2U15aTfzqfjQUbsdltEbHRpeahNRgeCbgvou3S7vxejQYjD498mDnr5zivq+oZz09+3kkuCgR2fKsfR0vNpKmeCX1JKtw/4n6nihT4V8l9ZNQjzu9Yi0irt3RJ6cKkjEnsPrHbJe9oGpFuG9/Ww97IzYNv5v1d7+vGD5fDFz2o32QkjQa3b9OeV658hV9+9UuvcdS2ptrKvD3rdm4ecrMHSdbSVLvCDYmkc3JnD5JM23/HGGL465S/OjdKnPdqxsOSqhJyS3MZ32u8R/qhRDTsUmnVeYUQAavXz8ycSVZ6FkaDkaeXPe0Mj4ZK78NfPcy8GfP8RwwD9OpKtQn7/OTnOXTqkItX0NT4VJe4ahvaVLiJTYWbeHTUo+EtsB9o+zbVuVE07aXpeYEMFAZh4Las2/jvjv8Crs/WIbEDcTFx/Pyin7s4R+nTrg81NleHec19fvd37m3+GBsT66LCPyBtgF+CTEUox+xnJzzLcyueI8GYwJ2f3hmydANFUmySU6LWIAxOG8D/3uIizMP9I+7n4i4XO72L+xsnIw2LxZKCYoNrGnAxirCJuwSCSmwVAbtRvFWecvzKdY7LHb/q7Ozs0He2Ui5DiOl4EmXVwPS1cA9wlxDiKhQj9utQpKPckQmoUlnDgEXAaw4HgUtQ7JYhhPjKcT0ROCKE+JWU8j0UO18j3BMVQlyAQsqlKKfiChTSKwX4wfEd2IGvAHUX+gtglhBiNwrBd72UupO4h4CvhRB1QL6UcpoQ4g0Um2Z7hBA1wE4p5e0OskxrKG8YDo+lKJ463xdCrHQc/1EIUYtCZP5cJ99WuCGckmRjgZ8CO4QQ6gv8HYq44SsoLPUXQoitUsorgHHAb4QQ9SgN6wGHbjRCiC+Be6SUTbYc7M3jo/vEwmgwegxEwaj8Lc5dzBtb3wAg15qLqY333QbVGHmDvYH/bv+vywQ6mEnvgZOuKhruCydfhiX1BjRfNtxAqcuiqiLn7qJ7/aiL3BgR41Lv7nmpdf+nH/9Ex8SOzDbPdokT6gmRWt85R3JCmm6w0JI9qnes3Sd2u3gnNGeYXSZM2np5bvlzFFUVIRC6BEeoob4nD1InwgRGg/RN+KjXtBNBIQTS3vgtFVYU0iAbdKWkIv08TTVK6+u76JbS6PFWSuli1wcan1FdjAuE7nOrRLc3hIvkUMscDbjne90F1/HqhlddwoZ2GsrEXhN54MsHPO6PpPcv9Z3a7LaI1legz6i2KW/f1Plkk0z7vWa0zeCnw35KYUUh87fM93h3AzsM5KZBN3l14vPYN49RXlvuQZKFGu79bCTel3vbCpQku2v4XQBsL240veJuED3cePCSB/nHhn/49SIcTviqKyEESbGuausd2nQIKN1oEVPnGpE+ufdkJ0mm/Z7ijfF8dJNiAkk75/Pnpb4p9fPAJQ8wOH0wSw8sZf/J/R5t5smxTzrneer/O4bewXWZgflICzUu6XYJo7qNcvm2w43fjP0Nf171Z8DRj6gOLYTg8j6X8+qGV+mS3IXS6lKq6hUiMdYQ6zJ/z2iXwdgeYzlaHh3VYC0sFksasBnF1rcVWIVC1uQB+4AjQHF2dnaN10SiBU+irBqYjpTLJCzzfbOahOysOV7iSEsv3pVekrgBeFAn/l4UKTx3WFG04PTysAE3+ykyUsoPgQ/dwmocZXGPO8ftfKLmeD6N3j8BAtboa4WCcHq3/BG8jq4eYn5SyndQvDHopTXNS3hyQGXxIUnmvlj++xV/9xg4gjFY6W7k2X0gyz+dT3V9Nf1N/ZUFhFTSX7hrYcB5+IM7mRGsjQyV5PK2IFJVIGsbanXjaSXJfOWtlvPY6WMcO32MqxdezYJrFniN31wYhKFFLM60XhrVunx62dP8++rG3SlfxGxFnWI41J0ACReEEBiEgQ0FG8Kel0u+bt2Hv0WT3jWBcPke7l1yLwCf3/p5iErZdDRVWssXmae9pvY9epNtd7XAUd1GsS6/0a9KIGULNamovr+FO0PXFwYL7TNd0u0SxXG49jrCaxuMpOdV9Z1uLNjIkfIjkcvXzzOq9ae2n0A9957L0LaptgltGdltJOvz1wP6koDu46l2DqHOL1QP0+GCXtrh3kRw7++l9D2+xcXEMfequbrXYmNiIyoNG0rbqU2FWld67ylGxPiVOm0JcyMttOVpaWVrCrRt2d8mFAQ+ngRDYibFJTGt/zS+3q/YQ3afG4zrOc7jngRjgs/NESkli3MXc3mfy5HIsJOq4U5/bM+x/D7u9zyz7Bllk1WdR2k2/dfmr3UhnfXMwqiaKy0AHVEIMoD3UVTxdgF52dnZtVErVaBoJMoWAHc7vFpGMHt5UyTza0XLwnnh/tPXZCutTRqf3/q506ZEr3a9KD/jSnTpGdzVy2PVsVUUVrjuJG4p2uJyrnpm+vzWz52dvbtIdXPh3jEXVwVnyFkdwL1N/OJjFLUI1dulh7qlY3JqEAavg+vOkp28sPoFj/A8a17YdhBbwqAlkS5kj7aO9SR+9KB9hkAnUg32Bg6fOhxkaRsRyCQ7nJBIFzVV8C6Z6C8MFDffqui8M70I75hrJ8rBLAICJUa1XpncofZx6jX3duT+rmvqayirKaNbajfCCTv2iJOx3qDX/wkhvG6aRIKwVqH2q+7jS7jhT7pB/YbUsWFd/joe9NyEjXo/HGm4f99qPRVUFFBaXcqIriOc79RfHYNDGjmMpKx7X7jm+BrS2qSFLT9w/X6kVMZJPeJMRa+2veiY1GjOQhs31hAbUW98kVS1njN1Dg9//bBHuJMk09TD9hJFAsdXv6WipUluacujHkdT/b650LbvQPo/vX5g3ubQqPL6mhsEix0lO5i/ZT551jwgPPMo7Xs/UR3+jRftukW7nlExqMMgl80pve9fK4UWTWRnZ+daLJYJwKMoKnaqGHyDxWI5imKzqgDF5pX6vwRF3bIUOJGdnR1dL4gKMZYR1TK04rzEeUGSQXCqMO4Dsb/JhV3auWbhNbrX1MWCHtR09SQnmtO5ut/7We5nQd2vup/3NllXB4Ram6ckWV1DHY9+86gz3NuAubFgo264dvIQDkmVptqBCiUa7A3OutUOri7P7qNcWtI2kAUVKBII5bXlFFYUBmxbwh/CvmPo9v61aqp6+avnWvJJtW3jjjs+uYMPb/rQIzyScO9XAm3vPtVqNHXibSIskTz/4/NA44RTT9VJi18v/TWHyw+HVQIvmvZm9MqgN/E1CIOubT5vaqvhQiQJOS0ClSTzdn4+Qtfup6Ne1h5fy8qjK1k4cyHGOOUb9JAk05kL2KWdGGKcfUh1fWjXMO7tS5UaDyfc24o/9frTta7O0rX3Gw1Gr3Oouoa6kDoEAldCvaa+xsURUajRu31vLul6icdmgr9xwZ+jDFW60ePeKH3D2jEo/3S+j5hnB7R9ZyDz+xgR4zNec9YIWpMozUV9gzIfdf8ew4VdJbvCnodaL1q1bfX76pjYkS4pXVxJMp0NNYMwtBjiOTs7eyWw0mKxxAMDUTw8DkYhnrqiGH+/AsVTpQcsFksNGtLM7Vj9bwVO42rAvyYs9spa0YoI4bwgyXypW+rBfVLrTzTaG0EWSLkAlh3Wlx5t6iD47s53nce+SDpvUBfOqsSYO9TwgkrFRJx2Uq/dwY0RMS4TLJvdxvs73/epWvqXVX8JuryBQrVJFm1oJ//ayfrPFwdmR1FLrgQ7yVlzfA3XZ14f1D0QmDRlqGEQBn5x8S+Yu2kuUkq/iya13Z6sabTtd+rMKY6dPuYRrl30RWu3T6sOVG+vD9gxgi8yaX+Z0ymPLknmJBIdeXuTZlXbWIO9gZNnTtK7fW8Olx92UfMKNaklhHBOuN3LGwm4twNdSTIv5Yk0aRVJ1U6XfAMk5f3BffGgtftyLsIpCYPrt+NcrGrqNZANvQ92f+Biq+f7g99z9cCrQ1beaLwHPfV6X+3Nl4S8N8muf6z/B18f+JqXp74cUi+J2u//pg9vCrs6v97Gjy91S4MwkJ6Uzue3fs6jXz/KgZMHXOZCB08e9LBtG+0F/p3D7mTl0ZXY7DZ+PPZjVMuixbrj6/jDyj+w4JoFTu/UgSBYSTJ/dgGb842q7745JkgOnTzE94e+Z0RXxab5zhM7GZA2wLuRnWZAuwmw60QESDJNv+M+j1I327Xfh14/JYRwmeOVVpdy92d3c+9F92LOMLs46ooUHOqV2x0/D1gslmSgC4qKZkegg5f/Axz//Zk8arBYLKq3S63nywqgxvE7ozn2FuYvzplWMs4LFC+aPlVVhRCvAFdLKXu5hU9AcU4wy2EnzUc24mJgLpAEfC6lfDIUxfeSV3dgrJRS34tWCBFO75ZvANOBEinlEEfYMJRKTEbxXHmblPK0ECID2IPikhVgrZTyFzppvo/CggO0A05JKYf7LYwMbnLvPji5L9y84ZKulzCs0zCuGnAV173v39Blv/b92H9yv+41bQfcnIXi/3z+P02+V/Wu+Ntxv+VkzUnmblLsf2i9UIErmaYduN3rfOmBpSG1vRYs1h5fG7W8Vahkj7rI9abSKkSjZIoveyfBLloXbF3gQZLV2mr556Z/cvfwu6MycPuCOcPsbHfu6pbeoP1+VYLsp5/8lFNnToWnkE3EE0ufIMGYEDSR7YuQKaosch7r7Rbb7DYaZANT+kzhwz0fOief7lIJS/KWkNkxk3sW34O1xsoNmYq90Ns+vo2xPcZSWVfpYQi6uRD4VwmKJPS+rTxrHoM6DvIIjzSpEC0yya8kmRfpTneoCzO7tGOz25i5aCbQMmwFhgM2u409pXucjjXUb1jrOU2FL5tkqmTDezvf472d7znDD546GFI7ZdGQVHTP072/t0s7udZc99ucUCXbQVmQgtLOztjOYJd2ahtq+fqAYotpY8HGkJJkkVYf1rO3pqduqUKvXdTUKxsjVXVVPPL1Ix7X1TqMFkyJJn4z9jf8YeUfoloOFTX1NSzatcjZBo+cOhJykkxLXMYYYiivLefTvZ9y7QXXungmVdOzS3uTvnk1f1/zx1kfz3I5P3jyIJ2SOpEUp4z7qsqv6pwsXGP37hO7Xc4jQd46JclEo8qk+v6Kq4opriomwZjgEV8LrSTZ75f/nvUFiqTmvM3zmLd5Xosc67KzsytRjPnvCyS+xWJJQCHO1F+K5pfs49wEtNH5NXngsVgstQRHttUDdY7/7schv5adnR15CQ2FIFOdHixBiOnuRJmDf5kExAkhUqSUFY7w7igqusdx9Z6pk41IBd4DrkfhcjYJId6SUu72dV8zcBmKNOTZS5IBbwKvAm9rwuYDT0gplwshfgb8CnjGce2AP8JLSun0CiGEeBHF/axfBCtJ5g5/nf+CaxawJG+J08sSKC6A526cyy9G/IKMdhk88tUjHDx1sLFMUtKrXS8PkuymQTexr2xfULslf7/i77y97W02F21mXI9xAe26/WHSH/x6YlI9L47pMQbASVbExcQxsutIZ6fvbee7jdFV5UB14x0oQi0tkdEuI6JGrrUY3mk4W4uVfkarbmmtserG13qufGr8U8xePls3nnsdB4J3tr3DT4f91Hn+w6EfWHpwKbGGWO6/5P6g04sU7NLuodqrB73vtaURZCqaIunpz4uZCj1Jsr1WxRO26vH2wz0fcudwT9fqq4+v5q5P73IaCv9wj7KJVFFX4Vxohto2yMbCjU5vUS0VlXWVupP0lkTuhRP+yBO1/akLiMwOmbrxvtz3JYM6DuL2T253CQ+3QfpoQPs8+RWK2pjazlVyWrsR4j6eHik/gs1uw2gwEhsT67Xfm7txbsj6b2u1/rgUTuipW2oX8Qt3LnQhBt2xt3SvR9gjXz9CfkW+R529s/0dbhocOnvMkZZQV+cSWqjtRu/7OXjyIP3S+rmElVSXsLNkJ7/9/re6eWwu3ByCkjYPLakveGf7O3ye10hsBOtZPBCSbPWx1c7jGBFDja2Gf2/5N/3S+rnkDcqGTVV9VZMI7abYJHvk60fokNiBEV1GOOcAAG9sfcN5vLt0N0PThwZdHl9QHVWpiISTDLW/OHb6mHOO7V5X2rmbN6dRJ6pPkGfNc66VzjVkZ2efQSFRjociPYvFEosraZaAPpmmF+4rLAVIdwuLBeIc/8PeqCwWiyS8pFwNisqrFbDO/PDD3oPhFaE8K3gnyizAH4D/QVHBXSuEiAdeAx5HcV/lKmbsiZ8CH0spdwIIIbYBmYALSSaE6Aq8AvRBKdcdUsr1QohfArNQhJ8+klI+LYRoC2wCPgWmoHg2vQy4CHgJOCWEuAKFmLsRuAXlPe6UUt7ip7wBI5zeLVc4GEotBgArHMdLgW9oJMkChlBGzpuAyYHe0xRJsmn9pvHl/i/9ToA6JHZwIchAIWT+fPmfned64v92aSc9MZ0pfafQPqE9k3tPJjYmlje3vsmuE7sC3jHpm9aXyb0ns7loM7cPvZ0nxylSjgUVBby05iWu6HsFc9bPYXinRg5yWOdhDGOYbnoqAXZpj0tdwoemD3Uagh3dfbSz4/c2kXGXOEuNb1R3H2ga6HNX2Fe6TcXjlz7O8iPLw5qHN1x7wbXOia3W6HKnpE5e7+ma0tXvbpN2N8sX+qf1Z1+Zsjm0aPciF5JM/TbcPbNqoXVuES00yAbiRKN66jvbG53htk9oz19/8lfAVTV04cyF3PKR7/4ykmolT49/mnX561h6cGnQ9z5+6ePsLd3LsM763y3AlD5T+GLfF4Dv3WKVIL+4i2Knpne73hw6dcgljq/2AN5JyqZCtYWoRSQXSYG0g4kZE5tEbJ4rCNRw/4GTB3h56stOySl3bC3e6kGQnU9wJ+217byqzpMoDkQyfWq/qc0vmAO66kMR9lqnlbgGVwJBD+6OWACPPi1ciJaNwFhDrHO8U0036BGcF3a+UPd+bwQZwJ7SPUD0ng0828SO4h3OzdtIw11yOli7c9pvypsNQS0hpK13vff0yd5PAMXm8D0X3RNUWdQNLl8b0UaDEZvd5rKRW1pd6kKQ6UFdI4QLwT5rU6B1chWI1J3enOC7Q98B8Pi3j4e2cIFhoBAixy1skZTyNSFEIvClzj1vSinfFEJ0APRU616XUr4vhOgBvKNz/UUp5edCiIHAP3Wu/0FK+Z0QYjjwd53rv5NSrhZCXAI8r3P9USnlViHE5cDTOtfvk1LmCiFmoJA7CCEwGAzCYDDU9+7d+65Zs2YVvf766zOrq6t/7ghXr4spU6Y8OmDAgDNffvnlzPLy8hmxsbEGo9FoUP+bzebX4+PjY/bs2TOxoqJimNFoNBiNRkNMTIwhNjY2ZsCAAT8CCWVlZQPq6urS1WuO/yIxMbEGhagKTlqkCcg4dIgBubl6I7YLUSaEGAwMAe4CxjmO1wJzgBeBNGCX9K+TPQt4THOejELeOSGEMAJfAU9JKZc42mGMEOIOFBXfsY6oq4QQ/wK6O/KfJ6V8QgjxGXCJQ8hqA4rA1U4hRCaKPb2LpJRSCNEukDoKFJG2SbYLuAaFGbwR6KG51lsIsQXF8N/TUsqVPtIZDxRLKb2KhAoh7gXuBUjunExhQSE5OTleE7SWKgNBTk4OdfY6rKVW9sv9WK1WYkSMz3sDwbFjx1wmL8tylpF3PI/immI6t+0MwKrjqwDYW7SXE9YT7Nq1C2uplRUrVviVqtp6aivWUiur16ymY3yjx6fpcdPhGCRUJXDs6LGAnmO0fTSDTYNZsXyFS/iUmClM6TKFnJwcYmQME+MncmH7Cz3SvCblGhYdW8T+Lfs5ajhKh9oO5FbksnHTRqylVn7R9xf0iO1B/+T+vHX4LZd7Z3afSVbbLCptlc2ucz1MiJ/AJ/mfOM9FhQhLPu7IrcjFWmpl3fp1FBUXsa9+HzkVOUgpGSAHsMa6hs4JnSk6o6jLLV+xnERj46RfbZ8j00YSa4hlVanSVtoWtSWnxH/5Z8TN4KnSpwAY22GsyzOvL12PtdTK4tLFjLaN9prG410eZ8WJFXxdpEySdu7cSduitkHVQ7CoaajBWmplx84dHDl1hCRjkrPss9rO4vk9ylj60KCH2L1hN7vZTb293llfK1au4NHOj5Jbkct/jvzHJW01nfyafKylVjZv3kzN/tB6mtXDUIaysFRRO85IyuBw1WEAJqVP8tsWL+ACflzhW1JUffblK5djLbWyc8dOEvOVtjQxfiIf538MKBPkwWmDycnJYWrsVL5q+IpNJzdxR687ePvI217T1yKU385NbW/i9QOvu4Rt376dhOOBEcHNRUFBAbJMkmPLcYbd3v52Xt73Mr/L/B1JMUns37wfKaWzjlVo22WkcGu7W3n9wOsumzjr16+nOCk4b8bBQPtt6T3vhsINzutHtx3lKEcB6GPvw4Yy/15Lly9f7jfO2Ya9xXtd2ktOTg5JMskZ1jOxp0tdfnToI6yVwUlyPZ/1PEe2HeEIoZGUllISWxVLUY0yHrWLa0dBRUFY27i2ba1bv46CwgIOVB0gp0bJM/l0svP61V2vZrRptEt56s803v9A3wd47cBrPvNbtmxZyEj4Btng8Y7DiTvT7uSlvJcYmDKQ3AplszF3dy45JY3zVxXPZz3Prg2NmgnHjx8nSSRxtPpoQHltW7ctajYQd5/e7fIsR/YeIacwJyplKT9d7ixLj8QeHvNjf9C2kfnfz8d6ysq27duIP964mdzN1o3tpxSS6ZjtGNZT/vuBpqxP1HKcaDiBVVgpqijySKO4RBlH5q2cR6+TvTzGPF8IZftPqnZtq6f2nyInP3Tp66JBqaNxHcax9sRabHYbq35cRZIxyaMeftL5J1TmVZKzL8dlbMwyZPmcm0d6vuAPBoNBWCyWRLPZ3H779u3xRqPREBcXZ4iNjTXExsYaevToMdRisdTef//93TZv3tzJYDCImJgYoZJMPXr0uNZisQz65S9/2Xnfvn29HNcNQghhMBhEt27dHrJYLDc+9thj6QUFBReo96m/jh07/tVisVT++te/NpWXl/fVXDMIIURKSsonFoul4amnnkquq6tr7zBJIxyXRWxs7GaLxWLIzs42SimNBoMHub8X4P77vUpbrwGYNm2at+t/B8jM1JeOR5FoIi3NqxdoDyPfUkrsdruUUtqNRmMlYKutrU2or683Siml3W6XdrtdAnXt27c/CNRbrdYeZ86cSdRej4mJqejRo8dWILWsrGzowx9/bIqrr/c2uCWi2CjLQJEge9ZBLu0BBgsh7gfyHGTUvcA2bw+kwRBc7dxlATvd4lwL7JFSLnE8ezWAEOIJx3W14tuhcFNZwFtSyjxHeCyg7i4OxPE+UezcDQT+KIR4W0rpKVLeDESaJPsZMEcI8QywmEamsRDoKaW0Ooy/fSqEGCyl9OYu5VYU/VevkFLOA+YBdB3YVfbq0Quz2ew1vqlQ0as3m83U2moxFZsYNHAQCd6qzwAAIABJREFUebl5zvDmYEndEqqtjbtHEydOZMPqDYhTwiPtI1uPkJuby+DBg9ncsJmJEyf6N+Z7GL6v+Z4xl46hW6rn7v3i2sWkxqc2+zm0mOxFkM+MmXto3O0Z0zAGa7WV/Ip8llQuYfSo0QwwDWASkxhVNIr/bP8PudZcPr7p47C7Ujdj5lf8iie+fYJcay5dU7qGtE68IaUghSWVSxg1chSfVX7GoAGDMA9X8p3EJGe8PGseX+77kitHXekygX+4w8OcOnOKX4xQTPWpUl2XT7484DKsnrSamz64iYF9B2K+yOwMjzsWx7of15GemO63Lnqe7MmGr5XFblZWFuaBvuM3F9X11bxW8hpDhgzhxKETmBJNmCc25mmeYObIqSMMTh/sct+c4jkATBg/gaS4JC7jMh7gAZ5d9ixbirYo9zqedX/ZfhaeWsjFF1/MyG4jw/o8KlZMWEFFbQXt27QHFOkR1dZHc/E/bf+HJfuWMGbsGEylJi4cdiHmvmZAaf/L31OIiMW3LHZpY/V76zm85TA/nfpT7jHewz7rPvqb+mM0GCmpKuHV9a86605FqL+dRe8tcjkfOnQo5n6hzcMbFlYupE/7PpjHuuZ389SbPeJONUxlQ8EGOiZ25I1r3vC4HinMYhZrjq1xeisdOXIkmR29TuKaDSklRaYixvUcR+/2vT2u9yjrwZ5v9jA5YzLmS83O8IaDDRxcp5gbmD9jPv/d8V+6p3Z3kQa9ou8VmEeaOddwbNsxdth3APDBjR84pX9XT9KXjBo6aih3ftqoAv3u9e/yq6W/YsaAGRw4eYDL+1zOoI6DOGM7Q2VdJfEx8WGxJTlpUuO49MKqF9hftj/sY2Wbfm34y6q/MHLkSL498y0X9LoA8wglz4IdBezauYsXprzgVdVt5k9mUlVfRWp8KuXry1mbv5aMthku6olpbdJ45cpXXCTbQ4HLJl3GrR/dSrv4dhGZU6xmNanxqdSX1VNeW84D0x+gXYKyiT7ZPJn/bP8Pdw6704MI/PTMp6S1SeP3Q37PY98qm/+q1oQeLpt0WXgfxAcMRwyYqkzO8xsvu5FOyd6l78OJhOMJfFX1VbOcPvy9SBGguWzYZRzfdpxhQ4e5jG9mzBw+dZhtRds4cPIA+Yd9e/X87bjfkpWeFfT3v3z8cq5fdD3DBw6npr6G8sJyjzY7vGq406ar2WzGVGTSSakRPVJ7uMQPFSZOnMjcjXOd7XPWlbP83BEaXHnZlQBcu/BaGmQD5glmkuKSWG1ezena09z28W0YhIHZN8523vNY+mO0jW+Lua8ZM2YA7vv8PgoqC/jgxg+48YMbnXHD3Efkzp49exIwFIXA6AL0s1gsC2fPnt0VT5tgCcBE4A2z2eytbMMBOnXqxJVXXql3vR+AyWTCZDIB2AEbiiqgDUVSyZaammpLTU2tcYTZNHGMQHJiYmJVYmLiJrfrznRiY2NtsbGxDYD2Z1OPhRANQgj365H82XxcU+uiHrDNnj07PMYs58zR2iJzRzVwtxBiFDAVuFAI8Q+UNrADRQipg0MFMg2oE0JsklL+20eOMVLKGgAhxCDAKqV0F+MejiKl5oRDuqydlLKne4JCiP8FtMJSmcBeh6RjuZTSBiClPCaEyEIh4T4XQvyvSsSFAhElyRwM308AhBADgKsc4bVAreN4kxDiAIpq5kb3NByVej3g25+1G4LybumYVITKk5ceJBK7tIdclL0l2XBQERcTR5eULhw/raita0Xoh3cezvDO/n0vnCuQSJ8G6AeYBjDANMAj/JYhoVGx1hoidYe7zRK/aUXY86BWTVVFclyyB0HmC89Nes5DbTQa3i2NBqOTIANCRpBpoRp3du9jvKnwatUNjQajC9mSnpTOc5OeY1PBJjI7ZnLzh57EUSgw96q5VNdXOxdvkUagbbpzcucwlyRwRFIdSgjhoqrtjr5pff2qiAsheOxS5f1e2e9K6u31JMYmBqw6frZicMfBAT1jWps0Ft+ymKsXKt4qU+JTmDt9rke8BGNCROssEnMLrd1Sd3XL+oZ6jAajT1tQMYYYF/JL7dszO2Typ8v+xIaCDYzqNipsz5LZIdPFi3Ik0CW5C73b9XYSZKD03+4mQNzR39Sfz2/9nKPlR+mR2sODJHvpJy9FjZBS0T5BGSMv630ZI7qOiGp5mmLHyx3PTniW51Y851UNHRRTLRntMnhpzUuA8p1/cOMHlFSVeHhAV+0FB4vYmFhd9WQt5lw5h18v/TU/u/BnzrnBbVm3kRSbxLzN81zi/nXKXxnYYSDbi7eHXOpQCMH9l9zPXcPvClrFNRRQJbW168GUOIWUvKjzRS5xbxh0g8f9/5yhp3kYXnTt2jUBRXNLu2NWDeQDBSjG1YP1KHlG878ON/LK7bwhOzs7st5MWtEIKZchxHQ8ibJqYLrj+vfADCnldwBCiE7AFillVzWyEGIZ8IyU8kfH+WPAAiml+yC3TwgxFKVd/Q1FVdMdRdBo40kI0VFKeUIIESeEGCal3CaESEARmMpDkSR7xRE3DaiSUtY6CLECTToDHPHfEEJMRLEzFzJElCQTQqRLKUuEEAYUneK5jvCOQJmUskEI0QfoDxz0kszlwF4pZcCGAiUyaJtkl3S9hO6p3QO+pykIB0nWkhGKScbZCnVSrnq3jFYdqN7RtGjJ70VLWoSq3p6f/LyuV6pIkn6RQFPfq68F5MVdlb2Jl6e+HLQjjkDQLbWbi5e6SCIaZGko0BI3RtzhrYwtzZtuOKA+ezDSJy3tnUbSbiM0bopo+65bs27lukz/ttlUOMdcR9ljDDGM7u7dnMDZBoH3Da9g0LOtson/+lWvc/8XjapI/U39m512c5HVKYsXprzAQNPAqH8Tal03Z57g3iaDQXpSepPz9QZf7cdoMPLSFQpRpzqm0Zs/AgzsMBCAoZ1Ca7Rfi2gQZFpotXmEEMyfMd+FmA4Eqm3gCT0nhLp4LrjmmmsyUAiR+1CkcfKBiuzs7LNzktOK4OFJlDkJModdtziVIFOiy2IhRLIQIk1KqRoIzsSh1iiE6Aw8hGKvzB2PAwsBAfxbSqln0+5N4F0hxC4UIvVZFI3Ce4H3hBCqE4IngTygN41eVoehSLnhKE8HIcROx73PCSG6oZC3y4BGe0ohQNhIMiHEe4AZ5WGOA9lAshDiQUeUj1H0YgEmoDxoPYqI5i/UlySEmA/MlVKqUmW34EfV0h1S+vduec3Aa5ySF0aDkWcnPovNbuPldS8Hk1VQZfJGkvmS9vGVXkuHOrhGe7ITTQRiBDRQePMcFyyCIVOiSSTZpT0k9ZbVKSsEpWm5UL8vdWIbDonYpqqbBAJt/3CuEZfhQGsdnR1oiZsQLQ3atuw+P4qLiXMapw82rUgiUoSiEAphEar8uqd25+9X/J2Mdhlh1aIIFsF6kQwX1Hpuznfc3DYZqPf6QMsSbNvRrk2uHnA1i/MWh6QsLRn3XXwfH+z+wMPkTVOkGl+64iUKKwrDLoneqVOnJOCB7OzswIzLtuLcRCNRtgC4W/Vq6SDHvvOMLlPdzrUN9T7gZ6qao1u8H4BBvosiK4GrdcIXo5Bl7uE9NcfLUAgwNR2tXZzAbQ41AeH0bnmrl0serJOU8iPgIy/p3ON2fldTyuPPppeex5RwGitV1S1D7UWqJS+YWrLEUrihvhen6HYz25Y/lSav5dAhYL2p5bUEaAmT803ysjlQJRbBf9/XirMb5/Omw9kAte9vyWNzS0Qo+nuJPCs2EJsKp3RTiPqAvml9Q5LOuQh1/hqKum5qm3xy3JNcW3otTyx9wn/kEEJb3vNts3v6gOlMHzA9ZOl1SekSsrT84LNIZdSKFgyFYMpofjLS0vzCnH04L1ZPUsomLRRDOQjoTZB9TQJDvRvZEibooRBXDzUiXRaVkIrWTq0vdcuWPOnxZ8utFY1Q27RTajHAOjuXF5OBoCl9bkvoy1pCGVrhHy25f/WHaPQNDfbmq9e3xDlHqHAuPlNLRrTVLVV0TenqP1KI4STGNCq+rRuWLReVlZX1L7zwQnm0y9GKVpztOC96OUnTSLJwwpe6pXr9XFu0XtTlIl6/6vWoDPLRhjo5UqV7ojnBcG9XwZAp0VroSSmxEx5Jskjb24kEVFIRmmCTrAUtviLd3s5GIuNsWKy0pDYVLZwN78kXIuqoxfHXLNW2s/BbDgaqumUrIoNQaEKE4htS7TheYGqeGmpTzLoIIbiwy4UAXNr90mbl34rwoaysrCbaZWhFK84FtCzmKExokSSZD++W7pO7c2WB0Sa2Dd1jw+sMoaXDKUkWJYkovYVDOAy8hwruNmrCKYF3ri2qwmmT7HxHS+qTz7V2e65BfT8tqc20VKh1FQrbnU2xuXS24VzbSG3JCIWaodaBU3Mwb/q8oI3Ge5QliP5IW96MdhlNNvfRisigpqbGw25UK1rRiuDRspijMKGp6pYqRndrvkekM7YzHmH19npiDbG68SWS/+74b8Dpn+uTwXMFeu6kIwn3hcMZ2xlqbMqmk0qSHTp5iIe/fpi5V82lW6p3V+WRhCoZdbZLY0QSobJ/F2loJ++RJhbORiLjbCzz+YhWMjNwhERqRyMpcy7Wfet3H1mE0qZuc+frobJrFWg5tOqWrWj56NOnTzuLxfJ7FOPsW7Ozs1tVL1vRiibgvCDJoOnGqz+75bOQDAyHyw+7nBdUFLCndA992nl6ifti3xdAcAOpKqHkazJ4uvY0/9z4T67LvK5Z7qRf3/A6nZM7B+WOvaUi0pPnluC8QF042KWdGz+40RkeG6MQtksPLgVgY8FGnyRZJCZM7ob7w0H4nK49DZw7u/KhlMY4n3C2vv+zjQA43xZa54rh/ki2s5ZESLRUaNUtz/a2dTYg1hBL2/i2EVe3DOe7bYq6ZStaPgoLCyt79uz5FPA0gMViqQCOAsc0/9XjI8DR7Ozs5kmfCTEJNy+KjZfEFY6ytAHigU3AE1LKUk2cWcB/gJFSyo2BZytmAY8DscAZ4HYpZZ7j2sfApcBRKeUozT1jgRcc5al23HMo6GcOvIyPSylfDFf6rQgfwkaSCSHeAKYDJVLKIY6w94GBjijtgFNSyuFCiCnAn4E4oA74lcOlqF66vwQeBBqAL6SUv/ZXlu6p3blr+F1Neo5wkRmPfP0IAImxiR7XKusqXc4DWeTOWT8H8D6g5pXlAbCvbB9L9i1plrj0l/u/BGBa/2nEG+ObnE40kV+RD8Cx08cimq+12gq0DOkedSGiIiVOsXVR11AHQFxMnM/7T1SfCE/BvKBBhl6SrLq+GstyxWlLnjWPS7pdEtL0o4n6hnoAr9KqLRXRnIifjYuAVunKlg2nuuVZ2LaihVB4XNZKTZ+LJJLWiHorwo+JGROZmDGxWWmESt0yFAimP/LmtODNa96ktqE2pOVqRfPxxhtv7Js9e/blwHiUNXcPoKfj/0WAu5REvcVi2Q/sBTYCa4AdgDU7O9t/Y1UIsiVAIrAEIaarRJkQ4kYUguw6KeVBIUQM8BsgofF2MQyYClQBO4N83HxgopSyUgjxBDAbmCWEiAX+CFwMOCf2Qmn4i4DLpZR7hBAPOcpzX5D5BgQhRC/gJqCVJDsLEU5JsjeBV4G31QAp5c3qsRDiRUAVAS0FZkgpC4QQQ4BvAA8RFqF8iNcAw6SUtUKIgMShjAaj09hlS8PTE572CHty7JP8ZdVfAk5DS6p1Su7kN35WelbAabuj1tY4IOZacxnaaWiT04om3InISGH+lvkAzbYn0VT4mhipkmQquaJHkmmJtYraihCXzjve2vYWoK+23FTMeG9GyNJqabDZbcxePhuA5LjkoO49HxfzUkpKqkvIJDPaRWnFOYZzwcNipKWx1P6+OWSCqm4ZaUKivqEem93W4uzgtqLloKIu8LmTag4jXAhkTuXNHpsp0RSWMrWi2Rg4e/bsT93CFkkpXxNCJMbGxn7dvn37+Pbt28enpaUldOjQITEjI6PGZDINBpwqQrW1tbYHH3ywpry8vCYhIWFdjx49visoKKj+9ttv7y8rKztTUVFRf7WU7RZCVkKjI0AnUSZgPfAKcJmU8iCAlLIBhbwCQAiRBjwPZAMXSymDmuRLKZdrTqtRJMqQUtYDm4QQt6EQfs4sHb8qB2E3FEWqzgVCiHhHOScDScDLjvqb7ChrW8d9N0kpa4QQm4GPgKuAzihcxT5gOdBGCLHV8ZwVgAVFoi4GRXKuOphnbkXkELZRXEq5QgiRoXfNweTehNL4kFJu0VzehdKg4qWU7lsU9wN/VsOllCWhLne48cyEZ/j9it87z5PikjzijOs5jgVbFlBSXcI9F97jN01VOunJsU8GVIY/Tv6j/0heoB3c2xjbNDmd8w1Hy4+6nGe0y4hKObS7694WD/V2hwRSjKcEkmoMHuDBkQ+GoYSucJck2F+2P6j7vS3u9Agyvec9G1FYUehyrtfH6OE/O/4TjuI0CyVV4e/i91n38di3jwGw4sgKHrv0sYDvLamO/hCkJV/Ub7cVLQdnu7pfWU0Zq46tYqBpoP/IIcKR8iMAbC7czMxBM5ucTqTrfkPBBgCue/+6sBs3F0JQXFkcdB/UYG/wkCJvRWSg9tX/3PRPoFFqXw8na06GtSyna087tUL8obpeWcOfzUR/KxpRX19vLykpqSkpKdEysG9KKd+cOHFiP6vV+nF6enpiWlpam/bt2yd26tQpNTk5eSowo2vXrtx1110AZBw6xK3vvktcvce8IxFYchO8sAi2SSl36ZXDQVL9C0VdchiwtanPJITogSIRdrvbpSzA2RlLKe0OTbcdKGTZQuD/dJL8O3AaRRINoKMQYiDwv8BVDsm1/wNuEkL8B8gEcqWUfxRC/C9wvZTSIoSYC1RKKV91lHMfChl4WgjRrpUga9mI1lbXeKBYSrlP59pMYLMOQQYwABgvhPgjiu7xE1LKDXoZCCHuBe4F6NSpEzk5OSEpeFNhLVWIrKq8KudxekK613INsg9iT+keEgsSySnSj6Nif+V+rKVW9u3Yh+2gvlq5mmecIY7ly5frxgkE5fXlzrR2b95Nfmx+k9OKJtRnuK3nbRFpG6WVpVhLrfRN7suotFHs2qA7ZoQdxcXF5FXnkVOdg81uw1pqZWyHsawqXcWuXbvoUNKBvCN5WE9b2bF1B/ZDnpPpEcYRXNz+YlYsXxGRMsdWxlJWX0bvxN5MjJkY0PuqLa+lsr6Sjav1TRuo71+Lnbt2kn6i6bb6Wgo+2/GZy/mWNVv8SofZ7DYKixVybcWKFcQZfKvahhMNssH5fnbt2UVOWU5Y8/vdjt85j6d1mRZQ+/pq31dYzyhljPbYkluR66yv/dv2UxZXFtXy6OHT/E+xlillXLlqJWlxaVEuUeSwq3gX1lIre/bsCaotq+802u1L/T5Wl64mJy4nrHkdrDzo0jfHGeOa/Py5hbmcKDtBXIXSl0WiHrVlD3d+Obtd0w8kv/2V+9l0aBMAZsyhL1QrfGLTyU0ubWTOD3NIKdTXcvl+1/fO43C0Jfc5kK88Xtn/CtYaK5+u/ZS2RW1DXpZWhBy5Ukqz3gUHKWO2WCwJgMnx6wC0t1gsd06ePDkFeBdIAZI1/1OB3ijrcACu+fRTPYJMRWJveBT4txoghJiDIhxTKaUcjSKp9Z6Ucq8Q4g5gW1MeVgjRF/gSxVST+8JkCBpJMiHEbxzPkgb0B94BXBbOQohOwDSgn2yUJigRQmSjkGE/OubUiY60+wN7pZQfOuLGAqccx0OBuZrkK4BXhRBvSClzmvK8rYgcokWS3Qq85x4ohBgM/AX4iZf7jCgNezSKjvEiIUQfqSMSI6WcB8wDGDFihDSbzaEpeRNhKlTEkidNmoSpSDnu0bYH3splxszjPB5Q2uPs47i65mraJbTzakdKzb+5u5uFFYWYSk08NvoxJvWe1Ky0ogm1Ph68JvzSUKC8z6sqryI9KT2q6mwLTi2gX5d+mEeZqWuow1RsYvjg4ezdtZfBgwdjHmRmxfIVFBcUM3LESF0bXZGeXDfl271kzCVU1lV6VT9W3/9z5ud4NudZADIzMzFnBZ9XS0On4k7Y7Db+OuWvDOwQmPRH/ul8TMVKnYwfP542sdGTEm2wNzj7yOdvfj7s+alt4bNbPgvYBtKLhS9iSjbxwIgHMPc3h7F0/jHePp7aDbVM7j2ZIelDoloWb7AdtHFw3UEArv/J9VEuTWRxbNsxttu306V3F8wjzQHfp7bLljJ3efXKV+nVrld48yo28UnFJ87zWZNn0d/Uv0lpHdxykEP7DtEzrScQmXpU6yoSnqHVvFQE8nwvvvcipg4to12djzi55ySmM43vTSC8vofH0x/nzW1vAuF5V8G0nxcLX8SUZKKU0tZ2cxbBYrHEAqOAkSjkTibQHYUU86di0IBC6FRq/uej2AwrA8pWjxnT7opvvrkrxm7XW3hWr4D3tQFSyoeFEFOBe4QQBuAO4BYhxF9R7KSdFkJsk1J+o1cgIYQJUNnjt6SUfxNCZAGfAQ9LKZe4xe/gyLdUE3wPMMGh9rlXCJGCYq9Na7g/C9jiUNnUYhhwhZTygFs+NwBrNUFDUZwYqGlt11wbjWJ/7RkhxLRA7Kq3InqIOEkmhDAC19MowqiGdwc+Ae5wb4AaHAc+dpBi64UQdpSPPbIWxJuAd69/N2zkiNFg9Out8sFLHiQ+pvlG9n2p4p1NaGNsE3EiIBB7ceGGnri8e7tUVTHOZttUSXFJAakZatWHzxUVlHYJ7SitLqVvWt+A7+mW2o3Xpr3GD4d+IMGY4P+GMCLSKlI3DrqRDokdmmQkfGq/qWEoUXCIMcTw8KiHo10Mn0iKVb7FQMwHnGvIteYC8PWBryOioh5qXNT5ImIMMWEnyMDVPtK86fPoktKlWel5s6UUboSbINPipZ+8RGp8akBxVXMLi29ZHOZStUIPo7uP5o2tbzjPfc2jZw6aydieY8k/HR5tjfkz5pMSn8LNH97sM55qoxbghswbwlKWVoQWMTExwmKx/BHFyZ0q+lcK7AFWOI5LAavm+CSupFhtgEb7F9JotF9FNTB9jZLmp0KIFx02xwUwBUVbzA50bUxGHALGSymPO9QwnwL+4IgHgJTSCgzX3DMGReDmTi9SWVm42iMDheAbAhQIISagkIXuNsmKgP5CiDgpZZ0QIt1h3qkQuBLF3jpCiKFSyu2OfLSqokNpJMbScdhfd0i8HZJSLnYQeMNpRYtGNCTJLkcRSzyuBggh2gFfAL+RUq7yce+nwCRgmRBiAIo3zFIf8VsM9BwHRJKkCdViLpSu2aOJ92Z6CDKeF1CNGbuE4epxaVPhJpfzcxFzr5pLvDGebUWN0t0N/8/efYdHUa1/AP+elE2B0BLpSCCg1BC6FAEREeWKFOkWBBFUEFFQUGmKV1GvFy6g/hAUrCgqgoKKKB0B6b0TaugEAum77++Pk012k5lNAptsNvl+nmeek5k9mTm70985c45YPVgi93n73rex78K+XDccXaVkFTwR9UQelSrn7D2/DmgwIF+W93iDx2/6f705kJyf7qp8F9685000KNfA00XJd/aeGr3VpHsm5duyribpvpzuDL3zlgNk6ec1SKFuSyk3Ne06VO+ArTFbedzykAohFfBz35+x/uR6vL327Wzb+StfvDzKFy+fJ2XJ6UNb+3VEpZBKBeL6gLLXpUuXKgBeBbAAuifH1RMmTMibBlRFVkCpfyEjUBYP4F8QWSEAlFKvAfhNKWUFkALde+YXjrNQSgUBKOMQF2gMXWPrjWyW/nnaMqemHdOOi8jDabXD9qZ9FqiUOgWgtYhEAxgG4OO09sSuAegqIk6vW4rIbqXUIgC7lVLxABYDGA/dS+dcpdQQ6Jp286CDYfWhX/eEUsoCoKSI2Cvv/ALggFJqBoByAHoqpeKga+UNzOb7kYflWZBMKfUNgHYAwtI20AkiMgdAH2R91XIYgBoAxiulxqdN6ygi55VSswF8LCKbAXwK4FOl1G4AydDRY6+9kz8d533teXnxz+3E18fX00XwmMwN9zteMPf5vo9HypTf7E/6HRs6D/YPNsvuVfLywjo/KKXyvNHrWzX9gem57kSiKFNKIao8H5rmxvPNns+2hnhhY28g/I7QO7LJmT3HB0KFMUg2tPHQ9N8rp6w2a/pDCPIce80/b6i9rpTC4j6LGVj1ImXLlg0GYAPw0oQJE7L03Oh2GYGyzwA8CZEVGR/JVwC+cv3vkoCMGm+ADh4Nyn6xUsNkehyAKiafbQLQKAfzfhU60Og47QCAFgZ5uzv8nQygqsN45u/xWnbLpoIjL3u37GsyfYDBtMkAJpvkf8rh72Rk7bnCa11LuubpIuRa+usLhfCisyhwXG+ZX2v77fBvuJFyI338SmLe9q5UENQvWz/97661unqwJORNwkuFe6yHWvIuJQP1tX+/ev1y9X/3RdyXF8XxCiEW48bMc8OxJ+fCqPMdnXP9P1axFukHhAWFPVDpLbVMGSDzLkFBQX4AfKBf+8v7IBmAtMBYuHtmJUPdMR+iW+WphvuLtOkPTMcry1/Bp10+zT5zAeXtr1sWVQrmr1tm7kp+x9kd6Bhh1odG4VCpRCWMbzMefj5+uX49kYgoO6k2/SYHg6rZ61SjE+KS4tCjTg+3zVNEkF/P9P7X6X9O7aoVNDax8dqtALDXZC8IbVqObjkaBy4e8HQxyI1KlSoVAOA9AL96uixE3ox3hR4QXioc3z7ybfYZCyBvqB5OrtmfrtufYpo9WS4qT5yNevAkInKHZGsyAO/v7CY/WHwt6B/Z3y3zMmp/M69VK10tX5eXW3zdsmAoEVCiwDQp0KZqG7Sp2sbTxSD32z9hwgTesBHdAj5SolwxaseKvIfjerPfvJn1etq5Zu5f5yAiogz242i5Yp7v3bgosb9uWZhfucwtvm5JVGQYttdFRDnHmmR0U9gmmXeyv27gqLLBAAAgAElEQVQ5dcNU/HnsTwBAcUtxw7x3hrnueYmIiFxrXrk5vn3k20LTMYg34UM9Zy+1eKnQ9OJMRMasVqv4+vq+OGnSpFgA702YMIFPCohuAmuSUa7YX7fkRad3Uko/XbcHyACgSknDTmCIiMgNGCDLf7xGySrIP8j0oRgRFQ5fffXVAQABAKYgBz05EpExBsnoprAmWeHBBqWJiKgwsb9uue/iPk8XhYgo3/To0SMi7c/vAezyZFmIvBlft6RcYU0y7yciCPQLhK/yxTsd3jEMeFYrVbAbICYiIsoJPtQjoqIiICDAD8BdEyZM2OjpshB5M9Yko5vCi07vZF9vVpsVnWp0Mq1F1rJKy3wsFRERkfs4Psg7cuWIB0tCRJR/fH19FYC9ni4HkbdjkIxyxd5TFGuSeSd7m2QpthREx0anT8vMR/HQQERE3snxQV5CaoIHS0JElH/SrulHTpo0qZKny0LkzXgnTLmS/rola5J5pRRrCtacWAMA2BKzBQADYkREVLg4PvxJtaV6sCRERPnn5MmTcQAmATg1adKkdZMmTQrydJmIvBHvjummsCaZd2oX3i79b4uvJf3vlpWdX69kEJSIiLzV7vO7PV0EIqJ8t2jRomgA29JGWwK4w3OlIfJeDJJRroikvW7JIIpXejTyUQCAv48/fuj1Q/r0sXePxUedP0L1UtUBMAhKRETeKyw4zNNFICLKd4MHD64DIALA/wA0mjBhwg4PF4nIK7F3S8oVtknm3ZRS+KLbFwjyy1r7unKJyhn5GAQlIiIvNbTJUPx57E88FvkYetXt5eniEBHli7i4uOSAgICGEyZMOOrpshB5MwbJKFfsNcnIe5UKLGX6mT0IynbKiIjIWwX6BeLnvj97uhhERPlq7dq1Mdu2bWOAjOgW8U6YcoVBlMKNQVAiIiIiIu+TkpJi83QZiAoDRjooV9gmWeHG12mJiIiIiLyPzWbj024iN2CQjHKFQZTCzSb6ARSDoERERERERFTUMEhGucKaZEUDg6BERERERN6D1+9E7sEgGeXKpYRLni4C5QMGQYmIiIiIvEexYsX8PV0GosKAQTLKsauJVzF903QAQLI12cOlobxw8tpJAHwSRURERETkTTp27Hj7pEmTenq6HETejkEyyrH4lPj0vyuEVPBgSSiv/X74d08XgYjywKVLlxAVFYWoqCiUL18elSpVSh9v2bJlnixz27ZtGDRokNvmN2PGDHz66adumx8REVFh4O/v7wNgmqfLQeTt/DxdAPIepYNKAwBCLCEICw7zcGkoLz1050OeLgIR5YHQ0FBs374dADBx4kQUL14co0aNytNl/vvf/8brr7/utvkNHDgQrVq1wsCBA902TyIiIm+XlJRkDQgIeMHT5SDydqxJRjkW6BeIH3r9gK+6f+XpolAe6xjR0dNFIKJ8Vrx4cQDAypUr0bZtWzz88MOoXr06xowZg6+++grNmjVD/fr1ceTIEQDAhQsX0KNHDzRt2hRNmzbFunXrsswzLi4OO3fuRIMGDQAAq1atSq+51rBhQ8TFxQEA3nvvPTRt2hSRkZGYMGFC+v9//vnniIyMRIMGDfDYY48BAIKDgxEeHo5Nmzbl6e9BRETkTVatWnVqwoQJ33m6HETejjXJKFcsvhZPF4GIiPLYjh07sG/fPpQpUwbVq1fHU089hU2bNmHatGmYPn06pk6dihEjRmDkyJFo3bo1Tpw4gfvvvx/79u1zms/mzZtRr1699PH3338fM2fORKtWrXD9+nUEBgZi2bJlOHToEDZt2gQRQZcuXbB69WqEhoZi8uTJWL9+PcLCwnD58uX0+TRp0gRr1qxBs2bN8u03ISIiKsiSkpJsni4DUWHAIBkRERE5adq0KSpU0G1PRkREoGNHXbu0fv36WLFiBQBg+fLl2Lt3b/r/XLt2DdevX0+vkQYAMTExuO2229LHW7VqhRdffBH9+/dH9+7dUblyZSxbtgzLli1Dw4YNAQDXr1/HoUOHsGPHDvTs2RNhYfr1/jJlyqTPp2zZsti/f38efXsiIiLvY7PZxNNlICoMGCQjIiIiJwEBAel/+/j4pI/7+PggNTUVAGCz2bBhwwYEBgaazicoKAiJiYnp42PGjEHnzp2xdOlStGrVCr///jtEBGPHjsWQIUOc/nf69Omm801MTERQUNBNfTciIqLCiEEyIvdgm2RERESUax07dnQKZNk7BHBUu3ZtHD58OH38yJEjqF+/Pl555RU0bdoU+/fvx/33349PP/0U169fBwCcPn0a58+fR/v27bFgwQJcunQJAJxetzx48KDTa5xERERFndVqZZCMyA1Yk4yI0o1rMw4KytPFICIv8L///Q/PPfccIiMjkZqaijZt2uDjjz92ylOrVi1cvXoVcXFxCAkJwdSpU7FixQr4+Pigbt26eOCBBxAQEIB9+/ahRYsWAHQHAl9++SXq1q2L1157DW3btoWvry8aNmyIuXPnAgDWrVuHiRMn5vM3JiIiKrhYk4zIPZRI4d+XmjRpIps3b/Z0MYiIiIqc//73vwgJCcFTTz3llvlt27YNH3zwAb744gu3zI+IiKgwCA8P3xcdHV3H0+Ug8nZ83ZKIiIjyzDPPPOPUxtmtunjxIt588023zY+IiKgwOH78eLyny0BUGLAmGREREREREZEXU0ptEZEmni4HkbdjTTIiIiIiIiIiIiryGCQjIiIiIiIiIqIir1AHyZRSnZRSB3bv3o133nnH08WhTE6ePIl77rkHderUQd26dTFt2jQAwMSJE1GpUiVERUUhKioKS5cu9XBJCQDCw8NRv359REVFoUkTXZP78uXLuO+++1CzZk3cd999uHLliodLSQcOHEjfd6KiolCiRAlMnTqV+1UBMnDgQJQtWxb16tVLn2a2L4kInn/+edSoUQORkZHYunWrp4pdJBmtq9GjR6NWrVqIjIxEt27dEBsbCwCIjo5GUFBQ+j42dOhQTxW7SDJaV66Oe2+//TZq1KiBO++8E7///rsnilxkGa2r3r17p6+n8PBwREVFAeB+5Wlm1+o8ZxFRYVZo2yRTSvkCOAjgvkaNGh1JTU3FN998gzp12OFHQRETE4OYmBg0atQIcXFxaNy4MX766Sd89913KF68OEaNGuXpIpKD8PBwbN68GWFhYenTXn75ZZQpUwZjxozBO++8gytXrmDKlCkeLCU5slqtqFSpEjZu3IjPPvuM+1UBsXr1ahQvXhyPP/44du/eDcB8X1q6dCmmT5+OpUuXYuPGjRgxYgQ2btzo4W9QdBitq2XLlqF9+/bw8/PDK6+8AgCYMmUKoqOj8a9//Ss9H+Uvo3U1ceJEw+Pe3r170bdvX2zatAlnzpxBhw4dcPDgQfj6+nqi6EWO0bpy9NJLL6FkyZIYP3489ysPM7tWnzt3Ls9ZBRDbJCNyj8Jck6wZgMMiclQphT59+mDRokWeLhM5qFChAho1agQACAkJQe3atXH69GkPl4pyY9GiRXjiiScAAE888QR++uknD5eIHP3555+IiIhA1apVPV0UctCmTRuUKVPGaZrZvrRo0SI8/vjjUErhrrvuQmxsLGJiYvK9zEWV0brq2LEj/Pz8AAB33XUXTp065YmiUSZG68rMokWL0KdPHwQEBKBatWqoUaMGNm3alMclJDtX60pE8N1336Fv3775XCoyYnatznMWERVmhTlIVgnASftI5cqVGYApwKKjo7Ft2zY0b94cADBjxgxERkZi4MCBfIWvgFBKoWPHjmjcuDFmzZoFADh37hwqVKgAAChfvjzOnTvnySJSJvPnz3e60eB+VXCZ7UunT59GlSpV0vPxXFawfPrpp3jggQfSx48dO4aGDRuibdu2WLNmjQdLRnZGxz3uVwXXmjVrUK5cOdSsWTN9GvergsHxWp3nLCIqzApzkIy8xPXr19GjRw9MnToVJUqUwDPPPIMjR45g+/btqFChAl566SVPF5EArF27Flu3bsWvv/6KmTNnYvXq1U6fK6WglPJQ6Siz5ORkLF68GD179gQA7ldehPuSd3jrrbfg5+eH/v37A9A1Lk6cOIFt27bhgw8+QL9+/XDt2jUPl7Jo43HP+3zzzTdOD3e4XxUMma/VHfGcRUSFTWEOkp0GkP4o49SpU6hUqZIHi0NGUlJS0KNHD/Tv3x/du3cHAJQrVw6+vr7w8fHB4MGD+QpEAWHff8qWLYtu3bph06ZNKFeuXHo1+piYGJQtW9aTRSQHv/76Kxo1aoRy5coB4H5V0JntS5UqVcLJk+mVonkuKyDmzp2LX375BV999VX6zWFAQABCQ0MBAI0bN0ZERAQOHjzoyWIWeWbHPe5XBVNqaip+/PFH9O7dO30a9yvPM7tW5zmLiAqrwhwk+wdATaVUNRHB/Pnz0aVLF0+XiRyICAYNGoTatWvjxRdfTJ/u2HbBwoULnXo/Is+4ceMG4uLi0v9etmwZ6tWrhy5dumDevHkAgHnz5uHhhx/2ZDHJQean8dyvCjazfalLly74/PPPISLYsGEDSpYsmf6KC3nGb7/9hnfffReLFy9GcHBw+vQLFy7AarUCAI4ePYpDhw6hevXqniomwfy416VLF8yfPx9JSUk4duwYDh06hGbNmnmqmJRm+fLlqFWrFipXrpw+jfuVZ5ldq/OcRUSFmogU2gHAgwAOWiwWmTx5slDBsmbNGgEg9evXlwYNGkiDBg1kyZIl8uijj0q9evWkfv368tBDD8mZM2c8XdQi78iRIxIZGSmRkZFSp06d9P3p4sWL0r59e6lRo4bce++9cunSJQ+XlERErl+/LmXKlJHY2Nj0adyvCo4+ffpI+fLlxc/PTypVqiSzZ8823ZdsNps8++yzUr16dalXr578888/Hi590WK0riIiIqRy5crp560hQ4aIiMj3338vderUkQYNGkjDhg1l8eLFHi590WK0rlwd9yZPnizVq1eXO+64Q5YuXerBkhc9RutKROSJJ56Qjz76yCkv9yvPMrtW5zmrYAKwWQrAPTgHDt4+KBHxZIwuX1StWlWOHz/u6WJQDsyaNQtPP/20p4tBOcB15V24vrwH15X34LryHlxX3oXry3twXRUcSqktItLE0+Ug8naF+XXLdBcvXvR0ESiH7L0mUsHHdeVduL68B9eV9+C68h5cV96F68t7cF0RUWFTJIJkRERERERERERErhSJ1y39/PwkICDA08WgHEhNTYWfn5+ni0E5wHXlXbi+vAfXlffguvIeXFfehevLe3BdFRzx8fEQEeXpchB5uyJxRIuKisLmzZs9XQwiIiIiIiIit1NKbfF0GYgKA75uSURERERERERERR6DZEREREREREREVOQxSEZEREREREREREUeg2RERERERERERFTkMUhGRERERERERERFXpEIku3atRhz52af78MPgYoVdUpEREREREREREWHEhFPlyHPKQUJDweOHXOdr2JFICYGqFABOHMmX4pGREREREREdEuUUltEpImny0Hk7YpETTKL5TQmTMg+3+uv6wDZ66/nfZmIiIiIiIiIiKjgKBI1yZo0aSKbN2/2dDGIiIiIiIiI3I41yYjco0jUJCMiIiIiIiIiInKFQTIiIiIiIiIiIiryGCQjIiIiIiIiIqIij0EyokLu2jVgzRqdEhEREREREZExP08XgIjyzrVrQO3aQFwcEBIC7NsHlCjh6VIRERERERERFTxFoibZnj3fYcGC7PN9+SUQEaFTosJgxw4dIIuL0wGzHTs8XSIiIiIiIiKigkmJiKfLkOeUgtSuDezd6zpfRARw9ChQvTpw5Ej+lI0oL9lrkl27pmuQsSYZEREREVHho5TaIiJNPF0OIm9XJGqSBQYexaRJ2eebNEkHyHKSl8gb2ANjS5cyQEZERERERETkSpGoSdakSRPZvHmzp4tBRERERERE5HasSUbkHkWiJhkREREREREREZErDJIREREREREREVGRxyAZERER3ZSUFE+XgIiIiIjIfRgkIyIiolzr1w+wWHRKRERERFQYMEhGREREuZKSAnzzjf77m29Yo4yIiIiICgcGyYiIiChXNm8G/P313/7+epyIiIiIyNsxSEZEREQ5ZrUCnTtn1B5LSdHjVqv5/8TFAevW6ZSIiIiIqKBikIyIiIhy7OJFICHBeVpCgp5uJC4OqF0b6NQJqFOHgTIiIiLKe0qpcKXU7jxexkSl1Ki8XAblPwbJiIiIKMfCwoCgIOdpQUF6upGdO4GrV4Hr14HYWD1ORERERFQQMUhGREREOebrCyxZApQuDQQG6nTJEj3dSGQkUKoUULy4TiMj87e8REREVDBlru2llBqllJqY9vfzSqm9SqmdSqn5adPaKqW2pw3blFIhSqniSqk/lVJblVK7lFIPGyynelr+ppmmt1NKrVJKLVJKHVVKvaOU6q+U2pQ2r4i0fA8ppTamzWO5UqqcwTIGK6V+VUoFpf39j1Jqh1LqB6VUcFqeuUqpj5RSG9KW104p9alSap9Saq47f1u6eX6eLgARERF5lxYtgAsX9CuWYWHmATIACAkB9u7VNcgiI/U4ERERUTbGAKgmIklKqVJp00YBeE5E1imligNITJveTUSuKaXCAGxQSi22z0QpdSeA+QAGiMgOg+U0AFAbwGUARwHMFpFmSqkRAIYDeAHAWgB3iYgopZ4C8DKAlxyWMQzAfQC6ppX3RxH5JO2zyQAGAZielr00gBYAugBYDKAVgKcA/KOUihKR7Tf/k5E7MEhGREREuebrC5TL8hzVWEgI0KpV3paHiIiICpWdAL5SSv0E4Ke0aesAfKCU+grAjyJySinlD+DfSqk2AGwAKgGwX6HcBmARgO4istdkOf+ISAwAKKWOAFiWNn0XgHvS/q4M4FulVAUAFgDHHP7/cQAnoQNkad0aoV5acKwUgOIAfnfI/3NasG0XgHMisitt2XsAhANgkMzD+Lqll3r2WcDPT6dEREREREREXiYVzjGJQIe/OwOYCaARdC0rPxF5B7rWVRCAdUqpWgD6QwfDGotIFIBzDvO5CuAEgNYuypDk8LfNYdyGjEpF0wHMEJH6AIZkKucu6OBWZYdpcwEMS8s/KVN+x/lnXjYrMRUADJJ5qVmzAKtVp0REREXNyy/rDgNeftnTJSEiIqKbdA5AWaVUqFIqAMC/AEAp5QOgioisAPAKgJIAiiulIkRkl4hMAfAPgFppn50XkRSl1D0AqjrMPxlANwCPK6X63UI5SwI4nfb3E5k+2wYdOFuslKqYNi0EQExaLbf+t7Bc8gAGybzU00/rV12eftrTJSEiIsp/06cDiYk6JSIiIu+T9nriGwA2AfgDwP60j3wBfJn2SuI2AP8TkVgALyildiuldgJIAfArgK8ANEnL+7jDPOzLuAEdfBuplOpyk0WdCGCBUmoLgIsG32MtdHtpS9LaRRsHYCP066H7M+engk2JiKfLkOeaNGkimzdv9nQxiPLdkSPA5MnAli1A48bA668DERGeLhWR9zt0CDh8GKhRA6hZ03Xey5eB48eBqlWBMmXyp3xFwcvDbmD6J0EYPjgB784o5p6ZHj2asWKrV3ed9/x54NgxoFo1oGxZ9yyfiIjoJimltohIE0+Xg8jbsSYZkZcRAebOBfr106lZnPvIEaBRI+CLL4Bdu3TaqJGeTkQ358oVoF07oEEDoG9fnbZrp6dnlpQEPPEEULGizlOxoh5PSsqal7TUVODsWZ2aio8HevXCu7NDkRBUBu/ODgV69dLTTSQkANu26dRQbCzQoQNQt66eV926ejw21nj5vXsDt98O3H+/Tnv3drl8IiIiIvIODJIReZl584DnngO++Uan8+YZ55s8GbhxQ7ddB+j0xg3grbfyr6xEeSmnFaFF9LbvjorT3boBf/+tgy1Xr+r077+BHj2y5n36aWDBAh0Uu3ZNpwsWAEOG3Ho5cmP4cMDfX6cF2fr1wG236YpZt92mxw0NGAD8/LP+Qa9e1enPPwNPPmmYPSEBqF8fuPtunRoGyh55BFizRr+/efWqTtesAXr2zJr3ySeBxYudl794senyAR2gmzYN2F6I+quyWvV55p57dGo/12QmAnz8sd5HPv7YPfshUV45d04fK0qU0Om5c67z22w6sG+z5U/5iIgo7zFIRuRlli3LqLAQH6/HjWzZkvWmxWoFCvKbxy++CAQE6JTIlaef1j38ZtcuowjQtSsQEqLTW7lBP3QI2LQJSE52np6cDGzYoN/Ss7t8Gfj226wBmYQEPf3y5ZsvR259/LGumfXxx/m3zMyyC9SlpgKdO+uKW4mJOu3c2SDwcv68DkglJjpPT0wEFi3Sn2eyfz9w6pQOlJ46pcedHD2qI3JGK3bdOv254/IXLcrV8rdtA1q3Bl55BWjVqvAEyt5+Ww8rV+r0nXeM8/3f/wGjRgE//qhTdjhEBdkjj+jjeVycTo3i5HY2G9C2LVClik4ZKMt/Y8cCwcE6JSJyFwbJHLzxhn5y9MYbni4JkbmOHfUFAaDTjh2N8zVurDt3cOTrCzTJ55YK3nsPCA3VqStJScDUqfq+dOpUvpJG5kSAOXP0DcmcOa4DX/HxupKRiE5v5Y24w4cBi8X4M4tFB9Hsjh/XAV+zvCdO3Hw5cmvoUB1QHDo0/5aZWXaBuosXjeNOFy5kynjsGBAYCEOBgUB0dJbJ+/ZlxL+Sk/W4k+xWrGP08yaWv3q1DvYlJenfYNUq43/3Nn/+6fzAZvly43x//KEDlIBOzR7sEBUEO3ZkvO6dmuo6qH3+vA6kpabq1CBGTnls2jT98GnaNE+XhG6WUqqrUkqUUrUMPiuhlDqllJrhhuWEKqVWKKWuZ56fUqq3UmqnUmqPUmqKw/QBSqkLSqntacNTBvMNcfh8u1LqolJqqkG+AQbLXamUYhtyBRCDZA7ef18/OXr/fU+XhMjcE08AM2fq9pBmztTjRl5/HShWLCNQ5uurx197Lf/KCujaBZcvm9cysDt8GPBJOyL5+DjflxI5UgoYNEhvJ089pcfNBAcDDz2k83TpkhFgvhk1amStbGSXnOzcgH/VquaB3uRk3YxVfpk+HUhJ8WwvkNkF6sLCssaeAgP1a5dOqlXLGk2zS0wEwsOzTF61KiOQKmIQpKpRw3xlJSXpz29h+W3b6uOvxaJ/g7Ztjf/d29x7r/N4hw7G+e67z3nc7MEOUUHQoIHeTwGdRkWZ5y1bFrjrLp3vrrvYf4cnjBgBBAXplLxWXwBr09LM3gSw2k3LSYTucXKU40SlVCiA9wDcKyJ1AZRXSjme4b4Vkai0YXbmmYpInMPnUQCOA/jRTWUmD2GQzMGoUfqVnNGjPV0SInNK6SZ5vv5ap2YBgogIYOtW4PHHdTs8jz+ux/O7d8sxY3SPfmPGuM5n7yUwKEinjvelRJnNmqWf3v/f/7nOpxTw00/6AcjCha4DatmpWRNo1ixrpSOLRd8gOW6zZcrottyDgpzzBgXp6UWtl8vsAnV+fsCSJUCpUjo4VqqUHs9cGxZly+pop1FE7eGHDe9Ss619W726fg/SaMW2bu3cy2XZsno5uVh+VJR+a/Pdd3Xq6qbbm4wdq9sj8/UF2rc3P8YPGaJfdfb31236ZfeKNJEnff890KKFvh9o0UK3I2nGx0cH3U+e1KkP76ry3dtv65qsb7/t6ZLQzVBKFQfQGsAgAH0yfdYYQDkAbql/LCI3RGQtdLDMUXUAh0TEXnd9OQCDlmazp5S6A0BZAGty+X9dHGqiHVBKHbuZ5ZP7KCkCLag2adJENhfkhpiICICutHH4sA42mL2qRuRJV67oBsg3bNAxlORkHSD74QegdGnnvElJOkAwf77enpOSgD59dGCP27cxq1W/YnnbbQYBMrv4eN1I/qJFOjiVmKgDVJ99ZlpV8Icf9Gt+HTsad7KA2Fjd+NDatRkrq3VrfYdcqtQtL5+IiCivKaW2iIjXvL6nlOoPoL2IDFJKrQcwXES2KKV8APwF4FEAHQA0EZFhBv//XwD3GMx6vogYvsOilBrgOD+lVGkAu6CDdacAfAvAIiIPpeV9G8AFAAcBjBSRky6+z3gAJURklMFnA6BrrJ12mFwDQDsR2eyQ7zsAq0RkptlyKO8xSEZERJRLhw7pgG5Oaj1evqzbILv99qJXgyxPnT8P65Fo+EaEu+89p6NHMyL1jjXITJaP6Gj9iiXfsyIiIg/zwiDZLwCmicgfSqnnAdwuIqOUUsMABIvIu5mDWm5YZpb5KaUeAvA6ABuA9QAiRKRr2quY10UkSSk1BEBvEWnvYt57ATwmIltyuNyVAEbZg2RKqZcB1BURk8Z0KL+wYjB5peef169uPP989nlfeEFXDHjhhbwvFxEVDTVrAg88kLPXgsuU0a/YuTtANm4cULy4TouaOXOAilFl4deyGSpGlcWcOW6acfXqurpZdgEyQAfGmjVjgIyokElK0g822IEQUd5RSpUB0B7AbKVUNIDRAHoppRSAFgCGpU1/H8DjSqksNcOUUv/N1Gi+fcimkRdnIvKziDQXkRYADkDXGoOIXBIR+5FgNoDGLr5PAwB+RgGynFBKdQDQE4AHu1kiOwbJyCvNmKHbI5qRg75OPvpIvxL10Ueu8129qtuUuHo1+3kmJwN795o34k3OzNq5zmzHDuB//9MpuZaSAhw4oNOCzmYDzpzRaVGV02OFvffFnOT/4APdW+AHH7jO5y3biog+TnfrplOziu5z5ugHJDExhwDsQ0yMHncVKPvuO93Jw3ffuV7+nDmC7t2vYvZsm8teUwHdvtjkyTotqnbt0h3I7NrlOt+RI8C8eTqloiklBTh4MGfHobNn9dvMZ8+6b/nr1gFvvZX9/rpqlX7du3ZtnRaW3mgLgpyu14sXgd9+02lOFOVrCy/3CIAvRKSqiISLSBUAxwDcLSL9ReR2EQmHbmj/cxHJEvgSkZGOjeY7DNl0Fygp+1gAACAASURBVOZMKVU2LS0N4FnogBiUUhUcsnUBkLl/bEd9AXyTm+U6LL8qgJkAeopIws3Mg9xMRAr90LhxY6HCRd/O6CE79erpfPXqmeeJjRUpX14kJESnsbHmeZOSRGrVEgkK0mlSUu7LX5T06KF//x49XOfbvl0kOFgkIECn27fnT/kcjRgh4u+vU0948UURi0WnriQn6+05MFCnycn5U76bYbWKtGypf9eWLfV4YfDMMyK+vjrNTq9eeh/o1ct1vqQkkTp19LGlTh3Xx5azZ/X6B3R69qxxvuRkkbp19Tzr1s1+WxkzRucdM8Z1vrzw4YcixYrp7xQcrMeNlC9vE+BNAZA2dBTgH6lQwTj/t9/q+dnnu2BB1jxWq1WGDJkvStVOm2dZadHiMfnmm2/k0qVLWfKvXavn5eOj07Vrb+GLe6mdO/V3DwzU6c6dxvkOHxYpXlyv2+LF9TiZGz1a/6ajR3u6JO6TnCwSGam/V2Sk6+NQTIxI6dL6eqx0aT1+q3K6vyYm6uU6XmOWKMHrPHfI6Xq9cEEkNFQfL0JD9bgrTz4popROSQTAZikA9945GQCsANAp07TnAXyUadoAADPctMxoAJcBXIduf6xO2vRvAOxNG/o45H8bwB4AO9LKW8vhs+2Z5n3U8XODZWf5HgBWAmgCYAKAiwC2pw1LPb1+ivrg8QLkx8AgWeHj46O3Xh8f1/lsNue8NptxvpUrMy6MQkL0uJk9e/RNJKDTPXtu/nsUdgkJ4nSxmZBgnnfaNB0gA3Q6bVr+lVNEX7A5bivZXZhfuyayd69O3cVi0cu3WFzn27/fOUCyf7/7yuBup0/rABmg09OnPV0i9/D11d/J19d1vqQk533A1c3W3r3Ox5a9e83zLl7sfMxavNg43/79zvPMbltxzJvfunZ1/q26ds2a58aNRAEedQiQOQ7dZMeO3Vn+Z9Ag5/kOGpTxmdVqlQULFkjdunVN5gnx8fGRFi1ayBtvvCGbN28Wq9Uqb77pfLx48808/GEKqBkznI9DM2YY55s7NyP4WayYHidzjr9pYXHggPP3OnDAPO9PPzkf23766daXP3my8/46ebJxvuPHMwLq9iE4WE+nW5PT9frzz87r6uefzedpteoAGaDTwvIQ7lZ4U5CMA4eCPPB1S/JKzz+ve5bLrk0ypYDBg3W33IMH63EjUVFAsWK6y+/ixfW4mRo1gKpVgaAgneakTaKiKjAwoye5Hj30uJm2bXVvdgEBOm3bNn/KCOiq//XrO0+rX9/8lYAlS4Dy5XVzROXL63F3GDZMb9fDsmmatHp1vd0FBuasfXFPKl8eaNpUtyHYtKkeLwyeflpvp0OGuM5nsQC9eum/e/XS42YiIoBq1fSxpVo1PW6mWTPAz08fs/z89LiR6tX1EBSk55fdtjJihM47YoTrfHmhY8eMziGDg/W4o4sXL+L++zsA+NJkDgsRFVUfjz32GI7Y3+u7ehUd/5mMYNzQ88UNdPrnTUhsLH766Sc0bNgQPXv2xJ49e0zLZbPZ8Pfff2P8+PFo0qQJKlSogHXrnoCf33wodRmBgcA9Rn1rFXJt2uhza2CgTtu0Mc7XurU+9xYrptPWrfO3nN5m+HD9mw4f7umSuE+1asAdd+jvdccdetxM8+bOx7bmzW99+e3aZWyngYF63Ei5cll71fXzKzznLU/K6XoNC8t4fdJm0+NmfHyAAQP0cWXAAD1OROQO7N2SigQR8wCZ3dWrwPbtOkBWsqTrvMnJGR2gubrpJS0x0XWAzG7HDt3+R9u2QIMGeV8uu1deAaZOdW4HymIBRo4E3snUqkFcnL5gjo/PmBYcrANqISH5U15At+ty9KgOevj7599yb4bNpn+f8uWL7kVscnLOjhXJybrdpoiI7POfOwds2qQDZOXKmefzlm1FBJg+HVi4ULdLNnx4xnF7//796Ny5M44ePerwH08DGA7gTQDOjY35+flh4MCBGLd/Pypv3Ijvk/6F33A/7sdvCPRbjAlBQdgWF+f0P8WKFYdSI3D9+gsICzuGRx/9FRs2/IqNGzfC7FrJ3z8YEyZ8jtde6+G238Gb7NoFrF6tA2SZHzQ4OnIEWLtWB8hcBX+p8EpJAY4d0wGy7I5DZ88CGzfqQIq7AlTr1gErV+oAWatW5vlWrQK6dNHt3vr5AYsX5+9Du8IsJ+s1JQWIjMw4D+7cmf32YrMV3WuLzLytd0uigopBMiIH9osiKlratTNunLddO2DFCudp+/bpoMT16xnTQkL0hV/t2nlZSvJGZ84A77+fEcwaNQqoWNHTpSqYEhIyanCWL68DMEFBwPLly/HII4/ganqvKgq6s6uRaX8DwHZYLK8jOdm5WmcAdAu8YwBsATAeQOargeDgYAwfPhyLFo3CwYNh6TdcdevqG7SLFy9i2bJl+PXXX/Hbb7/hYqbWpIODg7FlyxbUqlXLrb8HEeW/tWv1g7O9e4E6dYApU1zXfvz7b90hwR13AC1a5F85C7PDh3VQs1Urvq2RWwySEbkH4+7kdUR0L2a9e+vUVZw3Ph4YNEhfvAwa5Fz7x9G+fUB4uK65ER6ux13ZvRv48EOdZic5Gdi/33M9YSYm6hpyOe1hMidy2kuUiO6t8l//0qmrdXXyJFCpkn7VoVIlPZ5fmjfPWmvHYjF+HaBy5aw9KVmtevqtOncO+PlnnbqSkgKMHavLN3Zswe+10GoFLlzQaXauXQPWrNGpmcREYOhQHcR45hn3btvudOaMfiI+Y4beV2bO1ONnzhjnj47Wn1ssOo2Odk85tm3TvTpu2+ae+eWV/ft1gOzGDZ3u3w/MmjULnTp1Sg+QBQcHA1gI4EVkBMgAIAopKb9g1ap1aOfwLlUSgP8CqAjgQTgHyIICAjBq1CgcO3YM77zzDg4fDnN6zcd+HggLC0O/fv3wxRdf4Ny5c9i0aRMmTpyIKlWqAADi4+PRp08fJLrYEC9dApYt06kn5GYb2L8f+OUXnbpis+nvNGWKTl31MHfoEPDrrzot6K5e1QHanPR0nVOpqcC4cfqmf9w4PW7myBHg88917c/CJKc9iJ8/D7z6KnDvvTo9f948b3IycOqU+66v1q4F7r8fWL8eiI3V6f336+lGnn8e6NBBN5HQoYPrJkA8fQzwNBFdk++TT3RqdD147RrQqZN+WDJihE47dXJ9PUBFg1LqNaXUHqXUTqXUdqWUyxexlVIrlVI3FSxUSoUrpXJwh5ejebVTSv3ijnlRPvN0o2j5MbDh/sJl9mznnspmzzbPO3Cgc2OxAwca56taVdIbaVVKJDzcfJ67djn36LVrl3nepCTnnuXyu4ekhASRGjV0Y8k1arhuOF8kZ70rfv21c6PlX39tnnfaNOd15aox/ooVxamx3IoVXZfVnWJiRMLCnBvODwszb7z/l1/09wkJ0emSJbdehrNnnXt+MuutUET3POj4u44de+vLz60jR0Q+/1zk6FHX+f7+W3+fgACd/v23ed6rV/V6DwnR6dWrxvmGDHFuYH7oUPN5JifrXuKaNNFpdr07JieL7Nvnnh5DR47M6LTAPrjav+rXd26wODLSfN5Wq8j774vcf79OzRosHjvWeVvNblvZs0f3KpmTDkmSknTnAu46rsXHi0RE6ONV9eqpMmLES04N6FeqVEm2bNkq5co5/6b2oVw5PR+bzSZ//PGHNGvQwLAh/kBAXvD1lZhNm5yWX6+e8+9fv77r8u7YsUMCAgLS5zts2DDDfBcvitx2m14Ht92mx/PTq686bwOvvWae95139D5VooRO33nHOJ/VKvLgg7q3Sj8/vc4efNB4O3z/fT2vkiV1+v77rsvr7u1KRGTYMH2+MllF6RYuFAkOsklIcKoEB9lcNhofGyvSrZs+VnXr5rpX7Ndfdz5mv/66cb7MPYEeOeK6vDabyPnz5p0S3QybTZ+rO3fWqdm8jxzRvfD6+urUVVlz2oP4uXMiZcuKWCy2tOOlTcqW1dMzW71azy8gQKerV7v+Xn//LfL2267PQa1bGx9bWrfOmnf9+qyN/BcrpqdnlpfHgAMH9HWwq84QbDaRzz4T6ddPp+7cXnLCZtMdphQrpn+zYsVEnnoqa77778/owMk+BASIdOpkPu+zZ3XHNa6umezWrRN56y2dZmfDBn3827Ah+7wFCQphw/0AWgD4G0BA2ngYgIrZ/M9KAE1ucnnhAHa7qeztAPzi6d+Qw02sO08XID8GBskKlx49xOkE2qOHed4aNZzz1qiRNU9KSkbvOI6BspQU43nOnOkceJs503z5+/Y599i4b1/uvuutWrJE38AAOl261HX+7HpXPHAgI4/jTb/ZxVm7ds5527UzX7b95tQ+ZNdzqbvFxIi88oou4yuv5H/vljntrVBEpGlT59+qaVP3lCGnjhzRN3D2wezmKDVVB8Ycy1q6tJ5uxH7TA+j5mt301KnjPM86dczLOnq0883p6NHmeZOT9bwCAnTqKlD28sv6hv/ll83ztGqWZHjD1aqZ8Yzt+6p98PMzn/f77zt/r//8J2uerVuNe2rbutV4nnv26JuXoCCdugqUJSXp3ygoSKfuDJStXRsnnTt3cQpsNWzYUE6dOiUiIh9/bPy9Pv7YeV42m00WRUVJPaUEgFgAGQ7IaYtFpGPHLMs+dkwHxvz8dHrsWPbl/fDDD53KuXDhwix5fv/ded/+/feb+GFuUm62gX37MoLP9iEoyPi89fvveh91zFu8eNbvdvCg8TwPHjQub1KSfkjl66tTd21XOemNNjYmXoJ9E51/K99EiY2JN8zfrZvzObNbN/N5t2zp/Bu0bGmcb948555A580zn6fNJnLPPXp7vece9wU+cvpwq3Zt554Fa9c2n2dOexAf+4pVLD7JzkES32QZ+4pz9PXyZePj5ZUrxvNdv14f1318dGoUyBIRCS1jMzxmh4Zm/XHnzjXeB4x6b82rY8CBA86BJ7Nrsc8+c16n+d3D7IoVGdu1Y0BxxYqMPNHRGdfWmYfAQP15Zrl5uLhunf7uvr46dRUo27BB5/Hz06k3BcoKaZCsO4CfTT4bD+AfALsBzEJGU1IrAUwBsAnAQQB3G/xvcQB/AtgKYBeAh9OmhwPYB+ATAHsALAMQlPZZBIDfoFtwWAOgVtr0uQD+B2A9gKMAHkmbnh4kA9AUwDYAEZ7+TTlkPxSJ1y337v0aixdnn2/ePN2g6Lx5eV8myionrzAmJGR9xW/dOj3dSObG+o0a9vTzy9qbka+vedtkbdro+VosOjXr0QvQPTXaX4VLSdHj7vDll8Djj+vUzPHjQL9+Ga91pKYCffsCJ04Y5z93LuP3Usr4lb833sj6mkhqKvDmm8bz1OsyCcB1h3FjmRtmze8GxsuX1430r1ih0+waCw4J0W2Quaux/mbN9OWgxaJTs94KbTYgU5NIuHjR/FUnEWD2bN2z4uzZevxWrVunX528fl2nZq/dXr6c9TWJa9f0dCMNGmT0MFuihHnnDW3aZLwea7G43gf/+ivjNev4eD1uZv9+/YpdUpJOXb1uNn26Pu5Mn26ep9mJ7+GPJKdpFiSh+YnvsuQ9cybruhExfzXz22+dv9f8+VnzHDqU9VXclBTzV95WrdLbUUKCTo3a6bM7ckQ3wp2QoFN7Z5K36tKlUxg27G4sWZJx0u7atSvWrFmDSpUqAdA9in7wge6sQCmdfvBB1p5GlVLosnIldnTogI0BATgZEoL/BQWhYrt2+t3DTMLDdRtkKSk6DQ/PvrxDhw5F9+7d08cHDhyIE5kOso0b645LQkJ02rhxTn+NW3fokPH5zWgbOHw463HXYtHTM9u2LetrzomJWV/nNJqnv7/xPAG9WqKj9XElOtpwNeXaq686n9tefdU434kB4+Frdd5ffa1JOPHkBMP8GzdmvOaXnKzHzbRv79xza/v2xvlat3Y+D7hqC+viRf1qemqqTjOfF27WsmXOx5Zly7LmSUnRx0j7MUtEj5u9+h8RoT9TSqdmnTds/OoQkm3OG0yS1R+bvnLeYPv2Nb4W6dMn6zxFgOee08d1m02nzz1nfC6sHRxtWK7aQcezTLvjjqznXZtNT8+scWP93f39dequY8CaNXqZ8fE6XbPGON/PPzuv05zcE7nToe03IJku1CUhAYe230gfP3LEvEkGq9X4HLNpk17vcXE63bTJvAwrV+p1b7XqdOVK13mTk/U8k5Nd56V8sQxAFaXUQaXUh0qptg6fzRCRpiJSD0AQgH85fOYnIs0AvADA6ECeCKCbiDQCcA+A/yiVfudYE8BMEakLIBaAvXeeWQCGi0hjAKMAfOgwvwoAWqeVwanbL6VUSwAfQwfi3HTFRHmpSATJEhJqYty47PONH68vysaPz/MiUSa7d+v2lV56SadmwZSvvtLtRDi6ckVPN5L5IsgokGCzGV9smQUdlLqM1NSXkZr6FJQ6jDJljPMB+oTteBHp6gSeU19+qW8Gv/hCp2aBsilTnBuXB/R45t4a7TZudA48GF3wb91qfFG4dWvWvDqYswP6PFMCQG9cvLjHLUGavCACfPqpbuvu00+zDyZFR+vtzl3tRm3erC/ekpN1atbXyLJlWdtoOX/e+EYG0O32jRgBLFig0zlzbr2sPj4ZgemEBPNepfbuzXrRa7Wat/lXogTQs6f+DXr21ONGWrbMmK/VqsfNZG7011UjwJmD6q56xB08WO8ngwebZNi9G6Nix6EYdkLhPgCVofAw/PAeOl5+BdYdO5yyf/BB1t/Rx0dPzywpSfcE62jHDj3dUXy8cZDMrG3GFi0yAh+Jia4boY6I0A+VgoJ06o4eCxcuXIjGjRtj+/bt6dNGjx6NH374AcWKFXPKO2SIbrcsKUmnmQNk6UqWhM+yZWi2fz/K/vij3ih//z37LoxzSCmF2bNn4/bbbwcAXLlyBf369UOqw0klNFRv899/r9PQULcsGgkJur1Js4dEAFCzpvH5rWbNrHlr1Mi6vSQnG+8zDRsanwsaNsw6zxs3nKfduGG+H2Zu+8msLaic2rYNmDbN+WHRtGkGbbOdP4/bV34OK5wjilb44vYV83Sjiplk/q6Zxx2NHav3FR8fnY4da5zvxg39O4roNPNv5ygsDLj7bv1A7+679bg7dOyY0Rt1YKAez+zHH42Plz/+aDzPUaOcv9eoUQaZbtxA85hFsMA5+hqARDSLWZT+Y5w5k7VDHbsVK7I+WPjjj6znnH37gGXLBJcvX8a+ffuwatUqfPfll2h8thP88AqAZwC8B+ACgnEDb18cnCUq3KIFULWq83yrVjU+bq5apfct++DqAURuVKnifC5OayYxC6Pv7w4iumJB//46Nbtuqjn3NSib88WAsllRc97r6eMlS5oHWVNSjA/Z9oeJ9mtXs4eLgO6IKSBAPyQICNDjrvJaLHrfslhc56W8JyLXATSG7tL6AoBvlVID0j6+Rym1USm1C0B7AHUd/tV+RNoCXTssMwXg30qpnQCWA6gEwN5X+DERsV+MbAEQrpQqDqAlgAVKqe0A/g86MGb3k4jYRGSvw3wAoDZ0cO0hETGpqkAFTZEIkgE1ER39KjZt2mavXpnFlSt6UCrjb8o/q1frC6fERJ2uXm2cb+NG+3XKDSDtQiopCfjnH+P8bds6X+wZneh8fPQNg+OT5po1jW/8Fy1ahNat6yAl5T3YbHMQH98A48fPhM0kolaxonOQzB292uXkKS+gA3JGAQqz36p5c31BEBKiU6NG6xs1Mq6V0KhR1ry7du2Aj8+9AE5Cv4n0HYD66NOnN3YbREGN5pufPvsMGD5c11wYPlyPm7E3sD50qPsaWF++3Plid/ly43xGnTDYO2cwktPtJTcy17Ayq3F17FjG/mcXGOi6QepZs/SN+axZ5nlWrHAOkrl6yhsZ6Xrc0Z136t7MLBad3nmncb7YWF2TKyBAp5kD9wAgBw/iZ5xBMppDsBzAaQgWIx7j0Cn5NMq0bInOnTtjypQp2LBhA/7+O8UwoGUUrD571nh/OXvWeZqPj3EtHvuxLTU1FSdOnMDatWvx9ddfY+7cd+Dj8yyAZ+DrOxM7d65DXFyc4W9gsehAw5YtOs3c8UVuXLx4EX379kX37t1xPi0C7Ofnh08++QTvvvsufMyisMhFjdPwcN2ydk6qh+VS6dKl8fXXX8M3baWsW7cOkyZNcsoTGqqDDe4MkEVG6ppGkZHmgbKGDZHlQU5oqHFAp1Yt4Mknnac9+aSenlmHDsZBkg4dnKfVrJk1IGc0za5vX9fjuZXjmnTR0SgZmISv0RfBuIEQXEMwbuBr9EXJwCTDg/zgwRnz9vV1ETDXs0+v7RMfb37OsAdP7McCV8EUpYBSpXTgr3Rp10H93Khd2/n4atRr85YtxjVfjR6YJSUlYcWKFUhOHg/gWSQnD8OSJc9j5MiReOmllzB69GiMGTMGr770EhLUv+GH1+CLCQCWwYJ4lMRVvBDwUXr06+hRHWg0EhSU9fxy+DCg70tfA9AJQEMkJlbCgw9aEBoaijp16qBdu3bo/dhjmJZ6EKl4F7rCx8tQqIx7UB0lsNYwUJq5EwKzTgmWLXM+v7vjPAzoTgvsv0VQkB43EhPjevxmff458OyzwNdf6/Tzzw0yHTqEtgdmoTe+QiD+QCD+QTAuoy++RrsD/5derdRV7/J+fsa1zPbuzajtlZqqx820bKkDmFarTl09XGveXNc6nzxZp0bXw5S/RMQqIitFZAKAYQB6KKUCoWtyPSIi9aFfj3S86rQ/OrQCMNq6+gO4DUBjEYkCcM7h/x0fO9r/3wdArIhEOQyOR0jH/3E8IsdA37S6eJRCBY6n3/fMjwEO7YVERETI2LFjZevWrWJzaMBh+XLn9gKWL5eix2YTuX49/1v0lJw3hj9rlk0slukCBAkQKEBXsVi+kGnTjFuBjY/XjYPecYdO442bFpEDB0Rq1tTtatSsmbW9lEuXLsmjjz7q1PaM49ChQwc5fvx4lvkmJ+t2Ovz9deqOxsC/+MK5bYkvvjDO98wzxm12PPOM+bzPnhVZtMi8XYcDB/T+4di+S0hI1nYwtm3bJmXKlDH9vQBIz549ZZfDiu7d27nR7N69c/nD3KJevZx/q169zPN++WVGWyTFi+vxW2XvDMC+Xn/5xTjfr78at4Py66/G+T/5xHm+n3xy62X980/nef75p3G+NWuM2yFZu9Z83jlp62v+fJGgIJsAiRIcrMfNbNyo5+frq9ONG11/t+Rkkf37Xe+rf/3lfL746y/nz8+cOSMP3n23y+0/8+DnFyxK3SvAGwKsFiBJLBaRl17KuvzExIzl24eQED3d0fr1IkFBVgE2C/ChAGPE17efNGjQWqpUqSI+Pj45KltERIR0795d3njjDVm8eLEcP37c6fx5K3744QcpW7as0/IqVqwof5ptVLcgr09tb731Vvp3UErJX5k3DDfats257apt28zzFitmE2CxALMEOCTFi5vnvX5dN0QfEKDT69fN87Zp43zMbtPGON/Mmc5tc7pqx/PwYed2vg4fNs+bEzluk+3cufQGkWJRQnainsSihP6HwEDdOn4mly/rRuZDQnR6+bJ5OZKSRGrV0seg2rXN21rbudP5WmjnTvN5xsc7fy+z65vcGjvWeb5GnX3Mn298bJ8/X7cFuHPnTvnPf/4jnTp1kuDg4FwdCx2H0giVN1FMYgMD0zfG06ezNu5uHwIC9Od269evlzZtegnge9NlsA/t2rSRhQsXSqpDo5q1azsv36x9zB9+cD5n/vCDe9bVwYP6d7cPZm393XtvRicy/v563B369XP+/v36GWRaulSuligh9zr8lr5QUhuQHn5+Mr5fP5k/f76sXLlL/P0TDderxWLcecO4cc75xo0zL2tSknPe/O5IK7+gcLZJdieAmg7jkwHMAFAKOrAVBN2+2G4AE9PyrERaw/3QDf1HG8x3BIDpaX/fk7Z9hiNTw/3Qr1Xa57seQM+0vxWABml/z0VaO2Rp49fT0nYAfoGuWbYTQDtP/54ccrjdeboA+fIlTU54NWrUSA+YXbqke9AJDs7+YqfQsXdlFBqqoyihoa67NEr7lytX3HvTsWuXvng2C5Bdu3ZNHnmkt+G6tFgs0rlzZ/nss8/k0qVLzv+YnKxbYc9BhMqoZ65FixZJ+fLlnZZXtmx5qVy5jtO0EiVKyGeffZbl5jF5z0E5+MlKSd5jcvVyE774QuSxx8wDZCIix4+LlCrl3HB/qVJ6+q04cEDk0Uf1xeCjj2YNkG3dutUpQFayZEn5/PMvpUuXLobrzh4su35df6dq1XTq6uYsL8yZI+Lvpxvt9fezyZw5WfPYbDbZsmWLPPfca+Lj00z8fB6T4sHnsm/gOzVV5NIl8xbr0/zyi8gLL5gHyET0Ntqpk0jxYKv4+tqkeDGrdOpk3ruhzaYDYz176tRd++yff+oe2rKLZTz55Dmx+H8oFv8PJSjwoDz3XDYzvnFD3xnfuJHlI6vVKmvXrpWRI0dKWNjtAkBCiodK06ZNpU+fPvLqq6/K7Nmz5a+//pLo6Oj0m5mNi2JkytOHZeOibHpjyKErV3RPiiEhOnVsLHrBggUSGhrqtI3XBmQJIB8B0lcpqejvn4ObsmLi7/+gTJz4gezcuTPLccUeqLP3WugYjzl+/LjMnj1bevXqJYGBoTlY1k3cvJYuLe3atJHXnntOtm/YkOug2fnz56V376zH8wEDBsjlPDgBDxqkH4IMGuT2WadLTU2Ve++9N/27VKhQQc4bBFfcIT7euediswBJXFychIb2cfqNAwNryPDhw2Xp0qUSb/CP16+LbNxgc3kMTk1Nlb//Pij16v0igYGfS40a02XMmMkyatQoGTx4sPTq1Us6duwozZs3l1q1akmJEuUlJKS29O8/W6xW820lJUUkql6yBFqsElUv2bQDndzI3MPrq6+aZOzdW2yBgfIHIP8F5BdATgUGis3F/LSl5wAAIABJREFUE5vLGw/K8snr5fLG7M/vSReuyp5FhyTpgkm3vWl2Ljws05/eITt/yqZrS8noyMhVB0a59ccfzgGdP/7Imic5WaR5c5FiwVYBRIKCTkn16vOkX79Hs1wvuWMo5u8vQ4cOTX+w1rWrcS+IXbuKJCcnyzfffCPNmzfPdr4lSpSQGjVqSKtWraR79+4ytGFDmeDnJ+8B0tTkf6pVqyYffPCBxMbGynffifj66usGX1+bfPedyY9qs8kPk/fKkHb75YfJe9168XzwoL5+MQuQiegY771tkqR0iRS5t02SUcz3psydm32HADHr1klUWucp2Q1K+YpStQToLsBrAnws/v6L5b77NsuZM2ecApQi+rwXHGgVwCbBgVb56y99nXb27FlZvXq1zJ49W15++WXp2rWr1KlTR3x9gwVoJu1a/pb9l4uJ0a37Z9eLUwGDwhkkawJgA4C9AE5Av0YZlvbZZABHAKwD8BlyFyQLg+41c1fa/+5D9kGyatAN9+9IK8/4tOlz4SJIlvb37dAdATT39G/KIQfbnacLkC9fEt9LWFh/CQkJMT0wR0TUkJIlx4rFcljCw933RC6nbtwQGThQ13gaONDw/jDdxYu6m+TQUJ266kr60iWRZct0asqxKyP7UKyYaZdGmzeLhIXpJzthYXo8r+3evVvuvPPOHJ1k/fz8pGPHjjLr44/l/OjR+so4KEinEycaRxNOnNB9wzdtqtMTJ0xrjz3WvbtceuopSWzaVF5u1EhUppN/ly5dJCYmRv/obdvqZZcsqdO2bbNZGTmzefNmmTVrll6OC8eP65pjTZro9FYDZCIisnu3yCOPiERE6HT37vSPtm7dKqVLl07/LUoVKyb/RETo7at+fdk8dappsOzuu3vKnXfukmLFRKKiRH7LwTVMNjGnnEtOFtszz4of9FNMPySK7ZlnRZKTJTU1VdasWSMjR46UqlWrZil3RfjJ+qgo4/Vqs+k+50uW1DtMyZJ63OQC2WYTiYvL5vo5Pl6sffrJr/4PydsBE+RX/4fE2qdf/h+0snHw4EEZMnCgBGSqrRQRHCzPPPWULFy4UGJjHWqApqToCGFQkK4aFxQk8sILkpKQIH/++ac8++yzUqFChVzdWPn7+0vN4GC538dHxgUEyJWAAJEHHzTtkjQ2VuTdd3Uvde++q8fNXLmiL9DtAbIrV64YHi+eKV1eLqkgiUUJifcJlthazcV27pwcPnxY5syZI0888YRUq1Yt2+9Srlw56d+/v3z22Wdy8uRJkfnzJbFaLYn2qSbnq9aURaNGybBhw3J8nHScb9OmTaVH9+4ysmlT+a/FIu/5+8ujvr5S/7bbxNc3ZzUwapYuLa+NHSvbt2/PNmC2YMECue2225z3o4oVZcmSJbnaxl5/XZ+qXn/ddb6EBOfTW0JCrhaTK2fOnHH6bg888IDs3m2V3r31+b13b90rrjvEx+saZGa7/p49e6RWrVou11tgYKB06tRJpk2bJgcPHtTdKVapoiOKVaqIbe5ciYmJkWXLlsl//vMfGTBggDRp0kSCgoJytZ05Dn3at5erVw0CRTduiPTsKSmWYDlcvIGkWIJ1dN/VBVEObd0q8u235j27iogcPxAtdwdVylLe0DKh0r59exk5cqTMmzdPtm/fLkkxMSKtWzuf31u3FtvFixIbGyu7du2SpUuXyqxZs2Tcq6/KgDvukE4+PvKQr6/09fGRwbVqycjnn5dx48bJlClTZObMmfL5zJny4x13yDKLRXYUKya2wEDdDeaFCy6/W14c+v/4QwcXjQJkIiISFydX/r+98w6vouj++Lk19yY3hSIRBBIpUUoUIfReo4SiCAhBEFGpAQQE8QcvRaQKKkhT8VURAZUqAoJIUeGlF6UYei+JhHRS9/v7Y3KT3buzmxu4JGjm8zzzzJNksjvbZ858zznPdkR349Mobcj/vVzZ3x8DTSZ8bLVinsWCj5o2xQfvv4/Zs2dj5syZmD59OqZOnYopkydjUv36GGQywV9jWy1atMCyRf9FpxLb4EV34U/x8KK7aB/wA959+z8oX7489/9qGUrhVUt7zDM3wYWGjZB6/br6uLKygHfeQba3DzK97Pjdy4bu1apx34MOhwNDq1XDIlM7DLUswnpzF6BvX7Wk99Il9vA7HOyF5XAATzzhoQGZG9y9C/TqxayIfn6s7tXLIy9CSWKGschIVru++qOjo1Xftwr3+N4gIhiNRpQrVw5hYWHo1L49BlaogFeNldHM1AatDY8izM8Pfn5+bm2rV1AQbvJWOJOTgS5dmJzT35/VXboU/srtPUL/QiOZvBAzRpmKuh+i/PtLkXegUA6SmKvNuXN3sW7dOvTqpWcws8Nk+lJ3IPUg6NcvL/WxzcZ+1iI8XCmbDg/nt7t9W+kKwLXNSBKztslnEM5SqpTqiydJzDAmb1a6tPakPj2dTQjykzVLEktRHRnJavn2li1bppLrDyTCn0SYRoTaOitURiK0IsJ7xJQc1+12ZiiTc/kyyx0tO6k/+PigrIsL0KOPPor1S5ao2u729UWV4GDloLpUKXxXvXqe34izWK1Aixaa52HXLmDyZFZrsWTJklw3KavVitdeew0nTpzQP8Ge4vhxNsCT+9g4HMDx4zh06JDKQHbQdanXbgc2bsShQ4c0jWVEL4EoFna7tqHs1CmmODMYWH3qFL+dJAFLljDXySVLdIxPgwcD3t4YS9NgpxSMpnexxcsLA6pXR2BgYL6DLTMR3q9UCdmuBtjp0/m+PjNmqLpw5AhTJZnNrNZ0n+rZU50n3WbT8HNgtqfx49lca/x4eESZ4dzu2bPq7e3duxddunRRGY95xWQyoUmTJnj33Xex96WXkGW3A0RIJ8JmIrxmMqH0fUzGXUtFIuyyWJihzIX4eKBiRXaLOm/VihW1DWV79gDTprF627ZtqolZhQoVMGvWL/D2BmrQn+hMa1GD/oS3N9+d//Lzz2OZxYI3iFDJjWN5wmDAa0RoknP/6bUNJEIkESYT4QurFb+MHIkzZ87grnyS5JTbuNyraWPG4PDhw/jvf/+L4cOHo3nz5gjw8tLdX0hICMaNG4djx44pDGYxMTHo1q2bqn3fvn1xRy7HcxO5gkGLzEy29iE/rLp1+c+BJDEbUa9erM5P7JGSwhaJXG04mzdvVhyf1TpH9crUM5RpPVuufPMN8MorrFb/7RvVd7M+EbzzuVcqGwwYQoShRGhBhFIeevZcS6Vy5XDgwAFlp7t147/bdHzfJSl/G5okAfPnA506sZp3XdevXw+brYzb/bcYDHiKCL2J0I8IbYkpRh1uGpXdKQ2JcNhsZi9vDhcuAKGhbDgSGor8Fc0eQpIkfFO7Nh7T6XuJEiXQtWtXfPLJJzjfpw//OxgVpbmPlJhk7F12BPPen4uaNWty91GeCG+TLxbTU+hD3tx722q14tWyZXHUNfaE1arpc7h8ORBgu4tqjssIsN3FihXA5cuXMXbsWG4YCQMR2hFhORFSbTZmKMs7WcxA5oxR4SwmEzOUabxk4uLYdyI/UW1GBgtNULcuq7kOE7165X3YSDYW69VLc7vnzzNPhfPn9fcPsHXn69fV68/79+9H6dKlc8+TiQz4lCy4Q/6IJRt+rVkHX8yfj9GjR6N9+/YIdhlHF0bxt1iwYMECpULNaSBzfQ95Uq75AKF/sZGMmJrsOBF5FXVfRPn3lyLvQKEcJLHFG/kL/O5dfYPZq6++oZxAPGBCQqB4H4eEaLd1tWmVKsVvt3WrMm7O1q2cRsnJ6sBVzmI2q1ZO7txR230sFqW7kZP0dOaSZ7ezWs9Q9sUXasn23bt3MXDgQMV18SbCUk5fz/n5YdbMmW7J6wMNBoS3a4exY8di5cqViO7VC9k55yCO2KDX9X969erF3DijovIMZLITkDxgAKKiolT/14MIf7v2127n6uJ37WLHbjCwmmcoe//99zWPKyIiAjt27PBYrCAuXbuyDsqPx2jEodatFQayEiVK4GCVKvz7KjQ0d3OHDh1C586dOcdTDkS78Mwz/G48/nje5pyGMh5Llijj9ixZwmmUmJg7INqSc/0D9AZVRHiZCHM47Tq0bIm/ndLOrCy2Csk7BwEBChmcJDHDmLxJYCBn/BwTox68yQdxHMXB+PHKZys/1Y07ZGYytZ/Nxur09Gxs2LABTTXicNUhQgTlP0EvQWyyqaUgICI8QoQ3cq5VGhGuEeFXInxpsWDCO+/g5ZdfRsOGDRHoolSST2jGmkxIP3NGcUyzZvHnEbNmqY+fxfoCjMZUmEzDVfvo3bs37ty5g7Aw/qUKC3PZYGys6rqeI8InROhmNKKk7Nlyp9hsNrRr1w6zfX1xjAiSawcefVS5/7Q0dYAhZ/HxUSojUlMh2e24SITviRnfHDp9CQkJwfjx4/HZZ58pJkxEhMcee6zA6jEn168r41zxRCEAsGYNP3bSmjXqtl99pXxWvvpKe/8pKey94+PDaldDzVtvvSU7VguI9stfmZoxFzMzgWeeYcf0zDPahrJvvlH21WkoS0tLw6BBgxTnWf7dTCPCz0QYabWiWrVqBbqvXMujRGidcw8MIsI7BgNm1KmDRYsWYfny5di0aRN2h4biOBEuEKG/y/9bLBZ88MEH7Jt186Z+oClOoExJYu51BgNTf2oxf77yXM2fn/e3xMRE9OvXj3N8HUHUGETaHgiFUYxEiDKZcGfvXtVxhYYq16ueekr7HHiKAwcOoFFYmKqfFmJG1almM/b/+GOe0SEpSf1idRa7nf3dhZQUIDiYPVvBwUBysoSdO3eia9eubitby5Qpg0mTJuGmMyil1v7PKV1aY2L43wGne2JKSgo++eQTVNdQ7PoR4Q2jEb9v2sTu699+UwcRdRaHgxugsyCx7saMUd7bqliesbGK50rxLbDZuK4o58+zfTscrNYzlGVnA40bs2Fx48Z586zNmzcrjPR2ux1BQT8ixHAaz9ImVDWckQ8FGcePI8luxwEifEmEcUR4lQjhRHjKYEDpAnwHfR0OhIWFITIyEpNGj8ZyiwUHiXCaCC9x2oeFhTGj/Y0b+mMsjvfGuXPA0qXuGRQLA/oXG8lEEaUwS5F3oDCKwZCKd9+FJnfv3sWyr9fCaFAOGGvXro3zhfTW+zcqyU6eVKoy9FbOXYN/dup0AXXq1FFcjyeqVsWfWgY9iwXIcd+4dOkSPpw8GY3dDEpNRPAhQiMilHX5fWBgINatW5fX0fr1+fuvXx8AU5RUqFBBsY1HibBB3tbfH9i0SXUOJk/Osz8ZDOznvPMu4Z133nHptz/3WMLCwrBy5UpkekoyJKdyZdWxHyRCgOxclyhRAocOHVKvHDsLR/Zhsx0CkauxzAir9V1VDIrMTLWdzmDgu166FYz/5EmkOBx4Vef+KFOmDAYMGIAtU6Yg3c8vd4MXiKkz5G0rVKiAPXv2sAfO1aLsLFar4oFMSpLbqhNAdBYm0//w7bc/YMmSJZg+fTpGjhyJ3s89h2fNZtQhpopqSoT1lDPw9fcHXJUZYCIE+a41RAkF4uxZ5/sqDWbzf1GlSnXueXvObMYOyhuYpxFhOxHGenmhdkiI289nucBAREVFYcc33yBLy5jjcCgnPNu3I9nPD38SYQkxA5x8m3VCQvDXX3/lNn/hBf5mu3RRH//UqRKMxm0gUrqylSpVCqtWrcptJ7tVFMXPz2WDBw/qNs7evx+HDh3CjBkz0KZNG3hxzlEtIowxGPDzzz+zBZ7MTP72nA+M/P1w+bL+83rlSl7bCxdUVqdUIqwhQk+TCT5uBunu16/fPanHnKxdq1wEWruW3841sLOz8AI89+qlbKMjtMDBg0oDvGvYgfT0dNStW1d2zJVynm32P1oLYWfPKo1/WoHrX3lF2ddXXgEuXLiAMBcDxhMGA/7knQAvL+Dvv3HhwgUsWrQInTp1go/GtXIQoUGDBnj99dcxd+5cbN++HbFPPME/sTVrKjtasqTi7yuJGRLk24+IiEDs5s3aiwr+/gDHSJSSovxmainKOnVyHV+w3//6668c9UpZEG2Wtc+Gw3EOs2atxoQJE9C5c2cEl9FXnNmsVlStWhWtWrXCK507Y5zVisVE+IEIa4nwNbH4hO97eWHi4MEYNWoUBoSHo5fFgs5EaEnM6KT4Bvn748svv1QsgnHW6x4YN27cwKuvvqo61kBi79dk+bX69de8f4yO1jcSuQY1BfuMyZ8t+WftypUrGN+9O8poKJWfMhrxxfDhSHMa9rds0b+vtmxR7Hv/fvWr2M+P/V6OdOQItnp7I4LYwguvL1WrVsWUF17AJS0jncPBDeJVkERi1artA9FrIOoAojZwOJogLCwMNWvWRJUqVVA+MBClDQY4iCmOvYkwgAhJzgM7dEi1za+/ViYm0ot9e/26cj5y/TqwdOlSmM3m3PNQqlQp7N27l5tISsHatfofzbVrkZaWhosXL2LPvHlY5e2NeUR4mwhjiPAZEXYR4YavLyS5n/C+fap7YCsRqrpcL4PBgCFduuCOVh/8/dkKmYxz59g5cpZz+YcRfOAII5koonimFHkHCuUgCWhc6Zq274Qk4ZeG4+Cg6yDqqXhpBgQEYMOGDfz/8yBFHpOMt9SeT0wyi0U/Jtm9Ksm8vDbAx0e5YtS9e3ckxsczV0fex6tECaVUMCMD8PXFNSJ8ToQhxIxgWpMAXukVEqJOAqChJJO7DcTHx+PVF19Ub48IsUQFVpJlZWVhwIABLvdlUxiN8ahb93c8//zzXNe24OBgzJ07F0mcldp7pmvXvKVrIhwgpZoq10AGsGVu3rVSLR8yNRL780YQKQONt2rVCtddZCJOV0si95Vk3t58JdmpgwdRk3P+gogwwmTCb1u25BnqoqNVy8zpRBjp8r9msxnvz5yJbK3BlkxJlpaWhp9+2gK7fSiI8o9NxSsNibDDYik0JVlS0l2UKzcbTPGnPvY+ffrgj59/1l+RvXoVMTExWL58OV7p1UtloA4iwigi7LFake28h1NStFUB3t7KF+elS4r9XyVSZNciYqvbixcvhiRJbinJEhMTsWDBAjz+uNooGBERoYoReD9KMsW5crmuqcHB2EaEGUT4hgi3nG0rVVJu99FH+du8TyWZnkEtNS4Oa9asQY8ePeDj46M6T+XLl8dmrVSsBeD6dfYY+fqyWk9J5jpH97SSrFIl/jf73LlzsFjk8XF6gij7gSjJ3nrrR4Wal4jQvVs3JLqmQXUWPz/VxtPKl8c2IvyHWIiC9UQ4T4Ts8uXVHXjlFb4LmdzVDGDyEpd9nyNCmMOh6Gu5Rx/FTtdvq7N4eXFT292rkuzDD9MwevRo1XfTaOwOor/zfwT/+gvxNht+JcICIiwkthB2hAh/22yQ5P7/CQn6z7YzNtuJE4oX0F/EVLWuz0+TJk1w9OhRAIWjJEtLS8PMmTPhcLleFmKGiQTeMV29mrcBDyjJVM/WyZNIs9mwjAgNcvrSkdgCjGSzKVdkz53zqJIsl7i4XGv2pZznpQrnehExI1prYmrOZPmG70NJduLECbzwwgvc/blTniTCMavVo0qyRo0kzJgxU7GfoKCg3MWomjWV9ytPSaa7WCOLf4sLF/Sfq4sX89rKMtfKy10iTDab4eUSPiCQCMuIo8DmKMmWLlUaFJcu1T5XhYUwkokiimdKkXegMEpj+g1bre2VGns527Yhzqc8AukGHBQPX5qmWsV75513Howy5x6QJAlXr15FsqeCSOZkt8wMKIVsoxmZAZ7LblmQmGSff56J6tWVaimLxYKPP/44b/V0zhx+bIs5c9QbnTRJ1Tbbbkd0VBRWrlyJsWPHIjw8HGVcXIACibDWx4epK1zhxC9DiRLctj/UqIFAl/uoNBG+qVZN0yXSNSZZenq6KgNchw4dkJKSiri4vPMfHR2NAQMGwGazqQZDAQEBGD9+vGfch3NikklGo8pAVrJkSRyWB/PbuJE/2uSo6H76Sd70KoiaKY6hTJky2CrzFz51isWLMhhYfa8xyZYtW6aayEcS4RARJLudxSpzpXlzbqy59TVqqCapHZ58ErEu50Dy9sat8ePxxRdfoEuXLqrJx/2Utm3bYr/LkvfBg0DVqqzLVaveX6INSZKwZs0abqB5X19fjBo1Cpflz0LHjvwUZE4ph3zbw4fjT5sN3xBTJ0rOZ3vECGXDESP474CRI9Udbt9esf9sIswxmWB1UZl26tQJZ4+cQ0XbTdgphd2qlIIg203EX07AyZMnMWTIEK5rvtXqg8WLP+U+09u2qbtqt2soAwoSN2blSv45WLlS2e7TT/ntPvtMvU2NmGTcVICjR/Pbjh6taJaamorVq1ejR48eqFixIgYOHKhM1nCfXL/OxAdaBjKA2YGaNs2byPj4sJ8fZEwyOXPmrHS5ZxrAbt/nsZhkvXtnoXPncarv5rx589g9OW0a/1pNm6beoNxKKG/LsxaeOcNm0E5DmcnEfnZxY85d/XHZZvovv2DkyJGKfhuJMNFkRpb8m+2lH5MMKFhMsnfeOYbQ0FDVN/Kbb75Bz56S+6GbmjThxxxt0kTdtn9//rM9YICyXaNGikU4iQjfm0wob7Uqz5PRiGHDhuHYsXg89WQ6LOZsPFUt3aMxySRJwrp161C5cmXVO69Tp0443aqV2+92REUVPCZZClOQaV5b2bnKdm6TWWrUbVu35l+rNm24m16xgl0ePz9Wr1ih0Ye+fRXXVSLCbqsVb4SEaAaPdxChLxF2GI3IDgkpcEyyixcvom/fvrlxae+neBmNWLRoEffbVdCYZFevZmP48DcV23/qqadw7do1xTZDQ5mCLDRUY9v16/MXohs0ULcND+ffg88+q2770kv8Z7BHD5w5cwbh4eGq89OSmOEbRJoxyc6fVyrJHgaXS2EkE0UUz5Qi70ChlJyX3N1wzscbAN5/H7BYEEcB+IVaIo4CsNdkQoWAAOULs2VL3OTExfAU+Q2ML1y4gMmTJysGLRUrVkS7du0wbNgwLFy4ENu3b8f169cLHJfq228BIgnelAwiCd9+q902JgZo1YrZhlq14qywFZC///4bn3zyiYtrCnNb+9///qdsLEnMIOY0VJUowX7mHW92NjOU5ZPdUpIkXD9wAF817IBPS1TB8YjX+QYyJ5cvQ4qKQmZYfUg5mTC53L6N2EaN0IsTQyMiIkJpTJAdXkICq5OTk/Hss88q/q9Xr17I4EZmZdy6dQsTJ05EqVKlVPts1KhJXrwsDU6dYvNqLaMTAJz74ThmUCv4Ut4grWTJkjjCizS/cSOulAxFMnnjSslQroHMyYQJcjfKTDRrNkGx0m8wGHKN1V9+qRzE6ik+eKSmpqJ///6K82MzmbDEbIbkcLAB0eDB/Ci4t28jvWFzpJAd8eSPFLIjvWFz4PZtXLx4URUXr4zNHz+RAwfIjPFkQ/WAirpB7b28vBAUFISwsDA899xz6NOnD0aNGoUZM2bg888/x4ZVq/Bby3Y4bLBiEFlh5WyjS5cuOHHiBI4eZUYBuerOxwfIESIUiGPHjqFly5aqfZUtWxYzZszgu84lJbFJk80GyZklqlMnrnoAmZnMACbPbjlihPqFmJnJDGLydiNH8l+ciYnIbNce6UYbEgz+SDfakBneHkd370aNGjUUxxFoteJ7sx9m0VvoQqswnUZiqdkPrTiBmllxwGAYDLv9PDp04CfNBdhEJyyM3athYTquM84MZDYba2yz6WcgW7mSSZhMJla7GsicfPYZU44ZDKzmGcgAdgD/93/sBvH2ZvX//R//wLKygNGjkW3zRobVB9m2HAOZx9LNepbMTKYc+89/WF3Y610vvPC66v7p06ePYgJ5L9y4cQOtWrVSbLd8+fLK72aOoSzLxw+ZZi9kOvyZgUxrjOCS3VL35XrmDKRX+iKrek1Ir/RVG8ic7NqFWyGNkWgthVshjRXueD/++KPqe9WUDDhBvkglG1aZu+Pcn3xLiSQBixczl+jFi/WNmqmpWRgxYgYsFotiX23btmXZYlHAR/D2bW52S65sPz2dGcRsNki+vmzDAwaoVw9jY4FGjSDZ7cjw9mcLNY0aIenCBbz99tsKFzbnO+triwWSs7MREZqZewvCgQMH0KZNW9U9W716dWxxuifmvNuzLDakWP2RZcnn3R4VBcluR5a3gx1XVJTugyhJ7HRoXtPYWNyt0wgpZMednO/w3ToamUDv3IHUujUyLXYkW/yRabFDat2GH0wX7Hr37cuUon376iSBTEtDWmRfpBu9kGDwQ7rRC2mRfYG0NKSmpmL58uUIDw/X/N4HlS+P8ePHs6yy+XDr1i0MHz4cVheDKRHztFi7di22bNmCXbt2Yd++fTh27Biio6Nx6dIl3Lp0CQnduyPNywtf2Gyq2KDdunW7L9f3tLQ01WJuixYt7m1BJCaGGcS8vdlD6O3NfuZNNBISmEFMnoXy2Wfz1JlyUlOBHj3YM+iX07ZHj9zUsJIk4bvvvkO5cmp1fFODAd/Wr48MjXMkYpKJIsq/sxR5BwqlECGZvHG0v7aSLNtb6Y+R7e2D2NWrVasL5cqVw2+//cbfzn3gGgjbOXZISEjA559/jmbNmnE/slrFz88P9erVQ58+fTB9+nRs3rwZtzjuCgCbA7ku8rkmOpDTqhVgsUggYnWrVgU/3sTERHz99ddo3769auBHRAgPD0esXtrz7Gz2IdTqpJyMDCaR1jEu7dzJxrgGA6t37tTenCQBHTowuXiHDvmrDV5rdhp1aDJ8vJRp0n19fbFgwYLcjIiSxBbGDAagVas4NGrUSNE+Kioqt+3o0execRFu5JKSkoKFCxeicuUqim1UrRqCsxpBbk6dUs6PtQxldep8CaK8jINWq4aBLOeY5AYarXN15w5/oXndum149NFHFcdQt25jeHldVrS127nxVLmcPn0aTz/9tGKbISEhOHbsGJtgnDqV70RjwwbgGZ9oPEdK528/AAAgAElEQVQb8YxPNOQe2enp6Rg1alSBntdKlSph+PDh2LZtG9Lzk12CzUXKWmJRhw7gEfNhVKzYT7WybDQaUb78KyC6oDhXBgN/sV+LmJgY9O/fX7X9kiVLYt68eXmxX3QYHXkVzWgXRkdezbctUlLYqDM/eYib7Xr0AKpYL6EFbUcV6yX07Ml+n5qaimHDhqmuRRQRphDLnMa7VhUqPAmr9WPI40s5HIA8BIqcgkzkAUCKiUXC9oOQYnTefzmMHMkWznkiOhVuWoZGDE5DsPkKRgzWv65HjwIl7amoar2IEva792R4/aczfjx7V+bnwjx4cDrM5rEgUk5wfXx8MHXq1AKpfDMyMrBhwwZ07dpVNWFu164d97t5+DDga8/Eo5a/4WvPzDd796BBgMkoYdAg/XbOxH1ErNa6t5ctU7o7Llum/PvVq1fRvHlzxbGYyAEb9YXBsAd9+/I3vHixcruffKJuc/HiRUye/B6sVmUMRJvNhnnz5qkzEoN5ci1Z4ma2yL/+Yh8EWXxDLV7vnoBqdAKvd+dM4nOIiQEa+J/ES/b1aOB/UmEbOHnypMooSkRoTMztOtlqZYYyDmlpwNChwNNPs9r1tZ2eno5ly5Zxkx8FBARg3rx5qgW61auByraraEK/orLtKlav1j725GSgWoUkPG2PRrUKSa45oRRIEhtnmkys1rqvypYFnqST6EA/4Ek6ibJltbf55ZdAdds5tKUtqG47xwsFlsugQcp4unrPQUQEUMYSh1A6hjKWOHTooG5z5coVTJ82DU9UDNIcAzRs2BCLFy9GnIt0LCEhARMmTOAqzsPDw/PCW+SDJAE92/2N2nQIEY3/h6eeekqxreDgYOzbt8+tbTnJzs7Gr7/+qlo869q16/17Lhw/jszv1ypdLLW4eJGtPsldLDUY1jMGdWk/hvXkr+4nJCTgzTff5Cr1ypYti0mTJqnCf2RlsegOD8sakTCSiSKKZ0qRd6AwylrqhCGG+bhxXTsm2ZXaHZFEPkgjC5LIB1dqdwQkCVlZWZg0aZJiJchkMuVlZPIQeYGwAS+vLHzxxRZERkbCbrdrfFS9QVTwVOOPPfYYOnbsiIkTJ2L9+vW4cuUKYmMlrrqZKzpKT0cJa7KibQlrcv7+lGAJEtasWYNu3bppHpfZbMbkyZO5A9cHycSJSmPOxInabRMTlXEV9Owpf/0lP6+JiIwcojrmJk2a4K+//sK1a852N0CkHMBMmDBBcb/JkzzosWNHFoiUMSJKly6tVuhB6Znl7c1+lpOamorXXnvNpf8l8dRT+rNjp5eD1ard5tgxfli8Y8eAmzdvom1b5aq2gUqC6Ifctv7mZOz+OX/342+//VblMtejRw8kFnD1/epVpZfRVY7tZ/369Sr3S7kBq0mTJpg5cyZOnjxZ4HcJLxPmqVOn0K1bN87+LCCKAtFxEGWACAgOyn9/6enpmDNnDvz9lQkiTCYThg4dqo7Xp4EkKfv6IJOv8ggKUu4/KEj5959++gmPaqrF8q7XCy+8gF9++QUzZkjcAMS8LJiAexN5J5IEtG3L7qm2bfM/V/IA755C7kmux7x5efu32djPxQ15cHE9KlRw3itnQaSOIxQcHIxVq1bpvgeOHDmCESNGoAwnaLzBYMCkSZNUSU6cfPih8j384Yf6/ZW/2/RITlY+B1qGjz59lO369FG3ycrKwiOPTASRenJqtVbGxIkTVYqbLl2U23Um2UhISMB///tftGjRgvs816gRhlMaq0CJicBjjzHDd/nyHhFm5Ryfsq9aE+qNG/Nil/v5sZ/lSJKEbxcsQDnOcfkQoZfRiM1Ll6rCgwwdqjT8DBvGfn/t2jVMmDABgYGBnHNlhMEwGCdO8A32AwYoj8nVe1TOvn3K50XPHhMbq7wHtdZLZeFR4RyPaeGaHCoyUrtt9erKtjVqaLctU0bZtkwZfru7d4HKlSV4ee2Dv/8QlNT45nh5eaFbt2744YcfMGfOHK5XQIMGDbBjxw7tTnFITFT289atVFUWXLPZjNmzZ+uOv7OysrBr1y5ERUWhbNmyqr4NGTJE8z1UEHr0YP10Lmp5guxs5Thfb5oxefJRmEwvgUi9iG82m/HSSy/ht99+Q2amhHr12Hu1Xr2Hw1AmjGSiiOKZUuQdKIxiMKRyM1nJuXRRQrh5G0bR+wg3b8Oli8rB6k8//aT6WD399NOYP3/+fcmUnWRkSKhc+TiMxjEwm9VyX+dErX379lixYiX8/FJBlA6H4yRWr16DadOmoU+fPqhXr55mLAStUqpUaRC1A9FYEC0A0UcwGGZi3LgpGD9+PMaMGYM333wTgwYNwmshIQikNjBQVxD1hpHeQEXqjkm1a+P999/HokWL8PXXX2PNmjXYunUrfvllDyZN2ohevV7R7VeDBg3w0UcfqVZoCot7VZJ17Kg/kV22TDnYW7YM+P333/Hkk8qseF5eXujffyqMxmgQKWOAfPTRR6rt5qckc5KW5hwYfweivOCkNpsNq12WfU+dYpN4Ly9Wy+cQPPUV0ZOwWE5gxgz9PsyaxbxitYwIAFOS8Qy1zkcrOzsb06dP56R/bwGiATDTVHzxVDMcP34cqTnyefZ/wOzZQLt2aWjUaLDLxMuqGY8jP6KjlYZSToIuAEzFUKeOUxHoB6Lu+OCDr/N1e82PTp2Uk165MuzgwYMqN12lwSwUj3mHY+rUqVi3bh3Onj2bOyhOSgJ275bw3XcbEMLJPhkeHo4TJ04UuL/y61rY9OihNCbxBt0xBw+iM2fl+BEijDOZcHn37ty2W1clwMeYojgmhzEFP6/mq0O0JvI84uKU7wtewGY5BVKSucmwYezZc06itfjsM+VxaXlxAnmxAbt148cG/KfijpJs+XKojKpG4zZUqFBTdb81b94cR48exR9/sBhaO3bcxJw5c1SqD3kJCwtTxGvkcfgwe6dbrax2S0lm0lfQAJ5Tkjnp2BEg2gGWCVR7rDB//nzExsZizpy858VozMQbb2xGz549dRYXfVG69GQkJ2uryn//XRmImxNX/Z55+WW23Zdf1m4TE8OSMvn5sZobzmLHDiT4+WE0sYyFvGMtU6YMhg4din379kGSJDz9tPwelFClyu946aWXuEp+q9WKMmVehsn0B5o1076uq1crr2t+SrKgIPa8BAVpG1SBginJ5M9VfkoyeV89pSR77jnlWKB9e367VauUBpoVK9KwZs0adO7cmXsNeKVmzZpYv379PY1Z4uOV58rpCfn999+rxuYREREKRWpWVhZ27NiBwYMHq5T98jJ16lSPiAcyMpR91XECKTCvvsrO/6uv6rfr2dO5/2sgmgibjX/c1as/DZPpMxClwGplirKiRhjJRBHFM6XIO1AoB0na2e+cZGdD8VLmrTBcunQJ9erVU70kbTYbevfujV27dhXoA5GZmYldu3Zh1KhRqFSpquaHJzQ0FLNnz87NnDZnDkAk5Q52XGPWS5KEGzduYMeOHahdexGIBqNUqYY6A8eiKaGhoZg2bRrOOx35Y2JYpFY9N8t7YMoUNuCcMkW/XevW7Nq3bp3/NiWJrczld7kvXFAOoJzuG3fv3sW4ceM4gyOlYvErTjyYrCxgylvxaFErDlPeis935SotjYWA2bFjt8LQazAYFIrI27eBkiUkeFmzUbKElBtaZdWqVSr1lcEQCYMhCRaLfvyygjB0qPIZHDpU3eb3Vas03eDkpXz58mjRogXq138dFst0ENVW/L1SpUpuuynw0Bpw8li9WgJTkKSDiJ9Vr6DcucMMY4GBrObZ6Xdt2IBaBveSAnh7e6N27TB4e78Ck0kdiyYkJAQ//vjjPQ+ABwxgA1M9pcGDIimJDXiDglitlez1dlhbLCAzHidCU2LZreLJirh64Yp22Q0bo4NhAxyUCDOlw0GJ6GDYAKkhJ2A07l1J1q5d/u8Xdw1anubAAb6K48ABfnt5llkfH36W2X8rwcHK8+QsQUGZWLhwoWrxzWg0wmTqC6OxPbTU4uXKlcOYMWNw3B1XpBwOH2YKsvwMZECeYZcTp1rFvHlMPZOfkrBNG7ZNjVjpAICWLZ3nJxvMWPYa2OKC+hyYzWY88khHGAxLQTQKRPxJrNFoRHj4syhffjkslhTUqKE/6U5MZAoyTyvJnLijNImJYQoyzXivly+zGGBEuEiEqUSopvN+r1KlCkJCJoKpiT8HUS3N+2rKlCm4efMmsrOBmzfzfwetXs3e63oGMifJyUxB5k7OqXxjkoF515Uty949Zcvqe9tJEjOMRUayWm+7TPXF7sUqVXRikoEZ0ORjPC2DWvnyyudfnjA2NjYW8+bNQ1hYGPe6BAcHY+nSpfel0JIk9k0hYmE95Md//vx51dwmMPAx9OnzX7z00kCuetVZHnnkEQwYMAB79uy5577xcCrJevTw6GYBuBel5aOPlNd19ux0rFy5Ek2aNNE4Fz4wGmugdes26N27N0aPHo0PPvgAK1aswM6dOxEdHY2EhASPeiBpQcJIJoooHilF3oFCOUjK3y1t+nQoPmBa6pi0efMwymyGTeODERISgpkzZ7IA/3v2sFFfmTKs3rMHCQkJ+P7779G7d29NuTURwcvrEQwf/iaOHDmifKkmJSGizH5FXyPK7OPO/NKXfadol7p0JU6cOIGvv/4aI0aMQPPmzQusOrvfUqlSJYwbN045uE9NZbNXebTcnj1zA2reL07XBX9/7TYPcuVKvl1Xjh07pjEw8sILgWPUI/SkJEwJWZqTYAHwpmS898RXmjN/SQK2bgUWLmT16dNnUKWKMk5ZVFQUstLSsPX5BfDNibPkSwnY2Gku3nSJ2WS1WvHyy4tgszEjrbe35ya8M2YoByXcZ3DHDvzt64vn7+MefLFLF25A2Zs3gbFj2aM6diz7WYsDB5SKHy3jgCSxWM7ye6BJE40B+vLlbEZtNLJaM6WWmxw+DMnXF/MoGIFUFybiK1T1ir+/Pz744AN1nDRJAvbvZ+n19u//V0iDli1MwFZjOFLJhjvkj1SyYasxHMsWyhRiJ08C3t7IJgN+ptaYRW/hZ2rNMnHa7VyLcUYGc7czGlmd37tFkqDIXKtFcrLyHvRUsmN3CA9X3tPOEh7Ob9+tm7Jdt246Gz9zBti8WTsI/D8MV2Oiszhdw+Li4jB8+PB81SR2ux2RkZHYsmVL3mT5zh3mk+4BNbuT1FRlP/U+w5LEVMJErNa7Z91x4axYkXeuUlG69Hfo1KmT24obIpZZb/bs2bh+/TqiowGbF/tm2bwkTeWvk8REpiDztIHMk5yvHoFUysvsJxFhD1nwesnHucHH9UqzZs3w3XffKWOO/fEHW9H544+iO0g3ePNNppB8803PbbMg48FatZRta9VSt0lPlyclYsVg4EcpOXHiBEaMeBtEQSCqAKKP8fff+YQzOXuWpQjXiDcLsNCdwcFszBYcrA7lmZ6ejrfeesut+yUwMBCDBg3C9u3b2bvo2DEmlTt2TL+fbpKVxVwXLZaic2Fs3Fip/GvcOO9vR48exRtvvHFPwgOr1Rsm0xOoUaMDRowYgUWLFmHbtm24dOmSx8LMCCOZKKJ4phR5BwrlIIm5O+jRoQMUHzBe8E1cvJirwY4jwnwiPK2RtcZsMqGLyYRNRLiQ07ad0QiL7iDPG0RdQfQDbLYMTJ3K6UPHjphrGgFvSgIR4E1JmGsaoY7EvXIl4O2N7rQSREB3Yj+7ZkDLzs7GwIFnwdzx3gHRABANA9FoNGo0Du+++y5mzJiBDz74AAtmz8anZjO+IsIKInxJhAVEmEmE8SYTRg4Zgv79+yMyMhKdgoPRwmCGgeqCqA5MNBS7LH6QeFISp4FMfgFsNs2gEVlZLGZYkyas1vuAZmUxZZjJxGqttsePK+X1BVigz5e6ddl269bl/z0zMxOzq1WDMdcd0hdEO1DXcEAdhLdDB7Qw7FScqhaGndwbVpJY7BcfH3ZMPj7s55iYWDRu3Fhx73V8/HFctpVFGboJX0pAKTqEMIMyC9jjjz+OgwcPYtYs5aV6/33PnKd9+9gtajazmhuz5PJlSF7sXjlLhA1E+JAIQ4gQbjCiclAQxyXTWSx43tga0rhxqs3evAk88ojShfGRR7QNZc4Bp48Pf8DpZNYsfoZylevp8uX8zAU6hrKbN1m8aI18HEwa6LLzeCLsIcKnZjOG9e+PVq1aaawSG9GvQiXE8NI1XbsG1KzJDt7Xl9U1a7Lf/4P5/Xd2yoPoAlrRNgTRBfj4uLhb/fADy4wlv07OSaqfP/DDD6rtnjundPc8d06/H5LEFCR6BofkZObe5nTjM5vZzzxDWVYWU9G2aMFqT0w4ypXjngKUK8dpfPs2llSdDh+ZYX9J1RnqLIB37jALtTxbYMuWHjUAFQXBQdnccxUcpJwQnTx5EuGtW6uexTq+j+Lzjz9GgjxjW3o60K+fcmGpXz/d2KBJScDevdpKSifx8XnXt1w5bZWsJLFPtDyGXWSk9n07ZAj7Dg8Zor3vsDD+feX8dsbGxmLBggVo0KAB9x3vZSiFUVFROCrPIpGaioxukQg1/AEbpSLU8AcyukV6bBGuqNj8XSI2mSIURv1Npgj89H0isrKysG3bNrz66quaC6FeRgte79tXea4A9kGpW1eZXbBu3ftPY/6AcCfmaUE5fFj5DOipLwcPVj4Dgwfz2+XFJWSlQgWNDcbFAc2bowKx5EQV6DLQvDnf9/7OHeaPKn9ntmrFfWceOKBU82ot7G38/nv4mHxU98ujgYEYMmQIdu7cmWekv3WLPbTyeyUsTGdQ4h6XLimva1G4MPr6Kq+Xr6+6Tdz582ji1weuIVLutdhsNtSsWRMvvPAC3n77bSxZsgS7du0qcJZQYSQTRRTPlCLvQKEcJOXvbjlvntIl5uOPOY1++YV9iBQTIz8cXLQIAwcOVLmkuVPKlSsHi2UgiDaC6K7ipezn55KQ7OpVlr6YCHNpKCJoA+bSUKZisNmU0cMrVcrdUDrJgj1VqqQ6rF69FIeUW3hxM861fh0pZFc0TCE7zrXpn9coISHX6JVMdmykZ5Hs/B+bTbk8GxOjNpA5i83Gdb2cOFF5rSZN0r6uU6Yo2773Hr+dnhT+fnC6egUH67h6Xb4M2GwYQGNgobkgugBvSsbbNJ2dg8uXWbsrVwCbDVNonMJI+h69w9rlpLF3snUrPxj+zz8zd8/u3bsrJ2NEOEn+mEq1UJKUxt9OnTohLi4Ot27lGROdxW6/7zFRLnv3MgWZXlDf6KrtFSvoIEIqeSE6hBkUMzIycObECWz28sJcIjSlWgiizhhBkcgmA/OjcZlMjh2bNyhzFquV/V6LlBQ20NQykGVlAQEB/Fs7IMDFWKHlk6Xx4rp5kyk3fH1Zral669MHkk15wSSbHXjlFUWzW+Hh2G6xYDZZMYAqYC/ZmUWnc2fl9iSJGcScshBnMZuB0FDNGfKRI8x9QSMJqoLjx4EFC/I3VE+cyI5fL8lGQck3ftjJk8i02rnXKtPKV5JlZrLAz2Yzq/WSTEoSswuZzazWMjgsWMC3qS5YoG7r7juwIBRISda0KSSLFUuoH7rRt1hC/SBZrECzZsp2LVvyH8KWLe+/w0XI8vof5ip/c68VJWN5fXX0fKlNG/xoNuNZ8kcE1cUmqsrOQdu2yob9+vFfxP36cfuQlMRUWg4Hq/UMZS+8oDTqasXQ++kn9ffF4WC/v1cK8ho8U78+xhh9UJJC4UUvIowm45a5DFTptl96CbDZkEFmRFNVZJCZfS91/LhSU4FDh9y3o7mbyM/dMA3ubqtaNaA8XUZz2oHydBnVqqm3nZqaiu/r10dnoxF2MiGASuM1egK3tS5u3br8AKH16mn2Iz7e82LinTuBCRPyjw/bvDlT+jRv7pk+OOP3yVVEWnH84uPZ8yRXSQYF8Q3Lx48DpUuzdqVL63zfmjfPfQ8mko/+e7BVK/47kxMzJCWFPUc+PqzWTArdvj12mKvBRBEwUFWYaSCWmiohixdsLSxMHXDRbGa/55CdDUydyro9daq22+O33yrP/7ffavQ1h19/BSZPZrWnqFNHeVh16nAaNW+ODebnYackEN2AjXZjorEhPn/ySbz33nsYMmQIunTpgkaNGuHxxx+HzWZza17oWlYUMCuOMJKJIopnSpF3oDCKw3EQ+SWCkSRmGOvYkdXcj61MSaYYmOYscyQnJ+OLL75QqXRcyzPPPIMJEybg4MGDuHVLUilNnMXLy8VGtGuXykiXW/z92d+duE5inYXj6xAaym8aGqo+BRPGpuMTeh2pZEM8+SKVbFhM/TFhrMzocPKkehnGWXx92d+dHDiQ5w/JOybOchfPfU2LFi2UbVu0ULdJS3NfCl9QevbMP2g4duwA/P2RQWaMoemoS/vwNk1ng3l/f+TevDt3Av7+yCIj3qP/QwvajvfoHWSRkbVzGU0uXMi/XRcuZH/Pzs7G22+/rbg3HyGCQfaziQizxozJdfn93//Ut6C/P/u9R3DDbeDFdonYQMoV9A0UgRfbyYyvV6+qrQhya4JLOsq8WDjKcj/z87g49fhVPo5VLPbm55PlwoYNeY+Yry/7mUtaGjaVeQV3yQvx5Ie75IVNZV5hN738XOkZquUKsf371TNjZ/HxYX934cgRZUIIPUPZ8eNsMzYbq/UMZfLj9xTvvaccnPMMShcea4Q0Uk4i08iCC4/xY5JduMCMB97erHbGJeQRE6NUh2mJN1yzBTqLi+0TgHvvwIJy4AD/3aJ6XUdHqxvK/8Hp93bmjH67f6rr5c2bgJcXltNLCKbzMFIWguk8lhMz3Cis2+fP6z+HTlXnnTv67Tgqkr17lcHo9+7V7rKrSpCrDgRLLMATgM+fz28/eDAbfmgpbQC16tZZVNlb3b1fbt3SP1ecByw1lcWj8vFhdX6Gsq5d2ea6dtVvJ0/406GDZww6DRsqD6lhQ06j69f1z4E8WdIff+h/N11cLyUpL3D9c895zlC2c2eeoUovkdLcucoFgLlz73/fERH88SDPu+T99/nvwdmz1W3XrlX2de1azs5Pn9a/VvL34Nmz+s8AZwyV38IeLl3K3f9JehKLqT9O0pN5+3cu2ALMtVLvXuG4Xk6dqjwHPG+ZuDj1YXl7ayex+fXXvHvF29tzhjKZzgBEHH3B6dO5Hd1AERhGH2EDReh+s6QGDXDHaMRhInxrMGBKhQro06cPGjZsiNKlS0NrznhQL9MHB2EkE0UUz5Qi70BhFIfjoO5qVIFYtEgpbV68mNvsZL16GEWEQCJ4ESGcmGviJZdRTGYm4OfLd8fw883mKsk0P6AaSjL9Nz3L8uJqUzOZ+IvSq1axwaMfxaM6HYcfxcPHh/0+F5mSjNtPuZIsNvZfrSRzXRkPDuY0ylGSaZ4DFyWZZrsCKMnkLJ47F0bOh7kcEX61WhU+XA9MSXbnDlv9lD9brVtzJ3yTJjEDgnwF3WxmK4m5pKXlzQpdi4eUZPnxIJVkt24xBZmPD6v1zr/DAZSg23iajqAE3YbD4dLg11/1je/yUec33+gbwJcvV+3/o4+UhmJOstZcFizIu8VtNr4yysmDUJLt3s3eE0Yjq2VJLXNZu+Rv7DE2QgrZcYf8kUJ27DY2xrrP+RlLly1TGii0MvsB/xwlGcAmgnIFBW9iiB9/1L+3Nm5k7TZv1m+3ebNnOl3Y8FYV5Mclt1Zt3arf1pnB8tgx7YUlPz9uDKmHQUnmTkyyJ5/kH1a1ai4N3b1f9u3Tb8cx6h86pHRL08vvcveucpN6irLERGXMTU/EOxs/Xvlsc7Os7t6tfw7kL7k1a/TvLZesM/HxymMqoFeYJhMm5O3WYGA/84iIUHbRNTrFveDqFuksFSuq2774Ir8tL+biwIHKNtwA/wV5D/70k37be5F0bt+uv0252mDVKv17hZPJoVUrZTNX0SfAFtFcN+vnp724NnmycmFLMQ68RySJn5hG8T3etEn/XG3apN4wb0AuIy4uDvv27cOyZcswYcIE9OzUCWGBgUgoYFxAYSQTRRTPlCLvQKEcJHlm9TyXS5eY66Weo/yePbmjF0k+i+FkgHkv6FOuO8bUxz9Vb7djR2RblMut2RYvzZhkyo2qY5IBbOzNe8/zsspnZjLllnwQ2aQJx4Vo4EC+NWXgQPVGeTHJ7HbdmGSTJrH9TpqUf0yyyZOZB8HkyfoxycqWZR/ZsmU9F5PMmU3IWdq102gYEcEPXsWJScZtpxGTjLd/3sR7Q8dO8KE8A1lrIlwy2JE9VJ0278sv2STeaGS1Xip1t2nd2m23gRkz+Cu9qkD/48ZBcnkGJI2ZxM2b/PGLXvB+d5g1S71db29OTLIVKwoUk2zLFvZnm43VW7bw9x8dzR/sKQJXX7tWqEoy1xA4cgqiJHMXSQI+/ZRN9j/9VF/tkJQEjBgBPP00MHIk35iQkQE0aAA8Yz+FDvQDnrGfQoMG2sGdL1xgxjyHg9V6SjJnf92NSSY3ZujFJBsxAnjiCXZMngqCfOOG0uU3J/myEqEk05dHFZKSDChYTLIuXZiCrEuX/GOSORxstw4HC92gdd+6oyRbv56vTlu/3qXhP1xJ1rGjZ1RXmZnsc9aoEau5rtyeVJL9+afqmJxKsvbtPasks9uLoZLszJkHqiQD8sns6O6CLfCvV5KFhCiNbyEhLg1kSjK3v1kNGypXCxrx1ef3izCSiSKKZ0qRd6AwisNxwHNKsgIg7d6DoyVb4iaVwdGSLSHt5qRIjo6GZLNjKr0DP4qHF6XCj+Ixlcay2EEuaZgy7yRho7mTwtVso7kTsuLVI9+DY1binKESMsmEc4ZKODhGbSAD2ECXNyjo1Yt/XBkZeRl9atXSmBxmZDCDmM3GZlE2G/uZ1zg1lXXCZmMrMM6g/R4IrJuezlah7XZWa7lQXryYF4Pc11c/lbi7ZGfnZf5ylhIlNAYpiYm4EaZ0IbwRFsHNbokOHZTnqiOH9AAAAA2TSURBVEMH7syHJ6ZyOJSedk7Wr87EOHMv9CUTZpMViWTDAssw/LBGPeoeOVKp9hk16h5PkJMCDPYkKS+uh2spXdplkJ6djU21xyGJfJBM3kgiBzbWGc+9ACdO8Ae7J07c36FJEjOIBQQwm19AAPuZO5lYsYIpx4xGVmsYyFJS+AY9ngvFyy/zjWS9e7s07NSJb3zViknGi0PiRkwyPQOZE3djkrnLp58qB+afctYeAPYIVa2qbFu1qrahbNo0tvgybVr+GSsvXGAKsvwMZAVh40ZlwGinKMuVM2fYc+/jw2pP2ptu3ADWrdMwkDlp1oxvAC8mMcnQrRvf8sOTm7Rtyz8H9xmTrKCkprIYTPl9giWJGQQ6dGC1npEkM5MlrdCLyQewyaj8sJ54QqOhVjwmjZhkqvPvoZhko0ax3brzHYyKYnPjqCjtNs5FwKZN9Rf2ALZOW6sW23+tWjrrtl268M/BvyAm2dy5zLA1d27RxCQLDlYmfQoO5huWz57N81goX14nokTz5ixmo+z8S5b7j0l2+jT7nhkMrD59WmP/7dtDchkLSLwFW+CBxSRbtUqZC4AjSlPwIGKSnTnD3kVGI6u5301Z/Di3vlnXrzPDmI8Pq+UGag8ijGSiiOKZUuQdKIxShxtx8cHjlhR948ZcyW4mmRBLpZBJOSsNcneUHG7cYGObcnQVTWkXytFV2Gz8ScqwYVC8u4epRUEACuDiUJDjcpKQwGKQueNbEBvLAiZwXCzvFbnxQ8/osXy50iWK4zVWYP7+W213sFrZ73lERSldCIcO1dn4lSts9OjiYilHK4QezwA4aRL7uzclozKdyVU28lxZa9dWbrN2bf3zkC8FcBtISdEPt+dqKKpRA7BSGsrRVVgoHTVq8Luwbh1f4r9u3X0eWw5ZWUzg4QkVz+nTfOMnb9BbvTr/XFWv7tIwKYkZxOTG186d+Raia9eYQUye3TI09KHNbplvMP4c5s/ni/l4MZbcysT6gBk/XtlXrqsVgC++UCp/v/iiMHsJlsWyWTOlK3WzZsUmuyVSUvIMZc5nq1s3vlU7Pp4ZxORt27ZVf2TvIbuluxRESfXbb3nuyXY7+5lHZiZQqhS7B0uV0jeU5eORlIe7mf1SU5lBTH5Oe/TwWHZLuZozP+TxBrWYNElpqNdzH6tVK28sZjIBzzyj0TA5Oc9Q5jwHL77Il57GxDCDmNxKUa/eQ5vd8kFw+DAz/FasyGq97Jbx8cxA3K0bq7XGw9WqKQ1vWuNrxMXhj9ItFQumf5RuqZndUmrZCmlG5vqfZrRDasUPU+E0kDn3X7Wqxv4TE3G+unLB9nx1zoItwM9uWbeuRzI5xcWxxTUtBVlhoWt4jYvjf7OKuNPCSCaKKJ4pRd6BwihFZSRzS4rurjtKDpmZ6jhHqvhGOWzYoBxsaQX3jozkq020lGSSBDz7LGvz7LOeX0H0JAVVkjldojyRcjo7GyhZUnleS5bUXj1bv155vVQuJgXk+HGASHK5rSSuQmfdOv7iMa8PI0cqDY8PXEl27lxu0wIpycDifsj7quXqc+IE30Byv0qyB0FBlWS8eIMqJZmTa9fYcmx+Bi9JYq6Vy5ez+iF+CbirJHv5Zf591aePuu3MmcoJ78yZD/YYeGzfrjyu7dv57R6kkqxAREezRR+Xb5qKM2dY7J1/qoulFjdvMn9Hd3y4z59n8Q6cLpZa3LnDXOQ8aEg8fFhpVNUzEEyZkjfxNhrZzzzOnVM+U7JXugqnSt1ZatXKp8Pu3i8xMexd5WFjz6hRzEDmzndw6FD2vtBbAGvaVHn8TZtqt+WJWHS5fp3FIHNHwfLHHywGWQHjIQn48L7DWpQtC1SmMwinzahMZ1C2rHbb27eBqsazaEc/oarxrGrtAWBjTp63iNZnOyJCuWCbb6y3Y8eY3IvjYlksOH2axSB7SL5ZwkgmiiieKUXegcIoRWUkA9yUojdvjmwXeXW2xcqkvBx272aGMZuN1bzg0k42bGAKMs3sdwD++otNnuTqMIeD/f6+jushIT2dGTvyW2S/eJHN+T1hIHOyfz8zjFmtrOaEbFKwfj0bQN+vgQwAXu6cACNlKY2flInezyeo2h46pB5EGY38oMXp6WxCULs2qz2RBZQbk8zLi+s28PHHfIPWxx+rN3v3LjOM1ajBar3AylOnMkOac0GUFy/jYWHLljyjh4+PfkwyX19lGAxf3/ztFP8m3I1JpqUk4wXDfxiUZAAzjI0fr20gc3LmDFOQPSRjeMFDTEGVZHZ7wZRkpUvrK8kuXlQafz35Pf4nMHlywZRk8ne7ppJMUOTIlWRGo46SDEzE7RwOWa3qqAdyJImJTU0mVmt939xWkuHBxHoTFB7CSCaKKJ4pBgD0bycsLAwHDx4s6m5oExdH2Z27UMbu/ZQOK3kZMsjauB6Z1q8hKlmS+y/Z2USxsUSPPEJkMt1/F6KjiaZMITp8mKh2baL//IfoiSfuf7sCIkkiunOHqEQJIqOx8HZaw+ssncwKUf2puuU0nUiroujMwoVEQ4aoN7NgAdHgwQ+yoznExxN17Uq0Zw+R1UqUkUHUqBHRqlVEAQGKpgDr1+TJeed14kTWf4Ph/rpx8iTRmTNEVasSVa9+f9t60KSmEl27RvTYY0Te3trtTp8meu89okOHiOrUIRo/nihEfVsUe5KT2bvv2jV2br292bk9fJjI4VC337+faOdOohYtiOrVK+zeCgQPjrt3if76i+jJJ4nsdv22v/+e9xw0aaLdLiuL6PJloooVicxm/W1mZhJdvEgUHExkseTf38zM/NsB7DMTEHD/34kHSXY20dSpRNu2EbVpQzRunPYY7/JlouefJzpxgqhGDaJ169j5FTx8nDtH1LEj+x6HhBBt2EBUuTK/bXw8Ud++7BtTrx7Rl1+qhkEKgLyxkNa9feYMUUQE0dmzRFWqEG3axGqt7X38MdHWrUTt2hENHfpwPzMCJQaD4RCAsKLuh0DwT0cYyR4i0v44Tdd2naXHmlch21NiFiu4D7Zto97PxdKKrG6UTXkzEhNlUaT5O1q6uQwbgefQtSvR6tXqzbz4IrNTFRrnzuWN4rRGkDkAbDJnt4sBnMAzJCcTffUVm5zUr0/Upw/fQCYQCB4OevYkWrmS1cuX89sAROHhRNu3E7VqRbRli/hmCIonklSIi7WCIkEYyQQCzyCMZALBv5HFi+nMmwuoTvpuSiVvyiYzmSiLvCmVDnk1pqpzo4gGDMhtXrs20ZEj6s3Urs0USAKBQCAQPExkZjLhsZOMDL6i7M4dprrPzmaqrNhYproRCASCfxvCSCYQeAaxniDIJSmJebslJeXfNiuL6MIFNugUPIRUqUJVrZfoENWhSFpO1ek4RdI3dIjqUFXLRZVKq25dtQuM2UwUpvGZBZjqphjY2AUCgUDwEGKxMAUZEau1XC4DApiCzGQiat1a33VNIBAIBAKBQCjJBETEDGPVq+fF7Dh5ksjXl982K4u5IjnjYOzbl3+MEUEhI0lElSqxoCXyZ9xgYEFLzp9XaO4vXiSqVYsoMZE1NxiI/PyIjh5lcWHkACwOyoYNLMbGunXCdUUgEAgERcO/KSaZQCAQ3A9CSSYQeAahJBMQEdGff7IBZHIyq//8U7vtlSvMQJaezuorVwqvnwI3MRpZ5N+KFZm1025ndcWK7PcuQSmCg5lBzGlPA/gGMiKilBRmIANYnZLywI9GIBAIBAIu7gT3Nxj0A5sLBAKBQCAQOBFGMgEREYWGshVWh4PVoaHabStWZAoyLy9Wi2xKDylVqjDF2Jo1RB9+yOrz5zVTGgUHEw0axFxSBg3iG8iIiHx8mILMYGC1j88DOwKBQCAQCAQCgUAgEAgKjWLhbmkwGJKIKLqo+/HwE2AkCrMTHbxLFC/ptzUTUWUr0dkMIhGY7CGmNBH97fnNGojI10iUJBH9+98hgn8sD+j+FwgeesS9LyiuiHtfUJx5AoBGwByBQOAuxSWSVLTwzxYURwwGw0Fx7wuKK+L+FxRXxL0vKK6Ie19QnDEYDCIIt0DgAYS7pUAgEAgEAoFAIBAIBAKBoNgjjGQCgUAgEAgEAoFAIBAIBIJiT3Exkn1a1B0QCIoIce8LijPi/hcUV8S9LyiuiHtfUJwR979A4AGKReB+gUAgEAgEAoFAIBAIBAKBQI/ioiQTCAQCgUAgEAgEAoFAIBAINBFGMoFAIBAIBAKBQCAQCAQCQbFHGMkEAoFAIBAIBAKBQCAQCATFHmEkEwgEAoFAIBAIBAKBQCAQFHuEkUwgEAgEAoFAIBAIBAKBQFDsEUYygUAgEAgEAoFAIBAIBAJBsUcYyQQCgUAgEAgEAoFAIBAIBMUeYSQTCAQCgUAgEAgEAoFAIBAUe4SRTCAQCAQCgUAgEAgEAoFAUOwRRjKBQCAQCAQCgUAgEAgEAkGxRxjJBAKBQCAQCAQCgUAgEAgExR5hJBMIBAKBQCAQCAQCgUAgEBR7hJFMIBAIBAKBQCAQCAQCgUBQ7BFGMoFAIBAIBAKBQCAQCAQCQbFHGMkEAoFAIBAIBAKBQCAQCATFHmEkEwgEAoFAIBAIBAKBQCAQFHuEkUwgEAgEAoFAIBAIBAKBQFDsEUYygUAgEAgEAoFAIBAIBAJBsef/AawlO5wJNCxjAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 1440x576 with 5 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# NOTE: This will take several minutes depending on the performance of your machine\n", "audio_features = audioAnalyzer.analyze(audio_filename)\n", "\n", "# plot the features\n", "plt.rcParams['figure.figsize'] = [20, 8]\n", "\n", "audioAnalyzer.plot(audio_features)\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "... or call all the methods individually" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "- Getting relevant metadata of ../sample-data/ussak--sazsemaisi--aksaksemai----neyzen_aziz_dede/f970f1e0-0be9-4914-8302-709a0eac088e/f970f1e0-0be9-4914-8302-709a0eac088e.mp3\n", " The call took 1.48 seconds to execute.\n", "- Extracting predominant melody of ../sample-data/ussak--sazsemaisi--aksaksemai----neyzen_aziz_dede/f970f1e0-0be9-4914-8302-709a0eac088e/f970f1e0-0be9-4914-8302-709a0eac088e.mp3\n", " The call took 45.73 seconds to execute.\n", "- Filtering predominant melody of ../sample-data/ussak--sazsemaisi--aksaksemai----neyzen_aziz_dede/f970f1e0-0be9-4914-8302-709a0eac088e/f970f1e0-0be9-4914-8302-709a0eac088e.mp3\n", " The call took 7.13 seconds to execute.\n", "- Computing pitch distribution of ../sample-data/ussak--sazsemaisi--aksaksemai----neyzen_aziz_dede/f970f1e0-0be9-4914-8302-709a0eac088e/f970f1e0-0be9-4914-8302-709a0eac088e.mp3\n", " The call took 0.05 seconds to execute.\n", "- Identifying tonic from the predominant melody of ../sample-data/ussak--sazsemaisi--aksaksemai----neyzen_aziz_dede/f970f1e0-0be9-4914-8302-709a0eac088e/f970f1e0-0be9-4914-8302-709a0eac088e.mp3\n", " The call took 0.39 seconds to execute.\n", "- Identifying the transposition of ../sample-data/ussak--sazsemaisi--aksaksemai----neyzen_aziz_dede/f970f1e0-0be9-4914-8302-709a0eac088e/f970f1e0-0be9-4914-8302-709a0eac088e.mp3\n", " The call took 0.00 seconds to execute.\n", "- Computing the note models for ../sample-data/ussak--sazsemaisi--aksaksemai----neyzen_aziz_dede/f970f1e0-0be9-4914-8302-709a0eac088e/f970f1e0-0be9-4914-8302-709a0eac088e.mp3\n", " The call took 0.04 seconds to execute.\n", "- Computing the melodic progression model of ../sample-data/ussak--sazsemaisi--aksaksemai----neyzen_aziz_dede/f970f1e0-0be9-4914-8302-709a0eac088e/f970f1e0-0be9-4914-8302-709a0eac088e.mp3\n", " The call took 2.70 seconds to execute.\n" ] } ], "source": [ "# audio metadata extraction\n", "metadata = audioAnalyzer.crawl_musicbrainz_metadata(audio_filename)\n", "\n", "# predominant melody extraction\n", "pitch = audioAnalyzer.extract_pitch(audio_filename)\n", "\n", "# pitch post filtering\n", "pitch_filtered = audioAnalyzer.filter_pitch(pitch)\n", "\n", "# histogram computation\n", "pitch_distribution = audioAnalyzer.compute_pitch_distribution(pitch_filtered)\n", "pitch_class_distribution = copy.deepcopy(pitch_distribution)\n", "pitch_class_distribution.to_pcd()\n", "\n", "# tonic identification\n", "tonic = audioAnalyzer.identify_tonic(pitch_filtered)\n", "\n", "# get the makam from metadata if possible else apply makam recognition\n", "makams = audioAnalyzer.get_makams(metadata, pitch_filtered, tonic)\n", "makam = list(makams)[0] # for now get the first makam\n", "\n", "# transposition (ahenk) identification\n", "transposition = audioAnalyzer.identify_transposition(tonic, makam)\n", "\n", "# stable note extraction (tuning analysis)\n", "note_models = audioAnalyzer.compute_note_models(pitch_distribution, tonic, makam)\n", "\n", "# get the melodic progression model\n", "melodic_progression = audioAnalyzer.compute_melodic_progression(pitch_filtered)\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": "3.6.10" } }, "nbformat": 4, "nbformat_minor": 1 }	agpl-3.0
slundberg/shap	notebooks/api_examples/plots/scatter.ipynb	1	2028093	null	mit
ES-DOC/esdoc-jupyterhub	notebooks/fio-ronm/cmip6/models/sandbox-2/land.ipynb	1	173506	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Land \n", "MIP Era: CMIP6 \n", "Institute: FIO-RONM \n", "Source ID: SANDBOX-2 \n", "Topic: Land \n", "Sub-Topics: Soil, Snow, Vegetation, Energy Balance, Carbon Cycle, Nitrogen Cycle, River Routing, Lakes. \n", "Properties: 154 (96 required) \n", "Model descriptions: [Model description details](https://specializations.es-doc.org/cmip6/land?client=jupyter-notebook) \n", "Initialized From: -- \n", "\n", "Notebook Help: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "Notebook Initialised: 2018-02-15 16:54:01" ], "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', 'fio-ronm', 'sandbox-2', 'land')" ], "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 --> Conservation Properties](#2.-Key-Properties--->-Conservation-Properties) \n", "[3. Key Properties --> Timestepping Framework](#3.-Key-Properties--->-Timestepping-Framework) \n", "[4. Key Properties --> Software Properties](#4.-Key-Properties--->-Software-Properties) \n", "[5. Grid](#5.-Grid) \n", "[6. Grid --> Horizontal](#6.-Grid--->-Horizontal) \n", "[7. Grid --> Vertical](#7.-Grid--->-Vertical) \n", "[8. Soil](#8.-Soil) \n", "[9. Soil --> Soil Map](#9.-Soil--->-Soil-Map) \n", "[10. Soil --> Snow Free Albedo](#10.-Soil--->-Snow-Free-Albedo) \n", "[11. Soil --> Hydrology](#11.-Soil--->-Hydrology) \n", "[12. Soil --> Hydrology --> Freezing](#12.-Soil--->-Hydrology--->-Freezing) \n", "[13. Soil --> Hydrology --> Drainage](#13.-Soil--->-Hydrology--->-Drainage) \n", "[14. Soil --> Heat Treatment](#14.-Soil--->-Heat-Treatment) \n", "[15. Snow](#15.-Snow) \n", "[16. Snow --> Snow Albedo](#16.-Snow--->-Snow-Albedo) \n", "[17. Vegetation](#17.-Vegetation) \n", "[18. Energy Balance](#18.-Energy-Balance) \n", "[19. Carbon Cycle](#19.-Carbon-Cycle) \n", "[20. Carbon Cycle --> Vegetation](#20.-Carbon-Cycle--->-Vegetation) \n", "[21. Carbon Cycle --> Vegetation --> Photosynthesis](#21.-Carbon-Cycle--->-Vegetation--->-Photosynthesis) \n", "[22. Carbon Cycle --> Vegetation --> Autotrophic Respiration](#22.-Carbon-Cycle--->-Vegetation--->-Autotrophic-Respiration) \n", "[23. Carbon Cycle --> Vegetation --> Allocation](#23.-Carbon-Cycle--->-Vegetation--->-Allocation) \n", "[24. Carbon Cycle --> Vegetation --> Phenology](#24.-Carbon-Cycle--->-Vegetation--->-Phenology) \n", "[25. Carbon Cycle --> Vegetation --> Mortality](#25.-Carbon-Cycle--->-Vegetation--->-Mortality) \n", "[26. Carbon Cycle --> Litter](#26.-Carbon-Cycle--->-Litter) \n", "[27. Carbon Cycle --> Soil](#27.-Carbon-Cycle--->-Soil) \n", "[28. Carbon Cycle --> Permafrost Carbon](#28.-Carbon-Cycle--->-Permafrost-Carbon) \n", "[29. Nitrogen Cycle](#29.-Nitrogen-Cycle) \n", "[30. River Routing](#30.-River-Routing) \n", "[31. River Routing --> Oceanic Discharge](#31.-River-Routing--->-Oceanic-Discharge) \n", "[32. Lakes](#32.-Lakes) \n", "[33. Lakes --> Method](#33.-Lakes--->-Method) \n", "[34. Lakes --> Wetlands](#34.-Lakes--->-Wetlands) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "Land surface key properties" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of land surface model." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.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 land surface model code (e.g. MOSES2.2)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.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. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### General description of the processes modelled (e.g. dymanic vegation, prognostic albedo, etc.)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.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": [ "### 1.4. Land Atmosphere Flux Exchanges\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### Fluxes exchanged with the atmopshere." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.land_atmosphere_flux_exchanges') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"water\" \n", "# \"energy\" \n", "# \"carbon\" \n", "# \"nitrogen\" \n", "# \"phospherous\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.5. Atmospheric Coupling Treatment\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the treatment of land surface coupling with the Atmosphere model component, which may be different for different quantities (e.g. dust: semi-implicit, water vapour: explicit)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.atmospheric_coupling_treatment') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.6. Land Cover\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Types of land cover defined in the land surface model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.land_cover') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"bare soil\" \n", "# \"urban\" \n", "# \"lake\" \n", "# \"land ice\" \n", "# \"lake ice\" \n", "# \"vegetated\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.7. Land Cover Change\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe how land cover change is managed (e.g. the use of net or gross transitions)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.land_cover_change') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.8. Tiling\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the general tiling procedure used in the land surface (if any). Include treatment of physiography, land/sea, (dynamic) vegetation coverage and orography/roughness" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.tiling') \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. Key Properties --> Conservation Properties \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Energy\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe if/how energy is conserved globally and to what level (e.g. within X [units]/year)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.conservation_properties.energy') \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. Water\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe if/how water is conserved globally and to what level (e.g. within X [units]/year)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.conservation_properties.water') \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. Carbon\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe if/how carbon is conserved globally and to what level (e.g. within X [units]/year)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.conservation_properties.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": [ "# 3. Key Properties --> Timestepping Framework \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Timestep Dependent On Atmosphere\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is a time step dependent on the frequency of atmosphere coupling?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.timestepping_framework.timestep_dependent_on_atmosphere') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Time Step\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Overall timestep of land surface model (i.e. time between calls)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.timestepping_framework.time_step') \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. Timestepping Method\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### General description of time stepping method and associated time step(s)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.timestepping_framework.timestepping_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --> Software Properties \n", "Software properties of land surface code" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. 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.land.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": [ "### 4.2. 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.land.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": [ "### 4.3. 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.land.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": [ "# 5. Grid \n", "Land surface grid" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of the grid in the land surface" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Grid --> Horizontal \n", "The horizontal grid in the land surface" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the general structure of the horizontal grid (not including any tiling)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.horizontal.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. Matches Atmosphere Grid\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Does the horizontal grid match the atmosphere?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.horizontal.matches_atmosphere_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": [ "# 7. Grid --> Vertical \n", "The vertical grid in the soil" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the general structure of the vertical grid in the soil (not including any tiling)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.vertical.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": [ "### 7.2. Total Depth\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### The total depth of the soil (in metres)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.vertical.total_depth') \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. Soil \n", "Land surface soil" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of soil in the land surface" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.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. Heat Water Coupling\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the coupling between heat and water in the soil" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_water_coupling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Number Of Soil layers\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### The number of soil layers" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.number_of_soil layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Prognostic Variables\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### List the prognostic variables of the soil scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.prognostic_variables') \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. Soil --> Soil Map \n", "Key properties of the land surface soil map" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### General description of soil map" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.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": [ "### 9.2. Structure\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the soil structure map" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.structure') \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. Texture\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the soil texture map" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.texture') \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. Organic Matter\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the soil organic matter map" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.organic_matter') \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. Albedo\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the soil albedo map" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.albedo') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.6. Water Table\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the soil water table map, if any" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.water_table') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.7. Continuously Varying Soil Depth\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Does the soil properties vary continuously with depth?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.continuously_varying_soil_depth') \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": [ "### 9.8. Soil Depth\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the soil depth map" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.soil_depth') \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. Soil --> Snow Free Albedo \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Prognostic\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is snow free albedo prognostic?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.prognostic') \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": [ "### 10.2. Functions\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### If prognostic, describe the dependancies on snow free albedo calculations" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.functions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"vegetation type\" \n", "# \"soil humidity\" \n", "# \"vegetation state\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.3. Direct Diffuse\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.1\n", "### If prognostic, describe the distinction between direct and diffuse albedo" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.direct_diffuse') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"distinction between direct and diffuse albedo\" \n", "# \"no distinction between direct and diffuse albedo\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.4. Number Of Wavelength Bands\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### If prognostic, enter the number of wavelength bands used" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.number_of_wavelength_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": [ "# 11. Soil --> Hydrology \n", "Key properties of the land surface soil hydrology" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### General description of the soil hydrological model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.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": [ "### 11.2. Time Step\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Time step of river soil hydrology in seconds" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.time_step') \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. Tiling\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the soil hydrology tiling, if any." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.4. Vertical Discretisation\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the typical vertical discretisation" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.vertical_discretisation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.5. Number Of Ground Water Layers\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### The number of soil layers that may contain water" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.number_of_ground_water_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.6. Lateral Connectivity\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Describe the lateral connectivity between tiles" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.lateral_connectivity') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"perfect connectivity\" \n", "# \"Darcian flow\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.7. Method\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### The hydrological dynamics scheme in the land surface model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Bucket\" \n", "# \"Force-restore\" \n", "# \"Choisnel\" \n", "# \"Explicit diffusion\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Soil --> Hydrology --> Freezing \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Number Of Ground Ice Layers\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### How many soil layers may contain ground ice" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.freezing.number_of_ground_ice_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Ice Storage Method\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the method of ice storage" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.freezing.ice_storage_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.3. Permafrost\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the treatment of permafrost, if any, within the land surface scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.freezing.permafrost') \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. Soil --> Hydrology --> Drainage \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### General describe how drainage is included in the land surface scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.drainage.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": [ "### 13.2. Types\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### Different types of runoff represented by the land surface model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.drainage.types') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Gravity drainage\" \n", "# \"Horton mechanism\" \n", "# \"topmodel-based\" \n", "# \"Dunne mechanism\" \n", "# \"Lateral subsurface flow\" \n", "# \"Baseflow from groundwater\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Soil --> Heat Treatment \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### General description of how heat treatment properties are defined" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.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": [ "### 14.2. Time Step\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Time step of soil heat scheme in seconds" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.time_step') \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. Tiling\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the soil heat treatment tiling, if any." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.4. Vertical Discretisation\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the typical vertical discretisation" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.vertical_discretisation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.5. Heat Storage\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Specify the method of heat storage" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.heat_storage') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Force-restore\" \n", "# \"Explicit diffusion\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.6. Processes\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Describe processes included in the treatment of soil heat" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"soil moisture freeze-thaw\" \n", "# \"coupling with snow temperature\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Snow \n", "Land surface snow" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of snow in the land surface" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.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. Tiling\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the snow tiling, if any." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.3. Number Of Snow Layers\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### The number of snow levels used in the land surface scheme/model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.number_of_snow_layers') \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. Density\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Description of the treatment of snow density" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.density') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"constant\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.5. Water Equivalent\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Description of the treatment of the snow water equivalent" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.water_equivalent') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.6. Heat Content\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Description of the treatment of the heat content of snow" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.heat_content') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.7. Temperature\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Description of the treatment of snow temperature" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.temperature') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.8. Liquid Water Content\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Description of the treatment of snow liquid water" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.liquid_water_content') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.9. Snow Cover Fractions\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Specify cover fractions used in the surface snow scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.snow_cover_fractions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"ground snow fraction\" \n", "# \"vegetation snow fraction\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.10. Processes\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Snow related processes in the land surface scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"snow interception\" \n", "# \"snow melting\" \n", "# \"snow freezing\" \n", "# \"blowing snow\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.11. Prognostic Variables\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### List the prognostic variables of the snow scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.prognostic_variables') \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. Snow --> Snow Albedo \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Describe the treatment of snow-covered land albedo" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.snow_albedo.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"prescribed\" \n", "# \"constant\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.2. Functions\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### If prognostic, " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.snow_albedo.functions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"vegetation type\" \n", "# \"snow age\" \n", "# \"snow density\" \n", "# \"snow grain type\" \n", "# \"aerosol deposition\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 17. Vegetation \n", "Land surface vegetation" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 17.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of vegetation in the land surface" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.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": [ "### 17.2. Time Step\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Time step of vegetation scheme in seconds" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.3. Dynamic Vegetation\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is there dynamic evolution of vegetation?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.dynamic_vegetation') \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": [ "### 17.4. Tiling\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the vegetation tiling, if any." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.5. Vegetation Representation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Vegetation classification used" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_representation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"vegetation types\" \n", "# \"biome types\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.6. Vegetation Types\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### List of vegetation types in the classification, if any" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_types') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"broadleaf tree\" \n", "# \"needleleaf tree\" \n", "# \"C3 grass\" \n", "# \"C4 grass\" \n", "# \"vegetated\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.7. Biome Types\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### List of biome types in the classification, if any" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biome_types') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"evergreen needleleaf forest\" \n", "# \"evergreen broadleaf forest\" \n", "# \"deciduous needleleaf forest\" \n", "# \"deciduous broadleaf forest\" \n", "# \"mixed forest\" \n", "# \"woodland\" \n", "# \"wooded grassland\" \n", "# \"closed shrubland\" \n", "# \"opne shrubland\" \n", "# \"grassland\" \n", "# \"cropland\" \n", "# \"wetlands\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.8. Vegetation Time Variation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### How the vegetation fractions in each tile are varying with time" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_time_variation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"fixed (not varying)\" \n", "# \"prescribed (varying from files)\" \n", "# \"dynamical (varying from simulation)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.9. Vegetation Map\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### If vegetation fractions are not dynamically updated , describe the vegetation map used (common name and reference, if possible)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_map') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.10. Interception\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is vegetation interception of rainwater represented?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.interception') \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": [ "### 17.11. Phenology\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Treatment of vegetation phenology" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.phenology') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic (vegetation map)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.12. Phenology Description\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### General description of the treatment of vegetation phenology" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.phenology_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": [ "### 17.13. Leaf Area Index\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Treatment of vegetation leaf area index" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.leaf_area_index') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prescribed\" \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.14. Leaf Area Index Description\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### General description of the treatment of leaf area index" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.leaf_area_index_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": [ "### 17.15. Biomass\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Treatment of vegetation biomass " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biomass') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.16. Biomass Description\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### General description of the treatment of vegetation biomass" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biomass_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": [ "### 17.17. Biogeography\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Treatment of vegetation biogeography" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biogeography') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.18. Biogeography Description\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### General description of the treatment of vegetation biogeography" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biogeography_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": [ "### 17.19. Stomatal Resistance\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Specify what the vegetation stomatal resistance depends on" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.stomatal_resistance') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"light\" \n", "# \"temperature\" \n", "# \"water availability\" \n", "# \"CO2\" \n", "# \"O3\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.20. Stomatal Resistance Description\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### General description of the treatment of vegetation stomatal resistance" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.stomatal_resistance_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": [ "### 17.21. Prognostic Variables\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### List the prognostic variables of the vegetation scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 18. Energy Balance \n", "Land surface energy balance" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 18.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of energy balance in land surface" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.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": [ "### 18.2. Tiling\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the energy balance tiling, if any." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.3. Number Of Surface Temperatures\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### The maximum number of distinct surface temperatures in a grid cell (for example, each subgrid tile may have its own temperature)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.number_of_surface_temperatures') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.4. Evaporation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Specify the formulation method for land surface evaporation, from soil and vegetation" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.evaporation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"alpha\" \n", "# \"beta\" \n", "# \"combined\" \n", "# \"Monteith potential evaporation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.5. Processes\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Describe which processes are included in the energy balance scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"transpiration\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 19. Carbon Cycle \n", "Land surface carbon cycle" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 19.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of carbon cycle in land surface" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.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": [ "### 19.2. Tiling\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the carbon cycle tiling, if any." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.3. Time Step\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Time step of carbon cycle in seconds" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.4. Anthropogenic Carbon\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### Describe the treament of the anthropogenic carbon pool" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.anthropogenic_carbon') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"grand slam protocol\" \n", "# \"residence time\" \n", "# \"decay time\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.5. Prognostic Variables\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### List the prognostic variables of the carbon scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 20. Carbon Cycle --> Vegetation \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 20.1. Number Of Carbon Pools\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Enter the number of carbon pools used" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.number_of_carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.2. Carbon Pools\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List the carbon pools used" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.3. Forest Stand Dynamics\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the treatment of forest stand dyanmics" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.forest_stand_dynamics') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 21. Carbon Cycle --> Vegetation --> Photosynthesis \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 21.1. Method\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the general method used for photosynthesis (e.g. type of photosynthesis, distinction between C3 and C4 grasses, Nitrogen depencence, etc.)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.photosynthesis.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 22. Carbon Cycle --> Vegetation --> Autotrophic Respiration \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 22.1. Maintainance Respiration\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the general method used for maintainence respiration" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.autotrophic_respiration.maintainance_respiration') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.2. Growth Respiration\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the general method used for growth respiration" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.autotrophic_respiration.growth_respiration') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 23. Carbon Cycle --> Vegetation --> Allocation \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 23.1. Method\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the general principle behind the allocation scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.2. Allocation Bins\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Specify distinct carbon bins used in allocation" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.allocation_bins') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"leaves + stems + roots\" \n", "# \"leaves + stems + roots (leafy + woody)\" \n", "# \"leaves + fine roots + coarse roots + stems\" \n", "# \"whole plant (no distinction)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.3. Allocation Fractions\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Describe how the fractions of allocation are calculated" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.allocation_fractions') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"fixed\" \n", "# \"function of vegetation type\" \n", "# \"function of plant allometry\" \n", "# \"explicitly calculated\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 24. Carbon Cycle --> Vegetation --> Phenology \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 24.1. Method\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the general principle behind the phenology scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.phenology.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 25. Carbon Cycle --> Vegetation --> Mortality \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 25.1. Method\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the general principle behind the mortality scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.mortality.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 26. Carbon Cycle --> Litter \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 26.1. Number Of Carbon Pools\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Enter the number of carbon pools used" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.number_of_carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.2. Carbon Pools\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List the carbon pools used" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.3. Decomposition\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List the decomposition methods used" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.decomposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.4. Method\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List the general method used" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 27. Carbon Cycle --> Soil \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 27.1. Number Of Carbon Pools\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Enter the number of carbon pools used" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.number_of_carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.2. Carbon Pools\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List the carbon pools used" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.3. Decomposition\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List the decomposition methods used" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.decomposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.4. Method\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List the general method used" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 28. Carbon Cycle --> Permafrost Carbon \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 28.1. Is Permafrost Included\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is permafrost included?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.is_permafrost_included') \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": [ "### 28.2. Emitted Greenhouse Gases\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List the GHGs emitted" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.emitted_greenhouse_gases') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 28.3. Decomposition\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List the decomposition methods used" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.decomposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 28.4. Impact On Soil Properties\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the impact of permafrost on soil properties" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.impact_on_soil_properties') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 29. Nitrogen Cycle \n", "Land surface nitrogen cycle" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 29.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of the nitrogen cycle in the land surface" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.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": [ "### 29.2. Tiling\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the notrogen cycle tiling, if any." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 29.3. Time Step\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Time step of nitrogen cycle in seconds" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 29.4. Prognostic Variables\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### List the prognostic variables of the nitrogen scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 30. River Routing \n", "Land surface river routing" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 30.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of river routing in the land surface" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.2. Tiling\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the river routing, if any." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.3. Time Step\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Time step of river routing scheme in seconds" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.4. Grid Inherited From Land Surface\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is the grid inherited from land surface?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.grid_inherited_from_land_surface') \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": [ "### 30.5. Grid Description\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### General description of grid, if not inherited from land surface" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.grid_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": [ "### 30.6. Number Of Reservoirs\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Enter the number of reservoirs" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.number_of_reservoirs') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.7. Water Re Evaporation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### TODO" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.water_re_evaporation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"flood plains\" \n", "# \"irrigation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.8. Coupled To Atmosphere\n", "Is Required: FALSE    Type: BOOLEAN    Cardinality: 0.1\n", "### Is river routing coupled to the atmosphere model component?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.coupled_to_atmosphere') \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": [ "### 30.9. Coupled To Land\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the coupling between land and rivers" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.coupled_to_land') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.10. Quantities Exchanged With Atmosphere\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### If couple to atmosphere, which quantities are exchanged between river routing and the atmosphere model components?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.quantities_exchanged_with_atmosphere') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"heat\" \n", "# \"water\" \n", "# \"tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.11. Basin Flow Direction Map\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What type of basin flow direction map is being used?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.basin_flow_direction_map') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"present day\" \n", "# \"adapted for other periods\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.12. Flooding\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the representation of flooding, if any" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.flooding') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.13. Prognostic Variables\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### List the prognostic variables of the river routing" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 31. River Routing --> Oceanic Discharge \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 31.1. Discharge Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Specify how rivers are discharged to the ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.oceanic_discharge.discharge_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"direct (large rivers)\" \n", "# \"diffuse\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.2. Quantities Transported\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Quantities that are exchanged from river-routing to the ocean model component" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.oceanic_discharge.quantities_transported') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"heat\" \n", "# \"water\" \n", "# \"tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 32. Lakes \n", "Land surface lakes" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 32.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of lakes in the land surface" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.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": [ "### 32.2. Coupling With Rivers\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Are lakes coupled to the river routing model component?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.coupling_with_rivers') \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": [ "### 32.3. Time Step\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Time step of lake scheme in seconds" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.4. Quantities Exchanged With Rivers\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### If coupling with rivers, which quantities are exchanged between the lakes and rivers" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.quantities_exchanged_with_rivers') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"heat\" \n", "# \"water\" \n", "# \"tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.5. Vertical Grid\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the vertical grid of lakes" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.vertical_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.6. Prognostic Variables\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### List the prognostic variables of the lake scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 33. Lakes --> Method \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 33.1. Ice Treatment\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is lake ice included?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.ice_treatment') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.2. Albedo\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Describe the treatment of lake albedo" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.albedo') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.3. Dynamics\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Which dynamics of lakes are treated? horizontal, vertical, etc." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.dynamics') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"No lake dynamics\" \n", "# \"vertical\" \n", "# \"horizontal\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.4. Dynamic Lake Extent\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is a dynamic lake extent scheme included?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.dynamic_lake_extent') \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": [ "### 33.5. Endorheic Basins\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Basins not flowing to ocean included?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.endorheic_basins') \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": [ "# 34. Lakes --> Wetlands \n", "TODO" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 34.1. Description\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the treatment of wetlands, if any" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.wetlands.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": [ "### \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
google/nitroml	examples/visualize_tuner_plots.ipynb	1	11906	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "##### Copyright 2020 Google LLC." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "_K3eRbcw_SRI" }, "source": [ "# Visualize the MetaLearning pipeline built on top NitroML. \n", "# We are using NitroML on Kubeflow: \n", "\n", "This notebook allows users to analyze NitroML metalearning pipelines results." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Step 1: Configure your cluster with gcloud\n", "# `gcloud container clusters get-credentials <cluster_name> --zone <cluster-zone> --project <project-id>\n", "\n", "# Step 2: Get the port where the gRPC service is running on the cluster\n", "# `kubectl get configmap metadata-grpc-configmap -o jsonpath={.data}`\n", "# Use `METADATA_GRPC_SERVICE_PORT` in the next step. The default port used is 8080.\n", "\n", "# Step 3: Port forwarding\n", "# `kubectl port-forward deployment/metadata-grpc-deployment 9898:<METADATA_GRPC_SERVICE_PORT>`\n", "\n", "# Troubleshooting\n", "# If getting error related to Metadata (For examples, Transaction already open). Try restarting the metadata-grpc-service using:\n", "# `kubectl rollout restart deployment metadata-grpc-deployment` " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import sys, os\n", "PROJECT_DIR=os.path.join(sys.path[0], '..')\n", "%cd {PROJECT_DIR}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "both", "colab": {}, "colab_type": "code", "id": "gZQAacaeCfBh" }, "outputs": [], "source": [ "import json\n", "\n", "from examples import config as cloud_config\n", "import examples.tuner_data_utils as tuner_utils\n", "from ml_metadata.proto import metadata_store_pb2\n", "from ml_metadata.metadata_store import metadata_store\n", "from nitroml.benchmark import results\n", "import seaborn as sns\n", "import tensorflow as tf\n", "import qgrid\n", "\n", "sns.set()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "0vnwDmsobYGD" }, "source": [ "## Connect to the ML Metadata (MLMD) database\n", "\n", "First we need to connect to our MLMD database which stores the results of our\n", "benchmark runs." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "connection_config = metadata_store_pb2.MetadataStoreClientConfig()\n", "\n", "connection_config.host = 'localhost'\n", "connection_config.port = 9898\n", "\n", "store = metadata_store.MetadataStore(connection_config)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get trial summary data (used to plot Area under Learning Curve) stored as AugmentedTuner artifacts." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Name of the dataset/subbenchmark\n", "# This is used to filter out the component path.\n", "testdata = 'ilpd' " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_metalearning_data(meta_algorithm: str = '', test_dataset: str = '', multiple_runs: bool = True):\n", " \n", " d_list = []\n", " execs = store.get_executions_by_type('nitroml.automl.metalearning.tuner.component.AugmentedTuner')\n", " model_dir_map = {}\n", " for tuner_exec in execs:\n", "\n", " run_id = tuner_exec.properties['run_id'].string_value\n", " pipeline_root = tuner_exec.properties['pipeline_root'].string_value\n", " component_id = tuner_exec.properties['component_id'].string_value\n", " pipeline_name = tuner_exec.properties['pipeline_name'].string_value\n", " \n", " if multiple_runs:\n", " if '.run_' not in component_id:\n", " continue\n", " \n", " if test_dataset not in component_id:\n", " continue\n", " \n", " if f'metalearning_benchmark' != pipeline_name and meta_algorithm not in pipeline_name:\n", " continue\n", "\n", " config_path = os.path.join(pipeline_root, component_id, 'trial_summary_plot', str(tuner_exec.id))\n", " model_dir_map[tuner_exec.id] = config_path\n", " d_list.append(config_path)\n", " \n", " return d_list" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Specify the path to tuner_dir from above\n", "# You can get the list of tuner_dirs by calling: get_metalearning_data(multiple_runs=False)\n", "example_plot = ''\n", "if not example_plot:\n", " raise ValueError('Please specify the path to the tuner plot dir.')\n", " \n", "with tf.io.gfile.GFile(os.path.join(example_plot, 'tuner_plot_data.txt'), mode='r') as fin:\n", " data = json.load(fin)\n", " \n", "tuner_utils.display_tuner_data(data, save_plot=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Majority Voting" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "algorithm = 'majority_voting' \n", "d_list = get_metalearning_data(algorithm, testdata)\n", "\n", "d_list" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Select the runs from `d_list` to visualize. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_list = []\n", "\n", "for d in d_list:\n", " with tf.io.gfile.GFile(os.path.join(d, 'tuner_plot_data.txt'), mode='r') as fin:\n", " data_list.append(json.load(fin))\n", "\n", "tuner_utils.display_tuner_data_with_error_bars(data_list, save_plot=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Nearest Neighbor" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "algorithm = 'nearest_neighbor' \n", "d_list = get_metalearning_data(algorithm, testdata)\n", "\n", "d_list" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Select the runs from `d_list` to visualize. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_list = []\n", "\n", "for d in d_list:\n", " with tf.io.gfile.GFile(os.path.join(d, 'tuner_plot_data.txt'), mode='r') as fin:\n", " data_list.append(json.load(fin))\n", "\n", "tuner_utils.display_tuner_data_with_error_bars(data_list, save_plot=True)" ] } ], "metadata": { "colab": { "collapsed_sections": [], "last_runtime": { "build_target": "", "kind": "local" }, "name": "NitroML: Benchmark Overview", "provenance": [ { "file_id": "/piper/depot/google3/third_party/py/nitroml/notebooks/overview.ipynb", "timestamp": 1586208407782 }, { "file_id": "/piper/depot/google3/third_party/py/nitroml/notebooks/overview.ipynb?workspaceId=weill:fig-export-nitroml-change-46-553046179877::citc", "timestamp": 1585765690231 }, { "file_id": "/piper/depot/google3/third_party/py/nitroml/notebooks/overview.ipynb", "timestamp": 1582049042220 }, { "file_id": "/piper/depot/google3/third_party/py/nitroml/notebooks/overview.ipynb", "timestamp": 1581544369324 }, { "file_id": "/piper/depot/google3/third_party/py/nitroml/notebooks/overview.ipynb", "timestamp": 1581018369993 }, { "file_id": "/v2/notebooks/charts.ipynb", "timestamp": 1579279346196 } ], "toc_visible": true }, "environment": { "name": "tf2-gpu.2-1.m48", "type": "gcloud", "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-1:m48" }, "finalized": { "timestamp": 1594386744738, "trusted": false }, "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" }, "require": { "paths": { "buttons.colvis": "https://cdn.datatables.net/buttons/1.5.6/js/buttons.colVis.min", "buttons.flash": "https://cdn.datatables.net/buttons/1.5.6/js/buttons.flash.min", "buttons.html5": "https://cdn.datatables.net/buttons/1.5.6/js/buttons.html5.min", "buttons.print": "https://cdn.datatables.net/buttons/1.5.6/js/buttons.print.min", "chartjs": "https://cdnjs.cloudflare.com/ajax/libs/Chart.js/2.8.0/Chart", "d3": "https://d3js.org/d3.v5.min", "d3-array": "https://d3js.org/d3-array.v2.min", "datatables.net": "https://cdn.datatables.net/1.10.18/js/jquery.dataTables", "datatables.net-buttons": "https://cdn.datatables.net/buttons/1.5.6/js/dataTables.buttons.min", "datatables.responsive": "https://cdn.datatables.net/responsive/2.2.2/js/dataTables.responsive.min", "datatables.scroller": "https://cdn.datatables.net/scroller/2.0.0/js/dataTables.scroller.min", "datatables.select": "https://cdn.datatables.net/select/1.3.0/js/dataTables.select.min", "jszip": "https://cdnjs.cloudflare.com/ajax/libs/jszip/2.5.0/jszip.min", "moment": "https://cdnjs.cloudflare.com/ajax/libs/moment.js/2.8.0/moment", "pdfmake": "https://cdnjs.cloudflare.com/ajax/libs/pdfmake/0.1.36/pdfmake.min", "vfsfonts": "https://cdnjs.cloudflare.com/ajax/libs/pdfmake/0.1.36/vfs_fonts" }, "shim": { "buttons.colvis": { "deps": [ "jszip", "datatables.net-buttons" ] }, "buttons.flash": { "deps": [ "jszip", "datatables.net-buttons" ] }, "buttons.html5": { "deps": [ "jszip", "datatables.net-buttons" ] }, "buttons.print": { "deps": [ "jszip", "datatables.net-buttons" ] }, "chartjs": { "deps": [ "moment" ] }, "datatables.net": { "exports": "$.fn.dataTable" }, "datatables.net-buttons": { "deps": [ "datatables.net" ] }, "pdfmake": { "deps": [ "datatables.net" ] }, "vfsfonts": { "deps": [ "datatables.net" ] } } } }, "nbformat": 4, "nbformat_minor": 4 }	apache-2.0
darkomen/TFG	medidas/04082015/estudio.datos.ipynb	2	731397	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Análisis de los datos obtenidos " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Uso de ipython para el análsis y muestra de los datos obtenidos durante la producción.Se implementa un regulador experto. Los datos analizados son del día 11 de Agosto del 2015" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Importamos las librerías utilizadas\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Numpy v1.9.2\n", "Pandas v0.16.2\n", "Seaborn v0.6.0\n" ] } ], "source": [ "#Mostramos las versiones usadas de cada librerías\n", "print (\"Numpy v{}\".format(np.__version__))\n", "print (\"Pandas v{}\".format(pd.__version__))\n", "print (\"Seaborn v{}\".format(sns.__version__))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Abrimos el fichero csv con los datos de la muestra\n", "datos = pd.read_csv('841512.CSV')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Tmp Husillo</th>\n", " <th>Tmp Nozzle</th>\n", " <th>Diametro X</th>\n", " <th>Diametro Y</th>\n", " <th>MARCHA</th>\n", " <th>PARO</th>\n", " <th>RPM EXTR</th>\n", " <th>RPM TRAC</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>2819.000000</td>\n", " <td>2819.000000</td>\n", " <td>2819.000000</td>\n", " <td>2819.000000</td>\n", " <td>2819</td>\n", " <td>2819</td>\n", " <td>2819.000000</td>\n", " <td>2819.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>58.516602</td>\n", " <td>131.705534</td>\n", " <td>1.468372</td>\n", " <td>1.100644</td>\n", " <td>1</td>\n", " <td>0.3944661</td>\n", " <td>1.660163</td>\n", " <td>2.996379</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>9.536650</td>\n", " <td>21.267615</td>\n", " <td>0.545053</td>\n", " <td>0.727923</td>\n", " <td>0</td>\n", " <td>0.4888224</td>\n", " <td>0.677227</td>\n", " <td>0.834694</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>30.700000</td>\n", " <td>31.300000</td>\n", " <td>0.014000</td>\n", " <td>0.000342</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>57.600000</td>\n", " <td>137.200000</td>\n", " <td>1.264217</td>\n", " <td>0.000342</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>2.000000</td>\n", " <td>2.219072</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>63.000000</td>\n", " <td>138.000000</td>\n", " <td>1.585374</td>\n", " <td>1.459957</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>2.000000</td>\n", " <td>3.219072</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>64.400000</td>\n", " <td>138.600000</td>\n", " <td>1.757422</td>\n", " <td>1.678324</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2.000000</td>\n", " <td>3.219072</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>65.200000</td>\n", " <td>140.200000</td>\n", " <td>3.776121</td>\n", " <td>2.413878</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>2.000000</td>\n", " <td>5.899920</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Tmp Husillo Tmp Nozzle Diametro X Diametro Y MARCHA PARO \\\n", "count 2819.000000 2819.000000 2819.000000 2819.000000 2819 2819 \n", "mean 58.516602 131.705534 1.468372 1.100644 1 0.3944661 \n", "std 9.536650 21.267615 0.545053 0.727923 0 0.4888224 \n", "min 30.700000 31.300000 0.014000 0.000342 True False \n", "25% 57.600000 137.200000 1.264217 0.000342 1 0 \n", "50% 63.000000 138.000000 1.585374 1.459957 1 0 \n", "75% 64.400000 138.600000 1.757422 1.678324 1 1 \n", "max 65.200000 140.200000 3.776121 2.413878 True True \n", "\n", " RPM EXTR RPM TRAC \n", "count 2819.000000 2819.000000 \n", "mean 1.660163 2.996379 \n", "std 0.677227 0.834694 \n", "min 0.000000 0.000000 \n", "25% 2.000000 2.219072 \n", "50% 2.000000 3.219072 \n", "75% 2.000000 3.219072 \n", "max 2.000000 5.899920 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Mostramos un resumen de los datos obtenidoss\n", "datos.describe()\n", "#datos.describe().loc['mean',['Diametro X [mm]', 'Diametro Y [mm]']]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Almacenamos en una lista las columnas del fichero con las que vamos a trabajar\n", "columns = ['Diametro X', 'Diametro Y', 'RPM TRAC']" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([<matplotlib.axes._subplots.AxesSubplot object at 0x082580D0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x082963F0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x082A7D90>], dtype=object)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Isny3JEkdAKyTJKm/LMsNUII+GZqHZUCfEeQkNzfD093NYjDEn3yPTOTmZuCUU9oBAOLj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\ner1obm7GkCEn4Nxzx+LWW+9AdnZ3JCYm4osvPpVtJhwOBxyO8JCVev3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hNUdMTCxOPnk4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UMBALNns453/ftLXbEiIiKwbNnXeOyxJw2vo72wceMfeOWV1/D779t1j5ErfozDH9o4iQSHzWZD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\nf1W/Hy006c+vy+USc6J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5fjOtPC0RqMRP9IVmqgbg0JcDc88wToxec42WRIqG559/Jo6t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CmAvPTSL+PYY1+vvu+XqlNcqSRfD9nqKZlUhsXjh+CXubO+DZMaRCQXxPsnKk6DQFgWe4IA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\n0AaU4+pV/XO1EZvBnZPmScNhsHjk665eOszPz+GOO24ff85yC25CW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3T1ouKn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\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RVXBnmSYbxWXi+VCX92cTVd+oop2Gn+DecTdxGn4IIYQQQjxFFcegyZObuigPCGkS0W3S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\nLnXcHHglLe4cd9W9uaFezWT85DrYzj15dkuwnbt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"text/plain": [ "<matplotlib.figure.Figure at 0x82ef9b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Mostramos en varias gráficas la información obtenida tras el ensayo\n", "datos[columns].plot(subplots=True, figsize=(20,20))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Representamos ambos diámetros en la misma gráfica" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.LineCollection at 0x836bd10>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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96Tz2DfibK6KN5ZEfRNTiOEYIaA8Aej/qLcmSqvSls4Po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HJHWcKKrDkI5k04BJ4bhmcIsf+2kc9YWeuaFtdFgjbSAuVcskTOgZFufDpyrcWNIszwUS1vL2nj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\n7giAH9yPm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jkNZH4PoTbDqwi/etvLz0g2xH1j04bu+F7UfAaLSGOhJbK1BEdIex4yHLEo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"text/plain": [ "<matplotlib.figure.Figure at 0x836bcb0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "datos.ix[:, \"Diametro X\":\"Diametro Y\"].plot(figsize=(16,3),ylim=(1.4,2)).hlines([1.85,1.65],0,3500,colors='r')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x844f550>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x8457110>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "datos.ix[:, \"Diametro X\":\"Diametro Y\"].boxplot(return_type='axes')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Mostramos la representación gráfica de la media de las muestras" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([<matplotlib.axes._subplots.AxesSubplot object at 0x08430AB0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x08A1E110>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x08A46850>], dtype=object)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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TJs2ws7PD29uHqKhIiyuFJs9am7IATiZ3kbtxI9LkYxW0ixfPU7Sok640nDwDfO2apiCH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"text/plain": [ "<matplotlib.figure.Figure at 0x876b570>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pd.rolling_mean(datos[columns], 50).plot(subplots=True, figsize=(12,12))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comparativa de Diametro X frente a Diametro Y para ver el ratio del filamento" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x8b570b0>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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bmTatOOY3z0Xi88r4/c1Eo+UYRpR1647hdndSXz+F2toga9acIByeBryFyzWN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\npkssZlKWuDFSI3WR24joJ0GtkVsBpF4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"text/plain": [ "<matplotlib.figure.Figure at 0x8b047f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(x=datos['Diametro X'], y=datos['Diametro Y'], marker='.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Filtrado de datos\n", "Las muestras tomadas $d_x >= 0.9$ or $d_y >= 0.9$ las asumimos como error del sensor, por ello las filtramos de las muestras tomadas." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "datos_filtrados = datos[(datos['Diametro X'] >= 0.9) & (datos['Diametro Y'] >= 0.9)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Representación de X/Y" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x8b94db0>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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WKc0HzoIfcTt5P6tXg2lG8PudNUgTZxD2PDJu2xobF8Vsz3JGsGrVAaAaeIOO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\nlScXJotpIBu5SvmeMtUMKAU5FMVjSin4fIwsx2P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"text/plain": [ "<matplotlib.figure.Figure at 0x8b6fab0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(x=datos_filtrados['Diametro X'], y=datos_filtrados['Diametro Y'], marker='.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Analizamos datos del ratio" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "count 1897.000000\n", "mean 1.028405\n", "std 0.139681\n", "min 0.652023\n", "25% 0.963465\n", "50% 1.005803\n", "75% 1.070952\n", "max 2.214601\n", "dtype: float64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ratio = datos_filtrados['Diametro X']/datos_filtrados['Diametro Y']\n", "ratio.describe()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x929e790>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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LrMxijnQdRgNcfqGg0gDH4bt3X8Tbu+8pDUYPhkeF5u9onyjI+zNriGlM/pcG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\nAahTtwLZN9EZBJZh4sXDn8Zz+55OTwPsswFOyAkuwIRN53dtp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"text/plain": [ "<matplotlib.figure.Figure at 0x9298070>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rolling_mean = pd.rolling_mean(ratio, 50)\n", "rolling_std = pd.rolling_std(ratio, 50)\n", "rolling_mean.plot(figsize=(12,6))\n", "# plt.fill_between(ratio, y1=rolling_mean+rolling_std, y2=rolling_mean-rolling_std, alpha=0.5)\n", "ratio.plot(figsize=(12,6), alpha=0.6, ylim=(0.5,1.5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Límites de calidad" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculamos el número de veces que traspasamos unos límites de calidad. \n", "$Th^+ = 1.85$ and $Th^- = 1.65$ " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Th_u = 1.85\n", "Th_d = 1.65" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_violations = datos[(datos['Diametro X'] > Th_u) \| (datos['Diametro X'] < Th_d) \|\n", " (datos['Diametro Y'] > Th_u) \| (datos['Diametro Y'] < Th_d)]" ] }, { "cell_type": "code", "execution_count": 18, "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>Tmp Husillo</th>\n", " <th>Tmp Nozzle</th>\n", " <th>Diametro X</th>\n", " <th>Diametro Y</th>\n", " <th>MARCHA</th>\n", " <th>PARO</th>\n", " <th>RPM EXTR</th>\n", " <th>RPM TRAC</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>2263.000000</td>\n", " <td>2263.000000</td>\n", " <td>2263.000000</td>\n", " <td>2263.000000</td>\n", " <td>2263</td>\n", " <td>2263</td>\n", " <td>2263.000000</td>\n", " <td>2263.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>60.068758</td>\n", " <td>134.496818</td>\n", " <td>1.400948</td>\n", " <td>0.945249</td>\n", " <td>1</td>\n", " <td>0.3539549</td>\n", " <td>1.771100</td>\n", " <td>2.977024</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>7.446700</td>\n", " <td>18.607393</td>\n", " <td>0.588722</td>\n", " <td>0.732944</td>\n", " <td>0</td>\n", " <td>0.4783011</td>\n", " <td>0.526667</td>\n", " <td>0.811737</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>30.700000</td>\n", " <td>31.300000</td>\n", " <td>0.014000</td>\n", " <td>0.000342</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>59.300000</td>\n", " <td>137.500000</td>\n", " <td>1.183928</td>\n", " <td>0.000342</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>2.000000</td>\n", " <td>2.219072</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>63.300000</td>\n", " <td>138.000000</td>\n", " <td>1.482145</td>\n", " <td>1.276068</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>2.000000</td>\n", " <td>3.219072</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>64.400000</td>\n", " <td>138.600000</td>\n", " <td>1.688603</td>\n", " <td>1.563394</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2.000000</td>\n", " <td>3.219072</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>65.200000</td>\n", " <td>140.200000</td>\n", " <td>3.776121</td>\n", " <td>2.413878</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>2.000000</td>\n", " <td>5.899920</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Tmp Husillo Tmp Nozzle Diametro X Diametro Y MARCHA PARO \\\n", "count 2263.000000 2263.000000 2263.000000 2263.000000 2263 2263 \n", "mean 60.068758 134.496818 1.400948 0.945249 1 0.3539549 \n", "std 7.446700 18.607393 0.588722 0.732944 0 0.4783011 \n", "min 30.700000 31.300000 0.014000 0.000342 True False \n", "25% 59.300000 137.500000 1.183928 0.000342 1 0 \n", "50% 63.300000 138.000000 1.482145 1.276068 1 0 \n", "75% 64.400000 138.600000 1.688603 1.563394 1 1 \n", "max 65.200000 140.200000 3.776121 2.413878 True True \n", "\n", " RPM EXTR RPM TRAC \n", "count 2263.000000 2263.000000 \n", "mean 1.771100 2.977024 \n", "std 0.526667 0.811737 \n", "min 0.000000 0.000000 \n", "25% 2.000000 2.219072 \n", "50% 2.000000 3.219072 \n", "75% 2.000000 3.219072 \n", "max 2.000000 5.899920 " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_violations.describe()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([<matplotlib.axes._subplots.AxesSubplot object at 0x092D1730>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x09329CF0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x09352EB0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x09376A50>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x093A52D0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x093C53F0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x095DE6D0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x095E8390>], dtype=object)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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gWQLji3cAtyilJjZO4yA1NTXy6qsvU1Kyku3bvwAgJiaW7373Ki655DJOPvlU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Vs8MOO7DTTpOSLrt8+TLee2916HV1dQ0XXvg9fvazn3Dnnffi9/uZPHl35s79Jtdfv5Dbbrsz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\npjA1y5m6Y3o8zD7Mbcp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"text/plain": [ "<matplotlib.figure.Figure at 0x8b31cd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_violations.plot(subplots=True, figsize=(12,12))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 0 }	cc0-1.0
saketkc/hatex	2015_Fall/MATH-578B/Homework2/Homework2.ipynb	1	122690	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Problem 2\n", "\n", "Define $h(a) = P(\\tau_{\\phi} < \\tau_{\\dagger}) \| X_0=a)$\n", "\n", "To show: $P(X_{n+1}=b\|X_n=a\\ and\\ \\tau_{\\phi} < \\tau_{\\dagger}) = \\frac{h(b)}{h(a)}P_{ab}$\n", "\n", "LHS: $P(X_{n+1}=b\|X_n=a\\ and\\ \\tau_{\\phi} < \\tau_{\\dagger})$ \n", "#### Incorrect version\n", "LHS: \n", "~~\\begin{align}\n", "~~P(X_{1}=b\|X_0=a\\ and\\ \\tau_{\\phi} < \\tau_{\\dagger}) &= P(X_{1}=b\|X_0=a, \\tau_{\\phi} < \\tau_{\\dagger}\|X_0=a)~~\\\\\n", "&=P(\\tau_{\\phi} < \\tau_{\\dagger}\|X_0=a) \\times P(X_1=b\|X_0=a)\\ \\text{if $a \\notin \\phi,\\dagger$}\\\\\n", "&=h(a) \\times P_{ab} \\\\\n", "&\\neq RHS??\n", "\\end{align}~~\n", "\n", "#### Corrected Version\n", "Consider \n", "\n", "$P(A\|B,C)=P(X_{n+1}=b\|X_n=a\\ and\\ \\tau_{\\phi} < \\tau_{\\dagger})$\n", "Then\n", "$P(A\|B,C)= \\frac{P(A,B,C)}{P(B,C)}=\\frac{P(A,C\|B)P(B)}{P(C\|B)P(B)}=\\frac{P(A,C\|B)}{P(C\|B)} =\\frac{P(A\|C) \\times{P(B\|A,C)}}{P(B\|C)}$\n", "\n", "Thus,\n", "\n", "$$\n", "LHS=P(X_{n+1}=b\|X_n=a\\ and\\ \\tau_{\\phi} < \\tau_{\\dagger}) = \\frac{P(X_{n+1}=b,\\tau_{\\phi} < \\tau_{\\dagger}\|X_n=a)}\n", "{P(\\tau_{\\phi} < \\tau_{\\dagger}\|X_n=a)}\n", "$$\n", "\n", "Now, $n > min(\\tau_{\\phi},\\tau_{\\dagger})$\n", "and hence:\n", "\n", "$$\n", "\\begin{align}\n", "P(X_{n+1}=b\|X_n=a\\ and\\ \\tau_{\\phi} < \\tau_{\\dagger}) &= \\frac{P(X_{n+1}=b,\\tau_{\\phi} < \\tau_{\\dagger}\|X_n=a)}\n", "{P(\\tau_{\\phi} < \\tau_{\\dagger}\|X_n=a)} \\\\\n", "&= \\frac{P(X_{n+1}=b\|X_n=a)\\times P(\\tau_{\\phi} < \\tau_{\\dagger}\|X_n=a,X_{n+1}=b)}{P(\\tau_{\\phi} < \\tau_{\\dagger}\|X_n=a)}\n", "\\end{align}\n", "$$\n", "\n", "Using markov property and time homogeneity:\n", "$P(\\tau_{\\phi} < \\tau_{\\dagger}\|X_n=a,X_{n+1}=b)=P(\\tau_{\\phi} < \\tau_{\\dagger}\|X_0=b)$\n", "and hence:\n", "\n", "$$\n", "\\begin{align}\n", "P(X_{n+1}=b\|X_n=a\\ and\\ \\tau_{\\phi} < \\tau_{\\dagger}) &= \\frac{P(X_{n+1}=b\|X_n=a)\\times P(\\tau_{\\phi} < \\tau_{\\dagger}\|X_n=a,X_{n+1}=b)}{P(\\tau_{\\phi} < \\tau_{\\dagger}\|X_n=a)}\\\\\n", "&=\\frac{h(b)\\times P_{ab}}{h(a)}\\\\\n", "&= RHS\n", "\\end{align}\n", "$$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Problem 3\n", "\n", "If initial state $X_t=A$,\n", "\n", "$P(X_{t+1}=A) = 0.5$ and $P(X_{t+1}=A\\cup\\{b\\}-\\{a\\})=0.5$\n", "\n", "Observation 1: $X_t$ is irreducbile. The construction allows to reach every state from any state.\n", "\n", "Example: Let $A=\\{1,2,3,4,5\\}$ for $n=10$ and $k=5$. let $a$=3 and let $b=6$\n", "\n", "Then we have: $P(X_{t+1}=\\{1,2,3,4,5\\}) = 0.5$ and $P(X_{t+1}=\\{1,2,4,5,x\\})=0.51/5$ where $x\\ \\in \\{6,7,8,9,10\\}$\n", "\n", "Observation 2: For $X$ to be aperioidic, it is imporatant to have the $X_{t+1}=X_t$ with probabulity 0.5(any non-zero probability would do). Otherwise the diagonal of the trasition probability matrix will be zero, and in such cases it is possible for the chain to be periodic. An example (without taking into account the actual transition probabilities) is:\n", "For state space.$\\{1,2,3,4\\}$\n", "$$\n", "P = \\begin{bmatrix}\n", "0 & 0.5 & 0 & 0.5\\\\\n", "0.5 & 0 & 0.5 & 0\\\\\n", "0 & 0.5 & 0 & 0.5\\\\\n", "0.5 & 0 & 0.5 & 0\\\\\n", "\\end{bmatrix}\n", "$$\n", "\n", "and $(P^2)_{ii}>0$\n", "It is possible to return to the same state with a period of 2:\n", "$P(X_n=2\|X_0=1)= 0 \\ for\\ $n=2k$\\ and\\ 1\\ for\\ $n=2k-1$\\ where\\ k=1,2,3...$\n", "\n", "\n", "### About uniform stationary distribution\n", "\n", "From observations 1,2 we know that the markv chain is irreducible and aperiodic. There is another observation:\n", "\n", "Observation 3: $P$ the transition probabilty matrix is symmetric.\n", "\n", "$P_{ii} = 0.5$\n", "\n", "$P_{ij} = 0.5 \\underbrace{\\frac{1}{\|k\|}}_\\text{Probability of selecting 'i' uniformly} * \\underbrace{\\frac{1}{\|A\|-\|k\|}}_\\text{Probability of selecting 'j' uniformly}$ $\\forall j \\neq i$\n", "\n", "and hence $P_{ij} =P_{ji}$ $\\implies$ $P=P^T$ $\\implies$ $\\pi$ is uniformly distributed (Because $P$ is reversible)\n", "\n", "\n", " \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Problem 4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Part (4a)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.417022 0.5234847 0.47514525 ..., 0.41077488 0.47791837\n", " 0.52720653]\n", " [ 0.5234847 0.5270581 0.72129764 ..., 0.17310173 0.36455267\n", " 0.90610878]\n", " [ 0.47514525 0.72129764 0.91560635 ..., 0.29658343 0.8926068\n", " 0.43092462]\n", " ..., \n", " [ 0.41077488 0.17310173 0.29658343 ..., 0.83527618 0.36756992\n", " 0.06630671]\n", " [ 0.47791837 0.36455267 0.8926068 ..., 0.36756992 0.50837092\n", " 0.09638852]\n", " [ 0.52720653 0.90610878 0.43092462 ..., 0.06630671 0.09638852\n", " 0.76279378]]\n" ] } ], "source": [ "%matplotlib inline\n", "from __future__ import division\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "np.random.seed(1)\n", "D = np.random.rand(100,100)\n", "## This is not symmetric, so we make it symmetric\n", "D = (D+D.T)/2\n", "print (D)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import math\n", "N_steps = 10000\n", "\n", "def L(sigma):\n", " s=0\n", " for i in range(0, len(sigma)-1):\n", " s+=D[sigma[i], sigma[i+1]]\n", " return s\n", "\n", "def propose(sigma):\n", " r = np.random.choice(len(sigma), 2)\n", " rs = np.sort(r)\n", " j,k=rs[0],rs[1]\n", " x=(sigma[j:k])#.reverse()\n", " x=x[::-1]\n", " x0= sigma[:j]\n", " x1 = sigma[k:]\n", " y=np.concatenate((x0,x,x1))\n", " return y\n", "\n", "\n", "def pi(sigma,T):\n", " return math.exp(-L(sigma)/T)\n", "\n", "def metropolis(sigma,T,L_0):\n", " sigma_n = propose(sigma)\n", " L_n = L(sigma_n)\n", " pi_ab = math.exp(-(L_n-L_0)/T)\n", " q = min(1, pi_ab)\n", " b = np.random.uniform(size=1)\n", " if (b<q):\n", " return sigma_n\n", " else:\n", " return sigma\n", " " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[80 49 18 11 67 21 88 85 81 86 52 39 52 13 9 98 78 46 26 63 86 2 96 45 13\n", " 67 37 36 54 63 65 58 49 48 59 26 2 26 44 29 34 72 62 52 75 72 95 0 51 39\n", " 60 24 95 80 34 36 55 31 66 80 56 23 20 56 59 27 27 89 80 34 58 74 70 81 7\n", " 35 29 74 13 99 43 60 27 97 16 55 25 19 45 80 48 73 22 31 20 16 17 59 28 70]\n" ] } ], "source": [ "sigma_0 = np.random.choice(100,100)\n", "L_0 = L(sigma_0)\n", "print sigma_0" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "T = [0.05,10]\n", "def plotter(t):\n", " L_history = []\n", " sigma_history = []\n", " sigma_0 = np.random.choice(100,100)\n", " L_0 = L(sigma_0)\n", " L_history.append(L_0)\n", " sigma_history.append(sigma_0)\n", " sigma = metropolis(sigma_0,t,L_0)\n", " for i in range(1, N_steps):\n", " sigma_t = metropolis(sigma_history[i-1],t,L_history[i-1])\n", " L_1 = L(sigma_t)\n", " L_history.append(L_1)\n", " sigma_history.append(sigma_t)\n", " plt.figure(0)\n", "\n", " plt.hist(L_history, 20)\n", " #plt.xlim(min(L_history)-25, max(L_history)+0.5)\n", " plt.xlabel('Length')\n", " plt.ylabel('Frequency')\n", " plt.title('Frequency of L')\n", " plt.figure(1)\n", "\n", " plt.plot(range(1, N_steps+1),L_history)\n", " plt.ylim(min(L_history), max(L_history))\n", " plt.xlabel('N_steps')\n", " plt.ylabel('L')\n", " plt.title('Variation of L with N_steps')\n", " return L_history" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## T = 0.05" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f7e258eea50>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f7e099586d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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568XV4bvKwQshk9BDtwPVQirti8VlL1JQnH+++3ffMt55Z6z5mO4diKMqpxFM\nSc85j0ah31u/funalatubNq7aXLe16SoesXp962P6HkUnxRCnQUFvw8us6Eu6NV3yKVR1IJFVlCc\ndFIcnsKlUeT1elKFgM2rwpSn3hDTlsPU2dqEjxDptvg8+ujs6qyPoHB1PrwuIAl9AhIAVl9dfvJz\nsLka2ihjMtvHrv322/YV0scdJycq77ij8pq5c6vP5cjKaTSKMkepetrdu/vnN2+e21STJgSKS6Mw\nLZgzLbjTNQpfLYUFBbsxf/VVcjmZ3/wm/v+pp4Af/9idV5kssoJCXwhjW0eR5Jdtgz0Z1NXONjXU\n9HKWsZhGtzfr+U6dmj+PJFweJoyrI/PtSP773+pjp5wi1xHwfdt20NtvP/PxMiazkzxlOLTKNtuY\nr7/gAhk2QxUUQph99pPcPwHgqqvs5St6QabJ9OTbzvv2dU+cp9kMKe3ck0nj9xUUn35aKeCEkKvw\nbXNEKvpARR3o6AOoWpuhmkJQuNZRmPzwfeAAXWqY4003NZ9r0ij0EUueB2/bclLv+Nrbs+fhi8+o\nfNll7b/5vsy6BxMg779Xr+Q5CjXmlynvWbP8yuADawpqJ27qcFwbSZkmhU0duBquBah0VfUNC5GU\nt4kFC8xtK4+gSEJt67Z7+OUvgWHD/Ef/anpZBcXSSwOHHhp/T9PB87bMnI9LawqCwoLq++2DuqF7\n2nUUX3yRvBCJBYUaqqPsOQp1MlMIuTHM7Nn20Ypuq6+FS6w+d5P1+iQ4PLzuFtitW7KgOOggORmp\nU8bLx/XQvXsc/0f3nknCd6cz1hb4/i+9NP5t5kzzNUV03HfdZdaI9LT1yewDD8yep6pR2Orj3/+W\nC/R8NUU252V1j2XUrXR1K4OP1tfZKbVh157yQVBYSFpk5MK1jmLs2OrzN9+8ekMUHR4VP/BAfCyN\n11OWOQqezOTzV1hBdpQ2TyPOV1+IVia1EhQ8h6Kfr77Qrjp96KHseWdlww2r8/HJ0zSocN0bzw/5\nhNAvosOxPWubRsELRF3bByfhIyjS7iXP7q6zZ8dzCmpavnMUqjDgMviYZJnOzuq1HfWmdEFBRG8S\n0YtENImIxkfH+hHRI0T0GhE9TERLJaeTvQwurydTmO3Jk+2TsTNnyvRWW636tzyCQj3n9tuBAw4w\np2U639Y567v42VZvvvWWO680ZFmLoIYW0d2GbVx8sfzUXzx1MODqBF1ulGWjPqd9900+36RRuMqa\nRkhn8XrXQRoUAAAgAElEQVRibf2bb4Af/cj+bpo0irffjid008wz6NgiNavwXIFr4KBf++STcp7r\nwQcrj6cRFBMnVh/jZ6K216+/NvczPivYF0WNQgBoE0JsKIQYGh07AcAjQojVATwafXeijtyBStNS\nEi6vp7SssII0W5hexjSxnlxzFFdeCVx/vbsceqMlqu40fTttdaI0L1k0CnXSLu1ISl8HkEdQ5G0b\nNvOOKx81eKSNpIVreowhn1ErwwEkTfkAcr2KDntXzZ4tNTOboDBpFOrATH0HXKE/TKjX2traBx9U\nlsPUHn7728rvP/iBPT+ToPCFn4kqGL773TiagMo++wAvv+xOb1EUFACgN6VdAPC2G9cB2DVtgiap\nzehqo8vrKQsff2zuVGyCYu7cyk4kaY7CR3vSG223bnaN4p573Gmpi+/y1k/SZDZHOWWmTHHvL55E\njx6V3+slKJ5/Xg4ifEibj17WhQsr702PCpxmweF776UrCxCHtdfnvvT8dCHY0RG/m0cdVfm+JG2I\npKPWSdJ8YhHebLqgSJsmPxP1Hf3ss8otk02wy7fOoigoBICxRDSBiH4RHesvhIjkPT4AkHp9qKui\n9Ilvl9cTkK2DMC16sgmKP/2pshNJmqNIKyhGj5YjFdscxeWXJ6enlyNrQ0zSKPStNPv2BXr2rL4+\nCY6fo6vu9TI9+UZhbW9PP39j8mZT60n/vewNmPTBANdlUliYjg7p7gtI06Fabt8V+XoZAGD55eOd\n/HzKmwWfoIA6Y8fGXk/8TGzmtrRlq7WgyLRjNBEdJYS4xPP0LYUQs4hoWQCPEFGFN78QQhCR8bZH\njRqlfGuL/vg6e4b6by6vJ0Cq0GlDGZx5ZvUxk6A49tj45VDLl3cdhXo+u3PqJoe0HdI338Qj9KwN\nkd1Bfe/H5tKbBG8ypJsssgqKadPydSQmE4KJbbZJH+nTZHpS783mFVVWZ6LXk+/mSXp79HFxZRZf\nPDlWko28gx/ALihcZr6+feOFrrzvh82Tydb2/Nya2wG0o6K7LJhMggLAMQC8BIUQYlb0OZuI7gYw\nFMAHRDRACPE+ES0HwLguVRUUaVYWq3bVzz4zaxStrdVbYSbBk76+k3dAtWkESJ6jyLrpj22OwhfV\nZJe10+TtGX2FVNb1JOwqbJrM7uyU5kHTWgv1PJXp083PSufDD6VzQFIwPRd5NYrHH6/01TeZpoDy\nBQWnz/OFeQRF0vzOeuvJfa2zUIbpiev8pZfs16gLXNn8a1vgm1ZQVIYCaQPQ9q2gOC1NZ+lJqaYn\nIupJRL2j/5cEsD2AyQDuBcB+PQcASLCiV+N66Oo+wqym6xpFlokpV6MAgBNPrD5mWoiUNEfh44WU\nNLq57LJ8HVLWl+qKK+RnVkGRVkDpgoJfaFPAwSR88u7fH9h998pjaesq7xyFnoa+38GPfmTOR5+7\ny4re8drmKFT06KtA5TNKmoDX172kqfMyTE8+96yG62Cty+ay7Fs2NiPbQr6URdlzFP0BPE5EzwN4\nFsD9QoiHAZwDYDsieg3A8Oh7KnwbCpF5HYV6fVEjL1M6plW+SXMUPqE2kgTF2LH5XgxbnfzqV/ZG\n+sUX8X4BZZueGL7nf/xDfvo6LOSZzNZHvz6eSyp5NQqgcqHhPvtU/sadkV4PHDTQRtp9orm+2FPN\nVX8mQaGWJ2lL1tVXB4YOdZ9jg8uVJ7R+ljkK0xyMrY58295rr/mdVzRWQUFEnxPRZ6Y/AMv7JC6E\nmCGE2CD6W0cIcXZ0/BMhxLZCiNWFENsLIYxRXYrowIWoXkehp1vmxJBpkq7oOQpGFRQLF1a/mDvs\n4E7TR3hedpm9Y1QFSNmmJ8a2jsKn0+NNpzhfW95Jz0YN0LfXXsmdXhEaBUcGAGIvJD19/X6KWpmv\nl5+dEVz31dlZ3SbU8nAATxd6DCVf+Nwkl3MXaVZmq9ewEE0yB/q+L7WexGasgkII0UsI0dvyl2Op\njD+uSvF9aEJUr6Po7KzsoN59t/KarPGfkhg7Frjllur8uZxpsAkKTlf1tV9jDfmZFD7b1xznU/e1\n1iiYNBqFvjKY815KWf45ZowcZKhbxuob1qi7rt1xR/WWqzpZOpw0adgmbydNSpevDZsgctV79+7Z\nI9fee6/8XG45v/N10qyKtpFFo+jWLS5zVo2C099gg8rvi6J7bGZcm3zYKlYfzelzFK+8Ur3/rO6V\nZHPx5IejzoGk4aCDgJ/+VKaru8mlbYS2yWx10xMu/9SpMljZUgnr333NcT4dXb3mKNIICv1++fun\nnwKPPCL/3zVa4aOaLfRQ0dOmVaftIq3pKa2ZjO9DD0Nzia+fYgJFCQqO15UEtxG9rfhez+XKE8LG\nNkfh2ppVfccvukh+chp6ANEkQbF8ZMNJ8mg74wx7efLQ0IJCXTWqs/32fmmwysteTyZs6w9s7ngc\nCJAnDV2YXvLNN/cTFCa7rut8VaN49lm3r31Smi413acj9u3w9fpJG/Y9q6AAqsuofr/uusrf0mzE\nlCQoitAoXPsa8KrexRcHfvEL+3k6p57qd54+R8Hfb7jBfo1JUOiT8DZMe0gLIQW6j/dZlmgBpjKY\nBIUrTdNmQ1xX48eby6gzfDiw7rrVws7WxouMsqDS0IICSD8BZVpDoWoUJnwFBXdKHCfftHmMjhq7\nSC2bLihsD97mqWJqWAsW2EMbpHW5/cMf/M5TUfNIq1EUtatcVo0CcM+xuMwWaQVFR4d9xa2K6z6O\nO05+Pv109W9PPRXnkyee0oQJ5uO6gODPxx+3p2WKHOAL34MeqkT9zQWXL+s6DMAewoOPbbstMGhQ\n5TU//nG1IEtrerryykqzZ9Igo6xFlg0vKAYO9F/1ClT7wqtzFGmC9gHVaiU3dG6cPtE51YbiEhRp\n/aiTNAr9nLQaRZbzVLNfWkGhe+5khR0WsoRBOeQQ829AuolgH42itdUeV4jhAYmpDbC5S40ozKju\nsXkExSabVH7v0UPuVa27mJsC3umYBIXvLnV8Hu8pDVSuh0qC8zUFAPXFJijYkWDw4Op9vHv2rJ7v\ntL07vu9LEBQO8kxC8aSuS6OwmSAeeAD4+9/j47yrGv+uN3ReR6FOkto6erWBr7NOugesm5UYdY4C\nqGx8Pi+lr8nGdp66N0fWyey8pAn86BIGSS+uWgdqCBLffNm5wgULLjWvrbcGRoyQ/9s2ylLNFEXW\nrxCV5hSuI9MezzrqAlfmwgv98rXNUdiOAXHgQgC45hq/fJLKoLYX3uuDV1rb2p3JwmHillv8ypG0\nSLisSe4uISjy3Lzu9WTCZnr6v/8DDj88Pv6Xv1Ser49meHS3zjr2sjCtrbE/vmvjdNO92/zJdUFh\nsqm6yKtR+ET0dF2Thj59zF4wadxj9bpVO67Zsyuj2rombvVRfdJirCQNl3niCfmp1vfuu8eLt1Tt\neccdq8uW1fRkKve998YDLtschUtQdHTIv912A9ra5DHfsDl8D6YFobY6VAeXtpApvDjUhG5G0gUF\nm8Q5rpzN5Ok74e+7qyLXcVZX7qw0haBQ11Go8D4NNkFhg8/3sU2rZVfTbW0FBgyI/y/iAecxPSXt\ncaBiO099ucrWKDo7zaY/3zkKbhfq9z594u8TJlQufHS98Ppzv+km8zVq2VtbgXHj3GU0CZwXX4w1\nWzUwpWl72SIFxciR8rjqMah/ugQFr+tZddVYI/Itm0ujsKHG3uL3XOeww+zX33xzdRlcbZpID6sh\n8dUo9HvbaSfzeTfeCNx9t9zm1URTaxTLLGM+7vL8YFwaBUd0TevfzS+E3tD1HbWAWBjox1VTmN4I\nt97avywquqCYPj3+P2mEPWVKfo1CfU6uMPAqWReBCWFe2MaCwldY2L7r+53Y5rFMcLwrlz1a7xhM\no2uToFDTnDw5/l8VmuqIv6UF+Otf7WU14ZpwNQkKfn9MgoIX0rW0xOHR03b8WQSFutbF5T1pQ5/r\nVN9R033q7ZjnQ37968rjtrrVtVJ10AJUeojxlrommlqjUFFX0778MvDDH7rPVzWKtKYnG9xQ9PQ4\ndo36Yp9/fvy/6lWjChl++ebPl/77y3ute6+E94pWy3T66fH//JINGBAvwFPJ41Zqwtf+7OLzz+3z\nU52d8cY0KmkERWdnZdwm1zWuwYTeDthTzdXh6oMM3q5WRd94x5QXYwpoyBpF2jkUzk+PdMrl5vt6\n4434OGB+VnyfH3wgO83OzsoBkg8u05MPWbRW/RpVUPiYVXnl/EorSY8oxjaPoWvH+g6XZ50V/+8a\nXDW1RqGy0Ubx/88/nxxnx7WOQre1qteYYKnOL7D+svNCPPVh2fbeZqEhhEz3gw9khMntt/df+KbC\nXiW2RsT3PmOG3GpVJ80ksKmDBopvpL17A7/7nfk3W1lZUOi/b7NN9blvvx0/QxYurAHqL24aQaGP\nuHVMGoXr5e/slKFBTjnFfh67y6plY0Fhei4//ak7PwD4zW+qf1MjMLOnmktQ6KvUi9Yo0q7pyWoS\nVQWFKU/9uajX6+s/dBYskPOhKnq4Hd5/JYmgUUSoFX3eeZW/qUJEPd+mUXBa665bedxU2XPmVKuc\nJlfc/fYDfv5z+Z1t1SbUna2mTAH23LO6XGkwaRQqfLxHj8o4QYxtUycTZca913nhBfNxm8eSLZ4X\n/77eevGxKVMqI5gKEe/fnbS3h5r+n/5kLlsajcJV90JIN8zBg+3nqavu1QGQbf7LtH8GTzLzvZra\niSk9/q4Oik6INjfWXVJVjcLX7OjSKEz1seqq5vIB5nkEEy5BYUK/lzQbFF14YbqdBoNG4WClleSn\nWhGvvy4/DzxQfppi2fh4PekrrE2VbdrDVl9MI4ScbGIPFJ91FkBsDzfNcSQFmGNaW6UnhmnnPTVt\n/X+GCDj7bL+8aoktRAK7auqwZmSaf/jrX2NTIP+uzgO4vKV0QaHGB9NDzbhcGPfZJ547cKWv8txz\ncQe72mrmc0yrnLndm9qzqe64HfK+D6b229Ii13+oOzZy2U2Tw/pqb1WjSIIHcC6NwuTR5LIQ+Lra\nmzQEl0ahly2NoNBjh9lQtyxW4YHbxInAm2/6pZWWLiMoXCOI666zb3zi8np68EE5H6Cn6bMfBJA/\nsB/DL6TpRfAdvSe9ACZBoaq7ra3xZkA28oRASMPVV8d+5S7PIVPsKpvpqbNThkjn0C+crtrBuuY2\n1I2CgEpHAR2XoLjtNrPpyVW3V1wRC7GjjzZ3Qvp9cJo209PChcBVV8n4YwyHlRg92l4W7kDV903f\nBx2IXXvVcOhApUbBi9VMYTh6947DyHBd+XpJuRwPfN/RtKYnnTSCIq05TBViW24JnHRS/H9ZdBlB\nkVSZto1q1DkKfZQweLAUFKobZJptKpPMB76Nkl9yk0ahjxDUyXyVpMia6r1zPvo6i6SQJElqe1Fq\n7yGHxIvNTCP899+3rzq2TWbb3BTVNDo77XNKOq575bRN4TX4d73sLkF/5pnxSJzIPKBQR/8c78dl\nerrvPrsDA0dC8Bk5A7Er6afKZgHstaOHQFc1im7d5PfJk+V5HHpk9mxZBq6jtJPZ+v2qccuS+hGe\nGyhyjoIDTPK1vA7Gt0x6mmpeY8fG3/OEKEnMu7ykiyXJ7mvDpVEA0l9e3ZlOHwG5KEujcN3jnXea\nj5vsySqmybXddovnUTo6ktVW3WVUp0j7qOul5EGBzYRm0ihsgkM3PamL7HzKZ/vtnXfi7S91TBqF\nK7jd4ovHI3Fb566mx/M6LtPT+usnz0uZ7tFlNlLn3VhA6CYxVaNgWluliZVHxNzOdAHhO6ehO1uo\nzht8v7ZBkUko8XdXm9SPuTSKI4+sPuaz+NC0HKBHj+KjG5hoCkGRNEeRFdXtDcjuceDSKNRGsPTS\nlesyVGyeSIxJo1hjDen9ssYafmVnTy1bXKYiPS58OjC+p6WWisNZ2DQKvWymyWnXHAVQOTJNcqUd\nOLD6mWyxhfw87rjqjmTwYHt6POfiEhS2cpg0in32kfZum6cbD5Z8NQpGrTtu07qgMM1R2DRzfW6C\nCHj1VXvZAOnu7Fr4x9eZ5hyBOBBiWkGhe1+63Lr1ecTOznjy34UtFEnR/ZqJRUZQmB7eyivbvZ70\nhTBJmNLnCXZml10qv+v7FNjQBYVq8503r7IMPu6RKjxSMc1R2Bb82eAVwbbOogyNwrUXA//25pux\nN5NNo1DjUKlpDBwoP0eOlPMHrpdO9W13zZlxR6CbArjeXnghnTbKgoIHO771zO1eF4q33iq9u2xp\nsXZqahO+nRK3aX1C3KRR2OpCNz0ByVF3L7vM/burXQHxYCitoNCftU0Qmd7hefP8ninXKZHcJ4Wd\ncIKgUEiaSOI4TEy/fnLpvm0dBY/u8qDPiwwZUvndd0cxXVCoqKN3m6Do06d6cZSetuo9ldb1j+FR\nO3euOuqzMZnw0mgcaTQKtU4WLpQro5NePP5dXXw4blx1/dpMQr16yc80XiZqh6fvfZxkylJNT7Zz\n9fkVbvcHH2w+v6UlXjSn4hKCvmYOFhCuOQrGFknZZgZykXQuz7Mlda4uryfbojmVNNsD3H8/8NBD\nlREZTKj1tMIKcSiUWtBlBEVHh5zUU1coqlx9dfUxdXSpaxT6g7XF3neRNDLacEP5+alxR/Dq60yN\nXG1YNkHR2mq3ifILy67Eaj5JGoVtToLXGuiodbreetIvX50AT+M1ldRxAtXeM0DsjOArKEwbH6m0\ntEiTnw4LClc5//Wv6rQY3WvKR6NgQeE6D4g9mdj0xGXV+eYb84S7ug5Dx7aQDIjr8q234nY3cGCl\nsE2jUWRZaJckKNiVOem8tBqFvrLetKYLMN9/R4cUFOPGVYcqV1EHRnpfYBu8FUWXERSdncDPflYZ\nkz4JrlDTHIX+sB96KH2Z9Aeuf+fYR0lbNrpGTmrnahMUplXnjGkCzFdQ2DpR07mPPloZe18I2fBV\nU0AaQZFGo1AXPro6ORXuMHSNwfRMTWVJcy9synF1TrfdZv+to0NuCpQkKPR7t3mGMZyW6swBxCaN\nJI1CXUsBSE8qQHZanPYyy1SbT10axaRJbtNTEkkCgM3BpnpUQ+ykFRRq9F5XOUyCwhfd68s071gW\nXUZQfPZZuspgN0JVo1BJa083ne/ag3rgwMr9Alxuly6vDn75uAymOnB5PphGk7aXIGmBIPvOmzph\nVWPhsgKVtlubOp4WW5h3U/42t2F2AdW9xUzPwCUofNoR10HWl7mzU4bCePVVP0GhrqPwyfOccyrv\n4667ZJC6pDkK12JQ3Sx40klSE03SKDbaKJ609dEoTG3ZB1M9Lrts7PWWVlDobdH2nGxu/IyrPanr\nSXRBUfY8RZcRFEC6yuCKVOco1E6hiIlX0+5izFtvARtsEH83xRpiXCENHn00Ds1hG43MnGk3L+ij\nPsCuUag7vJngUBU+i4ZM9auuYNZ3T7Pxv/9VH2MNzSQoOF/XosmjjortwfvtV/mbSaMwuVKmWXzl\nuze0Dc5L9+bRQ4vrGkVSmHGbhn3vvXLxZZJGoUc4VfdJ0etxyBBZ5z5zFBwhQG2fNvSRvG8fYTtv\n553NeZrmKNT93X2dE845J3sIDlVwzppVKaiDoFDgyjDZjE3n6nMUtrj0gL9NW8/DNzKn64VN8hNX\nR4i2Ea/tWtOksklQ8E6Apnxt5VHRJ3X1dQr6/y530CQWW0zG83FpQKq5Q2fNNaVgHTDAHio+iTSm\nJxYUrvZnYvXVpQs2Pxe9bPp+8iaNwkfr4nN1kuYo9Fhnra12Txwe6NjWUZjg80y/m8y1I0bkFxS8\nGM5Ho1Db37BhleebBmg21C1x29rs56mCYcwY4Pjj4+9FehyaKF1QEFErEU0iovui70OJaHx07Dki\n8hxbxg/vhht88q2eo1DhitWjNNrgrQ91dBdZGy0tlaN+dYtVl+0fiMu6YEF8rt5J2hq+bU5DZfx4\nOensKyhM5fTZ06OoECA8KjWN1Dlf17qSlha5za26XSbj29GkMT1xfV9wgV/ajBCV+z/oZdPNZnpH\n1tlZ/awHDTLnxfez7rr+Xk96O1CDUtq8hkwaRdqJ5eefj4MNqvn4DtpmzbI7mPBeDy6vJ11QnHlm\ntdvuyivb89fTVqMt7Lqr/TrO3+TJWOQaJhO10Ch+C2AKAG525wE4WQixIYBTou9e+KiiKj5zFGrD\ndlW27h3CWo2vJNdXwap7TrBpw9aRqg2Uy6t3kmkEhellPukks6Do16/ahdKnUZrqxdZJpYVHpS7T\n0xln2K/3XTRm+s6kEXqcn48mrKKH4EgSYqusIj85pIZJo7At2OzokBpPz57xqDppjsK0iJF/150o\n1Hcxr0ax/vrxAE1fH8TCUzeLqSy/vH0fG5+V2ewNyHnZoj6Y2HTT6ugKnZ1SEzvvPODHP04eoJkE\nom98uqyUKiiIaEUAIwBcBYCbxywA7Ae0FADvALtpJ7P1OQoVVT3n7+eea09PDzrIgdN8BYUenE0t\nD0ePtHU+6ggvSfvQcWkU+m96mg89JMOrv/SS+zwTpnrp08c+lwLIsN+u39W0W1rssZ6Ayk3vdZIE\nhSucBtPRIWMUqbvM2ciyHgCo1iiSrn/iiUoPuzRzFCyUnn02HqkmaRT676qg0H9zaRRJgxzXfevu\nuuwqLYTb/KiiDro4MKJLUHAE3/XXj3/z9cwyhXdfZhk535QUxiNrdIoiKFujuBjAcQDUWzsBwIVE\n9DaA8wGcaLrQRNoJG3WOQn/wbBdU1wqowQGT0FevJgkMXaMw3YvPqnO+D/1cTu/3v3eXQ00jKf99\n9zUfN92remzHHc2mGSHcL9TSS9sXDurlZK82GxwZ13ROkqAw7T9tKsN661XuI+JK05Qv27FtPvBp\nNYq+fSvnpJLcY/U5Cr18SXMULo1C/407WpNGYbsv9hT01QD1spkEvr4AEKiMVsxC0iUoGJ5P+Oor\n/0GAqU7XWcfPQ011Uqg1pQkKItoZwIdCiEmItQkAGA3gN0KIgQB+B8CwVI4Zpfy1Z9IoeI5Cf2F4\nYQu/LM8+WxnLxyd99fokfASFqQHomogtXz6uutO6ymIqAzdEfeMXn61i1fL062cOtZ3UcdnWc5jy\nso3idJOi6Zykjufyy935DxiQzfSkl4VDguyxh/m6pDkKV14dHebOxzbXZnpH0s5RqIJCrx+XRmGD\n5wBc962bnmxlY/SAfIDs8H/yE3u6/N0k/AAZrddXo7ANspLeDSDOn127mfb2dlT2lcVTpkaxBYBd\niGgGgFsADCeiGwAMFULcHZ1zB4ChtgQqb77NW6O48EK5gVBLSxySOmkUrW8+k0QWQaE2NF9Bse++\nwMUXJ+ebZTLbJCjOPRd4+OHq467v+rG11or3trCtLLftsucjKFwahW5SzGJ6ctm3AdmB3Xtvcjn1\n/Gzmz1//2rxntq5R+HSwqrPDa69Vdz7qoki1bkyCIs8chU1QZFlw5jrfpVGkTUvFZh4zaffdu/un\nq5aLXeZtDjc6nDfvP8G0tbWhywoKIcRJQoiVhBCrAPgJgP8IIfYHMI2IIi92DAfwmjURDa5ItbGb\n2GsvYLvt5MMbOVJORNukddI+DkllSTNHkeT3bBIUQlRuXmQTFOq6hKQ9NVyCYuDA6sWBPoJCLbu6\nolsXFLxj3aGHxjupqeXS91g24dIoOD9eAGh6+ZI6Ht3urdO9uz2EuAmboFDzsGlH3brFHUMajQKQ\n22v6jnSL0Cheey0WoEVoFKY8Xb/5CIq8c3umwV6alePq9WPGSC8nX41C3ca31tTC64nhZvdLAOcR\n0fMAzoy+e8EPZtll3S+q7tUzZ469sSXtP8ETVnrnmXZS2ccl0ObLrh63uR+yF8icOXJyjBcDurye\nTPm3tFT/3tFR2WnwdqK2suvCQT3OaffoUS3w09h5bZ0r582eJVnmKNT7//LL6jmLGTMqwz0kpW8z\nPal5CiG9lf74x/i4vlo+raDQR6nrrGO/zqR1f/11dbBN9Rx1O1hTevp1WTUK07wCk1ajSDKbmtJS\nj5nadtJeMNddF/+vhhgnktEdbPOoOkmabpnURFAIIcYJIXaJ/p8ghNhUCLGBEGLzaA7Di6SRnv4b\nf86YEbvr6S6K5yU453Jj0Ee6eQVFkkZxxhlyIxebiWnyZHNIZV4b4IpdpWslbCN/8EGzoPBpxKoW\noYbtsAkNnzAINnhUagqHoD8P00ZEafz2v/rKb8LaVQZfQXHmmfHqdyDWKNTzklDzWLCg8ruro/nm\nm+p6GT26+v3QBZENk0bR0ZFu4heorgMd3z6BMb2vrkWp+jHT9UkLKfn3Pn2AV16p/M211ssGewbu\nvbff+UVQS40iN2pD0CvcdJ5a8X/4g/zUA92pL4++nwRgFwRZTE8q6qpak9AZNkzu8WwTFGuuCay9\ntj0/ta5+adHZOG02W33+ub+mwyxcKCfXOPQBEK/OBaons11zO2k1ClPMf/15qbub+eRz7rn2uYSs\nJJme2NHBZOJT241P/fzud/H/qjlpo42qw12orLaaNFXp6Bvq+GoD+nvW0iIDGz78sPk+9IHNz3/u\nl49NozA9sz//2fw+2+bLTMdMpic1VI+JDTaQ/cyZZ1Y+T9Xhxsf0xPkddJC0dOjBHMukywoK1+Sz\nyTzDo9yllqpc7GZLn0kKY5FVo1AD5OkTsJyu7iml4/PSckM0YfKmaWkxb19p4/jj5UughnP43vfi\n77pG4Vqo6CsoXHZuvaxcDnUFflobeV5M7VHtgHm9j8kNOa1Gsf76sbl04cL4XidOrDRr+dK3rwzt\nz27LvnV3wgmVix7Vsvs4GKgBNV2kERSudyGpPIB8Z01rrZKey+DBclFtt27V9ZBFo7j00tq7yHYp\nQaFWpMsuaHoxWVpPnBjvKWy7ToUn4HQ4QFvWhmfaKlEXFNyB2HA1UHXhmm1bRlM8ppaWalXaVQZe\nta2PtPhe2tvjDZyEiFdn5xEU774rn6MJ2/NQY+/o+ehb2pYdNweQIR/U+heiOl+fUaZOS0tsZs1y\nvTCyOGUAAB+lSURBVE5rqxy5svnNV6PYaqtKwWTaD0VFT9f3GajtvEhBYbvP006rPieNJ5W6qjqt\nRqEyZUr6uZ48NJ2gWHbZeJ8I23Vq+qYXGIgFha9GoTcCU+dtMtO4Gja7oJpQ7+WPfzSvEXEFW3PB\nnca8eXHMHX2thyr0eBX7sGHxzm4+gsJWFtNkOpNkKjTlkydAYRJrrZU8glY7CxWbff6UU+z5dXRU\n7p6XV1BwOXlHyKydE4cFsaGna9p5z4Q6wnetqL/zTj9BoQba8yWN8DGFI/fVKFyuyUcc4VeGrHQp\nQaFWlI8nhKtzSEqfvWZsGgXPMWTVKEwbwJs2KXKlP2VKcr5EcgHd/vtXHh89Op7Es6nvNvicxx+P\nj+kahS70GNcOdLYJf30/Yl51PXx4clkBaQpTtYakOQj9u6utMbYO+Ve/Mm+XqnpXpdUoXJ21vhgr\nr5mNr/eNQJCUDmCOS6SX0xaTSkcVDq7Okt3lk8qvBuhLwtTPuOAJffV6LtNHHyULdXXPdjX/WtCl\nBIX6QFy7xpk0Ch+bp0lim0Z6gIyro56XhE9jUrUMtsO7GrZvuHUTBx8cl2nWLP9y9uyZ7GLoY0Yw\nhcmw5a2HJenTRwZPM8XG4bxWWCH22Hn99cq9x5NeML28rgCDSWnaYkHpwlmIOOaXqqWZNIo0HURR\nGgVv0pU1ztB228X/28xCeUmKE9bSEofpUfdKVxk5Uu4Z4UMW05M+oOJBFUetdbH33sBf/1qdf5oy\nZKXLCgp1EZqO6QH6bJSjnq9OVpsaNm94k9X0ZBJ0alrspupK39czJAl1IaCps1bvv39/+8JAJklQ\nCBEHVlPRrzMJMiAeaZueC4dicKny+nGTtxvDHitJ2F7UcePs16hzFOpzHjtWfuoaxU47yc80WkIR\ncxSAFLTvvFO5JiANHNkW8BMUWTQXV72wht7e7k6jX7/05icu+8SJUnu0lZ01Cq5TFhS8tsLnuSa5\nmJdFQwsKtosyakX6RPjMalI54IBKN07TA2GzzZw5yemqaeubvajY1FIbeTsBRjWDJdVT9+7JITz0\nl/4f/8hWLttaFde6ji23lGFPfAQFmzd0t9EiO6kkrzmg+jnz//q1HIMrjaAoyvREJMOMZA2NoWpG\nZWkUSWmov5v2Isma32GHyc+NNkreh0IX/kRxYFKfOlAHHhzloBY0tKBwxdPxMT09+mh8zKdT5Qa8\n226Vttk8Hjr6+bzC29QoVJfZXr2SBYXPHtQ+je+mm6rLaaNbt/QaRV50L7WkBYA8mZ4kKLbc0vx7\nFkFhu+csgoLp7IxNnD55mX7zDTNuw7ed+64BAPwExZNP+uWr4iprz56Vedg2LkoDp+cSDiqsUagW\nD9Xxw6eu3367Ov9a0NCCQg+boWKy3fJ2hD5+2ibUkZyqUZhGUWp6rpWjjKpu2rj6avm5xx4yBAcR\n8Mwz9vPVcADMWWcll0VHfXF9BEVajQIAjj02fbk4nWnTYhv3vffKPQNc8zdJgoLT1YOrMVkFhRD2\nNTquPHRBoWpSqllQ/92Er+eYXgYbuiOBb74ufPL1iYKs46oXfQ1DEfjuqMe0tEhPRa5TFhSs0fvU\nYdJ7VhYNLSiAyg3Mk1DVZNtvLrgBq2EsbBqF6pWRxqzlOpcbDI9QXnvNHeDPVC7dQ8enMelzIy50\nzw29LAcfbL7uwguTy6Gjlp3t9iNHyi1Fk8KVz51rNws+9ZT8tKWRRTvi8/T9NPRn9M9/xv8fcICc\nqNcFxbRp5mvTlIPJa57U54dsZA2Mx9Ta9FQEK66YzoSl58+C4rbb5Hef/XCyOhPkpeEFhSuQmY7L\nCyGNtCaqDNGhvrBbbCFd2dSosz4NkDst07ns7skT7jbbvK28zFVXVbvQ+ZBGo7DFu+E0hgwp7oVM\nM3I2XXfjjebffbQ7xtRZmzRI1iR4G9Kk9ISQnjccMkTN58EH5aft+acxJ/nUkwtfAaBvferCtIYo\nT5thxxKVzTeP/+eFf2WMwJN2pVOxLTTk3TP1XTRN2GKnMccc41+eNDS8oLCtKjbhEhQ+ozN+CHPn\nxi/I3LmVD6etrdok5iOELrhAfpoWCl5+uTSb8QS5j/YBVN/TIYfYFxP6ppMU6+noo817PXAdrbFG\ncS+k69knmZ74HBOucBS77JLcOeohToDYRKgHmFPdQoG4nl58sbK8vlFNAbl/hc2bKo3pyYeiHCZU\nRo6sPmaK2+ULDwhM9fWnP8VarqldumLGFY1pnZBapjTzqIDZDFWWxtHwguLoo/3PzSsouAN8+mm5\nqcjo0XIBEwdL22KLyuieer4+HHec+bhq0vFdyOMzN5LV9KQGHFQ77OWWs3dq++wjF8EVJShcC918\nRtW2cnC6pt+PPNK+uQ9jEhS2bTv1oHrcDtXV067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"text/plain": [ "<matplotlib.figure.Figure at 0x7f7e097456d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "L_t1= plotter(T[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Correlation plots" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/saket/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:8: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n" ] } ], "source": [ "from scipy.signal import correlate\n", "def autocorr(x):\n", " xunbiased = x-np.mean(x)\n", " xnorm = np.sum(xunbiased**2)\n", " acor = np.correlate(xunbiased, xunbiased, \"same\")/xnorm\n", " #result = correlate(x, x, mode='full')\n", " #result /= result[result.argmax()]\n", " acor = acor[len(acor)/2:]\n", " return acor#result[result.size/2:]\n", "\n", "cov_t0 = autocorr(L_t0)\n", "cov_t1 = autocorr(L_t1)\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f7e0bb43cd0>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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xU1MTTU1NJYZmPcmgQfDNb6Zt1iy48Ua44gr46lfTDMR77gkjRsCQIR7waI2r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\n5NADI2LLbBscEZM6OEbAgoi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"text/plain": [ "<matplotlib.figure.Figure at 0x7f7e0993ba50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(cov_t0)\n", "plt.ylabel('Autocorrelation')\n", "plt.xlabel('N_steps')\n", "plt.title('Autocorrelation of L_i for T=0.05')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f7e09ad3c90>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Vec1RmkREKyqUxo2hrAwaN446R845V/flcq6tlCUSETlTVZ8QkSuIm6lXRARQVb0rFxnI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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f7e0998cdd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(cov_t1)\n", "plt.ylabel('Autocorrelation')\n", "plt.xlabel('N_steps')\n", "plt.title('Autocorrelation of L_i for T=10')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Result\n", "The autocorrelation seems to be high even for large values of $N_{step}$ for both the temperature values. I expected higher $T$ to yield lower autocorrelations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Problem 1\n", "Let the state space be $S = \\{\\phi, \\alpha, \\beta, \\alpha+\\beta, pol, \\dagger\\}$ \n", "Definitions:\n", "1. $\\tau_a = \\{ n \\geq 0: X_n=a\\}$\n", "\n", "2. $N = \\sum_{k=0}^{\\tau_{\\phi}}I_{X_k=\\dagger}$ \n", "\n", "3. $u(a) = E[N\|X_0=a] \\forall a \\in S $\n", "\n", "$u(a) = \\sum_{k=0}^{\\tau_{\\phi}}P(X_k=\\dagger\|X_0=a)=\\sum_{b \\neq a, \\dagger }P(X_1=b\|X_0=a)P(X_k=\\dagger\|X_0=b)$ $\\implies$ $u(a)=\\sum_{b \\neq a, \\dagger} P_{ab}u(b)$\n", "\n", "And hence $u$ solves the following set of equations:\n", "\n", "$u=(I-P_{-})^{-1}v$ where v is (0,0,0,1) in this case. and $P_{-}$ represents the matrix with that last and first row and columns removed.\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "k_a=0.2\n", "k_b=0.2\n", "k_p=0.5\n", "P = np.matrix([[1-k_a-k_b, k_a ,k_b, 0, 0, 0],\n", " [k_a, 1-k_a-k_b, 0, k_b, 0, 0],\n", " [k_b, 0, 1-k_a-k_b, k_a, 0, 0],\n", " [0, k_b, k_a, 1-k_a-k_b-k_p, k_p, 0],\n", " [0, 0, 0, 0, 0, 1],\n", " [0, 0, 0, 1, 0, 0]])\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.4 0. -0.2 0. ]\n", " [ 0. 0.4 -0.2 0. ]\n", " [-0.2 -0.2 0.9 -0.5]\n", " [ 0. 0. 0. 1. ]]\n", "[[ 2.85714286 0.35714286 0.71428571 0.35714286]\n", " [ 0.35714286 2.85714286 0.71428571 0.35714286]\n", " [ 0.71428571 0.71428571 1.42857143 0.71428571]\n", " [ 0. 0. 0. 1. ]]\n" ] } ], "source": [ "Q=P[1:5,1:5]\n", "iq = np.eye(4)-Q\n", "iqi = np.linalg.inv(iq)\n", "print(iq)\n", "print(iqi)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "U=[[ 0.35714286]\n", " [ 0.35714286]\n", " [ 0.71428571]\n", " [ 1. ]]\n" ] } ], "source": [ "print 'U={}'.format(iqi[:,-1])\n", "u=iqi[:,-1]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "PP = {}\n", "states = ['phi', 'alpha', 'beta', 'ab', 'pol', 'd']\n", "\n", "PP['phi']= [1-k_a-k_b, k_a ,k_b, 0, 0, 0]\n", "PP['alpha'] = [k_a, 1-k_a-k_b, 0, k_b, 0, 0]\n", "PP['beta'] = [k_b, 0, 1-k_a-k_b, k_a, 0, 0]\n", "PP['ab']= [0, k_b, k_a, 1-k_a-k_b-k_p, k_p, 0]\n", "PP['pol']= [0, 0, 0, 0, 0, 1]\n", "PP['d']= [0, 0, 0, 1, 0, 0]\n", "def h(x):\n", " s=0\n", " ht=0\n", " cc=0\n", " for j in range(1,100):\n", " new_state=x\n", " for i in range(1,10000):\n", " old_state=new_state\n", " probs = PP[old_state]\n", " z=np.random.choice(6, 1, p=probs)\n", " new_state = states[z[0]]\n", " s+=z[0]\n", " if new_state=='d':\n", " ht+=i\n", " cc+=1\n", " break\n", " else:\n", " continue\n", "\n", " return s/1000, ht/cc\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### $\\alpha$" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Simulation: 18.2323232323\t Calculation: [[ 0.35714286]]\n" ] } ], "source": [ "print('Simulation: {}\\t Calculation: {}'.format(h('alpha')[1],u[0]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## $\\beta$" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Simulation: 17.0\t Calculation: [[ 0.35714286]]\n" ] } ], "source": [ "print('Simulation: {}\\t Calculation: {}'.format(h('beta')[1],u[1]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## $\\alpha+\\beta$" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Simulation: 8.92929292929\t Calculation: [[ 0.71428571]]\n" ] } ], "source": [ "print('Simulation: {}\\t Calculation: {}'.format(h('ab')[1],u[2]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## pol" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Simulation: 1.0\t Calculation: [[ 1.]]\n" ] } ], "source": [ "print('Simulation: {}\\t Calculation: {}'.format(h('pol')[1],u[3]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Result\n", "\n", "The simulation and calculation do not agree. The simulation implementation doesn't look correct. However, looking at $\\alpha$ and $\\beta$ results, the simulation and calculated results seem to be in-sync." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
jastarex/DeepLearningCourseCodes	04_CNN_advances/cnn_mnist_simple.ipynb	1	160807	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 卷积神经网络示例与各层可视化" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "当前TensorFlow版本为 [1.3.0]\n", "所有包载入完毕\n" ] } ], "source": [ "import os\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import tensorflow as tf\n", "from tensorflow.examples.tutorials.mnist import input_data\n", "%matplotlib inline \n", "print (\"当前TensorFlow版本为 [%s]\" % (tf.__version__))\n", "print (\"所有包载入完毕\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 载入 MNIST" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting data/train-images-idx3-ubyte.gz\n", "Extracting data/train-labels-idx1-ubyte.gz\n", "Extracting data/t10k-images-idx3-ubyte.gz\n", "Extracting data/t10k-labels-idx1-ubyte.gz\n", "MNIST ready\n" ] } ], "source": [ "mnist = input_data.read_data_sets('data/', one_hot=True)\n", "trainimg = mnist.train.images\n", "trainlabel = mnist.train.labels\n", "testimg = mnist.test.images\n", "testlabel = mnist.test.labels\n", "print (\"MNIST ready\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 定义模型" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NETWORK READY\n" ] } ], "source": [ "# NETWORK TOPOLOGIES\n", "n_input = 784\n", "n_channel = 64 \n", "n_classes = 10 \n", "\n", "# INPUTS AND OUTPUTS\n", "x = tf.placeholder(\"float\", [None, n_input])\n", "y = tf.placeholder(\"float\", [None, n_classes])\n", " \n", "# NETWORK PARAMETERS\n", "stddev = 0.1\n", "weights = {\n", " 'c1': tf.Variable(tf.random_normal([7, 7, 1, n_channel], stddev=stddev)),\n", " 'd1': tf.Variable(tf.random_normal([141464, n_classes], stddev=stddev))\n", "}\n", "biases = {\n", " 'c1': tf.Variable(tf.random_normal([n_channel], stddev=stddev)),\n", " 'd1': tf.Variable(tf.random_normal([n_classes], stddev=stddev))\n", "}\n", "print (\"NETWORK READY\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 定义图结构" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "FUNCTIONS READY\n" ] } ], "source": [ "# MODEL\n", "def CNN(_x, _w, _b):\n", " # RESHAPE\n", " _x_r = tf.reshape(_x, shape=[-1, 28, 28, 1])\n", " # CONVOLUTION\n", " _conv1 = tf.nn.conv2d(_x_r, _w['c1'], strides=[1, 1, 1, 1], padding='SAME')\n", " # ADD BIAS\n", " _conv2 = tf.nn.bias_add(_conv1, _b['c1'])\n", " # RELU\n", " _conv3 = tf.nn.relu(_conv2)\n", " # MAX-POOL\n", " _pool = tf.nn.max_pool(_conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n", " # VECTORIZE\n", " _dense = tf.reshape(_pool, [-1, _w['d1'].get_shape().as_list()[0]])\n", " # DENSE\n", " _logit = tf.add(tf.matmul(_dense, _w['d1']), _b['d1'])\n", " _out = {\n", " 'x_r': _x_r, 'conv1': _conv1, 'conv2': _conv2, 'conv3': _conv3\n", " , 'pool': _pool, 'dense': _dense, 'logit': _logit\n", " }\n", " return _out\n", "\n", "# PREDICTION\n", "cnnout = CNN(x, weights, biases)\n", "\n", "# LOSS AND OPTIMIZER\n", "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n", " labels=y, logits=cnnout['logit']))\n", "optm = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost) \n", "corr = tf.equal(tf.argmax(cnnout['logit'], 1), tf.argmax(y, 1)) \n", "accr = tf.reduce_mean(tf.cast(corr, \"float\"))\n", "\n", "# INITIALIZER\n", "init = tf.global_variables_initializer()\n", "print (\"FUNCTIONS READY\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 存储" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAVER READY\n" ] } ], "source": [ "savedir = \"nets/cnn_mnist_simple/\"\n", "saver = tf.train.Saver(max_to_keep=3)\n", "save_step = 4\n", "if not os.path.exists(savedir):\n", " os.makedirs(savedir)\n", "print (\"SAVER READY\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 运行" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 004/020 cost: 0.035408186\n", "TRAIN ACCURACY: 0.980\n", "TEST ACCURACY: 0.986\n", "[nets/cnn_mnist_simple/net-4.ckpt] SAVED.\n", "Epoch: 008/020 cost: 0.014208857\n", "TRAIN ACCURACY: 0.990\n", "TEST ACCURACY: 0.987\n", "[nets/cnn_mnist_simple/net-8.ckpt] SAVED.\n", "Epoch: 012/020 cost: 0.006352575\n", "TRAIN ACCURACY: 1.000\n", "TEST ACCURACY: 0.987\n", "[nets/cnn_mnist_simple/net-12.ckpt] SAVED.\n", "Epoch: 016/020 cost: 0.003182677\n", "TRAIN ACCURACY: 1.000\n", "TEST ACCURACY: 0.987\n", "[nets/cnn_mnist_simple/net-16.ckpt] SAVED.\n", "Epoch: 020/020 cost: 0.001347903\n", "TRAIN ACCURACY: 1.000\n", "TEST ACCURACY: 0.987\n", "[nets/cnn_mnist_simple/net-20.ckpt] SAVED.\n", "OPTIMIZATION FINISHED\n" ] } ], "source": [ "# PARAMETERS\n", "training_epochs = 20\n", "batch_size = 100\n", "display_step = 4\n", "# LAUNCH THE GRAPH\n", "sess = tf.Session()\n", "sess.run(init)\n", "# OPTIMIZE\n", "for epoch in range(training_epochs):\n", " avg_cost = 0.\n", " total_batch = int(mnist.train.num_examples/batch_size)\n", " # ITERATION\n", " for i in range(total_batch):\n", " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", " feeds = {x: batch_xs, y: batch_ys}\n", " sess.run(optm, feed_dict=feeds)\n", " avg_cost += sess.run(cost, feed_dict=feeds)\n", " avg_cost = avg_cost / total_batch\n", " # DISPLAY\n", " if (epoch+1) % display_step == 0:\n", " print (\"Epoch: %03d/%03d cost: %.9f\" % (epoch+1, training_epochs, avg_cost))\n", " feeds = {x: batch_xs, y: batch_ys}\n", " train_acc = sess.run(accr, feed_dict=feeds)\n", " print (\"TRAIN ACCURACY: %.3f\" % (train_acc))\n", " feeds = {x: mnist.test.images, y: mnist.test.labels}\n", " test_acc = sess.run(accr, feed_dict=feeds)\n", " print (\"TEST ACCURACY: %.3f\" % (test_acc))\n", " # SAVE\n", " if (epoch+1) % save_step == 0:\n", " savename = savedir+\"net-\"+str(epoch+1)+\".ckpt\"\n", " saver.save(sess, savename)\n", " print (\"[%s] SAVED.\" % (savename))\n", "print (\"OPTIMIZATION FINISHED\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 恢复" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DO NOTHING\n" ] } ], "source": [ "do_restore = 0\n", "if do_restore == 1:\n", " sess = tf.Session()\n", " epoch = 20\n", " savename = savedir+\"net-\"+str(epoch)+\".ckpt\"\n", " saver.restore(sess, savename)\n", " print (\"NETWORK RESTORED\")\n", "else:\n", " print (\"DO NOTHING\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CNN如何工作" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "input_r = sess.run(cnnout['x_r'], feed_dict={x: trainimg[0:1, :]})\n", "conv1 = sess.run(cnnout['conv1'], feed_dict={x: trainimg[0:1, :]})\n", "conv2 = sess.run(cnnout['conv2'], feed_dict={x: trainimg[0:1, :]})\n", "conv3 = sess.run(cnnout['conv3'], feed_dict={x: trainimg[0:1, :]})\n", "pool = sess.run(cnnout['pool'], feed_dict={x: trainimg[0:1, :]})\n", "dense = sess.run(cnnout['dense'], feed_dict={x: trainimg[0:1, :]})\n", "out = sess.run(cnnout['logit'], feed_dict={x: trainimg[0:1, :]}) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 输入" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Size of 'input_r' is (1, 28, 28, 1)\n", "Label is 7\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f91a117e750>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print (\"Size of 'input_r' is %s\" % (input_r.shape,))\n", "label = np.argmax(trainlabel[0, :])\n", "print (\"Label is %d\" % (label))\n", "\n", "# PLOT\n", "plt.matshow(input_r[0, :, :, 0], cmap=plt.get_cmap('gray'))\n", "plt.title(\"Label of this image is \" + str(label) + \"\")\n", "plt.colorbar()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# CONV 卷积层" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SIZE OF 'CONV1' IS (1, 28, 28, 64)\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f91b46f5e50>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9188202fd0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f91881c68d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print (\"SIZE OF 'CONV1' IS %s\" % (conv1.shape,))\n", "for i in range(3):\n", " plt.matshow(conv1[0, :, :, i], cmap=plt.get_cmap('gray'))\n", " plt.title(str(i) + \"th conv1\")\n", " plt.colorbar()\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CONV + BIAS" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SIZE OF 'CONV2' IS (1, 28, 28, 64)\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f91786e0dd0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9188096890>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f91880fe550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print (\"SIZE OF 'CONV2' IS %s\" % (conv2.shape,))\n", "for i in range(3):\n", " plt.matshow(conv2[0, :, :, i], cmap=plt.get_cmap('gray'))\n", " plt.title(str(i) + \"th conv2\")\n", " plt.colorbar()\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CONV + BIAS + RELU" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SIZE OF 'CONV3' IS (1, 28, 28, 64)\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9188147b90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f91a117e710>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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EroH3kn4A3DvIvr1Ab7pva/ePzBqk1U8NhhUIJE2IiJ3p5gVA5w/7Ws1mzZpVMX3NmjWZ\nx/T19TWqOYVq+0Ag6U5gJslNFbcDXwRmSpoOBLAV+GwD22jW1prd7c8jz6zB/ArJtzagLWYdq+0D\ngZnVzoHAzLz60KzbdcQYgZnVzoGgzU2cWP5oucS+ffsyj9mzZ0+jmtPyjj/++My8c845p2L6Aw88\n0KjmtAwHAjNzIDAzBwKzrlevR541kgOBWQHcIzAzB4J2t2NH+ePnbTAnn3xyZt5hhx1WYEtaiwOB\nWZfzBUVmBrR+j6CWW5WZWU71ukORpNmSNknaLOn6QfZ7r6R+SZ/I0z73CMwKUI/pQ0k9wM3AucB2\nYKWkZRGxocJ+Xwf+K2/Z7hGYNVje3kCOHsEMYHNEbImI/cBSYG6F/a4GfgrszttG9wg6zPjx4zPz\ndu3alZlXLw899FBm3oEDBxpef6uq0xjBRODpku3twBmlO0iaSHL7wFnAe/MW7EBgVoAqAsG4sjt+\n96Y3AM7rO8B1EXFQUu6DHAjMClBFIOiLiNMz8nYAk0u2J6VppU4HlqZBYBxwvqT+iPj3wSp1IDAr\nQJ1ODVYCUySdSBIA5gGfKqvnxIH3kpYA9w4VBMCBwKzh6nVBUUT0S1oI3A/0AIsjYr2kK9P8RcMt\n24HArAD1Wn2YPnB4eVlaxQAQEZflLdeBwKwArX5lYZ4HnEwGbgfGkzzQpDcivitpLPAT4ASSh5xc\nGBFdc4+uY489NjPvpJNOqpi+du3aiuljx47NLKunp6di+tatWyumFzFFOFwrVqxodhOaptUDQZ4L\nivqBayNiKnAmcJWkqcD1wIMRMQV4MN02szJ1vKCoYYYMBBGxMyKeSN/vBTaSXNgwF7gt3e024KON\naqRZu2v1QFDVGIGkE4BTgceB8SUPQn2W5NTBzCpo9VOD3IFA0pEk1y9fExEvlV61FBGR9chzSQuA\nBbU21KyddUQgkDSSJAjcERF3p8m7Bh6PLmkCGQsc0ssje9NyWvunYdYAHXHzUiVf/bcCGyPiWyVZ\ny4BLga+l/97TkBa2qL1792bmPfPMMxXTX3755arSrXN0Qo/g/cDFwFpJa9K0G0gCwF2SrgCeAi5s\nTBPN2l/bB4KIWAFkLWOq/AwrM3uDtg8EZlabZk8N5uFAYFYABwIzcyAws/qtPmwUB4Jh+vOf/5yZ\nt23btgJb0p0OOaTy1fGt+AfnMQIzA3xqYGY4EJgZDgRmhgOBWdfriEVH1pqyHl5R72+eCRMmVEzf\ns6fyXeleeeWVutafpdX/sMq5R2BmDgRm5kBg1vV8QZGZAe4RmBkOBNYgRf1iTZs2rWL6zp07K6av\nW7eu6jrGjRuXmdfX11d1ea2o1Wc5HAjMGsxjBGYG+NTAzHAgMDNaPxDkeQiqmdWoXs8+lDRb0iZJ\nmyW96cHDki6S9KSktZIelXRKnva5R2DWYPUaLJTUA9wMnAtsB1ZKWhYRG0p2+wNwdkTskTSH5Clj\nZwxVdp4nHU0Gbid5yGkAvRHxXUk3Ap8Bnkt3vSEiluf/WNYOnnjiiYrpL774YtVljRhR+detv7+/\n6rLaTZ2mD2cAmyNiC4CkpSRPJX89EETEoyX7PwZMylNwnh5BP3BtRDwhaTSwWtIv0rxvR8Q/5anI\nrJvVaYxgIvB0yfZ2Bv+2vwL4eZ6C8zzpaCewM32/V9LGtEFmllMVgWCcpFUl273pg4SrImkWSSA4\nK8/+VY0RSDoBOBV4nOSZiFdLugRYRdJrqLxI3ayLVTlG0BcRp2fk7QAml2xPStPeQNK7gR8CcyLi\n+TyV5p41kHQkyaPRr4mIl4BbgJOA6SQ9hpsyjlsgaVVZlDPrKnWaNVgJTJF0oqRRwDySp5K/TtLx\nwN3AxRHx27zty9UjkDSSJAjcERF3px9sV0n+D4B7Kx2bdmt60/1aezLVrEHqMUYQEf2SFgL3Az3A\n4ohYL+nKNH8R8HfA0cD307tY9Q/Sw3hdnlkDAbcCGyPiWyXpE9LxA4ALgOpXm1hLOPzwwzPzshYE\nZc0ajB07NrOsffv2VUz/4x//OEjrOkO9LihKZ+aWl6UtKnn/aeDT1Zabp0fwfuBiYK2kNWnaDcB8\nSdNJphS3Ap+ttnKzbtARNy+NiBVApTtl+poBs5xa/RJjX1loVgAHAjNzIDAzBwKzruc7FFlbyJrW\nA9i0aVPF9Kwpx8GmIl944YXqGtZBHAjMrP2nD82sdu4RmHU5jxGYGeAegZnhQFCuD3gqfT8u3W4W\n119D/VkzDYPNQNSz/jqotf63VbOzA0GJiDhm4L2kVXmWRzaK63f9RdbvQGDW5Tpi9aGZ1c49gmxV\n35DR9bv+dq2/1QOBWr2BZu1u5MiRMWbMmFz79vX1rW7G2IlPDcwazBcUmRnQ+qcGDgRmBfCsgZm5\nR2DW7TxGYGaAewRmhgOBmeFAYGY4EJh1PS86MjPAPQIzw4HAzGj9QHBIsxtg1ukGLijK8xqKpNmS\nNknaLOn6CvmS9L00/0lJ78nTRgcCswLUIxBI6gFuBuYAU4H5kqaW7TYHmJK+FgC35GmfA4FZAerU\nI5gBbI6ILRGxH1gKzC3bZy5weyQeA8ZImjBUwR4jMCtAnaYPJwJPl2xvB87Isc9EYOdgBTsQmDXe\n/SS3T8/jLZJWlWz3RkTDb6vmQGDWYBExu05F7QAml2xPStOq3edNPEZg1j5WAlMknShpFDAPWFa2\nzzLgknT24EzgxYgY9LQA3CMwaxsR0S9pIcmpRg+wOCLWS7oyzV8ELAfOBzYD+4DL85TtuxibmU8N\nzMyBwMxwIDAzHAjMDAcCM8OBwMxwIDAzHAjMDPh/MX97MnzoRc8AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f9178128050>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print (\"SIZE OF 'CONV3' IS %s\" % (conv3.shape,))\n", "for i in range(3):\n", " plt.matshow(conv3[0, :, :, i], cmap=plt.get_cmap('gray'))\n", " plt.title(str(i) + \"th conv3\")\n", " plt.colorbar()\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## POOL" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SIZE OF 'POOL' IS (1, 14, 14, 64)\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9178133e50>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f91882ad0d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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04M2SLh/lPC9wYj2vsoEAOB94IiKei4jfAauBs+pPiogVEXH6OMs4mVVepweCZtoIdgEL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\nmeFAYGY4EJgZDgRmhgOBmeFAYGbA/wOKPf/qcTR0OQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f91786c3ed0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print (\"SIZE OF 'POOL' IS %s\" % (pool.shape,))\n", "for i in range(3):\n", " plt.matshow(pool[0, :, :, i], cmap=plt.get_cmap('gray'))\n", " plt.title(str(i) + \"th pool\")\n", " plt.colorbar()\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## DENSE" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SIZE OF 'DENSE' IS (1, 12544)\n", "SIZE OF 'OUT' IS (1, 10)\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0YAAACFCAYAAAB/sOifAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f9178663ed0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print (\"SIZE OF 'DENSE' IS %s\" % (dense.shape,))\n", "print (\"SIZE OF 'OUT' IS %s\" % (out.shape,))\n", "plt.matshow(out, cmap=plt.get_cmap('gray'))\n", "plt.title(\"OUT\")\n", "plt.colorbar()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CONVOLUTION FILTER 卷积核" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SIZE OF 'WC1' IS (7, 7, 1, 64)\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9178133590>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f91347b2c10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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EQojsKDflT8fmj9Y/crPE2mHasewy7KMlPQisAW6PiLcUajHrZIkXSBk2pRJCRLwREUeS\n3X99tKTD+24jaYGkbknd7ZApzRrVMQmhV0S8AiwF5vfzXlG5adQor8xmnacjEoKk6ZKm5j9PICse\n8XjVDTNrNe2QEMrMMswErs6ryYwCrouI6hc7MGsxI/3DXkaZWYaHyCo+m9kAWuHbvwxfqWiWSDsM\npnv0zyyRJld/lqR/z99/SNJRKY7BCcEskVQJoab680nALOAMSX0XED0JOCR/LAAuS3EMTghmCSS+\nMKlu9ef8+Y8i81tgqqSZQz0OJwSzRBpICF29F/HljwV9dtVf9ee9dmCbhnlQ0SyRBmYZXP3ZrN0l\nnHYsU/25kgrRlSSE0aNHs+uuu1ax6+0sW7as8hgXX3xx5TEAzj777MpjzJs3r/IYAJs2baq/0RC9\n+OKLlcfYunVr6W0TL7JaVH8m+5B/HPhEn21uAj4raTFwDLA+IlYNNbB7CGaJpOohRInqz2SFYE8G\nngI2A+emiO2EYJZIyisVo3715wA+kyxgzgnBLBFfumxmBScEMwN8c5OZ9eGEYGaFdrjb0QnBLBH3\nEMwMaJ8xhNI3N+VLsf9ekpdPM+tHp6yp2OsCYDkwpaK2mLW0kf5hL6NsoZa9gQ8Cl1fbHLPW1Uk9\nhEuBi4GdB9ogv6d7AcDYsWOH3jKzFpL45qZhU6YuwynAmogY9NbC2kIto0ePTtZAs1bRKT2EY4EP\nSzoZ2AmYIuknEfHJaptm1lpG+oe9jLo9hIi4JCL2joj9ye7L/rWTgdlbdUoPwcxKGOkf9jIaSggR\ncRdwVyUtMWthrfDtX4Z7CGaJOCGYWaEdph2dEMwScQ/BzACPIZhZH+2QEFzKzSyRZl2HIGlXSbdL\n+mP+57QBtrtS0hpJj5TdtxOCWSJNvDDpC8CdEXEIcGf+vD9XAfMb2XElpwyvvfYaTz75ZBW73s5e\new25tmVdS5YsqTwGwOLFiyuPMX78+MpjABx66KGVx3j44Ycrj7Ft27aGtm/iKcOpwPvyn68muzbo\n8/20525J+zeyY48hmCXQ4N2OXZK6a54vjIiFDYSbUVO2bTUwo4HfHZQTglkiKas/S7oD2KOft77Y\nJ2ZIStY1cUIwSyRxKbf3D/SepBclzYyIVZJmAmtSxfWgolkCZQcUEyWNm4Bz8p/PAX6eYqfghGCW\nTBMTwr8A8yT9EXh//hxJe0oqCsRKWgTcCxwqaYWk8+rt2KcMZok0a5YhIl4CTuzn9ZVkJeJ7n5/R\n6L6dEMwSaYcrFZ0QzBJol0VWSyUESc8CG4E3gG31pkzMOlGn9RBOiIh1lbXErMV1WkIws0G0Q0Io\nO+0YwB2SluUFWcysj05adfm4iHhB0u7A7ZIej4i7azeordxk1mla4cNeRqkeQkS8kP+5BrgROLqf\nbYrKTZLSttKsBbRDD6FMKbdJknbu/Rn4AFB6wQWzTtHT01PqMZKVOWWYAdyYf+uPAa6NiFsrbZVZ\nCxrp3/5l1E0IEfE0cEQT2mLWslrhdKAMTzuaJeKEYGYFJwQzKzghmBnQYTc3mVl97iGYWcEJwcwK\nTggDmDFjBueee24Vu97O0qVLK4+xZcuWymM0y2GHHdaUOLvsskvlMV599dXKYzQ6JuCEYGaAL0wy\nsz7aISF4GXazRJp1c1OZ6s+S9pG0VNJjkh6VdEGZfTshmCUywqo/bwMuiohZwLuBz0iaVW/HTghm\nCTS5ctOpZFWfyf/8SD/tWRURD+Q/bwSWA3XLpXsMwSyRBj7sTa3+nJeEnw3cV2/HTghmiYzE6s+S\nJgPXAxdGxIZ6DXNCMEtkpFV/ljSWLBlcExE3lInrMQSzREZS9WdlS5xdASyPiG+X3XGphCBpqqQl\nkh6XtFzSe8oGMOsEvXc7NmlNxTLVn48FzgLmSnowf5zc/+7eVPaU4TvArRFxmqRxwMSGD8GszY2k\n6s8RcQ/Q8PLndROCpF2A44FP5YFeB15vNJBZu+uUKxUPANYCP5T0e0mX58uxb0fSAkndkro3b96c\nvKFmI11H1GUg60UcBVwWEbOBP9HPlVG1hVomTvQZhXWWJl+YVJkyCWEFsCIiei9qWEKWIMysRkck\nhIhYDTwv6dD8pROBxyptlVkLaoeEUHaW4XzgmnyG4Wmg+tVPzFpMxyyyGhEPAoNeamnWyVrh278M\nX7pslogTgpkVnBDMrOCEYGYFJwQzAzyoaGZ9dMy0o5nV1w49BFVxEJLWAs818CtdwLrkDRmeOD6W\nkRlnR2LsFxHTy2w4bty46OrqKrXTVatWLau3hNpwqaSHUPYvsZek7mb8BTUjjo9lZMapOobHEMxs\nO04IZlZwQkinkTXpR3ocH8vIjFN5jHaYZahkUNGs04wZMyamTp1aatuXXnqpswYVzTpRO3y5ui6D\nWSLNWiClZPXnnST9TtIf8urPXyuzbycEs0SauGJSmerPrwFzI+II4EhgvqR319uxE4JZIk1MCGWq\nP0dEbMqfjs0fdYM7IZgl0OCqy129JQvyx4IGw5Wq/ixptKQHyWo/3l6zUPKAPKholkgD045Nqf4c\nEW8AR0qaCtwo6fCIeGSwuE4IZomknGWIBNWfa/b1iqSlwHxg0ITgUwazRJo4hlCm+vP0vGeApAnA\nPODxejt2QjBLoMExhKEqU/15JrBU0kPA/WRjCDfX27GvVDRLYNSoUTF+/PhS227ZssVXKpq1u3b4\ncnVCMEvECcHMgCwZtMPdjk4IZom4h2BmBScEMys4IZhZr9siotyyy81ZyXqH+DoEMyv4SkUzKzgh\nmFnBCcHMCk4IZlZwQjCzghOCmRWcEMys4IRgZgUnBDMr/D9RdipiQ1D9BAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f917814e290>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "wc1 = sess.run(weights['c1'])\n", "print (\"SIZE OF 'WC1' IS %s\" % (wc1.shape,))\n", "for i in range(3):\n", " plt.matshow(wc1[:, :, 0, i], cmap=plt.get_cmap('gray'))\n", " plt.title(str(i) + \"th conv filter\")\n", " plt.colorbar()\n", " plt.show()" ] } ], "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 }	apache-2.0
BYUFLOWLab/MDOnotebooks	SymbolicVsAD.ipynb	1	8617	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Symbolic Differentiation vs Automatic Differentiation\n", "\n", "Consider the function below that, at least computationally, is very simple." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from math import sin, cos\n", "\n", "def func(x):\n", " y = x\n", " for i in range(30):\n", " y = sin(x + y)\n", "\n", " return y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can compute a derivative symbolically, but it is of course horrendous (see below). Think of how much worse it would be if we chose a function with products, more dimensions, or iterated more than 20 times." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(((((((((((((((((((((((((((((2cos(2x) + 1)cos(x + sin(2x)) + 1)cos(x + sin(x + sin(2x))) + 1)cos(x + sin(x + sin(x + sin(2x)))) + 1)cos(x + sin(x + sin(x + sin(x + sin(2x))))) + 1)cos(x + sin(x + sin(x + sin(x + sin(x + sin(2x)))))) + 1)cos(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(2x))))))) + 1)cos(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(2x)))))))) + 1)cos(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(2x))))))))) + 1)cos(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(2x)))))))))) + 1)cos(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(2x))))))))))) + 1)cos(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(2x)))))))))))) + 1)cos(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(2x))))))))))))) + 1)cos(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(2x)))))))))))))) + 1)cos(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(2x))))))))))))))) + 1)cos(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(2x)))))))))))))))) + 1)cos(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(2x))))))))))))))))) + 1)cos(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(2x)))))))))))))))))) + 1)cos(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(2x))))))))))))))))))) + 1)cos(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(2x)))))))))))))))))))) + 1)cos(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(2x))))))))))))))))))))) + 1)cos(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(2x)))))))))))))))))))))) + 1)cos(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(2x))))))))))))))))))))))) + 1)cos(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(2x)))))))))))))))))))))))) + 1)cos(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(2x))))))))))))))))))))))))) + 1)cos(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(2x)))))))))))))))))))))))))) + 1)cos(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(2x))))))))))))))))))))))))))) + 1)cos(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(2x)))))))))))))))))))))))))))) + 1)cos(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(2x))))))))))))))))))))))))))))) + 1)cos(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(x + sin(2x))))))))))))))))))))))))))))))\n" ] } ], "source": [ "from sympy import diff, Symbol, sin\n", "from __future__ import print_function\n", "\n", "x = Symbol('x')\n", "dexp = diff(func(x), x)\n", "print(dexp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now evaluate the expression." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dfdx = 1.91770676038667\n" ] } ], "source": [ "xpt = 0.1\n", "\n", "dfdx = dexp.subs(x, xpt)\n", "\n", "print('dfdx =', dfdx)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's compare with automatic differentiation using operator overloading:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dfdx = [ 1.91770676]\n" ] } ], "source": [ "from algopy import UTPM, sin\n", "\n", "x_algopy = UTPM.init_jacobian(xpt)\n", "y_algopy = func(x_algopy)\n", "dfdx = UTPM.extract_jacobian(y_algopy)\n", " \n", "print('dfdx =', dfdx)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's also compare to AD using a source code transformation method (I used Tapenade in Fortran)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dfdx = 1.91770676039\n" ] } ], "source": [ "def funcad(x):\n", " xd = 1.0\n", " yd = xd\n", " y = x\n", " for i in range(30):\n", " yd = (xd + yd)*cos(x + y)\n", " y = sin(x + y)\n", " return yd\n", "\n", "dfdx = funcad(xpt)\n", "\n", "print('dfdx =', dfdx)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For a simple expression like this, symbolic differentiation is long but actually works reasonbly well, and both will give a numerically exact answer. But if we change the loop to 100 (go ahead and try this) or add other complications, the symbolic solver will fail. However, automatic differentiation will continue to work without issue (see the simple source code transformation version). Furthermore, if we add other dimensions to the problem, symbolic differentiation quickly becomes costly as lots of computations get repeated, whereas automatic differentiation is able to reuse a lot of calculations." ] } ], "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": "2.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
leftymitt/notebooks	random variables and modulo division.ipynb	1	4165826	null	gpl-2.0
fercarozzi/myseismicjulia	examples/POCS.ipynb	3	209550	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mPrecompiling module PyPlot.\n", "\u001b[39m" ] } ], "source": [ "using PyPlot,Seismic" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "#Create linear events\n", "d,ext = SeisLinearEvents(p1 = [-.001, 0.0015],tau=[1., 1./3.],dx1=5); \n", "\n", "#Randomly decimate\n", "dec = SeisDecimate(d);" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "param = Dict(:Niter=>100,:fmax=>60,:padt=>2,:padx=>2,:dt=>0.004)\n", "dpocs = SeisPOCS(dec;param...);" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f8a777aacd0>)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject <matplotlib.image.AxesImage object at 0x7f8a7768f850>" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "figure(1, figsize=(10, 5))\n", "subplot(121)\n", "SeisPlot(dec,cmap=\"seismic\",fignum=1,pclip=200,fignum=1)\n", "subplot(122)\n", "SeisPlot(dec,plot_type=\"FK\", cmap=\"seismic\", dy=0.004,fignum=1,hbox=5,pclip=200,fignum=1)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f8a776a10d0>)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject <matplotlib.image.AxesImage object at 0x7f8a77574990>" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "figure(2,figsize=(10, 5))\n", "subplot(121)\n", "SeisPlot(dpocs[:,:,1,1,1],cmap=\"seismic\",fignum=2,pclip=200)\n", "subplot(122)\n", "SeisPlot(dpocs[:,:,1,1,1],plot_type=\"FK\",cmap=\"seismic\",dy=0.004,fignum=2,pclip=200)" ] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.6.0", "language": "julia", "name": "julia-0.6" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
josdaza/deep-toolbox	TensorFlow/02_Linear_Regression.ipynb	2	59572	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Visualizando datos de entrada" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1.34796395 -0.14718261 -0.10057895 3.75246873 -0.20417813 -0.72438033\n", " 0.12010525 -0.01271722 -0.63606841 0.03770638 1.0712117 -0.33332108\n", " -1.03727673 1.41693218 0.36420673 -0.60076724 -0.1791213 0.73623532\n", " -0.73364601 -1.61082595 -0.60336734 2.59001401 1.02584569 -0.22528227\n", " 1.75829072 1.55086539 -0.57514712 0.23506313 -0.18276046 1.52998384\n", " -0.48651703 0.66247818 1.20296515 1.57657358 -0.52962705 -0.12984597\n", " -1.02361514 -0.943388 -0.09323011 -1.79043391 1.91259937 1.47954243\n", " -0.46446098 -0.70002989 -0.37753522 -0.11698503 -1.98889245 2.45751746\n", " -0.49267914 1.55561928 -1.21264763 -0.11783196 1.07828783 0.76515809\n", " 1.03403403 -0.45178788 1.94533072 -0.22920178 -0.58177145 -0.24433506\n", " 0.79919868 0.80227721 -0.39043291 -0.73305988 -1.06686383 0.87013976\n", " 0.34114806 1.8237653 -1.18236932 0.45187555 -0.21557263 0.08462563\n", " 2.80639847 0.20852661 -0.20356624 -1.26012413 0.43314302 0.0822964\n", " 1.8715585 0.50509674 0.35384347 1.67004801 -2.42315137 -0.66148324\n", " 0.12744404 1.34449831 1.56260653 -1.37443892 0.39043063 -0.76202123\n", " -1.0689417 -0.05735254 0.51150994 -1.02603319 0.84494066 0.0887639\n", " 0.48852519 1.07509876 0.69283938 0.46099975 0.11040374]\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10381f940>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# Regresa 101 numeros igualmmente espaciados en el intervalo[-1,1]\n", "x_train = np.linspace(-1, 1, 101)\n", "\n", "# Genera numeros pseudo-aleatorios multiplicando la matriz x_train * 2 y \n", "# sumando a cada elemento un ruido (una matriz del mismo tamanio con puros numeros random) \n", "y_train = 2 * x_train + np.random.randn(x_train.shape) 0.33\n", "\n", "print(np.random.randn(x_train.shape))\n", "\n", "plt.scatter(x_train, y_train)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Algoritmo de Regresion Lineal en TensorFlow" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10e13c7b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "learning_rate = 0.01\n", "training_epochs = 100\n", "\n", "x_train = np.linspace(-1,1,101)\n", "y_train = 2 x_train + np.random.randn(x_train.shape) 0.33\n", "\n", "X = tf.placeholder(\"float\")\n", "Y = tf.placeholder(\"float\")\n", "\n", "def model(X,w):\n", " return tf.multiply(X,w)\n", "\n", "w = tf.Variable(0.0, name=\"weights\")\n", "\n", "y_model = model(X,w)\n", "cost = tf.square(Y-y_model)\n", "\n", "train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n", "\n", "sess = tf.Session()\n", "init = tf.global_variables_initializer()\n", "sess.run(init)\n", "\n", "for epoch in range(training_epochs):\n", " for (x,y) in zip(x_train, y_train):\n", " sess.run(train_op, feed_dict={X:x, Y:y})\n", " \n", "w_val = sess.run(w)\n", "\n", "sess.close()\n", "\n", "plt.scatter(x_train, y_train)\n", "y_learned = x_trainw_val\n", "plt.plot(x_train, y_learned, 'r')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Regresion Lineal en Polinomios de grado N" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10ce4fcf8>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[ 0.83972043 3.11680341 5.68730021 4.13026142 3.75310993 3.78745461]\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10e4ca898>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "learning_rate = 0.01\n", "training_epochs = 40\n", "\n", "trX = np.linspace(-1, 1, 101)\n", "num_coeffs = 6\n", "trY_coeffs = [1, 2, 3, 4, 5, 6]\n", "trY = 0\n", "\n", "#Construir datos polinomiales pseudo-aleatorios para probar el algoritmo\n", "for i in range(num_coeffs):\n", " trY += trY_coeffs[i] np.power(trX, i)\n", " trY += np.random.randn(trX.shape) 1.5\n", " \n", "plt.scatter(trX, trY)\n", "plt.show()\n", "\n", "# Construir el grafo para TensorFlow\n", "X = tf.placeholder(\"float\")\n", "Y = tf.placeholder(\"float\")\n", "\n", "def model(X, w):\n", " terms = []\n", " for i in range(num_coeffs):\n", " term = tf.multiply(w[i], tf.pow(X, i))\n", " terms.append(term)\n", " return tf.add_n(terms)\n", "\n", "w = tf.Variable([0.] * num_coeffs, name=\"parameters\")\n", "y_model = model(X, w)\n", "cost = (tf.pow(Y-y_model, 2))\n", "train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n", "\n", "#Correr el Algoritmo en TensorFlow\n", "sess = tf.Session()\n", "init = tf.global_variables_initializer()\n", "sess.run(init)\n", "\n", "for epoch in range(training_epochs):\n", " for (x, y) in zip(trX, trY):\n", " sess.run(train_op, feed_dict={X: x, Y: y})\n", "\n", "w_val = sess.run(w)\n", "print(w_val)\n", "sess.close()\n", "\n", "# Mostrar el modelo construido\n", "plt.scatter(trX, trY)\n", "trY2 = 0\n", "for i in range(num_coeffs):\n", " trY2 += w_val[i] * np.power(trX, i)\n", "\n", "plt.plot(trX, trY2, 'r')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Regularizacion\n", "\n", "Para manejar un poco mejor el impacto que tienen los outliers sobre nuestro modelo (y asi evitar que el modelo produzca curvas demasiado complicadas, y el overfitting) existe el termino Regularizacion que se define como:\n", "\n", "$$ Cost(X,Y) = Loss(X,Y) + \\lambda \|x\| $$\n", "\n", "en donde \|x\| es la norma del vector (la distancia del vector al origen, ver el tema de Norms en otro lado, por ejemplo L1 o L2 norm) que se utiliza como cantidad penalizadora y lambda es como parametro para ajustar que tanto afectara la penalizacion. Entre mas grande sea lambda mas penalizado sera ese punto, y si lambda es 0 entonces se tiene el modelo inicial que no aplica reguarizacion.\n", "\n", "Para obtener un valor optimo de gama, se tiene que hacer un split al dataset y..." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "reg lambda 0.0\n", "final cost 0.0387668\n", "reg lambda 0.010101010101\n", "final cost 0.032114\n", "reg lambda 0.020202020202\n", "final cost 0.0290502\n", "reg lambda 0.030303030303\n", "final cost 0.0271835\n", "reg lambda 0.040404040404\n", "final cost 0.026038\n", "reg lambda 0.0505050505051\n", "final cost 0.0253773\n", "reg lambda 0.0606060606061\n", "final cost 0.025042\n", "reg lambda 0.0707070707071\n", "final cost 0.0249214\n", "reg lambda 0.0808080808081\n", "final cost 0.0249385\n", "reg lambda 0.0909090909091\n", "final cost 0.0250405\n", "reg lambda 0.10101010101\n", "final cost 0.0251916\n", "reg lambda 0.111111111111\n", "final cost 0.0253681\n", "reg lambda 0.121212121212\n", "final cost 0.025554\n", "reg lambda 0.131313131313\n", "final cost 0.0257393\n", "reg lambda 0.141414141414\n", "final cost 0.0259178\n", "reg lambda 0.151515151515\n", "final cost 0.0260859\n" ] } ], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "def split_dataset(x_dataset, y_dataset, ratio):\n", " arr = np.arange(x_dataset.size)\n", " np.random.shuffle(arr)\n", " num_train = int(ratio* x_dataset.size)\n", " x_train = x_dataset[arr[0:num_train]]\n", " y_train = y_dataset[arr[0:num_train]]\n", " x_test = x_dataset[arr[num_train:x_dataset.size]]\n", " y_test = y_dataset[arr[num_train:x_dataset.size]]\n", " return x_train, x_test, y_train, y_test\n", "\n", "learning_rate = 0.001\n", "training_epochs = 1000\n", "reg_lambda = 0.\n", "\n", "x_dataset = np.linspace(-1, 1, 100)\n", "\n", "num_coeffs = 9\n", "y_dataset_params = [0.] * num_coeffs\n", "y_dataset_params[2] = 1\n", "y_dataset = 0\n", "\n", "for i in range(num_coeffs):\n", " y_dataset += y_dataset_params[i] * np.power(x_dataset, i)\n", "y_dataset += np.random.randn(x_dataset.shape) 0.3\n", "\n", "(x_train, x_test, y_train, y_test) = split_dataset(x_dataset, y_dataset, 0.7)\n", "X = tf.placeholder(\"float\")\n", "Y = tf.placeholder(\"float\")\n", "\n", "def model(X, w):\n", " terms = []\n", " for i in range(num_coeffs):\n", " term = tf.multiply(w[i], tf.pow(X,i))\n", " terms.append(term)\n", " return tf.add_n(terms)\n", "\n", "w = tf.Variable([0.] * num_coeffs, name=\"parameters\")\n", "y_model = model(X, w)\n", "cost = tf.div(tf.add(tf.reduce_sum(tf.square(Y-y_model)),\n", " tf.multiply(reg_lambda, tf.reduce_sum(tf.square(w)))), \n", " 2*x_train.size)\n", "train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n", "\n", "sess = tf.Session()\n", "init = tf.global_variables_initializer()\n", "sess.run(init)\n", "\n", "i,stop_iters = 0,15\n", "for reg_lambda in np.linspace(0,1,100):\n", " i += 1\n", " for epoch in range(training_epochs):\n", " sess.run(train_op, feed_dict={X: x_train, Y: y_train})\n", " final_cost = sess.run(cost, feed_dict={X: x_test, Y:y_test})\n", " print('reg lambda', reg_lambda)\n", " print('final cost', final_cost)\n", " if i > stop_iters: break\n", "\n", "sess.close()" ] } ], "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
douglas-larocca/iocaml	notebooks/iocaml-symbolic-math.ipynb	2	5032	{ "metadata": { "language": "ocaml", "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Define a simple type representing expressions" ] }, { "cell_type": "code", "collapsed": false, "input": [ "type expr =\n", "\| Const of int\n", "\| Var of string\n", "\| Add of expr * expr\n", "\| Sub of expr * expr\n", "\| Mul of expr * expr\n", "\| Div of expr * expr\n", "\| Pow of expr * expr" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "type expr =\n", " Const of int\n", " \| Var of string\n", " \| Add of expr * expr\n", " \| Sub of expr * expr\n", " \| Mul of expr * expr\n", " \| Div of expr * expr\n", " \| Pow of expr * expr\n" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "An api for writing expressions" ] }, { "cell_type": "code", "collapsed": false, "input": [ "let c x = Const x\n", "let v x = Var x\n", "let (+) a b = Add(a,b)\n", "let (-) a b = Sub(a,b)\n", "let (/) a b = Div(a,b)\n", "let ( * ) a b = Mul(a,b)\n", "let (^) a b = Pow(a,b)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "val c : int -> expr = <fun>\n", "val v : string -> expr = <fun>\n", "val ( + ) : expr -> expr -> expr = <fun>\n", "val ( - ) : expr -> expr -> expr = <fun>\n", "val ( / ) : expr -> expr -> expr = <fun>\n", "val ( * ) : expr -> expr -> expr = <fun>\n", "val ( ^ ) : expr -> expr -> expr = <fun>\n" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Basic pretty printing function." ] }, { "cell_type": "code", "collapsed": false, "input": [ "let rec print x = \n", " let open Pervasives in\n", " match x with\n", " \| Const i -> string_of_int i\n", " \| Var x -> x\n", " \| Add(a,b) -> \"(\" ^ print a ^ \"+\" ^ print b ^ \")\"\n", " \| Sub(a,b) -> \"(\" ^ print a ^ \"-\" ^ print b ^ \")\"\n", " \| Mul(a,b) -> print a ^ \" \" ^ print b\n", " \| Div(a,b) -> print a ^ \"/\" ^ print b\n", " \| Pow(a,b) -> print a ^ \" ^{\" ^ print b ^ \"}\";;\n", "let pretty_print fmt x = \n", " let open Pervasives in\n", " Iocaml.display \"text/html\" (\"$$\" ^ print x ^ \"$$\");\n", " Format.fprintf fmt \"<expr>\";;\n", "#install_printer pretty_print;;" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "val print : expr -> string = <fun>\n", "val pretty_print : Format.formatter -> expr -> unit = <fun>\n" ] } ], "prompt_number": 27 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some examples" ] }, { "cell_type": "code", "collapsed": false, "input": [ "let _ = v \"a\" + v \"b\"\n", "let _ = c 10 * v \"a\"\n", "let _ = v \"a\" ^ (v \"b\" / c 2)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "$$(a+b)$$" ], "metadata": {}, "output_type": "display_data" }, { "html": [ "$$10 a$$" ], "metadata": {}, "output_type": "display_data" }, { "html": [ "$$a ^{b/2}$$" ], "metadata": {}, "output_type": "display_data" }, { "output_type": "stream", "stream": "stdout", "text": [ "- : expr = <expr>\n", "- : expr = <expr>\n", "- : expr = <expr>\n" ] } ], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "let x,y,z = v \"x\", v \"y\", v \"z\" in\n", "(((c 1 + x) * (c 7 - y)) ^ (z + c 2)) / (c 8)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "$$(1+x) (7-y) ^{(z+2)}/8$$" ], "metadata": {}, "output_type": "display_data" }, { "output_type": "stream", "stream": "stdout", "text": [ "- : expr = <expr>\n" ] } ], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
nikolaystanishev/traffic-sign-recognition	notebooks/Predict.ipynb	1	2501	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import json\n", "import skvideo.io\n", "import scipy.misc\n", "import numpy as np\n", "\n", "import os\n", "import sys\n", "sys.path.append('../')\n", "\n", "from aovek.network.network import YOLO\n", "from aovek.utils.image_processing import ImageProcessing\n", "from aovek.visualization.predict import Predict" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with open('../config.json') as config_file:\n", " config = json.load(config_file)\n", " \n", "config['network']['model_binary_data_file'] = '../models/model.h5'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "predict = Predict(config)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Predict Image\n", "print('Train')\n", "image_path = '../images/train.png'\n", "predict.predict_all_boxes(image_path)\n", "predict.predict(image_path)\n", "\n", "print('Validate')\n", "image_path = '../images/validate.png'\n", "predict.predict_all_boxes(image_path)\n", "predict.predict(image_path)\n", "\n", "print('Test')\n", "image_path = '../images/test.png'\n", "predict.predict_all_boxes(image_path)\n", "predict.predict(image_path)\n", "\n", "# Predict Video Frame By Frame\n", "video_path = '../videos/VID_20180302_1317044.mp4'\n", "video = skvideo.io.vread(video_path)\n", "for frame in video:\n", " scipy.misc.imsave('temp.png', frame)\n", " image_path = 'temp.png'\n", " predict.predict(image_path)\n", "os.remove('temp.png')" ] }, { "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": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
tjwei/PythonTutorial	Tutorial 3.ipynb	1	7357	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[Zebra Puzzle](http://en.wikipedia.org/wiki/Zebra_Puzzle)\n", "\n", "1. 有五間房子\n", "2. 英國人住紅色的房子\n", "3. 西班牙人養狗\n", "4. 住綠色房子的人喝咖啡\n", "5. 烏克蘭人喝茶\n", "6. 綠色房子緊鄰的左邊（你的右邊）是白色房子\n", "7. 抽「Old Gold」牌香菸的人養蝸牛\n", "8. 黃色房子的人抽「Kools」牌香菸\n", "9. 正中間房子的人喝牛奶\n", "10. 挪威人住左邊（你的右邊）第一間房子\n", "11. 抽「Chesterfields」牌香菸的人，住在養狐狸的人的隔壁\n", "12. 抽「Kools」牌香菸的人，住在養馬的人隔壁\n", "13. 抽「Lucky Strike」牌香菸的人，喝橘子汁\n", "14. 日本人抽「Parliament」牌香菸\n", "15. 挪威人住在藍色房子的隔壁\n", "\n", " Question：誰喝水？誰養斑馬？ " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import itertools \n", "屋子 = 第一間, _, 中間, _, _ = [1, 2, 3, 4, 5]\n", "所有順序 = list(itertools.permutations(屋子))\n", "所有順序" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def 在右邊(h1, h2):\n", " \"h1 緊鄰 h2 的右邊.\"\n", " return h1-h2 == 1\n", "\n", "def 隔壁(h1, h2):\n", " \"h1 h2 在隔壁\"\n", " return abs(h1-h2) == 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def zebra_puzzle(): \n", " return [locals()\n", " for (紅, 綠, 白, 黃, 藍) in 所有順序\n", " for (英國人, 西班牙人, 烏克蘭人, 日本人, 挪威人) in 所有順序\n", " for (咖啡, 茶, 牛奶, 橘子汁, 水) in 所有順序\n", " for (OldGold, Kools, Chesterfields, LuckyStrike, Parliaments) in 所有順序\n", " for (狗, 蝸牛, 狐狸, 馬, 斑馬) in 所有順序 \n", " if 英國人 is 紅 #2\n", " if 西班牙人 is 狗 #3\n", " if 咖啡 is 綠 #4\n", " if 烏克蘭人 is 茶 #5\n", " if 在右邊(綠, 白) #6 \n", " if OldGold is 蝸牛 #7\n", " if Kools is 黃 #8\n", " if 牛奶 is 中間 #9\n", " if 挪威人 is 第一間 #10\n", " if 隔壁(Chesterfields, 狐狸) #11\n", " if 隔壁(Kools, 馬) #12\n", " if LuckyStrike is 橘子汁 #13\n", " if 日本人 is Parliaments #14 \n", " if 隔壁(挪威人, 藍) #15 \n", " ]\n", "zebra_puzzle()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "時間太長！" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def zebra_puzzle(): \n", " return [locals()\n", " for (紅, 綠, 白, 黃, 藍) in 所有順序\n", " if 在右邊(綠, 白) #6\n", " for (英國人, 西班牙人, 烏克蘭人, 日本人, 挪威人) in 所有順序\n", " if 英國人 is 紅 #2\n", " if 挪威人 is 第一間 #10\n", " if 隔壁(挪威人, 藍) #15\n", " for (咖啡, 茶, 牛奶, 橘子汁, 水) in 所有順序\n", " if 咖啡 is 綠 #4\n", " if 烏克蘭人 is 茶 #5\n", " if 牛奶 is 中間 #9\n", " for (OldGold, Kools, Chesterfields, LuckyStrike, Parliaments) in 所有順序\n", " if Kools is 黃 #8\n", " if LuckyStrike is 橘子汁 #13\n", " if 日本人 is Parliaments #14\n", " for (狗, 蝸牛, 狐狸, 馬, 斑馬) in 所有順序\n", " if 西班牙人 is 狗 #3\n", " if OldGold is 蝸牛 #7\n", " if 隔壁(Chesterfields, 狐狸) #11\n", " if 隔壁(Kools, 馬) #12\n", " ]\n", "zebra_puzzle()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{1: ['Kools', '水', '黃', '挪威人', '狐狸'],\n", " 2: ['藍', '茶', '馬', '烏克蘭人', 'Chesterfields'],\n", " 3: ['紅', '蝸牛', 'OldGold', '牛奶', '英國人'],\n", " 4: ['LuckyStrike', '狗', '白', '橘子汁', '西班牙人'],\n", " 5: ['斑馬', '咖啡', '日本人', 'Parliaments', '綠']}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def result(d): return {i:[k for k,v in d.items() if v == i] for i in 屋子}\n", "def zebra_puzzle(): \n", " return [result(locals())\n", " for (紅, 綠, 白, 黃, 藍) in 所有順序\n", " if 在右邊(綠, 白)\n", " for (英國人, 西班牙人, 烏克蘭人, 日本人, 挪威人) in 所有順序\n", " if 英國人 is 紅\n", " if 挪威人 is 第一間\n", " if 隔壁(挪威人, 藍)\n", " for (咖啡, 茶, 牛奶, 橘子汁, 水) in 所有順序\n", " if 咖啡 is 綠\n", " if 烏克蘭人 is 茶\n", " if 牛奶 is 中間\n", " for (OldGold, Kools, Chesterfields, LuckyStrike, Parliaments) in 所有順序\n", " if Kools is 黃\n", " if LuckyStrike is 橘子汁\n", " if 日本人 is Parliaments\n", " for (狗, 蝸牛, 狐狸, 馬, 斑馬) in 所有順序\n", " if 西班牙人 is 狗\n", " if OldGold is 蝸牛\n", " if 隔壁(Chesterfields, 狐狸)\n", " if 隔壁(Kools, 馬) ]\n", "zebra_puzzle()[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Credit:\n", "基於 Udacity's CS212 的解答" ] } ], "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": "3.4.1" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
dennisobrien/PublicNotebooks	Spelling Bee Solver.ipynb	1	2846	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Spelling Bee Solver\n", "\n", "A solver for the New York Times puzzle \"Spelling Bee\"." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['annette',\n", " 'atman',\n", " 'bataan',\n", " 'batman',\n", " 'benet',\n", " 'bennett',\n", " 'bette',\n", " 'emmett',\n", " 'mamet',\n", " 'manet',\n", " 'menkent',\n", " 'nanette',\n", " 'tameka',\n", " 'abate',\n", " 'abatement',\n", " 'antenna',\n", " 'antennae',\n", " 'bantam',\n", " 'batten',\n", " 'beaten',\n", " 'betake',\n", " 'betaken',\n", " 'eaten',\n", " 'emanate',\n", " 'embankment',\n", " 'enemata',\n", " 'entente',\n", " 'manatee',\n", " 'matte',\n", " 'meant',\n", " 'taken',\n", " 'teammate',\n", " 'tenant',\n", " 'tenement',\n", " 'tenet']" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def get_matching_words(center_letter, other_letters, min_word_length=5):\n", " words = []\n", " allowed_letters = set(center_letter).union(set(other_letters))\n", " with open('/usr/share/dict/american-english', 'r') as f:\n", " for word in f:\n", " word = word.strip().lower()\n", " if len(word) < min_word_length:\n", " continue\n", " if center_letter not in word:\n", " continue\n", " if set(word).difference(allowed_letters):\n", " # the word contains letters other than the allowed letters\n", " continue\n", " words.append(word)\n", " return words\n", "\n", "matching_words = get_matching_words('t', ('a', 'b', 'e', 'k', 'm', 'n'))\n", "matching_words" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "py37", "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": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
sergey-tomin/workshop	1_introduction.ipynb	1	79638	{ "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" }, "name": "", "signature": "sha256:c8d2d8f915aad33d24944b2847a7b4fffcef70247e6363027ee7d89dec9cc5cc" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook was created by [Sergey Tomin](http://www.xfel.eu/organization/staff/tomin_sergey/) for Workshop: [Designing future X-ray FELs](http://www.xrayfels.co.uk/). Source and license info is on [GitHub](https://github.com/iagapov/ocelot/tree/dev/docs). August 2016." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# An Introduction to Ocelot\n", "\n", "Ocelot is a multiphysics simulation toolkit designed for studying FEL and storage ring based light sources. Ocelot is written in Python. Its central concept is the writing of python's scripts for simulations with the usage of Ocelot's modules and functions and the standard Python libraries. \n", "\n", "Ocelot includes following main modules:\n", "* Charge particle beam dynamics module (CPBD)\n", " - optics\n", " - tracking\n", " - matching\n", " - collective effects\n", " - Space Charge (true 3D Laplace solver) \n", " - CSR (Coherent Synchrotron Radiation) (1D model with arbitrary number of dipole) (under development).\n", " - Wakefields (Taylor expansion up to second order for arbitrary geometry).\n", " - MOGA (Multi Objective Genetics Algorithm). (under development but we have already applyed it for a storage ring [aplication](http://accelconf.web.cern.ch/AccelConf/ipac2016/papers/thpmb034.pdf))\n", "* Native module for spontaneous radiation calculation\n", "* FEL calculations: interface to GENESIS and pre/post-processing\n", "* Modules for online beam control and online optimization of accelerator performances. [Work1](http://accelconf.web.cern.ch/accelconf/IPAC2014/papers/mopro086.pdf), [work2](https://jacowfs.jlab.org/conf/y15/ipac15/prepress/TUPWA037.PDF), [work3](http://accelconf.web.cern.ch/AccelConf/ipac2016/papers/wepoy036.pdf).\n", "\n", "Ocelot extensively uses Python's [NumPy (Numerical Python)](http://numpy.org) and [SciPy (Scientific Python)](http://scipy.org) libraries, which enable efficient in-core numerical and scientific computation within Python and give you access to various mathematical and optimization techniques and algorithms. To produce high quality figures Python's [matplotlib](http://matplotlib.org/index.html) library is used.\n", "\n", "It is an open source project and it is being developed by physicists from [The European XFEL](http://www.xfel.eu/), [DESY](http://www.desy.de/) (Germany), [NRC Kurchatov Institute](http://www.nrcki.ru/) (Russia).\n", "\n", "We still have no documentation but you can find a lot of examples in ocelot/demos/ \n", "\n", "\n", "## Ocelot user profile\n", "\n", "Ocelot is designed for researchers who want to have the flexibility that is given by high-level languages such as Matlab, Python (with Numpy and SciPy) or Mathematica.\n", "However if someone needs a GUI it can be developed using Python's libraries like a [PyQtGraph](http://www.pyqtgraph.org/) or [PyQt](http://pyqt.sourceforge.net/Docs/PyQt4/). \n", "\n", "For example, you can see GUI for SASE optimization (uncomment and run next block)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import Image\n", "#Image(filename='gui_example.png') " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Outline\n", "* Preliminaries: Setup & introduction\n", "* #### Beam dynamics\n", "* [Tutorial N1. Linear optics.](#tutorial1). [Web version](http://nbviewer.jupyter.org/github/iagapov/ocelot/blob/master/test/workshop/1_introduction.ipynb).\n", " - Linear optics. DBA.\n", "* [Tutorial N2. Tracking.](2_tracking.ipynb). [Web version](http://nbviewer.jupyter.org/github/iagapov/ocelot/blob/master/test/workshop/2_tracking.ipynb).\n", " - Linear optics of the European XFEL Injector\n", " - Tracking. First and second order. \n", "* [Tutorial N3. Space Charge.](3_space_charge.ipynb). [Web version](http://nbviewer.jupyter.org/github/iagapov/ocelot/blob/master/test/workshop/3_space_charge.ipynb).\n", " - Tracking with SC effects.\n", "* [Tutorial N4. Wakefields.](4_wake.ipynb). [Web version](http://nbviewer.jupyter.org/github/iagapov/ocelot/blob/master/test/workshop/4_wake.ipynb).\n", " - Tracking with Wakefields\n", "* #### FEL calculation\n", "* [Tutorial N5: Genesis preprocessor](5_Genesis_preprocessor.ipynb). [Web version](http://nbviewer.jupyter.org/github/iagapov/ocelot/blob/master/test/workshop/5_Genesis_preprocessor.ipynb).\n", "* [Tutorial N6. Genesis postprocessor](6_Genesis_postprocessor.ipynb). [Web version](http://nbviewer.jupyter.org/github/iagapov/ocelot/blob/master/test/workshop/6_Genesis_postprocessor.ipynb).\n", "\n", "\n", "All IPython (jupyter) notebooks (.ipynb) have analogues in the form of python scripts (.py). \n", "\n", "All these notebooks as well as additional files (beam distribution, wakes, ...) you can download [here](https://github.com/sergey-tomin/workshop/archive/master.zip).\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preliminaries\n", "\n", "The tutorial includes 4 simple examples dediacted to beam dynamics. However, you should have a basic understanding of Computer Programming terminologies. A basic understanding of Python language is a plus.\n", "\n", "##### This tutorial requires the following packages:\n", "\n", "- Python version 2.7 or 3.4-3.5\n", "- `numpy` version 1.8 or later: http://www.numpy.org/\n", "- `scipy` version 0.15 or later: http://www.scipy.org/\n", "- `matplotlib` version 1.5 or later: http://matplotlib.org/\n", "- `ipython` version 2.4 or later, with notebook support: http://ipython.org\n", "\n", "The easiest way to get these is to download and install the (very large) [Anaconda software distribution](https://www.continuum.io/).\n", "\n", "Alternatively, you can download and install [miniconda](http://conda.pydata.org/miniconda.html).\n", "The following command will install all required packages:\n", "```\n", "$ conda install numpy scipy matplotlib ipython-notebook\n", "```\n", "\n", "##### Ocelot installation\n", "1. you have to download from GitHub [zip file](https://github.com/iagapov/ocelot/archive/master.zip). \n", "2. Unzip ocelot-master.zip to your working folder ../your_working_dir/. \n", "3. Rename folder ../your_working_dir/ocelot-master to ../your_working_dir/ocelot. \n", "4. Add ../your_working_dir/ to PYTHONPATH\n", " - Windows 7: go to Control Panel -> System and Security -> System -> Advance System Settings -> Environment Variables.\n", " and in User variables add ../your_working_dir/ to PYTHONPATH. If variable PYTHONPATH does not exist, create it\n", " \n", " Variable name: PYTHONPATH\n", " \n", " Variable value: ../your_working_dir/\n", " - Linux: \n", " ```\n", " $ export PYTHONPATH=../your_working_dir/:$PYTHONPATH\n", " ```\n", " \n", "#### To launch \"ipython notebook\" or \"jupyter notebook\"\n", "in command line run following commands:\n", "\n", "```\n", "$ ipython notebook\n", "```\n", "\n", "or\n", "```\n", "$ ipython notebook --notebook-dir=\"path_to_your_directory\"\n", "```\n", "\n", "or\n", "```\n", "$ jupyter notebook --notebook-dir=\"path_to_your_directory\"\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Checking your installation\n", "\n", "You can run the following code to check the versions of the packages on your system:\n", "\n", "(in IPython notebook, press `shift` and `return` together to execute the contents of a cell)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import IPython\n", "print('IPython:', IPython.__version__)\n", "\n", "import numpy\n", "print('numpy:', numpy.__version__)\n", "\n", "import scipy\n", "print('scipy:', scipy.__version__)\n", "\n", "import matplotlib\n", "print('matplotlib:', matplotlib.__version__)\n", "\n", "import ocelot\n", "print('ocelot:', ocelot.__version__)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "('IPython:', '2.4.1')\n", "('numpy:', '1.9.2')" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "('scipy:', '0.15.1')\n", "('matplotlib:', '1.4.3')" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "initializing ocelot..." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "('ocelot:', '16.8.rc')\n" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id=\"tutorial1\"></a>\n", "## Tutorial N1. Double Bend Achromat.\n", "\n", "We designed a simple lattice to demonstrate the basic concepts and syntax of the optics functions calculation. \n", "Also, we chose DBA to demonstrate the periodic solution for the optical functions calculation. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "from __future__ import print_function\n", "\n", "# the output of plotting commands is displayed inline within frontends, \n", "# directly below the code cell that produced it\n", "%matplotlib inline\n", "\n", "# import from Ocelot main modules and functions\n", "from ocelot import \n", "\n", "# import from Ocelot graphical modules\n", "from ocelot.gui.accelerator import " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating lattice\n", "Ocelot has following elements: Drift, Quadrupole, Sextupole, Octupole, Bend, SBend, RBend, Edge, Multipole, Hcor, Vcor, Solenoid, Cavity, Monitor, Marker, Undulator. " ] }, { "cell_type": "code", "collapsed": true, "input": [ "# defining of the drifts\n", "D1 = Drift(l=2.)\n", "D2 = Drift(l=0.6)\n", "D3 = Drift(l=0.3)\n", "D4 = Drift(l=0.7)\n", "D5 = Drift(l=0.9)\n", "D6 = Drift(l=0.2)\n", "\n", "# defining of the quads\n", "Q1 = Quadrupole(l=0.4, k1=-1.3)\n", "Q2 = Quadrupole(l=0.8, k1=1.4)\n", "Q3 = Quadrupole(l=0.4, k1=-1.7)\n", "Q4 = Quadrupole(l=0.5, k1=1.3)\n", "\n", "# defining of the bending magnet\n", "B = Bend(l=2.7, k1=-.06, angle=2pi/16., e1=pi/16., e2=pi/16.)\n", "\n", "# defining of the sextupoles\n", "SF = Sextupole(l=0.01, k2=1.5) #random value\n", "SD = Sextupole(l=0.01, k2=-1.5) #random value\n", "\n", "# cell creating\n", "cell = (D1, Q1, D2, Q2, D3, Q3, D4, B, D5, SD, D5, SF, D6, Q4, D6, SF, D5, SD, D5, B, D4, Q3, D3, Q2, D2, Q1, D1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "hint: to see a simple description of the function put cursor inside () and press Shift-Tab or you can type sign ? before function. To extend dialog window press + \n", "\n", "The cell is a list of the simple objects which contain a physical information of lattice elements such as length, strength, voltage and so on. In order to create a transport map for every element and bind it with lattice object we have to create new Ocelot object - MagneticLattice() which makes these things automatically. \n", "\n", "MagneticLattice(sequence, start=None, stop=None, method=MethodTM()): \n", " sequence - list of the elements,\n", "\n", "other paramenters we will consider in tutorial N2. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "lat = MagneticLattice(cell)\n", "\n", "# to see total lenth of the lattice \n", "print(\"length of the cell: \", lat.totalLen, \"m\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "length of the cell: 20.34 m\n" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Optical function calculation\n", "Uses: \n", "* twiss() function and,\n", "* Twiss() object contains twiss parameters and other information at one certain position (s) of lattice\n", "\n", "To calculate twiss parameters you have to run twiss(lattice, tws0=None, nPoints=None) function. If you want to get a periodic solution leave tws0 by default. \n", "\n", "You can change the number of points over the cell, If nPoints=None, then twiss parameters are calculated at the end of each element.\n", "twiss() function returns list of Twiss() objects.\n", "\n", "##### You will see the Twiss object contains more information than just twiss parameters. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "tws=twiss(lat)\n", "\n", "# to see twiss paraments at the begining of the cell, uncomment next line\n", "# print(tws[0])\n", "\n", "# to see twiss paraments at the end of the cell, uncomment next line\n", "print(tws[-1])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "emit_x = 0.0\n", "emit_y = 0.0\n", "beta_x = 0.527161369596\n", "beta_y = 0.51659778953\n", "alpha_x = -6.66133814775e-15\n", "alpha_y = 8.43769498715e-15\n", "gamma_x = 1.89695235211\n", "gamma_y = 1.93574192586\n", "Dx = 0.166739277081\n", "Dy = 0.0\n", "Dxp = -1.11022302463e-16\n", "Dyp = 0.0\n", "mux = 7.10073199212\n", "muy = 5.66988435162\n", "nu_x = 1.13011659612\n", "nu_y = 0.902390121319\n", "E = 0.0\n", "s = 20.34\n", "\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "len(tws)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "32" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "# plot optical functions.\n", "plot_opt_func(lat, tws, top_plot = [\"Dx\", \"Dy\"], legend=False, font_size=10)\n", "plt.show()\n", "\n", "# you also can use standard matplotlib functions for plotting\n", "#s = [tw.s for tw in tws]\n", "#bx = [tw.beta_x for tw in tws]\n", "#plt.plot(s, bx)\n", "#plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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GyyPb2XHItnYMWbWzyCbYpaaODkQ0jojKEZEvgPcA7CaiDwD8CSDQeFogAI3i\nWkskEonEkejF+05BMdN9A6CdEOIygNbGzxKJRCJxcbQ236VBRHsB7DXu3wXQ9lnXyPkjxxAgg9Y5\nBNnOjkO2tWOwpp11o5SsQSolxyA7sGOQ7ew4nK2tk5OTERISgoSEBFBmMb90iBACxYsXR3JyMjwt\nCKXu1EpJIpFIcgMhISEoXLgwqlevnq2TgJ4gIpw6dQohISGoUaNGjq/T25ySRCKRSDKQkJCAkiVL\nOo1CAkwjpYSEBIuuk0pJIpFIdA4ROZVCUhBCWGxulEpJIpFIJLpBKiWJRCKR6Abp6CCRSCSSHJOY\nmIgtW7YgT548CA8Px4ABA1QtX/ORkhDCRwhxRAhxSghxQQgx1Xh8ohDihjHP0kkhRAetZXU5tm7l\n3BSxsVpLIpHonwcPgEmTgA0btJZEU/7880907twZr776Ko4cOaJ6+ZqPlIgoUQjxMhHFCyE8AOwX\nQjQHR3eYQUQzNBbRNUlN5XScMTHA8uXAqlVAmzZaSyWR6JNDhziJWGgokCcP0LEj/81l3L59G76+\nvnBzc8PVq1fx/PPPq16H5iMlACCieOOuFwB3APeMn53P3cRZOHqUFRLAmfvatQM++wxIStJWLolE\nT6Sk8OioRQtWSACQkADs2aOtXEaEsG2zlJMnT6JRo0aYPHkyxo4di/79+6v+m3ShlIQQbkKIU+Dc\nSXuI6Lzxq6FCiNNCiKVZpUSXWMnff/PfQYO407m5AdOmAQEBnDdaIsnthIUBLVsCX3zB6ZY/+wwY\nY8w1unmztrJphOLe/fnnn6Nv375Yt26d6nVobr4DACIyAKgrhCgI4B9jbqUFACYZT/kKwHQAfc2v\nc3d3x7Bhw9I+BwQEOF34EM1ITQUCA4G33uI0sq1aARs3AvfuAdOnA6+8AjRoAACIi4tDWFiYtvLm\nAmQ7O45ntvXZs6x4qlThftClC1CxIlsVbt/mVLUO/F9FRkbi5s2bTx2/ccO2cjMpMluio6PT5Dh+\n/Di8vb0zlUvh4cOHiIyMxNq1a3OeGp2IdLUBmABgZIZjFQGczXhuYGAgSawgIoIIIMqXjygx0XT8\nwQOiwED+DiB6/XWi6GgKDQ3VTNTchGxnx5FlW9+7R9Sjh6kPvP02UUyM6fvUVKKSJfm7M2ccIywR\nBQUFOayurLh37x7Nnj2biIgMBgO9/vrrFBsbm+01N27cyFR2Vj2Z6wDNzXdCiGKKaU4IkQdAOwAn\nhRDm0VZWdoLKAAAgAElEQVS7ADirhXwuiWJ6aN8e8PY2HS9QAFixAvjlF6BQIeCvv4DatYGrVzUR\nUyJxKP/9B9StC6xZA+TLByxdCvz2G1C0qOkcNzfgtdd4XzGB5xJOnjwJf39/bNy4EbNnz8b//d//\nITIyElu3bsWiRYuwfPlyVerRg/muNICVQgg38BzXT0S0SwixSghRF+yFFwbgIy2FdCmUztSpU+bf\nv/su0KQJe+ft3QusXs2KbOpUwMfHcXJKJI4gOZnnVadM4bmjRo2An39m011mdOoELFvG/WjsWMfK\nqiEJCQno2LFjumMGgwGhoaFo1KgR6tSpo0o9mislIjoLoH4mx3tqII7rEx8P7NrF+xlusHSUL8/n\nTZsGXL4MzJrFn9esAV54wTGySiT2JiQE+N//2BtVCGD8eHZsyC7VQtu2gJcXu4nHxADFijlOXg1x\nc3vasLZw4ULcv38fhQoVwvXr11GxYkWb69FcKUkczO7dQGIi0LAh8Kx8VO7u7G104ACwfz9P/jZs\nCCxezKMoicSZ2bQJ+OAD4PFjfgn76SfgpZeefV2BAuyVt2MHsG0br1/KBXTo8HT8gkGDBqlej+Zz\nShIH8yzTXWaUKQOcPAl8+CHw5An/PSun+CROzL17JoX03nvA6dM5U0gKSv/JZfNKjkAqpdwEkcnJ\nwRKlBPDE748/AgMGsA2+Z0+50FbinKSmAn/8wQrpnXfYJF3IwmWQirPDtm3cHySqIZVSbuLMGV7Y\nULo0UK+edWVMmwb4+gKnTgGTJ6srn0TiCGbPBsLDgZIlgfnzrQttULkyUKMGcP8+m7clqiGVUm5C\nMTW89hq7tlpD/vzAypXckadMAY4dU08+icTeXLwIjBvH+0uWpHf3thRpwrMLUinlJqyZT8qMFi2A\nESNMQV0tTHcskWhCSgrfr0+esKXA1n6gXJ9LQw7ZC6mUcgvR0cCRI+zKqkY08MmT2XwRHMzpLyQS\nvTN1KhAUxJ52r7xie3lNm/JcVHAwu5ZLVEFzpZRNPqUiQogdQojLQojtMiCrjWzdyo4OL7/MJjhb\n8fFhM567OzBzJrBvn+1lSiT24uRJXiAL8MJX80gm1uLhAShu0nK0pBqaKyUiSgTwMhHVBVAbwMvG\nfEpjAOwgoqoAdhk/uzx37tgpqo9apjtzGjVi+zwR0KsX8OiRemVLJGrx5Amb7VJSgCFD1M0bZsd5\npWvXgMhI1YvVPZorJSDLfEpvAFhpPL4SQGcNRHM47doBfn5A587AuXMqFZqUBGzfzvuKK6tafP45\nxwsLCwNGjVK3bIlEDSZO5M7k5wd88426ZXfowE5De/dyZloVuHIF6N6dnVybNOHIR3oiMTERGzdu\nxNatW7Fw4ULVy9eFUsoin1JJIooynhIFoKRmAjqIqChewwfwMoratXl9n5JbzGr27+cO4+/Pd7qa\neHlx1lovL2DhQuCff9QtXyKxhUOHgO++Y8WxciWvt1OTokV5bik5mSM82MCNG0D//jxV+8svfOza\nNVZSesLl06EDmeZTejnD9ySEoIzXuVo+pZAQTnFUujRQrhxw/Dg7uH39NVC/Pi84t2o66MoVLrh5\nc6tywDwz90z+/BxReedOtq2XLy8Dt1qBzKekMsnJwPr1/GbXvDl3LGP7qtrWH33E65Zu3LCqf8XH\n83vjsWNsYfzgA3YOjI3l5VSXLgHu7pnnU3I0UVFRyJcvH27fvo1r166hYMGCuSefEoBgAKWMx0oD\nCM54rqvlU5o0idO0DB/On0NDiXr2JBKCj+fJQzR6NNHduxYWXKUKF7Bvn1Vy5SjPT0oKUUAA1/PB\nB1bVk9uR+ZRUZuhQvh/9/dPnDSOV2/rsWa6nRAnOt5RDHjwgmjiRqEABU/qmbt2IgoP5e/PnQZb5\nlJQLrd0sZPPmzURE9NVXX1G3bt0oIiIi2/NdKp8SgD8BBBpPCwTwuzYSOo7jx/mvMeErfH3Z4nD2\nLCe+TEgAvv2Wj0+ZkkO/gsuXeaRUuDAbqO2FuzsLmycPB7bctMl+dUkkz2L3bmDuXPaQW7VKHW+7\nrPD3BypU4GUXQUHPPD0xEZgxgxM+T5wIPHzIU1NBQcCvvwLVqvF5ynNAeS7oAdYn9k2HrrlSAo+C\ndhvnlI4A+IuIdgH4BkA7IcRlAK2Nn12ajEpJwd+fM5UfOcKOQ/fvc4T9ypW53z15kk2hilfQq69y\nB7UnVauy1gTYpHHnjn3rk0gy48EDoHdv3p8wgW3f9kSIHHnhpaRw+MgqVYBPP+WsF02bso/E1q1P\n93vl88mT2Tg72DpWspDU1NS0/atXr6JIkSIWl/EsNFdKRHSWiOoTUV0iqk1E04zH7xJRWyKqSkTt\niShOa1ntSXQ0m6Tz5+dne2Y0bszTNjt38n50NPDxx/xmtWIFzz89hT1cwbNj8GBeC3XnDgdvteLG\nl0hsYsQIICKCn+qOSsKXjVIyGIB164CaNdmR4cYNoE4dPnX//qyDk5csCZQtyyMpHUwnIS4uDteu\nXQPAI6Zt27ahc2f1naI1V0oSRhkl1a//7LB0bdoAhw+zhczfnydDe/cGatVib5004uM5xbObmzor\n2HOCmxsvTixQgId3a9c6pl6JBGBHm6VL2Vy3cmX2yfrUpFUrIG9eHtZERaUdjoriFGTvvcdWdD8/\n7hInTvDqjGfFgm3YkP/qwQMvs3TooaGhmDlzJq5du4b169erUo9USjpBMUVnHMJnhRC8lun0aTaZ\nV6zIsSa//97spOBgthlUqwbYYZidJRUrcpQHgEdOenjNk7g+sbGc6wvgMFj+/o6r28eH1+sB6RYY\n/vAD66kyZYBFi4ALF1hB5TQesvI8uHxZZXmtICEhAW3atMFbb72FTz75BA0bNkSJEiVQsGBB7Nmz\nBy1btlSlHqmUdEJW80nPwt2dXUjnz+fP58+bfXnxIv+tWdNm+SymTx9+FYyL4weFNONJ7M3QoRwC\noXlzYPhwx9ev9DOl38HUH7//nk13lg7clOeBHkLrZZYO/cmTJ3Bzc8OlS5dQvHhxdepRpRSJzVir\nlBSUl8J0SunCBf6rhVISgmd1CxfmRGhLljheBknu4bff2C6WNy9PsLq7O14GpZ8p/Q6m/mjtoM1c\nKWkd2SGzdOhBQUGoVq0aunTpolo9UinpgJw4OTyLcuX4+jt3zJzelDe2GjVUkdNiSpc2DeFGjLBq\nYaFE8kwiI4GBA3n/++/ZLVULlH5m7HdPnrAycXOzvl8rzg4JCey7oTe6d++OJk2a4MUXX1StTKmU\ndIAySqpXz/rce0Jk8qKm5UhJ4d13gW7deFFV797av+5JXAsiXn4QG8uBIwcM0E6WDB3w0iW+3f38\nbAtwooyWzKyCLo1USjpAUUqKp421pOsTSUn8miaE9a9paiAEj5ZKlOAFGXPnaieLxPVYtQr480/g\nuefY686a1OZqUa4cx9aLjgZiY1V7J1SeC8HBtpXjLGiulIQQ5YQQe4QQ54UQ54QQHxuPTxRC3BBC\nnDRuTxs0XQRLPe+yIp1SunKFFy5VqsRRFrSkWDGeXwKAMWNyT++S2Jfr13mhHgDMmcNKQUuESGfC\nU0spKc8Fs6kql0ZzpQQgGcBwIvIHEABgsBCiBgACMIOI6hm3bZpKaUdsdXJQSOfsoNzBWs0nZeSN\nNzjnUmIiB4dNSdFaIokzQ8Qeng8eAG++yfmS9IDS3y5csNnJQaFBA4BIIDiYnM76TUQQFo5eNVdK\nRBRJRKeM+48AXARQ1vi1hmNxx6CGk4NCupGSlu7gWTFrFr/NHj3K6QQkEmtZsIBDmxQtyguAtDTb\nmWPmFq7WSKlkSSB//jy4dy8K4eHOs7SCiHDnzh3ksdBSo4vUFQpCiIoA6gE4DKAZgKFCiJ4AggB8\n6oqhhtRwclAoX55N2lFRwJOTF+AN6EspFSzI0R7ateNIlK+9xvFWJBJLCAkxJZRcuJCf2nrB2N8M\n5y7gyhXu00qAVVuoXdsPO3eGYMOGW3j5ZedQTEII3L171+JFtbpRSkKI/ADWAxhGRI+EEAsATDJ+\n/RWA6QD6ml/jCvmU7txha1ZAgDoe0x9/DNy6BVwrUgFegYHsHmtjwarmnqlcmZe5HzsGrFnDWlSL\nNSU6ROZTygFE7NzQrRvH1WrQwD45wqylbFkgMBCp+Qvi/bJhKFJEnZTmXbsCRYv6oEABH4cGZ7EV\nIQRu3LiBw4cPO1c+JQCeAP4B8EkW31cEcDbjcVfIp9S5M4frXb1anfJ69iRyQwole3hzwQ8e2Fym\n6nl+Hj4kqlyZ5Rs/Xt2ynRiZTykHTJvG902pUkSxsVYXY7e2Tk4m8ua+lx8P6I031Cl282b+2S1b\nqlOeo8iqnaHzfEoCwFIAF4holtnx0mandQFw1tGyOQK1PO8U/P0BX4TBI+UJ8PzzHBhVb+TPz6vu\nhQCmTuWcHBLJszh/nnO2ABwhRI9DBg+PtMnh6ghWLfye8nw4ccL1l/pprpTAc0fvA3jZzP37VQDf\nCiHOCCFOA2gJQINgVvZFTScHhZo1gZrQwaLZZ9G8OSeVMRjYcyo+XmuJJHomOZnt3ElJQN++PB+p\nV4z9riYuqNYFzdNY6CFiuD2xak5JCNEIwDiwWU0pg4iotqVlEdF+ZK4ct1ojmzOhppODgr8/UAMa\nhxfKKV99BWzZwu6C48ebIotLJBmZOpU7TIUKnLZVzxj7XQ1cVDVQeYMGHHD/+HF1nCf0irWPwp8B\nLAfwNoDXjdsbagmVW1BrfZI5FSoAtdx5pPS4go5HSgDHXlm1ih0dZs0C/v1Xa4kkeuT4cX6BAYDl\nyzl6g45JrmoaKampPPSYHt0eWKuU7hDRn0QUSkTXlE1NwXID9lBKbm5APW9WSle9da6UAP7xn3/O\n+717s31CIlEwX2z98cec1VjnROTjflfH4wLy5lWvXKmUsudLIcRSIUR3IcTbxu0tVSXLBagV8y4d\nRKicxOa7k4k6N98pjB/PNsxr14CRI7WWRqInvviCHRyqVGETnhNw8nEVpMAdz6eEcXhvlcgtzg7W\nKqVAAHUAdADQybi9rpZQuYHoaA7dpaaTAwDg+nX4pDxGFErgZERRFQu2I56ebMbz8gIWLwa2uvx0\noiQnHDwITJvGw/+VK6HqsMOOnL/shRD4wR0GVVPGliqVO5wdrFVKDQE0IqJAIuqtbGoK5urYw8kB\nQFp4oYuo4VwBHF94wTRv0LcvcPeutvJItOXxYzbbEQGffQY0aaK1RDnmwgXufwBUzzeRG0x41j4O\nDwJwggkL/WKP+SQAaYFYL6Bm+iy0zsCnnwJNmwK3b5uiP0tyJ2PGcDihWrU4JJUTcf489z8Aqof2\nlkopa5oAOCWEuCyEOGvczqgpmKtjN6VkfDML8aiBW7eAOGeKFujubjLT/PwzsGGD1hJJtGDXLg5F\n5eHBZl1vb60lyjHJyWyxkyMl67FWKXUAUAVAe9joEp5NPqUiQogdRsW3XQhRyEpZdYm9R0rxFWua\nf3Qe/PxMEcQHDODJN0nu4f599sIE2Mmhbl1t5bGQkBBWTPfL2Hek5MrODlYpJXM3cBVcwrPKpzQG\nwA4iqgpgl/GzS2A3JweitE7gUYvf1JxOKQHAwIFAmzZATAynuibniIosUYHhw7lzNGrEJjwnQ+lv\nXrWMC5SuXGEtpRLmzg4hIaoVqys0DzNEWedTegPASuNpKwF01kZC9TF3clA1QHZ0NHDvHlCwIMo2\n5NCBTqmU3Nw4xUWBAsDvvwOrV2stkcQR/PUXL4719mYzrodukhjkGKW/+dXJB1SsyArp6lVV61BG\nS0rcTFdDc6Vkjlk+pSMAShJRlPGrKAA6SppiG/Y23aFmTdT056RnTufsoFC+PDB7Nu8PHcpBAiWu\nS2ws0K8f70+Zov8QWVmg9LeaNZEh66Z6uPq8kipKSQhRWghh02ykMZ/SBnA+pXTL+pVQ57aUryfs\n7eSAGjXSYm455UhJoVcv4PXXeZ6hb19pxnNlBg/m7JQvvQR88onW0liN0t/8/WFSrNLZwSLUGh+v\nBlBZCLGeiCxeki+E8AQrpJ+I6Hfj4SghRCkiijSmsXhqxttZk/yVL89LMKpVUyexXzoCA4E2beDm\nFoa+fTk6S3CwbQ5Mmiaf+/prNqInJLA3nuqaXD/k2iR/589zHMR+/di5JTzc7lXao60NBp4Kq1+f\nLc9hzZrxvKiPj6odXXl+eHsDoaH6yQSfGUo7a5LkDzzq8rfiOgFgFYCZGY5/B2C0cX8MgG8yXuuM\nSf6iozlZV758RCkpKhfeujUXvnkzERHVqcMfDx2yrVjNk8+tW2dqtKtXtZXFjmjezlpw6xZRkSL8\n/1240GHV2qOtg4P5Z1SoYDxw8CAfqFdP9brKlOGiL11SvWhV0TrJ31vIZDSTAzLLp9QBwDcA2gkh\nLgNobfzs9ChD7vr17ZAF/EL6PEouYcIDgHfeAd59l1f59+rlur6wuQ0ioH9/jt7Rvj3vOzHpTHeA\nyXwXHKz6PavEy3RFE56aSikIQCchxDdCiBdzehER7SciNyKqS0T1jNs2IrpLRG2JqCoRtSciZ1oG\nmiVqZ5pN4949IDKSF56WLw/ANM/qtM4O5sybx5nO/vuP01xInJ8VK4C//wYKFgSWLtW3HSoHpHNy\nAIBChYDSpdn0rLJJ0pU98NRUSl3Aprhglct1Kezu5FC9elowPZcZKQFA0aLAjz/y/rhxqk8eSxxM\neDigzAfPnQs8/7y28qjAUyMlQHrgWYGayuM/ADsBHABnpJVkgt2U0rlz/Ncs/7Kyq3zl9Lz+Oq/2\nf/KEU6inpGgtkcQaDAagTx9eAdq5M/D++1pLpAqZdEG7dUJXjuygmlIioiAiigCQF8Betcp1Je7c\n4cXq+fLZIZLD4sW837Rp2uHKlXmAceOGi5jwADbdlS/PdotvXGKaMfexYAGwezdQrBiwaJHTm+0A\n7mNnz7Kj3QsvmH3RrBn/XboUSE1Vrb5SpYAyZVwzsoM9zGxFAcTboVynx26RHLZt48JLlGBfUSPu\n7vwiCgC//aZifVry3HMc7QEAvvwSOHVKW3kklnHlCjBqFO8vWsT3rAuwfj3/7dgxQ9qnt98GKlXi\n371unap1uqoJTzWlJIToLYRwBxDqKk4JamOvTLNpeYhGjXoqEVq3bvzXZZQSwHHxhgxh813PnmzO\nk+if1FR+aUpIAP73P+At10lWrfQvpb+l4eEBjB3L+19/raqtzVU98NQcKXmBs9E6/4ylnbDLfNLu\n3cChQ2ynGzDgqa9btwaKFOF5Vpcx4QFsuvPzY5uJk+XbybVMn873apky7NzgIty4wUlyfXyATp0y\nOaFnTzY5X7gAbNyoWr1ypPRsIgCUAdBCxTJdCru4gyujpBEjOOx4Bjw9XdCEB/DE3MqV7Gn43Xf8\nsJPol3PngAkTeH/JEqBwYW3lUREl7VfHjpl2QcDLCxg9mvcnT1YtXJa5UnIlZweLlJIQYoAQYrkQ\n4itjHqSRQog+QogiAMIBbAFw3y6SOjl2cXL47z9g715eDzFkSJanuaQJD2CnjpEjuUcGBgLxcipT\nlyQn82ghKYlDCb36qtYSqUqWpjtz+vThNUunT3M0dBVwVWcHS0dK4UTUG8AKANMBnAXgY/ycl4gM\nRDTfkgKFEMuEEFFCiLNmxyYKIW5kiPDg1NjFyUEZJQ0bxg4AWdCmDb+YXrjgImuWzJk0iReGXLli\nst1L9MXXXwMnT3Iqh+nTtZZGVW7eBA4cyMZ0p+DjA3z2Ge9/9ZVdRkuugqVKKY8Qwo2IrgI4Q0T/\nGJXQmwCaWynDcnAmW3MIwAzzCA9Wlq0bVJ9POnIE2LGDIz+aBaXNDJc14QEclXLVKp5QnjMH2LNH\na4kk5hw/ziYrgCM4FCigqThqo5juXn01C9OdOf37s7dhUBDwzz+q1C+VEpvnugohGhHRZOWgMcBe\nhDUCENF/AO5l8pXzL14wQ3XPO2WUNGRIjuzzLmvCAziQoDJf0bs38OCBtvJImMRENtulpnI6ipYt\ntZZIdXJkulPImxf49FPeV2m05IoeeBYpJSJKJKJfAdwQQnQUQrwmhHhDCPEx1J9LGiqEOC2EWCqE\nKKRy2Q5H1ZHSiRPA5s18kw8fnqNL2rThqafz5100Qs/Ysdy44eHs9CHRngkT2F5crRon7nMxbt1i\n05239zNMd+YMHMjusAcPqjKqd8XIDlblUyKi2wBuK5+NSqOeEKIjAIMK5rYFACYZ978Cz1/1zXiS\ns+RTio8HXn6ZnXC8vVVIrbJjB0/sN20KPHrEWw744gtea3r0KJu4c4rT5PmZP58jW6SkANu3A1Wq\naC2RRThNO+eEiAjOJdSrFydojIzUWqJ0qNHWR4/yQLB6df6pMTE5vHDmTF7KsW8f4OtrkwwAG0se\nPmTFVLSozcWpiqb5lGzZwLHyzlr6nbPkU9q6lXOfNG+uQmFnznBhPj5Et29bdOnmzXzpCy9YVqVT\n5fmZNo1/ZKlSRDExWktjEU7Vztnx8CFRpUr8fxg3TmtpMkWNtm7Rgn/izz9beGFcHFHBgnzxf//Z\nLMfrr3NRa9bYXJTqaJ1PSTWMmWYVuoC9/JwWVU13X3/Nf/v1Y59QC2jblk14585xiheXZPhwoHlz\nfjMfOlRraXIno0dzStTatXl47oLcvg3s38+Wj9dft/DiggWBjz/mfWVu2AZczdlBc6UkhFgL4CCA\nakKI60KIPgC+FUKcEUKcBtASQM4mTnTKkSP812Ynh+Bg4Ndf2Q6ouJdagJcX8OabvO+SDg8A+9uv\nWMHzbWvXuvAP1Sk7drAZ1dOTvSK9vLSWyC5s2MB+Ch06WOlQ+Mkn7K63fTvbAW1Aea4ozxlnR3Ol\nRETdiagMEXkRUTkiWkZEPYmoNhHVIaLORBSltZzWcucOx0t1cwNatbKxsClTuCf07m11/hmX9sJT\nqFwZ+P573h84EIhy2tvHubh/nxeJAhz6qU4dTcWxJxZ53WVGkSLA4MG8b+NoqVkzniPev1+F+Wod\noLlScnVWruQF7R072pjH7OpVYM0aXo8zZozVxbRrx9aDs2eBS5dskEfvDBjAPzY2lteHqLRYUZIN\nw4ZxILjGja0ayTsLt29zMBWrTHfmjBgB5MnD2XdPnrS6mEKFTMpxyRIb5NEJUinZESJTstR+/Wws\nbOpUXu/xwQe8Mt5KcoUJD+AcPUuXcqSLP/9kU5LEfvzxB7+B+fjwXw+rHHudAsV098or2QZSeTYl\nSpiCKE+enP25z0B5vixfzi/BzoxUSnZk3z7g8mWOT9Wxow0FhYebgo+OG2ezXLnChAcA5cpxlAeA\nJ5avX9dWHlclJoZHowC/PFWvrq08dsZm0505o0bxkGvjRpuy0zZvzs1++zYvYXRmpFKyI0oy2L59\nbXxx/PZbXnvTvTuna7CRdu34De/MGVaaLk3PnsAbb3CUhz59pBlPbYh43i46miM2KF5lLopiuvPy\nstF0p1C6NPDhh7yveNZagRCm9wLlueOsSKVkJ2JjORulEKyUrObmTTZDCQGMH6+KbN7eucSEB3C7\nLV7Mqwp37gQWLtRaItdi3Tq+0fPnZ9uRm2s/UjZuNJnuChZUqdDRo9lbcd06myZ6P/iAleW2bWxc\ncVZc+w7SkFWrOFL/K68AFSrYUNC0aVxQ165AjRqqyffOO/zX5ZUSAJQsaVJGI0ey04jEdm7fBgYN\n4v0ZM1SJTqB3VDXdKZQrx5EviGwKx1SsGGdfJwKWLVNPPEcjlZIdMHdwUIbUVhEVBSxaxPuff26z\nXOYoJrzTp3OJYuralc2f8fEcoik1VWuJnBsiNjvdu8eLdRQTlAuzbRvPE3t5sUVYVcaM4TV2P//M\nC4+tRHneLF3KFn9nRHOllEU+pSJCiB1CiMtCiO3OFpD1wAEOelqqlAWBGjNj+nSOtPzmm7w6XkW8\nvU167n//A7ZsUbV4ffLDD/xPOXCA5+kk1rNoEd80hQqxH7JwqaD+T7FvH/DWW6yLP/1URdOdQqVK\nwPvv88vS1KlWF9OyJYd8vHkT2LpVRfkciOZKCZnnUxoDYAcRVQWwy/jZaVAmGvv0YVOxVcTE8Mp4\nwJSWQWVGjuQtOZmH/Xv32qUa/VCkiMmuMWECsGuXtvI4K0ePmnJ4zZ8PlC2rrTx2JiiIXy4TEnhA\naIM/QvaMG8dzcitXckBbKxDC5B6uWGucjqyC4jlyQ4agqwCCAZQ07pcCEJzZdXoMyBobS+TtzQES\nbYr5OH48F/Lqq6rJlhkGA1H//lxV/vxER448fY7LBApV+Pxz/sHFihGFh2stTRpO0c5RUUTPP8/t\nN3iw1tJYTU7b+uxZoiJF+Oe+9x5RSoqdBevRgysbNMjqIqKiiDw9idzciK5fV1E2K3CZgKxghaTE\nhokCUFJLYSxh9WrgyROes7F63jcuDpg7l/ftNEpSEIJfdnv04AwYHTpwtAeXZuJE9kCJieEhYmKi\n1hI5BykpwHvvcdSGpk3ZucGFCQnhfnz3Lrt/r1rF0z52Zfx408LvW7esKqJECaBLF86v5IwOD4J0\nsG5DCFERwF9EVMv4+R4RFTb7/i4RFcl4Xd++fSm/WQ5iPeRTWrCAl2y8847RWS4piQ3SERHsBVau\nHG/ZZYvdt48TgFWqxH6eDsBgYIeH4GD27u3dm61dAOdEKVTIqab1nk1CAttZ4+I4zLJNk3/qoPt2\n3rmT5+Py5+cZdSdObf6stn7wgD3c4+L45bJHDwcGqfjtN06OGBDAL09Zcf8+Lwi/fp09IUuX5gCb\nefIgLIyVaMGCbGnVaspPaeeM+ZTmzJkDIspcqqyGUI7ckLn5rpRxvzScxHx38CCPvEuUIHqSaCBa\nv56obFk+mHErVYrorbeIpk8nOnSI6MkTLuT+faLChfmcf/91qPwJCURt23LV5cubLFtOYVayhuPH\nOS8VQLRkidbS6Lud16/ndnJ3d/h9aQ+ya+uoKKKqVfnnBgRweiiHcuoUV54nDwtDRJSURHTsGNGs\nWd18wrUAABYlSURBVETvvJP1c6V4caKVKyk1xZCW0mrLFgfLb4Y15jvNFRJlrpS+AzDauD8GwDeZ\nXac3pdSrF7fotx9d5bkg5UZp1Iho0yaib78leuMNnsvIeDN5e3MWwFde4c8tWmjyGx49ImralEWo\nUoUoMlLnD0tbWb7c1P7Hjmkqim7b+eJFnnAEiGbM0FoaVciqre/eJapTh39qnTr8WRPeeIOFePll\nolatiPLmffqZUbgwUceORF9/TfT770QvvWT6rlUrWjz8AgFEnTtr9BvISZUSgLUAbgFIAnAdQG8A\nRQDsBHAZwHYAhTK7Vk9K6d49ooI+iTQOkynV2/j2XbAg0fz5T8+OGgxEly7xA/HDD4lq1nz6htu+\nXZPfQcS/pW5dFqNWLaLz53X6sFSLAQNMw8M7dzQTQ5dK6cEDourVuX3efZfvXRcgs7Z++JBHRgBR\ntWqmQYomHDv29DOhalV+8/3xR6Lz54lSU9NfYzAQrViR9tJr8PSkr8V4yucWTzdvavMznFIp2bLp\nSSltHPYvXUB10w30v/9Zlq48Npbo7785ffTMmZp3/uho07No/PhQevBAU3HsS2Ii0Ysv8o9t29YB\nLlaZozulZDAQvf02t4u/vwZ2LPuRsa0TEohat+afWqECUUSENnKlY9EiotGjeRQUHZ3z62Jjifr1\nS3sWXYUvrem51X5yZoNUSlqQkED3eg9PuwHul65KtHOn1lKpwvXrRBUrEgUGhlKrVkTx8VpLZEeu\nX2d7PMAvBhqgO6U0bRq3x3PP8cjehTBv66Qkotdfp7Sp3itXNBRMTQ4coAcVa6U9m2K79nf4i4Ur\nuYQ7BYYTpxBbqSEKLZ+JFLjjhyIT4B18BmjTRmvRVOH553l9aYECwL//cryvpCStpbITzz/PATHd\n3Dj+2B9/aC2RtuzZw4FCAXbjqlpVW3nsRGoqB5L/6y/2Nt2xQ5VA/PqgaVPkCz6O2WW+xRN4ocj6\nxbhXsS5S9x/SWrJskUrJGlJTETfmG6Q2bIyit8/jMqpg0isH0ePKJHg/5621dKqieKUXLcp5Wj74\nwIXDxr38MvDNN7zfs2cuyOuRBdevA+++y+sExo41hZR3MYg4x94vv/CL17ZtwAsvaC2Vurh5e6JP\n8Gf45u0gnEZtFI69CrRojruDJ+g3G2BWQyhn2LQw3xlCrlJklWZpQ+KlPoNo40+PHC6HIwkNDaWg\nILbiKA5B69ebvNhdCoOBqGtXTeZRdGG+M59fa9dOs/k1e5KcTLRtW2iag6yPD9HevVpLZX+2/ZFI\nP+T7jFIhiACKKteAUs9dsGudck7JnhgMFDd9CcW7s2vsLZSiiQFbKTLScSJohXJj/fcfUYECafqY\nihUjGjaM6PRpjQVUG408znShlHTiiWgPLlwgGjWK540CA0PTFNJWbXwANCE2lmhS270UhgpEACW6\n+VDsxNlPe/KphFRK9iIqim40fDPtabzJoyv9Mi9Gawc5h2F+Y925w+v3atc2KSeAqEEDoh9+0HBd\nh9qYr82ZOdMhVWqulHS0Zkst7t8nWryYqEmT9Pfrp5+G0nffWeYg60psWnmf1nj3SmuQmzXbkiFC\n/UB5UinZgQc//0lx3iWIAIrDc/TNCz9RRHgu0UZGMruxDAYOiDB4MFGhQqbO7u3NgSv/+ccFLD/m\nUQwcYN/RVCmdOKGr6Ba2kJpKtGcPUc+eHBRBuTcLFGBP6YMHdfACoAMiI4mmNNpI0eB1TQ89C9G9\nBWtVrcPllBKAawDOADgJ4GjG7+2qlB48oPBXPky7o/91a0WrJofnmtGROc/qwAkJRL/8QtS+PZEQ\npodAuXIckDskxEGC2oNRo/jHlCxJ9l6BqNmDMiaGff8BXsztpEREEH31FaWF1zELbkArV3K0EgWp\nlBiDgWjd7Nu01eO1tAYLb9ZdNZOHKyqlMABFsvreXkrp0fYDFFWA7+xEeNFc3+kUctk+NldnwJIO\nHB6e8weDU5CczJ4dAMdfsqN3hyYPypQUU2irhg35DcOJSEggWrvW8hciqZTSExFuoJk1FtEjcDij\nmDxl6f6GHTaX66pKqWhW36uulJ48obD/jaMUuBEBdErUoeWfnnV+M5SNWNOBc2JCcZpRp3kOoSFD\n7FaNJg/KCRP4dxUtqqvcUtlhMBAFBWVuOn733ZyZjqVSehqDgWj1xCt02C0grVFD3xhm06p5V1RK\noUbTXRCAfhm/V1MpxQedp4ji9YgASoWg5SVH0/kTiaqV78zY2oHv3+dwXRknm6tXJ+eZbD58mMjL\niwX/6Se7VOHwB+Wff/LvcXMj2mH7W7G9ycrJpn59drKJjc15WVIpZU1IcDItLv8VJcGDCKCbhWrQ\nw73HrSrLGqWki3xKWSGEKE1Et4UQxQHsADCUiP5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"text": [ "<matplotlib.figure.Figure at 0xdf110d0>" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "# you can play with quadrupole strength and try to make achromat\n", "Q4.k1 = 1.18\n", "\n", "# to make achromat uncomment next line\n", "# Q4.k1 = 1.18543769836\n", "# To use matching function, please see ocelot/demos/ebeam/dba.py \n", "\n", "# updating transfer maps after changing element parameters. \n", "lat.update_transfer_maps()\n", "\n", "# recalculate twiss parameters \n", "tws=twiss(lat, nPoints=1000)\n", "\n", "plot_opt_func(lat, tws, legend=False)\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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cH3frFocidO6sXtvKqOjSJfXa9GJcrUJrfwArTdtXAhiog2iegWKWa9RI3XY/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\n/Kw+PSuLJ8L37WPHhIAAdhdu0YInlIH8+zk/wsP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SVqaMsQ7myfOUpHN5TT8raUfKGOtA\n0qCk30s6Iemvksbz29uu6Uo0IknLgB8BO4AR4G5Jm9JGVWsBbI+ILRGxNXUwNfJzshpu9W3gcER8\nFHg8v27dmSvPAfwwr+ktEfHrBHHVzZvANyPiJmAM+Fr+vdx2TVeiEQFbgdMRcTYi3gQeBXYmjqnu\n5tzN0joXEU8BF99x8+3AdH55Grij0KBqaJ48g2u6pyJiJiL+nF++DJwE1tNBTVelEa0HXmq5fi6/\nzZZGAL+TdEzSV1IHU3OrI+JCfvkCsDplMDX3DUl/kbTXU6C9JWkjsAV4hg5quiqNyD92KtanI2IL\ncBvZ5vZnUgfUDyL7UZ9rfWn8GBgCPgGcB36QNpz6kHQ98CtgIiL+1XrftdZ0VRrRy8Bgy/VBsq0i\nWwIRcT7/9x/AfrKpUVsaFyStAZC0Fng1cTy1FBGvRg74Ka7pnpB0HVkTejgiDuQ3t13TVWlEx4Bh\nSRslNYC7gEOJY6olSe+V9L788gDweeC5hZ9lXTgENPPLTeDAAo+1DuVfiG/ZhWu6a5IE7AWej4iH\nWu5qu6Yrc4gfSbcBDwHLgL0R8WDikGpJ0hDZVhBkB8V9xLnuDUm/AD4LfIhs7vy7wEHgl8BHgLPA\nnRFxKVWMdTBHnieB7WTTcgGcAb7aso5hHZB0K/AkcJy3p98eAI7SZk1XphGZmVk9VWVqzszMasqN\nyMzMknIjMjOzpNyIzMwsKTciMzNLyo3IzMySciMyM7Ok/gcTY9bshHCXZQAAAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0xe48a190>" ] } ], "prompt_number": 9 } ], "metadata": {} } ] }	mit
augustot2/HPFEM.jl	work/static-condensation.ipynb	2	3134	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "HPFEM" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "using PyPlot\n", "include(\"../src/HPFEM.jl\")\n", "H = HPFEM" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "b = H.Basis1d(5,7);" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Nel = 3\n", "pts = [linspace(0, 5, Nel+1);]\n", "\n", "elems = [H.Element1d(1, pts[i], pts[i+1], b) for i in 1:Nel];" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dof = H.DofMap1d(H.nmodes(b), Nel+1, []);\n", "solver = H.CholeskySC(dof, H.BBSymTri);" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ii = H.interior_idx(b)\n", "ib = H.bndry_idx(b);" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "======================================\n", "PROCESSING ELEMENT \n", "-0.013888888888889006\n", "======================================\n", "PROCESSING ELEMENT \n", "-0.013888888888889006\n", "======================================\n", "PROCESSING ELEMENT \n", "-0.01388888888888895\n" ] } ], "source": [ "for e = 1:Nel\n", " println(\"======================================\")\n", " println(\"PROCESSING ELEMENT \", )\n", " M = H.mass_matrix(b, elems[e])\n", " Abb = M[ib,ib]\n", " Aii = M[ii,ii]\n", " Abi = M[ib,ii]\n", " println(H.add_local_matrix(solver, e, Abb, Abi, Aii))\n", "end" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "HPFEM.BBSymTri{Float64}(4,4,[0.06944444444444431,0.13888888888888878,0.1388888888888889,0.06944444444444448],[-0.013888888888889006,-0.013888888888889006,-0.01388888888888895])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solver.Abb" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.4.5", "language": "julia", "name": "julia-0.4" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.4.5" } }, "nbformat": 4, "nbformat_minor": 0 }	mit

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