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ioam/holoviews | examples/reference/elements/bokeh/Area.ipynb | 1 | 3678 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### **Title**: Area Element\n",
"\n",
"**Dependencies**: Plotly\n",
"\n",
"**Backends**: [Bokeh](./Area.ipynb), [Matplotlib](../matplotlib/Area.ipynb), [Plotly](../plotly/Area.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import holoviews as hv\n",
"hv.extension('bokeh')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"``Area`` elements are ``Curve`` elements where the area below the line is filled. Like ``Curve`` elements, ``Area`` elements are used to display the development of quantitative values over an interval or time period. ``Area`` Elements may also be stacked to display multiple data series in a cumulative fashion over the value dimension.\n",
"\n",
"The data of an ``Area`` Element should be tabular with one key dimension representing the samples over the interval or the timeseries and one or two value dimensions. A single value dimension will fill the area between the curve and the x-axis, while two value dimensions will fill the area between the curves. See the [Tabular Datasets](../../../user_guide/08-Tabular_Datasets.ipynb) user guide for supported data formats, which include arrays, pandas dataframes and dictionaries of arrays."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Area under the curve\n",
"\n",
"By default the Area Element draws just the area under the curve, i.e. the region between the curve and the origin."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"xs = np.linspace(0, np.pi*4, 40)\n",
"hv.Area((xs, np.sin(xs)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Area between curves\n",
"\n",
"When supplied a second value dimension the area is defined as the area between two curves."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X = np.linspace(0,3,200)\n",
"Y = X**2 + 3\n",
"Y2 = np.exp(X) + 2\n",
"Y3 = np.cos(X)\n",
"hv.Area((X, Y, Y2), vdims=['y', 'y2']) * hv.Area((X, Y, Y3), vdims=['y', 'y3'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Stacked areas \n",
"\n",
"Areas are also useful to visualize multiple variables changing over time, but in order to be able to compare them the areas need to be stacked. To do this, use the ``Area.stack`` classmethod to stack multiple ``Area`` elements in an (Nd)Overlay.\n",
"\n",
"In this example we will generate a set of 5 arrays representing percentages and create an ``Overlay`` of them. Then we simply call the ``stack`` method with this overlay to get a stacked area chart.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"values = np.random.rand(5, 20)\n",
"percentages = (values/values.sum(axis=0)).T*100\n",
"\n",
"overlay = hv.Overlay([hv.Area(percentages[:, i], vdims=[hv.Dimension('value', unit='%')]) for i in range(5)])\n",
"overlay + hv.Area.stack(overlay)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For full documentation and the available style and plot options, use ``hv.help(hv.Area).``"
]
}
],
"metadata": {
"language_info": {
"name": "python",
"pygments_lexer": "ipython3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| bsd-3-clause |
Jai-Chaudhary/computer-vision | HW2/HomeWork2.ipynb | 1 | 12208 | {
"metadata": {
"name": "",
"signature": "sha256:f22297580d7936bfad662d608318317210f7dabd3fb21c6164373a419f1648c5"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import scipy as sp\n",
"import cv2\n",
"from skimage.measure import ransac\n",
"from skimage.transform import warp, AffineTransform\n",
"from skimage import img_as_ubyte\n",
"\n",
"class SIFT:\n",
" MIN_MATCH_COUNT = 10\n",
" extractor = cv2.SIFT()\n",
"\n",
" \n",
" \"\"\"\n",
" returns list of DMatch Objects (matches) sorted by distance \n",
" that contains keypoints matched in images.\n",
"\n",
" :param keypoints1: list of keypoint objects for image 1\n",
" :param descriptorSet1: list of descriptor objects for image 1\n",
" :param keypoints2: list of keypoint objects for image 2\n",
" :param descriptorSet2: list of descriptor objects for image 2\n",
" :returns: list of DMatch Objects\n",
" \"\"\"\n",
" def findMatches(self,keypoints1, descriptorSet1, keypoints2, descriptorSet2): \n",
" # create BFMatcher object\n",
" bf = cv2.BFMatcher(cv2.NORM_L2, crossCheck=True)\n",
"\n",
" # Match descriptors.\n",
" matches = bf.match(descriptorSet1,descriptorSet2)\n",
"\n",
" # Sort them in the order of their distance.\n",
" matches = sorted(matches, key = lambda x:x.distance)\n",
"\n",
" # visualize the matches\n",
" print '#matches:', len(matches)\n",
" dist = [m.distance for m in matches]\n",
"\n",
" print 'distance: min: %.3f' % min(dist)\n",
" print 'distance: mean: %.3f' % (sum(dist) / len(dist))\n",
" print 'distance: max: %.3f' % max(dist)\n",
" \n",
" return matches\n",
"\n",
" \"\"\"\n",
" generate an image where the matches are visualizated by \n",
" connecting matching pixels by highlighted lines\n",
"\n",
" :param imageFilename1: path to image1\n",
" :param imageFilename2: path to image2\n",
" :returns: void\n",
" \"\"\"\n",
" def showImages(self, imageFilename1, imageFilename2):\n",
"\n",
"\n",
" # find the keypoints and descriptors with SIFT\n",
" img1 = cv2.cvtColor(cv2.imread(imageFilename1), cv2.COLOR_BGR2GRAY)\n",
" img2 = cv2.cvtColor(cv2.imread(imageFilename2), cv2.COLOR_BGR2GRAY)\n",
"\n",
" kp1, des1 = self.extractor.detectAndCompute(img1,None)\n",
" kp2, des2 = self.extractor.detectAndCompute(img2,None)\n",
" matches = self.findMatches(kp1, des1, kp2, des2)\n",
" self.drawMatches(img1, kp1, img2, kp2, matches)\n",
" \n",
"\n",
" \n",
" \n",
" \"\"\"\n",
" Visualization\n",
" My own implementation of cv2.drawMatches as brew binary for MacOSX\n",
" OpenCV 2.4.11 does not suppourt this function but it's supported in\n",
" OpenCV 3.0.0\n",
"\n",
" :param img1: image1 Object\n",
" :param kp1: list of keypoint objects for image 1\n",
" :param img2: image2 Object\n",
" :param kp2: list of keypoint objects for image 2\n",
" :param matches: list of DMatch Objects that contains keypoints matched in images.\n",
" :returns: void\n",
" \"\"\" \n",
" def drawMatches(self, img1, kp1, img2, kp2, matches, name = 'DrawMatches'):\n",
"\n",
" \n",
" h1, w1 = img1.shape[:2]\n",
" h2, w2 = img2.shape[:2]\n",
" view = sp.zeros((max(h1, h2), w1 + w2, 3), sp.uint8)\n",
" view[:h1, :w1, 0] = img1\n",
" view[:h2, w1:, 0] = img2\n",
" view[:, :, 1] = view[:, :, 0]\n",
" view[:, :, 2] = view[:, :, 0]\n",
"\n",
" \n",
" for m in matches:\n",
"\n",
" # Get the matching keypoints for each of the images\n",
" img1_idx = m.queryIdx\n",
" img2_idx = m.trainIdx\n",
"\n",
" # x - columns\n",
" # y - rows\n",
" (x1,y1) = kp1[img1_idx].pt\n",
" (x2,y2) = kp2[img2_idx].pt\n",
" color = tuple([sp.random.randint(0, 255) for _ in xrange(3)])\n",
" cv2.line(view, (int(x1), int(y1)) , ((int(x2) + w1), int(y2)), color)\n",
" cv2.circle(view, (int(x1),int(y1)), 4, (255, 0, 0), 1) \n",
" cv2.circle(view, (int(x2)+w1,int(y2)), 4, (255, 0, 0), 1)\n",
"\n",
" cv2.imwrite(name + \".png\", view)\n",
"\n",
" \n",
" def affineMatches(self, matches, kp1, kp2):\n",
" src_pts = np.float32([ kp1[m.queryIdx].pt for m in matches ])\n",
" dst_pts = np.float32([ kp2[m.trainIdx].pt for m in matches ])\n",
" model = AffineTransform()\n",
" model.estimate(src_pts, dst_pts)\n",
" \n",
" model_robust, inliers = ransac((src_pts, dst_pts), AffineTransform, min_samples=3,\n",
" residual_threshold=30, max_trials=500)\n",
" \n",
" print(\"Least Square Model(scale, translation. rotation) \", model.scale, model.translation, model.rotation)\n",
" print(\"RANSAC Least Square Model(scale, translation. rotation) \", model_robust.scale, model_robust.translation, model_robust.rotation)\n",
"\n",
" inlier_idxs = np.nonzero(inliers)[0]\n",
" \n",
" affineMatches = [matches[idx] for idx in inlier_idxs]\n",
" print (\"AFFINE MATCHES: \", len(affineMatches))\n",
"\n",
" return affineMatches, model_robust\n",
"\n",
" def alignAffine(self, imageFilename1, imageFilename2, affineTransform):\n",
" img1 = cv2.imread(imageFilename1)\n",
" img2 = cv2.imread(imageFilename2)\n",
" img_warped = warp(img2, affineTransform, output_shape=img1.shape)\n",
"\n",
" b1,g1,r1 = cv2.split(img1)\n",
" \n",
" b_warped,g_warped,r_warped = cv2.split(img_warped)\n",
" img_merged = cv2.merge((img_as_ubyte(b_warped), g1, r1 ))\n",
" cv2.imshow(\"AffineorigImage.png\", img1)\n",
" cv2.imshow(\"AffinewarpedImage.png\", img_as_ubyte(img_warped))\n",
" cv2.imshow(\"AffinemergedImage.png\", img_merged)\n",
"\n",
" print \"Affine Error: \", self.alignmentError(img1, img_warped)\n",
" \n",
" def alignmentError(self, img1, img2):\n",
" hist1 = cv2.calcHist([img1.astype('float32')],[0],None,[256],[0,256])\n",
" hist2 = cv2.calcHist([img2.astype('float32')],[0],None,[256],[0,256])\n",
" return cv2.compareHist(hist1, hist2, cv2.cv.CV_COMP_BHATTACHARYYA)\n",
" \n",
" def homographyMatches(self, matches, kp1, kp2):\n",
" src_pts = np.float32([ kp1[m.queryIdx].pt for m in matches ]).reshape(-1,1,2)\n",
" dst_pts = np.float32([ kp2[m.trainIdx].pt for m in matches ]).reshape(-1,1,2)\n",
"\n",
" transformMatrix, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,30.0)\n",
" \n",
" inliers = mask.ravel().tolist()\n",
" \n",
" inlier_idxs = np.nonzero(inliers)[0]\n",
" \n",
" homographyMatches = [matches[idx] for idx in inlier_idxs]\n",
" \n",
" print (\"Homography Matches\", len(homographyMatches))\n",
" return homographyMatches, transformMatrix\n",
" \n",
" \n",
" def alignHomography(self, imageFilename1, imageFilename2, homographyTransform):\n",
" img1 = cv2.imread(imageFilename1)\n",
" img2 = cv2.imread(imageFilename2)\n",
" img_warped = cv2.warpPerspective(img2, homographyTransform, img1.shape[:2], (1000,1000), flags = cv2.WARP_INVERSE_MAP)\n",
"\n",
" b1,g1,r1 = cv2.split(img1)\n",
" \n",
" b_warped,g_warped,r_warped = cv2.split(img_warped)\n",
"\n",
" img_merged = cv2.merge((b_warped,g1,r1))\n",
" cv2.imwrite(\"HomographyorigImage.png\", img1)\n",
" cv2.imwrite(\"HomographywarpedImage.png\", img_warped)\n",
" cv2.imwrite(\"HomographymergedImage.png\", img_merged)\n",
"\n",
" print \"Homography Error: \", self.alignmentError(img1, img_warped)\n",
" \n",
"\n",
"if __name__ == \"__main__\":\n",
" sift = SIFT()\n",
" imageFilename1 = 'StopSign1.jpg'\n",
" imageFilename2 = 'StopSign4.jpg'\n",
"\n",
" sift.showImages(imageFilename1, imageFilename2)\n",
"\n",
" \n",
" img1 = cv2.cvtColor(cv2.imread(imageFilename1), cv2.COLOR_BGR2GRAY)\n",
" img2 = cv2.cvtColor(cv2.imread(imageFilename2), cv2.COLOR_BGR2GRAY)\n",
"\n",
" kp1, des1 = sift.extractor.detectAndCompute(img1,None)\n",
" kp2, des2 = sift.extractor.detectAndCompute(img2,None)\n",
" matches = sift.findMatches(kp1, des1, kp2, des2)\n",
" affineMatches, affineTransform = sift.affineMatches(matches, kp1, kp2)\n",
"\n",
" sift.drawMatches(img1, kp1, img2, kp2, affineMatches, 'affineMatches')\n",
"\n",
" sift.alignAffine( imageFilename1, imageFilename2, affineTransform)\n",
"\n",
" homographyMatches, homographyTransform = sift.homographyMatches(matches, kp1, kp2)\n",
"\n",
" sift.drawMatches(img1, kp1, img2, kp2, homographyMatches, 'homographyMatches')\n",
"\n",
" sift.alignHomography( imageFilename1, imageFilename2, homographyTransform)\n",
"\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"#matches: 57\n",
"distance: min: 66.182\n",
"distance: mean: 248.593\n",
"distance: max: 419.063\n",
"#matches:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 57\n",
"distance: min: 66.182\n",
"distance: mean: 248.593\n",
"distance: max: 419.063\n",
"('Least Square Model(scale, translation. rotation) ', (2.2874197495623, 51.37786614914854), array([ 1192.76605199, 7763.4657654 ]), 0.869802576575406)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"('RANSAC Least Square Model(scale, translation. rotation) ', (2.593003672413597, 2.578301575009895), array([ 146.13314982, 62.798367 ]), 0.12204859657898029)\n",
"('AFFINE MATCHES: ', 18)\n",
"Affine Error: "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 0.430624746516\n",
"('Homography Matches', 18)\n",
"Homography Error: "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 0.881697945121\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"cv2.destroyAllWindows()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | gpl-2.0 |
brunez/dl_workshop_upm | notebooks/logistic_regression.ipynb | 1 | 207463 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Logistic regression\n",
"This notebook introduces logistic regression, implementing each of the examples we saw in the course."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"%matplotlib notebook\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def sigmoid(x):\n",
" return 1/(1+np.exp(-x))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def get_sample(N=50):\n",
" b_boys = 2\n",
" b_girls = .6 \n",
" x_boys = np.random.uniform(10, 18, size=N/2)\n",
" x_girls = np.random.uniform(10, 18, size=N/2)\n",
" y_boys = [3.5*i + b_boys*(i) + np.random.normal(0,i/2) for i in x_boys]\n",
" y_girls = [3.5*i + b_girls*(i) + np.random.normal(0,i/2) for i in x_girls]\n",
" return x_boys, x_girls, y_boys, y_girls"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def prepare(x_boys, x_girls, y_boys, y_girls):\n",
" X1 = np.concatenate([x_boys, x_girls])\n",
" X2 = np.concatenate([y_boys, y_girls])\n",
" X = np.vstack([X1, X2]).T\n",
" y = np.vstack([np.zeros(N/2), np.zeros(N/2)+1]).flatten()\n",
" return X,y"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# Generate data\n",
"# We have the height and weight of a bunch of young kids, and we must guess if they're a boy or a girl\n",
"N = 200\n",
"x_boys, x_girls, y_boys, y_girls = get_sample(N=N)\n",
"X,y = prepare(x_boys, x_girls, y_boys, y_girls)\n",
"\n",
"# Split train and test\n",
"split = 100\n",
"train_ids = np.random.choice(np.arange(N), split, replace=False)\n",
"test_ids = np.delete(np.arange(N), train_ids)\n",
"X_train = X[train_ids,:]\n",
"y_train = y[train_ids]\n",
"X_test = X[test_ids,:]\n",
"y_test = y[test_ids]"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted coefficients: [ 1.66677306 -0.34157436 -0.04564486]\n"
]
}
],
"source": [
"# Train a logistic regression model\n",
"from sklearn.linear_model import LogisticRegression\n",
"clf = LogisticRegression()\n",
"clf.fit(X_train,y_train)\n",
"w = np.concatenate([clf.coef_[0], clf.intercept_])\n",
"print 'Predicted coefficients: {}' .format(w)"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"'Boy'"
]
},
"execution_count": 125,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Predict a manually chosen instance\n",
"age = 12\n",
"weight = 59\n",
"x = np.array([age, weight]).reshape([1,2])\n",
"'Girl' if clf.predict(x) else 'Boy'"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
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"output_type": "display_data"
},
{
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\" width=\"639.999974568686\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# compute reference points for plotting the separating hyperplane\n",
"a = -w[0]/w[1]\n",
"b = w[2]\n",
"w\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"boys = np.array([X_test[i] for i in range(X_test.shape[0]) if y_test[i] == 0])\n",
"girls = np.array([X_test[i] for i in range(X_test.shape[0]) if y_test[i] == 1])\n",
"ax.scatter(boys[:,0], boys[:,1], label='Boys', color='red')\n",
"ax.scatter(girls[:,0], girls[:,1], label='Girls')\n",
"\n",
"xb = [10,18]\n",
"z = [a*x+b for x in xb]\n",
"ax.plot(xb, z, label='pred', color='black', linewidth=2)\n",
"plt.xlabel('Age')\n",
"plt.ylabel('Weight')\n",
"ax.legend(loc='best')\n",
"\n",
"ax.set_ylim(0,20)\n",
"ax.set_ylim(0,150)\n",
"\n",
"plt.show()\n",
"\n",
"#plt.savefig('../pres/images/logreg.pdf')"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initial w: [ 0.62606724 0.37281743 0.16866454]\n"
]
}
],
"source": [
"# Gradient ascent by hand\n",
"# first we choose a learning rate (alpha) and a random set of initial coefficients\n",
"# The algorithm is in the cell below\n",
"alpha = 0.005\n",
"X_train_1s = np.column_stack([X_train, np.ones(X_train.shape[0])])\n",
"w = np.random.rand(3)\n",
"print 'Initial w: {}'.format(w)\n",
"iterated = 0"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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width=\"639.999974568686\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final w: [ 1.6667813 -0.34157604 -0.04564509]\n"
]
}
],
"source": [
"# Here we run gradient ascent\n",
"# Note that the coefficients might not converge to the same ones as scikit-learn estimated. That is because\n",
"# they use a different optimization algorithm. The corresponding hyperplane, however, is roughly the same.\n",
"# After 100 iterations or so, with decreasing learning rate, we should reach more or less the same solution\n",
"import time\n",
"ITERATIONS = 50\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"plt.ion()\n",
"boys = np.array([X_test[i] for i in range(X_test.shape[0]) if y_test[i] == 0])\n",
"girls = np.array([X_test[i] for i in range(X_test.shape[0]) if y_test[i] == 1])\n",
"ax.scatter(boys[:,0], boys[:,1], label='Boys', color='red')\n",
"ax.scatter(girls[:,0], girls[:,1], label='Girls')\n",
"\n",
"xb = [10,18]\n",
"z = [a*x+b for x in xb]\n",
"ax.plot(xb, z, label='pred', color='black', linewidth=2)\n",
"plt.xlabel('Age')\n",
"plt.ylabel('Weight')\n",
"ax.legend(loc='best')\n",
"\n",
"ax.set_ylim(0,20)\n",
"ax.set_ylim(0,150)\n",
"\n",
"plt.show()\n",
"\n",
"for i in range(1,ITERATIONS):\n",
" grad = np.sum([xi*(yi-sigmoid(w.T.dot(xi))) for xi,yi in zip(X_train_1s, y_train) ], axis=0)\n",
" iterated += 1\n",
"\n",
" w = w+alpha/float(iterated)*grad\n",
" alpha = alpha*0.9 # The learning rate can be shrinked to help convergence\n",
" \n",
" a = -w[0]/w[1]\n",
" b = w[2]\n",
" z = [a*x+b for x in xb]\n",
" time.sleep(0.001) # Sleep to visualize the animation better\n",
"\n",
" ax.clear()\n",
" ax.set_title(str(iterated))\n",
" ax.set_ylim(0,20)\n",
" ax.set_ylim(0,150)\n",
" ax.scatter(boys[:,0], boys[:,1], label='Boys', color='red')\n",
" ax.scatter(girls[:,0], girls[:,1], label='Girls')\n",
" ax.plot(xb, z, label='pred', color='black', linewidth=2)\n",
" fig.canvas.draw() \n",
" \n",
"print 'Final w: {}'.format(w)"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.92\n",
"Precision: 0.9\n",
"Recall: 0.9375\n",
"F1: 0.918367346939\n"
]
}
],
"source": [
"#Measure accuracy\n",
"preds = clf.predict(X_test)\n",
"print 'Accuracy: {}'.format(len(filter(lambda x: x[0] == x[1], zip(preds, y_test)))/ float(len(preds)))\n",
"\n",
"from sklearn import metrics\n",
"print 'Precision: {}'.format(metrics.precision_score(y_test, preds))\n",
"print 'Recall: {}'.format(metrics.recall_score(y_test, preds))\n",
"print 'F1: {}'.format(metrics.f1_score(y_test, preds))\n"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" 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],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# A plot of the sigmoid function\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"xb = np.arange(-10,10,0.01)\n",
"z = [sigmoid(x) for x in xb]\n",
"ax.plot(xb, z, label='pred', linewidth=1)\n",
"ax.grid(True, which='both')\n",
"ax.axhline(y=0.5, color='k')\n",
"ax.axvline(x=0, color='k')\n",
"\n",
"plt.show()\n",
"#plt.savefig('../pres/images/sigmoid.pdf')"
]
}
],
"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
}
| unlicense |
DistrictDataLabs/intro-to-python | Workshop.ipynb | 1 | 60278 | {
"metadata": {
"name": "",
"signature": "sha256:788d450282aca14510b4fbb415c974ba9a033b999a6b00b6289ec70286d56a52"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"An Introduction to Python for new Programmers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Welcome to _An Introduction to Python for new Programmers_! This iPython notebook serves as an instructor-led guide to the Python programming language, particularly for those who are new to programming or have very limited programming experience. iPython notebooks are a novel form of communicating about programming; creating an environment where both text and live code can be executed in an explanatory fashion. Parts of this document will contain live Python code that is interpreted by the notebook and whose output will be rendered directly to the screen. If you would like to follow along on your laptop, you can install iPython and **download this workbook from** [github/DistrictDataLabs/intro-to-python](https://github.com/DistrictDataLabs/intro-to-python)."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Before we get Started...."
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Prerequisites:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Although this is an introductory course, some prerequisites are required. \n",
"\n",
"1. The student must have Python installed \n",
"2. Must be familiar with using Python on his or her operating system. \n",
"3. They must also have familiarity with the command line. \n",
"\n",
"The following are suggested tasks to perform before this course:\n",
"\n",
"* [Using the terminal](http://cli.learncodethehardway.org/book/)\n",
"* [Install Python](https://wiki.python.org/moin/BeginnersGuide/Download)\n",
"* [Python Basics](http://www.codecademy.com/tracks/python)\n",
"* [Python Hello World](http://www.learnpython.org/en/Hello,_World!)\n",
"\n",
"Installations:\n",
"\n",
"* [Install virtualenv and virtualenvwrapper](http://docs.python-guide.org/en/latest/dev/virtualenvs/) A *virtualenv* is an isolated working copy of Python which allows you to work on a specific project without worry of affecting other projects. *Virtualenvwrapper* provides a set of commands which makes working with virtual environments much more pleasant. It also places all your virtual environments in one place. \n",
"* [Get a Text editor, Sublime] (http://www.sublimetext.com/)\n",
"* [Get a Github account](https://github.com/)\n",
"\n",
"For Windows Users:\n",
"\n",
"* [Install Pip, Virtualenv and Virtualenv Wrapper on Windows] (http://www.tylerbutler.com/2012/05/how-to-install-python-pip-and-virtualenv-on-windows-with-powershell/)\n"
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Download the course materials from GitHub"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* The presentation is on github. [Github: Intro to Python] (https://github.com/DistrictDataLabs/intro-to-python)\n",
"\n",
"* The `Workshop.pynb` can be viewed on the iPython nbviewer application by using the following link: [nbviewer Intro to Python](http://nbviewer.ipython.org/github/DistrictDataLabs/intro-to-python/blob/master/Workshop.ipynb)\n",
"\n"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Learning how to Learn how to Code"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So, let's be clear- what can we accomplish in a two to three hour session? Obviously we're not going to make you an expert programmer in this time, nor are we going to impart a comprehensive theory of programming. \n",
"\n",
"However, there is one fundamental concept about programming that I would like ensure you all take away: **programming isn't about knowing syntax it's about learning**. Therefore, what were going to try to enable you to do today is help you **learn how to learn how to code**. \n",
"\n",
"In order to continually improve his or her craft, a programmer must constantly be in a state of education. From reading about new programming paradigms, languages, or frameworks to experimental approaches to reading other's code- programming is is a community activity that encourages the sharing of ideas. \n",
"\n",
"In particular we'll focus on clear expression of code and readability. **By ensuring you can write code in a readable form, it will help you to read other's code as well**!"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Getting Started"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's start by using the interactive interpreter to run the most basic of programs, \"Hello World!\" This interactive interpreter is also called the **\"REPL\" or the Read Evaluate Print Loop**. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print \"Hello World!\""
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The REPL is often used during program development. Let's try running this same basic program from a file. This is called a script or a program that is run from the command line (terminal). That first line is called the \"hash bang.\" Put simply, it indicates that the file can be run as a script. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#!/usr/bin/env python\n",
"\n",
"print \"Hello World!\""
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Output and Input"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# This is writing to standard out:\n",
"\n",
"print \"Python is awesome!\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Python is awesome!\n"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Basic input (These values will always be interpreted as strings)\n",
"\n",
"name = raw_input(\"What is your name?\")\n",
"print name"
],
"language": "python",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"What is your name?sarah\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"sarah\n"
]
}
],
"prompt_number": 19
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"What is Programming?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Programming is the act of creating **a set of instructions to transform input data to some kind of output**. \n",
"\n",
"Yeh- it's that simple. We're **making recipes of ingredients and instructions**.\n",
"\n",
"However, instead of food, we cook with numbers and strings. The instructions are defined as a formal language.\n",
"\n",
"The instructions are interpreted and executed from top to bottom, and when the interpreter runs out of instructions, the program is done!\n",
"\n",
"The **interpreter** is what takes your code instructions and translates them into something that your computer understands.\n",
"\n",
"Because of this python is called an **interpreted language** or a **scripting language**. \n"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Variables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Variables should follow some simple rules.\n",
"\n",
"* be lowercase\n",
"* use underscores for word separation\n",
"* not start with numbers\n",
"\n",
"In fact Python has it's own language style guide called PEP8.\n",
"\n",
"* [PEP8 Guide](http://legacy.python.org/dev/peps/pep-0008/)\n",
"\n",
"Consider the following simple program. This small program for computing the area of a triangle includes quite a few components:\n",
"\n",
"* Comments (`#`)\n",
"* Assignment (`a = 2`)\n",
"* Variables (`base`)\n",
"* Multiplication (`*`)\n",
"* Output \n",
"(`print`)"
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"# Compute the area of a triangle\n",
"\n",
"# Ingredients (aka: variables)\n",
"base = 4 \n",
"height = 7\n",
"\n",
"# Instructions (aka: an algorithm)\n",
"area = 0.5 * base * height\n",
"print area"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"14.0\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Interactivity is a powerful idea. It allows variables to be variable. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# The area of a circle\n",
"# Must input a number or a decimal\n",
"radius = input(\"Enter radius: \")\n",
"area = 3.14 * radius * radius\n",
"print area"
],
"language": "python",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"Enter radius: 10\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"314.0\n"
]
}
],
"prompt_number": 20
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we create a variable called radius. Note that **the name of the variable is expressive** (much better than x or y). The value of variable is assigned using the `=` operator. In this case it is assigned the value of the return value from the `input` function. **We can run this program over and over again and by changing the input we have changed the ouput- thus making our program dynamic.** We can then use the variable throughout the program and even modify its value through code during runtime. And through this very simple concept, very complex programs from web servers to self-driving cars are built from just a few simple building blocks!"
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Your Turn!"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Write code with variables to print the area of a rectangle"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Write code with variables to print the parameter of a rectangle"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Answers:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Area of a rectangle\n",
"\n",
"width = input(\"What is the width?\")\n",
"height = input(\"What is the hight?\")\n",
"\n",
"area_rectangle = width * height\n",
"\n",
"print area_rectangle\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"What is the width?5\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"What is the hight?10\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"50\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Parameter of a rectangle\n",
"\n",
"width = input(\"What is the width?\")\n",
"height = input(\"What is the hight?\")\n",
"\n",
"parameter_rectangle = 2 * width + 2 * height\n",
"\n",
"print area_rectangle"
],
"language": "python",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"What is the width?5\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"What is the hight?10\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"50\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Calculation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So let's look at what computers do best- compute things!"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 2 + 5 # Addition\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"7\n"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 10 - 3 # Subtraction\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"7\n"
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 5 * 5 # Multiplication\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"25\n"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 2 ** 2 # Power operator\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"4\n"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 5 % 2 # Modulus (aka: remainder)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1\n"
]
}
],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 11 / 2 # Division with an integer\n",
"print 11 / 2.0 # Division with a float"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"5\n",
"5.5\n"
]
}
],
"prompt_number": 26
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Comparison"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**True and False are booleans** in Python and are used to express truth. Let's explore some comparision operators. \n",
"\n",
"**Note:** Don't confuse assignment `=` with is equal to `==`; this is one of the most common programmer errors."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = 10 #ASSIGNMENT\n",
"b = 20\n",
"\n",
"print a > b # Greater than\n",
"print b >= a # Greater than or equal to\n",
"print a == b # EQUALITY symbol\n",
"print b < a # Less than\n",
"print a <= b # Less than or equal to"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"False\n",
"True\n",
"False\n",
"False\n",
"True\n"
]
}
],
"prompt_number": 27
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Simple Data Types"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have already seen three data types:\n",
"\n",
"1. Strings\n",
"2. Integers\n",
"3. Floats\n",
"4. Booleans\n",
"\n",
"These are all objects in Python. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#String\n",
"a = \"apple\"\n",
"print type(a)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<type 'str'>\n"
]
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Integer (whole number)\n",
"b = 1\n",
"print type(b)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<type 'int'>\n"
]
}
],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Float (real numbers with decimal places)\n",
"c = 2.2\n",
"print type(c)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<type 'float'>\n"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Boolean\n",
"d = True\n",
"print type(d)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<type 'bool'>\n"
]
}
],
"prompt_number": 31
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Strings"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = \"Hello\" # String\n",
"b = \" World\" # Another string\n",
"print a + b # Concatenation"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Hello World\n"
]
}
],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = \"World\"\n",
"# Slicing\n",
"print a[0]\n",
"print a[-1]\n",
"print \"World\"[0:4]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"W\n",
"d\n",
"Worl\n"
]
}
],
"prompt_number": 34
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"name = \"Jonesey\"\n",
"print \"Hello, %s!\" % name # Simple string formatting"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Hello, Jonesey!\n"
]
}
],
"prompt_number": 35
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Strings are an example of an **imutable** data type. Once you instantiate a string you cannot change any characters in it's set. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"string = \"string\"\n",
"string[1] = \"y\" #Here we attempt to assign the last character in the string to \"y\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "'str' object does not support item assignment",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-36-5cf8b6c54c2a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mstring\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"string\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mstring\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"y\"\u001b[0m \u001b[0;31m#Here we attempt to assign the last character in the string to \"y\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
]
}
],
"prompt_number": 36
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Formatting Strings"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# String Concatination\n",
"\n",
"first_name = \"Bob\"\n",
"last_name = \"Roberts\"\n",
"\n",
"print first_name + \" is my first name. \" + last_name + \" is my last name.\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Bob is my first name. Roberts is my last name.\n"
]
}
],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# String Formatting\n",
"\n",
"food = \"pizzas\"\n",
"number = 20\n",
"\n",
"print \"I'm so hungry I'm going to eat %i %s.\" % (number, food)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"I'm so hungry I'm going to eat 20 pizzas.\n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"String Methods"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"name = \"matt\"\n",
"name.capitalize() # Capitalizes the first letter of the string"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 38,
"text": [
"'Matt'"
]
}
],
"prompt_number": 38
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"name = \"matt\"\n",
"last = \"jones\"\n",
"\" \".join([name, last]) # Creates a new string by concatenating each item in the list"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 39,
"text": [
"'matt jones'"
]
}
],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# What other methods do strings have?\n",
"# You can use \"dir\" on any object to find out it's methods\n",
"dir(str)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 40,
"text": [
"['__add__',\n",
" '__class__',\n",
" '__contains__',\n",
" '__delattr__',\n",
" '__doc__',\n",
" '__eq__',\n",
" '__format__',\n",
" '__ge__',\n",
" '__getattribute__',\n",
" '__getitem__',\n",
" '__getnewargs__',\n",
" '__getslice__',\n",
" '__gt__',\n",
" '__hash__',\n",
" '__init__',\n",
" '__le__',\n",
" '__len__',\n",
" '__lt__',\n",
" '__mod__',\n",
" '__mul__',\n",
" '__ne__',\n",
" '__new__',\n",
" '__reduce__',\n",
" '__reduce_ex__',\n",
" '__repr__',\n",
" '__rmod__',\n",
" '__rmul__',\n",
" '__setattr__',\n",
" '__sizeof__',\n",
" '__str__',\n",
" '__subclasshook__',\n",
" '_formatter_field_name_split',\n",
" '_formatter_parser',\n",
" 'capitalize',\n",
" 'center',\n",
" 'count',\n",
" 'decode',\n",
" 'encode',\n",
" 'endswith',\n",
" 'expandtabs',\n",
" 'find',\n",
" 'format',\n",
" 'index',\n",
" 'isalnum',\n",
" 'isalpha',\n",
" 'isdigit',\n",
" 'islower',\n",
" 'isspace',\n",
" 'istitle',\n",
" 'isupper',\n",
" 'join',\n",
" 'ljust',\n",
" 'lower',\n",
" 'lstrip',\n",
" 'partition',\n",
" 'replace',\n",
" 'rfind',\n",
" 'rindex',\n",
" 'rjust',\n",
" 'rpartition',\n",
" 'rsplit',\n",
" 'rstrip',\n",
" 'split',\n",
" 'splitlines',\n",
" 'startswith',\n",
" 'strip',\n",
" 'swapcase',\n",
" 'title',\n",
" 'translate',\n",
" 'upper',\n",
" 'zfill']"
]
}
],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# What if you don't know how to use one of the methods?\n",
"\n",
"help(' matt '.strip)\n",
"\n",
"# Now that you know what it does, try it out!\n",
"' matt '.strip()\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Help on built-in function strip:\n",
"\n",
"strip(...)\n",
" S.strip([chars]) -> string or unicode\n",
" \n",
" Return a copy of the string S with leading and trailing\n",
" whitespace removed.\n",
" If chars is given and not None, remove characters in chars instead.\n",
" If chars is unicode, S will be converted to unicode before stripping\n",
"\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 41,
"text": [
"'matt'"
]
}
],
"prompt_number": 41
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Quick Stretch"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Conditionality: \"If Else\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Programs can make choices based on particular values of variables using conditional execution. Conditionality forks the flow of execution so that **some parts of the program may be executed but others will not**.\n",
"\n",
"This brings up a crucial point about Python in particular- **blocks of code are grouped by indentation**. Every time you get to a place where you have to define a contiguous block of code (usually following a `:`) then everything under must be indented with the same amount of space (usually 4 spaces). Dedented code does not belong to the indented block.\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Simple Conditionality\n",
"if 2 + 2 == 4:\n",
" print \"All is right in the world!\"\n",
"else:\n",
" print \"Pigs must be flying!\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"All is right in the world!\n"
]
}
],
"prompt_number": 42
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Evaluate Temperature\n",
"temperature = input(\"What is today's temperature? \")\n",
"\n",
"if temperature > 82:\n",
" print \"It's too hot, %i degrees will melt you.\" % temperature\n",
"elif temperature < 45:\n",
" print \"Are you crazy? You'll freeze at %i degrees\" % temperature\n",
"else:\n",
" print \"Perfect weather! Let's go for a picnic!\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"What is today's temperature? 30\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Are you crazy? You'll freeze at 30 degrees\n"
]
}
],
"prompt_number": 43
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Your Turn:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Use conditionality to determine if a number is even or odd"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Answer"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"number = input(\"What number should I test?\")\n",
"\n",
"if number % 2 == 0:\n",
" print \"The number is even!\"\n",
"else:\n",
" print \"The number is odd!\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"What number should I test?10\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"The number is even!\n"
]
}
],
"prompt_number": 44
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Logic with Booleans"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Both a and b are True\n",
"\n",
"a = True\n",
"b = True\n",
"\n",
"if a and b: \n",
" print \"a and b == True\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"a and b == True\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Either a or c is True\n",
"\n",
"b = True\n",
"c = False\n",
"\n",
"if b or c: \n",
" print \"b and c == True\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"b and c == True\n"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#If d is not true\n",
"\n",
"d = False\n",
"\n",
"if d is not True: \n",
" print \"d == False\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"d == False\n"
]
}
],
"prompt_number": 49
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# None denotes lack of value but it is not equal to false\n",
"\n",
"a = None\n",
"b = 0\n",
"\n",
"if a is None:\n",
" print \"None\"\n",
"\n",
"if b is not None: \n",
" print \"Not None\"\n",
"\n",
"print None == False"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"None\n",
"Not None\n",
"False\n"
]
}
],
"prompt_number": 50
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Repetition: For and While Loops"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**The power of computers is their ability to do a repetitive task over and over again** without tiring. Most programs don't just shut down on you have executing their instructions- they wait for input from the user and respond. In order to accomplish this some mechanism is required to continually execute a chunk of code."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for letter in \"a\", \"b\", \"c\", \"d\", \"e\":\n",
" print letter"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for number in range(1,10):\n",
" print number"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1\n",
"2\n",
"3\n",
"4\n",
"5\n",
"6\n",
"7\n",
"8\n",
"9\n"
]
}
],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# You are importing the sleep function from the time module\n",
"from time import sleep\n",
"\n",
"print \"Cooking the bacon.\"\n",
"\n",
"hot_enough = 180\n",
"temperature = 62\n",
"\n",
"while temperature < hot_enough:\n",
" print \"Not hot enough...\"\n",
" sleep(1)\n",
" temperature = temperature + 15\n",
"\n",
"print \"Bacon is done!\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Not hot enough..."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Not hot enough..."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Spam is done!"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Cooking the spam.\n",
"Not hot enough...\n",
"Not hot enough..."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Not hot enough..."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Not hot enough..."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Not hot enough..."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Not hot enough..."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Not hot enough..."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Not hot enough..."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Spam is done!"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 52
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Functions allow you to define proceedures that happen over and over again in your code, almost like mini-programs that take input and `return` an output. Let's start by looking at a very simple function."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# This function takes no arguments\n",
"# Every function starts with a definition, this is where \"def\" comes from\n",
"def hello():\n",
" # This is a docstring\n",
" '''say hi'''\n",
" print \"Hello!\"\n",
"\n",
"# Invoke the function\n",
"hello()\n",
"\n",
"# Help prints the docstring of the function\n",
"help(hello)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Hello!\n",
"Help on function hello in module __main__:\n",
"\n",
"hello()\n",
" say hi\n",
"\n"
]
}
],
"prompt_number": 53
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's refactor (aka: rewrite) the bacon code into a cooking function."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from time import sleep\n",
"\n",
"def cook(hot_enough, temperature, food):\n",
" print \"Cooking the %s.\" % food\n",
" while temperature < hot_enough:\n",
" print \"Not hot enough...\"\n",
" sleep(1)\n",
" temperature = temperature + 15\n",
" print \"The %s are done!\" % food\n",
"\n",
"cook(100, 50, \"coconuts\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Cooking the coconuts.\n",
"Not hot enough...\n",
"Not hot enough..."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Not hot enough..."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Not hot enough..."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"The coconuts are done!"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 54
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* **Arguments** to functions are named buckets that can have defaults associated with them. \n",
"\n",
"* So far we have only seen **Positional** arguments. Positional arguments (arguments without a default) must be specified in the order of the arguments. \n",
"\n",
"* **Keyword** arguments (those with a default) can be specified in any order, so long as they come after the positional arguments."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# postional and keyword arguments\n",
"# positional arguments must go before keyword (default) arguments\n",
"def writer(name, color=\"blue\"):\n",
" print name + \"'s favorite color is \" + color\n",
" \n",
"writer(\"Samantha\")\n",
"writer(\"Sarah\", \"red\")\n",
"writer(\"Susan\", color=\"green\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Samantha's favorite color is blue\n",
"Sarah's favorite color is red\n",
"Susan's favorite color is green\n"
]
}
],
"prompt_number": 55
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# A stub function that does nothing\n",
"def stub():\n",
" pass\n",
"\n",
"stub()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 56
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Your Turn:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Write a function that checks if a number is evenly divisible by 5"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Answer:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def by_4(number = 4):\n",
" '''Check if a number is evenly divisible by 4'''\n",
" if not number % 4:\n",
" print \"Yes\"\n",
" else:\n",
" print \"No\"\n",
"\n",
"by_4(10)\n",
"by_4()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"No\n",
"Yes\n"
]
}
],
"prompt_number": 58
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Lists, Tuples, Sets and Dictionaries"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Programmers can't live on numbers and strings alone. The currency of programming is data (information), therefore we need more significant structures to represent information.\n",
"\n",
"* Unlike integers, floats and strings these data types can hold multiple values"
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Lists: "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lists are **mutable** or able to be altered. Lists are a collection of data and that data can be of differing types."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
" # A new list\n",
" groceries = []\n",
"\n",
" # Add to list\n",
" groceries.append(\"Snails\") \n",
" groceries.append(\"Nutella\")\n",
" groceries.append(\"Cactus\")\n",
"\n",
" # Access by index\n",
" print groceries[2]\n",
" print groceries[0]\n",
"\n",
" # Find number of things in list\n",
" print len(groceries)\n",
"\n",
" # Sort the items in the list\n",
" groceries.sort()\n",
" print groceries\n",
"\n",
" # Remove from list\n",
" groceries.remove(\"Cactus\")\n",
"\n",
" #The list is mutable\n",
" groceries[0] = 2\n",
" print groceries"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Cactus\n",
"Snails\n",
"3\n",
"['Cactus', 'Nutella', 'Snails']\n",
"[2, 'Snails']\n"
]
}
],
"prompt_number": 57
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Tuples"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Tuples are an **immutable** type. Like strings, once you create them, you cannot change them. It is their immutability that allows you to use them as keys in dictionaries. However, they are similar to lists in that they are a collection of data and that data can be of differing types. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Tuple grocery list\n",
"\n",
"groceries = ('Cactus', 'Nutella', 'Snails')\n",
"\n",
"print groceries"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"('Cactus', 'Nutella', 'Snails')\n"
]
}
],
"prompt_number": 58
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Sets:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A set is a sequence of items that cannot contain duplicates. They handle operations like sets in mathmatics."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"numbers = range(10)\n",
"evens = [2, 4, 6, 8]\n",
"\n",
"evens = set(evens)\n",
"numbers = set(numbers)\n",
"\n",
"# Use difference to find the odds\n",
"odds = numbers - evens\n",
"\n",
"print odds\n",
"\n",
"# Note: Set also allows for use of union (|), and intersection (&)\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"set([0, 1, 3, 5, 7, 9])\n"
]
}
],
"prompt_number": 61
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Dictionaries"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A dictionary is a map of keys to values. Keys must be unique."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# A simple dictionary\n",
"\n",
"obvious = {'sky': 'blue'}\n",
"\n",
"# Access by key\n",
"print obvious['sky']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"blue\n"
]
}
],
"prompt_number": 62
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"# A longer dictionary\n",
"obvious = {\n",
" 'sky': 'blue',\n",
" 'dog': 'woof'\n",
"}\n",
"\n",
"# Check if item is in dictionary\n",
"print 'two plus two' in obvious\n",
"\n",
"# Add new item\n",
"obvious['two plus two'] = 4\n",
"print obvious['two plus two']\n",
"\n",
"# Print just the keys\n",
"print obvious.keys()\n",
"\n",
"# Print just the values\n",
"print obvious.values()\n",
"\n",
"# Print dictionary pairs another way\n",
"for key, value in obvious.items():\n",
" print key, value"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"False\n",
"4\n",
"['two plus two', 'sky', 'dog']\n",
"[4, 'blue', 'woof']\n",
"two plus two 4\n",
"sky blue\n",
"dog woof\n"
]
}
],
"prompt_number": 63
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Complex Data structures\n",
"# Dictionaries inside a dictionary!\n",
"\n",
"employees = {\n",
" \"eid0001\": {\n",
" \"name\": \"Bob Jones\",\n",
" \"department\": \"Marketing\",\n",
" \"interests\": [\"fishing\", \"deep breathing\", \"gatorade\",]\n",
" },\n",
" \"eid0002\": {\n",
" \"name\": \"Sherry Lorenzo\",\n",
" \"department\": \"Human Resources\",\n",
" \"interests\": [\"cooking\", \"steganography\", \"cycling\",],\n",
" }\n",
"}\n",
"\n",
"for employee in employees:\n",
" print employees[employee][\"name\"]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Bob Jones\n",
"Sherry Lorenzo\n"
]
}
],
"prompt_number": 64
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Quick Stretch"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"File I/O"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Writing to a file\n",
"with open(\"example.txt\", \"w\") as f:\n",
" f.write(\"Hello World! \\n\")\n",
" f.write(\"How are you? \\n\")\n",
" f.write(\"I'm fine.\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 66
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Reading from a file\n",
"with open(\"example.txt\", \"r\") as f:\n",
" data = f.readlines()\n",
" for line in data:\n",
" words = line.split()\n",
" print words"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"['Hello', 'World!']\n",
"['How', 'are', 'you?']\n",
"[\"I'm\", 'fine.']\n"
]
}
],
"prompt_number": 67
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Count lines and words in a file\n",
"lines = 0\n",
"words = 0\n",
"the_file = \"example.txt\"\n",
"\n",
"with open(the_file, 'r') as f:\n",
" for line in f:\n",
" lines += 1\n",
" words += len(line.split())\n",
"print \"There are %i lines and %i words in the %s file.\" % (lines, words, the_file)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"There are 3 lines and 7 words in the example.txt file.\n"
]
}
],
"prompt_number": 68
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"APIs"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Getting data from an API\n",
"\n",
"import requests\n",
"\n",
"width = '500'\n",
"height = '500'\n",
"response = requests.get('http://placekitten.com/g/' + width + '/' + height)\n",
"\n",
"print response\n",
"\n",
"with open('kitteh.jpg', 'wb') as f:\n",
" f.write(response.content)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Object Oriented Programming"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have covered the basic building blocks of a Python program, let's discuss a programming paradigm called Object-Oriented Programming (OOP). **OOP gives programmers a framework for modeling the real world by using classes - templates that describe an object's methods and attributes.** Methods can be thought of as the functions of the class while attributes are it's variables. Using this methodology, a programmer simply has to imagine the component interactions of the structure they're trying to model, then translate that into the executional components described above. \n",
"\n",
"* The names of classes should be \"camel cased.\" For example, \"FrogPrincess.\" \n",
"* The __init__ method constructs the class when it is instantiated, it takes self as it's first parameter. \n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class Person(object):\n",
" \"\"\"\n",
" Base class for all people\n",
" \"\"\"\n",
" \n",
" #An attribute of the class\n",
" population = 0\n",
" \n",
" def __init__(self, name, age):\n",
" self.name = name\n",
" self.age = age\n",
" Person.population += 1\n",
" \n",
" def displayCount(self):\n",
" print \"Total people: %d\" % Person.population\n",
" \n",
" def displayPerson(self):\n",
" print \"My name is %s and I am %i years old.\" % (self.name, self.age)\n",
"\n",
"## INHERITENCE ##\n",
"# Rude inherits from person\n",
"class Rude(Person):\n",
" \n",
" def displayPerson(self):\n",
" print \"I don't have to tell you anything!\"\n",
"\n",
"\n",
"# Create Person instance\n",
"bob = Person(\"Bob\", 35)\n",
"bob.displayPerson()\n",
"bob.displayCount()\n",
"\n",
"# Create Rude Person Instance\n",
"rude = Rude(\"None of your business\", None)\n",
"rude.displayPerson()\n",
"rude.displayCount()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"My name is Bob and I am 35 years old.\n",
"Total people: 1\n",
"I don't have to tell you anything!\n",
"Total people: 2\n"
]
}
],
"prompt_number": 77
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Your Turn:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Create a base class called animal with at least one attribute and one method.\n",
"# Then create two more classes that inherit from the animal class \n",
"# For example \"Mammal\" and \"Reptile.\""
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"A Time Formatter"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to get a feel for how simple programs can be constructed, let's take a look at simple program that I wrote and use every single day. We'll walk through each line of code together and discover how this code works. This exercise will be common as you continue to learn how to program."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#!/usr/bin/env python\n",
"# clock\n",
"# Prints out the time specially formatted\n",
"\n",
"\"\"\"\n",
"Prints out the time specially formatted\n",
"\"\"\"\n",
"\n",
"##########################################################################\n",
"## Imports\n",
"##########################################################################\n",
"\n",
"import sys\n",
"from datetime import datetime\n",
"from dateutil.tz import tzlocal\n",
"\n",
"##########################################################################\n",
"## A Clock Printer Object\n",
"##########################################################################\n",
"\n",
"class Clock(object):\n",
"\n",
" # The \"default\" formats. Add more formats via subclasses or in the\n",
" # instantation of a Clock object (or just add more here).\n",
"\n",
" # These formats are more complicated string formatting\n",
" FORMATS = {\n",
" \"code\":\"%a %b %d %H:%M:%S %Y %z\",\n",
" \"json\":\"%Y-%m-%dT%H:%M:%S.%fZ\",\n",
" \"cute\":\"%b %d, %Y\",\n",
" }\n",
"\n",
" # class method - bound to the class\n",
" # Get the current time\n",
" # tzlocal will get the local timezone\n",
" @classmethod\n",
" def local_now(klass):\n",
" return datetime.now(tzlocal())\n",
"\n",
" def __init__(self, formats={}):\n",
" self.formats = self.FORMATS.copy()\n",
" self.formats.update(formats)\n",
"\n",
" # strftime takes a format and returns a string representing the date\n",
" def _local_format(self, fmt):\n",
" return Clock.local_now().strftime(fmt)\n",
"\n",
" def get_stamp(self, name):\n",
" # strips \"-\" from beginning and end of string\n",
" # the string is \"-code\", \"-json\" or \"-cute\"\n",
" name = name.strip(\"-\")\n",
"\n",
" if name in self.formats:\n",
" return self._local_format(self.formats[name])\n",
"\n",
" return None\n",
"\n",
" def print_stamp(self, name):\n",
" stamp = self.get_stamp(name)\n",
" if stamp:\n",
" print stamp\n",
" else:\n",
" print \"No stamp format for name %s\" % name\n",
"\n",
"##########################################################################\n",
"## Main Method, handle inputs to program from command line\n",
"##########################################################################\n",
"\n",
"# print __name__ in the python shell to see how this works\n",
"if __name__ == \"__main__\":\n",
"\n",
" args = sys.argv[1:]\n",
" clock = Clock()\n",
" for arg in args:\n",
" clock.print_stamp(arg)\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"No stamp format for name -f\n",
"No stamp format for name /Users/SarahKelley/.ipython/profile_default/security/kernel-4d56b37b-9d68-482e-958c-317352f340c7.json\n",
"No stamp format for name --IPKernelApp.parent_appname='ipython-notebook'\n",
"No stamp format for name --profile-dir\n",
"No stamp format for name /Users/SarahKelley/.ipython/profile_default\n",
"No stamp format for name --parent=1\n"
]
}
],
"prompt_number": 102
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | apache-2.0 |
Saxafras/Spacetime | Domain 18.ipynb | 1 | 235574 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"from __future__ import division\n",
"from spacetime.CA_Simulators.CAs import *\n",
"from spacetime.Local_Measures.Local_Complexity import *\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Code for 0-wildcard tiling"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def wildcard_tiling(x,t):\n",
" '''\n",
" Returns spacetime field of dimension (x,t) sampled from 0-wildcard tiling language.\n",
" '''\n",
" field = np.zeros((t,x), dtype=int)\n",
" for i in xrange(t):\n",
" for j in xrange(x):\n",
" if i % 2 == 0 and j % 2 == 0:\n",
" field[i,j] = np.random.choice([0,1])\n",
" elif i % 2 == 1 and j % 2 == 1:\n",
" field[i,j] = np.random.choice([0,1])\n",
" \n",
" return field"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run machine inference on samples from this tiling"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tiling_field = wildcard_tiling(600,600)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tiling_states = epsilon_field(tiling_field)\n",
"tiling_states.estimate_states(3,3,1, alpha = 0.05)\n",
"tiling_states.filter_data()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3\n"
]
}
],
"source": [
"print tiling_states.number_of_states()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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f5cMf/nBYvV27djFz5sywev/lv/wXfvzjv++L5evxiU98guuuuy6sXnQvi3wO\nfO655/jjP/5j3v3ud4fU+9nPfsbv//7vp+0to0aN4nOf+1xYvejnFntLWdmfW1auXFlrbxkcHGRw\ncBCAU6dO8eMf/5iqqs7sb2lVVfW2L2ACsPdMf+70z1dRVq1aFVarqqpq9erVofU+85nPVEDY69Zb\nbw3Nt379+tB6GzZsCK03ceLE0PV78MEHw7INDAxUu3fvDqu3cePG6vjx42H1sveWvr6+0Hpr164N\nrWdvsbe8U9G95bbbbgtdu/nz54dlq6r8veXaa68NXb/oe8OmTZtC623evDms1sDAQHX++eeHrV13\nd7e9pSB7S3v3luzPLdG9BaiqdzDTeevrnf51sAZv+R5AjUZjzFt+7v8C9r3D30eSJEmSJEkt0PV2\n/0Oj0VgHfBS4tNFo/BD4MnBdo9HoAd4ADgP/d43XKEmSJEmSpF/S2w6Bqqpa9Pf8cF8N1yJJkiRJ\nkqSa/DL/OpgkSZIkSZLahEMgSZIkSZKkDuAQSJIkSZIkqQM4BJIkSZIkSeoADoEkSZIkSZI6wPAb\nAi1dCqNHw4wZ9dc6cgTmzYOpU2H6dLjzzvprBuYbBzwE7AP2Al+ou2Dk2kH8+gXnc/0Ks7eUk3nt\nIH0+e0thme/rYG8pyLNXmOtXjL2lMPdmUeYrb/gNgRYvhq1bY2p1dcGKFdDfDzt3wqpVcPBgvTUD\n8w0BtwDTgKuBZcCkOgtGrh3Er19wPtevMHtLOZnXDtLns7cUlvm+DvaWgjx7hbl+xdhbCnNvFmW+\n8obfEGjuXOjujqk1Zgz09DQ/HjECJk+Go0frrRmY7xjwxOmPB4EDNCeNtYlcO4hfv+B8rl9h9pZy\nMq8dpM9nbyks830d7C0FefYKc/2KsbcU5t4synzlDb8hUKscPgx79sCsWa2+klpMAHqAx1p9IXVx\n/dpb5vXLnA3M1+bsLe3LtWtvrl97y7x+mbMB7s02Z74yHAIBnDwJCxfCypXN6XAyFwD3ActpThfT\ncf3aW+b1y5wNzNfm7C3ty7Vrb65fe8u8fpmzAe7NNme+chwCDQ01m8HNN8OCBa2+muLOprmZ1gAP\ntPhaauH6tbfM65c5G5ivzdlb2pdr195cv/aWef0yZwPcm23OfGUNzyFQVTVfEZYsgSlTYPnymHoQ\nmu8uYD8Q8G8TNUWuHcSvX3A+168we0s5mdcO0ueztxSW+b4O9paCPHuFuX7F2FsKc28WZb6yht8Q\naNEimD2u4R9NAAAgAElEQVQbBgZg/Hjo66uv1vbtcM89sG0b9PbCzJmwZUt99SA032zgJmAesAt4\nHLi+tmrErh3Er19wPtevMHtLOZnXDtLns7cUlvm+DvaWgjx7hbl+xdhbCnNvFmW+8rpq/v3P3Lp1\ncbXmzIFTp+LqQWi+HQQvcOTaQfz6Bedz/Qqzt5STee0gfT57S2GZ7+tgbynIs1eY61eMvaUw92ZR\n5itv+H0lkCRJkiRJkopzCCRJkiRJktQBHAJJkiRJkiR1AIdAkiRJkiRJHcAhkCRJkiRJUgdwCCRJ\nkiRJktQBHAJJkiRJkiR1AIdAkiRJkiRJHcAhkCRJkiRJUgfoiihy+eWXR5Shu7ubK664IqQWwFNP\nPcU3vvGNsHqXX345999/f1i9O+64I2ztAH7rt36LESNGhNX7/d//fX73d383rN4//+f/nLlz54bV\n+973vkej0Qip9eyzz3LixAn+7u/+LqTeY489xuc//3m6ukJaWHhv+cu//Ev+4A/+IKzeBz/4QS67\n7LKwen/2Z3/Gv/23/zasXnRvefLJJzn//PPD6i1atIiZM2eG1fud3/mdsN4yNDTErbfemra3XHLJ\nJaH39UceeST0uSX6OSm6t9xwww3863/9r8Pq7d69O/TzuWvXLs46K+7Pi7/3ve/xxhtvhNR69tln\n+cpXvsLEiRND6j3yyCNMnTo1bW+56667Qt8zZH9uie4tt99+e+hzZ/b3RJG95Xvf+x5/9Ed/dMa/\nLqQTHT16NKIM3d3dzJ8/P6QWwN/93d+F1nvuuee48cYbw+r19fWxffv2sHrvf//7Qz+f/+7f/buw\nvQkwa9as0PU7//zzueGGG0JqPfXUUwwODtLT0xNS78UXX+TYsWMhtSC+t3zjG9/g61//eli966+/\nPjTfn/3Zn4Weveje8tprr4XWe+ONN/jN3/zNsHqf+9znOH78eFi9j370o6l7S+R94fjx4+HPLZl7\ny+TJk0PX74033gjvLZH1zjrrrLTPLSdPnmTFihUhtSC+t3zrW99i06ZNYfWyP7dE95b/8B/+g++J\nConuLVVV/UK/zr8OJkmSJEmS1AEcAkmSJEmSJHUAh0CSJEmSJEkdwCGQJEmSJElSB3AIJEmSJEmS\n1AEcAkmSJEmSJHUAh0CSJEmSJEkdwCGQJEmSJElSBxhWQ6BxwEPAPmAv8IWIokuXwujRMGNG/bWO\nHIF582DqVJg+He68s/6agfnC1y9y7cifL3x/Zt6bYL6CPHuF2TuLyX72fG4pzN5SVuJ82XuL+Qrz\nvl5W4t7ypmE1BBoCbgGmAVcDy4BJdRddvBi2bq27SlNXF6xYAf39sHMnrFoFBw/WWzMwX/j6Ra4d\n+fOF78/MexPMV5BnrzB7ZzHZz57PLYXZW8pKnC97bzFfYd7Xy0rcW940rIZAx4AnTn88CBygOWms\n1dy50N1dd5WmMWOgp6f58YgRMHkyHD1ab83AfOHrF7l25M8Xvj8z700wX0GevcLsncVkP3s+txRm\nbykrcb7svcV8hXlfLytxb3nTsBoCvdUEoAd4rNUXUpfDh2HPHpg1q9VXUovs65c9X+b9mX3tzNfm\nEp89yL1+mbMB7s12l3z9MufLvjfN196y58vaW4blEOgC4D5gOc3pYjonT8LChbByZXO6mEz29cue\nL/P+zL525mtzic8e5F6/zNkA92a7S75+mfNl35vma2/Z82XuLcNuCHQ2zc20BnigxddSi6Gh5ma6\n+WZYsKDVV1Nc9vXLni/z/sy+duZrc4nPHuRev8zZAPdmu0u+fpnzZd+b5mtv2fNl7i0wDIdAdwH7\ngYB/f+Lnqqr5irBkCUyZAsuXx9SD0Hzh6xe5duTPF74/M+9NMF9Bnr3C7J3FZD97PrcUZm8pK3G+\n7L3FfIV5Xy8rcW+BYTYEmg3cBMwDdgGPA9fXXXTRIpg9GwYGYPx46Ourr9b27XDPPbBtG/T2wsyZ\nsGVLffUgNF/4+kWuHfnzhe/PzHsTzFeQZ68we2cx2c+ezy2F2VvKSpwve28xX2He18tK3Fve1FV7\nhTOwgxZc0Lp1cbXmzIFTp+LqQWi+8PWLXDvy5wvfn5n3JpivIM9eYfbOYrKfPZ9bCrO3lJU4X/be\nYr7CvK+Xlbi3vGlYfSWQJEmSJEmS6uEQSJIkSZIkqQM4BJIkSZIkSeoADoEkSZIkSZI6gEMgSZIk\nSZKkDuAQSJIkSZIkqQM4BJIkSZIkSeoADoEkSZIkSZI6QFerL6CkU6dOcfLkybB6r732Wmi9V199\nNbTe66+/HlYL4j+fp06dCqsFMDg4GJovst7g4GBovVdffTWkzpuie8tPf/rTsFrQPOv2lnKie3V0\nb3njjTfCakFsvuy9JftZsLeU5XNLOdl7i88tZfmeqH3rtUtvCRkCbdu2LaIM//2//3fGjRsXUgvg\nqquuYvr06WH1fvSjH/H444+H1fvwhz/MF7/4xbB6jzzySGi+hQsX8l//638Nq/foo4+G5hsYGGDU\nqFEhtY4cOcK/+lf/KqQWwPnnn8/GjRu55JJLQupF95YZM2aE9U2Ahx56yN5S0OHDh0PrffGLX+R3\nfud3wur9m3/zb7jmmmtCah05coT58+eH1IL8vSX6uWXjxo3ceuutYfUWLFgQ2jvtLeXrRT63nDx5\nkqGhoZB6x44dC+0tDz74YOheec973hN69v7oj/4otHdG95bf+73f4z//5/8cVu9zn/tc6Huif/kv\n/yXLly8PqxfdW6LfE/0iQoZA1113XUQZvvnNb/Lyyy+H1AK48MILufbaa8Pq/eAHPwitd+TIkbC1\nA3j++edD873wwguh+QYHB0Pzvfrqq2H1nnrqKX72s5/x2muvhdQ7++yz+chHPsKll14aUi+6t1x0\n0UWhe/OZZ56xtxT0wgsvhNY799xzQ/fnBz/4wbD1s7eUFf3c8tWvfjU037hx4+wtBUX3lt7e3tDn\nlsHBQXp6ekLqvfjii6G95cCBA6F75amnngo9e3fddVfq3nLJJZeE5rvyyitD85133nmpe0v0c8sv\nwu8JJEmSJEmS1AEcAkmSJEmSJHUAh0CSJEmSJEkdwCGQJEmSJElSB3AIJEmSJEmS1AEcAkmSJEmS\nJHUAh0CSJEmSJEkdYPgNgZYuhdGjYcaM2kuNAx4C9gF7gS/UXpHQfBw5AvPmwdSpMH063HlnvfUi\ns4H5SgvOF37+7C3lJN+b2fNlPnuQO1/23pJ57QB7S2muXznR2cDeUpD5Csuej+E4BFq8GLZuDSk1\nBNwCTAOuBpYBk+ouGpiPri5YsQL6+2HnTli1Cg4erK9eZDYwX2nB+cLPn72lnOR7M3u+zGcPcufL\n3lsyrx1gbynN9SsnOhvYWwoyX2HZ8zEch0Bz50J3d0ipY8ATpz8eBA7QnMTVKjAfY8ZAT0/z4xEj\nYPJkOHq0vnqR2cB8pQXnCz9/9pZyku/N7Pkynz3InS97b8m8doC9pTTXr5zobGBvKch8hWXPx3Ac\nArXIBKAHeKzVF1KXw4dhzx6YNavVV1IP87W1zOcvczYg/d7Mni/7/sycL3M2yJ/P3tLmMq9f5mzk\n35vma29R+RwCARcA9wHLaU7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f5cILLwypZ28p65JLLuEDH/hAWL3vfOc7YbUgvrf89Kc/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\n+4V3g9z93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je/WX384x+vPv7xj1fve9/7KqCqXuNO52c/OngNVVV9FPgoQKPRuAK4oaqq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\nSZIkSZJ6gEMgSZIkSZKkHvD/AU4aXvJxObdzAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x120807190>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"state_overlay_diagram(tiling_field, tiling_states.get_causal_field(), t_min = 10, t_max = 50, x_max = 40)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.68002562571\n",
"0.739440226292\n",
"0.739599303525\n"
]
}
],
"source": [
"print tiling_states.entropy_rate('forward')\n",
"print tiling_states.entropy_rate('right')\n",
"print tiling_states.entropy_rate('left')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"rule 18 domain"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"np.random.seed(0)\n",
"domain = ECA(18,domain_18(600))\n",
"domain.evolve(600)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.random.seed(0)\n",
"domain_states = epsilon_field(domain.get_spacetime())\n",
"domain_states.estimate_states(3,3,1, alpha = 0.05)\n",
"domain_states.filter_data()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3\n"
]
}
],
"source": [
"print domain_states.number_of_states()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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F7bffHtrv7rvvDu33yU9+MrTf0qVL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wu99o4++mjvW0o0cODA0H7Nzc2hsyX6vuX8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\nJEmSJEmSGsD/BzveouaSAv/DAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f937ed0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"state_overlay_diagram(domain.get_spacetime(), domain_states.get_causal_field(), t_min = 10, t_max = 50, x_max = 40)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"22676\n"
]
}
],
"source": [
"plcs = domain_states.PLCs()\n",
"print plcs.map_to_label('0101100010010100')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0010100\n",
" 10001 \n",
" 101 \n",
" 0 \n",
"\n"
]
}
],
"source": [
"print plcs.map_to_shape(22676)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n"
]
}
],
"source": [
"print domain_states.epsilon_map()[22676].index()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.443796261542\n",
"0.739583703817\n",
"0.739583703817\n"
]
}
],
"source": [
"print domain_states.entropy_rate('forward')\n",
"print domain_states.entropy_rate('right')\n",
"print domain_states.entropy_rate('left')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"79\n"
]
}
],
"source": [
"print len(domain_states.all_transitions())"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(1, 'f:0000000', 2), (1, 'r:0000', 2), (1, 'l:0000', 3), (1, 'l:0000', 2), (1, 'r:0000', 3), (1, 'f:0000000', 3), (1, 'f:0000000', 0), (2, 'f:0110001', 1), (2, 'f:1101001', 1), (2, 'f:0000011', 1), (2, 'f:1010110', 1), (2, 'f:1100111', 1), (2, 'f:1011011', 1), (2, 'f:0111100', 1), (2, 'f:0110010', 1), (2, 'f:1100100', 1), (2, 'f:1011000', 1), (2, 'f:0111111', 1), (2, 'f:0001110', 1), (2, 'f:0001101', 1), (2, 'f:1101010', 1), (2, 'f:1010101', 1), (2, 'f:0000000', 3), (2, 'f:1010101', 0), (2, 'f:1100100', 0), (2, 'f:0001101', 0), (2, 'f:0001110', 0), (2, 'f:1011000', 0), (2, 'f:1101010', 0), (2, 'f:0000000', 0), (2, 'f:0110001', 0), (2, 'f:0111100', 0), (2, 'f:1100111', 0), (2, 'f:0111111', 0), (2, 'f:0110010', 0), (2, 'f:1011011', 0), (2, 'f:1101001', 0), (2, 'f:1010110', 0), (2, 'f:0000011', 0), (3, 'f:0000000', 3), (3, 'f:1010101', 1), (3, 'f:1101010', 1), (3, 'f:0111111', 1), (3, 'f:1101010', 0), (3, 'f:0000000', 0), (3, 'f:1010101', 0), (3, 'f:0111111', 0)]\n"
]
}
],
"source": [
"print domain_states.nonunifilar_transitions()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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AABnK/9v+AAAAAPgfjiEQAAAAQAUYAgEAAABU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\nmzYVEVH6tX79+pTcRqNRNJvNlF6sW7cupeZ9+/al1Lt///6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pcXd3d9pPGf3qr/5qSi+u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AAAADVgCAQAAABQA4ZAAAAAADVgCAQAAABQA4ZAAAAAADVgCAQAAABQA4ZAAAAAADXQdi9e5Ktf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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7dc39d4bd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"diagram(identity.get_spacetime(),t_max = 50, x_max =50)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"states = epsilon_field(identity.get_spacetime())\n",
"states.estimate_states(3,3,1)\n",
"states.filter_data()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.991619089679\n",
"0.994375130694\n",
"0.994375130694\n"
]
}
],
"source": [
"print states.entropy_rate('forward')\n",
"print states.entropy_rate('right')\n",
"print states.entropy_rate('left')"
]
},
{
"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
}
| bsd-3-clause |
ini-python-course/ss15 | notebooks/Linear Regression.ipynb | 1 | 6118 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Linear Regression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This tutorial shows how simple it is to implement a linear regression in python.\n",
"\n",
"So a regression is about fitting a function (in the linear case a line) to given data points. Therefore the reconstruction cost is minimized such that for all datapoints x the squared distanze between its target value y and the function value f(x) of the fitted curve is minimal.\n",
"\n",
"1) We need some data so create an array x with shape (100,1) that contains the numbers from -5 to 5.\n",
"\n",
"2) Initialize the random number generator of numpy to 42.\n",
"\n",
"3) Now add Gaussian random noise to x with a standard deviation of 1 and store the result in an array y.\n",
"\n",
"4) Plot the datapoints as yellow dots in the range $x\\in[-10,10]$ and $y\\in[-10,10]$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"# your code here"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# our solution\n",
"from solutions import *\n",
"decrypt_solution(solution_regression_1, 'foo')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A linear regression for datapoint matrix $X$ ($D \\times N$, D datapoints and N input dimensions) and target matrix $Y$ ($D \\times M$, D datapoints and M output dimensions) is defined as:\n",
"\n",
"$min \\langle \\frac{1}{2}\\left(\\vec{\\vec{A}}\\vec{x}-\\vec{y}\\right)^2 \\rangle_d = min \\frac{1}{2}\\frac{1}{D}\\sum_d^{D} \\sum_i^{N} \\sum_j^M \\left(a_{ij} x_{di}-y_{dj}\\right)^2$\n",
"\n",
"where $\\langle \\cdot \\rangle_d$ is the average over the Training data. \n",
"\n",
"We ignore the bias value for now!\n",
"\n",
"1) Set the derivative to zero and solve the equation for $A$ to get the optima $A^*$. (Hint: If you have problems with the closed form solution with abitray dimensions, first solve the equation for 1D input and output)\n",
"\n",
"2) Now plot the result"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"# your code here"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# our solution\n",
"from solutions import *\n",
"decrypt_solution(solution_regression_2, 'foo')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A common way to integrate a bias value for many machine learning methods is to add a dimension which is constant one for all datapoints!\n",
"\n",
"1) Modify the code by adding a second constant dimension to x and add 10 to y to shift the datapoints verctically.\n",
"\n",
"2) Now plot the result in the range $x\\in[-10,10]$ and $y\\in[0,20]$, notice that you have to select the first dimension of x in order not to plot the constant dimension!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"# your code here"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# our solution\n",
"from solutions import *\n",
"decrypt_solution(solution_regression_3, 'foo')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By using a polynomial expansion of x we can fit a polynome to the data.\n",
"\n",
"Fit a ploynome of degree 5 to the data "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y = np.cos(x[:,0])+np.random.randn(100)*0.5"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"# your code here"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# our solution\n",
"from solutions import *\n",
"decrypt_solution(solution_regression_4, 'foo')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now perform the same using the linear regression function np.polyfit(x,y,5) of numpy. Notice that x,y are 1D arrays here! "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"# your code here"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# our solution\n",
"from solutions import *\n",
"decrypt_solution(solution_regression_5, 'foo')"
]
}
],
"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 |
balmandhunter/jupyter-tips-and-tricks | notebooks/Data_Cleaning.ipynb | 4 | 23404 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Clean data\n",
"\n",
"Coal mining data from [eia.gov](http://www.eia.gov/coal/data.cfm#prices)\n",
"\n",
"Combining and cleaning the raw csv files into a cleaned data set and coherent database. \n",
"\n",
"Generally a good idea to have a separate data folder with the raw data.\n",
"\n",
"When you clean the raw data, leave the raw in place, and create cleaned version with the steps included (ideal situation for Notebook)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"application/json": {
"Software versions": [
{
"module": "Python",
"version": "2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]"
},
{
"module": "IPython",
"version": "3.1.0"
},
{
"module": "OS",
"version": "Darwin 14.3.0 x86_64 i386 64bit"
},
{
"module": "numpy",
"version": "1.9.2"
},
{
"module": "scipy",
"version": "0.15.1"
},
{
"module": "matplotlib",
"version": "1.4.3"
},
{
"module": "pandas",
"version": "0.16.1"
}
]
},
"text/html": [
"<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]</td></tr><tr><td>IPython</td><td>3.1.0</td></tr><tr><td>OS</td><td>Darwin 14.3.0 x86_64 i386 64bit</td></tr><tr><td>numpy</td><td>1.9.2</td></tr><tr><td>scipy</td><td>0.15.1</td></tr><tr><td>matplotlib</td><td>1.4.3</td></tr><tr><td>pandas</td><td>0.16.1</td></tr><tr><td colspan='2'>Tue Jun 02 19:34:55 2015 PDT</td></tr></table>"
],
"text/latex": [
"\\begin{tabular}{|l|l|}\\hline\n",
"{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
"Python & 2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)] \\\\ \\hline\n",
"IPython & 3.1.0 \\\\ \\hline\n",
"OS & Darwin 14.3.0 x86\\_64 i386 64bit \\\\ \\hline\n",
"numpy & 1.9.2 \\\\ \\hline\n",
"scipy & 0.15.1 \\\\ \\hline\n",
"matplotlib & 1.4.3 \\\\ \\hline\n",
"pandas & 0.16.1 \\\\ \\hline\n",
"\\hline \\multicolumn{2}{|l|}{Tue Jun 02 19:34:55 2015 PDT} \\\\ \\hline\n",
"\\end{tabular}\n"
],
"text/plain": [
"Software versions\n",
"Python 2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]\n",
"IPython 3.1.0\n",
"OS Darwin 14.3.0 x86_64 i386 64bit\n",
"numpy 1.9.2\n",
"scipy 0.15.1\n",
"matplotlib 1.4.3\n",
"pandas 0.16.1\n",
"Tue Jun 02 19:34:55 2015 PDT"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# %install_ext http://raw.github.com/jrjohansson/version_information/master/version_information.py\n",
"%load_ext version_information\n",
"%reload_ext version_information\n",
"%version_information numpy, scipy, matplotlib, pandas"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/jonathan/github/jupyter-best-practices/notebooks\r\n"
]
}
],
"source": [
"!pwd"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# The cleaned data file is saved here:\n",
"output_file = \"../data/coal_prod_cleaned.csv\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df1 = pd.read_csv(\"../data/coal_prod_2002.csv\", index_col=\"MSHA_ID\")\n",
"df2 = pd.read_csv(\"../data/coal_prod_2003.csv\", index_col=\"MSHA_ID\")\n",
"df3 = pd.read_csv(\"../data/coal_prod_2004.csv\", index_col=\"MSHA_ID\")\n",
"df4 = pd.read_csv(\"../data/coal_prod_2005.csv\", index_col=\"MSHA_ID\")\n",
"df5 = pd.read_csv(\"../data/coal_prod_2006.csv\", index_col=\"MSHA_ID\")\n",
"df6 = pd.read_csv(\"../data/coal_prod_2007.csv\", index_col=\"MSHA_ID\")\n",
"df7 = pd.read_csv(\"../data/coal_prod_2008.csv\", index_col=\"MSHA_ID\")\n",
"df8 = pd.read_csv(\"../data/coal_prod_2009.csv\", index_col=\"MSHA_ID\")\n",
"df9 = pd.read_csv(\"../data/coal_prod_2010.csv\", index_col=\"MSHA_ID\")\n",
"df10 = pd.read_csv(\"../data/coal_prod_2011.csv\", index_col=\"MSHA_ID\")\n",
"df11 = pd.read_csv(\"../data/coal_prod_2012.csv\", index_col=\"MSHA_ID\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dframe = pd.concat((df1, df2, df3, df4, df5, df6, df7, df8, df9, df10, df11))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Indepedent Producer Operator', 'Operating Subsidiary', 'Contractor'], dtype=object)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Noticed a probable typo in the data set: \n",
"dframe['Company_Type'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Average_Employees</th>\n",
" <th>Company_Type</th>\n",
" <th>Labor_Hours</th>\n",
" <th>Mine_Basin</th>\n",
" <th>Mine_County</th>\n",
" <th>Mine_Name</th>\n",
" <th>Mine_State</th>\n",
" <th>Mine_Status</th>\n",
" <th>Mine_Type</th>\n",
" <th>Operating_Company</th>\n",
" <th>Operating_Company_Address</th>\n",
" <th>Operation_Type</th>\n",
" <th>Production_short_tons</th>\n",
" <th>Union_Code</th>\n",
" <th>Year</th>\n",
" </tr>\n",
" <tr>\n",
" <th>MSHA_ID</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",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>102838</th>\n",
" <td>4</td>\n",
" <td>Independent Producer Operator</td>\n",
" <td>2712</td>\n",
" <td>Appalachia Southern</td>\n",
" <td>Bibb</td>\n",
" <td>Hebron Mine</td>\n",
" <td>Alabama</td>\n",
" <td>Permanently abandoned</td>\n",
" <td>Surface</td>\n",
" <td>Birmingham Coal & Coke Company</td>\n",
" <td>2477 Valleydale Rd. S. B3, Birmingham, AL 35244</td>\n",
" <td>Mine only</td>\n",
" <td>10572</td>\n",
" <td>NaN</td>\n",
" <td>2002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103184</th>\n",
" <td>5</td>\n",
" <td>Independent Producer Operator</td>\n",
" <td>2480</td>\n",
" <td>Appalachia Southern</td>\n",
" <td>Fayette</td>\n",
" <td>Berry Mine</td>\n",
" <td>Alabama</td>\n",
" <td>Temporarily closed</td>\n",
" <td>Surface</td>\n",
" <td>Midas Coal Company Incorporate</td>\n",
" <td>401 10th Avenue, S. E, Cullman, AL 35055</td>\n",
" <td>Mine only</td>\n",
" <td>9725</td>\n",
" <td>NaN</td>\n",
" <td>2002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100329</th>\n",
" <td>55</td>\n",
" <td>Operating Subsidiary</td>\n",
" <td>123618</td>\n",
" <td>Appalachia Southern</td>\n",
" <td>Jefferson</td>\n",
" <td>Concord Mine</td>\n",
" <td>Alabama</td>\n",
" <td>Active</td>\n",
" <td>Underground</td>\n",
" <td>U S Steel Mining Company Llc</td>\n",
" <td>8800 Oak Grove Mine Road, Adger, AL 35006</td>\n",
" <td>Preparation Plant</td>\n",
" <td>0</td>\n",
" <td>United Mine Workers of America</td>\n",
" <td>2002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100851</th>\n",
" <td>331</td>\n",
" <td>Operating Subsidiary</td>\n",
" <td>748182</td>\n",
" <td>Appalachia Southern</td>\n",
" <td>Jefferson</td>\n",
" <td>Oak Grove Mine</td>\n",
" <td>Alabama</td>\n",
" <td>Active</td>\n",
" <td>Underground</td>\n",
" <td>U S Steel Mining Company Llc</td>\n",
" <td>8800 Oak Grove Mine Rd, Adger, AL 35006</td>\n",
" <td>Mine only</td>\n",
" <td>1942153</td>\n",
" <td>United Mine Workers of America</td>\n",
" <td>2002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102354</th>\n",
" <td>28</td>\n",
" <td>Independent Producer Operator</td>\n",
" <td>55306</td>\n",
" <td>Appalachia Southern</td>\n",
" <td>Jefferson</td>\n",
" <td>Lindbergh</td>\n",
" <td>Alabama</td>\n",
" <td>Active</td>\n",
" <td>Surface</td>\n",
" <td>C & H Mining Company Inc</td>\n",
" <td>P.O. Box 70250, Tuscaloosa, AL 35407</td>\n",
" <td>Mine only</td>\n",
" <td>168446</td>\n",
" <td>NaN</td>\n",
" <td>2002</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Average_Employees Company_Type Labor_Hours \\\n",
"MSHA_ID \n",
"102838 4 Independent Producer Operator 2712 \n",
"103184 5 Independent Producer Operator 2480 \n",
"100329 55 Operating Subsidiary 123618 \n",
"100851 331 Operating Subsidiary 748182 \n",
"102354 28 Independent Producer Operator 55306 \n",
"\n",
" Mine_Basin Mine_County Mine_Name Mine_State \\\n",
"MSHA_ID \n",
"102838 Appalachia Southern Bibb Hebron Mine Alabama \n",
"103184 Appalachia Southern Fayette Berry Mine Alabama \n",
"100329 Appalachia Southern Jefferson Concord Mine Alabama \n",
"100851 Appalachia Southern Jefferson Oak Grove Mine Alabama \n",
"102354 Appalachia Southern Jefferson Lindbergh Alabama \n",
"\n",
" Mine_Status Mine_Type Operating_Company \\\n",
"MSHA_ID \n",
"102838 Permanently abandoned Surface Birmingham Coal & Coke Company \n",
"103184 Temporarily closed Surface Midas Coal Company Incorporate \n",
"100329 Active Underground U S Steel Mining Company Llc \n",
"100851 Active Underground U S Steel Mining Company Llc \n",
"102354 Active Surface C & H Mining Company Inc \n",
"\n",
" Operating_Company_Address Operation_Type \\\n",
"MSHA_ID \n",
"102838 2477 Valleydale Rd. S. B3, Birmingham, AL 35244 Mine only \n",
"103184 401 10th Avenue, S. E, Cullman, AL 35055 Mine only \n",
"100329 8800 Oak Grove Mine Road, Adger, AL 35006 Preparation Plant \n",
"100851 8800 Oak Grove Mine Rd, Adger, AL 35006 Mine only \n",
"102354 P.O. Box 70250, Tuscaloosa, AL 35407 Mine only \n",
"\n",
" Production_short_tons Union_Code Year \n",
"MSHA_ID \n",
"102838 10572 NaN 2002 \n",
"103184 9725 NaN 2002 \n",
"100329 0 United Mine Workers of America 2002 \n",
"100851 1942153 United Mine Workers of America 2002 \n",
"102354 168446 NaN 2002 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Correcting the Company_Type\n",
"dframe.loc[dframe['Company_Type'] == 'Indepedent Producer Operator', 'Company_Type'] = 'Independent Producer Operator'\n",
"dframe.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Average_Employees</th>\n",
" <th>Company_Type</th>\n",
" <th>Labor_Hours</th>\n",
" <th>Mine_Basin</th>\n",
" <th>Mine_County</th>\n",
" <th>Mine_Name</th>\n",
" <th>Mine_State</th>\n",
" <th>Mine_Status</th>\n",
" <th>Mine_Type</th>\n",
" <th>Operating_Company</th>\n",
" <th>Operating_Company_Address</th>\n",
" <th>Operation_Type</th>\n",
" <th>Production_short_tons</th>\n",
" <th>Union_Code</th>\n",
" <th>Year</th>\n",
" </tr>\n",
" <tr>\n",
" <th>MSHA_ID</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",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>103117</th>\n",
" <td>50</td>\n",
" <td>Independent Producer Operator</td>\n",
" <td>67199</td>\n",
" <td>Appalachia Southern</td>\n",
" <td>Cullman</td>\n",
" <td>Mine #2</td>\n",
" <td>Alabama</td>\n",
" <td>Active</td>\n",
" <td>Surface</td>\n",
" <td>Twin Pines Coal Company Inc</td>\n",
" <td>1874 County Road 15, Bremen, AL 35503</td>\n",
" <td>Mine only</td>\n",
" <td>177381</td>\n",
" <td>NaN</td>\n",
" <td>2003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103246</th>\n",
" <td>4</td>\n",
" <td>Independent Producer Operator</td>\n",
" <td>11075</td>\n",
" <td>Appalachia Southern</td>\n",
" <td>Franklin</td>\n",
" <td>Bear Creek</td>\n",
" <td>Alabama</td>\n",
" <td>Active</td>\n",
" <td>Surface</td>\n",
" <td>Birmingham Coal & Coke Co., In</td>\n",
" <td>P.O. Box 354, Lynn, AL 35575</td>\n",
" <td>Mine only</td>\n",
" <td>46049</td>\n",
" <td>NaN</td>\n",
" <td>2003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103006</th>\n",
" <td>3</td>\n",
" <td>Independent Producer Operator</td>\n",
" <td>5161</td>\n",
" <td>Appalachia Southern</td>\n",
" <td>Jackson</td>\n",
" <td>Bledsoe Mine No 1</td>\n",
" <td>Alabama</td>\n",
" <td>Mine closed by MSHA</td>\n",
" <td>Underground</td>\n",
" <td>A L Select Inc</td>\n",
" <td>P.O. Box 864, Stevenson, AL 35772</td>\n",
" <td>Mine only</td>\n",
" <td>500</td>\n",
" <td>NaN</td>\n",
" <td>2003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103183</th>\n",
" <td>8</td>\n",
" <td>Independent Producer Operator</td>\n",
" <td>19348</td>\n",
" <td>Appalachia Southern</td>\n",
" <td>Jackson</td>\n",
" <td>Henager</td>\n",
" <td>Alabama</td>\n",
" <td>Active</td>\n",
" <td>Surface</td>\n",
" <td>Gtm Mining Corporation</td>\n",
" <td>15693 Alabama Highway 71, Pisgah, AL 35765</td>\n",
" <td>Mine only</td>\n",
" <td>55187</td>\n",
" <td>NaN</td>\n",
" <td>2003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100329</th>\n",
" <td>23</td>\n",
" <td>Operating Subsidiary</td>\n",
" <td>52009</td>\n",
" <td>Appalachia Southern</td>\n",
" <td>Jefferson</td>\n",
" <td>Concord Mine</td>\n",
" <td>Alabama</td>\n",
" <td>Active</td>\n",
" <td>Surface</td>\n",
" <td>Oak Grove Resources, Llc</td>\n",
" <td>8800 Oak Grove Mine Road, Adger, AL 35006</td>\n",
" <td>Preparation Plant</td>\n",
" <td>0</td>\n",
" <td>United Mine Workers of America</td>\n",
" <td>2003</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Average_Employees Company_Type Labor_Hours \\\n",
"MSHA_ID \n",
"103117 50 Independent Producer Operator 67199 \n",
"103246 4 Independent Producer Operator 11075 \n",
"103006 3 Independent Producer Operator 5161 \n",
"103183 8 Independent Producer Operator 19348 \n",
"100329 23 Operating Subsidiary 52009 \n",
"\n",
" Mine_Basin Mine_County Mine_Name Mine_State \\\n",
"MSHA_ID \n",
"103117 Appalachia Southern Cullman Mine #2 Alabama \n",
"103246 Appalachia Southern Franklin Bear Creek Alabama \n",
"103006 Appalachia Southern Jackson Bledsoe Mine No 1 Alabama \n",
"103183 Appalachia Southern Jackson Henager Alabama \n",
"100329 Appalachia Southern Jefferson Concord Mine Alabama \n",
"\n",
" Mine_Status Mine_Type Operating_Company \\\n",
"MSHA_ID \n",
"103117 Active Surface Twin Pines Coal Company Inc \n",
"103246 Active Surface Birmingham Coal & Coke Co., In \n",
"103006 Mine closed by MSHA Underground A L Select Inc \n",
"103183 Active Surface Gtm Mining Corporation \n",
"100329 Active Surface Oak Grove Resources, Llc \n",
"\n",
" Operating_Company_Address Operation_Type \\\n",
"MSHA_ID \n",
"103117 1874 County Road 15, Bremen, AL 35503 Mine only \n",
"103246 P.O. Box 354, Lynn, AL 35575 Mine only \n",
"103006 P.O. Box 864, Stevenson, AL 35772 Mine only \n",
"103183 15693 Alabama Highway 71, Pisgah, AL 35765 Mine only \n",
"100329 8800 Oak Grove Mine Road, Adger, AL 35006 Preparation Plant \n",
"\n",
" Production_short_tons Union_Code Year \n",
"MSHA_ID \n",
"103117 177381 NaN 2003 \n",
"103246 46049 NaN 2003 \n",
"103006 500 NaN 2003 \n",
"103183 55187 NaN 2003 \n",
"100329 0 United Mine Workers of America 2003 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dframe[dframe.Year == 2003].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Final Cleaned Data Product"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dframe.to_csv(output_file, )"
]
},
{
"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 |
erinspace/share_tutorials | 1_SHARE_API_Basics_py3.ipynb | 1 | 42984 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Calling the SHARE API\n",
"----\n",
"Here are some working examples of how to query the current SHARE database for individual results, metrics, and statistics.\n",
"\n",
"These particular queries are just examples, and the data is open for anyone to use, so feel free to make your own and experiment!\n",
"\n",
"Soon, we'll need a URL to access the SHARE Search API\n",
"\n",
"If you want to learn more about Python, the language that we are using to access the API and play with the data, these are a few great guides:\n",
"+ [The Hitchhiker's Guide to Python](http://docs.python-guide.org/en/latest/)\n",
"+ [Code Academy Python](https://www.codecademy.com/learn/python)\n",
"+ [The Python Tutorial](https://docs.python.org/3/tutorial/index.html)\n",
"+ [SoloLearn Python -- App for Android](https://play.google.com/store/apps/details?id=com.sololearn.python&hl=en)\n",
"+ [SoloLearn Python -- App for iOS](https://itunes.apple.com/us/app/learn-python-pro/id953972812?mt=8)\n",
"\n",
"And a quick introduction to Jupyter Notebooks:\n",
"+ [Running the Jupyter Notebook](https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/execute.html)\n",
"+ [Basics of Jupyter Notebook](https://www.packtpub.com/books/content/basics-jupyter-notebook-and-python)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"SHARE_API = 'https://staging-share.osf.io/api/search/abstractcreativework/_search'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The SHARE Search Schema\n",
"\n",
"The SHARE search API is built on a tool called elasticsearch. It lets you search a subset of SHARE's normalized metadata in a simple format.\n",
"\n",
"Here are the fields available in SHARE's elasticsearch endpoint:\n",
"\n",
" - 'title'\n",
" - 'language'\n",
" - 'subject'\n",
" - 'description'\n",
" - 'date'\n",
" - 'date_created'\n",
" - 'date_modified\n",
" - 'date_updated'\n",
" - 'date_published'\n",
" - 'tags'\n",
" - 'links'\n",
" - 'awards'\n",
" - 'venues'\n",
" - 'sources'\n",
" - 'contributors'\n",
"\n",
"You can see a formatted version of the base results from the API by visiting the [SHARE Search API URL](https://staging-share.osf.io/api/search/abstractcreativework/_search)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Service Names for Reference\n",
"----\n",
"Each provider harvested from has a specific . Let's make an API call to generate a table to get all of those \"internal\" names, along with the official name of the repository that they represent.\n",
"\n",
"The SHARE API has different endpoints. One of those endpoints returns a list of all of the providers that SHARE is harvesting from, along with their internal names, official names, links to their homepages, and a simple version of an icon representing their service, in a parsable format called json.\n",
"\n",
"Let's make a call to that API endpoint using the requests libarary, get the json data, and print out all of the shortnames and longnames."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Here are the first 10 Providers:\n",
"Research Online @ University of Wollongong\n",
"http://ro.uow.edu.au\n",
"au.uow\n",
"\n",
"Ghent University Academic Bibliography\n",
"https://biblio.ugent.be/\n",
"be.ghent\n",
"\n",
"Pontifical Catholic University of Rio de Janeiro\n",
"http://www.maxwell.vrac.puc-rio.br\n",
"br.pcurio\n",
"\n",
"Lake Winnipeg Basin Information Network\n",
"http://130.179.67.140\n",
"ca.lwbin\n",
"\n",
"PAPYRUS - Dépôt institutionnel de l'Université de Montréal\n",
"http://papyrus.bib.umontreal.ca\n",
"ca.umontreal\n",
"\n",
"Western University\n",
"http://ir.lib.uwo.ca\n",
"ca.uwo\n",
"\n",
"BioMed Central\n",
"http://www.springer.com/us/\n",
"com.biomedcentral\n",
"\n",
"Social Science Research Network\n",
"http://papers.ssrn.com/\n",
"com.dailyssrn\n",
"\n",
"figshare\n",
"https://figshare.com/\n",
"com.figshare\n",
"\n",
"Nature Publishing Group\n",
"http://www.nature.com/\n",
"com.nature\n",
"\n"
]
}
],
"source": [
"# Requests library allows you to send organic, grass-fed HTTP/1.1 requests, no need to manually add query strings \n",
" # to your URLs, or to form-encode your POST data. Docs: http://docs.python-requests.org/en/master/\n",
"import requests\n",
"\n",
"# Json library parses JSON from strings or files. The library parses JSON into a Python dictionary or list. \n",
" # It can also convert Python dictionaries or lists into JSON strings. \n",
" # https://docs.python.org/2.7/library/json.html\n",
"import json\n",
"\n",
"# This takes the URL and puts it into a variable (so we only need to ever reference this variable, \n",
" # and so we don't have to repeat adding this URL when we want to work with the data)\n",
"SHARE_PROVIDERS = 'https://staging-share.osf.io/api/providers/'\n",
"\n",
"# this requests the data from the SHARE_PROVIDERS and uses the json library to parse it into this list variable\n",
"data = requests.get(SHARE_PROVIDERS).json()\n",
"\n",
"# literally prints out the sentence in the quotes on the screen\n",
"print('Here are the first 10 Providers:')\n",
"\n",
"# this is a for loop (https://wiki.python.org/moin/ForLoop) to repeat the same tasks for each of the items in the \n",
" # list of our SHARE providers (what we put into the variable \"data\").\n",
"# for every item (called 'source' below) in the list, we print out the title, website, and provider name, \n",
" # formatted so each is on a new line (\\n)\n",
"for source in data['results']:\n",
" print(\n",
" '{}\\n{}\\n{}\\n'.format(\n",
" source['long_title'],\n",
" source['home_page'],\n",
" source['provider_name']\n",
" )\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### SHARE Schema\n",
"\n",
"You can make queries against any of the fields defined in the [SHARE Schema](https://github.com/CenterForOpenScience/SHARE-Schema/blob/master/share.yaml). If we were able to harvest the information from the original source, it should appear in SHARE. However, not all fields are required for every document. \n",
"\n",
"Required fields include:\n",
"- title\n",
"- contributors\n",
"- uris\n",
"- providerUpdatedDateTime\n",
"\n",
"We add some information after each document is harvested inside the field shareProperties, including:\n",
"- source (where the document was originally harvested)\n",
"- docID (a unique identifier for that object from that source)\n",
"\n",
"These two fields can be combined to make a unique document identifier."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simple Queries"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's get the first 3 results from the most basic query - the first page of the most recently updated research release events in SHARE.\n",
"\n",
"We'll use the URL parsing library furl to keep track of all of our arguments to the URL, because we'll be modifying them as we go along. We'll print the URL as we go to take a look at it, so we know what we're requesting.\n",
"\n",
"We'll print out the result's title and sources where it appears."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The request URL is https://staging-share.osf.io/api/search/abstractcreativework/_search?size=3\n",
"----------\n",
"LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Shortgrass Steppe collected on 2011-06-17 -- from ['providers.org.datacite']\n",
"Test entry from ezid service for identifier: doi:10.6085//TEST/20152611351448160.0758426051207266 -- from ['providers.org.datacite']\n",
"LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Shortgrass Steppe collected on 1990-04-20 -- from ['providers.org.datacite']\n"
]
}
],
"source": [
"# furl is a Python library that allows you to easily manipulate URLs. https://github.com/gruns/furl\n",
"import furl\n",
"\n",
"# In cell 1, we put the URL for the SHARE API into the variable SHARE_API. We can use it even down here!\n",
" # We are parsing it using furl and putting it into a new variable called search_url\n",
"search_url = furl.furl(SHARE_API)\n",
"\n",
"# We are limiting the arguments that we can attach to the URL to 3 -- so we grab the first 3 entries from the API\n",
"search_url.args['size'] = 3\n",
"\n",
"# We are requsting the information from the search_url and parsing the JSON that we requested and got back \n",
" # (requests.get!) like we did in cell 2. We put it into this list variable \n",
"recent_results = requests.get(search_url.url).json()\n",
"\n",
"# This is called a 2 dimensional array -- a list that looks kind of like a matrix. A list can store other lists. \n",
" # This is a way to create two-dimensional (2D) lists in Python -- 2Dlist=[hi,my,name,is,erin][but,my,name,is,vicky]. We can print out 2Dlist[1][4] and we would see \"my Vicky\" because we start counting at 0 instead of 1\n",
"recent_results = recent_results['hits']['hits']\n",
"\n",
"# we need to do this to initialize/define the list -- look at this for more information: \n",
" # http://stackoverflow.com/questions/6667201/how-to-define-two-dimensional-array-in-python\n",
"recent_results\n",
"\n",
"#We are printing out on the screen the variable search_url\n",
"print('The request URL is {}'.format(search_url.url))\n",
"\n",
"#This is just so we have a nice visual queue between the url that we searched and the actual data we are grabbing\n",
"print('----------')\n",
"\n",
"#Another for loop! For all of the items in the results we just grabbed, we are printing out the variables \n",
" # called source and title\n",
"for result in recent_results:\n",
" print(\n",
" '{} -- from {}'.format(\n",
" result['_source']['title'],\n",
" result['_source']['sources']\n",
" )\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's limit that query to only documents mentioning \"giraffes\" somewhere in the title, description, or in any of the metadata. We'd do that by adding a query search term."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The request URL is https://staging-share.osf.io/api/search/abstractcreativework/_search?size=3&q=giraffes\n",
"---------\n",
"Genome reveals why giraffes have long necks -- from ['providers.org.crossref']\n",
"Odd creature was ancient ancestor of today’s giraffes -- from ['providers.org.crossref']\n",
"Genome reveals why giraffes have long necks -- from ['providers.com.nature']\n"
]
}
],
"source": [
"# we are reusing that variable search_url from the cell above! We are querying (hence the args['q']) the API to try \n",
" # and get items that have the word 'giraffes' in them\n",
"search_url.args['q'] = 'giraffes'\n",
"\n",
"# We are requsting the information from the search_url and parsing the JSON that we requested and got back \n",
" # (requests.get!) like we did in cell 2. We put it into this list variable \n",
"recent_results = requests.get(search_url.url).json()\n",
"\n",
"# This is called a 2 dimensional array -- a list that looks kind of like a matrix. A list can store other lists. \n",
" # This is a way to create two-dimensional (2D) lists in Python -- \n",
" # Exmaple: 2Dlist=[hi,my,name,is,erin][but,my,name,is,vicky]. We can print out 2Dlist[1][4] and we would see \n",
" # \"my Vicky\" because we start counting lists at 0 instead of 1 (computer science quirk)\n",
"recent_results = recent_results['hits']['hits']\n",
"\n",
"# We are printing out on the screen the variable search_url\n",
"print('The request URL is {}'.format(search_url.url))\n",
"\n",
"# This is just so we have a nice visual queue between the url that we searched and the actual data we are grabbing\n",
"print('---------')\n",
"\n",
"# Another for loop! For all of the items in the results we just grabbed, we are printing out the entries that have \n",
" # the keyword 'giraffes'\n",
"for result in recent_results:\n",
" print(\n",
" '{} -- from {}'.format(\n",
" result['_source']['title'],\n",
" result['_source']['sources']\n",
" )\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's search for documents from the source CrossRef"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The request URL is https://staging-share.osf.io/api/search/abstractcreativework/_search?size=3&q=sources:providers.org.crossref\n",
"---------\n",
"Communicating Accessibility Resources Benefits Everyone -- from ['providers.org.crossref']\n",
"The Devil Is in the Details -- from ['providers.org.crossref']\n",
"Progression of coronary artery calcification by cardiac computed tomography -- from ['providers.org.crossref']\n"
]
}
],
"source": [
"# we are reusing that variable search_url from the cell above! We are querying (see that arg again) for everything \n",
" # from the source provide CrossRef\n",
"search_url.args['q'] = 'sources:providers.org.crossref'\n",
"\n",
"# We are requsting the information from the search_url and parsing the JSON that we requested and got back \n",
" # (requests.get!) like we did in cell 2. We put it into this list variable \n",
"recent_results = requests.get(search_url.url).json()\n",
"\n",
"# This is called a 2 dimensional array -- a list that looks kind of like a matrix. A list can store other lists. \n",
" # This is a way to create two-dimensional (2D) lists in Python -- \n",
" # Example: 2Dlist=[hi,my,name,is,erin][but,my,name,is,vicky]. We can print out 2Dlist[1][4] \n",
" # and we would see \"my Vicky\" because we start counting at 0 instead of 1 (computer science quirk)\n",
"recent_results = recent_results['hits']['hits']\n",
"\n",
"# We are printing out on the screen the variable search_url\n",
"print('The request URL is {}'.format(search_url.url))\n",
"\n",
"# This is just so we have a nice visual queue between the url that we searched and the actual data we are grabbing\n",
"print('---------')\n",
"\n",
"# Another for loop! For all of the items in the results we just grabbed, we are printing out the entries that are \n",
" # from CrossRef\n",
"for result in recent_results:\n",
" print(\n",
" '{} -- from {}'.format(\n",
" result['_source']['title'],\n",
" result['_source']['sources']\n",
" )\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's combine the two and find documents from CrossRef that mention giraffes"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The request URL is https://staging-share.osf.io/api/search/abstractcreativework/_search?size=3&q=sources:providers.org.crossref+AND+giraffes\n",
"---------\n",
"Genome reveals why giraffes have long necks -- from ['providers.org.crossref']\n",
"Odd creature was ancient ancestor of today’s giraffes -- from ['providers.org.crossref']\n",
"Of Caucasians, Asians, and Giraffes: The Influence of Categorization and Target Valence on Social Projection -- from ['providers.org.crossref']\n"
]
}
],
"source": [
"# we are reusing that variable search_url from the cell above! We are querying (see that arg again) for entries \n",
" # that use the keyword \"giraffes\" from the source provider CrossRef\n",
"search_url.args['q'] = 'sources:providers.org.crossref AND giraffes'\n",
"\n",
"# We are requsting the information from the search_url and parsing the JSON that we requested and got back \n",
" # (requests.get!) like we did in cell 2. We put it into this list variable \n",
"recent_results = requests.get(search_url.url).json()\n",
"\n",
"# This is called a 2 dimensional array -- a list that looks kind of like a matrix. A list can store other lists. \n",
" # This is a way to create two-dimensional (2D) lists in Python -- 2Dlist=[hi,my,name,is,erin][but,my,name,is,vicky]. We can print out 2Dlist[1][4] and we would see \"my Vicky\" because we start counting at 0 instead of 1\n",
"recent_results = recent_results['hits']['hits']\n",
"\n",
"# We are printing out on the screen the variable search_url\n",
"print('The request URL is {}'.format(search_url.url))\n",
"\n",
"# This is just so we have a nice visual queue between the url that we searched and the actual data we are grabbing\n",
"print('---------')\n",
"\n",
"# Another for loop! For all of the items in the results we just grabbed, we are printing out the entries that are \n",
" # from CrossRef that are about giraffes\n",
"for result in recent_results:\n",
" print(\n",
" '{} -- from {}'.format(\n",
" result['_source']['title'],\n",
" result['_source']['sources']\n",
" )\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Complex Queries\n",
"The SHARE Search API runs on elasticsearch - meaning that it can accept complicated queries that give you a wide variety of information.\n",
"\n",
"Here are some examples of how to make more complex queries using the raw elasticsearch results. You can read a [lot more about elasticsearch queries here](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl.html)."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'https://staging-share.osf.io/api/search/abstractcreativework/_search'"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# reset the args so that we remove our old query arguments.\n",
"search_url.args = None \n",
"\n",
"# Show the URL that we'll be requesting to make sure the args were cleared\n",
"search_url.url "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Query Setup\n",
"\n",
"We can define a few functions that we can reuse to make querying simpler. Elasticsearch queries are passed through as json blobs specifying how to return the information you want."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#just like the json library from cell 2\n",
"import json\n",
"\n",
"#this is called a function -- we DEFINE it (def) and name it something useful for us. It makes it easy for us to \n",
" #reuse this later on by just calling the function by typng it's name, and adding the appropriate values in the \n",
" #parentheses (called arguments). To learn more about functions: \n",
" #http://www.tutorialspoint.com/python/python_functions.htm\n",
" #http://docs.python-guide.org/en/latest/writing/style/?highlight=function\n",
" \n",
"# This is a helper function that will use the requests library, pass along the correct headers, and make the query\n",
" # we want\n",
"def query_share(url, query):\n",
" headers = {'Content-Type': 'application/json'}\n",
" data = json.dumps(query)\n",
" return requests.post(url, headers=headers, data=data).json()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Some Queries\n",
"The SHARE schema has many spots for information, and many of the original sources do not provide this information. We can do a query to find out if a certain field exists or not within certain records. The SHARE API is set up to show an empty list if the field is empty.\n",
"\n",
"Let's query for the counts of documents that have a content in their tags field."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# we are making another list of all the items with tags\n",
"tags_query = {\n",
" \"query\": {\n",
" \"exists\": {\n",
" \"field\": \"tags\"\n",
" }\n",
" }\n",
"}\n",
"\n",
"\n",
"# we are making another list of all the items without tags\n",
"missing_tags_query = {\n",
" \"query\": {\n",
" \"bool\": {\n",
" \"must_not\": {\n",
" \"exists\": {\n",
" \"field\": \"tags\"\n",
" }\n",
" }\n",
" } \n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2443294 results out of 4914457, or 49.72%, have tags.\n",
"2471163 results out of 4914457, or 50.28%, do NOT have tags.\n",
"------------\n",
"As a little sanity check....\n",
"49.71645901062925 + 50.28354098937074 = 100.00%\n"
]
}
],
"source": [
"# we are making a list of the results from searching items with tags\n",
"with_tags = query_share(search_url.url, tags_query)\n",
"\n",
"# we are making a list of the results from searching items without tags\n",
"missing_tags = query_share(search_url.url, missing_tags_query)\n",
"\n",
"#Gets the total number of hits from each search\n",
"total_results = requests.get(search_url.url).json()['hits']['total']\n",
"\n",
"# getting the percentages of hits with and without tags, respectively, using the built-in float function, which\n",
" # returns a float number (written with a decimal point dividing the integer and fractional parts.)\n",
" # read more about float function: https://docs.python.org/3/library/functions.html#float\n",
"with_tags_percent = (float(with_tags['hits']['total'])/total_results)*100\n",
"missing_tags_percent = (float(missing_tags['hits']['total'])/total_results)*100\n",
"\n",
"\n",
"# this simply prints out the list of results that have tags by iterating over the list we already made \"with_tags\"\n",
" # it then prints out the percentage of total \n",
"print(\n",
" '{} results out of {}, or {}%, have tags.'.format(\n",
" with_tags['hits']['total'],\n",
" total_results,\n",
" format(with_tags_percent, '.2f')\n",
" )\n",
")\n",
"\n",
"# this simply prints out the list of results without tags by iterating over the list we already made \"missing_tags\"\n",
" # it then prints out the percentage of tota\n",
"print(\n",
" '{} results out of {}, or {}%, do NOT have tags.'.format(\n",
" missing_tags['hits']['total'],\n",
" total_results,\n",
" format(missing_tags_percent, '.2f')\n",
" )\n",
")\n",
"\n",
"# Visual cue, printing the percentage of results with tags + percent with no tags (we make sure it equals 100)\n",
"print('------------')\n",
"print('As a little sanity check....')\n",
"print('{} + {} = {}%'.format(with_tags_percent, missing_tags_percent, format(with_tags_percent + missing_tags_percent, '.2f')))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using SHAREPA for SHARE Parsing and Analysis\n",
"\n",
"While you can always pass raw elasticsearch queries to the SHARE API, there is also a pip-installable python library that you can use that makes elasticsearch aggregations a little simpler. This library is called [sharepa - short for SHARE Parsing and Analysis](https://github.com/CenterForOpenScience/sharepa#sharepa)\n",
"\n",
"### Basic Actions\n",
"\n",
"A basic search will provide access to all documents in SHARE in 10 document slices.\n",
"\n",
"#### Count\n",
"You can use sharepa and the basic search to get the total number of documents in SHARE"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4914457"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Sharepa is a python client for browsing and analyzing SHARE data specifically using elasticsearch querying.\n",
" # We can use this to aggregate, graph, and analyze the data. \n",
" # Helpful Links:\n",
" # https://github.com/CenterForOpenScience/sharepa\n",
" # https://pypi.python.org/pypi/sharepa\n",
" # here, we import the specific function from Sharepa called basic_search\n",
"from sharepa import basic_search\n",
"\n",
"# this performs a basic search over the SHARE dataset and returns a count of the items in it\n",
"basic_search.count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Iterating Through Results\n",
"Executing the basic search will send the actual basic query to the SHARE API and then let you iterate through results, 10 at a time."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Shortgrass Steppe collected on 2011-06-17\n",
"Test entry from ezid service for identifier: doi:10.6085//TEST/20152611351448160.0758426051207266\n",
"LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Shortgrass Steppe collected on 1990-04-20\n",
"Chemical composition of essential oils of three Pistacia cultivars in Khorasan Razavi, Iran\n",
"Test entry from ezid service for identifier: doi:10.6085//TEST/20152611351448190.5563383046761334\n",
"Test entry from ezid service for identifier: doi:10.6085//TEST/20152611351448200.9788127569002799\n",
"Test entry from ezid service for identifier: doi:10.6085//TEST/20152611351448150.3484667860365027\n",
"Compiled Tree-ring Dates from the Southwestern United States (Unrestricted)\n",
"None\n",
"Area-based Amino Acid Composition for three types of interactions in the BNCP-CS dataset\n"
]
}
],
"source": [
"# this captures 10 items in the SHARE dataset -- that's what the basic search function is for\n",
"results = basic_search.execute()\n",
"\n",
"# this prints out the 10 items we added to the list variable, results (above)\n",
"for hit in results:\n",
" print(hit.title)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we don't want 10 results, or we want to offset the results, we can use slices"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['providers.org.datacite']\n",
"['providers.org.datacite']\n",
"['providers.org.datacite']\n",
"['providers.org.datacite']\n",
"['providers.org.datacite']\n"
]
}
],
"source": [
"# this performs a basic search for items number 20-25 in the SHARE dataset (useful if you know where your things are\n",
" # in SHARE dataset) \n",
"results = basic_search[20:25].execute()\n",
"\n",
"# prints out the provider for the items capture in the list, results (above)\n",
"for hit in results:\n",
" print(hit.sources)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Advanced Search with sharepa\n",
"\n",
"You can make your own search object, which allows you to pass in custom queries for certain terms or SHARE fields. Queries are formed using [lucene query syntax](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-query-string-query.html#query-string-syntax), just like we used in the above examples.\n",
"\n",
"This type of query accepts an exists field. Other options include a query_string, a match query, a multi-match query, a bool query, and any other query structure available in the elasticsearch API.\n",
"\n",
"We can see what that query that we're about to send to elasticsearch by using the pretty print helper function. You'll see that it looks very similar to the queries we defined by hand earlier."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"query\": {\n",
" \"exists\": {\n",
" \"field\": \"tags\"\n",
" }\n",
" }\n",
"}\n"
]
}
],
"source": [
"# Sharepa is a python client for browsing and analyzing SHARE data specifically using elasticsearch querying.\n",
" # We can use this to aggregate, graph, and analyze the data. \n",
" # Helpful Links:\n",
" # https://github.com/CenterForOpenScience/sharepa\n",
" # https://pypi.python.org/pypi/sharepa\n",
" # here, we import the specific function from Sharepa called ShareSearch and pretty_print\n",
"from sharepa import ShareSearch\n",
"from sharepa.helpers import pretty_print\n",
"\n",
"#we just created a local name for ShareSearch function for us to use\n",
"my_search = ShareSearch()\n",
"\n",
"# Lucene supports fielded data. When performing a search you can either specify a field, or use the default field. \n",
"my_search = my_search.query(\n",
" 'exists', # Type of query, will accept a lucene query string\n",
" field='tags', # This lucene query string will find all documents that don't have tags\n",
")\n",
"\n",
"# this prints out (prettily!) our search, transformed into a dictionary data type\n",
" # read more about dictionaries here: http://learnpythonthehardway.org/book/ex39.html\n",
"pretty_print(my_search.to_dict())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When you execute that query, you can then iterate through the results the same way that you could with the simple search query."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['CDL.LTERNET', 'CDL', 'dataPackage', 'Dataset']\n",
"['CDL.PISCO', 'CDL']\n",
"['CDL.LTERNET', 'CDL', 'dataPackage', 'Dataset']\n",
"['CDL.DIGSCI', 'CDL', 'Paper', 'Dataset']\n",
"['CDL.PISCO', 'CDL']\n",
"['CDL.PISCO', 'CDL']\n",
"['CDL.PISCO', 'CDL']\n",
"['CDL.DIGANT', 'CDL', 'Dataset']\n",
"['TIB.R-GATE', 'TIB']\n",
"['CDL.DIGSCI', 'CDL', 'Image']\n"
]
}
],
"source": [
"# we are taking the my_search variable from the cell above and executing the search, placing the results into a \n",
" # new list called new_results\n",
"new_results = my_search.execute()\n",
"\n",
"# this for loop prints out the tags for each item in the results we gathered \n",
"for hit in new_results:\n",
" print(hit.tags)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Debugging and Problem Solving\n",
"\n",
"Not everything always goes as planned when querying an unfamillar API. Here are some debugging and problem solving strategies when you're querying the SHARE API.\n",
"\n",
"### Schema issues\n",
"The SHARE schema has a lot of parts, and much of the information is nested within sections. Making a query isn't always as straight forward as you might think, if you're not looking in the right part of the schema.\n",
"\n",
"Let's say you were trying to query for all SHARE documents that specify the language as not being in English.\n",
"\n",
"We'll guess as to what that query might be, and try to make it using sharepa."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# this creates a new search for us to use!\n",
"language_search = ShareSearch()\n",
"\n",
"# this sets the search query we are using for our new search! all the items that aren't in english\n",
"language_search = language_search.query(\n",
" 'query_string', # Type of query, will accept a lucene query string\n",
" query='NOT languages=english', # This lucene query string will find all documents that don't have tags\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'Result' object has no attribute 'languages'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/Users/erin/miniconda3/envs/share_tutorials/lib/python3.5/site-packages/elasticsearch_dsl/utils.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, attr_name)\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_wrap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_d_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mattr_name\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 121\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: 'languages'",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-17-f7e8b99a9f64>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhit\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlanguages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/erin/miniconda3/envs/share_tutorials/lib/python3.5/site-packages/elasticsearch_dsl/utils.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, attr_name)\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 122\u001b[0m raise AttributeError(\n\u001b[0;32m--> 123\u001b[0;31m '%r object has no attribute %r' % (self.__class__.__name__, attr_name))\n\u001b[0m\u001b[1;32m 124\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__delattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'Result' object has no attribute 'languages'"
]
}
],
"source": [
"# this allows us to search through 10 results and find the languages of each\n",
"results = language_search.execute()\n",
"\n",
"# for each item in results, print out the language it is in\n",
"for hit in results:\n",
" print(hit.languages)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So the result does not have an attribute called languages! Let's try to figure out what went wrong here.\n",
"\n",
"Step one could be that we are trying to find something that does NOT match a given parameter. Since languages is not required, this is returning results that do not include the languages result at all!\n",
"\n",
"So let's fix this up a bit to make sure that we're querying for items that specify language in the first place."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# let's try that again! creating a new search from the ShareSearch() function\n",
"language_search = ShareSearch()\n",
"\n",
"# this sets up our new query: if the field called 'language' exists, grab those results\n",
"language_search = language_search.filter(\n",
" 'exists',\n",
" field=\"language\"\n",
")\n",
"\n",
"# count the number of entries that have a language field\n",
"language_search.count()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# grab the results of the search for a language field!\n",
"results = language_search.execute()\n",
"\n",
"# Let's see how many documents have language results.\n",
"print('There are {} documents with languages specified'.format(language_search.count()))\n",
"\n",
"print('Here are the languages for the first 10 results:')\n",
"\n",
"# for each item in results, print out the language it is in\n",
"for hit in results:\n",
" print(hit.language)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So now we're better equipped to add on to this filter, and then narrow down to results that are not in English.\n",
"\n",
"When we printed out the first few results, we might have noticed a second problem with our query -- going back to the [SHARE Schema](https://github.com/CenterForOpenScience/SHARE-Schema/blob/master/share.yaml), we might notice that there is a restriction on how languages are captured - as a three letter lowercase representation. Instead of \"english\" let's look for the three letter abbreviation - \"eng\"\n",
"\n",
"We can modify our new and improved language query by adding on another query to our started language_search. We'll use the elasticsearch query object Q, and invert it with a ~ symbol, and search for the term \"eng.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"# Elasticsearch DSL is a high-level library whose aim is to help with writing and running queries against Elasticsearch.\n",
" # Read more about elasticsearch here:\n",
" # http://elasticsearch-dsl.readthedocs.io/en/latest/search_dsl.html\n",
" # https://pypi.python.org/pypi/elasticsearch-dsl\n",
" # https://github.com/elastic/elasticsearch-dsl-py\n",
" # this imports the function Q from the library \n",
"from elasticsearch_dsl import Q\n",
"\n",
"# sets up a new search -- for results that have english ('eng') in their language field\n",
"language_search = language_search.query(~Q(\"term\", language=\"eng\"))\n",
"\n",
"# execute our search and throw it into a new list\n",
"results = language_search.execute()\n",
"\n",
"# Let's see how many documents have language results that aren't eng\n",
"print('There are {} documents that do not have \"eng\" listed.'.format(language_search.count()))\n",
"\n",
"print('Here are the languages for the first 10 results:')\n",
"\n",
"# Check out the first few results, make sure \"eng\" isn't in there\n",
"for hit in results:\n",
" print(hit.language)\n",
" print(hit.title)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
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| apache-2.0 |
vbsteja/code | Python/math/.ipynb_checkpoints/Health outcomes with Linear Regression-checkpoint.ipynb | 1 | 2184 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from sklearn import datasets, linear_model, metrics\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import PolynomialFeatures\n",
"import math, scipy, numpy as np\n",
"from scipy import linalg"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"data_set = datasets.load_diabetes()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"x_trn,x_tst,y_trn,y_tst = train_test_split(data_set.data,data_set.target,test_size=0.2)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((353, 10), (89, 10), (353,), (89,))"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_trn.shape,x_tst.shape,y_trn.shape,y_tst.shape"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"feature_names=['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"lr = linear_model.LinearRegression()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def regr_metrics(act, pred):\n",
" return (math.sqrt(metrics.mean_squared_error(act, pred)), \n",
" metrics.mean_absolute_error(act, pred))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
selimnairb/2014-02-25-swctest | lessons/swc-setdict/setdict-json-instructor.ipynb | 1 | 11081 | {
"metadata": {
"name": "setdict-json-instructor"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Sets and Dictionaries in Python: JSON (Instructor Version)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Objectives\n",
"\n",
"* Correctly define \"JSON\" and give simple examples of valid JSON structures.\n",
"* Describe JSON's strengths and weaknesses as a storage format.\n",
"* Write code to read and write JSON-formatted data files using standard libraries."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lesson\n",
"\n",
"The example above used two data file formats: one for storing molecular\n",
"formulas, the other for storing inventory. Both formats were specific to\n",
"this application, which means we needed to write, debug, document, and\n",
"maintain functions to handle them. Those functions weren't particularly\n",
"difficult to create, but they still took time to create, and if anyone\n",
"ever wants to read our files in Java, MATLAB, or Perl, they'll have to\n",
"write equivalent functions themselves.\n",
"\n",
"A growing number of programs avoid these problems by using a flexible\n",
"data format called [JSON](glossary.html#json), which stands for\n",
"\"JavaScript Object Notation\". Despite the name, it is a\n",
"language-independent way to store nested data structures made up of\n",
"strings, numbers, Booleans, lists, dictionaries, and the special value\n",
"`null` (equivalent to Python's `None`)\u2014in short, the basic data types\n",
"that almost every language supports. For example, let's convert a\n",
"dictionary of scientists' birthdays to a string:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import json\n",
"birthdays = {'Curie' : 1867, 'Hopper' : 1906, 'Franklin' : 1920}\n",
"as_string = json.dumps(birthdays)\n",
"print as_string\n",
"print type(as_string)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{\"Curie\": 1867, \"Hopper\": 1906, \"Franklin\": 1920}\n",
"<type 'str'>\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`json.dumps` doesn't seem to do much, but that's kind of the point: the\n",
"textual representation of the data structure looks pretty much like what\n",
"a programmer would type in to re-create it. The advantage is that this\n",
"representation can be saved in a file:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"writer = open('/tmp/example.json', 'w')\n",
"json.dump(birthdays, writer)\n",
"writer.close()\n",
"\n",
"reader = open('/tmp/example.json', 'r')\n",
"duplicate = json.load(reader)\n",
"reader.close()\n",
"\n",
"print 'original:', birthdays"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"original: {'Curie': 1867, 'Hopper': 1906, 'Franklin': 1920}\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 'duplicate:', duplicate"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"duplicate: {u'Curie': 1867, u'Hopper': 1906, u'Franklin': 1920}\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(Note that strings are stored as Unicode.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data read in is the same as the original:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 'original == duplicate:', birthdays == duplicate"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"original == duplicate: True\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But it is *not* the same object in memory:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 'original is duplicate:', birthdays is duplicate"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"original is duplicate: False\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data file holds what we'd type in to create the data in a program,\n",
"which makes it easy to edit by hand:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cat /tmp/example.json"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{\"Curie\": 1867, \"Hopper\": 1906, \"Franklin\": 1920}"
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How is this different in practice from what we had? First, our inventory\n",
"file now looks like this:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cat inventory-03.json"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{\"He\" : 1, \"H\" : 4, \"O\" : 3}"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"while our formula files are:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cat formulas-03.json"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{\"helium\" : {\"He\" : 1},\r\n",
" \"water\" : {\"H\" : 2, \"O\" : 1},\r\n",
" \"hydrogen\" : {\"H\" : 2}}"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Those aren't as intuitive for non-programmers as the original flat text\n",
"files, but they're not too bad. The worst thing is the lack of comments:\n",
"unfortunately\u2014very unfortunately\u2014the JSON format doesn't support them.\n",
"(And note that JSON requires us to use a double-quote for strings:\n",
"unlike Python, we cannot substitute single quotes.)\n",
"\n",
"The good news is that given files like these, we can rewrite our program\n",
"as:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def main(inventory_file, formula_file):\n",
" with open(inventory_file, 'r') as reader:\n",
" inventory = json.load(reader)\n",
" with open(formula_file, 'r') as reader:\n",
" formulas = json.load(reader)\n",
" counts = calculate_counts(inventory, formulas)\n",
" show_counts(counts)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The two functions that read formula and inventory files have been\n",
"replaced with a couple of lines each. Nothing else has to\n",
"change, because the data structures loaded from the data files are\n",
"exactly what we had before. The end result is 51 lines long compared to\n",
"the 80 we started with, a reduction of more than a third."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>Nothing's Perfekt</h3>\n",
"\n",
"JSON's greatest weakness isn't its lack of support for comments, but the\n",
"fact that it doesn't recognize and manage aliases. Instead, each\n",
"occurrence of an aliased structure is treated as something brand new\n",
"when data is being saved. For example:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"inner = ['name']\n",
"outer = [inner, inner] # Creating an alias\n",
"print outer\n",
"print outer[0] is outer[1]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[['name'], ['name']]\n",
"True\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"as_string = json.dumps(outer)\n",
"duplicate = json.loads(as_string)\n",
"print duplicate\n",
"print duplicate[0] is duplicate[1]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[u'name'], [u'name']]\n",
"False\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The diagram below shows the difference between the original data\n",
"structure (referred to by `outer`) and what winds up in `duplicate`. If\n",
"aliases might be present in our data, and it's important to preserve\n",
"their structure, we must either record the aliasing ourself (which is\n",
"tricky), or use some other format. Luckily, a lot of data either doesn't\n",
"contain aliases, or the aliasing in it isn't important.\n",
"\n",
"<img src=\"files/json_alias.png\" alt=\"Lack of Aliasing in JSON\" />"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Key Points\n",
"\n",
"* The JSON data format can represent arbitrarily-nested lists and dictionaries containing strings, numbers, Booleans, and `None`.\n",
"* Using JSON reduces the code we have to write ourselves and improves interoperability with other programming languages.\n",
"* JSON doesn't allow for comments, and doesn't handle aliasing."
]
}
],
"metadata": {}
}
]
} | bsd-2-clause |
huazhisong/race_code | leetcode_ws/hash table/30. Substring with Concatenation of All Words.ipynb | 1 | 3580 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You are given a string, s, and a list of words, words, that are all of the same length. Find all starting indices of substring(s) in s that is a concatenation of each word in words exactly once and without any intervening characters.\n",
"\n",
"For example, given:\n",
"s: \"barfoothefoobarman\"\n",
"words: [\"foo\", \"bar\"]\n",
"\n",
"You should return the indices: [0,9].\n",
"(order does not matter)."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Solution(object):\n",
" def findSubstring(self, s, words):\n",
" \"\"\"\n",
" 每次判断单词是否在words_set里面\n",
" 需要注意的是bar的出现的次数比在words中多\n",
" :type s: str\n",
" :type words: List[str]\n",
" :rtype: List[int]\n",
" \"\"\"\n",
" words_set = {}\n",
" word_num = len(words)\n",
" for word in words:\n",
" if word not in words_set:\n",
" words_set[word] = 1\n",
" else:\n",
" words_set[word] += 1\n",
" word_len = len(words[0])\n",
" res = []\n",
" for i in range(len(s)+1-word_len * word_num):\n",
" curr, j = {}, 0\n",
" while j < word_num:\n",
" word = s[i+j*word_len: i + j*word_len + word_len]\n",
" if word not in words_set:\n",
" break\n",
" if word not in curr:\n",
" curr[word] = 1\n",
" else:\n",
" curr[word] += 1\n",
" if curr[word] > words_set[word]: break\n",
" j+=1\n",
" if j == word_num:\n",
" res.append(i)\n",
" return res\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0, 9]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Solution().findSubstring(\"barfoothefoobarman\", [\"foo\", \"bar\"])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def all_sorted(s):\n",
" if len(s) == 1:\n",
" return s[0]\n",
" else:\n",
" return s[0]+all_sorted(s[1:])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'foobar'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_sorted([\"foo\", \"bar\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
mrocklin/streams | examples/fibonacci.ipynb | 3 | 1268 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# A Stream of Fibonacci numbers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from streamz import Stream\n",
"source = Stream()\n",
"\n",
"s = source.sliding_window(2).map(sum)\n",
"s.rate_limit(0.5).sink(source.emit)\n",
"\n",
"L = s.sink_to_list()\n",
"s.sink(print)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"source.emit(0)\n",
"source.emit(1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"L"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| bsd-3-clause |
bloomberg/bqplot | examples/Applications/Feature_Vector_Distribution-Iris-Digits.ipynb | 2 | 18373 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tutorial for building a feature vector distribution plot\n",
"\n",
"In this tutorial we will build an interactive widget using bqplot and ipywidgets. bqplot is a powerful interactive plotting library for jupyter. Its main power comes from how well integrated it is into the ipywidgets library. There are a few things you should understand before diving into this tutorial.\n",
"\n",
"\n",
"### ipywidgets:\n",
"\n",
"* Widgets: Widgets python objects which link directly to their html counterpart allowing easy interaction between js,css,html and python.\n",
"\n",
"* Boxes: Boxes allow you to group widgets together, this can be either vertical or horizontally.\n",
"\n",
"### bqplot:\n",
"\n",
"\n",
"* Figures: Figures are a canvas that you will mark on. Its best to think of the figure as another widget (which it is)\n",
"\n",
"* Marks: marks are things that you draw onto the figure, these are composed of a variety of chart types such as bars, lines, histograms etc. You can put a bunch of marks on a single figure.\n",
"\n",
"If you are used to matplotlib, the paradigm of how axis and scales are used in bqplot can be somewhat counterintuitive at first, so take some time to read the documentation and play around until you understand them. Once you do, they are very powerful when you want to link multiple plots together.\n",
"\n",
"* Axis: Axis describe what the lines around a figure will look like. Only figures have axis, marks don't.\n",
"\n",
"* Scales: The scale describes how ranges should be displayed ie. linear or logarithmic. Scales are used by both axis and marks. Their max and min can auto aujust to the data, or be set. Be careful, you can add an axis to a figure that has a different scale than the one a mark you are adding to the figure has.\n",
"\n",
"* Tooltips: These allow you to add information on hover. They only accept three fields 'name', 'x' and 'y'. So in this tutorial we put all of the infomration we want to show into the name column as a string.\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from ipywidgets import HBox, VBox, Dropdown\n",
"from bqplot.marks import Scatter, Bars\n",
"from bqplot.scales import LinearScale, OrdinalScale\n",
"from bqplot.figure import Figure\n",
"from bqplot import Tooltip\n",
"from bqplot.axes import Axis\n",
"import numpy as np\n",
"\n",
"# simple function to return the bins for the plot\n",
"def get_h_bins(df, bins, f_lim):\n",
" if f_lim:\n",
" return np.arange(\n",
" f_lim[\"min\"], f_lim[\"max\"], (f_lim[\"max\"] - f_lim[\"min\"]) / float(bins)\n",
" )\n",
" scale_max = int(df.describe().loc[\"max\"].max() + 1)\n",
" scale_min = int(df.describe().loc[\"min\"].min() - 1)\n",
" return np.arange(scale_min, scale_max, (scale_max - scale_min) / float(bins))\n",
"\n",
"\n",
"def feature_vector_distribution(\n",
" features, label_column, bins=25, group_columns=None, f_lim=None, colors=None\n",
"):\n",
" \"\"\"\n",
" features (dataframe): a data frame of feature vectors along with a label column and other metadata\n",
" label_column (str): the name of the column in the features dataframe that refers to the label infomration\n",
" bins (int): the number of bins in the histograms\n",
" group_columns (list): if you want other metadata in the tooltip, these columns will be added\n",
" f_lim (dict): this sets the limits for max and min of the plots to a constant\n",
" {'max':10, 'min':10}. otherwise defaults to the values of the current features\n",
" which can be misleading.\n",
" colors (list): list of colors to use. Internally has a list of 10. If the labels\n",
" are longer you will need to pass your own\n",
"\n",
" \"\"\"\n",
" dist = \"640px\"\n",
" third_dist = \"213px\"\n",
"\n",
" if f_lim:\n",
" sc_x = LinearScale(min=f_lim[\"min\"], max=f_lim[\"max\"])\n",
" sc_y = LinearScale(min=f_lim[\"min\"], max=f_lim[\"max\"])\n",
" else:\n",
" sc_x = LinearScale()\n",
" sc_y = LinearScale()\n",
"\n",
" scale_y = LinearScale(min=0)\n",
"\n",
" x_ord_legend = OrdinalScale()\n",
" y_lin_legend = LinearScale()\n",
"\n",
" if group_columns is None:\n",
" count_column = features.columns[1]\n",
" group_columns = []\n",
" else:\n",
" count_column = group_columns[0]\n",
"\n",
" if colors is None:\n",
" colors = [\n",
" \"#E6B0AA\",\n",
" \"#C39BD3\",\n",
" \"#73C6B6\",\n",
" \"#F7DC6F\",\n",
" \"#F0B27A\",\n",
" \"#D0D3D4\",\n",
" \"#85929E\",\n",
" \"#6E2C00\",\n",
" \"#1A5276\",\n",
" \"#17202A\",\n",
" ]\n",
" box_color = \"black\"\n",
"\n",
" feature_x = Dropdown(description=\"Feature 1\")\n",
" feature_y = Dropdown(description=\"Feature 2\")\n",
"\n",
" feature_x.options = [\n",
" x for x in features.columns if x not in [label_column] + group_columns\n",
" ]\n",
" feature_y.options = [\n",
" x for x in features.columns if x not in [label_column] + group_columns\n",
" ]\n",
"\n",
" feature1 = feature_x.options[0]\n",
" feature2 = feature_y.options[1]\n",
"\n",
" feature_y.value = feature2\n",
"\n",
" tt = Tooltip(\n",
" fields=[\"name\"], labels=[\", \".join([\"index\", label_column] + group_columns)]\n",
" )\n",
"\n",
" scatters = []\n",
" hists_y = []\n",
" hists_x = []\n",
"\n",
" h_bins_x = get_h_bins(features[[feature1]], bins, f_lim)\n",
" h_bins_y = get_h_bins(features[[feature2]], bins, f_lim)\n",
"\n",
" for index, group in enumerate(features.groupby([label_column])):\n",
"\n",
" # put the label column and any group column data in the tooltip\n",
" names = []\n",
" for row in range(group[1].shape[0]):\n",
" names.append(\n",
" \"{},\".format(row)\n",
" + \",\".join(\n",
" [\n",
" str(x)\n",
" for x in group[1][[label_column] + group_columns]\n",
" .iloc[row]\n",
" .values\n",
" ]\n",
" )\n",
" )\n",
"\n",
" # create a scatter plot for each group\n",
" scatters.append(\n",
" Scatter(\n",
" x=group[1][feature1].values,\n",
" y=group[1][feature2].values,\n",
" names=names,\n",
" display_names=False,\n",
" opacities=[0.5],\n",
" default_size=30,\n",
" scales={\"x\": sc_x, \"y\": sc_y},\n",
" colors=[colors[index]],\n",
" tooltip=tt,\n",
" )\n",
" )\n",
"\n",
" # create a histograms using a bar chart for each group\n",
" # histogram plot for bqplot does not have enough options (no setting range, no setting orientation)\n",
" h_y, h_x = np.histogram(group[1][feature1].values, bins=h_bins_x)\n",
" hists_x.append(\n",
" Bars(\n",
" x=h_x,\n",
" y=h_y,\n",
" opacities=[0.3] * bins,\n",
" scales={\"x\": sc_x, \"y\": scale_y},\n",
" colors=[colors[index]],\n",
" orientation=\"vertical\",\n",
" )\n",
" )\n",
"\n",
" h_y, h_x = np.histogram(group[1][feature2].values, bins=h_bins_y)\n",
" hists_y.append(\n",
" Bars(\n",
" x=h_x,\n",
" y=h_y,\n",
" opacities=[0.3] * bins,\n",
" scales={\"x\": sc_x, \"y\": scale_y},\n",
" colors=[colors[index]],\n",
" orientation=\"horizontal\",\n",
" )\n",
" )\n",
"\n",
" # legend will show the names of the labels as well as a total count of each\n",
" legend_bar = Bars(\n",
" x=features.groupby(label_column).count()[count_column].index,\n",
" y=features.groupby(label_column).count()[count_column].values,\n",
" colors=colors,\n",
" opacities=[0.3] * 6,\n",
" scales={\"x\": x_ord_legend, \"y\": y_lin_legend},\n",
" orientation=\"horizontal\",\n",
" )\n",
"\n",
" ax_x_legend = Axis(\n",
" scale=x_ord_legend,\n",
" tick_style={\"font-size\": 24},\n",
" label=\"\",\n",
" orientation=\"vertical\",\n",
" tick_values=features.groupby(label_column).count()[count_column].index,\n",
" )\n",
"\n",
" ax_y_legend = Axis(\n",
" scale=y_lin_legend,\n",
" orientation=\"horizontal\",\n",
" label=\"Total\",\n",
" color=box_color,\n",
" num_ticks=4,\n",
" )\n",
"\n",
" # these are blank axes that are used to fill in the border for the top and right of the figures\n",
" ax_top = Axis(scale=sc_x, color=box_color, side=\"top\", tick_style={\"font-size\": 0})\n",
" ax_right = Axis(\n",
" scale=sc_x, color=box_color, side=\"right\", tick_style={\"font-size\": 0}\n",
" )\n",
" ax_left = Axis(\n",
" scale=sc_x, color=box_color, side=\"left\", tick_style={\"font-size\": 0}\n",
" )\n",
" ax_bottom = Axis(\n",
" scale=sc_x, color=box_color, side=\"bottom\", tick_style={\"font-size\": 0}\n",
" )\n",
" ax_top = Axis(scale=sc_x, color=box_color, side=\"top\", num_ticks=0)\n",
" ax_right = Axis(scale=sc_x, color=box_color, side=\"right\", num_ticks=0)\n",
" ax_left = Axis(scale=sc_x, color=box_color, side=\"left\", num_ticks=0)\n",
" ax_bottom = Axis(scale=sc_x, color=box_color, side=\"bottom\", num_ticks=0)\n",
"\n",
" # scatter plot axis\n",
" ax_x = Axis(label=feature1, scale=sc_x, color=box_color)\n",
" ax_y = Axis(label=feature2, scale=sc_y, orientation=\"vertical\", color=box_color)\n",
"\n",
" # count column of histogram\n",
" ax_count_vert = Axis(\n",
" label=\"\", scale=scale_y, orientation=\"vertical\", color=box_color, num_ticks=5\n",
" )\n",
" ax_count_horiz = Axis(\n",
" label=\"\", scale=scale_y, orientation=\"horizontal\", color=box_color, num_ticks=5\n",
" )\n",
"\n",
" # histogram bin axis\n",
" ax_hist_x = Axis(label=\"\", scale=sc_x, orientation=\"vertical\", color=box_color)\n",
" ax_hist_y = Axis(label=\"\", scale=sc_x, orientation=\"horizontal\", color=box_color)\n",
"\n",
" # create figures for each plot\n",
" f_scatter = Figure(\n",
" axes=[ax_x, ax_y, ax_top, ax_right],\n",
" background_style={\"fill\": \"white\"}, # css is inserted directly\n",
" marks=scatters,\n",
" min_aspect_ratio=1,\n",
" max_aspect_ratio=1,\n",
" fig_margin={\"top\": 0, \"bottom\": 60, \"left\": 60, \"right\": 0},\n",
" )\n",
"\n",
" f_hists_y = Figure(\n",
" axes=[ax_left, ax_count_horiz, ax_top, ax_right],\n",
" background_style={\"fill\": \"white\"},\n",
" marks=hists_y,\n",
" min_aspect_ratio=0.33,\n",
" max_aspect_ratio=0.33,\n",
" fig_margin={\"top\": 0, \"bottom\": 60, \"left\": 10, \"right\": 0},\n",
" )\n",
"\n",
" f_hists_x = Figure(\n",
" axes=[ax_count_vert, ax_bottom, ax_top, ax_right],\n",
" background_style={\"fill\": \"white\"},\n",
" marks=hists_x,\n",
" min_aspect_ratio=3,\n",
" max_aspect_ratio=3,\n",
" fig_margin={\"top\": 20, \"bottom\": 10, \"left\": 60, \"right\": 0},\n",
" )\n",
"\n",
" f_legend = Figure(\n",
" marks=[legend_bar],\n",
" axes=[ax_x_legend, ax_y_legend],\n",
" title=\"\",\n",
" legend_location=\"bottom-right\",\n",
" background_style={\"fill\": \"white\"},\n",
" min_aspect_ratio=1,\n",
" max_aspect_ratio=1,\n",
" fig_margin={\"top\": 10, \"bottom\": 30, \"left\": 20, \"right\": 20},\n",
" )\n",
"\n",
" # we already set the ratios, but it is necessary to set the size explicitly anyway\n",
" # this is kind of cool, inserts this into the style in html\n",
" f_legend.layout.height = third_dist\n",
" f_legend.layout.width = third_dist\n",
" f_hists_x.layout.height = third_dist\n",
" f_hists_x.layout.width = dist\n",
" f_hists_y.layout.height = dist\n",
" f_hists_y.layout.width = third_dist\n",
" f_scatter.layout.height = dist\n",
" f_scatter.layout.width = dist\n",
"\n",
" # we create some functions that allow changes when the widgets notice an event\n",
" def change_x_feature(b):\n",
" h_bins_x = get_h_bins(features[[feature_x.value]], bins, f_lim)\n",
" for index, group in enumerate(features.groupby([label_column])):\n",
" scatters[index].x = group[1][feature_x.value]\n",
" h_y, h_x = np.histogram(group[1][feature_x.value].values, bins=h_bins_x)\n",
" hists_x[index].y = h_y\n",
"\n",
" ax_x.label = feature_x.value\n",
"\n",
" def change_y_feature(b):\n",
" h_bins_y = get_h_bins(features[[feature_y.value]], bins, f_lim)\n",
" for index, group in enumerate(features.groupby([label_column])):\n",
" scatters[index].y = group[1][feature_y.value]\n",
" h_y, h_x = np.histogram(group[1][feature_y.value].values, bins=h_bins_y)\n",
" hists_y[index].y = h_y\n",
"\n",
" ax_y.label = feature_y.value\n",
"\n",
" # when the user selects a different feature, switch the data plotted\n",
" feature_x.observe(change_x_feature, \"value\")\n",
" feature_y.observe(change_y_feature, \"value\")\n",
"\n",
" # return the stacked figures to be plotted\n",
" return VBox(\n",
" [\n",
" HBox([feature_x, feature_y]),\n",
" HBox([f_hists_x, f_legend]),\n",
" HBox([f_scatter, f_hists_y]),\n",
" ]\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Iris Data Set"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Install scikit-learn\n",
"!pip install sklearn"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sklearn.datasets import load_iris\n",
"from sklearn.decomposition import PCA\n",
"from sklearn.preprocessing import scale\n",
"\n",
"np.random.seed(42)\n",
"\n",
"digits = load_iris()\n",
"data = scale(digits.data)\n",
"n_features = 4\n",
"# n_pca=3\n",
"# pca = PCA(n_components=n_pca).fit(data)\n",
"df = pd.DataFrame(data, columns=[\"feature_{}\".format(x) for x in range(n_features)])\n",
"df[\"leaf\"] = digits.target\n",
"df[\"extra_info\"] = [np.random.randint(100) for x in range(digits.target.shape[0])]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"feature_vector_distribution(\n",
" df, \"leaf\", group_columns=[\"extra_info\"], bins=25, f_lim={\"min\": -3, \"max\": 3}\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Digits data set, with PCA applied to reduce to 10 features"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.datasets import load_digits\n",
"from sklearn.decomposition import PCA\n",
"from sklearn.preprocessing import scale\n",
"\n",
"np.random.seed(42)\n",
"\n",
"digits = load_digits()\n",
"data = scale(digits.data)\n",
"\n",
"n_pca = 10\n",
"pca = PCA(n_components=n_pca).fit(data)\n",
"df = pd.DataFrame(\n",
" pca.transform(data), columns=[\"pca_{}\".format(x) for x in range(n_pca)]\n",
")\n",
"df[\"digit\"] = digits.target\n",
"df[\"test\"] = [np.random.randint(100) for x in range(digits.target.shape[0])]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"feature_vector_distribution(\n",
" df, \"digit\", group_columns=[\"test\"], bins=20, f_lim={\"min\": -7, \"max\": 7}\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": "3.8.2"
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"toc": {
"nav_menu": {},
"number_sections": true,
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"skip_h1_title": false,
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"toc_section_display": "block",
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"nbformat": 4,
"nbformat_minor": 2
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| apache-2.0 |
t-vi/pytorch-tvmisc | misc/gaussian_process_regression_basic.ipynb | 1 | 42930 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"cell_id": "05DE16FF3E9B483788D3B192E7AF6F73"
},
"source": [
"# Gaussian Process Regression in Pytorch\n",
"\n",
"*Thomas Viehmann*, <[email protected]>\n",
"\n",
"Bayesian time!\n",
"\n",
"I could not write a Gaussian Process introduction better than [Rasmussen and Williams in the canonical book](http://www.gaussianprocess.org/gpml/), so you will just follow the link.\n",
"\n",
"This notebook touches selected aspects of Chapter 2 (Regression) and Section 5.4 (Model Selection for GP regression, we use the marginal likelihood).\n",
"\n",
"As usual, the best part is that Pytorch will do the gradients for us.\n",
"\n",
"(Note that the explanations are in no way finished.)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"cell_id": "C56FA1DC11504021833EAF55BEF3E07D"
},
"outputs": [],
"source": [
"from matplotlib import pyplot\n",
"%matplotlib inline\n",
"import IPython\n",
"import torch\n",
"from torch import nn\n",
"from torch.autograd import Variable\n",
"import numpy"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_id": "7D0580E460BC4D258B714A255A53612C"
},
"source": [
"Let's have a very cheap cholesky layer.\n",
"\n",
"If you want to be a hero, write a autograd triangular solve function. I think that would be both more efficient and more stable."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"cell_id": "9BF8B14EE53A47A08E2A7082A831858F"
},
"outputs": [],
"source": [
"class Cholesky(torch.autograd.Function):\n",
" @staticmethod\n",
" def forward(ctx, a):\n",
" l = torch.potrf(a, False)\n",
" ctx.save_for_backward(l)\n",
" return l\n",
" @staticmethod\n",
" def backward(ctx, grad_output):\n",
" l, = ctx.saved_variables\n",
" # Gradient is l^{-H} @ ((l^{H} @ grad) * (tril(ones)-1/2*eye)) @ l^{-1}\n",
" # TODO: ideally, this should use some form of solve triangular instead of inverse...\n",
" linv = l.inverse()\n",
" \n",
" inner = torch.tril(torch.mm(l.t(),grad_output))*torch.tril(1.0-Variable(l.data.new(l.size(1)).fill_(0.5).diag()))\n",
" s = torch.mm(linv.t(), torch.mm(inner, linv))\n",
" # could re-symmetrise \n",
" #s = (s+s.t())/2.0\n",
" \n",
" return s"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_id": "B0752DA711874B3A9E068AB2F952EC8A"
},
"source": [
"So let's do Gaussian Process Regression. We use the basic RBF kernel but with automatic relevance detection and a gaussian likelihood (i.e. noisy data).\n"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"cell_id": "8E992D5F44DA489C89BDEF3E536700EA"
},
"outputs": [],
"source": [
"LOG2PI = numpy.log(2*numpy.pi)\n",
"class ARDRBFKernel(nn.Module):\n",
" def __init__(self, n, lambda0=None, sigma0=None):\n",
" super(ARDRBFKernel, self).__init__()\n",
" self.n = n\n",
" if lambda0 is None:\n",
" lambda0 = torch.ones(n)*0.05\n",
" self.lam = nn.Parameter(lambda0)\n",
" if sigma0 is None:\n",
" sigma0 = torch.ones(1)*0.1\n",
" elif numpy.isscalar(sigma0):\n",
" sigma0 = torch.FloatTensor(1).fill_(sigma0)\n",
" self.sigma = nn.Parameter(sigma0)\n",
" def forward(self, x, x2=None):\n",
" # todo stabilize\n",
" x = x/self.lam.unsqueeze(0).expand_as(x)\n",
" if x2 is None:\n",
" x2 = x\n",
" else:\n",
" x2 = x2/self.lam.unsqueeze(0).expand_as(x2)\n",
" x_ = x.unsqueeze(1).expand(x.size(0),x2.size(0),x.size(1))\n",
" x2_ = x2.unsqueeze(0).expand(x.size(0),x2.size(0),x.size(1))\n",
" sqdists = ((x_-x2_)**2).sum(2) #.squeeze(2)\n",
" return self.sigma.view(1,1).expand_as(sqdists)*(-sqdists/2).exp()\n",
"\n",
"class GP(nn.Module):\n",
" def __init__(self, K, x, y, var0=None):\n",
" super(GP, self).__init__()\n",
" if var0 is None:\n",
" var0 = torch.ones(1)\n",
" elif numpy.isscalar(var0):\n",
" var0 = torch.FloatTensor(1).fill_(var0)\n",
" self.var = nn.Parameter(var0)\n",
" self.x = x\n",
" self.y = y\n",
" self.K = K\n",
" def predict(self, xstar=None):\n",
" Kxx_inv = torch.inverse(self.K(self.x)+Variable(torch.eye(self.x.size(0)))*self.var)\n",
" Kxxstar = self.K(self.x, xstar)\n",
" Kxstarxstar = self.K(xstar)\n",
" mu = torch.mv(Kxxstar, torch.mv(Kxx_inv,self.y))\n",
" cov = torch.Kxstarxstar-torch.mm(Kxxstar.t(), torch.mm(Kxx_inv), Kxxstar)\n",
" return mu, cov\n",
" def forward(self, xstar=None):\n",
" Kxx_noise = self.K(self.x)+Variable(torch.eye(self.x.size(0)))*self.var\n",
" L = Cholesky.apply(Kxx_noise)\n",
" Linv = L.inverse()\n",
" alpha = torch.mv(Kxx_noise.inverse(),self.y)\n",
" if xstar is not None:\n",
" Kxxstar = self.K(self.x, xstar)\n",
" Kxstarxstar = self.K(xstar)\n",
" mu = torch.mv(Kxxstar.t(), alpha)\n",
" v = torch.mm(Linv,Kxxstar)\n",
" cov = Kxstarxstar-torch.mm(v.t(),v)\n",
" #print (self.K.lam.data[0], (0.5*self.y.dot(alpha)).data[0],L.diag().log().sum().data[0],self.y.size(0)/2.0*LOG2PI)\n",
" neg_logp = 0.5*self.y.dot(alpha)+L.diag().log().sum()+self.y.size(0)/2.0*LOG2PI\n",
" if xstar is not None:\n",
" return mu,cov,neg_logp\n",
" else:\n",
" return neg_logp\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_id": "582380B3DDE44415B06F652C6940AF71"
},
"source": [
"Let's have a regression example"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"cell_id": "7D9AA2647C2C4AD6A42AA374EFF07F28"
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fa246b15668>]"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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RtNv2SUl/KWmHJEXEJyTdJOk222cl/VTSzRERY6sYADDQwHCPiDcPeH2/ulMlAQBbBFeo\nYiwajYba7Xb/ebvdVqPRKLAiYHsZxWwZ4ByNRkPz8/NaWFhQlmWSpFqtplarJUmam5srsjxgWyDc\nMXL1el0LCwtqtVqanZ2VJHU6HVUqFdXr9YKrA7YHhmUwcuVyWVmWqVQqqdPpqNPpqFQqKcsylcvl\nossDtgXCHQASRLhj5Nrttmq1Wv+IffkIvlarnXOSFcD4EO4YuWazqVarpUqloqWlJS0tLalSqajV\naqnZbBZdHrAtcEIVI7c8G6Zer/fH2LMsU7PZZKYMMCEu6mLSarUai4uLhewbAKaV7aMRUR3UjmEZ\nAEgQ4Q4ACSLcASBBhDsAJIhwB4AEFTZbxnZH0vcl7Zb0w0KK2Dq2ex9s988v0QcSfSDl64PLI2Lg\nrewKC/d+AfZinmk9KdvufbDdP79EH0j0gTTaPmBYBgASRLgDQIK2QrgfKLqALWC798F2//wSfSDR\nB9II+6DwMXcAwOhthSN3AMCITSzcbV9n+zHbJ2y/b43XX2D77t7rR2zPTKq2Scjx+d9ju2X7Edv/\nZvvyIuocp0F9sKLdTbbDdnIzJ/L0ge0/7H0vPGr7nydd47jl+FnYazuz/a3ez8P1RdQ5LrY/Zbtt\ne2md1237473+ecT2qze0o4gY+0PSBZKekPRSSc+X9LCkyqo275T0id72zZLunkRtW+jz1yT9Um/7\ntpQ+f94+6LW7SNJXJT0oqVp03QV8H1wh6VuSfqX3vFx03QX0wQFJt/W2K5K+V3TdI+6D35b0aklL\n67x+vaT7JVnS1ZKObGQ/kzpyv0rSiYj4bkT8r6TPS7pxVZsbJd3V275H0utte0L1jdvAzx8RWUT8\nT+/pg5Ium3CN45bne0CS/krS30j62SSLm5A8ffDHkhoR8SNJiojUbl2Vpw9C0i/3tl8s6QcTrG/s\nIuKrkp4+T5MbJX06uh6UdLHtS4bdz6TC/VJJT654frL3tTXbRMRZSacl7ZpIdeOX5/OvdIu6v7lT\nMrAPbF8paU9E3DvJwiYoz/fByyW93PY3bD9o+7qJVTcZefrgA5LeYvukpPsk/clkStsyhs2LNU3q\nTkxrHYGvnqaTp820yv3ZbL9FUlXS74y1osk7bx/Yfp6kj0l6x6QKKkCe74ML1R2auUbdv96+Zns2\nIn485tomJU8fvFnSP0bE39p+jaTP9Prg/8Zf3pYwkiyc1JH7SUl7Vjy/TM/9U6vfxvaF6v45dr4/\nXaZJns8v22+Q9H5JN0TEsxOqbVIG9cFFkmYlHbb9PXXHGg8ldlI178/Bv0TEzyPiPyU9pm7YpyJP\nH9wi6aAkRcS/S3qhumuubBe58mKQSYX7f0i6wvav2n6+uidMD61qc0jS23vbN0n6SvTOLiRg4Ofv\nDUn8g7rBnto4qzSgDyLidETsjoiZiJhR97zDDRGR0r0Y8/wcfEndk+uyvVvdYZrvTrTK8crTB/8l\n6fWSZPvX1A33zkSrLNYhSW/rzZq5WtLpiHhq6HeZ4Bni6yU9ru6Z8vf3vnaHuj/AUvd/YFPSCUnf\nlPTSos9qT/jzf1nSf0s61nscKrrmSffBqraHldhsmZzfB5b0UUktSd+WdHPRNRfQBxVJ31B3Js0x\nSb9bdM0j/vyfk/SUpJ+re5R+i6RbJd264nug0eufb2/054ArVAEgQVyhCgAJItwBIEGEOwAkiHAH\ngAQR7gCQIMIdABJEuANAggh3AEjQ/wODCPxB2Js5WQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa24709a780>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"N = 12\n",
"X = torch.rand(N,1)\n",
"Y = (torch.sin(12*X) + 0.6*torch.cos(25*X) + torch.randn(N,1)*0.1 + 3).squeeze(1)\n",
"pyplot.figure()\n",
"pyplot.plot(X.numpy(), Y.numpy(), 'kx', mew=2)\n"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"cell_id": "37D848D2C2374C19894E433C3C5F1A58",
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final loss 15.55954647064209\n"
]
}
],
"source": [
"K = ARDRBFKernel(1)\n",
"gp = GP(K, Variable(X), Variable(Y), var0=0.1)\n",
"\n",
"def closure():\n",
" opt.zero_grad()\n",
" nlogp = gp.forward()\n",
" nlogp.backward()\n",
" return nlogp\n",
"\n",
"opt = torch.optim.LBFGS(gp.parameters(), lr=0.1)\n",
"for i in range(10):\n",
" opt.zero_grad()\n",
" nlogp = gp.forward()\n",
" nlogp.backward()\n",
" opt.step(closure)\n",
"print (\"Final loss\",nlogp.data[0])"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_id": "EF88856D8CAF446380AC9BFEADCE4647"
},
"source": [
"And we can plot with the credible interval"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"cell_id": "0912147330F74C12B0E6E1E38FBD8C60"
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fa244e59240>]"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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bWMa6Zs0arFmzZjzFdvbZS7BlSzY0mgFceqn9/aegoCDMnz8f4eHh7r4EhISE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\ncJekAhnuklSgH2s1VHaHhI0nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa246ed8dd8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"xstar = torch.linspace(0,1,1000)\n",
"mu, vsq, nll = gp.forward(xstar=Variable(xstar.unsqueeze(1)))\n",
"cred_size = vsq.diag()**0.5*2\n",
"pyplot.plot(xstar.numpy(),mu.data.numpy(),'b')\n",
"pyplot.fill_between(xstar.numpy(),mu.data.numpy()+cred_size.data.numpy(), mu.data.numpy()-cred_size.data.numpy(),facecolor='0.75')\n",
"pyplot.plot(X.numpy(), Y.numpy(), 'kx', mew=2)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_id": "B2A048522FDA4F3C8666F773BA8F809F",
"collapsed": true
},
"source": [
"I appreciate your feedback at <[email protected]>."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cell_id": "513CA294C85944AC9417C7C16DCEB8EC"
},
"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": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
thewtex/TubeTK | examples/CTP-Head/3-CreateBinaryVesselsFromCTPCTA.ipynb | 1 | 12798 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook is intended to demonstrate how select registration, segmentation, and image mathematical methods of ITKTubeTK can be combined to perform multi-channel brain extraction (aka. skull stripping for patient data containing multiple MRI sequences).\n",
"\n",
"There are many other (probably more effective) brain extraction methods available as open-source software such as BET and BET2 in the FSL package (albeit such methods are only for single channel data). If you need to perform brain extraction for a large collection of scans that do not contain major pathologies, please use one of those packages. This notebook is meant to show off the capabilities of specific ITKTubeTK methods, not to demonstration how to \"solve\" brain extraction."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import itk\n",
"from itk import TubeTK as ttk\n",
"\n",
"from itkwidgets import view\n",
"\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"InputBaseDir = \"../Data/CTP-MinMax/\"\n",
"\n",
"CTPMaxFilename = InputBaseDir + \"max3.mha\"\n",
"CTPMinFilename = InputBaseDir + \"min3.mha\"\n",
"CTPBrainFilename = InputBaseDir + \"max3-Brain.mha\"\n",
"\n",
"imMax = itk.imread(CTPMaxFilename, itk.F)\n",
"imMin = itk.imread(CTPMinFilename, itk.F)\n",
"imBrain = itk.imread(CTPBrainFilename, itk.F)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2130778f5f2d4b17aa7e2da544406563",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Viewer(geometries=[], gradient_opacity=0.22, point_sets=[], rendered_image=<itk.itkImagePython.itkImageF3; pro…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"view(imBrain)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"ImageType = itk.Image[itk.F, 3]\n",
"\n",
"imMath = ttk.ImageMath.New(Input=imBrain)\n",
"imMath.Threshold( 0.00001, 2000, 1, 0)\n",
"imMath.Erode(10,1,0)\n",
"imBrainMaskErode = imMath.GetOutput()\n",
"\n",
"imMath.SetInput(imMax)\n",
"imMath.AddImages(imMin,1,-1)\n",
"imDiff = imMath.GetOutput()\n",
"imMath.ReplaceValuesOutsideMaskRange(imBrain, 0.0001, 2000, 0)\n",
"imDiffBrain = imMath.GetOutput()\n",
"imMath.ReplaceValuesOutsideMaskRange(imBrainMaskErode, 0.5, 1.5, 0)\n",
"imDiffBrainErode = imMath.GetOutput()\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 590.79504\n"
]
}
],
"source": [
"tmpA = itk.GetArrayViewFromImage(imDiffBrain)\n",
"tmpAE = itk.GetArrayViewFromImage(imDiffBrainErode)\n",
"zMax = tmpA.shape[0]\n",
"clip = 0\n",
"while((np.amax(tmpA[clip:clip+1,:,:])>1000) | (np.amax(tmpA[clip:clip+1,:,:])==0)):\n",
" clip += 1\n",
"if(clip>0):\n",
" tmpA[0:clip,:,:]=0\n",
" tmpAE[0:clip,:,:]=0\n",
"clip = 1\n",
"while((np.amax(tmpA[zMax-clip:zMax-clip+1,:,:])>1000) | (np.amax(tmpA[zMax-clip:zMax-clip+1,:,:])==0)):\n",
" clip += 1\n",
"print(clip, np.amax(tmpA[zMax-clip:zMax-clip+1,:,:]))\n",
"clip = clip - 1\n",
"if(clip>0):\n",
" tmpA[zMax-clip:zMax,:,:]=0 #Happens to imDiffBrain since this array is a view of an itk image\n",
" tmpAE[zMax-clip:zMax,:,:]=0 #Happens to imDiffBrain since this array is a view of an itk image"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8273ce1297bf48f7bb5815995a744c44",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Viewer(geometries=[], gradient_opacity=0.22, point_sets=[], rendered_image=<itk.itkImagePython.itkImageF3; pro…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"view(imDiffBrain)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ -4.23602295 -145.46159363 296.73384094]\n",
" [ -33.55116272 -160.39572144 298.94630432]\n",
" [ -52.35710144 -206.85745239 296.73384094]\n",
" [ -25.80754089 -144.90847778 278.48101807]\n",
" [ -48.48529053 -193.02955627 305.03057861]\n",
" [ 0.74201965 -167.03311157 264.1000061 ]\n",
" [ -45.16659546 -175.88296509 317.75224304]\n",
" [ -5.34225464 -133.29304504 307.24304199]\n",
" [ -58.44137573 -155.97079468 292.86203003]\n",
" [ -6.44848633 -155.41767883 264.1000061 ]\n",
" [ -43.50724792 -140.48355103 327.70832825]\n",
" [ 26.18534851 -133.29304504 285.1184082 ]\n",
" [ -46.82594299 -157.63014221 276.82167053]\n",
" [ -18.61703491 -128.86811829 290.64956665]\n",
" [ -31.33869934 -128.86811829 297.28695679]]\n"
]
}
],
"source": [
"imMath = ttk.ImageMath[ImageType,ImageType].New()\n",
"imMath.SetInput(imDiffBrainErode)\n",
"imMath.Blur(1.5)\n",
"imBlur = imMath.GetOutput()\n",
"imBlurArray = itk.GetArrayViewFromImage(imBlur)\n",
"\n",
"numSeeds = 15\n",
"seedCoverage = 20\n",
"seedCoord = np.zeros([numSeeds,3])\n",
"for i in range(numSeeds):\n",
" seedCoord[i] = np.unravel_index(np.argmax(imBlurArray, axis=None), imBlurArray.shape)\n",
" indx = [int(seedCoord[i][0]),int(seedCoord[i][1]),int(seedCoord[i][2])]\n",
" minX = max(indx[0]-seedCoverage,0)\n",
" maxX = max(indx[0]+seedCoverage,imBlurArray.shape[0])\n",
" minY = max(indx[1]-seedCoverage,0)\n",
" maxY = max(indx[1]+seedCoverage,imBlurArray.shape[1])\n",
" minZ = max(indx[2]-seedCoverage,0)\n",
" maxZ = max(indx[2]+seedCoverage,imBlurArray.shape[2])\n",
" imBlurArray[minX:maxX,minY:maxY,minZ:maxZ]=0\n",
" indx.reverse()\n",
" seedCoord[:][i] = imDiffBrain.TransformIndexToPhysicalPoint(indx)\n",
"print(seedCoord)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"**** Processing seed 0 : [ -4.23602295 -145.46159363 296.73384094]\n",
"**** Processing seed 1 : [ -33.55116272 -160.39572144 298.94630432]\n",
"**** Processing seed 2 : [ -52.35710144 -206.85745239 296.73384094]\n",
"**** Processing seed 3 : [ -25.80754089 -144.90847778 278.48101807]\n",
"**** Processing seed 4 : [ -48.48529053 -193.02955627 305.03057861]\n",
"**** Processing seed 5 : [ 0.74201965 -167.03311157 264.1000061 ]\n",
"**** Processing seed 6 : [ -45.16659546 -175.88296509 317.75224304]\n",
"**** Processing seed 7 : [ -5.34225464 -133.29304504 307.24304199]\n",
"**** Processing seed 8 : [ -58.44137573 -155.97079468 292.86203003]\n",
"**** Processing seed 9 : [ -6.44848633 -155.41767883 264.1000061 ]\n",
"**** Processing seed 10 : [ -43.50724792 -140.48355103 327.70832825]\n",
"**** Processing seed 11 : [ 26.18534851 -133.29304504 285.1184082 ]\n",
"**** Processing seed 12 : [ -46.82594299 -157.63014221 276.82167053]\n",
"**** Processing seed 13 : [ -18.61703491 -128.86811829 290.64956665]\n",
"**** Processing seed 14 : [ -31.33869934 -128.86811829 297.28695679]\n"
]
}
],
"source": [
"# Manually extract a few vessels to form an image-specific training set\n",
"vSeg = ttk.SegmentTubes.New(Input=imDiffBrain)\n",
"vSeg.SetVerbose(True)\n",
"vSeg.SetMinRoundness(0.4)\n",
"vSeg.SetMinCurvature(0.002)\n",
"vSeg.SetRadiusInObjectSpace( 1 )\n",
"for i in range(numSeeds):\n",
" print(\"**** Processing seed \" + str(i) + \" : \" + str(seedCoord[i]))\n",
" vSeg.ExtractTubeInObjectSpace( seedCoord[i], i )\n",
" \n",
"tubeMaskImage = vSeg.GetTubeMaskImage()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6cf4e97b23284babbbea303ca890486a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Viewer(geometries=[], gradient_opacity=0.22, point_sets=[], rendered_image=<itk.itkImagePython.itkImageF3; pro…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"imMath.SetInput(tubeMaskImage)\n",
"imMath.AddImages(imDiffBrain, 200, 1)\n",
"blendIm = imMath.GetOutput()\n",
"view(blendIm)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"LabelMapType = itk.Image[itk.UC,3]\n",
"\n",
"trMask = ttk.ComputeTrainingMask[ImageType,LabelMapType].New()\n",
"trMask.SetInput( tubeMaskImage )\n",
"trMask.SetGap( 4 )\n",
"trMask.SetObjectWidth( 1 )\n",
"trMask.SetNotObjectWidth( 1 )\n",
"trMask.Update()\n",
"fgMask = trMask.GetOutput()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1c076e7382c44780af543f9ac8aa9882",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Viewer(geometries=[], gradient_opacity=0.22, point_sets=[], rendered_image=<itk.itkImagePython.itkImageUC3; pr…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"view(fgMask)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"enhancer = ttk.EnhanceTubesUsingDiscriminantAnalysis[ImageType,LabelMapType].New()\n",
"enhancer.AddInput( imDiff )\n",
"enhancer.SetLabelMap( fgMask )\n",
"enhancer.SetRidgeId( 255 )\n",
"enhancer.SetBackgroundId( 128 )\n",
"enhancer.SetUnknownId( 0 )\n",
"enhancer.SetTrainClassifier(True)\n",
"enhancer.SetUseIntensityOnly(True)\n",
"enhancer.SetScales([0.43,1.29,3.01])\n",
"enhancer.Update()\n",
"enhancer.ClassifyImages()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4c4cced2556c430d970025baaaad9545",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Viewer(geometries=[], gradient_opacity=0.22, point_sets=[], rendered_image=<itk.itkImagePython.itkImageF3; pro…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"im1vess = itk.SubtractImageFilter( Input1=enhancer.GetClassProbabilityImage(0), Input2=enhancer.GetClassProbabilityImage(1))\n",
"\n",
"imMath.SetInput(imDiffBrain)\n",
"imMath.Threshold(0.0001,2000,1,0)\n",
"imMath.Erode(2,1,0)\n",
"imBrainE = imMath.GetOutput()\n",
"\n",
"imMath.SetInput(im1vess)\n",
"imMath.ReplaceValuesOutsideMaskRange(imBrainE, 1, 1, -0.001)\n",
"im1vessBrain = imMath.GetOutput()\n",
"#view(enhancer.GetClassProbabilityImage(0))\n",
"view(im1vessBrain)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"itk.imwrite( im1vess, InputBaseDir + \"diff3-VesselEnhanced.mha\", compression=True)\n",
"\n",
"itk.imwrite( im1vessBrain, InputBaseDir + \"diff3-Brain-VesselEnhanced.mha\", compression=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
alandschaft/DataScienceMeetup | Medical_Text_Classification.ipynb | 1 | 292325 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"source": [
"# Medical Text Classification with IPython Notebook\n",
"\n",
"The purpose of this notebook is to show a simple medical text classification workflow using IPython notebook."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Standard imports and settings"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib as mpl\n",
"mpl.rcParams['font.size'] = 16.0\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd # process data with pandas dataframe\n",
"pd.set_option('display.width', 500)\n",
"pd.set_option('display.max_columns', 100)\n",
"pd.set_option('display.notebook_repr_html', True)\n",
"import seaborn as sns\n",
"sns.set_style(\"whitegrid\")\n",
"sns.set_context(\"poster\")\n",
"import time\n",
"import os"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Few words about IPython Notebook\n",
"\n",
"**Some of the main features of the IPython Notebook app include:**\n",
"\n",
"* In-browser editing for code, with automatic syntax highlighting, indentation, and tab completion.\n",
"* The ability to execute code from the browser, with the results of computations attached to the code which generated them.\n",
"* Displaying the result of computation using rich media representations, such as HTML, LaTeX, PNG, SVG, etc, up to publication-quality figures content.\n",
"* In-browser editing for rich text using the Markdown markup language, which can provide commentary for the code.\n",
"* The ability to easily include mathematical notation within markdown cells using LaTeX."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"**Personal advantages for my projects:**\n",
"\n",
"* Great support for ** Interpretable Data Science ** - This greatly contributes to the process of harnessing the medical expertise of the users to train the algorithms better. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Architecture of IPython notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"images/ipython_architecture.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The data \n",
"\n",
"The **Ohsumed** test collection (available at <ftp://medir.ohsu.edu/pub/ohsumed>) is a subset of the MEDLINE database, which is a bibliographic database of important, peer-reviewed medical literature maintained by the National Library of Medicine. The initial subset I consider in the project is the collection consisting of the first 20,000 documents from the 50,216 medical abstracts of the year 1991. The classification scheme consists of the 23 Medical Subject Headings (MeSH) categories of cardiovascular diseases group: \n",
"\n",
"| Category | Description | \n",
"| ------------- | ------------- |\n",
"| C01 | Bacterial Infections and Mycoses |\n",
"| C02 | Virus Diseases |\n",
"| C03 | Parasitic Diseases |\n",
"| C04 | Neoplasms |\n",
"| C05 | Musculoskeletal Diseases |\n",
"| C06 | Digestive System Diseases |\n",
"| C07 | Stomatognathic Diseases |\n",
"| C08 | Respiratory Tract Diseases |\n",
"| C09 | Otorhinolaryngologic Diseases |\n",
"| C10 | Nervous System Diseases |\n",
"| C11 | Eye Diseases |\n",
"| C12 | Urologic and Male Genital Diseases |\n",
"| C13 | Female Genital Diseases and Pregnancy Complications |\n",
"| C14 | Cardiovascular Diseases |\n",
"| C15 | Hemic and Lymphatic Diseases |\n",
"| C16 | Neonatal Diseases and Abnormalities |\n",
"| C17 | Skin and Connective Tissue Diseases |\n",
"| C18 | Nutritional and Metabolic Diseases |\n",
"| C19 | Endocrine Diseases |\n",
"| C20 | Immunologic Diseases |\n",
"| C21 | Disorders of Environmental Origin |\n",
"| C22 | Animal Diseases |\n",
"| C23 | Pathological Conditions, Signs and Symptoms |\n",
"\n",
"\n",
"## Downloading the data:\n",
"\n",
"* [Cardiovascular diseases abstracts](http://disi.unitn.it/moschitti/corpora/ohsumed-first-20000-docs.tar.gz) (the first 20,000 abstracts of the year 1991)\n",
"\n",
"** The following code assumes that the data is extracted into folder with the name 'Data' in the same folder of the IPython notebook**\n",
"\n",
"## Storing the data in a sql database\n",
"\n",
"The following code iterates over the extracted data and converts the ohsumed directory structure to a single sqlite databse with the data. The original dataset is already divided to Test and Training datasets. We will keep this division."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import sqlite3 as sqlite\n",
"\n",
"def get_all_tables(c):\n",
" \"\"\"\n",
" Helper function - Gets a list of all the tables in the database.\n",
" \"\"\"\n",
" all_tables = []\n",
" c.execute('SELECT name FROM SQLITE_MASTER WHERE type = \"table\"')\n",
" for tbl in c:\n",
" all_tables.append(tbl[0])\n",
" return all_tables\n",
"\n",
"def drop_tables(c, tables):\n",
" \"\"\"\n",
" Helper function - Checks that the specified tables exist, and for those that do, drop them.\n",
" \"\"\"\n",
" all_tables = get_all_tables(c)\n",
" for t in tables:\n",
" if t in all_tables:\n",
" c.execute('DROP TABLE %s' % t)\n",
"\n",
"def create_documents_table(c):\n",
" \"\"\"\n",
" Helper function - This function uses SQL to create the Documents table\n",
" \"\"\"\n",
" drop_tables(c, [ 'Documents' ])\n",
" c.execute(\"\"\"CREATE TABLE Documents ( \n",
" DOCID TEXT PRIMARY KEY,\n",
" NOTE_TEXT TEXT, \n",
" CATEGORY TEXT\n",
" )\"\"\")\n",
"\n",
"def add_document(c, docid, text, category):\n",
" \"\"\"\n",
" Helper function - Adds one document to sql Documents database\n",
" \"\"\"\n",
" c.execute('insert or replace into Documents values ( ?, ?, ? )', (docid, text, category))\n",
"\n",
"def ohsumed2sqlite(src_root_dir, dest_sqlite):\n",
" start_time = time.time()\n",
" print 'Converting ohsumed directory structure {0} to sqlite database'.format(src_root_dir)\n",
"\n",
" conn_out = sqlite.connect(dest_sqlite)\n",
" c_out = conn_out.cursor()\n",
"\n",
" fout = open(dest_sqlite, 'w')\n",
" fout.close()\n",
"\n",
" create_documents_table(c_out)\n",
" # Process the ohsumed directory\n",
" dict = {}\n",
" for root, dirs, files in os.walk(src_root_dir):\n",
" for f in files:\n",
" category = os.path.basename(root) # directory name is the category\n",
" with open (os.path.join(root, f), \"r\") as cur_file:\n",
" data=cur_file.read()\n",
" if f in dict:\n",
" category = dict[f] + ',' + category\n",
" dict[f] = category\n",
" add_document(c_out, f, data, category)\n",
" conn_out.commit()\n",
" c_out.close()\n",
" print(\"--- ohsumed2sqlite %s seconds ---\" % (time.time() - start_time))"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Converting ohsumed directory structure .\\Data\\ohsumed-first-20000-docs\\training to sqlite database\n",
"--- ohsumed2sqlite 1.50699996948 seconds ---\n",
"Converting ohsumed directory structure .\\Data\\ohsumed-first-20000-docs\\test to sqlite database\n",
"--- ohsumed2sqlite 1.83299994469 seconds ---\n"
]
}
],
"source": [
"# Convert the training data\n",
"ohsumed2sqlite('.\\\\Data\\\\ohsumed-first-20000-docs\\\\training', 'training.sqlite')\n",
"\n",
"# Convert the test data\n",
"ohsumed2sqlite('.\\\\Data\\\\ohsumed-first-20000-docs\\\\test', 'test.sqlite')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data exploration and preparation \n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Read the data from the sqlite databases into Pandas dataframes"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"con = sqlite.connect('training.sqlite')\n",
"\n",
"df_train = pd.read_sql_query(\"SELECT * from Documents\", con)\n",
"\n",
"con.close()\n",
"\n",
"con = sqlite.connect('test.sqlite')\n",
"\n",
"df_test = pd.read_sql_query(\"SELECT * from Documents\", con)\n",
"\n",
"con.close()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Output a random sample of 20 records"
]
},
{
"cell_type": "code",
"execution_count": 65,
"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>DOCID</th>\n",
" <th>NOTE_TEXT</th>\n",
" <th>CATEGORY</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1919</th>\n",
" <td>0004299</td>\n",
" <td>Sensitivity of digoxigenin and biotin labelled probes for detection of human papillomavirus by in situ hybridisation.\\n The sensitivity of digoxigenin and biotin labelled DNA probes for the detect...</td>\n",
" <td>C02,C04,C09,C13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3197</th>\n",
" <td>0000531</td>\n",
" <td>Absence of brown product FFI in nondiabetic and diabetic rat collagen.\\n Accumulation of brown products in long-lived proteins might be an important factor in determining long-term diabetic compli...</td>\n",
" <td>C18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3861</th>\n",
" <td>0007389</td>\n",
" <td>Immunoblastic T-cell lymphoma presenting as an eyelid tumor.\\n A 59-year-old white man presented with an ulcerating mass of the left upper eyelid of 6 months' duration.\\n A biopsy specimen of the ...</td>\n",
" <td>C04,C20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1791</th>\n",
" <td>0007955</td>\n",
" <td>The prevalence of subfertility: a review of the current confusion and a report of two new studies.\\n The difficulties inherent in measuring the prevalence of subfertility are discussed.\\n Four sub...</td>\n",
" <td>C12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>714</th>\n",
" <td>0000565</td>\n",
" <td>Phospholipids from rat, human, and canine gastric mucosa. Composition and metabolism of molecular classes of phosphatidylcholine.\\n To validate a recent proposal that a phospholipid lining with a ...</td>\n",
" <td>C06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>0005909</td>\n",
" <td>Early postoperative care of the cardiac transplantation patient: routine considerations and immunosuppressive therapy.\\n The authors have attempted to outline the current state of the art with res...</td>\n",
" <td>C01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5171</th>\n",
" <td>0004416</td>\n",
" <td>Serial left ventricular performance evaluated by cardiac catheterization before, immediately after and at 6 months after balloon aortic valvuloplasty.\\n Although impaired ventricular function has ...</td>\n",
" <td>C14,C23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5828</th>\n",
" <td>0007682</td>\n",
" <td>Longitudinal study of diagnoses in children of women with unipolar and bipolar affective disorder.\\n School-age children of unipolar depressed, bipolar, chronically medically ill, or normal women ...</td>\n",
" <td>C23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3193</th>\n",
" <td>0000521</td>\n",
" <td>Association of elevated fasting C-peptide level and increased intra-abdominal fat distribution with development of NIDDM in Japanese-American men.\\n The Japanese-American population of King County...</td>\n",
" <td>C18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5269</th>\n",
" <td>0005114</td>\n",
" <td>Pressure threshold for shock wave induced renal hemorrhage.\\n Studies were performed with an interest in determining a pressure threshold for extracorporeal shock wave induced renal damage.\\n Hist...</td>\n",
" <td>C12,C23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2428</th>\n",
" <td>0005912</td>\n",
" <td>Brucella endocarditis: the role of combined medical and surgical treatment.\\n Brucella endocarditis, although a rare complication of brucellosis, is the main cause of death related to this disease...</td>\n",
" <td>C01,C14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3818</th>\n",
" <td>0006685</td>\n",
" <td>Prehospital administration of inhaled metaproterenol.\\n STUDY OBJECTIVES: We conducted a study of the prehospital use of inhaled metaproterenol.\\n DESIGN, SETTING, TYPE OF PARTICIPANTS, AND INTERV...</td>\n",
" <td>C08,C20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5665</th>\n",
" <td>0006905</td>\n",
" <td>Squamous carcinoma metastatic to the sternum.\\n A 63-year-old man had a 10 x 16-cm sternal mass 18 months after a second aortocoronary bypass operation.\\n The resected lesion was a metastatic tumo...</td>\n",
" <td>C04,C23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3247</th>\n",
" <td>0003370</td>\n",
" <td>Early detection and treatment of hyperlipidemia: physician practices in Canada.\\n We surveyed primary care physicians in Canada to determine their current practices regarding the detection and tre...</td>\n",
" <td>C14,C18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5230</th>\n",
" <td>0004849</td>\n",
" <td>Subarachnoid hemorrhage caused by a fungal aneurysm of the vertebral artery as a complication of intracranial aneurysm clipping. Case report.\\n Intracranial aneurysms are an uncommon manifestation...</td>\n",
" <td>C01,C10,C14,C23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1502</th>\n",
" <td>0009007</td>\n",
" <td>Changes in thermal and mechanical pain thresholds in hand amputees. A clinical and physiological long-term follow-up.\\n In a previous study, allodynia to cold and vibratory stimuli was found in th...</td>\n",
" <td>C10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5565</th>\n",
" <td>0006590</td>\n",
" <td>Adverse haemodynamic effects of high-dose aprotinin in a paediatric cardiac surgical patient.\\n High-dose aprotinin for reduction of intra- and postoperative blood loss was associated with profoun...</td>\n",
" <td>C14,C23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>903</th>\n",
" <td>0008463</td>\n",
" <td>Primary sclerosing cholangitis.\\n Primary sclerosing cholangitis is a rare disease of unknown etiology.\\n Sclerosis of the bile ducts may actually be the final result of multiple factors such as a...</td>\n",
" <td>C06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>380</th>\n",
" <td>0005157</td>\n",
" <td>Prospective study of estrogen replacement therapy and risk of breast cancer in postmenopausal women [published erratum appears in JAMA 1991 Apr 10;265(14):1828] \\n We prospectively examined the us...</td>\n",
" <td>C04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3246</th>\n",
" <td>0003265</td>\n",
" <td>The electroretinogram in minimal diabetic retinopathy.\\n The pattern and diffuse flash electroretinograms were measured in 20 normal subjects and 40 diabetic patients who had either normal fundi o...</td>\n",
" <td>C18</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DOCID NOTE_TEXT CATEGORY\n",
"1919 0004299 Sensitivity of digoxigenin and biotin labelled probes for detection of human papillomavirus by in situ hybridisation.\\n The sensitivity of digoxigenin and biotin labelled DNA probes for the detect... C02,C04,C09,C13\n",
"3197 0000531 Absence of brown product FFI in nondiabetic and diabetic rat collagen.\\n Accumulation of brown products in long-lived proteins might be an important factor in determining long-term diabetic compli... C18\n",
"3861 0007389 Immunoblastic T-cell lymphoma presenting as an eyelid tumor.\\n A 59-year-old white man presented with an ulcerating mass of the left upper eyelid of 6 months' duration.\\n A biopsy specimen of the ... C04,C20\n",
"1791 0007955 The prevalence of subfertility: a review of the current confusion and a report of two new studies.\\n The difficulties inherent in measuring the prevalence of subfertility are discussed.\\n Four sub... C12\n",
"714 0000565 Phospholipids from rat, human, and canine gastric mucosa. Composition and metabolism of molecular classes of phosphatidylcholine.\\n To validate a recent proposal that a phospholipid lining with a ... C06\n",
"60 0005909 Early postoperative care of the cardiac transplantation patient: routine considerations and immunosuppressive therapy.\\n The authors have attempted to outline the current state of the art with res... C01\n",
"5171 0004416 Serial left ventricular performance evaluated by cardiac catheterization before, immediately after and at 6 months after balloon aortic valvuloplasty.\\n Although impaired ventricular function has ... C14,C23\n",
"5828 0007682 Longitudinal study of diagnoses in children of women with unipolar and bipolar affective disorder.\\n School-age children of unipolar depressed, bipolar, chronically medically ill, or normal women ... C23\n",
"3193 0000521 Association of elevated fasting C-peptide level and increased intra-abdominal fat distribution with development of NIDDM in Japanese-American men.\\n The Japanese-American population of King County... C18\n",
"5269 0005114 Pressure threshold for shock wave induced renal hemorrhage.\\n Studies were performed with an interest in determining a pressure threshold for extracorporeal shock wave induced renal damage.\\n Hist... C12,C23\n",
"2428 0005912 Brucella endocarditis: the role of combined medical and surgical treatment.\\n Brucella endocarditis, although a rare complication of brucellosis, is the main cause of death related to this disease... C01,C14\n",
"3818 0006685 Prehospital administration of inhaled metaproterenol.\\n STUDY OBJECTIVES: We conducted a study of the prehospital use of inhaled metaproterenol.\\n DESIGN, SETTING, TYPE OF PARTICIPANTS, AND INTERV... C08,C20\n",
"5665 0006905 Squamous carcinoma metastatic to the sternum.\\n A 63-year-old man had a 10 x 16-cm sternal mass 18 months after a second aortocoronary bypass operation.\\n The resected lesion was a metastatic tumo... C04,C23\n",
"3247 0003370 Early detection and treatment of hyperlipidemia: physician practices in Canada.\\n We surveyed primary care physicians in Canada to determine their current practices regarding the detection and tre... C14,C18\n",
"5230 0004849 Subarachnoid hemorrhage caused by a fungal aneurysm of the vertebral artery as a complication of intracranial aneurysm clipping. Case report.\\n Intracranial aneurysms are an uncommon manifestation... C01,C10,C14,C23\n",
"1502 0009007 Changes in thermal and mechanical pain thresholds in hand amputees. A clinical and physiological long-term follow-up.\\n In a previous study, allodynia to cold and vibratory stimuli was found in th... C10\n",
"5565 0006590 Adverse haemodynamic effects of high-dose aprotinin in a paediatric cardiac surgical patient.\\n High-dose aprotinin for reduction of intra- and postoperative blood loss was associated with profoun... C14,C23\n",
"903 0008463 Primary sclerosing cholangitis.\\n Primary sclerosing cholangitis is a rare disease of unknown etiology.\\n Sclerosis of the bile ducts may actually be the final result of multiple factors such as a... C06\n",
"380 0005157 Prospective study of estrogen replacement therapy and risk of breast cancer in postmenopausal women [published erratum appears in JAMA 1991 Apr 10;265(14):1828] \\n We prospectively examined the us... C04\n",
"3246 0003265 The electroretinogram in minimal diabetic retinopathy.\\n The pattern and diffuse flash electroretinograms were measured in 20 normal subjects and 40 diabetic patients who had either normal fundi o... C18"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.options.display.max_colwidth = 200\n",
"\n",
"df_train.sample(20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creating binary classification dataset\n",
"\n",
"** As we see, each document is assigned one class or more. ** In this exercise I would like to implement a simple binary classification workflow, so I define 2 binary classes:\n",
"* **Positive / True** - Documents that belong to class **C14 - Cardiovascular Diseases**\n",
"* **Negative / False** - Documents that do not belong to C14\n",
"\n",
"The following code converts that data to a binary classification dataset:\n"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import re\n",
"\n",
"def ConvertCategoryColToBinVal(df, poscat):\n",
" df['CATEGORY'] = df['CATEGORY'].apply(lambda x: bool(re.search(poscat, x, re.IGNORECASE) and True))\n",
" \n",
"ConvertCategoryColToBinVal(df_train, 'C14')\n",
"\n",
"ConvertCategoryColToBinVal(df_test, 'C14')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's take another look at the data:"
]
},
{
"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>DOCID</th>\n",
" <th>NOTE_TEXT</th>\n",
" <th>CATEGORY</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6060</th>\n",
" <td>0008845</td>\n",
" <td>Causes, diagnosis, and treatment of pharyngitis.\\n Pharyngitis is a common disease of the respiratory tract that can be caused by several different viruses and bacterial organisms.\\n Clinically sp...</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>978</th>\n",
" <td>0005872</td>\n",
" <td>Secondary correction of the unilateral cleft lip nose using a conchal composite graft.\\n The secondary deformity of the unilateral cleft lip nose has many components.\\n One is the dorsal dislocati...</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3655</th>\n",
" <td>0003185</td>\n",
" <td>Ability of anti-HIV agents to inhibit HIV replication in monocyte/macrophages or U937 monocytoid cells under conditions of enhancement by GM-CSF or anti-HIV antibody.\\n Monocyte/macrophages (M/M) ...</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4045</th>\n",
" <td>0002256</td>\n",
" <td>Alcohol and trauma. An endemic syndrome.\\n Injuries are a pervasive and costly problem, and alcohol use appears to be an important risk factor for injury.\\n We examined the blood alcohol levels an...</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2447</th>\n",
" <td>0006180</td>\n",
" <td>Dose-dependent reduction of myocardial infarct size with the perfluorochemical Fluosol-DA.\\n The perfluorochemical Fluosol-DA has been shown to reduce infarct size.\\n However, the dose-response re...</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3284</th>\n",
" <td>0004261</td>\n",
" <td>Clinical review 16: Parathyroid hormone-related proteins: coming of age in the 1990s.\\n The last 3 yr have yielded a fertile harvest of new information on the HHM clinical syndrome and on the nove...</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4031</th>\n",
" <td>0000997</td>\n",
" <td>Treatment of phenobarbital poisoning with multiple dose activated charcoal in an infant.\\n A 28-day-old infant developed lethargy, hypotonia, and hypothermia following a phenobarbital overdose sec...</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5510</th>\n",
" <td>0006332</td>\n",
" <td>Clinicopathologic features and long-term results of alpha-fetoprotein-producing gastric cancer.\\n During a 10-yr-period, 24 cases of alpha-fetoprotein-producing gastric cancer were experienced in ...</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1653</th>\n",
" <td>0002449</td>\n",
" <td>Geriatric pharmacokinetics and the kidney.\\n The general population is aging and, as a result, drugs are increasingly prescribed for a variety of medical conditions in a group of patients with mul...</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4737</th>\n",
" <td>0002332</td>\n",
" <td>Changes in circulating norepinephrine with hemofiltration in advanced congestive heart failure.\\n In congestive heart failure (CHF), hemofiltration is associated with an obvious decrease in circul...</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5448</th>\n",
" <td>0006046</td>\n",
" <td>Focal nodular hyperplasia of the liver.\\n Twenty-four patients underwent biopsy or resection of the liver for focal nodular hyperplasia (FNH) at Memorial Sloan-Kettering Cancer Center from 1978 to...</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4405</th>\n",
" <td>0009743</td>\n",
" <td>Subtle injuries of the Lisfranc joint.\\n In fifteen patients, a subtle injury of the Lisfranc joint (tarsometatarsal articulation) was found.\\n The lesion was defined as a diastasis of two to five...</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>490</th>\n",
" <td>0007578</td>\n",
" <td>Immunocytochemical profile of benign and carcinomatous effusions. A practical approach to difficult diagnosis.\\n One of the great challenges in the cytodiagnosis of effusions is the distinction be...</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>121</th>\n",
" <td>0004622</td>\n",
" <td>Measles incidence, vaccine efficacy, and mortality in two urban African areas with high vaccination coverage.\\n Measles incidence, vaccine efficacy, and mortality were examined prospectively in tw...</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5399</th>\n",
" <td>0005694</td>\n",
" <td>Abdominal trauma in pregnancy. When is fetal monitoring necessary? \\n The type and duration of observation and monitoring of mother and fetus after abdominal trauma are dependent on gestational ag...</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2139</th>\n",
" <td>0002320</td>\n",
" <td>Transient left ventricular filling abnormalities (diastolic stunning) after acute myocardial infarction.\\n A variety of experimental studies suggest that diastolic left ventricular (LV) function c...</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5899</th>\n",
" <td>0008051</td>\n",
" <td>Stress adaptation and low-frequency impedance of rat lungs.\\n At transpulmonary pressures (Ptp) of 7-12 cmH2O, pressure-volume hysteresis of isolated cat lungs has been found to be 20-50% larger t...</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4989</th>\n",
" <td>0003536</td>\n",
" <td>Perioperative arrhythmias after Fontan repair.\\n Arrhythmias are well-recognized sequelae of the Fontan repair.\\n A prospective analysis of perioperative arrhythmias after Fontan repair was perfor...</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4396</th>\n",
" <td>0009331</td>\n",
" <td>Motor unit numbers and contractile properties after spinal cord injury.\\n The number of motor units in the thenar muscle group was estimated in 11 patients with cervical spinal cord injuries.\\n Th...</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2280</th>\n",
" <td>0003481</td>\n",
" <td>Nonuniform regional deformation of the pericardium during the cardiac cycle in dogs.\\n We hypothesized that local contact forces between the pericardium and the heart cause regional variation in p...</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DOCID NOTE_TEXT CATEGORY\n",
"6060 0008845 Causes, diagnosis, and treatment of pharyngitis.\\n Pharyngitis is a common disease of the respiratory tract that can be caused by several different viruses and bacterial organisms.\\n Clinically sp... False\n",
"978 0005872 Secondary correction of the unilateral cleft lip nose using a conchal composite graft.\\n The secondary deformity of the unilateral cleft lip nose has many components.\\n One is the dorsal dislocati... False\n",
"3655 0003185 Ability of anti-HIV agents to inhibit HIV replication in monocyte/macrophages or U937 monocytoid cells under conditions of enhancement by GM-CSF or anti-HIV antibody.\\n Monocyte/macrophages (M/M) ... False\n",
"4045 0002256 Alcohol and trauma. An endemic syndrome.\\n Injuries are a pervasive and costly problem, and alcohol use appears to be an important risk factor for injury.\\n We examined the blood alcohol levels an... False\n",
"2447 0006180 Dose-dependent reduction of myocardial infarct size with the perfluorochemical Fluosol-DA.\\n The perfluorochemical Fluosol-DA has been shown to reduce infarct size.\\n However, the dose-response re... True\n",
"3284 0004261 Clinical review 16: Parathyroid hormone-related proteins: coming of age in the 1990s.\\n The last 3 yr have yielded a fertile harvest of new information on the HHM clinical syndrome and on the nove... False\n",
"4031 0000997 Treatment of phenobarbital poisoning with multiple dose activated charcoal in an infant.\\n A 28-day-old infant developed lethargy, hypotonia, and hypothermia following a phenobarbital overdose sec... False\n",
"5510 0006332 Clinicopathologic features and long-term results of alpha-fetoprotein-producing gastric cancer.\\n During a 10-yr-period, 24 cases of alpha-fetoprotein-producing gastric cancer were experienced in ... False\n",
"1653 0002449 Geriatric pharmacokinetics and the kidney.\\n The general population is aging and, as a result, drugs are increasingly prescribed for a variety of medical conditions in a group of patients with mul... False\n",
"4737 0002332 Changes in circulating norepinephrine with hemofiltration in advanced congestive heart failure.\\n In congestive heart failure (CHF), hemofiltration is associated with an obvious decrease in circul... True\n",
"5448 0006046 Focal nodular hyperplasia of the liver.\\n Twenty-four patients underwent biopsy or resection of the liver for focal nodular hyperplasia (FNH) at Memorial Sloan-Kettering Cancer Center from 1978 to... False\n",
"4405 0009743 Subtle injuries of the Lisfranc joint.\\n In fifteen patients, a subtle injury of the Lisfranc joint (tarsometatarsal articulation) was found.\\n The lesion was defined as a diastasis of two to five... False\n",
"490 0007578 Immunocytochemical profile of benign and carcinomatous effusions. A practical approach to difficult diagnosis.\\n One of the great challenges in the cytodiagnosis of effusions is the distinction be... False\n",
"121 0004622 Measles incidence, vaccine efficacy, and mortality in two urban African areas with high vaccination coverage.\\n Measles incidence, vaccine efficacy, and mortality were examined prospectively in tw... False\n",
"5399 0005694 Abdominal trauma in pregnancy. When is fetal monitoring necessary? \\n The type and duration of observation and monitoring of mother and fetus after abdominal trauma are dependent on gestational ag... False\n",
"2139 0002320 Transient left ventricular filling abnormalities (diastolic stunning) after acute myocardial infarction.\\n A variety of experimental studies suggest that diastolic left ventricular (LV) function c... True\n",
"5899 0008051 Stress adaptation and low-frequency impedance of rat lungs.\\n At transpulmonary pressures (Ptp) of 7-12 cmH2O, pressure-volume hysteresis of isolated cat lungs has been found to be 20-50% larger t... False\n",
"4989 0003536 Perioperative arrhythmias after Fontan repair.\\n Arrhythmias are well-recognized sequelae of the Fontan repair.\\n A prospective analysis of perioperative arrhythmias after Fontan repair was perfor... False\n",
"4396 0009331 Motor unit numbers and contractile properties after spinal cord injury.\\n The number of motor units in the thenar muscle group was estimated in 11 patients with cervical spinal cord injuries.\\n Th... False\n",
"2280 0003481 Nonuniform regional deformation of the pericardium during the cardiac cycle in dogs.\\n We hypothesized that local contact forces between the pericardium and the heart cause regional variation in p... True"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train.sample(20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How many records do we have in the test / train datasets ?"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training data has: 6286 documents\n",
"Test data has: 7643 documents\n"
]
}
],
"source": [
"\n",
"print 'Training data has: ', len(df_train.index), ' documents'\n",
"\n",
"print 'Test data has: ', len(df_test.index), ' documents'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data distribution"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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L3aZ+qYdG32Q6CpAwWMFGnbr44os1Y8aM3X6scePG+vnnn/f6Od5//32NHTtWU6dOVWZm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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x24a3b550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.axis('equal')\n",
"plt.pie(\n",
" df_train.CATEGORY.value_counts().tolist(), \n",
" labels=['False', 'True'], \n",
" autopct='%1.1f%%', \n",
" colors=(\"#E13F29\", \"#D69A80\"));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**The accuracy of a dumb classifier that classifies all the documents as False is >80%**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Baseline classifier"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## We define a trivial baseline classifier that simply look for the string 'Cardio' in the text"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import re\n",
"\n",
"def baseline_cpr_classifier(txt):\n",
" if re.search('Cardio', txt, re.IGNORECASE):\n",
" return True\n",
" else:\n",
" return False\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Testing our baseline classifier"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Ytrue = df_test.CATEGORY.tolist()\n",
"\n",
"Ypred = []\n",
"\n",
"for index, row in df_test.iterrows():\n",
" Ypred.append(baseline_cpr_classifier(row['NOTE_TEXT']))\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Comparing actual values to the predictions of the baseline classifier"
]
},
{
"cell_type": "code",
"execution_count": 91,
"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>Actual</th>\n",
" <th>Predicted</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1598</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6700</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7296</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4630</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>626</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6721</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>366</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>870</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2489</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3567</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3058</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3725</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7455</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7082</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6063</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7470</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4234</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>346</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4351</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3262</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Actual Predicted\n",
"1598 False False\n",
"6700 True True\n",
"7296 False False\n",
"4630 False False\n",
"626 False False\n",
"6721 False False\n",
"366 False False\n",
"870 False False\n",
"2489 False False\n",
"3567 False False\n",
"3058 True False\n",
"3725 False False\n",
"7455 True True\n",
"7082 False False\n",
"6063 False False\n",
"7470 False False\n",
"4234 False False\n",
"346 False False\n",
"4351 False False\n",
"3262 True False"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dictY = {'Actual' : Ytrue, 'Predicted': Ypred}\n",
"\n",
"dfY = pd.DataFrame.from_dict(dictY)\n",
"\n",
"dfY.sample(20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Assessing Classifier Performance\n",
"\n",
"### Confusion matrix and derivative estimators"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table border=\"1\" cellpadding=\"3\" cellspacing=\"0\" style=\"border:1px solid black;border-collapse:collapse;\"><tr><td style=\"background-color:White;border-left: 1px solid transparent;border-top: 1px solid transparent;\"><b></b></td><td style=\"background-color:LightGray;\"><b>Predicted 0</b></td><td style=\"background-color:LightGray;\"><b>Predicted 1</b></td></tr><tr><td style=\"background-color:LightGray;\"><b>Actual 0</b></td><td style=\"background-color:Ivory;\">True Negative</td><td style=\"background-color:Ivory;\">False Positive</td></tr><tr><td style=\"background-color:LightGray;\"><b>Actual 1</b></td><td style=\"background-color:AliceBlue;\">False Negative</td><td style=\"background-color:AliceBlue;\">True Positive</td></tr></table>"
],
"text/plain": [
"<ipy_table.IpyTable at 0x24c8d9e8>"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"from ipy_table import *\n",
"\n",
"confusion_matrix_binary = [\n",
" ['', 'Predicted 0', 'Predicted 1'],\n",
" ['Actual 0', 'True Negative', 'False Positive'],\n",
" ['Actual 1', 'False Negative', 'True Positive']\n",
"]\n",
"\n",
"make_table(confusion_matrix_binary)\n",
"apply_theme('basic_both')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Sensitivity (Recall) or true positive rate (TPR) $=\\frac{TP}{TP+FN}$\n",
"\n",
"#### Specificity (SPC) or true negative rate $=\\frac{TN}{TN+FP}$\n",
"\n",
"#### Precision or positive predictive value (PPV) $=\\frac{TP}{TP+FP}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Confusion matrix and derivatives of the baseline classifier"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[6226 116]\n",
" [ 922 379]]\n",
"6342\n"
]
}
],
"source": [
"from sklearn.metrics import confusion_matrix\n",
"\n",
"conf_mat = confusion_matrix(Ytrue, Ypred)\n",
"\n",
"print conf_mat\n",
"\n",
"print sum(conf_mat[0])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some code to prettify the printout of the confusion matrix in the notebook:"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def render_confusion_matrix(ytrue, ypred):\n",
" return pd.crosstab(pd.Series(ytrue), pd.Series(ypred), rownames=['Actual'], colnames=['Predicted'], margins=True)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Predicted</th>\n",
" <th>False</th>\n",
" <th>True</th>\n",
" <th>All</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Actual</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>False</th>\n",
" <td>6226</td>\n",
" <td>116</td>\n",
" <td>6342</td>\n",
" </tr>\n",
" <tr>\n",
" <th>True</th>\n",
" <td>922</td>\n",
" <td>379</td>\n",
" <td>1301</td>\n",
" </tr>\n",
" <tr>\n",
" <th>All</th>\n",
" <td>7148</td>\n",
" <td>495</td>\n",
" <td>7643</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Predicted False True All\n",
"Actual \n",
"False 6226 116 6342\n",
"True 922 379 1301\n",
"All 7148 495 7643"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"render_confusion_matrix(Ytrue, Ypred)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Accuracy: 0.864189454403\n",
"\n",
" precision recall f1-score support\n",
"\n",
" False 0.87 0.98 0.92 6342\n",
" True 0.77 0.29 0.42 1301\n",
"\n",
"avg / total 0.85 0.86 0.84 7643\n",
"\n"
]
}
],
"source": [
"from sklearn.metrics import classification_report, accuracy_score\n",
"\n",
"print\n",
"print 'Accuracy: ', accuracy_score(Ytrue, Ypred)\n",
"print\n",
"print classification_report(Ytrue, Ypred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Even though the accuracy is OK, the recall is misearable. Let's build a real classifier**"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"#Building a ML classifier"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Vectorizing textual data using Bag-of-Words (or Bag-of-Ngrams)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our vector space is a dictionary of all the N-grams in our set of documents."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Defining tokenizer to extract better features from the text"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from nltk.stem.porter import *\n",
"from nltk.tokenize import word_tokenize\n",
"import string\n",
"\n",
"stemmer = PorterStemmer()\n",
"def stem_tokens(tokens, stemmer):\n",
" stemmed = []\n",
" for item in tokens:\n",
" stemmed.append(stemmer.stem(item))\n",
" return stemmed\n",
"\n",
"def tokenize(text):\n",
" text = \"\".join([ch for ch in text if ch not in string.punctuation])\n",
" tokens = word_tokenize(text)\n",
" tokens = [item for item in tokens if item.isalpha()]\n",
" stems = stem_tokens(tokens, stemmer)\n",
" return stems"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Vectorize the test data"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X_train = df_train['NOTE_TEXT'].tolist()\n",
"Y_train = df_train.CATEGORY.tolist()\n",
"\n",
"vectorizer = CountVectorizer(tokenizer=tokenize, ngram_range=(1,2))\n",
"\n",
"wcounts = vectorizer.fit_transform(X_train)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### What is the dimentionality of our vector space?"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<6286x355860 sparse matrix of type '<type 'numpy.int64'>'\n",
"\twith 1452481 stored elements in Compressed Sparse Row format>"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wcounts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** We have 6286 documents in our training set, each has 355860 features (the combination of all the uni/bi-grams)**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The first 200 features"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[u'a',\n",
" u'a a',\n",
" u'a absolut',\n",
" u'a accuraci',\n",
" u'a acid',\n",
" u'a activ',\n",
" u'a adrenalectomi',\n",
" u'a after',\n",
" u'a alon',\n",
" u'a alpha',\n",
" u'a alphasubunit',\n",
" u'a amino',\n",
" u'a an',\n",
" u'a analog',\n",
" u'a analysi',\n",
" u'a and',\n",
" u'a antibodi',\n",
" u'a antigen',\n",
" u'a antiifnalpha',\n",
" u'a aortic',\n",
" u'a apic',\n",
" u'a aqueoussolubl',\n",
" u'a are',\n",
" u'a as',\n",
" u'a assay',\n",
" u'a assessmentreferr',\n",
" u'a ata',\n",
" u'a atmospher',\n",
" u'a atrial',\n",
" u'a attempt',\n",
" u'a averag',\n",
" u'a b',\n",
" u'a babi',\n",
" u'a baboon',\n",
" u'a background',\n",
" u'a backtoback',\n",
" u'a bacteremia',\n",
" u'a bacteri',\n",
" u'a bacteriolog',\n",
" u'a bad',\n",
" u'a baffl',\n",
" u'a bake',\n",
" u'a balanc',\n",
" u'a balloon',\n",
" u'a ban',\n",
" u'a band',\n",
" u'a bandlik',\n",
" u'a barbitur',\n",
" u'a bard',\n",
" u'a barium',\n",
" u'a baromet',\n",
" u'a baroreceptor',\n",
" u'a barrier',\n",
" u'a basal',\n",
" u'a base',\n",
" u'a baselin',\n",
" u'a basi',\n",
" u'a basic',\n",
" u'a basketweav',\n",
" u'a bath',\n",
" u'a batteri',\n",
" u'a bauermeist',\n",
" u'a bcc',\n",
" u'a bcell',\n",
" u'a bcrabl',\n",
" u'a bdvinfect',\n",
" u'a bdvrelat',\n",
" u'a bear',\n",
" u'a beat',\n",
" u'a becaus',\n",
" u'a becker',\n",
" u'a bed',\n",
" u'a begin',\n",
" u'a behavior',\n",
" u'a behaviour',\n",
" u'a below',\n",
" u'a belowthekne',\n",
" u'a bench',\n",
" u'a benefici',\n",
" u'a benefit',\n",
" u'a benign',\n",
" u'a bent',\n",
" u'a benzodiazepin',\n",
" u'a beta',\n",
" u'a betaadrenerg',\n",
" u'a betaadrenoceptor',\n",
" u'a betaagonist',\n",
" u'a betablock',\n",
" u'a betachain',\n",
" u'a betacl',\n",
" u'a betahemolyt',\n",
" u'a betasympathomimet',\n",
" u'a better',\n",
" u'a betweenheel',\n",
" u'a bewild',\n",
" u'a bfg',\n",
" u'a bgl',\n",
" u'a bia',\n",
" u'a biatrial',\n",
" u'a bicommissur',\n",
" u'a bicornu',\n",
" u'a bicycl',\n",
" u'a bidimension',\n",
" u'a bidirect',\n",
" u'a biexponenti',\n",
" u'a bifoil',\n",
" u'a bilater',\n",
" u'a bile',\n",
" u'a biliari',\n",
" u'a biliou',\n",
" u'a billingham',\n",
" u'a bimod',\n",
" u'a bind',\n",
" u'a bing',\n",
" u'a bioartifici',\n",
" u'a bioassay',\n",
" u'a biograph',\n",
" u'a biolog',\n",
" u'a biomechan',\n",
" u'a biomedicu',\n",
" u'a bioprosthesi',\n",
" u'a bioprosthet',\n",
" u'a biopsi',\n",
" u'a biotinyl',\n",
" u'a bipariet',\n",
" u'a bipedicl',\n",
" u'a biphas',\n",
" u'a biplan',\n",
" u'a bipolar',\n",
" u'a bipotenti',\n",
" u'a biraci',\n",
" u'a birth',\n",
" u'a bisexu',\n",
" u'a bisinu',\n",
" u'a bite',\n",
" u'a biventricular',\n",
" u'a black',\n",
" u'a blackout',\n",
" u'a blackpowd',\n",
" u'a blackpowderrifl',\n",
" u'a bladder',\n",
" u'a blade',\n",
" u'a blalocktaussig',\n",
" u'a blank',\n",
" u'a bleed',\n",
" u'a blind',\n",
" u'a block',\n",
" u'a blocker',\n",
" u'a blogt',\n",
" u'a blood',\n",
" u'a bloodbrain',\n",
" u'a bloodcontamin',\n",
" u'a bloodi',\n",
" u'a bloodless',\n",
" u'a bloodliquid',\n",
" u'a blunt',\n",
" u'a boari',\n",
" u'a bodi',\n",
" u'a bohr',\n",
" u'a bold',\n",
" u'a bolu',\n",
" u'a bone',\n",
" u'a boni',\n",
" u'a booster',\n",
" u'a borg',\n",
" u'a borna',\n",
" u'a bottom',\n",
" u'a bout',\n",
" u'a bovin',\n",
" u'a bowel',\n",
" u'a boy',\n",
" u'a boyfriendlov',\n",
" u'a bp',\n",
" u'a brachialjugular',\n",
" u'a bradycardia',\n",
" u'a brain',\n",
" u'a brainstem',\n",
" u'a branch',\n",
" u'a brdurd',\n",
" u'a breast',\n",
" u'a breastpreserv',\n",
" u'a breathhold',\n",
" u'a bridg',\n",
" u'a brief',\n",
" u'a broad',\n",
" u'a broaden',\n",
" u'a broader',\n",
" u'a broadli',\n",
" u'a broadspectrum',\n",
" u'a broken',\n",
" u'a bromin',\n",
" u'a bromodeoxyuridin',\n",
" u'a bronchial',\n",
" u'a bronchodil',\n",
" u'a bronchoplast',\n",
" u'a bronchu',\n",
" u'a brother',\n",
" u'a brown',\n",
" u'a brunetti',\n",
" u'a buccolingu']"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"feats = vectorizer.get_feature_names()\n",
"\n",
"feats[:200]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** At this point of the workflow, there are many feature selection and engineering techniques we could apply. Some generic, some based on natural language processing and some unique to the medical domain. However, to keep it simple for now, let's move on to the classifier:**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Naive Bayes classifier\n",
"\n",
"* **Naive** conditional independence assumption: The counts of individual features (or n-grams) are independent\n",
"* **Bayes' rule** is used to calculate the conditional probabilities for each class/feature pair: \n",
" $P(Y|W)=\\frac{P(W|Y) \\cdot P(Y)}{P(W)}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$\\implies P(Y|w_{1}, w_{2}, ..., w_{N})=\\frac{1}{P(W)} \\cdot P(Y) \\cdot \\prod_{k=1}^{N} P(w_{k}|Y)$\n",
"\n",
"Since the probability of all the features is not dependent on the probability of Y and all we care about is finding the Y that is most likely, we can drop $\\frac{1}{P(W)}$ and stay with:\n",
"\n",
"$P(Y|w_{1}, w_{2}, ..., w_{N})=P(Y) \\cdot \\prod_{k=1}^{N} P(w_{k}|Y)$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We estimate the probabilities from the training set using multinomial distribution with a smoothing factor alpha:\n",
"\n",
"$P(w_{i}) = \\frac{count_{i}+alpha}{overallcount_{i}+alpha \\cdot N}$\n",
"\n",
"<img width=\"575\" src=\"words_proba.png\"/>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fit (aka train) a classifier with some ad-hoc parameters on the test data"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"MultinomialNB(alpha=0.1, class_prior=None, fit_prior=True)"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.naive_bayes import MultinomialNB\n",
"\n",
"clf_nb = MultinomialNB(alpha=0.1)\n",
"\n",
"clf_nb.fit(wcounts, Y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Most Predictive Features\n",
"\n",
"A cool feature of the Naive Bayes classifier is that it can list for us most relevant features for each class. These are the features that are most relevant to the positive documents. Some of them are trivial English words, that will also appear in the list of features relevant to the Negative documents. However - we also see some domain specific features, such as **arteri** and ** coronari** :"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Positive word -3.82: the\n",
"Positive word -3.84: of\n",
"Positive word -4.15: in\n",
"Positive word -4.18: and\n",
"Positive word -4.77: to\n",
"Positive word -4.81: with\n",
"Positive word -4.86: patient\n",
"Positive word -4.89: a\n",
"Positive word -5.14: wa\n",
"Positive word -5.41: were\n",
"Positive word -5.57: of the\n",
"Positive word -5.58: for\n",
"Positive word -5.71: than\n",
"Positive word -5.73: in the\n",
"Positive word -5.85: by\n",
"Positive word -5.87: or\n",
"Positive word -5.96: arteri\n",
"Positive word -6.00: patient with\n",
"Positive word -6.01: that\n",
"Positive word -6.04: is\n",
"Positive word -6.09: after\n",
"Positive word -6.11: group\n",
"Positive word -6.15: coronari\n",
"Positive word -6.16: less\n",
"Positive word -6.19: from\n"
]
}
],
"source": [
"pf = [(clf_nb.feature_log_prob_[1, i], feats[i]) for i in range(len(feats))]\n",
"pf.sort(reverse=True)\n",
"for p in pf[:25]:\n",
" print 'Positive word %.2f: %s' % (p[0], p[1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Test the classifier with the test data\n"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Read the data from pandas dataframe to an array\n",
"X_test = df_test['NOTE_TEXT'].tolist()\n",
"Y_test = df_test.CATEGORY.tolist()\n",
"\n",
"# Convert the text to arrays of numbers\n",
"counts_test = vectorizer.transform(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<7643x355860 sparse matrix of type '<type 'numpy.int64'>'\n",
"\twith 1468293 stored elements in Compressed Sparse Row format>"
]
},
"execution_count": 118,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"counts_test"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**We have 7643 documents in our test set, each has 355860 features - exactly the same features we set while processing the training set of course.**"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Predict the values of the test set\n",
"predictions = clf_nb.predict(counts_test)"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, True, False, False, False, False,\n",
" False, False], dtype=bool)"
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Look at the first 20 predictions\n",
"predictions[:20]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Confusion matrix and derivatives of the classifier"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Predicted</th>\n",
" <th>False</th>\n",
" <th>True</th>\n",
" <th>All</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Actual</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>False</th>\n",
" <td>6064</td>\n",
" <td>278</td>\n",
" <td>6342</td>\n",
" </tr>\n",
" <tr>\n",
" <th>True</th>\n",
" <td>292</td>\n",
" <td>1009</td>\n",
" <td>1301</td>\n",
" </tr>\n",
" <tr>\n",
" <th>All</th>\n",
" <td>6356</td>\n",
" <td>1287</td>\n",
" <td>7643</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Predicted False True All\n",
"Actual \n",
"False 6064 278 6342\n",
"True 292 1009 1301\n",
"All 6356 1287 7643"
]
},
"execution_count": 122,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"render_confusion_matrix(Y_test, predictions)"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Accuracy: 0.92542195473\n",
"\n",
" precision recall f1-score support\n",
"\n",
" False 0.95 0.96 0.96 6342\n",
" True 0.78 0.78 0.78 1301\n",
"\n",
"avg / total 0.93 0.93 0.93 7643\n",
"\n"
]
}
],
"source": [
"print\n",
"print 'Accuracy: ', accuracy_score(Y_test, predictions)\n",
"print\n",
"print classification_report(Y_test, predictions)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**As expected we improved the accuracy compared to the baseline classifier, and dramatically improved the recall on the positive documents - from 29% to 78%.**\n",
"\n",
"**Before we move on to optimize the classifier, let's look at some other interesting output type of Naive Bayes - some insightful output besides the predictions on the test data**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get the indices of the elements that with wrong predictions"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[13,\n",
" 24,\n",
" 25,\n",
" 41,\n",
" 52,\n",
" 80,\n",
" 174,\n",
" 246,\n",
" 353,\n",
" 416,\n",
" 467,\n",
" 731,\n",
" 991,\n",
" 1005,\n",
" 1038,\n",
" 1047,\n",
" 1068,\n",
" 1120,\n",
" 1140,\n",
" 1176]"
]
},
"execution_count": 138,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iwrong_predictions = [i for i,v in enumerate(zip(Y_test, predictions)) if v[0] != v[1]]\n",
"\n",
"iwrong_predictions[:20]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Probabilities of a particular classification\n",
"\n",
"Bayesian models like the Naive Bayes classifier have the nice property that they compute probabilities of a particular classification -- the `predict_proba` and `predict_log_proba` methods of `MultinomialNB` compute these probabilities. "
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"proba = clf_nb.predict_proba(counts_test)\n",
"log_proba = clf_nb.predict_log_proba(counts_test)"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1.00000000e+000, 2.94055710e-037],\n",
" [ 1.00000000e+000, 2.56434674e-112],\n",
" [ 9.99999993e-001, 7.36050607e-009],\n",
" [ 1.00000000e+000, 3.67672356e-048],\n",
" [ 1.00000000e+000, 2.17669993e-030],\n",
" [ 1.00000000e+000, 1.25863592e-081],\n",
" [ 1.00000000e+000, 2.47597297e-173],\n",
" [ 1.00000000e+000, 2.14988957e-053],\n",
" [ 1.00000000e+000, 5.70562372e-108],\n",
" [ 1.00000000e+000, 4.42691208e-041]])"
]
},
"execution_count": 140,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"proba[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The output of `clf_nb.predict_proba(counts_test)` is a `(N example, 2)` array. The first column gives the probability $P(Y=0)$ or $P(False)$, and the second gives $P(Y=1)$ or $P(True)$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Commonly it is more comfortable to work with the log values of the probabilities - the results of `clf_nb.predict_log_proba`**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculate and plot the differences between the True and False probabilities"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"diff_prob = proba[:,1] - proba[:,0]\n",
"diff_log_proba = log_proba[:,1] - log_proba[:,0]"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"diff_prob:\n",
"\n",
"mean: -0.662949326315\n",
"std: 0.746432184454\n",
"\n",
"diff_log_prob:\n",
"\n",
"mean: -94.3395679123\n",
"std: 150.446111867\n"
]
}
],
"source": [
"print 'diff_prob:\\n'\n",
"print 'mean:',np.mean(diff_prob)\n",
"print 'std:', np.std(diff_prob)\n",
"\n",
"print '\\ndiff_log_prob:\\n'\n",
"print 'mean:', np.mean(diff_log_proba)\n",
"print 'std:', np.std(diff_log_proba)\n"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([-120.99198123, -105.33481755, -45.92763286, -115.47860148,\n",
" -126.40541505, -158.90170764, 30.44267742, -27.78738764,\n",
" 404.37342916, -157.70848569])"
]
},
"execution_count": 143,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"diff_log_proba[-10:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Distribution of the predictions on the test set, as function of the difference between the True/False log probabilities"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.lines.Line2D at 0x2962ad30>"
]
},
"execution_count": 149,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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APAj2gAAAAAAwDAEEAAAAgGEIIAAAAAAMQwABAAAAYBgCCAAAAADDEEAAAAAAGIYAAgAA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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2962a2b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot histogram.\n",
"plt.hist(diff_log_proba, range=[-500, 500], bins=30, normed=True, alpha=0.5)\n",
"plt.axvline(0, color='r')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Another view on the distribution of the prediction scores of our classifier"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Divide the test set into 2 sets - True (blue) / False(red) - and plot them in a scatter plot"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pospts = [v[1] for i,v in enumerate(zip(Y_test, diff_log_proba)) if v[0] == 1]\n",
"negpts = [v[1] for i,v in enumerate(zip(Y_test, diff_log_proba)) if v[0] == 0]"
]
},
{
"cell_type": "code",
"execution_count": 202,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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atUk2EhkaE4l4DcNoHqVWx+pOf6bqHHq6b1VFV15XZlAE2KG712tUJFpC6s9R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\nurTvAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x4ee50a20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import random \n",
"\n",
"plt.scatter(\n",
" pospts, \n",
" np.random.uniform(0.9, 1.1, len(pospts)),\n",
" color='blue')\n",
"\n",
"plt.scatter(\n",
" negpts, \n",
" np.random.uniform(0.9, 1.1, len(negpts)),\n",
" color='red')\n",
"\n",
"plt.xlim(-500, 450)\n",
"\n",
"plt.ylim(0.8, 1.2)\n",
"\n",
"plt.axvline(0, color='r')\n",
"\n",
"values = np.array(diff_log_proba[iwrong_predictions])\n",
"\n",
"plt.axvspan(\n",
" np.mean(values)-2*np.std(values),\n",
" np.mean(values)+2*np.std(values), \n",
" facecolor='b', alpha=0.1)\n",
"\n",
"plt.axvline(np.mean(values), linewidth=1);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Optimizing the classification pipeline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Scorer function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When optimizing parameters for classification pipeline we can write or define our own scoring function. The optimizing routine `GridSearchCV` which runs a **brute force parameter search**, will select the parameters that got the **highest** score.\n",
"\n",
"### What do we want to optimize exactly?\n",
"\n",
"When classifing medical documents, we sometimes want to **maximize sensitivity** (recall) while keeping specificity and accuracy in check. In other words - the sensitivity is our **target optimization parameter**. To maximize sensitivity, I'll define a scorer function that returns the recall score."
]
},
{
"cell_type": "code",
"execution_count": 199,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.metrics import recall_score, make_scorer\n",
"\n",
"# Define scorer function that returns the recall score\n",
"recall_scorer = make_scorer(recall_score)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we build a full pipeline and use the `GridSearchCV` sklearn utility to fit the best paramaters for the pipeline. Normally I use longer lists of parameters, but that takes hours to run.\n",
"\n",
"NOTE - we will use `GridSearchCV` default 3-fold cross-validation. Other cross-validation schemes can be defined if needed. "
]
},
{
"cell_type": "code",
"execution_count": 201,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=1)]: Done 1 jobs | elapsed: 17.2s\n",
"[Parallel(n_jobs=1)]: Done 36 out of 36 | elapsed: 10.9min finished\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 3 folds for each of 12 candidates, totalling 36 fits\n"
]
},
{
"data": {
"text/plain": [
"GridSearchCV(cv=None, error_score='raise',\n",
" estimator=Pipeline(steps=[('vect', CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',\n",
" dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',\n",
" lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
" ngram_range=(1, 1), preprocessor=None, stop_words=None,\n",
" st...28>,\n",
" vocabulary=None)), ('clf', MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True))]),\n",
" fit_params={}, iid=True, loss_func=None, n_jobs=1,\n",
" param_grid={'vect__ngram_range': ((1, 1), (1, 2)), 'vect__max_features': (20000, 30000), 'clf__alpha': (0.05, 0.1, 0.2)},\n",
" pre_dispatch='2*n_jobs', refit=True, score_func=None,\n",
" scoring=make_scorer(recall_score), verbose=1)"
]
},
"execution_count": 201,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.feature_extraction.text import TfidfTransformer\n",
"from sklearn.grid_search import GridSearchCV\n",
"from sklearn.pipeline import Pipeline\n",
"\n",
"pipeline = Pipeline([\n",
" ('vect', CountVectorizer(tokenizer=tokenize)),\n",
" ('clf', MultinomialNB())\n",
"])\n",
"\n",
"# Define some possible parameters for feature extraction and for the classifier\n",
"parameters = {\n",
" 'vect__max_features': (20000, 30000),\n",
" 'vect__ngram_range': ((1, 1), (1, 2)), # unigrams or bigrams\n",
" 'clf__alpha': (0.05, 0.1, 0.2)\n",
"}\n",
"\n",
"# find the best parameters for both the feature extraction and the\n",
"# classifier, based on the scorer function we defined\n",
"grid_search = GridSearchCV(pipeline, parameters, verbose=1, scoring=recall_scorer)\n",
"\n",
"grid_search.fit(df_train['NOTE_TEXT'].tolist(), df_train.CATEGORY.tolist())"
]
},
{
"cell_type": "code",
"execution_count": 203,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best parameters set found on development set:\n",
"\n",
"{'vect__ngram_range': (1, 1), 'vect__max_features': 30000, 'clf__alpha': 0.2}\n"
]
}
],
"source": [
"print(\"Best parameters set found on development set:\")\n",
"print\n",
"print(grid_search.best_params_)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Try the optimized classifier on the test set and output the performance parameters"
]
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Use the optimal classifier to make predictions on the test set\n",
"\n",
"opt_predictions = grid_search.predict(df_test['NOTE_TEXT'].tolist())"
]
},
{
"cell_type": "code",
"execution_count": 205,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Predicted</th>\n",
" <th>False</th>\n",
" <th>True</th>\n",
" <th>All</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Actual</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>False</th>\n",
" <td>5928</td>\n",
" <td>414</td>\n",
" <td>6342</td>\n",
" </tr>\n",
" <tr>\n",
" <th>True</th>\n",
" <td>240</td>\n",
" <td>1061</td>\n",
" <td>1301</td>\n",
" </tr>\n",
" <tr>\n",
" <th>All</th>\n",
" <td>6168</td>\n",
" <td>1475</td>\n",
" <td>7643</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Predicted False True All\n",
"Actual \n",
"False 5928 414 6342\n",
"True 240 1061 1301\n",
"All 6168 1475 7643"
]
},
"execution_count": 205,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"render_confusion_matrix(df_test.CATEGORY.tolist(), opt_predictions)"
]
},
{
"cell_type": "code",
"execution_count": 206,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Accuracy: 0.914431505953\n",
"\n",
" precision recall f1-score support\n",
"\n",
" False 0.96 0.93 0.95 6342\n",
" True 0.72 0.82 0.76 1301\n",
"\n",
"avg / total 0.92 0.91 0.92 7643\n",
"\n"
]
}
],
"source": [
"print\n",
"print 'Accuracy: ', accuracy_score(df_test.CATEGORY.tolist(), opt_predictions)\n",
"print\n",
"print classification_report(df_test.CATEGORY.tolist(), opt_predictions)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Comparing the performance of the optimized classifier to the performance of the the classifier with the ad-hoc parameters shows, that while the accuracy of the optimized classifier is a bit lower (91% compared to 92%), the sensitivity for True documents, which is our target performance parameter, increased from 78% to 82%."
]
},
{
"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 |
robblack007/clase-cinematica-robot | Practicas/practica5/Practica.ipynb | 1 | 7119 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Generación de trayectorias por medio de LSPB"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"El objetivo de esta práctica es generar una trayectoria para un robot manipulador, de tal manera que no tenga cambios bruscos de posición o velocidad.\n",
"\n",
"El algoritmo general que vamos a usar se llama LSPB (Linear segment with parabolic blend), en el cual vamos a tener una velocidad de crucero para el manipulador, así como un periodo en el que el manipulador acelerará constantemente y otro periodo en el que desacelererá.\n",
"\n",
"Una trayectoria generada por este método se ve asi:\n",
"\n",
"\n",
"\n",
"En la primer sección se tiene una aceleración constante, en la segunda una velocidad constante y en la tercera una aceleración constante de signo contrario a la primera.\n",
"\n",
"El método por el que se generó no es particularmente dificil, tan solo es un poco engorroso de programar, por lo que para facilidad de esta práctica, este método ya esta programado, tan solo hay que importar el código:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from generacion_trayectorias import grafica_trayectoria\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Vamos a hacer una prueba en primer lugar:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ts, qs, q̇s, q̈s = grafica_trayectoria(0, 2, 0, 1, 1000)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"En la gráfica anterior podemos ver no solo la posición en el primer cuadro, si no tambien la velocidad y la aceleración en el segundo y tercero, de tal manera que nos damos una mejor idea de la trayectoria.\n",
"\n",
"Vamos a generar un conjunto de trayectorias para un ejemplo, primero empecemos importando ```pi```:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from numpy import pi\n",
"τ = 2*pi"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Vamos a generar una trayectoria en la que en los primero dos segundos se mueva de $0^o$ a $90^o$, en los segundos dos segundos de $90^o$ a $-60^o$ y en los ultimos seis segundos de $-60^o$ a $240^o$:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ts, q1, q̇1, q̈1 = grafica_trayectoria(0, 2, 0, τ/4, 100)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ts, q2, q̇2, q̈2 = grafica_trayectoria(2, 4, τ/4, -τ/6, 100)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ts, q3, q̇3, q̈3 = grafica_trayectoria(4, 10, -τ/6, 2*τ/3, 300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Si quiero concatenar todos estos arreglos que generé, tan solo tengo que sumarlos:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"qs = q1 + q2 + q3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Esta trayectoria la podemos graficar:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from matplotlib.pyplot import figure, style"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from numpy import linspace\n",
"fig = figure(figsize=(17, 5))\n",
"ax = fig.gca()\n",
"ts = linspace(0, 10, 500)\n",
"ax.plot(ts, qs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pero mas importante, puedo generar una animación, tomando en cuenta que es un pendulo simple:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Se importa la funcion animation para crear la animacion, y rc para poder mostrar el video\n",
"# directamente en el notebook\n",
"from matplotlib import animation, rc\n",
"rc('animation', html='html5')\n",
"# Se importan las funciones necesarias para calcular la cinematica directa e inversa\n",
"from numpy import sin, cos, arange"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Se define una funcion para calcular la cinematica directa del sistema\n",
"def cinematica_directa_pendulo(q1):\n",
" # Se definen constantes utilizadas para graficar el sistema\n",
" l1, l2 = 1, 1\n",
" xs = [0, l1*cos(q1)]\n",
" ys = [0, l1*sin(q1)]\n",
" return xs, ys"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Se define el tamaño de la figura\n",
"fig = figure(figsize=(8, 8))\n",
"\n",
"# Se define una sola grafica en la figura y se dan los limites de los ejes x y y\n",
"axi = fig.add_subplot(111, autoscale_on=False, xlim=(-1.1, 1.1), ylim=(-1.1, 1.1))\n",
"\n",
"# Se utilizan graficas de linea para el eslabon del pendulo\n",
"linea, = axi.plot([], [], \"-o\", lw=2, color='gray')\n",
"\n",
"def inicializacion():\n",
" '''Esta funcion se ejecuta una sola vez y sirve para inicializar el sistema'''\n",
" \n",
" # Se inicializa la linea vacia para evitar que al principio exista una linea en la grafica\n",
" linea.set_data([], [])\n",
" return linea\n",
"\n",
"def animacion(i):\n",
" '''Esta funcion se ejecuta para cada cuadro del GIF'''\n",
" \n",
" # Se obtienen las coordenadas x y y para el eslabon\n",
" xs, ys = cinematica_directa_pendulo(qs[i])\n",
" # Se actualiza el estado de la linea con las coordenadas calculadas\n",
" linea.set_data(xs, ys)\n",
" \n",
" return linea\n",
"\n",
"# Se hace la animacion dandole la funcion que se debe ejecutar para cada cuadro, el numero de cuadros\n",
"# que se debe de hacer, el periodo de cada cuadro y la funcion inicial\n",
"ani = animation.FuncAnimation(fig, animacion, arange(1, len(qs)), interval=20, init_func=inicializacion)\n",
"ani"
]
}
],
"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": 1
}
| mit |
robertodias/study | python/recommender/recommender3.ipynb | 2 | 163587 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## BUILDING A RECOMMENDER SYSTEM ON USER-USER COLLABORATIVE FILTERING (MOVIELENS DATASET)\n",
"\n",
"We will load the data sets firsts."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import math\n",
"\n",
"#column headers for the dataset\n",
"data_cols = ['user id','movie id','rating','timestamp']\n",
"item_cols = ['movie id','movie title','release date','video release date','IMDb URL','unknown','Action',\n",
"'Adventure','Animation','Childrens','Comedy','Crime','Documentary','Drama','Fantasy','Film-Noir','Horror',\n",
"'Musical','Mystery','Romance ','Sci-Fi','Thriller','War' ,'Western']\n",
"user_cols = ['user id','age','gender','occupation','zip code']\n",
"\n",
"#importing the data files onto dataframes\n",
"data_df = pd.read_csv('ml-100k/u.data', sep='\\t', names=data_cols, encoding='latin-1')\n",
"item_df = pd.read_csv('ml-100k/u.item', sep='|', names=item_cols, encoding='latin-1')\n",
"user_df = pd.read_csv('ml-100k/u.user', sep='|', names=user_cols, encoding='latin-1')\n",
"\n",
"#dropping unecessary columns\n",
"#Voting Timestamp - Removed\n",
"data_df.drop(data_df.columns[[3]], axis = 1, inplace = True)\n",
"#Movie Title, Video Release Date and IMDB URL - Removed\n",
"item_df.drop(item_df.columns[[1,3,4]], axis = 1, inplace = True)\n",
"#Occupation and Zip Code - Removed\n",
"user_df.drop(user_df.columns[[3,4]], axis = 1, inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" user id movie id rating\n",
"0 196 242 3\n",
"1 186 302 3\n",
"2 22 377 1\n",
"3 244 51 2\n",
"4 166 346 1\n"
]
}
],
"source": [
"print(data_df.head())"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" movie id release date unknown Action Adventure Animation Childrens \\\n",
"0 1 01-Jan-1995 0 0 0 1 1 \n",
"1 2 01-Jan-1995 0 1 1 0 0 \n",
"2 3 01-Jan-1995 0 0 0 0 0 \n",
"3 4 01-Jan-1995 0 1 0 0 0 \n",
"4 5 01-Jan-1995 0 0 0 0 0 \n",
"\n",
" Comedy Crime Documentary ... Fantasy Film-Noir Horror Musical \\\n",
"0 1 0 0 ... 0 0 0 0 \n",
"1 0 0 0 ... 0 0 0 0 \n",
"2 0 0 0 ... 0 0 0 0 \n",
"3 1 0 0 ... 0 0 0 0 \n",
"4 0 1 0 ... 0 0 0 0 \n",
"\n",
" Mystery Romance Sci-Fi Thriller War Western \n",
"0 0 0 0 0 0 0 \n",
"1 0 0 0 1 0 0 \n",
"2 0 0 0 1 0 0 \n",
"3 0 0 0 0 0 0 \n",
"4 0 0 0 1 0 0 \n",
"\n",
"[5 rows x 21 columns]\n"
]
}
],
"source": [
"print(item_df.head())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"#Ajust release date to get only the year\n",
"item_df['release date'] = pd.to_datetime(item_df['release date'], errors='coerce').dt.year"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" movie id release date unknown Action Adventure Animation Childrens \\\n",
"0 1 1995.0 0 0 0 1 1 \n",
"1 2 1995.0 0 1 1 0 0 \n",
"2 3 1995.0 0 0 0 0 0 \n",
"3 4 1995.0 0 1 0 0 0 \n",
"4 5 1995.0 0 0 0 0 0 \n",
"\n",
" Comedy Crime Documentary ... Fantasy Film-Noir Horror Musical \\\n",
"0 1 0 0 ... 0 0 0 0 \n",
"1 0 0 0 ... 0 0 0 0 \n",
"2 0 0 0 ... 0 0 0 0 \n",
"3 1 0 0 ... 0 0 0 0 \n",
"4 0 1 0 ... 0 0 0 0 \n",
"\n",
" Mystery Romance Sci-Fi Thriller War Western \n",
"0 0 0 0 0 0 0 \n",
"1 0 0 0 1 0 0 \n",
"2 0 0 0 1 0 0 \n",
"3 0 0 0 0 0 0 \n",
"4 0 0 0 1 0 0 \n",
"\n",
"[5 rows x 21 columns]\n"
]
}
],
"source": [
"print(item_df.head())"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" user id age gender\n",
"0 1 24 M\n",
"1 2 53 F\n",
"2 3 23 M\n",
"3 4 24 M\n",
"4 5 33 F\n"
]
}
],
"source": [
"print(user_df.head())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"#Convert Gender column to numeric\n",
"user_df['gender'].replace('F', 1,inplace=True)\n",
"user_df['gender'].replace('M', 2,inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1989\n"
]
}
],
"source": [
"#Adjust columns replacing NaN with the mean\n",
"meanYear = int(round(item_df['release date'].mean()))\n",
"print(meanYear)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"item_df['release date'] = item_df['release date'].fillna(meanYear)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(item_df['release date'].hasnans)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"#merge it all\n",
"data_item = pd.merge(data_df, item_df, left_on = \"movie id\", right_on = \"movie id\")\n",
"data_item_user = pd.merge(data_item, user_df, left_on = \"user id\", right_on = \"user id\")\n",
"dataset = data_item_user"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" user id movie id rating release date unknown Action Adventure \\\n",
"0 196 242 3 1997.0 0 0 0 \n",
"1 196 257 2 1997.0 0 1 1 \n",
"2 196 111 4 1996.0 0 0 0 \n",
"3 196 25 4 1996.0 0 0 0 \n",
"4 196 382 4 1994.0 0 0 0 \n",
"\n",
" Animation Childrens Comedy ... Horror Musical Mystery Romance \\\n",
"0 0 0 1 ... 0 0 0 0 \n",
"1 0 0 1 ... 0 0 0 0 \n",
"2 0 0 1 ... 0 0 0 1 \n",
"3 0 0 1 ... 0 0 0 0 \n",
"4 0 0 1 ... 0 0 0 0 \n",
"\n",
" Sci-Fi Thriller War Western age gender \n",
"0 0 0 0 0 49 2 \n",
"1 1 0 0 0 49 2 \n",
"2 0 0 0 0 49 2 \n",
"3 0 0 0 0 49 2 \n",
"4 0 0 0 0 49 2 \n",
"\n",
"[5 rows x 25 columns]\n"
]
}
],
"source": [
"print(dataset.head())"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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>user id</th>\n",
" <th>movie id</th>\n",
" <th>rating</th>\n",
" <th>release date</th>\n",
" <th>unknown</th>\n",
" <th>Action</th>\n",
" <th>Adventure</th>\n",
" <th>Animation</th>\n",
" <th>Childrens</th>\n",
" <th>Comedy</th>\n",
" <th>...</th>\n",
" <th>Horror</th>\n",
" <th>Musical</th>\n",
" <th>Mystery</th>\n",
" <th>Romance</th>\n",
" <th>Sci-Fi</th>\n",
" <th>Thriller</th>\n",
" <th>War</th>\n",
" <th>Western</th>\n",
" <th>age</th>\n",
" <th>gender</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>100000.00000</td>\n",
" <td>100000.000000</td>\n",
" <td>100000.000000</td>\n",
" <td>100000.000000</td>\n",
" <td>100000.0000</td>\n",
" <td>100000.000000</td>\n",
" <td>100000.000000</td>\n",
" <td>100000.000000</td>\n",
" <td>100000.000000</td>\n",
" <td>100000.000000</td>\n",
" <td>...</td>\n",
" <td>100000.000000</td>\n",
" <td>100000.000000</td>\n",
" <td>100000.000000</td>\n",
" <td>100000.000000</td>\n",
" <td>100000.00000</td>\n",
" <td>100000.00000</td>\n",
" <td>100000.000000</td>\n",
" <td>100000.000000</td>\n",
" <td>100000.000000</td>\n",
" <td>100000.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>462.48475</td>\n",
" <td>425.530130</td>\n",
" <td>3.529860</td>\n",
" <td>1987.956310</td>\n",
" <td>0.0001</td>\n",
" <td>0.255890</td>\n",
" <td>0.137530</td>\n",
" <td>0.036050</td>\n",
" <td>0.071820</td>\n",
" <td>0.298320</td>\n",
" <td>...</td>\n",
" <td>0.053170</td>\n",
" <td>0.049540</td>\n",
" <td>0.052450</td>\n",
" <td>0.194610</td>\n",
" <td>0.12730</td>\n",
" <td>0.21872</td>\n",
" <td>0.093980</td>\n",
" <td>0.018540</td>\n",
" <td>32.969850</td>\n",
" <td>1.742600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>266.61442</td>\n",
" <td>330.798356</td>\n",
" <td>1.125674</td>\n",
" <td>14.154889</td>\n",
" <td>0.0100</td>\n",
" <td>0.436362</td>\n",
" <td>0.344408</td>\n",
" <td>0.186416</td>\n",
" <td>0.258191</td>\n",
" <td>0.457523</td>\n",
" <td>...</td>\n",
" <td>0.224373</td>\n",
" <td>0.216994</td>\n",
" <td>0.222934</td>\n",
" <td>0.395902</td>\n",
" <td>0.33331</td>\n",
" <td>0.41338</td>\n",
" <td>0.291802</td>\n",
" <td>0.134894</td>\n",
" <td>11.562623</td>\n",
" <td>0.437204</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.00000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1922.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>7.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>254.00000</td>\n",
" <td>175.000000</td>\n",
" <td>3.000000</td>\n",
" <td>1986.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>24.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>447.00000</td>\n",
" <td>322.000000</td>\n",
" <td>4.000000</td>\n",
" <td>1994.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>30.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>682.00000</td>\n",
" <td>631.000000</td>\n",
" <td>4.000000</td>\n",
" <td>1996.000000</td>\n",
" <td>0.0000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>40.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>943.00000</td>\n",
" <td>1682.000000</td>\n",
" <td>5.000000</td>\n",
" <td>1998.000000</td>\n",
" <td>1.0000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>...</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.00000</td>\n",
" <td>1.00000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>73.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 25 columns</p>\n",
"</div>"
],
"text/plain": [
" user id movie id rating release date unknown \\\n",
"count 100000.00000 100000.000000 100000.000000 100000.000000 100000.0000 \n",
"mean 462.48475 425.530130 3.529860 1987.956310 0.0001 \n",
"std 266.61442 330.798356 1.125674 14.154889 0.0100 \n",
"min 1.00000 1.000000 1.000000 1922.000000 0.0000 \n",
"25% 254.00000 175.000000 3.000000 1986.000000 0.0000 \n",
"50% 447.00000 322.000000 4.000000 1994.000000 0.0000 \n",
"75% 682.00000 631.000000 4.000000 1996.000000 0.0000 \n",
"max 943.00000 1682.000000 5.000000 1998.000000 1.0000 \n",
"\n",
" Action Adventure Animation Childrens \\\n",
"count 100000.000000 100000.000000 100000.000000 100000.000000 \n",
"mean 0.255890 0.137530 0.036050 0.071820 \n",
"std 0.436362 0.344408 0.186416 0.258191 \n",
"min 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 0.000000 \n",
"50% 0.000000 0.000000 0.000000 0.000000 \n",
"75% 1.000000 0.000000 0.000000 0.000000 \n",
"max 1.000000 1.000000 1.000000 1.000000 \n",
"\n",
" Comedy ... Horror Musical \\\n",
"count 100000.000000 ... 100000.000000 100000.000000 \n",
"mean 0.298320 ... 0.053170 0.049540 \n",
"std 0.457523 ... 0.224373 0.216994 \n",
"min 0.000000 ... 0.000000 0.000000 \n",
"25% 0.000000 ... 0.000000 0.000000 \n",
"50% 0.000000 ... 0.000000 0.000000 \n",
"75% 1.000000 ... 0.000000 0.000000 \n",
"max 1.000000 ... 1.000000 1.000000 \n",
"\n",
" Mystery Romance Sci-Fi Thriller \\\n",
"count 100000.000000 100000.000000 100000.00000 100000.00000 \n",
"mean 0.052450 0.194610 0.12730 0.21872 \n",
"std 0.222934 0.395902 0.33331 0.41338 \n",
"min 0.000000 0.000000 0.00000 0.00000 \n",
"25% 0.000000 0.000000 0.00000 0.00000 \n",
"50% 0.000000 0.000000 0.00000 0.00000 \n",
"75% 0.000000 0.000000 0.00000 0.00000 \n",
"max 1.000000 1.000000 1.00000 1.00000 \n",
"\n",
" War Western age gender \n",
"count 100000.000000 100000.000000 100000.000000 100000.000000 \n",
"mean 0.093980 0.018540 32.969850 1.742600 \n",
"std 0.291802 0.134894 11.562623 0.437204 \n",
"min 0.000000 0.000000 7.000000 1.000000 \n",
"25% 0.000000 0.000000 24.000000 1.000000 \n",
"50% 0.000000 0.000000 30.000000 2.000000 \n",
"75% 0.000000 0.000000 40.000000 2.000000 \n",
"max 1.000000 1.000000 73.000000 2.000000 \n",
"\n",
"[8 rows x 25 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Data distribution\n",
"display(dataset.describe())"
]
},
{
"cell_type": "code",
"execution_count": 14,
"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>user id</th>\n",
" <th>movie id</th>\n",
" <th>rating</th>\n",
" <th>release date</th>\n",
" <th>unknown</th>\n",
" <th>Action</th>\n",
" <th>Adventure</th>\n",
" <th>Animation</th>\n",
" <th>Childrens</th>\n",
" <th>Comedy</th>\n",
" <th>...</th>\n",
" <th>Horror</th>\n",
" <th>Musical</th>\n",
" <th>Mystery</th>\n",
" <th>Romance</th>\n",
" <th>Sci-Fi</th>\n",
" <th>Thriller</th>\n",
" <th>War</th>\n",
" <th>Western</th>\n",
" <th>age</th>\n",
" <th>gender</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>196</td>\n",
" <td>242</td>\n",
" <td>3</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>196</td>\n",
" <td>257</td>\n",
" <td>2</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>196</td>\n",
" <td>111</td>\n",
" <td>4</td>\n",
" <td>1996.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>196</td>\n",
" <td>25</td>\n",
" <td>4</td>\n",
" <td>1996.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>196</td>\n",
" <td>382</td>\n",
" <td>4</td>\n",
" <td>1994.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>196</td>\n",
" <td>202</td>\n",
" <td>3</td>\n",
" <td>1993.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>196</td>\n",
" <td>153</td>\n",
" <td>5</td>\n",
" <td>1988.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>196</td>\n",
" <td>286</td>\n",
" <td>5</td>\n",
" <td>1996.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>196</td>\n",
" <td>66</td>\n",
" <td>3</td>\n",
" <td>1995.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>196</td>\n",
" <td>845</td>\n",
" <td>4</td>\n",
" <td>1996.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>196</td>\n",
" <td>173</td>\n",
" <td>2</td>\n",
" <td>1987.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>196</td>\n",
" <td>238</td>\n",
" <td>4</td>\n",
" <td>1987.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>196</td>\n",
" <td>94</td>\n",
" <td>3</td>\n",
" <td>1990.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>196</td>\n",
" <td>762</td>\n",
" <td>3</td>\n",
" <td>1996.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>196</td>\n",
" <td>381</td>\n",
" <td>4</td>\n",
" <td>1994.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>196</td>\n",
" <td>306</td>\n",
" <td>4</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>196</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>1995.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>196</td>\n",
" <td>70</td>\n",
" <td>3</td>\n",
" <td>1994.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>196</td>\n",
" <td>655</td>\n",
" <td>5</td>\n",
" <td>1986.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>196</td>\n",
" <td>13</td>\n",
" <td>2</td>\n",
" <td>1995.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>196</td>\n",
" <td>692</td>\n",
" <td>5</td>\n",
" <td>1995.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>196</td>\n",
" <td>1022</td>\n",
" <td>4</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>196</td>\n",
" <td>287</td>\n",
" <td>3</td>\n",
" <td>1996.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>196</td>\n",
" <td>269</td>\n",
" <td>3</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>196</td>\n",
" <td>285</td>\n",
" <td>5</td>\n",
" <td>1996.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>196</td>\n",
" <td>110</td>\n",
" <td>1</td>\n",
" <td>1995.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>196</td>\n",
" <td>251</td>\n",
" <td>3</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>196</td>\n",
" <td>393</td>\n",
" <td>4</td>\n",
" <td>1993.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>196</td>\n",
" <td>663</td>\n",
" <td>5</td>\n",
" <td>1979.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>196</td>\n",
" <td>580</td>\n",
" <td>2</td>\n",
" <td>1995.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>2</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>99970</th>\n",
" <td>598</td>\n",
" <td>898</td>\n",
" <td>4</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99971</th>\n",
" <td>598</td>\n",
" <td>243</td>\n",
" <td>2</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99972</th>\n",
" <td>598</td>\n",
" <td>308</td>\n",
" <td>4</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99973</th>\n",
" <td>598</td>\n",
" <td>312</td>\n",
" <td>5</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99974</th>\n",
" <td>598</td>\n",
" <td>313</td>\n",
" <td>5</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99975</th>\n",
" <td>598</td>\n",
" <td>260</td>\n",
" <td>3</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99976</th>\n",
" <td>598</td>\n",
" <td>895</td>\n",
" <td>2</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99977</th>\n",
" <td>598</td>\n",
" <td>691</td>\n",
" <td>2</td>\n",
" <td>1998.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99978</th>\n",
" <td>598</td>\n",
" <td>349</td>\n",
" <td>4</td>\n",
" <td>1998.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99979</th>\n",
" <td>598</td>\n",
" <td>538</td>\n",
" <td>4</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99980</th>\n",
" <td>873</td>\n",
" <td>294</td>\n",
" <td>4</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>48</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99981</th>\n",
" <td>873</td>\n",
" <td>328</td>\n",
" <td>4</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>48</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99982</th>\n",
" <td>873</td>\n",
" <td>307</td>\n",
" <td>3</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>48</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99983</th>\n",
" <td>873</td>\n",
" <td>750</td>\n",
" <td>3</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>48</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99984</th>\n",
" <td>873</td>\n",
" <td>258</td>\n",
" <td>3</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>48</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99985</th>\n",
" <td>873</td>\n",
" <td>339</td>\n",
" <td>3</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>48</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99986</th>\n",
" <td>873</td>\n",
" <td>321</td>\n",
" <td>1</td>\n",
" <td>1996.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>48</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99987</th>\n",
" <td>873</td>\n",
" <td>879</td>\n",
" <td>2</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>48</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99988</th>\n",
" <td>873</td>\n",
" <td>286</td>\n",
" <td>2</td>\n",
" <td>1996.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>48</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99989</th>\n",
" <td>873</td>\n",
" <td>259</td>\n",
" <td>1</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>48</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99990</th>\n",
" <td>873</td>\n",
" <td>289</td>\n",
" <td>2</td>\n",
" <td>1996.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>48</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99991</th>\n",
" <td>873</td>\n",
" <td>292</td>\n",
" <td>5</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>48</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99992</th>\n",
" <td>873</td>\n",
" <td>269</td>\n",
" <td>2</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>48</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99993</th>\n",
" <td>873</td>\n",
" <td>875</td>\n",
" <td>1</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>48</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99994</th>\n",
" <td>873</td>\n",
" <td>300</td>\n",
" <td>4</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>48</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99995</th>\n",
" <td>873</td>\n",
" <td>313</td>\n",
" <td>5</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>48</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99996</th>\n",
" <td>873</td>\n",
" <td>326</td>\n",
" <td>4</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>48</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99997</th>\n",
" <td>873</td>\n",
" <td>348</td>\n",
" <td>3</td>\n",
" <td>1998.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>48</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99998</th>\n",
" <td>873</td>\n",
" <td>358</td>\n",
" <td>2</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>48</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99999</th>\n",
" <td>873</td>\n",
" <td>342</td>\n",
" <td>4</td>\n",
" <td>1997.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>48</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>100000 rows × 25 columns</p>\n",
"</div>"
],
"text/plain": [
" user id movie id rating release date unknown Action Adventure \\\n",
"0 196 242 3 1997.0 0 0 0 \n",
"1 196 257 2 1997.0 0 1 1 \n",
"2 196 111 4 1996.0 0 0 0 \n",
"3 196 25 4 1996.0 0 0 0 \n",
"4 196 382 4 1994.0 0 0 0 \n",
"5 196 202 3 1993.0 0 0 0 \n",
"6 196 153 5 1988.0 0 0 0 \n",
"7 196 286 5 1996.0 0 0 0 \n",
"8 196 66 3 1995.0 0 0 0 \n",
"9 196 845 4 1996.0 0 0 0 \n",
"10 196 173 2 1987.0 0 1 1 \n",
"11 196 238 4 1987.0 0 0 0 \n",
"12 196 94 3 1990.0 0 0 0 \n",
"13 196 762 3 1996.0 0 0 0 \n",
"14 196 381 4 1994.0 0 0 0 \n",
"15 196 306 4 1997.0 0 0 0 \n",
"16 196 8 5 1995.0 0 0 0 \n",
"17 196 70 3 1994.0 0 0 0 \n",
"18 196 655 5 1986.0 0 0 1 \n",
"19 196 13 2 1995.0 0 0 0 \n",
"20 196 692 5 1995.0 0 0 0 \n",
"21 196 1022 4 1997.0 0 0 0 \n",
"22 196 287 3 1996.0 0 0 0 \n",
"23 196 269 3 1997.0 0 0 0 \n",
"24 196 285 5 1996.0 0 0 0 \n",
"25 196 110 1 1995.0 0 1 1 \n",
"26 196 251 3 1997.0 0 0 0 \n",
"27 196 393 4 1993.0 0 0 0 \n",
"28 196 663 5 1979.0 0 0 0 \n",
"29 196 580 2 1995.0 0 0 0 \n",
"... ... ... ... ... ... ... ... \n",
"99970 598 898 4 1997.0 0 0 0 \n",
"99971 598 243 2 1997.0 0 0 0 \n",
"99972 598 308 4 1997.0 0 0 0 \n",
"99973 598 312 5 1997.0 0 0 0 \n",
"99974 598 313 5 1997.0 0 1 0 \n",
"99975 598 260 3 1997.0 0 1 0 \n",
"99976 598 895 2 1997.0 0 0 0 \n",
"99977 598 691 2 1998.0 0 0 0 \n",
"99978 598 349 4 1998.0 0 1 0 \n",
"99979 598 538 4 1997.0 0 0 0 \n",
"99980 873 294 4 1997.0 0 0 0 \n",
"99981 873 328 4 1997.0 0 1 0 \n",
"99982 873 307 3 1997.0 0 0 0 \n",
"99983 873 750 3 1997.0 0 0 0 \n",
"99984 873 258 3 1997.0 0 0 0 \n",
"99985 873 339 3 1997.0 0 1 0 \n",
"99986 873 321 1 1996.0 0 0 0 \n",
"99987 873 879 2 1997.0 0 1 0 \n",
"99988 873 286 2 1996.0 0 0 0 \n",
"99989 873 259 1 1997.0 0 0 0 \n",
"99990 873 289 2 1996.0 0 0 0 \n",
"99991 873 292 5 1997.0 0 0 0 \n",
"99992 873 269 2 1997.0 0 0 0 \n",
"99993 873 875 1 1997.0 0 0 0 \n",
"99994 873 300 4 1997.0 0 1 0 \n",
"99995 873 313 5 1997.0 0 1 0 \n",
"99996 873 326 4 1997.0 0 1 0 \n",
"99997 873 348 3 1998.0 0 0 0 \n",
"99998 873 358 2 1997.0 0 1 1 \n",
"99999 873 342 4 1997.0 0 0 0 \n",
"\n",
" Animation Childrens Comedy ... Horror Musical Mystery \\\n",
"0 0 0 1 ... 0 0 0 \n",
"1 0 0 1 ... 0 0 0 \n",
"2 0 0 1 ... 0 0 0 \n",
"3 0 0 1 ... 0 0 0 \n",
"4 0 0 1 ... 0 0 0 \n",
"5 0 0 1 ... 0 0 0 \n",
"6 0 0 1 ... 0 0 0 \n",
"7 0 0 0 ... 0 0 0 \n",
"8 0 0 1 ... 0 0 0 \n",
"9 0 0 1 ... 0 0 0 \n",
"10 0 0 1 ... 0 0 0 \n",
"11 0 0 1 ... 0 0 0 \n",
"12 0 1 1 ... 0 0 0 \n",
"13 0 0 0 ... 0 0 0 \n",
"14 0 0 1 ... 0 0 0 \n",
"15 0 0 0 ... 0 0 0 \n",
"16 0 1 1 ... 0 0 0 \n",
"17 0 0 1 ... 0 0 0 \n",
"18 0 0 1 ... 0 0 0 \n",
"19 0 0 1 ... 0 0 0 \n",
"20 0 0 1 ... 0 0 0 \n",
"21 0 0 0 ... 0 0 0 \n",
"22 0 0 0 ... 0 0 0 \n",
"23 0 0 1 ... 0 0 0 \n",
"24 0 0 0 ... 0 0 0 \n",
"25 0 0 1 ... 0 0 0 \n",
"26 0 0 1 ... 0 0 0 \n",
"27 0 0 1 ... 0 0 0 \n",
"28 0 0 1 ... 0 0 0 \n",
"29 0 0 1 ... 0 0 0 \n",
"... ... ... ... ... ... ... ... \n",
"99970 0 0 0 ... 0 0 0 \n",
"99971 0 1 1 ... 0 0 0 \n",
"99972 0 1 0 ... 0 0 0 \n",
"99973 0 0 1 ... 0 0 1 \n",
"99974 0 0 0 ... 0 0 0 \n",
"99975 0 0 0 ... 0 0 1 \n",
"99976 0 0 0 ... 1 0 0 \n",
"99977 0 0 0 ... 0 0 0 \n",
"99978 0 0 0 ... 0 0 0 \n",
"99979 1 1 0 ... 0 1 0 \n",
"99980 0 0 1 ... 0 0 0 \n",
"99981 0 0 0 ... 0 0 1 \n",
"99982 0 0 0 ... 1 0 1 \n",
"99983 0 0 0 ... 0 0 0 \n",
"99984 0 0 0 ... 0 0 0 \n",
"99985 0 0 0 ... 0 0 0 \n",
"99986 0 0 1 ... 0 0 0 \n",
"99987 0 0 0 ... 0 0 0 \n",
"99988 0 0 0 ... 0 0 0 \n",
"99989 0 1 1 ... 0 0 0 \n",
"99990 0 0 0 ... 0 1 0 \n",
"99991 0 0 0 ... 0 0 0 \n",
"99992 0 0 1 ... 0 0 0 \n",
"99993 0 0 0 ... 0 0 0 \n",
"99994 0 0 0 ... 0 0 0 \n",
"99995 0 0 0 ... 0 0 0 \n",
"99996 0 0 0 ... 0 0 0 \n",
"99997 0 0 0 ... 0 0 0 \n",
"99998 0 0 0 ... 0 0 0 \n",
"99999 0 0 1 ... 0 0 1 \n",
"\n",
" Romance Sci-Fi Thriller War Western age gender \n",
"0 0 0 0 0 0 49 2 \n",
"1 0 1 0 0 0 49 2 \n",
"2 1 0 0 0 0 49 2 \n",
"3 0 0 0 0 0 49 2 \n",
"4 0 0 0 0 0 49 2 \n",
"5 1 0 0 0 0 49 2 \n",
"6 0 0 0 0 0 49 2 \n",
"7 1 0 0 1 0 49 2 \n",
"8 1 0 0 0 0 49 2 \n",
"9 0 0 0 0 0 49 2 \n",
"10 1 0 0 0 0 49 2 \n",
"11 0 0 0 0 0 49 2 \n",
"12 0 0 0 0 0 49 2 \n",
"13 0 0 0 0 0 49 2 \n",
"14 1 0 0 0 0 49 2 \n",
"15 1 0 0 0 0 49 2 \n",
"16 0 0 0 0 0 49 2 \n",
"17 1 0 0 0 0 49 2 \n",
"18 0 0 0 0 0 49 2 \n",
"19 0 0 0 0 0 49 2 \n",
"20 1 0 0 0 0 49 2 \n",
"21 0 0 0 0 0 49 2 \n",
"22 0 0 0 0 0 49 2 \n",
"23 0 0 0 0 0 49 2 \n",
"24 0 0 0 0 0 49 2 \n",
"25 0 0 0 1 0 49 2 \n",
"26 0 0 0 0 0 49 2 \n",
"27 0 0 0 0 0 49 2 \n",
"28 0 0 0 0 0 49 2 \n",
"29 1 0 0 0 0 49 2 \n",
"... ... ... ... ... ... ... ... \n",
"99970 0 0 0 0 0 40 1 \n",
"99971 0 0 0 0 0 40 1 \n",
"99972 0 0 0 0 0 40 1 \n",
"99973 0 0 0 0 0 40 1 \n",
"99974 1 0 0 0 0 40 1 \n",
"99975 0 1 1 0 0 40 1 \n",
"99976 0 0 1 0 0 40 1 \n",
"99977 0 1 1 0 0 40 1 \n",
"99978 0 0 1 0 0 40 1 \n",
"99979 0 0 0 0 0 40 1 \n",
"99980 0 0 0 0 0 48 1 \n",
"99981 1 0 1 0 0 48 1 \n",
"99982 0 0 1 0 0 48 1 \n",
"99983 0 0 0 0 0 48 1 \n",
"99984 0 1 0 0 0 48 1 \n",
"99985 0 0 0 0 0 48 1 \n",
"99986 0 0 0 0 0 48 1 \n",
"99987 0 0 1 1 0 48 1 \n",
"99988 1 0 0 1 0 48 1 \n",
"99989 0 0 0 0 0 48 1 \n",
"99990 0 0 0 0 0 48 1 \n",
"99991 0 0 0 0 0 48 1 \n",
"99992 0 0 0 0 0 48 1 \n",
"99993 1 0 0 0 0 48 1 \n",
"99994 0 0 1 0 0 48 1 \n",
"99995 1 0 0 0 0 48 1 \n",
"99996 0 0 0 1 0 48 1 \n",
"99997 0 0 1 0 0 48 1 \n",
"99998 0 1 1 0 0 48 1 \n",
"99999 0 0 0 0 0 48 1 \n",
"\n",
"[100000 rows x 25 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Show the current Dataset Structure\n",
"from IPython.display import display\n",
"display(dataset)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Genre user id score = -0.462037184259\n",
"Genre movie id score = 0.103906128463\n",
"Genre rating score = -0.514671809931\n",
"Genre release date score = 0.981214096734\n",
"Genre unknown score = 0.199839967994\n",
"Genre Action score = 0.997266179049\n",
"Genre Adventure score = 0.998638681572\n",
"Genre Animation score = 0.997662265757\n",
"Genre Childrens score = 0.990137693577\n",
"Genre Comedy score = 0.99333890982\n",
"Genre Crime score = 0.994547788211\n",
"Genre Documentary score = 0.896100959944\n",
"Genre Drama score = 0.996313565788\n",
"Genre Fantasy score = 1.0\n",
"Genre Film-Noir score = 0.9976604411\n",
"Genre Horror score = 0.992888170977\n",
"Genre Musical score = 0.993823652939\n",
"Genre Mystery score = 0.997655839113\n",
"Genre Romance score = 0.991029666727\n",
"Genre Sci-Fi score = 0.999280954782\n",
"Genre Thriller score = 0.997210432658\n",
"Genre War score = 0.996194572353\n",
"Genre Western score = 0.997934902169\n",
"Genre age score = 0.976652941984\n",
"Genre gender score = 0.992743720756\n"
]
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"from sklearn.tree import DecisionTreeRegressor\n",
"\n",
"# Calculate Feature Relevance to the Dataset\n",
"for name, values in dataset.iteritems():\n",
" # Clone Dataset for Feature Relevance calculation\n",
" backupData = dataset.copy()\n",
" # Clone the Column to be predicted\n",
" y = backupData[name].copy()\n",
" # Drop column, that will be used for prediction\n",
" X = backupData.drop(name, 1)\n",
" # Split Data for Model calibration\n",
" X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=72)\n",
" regressor = DecisionTreeRegressor(random_state=72)\n",
" regressor.fit(X_train, y_train)\n",
" score = regressor.score(X_test, y_test)\n",
" print('Genre {} score = {}').format(name, score)\n",
" # User Id and Movie Id have a weak relation within the other features, but "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Based on Feature Relevance we are going to \n",
"# 1st Remove the user id from the dataset\n",
"smartdata = dataset.copy()\n",
"smartdata.drop(smartdata.columns[[0]], axis = 1, inplace = True)\n",
"print(smartdata.head())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 2nd Lets translate the Ratingo into a more discrete evalution (like and dislike)\n",
"# 1 - 2.9 : DILIKE\n",
"# 3 - 5 : LIKE\n",
"\n",
"for name, values in smartdata['rating'].iteritems():\n",
" print(values)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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3rHYA62sOcnTZoR6jcJyJhkeLUIqcp+lfqnyAMIdYnRtjTOpJpIHUCpyIM4nr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\nIlIwgjqC9ArwKPB14LfAHODKgLJFRGSQOmV0JVv2HKClrdPXnJd3ttG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2kIDdQGoI8GHJ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vlbn8HmUejHqR3Uj1MbzOZRkrzWzObhY9e52TwObYDi6s/Zc8RukeimUkh1TJBgOyYO4rzOt6xY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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a7c9e0190>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Produce a scatter matrix for each pair of features in the data \n",
"pd.plotting.scatter_matrix(dataset, alpha = 0.3, figsize = (14,8), diagonal = 'kde');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Feature Scaling\n",
"# Scale the data using the natural logarithm \n",
"log_data = np.log(dataset)\n",
"# Scale the sample data using the natural logarithm\n",
"log_samples = np.log(samples)\n",
"# Produce a scatter matrix for each pair of newly-transformed features\n",
"pd.scatter_matrix(log_data, alpha = 0.3, figsize = (14,8), diagonal = 'kde');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"item_list = (((pd.merge(item,data).sort_values(by = 'movie id')).groupby('movie title')))['movie id', 'movie title', 'rating']\n",
"item_list = item_list.mean()\n",
"item_list['movie title'] = item_list.index\n",
"item_list = item_list.as_matrix()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"recommendation_list = []\n",
"for i in recommendation:\n",
" recommendation_list.append(item_list[i-1])\n",
" \n",
"recommendation = (pd.DataFrame(recommendation_list,columns = ['movie id','mean rating' ,'movie title'])).sort_values(by = 'mean rating', ascending = False)\n",
"print(recommendation[['mean rating','movie title']])"
]
},
{
"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": "2.7.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
neuro-data-mining/materials | very_basic_python_code_testing.ipynb | 1 | 7067 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import warnings\n",
"import numpy.testing as npt\n",
"import numpy as np\n",
"import sys"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Resources\n",
"\n",
"- Errors & Exceptions in Python:\n",
" https://docs.python.org/3/tutorial/errors.html\n",
"\n",
"- Built-In Excpetions in Python:\n",
" https://docs.python.org/2/library/exceptions.html\n",
"\n",
"- Numpy Testing:\n",
" http://docs.scipy.org/doc/numpy/reference/routines.testing.html"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Assert statements"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Example: Assert that a specific version of Python is being used"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def check_python_version():\n",
" print 'Python version:\\n', sys.version\n",
" assert sys.version_info < (3,0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"check_python_version()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def improved_check_python_version():\n",
" print 'Python version:\\n', sys.version\n",
" try:\n",
" assert sys.version_info < (3,0)\n",
" except: \n",
" raise AssertionError('Incompatible version of Python: use Python version < 3.0')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"improved_check_python_version()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Raising errors\n",
"\n",
"- Anticipate and catch errors\n",
"- Raise a more informative error than the default error"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Example: Division in Python versions < 3.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def test_type_float(var):\n",
" if not isinstance(var, float):\n",
" raise TypeError('Expected input type == float')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"f = 1."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Since the input type is a float, no error is raised.\n",
"test_type_float(f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"i = 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Since the input type is a list, an error is raised.\n",
"test_type_float(i)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def incorrect_divide_by_two(var):\n",
" return var / 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print incorrect_divide_by_two(f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print incorrect_divide_by_two(i)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def correct_divide_by_two(var):\n",
" '''\n",
" Divides input by two.\n",
" \n",
" INPUT\n",
" var : float\n",
" '''\n",
" test_type_float(var)\n",
" return var / 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"correct_divide_by_two?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print correct_divide_by_two(f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print correct_divide_by_two(i)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Raising warnings"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Example: Division in Python versions < 3.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def divide_by_two(var):\n",
" if isinstance(var, int):\n",
" warnings.warn('Performing floor division. Input type == int', Warning)\n",
" return var / 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"divide_by_two(1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"divide_by_two(np.array([1]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def divide_by_two(var):\n",
" if isinstance(var, int):\n",
" warnings.warn('Performing floor division. Input type == int', Warning)\n",
" if isinstance(var, np.ndarray):\n",
" if var.dtype == int:\n",
" warnings.warn('Performing floor division. numpy.dtype == int', Warning)\n",
" return var / 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"divide_by_two(np.array([1]))"
]
},
{
"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 |
jmhsi/justin_tinker | data_science/courses/temp/courses/dl1/lesson5-movielens.ipynb | 1 | 20047 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"## Movielens"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"%reload_ext autoreload\n",
"%autoreload 2\n",
"%matplotlib inline\n",
"\n",
"from fastai.learner import *\n",
"from fastai.column_data import *"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"Data available from http://files.grouplens.org/datasets/movielens/ml-latest-small.zip"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"path='data/ml-latest-small/'"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"We're working with the movielens data, which contains one rating per row, like this:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"ratings = pd.read_csv(path+'ratings.csv')\n",
"ratings.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"Just for display purposes, let's read in the movie names too."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"movies = pd.read_csv(path+'movies.csv')\n",
"movies.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"## Create subset for Excel"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"We create a crosstab of the most popular movies and most movie-addicted users which we'll copy into Excel for creating a simple example. This isn't necessary for any of the modeling below however."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"g=ratings.groupby('userId')['rating'].count()\n",
"topUsers=g.sort_values(ascending=False)[:15]\n",
"\n",
"g=ratings.groupby('movieId')['rating'].count()\n",
"topMovies=g.sort_values(ascending=False)[:15]\n",
"\n",
"top_r = ratings.join(topUsers, rsuffix='_r', how='inner', on='userId')\n",
"top_r = top_r.join(topMovies, rsuffix='_r', how='inner', on='movieId')\n",
"\n",
"pd.crosstab(top_r.userId, top_r.movieId, top_r.rating, aggfunc=np.sum)"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"## Collaborative filtering"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"val_idxs = get_cv_idxs(len(ratings))\n",
"wd=2e-4\n",
"n_factors = 50"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"cf = CollabFilterDataset.from_csv(path, 'ratings.csv', 'userId', 'movieId', 'rating')\n",
"learn = cf.get_learner(n_factors, val_idxs, 64, opt_fn=optim.Adam)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"learn.fit(1e-2, 2, wds=wd, cycle_len=1, cycle_mult=2, use_wd_sched=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"Let's compare to some benchmarks. Here's [some benchmarks](https://www.librec.net/release/v1.3/example.html) on the same dataset for the popular Librec system for collaborative filtering. They show best results based on [RMSE](http://www.statisticshowto.com/rmse/) of 0.91. We'll need to take the square root of our loss, since we use plain MSE."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"math.sqrt(0.776)"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"Looking good - we've found a solution better than any of those benchmarks! Let's take a look at how the predictions compare to actuals for this model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"preds = learn.predict()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"y=learn.data.val_y\n",
"sns.jointplot(preds, y, kind='hex', stat_func=None);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analyze results"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"### Movie bias"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"movie_names = movies.set_index('movieId')['title'].to_dict()\n",
"g=ratings.groupby('movieId')['rating'].count()\n",
"topMovies=g.sort_values(ascending=False).index.values[:3000]\n",
"topMovieIdx = np.array([cf.item2idx[o] for o in topMovies])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"m=learn.model; m.cuda()"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"First, we'll look at the movie bias term. Here, our input is the movie id (a single id), and the output is the movie bias (a single float)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"movie_bias = to_np(m.ib(V(topMovieIdx)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"movie_bias"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"movie_ratings = [(b[0], movie_names[i]) for i,b in zip(topMovies,movie_bias)]"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"Now we can look at the top and bottom rated movies. These ratings are corrected for different levels of reviewer sentiment, as well as different types of movies that different reviewers watch."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"sorted(movie_ratings, key=lambda o: o[0])[:15]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"sorted(movie_ratings, key=itemgetter(0))[:15]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"sorted(movie_ratings, key=lambda o: o[0], reverse=True)[:15]"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"### Embedding interpretation"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"We can now do the same thing for the embeddings."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"movie_emb = to_np(m.i(V(topMovieIdx)))\n",
"movie_emb.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"Because it's hard to interpret 50 embeddings, we use [PCA](https://plot.ly/ipython-notebooks/principal-component-analysis/) to simplify them down to just 3 vectors. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"from sklearn.decomposition import PCA\n",
"pca = PCA(n_components=3)\n",
"movie_pca = pca.fit(movie_emb.T).components_"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"movie_pca.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"fac0 = movie_pca[0]\n",
"movie_comp = [(f, movie_names[i]) for f,i in zip(fac0, topMovies)]"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"Here's the 1st component. It seems to be 'easy watching' vs 'serious'."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"sorted(movie_comp, key=itemgetter(0))[:10]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"fac1 = movie_pca[1]\n",
"movie_comp = [(f, movie_names[i]) for f,i in zip(fac1, topMovies)]"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"Here's the 2nd component. It seems to be 'CGI' vs 'dialog driven'."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"sorted(movie_comp, key=itemgetter(0))[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"We can draw a picture to see how various movies appear on the map of these components. This picture shows the first two components."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"idxs = np.random.choice(len(topMovies), 50, replace=False)\n",
"X = fac0[idxs]\n",
"Y = fac1[idxs]\n",
"plt.figure(figsize=(15,15))\n",
"plt.scatter(X, Y)\n",
"for i, x, y in zip(topMovies[idxs], X, Y):\n",
" plt.text(x,y,movie_names[i], color=np.random.rand(3)*0.7, fontsize=11)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Collab filtering from scratch"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"### Dot product example"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"a = T([[1.,2],[3,4]])\n",
"b = T([[2.,2],[10,10]])\n",
"a,b"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"a*b"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"(a*b).sum(1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"class DotProduct(nn.Module):\n",
" def forward(self, u, m): return (u*m).sum(1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"model=DotProduct()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"model(a,b)"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"### Dot product model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"u_uniq = ratings.userId.unique()\n",
"user2idx = {o:i for i,o in enumerate(u_uniq)}\n",
"ratings.userId = ratings.userId.apply(lambda x: user2idx[x])\n",
"\n",
"m_uniq = ratings.movieId.unique()\n",
"movie2idx = {o:i for i,o in enumerate(m_uniq)}\n",
"ratings.movieId = ratings.movieId.apply(lambda x: movie2idx[x])\n",
"\n",
"n_users=int(ratings.userId.nunique())\n",
"n_movies=int(ratings.movieId.nunique())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"class EmbeddingDot(nn.Module):\n",
" def __init__(self, n_users, n_movies):\n",
" super().__init__()\n",
" self.u = nn.Embedding(n_users, n_factors)\n",
" self.m = nn.Embedding(n_movies, n_factors)\n",
" self.u.weight.data.uniform_(0,0.05)\n",
" self.m.weight.data.uniform_(0,0.05)\n",
" \n",
" def forward(self, cats, conts):\n",
" users,movies = cats[:,0],cats[:,1]\n",
" u,m = self.u(users),self.m(movies)\n",
" return (u*m).sum(1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"x = ratings.drop(['rating', 'timestamp'],axis=1)\n",
"y = ratings['rating'].astype(np.float32)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"data = ColumnarModelData.from_data_frame(path, val_idxs, x, y, ['userId', 'movieId'], 64)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"wd=1e-5\n",
"model = EmbeddingDot(n_users, n_movies).cuda()\n",
"opt = optim.SGD(model.parameters(), 1e-1, weight_decay=wd, momentum=0.9)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"fit(model, data, 3, opt, F.mse_loss)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"set_lrs(opt, 0.01)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"fit(model, data, 3, opt, F.mse_loss)"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"### Bias"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"min_rating,max_rating = ratings.rating.min(),ratings.rating.max()\n",
"min_rating,max_rating"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"def get_emb(ni,nf):\n",
" e = nn.Embedding(ni, nf)\n",
" e.weight.data.uniform_(-0.01,0.01)\n",
" return e\n",
"\n",
"class EmbeddingDotBias(nn.Module):\n",
" def __init__(self, n_users, n_movies):\n",
" super().__init__()\n",
" (self.u, self.m, self.ub, self.mb) = [get_emb(*o) for o in [\n",
" (n_users, n_factors), (n_movies, n_factors), (n_users,1), (n_movies,1)\n",
" ]]\n",
" \n",
" def forward(self, cats, conts):\n",
" users,movies = cats[:,0],cats[:,1]\n",
" um = (self.u(users)* self.m(movies)).sum(1)\n",
" res = um + self.ub(users).squeeze() + self.mb(movies).squeeze()\n",
" res = F.sigmoid(res) * (max_rating-min_rating) + min_rating\n",
" return res"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"wd=2e-4\n",
"model = EmbeddingDotBias(cf.n_users, cf.n_items).cuda()\n",
"opt = optim.SGD(model.parameters(), 1e-1, weight_decay=wd, momentum=0.9)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"fit(model, data, 3, opt, F.mse_loss)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"set_lrs(opt, 1e-2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"fit(model, data, 3, opt, F.mse_loss)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Mini net"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"code_folding": []
},
"outputs": [],
"source": [
"class EmbeddingNet(nn.Module):\n",
" def __init__(self, n_users, n_movies, nh=10, p1=0.05, p2=0.5):\n",
" super().__init__()\n",
" (self.u, self.m) = [get_emb(*o) for o in [\n",
" (n_users, n_factors), (n_movies, n_factors)]]\n",
" self.lin1 = nn.Linear(n_factors*2, nh)\n",
" self.lin2 = nn.Linear(nh, 1)\n",
" self.drop1 = nn.Dropout(p1)\n",
" self.drop2 = nn.Dropout(p2)\n",
" \n",
" def forward(self, cats, conts):\n",
" users,movies = cats[:,0],cats[:,1]\n",
" x = self.drop1(torch.cat([self.u(users),self.m(movies)], dim=1))\n",
" x = self.drop2(F.relu(self.lin1(x)))\n",
" return F.sigmoid(self.lin2(x)) * (max_rating-min_rating+1) + min_rating-0.5"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"wd=1e-5\n",
"model = EmbeddingNet(n_users, n_movies).cuda()\n",
"opt = optim.Adam(model.parameters(), 1e-3, weight_decay=wd)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fit(model, data, 3, opt, F.mse_loss)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"set_lrs(opt, 1e-3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fit(model, data, 3, opt, F.mse_loss)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
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"toc": {
"colors": {
"hover_highlight": "#DAA520",
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| apache-2.0 |
McDermott-Group/Simulation | Microwave Design/Transmission Lines/QuarterWave Resonators/QuarterWaveResonatorCalculations.ipynb | 1 | 8411 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Motivation\n",
"\n",
"We need to have a very good method of selecting resonator lengths such that we minimize the time it takes to fabricate suitable frequency-targeted qubit devices for readout with an amplifier. We must also ensure that these devices are spaced in frequency enough such that individual readout resonators are easily resolved in frequency and phase space for a given coupling rate $\\kappa$. \n",
"\n",
"# Method\n",
"## Capacitance and Inductance of Bare CPW (no crossovers)\n",
"\n",
"Using the geometry alone, one is able to calculate the phase velocity of the CPW by finding the the capacitance per unit length. (See [Goppel](http://arxiv.org/pdf/0807.4094v1.pdf) and [Gupta](https://www.scribd.com/doc/112426565/Gupta-Et-Al-1996-Microstrip-Lines-and-Slotlines-2nd-Ed) (p. 382) for details.) The calculation to be done are\n",
"\n",
"The total line capacitance per unit length (Gupta 7.8c) becomes\n",
"\n",
"\\begin{align}\n",
"C_l &= 2\\epsilon_0\\left(\\epsilon_r+1\\right)\\frac{K(k_0)}{K(k'_0)}\n",
"\\end{align}\n",
"\n",
"where $K(m)$ is the complete elliptic integral of the first kind and $k_0$ is a geometry dependent terms\n",
"\n",
"\\begin{align}\n",
"k_0 &= \\frac{w}{w+2s},\\\\\n",
"k'_0 &= \\sqrt{1-k_0^2}.\n",
"\\end{align}\n",
"\n",
"Using only the geometry and materials information, we are able to calculate the characteristic impedance of the CPW along with the phase velocity and relative permittivity of the CPW.\n",
"\n",
"## Lumped Element Approximation (LEA)\n",
"\n",
"We know that the resonance frequency of the cavity will be pulled by adding in extra capacitances (grounding straps, the coupling capacitance $C_{\\kappa}$, the qubit pocket, etc.) and inductances (say from coupling in inductively). The frequency that we specify in the function `calcQuarterWavelength()` below is the frequency we desire *after* taking these capacitances into account.\n",
"\n",
"For now just the basics: use the desired frequency $\\omega'$ and calculated $Z_0$ of the CPW resonator to calculate an effective $L_{LEA}$,$C_{LEA}$ (Pozar 6.34), resonant circuit. This lumped element circuit will ring at the target frequency $\\omega'$ following\n",
"\n",
"\\begin{align}\n",
"\\omega' &= \\frac{1}{\\sqrt{L_{tot}C_{tot}}} \\mbox{ where}\\\\\n",
"C_{tot} &= C_{LEA} + C_{parasitic} \\mbox{ and}\\\\\n",
"L_{tot} &= L_{LEA} + L_{parasitic}.\n",
"\\end{align}\n",
"\n",
"The desired action now is to strip the parasitics out and calculate $\\omega_0$, the original frequency of the resonator before it gets pulled down. From Pozar,\n",
"\n",
"\n",
"\\begin{align}\n",
"C_{LEA} &= \\frac{\\pi}{4\\omega_0Z_0},\\\\\n",
"L_{LEA} &= \\frac{1}{\\omega_0^2C_{LEA}},\\\\\n",
"\\end{align}\n",
"\n",
"which can be used to find\n",
"\n",
"\\begin{align}\n",
"\\omega_0 &= \\left(\\frac{1}{2}\\left(-\\frac{1}{\\delta\\omega} + \\sqrt{-4C_{parasitic}L_{parasitic}+\\frac{1}{(\\delta\\omega)^2} + \\frac{4}{\\omega'^2}}\\right )\\right)^{-1}\n",
"\\end{align}\n",
"\n",
"where\n",
"\n",
"\\begin{align}\n",
"\\frac{1}{\\delta\\omega}&=\\frac{L_{parasitic}\\pi}{4Z_0}+\\frac{4C_{parasitic}Z_0}{\\pi}.\n",
"\\end{align}\n",
"\n",
"(n.b.: in the $\\pm$ of solving the quadratic, the $-$ is non-physical, so the function below only uses $+$.) After we have the non-pulled frequency of the resonator, we can find the half-wavelength by using the already calculated phase velocity set by the geometry as\n",
"\n",
"\\begin{align}\n",
"\\frac{\\lambda}{4} &= \\frac{\\pi v_{ph}}{2\\omega_0}.\n",
"\\end{align}\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Coupling Rates\n",
"\n",
"We know that the coupling to the shorted end of the resonator will result in an additional inductance to the circuit above. The coupling quality will be \n",
"\n",
"\\begin{align}\n",
"Q_c &= \\frac{2 Z_0 L_{LEA}}{\\omega_0 M^2},\n",
"\\end{align}\n",
"\n",
"where $M$ is the mutual inductance between the resonator and the common feedline. This makes the coupling rate of the resonator to the cavity\n",
"\n",
"\\begin{align}\n",
"\\kappa &= \\frac{\\left(\\omega_0 M\\right)^2}{2Z_0L_{LEA}}.\n",
"\\end{align}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Some non-pythonic code"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import scipy as sp\n",
"from scipy import special\n",
"import re\n",
"import os\n",
"\n",
"from collections import namedtuple\n",
"from scipy.constants import speed_of_light,mu_0,epsilon_0,pi\n",
"\n",
"from QuarterWave import *\n",
"\n",
"### Resonator Parameters ###\n",
"# Center trace width (um)\n",
"CPW_w = 10\n",
"\n",
"# Gap from center trace to GND (symmetric on both sides) (um)\n",
"CPW_s = 6\n",
"\n",
"# Target resonator frequency (Hz)\n",
"LEA_f0 = 6.6 * 10**9 \n",
"\n",
"# Target coupling quality factor\n",
"RES_Qc = 1e5\n",
"\n",
"# Substrate relative permittivity\n",
"SUB_er = 11.7 # intrinsic silicon\n",
"\n",
"### Parasitics ###\n",
"\n",
"# Qubit pocket to ground is 10 fF in series with 60 fF\n",
"LEA_Cqb = 10.6*62/(10.6+62) * 1e-15 # (F)\n",
"\n",
"# Coupling capacitance to SFQ driver at the end of the resonator\n",
"LEA_Csfq = 12e-15 # (F)\n",
"\n",
"# Total parasitic capacitance\n",
"Cparasitic = LEA_Cqb + LEA_Csfq"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"---- Resonator 0 ----\n",
"\n",
"Coupling Q: 75000\n",
"Target Center F: 6.55 GHz\n",
"Coupling Mutual: 7.05 pH\n",
"Unpulled F: 6.644 GHz\n",
"lambda/4 length: 4476.47 um\n",
"Required ext. l: 346.87 um \n",
"\n",
"\n",
"---- Resonator 1 ----\n",
"\n",
"Coupling Q: 10000\n",
"Target Center F: 6.65 GHz\n",
"Coupling Mutual: 18.71 pH\n",
"Unpulled F: 6.885 GHz\n",
"lambda/4 length: 4320.14 um\n",
"Required ext. l: 146.53 um \n",
"\n",
"\n"
]
}
],
"source": [
"RES_Qc = [75000, 10000]\n",
"LEA_f0 = [6.55e9, 6.65e9]\n",
"LEA_Csfq = [0, 12e-15]\n",
"static_lengths = [3029+60, 3609+125]\n",
"for n in range(len(RES_Qc)):\n",
" Cparasitic = LEA_Cqb + LEA_Csfq[n]\n",
" RES_params = calcQuarterWavelength(CPW_w,CPW_s,LEA_f0[n],SUB_er,Cparasitic,0)\n",
" LEA_mutual = calcMutual(RES_params.z0, RES_params.L, 2*pi*RES_params.f0, RES_Qc[n])\n",
" resonator = calcQuarterWavelength(CPW_w,CPW_s,LEA_f0[n],SUB_er,Cparasitic,LEA_mutual)\n",
" \n",
" print '---- Resonator {0} ----\\n'.format(n)\n",
" print 'Coupling Q: {0}'.format(RES_Qc[n])\n",
" print 'Target Center F: {0} GHz'.format(LEA_f0[n]/float(1e9))\n",
" print 'Coupling Mutual: {:.2f} pH'.format(LEA_mutual*10**12)\n",
" print 'Unpulled F: {:.3f} GHz'.format(resonator.f0/float(1e9))\n",
" print 'lambda/4 length: {:.2f} um'.format(resonator.quarterlength*10**6)\n",
" print 'Required ext. l: {:.2f} um \\n\\n'.format((resonator.quarterlength*10**6 - static_lengths[n])/float(4))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "py-mcdermott",
"language": "python",
"name": "py-mcdermott"
},
"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": 2
}
| gpl-2.0 |
mne-tools/mne-tools.github.io | 0.20/_downloads/dfd4175ec1a2c7f21de3596573c74301/plot_multidict_reweighted_tfmxne.ipynb | 1 | 6294 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary\n\nThe iterative reweighted TF-MxNE solver is a distributed inverse method\nbased on the TF-MxNE solver, which promotes focal (sparse) sources [1]_.\nThe benefit of this approach is that:\n\n - it is spatio-temporal without assuming stationarity (sources properties\n can vary over time),\n - activations are localized in space, time and frequency in one step,\n - the solver uses non-convex penalties in the TF domain, which results in a\n solution less biased towards zero than when simple TF-MxNE is used,\n - using a multiscale dictionary allows to capture short transient\n activations along with slower brain waves [2]_.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Author: Mathurin Massias <[email protected]>\n# Yousra Bekhti <[email protected]>\n# Daniel Strohmeier <[email protected]>\n# Alexandre Gramfort <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport os.path as op\n\nimport mne\nfrom mne.datasets import somato\nfrom mne.inverse_sparse import tf_mixed_norm, make_stc_from_dipoles\nfrom mne.viz import plot_sparse_source_estimates\n\nprint(__doc__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load somatosensory MEG data\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data_path = somato.data_path()\nsubject = '01'\ntask = 'somato'\nraw_fname = op.join(data_path, 'sub-{}'.format(subject), 'meg',\n 'sub-{}_task-{}_meg.fif'.format(subject, task))\nfwd_fname = op.join(data_path, 'derivatives', 'sub-{}'.format(subject),\n 'sub-{}_task-{}-fwd.fif'.format(subject, task))\n\ncondition = 'Unknown'\n\n# Read evoked\nraw = mne.io.read_raw_fif(raw_fname)\nevents = mne.find_events(raw, stim_channel='STI 014')\nreject = dict(grad=4000e-13, eog=350e-6)\npicks = mne.pick_types(raw.info, meg=True, eog=True)\n\nevent_id, tmin, tmax = 1, -1., 3.\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,\n reject=reject, preload=True)\nevoked = epochs.filter(1, None).average()\nevoked = evoked.pick_types(meg=True)\nevoked.crop(tmin=0.008, tmax=0.2)\n\n# Compute noise covariance matrix\ncov = mne.compute_covariance(epochs, rank='info', tmax=0.)\n\n# Handling forward solution\nforward = mne.read_forward_solution(fwd_fname)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run iterative reweighted multidict TF-MxNE solver\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"alpha, l1_ratio = 20, 0.05\nloose, depth = 1, 0.95\n# Use a multiscale time-frequency dictionary\nwsize, tstep = [4, 16], [2, 4]\n\n\nn_tfmxne_iter = 10\n# Compute TF-MxNE inverse solution with dipole output\ndipoles, residual = tf_mixed_norm(\n evoked, forward, cov, alpha=alpha, l1_ratio=l1_ratio,\n n_tfmxne_iter=n_tfmxne_iter, loose=loose,\n depth=depth, tol=1e-3,\n wsize=wsize, tstep=tstep, return_as_dipoles=True,\n return_residual=True)\n\n# Crop to remove edges\nfor dip in dipoles:\n dip.crop(tmin=-0.05, tmax=0.3)\nevoked.crop(tmin=-0.05, tmax=0.3)\nresidual.crop(tmin=-0.05, tmax=0.3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate stc from dipoles\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"stc = make_stc_from_dipoles(dipoles, forward['src'])\n\nplot_sparse_source_estimates(forward['src'], stc, bgcolor=(1, 1, 1),\n opacity=0.1, fig_name=\"irTF-MxNE (cond %s)\"\n % condition)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Show the evoked response and the residual for gradiometers\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ylim = dict(grad=[-300, 300])\nevoked.pick_types(meg='grad')\nevoked.plot(titles=dict(grad='Evoked Response: Gradiometers'), ylim=ylim,\n proj=True)\n\nresidual.pick_types(meg='grad')\nresidual.plot(titles=dict(grad='Residuals: Gradiometers'), ylim=ylim,\n proj=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References\n.. [1] D. Strohmeier, A. Gramfort, J. Haueisen\n \"MEG/EEG Source Imaging with a Non-Convex Penalty in the Time-Frequency\n Domain\", 5th International Workshop on Pattern Recognition in\n Neuroimaging (PRNI), 2015\n DOI: 10.1109/PRNI.2015.14\n\n.. [2] Y. Bekhti, D. Strohmeier, M. Jas, R. Badeau, A. Gramfort\n \"M/EEG Source Localization with Multi-Scale Time-Frequency Dictionaries\"\n 6th International Workshop on Pattern Recognition in\n Neuroimaging (PRNI), 2016\n DOI: 10.1109/PRNI.2016.7552337\n\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.8"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | bsd-3-clause |
SGenheden/lammps | python/examples/ipython/interface_usage_bonds.ipynb | 1 | 184005 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using LAMMPS with iPython and Jupyter"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"LAMMPS can be run interactively using iPython easily. This tutorial shows how to set this up."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Installation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Download the latest version of LAMMPS into a folder (we will calls this `$LAMMPS_DIR` from now on)\n",
"2. Compile LAMMPS as a shared library and enable PNG support\n",
" ```bash\n",
" cd $LAMMPS_DIR/src\n",
" make yes-molecule\n",
" python2 Make.py -m mpi -png -a file\n",
" make mode=shlib auto\n",
" ```\n",
"\n",
"3. Create a python virtualenv\n",
" ```bash\n",
" virtualenv testing\n",
" source testing/bin/activate\n",
" ```\n",
"\n",
"4. Inside the virtualenv install the lammps package\n",
" ```\n",
" (testing) cd $LAMMPS_DIR/python\n",
" (testing) python install.py\n",
" (testing) cd # move to your working directory\n",
" ```\n",
"\n",
"5. Install jupyter and ipython in the virtualenv\n",
" ```bash\n",
" (testing) pip install ipython jupyter\n",
" ```\n",
"\n",
"6. Run jupyter notebook\n",
" ```bash\n",
" (testing) jupyter notebook\n",
" ```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from lammps import IPyLammps"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LAMMPS output is captured by PyLammps wrapper\n"
]
}
],
"source": [
"L = IPyLammps()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# 2d circle of particles inside a box with LJ walls\n",
"import math\n",
"\n",
"b = 0\n",
"x = 50\n",
"y = 20\n",
"d = 20\n",
"\n",
"# careful not to slam into wall too hard\n",
"\n",
"v = 0.3\n",
"w = 0.08\n",
" \n",
"L.units(\"lj\")\n",
"L.dimension(2)\n",
"L.atom_style(\"bond\")\n",
"L.boundary(\"f f p\")\n",
"\n",
"L.lattice(\"hex\", 0.85)\n",
"L.region(\"box\", \"block\", 0, x, 0, y, -0.5, 0.5)\n",
"L.create_box(1, \"box\", \"bond/types\", 1, \"extra/bond/per/atom\", 6)\n",
"L.region(\"circle\", \"sphere\", d/2.0+1.0, d/2.0/math.sqrt(3.0)+1, 0.0, d/2.0)\n",
"L.create_atoms(1, \"region\", \"circle\")\n",
"L.mass(1, 1.0)\n",
"\n",
"L.velocity(\"all create 0.5 87287 loop geom\")\n",
"L.velocity(\"all set\", v, w, 0, \"sum yes\")\n",
"\n",
"L.pair_style(\"lj/cut\", 2.5)\n",
"L.pair_coeff(1, 1, 10.0, 1.0, 2.5)\n",
"\n",
"L.bond_style(\"harmonic\")\n",
"L.bond_coeff(1, 10.0, 1.2)\n",
"\n",
"L.create_bonds(\"all\", \"all\", 1, 1.0, 1.5)\n",
"\n",
"L.neighbor(0.3, \"bin\")\n",
"L.neigh_modify(\"delay\", 0, \"every\", 1, \"check yes\")\n",
"\n",
"L.fix(1, \"all\", \"nve\")\n",
"\n",
"L.fix(2, \"all wall/lj93 xlo 0.0 1 1 2.5 xhi\", x, \"1 1 2.5\")\n",
"L.fix(3, \"all wall/lj93 ylo 0.0 1 1 2.5 yhi\", y, \"1 1 2.5\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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APKx1oqWF44/nhhvSBwzoZxhb43Gz9JbWRiRCnz4sWOAZN67I4fiypWUvLNca8HV1YRgs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\nTkSuQmCC\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.image(zoom=1.8)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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9u3YtHhgAOu32rEOHePbZ/V984YJNUH7++aUbN/7ZMN7UGvjCYhmzerXTYunw+2sNoxxC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g4WIgpetdvTi4okdCpKNXSAE170eAxwDQw7dMgoRFcgUN+fIbOuutoCZoiBtG3bIo8dc3d2lrrd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\nBIAQQggJACGEEBIAQgghJACEEEKc8n8BV6onOVwQeAcAAAAASUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.thermo_style(\"custom step temp epair press\")\n",
"L.thermo(100)\n",
"output = L.run(40000)\n",
"L.image(zoom=1.8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Queries about LAMMPS simulation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"System(ntypes=1, nimpropertypes=0, nbonds=1014, nangles=0, orthogonal_box=[58.2767, 40.3753, 1.16553], ndihedrals=0, yhi=40.3753, atom_style='bond', bond_style='harmonic', zlo=-0.582767, improper_style='none', xlo=0.0, style='lj/cut', xhi=58.2767, kspace_style='none', zhi=0.582767, nbondtypes=1, angle_style='none', dihedral_style='none', dimensions=2, natoms=361, boundaries='f,f f,f p,p', ylo=0.0, nimpropers=0, nangletypes=0, molecule_type='standard', atom_map='array', units='lj')"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.system"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"361"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.system.natoms"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1014"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.system.nbonds"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.system.nbondtypes"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Communication(comm_style='brick', comm_layout='uniform', proc_grid=[1, 1, 1], nthreads=1, mpi_version='MPI v3.0', nprocs=1, ghost_velocity=False)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.communication"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[{'group': 'all', 'name': '1', 'style': 'nve'},\n",
" {'group': 'all', 'name': '2', 'style': 'wall/lj93'},\n",
" {'group': 'all', 'name': '3', 'style': 'wall/lj93'}]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.fixes"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[{'group': 'all', 'name': 'thermo_temp', 'style': 'temp'},\n",
" {'group': 'all', 'name': 'thermo_press', 'style': 'pressure'},\n",
" {'group': 'all', 'name': 'thermo_pe', 'style': 'pe'}]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.computes"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.dumps"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[{'name': 'all', 'type': 'static'}]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.groups"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Working with LAMMPS Variables"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"L.variable(\"a index 2\")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'a': <lammps.Variable at 0x7faf041580f0>}"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.variables"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"L.variable(\"t equal temp\")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'a': <lammps.Variable at 0x7faf041580b8>,\n",
" 't': <lammps.Variable at 0x7faf04158240>}"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.variables"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2.0"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import sys\n",
"\n",
"if sys.version_info < (3, 0):\n",
" # In Python 2 'print' is a restricted keyword, which is why you have to use the lmp_print function instead.\n",
" x = float(L.lmp_print('\"${a}\"'))\n",
"else:\n",
" # In Python 3 the print function can be redefined.\n",
" # x = float(L.print('\"${a}\"')\")\n",
" \n",
" # To avoid a syntax error in Python 2 executions of this notebook, this line is packed into an eval statement\n",
" x = float(eval(\"L.print('\\\"${a}\\\"')\"))\n",
"x"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.363092663783386"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.variables['t'].value"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.181546331891693"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.eval(\"v_t/2.0\")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"L.variable(\"b index a b c\")"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'a'"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.variables['b'].value"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.0"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.eval(\"v_b\")"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['a', 'b', 'c']"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.variables['b'].definition"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"L.variable(\"i loop 10\")"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.variables['i'].value"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2.0"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.next(\"i\")\n",
"L.variables['i'].value"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.3620868669308006"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.eval(\"ke\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Accessing Atom data"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<lammps.Atom at 0x7faf04157a58>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.atoms[0]"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['charge',\n",
" 'force',\n",
" 'id',\n",
" 'index',\n",
" 'lmp',\n",
" 'mass',\n",
" 'mol',\n",
" 'position',\n",
" 'type',\n",
" 'velocity']"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[x for x in dir(L.atoms[0]) if not x.startswith('__')]"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(14.081609521719168, 0.920541427075243, 0.0)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.atoms[0].position"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.atoms[0].id"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(-0.08810676648167072, -0.8755151505832499, 0.0)"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.atoms[0].velocity"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(0.809523377179948, -1.4360462290451692, 0.0)"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.atoms[0].force"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.atoms[0].type"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-2.0 |
topgate/training-gcp | CPB102/tensorflow/tfstart.ipynb | 1 | 8552 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Getting Started with TensorFlow\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"何はともあれ TensorFlow を始めてみましょう！"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import numpy as np\n",
"\n",
"print(tf.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Hello TensorFlow"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Python を使って足し算をしてみましょう（決して馬鹿にしているわけではなく大真面目です）！"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"a = 1.\n",
"b = 2.\n",
"c = a + b\n",
"\n",
"print(c)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"当然ですが 3.0 と答えが表示されます。\n",
"\n",
"今度は TensorFlow で同じような足し算をやってみましょう。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"a = tf.constant(1.)\n",
"b = tf.constant(2.)\n",
"c = tf.add(a, b)\n",
"\n",
"print(c)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Tensor というオブジェクトが表示されますね。\n",
"実はまだこの時点ではデータフローグラフが作成されただけで、足し算は行われていません。\n",
"足し算を実行するにはセッションを介して実行する必要があります。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"with tf.Session() as sess:\n",
" result = sess.run(c)\n",
"\n",
"print(result)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## 行列演算"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"NumPy と TensorFlow で行列やベクトルの計算方法を比較してみましょう。"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### NumPy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"a = np.array([5, 3, 8])\n",
"b = np.array([3, -1, 2])\n",
"c = np.add(a, b)\n",
"\n",
"print(c)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### TensorFlow"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"a = tf.constant([5, 3, 8])\n",
"b = tf.constant([3, -1, 2])\n",
"c = tf.add(a, b)\n",
"\n",
"print(c)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"with tf.Session() as sess:\n",
" result = sess.run(c)\n",
"\n",
"print(result)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### TensorFlow + placeholder"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"計算をするたびにデータフローグラフを作っていると、データフローグラフがどんどん肥大化してしまいます。\n",
"そのため、 TensorFlow には一部の値を差し替えつつデータフローグラフの共通部分を再利用するための placeholder という仕組みが存在しています。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"a = tf.placeholder(dtype=tf.int32, shape=(None,))\n",
"b = tf.placeholder(dtype=tf.int32, shape=(None,))\n",
"c = tf.add(a, b)\n",
"\n",
"with tf.Session() as sess:\n",
" result1 = sess.run(c, feed_dict={a: [3, 4, 5], b: [-1, 2, 3]})\n",
" result2 = sess.run(c, feed_dict={a: [1, 2, 3], b: [3, 2, 1]})\n",
"\n",
"print(result1)\n",
"print(result2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"典型的な使い方として、学習時に使うデータを placeholder で定義しておくという方法があります。\n",
"placeholder に対して少しずつ学習用データを流していくというのが TensorFlow で機械学習のアルゴリズムを実装するときの定石です。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## tf.Variable"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"TensorFlow では、計算に使われた値は基本的に捨てられていきます。\n",
"`tf.add` の結果はセッションを介して実行するたびに計算し直すことになりますし、 `tf.constant` の値はメモリ上に保持されず必要に応じて再生成されます。\n",
"後から `tf.constant` の値を書き換えることもできません。\n",
"\n",
"ただし tf.Variable だけが例外で、値をメモリ上に保持し続けて後から書き換えることも可能になっています。\n",
"ニューラルネットやその他多くの機械学習手法で weight として使うことが想定されています。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"v = tf.Variable([1, 2])\n",
"assign_op = tf.assign(v, [2, 3])\n",
"init_op = tf.global_variables_initializer()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"tf.Variable 最初に初期化処理を行う必要があるので `init_op` を実行します。\n",
"その後 `v` の値を表示すると `assign_op` を実行する前後で値が書き換えられていることが確認できます。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with tf.Session() as sess:\n",
" sess.run(init_op)\n",
" print(sess.run(v))\n",
" sess.run(assign_v)\n",
" print(sess.run(v))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"tf.Variable に何の値が入っているかという情報はセッションと紐付けられています。\n",
"計算グラフは tf.Variable を計算にどう使うかという手順の情報だけを持っており、具体的な値の情報はセッションが持っているということを覚えておくと、コードを書く時に混乱せずに済むでしょう。"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
blehman/Data-Science-45min-Intros | model-selection-101/model-selection-part-1.ipynb | 25 | 12953 | {
"metadata": {
"name": "",
"signature": "sha256:2339f9b9331c1e7f79753f37c7ed2f30bb5197835d4a6595222c295c9148ee5f"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Model Assessment and Selection\n",
"##Ideas and Examples of Trade-offs\n",
"Scott Hendrickson\n",
"\n",
"2015 April 3\n",
"\n",
"(With very heavy borrowing from ESL!)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import matplotlib.pyplot as plt \n",
"%matplotlib inline\n",
"from IPython.display import Image"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Image(filename=\"Selection_003.png\", width=600)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Image(filename=\"Selection_001.png\", width=600)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Image(filename=\"Selection_004.png\")"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Image(filename=\"Selection_002.png\", width=600)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###In real life, things are messier..."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.neighbors import KNeighborsRegressor\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.linear_model import Lasso\n",
"from sklearn.linear_model import LassoCV\n",
"from sklearn.linear_model import Ridge\n",
"from sklearn import cross_validation\n",
"from sklearn import datasets\n",
"from sklearn.cross_validation import cross_val_predict\n",
"from sklearn import linear_model"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# boston house prices data set\n",
"boston = datasets.load_boston()\n",
"print boston.keys()\n",
"#print boston.feature_names\n",
"print boston.DESCR\n",
"print boston.target[:10]"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###KNN regression to for housing price prediction..."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Set up many trials on same data set to understand \n",
"# test vs. sample error\n",
"# variance in estimates\n",
"n_trials, n_max_neighbors = 50, 100\n",
"err_train, err_test = np.zeros(n_max_neighbors-2), np.zeros(n_max_neighbors-2)\n",
"for i in range(n_trials):\n",
" X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(boston.data, boston.target, test_size=0.3)\n",
" x_complexity, y_train, y_test = [], [], []\n",
" for k in range(n_max_neighbors, 2, -1):\n",
" clf = KNeighborsRegressor(n_neighbors=k)\n",
" clf.fit(X_train,Y_train)\n",
" x_complexity.append(k)\n",
" y_tmp = clf.score(X_train,Y_train)\n",
" y_train.append(y_tmp)\n",
" err_train[n_max_neighbors-k] += y_tmp\n",
" y_tmp = clf.score(X_test,Y_test)\n",
" y_test.append(y_tmp)\n",
" err_test[n_max_neighbors-k] += y_tmp\n",
" plt.plot(x_complexity, y_test, color=\"blue\", alpha=0.2)\n",
" plt.plot(x_complexity, y_train, color=\"red\", alpha=0.2)\n",
"plt.plot(x_complexity, err_test/n_trials, color=\"blue\")\n",
"plt.plot(x_complexity, err_train/n_trials, color=\"red\")\n",
"plt.xlabel(\"Number of Neighbors\")\n",
"plt.ylabel(\"Fraction Correct\")\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Image(filename=\"Selection_005.png\")"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The last term scales as the 1/number of nearest neighbors. As the complexity of the fitting model goes up (lower k!) the variance of the model goes up. Large k averages over a larger data set, smoothing our predictions.\n",
"\n",
"###Compare other comlexity... Linear"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# simple single value gut check\n",
"lr = LinearRegression()\n",
"boston = datasets.load_boston()\n",
"y = boston.target\n",
"# cross_val_predict returns an array of the same size as `y` where each entry\n",
"# is a prediction obtained by cross validation\n",
"predicted = cross_val_predict(lr, boston.data, y, cv=10)\n",
"fig,ax = plt.subplots()\n",
"ax.scatter(y, predicted)\n",
"ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4)\n",
"ax.set_xlabel('Measured')\n",
"ax.set_ylabel('Predicted')\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Image(filename=\"Screen Shot 2.png\", width=600)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"##############################################################################\n",
"# LassoCV: coordinate descent\n",
"diabetes = datasets.load_diabetes()\n",
"X = diabetes.data\n",
"y = diabetes.target\n",
"\n",
"rng = np.random.RandomState(42)\n",
"X = np.c_[X, rng.randn(X.shape[0], 14)] # add some bad features\n",
"\n",
"# normalize data as done by Lars to allow for comparison\n",
"X /= np.sqrt(np.sum(X ** 2, axis=0))\n",
"\n",
"# Compute paths\n",
"print(\"Computing regularization path using the coordinate descent lasso...\")\n",
"model = LassoCV(cv=20).fit(X, y)\n",
"\n",
"# Display results\n",
"m_log_alphas = -np.log10(model.alphas_)\n",
"\n",
"plt.figure()\n",
"ymin, ymax = 2300, 3800\n",
"plt.plot(m_log_alphas, model.mse_path_, ':')\n",
"plt.plot(m_log_alphas, model.mse_path_.mean(axis=-1), 'k',\n",
" label='Average across the folds', linewidth=2)\n",
"plt.axvline(-np.log10(model.alpha_), linestyle='--', color='k',\n",
" label='alpha: CV estimate')\n",
"\n",
"plt.legend()\n",
"\n",
"plt.xlabel('-log(alpha)')\n",
"plt.ylabel('Mean square error')\n",
"plt.title('Mean square error on each fold: coordinate descent ')\n",
"plt.axis('tight')\n",
"plt.ylim(ymin, ymax)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Now on the boston home price data..."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# is L1\n",
"n_trials, n_alpha = 30, 80\n",
"err_train, err_test = np.zeros(n_alpha), np.zeros(n_alpha)\n",
"for k in range(n_trials):\n",
" X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(boston.data, boston.target, test_size=0.3)\n",
" x_complexity, y_train, y_test = [], [], []\n",
" for i in range(n_alpha):\n",
" a = 5*(i+1)/float(n_alpha)\n",
" clf = Lasso(alpha=a)\n",
" clf.fit(X_train,Y_train)\n",
" x_complexity.append(-np.log(a))\n",
" y_tmp = clf.score(X_train,Y_train)\n",
" y_train.append(y_tmp)\n",
" err_train[i] += y_tmp\n",
" y_tmp = clf.score(X_test,Y_test)\n",
" y_test.append(y_tmp)\n",
" err_test[i] += y_tmp\n",
" plt.plot(x_complexity, y_test, color=\"blue\", alpha=0.2)\n",
" plt.plot(x_complexity, y_train, color=\"red\", alpha=0.2)\n",
"plt.plot(x_complexity, err_test/n_trials, color=\"blue\")\n",
"plt.plot(x_complexity, err_train/n_trials, color=\"red\")\n",
"plt.xlabel(\"-log(alpha)\")\n",
"plt.ylabel(\"Fraction Correct\")\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# simple single value gut check\n",
"lr = Lasso(alpha=5*1/float(40))\n",
"boston = datasets.load_boston()\n",
"y = boston.target\n",
"# cross_val_predict returns an array of the same size as `y` where each entry\n",
"# is a prediction obtained by cross validation\n",
"predicted = cross_val_predict(lr, boston.data, y, cv=10)\n",
"fig,ax = plt.subplots()\n",
"ax.scatter(y, predicted)\n",
"ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4)\n",
"ax.set_xlabel('Measured')\n",
"ax.set_ylabel('Predicted')\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Image(filename=\"Screen Shot 1.png\", width=600)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# is L2\n",
"n_trials, n_alpha = 20, 80\n",
"err_train, err_test = np.zeros(n_alpha), np.zeros(n_alpha)\n",
"for k in range(n_trials):\n",
" X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(boston.data, y, test_size=0.3)\n",
" x_complexity, y_train, y_test = [], [], []\n",
" for i in range(n_alpha):\n",
" a = 5*(i+1)/float(n_alpha)\n",
" clf = Ridge(alpha=a)\n",
" clf.fit(X_train,Y_train)\n",
" x_complexity.append(-np.log(a))\n",
" y_tmp = clf.score(X_train,Y_train)\n",
" y_train.append(y_tmp)\n",
" err_train[i] += y_tmp\n",
" y_tmp = clf.score(X_test,Y_test)\n",
" y_test.append(y_tmp)\n",
" err_test[i] += y_tmp\n",
" plt.plot(x_complexity, y_test, color=\"blue\", alpha=0.2)\n",
" plt.plot(x_complexity, y_train, color=\"red\", alpha=0.2)\n",
"plt.plot(x_complexity, err_test/n_trials, color=\"blue\")\n",
"plt.plot(x_complexity, err_train/n_trials, color=\"red\")\n",
"plt.xlabel(\"-log(alpha)\")\n",
"plt.ylabel(\"Fraction Correct\")\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# simple single value gut check\n",
"lr = Ridge(alpha=5*1/float(4))\n",
"boston = datasets.load_boston()\n",
"y = boston.target\n",
"# cross_val_predict returns an array of the same size as `y` where each entry\n",
"# is a prediction obtained by cross validation\n",
"predicted = cross_val_predict(lr, boston.data, y, cv=10)\n",
"fig,ax = plt.subplots()\n",
"ax.scatter(y, predicted)\n",
"ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4)\n",
"ax.set_xlabel('Measured')\n",
"ax.set_ylabel('Predicted')\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Image(filename=\"Screen Shot 3.png\", width=600)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Image(filename=\"Screen Shot 4.png\", width=600)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next Time:\n",
"* Better definition of DOF\n",
"* AIC and BIC\n",
"* ROC curves\n",
"\n",
"...and more!"
]
}
],
"metadata": {}
}
]
} | unlicense |
JamesClough/dagology | examples/calculations/causal_set_example.ipynb | 1 | 311254 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2017-04-04T17:11:05.222584Z",
"start_time": "2017-04-04T18:11:04.696720+01:00"
},
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import networkx as nx\n",
"import numpy as np\n",
"\n",
"import dagology as dag\n",
"from plot_utils import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2017-04-04T17:11:06.038514Z",
"start_time": "2017-04-04T18:11:06.033123+01:00"
},
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2017-04-04T17:11:07.792324Z",
"start_time": "2017-04-04T18:11:07.576067+01:00"
},
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# define causal set sprinkling parameters\n",
"N = 200 # number of points\n",
"D = 2 # spacetime dimension\n",
"R_m = dag.minkowski_interval(N, D, fix_ends=False) # create coordinates in Minkowski spacetime\n",
"G_m = dag.causal_set_graph(R_m) # create graph"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2017-04-04T17:11:09.549143Z",
"start_time": "2017-04-04T18:11:09.191802+01:00"
},
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# de Sitter spacetime\n",
"KT2 = 3.5\n",
"R_ds = dag.de_sitter_interval(N, D, KT2, fix_ends=False)\n",
"G_ds = dag.causal_set_graph(R_ds)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2017-04-04T17:11:14.738695Z",
"start_time": "2017-04-04T18:11:12.305385+01:00"
},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/james/Research/repos/networkx/networkx/drawing/nx_pylab.py:125: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n",
" Future behavior will be consistent with the long-time default:\n",
" plot commands add elements without first clearing the\n",
" Axes and/or Figure.\n",
" b = plt.ishold()\n",
"/home/james/Research/repos/networkx/networkx/drawing/nx_pylab.py:137: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n",
" Future behavior will be consistent with the long-time default:\n",
" plot commands add elements without first clearing the\n",
" Axes and/or Figure.\n",
" plt.hold(b)\n",
"/usr/local/lib/python2.7/dist-packages/matplotlib/__init__.py:917: UserWarning: axes.hold is deprecated. Please remove it from your matplotlibrc and/or style files.\n",
" warnings.warn(self.msg_depr_set % key)\n",
"/usr/local/lib/python2.7/dist-packages/matplotlib/rcsetup.py:152: UserWarning: axes.hold is deprecated, will be removed in 3.0\n",
" warnings.warn(\"axes.hold is deprecated, will be removed in 3.0\")\n"
]
},
{
"data": {
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MBgQHBzsMSokINWvWxJgxY+y+taNHjyIoKAjVq1dHhw4dkJWVJXncBx98ICpb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05VVRVFRUU8+OCDXq83gAACCKChSEpKYt++fdL/+/btS79+/Rg4cKDEoN+tWzc3ciFXLb9L37qu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LFYMBgMaNasGSQSCaNjlJ2dTSaDiooK6HQ6mM1mdO7cGYMHD0a1VOpeZrEmQ0nfm6ysLGzZ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EQqzhqASzIy4ujpxDFlw+gjRNIyMjA2FhYfjss8+g0WiIOuTs2bMxfvx48vlXXnkF06dP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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc54d9e53d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# draw the causal sets\n",
"f, axs = plt.subplots(1, 2, figsize=(16,8))\n",
"for i, G in enumerate([G_m, G_ds]): \n",
" draw_pos = {i:G.node[i]['position'][::-1] for i in range(N)}\n",
" nx.draw(nx.transitive_reduction(G), pos=draw_pos, ax=axs[i], node_size=100)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2017-04-04T17:14:27.377474Z",
"start_time": "2017-04-04T18:14:26.992048+01:00"
},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Minkowski spacetime\n",
"N: 200\n",
"E: 9766\n",
"LP: 25\n",
"C_2: 9766\n",
"MMD_2: 2.04\n",
"C_3: 207282\n",
"MMD_3: 2.04\n",
"C_4: 2399957\n",
"MMD_4: 2.05\n",
"MPSD: 2.0536379636\n",
"\n",
"\n",
"\n",
"de Sitter spacetime\n",
"N: 200\n",
"E: 11920\n",
"LP: 28\n",
"C_2: 11920\n",
"MMD_2: 1.77\n",
"C_3: 332819\n",
"MMD_3: 1.79\n",
"C_4: 5291228\n",
"MMD_4: 1.81\n",
"MPSD: 1.78923290378\n",
"\n",
"\n",
"\n"
]
}
],
"source": [
"names = ['Minkowski spacetime', 'de Sitter spacetime']\n",
"for i, G in enumerate([G_m, G_ds]):\n",
" print names[i]\n",
" N = G.number_of_nodes()\n",
" print 'N: ', N\n",
" print 'E: ', G.number_of_edges()\n",
" print 'LP: ', len(nx.dag_longest_path(G))\n",
" for k in [2,3,4]:\n",
" C_k = dag.count_chains(G, k)\n",
" print 'C_%s: %s' % (k, C_k)\n",
" print 'MMD_%s: %s' % (k, dag.mmd_estimate(C_k, k, N))\n",
" print 'MPSD: %s' % dag.mpsd(G)\n",
" print '\\n\\n'"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"ExecuteTime": {
"end_time": "2017-04-04T17:16:10.766439Z",
"start_time": "2017-04-04T18:16:10.688948+01:00"
},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Minkowski spacetime\n",
"N: 200\n"
]
},
{
"ename": "NameError",
"evalue": "name 'initial_guess' 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-13-8972bccf28d7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mC_k\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdag\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount_chains\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mchains\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mC_k\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mdag\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mde_sitter_param_estimate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchains\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial_guess\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m'\\n\\n'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'initial_guess' is not defined"
]
}
],
"source": [
"# measure curvature\n",
"for i, G in enumerate([G_m, G_ds]):\n",
" print names[i]\n",
" N = G.number_of_nodes()\n",
" print 'N: ', N\n",
" chains = [N]\n",
" for k in [2,3]:\n",
" C_k = dag.count_chains(G, k)\n",
" chains.append(C_k)\n",
" print dag.de_sitter_param_estimate(chains, initial_guess=)\n",
" print '\\n\\n'"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"ExecuteTime": {
"end_time": "2017-04-04T17:16:37.730923Z",
"start_time": "2017-04-04T18:16:37.728214+01:00"
},
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"None\n"
]
}
],
"source": [
"print dag.de_sitter_param_estimate.__doc__"
]
},
{
"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
}
| mit |
wmaciel/van-crime | src/classification.ipynb | 1 | 23210 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pickle as pk\n",
"import pandas as pd\n",
"import timeit as tm\n",
"import csv\n",
"import sys"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Loading data\n",
"## Loading training data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"training data dimensions: (295169, 63)\n",
"training label dimensions: (295169, 6)\n"
]
}
],
"source": [
"# Open training data to pandas\n",
"train_dat_pandas = pd.read_csv('../data/clean_data/train_vectors.csv', index_col=0, encoding='utf-8')\n",
"del train_dat_pandas['TYPE']\n",
"\n",
"# Open training labels to pandas\n",
"train_lbl_pandas = pd.read_csv('../data/clean_data/train_labels.csv', index_col=0, encoding='utf-8')\n",
"del train_lbl_pandas['YEAR']\n",
"\n",
"# Save headers\n",
"headers = [list(train_dat_pandas)]\n",
"\n",
"# Convert pandas to numpy matrix\n",
"train_dat = train_dat_pandas.as_matrix()\n",
"print 'training data dimensions:', train_dat.shape\n",
"\n",
"# Convert pandas to numpy matrix\n",
"train_lbl = train_lbl_pandas.as_matrix()\n",
"print 'training label dimensions:', train_lbl.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading test data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"testing data dimensions: (34142, 63)\n",
"testing label dimensions: (34142, 6)\n"
]
}
],
"source": [
"# Open test data\n",
"test_dat_pandas = pd.read_csv('../data/clean_data/test_vectors.csv', index_col=0, encoding='utf-8')\n",
"del test_dat_pandas['TYPE']\n",
"\n",
"# Open test labels\n",
"test_lbl_pandas = pd.read_csv('../data/clean_data/test_labels.csv', index_col=0, encoding='utf-8')\n",
"del test_lbl_pandas['YEAR']\n",
"\n",
"# Convert pandas to numpy matrix\n",
"test_dat = test_dat_pandas.as_matrix()\n",
"print 'testing data dimensions:', test_dat.shape\n",
"\n",
"# Convert pandas to numpy matrix\n",
"test_lbl = test_lbl_pandas.as_matrix()\n",
"print 'testing label dimensions:', test_lbl.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Concatenating test and train for final model"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"full_dat_pandas = pd.concat([train_dat_pandas, test_dat_pandas])\n",
"full_dat = full_dat_pandas.as_matrix()\n",
"\n",
"full_lbl_pandas = pd.concat([train_lbl_pandas, test_lbl_pandas])\n",
"full_lbl = full_lbl_pandas.as_matrix()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Converting one hot labels to numeric"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[4 4 4 ..., 4 2 3] [4 2 2 ..., 4 2 2] [[0 0 0 0 1 0]\n",
" [0 0 0 0 1 0]\n",
" [0 0 0 0 1 0]\n",
" ..., \n",
" [0 0 0 0 1 0]\n",
" [0 0 1 0 0 0]\n",
" [0 0 1 0 0 0]]\n"
]
}
],
"source": [
"# method to convert a one hot encoding array into a numeric array\n",
"def onehot_2_numeric(onehot):\n",
" numeric = []\n",
" for elem in onehot:\n",
" result = 0\n",
" for i, k in enumerate(elem):\n",
" result += i * k\n",
" numeric.append(result)\n",
" return np.asarray(numeric)\n",
"\n",
"\n",
"train_lbl_txt = onehot_2_numeric(train_lbl)\n",
"test_lbl_txt = onehot_2_numeric(test_lbl)\n",
"full_lbl_txt = onehot_2_numeric(full_lbl)\n",
"print train_lbl_txt, test_lbl_txt, full_lbl"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scaling data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[-1.43707687, -0.46160515, 0.88683494, ..., 0.25718323,\n",
" 0.46704734, -0.77932334],\n",
" [-1.43707687, -0.46160515, 0.88683494, ..., 0.25718323,\n",
" 0.46704734, -0.77932334],\n",
" [-1.43707687, -0.16683183, 0.88683494, ..., 1.03117731,\n",
" 1.0494166 , -0.51868547],\n",
" ..., \n",
" [ 1.57106923, -1.05115178, 0.07267337, ..., -0.45880567,\n",
" -0.35490798, 0.78072654],\n",
" [ 1.57106923, 0.42271481, -1.80602055, ..., 1.46893604,\n",
" 1.48036877, -0.50420559],\n",
" [ 1.57106923, -1.05115178, 0.79614277, ..., -0.45880567,\n",
" -0.35490798, 0.78072654]])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Feature vector scalling\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"scaler = StandardScaler()\n",
"\n",
"scaler.fit(train_dat)\n",
"train_dat = scaler.transform(train_dat)\n",
"test_dat = scaler.transform(test_dat)\n",
"\n",
"scaler.fit_transform(full_dat)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Dimensionality Reduction"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# from sklearn.decomposition import PCA\n",
"\n",
"# pca = PCA(n_components='mle')\n",
"# print pca"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# pca.fit(train_dat)\n",
"# print train_dat.shape\n",
"# train_dat = pca.transform(train_dat)\n",
"# print train_dat.shape\n",
"\n",
"# print test_dat.shape\n",
"# test_dat = pca.transform(test_dat)\n",
"# print test_dat.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Linear Regression"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.linear_model import LinearRegression\n",
"\n",
"# Fit Linear Regression\n",
"lin_reg = LinearRegression(n_jobs=-1, normalize=True)\n",
"lin_reg.fit(train_dat, train_lbl)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Generate predictions\n",
"predictions = lin_reg.predict(test_dat)\n",
"print predictions.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Compute RMSE\n",
"\n",
"import math\n",
"\n",
"errors = []\n",
"\n",
"# compute squared errors\n",
"for i in xrange(predictions.shape[0]):\n",
" p = predictions[i]\n",
" t = test_lbl[i]\n",
" \n",
" # compute distance\n",
" squared_distance = 0.0\n",
" for j in xrange(predictions.shape[1]):\n",
" squared_distance += (p[j] - t[j])**2\n",
" \n",
" errors.append(squared_distance)\n",
"\n",
"rmse = math.sqrt(sum(errors)/len(errors))\n",
"print 'Root mean squared error:', rmse"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Save model\n",
"from sklearn.externals import joblib\n",
"joblib.dump(lin_reg, '../models/linear_regression_model.p')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Logistic regression"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"# from sklearn.linear_model import LogisticRegression\n",
"\n",
"# clf = LogisticRegression(n_jobs=-1)\n",
"# clf.fit(train_dat, train_lbl_txt)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# predictions = clf.predict(test_dat)\n",
"# p_predictions = clf.predict_proba(test_dat)\n",
"\n",
"# print 'predictions dimensions:', predictions.shape\n",
"# print 'probabilities per class:', p_predictions.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# # Table of probabilities for each class\n",
"# for i in range(6):\n",
"# print str(i)+'\\t',\n",
"\n",
"# print ''\n",
"\n",
"# for i in xrange(len(p_predictions)):\n",
" \n",
"# for j in xrange(len(p_predictions[i])):\n",
"# print(\"%.2f\" % (p_predictions[i][j]*100))+'%\\t',\n",
" \n",
"# print ''"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# from sklearn.metrics import accuracy_score\n",
"# score = accuracy_score(test_lbl_txt, predictions)\n",
"# print score"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Logistic Regression Cross validation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.linear_model import LogisticRegressionCV\n",
"from sklearn.cross_validation import StratifiedKFold\n",
"\n",
"folder = StratifiedKFold(train_lbl_txt, n_folds=5, shuffle=False)\n",
"\n",
"clf = LogisticRegressionCV(n_jobs=-1, solver='liblinear', cv=folder, verbose=5)\n",
"print clf"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"clf = clf.fit(train_dat, train_lbl_txt)\n",
"print clf.score(test_dat, test_lbl_txt)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Save model\n",
"from sklearn.externals import joblib\n",
"joblib.dump(clf, '../models/logistic_regression_model.p')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Decision Tree Classifier"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.tree import DecisionTreeClassifier\n",
"\n",
"clf = DecisionTreeClassifier()\n",
"clf.fit(train_dat, train_lbl_txt)\n",
"predictions = clf.predict(test_dat)\n",
"\n",
"from sklearn.metrics import accuracy_score\n",
"score = accuracy_score(test_lbl_txt, predictions)\n",
"print score"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Decision Tree Cross Validation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.grid_search import GridSearchCV\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.cross_validation import StratifiedKFold\n",
"\n",
"folder = StratifiedKFold(train_lbl_txt, n_folds=5, shuffle=False)\n",
"parameters = {'max_depth':[None, 2, 4, 8, 16, 32, 64]}\n",
"dtc_clf = DecisionTreeClassifier()\n",
"\n",
"clf = GridSearchCV(dtc_clf, parameters, n_jobs=-1, pre_dispatch='n_jobs', cv=folder, refit=True, verbose=5)\n",
"clf.fit(train_dat, train_lbl_txt)\n",
"\n",
"print 'Score on test data:', clf.score(test_dat, test_lbl_txt)\n",
"\n",
"print 'best params:', clf.best_params_"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Save model\n",
"from sklearn.externals import joblib\n",
"joblib.dump(clf, '../models/decision_tree_model.p')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.cross_validation import StratifiedKFold\n",
"from sklearn.metrics import accuracy_score\n",
"\n",
"# Generate k-fold split in the training data\n",
"kf = StratifiedKFold(train_lbl_txt, n_folds=5, shuffle=True)\n",
"\n",
"# Do multiple runs and save'em to runs list\n",
"runs = []\n",
"depths = [None, 2, 4, 8, 16, 32, 64]\n",
"\n",
"print 'this will take a while...',\n",
"for d in depths:\n",
" clf = DecisionTreeClassifier(max_depth=d)\n",
" for t,v in kf:\n",
" trn = train_dat[t]\n",
" val = train_dat[v]\n",
" trn_lbl = train_lbl_txt[t]\n",
" val_lbl = train_lbl_txt[v]\n",
" clf.fit(trn, trn_lbl)\n",
" predictions = clf.predict(val)\n",
" #score = accuracy_score(val_lbl, predictions)\n",
" score = clf.score(val, val_lbl)\n",
" runs.append(tuple([d, score]))\n",
" print d, score\n",
"print 'done!'\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"best_result = max(runs, key=lambda run: run[1])\n",
"print 'Best result:', best_result\n",
"best_d = best_result[0]\n",
"\n",
"clf = DecisionTreeClassifier(max_depth=best_d)\n",
"clf.fit(train_dat, train_lbl_txt)\n",
"print 'Score on test data:', clf.score(test_dat, test_lbl_txt)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Random Forests"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.49753968718879971"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"\n",
"clf = RandomForestClassifier(n_estimators=50, n_jobs=-1)\n",
"clf.fit(train_dat, train_lbl_txt)\n",
"clf.score(test_dat, test_lbl_txt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cross validation on random forests"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"this will take a while...\n",
"run: None auto gini done!\n",
"run: None auto entropy done!\n",
"run: None log2 gini done!\n",
"run: None log2 entropy done!\n",
"run: None None gini done!\n",
"run: None None entropy done!\n",
"run: 2 auto gini done!\n",
"run: 2 auto entropy done!\n",
"run: 2 log2 gini done!\n",
"run: 2 log2 entropy done!\n",
"run: 2 None gini done!\n",
"run: 2 None entropy done!\n",
"run: 4 auto gini done!\n",
"run: 4 auto entropy done!\n",
"run: 4 log2 gini done!\n",
"run: 4 log2 entropy done!\n",
"run: 4 None gini done!\n",
"run: 4 None entropy done!\n",
"run: 8 auto gini done!\n",
"run: 8 auto entropy done!\n",
"run: 8 log2 gini done!\n",
"run: 8 log2 entropy done!\n",
"run: 8 None gini done!\n",
"run: 8 None entropy done!\n",
"run: 16 auto gini done!\n",
"run: 16 auto entropy done!\n",
"run: 16 log2 gini done!\n",
"run: 16 log2 entropy done!\n",
"run: 16 None gini done!\n",
"run: 16 None entropy done!\n",
"run: 32 auto gini done!\n",
"run: 32 auto entropy done!\n",
"run: 32 log2 gini done!\n",
"run: 32 log2 entropy done!\n",
"run: 32 None gini done!\n",
"run: 32 None entropy done!\n",
"run: 64 auto gini done!\n",
"run: 64 auto entropy done!\n",
"run: 64 log2 gini done!\n",
"run: 64 log2 entropy done!\n",
"run: 64 None gini done!\n",
"run: 64 None entropy done!\n",
"All done!\n"
]
}
],
"source": [
"from sklearn.cross_validation import StratifiedKFold\n",
"\n",
"# Generate k-fold split in the training data\n",
"kf = StratifiedKFold(train_lbl_txt, n_folds=5, shuffle=True)\n",
"\n",
"# Do multiple runs and save'em to runs list\n",
"runs = []\n",
"params = []\n",
"\n",
"depths = [None, 2, 4, 8, 16, 32, 64]\n",
"max_features = ['auto', 'log2', None]\n",
"criterions = ['gini', 'entropy']\n",
"for d in depths:\n",
" for mf in max_features:\n",
" for c in criterions:\n",
" params.append([d, mf, c])\n",
"\n",
"\n",
"print 'this will take a while...'\n",
"for d, mf, c in params:\n",
" clf = RandomForestClassifier(n_jobs=-1, max_depth=d, max_features=mf, criterion=c)\n",
" print 'run:', d, mf, c,\n",
" for t,v in kf:\n",
" trn = train_dat[t]\n",
" val = train_dat[v]\n",
" trn_lbl = train_lbl_txt[t]\n",
" val_lbl = train_lbl_txt[v]\n",
" clf.fit(trn, trn_lbl)\n",
" score = clf.score(val, val_lbl)\n",
" runs.append([score, d, mf, c])\n",
" print 'done!'\n",
"print 'All done!'"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['../models/random_forest_model.p',\n",
" '../models/random_forest_model.p_01.npy',\n",
" '../models/random_forest_model.p_02.npy',\n",
" '../models/random_forest_model.p_03.npy',\n",
" '../models/random_forest_model.p_04.npy',\n",
" '../models/random_forest_model.p_05.npy',\n",
" '../models/random_forest_model.p_06.npy',\n",
" '../models/random_forest_model.p_07.npy',\n",
" '../models/random_forest_model.p_08.npy',\n",
" '../models/random_forest_model.p_09.npy',\n",
" '../models/random_forest_model.p_10.npy',\n",
" '../models/random_forest_model.p_11.npy',\n",
" '../models/random_forest_model.p_12.npy',\n",
" '../models/random_forest_model.p_13.npy',\n",
" '../models/random_forest_model.p_14.npy',\n",
" '../models/random_forest_model.p_15.npy',\n",
" '../models/random_forest_model.p_16.npy',\n",
" '../models/random_forest_model.p_17.npy',\n",
" '../models/random_forest_model.p_18.npy',\n",
" '../models/random_forest_model.p_19.npy',\n",
" '../models/random_forest_model.p_20.npy',\n",
" '../models/random_forest_model.p_21.npy',\n",
" '../models/random_forest_model.p_22.npy',\n",
" '../models/random_forest_model.p_23.npy',\n",
" '../models/random_forest_model.p_24.npy',\n",
" '../models/random_forest_model.p_25.npy',\n",
" '../models/random_forest_model.p_26.npy',\n",
" '../models/random_forest_model.p_27.npy',\n",
" '../models/random_forest_model.p_28.npy',\n",
" '../models/random_forest_model.p_29.npy',\n",
" '../models/random_forest_model.p_30.npy',\n",
" '../models/random_forest_model.p_31.npy',\n",
" '../models/random_forest_model.p_32.npy',\n",
" '../models/random_forest_model.p_33.npy',\n",
" '../models/random_forest_model.p_34.npy',\n",
" '../models/random_forest_model.p_35.npy',\n",
" '../models/random_forest_model.p_36.npy',\n",
" '../models/random_forest_model.p_37.npy',\n",
" '../models/random_forest_model.p_38.npy',\n",
" '../models/random_forest_model.p_39.npy',\n",
" '../models/random_forest_model.p_40.npy',\n",
" '../models/random_forest_model.p_41.npy']"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"champion = max(runs, key=lambda run: run[0])\n",
"score, d, mf, c = champion\n",
"clf = RandomForestClassifier(n_jobs=-1, max_depth=d, max_features=mf, criterion=c)\n",
"clf.fit(full_dat, full_lbl_txt)\n",
"\n",
"from sklearn.externals import joblib\n",
"joblib.dump(clf, '../models/random_forest_model.p')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Support Vector Machine Crossvalidation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# from sklearn.svm import SVC\n",
"# from sklearn.grid_search import GridSearchCV\n",
"# from sklearn.cross_validation import StratifiedKFold\n",
"\n",
"# folder = StratifiedKFold(train_lbl_txt, n_folds=5, shuffle=False)\n",
"\n",
"# parameters = {'kernel':['linear', 'poly', 'rbf'], 'C':[64, 32, 16, 8], 'probability':[False], 'max_iter':[1000]}\n",
"# svm_clf = SVC()\n",
"\n",
"# clf = GridSearchCV(svm_clf, parameters, n_jobs=-1, pre_dispatch='n_jobs', cv=folder, refit=True, verbose=5)\n",
"# clf.fit(train_dat_scaled, train_lbl_txt)\n",
"# clf.score(test_dat_scaled, test_lbl_txt)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# print 'best score:', clf.best_score_\n",
"# print 'best params:', clf.best_params_"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
turbomanage/training-data-analyst | courses/machine_learning/deepdive/05_artandscience/labs/d_customestimator.ipynb | 1 | 63316 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1> Time series prediction using RNNs, with TensorFlow and Cloud ML Engine </h1>\n",
"\n",
"This notebook illustrates:\n",
"<ol>\n",
"<li> Creating a Recurrent Neural Network in TensorFlow\n",
"<li> Creating a Custom Estimator in tf.estimator\n",
"<li> Training on Cloud ML Engine\n",
"</ol>\n",
"\n",
"<p>\n",
"\n",
"<h3> Simulate some time-series data </h3>\n",
"\n",
"Essentially a set of sinusoids with random amplitudes and frequencies."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"PROJECT = 'cloud-training-demos' # REPLACE WITH YOUR PROJECT ID\n",
"BUCKET = 'cloud-training-demos-ml' # REPLACE WITH YOUR BUCKET NAME\n",
"REGION = 'us-central1' # REPLACE WITH YOUR BUCKET REGION e.g. us-central1\n",
"os.environ['TFVERSION'] = '1.8' # Tensorflow version"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# for bash\n",
"os.environ['PROJECT'] = PROJECT\n",
"os.environ['BUCKET'] = BUCKET\n",
"os.environ['REGION'] = REGION"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Updated property [core/project].\n",
"Updated property [compute/region].\n"
]
}
],
"source": [
"%%bash\n",
"gcloud config set project $PROJECT\n",
"gcloud config set compute/region $REGION"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.8.0\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"print(tf.__version__)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/envs/py3env/lib/python3.5/site-packages/matplotlib/font_manager.py:1320: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
" (prop.get_family(), self.defaultFamily[fontext]))\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd17c981da0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import seaborn as sns\n",
"import pandas as pd\n",
"\n",
"SEQ_LEN = 10\n",
"def create_time_series():\n",
" freq = (np.random.random() * 0.5) + 0.1 # 0.1 to 0.6\n",
" ampl = np.random.random() + 0.5 # 0.5 to 1.5\n",
" x = np.sin(np.arange(0, SEQ_LEN) * freq) * ampl\n",
" return x\n",
"\n",
"for i in range(0, 5):\n",
" sns.tsplot( create_time_series() ); # 5 series"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def to_csv(filename, N):\n",
" with open(filename, 'w') as ofp:\n",
" for lineno in range(0, N):\n",
" seq = create_time_series()\n",
" line = \",\".join(map(str, seq))\n",
" ofp.write(line + '\\n')\n",
"\n",
"to_csv('train.csv', 1000) # 1000 sequences\n",
"to_csv('valid.csv', 50)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"==> train.csv <==\r\n",
"0.0,0.41494536271196236,0.734451551569238,0.8850308030101335,0.8320492576193983,0.5876928930606661,0.20816469810142113,-0.2192422505391868,-0.5962225619744843,-0.836069183382574\r\n",
"0.0,0.16050934484878304,0.3187737388366111,0.4725796299246527,0.6197758245612592,0.7583035751751281,0.8862253746803034,1.0017520552610126,1.1032678124214037,1.1893528043033446\r\n",
"0.0,0.2019768145665784,0.38954713901631743,0.5493320613627289,0.6699345313575295,0.7427522829645161,0.7625914120164823,0.7280368441286417,0.6415532683388727,0.509309337136115\r\n",
"0.0,0.20513560202370504,0.38685799767444984,0.5244262531017174,0.6021389789663988,0.6111264126796244,0.5503627711095774,0.4267833286114507,0.2544928576485325,0.05315577684418399\r\n",
"0.0,0.20112443738388527,0.3910248758298022,0.559103685595879,0.6959810200217685,0.794018269517251,0.8477443436542725,0.8541609921610173,0.8129101260008574,0.726293800975636\r\n",
"\r\n",
"==> valid.csv <==\r\n",
"0.0,0.4518834761927216,0.8463432780938488,1.1332529187241658,1.2761529862603307,1.256884270714006,1.077895368911241,0.7619315258995626,0.3491442536614218,-0.10801097394423191\r\n",
"0.0,0.4790437748864075,0.8201581888176755,0.9251273884779285,0.7637279675979914,0.38243108423806044,-0.1089778135632325,-0.5690091192298001,-0.8652076640769141,-0.9122900603467594\r\n",
"0.0,0.3825159065598339,0.6359357547959016,0.6747323732958791,0.485812213690283,0.13293431847834153,-0.2648078457410006,-0.5731794804944685,-0.6881076260362654,-0.5708049526750818\r\n",
"0.0,0.09017440871015896,0.1785854105056497,0.26350408280046406,0.3432697973071416,0.4163226944765429,0.481234187353148,0.5367348983273075,0.5817394824668976,0.6153678519953671\r\n",
"0.0,0.7084840320523366,1.200134233070997,1.3244794677975964,1.0434634746979885,0.44309210797471377,-0.29288903247151693,-0.9392305365880832,-1.2981174951417012,-1.2597113929215498\r\n"
]
}
],
"source": [
"!head -5 train.csv valid.csv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2> RNN </h2>\n",
"\n",
"For more info, see:\n",
"<ol>\n",
"<li> http://colah.github.io/posts/2015-08-Understanding-LSTMs/ for the theory\n",
"<li> https://www.tensorflow.org/tutorials/recurrent for explanations\n",
"<li> https://github.com/tensorflow/models/tree/master/tutorials/rnn/ptb for sample code\n",
"</ol>\n",
"\n",
"Here, we are trying to predict from 9 values of a timeseries, the tenth value.\n",
"\n",
"<p>\n",
"\n",
"<h3> Imports </h3>\n",
"\n",
"Several tensorflow packages and shutil"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/envs/py3env/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" from ._conv import register_converters as _register_converters\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"import shutil\n",
"import tensorflow.contrib.metrics as metrics\n",
"import tensorflow.contrib.rnn as rnn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3> Input Fn to read CSV </h3>\n",
"\n",
"Our CSV file structure is quite simple -- a bunch of floating point numbers (note the type of DEFAULTS). We ask for the data to be read BATCH_SIZE sequences at a time. The Estimator API in tf.contrib.learn wants the features returned as a dict. We'll just call this timeseries column 'rawdata'.\n",
"<p>\n",
"Our CSV file sequences consist of 10 numbers. We'll assume that 9 of them are inputs and we need to predict the last one."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"DEFAULTS = [[0.0] for x in range(0, SEQ_LEN)]\n",
"BATCH_SIZE = 20\n",
"TIMESERIES_COL = 'rawdata'\n",
"# In each sequence, column index 0 to N_INPUTS - 1 are features, and column index N_INPUTS to SEQ_LEN are labels\n",
"N_OUTPUTS = 1\n",
"N_INPUTS = SEQ_LEN - N_OUTPUTS"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Reading data using the Estimator API in tf.estimator requires an input_fn. This input_fn needs to return a dict of features and the corresponding labels.\n",
"<p>\n",
"So, we read the CSV file. The Tensor format here will be a scalar -- entire line. We then decode the CSV. At this point, all_data will contain a list of scalar Tensors. There will be SEQ_LEN of these tensors.\n",
"<p>\n",
"We split this list of SEQ_LEN tensors into a list of N_INPUTS Tensors and a list of N_OUTPUTS Tensors. We stack them along the first dimension to then get a vector Tensor for each. We then put the inputs into a dict and call it features. The other is the ground truth, so labels."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"# Read data and convert to needed format\n",
"def read_dataset(filename, mode, batch_size = 512):\n",
" def _input_fn():\n",
" # Provide the ability to decode a CSV\n",
" def decode_csv(line):\n",
" # all_data is a list of scalar tensors\n",
" all_data = tf.decode_csv(line, record_defaults = DEFAULTS)\n",
" inputs = all_data[:len(all_data) - N_OUTPUTS] # first N_INPUTS values\n",
" labels = all_data[len(all_data) - N_OUTPUTS:] # last N_OUTPUTS values\n",
"\n",
" # Convert each list of rank R tensors to one rank R+1 tensor\n",
" inputs = tf.stack(inputs, axis = 0)\n",
" labels = tf.stack(labels, axis = 0)\n",
" \n",
" # Convert input R+1 tensor into a feature dictionary of one R+1 tensor\n",
" features = {TIMESERIES_COL: inputs}\n",
"\n",
" return features, labels\n",
"\n",
" # Create list of files that match pattern\n",
" file_list = tf.gfile.Glob(filename)\n",
"\n",
" # Create dataset from file list\n",
" dataset = tf.data.TextLineDataset(file_list).map(decode_csv)\n",
"\n",
" if mode == tf.estimator.ModeKeys.TRAIN:\n",
" num_epochs = None # indefinitely\n",
" dataset = dataset.shuffle(buffer_size = 10 * batch_size)\n",
" else:\n",
" num_epochs = 1 # end-of-input after this\n",
"\n",
" dataset = dataset.repeat(num_epochs).batch(batch_size)\n",
"\n",
" iterator = dataset.make_one_shot_iterator()\n",
" batch_features, batch_labels = iterator.get_next()\n",
" return batch_features, batch_labels\n",
" return _input_fn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3> Define RNN </h3>\n",
"\n",
"A recursive neural network consists of possibly stacked LSTM cells.\n",
"<p>\n",
"The RNN has one output per input, so it will have 8 output cells. We use only the last output cell, but rather use it directly, we do a matrix multiplication of that cell by a set of weights to get the actual predictions. This allows for a degree of scaling between inputs and predictions if necessary (we don't really need it in this problem).\n",
"<p>\n",
" \n",
"<p> You have two tasks to complete\n",
" \n",
"<ol>\n",
" <li>Firstly, define loss, train_op and eval_metric_ops as a function of mode</li>\n",
" <li>Secondly, use the defined variables to instantiate an EstimatorSpec</li>\n",
" </ol>"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"LSTM_SIZE = 3 # number of hidden layers in each of the LSTM cells\n",
"\n",
"# Create the inference model\n",
"def simple_rnn(features, labels, mode):\n",
" # 0. Reformat input shape to become a sequence\n",
" x = tf.split(features[TIMESERIES_COL], N_INPUTS, 1)\n",
" \n",
" # 1. Configure the RNN\n",
" lstm_cell = rnn.BasicLSTMCell(LSTM_SIZE, forget_bias = 1.0)\n",
" outputs, _ = rnn.static_rnn(lstm_cell, x, dtype = tf.float32)\n",
"\n",
" # Slice to keep only the last cell of the RNN\n",
" outputs = outputs[-1]\n",
" \n",
" # Output is result of linear activation of last layer of RNN\n",
" weight = tf.get_variable(\"weight\", initializer=tf.initializers.random_normal, shape=[LSTM_SIZE, N_OUTPUTS])\n",
" bias = tf.get_variable(\"bias\", initializer=tf.initializers.random_normal, shape=[N_OUTPUTS])\n",
" predictions = tf.matmul(outputs, weight) + bias\n",
" \n",
" # 2. Loss function, training/eval ops\n",
" # TODO: Implement training/eval ops for training, evaluation and prediction\n",
" \n",
" # 3. Create predictions\n",
" predictions_dict = {\"predicted\": predictions}\n",
" \n",
" # 4. Create export outputs\n",
" export_outputs = {\"predict_export_outputs\": tf.estimator.export.PredictOutput(outputs = predictions)}\n",
" \n",
" # 5. Return an EstimatorSpec\n",
" return None # TODO"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3> Estimator </h3>\n",
"\n",
"Distributed training is launched off using an Estimator. The key line here is that we use tf.estimator.Estimator rather than, say tf.estimator.DNNRegressor. This allows us to provide a model_fn, which will be our RNN defined above. Note also that we specify a serving_input_fn -- this is how we parse the input data provided to us at prediction time.\n",
"\n",
"You have one task to complete: instantiate an estimator using the model function we defined previously."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"# Create functions to read in respective datasets\n",
"def get_train():\n",
" return read_dataset(filename = 'train.csv', mode = tf.estimator.ModeKeys.TRAIN, batch_size = 512)\n",
"\n",
"def get_valid():\n",
" return read_dataset(filename = 'valid.csv', mode = tf.estimator.ModeKeys.EVAL, batch_size = 512)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"# Create serving input function\n",
"def serving_input_fn():\n",
" feature_placeholders = {\n",
" TIMESERIES_COL: tf.placeholder(tf.float32, [None, N_INPUTS])\n",
" }\n",
" \n",
" features = {\n",
" key: tf.expand_dims(tensor, -1)\n",
" for key, tensor in feature_placeholders.items()\n",
" }\n",
" features[TIMESERIES_COL] = tf.squeeze(features[TIMESERIES_COL], axis = [2])\n",
" \n",
" return tf.estimator.export.ServingInputReceiver(features, feature_placeholders)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"# Create custom estimator's train and evaluate function\n",
"def train_and_evaluate(output_dir):\n",
" # TODO: Instantiate an estimator using our model function\n",
" estimator = #\n",
" train_spec = tf.estimator.TrainSpec(input_fn = get_train(),\n",
" max_steps = 1000)\n",
" exporter = tf.estimator.LatestExporter('exporter', serving_input_fn)\n",
" eval_spec = tf.estimator.EvalSpec(input_fn = get_valid(),\n",
" steps = None,\n",
" exporters = exporter)\n",
" tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Using default config.\n",
"INFO:tensorflow:Using config: {'_model_dir': 'outputdir', '_service': None, '_keep_checkpoint_every_n_hours': 10000, '_task_type': 'worker', '_num_worker_replicas': 1, '_keep_checkpoint_max': 5, '_num_ps_replicas': 0, '_global_id_in_cluster': 0, '_log_step_count_steps': 100, '_save_checkpoints_secs': 600, '_evaluation_master': '', '_train_distribute': None, '_save_summary_steps': 100, '_tf_random_seed': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f65a2bc30b8>, '_master': '', '_task_id': 0, '_session_config': None, '_save_checkpoints_steps': None, '_is_chief': True}\n",
"INFO:tensorflow:Running training and evaluation locally (non-distributed).\n",
"INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after 600 secs (eval_spec.throttle_secs) or training is finished.\n",
"INFO:tensorflow:Calling model_fn.\n",
"INFO:tensorflow:Done calling model_fn.\n",
"INFO:tensorflow:Create CheckpointSaverHook.\n",
"INFO:tensorflow:Graph was finalized.\n",
"INFO:tensorflow:Running local_init_op.\n",
"INFO:tensorflow:Done running local_init_op.\n",
"INFO:tensorflow:Saving checkpoints for 1 into outputdir/model.ckpt.\n",
"INFO:tensorflow:step = 1, loss = 2.1511927\n",
"INFO:tensorflow:global_step/sec: 13.8533\n",
"INFO:tensorflow:step = 101, loss = 0.5264278 (7.220 sec)\n",
"INFO:tensorflow:global_step/sec: 14.175\n",
"INFO:tensorflow:step = 201, loss = 0.42215365 (7.055 sec)\n",
"INFO:tensorflow:global_step/sec: 14.2523\n",
"INFO:tensorflow:step = 301, loss = 0.34791386 (7.017 sec)\n",
"INFO:tensorflow:global_step/sec: 14.7247\n",
"INFO:tensorflow:step = 401, loss = 0.26609486 (6.791 sec)\n",
"INFO:tensorflow:global_step/sec: 14.6274\n",
"INFO:tensorflow:step = 501, loss = 0.21945082 (6.836 sec)\n",
"INFO:tensorflow:global_step/sec: 14.4637\n",
"INFO:tensorflow:step = 601, loss = 0.1646782 (6.914 sec)\n",
"INFO:tensorflow:global_step/sec: 14.5217\n",
"INFO:tensorflow:step = 701, loss = 0.13758004 (6.887 sec)\n",
"INFO:tensorflow:global_step/sec: 14.123\n",
"INFO:tensorflow:step = 801, loss = 0.1219064 (7.081 sec)\n",
"INFO:tensorflow:global_step/sec: 15.7489\n",
"INFO:tensorflow:step = 901, loss = 0.10583858 (6.350 sec)\n",
"INFO:tensorflow:Saving checkpoints for 1000 into outputdir/model.ckpt.\n",
"INFO:tensorflow:Loss for final step: 0.085023135.\n",
"INFO:tensorflow:Calling model_fn.\n",
"INFO:tensorflow:Done calling model_fn.\n",
"INFO:tensorflow:Starting evaluation at 2018-09-12-20:01:59\n",
"INFO:tensorflow:Graph was finalized.\n",
"INFO:tensorflow:Restoring parameters from outputdir/model.ckpt-1000\n",
"INFO:tensorflow:Running local_init_op.\n",
"INFO:tensorflow:Done running local_init_op.\n",
"INFO:tensorflow:Finished evaluation at 2018-09-12-20:01:59\n",
"INFO:tensorflow:Saving dict for global step 1000: global_step = 1000, loss = 0.069563285, rmse = 0.26374853\n",
"INFO:tensorflow:Calling model_fn.\n",
"INFO:tensorflow:Done calling model_fn.\n",
"INFO:tensorflow:Signatures INCLUDED in export for Classify: None\n",
"INFO:tensorflow:Signatures INCLUDED in export for Regress: None\n",
"INFO:tensorflow:Signatures INCLUDED in export for Predict: ['serving_default', 'predict_export_outputs']\n",
"INFO:tensorflow:Restoring parameters from outputdir/model.ckpt-1000\n",
"INFO:tensorflow:Assets added to graph.\n",
"INFO:tensorflow:No assets to write.\n",
"INFO:tensorflow:SavedModel written to: b\"outputdir/export/exporter/temp-b'1536782520'/saved_model.pb\"\n"
]
}
],
"source": [
"# Run the model\n",
"shutil.rmtree('outputdir', ignore_errors = True) # start fresh each time\n",
"train_and_evaluate('outputdir')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3> Standalone Python module </h3>\n",
"\n",
"To train this on Cloud ML Engine, we take the code in this notebook and make a standalone Python module."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/content/training-data-analyst/courses/machine_learning/deepdive/05_artandscience\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/envs/py3env/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" from ._conv import register_converters as _register_converters\n",
"INFO:tensorflow:Using default config.\n",
"INFO:tensorflow:Using config: {'_protocol': None, '_train_distribute': None, '_num_worker_replicas': 1, '_experimental_distribute': None, '_eval_distribute': None, '_model_dir': 'outputdir/', '_log_step_count_steps': 100, '_keep_checkpoint_every_n_hours': 10000, '_global_id_in_cluster': 0, '_task_id': 0, '_evaluation_master': '', '_save_summary_steps': 100, '_task_type': 'worker', '_master': '', '_session_config': allow_soft_placement: true\n",
"graph_options {\n",
" rewrite_options {\n",
" meta_optimizer_iterations: ONE\n",
" }\n",
"}\n",
", '_keep_checkpoint_max': 5, '_is_chief': True, '_service': None, '_tf_random_seed': None, '_device_fn': None, '_save_checkpoints_steps': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7efd011d6208>, '_num_ps_replicas': 0, '_save_checkpoints_secs': 600}\n",
"INFO:tensorflow:Not using Distribute Coordinator.\n",
"INFO:tensorflow:Running training and evaluation locally (non-distributed).\n",
"INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps None or save_checkpoints_secs 600.\n",
"WARNING:tensorflow:From /usr/local/envs/py3env/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Colocations handled automatically by placer.\n",
"INFO:tensorflow:Calling model_fn.\n",
"WARNING:tensorflow:From /content/training-data-analyst/courses/machine_learning/deepdive/05_artandscience/simplernn/trainer/model.py:90: BasicLSTMCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"This class is equivalent as tf.keras.layers.LSTMCell, and will be replaced by that in Tensorflow 2.0.\n",
"WARNING:tensorflow:From /content/training-data-analyst/courses/machine_learning/deepdive/05_artandscience/simplernn/trainer/model.py:91: static_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please use `keras.layers.RNN(cell, unroll=True)`, which is equivalent to this API\n",
"WARNING:tensorflow:From /usr/local/envs/py3env/lib/python3.5/site-packages/tensorflow/python/ops/losses/losses_impl.py:667: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use tf.cast instead.\n",
"INFO:tensorflow:Done calling model_fn.\n",
"INFO:tensorflow:Create CheckpointSaverHook.\n",
"INFO:tensorflow:Graph was finalized.\n",
"2019-04-04 22:20:25.388875: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n",
"2019-04-04 22:20:25.399578: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2200000000 Hz\n",
"2019-04-04 22:20:25.400981: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x5586480e4040 executing computations on platform Host. Devices:\n",
"2019-04-04 22:20:25.401122: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): <undefined>, <undefined>\n",
"INFO:tensorflow:Running local_init_op.\n",
"INFO:tensorflow:Done running local_init_op.\n",
"INFO:tensorflow:Saving checkpoints for 0 into outputdir/model.ckpt.\n",
"INFO:tensorflow:loss = 0.7419274, step = 1\n",
"INFO:tensorflow:global_step/sec: 19.6848\n",
"INFO:tensorflow:loss = 0.22478724, step = 101 (5.081 sec)\n",
"INFO:tensorflow:global_step/sec: 21.8056\n",
"INFO:tensorflow:loss = 0.120992295, step = 201 (4.586 sec)\n",
"INFO:tensorflow:global_step/sec: 19.5339\n",
"INFO:tensorflow:loss = 0.10022815, step = 301 (5.119 sec)\n",
"INFO:tensorflow:global_step/sec: 17.1955\n",
"INFO:tensorflow:loss = 0.07411742, step = 401 (5.815 sec)\n",
"INFO:tensorflow:global_step/sec: 23.2559\n",
"INFO:tensorflow:loss = 0.07213749, step = 501 (4.300 sec)\n",
"INFO:tensorflow:global_step/sec: 23.8249\n",
"INFO:tensorflow:loss = 0.07263194, step = 601 (4.197 sec)\n",
"INFO:tensorflow:global_step/sec: 24.2606\n",
"INFO:tensorflow:loss = 0.06830608, step = 701 (4.122 sec)\n",
"INFO:tensorflow:global_step/sec: 24.188\n",
"INFO:tensorflow:loss = 0.065488726, step = 801 (4.134 sec)\n",
"INFO:tensorflow:global_step/sec: 24.158\n",
"INFO:tensorflow:loss = 0.06435184, step = 901 (4.139 sec)\n",
"INFO:tensorflow:Saving checkpoints for 1000 into outputdir/model.ckpt.\n",
"INFO:tensorflow:Calling model_fn.\n",
"INFO:tensorflow:Done calling model_fn.\n",
"INFO:tensorflow:Starting evaluation at 2019-04-04T22:21:14Z\n",
"INFO:tensorflow:Graph was finalized.\n",
"WARNING:tensorflow:From /usr/local/envs/py3env/lib/python3.5/site-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use standard file APIs to check for files with this prefix.\n",
"INFO:tensorflow:Restoring parameters from outputdir/model.ckpt-1000\n",
"INFO:tensorflow:Running local_init_op.\n",
"INFO:tensorflow:Done running local_init_op.\n",
"INFO:tensorflow:Finished evaluation at 2019-04-04-22:21:14\n",
"INFO:tensorflow:Saving dict for global step 1000: global_step = 1000, loss = 0.05510318, rmse = 0.23474066\n",
"INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1000: outputdir/model.ckpt-1000\n",
"INFO:tensorflow:Calling model_fn.\n",
"INFO:tensorflow:Done calling model_fn.\n",
"WARNING:tensorflow:From /usr/local/envs/py3env/lib/python3.5/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:205: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.\n",
"INFO:tensorflow:Signatures INCLUDED in export for Regress: None\n",
"INFO:tensorflow:Signatures INCLUDED in export for Classify: None\n",
"INFO:tensorflow:Signatures INCLUDED in export for Eval: None\n",
"INFO:tensorflow:Signatures INCLUDED in export for Predict: ['predict_export_outputs', 'serving_default']\n",
"INFO:tensorflow:Signatures INCLUDED in export for Train: None\n",
"INFO:tensorflow:Restoring parameters from outputdir/model.ckpt-1000\n",
"INFO:tensorflow:Assets added to graph.\n",
"INFO:tensorflow:No assets to write.\n",
"INFO:tensorflow:SavedModel written to: outputdir/export/exporter/temp-b'1554416475'/saved_model.pb\n",
"INFO:tensorflow:Loss for final step: 0.060268052.\n"
]
}
],
"source": [
"%%bash\n",
"# Run module as-is\n",
"export parent_dir=$(dirname $(pwd))\n",
"echo $parent_dir\n",
"rm -rf outputdir\n",
"export PYTHONPATH=${PYTHONPATH}:$parent_dir/simplernn\n",
"python -m trainer.task \\\n",
" --train_data_paths=\"${PWD}/train.csv*\" \\\n",
" --eval_data_paths=\"${PWD}/valid.csv*\" \\\n",
" --output_dir=outputdir \\\n",
" --job-dir=./tmp"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Try out online prediction. This is how the REST API will work after you train on Cloud ML Engine"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting test.json\n"
]
}
],
"source": [
"%%writefile test.json\n",
"{\"rawdata_input\": [0,0.214,0.406,0.558,0.655,0.687,0.65,0.549,0.393]}"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"# local predict doesn't work with Python 3 yet.\n",
"# %%bash\n",
"# MODEL_DIR=$(ls ./outputdir/export/exporter/)\n",
"# gcloud ml-engine local predict --model-dir=./outputdir/export/exporter/$MODEL_DIR --json-instances=test.json"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3> Cloud ML Engine </h3>\n",
"\n",
"Now to train on Cloud ML Engine."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"jobId: simplernn_180912_200305\n",
"state: QUEUED\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"CommandException: 1 files/objects could not be removed.\n",
"Job [simplernn_180912_200305] submitted successfully.\n",
"Your job is still active. You may view the status of your job with the command\n",
"\n",
" $ gcloud ml-engine jobs describe simplernn_180912_200305\n",
"\n",
"or continue streaming the logs with the command\n",
"\n",
" $ gcloud ml-engine jobs stream-logs simplernn_180912_200305\n"
]
}
],
"source": [
"%%bash\n",
"# Run module on Cloud ML Engine\n",
"OUTDIR=gs://${BUCKET}/simplernn/model_trained\n",
"JOBNAME=simplernn_$(date -u +%y%m%d_%H%M%S)\n",
"gsutil -m rm -rf $OUTDIR\n",
"gcloud ml-engine jobs submit training $JOBNAME \\\n",
" --region=$REGION \\\n",
" --module-name=trainer.task \\\n",
" --package-path=$(dirname $(pwd))/simplernn/trainer \\\n",
" --job-dir=$OUTDIR \\\n",
" --staging-bucket=gs://$BUCKET \\\n",
" --scale-tier=BASIC \\\n",
" --runtime-version=1.4 \\\n",
" -- \\\n",
" --train_data_paths=\"gs://${BUCKET}/train.csv*\" \\\n",
" --eval_data_paths=\"gs://${BUCKET}/valid.csv*\" \\\n",
" --output_dir=$OUTDIR"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2> Variant: long sequence </h2>\n",
"\n",
"To create short sequences from a very long sequence."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"input= [ 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]\n",
"output= [[1. 2. 3. 4. 5.]\n",
" [2. 3. 4. 5. 6.]\n",
" [3. 4. 5. 6. 7.]\n",
" [4. 5. 6. 7. 8.]\n",
" [5. 6. 7. 8. 9.]]\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"import numpy as np\n",
"\n",
"def breakup(sess, x, lookback_len):\n",
" N = sess.run(tf.size(x))\n",
" windows = [tf.slice(x, [b], [lookback_len]) for b in range(0, N-lookback_len)]\n",
" windows = tf.stack(windows)\n",
" return windows\n",
"\n",
"x = tf.constant(np.arange(1,11, dtype=np.float32))\n",
"with tf.Session() as sess:\n",
" print('input=', x.eval())\n",
" seqx = breakup(sess, x, 5)\n",
" print('output=', seqx.eval())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Variant: Keras\n",
"\n",
"You can also invoke a Keras model from within the Estimator framework by creating an estimator from the compiled Keras model:"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"def make_keras_estimator(output_dir):\n",
" from tensorflow import keras\n",
" model = keras.models.Sequential()\n",
" model.add(keras.layers.Dense(32, input_shape=(N_INPUTS,), name=TIMESERIES_INPUT_LAYER))\n",
" model.add(keras.layers.Activation('relu'))\n",
" model.add(keras.layers.Dense(1))\n",
" model.compile(loss = 'mean_squared_error',\n",
" optimizer = 'adam',\n",
" metrics = ['mae', 'mape']) # mean absolute [percentage] error\n",
" return keras.estimator.model_to_estimator(model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%bash\n",
"# Run module as-is\n",
"echo $PWD\n",
"rm -rf outputdir\n",
"export parent_dir=$(dirname $(pwd))\n",
"export PYTHONPATH=${PYTHONPATH}:$parent_dir/simplernn\n",
"python -m trainer.task \\\n",
" --train_data_paths=\"${PWD}/train.csv*\" \\\n",
" --eval_data_paths=\"${PWD}/valid.csv*\" \\\n",
" --output_dir=${PWD}/outputdir \\\n",
" --job-dir=./tmp --keras"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright 2017 Google Inc. Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License"
]
}
],
"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": "3.5.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
luizirber/galGal | notebooks/05.Refining_RNA_data_Moleculo.ipynb | 1 | 41705 | {
"metadata": {
"name": "",
"signature": "sha256:080ba04a6f5882b509593d0be2a7f8c32e1e08af0f419067bd59d1f6412fea27"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Testing ortho"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- First, see if Likit has putative ortholog assignments between the chick RNAseq and human proteins. If not, the eel-pond protocol has some instructions on how to calculate this. Might take a few days. Then, we can recalculate the Venn diagram with a better curated set of mRNAseq transcripts, and also give people (Jerry in particular) the gene names that are in A, C, and D.\n",
"\n",
" - Likit: I only have homologs of chick RNASeq and human proteins of differential-expressed genes, which total ~1000 genes as of now.\n",
"\n",
" - Main goal right now: how many \"real\" mRNAseq genes, i.e. genes with orthology\n",
" to human, do/do not match in the genome? Nic has done exactly this analysis\n",
" for fungus."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"from matplotlib import pyplot as plt\n",
"from matplotlib_venn import venn3, venn3_circles"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 64
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Preparing all RNA data"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cd .. && make outputs/chicken_transcripts/global_merged.fa.clean.nr.renamed.fasta.nsq"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"make: `outputs/chicken_transcripts/global_merged.fa.clean.nr.renamed.fasta.nsq' is up to date.\r\n"
]
}
],
"prompt_number": 30
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Preparing 'only in RNA' data"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cd .. && make outputs/rna/only_rna.fa.renamed.fasta.nsq"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"make: `outputs/rna/only_rna.fa.renamed.fasta.nsq' is up to date.\r\n"
]
}
],
"prompt_number": 34
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Preparing Uniprot"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cd .. && make outputs/uniprot/uniprot_sprot.fasta.nsq"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"cd outputs/uniprot && \\\r\n",
"\tformatdb -i uniprot_sprot.fasta -o T -p T\r\n"
]
}
],
"prompt_number": 36
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cd .. && make outputs/uniprot/uniprot_sprot.namedb"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"cd outputs/uniprot && \\\r\n",
"\tpython /mnt/research/ged/irberlui/biodata/galGal/scripts/make-namedb.py uniprot_sprot.fasta uniprot_sprot.namedb\r\n"
]
}
],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cd .. && make outputs/uniprot/uniprot_sprot.fasta_screed"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"make: `outputs/uniprot/uniprot_sprot.fasta_screed' is up to date.\r\n"
]
}
],
"prompt_number": 42
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Uniprot ortho"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cd .. && make workdir/results/uniprot.x.chick.pbs"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"mkdir -p workdir/results\r\n",
"JOBID=`echo make workdir/results/uniprot.x.chick | cat pbs/header.sub - pbs/footer.sub | \\\r\n",
"\t qsub -l walltime=20:00:00,nodes=1:ppn=8,mem=400mb -A ged -N pbcr.uniprot.x.chick.pbs -o workdir/results/uniprot.x.chick.pbs -e workdir/results/uniprot.x.chick.pbs.err | cut -d\".\" -f1` ; \\\r\n",
"\twhile [ -n \"$(qstat -a |grep ${JOBID})\" ]; do sleep 600; done\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"^Cmake: *** [workdir/results/uniprot.x.chick.pbs] Interrupt\r\n",
"\r\n"
]
}
],
"prompt_number": 46
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cd .. && make workdir/results/chick.x.uniprot.pbs\n",
"!cd .. && make workdir/results/uniprot.x.chick.pbs"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"mkdir -p workdir/results\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"JOBID=`echo make workdir/results/chick.x.uniprot | cat pbs/header.sub - pbs/footer.sub | \\\r\n",
"\t qsub -l walltime=20:00:00,nodes=1:ppn=8,mem=400mb -A ged -N blast.chick.x.uniprot.pbs -o workdir/results/chick.x.uniprot.pbs -e workdir/results/chick.x.uniprot.pbs.err | cut -d\".\" -f1` ; \\\r\n",
"\twhile [ -n \"$(qstat -a |grep ${JOBID})\" ]; do sleep 600; done\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"^Cmake: *** [workdir/results/chick.x.uniprot.pbs] Interrupt\r\n"
]
}
],
"prompt_number": 47
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#!cp ../workdir/results/uniprot.x.chick ../outputs/rna/uniprot.x.chick\n",
"#!cp ../workdir/results/chick.x.uniprot ../outputs/rna/chick.x.uniprot"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cd .. && make outputs/rna/global_merged.fa.clean.nr.renamed.fasta.annot"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"cd outputs/rna && \\\r\n",
"\tpython /mnt/research/ged/irberlui/biodata/galGal/scripts/annotate-seqs.py /mnt/research/ged/irberlui/biodata/galGal/outputs/chicken_transcripts/global_merged.fa.clean.nr.renamed.fasta chick.x.uniprot.ortho chick.x.uniprot.homol\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Scanning sequences -- first pass to gather info\r\n",
"... 0\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... 25000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... 50000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... 75000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... 100000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... 125000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... 150000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... 175000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... 200000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... 225000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... 250000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... 275000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... 300000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... 325000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... 350000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... 375000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... 400000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"second pass: annotating\r\n",
"... x2 0\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... x2 25000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... x2 50000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... x2 75000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... x2 100000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... x2 125000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... x2 150000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... x2 175000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... x2 200000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... x2 225000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... x2 250000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... x2 275000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... x2 300000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... x2 325000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... x2 350000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... x2 375000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"... x2 400000\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"----\r\n",
"419276 sequences total\r\n",
"30421 annotated / ortho\r\n",
"182213 annotated / homol\r\n",
"0 annotated / tr\r\n",
"212634 total annotated\r\n",
"\r\n",
"annotated sequences in FASTA format: global_merged.fa.clean.nr.renamed.fasta.annot\r\n",
"annotation spreadsheet in: global_merged.fa.clean.nr.renamed.fasta.annot.csv\r\n",
"annotation spreadsheet with sequences (warning: LARGE): global_merged.fa.clean.nr.renamed.fasta.annot.large.csv\r\n"
]
}
],
"prompt_number": 56
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Recalculating Venn diagram with ortho"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cd .. && make outputs/rna/ortho.fa"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"python /mnt/research/ged/irberlui/biodata/galGal/scripts/extract.py outputs/rna/global_merged.fa.clean.nr.renamed.fasta.annot outputs/rna/ortho.fa ortho\r\n"
]
}
],
"prompt_number": 58
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cd .. && make outputs/reference/galGal4.fa.nsq"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"make: `outputs/reference/galGal4.fa.nsq' is up to date.\r\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cd .. && make outputs/moleculo/LR6000017-DNA_A01-LRAAA-AllReads.fasta.00.nsq"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"make: `outputs/moleculo/LR6000017-DNA_A01-LRAAA-AllReads.fasta.00.nsq' is up to date.\r\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cd .. && make workdir/results/Chick_RNA_BLAST.txt.pbs"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"blastn -query outputs/chicken_transcripts/global_merged.fa.clean.nr.renamed.fasta -out workdir/results/Chick_RNA_BLAST.txt -db outputs/reference/galGal4.fa -outfmt 6 -evalue 1e-3 -num_threads 8\r\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cd .. && make workdir/results/Moleculo_RNA_BLAST.txt.pbs"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"blastn -query outputs/chicken_transcripts/global_merged.fa.clean.nr.renamed.fasta -out workdir/results/Moleculo_RNA_BLAST.txt -db outputs/moleculo/LR6000017-DNA_A01-LRAAA-AllReads.fasta -outfmt 6 -evalue 1e-3 -num_threads 16\r\n"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cd .. && make outputs/rna/match_ortho.rna.ref.txt"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"cd outputs/rna && \\\r\n",
"\tpython /mnt/research/ged/irberlui/biodata/galGal/scripts/find_match_2.py ortho.fa Chick_RNA_BLAST.txt ortho.rna.ref\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Number of matches: 26766\r\n",
"Number of no matches: 3655\r\n"
]
}
],
"prompt_number": 54
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cd .. && make outputs/rna/match_ortho.rna.moleculo.txt"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"cd outputs/rna && \\\r\n",
"\tpython /mnt/research/ged/irberlui/biodata/galGal/scripts/find_match_2.py ortho.fa Moleculo_RNA_BLAST.txt ortho.rna.moleculo\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Number of matches: 23123\r\n",
"Number of no matches: 7298\r\n"
]
}
],
"prompt_number": 55
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"with open(\"../outputs/rna/match_ortho.rna.moleculo.txt\") as rna_mol_match:\n",
" rna_mol_set = set([line.split(' ')[0] for line in rna_mol_match if line.strip()])\n",
"\n",
"with open(\"../outputs/rna/match_ortho.rna.ref.txt\") as rna_ref_match:\n",
" rna_ref_set = set([line.split(' ')[0] for line in rna_ref_match if line.strip()])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 59
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"B_set = rna_mol_set & rna_ref_set\n",
"len(B_set)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 60,
"text": [
"23120"
]
}
],
"prompt_number": 60
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"B = len(rna_mol_set & rna_ref_set)\n",
"A = len(rna_ref_set) - B\n",
"C = len(rna_mol_set) - B\n",
"E = 30421\n",
"D = E - A - C - B\n",
"\n",
"print A, B, C, D, E"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"3646 23120 3 3652 30421\n"
]
}
],
"prompt_number": 61
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#scale = float(max([A, B, C, D]))\n",
"#v = venn3((1, 1, 1, D / scale, A / scale, C / scale, B / scale), set_labels=('Ref', 'Moleculo', 'RNA'))\n",
"\n",
"v = venn3((1, 1, 1, 1, 1, 1, 1), set_labels=('Ref', 'Moleculo', 'RNA'))\n",
"\n",
"v.get_label_by_id('100').set_text('')\n",
"v.get_label_by_id('010').set_text('')\n",
"v.get_label_by_id('001').set_text('D\\n%d' % D)\n",
"\n",
"v.get_label_by_id('011').set_text('C\\n%d' % C)\n",
"v.get_label_by_id('101').set_text('A\\n%d' % A)\n",
"v.get_label_by_id('110').set_text('')\n",
"\n",
"v.get_label_by_id('111').set_text('B\\n%d' % B)\n",
"plt.title('Filtered mRNAseq\\n(only genes with orthology to UniProt genes)')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 65,
"text": [
"<matplotlib.text.Text at 0x2b5617b25a90>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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Mpyq8kCRJOuOjw+ual7Ch5aTklJpN0Vl1r5ZVlnXm1bQKgH9+Dz3rKjztpykD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\nb/yQeBvH0psAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x2b5617ab5f90>"
]
}
],
"prompt_number": 65
}
],
"metadata": {}
}
]
} | bsd-3-clause |
huseinzol05/Deep-Learning-Tensorflow | deprecated/Deep Convolutional Network/mnist.ipynb | 2 | 6636 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import time\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from tensorflow.examples.tutorials.mnist import input_data\n",
"import tensorflow as tf"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting /home/huseinzol05/Documents/MNIST/MNIST_data/train-images-idx3-ubyte.gz\n",
"Extracting /home/huseinzol05/Documents/MNIST/MNIST_data/train-labels-idx1-ubyte.gz\n",
"Extracting /home/huseinzol05/Documents/MNIST/MNIST_data/t10k-images-idx3-ubyte.gz\n",
"Extracting /home/huseinzol05/Documents/MNIST/MNIST_data/t10k-labels-idx1-ubyte.gz\n"
]
}
],
"source": [
"mnist = input_data.read_data_sets('/home/huseinzol05/Documents/MNIST/MNIST_data', one_hot = True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Model:\n",
" \n",
" def __init__(self, learning_rate, y_shape):\n",
" self.X = tf.placeholder(tf.float32, (None, 28, 28, 1))\n",
" self.Y = tf.placeholder(tf.float32, (None, y_shape))\n",
" \n",
" def convolutionize(x, conv_w, h = 1):\n",
" return tf.nn.conv2d(input = x, filter = conv_w, strides = [1, h, h, 1], padding = 'SAME')\n",
"\n",
" def pooling(wx):\n",
" return tf.nn.max_pool(wx, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')\n",
" \n",
" w1 = tf.Variable(tf.random_normal([3, 3, 1, 16], stddev = 0.5))\n",
" b1 = tf.Variable(tf.zeros(shape = [16]))\n",
" w2 = tf.Variable(tf.random_normal([3, 3, 16, 8], stddev = 0.5))\n",
" b2 = tf.Variable(tf.zeros(shape = [8]))\n",
" w3 = tf.Variable(tf.random_normal([3, 3, 8, 8], stddev = 0.5))\n",
" b3 = tf.Variable(tf.zeros(shape = [8]))\n",
" w4 = tf.Variable(tf.random_normal([128, y_shape], stddev = 0.5))\n",
" b4 = tf.Variable(tf.zeros(shape = [y_shape]))\n",
"\n",
" conv1 = pooling(tf.nn.sigmoid(convolutionize(self.X, w1) + b1))\n",
" conv2 = pooling(tf.nn.sigmoid(convolutionize(conv1, w2) + b2))\n",
" conv3 = pooling(tf.nn.sigmoid(convolutionize(conv2, w3) + b3))\n",
" conv3 = tf.reshape(conv3, [-1, 128])\n",
" self.logits = tf.matmul(conv3, w4) + b4\n",
" \n",
" self.cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = self.logits, labels = self.Y))\n",
" self.optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(self.cost)\n",
" \n",
" self.correct_prediction = tf.equal(tf.argmax(self.logits, 1), tf.argmax(self.Y, 1))\n",
" self.accuracy = tf.reduce_mean(tf.cast(self.correct_prediction, \"float\"))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"learning_rate = 0.01\n",
"sess = tf.InteractiveSession()\n",
"model = Model(learning_rate, mnist.train.labels.shape[1])\n",
"sess.run(tf.global_variables_initializer())"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch: 1, loss: 0.27670943084, accuracy: 0.920928030303, s / epoch: 27.144755125\n",
"epoch: 2, loss: 0.188248241533, accuracy: 0.946896853147, s / epoch: 27.0147771835\n",
"epoch: 3, loss: 0.152987092488, accuracy: 0.95541958042, s / epoch: 26.8241751194\n",
"epoch: 4, loss: 0.129784707049, accuracy: 0.9622122669, s / epoch: 26.8857750893\n",
"epoch: 5, loss: 0.114598895856, accuracy: 0.967202068765, s / epoch: 27.4256939888\n",
"epoch: 6, loss: 0.101240242707, accuracy: 0.970880681818, s / epoch: 28.2523770332\n",
"epoch: 7, loss: 0.0919746177223, accuracy: 0.973594114219, s / epoch: 28.2267827988\n",
"epoch: 8, loss: 0.085458196739, accuracy: 0.975979749417, s / epoch: 28.3221931458\n",
"epoch: 9, loss: 0.0804469554527, accuracy: 0.977236305361, s / epoch: 28.1964430809\n",
"epoch: 10, loss: 0.0763231400005, accuracy: 0.978511072261, s / epoch: 28.5655319691\n"
]
}
],
"source": [
"EPOCH, LOSS, ACC = [], [], []\n",
"BATCH_SIZE = 128\n",
"\n",
"for i in xrange(10):\n",
" last = time.time()\n",
" EPOCH.append(i)\n",
" TOTAL_LOSS, ACCURACY = 0, 0\n",
" for n in xrange(0, (mnist.train.images.shape[0] // BATCH_SIZE) * BATCH_SIZE, BATCH_SIZE):\n",
" batch_x = mnist.train.images[n: n + BATCH_SIZE, :].reshape((-1, 28, 28, 1))\n",
" cost, _ = sess.run([model.cost, model.optimizer], feed_dict = {model.X : batch_x, model.Y : mnist.train.labels[n: n + BATCH_SIZE, :]})\n",
" ACCURACY += sess.run(model.accuracy, feed_dict = {model.X : batch_x, model.Y : mnist.train.labels[n: n + BATCH_SIZE, :]})\n",
" TOTAL_LOSS += cost\n",
" \n",
" TOTAL_LOSS /= (mnist.train.images.shape[0] // BATCH_SIZE)\n",
" ACCURACY /= (mnist.train.images.shape[0] // BATCH_SIZE)\n",
" LOSS.append(TOTAL_LOSS); ACC.append(ACCURACY)\n",
" print 'epoch: ' + str(i + 1) + ', loss: ' + str(TOTAL_LOSS) + ', accuracy: ' + str(ACCURACY) + ', s / epoch: ' + str(time.time() - last)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"testing accuracy: 0.9762\n"
]
}
],
"source": [
"from sklearn import metrics\n",
"testing_acc, logits = sess.run([model.accuracy, tf.cast(tf.argmax(model.logits, 1), tf.int32)], feed_dict = {model.X : mnist.test.images.reshape((-1, 28, 28, 1)), model.Y : mnist.test.labels})\n",
"print 'testing accuracy: ' + str(testing_acc)"
]
}
],
"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 |
ibm-cds-labs/pixiedust | notebook/PixieDust 5 - Stash to Cloudant.ipynb | 1 | 3447 | {"nbformat": 4, "nbformat_minor": 0, "cells": [{"source": "# Stashing Your Data\n\nWith PixieDust, you also have the option to export the data from your Notebook to external sources.\nThe output of the **`display`** API includes a toolbar which contains the **Download** button.\n\n<img style=\"margin:10px 0\" src=\"https://pixiedust.github.io/pixiedust/_images/downloadfile.png\">\n\n", "cell_type": "markdown", "metadata": {}}, {"source": "***\n### Stash to Cloudant\n\nOne export option is to save the data directly into a [Cloudant](https://cloudant.com/) or [CouchDB](https://couchdb.apache.org/) database.\n\n\n#### Prerequisites\n\nCollect your database connection information: the database host, user name, and password. \n \n> If your Cloudant instance was provisioned in [Bluemix](https://console.ng.bluemix.net/catalog/services/cloudant-nosql-db/) you can find the connectivity information in the Service Credentials tab.\n\n\n#### Steps \n\n* From the toolbar in the **`display`** output, click the **Download** button \n\n* Choose **Stash to Cloudant** from the dropdown menu \n \n> If you get an error that a library is missing, you may need to [install the cloudant-spark](https://github.com/ibm-watson-data-lab/pixiedust/blob/plotting-tools/docsrc/source/install.rst#stash-to-cloudant) library.\n\n* Click the dropdown to see the list of available connections \n\n* Select an existing connection or add a new connection \n\n * Click the **`+`** plus button to add a new connection\n * Enter your Cloudant database credentials in JSON format \n \n > If you are stashing to CouchDB be sure include the protocol. See the [sample credentials format](#Sample-Credentials-Format) below.\n\n * Click **OK**\n * Select the new connection\n \n* Click **Submit**\n\n\n#### Sample Credentials Format \n\n* **CouchDB**\n```\n{\n \"name\": \"local-couchdb-connection\",\n \"credentials\": {\n \"username\": \"couchdbuser\",\n \"password\": \"password\",\n \"protocol\": \"http\",\n \"host\": \"127.0.0.1:5984\",\n \"port\": 5984,\n \"url\": \"http://couchdbuser:[email protected]:5984\"\n }\n}\n```\n\n* **Cloudant**\n```\n{\n \"name\": \"remote-cloudant-connection\",\n \"credentials\": {\n \"username\": \"username-bluemix\",\n \"password\": \"password\",\n \"host\": \"host-bluemix.cloudant.com\",\n \"port\": 443,\n \"url\": \"https://username-bluemix:[email protected]\"\n }\n}\n```\n", "cell_type": "markdown", "metadata": {}}, {"source": "***\n### Download as File\n\nAlternatively, you may choose to save the data set to a various file formats (e.g., CSV, JSON, XML, etc.)\n\n#### Steps \n\n* From the toolbar in the **`display`** output, click the **Download** button\n* Choose **Download as File**\n* Choose the desired format\n* Specify the number of records to download\n<img style=\"margin:10px 0\" src=\"https://pixiedust.github.io/pixiedust/_images/save_as.png\">\n* Click **OK**\n", "cell_type": "markdown", "metadata": {"collapsed": true}}], "metadata": {"language_info": {"nbconvert_exporter": "python", "file_extension": ".py", "codemirror_mode": {"name": "ipython", "version": 2}, "pygments_lexer": "ipython2", "version": "2.7.11", "mimetype": "text/x-python", "name": "python"}, "kernelspec": {"language": "python", "display_name": "Python 2 with Spark 1.6", "name": "python2"}}} | apache-2.0 |
JaeGyu/PythonEx_1 | 머신러닝 딥러닝 실전개발 입문 책 연습/TensorflowEx1.ipynb | 1 | 30914 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.2.0\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"print(tf.__version__)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hello = tf.constant(\"Hello\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tensor(\"Const:0\", shape=(), dtype=string)\n"
]
}
],
"source": [
"print(hello)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"파이썬3 버전은 문자열(str) unicode가 기본이므로 str에서 encoding처리해 줘야 bytes 타입을 uncode type으로 변환함\n",
"안해줄 경우 b'Hello'이렇게 나옴"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello\n"
]
}
],
"source": [
"sess = tf.Session()\n",
"print(str(sess.run(hello),encoding = \"utf-8\"))\n",
"# print(sess.run(hello))\n",
"sess.close()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tensor(\"Const_1:0\", shape=(), dtype=float32)\n",
"Tensor(\"Const_2:0\", shape=(), dtype=float32)\n"
]
}
],
"source": [
"a = tf.constant(1234, dtype=tf.float32)\n",
"b = tf.constant(5000, dtype=tf.float32)\n",
"print(a)\n",
"print(b)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tensor(\"add:0\", shape=(), dtype=float32)\n"
]
}
],
"source": [
"add_op = a + b\n",
"print(add_op)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6234.0\n"
]
}
],
"source": [
"with tf.Session() as sess:\n",
" print(sess.run(add_op))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6234.0\n"
]
}
],
"source": [
"add_op2 = tf.add(a,b)\n",
"with tf.Session() as sess:\n",
" print(sess.run(add_op2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 마크다운으로 메모 작성하기...!\n",
"마크 다운으로 메모를 작성합니다.\n",
"\n",
"## 이번 절의 목표\n",
"\n",
"- jupyter notebook\n",
"- 시각적으로 기계 학습 살펴보기"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x11aec62e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"plt.hist([1,2,3])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x = np.arange(-20,20,0.1)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y = np.sin(x)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x11af5d828>]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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VAf9yr4AflTaO+nqeKXXuXH73CKk5feTtk4+9s6D8Y/CtwgL+BbQLF3hxSmNj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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11af01b70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(x,y)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"a = tf.constant(100)\n",
"b = tf.constant(50)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"add_op = a + b"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"v = tf.Variable(0)\n",
"let_op = tf.assign(v, add_op)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"150\n"
]
}
],
"source": [
"with tf.Session() as sess:\n",
" sess.run(tf.global_variables_initializer())\n",
" _, v_val = sess.run([let_op,v])\n",
" print(v_val)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
zunio/python-recipes | 90-NLP/Tokenization.ipynb | 1 | 10728 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Turn a string or document into **token**.\n",
"There are different theories and rules.\n",
"We want to perform tokenization in order to:\n",
"- easier to map part of speech\n",
"- matching common words\n",
"- removing unwanted tokens\n",
"\n",
"Usually we use nltk library with method:\n",
"- word_tokenie (word)\n",
"- sent_tokenize (sentence)\n",
"- regexp_tokenize (custom regexp pattern)\n",
"- TweetTokenizer (split hashtags, ecc)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"scene_one='''SCENE 1: [wind] [clop clop clop] \\nKING ARTHUR: Whoa there! [clop clop clop] \\nSOLDIER #1: Halt! Who goes there?\\nARTHUR: It is I, Arthur, son of Uther Pendragon, from the castle of Camelot. King of the Britons, defeator of the Saxons, sovereign of all England!\\nSOLDIER #1: Pull the other one!\\nARTHUR: I am, ... and this is my trusty servant Patsy. We have ridden the length and breadth of the land in search of knights who will join me in my court at Camelot. I must speak with your lord and master.\\nSOLDIER #1: What? Ridden on a horse?\\nARTHUR: Yes!\\nSOLDIER #1: You're using coconuts!\\nARTHUR: What?\\nSOLDIER #1: You've got two empty halves of coconut and you're bangin' 'em together.\\nARTHUR: So? We have ridden since the snows of winter covered this land, through the kingdom of Mercea, through--\\nSOLDIER #1: Where'd you get the coconuts?\\nARTHUR: We found them.\\nSOLDIER #1: Found them? In Mercea? The coconut's tropical!\\nARTHUR: What do you mean?\\nSOLDIER #1: Well, this is a temperate zone.\\nARTHUR: The swallow may fly south with the sun or the house martin or the plover may seek warmer climes in winter, yet these are not strangers to our land?\\nSOLDIER #1: Are you suggesting coconuts migrate?\\nARTHUR: Not at all. They could be carried.\\nSOLDIER #1: What? A swallow carrying a coconut?\\nARTHUR: It could grip it by the husk!\\nSOLDIER #1: It's not a question of where he grips it! It's a simple question of weight ratios! A five ounce bird could not carry a one pound coconut.\\nARTHUR: Well, it doesn't matter. Will you go and tell your master that Arthur from the Court of Camelot is here.\\nSOLDIER #1: Listen. In order to maintain air-speed velocity, a swallow needs to beat its wings forty-three times every second, right?\\nARTHUR: Please!\\nSOLDIER #1: Am I right?\\nARTHUR: I'm not interested!\\nSOLDIER #2: It could be carried by an African swallow!\\nSOLDIER #1: Oh, yeah, an African swallow maybe, but not a European swallow. That's my point.\\nSOLDIER #2: Oh, yeah, I agree with that.\\nARTHUR: Will you ask your master if he wants to join my court at Camelot?!\\nSOLDIER #1: But then of course a-- African swallows are non-migratory.\\nSOLDIER #2: Oh, yeah...\\nSOLDIER #1: So they couldn't bring a coconut back anyway... [clop clop clop] \\nSOLDIER #2: Wait a minute! Supposing two swallows carried it together?\\nSOLDIER #1: No, they'd have to have it on a line.\\nSOLDIER #2: Well, simple! They'd just use a strand of creeper!\\nSOLDIER #1: What, held under the dorsal guiding feathers?\n",
"\\nSOLDIER #2: Well, why not?\\n'''"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"SCENE 1: [wind] [clop clop clop] \\nKING ARTHUR: Whoa there! [clop clop clop] \\nSOLDIER #1: Halt! Who goes there?\\nARTHUR: It is I, Arthur, son of Uther Pendragon, from the castle of Camelot. King of the Britons, defeator of the Saxons, sovereign of all England!\\nSOLDIER #1: Pull the other one!\\nARTHUR: I am, ... and this is my trusty servant Patsy. We have ridden the length and breadth of the land in search of knights who will join me in my court at Camelot. I must speak with your lord and master.\\nSOLDIER #1: What? Ridden on a horse?\\nARTHUR: Yes!\\nSOLDIER #1: You're using coconuts!\\nARTHUR: What?\\nSOLDIER #1: You've got two empty halves of coconut and you're bangin' 'em together.\\nARTHUR: So? We have ridden since the snows of winter covered this land, through the kingdom of Mercea, through--\\nSOLDIER #1: Where'd you get the coconuts?\\nARTHUR: We found them.\\nSOLDIER #1: Found them? In Mercea? The coconut's tropical!\\nARTHUR: What do you mean?\\nSOLDIER #1: Well, this is a temperate zone.\\nARTHUR: The swallow may fly south with the sun or the house martin or the plover may seek warmer climes in winter, yet these are not strangers to our land?\\nSOLDIER #1: Are you suggesting coconuts migrate?\\nARTHUR: Not at all. They could be carried.\\nSOLDIER #1: What? A swallow carrying a coconut?\\nARTHUR: It could grip it by the husk!\\nSOLDIER #1: It's not a question of where he grips it! It's a simple question of weight ratios! A five ounce bird could not carry a one pound coconut.\\nARTHUR: Well, it doesn't matter. Will you go and tell your master that Arthur from the Court of Camelot is here.\\nSOLDIER #1: Listen. In order to maintain air-speed velocity, a swallow needs to beat its wings forty-three times every second, right?\\nARTHUR: Please!\\nSOLDIER #1: Am I right?\\nARTHUR: I'm not interested!\\nSOLDIER #2: It could be carried by an African swallow!\\nSOLDIER #1: Oh, yeah, an African swallow maybe, but not a European swallow. That's my point.\\nSOLDIER #2: Oh, yeah, I agree with that.\\nARTHUR: Will you ask your master if he wants to join my court at Camelot?!\\nSOLDIER #1: But then of course a-- African swallows are non-migratory.\\nSOLDIER #2: Oh, yeah...\\nSOLDIER #1: So they couldn't bring a coconut back anyway... [clop clop clop] \\nSOLDIER #2: Wait a minute! Supposing two swallows carried it together?\\nSOLDIER #1: No, they'd have to have it on a line.\\nSOLDIER #2: Well, simple! They'd just use a strand of creeper!\\nSOLDIER #1: What, held under the dorsal guiding feathers?\\n\\nSOLDIER #2: Well, why not?\\n\""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scene_one"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"showing info https://raw.githubusercontent.com/nltk/nltk_data/gh-pages/index.xml\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import nltk\n",
"#nltk.download()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['SCENE 1: [wind] [clop clop clop] \\nKING ARTHUR: Whoa there!',\n",
" '[clop clop clop] \\nSOLDIER #1: Halt!',\n",
" 'Who goes there?',\n",
" 'ARTHUR: It is I, Arthur, son of Uther Pendragon, from the castle of Camelot.',\n",
" 'King of the Britons, defeator of the Saxons, sovereign of all England!']"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from nltk.tokenize import word_tokenize,sent_tokenize\n",
"sentences = sent_tokenize(scene_one)\n",
"sentences[:5]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['ARTHUR',\n",
" ':',\n",
" 'It',\n",
" 'is',\n",
" 'I',\n",
" ',',\n",
" 'Arthur',\n",
" ',',\n",
" 'son',\n",
" 'of',\n",
" 'Uther',\n",
" 'Pendragon',\n",
" ',',\n",
" 'from',\n",
" 'the',\n",
" 'castle',\n",
" 'of',\n",
" 'Camelot',\n",
" '.']"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tokenized_sent = word_tokenize(sentences[3])\n",
"tokenized_sent"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Unique token\n",
"unique_tokens = set(word_tokenize(scene_one))\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tweets=['This is the best #nlp exercise ive found online! #python',\n",
" '#NLP is super fun! <3 #learning',\n",
" 'Thanks @datacamp :) #nlp #python']"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['#nlp', '#python']"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from nltk.tokenize import regexp_tokenize\n",
"from nltk.tokenize import TweetTokenizer\n",
"pattern1 = r\"#\\w+\"\n",
"regexp_tokenize(tweets[0],pattern1)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['@datacamp', '#nlp', '#python']"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pattern2 = r\"([#|@]\\w+)\"\n",
"regexp_tokenize(tweets[-1],pattern2)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[['This', 'is', 'the', 'best', '#nlp', 'exercise', 'ive', 'found', 'online', '!', '#python'], ['#NLP', 'is', 'super', 'fun', '!', '<3', '#learning'], ['Thanks', '@datacamp', ':)', '#nlp', '#python']]\n"
]
}
],
"source": [
"# Use the TweetTokenizer to tokenize all tweets into one list\n",
"tknzr = TweetTokenizer()\n",
"all_tokens = [tknzr.tokenize(t) for t in tweets]\n",
"print(all_tokens)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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
}
| apache-2.0 |
Jigsaw-Code/net-analysis | netanalysis/dns/analysis/DomainAnalysis.ipynb | 1 | 1466846 | null | apache-2.0 |
BadWizard/coalexploration | develop/2016-04-05-BadWizard-coal-predict.ipynb | 1 | 226099 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Make prediction about coal prediction"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"sns.set();"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Year</th>\n",
" <th>Mine_Name</th>\n",
" <th>Mine_State</th>\n",
" <th>Mine_County</th>\n",
" <th>Mine_Status</th>\n",
" <th>Mine_Type</th>\n",
" <th>Company_Type</th>\n",
" <th>Operation_Type</th>\n",
" <th>Operating_Company</th>\n",
" <th>Operating_Company_Address</th>\n",
" <th>Union_Code</th>\n",
" <th>Coal_Supply_Region</th>\n",
" <th>Production_(short_tons)</th>\n",
" <th>Average_Employees</th>\n",
" <th>Labor_Hours</th>\n",
" <th>log_production</th>\n",
" </tr>\n",
" <tr>\n",
" <th>MSHA ID</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",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>103381</th>\n",
" <td>2013</td>\n",
" <td>Tacoa Highwall Miner</td>\n",
" <td>Alabama</td>\n",
" <td>Bibb</td>\n",
" <td>Active, men working, not producing</td>\n",
" <td>Surface</td>\n",
" <td>Independent Producer Operator</td>\n",
" <td>Mine only</td>\n",
" <td>Jesse Creek Mining, Llc</td>\n",
" <td>1615 Kent Dairy Rd, Alabaster, AL 35007</td>\n",
" <td></td>\n",
" <td>Appalachia Southern</td>\n",
" <td>56004</td>\n",
" <td>10</td>\n",
" <td>22392</td>\n",
" <td>10.933178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103404</th>\n",
" <td>2013</td>\n",
" <td>Reid School Mine</td>\n",
" <td>Alabama</td>\n",
" <td>Blount</td>\n",
" <td>Permanently abandoned</td>\n",
" <td>Surface</td>\n",
" <td>Independent Producer Operator</td>\n",
" <td>Mine only</td>\n",
" <td>Taft Coal Sales & Associates,</td>\n",
" <td>3000 Riverchase Galleria Ste 1, Birmingham, AL...</td>\n",
" <td>UNIT</td>\n",
" <td>Appalachia Southern</td>\n",
" <td>28807</td>\n",
" <td>18</td>\n",
" <td>28447</td>\n",
" <td>10.268374</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100759</th>\n",
" <td>2013</td>\n",
" <td>North River #1 Underground Min</td>\n",
" <td>Alabama</td>\n",
" <td>Fayette</td>\n",
" <td>Active, men working, not producing</td>\n",
" <td>Underground</td>\n",
" <td>Independent Producer Operator</td>\n",
" <td>Mine and Preparation Plant</td>\n",
" <td>Jim Walter Resources Inc</td>\n",
" <td>3114 County Rd 63 S, Berry, AL 35546</td>\n",
" <td>UNIT</td>\n",
" <td>Appalachia Southern</td>\n",
" <td>1440115</td>\n",
" <td>183</td>\n",
" <td>474784</td>\n",
" <td>14.180234</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103246</th>\n",
" <td>2013</td>\n",
" <td>Bear Creek</td>\n",
" <td>Alabama</td>\n",
" <td>Franklin</td>\n",
" <td>Active</td>\n",
" <td>Surface</td>\n",
" <td>Independent Producer Operator</td>\n",
" <td>Mine only</td>\n",
" <td>Birmingham Coal & Coke Co., In</td>\n",
" <td>912 Edenton Street, Birmingham, AL 35242</td>\n",
" <td></td>\n",
" <td>Appalachia Southern</td>\n",
" <td>87587</td>\n",
" <td>13</td>\n",
" <td>29193</td>\n",
" <td>11.380388</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103451</th>\n",
" <td>2013</td>\n",
" <td>Knight Mine</td>\n",
" <td>Alabama</td>\n",
" <td>Franklin</td>\n",
" <td>Active</td>\n",
" <td>Surface</td>\n",
" <td>Independent Producer Operator</td>\n",
" <td>Mine only</td>\n",
" <td>Birmingham Coal & Coke Co., In</td>\n",
" <td>P.O. Box 354, Lynn, AL 35242</td>\n",
" <td></td>\n",
" <td>Appalachia Southern</td>\n",
" <td>147499</td>\n",
" <td>27</td>\n",
" <td>46393</td>\n",
" <td>11.901577</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Year Mine_Name Mine_State Mine_County \\\n",
"MSHA ID \n",
"103381 2013 Tacoa Highwall Miner Alabama Bibb \n",
"103404 2013 Reid School Mine Alabama Blount \n",
"100759 2013 North River #1 Underground Min Alabama Fayette \n",
"103246 2013 Bear Creek Alabama Franklin \n",
"103451 2013 Knight Mine Alabama Franklin \n",
"\n",
" Mine_Status Mine_Type \\\n",
"MSHA ID \n",
"103381 Active, men working, not producing Surface \n",
"103404 Permanently abandoned Surface \n",
"100759 Active, men working, not producing Underground \n",
"103246 Active Surface \n",
"103451 Active Surface \n",
"\n",
" Company_Type Operation_Type \\\n",
"MSHA ID \n",
"103381 Independent Producer Operator Mine only \n",
"103404 Independent Producer Operator Mine only \n",
"100759 Independent Producer Operator Mine and Preparation Plant \n",
"103246 Independent Producer Operator Mine only \n",
"103451 Independent Producer Operator Mine only \n",
"\n",
" Operating_Company \\\n",
"MSHA ID \n",
"103381 Jesse Creek Mining, Llc \n",
"103404 Taft Coal Sales & Associates, \n",
"100759 Jim Walter Resources Inc \n",
"103246 Birmingham Coal & Coke Co., In \n",
"103451 Birmingham Coal & Coke Co., In \n",
"\n",
" Operating_Company_Address Union_Code \\\n",
"MSHA ID \n",
"103381 1615 Kent Dairy Rd, Alabaster, AL 35007 \n",
"103404 3000 Riverchase Galleria Ste 1, Birmingham, AL... UNIT \n",
"100759 3114 County Rd 63 S, Berry, AL 35546 UNIT \n",
"103246 912 Edenton Street, Birmingham, AL 35242 \n",
"103451 P.O. Box 354, Lynn, AL 35242 \n",
"\n",
" Coal_Supply_Region Production_(short_tons) Average_Employees \\\n",
"MSHA ID \n",
"103381 Appalachia Southern 56004 10 \n",
"103404 Appalachia Southern 28807 18 \n",
"100759 Appalachia Southern 1440115 183 \n",
"103246 Appalachia Southern 87587 13 \n",
"103451 Appalachia Southern 147499 27 \n",
"\n",
" Labor_Hours log_production \n",
"MSHA ID \n",
"103381 22392 10.933178 \n",
"103404 28447 10.268374 \n",
"100759 474784 14.180234 \n",
"103246 29193 11.380388 \n",
"103451 46393 11.901577 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('../data/cleaned_coalpublic2013.csv',header=0,index_col='MSHA ID')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(1061, 16)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Year\n",
"Mine_Name\n",
"Mine_State\n",
"Mine_County\n",
"Mine_Status\n",
"Mine_Type\n",
"Company_Type\n",
"Operation_Type\n",
"Operating_Company\n",
"Operating_Company_Address\n",
"Union_Code\n",
"Coal_Supply_Region\n",
"Production_(short_tons)\n",
"Average_Employees\n",
"Labor_Hours\n",
"log_production\n"
]
}
],
"source": [
"for column in df.columns:\n",
" print(column)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1199add30>"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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k9XojPCoAAGOPfaRfEBf3n7b7/X4lJyfL6XTK5/PddvlwpKe7hj4oCsTCHtGyQ1eX0+oR\nEOMmTHAOOB+i5dwYTCzsIMXOHiMx4lBPmzZNJ0+e1KOPPqqjR48qPz9f2dnZqqurU09Pj7q7u9Xa\n2qrMzMxhXd+lS9F/zzs93RX1e0TTDp2dvqEPAu5AZ6cvdD5E07nx38TCDlJs7BHODY0Rh3r58uVa\ns2aNent7lZGRoaKiItlsNpWXl6u0tFTBYFDV1dVKTEwc8TAAAGCgYYV68uTJ2r9/vyTJ7XbL4/Hc\ndkxxcbGKi4sjOx0AAGMcL3gCAIDBCDUAAAYj1AAAGIxQAwBgMEINAIDBCDUAAAYj1AAAGIxQAwBg\nsBG/MhkAxLJgIKD29o9DH3d1OY162Vq3+/4Bb5SE2EeoAeAm//Ze0vYDHXKkXLB6lNtcu/KpXn1h\nnjIyhvdeCogNhBoAbuFImSRn2mSrxwAk8Rg1AABGI9QAABiMUAMAYDBCDQCAwQg1AAAGI9QAABiM\nUAMAYDBCDQCAwQg1AAAGI9QAABiMUAMAYDBCDQCAwQg1AAAGI9QAABiMUAMAYDBCDQCAwQg1AAAG\nI9QAABiMUAMAYDBCDQCAwQg1AAAGI9QAABiMUAMAYDBCDQCAwQg1AAAGI9QAABiMUAMAYDBCDQCA\nwQg1AAAGI9QAABiMUAMAYDBCDQCAwezhfuH3v/99OZ1OSdK9996ryspKrVixQnFxccrMzFRtbW3E\nhgQAYKwKK9Q9PT2SpN27d4cue/rpp1VdXa3c3FzV1taqqalJs2bNisyUAACMUWH96vuDDz7QtWvX\nVFFRoSVLlujUqVM6e/ascnNzJUmFhYU6fvx4RAcFAGAsCuse9T333KOKigoVFxerra1NP/7xjxUM\nBkOfT0pKktfrjdiQAACMVWGF2u12a8qUKaH/Tk1N1dmzZ0Of9/v9Sk5OHtZ1pae7whnBOLGwR7Ts\n0NXltHoEwDITJjjDOlej5fweSqzsMRJhhfqNN97Q3//+d9XW1urixYvy+XwqKCjQiRMnlJeXp6NH\njyo/P39Y13XpUvTf805Pd0X9HtG0Q2enz+oRAMt0dvpGfK5G0/k9mFjYI5wbGmGFesGCBVq5cqVK\nS0sVFxenrVu3KjU1VTU1Nert7VVGRoaKiorCuWoYpL+/X21trVaPcZv29o+tHgEA7pqwQp2QkKCf\n//znt13u8XjueCCYo62tVVXbGuVImWT1KANc/uScJt6bZfUYAHBXhP08aowNjpRJcqZNtnqMAa5d\nuWj1CABw1/DKZAAAGIxQAwBgMEINAIDBCDUAAAYj1AAAGIxQAwBgMEINAIDBeB41AESJYCAQ1ivz\ndXU5R/2ld93u+xUfHz+q32OsItQAECX+7b2k7Qc65Ei5YPUoA1y78qlefWGeMjIyrR4lJhFqAIgi\nJr5aIEYXj1EDAGAwQg0AgMEINQAABiPUAAAYjFADAGAwQg0AgMEINQAABiPUAAAYjFADAGAwQg0A\ngMEINQAABiPUAAAYjFADAGAwQg0AgMEINQAABiPUAAAYjFADAGAwQg0AgMEINQAABiPUAAAYjFAD\nAGAwQg0AgMEINQAABiPUAAAYjFADAGAwu9UDjHX9/f1qa2u1egx1dTnV2ekbcFl7+8cWTQMA+Ayh\ntlhbW6uqtjXKkTLJ6lFuc/mTc5p4b5bVYwDAmEaoDeBImSRn2mSrx7jNtSsXrR4BAMY8HqMGAMBg\nhBoAAIMRagAADMZj1ACAOxIMBO7Ks0Q+79kpQ3G771d8fPwoTXR3RDTUwWBQ69atU0tLixITE7Vp\n0yZ99atfjeS3uCO7972h7t7+iF+vw5Goa9d6wvrajkv/kpQW2YEA4C76t/eSth/okCPlgtWjDHDt\nyqd69YV5ysjItHqUOxLRUDc1Namnp0f79+/XqVOntGXLFu3YsSOS3+KOHD39TwVSHrJ6jAF8Xd1W\njwAAd8zUZ6/Egog+Rv3+++9rxowZkqSHH35YZ86cieTVAwAw5kT0HrXP55PL5frPldvtCgQCiosz\n42/Wer0XZOsLRPx64+1x6g/zegNXOnQ9LjXCE0XGv72dkmxWj3Eb5hoZ5hoZU+eSzJ3N1LmuXfnU\n6hEiIqKhdjqd8vv9oY+HE+n0dNegn4+ktw/8z137XgAAREJE7+p+/etf17vvvitJ+utf/6oHHngg\nklcPAMCYYwsGg8FIXdnNf/UtSVu2bNF9990XqasHAGDMiWioAQBAZJnxV14AAOBzEWoAAAxGqAEA\nMBihBgDAYJaEuq+vTz/72c+0aNEiPfHEE3rnnXesGCMiLl++rJkzZ+qjjz6yepSw7dq1SwsXLtT8\n+fP1xhtvWD3OiPX19en555/XwoULVVZWFpX/FqdOnVJ5ebkkqb29XaWlpSorK9P69estnmxkbt7j\n3LlzWrRokRYvXqwf/ehH6uzstHi64bl5h8+89dZbWrhwoUUThefmPTo7O7V06VKVl5ertLRU//jH\nPyyebnhu/Xn6wQ9+oEWLFmn16tUWTzY8n9e6cM5vS0Ld2NiotLQ07d27V7/61a+0YcMGK8a4Y319\nfaqtrdU999xj9ShhO3HihP7yl79o//798ng8unDBrBfVH453331XgUBA+/fv19KlS1VXV2f1SCPS\n0NCgmpoa9fb2SrrxtMbq6mrt2bNHgUBATU1NFk84PLfusXnzZq1du1a7d+/W7NmztWvXLosnHNqt\nO0jS2bNno+4G7K17bNu2TfPmzZPH41FVVZVaW1stnnBot+5QX1+vZcuWae/everu7taRI0esHXAY\nbm5dQ0ODNmzYENb5bUmo58yZo6qqKkk3Xr3Mbo/Od9t86aWXVFJSokmTJlk9Stj++Mc/6oEHHtDS\npUv19NNP65vf/KbVI42Y2+1Wf3+/gsGgvF6vEhISrB5pRKZMmaL6+vrQx83NzcrNzZUkFRYW6vjx\n41aNNiK37lFXV6evfe1rkm7cqB03bpxVow3brTt0dXXpF7/4RdTcg/vMrXv8+c9/1r/+9S/98Ic/\n1G9/+1t94xvfsHC64bl1h6ysLHV1dSkYDMrv90dFN25uXX9/v+Lj43X27NkRn9+WhHr8+PFyOBzy\n+XyqqqrSc889Z8UYd+TNN9/UxIkTVVBQoGh+KnpXV5fOnDmjX/7yl1q3bp2ef/55q0casaSkJH3y\nyScqKirS2rVrb/u1pelmz5494P1yb/55SkpKktfrtWKsEbt1jy984QuSbkTi9ddf15IlSyyabPhu\n3iEQCKimpkYrVqzQ+PHjo+o8v/Xf4vz580pNTdWvf/1rfelLX4qK327cuoPb7damTZs0d+5cdXZ2\nKi8vz8LphufzWhfO+W3ZH5NduHBBTz75pB5//HF9+9vftmqMsL355ps6duyYysvL9cEHH2j58uW6\nfPmy1WONWGpqqmbMmCG73a777rtP48aNi5rHEj/zm9/8RjNmzNDvf/97NTY2avny5erpCe/9wU1w\n8+vj+/1+JScnWzjNnXn77be1fv167dq1S2lp0fW+683NzWpvbw/dgP3www+1ZcsWq8cKS2pqaui3\nZY899piam5stnmjkNm3apNdff11vv/225s2bp61bt1o90rDc3Lq5c+eGdX5bEuqOjg5VVFTohRde\n0OOPP27FCHdsz5498ng88ng8evDBB/XSSy9p4sSJVo81Yjk5OfrDH/4gSbp48aKuX78edf9DTUlJ\nkdPplCS5XC719fUpEIj8u6TdLdOmTdPJkyclSUePHlVOTo7FE4Xn8OHD2rt3rzwejyZPjq73KQ4G\ng8rOztZbb72l3bt365VXXtHUqVO1cuVKq0cLS05OTuh9GE6ePKmpU6daPNHIpaamhs7zL37xi7p6\n9arFEw3t81qXlZU14vPbkl/y79y5U1evXtWOHTtUX18vm82mhoYGJSYmWjHOHbPZzHt7t+GaOXOm\n/vSnP2nBggUKBoOqra2Nun2efPJJrVq1SosWLQr9BXg0/4Hf8uXLtWbNGvX29iojI0NFRUVWjzRi\ngUBAmzdv1le+8hU988wzstlsysvL07Jly6webVii7RwYyvLly1VTU6N9+/bJ5XJp+/btVo80Yhs2\nbNCzzz4ru92uxMTEqPgj5M9r3erVq7Vx48YRnd+81jcAAAbjBU8AADAYoQYAwGCEGgAAgxFqAAAM\nRqgBADAYoQYAwGCEGgAAg/0/VnB2M24Kw2EAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115a4d0b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.log_production.hist()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Active, men working, not producing', 'Permanently abandoned',\n",
" 'Active', 'Temporarily closed', 'New, under construction'], dtype=object)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Mine_Status'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>log_production</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Mine_Status</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Active</th>\n",
" <td>11.977453</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Active, men working, not producing</th>\n",
" <td>10.499962</td>\n",
" </tr>\n",
" <tr>\n",
" <th>New, under construction</th>\n",
" <td>3.951244</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Permanently abandoned</th>\n",
" <td>9.896046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Temporarily closed</th>\n",
" <td>9.162933</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" log_production\n",
"Mine_Status \n",
"Active 11.977453\n",
"Active, men working, not producing 10.499962\n",
"New, under construction 3.951244\n",
"Permanently abandoned 9.896046\n",
"Temporarily closed 9.162933"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[['Mine_Status','log_production']].groupby('Mine_Status').mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Predict the Production of coal mines"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Year\n",
"Mine_Name\n",
"Mine_State\n",
"Mine_County\n",
"Mine_Status\n",
"Mine_Type\n",
"Company_Type\n",
"Operation_Type\n",
"Operating_Company\n",
"Operating_Company_Address\n",
"Union_Code\n",
"Coal_Supply_Region\n",
"Production_(short_tons)\n",
"Average_Employees\n",
"Labor_Hours\n",
"log_production\n"
]
}
],
"source": [
"for column in df.columns:\n",
" print(column)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([2013])"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.Year.unique()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([' ', 'UNIT', 'United Mine Workers of America', 'INTE',\n",
" 'International Union of Operation Engineers',\n",
" 'Scotia Employees Association', 'Western Energy Workers'], dtype=object)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.Union_Code.unique()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"features = ['Average_Employees',\n",
" 'Labor_Hours',\n",
" ]\n",
"\n",
"categoricals = ['Mine_State',\n",
" 'Mine_County',\n",
" 'Mine_Status',\n",
" 'Mine_Type',\n",
" 'Company_Type',\n",
" 'Operation_Type',\n",
" 'Union_Code',\n",
" 'Coal_Supply_Region',\n",
" ]\n",
"\n",
"target = 'log_production'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## first, look at the interplay between each possible predictor and the target variable"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Year', 'Mine_Name', 'Mine_State', 'Mine_County', 'Mine_Status',\n",
" 'Mine_Type', 'Company_Type', 'Operation_Type', 'Operating_Company',\n",
" 'Operating_Company_Address',\n",
" ...\n",
" 'Union_Code_United Mine Workers of America',\n",
" 'Union_Code_Western Energy Workers',\n",
" 'Coal_Supply_Region_Appalachia Central',\n",
" 'Coal_Supply_Region_Appalachia Northern',\n",
" 'Coal_Supply_Region_Appalachia Southern',\n",
" 'Coal_Supply_Region_Illinois Basin', 'Coal_Supply_Region_Interior',\n",
" 'Coal_Supply_Region_Powder River Basin',\n",
" 'Coal_Supply_Region_Uinta Region', 'Coal_Supply_Region_Western'],\n",
" dtype='object', length=989)"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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fdWlSCjBnzhyeeOIJXnjhBZYuXcrbb79NTU0NhYXJO2elpaXcfPPNPP30012a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4/JdOpGThcRORMpWIuIiMiQ19BQz2uvvcyGDe9/6gx1WNHoBSQnjMDdbhuE6s5sFlN4Duz05IJI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\nABhAsAYAAAAAwID/A3ksm6+t3lPDAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10a266940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.subplots(figsize = (14,8))\n",
"sns.set_context('poster')\n",
"sns.violinplot(y='Mine_Status',x='log_production', data=df,\n",
" split=True, inner = 'stick',)\n",
"plt.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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wjz/+OP54yJAhSktLU0lJiS677LL48OXLl2vhwoX61a9+FQ8ZH3/8sX74wx/G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yEiengM1kRERERPmbm5OeLjEyGTWQIAzqlVuKBWiVzV06comRn9/lrV7RATEydeQUREj4jBmoiI\niEgEHh6eiI29vwTX9wUK5NexJbhOlJm8rWnTZhg1agzXqiaiGol/uYiIiIhE0rlzV/1YYrUg4OfC\nggfsUTuoSpYZK+2j9/b25VrVRFSjMVgTERERiaR0CS4nJ2cAQFYd6bH+tcwXCPXru+Htt1NgY1NP\nxIqIiIzDYE1EREQkIrlcjvj4sfoluADgrrZ2Bmxlyezn+SjusXZ0dML48ZNgb+8gZllEREZjsCYi\nIiISWZMmgXjppZf1j39XFVaxdc2kFHQ4XKDQP7a3d8D48ZPg4uIqYlVERI8HgzURERGRCejduy+a\nNWsO4P7Y49rkYL4Cd3XFPdZyuRzvvDMZHh6eIldFRPR4MFgTERERmQAzMzPExyfWutui80rGjStK\nbgO3tbXDhAlT4enpJWZZRESPFYM1ERERkYmwt7fH6NFvGIy3vqEpErEi49zUFOFAfp7+sbOzCyZN\nmg4vL28RqyIievwYrImIiIhMSNOmzRAdPUj/+KfCfNzWaESs6NH8qVZhZ95dqEsee3h4YuLEaXBz\ncxe1LiKiJ4HBmoiIiMjE9OjRCx06dAIAaAF8pbiHQqHmzBT+p1qFvfl50JU8btIkEBMnTtcvK0ZE\nVNuYi10AERERERkqXt96JG7evIG//rqEfEGHHwryxS7rgVRC8TJaV8rcvt6hQycMGzYKFhYWYpVF\nRPTEsceaiIiIyATJZDK88UYyHB2dAEA/o7a2JLyamhtFRfip8H74l0gkGDhwEEaOHMNQTUS1HoM1\nERERkYlydHRCcnIKbGxs9G1HlQUmGa6PKPNR2k9ta2uH8eMnoWfP3gYTsRER1VYM1kREREQmzMvL\nG+PGjYe5eXGv7zVNETKUpnNb+I//ukU9ODgEM2bM1q/JTURUFzBYExEREZm4Jk0CkZDwJszNi6fH\nuWkCs4T/piwEAChR3HtuYWGB2NghSEp6Fw4OjmKWRkT01DFYExEREdUALVu2QmJisr7nulShoKtk\nj8dPVeYW9Du6+7OUN2vWHP/5z4fo1i0KZma8vCSiuod/+YiIiIhqiJCQlnjzzfGwtLTUtx0tLHji\n71sa3o8UGt72bW/vgBEjRmP8+Elcn5qI6jQut0VERERUgzRv3gKTJs3AwoUfIjc3B0VlntM95jnN\nsrQa/K4sxFm1qvj1S9otLCzQrVsUXnjhJVhbWz/eNyUiqoEYrImIiIhqGB8fX0yf/gGWLJmPS5cu\n6NtPqorHPWsFAdJHnI1bpdPhUpEaZ1RK3NIajuWWSqXo3LkLBg+Og5mZ1aN/ACKiWobBmoiIiKgG\nsrOzx6RJ0/Htt3uwbdtn0Go1+t7rrxX34CuTVfu1FDotTioLcbVIjWuaIvx71LalpSU6d45E9+5R\nCAjwBQDk5j75W9CJiGoKBmsiIiKiGsrMzAw9e/ZGSEgYPvlkI06dOgkAKISAcyW3b5f6VVkAO6kU\nuZriScdOq5RQ6Yoj9E8lM3z/m7u7B7p0icQzz3SBXC5/gp+EiKhmY7AmIiIiquE8Pb2QnJyCCxfO\nY+fO7Th9+iQEwXDA9cUiNcoOyM7UVrxkl52dPcLD26Jt2w5o3LgpJI94SzkRUV3CYE1ERERUSzRq\n1ATJyROQk5ODo0cz8Ntvx/H335dRUFD5bdv29g7w8fFF06ZBCApqDj+/Blwyi4joITFYExEREdUy\njo6O6N49Ct27R0EQBGRl3UFubg4KCwuhVqtgbW2DevXqwcnJGba2dmKXS0RU4zFYExEREdViEokE\nLi6ucHFxFbsUIqJai/f5EBERERERERmBwZqIiIiIiIjICAzWREREREREREZgsCYiIiIiIiIyAoM1\nERERERERkREYrImIiIiIiIiMwGBNREREREREZAQGayIiIiIiIiIjMFgTERERERERGYHBmoiIiIiI\niMgIDNZERERERERERmCwJiIiIiIiIjICgzURERERERGRESSCIAhiF0FERERERERUU7HHmoiIiIiI\niMgIDNZERERERERERmCwJiIiIiIiIjICgzURERERERGRERisiYiIiIiIiIzAYE1ERERERERkBAZr\nIiIiIiIiIiMwWBMREREREREZgcGaiIiIiIiIyAgM1kRERERERERGYLAmoirpdDqsW7cOvXr1QlhY\nGF544QV8/PHHYpdVY6jVakRFRWHixIlil1IjpKenY+DAgQgNDUVkZCSWLl0KQRDELsuk6XQ6rFmz\nBt27d0dYWBgGDhyIjIwMscsyWfv27UOrVq3Kta9cuRJdu3ZFy5Yt8dprr+HSpUsiVGe6KjpuKpUK\nCxcu1P/u9evXD3v27BGpQtNT2e9aqZycHHTs2BHLli17ilWZvsqO25dffok+ffogJCQEPXr0wKZN\nm0SoznRV9m903rx5iIyMRHh4OIYOHYozZ86IVKFpqM517aOeDxisiahKy5cvx6JFi9C3b1+sXLkS\nUVFR+OCDD/Df//5X7NJqhGXLluHy5ctil1EjHDt2DKNGjUKjRo2QmpqKuLg4rFmzBitWrBC7NJP2\n0UcfYdGiRRgwYABWrFgBHx8fjBw5EmfPnhW7NJNz/PhxvPvuu+Xaly1bhtWrV2PkyJFYuHAh8vLy\nMHz4cCgUChGqND2VHbdp06Zhy5YtGDZsGFasWIHw8HAkJyfj66+/FqFK01LZMStr1qxZyMnJeUoV\n1QyVHbc9e/Zg/PjxiIiIwJo1axAVFYVZs2bhiy++EKFK01PZcXv//fexZcsWjBo1CosXL4ZUKsXQ\noUNx69YtEao0DQ+6rjXqfCAQEVVCq9UKrVq1EpYsWWLQPmPGDKFjx44iVVVznD59WmjZsqXQoUMH\nISUlRexyTN6gQYOE0aNHG7TNnz9fGDJkiEgV1QxRUVEGv19arVaIiIgQZs6cKWJVpkWlUgmpqalC\ncHCw0LZtWyEsLEz/nEKhEMLCwoSPPvpI33b37l2hVatWwrp160So1nRUddyysrKEpk2bCtu2bTPY\nJz4+XoiOjn7apZqMqo5ZWfv27RPatWsnhIaGCkuXLn3KVZqeBx23iv6mjR8/XnjnnXeeZpkmp6rj\nptPphJYtWxr8fikUCqFFixbC2rVrxShXdA+6rjX2fMAeayKqlEKhQL9+/dCtWzeD9gYNGiA7OxtK\npVKkykyfVqvF5MmTMXLkSNSvX1/sckxednY2jh8/jldeecWgPTk5GWlpaSJVVTOo1WrUq1dP/9jM\nzAxyuRy5ubkiVmVaDh06hI8++ggpKSmIi4szeO63335DYWEhunbtqm+zs7NDmzZtcPjw4addqkmp\n6rjl5+cjNjYWzzzzjEF7gwYNcO3atadZpkmp6piVUigUmDFjBlJSUmBhYfGUKzRNVR23U6dOITMz\ns9z5Yd68eZg7d+7TLNPkVHXcdDodioqKDM4PNjY2kMlkdfb88KDr2oyMDKPOBwzWRFQpOzs7TJky\nBYGBgQbt+/fvh7u7O6ysrESqzPSlpqZCo9Hg9ddfF7uUGuH8+fMAAEtLS4wePRohISH6sYcCx1hX\nafDgwdixYwfS09OhUCiwYcMGXLx4Eb179xa7NJMREhKCffv2YfDgwZBIJAbPlQ7V8PX1NWj38fHB\nX3/99bRKNElVHTcfHx9MmzYNbm5u+jadTodDhw6hYcOGT7tUk1HVMSs1Z84cNG7cGH379n3K1Zmu\nqo7buXPnAABFRUUYMmQIgoODERERgS1btohRqkmp6rhJpVLExMRg48aN+P3333Hv3j3MnTsXKpUK\nPXv2FKlicT3oujYzMxPAo58PzB9bpURUJ2zduhXp6el47733xC7FZF28eBGrV69GWloazM35Z7Y6\nsrOzIQgCUlJS0Lt3b7z22mv4+eefsXLlSlhZWWHkyJFil2iyYmNjkZGRgeHDhwMAJBIJkpKSEBER\nIW5hJqSqu0by8/Mhk8nK/VutV69enR9j/bB32yxevBiXL1/GhAkTnlBFpu9Bxyw9PR179uzB7t27\nn1JFNUNVxy07OxtmZmZISEjAoEGDkJiYiL1792LGjBlwcHBAVFTUU6zUtDzo923s2LE4ceIEoqOj\nARSH7dmzZ6NZs2ZPo7waYevWrcjIyMCUKVOMPh/wio+Iqm3nzp2YPn06oqKiMHjwYLHLMUmCIGDK\nlCmIjo5GSEiI2OXUGBqNBgDw7LPP4p133gEAtG3bFjk5OVi5ciVGjBhRae9PXVc6Y+mMGTPQsGFD\nHDlyBEuXLoWtrS0GDRokdnkmTxCESn+3zMx4Y191paamYvXq1RgxYgS6dOkidjkmSalUYurUqRg3\nbhw8PT3FLqfG0Gg00Gq1eOWVVxAfHw8AaNeuHa5evYrly5fX6WBdFaVSiZiYGBQVFWHevHmoX78+\nvv32W0yaNAlyuRyRkZFilyi60uvanj17YvDgwVi9erVR5wMGayKqlnXr1mHu3Ll4/vnnMW/ePLHL\nMVlpaWnIzMzEmjVroNVqDW5j1mq1kEqlIlZnumxsbAAAnTp1Mmjv2LEjNm/ejGvXrsHHx0eM0kza\nsWPHcPz4cSxZsgTdu3cHALRp0wYajQbz5s1Dv379YG1tLXKVpk0ul0OtVpf795mfnw9bW1sRK6s5\nZs+ejQ0bNiAuLk7/xRiVt2DBAv0XXmXPD4Ig8PxQBRsbG0gkkgrPD3PnzoVGo+HdYRX45ptvcOXK\nFXz++edo3rw5gOIvJHJycjBr1qw6H6wruq419nzAr2KJ6IEWLFiADz/8EH379sXixYt5AqvC3r17\nkZmZifDwcDRv3hzBwcE4d+4ctm/fjuDgYNy4cUPsEk2Sn58fgOIxdGWV9mSzt7pimZmZkEgkCA0N\nNWhv3bo1lEolrl+/LlJlNYe/vz8EQSg34dbVq1fRoEEDkaqqGQRBwDvvvIO0tDSMGTMGU6ZMEbsk\nk7Z3716cOXMGISEh+vODQqHA8uXLERwcLHZ5Jquq84MgCLyzpBK3bt2CVCrVh+pSrVu3xs2bN1FY\nWChSZeKr7LrW2PMBfxOJqEobNmxAamoqhg0bhtmzZ/ME9gAzZ87E559/jm3btun/8/PzQ9euXbFt\n2zbOEF6JRo0awc3Nrdz6twcPHkT9+vXh7e0tUmWmrfQi4NixYwbtJ06cgFQqhbu7u0iV1RxhYWGQ\nyWTYu3evvu3u3bs4evQoOnToIGJlpm/27NnYvXs3UlJS8Oabb4pdjslbvXp1ufODtbU1Bg4ciG3b\ntoldnskKDw+HTCYrd344cOAAWrRoweuSSvj7+0Or1eLkyZMG7SdOnICTk1OdvZupqutaY88H7HYi\nokrdvn0b8+fPR9OmTREVFYXffvvN4Hme0Mrz9/cv12ZlZQUHBwcEBQU9/YJqCIlEgrfeegsTJ07E\n9OnT0aNHDxw5cgQ7duzAjBkzxC7PZDVv3hwRERGYMWMGcnNzERAQgJ9++gkfffQRhg4dCrlcLnaJ\nJs/GxgZxcXFYvHgxJBIJ/Pz8sGrVKtjZ2WHAgAFil2eyTp8+jY0bN6Jjx45o2bKlwfnBzMwMLVq0\nELE609S4ceNybVKpFPXr1+f5oQpyuRyvv/46li9fjnr16qFt27b48ssvcezYMaSmpopdnsmKjIxE\nYGAgkpKSMG7cOLi5uWHfvn3YvXt3nZ2A9kHXtcHBwUadDxisiahSP/zwA4qKinD+/HnExMSUez49\nPR0ODg4iVFazSCQS3spcDX379oVMJsOqVauwfft2uLu7Y8aMGfrZTKliS5YswcKFC7F69WrcvXsX\nfn5+mDp1KgYOHCh2aSbr3/8ek5OTIZVKsXbtWhQUFKBVq1aYO3cuv5iowoEDBwAAR44cwZEjRwye\ns7a2xvHjx8Uoy+Q86G8/zw8V+/cxSUhIgJ2dHTZu3Ii1a9fC398fS5cuLTfuuq4re9zMzc2xfv16\n/XrfSqUSAQEBWLJkSbl1nOuK6lzXGnM+kAhcIJSIiIiIiIjokfEeTiIiIiIiIiIjMFgTERERERER\nGYHBmoiIiIiIiMgIDNZERERERERERmCwJiIiIiIiIjICgzURERERERGRERisiRghuTgAAAn4SURB\nVIiIiIiIiIzAYE1EREQ1TmRkJEaNGiV2GaJ50p//6tWr+p9//vlnBAYGYs+ePU/s/YiIajoGayIi\nIiLSe++99zBz5kz944CAAMybNw9hYWEiVkVEZNoYrImIiIhI78cff4QgCPrHzs7O6NOnDzw8PESs\niojItDFYExERERERERmBwZqIiIhqvPT0dMTFxaFly5YIDw/H6NGjcf78+XLbbd26Fb169UJoaCj6\n9++PjIwMdO/eHRMnTnyo9ysdd3zkyBEkJSUhLCwMnTp1wuzZs6FSqfTbLV26FG3atMHu3bvRvn17\ntG3bFgcOHABQPI45KSkJ7dq1Q2hoKKKjo7F3795y73Xo0CEMGDAALVu2RO/evZGenl5um8rGXAcG\nBmL69OkGbfv370dsbCzCwsLQuXNnTJ06FXfv3tVvf/PmTRw+fBjNmjXD0aNHKxxjXVhYiA8//BAR\nERFo0aIFevTogdTUVOh0unKf/cKFCxg+fDjCwsLQsWNHzJo1C2q1+qGONxGRqWOwJiIiohrtu+++\nw4gRI3Dv3j0kJSVh5MiROH36NF555RWcPXtWv9369evx3nvvwcvLCxMmTEDjxo0RHx+PO3fuPPJ7\nT548GVeuXEFycjKee+45pKWlYdy4cfrnJRIJCgsLMWfOHIwePVofaK9evYro6GhkZGRgyJAhePvt\ntwEAiYmJ2Lp1q37/H374AWPGjIEgCBg/fjw6deqEhIQEZGVlPVK9O3bswNixY6HVapGcnIzo6Gjs\n2rULCQkJAIB58+bBwcEBgYGBmDdvHgICAvSfo5RarcawYcOwceNGREZGYtKkSQgMDMSCBQswYcIE\ng8+uUqkwbNgwuLu7Y9KkSWjTpg02bdqEZcuWPVL9RESmylzsAoiIiIgelVarxX/+8x/4+fnh888/\nh0wmAwD07dsXUVFRmDVrFjZt2gSFQoGlS5eiS5cuWL16NQBg0KBBcHZ2xtq1ax/5/a2srLBlyxZY\nWloCAFxcXLBixQpkZGSgffv2+hoTEhIwaNAg/X7Tpk2DQqHArl270KBBA309MTExmDNnDqKioiCX\ny7FgwQJ4e3tjy5Yt+s8WFBSEd99996Fr1el0mDt3LkJDQ7Fp0yaYmxdfBnp6emLKlCk4evQo+vTp\ng4ULF8LV1RW9e/fW71t2zPXWrVtx8uRJzJo1Cy+//DIAIDY2FrNmzcLHH3+Ml19+Wf/Z1Wo1YmJi\nkJiYCACIjo7GCy+8gK+++grJyckP/RmIiEwVe6yJiIioxjp9+jRu376NuLg4ffAEAHd3d7z00ks4\nfvw47t69i4yMDBQUFODVV1812H/kyJFGvX9cXJw+VAPA0KFDIQgCvv/+e4PtWrdurf9Zp9Ph0KFD\neO655/ShGgDMzc3x2muvoaCgABkZGcjOzsYff/yBF1980eCz9enTB/b29g9d66lTp5CVlYWBAwfq\nQ3Xp6/3vf/9DaGhotV7n4MGDcHJyQv/+/Q3aS3vW9+3bp2+TSCTo1q2bwXaBgYFG3SVARGSK2GNN\nRERENda1a9cAAP7+/uWeCwgIgCAIuHnzJq5cuQIA8PX1NdjGyckJdnZ2j/z+DRs2NHhsZ2cHe3t7\nXL9+vdz7lMrJyUFhYWGVNd+4cQNubm4AAG9vb4NtJBIJ/Pz8HrrWGzduQCKRlDsGMpkMzZo1q/br\nXL9+HT4+Pga3hwPFs4fb29vj5s2bBu1lP3vp+5Udi01EVBuwx5qIiIhqJa1WCwCwsLCARqPR//xv\nZXuDH4ZEIqnw9XQ6HaRSqUGbmdn9S66yt1VXtO+/66xooq/qBNN/b/O4wmxV9Wu12nLHpOxnJyKq\nrfiXjoiIiGosLy8vAMDly5fLPXfp0iVIJBLUr18fPj4+AIC//vrLYBuFQoHs7OxHem9BEHD16lWD\ntpycHOTl5ZXrFS7LyckJ1tbWldYMFN/K7uXlBYlEUq5mAOV6xKVSabkA/u8Jztzd3SEIgr6Xv5Ra\nrUZSUhIOHz5cac1leXl54erVq+UC9p07d6BQKODu7l6t1yEiqk0YrImIiKjGat68OVxcXLBp0yYo\nlUp9e2ZmJnbt2oVWrVrB1tYWnTp1gqWlJT755BOD/Tdv3mxUT+6nn35qsP/69eshkUjw/PPPV7qP\nmZkZnn32Wezfv18fpAGgqKgI69atg7W1Ndq1awcnJyeEhYVh+/btyMvL02/31VdfIScnx+A1XVxc\ncPHiRX0vfel2ZbVo0QKOjo7Ytm2bQc1ff/01vv76a/24a6lUWuUx6dq1K7KysrBt2zaD9tWrV0Mi\nkaBLly6V7ktEVFtxjDURERHVWObm5pg8eTLefvttREdH4+WXX0ZhYSE2b94MAJg0aRIAwNbWFmPH\njsX8+fMRHx+PiIgInDlzBrt27YJEIik3Xri6zpw5g6FDh6Jnz544deoUtm/fjn79+qFFixZV7vf2\n22/jp59+QmxsLOLi4mBvb4+dO3fi9OnTmDp1KmxsbAAAKSkpGDJkCKKjoxETE4OsrCxs2rSp3ORl\nvXv3xqxZsxAfH4+ePXvi3Llz+PLLL+Hs7KzfxsLCAu+++y4mTZqEIUOGoFevXrh16xY2btyIzp07\no0OHDgCKe9RPnz6NTz/9FJ07dy5Xe3R0NLZt24bp06fj9OnTaNKkCTIyMvDNN9+gV69e+hnBiYjq\nEvZYExERUY1TNgxHRUUhNTUVtra2WLx4MdavX4+wsDB89tlnCAoK0u8zatQoTJ48GX/99RfmzJmD\nM2fOIDU1FYIgVDhWujomTpwIe3t7/N///R9+/vlnJCcn44MPPnjgfn5+fvj000/Rrl07bNq0CYsW\nLYJMJsOKFSsQGxur3y4kJARpaWlwdXXF4sWL8c0332DmzJlo1KiRwZcBsbGxSEhIwMWLF/H+++/j\n/PnzWLduXbmJw/r164clS5ZAqVRi7ty52L17N2JjY7Fo0SL9NgkJCbCxscEHH3yAY8eO6Y93KZlM\nhrS0NAwaNAj79+/HnDlzcOHCBaSkpGD+/PnVOm6P+kUGEZGpkghVzUBBREREVAuo1Wqo1WrI5XKD\n9tzcXLRv3x4JCQkYN25ctV/v559/xquvvooFCxagV69ej7tcIiKqYdhjTURERLXe7du3ER4ejo0b\nNxq0f/XVV5BIJGjevLlIlRERUW3AMdZERERU63l5eSE8PByLFi3C7du34evriz///BOffPIJWrVq\nhcjISJw7dw7nzp174Gu5uLjoJ/oiIiICGKyJiIiojli5ciVWrFiBPXv24Pbt23BxcUFcXBwSExMh\nkUjw3XffYfny5Q98nTZt2uj3ISIiAjjGmoiIiIiIiMgoHGNNREREREREZAQGayIiIiIiIiIjMFgT\nERERERERGYHBmoiIiIiIiMgIDNZERERERERERmCwJiIiIiIiIjLC/wMTxO7SZvZeGAAAAABJRU5E\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115b974e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.subplots(figsize = (14,8))\n",
"sns.set_context('poster')\n",
"sns.violinplot(y='Company_Type',x='log_production', data=df,\n",
" split=True, inner = 'stick',)\n",
"plt.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Independent Producer Operator', 'Operating Subsidiary',\n",
" 'Contractor'], dtype=object)"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.Company_Type.unique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## create dummies\n",
"+ the function creates 3 new colunmns for each category"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Contractor</th>\n",
" <th>Independent Producer Operator</th>\n",
" <th>Operating Subsidiary</th>\n",
" </tr>\n",
" <tr>\n",
" <th>MSHA ID</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1517941</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1512753</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1519318</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3602733</th>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4103428</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Contractor Independent Producer Operator Operating Subsidiary\n",
"MSHA ID \n",
"1517941 0.0 0.0 1.0\n",
"1512753 0.0 0.0 1.0\n",
"1519318 0.0 0.0 1.0\n",
"3602733 0.0 1.0 0.0\n",
"4103428 0.0 0.0 1.0"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.get_dummies(df['Company_Type']).sample(50).head()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mine_State 29\n",
"Mine_County 164\n",
"Mine_Status 5\n",
"Mine_Type 3\n",
"Company_Type 3\n",
"Operation_Type 2\n",
"Union_Code 7\n",
"Coal_Supply_Region 8\n"
]
}
],
"source": [
"# turn eqch categorical variable into a dummy variable\n",
"\n",
"dummy_categoricals = []\n",
"for categorical in categoricals:\n",
" print(categorical,len(df[categorical].unique())) \n",
" drop_var = sorted(df[categorical].unique())[-1]\n",
" temp_df = pd.get_dummies(df[categorical],prefix=categorical)\n",
" df = pd.concat([df,temp_df],axis = 1)\n",
" temp_df.drop('_'.join([categorical, str(drop_var)]), axis = 1, inplace = True)\n",
" dummy_categoricals +=temp_df.columns.tolist()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Mine_State_Alabama',\n",
" 'Mine_State_Alaska',\n",
" 'Mine_State_Arizona',\n",
" 'Mine_State_Arkansas',\n",
" 'Mine_State_Colorado',\n",
" 'Mine_State_Illinois',\n",
" 'Mine_State_Indiana',\n",
" 'Mine_State_Kansas',\n",
" 'Mine_State_Kentucky (East)',\n",
" 'Mine_State_Kentucky (West)']"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dummy_categoricals[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Build our model"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.cross_validation import train_test_split\n",
"from sklearn.ensemble import RandomForestRegressor"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"213"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(dummy_categoricals)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(1061, 1210)"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(319, 1210)"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.shape"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train, test = train_test_split(df, test_size = 0.3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"rf = RandomForestRegressor(n_estimators=100, oob_score=True)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
" max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" n_estimators=100, n_jobs=1, oob_score=True, random_state=None,\n",
" verbose=0, warm_start=False)"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rf.fit(train[features + dummy_categoricals], train[target])"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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kHUqmTJmCt956C7fccotqvFBRFPzf//0fJk+enPIGEhGNV4oQ2PBGLWoOd8Kg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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1193af128>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.subplots(figsize = (8,8))\n",
"sns.regplot(test[target],rf.predict(test[features + dummy_categoricals]))\n",
"plt.ylabel('Predicted log_production')\n",
"plt.xlim(0, 22)\n",
"plt.ylim(0, 22)\n",
"plt.tight_layout()\n"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.metrics import explained_variance_score, r2_score, mean_squared_error"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.87932195036446992"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predicted = rf.predict(test[features + dummy_categoricals])\n",
"r2_score(test[target], predicted)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.88083361311273922"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"explained_variance_score(test[target], predicted)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.6987063758707428"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean_squared_error(test[target], predicted)"
]
},
{
"cell_type": "code",
"execution_count": 76,
"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>importance</th>\n",
" <th>name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.863181</td>\n",
" <td>Labor_Hours</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.030674</td>\n",
" <td>Average_Employees</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.007066</td>\n",
" <td>Coal_Supply_Region_Powder River Basin</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.005829</td>\n",
" <td>Coal_Supply_Region_Powder River Basin</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.003798</td>\n",
" <td>Mine_Type_Surface</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.003375</td>\n",
" <td>Mine_Type_Surface</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.001956</td>\n",
" <td>Mine_County_Boone</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.001802</td>\n",
" <td>Mine_State_West Virginia (Southern)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.001784</td>\n",
" <td>Mine_State_West Virginia (Southern)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.001746</td>\n",
" <td>Mine_Status_Active</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0.001709</td>\n",
" <td>Mine_Status_Active</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0.001623</td>\n",
" <td>Coal_Supply_Region_Appalachia Central</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0.001491</td>\n",
" <td>Mine_County_Boone</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0.001464</td>\n",
" <td>Coal_Supply_Region_Appalachia Central</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0.001442</td>\n",
" <td>Coal_Supply_Region_Illinois Basin</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>0.001388</td>\n",
" <td>Coal_Supply_Region_Illinois Basin</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0.001369</td>\n",
" <td>Mine_State_Refuse Recovery</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>0.001316</td>\n",
" <td>Mine_County_Buchanan</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0.001271</td>\n",
" <td>Mine_County_Buchanan</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>0.001258</td>\n",
" <td>Company_Type_Independent Producer Operator</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" importance name\n",
"0 0.863181 Labor_Hours\n",
"1 0.030674 Average_Employees\n",
"2 0.007066 Coal_Supply_Region_Powder River Basin\n",
"3 0.005829 Coal_Supply_Region_Powder River Basin\n",
"4 0.003798 Mine_Type_Surface\n",
"5 0.003375 Mine_Type_Surface\n",
"6 0.001956 Mine_County_Boone\n",
"7 0.001802 Mine_State_West Virginia (Southern)\n",
"8 0.001784 Mine_State_West Virginia (Southern)\n",
"9 0.001746 Mine_Status_Active\n",
"10 0.001709 Mine_Status_Active\n",
"11 0.001623 Coal_Supply_Region_Appalachia Central\n",
"12 0.001491 Mine_County_Boone\n",
"13 0.001464 Coal_Supply_Region_Appalachia Central\n",
"14 0.001442 Coal_Supply_Region_Illinois Basin\n",
"15 0.001388 Coal_Supply_Region_Illinois Basin\n",
"16 0.001369 Mine_State_Refuse Recovery\n",
"17 0.001316 Mine_County_Buchanan\n",
"18 0.001271 Mine_County_Buchanan\n",
"19 0.001258 Company_Type_Independent Producer Operator"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# find out the relative importance of each feature\n",
"\n",
"rf_importances = pd.DataFrame({'name':train[features + dummy_categoricals].columns,\n",
" 'importance':rf.feature_importances_\n",
" }).sort_values(by='importance', \n",
" ascending = False).reset_index(drop=True)\n",
"rf_importances[:20]"
]
},
{
"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": "3.4.4"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
mne-tools/mne-tools.github.io | 0.23/_downloads/b7659d33d6ffe8531d004e9d6051f16f/forward_sensitivity_maps.ipynb | 2 | 5873 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n\n# Display sensitivity maps for EEG and MEG sensors\n\nSensitivity maps can be produced from forward operators that\nindicate how well different sensor types will be able to detect\nneural currents from different regions of the brain.\n\nTo get started with forward modeling see `tut-forward`.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Author: Eric Larson <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport numpy as np\nimport mne\nfrom mne.datasets import sample\nfrom mne.source_space import compute_distance_to_sensors\nfrom mne.source_estimate import SourceEstimate\nimport matplotlib.pyplot as plt\n\nprint(__doc__)\n\ndata_path = sample.data_path()\n\nfwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'\n\nsubjects_dir = data_path + '/subjects'\n\n# Read the forward solutions with surface orientation\nfwd = mne.read_forward_solution(fwd_fname)\nmne.convert_forward_solution(fwd, surf_ori=True, copy=False)\nleadfield = fwd['sol']['data']\nprint(\"Leadfield size : %d x %d\" % leadfield.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute sensitivity maps\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"grad_map = mne.sensitivity_map(fwd, ch_type='grad', mode='fixed')\nmag_map = mne.sensitivity_map(fwd, ch_type='mag', mode='fixed')\neeg_map = mne.sensitivity_map(fwd, ch_type='eeg', mode='fixed')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Show gain matrix a.k.a. leadfield matrix with sensitivity map\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"picks_meg = mne.pick_types(fwd['info'], meg=True, eeg=False)\npicks_eeg = mne.pick_types(fwd['info'], meg=False, eeg=True)\n\nfig, axes = plt.subplots(2, 1, figsize=(10, 8), sharex=True)\nfig.suptitle('Lead field matrix (500 dipoles only)', fontsize=14)\nfor ax, picks, ch_type in zip(axes, [picks_meg, picks_eeg], ['meg', 'eeg']):\n im = ax.imshow(leadfield[picks, :500], origin='lower', aspect='auto',\n cmap='RdBu_r')\n ax.set_title(ch_type.upper())\n ax.set_xlabel('sources')\n ax.set_ylabel('sensors')\n fig.colorbar(im, ax=ax)\n\nfig_2, ax = plt.subplots()\nax.hist([grad_map.data.ravel(), mag_map.data.ravel(), eeg_map.data.ravel()],\n bins=20, label=['Gradiometers', 'Magnetometers', 'EEG'],\n color=['c', 'b', 'k'])\nfig_2.legend()\nax.set(title='Normal orientation sensitivity',\n xlabel='sensitivity', ylabel='count')\n\nbrain_sens = grad_map.plot(\n subjects_dir=subjects_dir, clim=dict(lims=[0, 50, 100]), figure=1)\nbrain_sens.add_text(0.1, 0.9, 'Gradiometer sensitivity', 'title', font_size=16)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compare sensitivity map with distribution of source depths\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# source space with vertices\nsrc = fwd['src']\n\n# Compute minimum Euclidean distances between vertices and MEG sensors\ndepths = compute_distance_to_sensors(src=src, info=fwd['info'],\n picks=picks_meg).min(axis=1)\nmaxdep = depths.max() # for scaling\n\nvertices = [src[0]['vertno'], src[1]['vertno']]\n\ndepths_map = SourceEstimate(data=depths, vertices=vertices, tmin=0.,\n tstep=1.)\n\nbrain_dep = depths_map.plot(\n subject='sample', subjects_dir=subjects_dir,\n clim=dict(kind='value', lims=[0, maxdep / 2., maxdep]), figure=2)\nbrain_dep.add_text(0.1, 0.9, 'Source depth (m)', 'title', font_size=16)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sensitivity is likely to co-vary with the distance between sources to\nsensors. To determine the strength of this relationship, we can compute the\ncorrelation between source depth and sensitivity values.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"corr = np.corrcoef(depths, grad_map.data[:, 0])[0, 1]\nprint('Correlation between source depth and gradiomter sensitivity values: %f.'\n % corr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Gradiometer sensitiviy is highest close to the sensors, and decreases rapidly\nwith inreasing source depth. This is confirmed by the high negative\ncorrelation between the two.\n\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | bsd-3-clause |
taneaki/Interpolations.jl | lin_interp_demo.ipynb | 1 | 2369966 | null | bsd-3-clause |
DTUWindEnergy/Python4WindEnergy | lesson 3/results/GFPE.ipynb | 1 | 234899 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import scipy as sp\n",
"import sympy\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib as mpl\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# activate pop up plots\n",
"#%matplotlib qt\n",
"# or change to inline plots\n",
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# some sample data\n",
"x = np.arange(0,10,0.1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"page_width_cm = 13\n",
"dpi = 200\n",
"inch = 2.54 # inch in cm\n",
"# setting global plot configuration using the RC configuration style\n",
"plt.rc('font', family='serif')\n",
"plt.rc('xtick', labelsize=12) # tick labels\n",
"plt.rc('ytick', labelsize=20) # tick labels\n",
"plt.rc('axes', labelsize=20) # axes labels\n",
"# If you don\u2019t need LaTeX, don\u2019t use it. It is slower to plot, and text\n",
"# looks just fine without. If you need it, e.g. for symbols, then use it.\n",
"#plt.rc('text', usetex=True) #<- P-E: Doesn't work on my Mac"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# create a figure instance, note that figure size is given in inches!\n",
"fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,6))\n",
"# set the big title (note aligment relative to figure)\n",
"fig.suptitle(\"suptitle 16, figure alignment\", fontsize=16)\n",
"\n",
"# actual plotting\n",
"ax.plot(x, x**2, label=\"label 12\")\n",
"\n",
"\n",
"\n",
"# set axes title (note aligment relative to axes)\n",
"ax.set_title(\"title 14, axes alignment\", fontsize=14)\n",
"\n",
"# axes labels\n",
"ax.set_xlabel('xlabel 12')\n",
"ax.set_ylabel(r'$y_{\\alpha}$ 12', fontsize=8)\n",
"\n",
"# legend\n",
"ax.legend(fontsize=12, loc=\"best\")\n",
"\n",
"##############################################################\n",
"\n",
"##Ploting Grid\n",
"plt.grid()\n",
"#Changing Legend\n",
"ax.legend(loc=4)\n",
"\n",
"ax.plot(x, x**2,color=\"purple\", lw=1, ls='-', marker='s',markevery=5, markersize=8,markerfacecolor=\"yellow\", markeredgewidth=2, markeredgecolor=\"blue\", label=\"label 12\")\n",
"\n",
"#Subplot\n",
"#SubPlot\n",
"fig, ax1 = plt.subplots(nrows=1, ncols=1, figsize=(8,6))\n",
"ax1.plot(x, x**3, label=\"label 12\")\n",
"ax1.set_title(\"Sub-plotting\", fontsize=14)\n",
"\n",
"\n",
"\n",
"\n",
"# saving the figure in different formats\n",
"fig.savefig('figure-%03i.png' % dpi, dpi=dpi)\n",
"fig.savefig('figure.svg')\n",
"fig.savefig('figure.eps')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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c+QtPnz6Fq6ureg5DmTJl4OHhASEE4uPjtd6TkJAAABp3PHve7du31V894kue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\nzPHuzSeY3eUPLKi6BKkXktH7l6H46sbraBVU2aB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ioqLw8ccf6zgy/lm0aBECAwNRUVGB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\nJUoUCCGEEKISJQqEEEIIUen/AQm564dFOwM7AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x76f1150>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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HDypUqEBgYCDz5s0D4PHHH+fChQsApKenA+Dt7Z3v/XI7EKalpRXpfBER8XzZ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\niJ2CgYiIiNj9f6xZUU6pD0hbAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7786f50>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"### Countour Plotting\n",
"\n",
"import math\n",
"from pylab import *\n",
"\n",
"alpha = 0.7\n",
"phi_ext = 2 * math.pi * 0.5\n",
"\n",
"def flux_qubit_potential(phi_m, phi_p):\n",
" return 2 + alpha - 2 * np.cos(phi_p)*np.cos(phi_m) - alpha * np.cos(phi_ext - 2*phi_p)\n",
"\n",
"phi_m = np.linspace(0, 2*math.pi, 100)\n",
"phi_p = np.linspace(0, 2*math.pi, 100)\n",
"X,Y = np.meshgrid(phi_p, phi_m)\n",
"Z = flux_qubit_potential(X, Y).T\n",
"\n",
"\n",
"fig, ax = plt.subplots()\n",
"\n",
"cnt = contour(Z, cmap=cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max(), extent=[0, 1, 0, 1])\n",
"\n",
"\n",
"\n",
"# Second Axis\n",
"ax2 = ax.twinx()\n",
"\n",
"ax2.set_ylabel(r\"Temperature ($^\\circ$C)\")\n",
"ax2.set_ylim(0, 35)\n",
"\n",
"\n",
"\n",
"#Add Line\n",
"axvline(x=0.5, ymin=0, ymax=1)\n",
"axhline(y=25,xmin=0,xmax=1)\n",
"\n",
"\n",
"#autoformat dates for nice printing on the x-axis using fig.autofmt_xdate()\n",
"fig.autofmt_xdate(bottom=0.2, rotation=30, ha='right')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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RXYRQVXzbttP45QqaV67Gt+VrHCOGkjl5IhmTJ+AaWYZs7Fwk3JF41m9k4z0P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\nCPQAzG8AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x64b3b10>"
]
}
],
"prompt_number": 8
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Advanced exercises"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"We are going to play a bit with regression\n",
"-Create a vector x of equally spaced number between x\u2208[0,5\u03c0] of 1000 points (keyword: linspace)\n",
"\n",
"-create a vector y, so that y=sin(x) with some random noise\n",
"\n",
"-plot it\n",
"\n",
"-Try to do a polynomial fit on y(x) using numpy.polyfit, with different polynomial degree"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"x= np.linspace(0, 5*math.pi, 1000)\n",
"\n",
"Y= np.sin(x)\n",
"noise = np.random.normal(-0.1,0.1,1000)\n",
"\n",
"y=Y+noise\n",
"\n",
"fig, ax1 = plt.subplots(nrows=1, ncols=1, figsize=(8,6))\n",
"fig.suptitle(\"Sin + Noise\", fontsize=16)\n",
"ax1.plot(x, y,color=\"black\",lw=2, ls='*', marker='.', label=\"Sin(x) + Noise\")\n",
"\n",
"\n",
"\n",
"plt.plot(x,Y,color=\"red\", linewidth=2.00,label=\"Sin(x)\")\n",
"\n",
"for i in [2,4,6,8,10]:\n",
" p = np.poly1d(np.polyfit(x,y,i))\n",
" plt.plot(x,p(x), linewidth=2.00,label=\"Fit order: \"+str(i))\n",
" \n",
"\n",
"ax1.legend(fontsize=12, loc=\"best\")\n",
"\n",
"\n",
"\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"<matplotlib.legend.Legend at 0x83f1c30>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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cqI3W5uEBkyZhtMwyGhgYSEBAAJs2bcozAYyPjw/e3t6cO3eOHj162ObbsBaw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\n5ctiYTSK3miUD4rHW7KNjWSeOHHDbdby1K1oQFQ2FqozRmp1h+Dxxx9n9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Pn56SyZ0oRu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\nGGOMMXBDwBhjjDFwQ8AYY4wxAP8HJVr1pkM+uzkAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x80ef230>"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
}
],
"metadata": {}
}
]
} | apache-2.0 |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/migration/UJ8 legacy AutoML Natural Language Text Sentiment Analysis.ipynb | 1 | 46495 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Xo4cz5r0c4yy"
},
"outputs": [],
"source": [
"# Copyright 2021 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": "text_title:migration,automl,icn"
},
"source": [
"# AutoML text sentiment analysis\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JE-aKjayJjkF"
},
"source": [
"## Installation\n",
"\n",
"Install the latest version of AutoML SDK."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7dxB6-B0JjkG"
},
"outputs": [],
"source": [
"! pip3 install google-cloud-automl"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Iddo-8HmJjkH"
},
"source": [
"Install the Google *cloud-storage* library as well."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xHvi6EFSJjkH"
},
"outputs": [],
"source": [
"! pip3 install google-cloud-storage"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ktRTgB8DJjkI"
},
"source": [
"### Restart the Kernel\n",
"\n",
"Once you've installed the AutoML SDK and Google *cloud-storage*, you need to restart the notebook kernel so it can find the packages."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "x1g4mBdlJjkI"
},
"outputs": [],
"source": [
"import os\n",
"\n",
"if not os.getenv(\"AUTORUN\"):\n",
" # Automatically restart kernel after installs\n",
" import IPython\n",
"\n",
" app = IPython.Application.instance()\n",
" app.kernel.do_shutdown(True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AGX8yszodHx_"
},
"source": [
"## Before you begin\r\n",
"\r\n",
"### GPU run-time\r\n",
"\r\n",
"*Make sure you're running this notebook in a GPU runtime if you have that option. In Colab, select* **Runtime > Change Runtime Type > GPU**\r\n",
"\r\n",
"### Set up your GCP project\r\n",
"\r\n",
"**The following steps are required, regardless of your notebook environment.**\r\n",
"\r\n",
"1. [Select or create a GCP project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\r\n",
"\r\n",
"2. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)\r\n",
"\r\n",
"3. [Enable the AutoML APIs and Compute Engine APIs.](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component)\r\n",
"\r\n",
"4. [Google Cloud SDK](https://cloud.google.com/sdk) is already installed in AutoML Notebooks.\r\n",
"\r\n",
"5. Enter your project ID in the cell below. Then run the cell to make sure the\r\n",
"Cloud SDK uses the right project for all the commands in this notebook.\r\n",
"\r\n",
"**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands.\r\n",
"\r\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "set_project_id"
},
"outputs": [],
"source": [
"PROJECT_ID = \"[your-project-id]\" # @param {type:\"string\"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "autoset_project_id"
},
"outputs": [],
"source": [
"if PROJECT_ID == \"\" or PROJECT_ID is None or PROJECT_ID == \"[your-project-id]\":\n",
" # Get your GCP project id from gcloud\n",
" shell_output = !gcloud config list --format 'value(core.project)' 2>/dev/null\n",
" PROJECT_ID = shell_output[0]\n",
" print(\"Project ID:\", PROJECT_ID)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "set_gcloud_project_id"
},
"outputs": [],
"source": [
"! gcloud config set project $PROJECT_ID"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jT0fijejJjkK"
},
"source": [
"#### Region\n",
"\n",
"You can also change the `REGION` variable, which is used for operations\n",
"throughout the rest of this notebook. Below are regions supported for AutoML. We recommend when possible, to choose the region closest to you.\n",
"\n",
"- Americas: `us-central1`\n",
"- Europe: `europe-west4`\n",
"- Asia Pacific: `asia-east1`\n",
"\n",
"You cannot use a Multi-Regional Storage bucket for training with AutoML. Not all regions provide support for all AutoML services. For the latest support per region, see [Region support for AutoML services]()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "CTBlncfrJjkK"
},
"outputs": [],
"source": [
"REGION = \"us-central1\" # @param {type: \"string\"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "J_croPBoJjkK"
},
"source": [
"#### Timestamp\n",
"\n",
"If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append onto the name of resources which will be created in this tutorial."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "KKxStk5bJjkL"
},
"outputs": [],
"source": [
"from datetime import datetime\n",
"\n",
"TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "c3yKEDsvdpFf"
},
"source": [
"### Authenticate your GCP account\r\n",
"\r\n",
"**If you are using AutoML Notebooks**, your environment is already\r\n",
"authenticated. Skip this step.\r\n",
"\r\n",
"*Note: If you are on an AutoML notebook and run the cell, the cell knows to skip executing the authentication steps.*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "chybg3Ap_i_2"
},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"\n",
"# If you are running this notebook in Colab, run this cell and follow the\n",
"# instructions to authenticate your Google Cloud account. This provides access\n",
"# to your Cloud Storage bucket and lets you submit training jobs and prediction\n",
"# requests.\n",
"\n",
"# If on Vertex, then don't execute this code\n",
"if not os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\n",
" if \"google.colab\" in sys.modules:\n",
" from google.colab import auth as google_auth\n",
"\n",
" google_auth.authenticate_user()\n",
"\n",
" # If you are running this tutorial in a notebook locally, replace the string\n",
" # below with the path to your service account key and run this cell to\n",
" # authenticate your Google Cloud account.\n",
" else:\n",
" %env GOOGLE_APPLICATION_CREDENTIALS your_path_to_credentials.json\n",
"\n",
" # Log in to your account on Google Cloud\n",
" ! gcloud auth login"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MDUAZaN3JjkL"
},
"source": [
"### Create a Cloud Storage bucket\n",
"\n",
"**The following steps are required, regardless of your notebook environment.**\n",
"\n",
"This tutorial is designed to use training data that is in a public Cloud Storage bucket and a local Cloud Storage bucket for your batch predictions. You may alternatively use your own training data that you have stored in a local Cloud Storage bucket.\n",
"\n",
"Set the name of your Cloud Storage bucket below. It must be unique across all Cloud Storage buckets.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bucket"
},
"outputs": [],
"source": [
"BUCKET_NAME = \"[your-bucket-name]\" # @param {type:\"string\"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "autoset_bucket"
},
"outputs": [],
"source": [
"if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
" BUCKET_NAME = PROJECT_ID + \"aip-\" + TIMESTAMP"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yU6VuylGJjkM"
},
"source": [
"**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "QVRixM4oJjkM"
},
"outputs": [],
"source": [
"! gsutil mb -l $REGION gs://$BUCKET_NAME"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Mi-Pdh6QJjkN"
},
"source": [
"Finally, validate access to your Cloud Storage bucket by examining its contents:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "aqDptKpYJjkN"
},
"outputs": [],
"source": [
"! gsutil ls -al gs://$BUCKET_NAME"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fR9geV9pJjkO"
},
"source": [
"### Set up variables\n",
"\n",
"Next, set up some variables used throughout the tutorial.\n",
"### Import libraries and define constants"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hIHTX-pkJjkO"
},
"source": [
"#### Import AutoML SDK\n",
"\n",
"Import the AutoM SDK into our Python environment."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qrSkIKVgJjkO"
},
"outputs": [],
"source": [
"import json\n",
"import os\n",
"import sys\n",
"import time\n",
"\n",
"from google.cloud import automl\n",
"from google.protobuf.json_format import MessageToJson\n",
"from google.protobuf.struct_pb2 import Value"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lbv411XjJjkP"
},
"source": [
"#### AutoML constants\n",
"\n",
"Setup up the following constants for AutoML:\n",
"\n",
"- `PARENT`: The AutoM location root path for dataset, model and endpoint resources."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Gx8Eos8PJjkP"
},
"outputs": [],
"source": [
"# AutoM location root path for your dataset, model and endpoint resources\n",
"PARENT = \"projects/\" + PROJECT_ID + \"/locations/\" + REGION"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hBaOc_HRc4zC"
},
"source": [
"## Clients\n",
"\n",
"The AutoML SDK works as a client/server model. On your side (the Python script) you will create a client that sends requests and receives responses from the server (AutoML).\n",
"\n",
"You will use several clients in this tutorial, so set them all up upfront."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "5t0zkxqPc4zC"
},
"outputs": [],
"source": [
"def automl_client():\n",
" return automl.AutoMlClient()\n",
"\n",
"\n",
"def perdictions_client():\n",
" return automl.PredictionServiceClient()\n",
"\n",
"\n",
"def operations_client():\n",
" return automl.AutoMlClient()._transport.operations_client\n",
"\n",
"\n",
"clients = {}\n",
"clients[\"automl\"] = automl_client()\n",
"clients[\"predictions\"] = perdictions_client()\n",
"clients[\"operations\"] = operations_client()\n",
"\n",
"for client in clients.items():\n",
" print(client)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "import_file:flowers,csv,icn"
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"\n",
"IMPORT_FILE = \"gs://cloud-samples-data/language/claritin.csv\"\n",
"with tf.io.gfile.GFile(IMPORT_FILE, \"r\") as f:\n",
" content = f.readlines()\n",
"\n",
"IMPORT_FILE = \"gs://\" + BUCKET_NAME + \"/claritin.csv\"\n",
"with tf.io.gfile.GFile(IMPORT_FILE, \"w\") as f:\n",
" for line in content:\n",
" f.write(\",\".join(line.split(\",\")[0:-1]) + \"\\n\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ed725169cbfa"
},
"outputs": [],
"source": [
"! gsutil cat $IMPORT_FILE | head -n 10"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "d3a0464dd6f3"
},
"source": [
"*Example output*:\n",
"```\n",
"@freewrytin God is way too good for Claritin,2\n",
"I need Claritin. So bad. When did I become cursed with allergies?,3\n",
"Thank god for Claritin.,4\n",
"\"And what's worse is that I reached my 3-day limit on the nose spray yesterday, which means I have to rely on Claritin.\",2\n",
"Time to take some Claritin or Allegra or something. I need my voice,3\n",
"Oh my RT @imsydneycharles: I just want it to be on record somewhere that I took Claritin and Benadryl together...just in case I pass out,2\n",
"Bouta take a Claritin _ÛªÛ_Ûª_ÛªÌâ FML !!,3\n",
"Commander Loratadine Generic A Sarcelles: Commander Loratadine Generic A Sarcelles Claritin =Ûª_Ûª__ http://t.co/mOleL8AM,2\n",
"\"Zyrtec, Claritin, Suddafed, Nasal Spray.. I feel like a drug addict taking these Allergy medicine. Please Allergy season.. DISAPPEAR!!\",1\n",
"\"Ûª_Ûª_ÛªÕ@SheLovesThatD: If she has allergies, give her the Claritin D.Ûª_Ûª_Ì_å @Sweeno_thakid41 @B_Original16 @luke_CYwalker14\",3\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "text_create_dataset:migration"
},
"source": [
"## Create a dataset"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "text_datasets_create:migration,old"
},
"source": [
"### [projects.locations.datasets.create](https://cloud.google.com/automl/docs/reference/rest/v1beta1/projects.locations.datasets/create)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ozaMOwdNJjkT"
},
"source": [
"#### Request"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "OMZr1t8mc4zF"
},
"outputs": [],
"source": [
"dataset = {\n",
" \"display_name\": \"claritin_\" + TIMESTAMP,\n",
" \"text_sentiment_dataset_metadata\": {\"sentiment_max\": 4},\n",
"}\n",
"\n",
"\n",
"print(\n",
" MessageToJson(\n",
" automl.CreateDatasetRequest(parent=PARENT, dataset=dataset).__dict__[\"_pb\"]\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "f1354773b89e"
},
"source": [
"*Example output*:\n",
"```\n",
"{\n",
" \"parent\": \"projects/migration-ucaip-training/locations/us-central1\",\n",
" \"dataset\": {\n",
" \"displayName\": \"claritin_20210304132912\",\n",
" \"textSentimentDatasetMetadata\": {\n",
" \"sentimentMax\": 4\n",
" }\n",
" }\n",
"}\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vHkuSZxkJjkT"
},
"source": [
"#### Call"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ifCavN75c4zG"
},
"outputs": [],
"source": [
"request = clients[\"automl\"].create_dataset(parent=PARENT, dataset=dataset)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FLqkZMD2JjkT"
},
"source": [
"#### Response"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "87xXwkaVc4zG"
},
"outputs": [],
"source": [
"result = request.result()\n",
"\n",
"print(MessageToJson(result.__dict__[\"_pb\"]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "f9c171ffceee"
},
"source": [
"*Example output*:\n",
"```\n",
"{\n",
" \"name\": \"projects/116273516712/locations/us-central1/datasets/TST1994716952680988672\"\n",
"}\n",
"```\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "86e845326428"
},
"outputs": [],
"source": [
"# The full unique ID for the dataset\n",
"dataset_id = result.name\n",
"# The short numeric ID for the dataset\n",
"dataset_short_id = dataset_id.split(\"/\")[-1]\n",
"\n",
"print(dataset_id)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "text_datasets_importdata:migration,old"
},
"source": [
"### [projects.locations.datasets.importData](https://cloud.google.com/automl/docs/reference/rest/v1beta1/projects.locations.datasets/importData)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "y3T7l1xAc4zH"
},
"source": [
"#### Request"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jjOtdiJBc4zH"
},
"outputs": [],
"source": [
"input_config = {\"gcs_source\": {\"input_uris\": [IMPORT_FILE]}}\n",
"\n",
"print(\n",
" MessageToJson(\n",
" automl.ImportDataRequest(name=dataset_id, input_config=input_config).__dict__[\n",
" \"_pb\"\n",
" ]\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "c0fd2b918d71"
},
"source": [
"*Example output*:\n",
"```\n",
"{\n",
" \"name\": \"projects/116273516712/locations/us-central1/datasets/TST1994716952680988672\",\n",
" \"inputConfig\": {\n",
" \"gcsSource\": {\n",
" \"inputUris\": [\n",
" \"gs://migration-ucaip-trainingaip-20210304132912/claritin.csv\"\n",
" ]\n",
" }\n",
" }\n",
"}\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7QSqElzPc4zH"
},
"source": [
"#### Call"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "FWm339Twc4zI"
},
"outputs": [],
"source": [
"request = clients[\"automl\"].import_data(name=dataset_id, input_config=input_config)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AQVw0rQsc4zI"
},
"source": [
"#### Response"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Fur_ANcMc4zI"
},
"outputs": [],
"source": [
"result = request.result()\n",
"\n",
"print(MessageToJson(result))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NT2ras8rhC_D"
},
"source": [
"*Example output*:\r\n",
"```\r\n",
"{}\r\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "text_create_and_deploy_model:migration"
},
"source": [
"## Train a model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "text_models_create:migration,old"
},
"source": [
"### [projects.locations.models.create](https://cloud.google.com/automl/docs/reference/rest/v1beta1/projects.locations.models/create)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bfPBtMYZc4zJ"
},
"source": [
"#### Request"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LbWL7tq2c4zJ"
},
"outputs": [],
"source": [
"model = {\n",
" \"display_name\": \"claritin_\" + TIMESTAMP,\n",
" \"dataset_id\": dataset_short_id,\n",
" \"text_sentiment_model_metadata\": {},\n",
"}\n",
"\n",
"print(\n",
" MessageToJson(automl.CreateModelRequest(parent=PARENT, model=model).__dict__[\"_pb\"])\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "e080c4730d86"
},
"source": [
"*Example output*:\n",
"```\n",
"{\n",
" \"parent\": \"projects/migration-ucaip-training/locations/us-central1\",\n",
" \"model\": {\n",
" \"displayName\": \"claritin_20210304132912\",\n",
" \"datasetId\": \"TST1994716952680988672\",\n",
" \"textSentimentModelMetadata\": {}\n",
" }\n",
"}\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "duJfz52Rc4zJ"
},
"source": [
"#### Call"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kHS2pwcec4zK"
},
"outputs": [],
"source": [
"request = clients[\"automl\"].create_model(parent=PARENT, model=model)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vmkA2d5-c4zK"
},
"source": [
"#### Response"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "o8P7a8nQc4zK"
},
"outputs": [],
"source": [
"result = request.result()\n",
"\n",
"print(MessageToJson(result.__dict__[\"_pb\"]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "498f8fd21640"
},
"source": [
"*Example output*:\n",
"```\n",
"{\n",
" \"name\": \"projects/116273516712/locations/us-central1/models/TST4078882474816438272\"\n",
"}\n",
"```\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "865fcdfa7262"
},
"outputs": [],
"source": [
"# The full unique ID for the training pipeline\n",
"model_id = result.name\n",
"# The short numeric ID for the training pipeline\n",
"model_short_id = model_id.split(\"/\")[-1]\n",
"\n",
"print(model_short_id)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "text_evaluate_the_model:migration"
},
"source": [
"## Evaluate the model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yfufMwAEJjkX"
},
"source": [
"### [projects.locations.models.modelEvaluations.list](https://cloud.google.com/automl/docs/reference/rest/v1beta1/projects.locations.models.modelEvaluations/list)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ro1Hr7Hbc4zL"
},
"source": [
"#### Call"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "hBEu4Ghoc4zL"
},
"outputs": [],
"source": [
"request = clients[\"automl\"].list_model_evaluations(parent=model_id, filter=\"\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "X9XycwTYc4zM"
},
"source": [
"#### Response"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ZbX6Z00uc4zM"
},
"outputs": [],
"source": [
"model_evaluations = [json.loads(MessageToJson(me.__dict__[\"_pb\"])) for me in request]\n",
"# The evaluation slice\n",
"evaluation_slice = request.model_evaluation[0].name\n",
"\n",
"print(json.dumps(model_evaluations, indent=2))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6835b78da85b"
},
"source": [
"*Example output*:\n",
"```\n",
"[\n",
" {\n",
" \"name\": \"projects/116273516712/locations/us-central1/models/TST4078882474816438272/modelEvaluations/54870628009945864\",\n",
" \"annotationSpecId\": \"8301667931964571648\",\n",
" \"createTime\": \"2021-03-04T17:15:51.851420Z\",\n",
" \"textSentimentEvaluationMetrics\": {\n",
" \"precision\": 0.33333334,\n",
" \"recall\": 0.16666667,\n",
" \"f1Score\": 0.22222222\n",
" },\n",
" \"displayName\": \"4\"\n",
" },\n",
" {\n",
" \"name\": \"projects/116273516712/locations/us-central1/models/TST4078882474816438272/modelEvaluations/1597159550285093673\",\n",
" \"annotationSpecId\": \"1384138904323489792\",\n",
" \"createTime\": \"2021-03-04T17:15:51.851420Z\",\n",
" \"textSentimentEvaluationMetrics\": {\n",
" \"precision\": 0.5,\n",
" \"recall\": 0.296875,\n",
" \"f1Score\": 0.37254903\n",
" },\n",
" \"displayName\": \"1\"\n",
" },\n",
" {\n",
" \"name\": \"projects/116273516712/locations/us-central1/models/TST4078882474816438272/modelEvaluations/3521790980763365687\",\n",
" \"createTime\": \"2021-03-04T17:15:51.851420Z\",\n",
" \"evaluatedExampleCount\": 452,\n",
" \"textSentimentEvaluationMetrics\": {\n",
" \"precision\": 0.6238938,\n",
" \"recall\": 0.6238938,\n",
" \"f1Score\": 0.6238938,\n",
" \"meanAbsoluteError\": 0.47566372,\n",
" \"meanSquaredError\": 0.69690263,\n",
" \"linearKappa\": 0.41007927,\n",
" \"quadraticKappa\": 0.45938763,\n",
" \"confusionMatrix\": {\n",
" \"annotationSpecId\": [\n",
" \"7148746427357724672\",\n",
" \"1384138904323489792\",\n",
" \"5995824922750877696\",\n",
" \"3689981913537183744\",\n",
" \"8301667931964571648\"\n",
" ],\n",
" \"row\": [\n",
" {\n",
" \"exampleCount\": [\n",
" 2,\n",
" 4,\n",
" 1,\n",
" 1,\n",
" 1\n",
" ]\n",
" },\n",
" {\n",
" \"exampleCount\": [\n",
" 3,\n",
" 19,\n",
" 14,\n",
" 28,\n",
" 0\n",
" ]\n",
" },\n",
" {\n",
" \"exampleCount\": [\n",
" 0,\n",
" 7,\n",
" 67,\n",
" 63,\n",
" 1\n",
" ]\n",
" },\n",
" {\n",
" \"exampleCount\": [\n",
" 1,\n",
" 8,\n",
" 19,\n",
" 191,\n",
" 4\n",
" ]\n",
" },\n",
" {\n",
" \"exampleCount\": [\n",
" 0,\n",
" 0,\n",
" 0,\n",
" 15,\n",
" 3\n",
" ]\n",
" }\n",
" ],\n",
" \"displayName\": [\n",
" \"0\",\n",
" \"1\",\n",
" \"2\",\n",
" \"3\",\n",
" \"4\"\n",
" ]\n",
" }\n",
" }\n",
" },\n",
" {\n",
" \"name\": \"projects/116273516712/locations/us-central1/models/TST4078882474816438272/modelEvaluations/3727703410992997127\",\n",
" \"annotationSpecId\": \"3689981913537183744\",\n",
" \"createTime\": \"2021-03-04T17:15:51.851420Z\",\n",
" \"textSentimentEvaluationMetrics\": {\n",
" \"precision\": 0.6409396,\n",
" \"recall\": 0.85650223,\n",
" \"f1Score\": 0.7332054\n",
" },\n",
" \"displayName\": \"3\"\n",
" },\n",
" {\n",
" \"name\": \"projects/116273516712/locations/us-central1/models/TST4078882474816438272/modelEvaluations/4692810493650008310\",\n",
" \"annotationSpecId\": \"7148746427357724672\",\n",
" \"createTime\": \"2021-03-04T17:15:51.851420Z\",\n",
" \"textSentimentEvaluationMetrics\": {\n",
" \"precision\": 0.33333334,\n",
" \"recall\": 0.22222222,\n",
" \"f1Score\": 0.26666668\n",
" },\n",
" \"displayName\": \"0\"\n",
" },\n",
" {\n",
" \"name\": \"projects/116273516712/locations/us-central1/models/TST4078882474816438272/modelEvaluations/8390011688796741170\",\n",
" \"annotationSpecId\": \"5995824922750877696\",\n",
" \"createTime\": \"2021-03-04T17:15:51.851420Z\",\n",
" \"textSentimentEvaluationMetrics\": {\n",
" \"precision\": 0.6633663,\n",
" \"recall\": 0.48550725,\n",
" \"f1Score\": 0.5606694\n",
" },\n",
" \"displayName\": \"2\"\n",
" }\n",
"]\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "i6T0bzuNJjkY"
},
"source": [
"### [projects.locations.models.modelEvaluations.get](https://cloud.google.com/automl/docs/reference/rest/v1beta1/projects.locations.models.modelEvaluations/get)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cu1lRCSOc4zM"
},
"source": [
"#### Call"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "XIIHbDYWc4zM"
},
"outputs": [],
"source": [
"request = clients[\"automl\"].get_model_evaluation(name=evaluation_slice)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aMgYNNWgc4zN"
},
"source": [
"#### Response"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "hSWpi4v1c4zN"
},
"outputs": [],
"source": [
"print(MessageToJson(request.__dict__[\"_pb\"]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "b11081c8c71e"
},
"source": [
"*Example output*:\n",
"```\n",
"{\n",
" \"name\": \"projects/116273516712/locations/us-central1/models/TST4078882474816438272/modelEvaluations/54870628009945864\",\n",
" \"annotationSpecId\": \"8301667931964571648\",\n",
" \"createTime\": \"2021-03-04T17:15:51.851420Z\",\n",
" \"textSentimentEvaluationMetrics\": {\n",
" \"precision\": 0.33333334,\n",
" \"recall\": 0.16666667,\n",
" \"f1Score\": 0.22222222\n",
" },\n",
" \"displayName\": \"4\"\n",
"}\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iRUvxxAKc4zN"
},
"source": [
"## Make batch predictions"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "caA77NMbc4zN"
},
"source": [
"### Make the batch input file\r\n",
"\r\n",
"To request a batch of predictions from AutoML Video, create a CSV file that lists the Cloud Storage paths to the videos that you want to annotate. You can also specify a start and end time to tell AutoML Video to only annotate a segment (segment-level) of the video. The start time must be zero or greater and must be before the end time. The end time must be greater than the start time and less than or equal to the duration of the video. You can also use inf to indicate the end of a video."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "5beaf1d4077c"
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"\n",
"gcs_input_uri = \"gs://\" + BUCKET_NAME + \"/test.csv\"\n",
"with tf.io.gfile.GFile(gcs_input_uri, \"w\") as f:\n",
" item_1 = \"gs://cloud-samples-data/language/sentiment-positive.txt\"\n",
" ! gsutil cp $item_1 gs://$BUCKET_NAME\n",
" f.write(\"gs://\" + BUCKET_NAME + \"/sentiment-positive.txt\" + \"\\n\")\n",
"\n",
" item_2 = \"gs://cloud-samples-data/language/sentiment-negative.txt\"\n",
" ! gsutil cp $item_2 gs://$BUCKET_NAME\n",
" f.write(\"gs://\" + BUCKET_NAME + \"/sentiment-negative.txt\")\n",
"\n",
"! gsutil cat $gcs_input_uri"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "031c110f4c79"
},
"source": [
"*Example output*:\n",
"```\n",
"gs://migration-ucaip-trainingaip-20210304132912/sentiment-positive.txt\n",
"gs://migration-ucaip-trainingaip-20210304132912/sentiment-negative.txt\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "text_models_batchpredict:migration,old"
},
"source": [
"### [projects.locations.models.batchPredict](https://cloud.google.com/automl/docs/reference/rest/v1beta1/projects.locations.models/batchPredict)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UNxo4fR5c4zO"
},
"source": [
"#### Request"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Vu-8O5Y2c4zO"
},
"outputs": [],
"source": [
"input_config = {\"gcs_source\": {\"input_uris\": [gcs_input_uri]}}\n",
"\n",
"output_config = {\n",
" \"gcs_destination\": {\"output_uri_prefix\": \"gs://\" + f\"{BUCKET_NAME}/batch_output/\"}\n",
"}\n",
"\n",
"print(\n",
" MessageToJson(\n",
" automl.BatchPredictRequest(\n",
" name=model_id, input_config=input_config, output_config=output_config\n",
" ).__dict__[\"_pb\"]\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9defd1c4d1a0"
},
"source": [
"*Example output*:\n",
"```\n",
"{\n",
" \"name\": \"projects/116273516712/locations/us-central1/models/TST4078882474816438272\",\n",
" \"inputConfig\": {\n",
" \"gcsSource\": {\n",
" \"inputUris\": [\n",
" \"gs://migration-ucaip-trainingaip-20210304132912/test.csv\"\n",
" ]\n",
" }\n",
" },\n",
" \"outputConfig\": {\n",
" \"gcsDestination\": {\n",
" \"outputUriPrefix\": \"gs://migration-ucaip-trainingaip-20210304132912/batch_output/\"\n",
" }\n",
" }\n",
"}\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kc_59mp8c4zP"
},
"source": [
"#### Call"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "TSk7yRiqc4zP"
},
"outputs": [],
"source": [
"request = clients[\"predictions\"].batch_predict(\n",
" name=model_id, input_config=input_config, output_config=output_config\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mcqKVj2Xc4zP"
},
"source": [
"#### Response"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bSTEmLgGc4zP"
},
"outputs": [],
"source": [
"result = request.result()\n",
"\n",
"print(MessageToJson(result.__dict__[\"_pb\"]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "c4CvqTBhtMNi"
},
"source": [
"*Example output*:\r\n",
"```\r\n",
"{}\r\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FQjooSKyc4zQ"
},
"source": [
"## Make online predictions\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nrRQoOCVc4zQ"
},
"source": [
"#### Prepare data item for online prediction\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mMdW9VbJc4zQ"
},
"outputs": [],
"source": [
"test_data = ! gsutil cat $IMPORT_FILE | head -n1\n",
"\n",
"test_item = str(test_data[0]).split(\",\")[0]\n",
"test_label = str(test_data[0]).split(\",\")[1]\n",
"\n",
"print((test_item, test_label))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8981b397c864"
},
"source": [
"*Example output*:\n",
"```\n",
"('@freewrytin God is way too good for Claritin', '2')\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "text_models_deploy:migration,old"
},
"source": [
"### [projects.locations.models.deploy](https://cloud.google.com/automl/docs/reference/rest/v1beta1/projects.locations.models/deploy)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "h0AxsMSjc4zQ"
},
"source": [
"#### Call"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "_Uxf6m8Cc4zR"
},
"outputs": [],
"source": [
"request = clients[\"automl\"].deploy_model(name=model_id)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Rj8jg_GVc4zR"
},
"source": [
"#### Response"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "q7rox1YCc4zR"
},
"outputs": [],
"source": [
"result = request.result()\n",
"\n",
"print(MessageToJson(result))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "s1ikNfXniG-3"
},
"source": [
"*Example output*:\r\n",
"```\r\n",
"{}\r\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "text_models_predict:migration,old"
},
"source": [
"### [projects.locations.models.predict](https://cloud.google.com/automl/docs/reference/rest/v1beta1/projects.locations.models/predict)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "g688Vva3c4zS"
},
"outputs": [],
"source": [
"payload = {\"text_snippet\": {\"content\": test_item, \"mime_type\": \"text/plain\"}}\n",
"\n",
"prediction_request = automl.PredictRequest(\n",
" name=model_id,\n",
" payload=payload,\n",
")\n",
"\n",
"print(MessageToJson(prediction_request.__dict__[\"_pb\"]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cd025e2420db"
},
"source": [
"*Example output*:\n",
"```\n",
"{\n",
" \"name\": \"projects/116273516712/locations/us-central1/models/TST4078882474816438272\",\n",
" \"payload\": {\n",
" \"textSnippet\": {\n",
" \"content\": \"@freewrytin God is way too good for Claritin\",\n",
" \"mimeType\": \"text/plain\"\n",
" }\n",
" }\n",
"}\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kgznWoQcc4zS"
},
"source": [
"#### Call"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "GUOkxQwic4zS"
},
"outputs": [],
"source": [
"request = clients[\"predictions\"].predict(request=prediction_request)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9MfWbNzhc4zS"
},
"source": [
"#### Response"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xZZliEjoc4zS"
},
"outputs": [],
"source": [
"print(MessageToJson(request.__dict__[\"_pb\"]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dc047af97666"
},
"source": [
"*Example output*:\n",
"```\n",
"{\n",
" \"payload\": [\n",
" {\n",
" \"textSentiment\": {\n",
" \"sentiment\": 3\n",
" }\n",
" }\n",
" ],\n",
" \"metadata\": {\n",
" \"sentiment_score\": \"0.30955505\"\n",
" }\n",
"}\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bQ-VVaSxJjkd"
},
"source": [
"# Cleaning up\r\n",
"\r\n",
"To clean up all GCP resources used in this project, you can [delete the GCP\r\n",
"project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\r\n",
"\r\n",
"Otherwise, you can delete the individual resources you created in this tutorial."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "oNJs94pKc4zT"
},
"outputs": [],
"source": [
"delete_dataset = True\n",
"delete_model = True\n",
"delete_bucket = True\n",
"\n",
"# Delete the dataset using the AutoML fully qualified identifier for the dataset\n",
"try:\n",
" if delete_dataset:\n",
" clients[\"automl\"].delete_dataset(name=dataset_id)\n",
"except Exception as e:\n",
" print(e)\n",
"\n",
"# Delete the model using the AutoML fully qualified identifier for the model\n",
"try:\n",
" if delete_model:\n",
" clients[\"automl\"].delete_model(name=model_id)\n",
"except Exception as e:\n",
" print(e)\n",
"\n",
"if delete_bucket and \"BUCKET_NAME\" in globals():\n",
" ! gsutil rm -r gs://$BUCKET_NAME"
]
}
],
"metadata": {
"colab": {
"name": "UJ8 legacy AutoML Natural Language - Text Sentiment Analysis.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
pcmagic/stokes_flow | head_Force/loop_table/generate_bash.ipynb | 1 | 60058 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import os\n",
"from codeStore import support_fun as spf\n",
"import importlib\n"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ecoB05_all_psi0.00\n",
"ecoB05_all_psi0.42\n",
"ecoB05_all_psi0.84\n",
"ecoB05_all_psi1.26\n",
"ecoB05_all_psi1.68\n",
"ecoB05_all_psi2.09\n",
"ecoB05_all_psi2.51\n",
"ecoB05_all_psi2.93\n",
"ecoB05_all_psi3.35\n",
"ecoB05_all_psi3.77\n",
"ecoB05_all_psi4.19\n",
"ecoB05_all_psi4.61\n",
"ecoB05_all_psi5.03\n",
"ecoB05_all_psi5.45\n",
"ecoB05_all_psi5.86\n",
"n_pbs = 15\n"
]
}
],
"source": [
"# case loop_table, each psi store in a independent folder. \n",
"n_tail = 1\n",
"ph = 2/3\n",
"ecoli_name = 'ecoB03'\n",
"\n",
"# sm = 'pf'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 0\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 1\n",
"# ch = 3\n",
"# nth = 15\n",
"# rh1 = 0.3\n",
"# rh2 = 0.1\n",
"# job_dir = 'planeShearRatex_1a'\n",
"# importlib.reload(spf)\n",
"# write_pbs_head = spf.write_pbs_head_newturb\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 10, endpoint=False)\n",
"# n_norm_theta = 25\n",
"# n_norm_phi = 25\n",
"\n",
"# sm = 'pf'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 0\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 1\n",
"# ch = 0.1\n",
"# nth = 30\n",
"# rh1 = 0.03\n",
"# rh2 = 0.01\n",
"# ph = 0.2\n",
"# n_tail = 4\n",
"# ecoli_name = 'ecoB003'\n",
"# job_dir = 'planeShearRatex_1b'\n",
"# importlib.reload(spf)\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 10, endpoint=False)\n",
"# n_norm_theta = 25\n",
"# n_norm_phi = 25\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoB01_all'\n",
"# job_dir = 'planeShearRatex_1c'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 1\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh1 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# importlib.reload(spf)\n",
"# write_pbs_head = spf.write_pbs_head_newturb\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 25\n",
"# n_norm_phi = 50\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoB01_all'\n",
"# job_dir = 'planeShearRatex_2c'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0.5\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh1 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# importlib.reload(spf)\n",
"# write_pbs_head = spf.write_pbs_head_newturb\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 25\n",
"# n_norm_phi = 50\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoB01_all'\n",
"# job_dir = 'planeShearRatex_4c'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0.25\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh1 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# importlib.reload(spf)\n",
"# write_pbs_head = spf.write_pbs_head_newturb\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 25\n",
"# n_norm_phi = 50\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoB01_all'\n",
"# job_dir = 'planeShearRatex_0.5c'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 2\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh1 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# importlib.reload(spf)\n",
"# write_pbs_head = spf.write_pbs_head_newturb\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 25\n",
"# n_norm_phi = 50\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoB01_all'\n",
"# job_dir = 'planeShearRatex_0.25c'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 4\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh1 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# importlib.reload(spf)\n",
"# write_pbs_head = spf.write_pbs_head_newturb\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 25\n",
"# n_norm_phi = 50\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoB01_all'\n",
"# job_dir = 'planeShearRatex_1c_passive'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh1 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# importlib.reload(spf)\n",
"# write_pbs_head = spf.write_pbs_head_newturb\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 25\n",
"# n_norm_phi = 50\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoB01_all'\n",
"# job_dir = 'planeShearRatex_1d'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 1\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh1 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# importlib.reload(spf)\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 64\n",
"# n_norm_phi = 128\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoB01_all'\n",
"# job_dir = 'planeShearRatex_1d_passive'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh1 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# importlib.reload(spf)\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 64\n",
"# n_norm_phi = 128\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoB01_all'\n",
"# job_dir = 'planeShearRatex_-1c'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = -1\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh1 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# importlib.reload(spf)\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"sm = 'pf'\n",
"ecoli_name = 'ecoB05_all'\n",
"job_dir = 'ecoB05_tau1c'\n",
"ksp_max_it = 300\n",
"main_fun_noIter = 1\n",
"planeShearRatex = 1\n",
"ecoli_velocity = 4.019587915525599\n",
"ch = 3\n",
"nth = 20\n",
"rh1 = 0.5\n",
"rh2 = 0.15\n",
"ph = 10/3\n",
"n_tail = 1\n",
"importlib.reload(spf)\n",
"write_pbs_head = spf.write_pbs_head_newturb\n",
"norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"n_norm_theta = 32\n",
"n_norm_phi = 64\n",
"\n",
"sm = 'pf'\n",
"ecoli_name = 'ecoB05_all'\n",
"job_dir = 'ecoB05_tau1c_passive'\n",
"ksp_max_it = 300\n",
"main_fun_noIter = 1\n",
"planeShearRatex = 1\n",
"ecoli_velocity = 0\n",
"ch = 3\n",
"nth = 20\n",
"rh1 = 0.5\n",
"rh2 = 0.15\n",
"ph = 10/3\n",
"n_tail = 1\n",
"importlib.reload(spf)\n",
"write_pbs_head = spf.write_pbs_head_newturb\n",
"norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"n_norm_theta = 32\n",
"n_norm_phi = 64\n",
"\n",
"PWD = os.getcwd()\n",
"if not os.path.exists(job_dir):\n",
" os.makedirs(job_dir)\n",
"\n",
"t_name = os.path.join(job_dir, 'run.sh')\n",
"n_pbs = 0\n",
"with open(t_name, 'w') as frun:\n",
" # create .pbs file\n",
" frun.write('t_dir=$PWD \\n')\n",
" for norm_psi in norm_psi_list:\n",
" job_name = '%s_psi%4.2f' % (ecoli_name, norm_psi)\n",
" print(job_name)\n",
" t_path = os.path.join(job_dir, job_name)\n",
" if not os.path.exists(t_path):\n",
" os.makedirs(t_path)\n",
" t_name = os.path.join(t_path, '%s.pbs' % job_name)\n",
" with open(t_name, 'w') as fpbs:\n",
" write_pbs_head(fpbs, job_name) \n",
" fpbs.write('mpirun -n 24 python ')\n",
" fpbs.write(' ../../../loop_table_ecoli.py ')\n",
" fpbs.write(' -f %s ' % job_name)\n",
" fpbs.write(' -pickProblem %d ' % 0)\n",
" fpbs.write(' -save_singleEcoli_vtk %d ' % 0)\n",
" fpbs.write(' -rh1 %f ' % rh1)\n",
" fpbs.write(' -rh2 %f ' % rh2)\n",
" fpbs.write(' -ch %f ' % ch)\n",
" fpbs.write(' -nth %d ' % nth)\n",
" fpbs.write(' -eh %f ' % -1)\n",
" fpbs.write(' -ph %f ' % ph)\n",
" fpbs.write(' -hfct %f ' % 1)\n",
" fpbs.write(' -n_tail %d ' % n_tail)\n",
" fpbs.write(' -with_cover %d ' % 2)\n",
" fpbs.write(' -left_hand %d ' % 0)\n",
" fpbs.write(' -rs1 %f ' % 1.5)\n",
" fpbs.write(' -rs2 %f ' % 0.5)\n",
" fpbs.write(' -ds %f ' % 0.07)\n",
" fpbs.write(' -es %f ' % -1)\n",
" fpbs.write(' -with_T_geo %d ' % 0)\n",
" fpbs.write(' -dist_hs %f ' % 0.5)\n",
" fpbs.write(' -ksp_max_it %d ' % ksp_max_it)\n",
" fpbs.write(' -plot_geo %d ' % 0)\n",
" fpbs.write(' -rel_wsz %f ' % 0)\n",
" fpbs.write(' -rel_whz %f ' % 100)\n",
" fpbs.write(' -ffweight %f ' % 2)\n",
" fpbs.write(' -sm %s ' % sm)\n",
" fpbs.write(' -zoom_factor %f ' % 1)\n",
" fpbs.write(' -planeShearRatex %f ' % planeShearRatex)\n",
" fpbs.write(' -ecoli_velocity %f ' % ecoli_velocity)\n",
" fpbs.write(' -n_norm_theta %d ' % n_norm_theta)\n",
" fpbs.write(' -n_norm_phi %d ' % n_norm_phi)\n",
" fpbs.write(' -norm_psi %f ' % norm_psi)\n",
" fpbs.write(' -main_fun_noIter %d ' % main_fun_noIter)\n",
" fpbs.write(' > %s.txt \\n\\n' % job_name)\n",
" # write to .sh file\n",
" frun.write('cd $t_dir/%s\\n' % job_name)\n",
" frun.write('qsub %s.pbs\\n\\n' % job_name)\n",
" n_pbs = n_pbs + 1\n",
" frun.write('\\n')\n",
" print('n_pbs = ', n_pbs)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ecoC01B05_all_psi0.00\n",
"ecoC01B05_all_psi0.42\n",
"ecoC01B05_all_psi0.84\n",
"ecoC01B05_all_psi1.26\n",
"ecoC01B05_all_psi1.68\n",
"ecoC01B05_all_psi2.09\n",
"ecoC01B05_all_psi2.51\n",
"ecoC01B05_all_psi2.93\n",
"ecoC01B05_all_psi3.35\n",
"ecoC01B05_all_psi3.77\n",
"ecoC01B05_all_psi4.19\n",
"ecoC01B05_all_psi4.61\n",
"ecoC01B05_all_psi5.03\n",
"ecoC01B05_all_psi5.45\n",
"ecoC01B05_all_psi5.86\n",
"n_pbs = 15\n"
]
}
],
"source": [
"rs1 = 1.5\n",
"rs2 = rs1 / 3\n",
"ds = 0.07\n",
" \n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01_all'\n",
"# job_dir = 'ecoC01_tau1c'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 1.6328647412856554\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# importlib.reload(spf)\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01_all'\n",
"# job_dir = 'ecoC01_tau1c_passive'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# importlib.reload(spf)\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC05_all'\n",
"# job_dir = 'ecoC05_tau1c'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 1.524781932318225\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 1.5\n",
"# rh12 = 0.5\n",
"# rh2 = 0.15\n",
"# ph = 10/3\n",
"# n_tail = 1\n",
"# importlib.reload(spf)\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC05_all'\n",
"# job_dir = 'ecoC05_tau1c_passive'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 1.5\n",
"# rh12 = 0.5\n",
"# rh2 = 0.15\n",
"# ph = 10/3\n",
"# n_tail = 1\n",
"# importlib.reload(spf)\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B05_all'\n",
"# job_dir = 'ecoC01B05_tau1c_passive'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# rs1 = 1.5 / 3\n",
"# rs2 = rs1 / 3\n",
"# ds = 0.07 / 3\n",
"# importlib.reload(spf)\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"sm = 'pf'\n",
"ecoli_name = 'ecoC01B05_all'\n",
"job_dir = 'ecoC01B05_tau1c'\n",
"ksp_max_it = 300\n",
"main_fun_noIter = 1\n",
"planeShearRatex = 1\n",
"ecoli_velocity = 1.6328647413699688\n",
"ch = 3\n",
"nth = 20\n",
"rh11 = 0.3\n",
"rh12 = 0.1\n",
"rh2 = 0.03\n",
"ph = 2/3\n",
"n_tail = 1\n",
"rs1 = 1.5 / 3\n",
"rs2 = rs1 / 3\n",
"ds = 0.07 / 3\n",
"importlib.reload(spf)\n",
"write_pbs_head = spf.write_pbs_head\n",
"norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"n_norm_theta = 32\n",
"n_norm_phi = 64\n",
"\n",
"PWD = os.getcwd()\n",
"if not os.path.exists(job_dir):\n",
" os.makedirs(job_dir)\n",
"\n",
"t_name = os.path.join(job_dir, 'run.sh')\n",
"n_pbs = 0\n",
"with open(t_name, 'w') as frun:\n",
" # create .pbs file\n",
" frun.write('t_dir=$PWD \\n')\n",
" for norm_psi in norm_psi_list:\n",
" job_name = '%s_psi%4.2f' % (ecoli_name, norm_psi)\n",
" print(job_name)\n",
" t_path = os.path.join(job_dir, job_name)\n",
" if not os.path.exists(t_path):\n",
" os.makedirs(t_path)\n",
" t_name = os.path.join(t_path, '%s.pbs' % job_name)\n",
" with open(t_name, 'w') as fpbs:\n",
" write_pbs_head(fpbs, job_name) \n",
" fpbs.write('mpirun -n 24 python ')\n",
" fpbs.write(' ../../../loop_table_ecoli.py ')\n",
" fpbs.write(' -f %s ' % job_name)\n",
" fpbs.write(' -pickProblem %d ' % 0)\n",
" fpbs.write(' -save_singleEcoli_vtk %d ' % 0)\n",
" fpbs.write(' -rh11 %f ' % rh11)\n",
" fpbs.write(' -rh12 %f ' % rh12)\n",
" fpbs.write(' -rh2 %f ' % rh2)\n",
" fpbs.write(' -ch %f ' % ch)\n",
" fpbs.write(' -nth %d ' % nth)\n",
" fpbs.write(' -eh %f ' % -1)\n",
" fpbs.write(' -ph %f ' % ph)\n",
" fpbs.write(' -hfct %f ' % 1)\n",
" fpbs.write(' -n_tail %d ' % n_tail)\n",
" fpbs.write(' -with_cover %d ' % 2)\n",
" fpbs.write(' -left_hand %d ' % 0)\n",
" fpbs.write(' -rs1 %f ' % rs1)\n",
" fpbs.write(' -rs2 %f ' % rs2)\n",
" fpbs.write(' -ds %f ' % ds)\n",
" fpbs.write(' -es %f ' % -1)\n",
" fpbs.write(' -with_T_geo %d ' % 0)\n",
" fpbs.write(' -dist_hs %f ' % 0.5)\n",
" fpbs.write(' -ksp_max_it %d ' % ksp_max_it)\n",
" fpbs.write(' -plot_geo %d ' % 0)\n",
" fpbs.write(' -rel_wsz %f ' % 0)\n",
" fpbs.write(' -rel_whz %f ' % 100)\n",
" fpbs.write(' -ffweight %f ' % 2)\n",
" fpbs.write(' -sm %s ' % sm)\n",
" fpbs.write(' -zoom_factor %f ' % 1)\n",
" fpbs.write(' -planeShearRatex %f ' % planeShearRatex)\n",
" fpbs.write(' -ecoli_velocity %f ' % ecoli_velocity)\n",
" fpbs.write(' -n_norm_theta %d ' % n_norm_theta)\n",
" fpbs.write(' -n_norm_phi %d ' % n_norm_phi)\n",
" fpbs.write(' -norm_psi %f ' % norm_psi)\n",
" fpbs.write(' -main_fun_noIter %d ' % main_fun_noIter)\n",
" fpbs.write(' > %s.txt \\n\\n' % job_name)\n",
" # write to .sh file\n",
" frun.write('cd $t_dir/%s\\n' % job_name)\n",
" frun.write('qsub %s.pbs\\n\\n' % job_name)\n",
" n_pbs = n_pbs + 1\n",
" frun.write('\\n')\n",
" print('n_pbs =', n_pbs)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.2864874730906016"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1 / 676.00372212 * 193.66659815"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.0000000000099165"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1.524781932318225 / 1.5247819323031044"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"n_pbs = 100\n"
]
}
],
"source": [
"# case loop_table, each (psi1 and psi2) store in a independent folder. \n",
"# swimmer with two tails in the ends. one is left hand and one is right hand. \n",
"importlib.reload(spf)\n",
"\n",
"sm = 'pf'\n",
"ecoli_name = 'ecoD01_all'\n",
"job_dir = 'dualTail_1c'\n",
"ksp_max_it = 300\n",
"main_fun_noIter = 1\n",
"planeShearRatex = 1\n",
"ch = 3\n",
"nth = 20\n",
"rh1 = 0.1\n",
"rh2 = 0.03\n",
"ph = 2/3\n",
"n_tail = 1\n",
"rel_tail1 = 193.66659814\n",
"rel_tail2 = 0\n",
"write_pbs_head = spf.write_pbs_head_newturb\n",
"norm_psi1_list = np.linspace(0, 2 * np.pi, 10, endpoint=False)\n",
"norm_psi2_list = np.linspace(0, 2 * np.pi, 10, endpoint=False)\n",
"n_norm_theta = 24\n",
"n_norm_phi = 48\n",
"\n",
"PWD = os.getcwd()\n",
"if not os.path.exists(job_dir):\n",
" os.makedirs(job_dir)\n",
"\n",
"n_pbs = 0\n",
"for norm_psi1 in norm_psi1_list: \n",
" t_name = os.path.join(job_dir, 'run_psi1-%4.2f.sh' % norm_psi1)\n",
" with open(t_name, 'w') as frun:\n",
" # create .pbs file\n",
" frun.write('t_dir=$PWD \\n')\n",
" for norm_psi2 in norm_psi2_list:\n",
" job_name = '%s_psi1-%4.2f_psi2-%4.2f' % (ecoli_name, norm_psi1, norm_psi2)\n",
"# print(norm_psi1, norm_psi2, job_name)\n",
" t_path = os.path.join(job_dir, job_name)\n",
" if not os.path.exists(t_path):\n",
" os.makedirs(t_path)\n",
" t_name = os.path.join(t_path, '%s.pbs' % job_name)\n",
" with open(t_name, 'w') as fpbs:\n",
" write_pbs_head(fpbs, job_name) \n",
" fpbs.write('mpirun -n 24 python ')\n",
" fpbs.write(' ../../../loop_table_dualTail_ecoli.py ')\n",
" fpbs.write(' -f %s ' % job_name)\n",
" fpbs.write(' -pickProblem %d ' % 0)\n",
" fpbs.write(' -save_singleEcoli_vtk %d ' % 0)\n",
" fpbs.write(' -rh1 %f ' % rh1)\n",
" fpbs.write(' -rh2 %f ' % rh2)\n",
" fpbs.write(' -ch %f ' % ch)\n",
" fpbs.write(' -nth %d ' % nth)\n",
" fpbs.write(' -eh %f ' % -1)\n",
" fpbs.write(' -ph %f ' % ph)\n",
" fpbs.write(' -hfct %f ' % 1)\n",
" fpbs.write(' -n_tail %d ' % n_tail)\n",
" fpbs.write(' -with_cover %d ' % 2)\n",
" fpbs.write(' -left_hand %d ' % 0)\n",
" fpbs.write(' -rs1 %f ' % 1.5)\n",
" fpbs.write(' -rs2 %f ' % 0.5)\n",
" fpbs.write(' -ds %f ' % 0.07)\n",
" fpbs.write(' -es %f ' % -1)\n",
" fpbs.write(' -with_T_geo %d ' % 0)\n",
" fpbs.write(' -dist_hs %f ' % 0.5)\n",
" fpbs.write(' -ksp_max_it %d ' % ksp_max_it)\n",
" fpbs.write(' -plot_geo %d ' % 0)\n",
" fpbs.write(' -rel_wsz %f ' % 0)\n",
" fpbs.write(' -ffweight %f ' % 2)\n",
" fpbs.write(' -sm %s ' % sm)\n",
" fpbs.write(' -zoom_factor %f ' % 1)\n",
" fpbs.write(' -planeShearRatex %f ' % planeShearRatex)\n",
" fpbs.write(' -n_norm_theta %d ' % n_norm_theta)\n",
" fpbs.write(' -n_norm_phi %d ' % n_norm_phi)\n",
" fpbs.write(' -norm_psi1 %f ' % norm_psi1)\n",
" fpbs.write(' -norm_psi2 %f ' % norm_psi2)\n",
" fpbs.write(' -rel_tail1 %f ' % rel_tail1)\n",
" fpbs.write(' -rel_tail2 %f ' % rel_tail2)\n",
" fpbs.write(' -main_fun_noIter %d ' % main_fun_noIter)\n",
" fpbs.write(' > %s.txt \\n\\n' % job_name)\n",
" # write to .sh file\n",
" frun.write('cd $t_dir/%s\\n' % job_name)\n",
" frun.write('qsub %s.pbs\\n\\n' % job_name)\n",
" n_pbs = n_pbs + 1\n",
" frun.write('\\n')\n",
"print('n_pbs = ', n_pbs)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hlxB01_psi0.00\n",
"hlxB01_psi0.42\n",
"hlxB01_psi0.84\n",
"hlxB01_psi1.26\n",
"hlxB01_psi1.68\n",
"hlxB01_psi2.09\n",
"hlxB01_psi2.51\n",
"hlxB01_psi2.93\n",
"hlxB01_psi3.35\n",
"hlxB01_psi3.77\n",
"hlxB01_psi4.19\n",
"hlxB01_psi4.61\n",
"hlxB01_psi5.03\n",
"hlxB01_psi5.45\n",
"hlxB01_psi5.86\n",
"n_pbs = 15\n"
]
}
],
"source": [
"# case loop_table for helix only, each psi store in a independent folder. \n",
"n_tail = 1\n",
"ph = 2/3\n",
"ecoli_name = 'hlxB03'\n",
"main_fun_noIter = 0\n",
"\n",
"# sm = 'pf'\n",
"# ksp_max_it = 300\n",
"# planeShearRatex = 1\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh1 = 0.3\n",
"# rh2 = 0.1\n",
"# ph = 0.666667\n",
"# n_tail = 1\n",
"# job_dir = ecoli_name + '_tau1a'\n",
"# importlib.reload(spf)\n",
"# write_pbs_head = spf.write_pbs_head_newturb\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 10, endpoint=False)\n",
"# n_norm_theta = 25\n",
"# n_norm_phi = 25\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'hlxB01'\n",
"# job_dir = ecoli_name + '_tau1a'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 0\n",
"# planeShearRatex = 1\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh1 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# importlib.reload(spf)\n",
"# write_pbs_head = spf.write_pbs_head_newturb\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 64\n",
"# n_norm_phi = 64\n",
"# main_fun_noIter = 1\n",
"\n",
"PWD = os.getcwd()\n",
"if not os.path.exists(job_dir):\n",
" os.makedirs(job_dir)\n",
"\n",
"t_name = os.path.join(job_dir, 'run.sh')\n",
"n_pbs = 0\n",
"with open(t_name, 'w') as frun:\n",
" # create .pbs file\n",
" frun.write('t_dir=$PWD \\n')\n",
" for norm_psi in norm_psi_list:\n",
" job_name = '%s_psi%4.2f' % (ecoli_name, norm_psi)\n",
" print(job_name)\n",
" t_path = os.path.join(job_dir, job_name)\n",
" if not os.path.exists(t_path):\n",
" os.makedirs(t_path)\n",
" t_name = os.path.join(t_path, '%s.pbs' % job_name)\n",
" with open(t_name, 'w') as fpbs:\n",
" write_pbs_head(fpbs, job_name) \n",
" fpbs.write('mpirun -n 24 python ')\n",
" fpbs.write(' ../../../loop_table_helix.py ')\n",
" fpbs.write(' -f %s ' % job_name)\n",
" fpbs.write(' -pickProblem %d ' % 0)\n",
" fpbs.write(' -save_singleEcoli_vtk %d ' % 0)\n",
" fpbs.write(' -rh1 %f ' % rh1)\n",
" fpbs.write(' -rh2 %f ' % rh2)\n",
" fpbs.write(' -ch %f ' % ch)\n",
" fpbs.write(' -nth %d ' % nth)\n",
" fpbs.write(' -eh %f ' % -1)\n",
" fpbs.write(' -ph %f ' % ph)\n",
" fpbs.write(' -hfct %f ' % 1)\n",
" fpbs.write(' -n_tail %d ' % n_tail)\n",
" fpbs.write(' -with_cover %d ' % 2)\n",
" fpbs.write(' -left_hand %d ' % 0)\n",
" fpbs.write(' -rs1 %f ' % 1.5)\n",
" fpbs.write(' -rs2 %f ' % 0.5)\n",
" fpbs.write(' -ds %f ' % 0.07)\n",
" fpbs.write(' -es %f ' % -1)\n",
" fpbs.write(' -with_T_geo %d ' % 0)\n",
" fpbs.write(' -dist_hs %f ' % 0.5)\n",
" fpbs.write(' -ksp_max_it %d ' % ksp_max_it)\n",
" fpbs.write(' -plot_geo %d ' % 0)\n",
" fpbs.write(' -rel_wsz %f ' % 0)\n",
" fpbs.write(' -rel_whz %f ' % 100)\n",
" fpbs.write(' -ffweight %f ' % 2)\n",
" fpbs.write(' -sm %s ' % sm)\n",
" fpbs.write(' -zoom_factor %f ' % 1)\n",
" fpbs.write(' -planeShearRatex %f ' % planeShearRatex)\n",
" fpbs.write(' -main_fun_noIter %d ' % main_fun_noIter)\n",
" fpbs.write(' -n_norm_theta %d ' % n_norm_theta)\n",
" fpbs.write(' -n_norm_phi %d ' % n_norm_phi)\n",
" fpbs.write(' -norm_psi %f ' % norm_psi)\n",
" fpbs.write(' > %s.txt \\n\\n' % job_name)\n",
" # write to .sh file\n",
" frun.write('cd $t_dir/%s\\n' % job_name)\n",
" frun.write('qsub %s.pbs\\n\\n' % job_name)\n",
" n_pbs = n_pbs + 1\n",
" frun.write('\\n')\n",
" print('n_pbs = ', n_pbs)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hlxC01_psi0.00\n",
"hlxC01_psi0.42\n",
"hlxC01_psi0.84\n",
"hlxC01_psi1.26\n",
"hlxC01_psi1.68\n",
"hlxC01_psi2.09\n",
"hlxC01_psi2.51\n",
"hlxC01_psi2.93\n",
"hlxC01_psi3.35\n",
"hlxC01_psi3.77\n",
"hlxC01_psi4.19\n",
"hlxC01_psi4.61\n",
"hlxC01_psi5.03\n",
"hlxC01_psi5.45\n",
"hlxC01_psi5.86\n",
"n_pbs = 15\n"
]
}
],
"source": [
"sm = 'pf'\n",
"ecoli_name = 'hlxC01'\n",
"job_dir = ecoli_name + '_tau1a'\n",
"ksp_max_it = 300\n",
"main_fun_noIter = 1\n",
"planeShearRatex = 1\n",
"ch = 3\n",
"nth = 20\n",
"rh11 = 0.3\n",
"rh12 = 0.1\n",
"rh2 = 0.03\n",
"ph = 2/3\n",
"n_tail = 1\n",
"importlib.reload(spf)\n",
"write_pbs_head = spf.write_pbs_head\n",
"norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"n_norm_theta = 32\n",
"n_norm_phi = 32\n",
"main_fun_noIter = 1\n",
"\n",
"PWD = os.getcwd()\n",
"if not os.path.exists(job_dir):\n",
" os.makedirs(job_dir)\n",
"\n",
"t_name = os.path.join(job_dir, 'run.sh')\n",
"n_pbs = 0\n",
"with open(t_name, 'w') as frun:\n",
" # create .pbs file\n",
" frun.write('t_dir=$PWD \\n')\n",
" for norm_psi in norm_psi_list:\n",
" job_name = '%s_psi%4.2f' % (ecoli_name, norm_psi)\n",
" print(job_name)\n",
" t_path = os.path.join(job_dir, job_name)\n",
" if not os.path.exists(t_path):\n",
" os.makedirs(t_path)\n",
" t_name = os.path.join(t_path, '%s.pbs' % job_name)\n",
" with open(t_name, 'w') as fpbs:\n",
" write_pbs_head(fpbs, job_name) \n",
" fpbs.write('mpirun -n 24 python ')\n",
" fpbs.write(' ../../../loop_table_FatHelix.py ')\n",
" fpbs.write(' -f %s ' % job_name)\n",
" fpbs.write(' -pickProblem %d ' % 0)\n",
" fpbs.write(' -save_singleEcoli_vtk %d ' % 0)\n",
" fpbs.write(' -rh11 %f ' % rh11)\n",
" fpbs.write(' -rh12 %f ' % rh12)\n",
" fpbs.write(' -rh2 %f ' % rh2)\n",
" fpbs.write(' -ch %f ' % ch)\n",
" fpbs.write(' -nth %d ' % nth)\n",
" fpbs.write(' -eh %f ' % -1)\n",
" fpbs.write(' -ph %f ' % ph)\n",
" fpbs.write(' -hfct %f ' % 1)\n",
" fpbs.write(' -n_tail %d ' % n_tail)\n",
" fpbs.write(' -with_cover %d ' % 2)\n",
" fpbs.write(' -left_hand %d ' % 0)\n",
" fpbs.write(' -rs1 %f ' % 1.5)\n",
" fpbs.write(' -rs2 %f ' % 0.5)\n",
" fpbs.write(' -ds %f ' % 0.07)\n",
" fpbs.write(' -es %f ' % -1)\n",
" fpbs.write(' -with_T_geo %d ' % 0)\n",
" fpbs.write(' -dist_hs %f ' % 0.5)\n",
" fpbs.write(' -ksp_max_it %d ' % ksp_max_it)\n",
" fpbs.write(' -plot_geo %d ' % 0)\n",
" fpbs.write(' -rel_wsz %f ' % 0)\n",
" fpbs.write(' -rel_whz %f ' % 100)\n",
" fpbs.write(' -ffweight %f ' % 2)\n",
" fpbs.write(' -sm %s ' % sm)\n",
" fpbs.write(' -zoom_factor %f ' % 1)\n",
" fpbs.write(' -planeShearRatex %f ' % planeShearRatex)\n",
" fpbs.write(' -main_fun_noIter %d ' % main_fun_noIter)\n",
" fpbs.write(' -n_norm_theta %d ' % n_norm_theta)\n",
" fpbs.write(' -n_norm_phi %d ' % n_norm_phi)\n",
" fpbs.write(' -norm_psi %f ' % norm_psi)\n",
" fpbs.write(' > %s.txt \\n\\n' % job_name)\n",
" # write to .sh file\n",
" frun.write('cd $t_dir/%s\\n' % job_name)\n",
" frun.write('qsub %s.pbs\\n\\n' % job_name)\n",
" n_pbs = n_pbs + 1\n",
" frun.write('\\n')\n",
" print('n_pbs = ', n_pbs)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ecoC01B03_T1/ecoC01B03_all_psi0.00/ecoC01B03_all_psi0.00.sh\n",
"ecoC01B03_T1/ecoC01B03_all_psi0.42/ecoC01B03_all_psi0.42.sh\n",
"ecoC01B03_T1/ecoC01B03_all_psi0.84/ecoC01B03_all_psi0.84.sh\n",
"ecoC01B03_T1/ecoC01B03_all_psi1.26/ecoC01B03_all_psi1.26.sh\n",
"ecoC01B03_T1/ecoC01B03_all_psi1.68/ecoC01B03_all_psi1.68.sh\n",
"ecoC01B03_T1/ecoC01B03_all_psi2.09/ecoC01B03_all_psi2.09.sh\n",
"ecoC01B03_T1/ecoC01B03_all_psi2.51/ecoC01B03_all_psi2.51.sh\n",
"ecoC01B03_T1/ecoC01B03_all_psi2.93/ecoC01B03_all_psi2.93.sh\n",
"ecoC01B03_T1/ecoC01B03_all_psi3.35/ecoC01B03_all_psi3.35.sh\n",
"ecoC01B03_T1/ecoC01B03_all_psi3.77/ecoC01B03_all_psi3.77.sh\n",
"ecoC01B03_T1/ecoC01B03_all_psi4.19/ecoC01B03_all_psi4.19.sh\n",
"ecoC01B03_T1/ecoC01B03_all_psi4.61/ecoC01B03_all_psi4.61.sh\n",
"ecoC01B03_T1/ecoC01B03_all_psi5.03/ecoC01B03_all_psi5.03.sh\n",
"ecoC01B03_T1/ecoC01B03_all_psi5.45/ecoC01B03_all_psi5.45.sh\n",
"ecoC01B03_T1/ecoC01B03_all_psi5.86/ecoC01B03_all_psi5.86.sh\n",
"ncase = 15\n",
"write parallel pbs file to ecoC01B03_T1/main_run.pbs\n",
"write myscript csh file to ecoC01B03_T1/myscript.csh\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"importlib.reload(spf)\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B03_all'\n",
"# job_dir = 'ecoC01B03_tau1c_passive'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# rs1 = 1.5 / 5\n",
"# rs2 = rs1 / 3\n",
"# ds = 0.07 / 5\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B03_all'\n",
"# job_dir = 'ecoC01B03_tau1c'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0.2864874730919643\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# rs1 = 1.5 / 5\n",
"# rs2 = rs1 / 3\n",
"# ds = 0.07 / 5\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"sm = 'pf'\n",
"ecoli_name = 'ecoC01B03_all'\n",
"job_dir = 'ecoC01B03_T1'\n",
"ksp_max_it = 300\n",
"main_fun_noIter = 1\n",
"planeShearRatex = 1\n",
"ecoli_velocity = 0.0014792842385756373\n",
"ch = 3\n",
"nth = 20\n",
"rh11 = 0.3\n",
"rh12 = 0.1\n",
"rh2 = 0.03\n",
"ph = 2/3\n",
"n_tail = 1\n",
"rs1 = 1.5 / 5\n",
"rs2 = rs1 / 3\n",
"ds = 0.07 / 5\n",
"write_pbs_head = spf.write_pbs_head\n",
"norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"n_norm_theta = 32\n",
"n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B01_all'\n",
"# job_dir = 'ecoC01B01_tau1c_passive'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# rs1 = 1.5 / 15\n",
"# rs2 = rs1 / 3\n",
"# ds = 0.07 / 15\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B01_all'\n",
"# job_dir = 'ecoC01B01_tau1c'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0.019882410135194976\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# rs1 = 1.5 / 15\n",
"# rs2 = rs1 / 3\n",
"# ds = 0.07 / 15\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B01_all'\n",
"# job_dir = 'ecoC01B01_T1'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0.00010266520127114365\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# rs1 = 1.5 / 15\n",
"# rs2 = rs1 / 3\n",
"# ds = 0.07 / 15\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B01_all'\n",
"# job_dir = 'ecoC01B01_T0.1'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0.000010266520127114365\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# rs1 = 1.5 / 15\n",
"# rs2 = rs1 / 3\n",
"# ds = 0.07 / 15\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B01_all'\n",
"# job_dir = 'ecoC01B01_T0.01'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0.0000010266520127114365\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# rs1 = 1.5 / 15\n",
"# rs2 = rs1 / 3\n",
"# ds = 0.07 / 15\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B05_all'\n",
"# job_dir = 'ecoC01B05_T193'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0.8960497908782866\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# rs1 = 1.5 / 3\n",
"# rs2 = rs1 / 3\n",
"# ds = 0.07 / 3\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B05_all'\n",
"# job_dir = 'ecoC01B05_T100'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0.46267647567407155\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# rs1 = 1.5 / 3\n",
"# rs2 = rs1 / 3\n",
"# ds = 0.07 / 3\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B05_all'\n",
"# job_dir = 'ecoC01B05_T50'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0.23133823783703578\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# rs1 = 1.5 / 3\n",
"# rs2 = rs1 / 3\n",
"# ds = 0.07 / 3\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B05_all'\n",
"# job_dir = 'ecoC01B05_T20'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0.09253529513481432\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# rs1 = 1.5 / 3\n",
"# rs2 = rs1 / 3\n",
"# ds = 0.07 / 3\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B05_all'\n",
"# job_dir = 'ecoC01B05_T10'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0.04626764756740716\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# rs1 = 1.5 / 3\n",
"# rs2 = rs1 / 3\n",
"# ds = 0.07 / 3\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B05_all'\n",
"# job_dir = 'ecoC01B05_T1'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0.004626764756740716\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# rs1 = 1.5 / 3\n",
"# rs2 = rs1 / 3\n",
"# ds = 0.07 / 3\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B05_all'\n",
"# job_dir = 'ecoC01B05_T2'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0.009253529513481431\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# rs1 = 1.5 / 3\n",
"# rs2 = rs1 / 3\n",
"# ds = 0.07 / 3\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B05_all'\n",
"# job_dir = 'ecoC01B05_T0.5'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0.0023133823783703577\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# rs1 = 1.5 / 3\n",
"# rs2 = rs1 / 3\n",
"# ds = 0.07 / 3\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B05_all'\n",
"# job_dir = 'ecoC01B05_T0.1'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0.00046267647567407156\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# rs1 = 1.5 / 3\n",
"# rs2 = rs1 / 3\n",
"# ds = 0.07 / 3\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B05_all'\n",
"# job_dir = 'ecoC01B05_T0.01'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0.000046267647567407156\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# rs1 = 1.5 / 3\n",
"# rs2 = rs1 / 3\n",
"# ds = 0.07 / 3\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B05_all'\n",
"# job_dir = 'ecoC01B05_T0.001'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0.0000046267647567407156\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# rs1 = 1.5 / 3\n",
"# rs2 = rs1 / 3\n",
"# ds = 0.07 / 3\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B05_all'\n",
"# job_dir = 'ecoC01B05_T0.25'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0.001156691189185179\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# rs1 = 1.5 / 3\n",
"# rs2 = rs1 / 3\n",
"# ds = 0.07 / 3\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B05_all'\n",
"# job_dir = 'ecoC01B05_T0.75'\n",
"# ksp_max_it = 300\n",
"# main_fun_noIter = 1\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0.003470073567555537\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# rs1 = 1.5 / 3\n",
"# rs2 = rs1 / 3\n",
"# ds = 0.07 / 3\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"\n",
"PWD = os.getcwd()\n",
"if not os.path.exists(job_dir):\n",
" os.makedirs(job_dir)\n",
"n_case = 0\n",
"job_name_list = []\n",
"for norm_psi in norm_psi_list:\n",
" job_name = '%s_psi%4.2f' % (ecoli_name, norm_psi)\n",
" job_name_list.append(job_name)\n",
" t_path = os.path.join(job_dir, job_name)\n",
" if not os.path.exists(t_path):\n",
" os.makedirs(t_path)\n",
" t_name = os.path.join(t_path, '%s.sh' % job_name)\n",
" print(t_name)\n",
" with open(t_name, 'w') as fsh:\n",
" fsh.write('mpirun -n 24 python ')\n",
" fsh.write(' ../../../loop_table_ecoli.py ')\n",
" fsh.write(' -f %s ' % job_name)\n",
" fsh.write(' -pickProblem %d ' % 0)\n",
" fsh.write(' -save_singleEcoli_vtk %d ' % 0)\n",
" fsh.write(' -rh11 %f ' % rh11)\n",
" fsh.write(' -rh12 %f ' % rh12)\n",
" fsh.write(' -rh2 %f ' % rh2)\n",
" fsh.write(' -ch %f ' % ch)\n",
" fsh.write(' -nth %d ' % nth)\n",
" fsh.write(' -eh %f ' % -1)\n",
" fsh.write(' -ph %f ' % ph)\n",
" fsh.write(' -hfct %f ' % 1)\n",
" fsh.write(' -n_tail %d ' % n_tail)\n",
" fsh.write(' -with_cover %d ' % 2)\n",
" fsh.write(' -left_hand %d ' % 0)\n",
" fsh.write(' -rs1 %f ' % rs1)\n",
" fsh.write(' -rs2 %f ' % rs2)\n",
" fsh.write(' -ds %f ' % ds)\n",
" fsh.write(' -es %f ' % -1)\n",
" fsh.write(' -with_T_geo %d ' % 0)\n",
" fsh.write(' -dist_hs %f ' % 0.5)\n",
" fsh.write(' -ksp_max_it %d ' % ksp_max_it)\n",
" fsh.write(' -plot_geo %d ' % 0)\n",
" fsh.write(' -rel_wsz %f ' % 0)\n",
" fsh.write(' -rel_whz %f ' % 100)\n",
" fsh.write(' -ffweight %f ' % 2)\n",
" fsh.write(' -sm %s ' % sm)\n",
" fsh.write(' -zoom_factor %f ' % 1)\n",
" fsh.write(' -planeShearRatex %f ' % planeShearRatex)\n",
" fsh.write(' -ecoli_velocity %e ' % ecoli_velocity)\n",
" fsh.write(' -n_norm_theta %d ' % n_norm_theta)\n",
" fsh.write(' -n_norm_phi %d ' % n_norm_phi)\n",
" fsh.write(' -norm_psi %f ' % norm_psi)\n",
" fsh.write(' -main_fun_noIter %d ' % main_fun_noIter)\n",
" fsh.write(' > %s.txt \\n\\n' % job_name)\n",
" n_case = n_case + 1\n",
"spf.write_main_run(write_pbs_head, job_dir, n_case)\n",
"spf.write_myscript(job_name_list, job_dir)\n"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.00046267647567407156"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"0.8960497908782866 / 193.66659815 * 0.1"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ecoC01B00_T10/ecoC01B00_all_psi0.00/ecoC01B00_all_psi0.00.sh\n",
"ecoC01B00_T10/ecoC01B00_all_psi0.42/ecoC01B00_all_psi0.42.sh\n",
"ecoC01B00_T10/ecoC01B00_all_psi0.84/ecoC01B00_all_psi0.84.sh\n",
"ecoC01B00_T10/ecoC01B00_all_psi1.26/ecoC01B00_all_psi1.26.sh\n",
"ecoC01B00_T10/ecoC01B00_all_psi1.68/ecoC01B00_all_psi1.68.sh\n",
"ecoC01B00_T10/ecoC01B00_all_psi2.09/ecoC01B00_all_psi2.09.sh\n",
"ecoC01B00_T10/ecoC01B00_all_psi2.51/ecoC01B00_all_psi2.51.sh\n",
"ecoC01B00_T10/ecoC01B00_all_psi2.93/ecoC01B00_all_psi2.93.sh\n",
"ecoC01B00_T10/ecoC01B00_all_psi3.35/ecoC01B00_all_psi3.35.sh\n",
"ecoC01B00_T10/ecoC01B00_all_psi3.77/ecoC01B00_all_psi3.77.sh\n",
"ecoC01B00_T10/ecoC01B00_all_psi4.19/ecoC01B00_all_psi4.19.sh\n",
"ecoC01B00_T10/ecoC01B00_all_psi4.61/ecoC01B00_all_psi4.61.sh\n",
"ecoC01B00_T10/ecoC01B00_all_psi5.03/ecoC01B00_all_psi5.03.sh\n",
"ecoC01B00_T10/ecoC01B00_all_psi5.45/ecoC01B00_all_psi5.45.sh\n",
"ecoC01B00_T10/ecoC01B00_all_psi5.86/ecoC01B00_all_psi5.86.sh\n",
"ncase = 15\n",
"write parallel pbs file to ecoC01B00_T10/main_run.pbs\n",
"write myscript csh file to ecoC01B00_T10/myscript.csh\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# active helix with rotlet\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B00_all'\n",
"# job_dir = 'ecoC01B00_T1'\n",
"# ksp_max_it = 300\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0.03131296037255681\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B00_all'\n",
"# job_dir = 'ecoC01B00_T0.1'\n",
"# ksp_max_it = 300\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0.003131296037255681\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B00_all'\n",
"# job_dir = 'ecoC01B00_T0.01'\n",
"# ksp_max_it = 300\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0.0003131296037255681\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B00_all'\n",
"# job_dir = 'ecoC01B00_T0.001'\n",
"# ksp_max_it = 300\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0.00003131296037255681\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"sm = 'pf'\n",
"ecoli_name = 'ecoC01B00_all'\n",
"job_dir = 'ecoC01B00_T10'\n",
"ksp_max_it = 300\n",
"planeShearRatex = 1\n",
"ecoli_velocity = 0.03131296037255681 * 10\n",
"ch = 3\n",
"nth = 20\n",
"rh11 = 0.3\n",
"rh12 = 0.1\n",
"rh2 = 0.03\n",
"ph = 2/3\n",
"n_tail = 1\n",
"write_pbs_head = spf.write_pbs_head\n",
"norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"n_norm_theta = 32\n",
"n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B00_all'\n",
"# job_dir = 'ecoC01B00_T2'\n",
"# ksp_max_it = 300\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0.03131296037255681 * 2\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"# sm = 'pf'\n",
"# ecoli_name = 'ecoC01B00_all'\n",
"# job_dir = 'ecoC01B00_T0.5'\n",
"# ksp_max_it = 300\n",
"# planeShearRatex = 1\n",
"# ecoli_velocity = 0.03131296037255681 * 0.5\n",
"# ch = 3\n",
"# nth = 20\n",
"# rh11 = 0.3\n",
"# rh12 = 0.1\n",
"# rh2 = 0.03\n",
"# ph = 2/3\n",
"# n_tail = 1\n",
"# write_pbs_head = spf.write_pbs_head\n",
"# norm_psi_list = np.linspace(0, 2 * np.pi, 15, endpoint=False)\n",
"# n_norm_theta = 32\n",
"# n_norm_phi = 64\n",
"\n",
"PWD = os.getcwd()\n",
"if not os.path.exists(job_dir):\n",
" os.makedirs(job_dir)\n",
"n_case = 0\n",
"job_name_list = []\n",
"for norm_psi in norm_psi_list:\n",
" job_name = '%s_psi%4.2f' % (ecoli_name, norm_psi)\n",
" job_name_list.append(job_name)\n",
" t_path = os.path.join(job_dir, job_name)\n",
" if not os.path.exists(t_path):\n",
" os.makedirs(t_path)\n",
" t_name = os.path.join(t_path, '%s.sh' % job_name)\n",
" print(t_name)\n",
" with open(t_name, 'w') as fsh:\n",
" fsh.write('mpirun -n 24 python ')\n",
" fsh.write(' ../../../loop_table_ecoli.py ')\n",
" fsh.write(' -f %s ' % job_name)\n",
" fsh.write(' -pickProblem %d ' % 0)\n",
" fsh.write(' -save_singleEcoli_vtk %d ' % 0)\n",
" fsh.write(' -rh11 %f ' % rh11)\n",
" fsh.write(' -rh12 %f ' % rh12)\n",
" fsh.write(' -rh2 %f ' % rh2)\n",
" fsh.write(' -ch %f ' % ch)\n",
" fsh.write(' -nth %d ' % nth)\n",
" fsh.write(' -eh %f ' % -1)\n",
" fsh.write(' -ph %f ' % ph)\n",
" fsh.write(' -hfct %f ' % 1)\n",
" fsh.write(' -n_tail %d ' % n_tail)\n",
" fsh.write(' -with_cover %d ' % 2)\n",
" fsh.write(' -left_hand %d ' % 0)\n",
" fsh.write(' -with_T_geo %d ' % 0)\n",
" fsh.write(' -dist_hs %f ' % 0.5)\n",
" fsh.write(' -ksp_max_it %d ' % ksp_max_it)\n",
" fsh.write(' -plot_geo %d ' % 0)\n",
" fsh.write(' -ffweight %f ' % 2)\n",
" fsh.write(' -sm %s ' % sm)\n",
" fsh.write(' -zoom_factor %f ' % 1)\n",
" fsh.write(' -planeShearRatex %f ' % planeShearRatex)\n",
" fsh.write(' -ecoli_velocity %f ' % ecoli_velocity)\n",
" fsh.write(' -n_norm_theta %d ' % n_norm_theta)\n",
" fsh.write(' -n_norm_phi %d ' % n_norm_phi)\n",
" fsh.write(' -norm_psi %f ' % norm_psi)\n",
" fsh.write(' -main_fun_rotlets %d ' % 1)\n",
" fsh.write(' > %s.txt \\n\\n' % job_name)\n",
" n_case = n_case + 1\n",
"spf.write_main_run(write_pbs_head, job_dir, n_case)\n",
"spf.write_myscript(job_name_list, job_dir)\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.03131296037255681"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"0.00288416382071854 / 0.09210767 * 1"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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| mit |
ComputationalModeling/spring-2017-danielak | past-semesters/spring_2016/day-by-day/day04-flint-water-data-analysis/day04-in-class-activity-SOLUTIONS.ipynb | 1 | 106697 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Get the Lead Out: Understanding The Water Crisis in Flint, MI"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Student Names"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Type the names of everybody in your group here!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Learning Goals (Why are we asking you to do this?)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, because data analysis is something that can and should be used for (among other things): \n",
"\n",
"- [improving local government][4] \n",
"- [improving the Federal government][5], and\n",
"- [serving humanity][3]\n",
"\n",
"Second, because data visualization is one of the most important parts of modeling and understanding a system. How we represent data affects everything from to [understanding poverty in the developing world][2] to [grappling with the global spread of lethal diseases][1]. We want you to be able to find things out about the models you create and use visual information to make convincing arguments. So, in this tutorial we'll learn some of the nuts and bolts of using Python to create data visualizations. When you're done, you'll be able to use the industry-standard `matplotlib` plotting package to:\n",
"\n",
"- **Create** plots from data (Use code to make pictures!)\n",
"- **Modify** plots using color, size, and shape to help pull out patterns in data\n",
"- **Combine** different sets of data into the same plot\n",
"- **Communicate** plots by supplying axis titles, legends, and annotations\n",
"\n",
"[1]: http://www.ted.com/talks/hans_rosling_the_truth_about_hiv\n",
"[2]: http://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen\n",
"[3]: http://www.datakind.org\n",
"[4]: http://www.codeforamerica.org\n",
"[5]: https://www.whitehouse.gov/digital/united-states-digital-service"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## In-Class Activity"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll be looking at the recently released [Flint Water Quality dataset](http://flintwaterstudy.org/2015/12/complete-dataset-lead-results-in-tap-water-for-271-flint-samples/). This is a dataset of nearly 300 tests run by volunteers at Virginia Tech on water samples obtained from Flint residents. The water testing method involves collecting three different bottles worth of water at timed intervals; our analysis will focus on just the first collection at each testing site. \n",
"\n",
"You'll be considering the following questions in the context of U.S. Environmental Protection Agency (EPA) guidelines about lead contaminants, which state:\n",
"\n",
"> Lead and copper are regulated by a treatment technique that requires systems to control the corrosiveness of their water. If more than 10% of tap water samples exceed the action level, water systems must take additional steps. For copper, the action level is 1.3 mg/L, and for lead is 0.015 mg/L. \n",
">\n",
"> Source: (http://www.epa.gov/your-drinking-water/table-regulated-drinking-water-contaminants#seven). "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For simplicity, the data we'll consider will be stored in two 1-dimensional numpy arrays. The code below loads the data, then creates those two numpy arrays for you to reference in your code."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Loading the data\n",
"\n",
"%matplotlib inline\n",
"from IPython.display import set_matplotlib_formats\n",
"set_matplotlib_formats('pdf', 'svg')\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas\n",
"\n",
"flint_data = pandas.read_json(\"\"\"[{\"SampleID\":1,\"Zip Code\":48504,\"Ward\":6,\"Pb Bottle 1 (ppb) - First Draw\":0.344,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.226,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.145},{\"SampleID\":2,\"Zip Code\":48507,\"Ward\":9,\"Pb Bottle 1 (ppb) - First Draw\":8.133,\"Pb Bottle 2 (ppb) - 45 secs flushing\":10.77,\"Pb Bottle 3 (ppb) - 2 mins flushing\":2.761},{\"SampleID\":4,\"Zip Code\":48504,\"Ward\":1,\"Pb Bottle 1 (ppb) - First Draw\":1.111,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.11,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.123},{\"SampleID\":5,\"Zip Code\":48507,\"Ward\":8,\"Pb Bottle 1 (ppb) - First Draw\":8.007,\"Pb Bottle 2 (ppb) - 45 secs flushing\":7.446,\"Pb Bottle 3 (ppb) - 2 mins flushing\":3.384},{\"SampleID\":6,\"Zip Code\":48505,\"Ward\":3,\"Pb Bottle 1 (ppb) - First Draw\":1.951,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.048,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.035},{\"SampleID\":7,\"Zip Code\":48507,\"Ward\":9,\"Pb Bottle 1 (ppb) - First Draw\":7.2,\"Pb Bottle 2 (ppb) - 45 secs flushing\":1.4,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.2},{\"SampleID\":8,\"Zip Code\":48507,\"Ward\":9,\"Pb Bottle 1 (ppb) - First Draw\":40.63,\"Pb Bottle 2 (ppb) - 45 secs flushing\":9.726,\"Pb Bottle 3 (ppb) - 2 mins flushing\":6.132},{\"SampleID\":9,\"Zip Code\":48503,\"Ward\":5,\"Pb Bottle 1 (ppb) - First Draw\":1.1,\"Pb Bottle 2 (ppb) - 45 secs flushing\":2.5,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.1},{\"SampleID\":12,\"Zip Code\":48507,\"Ward\":9,\"Pb Bottle 1 (ppb) - First Draw\":10.6,\"Pb Bottle 2 (ppb) - 45 secs flushing\":1.038,\"Pb Bottle 3 (ppb) - 2 mins flushing\":1.294},{\"SampleID\":13,\"Zip Code\":48505,\"Ward\":3,\"Pb Bottle 1 (ppb) - First Draw\":6.2,\"Pb Bottle 2 (ppb) - 45 secs flushing\":4.2,\"Pb Bottle 3 (ppb) - 2 mins flushing\":2.3},{\"SampleID\":15,\"Zip Code\":48507,\"Ward\":9,\"Pb Bottle 1 (ppb) - First Draw\":4.358,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.822,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.147},{\"SampleID\":16,\"Zip Code\":48505,\"Ward\":5,\"Pb Bottle 1 (ppb) - First Draw\":24.37,\"Pb Bottle 2 (ppb) - 45 secs flushing\":8.796,\"Pb Bottle 3 (ppb) - 2 mins flushing\":4.347},{\"SampleID\":17,\"Zip Code\":48505,\"Ward\":2,\"Pb Bottle 1 (ppb) - First Draw\":6.609,\"Pb Bottle 2 (ppb) - 45 secs flushing\":5.752,\"Pb Bottle 3 (ppb) - 2 mins flushing\":1.433},{\"SampleID\":18,\"Zip Code\":48503,\"Ward\":7,\"Pb Bottle 1 (ppb) - First Draw\":4.062,\"Pb Bottle 2 (ppb) - 45 secs flushing\":1.099,\"Pb Bottle 3 (ppb) - 2 mins flushing\":1.085},{\"SampleID\":19,\"Zip Code\":48507,\"Ward\":9,\"Pb Bottle 1 (ppb) - First Draw\":2.484,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.72,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.565},{\"SampleID\":20,\"Zip Code\":48507,\"Ward\":9,\"Pb Bottle 1 (ppb) - First Draw\":0.438,\"Pb Bottle 2 (ppb) - 45 secs flushing\":1.046,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.511},{\"SampleID\":21,\"Zip Code\":48503,\"Ward\":5,\"Pb Bottle 1 (ppb) - First Draw\":1.29,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.243,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.225},{\"SampleID\":22,\"Zip Code\":48504,\"Ward\":6,\"Pb Bottle 1 (ppb) - First Draw\":0.548,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.622,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.361},{\"SampleID\":23,\"Zip Code\":48504,\"Ward\":2,\"Pb Bottle 1 (ppb) - First Draw\":3.131,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.674,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.683},{\"SampleID\":24,\"Zip Code\":48504,\"Ward\":6,\"Pb Bottle 1 (ppb) - First Draw\":120,\"Pb Bottle 2 (ppb) - 45 secs flushing\":239.7,\"Pb Bottle 3 (ppb) - 2 mins flushing\":29.71},{\"SampleID\":25,\"Zip Code\":48505,\"Ward\":1,\"Pb Bottle 1 (ppb) - First Draw\":2.911,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.406,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.237},{\"SampleID\":26,\"Zip Code\":48505,\"Ward\":5,\"Pb Bottle 1 (ppb) - First Draw\":16.52,\"Pb Bottle 2 (ppb) - 45 secs flushing\":10.26,\"Pb Bottle 3 (ppb) - 2 mins flushing\":2.762},{\"SampleID\":27,\"Zip Code\":48505,\"Ward\":1,\"Pb Bottle 1 (ppb) - First Draw\":1.984,\"Pb Bottle 2 (ppb) - 45 secs flushing\":1.13,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.712},{\"SampleID\":28,\"Zip Code\":48503,\"Ward\":7,\"Pb Bottle 1 (ppb) - First Draw\":5.367,\"Pb Bottle 2 (ppb) - 45 secs flushing\":2.474,\"Pb Bottle 3 (ppb) - 2 mins flushing\":1.616},{\"SampleID\":29,\"Zip Code\":48504,\"Ward\":2,\"Pb Bottle 1 (ppb) - First Draw\":5.5,\"Pb Bottle 2 (ppb) - 45 secs flushing\":8.4,\"Pb Bottle 3 (ppb) - 2 mins flushing\":2.4},{\"SampleID\":30,\"Zip Code\":48506,\"Ward\":4,\"Pb Bottle 1 (ppb) - First Draw\":0.639,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.223,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.194},{\"SampleID\":31,\"Zip Code\":48503,\"Ward\":7,\"Pb Bottle 1 (ppb) - First Draw\":6.087,\"Pb Bottle 2 (ppb) - 45 secs flushing\":28.87,\"Pb Bottle 3 (ppb) - 2 mins flushing\":2.13,\"Notes\":\"*house sampled twice\"},{\"SampleID\":31,\"Zip Code\":48503,\"Ward\":7,\"Pb Bottle 1 (ppb) - First Draw\":10.32,\"Pb Bottle 2 (ppb) - 45 secs flushing\":13.47,\"Pb Bottle 3 (ppb) - 2 mins flushing\":18.19,\"Notes\":\"*house sampled twice\"},{\"SampleID\":33,\"Zip Code\":48503,\"Ward\":6,\"Pb Bottle 1 (ppb) - First Draw\":66.88,\"Pb Bottle 2 (ppb) - 45 secs flushing\":2.662,\"Pb Bottle 3 (ppb) - 2 mins flushing\":2.082},{\"SampleID\":34,\"Zip Code\":48505,\"Ward\":1,\"Pb Bottle 1 (ppb) - First Draw\":20.41,\"Pb Bottle 2 (ppb) - 45 secs flushing\":3.543,\"Pb Bottle 3 (ppb) - 2 mins flushing\":2.344},{\"SampleID\":35,\"Zip Code\":48504,\"Ward\":6,\"Pb Bottle 1 (ppb) - First Draw\":109.6,\"Pb Bottle 2 (ppb) - 45 secs flushing\":80.47,\"Pb Bottle 3 (ppb) - 2 mins flushing\":94.52},{\"SampleID\":36,\"Zip Code\":48503,\"Ward\":8,\"Pb Bottle 1 (ppb) - First Draw\":5.06,\"Pb Bottle 2 (ppb) - 45 secs flushing\":3.406,\"Pb Bottle 3 (ppb) - 2 mins flushing\":4.088},{\"SampleID\":37,\"Zip Code\":48504,\"Ward\":2,\"Pb Bottle 1 (ppb) - First Draw\":2.774,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.21,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.264},{\"SampleID\":38,\"Zip Code\":48505,\"Ward\":3,\"Pb Bottle 1 (ppb) - First Draw\":4.453,\"Pb Bottle 2 (ppb) - 45 secs flushing\":3.679,\"Pb Bottle 3 (ppb) - 2 mins flushing\":3.523},{\"SampleID\":39,\"Zip Code\":48505,\"Ward\":1,\"Pb Bottle 1 (ppb) - First Draw\":0.4,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.3,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.7},{\"SampleID\":40,\"Zip Code\":48529,\"Ward\":9,\"Pb Bottle 1 (ppb) - First Draw\":0.974,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.142,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.118},{\"SampleID\":41,\"Zip Code\":48505,\"Ward\":5,\"Pb Bottle 1 (ppb) - First Draw\":3.228,\"Pb Bottle 2 (ppb) - 45 secs flushing\":2.534,\"Pb Bottle 3 (ppb) - 2 mins flushing\":2.222},{\"SampleID\":42,\"Zip Code\":48505,\"Ward\":2,\"Pb Bottle 1 (ppb) - First Draw\":12.55,\"Pb Bottle 2 (ppb) - 45 secs flushing\":4.132,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.12},{\"SampleID\":43,\"Zip Code\":48505,\"Ward\":3,\"Pb Bottle 1 (ppb) - First Draw\":0.501,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.156,\"Pb Bottle 3 (ppb) - 2 mins flushing\":15.14},{\"SampleID\":44,\"Zip Code\":48504,\"Ward\":2,\"Pb Bottle 1 (ppb) - First Draw\":2.448,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.373,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.288},{\"SampleID\":45,\"Zip Code\":48505,\"Ward\":3,\"Pb Bottle 1 (ppb) - First Draw\":5.508,\"Pb Bottle 2 (ppb) - 45 secs flushing\":5.157,\"Pb Bottle 3 (ppb) - 2 mins flushing\":2.621},{\"SampleID\":46,\"Zip Code\":48504,\"Ward\":6,\"Pb Bottle 1 (ppb) - First Draw\":1.293,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.441,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.281},{\"SampleID\":47,\"Zip Code\":48505,\"Ward\":1,\"Pb Bottle 1 (ppb) - First Draw\":4.699,\"Pb Bottle 2 (ppb) - 45 secs flushing\":1.395,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.329},{\"SampleID\":48,\"Zip Code\":48504,\"Ward\":6,\"Pb Bottle 1 (ppb) - First Draw\":6.093,\"Pb Bottle 2 (ppb) - 45 secs flushing\":2.682,\"Pb Bottle 3 (ppb) - 2 mins flushing\":1.458},{\"SampleID\":49,\"Zip Code\":48504,\"Ward\":2,\"Pb Bottle 1 (ppb) - First Draw\":0.8,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.8,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.5},{\"SampleID\":50,\"Zip Code\":48503,\"Ward\":5,\"Pb Bottle 1 (ppb) - First Draw\":1.626,\"Pb Bottle 2 (ppb) - 45 secs flushing\":1.332,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.327},{\"SampleID\":51,\"Zip Code\":48507,\"Ward\":8,\"Pb Bottle 1 (ppb) - First Draw\":2.576,\"Pb Bottle 2 (ppb) - 45 secs flushing\":2.852,\"Pb Bottle 3 (ppb) - 2 mins flushing\":1.48},{\"SampleID\":52,\"Zip Code\":48504,\"Ward\":1,\"Pb Bottle 1 (ppb) - First Draw\":2.362,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.467,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.339},{\"SampleID\":53,\"Zip Code\":48503,\"Ward\":5,\"Pb Bottle 1 (ppb) - First Draw\":1.585,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.494,\"Pb Bottle 3 (ppb) - 2 mins flushing\":1.232},{\"SampleID\":54,\"Zip Code\":48506,\"Ward\":4,\"Pb Bottle 1 (ppb) - First Draw\":3.058,\"Pb Bottle 2 (ppb) - 45 secs flushing\":1.808,\"Pb Bottle 3 (ppb) - 2 mins flushing\":1.169},{\"SampleID\":55,\"Zip Code\":48506,\"Ward\":4,\"Pb Bottle 1 (ppb) - First Draw\":2.423,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.393,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.373},{\"SampleID\":56,\"Zip Code\":48503,\"Ward\":8,\"Pb Bottle 1 (ppb) - First Draw\":30.91,\"Pb Bottle 2 (ppb) - 45 secs flushing\":42.58,\"Pb Bottle 3 (ppb) - 2 mins flushing\":44.6},{\"SampleID\":57,\"Zip Code\":48503,\"Ward\":8,\"Pb Bottle 1 (ppb) - First Draw\":4.47,\"Pb Bottle 2 (ppb) - 45 secs flushing\":3.649,\"Pb Bottle 3 (ppb) - 2 mins flushing\":1},{\"SampleID\":58,\"Zip Code\":48505,\"Ward\":1,\"Pb Bottle 1 (ppb) - First Draw\":2.172,\"Pb Bottle 2 (ppb) - 45 secs flushing\":1.76,\"Pb Bottle 3 (ppb) - 2 mins flushing\":1.44},{\"SampleID\":59,\"Zip Code\":48505,\"Ward\":3,\"Pb Bottle 1 (ppb) - First Draw\":1.8,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.5,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.2},{\"SampleID\":63,\"Zip Code\":48503,\"Ward\":5,\"Pb Bottle 1 (ppb) - First Draw\":0.965,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.166,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.319},{\"SampleID\":65,\"Zip Code\":48505,\"Ward\":1,\"Pb Bottle 1 (ppb) - First Draw\":7.636,\"Pb Bottle 2 (ppb) - 45 secs flushing\":5.206,\"Pb Bottle 3 (ppb) - 2 mins flushing\":9.239},{\"SampleID\":66,\"Zip Code\":48506,\"Ward\":3,\"Pb Bottle 1 (ppb) - First Draw\":3.158,\"Pb Bottle 2 (ppb) - 45 secs flushing\":1.948,\"Pb Bottle 3 (ppb) - 2 mins flushing\":2.802},{\"SampleID\":67,\"Zip Code\":48503,\"Ward\":7,\"Pb Bottle 1 (ppb) - 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First Draw\":56.26,\"Pb Bottle 2 (ppb) - 45 secs flushing\":4.692,\"Pb Bottle 3 (ppb) - 2 mins flushing\":1.243},{\"SampleID\":263,\"Zip Code\":48504,\"Ward\":6,\"Pb Bottle 1 (ppb) - First Draw\":2.433,\"Pb Bottle 2 (ppb) - 45 secs flushing\":1.334,\"Pb Bottle 3 (ppb) - 2 mins flushing\":1.376},{\"SampleID\":264,\"Zip Code\":48503,\"Ward\":7,\"Pb Bottle 1 (ppb) - First Draw\":0.5,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.2,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.5},{\"SampleID\":265,\"Zip Code\":48504,\"Ward\":6,\"Pb Bottle 1 (ppb) - First Draw\":29.13,\"Pb Bottle 2 (ppb) - 45 secs flushing\":11.57,\"Pb Bottle 3 (ppb) - 2 mins flushing\":6.388},{\"SampleID\":266,\"Zip Code\":48505,\"Ward\":1,\"Pb Bottle 1 (ppb) - First Draw\":12.3,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.5,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.4},{\"SampleID\":267,\"Zip Code\":48504,\"Ward\":6,\"Pb Bottle 1 (ppb) - First Draw\":3.445,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.29,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.167},{\"SampleID\":268,\"Zip Code\":48507,\"Ward\":9,\"Pb Bottle 1 (ppb) - First Draw\":16.49,\"Pb Bottle 2 (ppb) - 45 secs flushing\":12.83,\"Pb Bottle 3 (ppb) - 2 mins flushing\":9.018},{\"SampleID\":269,\"Zip Code\":48503,\"Ward\":7,\"Pb Bottle 1 (ppb) - First Draw\":3.365,\"Pb Bottle 2 (ppb) - 45 secs flushing\":2.45,\"Pb Bottle 3 (ppb) - 2 mins flushing\":1.675},{\"SampleID\":270,\"Zip Code\":48503,\"Ward\":7,\"Pb Bottle 1 (ppb) - First Draw\":1.154,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.176,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.12},{\"SampleID\":271,\"Zip Code\":48507,\"Ward\":9,\"Pb Bottle 1 (ppb) - First Draw\":13.53,\"Pb Bottle 2 (ppb) - 45 secs flushing\":21.91,\"Pb Bottle 3 (ppb) - 2 mins flushing\":4.675},{\"SampleID\":272,\"Zip Code\":48504,\"Ward\":6,\"Pb Bottle 1 (ppb) - First Draw\":2.229,\"Pb Bottle 2 (ppb) - 45 secs flushing\":1.573,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.84},{\"SampleID\":273,\"Zip Code\":48503,\"Ward\":7,\"Pb Bottle 1 (ppb) - First Draw\":28.91,\"Pb Bottle 2 (ppb) - 45 secs flushing\":5.471,\"Pb Bottle 3 (ppb) - 2 mins flushing\":3.056},{\"SampleID\":274,\"Zip Code\":48503,\"Ward\":7,\"Pb Bottle 1 (ppb) - First Draw\":6.601,\"Pb Bottle 2 (ppb) - 45 secs flushing\":1.929,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.417},{\"SampleID\":275,\"Zip Code\":48507,\"Ward\":9,\"Pb Bottle 1 (ppb) - First Draw\":0.948,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.27,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.207},{\"SampleID\":276,\"Zip Code\":48505,\"Ward\":3,\"Pb Bottle 1 (ppb) - First Draw\":3.484,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.434,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.306},{\"SampleID\":278,\"Zip Code\":48503,\"Ward\":7,\"Pb Bottle 1 (ppb) - First Draw\":1.888,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.359,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.322},{\"SampleID\":279,\"Zip Code\":48503,\"Ward\":7,\"Pb Bottle 1 (ppb) - First Draw\":13.95,\"Pb Bottle 2 (ppb) - 45 secs flushing\":12.2,\"Pb Bottle 3 (ppb) - 2 mins flushing\":8.251},{\"SampleID\":280,\"Zip Code\":48504,\"Ward\":6,\"Pb Bottle 1 (ppb) - First Draw\":6.27,\"Pb Bottle 2 (ppb) - 45 secs flushing\":4.036,\"Pb Bottle 3 (ppb) - 2 mins flushing\":1.182},{\"SampleID\":281,\"Zip Code\":48506,\"Ward\":7,\"Pb Bottle 1 (ppb) - First Draw\":19.12,\"Pb Bottle 2 (ppb) - 45 secs flushing\":22.02,\"Pb Bottle 3 (ppb) - 2 mins flushing\":7.968},{\"SampleID\":282,\"Zip Code\":48507,\"Ward\":9,\"Pb Bottle 1 (ppb) - First Draw\":1.633,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.465,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.238},{\"SampleID\":283,\"Zip Code\":48503,\"Ward\":7,\"Pb Bottle 1 (ppb) - First Draw\":1.114,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.605,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.255},{\"SampleID\":284,\"Zip Code\":48503,\"Ward\":7,\"Pb Bottle 1 (ppb) - First Draw\":3.9,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.558,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.504},{\"SampleID\":285,\"Zip Code\":48504,\"Ward\":1,\"Pb Bottle 1 (ppb) - First Draw\":3.521,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.45,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.321},{\"SampleID\":286,\"Zip Code\":48507,\"Ward\":9,\"Pb Bottle 1 (ppb) - First Draw\":3.832,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.794,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.339},{\"SampleID\":287,\"Zip Code\":48505,\"Ward\":3,\"Pb Bottle 1 (ppb) - First Draw\":3.243,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.738,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.27},{\"SampleID\":289,\"Zip Code\":48503,\"Ward\":7,\"Pb Bottle 1 (ppb) - First Draw\":0.99,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.25,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.263},{\"SampleID\":290,\"Zip Code\":48507,\"Ward\":9,\"Pb Bottle 1 (ppb) - First Draw\":1.203,\"Pb Bottle 2 (ppb) - 45 secs flushing\":19.26,\"Pb Bottle 3 (ppb) - 2 mins flushing\":1.626},{\"SampleID\":291,\"Zip Code\":48506,\"Ward\":3,\"Pb Bottle 1 (ppb) - First Draw\":2.261,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.102,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.407},{\"SampleID\":292,\"Zip Code\":48503,\"Ward\":4,\"Pb Bottle 1 (ppb) - First Draw\":16.99,\"Pb Bottle 2 (ppb) - 45 secs flushing\":6.32,\"Pb Bottle 3 (ppb) - 2 mins flushing\":3.585},{\"SampleID\":293,\"Zip Code\":48503,\"Ward\":7,\"Pb Bottle 1 (ppb) - First Draw\":3.322,\"Pb Bottle 2 (ppb) - 45 secs flushing\":2.559,\"Pb Bottle 3 (ppb) - 2 mins flushing\":1.512},{\"SampleID\":294,\"Zip Code\":48506,\"Ward\":4,\"Pb Bottle 1 (ppb) - First Draw\":14.33,\"Pb Bottle 2 (ppb) - 45 secs flushing\":1.284,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.323},{\"SampleID\":295,\"Zip Code\":48503,\"Ward\":7,\"Pb Bottle 1 (ppb) - First Draw\":18.11,\"Pb Bottle 2 (ppb) - 45 secs flushing\":20.21,\"Pb Bottle 3 (ppb) - 2 mins flushing\":4.263},{\"SampleID\":296,\"Zip Code\":48503,\"Ward\":7,\"Pb Bottle 1 (ppb) - First Draw\":12.81,\"Pb Bottle 2 (ppb) - 45 secs flushing\":7.874,\"Pb Bottle 3 (ppb) - 2 mins flushing\":1.78},{\"SampleID\":298,\"Zip Code\":48503,\"Ward\":7,\"Pb Bottle 1 (ppb) - First Draw\":1.083,\"Pb Bottle 2 (ppb) - 45 secs flushing\":0.322,\"Pb Bottle 3 (ppb) - 2 mins flushing\":0.26},{\"SampleID\":299,\"Zip Code\":48503,\"Ward\":7,\"Pb Bottle 1 (ppb) - First Draw\":29.59,\"Pb Bottle 2 (ppb) - 45 secs flushing\":3.258,\"Pb Bottle 3 (ppb) - 2 mins flushing\":1.843},{\"SampleID\":300,\"Zip Code\":48505,\"Ward\":1,\"Pb Bottle 1 (ppb) - First Draw\":4.287,\"Pb Bottle 2 (ppb) - 45 secs flushing\":4.345,\"Pb Bottle 3 (ppb) - 2 mins flushing\":4.905}]\"\"\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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>Notes</th>\n",
" <th>Pb Bottle 1 (ppb) - First Draw</th>\n",
" <th>Pb Bottle 2 (ppb) - 45 secs flushing</th>\n",
" <th>Pb Bottle 3 (ppb) - 2 mins flushing</th>\n",
" <th>SampleID</th>\n",
" <th>Ward</th>\n",
" <th>Zip Code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>266</th>\n",
" <td>NaN</td>\n",
" <td>18.110</td>\n",
" <td>20.210</td>\n",
" <td>4.263</td>\n",
" <td>295</td>\n",
" <td>7</td>\n",
" <td>48503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>267</th>\n",
" <td>NaN</td>\n",
" <td>12.810</td>\n",
" <td>7.874</td>\n",
" <td>1.780</td>\n",
" <td>296</td>\n",
" <td>7</td>\n",
" <td>48503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>268</th>\n",
" <td>NaN</td>\n",
" <td>1.083</td>\n",
" <td>0.322</td>\n",
" <td>0.260</td>\n",
" <td>298</td>\n",
" <td>7</td>\n",
" <td>48503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>269</th>\n",
" <td>NaN</td>\n",
" <td>29.590</td>\n",
" <td>3.258</td>\n",
" <td>1.843</td>\n",
" <td>299</td>\n",
" <td>7</td>\n",
" <td>48503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>270</th>\n",
" <td>NaN</td>\n",
" <td>4.287</td>\n",
" <td>4.345</td>\n",
" <td>4.905</td>\n",
" <td>300</td>\n",
" <td>1</td>\n",
" <td>48505</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Notes Pb Bottle 1 (ppb) - First Draw \\\n",
"266 NaN 18.110 \n",
"267 NaN 12.810 \n",
"268 NaN 1.083 \n",
"269 NaN 29.590 \n",
"270 NaN 4.287 \n",
"\n",
" Pb Bottle 2 (ppb) - 45 secs flushing \\\n",
"266 20.210 \n",
"267 7.874 \n",
"268 0.322 \n",
"269 3.258 \n",
"270 4.345 \n",
"\n",
" Pb Bottle 3 (ppb) - 2 mins flushing SampleID Ward Zip Code \n",
"266 4.263 295 7 48503 \n",
"267 1.780 296 7 48503 \n",
"268 0.260 298 7 48503 \n",
"269 1.843 299 7 48503 \n",
"270 4.905 300 1 48505 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Creating Numpy Arrays from the Data\n",
"\n",
"## lead_levels are in \n",
"lead_levels_first_draw = np.array(flint_data[\"Pb Bottle 1 (ppb) - First Draw\"])\n",
"\n",
"## zip_code data are in\n",
"zip_code = np.array(flint_data[\"Zip Code\"])\n",
"\n",
"## For reference, the full dataset looks like this\n",
"flint_data.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data Cleaning\n",
"\n",
"The sample readings are in ppb (parts per billion). But, the U.S. Environmental Protection Agency's guidelines are expressed in mg/L, or milligrams per liter. What will we need to do in order to compare the data collected to the EPA's guideline threshold?"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.000344, 0.008133, 0.001111, 0.008007])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Write any python you need to here\n",
"conversion_factor = 1 / 1000\n",
"# print(conversion_factor)\n",
"\n",
"converted_lead_levels = conversion_factor * lead_levels_first_draw\n",
"converted_lead_levels[0:4] # checking the data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ x \\text{ ppb} \\times \\frac{1 \\text{ ppm}}{1000 \\text{ ppb}} \\times \\frac{1 \\text{ mg/L}}{1 \\text{ ppm}} $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Getting a Sense of the Data\n",
"\n",
"What's the mean value of all the lead readings?"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.010645992619926199"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Write any python you need to here\n",
"\n",
"converted_lead_levels.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"// write any markdown here - explain what you did in the cells above!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Do you think the mean value is representative of how good (or bad) the overall lead levels in Flint water? If so, why; if not, why not? **Really take some time to think this one through.** Try to justify your group's opinions by using plots, calculations, or anything else you feel appropriately supports your point."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"45"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
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"source": [
"# Write any python you need to here\n",
"\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"\n",
"\n",
"\n",
"converted_lead_levels = pd.Series(converted_lead_levels)\n",
"epa_action_threshold = 0.015\n",
"critical_lead_levels = pd.Series(\n",
" converted_lead_levels[converted_lead_levels > epa_action_threshold])\n",
"\n",
"critical_lead_levels.hist()\n",
"critical_lead_levels.count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"// write any markdown here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How does the mean value for all the readings compare to the EPA's \"action level\"? What about the range of values from the readings? As a reminder, here's what the EPA guidelines say:\n",
"\n",
"> Lead and copper are regulated by a treatment technique that requires systems to control the corrosiveness of their water. If more than 10% of tap water samples exceed the action level, water systems must take additional steps. For copper, the action level is 1.3 mg/L, and for lead is 0.015 mg/L. \n",
">\n",
"> Source: (http://www.epa.gov/your-drinking-water/table-regulated-drinking-water-contaminants#seven). "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Write any python you need to here\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"// write any markdown here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Is comparing the mean to the action level enough to tell us whether Flint had a definite problem with its drinking water? If so, why? If not, why not? Take some time to think this through."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Write any python you need to here\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"// write any markdown here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"# Homework - Informing Government\n",
"\n",
"As part of this week's homework, **You're going to create a letter to [send to the Governor's office](http://michigan.gov/snyder/0,4668,7-277--267869--,00.html)** based on your data anlysis here. You will consider this core question: **Did water lead levels exceed the EPA's action limits? And if they did, how can we understand how badly it exceeded the limits?**\n",
"\n",
"So that you're prepared to work on the homework, **make sure to have the person who creates this notebook email it to everybdoy in the group!**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Feedback (required!)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How far did you get on this in class?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"// Write your answer here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What questions do you (or does your group) have after this assignment?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"// Write your answer here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How to submit this assignment\n",
"\n",
"Log into the course Desire2Learn website (d2l.msu.edu) and go to the \"In-class assignments\" folder. There will be a dropbox labeled \"Day 4\". Upload this notebook there (but not pictures of drawings, etc.). You only have to upload one notebook per group - just make sure that everybody's name is at the top of the notebook!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Want to learn more?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- [Two][1] [talks][2] by Hans Rosling on visualizing public health data\n",
"- [DataKind][3]: data analysis for humanity\n",
"- [Code For America][4]\n",
"- [The U.S. Digital Service][5]\n",
"\n",
"[1]: http://www.ted.com/talks/hans_rosling_the_truth_about_hiv\n",
"[2]: http://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen\n",
"[3]: http://www.datakind.org\n",
"[4]: http://www.codeforamerica.org\n",
"[5]: https://www.whitehouse.gov/digital/united-states-digital-service"
]
}
],
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"name": "python3"
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| agpl-3.0 |
mjones01/NEON-Data-Skills | code/Python/remote-sensing/hyperspectral-data/Plot_Spectral_Signature_Flightlines_py.ipynb | 1 | 1372019 | null | agpl-3.0 |
fermiPy/fermipy-extra | notebooks/gtools_customize.ipynb | 1 | 375274 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Configuring the model, running g-tools and output files and infos"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This tutorial shows how to configure the model and how to run Fermipy-LAT g-tools with Fermipy.\n",
"Many parts of this tutorial are taken directly from the documentation page of Fermipy: [fermipy.readthedocs](http://fermipy.readthedocs.io/en/latest/). \n",
"I suggest to visit it to find further informations."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Configuring the model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The model can be configured with a configuration file or directly running Fermipy with your script."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The configuration file defines the data selection and analysis parameters. The configuration file has a hierarchical structure that groups parameters into dictionaries that are keyed to a section name (data, binning, etc.). "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When creating a class instance, the configuration is initialized by passing either a configuration dictionary or configuration file path to the class constructor. \n",
"Keyword arguments can be passed to the constructor to override configuration parameters in the input dictionary. \n",
"In the following example the config dictionary defines values for the parameters emin and emax. By passing a dictionary for the selection keyword argument, the value of emax in the keyword argument (10000) overrides the value of emax in the input dictionary."
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"# config = {\n",
"'selection' : { 'emin' : 100,\n",
" 'emax' : 1000 }\n",
"}\n",
"\n",
"gta = GTAnalysis(config,selection={'emax' : 10000})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The configuration file can be created also with a yaml file.\n",
"Below I report a sample of configuration applied for an analysis of the SMC:"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"data:\n",
" evfile : 'ft1.fits'\n",
" scfile : '/u/gl/mdimauro/kipac/workdir/files/SC/P8_104months_ft2.fits'\n",
" ltcube : '/u/gl/mdimauro/dmcat/workdir/mattia/LogNLogS_Ebins/files_bins/bin13/P8_SOURCE_zmax105_1_3_ltcube.fits'\n",
"\n",
"binning:\n",
" roiwidth : 12.0\n",
" binsz : 0.08\n",
" binsperdec : 8\n",
" coordsys : 'GAL'\n",
"\n",
"selection :\n",
" emin : 1000\n",
" emax : 500000\n",
" tmin : 239557417\n",
" tmax : 512994417\n",
" zmax : 105\n",
" evclass : 128\n",
" evtype : 3\n",
" glon: 302.25\n",
" glat : -44.37\n",
" \n",
"gtlike:\n",
" edisp : True\n",
" irfs : 'P8R2_SOURCE_V6'\n",
" edisp_disable : ['isodiff','galdiff']\n",
"\n",
"model:\n",
" src_roiwidth : 12.0\n",
" galdiff : '$FERMI_DIFFUSE_DIR/gll_iem_v06.fits'\n",
" isodiff : '$FERMI_DIFFUSE_DIR/iso_P8R2_SOURCE_V6_v06.txt' \n",
" catalogs: gll_psc_v16.fit\n",
"\n",
"fileio:\n",
" usescratch: False"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The configuration file has the same structure as the configuration dictionary such that one can read/write configurations using the load/dump methods of the yaml module:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"DATA"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data section defines the input data files for the analysis (FT1, FT2, and livetime cube). \n",
"evfile and scfile can either be individual files or group of files. The optional ltcube option can be used to choose a pre-generated livetime cube. \n",
"If ltcube is null a livetime cube will be generated at runtime with gtltcube."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below an example of the data part:"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"data :\n",
" evfile : ft1.lst\n",
" scfile : ft2.fits\n",
" ltcube : null"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The options for the data component are the following:\n",
"* cacheft1: Cache FT1 files when performing binned analysis. If false then only the counts cube is retained.\n",
"* evfile: Path to FT1 file or list of FT1 files.\n",
"* ltcube: Path to livetime cube. If none a livetime cube will be generated with gtmktime.\n",
"* scfile: Path to FT2 (spacecraft) file."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"BINNING"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Options in the binning section control the spatial and spectral binning of the data. "
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"binning:\n",
"\n",
" # Binning\n",
" roiwidth : 10.0\n",
" npix : null\n",
" binsz : 0.1 # spatial bin size in deg\n",
" binsperdec : 8 # nb energy bins per decade\n",
" projtype : WCS"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The roiwidth is the ROI width, npix specifies the number of pixels, binsz indicates the pixel size, binsperdec is the number of energy bins per decade and projtype is the type of projection and can be WCS and HEALPIX."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Other options are: \n",
"* coordsys: for the coordinate system Galactic or Celestial (CEL or GAL), \n",
"* hpx_order: is the healpix order, hpx_ordering_scheme is the HEALPix Ordering Scheme, \n",
"* proj: is the Spatial projection for WCS mode, Width of the ROI in degrees. \n",
"* roiwidth: is the number of pixels in each spatial dimension will be set from roiwidth / binsz (rounded up)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"COMPONENTS"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The components section can be used to define analysis configurations for independent subselections of the data. Each subselection will have its own binned likelihood instance that is combined in a global likelihood function for the ROI (implemented with the SummedLikelihood class in pyLikelihood). The components section is optional and when set to null (the default) only a single likelihood component will be created with the parameters of the root analysis configuration.\n",
"\n",
"The component section is defined as a list of dictionaries where each element sets analysis parameters for a different subcomponent of the analysis. The component configurations follow the same structure and accept the same parameters as the root analysis configuration. Parameters not defined in a given element will default to the values set in the root analysis configuration.\n",
"\n",
"The following example illustrates how to define a Front/Back analysis with two components. Files associated to each component will be given a suffix according to their order in the list (e.g. file_00.fits, file_01.fits, etc.)."
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"# Component section for Front/Back analysis\n",
" - { selection : { evtype : 1 } } # Front\n",
" - { selection : { evtype : 2 } } # Back"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following example illustrates how to define an analysis divided in four components. Each of them has a different zmax and uses a different evtype and isotropic template."
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"components:\n",
" - model: {isodiff: $FERMI_DIFFUSE_DIR/iso_P8R2_SOURCE_V6_PSF0_v06.txt}\n",
" selection: {evtype: 4, zmax: 80}\n",
" data: {ltcube: ltcube_00.fits}\n",
" - model: {isodiff: $FERMI_DIFFUSE_DIR/iso_P8R2_SOURCE_V6_PSF1_v06.txt}\n",
" selection: {evtype: 8, zmax: 85}\n",
" data: {ltcube: ltcube_01.fits}\n",
" - model: {isodiff: $FERMI_DIFFUSE_DIR/iso_P8R2_SOURCE_V6_PSF2_v06.txt}\n",
" selection: {evtype: 16, zmax: 95}\n",
" data: {ltcube: ltcube_02.fits}\n",
" - model: {isodiff: $FERMI_DIFFUSE_DIR/iso_P8R2_SOURCE_V6_PSF3_v06.txt}\n",
" selection: {evtype: 32, zmax: 100}\n",
" data: {ltcube: ltcube_03.fits}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"EXTENSION"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The options in extension control the default behavior of the extension method. For more information about using this method see the Extension Fitting page. The main options are the following:\n",
"* fit_ebin: Perform a fit for the angular extension in each analysis energy bin.\n",
"* fit_position: Perform a simultaneous fit to the source position and extension.\n",
"* fix_shape: Fix spectral shape parameters of the source of interest. If True then only the normalization parameter will be fit.\n",
"* free_background: Leave background parameters free when performing the fit. If True then any parameters that are currently free in the model will be fit simultaneously with the source of interest.\n",
"* free_radius: Free normalizations of background sources within this angular distance in degrees from the source of interest. If None then no sources will be freed.\n",
"* sqrt_ts_threshold: Threshold on sqrt(TS_ext) that will be applied when update is True. If None then nothreshold is applied.\n",
"* width: Sequence of values in degrees for the likelihood scan over spatial extension (68% containment radius). If this argument is None then the scan points will be determined from width_min/width_max/width_nstep.\n",
"* width_max: Maximum value in degrees for the likelihood scan over spatial extent.\n",
"* width_min: Minimum value in degrees for the likelihood scan over spatial extent.\n",
"* width_nstep: Number of scan points between width_min and width_max. Scan points will be spaced evenly on a logarithmic scale between width_min and width_max."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is a notebook dedicated to this analysis."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"FILEIO"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The fileio section collects options related to file bookkeeping. The outdir option sets the root directory of the analysis instance where all output files will be written. If outdir is null then the output directory will be automatically set to the directory in which the configuration file is located. Enabling the usescratch option will stage all output data files to a temporary scratch directory created under scratchdir."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sample fileio Configuration"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"fileio:\n",
" outdir : null\n",
" logfile : null\n",
" usescratch : False\n",
" scratchdir : '/scratch'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The options for the fileio are the following:\n",
"* logfile: Path to log file. If None then log will be written to fermipy.log.\n",
"* outdir: Path of the output directory. If none this will default to the directory containing the configuration file.\n",
"* outdir_regex: Stage files to the output directory that match at least one of the regular expressions in this list. This option only takes effect when usescratch is True.\n",
"* savefits: Save intermediate FITS files.\n",
"* scratchdir: Path to the scratch directory. If usescratch is True then a temporary working directory will be created under this directory.\n",
"* usescratch: Run analysis in a temporary working directory under scratchdir.\n",
"* workdir: Path to the working directory.\n",
"* workdir_regex: Stage files to the working directory that match at least one of the regular expressions in this list. This option only takes effect when usescratch is True."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"GTLIKE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Options in the gtlike section control the setup of the likelihood analysis include the IRF name (irfs)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A subsample of options for the gtlike section is listed below:\n",
"* edisp: Enable the correction for energy dispersion.\n",
"* edisp_disable: Provide a list of sources for which the edisp correction should be disabled.\n",
"* use_external_srcmap: Use an external precomputed source map file.\n",
"* use_scaled_srcmap: Generate source map by scaling an external srcmap file.\n",
"* wmap: Likelihood weights map."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Likelihood wights map is created and used in order to account for example for the uncertainty in the interstellar emission."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MODEL"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The model section collects options that control the inclusion of point-source and diffuse components in the model. galdiff and isodiff set the templates for the Galactic IEM and isotropic diffuse respectively. catalogs defines a list of catalogs that will be merged to form a master analysis catalog from which sources will be drawn. Valid entries in this list can be FITS files or XML model files. sources can be used to insert additional point-source or extended components beyond those defined in the master catalog. src_radius and src_roiwidth set the maximum distance from the ROI center at which sources in the master catalog will be included in the ROI model."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sample for the Model section:"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"model :\n",
"\n",
" # Diffuse components\n",
" galdiff : '$FERMI_DIR/refdata/fermi/galdiffuse/gll_iem_v06.fits'\n",
" isodiff : '$FERMI_DIR/refdata/fermi/galdiffuse/iso_P8R2_SOURCE_V6_v06.txt'\n",
"\n",
" # List of catalogs to be used in the model.\n",
" catalogs :\n",
" - '3FGL'\n",
" - 'extra_sources.xml'\n",
"\n",
" sources :\n",
" - { 'name' : 'SourceA', 'ra' : 60.0, 'dec' : 30.0, 'SpectrumType' : PowerLaw }\n",
" - { 'name' : 'SourceB', 'ra' : 58.0, 'dec' : 35.0, 'SpectrumType' : PowerLaw }\n",
"\n",
" # Include catalog sources within this distance from the ROI center\n",
" src_radius : null\n",
"\n",
" # Include catalog sources within a box of width roisrc.\n",
" src_roiwidth : 15.0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the sample above the official IEM and isotropic template are used together with the 3FGL catalog given with a fits file and by an additional catalog given with an xml file following the notation of the Fermi Science Tools [fermi.fssc.spectralmodel](https://fermi.gsfc.nasa.gov/ssc/data/analysis/scitools/xml_model_defs.html#xmlModelDefinitions).\n",
"Then two sources named as SourceA and SourceB are added."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To define two or more galactic diffuse components you can optionally define the galdiff and isodiff parameters as lists. A separate component will be generated for each element in the list with the name galdiffXX or isodiffXX where XX is an integer position in the list."
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"model:\n",
" galdiff :\n",
" - '$FERMI_DIFFUSE_DIR/diffuse_component0.fits'\n",
" - '$FERMI_DIFFUSE_DIR/diffuse_component1.fits'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To explicitly set the name of a component you can define any element as a dictionary containing name and file fields:"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"model:\n",
" galdiff :\n",
" - { 'name' : 'component0', 'file' : '$FERMI_DIFFUSE_DIR/diffuse_component0.fits' }\n",
" - { 'name' : 'component1', 'file' : '$FERMI_DIFFUSE_DIR/diffuse_component1.fits' }"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The list of sources for inclusion in the ROI model is set by defining a list of catalogs with the catalogs parameter. Catalog files can be in either XML or FITS format. Sources from the catalogs in this list that satisfy either the src_roiwidth or src_radius selections are added to the ROI model. If a source is defined in multiple catalogs the source definition from the last file in the catalogs list takes precedence."
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"model:\n",
"\n",
" src_radius: 5.0\n",
" src_roiwidth: 10.0\n",
" catalogs :\n",
" - 'gll_psc_v16.fit'\n",
" - 'extra_sources.xml'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fermipy supports four spatial models which are defined with the SpatialModel property:\n",
"\n",
"* PointSource : A point source (SkyDirFunction).\n",
"* RadialGaussian : A symmetric 2D Gaussian with width parameter ‘Sigma’.\n",
"* RadialDisk : A symmetric 2D Disk with radius ‘Radius’.\n",
"* SpatialMap : An arbitrary 2D shape with morphology defined by a FITS template.\n",
"\n",
"The spatial extension of RadialDisk and RadialGaussian can be controlled with the SpatialWidth parameter which sets the 68% containment radius in degrees. Note for ST releases prior to 11-01-01, RadialDisk and RadialGaussian sources will be represented with the SpatialMap type."
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"model:\n",
" sources :\n",
" - { name: 'PointSource', glon : 120.0, glat : 0.0,\n",
" SpectrumType : 'PowerLaw', Index : 2.0, Scale : 1000, Prefactor : !!float 1e-11,\n",
" SpatialModel: 'PointSource' }\n",
" - { name: 'DiskSource', glon : 120.0, glat : 0.0,\n",
" SpectrumType : 'PowerLaw', Index : 2.0, Scale : 1000, Prefactor : !!float 1e-11,\n",
" SpatialModel: 'RadialDisk', SpatialWidth: 1.0 }\n",
" - { name: 'GaussSource', glon : 120.0, glat : 0.0,\n",
" SpectrumType : 'PowerLaw', Index : 2.0, Scale : 1000, Prefactor : !!float 1e-11,\n",
" SpatialModel: 'RadialGaussian', SpatialWidth: 1.0 }\n",
" - { name: 'MapSource', glon : 120.0, glat : 0.0,\n",
" SpectrumType : 'PowerLaw', Index : 2.0, Scale : 1000, Prefactor : !!float 1e-11,\n",
" SpatialModel: 'SpatialMap', Spatial_Filename : 'template.fits' }"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OPTIMIZER"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This section provides informations for the setup used for the optimization. The list of options are the following:\n",
"* init_lambda: Initial value of damping parameter for step size calculation when using the NEWTON fitter. A value of zero disables damping.\n",
"* max_iter: Maximum number of iterations for the Newtons method fitter.\n",
"* min_fit_quality: Set the minimum fit quality.\n",
"* optimizer: Set the optimization algorithm to use when maximizing the likelihood function.\n",
"* retries: Set the number of times to retry the fit when the fit quality is less than min_fit_quality.\n",
"* tol: Set the optimizer tolerance.\n",
"* verbosity"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"PLOTTING"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This section selects the options to be used to make the plots:\n",
"* cmap: Set the colormap for 2D plots. Default magma\n",
"* cmap_resid: Set the colormap for 2D residual plots.\n",
"* figsize: Set the default figure size.\n",
"* format: format of images\n",
"* interactive: Enable interactive mode. If True then plots will be drawn after each plotting command.\n",
"* label_ts_threshold: TS threshold for labeling sources in sky maps. If None then no sources will be labeled."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"SELECTION"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The selection section collects parameters related to the data selection and target definition. The majority of the parameters in this section are arguments to gtselect and gtmktime. The ROI center can be set with the target parameter by providing the name of a source defined in one of the input catalogs (defined in the model section). Alternatively the ROI center can be defined by giving explicit sky coordinates with ra and dec or glon and glat."
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"selection:\n",
"\n",
" # gtselect parameters\n",
" emin : 100\n",
" emax : 100000\n",
" zmax : 90\n",
" evclass : 128\n",
" evtype : 3\n",
" tmin : 239557414\n",
" tmax : 428903014\n",
"\n",
" # gtmktime parameters\n",
" filter : 'DATA_QUAL>0 && LAT_CONFIG==1'\n",
" roicut : 'no'\n",
"\n",
" # Set the ROI center to the coordinates of this source\n",
" target : 'mkn421'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A sample of options for this section are the following:\n",
"* emax/emin: Maximum/Minimum Energy (MeV)\n",
"* evclass: Event class selection.\n",
"* evtype: Event type selection.\n",
"* filter: Filter string for gtmktime selection.\n",
"* logemax/logemin: Maximum/Minimum Energy (log10(MeV))\n",
"* phasemax/phasemin: Maximum/Minimum pulsar phase\n",
"* radius: Radius of data selection. If none this will be automatically set from the ROI size.\n",
"* target: Choose an object on which to center the ROI. This option takes precendence over ra/dec or glon/glat.\n",
"* tmax/tmin: Maximum/Minimum time (MET).\n",
"* zmax: Maximum zenith angle."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Running g-tools"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First of all you need to load the configuration file, create the object gta and run the tool gta.setup that implements the ST gtselect, gtmktime, gtbin, gtexpcube, gtsrcmap tools"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With the commands below we import the packages needed to run the script."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import os\n",
"import numpy as np\n",
"from fermipy.gtanalysis import GTAnalysis\n",
"from fermipy.plotting import ROIPlotter, SEDPlotter\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
"from IPython.display import Image"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we untar the file ../data/SMC_data.tar.gz. This will copy the config.yaml and ft1 file in the notebook directory."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"if os.path.isfile('../data/SMC_data.tar.gz'):\n",
" !tar xzf ../data/SMC_data.tar.gz\n",
"else:\n",
" !curl -OL https://raw.githubusercontent.com/fermiPy/fermipy-extras/master/data/SMC_data.tar.gz\n",
" !tar xzf SMC_data.tar.gz"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-31 09:02:08 INFO GTAnalysis.__init__(): \n",
"--------------------------------------------------------------------------------\n",
"fermipy version 0.16.0+175.ge34f \n",
"ScienceTools version ScienceTools-11-07-00\n",
"WARNING: UnitsWarning: 'photon/cm**2/MeV/s' contains multiple slashes, which is discouraged by the FITS standard [astropy.units.format.generic]\n",
"WARNING: UnitsWarning: 'photon/cm**2/s' contains multiple slashes, which is discouraged by the FITS standard [astropy.units.format.generic]\n",
"WARNING: UnitsWarning: 'erg/cm**2/s' contains multiple slashes, which is discouraged by the FITS standard [astropy.units.format.generic]\n",
"2018-03-31 09:02:11 INFO GTAnalysis.setup(): Running setup.\n",
"2018-03-31 09:02:11 INFO GTBinnedAnalysis.setup(): Running setup for component 00\n",
"2018-03-31 09:02:11 INFO GTBinnedAnalysis._select_data(): Skipping data selection.\n",
"2018-03-31 09:02:11 INFO GTBinnedAnalysis.setup(): Using external LT cube.\n",
"2018-03-31 09:02:13 INFO GTBinnedAnalysis._create_expcube(): Skipping gtexpcube.\n",
"2018-03-31 09:02:13 INFO GTBinnedAnalysis._create_srcmaps(): Skipping gtsrcmaps.\n",
"2018-03-31 09:02:13 INFO GTBinnedAnalysis.setup(): Finished setup for component 00\n",
"2018-03-31 09:02:13 INFO GTBinnedAnalysis._create_binned_analysis(): Creating BinnedAnalysis for component 00.\n",
"WARNING: AstropyDeprecationWarning: \"clobber\" was deprecated in version 2.0 and will be removed in a future version. Use argument \"overwrite\" instead. [astropy.utils.decorators]\n",
"2018-03-31 09:02:35 INFO GTAnalysis.setup(): Initializing source properties\n",
"2018-03-31 09:03:30 INFO GTAnalysis.setup(): Finished setup.\n"
]
}
],
"source": [
"gta = GTAnalysis('config.yaml')\n",
"matplotlib.interactive(True)\n",
"gta.setup()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The setup() method performs the data preparation and response calculations needed for the analysis (selecting the data, creating counts and exposure maps, etc.). Depending on the data selection and binning of the analysis this will often be the slowest step in the analysis sequence. The output of setup() is cached in the analysis working directory so subsequent calls to setup() will run much faster."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The g-tools performed with gta.setup() are the following:\n",
"* gtselect: [gtselect_fssc](https://fermi.gsfc.nasa.gov/ssc/data/analysis/scitools/help/gtselect.txt)\n",
"* gtmktime: [gtmiktime_fssc](https://fermi.gsfc.nasa.gov/ssc/data/analysis/scitools/help/gtmktime.txt)\n",
"* gtbin: [gtbin_fssc](https://fermi.gsfc.nasa.gov/ssc/data/analysis/scitools/help/gtbin.txt)\n",
"* gtsrcmaps: [gtsrcmaps_fssc](https://fermi.gsfc.nasa.gov/ssc/data/analysis/scitools/help/gtsrcmaps.txt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to have a summary of the source model you can use the command:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-31 09:03:55 INFO GTAnalysis.print_model(): \n",
"sourcename offset norm eflux index ts npred free\n",
"--------------------------------------------------------------------------------\n",
"3FGL J0059.0-7242e 0.088 1.059 1.45e-05 2.22 nan 1434.2 \n",
"3FGL J0112.9-7506 2.572 1.172 1.31e-06 2.17 nan 120.0 \n",
"3FGL J0023.9-7203 2.662 0.714 9.3e-06 2.65 nan 1675.8 \n",
"3FGL J0029.1-7045 3.008 0.506 2.49e-06 2.28 nan 269.9 \n",
"3FGL J0021.6-6835 5.122 1.859 5.27e-07 2.65 nan 84.0 \n",
"3FGL J2351.9-7601 5.495 1.027 3.74e-06 1.69 nan 117.5 \n",
"3FGL J2338.7-7401 5.777 0.677 4.38e-06 1.89 nan 213.2 \n",
"3FGL J0146.4-6746 6.423 0.420 1.03e-06 2.39 nan 123.7 \n",
"3FGL J2336.5-7620 6.454 0.557 1.66e-06 2.33 nan 186.9 \n",
"3FGL J0002.0-6722 7.153 0.483 2.07e-06 1.95 nan 113.2 \n",
"isodiff --- 1.000 0.0313 2.12 nan 9076.7 \n",
"galdiff --- 1.000 0.126 0.00 nan 17968.1 \n",
"\n"
]
}
],
"source": [
"gta.print_model()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The table above contains:\n",
"* sourcename as given in the catalog\n",
"* offset from the center of the roi. Sources are ranked with respect to this parameter\n",
"* normalization of the SED\n",
"* energy flux of the source\n",
"* index of the SED with a PowerLaw SED\n",
"* Test Statistics\n",
"* npred is the number of sources associated to the source\n",
"* free column that tells if the SED parameter of the sources are free (if the column is filled with an asterix) or fixed without no asterix."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Source dictionary"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First of all, let's make a fit using gta.fit tool.\n",
"Source fitting with fermipy is generally performed with the optimize and fit methods. fit is a wrapper on the pyLikelihood fit method and performs a likelihood fit of all free parameters of the model. This method can be used to manually optimize of the model by calling it after freeing one or more source parameters."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-31 09:04:41 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0059.0-7242e : ['Prefactor', 'Index']\n",
"2018-03-31 09:04:41 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0112.9-7506 : ['Prefactor', 'Index']\n",
"2018-03-31 09:04:41 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0023.9-7203 : ['norm', 'alpha', 'beta']\n",
"2018-03-31 09:04:41 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0029.1-7045 : ['Prefactor', 'Index']\n",
"2018-03-31 09:04:41 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0021.6-6835 : ['Prefactor', 'Index']\n",
"2018-03-31 09:04:41 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J2351.9-7601 : ['Prefactor', 'Index']\n",
"2018-03-31 09:04:41 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J2338.7-7401 : ['Prefactor', 'Index']\n",
"2018-03-31 09:04:41 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0146.4-6746 : ['Prefactor', 'Index']\n",
"2018-03-31 09:04:41 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J2336.5-7620 : ['Prefactor', 'Index']\n",
"2018-03-31 09:04:41 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0002.0-6722 : ['Prefactor', 'Index']\n",
"2018-03-31 09:04:41 INFO GTAnalysis.free_source(): Freeing parameters for isodiff : ['Normalization']\n",
"2018-03-31 09:04:41 INFO GTAnalysis.free_source(): Freeing parameters for galdiff : ['Prefactor', 'Index']\n",
"2018-03-31 09:04:41 INFO GTAnalysis.fit(): Starting fit.\n",
"/u/gl/mdimauro/kipac/software/anaconda/lib/python2.7/site-packages/scipy/interpolate/fitpack2.py:226: UserWarning: \n",
"The maximal number of iterations maxit (set to 20 by the program)\n",
"allowed for finding a smoothing spline with fp=s has been reached: s\n",
"too small.\n",
"There is an approximation returned but the corresponding weighted sum\n",
"of squared residuals does not satisfy the condition abs(fp-s)/s < tol.\n",
" warnings.warn(message)\n",
"2018-03-31 09:04:57 INFO GTAnalysis.fit(): Fit returned successfully. Quality: 3 Status: 0\n",
"2018-03-31 09:04:57 INFO GTAnalysis.fit(): LogLike: -77883.041 DeltaLogLike: 80.036 \n"
]
}
],
"source": [
"gta.free_sources()\n",
"fitresult=gta.fit()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-31 09:04:57 INFO GTAnalysis.print_model(): \n",
"sourcename offset norm eflux index ts npred free\n",
"--------------------------------------------------------------------------------\n",
"3FGL J0059.0-7242e 0.088 1.512 1.12e-05 2.47 753.09 1532.2 *\n",
"3FGL J0112.9-7506 2.572 1.268 2.14e-06 2.00 152.12 140.7 *\n",
"3FGL J0023.9-7203 2.662 0.590 9.68e-06 2.70 4889.10 1771.0 *\n",
"3FGL J0029.1-7045 3.008 0.746 3.1e-06 2.38 89.09 378.7 *\n",
"3FGL J0021.6-6835 5.122 2.072 2.11e-07 3.26 10.91 46.4 *\n",
"3FGL J2351.9-7601 5.495 1.429 2.81e-06 2.04 237.29 198.5 *\n",
"3FGL J2338.7-7401 5.777 0.574 2.82e-06 2.01 257.48 179.2 *\n",
"3FGL J0146.4-6746 6.423 0.529 1.65e-06 2.28 192.10 170.3 *\n",
"3FGL J2336.5-7620 6.454 0.449 1.28e-06 2.35 119.61 147.9 *\n",
"3FGL J0002.0-6722 7.153 0.395 1.7e-06 1.94 86.64 92.7 *\n",
"isodiff --- 0.913 0.0286 2.12 1145.04 8289.4 *\n",
"galdiff --- 0.944 0.122 -0.02 5109.11 17185.3 *\n",
"\n"
]
}
],
"source": [
"gta.print_model()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now you see that the ts column is filled and the free column has all asterix because we have freed the sources."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fermipy gives the possibility to access many informations on the sources in the model.\n",
"Below a few examples."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"NAME AND CHARACTERISTICS OF THE SOURCE"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3FGL J0059.0-7242e\n",
"3FGL J0059.0-7242e\n",
"SpatialMap\n",
"None\n",
"SpatialMap\n",
"DiffuseSource\n",
"PowerLaw\n",
"$FERMIPY_DATA_DIR/catalogs/Extended_archive_v15/Templates/SMC.fits\n",
"None\n",
"{'3FGL J2336.5-7620': 0.021540975517969452, '3FGL J0029.1-7045': -0.018737972377049942, 'isodiff': -0.011270554190343823, 'galdiff': -0.16130718991869766, '3FGL J2338.7-7401': 0.01187470654461815, '3FGL J0059.0-7242e': 0.9999999999999998, '3FGL J0112.9-7506': -0.010991866042965983, '3FGL J0146.4-6746': 0.01590697189126327, '3FGL J0023.9-7203': -0.02136376358912324, '3FGL J0021.6-6835': 0.01579921758235634, '3FGL J0002.0-6722': 0.011830993863816544, '3FGL J2351.9-7601': 0.00401017952234348}\n",
"[4.95790377e+02 3.41435355e+02 2.31934703e+02 1.55836591e+02\n",
" 1.03590847e+02 6.85066795e+01 4.54694907e+01 3.03664598e+01\n",
" 2.00853639e+01 1.32773079e+01 8.77338782e+00 5.83038311e+00\n",
" 3.90279960e+00 2.59778026e+00 1.71628875e+00 1.12902221e+00\n",
" 7.43126007e-01 4.87775565e-01 3.21625630e-01 2.11836077e-01\n",
" 1.38999521e-01 8.97531536e-02]\n"
]
}
],
"source": [
"print gta.roi.sources[0]['name'] #name of the source\n",
"print gta.roi.sources[0]['Source_Name'] #name of the source\n",
"print gta.roi.sources[0]['SpatialModel'] #spatial model\n",
"print gta.roi.sources[0]['SpatialWidth'] #spatial size parameter\n",
"print gta.roi.sources[0]['SpatialType'] #spatial size parameter\n",
"print gta.roi.sources[0]['SourceType'] #Source type\n",
"print gta.roi.sources[0]['SpectrumType'] #Spectrum type string\n",
"print gta.roi.sources[0]['Spatial_Filename'] #Path to spatial template\n",
"print gta.roi.sources[0]['Spectrum_Filename'] #Path to the SED source template\n",
"print gta.roi.sources[0]['correlation'] #Dictionary of correlation coefficients.\n",
"print gta.roi.sources[0]['model_counts'] #Vector of predicted counts for this source"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"CHARACTERISTICS OF THE POSITION OF THE SOURCE"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"14.75\n",
"-72.7\n",
"302.14493\n",
"-44.4167\n",
"nan\n",
"nan\n",
"nan\n",
"nan\n",
"nan\n",
"nan\n",
"nan\n",
"nan\n",
"nan\n",
"nan\n",
"-0.07331403829880656\n",
"0.04939684436004943\n",
"0.0750319866158955\n",
"-0.046748398465821905\n",
"0.0884039\n"
]
}
],
"source": [
"print gta.roi.sources[0]['ra'] #ra\n",
"print gta.roi.sources[0]['dec'] #dec\n",
"print gta.roi.sources[0]['glon'] #glon\n",
"print gta.roi.sources[0]['glat'] #glat\n",
"print gta.roi.sources[0]['ra_err'] #error for ra\n",
"print gta.roi.sources[0]['dec_err'] #error for dec\n",
"print gta.roi.sources[0]['glon_err'] #error for glon\n",
"print gta.roi.sources[0]['glat_err'] #error for glat\n",
"print gta.roi.sources[0]['pos_err'] #error for the position in deg\n",
"print gta.roi.sources[0]['pos_r68'] #68% CL error for the position\n",
"print gta.roi.sources[0]['pos_r95'] #95% CL error for the position\n",
"print gta.roi.sources[0]['pos_r99'] #99% CL error for the position\n",
"print gta.roi.sources[0]['pos_err_semimajor'] #1-sigma uncertainty (deg) along major axis of uncertainty ellipse.\n",
"print gta.roi.sources[0]['pos_err_semiminor'] #1-sigma uncertainty (deg) along minor axis of uncertainty ellipse.\n",
"print gta.roi.sources[0]['offset_ra'] #Right ascension offset from ROI center in local celestial projection (deg).\n",
"print gta.roi.sources[0]['offset_dec'] #Declination offset from ROI center in local celestial projection (deg).\n",
"print gta.roi.sources[0]['offset_glon'] #Galactic longitude offset from ROI center in local galactic projection (deg).\n",
"print gta.roi.sources[0]['offset_glat'] #Galactic latitude offset from ROI center in local galactic projection (deg).\n",
"print gta.roi.sources[0]['offset'] #Angular offset from ROI center (deg)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The error for the position is nan because we have not calculated yet the relocalization. \n",
"Let's run the localization for a source with gta.localize tool."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-31 09:06:10 INFO GTAnalysis.print_model(): \n",
"sourcename offset norm eflux index ts npred free\n",
"--------------------------------------------------------------------------------\n",
"3FGL J0059.0-7242e 0.088 1.512 1.12e-05 2.47 753.09 1532.2 *\n",
"3FGL J0112.9-7506 2.572 1.268 2.14e-06 2.00 152.12 140.7 *\n",
"3FGL J0023.9-7203 2.662 0.590 9.68e-06 2.70 4889.10 1771.0 *\n",
"3FGL J0029.1-7045 3.008 0.746 3.1e-06 2.38 89.09 378.7 *\n",
"3FGL J0021.6-6835 5.122 2.072 2.11e-07 3.26 10.91 46.4 *\n",
"3FGL J2351.9-7601 5.495 1.429 2.81e-06 2.04 237.29 198.5 *\n",
"3FGL J2338.7-7401 5.777 0.574 2.82e-06 2.01 257.48 179.2 *\n",
"3FGL J0146.4-6746 6.423 0.529 1.65e-06 2.28 192.10 170.3 *\n",
"3FGL J2336.5-7620 6.454 0.449 1.28e-06 2.35 119.61 147.9 *\n",
"3FGL J0002.0-6722 7.153 0.395 1.7e-06 1.94 86.64 92.7 *\n",
"isodiff --- 0.913 0.0286 2.12 1145.04 8289.4 *\n",
"galdiff --- 0.944 0.122 -0.02 5109.11 17185.3 *\n",
"\n",
"2018-03-31 09:06:10 INFO GTAnalysis.print_model(): \n",
"sourcename offset norm eflux index ts npred free\n",
"--------------------------------------------------------------------------------\n",
"3FGL J0059.0-7242e 0.088 1.512 1.12e-05 2.47 753.09 1532.2 *\n",
"3FGL J0112.9-7506 2.572 1.268 2.14e-06 2.00 152.12 140.7 *\n",
"3FGL J0023.9-7203 2.662 0.590 9.68e-06 2.70 4889.10 1771.0 *\n",
"3FGL J0029.1-7045 3.008 0.746 3.1e-06 2.38 89.09 378.7 *\n",
"3FGL J0021.6-6835 5.122 2.072 2.11e-07 3.26 10.91 46.4 *\n",
"3FGL J2351.9-7601 5.495 1.429 2.81e-06 2.04 237.29 198.5 *\n",
"3FGL J2338.7-7401 5.777 0.574 2.82e-06 2.01 257.48 179.2 *\n",
"3FGL J0146.4-6746 6.423 0.529 1.65e-06 2.28 192.10 170.3 *\n",
"3FGL J2336.5-7620 6.454 0.449 1.28e-06 2.35 119.61 147.9 *\n",
"3FGL J0002.0-6722 7.153 0.395 1.7e-06 1.94 86.64 92.7 *\n",
"isodiff --- 0.913 0.0286 2.12 1145.04 8289.4 *\n",
"galdiff --- 0.944 0.122 -0.02 5109.11 17185.3 *\n",
"\n",
"2018-03-31 09:06:10 INFO GTAnalysis.localize(): Running localization for 3FGL J0023.9-7203\n",
"WARNING: AstropyDeprecationWarning: \"clobber\" was deprecated in version 2.0 and will be removed in a future version. Use argument \"overwrite\" instead. [astropy.utils.decorators]\n",
"WARNING: AstropyDeprecationWarning: \"clobber\" was deprecated in version 2.0 and will be removed in a future version. Use argument \"overwrite\" instead. [astropy.utils.decorators]\n",
"2018-03-31 09:06:28 INFO GTAnalysis._localize(): Localization succeeded.\n",
"2018-03-31 09:06:28 INFO GTAnalysis._localize(): Updating source 3FGL J0023.9-7203 to localized position.\n",
"2018-03-31 09:06:28 INFO GTAnalysis.delete_source(): Deleting source 3FGL J0023.9-7203\n",
"2018-03-31 09:06:28 INFO GTAnalysis.add_source(): Adding source 3FGL J0023.9-7203\n",
"2018-03-31 09:06:31 INFO GTAnalysis._localize(): Localization completed with new position:\n",
"( ra, dec) = ( 5.9919 +/- 0.0054, -72.0832 +/- 0.0053)\n",
"(glon,glat) = ( 305.9080 +/- 0.0054, -44.8859 +/- 0.0053)\n",
"offset = 0.0183 r68 = 0.0081 r95 = 0.0131 r99 = 0.0163\n",
"2018-03-31 09:06:31 INFO GTAnalysis._localize(): LogLike: -77883.001 DeltaLogLike: 0.040\n",
"2018-03-31 09:06:31 INFO GTAnalysis.localize(): Finished localization.\n",
"WARNING: AstropyDeprecationWarning: \"clobber\" was deprecated in version 2.0 and will be removed in a future version. Use argument \"overwrite\" instead. [astropy.utils.decorators]\n",
"2018-03-31 09:06:35 INFO GTAnalysis.localize(): Execution time: 25.06 s\n",
"2018-03-31 09:06:35 INFO GTAnalysis.print_model(): \n",
"sourcename offset norm eflux index ts npred free\n",
"--------------------------------------------------------------------------------\n",
"3FGL J0059.0-7242e 0.088 1.512 1.12e-05 2.47 753.09 1532.2 *\n",
"3FGL J0112.9-7506 2.572 1.268 2.14e-06 2.00 152.12 140.7 *\n",
"3FGL J0023.9-7203 2.654 0.590 9.68e-06 2.70 4997.47 1770.8 *\n",
"3FGL J0029.1-7045 3.008 0.746 3.1e-06 2.38 89.09 378.7 *\n",
"3FGL J0021.6-6835 5.122 2.072 2.11e-07 3.26 10.91 46.4 *\n",
"3FGL J2351.9-7601 5.495 1.429 2.81e-06 2.04 237.29 198.5 *\n",
"3FGL J2338.7-7401 5.777 0.574 2.82e-06 2.01 257.48 179.2 *\n",
"3FGL J0146.4-6746 6.423 0.529 1.65e-06 2.28 192.10 170.3 *\n",
"3FGL J2336.5-7620 6.454 0.449 1.28e-06 2.35 119.61 147.9 *\n",
"3FGL J0002.0-6722 7.153 0.395 1.7e-06 1.94 86.64 92.7 *\n",
"isodiff --- 0.913 0.0286 2.12 1145.04 8289.4 *\n",
"galdiff --- 0.944 0.122 -0.02 5109.11 17185.3 *\n",
"\n"
]
}
],
"source": [
"gta.free_sources(free=True)\n",
"gta.print_model()\n",
"gta.free_sources(skydir=gta.roi[gta.roi.sources[2].name].skydir,distance=[3.0],free=True)\n",
"gta.print_model()\n",
"localsmc = gta.localize(gta.roi.sources[2].name, update=True, make_plots=True)\n",
"gta.print_model()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the option make_plots=True a few control plots are created like 3fgl_j0023.9-7203_localize_peak.png with the result for the likelihood analysis for the besy fit position and error for the position."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename='3fgl_j0023.9-7203_localize_peak.png') "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5.991891558666937\n",
"-72.08324039663697\n",
"305.90801515198206\n",
"-44.88593302102324\n",
"0.005397242484279103\n",
"0.005337532530487048\n",
"0.005397242484279103\n",
"0.005337532530487048\n",
"0.005366221368316228\n",
"0.008138277648229967\n",
"0.013133545877823873\n",
"0.016285398847939087\n",
"0.005448724558888491\n",
"0.005284967419907161\n",
"2.609705201380567\n",
"0.48092218927044694\n",
"-2.5908516158641124\n",
"-0.573805547525784\n",
"2.6537356812\n"
]
}
],
"source": [
"print gta.roi.sources[2]['ra'] #ra\n",
"print gta.roi.sources[2]['dec'] #dec\n",
"print gta.roi.sources[2]['glon'] #glon\n",
"print gta.roi.sources[2]['glat'] #glat\n",
"print gta.roi.sources[2]['ra_err'] #error for ra\n",
"print gta.roi.sources[2]['dec_err'] #error for dec\n",
"print gta.roi.sources[2]['glon_err'] #error for glon\n",
"print gta.roi.sources[2]['glat_err'] #error for glat\n",
"print gta.roi.sources[2]['pos_err'] #error for the position in deg\n",
"print gta.roi.sources[2]['pos_r68'] #68% CL error for the position\n",
"print gta.roi.sources[2]['pos_r95'] #95% CL error for the position\n",
"print gta.roi.sources[2]['pos_r99'] #99% CL error for the position\n",
"print gta.roi.sources[2]['pos_err_semimajor'] #1-sigma uncertainty (deg) along major axis of uncertainty ellipse.\n",
"print gta.roi.sources[2]['pos_err_semiminor'] #1-sigma uncertainty (deg) along minor axis of uncertainty ellipse.\n",
"print gta.roi.sources[2]['offset_ra'] #Right ascension offset from ROI center in local celestial projection (deg).\n",
"print gta.roi.sources[2]['offset_dec'] #Declination offset from ROI center in local celestial projection (deg).\n",
"print gta.roi.sources[2]['offset_glon'] #Galactic longitude offset from ROI center in local galactic projection (deg).\n",
"print gta.roi.sources[2]['offset_glat'] #Galactic latitude offset from ROI center in local galactic projection (deg).\n",
"print gta.roi.sources[2]['offset'] #Angular offset from ROI center (deg)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"SED PARAMETERS FLUX AND SCAN OF FLUX"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Prefactor' 'Index' 'Scale' '' '' '' '' '' '' '']\n",
"[ 1.51191261e-11 -2.46682153e+00 6.65532043e+02 nan\n",
" nan nan nan nan\n",
" nan nan]\n",
"[1.25024392e-12 6.17596151e-02 nan nan\n",
" nan nan nan nan\n",
" nan nan]\n",
"753.08940408\n",
"-77883.0461087\n",
"[0.00000000e+00 3.45304615e-09 3.49502174e-09 3.53699734e-09\n",
" 3.57897294e-09 3.62094854e-09 3.66292414e-09 3.70489974e-09\n",
" 3.74687534e-09 3.78885093e-09 3.83082653e-09 3.87280213e-09\n",
" 3.91477773e-09 3.95675333e-09 3.99872893e-09 4.04070453e-09\n",
" 4.08268013e-09 4.12465572e-09 4.16663132e-09 4.20860692e-09]\n",
"[0. 1.38385295 1.40067521 1.41749748 1.43431974 1.45114201\n",
" 1.46796427 1.48478654 1.5016088 1.51843106 1.53525333 1.55207559\n",
" 1.56889786 1.58572012 1.60254239 1.61936465 1.63618692 1.65300918\n",
" 1.66983145 1.68665371]\n",
"1532.23595176\n",
"3.77258581354e-09\n",
"1.6566462673e-10\n",
"4.03860906082e-09\n",
"9.1858070747e-13\n"
]
}
],
"source": [
"print gta.roi.sources[0]['param_names'] #Names of spectral parameters.\n",
"print gta.roi.sources[0]['param_values'] #Spectral parameter values.\n",
"print gta.roi.sources[0]['param_errors'] #Spectral parameters errors.\n",
"print gta.roi.sources[0]['ts'] #Source test statistic.\n",
"print gta.roi.sources[0]['loglike'] #Log-likelihood of the model evaluated at the best-fit normalization of the source.\n",
"print gta.roi.sources[0]['flux_scan'] #Flux values for scan of source normalization.\n",
"print gta.roi.sources[0]['norm_scan'] #Normalization parameters values for scan of source normalization.\n",
"print gta.roi.sources[0]['npred'] #Number of predicted counts from this source integrated over the analysis energy range.\n",
"print gta.roi.sources[0]['flux'] #Photon flux (cm−2 s−1) integrated over analysis energy range\n",
"print gta.roi.sources[0]['flux_err'] #Photon flux uncertainty (cm−2 s−1) integrated over analysis energy range\n",
"print gta.roi.sources[0]['flux_ul95'] #95% CL upper limit on the photon Differential photon flux (cm−2 s−1 MeV−1tegrated over analysis energy range\n",
"print gta.roi.sources[0]['dnde'] #Differential photon flux (cm−2 s−1 MeV−1) evaluated at the pivot energy."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-31 09:09:31 INFO GTBinnedAnalysis.write_xml(): Writing /nfs/slac/kipac/fs1/u/mdimauro/software/fermipy-extra/notebooks/model_test_00.xml...\n",
"2018-03-31 09:09:31 INFO GTAnalysis.write_fits(): Writing /nfs/slac/kipac/fs1/u/mdimauro/software/fermipy-extra/notebooks/model_test.fits...\n",
"2018-03-31 09:09:32 INFO GTBinnedAnalysis.write_model_map(): Generating model map for component 00.\n",
"2018-03-31 09:09:34 INFO GTAnalysis.write_roi(): Writing /nfs/slac/kipac/fs1/u/mdimauro/software/fermipy-extra/notebooks/model_test.npy...\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f95d449ccd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gta.write_roi('model_test',make_plots=True,save_model_map=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Customizing your model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we saw before you can customize your model directly in the config.yaml file.\n",
"However, sometimes you want to change something in your model directly in your Python script."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can free or fix sources using gta.free_sources.\n",
"First let's fix the SED parameter of all the sources."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-31 09:10:09 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0059.0-7242e : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:09 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0112.9-7506 : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:09 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0023.9-7203 : ['norm', 'alpha', 'beta']\n",
"2018-03-31 09:10:09 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0029.1-7045 : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:09 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0021.6-6835 : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:09 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J2351.9-7601 : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:09 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J2338.7-7401 : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:09 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0146.4-6746 : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:09 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J2336.5-7620 : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:09 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0002.0-6722 : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:09 INFO GTAnalysis.free_source(): Fixing parameters for isodiff : ['Normalization']\n",
"2018-03-31 09:10:09 INFO GTAnalysis.free_source(): Fixing parameters for galdiff : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:09 INFO GTAnalysis.print_model(): \n",
"sourcename offset norm eflux index ts npred free\n",
"--------------------------------------------------------------------------------\n",
"3FGL J0059.0-7242e 0.088 1.512 1.12e-05 2.47 753.09 1532.2 \n",
"3FGL J0112.9-7506 2.572 1.268 2.14e-06 2.00 152.12 140.7 \n",
"3FGL J0023.9-7203 2.654 0.590 9.68e-06 2.70 4997.47 1770.8 \n",
"3FGL J0029.1-7045 3.008 0.746 3.1e-06 2.38 89.09 378.7 \n",
"3FGL J0021.6-6835 5.122 2.072 2.11e-07 3.26 10.91 46.4 \n",
"3FGL J2351.9-7601 5.495 1.429 2.81e-06 2.04 237.29 198.5 \n",
"3FGL J2338.7-7401 5.777 0.574 2.82e-06 2.01 257.48 179.2 \n",
"3FGL J0146.4-6746 6.423 0.529 1.65e-06 2.28 192.10 170.3 \n",
"3FGL J2336.5-7620 6.454 0.449 1.28e-06 2.35 119.61 147.9 \n",
"3FGL J0002.0-6722 7.153 0.395 1.7e-06 1.94 86.64 92.7 \n",
"isodiff --- 0.913 0.0286 2.12 1145.04 8289.4 \n",
"galdiff --- 0.944 0.122 -0.02 5109.11 17185.3 \n",
"\n"
]
}
],
"source": [
"gta.free_sources(free=False)\n",
"gta.print_model()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we free all parameters:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-31 09:10:13 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0059.0-7242e : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:13 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0112.9-7506 : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:13 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0023.9-7203 : ['norm', 'alpha', 'beta']\n",
"2018-03-31 09:10:13 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0029.1-7045 : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:13 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0021.6-6835 : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:13 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J2351.9-7601 : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:13 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J2338.7-7401 : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:13 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0146.4-6746 : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:13 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J2336.5-7620 : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:13 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0002.0-6722 : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:13 INFO GTAnalysis.free_source(): Freeing parameters for isodiff : ['Normalization']\n",
"2018-03-31 09:10:13 INFO GTAnalysis.free_source(): Freeing parameters for galdiff : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:13 INFO GTAnalysis.print_model(): \n",
"sourcename offset norm eflux index ts npred free\n",
"--------------------------------------------------------------------------------\n",
"3FGL J0059.0-7242e 0.088 1.512 1.12e-05 2.47 753.09 1532.2 *\n",
"3FGL J0112.9-7506 2.572 1.268 2.14e-06 2.00 152.12 140.7 *\n",
"3FGL J0023.9-7203 2.654 0.590 9.68e-06 2.70 4997.47 1770.8 *\n",
"3FGL J0029.1-7045 3.008 0.746 3.1e-06 2.38 89.09 378.7 *\n",
"3FGL J0021.6-6835 5.122 2.072 2.11e-07 3.26 10.91 46.4 *\n",
"3FGL J2351.9-7601 5.495 1.429 2.81e-06 2.04 237.29 198.5 *\n",
"3FGL J2338.7-7401 5.777 0.574 2.82e-06 2.01 257.48 179.2 *\n",
"3FGL J0146.4-6746 6.423 0.529 1.65e-06 2.28 192.10 170.3 *\n",
"3FGL J2336.5-7620 6.454 0.449 1.28e-06 2.35 119.61 147.9 *\n",
"3FGL J0002.0-6722 7.153 0.395 1.7e-06 1.94 86.64 92.7 *\n",
"isodiff --- 0.913 0.0286 2.12 1145.04 8289.4 *\n",
"galdiff --- 0.944 0.122 -0.02 5109.11 17185.3 *\n",
"\n"
]
}
],
"source": [
"gta.free_sources(free=True)\n",
"gta.print_model()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we free all the SED parameters of sources within 3 degrees from 3FGL J0059.0-7242e:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0059.0-7242e : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0112.9-7506 : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0023.9-7203 : ['norm', 'alpha', 'beta']\n",
"2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0029.1-7045 : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0021.6-6835 : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J2351.9-7601 : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J2338.7-7401 : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0146.4-6746 : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J2336.5-7620 : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Fixing parameters for 3FGL J0002.0-6722 : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Fixing parameters for isodiff : ['Normalization']\n",
"2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Fixing parameters for galdiff : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0059.0-7242e : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0112.9-7506 : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Freeing parameters for 3FGL J0023.9-7203 : ['norm', 'alpha', 'beta']\n",
"2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Freeing parameters for isodiff : ['Normalization']\n",
"2018-03-31 09:10:19 INFO GTAnalysis.free_source(): Freeing parameters for galdiff : ['Prefactor', 'Index']\n",
"2018-03-31 09:10:19 INFO GTAnalysis.print_model(): \n",
"sourcename offset norm eflux index ts npred free\n",
"--------------------------------------------------------------------------------\n",
"3FGL J0059.0-7242e 0.088 1.512 1.12e-05 2.47 753.09 1532.2 *\n",
"3FGL J0112.9-7506 2.572 1.268 2.14e-06 2.00 152.12 140.7 *\n",
"3FGL J0023.9-7203 2.654 0.590 9.68e-06 2.70 4997.47 1770.8 *\n",
"3FGL J0029.1-7045 3.008 0.746 3.1e-06 2.38 89.09 378.7 \n",
"3FGL J0021.6-6835 5.122 2.072 2.11e-07 3.26 10.91 46.4 \n",
"3FGL J2351.9-7601 5.495 1.429 2.81e-06 2.04 237.29 198.5 \n",
"3FGL J2338.7-7401 5.777 0.574 2.82e-06 2.01 257.48 179.2 \n",
"3FGL J0146.4-6746 6.423 0.529 1.65e-06 2.28 192.10 170.3 \n",
"3FGL J2336.5-7620 6.454 0.449 1.28e-06 2.35 119.61 147.9 \n",
"3FGL J0002.0-6722 7.153 0.395 1.7e-06 1.94 86.64 92.7 \n",
"isodiff --- 0.913 0.0286 2.12 1145.04 8289.4 *\n",
"galdiff --- 0.944 0.122 -0.02 5109.11 17185.3 *\n",
"\n"
]
}
],
"source": [
"gta.free_sources(free=False)\n",
"gta.free_sources(skydir=gta.roi['3FGL J0059.0-7242e'].skydir,distance=3.0)\n",
"gta.print_model()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we want to delete the source 3FGL J0021.6-6835:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-31 09:10:23 INFO GTAnalysis.delete_source(): Deleting source 3FGL J0021.6-6835\n",
"2018-03-31 09:10:23 INFO GTAnalysis.print_model(): \n",
"sourcename offset norm eflux index ts npred free\n",
"--------------------------------------------------------------------------------\n",
"3FGL J0059.0-7242e 0.088 1.512 1.12e-05 2.47 753.09 1532.2 *\n",
"3FGL J0112.9-7506 2.572 1.268 2.14e-06 2.00 152.12 140.7 *\n",
"3FGL J0023.9-7203 2.654 0.590 9.68e-06 2.70 4997.47 1770.8 *\n",
"3FGL J0029.1-7045 3.008 0.746 3.1e-06 2.38 89.09 378.7 \n",
"3FGL J2351.9-7601 5.495 1.429 2.81e-06 2.04 237.29 198.5 \n",
"3FGL J2338.7-7401 5.777 0.574 2.82e-06 2.01 257.48 179.2 \n",
"3FGL J0146.4-6746 6.423 0.529 1.65e-06 2.28 192.10 170.3 \n",
"3FGL J2336.5-7620 6.454 0.449 1.28e-06 2.35 119.61 147.9 \n",
"3FGL J0002.0-6722 7.153 0.395 1.7e-06 1.94 86.64 92.7 \n",
"isodiff --- 0.913 0.0286 2.12 1145.04 8289.4 *\n",
"galdiff --- 0.944 0.122 -0.02 5109.11 17185.3 *\n",
"\n"
]
}
],
"source": [
"gta.delete_source('3FGL J0021.6-6835')\n",
"gta.print_model()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also delete all the sources as a function of the npred/ts or position using the options minmax_npred/minmax_ts/skydir options. Finally, you can delete sources in a given list using the option names."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on method delete_sources in module fermipy.gtanalysis:\n",
"\n",
"delete_sources(self, cuts=None, distance=None, skydir=None, minmax_ts=None, minmax_npred=None, exclude=None, square=False, names=None) method of fermipy.gtanalysis.GTAnalysis instance\n",
" Delete sources in the ROI model satisfying the given\n",
" selection criteria.\n",
" \n",
" Parameters\n",
" ----------\n",
" cuts : dict\n",
" Dictionary of [min,max] selections on source properties.\n",
" \n",
" distance : float\n",
" Cut on angular distance from ``skydir``. If None then no\n",
" selection will be applied.\n",
" \n",
" skydir : `~astropy.coordinates.SkyCoord`\n",
" Reference sky coordinate for ``distance`` selection. If\n",
" None then the distance selection will be applied with\n",
" respect to the ROI center.\n",
" \n",
" minmax_ts : list\n",
" Select sources that have TS in the range [min,max]. If\n",
" either min or max are None then only a lower (upper) bound\n",
" will be applied. If this parameter is none no selection\n",
" will be applied.\n",
" \n",
" minmax_npred : list\n",
" Select sources that have npred in the range [min,max]. If\n",
" either min or max are None then only a lower (upper) bound\n",
" will be applied. If this parameter is none no selection\n",
" will be applied.\n",
" \n",
" square : bool\n",
" Switch between applying a circular or square (ROI-like)\n",
" selection on the maximum projected distance from the ROI\n",
" center.\n",
" \n",
" names : list \n",
" Select sources matching a name in this list.\n",
" \n",
" Returns\n",
" -------\n",
" srcs : list\n",
" A list of `~fermipy.roi_model.Model` objects.\n",
"\n"
]
}
],
"source": [
"help(gta.delete_sources)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the example below we delete all the sources that have an npred=[0,500]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-31 09:10:36 INFO GTAnalysis.delete_source(): Deleting source 3FGL J0112.9-7506\n",
"2018-03-31 09:10:36 INFO GTAnalysis.delete_source(): Deleting source 3FGL J0029.1-7045\n",
"2018-03-31 09:10:36 INFO GTAnalysis.delete_source(): Deleting source 3FGL J2351.9-7601\n",
"2018-03-31 09:10:36 INFO GTAnalysis.delete_source(): Deleting source 3FGL J2338.7-7401\n",
"2018-03-31 09:10:36 INFO GTAnalysis.delete_source(): Deleting source 3FGL J0146.4-6746\n",
"2018-03-31 09:10:36 INFO GTAnalysis.delete_source(): Deleting source 3FGL J2336.5-7620\n",
"2018-03-31 09:10:36 INFO GTAnalysis.delete_source(): Deleting source 3FGL J0002.0-6722\n",
"2018-03-31 09:10:36 INFO GTAnalysis.print_model(): \n",
"sourcename offset norm eflux index ts npred free\n",
"--------------------------------------------------------------------------------\n",
"3FGL J0059.0-7242e 0.088 1.512 1.12e-05 2.47 753.09 1532.2 *\n",
"3FGL J0023.9-7203 2.654 0.590 9.68e-06 2.70 4997.47 1770.8 *\n",
"isodiff --- 0.913 0.0286 2.12 1145.04 8289.4 *\n",
"galdiff --- 0.944 0.122 -0.02 5109.11 17185.3 *\n",
"\n"
]
}
],
"source": [
"gta.delete_sources(minmax_npred=[0,500])\n",
"gta.print_model()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can aldo add a new source in the model using the gta.add_source function."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on method add_source in module fermipy.gtanalysis:\n",
"\n",
"add_source(self, name, src_dict, free=None, init_source=True, save_source_maps=True, use_pylike=True, use_single_psf=False, **kwargs) method of fermipy.gtanalysis.GTAnalysis instance\n",
" Add a source to the ROI model. This function may be called\n",
" either before or after `~fermipy.gtanalysis.GTAnalysis.setup`.\n",
" \n",
" Parameters\n",
" ----------\n",
" name : str\n",
" Source name.\n",
" \n",
" src_dict : dict or `~fermipy.roi_model.Source` object\n",
" Dictionary or source object defining the source properties\n",
" (coordinates, spectral parameters, etc.).\n",
" \n",
" free : bool\n",
" Initialize the source with a free normalization parameter.\n",
" \n",
" use_pylike : bool\n",
" Create source maps with pyLikelihood.\n",
" \n",
" use_single_psf : bool \n",
" Use the PSF model calculated for the ROI center. If false\n",
" then a new model will be generated using the position of\n",
" the source.\n",
"\n"
]
}
],
"source": [
"help(gta.add_source)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the example below we add a Source called Source_PL with a PLSuperExpCutoff and pointlike and a source called Source_Gauss that is spatial extended with a PowerLaw SED and with a RadialGaussian template with an extension of 1 deg."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-31 09:10:44 INFO GTAnalysis.add_source(): Adding source Source_PL\n",
"2018-03-31 09:10:47 INFO GTAnalysis.add_source(): Adding source Source_Gauss\n",
"2018-03-31 09:11:00 INFO GTAnalysis.print_model(): \n",
"sourcename offset norm eflux index ts npred free\n",
"--------------------------------------------------------------------------------\n",
"3FGL J0059.0-7242e 0.088 1.512 1.12e-05 2.47 753.09 1532.2 *\n",
"Source_Gauss 0.655 1.000 0.00621 2.00 nan 410827.7 *\n",
"Source_PL 2.274 1.000 0.00319 4.00 nan 471919.8 *\n",
"3FGL J0023.9-7203 2.654 0.590 9.68e-06 2.70 4997.47 1770.8 *\n",
"isodiff --- 0.913 0.0286 2.12 1145.04 8289.4 *\n",
"galdiff --- 0.944 0.122 -0.02 5109.11 17185.3 *\n",
"\n"
]
}
],
"source": [
"gta.add_source('Source_PL',{ 'glon' : 300., 'glat' : -46.,'SpectrumType' : 'PLSuperExpCutoff', 'Index1':-1.5, 'Index2' : 1.0,'Scale' : 1000,'Prefactor':1e-9,'SpatialModel' : 'PointSource' })\n",
"gta.add_source('Source_Gauss',{ 'glon' : 302., 'glat' : -45.,'SpectrumType' : 'PowerLaw', 'Index':2.0,'Scale' : 1000,'Prefactor':1e-9,'SpatialModel' : 'RadialGaussian', 'SpatialWidth': 1.0 })\n",
"gta.print_model()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now I load the model saved into model_test to have back the intial model."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-31 09:11:04 INFO GTAnalysis.load_roi(): Loading ROI file: /nfs/slac/kipac/fs1/u/mdimauro/software/fermipy-extra/notebooks/model_test.npy\n",
"2018-03-31 09:11:04 INFO GTBinnedAnalysis._create_binned_analysis(): Creating BinnedAnalysis for component 00.\n",
"2018-03-31 09:11:23 INFO GTAnalysis.load_roi(): Finished Loading ROI\n",
"2018-03-31 09:11:23 INFO GTAnalysis.print_model(): \n",
"sourcename offset norm eflux index ts npred free\n",
"--------------------------------------------------------------------------------\n",
"3FGL J0059.0-7242e 0.088 1.512 1.12e-05 2.47 753.09 1532.2 *\n",
"3FGL J0112.9-7506 2.572 1.268 2.14e-06 2.00 152.12 140.7 *\n",
"3FGL J0023.9-7203 2.654 0.590 9.68e-06 2.70 4997.47 1770.8 *\n",
"3FGL J0029.1-7045 3.008 0.746 3.1e-06 2.38 89.09 378.7 *\n",
"3FGL J0021.6-6835 5.122 2.072 2.11e-07 3.26 10.91 46.4 *\n",
"3FGL J2351.9-7601 5.495 1.429 2.81e-06 2.04 237.29 198.5 *\n",
"3FGL J2338.7-7401 5.777 0.574 2.82e-06 2.01 257.48 179.2 *\n",
"3FGL J0146.4-6746 6.423 0.529 1.65e-06 2.28 192.10 170.3 *\n",
"3FGL J2336.5-7620 6.454 0.449 1.28e-06 2.35 119.61 147.9 *\n",
"3FGL J0002.0-6722 7.153 0.395 1.7e-06 1.94 86.64 92.7 *\n",
"isodiff --- 0.913 0.0286 2.12 1145.04 8289.4 *\n",
"galdiff --- 0.944 0.122 -0.02 5109.11 17185.3 *\n",
"\n"
]
}
],
"source": [
"gta.load_roi('model_test')\n",
"gta.print_model()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is also possible to modify the SED parameters using the functions gta.set_norm, gta.set_parameter, gta.set_parameter_bounds, gta.set_parameter_error ."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on method set_norm in module fermipy.gtanalysis:\n",
"\n",
"set_norm(self, name, value, update_source=True) method of fermipy.gtanalysis.GTAnalysis instance\n",
"\n",
"Help on method set_parameter in module fermipy.gtanalysis:\n",
"\n",
"set_parameter(self, name, par, value, true_value=True, scale=None, bounds=None, error=None, update_source=True) method of fermipy.gtanalysis.GTAnalysis instance\n",
" Update the value of a parameter. Parameter bounds will\n",
" automatically be adjusted to encompass the new parameter\n",
" value.\n",
" \n",
" Parameters\n",
" ----------\n",
" \n",
" name : str\n",
" Source name.\n",
" \n",
" par : str\n",
" Parameter name.\n",
" \n",
" value : float\n",
" Parameter value. By default this argument should be the\n",
" unscaled (True) parameter value.\n",
" \n",
" scale : float\n",
" Parameter scale (optional). Value argument is interpreted\n",
" with respect to the scale parameter if it is provided.\n",
" \n",
" error : float\n",
" Parameter error (optional). By default this argument should be the\n",
" unscaled (True) parameter value.\n",
" \n",
" update_source : bool\n",
" Update the source dictionary for the object.\n",
"\n"
]
}
],
"source": [
"help(gta.set_norm)\n",
"help(gta.set_parameter)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Prefactor' 'Index' 'Scale' '' '' '' '' '' '' '']\n",
"[ 1.51191261e-11 -2.46682153e+00 6.65532043e+02 nan\n",
" nan nan nan nan\n",
" nan nan]\n",
"[ 1.25024392e-12 -6.17596151e-02 nan nan\n",
" nan nan nan nan\n",
" nan nan]\n"
]
}
],
"source": [
"print gta.roi['3FGL J0059.0-7242e']['param_names']\n",
"print gta.roi['3FGL J0059.0-7242e']['param_values']\n",
"print gta.roi['3FGL J0059.0-7242e']['param_errors']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the example below we fix the normalization to 1e-11 and the slope to 2.0."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/u/gl/mdimauro/kipac/software/anaconda/lib/python2.7/site-packages/scipy/interpolate/fitpack2.py:226: UserWarning: \n",
"A theoretically impossible result was found during the iteration\n",
"process for finding a smoothing spline with fp = s: s too small.\n",
"There is an approximation returned but the corresponding weighted sum\n",
"of squared residuals does not satisfy the condition abs(fp-s)/s < tol.\n",
" warnings.warn(message)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Prefactor' 'Index' 'Scale' '' '' '' '' '' '' '']\n",
"[1.00000000e-11 2.00000000e+00 6.65532043e+02 nan\n",
" nan nan nan nan\n",
" nan nan]\n",
"[ 0. 0. nan nan nan nan nan nan nan nan]\n"
]
}
],
"source": [
"gta.set_norm('3FGL J0059.0-7242e',value=1.)\n",
"gta.set_parameter('3FGL J0059.0-7242e',par='Index',value=2.0)\n",
"print gta.roi['3FGL J0059.0-7242e']['param_names']\n",
"print gta.roi['3FGL J0059.0-7242e']['param_values']\n",
"print gta.roi['3FGL J0059.0-7242e']['param_errors']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is also possible to set the SED shape of a source using gta.set_source_spectrum tool."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on method set_source_spectrum in module fermipy.gtanalysis:\n",
"\n",
"set_source_spectrum(self, name, spectrum_type='PowerLaw', spectrum_pars=None, update_source=True) method of fermipy.gtanalysis.GTAnalysis instance\n",
" Set the spectral model of a source. This function can be\n",
" used to change the spectral type of a source or modify its\n",
" spectral parameters. If called with\n",
" spectrum_type='FileFunction' and spectrum_pars=None, the\n",
" source spectrum will be replaced with a FileFunction with the\n",
" same differential flux distribution as the original spectrum.\n",
" \n",
" Parameters\n",
" ----------\n",
" \n",
" name : str\n",
" Source name.\n",
" \n",
" spectrum_type : str\n",
" Spectrum type (PowerLaw, etc.).\n",
" \n",
" spectrum_pars : dict\n",
" Dictionary of spectral parameters (optional).\n",
" \n",
" update_source : bool\n",
" Recompute all source characteristics (flux, TS, NPred)\n",
" using the new spectral model of the source.\n",
"\n"
]
}
],
"source": [
"help(gta.set_source_spectrum)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-31 09:14:18 INFO GTAnalysis.load_roi(): Loading ROI file: /nfs/slac/kipac/fs1/u/mdimauro/software/fermipy-extra/notebooks/model_test.npy\n",
"2018-03-31 09:14:18 INFO GTBinnedAnalysis._create_binned_analysis(): Creating BinnedAnalysis for component 00.\n",
"2018-03-31 09:14:37 INFO GTAnalysis.load_roi(): Finished Loading ROI\n",
"2018-03-31 09:14:37 INFO GTAnalysis.print_model(): \n",
"sourcename offset norm eflux index ts npred free\n",
"--------------------------------------------------------------------------------\n",
"3FGL J0059.0-7242e 0.088 1.512 1.12e-05 2.47 753.09 1532.2 *\n",
"3FGL J0112.9-7506 2.572 1.268 2.14e-06 2.00 152.12 140.7 *\n",
"3FGL J0023.9-7203 2.654 0.590 9.68e-06 2.70 4997.47 1770.8 *\n",
"3FGL J0029.1-7045 3.008 0.746 3.1e-06 2.38 89.09 378.7 *\n",
"3FGL J0021.6-6835 5.122 2.072 2.11e-07 3.26 10.91 46.4 *\n",
"3FGL J2351.9-7601 5.495 1.429 2.81e-06 2.04 237.29 198.5 *\n",
"3FGL J2338.7-7401 5.777 0.574 2.82e-06 2.01 257.48 179.2 *\n",
"3FGL J0146.4-6746 6.423 0.529 1.65e-06 2.28 192.10 170.3 *\n",
"3FGL J2336.5-7620 6.454 0.449 1.28e-06 2.35 119.61 147.9 *\n",
"3FGL J0002.0-6722 7.153 0.395 1.7e-06 1.94 86.64 92.7 *\n",
"isodiff --- 0.913 0.0286 2.12 1145.04 8289.4 *\n",
"galdiff --- 0.944 0.122 -0.02 5109.11 17185.3 *\n",
"\n"
]
},
{
"data": {
"text/plain": [
"'PowerLaw'"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gta.load_roi('model_test')\n",
"gta.print_model()\n",
"gta.roi['3FGL J0059.0-7242e']['SpectrumType']"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'LogParabola'"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gta.set_source_spectrum('3FGL J0059.0-7242e',spectrum_type='LogParabola')\n",
"gta.roi['3FGL J0059.0-7242e']['SpectrumType']"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-31 09:15:23 INFO GTAnalysis.fit(): Starting fit.\n",
"2018-03-31 09:15:38 INFO GTAnalysis.fit(): Fit returned successfully. Quality: 3 Status: 0\n",
"2018-03-31 09:15:38 INFO GTAnalysis.fit(): LogLike: -77883.001 DeltaLogLike: 0.000 \n"
]
},
{
"data": {
"text/plain": [
"{'config': {'covar': True,\n",
" 'init_lambda': 0.0001,\n",
" 'max_iter': 100,\n",
" 'min_fit_quality': 2,\n",
" 'optimizer': 'MINUIT',\n",
" 'reoptimize': False,\n",
" 'retries': 3,\n",
" 'tol': 0.001,\n",
" 'verbosity': 0},\n",
" 'correlation': array([[ 1.00000000e+00, 4.40010977e-01, -1.80903032e-03,\n",
" -1.29673069e-03, 3.87817026e-03, 2.64332600e-03,\n",
" -2.04198717e-03, 9.41038275e-03, 6.18965916e-04,\n",
" 1.18313905e-02, 2.62916494e-03, 4.12849233e-03,\n",
" 2.26000858e-03, 6.23891541e-03, 3.04105095e-03,\n",
" 5.05645412e-03, 3.00353477e-03, 4.90626867e-03,\n",
" 2.82449046e-03, 2.82031889e-03, 2.93014665e-03,\n",
" -1.19680073e-02, -2.71265943e-02, -3.41242300e-02],\n",
" [ 4.40010977e-01, 1.00000000e+00, -1.81621670e-03,\n",
" -7.32754724e-04, 2.44740751e-03, 1.49292747e-03,\n",
" -8.93745178e-04, 5.79304529e-03, 4.32350178e-03,\n",
" 1.14652827e-02, 8.09005714e-03, 3.14397563e-03,\n",
" 2.46443004e-03, 3.52454107e-03, 2.37096614e-03,\n",
" 4.72386017e-03, 3.46125929e-03, 2.76468822e-03,\n",
" 2.53278353e-03, 1.15894276e-03, 3.11172827e-03,\n",
" -3.39077196e-02, -2.77540909e-02, -1.89906219e-03],\n",
" [-1.80903032e-03, -1.81621670e-03, 1.00000000e+00,\n",
" 8.61787939e-01, 5.57550572e-03, 3.44059022e-03,\n",
" -2.20704334e-03, -1.68151354e-02, -1.15186150e-02,\n",
" 1.56722554e-02, 1.05487303e-02, 2.77193258e-03,\n",
" 2.56573839e-03, 5.95117489e-03, 3.78280704e-03,\n",
" 4.66781225e-03, 4.01891459e-03, 3.48241472e-03,\n",
" 3.20035094e-03, 6.91485427e-04, 3.48455951e-03,\n",
" -1.95753025e-02, -6.10088327e-02, -3.71947727e-02],\n",
" [-1.29673069e-03, -7.32754724e-04, 8.61787939e-01,\n",
" 1.00000000e+00, 2.21844270e-03, 1.44347366e-03,\n",
" -7.90470631e-04, -7.08738255e-03, -5.00629583e-03,\n",
" 7.50301160e-03, 8.19997549e-03, 2.34604520e-04,\n",
" 1.10416771e-03, 1.99669462e-03, 1.79391848e-03,\n",
" 1.45040587e-03, 2.02224228e-03, 4.46055027e-04,\n",
" 1.36791422e-03, -9.66890171e-04, 1.56792928e-03,\n",
" -1.11987392e-02, -4.14265699e-02, -1.53951058e-02],\n",
" [ 3.87817026e-03, 2.44740751e-03, 5.57550572e-03,\n",
" 2.21844270e-03, 1.00000000e+00, 9.12794676e-01,\n",
" -7.87387195e-01, -1.12933332e-01, -7.75789895e-02,\n",
" -2.13540610e-02, -1.62704772e-02, 2.04205710e-03,\n",
" 2.10561763e-03, 5.79314587e-03, 3.58099264e-03,\n",
" 2.64602281e-03, 2.79123264e-03, 1.24610943e-03,\n",
" 1.19157719e-03, -1.96345718e-04, 1.90464271e-03,\n",
" -1.51143517e-03, -6.58797942e-02, -5.19246295e-02],\n",
" [ 2.64332600e-03, 1.49292747e-03, 3.44059022e-03,\n",
" 1.44347366e-03, 9.12794676e-01, 1.00000000e+00,\n",
" -9.53006611e-01, -5.66935236e-02, -4.82988376e-02,\n",
" -1.41035967e-02, -1.23516314e-02, 4.24102285e-04,\n",
" 9.65151924e-04, 4.21371130e-03, 2.60739988e-03,\n",
" 7.85177633e-04, 1.50411220e-03, 5.53569374e-04,\n",
" 6.10302575e-04, -7.05319173e-04, 8.66909407e-04,\n",
" 1.10796568e-02, -5.58470765e-02, -4.97881237e-02],\n",
" [-2.04198717e-03, -8.93745178e-04, -2.20704334e-03,\n",
" -7.90470631e-04, -7.87387195e-01, -9.53006611e-01,\n",
" 1.00000000e+00, 3.26407713e-02, 2.44138611e-02,\n",
" 9.72780037e-03, 9.03431459e-03, 9.90441985e-05,\n",
" -3.56367019e-04, -3.19245662e-03, -1.85352276e-03,\n",
" 2.40927129e-05, -6.85183821e-04, -3.64337719e-04,\n",
" -2.50450372e-04, 5.82240863e-04, -2.70585849e-04,\n",
" -1.64014563e-02, 4.18856752e-02, 4.38643321e-02],\n",
" [ 9.41038275e-03, 5.79304529e-03, -1.68151354e-02,\n",
" -7.08738255e-03, -1.12933332e-01, -5.66935236e-02,\n",
" 3.26407713e-02, 1.00000000e+00, 5.80487472e-01,\n",
" -1.87595578e-02, -1.23060789e-02, 1.19052085e-02,\n",
" 7.64558006e-03, 1.27493248e-02, 7.06093880e-03,\n",
" 1.36421567e-02, 8.84446196e-03, 1.02288328e-02,\n",
" 7.10200432e-03, 5.64930029e-03, 8.29629772e-03,\n",
" -6.92002424e-02, -6.61472086e-02, -3.83361561e-02],\n",
" [ 6.18965916e-04, 4.32350178e-03, -1.15186150e-02,\n",
" -5.00629583e-03, -7.75789895e-02, -4.82988376e-02,\n",
" 2.44138611e-02, 5.80487472e-01, 1.00000000e+00,\n",
" -1.33306016e-02, -9.83690603e-03, 5.25041106e-03,\n",
" 6.17726862e-03, 3.77239953e-03, 4.51690976e-03,\n",
" 9.17089932e-03, 8.34603639e-03, 2.16761030e-03,\n",
" 5.03044211e-03, -8.93480298e-04, 7.00248571e-03,\n",
" -1.02228467e-01, -7.82270683e-02, 3.42990225e-02],\n",
" [ 1.18313905e-02, 1.14652827e-02, 1.56722554e-02,\n",
" 7.50301160e-03, -2.13540610e-02, -1.41035967e-02,\n",
" 9.72780037e-03, -1.87595578e-02, -1.33306016e-02,\n",
" 1.00000000e+00, 8.47824788e-01, -1.09930376e-02,\n",
" -7.48634299e-03, 1.59055343e-02, 1.11948590e-02,\n",
" 2.15383897e-02, 1.64886598e-02, 1.18746541e-02,\n",
" 1.17888253e-02, 4.00708119e-03, 1.42861194e-02,\n",
" -1.61261963e-01, -1.47766781e-01, -1.12714296e-02],\n",
" [ 2.62916494e-03, 8.09005714e-03, 1.05487303e-02,\n",
" 8.19997549e-03, -1.62704772e-02, -1.23516314e-02,\n",
" 9.03431459e-03, -1.23060789e-02, -9.83690603e-03,\n",
" 8.47824788e-01, 1.00000000e+00, -5.31363796e-03,\n",
" -3.81314007e-03, 6.94723247e-03, 7.84505557e-03,\n",
" 1.35674397e-02, 1.32308281e-02, 3.24757136e-03,\n",
" 8.17782104e-03, -2.50550792e-03, 1.07313695e-02,\n",
" -1.47850432e-01, -1.51047458e-01, 2.83374484e-02],\n",
" [ 4.12849233e-03, 3.14397563e-03, 2.77193258e-03,\n",
" 2.34604520e-04, 2.04205710e-03, 4.24102285e-04,\n",
" 9.90441985e-05, 1.19052085e-02, 5.25041106e-03,\n",
" -1.09930376e-02, -5.31363796e-03, 1.00000000e+00,\n",
" 6.09604262e-01, 4.22991217e-03, 2.33096454e-03,\n",
" 8.14367940e-03, 4.57041968e-03, 5.08251972e-03,\n",
" 3.42510687e-03, 3.76244490e-03, 4.37764552e-03,\n",
" -6.18951575e-02, 3.48306679e-03, 2.57515666e-02],\n",
" [ 2.26000858e-03, 2.46443004e-03, 2.56573839e-03,\n",
" 1.10416771e-03, 2.10561763e-03, 9.65151924e-04,\n",
" -3.56367019e-04, 7.64558006e-03, 6.17726862e-03,\n",
" -7.48634299e-03, -3.81314007e-03, 6.09604262e-01,\n",
" 1.00000000e+00, 2.86373426e-03, 2.07338410e-03,\n",
" 5.47517823e-03, 3.76054540e-03, 2.79451239e-03,\n",
" 2.61953324e-03, 1.45883310e-03, 3.37000180e-03,\n",
" -4.70426189e-02, -1.68863278e-02, 1.54644100e-02],\n",
" [ 6.23891541e-03, 3.52454107e-03, 5.95117489e-03,\n",
" 1.99669462e-03, 5.79314587e-03, 4.21371130e-03,\n",
" -3.19245662e-03, 1.27493248e-02, 3.77239953e-03,\n",
" 1.59055343e-02, 6.94723247e-03, 4.22991217e-03,\n",
" 2.86373426e-03, 1.00000000e+00, 7.32648960e-01,\n",
" 5.84270803e-03, 4.15812081e-03, 5.32235340e-03,\n",
" 3.68420001e-03, 2.27864484e-03, 3.82471168e-03,\n",
" -1.56427340e-02, -5.36847541e-02, -4.76668130e-02],\n",
" [ 3.04105095e-03, 2.37096614e-03, 3.78280704e-03,\n",
" 1.79391848e-03, 3.58099264e-03, 2.60739988e-03,\n",
" -1.85352276e-03, 7.06093880e-03, 4.51690976e-03,\n",
" 1.11948590e-02, 7.84505557e-03, 2.33096454e-03,\n",
" 2.07338410e-03, 7.32648960e-01, 1.00000000e+00,\n",
" 3.80466597e-03, 3.14837825e-03, 2.63601650e-03,\n",
" 2.45828768e-03, 6.37329113e-04, 2.75366311e-03,\n",
" -1.98045470e-02, -4.16937207e-02, -2.11093803e-02],\n",
" [ 5.05645412e-03, 4.72386017e-03, 4.66781225e-03,\n",
" 1.45040587e-03, 2.64602281e-03, 7.85177633e-04,\n",
" 2.40927129e-05, 1.36421567e-02, 9.17089932e-03,\n",
" 2.15383897e-02, 1.35674397e-02, 8.14367940e-03,\n",
" 5.47517823e-03, 5.84270803e-03, 3.80466597e-03,\n",
" 1.00000000e+00, 7.52998489e-01, -4.19339831e-05,\n",
" 1.46617404e-04, -3.57858050e-02, -5.78234433e-02,\n",
" -9.11410155e-02, -1.80565433e-02, 3.29270656e-02],\n",
" [ 3.00353477e-03, 3.46125929e-03, 4.01891459e-03,\n",
" 2.02224228e-03, 2.79123264e-03, 1.50411220e-03,\n",
" -6.85183821e-04, 8.84446196e-03, 8.34603639e-03,\n",
" 1.64886598e-02, 1.32308281e-02, 4.57041968e-03,\n",
" 3.76054540e-03, 4.15812081e-03, 3.14837825e-03,\n",
" 7.52998489e-01, 1.00000000e+00, -4.44307202e-04,\n",
" 2.51184832e-04, -1.75598835e-02, -3.99710132e-02,\n",
" -6.05610510e-02, -3.55894924e-02, 1.29882217e-02],\n",
" [ 4.90626867e-03, 2.76468822e-03, 3.48241472e-03,\n",
" 4.46055027e-04, 1.24610943e-03, 5.53569374e-04,\n",
" -3.64337719e-04, 1.02288328e-02, 2.16761030e-03,\n",
" 1.18746541e-02, 3.24757136e-03, 5.08251972e-03,\n",
" 2.79451239e-03, 5.32235340e-03, 2.63601650e-03,\n",
" -4.19339831e-05, -4.44307202e-04, 1.00000000e+00,\n",
" 2.69314517e-01, -4.70052066e-04, -1.81469896e-03,\n",
" -3.16592750e-02, -1.13876227e-02, -8.64561610e-03],\n",
" [ 2.82449046e-03, 2.53278353e-03, 3.20035094e-03,\n",
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" -6.90639031e-05, 8.57776731e-05, 2.60413254e-04,\n",
" 8.45897653e-05, -2.02608509e-04, -1.23835086e-04,\n",
" 1.48978993e-04, 9.75446086e-05, -2.65016650e-05,\n",
" -3.61509901e-07, 9.14133333e-05, 6.70916578e-05,\n",
" -6.88413280e-04, 7.05461730e-04, 2.40222644e-03]]),\n",
" 'dloglike': 0.0001458001061109826,\n",
" 'edm': 0.0002503984850541394,\n",
" 'errors': array([0.07113003, 0.13876951, 1.4103957 , 0.56327771, 0.05534538,\n",
" 0.1889185 , 0.08061457, 0.10888297, 0.13831382, 0.12501587,\n",
" 0.06175989, 0.20632529, 0.11160342, 0.08672306, 0.11969093,\n",
" 0.09231346, 0.15323099, 0.06254178, 0.09602424, 0.16350701,\n",
" 0.10045753, 0.02025255, 0.02364644, 0.04901251]),\n",
" 'fit_quality': 3,\n",
" 'fit_status': 0,\n",
" 'fit_success': True,\n",
" 'indices': array([ 0, 1, 3, 4, 6, 7, 8, 10, 11, 13, 14, 17, 18, 20, 21, 23, 24,\n",
" 26, 27, 29, 30, 32, 33, 35]),\n",
" 'is_norm': array([ True, False, True, False, True, False, False, True, False,\n",
" True, False, True, False, True, False, True, False, True,\n",
" False, True, False, True, False, True]),\n",
" 'loglike': -77883.00088192237,\n",
" 'niter': 1,\n",
" 'par_names': ['Prefactor',\n",
" 'Index',\n",
" 'Prefactor',\n",
" 'Index',\n",
" 'norm',\n",
" 'alpha',\n",
" 'beta',\n",
" 'Prefactor',\n",
" 'Index',\n",
" 'norm',\n",
" 'alpha',\n",
" 'Prefactor',\n",
" 'Index',\n",
" 'Prefactor',\n",
" 'Index',\n",
" 'Prefactor',\n",
" 'Index',\n",
" 'Prefactor',\n",
" 'Index',\n",
" 'Prefactor',\n",
" 'Index',\n",
" 'Prefactor',\n",
" 'Index',\n",
" 'Normalization'],\n",
" 'src_names': ['3FGL J0002.0-6722',\n",
" '3FGL J0002.0-6722',\n",
" '3FGL J0021.6-6835',\n",
" '3FGL J0021.6-6835',\n",
" '3FGL J0023.9-7203',\n",
" '3FGL J0023.9-7203',\n",
" '3FGL J0023.9-7203',\n",
" '3FGL J0029.1-7045',\n",
" '3FGL J0029.1-7045',\n",
" '3FGL J0059.0-7242e',\n",
" '3FGL J0059.0-7242e',\n",
" '3FGL J0112.9-7506',\n",
" '3FGL J0112.9-7506',\n",
" '3FGL J0146.4-6746',\n",
" '3FGL J0146.4-6746',\n",
" '3FGL J2336.5-7620',\n",
" '3FGL J2336.5-7620',\n",
" '3FGL J2338.7-7401',\n",
" '3FGL J2338.7-7401',\n",
" '3FGL J2351.9-7601',\n",
" '3FGL J2351.9-7601',\n",
" 'galdiff',\n",
" 'galdiff',\n",
" 'isodiff'],\n",
" 'values': array([ 0.39538392, 1.94362954, 2.07191975, 3.25877604, 0.59012212,\n",
" 1.19264762, 0.57570313, 0.74811706, 2.37595198, 1.51185817,\n",
" 2.46682951, 1.26822259, 1.99775289, 0.52935079, 2.27543711,\n",
" 0.4487337 , 2.3544816 , 0.57386554, 2.01149183, 1.42865223,\n",
" 2.03646131, 0.94353891, -0.02082148, 0.91325844])}"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gta.fit()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['norm' 'alpha' 'beta' 'Eb' '' '' '' '' '' '']\n",
"[1.51185817e-11 2.46682951e+00 0.00000000e+00 6.65532043e+02\n",
" nan nan nan nan\n",
" nan nan]\n",
"[1.25015869e-12 6.17598948e-02 nan nan\n",
" nan nan nan nan\n",
" nan nan]\n"
]
}
],
"source": [
"print gta.roi['3FGL J0059.0-7242e']['param_names']\n",
"print gta.roi['3FGL J0059.0-7242e']['param_values']\n",
"print gta.roi['3FGL J0059.0-7242e']['param_errors']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see above the tool gta.set_source_spectrum has modified the SED shape from PowerLaw to LogParabola."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is also possible to set the spatial morphology of a source using gta.set_source_morphology"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on method set_source_morphology in module fermipy.gtanalysis:\n",
"\n",
"set_source_morphology(self, name, **kwargs) method of fermipy.gtanalysis.GTAnalysis instance\n",
" Set the spatial model of a source.\n",
" \n",
" Parameters\n",
" ----------\n",
" name : str\n",
" Source name.\n",
" \n",
" spatial_model : str\n",
" Spatial model name (PointSource, RadialGaussian, etc.).\n",
" \n",
" spatial_pars : dict\n",
" Dictionary of spatial parameters (optional).\n",
" \n",
" use_cache : bool \n",
" Generate the spatial model by interpolating the cached source\n",
" map.\n",
" \n",
" use_pylike : bool\n",
"\n"
]
}
],
"source": [
"help(gta.set_source_morphology)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"gta.set_source_morphology(name='3FGL J0029.1-7045',spatial_model='RadialGaussian',spatial_pars={'SpatialWidth': 1.0} )"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-31 09:17:12 INFO GTAnalysis.print_model(): \n",
"sourcename offset norm eflux index ts npred free\n",
"--------------------------------------------------------------------------------\n",
"3FGL J0059.0-7242e 0.088 1.512 1.12e-05 2.47 753.09 1532.2 *\n",
"3FGL J0112.9-7506 2.572 1.268 2.14e-06 2.00 152.13 140.7 *\n",
"3FGL J0023.9-7203 2.654 0.590 9.68e-06 2.70 4888.53 1770.7 *\n",
"3FGL J0029.1-7045 3.008 0.748 3.1e-06 2.38 89.43 379.6 *\n",
"3FGL J0021.6-6835 5.122 2.072 2.11e-07 3.26 10.91 46.4 *\n",
"3FGL J2351.9-7601 5.495 1.429 2.81e-06 2.04 237.30 198.5 *\n",
"3FGL J2338.7-7401 5.777 0.574 2.82e-06 2.01 257.49 179.2 *\n",
"3FGL J0146.4-6746 6.423 0.529 1.65e-06 2.28 192.11 170.3 *\n",
"3FGL J2336.5-7620 6.454 0.449 1.28e-06 2.35 119.62 148.0 *\n",
"3FGL J0002.0-6722 7.153 0.395 1.7e-06 1.94 86.64 92.7 *\n",
"isodiff --- 0.913 0.0286 2.12 1145.15 8289.4 *\n",
"galdiff --- 0.944 0.122 -0.02 5108.78 17184.0 *\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"RadialGaussian\n",
"1.0\n"
]
}
],
"source": [
"gta.print_model()\n",
"print gta.roi['3FGL J0029.1-7045']['SpatialType']\n",
"print gta.roi['3FGL J0029.1-7045']['SpatialWidth']"
]
}
],
"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
}
| bsd-3-clause |
rdhyee/working-open-data-2014 | notebooks/Day_23_B_folium-ipython.ipynb | 1 | 9904 | {
"metadata": {
"name": "",
"signature": "sha256:26f9c5115f67d82016ff64d0bb66b8f16ab725c0df35c7c424618e520210cbef"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A promising route for using leaflet.js maps in the IPython notebook -- use [Folium: Python Data. Leaflet.js Maps. \u2014 Folium 0.1.2 documentation](https://folium.readthedocs.org/en/latest/). Easiest way to install Folium:\n",
"\n",
" pip install folium\n",
"\n",
"This notebook is a tiny modification of http://nbviewer.ipython.org/gist/bburky/7763555/folium-ipython.ipynb. (See https://gist.github.com/bburky/7763555) Specifically, I host the us_counties_20m_topo.json file on my server. I confirm that this notebook works in IPython 2.0 "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.display import HTML\n",
"import folium"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def inline_map(map):\n",
" \"\"\"\n",
" Embeds the HTML source of the map directly into the IPython notebook.\n",
" \n",
" This method will not work if the map depends on any files (json data). Also this uses\n",
" the HTML5 srcdoc attribute, which may not be supported in all browsers.\n",
" \"\"\"\n",
" map._build_map()\n",
" return HTML('<iframe srcdoc=\"{srcdoc}\" style=\"width: 100%; height: 510px; border: none\"></iframe>'.format(srcdoc=map.HTML.replace('\"', '"')))\n",
"\n",
"def embed_map(map, path=\"map.html\"):\n",
" \"\"\"\n",
" Embeds a linked iframe to the map into the IPython notebook.\n",
" \n",
" Note: this method will not capture the source of the map into the notebook.\n",
" This method should work for all maps (as long as they use relative urls).\n",
" \"\"\"\n",
" map.create_map(path=path)\n",
" return HTML('<iframe src=\"files/{path}\" style=\"width: 100%; height: 510px; border: none\"></iframe>'.format(path=path))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"map = folium.Map(location=[40, -99], zoom_start=4)\n",
"map.simple_marker([40.67, -73.94], popup='Add <b>popup</b> text here.')\n",
"inline_map(map)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<iframe srcdoc=\"<!DOCTYPE html>\n",
"<head>\n",
" <link rel="stylesheet" href="http://cdn.leafletjs.com/leaflet-0.5/leaflet.css" />\n",
" <script src="http://cdn.leafletjs.com/leaflet-0.5/leaflet.js"></script>\n",
" \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"<style>\n",
"\n",
"#map {\n",
" position:absolute;\n",
" top:0;\n",
" bottom:0;\n",
" right:0;\n",
" left:0;\n",
"}\n",
"\n",
"</style>\n",
"</head>\n",
"<body>\n",
"\n",
" <div id="map" style="width: 960px; height: 500px"></div>\n",
"\n",
"<script>\n",
"\n",
"\n",
"\n",
"var map = L.map('map').setView([40, -99], 4);\n",
"\n",
"L.tileLayer('http://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {\n",
" maxZoom: 18,\n",
" attribution: 'Map data (c) <a href="http://openstreetmap.org">OpenStreetMap</a> contributors'\n",
"}).addTo(map);\n",
"\n",
"\n",
"var marker_1 = L.marker([40.67, -73.94]);\n",
"marker_1.bindPopup("Add <b>popup</b> text here.");\n",
"map.addLayer(marker_1)\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"</script>\n",
"\n",
"</body>\" style=\"width: 100%; height: 510px; border: none\"></iframe>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"<IPython.core.display.HTML at 0x106b99890>"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"\n",
"#Grab the geojson from github\n",
"#county_geo = r'us_counties_20m_topo.json'\n",
"# https://gist.githubusercontent.com/wrobstory/5609959/raw/17e222ecd9e26348f50a04fa484485a0e0f54a58/us_counties_20m_topo.json\n",
"county_geo = 'http://mashupguide.net/wwod14/us_counties_20m_topo.json'\n",
"county_data = 'https://raw.github.com/wrobstory/folium/master/examples/data/us_county_data.csv'\n",
"\n",
"df = pd.read_csv(county_data, na_values=[' '])\n",
"df['FIPS_Code'] = df['FIPS_Code'].astype(str)\n",
"\n",
"def set_id(fips):\n",
" '''Modify FIPS code to match GeoJSON property'''\n",
" if fips == '0':\n",
" return None\n",
" elif len(fips) <= 4:\n",
" return ''.join(['0500000US0', fips])\n",
" else:\n",
" return ''.join(['0500000US', fips])\n",
"\n",
"#Apply set_id, drop NaN\n",
"df['GEO_ID'] = df['FIPS_Code'].apply(set_id)\n",
"df = df.dropna()\n",
"\n",
"map = folium.Map(location=[40, -99], zoom_start=4)\n",
"map.geo_json(geo_path=county_geo, data_out='data2.json', data=df,\n",
" columns=['GEO_ID', 'Unemployment_rate_2011'],\n",
" key_on='feature.id',\n",
" threshold_scale=[0, 5, 7, 9, 11, 13],\n",
" fill_color='YlGnBu', line_opacity=0.3,\n",
" legend_name='Unemployment Rate 2011 (%)',\n",
" topojson='objects.us_counties_20m')\n",
"\n",
"embed_map(map)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<iframe src=\"files/map.html\" style=\"width: 100%; height: 510px; border: none\"></iframe>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"<IPython.core.display.HTML at 0x10728e390>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Blending folium with interact"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.html import widgets\n",
"from IPython.display import display, Image, HTML, clear_output"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# not the most interesting demo --> but a proof of concept on how we can control map using interact\n",
"\n",
"def plot_map(lat, long, zoom):\n",
" map = folium.Map(location=[lat, long], zoom_start=zoom)\n",
" map.simple_marker([lat, long], popup='lat:{lat} long:{long}'.format(lat=lat,long=long))\n",
" display(inline_map(map))\n",
" \n",
"widgets.interact(plot_map, \n",
" lat=widgets.FloatSliderWidget(min=-90,max=90,step=0.1,value=0),\n",
" long=widgets.FloatSliderWidget(min=-180,max=180,step=0.1,value=0),\n",
" zoom=widgets.IntSliderWidget(min=0,max=20,step=1,value=2))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<iframe srcdoc=\"<!DOCTYPE html>\n",
"<head>\n",
" <link rel="stylesheet" href="http://cdn.leafletjs.com/leaflet-0.5/leaflet.css" />\n",
" <script src="http://cdn.leafletjs.com/leaflet-0.5/leaflet.js"></script>\n",
" \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"<style>\n",
"\n",
"#map {\n",
" position:absolute;\n",
" top:0;\n",
" bottom:0;\n",
" right:0;\n",
" left:0;\n",
"}\n",
"\n",
"</style>\n",
"</head>\n",
"<body>\n",
"\n",
" <div id="map" style="width: 960px; height: 500px"></div>\n",
"\n",
"<script>\n",
"\n",
"\n",
"\n",
"var map = L.map('map').setView([50.6, -87.5], 2);\n",
"\n",
"L.tileLayer('http://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {\n",
" maxZoom: 18,\n",
" attribution: 'Map data (c) <a href="http://openstreetmap.org">OpenStreetMap</a> contributors'\n",
"}).addTo(map);\n",
"\n",
"\n",
"var marker_1 = L.marker([50.6, -87.5]);\n",
"marker_1.bindPopup("lat:50.6 long:-87.5");\n",
"map.addLayer(marker_1)\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"</script>\n",
"\n",
"</body>\" style=\"width: 100%; height: 510px; border: none\"></iframe>"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x106f16590>"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | apache-2.0 |
kristinriebe/cosmosim-uws-notebook | cosmosim-uws-intro.ipynb | 1 | 20799 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Exploring cosmological simulations with CosmoSim\n",
"## Introduction to job handling with uws-client\n",
"\n",
"CosmoSim is a web application available at http://www.cosmosim.org/ where data from cosmological simulations is available. This includes catalogues of dark matter halos (clusters) and galaxies for different time steps during the evolution of the simulated universe, merging information, substructure data, density fields and more.\n",
"\n",
"In this tutorial, we will use the [uws-client](https://github.com/aipescience/uws-client) for connecting with CosmoSim's UWS-interface for seeing your list of jobs, sending jobs and retrieving results."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Imports\n",
"Import the necessary libraries and the UWS module from the uws-client:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# load astropy for reading VOTABLE format\n",
"from astropy.io.votable import parse_single_table\n",
"\n",
"# import matplotlib for plotting results, mplot3d for 3D plots\n",
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits.mplot3d import Axes3D"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# import sys\n",
"# sys.path.append('<your own path>/uws-client')\n",
"\n",
"from uws import UWS"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Setup the connection\n",
"\n",
"The URL for connecting with CosmoSim's uws-client is 'https://www.cosmosim.org/uws/query/'. You also need to define your username and password, either by inserting it directly below or by saving your credentials in a local cosmosim-user.json file and reading it here. The credentials are the same as on the CosmoSim webpage. If you do not have an account yet, please register at [CosmoSim registration](https://www.cosmosim.org/auth/registration/register). Alternatively, you can use the user `uwstest` with password `gavo` for testing purposes. (Be aware that anyone can use this user and delete your results at any time!)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# set credentials here:\n",
"# username = 'uwstest'\n",
"# password = 'gavo'\n",
"\n",
"# or read your own username and password from a json-file,\n",
"# format: { \"username\": \"<yourname>\", \"password\": \"<your password>\" }\n",
"import json\n",
"with open('cosmosim-user.json') as credentials_file: \n",
" username, password = json.load(credentials_file).values()\n",
"\n",
"url = 'https://www.cosmosim.org/uws/query/'\n",
"c = UWS.client.Client(url, username, password)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## List previous jobs\n",
"Once the connection is set up, you can retrieve the list of previously run jobs with `c.get_job_list()`. You can also provide extra filters for the job list, e.g. filtering by phase or creation time of the job or just output the most recent ones using the `last` keyword."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"filters = {'phases': ['PENDING', 'COMPLETED', 'ERROR'], 'last': 5}\n",
"jobs = c.get_job_list(filters)\n",
"\n",
"# printing the returned resulting jobs-object gives a list of jobs\n",
"print jobs"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"For each job, its unique id, ownerId, creationTime and phase are stored within this job list. \n",
"At CosmoSim, we store the table name as the `runId` for each job. If no table name was given during job creation, the current timestamp is used."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"print \"# jobId, ownerId, creationTime, phase, runId:\"\n",
"for job in jobs.job_reference:\n",
" print job.id, job.ownerId, job.creationTime, job.phase[0], job.runId"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Create, check and run a job\n",
"\n",
"For creating a new job, first define the necessary parameters. For CosmoSim this is `query`, which is the SQL string and the optional parameters `queue` (long or short) and `table` (a unique table name). We set here an SQL query that will select the 10 most massive clusters from the MDPL2 simulation, Rockstar catalog."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"parameters = {'query': \n",
" 'SELECT rockstarId, x,y,z, Mvir FROM MDPL2.Rockstar'\\\n",
" + ' WHERE snapnum=125 ORDER BY Mvir DESC LIMIT 10',\n",
" 'queue': 'short'}"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Now **create a new job** with these parameters:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"job = c.new_job(parameters)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"And print the job's id and phase:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"print job.job_id, job.phase[0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"The job is created now, but it is not started yet - you can still adjust its parameters with c.set_parameters_job. E.g. let's change the queue to long:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"update_params = {'queue': 'long'}\n",
"job = c.set_parameters_job(job.job_id, update_params)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Print the parameters to check this:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"for p in job.parameters:\n",
" print p.id, p.value"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Now **start the job**, i.e. put it into the job queue using `run_job`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"run = c.run_job(job.job_id)\n",
"print run.job_id"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"The job should now also be visible in the web interface, at the Query Interface, left side, under 'Jobs'. \n",
"Let's check the job's phase: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"job = c.get_job(run.job_id)\n",
"print job.phase[0]\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"You can also use the `wait` parameter to wait at most the specified number of seconds until the phase has changed:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"job = c.get_job(run.job_id, '10', 'QUEUED')\n",
"print job.phase[0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Repeat the step above using \"EXECUTING\" as job phase until the job phase is \"COMPLETED\"."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"job = c.get_job(run.job_id, '10', 'EXECUTING')\n",
"print job.phase[0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Get the results"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Once your job is in \"COMPLETED\" phase, you can retrieve the results.\n",
"\n",
"Print the job result entries:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"for r in job.results:\n",
" print r"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"With `r.reference` you can access the URL of each result. Let's download the results in VOTABLE format:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"fileurl = str(job.results[1].reference)\n",
"resultfilename = \"result.xml\"\n",
"success = c.connection.download_file(fileurl, username, password, file_name=resultfilename)\n",
"if not success:\n",
" print \"File could not be downloaded, please check the job phase and result urls.\"\n",
"else:\n",
" print \"File downloaded successfully.\""
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Since there is only one table, we can quickly read the VOTABLE into a numpy array using astropy:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"table = parse_single_table(resultfilename, pedantic=False)\n",
"data = table.array"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Print the results row by row:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"print data"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Or print only a column:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"print data['x']"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Get the units for x and y values:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"field = table.get_field_by_id('x')\n",
"unit_x = field.unit\n",
"field = table.get_field_by_id('y')\n",
"unit_y = field.unit\n",
"\n",
"print \"Units for x and y: \", unit_x, unit_y"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Plot results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"ax = plt.subplot(111)\n",
"\n",
"# set axis labels\n",
"ax.set_xlabel('x [' + str(unit_x) + ']')\n",
"ax.set_ylabel('y [' + str(unit_y) + ']')\n",
"\n",
"# set axis range\n",
"ax.set_xlim(0, 1000)\n",
"ax.set_ylim(0, 1000)\n",
"\n",
"# plot data,\n",
"# using decreasing point size,\n",
"# so the biggest point is the most massive object\n",
"s = range(20,0,-2)\n",
"ax.scatter(data['x'], data['y'], s, color='b');\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Delete job\n",
"\n",
"Delete the job on the server, because we don't need it anymore:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"deleted = c.delete_job(job.job_id)\n",
"deleted"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Example: Retrieve progenitors of a halo\n",
"\n",
"Let's do a more elaborate example: for the most massive dark matter halo from the previous query, get its progenitors that merged into this halo and plot their positions over time. We restrict the progenitors to those with mass > 1.e12/h solar masses."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# store id of most massive dark matter halo from query before\n",
"most_massive_rockstarId = data[0]['rockstarId']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"query = \"\"\"\n",
"SELECT p.rockstarId, p.snapnum as snapnum, p.x as x, p.y as y, p.z as z, p.Mvir as Mvir, p.Rvir as Rvir\n",
"FROM MDPL2.Rockstar AS p,\n",
" (SELECT depthFirstId, lastProg_depthFirstId FROM MDPL2.Rockstar\n",
" WHERE rockstarId = \"\"\" + str(most_massive_rockstarId) + \"\"\") AS m\n",
"WHERE p.depthFirstId BETWEEN m.depthFirstId AND m.lastProg_depthFirstId\n",
"AND p.Mvir > 5.e11\n",
"ORDER BY snapnum\n",
"\"\"\"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Create and start the job:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"job = c.new_job({'query': query, 'queue': 'long'})\n",
"if job.phase[0] != \"PENDING\":\n",
" print \"ERROR: not in pending phase!\"\n",
"else:\n",
" run = c.run_job(job.job_id)\n",
"print job.phase[0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Check the status and wait until it is finished (this can take a couple of minutes!!):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"job = c.get_job(run.job_id, '60', 'QUEUED')\n",
"print \"Time out or job is not in QUEUED phase anymore.\"\n",
"job = c.get_job(run.job_id, '60', 'EXECUTING')\n",
"print \"Time out or job is not in EXECUTING phase anymore.\"\n",
"print \"Job phase: \", job.phase[0]\n",
"print job"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Retrieve the results:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"fileurl = str(job.results[1].reference)\n",
"resultfilename = \"result.xml\"\n",
"success = c.connection.download_file(fileurl, username, password, file_name=resultfilename)\n",
"if not success:\n",
" print \"File could not be downloaded, please check the job phase and result urls.\"\n",
"else:\n",
" print \"File downloaded successfully.\""
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Plot the positions of the progenitor halos, colored by snapshot number (increasing time):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"table = parse_single_table(resultfilename, pedantic=False)\n",
"data = table.array\n",
"\n",
"field = table.get_field_by_id('x')\n",
"unit_x = field.unit\n",
"field = table.get_field_by_id('y')\n",
"unit_y = field.unit\n",
"field = table.get_field_by_id('z')\n",
"unit_z = field.unit\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"ax = plt.subplot(111)\n",
"\n",
"# set axis labels\n",
"ax.set_xlabel('x [' + str(unit_x) + ']')\n",
"ax.set_ylabel('y [' + str(unit_y) + ']')\n",
"\n",
"# plot data,\n",
"# color by snapnum, i.e. snapshot number, i.e. increasing time\n",
"cm = plt.cm.get_cmap('viridis')\n",
"sc = ax.scatter(data['x'], data['y'], s=0.7, c=data['snapnum'], alpha=0.5, cmap=cm)\n",
"plt.colorbar(sc)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Now use an interactive 3D plot instead:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# Make an interactive 3D plot\n",
"%matplotlib notebook\n",
"fig = plt.figure()\n",
"\n",
"ax = fig.add_subplot(111, projection='3d')\n",
"\n",
"# set axis labels\n",
"ax.set_xlabel('x [' + str(unit_x) + ']')\n",
"ax.set_ylabel('y [' + str(unit_y) + ']')\n",
"ax.set_zlabel('z [' + str(unit_z) + ']')\n",
"\n",
"# plot the data\n",
"cm = plt.cm.get_cmap('plasma')\n",
"\n",
"ax.scatter(data['x'], data['y'], data['z'], s=0.7, c=data['snapnum'], depthshade=True, cmap=cm)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Delete your job on the server:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"deleted = c.delete_job(job.job_id)\n",
"deleted"
]
}
],
"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
}
| apache-2.0 |
kit-cel/wt | sigNT/spectral_estimation/psd_Bartlett.ipynb | 2 | 275818 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Content and Objective\n",
"\n",
"+ Show estimation of psd w. Bartlett\n",
"+ Method: Get noise, filtered noise and sinusoid, and perform psd estimation"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# importing\n",
"import numpy as np\n",
"from scipy import signal\n",
"import scipy as sp\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
"\n",
"# showing figures inline\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# plotting options \n",
"font = {'size' : 30}\n",
"plt.rc('font', **font)\n",
"plt.rc('text', usetex=True)\n",
"\n",
"matplotlib.rc('figure', figsize=(30, 8) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Helper Functions"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"########################\n",
"# periodogram estimator\n",
"########################\n",
"def find_periodogram(y, omega):\n",
" \"\"\"\n",
" estimates periodogram out of the given observation at the frequencies specified in omega\n",
" \n",
" IN: observation y, frequencies\n",
" OUT: psd estimator\n",
" \"\"\"\n",
" N = len(y)\n",
" per = np.zeros(len(omega), dtype=complex) \n",
" \n",
" for p in np.arange(0, N):\n",
" per += y[p] * np.exp( -1j * omega * (p+1) )\n",
" \n",
" per = ( abs(per)**2 )/ N\n",
" \n",
" return per \n",
"\n",
"\n",
"########################\n",
"# Bartlett periodogram estimator\n",
"########################\n",
"def find_bartlett_estimate( y, M, omega):\n",
" \"\"\"\n",
" estimates periodogram out of the given observation at the frequencies specified in omega\n",
" using Bartlett's method\n",
" \n",
" IN: observation y, group size M, frequencies Omega\n",
" OUT: psd estimator\n",
" \"\"\"\n",
" \n",
" N = len(y)\n",
" K = int( float(N)/M )\n",
" \n",
" per = np.zeros(len(omega) )\n",
" \n",
" k = 0\n",
" while k<K:\n",
" \n",
" yk = y[ k*M : (k+1)*M ] # mind that the upper limit is not included \n",
" Yk = find_periodogram( yk, omega )\n",
" per = 1.0/(k+1) * ( k * per + Yk ) \n",
"\n",
" k += 1 \n",
"\n",
" return per"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Parameters"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# parameters: number of samples and according length of acf\n",
"N = int( 1e2 )\n",
"N_range = np.arange( 0, N )\n",
"\n",
"# width of segments\n",
"M = N // 10\n",
"\n",
"# number of realizations for averaging \n",
"N_real = int( 1e2 )\n",
"\n",
"# number of freq. points and freq. range\n",
"N_freq = 512 \n",
"Ome = np.linspace(-np.pi, np.pi, N_freq)\n",
"\n",
"\n",
"# filtering noise?!\n",
"filtered = 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Loop for realizations"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# initialize arrays for psd\n",
"psd_noise_per = np.empty( [ N_real, N_freq ], dtype=float )\n",
"psd_noise_bart = np.empty( [ N_real, N_freq ], dtype=float )\n",
"\n",
"psd_sin_per = np.empty( [ N_real, N_freq ], dtype=float )\n",
"psd_sin_bart = np.empty( [ N_real, N_freq ], dtype=float )\n",
"\n",
"# avtivate parameter \"filtered\" in parameters if you like to see filtered noise\n",
"if filtered == 1:\n",
" # filter parameters\n",
" cutoff_freq = 1.0/4.0\n",
"\n",
" ripple_db = 60 # ripples and transition width of the filter\n",
" width = 1 / 5.0\n",
"\n",
" N_filter, beta = signal.kaiserord(ripple_db, width) # find filter order and beta parameter\n",
" \n",
" taps = signal.firwin( N_filter, cutoff=cutoff_freq, window=('kaiser', beta))\n",
"\n",
" \n",
"# loop for realizations\n",
"for _k in range( N_real ):\n",
" \n",
" # generate noise\n",
" noise = np.sqrt(2) * np.random.normal( 0.0, 1.0, N )\n",
"\n",
" # activate to have filtered noise\n",
" if filtered == 1:\n",
" noise = signal.lfilter( taps, 1.0, noise ) \n",
" noise /= np.linalg.norm( noise )\n",
"\n",
" # find estimations\n",
" psd_noise_per[ _k, :] = find_periodogram( noise, Ome ) \n",
" psd_noise_bart[ _k, :] = find_bartlett_estimate( noise, M, Ome )\n",
"\n",
"\n",
" Omega_0 = 1.0\n",
" Omega_1 = 1.2\n",
" y = np.sin( Omega_0 * N_range ) + np.sin( Omega_1 * N_range) + np.random.normal(0.0, 1.0, size = N)\n",
"\n",
" psd_sin_per[ _k, :] = find_periodogram( y, Ome ) \n",
" psd_sin_bart[ _k, :] = find_bartlett_estimate( y, M, Ome ) \n",
" \n",
" \n",
" \n",
"# get mean and std along realizations\n",
"psd_noise_per_average = psd_noise_per.mean( axis=0 ) \n",
"psd_noise_per_tria_std = psd_noise_per.std( axis=0 ) \n",
"\n",
"psd_noise_bart_average = psd_noise_bart.mean( axis=0 ) \n",
"psd_noise_bart_std = psd_noise_bart.std( axis=0 ) \n",
"\n",
"\n",
"psd_sin_per_average = psd_sin_per.mean( axis=0 ) \n",
"psd_sin_per_tria_std = psd_sin_per.std( axis=0 ) \n",
"\n",
"psd_sin_bart_average = psd_sin_bart.mean( axis=0 ) \n",
"psd_sin_bart_std = psd_sin_bart.std( axis=0 ) \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plotting"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, '$\\\\hat{\\\\Phi}_B(\\\\Omega)$')"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 2160x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 2160x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"\n",
"plt.subplot(121) \n",
"plt.plot(Ome, psd_noise_per_average) \n",
"plt.plot(Ome, psd_noise_per_average - psd_noise_per_tria_std) \n",
"plt.plot(Ome, psd_noise_per_average + psd_noise_per_tria_std) \n",
"\n",
"plt.title('Periodogram') \n",
"plt.grid(True); \n",
"plt.ylabel('$\\hat{\\Phi}_p(\\Omega)$') \n",
"\n",
"\n",
"plt.subplot(122) \n",
"plt.plot(Ome, psd_noise_bart_average) \n",
"plt.plot(Ome, psd_noise_bart_average - psd_noise_bart_std) \n",
"plt.plot(Ome, psd_noise_bart_average + psd_noise_bart_std) \n",
"\n",
"plt.title('Bartlett') \n",
"plt.grid(True); \n",
"plt.ylabel('$\\hat{\\Phi}_B(\\Omega)$') \n",
"\n",
"\n",
"\n",
"plt.figure()\n",
"\n",
"plt.subplot(121) \n",
"plt.plot(Ome, psd_sin_per_average) \n",
"plt.plot(Ome, psd_sin_per_average - psd_sin_per_tria_std) \n",
"plt.plot(Ome, psd_sin_per_average + psd_sin_per_tria_std) \n",
"\n",
"plt.title('Periodogram') \n",
"plt.grid(True); \n",
"plt.ylabel('$\\hat{\\Phi}_p(\\Omega)$') \n",
"\n",
"\n",
"plt.subplot(122) \n",
"plt.plot(Ome, psd_sin_bart_average) \n",
"plt.plot(Ome, psd_sin_bart_average - psd_sin_bart_std) \n",
"plt.plot(Ome, psd_sin_bart_average + psd_sin_bart_std) \n",
"\n",
"plt.title('Bartlett') \n",
"plt.grid(True); \n",
"plt.ylabel('$\\hat{\\Phi}_B(\\Omega)$') "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
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"metadata": {
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| gpl-2.0 |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session04/Day1/LSSTC-DSFP4-Juric-FrequentistAndBayes-01-Probability.ipynb | 1 | 142650 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Frequentism and Bayesianism I: a Practical Introduction\n",
"\n",
"**Mario Juric & Jake VanderPlas, University of Washington**\n",
"\n",
"**e-mail: <[email protected]>, twitter: [@mjuric](http://twitter.com/mjuric)**\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*This lecture is based on a [post](http://jakevdp.github.io/blog/2014/03/11/frequentism-and-bayesianism-a-practical-intro/) on the blog [Pythonic Perambulations](http://jakevdp.github.io), by [Jake VanderPlas](https://staff.washington.edu/jakevdp/). The content is BSD licensed. See also VanderPlas (2014) [\"Frequentism and Bayesianism: A Python-driven Primer\"](http://arxiv.org/abs/1411.5018).*\n",
"\n",
"*Slides built using the excellent [RISE](https://github.com/damianavila/RISE) Jupyter extension by [Damian Avila](https://github.com/damianavila).*"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<!-- PELICAN_BEGIN_SUMMARY -->\n",
"\n",
"One of the first things a scientist hears about statistics is that there is are two different approaches: ***Frequentism*** and ***Bayesianism***. Despite their importance, many scientific researchers never have opportunity to learn the distinctions between them and the different practical approaches that result.\n",
"\n",
"The purpose of this lecture is to synthesize the philosophical and pragmatic aspects of the frequentist and Bayesian approaches, so that scientists like you might be better prepared to understand the types of data analysis people do.\n",
"\n",
"We'll start by addressing the philosophical distinctions between the views, and from there move to discussion of how these ideas are applied in practice, with some Python code snippets demonstrating the difference between the approaches.\n",
"\n",
"<!-- PELICAN_END_SUMMARY -->"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Prerequisites\n",
"\n",
"* Python Version 2.7\n",
"* The \"PyData\" data science software stack (comes with [Anaconda](https://www.anaconda.com/download/)).\n",
"\n",
"\n",
"* [emcee](http://dan.iel.fm/emcee/) -- a pure-Python implementation of Goodman & Weare’s Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler.\n",
"* [AstroML](http://www.astroml.org/) -- a library of statistical and machine learning routines for analyzing, loading, and visualizing astronomical data in Python."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Frequentism vs. Bayesianism: a Philosophical Debate"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<br>\n",
"<center>Fundamentally, the disagreement between frequentists and Bayesians concerns the definition (interpretation) of probability.</center>\n",
"<br>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Frequentist Probability\n",
"\n",
"For frequentists, probability only has meaning in terms of **a limiting case of repeated measurements**.\n",
"\n",
"That is, if I measure the photon flux $F$ from a given star (we'll assume for now that the star's flux does not vary with time), then measure it again, then again, and so on, each time I will get a slightly different answer due to the statistical error of my measuring device. In the limit of a large number of measurements, the *frequency* of any given value indicates the probability of measuring that value."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"For frequentists **probabilities are fundamentally related to frequencies of events**. This means, for example, that in a strict frequentist view, it is meaningless to talk about the probability of the *true* flux of the star: the true flux is (by definition) a single fixed value, and to talk about a frequency distribution for a fixed value is nonsense."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Bayesian Probability\n",
"\n",
"For Bayesians, the concept of probability is extended to cover **degrees of certainty about statements**. You can think of it as an **extension of logic** to statements where there's uncertainty.\n",
"\n",
"Say a Bayesian claims to measure the flux $F$ of a star with some probability $P(F)$: that probability can certainly be estimated from frequencies in the limit of a large number of repeated experiments, but this is not fundamental. The probability is a statement of my knowledge of what the measurement reasult will be."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"For Bayesians, **probabilities are fundamentally related to our own knowledge about an event**. This means, for example, that in a Bayesian view, we can meaningfully talk about the probability that the *true* flux of a star lies in a given range.\n",
"\n",
"That probability codifies our knowledge of the value based on prior information and/or available data."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"The surprising thing is that this arguably subtle difference in philosophy leads, in practice, to vastly different approaches to the statistical analysis of data. Below I will give a few practical examples of the differences in approach, along with associated Python code to demonstrate the practical aspects of the resulting methods."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Frequentist and Bayesian Approaches in Practice: Counting Photons"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we'll take a look at an extremely simple problem, and compare the frequentist and Bayesian approaches to solving it. There's necessarily a bit of mathematical formalism involved, but we won't go into too much depth or discuss too many of the subtleties.\n",
"\n",
"If you want to go deeper, you might consider taking a look at chapters 4-5 of the [Statistics, Data Mining, and Machine Learning in Astronomy](http://www.amazon.com/dp/0691151687/) textbook."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### The Problem: Simple Photon Counts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Imagine that we point our telescope to the sky, and observe the light coming from a single star. For the time being, we'll assume that the star's true flux is constant with time, i.e. that is it has a fixed value $F_{\\rm true}$ (we'll also ignore effects like sky noise and other sources of systematic error). We'll assume that we perform a series of $N$ measurements with our telescope, where the $i^{\\rm th}$ measurement reports the observed photon flux $F_i$ and error $e_i$.\n",
"\n",
"**The question is, given this set of measurements $D = \\{F_i,e_i\\}$, what is our best estimate of the true flux $F_{\\rm true}$?**"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"#### Aside on measurement errors\n",
"\n",
"We'll make the (reasonable) assumption that errors are Gaussian:\n",
"* In a Frequentist perspective, $e_i$ is the standard deviation of the results of a single measurement event in the limit of repetitions of *that event*.\n",
"* In the Bayesian perspective, $e_i$ is the standard deviation of the (Gaussian) probability distribution describing our knowledge of that particular measurement given its observed value)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Here we'll use Python to generate some toy data to demonstrate the two approaches to the problem.\n",
"\n",
"Because the measurements are number counts, a Poisson distribution is a good approximation to the measurement process:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Generating some simple photon count data\n",
"import numpy as np\n",
"from scipy import stats\n",
"np.random.seed(1) # for repeatability\n",
"\n",
"F_true = 1000 # true flux, say number of photons measured in 1 second\n",
"N = 50 # number of measurements\n",
"F = stats.poisson(F_true).rvs(N) # N measurements of the flux\n",
"e = np.sqrt(F) # errors on Poisson counts estimated via square root"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Now let's make a simple visualization of the \"measured\" data:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
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eM3t7dpEnr865+YSZvWBmO6PbO2ueX9hzY2Z/ZmZHzGx3zbGGvydmttrMHo9+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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10b403890>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"\n",
"fig, ax = plt.subplots()\n",
"ax.errorbar(F, np.arange(N), xerr=e, fmt='ok', ecolor='gray', alpha=0.5)\n",
"ax.vlines([F_true], 0, N, linewidth=5, alpha=0.2)\n",
"ax.set_xlabel(\"Flux\");ax.set_ylabel(\"measurement number\");"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"These measurements each have a different error $e_i$ which is estimated from [Poisson statistics](http://en.wikipedia.org/wiki/Poisson_distribution) using the standard square-root rule.\n",
"\n",
"In this toy example we already know the true flux $F_{\\rm true}$, but the question is this: **given our measurements and errors, what is our best estimate of the true flux?**\n",
"\n",
"Let's take a look at the frequentist and Bayesian approaches to solving this."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Frequentist Approach to Simple Photon Counts"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"We'll start with the classical frequentist **maximum likelihood** approach. Given a single observation $D_i = (F_i, e_i)$, we can compute the probability distribution of the measurement given the true flux $F_{\\rm true}$ given our assumption of Gaussian errors:\n",
"\n",
"$$ P(D_i~|~F_{\\rm true}) = \\frac{1}{\\sqrt{2\\pi e_i^2}} \\exp{\\left[\\frac{-(F_i - F_{\\rm true})^2}{2 e_i^2}\\right]} $$\n",
"\n",
"This should be read \"the probability of $D_i$ given $F_{\\rm true}$ equals ...\". You should recognize this as a normal distribution with mean $F_{\\rm true}$ and standard deviation $e_i$."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"We construct the **likelihood function** by computing the product of the probabilities for each data point:\n",
"\n",
"$$\\mathcal{L}(D~|~F_{\\rm true}) = \\prod_{i=1}^N P(D_i~|~F_{\\rm true})$$\n",
"\n",
"Here $D = \\{D_i\\}$ represents the entire set of measurements. Because the value of the likelihood can become very small, it is often more convenient to instead compute the log-likelihood. Combining the previous two equations and computing the log, we have\n",
"\n",
"$$\\log\\mathcal{L} = -\\frac{1}{2} \\sum_{i=1}^N \\left[ \\log(2\\pi e_i^2) + \\frac{(F_i - F_{\\rm true})^2}{e_i^2} \\right]$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"What we'd like to do is determine $F_{\\rm true}$ such that the likelihood is maximized. For this simple problem, the maximization can be computed analytically (i.e. by setting $d\\log\\mathcal{L}/dF_{\\rm true} = 0$). This results in the following observed estimate of $F_{\\rm true}$:\n",
"\n",
"$$ F_{\\rm est} = \\frac{\\sum w_i F_i}{\\sum w_i};~~w_i = 1/e_i^2 $$\n",
"\n",
"Notice that in the special case of all errors $e_i$ being equal, this reduces to\n",
"\n",
"$$ F_{\\rm est} = \\frac{1}{N}\\sum_{i=1}^N F_i $$\n",
"\n",
"That is, in agreement with intuition, $F_{\\rm est}$ is simply the mean of the observed data when errors are equal."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"We can go further and ask what the error of our estimate is? In the frequentist approach, this can be accomplished by fitting a Gaussian approximation to the likelihood curve at maximum; in this simple case this can also be solved analytically.\n",
"\n",
"It can be shown that the standard deviation of this Gaussian approximation is:\n",
"\n",
"$$ \\sigma_{\\rm est} = \\left(\\sum_{i=1}^N w_i \\right)^{-1/2} $$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"These results are fairly simple calculations; let's evaluate them for our toy dataset:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" F_true = 1000\n",
" F_est = 998 +/- 4 (based on 50 measurements)\n",
" \n"
]
}
],
"source": [
"w = 1. / e ** 2\n",
"print(\"\"\"\n",
" F_true = {0}\n",
" F_est = {1:.0f} +/- {2:.0f} (based on {3} measurements)\n",
" \"\"\".format(F_true, (w * F).sum() / w.sum(), w.sum() ** -0.5, N))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We find that for 50 measurements of the flux, our estimate has an error of about 0.4% and is consistent with the input value."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Bayesian Approach to Simple Photon Counts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Bayesian approach, as you might expect, begins and ends with probabilities. It recognizes that what we fundamentally want to compute is our knowledge of the parameters in question, i.e. in this case,\n",
"\n",
"$$ P(F_{\\rm true}~|~D) $$\n",
"\n",
"N.b.: that this formulation of the problem is *fundamentally contrary* to the frequentist philosophy, which says that *probabilities have no meaning for model parameters* like $F_{\\rm true}$. Within the ***Bayesian interpretation of probability***, this is perfectly acceptable. "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"To compute this result, Bayesians next apply [Bayes' Theorem](http://en.wikipedia.org/wiki/Bayes\\'_theorem), a fundamental law of probability:\n",
"\n",
"$$ P(F_{\\rm true}~|~D) = \\frac{P(D~|~F_{\\rm true})~P(F_{\\rm true})}{P(D)} $$\n",
"\n",
"Though Bayes' theorem is where Bayesians get their name, it is not this law itself that is controversial, but the Bayesian *interpretation of probability* implied by the term $P(F_{\\rm true}~|~D)$."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Let's take a look at each of the terms in this expression:\n",
"\n",
"- $P(F_{\\rm true}~|~D)$: The **posterior**, or the probability of the model parameters given the data: this is the result we want to compute.\n",
"- $P(D~|~F_{\\rm true})$: The **likelihood**, which is proportional to the $\\mathcal{L}(D~|~F_{\\rm true})$ in the frequentist approach, above.\n",
"- $P(F_{\\rm true})$: The **model prior**, which encodes what we knew about the model prior to the application of the data $D$.\n",
"- $P(D)$: The **data probability**, which in practice amounts to simply a normalization term."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"If we set the prior $P(F_{\\rm true}) \\propto 1$ (a *flat prior*), we find\n",
"\n",
"$$P(F_{\\rm true}|D) \\propto \\mathcal{L}(D|F_{\\rm true})$$\n",
"\n",
"and the Bayesian probability is maximized at precisely the same value as the frequentist result! So despite the philosophical differences, we see that (for this simple problem at least) the Bayesian and frequentist point estimates are equivalent."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### But What About the Prior?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We glossed over something here: the prior, $P(F_{\\rm true})$.\n",
"\n",
"The prior ***allows inclusion of other information into the computation***, which becomes very useful in cases where multiple measurement strategies are being combined to constrain a single model (as is the case in, e.g. cosmological parameter estimation).\n",
"\n",
"The necessity to specify a prior, however, is one of the more controversial pieces of Bayesian analysis."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### But What About the Prior?\n",
"\n",
"A frequentist will point out that the ***prior is problematic when no true prior information is available***. Though it might seem straightforward to use a **noninformative prior** like the flat prior mentioned above, there are some [surprisingly subtleties](http://normaldeviate.wordpress.com/2013/07/13/lost-causes-in-statistics-ii-noninformative-priors/comment-page-1/) involved. It turns out that in many situations, a truly noninformative prior does not exist!\n",
"\n",
"***Frequentists point out that the subjective choice of a prior which necessarily biases your result has no place in statistical data analysis***. A Bayesian would counter that frequentism doesn't solve this problem, but simply skirts the question.\n",
"\n",
"Frequentism can often be viewed as simply a special case of the Bayesian approach for some (implicit) choice of the prior: ***a Bayesian would say that it's better to make this implicit choice explicit, even if the choice might include some subjectivity***."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Photon Counts: the Bayesian approach"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Leaving these philosophical debates aside for the time being, let's address how Bayesian results are generally computed in practice.\n",
"\n",
"For a one parameter problem like the one considered here, it's as simple as computing the posterior probability $P(F_{\\rm true}~|~D)$ as a function of $F_{\\rm true}$: this is the distribution reflecting our knowledge of the parameter $F_{\\rm true}$.\n",
"\n",
"But as the dimension of the model grows, this direct approach becomes increasingly intractable. For this reason, Bayesian calculations often depend on sampling methods such as [Markov Chain Monte Carlo (MCMC)](http://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo). Here, we'll be using Dan Foreman-Mackey's excellent [emcee](http://dan.iel.fm/emcee/current/) package. Keep in mind here that the goal is to *generate a set of points drawn from the posterior probability distribution*, and to use those points to determine the answer we seek."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"To perform this MCMC, we start by defining Python functions for the prior $P(F_{\\rm true})$, the likelihood $P(D~|~F_{\\rm true})$, and the posterior $P(F_{\\rm true}~|~D)$, noting that none of these need be properly normalized. \n",
"\n",
"Our model here is one-dimensional, but to handle multi-dimensional models we'll define the model in terms of an array of parameters $\\theta$, which in this case is $\\theta = [F_{\\rm true}]$:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def log_prior(theta):\n",
" return 1 # flat prior\n",
"\n",
"def log_likelihood(theta, F, e):\n",
" return -0.5 * np.sum(np.log(2 * np.pi * e ** 2)\n",
" + (F - theta[0]) ** 2 / e ** 2)\n",
"\n",
"def log_posterior(theta, F, e):\n",
" return log_prior(theta) + log_likelihood(theta, F, e)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Now we set up the problem, including generating some random starting guesses for the multiple chains of points."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"ndim = 1 # number of parameters in the model\n",
"nwalkers = 50 # number of MCMC walkers\n",
"nburn = 1000 # \"burn-in\" period to let chains stabilize\n",
"nsteps = 2000 # number of MCMC steps to take\n",
"\n",
"# we'll start at random locations between 0 and 2000\n",
"starting_guesses = 2000 * np.random.rand(nwalkers, ndim)\n",
"\n",
"import emcee\n",
"sampler = emcee.EnsembleSampler(nwalkers, ndim, log_posterior, args=[F, e])\n",
"sampler.run_mcmc(starting_guesses, nsteps)\n",
"\n",
"sample = sampler.chain # shape = (nwalkers, nsteps, ndim)\n",
"sample = sampler.chain[:, nburn:, :].ravel() # discard burn-in points"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"If this all worked correctly, the array ``sample`` should contain a series of 50000 points drawn from the posterior. Let's plot them and check:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10f9a9a50>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x10f9365d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot a histogram of the sample\n",
"plt.hist(sample, bins=50, histtype=\"stepfilled\", alpha=0.3, normed=True)\n",
"\n",
"# plot a best-fit Gaussian\n",
"F_fit = np.linspace(975, 1025)\n",
"pdf = stats.norm(np.mean(sample), np.std(sample)).pdf(F_fit)\n",
"\n",
"plt.plot(F_fit, pdf, '-k')\n",
"plt.xlabel(\"F\"); plt.ylabel(\"P(F)\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"We end up with a sample of points drawn from the (normal) posterior distribution. The mean and standard deviation of this posterior are the corollary of the frequentist maximum likelihood estimate above:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" F_true = 1000\n",
" F_est = 998 +/- 5 (based on 50 measurements)\n",
" \n"
]
}
],
"source": [
"print(\"\"\"\n",
" F_true = {0}\n",
" F_est = {1:.0f} +/- {2:.0f} (based on {3} measurements)\n",
" \"\"\".format(F_true, np.mean(sample), np.std(sample), N))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that as expected for this simple problem, the Bayesian approach yields the same result as the frequentist approach!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Discussion"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, you might come away with the impression that the Bayesian method is unnecessarily complicated, and in this case it certainly is. Using an Affine Invariant Markov Chain Monte Carlo Ensemble sampler to characterize a one-dimensional normal distribution is a bit like using the Death Star to destroy a beach ball.\n",
"\n",
"But we did it here because it demonstrates an approach that can scale to complicated posteriors in many, many dimensions, and can provide nice results in more complicated situations where an analytic likelihood approach is not possible."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"As a side note, you might also have noticed one little sleight of hand: at the end, we use a frequentist approach to characterize our posterior samples! When we computed the sample mean and standard deviation above, we were employing a distinctly frequentist technique to characterize the posterior distribution. The pure Bayesian result for a problem like this would be to report the posterior distribution itself (i.e. its representative sample), and leave it at that. That is, in pure Bayesianism the answer to a question is not a single number with error bars; the answer is the posterior distribution over the model parameters!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Adding a Dimension: Exploring a more sophisticated model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's briefly take a look at a more complicated situation, and compare the frequentist and Bayesian results yet again. Above we assumed that the star was static: now let's assume that we're looking at an object which we suspect has some stochastic variation — that is, it varies with time, but in an unpredictable way (a Quasar is a good example of such an object)."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"We'll propose a simple 2-parameter Gaussian model for this object: $\\theta = [\\mu, \\sigma]$ where $\\mu$ is the mean value, and $\\sigma$ is the standard deviation of the variability *intrinsic to the object*. Thus our model for the probability of the true flux at the time of each observation looks like this:\n",
"\n",
"$$ F_{\\rm true} \\sim \\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp\\left[\\frac{-(F - \\mu)^2}{2\\sigma^2}\\right]$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Now, we'll again consider $N$ observations each with their own error. We can generate them this way:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(42) # for reproducibility\n",
"N = 100 # we'll use more samples for the more complicated model\n",
"mu_true, sigma_true = 1000, 15 # stochastic flux model\n",
"\n",
"F_true = stats.norm(mu_true, sigma_true).rvs(N) # (unknown) true flux\n",
"F = stats.poisson(F_true).rvs() # observed flux: true flux plus Poisson errors.\n",
"e = np.sqrt(F) # root-N error, as above"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Varying Photon Counts: The Frequentist Approach"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The resulting likelihood is the convolution of the intrinsic distribution with the error distribution, so we have\n",
"\n",
"$$\\mathcal{L}(D~|~\\theta) = \\prod_{i=1}^N \\frac{1}{\\sqrt{2\\pi(\\sigma^2 + e_i^2)}}\\exp\\left[\\frac{-(F_i - \\mu)^2}{2(\\sigma^2 + e_i^2)}\\right]$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Analogously to above, we can analytically maximize this likelihood to find the best estimate for $\\mu$:\n",
"\n",
"$$\\mu_{est} = \\frac{\\sum w_i F_i}{\\sum w_i};~~w_i = \\frac{1}{\\sigma^2 + e_i^2} $$\n",
"\n",
"And here we have a problem: the optimal value of $\\mu$ *depends on* the optimal value of $\\sigma$. The results are correlated, so we can no longer use straightforward analytic methods to arrive at the frequentist result."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Nevertheless, we can use numerical optimization techniques to determine the maximum likelihood value. Here we'll use the optimization routines available within Scipy's [optimize](http://docs.scipy.org/doc/scipy/reference/optimize.html) submodule:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization terminated successfully.\n",
" Current function value: 502.839505\n",
" Iterations: 58\n",
" Function evaluations: 114\n",
"\n",
" Maximum likelihood estimate for 100 data points:\n",
" mu=999, sigma=19\n",
" \n"
]
}
],
"source": [
"def log_likelihood(theta, F, e):\n",
" return -0.5 * np.sum(np.log(2 * np.pi * (theta[1] ** 2 + e ** 2))\n",
" + (F - theta[0]) ** 2 / (theta[1] ** 2 + e ** 2))\n",
"\n",
"# maximize likelihood <--> minimize negative likelihood\n",
"def neg_log_likelihood(theta, F, e):\n",
" return -log_likelihood(theta, F, e)\n",
"\n",
"from scipy import optimize\n",
"theta_guess = [900, 5]\n",
"theta_est = optimize.fmin(neg_log_likelihood, theta_guess, args=(F, e))\n",
"print(\"\"\"\n",
" Maximum likelihood estimate for {0} data points:\n",
" mu={theta[0]:.0f}, sigma={theta[1]:.0f}\n",
" \"\"\".format(N, theta=theta_est))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"#### Error Estimates\n",
"\n",
"This maximum likelihood value gives our best estimate of the parameters $\\mu$ and $\\sigma$ governing our model of the source. But this is only half the answer: we need to determine how confident we are in this answer, that is, we need to compute the error bars on $\\mu$ and $\\sigma$.\n",
"\n",
"To see how this is done in the frequentist paradigm, see the sub-slides."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"There are several approaches to determining errors in a frequentist paradigm. We could:\n",
"* as above, fit a normal approximation to the maximum likelihood and report the covariance matrix (here we'd have to do this numerically rather than analytically).\n",
"* Alternatively, we can compute statistics like $\\chi^2$ and $\\chi^2_{\\rm dof}$ to and use standard tests to determine confidence limits, which also depends on strong assumptions about the Gaussianity of the likelihood. \n",
"* We might alternatively use randomized sampling approaches such as \"Jackknife\"...\n",
"* or \"Bootstrap\", which maximize the likelihood for randomized samples of the input data in order to explore the degree of certainty in the result."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"All of these would be valid techniques to use, but each comes with its own assumptions and subtleties. Here, for simplicity, we'll use the basic bootstrap resampler found in the [astroML][4] package:\n",
"\n",
"[1]: http://en.wikipedia.org/wiki/Pearson%27s_chi-squared_test\n",
"[2]: http://en.wikipedia.org/wiki/Jackknife_(statistics)\n",
"[3]: http://en.wikipedia.org/wiki/Bootstrapping_(statistics)\n",
"[4]: http://astroML.org"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"from astroML.resample import bootstrap\n",
"\n",
"def fit_samples(sample):\n",
" # sample is an array of size [n_bootstraps, n_samples]\n",
" # compute the maximum likelihood for each bootstrap.\n",
" return np.array([optimize.fmin(neg_log_likelihood, theta_guess,\n",
" args=(F, np.sqrt(F)), disp=0)\n",
" for F in sample])\n",
"\n",
"samples = bootstrap(F, 1000, fit_samples) # 1000 bootstrap resamplings"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Now in a similar manner to what we did above for the MCMC Bayesian posterior, we'll compute the sample mean and standard deviation to determine the errors on the parameters."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" mu = 999 +/- 4\n",
" sigma = 18 +/- 5\n"
]
}
],
"source": [
"mu_samp = samples[:, 0]\n",
"sig_samp = abs(samples[:, 1])\n",
"\n",
"print \" mu = {0:.0f} +/- {1:.0f}\".format(mu_samp.mean(), mu_samp.std())\n",
"print \" sigma = {0:.0f} +/- {1:.0f}\".format(sig_samp.mean(), sig_samp.std())"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"I should note that there is a **huge** literature on the details of bootstrap resampling, and there are definitely some subtleties of the approach that I am glossing over here. One obvious piece is that there is potential for errors to be correlated or non-Gaussian, neither of which is reflected by simply finding the mean and standard deviation of each model parameter. Nevertheless, I trust that this gives the basic idea of the frequentist approach to this problem."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Varying Photon Counts: The Bayesian Approach"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Bayesian approach to this problem is almost exactly the same as it was in the previous problem, and we can set it up by slightly modifying the above code."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"def log_prior(theta):\n",
" # sigma needs to be positive.\n",
" if theta[1] <= 0:\n",
" return -np.inf\n",
" else:\n",
" return 0\n",
"\n",
"def log_posterior(theta, F, e):\n",
" return log_prior(theta) + log_likelihood(theta, F, e)\n",
"\n",
"# same setup as above:\n",
"ndim, nwalkers = 2, 50\n",
"nsteps, nburn = 2000, 1000\n",
"\n",
"starting_guesses = np.random.rand(nwalkers, ndim)\n",
"starting_guesses[:, 0] *= 2000 # start mu between 0 and 2000\n",
"starting_guesses[:, 1] *= 20 # start sigma between 0 and 20\n",
"\n",
"sampler = emcee.EnsembleSampler(nwalkers, ndim, log_posterior, args=[F, e])\n",
"sampler.run_mcmc(starting_guesses, nsteps)\n",
"\n",
"sample = sampler.chain # shape = (nwalkers, nsteps, ndim)\n",
"sample = sampler.chain[:, nburn:, :].reshape(-1, 2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Now that we have the samples, we'll use a convenience routine from astroML to plot the traces and the contours representing one and two standard deviations:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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8JyoqimzcuNGhSVdBO661PwuKGVBBT0DOjryxsREfffQR/vWvf2H//v3o3bs3EhIScPfd\nd+PHH3/EL37xCzQ1NSEwMBC9e/fGhQsXoNVq0bt3b/aprKyEt7c3xo8fj969e8Pb29vueJ8+fdhO\n+lqMU/jdZDLBZrN1OhfHHS9IdBYV6W64S1Ppag5TW1sbmpqacP78eRw7dgx79uzB+vXrUVVVhYiI\nCKSnp+M3v/mNZFh9Z+EJkbLXo7bY45oVgL4A9gMoA3AEwMtXfg8AsB1A5ZW//s7aUjQr1yG24+1O\nJ68zB2xbWxvZuHEjWbhwIavaEBERQd544w1y7ty5a0aHFJztJjvTrqNrnPVHNSoevEYj1o+Y9uUq\n3Z2trSj3uq7UcewKaHWRiIgIAoDcdtttZO3ateS9997rUvBAT0X/CQN4rmegp6MBAagA+Fz5vzeA\nrwFMAfAagD9f+f3PALKctaUIq+sXZ8+eJX/9619JYGAgi/567LHHyL59+0RDrmkkXU9DipFK/b5y\n5UrRDYLUWITt8AJHCq6WYZL6LtVXZ5mfIwEq9hsVqs7MkY7Mkp2lt7W1lWzcuJGMHDmSACC33nor\nWbVqFfnxxx/tzvP0mog3EnpcWNl1AvQHYAYwGcAxAEFXfg8CcMzZ9Yqw6n7IfTjlMNWWlhby+eef\nk4SEBOLl5UUAkGnTppHc3FzS3Nwsqx8hY1qxYkWH/jtT/6+zDLoz/Ym1IQTVoKSEmvAaMYYt/M2V\n/C2x6+WC9iMmrOSsJ3cJhM7cl7a2NrJ161ZiMBgIABIUFETeeOMN2etTCHelYdyM8AhhBcALwCEA\nzVSDAtDAHVfx3wXXPg6gBEDJkCFDum+mFDiFHAFFCCHHjx8nf/7zn0lQUBABQAIDA8myZctIRUWF\n22gpLS3tdgdwbW0tC5RwRouUliB2rRyhsG7dOtaeI6EjJrR4k5yze8W3TefTFSEnpU1JbQbosc7m\nldFgFbHz+P+l1oZUu21tbaSgoIDExsYSAGTw4MEkIyODXLhwocO5rqy768lE19O0eoSwYp0AgwDs\nBDBGKJwA1Du7XtGspOHu3Zqru+yffvqJfPjhh+TOO+8kAIiXlxe59957yd/+9jfy888/i17jDpr5\nXbm73r0kdq4cxu+oH0dmQ7HrnM1NfHx8h+s6Yx4TY7y0HbnzKVf4ukIXD3dUkBCjUardvXv3krlz\n5zJ/qjMtq7MapQJ7eJSwaqcHLwB4RjEDug+uvGPJHaAax5YtW8j27dvJI488wkLOhw0bRjIyMojF\nYpFFk7OwZf48qWscnesMcv0sYsfEXofRVZrkaq9y2hULK5eCGHOV8jdNmzaNaX6u0uUoDF54He/v\nE861MzOuO/xaW7ZsISqVijz66KMuX3stIDXmntaQOoseF1YANAAGXfm/H4DdAO4B8LogwOI1Z21d\nC2ElFn11M0Buzs1PP/1EtmzZQpKTk0lAQAABQAYMGEB+85vfkC+//JLU1NQ4vJ7CFUYqpFNMuHWF\nuTvK+xH+dWQCckUblRLQzkxk9LiUwJYjfN2thcuNoCwtLWU+R96MK9RahaZPuZC78aGQEpw8nn/+\neQKARQu6W2NyRxDR9RjFKAZPEFZjARwEcBjANwBeuPK7GsCOK6Hr/wUQ4Kytrgor+qDIhVzTz40K\nOvaLFy+SjRs3kocffpj4+fkRAMTPz4/89re/JXl5eeTSpUudapeCn2ch45bjH3DmY3Hk3xLbncpN\n/HUEXsitWLFCUkuQigak/zsLmuiMZiX8vnLlSlHG3ZW174rGKmcDIdUmPdcdTF9MEF2+fJnMnDmT\n9O/fnxw9elSSNgVdR48LK3d+FDNg1yDHJEfR2NhIPvzwQzJv3jz25li1Wk0WLVpEvvjiC/LTTz91\nuNYVBuOof7m+ls607eg4L4ikmK3cDQ/dhfOCWMxsKByHULDyjNgVZs8LXSog5VzPC1VX+hOrViJ3\nvdGgCTl+MEdtSs2Ts+/OUFtbSwYPHkz0ej3bmAnvmRiUqD/XoAgrN+NG3VXV1taS8+fPk/fee4/c\nd999LGE3MDCQLFmyhPz3v/8lLS0tDq8nRPwBdSYE5LbjCO5iDHJ9Hdu2bWM7cZ5J8h9HtLlirhK7\nRup/HpQ+XmgJhZHUJoMX3s40ODEISzFJ9eXoNTLCyD9XIVdjk9NGfn4+AUAee+wxWe3cqHyiO6EI\nqxsA3bXwV65cSc6dO0feffddEhcXR7y9vVmQRGpqKtm1a5fD9wx11VbuyATmSh/8ObzJzRWI7crl\nmsPkMHMxTUOO6YrOk9j1UszdFQbvyITaFXOgVB4Yrz2L3X8pjYw30cnRroTtyvnNGf785z8TAMRo\nNHY45o7N0s2uiSnC6jpFd1ZvaG1tJZ999hmJjY0lKpWKACAjRowgy5YtI19//TU5c+aM0zZczWNx\nBWLmIHfnVDnSTCjTkIr2EzJasTwr3jfFtyF2HWXivKlOaCoUajpyBZ0QQi2QEEKWLFliVy2itLSU\nLFu2TLaWKxQwvJCTsyGRm36wYsWKDmZYR6ZjV5i/2L0SttnS0kJGjx5NfHx8yPHjxx1q3VJw5fkQ\nm78bGYqwus7QneaFlpYW8pe//IXVRQsJCSHPPfccOXjwoNO3zEpVKZDDyOQwVmc+Cp4hujIHjnwn\nPMN2xhiktCJem5HKTxIKMerPkupDTJDSa3g6nfmJ5AgbudGNYveB/i7Wxrx589j5Uv0724DIeVOx\nHN+RFDrzLGVnZxM/Pz8yZswYh5s6TygVdr1BEVbdhOtpx3Pp0iWSlpbGcqGGDBlC1q1bJ+mDcsWx\n7so5UuYysbnsiglKqOXQvzSYQciExYIJ6Dk0SMIR/bQNftfPM2kxQevMdCg0e9GPs2hFqeuF9Ar/\nF2prYhqYM0iZY4VCqbS0lCxZssTpOIRpJELtTY5ZVOq42FjF2hAb+/bt24mPjw8JDAwku3btcjgG\nIdatWydbkMm1JnjSq+m7AkVY3aBYsmSJ08i0Cxcu2BWPnTJlCvn888875ELxcMfC70rFATnak1hk\nm9T5cs2VznbtUrSJmb/E+hIKHOFxqeg7qcoLUozZkSYp1HKE46AfKc1NTCPkzxcG0gjb4u+bs3dm\niQkQZ3lRjtad3MAeZ6DnHTlyhISFhRFvb2+SnZ3t1DLhSh9SuNFTaRRhdYNBTlLiuXPnSHp6Ohk4\ncCABQGbPnk127twp64FyBGfajpSgkWKKjiBHeDliysJr+Yg4/hxnkNOvMMrO0bliviBH88n7qfgX\nOkpdw7cvhFhpJzFNT0wrdSTM5filKPj7ILwnzrRyZ+jq9XKu5ROZGxoayL333ksAkEceeYRcvHhR\nso3r1Sx4LYWjIqxuIMhhsC+88ALp168fUalU5Ne//jXJz88XPc9di9CZ5iJlXiOkozbAt+WIgXWW\ndrq75zVSOYyVBzXjSCXnCk1NUkxeqGE5G6cjrU0IYci6EAsWLOgQBCGlNQqPCcfkaqCHUBPjhTw/\nL0uWLLET6mL5akIaeDqFWp6jdeooItLZhqi1tZU8/fTTRKVSkcjISHLo0CFHU6HAARRh5aHorh2L\nWq0mY8eOJV999ZXD85YsWdKtdPBty+lDbsixXEgxIEeBHI4YFWWWvKCTqkRBv/PCWErAiQksnhaq\nKfF9iNFK2+ffpyU2bp4GYSi58Bo6Xr4/YWAJPz+0b0oLTxN/rXBOpdDVtSllVpVb8d2V/rds2UIC\nAgJIr169yOOPP06+//57p7QosIcirLqIzizcawWxhy42NpbcdttthBDHNMuJvuP/OoOUFsK3K8bQ\n5bTHM0s59PA7cSFjF353piVJMVi6e1+xYoWkaU5s7fAMmtcI7rrrLrJixQqyZMmSDuY+oXYjpFdM\ns5Bat8L7umLFCocaoPA6+j9Nil65cmWHKhxSJkKp+RC+y8vZs+bIDOpozMLj7vQB1da2J9WnpqYS\nLy8v4ufnR1577TVy8uRJh9d1xRx9o0ERVjcZnnnmGdKnTx+713JILXRHv4sxajGIMXPKfPh3Msnp\nX2iqEe7SxcxichztzoQ21QjEBAG9Viy/R4rJC/sUE8y8/0hotuI1M6k+KN1S4d1iApkvuyQm5IRj\nFv5G6eKL0dL2hbSJQWrDwQs7MW3T1cAJKe1JbP2IfXd0TNgO7Y/H0aNHSXx8PAFARo4cSfLy8rrs\nL+4KnM2hp0ARVjcZPvzwQwKgg+3c2W5UzmKm1zt7PQQh0kWDxQSSHPooXGWEy5Yt6xCZJqVF1NZ2\nzGXihZKjslFiJjqhKVBIl/Ac4Vj471IFbTdv3kwuXrxIdu3aRerr68k//vEP8v3335PS0lJy+vRp\nUl1dTaqqqsjzzz9Pvv32W5KZmUkWLlxISkpKyJYtW8jatWvJ7t27ySeffEIqKioYU5XSPhxpI8Lz\n+LE5ezWIWFvOAkLE5kqMFrFzpNaL3HXoqG3a7rZt28jo0aMJABIdHU3Kysokz1WgCCu3wtNV8dra\nWnLs2DECgKxdu9bp+XKEjrP+eDiKhKOM3llQAX8O9XfIoVNoipNjShJ+F1aIENNshCY6KZOXI/8S\n/7sYoxIKPEIIOXXqFPnvf/9LPvjgA7JgwQISExPD8ubc+QkICCALFy4kH3zwATl48KCdhiekTzjf\nYt+Fc003MXLLJ/HHxXxkFHJ8nlKbAqncNSmt2RW0tLSQlStXkoCAAKJSqUT9Wc7QE3ynJwSoXGGl\naj/XsxEdHU1KSkp6mgyPRltbGwYOHIhHH30Ub7/9tsNzLRYLgoODO/wvhMlkQlxcXKfaMZvNiIqK\nYscAoLy8HHq9HsHBwaxtYR85OTlISUmR7JM/PycnBwaDgfVD+6L9AHBpbHQMJpMJer0e5eXldufw\n1/Djo98BdKAlODiYjV+MFnoMAHr16oWdO3fi888/R0tLCwoKCtDc3IyWlhYAgLe3N0aMGIGxY8di\n7NixuOWWW3D27FnceuutaG5uRkBAAJqbm6FWq9HY2Ijm5mZ4eXnB398f3t7e0Gg08PLyQnV1NZqb\nmzFp0iR4eXnh888/x8mTJ1FUVASbzQYAuPXWW/GrX/0Ks2bNwowZMzBgwABkZGQgPT3djn6j0Yjq\n6mokJycjODgYRqMRSUlJHcZpNptRV1fH5o/eJ4q4uDi7NSR2f4S/Cdcc/U7vjdj8O1rvroD2IVwH\nQtTX1+OVV17BypUr0a9fPzz//PN46qmn0KdPH9ExCMfi7BmUA3eNubugUqlKCSHRTs9ThNX1D7oY\nZ8yYgdbWVuzdu7fTbdCHT/iQyHloHDFlR30C7Q+/VquVvNZiscBqtaKiooIxw7S0NGRlZXVgShTC\n3+j4KLRaLQDAarWyvs1mM7777jvccsst+Pzzz1FWVoaff/4Zv/jFL+Dv74/evXvDy8sLo0ePBgCE\nhITA398fgwYNwjfffIN7770XFy9eREVFBQCwOaNM3GKxoLS0FEOHDsXhw4exfft2VFZW4tSpUzh3\n7pwdbZGRkUwwjR07Fjt27EBqaioTGhaLBXl5eTAYDIx+k8kEjUaD4uJipKSkwGg0IiYmxm5TYbVa\nAQB1dXVs82CxWKDVanHo0CF8+umn2Lp1K44ePYr//e9/8Pb2xoQJEzBjxgxMnz4dXl5e0Gq1dpsR\n/r5RGnjhxN9Hei/E7rHw3gmFT2Fhod14hBATHnTtitEptWGRaovvh64foZAWEw7Hjh3D008/jS++\n+ALDhw/Hm2++ifvvvx8HDx50KOzkQIxOTxdQPOQKqx438cn5yDEDdjb5zp3msO6Gs/6WLl1K+vfv\n77BiOg+5VRLEzqfniCUrC3N5xCBmKpIKr5a6XsocyfuieJ8UPUbbpucXFRWRjz/+mDz44INkwoQJ\nxMvLiwAgvXv3JlFRUWT27Nlk/PjxZNiwYUSj0cgyq/n4+JChQ4eSUaNGkenTp5NZs2aRX//616w+\nI/307duXREdHk9mzZ5OXX36ZPPvss2T58uWSZk3h73zYOB0vzVUSBls48uEIz6+trSVr164laWlp\n5IknniCjRo1ixY8HDhxIZs6cSZYsWUJ27dpF8vPzO/jrxPriowmF91HKNyekTWpN8KH7Yn5Tfi0I\nxy4Hjuo/8jTIaef2228nAMi8efNkP6s3MqD4rNyL7hZMnX2IeLz//vsEAPn222+7TIscH5DYMVeE\njFDgCZ3dkp9oAAAgAElEQVT7fK6RWD9S7fO+JL4P+v2LL74gxcXF5IEHHiCxsbHsHV4qlYpERUWR\n9PR08o9//INcunTJzidDx3bmzBmyZ88eMm/ePLJjxw6ye/du8sc//pG88sorZMmSJeS3v/0tmTlz\nJklOTib3338/MRgMZNy4cWTEiBHk3nvvZe1/+umndsxKSDftU5jjxOcvSQVlUF8c75fhjwn74yHs\ng7ZRV1dHMjMzyWOPPUZCQkKYwB02bBhZtGgRMRqN5LPPPutwb6hPUCyysrb2ag4bv4ngaeKjNsXo\nFcLRceF8OUtuF2tLKn/MWX+0LVpYGgBZunSp0zZudCjCysPQncKOPnDffPMNAUD+85//dLk/ISPj\nAxF4SO3WS0tLnRbvFBNE9DrhjlUoyIQ7b7HXelDmXlNTQ7788kvy4osvkkmTJhFfX1/GaMeNG0ce\ne+wxsmXLFnLhwgVCCOlQ7YKQq4xJmCwrrL5B6aMBGcJcKX7cziIceWHDX8trUfxfYfDGunXrGH3C\n15Dwgo/+z4e30/B42pdwk/HBBx+Qqqoqsnz5cjJ37lzi5+fHQrY//fTTDrSI/ZWK0hSbCzHhQe8F\nX3SY/k4hN6fKUdi6OzaSYnjqqacIALJq1Sq3tSk3Z82ToAgrD0d3LKaWlhbSr18/8oc//IH95kp0\nj7PwZOHO3BGzFVZJEFZ9ENOwpHazPMPkaaH/03Bw+tuZM2fIxo0bSWZmJnnggQeIv78/E04jRowg\njzzyCPn444/Jjh077KLehEyO71tYlUHIwHgahdqQ2DuT+PnmhRMvNCgNvLAWzg/tizJueoyOhc49\nLxyFgomnmRcyVGivW7dOVLvh6bh8+TJ58sknibe3N5kxYwa5dOkSS5zmBSV/ndh7qqQEPA+xtSic\nG6l1L7wfjs6VA2dRnY5w+fJlEh8fT7y8vGS7MbrKNzxRiCnCqgchd0G4I0xUuAOcMmUKmTlzpkt0\nSEEolIQajfBcPtTZWd+85uEorJ3XAniGK9ZPY2Mjeeutt0hoaCgTTkOHDiUPPPAAefrpp8n+/fvt\nzFr89WI5QHwfvFCgEAouXuhICWWxcldCbUlMOAuFi9BUyIOfL7GcJ9oH/SuWC0U3Afxc0aoXUveq\ntLSU5OTkEAAkPj6enDp1qsNxVyGmRQuFgZQWJNW3WMi6HH+TVD+deWMBncfGxkYyduxY4ufnR775\n5psO/dwMUITVTYrExEQSGBho95uzXZtQixDubMVMXhRizKGzPgUhnVI+K6Fvp7W1lTz22GOkX79+\n7JUo/+///T9y/PhxluwqpuEItQO+Hz55d926dXaMWyjgxKpsCBkib5qjAobPJeOrTAiv4bUa2hev\nOfEJ0Py4pMx4VFNatmxZh7JLUtorLzT4Y0LBXlpaSv75z3+y+2Cz2UQFHD82qWM8hNUuhH+lcqaE\nc8nT6g6Iba5cvea7774jgYGB5I477ug0HdezgFOElYdBuJPuDuzevZuoVCry5JNPyu7H2Tm8AHMV\nru4+pcyCtbVXfSiUEdPPwYMHSXR0NAFARo8ebZcUzWtjfC0/oYASBjCIma2E33mNkPf70PbFTIpS\nDJPvT/hqEPoRmuzEzHv8ebxA4ukT+rkoLdTkt3LlSrtoQrHx82WXxII9Vq5cSV577TXi7e1NBg8e\nTMxmc4e55ueNh9h6E1ujvAlRKFjlQCzQRAhHPjVHv0kVixZqb/xc3HHHHcRgMDik50aFIqxuMly8\neJHcdtttJDQ0lDQ3N8u+To4Dmt89O9OkKLMXtsubtwjpaGJ0VAVDrJ/6+npy3333kV69ehGNRkPe\neOMN0tra2oEO3pTHv5OIZyiUOQp9Q7w5T8pvJhRUYmPj2xEKQkKuak7z5s1jbYlpHLzA4v1IVKDx\ndPDjoMKIp0foi+MFjaP7wgtoMT8dP9/79u0jGo2G9O3bl7z33nt2AR9i60PMpCnsW0yLdUQvP3/8\n985uwMTaE/tdykwpBZ1OR+bPn+/wWeyuTW5PQ66w6tX5VC7HUKlUw1Qq1U6VSvWtSqU6olKpUq/8\n/pJKpapVqVSHrnzmdhcNngJhsmp34IUXXkBlZSXWrFmDCxcuyL6urq5O8hilmyZOxsXFiSYa0uRI\niqSkJFRUVNgl5UZFRdklYNJ2aKWJuLg4GI1GdpwmrgLtSY9msxn5+fmora3FW2+9hfDwcGzevBmP\nPPIIjh07hpkzZ8JqtcJkMsFisSAqKgrh4eEsGVRI77Rp01jiZHl5Oerq6mAwGOyqKoSHhyM4OBha\nrRZ6vR5WqxXl5eUwm83svJSUFKxfvx51dXUoLCzsUAWDzkd5eTkqKioQHh7OxkQrIPj7+8NqtWLx\n4sVsLk+fPg2j0Qij0Qir1cqSmfPy8libiYmJjM7q6uoO94HeQ41Gg/DwcOj1elgsFuzZswcajQZW\nq9Wu0ofFYkFKSgosFguqqqpgsVjYcdp/XFwczGYzEhMTodFoUF5ejuDgYMTFxUGj0UCj0TA6hw0b\nhrfeegsRERFYuHAhvvzyS8ybNw85OTkdqpvQ78JEdB56vd6uAgq9zzydfIUM+uGrYtCk6ODg4C4l\n4/LJ5RT8s0H/NxqN0Gq1ducLr21ra8Pp06dx6623Okzk7UySrxid1y3kSLTOfAAEAYi68r8vgOMA\nbgfwEoBnXGnL0zWrnlbbi4uLSa9evcjvfvc7l67r6k7Nkb2e11R4DUDMfyHURKR2pYcPHyYjRowg\nAMj48eNJSUlJB01OaNITms14PxWvFS1YsMCpCUpsN041BX6s9Fyp6DsxU5yw9iD9zoeQ03Z5LU3o\nR6JaFO1327ZtZMGCBR00wSVLlrD++GhKof+H9iHsn84V/8LHbdu22bVL22hpaSG//vWvCQAyatQo\ncubMmQ4mUX4e+LkTrgm52gs/987acZZeIQdCzVTsWqm2LBYLAUBycnJk9dXTcKfPjxAP0KwIIWcJ\nIeYr/zcBOApgSHf15wjdvbvoarkUoPPa108//YTk5GQMGTIEr732msP26G/CkjfO+qbzZzabRUvi\n0DaoNkTrwFEYDAYEBwcjKSmJ7YCBq9oT1VLorpiWrwHa53bXrl14+umnERkZifPnz2PRokVYvXo1\nVCoVoqKiEBMTY1feiO64tVotrFYr0wjVajXrOy0tjWkmWq0WqampiIuLQ2FhISs3ZTKZmLZGtam6\nujpkZGQwbU+tVsNisUCn0wEA/P392XxRLYNqPCtWrGCaGp17OgcxMTF2c56UlMS0Paq1UBpoeSiq\nTVHtITc3FzabDWq1GiaTid2D1NRUppHpdDpotVoMGTIEUVFRKC8vx/Dhw+3uKy2VRLUwWpaJr/EY\nFxcHq9UKg8GAnJwcZGdnAwASEhLYeLVaLUwmEz7++GN88skn2LBhA2pqahAVFYXS0lJYLBYYjUa7\nNaFWq9k9A9q1KZPJxNYaHQddJxRUw6JrkV8HdF0YjUZ2TWFhod18C58BqtUJtRmpZ4WeR+taipU7\nktKMTp8+DQAYMGCA6HEhTY7o6CrktNvVWoWdRbcJKx4qlSoEQCSAr6/89KRKpTqsUqnWqlQq/+7u\n3x3CpLvhSj09/u8zzzyDiooKrF69Gn5+fuw8+jBSpsr3kZeXZyfAhULLZDIhLS2NHaeMKyoqyu5c\n/oGkTBi4avJLSUlh5haj0WjHdCwWC2OmlFnRc4B2Zk0IwauvvooHH3wQb7zxBu69915UVVUhKioK\n0dHR7HqeBv5h4016FosFNpsN+fn5qKiowPz58xmt9C8VPmazmZm0srOzERcXh6ioKJSUlNg9qJSJ\n5+bmMvOUsG/KIMPDw5npkd6XiooKJhjLy8sZLXl5eTAajazGHzUZ0v6ioqKQm5uL5ORkZoYDgOjo\naKjVaiYk9Xo9SkpKoNVqkZ+fj/LycmzatAm5ubmstiDQLsByc3ORm5uL4OBgu43G4sWLAbQLPLqm\nNBqN3TzrdDokJSXBZrNBo9EgODgYwcHBKCwstJuvefPmoaSkBL6+vliyZAneeecdzJw5k907s9nM\nxsevzZKSEjaPdIPBQ7gWFyxYwNYanWth7T6+2C6lmYfQfEw3T/yGzBGENQgd4bvvvgPgnE8Jzei8\nqdMR+OOu0O1xkKN+deUDwAdAKYBfXfkeCMAL7YIyA8BaieseB1ACoGT48OFuVTs9Ga7kbBw4cIB4\neXmRRYsWEUKkzZGdMfcJX+3Bm4Xod1ei/XjTkZBWPqqL9vntt9+SO+64g1VF2Ldvn2TbjoJEhIER\n27ZtY2Howqg6ocmSp4kGQPD0C6PFaDg6Na2VlpaS+Ph4Rh9v0hNGNdI55EPYhZGBy5Yt65BcvWDB\nAjtTHf9XLOqPmqnWrVvHaOPvoTDIQRipWFpaylIK+Nwz4Xw4yme7cOECMRgMBACZP38+aWpqYmZN\n/p5KzbPY/Rdb+/wcSl3P0+oswIPvR5hzJjZfcvH6668TAKShoUHW+TdaoAV62gwIACqVqjeATwGs\nI4RsvCIcvyeEtBJC2gCsBjBJ7FpCyLuEkGhCSDTdKXoausO8KPZ6BbGd088//4xFixYhMDAQf/vb\n3wB0DHSQgthOS7j7o3RQhzrd9SUlJbHvvLkuKSkJGRkZsFgsos5k6oTnK3zT46GhoQDad62VlZXY\ntGkTxo4diyNHjmDVqlU4duwYhg0bBqPRCIvFYkeryWRi5j7hPGVkZLA+6Y4xLi6OaVVUG1Kr1Uxr\n0Ol0CA4ORkxMDMxmM2s3ODgYixcvZgEWFGlpacjNzWV9JyYmIjc3F7GxsdBqtXj33XfZWP39/aHR\naOzMXnV1dUhNTWVt2mw2hIeHw2q1Ys+ePcwUWV9fj5MnTyI8PNxOE8vMzER9fT3ThioqKqDRaJiJ\nb/369bBarUhLS4Ner8fp06eZBjlnzhx2XzMyMlg1c3rPLRYLCzIxGo0IDw9HXV0dYmNjAbRrJFVV\nVXZjMZlMKCwsRFVVFdPw+Orp+fn5qKqqwttvv40nn3wSGzZswIQJE+Dj48Oq0gNXtQw6d3zwDV03\n1IxIK8DTa/lz+edJqDVQ7SsuLo6tE+ErSChoUAilKz09XfTcpKQkp9qJkG989913GDhwIAYOHIh7\n7rnH4bVi47hpIEeideYDQAXgfQBvCn4P4v7/A4CPnLXl6QEWPYEXXniBACCbN292+VrhbtuVnZqj\n3a1chzI9xgcOlJSUkE8++YQMHTqUACCzZs0iOTk5djtyuqMnhLD8ITFHOX+N0HHP08vnC/GaH68Z\nCQMJ+EAIYfAB344wiIMPZBBqHDQwQdhObW0tK6NE6efD22k7fE4X1aZ4unkNi6/Szs8rTVDmtT1e\n4+SDT3ja582bZxfGLhYkwx8Tatjbt28nAwcOJH5+fmTt2rV2c03pE9PQ+OP82B1p+I7WuqN13RmI\nBeNI4d577yXh4eFO27tRAQ/QrO4A8AiAWEGY+msqlapcpVIdBnDnFYHl0XCXM9NdmtiqVavw6quv\n4re//S3uuecel2zWAJhWRB3R1A5Pz8vJyQFwVduif6nfgO5cjUYj84nRNsV8YTxo+DDQrkVERETg\n6aefxtKlSzFv3jx4e3tjz549+Otf/4r777+f0RMVFYXY2FgWtpyVlQWDwcDopmNJS0tjIeR0V041\nhMLCQua/iIqKQnJyMsxmM4qLiwEAJSUlLIBh3LhxyM/Ph81mQ3BwMHQ6HUwmE9up5+bmsjB0GvxA\ntRm9Xs/6TkxMRFRUFKxWK1JTU1lAQV1dHZtnjUaDxYsXQ6PRICMjg7VTXl6O6OhoFlRA3+VUXFwM\nrVbL/HfJycnIyclhWk1dXR3TePz9/ZGXlwetVmsXnKDRaNh9zMrKQmJiIgvyoL60+vp6Nh8FBQVM\nm6urq0NaWhqsVis2bNgAnU6HFStWAAB7oWZ5eTm7L3TdxMXFITs7GwUFBWxtBAQE4PDhwwgJCcGi\nRYuQk5OD3//+9+zaqKgoaDQaFkhCw/jpcV5Dp1qylLVAKvVCuFb556Wz/m6h788RTp8+zQJ0HLUH\ntGvA3Q1nPraeQndGA+4hhKgIIWMJIeOvfLYSQh4hhOiv/H4fIeRsd9HgDHKFkLvUbrEXpLmKlpYW\nrF69Gmq1Gn/+858d0icMrBCCmr4oXcKIJj53xWw2M0YdExPDnNRRUVEoLi5mC5w3x6SlpcFisTBG\nyqOmpgY///wzZsyYgTfeeAPfffcdVq5cicrKSvTr148xeABMKAFX84Z40yT/IMfGxqK6upqZ/qij\nX6vVIjw8nOVf8dGLiYmJsFqtmDNnDuLi4qDVarFnzx4kJyezscbFxbHItLq6OkRHR7Px5ufnswAN\nmr9EhSrQLtQrKiqwfPlyqNVqFk2XmJjIIgzr6upQXFyMOXPmsHb0ej00Gg10Oh1iYmJQVlYGnU7H\nGFtiYiKba4PBAI1GwwSOTqezG19eXh4SExNZLllUVBSqq6uxfv16RicVZtQEyjPQ2NhYFBQUwGKx\nYNOmTYiNjUVdXR3MZjNKSkqwe/duLF++HMDVKEjaf2ZmJjPfZmVlMTMsnb8ffvgBf/nLX7B48WK8\n+uqriI2Nxfvvvw8AeOKJJ+xeckivoWuX9sFH/dHz6boFnDNgYZQr4HxzKXZcGFUpB9999x1uvfVW\nWecK39TcHeipaD9nuCbRgJ6KzgohfkG6InCED4zUbs4R/vrXv+LQoUNYtWoVIiIiHJ4rtSvk7euO\njvPRdBQmkwl5eXlMw6IJogCYT4mOKysrC0C78KMPQFVVFd566y386le/QnJyMnx9ffHBBx9g9+7d\nMBgM8Pb2tqObMlaz2YyqqirWNtWGKIKDg5GcnAyNRoPk5GQ7/wUfsn7PPfegrq6OJemKzVdeXh4W\nL14Mq9XKPiaTiUUGFhQUwGazsfOTk5MBtPtXeL8hFVzh4eGIiYlBamoqqqqq7MKxqd+HD3PPzMwE\n0M6I6+rqoNFokJeXh9jYWNhsNhaByIe/0118eXk5jEYjCgoKkJmZyULB6XnFxcWw2WwwmUxITk7G\n/PnzWfv+/u2BudnZ2axfGp1ns9nY/Vy8eDH0ej3i4uJQUVHB5jszM5OtcaoN0c0CFdBUS6ZaHWX4\nQ4cOxerVq/Huu+9i9+7d+Oc//4mnnnrK7j7QCFI+so/eA7oW6VoAwDQvmqBNtTyaikDP4a+h4N9O\nzIP/LqY5CcPphRDyjmPHjqGhoQHDhw/vcG5XcC0KEVxzyLEV9vRH8Vm144svviC9evUiDz30UKeu\n530brlaKFkZK8VFyQn8V9UXxPh0+mXXgwIFEq9WSjIwMcubMmQ6JnmIvtxOWTuL/5yPJeJ+SWJQY\njV7j/Re0rXnz5tlFhtF2+Ndv8H6dBQsW2NFD/8bHx5OVK1faJQovW7bMzudG+6F+LELsS1IJS1zx\n34WvEqH3lc4RPwbet8bfd2FZJ+pbo3/59UHnS9imcG0I/XfCMlTCd4EJz6V4//33yeDBg4mXlxf5\n+9//zpKIxfxh/H2nbfF08f058mmJJRuL+WCF/j5ncFb8ee/evQQA2bhxo9O2ugpP9XtBps9K1X6u\nZyM6OpqUlJT0NBldgliSoCOYTCY7dfybb77B1KlTodPpsHv3bqcJhLyZzNFvUnTyO0u+dEx4eLid\n9iA1JuF46fdjx44hPDwczz77LCIiIlgEGPX10DZ5M11FRYVdjkx5ebmdiUVID79bzsnJYeY2mhha\nX18Pg8GAuro6Fn1H54VqBjabjUXH0V047ae8vJz5hE6fPo1x48YhJiYG5eXl7DpqplKr1axUk1ar\nZdoqTbylPp6SkhIkJyez/qiPyWAwsP91Oh1L+tVoNKioqEBMTAxWrFiB5557DkC79kTLUiUkJLC+\niouLodPpsGbNGqYJ03b40ke5ubmYM2cOm8/i4mKkpKQgIyMDoaGhbGwxMTFMo+PHR0HvJ72HfCI0\ncHUtZmRksJwvft2dP38ec+fOxddff4377rsPubm52LZtG1sHdI7o/ab312QywWazYc+ePXjuuefs\nTHpr1qzBqlWrOlwvpIn+D1zNpaP98H/F1rmrePXVV5Geno66ujr8/PPPN2Wkn0qlKiWERDs776Y2\nA15LOFqEYrZvXlAdOXIE8fHx8PHxweeff95BUFHzBg9eKAnDgfnfpECTVXnQqgp8Ei6PhQsXMue3\nMGiDmg1ramoAAI2NjYiJiWGh2SUlJcxkBLQzXUov9cPQtqhQ40OJqRDIyclhviqLxcJ8NNTPFRMT\nw8xdcXFxUKvVjMmaTCZs2rQJmzZtYsLHZDKxfmi71I+UkpKC+fPno7q6mvl7eLNbfX29XQBLbm4u\nM/lptVomcKuqqhAaGsqCBzIzM+0EVWFhIRITE0E3bBqNBvn5+UhKSkJubi4zlQFgfq+EhATYbDYU\nFBSgrq4OOp0OGo0G48ePh1qtRllZGWw2Gws3z8nJQXl5OQYNGoQ1a9awII6UlBQYjUY0NjYyQUFr\nHdK5p+ZF6qujNQf5c+haycnJYYLCbDazIBfeH2U2m5GVlYUnn3wSjz/+OLZu3YpRo0YhNDSUBaXw\ngopWvKC+xaSkJKxatcpufWq1WibQqVCigTh0bfHBGjSQhA/KoH9587hwQ+bIbAh0DMH/6quvMGbM\nGAwePPimFFSuQBFW3QThIjWbzTCZTKLRPI5s2xs3bsTkyZPR3NyMLVu2YOjQoQDsF31wcHAH/5PQ\nYSwUPHyhWB70XFpBQSh0qO1f+LvFYkFqaqpdlQsqOGgEYExMDHbv3g0AmDhxIoKDg5ngoUyLBjRQ\nJlpXV8fymPiHWcxfUF5ezpgr/c5X1aAMNSYmhjFFvlCrRqPBc889x5g/3TDwZaYsFguKi4uZ854K\nB4PBgE2bNrFIwJiYGBgMBhiNRsb4k5OTkZiYyIre0vZOnz6N+vp6rFmzBvn5+SxQYf369SgsLERZ\nWRmsViuio6NZNGBoaCjMZjMGDRqE7Oxs5Ofns/JHFRUV0Ov1qK+vx/z586HX61FVVQWtVsu0pvnz\n5yMmJgY2mw3A1SAWnU7HmPqKFStgsVigVqtZUATVfqiWSgUvjXrkK52UlJTAZDKhoqKC3ZOUlJQO\nGx56L+hcA8C4cePw8MMP48UXX0RRURF8fX0xY8YMXLp0CW1tbQCu5kkJ1zR9xvgKD3SjQXO9gI45\nUfR/6ucSK4IMwC5PDrgaPcvn8wnb5CuzULS0tGDv3r0sz5DClWg8ofC7kaGYAXsAckwHbW1teOGF\nF5CRkYHJkydj48aNkiYLYbtiJjx6HLB3QFPzjZxERmrGEosW4gUY7/SmpjSK7OxsLF26FB999BGm\nT58u2T8/FmrC0uv1KCwsZKZDoXmyoqIC9fX1LDE3PT2dmdiio6MZk+FNcQCQn5/Povtoe5mZmbBa\nraxSen5+PubMmYOKigpm2issLIRarUZBQQGysrJYTURqqrPZbKiursagQYOYNkfNZ8XFxWhoaEBo\naCgLgefNaNnZ2cjKyoLJZEJBQQHmz5/PtCwALGikpKQEjY2NsFqtTOOmWpXQ3EnnZvny5bh06RKW\nL1/OhD4VvvwmIC4ujjH+5OTkDiY9Orfp6el25lWtVovly5fbzSFvxhWaDKUCjXgT3o4dO7Bt2zYU\nFBRg7ty5yMrKws8//8zWpNBUJ7aW6L3laRH2L4TQHM+vEakEfjkakslkwsCBA2EwGLBhwwbMmzdP\n8lxHz/qNAMUM2I3gtSNX6m4B7eHczhbZV199xez5//d//4evvvqqwzVi2hhvSuG/88fpb7T2H32V\nBz8esTHR3TL1C9Bz+YKf1FxGw9Vp9QO+3fLycgwYMADTpk2z61+4g6XXVVRUICUlhfk/hAVfaQg9\n0P6qDFqPkEboAe2MNi4ujuUMaTQaFsmm1WqRnp7OftNoNEhKSkJeXh7q6uqYEKQaSXV1NYCrOU98\nlFxmZiaqqqqgVqvZ3zlz5qChoQEAsHbtWqSmpjLhSwUkLyCoMBo3bhzbNdOaj+Xl5YiJiWHmRaPR\niOjoaKSmpqK2thaLFy/GkCHttaLLyspw5MgRptXU19fD398fhYWFyMzMZIVnaQ1EXtBSU6XZbEZ6\nejrS09NRXl6OzMzMDuaz6OhoLFy4kN2X7OxslJeXIzU1lYXl06onVBOi46WmU1o0mAoyXgui8/Pw\nww/jtddeQ3x8PLZv345Zs2bh0KFDAKRDrcWidqnmLmb5ECInJ0eybalIWrkChBZNBoCwsDBJGgD3\n1Da9EV4Vct0Kq56cfD7XwVmoqlBNp4xNCkePHsVjjz3GSg2tXr0affr06XCecPxC8yJvthJDVlYW\nMx/yRWuFDxtvx6fj4c0gYqaS4OBg+Pv7sxBlfgdbWVmJIUOGQKVSMeZLfRaUoQFXTSGUCVJnPt3R\ni5k2Ka05OTmwWq3IyMiAzWZjviCbzWZXPJa2nZOTw0w/QDsD9/f3Zya9+vp6VFRUMPMbDYe3Wq0s\nCTUtLQ3Lly/He++9h02bNjEfUV1dHQYNGoSkpCRMmDAB/fv3h06nw9tvv820naqqKhQXFyM4OJgF\nMPCBHlRQ2mw25OXlsZJJYWFhjOn96U9/YkLVZrNh+PDhSEhIgFqtxqRJk9DQ0IDq6mpmUszNzQVw\nNQWAhqfHxMQgJSUFxcXFyM/PxxNPPAGj0QibzYbs7Gw7kxcVdJTZ6nQ6ZGZmsuAPGmaflpbG+qFh\n5NQnRzcPdE3R5F9qTqR5deXl5ZgwYQLeffdd7Nu3DwMGDMDixYvxhz/8gfk7hYEPdN2K5U0tX74c\n5eXldgnkfDi7xWKxy2OjEJoru5JA+9VXX+H222/H2LFj2T0QtifF54Tj9ES4m0d7vfTSS25tsDvw\n7rvvvvT444/b/RYUFNRD1LgGoX3bETZt2oS5c+eitbUVX3zxBQwGA5qbm+Hr68vOMZlM0Ol0IITY\n/T5jxgy7tpqamuDr6wtfX1+YzWY2XxaLxe46AJg1a5bd98rKStY+PZe/PjIyEgDg6+sLi8XC8mjU\namwNlGMAACAASURBVDX7berUqexvU1MTO//1119HS0sLnnvuOeh0OjQ1NcHHxwdNTU1Ma9HpdOjf\nvz/r32KxoLq6mmkTkydPRlNTEwIDAxmzOXz4MCoqKjBp0iTU19dj5MiRuHDhAov027lzJ2JiYhAS\nEoL3338fM2fOZLlFCQkJaGpqAiEEFRUV+OyzzzB79myEhYWhsrISRUVFiIiIwKhRozB79mzs27cP\nfn5+GDNmDI4ePYoxY8YgJCQEWq0Wf/zjH3HhwgUkJibi7NmzGDlyJO666y6YTCb4+flBrVZj48aN\nmDVrFnbt2oWZM2fi/PnzaGhowO7du3Hq1CnMmTMH+/btw+zZs3HgwAFMnz4dJSUlUKvVGDVqFLy9\nvTF48GCYzWbk5eUhMjISer0er776Ku68807Mnj0bQ4cORWBgIAghKCoqwg8//IBx48ahT58+uHjx\nIgCgqKgIAwcOxMSJE1FUVITJkyfDarWyeXj66afh7e2Nffv24cEHH4TVasXEiRMREhKCyspK1NXV\nYeTIkfD29sZHH32E0NBQREZGIigoCHq9npkqp0yZgsuXLyMuLg779+/HwYMHodPpcOnSJfY5evQo\npk6dyu755s2b2fU+Pj4IDAyEr68vioqKMH36dPTv3x9Au0+3pKQEI0eOhEajQVFRERobGxEWFgZf\nX18EBQUxIRYUFISmpiZYrVbcf//9bN7o+mxubkZQUBCjwdfXF01NTSgqKoJOp4PFYoGPjw8bO5+g\nLQT/zNHnhrYDAJcvX0ZKSgoSEhIQHx/PzqPH6XMqfM55uGIK7Al+KbfPl19++exLL730rrPzrlth\ndSPgiSeeQHx8PNra2vDyyy9jyZIlGDduHAoKCqDX6+2EBQVdzFILmII/zi8aKsTEYLFYUFlZiaio\nKFRWViIoKIg9dPThof2bTCb079+fBUno9Xq8//77GDp0KHuAmpqaGHPw8fHB8ePHkZ2djfj4eCQk\nJDCzYGBgIIKDg9HY2IgBAwYwpuLr6wuTyYTIyEjGLPr27YuSkhL07dsX//rXvxAREQG9Xo/Ro0dj\n9OjRaGpqwrlz50AIwZgxY5ggDAkJQXBwMHx9fXHgwAGMHj0aX375Jfbt24fIyEgWfWixWPDAAw9g\n+/bt+Pe//w2z2YwFCxZgwIABWLNmDXbt2oVx48YhPDwczc3N+OGHH3Dq1Cns2LEDAwYMgE6nQ3l5\nOT7//HOYzWasWrUKTU1NeOihh9DS0oKJEycyIRUbG4stW7aAEILGxkb06dMHQUFBmDp1KoKCgvDC\nCy/gxx9/RE1NDQtb37FjB9MIm5qaMGrUKNx6660ICwtDTEwMqqursX//fvTt2xfV1dWoqalBWVkZ\nG+NDDz2ETZs24dlnn8WsWbPw6aefYsCAAYiLi8PmzZtZTgsV0HQeN2zYgNbWVhw8eBCXL1/GpUuX\nUFVVhdGjR6OoqAh33303PvzwQ0RGRqKyshKHDx8GIQQ+Pj6wWq3YtWsXZsyYAbVaje+//x6JiYk4\nceIE9Ho9AgMDcerUKbS0tIAQgsrKSsyePZsJEaqRE0IQGBiIoqIiTJs2DRMmTEBLSwsKCgrwxRdf\n4MKFC5g6dSq0Wi2Kiopw4sQJJrjo+qZCi38GKisrUVJSAkII9u/fD7VazdY/v+aLiooQGRmJs2fP\nYvr06XbPjlA4CRk1bYdaJkpKSrBq1So888wz+N///tdBKNH/6V+hRUP4jLsDwjFcKyjCqoch58bH\nx8ejsbEREydOxIYNG5CcnIxPPvkEAQEBXe5PTIMCOi5w/jy6EwXAHh76lwoenmnQAAN6PRVUOTk5\n+Pe//40HH3yQ7YR9fX1RUVGBNWvWICEhAX5+fggLC4NOp8Nbb70FnU6HsLAwHD58GP3790d5eTka\nGxtx6dIlrF+/nlVDf/PNNxEREYFLly7hvvvuY8wxLCyMMZMTJ06gpqYGU6dOZb9RDfPNN9/EhQsX\nMGXKFISEhKB///44ePAgJk6cyLSr6upqBAQEYPr06Rg2bBhqamrYbvq2227DnDlz4OPjg+rqalgs\nFvz000949NFHQQjBzp07ER4ejttuuw379u1DQkICIiMj8Ze//AVz585FcHAwvv32W8TFxWH79u0Y\nOHAgnn76abS1tTFTlre3N4vo8/LyQmNjI9PKWltbcfHiRQwePBhFRUUoKytjNfz8/f1RU1PD/Gzb\nt2/H8OHDodPpEBkZiUGDBoEQgn379sHb2xsnTpxAa2srCCFMUNhsNhw4cACvvPIK03qrq6tRUVGB\nPn36wGAwYOTIkTh37hwCAgJACMHFixdBCEFqaio2b96MyspKzJw5E9XV1YiMjMTOnTuRmJjINl+U\n6Z44cQJFRUW46667oNfrsXPnTkydOhWHDx/GiRMnQAjB9u3bUV9fj5qaGoSEhMBqtaKmpgZ9+/bF\nhAkTMGzYMDz++OPIy8vDtm3boFKpMHDgQIwbNw6EEGi1Wnbved8Q3YA1NTUhLCwMer0e/fv3x9Sp\nU7F582bMnj0bOTk5mDTp6ksheO1K+BzJZfJ07OvWrcN///tfrFy5ErfddptTwePMQiP1vLuCnrJW\nyRVW163PytMhxym6a9cuTJ48GRUVFVi5ciXuuusu9O3bF4A8WzhvE+ZL2ADO87rouWLn8XZ+4Xc+\n30gYVUV9QykpKVi1ahUsFguqqqpgsViQkZGBs2fby0COGzeOvdARaPcBUmczzUuijm2NRoP09HTW\nf2pqKiv1IwzMoGWM9Ho9wsPDO/jxiouLMX/+fGRlZSE1NRVr1qwBAKYN0Sg1WgeQvm6juroaBoMB\nixcvxv79+1l7RqMROp0ODQ0NyM3NhVarRVlZGdasWQOtVotVq1ahuroadXV18PX1xYoVK2A2mxEd\nHQ2r1QqdTgeDwcDuY1VVFYveo/c/Ojoaw4cPR3V1NQoKCpjvaf369Zg/fz4yMzMRHR3N6jkC7dGN\nZWVliIiIQHV1NTOn0ZdPLl68mAVS0ARh+sLMmJgYhIaGMq03NzcXGo0GWVlZaGhoYKWq+Dnas2cP\nCgoKWOoBXQs0LF6tVrMXfgrXdUpKCiyW9tfK1NfXs7B4+tLIlJQU2Gw2FmYeFRXFalLSHDir1YpD\nhw7h4YcfxjvvvIPly5ejV69eWL9+fYdwdOoLE+bj0eAOk8mE8PBwto75tAX6HPDPhnA8cn1Ie/fu\nxahRo1BbW+vwPLnt3SiRgY6gCKtuhCMH4+bNm3Hvvfcy005KSgoefvhhAO1MkDJrsTwKqQXMRyg5\ne4jE8rL4yD8KyiDo/8K2+AeeT9KllbZpQdXk5GQWKBIaGsqEEs09o6+Hp+8nAq7W2hPOAZ+7wtNE\nQ5jz8vIQFRXFAhNoLTh/f3/k5+czpvrcc8+xN9wWFBSwIACj0cgCECoqKhAaGgqtVsuSRLOzs2G1\nWvHee+9Br9djzpw5LHw8KysLCQkJsFqtMBqNGDRoEEpKSnDkyBFMmzaNCUBa/2/NmjUsGIHPkUpN\nTWVCxt/fH4MGDWLzCLRHCFKBZbPZ8MADD7ACvKGhofDz80NDQwNqa2tZdfb6+npER0ejuLgY1dXV\nOHLkCPLy8hAeHg6j0cjmma/GMWfOHCY8Q0NDsWbNGmg0GhbEkpSUhMWLF2P48OF2xYe1Wi1LMdBo\nNCyPi3/LMA2XDw5uf8s0NbfR/DEadMNvivgPvbdxcXHw9fXFf/7zHyxduhSVlZUYP348Zs2axYRN\nYWGh3RuBqZDkc7BokAldx1QY0lQHsQ0ofU75tsTACzygvfIJDUJyBFeFkCcHXHQVSp5VD+Dw4cMY\nN24cdDodCgoKMGzYsG7tzx05GcK8ErEcLj4iiz40tNo3ADzzzDP48MMPcf78efz4449shy6WR0LL\nJBUWFiI8PJyZD/nzaVQeLRnE5/Dk5ubi0KFDLMGWliaiQRXh4eEoLi5mtNHXxFMNj+aH8YVwaUkk\nq9WK7OxshIWFYdCgQayq+enTp5GVlYW0tDQMHz6caUk0KvDtt9/GK6+8wnK1Xn/9dSxatIiNmY4z\nIiKCXVtSUoI5c+awMQ0aNMiuCj3NLQPAQuQHDRpkl5cGgIXTHzlyBL6+viy0nKYE0LJClDHbbDaU\nlZXBz8+PCUi+2C2dE5oUTO8lvf+84CssLMSePXtY+SeqcVHQ0Ha+HBWlSyy/iObaCdcd0M7cP/74\nY7z00ks4evQoVqxYwfLt+CRvoKPwEcuponTRth3lcUnlZIlh9OjRGDlyJLZs2SLrfDml0q5XKHlW\nHgj6QI0cORKTJ09GdXU1du3aJXqeMzMgr7UJNQ/h9fzDJax27Qi8GY0veUTboLtJ3kzI/6UVFeh3\n+rB5e3sjLy+vQ9g7Hw5sMBjs3t+0ePFidh59NUVSUhLi4uJYJW6atxMcHIzQ0FBWPbyurs4uP0ut\nVrOad8DV3CZhGZ2cnBwkJSWxV3UA7XlExcXFSE1NBQAmUBsaGtDU1ASz2YympiZmMqOCCgATOjTq\n8bXXXkNZWRnCw8NRX1+P4uJirFq1ys6kd/z4ccYsk5OT4e/vjzVr1mD9+vXIz89ngur06dNMk2xo\naEBVVRUef/xxVFdXszEAYNUpqOZI88mo1kGDZYD214JQEyytTcgLKXpeYWEhSkpK7EK/6+vr2RuP\nw8PDmaCiJa7UajXCw8OZuba4uJjRVFVVxaqPLFy40G7N5uXlsTJd9J7xAoTmiy1duhQAWKUPShdg\nX6aLBx0P/zxRzUq4vnnQ33gti0LqOQsICMCFCxdEjwnboHRcT+iOd2LdFAEW7nA+CkGdtK5E0FAa\nbrnlFsyfPx9FRUX4+9//zioC8OHmUiGxdKdPncUAWC4LDUHfvHkz0xiE0Ov1HXZpwog/s9kMQggO\nHTrEQuJpcEVYWBhaWlrQ3NxsFx5MndF8iC4hhJ0DtIfm7927F0uXLsWdd97J5oQ61Gkk4P79+zF7\n9mzo9Xps3rwZc+bMQUlJCSZPnozKykrU1NRg8ODBOHv2LIte27FjBx599FGEhYUhIyMDDQ0NuHDh\nArZv385Ckpubm9G3b1/YbDZERkbi8OHDKCoqQr9+/ZCSkoLVq1fjxx9/BAAcOHCAhdDTRObW1lbE\nxMRg4sSJKCkpQXZ2Nu6++24MHjwYP/zwA15++WXs3LmTve4hICAAa9asQWxsLAYMGIAzZ84wk57N\nZsPs2bPR1taGmpoaREZGom/fvjCZTKivr8elS5dgs9mg0+lw6tQpnDp1Cjt37oROp4O3tzcmTJiA\n4OBgnDx5EidOnECfPn0QGBgIi8WCOXPm4OLFi9Dr9TAYDCCEYMWKFRg1ahRCQkLw9ddf44477kBc\nXBwTdlu2bMHx48fx/fff49SpUwgPD8eAAQPw5ptvYv/+/XjooYdQVFSEUaNG4cCBAzCbzSxc/+DB\ngzh37hx++ctfwmq1IiwsjAVFEEIwYMAA1NTUMCFGE8snTpwIHx8feHt7Y+bMmbhw4QJuvfVWTJw4\nkQVXzJw5E/3790f//v0RFhaGoUOHorCwEJMnT2bPSWVlJc6ePYuwsDAQQjB69Gjs3bsXBQUFeO+9\n92Cz2TB9+nT2fEk9uzRNIiQkBJs3b2bCi6ZRiD3r9JkRBipRSPGGyspKbNiwAREREbj99ts7HJfD\nr7qDr7mrTWcvk+ShBFhwENbncgcos+/sjsfX1xdbt27F3Llz8fvf/575eMTA786E9cwo+GRj3kQi\n3NnxPiHhWGi7tHYaTX6mO01+zPy4TSYT/P39YTabRc1B9HoqYE+dOmXXP9VcKF1JSUks+ZTuztVq\nNYKDg1npHlpoFGivoefn54fly5fDaDQiPT2dJeIC7bvruro6VjyWljfS6/Xw9/dHWVkZgPbADzpm\n6tin2hqt90c1gKSkJHz00UdsDLQOYH19PcrKyuDv74+qqiokJSUxvxRtR6PRQK1Ws3JFQHvwBw14\noDUGY2JiEB4ejrKyMlatoqqqijEC6lOaNm0aq903aNAg9qLJ6upqVnFiyJAhzBeVlZXFctZo4d7Y\n2FjodDpGP63uQefEarWioaEBWq0WmzZtwrhx4xAVFYW8vDykpKQwTRO4WmcxKSmJ3Veq2VJBZTAY\nmPZNNRK1Wo01a9Zg+fLl0Gq1MBgMLJCD3ofy8vIO/lZa95FqXnSdDBs2DD4+PgDACvUK1zG/hmn7\nfPAQYF8zkD+f9kfXek5OjmyfUUJCAi5fvoz//e9/oselNDKhv9jd8ORAjZtCs6JwRdpfC/Tu3Rvz\n5s3D8ePH8eabb6KtrQ0xMTE4e/as3W6tsyGlYte2tLTY5Z2I7aKEuVwWiwWEELtwXz5fi/pHgoKC\noNPpYDab7fKb+vbti6CgIBQXF2Pr1q2YM2cORo8ezXaqK1aswC9/+UsWrr527Vrceeed2LlzJ8aM\nGYPm5mYMGDAAO3fuZPT079+f5fFMmTIFkf+fvfePb7I898ffAZRaaG2IOU0DFGqzWoSc0q7CgqBZ\n5RAznTVHWKXbZB0dUzpWP3qww3Z+1bX2VM/HGSXgSzuqKNnywUPt3DFEZonYQx2GYBsrGVSDgYZC\nKKktPyoKz/ePel3ceZr+AGWi43q9+mr7/Lif+3me+7mv+7qu9/W+srNx11138b7PP/8c8fHxuOGG\nG7Br1y68/fbbOHPmDI4cOYIZM2agqqqK2a7nz5+P3t5e7Nq1C5FIBBMmTMCHH36IkydPcol7siTf\nffddfP/730dKSgoOHjwIh8OB5ORktLa2oq+vDwaDAW1tbTAajXj//fcZUKFUKjFp0iROuH3nnXeQ\nlpaGsWPHwmg04tChQ5g0aRJ2796Nu+66i9+Fx+OB0WhEX18f/H4/Dhw4gFAohLS0NGzZsgWffvop\nli5diq1btzLrgtVqxYwZM3DkyBHMmjUL27Ztw/e//312P/b09GDr1q0IBALIysrCggUL8Mc//hFq\ntRp9fX24+eabMWPGjKh4V1VVFXJzczFnzhycOnUKZrMZe/fuxfvvvw+fz4dp06ZxvPDYsWOsCCjf\nimJvZA1JksTQeEr27erqQkJCAn71q1+hrq4Op0+fhs/nw5w5cxCJRBAfH4/k5GT09vbC7XZHwbnH\njBmDcDiMxYsXAwDuvfdepKSkIDU1Fenp6ZyU7PV6cfDgwagk971792LcuHGQJIkT3mMJ5V7RmE9J\nScGsWbM4H3DChAnYvXs3VCrVkPmMADBx4kQ8++yzOHXqVExewMGSgb8Kq4eIBS4WuWRZfY1CVsFI\n5PLLL4fdbsedd96J3/3ud7jvvvv4gxD50YaSoVZzQ/m+Y62ixEB1VVUVQqEQV4sF+uMTWq12QBVh\nsp7sdnsUvJ2C93a7ndGA48eP5+vY7XbU1NTA7XZz/EkklQXOogKNRiOMRmNUiRBCn1GFXEIZirWx\ndDod1qxZA5VKxdYFlT/XaDSorKzkPCViKCcy2qSkJL53m82G7u5uOJ1OuFwuaDQa5OXlcW2rkpIS\n+P1+5vKjSrxEVkuuQ5VKhcTERDQ0NCAzMxM+nw8tLS1ob2+H0WiE1WplYEhLSwvTPJWWlmL69OlM\nUTR37tyo+BaR1NbU1KC6uhoWi4UtBqfTifb2dkYzBgIBmM1mGI1G+P1+jikFg0E4nU6mwgoEAnC7\n3SgtLWV0H9UHC4fDKCoq4hw4jUYTxcYfCoWiwBQipRaNn/r6eixZsoSPo7aodpZoBVMbZF2JMdOc\nnBzmM5QkCYcOHcLUqVOZ4DbW+I8VwxKRhrGOG0xMJhOXJ5HD28X26O9Ro0bhhz/8IV577TWcOnUq\nqi05m/z5ymBzx0i4FC9GuaSsLoCIE8hgIg6M0aNH4/7778evf/1rPPXUU1i2bBlOnz4NYHg3I+WL\nxBJ5vpS4PVZ/qLYUnSNyIJKQe0QER4RCZ8lkY7loKH+H4kF9fX0D7o0mJfrogbNIss7OzijlSNuI\nkb2zsxPt7e3cBgkpMKCfuLerq2sAwm/ZsmVMqgv0l8Xo7u6GSqVCQ0MDlEolli9fHsWSTiUdCN7d\n3d0dVdBQFCLDdTqdSEtLw6xZs9DV1YWioiJMnDiRy8wnJibyarempgYdHR18z0qlkq2iYDCIjIwM\nhMNhrFu3jrkXA4EAE8ja7XZs3LiR64QZDAakpaWhra2N31VaWhqzomdmZkYpFbPZDLVajdtvv523\nNTc3M4hFBELQwkAknW1ubmaFSy5bQvwRmIP4/gwGAwoLC9nd2dXVxX10u91wOBxR47WqqipKcWm1\nWjgcDrhcLjidToTDYRw8eBCnTp3C8ePHeSzTOaFQiN3HtJii7fR/rFpschGh87G2D+auE//Oz8/H\niRMn2IIlGSmicDg51xDFxewCBC4pq69N5ANj3bp1eOqpp1BRUYHa2lr8+Mc/xmeffTZsO6QYY62K\nxJwnOSJKfrxW28+oTvEqOoYsKVJmYq0gmkzFXCdxZSr+1mq1zJL96aef8jmk3ETCXLLOaD+tmkWl\nRVaRxWLBQw89FLVA8Pv9qK+vZ4tHrVYjKysLkUgEoVCI4yt1dXX4y1/+wiU/jEYj8vPzGapdUVEB\nl8vFSMTc3Fy2zsgCCwQCjDwsKyuDWq1Gbm4uwuEwVq1axaSuRUVFUKlUDJunQoeEsDObzWhvb4fV\nasXMmTPx85//HF1dXcjLy+Nr+Hw+FBQUIDc3Fw0NDbBYLAgEAlzriqxFeucajQY9PT1obm6GSqVC\ncXEx3G43Ghsb0dTUhFWrVnENrczMzChFGw6H8eyzz3LRRZ1Oh8zMTAaIEHgjHA5zPKmurg4mkwkW\niwWJiYmsoEioDpjT6UROTg70ej2am5s5rlVTU8OWL6VK1NTURBV5JHQijbOysjKUlpbyc6f3DwA/\n/elP4fV6UVVVFTVWxVwvEjE2KcZwxe9mMIQtCS0OR5oqMn/+fMTFxeGJJ54Y9thLcinP6qIUk8mE\nN954A/PmzcMbb7zBrBZyGeqjsNlsg1p455J3FSt/iqwTsmTEUuDy1dzy5cu5vPgLL7yAoqIi/Md/\n/AfHFsSy4bTypiC86MLxer08CVG+lMFg4HIbFI+gNoH+lT8pB7FuE7VttVpRUFAAh8PBKLa0tDRG\nyBEsXaxNlZaWhqamJkyfPh06nQ4NDQ2YPn06AET1B+hfzVO+F7FGUNsGg4GLDlJyr0ajgdVqZcUa\nDAYBgJNvPR4POjo6MHHixKgaWUD/ZDtz5kwkJSVxMvD06dOZcSMjI4Npm3Jzc6FWq+FwOAD0Q9QB\nMMyemDTIRVpcXMx5bDTpNzY2cr8aGxsBgDkTY9W7omdOVovD4YiqAWYymXjM0nuMVecs1rim/Dp6\nnuvWrYPNZsOBAwfw/vvvsxeAwCBiG+eagygqucHywICRWyl6vR5dXV3o6OiAQqEA8O3OqYoll/Ks\nvsHicrmwdu1aNDU14ZZbbsGxY8ei9pEM9UEM5YqMldQ41LHkQ6cP2+v1Mpu5RqPhEiP0gVGsCwCX\nF7fZbFxr6Xvf+x5ycnLg9/thMpnYraTRaNhdI1bwJSYBQrjV1dVBqVQyHQ9NRtQmLWwsFgtPnpQX\nVFVVxTlTQL9iS01NRWlpKcxmM1QqFdra2titFwgEmO7nvffeQ2ZmJoqLi2GxWKDX6zF9+nR0d3ej\nra0N4XAYSqUSXV1dUKvVqKysBNCvXH/7299G5XpVV1ejrKwMDocDWVlZAPoVTmpqKux2O7q7u5GV\nlYWCggJWVD09PUhISODEYHINNjU1IT4+nhOUk5KSkJ+fD4PBgNLSUq4QnJqaip6eHrY4S0tLGUWo\nVqvx0EMPscXmdDq5/83NzXC5XHC73Vz2IysrC4WFhWhvb0deXh4KCgr4WRNCrr29HWazGVVVVVxP\njCwXsm5JOdrt9ijqKbFsPbFQiPl9RJ0kKioag6NHj8bo0aM5tgf0KxYq+WGxWKJKnYjXkH8XZWVl\nUdaVmG8VK5FddKPTOUPJ/fffj4MHDw6gTrsYZDAvzNcll5TV1yhDgSfuvvturF+/Hm+99Rb+7d/+\njVfkX8afPdigEz/YWELXFCG8NIFptVqGzdPkYTab2fqibQaDAVOmTAEA5ggkiiWKjdGHTv10u93M\n1QeAA/pFRUXsSqNJh1bshYWFXExRDOITGIOq/XZ2diIvLy+qjhKhsubOnctcgUC/sqGqx+Rm8/l8\nzOiwZ88eTJw4kbkGA4EAnE4npk+fjrS0NBgMBrz66qvcnk6ng9VqRW9vL0/yQH88qru7G2azmcEP\nNJl3dHSwBRQMBrm+VlNTExISEjBr1iyO5TmdTphMJjzwwAPQaDSsGJRKJXP8UbKrWPjxueeeY7RZ\neXk58vPzOYmYal0FAgFotVpmxKAEZIfDwcAaSujW6XTsArXZbFEMGZ2dnQzeoUUFLX6IrYPcauFw\nmJ+dxWJhV54cUk4K57333sPMmTPx3e9+l+tjUd/o3YmLOVJ2sRhZSktLB9SZor7IpaSkJErRxEpH\nkQM44uPjMWrUKDQ0NESddzHIUInQX4dcMGWlUCgmKxSKrQqF4gOFQtGmUChKv9g+QaFQbFEoFHu/\n+K0crq2LWc531TGUqU9K4yc/+Qk2btwIr9eL73//+zh8+PCI2h1KRKQiVWgFzq4UB7sfOo7at1gs\nnKsjuj4IFRYKhZiyiCDN5M6k32IpcvF5EOKLFBJ94KJbkDjqlEplVIwL6H8n5DL0+/28YifuP1Im\ndrud84eAfnCFx+Ph/CBqo76+HpFIhJFq5D4j66ujowM9PT1Qq9VwuVzIzc2F2WxmYERtbS3HRIjR\no7OzE3PnzuV+E7KQKgsvXLiQ+ex0Oh0SEhLQ3t6O1NRU5OXloaWlBQaDAXPnzmVS3FWrVsHpdMJs\nNsNms2HlypVwu92wWq1M8WSz2ZCbm8vFKMkKJIWQmpoaFashK5biWR0dHVi+fDkaGxu52rHFYmFF\nSkLHkyI2GAxM70QuXVoQ1NXVMX2WCO6hcUTgD1KUoVAoigJJVB4UG6W4lM/nYwVJ90R9CIX6RRc9\n/wAAIABJREFUCZYHc+VRe0SjRH2TK6TBRA74iUWE+6Mf/QjXX399lLIarArxP7tcSMvqcwD3S5J0\nLYDvAShRKBTXAvgNgDclSfoOgDe/+P8bK+e76pB/IIO5ASwWC1544QXs2bMHt9xyCyPpRtquvK9E\n4AmAy7jLj5GLyHkmThLA2SRlUZnRB0mTHq1aiV6GJhcishXjUpRsqdfredW/atUqthqAfuWxZs0a\ndg3SxEiuIkoiBsCuqXA4zBRIRUVFSEtLQ2FhIfx+PwftVSoVcnNzEQqFUFtbi8zMTJjNZrS1tcFg\nMKCxsZGh9lQ5NxKJ4PHHH0deXh6CwSCj/RwOB4xGI3p6etg6aW5uRjgcxooVK+D3+znZt7m5GfX1\n9ejq6kJzczPMZjNuvvlmTlYGgIKCAhgMBiiVSng8HpSWlmLPnj1oampCaWkpGhsb2bJTKpVR/IGp\nqanMoUjJzpQ4nJmZCYfDwQqru7ubFbvH40FSUhKPlbq6OkyfPh0VFRVRysnn88Hj8cBut/NiSK1W\nMymsVquNAu7k5OREgTnKy8ujEKXkRhQTdEViYrLAq6qqoiyenJwcnDhxAj09PWwxi+hSQo76fD7+\nTgiUIX5/coUiIl/li8GhFIu4+Iu1OKV9+fn5aG1tZbfuxeZ+u1jkgikrSZIOSpLk/eLvXgC7AUwE\nkA/gxS8OexHA7bFb+HbISPKkgNhKhgbr4sWL8Yc//AEejwd//etfo9o9nwEtutrkFkms9kTXo3wl\nG+sYUUREYnJyMgDgyiuvjAqsiytlUXmq1WqYTCaUlpYy/BtAVMxBo9Hwyptg2HQubSe3EUHOKc+I\n9hNaUiw9UlxcDL/fzxYAWT4tLS2w2WwwGo1ITU1FMBjkvq9YsQJAf25UXl4eJxSTu5AAEc888wxU\nKhVSU1PR0tKC7u5uRgW63W7U1tZi4sSJqKmpQW1tLbq6uuD3+xEOhxGJRJCWlsbxI4LdZ2VlsVKk\n+lMejweZmZkIBoOcwwb0uzUp/625uRnbt29HOBxGY2MjT94lJSVIS0vDjh07kJmZCZvNhs2bN2PH\njh2orKwc8L7JNUuuP4KrA2c5Jj0eD3w+H8rKyqI4CEXgBl27vr6eacRoTFLpeQJSlJeXw2QyReVI\nHThwAEA/Kz2lNwD934vFYuFUCjmwglx9sZCsNIZGKrTgEq8R6/umfbNnzwYA7N69e0BcTJRzyd/8\nNso/JGalUCimAsgG8DcAyZIkHfxiVyeA5EHOWaZQKDwKhcIj+tW/aXI+wdJYtaYuv/xyAMD7778f\n1e5QOSCDKTLxgxDh5kMlM4oiTg7iB0QM1bTaFZVfYWEhx6rI5dje3h5VV4gmZFqN08qUkjaTkpJ4\n8tHpdGyVFRYWcpIuxXKoDIbYn8zMTJhMJqSmpvKkbDQaUVhYyHWaqqqqsHz5coTDYahUKrS0tMDh\ncKC0tBQNDQ2cD0Vs8llZWfD5fHjiiSfYcklNTWWQB9XCIjcgKWMqCZKamorm5mZG1M2cORPhcBh7\n9uxBWVkZjh8/jqampijCWnqeK1asQFNTE1tJxMqelJSElpYWmM1mOJ1OpKamwuv1MpDB5/NxWZKS\nkhI4HA7o9XqkpqYyg73L5UIgEODigxRnq66uRn5+PpYsWQK1Wg2Px8PISmqTYpFlZWWw2+0oKipC\nbm4uioqKoNfrkZeXx65eetcmkwlLlizhMaVUKqHValFQUBA1eVOCOdEryYERtJibNm3aoIS11BZ5\nDMgiJxAI7Rd/y8f+cCIiGWMtAuWoQqCf1eJ8QVNie4MtOr/pcsGVlUKhGA/gvwHcK0lSj7hP6sfN\nx8TOS5L0nCRJuZIk5cpdVd9kGckgiuVauOOOOzBx4kS0tbUNe774ocVCOYkxCTl6CQBPAhQEp20i\naooUhfgB0YcsIrDEmNSTTz4ZdX1ya4lKT14RlSb5UCjE16J8Hr1eD7vdzit3rVbLx5NlQAnDZCXZ\nbDYYDAa+DhUFJJRbT08P1qxZw+jB0tJSuN1uWCwWBkyQECu8Xq/HypUruVaWwWBAT08PwuEw/xaR\niR6PBy6XC1arlYEYO3fuhF6vR1JSEq6++mqYTCb09vZCo9Fg7ty56O7uRldXF1JTU5mpvLGxERUV\nFVi1ahWam5vR09MDpVKJ7u5uRvkB/Zaow+Hg2BOhASORCOcg1dXVcd7UkiVLAJx1kTkcDnZRAv0p\nAdXV1Ry7A8DXy8zMZC6/0tJSRCIRnozdbjfq6+t5fNA7cLlcUTXcgsEgL0DEMQmAE8x1Oh3zQ4rj\nnbgA4+LiWAmJlrvYpmhhicnug8lIvSSUfE4iXwTSNpL9+/cD6K+0Lcq5KJzhrvdtkAuqrBQKxWXo\nV1QbJEna9MXmQwqFIuWL/SkAhkcNfIvkywyi6dOnj0hZyRMxY11ThPoS/FfcR64cedxJXAkDA1d0\nZCGJbdPfxOC+ZcsWXvXSJEFMBp2dnQx9JwVFgfdQKMR0TASlJvQf3bfVao2CQAP9kzVRNel0Op64\nVCoVu63IfZiYmBhFarpq1SouZ0FCoId169bxSpySUuneKVEV6J/ciaS1tLQUubm5qK6uxqxZs2Aw\nGJCVlYWVK1cC6LdgEhMTEYlEUFFRgYKCAqhUKnazUSyKCiz6fD5kZGSwlSbGgkiBEV0SKXIChjid\nTk6ATkpKYh5Derd6vT5K8RHKj/qQl5eH3NxcuN1uNDc3c/yLFjFkfdJYMxqNjJgT0yDUajXD+sma\nou3y8Sq+A7vdzpWoSWgx8fbbbzMtkzh5i4shMcGdZCiy61gxp6qqKl7c0UKOnvNIZf/+/bjiiisw\nYcKEqO0Xo8KhNJWvQ4ZNClYoFA9LkvTwOTfcn+H2IoCjkiTdK2x/AkCXJEn/qVAofgNggiRJDwzV\n1j9bUvBgct9992Ht2rWoqanBr3/966+kTbI0gNgfI3D2oxksSCwqNPEYeTG6hQsX4r//+7/R19eH\nsWPHcqIocNYVQknCVAyRthPbuvya5CKmJFdKrqU2ia+O4mNlZWWcdKvT6ZjF3OPxIDc3lwlXm5ub\n2dJobGxEamoqdDodQ7jr6uo44E99IEBCW1sb519RAq7T6eREXuLlEwsnUjyLEo8DgQA6OjqQkJCA\nrKysKGaHWbNmIRgMoqCgAH6/Hy6XC7NmzeLric+UrtHd3Y2tW7di5cqVDKYQa215PB5s3boV69ev\n53PFZ/bRRx9xYjMpbsqpAsB8gSRqtZprhpWVlXERR1JgtPgghCDQbzH39PQgLy+PrysmMYvcg9Rv\neh/E9/ib3/wGNTU1nLNG/RTzBIeSwZKORZderGTgoWS46y5atAitra34+9//PuI2v00y0qTgMSNo\n6yGFQnEFgAkAvAD+JElSZATnXQ/gpwB8CoXivS+2PQjgPwH8P4VCsRTAxwB+NIK2Lgn6Lau+vj7c\ncsstX1mb4oc5XGZ/rA9UrNoK9E8isT5uSsxVKpXMmE3QdxIqDdLY2IiamhouuUFxlUgkEgX9JuVI\nfaOYlzzGqdFoUFtbC7vdjhdf7Mf22O12dvX5fD6YzWbk5OTA4/GwYqEcJ8pTosmYuNwIfTd37lwm\nvd2xYwc0Gg0rB0LT0YTr9/s5lpSWloZgMIisrCxWUklJSVyMkSZ3oN9SW7VqFWbNmsXWNRWVJIYN\npVLJDB0mk4mrLPv9fuh0OuzZs4erB5ObjVg+zGYzkpKSYLVamYg3KSkJHo8HH330Eacq2Gw2VkI2\nm40VODF9kALUaDSsgCmfjbgASeFUVlYyKKSzs5OtPHrO5IIVxx7FmkSF7PP52Aoj5T927NgB41XM\n8Yo1nl0u17DMFmSlyfeLSky+qBtOQe7fv5+rhZ8PoOOrkJEo8q/7WiNxA0oA+gC4AEwGsF2hUGQN\ne5IkNUmSpJAk6V8lSZr5xc/rkiR1SZJ0kyRJ35Ekab4kSUfPudffABmJv3kwQtnBjiFan5G4AkVZ\nvnz5sP3xer1RsHZxMFEfxJwqEpVKxcAKAFH5KPQ3JeGGQiGMGzeOt7tcLiYxJYVnsVhQWlrKeVrE\ncP7RRx+hpKQEGo0GTqeT86TouqFQiOMlarUa7e3trBzcbjfWrFmDWbNmMVecSqViUAXB5Ok+i4qK\n0N3djYaGhqi6S8QFGIlEYDabUVxcjISEBGZUJ5olmuypZD0BEUiIUSIQCEQpBqPRiGAwyEwYQL/F\nVV9fj+LiYtTW1iIYDKKiogLFxcVoaGhgDkSVSoVAIIDly5ejq6sLLpeLnx8JKbCWlhZUVVXB4/Ew\nHZXf74dSqURqaipbjESAS9yIoVCISXtdLhcsFgtTSEUikSgXW2dnJwKBALxeb5RbEehn7wCA4uJi\nzoMSrSY6vrKykseFzWbjhQ0pJsrpI6srFApxAVIx/kPoPBp35IKWf3+xStqLx4jAJdHbQH2JVSdr\nKKH2Dhw4gPj4eG5HTmwr79OFkH+kcjzfa41EWfklSfr/JEl6RZKkB9EPPf/9eV3tIpAL9bLlMpIX\nMhScNdYxVFG0ra1twH3EUkZ0DFEeDdcXOUu7aLUQXFt+LYIOx2qfJpfCwkKeTGbMmMFt0ORACaFA\nP+BBjJ/95S9/YZYD4CyZaXl5ORdkpGdE8ZJwOIySkhKEw2EEAgG0tLRwLILiOCaTCY2NjTzxkUVF\nMY+0tDSsWbOG74MUCFEdNTc3w+FwIDU1FeFwGGazmaHwPT09DE7Q6XQIh8McEyJ4ejgc5mupVCqU\nlJSgs7MTBQUFrHyodMjMmTPh8Xgwffp0pKamMpqvuLgYSqWSqZ86OjpQUVHBLOpWqxW5ubmIRCLM\nbUiWR1JSEoqKigYoNKVSCb/fz6VRyPqKRCJs5RHTOlloBoMBJSUlfF/A2Xw8gptrtVqm1bJarTwh\n06JCq9WitraWFzFerxcVFRUIhUIcIzQajVFgCSAaTKTVahlx+uGHHwLoV3JqtZqtYAIHie2IiFix\nXaKGkltQ4v8UoxxOaPzJ6dI+//xzHDx4EDNnzuTtIi0XCQGQLhYqpq9DRqKsjigUiu/SP5Ik7QHw\njYXnXciX/VXDRcX2vF4vEhMTMXnyZLS1tUWRxwLRFDCxcqiG6qOolEQR/y8pKeGPVF7LSlxxUmIv\nEI1q1Gq12LdvH7vFRGYKOjYUCrFSomsRkIImKrHP5Ea02+1YtmwZ8vLyOF8K6J8Ic3NzGSBgsVhg\nMBgYRNHb2wun0wmtVouenh52wZFLq6qqiicjUmoUxyopKUFBQUEUmWwgEGBovMfjYeQdlekAgIaG\nBgZBtLS0ICkpiZkvyIUpEuEWFBQgKSkJHR0dUCqV2LFjBxobG9HS0sKKgawTsrzpueXl5UGtVsNi\nsXDCM1lP3d3dzG5BjPDUL4rn0TMOBoMoKSmBUqlEbm5uFCM+jZ2qqqooy1ar1cJsNrNi9Xq97Oaj\nBOycnBzmVHS5XJg7dy5cLhfD5+neKHYG9LtgifmCxhkpRJvNxhZKb28vj1sAvPAgRSvmC9Lfouta\nPrbF40Tuy5GwTYgLM7n1FgqFcObMGXYD0nXkcq5lh76NMhJl9WsALysUipcVCkWZQqHYACBwgfv1\njZQva0rHWt2R0McrRwTGcj0M1Q8Rtg6cBU0MRuQpJkqKdDi0T7werUYJGk3nAMDTTz+No0f7Pb7E\nEwechY4D/ROJ2+3myY1KnhOiq76+Hs3NzSgrK+M+0yS7YsWKKNogr9fLHHnkKrJardBoNMjNzUVj\nYyOKi4uRlpaGqqoqBlAQqk2tVrPyCgQCnHdFABB5XCInJwc9PT1cRsNsNnMZDQBs5VEcDOiHexMa\nkVyX5C4kbsNwOIxgMIiEhASoVCrMmjWLiW+pHbvdzufV19fD4XDAZDKhvb0dTqeT2TGeeeYZVkTk\nhiwtLeXFB7k5KSG3ubkZkUgEBQUFsNlsUKlU8Hg8/G7oGRBCk9y9pDyam5uh1+uhUqng9/vZ6hWR\nfsXFxazggH4FTVY4PYf29nZ4vV627PR6PZMgk6WTk5MDg8HADC+kFMhFLS7IxDgYucjJeyD/fmJ5\nOojxhL4D+Tcj/77kbYjWFcHWRWU1nAylTGP16dsiwyorSZJaAMwE8McvNm0FsPhCduofIcO90AtF\nJjmUG3Ik+RHTp0+H3+/H6dOnBx20osjzpICBkHJiNafAsSgU5wDOWj/k+6eyHHLFRWUeQqEQr74X\nLVqEM2fO4IYbbuDSGwC4FAVNKi0tLQgEAkxqunz5cs7D2bFjB3Q6HbKysqKURWNjIzweD7NZkMWx\nYsUKpl+iekl0TwSaMBqNzG9HyEByi5WUlKC2thbl5eVcv4rqaonvUq1Wo6ysjNFnRONErq20tDSo\n1Wp2uwWDQej1ejgcDmaZoD4Tmo/ojkwmEwoKCthKAvotH7oXQuYRq0VJSQmX6iCgiMViQUtLCx59\n9FEYDAauWJyUlASfzxelJIuKivD4448DALq7u6FUKjmfqquri8EhZHF6vV7up0qlwkMPPcRVjoPB\nIKP0mpqa2FIT3W8ajYaVpQi7J0uHrFiqXRaJRBg4AvRbWmRd5+TksPvP7/ezEqJFFEHVaRwD/cqS\nvjkxZktKRlQ+8hgW9VMU0S0NxPZuiNbV+SiroZTpYNu+DTKiPCtJkj6VJOl/JEmqkSSpVpKk4xe6\nYxdahnuhg5n3X5by5Mu6IceNG4e+vj589NFHMQftUKwT4vGxKJNoxQz051PQ8UajcYAVJSo7rVbL\n+S60nWiKRBchnV9fX88ktxqNhmMdWm0/y3VRURFbcRUVFdDr9SgsLERpaSlPitRmV1cXCgoKeLVL\nSLicnJwomLXb7Y6KN5AFR0mqVEKCLKrm5mbYbDZMnz4dXq+Xk2aB/rgXIQxVKhWam5uRlZXF+71e\nL4xGI7xeL4qLi6NiEKRs6+rqkJWVhbS0NKZUamtr4+q75HIjtgYCEQSDQSiVSrhcLuY6HDduHFQq\nFaxWK8cWHQ4HDAYDgsEgtNqzzPjhcBhbt25FRkYGuxstFgt6enoYfk7KhIpNlpaWMh2URqNhMEhz\nczP8fj9bpyaTCY8++ihPxmQBhkIhVFRUwOFwwGazsWuQFgwmk4mtMADMbGG32wckiet0uqgYqNFo\njALoXHXVVQCAjz76aEB16Z6enmFpkMTt8vhULEYMAvqIYxwYfLErKjuXyzUiZTUSINY/g1wqEXKO\nMhLfsShf9aAi2Po777wD4GxiI8lQmfJiX2IpTfFjpPgPtSGuBuWuDZFdglajVH2XVpqHDh0CAJw6\ndQolJSXw+XysAI1GI8efCIpMConITEVXJLn7KOmTrBKbzYbS0lKuQSQvDkmuK5vNxu49i8XCzAzi\nRK3T6WCxWLicBiUeE9Civr6eefkMBgMnxBJsm3KcnE4n3G43W2vkFisvL2ci2YqKCqhUKo45ORwO\nnnxdLhe7Ct1uN/Ly8rB27Vp0dXVxxeCEhAS2jHbs2IHOzk6UlpYiLS0NpaWlPEFbrVao1Wr8/Oc/\nR3l5Odrb29HY2MjtkuL2+Xxwu91ob29n0AgR1VLpD7LiSIqKivgdEVMHQeorKyu5JIrBYEB7ezuM\nRiOjDOkeRRQclWQhoYrVYrmazs5OVFZWsnfg448/xp///Gd+zgTjp8WDWHU41nd5Pt9qeXk5L0zE\nb2Kwxa743ZlMJuzfvx8JCQmMCh3qHHGhHGux/Y8Cj31dMvrhhx/+uvswrDz33HMPL1u27OvuxnkJ\n1Qf6qiQ5ORmvvPIK3nzzTfzyl78cUJoB6P/o5NelDzFWf2iFLd8mSRJ6e3uRkJAAr9eLlJQUhEIh\nZGRkAABvo/2BQAAffvghtm/fjsWLF2Pv3r1obW2FTqfDlVdeibVr16K3txfXXHMN0tPTkZ2djYSE\nBO6TSqXiFX9PTw8OHjyIEydOQKfT4bXXXsO7774LnU6HEydOQK1W449//CPGjBkDtVqNgwcPIj8/\nHwcPHsSMGTMQDoexf/9+XHXVVdi0aRP6+vowefJk+P1+TJo0Cdu2bUN5eTmefvpppKSkYMaMGXjs\nscfQ19eHtrY2zJs3Dx6PB93d3Rg/fjyeeuopjBkzBvHx8fjggw/Q19cHg8GA/fv3Y+/evThw4AA+\n+eQTJCYmYt++fbjqqqvw5ptv4vbbb8eMGTPwySefYNeuXXjnnXdw4sQJjBkzBs3NzYiLi8P27dtx\n9OhRjnVNnToVmzZtwnvvvYdrr70WmzdvxuzZszFlyhSkp6cjMzMTFosFzz//PNRqNe6++26kpKRw\nCY0ZM2bAarVi+vTpyM7OxtatW/Hyyy9j0aJFSE9Px65du9De3o7s7GyMGTMGmZmZGDduHDZt2oSk\npCS8+uqrKC4uxrRp07Bv3z78/ve/ZxfrQw89hJ6eHhw5cgSfffYZZsyYgV27dkGpVGL8+PH4wx/+\ngDFjxmDq1KnYvXs3s3/cfffd+OSTT7BgwQK0t7djzpw56Onpwfjx4/H8889j0aJFmDp1KgBAkiTM\nmDEDWq0WCQkJXOLe6/Xi4MGDSElJAQCkpKTwO7nssstw++2347333sO9997LYA6dTsdjlCQhIQHb\nt29HfHw8b7fb7Zg6dSp6e3t5zCckJCAUCiEQCKCnp4evK/++EhISeF8scblcUdcSz1+3bh36+vrw\nq1/9atDzSYincTAZqg8XszzyyCMHH3744eeGO+6SsvqGCA3wUaNGITMzE1arFXFxcUxhBEQrD/F/\ncmfIFRWtaAcTUZFQO7SdlBb1KyUlBTqdDvHx8bjpppt4P6HKdDod+vr68Pzzz+Po0aMwm83o7e3F\n9u3bodPpuI+hUAi9vb04duwYcnJymLD2nXfewe2334558+YhPj4e48ePxw033ABJktDc3IzJkyej\ntbUVJ06cwP79+xnOvXXrVlxxxRUwm83YsmUL9uzZw2S08fHxyM7OxtSpU6HVaqHT6XD8+HGkpqZC\nkiRMmTIF8+bNQyAQwG233YatW7ciFAohLS0NFouFa19t2bIF999/P86cOYPZs2dj6tSp8Hg8OHny\nJL7zne8gEAggPj4ekiTBYrHg888/x/79+5mV/cCBA9DpdDh58iSmTp2KLVu2QKlUYuzYsTAYDEhP\nT4fdbsfWrVuRnZ2Np59+mmtdvffee9izZw/279+PrKwsXHXVVQgEAjh9+jRuvPFGvPbaa4hEIvjg\ngw/Q3d2NqVOnYvLkyXj33Xfh9Xpx5513YsuWLcjPz8fJkycRiUQwceJETJs2jWNDY8aMweLFizF/\n/ny89tprOHLkCBdLXL9+PXQ6HdLT0+Hz+dgS+/zzzxEKhXDNNdfgxIkT+PDDD3Hy5EmsXbsW2dnZ\n0Ol02LRpE2666Sbccsst2L59O7Kzs9Hb24stW7Yw88aHH36I2bNnY/v27Zg3bx4kSYpaPOl0Ouzc\nuRM/+tGPEAwG8V//9V9YtmxZ1NhasmQJZs+ejYSEBLhcLvT09CA9PT0qV2rBggU83nt7e7F3715I\nksTjQlSSNP6HWoiK+6keWSgUwt69e6O+m6effhpXXnklfvazn53LdBBT6Jl802SkyuqSG/BLyoX0\nHYtmvegKu+mmm7Bw4UI89thj+Pjjj/kYCnrLoeuxcqcof0VM9B3KNTJUsrD4t4gMJCHk3vz585GQ\nkIDW1lYAZ+NmYk0rcvs1NzfzNYkRnNx9hBokSiCCk5OLiALoy5cvZ1Z1n8+Htra2qDgJuRwBYMmS\nJQiHw2hpacG6deuY/JagzpTflZSUBJVKFcX/Zjab4XK5ooosNjU1ISkpiWtFUaJuXV0du+zMZjPX\nkFKr1VEFGtetW4e2tjbOD9JoNBwnuvrqq1FRUYGkpCSsWrWK74kSptVqNYLBIBPHtrW1wWq1IjU1\nlcER5CKsr6+HwWBgtJ+4eCF3HiVGk5uOFJXL5UIwGERXVxc/R7PZzGwXlGMGgIEkc+fOZWCOTqfj\n2A4hG4k0uKSkBHq9ntkx9Hp9FJs/je3169fj1ltvxaFDh/Dss8/isssug1arjaJxqq6uHhDPFMe3\n6BoU41RifFZ0xYnHiHHawZC8JG63e0As7MCBA1+JghH7+HXLhZoTh+UGvBjkEjdgv4iDPBgMIjMz\nEz/4wQ/wyiuvfKm2gNi8f7SNJgmRyieWiMpL/lES2m/jxo249957YbPZOFAPxOYfFP8WPwCitamq\nqoLZbI5ClMlja8SBJ08kDYVCHFcCztZ4AsCregJM0DGE2iPWBp1Oxxx4BE6ga5GSII7APXv2YNas\nWcx8TuAPShSmOByV2CAWcpVKxfE8SuglIaomEqJsor8pp434CJOSktDd3Y2enh6kpqZGAUaoX8SD\nCCBqEbBo0SJmdwfOcgFScjAVDqT3Qc+WFg6ECiTlRPcVCoU4j45Qm+K4pLHX2NiI0tJS3idJEn77\n29/iscceQ3Z2Np599llcd911fB6NPRqvscYtjSmR60+08OXfh9g3ck2eq9D9FhYWQpIkjB8/Hr/8\n5S+5IsE/o4yUG/CSsvqGisvlgsfjQUVFBbZs2YL58+cPOGa4D0r+QZ6rDPZhD3U8xTAmTJiAyspK\nJCcn80QhxiYeeOAB/PWvf+VJhvKmioqKYipZkfKJJh+aGAAw4IMmUSI/BTBg0iSlQ9V8e3t7MXHi\nRKSlpUWRuFKFXBHoQHx2hYWFWL58OebOnYtIJAKlUsmEsnICXFJQNMZJ4ZBCpP3E4yeS34qkuHSM\nxWJBfX09K0myvKjfANhlR+cDYPb55ubmKEWsVCqjlImoiImuie7H4/EwAbGc0khUYvQ+SInJrRX6\nm4iWCR2oUqlw4403YtmyZXjppZcwb948bN68GW+//TYnE9NChJQLjRFqN9bCLNZYFZXYYIu0oYib\nh5Pe3l4kJiaivLwclZWVIz7v2yYjVVaX3IDfUDGZTLj//vtx9dVX4+6778apU6cGHDPcyi9WXpW8\nRIIIwRUReaLIGS3ECUrM9vf5fBg7diweffRReL1erF27Nirni65BsRByVQJgSiM6JhQ+Gcr+AAAg\nAElEQVQKRfWDIPTihOfz+Xhi93g8qK+vh0ajiaqxRO2TogLAtZtUKhVqamqQn5/PMO7a2tqoiY4I\naouLi5ntm/gS16xZg0AgAKVSicLCQrS1tSEpKYlde0SkS7x6ubm5XDixpaWFqwET5RGJwWCAwWCA\n3W5nGqd169ahrKyMiYKVSiWKiopw4sQJ5lIkC5HcjZFIJKqsSSAQgNPpZIsLANfhEkl8LRYL1qxZ\nA4vFwmOMCi+KTPmUR9Xc3MzJ2hqNBoWFhfzcRZ4/ep8iKlAsKVJYWIijR49izpw5eOmll/CDH/wA\nb731FuLj46OeD6VEkFDOlZwUl/ooR9RSG+LYHMwqk6P7zkWIGuqaa645p/MuJvmKEIgjSjK7BLD4\nBsuYMWOQnp7OLrU5c+Zw4Ha4ADCJiIby+XxMd0Qilh4n5TZnzpyocwkgQQHtgwcPYtOmTdiyZQvM\nZjMHlLOzs2G329HS0oJgMIgDBw7ghhtuQGZmJlwuF6ZMmcJIQ6LLycjIgM1mw+LFi5GSkoLt27cj\nOTkZnZ2dePXVV5GTk8PBb7vdjs8++wzhcBjPP/885s2bhwMHDmDp0qUYP3484uLikJycDKVSCY1G\ng927d2Pv3r2YNGkSXn75ZXR1dWHatGl4+eWX0dfXh0AggDNnzkCtVjNog0AA9fX1OHr0KI4ePYq4\nuDikp6dj7969OH78OD744AOcPn0a27Ztw+HDh6HRaKDX6/HJJ59g0qRJWLRoEcaPH49jx44hEAjg\nmmuuwbFjxziW98knnyArKwsWiwVXXXUVRO/Htm3b8Le//Q3p6ek4fvw4rrzySixduhT79+/H9ddf\nD7PZjJdffhkLFixg+qfvfOc7bJXFxcXhuuuuw/bt26FUKvHOO+9g7NixuO6667Bv3z7MmDEDJ06c\ngF6vx8aNG3HLLbegtbUVHo8Hp0+fZgtLkiR0dnaitbUV8+bNw5///Gcu/dHa2or9+/ejt7cXycnJ\nuOmmm+ByudhNt379esTFxeH555/HoUOHcODAAahUKh5Xer2egTaEzNu7dy9+97vfYfXq1fj444/x\ns5/9DL///e+RmJiIjIwMvPTSSxz78Xq9yMjI4PGp1+uZAaS3txfr169nUuPx48dzOogccBTLY0Df\nlfht2Ww2jBkzZkDsKRQK4bXXXuN4m7ztYDCIF154AT/60Y+YM3MoGek3/Y+UryLe9sgjj/hHArC4\n5Aa8SCWWWyTWx+P1evHQQw/hrbfewrZt25CdnT2grZG6PchFMlT9H3LVyfsHIKYbR2yfpLOzE01N\nTSgtLYXNZsPy5csHvW86HhhYKJKojyh2Qi6lsrIylJaW8kpevK7VasWLL74Y5T4EzrqoSKgMht1u\nRyAQiCrx0dTUhIqKCvh8Pi5HX1BQwG49sf6VWDOKzhdjT1RLq6OjI2Z8S2S3IKQdlVIRXYS1tbUw\nGo0ck6K8ndzcXI7ZUKzNYrEwOIUsG/E50X1RvSwCWsj3EXWUWFZDdOsBZy1WMU5I90MLIRp34rXo\neHJX+nw+PPHEE7jqqquYP1AcJ/LrkCt2MDddKNRfJJH6HquO1bkK5YbFconH+iYcDgfuvPNOvP/+\n+1G8jrHATN9mueQG/Arkq0C1nG8bsdgpYg3enJwcPPXUUzh16hR+//vYZPixCiaK/ZIDI0SCT/l5\nlKwr759ITUMuI/m9E+s2AFx//fW44YYb8Oijj2Lv3r2w2+1RaC9CCGq1WlRXVzMlE/WNrBXisQPA\nnH01NTXo7OxEc3PzAKaO6upquFwuaDQaLFq0iJm13W43NBoNI+osFgsnk/b09DA6MBAIYO7cuVya\nIy8vD3l5eVyuXq/Xo7e3lxUJlcPo6uriRFly4ymVSjQ2NiI3Nxf5+fkIBoPIzc1FRkYGAx4IdAGc\nZeN2uVxQKpVoaGjgiXvmzJnYsWMHFy+k2liNjY3wer1oaGhAZmYmdDodfD4fJwgTlVJ9fT0TxALA\nzp07AYAJZwl9SeS6BLSg1AJSRPI6U0ajkd1v9fX1zCwiF6fTGaUUaVxNnz4d7777Lv7zP/8TOTk5\neOONN5CVlRU1/qxW66BKAjjrthbddFqtFgaDgb+FWIpqOBcXjW+RWmywRVqsvoljWRR5IvxXKUNV\nQr7Y5ZIbcAj5KkzuWMm5X6bdWBQ0EyZMQF9fH2w2G+bPn89orqH6JPZBnjS5adOmqARE0fUhuvwo\nUbKqqgo33HBDVL4VrYrJ9ZGQkMDHkNuOqIb+5V/+BRaLBZIkYcuWLZg2bRokSUJrayvi4+OxdOlS\n9Pb2Yvz48QgEApg3bx56e3vhdruxdOlS7N27F4FAAMnJyRg/fjwfq1QqkZOTg7179+LYsWMIh8OQ\nJAn79+/H1KlTkZ2djWPHjnHy8+HDh7F79262PJxOJ7Kzs3Hw4EEYDAZcddVVSEpKwpQpUzB58mQc\nOHAAN954I5KTk+Hz+biIIrGPx8XFQa1WIxQKoa+vD9OmTUNycjICgQDS09MRFxeHMWPGIBQKYcKE\nCZg1axZOnDgBrVaLDz74ACaTCcnJyWhoaMDp06exfv16PPTQQ9ixYwdaWlpgNptRWFgIj8eDkpIS\nfPDBB7j99tvR2tqKvr4+JuZNSkrCggULAIATqiVJwvHjx3HnnXdi/PjxOHToEOLj4zFu3DjU1tZi\n0aJFePPNNzFr1iz87ne/4/OPHTsGjUYDv9+PG2+8kROap02bhmPHjrE7c//+/QiFQpg2bRp27dqF\nSZMmYdq0aZy/JFIrxcfH45ZbbkFKSgp6e3t5olYoFFi2bBleeOEFLFiwAC+++CISExOjcpZSUlLY\nKiGX4fr16/m95eTkDPhexHGuVCrZ9SyOdfouYm0XvyP59+P1eqOS6el3LHn11VexY8cOPPbYY+gv\nrN4v4jcjylfhBhwqr/Lrkkt5VhepDLb6G+kqajDQxIMPPohJkyZhxYoVOH369IB2z2WVNpQ7RMx9\nIouNqIhIROoZoogR+0Gr8h/+8IewWCx4/PHH4ff7odFouK5TTk4O50KRW0eEDWu1WmRmZjIgQwRK\nEBUPHQuAWbopwA+A+6HVaqHX67msB1kCZrOZAQXNzc1wOp3o6uqC3+/nYoV1dXVwu91obGxEOByG\n3W5HV1cXo96ampr4mfp8PrYuyfoMBALo7u5mUAeRyqalpaG+vh6lpaVITEyExWJBdXU1r+BLS0vR\n1dWFuro6ZGZmwmazITc3F06nE8FgEMFgkJOWI5EIlx+h2k4GgwGFhYVccqOlpYU5+jIyMpieyOFw\nIBgMsgVKz5gUusViQVtbWxR4oauri+tcud1utLW1cZ5WaWkpvyti1SfeRrJkXC4XNm7ciOzsbDQ0\nNOCpp57CY489hj179kR5GcgtbLVamfiWikJqNBp2N8bKhSJrajD3uJiPSNeTfwPyv+n+RU9DLLc9\n9aezsxOXX355lKL6KuRC5Tl93XLJsrpI5MuumC6//HJMnjwZzzzzDJKTk3HddddxoNnlcsWMZQ0l\nBLig4DStZDMyMphtQBSytuLj43Hs2DG2sMhCo/tLSEjArFmzGIwRDAbxP//zPxgzZgzy8vLYOqNk\n5Xnz5mH9+vWYNGkSenp6MG7cOCxYsIAnk4yMDI5LJCQk4LPPPkNKSgpSUlLQ2tqK2bNnc0LqwYMH\n4fF4EAqFsHv3bnR2duL06dNc6JAmlilTpsDpdDIoYc6cOZg0aRI++eQTvl+j0Yh9+/axFSlJEkKh\nEJ588kls27YNx48fZ5j+Rx99hLfffhtjx44F0E+yeuLECeTn58Pn8yEuLg5XXHEF+vr6oFQqYTAY\nsGHDBkycOBFTp07FjBkzsHv3bmzduhX79u1DJBLB66+/jra2NmatOH78OKZMmYIjR47gk08+QV5e\nHsaOHctWIFlUAPDpp5/iuuuuQ29vL/bt24cpU6ZEWXi0SGhtbcXdd9+NzMzMKIupsLAQvb29CAQC\nkCQJn376KSZNmoQTJ06gp6cHkiQhPj4eGRkZ+OyzzxAfH8+ciGPGjEF+fj6zlGzduhWZmZnIyMhg\nIMmePXuwatUq9Pb24g9/+ANSU1Mxb968KKuAqJcyMjLYVUnvgazs5ORkBuTQuxXHoWip0PilcR2L\nrUL0MIh/D8YEE+ubJmBKRkYG1q5di8TERPziF78YlH1C3D5SS+tiA2EMJ5csq2+AfNUroIULFyI7\nOxt//GN/NRdaNQ4HqZX3w+v1orCwMMqKo3iD2B5BfmkFaTKZmAw2lr9f3Ea5UytXrsQdd9yBdevW\nYe3atdi8eTOAfgtSdN/o9Xo4nc6Y5cip/pUYQwDAQfjCwkLk5OQwRJuIVMkipL7r9Xq+ptlsRkdH\nB5dmr6+vR319PVpaWgCAqYioii1V3vV6vZyX1NXVxc8qLS0NRqMRFouF6ZqIxYEmOoPBwOzpZJlQ\nOREx/wnoB04UFxcjMzMT1dXVbD11d3cjLy8Per0eRqMRGo0G1dXVbFU6HA4uvCimHBiNxgElUBoa\nGrj0BtUpy8zMRFVVFerq6jgBWqlUcgkVsnJFEAwBctxuN8eJgP7xSe9GZEB55plnMGrUKK56TEo2\nFArBZrPxsU6nMwp2TmztlE9H71UO2KExIsaF3G73oN8JnU9EyHS8ODZHKmQRbtmyBVu3buXq34NZ\neEMBo/5ZABgklyyrr1G+7ApIvrJSKBSor6/H8ePH8Ytf/GJEbcSyulJSUqJWdKFQCDfddNOAc3U6\nHccY5NvIpSbyrImxAQBMcjt79mz09vbCarVCoVDg1ltvxbPPPotDhw5x4T6/34/Fixejt7cXGRkZ\nHI8gBBat8kUouyRJWL9+PZeJ37dvHwBg6dKlvCKePXs2nn76afh8Pk6yTU9PR3l5Oaqrq5GSkgKN\nRoNDhw7hnnvuYUtnzpw5OHjwIEPgN2/ejOzsbGg0GoZs9/b24sMPP4TJZEJcXBw2btyIlJQUvPvu\nu9i3bx/ef/99vP766zhz5gyAfuqdCRMmoLCwEIcPH0ZtbS1uu+027N69m2NGoVAIR48exYQJE5Ce\nns4J4TfeeCMOHz6MEydOYPHixaisrMTUqVOxceNGFBUVobW1lYlvi4qK0Nvby3E1p9OJlJQUJvrd\nu3cvJk+ejB/84AeYM2cO7HY74uLiEBcXh3nz5sHn8zH5b11dHXQ6HZKTkzkmpdFo+F0TN2QgEMDs\n2bNRVVXFVg+N3bKyMsyfP59pth5++GGUlZVhyZIlbHGRhURpBxkZGRg/fjySkpKQnp7Oz5ysI+Ky\nFGNGZBFVVlbirrvu4m3AWYVDVlIsa4ksdvF4amM4nk1RNm3ahH//939HZmYm/vSnP+GKK66IedyX\n4fq7GGHug8kly+pbKuLqMNbK6tSpUzz5jaQNEWYsWiZiQqRIhUS/aWUay5dP1gxl5efk5ERx6ZWV\nlYESfr1eLy677DLMnDkTd911F15++WUYjUYkJyczwovK0YtoNaB/YiEElggRF5+PTqdjRFphYSEC\ngQDsdjtXK+7s7ERHRwef09DQgM7OTsycOZMr9ZIFQvfg8/mwZMkSAP2QdSIWJi5BtVodtfKuq6vj\npOZwOIykpCS43W7k5ubi6quvRlFREd8jAKxatSqqSKHRaORn293dzcdVVlZCp9NxKRa73Q6lUsmI\nSIfDgbS0NFRXV8Pj8SAhIQFJSUlcObizsxMOhwNFRUXw+/38/CjXjsq4kDXp8XgYiWg0GrmAI5UV\nES0Q4tCz2Wwcf/T5fFyTTKwQTejNUCiElStXQqlU4s4774yK+5CFRMwhNEbFkiF0HI0N6ot8fNbU\n1MDr9UahT0loTJI1N1IZaULws88+i4ULF+K73/0utm7dCqVSOeAYsarx+cpXbXVdDHGwS3lWF6mM\nJDcqlvzwhz9ER0fHoAXbzmcQy3nRhms3Fu0M5T6JrhdxMqJtjY2N+OlPf4pJkybhlVdeYZJX8VlU\nVVWhvLycg9Qirxvl8RAPHSkqSmYlKDiRsdJzknP+UdVgkb6IqI5KSkqiWCXkeUvUrvh/WVkZsrKy\nmHoJQNS1ROokKkhJpSkKCgrgdDqRlpbGlEptbW2YO3cuc+xR3pVarUZ1dTWMRiPnaYn8jgAG8Bk2\nNjYiLy8PXV1dMBqNTG5rtVoxbtw4rjtF7YjPj943uQTdbncU1yEtCEjxUk4Vibh9+/btuP766/Hg\ngw+ipKQkKu+PFhvieBtuPFO/yAKXg5PkeVcXyq1mt9uxePFiPPLII3jkkUdw6623wuFwID4+/oJc\nj+TL3tOFfCaiXMqz+obLuSoqmuxHjRo1wLIi9NFIB55IY0RKU4xvUHtypSPP3xInyJqamigkl5i8\nKfbtJz/5CbZu3Ypjx47h1ltv5cqvopAlRPExyvOhNkhRkdWVmZnJLiYAXPEWOFtw0mQycR6TXq9H\nd3c3gH431qpVq7ik+o4dOwD0x70oV8rn83GlW2KLdzgcjBz0er3o7e1FZmYmSkpK0NLSwsm5K1as\ngE6nQ0tLC9LS0tDc3BxF/ZSYmIjq6mrk5uZyXGnHjh24+TvfwW0uF/5t4UJICgWW3ncfpq1ejZwr\nr4TFYoFOp+OCkIsWLWKKJ4/Hw1WEyeooKCiAyWRCYWEhVyn2+/2orq5GcXFxVFFOUtB+vx8ul4vf\nsd/vZ4sa6EdakjXkdDo5hlRVVcUxMI1GA6vVymPBbrfjsssuw8KFC9kypTijXNHIFzm0TRS9Xs/v\nWVR0JPK8q1htDLaNZCSVwwsKCnD33XfjkUceQVFREerr6y+4ogKGt66Gs5YutpjYBVNWCoVinUKh\nOKxQKN4Xtj2sUCg6FArFe1/8/OBCXf+fTWhgnTp1Kgq6TvvOZeCKEwNNUqKPntqLlRgMnHUtiqtW\nIBoOTMnDpFC8Xi+sViuAfrfX//7v/2Ls2LG44YYbBiRfrlmzht2I1Defz8cuJ6BfIYmB/FAoBIPB\nAJPJFNUvcZVP1pPb7YbZbObjXnzxRU4ira6uZnJVKtsB9Cs1it8AYCsqEolAo9GguLiYJ/iCggIE\ng0EAwJ/+9CfU1tYiNTUVTU1NbA1ZrVYUFRUhKSkJxcXFUW4pM4Cby8pwxYYNGHXsGBQA4k6dQqrL\nhc+vvRaBNWsYSl9TU4OZM2fCaDQy/2BiYiKnCojuU5fLhXA4zMS0xKVos9mwaNEihEIhti4JQEKg\nDGJdp/dCAJJQKASz2czWDTHBA/0uupqaGoRCIfj9fjz//PP46U9/CkmSUFhYyAsO0aUqjjVxgSJ3\nZVN6g9ziF9MfYkms72Sob2c41ou+vj4sWrQIzz33HFatWsXFKQeTf6S77WJTRsPJhbSsXgBwc4zt\nv5ckaeYXP69fwOt/q2WwQT1u3Di2rOTZ6hRrEid/+p8GLhHPni9BpRi3oAlBRFERCwUxUNC9aDQa\n1NTUcDvXXnstPB4P0tPTkZ+fz6S34iRFiokofkpKSrjv5H4qKyvjcuwajYYh8Z2dnVzyQoxfqFQq\nBnUA4KReqlcF9Csmip0QQ7tareZS8eQKKykpYRciUQlRLlNeXh6sVitycnIQHx/PLObEWkEVoA0G\nA7vYOjs7MfnUKfy73Y7LP/sMo2WLEnz2GcacOoUH3n0X9+XnA+iflNPS0hgBCPTnxalUKnahEncd\nWU2khKnvFosFq1atQl1dHYxGI5egp79zcnKQlJQUpQxIrFYro0OpwjGNE41Gw+9y8+bNOHXqVFQ+\nFy0ESHmK41a0aCh+SnEzkb1/MEtKbmnJvye5p4Cs93OR7u5uLFiwAK+++iqsVuuA5N9Y39g3TYH8\nI+WCxqwUCsVUAH+RJGnGF/8/DOCYJEn/dS7t/DPGrM5XCgoK0Nrait27dwP48sFauftwsFiavAyD\nvA0AA1bD4r7Ozk7U1taioqICWq0WZWVlqKmpwd69e7F06VK8/fbbuOeee/Dggw/i8OHDUX0QP3qq\noTSciIqcajKJEGs5nx0A1NbWIj8/n60ch8PBMHFyk4oWkNPpRHl5eRSPn7wm1o4dO9ht+eijj3Ii\ncXFxMZxOJ4MyKFH4NpcLV2zYMFBRCXJm9Gi886//il1Ll3JciuJR9B5IORFAg4Qg+eLzpPpbRLVF\n90HlQmiRQO9RjEPR+xHLtxDvIo2ZJ598EpWVlUhPT0dlZSXUajU/u1hSVVUVxXlI/a+srMSaNWv4\nmmIbcs5LsX8ksWJhI42PySUUCuHmm2+G3+/HSy+9hIKCghGf+88mF3PMaoVCoWj9wk04EArzTyAj\nWaGdrztg1KhR7AakHJmRKqpY15S7WQg8IK8SHKsqsSiD8Q3SajgnJwfFxcW8nz7ucePG4Te/+Q3u\nuecerF27Fj//+c9x7bXXDmAYIMYCKlxIFpZ4HFlIhPYiNgetVotIJAKXyxUFxgiFQhwX02g0WLNm\nDfPpOZ1O5OXlQa1Ww2q1Qq/Xs0uNfijviqyJwsJCPPTQQzCZTGhpaYHFYsG4ceOgUqmwYsUK5ukj\nQENRURErttzc/m/58o0bh1RUADDq9Glc9/e/8/96vZ7jUYTwa2xsjEISEsOE+Cyp7hflbFHF48LC\nQlY+5AYjZU3b3W53FN8jtZ+TkxOVawUAZ86cQSQSYXdnTk4OzGZzlGUkWjnl5eVsJVEunVarRf4X\n1iRxP1JsUwRpkIjoVLnI3dskYl4hSaxv5u9//zvmzJmDQCCA119//ZKi+orkH62s1gK4GsBMAAcB\n/N/BDlQoFMsUCoVHoVB45JDki03OVbGMZIV2vu6ATz/9lN2A51rJdKhrivvkClBekwqIdq/IyWRF\nEV0zYpvhcBgulwudnZ3YtWsXysvL8fjjj3NeEQEgAPCKmeJRFAejyZ/iF5T4Sdx2AHD11VfDbrfD\nYDCwG0yr1XL9JWJzIPcSAOZeNJlMcDqdDB7x+/1oaGjgWA71ze1245lnnoHL5cJf/vIXVFVVISsr\nC3V1dZwLo1ar2fVHfaPijpFIhJ/TZX19gz5LUcacPMkgCp/Px32nxGe6Fr2jUCiEzMxMLj5JZLP1\n9fWorKxklyHB6Ovq6tjisNlsUUoD6B97YkIvkQKL78vv9+PMmTN49tlnkZOTg9zcXGg0GpSVlbE7\nUFzkyMeR3W5HOBzmsUbPiCwtsrLkqRNarZYh/nT/tD2WkJuxqKhowHHyc/72t7/h+uuvx7Fjx/DW\nW29xUdSLAfr9TZd/qBtwpPvkcskNOLjI3XJ33XUX3n77bQ54D3WsKMN9sLGEoMHA2QlosGvGCoZT\nvEeEeNM5JPJ2H3zwQTzxxBPQarW444478OSTTw4o9wGAodnyUumiW1QeMxAr2QJny9gDZxk36Bjq\ns1imhCDoBA4Q3W7Lly/nEiBiZV45lJ1ymNxuNyKRCFfJpcrQKysrcfkIFNaZhARs2bgxCjpOSoja\nlZfiIFejyJZBlpf8vYjVmemZkWvQarWioKCA9xF0n/KbKLWAQDY/+9nPBpSKEUU+Ngcbq8OVtxlM\nhqqoPVQ78n1OpxMLFy6ERqPBG2+8gfT09PNOQflnkovSDahQKMR0bAuA9wc79pJEi3xipdWwPIdm\n9OjRMQEWBBmOJeerqEwmU1SbFIT2er28ko/lHhQTOAk2LlI0kduNABXifcyYMQNutxtHjhzByy+/\njNdee43vgc7TaDSsqEjIpUSVaylATy47EkLDkavLZDKhvb0ddrudJ0JRuYqIRHL3EZiAABgAMHfu\nXOh0OhQVFXHVXgIoRCIRLt9B57W0tKCkpAQajYbdT0lJSdg3dy7ODIEmAwBcdhmO3NyPbfL5fJys\nDPQDSAixSNZiZWUluxoLCwvh9/uh0+miaLVIyYjksKQAiVLJ6XTC5/MxUIbcfi6XC6WlpfxuCbSi\n0WjYQvvFL34RheQjF65YFobGPJEVy0EWBIAB+i1CERwRqzQGtScqKjkUfaTehvXr1+O2227DNddc\ng+3btyM9PR3Al0vsvSQykSTpgvwA+CP6XX2fATgAYCmAlwD4ALQC+DOAlJG09d3vfle6JMNLR0eH\nVFxcLCUmJkoHDhwY0fGDyebNm6P+37lz54j2ydvt6OiQOjo6pJ07d/Lfw52zc+dOaefOndLmzZv5\nPPk527Ztk7RarRQXFye99NJLfAz93rx5s9TR0SGtXr16QN8rKyv52A0bNnD/xPui69J++b3Q/srK\nSu7n5s2bpc2bN0urV6/ma6xevVrq6OiIOob6Je8zXU+8d/pZvXq1tGHDBqnhySelz8aOlSRg0J/P\n4+Kk1f/n/0Q9R0mSpMrKSr42PRe6Jl1PvM8NGzZE/U/niu9CfBbi81u9erU0d+7cqGPp3iVJkp55\n5hnpgQcekABI9913X1Rb8nEx2FiRjxnxGY5UYn0DQ30X8vs4c+aMVFNTIwGQbrrpJsnv9w849pIM\nLQA80kh0ykgO+rp/LimrkUtTU5M0fvx4SavVSh6PZ0TnyD96+hgH+2jlH6B8ohrqYx/J5CAqKNov\n9oeUxOHDh6UZM2ZIl19+ufTCCy8MaJcmP1IA8nsQt4sTHN2LqJjEyZnOk0/CHR0d0sKFC6UHHniA\nn8nq1aulW265JUrxioqIlNDChQtZ2VE71A9R6XR0dEh7n366X2FddlmUkjo9Zox0+oorpCMvvSTd\nc889fF/iQkFsh5SU+Gzp/8rKyijFJD6vDRs2SHfddRcfLz4b8dmJ+0hI+d98880SACk7O1tqamqK\nuhb1V2xX7D9dV3ymsZSaOHYkSeJrDCaxzh9KPv/8c6mkpEQCIBUUFEh9fX3n3dZXKcMp24tNRqqs\nLjFYfMvk+uuvx/bt23HZZZdh3rx5eOWVV4Y9Rw6eEPnWYkksHrRYbAAABsQh5KwDAJjrjdyIouuE\n4gJiIqjP50N5eTnUajW2bduGzMxMFBcXo6Ghgc+z2WxRNY0oKVXsLzGVE7iA+tDV1cUuSWK9EBkv\nCMItdzu53W5YrVbk5eWx68pisXCMCjhbLZnQdgaDAZmZmcwHSM9j1apV0Ov1UV8NrD0AACAASURB\nVInOdB3dihXwOxz4+4034tO4OGDUKHwWH48jFgs2PPAAXF8wlldVVTFbR2dnJ+rq6qLaoiKRYu0v\ntVqN+vp6ZoZ3uVyci0WxrEgkgtLSUk4ApmcEnAU3UHkZeRwyGAxi2rRpcLvdeOCBB7Bz505cf/31\nsFgsCIVCCIfD7P6lWCf9T8+NEs5NJhPC4TDvE2m35GMnFApFQezFPpHIx91QcuLECdxxxx2w2WxY\nuXIl7HY7l4ChZ/ZVy0hBGt/aXK2RaLSv++eSZTVQBlsl0qrq0KFDksFgkABI99xzj3TmzJmo/ecq\ng61ahzt2KOuMVvHi8WSVyF1LsdqhfZFIRMrOzpbGjBkjvfLKK1EuIXGlLkkSu+dE64a2y91fse6X\n3GfkxhPdhPfcc0+Ua02SzloSGzZskO655x52Q4r9Ee+NrB358xOtDEmS2CIT3YyiC1G0RslKouOo\nffEdiO9i8+bNbJnFcs+KFhltF4+Lde7Ro0elxYsXSwCkmTNnSn6/P6oNei/y5y2/7+FksPF2rtuH\nknA4LH3ve9+TFAqF9PTTTw96nDg2xG3nIl+nhfaPElxyA377Rf4hyN1zJ0+elH784x9LAKQf//jH\n0smTJ0fc1nDyZSYPuVtLnDxjiTihx3LNbd68WXrrrbekadOmSaNHj5YcDof0wAMP8PnipChJZxWi\nPJYmugRpUo4VZ6NzxHZouxibEid1UgyighBdhaTIRBeYeH9iTIz6Rwpo9erVAxTczp07uU1x0hSf\n+1133TUgLig+E/EasZ6R/H3I3cEkVVVVkkajkUaPHi09+uij0scffzzYcBkwTsT3NtL4Dyk+UTGI\nSj6WjHT8t///7Z17dJTF/f/fkxBIIEiUe4KIX0QURUqgATxgaS2N/NDWKwJa5EBLq6CIIpdv5KeH\ngj8vx6NYOAU9RaxcxKqABCRyx0tIhUC5FSoCEhKojYhclUs+vz+ys8xOZp7L7ia7m3xe5+zJ5tl5\nZuaZfXbez+czn5nZt486dOhAqamp9MEHH4SVh0pdECM3vIoVuwFrGN2Ut61R5gXd3Nfdc6mpqXj7\n7bcxdepUzJ8/H7feeiu++eYbT3np9dSjqUxuDic3hR72LCPtgEuuI/m/uvwRUDkJVNZPuodUF1/z\n5s1x+PBhFBUVoVu3bhg0aBC6du0anAAs50pNmzYNCxYsCK7+DlwKV5fL/xQUFATdg3LOkXr9xcXF\nwei6MWPGYPHixSETYvft24c9e/YEV3lQr23fvn349ttvgxGEMnS/V69eePrpp0NWLpfh82VlZVi7\ndi327NmD5s2bByfoZmdnByfkyiWZ1HoWFhbi6aefDrahdLXKUPvMzMzgBpfSlTZt2rTgBobqZptA\n5aRuGd0HXFp7MTOzcqFaGRkoV5WYOXMmli5dikcffRR5eXlo1KgRNm3ahMmTJ6Nt27bBCEPTvaMe\nP3r0aPCa1q5da7y39N+QXLXCtMal3F7loYceCllWzIvrbPv27cjJycG3336LNWvWhER/yjz8zqfi\naEEfeFG0WL/Ysoqc2bNnU1paGmVlZdH27dt9nau7YkxRWCpuT5j6E7hqxUjUJ2D53qlc+YS/ZMkS\n6tixIyUlJdEHH3wQfDqXFoJ+LfKvdF2pbjKZr2rJ2CwKmZdqBUnrRlphap3l9apReXr0n/pXdTnq\n1pwsT02rt4vq9tStKDWtzEMPqDC1/b333huSl2oxFhUV0bXXXksAaMyYMXTmzJngOXqEpfyrXpeK\nHiWo19nmKjbdr06uazXIxESPHj2oZcuWIRF/kQQzsFVVCdgNGF/Ew435xRdfUOvWrSk9PZ1WrFhh\nTOPnB+4Fk9A45Wdy+5jEUnXr6C6qjRs3Uvv27Sk5OZk++uijkHEqU+coy1HD2FV3mZpeH2ebP39+\nFbec7mrT3Y16x6yP/Uj3oe4WVMVIPVetg+6GVJFjXKaIO1OdVHHWy9LdqLINi4qKaOzYsZSUlERt\n2rSh1atXV/k+1f9NDx2yrqbPZH2cIvv0BwMdP+Hksj6ffPIJAaDp06cTUVUXZbR/39HML5Lfb03A\nYlXD+LkhavrmUTuckpIS+slPfkJJSUk0c+bM4HHZwRFVHeNR8TuGoP6o1Q7GJF665UFkHni31Uc9\n//XXX6euXbtSgwYNaPbs2SFCY0N2wvpYmm6ZqNdlGvdSz1PnecnjcoxJF0x5zbKz1QVLCoU8PnXq\nVKvVpR6T7STzVUVLP0cPnFDbWRezlStXhgjDu+++S507dyYAdPfdd9Pu3butYqg/iEhhV9Oq94uX\ne01asV4fjLwIgjz/tttuo+bNm9Pp06ddz2H8wWLFWDl58iTdfvvtBIDGjh1LFy5cCH5mc6XoQRBO\nEXMmd45pbpZehi0vFdUNJ89VOzz1aX3RokXUqVMnatSoEb388stVggWIKKQD191j+jE1oEHtTFUL\nSrU81DxU0ZEBFaoVpaZRhUzmobaTelwVNfVaevfuXSVCUH4PuuCo7kd9Pphu/annERFt376dpk2b\nRjfffDMBoKZNm9Jjjz1WpSw1D/27k+KpumB1a1mvgz5HzFZP0z3ll82bNxMAmjRpUvC6wkEN+mEu\n4VWsOMCiFmNaYgYA0tPTsWTJEjz22GN45ZVXcPfdd+P06dMAqg40ywFgGfwg13QbMmSIMX8ZrKAP\nNqtbZ8h0ehmmvPTFS+U2HrKee/bsCTlfBikAlXNynn76aaSmpmLKlCkAEFyqR274KJcBatq0aXBF\ncuDSnk5q+b169UJmZmZwPycAwXlfOTk5ACr3YJJ5ZWdnB3cplnnu2LEjGFAhy5WL3sqdbWVQhlzs\ntW/fvigoKAjuQiyXfJJr8QGVOwB36dIl2EaffPIJmjdvHrL78YIFCyDX2JTfjfxe1MWJ1fUQZfCI\n3JOsrKwMZ86cwdq1a/HTn/4UXbt2RV5eHg4ePIiJEydi165dmDBhQvD8N998M2Q5L9OeV3Lvr9zc\n3GB91LlTMhBCPW/IkCHIzc0Nfg9qAI5EfndqcEw4c5CmTZuGjIwMTJw4MXhd4aDu18aEgRdFi/WL\nLavq47XXXqOkpCTKzs42Pp2aUJ/U/aCfZ7KoTHOv3PLTLTB1PKm0tJSWL19OWVlZ1KRJE9qxY0fI\nU7secFBaWhq0elSXn2lemLSi5HuZVreAdPdgaWlpSHDC+PHjQ+Zjyfx0y0O12ExuO/mZXE5Jvwab\nK85kNen3QFFREY0bN4569uxJDRo0IACUmZlJ48ePp1WrVlFFRYVxXpFqzerto2NagsqG6XOnfL1g\nynPnzp0EgCZPnhxyXA/EqWliWXa0AbsBGa/k5+dTo0aNqE2bNrRt2zZP54TTmRD5GzjWgz3c8tQ7\ncvXYvn37qHHjxtSkSRN6//33q7iZTONpuojpIqpPYLaNbZkCEnQXnx4VqI8lqe5P3T2pzr9S85Pr\nFqqY3LF6/VTeeecdeuihh6hZs2YEgC6//HIaOXIkvf/++/TFF184fi9qmWq++gOGLmjqg4QX9PKd\n6uPHDXj48GEaNGgQNWrUiMrLyz3Vw0/+ficIe0Ud561uUYtGIAiLVQIRjh89Gqg38tatWykrK4sa\nNWpES5cuDUln6kxs2D4zTdDU/zd1ljIk2+sTtzxHtyKIiHbv3k1XXHEFNWnShJYtWxYiDgMGDAiO\nKeirPKjCoraZKiD6mJReVz2iUbVe1HBxNb3peGlpabAzkmnUYAtTO6jnyr+6yOpRfV999RU98MAD\nJISg1NRUGjhwIC1dupQOHDhQ5Tr0c/Vr1VGtWa8WtHoNXsRJz1efwE10abqCia+++opyc3MJAE2c\nONG1jkz4sFjVYqIV1moKDe7evTsJIejFF1+kefPmRVyeVyE2rW5uy09/Utddb3pHqOZdWFhI7du3\nJwA0fPhwOnTokNF9JfO69dZbQ/JVXY9qfXRBV/PQgzX09tTde1IEZb560Ib619Ym+uobujCq+ati\nU1xcTPfddx/Vq1ePGjRoQKNHj6Zjx46F5KMvEaUGaKhlqXWU5Xm1guRx1UoI96FOfQhyy+PcuXP0\n/PPPU1paGqWnp9M999xDy5cvd6wjExksVglGtAQoUk6fPk0DBw4kADRs2LCQlaRN6G4Gp47fr+tQ\n7+hMriHThFWTpaam+/zzz2nEiBEEgPr160dr1qwJWbJIFzx9/EePSJw6dWrIOn1qHrqImtx9+jww\nmU4VKCkQqhUkRczkTlIFSe/w1chFyfr162nSpEmUlJREAGjo0KFUVlYW0p56tJ5qfZrGItUIQ7UN\nbPeBLfJPz9PpPBWTVemUftOmTXTTTTcRALrzzjuppKTEmM4v8SBq8dK/mGCxiiHV5YuuKSoqKuiJ\nJ54gANS7d2/65ptvfJ1v8pPr4xNOab38sGxzprz46GWH+cYbb1C9evUoMzOTpkyZUsUS0MeJ9LEW\n1dJS3YRqiLLqOjQJmLxetQz1OvT5U6oA6J2/GpShW54yjVqXLVu20OnTpykvL48uv/zyYCc9duxY\nIiJjqL+pLeW16w8K+u9Az0e3EGVdVVRxtn2PXu4XJ8H4/vvvadSoUSSEoKysrCpr/oWbbzjpokk8\niKQXWKwYI36esBYuXEipqanUrl072rFjR/B4df8IbJaS/N92ju4GU5/4Tcs5SasjKyuL6tWrR7Nm\nzQpZnV51k8k81LJkGif3pR7wobsR1Ym/6vWp+0XJukorT28nvQxVOHRxnT9/Pq1YsYJmz55Nw4cP\np+TkZAJAv/jFL2jr1q3Gzl8fMzOVL8vV9wV7+OGHQyxEN3RR1ctUcRoTc8tf8sEHH1BmZiYJIWj0\n6NH0/fffu9bRa95OaUwrjNRVWKyYqFBUVEStW7emxo0bW333ErfxB7fzdKtG7/hs6fUnb70T1fPQ\nLZvy8vLgZoD33Xcf7du3r0p++uoTNhE1CZl6vmmsTReqLVu2BLcbUettaid16w+1HNXqk+353nvv\n0ejRo6lly5YEgBo0aEDDhg2jTz/91LHt9EnJpsnCuijqFpnNJedmZatpnFb98MuhQ4foN7/5DQGg\nLl26UFFRUVj5MJHDYsUEidRfXVJSQl27dqWkpCQaOXJk0PqIBJsLz0nY9M7Y1BFKpHVh+kwXqxkz\nZtD06dNp5MiRwf2WPv/885C0+rJHej4mwSGikG3v1fXu1L96B6yn1S1NaV2Z5knprruFCxfSq6++\nGgwqSU5Opv79+9OECRPos88+qyL4pnlr+jyl0tJLEY/6+J3exuo5Km7z7WyfObl53VzAW7ZsoQsX\nLtD06dMpPT2d0tLSKC8vj86dO+d4XnWQKC66moDFKoGJ9Eb2Kk5uEVLq8S+//JLuvvtuAkC/+93v\n6Mcff3QVFx1bKLWpXHXNOT2dl/qaOkenz+X/06dPpyZNmlBGRgYtW7YsRIhUcdOj8dT66u430/qA\nJktLLUtd9880lqYLpSpcp0+fpkceeYRuuummoJsvOzubnn322ZD1IHWrRgqxXif1Om0CqmJaEsuE\nl3vBRji/ka1bt1KnTp0IAN12221UWFhoHCOLhpBEU4xqu7CxWDFV8HrTm57YiYguXrxIeXl5BIB+\n9rOf0Zo1a3zXQVoNXuqod45+OxK1o9XdiCY3lvy7f/9+ys7OJgD04IMP0scff0ybN28Ocdup4zSq\nOOmfyb82K1C9Rn0TRfV69UAOvc5ffPEFLVq0iAYOHEiNGjUiANSyZUuaOHEi7dq1y1GApEiZIv1U\ndKHS29bJWtYXJLa5Dd0eoEz1cWPJkiU0btw4Sk5OphYtWtDChQuj4h1gogOLVR3Ej7svkpntf/7z\nn6lBgwaUlZVFu3fvDh43dTC2TkcNfvAyIG4SKtVtZcpf7SRt56uRe1Is5s+fT2fPnqVBgwYRAAJA\naWlp1L59e+rXrx8NHjyY/vjHP9LcuXPp3XffpaVLl9KmTZuqhHbrnbBuAamrr8s6qmNPNoFRO/S1\na9fSI488Qm3atCEA1KhRI/r1r39Ns2bNopKSkirRdKapBjYhN6U3pTM9FFS3pWRzP6r/X7x4kRYt\nWkTt2rUjAPT73/+ejh07FlG5TPRhsWJ84beTKSwspJYtW1JGRkaIYDkR7hpturWljpkQmdebs1lw\neuCAFDNVVNQn/LVr19KUKVPoySefpAEDBlBOTg5lZGQERUx9tW7dmnr06EF33HEHjRs3jp566ika\nMmQIFRQU0LfffhsSaThlyhTav38/nTx5knbt2kXTpk2jjz76iA4cOECLFy+m2bNn07Zt2+ijjz6i\nwsJC2rhxI61Zs4ZWrlxJc+fOpbFjx1JmZiYBoKSkJOrduze98847NGfOHMe21NtLt2bUOV8StY1M\n35XTg4/pQURd4cNp9X6nPGz1kaxatYq6detGAKhz5860ceNGa1omtrBYMWHjVbD2799PLVu2pPbt\n23taO80rXkVNRbViVHRXmn6Oeq4eRq7mQRS6xcPZs2dpzpw5NGnSJHrmmWeob9++9Ktf/Yr69OlD\nbdu2DS72qr7q1atHKSkpRqEL59WuXTuaMmUK/ec//wnWUw2wUJeP0sfQ5HWrFp/JXWdqB7XNbJgm\nh6suRy/5OH2nNjZv3ky//OUvCQC1bduW3nrrrZAtcKoLttDCx6tYicq08U337t1Jbm3AhIe6rYb6\n3omCggLX7RAKCwvRt29f9OnTBx9//DGSkpKCZQBVtxzxUlZxcbFx2xD1+IIFC/Ddd99h1KhRIWnk\ntenl69dcXFwcshWGU7nyXLnVROfOnYNlHD16FHv27Alu1yGPb9++HdnZ2Th06BC+/vpr7NixAwcO\nHEB6ejqOHTuG66+/HikpKTh9+jT++9//IiMjA+3bt0dKSgpKS0txzTXXICUlBSkpKfj6669RWlqK\n/v374/jx42jdujV2796NYcOGBesm615cXBysh2ynIUOGYMGCBWjatGlw+xGJPFeeU1BQELLNyvr1\n64PbjMg81bIAhJRn+y7Uz/X7yst9BgAzZ84Mft+m73PlypXIy8tD06ZNkZeXh4cffhipqalV6sPE\nF0KILUTU3TWhF0UL5wVgDoBvAOxUjl0BYBWALwN/L/eSF1tWscFt3ovk9ddfJwD06quvhqQxzcvx\ngsntp+JnbE6th+4uNI0Bqej1Ns2Jktg2hTS5KPUlkNR5W7olaLt+3ZVnskJMY0pux/WxNR1Tm6l1\ndlocVq2/LV+nY04Wd3l5OaWlpdEdd9xBx48fJ6LatY1GbQaxdgMCuAVAtiZWLwKYGHg/EcALXvJi\nsapeInVhVFRU0IABAyg1NZX+9a9/RaUOTkETfvKxpfEaDKKHqOvp9C3qTWNBJmwRhG4TbvW66mKq\nh8bbrstUhizb5O4zlaeWYRIpLwE0tnqZHirkcdP1/OlPfyIAtHPnzojnFTI1S8zFqrIOaKeJ1V4A\nrQPvWwPY6yUfFqvY4uUJtaysjK644grKycmh8+fPh3zmFKru5SlbjTRTO01bh6b/rwdVqGnUdE6D\n/SbB0Nft0z9Ty1c7Ydvaibq1pNdRP0+f16Wet2XLlirbqKvtIBfGNVlcalo9mEXWwzR5WL8eL9jG\ntWzji6b75ezZs9SyZUu67bbbPJXJxBfxKlbHlfdC/d/pxWIVf5iesBctWkQAHAfpo1W27n5yG/C3\nuQBNecv0buWrW3eoq1rYXG62cvW6S7egbQKxKX99/T3TOoYyL5M7zSZaTscktvlX4WJre1uE51//\n+lcCQKtWrYqo3HAFlomMuBerwP/fOZw7EsBmAJvbtm1bTc1Udwl3HMj2xCu5//77KSUlhbZu3Vrl\nXJNF4bTunQmnNQK9nC/xcr02UTC1gT5fSaKvMq9G3klsezbp73VR08VXt7RM37FtfM4k0nrddfyO\nRerXYvoO1GNeJoFXVFRQhw4dqEuXLtaJvuwWjG/iVazYDVjLKS8vp1atWtGNN95I+/fvd01vWsMv\nnDEOL52vnk4XF5OrzuQq0/93E3DT2I3e0euTpNVdgk0iYxsbc3KHmsROnq9PTjald8KtTWz1i5QV\nK1YQAPrb3/4W1XxrC+FMA6lp4lWsXtICLF70kk8ii1W4q0LHaySTU6SYJD8/37gduBf3kltn5udz\n2/iWl3LVjtp0rqlD18eWTNF9Mp1tMVo3C0oiLTnbmJ9TPl7uLad8ndKFK3Bejpu49dZbKTMzk378\n8cewymJiT8zFCsBCAEcAnAdwGMAIAE0BrAmErq8GcIWXvBJZrGqCmvgh+i1jxIgRlJSURJ999plj\nfqpLyKkTdXpaN6XVt8owlW0aazG5+Uz1cHOz6fnqeehjSqZ6EdndpDYXqFPIuS3c3pafanmZqAn3\nmtN9t3XrVgJAL7zwgmtanXh9GKyLxFysovlisfJHPPjov//+e7rqqqvommuuoVOnTvk+P1w3kh9L\nTe2g1TUETajuSpuLUP1ctbJkOtNWG7Z5W2oYucmVY1vw1mRRmSw/J6F1EktTHdzE3Qs2V6aTqDz4\n4IPUsGFD+u6773yXp5fJxA4WqzqCbdzFhtugdjRZt24dAaDRo0dbOx1TwISXcHa9szbNT3LKw+Qe\nU883zTnSyzTVU+3A9WuWe1t5sX5ME35N16JP4tWtKyd3oi1vG3rwg15nv3no9TNhGnMrLS2lkpIS\nqlevHj3++OPWvJnEgMWKiQgvYd5eGDNmDAGg1atXV/nMi9Xjhp+66R2rTbzUtfN0i4XIHoCgd5Zq\ngITucjO5EtVybddoEyI9L6cJvmrdvIqFycXo9nBgw4sLzi3PoUOHUlJSEh04cMBX2Uz8wWLFEJHz\nk7UX/J6jdzJnzpyhjh070pVXXhnirtHLcBpvCbd8kzCZOmgnl5leT4nuCjRt6WGrm2ohlJZeWnBW\niovJgtHzMC0UKwl3IWCvn/t5yHCyck1WmRfhO3HiBDVp0oQGDhxoTcNWVuLAYsVETLQGofPz8yk5\nOZk6depEb7zxRpXPI7HcnPAaiWmybry4IvWxIlvghY5q1ci0+saLtjL1fEzWnH6OnC+lplUDPPxY\nSF7dhrY5YLZzTXUzcfLkSerXrx8BoOXLl7Mo1QJYrJgQ/IxNRKMsndWrV1NGRgalp6dTXl5ejdSD\nqGpEm1Pnpnf0+vtwrEwn95n+vzr2ZKqvW92d6qB//+ouxk6EY/mY6mCrlx/Ky8spJyeHkpOT6c03\n3zTWL1xq6rfBVIXFiqlCNH6Qbp2DUxklJSXUs2dPAkBPPvkknTt3LuJ6eY0c85KPU4CHmr/83LRV\nu46+RqA6jmWqg/5XtdZMY2gmS0rPzyZITgLnJQTfFN3oVI9IKCkpoeuvv57q169PS5YsqZI/i03i\nwmLFVCteXUY6P/74Iw0bNowAUJ8+fejIkSOueZoWUnXCzVrQx0tUd5rbYra2//W06l89MtAmMKat\n2m2CaCvbNCYn81Vdj05javK9bBebsLq5StXrUb9Dv+Kyd+9eatu2LaWlpdG6des8n2fDy8R223nR\nSMOEwmLFxDXz5s2j1NRUatWqlXXLcS9jOG7WkPzMqXPX83OzVkyf24TMSeBMLkJbnfRzvIiN3w7Z\nFPTgZk25oS7wa8PNwmvevDk1b97c8Xsx1ZVJDFis6jDx8KP18oS5fft2uuaaayg5OZkeeOAB40Kk\nbp2TV1eWjtwiwwmbIJkEUoqDaol4saZM5eh4DTF3s/ZsnzuJk5so6se8uFNN5w4dOrRKvuvWraPU\n1FRq27Yt7d2713p+uEuaMfEBixVTbURTDI8fP0533nknAaChQ4daV872g5OAqSHfXsXK1BGb0rpt\neOi20oPJanMTIRPqflNeXKK2+nrFzeJxK9vE4sWLqUGDBtSpUycqKSnxXA/bsXh4gGPMsFgxEePl\nB677//0+VRNVbvMwefJkAkDPP/+8Y1nhYNpoMBxM5atuMZNFJVdPJ6oqPPpqFvo8rpUrV1YRGz+u\nPSfRVv+GQzjuOC/f36FDh+i3v/0tAaAePXpQeXk5DRgwwFOdeLwoMWGxYnwRa1dKRUUF3X///ZSU\nlBT2Jnp+Oitb4IKX+VXqcS8i6LbElel8p2WZ9PqqOwKreenr+nlZkipa+M3/+PHjNHHiREpNTaUG\nDRrQhAkTfK8pGa1rqu7NQ5lQWKyYhOPkyZN0ww03UNOmTengwYPB405jJF4G/J3GnrwEANhw6tR0\nl55phXMVXVhkejmh1+aKNAVSOK096HTMr1tUoj/o+BGNc+fO0YwZM6hZs2YEgB588MGQ7z5c2O2X\nOLBY1VHC6XTjabuEf//733TZZZdRdnY2nTlzJuQzpw7IFI7s1Y0p06pBGbbJsLZIP30szOvYlnrM\ntlCs7Zr0gA5TGjdiFV1XUVFBixcvpmuvvZYAUK9evWjz5s3GtKorNdFg0XSHxaoOEqkrz8sPqyZ+\nfB9++CEBoDvuuMMx4MLWgZmsLVnv8ePHRz0YwFamCduDgU1w3Mad9GNeAjsiFTMvY1JOZRQVFVGf\nPn0IAF133XW0bNkyOnz4sOc6OcHikHiwWDFRIxYdgAy4ePnll6ulDurisV6sC909ZzvHLSJQT+NU\nlsTvkkhOefnJw4RtrEzidv2PP/44AaAWLVrQrFmz6Ouvv/ZULlN7YbFiEpoLFy7QPffcE4wQdLI0\nZAfpRQSky802VuPkvnNLo9fPJoR+hde0WK3tM1M5Xt2hfif8mlyaTrz99tsEgP7whz/QiRMnjGlM\nAhgJTqvTM/EBixVTBb8/2miEe0fCuXPnaPDgwQSAhgwZYnUVOQmCySoJ1y1mG69yqpOTKHqpm1v+\ntmP6506L9HrJ2y/6vbNv3z5KT0+nPn360IULF6JafqwjWZnIYLFiok51C5epoz506BANHz6cANBT\nTz3la9KwHpEnjzlZA06TSPWndJO151VsZJSfnpf+Xl0818mq1K1H3Yr00wZu+L0Pzp07Rzk5OZSR\nkWF1+yVqAAUTOSxWTEwIR9D0jlN3R128eDG4+O2oUaPo4sWLVfIIt7Nz5Pr7cAAAEdVJREFUirbz\ncy1umyb6zc+E2xiYn2AMWzovbkU39O9z0qRJBID+/ve/ezrfiWg/MLGLMPawWNUiauoHFc8/3IqK\nCho3bhwBoOHDh9M//vGPiPLzMjYVDXR3oB8XpLoXl5eJqqoVabMQTeVXp1Uza9YsEkLQiBEjjPV0\nws934XfjSiZ+YLFiEh69E62oqKBnnnmGANDgwYOD+2HpRNpJmcZAdHebWkd1Cw79M9UFp7v+ZH6m\n8nSR0y0+rwKjp4vGnDqb8OoWcXl5OWVlZVHHjh19r0bB1B1YrJi4JBpBG88//zwBoLvuuot++OGH\nkM/CcWPp4zx6fmrnbOrsbWU6TexVhcs2HiXxu/yPlyAQL0EJft2gKhUVFXTnnXdSSkoKFRcXR+xe\nZCup9sJixdRqXnvtNQJA/fv3r7LShVeiuXKH09iXW9pw8zFhW8WjOjr70tLSkEVm1TL+8pe/hMyT\ni6QMpnbjVaySEAOEEAeFEDuEENuEEJtjUYe6SllZmfF9deRfHcj8H330UbzxxhtYuXIlBgwYgFOn\nTvnOKzc311hfeczvtajpMzMzUVBQgOLiYk95lZWVYebMmTh69GjI8fXr16OsrCzk/IKCgpC/ah6d\nO3cOKVPml5mZGfa12MjMzMSUKVOCaWUZu3fvxtixY5Gbm4vHH3/cV7mmMhgGQGwsKwAHATTzmp4t\nq7qD3yfpefPmUXJyMnXv3p2OHz8eUV5u9ZLuOKd1AyXq+I0MeZfuP7c62ha9jfbcqHAtS6eyzp49\nS507d6YWLVrQkSNHwso/3IAPDn9PTBDPbkAWq+gRT4vQhoOsv5+JnXpn+d5771FKSgp169aNysvL\njefYVgZ3aj+/C+PaJiKHszlhpO4/p1UvTGXp5XkRVTVvWb6cE7d8+fKQNDxxl7ER72J1AMA2AFsA\njLSkGQlgM4DNbdu2raZmYmJBJKs02MjPz6f69etTx44d6ejRo475ueVrivxzmiysR8eZ0trOHz9+\nfMimjZFaSHIldluEoum8SOaoqfXNz88nADRmzJiw8gvnwctPe1XX+BePq0VGvItVVuBvCwD/BHCL\nU3q2rOKLaP04I3Xb6Cs9rF69mho2bEjXXXedZytInqtjso7089Tj6mfqWoVu1xhJG/i5Rr28aLrM\nKioqaOnSpZSenk433HBDlQjN6iIaE5iZ2BPXYhVSAeBZAOOc0rBY1R6i0YmY8pACsWHDBmrYsCFd\ndtllNGXKlOCCqV4nAXuxNpxWqjBF8+nb1cu/JjejF+vPrQ29WIF6eV7LUI9LkeratSsBoPbt21Nh\nYaFj3aqLRHeHm6grY3BxK1YAGgForLz/HMBtTuewWCU+1fHDM7nZiIh27dpFubm5BIAaN25ML7/8\ncjC8PZwVG2zlhFNfJzFyE6jSUvOafiZryU1sIhlDM4nUK6+8QufPn/eVTyRpbNRG0artxLNY/U/A\n9fdPALsA5Lmdw2KVeITTGbthG6S3Hc/Pz6d+/foRAMrKyqJZs2ZZV70Ip+xoudRUAfEqJnIisVP6\naD8gSJG68cYbgyI1d+7coEjpdfA7mTnasCswMYhbsQrnxWKVOPh1Kzlh6my9dMB6mrVr11KvXr2C\nHey8efPowoULxnOdotbUfL1u7+EkaLb9oyK1PpzKCkfATJaUKlJqfdweUvxEKYZLbXWf1dbrYrFi\n6hRuGy9WVFRQfn4+denShQDQjTfeSIsXL66y5YgXoXAqa+XKlda1/pyi/bys/ReNzspPxGFFRQXN\nmTPHUaTCxWkpqmjBllViwGLFxD3VMSnVLd3FixfpnXfeoQ4dOhAAysnJoZkzZ9Lhw4dd3ZQzZsyw\nTtj1UzfbfKxw0MXHbW6YU3CKxM3dx4TCohgZLFZM3BOJ6ERaxvnz52nw4MF05ZVXEgD6+c9/Tp9/\n/nnE83ZM4hDtQf9wdzqW2Orjxd3nVA91jCrc76y2uroYOyxWTMLjFkCg4nWir84PP/xA06dPpxYt\nWhAAuv3222nbtm0heYbb8ZqsHKewd6eOWs/LtvKHaRzMaYdhoksi1bFjR2N0X12LsGPBrFlYrJgq\n1JYfoVfx8ONmO3nyJD333HOUkZFBAGjQoEG0d+9e3/VRO3YptnI7EKcJxbY8va4NGC6bNm2qljEp\nIu9z1Zi6DYsVw4TBsWPH6H//93+pYcOGlJycTCNGjKA9e/a4nmcTkmh0yn4Ey4+gVVRUUMeOHSkr\nK8sqUvPnzw+u/efVwgp32aiahMUyfmCxYqJCPLqAbAvD+rFcnPImIjp69CiNGTOG6tevTwCob9++\ntHDhwhpbSojILDwmN5/TShROeX766acEgObMmRNxvbjzZ8KFxYphosCRI0foueeeo6uvvpoAULNm\nzWjZsmXWOVKSWCya6ub21AVl5MiRlJaWRidPnvRVB15BnYkmXsUqJpsvMgwQ2UaNXs+VGxHKv35p\n1aoVJk2ahH379uHdd99FeXk5tmzZglGjRlnPmTlzZsimgdHckNJpM8IhQ4Y4lpednR3yf1ZWFs6e\nPYsFCxa4lqvmKcsBKq81moT7PUWDaG8cqm+OyUSIF0WL9Ystq8QlVmMX1bENybJlywgArVu3Lsxa\nhaLX0c1aizbnz5+nW265hRo2bEglJSXWdNH4Dr2uPBItt3NNtyUTPmDLiokHnCyBaD5F69aBbkXY\n8LNt+oYNG1C/fn306NHD+LmX61GftrOzs0Oe5p2sNR15XllZGQoKCnxZBTJtvXr1MHfuXFy8eBGT\nJk2ypndqowkTJngqU34fTvXMzs5G586dPeXnhp+2ZBIDFismZpgExdbhu7mbVNdUOHgRmvXr16Nn\nz55IS0szfu5FIHNzc4PllZWV4ejRo57qp1+/PC8zMxO5ublGQfHihrr66qvxxBNPYN68eSgqKvJ8\nnuT+++/3nBZwfzjw8/DA1DG8mF+xfrEbMD5JFFdLNFxLL730EiUlJdHkyZMjyidablG3aEgvi+TK\ndjlx4gS1atWKevbsSYcPH45K/apzU0mmdgF2AzLVTTRcLQsWLIj6wLaOtGb8oFtanTp1QkVFBfr2\n7QvAf2BBWVmZ43U6BTmoLj+JyQKRx8rKyqzfjWrJ5ebmoqysDI0bN8Zzzz2HTZs2YcOGDVXOCSdQ\nwKsb1oTp2mIZeMHECV4ULdYvtqwSg+pYwy/a5YTL+PHjKSUlhU6fPm2d9Kpuaz906FDfZTz88MOO\nn0dr+xVpUannXbx4kbKzs6lNmzZ06tQpX/nVFdjiqx7A86yYaBDvP9Bo18+2qnpOTg717t07xL3m\nZTKuzR2mHtddduFM8I2G2G/cuJEA0LPPPluljn7zSjR47ljsYLFiEoZIVhGv7s6ytLSUTpw4QcnJ\nyZSXl1djZZv2tPKyWK9+jm28ziZE9913H6WkpNCKFSt8nccw4eJVrERl2vime/futHnz5lhXg0kA\niouLIxov0SkrK8P27dvRv39/rFq1Cp06dYp6xFpZWVncRMEdPHgQ1113He69917MmzevRsosKCgI\na1yRqR0IIbYQUXe3dBxgUcepbbPsdaGKdGA+MzMTGzZsQEpKCnr16hUSxOBGtFd3kHhZcUJ+r37r\n0K5dOzz55JOYP38+Nm3aFFb9/BIvQlXdgT5MZLBlxcTVk31143Stts9uvvlmCCHw2WefOeYdiYUQ\nLYvQ73dpSn/q1Cl06NABV111FQoLCyGEiCi/unR/Mf5hy4rxTCJ1JJE+/Tpdq+2zu+66CyNHjnTN\ne+3atZ7qoFt7xcXF2LNnj+t5NitYPe7lu1RXnTClT09Px0svvYRrr70W586dc81PxSmknmEiwsvA\nVqxfHGBR+5AD/zUxsbg6AiHUwAW/+bttuuhlb6zqCu6QUXHh5B+tdf3icVsapvoARwMyiYaXPaqi\nla8fIuk89ZDocMPBvdRBnjd16lRrPl72ovJ6vX7Cvat7x+NoMH78+FhXoU4S12IF4DYAewHsAzDR\nLT2LFeMFPx16uES6VbtTHatjpXg/+YZLdeRvCt2vaWJdfl3Bq1jV+JiVECIZwEwA/QF0AjBYCNGp\npuvBxDfquI5tnEof+/ES3GAaP5k2bZrnujgFQcjP1Ag8WXcvUYluARZyySZTAIMTrVq1cvzcS3Sh\nE3q99fqEE5GZmZkZ1SkI4RDr8plQajwaUAjRC8CzRJQb+H8SABDR/7Odw9GATCIzbdo05OXlAXCO\nGPQaNXf77bcjPz/fNT+GSQTiORowC0CJ8v/hwDGGiTuisYCqFCrA2frThcpWthQqt/zciNSiYpia\npF6sK2BDCDESgIwXPiWE2BvL+sQJzQCUx7oScQK3xSXCbosHHnggylWJOXxfXCJR2uIqL4liIVal\nAK5U/m8TOBYCEb0O4PWaqlQiIITY7MVcrgtwW1yC2+IS3BaXqG1tEQs34BcAOgghrhZC1AcwCMCH\nMagHwzAMkyDUuGVFRBeEEKMBFABIBjCHiHbVdD0YhmGYxCEmY1ZEtALAiliUneCwW/QS3BaX4La4\nBLfFJWpVWyTEQrYMwzBM3YYXsmUYhmHiHharOEIIMUYIsVMIsUsI8XjgWBchRKEQYocQYpkQ4jIl\n/SQhxD4hxF4hRK2aGeqnLYQQ7YQQZ4UQ2wKvWbGtfWQIIeYIIb4RQuxUjl0hhFglhPgy8Pdy5TPj\nfSCE6BZoq31CiNeEn70+4ogotsf6wDF5n7So6WuJFD9tIYRoKoRYJ4Q4JYSYoeWTePeGlzWZ+FUj\n6yXeCGAngIaoHEtcDeAaVEZP/iyQZjiAPwXedwLwTwANAFwN4CsAybG+jhi1RTsAO2Nd7yhe/y0A\nstVrAvAiAutoApgI4AW3+wDAPwD0BCAAfASgf6yvLcbtsR5A91hfTw22RSMAvQH8EcAMLZ+EuzfY\nsoofrgdQRERniOgCgA0A7gZwLYCNgTSrANwTeP8bAO8Q0Y9EdACViwLn1HCdqwu/bVGrIKKNAI5p\nh38D4K3A+7cA3Kkcr3IfCCFaA7iMiDZRZe/0N+WchCIa7VEjFa0B/LQFEZ0mok8B/KAmTtR7g8Uq\nftgJoE/AdG8I4P+gcvL0LlTejABwHy5NqK7Ny1b5bQsAuDrg2tkghOhTs9WtEVoS0ZHA+6MAWgbe\n2+6DrMB7/XhtwW97SN4K3CeTE8L15Q1bW9hIyHuDxSpOIKJ/AXgBwMcAVgLYBuAiKt1djwghtgBo\nDMDf1q0JSBhtcQRAWyL6CYAnACxQx/ZqG4GnYQ7jDeCjPR4gohsA9Am8flutFYsBtfneYLGKI4jo\nr0TUjYhuAfAdgH8T0R4i+hURdQOwEJU+eMDjslWJip+2CLh8vg283xI4fm2s6l5N/CfgvpFunG8C\nx233QWngvX68tuC3PUBE8u9JAAtQe9yDtrawkZD3BotVHCGjk4QQbVE5RrNAOZYE4GkAMtLtQwCD\nhBANhBBXA+iAykHTWoGfthBCNBeV+6RBCPE/qGyL/bGodzXyIYCHAu8fArBUOV7lPgi4hU4IIXoG\n3F1DlXNqA77aQwhRTwjRDACEECkAbkelu7k2YGsLIwl7b8Q6woNfl14APgGwG5XRTLcGjo0B8O/A\n63kEJnIHPstDpRWxFwkQzVNdbYHKQItdqHQXFgO4I9b1j/DaF6LStXkeleMJIwA0BbAGwJeojI68\nwu0+ANAdlR3yVwBmqPdOIr2i0R6ojIzbAmB74F6ZjgSMng2jLQ6iMiDjVCB9p0S9N3gFC4ZhGCbu\nYTcgwzAME/ewWDEMwzBxD4sVwzAME/ewWDEMwzBxD4sVwzAME/ewWDEMwzBxD4sVwzAME/ewWDFM\nnBDYb+m6wPum6p5FDFPXYbFimPjhGlSuzgEANwHYEcO6MExcwWLFMHGAEOIqAKVEVBE4dBMqlwZi\nGAYsVgwTL3RBqDh1A4sVwwRhsWKY+OAnAFIBQAjRAZWbTLIbkGECsFgxTHzQBUCSEOKfAP4vKlec\nf8j5FIapO/Cq6wwTBwghvgSQTZUbAzIMo8GWFcPEGCFEY1TuSM5CxTAW2LJiGIZh4h62rBiGYZi4\nh8WKYRiGiXtYrBiGYZi4h8WKYRiGiXtYrBiGYZi4h8WKYRiGiXtYrBiGYZi4h8WKYRiGiXv+P19y\nmhxegkPHAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f9a9550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from astroML.plotting import plot_mcmc\n",
"fig = plt.figure()\n",
"ax = plot_mcmc(sample.T, fig=fig, labels=[r'$\\mu$', r'$\\sigma$'], colors='k')\n",
"ax[0].plot(sample[:, 0], sample[:, 1], ',k', alpha=0.1)\n",
"ax[0].plot([mu_true], [sigma_true], 'o', color='red', ms=10);"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"The red dot indicates ground truth (from our problem setup), and the contours indicate one and two standard deviations (68% and 95% confidence levels). In other words, based on this analysis we are 68% confident that the model lies within the inner contour, and 95% confident that the model lies within the outer contour.\n",
"\n",
"Note here that $\\sigma = 0$ is consistent with our data within two standard deviations: that is, depending on the certainty threshold you're interested in, our data are not enough to confidently rule out the possibility of a non-varying source!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"***The other thing to notice is that this posterior is definitely *not* Gaussian***: this can be seen by the lack of symmetry in the vertical direction.\n",
"\n",
"That means that the Gaussian approximation used within the frequentist approach may not reflect the true uncertainties in the result. This isn't an issue with frequentism itself (i.e. there are certainly ways to account for non-Gaussianity within the frequentist paradigm), but the *vast majority* of commonly applied frequentist techniques make the explicit or implicit assumption of Gaussianity of the distribution.\n",
"\n",
"Bayesian approaches generally don't require such assumptions."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"(Side note on priors: there are good arguments that a flat prior on $\\sigma$ subtley biases the calculation in this case: i.e. a flat prior is not necessarily non-informative in the case of scale factors like $\\sigma$. There are interesting arguments to be made that the [Jeffreys Prior](http://en.wikipedia.org/wiki/Jeffreys_prior) would be more applicable. Here I believe the Jeffreys prior is not suitable, because $\\sigma$ is not a true scale factor (i.e. the Gaussian has contributions from $e_i$ as well). On this question, I'll have to defer to others who have more expertise. Note that subtle — some would say subjective — questions like this are among the features of Bayesian analysis that frequentists take issue with)."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Conclusion"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Philosophical differences underlying frequentism and Bayesianism lead to fundamentally different approaches to simple problems, which nonetheless can often yield similar or even identical results."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"***The root of all differences is in a different interpretation of probability***:\n",
"\n",
"- Frequentism considers probabilities to be relative frequencies of a large number of (real or hypothetical) events.\n",
"- Bayesianism considers probabilities to measure degrees of knowledge (belief) about something.\n",
"\n",
"... and differences in methodology follow:\n",
"\n",
"- Frequentist analyses generally proceed through use of point estimates and maximum likelihood approaches.\n",
"- Bayesian analyses generally compute the posterior either directly or through some version of MCMC sampling."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"As we've seen, in simple problems the two approaches can yield similar results.\n",
"\n",
"But as data and models grow in complexity, the two approaches can diverge greatly. We turn to that next..."
]
}
],
"metadata": {
"anaconda-cloud": {},
"celltoolbar": "Slideshow",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
},
"livereveal": {
"scroll": true,
"start_slideshow_at": "selected",
"theme": "sky"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
openconnectome/ndreg | notebooks/demo-ndreg-2.ipynb | 1 | 3096623 | null | apache-2.0 |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/gapic/automl/showcase_automl_text_multi-label_classification_online.ipynb | 1 | 62331 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "copyright"
},
"outputs": [],
"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": "title"
},
"source": [
"# Vertex client library: AutoML text multi-label classification model for online prediction\n",
"\n",
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_text_multi-label_classification_online.ipynb\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_text_multi-label_classification_online.ipynb\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
"</table>\n",
"<br/><br/><br/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "overview:automl"
},
"source": [
"## Overview\n",
"\n",
"\n",
"This tutorial demonstrates how to use the Vertex client library for Python to create text multi-label classification models and do online prediction using Google Cloud's [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dataset:mcdonalds,tmcn"
},
"source": [
"### Dataset\n",
"\n",
"The dataset used for this tutorial is the [McDonald's Service](https://TODO). The version of the dataset you will use in this tutorial is stored in a public Cloud Storage bucket."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "objective:automl,training,online_prediction"
},
"source": [
"### Objective\n",
"\n",
"In this tutorial, you create an AutoML text multi-label classification model and deploy for online prediction from a Python script using the Vertex client library. You can alternatively create and deploy models using the `gcloud` command-line tool or online using the Google Cloud Console.\n",
"\n",
"The steps performed include:\n",
"\n",
"- Create a Vertex `Dataset` resource.\n",
"- Train the model.\n",
"- View the model evaluation.\n",
"- Deploy the `Model` resource to a serving `Endpoint` resource.\n",
"- Make a prediction.\n",
"- Undeploy the `Model`."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "costs"
},
"source": [
"### Costs\n",
"\n",
"This tutorial uses billable components of Google Cloud (GCP):\n",
"\n",
"* Vertex AI\n",
"* Cloud Storage\n",
"\n",
"Learn about [Vertex AI\n",
"pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage\n",
"pricing](https://cloud.google.com/storage/pricing), and use the [Pricing\n",
"Calculator](https://cloud.google.com/products/calculator/)\n",
"to generate a cost estimate based on your projected usage."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "install_aip"
},
"source": [
"## Installation\n",
"\n",
"Install the latest version of Vertex client library."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "install_aip"
},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"\n",
"# Google Cloud Notebook\n",
"if os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\n",
" USER_FLAG = \"--user\"\n",
"else:\n",
" USER_FLAG = \"\"\n",
"\n",
"! pip3 install -U google-cloud-aiplatform $USER_FLAG"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "install_storage"
},
"source": [
"Install the latest GA version of *google-cloud-storage* library as well."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "install_storage"
},
"outputs": [],
"source": [
"! pip3 install -U google-cloud-storage $USER_FLAG"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "restart"
},
"source": [
"### Restart the kernel\n",
"\n",
"Once you've installed the Vertex client library and Google *cloud-storage*, you need to restart the notebook kernel so it can find the packages."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "restart"
},
"outputs": [],
"source": [
"if not os.getenv(\"IS_TESTING\"):\n",
" # Automatically restart kernel after installs\n",
" import IPython\n",
"\n",
" app = IPython.Application.instance()\n",
" app.kernel.do_shutdown(True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "before_you_begin"
},
"source": [
"## Before you begin\n",
"\n",
"### GPU runtime\n",
"\n",
"*Make sure you're running this notebook in a GPU runtime if you have that option. In Colab, select* **Runtime > Change Runtime Type > GPU**\n",
"\n",
"### Set up your Google Cloud project\n",
"\n",
"**The following steps are required, regardless of your notebook environment.**\n",
"\n",
"1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
"\n",
"2. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)\n",
"\n",
"3. [Enable the Vertex APIs and Compute Engine APIs.](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component)\n",
"\n",
"4. [The Google Cloud SDK](https://cloud.google.com/sdk) is already installed in Google Cloud Notebook.\n",
"\n",
"5. Enter your project ID in the cell below. Then run the cell to make sure the\n",
"Cloud SDK uses the right project for all the commands in this notebook.\n",
"\n",
"**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "set_project_id"
},
"outputs": [],
"source": [
"PROJECT_ID = \"[your-project-id]\" # @param {type:\"string\"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "autoset_project_id"
},
"outputs": [],
"source": [
"if PROJECT_ID == \"\" or PROJECT_ID is None or PROJECT_ID == \"[your-project-id]\":\n",
" # Get your GCP project id from gcloud\n",
" shell_output = !gcloud config list --format 'value(core.project)' 2>/dev/null\n",
" PROJECT_ID = shell_output[0]\n",
" print(\"Project ID:\", PROJECT_ID)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "set_gcloud_project_id"
},
"outputs": [],
"source": [
"! gcloud config set project $PROJECT_ID"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "region"
},
"source": [
"#### Region\n",
"\n",
"You can also change the `REGION` variable, which is used for operations\n",
"throughout the rest of this notebook. Below are regions supported for Vertex. We recommend that you choose the region closest to you.\n",
"\n",
"- Americas: `us-central1`\n",
"- Europe: `europe-west4`\n",
"- Asia Pacific: `asia-east1`\n",
"\n",
"You may not use a multi-regional bucket for training with Vertex. Not all regions provide support for all Vertex services. For the latest support per region, see the [Vertex locations documentation](https://cloud.google.com/vertex-ai/docs/general/locations)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "region"
},
"outputs": [],
"source": [
"REGION = \"us-central1\" # @param {type: \"string\"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "timestamp"
},
"source": [
"#### Timestamp\n",
"\n",
"If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append onto the name of resources which will be created in this tutorial."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "timestamp"
},
"outputs": [],
"source": [
"from datetime import datetime\n",
"\n",
"TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gcp_authenticate"
},
"source": [
"### Authenticate your Google Cloud account\n",
"\n",
"**If you are using Google Cloud Notebook**, your environment is already authenticated. Skip this step.\n",
"\n",
"**If you are using Colab**, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.\n",
"\n",
"**Otherwise**, follow these steps:\n",
"\n",
"In the Cloud Console, go to the [Create service account key](https://console.cloud.google.com/apis/credentials/serviceaccountkey) page.\n",
"\n",
"**Click Create service account**.\n",
"\n",
"In the **Service account name** field, enter a name, and click **Create**.\n",
"\n",
"In the **Grant this service account access to project** section, click the Role drop-down list. Type \"Vertex\" into the filter box, and select **Vertex Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n",
"\n",
"Click Create. A JSON file that contains your key downloads to your local environment.\n",
"\n",
"Enter the path to your service account key as the GOOGLE_APPLICATION_CREDENTIALS variable in the cell below and run the cell."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "gcp_authenticate"
},
"outputs": [],
"source": [
"# If you are running this notebook in Colab, run this cell and follow the\n",
"# instructions to authenticate your GCP account. This provides access to your\n",
"# Cloud Storage bucket and lets you submit training jobs and prediction\n",
"# requests.\n",
"\n",
"# If on Google Cloud Notebook, then don't execute this code\n",
"if not os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\n",
" if \"google.colab\" in sys.modules:\n",
" from google.colab import auth as google_auth\n",
"\n",
" google_auth.authenticate_user()\n",
"\n",
" # If you are running this notebook locally, replace the string below with the\n",
" # path to your service account key and run this cell to authenticate your GCP\n",
" # account.\n",
" elif not os.getenv(\"IS_TESTING\"):\n",
" %env GOOGLE_APPLICATION_CREDENTIALS ''"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "setup_vars"
},
"source": [
"### Set up variables\n",
"\n",
"Next, set up some variables used throughout the tutorial.\n",
"### Import libraries and define constants"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "import_aip:protobuf"
},
"source": [
"#### Import Vertex client library\n",
"\n",
"Import the Vertex client library into our Python environment."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "import_aip:protobuf"
},
"outputs": [],
"source": [
"import time\n",
"\n",
"from google.cloud.aiplatform import gapic as aip\n",
"from google.protobuf import json_format\n",
"from google.protobuf.json_format import MessageToJson, ParseDict\n",
"from google.protobuf.struct_pb2 import Struct, Value"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aip_constants"
},
"source": [
"#### Vertex constants\n",
"\n",
"Setup up the following constants for Vertex:\n",
"\n",
"- `API_ENDPOINT`: The Vertex API service endpoint for dataset, model, job, pipeline and endpoint services.\n",
"- `PARENT`: The Vertex location root path for dataset, model, job, pipeline and endpoint resources."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "aip_constants"
},
"outputs": [],
"source": [
"# API service endpoint\n",
"API_ENDPOINT = \"{}-aiplatform.googleapis.com\".format(REGION)\n",
"\n",
"# Vertex location root path for your dataset, model and endpoint resources\n",
"PARENT = \"projects/\" + PROJECT_ID + \"/locations/\" + REGION"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "automl_constants"
},
"source": [
"#### AutoML constants\n",
"\n",
"Set constants unique to AutoML datasets and training:\n",
"\n",
"- Dataset Schemas: Tells the `Dataset` resource service which type of dataset it is.\n",
"- Data Labeling (Annotations) Schemas: Tells the `Dataset` resource service how the data is labeled (annotated).\n",
"- Dataset Training Schemas: Tells the `Pipeline` resource service the task (e.g., classification) to train the model for."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "automl_constants:tmcn"
},
"outputs": [],
"source": [
"# Text Dataset type\n",
"DATA_SCHEMA = \"gs://google-cloud-aiplatform/schema/dataset/metadata/text_1.0.0.yaml\"\n",
"# Text Labeling type\n",
"LABEL_SCHEMA = \"gs://google-cloud-aiplatform/schema/dataset/ioformat/text_classification_multi_label_io_format_1.0.0.yaml\"\n",
"# Text Training task\n",
"TRAINING_SCHEMA = \"gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_text_classification_1.0.0.yaml\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tutorial_start:automl"
},
"source": [
"# Tutorial\n",
"\n",
"Now you are ready to start creating your own AutoML text multi-label classification model."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "clients:automl,online_prediction"
},
"source": [
"## Set up clients\n",
"\n",
"The Vertex client library works as a client/server model. On your side (the Python script) you will create a client that sends requests and receives responses from the Vertex server.\n",
"\n",
"You will use different clients in this tutorial for different steps in the workflow. So set them all up upfront.\n",
"\n",
"- Dataset Service for `Dataset` resources.\n",
"- Model Service for `Model` resources.\n",
"- Pipeline Service for training.\n",
"- Endpoint Service for deployment.\n",
"- Prediction Service for serving."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "clients:automl,online_prediction"
},
"outputs": [],
"source": [
"# client options same for all services\n",
"client_options = {\"api_endpoint\": API_ENDPOINT}\n",
"\n",
"\n",
"def create_dataset_client():\n",
" client = aip.DatasetServiceClient(client_options=client_options)\n",
" return client\n",
"\n",
"\n",
"def create_model_client():\n",
" client = aip.ModelServiceClient(client_options=client_options)\n",
" return client\n",
"\n",
"\n",
"def create_pipeline_client():\n",
" client = aip.PipelineServiceClient(client_options=client_options)\n",
" return client\n",
"\n",
"\n",
"def create_endpoint_client():\n",
" client = aip.EndpointServiceClient(client_options=client_options)\n",
" return client\n",
"\n",
"\n",
"def create_prediction_client():\n",
" client = aip.PredictionServiceClient(client_options=client_options)\n",
" return client\n",
"\n",
"\n",
"clients = {}\n",
"clients[\"dataset\"] = create_dataset_client()\n",
"clients[\"model\"] = create_model_client()\n",
"clients[\"pipeline\"] = create_pipeline_client()\n",
"clients[\"endpoint\"] = create_endpoint_client()\n",
"clients[\"prediction\"] = create_prediction_client()\n",
"\n",
"for client in clients.items():\n",
" print(client)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_aip_dataset"
},
"source": [
"## Dataset\n",
"\n",
"Now that your clients are ready, your first step in training a model is to create a managed dataset instance, and then upload your labeled data to it.\n",
"\n",
"### Create `Dataset` resource instance\n",
"\n",
"Use the helper function `create_dataset` to create the instance of a `Dataset` resource. This function does the following:\n",
"\n",
"1. Uses the dataset client service.\n",
"2. Creates an Vertex `Dataset` resource (`aip.Dataset`), with the following parameters:\n",
" - `display_name`: The human-readable name you choose to give it.\n",
" - `metadata_schema_uri`: The schema for the dataset type.\n",
"3. Calls the client dataset service method `create_dataset`, with the following parameters:\n",
" - `parent`: The Vertex location root path for your `Database`, `Model` and `Endpoint` resources.\n",
" - `dataset`: The Vertex dataset object instance you created.\n",
"4. The method returns an `operation` object.\n",
"\n",
"An `operation` object is how Vertex handles asynchronous calls for long running operations. While this step usually goes fast, when you first use it in your project, there is a longer delay due to provisioning.\n",
"\n",
"You can use the `operation` object to get status on the operation (e.g., create `Dataset` resource) or to cancel the operation, by invoking an operation method:\n",
"\n",
"| Method | Description |\n",
"| ----------- | ----------- |\n",
"| result() | Waits for the operation to complete and returns a result object in JSON format. |\n",
"| running() | Returns True/False on whether the operation is still running. |\n",
"| done() | Returns True/False on whether the operation is completed. |\n",
"| canceled() | Returns True/False on whether the operation was canceled. |\n",
"| cancel() | Cancels the operation (this may take up to 30 seconds). |"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "create_aip_dataset"
},
"outputs": [],
"source": [
"TIMEOUT = 90\n",
"\n",
"\n",
"def create_dataset(name, schema, labels=None, timeout=TIMEOUT):\n",
" start_time = time.time()\n",
" try:\n",
" dataset = aip.Dataset(\n",
" display_name=name, metadata_schema_uri=schema, labels=labels\n",
" )\n",
"\n",
" operation = clients[\"dataset\"].create_dataset(parent=PARENT, dataset=dataset)\n",
" print(\"Long running operation:\", operation.operation.name)\n",
" result = operation.result(timeout=TIMEOUT)\n",
" print(\"time:\", time.time() - start_time)\n",
" print(\"response\")\n",
" print(\" name:\", result.name)\n",
" print(\" display_name:\", result.display_name)\n",
" print(\" metadata_schema_uri:\", result.metadata_schema_uri)\n",
" print(\" metadata:\", dict(result.metadata))\n",
" print(\" create_time:\", result.create_time)\n",
" print(\" update_time:\", result.update_time)\n",
" print(\" etag:\", result.etag)\n",
" print(\" labels:\", dict(result.labels))\n",
" return result\n",
" except Exception as e:\n",
" print(\"exception:\", e)\n",
" return None\n",
"\n",
"\n",
"result = create_dataset(\"mcdonalds-\" + TIMESTAMP, DATA_SCHEMA)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dataset_id:result"
},
"source": [
"Now save the unique dataset identifier for the `Dataset` resource instance you created."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "dataset_id:result"
},
"outputs": [],
"source": [
"# The full unique ID for the dataset\n",
"dataset_id = result.name\n",
"# The short numeric ID for the dataset\n",
"dataset_short_id = dataset_id.split(\"/\")[-1]\n",
"\n",
"print(dataset_id)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "data_preparation:text,u_dataset"
},
"source": [
"### Data preparation\n",
"\n",
"The Vertex `Dataset` resource for text has a couple of requirements for your text data.\n",
"\n",
"- Text examples must be stored in a CSV or JSONL file."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "data_import_format:tmcn,u_dataset,csv"
},
"source": [
"#### CSV\n",
"\n",
"For text multi-label classification, the CSV file has a few requirements:\n",
"\n",
"- No heading.\n",
"- First column is the text example.\n",
"- Remaining columns are the labels."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "import_file:u_dataset,csv"
},
"source": [
"#### Location of Cloud Storage training data.\n",
"\n",
"Now set the variable `IMPORT_FILE` to the location of the CSV index file in Cloud Storage."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "import_file:mcdonalds,csv,tmcn"
},
"outputs": [],
"source": [
"IMPORT_FILE = \"gs://ucaip-test-us-central1/dataset/ucaip_multi_tcn_dataset.csv\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "quick_peek:csv"
},
"source": [
"#### Quick peek at your data\n",
"\n",
"You will use a version of the McDonald's Service dataset that is stored in a public Cloud Storage bucket, using a CSV index file.\n",
"\n",
"Start by doing a quick peek at the data. You count the number of examples by counting the number of rows in the CSV index file (`wc -l`) and then peek at the first few rows."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "quick_peek:csv"
},
"outputs": [],
"source": [
"if \"IMPORT_FILES\" in globals():\n",
" FILE = IMPORT_FILES[0]\n",
"else:\n",
" FILE = IMPORT_FILE\n",
"\n",
"count = ! gsutil cat $FILE | wc -l\n",
"print(\"Number of Examples\", int(count[0]))\n",
"\n",
"print(\"First 10 rows\")\n",
"! gsutil cat $FILE | head"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "import_data"
},
"source": [
"### Import data\n",
"\n",
"Now, import the data into your Vertex Dataset resource. Use this helper function `import_data` to import the data. The function does the following:\n",
"\n",
"- Uses the `Dataset` client.\n",
"- Calls the client method `import_data`, with the following parameters:\n",
" - `name`: The human readable name you give to the `Dataset` resource (e.g., mcdonalds).\n",
" - `import_configs`: The import configuration.\n",
"\n",
"- `import_configs`: A Python list containing a dictionary, with the key/value entries:\n",
" - `gcs_sources`: A list of URIs to the paths of the one or more index files.\n",
" - `import_schema_uri`: The schema identifying the labeling type.\n",
"\n",
"The `import_data()` method returns a long running `operation` object. This will take a few minutes to complete. If you are in a live tutorial, this would be a good time to ask questions, or take a personal break."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "import_data"
},
"outputs": [],
"source": [
"def import_data(dataset, gcs_sources, schema):\n",
" config = [{\"gcs_source\": {\"uris\": gcs_sources}, \"import_schema_uri\": schema}]\n",
" print(\"dataset:\", dataset_id)\n",
" start_time = time.time()\n",
" try:\n",
" operation = clients[\"dataset\"].import_data(\n",
" name=dataset_id, import_configs=config\n",
" )\n",
" print(\"Long running operation:\", operation.operation.name)\n",
"\n",
" result = operation.result()\n",
" print(\"result:\", result)\n",
" print(\"time:\", int(time.time() - start_time), \"secs\")\n",
" print(\"error:\", operation.exception())\n",
" print(\"meta :\", operation.metadata)\n",
" print(\n",
" \"after: running:\",\n",
" operation.running(),\n",
" \"done:\",\n",
" operation.done(),\n",
" \"cancelled:\",\n",
" operation.cancelled(),\n",
" )\n",
"\n",
" return operation\n",
" except Exception as e:\n",
" print(\"exception:\", e)\n",
" return None\n",
"\n",
"\n",
"import_data(dataset_id, [IMPORT_FILE], LABEL_SCHEMA)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "train_automl_model"
},
"source": [
"## Train the model\n",
"\n",
"Now train an AutoML text multi-label classification model using your Vertex `Dataset` resource. To train the model, do the following steps:\n",
"\n",
"1. Create an Vertex training pipeline for the `Dataset` resource.\n",
"2. Execute the pipeline to start the training."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_pipeline:automl"
},
"source": [
"### Create a training pipeline\n",
"\n",
"You may ask, what do we use a pipeline for? You typically use pipelines when the job (such as training) has multiple steps, generally in sequential order: do step A, do step B, etc. By putting the steps into a pipeline, we gain the benefits of:\n",
"\n",
"1. Being reusable for subsequent training jobs.\n",
"2. Can be containerized and ran as a batch job.\n",
"3. Can be distributed.\n",
"4. All the steps are associated with the same pipeline job for tracking progress.\n",
"\n",
"Use this helper function `create_pipeline`, which takes the following parameters:\n",
"\n",
"- `pipeline_name`: A human readable name for the pipeline job.\n",
"- `model_name`: A human readable name for the model.\n",
"- `dataset`: The Vertex fully qualified dataset identifier.\n",
"- `schema`: The dataset labeling (annotation) training schema.\n",
"- `task`: A dictionary describing the requirements for the training job.\n",
"\n",
"The helper function calls the `Pipeline` client service'smethod `create_pipeline`, which takes the following parameters:\n",
"\n",
"- `parent`: The Vertex location root path for your `Dataset`, `Model` and `Endpoint` resources.\n",
"- `training_pipeline`: the full specification for the pipeline training job.\n",
"\n",
"Let's look now deeper into the *minimal* requirements for constructing a `training_pipeline` specification:\n",
"\n",
"- `display_name`: A human readable name for the pipeline job.\n",
"- `training_task_definition`: The dataset labeling (annotation) training schema.\n",
"- `training_task_inputs`: A dictionary describing the requirements for the training job.\n",
"- `model_to_upload`: A human readable name for the model.\n",
"- `input_data_config`: The dataset specification.\n",
" - `dataset_id`: The Vertex dataset identifier only (non-fully qualified) -- this is the last part of the fully-qualified identifier.\n",
" - `fraction_split`: If specified, the percentages of the dataset to use for training, test and validation. Otherwise, the percentages are automatically selected by AutoML."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "create_pipeline:automl"
},
"outputs": [],
"source": [
"def create_pipeline(pipeline_name, model_name, dataset, schema, task):\n",
"\n",
" dataset_id = dataset.split(\"/\")[-1]\n",
"\n",
" input_config = {\n",
" \"dataset_id\": dataset_id,\n",
" \"fraction_split\": {\n",
" \"training_fraction\": 0.8,\n",
" \"validation_fraction\": 0.1,\n",
" \"test_fraction\": 0.1,\n",
" },\n",
" }\n",
"\n",
" training_pipeline = {\n",
" \"display_name\": pipeline_name,\n",
" \"training_task_definition\": schema,\n",
" \"training_task_inputs\": task,\n",
" \"input_data_config\": input_config,\n",
" \"model_to_upload\": {\"display_name\": model_name},\n",
" }\n",
"\n",
" try:\n",
" pipeline = clients[\"pipeline\"].create_training_pipeline(\n",
" parent=PARENT, training_pipeline=training_pipeline\n",
" )\n",
" print(pipeline)\n",
" except Exception as e:\n",
" print(\"exception:\", e)\n",
" return None\n",
" return pipeline"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "task_requirements:automl,tmcn"
},
"source": [
"### Construct the task requirements\n",
"\n",
"Next, construct the task requirements. Unlike other parameters which take a Python (JSON-like) dictionary, the `task` field takes a Google protobuf Struct, which is very similar to a Python dictionary. Use the `json_format.ParseDict` method for the conversion.\n",
"\n",
"The minimal fields you need to specify are:\n",
"\n",
"- `multi_label`: Whether True/False this is a multi-label (vs single) classification.\n",
"\n",
"Finally, create the pipeline by calling the helper function `create_pipeline`, which returns an instance of a training pipeline object."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "task_requirements:automl,tmcn"
},
"outputs": [],
"source": [
"PIPE_NAME = \"mcdonalds_pipe-\" + TIMESTAMP\n",
"MODEL_NAME = \"mcdonalds_model-\" + TIMESTAMP\n",
"\n",
"task = json_format.ParseDict(\n",
" {\n",
" \"multi_label\": True,\n",
" },\n",
" Value(),\n",
")\n",
"\n",
"response = create_pipeline(PIPE_NAME, MODEL_NAME, dataset_id, TRAINING_SCHEMA, task)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pipeline_id:response"
},
"source": [
"Now save the unique identifier of the training pipeline you created."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "pipeline_id:response"
},
"outputs": [],
"source": [
"# The full unique ID for the pipeline\n",
"pipeline_id = response.name\n",
"# The short numeric ID for the pipeline\n",
"pipeline_short_id = pipeline_id.split(\"/\")[-1]\n",
"\n",
"print(pipeline_id)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "get_training_pipeline"
},
"source": [
"### Get information on a training pipeline\n",
"\n",
"Now get pipeline information for just this training pipeline instance. The helper function gets the job information for just this job by calling the the job client service's `get_training_pipeline` method, with the following parameter:\n",
"\n",
"- `name`: The Vertex fully qualified pipeline identifier.\n",
"\n",
"When the model is done training, the pipeline state will be `PIPELINE_STATE_SUCCEEDED`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "get_training_pipeline"
},
"outputs": [],
"source": [
"def get_training_pipeline(name, silent=False):\n",
" response = clients[\"pipeline\"].get_training_pipeline(name=name)\n",
" if silent:\n",
" return response\n",
"\n",
" print(\"pipeline\")\n",
" print(\" name:\", response.name)\n",
" print(\" display_name:\", response.display_name)\n",
" print(\" state:\", response.state)\n",
" print(\" training_task_definition:\", response.training_task_definition)\n",
" print(\" training_task_inputs:\", dict(response.training_task_inputs))\n",
" print(\" create_time:\", response.create_time)\n",
" print(\" start_time:\", response.start_time)\n",
" print(\" end_time:\", response.end_time)\n",
" print(\" update_time:\", response.update_time)\n",
" print(\" labels:\", dict(response.labels))\n",
" return response\n",
"\n",
"\n",
"response = get_training_pipeline(pipeline_id)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wait_training_complete"
},
"source": [
"# Deployment\n",
"\n",
"Training the above model may take upwards of 240 minutes time.\n",
"\n",
"Once your model is done training, you can calculate the actual time it took to train the model by subtracting `end_time` from `start_time`. For your model, you will need to know the fully qualified Vertex Model resource identifier, which the pipeline service assigned to it. You can get this from the returned pipeline instance as the field `model_to_deploy.name`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wait_training_complete"
},
"outputs": [],
"source": [
"while True:\n",
" response = get_training_pipeline(pipeline_id, True)\n",
" if response.state != aip.PipelineState.PIPELINE_STATE_SUCCEEDED:\n",
" print(\"Training job has not completed:\", response.state)\n",
" model_to_deploy_id = None\n",
" if response.state == aip.PipelineState.PIPELINE_STATE_FAILED:\n",
" raise Exception(\"Training Job Failed\")\n",
" else:\n",
" model_to_deploy = response.model_to_upload\n",
" model_to_deploy_id = model_to_deploy.name\n",
" print(\"Training Time:\", response.end_time - response.start_time)\n",
" break\n",
" time.sleep(60)\n",
"\n",
"print(\"model to deploy:\", model_to_deploy_id)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "model_information"
},
"source": [
"## Model information\n",
"\n",
"Now that your model is trained, you can get some information on your model."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "evaluate_the_model:automl"
},
"source": [
"## Evaluate the Model resource\n",
"\n",
"Now find out how good the model service believes your model is. As part of training, some portion of the dataset was set aside as the test (holdout) data, which is used by the pipeline service to evaluate the model."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "list_model_evaluations:automl,tmcn"
},
"source": [
"### List evaluations for all slices\n",
"\n",
"Use this helper function `list_model_evaluations`, which takes the following parameter:\n",
"\n",
"- `name`: The Vertex fully qualified model identifier for the `Model` resource.\n",
"\n",
"This helper function uses the model client service's `list_model_evaluations` method, which takes the same parameter. The response object from the call is a list, where each element is an evaluation metric.\n",
"\n",
"For each evaluation (you probably only have one) we then print all the key names for each metric in the evaluation, and for a small set (`logLoss` and `auPrc`) you will print the result."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "list_model_evaluations:automl,tmcn"
},
"outputs": [],
"source": [
"def list_model_evaluations(name):\n",
" response = clients[\"model\"].list_model_evaluations(parent=name)\n",
" for evaluation in response:\n",
" print(\"model_evaluation\")\n",
" print(\" name:\", evaluation.name)\n",
" print(\" metrics_schema_uri:\", evaluation.metrics_schema_uri)\n",
" metrics = json_format.MessageToDict(evaluation._pb.metrics)\n",
" for metric in metrics.keys():\n",
" print(metric)\n",
" print(\"logloss\", metrics[\"logLoss\"])\n",
" print(\"auPrc\", metrics[\"auPrc\"])\n",
"\n",
" return evaluation.name\n",
"\n",
"\n",
"last_evaluation = list_model_evaluations(model_to_deploy_id)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_endpoint:automl"
},
"source": [
"## Deploy the `Model` resource\n",
"\n",
"Now deploy the trained Vertex `Model` resource you created with AutoML. This requires two steps:\n",
"\n",
"1. Create an `Endpoint` resource for deploying the `Model` resource to.\n",
"\n",
"2. Deploy the `Model` resource to the `Endpoint` resource."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_endpoint"
},
"source": [
"### Create an `Endpoint` resource\n",
"\n",
"Use this helper function `create_endpoint` to create an endpoint to deploy the model to for serving predictions, with the following parameter:\n",
"\n",
"- `display_name`: A human readable name for the `Endpoint` resource.\n",
"\n",
"The helper function uses the endpoint client service's `create_endpoint` method, which takes the following parameter:\n",
"\n",
"- `display_name`: A human readable name for the `Endpoint` resource.\n",
"\n",
"Creating an `Endpoint` resource returns a long running operation, since it may take a few moments to provision the `Endpoint` resource for serving. You call `response.result()`, which is a synchronous call and will return when the Endpoint resource is ready. The helper function returns the Vertex fully qualified identifier for the `Endpoint` resource: `response.name`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "create_endpoint"
},
"outputs": [],
"source": [
"ENDPOINT_NAME = \"mcdonalds_endpoint-\" + TIMESTAMP\n",
"\n",
"\n",
"def create_endpoint(display_name):\n",
" endpoint = {\"display_name\": display_name}\n",
" response = clients[\"endpoint\"].create_endpoint(parent=PARENT, endpoint=endpoint)\n",
" print(\"Long running operation:\", response.operation.name)\n",
"\n",
" result = response.result(timeout=300)\n",
" print(\"result\")\n",
" print(\" name:\", result.name)\n",
" print(\" display_name:\", result.display_name)\n",
" print(\" description:\", result.description)\n",
" print(\" labels:\", result.labels)\n",
" print(\" create_time:\", result.create_time)\n",
" print(\" update_time:\", result.update_time)\n",
" return result\n",
"\n",
"\n",
"result = create_endpoint(ENDPOINT_NAME)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "endpoint_id:result"
},
"source": [
"Now get the unique identifier for the `Endpoint` resource you created."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "endpoint_id:result"
},
"outputs": [],
"source": [
"# The full unique ID for the endpoint\n",
"endpoint_id = result.name\n",
"# The short numeric ID for the endpoint\n",
"endpoint_short_id = endpoint_id.split(\"/\")[-1]\n",
"\n",
"print(endpoint_id)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "instance_scaling"
},
"source": [
"### Compute instance scaling\n",
"\n",
"You have several choices on scaling the compute instances for handling your online prediction requests:\n",
"\n",
"- Single Instance: The online prediction requests are processed on a single compute instance.\n",
" - Set the minimum (`MIN_NODES`) and maximum (`MAX_NODES`) number of compute instances to one.\n",
"\n",
"- Manual Scaling: The online prediction requests are split across a fixed number of compute instances that you manually specified.\n",
" - Set the minimum (`MIN_NODES`) and maximum (`MAX_NODES`) number of compute instances to the same number of nodes. When a model is first deployed to the instance, the fixed number of compute instances are provisioned and online prediction requests are evenly distributed across them.\n",
"\n",
"- Auto Scaling: The online prediction requests are split across a scaleable number of compute instances.\n",
" - Set the minimum (`MIN_NODES`) number of compute instances to provision when a model is first deployed and to de-provision, and set the maximum (`MAX_NODES) number of compute instances to provision, depending on load conditions.\n",
"\n",
"The minimum number of compute instances corresponds to the field `min_replica_count` and the maximum number of compute instances corresponds to the field `max_replica_count`, in your subsequent deployment request."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "instance_scaling"
},
"outputs": [],
"source": [
"MIN_NODES = 1\n",
"MAX_NODES = 1"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "deploy_model:automatic"
},
"source": [
"### Deploy `Model` resource to the `Endpoint` resource\n",
"\n",
"Use this helper function `deploy_model` to deploy the `Model` resource to the `Endpoint` resource you created for serving predictions, with the following parameters:\n",
"\n",
"- `model`: The Vertex fully qualified model identifier of the model to upload (deploy) from the training pipeline.\n",
"- `deploy_model_display_name`: A human readable name for the deployed model.\n",
"- `endpoint`: The Vertex fully qualified endpoint identifier to deploy the model to.\n",
"\n",
"The helper function calls the `Endpoint` client service's method `deploy_model`, which takes the following parameters:\n",
"\n",
"- `endpoint`: The Vertex fully qualified `Endpoint` resource identifier to deploy the `Model` resource to.\n",
"- `deployed_model`: The requirements specification for deploying the model.\n",
"- `traffic_split`: Percent of traffic at the endpoint that goes to this model, which is specified as a dictionary of one or more key/value pairs.\n",
" - If only one model, then specify as **{ \"0\": 100 }**, where \"0\" refers to this model being uploaded and 100 means 100% of the traffic.\n",
" - If there are existing models on the endpoint, for which the traffic will be split, then use `model_id` to specify as **{ \"0\": percent, model_id: percent, ... }**, where `model_id` is the model id of an existing model to the deployed endpoint. The percents must add up to 100.\n",
"\n",
"Let's now dive deeper into the `deployed_model` parameter. This parameter is specified as a Python dictionary with the minimum required fields:\n",
"\n",
"- `model`: The Vertex fully qualified model identifier of the (upload) model to deploy.\n",
"- `display_name`: A human readable name for the deployed model.\n",
"- `disable_container_logging`: This disables logging of container events, such as execution failures (default is container logging is enabled). Container logging is typically enabled when debugging the deployment and then disabled when deployed for production.\n",
"- `automatic_resources`: This refers to how many redundant compute instances (replicas). For this example, we set it to one (no replication).\n",
"\n",
"#### Traffic Split\n",
"\n",
"Let's now dive deeper into the `traffic_split` parameter. This parameter is specified as a Python dictionary. This might at first be a tad bit confusing. Let me explain, you can deploy more than one instance of your model to an endpoint, and then set how much (percent) goes to each instance.\n",
"\n",
"Why would you do that? Perhaps you already have a previous version deployed in production -- let's call that v1. You got better model evaluation on v2, but you don't know for certain that it is really better until you deploy to production. So in the case of traffic split, you might want to deploy v2 to the same endpoint as v1, but it only get's say 10% of the traffic. That way, you can monitor how well it does without disrupting the majority of users -- until you make a final decision.\n",
"\n",
"#### Response\n",
"\n",
"The method returns a long running operation `response`. We will wait sychronously for the operation to complete by calling the `response.result()`, which will block until the model is deployed. If this is the first time a model is deployed to the endpoint, it may take a few additional minutes to complete provisioning of resources."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "deploy_model:automatic"
},
"outputs": [],
"source": [
"DEPLOYED_NAME = \"mcdonalds_deployed-\" + TIMESTAMP\n",
"\n",
"\n",
"def deploy_model(\n",
" model, deployed_model_display_name, endpoint, traffic_split={\"0\": 100}\n",
"):\n",
"\n",
" deployed_model = {\n",
" \"model\": model,\n",
" \"display_name\": deployed_model_display_name,\n",
" \"automatic_resources\": {\n",
" \"min_replica_count\": MIN_NODES,\n",
" \"max_replica_count\": MAX_NODES,\n",
" },\n",
" }\n",
"\n",
" response = clients[\"endpoint\"].deploy_model(\n",
" endpoint=endpoint, deployed_model=deployed_model, traffic_split=traffic_split\n",
" )\n",
"\n",
" print(\"Long running operation:\", response.operation.name)\n",
" result = response.result()\n",
" print(\"result\")\n",
" deployed_model = result.deployed_model\n",
" print(\" deployed_model\")\n",
" print(\" id:\", deployed_model.id)\n",
" print(\" model:\", deployed_model.model)\n",
" print(\" display_name:\", deployed_model.display_name)\n",
" print(\" create_time:\", deployed_model.create_time)\n",
"\n",
" return deployed_model.id\n",
"\n",
"\n",
"deployed_model_id = deploy_model(model_to_deploy_id, DEPLOYED_NAME, endpoint_id)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "make_prediction"
},
"source": [
"## Make a online prediction request\n",
"\n",
"Now do a online prediction to your deployed model."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "get_test_item"
},
"source": [
"### Get test item\n",
"\n",
"You will use an arbitrary example out of the dataset as a test item. Don't be concerned that the example was likely used in training the model -- we just want to demonstrate how to make a prediction."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "get_test_item:automl,tmcn,csv"
},
"outputs": [],
"source": [
"test_item = ! gsutil cat $IMPORT_FILE | head -n1\n",
"\n",
"cols = str(test_item[0]).split(\",\")\n",
"test_item = cols[0]\n",
"test_label = cols[1:]\n",
"\n",
"print(test_item, test_label)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "predict_item:automl,tmcn"
},
"source": [
"### Make a prediction\n",
"\n",
"Now you have a test item. Use this helper function `predict_item`, which takes the following parameters:\n",
"\n",
"- `filename`: The Cloud Storage path to the test item.\n",
"- `endpoint`: The Vertex fully qualified identifier for the `Endpoint` resource where the `Model` resource was deployed.\n",
"- `parameters_dict`: Additional filtering parameters for serving prediction results.\n",
"\n",
"This function calls the prediction client service's `predict` method with the following parameters:\n",
"\n",
"- `endpoint`: The Vertex fully qualified identifier for the `Endpoint` resource where the `Model` resource was deployed.\n",
"- `instances`: A list of instances (text files) to predict.\n",
"- `parameters`: Additional filtering parameters for serving prediction results. *Note*, text models do not support additional parameters.\n",
"\n",
"#### Request\n",
"\n",
"The format of each instance is:\n",
"\n",
" { 'content': text_item }\n",
"\n",
"Since the `predict()` method can take multiple items (instances), you send your single test item as a list of one test item. As a final step, you package the instances list into Google's protobuf format -- which is what you pass to the `predict()` method.\n",
"\n",
"#### Response\n",
"\n",
"The `response` object returns a list, where each element in the list corresponds to the corresponding text in the request. You will see in the output for each prediction -- in this case there is just one:\n",
"\n",
"- `confidences`: Confidence level in the prediction.\n",
"- `displayNames`: The predicted label."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "predict_item:automl,tmcn"
},
"outputs": [],
"source": [
"def predict_item(data, endpoint, parameters_dict):\n",
"\n",
" parameters = json_format.ParseDict(parameters_dict, Value())\n",
"\n",
" # The format of each instance should conform to the deployed model's prediction input schema.\n",
" instances_list = [{\"content\": data}]\n",
" instances = [json_format.ParseDict(s, Value()) for s in instances_list]\n",
"\n",
" response = clients[\"prediction\"].predict(\n",
" endpoint=endpoint, instances=instances, parameters=parameters\n",
" )\n",
" print(\"response\")\n",
" print(\" deployed_model_id:\", response.deployed_model_id)\n",
" predictions = response.predictions\n",
" print(\"predictions\")\n",
" for prediction in predictions:\n",
" print(\" prediction:\", dict(prediction))\n",
" return response\n",
"\n",
"\n",
"response = predict_item(test_item, endpoint_id, None)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "undeploy_model"
},
"source": [
"## Undeploy the `Model` resource\n",
"\n",
"Now undeploy your `Model` resource from the serving `Endpoint` resoure. Use this helper function `undeploy_model`, which takes the following parameters:\n",
"\n",
"- `deployed_model_id`: The model deployment identifier returned by the endpoint service when the `Model` resource was deployed to.\n",
"- `endpoint`: The Vertex fully qualified identifier for the `Endpoint` resource where the `Model` is deployed to.\n",
"\n",
"This function calls the endpoint client service's method `undeploy_model`, with the following parameters:\n",
"\n",
"- `deployed_model_id`: The model deployment identifier returned by the endpoint service when the `Model` resource was deployed.\n",
"- `endpoint`: The Vertex fully qualified identifier for the `Endpoint` resource where the `Model` resource is deployed.\n",
"- `traffic_split`: How to split traffic among the remaining deployed models on the `Endpoint` resource.\n",
"\n",
"Since this is the only deployed model on the `Endpoint` resource, you simply can leave `traffic_split` empty by setting it to {}."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "undeploy_model"
},
"outputs": [],
"source": [
"def undeploy_model(deployed_model_id, endpoint):\n",
" response = clients[\"endpoint\"].undeploy_model(\n",
" endpoint=endpoint, deployed_model_id=deployed_model_id, traffic_split={}\n",
" )\n",
" print(response)\n",
"\n",
"\n",
"undeploy_model(deployed_model_id, endpoint_id)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cleanup"
},
"source": [
"# Cleaning up\n",
"\n",
"To clean up all GCP resources used in this project, you can [delete the GCP\n",
"project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
"\n",
"Otherwise, you can delete the individual resources you created in this tutorial:\n",
"\n",
"- Dataset\n",
"- Pipeline\n",
"- Model\n",
"- Endpoint\n",
"- Batch Job\n",
"- Custom Job\n",
"- Hyperparameter Tuning Job\n",
"- Cloud Storage Bucket"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cleanup"
},
"outputs": [],
"source": [
"delete_dataset = True\n",
"delete_pipeline = True\n",
"delete_model = True\n",
"delete_endpoint = True\n",
"delete_batchjob = True\n",
"delete_customjob = True\n",
"delete_hptjob = True\n",
"delete_bucket = True\n",
"\n",
"# Delete the dataset using the Vertex fully qualified identifier for the dataset\n",
"try:\n",
" if delete_dataset and \"dataset_id\" in globals():\n",
" clients[\"dataset\"].delete_dataset(name=dataset_id)\n",
"except Exception as e:\n",
" print(e)\n",
"\n",
"# Delete the training pipeline using the Vertex fully qualified identifier for the pipeline\n",
"try:\n",
" if delete_pipeline and \"pipeline_id\" in globals():\n",
" clients[\"pipeline\"].delete_training_pipeline(name=pipeline_id)\n",
"except Exception as e:\n",
" print(e)\n",
"\n",
"# Delete the model using the Vertex fully qualified identifier for the model\n",
"try:\n",
" if delete_model and \"model_to_deploy_id\" in globals():\n",
" clients[\"model\"].delete_model(name=model_to_deploy_id)\n",
"except Exception as e:\n",
" print(e)\n",
"\n",
"# Delete the endpoint using the Vertex fully qualified identifier for the endpoint\n",
"try:\n",
" if delete_endpoint and \"endpoint_id\" in globals():\n",
" clients[\"endpoint\"].delete_endpoint(name=endpoint_id)\n",
"except Exception as e:\n",
" print(e)\n",
"\n",
"# Delete the batch job using the Vertex fully qualified identifier for the batch job\n",
"try:\n",
" if delete_batchjob and \"batch_job_id\" in globals():\n",
" clients[\"job\"].delete_batch_prediction_job(name=batch_job_id)\n",
"except Exception as e:\n",
" print(e)\n",
"\n",
"# Delete the custom job using the Vertex fully qualified identifier for the custom job\n",
"try:\n",
" if delete_customjob and \"job_id\" in globals():\n",
" clients[\"job\"].delete_custom_job(name=job_id)\n",
"except Exception as e:\n",
" print(e)\n",
"\n",
"# Delete the hyperparameter tuning job using the Vertex fully qualified identifier for the hyperparameter tuning job\n",
"try:\n",
" if delete_hptjob and \"hpt_job_id\" in globals():\n",
" clients[\"job\"].delete_hyperparameter_tuning_job(name=hpt_job_id)\n",
"except Exception as e:\n",
" print(e)\n",
"\n",
"if delete_bucket and \"BUCKET_NAME\" in globals():\n",
" ! gsutil rm -r $BUCKET_NAME"
]
}
],
"metadata": {
"colab": {
"name": "showcase_automl_text_multi-label_classification_online.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
gfrias/udacity | 2_traffic_signs/Traffic_Sign_Classifier.ipynb | 1 | 145123 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Self-Driving Car Engineer Nanodegree\n",
"\n",
"## Deep Learning\n",
"\n",
"## Project: Build a Traffic Sign Recognition Classifier\n",
"\n",
"In this notebook, a template is provided for you to implement your functionality in stages which is required to successfully complete this project. If additional code is required that cannot be included in the notebook, be sure that the Python code is successfully imported and included in your submission, if necessary. Sections that begin with **'Implementation'** in the header indicate where you should begin your implementation for your project. Note that some sections of implementation are optional, and will be marked with **'Optional'** in the header.\n",
"\n",
"In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a **'Question'** header. Carefully read each question and provide thorough answers in the following text boxes that begin with **'Answer:'**. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.\n",
"\n",
">**Note:** Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## Step 0: Load The Data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Image shape: (32, 32, 3)\n",
"\n",
"Training Set: 31367 samples\n",
"Validation Set: 7842 samples\n",
"Test Set: 12630 samples\n"
]
}
],
"source": [
"# Load pickled data\n",
"import pickle\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"# TODO: Fill this in based on where you saved the training and testing data\n",
"\n",
"training_file = '/Users/gfrias/Downloads/traffic-signs-data/train.p'\n",
"testing_file = '/Users/gfrias/Downloads/traffic-signs-data/test.p'\n",
"\n",
"with open(training_file, mode='rb') as f:\n",
" train = pickle.load(f)\n",
"with open(testing_file, mode='rb') as f:\n",
" test = pickle.load(f)\n",
" \n",
"X_train, y_train = train['features'], train['labels']\n",
"\n",
"X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=0.2, random_state=0)\n",
"X_test, y_test = test['features'], test['labels']\n",
"\n",
"assert(len(X_train) == len(y_train))\n",
"assert(len(X_validation) == len(y_validation))\n",
"assert(len(X_test) == len(y_test))\n",
"\n",
"print()\n",
"print(\"Image shape: {}\".format(X_train[0].shape))\n",
"print()\n",
"print(\"Training Set: {} samples\".format(len(X_train)))\n",
"print(\"Validation Set: {} samples\".format(len(X_validation)))\n",
"print(\"Test Set: {} samples\".format(len(X_test)))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"## Step 1: Dataset Summary & Exploration\n",
"\n",
"The pickled data is a dictionary with 4 key/value pairs:\n",
"\n",
"- `'features'` is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels).\n",
"- `'labels'` is a 2D array containing the label/class id of the traffic sign. The file `signnames.csv` contains id -> name mappings for each id.\n",
"- `'sizes'` is a list containing tuples, (width, height) representing the the original width and height the image.\n",
"- `'coords'` is a list containing tuples, (x1, y1, x2, y2) representing coordinates of a bounding box around the sign in the image. **THESE COORDINATES ASSUME THE ORIGINAL IMAGE. THE PICKLED DATA CONTAINS RESIZED VERSIONS (32 by 32) OF THESE IMAGES**\n",
"\n",
"Complete the basic data summary below."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Summary for feature set: train\n",
"-----------------------\n",
"features shape: (39209, 32, 32, 3)\n",
"labels shape: (39209,)\n",
"sizes shape: (39209, 2)\n",
"coord shape: (39209, 4)\n",
"\n",
"Summary for feature set: test\n",
"-----------------------\n",
"features shape: (12630, 32, 32, 3)\n",
"labels shape: (12630,)\n",
"sizes shape: (12630, 2)\n",
"coord shape: (12630, 4)\n",
"\n"
]
}
],
"source": [
"def print_summary(feature_set, feature_set_name):\n",
" print(\"Summary for feature set: {}\".format(feature_set_name))\n",
" print(\"-----------------------\")\n",
" print(\"features shape: {}\".format(feature_set['features'].shape))\n",
" print(\"labels shape: {}\".format(feature_set['labels'].shape))\n",
" print(\"sizes shape: {}\".format(feature_set['sizes'].shape))\n",
" print(\"coord shape: {}\".format(feature_set['coords'].shape))\n",
" print()\n",
" \n",
"print_summary(train, 'train')\n",
"print_summary(test, 'test')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Visualize the German Traffic Signs Dataset using the pickled file(s). This is open ended, suggestions include: plotting traffic sign images, plotting the count of each sign, etc.\n",
"\n",
"The [Matplotlib](http://matplotlib.org/) [examples](http://matplotlib.org/examples/index.html) and [gallery](http://matplotlib.org/gallery.html) pages are a great resource for doing visualizations in Python.\n",
"\n",
"**NOTE:** It's recommended you start with something simple first. If you wish to do more, come back to it after you've completed the rest of the sections."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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jUIhCHlkD1kBokERRVhTVgGpwjGp4jHLlKFo5gq2sYEnTMQJN8Pjgp5901ugp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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11f910cf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import random\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib as mpl \n",
"\n",
"mpl.rcParams.update({'figure.max_open_warning': 0})\n",
"%matplotlib inline\n",
"\n",
"def load_label_descriptions(filename):\n",
" ret = {}\n",
" \n",
" f = open(filename)\n",
" for line in f:\n",
" v = line.rstrip().split(',')\n",
" ret[v[0]] = v[1]\n",
" \n",
" return ret\n",
"\n",
"def show_sample(X, y, index):\n",
" s = \"Sample: {} with Label: {} ({})\".format(index, y[index], label_descriptions[str(y[index])])\n",
" \n",
" image = X[index].squeeze()\n",
" \n",
" plt.figure(figsize=(1,1))\n",
" plt.text(0, -10, s)\n",
" plt.imshow(image, cmap=\"gray\") \n",
" \n",
"label_descriptions = load_label_descriptions('signnames.csv')\n",
"index = random.randint(0, len(X_train))\n",
"\n",
"show_sample(X_train, y_train, index)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----\n",
"\n",
"## Step 2: Design and Test a Model Architecture\n",
"\n",
"Design and implement a deep learning model that learns to recognize traffic signs. Train and test your model on the [German Traffic Sign Dataset](http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset).\n",
"\n",
"There are various aspects to consider when thinking about this problem:\n",
"\n",
"- Neural network architecture\n",
"- Play around preprocessing techniques (normalization, rgb to grayscale, etc)\n",
"- Number of examples per label (some have more than others).\n",
"- Generate fake data.\n",
"\n",
"Here is an example of a [published baseline model on this problem](http://yann.lecun.com/exdb/publis/pdf/sermanet-ijcnn-11.pdf). It's not required to be familiar with the approach used in the paper but, it's good practice to try to read papers like these.\n",
"\n",
"**NOTE:** The LeNet-5 implementation shown in the [classroom](https://classroom.udacity.com/nanodegrees/nd013/parts/fbf77062-5703-404e-b60c-95b78b2f3f9e/modules/6df7ae49-c61c-4bb2-a23e-6527e69209ec/lessons/601ae704-1035-4287-8b11-e2c2716217ad/concepts/d4aca031-508f-4e0b-b493-e7b706120f81) at the end of the CNN lesson is a solid starting point. You'll have to change the number of classes and possibly the preprocessing, but aside from that it's plug and play!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Implementation\n",
"\n",
"Use the code cell (or multiple code cells, if necessary) to implement the first step of your project. Once you have completed your implementation and are satisfied with the results, be sure to thoroughly answer the questions that follow."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import cv2\n",
"from sklearn.utils import shuffle\n",
"\n",
"#normalize image: (values around zero mean as discussed in the lecture)\n",
"def normalize(Xs):\n",
" return np.array(list(map(lambda x: (x.astype('float32')-128)/128, Xs)))\n",
"\n",
"#as in the paper cited, converting images to grayscale improved signals recognition, \n",
"#reduced the amount of input parameters\n",
"def to_grayscale(rgb):\n",
" #converting to grayscale using a similar approach to MatLab\n",
" #https://nl.mathworks.com/help/matlab/ref/rgb2gray.html\n",
" ret = np.dot(rgb[...,:3], [0.299, 0.587, 0.114])\n",
" \n",
" return np.expand_dims(ret, axis=4)\n",
"\n",
"X_train = normalize(to_grayscale(X_train))\n",
"X_validation = normalize(to_grayscale(X_validation))\n",
"X_test = normalize(to_grayscale(X_test))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 1 \n",
"\n",
"_Describe how you preprocessed the data. Why did you choose that technique?_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Answer:**\n",
"First of all, I converted the images to grayscale, after reading the cited paper for the base line model since it ackknowledges better results than using the RGB space which I also confirmed while exploring with the validation set.\n",
"After that, I also normalized the values around 0 mean as discussed during the lectures since this it is believed to provide better numerical stability and also improved (although slightly) the results.\n",
"Since the images provided were all 32x32, no addional padding was required as in the LeNet lab (where they were 28x28)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 2\n",
"\n",
"_Describe how you set up the training, validation and testing data for your model. **Optional**: If you generated additional data, how did you generate the data? Why did you generate the data? What are the differences in the new dataset (with generated data) from the original dataset?_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Answer:**\n",
"\n",
"The validation set was 'generated' by splitting the given train set in 80/20 for training and validation respectively by using the _train_test_split_ function\n",
"The test set was left untouched from the one provided (except for grayscale and normalization transformations)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"from tensorflow.contrib.layers import flatten\n",
"\n",
"def LeNet(x):\n",
" mu = 0\n",
" sigma = 0.1\n",
" \n",
" #Layer 1: Convolutional, input = 32x32x1, output = 28x28x6\n",
" conv1_W = tf.Variable(tf.truncated_normal(shape=(5,5,1,6), mean=mu, stddev=sigma))\n",
" conv1_b = tf.Variable(tf.zeros(6))\n",
" conv1 = tf.nn.conv2d(x, conv1_W, strides = [1,1,1,1], padding='VALID') + conv1_b\n",
" \n",
" #Activation 1\n",
" conv1 = tf.nn.relu(conv1)\n",
" \n",
" #Pooling. Input=28x28x6, output=14x14x6\n",
" conv1 = tf.nn.max_pool(conv1, ksize=[1,2,2,1], strides=[1,2,2,1], padding='VALID')\n",
" \n",
" #Layer 2: Convolutional. Output 10x10x16\n",
" conv2_W = tf.Variable(tf.truncated_normal(shape=(5,5,6,16), mean=mu, stddev=sigma))\n",
" conv2_b = tf.Variable(tf.zeros(16))\n",
" conv2 = tf.nn.conv2d(conv1, conv2_W, strides=[1,1,1,1], padding='VALID') + conv2_b\n",
" \n",
" #Activation 2\n",
" conv2 = tf.nn.relu(conv2)\n",
" \n",
" #Pooling. Input=10x10x16. Output=5x5x16\n",
" conv2 = tf.nn.max_pool(conv2, ksize=[1,2,2,1], strides=[1,2,2,1], padding='VALID')\n",
" \n",
" #Flatten. Input=5x5x16. Output=400\n",
" fc0 = flatten(conv2)\n",
" \n",
" #Layer 3: Fully connected. Input = 400. Output = 120\n",
" fc1_W = tf.Variable(tf.truncated_normal(shape=(400,120), mean=mu, stddev=sigma))\n",
" fc1_b = tf.Variable(tf.zeros(120))\n",
" fc1 = tf.matmul(fc0, fc1_W) + fc1_b\n",
" \n",
" #Activation\n",
" fc1 = tf.nn.relu(fc1)\n",
" \n",
" #Layer 4: Fully connected. Input = 120. Output = 84\n",
" fc2_W = tf.Variable(tf.truncated_normal(shape=(120,84), mean=mu, stddev=sigma))\n",
" fc2_b = tf.Variable(tf.zeros(84))\n",
" fc2 = tf.matmul(fc1, fc2_W) + fc2_b\n",
" \n",
" #Activation\n",
" fc2 = tf.nn.relu(fc2)\n",
"\n",
" #Layer 5: Fully Connected. Input = 84. Output = 43\n",
" fc3_W = tf.Variable(tf.truncated_normal(shape=(84,43), mean=mu, stddev=sigma))\n",
" fc3_b = tf.Variable(tf.zeros(43))\n",
" logits = tf.matmul(fc2, fc3_W) + fc3_b\n",
" \n",
" return logits"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x = tf.placeholder(tf.float32, (None, 32, 32, 1))\n",
"y = tf.placeholder(tf.int32, (None))\n",
"\n",
"one_hot_y = tf.one_hot(y, 43)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rate = 0.001\n",
"\n",
"logits = LeNet(x)\n",
"cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits, one_hot_y)\n",
"loss_operation = tf.reduce_mean(cross_entropy)\n",
"optimizer = tf.train.AdamOptimizer(learning_rate=rate)\n",
"training_operation = optimizer.minimize(loss_operation)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))\n",
"accuracy_prediction = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
"\n",
"def evaluate(X_data, y_data, batch_size):\n",
" num_examples = len(X_data)\n",
" total_accuracy = 0\n",
" sess = tf.get_default_session()\n",
" for offset in range(0, num_examples, batch_size):\n",
" batch_x, batch_y = X_data[offset:offset+batch_size], y_data[offset:offset+batch_size]\n",
" accuracy = sess.run(accuracy_prediction, feed_dict={x: batch_x, y: batch_y})\n",
" total_accuracy += (accuracy * len(batch_x))\n",
" \n",
" return total_accuracy / num_examples"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training...\n",
"\n",
"EPOCH 1...\n",
"Validation accuracy = 0.798\n",
"\n",
"EPOCH 2...\n",
"Validation accuracy = 0.887\n",
"\n",
"EPOCH 3...\n",
"Validation accuracy = 0.909\n",
"\n",
"EPOCH 4...\n",
"Validation accuracy = 0.948\n",
"\n",
"EPOCH 5...\n",
"Validation accuracy = 0.953\n",
"\n",
"EPOCH 6...\n",
"Validation accuracy = 0.966\n",
"\n",
"EPOCH 7...\n",
"Validation accuracy = 0.962\n",
"\n",
"EPOCH 8...\n",
"Validation accuracy = 0.972\n",
"\n",
"EPOCH 9...\n",
"Validation accuracy = 0.968\n",
"\n",
"EPOCH 10...\n",
"Validation accuracy = 0.976\n",
"\n",
"Model saved\n"
]
}
],
"source": [
"EPOCHS = 10\n",
"BATCH_SIZE = 128\n",
"\n",
"with tf.Session() as sess:\n",
" sess.run(tf.global_variables_initializer())\n",
" num_examples = len(X_train)\n",
" \n",
" print(\"Training...\")\n",
" print()\n",
" for i in range(EPOCHS):\n",
" X_train, y_train = shuffle(X_train, y_train)\n",
" for offset in range(0, num_examples, BATCH_SIZE):\n",
" end = offset + BATCH_SIZE\n",
" batch_x, batch_y = X_train[offset:end], y_train[offset:end]\n",
" sess.run(training_operation, feed_dict={x:batch_x, y:batch_y})\n",
" \n",
" validation_accuracy = evaluate(X_validation, y_validation, BATCH_SIZE)\n",
" \n",
" print(\"EPOCH {}...\". format(i+1))\n",
" print(\"Validation accuracy = {:.3f}\".format(validation_accuracy))\n",
" print()\n",
" \n",
" try:\n",
" saver\n",
" except NameError:\n",
" saver = tf.train.Saver()\n",
" saver.save(sess, 'lenet')\n",
" print(\"Model saved\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test Accuracy = 0.906\n"
]
}
],
"source": [
"done_training = 1\n",
"\n",
"if (done_training):\n",
" with tf.Session() as sess:\n",
" saver.restore(sess, tf.train.latest_checkpoint('.'))\n",
"\n",
" test_accuracy = evaluate(X_test, y_test, BATCH_SIZE)\n",
" print(\"Test Accuracy = {:.3f}\".format(test_accuracy))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 3\n",
"\n",
"_What does your final architecture look like? (Type of model, layers, sizes, connectivity, etc.) For reference on how to build a deep neural network using TensorFlow, see [Deep Neural Network in TensorFlow\n",
"](https://classroom.udacity.com/nanodegrees/nd013/parts/fbf77062-5703-404e-b60c-95b78b2f3f9e/modules/6df7ae49-c61c-4bb2-a23e-6527e69209ec/lessons/b516a270-8600-4f93-a0a3-20dfeabe5da6/concepts/83a3a2a2-a9bd-4b7b-95b0-eb924ab14432) from the classroom._\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Answer:**\n",
"\n",
"The architecture used is based on the LeNet lab, it consists of 2 convolutional layers (with ReLu activation and Max pooling), flattening and 3 fully conectted layers for the final classification.\n",
"\n",
"* Layer 1: Convolutional, input = 32x32x1, output = 28x28x6\n",
"* ReLu Activation 1\n",
"* Max Pooling. Input=28x28x6, output=14x14x6\n",
"\n",
"* Layer 2: Convolutional. Output 10x10x16\n",
"* ReLu Activation 2\n",
"* Max Pooling. Input=10x10x16. Output=5x5x16\n",
"\n",
"* Flatten. Input=5x5x16. Output=400\n",
"\n",
"* Layer 3: Fully connected. Input = 400. Output = 120\n",
"* ReLu Activation 3\n",
"\n",
"* Layer 4: Fully connected. Input = 120. Output = 84\n",
"* ReLu Activation 4\n",
"\n",
"* Layer 5: Fully Connected. Input = 84. Output = 43\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 4\n",
"\n",
"_How did you train your model? (Type of optimizer, batch size, epochs, hyperparameters, etc.)_\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Answer:**\n",
"\n",
"The used optimizer is the AdamOptimizer since it provided a slightly better performance than gradient descent. Batch size = 128 and epochs = 10, several tests were done increasing the batch size and number of epochs without significant accuracy improvement and making the training phase longer so they were ruled out.\n",
"As for the learning rate hyperparameter, by making it larger (in an order of magnitude) it increased the accuracy in the earlier epoch stages but made it unstable and less reliable by the last couple of epochs. Reducing the value in 10x would cause the training rate to be slower and less accurate, even extending the # of epochs.\n",
"\n",
"The original value of 0.001 turned out to be a good compromise."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 5\n",
"\n",
"\n",
"_What approach did you take in coming up with a solution to this problem? It may have been a process of trial and error, in which case, outline the steps you took to get to the final solution and why you chose those steps. Perhaps your solution involved an already well known implementation or architecture. In this case, discuss why you think this is suitable for the current problem._"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Answer:**\n",
"\n",
"I started of by using the LeNet archicture from the MNIST digit recognition and extending it so that it will fit the new input and the amount of final clasification labels.\n",
"After running it a few times with default parameters, I tried changing learning rate to a faster one, to a slower one, changing the number of epochs and batch sizes.\n",
"Also I tried normalizing the RGB image as discussed in the lectures, converting it to graycale with and without normalization and found that the best combination was the normalized grayscale on the validation set."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"## Step 3: Test a Model on New Images\n",
"\n",
"Take several pictures of traffic signs that you find on the web or around you (at least five), and run them through your classifier on your computer to produce example results. The classifier might not recognize some local signs but it could prove interesting nonetheless.\n",
"\n",
"You may find `signnames.csv` useful as it contains mappings from the class id (integer) to the actual sign name."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Implementation\n",
"\n",
"Use the code cell (or multiple code cells, if necessary) to implement the first step of your project. Once you have completed your implementation and are satisfied with the results, be sure to thoroughly answer the questions that follow."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.image as mpimg\n",
"import os\n",
"\n",
"directory = 'streetview_imgs/'\n",
"\n",
"imgs = []\n",
"labels = []\n",
"\n",
"files = [f for f in os.listdir(directory) if os.path.isfile(directory + f) and f.endswith('png')]\n",
"for f in files:\n",
" img = cv2.imread(directory + f, 1)\n",
" label, _ = f.split('.')[0].split('_')\n",
" \n",
" img = normalize(to_grayscale(img))\n",
" imgs.append(img)\n",
" labels.append(label)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 6\n",
"\n",
"_Choose five candidate images of traffic signs and provide them in the report. Are there any particular qualities of the image(s) that might make classification difficult? It could be helpful to plot the images in the notebook._\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Answer:**\n",
"The images were taken from Google Streetview, manually centered cropped in the sign and reduced to 32x32 RGB png color. The rest of the preprocessing is of course the same as for the training/validation/test sets.\n",
"The images in general are sharp and not twisted/tilted (were taken when front facing the signs as much as possible) "
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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MpJ9l6kYn1cNPR18sFp0HQz0bOvFVTdAIzFqt1vbmDl2s/qTWcWm+itnZWebn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\nK/4fOEOIjKB6X88AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c41e128>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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Te61Tkz9lMpnY9aFUKpUeCzc5I1GtYCWMG0/2u98NJ/k5+7lSVLi1H5qpMDwv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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c3477b8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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fiHwb2I1tDl29RmW6U+/eLyL3Ao8CXwLOBi7uMqsLsOLblj4h/wew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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c3acfd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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EmPAahmEsMSa8hmEYS4wJr2EYxhJjwmsYhrHELJrwJnNebRCRCRH5lYgcOftRhmEYT30W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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10bba59e8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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5JfAB4C0icjewD3g7cB/w5UH1joyMMDo6SqPRYGRkhPHxcUZHR8lms2SzWVqt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\nmE0kYoPDrMVpRRlwAWW+RW3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Tfl100UUXXTbPclwtaBFJEFiK/hzSBriW/nNInx66o/eI\nyD+KyIOWua8HMl/1RaGL+nYR+YiITITrxwis7rll7qOr/KD6ZY3n21YURVFOLNZisowHwhYgRu85\npM/oUf4HwKXAHQTpRt8KXC8ijzTGLC6xr+2sbr7qq4EvAnuBXcDfAF+XYP7rDwA3GGNsP/hS+4iW\n71X/+4BXEFjaazbftqIoinJicbwFekUYY67xPv5CRH4E3EvgPr5infb5ee/jLSJyM7AHuBI4E3jy\nMqt6LMHLSFf5HvXfCtxJ0Cd+Kms037aiKIpyYnG8g8RmgDZBgJTPFHBoqY2NMQUCMVtOJPMhjs5X\nveJ9efvcS5Az/DzgImPMwWXs47xwXbR8r/rvJjguGGPeTBA097q1ar+iKIpyYnBcBdoY0wT+nWAO\naQAkmHPxqcD3l9peRHIE4jxQ9MJ97SUQMn9fdr7qJfflbXMFkCYIYtu/jH18lMAd/6Zo+T71nwJM\ncvQ3ufm216L9iqIoyonBRnBxvw/4lIj8O/Aj4PUEgVGfihYUkXcDVxG4tU8G/gpoAp8Nv88SCLaE\nm5wmIucAc8aYX9J7vur7gXtE5FHRbcLlcoI+4kPAh4D/FO5/t4hYa7ZgjLEzcfn7eCHwu+E+vhwt\nH7bXr/+vCYLm7gHuE5G/4YHPt60oiqKciBzvMPIgaJtXEwhOFfg34LF9yn2WQJCqwH7gM8CDve8v\nJAiuakeWT3pl3kowXKkCXAP8Qb9tCKZ03E0gnjWCIK1eZV8caafdhwmXnuV71F8Ml2q47l+Bp/Sp\n27b/Icf7/Omiiy666LL2i043qSiKoigbkOMdJKYoiqIoSg9UoBVFURRlA6ICrSiKoigbEBVoRVEU\nRdmAqEAriqIoygZEBVpRFEVRNiAq0IqiKIqyAVGBVhRFUZQNiAq0oiiKomxAVKAVRVEUZQOiAq0o\niqIoGxAVaEVRFEXZgPx/mqzUs7A2tmwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10bc3c898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"idx = np.random.choice(np.arange(len(imgs)), 5, replace=False)\n",
"\n",
"test_imgs = [imgs[i] for i in idx]\n",
"test_labels = [int(labels[i]) for i in idx]\n",
"\n",
"for i in range(len(test_imgs)):\n",
" show_sample(test_imgs, test_labels, i)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 7\n",
"\n",
"_Is your model able to perform equally well on captured pictures when compared to testing on the dataset? The simplest way to do this check the accuracy of the predictions. For example, if the model predicted 1 out of 5 signs correctly, it's 20% accurate._\n",
"\n",
"_**NOTE:** You could check the accuracy manually by using `signnames.csv` (same directory). This file has a mapping from the class id (0-42) to the corresponding sign name. So, you could take the class id the model outputs, lookup the name in `signnames.csv` and see if it matches the sign from the image._\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### **Answer:**\n",
"Based on 22 images taken from the street signs in Google Street View, the accuracy is around 85% which is less than the ~90% from the test set.\n",
"For the 5 randomly picked images, it is 80%.\n",
"In order to see what to do with this differences, things like brightness, contrast and image distortion should be thought of by adding preprocessing or making the network more robust by adding layers. The last image (Right-of-way in next intersection) is considerably sharper and brighter than the samples. We could consider some image processing techniques like histogram normalization preprocessing for the training set."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing accuracy on 21 images...\n",
"accuracy: 0.8571428656578064\n",
"\n",
"Testing accuracy on 5 images...\n",
"accuracy: 0.800000011920929\n",
"\n"
]
}
],
"source": [
"def test_accuracy(imgs, labels):\n",
" X_new = np.array(imgs)\n",
" y_new = np.array(labels)\n",
"\n",
" print (\"Testing accuracy on {} images...\".format(len(imgs)))\n",
" with tf.Session() as sess:\n",
" tf.train.Saver().restore(sess, tf.train.latest_checkpoint('.'))\n",
" accuracy = evaluate(X_new, y_new, len(X_new))\n",
" print(\"accuracy: {}\".format(accuracy))\n",
" print()\n",
" \n",
"test_accuracy(imgs, labels)\n",
"test_accuracy(test_imgs, test_labels)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 8\n",
"\n",
"*Use the model's softmax probabilities to visualize the **certainty** of its predictions, [`tf.nn.top_k`](https://www.tensorflow.org/versions/r0.12/api_docs/python/nn.html#top_k) could prove helpful here. Which predictions is the model certain of? Uncertain? If the model was incorrect in its initial prediction, does the correct prediction appear in the top k? (k should be 5 at most)*\n",
"\n",
"`tf.nn.top_k` will return the values and indices (class ids) of the top k predictions. So if k=3, for each sign, it'll return the 3 largest probabilities (out of a possible 43) and the correspoding class ids.\n",
"\n",
"Take this numpy array as an example:\n",
"\n",
"```\n",
"# (5, 6) array\n",
"a = np.array([[ 0.24879643, 0.07032244, 0.12641572, 0.34763842, 0.07893497,\n",
" 0.12789202],\n",
" [ 0.28086119, 0.27569815, 0.08594638, 0.0178669 , 0.18063401,\n",
" 0.15899337],\n",
" [ 0.26076848, 0.23664738, 0.08020603, 0.07001922, 0.1134371 ,\n",
" 0.23892179],\n",
" [ 0.11943333, 0.29198961, 0.02605103, 0.26234032, 0.1351348 ,\n",
" 0.16505091],\n",
" [ 0.09561176, 0.34396535, 0.0643941 , 0.16240774, 0.24206137,\n",
" 0.09155967]])\n",
"```\n",
"\n",
"Running it through `sess.run(tf.nn.top_k(tf.constant(a), k=3))` produces:\n",
"\n",
"```\n",
"TopKV2(values=array([[ 0.34763842, 0.24879643, 0.12789202],\n",
" [ 0.28086119, 0.27569815, 0.18063401],\n",
" [ 0.26076848, 0.23892179, 0.23664738],\n",
" [ 0.29198961, 0.26234032, 0.16505091],\n",
" [ 0.34396535, 0.24206137, 0.16240774]]), indices=array([[3, 0, 5],\n",
" [0, 1, 4],\n",
" [0, 5, 1],\n",
" [1, 3, 5],\n",
" [1, 4, 3]], dtype=int32))\n",
"```\n",
"\n",
"Looking just at the first row we get `[ 0.34763842, 0.24879643, 0.12789202]`, you can confirm these are the 3 largest probabilities in `a`. You'll also notice `[3, 0, 5]` are the corresponding indices."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Answer:**\n",
"\n",
"As it can be seen in the last block code below, for image #5, the label prediction is far off ('11' is 5th place)\n",
"This means the prediction is really innacurate for this image, and leaves room for further improvements discussed in Answer 7."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> **Note**: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to \\n\",\n",
" \"**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing on 5 images...\n",
"accuracy: TopKV2(values=array([[ 1.00000000e+00, 6.13789184e-12, 3.47589631e-17,\n",
" 2.75008913e-18, 2.09709440e-19],\n",
" [ 9.99547184e-01, 1.63430173e-04, 1.04088809e-04,\n",
" 6.26783149e-05, 6.10630450e-05],\n",
" [ 1.00000000e+00, 3.30285026e-08, 2.78208762e-10,\n",
" 1.02531048e-12, 1.71245288e-13],\n",
" [ 9.96130466e-01, 3.85568733e-03, 7.11382017e-06,\n",
" 4.89867534e-06, 1.42611668e-06],\n",
" [ 4.84159917e-01, 3.80829424e-01, 1.30196035e-01,\n",
" 2.16162438e-03, 1.03527424e-03]], dtype=float32), indices=array([[13, 35, 33, 34, 12],\n",
" [36, 12, 17, 14, 38],\n",
" [17, 14, 40, 12, 36],\n",
" [29, 24, 22, 28, 25],\n",
" [24, 27, 20, 30, 11]], dtype=int32))\n",
"Test labels [13, 36, 17, 29, 11]\n"
]
}
],
"source": [
"sm = tf.nn.softmax(logits)\n",
"top_k = tf.nn.top_k(sm, 5)\n",
"\n",
"print (\"Testing on {} images...\".format(len(test_imgs)))\n",
"with tf.Session() as sess:\n",
" tf.train.Saver().restore(sess, tf.train.latest_checkpoint('.'))\n",
" res = sess.run(top_k, feed_dict={x: test_imgs})\n",
" print(\"accuracy: {}\".format(res))\n",
" \n",
"print (\"Test labels\", test_labels)"
]
}
],
"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": 1
}
| mit |
oliverlee/antlia | python/notebooks/find_avoidance_event.ipynb | 1 | 3455041 | null | bsd-2-clause |
szczeles/spark-oktawave | spark_oktawave/templates/Spark.ipynb | 1 | 833 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pyspark.sql.session import SparkSession\n",
"from pyspark.sql.functions import *\n",
"from pyspark.sql.types import *\n",
"\n",
"spark = (SparkSession.builder\n",
" .appName('appname')\n",
" .getOrCreate())"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
mlperf/training_results_v0.6 | Fujitsu/benchmarks/resnet/implementations/mxnet/3rdparty/onnx-tensorrt/third_party/onnx/onnx/examples/np_array_tensorproto.ipynb | 1 | 4067 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original Numpy array:\n",
"[[ 1. 2. 3.]\n",
" [ 4. 5. 6.]]\n",
"\n"
]
}
],
"source": [
"from __future__ import absolute_import\n",
"from __future__ import division\n",
"from __future__ import print_function\n",
"from __future__ import unicode_literals\n",
"\n",
"import numpy\n",
"import onnx\n",
"import os\n",
"from onnx import numpy_helper\n",
"from distutils.version import LooseVersion\n",
"\n",
"\n",
"# Preprocessing: create a Numpy array\n",
"numpy_array = numpy.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], dtype=float)\n",
"if LooseVersion(numpy.version.version) < LooseVersion('1.14'):\n",
" print('Original Numpy array:\\n{}\\n'.format(numpy.array2string(numpy_array)))\n",
"else:\n",
" print('Original Numpy array:\\n{}\\n'.format(numpy.array2string(numpy_array, legacy='1.13')))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TensorProto:\n",
"dims: 2\n",
"dims: 3\n",
"data_type: DOUBLE\n",
"raw_data: \"\\000\\000\\000\\000\\000\\000\\360?\\000\\000\\000\\000\\000\\000\\000@\\000\\000\\000\\000\\000\\000\\010@\\000\\000\\000\\000\\000\\000\\020@\\000\\000\\000\\000\\000\\000\\024@\\000\\000\\000\\000\\000\\000\\030@\"\n",
"\n"
]
}
],
"source": [
"# Convert the Numpy array to a TensorProto\n",
"tensor = numpy_helper.from_array(numpy_array)\n",
"print('TensorProto:\\n{}'.format(tensor))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"After round trip, Numpy array:\n",
"[[ 1. 2. 3.]\n",
" [ 4. 5. 6.]]\n",
"\n"
]
}
],
"source": [
"# Convert the TensorProto to a Numpy array\n",
"new_array = numpy_helper.to_array(tensor)\n",
"if LooseVersion(numpy.version.version) < LooseVersion('1.14'):\n",
" print('After round trip, Numpy array:\\n{}\\n'.format(numpy.array2string(numpy_array)))\n",
"else:\n",
" print('After round trip, Numpy array:\\n{}\\n'.format(numpy.array2string(numpy_array, legacy='1.13')))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Save the TensorProto\n",
"with open(os.path.join('resources', 'tensor.pb'), 'wb') as f:\n",
" f.write(tensor.SerializeToString())"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"After saving and loading, new TensorProto:\n",
"dims: 2\n",
"dims: 3\n",
"data_type: DOUBLE\n",
"raw_data: \"\\000\\000\\000\\000\\000\\000\\360?\\000\\000\\000\\000\\000\\000\\000@\\000\\000\\000\\000\\000\\000\\010@\\000\\000\\000\\000\\000\\000\\020@\\000\\000\\000\\000\\000\\000\\024@\\000\\000\\000\\000\\000\\000\\030@\"\n",
"\n"
]
}
],
"source": [
"# Load the TensorProto\n",
"new_tensor = onnx.TensorProto()\n",
"with open(os.path.join('resources', 'tensor.pb'), 'rb') as f:\n",
" new_tensor.ParseFromString(f.read())\n",
"print('After saving and loading, new TensorProto:\\n{}'.format(new_tensor))"
]
}
],
"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": "2.7.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
FavioVazquez/Electrodinamica-Clasica-PCF | Tarea9/.ipynb_checkpoints/Problema3-checkpoint.ipynb | 1 | 444280 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"import numpy as np\n",
"from numpy import arccos,sin, cos, pi, array, sqrt, log, exp\n",
"import matplotlib\n",
"matplotlib.use('nbagg')\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import rc\n",
"\n",
"rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})\n",
"## for Palatino and other serif fonts use:\n",
"#rc('font',**{'family':'serif','serif':['Palatino']})\n",
"rc('text', usetex=True)\n",
"\n",
"#LaTeX\n",
"plt.rc('text', usetex=True)\n",
"plt.rc('font', family='serif')"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
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"output_type": "display_data"
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{
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\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
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"output_type": "display_data"
},
{
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\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
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"output_type": "display_data"
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{
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\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
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\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Linespace for theta\n",
"\n",
"theta = np.linspace(-pi,pi,360)\n",
"\n",
"# Variable definitions\n",
"x1 = 3/(8*pi)*(sin(theta))**2 #l=1,m=0\n",
"x2 = 3/(16*pi)*(1 + (cos(theta))**2)#l=1,m=+-1\n",
"x3 = 15/8*pi * (sin(theta))**2*(cos(theta))**2 #l=2,m=0\n",
"x4 = (5/8*pi) * (1 - 3*(cos(theta))**2 + \n",
" 4*(cos(theta))**4) #l=2, m=+-1\n",
"x5= \n",
"\n",
"# l=1, m=0\n",
"ax = plt.subplot(111, polar=True)\n",
"ax.set_theta_offset(pi/2)\n",
"ax.plot(theta,x1)\n",
"plt.show()\n",
"\n",
"# l=1, m=+-1 \n",
"ax = plt.subplot(111, polar=True)\n",
"ax.set_theta_offset(pi/2)\n",
"ax.plot(theta,x2)\n",
"plt.show()\n",
"\n",
"# l=2, m=0\n",
"ax = plt.subplot(111, polar=True)\n",
"ax.set_theta_offset(pi/2)\n",
"ax.plot(theta,x3)\n",
"plt.show()\n",
"\n",
"# l=2, m=+-1\n",
"ax = plt.subplot(111, polar=True)\n",
"ax.set_theta_offset(pi/2)\n",
"ax.plot(theta,x4)\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
shashank14/Asterix | 1-Python Crash course/Python-Crash-Course/.ipynb_checkpoints/Python-Csv-Files-checkpoint.ipynb | 1 | 12292 | {
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[{'': '1',\n",
" 'class': 'compact',\n",
" 'cty': '18',\n",
" 'cyl': '4',\n",
" 'displ': '1.8',\n",
" 'drv': 'f',\n",
" 'fl': 'p',\n",
" 'hwy': '29',\n",
" 'manufacturer': 'audi',\n",
" 'model': 'a4',\n",
" 'trans': 'auto(l5)',\n",
" 'year': '1999'},\n",
" {'': '2',\n",
" 'class': 'compact',\n",
" 'cty': '21',\n",
" 'cyl': '4',\n",
" 'displ': '1.8',\n",
" 'drv': 'f',\n",
" 'fl': 'p',\n",
" 'hwy': '29',\n",
" 'manufacturer': 'audi',\n",
" 'model': 'a4',\n",
" 'trans': 'manual(m5)',\n",
" 'year': '1999'},\n",
" {'': '3',\n",
" 'class': 'compact',\n",
" 'cty': '20',\n",
" 'cyl': '4',\n",
" 'displ': '2',\n",
" 'drv': 'f',\n",
" 'fl': 'p',\n",
" 'hwy': '31',\n",
" 'manufacturer': 'audi',\n",
" 'model': 'a4',\n",
" 'trans': 'manual(m6)',\n",
" 'year': '2008'}]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import csv\n",
"\n",
"%precision 2\n",
"\n",
"with open('mpg.csv') as csvfile:\n",
" mpg = list(csv.DictReader(csvfile))\n",
" \n",
"mpg[:3] # The first three dictionaries in our list."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[{'': '1',\n",
" 'class': 'compact',\n",
" 'cty': '18',\n",
" 'cyl': '4',\n",
" 'displ': '1.8',\n",
" 'drv': 'f',\n",
" 'fl': 'p',\n",
" 'hwy': '29',\n",
" 'manufacturer': 'audi',\n",
" 'model': 'a4',\n",
" 'trans': 'auto(l5)',\n",
" 'year': '1999'},\n",
" {'': '2',\n",
" 'class': 'compact',\n",
" 'cty': '21',\n",
" 'cyl': '4',\n",
" 'displ': '1.8',\n",
" 'drv': 'f',\n",
" 'fl': 'p',\n",
" 'hwy': '29',\n",
" 'manufacturer': 'audi',\n",
" 'model': 'a4',\n",
" 'trans': 'manual(m5)',\n",
" 'year': '1999'},\n",
" {'': '3',\n",
" 'class': 'compact',\n",
" 'cty': '20',\n",
" 'cyl': '4',\n",
" 'displ': '2',\n",
" 'drv': 'f',\n",
" 'fl': 'p',\n",
" 'hwy': '31',\n",
" 'manufacturer': 'audi',\n",
" 'model': 'a4',\n",
" 'trans': 'manual(m6)',\n",
" 'year': '2008'}]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import csv\n",
"%precision 2\n",
"with open('mpg.csv') as csvfile:\n",
" mpg = list(csv.DictReader(csvfile))\n",
"mpg[:3]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"234"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(mpg)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['', 'year', 'model', 'class', 'cty', 'trans', 'hwy', 'displ', 'manufacturer', 'cyl', 'fl', 'drv'])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mpg[0].keys()"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"16.86"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(float(d['cty']) for d in mpg)/len(mpg)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'18'"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mpg\n",
"mpg[0][\"cty\"]\n"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"23.44"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(float(d[\"hwy\"]) for d in mpg)/len(mpg)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'4', '5', '6', '8'}"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cylinders = set(d[\"cyl\"] for d in mpg)\n",
"cylinders"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'audi',\n",
" 'chevrolet',\n",
" 'dodge',\n",
" 'ford',\n",
" 'honda',\n",
" 'hyundai',\n",
" 'jeep',\n",
" 'land rover',\n",
" 'lincoln',\n",
" 'mercury',\n",
" 'nissan',\n",
" 'pontiac',\n",
" 'subaru',\n",
" 'toyota',\n",
" 'volkswagen'}"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cars_types = set(d[\"manufacturer\"] for d in mpg)\n",
"cars_types"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'4', '5', '6', '8'}"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cylinders"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'4runner 4wd',\n",
" 'a4',\n",
" 'a4 quattro',\n",
" 'a6 quattro',\n",
" 'altima',\n",
" 'c1500 suburban 2wd',\n",
" 'camry',\n",
" 'camry solara',\n",
" 'caravan 2wd',\n",
" 'civic',\n",
" 'corolla',\n",
" 'corvette',\n",
" 'dakota pickup 4wd',\n",
" 'durango 4wd',\n",
" 'expedition 2wd',\n",
" 'explorer 4wd',\n",
" 'f150 pickup 4wd',\n",
" 'forester awd',\n",
" 'grand cherokee 4wd',\n",
" 'grand prix',\n",
" 'gti',\n",
" 'impreza awd',\n",
" 'jetta',\n",
" 'k1500 tahoe 4wd',\n",
" 'land cruiser wagon 4wd',\n",
" 'malibu',\n",
" 'maxima',\n",
" 'mountaineer 4wd',\n",
" 'mustang',\n",
" 'navigator 2wd',\n",
" 'new beetle',\n",
" 'passat',\n",
" 'pathfinder 4wd',\n",
" 'ram 1500 pickup 4wd',\n",
" 'range rover',\n",
" 'sonata',\n",
" 'tiburon',\n",
" 'toyota tacoma 4wd'}"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"models = set(d[\"model\"] for d in mpg)\n",
"models"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'audi',\n",
" 'chevrolet',\n",
" 'dodge',\n",
" 'ford',\n",
" 'honda',\n",
" 'hyundai',\n",
" 'jeep',\n",
" 'land rover',\n",
" 'lincoln',\n",
" 'mercury',\n",
" 'nissan',\n",
" 'pontiac',\n",
" 'subaru',\n",
" 'toyota',\n",
" 'volkswagen'}"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"manufacturers = set(d[\"manufacturer\"] for d in mpg)\n",
"manufacturers"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"16.86"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"avg_mpg_in_cty = sum(float(d[\"cty\"]) for d in mpg)/len(mpg)\n",
"avg_mpg_in_cty"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"23.44"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"avg_mpg_in_hwy = sum(float(d[\"hwy\"]) for d in mpg)/len(mpg)\n",
"avg_mpg_in_hwy"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%precision 2\n",
"def Avg_Mpg():\n",
" avg_mpg_in_cty = sum(float(d[\"cty\"]) for d in mpg)/len(mpg)\n",
" avg_mpg_in_hwy = sum(float(d[\"hwy\"]) for d in mpg)/len(mpg)\n",
" avg_mpg_in_cty\n",
" avg_mpg_in_hwy\n",
" if avg_mpg_in_cty > avg_mpg_in_hwy:\n",
" print(\"The Avg Mpg in city is greater than in Highways {}\".format(float(avg_mpg_in_cty)))\n",
" else: \n",
" print(\"The Avg Mpg in Highways is greater than in City {}\".format(float(avg_mpg_in_hwy)))\n",
" \n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The Avg Mpg in Highways is greater than in City 23.44017094017094\n"
]
}
],
"source": [
"Avg_Mpg()"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"7"
]
},
"execution_count": 131,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class_of_vhl = set(d[\"class\"] for d in mpg)\n",
"class_of_vhl\n",
"len(class_of_vhl)"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"7"
]
},
"execution_count": 132,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"HwyMpgByClass = []\n",
"\n",
"for vehicleclass in class_of_vhl:\n",
" summpg = 0\n",
" vclasscount = 0\n",
" for d in mpg:\n",
" if vehicleclass == d[\"class\"]:\n",
" summpg += float(d['hwy']) # add the hwy mpg\n",
" vclasscount += 1 # increment the count\n",
" HwyMpgByClass.append({vehicleclass, summpg / vclasscount}) # append the tuple ('class', 'avg mpg')\n",
"\n",
"#HwyMpgByClass.sort(key=lambda x: x[1])\n",
"HwyMpgByClass\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| apache-2.0 |
tommytwoeyes/continuity | Homework_Assignments/S11.8_Power_Series_Examples.ipynb | 2 | 4597 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<hgroup>\n",
" <h1>Power Series</h1>\n",
" <h5>Section 11.8</h5>\n",
"</hgroup>\n",
"\n",
"Although the section number is from our official course textbook, _Stewart Calculus: Early Transcendentals,_ these examples are from the (vastly superior) textbook, _Thomas Calculus, Twelfth Edition._ Its author, George Thomas, was an MIT professor and a master of explaining calculus concepts.\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example 3\n",
"\n",
"<p class='lead'>How to test a power series for convergence by using the _Ratio Test_ to determine for which values of $x$ it converges/diverges.</p>\n",
"\n",
"For what values of $x$ do the following power series converge?\n",
"\n",
"___\n",
"\n",
"__a)__ $\\quad \\sum_{n=1}^{\\infty} (-1)^{n-1} \\frac{x^n}{n} = x - \\frac{x^2}{2} + \\frac{x^3}{3} - ... + \\frac{x^n}{n}$\n",
"\n",
"First, examine a larger list of terms:\n",
"\n",
"$$\\sum_{n=1}^{\\infty} (-1)^{n-1} \\frac{x^n}{n} = x - \\frac{x^2}{n} + \\frac{x^3}{n} - \\frac{x^4}{4} + \\frac{x^5}{n} - \\frac{x^6}{n} + \\frac{x^7}{7} - ...$$\n",
"\n",
"The Ratio Test says that for series with positive terms, you take the limit of the ratio of two consecutive terms. If the result, $p$ is less than one, the series converges:\n",
"\n",
"$$\n",
"\\require{cancel}\n",
"\\color{blue}p\\color{black} = \\lim_\\limits{n \\to \\infty} \\left|\\frac{a_{n+1}}{a_n} \\right| \\\\\n",
"\\text{If } \\color{blue}p\\color{black} < 1 \\text{, the series converges.} \\\\\n",
"\\text{If } \\color{blue}p\\color{black} > 1 \\text{ or } \\color{blue}p\\color{black} \\to \\infty \\text{ as } n \\to \\infty \\text{, the series diverges.} \\\\\n",
"\\text{If } \\color{blue}p\\color{black}=1 \\text{, the result is inconclusive, meaning } \\\\\n",
"\\text{you can't tell if the series is convergent or divergent.}\n",
"$$\n",
"\n",
"For the series in __a),__, the ratio test, is:\n",
"\n",
"$$\n",
"\\lim_\\limits{n \\to \\infty} \\left| \\frac{ \\frac{x^{n+1}}{n+1} }{ \\frac{x^n}{n} }\n",
"\\right| = \\color{blue}p \n",
"$$\n",
"\n",
"$$\n",
"= \\lim_\\limits{n \\to \\infty} \\left| \\frac{x^{n+1}}{n+1} \\cdot \\frac{n}{x^n} \\right| = \\lim_\\limits{n \\to \\infty} \\left| \\frac{x^{\\cancel{n+1}}}{n+1} \\cdot \\frac{n}{\\cancel{x^n}} \\right|$$\n",
"\n",
"$$\n",
"= \\lim_\\limits{n \\to \\infty} \\frac{n}{n+1} \\cdot |x|\n",
"$$\n",
"\n",
"$$\n",
"= \\left( \\frac{n}{n} \\right) \\cdot |x| \\quad \\text{ (<= n / (n+1) behaves like 1 as } n \\to \\infty \\text{)}\n",
"$$\n",
"\n",
"$$= |x|$$\n",
"\n",
"So, the series converges for $|x| < 1$, which is the same as saying, $-1 < x < 1$.\n",
"\n",
"It diverges if $|x| > 1$, because in that scenario the _nth term_ does not converge to zero.\n",
"\n",
"Therefore, the radius of convergence $R$ is equal to one: $R = 1$.\n",
"\n",
"___\n",
"\n",
"__b)__ $\\quad \\sum_{n=1}^{\\infty} (-1)^{n-1} \\frac{x^{2n-1}}{2n-1} = \\left[ (-1)^{1-1} \\cdot \\frac{x^{2(1)-1}}{2(1)-1} \\right] + \\left[ (-1)^{2-1} \\cdot \\frac{x^{2(2)-1}}{2(2)-1} \\right] + \\left[ (-1)^{3-1} \\cdot \\frac{x^{2(3)-1}}{2(3)-1} \\right]$\n",
"\n",
"$$= \\sum_{n=1}^{\\infty} \\left( (-1)^{n-1} \\cdot \\frac{x^{2n-1}}{2n-1} \\right) = x - \\frac{x^3}{3} + \\frac{x^5}{5} - \\frac{x^7}{7} + \\frac{x^8}{8} - ... $$\n",
"\n",
"The Ratio Test for this power series looks like this:\n",
"\n",
"$$\n",
"\\color{blue}p\\color{black} = \\lim_\\limits{n \\to \\infty} \\left| \\frac{ \\frac{x^{2(n+1)-1}}{2(n+1)-1} }{ \\frac{x^{2n-1}}{2n-1} } \\right| = \n",
"\\lim_\\limits{n \\to \\infty} \\left| \\frac{x^{2(n+1)-1}}{2(n+1)-1} \\cdot \\frac{2n-1}{x^{2n-1}} \\right|\n",
"$$"
]
},
{
"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": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
gcallah/Indra | notebooks/IntroToABM.ipynb | 1 | 155863 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Agent-Based Modeling\n",
"### What Is It? What's It For?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is an introduction to Agent-Based Modeling (ABM). ABM is a type of computer programming with applications to physics, chemistry, biology, epidemiology, weather forecasting, forestry, sociology, economics, business, and even film making. But to understand what makes it different from other modeling, we need a little background."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### What Is a Model?\n",
"\n",
"A model is a construction that \"resembles\" something we want to investigate, and which, by manipulation of the construct, we hope to learn about the subject we are investigating."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Three Types of Modeling"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Simple Orders"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first successes of mathematical modeling of reality were in describing *simple orders*. A classic expression of a simple order is Newton's second law of motion, *F* = *ma*: it relates one quantity to only two others, in a very straightforward manner. Similar examples are models for springs, and pendulums, and heat diffusion. Models of simple orders usually take the form of *differential equations*. These types of models were developed extensively in the 18th and 19th centuries, and are still very important today.\n",
"\n",
"<div align=\"center\">\n",
"<figure>\n",
"<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/f/fc/Hookes-law-springs.png/440px-Hookes-law-springs.png\" width=\"25%\">\n",
" <figcaption>\n",
" <i>Hooke's Law is a model of the simple order of an isolated spring.</i>\n",
" </figcaption>\n",
"</figure>\n",
"</div>\n",
"<br/>\n",
"<div align=\"center\">\n",
"<figure>\n",
"<img\n",
"src=\"https://upload.wikimedia.org/wikipedia/commons/f/f2/Orbit2.gif\" width=\"25%\">\n",
" <figcaption>\n",
" <i>A simple order: planetary orbits.</i>\n",
" </figcaption>\n",
"</figure>\n",
"</div>\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Disorder"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The second class of phenomena to be successfully modeled were *random* systems:\n",
"\n",
"\"Whereas ordinary mechanics only considers the behaviour of a single state, statistical mechanics introduces the statistical ensemble, which is a large collection of virtual, independent copies of the system in various states. The statistical ensemble is a probability distribution over all possible states of the system.\" -- *Wikipedia*\n",
"\n",
"The study of such systems was conducted by the use of *statistical models*. Examples of phenomena successfully modeled in this way includes thermodynamical systems, gas diffusion, Brownian motion, the evolution of a quantum wave function, height and weight distributions in a population, and games of chance.\n",
"\n",
"<img src=\"https://upload.wikimedia.org/wikipedia/commons/c/c2/Brownian_motion_large.gif\" width=\"25%\">\n",
"\n",
"<div align=\"center\">*Brownian motion: gas molecules producing random motion of a dust particle.*</div>\n",
"\n",
"In the mid-twentieth century, many scientists believed that these two types of models were all that were needed to capture all of reality: if some phenomenon could not be captured exactly by a simple model, then it must be random, and could be described by statistical analysis. But this view was soon to collapse, as, from multiple fields, researchers discovered..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Complex orders"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The discovery of complex orders had been hinted at in work on the three-body problem: Newton had \"solved\" the problem of the planet's orbits by assuming that only the sun influenced their orbits, which is a good approximation. But once another planet was allowed to sneak into the system, the possible behavior became far more complex:\n",
"\n",
"<img src=\"https://upload.wikimedia.org/wikipedia/commons/1/11/3bodyproblem.gif\" width=\"74%\">\n",
"\n",
"Well, at least for our solar system, these complexities could (mostly) be ignored. But for other systems this was not the case: as researchers looked into \"far-from-equilibrium\" systems like our weather, or chaotic fluid dynamics, or the behavior of asset markets, they found that these exhibited patterns that could not be captured by models of simple orders, nor models of pure randomness. They were discovering the existence of *complex orders*.\n",
"\n",
"<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/6/6a/LogisticTopMixing1-6.gif/440px-LogisticTopMixing1-6.gif\" width=\"25%\">\n",
"\n",
"The behavior of these orders could not be captured by analysis of a differential equation, nor by statistical procedures. Instead, in general, one creates a model based on some (seemingly) simple rules, and then *runs the model*, and only by doing so does one discover what the system does.\n",
"\n",
"A milestone along this path was the discovery, by the mathematician Benoit Mandelbrot, of the *Mandelbrot set*. Mandelbrot took the \"simple\" equation, *f*<sub>*c*</sub>(*z*) = *z*<sup>2</sup> + *c*, where *z* and *c* are complex numbers, and *iterated* it (feeding the result of one iteration into the next), with *z* at the start equal to 0, for *c* near the origin, and found that this seemingly simple equation produced fantastically complex results. In a *simple order*, small differences in the initial value of *c* ought to produce small differences in the final outcome. But Mandelbrot discovered that *extremely* small differences in the initial value of *c* could produce wildly divergent outcomes: this is a characteristic of complex systems: small differences in initial values can produce very big differences in the development of the system. If we color each point *c* by how fast the above equation moves away from the origin, we get a result like:\n",
"\n",
"<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/2/21/Mandel_zoom_00_mandelbrot_set.jpg/644px-Mandel_zoom_00_mandelbrot_set.jpg\" width=\"25%\">\n",
"<div align=\"center\">*The Mandelbrot set*</div>"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/pCpLWbHVNhk\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import HTML\n",
"\n",
"HTML('<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/pCpLWbHVNhk\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similar phenomena were discovered in many fields... see the list of applications of ABMs above!... and a new field was opened to science: the study of *complex orders*. In general, we cannot either \"solve\" a problem in such an order by solving a differential equation, as we can for simple orders, or by purely statistical analysis, as we can for random phenomena. Instead, we must specify the rules the parts of the system must follow, and then we must *run the system*, most often on a computer, and see what *emerges*. (Another name for this field of study is *emergent phenomena*.)\n",
"\n",
"One of the important tools in exploring this (relatively) new field of complex orders has been *agent-based models*. In such models, instead of trying to find a \"top-down\" equation that we can solve, and therefore say exactly what the model predicts at time *t*, we instead create a number of *agents*, which we program to follow some rules of behavior, and then set those agents \"loose\" in an \"environment\" (our model), and allow them to interact. Such models are also sometimes called *cellular automata*.\n",
"\n",
"An early example of such a model was John Conway's *game of life*, where each \"cell\" on a grid evolved according to a simple rule, based on the state of its neighbors. Although the rules were simple, the \"game\" could produce very complex patterns:\n",
"\n",
"<img src=\"https://upload.wikimedia.org/wikipedia/commons/e/e5/Gospers_glider_gun.gif\" width=\"33%\">\n",
"<div align=\"center\">*A \"glider\" in the game of life*</div>\n",
"\n",
"The pattern produced is clearly not \"random,\" but it also could not be predicted by any \"top-down\" equation attempting to model the system. The system needs to be *run* in order to see what patterns *emerge*."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Applications of Agent-Based Modeling"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An early area in which ABMs were successfully applied was that of *flocking* or *schooling*. Birds form \"flocks,\" and fish \"schools,\" and these groups exhibit behavior that clearly is not random, but also cannot be described by any simple equation. For instance, check out this school of fish:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/15B8qN9dre4\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"HTML('<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/15B8qN9dre4\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that there is no fish \"choreographer\": the school must emerge \"bottom-up\" from the behavior of inidividual fish.\n",
"\n",
"Using ABMs, researchers discovered that they could create agents that followed simple rules, and yet produced this sort of complex phenomena, like \"boids\":"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/QbUPfMXXQIY\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"HTML('<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/QbUPfMXXQIY\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ABM models have also been useful in many other fields, as noted above. Perhaps of particular interest is their application, over the past two decades, in film making. A common difficulty faced by film makers trying to shoot such scenes was whether to employ large numbers of real humans, all of whom would need to be directed, fed, and so on, or to try to animate these scenes with top-down control.\n",
"\n",
"Massive realized that ABM could cut this Gordian knot: instead of trying to control all of the animation top down, film makers could instead create \"agents\" who would follow simple rules, and then instantiate a whole host of such agents, and create the movie episode by simple allowing all of those agents to follow their own rules.\n",
"\n",
"This technique was so successful in the *Lord of the Rings* movies that since then [most film makers](http://www.massivesoftware.com/gallery.html) desiring huge crowd scenes have used ABM."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/EmTz7EAYLrs\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"HTML('<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/EmTz7EAYLrs\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Our ABM Efforts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's look at a few agent based models we've made here at Tandon.\n",
"\n",
"The first one is of the spread of a forestfire."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from models.forestfire import main as ffmain\n",
"\n",
"ffmain()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another model we've built is of bacteria avoiding toxins and moving towards nutrients:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from models.bacteria import main as bamain\n",
"\n",
"bamain()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, let's look at a model of trends in fashion.\n",
"\n",
"In this model, there are trendsetters and followers. The trendsetters want to wear something that only other trendsetters wear. Meanwhile, the followers want to be just like the trendsetters.\n",
"\n",
"This leads to waves of fashion.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/scipy/__init__.py:115: UserWarning: Numpy 1.13.3 or above is required for this version of scipy (detected version 1.13.1)\n",
" UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"What is the grid height? [2-100] (8) \n",
"What is the grid width? [2-100] (8) \n",
"How many fashion trendsetters do you want? [1-100] (4) \n",
"How many fashion followers do you want? [1-100] (4) \n",
"Welcome to Indra, gcallah!\n",
"NOTIFICATION: Running model fashion\n",
"\n",
"***************\n",
"Menu of Actions\n",
"***************\n",
"1. Run for N periods\n",
"2. Display a population graph\n",
"3. Display a scatter plot\n",
"4. Examine model data\n",
"6. View log\n",
"0. Quit\n",
"Please choose a number from the menu above:\n",
"\n",
"How many periods? 24\n",
"Steps = 24\n",
"NOTIFICATION: Running env fashion for 24 periods.\n",
"NOTIFICATION: Switching 0 agents between groups\n",
"NOTIFICATION: Agent tsetter1 is scheduled to switch from Red Trendsetters to Blue Trendsetters\n",
"NOTIFICATION: Agent tsetter2 is scheduled to switch from Red Trendsetters to Blue Trendsetters\n",
"\n",
"Total census for period 1:\n",
"==================\n",
"Group census:\n",
"==================\n",
" Blue Trendsetters: 0\n",
" Red Trendsetters: 4\n",
" Red Followers: 0\n",
" Blue Followers: 4\n",
"==================\n",
"Agent census:\n",
"==================\n",
" Total agents moved: 6\n",
" Total agents who switched groups: 0\n",
"NOTIFICATION: Switching 2 agents between groups\n",
"NOTIFICATION: Agent tsetter2 is scheduled to switch from Blue Trendsetters to Red Trendsetters\n",
"NOTIFICATION: Agent tsetter0 is scheduled to switch from Red Trendsetters to Blue Trendsetters\n",
"NOTIFICATION: Agent tsetter3 is scheduled to switch from Red Trendsetters to Blue Trendsetters\n",
"NOTIFICATION: Agent follower3 is scheduled to switch from Blue Followers to Red Followers\n",
"\n",
"Total census for period 2:\n",
"==================\n",
"Group census:\n",
"==================\n",
" Blue Trendsetters: 2\n",
" Red Trendsetters: 2\n",
" Red Followers: 0\n",
" Blue Followers: 4\n",
"==================\n",
"Agent census:\n",
"==================\n",
" Total agents moved: 10\n",
" Total agents who switched groups: 2\n",
"NOTIFICATION: Switching 4 agents between groups\n",
"NOTIFICATION: Agent tsetter1 is scheduled to switch from Blue Trendsetters to Red Trendsetters\n",
"NOTIFICATION: Agent follower3 is scheduled to switch from Red Followers to Blue Followers\n",
"\n",
"Total census for period 3:\n",
"==================\n",
"Group census:\n",
"==================\n",
" Blue Trendsetters: 3\n",
" Red Trendsetters: 1\n",
" Red Followers: 1\n",
" Blue Followers: 3\n",
"==================\n",
"Agent census:\n",
"==================\n",
" Total agents moved: 16\n",
" Total agents who switched groups: 4\n",
"NOTIFICATION: Switching 2 agents between groups\n",
"NOTIFICATION: Agent tsetter0 is scheduled to switch from Blue Trendsetters to Red Trendsetters\n",
"NOTIFICATION: Agent tsetter3 is scheduled to switch from Blue Trendsetters to Red Trendsetters\n",
"\n",
"Total census for period 4:\n",
"==================\n",
"Group census:\n",
"==================\n",
" Blue Trendsetters: 2\n",
" Red Trendsetters: 2\n",
" Red Followers: 0\n",
" Blue Followers: 4\n",
"==================\n",
"Agent census:\n",
"==================\n",
" Total agents moved: 22\n",
" Total agents who switched groups: 2\n",
"NOTIFICATION: Switching 2 agents between groups\n",
"NOTIFICATION: Agent follower1 is scheduled to switch from Blue Followers to Red Followers\n",
"NOTIFICATION: Agent follower2 is scheduled to switch from Blue Followers to Red Followers\n",
"NOTIFICATION: Agent follower3 is scheduled to switch from Blue Followers to Red Followers\n",
"\n",
"Total census for period 5:\n",
"==================\n",
"Group census:\n",
"==================\n",
" Blue Trendsetters: 0\n",
" Red Trendsetters: 4\n",
" Red Followers: 0\n",
" Blue Followers: 4\n",
"==================\n",
"Agent census:\n",
"==================\n",
" Total agents moved: 27\n",
" Total agents who switched groups: 2\n",
"NOTIFICATION: Switching 3 agents between groups\n",
"NOTIFICATION: Agent tsetter3 is scheduled to switch from Red Trendsetters to Blue Trendsetters\n",
"\n",
"Total census for period 6:\n",
"==================\n",
"Group census:\n",
"==================\n",
" Blue Trendsetters: 0\n",
" Red Trendsetters: 4\n",
" Red Followers: 3\n",
" Blue Followers: 1\n",
"==================\n",
"Agent census:\n",
"==================\n",
" Total agents moved: 34\n",
" Total agents who switched groups: 3\n",
"NOTIFICATION: Switching 1 agents between groups\n",
"NOTIFICATION: Agent tsetter2 is scheduled to switch from Red Trendsetters to Blue Trendsetters\n",
"\n",
"Total census for period 7:\n",
"==================\n",
"Group census:\n",
"==================\n",
" Blue Trendsetters: 1\n",
" Red Trendsetters: 3\n",
" Red Followers: 3\n",
" Blue Followers: 1\n",
"==================\n",
"Agent census:\n",
"==================\n",
" Total agents moved: 41\n",
" Total agents who switched groups: 1\n",
"NOTIFICATION: Switching 1 agents between groups\n",
"NOTIFICATION: Agent tsetter2 is scheduled to switch from Blue Trendsetters to Red Trendsetters\n",
"\n",
"Total census for period 8:\n",
"==================\n",
"Group census:\n",
"==================\n",
" Blue Trendsetters: 2\n",
" Red Trendsetters: 2\n",
" Red Followers: 3\n",
" Blue Followers: 1\n",
"==================\n",
"Agent census:\n",
"==================\n",
" Total agents moved: 48\n",
" Total agents who switched groups: 1\n",
"NOTIFICATION: Switching 1 agents between groups\n",
"NOTIFICATION: Agent tsetter1 is scheduled to switch from Red Trendsetters to Blue Trendsetters\n",
"\n",
"Total census for period 9:\n",
"==================\n",
"Group census:\n",
"==================\n",
" Blue Trendsetters: 1\n",
" Red Trendsetters: 3\n",
" Red Followers: 3\n",
" Blue Followers: 1\n",
"==================\n",
"Agent census:\n",
"==================\n",
" Total agents moved: 55\n",
" Total agents who switched groups: 1\n",
"NOTIFICATION: Switching 1 agents between groups\n",
"NOTIFICATION: Agent follower2 is scheduled to switch from Red Followers to Blue Followers\n",
"\n",
"Total census for period 10:\n",
"==================\n",
"Group census:\n",
"==================\n",
" Blue Trendsetters: 2\n",
" Red Trendsetters: 2\n",
" Red Followers: 3\n",
" Blue Followers: 1\n",
"==================\n",
"Agent census:\n",
"==================\n",
" Total agents moved: 62\n",
" Total agents who switched groups: 1\n",
"NOTIFICATION: Switching 1 agents between groups\n",
"NOTIFICATION: Agent tsetter3 is scheduled to switch from Blue Trendsetters to Red Trendsetters\n",
"\n",
"Total census for period 11:\n",
"==================\n",
"Group census:\n",
"==================\n",
" Blue Trendsetters: 2\n",
" Red Trendsetters: 2\n",
" Red Followers: 2\n",
" Blue Followers: 2\n",
"==================\n",
"Agent census:\n",
"==================\n",
" Total agents moved: 69\n",
" Total agents who switched groups: 1\n",
"NOTIFICATION: Switching 1 agents between groups\n",
"NOTIFICATION: Agent tsetter1 is scheduled to switch from Blue Trendsetters to Red Trendsetters\n",
"NOTIFICATION: Agent tsetter0 is scheduled to switch from Red Trendsetters to Blue Trendsetters\n",
"NOTIFICATION: Agent tsetter2 is scheduled to switch from Red Trendsetters to Blue Trendsetters\n",
"NOTIFICATION: Agent follower0 is scheduled to switch from Blue Followers to Red Followers\n",
"\n",
"Total census for period 12:\n",
"==================\n",
"Group census:\n",
"==================\n",
" Blue Trendsetters: 1\n",
" Red Trendsetters: 3\n",
" Red Followers: 2\n",
" Blue Followers: 2\n",
"==================\n",
"Agent census:\n",
"==================\n",
" Total agents moved: 73\n",
" Total agents who switched groups: 1\n",
"NOTIFICATION: Switching 4 agents between groups\n",
"NOTIFICATION: Agent tsetter3 is scheduled to switch from Red Trendsetters to Blue Trendsetters\n",
"NOTIFICATION: Agent follower2 is scheduled to switch from Blue Followers to Red Followers\n",
"\n",
"Total census for period 13:\n",
"==================\n",
"Group census:\n",
"==================\n",
" Blue Trendsetters: 2\n",
" Red Trendsetters: 2\n",
" Red Followers: 3\n",
" Blue Followers: 1\n",
"==================\n",
"Agent census:\n",
"==================\n",
" Total agents moved: 79\n",
" Total agents who switched groups: 4\n",
"NOTIFICATION: Switching 2 agents between groups\n",
"NOTIFICATION: Agent tsetter1 is scheduled to switch from Red Trendsetters to Blue Trendsetters\n",
"\n",
"Total census for period 14:\n",
"==================\n",
"Group census:\n",
"==================\n",
" Blue Trendsetters: 3\n",
" Red Trendsetters: 1\n",
" Red Followers: 4\n",
" Blue Followers: 0\n",
"==================\n",
"Agent census:\n",
"==================\n",
" Total agents moved: 86\n",
" Total agents who switched groups: 2\n",
"NOTIFICATION: Switching 1 agents between groups\n",
"NOTIFICATION: Agent follower2 is scheduled to switch from Red Followers to Blue Followers\n",
"\n",
"Total census for period 15:\n",
"==================\n",
"Group census:\n",
"==================\n",
" Blue Trendsetters: 4\n",
" Red Trendsetters: 0\n",
" Red Followers: 4\n",
" Blue Followers: 0\n",
"==================\n",
"Agent census:\n",
"==================\n",
" Total agents moved: 93\n",
" Total agents who switched groups: 1\n",
"NOTIFICATION: Switching 1 agents between groups\n",
"NOTIFICATION: Agent follower3 is scheduled to switch from Red Followers to Blue Followers\n",
"NOTIFICATION: Agent follower0 is scheduled to switch from Red Followers to Blue Followers\n",
"\n",
"Total census for period 16:\n",
"==================\n",
"Group census:\n",
"==================\n",
" Blue Trendsetters: 4\n",
" Red Trendsetters: 0\n",
" Red Followers: 3\n",
" Blue Followers: 1\n",
"==================\n",
"Agent census:\n",
"==================\n",
" Total agents moved: 99\n",
" Total agents who switched groups: 1\n",
"NOTIFICATION: Switching 2 agents between groups\n",
"NOTIFICATION: Agent tsetter3 is scheduled to switch from Blue Trendsetters to Red Trendsetters\n",
"NOTIFICATION: Agent tsetter1 is scheduled to switch from Blue Trendsetters to Red Trendsetters\n",
"\n",
"Total census for period 17:\n",
"==================\n",
"Group census:\n",
"==================\n",
" Blue Trendsetters: 4\n",
" Red Trendsetters: 0\n",
" Red Followers: 1\n",
" Blue Followers: 3\n",
"==================\n",
"Agent census:\n",
"==================\n",
" Total agents moved: 105\n",
" Total agents who switched groups: 2\n",
"NOTIFICATION: Switching 2 agents between groups\n",
"NOTIFICATION: Agent tsetter2 is scheduled to switch from Blue Trendsetters to Red Trendsetters\n",
"\n",
"Total census for period 18:\n",
"==================\n",
"Group census:\n",
"==================\n",
" Blue Trendsetters: 2\n",
" Red Trendsetters: 2\n",
" Red Followers: 1\n",
" Blue Followers: 3\n",
"==================\n",
"Agent census:\n",
"==================\n",
" Total agents moved: 112\n",
" Total agents who switched groups: 2\n",
"NOTIFICATION: Switching 1 agents between groups\n",
"NOTIFICATION: Agent tsetter0 is scheduled to switch from Blue Trendsetters to Red Trendsetters\n",
"NOTIFICATION: Agent follower2 is scheduled to switch from Blue Followers to Red Followers\n",
"NOTIFICATION: Agent follower0 is scheduled to switch from Blue Followers to Red Followers\n",
"\n",
"Total census for period 19:\n",
"==================\n",
"Group census:\n",
"==================\n",
" Blue Trendsetters: 1\n",
" Red Trendsetters: 3\n",
" Red Followers: 1\n",
" Blue Followers: 3\n",
"==================\n",
"Agent census:\n",
"==================\n",
" Total agents moved: 117\n",
" Total agents who switched groups: 1\n",
"NOTIFICATION: Switching 3 agents between groups\n",
"NOTIFICATION: Agent tsetter0 is scheduled to switch from Red Trendsetters to Blue Trendsetters\n",
"NOTIFICATION: Agent follower3 is scheduled to switch from Blue Followers to Red Followers\n",
"\n",
"Total census for period 20:\n",
"==================\n",
"Group census:\n",
"==================\n",
" Blue Trendsetters: 0\n",
" Red Trendsetters: 4\n",
" Red Followers: 3\n",
" Blue Followers: 1\n",
"==================\n",
"Agent census:\n",
"==================\n",
" Total agents moved: 123\n",
" Total agents who switched groups: 3\n",
"NOTIFICATION: Switching 2 agents between groups\n",
"NOTIFICATION: Agent tsetter3 is scheduled to switch from Red Trendsetters to Blue Trendsetters\n",
"NOTIFICATION: Agent tsetter1 is scheduled to switch from Red Trendsetters to Blue Trendsetters\n",
"\n",
"Total census for period 21:\n",
"==================\n",
"Group census:\n",
"==================\n",
" Blue Trendsetters: 1\n",
" Red Trendsetters: 3\n",
" Red Followers: 4\n",
" Blue Followers: 0\n",
"==================\n",
"Agent census:\n",
"==================\n",
" Total agents moved: 129\n",
" Total agents who switched groups: 2\n",
"NOTIFICATION: Switching 2 agents between groups\n",
"NOTIFICATION: Agent tsetter2 is scheduled to switch from Red Trendsetters to Blue Trendsetters\n",
"NOTIFICATION: Agent follower0 is scheduled to switch from Red Followers to Blue Followers\n",
"NOTIFICATION: Agent follower3 is scheduled to switch from Red Followers to Blue Followers\n",
"\n",
"Total census for period 22:\n",
"==================\n",
"Group census:\n",
"==================\n",
" Blue Trendsetters: 3\n",
" Red Trendsetters: 1\n",
" Red Followers: 4\n",
" Blue Followers: 0\n",
"==================\n",
"Agent census:\n",
"==================\n",
" Total agents moved: 134\n",
" Total agents who switched groups: 2\n",
"NOTIFICATION: Switching 3 agents between groups\n",
"NOTIFICATION: Agent tsetter1 is scheduled to switch from Blue Trendsetters to Red Trendsetters\n",
"\n",
"Total census for period 23:\n",
"==================\n",
"Group census:\n",
"==================\n",
" Blue Trendsetters: 4\n",
" Red Trendsetters: 0\n",
" Red Followers: 2\n",
" Blue Followers: 2\n",
"==================\n",
"Agent census:\n",
"==================\n",
" Total agents moved: 141\n",
" Total agents who switched groups: 3\n",
"NOTIFICATION: Switching 1 agents between groups\n",
"NOTIFICATION: Agent tsetter3 is scheduled to switch from Blue Trendsetters to Red Trendsetters\n",
"NOTIFICATION: Agent tsetter2 is scheduled to switch from Blue Trendsetters to Red Trendsetters\n",
"NOTIFICATION: Agent tsetter1 is scheduled to switch from Red Trendsetters to Blue Trendsetters\n",
"\n",
"Total census for period 24:\n",
"==================\n",
"Group census:\n",
"==================\n",
" Blue Trendsetters: 3\n",
" Red Trendsetters: 1\n",
" Red Followers: 2\n",
" Blue Followers: 2\n",
"==================\n",
"Agent census:\n",
"==================\n",
" Total agents moved: 146\n",
" Total agents who switched groups: 1\n",
"\n",
"***************\n",
"Menu of Actions\n",
"***************\n",
"1. Run for N periods\n",
"2. Display a population graph\n",
"3. Display a scatter plot\n",
"4. Examine model data\n",
"6. View log\n",
"0. Quit\n",
"Please choose a number from the menu above:\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2\n",
"Drawing a line graph.\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"***************\n",
"Menu of Actions\n",
"***************\n",
"1. Run for N periods\n",
"2. Display a population graph\n",
"3. Display a scatter plot\n",
"4. Examine model data\n",
"6. View log\n",
"0. Quit\n",
"Updating the line graph.\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Please choose a number from the menu above:\n"
]
}
],
"source": [
"%matplotlib inline\n",
"from models.fashion import main as famain\n",
"\n",
"famain()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"0"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| gpl-3.0 |
globus/globus-sample-data-portal | notebook/mrdp-notebook.ipynb | 2 | 13584 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The Modern Research Data Portal: A Design Pattern for Networked, Data-Intensive Science\n",
"\n",
"In this notebook we demonstrate the core logic for developing a Modern Research Data Portal (MRDP). This code leverages the Globus platform to manage identities and data access. We first demonstrate how to use the Globus SDK before stepping through the MRDP logic. \n",
"\n",
"The following notebook contains a brief introduction to the Globus SDK. More complete documentation and example notebooks are avaialble in the following locations:\n",
"\n",
"* https://docs.globus.org/\n",
"* https://docs.globus.org/research-data-portal\n",
"* https://github.com/globus/globus-jupyter-notebooks\n",
"* https://github.com/globus/globus-sample-data-portal"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"To use the following notebook you must first install the Globus Python SDK. This can be done by downloading the SDK and installing it manually (https://github.com/globus/globus-sdk-python) or via Python pip as follows. \n",
"\n",
"```\n",
"pip install globus-sdk\n",
"```\n",
"\n",
"To access the SDK you must authenticate using your Globus identity. In this notebook we use the NativeAppAuthClient as a way of acquiring tokens. If the MRDP code is deployed in a service web-based authentication flows should be used. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from globus_sdk import AuthClient, TransferClient, AccessTokenAuthorizer, NativeAppAuthClient, TransferData\n",
"\n",
"\n",
"CLIENT_ID = '2f9482c4-67b3-4783-bac7-12b37d6f8966'\n",
"\n",
"client = NativeAppAuthClient(CLIENT_ID)\n",
"client.oauth2_start_flow()\n",
"\n",
"authorize_url = client.oauth2_get_authorize_url()\n",
"print('Please go to this URL and login: {0}'.format(authorize_url))\n",
"\n",
"# this is to work on Python2 and Python3 -- you can just use raw_input() or\n",
"# input() for your specific version\n",
"get_input = getattr(__builtins__, 'raw_input', input)\n",
"auth_code = get_input(\n",
" 'Please enter the code you get after login here: ').strip()\n",
"token_response = client.oauth2_exchange_code_for_tokens(auth_code)\n",
"\n",
"AUTH_TOKEN = token_response.by_resource_server['auth.globus.org']['access_token']\n",
"TRANSFER_TOKEN = token_response.by_resource_server['transfer.api.globus.org']['access_token']\n",
"\n",
"tc = TransferClient(AccessTokenAuthorizer(TRANSFER_TOKEN))\n",
"ac = AuthClient(authorizer=AccessTokenAuthorizer(AUTH_TOKEN))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Using the Globus SDK\n",
"\n",
"We first show how the Globus SDK can be used to discover endpoint IDs. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# discover an Endpoint ID\n",
"search_str = \"Globus Tutorial Endpoint\"\n",
"endpoints = tc.endpoint_search(search_str)\n",
"print(\"==== Displaying endpoint matches for search: '{}' ===\".format(search_str))\n",
"for ep in endpoints:\n",
" print(\"{} ({})\".format(ep[\"display_name\"] or ep[\"canonical_name\"], ep[\"id\"]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The Research Data Portal function\n",
"\n",
"The following code uses the Globus SDK to create, manage access to, and delete shared endpoints, as follows. \n",
"\n",
"It first sets up variables for the host endpoint on which the shared enpoint will be created (in this case the \"Globus Tutorial Endpoint\"), the source path for the data to be copied and shared, and the email address of the user to be shared with. \n",
"\n",
"import sys, random, uuid\n",
"It then creates a TransferClient and an AuthClient object and uses the Globus SDK function endpoint_autoactivate to ensure that the portal admin has a credential that permits access to the endpoint identified by host_id. Activation of the endpoint assumes that the endpoint is configured to trust the Globus IdP (as is the case with Globus Connect Personal)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sys, random, uuid\n",
"from globus_sdk import AuthClient, TransferClient, AccessTokenAuthorizer, TransferData\n",
"\n",
"host_id = 'ddb59aef-6d04-11e5-ba46-22000b92c6ec' # Endpoint for shared endpoint\n",
"source_path = '/share/godata/' # Directory to copy data from\n",
"email ='[email protected]' # Email address to share with\n",
"\n",
"tc = TransferClient(AccessTokenAuthorizer(TRANSFER_TOKEN))\n",
"ac = AuthClient(authorizer=AccessTokenAuthorizer(AUTH_TOKEN))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We use the Globus SDK function operation_mkdir to create a directory (in our example call, a UUID) on the existing endpoint with identifier host_id. \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"share_path = '/~/' + str(uuid.uuid4()) + '/'\n",
"r = tc.operation_mkdir(host_id, path=share_path)\n",
"print (r['message'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we use the Globus SDK function create_shared_endpoint to create a shared endpoint for the new directory. At this point, the new shared endpoint exists and is associated with the new directory. However, only the creating user has access to this new shared endpoint at this point."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"shared_ep_data = {\n",
" 'DATA_TYPE': 'shared_endpoint',\n",
" 'host_endpoint': host_id,\n",
" 'host_path': share_path,\n",
" 'display_name': 'RDP shared endpoint',\n",
" 'description': 'RDP shared endpoint'\n",
"}\n",
"\n",
"r = tc.create_shared_endpoint(shared_ep_data)\n",
"share_id = r['id']\n",
"print(share_id)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To provide access to the requested data we copy data to the shared endpoint. We use sample data contained on the Globus Tutorial Endpoint under path \"/share/godata\".\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tc.endpoint_autoactivate(share_id)\n",
"tdata = TransferData(tc, host_id, share_id, label='RDP copy data', sync_level='checksum')\n",
"tdata.add_item(source_path, '/', recursive=True)\n",
"r = tc.submit_transfer(tdata)\n",
"o = tc.task_wait(r['task_id'], timeout=1000, polling_interval=10)\n",
"print (r['task_id'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To confirm all data is in place for sharing we check the contents of the shared endpoint. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for f in tc.operation_ls(share_id):\n",
" print (f['name'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now share the endpoint with the appropriate user. We first use the Globus SDK function get_identities to retrieve the user identifier associated with the supplied email address; this is the user for whom sharing is to be enabled. (If this user is not known to Globus, an identity is created.) We then use the function add_endpoint_acl_rule to add an access control rule to the new shared endpoint to grant the specified user readonly access to the endpoint. The various elements in the rule_data structure specify, among other things:\n",
"\n",
"* principal_type: the type of principal to which the rule applies: in this case, ’identity’ —other options are ’group’, ’all_authenticated_users’, or ’anonymous’;\n",
"* principal: as the principal_type is ’identity’, this is the user id with whom sharing is to be enabled;\n",
"* permissions: the type of access being granted: in this case read-only (’r’), but could also be read and write (’rw’);\n",
"* notify_email: an email address to which an invitation to access the shared endpoint should be sent; and\n",
"* notify_message: a message to include in the invitation email.\n",
"\n",
"As our add_endpoint_acl_rule request specifies an email address, an invitation email is sent to the user. At this point, the user is authorized to download data from the new shared endpoint. \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"r = ac.get_identities(usernames=email)\n",
"user_id = r['identities'][0]['id']\n",
"rule_data = {\n",
" 'DATA_TYPE': 'access',\n",
" 'principal_type': 'identity', # Grantee is\n",
" 'principal': user_id, # a user.\n",
" 'path': '/', # Path is /\n",
" 'permissions': 'r', # Read-only\n",
" 'notify_email': email, # Email invite\n",
" 'notify_message': # Invite msg\n",
" 'Requested data is available.'\n",
"}\n",
"r = tc.add_endpoint_acl_rule(share_id, rule_data)\n",
"print (r['message'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The shared endpoint will typically be left operational for some period, after which it can be deleted. Note that deleting a shared endpoint does not delete the data that it contains. The portal admin may want to retain the data for other purposes. If not, we can use the Globus SDK function submit_delete to delete the folder."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"r = tc.delete_endpoint(share_id)\n",
"print (r['message'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Putting it all together\n",
"\n",
"The following code integrates the code above into a single callable function."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from globus_sdk import TransferClient, TransferData, AccessTokenAuthorizer\n",
"from globus_sdk import AuthClient\n",
"import sys, random, uuid\n",
"\n",
"def rdp(host_id, # Endpoint for shared endpoint\n",
" source_path, # Directory to copy data from\n",
" email): # Email address to share with\n",
" \n",
" # Instantiate transfer and auth clients\n",
" tc = TransferClient(AccessTokenAuthorizer(TRANSFER_TOKEN))\n",
" ac = AuthClient(authorizer=AccessTokenAuthorizer(AUTH_TOKEN))\n",
" tc.endpoint_autoactivate(host_id)\n",
"\n",
" # (1) Create shared endpoint:\n",
" # (a) Create directory to be shared\n",
" share_path = '/~/' + str(uuid.uuid4()) + '/'\n",
" tc.operation_mkdir(host_id, path=share_path)\n",
" \n",
" # (b) Create shared endpoint on directory \n",
" shared_ep_data = {\n",
" 'DATA_TYPE': 'shared_endpoint',\n",
" 'host_endpoint': host_id,\n",
" 'host_path': share_path,\n",
" 'display_name': 'RDP shared endpoint',\n",
" 'description': 'RDP shared endpoint'\n",
" }\n",
"\n",
" r = tc.create_shared_endpoint(shared_ep_data)\n",
" share_id = r['id']\n",
"\n",
" # (2) Copy data into the shared endpoint\n",
" tc.endpoint_autoactivate(share_id)\n",
" tdata = TransferData(tc, host_id, share_id, label='RDP copy data', sync_level='checksum')\n",
" tdata.add_item(source_path, '/', recursive=True)\n",
" r = tc.submit_transfer(tdata)\n",
" tc.task_wait(r['task_id'], timeout=1000, polling_interval=10)\n",
"\n",
" # (3) Enable access by user\n",
" r = ac.get_identities(usernames=email)\n",
" user_id = r['identities'][0]['id']\n",
" rule_data = {\n",
" 'DATA_TYPE': 'access',\n",
" 'principal_type': 'identity', # Grantee is\n",
" 'principal': user_id, # a user.\n",
" 'path': '/', # Path is /\n",
" 'permissions': 'r', # Read-only\n",
" 'notify_email': email, # Email invite\n",
" 'notify_message': # Invite msg\n",
" 'Requested data is available.'\n",
" }\n",
" tc.add_endpoint_acl_rule(share_id, rule_data)\n",
"\n",
" # (4) Ultimately, delete the shared endpoint\n",
" #tc.delete_endpoint(share_id)\n",
" \n",
"rdp('ddb59aef-6d04-11e5-ba46-22000b92c6ec', '/share/godata/' , '[email protected]')"
]
}
],
"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",
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"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| unlicense |
nlehuby/OSM_snippets | conflate/201812_BATO/partition_by_ddtm.ipynb | 1 | 15009 | {
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import pandas as pd\n",
"\n",
"import geopandas\n",
"from shapely.geometry import Point, Polygon\n",
"\n",
"pd.options.display.max_rows = 10"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# xsv partition transport_mode data BATO_GTFS.csv"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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>source</th>\n",
" <th>stop_id</th>\n",
" <th>latitude</th>\n",
" <th>longitude</th>\n",
" <th>stop_name</th>\n",
" <th>transport_mode</th>\n",
" <th>geometry</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>opendata_GTFS_fr-se-open-GTFS</td>\n",
" <td>StopPoint:DGL46132</td>\n",
" <td>45.716301</td>\n",
" <td>4.814736</td>\n",
" <td>Gare d'Oullins</td>\n",
" <td>bus</td>\n",
" <td>POINT (4.814736 45.716301)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>opendata_GTFS_fr-se-open-GTFS</td>\n",
" <td>StopPoint:DGL46133</td>\n",
" <td>45.713623</td>\n",
" <td>4.817385</td>\n",
" <td>Kellermann</td>\n",
" <td>bus</td>\n",
" <td>POINT (4.817385 45.713623)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>opendata_GTFS_fr-se-open-GTFS</td>\n",
" <td>StopPoint:DGL3167</td>\n",
" <td>45.710812</td>\n",
" <td>4.820113</td>\n",
" <td>Yon Lug</td>\n",
" <td>bus</td>\n",
" <td>POINT (4.820113 45.710812)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>opendata_GTFS_fr-se-open-GTFS</td>\n",
" <td>StopPoint:DGL3026</td>\n",
" <td>45.706046</td>\n",
" <td>4.820918</td>\n",
" <td>Vaillant Couturier</td>\n",
" <td>bus</td>\n",
" <td>POINT (4.820918 45.706046)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>opendata_GTFS_fr-se-open-GTFS</td>\n",
" <td>StopPoint:DGL2318</td>\n",
" <td>45.702555</td>\n",
" <td>4.821277</td>\n",
" <td>Pierre-Bénite Centre</td>\n",
" <td>bus</td>\n",
" <td>POINT (4.821276999999999 45.702555)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149200</th>\n",
" <td>opendata_GTFS_fr-idf-OIF</td>\n",
" <td>StopPoint:92:7</td>\n",
" <td>48.852338</td>\n",
" <td>2.601715</td>\n",
" <td>MAIRIE DE CHAMPS</td>\n",
" <td>bus</td>\n",
" <td>POINT (2.601715 48.852338)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149201</th>\n",
" <td>opendata_GTFS_fr-idf-OIF</td>\n",
" <td>StopPoint:92:15</td>\n",
" <td>48.848283</td>\n",
" <td>2.585516</td>\n",
" <td>BOIS DE GRACE</td>\n",
" <td>bus</td>\n",
" <td>POINT (2.585516 48.848283)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149202</th>\n",
" <td>opendata_GTFS_fr-idf-OIF</td>\n",
" <td>StopPoint:92:9</td>\n",
" <td>48.844801</td>\n",
" <td>2.582994</td>\n",
" <td>AMPERE</td>\n",
" <td>bus</td>\n",
" <td>POINT (2.582994 48.844801)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149203</th>\n",
" <td>opendata_GTFS_fr-idf-OIF</td>\n",
" <td>StopPoint:92:11</td>\n",
" <td>48.841617</td>\n",
" <td>2.584096</td>\n",
" <td>CROUS</td>\n",
" <td>bus</td>\n",
" <td>POINT (2.584096 48.841617)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149204</th>\n",
" <td>opendata_GTFS_fr-idf-OIF</td>\n",
" <td>StopPoint:92:13</td>\n",
" <td>48.794042</td>\n",
" <td>2.449382</td>\n",
" <td>CRETEIL L'ECHAT</td>\n",
" <td>bus</td>\n",
" <td>POINT (2.449382 48.794042)</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>149205 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" source stop_id latitude \\\n",
"0 opendata_GTFS_fr-se-open-GTFS StopPoint:DGL46132 45.716301 \n",
"1 opendata_GTFS_fr-se-open-GTFS StopPoint:DGL46133 45.713623 \n",
"2 opendata_GTFS_fr-se-open-GTFS StopPoint:DGL3167 45.710812 \n",
"3 opendata_GTFS_fr-se-open-GTFS StopPoint:DGL3026 45.706046 \n",
"4 opendata_GTFS_fr-se-open-GTFS StopPoint:DGL2318 45.702555 \n",
"... ... ... ... \n",
"149200 opendata_GTFS_fr-idf-OIF StopPoint:92:7 48.852338 \n",
"149201 opendata_GTFS_fr-idf-OIF StopPoint:92:15 48.848283 \n",
"149202 opendata_GTFS_fr-idf-OIF StopPoint:92:9 48.844801 \n",
"149203 opendata_GTFS_fr-idf-OIF StopPoint:92:11 48.841617 \n",
"149204 opendata_GTFS_fr-idf-OIF StopPoint:92:13 48.794042 \n",
"\n",
" longitude stop_name transport_mode \\\n",
"0 4.814736 Gare d'Oullins bus \n",
"1 4.817385 Kellermann bus \n",
"2 4.820113 Yon Lug bus \n",
"3 4.820918 Vaillant Couturier bus \n",
"4 4.821277 Pierre-Bénite Centre bus \n",
"... ... ... ... \n",
"149200 2.601715 MAIRIE DE CHAMPS bus \n",
"149201 2.585516 BOIS DE GRACE bus \n",
"149202 2.582994 AMPERE bus \n",
"149203 2.584096 CROUS bus \n",
"149204 2.449382 CRETEIL L'ECHAT bus \n",
"\n",
" geometry \n",
"0 POINT (4.814736 45.716301) \n",
"1 POINT (4.817385 45.713623) \n",
"2 POINT (4.820113 45.710812) \n",
"3 POINT (4.820918 45.706046) \n",
"4 POINT (4.821276999999999 45.702555) \n",
"... ... \n",
"149200 POINT (2.601715 48.852338) \n",
"149201 POINT (2.585516 48.848283) \n",
"149202 POINT (2.582994 48.844801) \n",
"149203 POINT (2.584096 48.841617) \n",
"149204 POINT (2.449382 48.794042) \n",
"\n",
"[149205 rows x 7 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stops = pd.read_csv(\"data/BATO_bus.csv\")\n",
"stops['geometry'] = stops.apply(lambda z: Point(z.longitude, z.latitude), axis=1)\n",
"stops = geopandas.GeoDataFrame(stops)\n",
"stops"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"dptm = geopandas.read_file(\"data/departements-20140306-5m.shp\")\n",
"dptm.dropna(inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/pclf/.local/share/virtualenvs/tt_bato_maproulette-_HzSl9D-/lib/python3.6/site-packages/geopandas/tools/sjoin.py:56: UserWarning: CRS of frames being joined does not match!(None != {'init': 'epsg:4326'})\n",
" '(%s != %s)' % (left_df.crs, right_df.crs))\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>source</th>\n",
" <th>stop_id</th>\n",
" <th>latitude</th>\n",
" <th>longitude</th>\n",
" <th>stop_name</th>\n",
" <th>transport_mode</th>\n",
" <th>geometry</th>\n",
" <th>index_right</th>\n",
" <th>nom</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>opendata_GTFS_fr-se-open-GTFS</td>\n",
" <td>StopPoint:DGL46132</td>\n",
" <td>45.716301</td>\n",
" <td>4.814736</td>\n",
" <td>Gare d'Oullins</td>\n",
" <td>bus</td>\n",
" <td>POINT (4.814736 45.716301)</td>\n",
" <td>69</td>\n",
" <td>b'Rh\\xf4ne'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>opendata_GTFS_fr-se-open-GTFS</td>\n",
" <td>StopPoint:DGL46133</td>\n",
" <td>45.713623</td>\n",
" <td>4.817385</td>\n",
" <td>Kellermann</td>\n",
" <td>bus</td>\n",
" <td>POINT (4.817385 45.713623)</td>\n",
" <td>69</td>\n",
" <td>b'Rh\\xf4ne'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>opendata_GTFS_fr-se-open-GTFS</td>\n",
" <td>StopPoint:DGL3167</td>\n",
" <td>45.710812</td>\n",
" <td>4.820113</td>\n",
" <td>Yon Lug</td>\n",
" <td>bus</td>\n",
" <td>POINT (4.820113 45.710812)</td>\n",
" <td>69</td>\n",
" <td>b'Rh\\xf4ne'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>opendata_GTFS_fr-se-open-GTFS</td>\n",
" <td>StopPoint:DGL3026</td>\n",
" <td>45.706046</td>\n",
" <td>4.820918</td>\n",
" <td>Vaillant Couturier</td>\n",
" <td>bus</td>\n",
" <td>POINT (4.820918 45.706046)</td>\n",
" <td>69</td>\n",
" <td>b'Rh\\xf4ne'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>opendata_GTFS_fr-se-open-GTFS</td>\n",
" <td>StopPoint:DGL2318</td>\n",
" <td>45.702555</td>\n",
" <td>4.821277</td>\n",
" <td>Pierre-Bénite Centre</td>\n",
" <td>bus</td>\n",
" <td>POINT (4.821276999999999 45.702555)</td>\n",
" <td>69</td>\n",
" <td>b'Rh\\xf4ne'</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" source stop_id latitude longitude \\\n",
"0 opendata_GTFS_fr-se-open-GTFS StopPoint:DGL46132 45.716301 4.814736 \n",
"1 opendata_GTFS_fr-se-open-GTFS StopPoint:DGL46133 45.713623 4.817385 \n",
"2 opendata_GTFS_fr-se-open-GTFS StopPoint:DGL3167 45.710812 4.820113 \n",
"3 opendata_GTFS_fr-se-open-GTFS StopPoint:DGL3026 45.706046 4.820918 \n",
"4 opendata_GTFS_fr-se-open-GTFS StopPoint:DGL2318 45.702555 4.821277 \n",
"\n",
" stop_name transport_mode geometry \\\n",
"0 Gare d'Oullins bus POINT (4.814736 45.716301) \n",
"1 Kellermann bus POINT (4.817385 45.713623) \n",
"2 Yon Lug bus POINT (4.820113 45.710812) \n",
"3 Vaillant Couturier bus POINT (4.820918 45.706046) \n",
"4 Pierre-Bénite Centre bus POINT (4.821276999999999 45.702555) \n",
"\n",
" index_right nom \n",
"0 69 b'Rh\\xf4ne' \n",
"1 69 b'Rh\\xf4ne' \n",
"2 69 b'Rh\\xf4ne' \n",
"3 69 b'Rh\\xf4ne' \n",
"4 69 b'Rh\\xf4ne' "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stops = geopandas.sjoin(stops, dptm[['nom', 'geometry']].copy(), op='within')\n",
"stops.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"stops.to_csv(\"data/BATO_by_regions.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#xsv partition --filename BATO_{}.csv nom . BATO_by_regions.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": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
myinxd/cavdet | levelset/mock-samples.ipynb | 1 | 503710 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Mock samples\n",
"1. Generate beta-models according to given parameters\n",
"2. Generate the mock samples with additive background noises\n",
"3. The total number of counts is restricted"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib\n",
"matplotlib.use('Agg')\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import utils"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"matshape = [201,201]\n",
"cen = [101, 101]\n",
"betaparam = {\"A\": 2,\n",
" \"r0\": 10,\n",
" \"theta\": 45/180 * np.pi,\n",
" \"beta\": 0.2,\n",
" \"majaxis\": 50,\n",
" \"minaxis\": 30,}\n",
"matbeta = utils.genBetaModel(matshape=matshape,cen=cen,betaparam=betaparam)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7feff2bf2198>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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SrIXGeY7zHQEjAkjIzwExEMhjqPcrSPGeFKV2zHk2MHdpVPVvykr5HGswiu8E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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ff01c4c2c88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(matbeta)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-21.21320344 -21.21320344]\n",
"[ 21.21320344 21.21320344]\n",
"[-13.30750929 -13.30750929]\n",
"[-29.11889759 -29.11889759]\n"
]
}
],
"source": [
"deprate = 0.2\n",
"cavparam = {\"majaxis\": 30,\n",
" \"minaxis\": 20,\n",
" \"theta\": 165/180 * np.pi,\n",
" \"phi\": 45/180 * np.pi,\n",
" \"dist\": 30,\n",
" }\n",
"angbeta = 60/180 * np.pi\n",
"matcav,rot1,rot2 = utils.genCavDepression(matbeta=matbeta, cen=cen, \n",
" cavparam=cavparam, \n",
" angbeta = angbeta,\n",
" deprate=deprate)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7ff01c4c2b00>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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/CeD/dkU/A+CbMZglXwXw/ky/h4noESJ65LR/yWpQVvDpC43HKJkcJmNQ5Tm/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\n/weWnaJ6TNwetQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ff01c4c2e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = plt.subplot(111)\n",
"ax.imshow(matcav)\n",
"# ax.invert_yaxis()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# generate Gaussian distributed background\n",
"matbkg = np.random.normal(0.8, 0.12, size=matshape)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7feff0acf7b8>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ZHCKBOK8QAwFqzSGzJInyinjSJ92qOTIaJqmTIiNB9YKgvl+TsVQtRHkmmQWT\nwqzC6xf3wtXW/ojcLQvxcIP4fJnyjZRT//kffH+HHsQC4UtIBXMz/ZQvS8yuwOwKZALBcpb0Ygl5\n12Xosw4rayVGDq5QGWhTuWAQ9cUoqe6FBL2ugxHCPz38FZC6IKn98iCGoZAxfPqHv4T0dEhhfaaK\n9ZkqaS4hjg3sVZNjv3KOyLMgk+Aum/hNh8pQi+p5Qfq5Pp1/TzUhyOwIMksmN+4MYc3bpGdKXP/m\nDrxBqB1e49LCMN3JmKhPf2QIXs/B6mgvpXu2xmyrghAKebjJEw9ewZx3CIsp2bEO2TEdp7vnsmw+\n5WN4ghMP3sRugtWWdMe1gkxXXArTBoVpncZxrmfwQguhIMko1no5zWzd7fPIE5eRxYhoKMIxYhwj\npjeS0htWFO6Aa+sDf3uhj9Yrg7huhPvoOuaig9GRlM9blM9btLsubj5k8amU7LMrPHhgBqwUIxTU\nzgkKOR/viMeNdydQDzZRDzbJzUs+8cBZopIiLKWYZsJLbx7CahmYT26AUPR2BYwP1CkXPMoFD3mo\nyeaVPlQhJmlZtG6V6W2PCKoJTkPQvljD9CBrR2TtiL7963S2pcQbLlYh5O5yld5+n96o0oB5y0JE\nel98B+QNbhSJTlcIytobclYNgjIMPLLEynQfwjOoXNUku8LdlMLdFG885uaFcboLBfryXZKWTWHa\nYNu+ZcKyICoAqSAtxFqp2ZqJ66wbuO9oAxYOxqSZlFxOc4SmPjqjC/0MhQgkIpAkhYShN7Qn2tgD\natwjzSW6jicTM/Wh26w9HfLxn3pNexAOOIsW3gEPy0iQAbQm/3ZH/X3hUeSr46r227+Is2Zw+Okb\nvHNtO1Zui2V3OwsCorKm7ho9yYefOsvXXz2qU6XZBCOTkDmfYd8P6lqP6c0+uue1xXF2teiu5sjM\nmUQHepReytDYp0hyKbmBLl5Xk3PMWRcUuIcatJYLmC2D7O4GrdU87oJFtNujr9ImSiTuH+liJfGp\nNTbeHMIfjShesWjvSCiMt2jfLVKebNC5UmXk+BLzaxWMGV0fEvZr0E94EuRWBWgmxer3ODE2x6m3\n9iBiQTriM/S89hDWfriHupOj78gqa1f6MXo6rfuJZ9/m+ZsHUXdzmD2Bv1UbYG8aiN0d0pk8w68n\nrB8yiQqKdJuH60YEvkWy5moy2Zalsx/ZpHW7rLMrqUD5BrJjYHW0wu7u1O8jO2PR271VrBYYZO+a\nqOMtotAP18ZtAAAgAElEQVTEvpglOOhh3chgHavjXy2jDLAbuggPYOczM1w5PUlSjRBtk+xYB+Pb\nJRIXgorC2rlFk4/FPa/S2ZT4wzH5GZNHfuQ833rlMKmrqeVJJkVlEtw5+x4hS+USzDWLuJyAVBQG\nOmTtiLbn0FvL4S6a+NsDRM9kZMcagC7AmvAQdzOklqJ4U9AbFthH6vjXykTVmOxti9SG6kmdjVhc\nrGIvWIg9HcLVLP2nJOvHFJltbSarm1ydG0IaCvtiFm9Yj23oDcHkL1znzes7yBR9kqsFwoEYe90k\nsyJonfAx7QT3nRzdCe0eVHdu0n2zj7CsKNwWyOfW2VwpUrpg05lIMXxB9lCddieDWtV7RtmKoVcF\nS08n2GsmtYuKt//k+9yjiHKQG+wS7/DZ8HNs+6wg6thEHZu4uOVLGYrsvIEyFC9O72H0wApmR9I/\n3CRpW3QP+rxzYQfvXNhB92IVZcLAkRX8W0XMpgHHWqiFDEaoyXjusonv2TjXMjoVVkmJKqlmyA10\n2fHAXeJEIjsG0W6P8T8ycP9thQN9y2wcNNg4aLB0dUCXUAeS9lSC0ZVEsYHKpESv1IjKCStvjFAs\n9EgyiiSjMBsG/SMNzJ5EuVu118WIyDe59p/2kr8jKc4AAoxPrWB8agXnTJ6xE4sEXxwgu6NJOBLh\nbAq++oVHqHw5R+XAOv5ohNPn4fR5ushqPYOc6rB+0MQIILOvgXU9S28xTxIaKCcluyApfnCF4gdX\n6F2s8NjDV1B1m8K7LplZi9RNSXZ69Pb7WGsm7pxFbzIiM+OQmXHATIkKil898CJjf2jS2xajUgjL\nKdHZClElIR3xUUIDeklWcfHqBKmrsJZtlK3wbxfojmnAM67GeG2H4lWTyrY6ItUpS380AichKCtO\nL+uMk6iEKENBMUJYKYVZxVMPXeaphy5Tecciyadk75qY+Yj2ap6V+Qr+bIHycIuomFJ73Ub2JC3f\noeU7xBUN3EaVmOyOJplPrhBWEzp3i0S1mMpZ856SWD89yPrpQXJlD7MniEKT3GibjSOK1EkJQwPv\n/xih+HaGJJb4Aym5eYPcvEGvX/LuwhjSSvCXcsQ5xcmD0yQ2dLalVF91KBU8usc9xl9IGH8hoXW+\nRuIqMjubJBlIv9oHStCeSnnmiXO464LwrSpqTVeJ2g1JebyB9xMNhC8xO4Ll58K/1Rl9X3gUzrYx\nNfxrv4yohqhNm8Jtg/bUd+Pz3Jyke9xDbdqalqogt7NJ+nqFzv6A4jkH+8PrbMxqS7/tSwkLf08X\nGcnredTeDup2jsTeIvAsSnpHPeSCe69PgtUW9KYiSATlSybdx7pETUenRjMaF/hOgVT6nfssGHQn\nY0hh554lbp8bxRzvUin0aHYzpFcKhJMB5rxDdklbxyQD4tE6Hxyd4cWvnCC7rGgcTDHbOhugLEXp\nmrZm4RZG4awbiFgftiivcEa61Apdli8N4E61yX+uwMZBQeGOnktmI6Wx0yAqqq2eCYrh1xSNn+yg\nFPTWdH2LtWkQlbcWQGqFlxlrU8z6fHLsAr/38tM8dPwmb1/cibtoEkwFWAs2yaRuyJB/K4MyoX0k\nwMmFBMtZRCKgFjA5vMHm58dIbYgfb+LPavq36gv58N6rfPPVI1tzk/QmYh47coPXr+7EyMTYV7OE\npRR3baveZ7uuj3j48DSnzu0id9ugM6WzH3FoUHrHJSxA5Qlt6RMlWL84QFyLkE1TZwdGErbtXUb+\nn33Uf6lDfSNPsdKjtaYB8OIlC/VUnfZigep5g+Yuzb8IRyKEb+CsGCT7OzhOTLetvUNppmTPZEls\n8AdSlK2gECGEIo0M8pUevTtFytcE0XOae9OZL0IxYuJPDOp7LH7nF36Pn//8/6JJU4UI+2qG9HCb\ncDVLaVzzYnoXKkTjIaJukeZ1LU3/eJ32O/34AzFGOcSczpBZFmQ2tWFt/Vgb66USMlL0fqCDv+ly\n9+f+4fe3R1HKeYhEMPkHkup5iTeoKF01KF01MEd6VK9GpIGBM9wjySeooQDPszUvYMW+56KZbYnZ\nlkSf3qRW7mBcy5Pu6RB2bc2zGPI58dBNUgtUw6bvvEJNeqhJj6igY0KEIqhA5JsUbpiUjq6TXZL8\nrx95geJIm9RRDLwtGHhbIEN9uPpPGSx9Y5zMkiRcy7Jyox9/OUfmSB3aFmZX0HusQ++xDu6aIggs\nvvW5E9gtaO5SfPShc9pirAh+7ZnnaX1A112YHYnZkUTFFG9HqJmKhkIpaLw8hLMhSc+U6A1KEFB/\nOKT+cEjjxzukDpSvQVKOESk0txt4PZvMV4tYDYNfePxFMmtCZ5G2PpmxNr5ns7JW4t+fegK7Lrn4\n53uRnsQfiXFmHML+mDQRpIkgqEB7MkXFAi4WyCwaZBclqWey+jVN5OpMpPieDf0B9AdYcw7vro4j\noq0CMgHlyyYXP7Mfe8kiDQ2qVxKScqy5LXlFZt7EKIW88/Zu8jNaARpdgw9sv4Wo27RPekQlRf2V\nIeqvDNF8bRAZweBwg9yCpDcZUZmsY8uE2Y9a9HwbIRWt9RzCThB2QnuHxqgMT9KZ2AKQI4G5ZmH1\nedgtSBODzBdKGIsOxqKDFIr2vpCwoijelJSuGmSuuZh3XDK3bTqbWdxVSWs7GN+oYHxDtwSQKw7z\nPxXjPdzl0xd/DMMTWKWAxDPIPbJO5uUCKpvgv1vFf7dKNBEg12zSTErxskVlpEnODol2eZhtA7Xi\nEvQlyFixfliwfliQ+0KR7qgiygmMMwWePnL1b3VG3xceRXZwXD3+Zz/C0pe2EVQgu6xoT+q/jR5b\nYvbWgEbC+1PsliQspihHkZs1QEHtw4ssNwoEbR2fPXf4El+9eBBz3dJMwgWLqJRSvSBp7YTE1mXX\nx/bdYdPX1nX1lRFdSt4V+KMRpcsWvSFFNKiBPXNNM92Mie69cYebLiKUqGyCU/KRFwpklxUbD8YU\nBjtEkUk8kydx9X0BTE9XoVp7WnhdB+UbWBsmIoGopLsfORs6Ji8Oa0TeNhM21guU37bxBjSyHdRS\nyjs2dUHaTBHDE0Rj32UyFl/J0J6CuKqrZ61KQLKSIS3EmBsWpBCXE40FoEv0k1yKCCTOhiTe28O8\nkcU43KTbyFC8aMMTddI3KlQ+pFOqcws17EWL3Dw096h7aUqRghoMeHLXTW791j7mnjGwG9omFW8p\nGrvhQx85yzdeOoZQUL4K6w/o8m9/LMQpBojLBYq39d7cPAjuqqA3lqKkQn5HyZgp0kpJOxYiG2Pa\nGqOJGi52xcc+nSfOQOooEkeRVGNEV4OJ/nBMZsHEG9fYy8CrJq0f7BDO5XSTGUM3wBEvVWid8HW6\n1pOIRJBmtgxT07iXAjc6kqG3U+afSxka32TzzAC7Hr/D7Je30xvVDFIAq61o7lFkViQjz97VIPia\nRTrhUcx7NO6Wyd01CE90SG/rvek0BEFZNxsaPB3QGbUJSoLmkZC+1y2MULFxWOBsCnqj2kM0e5LE\nVtgtSby7h2kl3PjRf/b97VHcl/tyX97f8r5QFIkFiZL4Jzs89uwF9v/0VRj3YNwjSAyePKbdph2f\n0amy7JKkdkbS2RnhDShanxsmXMtCKCGUvP5fjiPthMqBdVBQOr7OyCsQVARqwsMY6ZGZs1j4DztZ\neW2ElddGKN3SVsIIQeZizd6c6oJUOHM2hi/ILQqitQzm+Tzm+Tzusok72qH/NZNgPUN2RVH7iTmc\nZYvgUhkpUzJ7GogUBs6kDJxJsVo6gxPcKfDA1CzuvPXdfpLZBKsj8McjMgsm/sUy/sUySSqovmbT\nmdD9JUde9XHWJI2bVboLBQxPMPjgMvasgz3rkHs7S/1IgrsmsHIRxesmI39sM/gWHNk5x9SJOeKB\nCKMQbfXHFCTVCKfq3fNWauUOzga6sU8g+dmf/TLpGxWivGL++gDz13Xpf2EWorygf886mTXNCM3u\nbGLMu7z81kGWHjMpbm8gA5ABbBxRKAu+9uYRElcRZxT1A1A9J4kKKdaahW3H+IMxflXgVwX5PXX8\nPkWaTShMtEiqunPUv3z8z0hDA6MjMRcdSnmfUt5HBFIXdZ1skrgKuyHI7WySu2FTuSjJLSpGJtcJ\ndnuUB9uUB9ukJohzBZStENUQoxLQvlukdcLnkZ23ka5moDrrkiePXuXJo1dx6oLunw9hNSUDZ1I2\n9xrs/KOI1fUi0XjA5WvjBFUFqW4gFJYU235qGhEJ7IbSKfGWSTruoxJBGJuobEJnX0Du5RylW1C6\nBYM/ME9mTZBkYOkRB6ub0jygPcPekGD1pEKG4G4oStcMStcM3YAoEvgTIe7ZLNFWHczfVN4XoUd+\n95Dq/81fRG3aGH0B9qUsQUWPK7OzSWclj7VpgIR4KEQFkvHJdY7W5nn+1DHcVRMl1b2qyNRJySwZ\n5JYUQVlg/8A6vTf6dNFWXlG6oXPNtR+dZ/nrGkXvjaSkRd2xSbQsCnckvSFNmQ4rmiZefDFLczfk\n92pij2tHdAObXtdFCEXcshHZmB2ja2z+1zGyqylen2TzMQ1EAeTvStpHAuy7NvFOD7XqkuYSCv0d\nOnNF3BWDnR+e4for23E3dFjQPu5DXZPNlJNybN8dzp/ZQf6upDuqiTiFgxs0buo+F1ZHkNjgbgq6\n+wOcGUc3bG0KcguKtZOJLkx7KARP8y5ysyZhWZGM+aSxxFyzyS7qoqbOzhir7GNdyBMe7JFEW014\nYskzhy7rgraWg7VpEvVFVN+xSGxBd1RRvgGdcYE/qMOCA/vnWGiWaN4t6RZ+iUBWA9JYsnd8Gf+3\nR+iMWDSf62JuEa4AxisNZk5NsPPhWZr/boKVhzWPJLOsXXmzJwgHt1LqsabxD7wlaG3XzWXjnEIk\nmk0LkIwEFIoenTu6yKt0TaCeq9O+WSZ1FdmRDlknpPt6P2FZkb8raD4Q4N50GHxyAYDZ60O4Kwa5\nh9cZL9a5tjqIulAk84BukJNb0E2Ov8NtAN3hLOhPdDe3yZTqrk02ZipQirAzEaGnaeh2Q5Ls1A09\nM2eyBA91+PDUdb55Zzd+08FaszA8QXpAY3CZGRt/OOHgQd0W8+LVCU0UbDkQSQo3TC7/i1/5/mZm\nuiPjavA3fxGrbmgq8u6A8jt6ZZt7E1QmRWZi5IJLdlnQ3pFQGm8Sv1ZFhpBbTtk4KO41cHUe2kR8\nrULmh1ZYqxeIehZ9gy2Cl/ronfAwrRiu5cke3aToak7Ao/0zvL0xyczsgK62yyhULqb2tkX0sQbd\nnoM5nSEYiXQHaSB3xyQ82iFeyaJcna8uHt2g3XMINjI6xdk2kDFEpa3uWRsGmTVBc39M7YxB58Md\ngpZD7U2LzcMKd7RDfKMA23tEXa1czA2d7iuNNfHPVHVzlRUDd11RPxFTHWrSvF5l/4N3AFhoFcn8\nYYXVH/WY/F3B+j/yCV+vEdQUpeuQ/x+WWH5zhNzxdTaX9EERvqS0rUnvUkVbwIEYLEXxgo3VVfg1\nQenJZRaXKoitpizmgkOcVZrnMBjTN9Fg/W6Z7JxJb1fI1MQqM9ND5Aa6fGeb+Z6NWHJJnRRRDTk5\ndZtb/89elj8U6ybAuRhr2dZZnoy+KLUAoSjdMGju0XNXBiT7OkR1F+lJZCwYO7YIwPy7I+T31mks\nFMFOQQnyNy22ffQ2Vy5OoHIJ/YNNuq/10xvXCqx/W5322/1Ur6UsPpVibxi4BxtEZypE+3q4mZDO\nUh6RCCqT2lA4/7nK8uOKgakN2q8OUHtyidVmnmA9g1GKGOlr6LKBrG44BODccik9vMr61T4yKxL/\naI/sO1laB0OceZs4q+dc2F3H+JLO4m1+MECFBuVzFs7HVlmdqZEfa9G9UwIF+TuS3ErK0lMJmXm9\nZ7wR3fRpZPcarW8MEZ9scf1HfvP7G6MwPTh56KZudXasjbFp0Z1QdCd0yzujYcK6w4efPkvrQEhm\nwaA5V6J4JyEswcrDGiT8DlfBO1fFrwkarwyRzmcxGib1qzXahwL6Km24lidxoL5SZHa+j9n5Pr7+\n7x5jdrWKtBNkCIeP3qbvDYuNYwn+5TJiLkMwFkEk+eJHfpcvfuR3ifIKMa0Bp2xfDwRs3qjyzNQ1\nrHKAs2rgbApNL491IVLqgP3sGrnbJhsnI8LlLFY+ZOPBhPxdSTxdYOD4CuaVnC6wMrXrXLxm0LpV\nJrUVRk+Sn1NEBUF1qMnmSpHyVcGVU9u5cmo74at9LD0miFoOrakMwVs1uttj4nzCyM/cxo9N2Nem\nea1GYaBDYaCD2ZWEscH2k3eJtgXIngGBxHukQ3Y1ITzawf5XNfJXHfZPLLF/Yol4OMRqCfIPrlO8\nYdK4UiMz0CM60uHA1AILr48xuWOFzJ8XCWb0x3EjxKgHEszbLp3IoTUpsVcs8jPau4lHAsL+RP/b\nBgnmYA+VSUlsKIy3+Lt/99sEAwmmmZLp71G6IYiLCWvfHGXtm6OaufhSlcyiiQg04J0asNgqYtQC\nstM2Gzdr/y977xls2XWe6T1rp7P3yenm2Pfe7r6d0Gh0QCMHEiAFZlIUKVFprDBVkiwNpRqPa8oz\n5Rm5xmXPeGSXrbFKM5Il0aJEimIQKZIACTRSoxudc7w5x5PjTss/1sHFuMqWbELloVzcVacK1bgn\n7r3WXt+33vd5aexpY1R0jIrO1kwWb2+T1Q+7mGX1nOpiEndfA79hUK/a9IwWiGzryG/nkN/O0fzJ\nEtIMEUKSWAzZPN1H5K0EkU2DoGLS8g28vU363wwwliMYyxHcdMj6YoauC+ra92smzS6JtWIidam2\n3oVkX35djU4N4tdsMj0VypMB63NZpBVivJhGZl2MhqB6pM3Wx5oYCQ9nXeKsq12s5H2dwulefBuG\ns8X3NEZ/4BWFEGII+BOgB2Uu/n0p5f8khPivgV8CNjt/+k+llN/+m17LHh+Q/b/yW9DfIixZpG7q\nlA6rZaRW1zGaAr2puv3Opqr5KhMqVyGMhsioT/xWhNqYujuYRZ2feOFNvnznCF7JRmtoiJ429jWH\niRemuToziBX1sCwf6zvqjlreq3B1rf0KpT7+oWmuXRsleU+n+bjC6zsbktLJNskLah/daEkq4+BM\nljjQtca9/22S0jMtxLLNwCmfRo9B4QA8ePI+V851zGejVbQzKVp5iVURtLpDeic3WJ3qIrKp46ZC\nkjMa9X5J/OC7PhQRQJAKMAoG3RdCVh8X6G2BGK5jXY4z/ME5pk+PAOCPtDBnbZis4W5G0VoCo6bR\nHm7T96KJs+kx9/MhMhQ7BqfYOYf6oCpPWhPKnalZAXbUpbkaR6RdwqZB/ozB9pNqdyXfVeFAbo1b\nv3uQ4j6Ugrbbw563yD26RuV7vXhxdXU4x7YBNTnbSyZmVflK7G2lMpUnFZ9CShA1g8im/u6uxwHw\nMz7OkoneVtBdaYZYGwZeJiQxrVOdCEjeVRNNbUjibKhcFP/pMp8av8LV0iCb/8sohQMK0yfSLsaC\nreT4KMK1uWzBRB1vyyF3QcOqS8q7NPyjVZKxFrW3uui66tPoVu/T6FEWg9qkWj2tvD6IH5fYW4La\nuIfwNfS6htSkyv0AtLSLEJL09x22HvGJ5+vUyw7WgkXmjuTR3zrH1946jlXQScyp51R/rIZhqAlJ\nnk3TGAyID1VwXeUFCafiBEMtjDkb0RnOzuEi9UYEf9smNq/Tzkmm/8l/AlOYEKIP6JNSXhJCJICL\nwMeBnwBqUsp/8//0teyBITn6C7+JfWKb0mwGmfTQI+/Wp4k3o1T2hHTv3WRtKUvPQJGNmRyRTZ12\nNsRZ0whsdgw+e3o2mf/6GJUDLta6iZv3MVNtxHSM5DS0P1oi9qUUGy+0OTyyBMBWM477hR4qIxrN\nEQ897mHdiiICaIz4JG8b1E+oskW/rMRDjRGf+JRS7LVzIc66Rm2yA7VdhuLBEBkJMbcNuo4o9+TK\naoaPHLpGr1Xhr3/7adyfK1K4nyV0Qo4fnGaqkKd6K0uQCNk9qWrhjVoc/ZsZjE9u0vYMjK9nECFU\nxgRmXQm4Wk0L2YHrRpcFjX5J0Nsm+0YENyWojitS876hNea/tQs3Jek5scbyhoIBy0KESH8deSuh\n7mxjTVKnHGqDSi9iNCF4rIw7ldwhL2kjdeyIR20uhehuYV+NqgmwJGhOtLGWLNweX/E5OgwLbcUm\nUhDUd7tE000a6zFlLdck6f4K/htZAhse/LHbnLk3ps7/jQjV/R0ASyJEtNUWsnm8SMpp0fRMvBfz\n6M+rLJhSOYZhBhhX40pwdrCKZfn4FzI4m5LW8xXk5RReUip1JyhPhtUBylgSsyZwOxAYfdskOaMY\nE7mfWaD275RGZP2TbQJPQ2xbqhcRD4jNmrQfaBD4GrKlo8c9WLF3nK2tQSXqi6yrQR5flNQ/UMOf\njuPnfEZHN1h/dQAvpcC9gBroR2uES1HlKA0FIhLg3LFpH2jCik2Q8zA3TLysulkOjWyxfLsHvSXo\nPRvQ/MUSlz70r37gieIHDgCSUq4Cq53/rgohbqMSwv5fH9FkS7kKmxHsgRpCSJprHVpRJKD1TBW5\nEqNwqRvdlhQSUZBgNFDGGwHtQRd9UZUB82cSaE8XMG5ncHs8hCHpz1bYjvgMPL7NtdlBsuUAY8Hm\n1oxKEmr3+BiHBH7aw1kwmXhuEbdX5971IaLzBtUjbezbDl5concGirWpv3uHi4SEo1VM18AbCyh0\nm+TP6Ww94dP70BqrV3oB0CzJ+c1h1qfz2Ht15Lk8kRDaeTh/dxfOrEX0WJHMv49zz1Qgmp88/jZf\nfuYokWaERtlBHA0xixpmTb23fT6DeKCGUe1kdPRJzKogsC0Kh0O0XBuxaRPWTWZf3IVTlrhpMPWA\n7Cm1Oqp8oI5l+oQ18GMC7U6U8m6JvdW5208nieoh0TWBUVeDy9/jEY24VKyQ94/f55VwD6nTNlKD\nthniDrrsHVlj5u1hRMcx/U4fyYy5HOhZ42JjmOQZBz8KqfEW3nxA7XMVzr01iegoYGuH2iBVGSJc\n5T3xUpLmaoLWdprQgL2fmmV2WzVzdSNAux0n9tgmzVe7aM3EsabBaUnSP7/I1LVBZG9A4r5OQ/3E\n+N0ez+6/w6kLB9BaapWTumxRGw2RhqSV13BObHF3to/uqBr0ft2EQGDVBI88f4OLXz6EFwfzVpSg\nNwBDIpYcdh+fZ67z2SjZ2KsmbjpEbwsq72sgZ+JEJ5Vyc7MaVxNxCGZHe1Lb46GtRQkTAT9+5CLf\n/fNHqO0S+FHJyV2znK7vxp6z0A6XCduqR7H9ah/hLg9roMWyHSP6Wv4HGZo7x99Jj0IIMQocAd7u\n/NOvCSGuCSH+UAiR+dueX29GSMxpxJw2/v2EcvRZIdIK6X7NRNNCEnMacrxOmHexLscRnqC226Pn\n4AZeXGJumCR3F0nuLuLb4PxZmuQMRGcszGWLjUqcINC4eWYMw/bY3m/iJUP0tuJyCl9gTVQQdoAX\nl9yYHeDu3QFkwkd3VdhPO6tO7jtd7HC8SXm/z2efOIOR8GhsRdH0kMhdBz3hsX1ENdKWb/cgB1vq\nkfHYuN2Fs6zT6gpp9wToLXCGq0yMrtMcc/EuZlj6nBJ6IeBb8wcI2jrNlTj57gqxOZ0jz96lnZNI\nKyRzX7kwQ1MSmgqS0trdYnjfGjLu89DwItEVjexAicaYx/ZxHz8mmVnopvKBOpUP1On/Q4vW9fQO\nGt+qQHr/Nkc+cYPRbAEhIWIEuAkoHPcpHPepNyJsl2NgSl4+f1DV9SPKem/MK2fpaiWJPlHDG2nj\njbQxG6jdqbkYV1/dg6xYtLrAf7jK/Ew3xc/U8XydIOUjzU4eSNnkYw9cJXMTZNZFhIL4nEBra5j7\nK/jJgMVSmvg3ksS/kYSZmNpd+H4XzW6JPlZTqMQJwczFIfS2YP/BBQKHndhJUdM5vbALLdum68Am\nXipg8FOzZPYWePbR63gJSeVWDmGEuJ8q4n6qSOaioRq1upKNtx+uEUZU2E5kQ1c7OiN1Vr46Smst\nRmstRiTZJjQkVlnDj4ZYV2MgoLIZp163aTUtWhNt/FRAfBHii2AUDbS2YGh0i1d+7yS1PaqPEikK\n3rqwl+isupbNV1PK0BcK0k+tEZ01aa9H0VuC2rj/nsb4e54ohBBx4C+BfySlrAD/KzAOPIhacfwP\n/zfP+2UhxAUhxIWgWkf4qhaPrgrc2QROpomTaVJ8oUFzJU69XxKsRDGsAPdIDaMpSNwxsX4ni1kV\nxJbFjmU49/Qqmw8JvBdKNHa3CWzJcLZIaz2G5kHg6wQOmBXt3eZP3CcINOJXbcWMiLchEmJsmPBE\nkeQZB6MhaA+52FtgbwELDtEFgy+9+iijPduYSZd22cZ3JNYth9yuIqKhkxorErvgELvgkMtXscoq\nZs5oCKQTUNvr0VhMMHW/D1oa0TVJULE4vm+G4/tmaN5LM/GHah+/eDtH7NkN7v7pJNEVQfK2ycYx\ngXUqheYp2bGXlMSv2kR0H2PD4uZf76WVlzRaEYxtg6GRLWJLGpoV4G04eBsOSz/nY28JUs+skTmw\nReUBl63NBGdfPcBKJYmfDNjaTOAlQ6x1A2vdIJVoIKZiTIytEV3SSb1l46cDEpMFrP1lfE+nXrcJ\n78aJXbeJXbepHm4rvF48xNhXQa9pmFXwPZ30dQOuJnHn42CGJO8bJO8biGyb783txf9UgdgNW+Wv\nPl8kjPtYLyURMZ9a1aaVEbQyQrEqTQUdEiGEM3HKB3yGX6zjrAsI4f6bo1iPbhOkfDUp2SGPDc+i\nCUmlYWOvGxR+b4Ti7Rwv357k08+dxt4SyLqB/aU09pfSVJ9swrkUbq/H/VIXwZIiroWWxHyoiNQk\nciZGZUI5XKUTYJ5LoHkC88EiZk+TIAL2pkCPeaSSdWLnHaJ3IhAJCSKCICLouiQREtZLCUpPthBm\nSO/ZgMgzWyptDOVDaj1RRfoa0tdYvdut4MyBUNfJ3feWHvqetkeFECbwLeBFKeW//b/4/6PAt6SU\nB4aSN7IAACAASURBVP+m14mMDsrxn/otzEcKlLbipPM1ykVVRlAzkLokf06ncFgisy7S1Yhmmniu\ngVe1sJdMWv0+wlGzZvKiMgnFHtmicC+LURfI3XXC5SiRLY3HP3GZO799iKVnNR4+oazpq40kpW8M\nEPvwGu6f9dDsEfjHq3T/scPyUzphNCQ2p+Om5I7BqzoW0ntGUv5cleZUqrM6ETjrUmWj9rcI6gbC\nCjEX1Qa+7graOVV7SkNidjWxLsRpDIRENtUEEu6t4ZyJ0+5wJt1MSHRZJzUdUB7XkRo0dnl88qGL\nfP8LJ0nN+awf14kuq89VesDf2ZaVGlgTFTRNElxI0xzy0RMeQcUkumjs2L/3PD9Nqe2w+nYfwVgL\nY9pm7Il5ZrdyKix3K4I0JenrmkoIQ93pug+vUzjbS3RFUhtSk5/+cJFG3UaGELvsIJ4sYn1D9UK2\nHvdUv6JmYhY0zLpqOjb6JPbuMq5rsK93nav3h4hNqd/Mi0nMAxW0N1PUDrdIXlTljRdXIJz4SJmk\n3abeVn9fLkXRzBBdD/E2HYQrSN0TeAlB6n1rrGykkU2d+LSJd0z1tdz1KLv3L3P/9gB7f79M6b/z\nKL/Vg39AnYv6oCTIeFirJm5HKp9/3SL5uWW1BTxnIDVFVq/tbzPUX2Bxtot4Tw3zpRSVcXUu03cE\nhSfaxK/atI7X0e/E8PY0sa87tB9o0P21CG5Co3DwXdm/26c4o+2cRAzXyX0jSuMzZSKmj/tiF9UT\nTTRNYl+OUh8Md8bVsaP3uXpqD/q+Kg/0rfAXj/7+//c9CiGEAP4AuP0fTxJCiL5O/wLgE8CNv+21\nNCOkMRhgtRXEtbWQRUuoL/wOBk5qOporcJJNgnMZQtPCzwdEV3RaPSHJ2wbtnJpdy/vVHnJwJYdm\nqsRpf9MhNVGiPmDx8uuHCT4UoifaXHpZ5fbJvXXc/T7t0720D4dYFYE/E6f0iyUigUZrIUFtr8uz\nB+/wxpxqsmUTTZbTKZKvponEwHq4QHUqTXtPm76uMssLObS6jrlq0u5cXGbUQxZsPv3o23z924/g\nRi10S5Ue9biDXjSJnotT2avSvwCGMxW2lvsp7tFJPrZB/ZVu0CXXSgMK17ekmKPN3ndi1tVFnXnL\npN4vcNsm5s0orT0tnCmbVrdAgNKKdOIKbiz3KTpTJiR1xiH24TXuXh5GBCrFXSZ8ul81KU90IEOo\nuMf16z0EQy6tEYlWVp4a29exbjk7sBYh1SAFsNZMMkc2SQy2mb7dj5uTRBcNfuoDr/Mn5x8hfs9i\n1oujDYfUx1QzMT5lUluPEbUBoXa73lGQyriPqQdsXO4hHFECJWM1ghTgxUNyo0WarkmtlcTL+rgX\nexg/ucT0nX58G9rFjhPUE8yeHyLSFNz/Jw7hVArG2wx82WHlyZDIloa+btHqkhibakJqdQm25rox\nSjpmFYLnikSMAPurOWoft8hd0Mn/VJmFbJqfeN9pAL5eehz7nk3tcIuE41LpjdCbrbCVtmHJYeV9\nAT3Dm4jbeeSEQpfHbY/x3SpZTW46FA4I/LkUblGjfawFoWD3wDp33D60Dj5wpGebxd/djfusj942\nufbtyb9tGP7NY/Q9PPcx4GeAZ4UQVzqPF4D/XghxXQhxDXgG+Pzf9kKWHiCtEG4nVOx7VOJsaDgb\nGru/4KKvRqiOKgBK0m7TGPTx9zZUTJ9QuLz2Y1WFZW8K7DUDraWhNwVGU+D1ukhTUqk6/Pz+t3HG\nK9irBub9dy9m7VYcLeHR2t1SVOlOrekHGq2mhVnRSObrvHJ7L/v71tnft04+WsfY6ghcekIqs2ms\nooYMBda/zZJ721Cg33yAuWFibpj058qkr2t8+eIxvAGXXX+uhEWtmQQEgtQU1I80wZS4VQu3arF8\nqQ//oSrNXS7GH+So7fEQmmTtu0P0nlxl45hGa8Sl9+QqvSdXeezgfRX0+5hHc7yNfSlKc8Qjdt1W\nDtRQkLyvlK7vcCKCpoGMKLv7wI/PIn6/i8i2ctPKaECmq0r9ExWC3Q3MioZZ0WiNuoR2iKip0GWk\n6vV4riKIGw0lXa6txyk/6FJ+0MWsCtbnsiy9NsTPPPEmybsGrQNNvnjzOPFcg+6Lbayy5KeffQNr\nw8DaMKjtcbHXDLwHahiWT3WPj5sGqUsiyyaO2UmKvxzFuRwlGFCAWwLY3kpQ34yi+WBlWgS7msxc\nH0Cvqs/vLJk4SyYy65G6p9LI9g+uEhusYtgeS88r2nWkqPio2RsQ2RZEthU71Ix5xJcE5f0Bo5ki\nWwtp3I+WKC6lsIsh968M0c6FnFrbzam13crYWAVzMYL2vQxGyqV0pofxEwsMfd8j/7ZO7bVugqzH\n4B8YDP6BgXctzdW7w0QKGseOTOGlQ/SGwN6GRydm0HTJwndHSV6KEFRMgopJ4w/6lYgtEqDP2TTH\n3xuP4r3serzJzkbZ/+n4GzUTPzp+dPzo+Pt3/FBIuCOjg7L3n/06etRHNwJVV6bVDBi56+zU9JHB\nGjHbpXoxj5sOlZmqq03qLZvyHqnAIahkpcjFuEoXbwtkJ/uz2asSpaJ9NcxXUvhRqB9Qy3tRtJBZ\nl+xrEYXKSwQ4iyZ+VBJbEmSmXFYeN0nMQn3gHQiNJD4P0a2QxH++yN3Lw9ibGv6DNZiKQQheRmHu\nMgm1jGy+2I1VkWw97rFvbIXZU6NqCR3ziU5bSF1Be1MfX6HwYj8A5tNbJP5dioXPBlAzGZ9cYf7c\nINnDm6QiLe4t9CB0iayped/uapL8qzhmI2TlGUgPl6hfy+LmA6JddRrbUYyCgZ8MSN5Tz/ESwOEK\nvq9hWQHtlomYc3jwyXvc2eomCDR0PaQ+k9rZ4vR6PB7bN8XpG7tx5k38QzUeG53l1WuTCDsgn6uy\nXYwjA0H0tlrit3IKAiSGGwRrjvIrjLTIpOqUbuYwxhTYN/Oqzfbj6hqwoh66HuK8lKD6TEPlaTZN\nkALd9un7iwhP/PMzfOnUowDExsp4lzO0uwIy1zUKR332TqwwdWkIc6ROMBXH73Pp7i5TaajPFf9m\ngu0jEr0l8Hpc0hcjhAZU9voIX2WM6kWDyWPz3JpT58WOt5FSYL+aQPvgFsH38pQnlX3dfnib+rUs\ngSNVGRx/l5kSDKnMkXxXhc21lOJjFk3MisbEU7Os/8ko5ecapJPqmgm+kafZI2gO+BgVnYEHV1m6\n2sfk8Tna/1UPU5+zyJ/XqQ2KHdq7M1CjtZhAc4Xyugz8/yEAaHhI9v0Xv6HCf6IBQpMqowHVHIzP\nCWqjEgab+E0De9EifS+k5x/Ocv+744Qm2EcLtM+rverQUrW31ODHP3iaP7t4QhVZvsDcNghNlRZt\nFowd7UHskS1q5/PK0djnolkBQcnCzLYY6Soyc20AESjfgezUgZnBMv5rObwEtPo8tJhP6GngaQwM\nbyOBtc0Uo3+kUR1Sde3YL93lwtk9SB3MsjIs6Q+WVUzBQ2qpmM7Uqd3M7hCVpa7wfT/1wdf5kwuP\noJUNIgWN5qiLaOvoVQ2/22P0S+rvN365SWMzRvqGoki7+xsM5ksUGw6O5bG+mMFKt3GrFs68+lyh\nAcahMvXNKM6SSXuvIoD5eQ+tYijeRc4nPm0SmJ3PZUBoKHVlK6+IUK09auKNzCqYSn5XgXrL2iFc\nmYN1vJUYySkNnitQWk+QvGWiP7uNlALrLzNsPKWk8pmrnY5+RFCZ9DFKOn6Xh24HhL5Auh2XZMxH\nrEdUqQR4CclHnn+br54/hrNoYDQh8fwa6ze6QQq0oTqmGdBciaN3ktk1D+wtQeZDKyzc7cGoaugt\ngdEAqyLhI9sUZ7I7KW0AoROgNXUmDi3x22Nf4zMv/Qp6xSBIK75mKx8ydniZqaletLr6LkZd4A6o\nXJbGmEf8rkltQjXipafR01+ifLYbebCKu6YEdHZ/nWbRAV+V1fY2VB9tYN6L4o43iV5zaOUkmgfZ\nW2o8r73fJ33RotGvKGf23jK3Pv4v/357PUSgcG9ato0za2HfjyATPjLho3nQyqM8E3UTYYToTaj1\na1y9M0xzb5vWsEvtbgY/LhX63BfYG4L0A1v8+euPkrxpMTSwza6vSOILivqkV9TuwTv76NvTWdyJ\nJv1HVzFWLPLpGnrKw7oSxwt0jJryaiTmNDLXdDLXdPzXcognirRzgdrrXrHRzBB7xcQPNQ7nVhAb\nERbfZ9H8WJnmx8pcnB9m8ug8sQUNZ1PxJV1X5+QvXEZ4GulMneJGgqHjy/gxiR9Tuojx4wv86c3j\nOLMWYdKnnQvJv2WSvKvjZ3zsRJu5T8Pcp0HXQ4yyTr1f0trTQq7ZLNzpwQ80tq93EZ03ibwdJ3PB\nZPTZOUafncM4VMa/kSSabxA5ViCXqdF7NiRxS+EHw9Em0TmT+lBA91MrdD+1gn64THzx3eaiH5c4\n8TbmQgRC0Osapat5gkDDqqiHux4ljAa001BaSRKdMxn/5H2K60lKy0k2nvaI31ZOWSQgwSpLlavR\n38ZaMYnGWiSu2HzkyBUIBMaczeArAY1Bn8agT+rQNi//0Ul6R7Zp9QZ0vbCE/8UeklMC3YXkyzHE\nuRT5i//R5a9B70cXWLnYB5qCxYgQRj48S+FIQP1yjp95+g0yN8ROMj2A6Gqz8MYw//jXf5X4fVPl\nsV4xaR9oEkZDpm/2k+mroPkCreP1MddMdn1gFjRJ/VCL0fF1ZFtHGJLS+W6CqES/ktjpBfn31CT7\n84++iZcM8W0IyhZSQOjp1Hb5BI6S329/tMn2R5v0DxQIbDVpGuM1kk7rvY3RH4oVxdiA7P3nv67u\n+AkXr2ah1dQMrLlqT/wzz7zFXCPHhdcm0TzBiedvcHZuFw8MLrNWT1J4sxd7u/NdJOQ+vcTSG0O0\nRtrQ1jl+cJrZUo5CMcbE/+xz7x9GIFT71wBBxWLP7hXuTfdhbhuk70Dz4yoExpmz8OKScKClSqOC\nWq7aXU2kBONSgsCGcF9NLdFnHbxkqC64DUVTElHVcEun69hfzLBxXKkUBx9exv3dPkq71d2xts+l\n55TB5gfbdOdUdN3WdZUjoXngZgMO75/n2rVRnFWdSFH5T8yFCFalo8zsCUnfFVSebeCVI3Sf1tl4\nskORNpXV2csEiKiP2FIrinci6vSWar7mzutUd4F1oIx+Kk1tSKK3FEX8nWBjaUjcbEj32Dab9/Lo\nPU00PaRdt+j5vsnmUZA5FwQkLqrfrD4o+dizb/PqyoQyVvWIHdVme7KJLFmKMBUq6zuAHwU3FxCb\nU54FbbxGe8vBzLaIRHy8a2mcBwvK+YpCALpJgZsEjpXx7ySVBd1X+HvNVVuyelMw9twsoAxjpbk0\nIuti33QwmtDslsq7smhhPFhid26TW2u9hFNKNdx7dI2Ns31EDhfh1Qy1kVDFFFYstJiHLFo4qzrt\ng02CSmeF3CGbB5N1tYK6HmX3B6e5enMEvamRvC+QhsCLo0plID5apvdfGdz/NZPU2zbdn1xg7ZvD\nVMcCjK4WzrkY9cEQ2dNWNHkgf3SdjUKSYCuCva7T7gqZ+40fnML9QzFRxPJDsut3foXItK2UijWN\nSLEj9X2kgB9quK6BfTZO9XCb/aMr3Lw/iJ1u0ZuuMDfTTeKuucOwCMab6LMO+t4qTsSlsJFkaHCb\n+l/20nxeBRd7rkEmVad8TeVVap7abQkySjMvR5uw4IAGPW+HuAmN7M0a8x9K7FiB9aagPegSv22p\nbcBjZeplG3PdUri9aIiwAz57+Dx//tYjgMp1aOzyEE0dratF4nSUykRIarxI+2yOdjYktCSJGZ3o\nB5Q/pHChGzToP7HC2psDRFclhWM+Txy6y1tv7UdzhUrQ7vQozLJSsYqpGM5GJ5ns6SKNRoT091Q4\nc23SJXnd2tFqaG2BfLCKuxyjb3KD9Ss9CGDsxAK27nP13jCJrhrWX6c58ovXAHjt1AOEhgSB8iC0\nNOIzBuaT25h/kaXeL+h5boniVwZoZ9T5FCHUxzzMgoEcaaJPObi5ECkkOAHWkurTPPP+K9wsKH31\n6lYKTZPEzkTRntumdiNLMNQiecahdMRDuBrJO7oyoKEGmNEU+H1tJv+bEnd+rZsTx+5xfa0P2/Jo\nvaXkzI0hH/EOWyPp0fuSiRbA6pMSI9disn+duWKG2lKS2ECVWjGKPWfhT6reQe6vbdafClTAUDVO\n+14SPx2ozFa7I7wqa3gjbfYMqnO5+o0RwqdL6KfSVE80iV5xaAyE6hymfbUVPO7jLBo7ZZx9X4nM\nvJjqoWTvBqw8qSbqQwcWuD49SO60SX3g3R6FtFUGrmyqeMfeUzrnvviDTxTvTa71d3T4yRB9LYK3\nt4msmqSmoLi/U6CfyaK3QI9D/mqb5gmfm1MDHNk7x+2Xd7O4x1A1aqDyFQDChkV6FspGAjlWo+sN\nk8WHuzAmIGya5LI1qje64Hidx55VMo83Xz8ImgRPw8sG9GaqrBcjGNkWhUqMVk/A5kkHM13D7+y9\n+10BkUWL+PvWWVvJELmRRORCgoik/3XJ0o9Jer9j8t3svp1gHinAKKmaPzIXpbwvQG9oNC/liNSg\nMRxiJF2CQY/1jQ4rYrRF/ILD3FQPXSc2mUhvceH1SSJaQGJao9UFXksn1q++f12LQTVCfhr8GMog\nNpNC6lD+QB2vZGPGXdAszE6Pxo+BvJUgsQXaPmWKkoerTK12E5RNYvMG4maG6IbP219SBO1wIGTP\nQwvMvTxKM6YxtmeN8uUBCoU43VLiH60xu5InpYkd7kNs3iDW1SDxaoKNIYEuBfgCIuq3f8dafer7\nDzL4impmyp9VSlepq5CfYs4necmh8VQNsemQP68Bkv2fvQPAxVOTylV6xeb2b+ZxVjTOn9+D3tsg\nuJGCI+pm0RVv4gdqouiO17j7TD/mlkH3Wdh4Tuf22V3YmwK5zyW4mMayJfJgFbmgxIDVYY3RXavM\n3+pDxnwiLsiqKmmDuMoEDaKS+BWbOVv1z2Reogca7UfqhHWT1kMN9vWvM/viLoK4RmigUtRMiN7q\nNIAPN0icjlLf7bFndI33/+RtTm9PcHVmkOt3h8AM2T6p+luR6Y4uxNUREvSTRWpVm9D4T6jM/Ls6\n4rkhOfoPfhMhVb3b6pIEPQr04dyxVZbjtlItuhNNZKCRyVUpzmRVPkVXnUbRwVpTyzu3TzXDtJaG\n2V9XGReuRmy0DG9kqE6oTrbIuMhiZ+kdC3CmLeV6XLbw45LIpoa9LSk+5BNZVWKi7nOKfwGqqamn\nXcLCO/gigUx6RJMt7JeStHKCD3/6Lb5y6iRhQs30Wk3fSfzWe5sk4k0KaykylwyaXUIZe66mCPbX\noANXzd6U1D9Voe9fm8z8qkbQVPVs6kKEp/7BOd5a20X9zS7CDr3JSypIr1VWQb6xaZP6iK8ClRct\nWv0eGJLUVYtGXyfqcKRJGAicGw71XYpolXw5Ru25GuJmgtCQeEnJ7oNLrLw4rD7XbZ+Fj4Xga8S6\nFXTYvZPEy/sYJWMH8eelAkS8U3q9FaF4wkMrGUSKGkFE4vZ7jA5tMjfdQ3zGoNkdkpwWO6sQ74Ea\n4WKMvrdCKqM6rZM1goUYYTQkfUOjdLJN7tUIpY6mKLTUzoreEGTuSmoDagA2Rz3i903qAyFWX53I\nWwmVQA/0vKlRHdbwY5JgtIVcjxDGAoQdELthw6Ml6gtJSLuId64ZQypKV1sBlq01A7fb58i+Oaa+\nsZvargB7TcdogPmMcrY+0LXK2e8c4lOfeIM/PfMI1lYHZHTAY+QbUB00qO4Cc08F7W11o6gPBmRu\nauR/YpF7Cz3oW9a7sZhJl4EvmGwdNvESksDunM9uNX4yr9nUBwSJE5tcfOG//fvdzPQd1dBsnqjT\nPqruitnXI2Rfj9AY8clMFggfqiJ8iDge2rZJ5VaOyLYyOvm3kuTOmkQPFYkeKmKtmpglndwVgbsR\nBV2i9zSRUhCaYGZaJO/pyIJFZFtXjyWTzBNrdL1mIQ1IjRVp50PKkxI95pOYk8RGy2wdFkSHqkSH\nqmgtjaBmYlQ1+ic2Ebk2xrpF4qsJCg97tA80+fK548TGyjvftW/fBmEiILKl4RdsumJ1egaKFA8H\nClf3Sgp7C0wzQHOV4nLrsMDUAxY+GCUsWMSmLGRbo7or5MLmMJsraUaen8OPSvyoRG8IPvzseUXE\nbqudFaugk+6qET22Rfy+iVY2aDxSJxxpEo408ds6Qpc0hoJO0rfO9iMe7ZKNm1ZLeRnzWXhtmNqk\nS23SZfVRnZ7+kpqQX0nh30gSRCX4Amu8gt/n4qwL7HWDVKpBKtWg/kwdvWgQRkMCSzVr45kGa28O\nIDyB1gZ9qIHUBFYJrBJomirjll4IqfdLwlmVH/vgoRlKB0KSF20KhyV+3sPPe+htQX7fFu6Ax+Yx\nlbnRHFacTamBtAOV2v6BdbSWuqEUDgrctMQbapNK1unbv4Fo6dj3bPwTVfwraWVU7IBwkKC1BHrC\nQ1ohWlVHdwWirXF5eph2ViqnaxP6PrSAY/o4ps+5vzpEq8/nSy89jp5Uxr9Gr8ReNSlMmjS7BPa+\nEl2JOs2ekGZPSHxep/iIS+mPhzBX1Sow2NVCi3uIOYfNX2xQ39smOcUO7EdWLGTFovZ8jdCSbK6l\n3tMY/aGYKKQV0hiQxKJttT8ObB8P2D4e4Cwb1C7kMYwALykZyJR3tj79qKSwnMY8UKHwZJvSsuqc\nB7taBAMt3IQgtqAjnADragz5dpr6qE/McXGfqqA3lSHJrEJ7yGVlJUt1VOCsC7RvZolsaUSXNCI3\nHIr7oXk3TeCE+L6G7ysYTs9gEa/Lww81InccnMkS9QGNeKZB4CrlaLUYxV4ysZdMiq/1Yi+ZWBXV\n6Jw5O8y+7DoYIXKwReVkk8aAJLyaoj3cpj3cJkj61G5mMQ+VGd67Tn2Xh1EyCA1YXs2gVXU26/Gd\ngRJbgVOLu5VSta5RHwkIx5o02yb5347Q6A8ZeD3Ea5qkX3FIv+JAS0fTFIlL6hJZsrAXLNJXTezh\njurSkAT76hzdPcfR3XMqr+TlbpDQyoGbC4muaKqUWo4jPRVc3M6GtD2DtmdgXYyjeQIr00JvC4X4\nv58iemwLq6hRHQuJnYpR3htQesil9JBLOBOHmE/f93XobzF4dAUvGXLlyhi7JldpPFJHcwWE7zxg\nYyMFnmDs0DKaJxh4SSM+p9EYDNCiPqk7gtKZHkIrJLRCIgVlJpM1g+JMlkorAoZEGiCuJnA2JKlb\nBsLTCOMBYTwguqph3nNwlgysssbos3NEB2vEb6ugYquiURsO2fjLYdxAxw10rDJoLY38oQ30ORVj\n2X9sFf2BMrWxgFZfQHU1wdK1XqyChlVQr5E9bdH4RJnM7U78QMXksfFpjLrA+U4SUTHZetLdMQY6\nvTUm9y/iNk2MmkB33pt79Iei9HD6huTAb3xe7cv3t7CmHLU8BhVgHPexoy6tWgR9zSKyp0JjMYGM\n+xiOj1+xMJIuclXVZ2ZFwzpSpLqcVAQjCTISIlrKKKU3BOF4U0XrdeCyP/voaf7s9lGG8iXqf9TP\n+tM+2Z4K9Ut5MifWCUKN7WKc0NPQiu9qPLysT+6CQfuFMvJsGj+qBFPVoy3sezbBoRrutk3qjqoR\nFegV2g/ViJ6O42yFFPcJzIpAGmor0PqISsyOFDuEo13gpwJ63tQo7Bc8+OQ9rn9/L+1MiNbVQiw5\n9DywzuqWumvoCzbBUAuhQViyyF3QKO+GriuSdlJgfmqD9em8ch6+s1F0oIoQEHfaeC/lKe/3Mcq6\nyjkZcTE3TMRYnXA+tqPv8BMhxHxEwSQ+r1HbFTL9E7/Hb60+xFfPHSNx3yC2GrJ5DIJOFkZi2qA+\nEDJ8aJWcXefStXH1/prEzjcJpuJ46YD+Uxprj3Y+W8Yln6/SermL+pEmMhQYyxGcdaF6Kzo0hzyi\nc+q8JOdCnvzHZ/mLaw/h3LHx41JBdTUFpvEyKhPk4X0z1DxVNt49N4pRE9hHC9SbFrlUnYPZNb5/\n8QDZKzqFEx5azUCmvR0IT+Y1m9qgINyr2J0iEJhFDb0tMI8VidttglDdi8tn1c6VP9nAuBtFP1xG\nO51CPFGkOp8idU9j/LP3KPyLERafszDGaphn1LaoVZU084LoY1tsz2aQVkh03sQqg10MWXtCbYf7\n3R6a1cn1mLNx+z0Gv6WzfkLD739vgqsfihWFCFQdblYEsmjR6vHpGSzSM1gk2lPnxPgcXE1iRV0+\n9Nx56gUHq68ObY18porwhYKVtAVaWwXshG9lsNd0nBVd+TR6amg+hDkPaUDvVyJomxaJ+waJ+wYv\nLu8jEWuxeGGA7QdUglZhJYUflazN5mi93EX8ooPQJJGiRqTY+el0yYlfvgygfBT7qtQfbtD3TZPm\nsIdbUnemwIbAhvL+gMYDTawrcfo/Ocfa8x7t3k5wTUJtda6vpYmtSrwPlPE+UMZPqVT17RdaJGfh\n/J1d9D++BJokLETw0z7F13p3lsReOiCbqSOEZNfeVSpj4PW5lMc0Gn2CzWKC6KK6u9nb6uFEPNp1\ni8bpPJVjLdLXDMSw4oAmr1mYFUGwFFV6lliIH1O8EFE0kTlXNd9GKuz661/iq1cfAjvAPVml8PGG\nErdVdMyKzvBHZokUNbZeHKDxCylEqpMhagd48zG8TEDfqxriP9vY0Sv09ZSI/Ics1T0+xoJN7IaN\nNVlBe7aAUQd/fx18QWCrGv3R3zrH/WoX8Ws2z3zyIvEFGHzFJzJaxd5XIrpgkHvb5PLSIBv1OBv1\nOEZdka2Mr2fQb8fZ2E7yxvwYIu6r5DAzRO9pgibJvGaTec2muF+SuRviNSzi0waTBxZVWHRXSG0m\nxfp2imI1yua9/E5ZmHjDwUuEON9MUp3wadxLE1vWqEyEXF/pZ/4Fk+iaoF23aPZImj2SVlbQGHep\nv51XWaue6p1JHTaPoq4DW2JsmehGgG4EBBF13pbeLwksSfb1yHsboz8MK4rI0JAc+EefJ8h4dwnS\nDQAAIABJREFU6FEf8150pxHWHHF5+uBdLq0NUllNQCTc0VswF1US1Q7tKn9NrUIWftrHvunQzoeE\nBkw+sMDtmX70kkF0RWP4I7P0ORVevj2Jc7eT/ny0gjedILl/m9KU6lDnLwm0n9qgeK5HZYQeCDFy\nLaUcBQwjoLEZY/AlQfGnazTnEoTxgOFvCtopjY1HlC1bd6GVV4256JqkNihU2G+2iTedYPz4Altf\nHKY6opSfsRWBH1XfCaCdhfaYyn5IXI/QONZAAIGro5khcjMC+Ta7/oN6j8JkhGaPwDxSpDGdYvzB\nJea2sopHKUC/EUdzVe3+xGM3ATj/tUOYNSgd8kGT2Msm7VyA3tLoPROy9FEfZypCqyege0LxLwuV\nKJomiZ5Sr2fVJYX9KgO1/FwD+0IMN6WaoGFKnRtr2UJOKM7ns58+z7dOH0UmPdIXI1QfbcCKTdeB\nTSqv92A8rICwlZUEaDCxW+W7WkWNgWcWmV7qQvoadrJN1//u0Ppl9feb8xkSU4YCDB2pEn0lTnVU\nrQD93Q2OjSxwcX4YuWoTHVf9o1opij0VoZ1TSWdhl4u+FkEbqeMWbHpGCqwvZTAKBnJYuVS7MlW2\nignCdRvNU4ntzkQZ7dU0jX5JdE1QO9rEmH2XZWlWBV5CYj1Qwvd19nRtsvzHY2wdC8gMlimuJUnc\nMbHft4nxBbV1v/ZBD2vBws2GJO/qlB9qI6oG0g7J95fZ2kiCqyIKhKeuAZl1kb5GJNFmIFtmtZT8\n+0/h/tHxo+NHxw/38cOxohgZlP2/9Xl6Jzcon+rFP1plf69Kpp4u5NFeyhBGVHiLPFGmuRpHa2qk\n70H1/XVir8YoHg7QOpwEvbdBGGrKd1E20fNt/tlD3+JffvPThJEQGQ+wFyz0B8qIs6qu9+KS0FTL\nduwArWiiu4LIliJG+TFJfF5TAJNIR6TU1SLcjmCWNBUVkAzI9pUpzmf47GNn+OYXHyc0oDHmITr+\nEOlqSkcxWqNdcNT3uCMoPBgQ763Rup8iMQPVMeg9o56z/AzE53WqezwmJtaYvjFA6q5Go1/Jdps9\nIbuOLDM13wNA/I6FPFnGbRv42zZ6tk30fBQvpmr57tMGm8dC0NkB+O5ObvLtsw+SGi4TvJ6lOq7S\nwoNVhzClMjeI+VhRl2BGKZvCgRaROw5eSuKnfLSGjt5Rd4amxO3zsOcs3ExI7qq60+mf2WCzkCRo\n6xi2j98ysBcsErOSer+gtb/Jk7unuPKFQ7SeVlCZ/kyZ2Vt9SFOi1zV6Dm7Q/HoPvFCgVIxh2j6G\nEbC3awOAyzd28fDhKd6+PcbI1wRrP9dC3lUsyuiaoHTQJ9NfpnU+h9ZxXwcRaA14HJ5c4NqNUaQV\n4iyYGEeL1GdThJGQ33nui/yL//FnaXZ3rtuiel47J8kc2KJ0Pb8jtNPrGlJAbEmj70ML3F9U52bi\n3wds/5cNqjVHmdtKKpdEZt2dHZVIpoUQ0Opk6eILhKehNRVU+KlPXuKl1x5E6pCYUaHeRqODZ1jv\nwG6eqmC+maSVk8Qf3KY0nX1PyswfihWF8AVaS9D8eg/tjETcSrBYybBYyVCfTtHslbiPVpEajGSL\npIbKaIMNvJjALdrIDxYxyjrpW4L0LYHfMskkG8Ru2OQvaej3o/zLb34aISExq6MXDDQXPr/vZZqH\nmzQPNwkt6LoUIlyN/r4iuisIBlvUJnwScxBb1Cjv9/HTPnpLDYagaWDUNNweH4QK0WmfzhPZ1Pmr\nP3+c2qRLbFWS7yuTOh8hdT6ivAl1QTgdZ+D7gkhBpWdjB7TvpJCaxNkO8btcrF9dxfrVVdAUoyI6\nZzJ7aZDoqkbpIRe338NNSUQomJrp5cDYMgfGlrGe2KI5lyByKYaRbxKxPXxH8RTQUGj9tEfX2xoh\nghDBd944glXUKC+m6HlhEbOkE7kUo3f/BtnuClpDo7u7TKTTYAMIG0o9OPzQMtamgdHfQHhg1JX0\nXugh9jZERyvon9lA/8wGPdEa1i2H7FkTv2RxdPccZhUKz7foemYFfdHm1Sv7FEj3agJxNcGx3AJ9\nezaJ5JpYJY3V6S6qY9A6nyP1to12K05jM8bdzW7ubnbzk4+c5eLpveglg8Wf9Im/FCe0VIxB6qMr\n6EmXxpUswcEa1mPbWI9tE+yvodV1pr4zTmxeR7MVVzT2pRRde7fQGxrfLx3ATSoOhtRV/wCh1Kal\nShQvGdA1uUX6mkH2miBS1Kju8dn42vDOb1b6p3WsP8uS/m6UeLSNXtXVzcvtELsDQTATp7Xt7ERk\n6hU1gehDDRrDPt89d5gg5xGmfMoPuthbqi9nHSiT+egymY8u88jgHEj4+IfPUFzIEF15b0P9h2JF\n4fQNycFf/zyhrjwHIhA7iLYwEiItiVHRYbCJfUkh4R956ibDTpGvfOMJ/FiosHOlDm6uqZGd3GZz\nIYOI+shAI3HDorYrwMirHkP3lxw2HtIYPKnuqOvfG6TVpfTyiXMO9UcasOTgp33SV01KB5TcN1LU\n0JSWhcZuF1HTifQ1aFUj2HMWB567x92/2sPAj80z/9oI7fEW5nwEt9OwjE2Z5N+3wuJ6Bsv2aRVs\n0tdN2ilIPb7O5o1u5ano9TG2O16HrI+TbSpS1V2H+JJk+7AkMavx2Ocu8eKdfYQ1k2RnZ6Vy8F2R\nlVnSESHEF8CPCip7lfBqaGCbwit9tLrVqiU6XqY/WeHubB9mzCXxSozaEPSfXGH1TD+aK2j1BRD3\n+LWjrwLwu1eeIn7BoTYcMnF4iXszfQo2U1QxjMIXCmDcFHRdVu9T+1yFymoCs6gTRCVWQSOwlTTZ\nzYSE0YBkT43qfIrBSSV7XrrfjVnSkBN1hIBgKUrmhiCx7DH/MwFiPcKuI8vMbyqvh1e3yJ4zKe+R\nRFc0/GhHpzPZ4l+f/Ar/Zvo51tfTpM5HVMYsSjyVGiljWx7eV7sZ+7l7XD69h+T+bcJQo7SWQK/q\nRFc1qgc7F4CrYW0YihI2VsffcpB2QO5tk+L+/4O9Nw2S7DrPM59z95t7VmXta1dX9b6gFyyNjSBI\ngABBQpQ4pERRtqhRSDNjWROyLFt2hMOB8XhsecIjydZEyJJFS0NLpFaSogGRBImNWBroDei9u6q7\nuvY9K/fMu5/5cYpNOSb0Y0hFCIrQjaiIXiIr82bee/I73/e+zys5eHyOqwsDONdcvD71PFZF49iT\n1zl9fbeq0mKhvB+OxBxtMfJrGgu/kOC8kcXfwVJ7o8om0B5MsEebGEbMEyM3+eq1o8iKRWpZpz2Y\n0HVJUL5XNfdy19Xr8nqlMo+Zkrl/9Lfc65GaGpRDn/slnMNV4tNF2iMx5FTzS/o6qVmT2EEZwV45\niO6pBqbUIX/fBsd7ljj3fx+jckD9vu7LSo3X2hVBglIFSujrrdHyLfQXi7RGJVE6IbuTTvXAZ97l\nxXcPgS7JllqqWXi6iF9UQT1PfPIML375PtxNid+lVrHEAO6p462ncdZ0OhM+dPS7JXvXnm0abZvc\nC4oNAXDgl67w9p8cpTkR4a4YSsfRVrzJoCtRNnsjgbqJta2+Bfz+iGxvk9FClas3hzm4d4lrF8dw\n1zTCvGTsBY+Vh126bqqLZN8vX+HKdj9Nzya8nCcY95UBbt3i2EPTnD87xfjhFap/PMT2Peoxqb4W\nYahj2xGthRxWTWAeruHN5JFCTQ2W/l5I7nWXILezjQigMRmrcnlLx7ynwmihyuZ/GWfjVIxZ1Snc\nVAE+qbUdlWUawn1tNCFx3YDGQo6uSxr+MzU0IWnUXcS2Rc852LhXfZ7umkZnOKbrgkb3ZeW3Sa1J\nGuMQdasGePFbLp2PKxNdGOo8On6bl08fhm6fXE4lh9UPhnz8+Huc+/cnqI9rtPf50FKLq1HTiFNK\naWmUOjhnM3ROtMm/5KqbT5egST5z/AxfntmRsMcaUgqymQ4fH7vC1/7zB6geiLC2dXKzKpBZiyDu\nCsld3Gma7wiimlMhZtlQ1UAaBt/wWH3AITjcJt620bp8nEvKZt7aEyCMhOIbNvUJsKbqZF0f4/Pd\nrDyqRuupVUFjt1LkAiAkfYc28P+sj/ZHGnS2XRZ+5pf/di8U6e4R2fPcL2Bva6SXJE/9/Bt86dWH\nAFVhSE2CmSA6Ou5gE0NP6PkPLsuPOjibCiY7ObXK2tdV4HBrl/rm09fUrL1zso1YcEEqgIe2YeFs\nabQmA8wd96hzNk3jUIDo6IhQIAshUyPr6FpC5T+P0vp0jXbLQZ91yCyq112fgCinvBrmWItwLkNm\nUbEZjjw+zfq/283CsxK9rt8FpeqeIEorBaCMNEjAXjEJxn0enJxl5jf30xwWdAZinE21iHl9MVpH\n45FHrnDma4cJ8uozc/bUEELSrKYQesL4gJpGzM70qwsbKPbX6ZzrJj+bsP6BmOx1E69HMTfy52zq\nEzsp7oNt9GsZzOMVGqtZikM1mm0b/UaGIJ/gjDaIbuR4+EOXeXtZJZJ11jJKp6JB/rpOfSJBlgI+\nduAyz185QvdrFm4lZusn2nhrSo5ub+oExYTHHrjCbKObrOVzZX4Q2Taw1w2MNjjbKuv0u6HTWrTT\nQ7IkSSbGyvlEKyn0nXG4fbRCvZKi79tKR9EY02iPh3cnN6llndY+n+xlWyWuucphm5vRqO9R5z96\ncJXVSo74TgZtvEXQsOh71aDdp9HcFSv25uslWlMB2Wuqcu16aoWF6/073AqBNxySvW4S5CG2FTnb\nrCtr+Xcdsp3BiOFvCVo/VaW6neHIriUuzQ1hzdu4Ryu0LxeJXUnvGVh9/Lsx9wKzqiuOSjrBWTM4\n+sQNzlyYwuprY72TJTHVxEzfcZO3BxKskRbepouQAntDZ/q5v8GQYiHEHNAAYiCSUp4UQnQBfwSM\nA3PAp6WUf2X4oT0xJA/8xudovdut8jwl9B1UjanVmR6yt3WaJzv81JHTfOkPHwcJ6Yc32VwqkLlt\n0twTcHRqkaunJ3ZOCjLz4BfVKKowDa0BdfNJSzL0bcHGcaWajHbCY40Vm5/5+Iv81sVHcdyA5Hwe\ncbxGkmiYZoRjRpS3MwgNugpKZl65UiLKxaQWDeJ7GkSLaeJcjOhooIO9rqOF4JcSHjilDEvvrg4R\n3cjRfVlSH9OY+OgsVy6NQTbinokFbn59is5en0yhTXRe1Z7ebh+xbWLVlTfC3hY09weUeutsV9Mk\nnk7XGZPqvp1QXychtWjQ/6ElZud679rko1yMkIol2fuOQsFvH/oukBcVqjTYJqjZ6A2dwnXBxOem\nFe6/L8FoCsJigrOmFjC/S9mq40ij7xsW5UOCsEu5J4OSqijcdcHID93h2oJygvZ822bjUWX5tso6\nQUklXxkNHX28SbCeQuoKuBtldnwLE23SKR++1UX1UITmadhlDW2nH1I7GGFWdKyaWoy7PrjK9ssD\ntCZDcldNmmMqxa2112d8eIuNl4fw+pK7FHSAoGbjLph0xgOIBb/48Iv82tkPY8/aWDUICuD3KOT+\nsSmVGG5oCefOT5G7rWG0JNqPbLG5lid1y6K9K6TrnAECapMQ73h9jKrihwBkZky1nShraCdqdNoW\n2oqjxGdbSkUKoHU0UlNVwtAgms1g7m5gvJUjSoE82sBrWuDpWNu60vIApckym+t5el8x8T5ZxbVC\nzj79K3/jzcwPSinv+Usv4p8BL0kpp4CXdv7+Vx+hhheYBEX1jWRvaThGpH7WdYICDPVU+d1vP8b4\n787SHosoz3SjN3TMhsQteFy6Mo6xu4mxu8lHnzxL49EO4dEmaKrTHjuQHmmQuWWw+akOUSZR2LlZ\nB3vWISzE/NY3n6Cr0MSfzmFXQUrBl07+Du3beZodG1m36O+uUbtQonahhBYoOW6nL8HfchGxoP9l\nHQohqUX1zemXEnJTFRIpSKSgs5zB2RQ0RjQmP3qb2XI3zoaOuWxxaXGY9liE6YY0tlUGiRYCTQN9\noIO9DdFAgNcjceYtlfFxwWVoeBvdB3u0iT3aBENieDC72IM7ZyEScLaViMwqa+Rv6mwdh/IJJblO\nrWhMHVlUTeWrGYyKoXoGH6vy7ht7SGwwGgJ7WzWd08uS9LKaAiVrDiSC9QclUT7BLbUV76Gp1IBS\ng6vXR8iddciddWj3C9w5i56RCsFgoEA5Gwa9R9bRNIl0Yyb2rOFsSw4+couDj9wi2nTuxjek5lXE\ngF9MME9tUzsaML57Hd0XDD2xwNATC8Sf70PfmWaIBJJcpKowX2fudh9enzrv4lgF53QG53SG8V0b\neFMe9opJ30iFX33rSUqvWGghPPDZd9GO1GAnj/XWn09x68+nsLSY0gVBe0DS6RNsVzNodUPFSy6a\nDPz4HJUHfWU3r+iYFR3DEwhPZ3CsTGE2xqpp+N0J7YbNwFctNB/c3XWMFuw7uMi+g4skfT6OGRHf\nzpCYkvBWFr8oMZsQz2TIXLdxe5To5ruA6VrTRbNimp+o07lWQPzXHywp7K+rojgppdz6S/92E3hM\nSrm6k1H6qpRy71/1OzJ7+mXv//JPSC9otO5vE9ctxr6mXtf6SRO7Ct7DDfyqQ6anRXyhQHSwSdgx\nuXdqjrPXJ3DnTOIj6ps+Xkqh+aqPEZQihsbKlE/30/92wPY+i+DhBpYZEZ4r4tynyvU40YjeKRLm\nJWF3hGjrjH09ZuuIifPoFomExtVuwmKEWdlpMqYT9h1aZO2Px2iMgxZA+ug2leU89oaBVYNOn2T4\n+Arz19Q3qt4RZBYEtQc8jGVbgXlseRezp3uqJ4GZYDo7is2ajVHVyd+GTo+gPeWjbylzkDcUkult\n0VzL4O4AZbxJD9OJGP5tkzs/bKhA3jkHZ1MQPNwgm/LovNKD+WiZ+s0dcdnBTTanS8hCiPQ10ndM\nvCNK2GVaEZ3NFDix4jvuqIdk1cJd0fEOdhCrDp/7yCt88Y8ex91UvAwSQba/wb7SBucuqJBmCiFi\n20LqktGvJwRZncpeDe1ojXbF5aEDt1hoFFm50gcDqo7OnE4pl6tQsvHUoo7uQfJYFW8mz8H7Z7l4\ndezu9WRWdcKBgGyxjfvlAu1egfWBLbZX80zuXuPW9ADYCelpC6/7e1VY5o5OcyLGrGlEYx65t11q\n+5RJzlnXiDKSZFeHeHMHXDTUJAwMHDdA+04B/5TKOA3fLSIitWU68ux13p6ZQH63d+BrmDUdsUu5\nbVNOQG0xj72pY1UVEiDqDnHvWHh7d3iuO2rQRIfKkRi3t41pxDSqKfJnbVrDCneX3aOCiQFKX3XZ\nOiYIczGFawbVgxEL//M//RutKCTwohDivBDiZ3f+re8vZXusoRLP/8ojjHQSR9I45mMYMUZVZ/5T\nCfOfShTAdiUmjnRKbxt02japVUlYs9EqJrd+fw+ZGRNvMEbOppGzaZIeJSm2KgKny2P1ei8fffZt\n5j6h0RmQpL6doV5JEWUk1fkC1fkC+d/LEhQkjLewcj5aj8fWYZPWIY/a5W68t0qEuRjha4S9IWFv\nSHpeZ/VPx9EiSK0KsvOQsQMwE6KUpNMriboiml8aVNkYdkKUj7E/tkEq4xP2hcoOvbup2AVOQjDu\nk7tu4mZ9hCYRmmTvb7XQfYGfFyTHGtiLFk89doE4JSlcMjG+VVAycUv9pK47xEsp+OebZGd0xJrN\n4P0rtEYTotkMlVoaw4PKZhZrVwNrV4PypR4mDy9By8BZNolcsO2QqGbR2UghQoHpRMhIU8i/FQej\n5JFdSNCW1I3zuy89plLWqgqUImKBfKPIrT/Yg7Oh42zo5M45uGsa7orO4o9HbB4XquF3JY+5YXL6\nnX3UvjFAUgoY+x2dsd/R1QKRTzD31EGCU5Ykj1VJEoEzVePi9Cj9u8qklgxSS4rlQCxozxQwf3wd\nv1tSWSjiLpjcutWPvakzOLhNayrA6KgAZndFx/zgFu5AExGDtuzQfLDNE/ddUqPQ/R7pnd6UzETK\nZ2TEJBvq3B/4zLv42y7tmQL+bg9/fwe/S3L5z/erhfemRfqmhbuiKiLzQoawYxJ/p4v0nM6uL63T\nHlDBxEYqwmyB7Cgvkq4nxKYgfqqKvWngraSVWrVu0O5XtDKzIRjI1dFmUmgzKTZPCMJiROaOQW1f\njFnTf6Cb/K9joXhYSnkceBr4OSHEo3/5P6UqWf4/ZctfjhSUzSbWlk7PyxbBeoqoEIOvg68Tj3gs\nf0gSb9k0RwRxzaQ+AcKJSfIRflEovkBZu2uZNudVqlWnPyGcT5OZ1/jWFx/AXTE4cGqWdr8AT6Hn\nvpvvuX5CJ7El2nQaptOYN1KEWYkoWxhTDbXwdHuKO7BsYi2rSUxrWNLuU9i1+gTUnx8ge83CbAkG\n3o6x1g3CjEBra2htjcMHFmi83ovxap7Du5d44uNn8bZc7MNVpJ2Qfdehvj9EXsmp6MNIZ/p/zKAf\nqjH00Xn8loU/FPKN147Re886zTFJ/cEO5qahRF+uJPXIJqULcGeph/R6gruhsfbmEKV3VWrYJ/Zd\npLYnxr1j0Sm7dMou7oagFVq4SzphXjX49vZskB1okF4w0NsayVIKAg2nLNTPuTSNUUUj++yT3+Hx\nBy+jhWD9zCrrTwWcPHqL3LzKVu3sCujsCr5nKEurRdBoCfxDbfzBUAGMOoIgC2LbwvwXa5j/Yo3a\n4RB7U+djE1eRuqR8X0Tndg5vPot/PY9e09m62EtnKKYzFFPcX8YteBgd2HivD2OqgVnq4O3xSM2b\niFjw90bfoX+wQuQqzIEWqgzcznJGcSZbgrhs89Jr9ygr+oZFawRYchkb2WJsZIvwvSKJlRBczfPm\n0gS7/yhS24x5GzcVkJ8BryQRocbwR+YZ/sg8UoBTFnT6E/R1i/SH1Qh49l+nkLpasGwnpL4vInfd\nJHfdJFl28XqhOZdXYr+c0u2kRxoEPTF6QydKS+5sdd/V+IjRFva6QXNSvXdhKfyBbvK/1qmHEOI5\noAn8DP8/th726Ijsf+4fIjydD5y8xtvfOEzPKVWQrJTzuBdStAcTkp0YeL2pUbwqqE9COOKjGQnG\nTOp7ryOBYKrDaN82a68PEWUUodg5WCV4r0iYTTCaGtk5ydb9Ow2jTEhXoUWt4RKFOoYVkft2mu1H\n1WhR6AmDX7Gojes0DqkNcO+rJtpnNgi/3EtsqcWiPREiOhpGUyM/A3yyjPP5IkvPqm1E9rJN9GAd\nbzWNCAWfefxNvvTKQyCUg1ILBXEmpme4ysFupU597fwBCiNVKst5hnZt4f1JH+WTMXv2rDA9M6j0\n/RJSw2rr5d3JYo62iGYzxOmE/tcFaw9LzLpGMBjQdVp17ZsfaqHt+Fa8poVhxzy79xIvf/4BUs+q\n516/0ovUIUnFmBUDY7KBt6yUmYfumePmG7sQU02yKZ+tpYJyMXZFmGWDKJNglXUOPT7N1Zf2AND/\n0DLzyyWcW2oCkVhgHqsQRTreahq9oxHlYkrDVY6UVgB49a1DSh2qS2QkKPSoyMndvxNT2e9Sf7yN\neTlNeyfkJtfdon2joAKlXUnulnLgplZUmlpsQZyO/7uvSae7Q+rbGSqHEkoT2xzuXuW1Nw6R2JL0\nvI7ZlEx8dgZNSC6cVueSWhU0d8cM7N5k9WYvMh2hOTFJx8DM+sQr6pos3BBUju7g+msaz3/23/Mj\n538W1wqxjIiMGTA9O0DumknwYANxMUtQTBg7qs4/bQbceX6C1oiasI1/3WP5YZcwJ7Ergn3PTDP9\n1T1Ep+rEN5Qgzl0Xqsm7raOFqq/03m/947+ZqYcQIg1oUsrGzp+/Bfwr4ENAWUr5K0KIfwZ0SSn/\n6V/1e+yJIdn/y79AasGgdDli8X+IOTk5B8CNr+zFv69JFOjIbRs0pYrTsiFJxyA9a+IXJHE2wd7Y\n6caXYqVH8HSkkZCeNdF98E416ck375Y3Gxf7lHMRFfKqdzTiVEJ63qA9EqO3NLL7tvHOdpOYEn8o\nJHXb4oFnFTPy5Wv7ODE1x3vvTJK4Er2pkZiS8RdCtv/XFif7F7le6aP+Yj+Ng+oidmctYkdNdvzh\nkGKpQetilxqvHVQqyuRalqEHl1l7aRhQDEjpxphlg5F7l9n8i2Ea+0L0hq5yMSyIsjHpOdWjcDck\n1X0gxlpEGy7okB+u4Z/rojMQoeVCul5y6PQKRp9S7/P1W0Pkrpi0hiXFa8Any0RfL5Fej1l+JsK5\nY+OeLFOrp8icU3PL+sEQo6xMUrGvYy9aCjJs7EQV6hKtGCCWnbud/kJvg9bVImEpUmKlSSVU6n9D\nENmCrRMJFEJ6X7SoTag7ufvBNX505Dy/fuFxzFkXpww//T+9wK9/82nyM4LKfQFCAHV1/sWrGu1+\ngdcXqSSyHkkyod5b3snfRQEkNndr3TADpfvV4rg8W+LeI7c5e2k3IlTY/vTSd7NcFOwXwN/lQdPE\n7m3jWCHiG0VqU0o8pvvq+fWORnp3jcZCDoAnHrjEt945Aij0gbNiUrh/nerbfXhDIbneJh3PJJPy\nqdXUE6Xfc+mcaDPQXWNpupfMSB3fN8l+K02QFxgtSeWemExfk8yfqOdJdEH92Sbivayq5MyE+b//\n/dvMf1BmZh/wFRVDigF8UUr5DSHEWeCPhRA/DcwDn/4Bn+fvjr87/u74GzzeF4KrVK+ymWuhINrb\nJpdt03xPWWwTC/Y/cIc7/22CTn9C4krsHSGSuy6p3Kuo2WP3LpHs6L5n53rp6qtTmVdMip/6sW/y\ne1/8CO3dAYPf0Nn8EQ/zUhqrDq0hdf5hjyqXjaYgOdJAu6iszfJIg2ApjVXR8Hd7ODMO2Xn1mM0P\nBWQu2TQO+xBo5K+Y+EWlGA13d9CNGPNihtRDW/ivqPFUczQhO1bDfKGA93Qd89t5END5QAM5nUFq\nMPGVJvGvVJm9NARAZk7DfnKTrTtdauxYiDFqOnG/rwJ5RzvEZdWXAbDKOiKG4g1Jq19TSsDJCBEL\nRC7AdkM6dQfR1u8G4DhlgX+sRbLsEhci3PmdyidWnAxntEHW9am/2UtQVGX08EsxsaOu9FOUAAAg\nAElEQVRR2aPT8+Fltp8fwt1K2P54m+58i8ZrfUTHG/gNW8mVUTQzt9gh+7Us1b07gUpDnhK/rDok\nPQHZCw6NY97dqY9+JUNnOGTgZR2rGTP/SYmb83hy1w2+9sZJCrsqZP5LnuXH1LkkmYix0S38WKdc\nzRDHGmLVQRtuIzRJuOFiNDXSS+KuTJp76ox2VZg9M0pYiBmb2GB+vgciQd/YNlvXSySGUomeeFYB\nma+X+9nayOHMW9jboIWS6oGE1IpObIFVg94LHRY+4mA21bVZeGyNciPN0cFlLry+l/5ja5SbKcSZ\nPGFOEvREZKZNWiMJ6VGlND3cu8pMpQfrC11sH9DwBkIVNJSNcRcNkqMqLMhZ18k8uAlAebobsyHw\ne5VyNj1ncP1Xvn/Blf7cc899P4/7az3+1W/+X8+VfuIQXmxirFp0qininFoUzKEWG5f7CPZ4JGik\nFg20SMXdmydqhGspjI7Au5LHv5KjcyMHoc49R2dJvlAitZHwzuIeWpMhek0BXTuWSeHeTTobaYIJ\nnyQX487axBYUT27SvF0g6InQfA26A2JDICOd9G2T1KOb1KIUXgmMisHYk3PYbkRiQSOrM35shU0/\nzYcO3uD2Ri+FfRX0P+xGPrONNt7Br7gYV1N0+sHq66DvadEIXaLAIDVZI6w4NMZsiruqtG0NIx8g\nJjpU5wuKZdkd4c6ZSBNE28CqCY7fe4uNq730vy3JzSrvgFWFjcdDzIqBAIy9DcybLuaGiTbSIWhZ\n2N0dQqkmTky0SZZTCt6SDyntLdPayNB1ReIXBFHdRl7LEOakCrOJBfKZKhs9NiIRlP0UsSXwegWR\nA9pbeYKCRF+20Vs6VkXDbGgqW6MrxDpWx19NkZ0DrWISlGKsdZP7751m81IPSaTjTNsYSxbtgx7O\ngk31aESr1+Do0TssX+9nScsQBAZMZ6gclgy9Atl5qI8LxBt5tkwHMxOSrLrk5jQ6joE1a8N4h9CE\nrktQ26OiIaWbcKx/idurKiWs8AWb5oDJqftvcmujhF1SQUpBT8IWLqvNPO2rRezBNmExZvKBReZT\nGYQbc++D0zRfKhHmBObntgims6RXJFYDNgcN4lBjaakHvaPRWs4glhwSS4GNcjd0vJJEDvhEkUEU\nGqyf7yfpjqg7DlFaord0/t0n/oCXLh0lHPUpveBiPlylGdu0mg7tuovUJV1XNKyKztEPT2P8jsud\nxVdWn3vuud/+fu7R90VF4QyNyE/86dOcvrkbrWaQ311BvrhTURjQ//EF5k6PEE900OZc2NUmWXaZ\nPL6IF5nsLazz0mv3kJtVv69yLKJ4waC5M1pPL6lmX3NMEuVjekYqlFItOpHJ8lmVI5laFRSfXWbl\nzCBBr/omS82atHeF7PnZs5R/+hR+l6C5K0LLqg5yodDCD03adYdsoU1zNk9+RtBWkglFter3oWEi\n/O/lOZtNpRgVieqNpOcUZMUbV30Mc80kSivxEYAINFKLOoUPrrH+Xh/JsJrxi1gJffL3bbB1tQdt\nR/GbjHYovOKie1DfLVTs35agfihAeDp6W8PZErRGlLwZVOc/MSVhIUbzNZJ0jJX3CbYdSmd0th4K\n0asGpQNb1E8rn7VzbxnxfBfV/epcSvu31DdvT4CmS7QFhw9/+F3O/KdjPPwPzgLwtTdPIN0EYgFC\nIpyYgb4q5Xf6lUTbklhVDa8nZmy/6hlsPz9E45gPNVP5Pvb6pPMd+v6DQ+2XmjTPlFSi3I7xKnES\nxfhMBNkZpY61GpLtQzshSqWIwmWTxphqcoPC6v/ch1/k/7l1P+kv5dk6qlSm+WsG9T0xRl0jyicU\nLu+4fVHZq/ga2Alu3iO4kyV7R1Upfk9CYieMTGyy+dYAwaRSgJqzLvGUyhVtv1mis9en0NVU4UPF\nAHPWJRhTHpTeXUrjU2269P1XlzCtsfbRgOx5h/aQSlKLukOcBUuNedvAk9vqMRtZiATusqFAOVXB\njX/zt7yi+N9/41efC+1nSYTA6Aj0aylaw5IgD8WH1llY7yLSBf3fNGmf6hCvuchSQHW6m86dHDdb\nJUQx4PEnLjFybJWycGgGKcKeUKUuTfm4dwzsh8sYFzJU0ga6nbA810PhuoZdhdo+SWMlS2ZRwxuI\nsTZMYlvla24/M0xnKkDf1SZuWqS6OphWTN71sb5Q5MjHZni4d5bttM2amSIpRUQm6G0Na80kvaAR\nOypkaPj4KqI3oI2usjM1idSEikIMNKQhOXXfTRa2i5h5H92OKbxlUzsYo72VY8+Ts2zMlgjykiAr\nyB/bonK5B7MuGH9kgeJQja3tHNlpDffvr1Gpp7G3lVxdn2whlh0GT6yy5ZpYayb6jjAtdiRhvzIq\nMayadONfgMaISXMyZmi0jPGdDNUgTWypLWGnY1N8aINmOY3REdSlBbkIc94mCXTcqTqrrRzOWZvV\nvSYr7Tx1Tyk59UyEsW5BV0iz6TD8FxA81cBPdGIXRDFQMYOVDOahOkHdxip5iC0Lo2LQNnW6LkuW\nR1OIkTaiahF2R0hL4i6YxOmdRmrdpDOQ0NwTI0o+0jOQlkRKne5jG3graYRUn83VN6doJjZS1wj3\nd5ChRu+9G8RuQmawRXQnTWKrz0oaKvUrSSWY2QDn9SzeZECCjjQBBLIY4qYCfvLB73Du3H5Ew6Dn\nYkJtRMNfSeNs7SwqFRdpSrpOW0z80CyNt3sIuhJabVUhCB083SZyBZGuKXVnJDA8QVhIsDd12mOx\nShdLDLy2hbnjdcrfgvaAJLGh8q0Xv++K4n2xUPzrX//V58b+4W6ckod5JkX1YIwUCja7b3yFtUYO\nGWs0pxJG/tDAeKpKmOy4/UINzdMZfkFwrruXmY0+0hmf/FCDe0YWWZrtxe7pUM8adAJLzZ3LFu3N\nNOlFndYQ+EXovQDNcUnsCIymTu5omWZgYzR0rKpGas6gbRponkb3cBXHjFhZ66IxKqh9p48zcoDO\n1QIi0XCWDMyGhkAQ7mvjZTTcDQVdHTm4xsK5YZJE+UGsiq5gr4lA6/ExVizWpntJ+gLihk3iGZg1\nnSefPs/WkM5GM4t5OYU3GCE1aNdc0gsafrdEFiM6oUU67bOtpYku5rArgvY9HegYRA2bsBiT+2Ia\n37Z4/Jl32XqpH7MJYVZw4tQMC508u4a2aCQWzfsj4nSCkQrxz3WRWZHUDiQkjmI0do1UqZ/p5Z5H\npgm6EjJZj1bbxh5qk3/dwW+67Dm8xHRXnuRcHm8uQ25GI8gK4kQjySTqxkVgbZp0PBcRaCSWYnh0\nyilEIggCEzOrJhvaukVnMCI1b1J+OkQGOomvE2UTRKgpkdeox4m9cyytdWFWdXovJES6QRIayAEf\niSDOxYTX8/S+m5Belfg5jd6PLNE3WGFFT5G+6hANBzQ7DsVsG9eMCLtiChM16pU0UgNnS8Pe0yBY\nUmTxWNeI8rHayggoDDTYWi1wsTFEGKgFyvngNubpLI8/e4ErssRjR26w/Vo/+oEG3q6IcitNUrUZ\nObpKtZECAYlvEGUVfiHsigmzYJc12kMxhaE6TWHxoZNXmIvyiG1LvYe+gVnTOPSZ66xf6yUoxdSe\n/9b3vVC8L8A1ektQvthL860eqgckIh3dBdgGsYEmJEY6xEoHrN9rsrWS54f3qhFlnEmISwEbxw1E\nW0e0dcJvllhe6eLO/7lfjR07Fj1jFSbH1tHdCDngUbihJLZRd0TUHVF+to3maYRdEUZLUPy3KfRc\niLuvSmJJPvXTL4OuWIoNz6bh2UyNrIOVUN8T0fWmxVPPnEVEChDjdyfIAw1S51NYZZ2f/6mv8vM/\n9VXO3xwndiTpBZ3CaJUoJcn0N0kvaUgJdkXgjfkYCw7ZaYPstEH/swuc2Rhj63wf9a00yUM1iv11\njJbGnv1L+EX1uM7rJTqvl9i61U33oU28kqTzQBNj3iGZamNP1ikM1Fm7X+Whvvbl42ycitk4FeNN\nepSsFlY2YPZWP8maQzSfUdJvJ0T3oDmgYW7rGLkAIxdQneli+NFFLr20Fz80aL7WSyrjY+gJm4+E\neH0xS40Coqkz/vQdxp++g/HDm2TnNNxlg/Rt5fY0yiaJAf5uD3d/ldSSTinVQhQCRCEgdcdEzqcI\nt1ycskTzNAofWKPrWw7CjXjm+CVVje0AZSw75Py5KUr9dextWD2lEeQlcsjDcQNylyxljd+GTkmj\nU9Jo7wqZXezh1ttjmJsm7YEE+7pLEmqsLXQxP9tLHGssL3QripcvCPOS1kqW7KyG16e2GiLQKL2j\nYzQ1Oue6yV0zeXDkDpk5ncycTqPtEOTg1T89gbVq8p3TB+n0S9JOADNp+n/PIdzdYevFIYhUTyfX\n00RLh/zQx0/TfdZQRDUH9JJPo+niLum8dO4QccNExIq9kZ5XvI+5ehd+t2rO/iDH+6ZH0fdvfo77\n99zhnRsT9L5qUp/YgdGe3EL7SrdqOlmSzESN+kYGe81g4pF5tj8/yuGfv8w7q6M8NnwLgP927hj7\n9y4xfWGU/LSg+oja01cPK+3BxJc7OP92nZlXJu4CbFuHPNzrDt6BDtrO3N8sG8qHEQniAR8nFSDO\n5+52vW//6n42j2mEpUjF4hkSzY3Qlx3CbgW88brBOlahXlampswNi9ZhD9nRVYmckkSjHrlch8ZM\nQUXRGQmpWYv21PcAKfv3LHP95jBaJiRpG0xMrHPn2gB6W8PYrTwH2YzaB48Uqtz+5gSdvR6FYotq\nNY1mJCQVG5ELSAIdJ+fjb6TYd0DpkherBVo1F8MJyb6SJswIdXPpEPSHpG5ZFD6wxspc6b/Ta2wf\nkeRvCNqPNwk6Js4tB2dTsSIKh8psV9J84sBF3lxXzt7ylR7i3gDZVr0Rq6ITj3uUig06gYmuJQSR\nQXQlp3gfqG2BdrBOOJtVFU5D6VWSUoAz4xClFMbQ2VLXzNM/eprnbx8i8A3SF1z6nlnk9vVBHjg2\nzZm395KZ00g/s8bq7R4yOzySdr/EKQvCtEL4ld4y2X7cI2kbECl3a34ato8nyB2soeZGiA0bzReE\nvSFjfyZo9xlokaQ2qeENKoCyv5Bh8uiSep9fHcXf2+HY2CL3F+/w+7fuIzxfxOuLEYFADwRRNtlR\nyO5AnBsK6hOnE3BU9alvmxy5/xbTW720yimMbYOuq1B5Wl3Q0ZbDgcMLVH9jlJUPqPfwzi/+LQfX\ndO8vyRO/+RPcvDkEmsQq+Oi6+jC89TTDkxtsvNNPkJdkx2qE54voPhQ/tMryRoHE1xEdHensKDdr\nOkjFCEj2KNGRtBKVlnUTBVWp6Hzmme/whdOKe2HUdeRwB11P6Mk3qb3cj1WTFD61zJ0bA+Rv6Hgl\nNfJqnlA3ZH+pxspsCZGJMFZsxQsoRqRmLLxeRXPWh9tY5zJ3E9DCho0INNJ3dJqTEYf3L3DrxQm0\nSDESJo8uMbteIq7YkFZNVTfrId/LY7R2kHgZn9ZCDmkqNoF7skytllIXNZC5ZdI66Cty82iAPWsj\nDUmyp0X6jQxeF+z64By31kskizuuzDXB4Ks1GruzVCc10g9t0uzYPDZ2i5u1Xhq/P8TWiQS9rd0V\nHzXu65DOejQqKex0gN+w0ewYw4w5OrTM2esTWDmfh8dmeeXGjjC3YXD8ntvcqXZRvd1Fkg/RaiZJ\nLlIk6VjZ4K2KTrJHGafidcUSGdi/wea5PsSeJnGkY19K4XfJu6K478bpRWkJ3T5SCvJnHEY+Ncvl\n66Ps/qOIpcccpK4YqEk2wlrfqWpaAq+UkJ3TqB3z2Tu2RpfdxtYjXr89iZvysb6eJygIwpOK5Rku\np9l7zwLX5wZI5z3aDRtrzqHnvZjlD6OSwtoCZ0uQPFoFwHwxj9ctyJzaZGtOGfJyN3SCPHgDMaNT\n65S/PYizJdm6/3uhPZkZ1WtJLWsEBamCnZ2dZndHR1oJekNXQUhA2BuSu2ThP9QgWE/hDja58SP/\n29/uhSI1NSB7/vkvoNUNsnMqKq/rkjph/4eq6EJSv1PA2tY4+pEbvPfSXsT+JqNdFQUtbRqQC9E2\nlDQ5ziQUBup3nXRdL7hoEdQ/3aC17TI2usX8bC9GdWc0iCrX+g9usDxXUu7DQky2v4F/pYDZFOie\n+tYpHCrjvao0Eb0XfOq/2KDzeon2YMJPPvYd/vT3H6M5oRB0ui/of2iZMNZZ3dwJHNYlMhE8tPs2\nb792UMFYMzFaW1cSbl+V0PT6GHeU4Sjoj+j9jkFjVNAZC3GWTOL9LcZ6t7k1PUDuhsH+T9/g2p+p\n8M3m2E7mhhsjPV2d57CHfdPFPrlNdTWHVdaxqoL2wE6YzVVB9YAkzkcIX4dMSKnU4MfGzvNqeQ9X\n3x2HRLEwzR0IT2wrT0T2kQ360+rmubo8QBILUpdcpAbaKQXCKVxRi1jrkSbu2xnsJ3ZulEwIDZPd\n+1e4NT3A4K4tVm/3YPe2iUL1bb+7f5N2aLG0XkS2DYyaTmpNUHx6hXZoon2xm/VHkrtw5dSKhrMl\nMVuSxo/Vyf9+lo2TGk89cY7n3z5OZlbJ8DUrJtkhXGkdHWlKjLpG+kCF5o0ixWvQGlJYxs54SGrW\nVKyTR1UZf7JnkVe+fILgcJvUmRT+gw38pq1u0KKyEnijCoaUv7ajGn68zqH+Vc7NjpG+7NDpT8jv\n2aYy2wWGRG/sBDUnkNmvEC61WgrqJs6qrjQTbYtsoc3BnjVWWnmGM1XOvrJfbWN3qfiBxnwea6CF\nv+2Sv2qgB5L3/tP3L+F+X/QopBRkutto/R5BHpJCyNb9MVv3x0TniwghSY3VCfd2uLI+wOSjcwx3\nVbm10kP6ig2JAs9IXYmdrE2dtmehX0+DkNQmNNaeCmlVXDI3LeZv9+J0d4jTCYXrgsJ15a9Yu9qr\nzFu+IgrlHB852cLrSejc36LnXUmllia6v0F0f4PZT+u03inR6UsY2LvBi//Ho4QZSC0aiIkWQS7h\nvtI8y3Ml9GUHfdnho3uuktRNXr+u/AKJm6CnI6QmkU5C3B3y0KlrlF50MA/WMQ/WmZxYY+MDIZ3d\nAXo6It7fImyZTGS3MOo62afXuPrn+2gcCmgcClRS+oaBMBK0jJr//ZPjLyJ1KGVapOYNYlvS3BWR\nZGOSbEzrqSb6cButYTDwmsCacyhXMpxMzTL92i70vg5JPsJsCry+GK9PORbTD2+yMdvNpUvjTG/2\nkCQC67arLPMBeDcKZG8pm3P1YIR9LkNrJCFr+2iewLBiUks6WdOjeFFn+3Q/Ukii2xkMU1Un0wv9\njGYrGGZMqqeF7gkaxzyWLg5QqaXhs1tkZ4y7Wa2xDZ1eQWtAQ7xRoN2rYVUFr/zxvXSPVxTIZtZC\nxkLJ4BsKXpu9pdNzbJ3qepbEktT2QHvKZ/RD85ibBqk1iVdKWF/oYn2hi1cXJ+kMxWi3XZUAt5jG\nzvh4p5oEpZiPPHsGZ97CWdfRfYnuS+xXcjRDm/QlhygNcTrB+mIXWo+H3tQw2oK9983Rd8869dsF\n6rcLyLZBel5X/JTTWaiZNBZzXP3T/ay+M8CFFw4QFGP63obonSLRO0XuOzlNHOlk+ppMfnoa54fX\nf6B79H1RUdgTw3LgX/0c2fMOrWGJs7tOeF1p1s39dUaLFWbOjHH0wRnee3sKbbiNpif4ZZenT17i\nm2/cAz0+5m3lQUhMSfexDcoXewn7Aox1C8NTmZTa0Rr2t3PEtqAxEdP9nlorox+qoL1Q5P6feZfT\nK+Ok7YD1S33EaTV+8gZCtI4q7dwd/mNzXLELdE/gPrBFT7pFzXeIYp34hW7sZzfwnu/b2bOqc9UD\nCLqVlT6zv0J1KwO+hkhHuDcd2uMhZjbAPZNWIFwUpVzaCT9y/DxfeetezN4OSaxhXk0hTeg5tcry\n1T7lrwB6p7ZovN6LPFHHW0tjbatwmCitdANiTxPTjAkv5zn1pOq33PiPB2n3akx9cpqr396jEsSe\nWWfrYi8HT81S9dV7u/zewN14wMGpTaptl1ZFQVKKrzloEWw+oOz43e8Jqk900PQE4z1lJPP6EhJH\nlcliaEdbYEV0qg7OoqVQdbYkfxuqH1H7bfNyWrlNp5poF7N0xkN6Bqtszhfpe1OjPqHRGQ8wymob\nEeViPnfqDf741jEFw3m7oFycO85VLQRnU9AaTRg8pG6gxTs9AGT7GzTKaYSRIGMNY9Mk6g0QuoSq\nyT/+8F/w27/1cfVZfqhM7XZRKYqLEd1vG3g9CptvV9SCJe+t0V5Pkx1UFVezmiJ91SbMSvyemJ63\ndTo9Aq9XoncEZgtIwHy0TOeC0hIZbQhONNnbv8Hc1yYQkWKVju5dZ+X8APpkE79jQsO863cK97bV\niPqmjX+4jdxwfiBc//tioXAHRuSuz/0i/tE2mTdSeD3fY//ZD2+xvVRAb+qYTUH+VsL6h0OQgvS0\nRXsgwdlUUBGj8T1RU5RV4iFQzkCuZLEr0JhQVmZnf5X27bxSCwL5WwnbH2vjvpMheriGrid0pgtY\nFUHPB1dYPTNAvMsjfcbF2VbvWXWvKsWjfW2K+RadwKS1kEMLlf4iyUQYZZOuK9AcVq+tPRZxdP88\nF2dGIBEU3zWonAwRuiSd7xAEBuFaCpFA8Zp6zMRPTvPed/YQdsWMfU2yespQDbf+OltLBQAGx7do\n+2rrFZ7ugvtqxO/lGXtsnu1OijgRVCoZtA2LnoObNF/pozkVkr6lbq7Rp+ZYqBQxX84TZMHv3qFG\njzfxF9X4Lz+tzvm7Ww+jpS7i6vEANIm9aJE+VmZ7K6v6DaGG5gkSVypQIiAtqfbSIYRdMe6SQWIp\nvF/jmKdm/zNgfnKDjZvq5p04vMx22yV6uURigt8lSUY7JBUbe10nKCSYDY1gl7poBr9msr1PEcay\ns5rK7uwR3PupS7xy7iDSTBBOjGHFRGW1vRP5gNxpF+fjyqAlYggKCflpofoSGUlQTEgv6MQ7cRvB\n/g6aHiNupRVOf38D8/Ucrfs6aPMOdkUhC4de0Nk4qa4za2+df3noeT6/+Ah3zo6QGJK4O+SRfTOc\nXxnBMmKqa1mGxspsN5UprFN3EA2Dwq4Khf+YoXxIvQD98TL1Rgpz2lXX9nhyV8YvUzG5yxZmQzWc\ns3c0rvza9y+4el9sPf7u+Lvj74739/H+WCjSMV5J0v2CoyAsDWiNRbTGIpJvlhChILETvKGQyBFk\nCh20mgH31cjOasSHFZWoM+nTmfSJ0pJ4yEPraFhbOt6mi70NtUMh+++Zp+fYOo31DLLXp3g9oXg9\noXxEoE2n8YsS3zdp385jlwW6D4PpGkFfhDnj0jjuUZuC2pQyNEUpSbLhsK9rneBaHpmJGHotwd7U\n6DpjEpVCNu9N7oYUmxWdmW/sRq8aFPvrtD/YxF42oWbSXM8QL6bYdXCFJBdR3Sep7pOcvbFLld6x\nQPyjDYKeGHPTpDxXJNPXRASCB3rm7r6dBz52k9ZGGj2A+dfGKM90E+6Y0nRfUDnbq/wYDZ34ZIP4\nZANLi9HeyNMeUKpDEQmcTY1gPoM+0Gbo1QinmmDVBMFkh2CyQ2LA2KdvU+xpoNVMUvdsU7vRjWga\nDI2V0ZsacSYhe0tH72jqJ6uoXv1nVKK4PNogcqE1rKA9UU9I+YEQ8Xs9d3meS9sFKrNd1PdGjH30\nDmEhJq5baIWA5GCT7ksCvzcilfFJZXy29+tEB9XEpPFQm8p+yC4mvHx9L9KNsdcNaJoYV9MqrzMS\n9P6FjZCSotMhciVBXknJt+8P4VSV3Ikt0KB9tENnKKIzFKHPOhwfWSIsJiQWGKdz1PdFWDdcUquC\noafnIRJs3KuRjHokox5ex+JffumzzNwYIh7ySE3W0KqqZyWlQHyjSNdgjc3zfTy96xpP77pG9qqF\n3hG0LnWx8Q88WsMJz/zkG1TWc6QuuBgtdS1mFpWyVxoSa8Wk0y/xnqlTmiwTFH6wW/R9sVDoWkJ2\nXtAc0TBvu9gVlX4lEkFtvyJnS0PSNVCjNSQwX8yjhYIkEbTvbxOupYjSEjfr42Z9NYe+4qL7Autg\nDSEFB37sOvkrJnPbXWy/1U+q1EbWLMpHBOUjqqyOXFVuJpFGbqpCUJC0ByTNyMaoGHiDITIR2GX1\nk1kUSFOS21XlrbdUqIiT86lOGOgBbD8Q4s5aGE2NZG+TZG8TBIR5SXaqSmUth/N6Vu1lrQSto5GU\nQlbeGCZz06Lv4AZ9BzfIX7LIzGsULmssnxtk9Hm1xyYT0VrOYpd13lyfoF53qdddzl6bUFuBh7YI\nCgnWUIvGgQDDihCRwO+JCboSknRMsJgmWExz8fqYaiTrYJ6oEGcTlScRCcxLGVYfNNjeryM1SJom\nSdPErkiuLg9Qv9lF4sYYeoIWKen6ykwPpUuSzKxB+9723c86DjSyc7DyiMaxPfP0/IHLruNLKslK\nwsToBkQa9R9tMPTUPENPzWO9mcWuaAgn5saySm03cgFy3SZaSfHkL7wBQHI+r+jpMSRLKRJb4r6X\novfYOrVdKnUrPWP9v+y9d5Cc6X3f+Xne/Hbu6Z4cMTPIabEANmAX3EBqE5cil0GiZNFKlHQ6le8U\n7FKVrkon2ee6k0/yXcnyyZIsWtLJosUjKR65u+SSm7F5F2GBATDAABjMYELPTM907n77Tc/98Qwh\n/+GyfSKvSF3xrXr/QBe633fe8Ht+4RvwczE9ZzTEkRofOvE+HzrxPmuP+9R2Sha+NYG5LSRsV8Be\nNonO5Ni6VIQYsq87GA0do6EATW/PTJFY1Jk4toR3XN3fzrhP7iMrXCv1ojV1ek/HGGaIYargMnFy\nAWkoJGtjLcXo/hJ6xcC1fZpjYP9lD7EJX5/fx9fn99Eci4kSMdFkh+5chv/jY3/Ky79zAmFHNMfV\nItsekAQJMPMeZt5j8E2F3E05XSozxdt4ob/r9n3Ro7DHRuXQ//yLsGmj9Xn0/t8Omwe3TXZMZehT\nfryLcc2lOxhgVAylaxgIHnj8LM9f2UPifZfm9DbuoNjGW01iVjW0PcpZiTdzdIPTw10AACAASURB\nVHol+b2bVOsJEm8nlEbnqqrrB9+IWPxYjOGGDBZq3FFY4ltPH6f/xArrrwyRvLfMoeIKr7x2gIdO\nXgDghXcPqPl1XSfKRApsZUZkXkiyecJnYKBKqZSDjk5yQY3homMNvIoDkWDglEb0Dzapny5ibwqC\nlKJ0P/rBM3z9yj6SZ1QD0c8pb8no6LY0eyRwlizSN1WT1B8IMMvmbe+IIBPjrupqVawr79T0/k2a\n5wv4vSF6QydKKEn/zrBqHoxMr7P58iDt0ZD+NzS2nmxj2yHRezmOPTnD+184QOtYh6htkLqi+hqR\nC30nV1hcKWA4AdFKQuEQDgRgSDLnLRpHPPKv2+z9qcsAvP/lfXSPtkglPRqXe2C0Q/6bLuUHfUTF\nxGgK7Ipyc5fbgCspIMiqkfnwT97g/IUJnJJO8eQqPU6b8zeHcWcdOntUjyKR7tJeTiFC5dWZObFO\n5UzvdqYEcqpNULMxK/ptuwYZamRmLKSAXU9d5WJpkCDQyX/LpfpDHVh0EbFyI0+fVOPR6nu96J5g\n92NzzG320iylyM0YGG2JVxR086r34mcl+l7VzCymW9xZvMU3njlOdyBk4GWdtUd9pK9jprtwM0ms\nQ9yvyIQAWlvD3lLZrdcncfZU8S7lCDNKQUz3YPyRm9z6+sTtZ2DfJ2Z5571dypM0pYSDL/7e33NS\n2O/80//tt8aNk5h1gT8e0ijq5GYFdhXaYzGdosDu7RCXbWJdoA+3CTSN5KLOlWo/ycEW2ryLFAK9\no9HVdcan1nnq2Htc/coevK7NyANLfPjgOS58dR9eXqK1dCaPLLO1qvQgulkdfaJNsO6iZ3zW/2iK\n2jEf70Ke40/MkLa7zPyH/XTGQqrCYrWVwS8lkXaMyAeImons6khb8tjjp1l+boJNw6HwlklnWBJk\nY0XgmXUVO7MYMvBgieUrfegdjSAnmX7gJptLOVbfGiLQdIK0JEhLCndsUHVNbDcgLDskFk30o1Wq\neRMGuhgrNtGoRxzqxBaQCzDLBt2BiHDQJ3fOYKvH5J7jV9ESMZVmAi2tfEu1RIiwI7wLeaJ9LaWQ\nLTQGDm6wMV+g53CZubcmsBqCjzzyLutfGMfrFUrAVoNaOUXqmkHcNrF3Nei2bKJUTKrQJi45JHbW\nqfZpVF4aZO1SL35OEqZjur5JesYkbpuYbUXZdzYE7bEY+2CNuC8gMdrAHGrT3XQBQZCFimsQNi2i\n4S711QxbV3qIcxFG1cDYNDC2DPyGTZyOAFVWhNdSiFjw2Iff5dq1YQKhkX9fZ/CRJUKhYTsh/qZL\ndzgkHPFZu9rH9FSJStulsyNm8Is2raFtNumBLcrlNK22Tfa8QWwJltwEQWCQ72tQ6xW0egWxIQj7\nAvxCRHrO4PB91xlM15mZHWO+kyfAwNrSuf8nznJ1dYDewZpCpLZMzLYgtAR2sYOeDIiTMX4SgqQA\nKdDnXLrFmJ37ltnQbZxlg+rNHKkH1qm3E4QpiPMRgSORuZDk+w6JjZiV8393Utj3R0YxOSx3fOYf\nE6QUHbZbjNG2admJkqB+1KPnlI34mMoGhBYTBTqyYqEXlVN3ZEN7e3W0+ttoF9J0xgJOHrjCm6/u\nR4y10Y0Yb0uxFxFK4CV7h3IZqDVdgo5J7i2LynFF3Q1SkuSSoDmuuuqRLdF2tNg9oFaU0ud2sHGP\nQnkCBMUQZ9lE86F7oIO45RAWA3r66zh/oRRSVp4IsRIK2+DXbHZNrXL1pkqnEfCRw+/z9Ft3MrZr\njcozigLfLUgKd62xcquAnfXwO6Zy1ip0KGZaWP+yh8VHjduybtN/3WL+h1PEJvTdsUbl1ACDDy3R\n/twQzSENuyYJHUFzTJJcVte5frjLyT1zvH5tClFyQJNow22Kf5Ng/aNKiyFORdirJn4+un3vtK5G\nYlWQWomV7L4PmYUYoyPZuEPHbEHnjjZxZdtSz1XixGL3NuR7zsHfp2wBjKsJRABeX6wWjYI6Tt9b\nOhvHY3rOaTTHlL3BP3ziJf7i6YfQppvIKynFvt0ef+qeIJrw+M2jX+Ofv/8EmaTHVjVJ37M26090\n+dlDb/Anrz3A0GSZrYZCpkZzKQozipHbzQnawxGpsTqNlTRYMc7i9kQprWwCAYy2oDPZpbevzsZy\nDjRJ4U2TzaMR2jb3JHVTp3mwC9vapNRNpB1DKNDbGlN3LHHrpTHsClTvUKCu6IiS/W/NK5CevaWh\nHalhvpileiAksWAQHGrxG0e+zm+//DEmp0ssvTlMbHN7dK13NIyW8n6VAqya9r11CvtubO70kDz4\nBz9J+VKRKKOUq/1B9TK58xbJe8u03iyCpqzlIjfG3lIpV2ssxO1rM5yvcW1OCUHs3b3E7MIgdtJH\nfy9N50AHe9alMxwy+hys/qiPWHBxS4LmcTXL/zZCrzhcY/NmHmtLI3QhfVMQfqhKu2UTN0yMuk7U\nr3Qj7KSP9UYa/0SDKBIYMykQ4GckcZ+yDDSTPkHHVHN4QFuzFa3blrdt/2QyJHXZJkiC3NNECIlf\nSsA2LkIasTIyqhrYk3V2FsvMvjJJbKiSqf5zdbzTPfg71d+SSHVpraRJDDYJLmVIHtoieK1Aazxi\naGqD9K/bLP82dK7kCDPbDlZ1nSgVkx+rEEQ61tM5RATluyOSfS28G2minhAr6eNX1EgRXXFb4q6u\nRr39dXWcsYhDB29y/WtTtCYiCqc1Kj+kzi35ToLGEY+pkQ3WnhklsiG5Ktm4L0RYkeKKlCW9n7zF\n1SsqUPa9pbPxsPLflFsWn3ngNf7qGx8gGuhyYGKFxS9N0phUDWRA9Qmmureh4bsnVrl6qx+xZWFV\nNaw6dHol1p46rXUVKIQToZcsxESLB3Zc5/mz+8mdN6ge89G3jNvKat6or1zGgcJpnc1jKmNo7AnI\nXDLp3ttAbiut7R1Y49JqP/aZFM2d6nnuf0Vn7cEQIxGiXXfpPb7GyvVeet/W2HjYR3Y1JqfWWDw9\njDWtFK6EgPBCFqOtBJvSO6s05nJYNaXbYfR1FBRfU/wXADfVxVtMY7QFuVmoTQvmfvN7MB4VQuwW\nQpz7j/a6EOKXhRC/JYRY/o8+f+K/+Ft1na1zvbglDTSJPxBibpiYGyZ+TjKe3aKzw1cIzLZAWhKr\nBpOP38Dta9OTarP0yijukoG7ZDD/8gTuVRt/KUl+LqK30KAzFoAbUbpHx3F9pIDGlILwxi0DPRXi\n3jI53r+I1tPl3kdmsLcEPU8t0Swnyb3qYJWVh6jsamq/lKZ+qEs8m8J8P0XkSDo7fNw1gWGHZC5Y\nRIGOaBi4yS5usovRgeLBdWR6G41pxZirFs0plaYmXknheyZmQ0PaEdKOMNIBo2NlrKpgINvg6vNT\nFO9aI8zE3PpkRKdrogUQdxXlWr6Vwyh0EG9m8XsiOu8VMNqgeYLV2T7mft2mUUojR7y/fRAmWoxM\nr1NvJGiU0mzeFdKYEBg1HefZDHExUHJ/28QzgMO7FrHsEAKNVLFFdSGHFoBZ1bi4PEhzZ4BR7FDd\nA8Y1F+OaS2NnRH9fjaVXR1Wm1oLysZi908v0vGYrluooXFvpJTXYJDXYZPNRJZUnSjZ6W+NL1+9g\n7z3zZE47zL6+g+bd6pzCpOJwDD64hGiqwN/bX+M3Jp7BuerQt2eDaG8T/0SDeNwjvJAFM1a7kMhR\nD02TvHh1F8mbBn4WcoUmsr8LsWp0J3IdUjcMUjcMoo9UMHOeEtsNFZ+ju+WiXU5hGBGlP95BtJqg\ne2eT1JxJas4k+VMr5M5auKcTGG3BSimP3tLYPCSV/+iSyepLIxQPrdNZStNZStNaS5I+WqZzoENs\nScQ38phNDW+qy9ArMNRT3/aykdiJADsR0K473H3XFcXKzQnSR8t8J9t3JaMQQujAMnA38NNAU0r5\nu/+137cnh+Xe3/9parUE+VcdCp++xdWrajURkUC6EaMjm6xuZhn5c4PSvRaRLdGnmwzlayycHSZz\nHeyPqpJg7VoRZ7BFdCVNtKND1DI5eeAKp97fQ+qGQWxAZMPkB26y+I0JAFrTAVpLqVobLcHEB28y\n/9IEyVVJ/WHV/HJKBr33rlKqKEl0MZfEaKtSSfZ1seZcusUIESvrvex1FBr042UqF7ct3WKILcnH\nH3ybv3npbsYPr7Dx7IiCHQ9FyFSIXraIUtFt7oLeUfJ24z90k/KfjVM+rlS5iQTC00gt6DR2hrcz\nlMQNpZDlD/tkz9qKcDTt4VxzttNQ1RQLcvFtzcwwH+LcMgl2d9BvODhbguZYTJwLoKtjr+t0hwLM\nZED2W9tAoD5BazJAeBrucBPbDPHeKZBYk7QGVYmQvqH+37e77qNPzXPp4hjmlobc2SIqJZCmksSX\nGnTzEmOySXwlhT/0t1ml1CB0JWZT0N3doSffonGmQLDDQ27ZJJeUOA+AVZX4j9cYz1e4ODuqIOsO\n/Nln/hU//trPIZsG6BI77+F7qmFIxeL+uy7x5vwkiUSX5nwWo6k0UYJ8BJrEyXv0ZpqsbqqSQK46\nSA2G963R47SZ/8oUreGYKB3xPz34ZX77zJNwM8EDD5/n9Vs7ANS4uSOwD1Rp3sqABu6y0jjVfajv\nC0gW2rSXU2g9KjuIWgbWhsHjj73L07MHMcyQYCVJ+oamdDoPxBCDyPk4l1QDvDOkUMPJJajcFaDb\nEfM//j98b0sPIcQjwP8opbzv294e/68CxdioHPrVX8adaNCfaXDjej+iqx5gt6Qr1uRYjFXRGH7o\nFp3AZHm7Xo+vpUjuryC+nlcGLSiCl3WoivlsjvjDFawv5qlPCswGNPYGmCmfwDOwViyiHWo1cpwA\n8UaW1gHFuuyO+mhGTCKlWIjxmextL9D0I0qibfV6L3pTw6wLvJ1d9DULzQe/NwJdoidCku+oVTQx\npNijrS0XMxmQfzZB5cMt3LdSdPok7rqS66vvDZmcLlFuJmlfUcNvq6o6586WIDYg94ES5VoKMZui\nOxAwvmOD5hcGsT+p4Mirazlkx1BMzBiMlkasQ/Ya1Hco0lx2tIZjBaxdVwFsx55V5i8Pkrqp49/d\noLvpYtR0zJbA2+mRvOCg3VdBnsrDSUVWai5mcDZ0Qleid8XtPku0t0lQt1Vp0jCQhsRobievEy2C\nisM/Ovk8f/rvH+NHPv0y3/hfPsDmR9uYZoRlRMQv9dA44im3dwAJZtkk1iGxs0pjM4mV8okXkqR2\nV+gGBuJ05vaUxM8pv9fGbA/EMHF8icU3RogSUpW2JQMtFIw/sMCVG6pcFWaMvmZTPLiOrUdU2i5R\nrCHeyOL1KfKb5issTLyNfjQb6r4ICdpUEyHAeDeNFkBzNCZORxzfe4Nzr+66fW4iBhGg5BaluN1X\nGjilsf5El96v2+ifWccLDGqzCsJtbwnE8Rqt9ST9Y1us3ygw/AJs7dGZeuwGF89MkL8kbtME1IFA\n60LQEyMyPrJmsfBL/+R7Hig+B5yRUv7BdqD4KaAOvAf82n/OyRzAHh+Ruz/2aziPr1NpJGAuibZb\nvVj2qTT1qVjdlN4QLRESN1WvQESq2VnbG4IT4aRUBN7Zt8G1b07SLcbEboy1oWbeUS5EaxgYLUHv\n2ZjNH23jb9Os42zI3XtucObUbsyawGoAErTHyshnCjTHlUGwvm4hxhSYh/kkZkPQHg1JXzPY93HF\nbO25KNnap9Ed9cHTwJQIextOfsWhu7fD/tFVZhaGuHPHIqv/aprV+yUyqbw9aRkkFnXa+1VpMPw3\nJpXdOto9FVoNBzZtUvMa7WGJPtGkW3UQHZ3E6rYKtQHanTVyiQ62EXLrnWEG3om49RiIRIimS5wL\nLrGhAGMAxfcl1s+WqH5tSI03tx9iEQiMtkZkKe9Pil0OjiljmvMzE4hAIPM+QpPYboA4k2Hg4SUW\nzg1hjrcI51OE2ZDkvFq5RaSua3Ovj+GGOOcSGC2oT8VK+HiHj9AVzyJ1aZsN7MDkh+a5tDCIbsYY\nZoTfNci84apsaX+H3q/bbG2P1MNkjMj7yC0L6cSIjs6O/StsfnWE1j1tMq+4VE500QxJtK0Obi9Z\ndEd8hZuY7hCFGuaiTTju8U/v+iq/dfojcMtF7257wwLCjigUmlQuF9A9SC2B1yM4+OQs794Yx73o\nKkew/o5SGQcODK1y4a1ppCZVg9xRloCj34DNz7SQZ7O3Vc73HFWu6RevDWOvmARpibumYTageiCk\ncEanPSDoua/E+pl+jF0NvKZqGmubJu5kne5sFikgGux+R74e3w2TYgtYAfZLKdeEEP1AGRUr/xkw\nKKX8mf/E934e+HkAPZ8/+vCX/yF9boNLn9vP5p3R7TSy+I6OV1DjuKFTHeZ/2CF2YkQoSM9rOI+t\n03qxj+YeH237ZYw9Q+lapHz6cw2aXxmgui9mfE+J1UqGdKJLpZ5Qno5LKo3u3bdB9e1+EkfL1K72\nIAY9wrqFs2IoH8j7qzQrCRLZDt6iKj10TxCNeIg1Gwa7yHWb1I4a//LgF/i5Zz+LPdBGnEuTvLdM\nY5vyHs2nMOuC1LKkNqV+ozgTsvyAhlPWiI806JYSiFCQmazevl5tz0LeSBImYwZPQe3HmvQk26xc\n6Cfu9RGbFvq2FoFbUi95a1QBdXBi3HmLiYfVrH30mU2u/kaC/kLtdte/kG5RmukjfVPDz0BwsEUY\n6Mi2gd7UQAqcqTr+bAajo46TumeDrcsFolRM72iF9qlesg+WqLw+gDzUoJBuIYFKM0F0RV2z4w9d\n5vWZneipgGSiS/BuHi2C1k4frWYgewLG/lrj1qO6esGA+3bc4LUbU8RrDvpgG/N8CrMBtf0hVt7D\nb1j0D1XxvqFEf2t7QtxlA7OJItZp0HNGx3+8RnM9iV43MBvKReyBh5VS2rV6kZtLRbSKyX/zyLf4\ns796lM5ABFJgb2l4wwob4qS7txcXeruIdZvCOWW2E8+mCHd4jPZVWHttSIHarBgz371NmZdt1Ufz\nsxJpKiKY7ilZfXdJp7O7izNnkz9ZYm1LESNlLHAuqpGo7qlpkN3TQcykyd6zTvfpPsRjm1QXchQn\nlbiu90IvzUllsKz5Am28xbUf+c3vKdfjcVQ2sQYgpVyTUkZSyhj4E+Cu/9SXpJR/LKU8JqU8pqeS\n34XT+MH2g+0H2/9X23cjUPwY8Plv/2Pba/Tb21PAzH/Nj1wr9XLq/T24mzGDr2gkelskeltU9irl\np85ujxtP2ZhNQWJZx97S8D9QZ+1mD0YH7BWTvmKdvmId4WtKvuz9FJ3ApDmuVJ0XLw7Sbdo0Ozam\nGZF7JknukiB3SbAx08fOh27QPl3EHGsRrzn86snneOqp1+j2SFo1F2fBwr+eYWDvOgN710nuq6Df\ncijuK7NnuITMB3gXc/za//4LFCe3eHJqBjSoXiwQBsqjIR72SN+SVB9t89994mmcuzdZfkgjzge0\np32s19KK2+LE1Baz1Baz2F/IEUVKWUq6MWtPdWk3bdbeGQApSFx2SOyoEzlSTV7ubvHZn3+GKKUy\nL/uWhZ+NufXsBK3hmGs/0UPyXZeU6RPcTBHcTFFtu8TFQCk5D0ToV5JQNdEzPifvv6g0ORfS5A6X\n6RZiuoWY6oUiTlnpX9ZPF+n2SOodh9gA/1aSlfUcK8s9dGqOMoSO4fTyKAd330JbdIlfyyNi6Hlo\nlSM7F8hMVZESUr++hIhALiSQCwlen58k+6LL0eNzuG+l6HtomdrBAHfZIFpKMD5W5q6+BdoDCsqs\n53y8/oj6gYDUTYP+Uxr1KWhuJii8a6B7IA80MFqC1xYneW1xkuXTQyTmbHKzgj/56iNwtIZZ05DJ\nkG5fROqaiXvdYiBXR9+WrDMWHaJ0RGNcEAY64YTH0YlFRlJVzBbEdkzxXZ2gYWEu2mrf0kkuK4Sw\n6POIDSjMRGhtDalD4RULd0PyocErFJ91KD7rEPs6vQ+tECUV89m9ZRCFKkNZL2eon+hQWc1gbels\nLObZWMyjP7AFkUD2+ETJmKDsfkcv+XfDe3QRmJRS1rY/+z+BO1Clx03gF6SUq/+533EHR+XEz/wq\nUlf0ZpkPsBZUqp5eUAIihfclpYcUAu/wvXNc+eoumnt90j0tGuspEjdN9j5+FYDTcxOkZi2aOwPM\nDYMgF+GUDILdCgSVv6zo0nKiQ+p1VXrU9qg0TVqSH7/vDb70lZPIfQ2YTak00Y7R2hqxGzP4soqv\n68cEUU9AMt8hnMkwft8trp8ZJUpGiIRSl7LXlSwf2xDeOBaEvkHPKzYDn7lJuZ2kcrYXfyBAdLYd\n1rMBxqpNel5dH++ROvbzGWq7FKgou2uLzttFsvetUVoo4JQMNB/iI+oY8lKayJaY0w0yCY+NK0US\nE3U6bZu4amHUNZy9VQwtptVR19l6N0VzOlQPbNGHikVyWaOblwQFhd7UDEn+BYfKXnVe6V0VhrM1\n1lspNucKJMbrhOdy2Fuw79OXWWrmWLnQjxzokkmrMmI4W2Otmab1dpHu7g7WVRdvNEBYEckZFWQM\nD9V72PZXGXsWlh7WkAWf3Fs2lTtDEJKRZ3S29uh4+zsM91a5taKk5YgEWtMgeUsjvLdO8tk09Uno\nfy/iU//8Of71l57Az8eISEG8Abq7t7VS02pqJQ2JUdeJLfV+6J7AmG5gv5ShcZ8a4bjnEnRz6p5Y\n+2uEZ3PIAw0Gcg1uXRwgdmLMmk5iWdAa3VZSG20z2lvh5syQCobbYKj8lZi1D3dxZ1y8vm3Q1Ig6\njmMHdH0D99U0jYkYY6yFfjaNuyHRfdh81MM969I91lSUd8DviTCaOtkrcP9/+y5X63089+Dvf29K\nDyllS0pZ+HaQ2P7sM1LKg1LKQ1LKH/4vBQlQfI5/8bOfw9vpoXsC+6ZNckWSXFFmu+GAz8YxIBak\nbgpmXtyl5OZKJp3ZHOaW4ixcXu/n8no/+paqAe2sh7MpuPvwNawaRJ6OFqq5fTjSxbqQuL0KSStm\nYO86hfEKzyzsZ8cDN+luuYRTHvZQCxEo5WWzqlP5ZIvKJ1s4mwKhS8KZDN2BkLFUBWO8qa5q3SBz\nxcCuCLq9Id0tl+6WS7ycIH1aOUNt/OkETc/GH/LR3ZD0NR1px+wbX0WfavKJX3qRT/zSi4SXMtSn\nJLnpLTRfULvag7+3Q6WRQEsFjJ1cpDMU4TUtvKaFkJC5AZ2aQyzVuNe7kmXPcIk7D10nyEc0Smkq\npQzWuykVJPYr9KCIYXxwUxnlZCT5y5C8YTLUX0UsO5TvD4gdJT5Tv57jxgs70P99AYpd9veViE1J\np0/y1twkXmgQZSP+yZ3fhOd64LkeThauUZtRps97R0rE+5toTZ30WYfWSEx7R4B3TxOrBtgx2DG/\n8/t/SJwLSWc7VA5GmCmf3ZOreD9bweuLiRsmGdtDtyN0O8JMBsSZkE6vpNsxyfz4MnpHULpL519/\n8cNo+xrYZR19oE1+NiI/G5F93SGzTxnupOd0RCgYu3MZJFgVDWdPFeuVDP7DSqtE12OCJMQTHdLz\nkE90FFZFkyxc6yNORBhZn6A3IEjD7z315/zeU39OuO6yUsmidQXOuobsCfDGfWo7NPQlB/v+Mj0X\nBLElMc+mMM+mSPx1Fn81SfShCu6aRnwthZ+TbJ3ssnEs5sDICs3dAXeMLJO5Y5PMHZsUzioN2NaI\n4OnZg9x6ZuI7edW/T5CZA6Ny5L//FcUALWn0fnCZvP23dLe5r+6kdcijWGgoJlxH0C2ohmd2VidM\nKIfpofuV0vHqKyN0xn3MsknkSGSPT+5Nm8pBtdI71238jMRsKIgrQJRRI03DDQk7htK27Gokbpq0\nJwP0mo67rhFZat4N0Nzts3dyhfW/HGfzREDqskXrQBf3ik365DrBV3qpTwGjndtovTgU0DCRmiRx\ny6A9HmJt6MQ2WBVBZ3cXGWg4OY9fP/gcAM+WD/LupUlEoOEu6YRJhYFAKAHX2FaeFjK13Y03lVOW\nbsZYdgCns7QnAjBjRNtAz3fp66lTmu0j3nYj09o6A3vX8UOD9htF2lNqAuCP+ViLFk5ZTZcGXtWo\n7FHri10Br1fi9wcQCzKXTfwTDdwXUnTzCjvRONIlkfGIz2Rv308/FxMXA7SyiVXTEBF4+zoUv6lw\nHp1egVeUJLaVxOqH1PRISwe4F11FsppuwsU0hXtKLC8UsNcM9O0mq3+wzVjfFrW/Hmbz3kBpY7YN\nhBORuOigdyE8WaO9kbyNPRGehtHUsCuC1niEdCOOTC9w7twk6bE6retZ9LZQo+9tFW7R1tU1DzXc\nBROjA5Gl3OeTyxrdnAQNnD1VMn+pGpPrRzV6j6yxcaaf8buXFJpYl0rVaku51d96Z5jUga3b2d7w\nn1rU/lGdajVJMu3RuZJTlg/LGs1ppSwfG5C9olG7W03KTCckvpkkzKrGZ+bZFKf/3d9zzczYAL0t\nkLokvRiz2Upw7twk585Ncv7dKVoHupyYvkG5nCYsBnR7Iz541wzSjqlPxTR3+xgtmF/qZX6pl8Sq\nRK8ZGNMNeneVGXzaoro3xip4CCMmTEiK70uMI1XlC5JSL1pq1iLatDHcEG3TRHQ1fuiT70AgGHkx\nQjtRIchIWsMxreGYXLHJ9fUi1T1gJgLahztYCZ++h5epnO0leEJ5goQNk6hlELUMsm87SE2SXDQ4\n8pFLYCotgzAT3WY/Agz9ocVX1o7wlbUjnH9hN5OTa5hVjfa48jktTFTonywzuHcdva0hnYjknEVy\nzsK56iBbBualBOFshvZoSKLQhq6ugE6rDu2uheYLHjkywyNHZpD5gJWVHryXiwr0VFZjwl1jJaRQ\ngVhEgtIPhZhN1EThcBerIsDXSBTapB8tIS+nqO6P0SJoHPUQVRP5Xva2hkNqaVtXQ4+xtzSyJ9bw\n9qogUT4iCRPgFZW1YGNnRGNnhLFuomUCxLrNZz/zLIlVgX46jYig8dyAEhHWlMye1x8jVxxqfz2M\n/Yk1fvf+L5DNtNFrBk/uu0B7JKIxHZH9QhprQ1fWhpEiE4bJmMLFQIkbBxB16QAAIABJREFUx3D5\nhZ1Kfb1r0r9/HS1QNohmIsBMBLhDTbTtoCENpcoWJiQyFRImFP6heE7Sbtvw2Q347Aa5Q2Uqrw3A\njjbzZ4dxCh2Ep2DbQTbixvV+dF+Qcbro51Po51PM/yhsLefIvu4Qns5jVVWWiASjatD/luTQHfNK\nS8TTkZ6OZYWE2Qh32WBX/8ZthbW/6/Z9ESjS6Q5hUpIYbrJ2QtLYTGLWNcy6RmZOoJVNzpWGyb7j\nkDtjYTQ0nr+wFzPlM3ZgFfemhXekjWzryLbO5p0RUS4k8VyatdUcG0910D2NR6cuY5gR+UtQ/1iT\n5lqK9ECD9EADQoHZlBw7cg1Nj9GGOgy9Aqf+6Dhmjwe/vEFjMYMc9pCWUrkGKHw5wa98+GnMC0mk\nFPRlm1S/MoyzLujO5Hj4xAV+5K532TW1yq6pVboFyM0YtMZCXr80jb1s4mwKJqbWoGkiAw171WDl\npM3Ftya5+NYk3WGf+YtDyqA243PvyYu03yiycamX1ct9WBNNrLJBkJIEKal0F1GYhWDYJ7Fo4C2m\nyQ/V8PtC4kLAjvwmU19o8MKrh3nh1cPsm1hRWIiKJHZj7Iog8XaCdmARZGPE3VXMitJ0aE4HNKcD\nnHmb9pC6DuHlDKWZPtx1gdHS6BzuoK1bJFY0Ygu0VICWCmiMC9x1wWh/BbOhULTGokNzWKXi1b0x\nYSEkTkYK4q5JEnuqWFddnnrobf7DwjG0QCLuruJsSOwPbeBcdbDqAopdKHaJiwG1neAYIb918Unc\nv8wjTcnXnz+mjH/KOo1P14mnOqTnDNJzBn2H1sjM6azfqfAeZjLAqivP0uOji/ihAQcbCCei8DWX\nwtdcup6FseCAkDjrCtvgj/gU3jTRDtbQfTB+eg3X9dmopdR+K483GGGfTrLr6CLOK2mkLpm/MghW\nTPqKSf5yzHI5hxahditi4FWNICPojASklhQDtzUeYTQF5cMa718ZI0xJ0ldN0ldN+tJNjKqOVYWN\ndhJ34zuEQXw/lB726Kgc/he/SH+hRhRrrC323FaPtmdd/H1ttAUX3RN44z75YoPKagbR1ciO1xBC\n0pjtYTu7Jyr66HaEcc1VrMuFAvlzBtUDMeaWht8fYpdUqvptY1tQ+pfJWwKvCNHONrYT4M2n6d23\nwdbpPnqOrlNaKJC+qmB2Xq8kHu8Q1Szc3jacyxAfahDNp9RL2h8gzJh0pkP3fcUe7Q77pIstpBQ0\ny0kwYo5OLzD3pV00x2L0wQ62E2BoMeLr6jv6k5t0Q6UKXprp48R9l3jnW/vp9kWYOY+gY6JbMXeM\nKTOf+WoP7dNF5UFyZwf7qktnJFArb1cpTgtPw2hrqnkKuBsKsp3cUUO+kcfPSJwDVVpzObSRNiP/\nzqQ+ZlKfBHuvaknJt3OEdzYIA4P0my7VwwFOzkOcS5NelAx+9gYX35wkGvAp9iqCU+2MKh3bE4Gy\nAPQFdlkjeW+ZjONx49oAA+Ob+H/TR223ejZP3neR91ZHkVLQvZohHvHQ9Vj1X4yI+EYKe0vQObyN\nsj3vEh1r0F1LoPd0CesWmVmDxKNrlM/3MXFsibVnRglP1JVWCSDfyNPa10W3Ior5Bo2X+2mPRhgN\nZQVpHK7SuZ7BbGgM3L8MwM25fty+NvJCBrsC9WklsvTho+9z6q+Ocs+PneWlGzux7VDda1S54pY0\n3LIkerJCq21zbGyRa3+8h83Dyrqh57RBa0hB4AEy16D+oRbiehK/EGFt6tibgtZoTJwOsVdMuiMB\n2bMWnb5tb5O0RG8rajxWTOa89f8DzUxDYs+4rJ/vp/lqn5o+VCxkxcLri4gDDRGCdqiGO2/RPlvA\nzntqNGj7mF/sIUrE5GZR+3s2UdWiW4yoNBPYeY/sx1cwahp2VSinpY6gvaurZjNSGcCk92zR6ZeE\nO9vEaw7hhSzOjgbr5QxGSzCcqqF1NLo9km6PVLTzRRd7Q6e7mEJqkHB8jLYgKCoUqHvZobGYIbUk\nSS1JpSfwrTz6SzncRRPRMjhzbgrv3iZ771xgtFehLz82cZ76SY/6SY/yapbObI7qywPEruTUzG78\nnpj+sS2cd1OYJQt7xuXsu9OcfXeavYV13JKkdayj7AQPNtAbOm6yS2rOJHPJxGhp/IPHX0H3FOCn\nsSNC9HVpNdTkof94ia5vEOUDhgo1Vu6z6PYoJe/gfI7gfA6jDcM9NRzXZ/RTN8j0KjVoPydpDQm8\nyCDsCTGXLXrcNj1uG3N/nRMffZ+B0S363hBYFY3UkqS8lGPh3BDpgQZrc0W2jkTI/i6yv8srb+/H\nv5ilteUS9oQcnVjEPJ8kqlpYVsiO47fws5L+r1j0f8XCv6NFdD1F8qaOcyaBng5ojcRUmwm0AKod\nl/bRDqP5vwW0xQYqbV92WVvPEjnwbx77HO66QO5r0Go4/OMnvoafjVl/cZj1F4fZ8eUIcTrDQ0+e\noTkeY1U1zIpOwWrS7pd8661DhJ5JayWNnfWwsx4yFeLnJXt++jJhrJF+OcFb7+2mNg3JyRrjExtU\n7vUxDtYwmgKjKWgPCIK6jb2/CjHIyTbN8ZjMnIZZNpECtLpBe0ji90b4vRHF09vliS5BSKwPfR+Q\nwr7TzR4dlXv/8CeprGXQnJC4YZK6oVZtuyppjKsIKfM+CBh82qL8iTZTfWUuzw+Rf8ekmxfI7bAX\nZCXxoIdx00ELVVPNrij9ydiWGA2NYECxId1b6jhhWhIO+GhmTDrVQXs2T2V/jNHXIag4HD94nStf\n3I3uSWq71DVLLmvkHlll5UI/ekdgHagRRRrxxQz24Qr6czkiW+BnodunVq6de5ZZb6awzZC1tSzO\nTRu7An4Geu4v0Xx2gMiGnR+ZY/nfKvB+5+NVxCt5RdJyY5wVg+60Rz7fxA8NkrbP1vlesgdU175S\nS6IbkaKDS8hdNKgeDrh733Xee3MXD9w/w6svH+ToySvMbSmuR222QOY6ND7QIWwbEGoYNUXUyu7Z\npO3ZpJ9OUb4rxiyqlTtaSjB6aJWC0+L0zCSpeYNOr4Qhj8wpB7smKT0SkJy1aY+ovz95SycywWpA\n/ahH4rKDiCE41sDfSCB1yfED1zl3ahfOXvUip/8qi/PzKyxvZfE7Sl80NVYnijQ6LRvqJtamTndE\ndZn1qkFyqkYx1eLGjX7MTYPEiiC9FLF+VEnxbd6rsj1jZVvRuq3QkUZTwy0JuicaxNdSyIkO2Rdd\nKg966LccJu9eZOmb4+q+9KmGcs/OLcprGRLXlYbJnQ9c4e0L04hQ4C7rBBl5e8w68nzEwsdArxtY\nVUFnLMBZNTHaEB5v0O2YJM877H9qlpmvKUOn+FidsZ4Ki1t59LczBMcb2FZIo+5iX3cUv2Z3C/ts\nEruijuOnBfH9NaxvZggerdH/+w4vvfg9hHB/NzZ7dFQO/dovk1rQaE7E2GWNYP/2DPlMgsyHlOfl\n0f03WG+nuXW9V7mApyTWYIvoZop40ENGKlIIIyaV8ujM5tRosDdCRILEssZDn36XdzfGaL7YT+dI\nm6irgCsDA1VKCwVS1w38Y03kjSRmXdAeCxGxQG9phD0B9oqJ2VQ1jleQ3PuBi7x+fQrKNtKUJAab\n+FcyCprbheknrjOzNIT1bVbfSEhi0SA4rLgs2hVFT4/6fQw7JF520UKBCNXYGBT56FOfeoXPP/MB\njF0NfN8g9VaC+s4IvaWRWBMMPLl4m3Fr1HXCQsCuHSVKT4/hFSThuIcoOei+0sUMc0pkNkhv+2j6\ngsw8tAcE3phPatZSug59MflLAj8jqO8L6HnPoLpPPTPfhtK7K7rybF1xlCfHnEbr/iZBVelWGFUd\ne5cqPVqlJNamjlDJFca+OuHlDLEFiV1VGtUEmhUReQYDg4oiVFrsIbFg4u3tsHtkjYVvTtAZVHgH\nESnjaNkyGHpJ3f/N/Rrpo2Wq9QTGlQTeYEjhtE67Xyhp/7qGN+FDJLDWtm0YD29SWcyTGq4TnM3j\nDYaY2S6GGeGtJHFXddqjIdamftvjdMfHrzP/5Snqh7fHYL4qpdLX1SQuONwkqCkLSaNXBVcxlyRy\nlfXkw/tnee/zh+h5cpnyt4ZpjUeYVQ2rJghdpb0CIE2Ju6px91PnGXErfOnzD5B9sETpch/2lkZy\nWbJ1UNK3Z4O1ayrwp2/ojH/sBjMz4xROa/g/XGXmo//s73mgGB+R+x/5VbYe6JJMewSBTuIlZRhT\nPRAztlMZnYgIwpQS2tV7ujy68zIvPHMUb8THXrYYvk+NR6NYo/TmEGZDsfWaUxF6UyPsDVQ3v79L\nz2s2zlbM2t3bwSVSpC9t01Sj0kiQnDcI7mzC1SRT9yuCzuyFUURePRjTQxssVbPE57MEGcVYrM/l\nQUJqQaP7gQb6e2naoxHa9nesSy7G8QqmHrG1luHk/qvM/MV+KgeVhf3e4hpX/nyPyloGlZ6jZQfo\nb2do7vURDQNpx7dJccbOBnEs8NcStz0dxifXWX1nELsicB7eQEpBIdFi7YvjeA81EELiWAHVSpKp\nP1GBonQigXHfFu3zeeR0G+tckvZIxBP3nOMbL99J3NeFmolV0Ql3qiDem28ok6W+AGvZpHh8jZWV\nHlL5NvqLOfZ8epYLT++hMxypRihgNQStfV2S2Q7NUorCGZ3WsMDPqmwp2deiN91k44VhOgPbDZQY\npCFxh5t4i2mcsQbh5YzqGd0sIAKBW9JvK0+FDkw9PM+l98cpvC+o7ga9C35eeadaNWUT6PXFyITK\ndNxFk9zVmNVHQwgE7qKpNFCkwKgo9bD0PDTHlZIZgJ31MM2IKNKIrqQRobKe1O6q4s1lsSoC50SZ\nynweq6r+fj8fk76u05iMIBegGTHZdJvW2QKTJxeoeC61lktnPQHbo2tiwcTYBosXBul7DzYPCMLh\nLp84eJYvnjkKAnQnwp5x8fPbUgPLgsad3m0WceqGzsXf/XuucJXaNSALv/ErIMG5ZTH14DzrfzYB\nwOZhiT7YJl5O0Ltvg42ZPg7dfY0Lb00TZiImp0usVjN0ygn0jHoZB3rqrF7qw9nQCA4p+z133qIz\nHGL2eDhOQHAujzhQR17YdiRroiTLGgZ3HL7BuXOTCiG5YqMFqnTInTdoP9AkKCk058889DKf//zD\nqqMsoP1og+5SCpn36e+rsXGxlygbkbpqom8bk9d3bzfPTDWStco6/rg6rtbjo2kx+pUk3qjCMYCS\nX8tdFlQOx6SH69QrCUw3wJhJYW9BY4dEH2sRrqjzcksarWmF/RBDHlGo0fOqTXWPkuKXplQybTcM\nWuN/K2uXvqZT3xmCE2Oum1gVgdcbY+9o0L2RQRttsWdwnbmXJrfPK8ZoCXX+NROZiOh71cT48TVK\nl/rITFVpXcqrGnr7MFZNYSSsiiDIKtTnI4dnmNkaZHU9RzLtYTyfoz4VM3Fo5fa5rbw6gn5HDd/X\niVYSmCMtwlAn96LL5vEQYcfIbSaoCAV79t8iiHWuXR1kdHKDciOJ8UaGxnSIVdHxh3wMO2KiX5Vr\n124MIOwIJ+HTXUzhTjRo30qje4KwEJDId4gvZPFGFNgL4OrFERVohMpiRdkiTimXtPuPXWb23+yn\nPqnUvI0Pqh5B560inbEAc0vZOhodQbSjQ7xpYzQ1gt6A9CWLyN42W/72O7KoVN2TVy2FzxAwcWyJ\n67f6GB8us3hxkNS8Rn2vCmKp/ibmc1lEDIlyTOUnmlx+6u9uUvz90cz8wfaD7Qfb9/X2fREogq6B\nO28hdMkffOaPlHfDp8rwqTK9u8vEywmidMTWmT6cTYGlRySWBbnzBrYeor2XUY23koMsOWy+OcD+\nozdpT/lEkcbE+Abezi5WwWNH7xZhqBObEq9tMXD/MgP3L9OYDqFmkr6hc/HUNDIVkX3XQfMF3QFV\nm9Z3xgTrLnZFw65onK8P4+cl8qObbB2K6d5K8eh956BpEsUad917BbNs0Nzj0+lT0GajriGFxNw0\nEFkfIcG+aUM2IK5aWHaIOFAnfdHCL8T4hRhnXaNyf5fcaJX6RgpzzUIuJhExtIclI4dXSTi+GruV\nNFoT4e1rm3g7QbHQYOuQMmR2S6qO1jyN+O6aMmVua2gdQXi/Gns+uO8K+lSTzqBCULY3ktgVgZhL\ncm2jSGRJ5cJuS7pDAelzDkavAotpoSRtdUnf1KiuZojHPMLeAL8Y4hdDvILS0GhPBkSWxFo3+OaZ\ng4ymq4wPbuJdzVK/p0OcD1g5NcLKqRFuXhgiciT9mQbxUoIdX/Wx30xjXUxQn1Jlg2zr9JxRu97S\n2Pq3Y6w8P4pZ0ak9O4j+TobGLpVNPProe2TybZJvJZhfKzC/VsBdMKFqEvgGySWNOFb0cmdXDaNs\nor+RRevC7slVrpV6uVbqJXtZp9hf5+F9s6QzHYy2mjLIRMSbr+xn44GAzLykccQjfL5I+HwRry8m\nc8kk6A0onFe9DutyguRIg9yhMpkLFn5O4pwoE9lKic2qCcInqmg1g9aOgDAXYVUF164P8NCeK9yc\n70PzBN0ecJcN3GWDMNRpDwr2/tRljF8s0ao539E7+n0RKPSOajjuGivxC29/hsEvWZQ305Q302xe\n6EUEaqRp1QTdQ22agY376Dq1vRHt/3UYPyfRPaWsjVBSc5eWBzA2TXKnHMI/HEAGCnC1+swY9osZ\n/CEfJJRODVM6NYy1qTO0ex2jJcneUWZ4aAspIHlnmdQ1kyAbk5veQpqSWFf7lf9rN5EbE8eaIo0F\ngteXd6A3NbwXenlnYRzNF/QO1DBaalwVZmKMps7g6xHORRc/G3PisfPIpkFquE5nIY3XtGmNxmRm\ndTKzOl5fTPYth/qVHuwVk8jaftkmAvzekKX3BzH/Jk/6wTXSD67hrBoMfUsntagYifWWA9mAzXtC\nOgc6WDUNa0vDeDWL0RYY7f+HvfcOkiQ9z/x+X/osX13tfY/3fmdmvcMuFgBhScKQOIJHzxNPJEEd\njShRilBIcRGMO4ruKJA68gjQHAAS5ALEAgtgsYt1s7vjd7zpnvbd1bZ8ZlaaT398hVkcdZJ4xDEE\nRCAjKma6ZrK7OqvyM+/7PL9HYNY0gqkcelPnwif3o53PIotttv+7NtaaTu7BMvEWH+tFhW9Dg/w1\nnXSXR5gB60Iad8Zi/T0tbl4aoXbMhwRyLzukix6pGZPUjInRFAQbLuaqgTtep/uiJN3b5Npf7aLy\nN0Pkb0HmrIs7ZdMuJrSLKqhISFiq5IhLIauHHfxuyY4nJ0kM8EZUsJFfEh12iaT8YEJ6UUmopQHN\nfT52l4ezKnj2S8d4auwajRGJTAQyUaQqdIhaBtKAXb1l2sWE1kwOPRA0Dvi0toRUfFfFRqzZtAsg\n/qrEY8VrhGeK7H/4Ft0vm+RKTVJLSn3qdwmy51QHyK4qUlbtYBtzxaTyVJOwkChYz0sFpFR5Ju6K\noFpP0be/TN/+MgjlJXFWNMx1Ay2tNEZ61eDrl3dhF3wKN0HbW7sb8RCUVX7tq+d3Mn27D1Ezv617\n9DuiRpHf2ScHPvirbHtyiuuLfTw4Mckrs4oxGM2m0T3B6H3zTC12k3gGpdMG9VFFFGo81sS2Q+qb\nKbaNKmamrUdcm+8nc9ZF9yWbRyIO75zmwqUtGFWNKJ8wsXOJci2L9RVVoxj88DRXro2ALnG7POJY\n43eP/iW//Ns/STsP3liInoroL1UJPtMHgOFJNvYIMofWaZ3uxh9UM7mzbOD3RqT7m7QaNuaMTdwZ\n0K2qYOCReSqfGaL1RAN/00Fr6Tx08gqv/91+YlsSFhPIREivg+Hu+BHMdYNoMEAzE+Syg1XT7upA\n/CHltwBwuj0OD85z69/vov2eCrxQxPAkzcc7Keahjn0qS2MsUQldQGNLRO6agXeySRQY3Ldjkmvr\nvdQbLsmCS1wK6e6pU32zpBiSqO6Ku1O1ML2bBVV01JV0eftTk1ycGkbrpIKn852W6pkCzol1glMl\n1bK+N0BbtTBGm7SXU+Rv6LQfq+J+KUf/R6cBuP3iOOEWn3TGpzGfQ9oJ7qxJlJJEuQS9rmH4gmCL\nWtWkLztMvGuKyWe3II/USK7kcNZAf3IN/a9L1EcFwZaAx3df55XPHwRQERF96vyoYUIiGPqqYOHJ\nRLlRsyFi2aF/f5nGM/0AtO5tErZM3LyP+9UsXo/SmcSOUrf2jGxifLJE+SQcPnYbgPOnt5Fa1kgM\nyMxJNvYrtJ5VUZ6X2FXemfQtC2+/umb2dZfEUgOASMDa0BQUp6etcIESto2XWX52BHFfB1M4rz7X\n6RldOYC7I2Z/8le+u4uZ9pYhOfRzH0f3BOP3zbH62REqu9Xrcpc1OFaltZ4ie8NUIz+QmYPaYy3i\nssu/f/cf8ou/9TM0htW/xa4SmUgnQQQaP/HwC3zqc4/j90eqdZiJEekIa8am66o6pzauEGOtQcno\nyXnuLHcjJQz1VJi/3UtqVoXrSO2tIlOcSnAXdVpblGy38riHNuMSFmPyVw1SK4mKsjtWRZxWpiiv\nL+GfP/4Cn/r8owpttqygsMbxTTzfVO2zrR4yFsiqQsH1vK5R2QntUkzfyxp+Sd3c5pNrbNzqonRB\nIH9wneTzirFYfdjHuuqSn0pYPygoXIPNPZBeENQnOp0EqXJI7V1qu5EkAm8hQ/6mzsD3T1P5xCju\nTywyvVRCNxKMy2mCXR7dX3UY+PEpAG49uxWvL0FIyE4qqXaksnow66A9ukHTszg0vMBYSpGXvji1\nl2A+gzXUJPBMsjl1M4SRjjibo52XRAUFFv74k18E4LcvPoacTSFHPGw7JAhMZCwYeNpifY9O35mQ\npY8Fd4u5UpfY6zrGoQpBYBCtuJh9HqFvoC/ZGFsb6Gez+N0JcbqT2r6mK6OdBHeoQXQ1R2KCNtEk\nXHMZ3a5yVrRYIQkApBOjVw263hQ0RlWr1huI2bN/luuLffQU6/SlGtx6dutd1TAC/IFYnetGsOjc\nXQVrpYD+v7YRCawd0DEPq5u+vpJRERa9EWMTqyy/NkDsKMt65hspGqMQDigc4aM7FWrhRqWXjZf6\ncdYlmwdiBl/QeO3T/91390CR3dkvtz/yy4QpQe1Amx+/52XOVRQpd3Kjm/aFIv5AyNE9d7j2zA68\nXT7aqoUY9On5vMPSI4maxTuCm/GxVRZODxKP+BRectjc34mDNxJKr5to71tj7U4XmqcRd7+V0dFu\nWchAU5GFCcQplSvanWlyZ7mbONDRnYht/avqtb0xSlRUmP93feAUz3z2XsXT0JUuwWyoZXC7K6Ew\nqmZe/40S3tZAka27PNJfy1DfAoX9awShQa2cwcwHxKGONam0F5ErifIx/WPrrFfTxIsphWfri8lO\n6koEtvOtAJz0vMaxD73JeqAyRTVPyZBzt6A1KNSqJSuRZsLQc+oTvPCEpGdkk42r3cSlkKHBDZYv\n9SH7fbQFB6lDXIgo9NapLKjZyu720C5m8ba0sectzAMVMk7A+vlewlJE8bxBZX+C5gniLrXa0ioG\nhicIswmjzybMvkPj8Xsu8/yLB3C21lQUYChIUsld0V1jd0Cx1GBzNcuPHDvF5/78YexNSf1tTdp1\nC61hsP3AHDcmlY4EIdk2UWa6XCLyDd594CLPPH+M7LQyUlUOROgNjYcfuMzzp/cCYG3oBD0RWAl2\nLqC9msLqadFeSqNFkHS3cW46+P3xWy5dAcVTFtUdktJFoQA2KYlQuAyydyD9kSXmL/XTfU69tPV3\n+SRlhySnYg7cOYP23hZJqKGbCdqki9zWInUqTXWv2mKgS5xZiyiluiTRzhZxW8dOt/ErDhgJuh0z\nWKpSrijkYD7jsVlNk6w4pOY1muPx//9w3W/3yO3sk8Vf/TjCUJ90w47eEuvk2uSyLbzXuzGPbaIJ\nSf1WATHokyw72KONu22v+w+p0fTV07uQZkLfqxrNAXWRAMiGOKk24VSW8aPzrDXSVFbUhUWC1tQV\nNGfWuktYjlISd7xOeDVH8bpk7RDYEwoQ4z6Tw/j+Vbxne6ntjNi5a4Glp8fQH1+nOlVEakpVpLUF\n+V2qDee/2o23y2fHSJnbZ0fRR5uEgYG2apGUVAgyQoKnv4Xrb6v8SKOmERZjctcNBt47w8wLY8R7\nGnd7+Vt61M+4dnsIEWg4S7pq3SWQvn9VKTZvqb1r0JWQH68QnFGwF6l3TGRpyQ+940VW21mee/Yw\nIoHEgtiSGEMtosUUSV7dKH3PGZQfiTFXDZw9FRpV9y5wSGrQ7o7pGdlkdaHAwHNqG7WxR0OLINjh\n4VxVkBbFPxUc/9HzvLk+yPJsFz3DFdam1Guz13TE/hqZL2TxS4LWsRZJpHFofI6L57aSvyGo7E0U\n2xO1x49S6jX4gxGiLRDFNpYTKf3IegatqvJrnU5okFWTbB6KKFw0qe5M+NgjL/K3n3gE451rarvV\nFSPciEKxSfCaWrk9+v6zfP3pozjH12l6NqFvKGBv08CdU5EJyYiPmwrIOKo/vnaxl+6LkvX3tghr\nSoyFhPSsTnM4wV3WiA/XyaYCjD9Xv7/5o2VWXhtAi8DfGqhJbSFNYqv82NZohHAjpK+T61NCvt5s\ng8qfDRO5Shn8Kx/7DP9852vf3QNFqndE9v6bf4FmJtiXUspuXVHLbrsj1MkcW6PacNB1SXshzYce\nfpXPPHcfzpqmRFXjEd1n1Idx6GNTXLyqZLboksHRdRanuhl4UaN8HGSpjTlno4WC7HG1Omi+0kN7\nX4vIM7DnLWJHBQNvf2yKGy9OEI37KsLQ14iynYFHgFHViXJqS9Dq0whKEn2noiGnrts0t4ZoDV3l\nYwD6usnDD1zm1S8fYOLhaa7dHKLrrEE7L4gd+F/+2Z/xa5/7YRhrEXZi+IQU3Hf4BpdWBqgtZ9HS\nIV1fd6jugD33TXHp7ATveugsp//tUQDiH16n/kYP6QXJ+klFzrJXdfQAmltDUqUW/lxW0ZUGlNW0\n9Fdp2mklTnLWBY2JiN5TOhv7wWgIHnjXRV5+5iBBT3wXvZ+YksyWsmCSAAAgAElEQVSMQhL6Cxk1\n8zoSZ1mnXUjo37vC4mQPvacEwQ+qFVWUaISX8ohIsP3xKe58YQt+tyTKxaT6mrRW0zhLBplZSa2D\nnw/zSiptr6hoAMNT6lF3qkPpPtBAv5R5K7H7oU38qwVyd1SYULuYIDMR/f0VelJNLl8ao3fLOkFo\nUFlWE8XQ2DpHuud4bmYHrdU06TsGze1tBoc3WLnQR9ShpFmb0BhTE1qSiclfNgmKsP2xKS5dG2X/\n7lkuXR1V/qW8j2EktG/kOP6wCmk+/dxuoqwkcRLcBQNvvA2BRrq/ianHhLGO9nKe1rEWXXn13qyt\nZ8lcdKhvj3C7WwSzGXK3NaIMuA+vUj3XTWJClI/Rc+pzZt5y7ybpmVUdqcPUv/rH8yi+IwYKZ2hE\nDvyrX0APBOk9m9RvFejpLNXWvs8nDjUKr9lU7g2wpxwyx9ZonO4mc88a4rMlNveorAp3Xg0UI0/M\nMPnGKNKUxF0homWg+YI4G2NsGsqnP2fgD8ZYa+qc2JVoIYTDAbKtI+wYfclWEmETkt4AfdnGqgpa\n29R2pXDGIiiBvQHh41Vyn8nSHNAY+sISdz46QLijhZh3ibIxXcPqRqnVUxS+5uJ3CXJvW2ZpuQhV\nE7Mm1GAkBbJlkOuvU1vqrHasBHfaQg9UjUN2apyZOxq1g21+/sTX+OTvvYO6qv8SFSIwE370yCn+\n8umHCbNKtNbzuRRLDyhMfLsUY+YDwrq62bSWztDuMtGf9LH+fpWxEfgmUcPELvgEVYeDO2ZZ/f0J\nlh7uSIuNBGEnOLeUX6VyICJ706A5miB1SXpWpzERkbtlkF5UN1dlu4Y8WCeazBBnEyXy2hkhnBjZ\n0tHaGomTYJeNu/kZaOCsCsy6pDUg8Aci7C6PeCpDMuRT+prD2j0J2VvqwmQXYhbeGbNtrMzkQg/6\nkmKtDjw8T9pss/JH45TvU90n+c1YRwG9Z6A2plF8eJnFmz2qi5YPsaccIkdRszMzGl6fOqfryAof\nG3uNHqPO//THH2X07dPcPjVGMuYj5h2ifMfpuSGI1ZhPazQic9tAi1XrUz+5yW/u+yt+5os/jhZC\nnI3Raway38e6pbaemTnJ2oMhE38hWXzIQttTx29ayKaB5mv0noHyfRKRb5N/Ra3EG6Oo2seoT/oN\nlwc/epY/OPbn/7QDRSe34/uAFSnlvs5zXcCngXEUG/ODUspNIYQAfht4J9ACflRKee7/7fvbE8Ny\n8Od/kaG9Zcpv9NPuiSl1Vgf1CeVN0ELouiop36s+6On9G3hnS8i9ykz0zeouQGbvBtZniqyclKSH\n6jw1do2vzO4iPFPEm2ijVwzcJY2hd84w9fooALEjlTS4Ay2NYw3HCfEnc2gRpOcEYVbNyN+MEtCc\nCPeKy8ATcyy8MIIUSuFZ36FSu9516E1e/4MjrN0boVfUflv2+xwcm+fNuWHc8y6tQx7avENuCvyS\nwBuKcQca+DNZ7jmhtlKvX+pMrR1ICgL0DUMtU20QR1Sx151VLbCgKyE7rVEfV7UZs9ejve4w9kXJ\n2o+3CK/lMBqCdkHJ4QH+2dte5FNfe0iZ5moaYSEGTXEk7BsuXfcvs3ijF90TRPm3VlTunEFiS+Xb\n8ASDj8+x/OwIjfEILRuizzkUboD8QbUtWivnEL5O/qpOdWdM8bLGxpEYva6DJjFHm/h1G2vBZPw+\nZZu/vdRDId8kbYXM3+xV+SdNHZmO0arqupYuCJpDHQ9OT4K9rhHs9EhdcvFLktwU1LZC3B8gfR3h\na4pcdUuFLIkhRSFLKpbKejUlO8eXmH92DO3eTaQUGFpC8vUurJq6ZtaHyvDHvZiNmAf+t9f49NWj\n2BdTNCdUXIDUob3Vw7nmIo6ponGz6uDetrFq4D9YJ450tEmXaMKnkG8ShCbhlRxRVmINqRWFX3FI\nTZkE3QnOqkY7JxVvU4f03k0MPSH9+3kWHjZIL6prUN+SkLutUb3HJ3vBobYzYvanf/mfXJn5H4Cn\n/t5zvwo8J6XcDjzX+RoUvn975/FTwB/8f31zvSHIb9tk/YUB2NEkPW1gfGAV4wOrtAdCxu+ZJ7Fg\n+cEEkQgiV1JruIQZibiSVTbvgiQzA5kZiJ8vsbFPYPe1iM8W+PLMbupzObyRkC1jK5h1jea+gBuT\ng1gVgVVR0JPkzbzKzmjrsOCSJIKBUwk/8NQrBCXwBhKMDQPR0hUGLdbwS5I754fgYI30omTouU30\nXBtj3eDLL6hBglggVCMG+6bLlRe3kUSCxs4QVm2kDt6TdcKDDWQ6IooUdk9Dqkenb24tmxAJrCUT\nLXorpSq+nKd43sAbjPAGIzJbqsRWR6rtJJhmTO6mwfwPh0SXc0SjPt5ATGpZsSDsNY1PXTmurNZm\notqfToKeDdk+uEJQTGjHOoWJTWJXsnvXPLt3zWPUdOzjG8qcJaGdl3j/bpAoBfcfuommS9yyoD4u\nWJstsDZboHDOQuqS4NEa0pRsHI3QsyHutipiwMd4PUtffwXDE0yVu5kqdyMTQWWyi43nB5DpmOJp\nk+xtnZ4+ZcXue12t+lojEa2RCGdFwxuKcNNtRATOmmDjSETU08a54WBm24iu9l0bfVwM0fQE+7KL\nCAXmugFCcvPaMP4Bj+hskWLKozpdIMxyVwi1draPzQ82WLrf4DNffgBmXZx1ye6d8+RvJ0hNos85\n/I8f+0vCaznCazlyb9p42wLqWxLElSzWpRTZaWDFJvUnRbzpLKmywBmp370/nDmT1rY2SXebrusR\n8YjP9genCUsR7UindqHE3BM6sSup7o6o7o4w6oKtH7qJDHTqW9+aeP+xxz9ooJBSvghs/L2n3wv8\naefvfwq871ue/6RUx2tA4e8h/P9vR+xA7VaRKCPR9YT2wSbNwKIZWBAL5l4eQSTg9HhYGxpJLqLn\nCw7ZaYHdeVVRWlJ7yKP2kBIAAVgvZ4lSHdT+QBMEzFwYxO+PEEaC1lAW4DAnsW+6quawvc7h8Tn6\nTkukFGzs1Pn05aO4x9fQ2kr0teNTDXZ8qkH38wpka441aU9nqG2B+SeKmGZMelGgDbcw1wx6RjaJ\n+wLivoBoT5OwmJC+5CACVV8RMQwUakTrLlYqJLmTpjkWceNPd3HjT3cpV6wdK82EpnBrVkXdnK3R\nCMMD8c510jMG6RmD8GyRrhsRpfMamhsRXskROyDLDkF/hHPVJT1Sp74txhtWjyTSSM9riEBDz3XM\nc77OwpfGsCoawVd7aFztwt7QWP7MGMufGUNEEL7aRenksvImFCKqH60T7mhxamqCYr5JmFWtP3T1\naOfAWTQY+l0TZ8mgeMEgDnQC38S4kaKxu03aaqNFIBdc5IILEvpeg/bBJpmrFuGTVepHfLrcFu2+\niESH2hbAkGBIvP6ED558AyEkraGExvYQZ8GEtobhgXMmTX+3muH7nzPof86g3bTY+tQUsquNWRfc\nu2OKP3rq/0SWbfQjFcpv9COdmEPvuHY3WrI92MZvWWihao3GqYTaNrjz/Di1H6jTf3KJ449c43/+\n9IdJdLUybufBnrVBws7HJvF7EtbvDbEqGpUtOlq/T5CH6GoOf93FX3cJtgRYmTbmvM3ce2NyOY8b\nr41jrRkYL+VVq3uwhVkTd4O620MhN9d7SE2bkA1ZPx79Z++9f+jx7Sgz+76FsL0M9HX+PgTMfcv/\nm+889/94iA7pOEpJ7Odz9BbrlNItSukWwooJ+kPiYR9/3WXi0Wn6Biusv8ejti2hcDvE2oTCdQEL\nLiy4eBNt4iGfxliCvr2B9HT8poXWUkUd7Bjrtos90kBEAhEJvJEQrQ3GqznOn93G6gdahKFyJFpO\nxMZaFm2sSZiTrPxGyMpvhDg/tExmVuBXbeJMomoZFoShTtAF5ptpwt6QtbUsetlWjxtppC5pF9US\n3xxtYm8K7lwZRPME7YpN3N9GxILIVQ/3hs3Y0DrhaIC1YpDfuklrt4+7InAXDFq7ffxXu+8qM5Gw\n9MM+1e2ogXcgpDUSM7J3GWElhAcbeFM5nL4mdlnHLutkzyq1I7kIseCw5egchhuBgKAvpp1XzAa3\nLKltU4/YlTR3tFmYKRGnE3pfMWispDGvp3j37kusrnREP/s30DwdzVOzXrirRf1X6iSmpDWAyp+Y\nS6G1oesNk+Q3e7Gqkt4z6iE9A/mxVcTtFI09SsVoLNksPDMGmmTtMLhlAZpUJPGugM++eYT4bEEV\nXiX4EwF6NqS+IyI7l9B4th+uZalNaNQmNB7Zc4PbX9vCkYlZvN0+F7+4m3/5xz9NUgxpLGfQYkGu\nr8GNT+5i/VjM+rGY/Hl1ww8/76GFKnPF3F4j3t1E0yS9qTqvnt9JMNJGGmqb5w+F5I6tYtY1Jr+4\nld95z38g292keFMNaFGgIw3ubrsAnthzlXZTDUiEGl5g4uysEqUljWMe7f4I/XpGQZk66mR7zsJr\n2Xgdglvm9renzPyvIuGWqtDxX1QVFUL8lBDijBDiTNxs/td4Gd87vnd87/gnOr6dgaL8zS1F58+V\nzvMLwMi3/L/hznP/yfGtkYKmkcYbishOadS2JywuF1l5cZCVFwfJXnBwSwqKIgKNyVfG2KilMK6k\ncUbr+P9yU4XFdAtKl6B0CfA10uddDh6bJLqTUWnYszaiJ8Be19AqJv5QSHQze3ekH/miwDi+SWtA\nqSXTrmIRBEWJX7NJ3bJptxR2rDJXoDJXYOPrA9THE+7dPYloC9xtVVrbA3IZj/1vu4E3EGMtmtiT\nDva6wF4XBN0xQ+NrSgW4pYn5WpbWXh+tx8doCURbQ1s3Mbo9GveoR9f1mDDRyFx0sPdWqNZSyFCj\nMaxgutIzaI1EbJzrZeNcL2FOknsuTdjfJpP2sZZM3AWdINYx5yyYTpOYEvOVHP5EgD8RIB/dVECe\npkGcTpjdKJLEgtgEva4hdYl7bJ3K/oQ4FxPnYoyWQHcjNE+nMFrB7xJoqYjhh+d4+sxhnDuq/jKa\nr2BVNMVk2NUg9g3qL/cSpSW9J5ZJ3zaJ3QRvl8/AR6ZZ/WmPzfsDBn/uNoM/dxvN67AcSjHOrEXz\nsIfUoDkaIzydzIxG80SL3m+Y9H7DpKvYhLqJe2JNJZtfN3EnbbRpF7Pg0xhSnYsok9AuSNoFyUsv\n7UMcqnLuzihmZ2sQZSS6lYAhSS1J6rM53v/fPA9ODE7M6PdPMTa4zu2PGhSvJXT1V/Hms0RrDkki\nuDA/hFZoUzhjk9+5QX7nBlpdx2ub2Ac3afUn/Pe/92PUN1Os7xWISEOrqoiJ2RdHKVw2KFw2eO7F\ng2S7mnzfe06hpyPiqQyeZ5HYCbYTMvgVja6TyzT2B0QZSZSRpBck2qSLvaFh2BHx8dq3cav/F7RH\nhRDjwN99S9fjN4F1KeW/FkL8KtAlpfxlIcS7gJ9DdT1OAL8jpfzP5o9+83CGR+Twv/lp4lhTrExT\nkruhqtlhBlJLko2HAmw3xH0uQ+W+ALFmIfp9zBspHnrXeb5yZj+pDtauvb8F8y5bjs4xWe4m/w2X\n2gRYNYG/Wwl9hFR73mhNtZOcAVVn6N6zxsqdEsU3VQydMdEgDHVk2SEzq1HfEuMsdwJnDcVkSByJ\nFgiMkSbyRoauYytsnu4l6FG4dG9Y9b8B/IbNoS2znL8xjlU2MFqC+FAdw0hoNWykr5OaNglKyd0U\nq9ZWZSIyGgJ/h49Yt5C2JD2t09gaKdOcBkZJyaHd1zOYNaVDcMuCgffMMLdZILyZIzElcTa+6x8x\n1tSSNE4lpBYU+k4k0NwWUjxnUH9QdQM0PcZ8M6Nw9OoyYzQF3pYAfJ3sQJ34jSKlh5eYWyipIB4r\nIkmEwgNG6ndJXVHhR9kZyeZupdEIcxIx1iReTmHUBVZd0D6qtBGgCpX2wU3qlRQD/ZusXOjDqgq0\n4xWadQchJKzbd1udZkWj3RuRu2ZiNiXNIYF7ZJ1qNYV93SXKStLzgsrRAEL1ugoXTIKS6lp5JxrE\nSykeufcyz9/cQamrwdpKDtFQAU3Dz6pzvC6NzX2S4mXB5j5JaqxGMeWxfK4foyVwj6+xsVBAb2hk\n73QGu8eqBHeyFK4J1u8PIRRgJSAF6RsWUusIxfpiHj1+BYCvX9yNmWsz1F1h5etDtPOSOKWEaplZ\nxXCNtvoUvu6Q/4iak2fKJVJpn/j1oqrD5WNmf/afuOshhPhL4BSwUwgxL4T4ceBfA08IIW4Bb+t8\nDfAMMAXcRoUU/4t/yM+I1h0yGZ/0cJ3sLYPavja1fW0OPHWd2tua2FMOYagTvr3KofE5stMaxVwL\nfzDkuVu7yAw0kLpqGbmpAGtrjbmvj+KeT1F92EPf0sDvTjBmHFpjEflHlvmtez6NVgrQSgHjJVUV\nrb7ei3RjNvcmKkOknEJ2YLxerwK+fOiDL/ChD76A1MBZ08gM1xBRJ+DHguWZEva6oHukgtECc0Pn\nsbFbPDZ2C/eGzY0vbcdaVoOE4UFQt2mupDk0Psf4xAp+T0J2W+VuWtrQ0AbWzhrtrgQapsL6zeo0\nRxTOTm9piECQejVD6tUMkQt2LSF/CxpHPRZrOZJEY9/9t9UeVoPMDYvUpEX3wRW6D67w/vtPk5tJ\nVGJ2AFpdEbRSqYDYV7CYoCuB7U1E3FFx7m6RzvuIdIR/vYA4VqV8pp/+gU20OQf9TBbjQgbzZgoq\nysadWpboAVS/r6FSxbYFpBYF+vUMzkidxFLdE+2qinJs56UKDHq1CA2DxdkSiS1pTYSYX8njXneQ\nmxZGQ2Vz6J5GuydGhBqt4y0290q0NgSnSjhXXfQAwu6QrvfNoxkJeiZEz4S0C+CuSGrbI2w7Qu/3\nePmr+8nn1QBvLajrTiJY26+ztl9n/Z6Ivteg1SeU32MlzdxiFyKCJ9/3BlGss2P7IlZVo3F/i8b9\nLZUyNtJi40iC0BM+cOwsRtnqWMgjOFbF29rGrGm89vQBXnv6AMLXsc+lmb08gF2Rd+ncRlPgnWwQ\n2yAlVPZI1r4wzNoXhombBilLRQ6YNfFtFxm+IwRX9siIHPupj5PsaJL/Soq1kxGigwFzbjkEXQkj\ne5dZPDNAYku0tiAZ9cm85lLdrWLt9YZG127Vq/e+0UNzr8+B8QUu3hpBrxgUbqjcjvxPzlH+3BjV\n3TFGTSPuXPTeM3DgFy7y3DcOERcVGzPoi8hdN+l+1zz+JwZo9uuEj3QSplAsyA899TJ/8eJ9yExM\n6ZSJ1y2IXYm+t4b+ap7mIQ9ZtXj42FUATv/tflojMcSQHa0RvVFUBdAOuzJOJdglD3Eli3u0A8ud\nz5OeMQiKkmTIJ5fzqN8uqMJsAg/dd4WXXt57F+DadVFj/WjM/r2z3PnCFh780DnOrw3RDKy7Ltul\nag5NSzCfUTqCjSMx1qoq9saORPQFuBddGtuUbsRcNcjfgvVjCZqvBsXu87ByQiLdGGPDxKwrL4Vd\ngdqWBGdNozUakZox7r7XQoLfrQq/7rJGUErQIkHp4AobtTTtlkn3SxbBeyrUN9R1zl+wqO6OsDZ0\nwpGA9JsOVk3SGIGwK8Hc1HjkyQucXx0GYG01hzlnqViArW2V+7LNx3JD2kuKhWpvCppHPPTZjlVg\nR51jQ7O8/MYeDE/wA29/hXm/wBtf2UfQH4IGpdcNqtvAbKrfv+t+BUOubYuRGbUtOvrkVV69tQXZ\nNJR8PhXx+K4bvPbXyqXaPtpAu5Yh6I4hAXdJxxuIef/9p/n8cydg2CMKdEwngmllctPaakIRJyq0\nL+dhh3JMey1baTA64GmhybsirXZXQnZSo+d9c4SxjvepAc588rtdmbl1SI782C/R7ol58sglTv3H\nw+SeXAZg/VQ/hgeNbSFmLmDgzxy8n91E//MSa4cEPWclS0+FPLn3Kmf/6BCgZLtBX0TfyCYb1TSp\nU2m8+xt3L66YcYlHfMSyfRdgaw82cZ9TBq0nHj3Pl6/tYeSzBkv36pg7a6TskLXFPE7RRzunFJN+\nX0KSihG+Rmq4QftmjtiWuGWNA+++xumXdiFNiT7col1R0jxnyUQLIDzQJNpwMCsaYUaSGaviXy0g\nYkDAzgfvsPnbSoZu/OwyXmiydqUHLVTCpvxkQvnJkMybNmZT0hiFwg31uzSG1GAVZiRmQ9DuStAC\nZVDrOyXwP1yhNllAdoVYqY4p7tUsve+e4865YUSsgL6psiTMChojCdZIk2AxjdEUd39O18dmmVnv\noifXIEw01i71ovsQ5pSKsfu0xuZuKO5bYzCj9sjXllVzLAp15KZF/ppO5R5lkisOVvFfLymrdU+E\nllItvfwrDq3HGnTnmlRaLq2GjVa26dm3Qv0bfbQGY3q3rVNeVoNe5qpFY0eIlQ/o+YzL8n0C+gOy\nGQ//bBf+YISWCUnq5l0R27aJMneWu3EvuMh7q7RqDtQNpCmxV3SK1ySZn+jkeZSV1yPesLHXdOSe\nOtFMBjRlDtv+M9e4udlD5WI3qSVBc1jirKvBJfNomZVrPQztLbO4nufDe87ylYVdrM6oDBctG2Ld\ncsmfXGH9zR4AUsuCxFRbwsiFA++4zoWv7SIYbuPMWvijbbSagTTkXX9QYie4SzpRSpKdUWKzyV/9\nLh8o7PFhueMDv4TXr7IyBnavUPPVjWU8W2D8I7e5udZLcCtH78Eym40UftOi9ysW4qOrLM93UXrD\nQL6nMwOvZ3EyAcF8hp4da2zWUwwUayyeGSAaCsidcQgzapsQ9HQsw+kIoUt0Q+V1mi/kaQ6rG033\nVZ5kZkajMSLZekS1rhaqeQopj4XpboyqjlURtMY61kEBek2HgQBNjxG31OyYWBCVQpx5i9iW6J5Q\nmgEB7sk1fmzrKX7ri9+HWRfEe5TBxz6TobGrjTtjEez06PmyTWQL1k9GaA2d4mVB70dnmP7aOAB6\nCI1dbew5i7EHZrk5NYC9ZNAuJEhL4iwaxLubJIsuccfgRajs7nbZUOyDUoQzbxL0xmzdvcjaX49Q\nu9ej74u2ss6jrom9oZGYqm6RmFIBhioCeajORPc6018bV67WjprVG4xxF9Us2vu6YOWkJDOlU98Z\nqkCnmzqVfRG9r+g0B9VJXl+CO14nuJkjOw3tvKA1rAjk6QVBbUeMvaYT7lA1Gn3aoedcwsI7YkTT\nwBpoEs6nGT+wyNRcD9aiwurfd891Vj1VB/lmhEL1jV4ys5LKDvV6/9cP/AW/fub9RE1TiezshNSs\nWiF5AzHWhq4+HztV5mx0SMGYw7y61qkZA78nwayra6a1O96T/gDniovXn1C6IBA/sEal7iqjmK8h\n3YRfuf8ZAH7v+iMEN3NosWo1e+dKRK7ErAkyC5K1RwO1AgHklPqcmbVODmuHrxn1tJn90V/77h4o\nsoVh2ferH0ckqpCTWhb4DyhlWjydwdpao30rhxhr0f20S/k4nDhxg9duT6CtWTirKiDomx6I1mCC\n4anwFHmwjnY+S5iRxCmJs6Ix9sQ0G16K8mKBLeOqWTP95iD0BhjTKgAnGfaR6zb2pkZ7q4co28rl\nOA1+j3oT7A2oj6vioFHRycwrTb/XJ9l7YorLpyfITQq8XmViAjDciPRrKdp5sCrgd0P3iWW63BbX\nX5kgdiW5Wxpev3KugtqO4HSCcetKup2Y0N7hIQScnLjDhaf38PYPvgbAM1N7MU5nae7pyJVTEW46\nQF7IIwXkJxNqWzT83pj0rLpojd1tcpcsWn0qvDnKSoxtdewXctROeIhlh+9/2yn6zBq/9/wTAJTO\nadTf0SCZzCjeggZRMSJ1xyQ/lRDkBIklqG1L2HlwFoCp1RKlXJOVC330HioTxjprqzmcWzYcrhHe\nzmJVBeGBJkPdyh8zt1pErjgcPnqb63+3A6mrUOP0eWV8atzfQrvj3n3/E10h/NNX1GTjH24p1eSG\n6mKJRBnNjIbG4BElBVo4N8DQkSVmbvfSM7ZJ8JUe2kUISsoEl78FwXsqRGeK+Ns7GbE1U1nO24o8\npQeC3nMR849quGUNe1MSvrNCfTOFvqGWrv2vShYelwgpGN62Qu2LA1T3RGiepjpwpiTbX8e/WuCd\nbz8NwPN/dhztkQ3qDbWtGPysydpHlTnMWdWwK1IFO4+E0KmV2as64RafJFSdlCQdM/sT/3hwzXcE\nCq+dF9gVgbOvglsWWG9fRbuURbuklvjul3IkjsQ+m2b1iMAebXDp6d3Its7AK1Lh0AYSmhMRzYkI\ne0PDrAq84YhwOkN4oIG9u8rInmVaEyHXpgZpRzqZUovppRLTSyWSlCp0hiOBChnqriJzId0XY37q\n0Mt071kj7m+TXkkwj25iHt2kNSjJzAr6Rjcw64LqPb6KGbQkF6+Pkp7XqG5XN7y+aaBvGtgXU4jH\nN3DuWae2Kya2JSvn+0ikspIjUTNAWmJvaCrI95pBd28NJxuQHq0pmfIaJDUT+7LLnVoXzbGIr33q\nJF/71EkODi7Q2NVGVEwGx9cQ6xatTZfUssTwYPWpgPSJNfSWRnS8TnS8jm7HiEjBbfQASgdX0E7n\nFC6uYhEXIp5f3M7vnXuE/GiV/GiV9ErEgcFFitdg+Ogiug/OvEliQfk4bNzbpjEqEb0+ky+PMfny\nGEHTYiK3QdTXpv7VflYXCpzcPqU6SG0DbaKJFoOcTbFcybFcyZFsqtZ24xf6+IkfeUbJmUON1kBC\nUADdiPnEhz9BlE2IsgnutirFVy1FFbuvQubVFHEmoTWgAqqRMPCiIHYky5Usy5UsyZBP9fODiFAj\nijXCDBgHK6CBvavK2n0hrdt5/B0+6csO6csO2/csIFoG6SmTsBjT3ubR7NMxWgKvL6E1KIhOF3En\nbcyqhlnVWDmm4SwbkAlZ3siRe9cSZsFXYqpciNvdor6UJbElT58+wtOnj9AcSqjfLMKyTVw3mXt3\ngnYmR2pZ4+h7LlPbAsbhCqmuFqlZg9SsoVgoncyaxEpITX0HCK6+7SMG76CH8zm1x1ydL2Af28A+\ntoG1tUbQJUgtajiPrOEuC0wjJrqnTtcZg/JxDW8sJM7FCEr3yW8AACAASURBVEc93KPrGCc2wY5J\nUoppmf/zLAvnB8h0N9HqBpVKGtcKSTyDxDNwlgzsDYFeton72ixf7sW5Y7P4oOATzz9G9Dc9ICQD\nH79NczJPczJP5EqqeyM2ailiR6Iv23dVZ5qn076/jtkQRN0h+kgLfaSF++Aa9ZtF7umfxahpaDGE\nvSFX7gwysGMVrS2obVWYM/vkOvbJdUXW+psS/qZDczFLz+s6zYcaWBs6/l6P1UoGo67f9SBc/Mou\n5QnZ0FirZlR2Zme/3xpMlHficglnR5UHRqd4YHSKpGLRzis5/eA7ZlmvZEiVJdp4E5mOMdcN0lYb\nw4ypzuepzueZe0Lnxmd3EqZVDmcw0sY4pDo9Vk3DWLEYP6qyVtoDIe2BkPw5m1dubMWZsanvCjE2\nDE5d24o/HtBdrCMn07SzktSyII404kgjO1Qj2bS4/XGL33n+7RgrJt0vqsFr25NTlHJNfvHyB9GK\nAVoxoNWwCYqqdRv4Jtrb15BOTOGawBlqIGJB7SM1NF+wb2CJfQNLsG7jd8NT912g6dnoxyq0mjYi\nEBQ+lcWZtUgcyW8c/zv8wy21SgGMqqaUxRs6H9p3lqBL0O6KMYZa+P0R3kiE0QL32DrusXXCvhDn\n6Aapaw7pV9JsfnWA3kJDdW3MhPZ0BnfeILd9k9SMQWrGwJ6oI2JIz2pKfRpqREfqhBnJ2cURSm9C\nYzOF8VL+rqfIfGCdwlmLXLGFlg1pbW9/W7fod8TWwx0YkTvf/4s0h0FubZEsunRdUkso88NllsoF\n3ExAT7bJ/FqBpK0r0IvdyXusqayInSenAbj56jhhSXVOzDkb3ROYDWgeb5FKBezqXuHCSzsIS5Hy\nB6ASo3U9IV52GdpTJvizftYOS3rPgPzoGqvrWdIXXPxuiRxTe2HzaopUWbKxP0F0tbFvqOVv0BOT\nvqMTZiHoD9m5fZE7ryqXampRUDkQIVIRO4bLzKx34byQpTmilvqu3ab9YjetgYQkpbYbbneL+EaW\n3MF1Wq9003MhZHOXSf2wj75kU7gB1gfLLK2ogVbGgp859iJ/ePEBUumA9uU8qbKguiPm+x94g796\n/R66zutsHIsY/pKaK1aOaYQjAbnTDq0BSf42VLdDmI/JDdZJXi7S2BFiZEL0W6oaH9uSzO5NKstZ\n9EwESzZdu9dZnS9gbhjo2xo4L2QJMxCpU7Cq0DzikX3Dxevr5LeGgp5zCYuPS1KzBsWHlilf6MPa\nrgqgrY0UxrrKMXUW1czoD4V3w5alnaC1dNwxtV3VX8zTGE/ouiio7Ops3XIhO0bKLFTzNDZT9PRV\naXg2/mKng9Xr8ZHdZ/mbTz7MAx8+x0ufVTP5wM4VFmdKmBsG2tYGhpEgXlNYQxHDPT/4Jt94aT9i\nyCOqWSo0uuKg+RoiVODioD+k0K9eW7WaQrYMdmxf5NZ8L9kLDuH9NYZ+12TqvTZJNsadNpWPZ0KZ\nAYWnobVVRuzRR69z7mu7ibYrhEHhOlSfbBKVUxhNQe+RMgDLl/ro3rtK9XWl5+m6qHH+D7/Li5nO\n1iG576FfwvxImfhTvazeA5QUESj3ukvtpEr4Dm9nkSM+xm2X3pNLlE/3kzu0TrXu8uDEJC+8ug9Q\nRCipqZsySqloNmmAsyKITtRxvpGleX+TrnyTRqdoGtzMsf3EDAvVPOGZIs66xOtVEXTWcBOuZEl0\nSdexlbfOCQyicgpnVcMbjLjnwCSrXobyC0OgQdBB7VsnNqjV1P4yaeuq3bc3QgQa2bEqnm8Sh2qZ\naF930Y+ppKlvAnK0QMNd0mgXlZqQbIi2ZiH7An7h8Nf53599BwN7VliYVnFywo1wbjt4E226e2u0\nXu0mKEriYkhq0iLY52HZIX45/ZbwPhtizdiULiW0+nQq+1VLsNhXozJXuCtmstZ1cpPqlPpTDfTz\nWfJTCeP/7Q3OzI4SL7mIvgCx4JCZFlQOhhQHalRraqTYP7LImxfHyU7pGI+vkTzbTe2ER+Flh80D\nCSLfJp1RNYBvAmKlIek5pbN6f6TMXaj31FkTmHVlqY6zMfay+rconZDeVqV9vkh6UQ3k2Umd2q4I\np2wQW5LMHHg9AvZ3amGTGRhrcXL8Dm98ZR96gGpHW5LcpEb9Hg833b7bcYHOzF0X+H2qjhH2tREC\naBhoXQFi1oVRj2TFIbHfYnO2h0LcKRVINbJllc2WS3AtT8+RMqsX+tB9FSn5zSzdYKvPYF+FzaaL\nP5sltaTR++Q8G387TOVIG33TQAsVG9V7nzK7RZGO/VKWbT94k7O3x8DXmf2Zf7zg6jtioLBHR+TA\nr/08elMju2uDSiXNrhHVHr12c0glgDfVVctOaVTvCZChhl7VidMqXs/ZUaX/36o38PYPmezYscj0\nayPoO+vor6k4PmPVpHgdvPdWaSxlOLzvDleXFVE52HARgcbDJ67wwvnd5AbqRG8U2f72SWb/4xZq\nD/gkkUAGOprbqTDXLPS6RpxNkFaCsGIMK0bOqHi/1IJGmFMzT+myuukTU7B8LySFEM1MSGomuZuG\nCjKeCMifsWn1qxk9fL+Cq9amCqTnNbweiRzx6S7WWatkiKsWZtFHTKbJTcLag2p52fOCRWUXhMVY\nqTYFSDvBWTAZfnCOjZbL5p0iZp+HdbZjtZXQHItxF3T8voTC1g2ir3dT2xWq659rI8sObllT4bwo\njFxrJMKs6CRjPoUXHBIdmkNKbq1lQ/R5Fex7ojQNwKeffoh2l7pexoZBallQP+xj33FIDEl6QTlh\n5RdLbB7udGSsBGPFwqoql2ZzPCLT36B9JY/UlHIzzsZkb6luRFBUqxTtQBUhQP9Gnvr2GKPbUx2k\nrWq1mb5qM/bOOwBcuT6CUdNJDNXebg0myKwqVIpYMLh1lcXJHpWJEqjV7iOPvsnzLx4gzsSYhYBk\nPoU53oDLWZCQKkvq42owaec6helMgt3X4vHxm7zx+0cI04LmA036u2rMzZUo9tZpRwbxxfxdBqrR\nUjm3bGlinc/QGo1x+ppkvpDFe1+VxkYKYSa46YBWVU1I9pyFe0jBjaNyCrMmuP0//ONXFN8ZNYrv\nHd87vnd8Rx/fGSuK4RF59I9/iObf9eM/XMdfc9EbHdJUNu5o4cHKtNGuZfBH24iWjt7q8DRnBPET\nmzTvqL1jZlqjtivinv2TnDm7ndLWDX595zP8+p/8CMnhOrnPZ6htEQSlmOwd9XNqOxU+buCrBivv\n8RnurjAz04M7beJtC0hft5HHq1hGjPXXShyzdkTp/L131Oj54xTzj+rcd/9VTt3ZQrxpIw1lva5N\nqKUkgL/TR8YCAh2tpanszl4lpHDnDPy+GC0U/PDbXuJTF06ok+omA9tWWd3MYjshyekCrdEIu2wg\ndYm+q46uJzRW1H4bKeh5TWftsQDLCTHPZBl6aoYgNpie6UE0DaQbY+YC7h/vzKj/xz5WHwgZHN6g\nfLkXe12jXVBy4SQVqyIaoDUM8lvUSmdHaZXXL21Dr+skfQFa2caqqExRqUkSR2J0exhXMgRFNT3K\nUpv86w5RSq0Egn0e6dMuqbeX2aynCP4v9t40SLLsPM97zt1zz6zKytqruqu7q/fpHbMPMAswGIBD\nAqBIBACSBmguoswwTUomRdFSWBEKKxQSbUdQP0hTXGQHCUjY98HMYPalp3t6md67urq69j2zcr/7\nPf5xCg2KIYo0x7aGEbgRN6I7ozLz5r33nPud7/ve523b5Hs6hOdKeP0/9EUd2bPOVjfFo6O3+NbV\nw2gbFnEhJj1j0h1XKD1rfpuzWhW0dyTsPzbHtUtjFG7qSuy3LgjysPOPZrj2L0YY+p7B2vYp1oa7\nZFIB8vkemic9Ks9Z8OlNNq+VyexuEMUa7mIOhKQwpe4Zv6Cup9Ak5oxDUFD+HOljVTqXe2B3B3kn\ng9jZUXoUVLlfxDDx5RbzT+XvEtGzc0qzEQwHGBsWzp4G3Tm19HrqwQt8+9wR9JZO/+E1oj/vZ/QX\nb3FhbhRrKoU3EmLUDdLLQkUegEgEyd42SaIRNy2cFYOp//lvb1L8nogohJ2wtNxDbEE4l0GEAqup\n9txACyKBsWkibmbwe2PStyxEMSDKxnzuwy/Q2B/TmS2gBYp43dwboXc0Lrw2yeDedeJE8OsvfYoo\nIzk2tEh7RKAFYDY0+i569F30+Ocf+ArGhokWSTJn0qy/MsRnTp2m8v5lim/b+L0St+XQulW8K9wx\nWoLakQTj1QID/+Q2WiR4/c0DRE0LsiHoktJn5wmzEuPUFsapLUgEWt3E3NJJSiFhKWH4WY3xb0rs\nOoztWyMx4AvffIRST5tSTxvyIavXKwBkvpzHO+CidzTKl2PSh7dwN9J4NwsYDQOjYYAd45cEomph\nv5EjOtVi8XvjzN7qR/g6WtnHrBmELZtG4NAIHFofbZO+bbH1+gBxISY43MVwVZid7u1SuGShOTEi\nhiOVZY5UljlzcQ8iFRHnYtJXHcwdSm9z5P5bjNyzSnpRJ/1GVoXd25wE2TGon/JJLBVSG7cd/B7J\n6kIP8mYW0TJoruZwhyPIbu8JbLYylNIu37u9n3K5RVyKyE6ZSENBd81UqNrmDzZp7VYTzNx3diIS\nwfGfvUTmnhqNowF2Ha7/y2F271hj46hg5PAqI4dX0aYy1DeyNI77DH3DZGu/4P2D08TZBPdakW4t\njRaB2dIQkUREksr7l5UxcccgtSoo3NTwh0K2NhW4WNcTUvvqHB5axjASDCNh8MgqUS7h1q+ZuIMR\nUUaSm9XoHHPJLIFhx/Tfs4Z4tYjmq/P//W+fACvB2RQIYOupLu8sjCC3rLuTRFSIaB33sA41sA41\niB1J5JloWoKI1ZLtXY3R90JE4ewaliO/+A+xmoLuYIIWKBAuwOgzsPywRvGaoD2uyneJqToaoxGf\n1HUHaahsenpNPbVWH5SqpLdmktztfhQEhYT/7oPP8odf/DBWHXRfsnVym1q8YSImOhSyLhsLJUQg\nGJrcYOVGBWtLIyglpMZalLMdNl5S/hF+SdJ3aJ3+dJup53fhTvgYaxZOTTDw5ALr3xzF65UEZYWi\nA2geCLHWlA4g6FOeIH6PcqnKv5yiuUs9EWObu+KfcDhABhrZWyZOVVI9mkA+wly0SK0LOkOSeNCn\ncFbpFsSHqmwtKX2Id9hVWTfAuJUmtQHdQcnOr7Y4+AfX+OoV1fbe+5KN1ZF0KxpBDnILCcmnqpQc\nFz82aPkWtZUCe3cvEybqiTozPYBV8ghX1RpYiwWaD1qEqto4Cdk7BtH7WmrdjnKFl6kYY8PEqQo6\nwwnSTsjfNPDKkjCfkBlp0d5KU66oqkf7TFmh721J7rZG44CK/voGVeXCbdmIjqHyMYCzvm1GlJbs\nOjWPo0dsuBk627oMkQj0riBOy7vvMVsCvxLT/4Zg9ZGE0lCDrmfh11KwnSswGzq5WWgrSQm/96l/\nx29d/UmKaRf33w+y9mCCUQiINxxSqxrd3QFWNiBo2IoJ+oP7fWdLofHyiWphP6m6aMNcQpKLGR6t\nsvZOP3JEJXUHv2Rh/INVls8MqVbuWHmG9HxykY2vj9IdkNhbgu5Qgr6dP0GCmOiQey5D9f4Q0dWZ\n+9W/474eqd1DsvJPfk2hypo6UX+AmVIDOFlMIxKB1CSpdQ2vLCkfXqf7vX6a+0P0bIRzKaUcq7Yv\naFxQ+gvpJOyeWGXmnWEQiqI19Mgid24Mkl7UefATF3jhpaN3j8OuqpMtUzGpHpdgNovuCsIxH5om\neq9P5BoMDSul6fJMGbPkk305jdQFqWpCZ0CjtTNB9Ppk306hPVrDO9dDclC1Y5vns4r1mQjCfILu\nCaJcgigGZM6naB/1yOZd3JvFu3Z9rVqG/GWLzpAihUdZNZnqo12YyhCnJFKH/IT6+65n0fcfU6w8\nKJCWMs1N5T2iSMO2IzoLOcrnNDZPJmS3l15RBu796GVendmFdT1NsM9Fbtg4axpGF6JHGvi+iXU1\nTaIifKK0JC5GZG6bdPb6pKZtCg+tEcU6mxs5sldt/BMdBr5gUz24jQAoqJv6B0bKuVsGrV0RZo9H\n2LRJzZkMPrrI3FovpR/g6ueLFK8YdIclzoayNYhSYBxu0G3b6Ms2ZksgjquMf3cjA2ZyV0JvTChn\nMCnAHY6x13USWyL2tJHbFl7JnQwjJ5ZZuDSINrTtKmYlaPMOuqci0O62x+nYPlWCXH95iCirLAR1\nV6DtbhOsppFOojQkvo61oRNlJD1X1Pd0Brcnpq5CKFgNyKwm1D/ZottIkZmyiI638P+CqbC9bGLX\nwdlUlTi3XyJHXGJXNfH17K9Sv1Rm6MQKy1W1/L5nZImb35ykvTtk/54lbr01zsxv/R1PZiaBjpkN\nGJ7YRJoSbcskdNWuj3ZJDEnfwQ3cPknUF7B5WaHShJ0gFhw6OyOyc5paT9uSdE+XnftXMDIh03f6\nySwqaIo3FLFUK5Ba0gnykmfPHUbqSjo+dnyJ7lDCoaOzlIcahNM50ssa2r422rqNKAWqq7JtsFbL\ns1bLY2/qHBhaJf2xNRoHYtZPQXNfBJok8XSakxE5x4fDLcSNLOJGFq8s0X1BYksyCxrxqKey8ddT\ntCditDWb9lIeJLTWsrTWsqSnLVoTKuyPRz3sTZ24qAaavr+FNtJl7JkI92IP7sUedvZV8UoqWy+t\nhIHnDILZLGHLxr9RoHdii+oHPTRf0BlL6IwluCMhL799gMnBdby+mMIrDkk6Rgvh5Kcu0a2lSUIN\nbyhm4EzIwJmQvkPrGFsGnfGI1G0bdyJg9U4v7TNlij0dfua/eY5EChY+IjHet4Xxvi0Q0Pf4Es6K\ngdHUaU2GpJYMHEfd0FKH3931ReS6Ted0mc7pMuO71+k83CHoi+iMqqeB3x/x4MgM6asOzt4GQSnB\nNiNsU5nhkAjEeIeoHBIGBt19PuERtYzTfeg7sYaUgmzaI5v2EMDyW0Now13Cuo29YBG3DYyOwDle\n4/O//L9i9XogBY2vD9H4+hCDjy5i1wTOWEs5u7dseie2qLxm3BW5xRMeVkOjsUf1pdhbYDWhNRET\n7O9iPbnB8uMx3XqKzE2L4EiHeCaLnglVJJPA5AdmaB31WH8gpjOsfn/p+RQ9b5lYdY3atTLJuMs/\n3fVNwi2bcMvm3HXl3aB1dWZe2cGTj/8XQfh/7faemCiEmZBJBaxe7kfzBfldddJTNukpG8cOyc5r\nrN7pxdkUZK/ZGDvbDH72DrkLNuUj69hrBlEaBvdsMLhng13lKneWy8ilFJVXTNyTXcYemSd/0yBY\nT9PdERL2KHhLnEqIUwkb3x0BIbnx5k7qzTRWQ9wFtBRvgr7o4B3pKk/Tv+CAPfcfd7G8VsRoaUgB\nwonRen3MTZPhiU00ITkxvIB9ZAv7yBZxT4jhAlI9UdiwCZs2UVZCLHCqAmdVx64LMjMmmRmT9ANq\nAs3Nwc4/FohEOVmnnYDoVg5tKkO3YhLs9Ah2eqx8c5xuv8CuCT7zvtOEGUGcSeh9yyA3q9zNZUN1\nNopQIEJB/rpJ/65N5p/ZAUB9n0RvGAw8tcDLrx1SXMuOQeaOztIjBkuPGGxc60N3Bb1v67hjIcJI\n6N9Rw+yApiX84bOPk4QaRi6kuZyjuZzDnGix9tIwRz50Qzl+p2K8PT7h+RLLX92BFsInXv0VsrMa\n4YEu4YEuK28Nok1lMGvKGsAvKdL1mT89xk9++mW6bZs4nbC1kWNrI4duJpg5nyd2TdE30CBumNy3\nZ4bMq1msWWXKVH9xAOd0lq2a2s2GICwlRNUU+SmDxASjrgx6/LM9fPr8f4sQyhTK6wWvF+qug9cr\nKWZcdFdgLZtU75SoHlY2BlGiEXs6/k5fQZQTCB5v4NRUA1jqQpqtq2XKbxmkZi06OyMGvmATFWPi\nuoW1YWBtGFxbGqDnNZuPnnyH0lV1LfOfXiJ4SkVQcSHCvpLmd+efpHDDoHDDQM+GdA+r45KG5Dtv\nHf3PDb2/+Rh9Lyw9sj2jcuA3fx2zJQhzkmjYJ7vtft2dLtB3cIPqxQp9FyQrHw1wMgHx9RxRTrEp\nkBD3B4ht4Y3RFTgbAuOJTWpreZwFpdTMLAkaB2LsDZ1El8g9HayLqo+gsyPCWTbof2iZuZnK3dBV\njHdACsTtNJklRXvuP6xCz416liTRYMVBCxXcRj/S4OjAEivdPLNXhkicBGtTR+5WYXTuhQydYYE/\nGCLsGGfKwR1TCak4pZYUjz/yDi+8cFT5pQKJAZkltezK7avRvdijrAmWI1YeMAjHfQpvOTT2q7yO\nvanjbEDjXg8aJtnRJt3bBZJyQPq6g19SblvOsRrtm6qCk5gKAdg94GFYMbkX0yCg8YhHJuMRvVXC\n71U0r6Hdyl1teb2Ic9Mhc/8m1VqWzGWH9mRIat7E64uV/4anc8+hWS7Pq7yOjDWkqzP8fcHqfQKr\noeGOhfScNXA/1MJdT+OsGXiDkUpkA91bRVLrAq9PcuyBKd55afKHsuu0RGYjeipNOudVw5lfUROg\nzCpUnt7WlC/MVYn52TUW10qUX7CpT0LYv+3vmQi0VETSNNGLAYVcl2YrTdS0lNo2JZm4d57p86MU\n9m5DjqZ6yE8Lto5G9L1p4PUIvIpUorR+H+emg+FCa3/4w5s9FvSe1WnuVo1hyqNW23ZdU4gE3RN4\ng/FdxW16Xif18CadN9XvMzywa5L8Z5aofn2EoADuUERm3iA4ou6z/i861HfpVJ5cpPGFYaoPvjv1\n6HsiotD7Aj7y5FmirKSknNfwLxfxLxfpuSJYne8hyiY0x5UU2lvNkNhA2cdoCypH1jBWrLuEq/Sy\noHkwJH62TO66hd8bE/ZGdAck2eGmEgZNdogj/S4z0azp+JWY6vNDmAUfo2oS9YbY57LoNzIUbgES\nEif5QQKfZCHDh/dcJ06pcFD3oLOR5vUre1h5cYSeS4JMpYPc1UW/kUW/oXgXYSHhU6feQroG7q6A\nHRPr227WAXEh5vvTe1WGe5sQrkXqu50NQdYOiG3QYqgdMOl/3ypsWTQOKBCP0dRwNkGLJMaijeYL\n4jMldh1dJHVDlfCcDYE80GK0WCfKx0T5GGddozsco21YmJcyyB+rUbgTwsq2l+iJJvnJLWQmYuVG\nhZUbFUTNov8DS4TfK6OtW7R3KuVoUEiwazo9lSb5mzp3vraL/Jsp8m+m0NctrJrO0kdi4lzM7idm\nMHM+sSNwmw5mQ6fvgRV6hut4nonnmew8vog7kBD1Bcz8ySR7HpqlfFGSjLuYA11EV6e2VCS7KMku\nSnZPrtBzWUO3YzRXEOVjilOw+v6E5Sv9GMs2+375KpllwdhXNca+qtH3moG+4FC6rCMWUjRu9qBP\np7j38DRRWhIOBMzXSsSliEYzQ6OZIXdHI3qyTmrRpLFb3XvRQIA22oFEJUvdiiQ1Z2LmfMycj/A1\nao/43PeBqwxObqikdjEhtlD3xrjPoadukl7Q0TyB5gmy71+nfq0XkUBqQ9IdSkgsmD+n4PZef8yJ\nwzN0h2PMSxnMSxlW79VoH/BZbeSUa1gieDfbe2Ki8FyLb1w+grMuaH+sCQLCUkJYSij87CJGLsTe\n0IkdqJSbZGZ1dFeQeDrakQbdbwwohaarnuoI6Bls0PexBcTDW5TPaaRnTaKdHq3VHFGf6scwb6cI\nizFhMSbZoSKYyocWCVuWGjzzFu5Rl/TJTTYf82k+7EE+ZPl2H8u3+4jzEVfrA+zfv0g0ECjtfywQ\noRKqhRkBbxXIvJ7BGwnwRgJ0V2AMdnl+eS8iEOg1g/rXhuk7B3HLRG/oFF5K0d4fEFXULis+RgcM\nFzSh0G7pT6/gbEo2mlmMikv/xCbFKShOQXtMUjsac+ihafrOQXePz/IzYwRFxU7Mz8UYRsLcVonc\nlEFuyiAsqOhMRAL/kEvjdonZn9BJ+gLCsyWy38qxtZnD2LBIL2mklzSs4Q6L54Zovc9FxIL8lEGY\nk4hYYHSgtlIgyEN7LMHvAb9HRXtBT0zxbYvMjImjhxRzLo3DIeaaSZRLWLk4QM4OSBbTJItpbl8d\nUnYMgUaUFtw4uwPn51dINhyYyqjynxNTvS+kel/I9PQA3SdbFF90iHtDnLKLSCR6S3XyIuG1c/sJ\ncuD26ri9OpsnlVWj16MGlBYoZ/J2aBPnEuwFi8A3Ea6OcymFcylF816X3T2bBHkF2une40LL4OTo\nAtLTMfc3icrbmpTpDGI6Q2XPJkPfNHn1nX0sL/WQlAMSJ1HLh6rAuuNw9vIuklNNSlcEpSuC6uU+\nfuGp5wmKktajXcymhvZ0FUZdGvsjNE/j4uk9FK9pd02Q4gGfnz56Du2tAh/96Fv0DTTe1Rj9aycK\nIcQfCyHWhRBX/sJr/1oIcUMIcUkI8VUhRHH79R1CCFcIcXF7//2/2VFI0nmP9kkXz7XInXMov61R\nfltj6cVRDo8s4e/yMFtQP1vh4MdvEO3pkr5toZ0uoEUSZ1MjTifE6YTjP3uJ5vVe/N8bpLmco/Yh\nj/BIG6Ep0dH9kzM4G+APhcpYx45JtmzMPpe150dAUzeV4UFStTheWUJULVh2+MfvewZRCJQm4Y7J\n/OVBblwdRQYaqXWBXXYxtzT6hus0DkQUHl2lPSLvuotFaUnQsag1Msi0IlnXD0VsHhEUrxgw5NHa\nCc68ReGCrbwjgPrRkG6/ZPOFIYL+iPnVHryyULX6VEAQ6WweT9g8nhAP+GTvGKx3c9Q+1kXokv0/\ncZPewxvYGwY7f/0m8qxSwHaOuXSObYvcWkrNqs84UPaRVoI162B2IPOZZUTL4KOPn6UzktAZSfC7\nJmJcOYsbHZU7+YFfaOZRlei0mqr0+APTHKRiJbgDEq+S8M6Lk1h/3IOzpKTaPe8ot/PO5wcVU7Qr\nsGs6Xq/ArBm0x5WMvOE65GY0wjGfJB1TftGicHF7v2IShTrdQUGm4LGzXGVrv8DsqAgvLMVUTguC\nw12qxyTVYxKzqeGsC+Wuvq3o1YZdNrsZsjM6hx69lH1kmAAAIABJREFURVK1yN/U6R7w6B7wSF9K\ncWF6XJlTrZhkz6WQdsLFlWEGxqvEFwtodoweQvZIleyRKvILfax+PEDzNMxMgD3j8MSxq1jrBgMf\nXiDe08Vo6pRzHaxPrmF9cg0tgD/7kw8iYqiUWjjHaxztWyLetHH6XDUZVgUf+eXXENkIkY2QXYMv\nv3AfQUHylQvHkV8u/z+cGv7SPPDX5SiEEI8AbZT71w8I3B8CXpBSRkKIfwUgpfytv0zq/ptugwdL\nMvOz/xTdF4R7u+i3U8jdSsYbtiyMLUPNygmqYmBJwkLC5IFF7rw+xo4HFlj9xhide9V7kqpNelHH\n7KjGqMYxH2KBvWyqPgxLUrosCPOC7uA2ubkl6LsQsX7cQD/aIIo0wsUMqXWNkQ/NcfPOIMamSVSM\nlOUcMHAmxvzVVWbmK5ipUD0BnYTMnI4eQHdAMvnALLde3UHYs1273R4o4aRLHCgptkhQScb3u4j5\nFNlZhS6zdqo1eubbOTbvjdHbGuUDm6wtlBh8Uac5rimv052KyJxZUk9DqUHrhEcm59FeyWKXXSb+\necCN/1Hh2lI3HJAqjHW3ITwIkMeb5NMetUt9RNuU5yMnb7Plp5m7PoDR1VRy7KA6Lr+aonRJx32s\nTXIjy9B9y8zO9XFw9xLz9SI/v+dN/o8//wgiUTQsUD0WVk3D3+dizKvcjjeo8hpBKSF/W+D1Cvze\n5G6T0M9+8BX+/Tv3QcOEfIgMdB48eIvrf7qf6CN1gtAgCnWMm0p45g2HlE8biJ/cZGOhRN/oFl5o\n4JgR7ot9JPc3CKfyZA/W0L7eAygY8er9guxEg+T1El6vJCopEZo3EmJuGsgdLtk30jT2bVPRrIRM\nuUv2yznWH1DVMz0fYF9J0x2JSC8YdMcj0n0dwpuqyzLZ4ZLPuoSv9DL01Dzr7SwH+1Z5/eIkeiFE\nn3FI36M6X7tXVP7oAx+8yLNvHyZ/yyB6qIFtRri+RRjqDP0Hi+CXazhGxOKlAfRtnqkWCbyBCKOp\nU7gFYUZw5X/723dm/k1NinfwV0wAQoiPA39PSvmZv+1E4YyMyomf+w06OyJSiwbueIiZU+rRsOag\n+Rp9b4PXo9G+z8V2AnzfZKi3Qe3FQdxKQnpZI1Z6GOURMtCitb7t6VHw6flWmrVHYjAT0nkPbz6n\nuiu72006GwK/R6JFKuTUd7SJ5jOYbQ1vKERvKf+I7pAkGVNP4NJzKfSf2mBtvgfsGHPFIsonWFVd\nRSsStI5O4iR3282REPdECoSaDkg7AY1GmkKhS8e1Sb+apXHK4yMHrvLd148BoA90mahUWXpmnDAr\nOf74DS6tDZH6Zp7Gh7pEoU5vT5v6lW2WY0oien1kLMhccdSSKIGR+5eYmR5QbMe3DOqPKUAxQHtH\nwtChNZYv95OYsOObIbMfN7ArXbymDZEgf8PE3pJE2+dZ/6iSvWcXJVKD6lFJklLNSs2pEqldTTot\nB8OMKebVJL4xV0IvBshVh4E3Jd2KTv2I4lIUbqnesOojAfq6hTauEnPO6Sy6J2nthOKBKrV6Bmsq\nhVNT1QctUKCfcGxbcfy2w//+a7/Pl2qn+PaVQzhzNt5AhFXVCUd8CmccOg91lBH1VfVjuuMRRkMn\nKsSMfRs6/TqJAY19CjmnBYJgv0v6fIpw22R+8DWf+n/fZquaVcnQlkFiJxgtnagvQK+ZSF3ltQbG\nFaax9VI/QUmdr/571lie6iO9rJNelVSf8BArDkkqUVWmtJqQsjdNOjuUwM8Za3FiaIFXL+xDZCIF\nDF5NU9ldxXumQu+PK/5H4wvDTHx2ircv7Mbq7xJFOnc+9T/9V01m/jzw3b/w/51CiAtCiJeFEA//\nv/D5P9p+tP1o+6+8vauJQgjxO0AE/Nn2SyvAmJTyGPAbwJ8LIfJ/xXv/E0tB96BL5Q2d5J4Wmdsm\njhOqfdXAaAvWty2EMhlPCV1cg9VzA3TGI5JSiDuY4O918fe6aK7GZO8G9qqBCDTSr2eZ+JWbTEys\nYS9Yqhuv7Kuuy0pIWAlJP7FOWEjIndogvSzIZzzibIIUCtOemmjSvb9DPOKxs7/Kzv4q1aOSjaky\nTq/LwZ3LhMUYs6GROlqjWGmptl2pWn+14S7acBejo0EkSOU8wuUMzVaa4qsOW8sFit/KEOTBmrN5\nZuoARkdgdATWxSxzL4/THUrwRwMiqdFt2XQHBKlUAE2TrZs9RMWYqBgj8yG2E5I771B4fFXlRfpi\nFjZKCCeGSFA7mpCE2l2PDtnvs3Z2gKQvwBluM/9hE2nHBL5Buuji9HiEGbX8SwxBYgi80KA76bP+\nUERzQpCkEsyajheYpHYpM5zCaYe+Uov2m3203+zjE/e+DUspRALLT8YkFgyNVTF2tWnugSD/A38U\nSealDJmXMjQPBeQ/vkLc71OtZtEXHUZedhn9qRn88rbJsgFsWbBloT1R5XPP/gLPTB1AtAzMJiCV\n9L1Q7FL5xDz6TRWVJDYkNmiexv777tBzQWf5IZ3aIz6NvVIZEt1TJ8xJyt+xad/j4Q2GeIMhsz8D\n9XoGraHAu1ogmJxcJipGWEsWcS4mO6thlTxW7/SyeqeX1MObyg4hgaU7Zay6hri3Tu2DHsVXHVIb\nAmdNx1n/4dB0BxP63xAUd9fQ3ixQD9KYdZ1U1sdv2Zgtjc16luYJj9nLQ8xeHqL+qMe5s3sU98NI\nGP689W6G+t9+6SGE+Czwy8DjUsruX/G+l4B/JKV8+7/0+faOEXnwyd8gNhX9KVPpEGyv6YyuUHzJ\nVpq4YWI2dcJCzMQelRtw5i12feAOt9b60K6rnogwm6gbbVHHrSjDFccOadXT2LM23qDqW5AjHsmW\nOoGaL7DqGu5ghF12lcovgbD0Q+x/9uU0+o9V6bylEkNRRp07fVcbLufQjzYw9ZjWdJGkHCCqFj2X\nBfUPukSuymvkL1l0RlRPR1CAMJ+QmJLijjr1WpZMwcW/WSCzf4vGllKDylDDyvtEy2mSTMzotwXe\nL22xdbmM1RS4AzHSTjALKvTWpjJoB5SitNNIkbto4w5IrC1Vau2MJCSFiEzRRdNU7iDn+LSeHSB5\nqEGnlsLYNMnNwaO/8BZfOXuS0lAD+WwvRldS36euW2pV0B1OiLMqRNbb+nYJWVK8qtF9tI12KQcC\n3B2KlSG6OiJW1answRp8twe3T+DUoHHKQzMkccukdFGnNaG+p3Rwk42VAuaGSfGeTYJn+lSPx4FI\ntUjv8JBSsGtY9XfcuTiMiARxKkGmEoSv0XtOozEJ0bBP6TWb+n6J7glligRcmBpn4PsGm8cUAKb2\nmEf2fAqzLak9qI5dX7ewaz8sM3aHlG5E81TezBzp4FdTiFSMNWdjuKplfcc3uiw+pq6l3YCejy2y\nUs8T+CZJ1WJs3xpLm0WsK2ncfR6p6w7dsejucnXo0BrLm0VY/QGtTRBmJOkVwa5PTfHOwgj6dAot\nFIRZdU9OfLXD+m8HRImmnO6mskz/zv/PLdxCiA8Dvwn8+F+cJIQQfUIIffvfE8AelGvYf/nzIkFn\nSOD2w/jEOoW0e/dJ544HbG3kyGVd7LJLWIrATpi508+pyTuIEKpuGu1qliCfEORVEk5akvZEhDHW\nIVpJ05orIBNBNNkle8skGVKEYpmJkJlIlc2ONMkNteB6jsJNCIsxqXmTxukKhhkTO4Lq7R7sKthV\n1RiTWRQYZ9Wi1Z3L0VgokFgSZ8qh/y0IioKoaVE8p/adn7hNXIxwByTJgTbGaAejz6N7oRfpa/CG\nUqc25gvIUEOGmqqWLKUZ+b666Rd+PKFxvoy5u8WeJ2/T847GkX3zRBspoo0Ujz11AXEuj/ZyEdoG\nRz55BbMh6A7HdAeVcCh/2aJTTdP/b2z6/43N8myZzjGX5EIBe9kkKsbUDyW88m/vRaQjvDO9kEB7\nVLDn1Bx7Ts0Byq3KyIWIVExiS+JsgtnQlLfKZorSQ6ukVyU7xjbYMbZBaqitkn7b4iW3ovoNmrsT\npGswObRGuq9D/YCq3sQDPq2zfVirJvG2SKozKjFbEqOhaFGJa/wnFtnpZQ27JsiONbELHiTQ+WiL\nsD/EmrXpDgoq+zZIrQtmP7+b2c/vZuAFg/aIRuwkHPrlK1C1SX9oDecn1yicVVaP1pbAeqBKmJV3\nB+QH7ruyLcVP8BsO5dE6xZ42WohyJeuNuPVz9t0eD/8DTVa/P4K3nCEONNBgfrkXMZvCqyRIV3FQ\nRToiO6uRndXYen4QTY9J72oQ9MakVyX5Y1XqJwMuLozQ39MkdsDb4zFyfJmR48ss/WZMY75AdKGI\nvJklzL27xsq/SXn0P2cn+G+BHPDcXyqDPgJcEkJcBL4E/H0pZe2v+w49FZFekdhHtljcKFE9009Q\njgnKMenbFvkrFvWNLI/tvEX2tqnAsasGq508g48uUjtfQUjILGpkFjU1uxc98jcMwuWMygQbkv07\nlzGvpekOJQhdIl0dY91SlYdYEEUa/tUi4YRLczcMfV8oi72jDQLf5OnPvkp6tIXuS3RfUrqekHt6\nBb9Hoh9tKJJyomrwAJvHBK0jPiJSJdD6oYirS4P0D28RDfoEXdVJuntgg2CnR7rcVQTubKwIWTMm\n6RkTrexj1zSaOwyFh08EQV+Mt5Rl+Y8n+KV/9HWuvjnB5MFFJg8u8szVg/g9EndAMvCaYGqrgn1/\nFYohcTrhwQ9coT2agCbx+my8PpuJ3YooZlfhiafOk1o0MOsatcc87GmH5J4W7oDE742ZOjfG1Lkx\nYhushoD5FL09bYZ2b+AsG4SFBKshyMwb1FoZqveFzN0cYO7mAKWMS887Gv5ODy9Qytkom0DZx6rq\nzNVKJJcKDL4KmUsOmUsO+j0NZZvXsGh2HHRX0JhUCsrcVYvdu1bZNbLB9Fw/03P9yIfquIddHhu9\nhW1F6J6GP58ld8Ui6E0IdrvUT/fTOenS2Ctp7JXc+w/fJvPoOullnfcXb5I4CWvrBbwv94OEew9P\nk5iQ+8PCXcm8XdO59AeHSY23+NS9p9HaOt4rZbyzvfS9E6lk+ZbB+K519ECiBxK34eD3KsVsatrG\nqnRJ37Ax2wog/cTRa8hIQ1+xaU7GNCdjOuMxcjaDf7mICAV+QRBEOs68xfsnplm72E9qTVDubbHy\n5hArbw7RqacwKy6JrSaV4o1313D1nmjhtneMyKf//MeYrpVpTJdIr2p0RlQ4aw10CdbSmHUNw1V1\n7sLEFu2rPdj7GvjXC8rCb7CLmFbhXVCOcZYNgkmXpGNQuGLSub+LmE+pSUOCNx5gbJp3M+va9Sxe\nJcJs6JSug/apdWrnKkRZiTnUgRtZgp6Eew7P8s515eBl97iEyxlEn0+yaSNTMUNjVdqeTftOgb69\nmzTfrGC2UQMTxfPUXUFyoI1zOktrV4w0FaZu7+O3mfviLpr3uehGjL2NqfvIp9/g2T96gPqhCIwE\ne9nk5BPXufCtA3THFIouyv7Q6s/oaPj9KosvdUmcj7HWDbRA4PcpmbdeN9A9QTCw3V6ssb2eF5x4\n7Abrv7OTmc8KZCww101S++vIV0p0T7jY25UCv1dFD34lpnRZI/OJVZY3i6QupnCPuuTeSOH1QZRS\nTWIAlQsJS4/BvoMLXJ8a5uDeRW6e2aHKtevKKqB+XDlf5ae3jY1/bJX1Wh4xmyLsiRGpCHPRJrbl\n3d8qQoGxbbJj7WtiPZdn64RihI5/aJalr+3Aq6jqT1hKSC3plKZiVj6uDixpmQrGvGih72vR8/kM\n8hc32Hp1gOiQMktK+gKyeRdeUWXL1t6QzB2TRIfsAxtUa1mSlkl6QfWP1E/66oHk6yqyAYK1NKll\nnbCgmt/0rkacj3EWVRlW66hrlpvR8XrU2IwyksLuLTqXetD2tkmmspBAbg46wwLjaB3HjNhcyzMw\npEqr9bf6CTPKoSzR1Wfc/u2/4+pRYsHbM+Nk/6iA1KFzwKd4XaN4XSPz/SzOqk72aJXMgxsMv5Tg\nvdULEtyZPMUjm9hVjYlKlaA/IuhX0FpvPCB9MQWmKkXJpRS6K8id2sAvx6TuWDDiKtNYPSGc7OKs\nG4w/42F8eo2tM/2UbqgLFaylSfZ0GXlecvn8Tn763jP89L1nSBJBkomJPZ2RFxLMTYOS4+J6Jkkq\nYaOWw+9JaB4OSK0pU5j8bZRiVarJwyi72CUP50SNS1d20NqRIATkn8sQZZT8+yvfux/7I+uk+zpk\nb1gYXcGbZ/Zh31cltWhAAuN7V0nKIUk5VH6qZkJUCdCGXewVk8wSBHtcTh6bxsoFHDp1h6Ac3306\npmYsjP4uQ/ct8+Y7e6gecshcs+mpNEksSXCxhK5SIHdD77gYKXs+TxDbAlOPKb7ogECF1RLkgRZi\nrEuUkUQZSfPnmkgz4frMEM6Kyc0zO7B2NYnTieJRZCF73aK4q0b54wuUP76A+7V+yt9ySEzQWzpC\nk8SWiiB7713FKnlkFnQK01CYhsA3kEItr9yRmIXv7iB4qIVdU8Ivs67h7vdYO6WRyXpksh6F6wa5\nizaFWxBN51h5UND8/oDSXMymEbHAXLDp3i7Q2hfS2hdiFny6IzFBKWFjvsSpiTmyMwbuYMzDnzsL\nrq6c59YNxJUc4kqO1FAbdzBGCpXkTu9qQKwa0oaf0Ugvawy9BK2jHkElUvSzPp/o5V4SE7wt5y6m\nwOxK+s+GPD42ReNKL8f2zLG5lWNzK4fUVMLULyoZfJx+dwGB8df/yf/3m0hA1CzGfusGC1f3QCyo\nH9hG1S/rIKDj2miXc1Q/kJCZhwMfvsVqJ0/t9QHcoYipy6Po2yG/iEF0Tcy2xJmzMLuqvh8YAv+5\nPiqbCYYXs7TDYPRP1Sm48/cEcSVi+pMmrJQQPTEbJwRJLkJrGEQ1h/qEjtGVdCLVLandzMJwAIlg\n4emE9G2Da1fHkGaiYLTrDtKS5Hs6xKik6dZByf6jc8zXiwQjHXJpn+7rZVo9kvKBTcw/7WH5CYPm\nBzt3z08u7VO93KdwgPc3eHRsmhe/coJmJUW8I0BYCRstlQgF6ADGuqUcvzIWfl9E0CP44N4bPD+1\nj+Evmlw/OYG2U+VpAPJ3Ejb26SyfHkKMBvglQ1nrdRzSyxpuv6Q9DpmzKdon1fcQaohIkPQF+C2H\n5ddG4MkWgWew/1+0uPkrZfTbqpcl1VDXphsVSW8p012vrFzJ3MUc0okRiUZ3KEY6Cff3bHD5q/sB\nCEYk+tEaxvleRCwYH15n5dw4rV0x7WuVbeNjSfce9dTuK3ToOhlae0MwJFLoVP7PFO1f2kQuFwgG\nY07snOfixm7GiorhcbunRLK/jTyTJTETZDmgndURVkIcmEx81Wf2ow6JKRHb/p7W7SymDvHRFnHb\n5uyZSZIDAbmeDt+6dpjeczq192sYgVJwArgdG93TePKx87z6Zycwp4twQlVt1u7VmDgxT8N3yAUm\n7oqyX4h8jaCgvE60BZPDn7nCQrvEojNEVBAsTx0ktSJIpEbxORXtbd4XUbxsUj8SUnzHpF5+d4ir\n98TSwxkZlTs/+xuEhzvI+TRyxENuNzIKXVJ8MaUyzx2D3nMaWwcl5f2bxF/qw+tRBq7GvVu0t5mZ\niZ1g1nUyy4LmREJ2QSNMQ5xWyr6oEuLMWmRWJO//+28BcPlXD7P1Oy6bSwUKV00a+yPS84bylJyy\n8fa72FMpvIqqMADkr5l03tcl7pjodUPpGIrKzjAe9tgzvM7N6SFO7L/DuZs7ANDrBv/Lj3+e33r5\npxWQ5MgWPZkuS2eHYBuxZzYF7riiEgGkVzQ6kwHWiknQGyMigdnnUsi6tN7qU1ZygLbtP6mbMaFr\nghTYCxZaqMqN/mCE5kSksz7erQLZOYH/qKJIGUaMY0acrCzw3fOHGd+5QePrQ9SPBZgbJlEmITfW\n5GDfKrO/txeAbkUjSkF3TLmzGxWX3m+kiT9TZWOlwI7xDZY2iyDkXe/V2FEcVOFryFyE1jTQB7rE\nkY5sWEhNgpWQuWWxDdJCHm3htWyK5yz6f3KO+e+P4/cmFG4Jhj45y51ndxIfbeHXFexFz4YUcl1a\nV3uJhnzYUtWn5hMdoo2UUpbqKGTAdsUme83CHUywd7RI2yEdz6L4pSy1T3TIZzy6r/Spia0jMLbT\n9/aWpPtkC69tk7li07nHY3ywSs7ymf3GBP/yV/6Y/+ErnyOzKGjt2GaG2pLUQJtgOk/UF6JXTaQu\nMYa7OKezxA83kGcLeAddkrbKYWmeRmIp2rzVEKrLtRCSvmHj3dMl/XYa3QORSKKMmpBbBwOy1y3c\niqKw1ZoZpn/6n/0dX3okCisXr6R48rHzxL5O8TWH4msO+u0UydM1UlM2RlOjeiKhdEWwPt2raD/H\nXBIbul0bfbCLPtglM2cwdHKF+qFIMTT3hegBlE+sKTOY7UmoPQov/cG9vPQH97L2vgzNd3oxtgy6\n93ewejy64xFa1aQ7FiHWbcymWv+jS9Al7fGE3mdSOIsmcTbGaqpBHfaFiDWbhefGGR6rcu7KBJ84\ndp5PHDtP5W347e98isy0iT8Ukvl8gfUXhomyiTLDGe/gjofomZCx/auM7V8l9dAmoq0TW5CeUxg9\n6+0s9XfKcLiFkQk5tX8GcyaFOZMi92IGfdPCWlYTizscEfQmlN80SCKNrOMjIug+0iacyRHO5PAv\nF4m/Xua5F44hUjHid8sM/9QdSpUW6RVBeVeN7o0iKT2kvkejvkejOyhJP7JB7/gW6IAUVJ922Zgr\noTkx680s1sUMKSfE3hLYW4Li1PY1NyS5qxanTk2ReTWLWLMhAaOlMzC0hRQQ5SRRTpLczJIuuNSP\nhjR8B28g5pOPvkF3EJa+uBN5vEngmWSn1W5OpWk0Mzzx+AUA+ic3cLYSwq5FdlYnu6BRuKVhVFyc\neQtn3kL3lROX23KIvlvGa9usn4Ri1iWMdLrDMXExIpzs0hmN6YzG1PdJ3LoDDVM9rBZtPjf6Otfe\n3oF3ssM//v2fJ+oLKD29dHfpKQoB5ssFxHiHdMHlnz39RWQ5YKinQZAH5zt5upM+QkDlDZ3KGzpG\na7t7uCZIP7gJmkRG23iFjkmUhvqhiPwnVmgdCGgdCOh7xaQ9ETH0Wkz9dD/JbOZdDdH3RkQxNCoH\n/tU/IFdwaS3mkXbC+LiqiS9cGyDJRvS/aOAXNeIPbjFRqrH8JxN4H6sTBGrpIAQES+pk3HvqJm+d\n3QuJMo8pXdVoTCozYa2zXevPxpQuGLRH1THonmDXY3e4emMUEQmMtkZqXx3/nZLyqPAESEFcDu5K\ndke/obHwdIK+ZRJnYnpG66TMiLV3+jnywC0unN3N7iOL3FNa4vl/d786zkTSeNCj5wUH/ac2SKRg\nLL/F+ZkxaJmIYsDg1yzkL2zcdf7SVxWYRaZiek+bVE/ElM/obD4Y0lNp0rqqSpdhRUUWxqZJ1Bcy\n+Qc+y+/PqZZrAelVSWOPgq+ISJC/I+kMbzc4nWzibqYZfElj5QOqDGt0BWF/SO6KRWYlYWufht8X\n3X28iFjg9HfwljPoXe2uSXCci7FKHvZbWVq7Iypv6myc2u7XmNEJM3D/05d44eIBUgvb/NCCRA56\nyC0Lo6URj3l3S576kkOUSUgv6VgNSX1/oiamTETmmo1flPQeW6d6UQGItV1tjHOqRT8+1iJYyeAM\nt3FbNlrdRJZCpe7c0smrNgrCp+ukrJDwa30kpsCtKNGgkQn58b2X+MqZkxSHmvBcD8WfWAJgZStP\nuJjBrmpYTWU4bR/Zwr1eJCyqluv+04ITv36BZ58/rs7NqEf6YgqvT0GTdFejeLDK5nqecqVJFGvU\n13LkKm1FRwPqnRTdWpq9u5a5OTeAvWDhD4UIM0GGGoVLFq1TLtmcx4PDiqp+emWc+kJR+c6kEzLz\nOtf+9d9xCrc0JfYdh5QV8r5jtzC2DIq2S9F20Soe9rKJX9LgQzXa9TSL/9cEiQ6Z/1DAtiLE1RzR\nnSxJOiZJx5x7aR8nTt5i4sgSdlWndiQmcSSl8wa9k1XKFwTC1YnSgnAwIBwM2PXYHW6c3UF6zkAE\ngmggIIyUsExvKw7Gju/4OHdshr5nMPQ9g/agWsPKAQ+9o7N1p8R6PYs0JFefn0Sakpm1Ml979j7a\n45L2uMQrC5K2ydYTHrV3+hjKNrl4eg+FYpfeHVvIRLB6r8bKdB/6suJB/syTLzPwmiB7w6KxS/Wa\ndPsFoqvTl+lgtITyF62ZGDWTxJIYVZP5p3K0J0MSU+KNhNSOxTCoOlK9oQjt0+uEx9qEx9q4TQer\nqtP6ZJP8deOuVWN62qK1J6J2UPDRHztNetHgBwaXqSUdbRvjH5UiRAzZBcGJgzOEvoHVlORvGmy8\nLyE7p5Od0/EfaPHUJ07z0huHyN8w8CY93NFQmeq6BtKQPPzYZYxZh3TWJ531iXJqkgBwH2uz88CK\nIopt51cqx9dovtJPMu6SjLuIazliW00+SaxhtgXeaoZju+bJ39aQkaBwzSAxJbWHA2oPB+jPFGmc\nqeCVFdxISLj/wDSRZ/DMl+6jf7xGY7FAYsLctUHmrg0SrKYRCeRnEzqjEr+i0Iel62A0dfSyT/3j\nHb59+TB95xP6zidY0ynauyOQkL+lY7iCrUaGTNGlfqWX7hXVPdufa7OyVmRlrcie8ibZWybT50cx\nUyF2TWCtmVizDsLX6T7QRjdjxAslnnnjKM+8cZT6nRKpgTbxQEBqVVfK3XexvScmih9tP9p+tL23\nt/fE0sMeHZU7P/cbOFVJfZ9iS6RX1HHVDksoBugrtqI374wwazrGZIvc13NsHpckdsLuvSvcvqpw\na1Zd1fYRkp7zBt1BQc99q6xuFHhscooXbk2SfyNF/ZTPwf+bvTcPliu77/s+5+639+637w8PwMM6\nwAAYADOYFZyNGlLkSCRti6KqFNFRLGtxJDux7MgVRqlYiUsqVRImsaIokWSZlCiKY25DDgecfYiZ\nAQb78oCHh7fvW+9995M/ThNkObbkoqIKmXIwBMDxAAAgAElEQVRXdRWA16/Rfe89557z+32/n++O\nJQCu3xzCrLRDhyyJtCVkQoV32xNirRlEIx77Ble49c4oAP1vxcx9CMyyTjzk4bgBOzs3uXZlBLu3\niXEui/PYBvX3Onn6I+cAeGlqH5mUR+V2ibgQsePPYO4Zg96zkqXT8OTxa1zd7GN1vqi2SbTt9Z4g\n6A/RyuquW7qsirrOhoZ/sMnR4XkuvTEOKK1Gx4MrtF7oQUSg/eQG60sFzHUD0RaE7X7yLtcujVK4\nqbYe9RGQI+2czDa+z6oIohM1rLez+KdqBO0CqdDVNkLoknTKp1Z16XrZpj4oaO4I6R/epHqml/qY\nYiOILYued9S53ni+CdNphZW7OIQ2oJyxS9Uc4aUiuSnJ2qMR6c4mjQ1lG9dSEUnLwF4yyc5Ktg5J\n6PApFBpoL3SweSRBpmIe3qf2Ed+5uAfZjihIzyqe6v7Tk0y9sBsSaJ5sEgc6xpJFWGh3A3QFa5ae\njrtoUHx4hY33e+g8tkql6dJcznD00BRTnx/H2W4Dbn9+jpt3BtDLBrLXQ8Ya1qyNNCSFCSjvUedO\n219Df09ZErySJM4mpPtrBIGBpqkaTDLWUjstKdDmHcXEyLWLaRpoTQ36PWJfB1+j9w2N9WOKOI8m\nsdtu66EO1cWZXulEN2JOjUzznZcPEvSFzH3613+0cf1O/5Ds+c1fptBTw3yhyOb98l476eB9s1y9\nOazoRB0hpXdNqo+3yKQ9ordL5D+wwuJCSVXY26jyZCGFta0RZiV2WdDY4yMaBsUrGkFe0OpJiDtC\nUjmP7Jfa8usuDfvpddaXChzYvcD124NgJmSv2sQPV9DeyRNmoON6wvbfUuh9r24jI7WNSQ3UaVYd\n0tdtEhOaIyEi0DDLGuaBKt3/UrWttn+xTvxGidouJUUvdtYw/qJE+lNL1H2b6kVlFQ9LCfsPzAHQ\n+K0BKr9YU3ZmTyczZRDmJIkBUS4he0dZov2O7wp0ErJ3dRoDEiEhTidIM2F0xxozC51kCi0ac+1a\n0NgaoNDzrR0Bxe4a5XIaGWj0vWzQ+mSZp4Zu8cVzDyDshNwFG/OZDQA2VnP3uI7dr5qsH0+QlvoM\npQu6Kjb3R4zvWWL+FZXmXriTUB3VOP4TV3n7tYOEhRgtHWLMOgplqEGUj+kZ3qJ8vgtQCsyt+1SI\nUG6sTOtSiXCshaZLjNsprPu3CS4UCdrMD3uwTnIji9+rNDVGp4e4m0LE4PdGoEvMdEC8nLoXBEwx\nQDcShJCU8g2aZ7ppdUu6718l+aNuVj8YQFlpTe4fVDWK9VaG2cVOZEtHa2mkljVavYkCA2chP5XQ\n6NNIDDAeVQJl04gpX+5k6MQid2e7sRcsElOSvW+T4M1OWt0JiS2Rboww2wwPQ2LccQlGPdLXHFpH\nmiAFhhmRezHDh371df7wO4+AhEyfujYb81mkIXEXFb0sTiXM/uKPeK6HPTYgB//538e6miLMK/9/\nc1i1+jrO61THVBEonfVoNhyyZ12crYTVZ1SLyC9I0L5HV/pu8nb2rk5QQJnG3hxF31ejO1dn8UKf\nigUIBYcengRQ7ctQQ29opBc1tCe2KK9l6XrL4MAvXOP19/aTu6OKadUxNTqitDKf2X1N4kgjjjWE\nQMXHzbk4W4LYBOP49+IORSTQA4iGPcSqjez1MaYdUsuC1hM1okineMalNiIonlAQX+9rPZTvi3CW\nDcwj2wwXytxa6sG+nMLrSkDAwQemuXJJIdqddXX1px/coPFuJ/YmtHogzKockbAUY23oiPE69nfa\nwTwlFblX2ReT6qvjtSxMK8JfS5Ge0dEe3qa6kUb4OsUr6v1rO0AKcFcF5ukNqrUUj4xN8fZrB9HG\n6iR3MyRDHk/unuC1lxUFOswn7N63yOK3hhl77i53XxxDSKjvCTDTAdFqCvIhQk9IX1KTq1lXeSb1\nQaW/SEzQjpfxJ1Q6nF2GxvEm+l31+uws2B9bxQsNthZVe5ZYYG2qjI3clEblhIe+atOO9SA7LfBO\n1zDNCF1IKvN5pBtDoGFWdOJBD33BwVkXJA8rrFxwO0fYE+BmfQwjJm0HbFzpRiSQnhdUDsQ4yzri\nSIXcX6jjvPOXJnhnegeP75zk9bcOsvPIApPXB8hN6lTuD9CsGH3RQQ630NrfR/cUEd4vKu5KY0+A\nO2URHGjCksPI/UssbuVVgrmtihG+byLmXSV+cxPMqsadf/qjrsxMBPG6Q2EqIR708LpVz9io6vR+\naobsLKQyPsGVAvk3HHb8rUm2n28gE0HnB5YwG4LMrCA9r5Ge1zCaGpkZneqeiNKDK1y/OURmHsa7\n1pmd7lICrz6Va3HzxXFuvjhOaspSlCcDqgcCUnZAqbfC9j547dI+yEU0T9Xxi4LRR2cZfVSZojIz\nOoFnEAU6rNt0FmuIORfDE9RHI+JDdewvF0gclZxljNT5pedfRFt0sDc1Rns3kSbs/KnbiOtZ4pbB\nxoMRQSGhfLaH8tke6kNSuSRdSW0zzfRLOzg+MktzMObRR66z68g8c5UCMhcicyGtsQDdh+1KGn9P\ni/qoxFBJA5g1gVFR24pwKU3vj8/R++NzGE1BdZfiSXp3s6QuuHBdCaHCrER7uUi2s0F2oEplt+qe\ndN+/SmJK6jtjHu6bJmqYvL8ySNgdIq5nKR1aR5t1eOXV+zn2xATHnpjALGssnBlGJDBxdgdRWtL9\nzAJ93zLQb2RAl6Ru2qQuu7S6JK0uSXYuYuNkRJRS3yPMSeplVwFlQtA9SeZsitKxNUrH1vB+rMrW\n2V62lvOYWzrmtoFRCGC8gUjgvk9dg7KFiKB4Uz39EoRzafzrBcqLOdIDNdy8x9ifx3QcXsO+6SJU\nRCzH++c43j9Het826Qkb640crYkClbd6MOuCKJdQPhYgjQQ9UB25lSdiVp6IefetfcRVkwurgxhN\nwe3Jfnb9qUflQETHOyZJyyC1KLCdkPQCpBdUUdbrDxElpQQ1UwEigefGryMSQbnlEC6kEfPuPaVx\n7Ot0XpZ0n5foHT6pxf8/eD0Gh+SOT/8aPe8FrB63KD6ygtm2P0eJxvJGXikIE0HPqwabBxWf0awr\niKv+5CbJyx1U96j9prOi2JRdlySNXo34dJn6ZopCV53qVIGkEDH6ZzD70wmiXbXvKNZZXyzwU8ff\n5aszB4ljDa9h0f2yRaNPcRvy0zELH46xlpQQRvcE0mwj4zVJnE7IThp4xxuYV9JkH13D+r0SS387\nULM70He/Ml9t1lO0ljOIfEDuXZfqrgS3vXR1VzQaIxH2umr9JrbErCjyllkXNAdj0oM1WnfyKpZv\nNEJLh4j2Ssq5nKIxHqBVDNWgCAVjx+dpRSYbb/bR/dgS82tF0hdc/JNqqZrMpJGDHmLRIenzsG+7\nig6dSkjPGiQPVNE0iTyfx1IaLcrHfFK3bZ77xFm+eOEYeiqCZYfEkdirOn5XTGqgjqYlNJtKzWpO\npHAf2ER/oURtVCVwhQeUgil9NkWQg9YO1YLO3lLHef9PTnD9hb0YHpT3R2RmDOpjEQ8fus27b+xD\niyA/CdUPqe+i6wnJlTwiAm9c1Q66eipszJQwahq978Q0enRaT9dIrufa5xJaez0QkLnkENsw+NQc\nU5cGsbbbrV8Bwa4WXd9Q32XrgLJ82w9uUt5OY87bGA2Bt78Fmzai20OfcgkGA7RKWzzlCw6fmuTu\n53YjhUINZOcTVp+IcOYsDA/q4wHZGxbZZ9S1snG+B31vjdaWy08df5ev/9Ej1Haq9qvuC5KRFq4b\n4Hsm0UY7YUyAs6ryVogFdqnF5Cf+6x/9rUfvb/yKmq5jgbNs4I8pOa6+YpNYksJNQbNXkFmQdP3M\nLEtfGmX041NMfX0njYEEq6yhH1RLQn86izbYJJ/xKLlNtT8+UiW8m723VNYilWAuArWo0iK1l9fS\nIayrgpS9peMNBjy0b4qLL+8j0SWJxb1MyO8+tDmHsCMiNWOi+xCcrOG8lcVvr3pLD6/gR+3s0YZD\nsO1QuGZQ2RfTObpF07f42M5LfO6lxzj80CRX3txN2BdgLSjZd9AbMTKyzs7cBq9O7IGKyeMnr3Nu\naRj3aznqw8q16ijaGuX72li3YkTv0BZxotKknJ1V5Lk8QVHi7K7QalnkMkqOXZ0sMvBqwtxPJEot\n2dfEcUKSd4pIrV1QfXCb2myewg5lPLKMmOa3u/FP1AmaJrodk0Qa47/rU/jsMje+tJfGUEKSD6Gl\nCrMDrwjWjmkw2mSoa5uZq/0Ubgr+l1//LL9+52OsvtVPcqBO95+5rB1V50bVLRLsNR3n6BZ+aCDe\nz2FVoP5IE23axagLnC11LTd7BVKXdJxYZWmxBJokc8Om1ZOgDbTUFnHZQRqK3A6QXhIc+OQNzn17\nH8GgwtjFmRhn2SQaVxOZdS2Ft69F0lTnsmuwzMZGlvR1h46nllg634ceCOxNcJ9bZW0jh5SCjlKd\nzbvKSCbTKmtEGpLCVYPy4RDNVWrZVsviyV23mPjN+9j6dJ30F9UktvkhjyQRGLMOYncd7XKW1lCE\nua0TDfhQNjGaGta2IH9aTS7Vb/fidUqizhAn55P/N2nO/ck/+hGfKIaH5PDv/DzG5QyJobwaQfsu\n80uHX+N8ZZSz7yhaitRR9OUcKiyoEOGWWniLGayyurD87ojiQIVq3UXTEroKdWqezcm+Oc7c2Evm\npo1fkux9aJrbryk6ilWF+g4lzx4Y2qT6cq8K2u0KcDM+zks5/KIKKLIPqspya6LAfafucOnyGPam\nTurYBtt3S1AMcCccmjtCUqUmXssi/7aa6cOM4MDzE5yfHSb1for6IU/lbRQkIgZjvIb1eo7azgRZ\nVM5GGeh0vGewfUAiiwFiy0L3BfnbsHEiJjdhkDxepr6q3KbfZUPqyzZRZ0jqrkVigt8Zo3f4PL17\ngqlqJ5M3BtA6VLU8bpiIQMPpaaCdy5FYij36XTDM0OcMakMGtR2qXgTgXnVp7FC1pEJflfJmBmPd\nRA56KnogF5BEGjIWDPWrYt7KdpZo0yXVV+ehgRlen95JvJJS6d++IO4O2Dm0xvxbQ4TZtt9ntEYh\n1WLtYg+JJUlMyd994jX+j9eewKxqxDagSfK32h2cYcjdr3iezZEIva5hNDRyD6yzVUkT102MbYPM\nrKA2pv6PB09N8Pa13fS9opPoUBvWaA3GnLh/kvPf2YO7SzmVo2yCvdnuRoXQGozoeVtj/YM+uXdc\ntGc3KJfTFN5yCHKC2FLb2e8a6kQM/tEGqe+kqT7gQdlSnauS4oTYWyqo21nT8Hq+B2Tu3rvO1vvd\nBD0RRAKjpqONNND1hLQTsLWdxnZDWmuqU1S8qpP7iWUWL/QRlRQQeurXf/AaxQ/HRDEyJAd/8VcR\now2KL6apf7RK6mvt2fS0j7Zu8eEnzvPqn5zA65QExfhemtTYrhXWahmiSwU4qOjQgWeQBDrHxme4\nPD+INu1i7qsS3szRf2KJlTcH8Hti9IZGalwNel1L2N7MoJVNdF/w6BNXeXViD84dm6CgErKkmWBk\nQ5IVNegzY+qu3PUlh9pPVxGgADl2olyEh1u4l12avQlDB9VMX7BbVAKHuat9qu3lCVIrgshVS9/0\nNYf06TXiL3axeURdKKVLGpsPhdy3e4E7Z8Ywj21Tn8krB2JVEO3wkJv2PVBwYkkeffg6r13dy33j\n89x6YwfG/irFdIuGb6F/rUhtpG3Gi9TvZOYlAz97l0ZksVTOEV/PkT6ySfVmB7GbkJnR8QuScMTH\nvqO+v72NAvDoanvUu2+NpZlOBTZeyt5rOVpzFlobVOP1xZANMeds5O4G8XKK/jclqw9oyNEWyapD\nko4hFiroF+j9tsHaie9i+AAhyd/WqRxXk6yQ0Nzv0d+tzuXapR50T+AXlZpVr+qYVa1NcYdn/s47\nfOvzD9IYiRk/oGC0t+72UXzfRPcgtsH+6Bori0WsbED2jGJqGOsmiSPvka5BiUeTAY/er9osP9YO\nhJ6y+IVPfp3Xt3Zz4cIudh5c5O6yoqIlga4Co0OdsGrRf0Zj/Sc9rMtp+p6eZ+nMEFrcfuP20AyK\nkq5jq6xd7CHsCjFSEUksMGYcek6sUPtyH5WTHtaMg9yrtl/p19KU9yppwf6RZa5PDjL3d//xj/hE\nMTwkj//BT7G0mWews8xgpszbV5QmQGtpikqcAE6ilm2aVMao7hahb2DO2pRuSio71Yqi+PAKG+d6\niHZ46HpC4ZspqmOCrssx9T6d8n0Rne/phGlBY7DdUuwOINAo9Naw/7zA6qmEzLShlm8F5cSrnPCQ\ngYZWbcvGEyjt22R9rkhm2sCsSqo7gYEW1vUU0cEGQpNoNxV9C5SkXO/00SdTBIUEmY5UizESauKo\n6UhbIu0YK6tWFEHZJttTJ7hSoPediLVjJvrRMo2aowZce/9sjaviQXg9R8/5mMWPh+hGQpJo9HRU\nKL/ei9FSW66ekS38r3djVdX33zgikR0BYtsiyajCmtch8A83cdwA86U8xY8vMr3Yid5OD7Mqig/S\ncUnw+D94h39z5kHlVwkhcSXmtobU1CTyqz/2dQB+++0PYm4aRL3K5Ocs6TgnN9leySmjWCrmAwcm\nOL8yRMtT+/o4VHdw+5ZLmJPEtsSqCJUIn03YdWSekcwWr7yiOitRIQYjuddelC0Ds+AhJtN0HV9l\ncbGknK9OjNZ+jXvRpf/H5rh7boi43ydzySHMqFVY/xuw+EyCsGOomGgd6rzYTkBrMYPR1NS4Ntq6\nlnYXzt4U5KcTlk4n9yZko9NDn0gjIkVLTy9AY0i1gJOPbFEupzm16y6rrSwz5wbVANGgdBWaP1mh\nvp3CXDEJSzFPHbnO668c4oNPn+dWpYeNPxvC62xPyPtapC67NHbEWF1NzHNZbvyLv2Fc/9/0w94x\nKPf9zz+L924HUVYqs4ujPpcWCOJUguZpxLkIp+QRT2XQAhh9ZI5btwdIzRkMPTXL7RvqwJpdLZKZ\nNFFniFYzcNY0/I6E7LRGkIXUqmTzcR9rxiEoqgKoiAXpHRVsI6bpm7TqNu6EQ2ufh2woP8LI2BqP\ndE/xlX+l4OKNgQRpSGRagWDo9TFvuwTFhPScRmotQWqC8h7QxttFw1sZnHUVeYguGf1zwcoJE28g\nRDgxYtNCWpLusU3WNtSqSluziNMJXe/q+M+X0YSkMptXxaqxAGvJJMpInOF2VudmCr2qY++oYb6e\np9UtCTpjShd1tk/5nNg1w/mz4yoNq839VFVPIK9wcX5nzBNHb3J2bpRwKU3X+AarKwXMFWWAAkhS\nCe6CjteTkLQdtYWrBrWxBHOkAdeyeH0qgsHrVsdZ6/T5uYNn+eMXnlTHTocwFyNSMTLQyNwxVWzC\nWB1xo83Fl+Dv9NCNhKhsYW3riEhQmExY/UCElQ6IF1LkdqvaSfV2kdwdQW0Uuu9fpdpy8H0D1wmp\nz+Qp7Nwi/lYn7nOr1F7rUedyh/qcmQXJ+kMRD983ybtv7MOqCsL7GjjnlG091dnEflmdF78oKNyJ\nqe5QEunCZMTqcQPdU85SLYbNh5RT2TiiVjv+rTzuisB9Zo21yU7MmmDPo9NcvTOI0CUHdyyy/T+O\nkP3leaa+owBJpaNr1F7rIfXYOhtTJWQ2QtQNRKiAxtjqRirMRGWfAFpHQPGMg9elCsbdPz7PKx/4\n3R/tiSI93ie7/tmvkL7q0PnsIrYecfddJdCxKgKpQXNXgKjr5EcqBJGObUZUJ4tk7ypBS/Nkk6jR\nvoJjgVVUq4nWlosINZwVndaAuhjEkQqtpQyiGCDLqmBoVDX2PjzN1DfH1OTQNBCOgqQGOzyEplLM\nuzurrG+qC3igu8zCcgln0sbricmPVGjcLGJWBf7BJuN9a0x9Z4SwkJAfVIXW8nIOZ9mg9NAKS8tF\n8hdtClMha0dNIrctkLIlSKEwc6guwIcOXeXbXzuGv9ODbYvOXZvU3u0itaJWMVZZ0Nyt7nTutEWU\nUjmuVj1h8Ql1txvfs8TtyX4Fm925hfZnHWx+UNUbklBDlE2OHLvDhQu70D1FAA/zCSJUzk+vKyEu\nRRjr7YtxtEFQt9CqBlJXMOPEaEcfBtB6vEYc6XzywDle/i01uVY+UYPzeeyy5OO/8Aq//+5jmFmf\nsOyoMOHBJnI2jVURZOfU5OMVNSr3qYk119GgMZWneE1Q+7E6vYUaa2/0E+YUqAVQ0ZSHfAw7Ilp3\nMBoavUcVfUuu24pXEqobkLuiVitGCx7/6XO88sXjhFlJmFXiMaOiCo+xm9BxUaf5bI2HBmcAeO2d\ng5gVDaMJiQXB3hbJtkVhuEzrfAfm0W1sIyb6ZidBm0UfZiRhVwShQPM1knxE8ZzJ3/uVL/PZ33+e\n+s4Yacf0vGbQ6PteMXfw2Vlu3RpApCIOjS6y9H+OUd4DYSlGzwWKPyQktqN0FPWVDKk5g+ZoyKOH\nbnFuYYTbH//Bux4/FDqKqGUgfZ3Mk6vMX+pn5YWRe/3tVk9ClJaImoG0E/SvFimmW9TqLnFecSgT\nG1IpHxFoiEDDKOtwJ03pT9O4cyZubx1/vKUYkT0xv7zvNdKzOtZtF62lobU0ihOwUMnjdybsH15G\nz4ZkL9vIvXW0dQvHDfivjr9I3vbuUaEWlkuYCxajT80gLUnjepHYkow8PYN1I8XKF0bQmwKjo4X4\nRhHxjSJ2qYXRhOWVIkP9W8QOzD+pM/7UFDtOzhO5kuLeLRJLwv1VuL/KwMAW728MkliSTK6FM1DH\nDw20CMqPqYEeZSTGhqnwfgGEpYi1B2M2D+iKFJ0NuXN5kGJ/BbMmCF7u5B/+s8+RhJqCDDd18rcE\nF67sxFnXiPt8YkclnMe9gcLM52O0ikFqSZBaEvQVq1jLpgLTLOs0exN0TyEDqrsSwsU0ncUa//rl\nR+9Z05OreZ7++HuUTwb84dc/QLarjnE1g9bSiDtConUXq6zUsxtHYOMIFD+yiFbXMTZNdnes46xr\nVJ5pEt/NsPp2P/6eFh0H1wm7QvXMgW7FlPIN+sfX6T26wsrFXuxr7r02ttQVZnH084uMfn6Rzg8t\n8PrCLvyiVNJpTalLM3vVKiV/02DzWExr0+XsVw9x9quHFFqwJyI8WsfZkFgTLmZF40j3InYZdCEJ\nXu6kerJFkJcEeUl6QWDnPQoDVTKzGu60xfbxkN96+zm8TonV1SR7w6LRqxEerxEerxEUJEvVHB8/\neY7OzhrT2yU2HkhIRjy6v6NTfNnFmEjR8wUHr2XhtSxwYpojEfaqwd3f3odXsf9aY/QHzR79jBBi\n8fsyRp/7vp/9EyHEHSHELSHEs3+tT/cfH//x8R8fPxSPHzR79DNAXUr52//Wa/cDnwdOAP3AGWBc\nSvmXcrjs0UHZ94//c9zeOs21NOmeBs1l1erTPJW3EaaVgKq8S3n/m/2S0qF1+jMVDC3h9hf2UD+p\nNAGGqaznsSNJRloYRoxftbFWTEQssKpQ2x0hXBXmCmopKtNKsmuVPJLZNEZD4HfEWGWNYLTdbuwK\nGRpQgoXly73EtiQ/WqZxo6jksoNqy5NJexQ+m2X64wowkplSy/VWl0K7G1WN9KKgfDgk21UninSE\nkPiehRASbdolctu+iWuCjVOKeGWVNaK0JLGUH0CvqL3qI09c4+K/vg+Ayr4IZ9UgzCb83DOv8scv\nPIl+sIJ4L09yvIq3nKY4us32dBFnXS29/T0txIqtWIw1QWswREtFiBWHwcPLLJ/rIyjGpOcMGjvU\n8lYEGvaGkkXLAbWy0eYcwnyswnMnM8g2bFEMtbUIl9M0B2Nyt3Tc51ZZ38rR90WLtaMaWihIL0qS\nj27hWiGbVcUXKWabbJYzxMsquyK3fxPxlQ7KeySy1yeT8XC+VGDtUdWq1Wo6xkATOZVm10OzzL84\nirMh2XgkJHVHEb+cJzY41LnEmzOqPa7dyuD3hZhbBmFneI/q1XNW0OzWVBtVl2pV2zYPWtsa9rYK\nkP7Zk2/zh2cfYc/4IndXO4kjjdRll/pYhNXhkXtJfZf6kFC5sy2djrMmW4+2C6PpgOS2ql9l52P4\nT9fZPNsLqK1oakXgdUoGXg9YetTmY8+/yRe++QhRb0Aq5yGEpL6VYmBAtaG36imiySx6S3D42Qlm\nq0Xe++D/8De39ZBSvgH8lcj99uOjwJ9KKX0p5TRwBzVp/OUfwkgwOloUP5fh2MG7+HdyWJsa1qbG\n0ZOTSA2efvIi5Z0aXndM5YACyK5NdnL5wk7OXdtJ5rkVrBsu1g2XaD4NAgq3lBLQspT2Vu5qIGL4\nzz79VdLTBqaj8iitTV2ZmTRJabCMmMjgrAmyM5LMrE7kgjXtYO+uIpo6C7e6WbjVjbsqOH3yGrU7\nBcLegNSiIGkY2E5IGOssPmoiWjpP3X8DcWobcWqbOB9hbmnEQx71QYlbbBFeKGIYMVIKklDDupYi\n2dGCLh+6fIKcIDVlMX5wQRG8OwPS8zp6WTEVxEiTd79+H/UHW9QfbGFWdIKiKih+9b8/rfbbF/L4\nHRJvMYPUJduzRTIzOvlTq+r5ltrLG02Bt8vjicMTyLKF7sHixT6iYQ/cmMaOEGEnCDvBqGtKvdkd\nYF93ybzjYm8JsncMNE1tGaNcjO5BtOUQbTm0uhOMmkZtZ0LrxR5O77rN2tH2/zsYYnxsncrdImtb\nOcLFNOFimtXVAqd33iYpKAT+9naGZo+APh9t0cHzTRID0lMm6SkTaUiipRTuumD+66MKWffhKkKX\nbcCMpHy9g1ffP4A+kUGfyOB3RTx1+AZhKUIYkrArxF3RWT2lMPvOhoZR0yj1VqDPhz4fswlBAcZ3\nLvN+eZjCVYM7y90wnSLxDKQO7qIBt9O0Plyl9eEqugdJrHgSzT4BFZPuMyZ+w6J4ZF0liH04onKm\nF3+Hj7/DJxr0afWoTt/0Rw28noiX/qdHiDIJ+oqFP5kj9UKewvsWi9OdLE53Ii7k6D+2DMB7l3az\nMlf6DxzC/54x+tf43V8SQlxpb02K7ROqi2AAACAASURBVH8bAOa/7zUL7X/7fzy+P1Iw2vDIpHyW\nfjzi8tnduCuCYNQnGPU5N7GD2JW8Mr0brz9m+BsJQy8KnHUNISFJxWAliN/rojUS0hoJSQwIdrbY\n3qdEWbXlLKlSEzmXxhsJ+J0zz6lA48k0QW9I0Buya/cy+pZJuZym+8Flmv0Jrecr+CVVJEssqejO\nhkRa6ul1Sd46cx8iFJTOWiRPbpPrqXNqYJr4fIH80Q2cNY3JShf6mSL6mSKFnhq7H53Buu0SZxL2\n96wQ7W0SXCkQxxoP7r5LbIN5I0VSN0nqJs1eSWsgZu7VEbQIZKLMZplZDS0QOOeUp0OfdtCnHaK+\ngMJNQRTqrB1XIT1BQZJaFEg7QaRiyIXUdkdU3u6h8nYP5QcDRh6ZQ/Mhe8HhvS/fR3pWgY3NqmCg\nu0xvbxm9ajA+vML48Aq5O6or9QsPvEarL0aLJN3PLtDskzTLLsWxLfRsiIgFmSmdzJSOFgnCYsyB\nIzOIGM5c34fUoTkaUrhisjbdwehXQpJVBxEpEpcwEt788hFSkxZxISKd9eh+bIkTozMklqT05RSb\njwU0hiMaw5HqQvmCMA2ZJ1fZfsKjuZGCukFjSCJNSTLgkb2t/DORK9HzIW+8fAgSQf6cTf6yhVUG\np69B7AjsbQh7A7aW8sSBRhxoVPeG2A9usu25pIyAHX97kvQ5l9iVOAsmjT0BcUpi1YSKgKzZeJ1S\nrRgDgbfbI72oURlT5311qUDlUEDprEXzcAtrzsKaszDnbDqPrRJmJTv2L4Mm2TyaYHU3leZiqEWz\nRxn6NE9D8zSawxFzSx24D2wiUxGi3ZX6QR8/UKSgEKIH2EBJQv5boE9K+XNCiM8C70gp/6T9uj8A\nviGl/OJf9v72yKAc/OVfI7NnG/98iSCfMHRG7VZmnlcXS/fYJtELXbQ623BRXVnRC31Vyos5RCrG\nnlEFm8hV6r3S+BZbt0tkpzUq+yLsDV2FsjRUBd8drd1jHtjLJkYLes75LJxWBrEwmyBKAfqcg9FS\nMunYleh7VRsyuZ4jNy0Z/Lk7TH5tN40B9blKlzRqI+CuCyoHQrrfMtjz968D8OaVPUpHkE1Iz+o0\nhlXYj+ZpHDs+yfn3d6N3edhOSLPepn2v2pgNJQ1uPlwndyZNq0sQFCVRV4C5YhF2RpTOqXX+1slQ\n8QkmMgR5idapckfSixrN/gS9KUgtK5+B16uOc3qgRqtp09tRofpyL2FGnd3UqmTrWETmtolVk2w/\n7KMZbfuzlhC2TDKFFs8MT/D1r58kzEmOPTDJ+9PDdH3TZvURdUy+iw+0NvU2al/gdUuilCTJR/R/\nQ2f5CSAbIrYsOi4K1h9RWwl3ziT30Bqri0U0N4INm8SNSXU2sV7NY1UU0u/481cBeOPsAYy6IMpK\njIZSR544NcH7r+xFJIKgI0ZqksMHZln4Y7X16PjkPHffH8KoC+L9dZhOw44G5pUMzZ0B7rSlVLm7\nq/htfYdhxoz884TJX7PI55tUb5WUJ+iOctbGO1qw6KpAqjb30t4SDH9kmomlHtxLyubuPrLB9s0O\n9OEGQc1CmAnGon2vDW2VBbmH16i92Y1dllQfbVE847L5WIDQE2TVIjOl0/Fji6y8pe7L9jZUj3mI\nLQsRA1Iw/Y/+hpWZ/67s0X/Xz4QQ/wRASvlb7Z+9BHxGSnn2L3t/Z3BI9vzmL5G+Y5FelNQHBa0+\ndQFLU+kqsBPMdVMF6uqKD2msmVgVoYJcHhb3FHO/9vxX+F9vP4Y3USC2JElWeftlJEhPWvgdUlXn\nO2OGXlKfof7pCo0rJTJz4HUIrAe3lIJxVKkBD5+a5Pq3x/F6IsyC0uNGmw4yFVPsrBElGv61ArEN\nzoYSKsWRRvaCg3Z6i/JyWxORDsm9694L8xGW6n1LWwl6zAWboDsiM2nSGGqb3NZ0WmM+djog7fpU\nbpco7tmi9VYng8/McmuqH62uk5lVC8TCnYj5pzVEh491x4UDNfyGhbFs4awLWg+oz5bOemTaXMb1\n7SzZtEd5M0Nq0iKxwO+I+cjD7/PqH58gKEBiKD3GR09cAOAr7xxTXFI7wdg2iLoDrEWLwi3lt/CP\n1Xl8xxSvvXaIpF/VMMxpB6MpaHUn2Jsahgc/+598k8//zrNsHk1wl3UOf+gmZy/vRmtj8RM3IdtX\nQ75dpL4rpHOgQuVKB7m7sPmAUti6Y1XMb7V5JKZSugYFJf4KCglGd4vuQp0P9t9gutnJK9f3kpqy\n0E+orkZzsoDU1aTV1Vvh8b47fOnG/dhOSBTqROsOMhOjVY17x7m2I2bnnwcsPeq2b05K7CVSEbKl\nQyLQPA1nXaP7tGJYfD9tPe7xcTM+xlt59NObhG904B1tEm/bEIPe/v65fZtszRfo+Y7G6sMJxSs6\n0bNlHhu4y7e+fZR40CNpGErN2haNWpu6sjsc3MT6XIn1o/8fTBRCiD4p5XL7z78KnJRS/h0hxAHg\nc3yvmPltYPdfVcx0dvXLnt/4B4imTt+udZbmOkhNq+k0zEui3gBhJMiyxd4D82w006zPq1ajv+3g\nzptoRyvfIyI1dJWsXdZVcnQMekuotltvorwiWdXGi0rqruUsmCDA6w/RWjrOUI3mehp3weATn3id\nz3/jMUC9jzekClBmJkAI2N2zztQrOwjzCe5YlXrZxZm18YsJ6eEq+isFKodUATB9x0TqEBxsEm/Z\nmBWNxITYUeItLReS1E3MbZ2xkwpcc+v2ACIWFIbKlOcLSCMh11NHO1MktqE2HoKVYNjtsOA7LpEL\n+09MM/XSGM3BCGfFINzTQq7ZZHZUqM/kSVLxvYwKo6Ho4c6CRe6uZO3BGHdJCaVkNsJOB+gXs3hd\nCUkbce8smZCANxhibhjoviDa00TMuoSdEcJM0Ncsomx8jx4WdoX09JVZnS2ptvR1wdZjPr0vWkhN\nOTKVfkPeo08LCe6qpPxkiyTS0NYs6Pc4OjzPuSs70Rs62Rmh7OPAW+f2KYOhkBRuaJTvS5TyVZfY\nazrZWUmYFZQPRN8bWBs60lBME2tLUystAea2RjzmoU87iKQdN+C0FZ/pGN2NyH4nRf1UE30yhT8Y\nMP57PvNPZ9EDaB5qYU47BB3qd8wtjWiHx+HhBaa/sJtmj6T0wBor0x2IVIwzadMajDiwb57rd9u7\ndl8jf8Ogcijk5P4pLr6yh8SE1LIgzCruSHCmE7Mu2X5cTciZ8y6NEy3say6t/hh3WWfiv/sbhOv+\ne7JH/4UQ4qoQ4gpwGvhVACnldeALwA3gm8Av/lWTBLTPlQS9obE034GeCWmOqWfsSLRNk8TT+YmH\nznG8NEvtPRWG4zcsMJTeoDmfxcz5mDl1h8xNGBgNocAmG4p2VT/gkxvfJniyopKussk97YXfE+ON\n+jjLJs8/+h5pJ8Da1LGPb/HKf/MI8aCHURP4Y949vUIS64hbaSbOj5BelohYsLtjnZ5vqwHkbGg0\nFrNIHcx1A3NdFbiKt2KiqoVZ0Qg6Yoo3lOdCy6rJRM+GWBXBrZk+bs30Ubqgk7+uU62lEPkAYkFw\noUj5/oDIBeFr6FaCXHKQS+qOvfPoPNdm+mmN++QnDDWYb7nQ6dNoKmCLkQnROn20Tp/8JJhrJn4p\nZuOIJLWgdBpGt+okSSlojoaYgw21jUgE/qiPNxJgbhjsekjxOcZ6NrC3BMaWQfGsRXbvFkiVCh8W\nIzITFolUpia6fDYfUGrU1ifLrHygrd48UcZdFnjjHt64R+FOQuu5KubtFO6Eg1XRMCdSXHh3N+6y\ngbsuKB8LOLcwwrmFEVVDcmPMqkb5lA8SjKbALGuE+YTtZ1uUj/lonnaPexLmE8JcjDVSx+tOKF3S\nSM3r7Hlsms5v2AS9IfamQPdAfjclvW6gT7tET5YxJlL4/YqKPvdfqFXvd4ljsQPSVkIqxhsUX3W4\ndHWM+qAk3tlifSuHWfI4vfcW/+XPfBERC9YaGQgFhIKTh+5QOaJuTpcWB0h2thQNTAdvl0/4Uie1\nsZjClM9Ad5mB7jJ+h0Su2iS2GmBW5T9gNvjLxugPgzLTHh6Sn37hcV7+lkKah7kEt19Jno038tTG\nYwpXNaQm8IuAUM5GdIm1biha1JqNCNXtoXAbyk+1yLyVUsG6fRGF7hqV+TyF4TKD+QrXrowgUzHO\nvFJmdjy4wuJiCWPDpPfdhOWHVLFUCojzKhjYzPvYF9IEeXXMdj8yw8T5EbRIqBtYDGExVlSup5qE\nFRsRC/7h6Rf5099QUpOFDyZ84NBNpqqdzN3sBSFx1nU+9Ylv869unUA/n8UuS7b3S0RJTXqpyy71\nPSHpOybZJ1bZrqXwN1z63tDY+skG/raD1tJxV9S8H2YkyViLqGUg6gbSStAyIUnTIDVjEh5qYF9I\n0+z7HrpOahK9qZSR8XKK3n1rrF3uQQtUcJLs9nGvuwT31+GuavXl74BXEjQOeRSLdWrXOogdqQZy\nDWIL+p+aZ2q++56nIvZ1RNtyTjaEskl2uMrhnkWub/RSniqBVEVSs6rOp9+R4K5peIeaaEIi51M4\nG4pF0uyXpJYF9SHF6gAIcpLOA+usTXZSuioI8oLIUddNUEjYeWSBemixfKfrnllNbwrssqC2L0B4\nOukZnfBEDf1SlsQCr0cRxvye77vvJaB3+GjTLkFHezXXUAVmqyLwOhXPIzdp8OBPqYyRN148gnNk\ni+p0gdSixoMfv8ybs2PEdzNEpUipgWdszKogPKFqYSMd2yy/OEzj/hbP7r3JN88epn98nbVLPRgN\npVz2+iLcRUOxPAA8jdGvSGY+qibl/G24+Ps/6oQrCXfrHfdSsjRfYJsRthlRG4+RusT6yDpRSnlA\nWoMRhasGItQIM5K/d+QN3FWNzIIgsyBo9ggyaY/KgZjedyOyEybV6QJSk1TrLguVPKmBOqX3lMfC\nGwhZnOuAWB30xScgt2cLe1PgrgmMsoG9ppPPtvC65L2M05uXRhCJwFlX25ool2DUdPySwH0njb2m\nY21q/NH0gyyfEiyfEnT2V3jj9ftImQH3H5nCrCnj1B9cOUV8JwMCth8KyE1pGHMOxpyDeGQbjITE\nhLWbXSR3M4hEsHFYEIWGwgDmQxq7Axq7A4KBAKEluHcthZO/Zai8jIpa0egTaWXWSineZvaOjrWt\nk17QsN/PMHRghYwZYFUEwUDI4w9fQ6zZigsRGFhVgVUVtLoE3tEm2fcdtrczjJxYQMRKvv1de/XG\nXwwxMrBB6n2X1Psu+YsWIh+QGahiLNiYPS2id4vc+IMDDOUq5Me2kZYkvSgI9rcI9rewqhqtAy3M\niRT6zTTOhrJi+yVgV4MwA1EuJj5YJz5Yx10V2P9bCbMu2DwZEZ+q4GxKnBObxJmEO5cHqXk27pKO\nPVrDHq2hB99D3A1/I1EF85tZtEBNLqnuBs6xLbSmRm5CJzehI2KBlKpYWriqjGckgrAzwi+pzFt7\nQ6c+lPDtNw7z7TcOox+qsLO0gWxn4r791cP4FQdjZ509u5Yw52zVtctLxPUs4nqWtXqG+mGPpGZy\nbasPEQkWZzqJbVUMjvY00esa+tEyHW+bdLxtgiGZf1onN2GordJfcz3wQzFRDBa2mN7owF1RQBmj\nKZDf6kB+qwMRCB66b5LNK13UD/jKNCYktbEEe10HTfJ//emzBDlJs7f9HAsZKWwjUxHzT+sc+tgN\n9JZg8GVBXLWwzYjoap7KYx65aya5a0qGTKwGABpsL+Vp9idkPqBwb+EOj0rNJeoIsVYMrBWDzIyG\nvSUoPLOM15Mwvn9B3QUFNPsk0a4WUkDlYie6r2hEmzNFjIag/C+HuXx+J+GQj98VY8w4aIGKR5SJ\noDGgkrdJoLacJXfZJjvbJpQvCYZeUoaqpL331som1op69vdtY13M4Hcl5KYUnevJk9fUZJaRJPvr\nNHYH99LO6iMJxSPrVA/7WI9tMLvYwexmEamrwOB3l0aICxGDT85huyHN/phmf0xqRaLfcen+yDxi\ny+LOZJ+CvjbVcjsxFKuz3HQxmhKjKamdaFHIN6jP5QhLMe7bGbQIGoOCidfHSF7uQPMErW5JEguS\nWHWbEl+n6+Fl9ECJ1uyhusL7CUljj0+qu0HQsAgaFu7pdRZOa8QW7PxcTBjqKrD3nQ6OHbhLUgzR\nXinibEq82SzebJb8qVUaWy79g1s0fqFC4kqV2Xq8jggFgW/iXShhjtaJbWVFT9wYbdZh17E5Kntj\n7PMZnE3BM4evEduS9FiFxFRhU3EpIi5FWN/KceHKTuWfeWQDrzcm09nAeDfL1HvDiFig7aoT9IX4\nHTF+R0z0Vgl92cba1tl8TfFe0zMGiZvQ/X5CVLHIzGk0Gzbdn5ql+1OzZG6bOGsa+eeWkamYIz9/\n5a81Rn8oth7priE5/PO/hhZD46CHsWgTZdRS1VnTkUIRqIK8JDsN20cjxncuM3lzgMJ1jfLJAFE2\nMRptWtF9Stm3NN1Jtq9GKdVi8VIfsa2IRmZdMPrkDAA37yrEv/B07DUd3YPxD01ybbEfMZVS9YWa\noON6xPyzMPJ1ydr9qtB66LkJLr66B21PHf18Fu9wE11P2N2zzo2rw+gtjcSQWGXtXgBL8fEVWl/q\nIXYE9eEEaSg8mtQg7lSg1lbVQdR10vNqiZ4+vYb3zW6aDzXQ9IQHBud59w2lP0hSCcTQN75O8ytK\nyVfdnagEdzMh3dsgeT9P7LZ790MhSBgZXWf5vT5S96mqf2VWdQyMLg+EJGyZ9PaWWZnpIHvHILWa\nsHo6Ymhwk/WqUs0mtzOISBBlEnKTgu0jEUY2RJ9MUTi+RvSlLrbuT+ge26TWUq1er2nBumrVBjlI\nHdlkeyOLVjFICiH2nPLXhBsuRlXdx0QE2Tno/9Q0a//7KOvHQO9vqi1QG6psDDWwLVWY9q4VCLNK\nMbu7Y50L7+zGXdXglPr7tbd2qdVfh8d4n6KQ37g+TG5CJ8iB2YTarhgRCAXifcDHWLYIizFmWb+H\n+NfSoSqwH5xn6q0RtFjg9YXkr5tU98QIX6CFgsGjSyxtqeObdn1F6DqXp9WbIDWJ0eWRfS1F+VEP\nw4zRbmTIt2nlAK0RRcEypx3sLUHtqIdpRwRVG3PTwN1bJuv4VF/upTGoxo3uqRsTB2pkXJ/NrQyz\nP/NPf+Cth/6Zz3zmB/m9/1cfv/nZ3/1Mz46HqB4I6eyu0TB19G0TLRTo+2oE+YTOfZvE+Zig7qIP\nNylf7kILBM39PtqGRe6OStxOTCATU54vgJ0QxTrOnxeo7ZLoLQ0x0sA3NCp3izQvFgm62iG1d0y8\n/S1CU6NysRPf0YhdydihJdZMB/2RKtFCmma3jjcYEuUTNsIUctsisMFeN4gSnZ6XDOaGbbIXbIae\nmqN+q0j3hYjtpzzinpDaTB53VWB4qtMQ7PUgHyLTMbnzDk3NZP/+ebZmini9sVJYvp1Ff3YDfypP\nKDSWprqxqhrZOfD3eSSRTm07RbKvSTgaEDcNsnd1dE/jow+f54ZZpPSmidQEmqeT+b/Ze+8gy677\nvvNzbn45dffrHCb05IxBGgADAiRIUAQpgitR1MqUbVkql+UoW7XrtWyrXLt2lS1bsqRdybJlaxUp\nygySaAYQgcgYADOYnHs659cvv3ffjWf/OM2GXLUrqaiqNVmlW9U10/G9d989557z+32/n+90gx+a\nfJdrr0zT7TkEmwlELDjz0HVmlwdIXkmgTbl0rxZI7mqSeNdh62mP/hdsmpUMP/yB1znat8J6NgHn\n0rj7An7k4y9z76t7MJdVFmanmiS+v4VcdxCFgH39G/SnOqws9oEAsb/D4SPzzFdKyK5BeU+FQGpY\nMzZRzyS1pHHmmctMTq3j98Vs+hkaN4u0H3aJNNDWHHRfaWKEBGvGppU08LoWiVVVhNx4Yxj/G0Va\ne2PSR6roWsz89RGcimpZBtKkZphUOykiqeEVJIn9DbqhjbOu0/9eTGdYw1kx8Pb1MNYtBk6s02o7\noEkyl1VhtbKew2oL3MEIkQqRvkGYj7A3DPzBkMZGhtRFB2PRouMlyL1iI2IIU4JPPnGOWxem8AuQ\nmDVxsxBmYmIMxp+cJ7erwXB/nehL/USOwB2KyV0ySB5p4vomBw8vst7I4oUG06cWWOtlIBGDpxEM\n+eSfT9AIkxQmG6z+7qurP/uzP/tr38kY/a7Yevzl8ZfHXx7f3cd3xdbDGRmTE7/44yReTFM/EFO4\npqmcTCA1Z9CZDMGKlaAEOLFvjuvf2kPyeBXxlSLtMUgf36K2zYnQKiZRJsKsGSpha4+LbFiYVY0T\nT9wiloKLr0xjtgXpRfX63X5Bazokfdege9wFCU/vv85LXzxFdzzEqirzkzPWUstnwLyXUMteV2A1\nwOhImnshyEfkrxr0SkohZ9clvdJ2wz6GgY8vsvTyGONnF1ioFuBKhighKR3foPlqGa8Uk9rdoHtT\nhRTHIz2sOwl6kz5CkxRfs2h+sENYSeAMdvAW0iTXlOoSULbvdQ13IEb2+Zh2iJxNEdlKen78yD1u\nrA9SzHRYnVEhO1KTJBcMegdd/uaJV/jdX/kw3WHJ1EMLLD4/QWyAFoB3RGVJAPQf2mR9I4f0dLSu\nzr/+6O/yv/3+/4zVEvRKksiRlKc3Od2/wJv/l1rxdkYEXjHGGu2gaTGHymss//s9rH/Sw7qRxM/F\nZPbW2de3wbkbSjVpb7e8zXcy2zZ2VeXP3TDQAok7IMjNxNT2qXMcjPno6xZaqJSQ/gMtvK2E6loM\nREzsW6PaSRK+XSBWbyW5OzGVkxBlI5L3TNzRCCQUJmrUZ4rIfIC2aZFaEbRPqZaxvuhgNRQFPTXQ\nobuYQWZCJVg72eLBsTlu/NIhKt/3/hbn5sKgUl1OuphmxESpSvPXxtg8obYoC2tFLCfENEOCi8oZ\nEU53sa4kcfd5THxOw/27Ndbnioh0iIwE+XdsogSMPzPLtVm1lU7nXUVAm7dBA38wYOGvf48nhaX6\nxuTQT/8DjK7Avq+K9YUCWx9+H6giuzpPnLzOS+cOY7iCgXdjin9nnutLQ8gY4o6JWX1/75hYVpj6\n7qhK4yq9YVJ7vEfycoLEpqQ1KegNhohI0Dep/G6b8wWFKxNANthBvDc7DmGoEzUtnj51mZf++CS9\nIbUXzg61KKa6zC31qSLepknpsiRICdrj0HdZUtur/XfrtiAtGTi2Tv3lQbKPrdN+oYxbVgVasd11\n0T0VlGM2ttF+JxUUNmhbnDl0hzdnpji9a54Lr+wjzMTcd/wuV57bx+mnleCoF5m8e3EPhcsarUkF\nN0nPGLSnQvrf1vEKgtauCBEKinvV63d9E3chs8OrdEouUSSIVpM4Gxru/h75t2wKn1rm3pxKDRcd\ng8J15Y6tPaIChU09or6W4Wce+2N+7veeJT7UJlpIEZW2NSJbJlGfj+ga5K7phB+q064mwddAl6T6\nunTbNtLXKA8rKtTWlX6idEz2plIb6j1J/WGPdNbFu5pXxLB9IXpetQY/se8yL/7nBxEf3qI+WyC5\nqhGebBEsp7BGOwRz2zUWR+klANKfXqXywjD2IxVaV0qEwz7pfJdO20GsOmRnIMioiMVwG80X+TqJ\nW2ob0R1W4rp2JYXW0UktajgVyeBfn+XOi7vojavnlrlmUbwV8NS/eoXf/oMnCQ+10XWJt55E+ELV\nnFD1D9NW15m/msKqa0hNEuQVvzRIge6D2YTGoYjMXZ32uKK4ATT3RSSWdZyHKtRqaRI3nb+Q4Oq7\nYqKwx8fk/k/8FPXjAeZ2loW+dzu2r2mTyPUIZjIMHFtnZa6P9Iyh1JYeNB9y+fSh87zw787QK6o7\nijQUpg5NXQxmVeezH3uJ3/jm45QPbbB2YwCrsX3ip9SE9HdOvsSv336Y9maK5KzJqY9f5cpvH6Z+\nKEQkI8xEgH4ljX/AJfWOyujwipJ4ukPq1TTtcZVwNv7kPLcXBlWuZSmkMNTEe7OE8YAqGgbvFvDz\nMborCIoxWsEjapuYWY9wM6Hs7qbkYw9d4KXPnwbAbEp0HyoPhThLplKPdnUSEy1V9V/J8OR9V3n+\n8kEAcpdN/KwS5GgBdPf6TI5tUvnmCIUnV6m8OkRmXk2Y36ZD/9iPfpX/8AcfJbKVPyI8rPQS0gA5\n6lLMdai3EoiZFP6AuoCFq/GhBy/zwivHyN0W1PdLCjcE7keaBLezyAmXsG4hIkFufJvwtZ4hX27R\nO18kfX+F+vUS4yeXmVstEbdMypNVahf6kQbYle1VmKY6Hf0XJJVnerCYIB7ukUx7aC/n8R5uMVGq\nsfI1RUXrjEdISyKcCOeujbfPxXJCLDMkijXCUFM5qk0TsmoCy7zn0DwUYG4Z9F+QbNynFKJhQjJ5\neom7t4bQcwFRy1SrW0BrGjz24DU6ocWdrX7cSwW0gy16Kynsik5iQxImBf2XPOaeUQXwn3rqv/FL\nVx9HSkFfrk3lfBnrYIPkl3JsHVFq2mvnJ0muabR3qfOsZ30+uf8S31rZSznd4sbFCZITTbqzWcae\nj1j8oE72rkb9RIDWVpNYYk2DBxp01lMkVgyClOTe//I9TuFODI3JT37pIyz+y2mWPh0Qh9oO3n3r\n5SF6B1zEmkOUDyESiETI4FctWmMa5mNb1JZyYEqsje1JZl+L0Z83uPtDNlZVx2qwYxwK0xIRQjjs\nc3L3vIoSBPSGQWwo9kPv6SbRxRzHPnyTc1d3k5o1Sa1IavvV3blwaRuf1oX2My3klexOXJ/RVYPP\nrkoa++DsBy5z5RePsHVsO4awL8BZsNA88I90Gfktk+oBk/YxhduLQw1jQ+kf7Nq2kerRCtX1LKJj\nqIzMFbWc1AJ2GA6ZH1zBj9Tzqr02SPqhTYzfLLH+MY841GBbgYoE3VXaDbP1PjPz2zF49ft8+gaa\n1G4Wsbc0Jp6aY62ltnStdoL060mau9VAGXgXGs+20d/O4hVVSFF3POSho3c4984+pCHZf2iR+ecm\n+anPfhGAX/j1Z+lMKW2MvW6QrBpfMAAAIABJREFUnZVsPhhh1nSCUsjEH8LS4wZjx1eofENJmLUQ\n4rN1kl/MUd8PxauS2ic7mG8rMdSR77vJO+emd+IaRs8ustFK417PK1/Jbo/COYvafQGaE2HfUqlf\n3wYDAwTP1oheKdIrSYJihOZqJNY1ldOSgU9+8jU+98rDJFc1FeMIaMMuoa8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pEGO7h+xU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R9tw6Z4UN1da9f6lG9ipMlorsF8tcCDI/O8eOEgj564yetvHSS2YwVnPaYcW/XVLOm7Bu5g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\nNrGJt8TmQrGJTWziLbG5UGxiE5t4S/z/SPu6+MPxLO8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7feff0b73cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(matbkg)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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VHPHimLTgRVrqkXG+FgAMblAeVE3JO+0kjsRPOR4YjDP0PrMahKwtyGNqei6U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h9+awPRkA607ls1Czr+cKSx/2uGzdh8mIpEFKdbt7+X4PDrhskTDQu075rj6n3DpOnWv3cI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\nj3KyKlGiRIkSJx7/C3qN5QLcmJOAAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fefeae05b38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.rcParams[\"figure.figsize\"] = [7.2, 6.0]\n",
"plt.imshow(matbkg+matcav)\n",
"plt.colorbar()\n",
"plt.axis(\"off\")\n",
"plt.savefig(\"fig_2d.png\",bbox_inches=\"tight\",dpi=200)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"33567.269963556697"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"matcav.sum()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(201, 201)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"matbkg.shape"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x = np.arange(0,200+1,1)\n",
"y = np.arange(0,200+1,1)\n",
"X,Y = np.meshgrid(x,y)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(201, 201)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X.shape"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-21.21320344 -21.21320344]\n",
"[ 21.21320344 21.21320344]\n",
"[-13.30750929 -13.30750929]\n",
"[-29.11889759 -29.11889759]\n"
]
}
],
"source": [
"row,col,height = utils.genCavProfile(matbeta=matbeta, cen=cen, \n",
" cavparam=cavparam, \n",
" angbeta = angbeta,\n",
" deprate=deprate)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7feff01a1438>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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CMLXKJwmLObchsUDNSABAITsGuXldYBtj3Oeb4WCSBMEYcpoBBuCtk1QffiHg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MRw5JU3uFTwTk0Mx/mrWqRr1Oix2HClthlxoUqXOoX0GDU++92qAX10hgJjDLNnz0ZOTg7kcrnb\n11ksFixbtgxLlizBhg0b+n2/srISy5YtQ0FBwYjmd4PhVjCiFfwvwB1TErAoMxZ2u50z8hpDqeVG\nQCoRY3oSlyb44PxJsNsZrtQ24VRxLS5V6FCua+HdL2PU/S2KJWKuybNT41fLfSGTemFCRCB/LtfM\n2nVjWCAU4EwpJ3+kxajhJRLCNoBtsNbQwa+wBQIuiJrMVhR0NvIrHLWCC/CcfS+XfZMcEdzPj73N\ncVMorOaORQcHQOErdemoFBUcgOqmNv4GlRwVjBC5rF+At1htOF/mKL4KVaKoutHlplPV2IrqxjaE\nqvwwPSkCxXXNaO3sQbRjfKc8w/m6Kzm/fcdegXNl3trJrczbunpw1VHkZGeM1/QTwlVQyLxR12v+\nUol4xMEdAIqKihAdHT2k4M4YwyOPPILk5GSX4K7Vanlt/ssvv0RaWtqI5zceoQD/C8AYo+B+HYRC\nAVKi1UiJ5nxMrDY78it1OFFUjZqmVlx1ZNc4ae9jfdze3YMfiqrBGBz54UpIJV5obK1yOc/52dsZ\n44NwUnggpiaEo7XLhNJ6A+S+3v0qSXvMNuRXNvJyT4WupZ8lLmNAfUs7rtQ2I1zlz9ka1DT122gG\nONMwfx8JJsWGotLRmrG31cHlmib4SSMQrgpARKA/Cqt06DJbeTsCXt7x9oLCV+qSLRQdHICqpjZo\nDR2QiEWYmhAOqbfIZfzqplaoFb6QeXthQmSgY++hCy0d3S6NUiZGBvXbNBY5WgU651/b3Dpq+e+f\nf/75kOWZH374AR999BHS09ORmZkJANi0aRN27dqFvLw8CAQCaDQabN26dVTmON4gicYNRirR2Gw2\n2O12CG9iWWYktHf34OTlGhzNr0ROfiXau3tcLIAnRAb2sy2e7iizjwtVoq2rB8V1+n6ZSX3ln8ig\nACSGqxxe510uY/VuliIUCDA7NRotHd0oqLy2qpd5e7nk8MeGKBDoL4O+vYvfKJ2SEOayKSoUCHB7\nuga6lnYX987E8EB+rr5SL6REB6PR2OGyYRujVvCv02KCAXC1EOeuuspEUxPD0WkyQyIW4VKFDgxc\n+qPTI0gkFGCSJhQ9VqtL8Zrz8+I8fFSo1LUiRi13aRAOAO8/9SssyUrASLDb7ZgzZw6OHj2KgAD3\nNmw1Gg38/f0hEokgFotx9uxZGAwGrF69GpWVldBoNNi9e7enbq6SRHMj0Hv1TgwPfx9vLMlKwJKs\nBNgdhUqHL1bg8MVy5FU0IMCnf/51p8nsKAbi9ORQpS9UfjLEhzLedyVarXDRwmub2xCu8kdzWxdS\no4MhEYtQUNXYr/DKzhjOl2nR0W3mV/Ud3T397JWVfj58O8PEcC5PXNjn79LOGGqb21Bab0BUkByh\nSl9cqWlyCeSdJguajF2oamxDWgyX2llQ1cgXPQHgW+7NTonGNIdfkfMmaGjv5jVztdwXmhA5yrXX\nMnNsdoZ2Uw9K6w2IDpYjxOEcqXAUbXEePlxxV0p0ENI1ar7CGuD2TUbCnj170NTUBLlcDp1OB7PZ\nDIVCAbH4p8PTkSNHEBR0rYPUli1bsGDBArz44ovYsmULtmzZgjfffHNE8xvP0AreDYa7gmeMwWaz\ncQ21afX+s9Dc1oUfiqqx70wJjhdV8y6Ycpk3WnsVhnHphteMuCIC/SERC3GiyDUffVJsiEsOua/U\nC+kxIdC3d/Er6oE8dibHcd74IqEA+ZU62BnDtMTwfqvdDE0IvL3EqDe0oU7fDpFQAJFQxKeSAtzN\nQO4rRZOxk9fhpyaE4WyvlX+Y0g8p0cHILalzkaycG6BSLzHSYoLR0tGNqsY2WHt1k0oMV6FSZ0Sa\nRo1OkwUldfp+c/WRiDFzYiQqG428nTHAefDrjJ1Qy2XQhCjQYbLg21fXXvff6Kd499138dVXX8HX\n1xdBQUFoaWnBwoUL8cILL1z3Oo1Gg7Nnz7oE+AkTJiAnJwdhYWHQarWYO3cuiouLRzS/GxS3ghEF\neDcYboB3SjOkvf8ydPdYcLywCieKqvFtbomLDQTXHNw1y2ZyPOcAqZbL+Bz4vrYMXNco7nVkUADC\nVX4QCoBTxa6ZN1w2DhcgVf4+SAhTwmy91kMXQL9gnhwVhEB/H5y6UstbEl+bKzdWUkQgfKVeEELA\nN+F2EhuqQF1zu6M/ag8qGloACPoFc4BrSZhfqXN0ZXIN5jFqOeLDlMgtrkNHL+nLaToXF6qEys+H\ntyHuzZKseLz/1LIB/z3cxW63Y/bs2Th27Jjb8gwAxMbGQqlUQiAQ4PHHH8djjz0GhUIBo5G7ITHG\noFQq+dceBkk0YwltrP7y+Hh7YXFWAhZnJeCVNXORW1KH785fxffnrw54vq6lE/WGdr4heGZcKMQi\nocMUjVv997ZpcLa6m54UjtRotUPC4ZqIGHtZShjau3Gu0wSxSIjkqGD4SMQoqNIhMkjushq+XNOM\nqYnh8PPxRlJEIOr0bQ6bhmvB3ikhpUQFY3pSBH+ORCxEdWMrbHbGb4pOTQwDIMDlXt26FL5SPphz\nG6NB/QrOqhpbYbbYYLHZkRUfhg6TGY2OZh8A5+lTjhZkxoUgTOWHju5rDWUmx/cvehsqBQUFiIuL\nG1JwB4ATJ04gIiICjY2NWLRoESZOnOjyffrbowD/s2F3rKJu9l+wsUIsEmJWchRmJUfh1TVzkVfR\ngANnr2L/uVLU6dvh7yPptxq12RnyyrnWc07DMWcFaG9aO3v4AOcnlWBSXJBLQ2qAWxWXNxj56mU/\nHwk0agUEELg0ZGGM8T71AJe54i1xzVyReolwpbaZL1aaGBkEtUKGHy/XwtbrZiAQCHCmpB5SLzGm\nJHCpjpZe0k97txlnSusRpvTj9wQKqxoh9fLi2ws6bxbTJ4QDjCuIcvohScRi5Dr89iOC/BGh8kdW\n/MgzaIZb3OS0AFar1cjOzkZubi5CQkL4FEmtVgu1enQ6TI1XSKJxg56eniG5STLGYLVaaQVxA8IY\nQ35VI04UVOPTHwpdNjP7ZssAnGYuFglhttpQVN0Ixjjbht49cZ12Apy2H4AKXQti1HKcLXWVVJwZ\nNBq1AsFyGUrq9ZDLpC6plFIvEcxWO2RSL0yMDIKhvQtioRAl9a6FS9McbRgnRgahuY3L0pkcF+rS\n3g8A0jVqSL3EKK0zwNhlgkLGNW1x4iMR45YJkajXt/OtIAFg+oQIvpVfarQaAgBmqxWXe2UriUVC\nXHz3CfgM4JbpLk555vjx4/D3d78bVGdnJ+x2O/z9/dHZ2YlFixbhlVdewaFDhxAYGMhvshoMBvzh\nD38Y9vxuYEiiGQucG6sArd5vRAQCATI0XEenJ381FUXVTdh/7ir2nS1FR59VOMC18HN6vyh8pcjQ\nqKFt6XBJtwwM4JqDOP3RASA1KhhTEsJQVN3EX++UeiobjahsNCJAJoHSTwq5L7eStjNAE6rElZpm\ndHSbeavlOanRkPtKUVyn56Uji83Or8iBgb3vlb5SPttFLBRiUmwIFL7eOFZQza/Uus1WdPVYUFKv\n5/cYyrQtvM+Ozc64ZuLgNlinJYbD0NaNMl0LJkYEjSi4A0B+fj4SEhKGFNwBznkyOzsbAGC1WrF2\n7VrccccdmDZtGlatWoVt27YhJiYGu3fvHtH8xjsU4EcZu91OOe/jBIFAgNQYNVJj1HgueyYu1zRj\n39lS7Dtbikqd0aUFHsClC3b1cBbBzkbeWkP7gF2jyhpaUNPcBqmjIbrNZkNBnwYpkYEBfMaO0k+K\nxHDOdvgKXNMt27p6cLFCBy8RF6RtdgZtH5fMlg4uFdJLLERmXCisjoyelgputW6123GxQofpSRGO\nrldK6Fq4LB3njc25xyBy+ApNjgtFUU0Teiw2aBxPKQ0OT5roYDnmj4KD5BdffIF7773XrXOLi4ux\nevVq/nVFRQV+//vfw2g04i9/+Qs+/fRTAFyh09KlS0c8N0+AJBo3cFeiIWnGM2CM4XJNM04UVeOT\nYwX8JizQv1AJALLiwyAWCXnfGl+pl0shFsBlzDQaOxEfpkJTaycqdMYBJaFJsSFo6+pBkFyGqw79\nOyhA5tJiUeUvBSDgWhM6ip/6evgAwK3JkbDY7HwgB4CU6GAU9brRxIcqEayQobhWz2vtMSHXbmwy\nby8kRwbBTybB0XzXyuC3/88SrJjhurHpLrW1tQCAlStX4scff4Sfn9+QrrfZbIiIiMDp06exfft2\n+Pn53Wze7yTR/NLQxqpnIBAIkBIdjJToYDy6JItf2e89U9Kv8Ymv1MvF5GxCRCDCVH64UN7g4hPj\n7+ONyzXN0DvaCXI2yF79PHNqm7nm1hU6I0RCAaYmhsNms6Oty8R3bIoKkuNihY5vTagJUUAt94XK\nX+rSdMXQYeI3eWNDFQjy9+mXv2+123GqmNPa0zScP4+XWMgH+K4eC86VaZEVH+bwwFGgua0L5Q2c\n5j9cdu/ejQMHDkCr1eL2228HAISGhmLv3r1uXX/o0CHEx8cjJiZm2HO4GaAAP0pQxapn0jvYP5c9\nE4XVjdh/ltPsqxpboVErXMr7i+v0kPtK0dFtRmp0MKQSMa7UNPezA65qbEVbF9f7dGJkEPx8JNC3\ndbl4v9vsDFabHXnlDZB5cwVX3RYr33XJSaXOCKmXGMaOHqRGB8PbS4wrtU0uTx4VDUbYGYO2pQOp\nMY5zqpsQLPdFVROXbun055meFI6s+DB+Y9nOAJ3xmgEbwNkqRwe7bwrWlw0bNsBoNOKZZ57BqlWr\nhnz9J598gjVr1vCvyft9YEiicYOfkmioYvXmgzGGouom/FBUg13H8l20+r6Okpx2Hgqr3Y7LDk07\nTOnHpyY6mRwfCpuNQSQU8Nr3QDJOVnwoREIh9A4LAgH6Z/YkhKngL5PAarPjcnUTrHaGqYnXCqgA\nzrdm1sQo6Nu7+S5YALc3UOvQ+OW+3kiODIaxsxtXemXQLMyMwwdP3zXsz89ut+PWW2/Fjz/+CJlM\nNqRrzWYzwsPDUVhYiJCQEOh0OgQFBfHe71qt9mbwfieJZrQQCAT9mkz3xm63j0pTbmL80HuD9tE7\nsq5l45wpQVWja+VksFzGB1YfiRiT40Mhl3mjua2Lz3UHuLTDC2Wu53mJhRALBXzwFQkFKKxu4q16\nIwL9ER+mRIXO6NLlSuHnzVfuch2uAiHzdv1z77HYUFKvR52+HX6Oc5xOnk5aO3vQaTLjSq0eQQEy\nxIYoYOw0IWuEDpJ5eXlISkoacnAHgP379yMrKwshIZwHjvP/APDoo49i2bKRVdZ6EhTgRwhVrBK9\ng/2GFTNQXKfnZZzyhhaEKv157bvbbMWFsgZMSwyHt5cY6ZpAmK02XKlpQkuvRhvdZivyK3QQiASQ\nSr0wITwQJrMVFqvNJSe+Tt+OcJU/aprboAlVQB0gQ3VTG3o5Hzg6XGkREejP2ym3dfZAa7yW1tnR\nbca5q1qkxagRIJMiIUyFThPXAN7HcWPoLdFkxYeP6DP74osvhiXNAMCuXbtc5Bnyfh8ckmjcwGw2\nD6qvkxUwMRiMMZTU6XGiqBqfHi9yKSSKDVWgopdtQaC/DzQhCvRYrbhc3QybnXF2vVrXAqcZEyJh\ntdnR5Qi+jAGTE0L7dZLKiguFWCRChaMna9+OVABnYCYQCGDsNPF5/X0lIaWfFBmaEDQaO3GlthkM\nIy9wstvtmDVrFk6dOjXkFXxnZyeio6NRXl7ONwb5l3/5l37e786A78GQRDOaDCTB0MYqcT0EAgEm\nRAZhQmQQHlmchfKGFuw/W4rDlypwqcI1pTFILuPNxPx9JEgKD4RcJkGFrsXFiMxksSLPUa2q9JMi\nPlQFMLj0kFX6SnG+V0VrYpgKkcEBKKxqcmlmIhQK+WCulssQo1ZA1Od3ubXThNPFdTBZrPzPC5bL\nRlTgdOHCBSQnJ183uD/88MP49ttvoVar+VZ7Tq93lUqFe++9l/d637lzJ377299i3759qKysdFnR\n3+zQsnOYUMUqMVTiQpV4atl0fP671Ti8aR1+t2oOshxmXXLZNU/79m4zzpVpoWieV6gAABjkSURB\nVO8wQSrxQlZ8GGeZIBTwnZsAoKXDhApdC86W1UPhJ8XUhHCkRAUjRu2a3VKqNaDTZEFjaycSwpSY\nnhSBMJUfXxULAI2tXTh3tR55FQ0ICpBhWmI4kiODEBeq5DdvWzpMOHu1HiGKoeWs98Udeeahhx7C\ngQMHXI45vd5LS0uxYMECbNmyBQCnyZeWlqK0tBQffPABnnzyyRHNz5MgicYNLBYLrFariwxD0gwx\nWuhaOnC0sArfnC7G6ZI6WB0br0o/KV98BABRQQEIDJBBAOBKbTO6zVZkxoXyK3onsyZGosdig9lm\nw5WaZlhs9n5dr0RCAeJClVD6+UDX0oGqptZ+zbQBThKy2GyOfQJurP/3+J341bSkIb/P999/H3v2\n7MHFixexYsUKqNVqKJVKPP300wM6SfZtlj2Y1/vjjz+OuXPn8rp87/M8GJJofi5ImiFGkxClH1bN\nTsWq2akwdphw+FIFTl6uxr5zrjbHQQEy3kzMSyxEeowagQ4vm976enNbN6/3+0jESNeEQCAAZBIx\nuhy+OJoQhYufTkSgP+JCFBAJBS7HeyxW/mf6SMRIjVEjK2F4gfOJJ55ASkoK/vKXv+DZZ59FS0sL\nDAYDvLzck3t0Oh0ftENDQ6HTcTJXXV0doqKi+PMiIyNRV1fn6QHeLSjADwOqWCV+LhR+UtwzKxn3\nzErG/31gPk4UVeP7C2U4fKnCxcfdYrUjv6oRCWFKtHeZMTEyCP4+EtQZ2lChu7YK7zZb0dZlwlVt\nC7xEQqTFcO6SErGQ78kKcNk4arkvSusNCPT3QWyIEt1mC7S9LJW7zVbojB0IUw7NGMyJQCDAd999\nh/Xr12Py5MnDGqP3WPT399NQgB8iTjMx+uUifm58vL2waHI8Fk2Oh9Vmx/myevzzQjn+mVeO6qZW\neHuJUKHjKlSv1HKWBEkRgZAEiaGW+/KeNyo/HwAtsNjsfAFWZlwoEsNVUPpKoW3pQE1zG2qaOX3f\n2cs2WC5DR7cZk2JDIBIKcVVrwJSE4adH2u12HDp0CJs3bx7W9YN5vUdERKCm5lrrxdraWt4r/maH\nBOQhQDnvxFghFgkxPSkSL62+DUc2rcOB1x/ACytvRYZDfnGikHmjUmdEbkkdKnRGqOUy+PlIkBod\nDLHw2ok1Ta0orTcgt7QeNc1tSI4KQlyIEhMjg+A8LTIwAN1mKy5W6Pgm4zMnRg77PZw9exYZGRnw\n8fEZ1vXLly/Hjh07AAA7duzA3XffzR/fuXMnGGM4deoU5HI5yTMOaAU/BJwVq7SxSowlAoEASRGB\nSIoIxPqFk9HU2okj+ZU4fLHCxXUS4LJjzpc1wNhpgszbC2kRgfCRiFFU42pdLPW61q3J30eCBMfq\nvre/vJ0xZGhCMFyGYg28Zs0a5OTkoLm5GZGRkXj99dfx4osvDuj1vnTpUuzbtw8JCQmQyWTYvn37\nsOfoaVAWjRs4s2hsNhut3okbmh6LFaeKa3H4YgUOX6oAAL5a1UlWfBgulGuREKaC0k+KRmMXggJ8\n+jUljw1RoKqxFYkRKshlUrR2mfDNy2v79XR1B7vdjpkzZ+LMmTOQSqUDnjNQ7vvGjRvxzTffQCKR\nID4+Htu3b4dCoUBlZSWSk5MxYcIEAMCMGTPw17/+dcjzGse4FYQowLuBxWKB2Wym1TsxrmCMobRe\nj5z8Khy5VIGzV+thszNMTQjrF8zTY9Tw9hLDbLWipE4PkVCILrPFpZnJ7JRo7NyQPay5nD59Gtu2\nbcP//u//DnrOsWPH4OfnhwcffJAP8N9//z3mz58PsViMf//3fwcAvPnmm/1SKG9CKE1ytMjPz8fp\n06ehUqmgVCqhUqmgUqmgUCjg7c0VqPRd1Xd1dcFkMkGlUo3FlAnCIeUEISkiCI/dMQVtXT04UVSN\nM6V1qGpq5b3t/X0kyO/tfikWYmp8GHosVmhbOlDr2HydMoz0SK1WC61Wi+3bt+POO++8bu3Ibbfd\nhsrKSpdjixcv5r+eMWMGPvvssyHP4WaGArwbSCQSmEwmFBQUwGAw8Pm7LS0tMJuv9fH09/eHUqmE\nUqnElStXEBgYiCVLlvA3BufNQalUwtfXl+Qe4hclQOaNpVMTsXRqIl5efTuKappwNL8SV7UGfHum\nBDaHJYLFaofZauP7vYYqfBEZJMctSUPPTCkrK8OuXbuwZ88e1NTU4O233wZjDO+99x5mzZo1pLE+\n/PDDfi37Jk+ejICAALzxxhuYM2fOkOfn6VCAd4O0tLTrOtQxxsAYQ1tbG/R6PfLz8/HCCy/gqaee\nQmtrKyoqKnDu3DmXm0Nn5zVPEG9vb/4G4LwJyOVylxuC8+uLFy/CZrNh0aJFv8RbJzwUoVCAtBg1\n0mK4VMPX75+HHy5X41hBFY4VVLnYGDQYO9HU1oWM2KFbBM+ePRsikQhWqxUfffTRsOf7X//1XxCL\nxbj//vsBAGFhYaiurkZgYCDOnTuHFStWoLCwcMCK2JsZCvCjgHMlrlAooFAo0NDQgG3btv3kisK5\n/9HV1YWWlhbo9XoYDAYYDAbo9Xo0NzejpKSEvyno9Xrk5uYiJiYGr732GkQiEf8znZJR7xuFu3KS\ncw5CoXDQDTDCswmQeePOKYm4c0oiGGO4Wm/A8aJqHC+swumSOiSGqyAbpsHYl19+iV//+tfDntvf\n//53fPvttzh06BD/u+vt7c3/Pk+ZMgXx8fEoKSnB1KlTXa595ZVXoFKp8OyzzwIAXnrpJajVavz2\nt78d9nzGE7TJOo74+OOPcfHiRd5kyWKxwGg0Qq/XQ6/X8zcJ5w3BXTlJqVTiwoULCA8Px9KlS0lO\nIlzosVjR0NKBGLViyNfabDbMnDkT586d4wPy9ei7eXrgwAFs2LABR48eRXBwMH9eU1MTVCoVRCIR\nysvLMWfOHOTn5/fb86qsrMQ999yD8+fPw263IzExEbm5uQgMDBzye7nBoE1WT2PlypW4++67+UAr\nkUigVqv5ij536CsnGQwGlJeX46uvvkJ2dvaoyklyuRxVVVUoKCjgi1KI8Ye3l3hYwR3gsmeysrLc\nCu4D5b5v3rwZPT09vCTpTIc8duwYXnnlFXh5eUEoFOKvf/3rgAkNGo0GgYGBuHDhAnQ6HSZPnuwJ\nwd1taAVP4OTJk2hubsby5csHPeen5KTeTwrO462traisrOSlo9GSk5yYzWZIJJLR/TCIUeX555/H\n0qVLx7SN3j/+8Q+cPHkSDQ0NWLduHZYuXTpmcxlFKA+eGFvKysqwfv165OTkwGq1jpqcpFQqYTKZ\ncPDgQbz66qskJ92gOOWZ8+fPD3ojHqi46bXXXsPf/vY3XpLZtGkTH5Q3b96Mbdu2QSQS4d1338WS\nJUt+ch5msxnp6emwWCwoLS2FSCQapXc4ppBEQ4wt0dHR+PjjjyEUCkdNTnI+MfzpT39CSkoKDh48\nOGpykkgkgkAgwGeffYa77rrLLVmBGJxTp05hypQp133Keuihh/D000/jwQcfdDn+b//2b3j++edd\njhUVFeGTTz5BYWEh6uvrsXDhQpSUlPxkwJZIJJg3bx4UCoWnBHe3oQBP/Gx4eXkhMnL45lR9s5Pi\n4+MBAFarFTk5Odi6deuAK/WhZic55SS73Q6LxYLKykrs2LGDD/6jISc553UzPFlcunQJBw8exMGD\nB3HrrbciJycHSqUSsbGx/dIYBypuGow9e/bgvvvug7e3N2JjY5GQkIDc3FzMnDnzutfZ7XacOnUK\nn3766XDf0riFAjwx7hCLxfjggw8G/b4ziPr6+sLX19ftmwxjDK+88gpiY2OxbNmyAeWkurq6IctJ\nzn2HI0eOYMKECVi+fLlHy0lKpRLh4eEoKSnBvHnz8PXXX8NgMGDdunWYN2+eW2O899572LlzJ6ZO\nnYq33noLSqUSdXV1mDFjBn+Os7HH9SgqKsKyZcuQnZ2NxMTEEb2v8QgFeIJwIBAIsHz5cmRkZMDb\n23vU5CSDwYCysjJ888036O7uxokTJ0ZNTnKSk5ODuXPnjubHMWyioqIQERGBJUuW4NVXXx3y9U8+\n+SRefvllCAQCvPzyy3juuefw4YcfDmsuKSkpKC8vH9a1ngAFeILoxbRp04Z13WBykpPCwkK0tbXh\nrbfecjk+EjkJAEQiEcRiMaqrq7Fs2bJRk5NGijuNtQcjJOSaJfGjjz7KZ+BQY4+hQ1k0BDEOcf7d\nWiwWPPvss0hJSUFmZuaIs5OccpLz9YkTJxAbG4s777zTbTnJarVi1qxZuHDhglv9VvsWNzm7NgHA\nH//4R5w+fZrfXF27di1yc3NRX1+PBQsWeFJWzFChLBqC8FR6F7vNmzcPK1ascLt59U/JSb2L3fbu\n3YvY2Fhs2rSJv/6n5KSqqipkZma6NZ+BiptycnKQl5cHgUAAjUaDrVu3AgBSU1OxatUqpKSkQCwW\n489//vPNGtzdhlbwBEEMiE6nwwMPPIB//vOfANwvdrt06RKys7Px2GOPjeX0PR0qdCIIYmT09PSM\nej3AQMVNq1evRnFxMQDAaDRCoVAgLy+POjcNDkk0BEGMjJ+j2Gug4qZ//OMf/NfPPfcc5HI5/zo+\nPh55eXmjPo+bAQrwBEH8olyvuIkxht27d+Pw4cO/7KQ8FGowShDEDcPx48cREhLiUpTk7Nx0++23\n4/jx42M4u/EHBfhR5MCBA5gwYQISEhJ4z3aCINxn165dWLNmDf/a2bnpwoULePvtt7F27Vq0tbWN\n4QzHFxTgRwmbzYannnoK+/fvR1FREXbt2oWioqKxnhZBjBusViu++OILl76r3t7evH97785NhHtQ\ngB8lcnNzkZCQgLi4OEgkEtx3333Ys2fPsMaqqanBvHnzkJKSgtTUVPzpT38CwNmoRkREIDMzE5mZ\nmdi3b99ovgWCGFMOHjyIiRMnungHNTU1wWazAQDKy8tRWlqKuLi4sZriuIM2WUeJuro6REVF8a8j\nIyNx+vTpYY0lFovx1ltvISsrC+3t7ZgyZQrf0WYgG9WhoNFo4O/vz5e4nz17FgaDAatXr0ZlZSU0\nGg12794NpVI57J9BENdjoOKmRx55BJ988omLPAPA7c5NxMBQgL8BCQsL40u1/f39kZyc/JOueUPh\nyJEjCAoK4l9v2bIFCxYswIsvvogtW7Zgy5YtePPNN0ft5xFEb3bt2jXg8b///e/9jq1cuRIrV678\nmWfkuZBEM0r8XEZIlZWVuHDhAm655RYAnI1qRkYGHn74YbS0tIx4fIDz2V63bh0AYN26dfjqq6+G\ndH1xcTEvG2VmZiIgIADvvPMOSUoEz2Cyo8FgwKJFi5CYmIhFixbxv9OMMTzzzDNISEhARkYGzp8/\nP5bTH784fSnc/I8YBIvFwmJjY1l5eTnr6elhGRkZrKCgYERjtre3s6ysLPb5558zxhhraGhgVquV\n2Ww29rvf/Y6tX79+yGNqNBo2efJklpWVxbZu3coYY0wul/Pft9vtLq+HitVqZSEhIayyspK9+uqr\n7L//+7+HPMb69etZcHAwS01N5Y/p9Xq2cOFClpCQwBYuXMgMBgM/33/9139l8fHxLD09nZ07d27Y\ncyd+Purr6/l/m7a2NpaYmMgKCwvZxo0b2ebNmxljjG3evJm98MILjDHG9u7dy+644w5mt9vZjz/+\nyKZPnz5mc79BcStmU4AfRfbu3csSExNZXFwce+ONN0Y0ltlsZosXL2ZvvfXWgN+vqKhwCYDuUltb\nyxhjTKfTsYyMDHb06NF+AV2hUAx9wg6+++47NmvWLMYYG3aAP3r0KDt37pzL+6NA4FksX76cff/9\n9ywpKYnV19czxribQFJSEmOMsccee4x9/PHH/Pm9zyMYY27GbJJoRpGlS5eipKQEZWVleOmll4Y9\nDmMMjzzyCJKTk7Fhwwb+uFar5b/+8ssvkZaWNuSxnbKRWq1GdnY2cnNzERISwo+t1WqH1OiiL303\nyoYjKd122239NtIGk5H27NmDBx98EAKBADNmzIDRaHT5nHrz8MMPQ61Wu3xuGzduxMSJE5GRkYHs\n7GwYjUYAnDTm4+PDy0tPPPGE+x8CcV16y446nY7fbwoNDYVOpwMwcNLCaO5D3SxQgL8B+eGHH/DR\nRx/h8OHDLvr1Cy+8gPT0dGRkZODIkSP44x//OKRxOzs70d7ezn/9/fffIy0tDcuXL8eOHTsAADt2\n7MDdd989rHmbzWZ8/fXX+PWvfw2A68xTVlaGvLw8hIWF4bnnnhvWuABGJRA89NBDOHDggMuxRYsW\noaCgAJcuXUJSUhI2b97Mf8/pgZKXl0cGV6NER0cHVq5ciXfeeadff1ZPbF841lAWzQ3I7NmzeWvW\n3ixdunRE4+p0OmRnZwPgikrWrl2LO+64A9OmTcOqVauwbds2xMTEYPfu3cMaf//+/cjKyuI78gzW\nmWekDDcQDOSBsnjxYv7rGTNm4LPPPhvyuAO5I7722mv429/+huDgYADApk2b+H+/zZs3Y9u2bRCJ\nRHj33XexZMmSIf/M8YjFYsHKlStx//3345577gEA/ukxLCzM5emRujeNDrSCv4mIi4vDxYsXcfHi\nRRQWFvIyUmBgIA4dOoTS0lIcPHhw2HnGfcvMR0NScjKYjDSageDDDz/EnXfeyb921wNloCcDgKtZ\ncD4BOIN7UVER353owIED+M1vfsMX8gzEQLLS6tWr+Sc7jUaDzMxMADe2rDSY7DjY0+Py5cuxc+dO\nMMZw6tQpyOVy/gmOGALuivWMNlmJ69DR0cFUKhUzGo38sQceeIClpaWx9PR0dtdddw1pk6zvJvLz\nzz/vssm6ceNGxhhj3377rcsm67Rp04Y0rpM33niDrVixgtntdsYYYyaTiTU3NzPGGDt79iyLjIxk\nra2tbo872Abzpk2b2KZNm/jXixcvZidPnhx03IE2nHuzYcMG9vrrr1/3vd0IHD9+nAFg6enpbNKk\nSWzSpEls7969rLm5mc2fP58lJCSwBQsWML1ezxjjsqN+85vfsLi4OJaWlsbOnDkzxu/ghoOyaIjx\nyX333cdCQ0OZWCxmERER7H/+539GLRAMFAS3b9/OZsyYwTo7Owe97vbbb7/u2AMF+JiYGJaens7W\nr1/Pp3U+9dRT7KOPPuLPe/jhh9mnn3465Dkzxr33yMhIVlJSct3zBqK6uprNnTuXJScns5SUFPbO\nO+8wxigddRxBAZ4g+tI3CO7fv58lJyezxsZGl/MaGxuZ1WpljDFWVlbGwsPD+ZuKO+MOVrMwmgH+\n6NGjbMqUKS7nyWQylpmZyW677TZ27NixQcekvPRxD6VJEkRv1qxZg5kzZ6K4uBiRkZHYtm0bnn76\nabS3t2PRokUuuvWxY8eQkZGBzMxM3HvvvUP2QAkJCYFIJIJQKMSjjz6K3NxcAKO7ZzASa92wsDBk\nZWUBcLXDGI10VOIGwt07AaMVPEEMSt9Vdu/9hrfffputXr2aMcZYQUEBy8jIYCaTiZWXl7PY2Fj+\nScHdsRnjKqfVajWrqakZ9LqfkpV6jx8VFcVaW1sHrWr+1a9+xY4fP85/b/78+aSLjy1uxWxKkySI\nETKQO2JOTg7y8vIgEAig0WiwdetWAEBqaipWrVqFlJQUiMVi/PnPf4ZIJBryzxzMWlelUkEkErlt\nrUt56R6Ou3cCRit4gvjFGWjDmTHG1q1bx95//32Xcz/77DOWkpLCJk2axCZPnsy+/vrr6449kB0G\nWQeMG9yK2QI2QEHN9e4HP9eNhiCIXw7GGNatWweVSoV33nmHP75x40YEBgby1tEGgwF/+MMfsHfv\nXrz33nvYt28fTp8+jWeeeYbfVyDGBLcerSjAE8RNyIkTJzBnzhykp6dDKORyLTZt2oRbbrkFq1at\nQnV1NV/VrFKpwBjD008/jQMHDkAmk2H79u2YOnXqGL+LmxoK8ARBEB6KWwGe0iQJgiA8FArwBEEQ\nHgoFeIIgCA+FAjxxQ3PmzBlkZGTAZDKhs7MTqampvCUvQRDXhzZZiRue//zP/4TJZEJ3dzciIyPx\nH//xH2M9JYIYayiLhvAMzGYzpk2bBqlUipMnTw6r8pMgPAzKoiE8A71ej46ODrS3t8NkMo31dAhi\n3EAreOKGZ/ny5bjvvvtQUVEBrVaL9957b6ynRBBjjVsreDIbI25odu7cCS8vL6xduxY2mw2zZs3C\n4cOHMX/+/LGeGkHc8NAKniAIYvxBGjxBEMTNDAV4giAID4UCPEEQhIdCAZ4gCMJDoQBPEAThoVCA\nJwiC8FAowBMEQXgoFOAJgiA8FArwBEEQHgoFeIIgCA+FAjxBEISHQgGeIAjCQ6EATxAE4aFQgCcI\ngvBQKMATBEF4KBTgCYIgPBQK8ARBEB4KBXiCIAgPhQI8QRCEh0IBniAIwkOhAE8QBOGhUIAnCILw\nUCjAEwRBeCgU4AmCIDwU8RDPF/wssyAIgiBGHVrBEwRBeCgU4AmCIDwUCvAEQRAeCgV4giAID4UC\nPEEQhIdCAZ4gCMJDoQBPEAThoVCAJwiC8FAowBMEQXgoFOAJgiA8lP8Pv+L6FLv6IsUAAAAASUVO\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7feff019bac8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## Surface plot\n",
"# http://blog.csdn.net/u011276025/article/details/60883114\n",
"plt.rcParams[\"figure.figsize\"] = [14.0, 8.0]\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"fig = plt.figure()\n",
"ax1 = fig.add_subplot(121, projection='3d')\n",
"ax1.plot_surface(X,Y,matcav,facecolor='blue')\n",
"ax1.invert_xaxis()\n",
"ax1.view_init(elev=15,azim=100)\n",
"ax1.grid('off')\n",
"ax1.set_xlabel('x')\n",
"ax1.set_ylabel('y')\n",
"ax1.set_zlabel('z')\n",
"# ax1.scatter3D(col,row,height,c='0',s=50)\n",
"\n",
"#ax2 = fig.add_subplot(122)\n",
"#ax2.contourf(X,Y,matcav)\n",
"#ax2.invert_yaxis()\n",
"#ax2.scatter(col,row,c='r')\n",
"# ax2.view_init(elev=30,azim=-45)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7fefea9dceb8>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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KU/2jFFgW4ZtuMg+sbtXg85l2DEoRvvnmxE4+Ti655BLmzJnDhAkTuPDCC/n222859zBV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\nO8+Dr67m5YJv3IDSuzcvPtYMW/Nt289HcnIyvvvuO5SXl8NoNGLz5s2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1dG5Xlpd11rVW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\n1P8DfrTnzSdZ56gAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fefeac2d6d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"ax1 = fig.add_subplot(121, projection='3d')\n",
"ax1.scatter3D(col,row,height,c='r')\n",
"# ax1.plot_trisurf(col,row,height+0.2)\n",
"ax1.view_init(elev=15,azim=90)\n",
"ax1.grid('off')\n",
"ax1.invert_xaxis()\n",
"ax1.set_xlabel('x')\n",
"ax1.set_ylabel('y')\n",
"ax1.set_zlabel('z')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from scipy.io import savemat"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"save_dict = {\"matcav\": matcav,\n",
" \"matbkp\": matbkg,\n",
" \"row\":row,\n",
" \"col\":col,\n",
" \"height\":height}\n",
"savemat(\"3d_demo.mat\",save_dict)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2+"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
QuLogic/folium | examples/VideoOverlayLayer.ipynb | 1 | 6474 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.5.0+27.g2d457b0.dirty\n"
]
}
],
"source": [
"import os\n",
"import folium\n",
"\n",
"print(folium.__version__)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"data:text/html;charset=utf-8;base64,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\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7fe7055c0d68>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"m = folium.Map(location=[22.5, -115], zoom_start=4)\n",
"\n",
"video = folium.raster_layers.VideoOverlay(\n",
" video_url='https://www.mapbox.com/bites/00188/patricia_nasa.webm',\n",
" bounds=[[32, -130], [13, -100]],\n",
" opacity=0.65,\n",
" attr='Video from patricia_nasa',\n",
" autoplay=True,\n",
" loop=False,\n",
")\n",
"\n",
"video.add_to(m)\n",
"\n",
"m.save(os.path.join('results', 'VideoOverlayLayer.html'))\n",
"\n",
"m"
]
}
],
"metadata": {
"_draft": {
"nbviewer_url": "https://gist.github.com/629da6f621481ed4d513258d2ec64589"
},
"gist": {
"data": {
"description": "folium video test",
"public": true
},
"id": "629da6f621481ed4d513258d2ec64589"
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
superbobry/pymc3 | pymc3/examples/profiling.ipynb | 1 | 20583 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Profiling\n",
"Sometimes computing the likelihood is not as fast as we would like. Theano provides handy profiling tools, which pymc3 provides a wrapper `model.profile` which returns a `ProfileStats` object. Here we'll profile the likelihood and gradient for the stochastic volatility example.\n",
"\n",
"First we build the model."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"from pymc3 import *\n",
"from pymc3.distributions.timeseries import *\n",
"\n",
"n = 400\n",
"returns = np.genfromtxt(get_data_file('pymc3.examples', \"data/SP500.csv\"))[-n:]\n",
"\n",
"model = Model()\n",
"with model:\n",
" sigma = Exponential('sigma', 1. / .02, testval=.1)\n",
"\n",
" nu = Exponential('nu', 1. / 10)\n",
"\n",
" s = GaussianRandomWalk('s', sigma ** -2, shape=n)\n",
"\n",
" r = StudentT('r', nu, lam=exp(-2 * s), observed=returns)\n",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then call profile and summarize it."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Function profiling\n",
"==================\n",
" Message: /Users/fonnescj/GitHub/pymc3/pymc3/model.py:197\n",
" Time in 1000 calls to Function.__call__: 6.456351e-02s\n",
" Time in Function.fn.__call__: 3.473592e-02s (53.801%)\n",
" Time in thunks: 2.467275e-02s (38.215%)\n",
" Total compile time: 3.600923e+00s\n",
" Number of Apply nodes: 26\n",
" Theano Optimizer time: 9.634581e-01s\n",
" Theano validate time: 2.970219e-03s\n",
" Theano Linker time (includes C, CUDA code generation/compiling): 2.587607e+00s\n",
" Import time 5.669570e-02s\n",
"\n",
"Time in all call to theano.grad() 2.964401e-02s\n",
"Class\n",
"---\n",
"<% time> <sum %> <apply time> <time per call> <type> <#call> <#apply> <Class name>\n",
" 85.4% 85.4% 0.021s 1.50e-06s C 14000 14 theano.tensor.elemwise.Elemwise\n",
" 5.8% 91.2% 0.001s 3.59e-07s C 4000 4 theano.tensor.elemwise.Sum\n",
" 3.0% 94.2% 0.001s 3.76e-07s C 2000 2 theano.tensor.subtensor.Subtensor\n",
" 2.4% 96.7% 0.001s 3.00e-07s C 2000 2 theano.tensor.elemwise.DimShuffle\n",
" 2.1% 98.7% 0.001s 2.54e-07s C 2000 2 theano.tensor.basic.Flatten\n",
" 1.3% 100.0% 0.000s 1.58e-07s C 2000 2 theano.compile.ops.ViewOp\n",
" ... (remaining 0 Classes account for 0.00%(0.00s) of the runtime)\n",
"\n",
"Ops\n",
"---\n",
"<% time> <sum %> <apply time> <time per call> <type> <#call> <#apply> <Op name>\n",
" 62.0% 62.0% 0.015s 1.53e-05s C 1000 1 Elemwise{Composite{Switch((GT(Composite{exp((i0 * i1))}(i0, i1), i2) * i3 * GT(inv(sqrt(Composite{exp((i0 * i1))}(i0, i1))), i2)), (((i4 + (i5 * log(((i6 * Composite{exp((i0 * i1))}(i0, i1)) / i7)))) - i8) - (i5 * i9 * log1p(((Composite{exp((i0 * i1))}(i0, i1) * i10) / i7)))), i11)}}\n",
" 6.0% 68.0% 0.001s 7.36e-07s C 2000 2 Elemwise{Composite{log(Abs(exp(i0)))}}\n",
" 5.8% 73.8% 0.001s 3.59e-07s C 4000 4 Sum{acc_dtype=float64}\n",
" 5.0% 78.8% 0.001s 1.23e-06s C 1000 1 Elemwise{Composite{Switch(i0, (i1 * ((-(i2 * sqr((i3 - i4)))) + i5)), i6)}}\n",
" 2.6% 81.3% 0.001s 3.18e-07s C 2000 2 Elemwise{exp,no_inplace}\n",
" 2.6% 83.9% 0.001s 3.17e-07s C 2000 2 Elemwise{Composite{scalar_gammaln((i0 * i1))}}\n",
" 2.4% 86.3% 0.001s 3.00e-07s C 2000 2 InplaceDimShuffle{x}\n",
" 2.1% 88.4% 0.001s 2.54e-07s C 2000 2 Flatten{1}\n",
" 2.0% 90.4% 0.000s 4.84e-07s C 1000 1 Elemwise{Composite{(Switch(GT(i0, i1), (i2 - (i3 * i0)), i4) + i5 + Switch(GT(i6, i1), (i7 - (i8 * i6)), i4) + i9 + i10 + i11)}}[(0, 0)]\n",
" 1.8% 92.2% 0.000s 4.42e-07s C 1000 1 Subtensor{int64::}\n",
" 1.3% 93.4% 0.000s 1.58e-07s C 2000 2 ViewOp\n",
" 1.3% 94.7% 0.000s 3.10e-07s C 1000 1 Subtensor{:int64:}\n",
" 1.2% 95.8% 0.000s 2.85e-07s C 1000 1 Elemwise{Composite{log((i0 * i1))}}\n",
" 1.1% 97.0% 0.000s 2.74e-07s C 1000 1 Elemwise{Composite{(GT(i0, i1) * GT(inv(sqrt(i0)), i1))}}\n",
" 1.1% 98.0% 0.000s 2.60e-07s C 1000 1 Elemwise{Composite{inv(sqr(i0))}}\n",
" 1.1% 99.1% 0.000s 2.60e-07s C 1000 1 Elemwise{add,no_inplace}\n",
" 0.9% 100.0% 0.000s 2.32e-07s C 1000 1 Elemwise{gt,no_inplace}\n",
" ... (remaining 0 Ops account for 0.00%(0.00s) of the runtime)\n",
"\n",
"Apply\n",
"------\n",
"<% time> <sum %> <apply time> <time per call> <#call> <id> <Apply name>\n",
" 62.0% 62.0% 0.015s 1.53e-05s 1000 22 Elemwise{Composite{Switch((GT(Composite{exp((i0 * i1))}(i0, i1), i2) * i3 * GT(inv(sqrt(Composite{exp((i0 * i1))}(i0, i1))), i2)), (((i4 + (i5 * log(((i6 * Composite{exp((i0 * i1))}(i0, i1)) / i7)))) - i8) - (i5 * i9 * log1p(((Composite{exp((i0 * i1))}(i0, i1) * i10) / i7)))), i11)}}(TensorConstant{(1,) of -2.0}, s, TensorConstant{(1,) of 0}, Elemwise{gt,no_inplace}.0, Elemwise{Composite{scalar_gammaln((i0 * i1))}}.0, TensorConstant{(1,) of 0.5}, Te\n",
" 5.0% 67.0% 0.001s 1.23e-06s 1000 21 Elemwise{Composite{Switch(i0, (i1 * ((-(i2 * sqr((i3 - i4)))) + i5)), i6)}}(Elemwise{Composite{(GT(i0, i1) * GT(inv(sqrt(i0)), i1))}}.0, TensorConstant{(1,) of 0.5}, Elemwise{Composite{inv(sqr(i0))}}.0, Subtensor{int64::}.0, Subtensor{:int64:}.0, Elemwise{Composite{log((i0 * i1))}}.0, TensorConstant{(1,) of -inf})\n",
" 5.0% 71.9% 0.001s 1.22e-06s 1000 7 Elemwise{Composite{log(Abs(exp(i0)))}}(Flatten{1}.0)\n",
" 2.3% 74.3% 0.001s 5.76e-07s 1000 24 Sum{acc_dtype=float64}(Elemwise{Composite{Switch((GT(Composite{exp((i0 * i1))}(i0, i1), i2) * i3 * GT(inv(sqrt(Composite{exp((i0 * i1))}(i0, i1))), i2)), (((i4 + (i5 * log(((i6 * Composite{exp((i0 * i1))}(i0, i1)) / i7)))) - i8) - (i5 * i9 * log1p(((Composite{exp((i0 * i1))}(i0, i1) * i10) / i7)))), i11)}}.0)\n",
" 2.3% 76.6% 0.001s 5.68e-07s 1000 23 Sum{acc_dtype=float64}(Elemwise{Composite{Switch(i0, (i1 * ((-(i2 * sqr((i3 - i4)))) + i5)), i6)}}.0)\n",
" 2.0% 78.5% 0.000s 4.84e-07s 1000 25 Elemwise{Composite{(Switch(GT(i0, i1), (i2 - (i3 * i0)), i4) + i5 + Switch(GT(i6, i1), (i7 - (i8 * i6)), i4) + i9 + i10 + i11)}}[(0, 0)](Elemwise{exp,no_inplace}.0, TensorConstant{0}, TensorConstant{3.9120230674743652}, TensorConstant{50.0}, TensorConstant{-inf}, Sum{acc_dtype=float64}.0, Elemwise{exp,no_inplace}.0, TensorConstant{-2.3025850929940455}, TensorConstant{0.1}, Sum{acc_dtype=float64}.0, Sum{acc_dtype=float64}.0, Sum{acc_dtype=float64}.0)\n",
" 1.8% 80.3% 0.000s 4.42e-07s 1000 5 Subtensor{int64::}(s, Constant{1})\n",
" 1.5% 81.8% 0.000s 3.74e-07s 1000 10 InplaceDimShuffle{x}(sigma)\n",
" 1.5% 83.3% 0.000s 3.60e-07s 1000 0 Elemwise{exp,no_inplace}(sigma_log)\n",
" 1.3% 84.6% 0.000s 3.18e-07s 1000 16 Elemwise{Composite{scalar_gammaln((i0 * i1))}}(TensorConstant{(1,) of 0.5}, InplaceDimShuffle{x}.0)\n",
" 1.3% 85.9% 0.000s 3.16e-07s 1000 20 Elemwise{Composite{scalar_gammaln((i0 * i1))}}(TensorConstant{(1,) of 0.5}, Elemwise{add,no_inplace}.0)\n",
" 1.3% 87.1% 0.000s 3.10e-07s 1000 4 Subtensor{:int64:}(s, Constant{-1})\n",
" 1.2% 88.3% 0.000s 3.03e-07s 1000 1 Flatten{1}(sigma_log)\n",
" 1.2% 89.5% 0.000s 2.85e-07s 1000 18 Elemwise{Composite{log((i0 * i1))}}(TensorConstant{(1,) of 0...9154943092}, Elemwise{Composite{inv(sqr(i0))}}.0)\n",
" 1.1% 90.6% 0.000s 2.77e-07s 1000 2 Elemwise{exp,no_inplace}(nu_log)\n",
" 1.1% 91.7% 0.000s 2.74e-07s 1000 19 Elemwise{Composite{(GT(i0, i1) * GT(inv(sqrt(i0)), i1))}}(Elemwise{Composite{inv(sqr(i0))}}.0, TensorConstant{(1,) of 0})\n",
" 1.1% 92.8% 0.000s 2.60e-07s 1000 15 Elemwise{add,no_inplace}(TensorConstant{(1,) of 1.0}, InplaceDimShuffle{x}.0)\n",
" 1.1% 93.8% 0.000s 2.60e-07s 1000 14 Elemwise{Composite{inv(sqr(i0))}}(InplaceDimShuffle{x}.0)\n",
" 1.0% 94.9% 0.000s 2.49e-07s 1000 9 Elemwise{Composite{log(Abs(exp(i0)))}}(Flatten{1}.0)\n",
" 0.9% 95.8% 0.000s 2.32e-07s 1000 17 Elemwise{gt,no_inplace}(InplaceDimShuffle{x}.0, TensorConstant{(1,) of 0})\n",
" ... (remaining 6 Apply instances account for 4.21%(0.00s) of the runtime)\n",
"\n"
]
}
],
"source": [
"model.profile(model.logpt).summary()\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Function profiling\n",
"==================\n",
" Message: /Users/fonnescj/GitHub/pymc3/pymc3/model.py:197\n",
" Time in 1000 calls to Function.__call__: 1.570358e-01s\n",
" Time in Function.fn.__call__: 1.222701e-01s (77.861%)\n",
" Time in thunks: 9.528661e-02s (60.678%)\n",
" Total compile time: 5.955295e+00s\n",
" Number of Apply nodes: 55\n",
" Theano Optimizer time: 8.904371e-01s\n",
" Theano validate time: 1.370955e-02s\n",
" Theano Linker time (includes C, CUDA code generation/compiling): 4.988965e+00s\n",
" Import time 1.071918e-01s\n",
"\n",
"Time in all call to theano.grad() 1.577589e+00s\n",
"Class\n",
"---\n",
"<% time> <sum %> <apply time> <time per call> <type> <#call> <#apply> <Class name>\n",
" 47.2% 47.2% 0.045s 2.25e-05s Py 2000 2 theano.tensor.basic.Split\n",
" 31.9% 79.1% 0.030s 1.12e-06s C 27000 27 theano.tensor.elemwise.Elemwise\n",
" 4.7% 83.8% 0.005s 6.45e-07s C 7000 7 theano.tensor.elemwise.Sum\n",
" 4.5% 88.3% 0.004s 2.14e-06s C 2000 2 theano.tensor.subtensor.IncSubtensor\n",
" 3.9% 92.3% 0.004s 3.76e-06s C 1000 1 theano.tensor.basic.Join\n",
" 2.6% 94.9% 0.002s 2.49e-06s C 1000 1 theano.tensor.basic.Alloc\n",
" 1.3% 96.2% 0.001s 2.55e-07s C 5000 5 theano.tensor.basic.Flatten\n",
" 1.2% 97.4% 0.001s 5.80e-07s C 2000 2 theano.tensor.subtensor.Subtensor\n",
" 0.9% 98.4% 0.001s 4.49e-07s C 2000 2 theano.tensor.basic.Reshape\n",
" 0.9% 99.3% 0.001s 2.94e-07s C 3000 3 theano.tensor.elemwise.DimShuffle\n",
" 0.4% 99.7% 0.000s 2.08e-07s C 2000 2 theano.compile.ops.ViewOp\n",
" 0.3% 100.0% 0.000s 2.51e-07s C 1000 1 theano.compile.ops.Shape_i\n",
" ... (remaining 0 Classes account for 0.00%(0.00s) of the runtime)\n",
"\n",
"Ops\n",
"---\n",
"<% time> <sum %> <apply time> <time per call> <type> <#call> <#apply> <Op name>\n",
" 47.2% 47.2% 0.045s 2.25e-05s Py 2000 2 Split{1}\n",
" 7.4% 54.6% 0.007s 7.04e-06s C 1000 1 Elemwise{Composite{Switch(i0, (-log1p((i1 / i2))), i3)}}\n",
" 4.7% 59.4% 0.005s 6.45e-07s C 7000 7 Sum{acc_dtype=float64}\n",
" 3.9% 63.3% 0.004s 3.76e-06s C 1000 1 Join\n",
" 3.1% 66.4% 0.003s 2.94e-06s C 1000 1 Elemwise{Composite{(GT(i0, i1) * i2 * GT(inv(sqrt(i0)), i1))}}\n",
" 3.1% 69.5% 0.003s 2.93e-06s C 1000 1 IncSubtensor{InplaceInc;int64::}\n",
" 3.0% 72.4% 0.003s 7.03e-07s C 4000 4 Elemwise{Switch}\n",
" 2.8% 75.2% 0.003s 2.65e-06s C 1000 1 Elemwise{Composite{Switch(i0, ((i1 * i2 * i3 * i4) / i5), i6)}}\n",
" 2.8% 78.0% 0.003s 2.64e-06s C 1000 1 Elemwise{Composite{(i0 * (Switch(i1, i2, i3) + Switch(i1, ((i4 * i5 * i6 * i7) / i8), i3)))}}[(0, 7)]\n",
" 2.6% 80.6% 0.002s 2.49e-06s C 1000 1 Alloc\n",
" 2.3% 82.9% 0.002s 2.22e-06s C 1000 1 Elemwise{Composite{exp((i0 * i1))}}\n",
" 1.4% 84.3% 0.001s 1.34e-06s C 1000 1 IncSubtensor{InplaceInc;:int64:}\n",
" 1.3% 85.7% 0.001s 2.55e-07s C 5000 5 Flatten{1}\n",
" 1.2% 86.8% 0.001s 1.13e-06s C 1000 1 Elemwise{Composite{Switch(i0, (i1 * sqr(i2)), i3)}}\n",
" 1.1% 88.0% 0.001s 1.09e-06s C 1000 1 Elemwise{Composite{((((i0 * i1 * scalar_psi((i0 * i2))) + (i3 * (i4 / sqr(i5))) + (i0 * i6 * scalar_psi((i0 * i5))) + (i0 * i7) + (i8 / i5)) * i9) + Switch(GT(i9, i10), (i11 * i9), i10) + (i12 * i9))}}[(0, 1)]\n",
" 1.1% 89.1% 0.001s 1.05e-06s C 1000 1 Elemwise{Composite{Switch(i0, (i1 * i2 * i3), i4)}}\n",
" 1.1% 90.2% 0.001s 5.16e-07s C 2000 2 Elemwise{mul,no_inplace}\n",
" 0.9% 91.1% 0.001s 4.49e-07s C 2000 2 Reshape{0}\n",
" 0.9% 92.0% 0.001s 2.94e-07s C 3000 3 InplaceDimShuffle{x}\n",
" 0.9% 93.0% 0.001s 4.41e-07s C 2000 2 Elemwise{exp,no_inplace}\n",
" ... (remaining 14 Ops account for 7.03%(0.01s) of the runtime)\n",
"\n",
"Apply\n",
"------\n",
"<% time> <sum %> <apply time> <time per call> <#call> <id> <Apply name>\n",
" 25.7% 25.7% 0.024s 2.45e-05s 1000 16 Split{1}(Elemwise{Composite{(sgn(exp(i0)) / Abs(exp(i0)))}}.0, TensorConstant{0}, TensorConstant{(1,) of 1})\n",
" 21.5% 47.2% 0.021s 2.05e-05s 1000 19 Split{1}(Elemwise{Composite{(sgn(exp(i0)) / Abs(exp(i0)))}}.0, TensorConstant{0}, TensorConstant{(1,) of 1})\n",
" 7.4% 54.6% 0.007s 7.04e-06s 1000 31 Elemwise{Composite{Switch(i0, (-log1p((i1 / i2))), i3)}}(Elemwise{Composite{(GT(i0, i1) * i2 * GT(inv(sqrt(i0)), i1))}}.0, Elemwise{mul,no_inplace}.0, InplaceDimShuffle{x}.0, TensorConstant{(1,) of 0})\n",
" 3.9% 58.6% 0.004s 3.76e-06s 1000 54 Join(TensorConstant{0}, Flatten{1}.0, Flatten{1}.0, Flatten{1}.0)\n",
" 3.1% 61.6% 0.003s 2.94e-06s 1000 27 Elemwise{Composite{(GT(i0, i1) * i2 * GT(inv(sqrt(i0)), i1))}}(Elemwise{Composite{exp((i0 * i1))}}.0, TensorConstant{(1,) of 0}, Elemwise{gt,no_inplace}.0)\n",
" 3.1% 64.7% 0.003s 2.93e-06s 1000 48 IncSubtensor{InplaceInc;int64::}(Elemwise{Composite{(i0 * (Switch(i1, i2, i3) + Switch(i1, ((i4 * i5 * i6 * i7) / i8), i3)))}}[(0, 7)].0, Elemwise{Composite{Switch(i0, (i1 * i2 * i3), i4)}}.0, Constant{1})\n",
" 2.8% 67.5% 0.003s 2.65e-06s 1000 43 Elemwise{Composite{Switch(i0, ((i1 * i2 * i3 * i4) / i5), i6)}}(Elemwise{Composite{(GT(i0, i1) * i2 * GT(inv(sqrt(i0)), i1))}}.0, TensorConstant{(1,) of 0.5}, InplaceDimShuffle{x}.0, Elemwise{Composite{exp((i0 * i1))}}.0, TensorConstant{[ 4.05769..48400e-06]}, Elemwise{Add}[(0, 1)].0, TensorConstant{(1,) of 0})\n",
" 2.8% 70.3% 0.003s 2.64e-06s 1000 45 Elemwise{Composite{(i0 * (Switch(i1, i2, i3) + Switch(i1, ((i4 * i5 * i6 * i7) / i8), i3)))}}[(0, 7)](TensorConstant{(1,) of -2.0}, Elemwise{Composite{(GT(i0, i1) * i2 * GT(inv(sqrt(i0)), i1))}}.0, TensorConstant{(1,) of 0.5}, TensorConstant{(1,) of 0}, TensorConstant{(1,) of -0.5}, InplaceDimShuffle{x}.0, TensorConstant{[ 4.05769..48400e-06]}, Elemwise{Composite{exp((i0 * i1))}}.0, Elemwise{Add}[(0, 1)].0)\n",
" 2.6% 72.9% 0.002s 2.49e-06s 1000 38 Alloc(Elemwise{Switch}.0, Elemwise{Composite{(i0 - Switch(LT(i1, i0), i1, i0))}}[(0, 0)].0)\n",
" 2.3% 75.2% 0.002s 2.22e-06s 1000 7 Elemwise{Composite{exp((i0 * i1))}}(TensorConstant{(1,) of -2.0}, s)\n",
" 1.4% 76.6% 0.001s 1.34e-06s 1000 51 IncSubtensor{InplaceInc;:int64:}(IncSubtensor{InplaceInc;int64::}.0, Elemwise{Composite{Switch(i0, (i1 * i2), i3)}}[(0, 2)].0, Constant{-1})\n",
" 1.2% 77.8% 0.001s 1.13e-06s 1000 29 Elemwise{Composite{Switch(i0, (i1 * sqr(i2)), i3)}}(Elemwise{Composite{(GT(i0, i1) * GT(inv(sqrt(i0)), i1))}}.0, TensorConstant{(1,) of 0.5}, Elemwise{sub,no_inplace}.0, TensorConstant{(1,) of 0})\n",
" 1.1% 79.0% 0.001s 1.09e-06s 1000 49 Elemwise{Composite{((((i0 * i1 * scalar_psi((i0 * i2))) + (i3 * (i4 / sqr(i5))) + (i0 * i6 * scalar_psi((i0 * i5))) + (i0 * i7) + (i8 / i5)) * i9) + Switch(GT(i9, i10), (i11 * i9), i10) + (i12 * i9))}}[(0, 1)](TensorConstant{0.5}, Sum{acc_dtype=float64}.0, Elemwise{add,no_inplace}.0, TensorConstant{3.141592653589793}, Sum{acc_dtype=float64}.0, nu, Sum{acc_dtype=float64}.0, Sum{acc_dtype=float64}.0, Sum{acc_dtype=float64}.0, Elemwise{exp,no_inplace}.\n",
" 1.1% 80.1% 0.001s 1.05e-06s 1000 28 Elemwise{Composite{Switch(i0, (i1 * i2 * i3), i4)}}(Elemwise{Composite{(GT(i0, i1) * GT(inv(sqrt(i0)), i1))}}.0, TensorConstant{(1,) of -1.0}, InplaceDimShuffle{x}.0, Elemwise{sub,no_inplace}.0, TensorConstant{(1,) of 0})\n",
" 1.0% 81.0% 0.001s 9.33e-07s 1000 34 Elemwise{Switch}(Elemwise{Composite{(GT(i0, i1) * i2 * GT(inv(sqrt(i0)), i1))}}.0, TensorConstant{(1,) of 1.0}, TensorConstant{(1,) of 0.0})\n",
" 0.9% 81.9% 0.001s 8.37e-07s 1000 33 Elemwise{switch,no_inplace}(Elemwise{Composite{(GT(i0, i1) * i2 * GT(inv(sqrt(i0)), i1))}}.0, Elemwise{mul,no_inplace}.0, TensorConstant{(1,) of 0})\n",
" 0.7% 82.7% 0.001s 7.03e-07s 1000 44 Sum{acc_dtype=float64}(Alloc.0)\n",
" 0.7% 83.4% 0.001s 6.97e-07s 1000 46 Sum{acc_dtype=float64}(Elemwise{Composite{Switch(i0, ((i1 * i2 * i3 * i4) / i5), i6)}}.0)\n",
" 0.7% 84.1% 0.001s 6.86e-07s 1000 39 Sum{acc_dtype=float64}(Elemwise{Composite{Switch(i0, (-log1p((i1 / i2))), i3)}}.0)\n",
" 0.7% 84.8% 0.001s 6.59e-07s 1000 42 Sum{acc_dtype=float64}(Elemwise{Switch}.0)\n",
" ... (remaining 35 Apply instances account for 15.20%(0.01s) of the runtime)\n",
"\n"
]
}
],
"source": [
"model.profile(gradient(model.logpt, model.vars)).summary()"
]
}
],
"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"
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"nbformat_minor": 0
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| apache-2.0 |
ES-DOC/esdoc-jupyterhub | notebooks/nuist/cmip6/models/sandbox-3/atmos.ipynb | 1 | 208999 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"source": [
"# ES-DOC CMIP6 Model Properties - Atmos \n",
"**MIP Era**: CMIP6 \n",
"**Institute**: NUIST \n",
"**Source ID**: SANDBOX-3 \n",
"**Topic**: Atmos \n",
"**Sub-Topics**: Dynamical Core, Radiation, Turbulence Convection, Microphysics Precipitation, Cloud Scheme, Observation Simulation, Gravity Waves, Solar, Volcanos. \n",
"**Properties**: 156 (127 required) \n",
"**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/atmos?client=jupyter-notebook) \n",
"**Initialized From**: -- \n",
"\n",
"**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n",
"**Notebook Initialised**: 2018-02-15 16:54:34"
],
"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', 'nuist', 'sandbox-3', 'atmos')"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Authors \n",
"*Set document authors*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set as follows: DOC.set_author(\"name\", \"email\") \n",
"# TODO - please enter value(s)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Contributors \n",
"*Specify document contributors* "
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set as follows: DOC.set_contributor(\"name\", \"email\") \n",
"# TODO - please enter value(s)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Publication \n",
"*Specify document publication status* "
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set publication status: \n",
"# 0=do not publish, 1=publish. \n",
"DOC.set_publication_status(0)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Table of Contents \n",
"[1. Key Properties --> Overview](#1.-Key-Properties--->-Overview) \n",
"[2. Key Properties --> Resolution](#2.-Key-Properties--->-Resolution) \n",
"[3. Key Properties --> Timestepping](#3.-Key-Properties--->-Timestepping) \n",
"[4. Key Properties --> Orography](#4.-Key-Properties--->-Orography) \n",
"[5. Grid --> Discretisation](#5.-Grid--->-Discretisation) \n",
"[6. Grid --> Discretisation --> Horizontal](#6.-Grid--->-Discretisation--->-Horizontal) \n",
"[7. Grid --> Discretisation --> Vertical](#7.-Grid--->-Discretisation--->-Vertical) \n",
"[8. Dynamical Core](#8.-Dynamical-Core) \n",
"[9. Dynamical Core --> Top Boundary](#9.-Dynamical-Core--->-Top-Boundary) \n",
"[10. Dynamical Core --> Lateral Boundary](#10.-Dynamical-Core--->-Lateral-Boundary) \n",
"[11. Dynamical Core --> Diffusion Horizontal](#11.-Dynamical-Core--->-Diffusion-Horizontal) \n",
"[12. Dynamical Core --> Advection Tracers](#12.-Dynamical-Core--->-Advection-Tracers) \n",
"[13. Dynamical Core --> Advection Momentum](#13.-Dynamical-Core--->-Advection-Momentum) \n",
"[14. Radiation](#14.-Radiation) \n",
"[15. Radiation --> Shortwave Radiation](#15.-Radiation--->-Shortwave-Radiation) \n",
"[16. Radiation --> Shortwave GHG](#16.-Radiation--->-Shortwave-GHG) \n",
"[17. Radiation --> Shortwave Cloud Ice](#17.-Radiation--->-Shortwave-Cloud-Ice) \n",
"[18. Radiation --> Shortwave Cloud Liquid](#18.-Radiation--->-Shortwave-Cloud-Liquid) \n",
"[19. Radiation --> Shortwave Cloud Inhomogeneity](#19.-Radiation--->-Shortwave-Cloud-Inhomogeneity) \n",
"[20. Radiation --> Shortwave Aerosols](#20.-Radiation--->-Shortwave-Aerosols) \n",
"[21. Radiation --> Shortwave Gases](#21.-Radiation--->-Shortwave-Gases) \n",
"[22. Radiation --> Longwave Radiation](#22.-Radiation--->-Longwave-Radiation) \n",
"[23. Radiation --> Longwave GHG](#23.-Radiation--->-Longwave-GHG) \n",
"[24. Radiation --> Longwave Cloud Ice](#24.-Radiation--->-Longwave-Cloud-Ice) \n",
"[25. Radiation --> Longwave Cloud Liquid](#25.-Radiation--->-Longwave-Cloud-Liquid) \n",
"[26. Radiation --> Longwave Cloud Inhomogeneity](#26.-Radiation--->-Longwave-Cloud-Inhomogeneity) \n",
"[27. Radiation --> Longwave Aerosols](#27.-Radiation--->-Longwave-Aerosols) \n",
"[28. Radiation --> Longwave Gases](#28.-Radiation--->-Longwave-Gases) \n",
"[29. Turbulence Convection](#29.-Turbulence-Convection) \n",
"[30. Turbulence Convection --> Boundary Layer Turbulence](#30.-Turbulence-Convection--->-Boundary-Layer-Turbulence) \n",
"[31. Turbulence Convection --> Deep Convection](#31.-Turbulence-Convection--->-Deep-Convection) \n",
"[32. Turbulence Convection --> Shallow Convection](#32.-Turbulence-Convection--->-Shallow-Convection) \n",
"[33. Microphysics Precipitation](#33.-Microphysics-Precipitation) \n",
"[34. Microphysics Precipitation --> Large Scale Precipitation](#34.-Microphysics-Precipitation--->-Large-Scale-Precipitation) \n",
"[35. Microphysics Precipitation --> Large Scale Cloud Microphysics](#35.-Microphysics-Precipitation--->-Large-Scale-Cloud-Microphysics) \n",
"[36. Cloud Scheme](#36.-Cloud-Scheme) \n",
"[37. Cloud Scheme --> Optical Cloud Properties](#37.-Cloud-Scheme--->-Optical-Cloud-Properties) \n",
"[38. Cloud Scheme --> Sub Grid Scale Water Distribution](#38.-Cloud-Scheme--->-Sub-Grid-Scale-Water-Distribution) \n",
"[39. Cloud Scheme --> Sub Grid Scale Ice Distribution](#39.-Cloud-Scheme--->-Sub-Grid-Scale-Ice-Distribution) \n",
"[40. Observation Simulation](#40.-Observation-Simulation) \n",
"[41. Observation Simulation --> Isscp Attributes](#41.-Observation-Simulation--->-Isscp-Attributes) \n",
"[42. Observation Simulation --> Cosp Attributes](#42.-Observation-Simulation--->-Cosp-Attributes) \n",
"[43. Observation Simulation --> Radar Inputs](#43.-Observation-Simulation--->-Radar-Inputs) \n",
"[44. Observation Simulation --> Lidar Inputs](#44.-Observation-Simulation--->-Lidar-Inputs) \n",
"[45. Gravity Waves](#45.-Gravity-Waves) \n",
"[46. Gravity Waves --> Orographic Gravity Waves](#46.-Gravity-Waves--->-Orographic-Gravity-Waves) \n",
"[47. Gravity Waves --> Non Orographic Gravity Waves](#47.-Gravity-Waves--->-Non-Orographic-Gravity-Waves) \n",
"[48. Solar](#48.-Solar) \n",
"[49. Solar --> Solar Pathways](#49.-Solar--->-Solar-Pathways) \n",
"[50. Solar --> Solar Constant](#50.-Solar--->-Solar-Constant) \n",
"[51. Solar --> Orbital Parameters](#51.-Solar--->-Orbital-Parameters) \n",
"[52. Solar --> Insolation Ozone](#52.-Solar--->-Insolation-Ozone) \n",
"[53. Volcanos](#53.-Volcanos) \n",
"[54. Volcanos --> Volcanoes Treatment](#54.-Volcanos--->-Volcanoes-Treatment) \n",
"\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"# 1. Key Properties --> Overview \n",
"*Top level key properties*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 1.1. Model Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of atmosphere model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.overview.model_overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.2. Model Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Name of atmosphere model code (CAM 4.0, ARPEGE 3.2,...)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.overview.model_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.3. Model Family\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of atmospheric model.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.overview.model_family') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"AGCM\" \n",
"# \"ARCM\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.4. Basic Approximations\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Basic approximations made in the atmosphere.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.overview.basic_approximations') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"primitive equations\" \n",
"# \"non-hydrostatic\" \n",
"# \"anelastic\" \n",
"# \"Boussinesq\" \n",
"# \"hydrostatic\" \n",
"# \"quasi-hydrostatic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 2. Key Properties --> Resolution \n",
"*Characteristics of the model resolution*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 2.1. Horizontal Resolution Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *This is a string usually used by the modelling group to describe the resolution of the model grid, e.g. T42, N48.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.resolution.horizontal_resolution_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.2. Canonical Horizontal Resolution\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Expression quoted for gross comparisons of resolution, e.g. 2.5 x 3.75 degrees lat-lon.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.resolution.canonical_horizontal_resolution') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.3. Range Horizontal Resolution\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Range of horizontal resolution with spatial details, eg. 1 deg (Equator) - 0.5 deg*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.resolution.range_horizontal_resolution') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.4. Number Of Vertical Levels\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Number of vertical levels resolved on the computational grid.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.resolution.number_of_vertical_levels') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.5. High Top\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Does the atmosphere have a high-top? High-Top atmospheres have a fully resolved stratosphere with a model top above the stratopause.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.resolution.high_top') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 3. Key Properties --> Timestepping \n",
"*Characteristics of the atmosphere model time stepping*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 3.1. Timestep Dynamics\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Timestep for the dynamics, e.g. 30 min.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_dynamics') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.2. Timestep Shortwave Radiative Transfer\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Timestep for the shortwave radiative transfer, e.g. 1.5 hours.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_shortwave_radiative_transfer') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.3. Timestep Longwave Radiative Transfer\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Timestep for the longwave radiative transfer, e.g. 3 hours.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_longwave_radiative_transfer') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 4. Key Properties --> Orography \n",
"*Characteristics of the model orography*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 4.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Time adaptation of the orography.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.orography.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"present day\" \n",
"# \"modified\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.2. Changes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *If the orography type is modified describe the time adaptation changes.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.orography.changes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"related to ice sheets\" \n",
"# \"related to tectonics\" \n",
"# \"modified mean\" \n",
"# \"modified variance if taken into account in model (cf gravity waves)\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 5. Grid --> Discretisation \n",
"*Atmosphere grid discretisation*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 5.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of grid discretisation in the atmosphere*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.grid.discretisation.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 6. Grid --> Discretisation --> Horizontal \n",
"*Atmosphere discretisation in the horizontal*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 6.1. Scheme Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Horizontal discretisation type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"spectral\" \n",
"# \"fixed grid\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.2. Scheme Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Horizontal discretisation method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"finite elements\" \n",
"# \"finite volumes\" \n",
"# \"finite difference\" \n",
"# \"centered finite difference\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.3. Scheme Order\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Horizontal discretisation function order*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_order') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"second\" \n",
"# \"third\" \n",
"# \"fourth\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.4. Horizontal Pole\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Horizontal discretisation pole singularity treatment*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.horizontal_pole') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"filter\" \n",
"# \"pole rotation\" \n",
"# \"artificial island\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.5. Grid Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Horizontal grid type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.grid_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Gaussian\" \n",
"# \"Latitude-Longitude\" \n",
"# \"Cubed-Sphere\" \n",
"# \"Icosahedral\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 7. Grid --> Discretisation --> Vertical \n",
"*Atmosphere discretisation in the vertical*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 7.1. Coordinate Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Type of vertical coordinate system*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.grid.discretisation.vertical.coordinate_type') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"isobaric\" \n",
"# \"sigma\" \n",
"# \"hybrid sigma-pressure\" \n",
"# \"hybrid pressure\" \n",
"# \"vertically lagrangian\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 8. Dynamical Core \n",
"*Characteristics of the dynamical core*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 8.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of atmosphere dynamical core*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.2. Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Commonly used name for the dynamical core of the model.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.3. Timestepping Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Timestepping framework type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.timestepping_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Adams-Bashforth\" \n",
"# \"explicit\" \n",
"# \"implicit\" \n",
"# \"semi-implicit\" \n",
"# \"leap frog\" \n",
"# \"multi-step\" \n",
"# \"Runge Kutta fifth order\" \n",
"# \"Runge Kutta second order\" \n",
"# \"Runge Kutta third order\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.4. Prognostic Variables\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *List of the model prognostic variables*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.prognostic_variables') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"surface pressure\" \n",
"# \"wind components\" \n",
"# \"divergence/curl\" \n",
"# \"temperature\" \n",
"# \"potential temperature\" \n",
"# \"total water\" \n",
"# \"water vapour\" \n",
"# \"water liquid\" \n",
"# \"water ice\" \n",
"# \"total water moments\" \n",
"# \"clouds\" \n",
"# \"radiation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 9. Dynamical Core --> Top Boundary \n",
"*Type of boundary layer at the top of the model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 9.1. Top Boundary Condition\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Top boundary condition*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_boundary_condition') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"sponge layer\" \n",
"# \"radiation boundary condition\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 9.2. Top Heat\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Top boundary heat treatment*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_heat') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 9.3. Top Wind\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Top boundary wind treatment*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_wind') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 10. Dynamical Core --> Lateral Boundary \n",
"*Type of lateral boundary condition (if the model is a regional model)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 10.1. Condition\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Type of lateral boundary condition*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.lateral_boundary.condition') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"sponge layer\" \n",
"# \"radiation boundary condition\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 11. Dynamical Core --> Diffusion Horizontal \n",
"*Horizontal diffusion scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 11.1. Scheme Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Horizontal diffusion scheme name*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.diffusion_horizontal.scheme_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 11.2. Scheme Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Horizontal diffusion scheme method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.diffusion_horizontal.scheme_method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"iterated Laplacian\" \n",
"# \"bi-harmonic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 12. Dynamical Core --> Advection Tracers \n",
"*Tracer advection scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 12.1. Scheme Name\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Tracer advection scheme name*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.scheme_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Heun\" \n",
"# \"Roe and VanLeer\" \n",
"# \"Roe and Superbee\" \n",
"# \"Prather\" \n",
"# \"UTOPIA\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 12.2. Scheme Characteristics\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Tracer advection scheme characteristics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.scheme_characteristics') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Eulerian\" \n",
"# \"modified Euler\" \n",
"# \"Lagrangian\" \n",
"# \"semi-Lagrangian\" \n",
"# \"cubic semi-Lagrangian\" \n",
"# \"quintic semi-Lagrangian\" \n",
"# \"mass-conserving\" \n",
"# \"finite volume\" \n",
"# \"flux-corrected\" \n",
"# \"linear\" \n",
"# \"quadratic\" \n",
"# \"quartic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 12.3. Conserved Quantities\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Tracer advection scheme conserved quantities*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.conserved_quantities') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"dry mass\" \n",
"# \"tracer mass\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 12.4. Conservation Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Tracer advection scheme conservation method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.conservation_method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"conservation fixer\" \n",
"# \"Priestley algorithm\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 13. Dynamical Core --> Advection Momentum \n",
"*Momentum advection scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 13.1. Scheme Name\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Momentum advection schemes name*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"VanLeer\" \n",
"# \"Janjic\" \n",
"# \"SUPG (Streamline Upwind Petrov-Galerkin)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.2. Scheme Characteristics\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Momentum advection scheme characteristics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_characteristics') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"2nd order\" \n",
"# \"4th order\" \n",
"# \"cell-centred\" \n",
"# \"staggered grid\" \n",
"# \"semi-staggered grid\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.3. Scheme Staggering Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Momentum advection scheme staggering type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_staggering_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Arakawa B-grid\" \n",
"# \"Arakawa C-grid\" \n",
"# \"Arakawa D-grid\" \n",
"# \"Arakawa E-grid\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.4. Conserved Quantities\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Momentum advection scheme conserved quantities*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.conserved_quantities') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Angular momentum\" \n",
"# \"Horizontal momentum\" \n",
"# \"Enstrophy\" \n",
"# \"Mass\" \n",
"# \"Total energy\" \n",
"# \"Vorticity\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.5. Conservation Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Momentum advection scheme conservation method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.conservation_method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"conservation fixer\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 14. Radiation \n",
"*Characteristics of the atmosphere radiation process*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 14.1. Aerosols\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Aerosols whose radiative effect is taken into account in the atmosphere model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.aerosols') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"sulphate\" \n",
"# \"nitrate\" \n",
"# \"sea salt\" \n",
"# \"dust\" \n",
"# \"ice\" \n",
"# \"organic\" \n",
"# \"BC (black carbon / soot)\" \n",
"# \"SOA (secondary organic aerosols)\" \n",
"# \"POM (particulate organic matter)\" \n",
"# \"polar stratospheric ice\" \n",
"# \"NAT (nitric acid trihydrate)\" \n",
"# \"NAD (nitric acid dihydrate)\" \n",
"# \"STS (supercooled ternary solution aerosol particle)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 15. Radiation --> Shortwave Radiation \n",
"*Properties of the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 15.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of shortwave radiation in the atmosphere*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.2. Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Commonly used name for the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.3. Spectral Integration\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Shortwave radiation scheme spectral integration*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.spectral_integration') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"wide-band model\" \n",
"# \"correlated-k\" \n",
"# \"exponential sum fitting\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.4. Transport Calculation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Shortwave radiation transport calculation methods*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.transport_calculation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"two-stream\" \n",
"# \"layer interaction\" \n",
"# \"bulk\" \n",
"# \"adaptive\" \n",
"# \"multi-stream\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.5. Spectral Intervals\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Shortwave radiation scheme number of spectral intervals*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.spectral_intervals') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 16. Radiation --> Shortwave GHG \n",
"*Representation of greenhouse gases in the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 16.1. Greenhouse Gas Complexity\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Complexity of greenhouse gases whose shortwave radiative effects are taken into account in the atmosphere model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.greenhouse_gas_complexity') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"CO2\" \n",
"# \"CH4\" \n",
"# \"N2O\" \n",
"# \"CFC-11 eq\" \n",
"# \"CFC-12 eq\" \n",
"# \"HFC-134a eq\" \n",
"# \"Explicit ODSs\" \n",
"# \"Explicit other fluorinated gases\" \n",
"# \"O3\" \n",
"# \"H2O\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 16.2. ODS\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Ozone depleting substances whose shortwave radiative effects are explicitly taken into account in the atmosphere model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.ODS') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"CFC-12\" \n",
"# \"CFC-11\" \n",
"# \"CFC-113\" \n",
"# \"CFC-114\" \n",
"# \"CFC-115\" \n",
"# \"HCFC-22\" \n",
"# \"HCFC-141b\" \n",
"# \"HCFC-142b\" \n",
"# \"Halon-1211\" \n",
"# \"Halon-1301\" \n",
"# \"Halon-2402\" \n",
"# \"methyl chloroform\" \n",
"# \"carbon tetrachloride\" \n",
"# \"methyl chloride\" \n",
"# \"methylene chloride\" \n",
"# \"chloroform\" \n",
"# \"methyl bromide\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 16.3. Other Flourinated Gases\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Other flourinated gases whose shortwave radiative effects are explicitly taken into account in the atmosphere model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.other_flourinated_gases') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"HFC-134a\" \n",
"# \"HFC-23\" \n",
"# \"HFC-32\" \n",
"# \"HFC-125\" \n",
"# \"HFC-143a\" \n",
"# \"HFC-152a\" \n",
"# \"HFC-227ea\" \n",
"# \"HFC-236fa\" \n",
"# \"HFC-245fa\" \n",
"# \"HFC-365mfc\" \n",
"# \"HFC-43-10mee\" \n",
"# \"CF4\" \n",
"# \"C2F6\" \n",
"# \"C3F8\" \n",
"# \"C4F10\" \n",
"# \"C5F12\" \n",
"# \"C6F14\" \n",
"# \"C7F16\" \n",
"# \"C8F18\" \n",
"# \"c-C4F8\" \n",
"# \"NF3\" \n",
"# \"SF6\" \n",
"# \"SO2F2\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 17. Radiation --> Shortwave Cloud Ice \n",
"*Shortwave radiative properties of ice crystals in clouds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 17.1. General Interactions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *General shortwave radiative interactions with cloud ice crystals*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.general_interactions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"scattering\" \n",
"# \"emission/absorption\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.2. Physical Representation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Physical representation of cloud ice crystals in the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.physical_representation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"bi-modal size distribution\" \n",
"# \"ensemble of ice crystals\" \n",
"# \"mean projected area\" \n",
"# \"ice water path\" \n",
"# \"crystal asymmetry\" \n",
"# \"crystal aspect ratio\" \n",
"# \"effective crystal radius\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.3. Optical Methods\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Optical methods applicable to cloud ice crystals in the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.optical_methods') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"T-matrix\" \n",
"# \"geometric optics\" \n",
"# \"finite difference time domain (FDTD)\" \n",
"# \"Mie theory\" \n",
"# \"anomalous diffraction approximation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 18. Radiation --> Shortwave Cloud Liquid \n",
"*Shortwave radiative properties of liquid droplets in clouds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 18.1. General Interactions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *General shortwave radiative interactions with cloud liquid droplets*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.general_interactions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"scattering\" \n",
"# \"emission/absorption\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 18.2. Physical Representation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Physical representation of cloud liquid droplets in the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.physical_representation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"cloud droplet number concentration\" \n",
"# \"effective cloud droplet radii\" \n",
"# \"droplet size distribution\" \n",
"# \"liquid water path\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 18.3. Optical Methods\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Optical methods applicable to cloud liquid droplets in the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.optical_methods') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"geometric optics\" \n",
"# \"Mie theory\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 19. Radiation --> Shortwave Cloud Inhomogeneity \n",
"*Cloud inhomogeneity in the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 19.1. Cloud Inhomogeneity\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Method for taking into account horizontal cloud inhomogeneity*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_inhomogeneity.cloud_inhomogeneity') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Monte Carlo Independent Column Approximation\" \n",
"# \"Triplecloud\" \n",
"# \"analytic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 20. Radiation --> Shortwave Aerosols \n",
"*Shortwave radiative properties of aerosols*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 20.1. General Interactions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *General shortwave radiative interactions with aerosols*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.general_interactions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"scattering\" \n",
"# \"emission/absorption\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 20.2. Physical Representation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Physical representation of aerosols in the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.physical_representation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"number concentration\" \n",
"# \"effective radii\" \n",
"# \"size distribution\" \n",
"# \"asymmetry\" \n",
"# \"aspect ratio\" \n",
"# \"mixing state\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 20.3. Optical Methods\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Optical methods applicable to aerosols in the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.optical_methods') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"T-matrix\" \n",
"# \"geometric optics\" \n",
"# \"finite difference time domain (FDTD)\" \n",
"# \"Mie theory\" \n",
"# \"anomalous diffraction approximation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 21. Radiation --> Shortwave Gases \n",
"*Shortwave radiative properties of gases*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 21.1. General Interactions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *General shortwave radiative interactions with gases*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_gases.general_interactions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"scattering\" \n",
"# \"emission/absorption\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 22. Radiation --> Longwave Radiation \n",
"*Properties of the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 22.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of longwave radiation in the atmosphere*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_radiation.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 22.2. Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Commonly used name for the longwave radiation scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_radiation.name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 22.3. Spectral Integration\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Longwave radiation scheme spectral integration*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_radiation.spectral_integration') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"wide-band model\" \n",
"# \"correlated-k\" \n",
"# \"exponential sum fitting\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 22.4. Transport Calculation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Longwave radiation transport calculation methods*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_radiation.transport_calculation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"two-stream\" \n",
"# \"layer interaction\" \n",
"# \"bulk\" \n",
"# \"adaptive\" \n",
"# \"multi-stream\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 22.5. Spectral Intervals\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Longwave radiation scheme number of spectral intervals*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_radiation.spectral_intervals') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 23. Radiation --> Longwave GHG \n",
"*Representation of greenhouse gases in the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 23.1. Greenhouse Gas Complexity\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Complexity of greenhouse gases whose longwave radiative effects are taken into account in the atmosphere model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_GHG.greenhouse_gas_complexity') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"CO2\" \n",
"# \"CH4\" \n",
"# \"N2O\" \n",
"# \"CFC-11 eq\" \n",
"# \"CFC-12 eq\" \n",
"# \"HFC-134a eq\" \n",
"# \"Explicit ODSs\" \n",
"# \"Explicit other fluorinated gases\" \n",
"# \"O3\" \n",
"# \"H2O\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 23.2. ODS\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Ozone depleting substances whose longwave radiative effects are explicitly taken into account in the atmosphere model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_GHG.ODS') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"CFC-12\" \n",
"# \"CFC-11\" \n",
"# \"CFC-113\" \n",
"# \"CFC-114\" \n",
"# \"CFC-115\" \n",
"# \"HCFC-22\" \n",
"# \"HCFC-141b\" \n",
"# \"HCFC-142b\" \n",
"# \"Halon-1211\" \n",
"# \"Halon-1301\" \n",
"# \"Halon-2402\" \n",
"# \"methyl chloroform\" \n",
"# \"carbon tetrachloride\" \n",
"# \"methyl chloride\" \n",
"# \"methylene chloride\" \n",
"# \"chloroform\" \n",
"# \"methyl bromide\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 23.3. Other Flourinated Gases\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Other flourinated gases whose longwave radiative effects are explicitly taken into account in the atmosphere model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_GHG.other_flourinated_gases') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"HFC-134a\" \n",
"# \"HFC-23\" \n",
"# \"HFC-32\" \n",
"# \"HFC-125\" \n",
"# \"HFC-143a\" \n",
"# \"HFC-152a\" \n",
"# \"HFC-227ea\" \n",
"# \"HFC-236fa\" \n",
"# \"HFC-245fa\" \n",
"# \"HFC-365mfc\" \n",
"# \"HFC-43-10mee\" \n",
"# \"CF4\" \n",
"# \"C2F6\" \n",
"# \"C3F8\" \n",
"# \"C4F10\" \n",
"# \"C5F12\" \n",
"# \"C6F14\" \n",
"# \"C7F16\" \n",
"# \"C8F18\" \n",
"# \"c-C4F8\" \n",
"# \"NF3\" \n",
"# \"SF6\" \n",
"# \"SO2F2\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 24. Radiation --> Longwave Cloud Ice \n",
"*Longwave radiative properties of ice crystals in clouds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 24.1. General Interactions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *General longwave radiative interactions with cloud ice crystals*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.general_interactions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"scattering\" \n",
"# \"emission/absorption\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 24.2. Physical Reprenstation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Physical representation of cloud ice crystals in the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.physical_reprenstation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"bi-modal size distribution\" \n",
"# \"ensemble of ice crystals\" \n",
"# \"mean projected area\" \n",
"# \"ice water path\" \n",
"# \"crystal asymmetry\" \n",
"# \"crystal aspect ratio\" \n",
"# \"effective crystal radius\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 24.3. Optical Methods\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Optical methods applicable to cloud ice crystals in the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.optical_methods') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"T-matrix\" \n",
"# \"geometric optics\" \n",
"# \"finite difference time domain (FDTD)\" \n",
"# \"Mie theory\" \n",
"# \"anomalous diffraction approximation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 25. Radiation --> Longwave Cloud Liquid \n",
"*Longwave radiative properties of liquid droplets in clouds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 25.1. General Interactions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *General longwave radiative interactions with cloud liquid droplets*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.general_interactions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"scattering\" \n",
"# \"emission/absorption\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 25.2. Physical Representation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Physical representation of cloud liquid droplets in the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.physical_representation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"cloud droplet number concentration\" \n",
"# \"effective cloud droplet radii\" \n",
"# \"droplet size distribution\" \n",
"# \"liquid water path\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 25.3. Optical Methods\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Optical methods applicable to cloud liquid droplets in the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.optical_methods') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"geometric optics\" \n",
"# \"Mie theory\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 26. Radiation --> Longwave Cloud Inhomogeneity \n",
"*Cloud inhomogeneity in the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 26.1. Cloud Inhomogeneity\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Method for taking into account horizontal cloud inhomogeneity*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_inhomogeneity.cloud_inhomogeneity') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Monte Carlo Independent Column Approximation\" \n",
"# \"Triplecloud\" \n",
"# \"analytic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 27. Radiation --> Longwave Aerosols \n",
"*Longwave radiative properties of aerosols*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 27.1. General Interactions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *General longwave radiative interactions with aerosols*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.general_interactions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"scattering\" \n",
"# \"emission/absorption\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 27.2. Physical Representation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Physical representation of aerosols in the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.physical_representation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"number concentration\" \n",
"# \"effective radii\" \n",
"# \"size distribution\" \n",
"# \"asymmetry\" \n",
"# \"aspect ratio\" \n",
"# \"mixing state\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 27.3. Optical Methods\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Optical methods applicable to aerosols in the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.optical_methods') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"T-matrix\" \n",
"# \"geometric optics\" \n",
"# \"finite difference time domain (FDTD)\" \n",
"# \"Mie theory\" \n",
"# \"anomalous diffraction approximation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 28. Radiation --> Longwave Gases \n",
"*Longwave radiative properties of gases*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 28.1. General Interactions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *General longwave radiative interactions with gases*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_gases.general_interactions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"scattering\" \n",
"# \"emission/absorption\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 29. Turbulence Convection \n",
"*Atmosphere Convective Turbulence and Clouds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 29.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of atmosphere convection and turbulence*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 30. Turbulence Convection --> Boundary Layer Turbulence \n",
"*Properties of the boundary layer turbulence scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 30.1. Scheme Name\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Boundary layer turbulence scheme name*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.scheme_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Mellor-Yamada\" \n",
"# \"Holtslag-Boville\" \n",
"# \"EDMF\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.2. Scheme Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Boundary layer turbulence scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.scheme_type') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"TKE prognostic\" \n",
"# \"TKE diagnostic\" \n",
"# \"TKE coupled with water\" \n",
"# \"vertical profile of Kz\" \n",
"# \"non-local diffusion\" \n",
"# \"Monin-Obukhov similarity\" \n",
"# \"Coastal Buddy Scheme\" \n",
"# \"Coupled with convection\" \n",
"# \"Coupled with gravity waves\" \n",
"# \"Depth capped at cloud base\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.3. Closure Order\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Boundary layer turbulence scheme closure order*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.closure_order') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.4. Counter Gradient\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Uses boundary layer turbulence scheme counter gradient*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.counter_gradient') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 31. Turbulence Convection --> Deep Convection \n",
"*Properties of the deep convection scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 31.1. Scheme Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Deep convection scheme name*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 31.2. Scheme Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Deep convection scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_type') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"mass-flux\" \n",
"# \"adjustment\" \n",
"# \"plume ensemble\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 31.3. Scheme Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Deep convection scheme method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_method') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"CAPE\" \n",
"# \"bulk\" \n",
"# \"ensemble\" \n",
"# \"CAPE/WFN based\" \n",
"# \"TKE/CIN based\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 31.4. Processes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Physical processes taken into account in the parameterisation of deep convection*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.processes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"vertical momentum transport\" \n",
"# \"convective momentum transport\" \n",
"# \"entrainment\" \n",
"# \"detrainment\" \n",
"# \"penetrative convection\" \n",
"# \"updrafts\" \n",
"# \"downdrafts\" \n",
"# \"radiative effect of anvils\" \n",
"# \"re-evaporation of convective precipitation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 31.5. Microphysics\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Microphysics scheme for deep convection. Microphysical processes directly control the amount of detrainment of cloud hydrometeor and water vapor from updrafts*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.microphysics') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"tuning parameter based\" \n",
"# \"single moment\" \n",
"# \"two moment\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 32. Turbulence Convection --> Shallow Convection \n",
"*Properties of the shallow convection scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 32.1. Scheme Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Shallow convection scheme name*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 32.2. Scheme Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *shallow convection scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_type') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"mass-flux\" \n",
"# \"cumulus-capped boundary layer\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 32.3. Scheme Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *shallow convection scheme method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"same as deep (unified)\" \n",
"# \"included in boundary layer turbulence\" \n",
"# \"separate diagnosis\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 32.4. Processes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Physical processes taken into account in the parameterisation of shallow convection*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.processes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"convective momentum transport\" \n",
"# \"entrainment\" \n",
"# \"detrainment\" \n",
"# \"penetrative convection\" \n",
"# \"re-evaporation of convective precipitation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 32.5. Microphysics\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Microphysics scheme for shallow convection*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.microphysics') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"tuning parameter based\" \n",
"# \"single moment\" \n",
"# \"two moment\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 33. Microphysics Precipitation \n",
"*Large Scale Cloud Microphysics and Precipitation*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 33.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of large scale cloud microphysics and precipitation*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.microphysics_precipitation.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 34. Microphysics Precipitation --> Large Scale Precipitation \n",
"*Properties of the large scale precipitation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 34.1. Scheme Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Commonly used name of the large scale precipitation parameterisation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_precipitation.scheme_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 34.2. Hydrometeors\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Precipitating hydrometeors taken into account in the large scale precipitation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_precipitation.hydrometeors') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"liquid rain\" \n",
"# \"snow\" \n",
"# \"hail\" \n",
"# \"graupel\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 35. Microphysics Precipitation --> Large Scale Cloud Microphysics \n",
"*Properties of the large scale cloud microphysics scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 35.1. Scheme Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Commonly used name of the microphysics parameterisation scheme used for large scale clouds.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_cloud_microphysics.scheme_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 35.2. Processes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Large scale cloud microphysics processes*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_cloud_microphysics.processes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"mixed phase\" \n",
"# \"cloud droplets\" \n",
"# \"cloud ice\" \n",
"# \"ice nucleation\" \n",
"# \"water vapour deposition\" \n",
"# \"effect of raindrops\" \n",
"# \"effect of snow\" \n",
"# \"effect of graupel\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 36. Cloud Scheme \n",
"*Characteristics of the cloud scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 36.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of the atmosphere cloud scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 36.2. Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Commonly used name for the cloud scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 36.3. Atmos Coupling\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Atmosphere components that are linked to the cloud scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.atmos_coupling') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"atmosphere_radiation\" \n",
"# \"atmosphere_microphysics_precipitation\" \n",
"# \"atmosphere_turbulence_convection\" \n",
"# \"atmosphere_gravity_waves\" \n",
"# \"atmosphere_solar\" \n",
"# \"atmosphere_volcano\" \n",
"# \"atmosphere_cloud_simulator\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 36.4. Uses Separate Treatment\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Different cloud schemes for the different types of clouds (convective, stratiform and boundary layer)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.uses_separate_treatment') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 36.5. Processes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Processes included in the cloud scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.processes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"entrainment\" \n",
"# \"detrainment\" \n",
"# \"bulk cloud\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 36.6. Prognostic Scheme\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is the cloud scheme a prognostic scheme?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.prognostic_scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 36.7. Diagnostic Scheme\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is the cloud scheme a diagnostic scheme?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.diagnostic_scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 36.8. Prognostic Variables\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *List the prognostic variables used by the cloud scheme, if applicable.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.prognostic_variables') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"cloud amount\" \n",
"# \"liquid\" \n",
"# \"ice\" \n",
"# \"rain\" \n",
"# \"snow\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 37. Cloud Scheme --> Optical Cloud Properties \n",
"*Optical cloud properties*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 37.1. Cloud Overlap Method\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Method for taking into account overlapping of cloud layers*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.optical_cloud_properties.cloud_overlap_method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"random\" \n",
"# \"maximum\" \n",
"# \"maximum-random\" \n",
"# \"exponential\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 37.2. Cloud Inhomogeneity\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Method for taking into account cloud inhomogeneity*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.optical_cloud_properties.cloud_inhomogeneity') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 38. Cloud Scheme --> Sub Grid Scale Water Distribution \n",
"*Sub-grid scale water distribution*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 38.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Sub-grid scale water distribution type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prognostic\" \n",
"# \"diagnostic\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 38.2. Function Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Sub-grid scale water distribution function name*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.function_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 38.3. Function Order\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Sub-grid scale water distribution function type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.function_order') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 38.4. Convection Coupling\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Sub-grid scale water distribution coupling with convection*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.convection_coupling') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"coupled with deep\" \n",
"# \"coupled with shallow\" \n",
"# \"not coupled with convection\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 39. Cloud Scheme --> Sub Grid Scale Ice Distribution \n",
"*Sub-grid scale ice distribution*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 39.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Sub-grid scale ice distribution type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prognostic\" \n",
"# \"diagnostic\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 39.2. Function Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Sub-grid scale ice distribution function name*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.function_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 39.3. Function Order\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Sub-grid scale ice distribution function type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.function_order') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 39.4. Convection Coupling\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Sub-grid scale ice distribution coupling with convection*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.convection_coupling') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"coupled with deep\" \n",
"# \"coupled with shallow\" \n",
"# \"not coupled with convection\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 40. Observation Simulation \n",
"*Characteristics of observation simulation*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 40.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of observation simulator characteristics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 41. Observation Simulation --> Isscp Attributes \n",
"*ISSCP Characteristics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 41.1. Top Height Estimation Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Cloud simulator ISSCP top height estimation methodUo*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.isscp_attributes.top_height_estimation_method') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"no adjustment\" \n",
"# \"IR brightness\" \n",
"# \"visible optical depth\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 41.2. Top Height Direction\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Cloud simulator ISSCP top height direction*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.isscp_attributes.top_height_direction') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"lowest altitude level\" \n",
"# \"highest altitude level\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 42. Observation Simulation --> Cosp Attributes \n",
"*CFMIP Observational Simulator Package attributes*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 42.1. Run Configuration\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Cloud simulator COSP run configuration*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.run_configuration') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Inline\" \n",
"# \"Offline\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 42.2. Number Of Grid Points\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Cloud simulator COSP number of grid points*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_grid_points') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 42.3. Number Of Sub Columns\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Cloud simulator COSP number of sub-cloumns used to simulate sub-grid variability*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_sub_columns') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 42.4. Number Of Levels\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Cloud simulator COSP number of levels*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_levels') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 43. Observation Simulation --> Radar Inputs \n",
"*Characteristics of the cloud radar simulator*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 43.1. Frequency\n",
"**Is Required:** TRUE **Type:** FLOAT **Cardinality:** 1.1\n",
"### *Cloud simulator radar frequency (Hz)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.frequency') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 43.2. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Cloud simulator radar type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"surface\" \n",
"# \"space borne\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 43.3. Gas Absorption\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Cloud simulator radar uses gas absorption*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.gas_absorption') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 43.4. Effective Radius\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Cloud simulator radar uses effective radius*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.effective_radius') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 44. Observation Simulation --> Lidar Inputs \n",
"*Characteristics of the cloud lidar simulator*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 44.1. Ice Types\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Cloud simulator lidar ice type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.lidar_inputs.ice_types') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"ice spheres\" \n",
"# \"ice non-spherical\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 44.2. Overlap\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Cloud simulator lidar overlap*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.lidar_inputs.overlap') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"max\" \n",
"# \"random\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 45. Gravity Waves \n",
"*Characteristics of the parameterised gravity waves in the atmosphere, whether from orography or other sources.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 45.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of gravity wave parameterisation in the atmosphere*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 45.2. Sponge Layer\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Sponge layer in the upper levels in order to avoid gravity wave reflection at the top.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.sponge_layer') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Rayleigh friction\" \n",
"# \"Diffusive sponge layer\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 45.3. Background\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Background wave distribution*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.background') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"continuous spectrum\" \n",
"# \"discrete spectrum\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 45.4. Subgrid Scale Orography\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Subgrid scale orography effects taken into account.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.subgrid_scale_orography') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"effect on drag\" \n",
"# \"effect on lifting\" \n",
"# \"enhanced topography\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 46. Gravity Waves --> Orographic Gravity Waves \n",
"*Gravity waves generated due to the presence of orography*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 46.1. Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Commonly used name for the orographic gravity wave scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 46.2. Source Mechanisms\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Orographic gravity wave source mechanisms*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.source_mechanisms') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"linear mountain waves\" \n",
"# \"hydraulic jump\" \n",
"# \"envelope orography\" \n",
"# \"low level flow blocking\" \n",
"# \"statistical sub-grid scale variance\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 46.3. Calculation Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Orographic gravity wave calculation method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.calculation_method') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"non-linear calculation\" \n",
"# \"more than two cardinal directions\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 46.4. Propagation Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Orographic gravity wave propogation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.propagation_scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"linear theory\" \n",
"# \"non-linear theory\" \n",
"# \"includes boundary layer ducting\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 46.5. Dissipation Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Orographic gravity wave dissipation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.dissipation_scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"total wave\" \n",
"# \"single wave\" \n",
"# \"spectral\" \n",
"# \"linear\" \n",
"# \"wave saturation vs Richardson number\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 47. Gravity Waves --> Non Orographic Gravity Waves \n",
"*Gravity waves generated by non-orographic processes.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 47.1. Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Commonly used name for the non-orographic gravity wave scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 47.2. Source Mechanisms\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Non-orographic gravity wave source mechanisms*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.source_mechanisms') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"convection\" \n",
"# \"precipitation\" \n",
"# \"background spectrum\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 47.3. Calculation Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Non-orographic gravity wave calculation method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.calculation_method') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"spatially dependent\" \n",
"# \"temporally dependent\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 47.4. Propagation Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Non-orographic gravity wave propogation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.propagation_scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"linear theory\" \n",
"# \"non-linear theory\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 47.5. Dissipation Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Non-orographic gravity wave dissipation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.dissipation_scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"total wave\" \n",
"# \"single wave\" \n",
"# \"spectral\" \n",
"# \"linear\" \n",
"# \"wave saturation vs Richardson number\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 48. Solar \n",
"*Top of atmosphere solar insolation characteristics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 48.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of solar insolation of the atmosphere*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 49. Solar --> Solar Pathways \n",
"*Pathways for solar forcing of the atmosphere*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 49.1. Pathways\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Pathways for the solar forcing of the atmosphere model domain*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.solar_pathways.pathways') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"SW radiation\" \n",
"# \"precipitating energetic particles\" \n",
"# \"cosmic rays\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 50. Solar --> Solar Constant \n",
"*Solar constant and top of atmosphere insolation characteristics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 50.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Time adaptation of the solar constant.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.solar_constant.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"fixed\" \n",
"# \"transient\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 50.2. Fixed Value\n",
"**Is Required:** FALSE **Type:** FLOAT **Cardinality:** 0.1\n",
"### *If the solar constant is fixed, enter the value of the solar constant (W m-2).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.solar_constant.fixed_value') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 50.3. Transient Characteristics\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *solar constant transient characteristics (W m-2)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.solar_constant.transient_characteristics') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 51. Solar --> Orbital Parameters \n",
"*Orbital parameters and top of atmosphere insolation characteristics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 51.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Time adaptation of orbital parameters*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.orbital_parameters.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"fixed\" \n",
"# \"transient\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 51.2. Fixed Reference Date\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Reference date for fixed orbital parameters (yyyy)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.orbital_parameters.fixed_reference_date') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 51.3. Transient Method\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Description of transient orbital parameters*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.orbital_parameters.transient_method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 51.4. Computation Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Method used for computing orbital parameters.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.orbital_parameters.computation_method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Berger 1978\" \n",
"# \"Laskar 2004\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 52. Solar --> Insolation Ozone \n",
"*Impact of solar insolation on stratospheric ozone*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 52.1. Solar Ozone Impact\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Does top of atmosphere insolation impact on stratospheric ozone?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.insolation_ozone.solar_ozone_impact') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 53. Volcanos \n",
"*Characteristics of the implementation of volcanoes*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 53.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of the implementation of volcanic effects in the atmosphere*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.volcanos.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 54. Volcanos --> Volcanoes Treatment \n",
"*Treatment of volcanoes in the atmosphere*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 54.1. Volcanoes Implementation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *How volcanic effects are modeled in the atmosphere.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.volcanos.volcanoes_treatment.volcanoes_implementation') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"high frequency solar constant anomaly\" \n",
"# \"stratospheric aerosols optical thickness\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### \u00a92017 [ES-DOC](https://es-doc.org) \n"
],
"cell_type": "markdown",
"metadata": {}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"name": "python2",
"language": "python"
},
"language_info": {
"mimetype": "text/x-python",
"nbconvert_exporter": "python",
"name": "python",
"file_extension": ".py",
"version": "2.7.10",
"pygments_lexer": "ipython2",
"codemirror_mode": {
"version": 2,
"name": "ipython"
}
}
}
} | gpl-3.0 |
ioam/geoviews | examples/Homepage.ipynb | 1 | 3480 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"GeoViews is a [Python](http://python.org) library that makes it easy to explore and visualize geographical, meteorological, and oceanographic datasets, such as those used in weather, climate, and remote sensing research. \n",
"\n",
"GeoViews is built on the [HoloViews](http://holoviews.org) library for building flexible visualizations of multidimensional data. GeoViews adds a family of geographic plot types based on the [Cartopy](http://scitools.org.uk/cartopy) library, plotted using either the [Matplotlib](http://matplotlib.org) or [Bokeh](http://bokeh.pydata.org) packages. \n",
"With GeoViews, you can now work easily and naturally with large, multidimensional geographic datasets, instantly visualizing any subset or combination of them, while always being able to access the raw data underlying any plot. Here's a simple example:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import geoviews as gv\n",
"import geoviews.feature as gf\n",
"import xarray as xr\n",
"from cartopy import crs\n",
"\n",
"gv.extension('bokeh', 'matplotlib')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"(gf.ocean + gf.land + gf.ocean * gf.land * gf.coastline * gf.borders).opts(\n",
" 'Feature', projection=crs.Geostationary(), global_extent=True, height=325).cols(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"GeoViews is designed to work well with the [Iris](http://scitools.org.uk/iris) and [xarray](http://xarray.pydata.org) libraries for working with multidimensional arrays, such as those stored in netCDF files. GeoViews also accepts data as NumPy arrays and Pandas data frames. In each case, the data can be left stored in its original, native format, wrapped in a HoloViews or GeoViews object that provides instant interactive visualizations.\n",
"\n",
"The following example loads a dataset originally taken from [iris-sample-data](https://github.com/SciTools/iris-sample-data) and quickly builds an interactive tool for exploring how the data changes over time:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataset = gv.Dataset(xr.open_dataset('./data/ensemble.nc'))\n",
"ensemble = dataset.to(gv.Image, ['longitude', 'latitude'], 'surface_temperature')\n",
"\n",
"gv.output(ensemble.opts(cmap='viridis', colorbar=True, fig_size=200, backend='matplotlib') * gf.coastline(),\n",
" backend='matplotlib')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"GeoViews also natively supports geopandas datastructures allowing us to easily plot shapefiles and choropleths:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import geopandas as gpd\n",
"gv.Polygons(gpd.read_file(gpd.datasets.get_path('naturalearth_lowres')), vdims=['pop_est', ('name', 'Country')]).opts(\n",
" tools=['hover'], width=600, projection=crs.Robinson()\n",
")"
]
}
],
"metadata": {
"hide_input": false,
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| bsd-3-clause |
cdtait/simulation | md_processor/md_example.ipynb | 1 | 68047 | {
"metadata": {
"name": "",
"signature": "sha256:b07dd0fa4f107d0cb9d5f84b7bad6d89bb34bf4d7817fa84bf9aa717f5213e57"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Market Data Example"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Quick Notebook example of using panadas and ploting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the data from the csv and plot mid price with buy/sell trades"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"pd.options.display.max_columns=40\n",
"pd.options.display.max_rows=1000"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"path = 'data/md-test-2.C-M.csv'\n",
"book_data = pd.read_csv(path, index_col=False,header=None,nrows=50000,skiprows=10)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ask_trade_prices = book_data[book_data.iloc[:,1]=='S'].iloc[:,3]\n",
"bid_trade_prices = book_data[book_data.iloc[:,1]=='B'].iloc[:,3]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 50
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"book=book_data[book_data.iloc[:,1]=='U']\n",
"book['mid_price']=(book.iloc[:,6]+book.values[:,7])/2.0"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 51
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.figure(figsize=(10,5))\n",
"book['mid_price'].plot(color='g')\n",
"plt.scatter(ask_trade_prices.index,ask_trade_prices.values,color='r',s=5)\n",
"plt.scatter(bid_trade_prices.index,bid_trade_prices.values,color='b',s=5)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 52,
"text": [
"<matplotlib.collections.PathCollection at 0x7677210>"
]
},
{
"metadata": {},
"output_type": "display_data",
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pV98obJYP9u5F1DSXwIv27eG996BBA/juO1BzX2oFVMTZsyEqyvwYGTIkB2PE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\nS1dX1/Ly8nLX+fPnf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"text": [
"<matplotlib.figure.Figure at 0x469a890>"
]
}
],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | gpl-2.0 |
autism-research-centre/Autism-Gradients | 6a-gradient-network-fit.ipynb | 1 | 341932 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## 6a Calculate gradients inside standard networks\n",
"\n",
"##### written by J. Freyberg for the Autism Gradients project at Brainhack Cambridge 2017\n",
"\n",
"__Note: This is a matlab notebook!__\n",
"\n",
"You need to have both a valid matlab installation (2016a or newer) and the python package `matlab_kernel` installed. You also need to have SPM installed.\n",
"\n",
"In the first cell, we transform all of the gradient files into the same voxel space as the standard network maps from neurosynth."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"% list the correct files:\n",
"indivfiles = dir(fullfile(pwd, 'data', 'Outputs', 'individual', '*npy0.nii'));\n",
"network_files = dir(fullfile(pwd, 'ROIs_mask', '*FDR_0.01.nii*'));\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%plot native\n",
"network_file = fullfile(pwd, 'ROIs_mask', network_files(1).name);\n",
"\n",
"% transform into different voxel space\n",
"MatlabFuncs.progressbar(0);\n",
"for i = 1:numel(indivfiles)\n",
" gradient_file = fullfile(pwd, 'data', 'Outputs', 'individual', indivfiles(i).name);\n",
" if ~exist(fullfile([gradient_file(1:end-4), 'transformed.nii']))\n",
" MatlabFuncs.Transform_into_the_same_voxelspace(network_file, gradient_file);\n",
" end\n",
" MatlabFuncs.progressbar(i/numel(indivfiles));\n",
"end"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the next cell, we use the different network files to calculate a goodness-of-fit (ratio between inside-of-the-network to outside-of-the-network)."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%plot native\n",
"% define the inputs\n",
"mask_file = fullfile(pwd, 'ROIs_mask', 'rbgmask.nii');\n",
"network_files = dir(fullfile(pwd, 'ROIs_mask', '*FDR_0.01.nii*'));\n",
"\n",
"% list the new, transformed files\n",
"transformfiles = dir(fullfile(pwd, 'data', 'Outputs', 'individual', '*transformed.nii'));\n",
"\n",
"% reset the goodness of fit result\n",
"ratio = [];\n",
"\n",
"MatlabFuncs.progressbar(0);\n",
"for inetwork = 1:numel(network_files)\n",
" \n",
" % define network file for this loop\n",
" network_file = fullfile(pwd, 'ROIs_mask', network_files(inetwork).name);\n",
" \n",
" % loop over participants\n",
" for i = 1:numel(transformfiles)\n",
" % update a progbar\n",
" MatlabFuncs.progressbar(((inetwork-1)*numel(transformfiles)+i)/(numel(transformfiles)*numel(network_files)));\n",
" % define gradient for this loop\n",
" gradient_file = fullfile(pwd, 'data', 'Outputs', 'individual', transformfiles(i).name);\n",
" % run the goodness-of-fit analysis\n",
" [ratio(inetwork, i)] = MatlabFuncs.gradient_goodness(gradient_file, network_file, mask_file);\n",
" end\n",
"end\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To avoid having to run everything _again_, we can save the ratio file to disk:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"save(fullfile(pwd, 'data', 'ratios.mat'), 'ratio');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, make one big vector that indexes what group people are in"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Warning: Variable names were modified to make them valid MATLAB identifiers. The original names are saved in the VariableDescriptions property.\n"
]
}
],
"source": [
"phenodata = readtable(fullfile(pwd, 'data', 'Outputs', 'Phenotypic_V1_0b_preprocessed1.csv'));\n",
"for i = 1:numel(transformfiles)\n",
" [~, loc] = ismember(transformfiles(i).name(4:end-34), phenodata.FILE_ID(:));\n",
" if loc\n",
" group(i) = phenodata.DX_GROUP(loc);\n",
" else\n",
" group(i) = NaN;\n",
" end\n",
"end\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the next cell, I create a boxplot that ranks the networks by fit"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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pKSlaWlrr1q2jMlFCiJ6eXnR0NP2Wtnr1anq51MnJSVtbm8Vi+fn5UZkoIWT06NGEkPLy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\nAAAAAGAMklEAAAAAYAySUQAAAABgzP8DspVgmakJF/QAAAAASUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%plot inline -s 900,1000\n",
"network_files = dir(fullfile(pwd, 'ROIs_mask', '*FDR_0.01.nii*'));\n",
"[~, sortorder] = sort(median(ratio, 2));\n",
"figure;\n",
"labels = {network_files(:).name};\n",
"for i = 1:numel(labels)\n",
" labels{i} = labels{i}(1:end-23);\n",
"end\n",
"\n",
"grouplabels = {'control', 'autism'};\n",
"for igroup = 1:2\n",
" subplot(2, 1, igroup);\n",
" boxplot((ratio(sortorder, group==igroup)'), 'orientation', 'horizontal', 'Label', labels(sortorder));\n",
" xlim([0, 300]);\n",
" xlabel('Gradient_i_n_s_i_d_e / Gradient_o_u_t_s_i_d_e of the network');\n",
" title([grouplabels{igroup}, ' group']);\n",
"end\n",
"% MatlabFuncs.xticklabel_rotate([1:inetwork],90,{network_files(:).name},'interpreter','none')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also do this for each bin of the gradient. The bin boundaries (in percentiles) can be given as a last argument to the gradient_goodness function. Note that this will roughly take 10x as long as the previous steps."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%plot native\n",
"% define the inputs\n",
"mask_file = fullfile(pwd, 'ROIs_mask', 'rbgmask.nii');\n",
"network_files = dir(fullfile(pwd, 'ROIs_mask', '*FDR_0.01.nii*'));\n",
"\n",
"% list the new, transformed files\n",
"transformfiles = dir(fullfile(pwd, 'data', 'Outputs', 'individual', '*transformed.nii'));\n",
"\n",
"% reset the goodness of fit result\n",
"binned_ratio = [];\n",
"\n",
"MatlabFuncs.progressbar(0);\n",
"a = 0;\n",
"for inetwork = 1:numel(network_files)\n",
" \n",
" % define network file for this loop\n",
" network_file = fullfile(pwd, 'ROIs_mask', network_files(inetwork).name);\n",
" \n",
" % loop over participants\n",
" for i = 1:numel(transformfiles)\n",
" % update a progbar\n",
" a = a+1;\n",
" MatlabFuncs.progressbar(a/(numel(network_files)*numel(transformfiles)));\n",
" % define gradient for this loop\n",
" gradient_file = fullfile(pwd, 'data', 'Outputs', 'individual', transformfiles(i).name);\n",
" % run the goodness-of-fit analysis\n",
" [binned_ratio(inetwork, i, 1:10)] = MatlabFuncs.gradient_goodness(gradient_file, network_file, mask_file, true);\n",
" end\n",
"end\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since this takes for-absolutely-ever, we should save this file so that we don't have to repeat anything!"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"save(fullfile(pwd, 'data', 'binned_ratios.mat'), 'binned_ratio');"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"load(fullfile(pwd, 'data', 'binned_ratios.mat'));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also do the same visualisation as above for these binned results"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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IGO4AAAAAABAw3AEAAAAAIGC4AwAAAABAwHAHAAAAAICA4Q4AAAAAAAHDHQAAAAAAAoY7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\nAAAAAAAYCHcAAAA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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Columns 1 through 7\n",
"\n",
" 0.9410 0.9836 0.9496 0.9452 1.0110 0.9753 1.0224\n",
" 0.9590 1.0113 0.9206 0.9472 0.9927 1.0654 0.9856\n",
" 0.9481 0.9488 1.0225 1.0494 0.9598 0.9968 1.0671\n",
" 0.9843 1.0370 0.9569 0.9854 1.0216 0.9691 1.0070\n",
" 1.0390 1.0107 0.9450 0.9092 0.9613 0.9554 0.9536\n",
" 0.8369 0.8984 0.8849 0.9142 0.9478 0.9700 0.9328\n",
" 1.0112 0.9989 1.0131 0.9479 0.9984 0.9819 1.0193\n",
" 0.9198 0.9301 0.9986 0.9637 1.0304 0.9568 1.0220\n",
" 0.9307 0.9759 1.0557 0.9670 0.9843 1.0265 1.0684\n",
" 1.1448 1.0647 1.0054 0.9934 1.0478 0.9593 0.9548\n",
" 0.9015 0.9566 0.9703 1.1139 0.9305 0.9720 0.8967\n",
" 0.8437 0.9769 0.9479 0.9984 1.0054 0.9788 1.0115\n",
" 0.8234 0.9280 0.9231 1.0750 0.9490 0.9619 0.9827\n",
" 1.0364 0.9771 1.0050 0.8957 1.0066 1.0322 0.9931\n",
" 0.9294 0.9516 0.9215 0.9893 0.9893 1.0177 1.0111\n",
" 0.8307 0.9660 0.9560 0.9694 1.0450 0.9643 0.9910\n",
" 0.9537 0.9620 0.9666 0.8263 0.9638 0.9673 1.0018\n",
" 0.9397 0.9342 0.8848 0.9832 0.9851 0.8933 0.9782\n",
" 1.0364 0.9110 1.0950 0.8231 0.9350 0.9563 1.0485\n",
" 0.9282 0.9509 0.9438 1.0222 0.9422 1.0264 1.0028\n",
" 0.9065 0.9818 1.0738 1.0051 0.9260 1.0132 1.0523\n",
" 0.9663 0.9408 0.8669 0.8849 0.8916 0.9882 0.9601\n",
" 0.9774 1.0055 0.9980 0.9028 1.0024 0.9744 0.9812\n",
" 1.0673 0.9864 1.0387 1.0038 0.9673 0.9558 0.9434\n",
" 0.8585 0.9314 0.9961 0.8764 0.9628 0.9396 0.9327\n",
" 1.0027 0.9786 0.9927 0.9498 1.0085 1.0287 1.0179\n",
" 0.9456 0.9975 0.9437 1.0511 0.9531 0.9417 0.9284\n",
" 1.0109 0.9670 1.0141 0.9228 0.9675 1.2094 1.0524\n",
" 0.9901 0.9662 0.9428 0.9673 0.9446 0.9506 0.9810\n",
" 0.9398 0.9817 0.9632 0.9706 0.9651 0.9790 0.9743\n",
" 0.9864 0.9752 0.9739 0.9215 0.9212 0.9867 1.0845\n",
" 1.0856 1.0151 0.9595 1.0653 0.9915 1.0137 0.9665\n",
" 0.8366 0.8463 1.1111 0.8905 0.8525 0.9200 1.0680\n",
" 0.9259 0.9355 0.9977 1.0026 0.9595 1.0342 1.0263\n",
" 1.0224 0.9992 1.0256 1.0142 0.9820 0.9433 0.9521\n",
" 0.8903 0.8664 0.8994 1.0226 0.9467 1.0541 0.9161\n",
" 0.9937 0.9780 0.9239 0.9716 0.9890 0.9872 0.9679\n",
" 0.8776 0.8355 0.9678 0.9999 0.9662 0.9801 0.9079\n",
" 0.9232 0.9004 0.9206 0.9655 1.0114 1.0421 0.9811\n",
" 0.9188 0.9210 0.9648 0.9209 0.8962 0.9441 1.1041\n",
" 1.0446 1.0263 0.9852 0.9161 1.0059 0.9344 1.0113\n",
" 0.9472 0.9344 1.0024 0.9798 0.9592 0.9717 1.0277\n",
" 1.0732 1.0124 0.8659 1.0145 0.8826 1.0913 0.9994\n",
" 0.9472 0.9149 0.9665 0.8282 0.9184 0.9524 0.9739\n",
" 0.8617 0.9031 0.9049 0.9922 0.9793 0.9842 0.9829\n",
" 1.0207 0.9913 0.8702 0.9634 1.0219 0.9805 0.9358\n",
" 1.0021 1.0590 1.0074 1.0848 0.9235 1.0441 1.0269\n",
" 0.8298 0.9779 0.8343 0.9967 1.0088 0.9551 1.0416\n",
" 0.8970 0.9384 0.9088 0.9489 0.9561 1.0212 0.9734\n",
" 0.8717 0.8997 0.9139 0.9909 1.0239 0.9535 0.9797\n",
" 0.9794 1.0367 1.0574 0.8976 1.0040 1.1024 1.1589\n",
" 1.0408 0.9564 0.9409 1.0016 0.9432 0.9727 0.9318\n",
" 0.9847 0.9736 0.9518 1.0781 1.0347 0.9610 0.9661\n",
" 0.9825 0.9397 1.0015 0.9499 0.9649 0.9399 0.9916\n",
" 0.8983 0.9846 0.9900 0.9905 0.9578 0.9083 1.0255\n",
" 1.0493 1.0173 0.9230 0.9971 1.0002 0.9455 0.9564\n",
" 1.0147 0.9307 0.9809 0.9270 1.0027 1.0055 0.9815\n",
" 0.7987 0.8486 1.0371 0.9449 0.9746 1.0831 0.9711\n",
" 0.8844 1.0146 0.8822 1.0517 0.9787 0.9961 0.9667\n",
" 0.8912 0.9304 0.9983 1.0403 0.9839 0.9577 1.0587\n",
" 0.8439 0.9151 0.9674 0.9737 0.9464 1.0035 1.0140\n",
" 1.0400 1.0172 0.9852 0.9171 1.0157 0.9914 0.9903\n",
" 1.1116 0.9866 0.9521 0.8748 1.0825 0.9417 0.9214\n",
" 0.8354 0.8826 1.0427 0.9228 0.9271 0.9895 1.0804\n",
" 0.8960 0.9540 0.9430 0.9685 0.9586 0.9623 0.9875\n",
" 0.9784 0.9317 0.9710 0.9000 0.9477 0.8950 0.9829\n",
" 1.0377 1.0151 0.9398 0.8755 1.0170 0.9320 0.9794\n",
" 1.0578 1.0521 0.8985 0.9501 1.1147 0.9250 0.9002\n",
" 0.8934 0.9972 0.9211 0.9450 0.9863 1.0278 1.0189\n",
" 0.9610 0.9612 0.9319 0.9227 1.0248 1.0366 1.0367\n",
" 0.9248 0.9407 0.8644 0.8410 0.9705 0.8474 0.9016\n",
" 0.9968 0.9042 1.0100 0.9051 1.0053 1.0025 1.0118\n",
" 0.9546 0.9494 1.0483 0.8804 0.9422 0.9745 1.0433\n",
" 0.9338 0.9579 0.9182 0.9554 0.9964 0.9330 0.9514\n",
" 1.0140 1.0197 0.9519 0.9824 0.9204 0.9737 0.9539\n",
" 1.0481 1.0254 0.9817 1.0583 1.0279 0.9387 0.9970\n",
" 0.9746 0.9442 1.0222 1.0770 0.9621 0.9669 0.9698\n",
" 0.8555 0.9095 0.9863 1.0677 0.9409 1.0133 0.9836\n",
"\n",
" Columns 8 through 14\n",
"\n",
" 0.9812 0.8969 0.9966 1.0085 1.0093 0.9379 1.0339\n",
" 1.0038 1.1182 0.9665 0.9733 1.0641 1.0120 1.0269\n",
" 1.0922 1.0029 1.0853 0.9983 1.0446 0.9933 1.0374\n",
" 0.9945 1.0277 1.0300 1.0212 0.9634 0.9973 0.9575\n",
" 0.9371 0.9673 0.9748 0.9769 0.9757 0.9934 0.9892\n",
" 0.9919 0.9540 0.9455 1.0175 0.9622 1.0185 1.0012\n",
" 1.0219 0.9033 1.0339 0.9900 1.0044 0.9842 1.0043\n",
" 1.0089 0.9005 1.0542 1.0052 1.0063 0.9550 1.0125\n",
" 1.0407 0.9594 1.0371 0.9882 1.0487 0.9845 0.9644\n",
" 0.9530 0.9236 0.9394 0.9569 1.0396 0.9775 1.0234\n",
" 1.0930 1.1622 0.9355 0.9410 0.9681 1.0410 0.9291\n",
" 0.9653 0.9802 1.0201 0.9866 1.0330 0.9695 1.0142\n",
" 0.8823 0.9537 0.9900 1.0768 0.9546 0.9990 0.9571\n",
" 0.9736 1.0245 1.0517 0.9784 1.0491 0.9817 1.0224\n",
" 0.9214 0.9712 0.9643 0.9896 0.9981 1.0034 1.0023\n",
" 0.9589 1.0288 1.0073 0.9822 0.9776 0.9997 1.0076\n",
" 1.0359 1.1047 1.0329 0.9924 0.9276 1.0214 1.0236\n",
" 1.0106 0.9072 0.9411 1.0378 0.9469 1.0035 0.8963\n",
" 0.9811 0.9540 1.0350 1.0463 0.9917 1.0459 0.9942\n",
" 0.9877 0.9914 0.9859 0.9841 0.9838 1.0016 0.9733\n",
" 1.0401 1.0372 1.0570 1.0190 1.0112 0.9543 0.9926\n",
" 0.9689 0.9288 0.9576 0.9988 0.9980 0.9935 0.9796\n",
" 1.0120 1.0862 0.9840 0.9760 1.0209 0.9828 1.0030\n",
" 1.0854 0.8969 0.9883 0.9686 0.9593 1.0106 1.0567\n",
" 0.9298 1.0552 0.8781 0.9702 0.9465 1.0137 0.9643\n",
" 1.0645 0.9251 1.0307 0.9909 1.0059 0.9760 1.0135\n",
" 0.9407 0.9576 0.9313 0.9729 0.9372 1.0025 0.9919\n",
" 0.9367 1.0528 1.0351 1.0171 1.0230 1.0802 1.0289\n",
" 0.9838 0.9965 0.9924 0.9829 1.0010 1.0003 0.9709\n",
" 1.0125 0.9964 0.9682 0.9993 0.9361 0.9726 1.0148\n",
" 1.0169 1.0196 1.0572 1.0084 0.9949 1.0229 0.9870\n",
" 1.0728 1.1761 0.9845 0.9722 0.9426 0.9982 1.0299\n",
" 1.0680 1.5116 1.1524 0.8856 1.3106 1.0247 0.9523\n",
" 1.0213 1.0127 1.0040 0.9958 1.0079 0.9790 1.0116\n",
" 1.0308 0.9719 1.0028 0.9699 0.9553 1.0034 1.0134\n",
" 0.8956 0.8318 0.8903 0.9579 1.0609 1.0429 1.0295\n",
" 0.9771 0.9481 0.9489 0.9523 0.9897 0.9952 1.0018\n",
" 0.9974 0.8680 0.9640 0.9852 1.0336 0.9897 1.1355\n",
" 1.0015 0.9646 1.0009 1.0131 1.0215 0.9782 1.0462\n",
" 1.0088 0.9669 1.0852 1.0264 0.9697 1.0291 0.9928\n",
" 1.0080 1.1048 0.9961 0.9948 0.9118 1.0181 0.9915\n",
" 1.0499 1.0276 1.0165 0.9905 1.0228 0.9701 1.0190\n",
" 0.9647 1.0406 1.0597 1.0215 0.9125 0.9536 1.0234\n",
" 0.9658 1.0263 0.9394 0.9980 0.9753 1.0621 1.0209\n",
" 0.9465 1.0653 0.9876 0.9648 1.0297 0.9871 0.9536\n",
" 0.9412 0.9671 0.9202 0.9886 0.9293 0.9934 1.0248\n",
" 1.0339 1.0402 1.0516 0.9998 1.0100 1.0316 0.9730\n",
" 0.9704 1.0506 0.9290 1.0333 0.9649 1.0372 0.8944\n",
" 0.9086 1.0016 0.9846 1.0093 1.0635 0.9996 0.9864\n",
" 0.9311 0.9339 0.9177 1.0219 0.9854 0.9982 0.9822\n",
" 1.0416 1.2605 1.0832 1.0472 1.0728 1.0125 0.9840\n",
" 0.9600 0.9598 0.9462 0.9602 0.9637 0.9812 1.0257\n",
" 1.0688 0.9375 1.0143 0.9673 0.9570 0.9715 1.0074\n",
" 1.1109 0.9669 1.0176 0.9526 1.0158 0.9888 0.9981\n",
" 0.9646 0.8894 1.0489 1.0101 1.0442 0.9729 0.9768\n",
" 0.9394 1.0927 0.9405 0.9899 0.9795 1.0207 0.9848\n",
" 1.0309 0.9168 0.9922 0.9995 0.9655 0.9881 1.0203\n",
" 0.9298 0.7739 0.9502 0.9437 1.0711 0.9159 1.1428\n",
" 0.9127 1.0196 0.9446 1.0042 0.9897 1.0080 1.0086\n",
" 0.9374 0.9362 0.9873 1.0029 1.0116 1.0397 0.9336\n",
" 0.9066 1.0289 0.9949 0.9935 1.0100 1.0227 1.0031\n",
" 1.0013 0.9724 0.9993 0.9936 0.9765 0.9986 1.0466\n",
" 0.9430 0.9218 0.9552 0.9578 0.9548 0.9898 0.9557\n",
" 0.9726 0.8869 1.0441 1.0482 1.0828 1.0398 1.0478\n",
" 0.9127 0.9920 0.9891 1.0323 0.9942 1.0021 0.9361\n",
" 0.9703 1.0229 0.9929 0.9742 0.9641 1.0427 0.9717\n",
" 0.9636 1.0755 0.9423 0.9907 0.9712 1.0443 0.9655\n",
" 0.9139 0.9510 0.9081 0.9865 0.9524 1.0213 0.9405\n",
" 0.8945 1.0119 0.9305 1.0200 0.9892 1.0010 1.0267\n",
" 1.0149 0.9541 1.0266 1.0213 1.0303 0.9716 0.9751\n",
" 0.9505 0.9070 0.9832 1.0411 0.8486 0.9453 0.9285\n",
" 1.0839 1.0256 1.0491 0.9535 1.0055 0.9949 1.0180\n",
" 1.0221 0.9418 1.0480 1.0122 1.0024 1.0069 1.0348\n",
" 0.9895 1.0431 0.9634 1.0186 0.9410 0.9702 1.0350\n",
" 1.0504 0.9971 0.9583 1.0028 0.9436 1.0075 0.9904\n",
" 0.9730 1.0242 1.0042 0.9442 1.0254 0.9378 0.9733\n",
" 1.0618 1.0332 1.0127 1.0033 1.0322 1.0387 0.9637\n",
" 1.0103 1.0251 0.9993 0.9869 1.0791 0.9746 1.0169\n",
"\n",
" Columns 15 through 19\n",
"\n",
" 1.0094 1.0526 0.9891 0.9429 1.0284\n",
" 1.0478 1.0121 1.0478 1.0863 0.9677\n",
" 0.9787 0.9837 1.0058 0.8783 1.0368\n",
" 1.0230 0.9736 1.0136 1.0088 0.9902\n",
" 0.9866 0.9838 0.9777 1.0435 1.0358\n",
" 1.0347 1.0204 1.0442 0.8652 0.9625\n",
" 0.9920 1.0029 0.9859 0.9709 1.0516\n",
" 1.0248 0.9874 1.0337 0.9018 1.0282\n",
" 0.9741 1.0288 1.0069 1.0542 1.0682\n",
" 1.0606 1.0353 1.0930 0.9807 1.1222\n",
" 0.9771 0.9141 1.0126 1.1302 1.0139\n",
" 1.0551 1.0587 1.0675 0.9652 1.0641\n",
" 0.9905 0.9377 1.0118 0.9440 0.8907\n",
" 0.9960 1.0443 1.0062 1.0556 1.1299\n",
" 1.0397 0.9839 1.0399 1.0317 0.9560\n",
" 1.0370 1.0048 1.0142 1.0606 1.0928\n",
" 0.9605 1.0535 0.9608 0.9920 0.8752\n",
" 1.0028 0.9102 1.0425 0.8460 1.0263\n",
" 1.0277 1.0785 1.0272 0.9110 0.9733\n",
" 0.9735 1.0163 0.9919 0.9301 0.9778\n",
" 0.9199 1.0911 0.9150 1.0043 1.1557\n",
" 0.9685 0.9969 1.0592 1.0150 0.8642\n",
" 1.0302 0.9926 1.0206 1.0038 1.0624\n",
" 0.9960 1.1402 1.0058 1.0412 0.8712\n",
" 0.9396 1.1128 0.9568 0.9936 1.1818\n",
" 1.0281 1.0080 0.9811 1.0386 0.9857\n",
" 1.0030 1.0445 1.0423 1.1014 0.9695\n",
" 0.9406 1.0313 0.9391 0.9700 1.0940\n",
" 1.0290 1.0166 1.0317 1.0565 0.9501\n",
" 0.9957 0.9906 0.9694 0.9636 1.0201\n",
" 0.9775 0.9702 0.9493 1.0237 1.1195\n",
" 0.9498 0.9992 0.9427 1.1187 0.9627\n",
" 0.9794 1.1593 1.0728 1.1212 1.2051\n",
" 0.9895 1.0365 0.9908 1.0194 1.0517\n",
" 1.0064 1.0089 0.9936 0.9880 0.9609\n",
" 1.0029 0.9722 1.0676 0.9227 1.1359\n",
" 1.0484 0.9688 1.0461 1.0267 1.0188\n",
" 1.0658 1.1386 1.0445 0.9764 0.9986\n",
" 1.0355 1.0635 1.0499 1.0088 1.0198\n",
" 0.9615 0.9416 0.9945 0.9278 1.1121\n",
" 0.9778 1.0629 0.9511 1.0764 1.0202\n",
" 1.0604 0.9835 1.0873 1.0258 1.0659\n",
" 0.9538 0.9806 1.0065 1.0468 0.9161\n",
" 0.9571 0.9643 1.0101 0.8207 0.9344\n",
" 0.9983 0.9316 1.0591 1.0293 1.0525\n",
" 1.0365 1.0562 1.0275 1.0121 0.8935\n",
" 0.9672 1.0064 1.0137 0.9617 0.9035\n",
" 1.0439 0.9599 1.0426 1.1527 0.9774\n",
" 1.0977 0.9479 1.1285 1.0884 0.9126\n",
" 1.0732 1.0443 1.0850 1.0432 0.9679\n",
" 1.0160 1.0526 1.0153 1.1036 1.1472\n",
" 0.9856 0.9226 1.0265 0.9985 1.0125\n",
" 1.0137 1.0269 1.0364 1.0327 0.9550\n",
" 1.0264 1.0217 1.0657 0.9270 0.9975\n",
" 0.9938 0.9370 0.9967 0.9751 1.1058\n",
" 1.0084 0.8649 1.0122 1.0643 1.0132\n",
" 1.0192 0.9917 0.9981 0.9320 0.9688\n",
" 1.1301 1.0864 1.2221 1.0704 1.0825\n",
" 1.0145 1.0370 1.0210 1.0975 1.0019\n",
" 1.0015 0.9336 1.0545 0.9650 1.0909\n",
" 0.9628 1.0637 0.9852 1.0331 1.0618\n",
" 1.0257 1.0249 1.0166 0.8963 1.0978\n",
" 1.0866 0.9142 0.9917 1.0466 0.8534\n",
" 0.9746 1.0988 0.9936 0.8947 1.1302\n",
" 0.9784 0.9644 0.9606 1.0355 1.0242\n",
" 0.9339 1.0124 0.9745 0.9522 1.1098\n",
" 1.0210 0.9911 1.0033 1.1704 1.0132\n",
" 1.0795 0.9190 1.1025 1.0430 1.0622\n",
" 1.0012 0.9883 0.9382 1.0396 1.1538\n",
" 1.0749 1.0066 1.0923 0.9619 1.0860\n",
" 0.9545 0.8210 0.9524 1.0356 0.8544\n",
" 0.9968 1.0430 0.9987 1.1098 1.0365\n",
" 0.9787 0.9427 1.0078 0.9151 0.9728\n",
" 1.0468 1.0287 1.0475 0.9925 0.8880\n",
" 0.9532 0.9331 0.9953 1.0659 0.9703\n",
" 1.0683 0.9641 1.0476 1.1023 1.0276\n",
" 0.9893 0.9612 0.9961 0.9555 1.0515\n",
" 0.9756 0.9991 1.0374 0.9416 1.1405\n",
"\n",
" Columns 1 through 7\n",
"\n",
" 0.9954 0.9624 0.9235 0.9626 1.0349 0.9505 0.8431\n",
" 1.0403 0.9432 0.9294 0.9168 0.9807 0.8400 1.0028\n",
" 0.8737 0.8891 0.9048 0.9100 0.9789 0.9504 1.0487\n",
" 0.9768 0.9342 1.0130 1.0267 1.0246 1.0877 0.9461\n",
" 1.1135 1.0576 1.0897 0.9933 1.0114 1.0045 0.9229\n",
" 0.9890 0.9579 1.0752 1.0095 1.0138 0.9275 0.9158\n",
" 0.9302 0.9720 1.1355 1.1005 0.9312 0.8926 0.9960\n",
" 0.9278 0.9830 0.9665 0.9437 1.0190 1.1119 0.9700\n",
" 0.9315 0.9561 1.0050 0.9883 0.9221 0.9679 1.0466\n",
" 0.9474 0.9618 0.9522 0.9760 1.0043 0.9268 0.9661\n",
" 0.9237 0.9640 0.9676 0.9730 0.9598 0.9954 0.9940\n",
" 1.0212 1.0169 1.0016 1.0094 0.9267 0.9473 0.9218\n",
" 0.9103 0.9538 0.9762 0.9417 1.0011 1.0101 0.9670\n",
" 0.9723 1.0159 1.0547 1.0645 0.9735 0.9103 1.0324\n",
" 0.9691 0.9349 0.9764 0.9295 0.9226 0.9656 0.9785\n",
" 0.9487 0.9732 0.9802 0.9616 0.9618 0.9414 1.0160\n",
" 0.8784 0.9531 0.9029 0.8821 0.9393 1.0632 0.9460\n",
" 0.9543 0.9939 1.0485 1.0076 1.0204 1.1165 0.9263\n",
" 0.9163 0.9407 1.0277 0.9514 0.9340 0.9333 1.0293\n",
" 0.9906 0.9405 0.8976 0.9077 0.9035 1.0670 1.0100\n",
" 0.8746 0.9426 0.9666 0.9818 1.0081 0.9925 1.0185\n",
" 0.9580 0.9767 1.0947 0.9712 1.0152 0.9509 1.0114\n",
" 0.9534 1.0104 1.0178 0.9805 0.9823 1.0052 1.0079\n",
" 1.0985 0.9624 0.8920 0.9989 1.0488 1.0970 0.9574\n",
" 0.9102 0.9840 0.9227 0.9896 0.9576 1.0621 1.0640\n",
" 0.9242 0.8691 0.8219 0.9355 0.9639 0.9746 0.8600\n",
" 1.0173 0.9204 0.9280 1.0325 0.9584 1.0269 0.9310\n",
" 0.9463 1.0901 1.2253 1.0721 0.9186 1.1345 0.9368\n",
" 0.9471 0.9595 1.0689 0.9693 0.9904 0.9485 0.9876\n",
" 0.8727 0.9775 0.9574 0.9743 0.9734 0.8972 0.9799\n",
" 0.9180 0.9790 0.8289 1.0442 0.9462 0.9528 0.9462\n",
" 0.8568 1.0323 0.9324 0.9554 0.9848 1.0975 0.8915\n",
" 0.9125 1.0008 0.9483 0.9394 0.9891 0.9629 0.9630\n",
" 1.0303 1.0101 1.0180 0.9742 0.9815 0.9715 0.9457\n",
" 0.8895 0.9464 1.0275 0.9389 1.0039 0.9948 0.9743\n",
" 0.8047 0.8528 0.9735 0.9096 1.0894 0.8461 0.9513\n",
" 0.9248 0.9514 1.0316 0.9644 0.9790 0.8167 0.9492\n",
" 0.8868 0.9812 1.0081 1.0038 0.9764 0.8769 1.0162\n",
" 0.9744 1.0232 1.0800 1.0521 1.0044 0.8728 1.0682\n",
" 1.2871 0.9231 0.9604 0.9138 0.9398 0.9578 0.9786\n",
" 0.8621 1.0255 1.1114 0.9997 0.9758 0.9836 1.0126\n",
" 0.9354 0.9770 0.9040 0.9552 0.9752 1.0492 0.9216\n",
" 0.8770 0.9417 0.9319 0.8955 0.9171 1.0663 1.0132\n",
" 0.9482 0.9465 1.0364 0.9341 0.9677 0.9857 1.0169\n",
" 1.0257 0.9295 0.9134 0.9972 0.9833 0.8736 0.9373\n",
" 0.8493 0.9382 0.9998 1.0111 0.8855 0.9062 1.0510\n",
" 0.8744 0.9425 0.9320 0.9548 1.0276 1.0089 0.9459\n",
" 1.0413 0.9819 0.9677 1.0115 1.0071 1.0447 0.9702\n",
" 1.0340 0.9865 1.0439 0.9780 1.0447 0.9921 1.0203\n",
" 0.7759 0.9029 0.9166 0.9900 0.9066 0.9258 0.9442\n",
" 0.9660 0.9074 0.9071 0.9747 0.9507 0.9781 0.9458\n",
" 1.0376 0.9827 0.9601 0.9033 1.0517 0.9766 0.9716\n",
" 0.8992 0.9838 0.9827 0.9660 1.0139 0.9400 0.9360\n",
" 0.9416 0.9967 1.0489 0.9590 0.9840 1.0512 1.0057\n",
" 1.0332 1.0179 0.9673 0.9880 1.0135 0.9352 1.0387\n",
" 1.0018 1.0143 1.0933 0.9853 0.9589 0.9777 1.0562\n",
" 1.0087 0.9375 0.8685 0.9459 0.9660 0.9803 0.9539\n",
" 0.9930 0.9346 0.9961 1.0040 1.0699 0.9248 1.0043\n",
" 0.9506 1.0155 1.0174 0.9755 0.9592 0.9996 1.0001\n",
" 0.9039 0.8947 0.9899 1.0020 1.0050 0.9603 0.9942\n",
" 0.9491 0.9446 0.9502 0.9277 0.9768 0.9814 0.9152\n",
" 0.8832 0.9399 1.0092 1.0051 1.0249 0.9343 0.9975\n",
" 0.9591 0.9286 0.8617 0.9227 0.9315 1.0182 0.9601\n",
" 0.9468 0.9411 0.9379 0.8932 0.8633 1.0166 1.0972\n",
" 0.8726 0.9306 0.9908 0.9288 0.9376 0.9270 0.9593\n",
" 0.9112 1.0050 0.9580 0.9607 1.0224 1.1301 0.9615\n",
" 1.0608 0.9921 0.9214 0.9449 0.9729 0.9747 0.9446\n",
" 0.9615 0.9636 0.8953 0.9728 0.9980 0.9965 0.9998\n",
" 0.9193 0.9201 0.9819 0.8934 0.9413 0.9708 1.0259\n",
" 0.8955 0.8770 0.8567 0.9662 0.9786 0.8793 0.9920\n",
" 0.9705 0.9392 0.9553 1.0286 0.9243 0.9262 0.9581\n",
" 0.9673 0.9326 1.0214 0.9650 0.9630 1.0378 0.9985\n",
" 0.9228 0.8426 0.7828 0.9252 0.9655 0.9730 0.9655\n",
" 1.0677 0.9359 0.8563 1.0097 1.0100 0.8630 0.9386\n",
" 1.0181 0.9225 0.9857 1.0382 0.9853 0.9895 1.0375\n",
" 0.8391 0.9541 1.0046 1.0169 0.9631 1.0911 0.9624\n",
" 1.0286 0.9358 0.9579 1.0062 1.0119 0.9117 0.9323\n",
" 0.8821 0.9510 0.9219 0.8954 0.9422 0.9968 1.0350\n",
" 0.8770 0.9554 0.9509 0.9579 0.9664 1.0332 1.0836\n",
" 0.8709 0.9334 0.9665 0.9629 0.9294 0.9267 0.9361\n",
" 0.9649 1.0264 1.0611 0.9582 0.9456 1.0013 1.0056\n",
" 0.9051 0.9009 0.9341 0.9534 0.9588 0.9533 0.9831\n",
" 0.9431 0.9744 1.0463 1.0397 0.9162 1.0448 1.0025\n",
" 0.8203 0.9306 0.9495 0.9456 0.9559 1.0721 0.9696\n",
" 0.9379 0.9050 0.9706 0.9811 0.9614 0.8621 0.9884\n",
" 0.9668 1.0054 0.9966 0.9904 0.9940 0.9585 1.0125\n",
" 1.0126 1.0215 1.0068 0.9138 1.0452 0.9604 1.0790\n",
" 1.0036 1.0979 1.0947 0.9857 1.0139 0.9772 0.9708\n",
" 0.9364 0.9188 0.9161 0.9885 0.9313 0.9902 0.9526\n",
" 0.9195 0.9667 0.9740 0.9935 0.9593 1.0719 0.9885\n",
" 1.0609 1.0748 1.0280 0.9816 0.9402 0.9739 1.0842\n",
" 0.9845 1.0302 0.9140 0.9600 0.9732 1.0474 0.9843\n",
" 0.8842 0.9119 0.9181 0.9581 0.9429 0.9709 0.9126\n",
" 0.9238 0.9353 0.9975 0.9219 0.9524 1.0436 0.9760\n",
" 1.0276 1.0788 0.8735 0.9903 1.0238 1.0754 0.9460\n",
" 1.0362 0.9928 0.9577 0.9841 1.0252 0.8895 0.9101\n",
" 0.9270 0.9109 0.8803 0.9568 1.0396 0.9153 0.9260\n",
" 0.9578 0.9436 0.9055 0.9593 0.9851 1.0383 0.9584\n",
" 0.9709 0.9982 0.9783 0.9783 0.9976 0.8943 1.0087\n",
" 0.9481 1.0361 1.0745 1.0610 0.9290 1.0092 1.0654\n",
"\n",
" Columns 8 through 14\n",
"\n",
" 0.9893 1.0291 0.9527 0.9314 1.0184 1.0335 0.9825\n",
" 0.9601 1.0517 0.9705 0.9997 1.0524 0.9786 0.9321\n",
" 0.9738 0.9748 1.0022 1.0169 0.9918 0.9879 0.8942\n",
" 0.9708 0.9481 0.9605 1.0417 0.9410 1.1302 1.1311\n",
" 0.9768 0.9704 0.9735 0.9273 1.0278 1.0063 1.0190\n",
" 0.9991 1.0517 0.9494 1.0045 0.9675 1.0333 0.9146\n",
" 0.9281 0.9504 0.9911 1.0256 0.9962 1.1018 0.9490\n",
" 0.9804 0.9822 1.0261 0.9527 0.9784 0.9763 1.0935\n",
" 0.9873 1.0115 1.0444 1.0267 0.9972 0.9613 0.9093\n",
" 0.9749 1.0029 0.9610 0.9754 0.9992 0.9885 1.0218\n",
" 0.9713 1.0083 1.0088 0.9525 1.0182 0.9761 1.0701\n",
" 1.0273 1.0742 1.0283 0.9933 1.0063 1.0245 0.9741\n",
" 0.9854 0.9790 1.0109 0.9367 0.9752 0.9727 1.0027\n",
" 0.9254 0.9547 1.0212 0.9569 0.9355 1.0316 0.9955\n",
" 0.9686 1.0039 0.9856 0.9864 1.0082 0.9483 1.0116\n",
" 0.9966 1.0154 1.0099 0.9966 1.0325 0.9635 1.0743\n",
" 1.0009 0.9593 0.9426 0.9943 0.9696 0.9890 1.0301\n",
" 1.0470 1.0159 0.9618 0.9969 1.0509 1.0424 1.1972\n",
" 1.0781 1.0131 0.9969 1.0244 1.0628 0.9654 0.9929\n",
" 0.9540 0.9870 1.0054 1.0191 0.9986 1.0579 0.8730\n",
" 0.9661 0.9731 0.9965 1.0911 0.9449 1.0440 1.1309\n",
" 1.0969 1.0411 1.0855 1.0202 0.9920 0.9729 0.8891\n",
" 0.9951 0.9911 1.0055 0.9996 0.9780 0.9774 1.0369\n",
" 0.9631 1.0361 0.9707 0.9678 1.0256 0.9962 1.0101\n",
" 0.9530 0.9799 1.0205 1.0287 0.9704 1.0701 1.0107\n",
" 0.9397 1.0527 0.9896 1.0146 1.0656 0.9752 0.9182\n",
" 0.9263 1.0140 0.9097 0.9672 0.9939 1.0497 0.9585\n",
" 0.9679 1.0153 0.9647 0.9260 1.0289 1.0020 1.2145\n",
" 0.9974 0.9852 0.9664 1.0305 1.0244 0.9455 0.9105\n",
" 0.9724 1.0285 1.0221 1.0389 0.9447 1.0853 1.0826\n",
" 0.9354 0.9983 0.9691 0.9790 0.9713 1.1456 1.0904\n",
" 0.9101 1.0067 0.9333 0.9013 1.0170 0.9879 1.0933\n",
" 1.0069 0.9842 1.0212 0.9401 0.9821 0.9503 0.9963\n",
" 0.9781 0.9890 0.9558 0.9823 0.9566 1.0201 1.0264\n",
" 0.9946 0.9927 0.9977 1.0165 1.0159 1.0104 1.0602\n",
" 0.9688 0.8779 0.9896 1.0050 0.9570 1.1241 0.9359\n",
" 0.9725 0.9960 0.9977 0.9291 1.0377 1.0102 0.9899\n",
" 0.9366 0.9689 0.9985 1.0256 0.9866 1.0364 0.9716\n",
" 1.0035 0.9888 0.9776 0.9416 0.9814 1.0185 1.0604\n",
" 0.8791 0.9444 0.9053 1.0118 1.0485 0.9859 1.0008\n",
" 1.0497 1.0397 1.0418 0.9490 1.0133 1.0440 1.0292\n",
" 0.9799 0.9562 0.9627 0.9405 0.9709 1.0058 1.0575\n",
" 0.9796 0.9507 0.9624 1.0143 1.0058 0.9319 1.1173\n",
" 0.9658 0.9712 1.0220 1.0181 0.9909 0.9862 1.0959\n",
" 0.9560 0.9817 0.9587 0.9779 1.0111 1.0014 1.0100\n",
" 0.9892 0.9947 1.0509 1.0391 0.9551 1.0127 0.9845\n",
" 0.9383 0.9902 1.0008 1.0527 0.9846 1.0124 0.9728\n",
" 0.9748 0.9863 0.9852 0.9988 1.0322 1.0129 1.0946\n",
" 1.0011 1.0192 1.0400 1.0107 1.0057 0.9825 0.9998\n",
" 1.0207 1.0342 1.0411 0.9371 0.9585 0.9542 1.0066\n",
" 0.9860 1.0199 0.9557 0.9402 1.0346 0.9411 0.9949\n",
" 0.9443 0.9792 0.9846 0.9097 0.9733 0.9304 1.0092\n",
" 1.0544 1.0156 1.0524 1.0642 0.9787 1.0382 1.0338\n",
" 0.9594 0.9541 0.9937 1.0343 1.0019 1.0088 1.1454\n",
" 1.0566 1.0203 1.0433 1.0227 0.9860 0.9729 0.9577\n",
" 1.0671 0.9858 1.0830 1.0758 0.9479 0.9506 0.9884\n",
" 0.9837 0.9974 0.9517 1.0059 1.0591 0.9936 1.0249\n",
" 1.0767 0.9646 1.1368 1.0799 0.9435 1.0103 0.9366\n",
" 0.9685 1.0124 0.9698 0.9889 0.9953 1.0236 0.9451\n",
" 1.0259 0.9630 0.9837 1.0215 0.9581 1.1112 0.8913\n",
" 1.0802 1.0739 1.0153 0.9750 1.0513 0.9822 0.9024\n",
" 0.9873 0.9609 0.9960 1.0368 0.9622 1.0525 0.9239\n",
" 1.0617 0.9878 0.9789 0.9834 1.0333 0.9793 1.0599\n",
" 1.0065 0.9863 1.0668 0.9298 1.0004 0.8988 0.9414\n",
" 0.9912 0.9686 1.0158 0.9521 0.9679 0.9605 1.0411\n",
" 1.0534 1.0110 1.0331 1.0471 0.9991 1.0524 1.0311\n",
" 0.9600 0.9720 0.9663 0.9595 0.9924 0.9952 0.8969\n",
" 1.0018 1.0075 1.0272 0.9880 1.0150 0.9409 1.0261\n",
" 0.9950 1.0395 0.9679 0.9595 1.0525 0.8995 1.0082\n",
" 1.0001 1.0094 0.9801 1.0615 1.0196 1.0285 0.9853\n",
" 1.0329 0.9566 0.9658 1.0775 0.9440 1.0855 0.8988\n",
" 1.0031 1.0126 0.9757 1.0112 1.0177 1.0066 0.9375\n",
" 0.9989 0.9761 0.9258 1.0053 1.0103 0.9911 0.8918\n",
" 1.0335 1.0400 0.9738 0.9758 1.0306 0.9958 0.9625\n",
" 0.9298 0.9830 1.0038 0.9475 0.9552 0.9413 1.1557\n",
" 0.9031 1.0072 0.9709 1.0002 0.9769 1.1131 1.0812\n",
" 0.9884 0.9816 0.9736 0.9901 0.9560 1.1016 0.9317\n",
" 0.9843 1.0411 1.0259 1.0224 0.9645 0.9523 0.9761\n",
" 0.9847 0.9826 1.0397 0.9954 1.0068 0.9642 0.9702\n",
" 0.9356 0.9730 0.9151 0.9752 1.0202 0.9630 0.9327\n",
" 0.9664 0.9743 0.9999 0.9722 1.0048 1.0297 1.0936\n",
" 0.9905 0.9995 0.9668 0.9404 1.0122 0.9830 0.9893\n",
" 0.9550 0.9779 1.0148 1.0360 0.9937 1.0488 0.9754\n",
" 1.0063 1.0196 1.0328 1.0058 1.0495 0.9828 1.0094\n",
" 0.9755 0.9625 1.0300 1.0021 0.9154 1.0230 1.0101\n",
" 1.0240 1.0214 1.0192 1.0151 1.0007 1.0096 1.0107\n",
" 1.0286 0.9947 1.0452 1.0277 1.0122 0.9682 1.0358\n",
" 1.0461 1.0248 1.0620 0.9946 1.0091 1.0168 1.0747\n",
" 0.9993 0.9994 0.9566 0.9690 1.0170 1.0032 0.9909\n",
" 0.9962 1.0076 1.0084 0.9782 0.9642 1.0011 1.0820\n",
" 0.8983 0.9625 0.9929 0.9315 1.0365 0.9109 1.0811\n",
" 1.0041 1.0034 0.9749 0.9979 1.0356 1.0048 1.1366\n",
" 0.9525 1.0194 0.9853 0.9327 0.9735 1.0141 0.9246\n",
" 0.9480 0.9859 0.9582 0.9018 1.0045 0.9679 0.9149\n",
" 0.9857 0.9980 1.0119 0.9776 0.9638 1.0246 1.0920\n",
" 0.9987 1.0403 0.9657 0.9358 1.0206 1.0060 0.9068\n",
" 0.9035 0.9761 0.9359 0.9088 0.9695 1.0058 1.0734\n",
" 0.9727 1.0024 1.0137 0.9527 0.9888 0.9795 0.9862\n",
" 1.0102 0.9786 1.0130 1.0185 0.9602 1.0114 0.8841\n",
" 0.9782 0.9356 1.0722 1.0536 0.9461 1.0903 1.1214\n",
"\n",
" Columns 15 through 19\n",
"\n",
" 1.0499 0.9035 1.0554 1.0677 0.9503\n",
" 1.0356 1.0854 1.0070 1.0557 0.9981\n",
" 0.9922 0.9006 0.9837 1.0030 1.0683\n",
" 1.0396 1.0526 1.0884 1.0950 0.9715\n",
" 1.0126 0.8751 0.9845 0.9893 1.0003\n",
" 1.0403 0.9043 1.0611 1.0407 0.9166\n",
" 0.9498 1.0004 0.8788 1.1467 1.0509\n",
" 1.0370 1.0198 0.9574 0.9998 1.0738\n",
" 0.9485 1.0552 0.9578 0.9698 1.0448\n",
" 0.9926 0.9570 1.0084 0.9660 0.9357\n",
" 0.9841 1.0664 0.9885 0.9560 1.0522\n",
" 0.9523 0.9475 0.9998 1.0218 1.0529\n",
" 1.0308 1.0477 1.0006 1.0024 1.0414\n",
" 1.0407 1.0935 0.9760 0.9774 1.0846\n",
" 0.9406 1.0674 0.9537 0.9873 0.9556\n",
" 1.0149 0.9425 0.9987 0.9582 0.9760\n",
" 1.0040 1.1498 1.1252 1.0529 0.9686\n",
" 0.9589 0.9238 0.9412 1.0751 0.9735\n",
" 0.9650 0.9585 0.9289 0.9801 1.0498\n",
" 1.0455 0.9625 0.9759 1.0677 1.1510\n",
" 1.0166 0.9906 0.9960 1.1066 0.9796\n",
" 1.0627 1.1757 1.0488 0.9522 0.9850\n",
" 0.9997 1.0117 1.0071 1.0043 1.0570\n",
" 1.0289 1.1213 1.0154 0.9800 0.9321\n",
" 0.9478 1.0598 1.0200 1.0974 0.9418\n",
" 1.0159 1.1041 1.0020 1.0468 0.8981\n",
" 0.9551 0.8967 0.9742 1.0640 0.9477\n",
" 0.9943 0.9183 1.0354 0.9896 1.0254\n",
" 1.0584 1.0301 1.0302 1.0069 0.9628\n",
" 0.9500 1.0442 0.9743 1.1184 1.0423\n",
" 0.9929 0.8889 1.0193 1.1249 0.9703\n",
" 1.0659 0.9323 0.9801 0.8949 1.0216\n",
" 1.0302 0.9771 1.0782 0.9413 1.0287\n",
" 1.0465 1.0164 1.0688 1.0202 0.9797\n",
" 0.9746 1.0430 1.0696 1.0158 0.9987\n",
" 1.0963 1.1277 1.1055 1.1780 1.0808\n",
" 1.0365 0.9152 1.0515 0.9855 1.1121\n",
" 0.9375 0.9784 1.0304 1.1162 1.0266\n",
" 0.9924 1.2008 0.9859 1.0033 1.0327\n",
" 1.1017 0.8852 1.0937 1.0214 0.9295\n",
" 1.0159 0.9368 0.9618 0.9740 1.1414\n",
" 1.0376 0.9094 1.1071 0.9641 0.9788\n",
" 0.9731 1.1126 1.0515 1.0357 0.9731\n",
" 0.9662 0.9604 0.9927 1.0417 1.1663\n",
" 0.9959 1.0722 1.0468 1.0512 0.9451\n",
" 1.0138 1.0920 0.9131 1.0316 1.0333\n",
" 1.0125 0.9397 0.9703 1.0829 0.9620\n",
" 0.9913 0.9925 0.9860 1.0415 1.0240\n",
" 0.9924 1.0609 0.9625 0.9470 1.0565\n",
" 0.9919 0.9809 0.8787 0.9561 1.0345\n",
" 1.0089 1.0230 1.0681 0.9078 0.8993\n",
" 1.0720 1.0860 1.0574 0.8930 1.0312\n",
" 0.9849 1.1573 1.0059 1.0553 1.0881\n",
" 0.9964 1.1160 0.9064 1.0461 0.9913\n",
" 0.9523 1.2197 1.0360 0.9564 0.9790\n",
" 1.0267 1.0827 1.0259 0.9354 1.0662\n",
" 0.9681 0.9531 0.9597 1.0467 0.9708\n",
" 0.9894 1.1516 1.0578 1.0988 1.0474\n",
" 0.9937 1.0611 0.9964 1.0545 1.0790\n",
" 1.0610 0.9858 1.0268 1.0878 1.0780\n",
" 1.0161 0.8900 1.0331 0.9860 1.0480\n",
" 1.0287 1.0593 1.0131 1.0697 1.0212\n",
" 0.9916 0.9017 0.8961 0.9971 0.9913\n",
" 1.0247 1.0019 1.1200 0.7734 1.1257\n",
" 1.0324 1.0461 1.0326 0.9861 1.0252\n",
" 0.9753 1.1022 0.9610 1.0891 0.9837\n",
" 1.0547 0.9521 1.0169 0.9488 0.9528\n",
" 1.0074 1.0166 1.0493 0.9472 1.0212\n",
" 1.0299 0.9285 1.0470 0.9608 0.9419\n",
" 0.9929 1.2082 1.0394 1.0622 0.9491\n",
" 0.9857 1.0718 0.9966 1.1235 0.9765\n",
" 0.9824 0.9914 1.0662 1.0590 0.9651\n",
" 0.9948 1.0728 1.0242 1.0513 0.9561\n",
" 1.0064 0.9685 1.0847 1.0074 0.9268\n",
" 0.9249 1.1630 0.9534 0.9320 1.0647\n",
" 0.9302 0.9119 0.9126 1.1661 1.0530\n",
" 1.0172 1.0458 1.0023 1.1460 1.0252\n",
" 1.0419 0.9639 1.0421 1.0412 1.0235\n",
" 0.9749 1.0627 1.0741 0.9914 1.1108\n",
" 1.0458 0.9586 1.1696 0.9597 0.9389\n",
" 1.0127 1.0362 1.0645 1.0401 0.9940\n",
" 1.0392 0.9234 1.0353 0.9445 1.0385\n",
" 0.9956 1.0615 0.9879 1.1145 1.0179\n",
" 0.9938 1.0485 0.9790 1.0151 0.9637\n",
" 1.0112 1.1698 1.0646 1.0405 0.9756\n",
" 1.0071 0.9771 0.9733 1.0281 1.0347\n",
" 0.9904 1.1132 0.9733 0.9755 1.0604\n",
" 0.9670 1.0463 0.9702 1.0347 1.0588\n",
" 1.0139 0.9782 1.0032 1.0285 1.0174\n",
" 0.9735 1.1312 1.0336 0.9807 0.9863\n",
" 1.0279 1.0128 1.0048 0.8895 1.0619\n",
" 0.9659 1.0020 0.9914 1.0414 0.9584\n",
" 1.0210 0.9121 1.0730 1.0386 1.0042\n",
" 1.0715 0.8621 1.1024 0.9583 1.0856\n",
" 0.9985 0.8651 1.0527 0.9955 0.9771\n",
" 1.0223 0.9709 1.0978 1.0056 0.9970\n",
" 1.0355 0.8533 1.0462 1.0059 0.9380\n",
" 0.9970 0.9935 0.9634 0.9686 1.0367\n",
" 1.0530 1.0121 1.0260 0.9926 0.9940\n",
" 1.0092 1.0116 1.0271 1.0183 1.0577\n"
]
}
],
"source": [
"%plot inline -s 2000,1000\n",
"% network_files = dir(fullfile(pwd, 'ROIs_mask', '*FDR_0.01.nii*'));\n",
"figure;\n",
"labels = {network_files(:).name};\n",
"for i = 1:numel(labels)\n",
" labels{i} = labels{i}(1:end-23);\n",
"end\n",
"\n",
"grouplabels = {'control', 'autism'};\n",
"\n",
"for ibin = 1\n",
"figure;\n",
"for igroup = 1:2\n",
" \n",
" subplot(1, 2, igroup);\n",
" \n",
" [~, sortorder] = sort(median(binned_ratio(:, group==igroup, ibin), 2));\n",
" disp(binned_ratio(sortorder, group==igroup, ibin)');\n",
" \n",
" boxplot(squeeze(binned_ratio(sortorder, group==igroup, ibin))', 'orientation', 'horizontal', 'Labels', labels(sortorder));\n",
" % xlim([0, 300]);\n",
" xlabel('Gradient_i_n_s_i_d_e / Gradient_o_u_t_s_i_d_e of the network');\n",
" title([grouplabels{igroup}, ' group']);\n",
"end\n",
"end\n"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Matlab",
"language": "matlab",
"name": "matlab"
},
"language_info": {
"codemirror_mode": "octave",
"file_extension": ".m",
"help_links": [
{
"text": "MetaKernel Magics",
"url": "https://github.com/calysto/metakernel/blob/master/metakernel/magics/README.md"
}
],
"mimetype": "text/x-matlab",
"name": "matlab",
"version": "0.13.1"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
superliaoyong/plist-forsource | python 第五课课件.ipynb | 1 | 218903 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
" 人生苦短，我用python\n",
"# 泰坦尼克数据处理与分析"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"<img src='https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1502440065892&di=51db15bf76374068735a690806ad66a2&imgtype=0&src=http%3A%2F%2Fwww.pp3.cn%2Fuploads%2F201607%2F20160708007.jpg'>"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 导入数据"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# 如果不知道函数名是什么，可以只敲击函数前几个，然后按tab键，就会有下拉框提示\n",
"titanic = pd.read_csv('train.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 快速预览"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked \n",
"0 0 A/5 21171 7.2500 NaN S \n",
"1 0 PC 17599 71.2833 C85 C \n",
"2 0 STON/O2. 3101282 7.9250 NaN S \n",
"3 0 113803 53.1000 C123 S \n",
"4 0 373450 8.0500 NaN S "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"| 单词 | 翻译 \n",
"| ---: | :--- \n",
"| pclass | 社会阶层(1，精英；2，中层；3，船员/劳苦大众) \n",
"| survived | 是否幸存 \n",
"| name | 名字 \n",
"| sex | 性别 \n",
"| age | 年龄 \n",
"| sibsp | 兄弟姐妹配偶个数 sibling spouse\n",
"| parch | 父母儿女个数 \n",
"| ticket | 船票号 \n",
"| fare | 船票价钱 \n",
"| cabin | 船舱 \n",
"| embarked| 登船口"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 891 entries, 0 to 890\n",
"Data columns (total 12 columns):\n",
"PassengerId 891 non-null int64\n",
"Survived 891 non-null int64\n",
"Pclass 891 non-null int64\n",
"Name 891 non-null object\n",
"Sex 891 non-null object\n",
"Age 714 non-null float64\n",
"SibSp 891 non-null int64\n",
"Parch 891 non-null int64\n",
"Ticket 891 non-null object\n",
"Fare 891 non-null float64\n",
"Cabin 204 non-null object\n",
"Embarked 889 non-null object\n",
"dtypes: float64(2), int64(5), object(5)\n",
"memory usage: 83.6+ KB\n"
]
}
],
"source": [
"titanic.info()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Fare</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>714.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>446.000000</td>\n",
" <td>0.383838</td>\n",
" <td>2.308642</td>\n",
" <td>29.699118</td>\n",
" <td>0.523008</td>\n",
" <td>0.381594</td>\n",
" <td>32.204208</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>257.353842</td>\n",
" <td>0.486592</td>\n",
" <td>0.836071</td>\n",
" <td>14.526497</td>\n",
" <td>1.102743</td>\n",
" <td>0.806057</td>\n",
" <td>49.693429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.420000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>223.500000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>20.125000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>7.910400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>446.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.000000</td>\n",
" <td>28.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>14.454200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>668.500000</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>38.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>31.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>891.000000</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>80.000000</td>\n",
" <td>8.000000</td>\n",
" <td>6.000000</td>\n",
" <td>512.329200</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass Age SibSp \\\n",
"count 891.000000 891.000000 891.000000 714.000000 891.000000 \n",
"mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n",
"std 257.353842 0.486592 0.836071 14.526497 1.102743 \n",
"min 1.000000 0.000000 1.000000 0.420000 0.000000 \n",
"25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n",
"50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n",
"75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n",
"max 891.000000 1.000000 3.000000 80.000000 8.000000 \n",
"\n",
" Parch Fare \n",
"count 891.000000 891.000000 \n",
"mean 0.381594 32.204208 \n",
"std 0.806057 49.693429 \n",
"min 0.000000 0.000000 \n",
"25% 0.000000 7.910400 \n",
"50% 0.000000 14.454200 \n",
"75% 0.000000 31.000000 \n",
"max 6.000000 512.329200 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 把所有数值类型的数据做一个简单的统计\n",
"titanic.describe()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PassengerId 0\n",
"Survived 0\n",
"Pclass 0\n",
"Name 0\n",
"Sex 0\n",
"Age 177\n",
"SibSp 0\n",
"Parch 0\n",
"Ticket 0\n",
"Fare 0\n",
"Cabin 687\n",
"Embarked 2\n",
"dtype: int64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 统计None值个数\n",
"titanic.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 处理空值"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"# 可以填充整个datafram里边的空值, 可以取消注释，实验一下\n",
"# titanic.fillna(0)\n",
"\n",
"# 单独选择一列进行控制填充，可以取消注释，实验一下\n",
"# titanic.Age.fillna(0)\n",
"\n",
"# 年龄的中位数\n",
"titanic.Age.median()\n",
"\n",
"# 按年龄的中位数去填充，此时是返回一个新的Series\n",
"# titanic.Age.fillna(titanic.Age.median())\n",
"\n",
"# 直接填充，并不返回新的Series\n",
"titanic.Age.fillna(titanic.Age.median(), inplace=True)\n",
"\n",
"# 再次查看Age的空值\n",
"titanic.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 尝试从性别进行分析"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"male 577\n",
"female 314\n",
"Name: Sex, dtype: int64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 做简单的汇总统计，经常用到\n",
"titanic.Sex.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"# 生还者中，男女的人数\n",
"survived = titanic[titanic.Survived==1].Sex.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"# 未生还者中，男女的人数\n",
"dead = titanic[titanic.Survived==0].Sex.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11aaa1b00>"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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CL/Va76naJEk12LquA0fEdGB6tboqIp6uq5bSBOwE/L7uOvp0YdRdgTYzfzcH3V8302ko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\npMIZ9JJUOINekgpn0EtS4f4PCXghPOYVCUMAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1168b9080>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df = pd.DataFrame([survived, dead], index=['survived', 'dead'])\n",
"df.plot.bar()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11abc1940>"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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qliQNwoBBn5nL9rHp9L30TeAjB1uUJGnoeAsESSqcQS9JhfNeNyXqmFx3BY3p6Km7AmlM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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11aa9a320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 绘图成功，但是不是我们想要的效果\n",
"# 把dataframe转置一下，就是行列替换\n",
"df = df.T\n",
"df.plot.bar() # df.plot(kind='bar') 等价的"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11ace8e10>"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x11a9779b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 仍然不是我们想要的结果\n",
"df.plot(kind='bar', stacked=True)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11ae65c88>"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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JKpDhLkkFMtwlqUCGuyQVyHCXpAIZ7pJUIMNdkgpkuEtSgQx3SSqQ4S5JBTLcJalAhrsk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\n3CWpQIa7JBXIcJekAv1/tHLeUX9DZ+IAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b157e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 男女中生还者的比例情况\n",
"df['p_survived'] = df.survived / (df.survived + df.dead)\n",
"df['p_dead'] = df.dead / (df.survived + df.dead)\n",
"df[['p_survived', 'p_dead']].plot.bar(stacked=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"通过上面图片可以看出：性别特征对是否生还的影响还是挺大的"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 从年龄进行分析"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [],
"source": [
"# 简单统计\n",
"# titanic.Age.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11b4234e0>"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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U6e2+/x64G/h5ahoC3J9XUWZmVrxKD1JfROmYwtuw/eFBn8yrKDMzK16lAbElIt5vGZG0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\nmRwQZmaWyQFhZmaZHBBmZpbp/wNVy+pJniLCWQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11ae21748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"survived = titanic[titanic.Survived==1].Age\n",
"dead = titanic[titanic.Survived==0].Age\n",
"df = pd.DataFrame([survived, dead], index=['survived', 'dead'])\n",
"df = df.T\n",
"df.plot.hist(stacked=True)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11b754fd0>"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x11b6874e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 直方图柱子显示多一点\n",
"df.plot.hist(stacked=True, bins=30)\n",
"# 中间很高的柱子，是因为我们把空值都替换为了中位数"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11b8c5eb8>"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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eL/cV25WFMR6WLEzfVpwNUUngcjet+nxnAacOj8XtklZfkp46EBFYtbvAc2VhycIYSxam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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b7dbc18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 密度图, 更直观一点\n",
"df.plot.kde()"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 891.000000\n",
"mean 29.361582\n",
"std 13.019697\n",
"min 0.420000\n",
"25% 22.000000\n",
"50% 28.000000\n",
"75% 35.000000\n",
"max 80.000000\n",
"Name: Age, dtype: float64"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 可以查看年龄的分布，来绝对我们图片横轴的取值范围\n",
"titanic.Age.describe()"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11cfcb630>"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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WnD40Z1wsVbU23t+gfR1KnUtTy6qfMteRQSgnMga2zLfmNYSeWXLjyMlylmw9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\nw8zMqrhwmJlZFRcOMzOrcgG45npSWvJftAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11cfe3438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 限定范围\n",
"df.plot.kde(xlim=(0,80))"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11d8a14e0>"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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m/tfMHJ+ZrcBs4OeZ+R+BXwDnlmFzgZtLe0XZpuz/eWZmv1YtSWpYX9bRXwZcEhHr6JqD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\neyz5HqYADFYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11d9f5eb8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"age = 16\n",
"young = titanic[titanic.Age<=age]['Survived'].value_counts()\n",
"old = titanic[titanic.Age>age]['Survived'].value_counts()\n",
"df = pd.DataFrame([young, old], index=['young', 'old'])\n",
"df.columns = ['dead', 'survived']\n",
"df.plot.bar(stacked=True)"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11dbdc9e8>"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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AhrskFchwl6QCGe6SVCDDXZIKZLhLUoEMd0kqkOEuSQUy3CWpQIa7JBXIcJekAhnuklQg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\nCmS4S1KBDHdJKtD/B9to3QAAuPPjAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11d9f8c50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 男女中生还者的比例情况\n",
"df['p_survived'] = df.survived / (df.survived + df.dead)\n",
"df['p_dead'] = df.dead / (df.survived + df.dead)\n",
"df[['p_survived', 'p_dead']].plot.bar(stacked=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 分析票价"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11de1aa90>"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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5fbxdbt7oYwLJ4fYIG3c3pLiExhivWSAxSdm+v5ks2sjJ7WsgGQMtdRBpB2De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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11dd413c8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 票价跟年龄特征相似\n",
"survived = titanic[titanic.Survived==1].Fare\n",
"dead = titanic[titanic.Survived==0].Fare\n",
"df = pd.DataFrame([survived, dead], index=['survived', 'dead'])\n",
"df = df.T\n",
"df.plot.kde()"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 891.000000\n",
"mean 32.204208\n",
"std 49.693429\n",
"min 0.000000\n",
"25% 7.910400\n",
"50% 14.454200\n",
"75% 31.000000\n",
"max 512.329200\n",
"Name: Fare, dtype: float64"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 设定xlim范围，先查看票价的范围\n",
"titanic.Fare.describe()"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11de30518>"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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fNYbpY/P44fJ11NTrvhIRGXwUJN3Vzgl3CHol158zjc17qvnVK5t6t20iIr1A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\niIh0y/8P1AkqU6b3WKkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11da06dd8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot(kind='kde', xlim=(0,513))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"可以看出低票价的人的生还率比较低"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 组合特征"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x11e0b2ac8>"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Tl4xyz9MHWo6laC6lpv1cnXxCfEk1TquG2k5nq+y0SjZuH50WAhuPbXecTK/X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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11e068588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 比如同时查看年龄和票价对生还率的影响\n",
"import matplotlib.pyplot as plt \n",
"\n",
"plt.scatter(titanic[titanic.Survived==0].Age, titanic[titanic.Survived==0].Fare)"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x11e6a9f98>"
]
},
"execution_count": 138,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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5lW0QcRyjRke57nlkwxDXcUgfPUoymbzvz6y9yqlSDTACj8OHj2EYBoaxfNK7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\nf0dLjWOXoAXrAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11fa53eb8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 不美观\n",
"ax = plt.subplot()\n",
"\n",
"# 未生还者\n",
"age = titanic[titanic.Survived==0].Age\n",
"fare = titanic[titanic.Survived==0].Fare\n",
"plt.scatter(age, fare, s=20, marker='o', alpha=0.3, linewidths=1, edgecolors='gray')\n",
"\n",
"# 生还者\n",
"age = titanic[titanic.Survived==1].Age\n",
"fare = titanic[titanic.Survived==1].Fare\n",
"plt.scatter(age, fare, s=20, marker='o', alpha=0.3, linewidths=1, edgecolors='gray', c='red')\n",
"\n",
"ax.set_xlabel('age')\n",
"ax.set_ylabel('fare')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 隐含特征"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 891\n",
"unique 891\n",
"top Williams, Mr. Howard Hugh \"Harry\"\n",
"freq 1\n",
"Name: Name, dtype: object"
]
},
"execution_count": 141,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.Name.describe()"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"titanic['title'] = titanic.Name.apply(lambda name: name.split(',')[1].split('.')[0].strip())"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Mr'"
]
},
"execution_count": 146,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = 'Williams, Mr. Howard Hugh \"Harry\"'\n",
"s.split(',')[-1].split('.')[0].strip()"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"Mr 517\n",
"Miss 182\n",
"Mrs 125\n",
"Master 40\n",
"Dr 7\n",
"Rev 6\n",
"Col 2\n",
"Major 2\n",
"Mlle 2\n",
"Mme 1\n",
"Lady 1\n",
"Ms 1\n",
"Jonkheer 1\n",
"Sir 1\n",
"Don 1\n",
"Capt 1\n",
"the Countess 1\n",
"Name: title, dtype: int64"
]
},
"execution_count": 149,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.title.value_counts()\n",
"# 比如有一个人被称为 Mr，而年龄是不知道的，这个时候可以用 所有 Mr 的年龄平均值来替代，而不是用我们之前最简单的所有数据的中位数"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### gdp"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# 夜光图，简单用灯光图的亮度来模拟gdp"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" <th>title</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>Mr</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" <td>Mrs</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>Miss</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" <td>Mrs</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>Mr</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked title \n",
"0 0 A/5 21171 7.2500 NaN S Mr \n",
"1 0 PC 17599 71.2833 C85 C Mrs \n",
"2 0 STON/O2. 3101282 7.9250 NaN S Miss \n",
"3 0 113803 53.1000 C123 S Mrs \n",
"4 0 373450 8.0500 NaN S Mr "
]
},
"execution_count": 152,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.head()"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"titanic['family_size'] = titanic.SibSp + titanic.Parch + 1"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1 537\n",
"2 161\n",
"3 102\n",
"4 29\n",
"6 22\n",
"5 15\n",
"7 12\n",
"11 7\n",
"8 6\n",
"Name: family_size, dtype: int64"
]
},
"execution_count": 155,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.family_size.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def func(family_size):\n",
" if family_size == 1:\n",
" return 'Singleton'\n",
" if family_size<=4 and family_size>=2:\n",
" return 'SmallFamily'\n",
" if family_size > 4:\n",
" return 'LargeFamily'\n",
" \n",
"titanic['family_type'] = titanic.family_size.apply(func)"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Singleton 537\n",
"SmallFamily 292\n",
"LargeFamily 62\n",
"Name: family_type, dtype: int64"
]
},
"execution_count": 158,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.family_type.value_counts()"
]
},
{
"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": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
bbglab/adventofcode | 2020/ferran/18/18.ipynb | 1 | 5564 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Day 18"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"! cat README.md"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with open('input.txt', 'rt') as f:\n",
" lines = f.read().splitlines()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import re\n",
"from functools import reduce\n",
"import numpy as np\n",
"\n",
"od = {'+': lambda x,y: x+y, '*': lambda x,y: x*y}\n",
"\n",
"def split(expr):\n",
" \"\"\"splits expression into left, operator and reminder\"\"\"\n",
" c = expr[0]\n",
" if re.match(r'\\d', c):\n",
" if len(expr) == 1:\n",
" return c, None, None\n",
" else:\n",
" return c, expr[2], expr[4:]\n",
" elif c == '(':\n",
" cs = np.cumsum(list(map(lambda x: dict(zip('()', [1, -1])).get(x, 0), expr)))\n",
" close = min([i for i, val in enumerate(cs) if val == 0])\n",
" if len(expr) == close + 1:\n",
" return expr[1: close], None, None\n",
" else:\n",
" return expr[1: close], expr[close + 2], expr[close + 4:]\n",
"\n",
" \n",
"def fold(queue):\n",
" acc = queue[0]\n",
" for i, operator in enumerate(queue[1::2]):\n",
" value = queue[2*i+2]\n",
" acc = od[operator](acc, value)\n",
" return acc\n",
" \n",
" \n",
"def evaluate(expr):\n",
" if len(expr) == 1:\n",
" return int(expr)\n",
" queue = [expr]\n",
" while True:\n",
" s = queue.pop()\n",
" left, op, right = split(s)\n",
" queue.append(evaluate(left))\n",
" if op is None:\n",
" return fold(queue)\n",
" else:\n",
" queue.append(op)\n",
" queue.append(right)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# test\n",
"\n",
"assert(evaluate('2 * 3 + (4 * 5)') == 26)\n",
"assert(evaluate('5 + (8 * 3 + 9 + 3 * 4 * 3)') == 437)\n",
"assert(evaluate('5 * 9 * (7 * 3 * 3 + 9 * 3 + (8 + 6 * 4))') == 12240)\n",
"assert(evaluate('((2 + 4 * 9) * (6 + 9 * 8 + 6) + 6) + 2 + 4 * 2') == 13632)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# solution\n",
"\n",
"reduce(lambda x,y:x+y, list(map(evaluate, lines)), 0)"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"--- Part Two ---\n",
"You manage to answer the child's questions and they finish part 1 of their homework, but get stuck when they reach the next section: advanced math.\n",
"\n",
"Now, addition and multiplication have different precedence levels, but they're not the ones you're familiar with. Instead, addition is evaluated before multiplication.\n",
"\n",
"For example, the steps to evaluate the expression 1 + 2 * 3 + 4 * 5 + 6 are now as follows:\n",
"\n",
"1 + 2 * 3 + 4 * 5 + 6\n",
" 3 * 3 + 4 * 5 + 6\n",
" 3 * 7 * 5 + 6\n",
" 3 * 7 * 11\n",
" 21 * 11\n",
" 231\n",
"Here are the other examples from above:\n",
"\n",
"1 + (2 * 3) + (4 * (5 + 6)) still becomes 51.\n",
"2 * 3 + (4 * 5) becomes 46.\n",
"5 + (8 * 3 + 9 + 3 * 4 * 3) becomes 1445.\n",
"5 * 9 * (7 * 3 * 3 + 9 * 3 + (8 + 6 * 4)) becomes 669060.\n",
"((2 + 4 * 9) * (6 + 9 * 8 + 6) + 6) + 2 + 4 * 2 becomes 23340.\n",
"What do you get if you add up the results of evaluating the homework problems using these new rules?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def fold(queue):\n",
" if '*' not in queue:\n",
" return reduce(lambda x,y: x+y, queue[::2])\n",
" else:\n",
" chunks = [list(g[1]) for g in itertools.groupby(queue, key=lambda x: x != '*') if g[0]]\n",
" return reduce(lambda x,y: x*y, list(map(fold, chunks)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# test\n",
"\n",
"assert(evaluate('1 + (2 * 3) + (4 * (5 + 6))') == 51)\n",
"assert(evaluate('2 * 3 + (4 * 5)') == 46)\n",
"assert(evaluate('5 + (8 * 3 + 9 + 3 * 4 * 3)') == 1445)\n",
"assert(evaluate('5 * 9 * (7 * 3 * 3 + 9 * 3 + (8 + 6 * 4))') == 669060)\n",
"assert(evaluate('((2 + 4 * 9) * (6 + 9 * 8 + 6) + 6) + 2 + 4 * 2') == 23340)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# solution\n",
"\n",
"reduce(lambda x,y:x+y, list(map(evaluate, lines)), 0)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:stats_env]",
"language": "python",
"name": "conda-env-stats_env-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": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
CompPhysics/ComputationalPhysics2 | doc/pub/intro/html/intro.ipynb | 1 | 19175 | {
"metadata": {},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Slides from FYS4411/9411 Computational Physics II Lectures\n",
"**Spring 2015**\n",
"\n",
"### Aims\n",
"* Be able to apply two central methods, Variational Monte Carlo and Hartree-Fock (and density functional theory) to properties of atoms and molecules.\n",
"\n",
"* Understand how to simulate qauntum mechanical systems with many interacting particles. The methods are relevant for atomic, molecular, solid state, materials science, nanotechnology, quantum chemistry and nuclear physics.\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"### Overview of first week\n",
"\n",
" * Monday 9.15am-12pm: First lecture: Presentation of the course, aims and content\n",
"\n",
" * Monday 9.15am-12pm: Introduction to Monte Carlo methods and basic many-body physics\n",
"\n",
" * Tuesday 9.15am-12pm: Basic many-body physics and variational Monte Carlo methods\n",
"\n",
" * Wednesday 9.15am-12pm: Variational Monte Carlo methods and presentation of project 1\n",
"\n",
" * Thursday 2.15pm-4pm: Continued discussion of first project, variational Monte Carlo methods and codes\n",
"\n",
" * Computer lab: Thursday 4pm-7pm. First time: Thursday this week at room FV329.\n",
"\n",
"\n",
"\n",
"### Lectures and ComputerLab\n",
"\n",
" * Lectures: Thursday (2.15pm-4pm), remotely via adove connect every second week.\n",
"\n",
" * Computerlab: Thursday (2.15pm-7pm), first time January 22, last lab session May 14.\n",
"\n",
" * Weekly plans and all other information are on the webpage, see URL: ''\n",
"\n",
" * Second intensive week starts April 7 and ends April 10.\n",
"\n",
" * First project to be handed in February 27 at noon.\n",
"\n",
" * Second project to be handed in March 27 at noon.\n",
"\n",
" * Third and final project to be handed in June 1 at noon.\n",
"\n",
"\n",
"\n",
"### Course Format\n",
"\n",
" * Three compulsory projects. Electronic reports only. You are free to choose your format.\n",
"\n",
" * Evaluation and grading: The first two projects count 25% while the last project counts 50% of the final grade. There is no oral or written exam.\n",
"\n",
" * The computer lab (room FV329) consists of 16 Linux PCs, but many prefer own laptops. C/C++ is the default programming language, but Fortran2008 and Python are also used. All source codes discussed during the lectures can be found at the webpage of the course. We recommend either C/C++, Fortran2008 or Python as languages.\n",
"\n",
"\n",
"\n",
"\n",
"### Topics covered in this course\n",
" * Parallelization (MPI and OpenMP), high-performance computing topics. Choose between Fortran2008 and/or C++ as programming languages. Python also possible as programming language. \n",
"\n",
" * Algorithms for Monte Carlo Simulations (multidimensional integrals), Metropolis-Hastings and importance sampling algorithms. Improved Monte Carlo methods.\n",
"\n",
" * Statistical analysis of data from Monte Carlo calculations, blocking method. (exercise 1 of part 1)\n",
"\n",
" * Eigenvalue solvers, efficient computations of integrals\n",
"\n",
"\n",
"\n",
"### Topics covered in this course\n",
" * Search for minima in multidimensional spaces (conjugate gradient method, steepest descent method, quasi-Newton-Raphson, Broyden-Jacobian).\n",
"\n",
" * Iterative methods for solutions of non-linear equations.\n",
"\n",
" * Object orientation\n",
"\n",
" * Variational Monte Carlo for 'ab initio' studies of quantum mechanical many-body systems.\n",
"\n",
" * Simulation of three-dimensional systems like atoms or molecules.\n",
"\n",
" * Hartree-Fock method to study atoms and molecules\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"### Quantum Monte Carlo Motivation\n",
"Most quantum mechanical problems of interest in for example atomic, molecular, nuclear and solid state \n",
"physics consist of a large number of interacting electrons and ions or nucleons. \n",
"\n",
"The total number of particles $N$ is usually sufficiently large\n",
"that an exact solution cannot be found. \n",
"\n",
"Typically, \n",
"the expectation value for a chosen hamiltonian for a system of $N$ particles is"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\langle H \\rangle =\n",
" \\frac{\\int d\\boldsymbol{R}_1d\\boldsymbol{R}_2\\dots d\\boldsymbol{R}_N\n",
" \\Psi^{\\ast}(\\boldsymbol{R_1},\\boldsymbol{R}_2,\\dots,\\boldsymbol{R}_N)\n",
" H(\\boldsymbol{R_1},\\boldsymbol{R}_2,\\dots,\\boldsymbol{R}_N)\n",
" \\Psi(\\boldsymbol{R_1},\\boldsymbol{R}_2,\\dots,\\boldsymbol{R}_N)}\n",
" {\\int d\\boldsymbol{R}_1d\\boldsymbol{R}_2\\dots d\\boldsymbol{R}_N\n",
" \\Psi^{\\ast}(\\boldsymbol{R_1},\\boldsymbol{R}_2,\\dots,\\boldsymbol{R}_N)\n",
" \\Psi(\\boldsymbol{R_1},\\boldsymbol{R}_2,\\dots,\\boldsymbol{R}_N)},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"an in general intractable problem.\n",
"\n",
" This integral is actually the starting point in a Variational Monte Carlo calculation. **Gaussian quadrature: Forget it**! Given 10 particles and 10 mesh points for each degree of freedom\n",
"and an\n",
" ideal 1 Tflops machine (all operations take the same time), how long will it take to compute the above integral? The lifetime of the universe is of the order of $10^{17}$ s.\n",
"\n",
"\n",
"\n",
"\n",
"### Quantum Monte Carlo Motivation\n",
"As an example from the nuclear many-body problem, we have Schroedinger's equation as a differential equation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\hat{H}\\Psi(\\boldsymbol{r}_1,..,\\boldsymbol{r}_A,\\alpha_1,..,\\alpha_A)=E\\Psi(\\boldsymbol{r}_1,..,\\boldsymbol{r}_A,\\alpha_1,..,\\alpha_A)\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{r}_1,..,\\boldsymbol{r}_A,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"are the coordinates and"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\alpha_1,..,\\alpha_A,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"are sets of relevant quantum numbers such as spin and isospin for a system of $A$ nucleons ($A=N+Z$, $N$ being the number of neutrons and $Z$ the number of protons).\n",
"\n",
"\n",
"\n",
"\n",
"### Quantum Monte Carlo Motivation\n",
"There are"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"2^A\\times \\left(\\begin{array}{c} A\\\\ Z\\end{array}\\right)\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"coupled second-order differential equations in $3A$ dimensions.\n",
"\n",
"For a nucleus like ${}^{10}\\mbox{Be}$ this number is **215040**.\n",
"This is a truely challenging many-body problem.\n",
"\n",
"Methods like partial differential equations can at most be used for 2-3 particles.\n",
"\n",
"\n",
"\n",
"\n",
"### Quantum Monte Carlo Motivation\n",
"* Monte-Carlo methods\n",
"\n",
"* Renormalization group (RG) methods, in particular density matrix RG\n",
"\n",
"* Large-scale diagonalization (Iterative methods, Lanczo's method, dimensionalities $10^{10}$ states)\n",
"\n",
"* Coupled cluster theory, favoured method in quantum chemistry, molecular and atomic physics. Applications to ab initio calculations in nuclear physics as well for large nuclei.\n",
"\n",
"* Perturbative many-body methods \n",
"\n",
"* Green's function methods\n",
"\n",
"* Density functional theory/Mean-field theory and Hartree-Fock theory\n",
"\n",
"The physics of the system hints at which many-body methods to use.\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"### Quantum Monte Carlo Motivation\n",
"**Pros and Cons of Monte Carlo.**\n",
"\n",
"* Is physically intuitive.\n",
"\n",
"* Allows one to study systems with many degrees of freedom. Diffusion Monte Carlo (DMC) and Green's function Monte Carlo (GFMC) yield in principle the exact solution to Schroedinger's equation.\n",
"\n",
"* Variational Monte Carlo (VMC) is easy to implement but needs a reliable trial wave function, can be difficult to obtain. This is where we will use Hartree-Fock theory to construct an optimal basis.\n",
"\n",
"* DMC/GFMC for fermions (spin with half-integer values, electrons, baryons, neutrinos, quarks) has a sign problem. Nature prefers an anti-symmetric wave function. PDF in this case given distribution of random walkers ($p\\ge 0$).\n",
"\n",
"* The solution has a statistical error, which can be large. \n",
"\n",
"* There is a limit for how large systems one can study, DMC needs a huge number of random walkers in order to achieve stable results. \n",
"\n",
"* Obtain only the lowest-lying states with a given symmetry. Can get excited states.\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"### Quantum Monte Carlo Motivation\n",
"**Where and why do we use Monte Carlo Methods in Quantum Physics.**\n",
"\n",
"* Quantum systems with many particles at finite temperature: Path Integral Monte Carlo with applications to dense matter and quantum liquids (phase transitions from normal fluid to superfluid). Strong correlations.\n",
"\n",
"* Bose-Einstein condensation of dilute gases, method transition from non-linear PDE to Diffusion Monte Carlo as density increases.\n",
"\n",
"* Light atoms, molecules, solids and nuclei. \n",
"\n",
"* Lattice Quantum-Chromo Dynamics. Impossible to solve without MC calculations. \n",
"\n",
"* Simulations of systems in solid state physics, from semiconductors to spin systems. Many electrons active and possibly strong correlations.\n",
"\n",
"\n",
"\n",
"### Quantum Monte Carlo Motivation\n",
"Given a hamiltonian $H$ and a trial wave function $\\Psi_T$, the variational principle states that the expectation value of $\\langle H \\rangle$, defined through"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"E[H]= \\langle H \\rangle =\n",
" \\frac{\\int d\\boldsymbol{R}\\Psi^{\\ast}_T(\\boldsymbol{R})H(\\boldsymbol{R})\\Psi_T(\\boldsymbol{R})}\n",
" {\\int d\\boldsymbol{R}\\Psi^{\\ast}_T(\\boldsymbol{R})\\Psi_T(\\boldsymbol{R})},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"is an upper bound to the ground state energy $E_0$ of the hamiltonian $H$, that is"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"E_0 \\le \\langle H \\rangle .\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In general, the integrals involved in the calculation of various expectation values are multi-dimensional ones. Traditional integration methods such as the Gauss-Legendre will not be adequate for say the computation of the energy of a many-body system.\n",
"\n",
"\n",
"\n",
"### Quantum Monte Carlo Motivation\n",
"The trial wave function can be expanded in the eigenstates of the hamiltonian since they form a complete set, viz.,"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\Psi_T(\\boldsymbol{R})=\\sum_i a_i\\Psi_i(\\boldsymbol{R}),\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and assuming the set of eigenfunctions to be normalized one obtains"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\frac{\\sum_{nm}a^*_ma_n \\int d\\boldsymbol{R}\\Psi^{\\ast}_m(\\boldsymbol{R})H(\\boldsymbol{R})\\Psi_n(\\boldsymbol{R})}\n",
" {\\sum_{nm}a^*_ma_n \\int d\\boldsymbol{R}\\Psi^{\\ast}_m(\\boldsymbol{R})\\Psi_n(\\boldsymbol{R})} =\\frac{\\sum_{n}a^2_n E_n}\n",
" {\\sum_{n}a^2_n} \\ge E_0,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where we used that $H(\\boldsymbol{R})\\Psi_n(\\boldsymbol{R})=E_n\\Psi_n(\\boldsymbol{R})$.\n",
"In general, the integrals involved in the calculation of various expectation\n",
"values are multi-dimensional ones. \n",
"The variational principle yields the lowest state of a given symmetry.\n",
"\n",
"\n",
"\n",
"\n",
"### Quantum Monte Carlo Motivation\n",
"In most cases, a wave function has only small values in large parts of \n",
"configuration space, and a straightforward procedure which uses\n",
"homogenously distributed random points in configuration space \n",
"will most likely lead to poor results. This may suggest that some kind\n",
"of importance sampling combined with e.g., the Metropolis algorithm \n",
"may be a more efficient way of obtaining the ground state energy.\n",
"The hope is then that those regions of configurations space where\n",
"the wave function assumes appreciable values are sampled more \n",
"efficiently.\n",
"\n",
"\n",
"\n",
"\n",
"### Quantum Monte Carlo Motivation\n",
"The tedious part in a VMC calculation is the search for the variational\n",
"minimum. A good knowledge of the system is required in order to carry out\n",
"reasonable VMC calculations. This is not always the case, \n",
"and often VMC calculations \n",
"serve rather as the starting\n",
"point for so-called diffusion Monte Carlo calculations (DMC). DMC is a way of\n",
"solving exactly the many-body Schroedinger equation by means of \n",
"a stochastic procedure. A good guess on the binding energy\n",
"and its wave function is however necessary. \n",
"A carefully performed VMC calculation can aid in this context.\n",
"\n",
"\n",
"\n",
"\n",
"### Quantum Monte Carlo Motivation\n",
"* Construct first a trial wave function $\\psi_T(\\boldsymbol{R},\\boldsymbol{\\alpha})$, for a many-body system consisting of $N$ particles located at positions \n",
"\n",
"$\\boldsymbol{R}=(\\boldsymbol{R}_1,\\dots ,\\boldsymbol{R}_N)$. The trial wave function depends on $\\alpha$ variational parameters $\\boldsymbol{\\alpha}=(\\alpha_1,\\dots ,\\alpha_M)$.\n",
"* Then we evaluate the expectation value of the hamiltonian $H$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"E[H]=\\langle H \\rangle =\n",
" \\frac{\\int d\\boldsymbol{R}\\Psi^{\\ast}_{T}(\\boldsymbol{R},\\boldsymbol{\\alpha})H(\\boldsymbol{R})\\Psi_{T}(\\boldsymbol{R},\\boldsymbol{\\alpha})}\n",
" {\\int d\\boldsymbol{R}\\Psi^{\\ast}_{T}(\\boldsymbol{R},\\boldsymbol{\\alpha})\\Psi_{T}(\\boldsymbol{R},\\boldsymbol{\\alpha})}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Thereafter we vary $\\alpha$ according to some minimization algorithm and return to the first step.\n",
"\n",
"\n",
"\n",
"\n",
"### Quantum Monte Carlo Motivation\n",
"**Basic steps.**\n",
"\n",
"Choose a trial wave function\n",
"$\\psi_T(\\boldsymbol{R})$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"P(\\boldsymbol{R})= \\frac{\\left|\\psi_T(\\boldsymbol{R})\\right|^2}{\\int \\left|\\psi_T(\\boldsymbol{R})\\right|^2d\\boldsymbol{R}}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is our new probability distribution function (PDF).\n",
"The approximation to the expectation value of the Hamiltonian is now"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"E[H(\\boldsymbol{\\alpha})] = \n",
" \\frac{\\int d\\boldsymbol{R}\\Psi^{\\ast}_T(\\boldsymbol{R},\\boldsymbol{\\alpha})H(\\boldsymbol{R})\\Psi_T(\\boldsymbol{R},\\boldsymbol{\\alpha})}\n",
" {\\int d\\boldsymbol{R}\\Psi^{\\ast}_T(\\boldsymbol{R},\\boldsymbol{\\alpha})\\Psi_T(\\boldsymbol{R},\\boldsymbol{\\alpha})}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Quantum Monte Carlo Motivation\n",
"Define a new quantity"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"eq:locale1\"></div>\n",
"\n",
"$$\n",
"E_L(\\boldsymbol{R},\\boldsymbol{\\alpha})=\\frac{1}{\\psi_T(\\boldsymbol{R},\\boldsymbol{\\alpha})}H\\psi_T(\\boldsymbol{R},\\boldsymbol{\\alpha}),\n",
" \\label{eq:locale1} \\tag{1}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"called the local energy, which, together with our trial PDF yields"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"eq:vmc1\"></div>\n",
"\n",
"$$\n",
"E[H(\\boldsymbol{\\alpha})]= = \\int P(\\boldsymbol{R})E_L(\\boldsymbol{R}) d\\boldsymbol{R}\\approx \\frac{1}{N}\\sum_{i=1}^NP(\\boldsymbol{R_i},\\boldsymbol{\\alpha})E_L(\\boldsymbol{R_i},\\boldsymbol{\\alpha})\n",
" \\label{eq:vmc1} \\tag{2}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"with $N$ being the number of Monte Carlo samples."
]
}
],
"metadata": {}
}
]
}
| cc0-1.0 |
google/or-tools | examples/notebook/contrib/magic_square_mip.ipynb | 1 | 8461 | {
"cells": [
{
"cell_type": "markdown",
"id": "google",
"metadata": {},
"source": [
"##### Copyright 2021 Google LLC."
]
},
{
"cell_type": "markdown",
"id": "apache",
"metadata": {},
"source": [
"Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"you may not use this file except in compliance with the License.\n",
"You may obtain a copy of the License at\n",
"\n",
" http://www.apache.org/licenses/LICENSE-2.0\n",
"\n",
"Unless required by applicable law or agreed to in writing, software\n",
"distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"See the License for the specific language governing permissions and\n",
"limitations under the License.\n"
]
},
{
"cell_type": "markdown",
"id": "basename",
"metadata": {},
"source": [
"# magic_square_mip"
]
},
{
"cell_type": "markdown",
"id": "link",
"metadata": {},
"source": [
"<table align=\"left\">\n",
"<td>\n",
"<a href=\"https://colab.research.google.com/github/google/or-tools/blob/master/examples/notebook/contrib/magic_square_mip.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/master/tools/colab_32px.png\"/>Run in Google Colab</a>\n",
"</td>\n",
"<td>\n",
"<a href=\"https://github.com/google/or-tools/blob/master/examples/contrib/magic_square_mip.py\"><img src=\"https://raw.githubusercontent.com/google/or-tools/master/tools/github_32px.png\"/>View source on GitHub</a>\n",
"</td>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"id": "doc",
"metadata": {},
"source": [
"First, you must install [ortools](https://pypi.org/project/ortools/) package in this colab."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "install",
"metadata": {},
"outputs": [],
"source": [
"!pip install ortools"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "code",
"metadata": {},
"outputs": [],
"source": [
"# Copyright 2011 Hakan Kjellerstrand [email protected]\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# http://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License.\n",
"\"\"\"\n",
"\n",
" Magic square (integer programming) in Google or-tools.\n",
"\n",
" Translated from GLPK:s example magic.mod\n",
" '''\n",
" MAGIC, Magic Square\n",
"\n",
" Written in GNU MathProg by Andrew Makhorin <[email protected]>\n",
"\n",
" In recreational mathematics, a magic square of order n is an\n",
" arrangement of n^2 numbers, usually distinct integers, in a square,\n",
" such that n numbers in all rows, all columns, and both diagonals sum\n",
" to the same constant. A normal magic square contains the integers\n",
" from 1 to n^2.\n",
"\n",
" (From Wikipedia, the free encyclopedia.)\n",
" '''\n",
"\n",
" Compare to the CP version:\n",
" http://www.hakank.org/google_or_tools/magic_square.py\n",
"\n",
" Here we also experiment with how long it takes when\n",
" using an output_matrix (much longer).\n",
"\n",
"\n",
" This model was created by Hakan Kjellerstrand ([email protected])\n",
" Also see my other Google CP Solver models:\n",
" http://www.hakank.org/google_or_tools/\n",
"\"\"\"\n",
"import sys\n",
"from ortools.linear_solver import pywraplp\n",
"\n",
"#\n",
"# main(n, use_output_matrix)\n",
"# n: size of matrix\n",
"# use_output_matrix: use the output_matrix\n",
"#\n",
"\n",
"\n",
"\n",
"# Create the solver.\n",
"\n",
"print('Solver: ', sol)\n",
"\n",
"# using GLPK\n",
"if sol == 'GLPK':\n",
" solver = pywraplp.Solver('CoinsGridGLPK',\n",
" pywraplp.Solver.GLPK_MIXED_INTEGER_PROGRAMMING)\n",
"else:\n",
" # Using CLP\n",
" solver = pywraplp.Solver('CoinsGridCLP',\n",
" pywraplp.Solver.CBC_MIXED_INTEGER_PROGRAMMING)\n",
"\n",
"#\n",
"# data\n",
"#\n",
"print('n = ', n)\n",
"\n",
"# range_n = range(1, n+1)\n",
"range_n = list(range(0, n))\n",
"\n",
"N = n * n\n",
"range_N = list(range(1, N + 1))\n",
"\n",
"#\n",
"# variables\n",
"#\n",
"\n",
"# x[i,j,k] = 1 means that cell (i,j) contains integer k\n",
"x = {}\n",
"for i in range_n:\n",
" for j in range_n:\n",
" for k in range_N:\n",
" x[i, j, k] = solver.IntVar(0, 1, 'x[%i,%i,%i]' % (i, j, k))\n",
"\n",
"# For output. Much slower....\n",
"if use_output_matrix == 1:\n",
" print('Using an output matrix')\n",
" square = {}\n",
" for i in range_n:\n",
" for j in range_n:\n",
" square[i, j] = solver.IntVar(1, n * n, 'square[%i,%i]' % (i, j))\n",
"\n",
"# the magic sum\n",
"s = solver.IntVar(1, n * n * n, 's')\n",
"\n",
"#\n",
"# constraints\n",
"#\n",
"\n",
"# each cell must be assigned exactly one integer\n",
"for i in range_n:\n",
" for j in range_n:\n",
" solver.Add(solver.Sum([x[i, j, k] for k in range_N]) == 1)\n",
"\n",
"# each integer must be assigned exactly to one cell\n",
"for k in range_N:\n",
" solver.Add(solver.Sum([x[i, j, k] for i in range_n for j in range_n]) == 1)\n",
"\n",
"# # the sum in each row must be the magic sum\n",
"for i in range_n:\n",
" solver.Add(\n",
" solver.Sum([k * x[i, j, k] for j in range_n for k in range_N]) == s)\n",
"\n",
"# # the sum in each column must be the magic sum\n",
"for j in range_n:\n",
" solver.Add(\n",
" solver.Sum([k * x[i, j, k] for i in range_n for k in range_N]) == s)\n",
"\n",
"# # the sum in the diagonal must be the magic sum\n",
"solver.Add(\n",
" solver.Sum([k * x[i, i, k] for i in range_n for k in range_N]) == s)\n",
"\n",
"# # the sum in the co-diagonal must be the magic sum\n",
"if range_n[0] == 1:\n",
" # for range_n = 1..n\n",
" solver.Add(\n",
" solver.Sum([k * x[i, n - i + 1, k]\n",
" for i in range_n\n",
" for k in range_N]) == s)\n",
"else:\n",
" # for range_n = 0..n-1\n",
" solver.Add(\n",
" solver.Sum([k * x[i, n - i - 1, k]\n",
" for i in range_n\n",
" for k in range_N]) == s)\n",
"\n",
"# for output\n",
"if use_output_matrix == 1:\n",
" for i in range_n:\n",
" for j in range_n:\n",
" solver.Add(\n",
" square[i, j] == solver.Sum([k * x[i, j, k] for k in range_N]))\n",
"\n",
"#\n",
"# solution and search\n",
"#\n",
"solver.Solve()\n",
"\n",
"print()\n",
"\n",
"print('s: ', int(s.SolutionValue()))\n",
"if use_output_matrix == 1:\n",
" for i in range_n:\n",
" for j in range_n:\n",
" print(int(square[i, j].SolutionValue()), end=' ')\n",
" print()\n",
" print()\n",
"else:\n",
" for i in range_n:\n",
" for j in range_n:\n",
" print(\n",
" sum([int(k * x[i, j, k].SolutionValue()) for k in range_N]),\n",
" ' ',\n",
" end=' ')\n",
" print()\n",
"\n",
"print('\\nx:')\n",
"for i in range_n:\n",
" for j in range_n:\n",
" for k in range_N:\n",
" print(int(x[i, j, k].SolutionValue()), end=' ')\n",
" print()\n",
"\n",
"print()\n",
"print('walltime :', solver.WallTime(), 'ms')\n",
"if sol == 'CBC':\n",
" print('iterations:', solver.Iterations())\n",
"\n"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
| apache-2.0 |
colour-science/colour-hdri | colour_hdri/examples/examples_absolute_luminance_calibration_and_photometric_exposure_conversion.ipynb | 1 | 3165424 | null | bsd-3-clause |
james-prior/cohpy | 20160527-cohpy-set-order.ipynb | 1 | 151914 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook explores the order of items in sets.\n",
"It seems that when Jupyter prints out the value of a set,\n",
"if the set has less than 1000 items\n",
"the output is sorted.\n",
"Sets with 1000 or more items seem to not be sorted.\n",
"\n",
"This seems to be just Jupyter behavior, not of Python itself.\n",
"str(b), repr(b), print(b), print(str(b)), and print(repr(b))\n",
"are seem to always be unordered."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'city': None, 'hello': 324, 'world': 98743}"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"{'hello': 324, 'world': 98743, 'city': None}"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"b = {'hello', 'world', 'city', 'zebra', 'aardvark', 'Aaron', 'aaa', 'foo', 'xray', 'alex', 'jim', 'chris'}"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(set, 99171)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"words = set(open('/usr/share/dict/american-english').read().split())\n",
"type(words), len(words)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from itertools import islice"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{\"AB's\",\n",
" \"Abe's\",\n",
" 'Adela',\n",
" \"Adolf's\",\n",
" 'Ahriman',\n",
" 'Alicia',\n",
" \"Allison's\",\n",
" 'Alyce',\n",
" 'Anubis',\n",
" 'Arcadia',\n",
" 'Armando',\n",
" 'Aurora',\n",
" 'Avalon',\n",
" 'Barclay',\n",
" 'Blucher',\n",
" 'Brahmans',\n",
" 'Brits',\n",
" \"Bundesbank's\",\n",
" \"Byers's\",\n",
" \"CPA's\",\n",
" \"Cancun's\",\n",
" 'Carlene',\n",
" 'Casals',\n",
" 'Cheetos',\n",
" 'Chou',\n",
" 'Christina',\n",
" 'Confucians',\n",
" 'Cormack',\n",
" \"Cr's\",\n",
" 'Croats',\n",
" \"Cupid's\",\n",
" 'Cyrano',\n",
" \"Danielle's\",\n",
" 'Delgado',\n",
" 'Delphic',\n",
" \"Descartes's\",\n",
" 'Dexter',\n",
" 'Drano',\n",
" 'Duncan',\n",
" \"Dunkirk's\",\n",
" \"Earle's\",\n",
" \"Emacs's\",\n",
" \"Eniwetok's\",\n",
" \"Eocene's\",\n",
" \"Eurasia's\",\n",
" 'Fafnir',\n",
" \"Felice's\",\n",
" 'Francisco',\n",
" 'Freud',\n",
" 'Gadsden',\n",
" 'Gandhian',\n",
" 'Gantry',\n",
" \"Gertrude's\",\n",
" 'Getty',\n",
" 'Gillette',\n",
" 'Ginny',\n",
" 'Godiva',\n",
" 'Gorey',\n",
" \"HP's\",\n",
" 'Hangzhou',\n",
" \"Hitler's\",\n",
" 'Ingres',\n",
" 'Innocent',\n",
" 'Iroquois',\n",
" 'Joaquin',\n",
" 'Joplin',\n",
" \"Juanita's\",\n",
" \"Judaism's\",\n",
" 'June',\n",
" \"Kara's\",\n",
" \"Khwarizmi's\",\n",
" \"Knesset's\",\n",
" 'Knickerbocker',\n",
" 'Kurosawa',\n",
" \"Laocoon's\",\n",
" 'Lawson',\n",
" 'Leeds',\n",
" 'Lela',\n",
" \"Lew's\",\n",
" \"Liege's\",\n",
" 'Long',\n",
" 'Luann',\n",
" 'Lucile',\n",
" \"Mackinac's\",\n",
" \"Maidenform's\",\n",
" 'Mancini',\n",
" 'Maracaibo',\n",
" \"Mark's\",\n",
" 'Mather',\n",
" 'Melville',\n",
" \"Merriam's\",\n",
" 'Midwest',\n",
" 'Mississippian',\n",
" 'Mitterrand',\n",
" 'Mohammedans',\n",
" \"Mohorovicic's\",\n",
" \"Morpheus's\",\n",
" \"Naphtali's\",\n",
" \"Naziism's\",\n",
" 'Niger',\n",
" \"Nobelist's\",\n",
" \"Oise's\",\n",
" 'Otto',\n",
" \"Patterson's\",\n",
" 'Paula',\n",
" 'Perseid',\n",
" \"Persia's\",\n",
" 'Petersen',\n",
" 'Piaget',\n",
" \"Podhoretz's\",\n",
" 'Polanski',\n",
" 'Ponce',\n",
" 'Popocatepetl',\n",
" 'Prut',\n",
" \"Psalter's\",\n",
" 'Ringo',\n",
" 'Roosevelt',\n",
" \"Rosalyn's\",\n",
" 'Rosie',\n",
" \"Rostov's\",\n",
" 'Rover',\n",
" 'Sakhalin',\n",
" 'Scripture',\n",
" 'Siberia',\n",
" \"Snyder's\",\n",
" 'Sq',\n",
" 'Stamford',\n",
" \"Starr's\",\n",
" \"Stephan's\",\n",
" 'Sterno',\n",
" 'Subaru',\n",
" 'Sumerian',\n",
" 'Teri',\n",
" \"Tibetan's\",\n",
" \"Trey's\",\n",
" \"Trojan's\",\n",
" 'Trujillo',\n",
" 'Tubman',\n",
" 'Turner',\n",
" \"Valentine's\",\n",
" \"Vulcan's\",\n",
" \"Walmart's\",\n",
" 'Westinghouse',\n",
" 'Xanthippe',\n",
" \"Xingu's\",\n",
" \"Yamagata's\",\n",
" 'Yaobang',\n",
" 'Yellowstone',\n",
" \"Yuletide's\",\n",
" 'Zamora',\n",
" 'abscessing',\n",
" 'accessing',\n",
" 'aches',\n",
" 'addle',\n",
" 'adjurations',\n",
" 'advents',\n",
" 'advertisement',\n",
" 'advertisers',\n",
" 'airbrushing',\n",
" 'alleluia',\n",
" 'alms',\n",
" \"ambrosia's\",\n",
" \"amiability's\",\n",
" \"anaemia's\",\n",
" 'anaesthetic',\n",
" 'analogues',\n",
" \"analogy's\",\n",
" \"aneurism's\",\n",
" 'annoyances',\n",
" 'anonymity',\n",
" 'anorexic',\n",
" 'antiquaries',\n",
" 'anxiously',\n",
" 'appertain',\n",
" 'applicable',\n",
" 'appreciate',\n",
" 'armistice',\n",
" 'armlet',\n",
" \"armlet's\",\n",
" 'astrophysicist',\n",
" 'athletically',\n",
" 'aureolas',\n",
" 'authorship',\n",
" 'automatons',\n",
" 'avenge',\n",
" 'avows',\n",
" 'awoke',\n",
" 'babysat',\n",
" \"backstroke's\",\n",
" 'bailiff',\n",
" 'barbarity',\n",
" 'barber',\n",
" 'barrener',\n",
" 'battlements',\n",
" 'benign',\n",
" 'berates',\n",
" 'betrothals',\n",
" 'biassed',\n",
" 'bib',\n",
" 'big',\n",
" 'bills',\n",
" \"birch's\",\n",
" 'blazed',\n",
" \"bleach's\",\n",
" 'blindfolding',\n",
" 'blinding',\n",
" \"blotter's\",\n",
" 'blowtorch',\n",
" 'bold',\n",
" 'bolted',\n",
" 'bookend',\n",
" 'born',\n",
" 'boroughs',\n",
" 'bounces',\n",
" 'boundless',\n",
" 'bounds',\n",
" 'bourgeois',\n",
" 'boxing',\n",
" 'brainy',\n",
" \"brat's\",\n",
" \"bronchitis's\",\n",
" 'broth',\n",
" \"browse's\",\n",
" 'bulkier',\n",
" 'bushwhackers',\n",
" 'bussing',\n",
" 'buttoned',\n",
" 'caliphate',\n",
" 'canoeing',\n",
" \"cantaloup's\",\n",
" 'capacitance',\n",
" 'castigation',\n",
" 'catalogers',\n",
" 'caucused',\n",
" 'cedes',\n",
" 'celibates',\n",
" 'cemeteries',\n",
" 'chagrining',\n",
" 'cheapening',\n",
" 'checklist',\n",
" \"chemical's\",\n",
" \"cherry's\",\n",
" \"chill's\",\n",
" 'chillers',\n",
" 'chiropractors',\n",
" 'chlorine',\n",
" \"chock's\",\n",
" 'chokers',\n",
" 'chorale',\n",
" \"chromosome's\",\n",
" \"cigarillo's\",\n",
" 'cinema',\n",
" 'circlets',\n",
" 'circularizing',\n",
" 'civics',\n",
" 'clapboard',\n",
" 'clarified',\n",
" 'clasping',\n",
" 'classes',\n",
" \"classification's\",\n",
" 'classmate',\n",
" \"clime's\",\n",
" 'clitoral',\n",
" 'close',\n",
" \"clothespin's\",\n",
" 'clothespins',\n",
" 'coding',\n",
" 'coeducational',\n",
" 'cognac',\n",
" \"collation's\",\n",
" 'collectable',\n",
" \"colonialism's\",\n",
" 'comediennes',\n",
" 'commencing',\n",
" 'commendable',\n",
" 'commissariats',\n",
" 'commitments',\n",
" 'communicator',\n",
" 'compelled',\n",
" 'competences',\n",
" \"competitiveness's\",\n",
" \"confine's\",\n",
" 'confiscating',\n",
" 'consider',\n",
" 'constable',\n",
" 'constitution',\n",
" \"consumerism's\",\n",
" 'consummate',\n",
" 'converses',\n",
" 'cookouts',\n",
" 'cooky',\n",
" 'coordinating',\n",
" \"corespondent's\",\n",
" 'corpses',\n",
" 'corroded',\n",
" 'cosmically',\n",
" \"counselor's\",\n",
" 'county',\n",
" 'couriers',\n",
" 'crave',\n",
" 'creamed',\n",
" 'crisps',\n",
" 'crunchier',\n",
" 'crypt',\n",
" 'customized',\n",
" 'daiquiris',\n",
" 'damages',\n",
" 'dashboards',\n",
" \"dateline's\",\n",
" 'daughter',\n",
" 'daze',\n",
" 'deadest',\n",
" 'debarment',\n",
" 'deceived',\n",
" 'decencies',\n",
" 'decentralizing',\n",
" \"decompression's\",\n",
" 'decriminalized',\n",
" 'dedications',\n",
" 'defiled',\n",
" 'deformities',\n",
" \"deism's\",\n",
" \"delinquent's\",\n",
" 'denounces',\n",
" \"dentist's\",\n",
" 'depressingly',\n",
" \"desecration's\",\n",
" 'despoiling',\n",
" 'destabilize',\n",
" 'destructive',\n",
" 'detraction',\n",
" 'devaluations',\n",
" \"deviance's\",\n",
" 'devilries',\n",
" 'devises',\n",
" \"dick's\",\n",
" 'different',\n",
" 'dire',\n",
" 'dis',\n",
" 'disadvantageously',\n",
" \"disaster's\",\n",
" 'discounts',\n",
" 'disease',\n",
" 'disestablishing',\n",
" 'disfigured',\n",
" 'disillusioned',\n",
" 'disillusioning',\n",
" 'dislocating',\n",
" \"disposable's\",\n",
" \"distillery's\",\n",
" 'distinction',\n",
" 'district',\n",
" 'dithers',\n",
" 'dominoes',\n",
" 'dormice',\n",
" 'drams',\n",
" 'drank',\n",
" 'dress',\n",
" 'drown',\n",
" 'drudged',\n",
" 'dryads',\n",
" 'ducats',\n",
" 'duelling',\n",
" 'dumbness',\n",
" 'durability',\n",
" 'dwarfed',\n",
" 'dwelling',\n",
" \"dying's\",\n",
" 'dynastic',\n",
" 'earthward',\n",
" \"easel's\",\n",
" \"easterner's\",\n",
" 'eclipsed',\n",
" 'ecstatically',\n",
" 'eddying',\n",
" \"edit's\",\n",
" 'editorializes',\n",
" 'eighteen',\n",
" 'eighth',\n",
" 'elapsed',\n",
" 'electrocution',\n",
" \"elf's\",\n",
" 'elks',\n",
" 'embedding',\n",
" 'embolden',\n",
" 'emolument',\n",
" \"empire's\",\n",
" 'emporium',\n",
" \"emulsion's\",\n",
" 'encores',\n",
" 'enfolded',\n",
" \"enlightenment's\",\n",
" 'enmeshed',\n",
" 'entrails',\n",
" 'errs',\n",
" \"euphemism's\",\n",
" 'evinces',\n",
" 'exact',\n",
" 'exalts',\n",
" 'excess',\n",
" 'excruciatingly',\n",
" 'execrating',\n",
" 'exercising',\n",
" 'exorcised',\n",
" 'exorcize',\n",
" 'expanding',\n",
" 'expelling',\n",
" 'explorers',\n",
" 'extendable',\n",
" 'extenuate',\n",
" 'eyeliners',\n",
" 'eyestrain',\n",
" 'failure',\n",
" \"falconer's\",\n",
" 'fallowing',\n",
" \"farewell's\",\n",
" 'fasted',\n",
" 'fate',\n",
" 'fathomed',\n",
" 'feds',\n",
" 'ferocity',\n",
" \"fetus's\",\n",
" \"figurehead's\",\n",
" \"file's\",\n",
" 'findings',\n",
" 'fireman',\n",
" 'flabbergasting',\n",
" 'flatted',\n",
" 'flatulence',\n",
" 'floggings',\n",
" 'floodlight',\n",
" 'floras',\n",
" \"flounder's\",\n",
" 'flouting',\n",
" 'fluids',\n",
" 'flyspecked',\n",
" \"fondness's\",\n",
" 'footbridge',\n",
" \"footman's\",\n",
" 'fora',\n",
" 'forbids',\n",
" 'freedoms',\n",
" 'frizzle',\n",
" \"frog's\",\n",
" \"frustration's\",\n",
" 'fumigation',\n",
" 'funniest',\n",
" 'furors',\n",
" \"furze's\",\n",
" 'gabbier',\n",
" \"gallantry's\",\n",
" \"gamekeeper's\",\n",
" 'gamekeepers',\n",
" 'gantries',\n",
" 'gazing',\n",
" 'genetic',\n",
" 'gibbeting',\n",
" 'girdling',\n",
" \"goalpost's\",\n",
" \"gobbledygook's\",\n",
" \"golf's\",\n",
" 'gooier',\n",
" \"gosling's\",\n",
" 'gougers',\n",
" 'graves',\n",
" \"gravity's\",\n",
" 'greasier',\n",
" 'griddlecakes',\n",
" 'griped',\n",
" 'gritting',\n",
" 'grotto',\n",
" 'grout',\n",
" 'gulped',\n",
" 'halitosis',\n",
" 'handled',\n",
" 'hangmen',\n",
" 'hardy',\n",
" \"hastiness's\",\n",
" 'have',\n",
" 'haw',\n",
" 'hawkish',\n",
" 'hayseeds',\n",
" 'healers',\n",
" 'heckled',\n",
" 'heckling',\n",
" 'herbivorous',\n",
" 'herded',\n",
" \"hermitage's\",\n",
" 'homeopathy',\n",
" 'homesteader',\n",
" \"hooter's\",\n",
" 'hose',\n",
" \"housetop's\",\n",
" 'humbugged',\n",
" 'humiliated',\n",
" 'hunger',\n",
" \"hush's\",\n",
" 'hydrangeas',\n",
" 'hydroplaning',\n",
" \"hyperbola's\",\n",
" 'hyphening',\n",
" \"idea's\",\n",
" 'iffiest',\n",
" 'impinge',\n",
" 'impressionistic',\n",
" 'inadvertent',\n",
" \"incantation's\",\n",
" 'incarnation',\n",
" 'incorporate',\n",
" 'incremental',\n",
" 'increments',\n",
" 'incurable',\n",
" 'indict',\n",
" 'indoctrinating',\n",
" 'inducements',\n",
" 'indulged',\n",
" 'industrialists',\n",
" \"inertia's\",\n",
" 'infielders',\n",
" 'ingenuous',\n",
" 'inhabit',\n",
" 'initials',\n",
" \"inmate's\",\n",
" 'insincerely',\n",
" 'instigate',\n",
" 'instills',\n",
" 'institutionalize',\n",
" 'insurer',\n",
" 'intelligible',\n",
" 'intending',\n",
" 'interments',\n",
" 'into',\n",
" \"intransigence's\",\n",
" 'intrudes',\n",
" 'invaders',\n",
" 'inveigled',\n",
" 'inventions',\n",
" 'investigation',\n",
" 'invincibility',\n",
" 'invokes',\n",
" 'island',\n",
" 'jejune',\n",
" \"jeopardy's\",\n",
" \"jet's\",\n",
" 'joggles',\n",
" 'joiners',\n",
" 'junking',\n",
" 'keels',\n",
" \"killdeer's\",\n",
" 'kilter',\n",
" 'kinking',\n",
" 'kinky',\n",
" 'kleptomaniacs',\n",
" 'knacker',\n",
" \"knitwear's\",\n",
" 'knobbier',\n",
" 'labels',\n",
" 'laburnums',\n",
" 'ladybirds',\n",
" 'lamed',\n",
" 'lamentations',\n",
" \"landlubber's\",\n",
" 'lapwing',\n",
" 'larboard',\n",
" 'lariat',\n",
" \"lassie's\",\n",
" 'leafier',\n",
" 'leak',\n",
" \"leapfrog's\",\n",
" 'led',\n",
" 'lees',\n",
" \"leitmotif's\",\n",
" 'lepers',\n",
" \"lichee's\",\n",
" \"lightning's\",\n",
" 'likelier',\n",
" \"linoleum's\",\n",
" 'liter',\n",
" 'loges',\n",
" 'loosened',\n",
" 'lopsidedness',\n",
" \"lube's\",\n",
" 'lustily',\n",
" 'luxuries',\n",
" 'luxurious',\n",
" \"madder's\",\n",
" \"mahatma's\",\n",
" 'mailer',\n",
" 'maligned',\n",
" 'manageable',\n",
" 'maneuvered',\n",
" 'manifest',\n",
" \"manufacture's\",\n",
" 'marches',\n",
" 'materialistic',\n",
" \"melon's\",\n",
" 'messiah',\n",
" 'metamorphism',\n",
" 'mewls',\n",
" 'miasmata',\n",
" 'midyear',\n",
" \"militiaman's\",\n",
" \"milkmaid's\",\n",
" 'miniscule',\n",
" 'minuter',\n",
" 'mirrors',\n",
" 'miscellaneous',\n",
" 'misdoings',\n",
" 'misdone',\n",
" \"mishmash's\",\n",
" \"mistake's\",\n",
" 'moan',\n",
" 'mobilized',\n",
" 'modelings',\n",
" \"mommy's\",\n",
" \"monologue's\",\n",
" \"monstrance's\",\n",
" 'mortal',\n",
" 'moth',\n",
" 'motormouth',\n",
" 'mountaineers',\n",
" \"mukluk's\",\n",
" 'multitasking',\n",
" \"municipal's\",\n",
" \"mush's\",\n",
" 'musketeers',\n",
" 'mutilated',\n",
" \"mutilation's\",\n",
" 'mysterious',\n",
" 'narcissistic',\n",
" \"native's\",\n",
" 'necklaces',\n",
" \"neglig's\",\n",
" 'negligee',\n",
" 'nerving',\n",
" 'never',\n",
" \"nickel's\",\n",
" 'nightclothes',\n",
" 'nincompoops',\n",
" 'nonprofessional',\n",
" 'nonwhites',\n",
" 'northbound',\n",
" \"nuke's\",\n",
" 'objectors',\n",
" 'obstructive',\n",
" 'obtainable',\n",
" 'occludes',\n",
" 'oftener',\n",
" 'opaques',\n",
" 'operations',\n",
" 'opposes',\n",
" 'orgasm',\n",
" 'orphan',\n",
" 'orthopaedists',\n",
" 'otiose',\n",
" 'outstripping',\n",
" 'outtakes',\n",
" \"overachiever's\",\n",
" 'overbore',\n",
" 'packet',\n",
" \"packet's\",\n",
" 'painted',\n",
" 'palisades',\n",
" 'palsied',\n",
" \"panderer's\",\n",
" \"parabola's\",\n",
" 'parading',\n",
" 'parentheses',\n",
" \"paring's\",\n",
" 'participating',\n",
" 'passenger',\n",
" 'pauses',\n",
" 'payroll',\n",
" 'peculiar',\n",
" 'pedestals',\n",
" 'pediatrician',\n",
" 'pediatrics',\n",
" 'pellucid',\n",
" 'pelt',\n",
" 'pelvis',\n",
" 'peony',\n",
" \"pepsin's\",\n",
" 'perceptive',\n",
" 'peritoneums',\n",
" 'permissible',\n",
" \"pertness's\",\n",
" 'perturb',\n",
" 'petioles',\n",
" \"petrel's\",\n",
" 'petting',\n",
" \"phallus's\",\n",
" 'phantasmagorias',\n",
" \"phenomenon's\",\n",
" 'phial',\n",
" 'philologists',\n",
" \"phobia's\",\n",
" 'phosphors',\n",
" 'physiognomy',\n",
" \"physiotherapist's\",\n",
" \"pictograph's\",\n",
" \"pine's\",\n",
" 'piranha',\n",
" 'piston',\n",
" 'placidity',\n",
" 'plague',\n",
" 'plaice',\n",
" 'platooning',\n",
" 'pleads',\n",
" 'plenary',\n",
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" \"plow's\",\n",
" 'plunge',\n",
" 'pluralistic',\n",
" 'policies',\n",
" 'pompadour',\n",
" \"poncho's\",\n",
" \"pornography's\",\n",
" 'possibilities',\n",
" 'postmark',\n",
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" 'pragmatists',\n",
" 'preaching',\n",
" 'preeminently',\n",
" \"presence's\",\n",
" \"presumption's\",\n",
" 'preteen',\n",
" 'prettier',\n",
" 'privateers',\n",
" \"profession's\",\n",
" 'professorship',\n",
" \"projectionist's\",\n",
" 'proletariat',\n",
" 'prolongation',\n",
" 'prolonging',\n",
" 'promptest',\n",
" 'propagandizes',\n",
" 'prophylactics',\n",
" 'prosperous',\n",
" 'protective',\n",
" 'provides',\n",
" \"puberty's\",\n",
" 'pudgy',\n",
" 'pulse',\n",
" 'pumped',\n",
" 'purpose',\n",
" 'puttied',\n",
" 'qua',\n",
" \"quadruped's\",\n",
" 'quarantining',\n",
" 'quickened',\n",
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" 'racetrack',\n",
" \"ragout's\",\n",
" 'railroaded',\n",
" \"ramble's\",\n",
" 'ratcheted',\n",
" 'ration',\n",
" 'rawest',\n",
" 'reapportionment',\n",
" 'reasonableness',\n",
" 'recall',\n",
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" 'reckons',\n",
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" 'reddish',\n",
" \"referee's\",\n",
" 'refill',\n",
" 'refit',\n",
" 'reformulating',\n",
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" 'refueled',\n",
" 'regenerate',\n",
" 'regrettable',\n",
" 'relishing',\n",
" 'remake',\n",
" 'remakes',\n",
" 'remedying',\n",
" 'repeals',\n",
" \"reprint's\",\n",
" 'reprobates',\n",
" 'rescind',\n",
" 'researches',\n",
" 'resenting',\n",
" 'resigned',\n",
" 'respect',\n",
" 'respell',\n",
" 'respires',\n",
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" \"retardant's\",\n",
" 'retrieves',\n",
" 'revues',\n",
" 'rewriting',\n",
" 'rickshas',\n",
" \"riddle's\",\n",
" 'ridiculously',\n",
" \"rifleman's\",\n",
" 'rigidness',\n",
" 'ringers',\n",
" 'roping',\n",
" \"roughneck's\",\n",
" 'rowdiness',\n",
" 'sailcloth',\n",
" 'sailors',\n",
" 'salve',\n",
" 'sancta',\n",
" 'sanded',\n",
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" \"scholarship's\",\n",
" \"scratchiness's\",\n",
" 'scripts',\n",
" 'scrotums',\n",
" 'sealer',\n",
" 'searing',\n",
" 'secedes',\n",
" \"secret's\",\n",
" 'semipermeable',\n",
" 'sensitives',\n",
" 'sensuously',\n",
" 'sermonize',\n",
" 'sermons',\n",
" 'servility',\n",
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" 'seventeens',\n",
" 'sexiest',\n",
" \"shirtwaist's\",\n",
" \"shiver's\",\n",
" \"showgirl's\",\n",
" 'shrinks',\n",
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" \"sidewall's\",\n",
" 'siesta',\n",
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" 'sloshes',\n",
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" 'tomboys',\n",
" \"torpedo's\",\n",
" 'torts',\n",
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" 'toxicity',\n",
" 'transceivers',\n",
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" 'transmissions',\n",
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" 'trochee',\n",
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" 'trustworthiness',\n",
" 'tuckers',\n",
" \"turbojet's\",\n",
" 'turd',\n",
" 'turnabouts',\n",
" \"turner's\",\n",
" \"turquoise's\",\n",
" \"tweet's\",\n",
" \"twitch's\",\n",
" 'type',\n",
" 'umber',\n",
" 'umping',\n",
" 'umpired',\n",
" 'uncompromisingly',\n",
" 'underlain',\n",
" 'underrated',\n",
" 'undersell',\n",
" \"undesirable's\",\n",
" 'uneconomical',\n",
" 'unexceptionable',\n",
" 'unfit',\n",
" \"unhappiness's\",\n",
" 'unimaginative',\n",
" 'unkindness',\n",
" 'unloosing',\n",
" 'uprising',\n",
" \"urge's\",\n",
" 'utterance',\n",
" 'valises',\n",
" 'vantages',\n",
" 'variables',\n",
" 'ventriloquist',\n",
" \"venue's\",\n",
" 'vibration',\n",
" 'victims',\n",
" 'vindicated',\n",
" 'visions',\n",
" 'vitality',\n",
" 'vivaciousness',\n",
" 'vocalize',\n",
" \"vowel's\",\n",
" 'vulcanizing',\n",
" 'waking',\n",
" \"walkway's\",\n",
" \"washable's\",\n",
" 'weapon',\n",
" \"weaver's\",\n",
" 'westerns',\n",
" 'whites',\n",
" 'whizzed',\n",
" 'wicker',\n",
" \"willpower's\",\n",
" 'wish',\n",
" \"wisher's\",\n",
" 'wobblier',\n",
" \"wordiness's\",\n",
" 'wormwood',\n",
" \"yen's\",\n",
" 'yews',\n",
" 'yield',\n",
" 'yuletide',\n",
" 'zoologists'}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b = set()\n",
"for word in islice(words, 999):\n",
" b |= set([word])\n",
" # print(word)\n",
" \n",
"b"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'{\\'armlet\\', \"figurehead\\'s\", \"deviance\\'s\", \\'palisades\\', \"monstrance\\'s\", \\'nightclothes\\', \\'packet\\', \\'miniscule\\', \\'evinces\\', \\'orphan\\', \"torpedo\\'s\", \\'drank\\', \\'denounces\\', \"leapfrog\\'s\", \\'Mancini\\', \\'intending\\', \\'yield\\', \"classification\\'s\", \\'gougers\\', \\'Ponce\\', \\'circularizing\\', \\'pulse\\', \"delinquent\\'s\", \\'errs\\', \\'quickened\\', \\'operations\\', \\'sublimating\\', \\'exact\\', \\'gantries\\', \"sweetbrier\\'s\", \\'exercising\\', \\'midyear\\', \\'dislocating\\', \\'phantasmagorias\\', \\'classes\\', \\'resigned\\', \\'slewing\\', \"knitwear\\'s\", \\'reasonableness\\', \\'Yellowstone\\', \"euphemism\\'s\", \\'floggings\\', \\'respect\\', \\'underlain\\', \"split\\'s\", \\'aureolas\\', \\'destructive\\', \\'nincompoops\\', \\'wobblier\\', \\'daughter\\', \\'piranha\\', \\'lamed\\', \\'torts\\', \\'overbore\\', \\'plague\\', \\'ferocity\\', \"dying\\'s\", \\'blinding\\', \\'labels\\', \\'plenary\\', \"browse\\'s\", \\'feds\\', \\'extenuate\\', \\'purpose\\', \\'flabbergasting\\', \\'rewriting\\', \\'annoyances\\', \\'appreciate\\', \\'babysat\\', \"troublemaker\\'s\", \\'spreadsheets\\', \\'insurer\\', \"clime\\'s\", \"Dunkirk\\'s\", \\'classmate\\', \\'consummate\\', \\'utterance\\', \\'lapwing\\', \\'confiscating\\', \"melon\\'s\", \"venue\\'s\", \"spleen\\'s\", \\'salve\\', \\'handled\\', \\'snored\\', \\'preaching\\', \\'inventions\\', \\'devilries\\', \\'Polanski\\', \"gosling\\'s\", \\'westerns\\', \\'bold\\', \"cigarillo\\'s\", \\'protective\\', \\'discounts\\', \"phallus\\'s\", \\'marches\\', \"thong\\'s\", \"Knesset\\'s\", \\'palsied\\', \"presumption\\'s\", \"Persia\\'s\", \\'ducats\\', \\'sermons\\', \"enlightenment\\'s\", \\'greasier\\', \"turbojet\\'s\", \\'whites\\', \"gravity\\'s\", \"backstroke\\'s\", \\'incorporate\\', \\'customized\\', \"Cupid\\'s\", \\'Mitterrand\\', \\'decentralizing\\', \"distillery\\'s\", \"Oise\\'s\", \\'strait\\', \\'abscessing\\', \\'retrieves\\', \\'haw\\', \\'eclipsed\\', \"collation\\'s\", \"blotter\\'s\", \\'narcissistic\\', \\'possibilities\\', \\'spireas\\', \\'vulcanizing\\', \\'bourgeois\\', \\'tomboys\\', \\'Cheetos\\', \\'manifest\\', \"lassie\\'s\", \"ramble\\'s\", \"wordiness\\'s\", \"Felice\\'s\", \\'dire\\', \\'disfigured\\', \\'damages\\', \\'provides\\', \\'regenerate\\', \\'parentheses\\', \\'reddish\\', \\'jejune\\', \"nuke\\'s\", \"tanner\\'s\", \\'puttied\\', \\'smelliest\\', \"plow\\'s\", \\'metamorphism\\', \\'liter\\', \\'deformities\\', \\'sidelight\\', \\'initials\\', \\'Cormack\\', \"mahatma\\'s\", \\'yuletide\\', \"jet\\'s\", \\'Zamora\\', \\'instills\\', \\'griped\\', \\'sesame\\', \"skim\\'s\", \\'misdone\\', \\'fireman\\', \\'materialistic\\', \\'rawest\\', \"tare\\'s\", \\'Melville\\', \\'county\\', \\'floodlight\\', \"clothespin\\'s\", \\'humbugged\\', \"edit\\'s\", \\'swooned\\', \\'Lawson\\', \\'ecstatically\\', \\'eddying\\', \\'phial\\', \\'mirrors\\', \"mistake\\'s\", \\'fumigation\\', \\'trenches\\', \\'different\\', \\'suspenseful\\', \\'orgasm\\', \\'corpses\\', \\'never\\', \\'prettier\\', \\'submission\\', \\'indoctrinating\\', \\'pumped\\', \\'pedestals\\', \\'pellucid\\', \\'bailiff\\', \\'devises\\', \"Snyder\\'s\", \"footman\\'s\", \"confine\\'s\", \"militiaman\\'s\", \\'civics\\', \"profession\\'s\", \\'aches\\', \"lichee\\'s\", \\'Ahriman\\', \"sirup\\'s\", \\'superscript\\', \\'Mohammedans\\', \\'antiquaries\\', \\'rigidness\\', \\'knacker\\', \\'skylines\\', \\'keels\\', \\'defiled\\', \"mommy\\'s\", \"easel\\'s\", \\'tap\\', \"urge\\'s\", \\'Mather\\', \"pertness\\'s\", \\'distinction\\', \\'sensuously\\', \\'catalogers\\', \\'Long\\', \\'telecommuted\\', \\'enmeshed\\', \"flounder\\'s\", \\'island\\', \\'anorexic\\', \"spreadsheet\\'s\", \"trial\\'s\", \\'clapboard\\', \\'Croats\\', \"simplification\\'s\", \"linoleum\\'s\", \\'Lela\\', \\'Hangzhou\\', \"potluck\\'s\", \\'impressionistic\\', \\'prolonging\\', \\'dedications\\', \\'tameness\\', \\'transceivers\\', \"physiotherapist\\'s\", \\'Sakhalin\\', \\'fate\\', \\'Iroquois\\', \\'unimaginative\\', \\'blindfolding\\', \\'destabilize\\', \"dick\\'s\", \\'accessing\\', \\'institutionalize\\', \\'Adela\\', \\'staggers\\', \"disposable\\'s\", \"cherry\\'s\", \"Mark\\'s\", \\'impinge\\', \"Descartes\\'s\", \\'decriminalized\\', \"gallantry\\'s\", \\'larboard\\', \\'weapon\\', \\'dwarfed\\', \\'excruciatingly\\', \\'Godiva\\', \"mishmash\\'s\", \\'slue\\', \\'adjurations\\', \\'indict\\', \\'trustworthiness\\', \\'Gantry\\', \\'gooier\\', \"elf\\'s\", \\'inhabit\\', \\'disadvantageously\\', \\'deceived\\', \"sled\\'s\", \"Bundesbank\\'s\", \\'ventriloquist\\', \"Hitler\\'s\", \"gamekeeper\\'s\", \\'inveigled\\', \\'motormouth\\', \"monologue\\'s\", \\'undersell\\', \\'dominoes\\', \\'slovenliness\\', \\'bounds\\', \\'stoning\\', \\'tankers\\', \\'blazed\\', \\'athletically\\', \\'semipermeable\\', \\'Fafnir\\', \"sparkler\\'s\", \"madder\\'s\", \\'advertisement\\', \\'detraction\\', \\'leak\\', \\'musketeers\\', \"phobia\\'s\", \\'occludes\\', \\'knobbier\\', \\'umping\\', \\'dashboards\\', \\'sensitives\\', \\'flouting\\', \\'intrudes\\', \\'pragmatists\\', \\'led\\', \\'Stamford\\', \\'physiognomy\\', \\'Chou\\', \\'pauses\\', \\'pluralistic\\', \"Eocene\\'s\", \\'Brits\\', \\'preeminently\\', \\'funniest\\', \\'miasmata\\', \\'rowdiness\\', \"summer\\'s\", \\'spans\\', \\'eyeliners\\', \\'umpired\\', \\'Gandhian\\', \\'laburnums\\', \\'lariat\\', \\'Xanthippe\\', \\'racetrack\\', \"Merriam\\'s\", \\'dynastic\\', \\'recall\\', \\'lees\\', \\'reapportionment\\', \"puberty\\'s\", \\'permissible\\', \\'inducements\\', \\'pediatrics\\', \\'biassed\\', \\'capacitance\\', \\'chillers\\', \\'Alyce\\', \\'maligned\\', \\'healers\\', \"goalpost\\'s\", \"spokesperson\\'s\", \\'barbarity\\', \\'embolden\\', \\'nerving\\', \\'have\\', \\'cemeteries\\', \\'oftener\\', \\'moth\\', \\'born\\', \\'Kurosawa\\', \\'competences\\', \"housetop\\'s\", \\'Trujillo\\', \\'petting\\', \\'Tubman\\', \\'mysterious\\', \\'relishing\\', \\'appertain\\', \\'cedes\\', \\'vibration\\', \\'gibbeting\\', \\'anxiously\\', \\'intelligible\\', \"idea\\'s\", \\'industrialists\\', \"quadruped\\'s\", \"furze\\'s\", \\'participating\\', \\'proletariat\\', \"landlubber\\'s\", \"Allison\\'s\", \\'addle\\', \\'dress\\', \"lightning\\'s\", \\'barrener\\', \\'sailors\\', \\'multitasking\\', \\'Carlene\\', \\'collectable\\', \"amiability\\'s\", \\'floras\\', \"twitch\\'s\", \\'vitality\\', \"disaster\\'s\", \\'Alicia\\', \\'Ingres\\', \\'hydrangeas\\', \\'bills\\', \"manufacture\\'s\", \\'celibates\\', \\'commitments\\', \\'Luann\\', \\'unloosing\\', \\'cooky\\', \\'clothespins\\', \\'perturb\\', \\'trochee\\', \"walkway\\'s\", \\'Leeds\\', \\'victims\\', \\'Westinghouse\\', \\'springtime\\', \\'platooning\\', \\'benign\\', \\'emporium\\', \\'hardy\\', \\'pudgy\\', \\'dithers\\', \\'heckled\\', \\'rescind\\', \\'umber\\', \\'turnabouts\\', \"HP\\'s\", \"stream\\'s\", \\'Casals\\', \\'extendable\\', \\'Prut\\', \"hermitage\\'s\", \\'leafier\\', \\'consider\\', \\'exorcize\\', \"weaver\\'s\", \\'execrating\\', \\'heckling\\', \\'elks\\', \\'sermonize\\', \"chromosome\\'s\", \\'kinking\\', \\'philologists\\', \"brat\\'s\", \\'lopsidedness\\', \\'communicator\\', \\'chagrining\\', \\'ingenuous\\', \\'pediatrician\\', \"nickel\\'s\", \\'Mississippian\\', \\'anonymity\\', \\'clitoral\\', \\'professorship\\', \\'caucused\\', \"mutilation\\'s\", \"riddle\\'s\", \"pepsin\\'s\", \\'bookend\\', \\'mailer\\', \\'sovereign\\', \\'explorers\\', \"retardant\\'s\", \\'drudged\\', \\'wicker\\', \\'sniffed\\', \"falconer\\'s\", \\'findings\\', \\'Christina\\', \\'researches\\', \\'objectors\\', \\'valises\\', \\'humiliated\\', \\'pompadour\\', \\'homeopathy\\', \\'toughened\\', \\'Blucher\\', \\'necklaces\\', \\'close\\', \\'siesta\\', \\'analogues\\', \\'exorcised\\', \\'remake\\', \\'minuter\\', \\'Duncan\\', \\'dis\\', \\'repeals\\', \\'infielders\\', \"emulsion\\'s\", \"showgirl\\'s\", \\'slashed\\', \\'duelling\\', \\'trimly\\', \\'technocracy\\', \"overachiever\\'s\", \\'outstripping\\', \"dentist\\'s\", \"Judaism\\'s\", \\'insincerely\\', \\'pleads\\', \"Rostov\\'s\", \\'privateers\\', \\'boroughs\\', \\'Barclay\\', \\'slithering\\', \\'lamentations\\', \\'Delgado\\', \\'incurable\\', \\'obstructive\\', \"Adolf\\'s\", \\'avows\\', \"mush\\'s\", \\'tier\\', \"chock\\'s\", \\'district\\', \\'enfolded\\', \\'soda\\', \\'big\\', \\'cinema\\', \"Morpheus\\'s\", \\'junking\\', \\'subterranean\\', \"turner\\'s\", \\'hayseeds\\', \"desecration\\'s\", \\'fallowing\\', \\'clarified\\', \\'ratcheted\\', \\'negligee\\', \\'Perseid\\', \\'crunchier\\', \\'slavered\\', \"birch\\'s\", \\'suitable\\', \\'miscellaneous\\', \\'ladybirds\\', \"hastiness\\'s\", \"turquoise\\'s\", \"CPA\\'s\", \\'refit\\', \\'servility\\', \\'Scripture\\', \\'hunger\\', \"phenomenon\\'s\", \"Yamagata\\'s\", \"Xingu\\'s\", \\'Anubis\\', \"consumerism\\'s\", \\'dumbness\\', \"Liege\\'s\", \"sidewall\\'s\", \\'kinky\\', \\'cognac\\', \\'luxurious\\', \"milkmaid\\'s\", \\'stormy\\', \"frog\\'s\", \"panderer\\'s\", \\'brainy\\', \\'incremental\\', \\'Ginny\\', \\'prosperous\\', \\'instigate\\', \\'scripts\\', \\'joiners\\', \\'furors\\', \\'policies\\', \\'elapsed\\', \\'authorship\\', \\'bushwhackers\\', \"reconnaissance\\'s\", \\'quid\\', \"inmate\\'s\", \\'messiah\\', \\'misdoings\\', \"Psalter\\'s\", \"teacher\\'s\", \\'Cyrano\\', \\'bib\\', \\'thirds\\', \"yen\\'s\", \\'berates\\', \"undesirable\\'s\", \\'Delphic\\', \\'bulkier\\', \"bronchitis\\'s\", \\'commissariats\\', \\'underrated\\', \\'armistice\\', \"washable\\'s\", \\'astrophysicist\\', \\'syllabication\\', \"ambrosia\\'s\", \"colonialism\\'s\", \\'placidity\\', \\'Gillette\\', \"municipal\\'s\", \\'Lucile\\', \"spear\\'s\", \\'Gadsden\\', \\'fora\\', \"Naphtali\\'s\", \"chemical\\'s\", \\'dwelling\\', \\'invaders\\', \\'debarment\\', \\'qua\\', \\'drown\\', \\'hydroplaning\\', \\'Armando\\', \\'waking\\', \\'gamekeepers\\', \\'Arcadia\\', \"jeopardy\\'s\", \\'situated\\', \\'propagandizes\\', \"armlet\\'s\", \\'promptest\\', \"Kara\\'s\", \\'Otto\\', \"Naziism\\'s\", \\'fasted\\', \\'scrotums\\', \\'bolted\\', \\'chokers\\', \\'prophylactics\\', \\'interments\\', \\'Maracaibo\\', \\'opaques\\', \"referee\\'s\", \\'invincibility\\', \"Podhoretz\\'s\", \\'earthward\\', \"tiptop\\'s\", \\'gritting\\', \\'Subaru\\', \\'respell\\', \\'daiquiris\\', \\'lustily\\', \"decompression\\'s\", \"bleach\\'s\", \\'postmark\\', \\'commendable\\', \\'couriers\\', \\'advents\\', \\'railroaded\\', \\'mortal\\', \\'creamed\\', \\'iffiest\\', \\'seventeens\\', \"analogy\\'s\", \"refreshment\\'s\", \\'visions\\', \\'hangmen\\', \"Nobelist\\'s\", \\'revues\\', \"Cancun\\'s\", \\'halitosis\\', \\'herded\\', \\'dormice\\', \\'snivels\\', \"projectionist\\'s\", \"wisher\\'s\", \\'blowtorch\\', \"packet\\'s\", \\'reckons\\', \\'cosmically\\', \\'genetic\\', \\'spider\\', \\'cookouts\\', \\'indulged\\', \\'tinnier\\', \\'barber\\', \"scratchiness\\'s\", \"dateline\\'s\", \\'plaice\\', \\'strumpet\\', \\'Joplin\\', \\'tuckers\\', \\'joggles\\', \"Maidenform\\'s\", \"Trojan\\'s\", \"empire\\'s\", \\'flatted\\', \"unhappiness\\'s\", \"Valentine\\'s\", \"plenary\\'s\", \"Mohorovicic\\'s\", \\'cheapening\\', \\'electrocution\\', \\'modelings\\', \\'refueled\\', \\'betrothals\\', \\'uprising\\', \\'restfulness\\', \"teacup\\'s\", \\'disease\\', \\'dryads\\', \\'incarnation\\', \\'tango\\', \"file\\'s\", \"cantaloup\\'s\", \"tautology\\'s\", \\'nonprofessional\\', \"killdeer\\'s\", \\'hawkish\\', \\'devaluations\\', \\'grout\\', \\'bussing\\', \"slack\\'s\", \"fondness\\'s\", \"tetrahedron\\'s\", \\'crisps\\', \\'vivaciousness\\', \\'transmissions\\', \\'sorely\\', \\'telekinesis\\', \\'homesteader\\', \\'parading\\', \"tillage\\'s\", \\'frizzle\\', \\'comediennes\\', \"tweet\\'s\", \\'gabbier\\', \\'grotto\\', \\'flyspecked\\', \"hooter\\'s\", \\'footbridge\\', \\'boundless\\', \"Danielle\\'s\", \\'mutilated\\', \\'regrettable\\', \"pine\\'s\", \\'zoologists\\', \"easterner\\'s\", \\'bounces\\', \\'girdling\\', \\'circlets\\', \\'otiose\\', \"poncho\\'s\", \\'maneuvered\\', \\'Knickerbocker\\', \\'Innocent\\', \"Eniwetok\\'s\", \\'graves\\', \\'hyphening\\', \\'outtakes\\', \\'broth\\', \"pictograph\\'s\", \\'griddlecakes\\', \\'sancta\\', \\'anaesthetic\\', \\'corroded\\', \\'piston\\', \"synchronization\\'s\", \\'transmigrated\\', \\'Roosevelt\\', \"neglig\\'s\", \\'expanding\\', \"shirtwaist\\'s\", \\'variables\\', \\'Sq\\', \"Vulcan\\'s\", \\'respires\\', \\'soy\\', \\'Teri\\', \\'orthopaedists\\', \"competitiveness\\'s\", \"Trey\\'s\", \\'castigation\\', \\'gazing\\', \"Earle\\'s\", \"inertia\\'s\", \\'herbivorous\\', \\'obtainable\\', \\'battlements\\', \\'sanded\\', \\'June\\', \"stipend\\'s\", \\'refill\\', \\'quarantining\\', \\'storms\\', \\'commencing\\', \"Emacs\\'s\", \"frustration\\'s\", \\'fluids\\', \"leitmotif\\'s\", \"hush\\'s\", \\'Yaobang\\', \"golf\\'s\", \\'Turner\\', \"intransigence\\'s\", \\'opposes\\', \"parabola\\'s\", \\'crypt\\', \\'depressingly\\', \\'unexceptionable\\', \"vowel\\'s\", \\'vantages\\', \\'Brahmans\\', \\'drams\\', \\'reformulating\\', \\'boxing\\', \\'vocalize\\', \"Eurasia\\'s\", \"Laocoon\\'s\", \"aneurism\\'s\", \\'kilter\\', \"something\\'s\", \\'flatulence\\', \\'entrails\\', \\'likelier\\', \\'moan\\', \"slanderer\\'s\", \\'investigation\\', \\'shrinks\\', \"snip\\'s\", \\'loosened\\', \"petrel\\'s\", \\'swiping\\', \"Patterson\\'s\", \\'advertisers\\', \\'remakes\\', \\'Dexter\\', \\'pelt\\', \\'Francisco\\', \\'Paula\\', \\'alleluia\\', \"mukluk\\'s\", \\'gulped\\', \"anaemia\\'s\", \\'tarring\\', \\'searing\\', \\'slides\\', \"Abe\\'s\", \"shiver\\'s\", \\'coordinating\\', \\'Getty\\', \\'constable\\', \"Rosalyn\\'s\", \\'Rosie\\', \\'luxuries\\', \\'whizzed\\', \\'disestablishing\\', \"counselor\\'s\", \"native\\'s\", \\'sloshes\\', \\'phosphors\\', \\'wormwood\\', \"chill\\'s\", \\'avenge\\', \\'mobilized\\', \\'encores\\', \\'kleptomaniacs\\', \\'totters\\', \\'chlorine\\', \\'clasping\\', \\'into\\', \\'manageable\\', \\'type\\', \\'Drano\\', \\'peculiar\\', \\'rickshas\\', \\'deadest\\', \"pornography\\'s\", \\'yews\\', \\'Siberia\\', \"AB\\'s\", \\'nonwhites\\', \"Byers\\'s\", \\'hose\\', \"Starr\\'s\", \\'perceptive\\', \\'Popocatepetl\\', \"Juanita\\'s\", \"sonnet\\'s\", \\'emolument\\', \"incantation\\'s\", \\'failure\\', \\'Sterno\\', \"Lew\\'s\", \\'payroll\\', \\'peony\\', \\'embedding\\', \\'unkindness\\', \\'tamper\\', \"reprint\\'s\", \\'chorale\\', \"paring\\'s\", \\'trisects\\', \\'uncompromisingly\\', \\'buttoned\\', \\'peritoneums\\', \\'freedoms\\', \\'eighteen\\', \\'Ringo\\', \"Khwarizmi\\'s\", \\'exalts\\', \\'wish\\', \\'resenting\\', \\'vindicated\\', \\'invokes\\', \\'mewls\\', \\'eyestrain\\', \\'decencies\\', \\'preteen\\', \\'Piaget\\', \\'alms\\', \\'caliphate\\', \\'excess\\', \\'transferal\\', \\'plunge\\', \\'reprobates\\', \\'coeducational\\', \\'eighth\\', \"rifleman\\'s\", \\'transposes\\', \\'spanners\\', \\'awoke\\', \\'pelvis\\', \\'disillusioned\\', \"deism\\'s\", \\'trifle\\', \\'Confucians\\', \\'Petersen\\', \\'inadvertent\\', \\'sexiest\\', \"farewell\\'s\", \"fetus\\'s\", \\'northbound\\', \\'loges\\', \"hyperbola\\'s\", \\'ration\\', \\'ridiculously\\', \\'sailcloth\\', \\'Niger\\', \\'Midwest\\', \\'Avalon\\', \"Gertrude\\'s\", \\'mountaineers\\', \"scholarship\\'s\", \"lube\\'s\", \\'secedes\\', \\'editorializes\\', \\'swankest\\', \\'lepers\\', \\'airbrushing\\', \\'spillages\\', \"Tibetan\\'s\", \"powerlessness\\'s\", \"secret\\'s\", \\'constitution\\', \"Stephan\\'s\", \\'prolongation\\', \\'Rover\\', \\'roping\\', \\'chiropractors\\', \\'recites\\', \"settle\\'s\", \\'applicable\\', \\'coding\\', \\'Joaquin\\', \\'sledgehammers\\', \\'toxicity\\', \\'daze\\', \"gobbledygook\\'s\", \\'unfit\\', \\'forbids\\', \"ragout\\'s\", \\'fathomed\\', \\'ringers\\', \\'spared\\', \\'durability\\', \\'checklist\\', \\'remedying\\', \"presence\\'s\", \"Cr\\'s\", \"Yuletide\\'s\", \\'disillusioning\\', \\'automatons\\', \\'compelled\\', \\'passenger\\', \"roughneck\\'s\", \\'Aurora\\', \\'expelling\\', \\'crave\\', \\'Sumerian\\', \"Walmart\\'s\", \\'canoeing\\', \\'sealer\\', \\'petioles\\', \\'sinkholes\\', \\'uneconomical\\', \\'painted\\', \"corespondent\\'s\", \"tidbit\\'s\", \\'increments\\', \"willpower\\'s\", \\'converses\\', \\'schematically\\', \\'Freud\\', \\'despoiling\\', \"Mackinac\\'s\", \\'turd\\', \\'Gorey\\'}'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"str(b)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'{\\'armlet\\', \"figurehead\\'s\", \"deviance\\'s\", \\'palisades\\', \"monstrance\\'s\", \\'nightclothes\\', \\'packet\\', \\'miniscule\\', \\'evinces\\', \\'orphan\\', \"torpedo\\'s\", \\'drank\\', \\'denounces\\', \"leapfrog\\'s\", \\'Mancini\\', \\'intending\\', \\'yield\\', \"classification\\'s\", \\'gougers\\', \\'Ponce\\', \\'circularizing\\', \\'pulse\\', \"delinquent\\'s\", \\'errs\\', \\'quickened\\', \\'operations\\', \\'sublimating\\', \\'exact\\', \\'gantries\\', \"sweetbrier\\'s\", \\'exercising\\', \\'midyear\\', \\'dislocating\\', \\'phantasmagorias\\', \\'classes\\', \\'resigned\\', \\'slewing\\', \"knitwear\\'s\", \\'reasonableness\\', \\'Yellowstone\\', \"euphemism\\'s\", \\'floggings\\', \\'respect\\', \\'underlain\\', \"split\\'s\", \\'aureolas\\', \\'destructive\\', \\'nincompoops\\', \\'wobblier\\', \\'daughter\\', \\'piranha\\', \\'lamed\\', \\'torts\\', \\'overbore\\', \\'plague\\', \\'ferocity\\', \"dying\\'s\", \\'blinding\\', \\'labels\\', \\'plenary\\', \"browse\\'s\", \\'feds\\', \\'extenuate\\', \\'purpose\\', \\'flabbergasting\\', \\'rewriting\\', \\'annoyances\\', \\'appreciate\\', \\'babysat\\', \"troublemaker\\'s\", \\'spreadsheets\\', \\'insurer\\', \"clime\\'s\", \"Dunkirk\\'s\", \\'classmate\\', \\'consummate\\', \\'utterance\\', \\'lapwing\\', \\'confiscating\\', \"melon\\'s\", \"venue\\'s\", \"spleen\\'s\", \\'salve\\', \\'handled\\', \\'snored\\', \\'preaching\\', \\'inventions\\', \\'devilries\\', \\'Polanski\\', \"gosling\\'s\", \\'westerns\\', \\'bold\\', \"cigarillo\\'s\", \\'protective\\', \\'discounts\\', \"phallus\\'s\", \\'marches\\', \"thong\\'s\", \"Knesset\\'s\", \\'palsied\\', \"presumption\\'s\", \"Persia\\'s\", \\'ducats\\', \\'sermons\\', \"enlightenment\\'s\", \\'greasier\\', \"turbojet\\'s\", \\'whites\\', \"gravity\\'s\", \"backstroke\\'s\", \\'incorporate\\', \\'customized\\', \"Cupid\\'s\", \\'Mitterrand\\', \\'decentralizing\\', \"distillery\\'s\", \"Oise\\'s\", \\'strait\\', \\'abscessing\\', \\'retrieves\\', \\'haw\\', \\'eclipsed\\', \"collation\\'s\", \"blotter\\'s\", \\'narcissistic\\', \\'possibilities\\', \\'spireas\\', \\'vulcanizing\\', \\'bourgeois\\', \\'tomboys\\', \\'Cheetos\\', \\'manifest\\', \"lassie\\'s\", \"ramble\\'s\", \"wordiness\\'s\", \"Felice\\'s\", \\'dire\\', \\'disfigured\\', \\'damages\\', \\'provides\\', \\'regenerate\\', \\'parentheses\\', \\'reddish\\', \\'jejune\\', \"nuke\\'s\", \"tanner\\'s\", \\'puttied\\', \\'smelliest\\', \"plow\\'s\", \\'metamorphism\\', \\'liter\\', \\'deformities\\', \\'sidelight\\', \\'initials\\', \\'Cormack\\', \"mahatma\\'s\", \\'yuletide\\', \"jet\\'s\", \\'Zamora\\', \\'instills\\', \\'griped\\', \\'sesame\\', \"skim\\'s\", \\'misdone\\', \\'fireman\\', \\'materialistic\\', \\'rawest\\', \"tare\\'s\", \\'Melville\\', \\'county\\', \\'floodlight\\', \"clothespin\\'s\", \\'humbugged\\', \"edit\\'s\", \\'swooned\\', \\'Lawson\\', \\'ecstatically\\', \\'eddying\\', \\'phial\\', \\'mirrors\\', \"mistake\\'s\", \\'fumigation\\', \\'trenches\\', \\'different\\', \\'suspenseful\\', \\'orgasm\\', \\'corpses\\', \\'never\\', \\'prettier\\', \\'submission\\', \\'indoctrinating\\', \\'pumped\\', \\'pedestals\\', \\'pellucid\\', \\'bailiff\\', \\'devises\\', \"Snyder\\'s\", \"footman\\'s\", \"confine\\'s\", \"militiaman\\'s\", \\'civics\\', \"profession\\'s\", \\'aches\\', \"lichee\\'s\", \\'Ahriman\\', \"sirup\\'s\", \\'superscript\\', \\'Mohammedans\\', \\'antiquaries\\', \\'rigidness\\', \\'knacker\\', \\'skylines\\', \\'keels\\', \\'defiled\\', \"mommy\\'s\", \"easel\\'s\", \\'tap\\', \"urge\\'s\", \\'Mather\\', \"pertness\\'s\", \\'distinction\\', \\'sensuously\\', \\'catalogers\\', \\'Long\\', \\'telecommuted\\', \\'enmeshed\\', \"flounder\\'s\", \\'island\\', \\'anorexic\\', \"spreadsheet\\'s\", \"trial\\'s\", \\'clapboard\\', \\'Croats\\', \"simplification\\'s\", \"linoleum\\'s\", \\'Lela\\', \\'Hangzhou\\', \"potluck\\'s\", \\'impressionistic\\', \\'prolonging\\', \\'dedications\\', \\'tameness\\', \\'transceivers\\', \"physiotherapist\\'s\", \\'Sakhalin\\', \\'fate\\', \\'Iroquois\\', \\'unimaginative\\', \\'blindfolding\\', \\'destabilize\\', \"dick\\'s\", \\'accessing\\', \\'institutionalize\\', \\'Adela\\', \\'staggers\\', \"disposable\\'s\", \"cherry\\'s\", \"Mark\\'s\", \\'impinge\\', \"Descartes\\'s\", \\'decriminalized\\', \"gallantry\\'s\", \\'larboard\\', \\'weapon\\', \\'dwarfed\\', \\'excruciatingly\\', \\'Godiva\\', \"mishmash\\'s\", \\'slue\\', \\'adjurations\\', \\'indict\\', \\'trustworthiness\\', \\'Gantry\\', \\'gooier\\', \"elf\\'s\", \\'inhabit\\', \\'disadvantageously\\', \\'deceived\\', \"sled\\'s\", \"Bundesbank\\'s\", \\'ventriloquist\\', \"Hitler\\'s\", \"gamekeeper\\'s\", \\'inveigled\\', \\'motormouth\\', \"monologue\\'s\", \\'undersell\\', \\'dominoes\\', \\'slovenliness\\', \\'bounds\\', \\'stoning\\', \\'tankers\\', \\'blazed\\', \\'athletically\\', \\'semipermeable\\', \\'Fafnir\\', \"sparkler\\'s\", \"madder\\'s\", \\'advertisement\\', \\'detraction\\', \\'leak\\', \\'musketeers\\', \"phobia\\'s\", \\'occludes\\', \\'knobbier\\', \\'umping\\', \\'dashboards\\', \\'sensitives\\', \\'flouting\\', \\'intrudes\\', \\'pragmatists\\', \\'led\\', \\'Stamford\\', \\'physiognomy\\', \\'Chou\\', \\'pauses\\', \\'pluralistic\\', \"Eocene\\'s\", \\'Brits\\', \\'preeminently\\', \\'funniest\\', \\'miasmata\\', \\'rowdiness\\', \"summer\\'s\", \\'spans\\', \\'eyeliners\\', \\'umpired\\', \\'Gandhian\\', \\'laburnums\\', \\'lariat\\', \\'Xanthippe\\', \\'racetrack\\', \"Merriam\\'s\", \\'dynastic\\', \\'recall\\', \\'lees\\', \\'reapportionment\\', \"puberty\\'s\", \\'permissible\\', \\'inducements\\', \\'pediatrics\\', \\'biassed\\', \\'capacitance\\', \\'chillers\\', \\'Alyce\\', \\'maligned\\', \\'healers\\', \"goalpost\\'s\", \"spokesperson\\'s\", \\'barbarity\\', \\'embolden\\', \\'nerving\\', \\'have\\', \\'cemeteries\\', \\'oftener\\', \\'moth\\', \\'born\\', \\'Kurosawa\\', \\'competences\\', \"housetop\\'s\", \\'Trujillo\\', \\'petting\\', \\'Tubman\\', \\'mysterious\\', \\'relishing\\', \\'appertain\\', \\'cedes\\', \\'vibration\\', \\'gibbeting\\', \\'anxiously\\', \\'intelligible\\', \"idea\\'s\", \\'industrialists\\', \"quadruped\\'s\", \"furze\\'s\", \\'participating\\', \\'proletariat\\', \"landlubber\\'s\", \"Allison\\'s\", \\'addle\\', \\'dress\\', \"lightning\\'s\", \\'barrener\\', \\'sailors\\', \\'multitasking\\', \\'Carlene\\', \\'collectable\\', \"amiability\\'s\", \\'floras\\', \"twitch\\'s\", \\'vitality\\', \"disaster\\'s\", \\'Alicia\\', \\'Ingres\\', \\'hydrangeas\\', \\'bills\\', \"manufacture\\'s\", \\'celibates\\', \\'commitments\\', \\'Luann\\', \\'unloosing\\', \\'cooky\\', \\'clothespins\\', \\'perturb\\', \\'trochee\\', \"walkway\\'s\", \\'Leeds\\', \\'victims\\', \\'Westinghouse\\', \\'springtime\\', \\'platooning\\', \\'benign\\', \\'emporium\\', \\'hardy\\', \\'pudgy\\', \\'dithers\\', \\'heckled\\', \\'rescind\\', \\'umber\\', \\'turnabouts\\', \"HP\\'s\", \"stream\\'s\", \\'Casals\\', \\'extendable\\', \\'Prut\\', \"hermitage\\'s\", \\'leafier\\', \\'consider\\', \\'exorcize\\', \"weaver\\'s\", \\'execrating\\', \\'heckling\\', \\'elks\\', \\'sermonize\\', \"chromosome\\'s\", \\'kinking\\', \\'philologists\\', \"brat\\'s\", \\'lopsidedness\\', \\'communicator\\', \\'chagrining\\', \\'ingenuous\\', \\'pediatrician\\', \"nickel\\'s\", \\'Mississippian\\', \\'anonymity\\', \\'clitoral\\', \\'professorship\\', \\'caucused\\', \"mutilation\\'s\", \"riddle\\'s\", \"pepsin\\'s\", \\'bookend\\', \\'mailer\\', \\'sovereign\\', \\'explorers\\', \"retardant\\'s\", \\'drudged\\', \\'wicker\\', \\'sniffed\\', \"falconer\\'s\", \\'findings\\', \\'Christina\\', \\'researches\\', \\'objectors\\', \\'valises\\', \\'humiliated\\', \\'pompadour\\', \\'homeopathy\\', \\'toughened\\', \\'Blucher\\', \\'necklaces\\', \\'close\\', \\'siesta\\', \\'analogues\\', \\'exorcised\\', \\'remake\\', \\'minuter\\', \\'Duncan\\', \\'dis\\', \\'repeals\\', \\'infielders\\', \"emulsion\\'s\", \"showgirl\\'s\", \\'slashed\\', \\'duelling\\', \\'trimly\\', \\'technocracy\\', \"overachiever\\'s\", \\'outstripping\\', \"dentist\\'s\", \"Judaism\\'s\", \\'insincerely\\', \\'pleads\\', \"Rostov\\'s\", \\'privateers\\', \\'boroughs\\', \\'Barclay\\', \\'slithering\\', \\'lamentations\\', \\'Delgado\\', \\'incurable\\', \\'obstructive\\', \"Adolf\\'s\", \\'avows\\', \"mush\\'s\", \\'tier\\', \"chock\\'s\", \\'district\\', \\'enfolded\\', \\'soda\\', \\'big\\', \\'cinema\\', \"Morpheus\\'s\", \\'junking\\', \\'subterranean\\', \"turner\\'s\", \\'hayseeds\\', \"desecration\\'s\", \\'fallowing\\', \\'clarified\\', \\'ratcheted\\', \\'negligee\\', \\'Perseid\\', \\'crunchier\\', \\'slavered\\', \"birch\\'s\", \\'suitable\\', \\'miscellaneous\\', \\'ladybirds\\', \"hastiness\\'s\", \"turquoise\\'s\", \"CPA\\'s\", \\'refit\\', \\'servility\\', \\'Scripture\\', \\'hunger\\', \"phenomenon\\'s\", \"Yamagata\\'s\", \"Xingu\\'s\", \\'Anubis\\', \"consumerism\\'s\", \\'dumbness\\', \"Liege\\'s\", \"sidewall\\'s\", \\'kinky\\', \\'cognac\\', \\'luxurious\\', \"milkmaid\\'s\", \\'stormy\\', \"frog\\'s\", \"panderer\\'s\", \\'brainy\\', \\'incremental\\', \\'Ginny\\', \\'prosperous\\', \\'instigate\\', \\'scripts\\', \\'joiners\\', \\'furors\\', \\'policies\\', \\'elapsed\\', \\'authorship\\', \\'bushwhackers\\', \"reconnaissance\\'s\", \\'quid\\', \"inmate\\'s\", \\'messiah\\', \\'misdoings\\', \"Psalter\\'s\", \"teacher\\'s\", \\'Cyrano\\', \\'bib\\', \\'thirds\\', \"yen\\'s\", \\'berates\\', \"undesirable\\'s\", \\'Delphic\\', \\'bulkier\\', \"bronchitis\\'s\", \\'commissariats\\', \\'underrated\\', \\'armistice\\', \"washable\\'s\", \\'astrophysicist\\', \\'syllabication\\', \"ambrosia\\'s\", \"colonialism\\'s\", \\'placidity\\', \\'Gillette\\', \"municipal\\'s\", \\'Lucile\\', \"spear\\'s\", \\'Gadsden\\', \\'fora\\', \"Naphtali\\'s\", \"chemical\\'s\", \\'dwelling\\', \\'invaders\\', \\'debarment\\', \\'qua\\', \\'drown\\', \\'hydroplaning\\', \\'Armando\\', \\'waking\\', \\'gamekeepers\\', \\'Arcadia\\', \"jeopardy\\'s\", \\'situated\\', \\'propagandizes\\', \"armlet\\'s\", \\'promptest\\', \"Kara\\'s\", \\'Otto\\', \"Naziism\\'s\", \\'fasted\\', \\'scrotums\\', \\'bolted\\', \\'chokers\\', \\'prophylactics\\', \\'interments\\', \\'Maracaibo\\', \\'opaques\\', \"referee\\'s\", \\'invincibility\\', \"Podhoretz\\'s\", \\'earthward\\', \"tiptop\\'s\", \\'gritting\\', \\'Subaru\\', \\'respell\\', \\'daiquiris\\', \\'lustily\\', \"decompression\\'s\", \"bleach\\'s\", \\'postmark\\', \\'commendable\\', \\'couriers\\', \\'advents\\', \\'railroaded\\', \\'mortal\\', \\'creamed\\', \\'iffiest\\', \\'seventeens\\', \"analogy\\'s\", \"refreshment\\'s\", \\'visions\\', \\'hangmen\\', \"Nobelist\\'s\", \\'revues\\', \"Cancun\\'s\", \\'halitosis\\', \\'herded\\', \\'dormice\\', \\'snivels\\', \"projectionist\\'s\", \"wisher\\'s\", \\'blowtorch\\', \"packet\\'s\", \\'reckons\\', \\'cosmically\\', \\'genetic\\', \\'spider\\', \\'cookouts\\', \\'indulged\\', \\'tinnier\\', \\'barber\\', \"scratchiness\\'s\", \"dateline\\'s\", \\'plaice\\', \\'strumpet\\', \\'Joplin\\', \\'tuckers\\', \\'joggles\\', \"Maidenform\\'s\", \"Trojan\\'s\", \"empire\\'s\", \\'flatted\\', \"unhappiness\\'s\", \"Valentine\\'s\", \"plenary\\'s\", \"Mohorovicic\\'s\", \\'cheapening\\', \\'electrocution\\', \\'modelings\\', \\'refueled\\', \\'betrothals\\', \\'uprising\\', \\'restfulness\\', \"teacup\\'s\", \\'disease\\', \\'dryads\\', \\'incarnation\\', \\'tango\\', \"file\\'s\", \"cantaloup\\'s\", \"tautology\\'s\", \\'nonprofessional\\', \"killdeer\\'s\", \\'hawkish\\', \\'devaluations\\', \\'grout\\', \\'bussing\\', \"slack\\'s\", \"fondness\\'s\", \"tetrahedron\\'s\", \\'crisps\\', \\'vivaciousness\\', \\'transmissions\\', \\'sorely\\', \\'telekinesis\\', \\'homesteader\\', \\'parading\\', \"tillage\\'s\", \\'frizzle\\', \\'comediennes\\', \"tweet\\'s\", \\'gabbier\\', \\'grotto\\', \\'flyspecked\\', \"hooter\\'s\", \\'footbridge\\', \\'boundless\\', \"Danielle\\'s\", \\'mutilated\\', \\'regrettable\\', \"pine\\'s\", \\'zoologists\\', \"easterner\\'s\", \\'bounces\\', \\'girdling\\', \\'circlets\\', \\'otiose\\', \"poncho\\'s\", \\'maneuvered\\', \\'Knickerbocker\\', \\'Innocent\\', \"Eniwetok\\'s\", \\'graves\\', \\'hyphening\\', \\'outtakes\\', \\'broth\\', \"pictograph\\'s\", \\'griddlecakes\\', \\'sancta\\', \\'anaesthetic\\', \\'corroded\\', \\'piston\\', \"synchronization\\'s\", \\'transmigrated\\', \\'Roosevelt\\', \"neglig\\'s\", \\'expanding\\', \"shirtwaist\\'s\", \\'variables\\', \\'Sq\\', \"Vulcan\\'s\", \\'respires\\', \\'soy\\', \\'Teri\\', \\'orthopaedists\\', \"competitiveness\\'s\", \"Trey\\'s\", \\'castigation\\', \\'gazing\\', \"Earle\\'s\", \"inertia\\'s\", \\'herbivorous\\', \\'obtainable\\', \\'battlements\\', \\'sanded\\', \\'June\\', \"stipend\\'s\", \\'refill\\', \\'quarantining\\', \\'storms\\', \\'commencing\\', \"Emacs\\'s\", \"frustration\\'s\", \\'fluids\\', \"leitmotif\\'s\", \"hush\\'s\", \\'Yaobang\\', \"golf\\'s\", \\'Turner\\', \"intransigence\\'s\", \\'opposes\\', \"parabola\\'s\", \\'crypt\\', \\'depressingly\\', \\'unexceptionable\\', \"vowel\\'s\", \\'vantages\\', \\'Brahmans\\', \\'drams\\', \\'reformulating\\', \\'boxing\\', \\'vocalize\\', \"Eurasia\\'s\", \"Laocoon\\'s\", \"aneurism\\'s\", \\'kilter\\', \"something\\'s\", \\'flatulence\\', \\'entrails\\', \\'likelier\\', \\'moan\\', \"slanderer\\'s\", \\'investigation\\', \\'shrinks\\', \"snip\\'s\", \\'loosened\\', \"petrel\\'s\", \\'swiping\\', \"Patterson\\'s\", \\'advertisers\\', \\'remakes\\', \\'Dexter\\', \\'pelt\\', \\'Francisco\\', \\'Paula\\', \\'alleluia\\', \"mukluk\\'s\", \\'gulped\\', \"anaemia\\'s\", \\'tarring\\', \\'searing\\', \\'slides\\', \"Abe\\'s\", \"shiver\\'s\", \\'coordinating\\', \\'Getty\\', \\'constable\\', \"Rosalyn\\'s\", \\'Rosie\\', \\'luxuries\\', \\'whizzed\\', \\'disestablishing\\', \"counselor\\'s\", \"native\\'s\", \\'sloshes\\', \\'phosphors\\', \\'wormwood\\', \"chill\\'s\", \\'avenge\\', \\'mobilized\\', \\'encores\\', \\'kleptomaniacs\\', \\'totters\\', \\'chlorine\\', \\'clasping\\', \\'into\\', \\'manageable\\', \\'type\\', \\'Drano\\', \\'peculiar\\', \\'rickshas\\', \\'deadest\\', \"pornography\\'s\", \\'yews\\', \\'Siberia\\', \"AB\\'s\", \\'nonwhites\\', \"Byers\\'s\", \\'hose\\', \"Starr\\'s\", \\'perceptive\\', \\'Popocatepetl\\', \"Juanita\\'s\", \"sonnet\\'s\", \\'emolument\\', \"incantation\\'s\", \\'failure\\', \\'Sterno\\', \"Lew\\'s\", \\'payroll\\', \\'peony\\', \\'embedding\\', \\'unkindness\\', \\'tamper\\', \"reprint\\'s\", \\'chorale\\', \"paring\\'s\", \\'trisects\\', \\'uncompromisingly\\', \\'buttoned\\', \\'peritoneums\\', \\'freedoms\\', \\'eighteen\\', \\'Ringo\\', \"Khwarizmi\\'s\", \\'exalts\\', \\'wish\\', \\'resenting\\', \\'vindicated\\', \\'invokes\\', \\'mewls\\', \\'eyestrain\\', \\'decencies\\', \\'preteen\\', \\'Piaget\\', \\'alms\\', \\'caliphate\\', \\'excess\\', \\'transferal\\', \\'plunge\\', \\'reprobates\\', \\'coeducational\\', \\'eighth\\', \"rifleman\\'s\", \\'transposes\\', \\'spanners\\', \\'awoke\\', \\'pelvis\\', \\'disillusioned\\', \"deism\\'s\", \\'trifle\\', \\'Confucians\\', \\'Petersen\\', \\'inadvertent\\', \\'sexiest\\', \"farewell\\'s\", \"fetus\\'s\", \\'northbound\\', \\'loges\\', \"hyperbola\\'s\", \\'ration\\', \\'ridiculously\\', \\'sailcloth\\', \\'Niger\\', \\'Midwest\\', \\'Avalon\\', \"Gertrude\\'s\", \\'mountaineers\\', \"scholarship\\'s\", \"lube\\'s\", \\'secedes\\', \\'editorializes\\', \\'swankest\\', \\'lepers\\', \\'airbrushing\\', \\'spillages\\', \"Tibetan\\'s\", \"powerlessness\\'s\", \"secret\\'s\", \\'constitution\\', \"Stephan\\'s\", \\'prolongation\\', \\'Rover\\', \\'roping\\', \\'chiropractors\\', \\'recites\\', \"settle\\'s\", \\'applicable\\', \\'coding\\', \\'Joaquin\\', \\'sledgehammers\\', \\'toxicity\\', \\'daze\\', \"gobbledygook\\'s\", \\'unfit\\', \\'forbids\\', \"ragout\\'s\", \\'fathomed\\', \\'ringers\\', \\'spared\\', \\'durability\\', \\'checklist\\', \\'remedying\\', \"presence\\'s\", \"Cr\\'s\", \"Yuletide\\'s\", \\'disillusioning\\', \\'automatons\\', \\'compelled\\', \\'passenger\\', \"roughneck\\'s\", \\'Aurora\\', \\'expelling\\', \\'crave\\', \\'Sumerian\\', \"Walmart\\'s\", \\'canoeing\\', \\'sealer\\', \\'petioles\\', \\'sinkholes\\', \\'uneconomical\\', \\'painted\\', \"corespondent\\'s\", \"tidbit\\'s\", \\'increments\\', \"willpower\\'s\", \\'converses\\', \\'schematically\\', \\'Freud\\', \\'despoiling\\', \"Mackinac\\'s\", \\'turd\\', \\'Gorey\\'}'"
]
},
"execution_count": 7,
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"output_type": "execute_result"
}
],
"source": [
"repr(b)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'armlet', \"figurehead's\", \"deviance's\", 'palisades', \"monstrance's\", 'nightclothes', 'packet', 'miniscule', 'evinces', 'orphan', \"torpedo's\", 'drank', 'denounces', \"leapfrog's\", 'Mancini', 'intending', 'yield', \"classification's\", 'gougers', 'Ponce', 'circularizing', 'pulse', \"delinquent's\", 'errs', 'quickened', 'operations', 'sublimating', 'exact', 'gantries', \"sweetbrier's\", 'exercising', 'midyear', 'dislocating', 'phantasmagorias', 'classes', 'resigned', 'slewing', \"knitwear's\", 'reasonableness', 'Yellowstone', \"euphemism's\", 'floggings', 'respect', 'underlain', \"split's\", 'aureolas', 'destructive', 'nincompoops', 'wobblier', 'daughter', 'piranha', 'lamed', 'torts', 'overbore', 'plague', 'ferocity', \"dying's\", 'blinding', 'labels', 'plenary', \"browse's\", 'feds', 'extenuate', 'purpose', 'flabbergasting', 'rewriting', 'annoyances', 'appreciate', 'babysat', \"troublemaker's\", 'spreadsheets', 'insurer', \"clime's\", \"Dunkirk's\", 'classmate', 'consummate', 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]
}
],
"source": [
"print(b)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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'Otto', \"Naziism's\", 'fasted', 'scrotums', 'bolted', 'chokers', 'prophylactics', 'interments', 'Maracaibo', 'opaques', \"referee's\", 'invincibility', \"Podhoretz's\", 'earthward', \"tiptop's\", 'gritting', 'Subaru', 'respell', 'daiquiris', 'lustily', \"decompression's\", \"bleach's\", 'postmark', 'commendable', 'couriers', 'advents', 'railroaded', 'mortal', 'creamed', 'iffiest', 'seventeens', \"analogy's\", \"refreshment's\", 'visions', 'hangmen', \"Nobelist's\", 'revues', \"Cancun's\", 'halitosis', 'herded', 'dormice', 'snivels', \"projectionist's\", \"wisher's\", 'blowtorch', \"packet's\", 'reckons', 'cosmically', 'genetic', 'spider', 'cookouts', 'indulged', 'tinnier', 'barber', \"scratchiness's\", \"dateline's\", 'plaice', 'strumpet', 'Joplin', 'tuckers', 'joggles', \"Maidenform's\", \"Trojan's\", \"empire's\", 'flatted', \"unhappiness's\", \"Valentine's\", \"plenary's\", \"Mohorovicic's\", 'cheapening', 'electrocution', 'modelings', 'refueled', 'betrothals', 'uprising', 'restfulness', \"teacup's\", 'disease', 'dryads', 'incarnation', 'tango', \"file's\", \"cantaloup's\", \"tautology's\", 'nonprofessional', \"killdeer's\", 'hawkish', 'devaluations', 'grout', 'bussing', \"slack's\", \"fondness's\", \"tetrahedron's\", 'crisps', 'vivaciousness', 'transmissions', 'sorely', 'telekinesis', 'homesteader', 'parading', \"tillage's\", 'frizzle', 'comediennes', \"tweet's\", 'gabbier', 'grotto', 'flyspecked', \"hooter's\", 'footbridge', 'boundless', \"Danielle's\", 'mutilated', 'regrettable', \"pine's\", 'zoologists', \"easterner's\", 'bounces', 'girdling', 'circlets', 'otiose', \"poncho's\", 'maneuvered', 'Knickerbocker', 'Innocent', \"Eniwetok's\", 'graves', 'hyphening', 'outtakes', 'broth', \"pictograph's\", 'griddlecakes', 'sancta', 'anaesthetic', 'corroded', 'piston', \"synchronization's\", 'transmigrated', 'Roosevelt', \"neglig's\", 'expanding', \"shirtwaist's\", 'variables', 'Sq', \"Vulcan's\", 'respires', 'soy', 'Teri', 'orthopaedists', \"competitiveness's\", \"Trey's\", 'castigation', 'gazing', \"Earle's\", \"inertia's\", 'herbivorous', 'obtainable', 'battlements', 'sanded', 'June', \"stipend's\", 'refill', 'quarantining', 'storms', 'commencing', \"Emacs's\", \"frustration's\", 'fluids', \"leitmotif's\", \"hush's\", 'Yaobang', \"golf's\", 'Turner', \"intransigence's\", 'opposes', \"parabola's\", 'crypt', 'depressingly', 'unexceptionable', \"vowel's\", 'vantages', 'Brahmans', 'drams', 'reformulating', 'boxing', 'vocalize', \"Eurasia's\", \"Laocoon's\", \"aneurism's\", 'kilter', \"something's\", 'flatulence', 'entrails', 'likelier', 'moan', \"slanderer's\", 'investigation', 'shrinks', \"snip's\", 'loosened', \"petrel's\", 'swiping', \"Patterson's\", 'advertisers', 'remakes', 'Dexter', 'pelt', 'Francisco', 'Paula', 'alleluia', \"mukluk's\", 'gulped', \"anaemia's\", 'tarring', 'searing', 'slides', \"Abe's\", \"shiver's\", 'coordinating', 'Getty', 'constable', \"Rosalyn's\", 'Rosie', 'luxuries', 'whizzed', 'disestablishing', \"counselor's\", \"native's\", 'sloshes', 'phosphors', 'wormwood', \"chill's\", 'avenge', 'mobilized', 'encores', 'kleptomaniacs', 'totters', 'chlorine', 'clasping', 'into', 'manageable', 'type', 'Drano', 'peculiar', 'rickshas', 'deadest', \"pornography's\", 'yews', 'Siberia', \"AB's\", 'nonwhites', \"Byers's\", 'hose', \"Starr's\", 'perceptive', 'Popocatepetl', \"Juanita's\", \"sonnet's\", 'emolument', \"incantation's\", 'failure', 'Sterno', \"Lew's\", 'payroll', 'peony', 'embedding', 'unkindness', 'tamper', \"reprint's\", 'chorale', \"paring's\", 'trisects', 'uncompromisingly', 'buttoned', 'peritoneums', 'freedoms', 'eighteen', 'Ringo', \"Khwarizmi's\", 'exalts', 'wish', 'resenting', 'vindicated', 'invokes', 'mewls', 'eyestrain', 'decencies', 'preteen', 'Piaget', 'alms', 'caliphate', 'excess', 'transferal', 'plunge', 'reprobates', 'coeducational', 'eighth', \"rifleman's\", 'transposes', 'spanners', 'awoke', 'pelvis', 'disillusioned', \"deism's\", 'trifle', 'Confucians', 'Petersen', 'inadvertent', 'sexiest', \"farewell's\", \"fetus's\", 'northbound', 'loges', \"hyperbola's\", 'ration', 'ridiculously', 'sailcloth', 'Niger', 'Midwest', 'Avalon', \"Gertrude's\", 'mountaineers', \"scholarship's\", \"lube's\", 'secedes', 'editorializes', 'swankest', 'lepers', 'airbrushing', 'spillages', \"Tibetan's\", \"powerlessness's\", \"secret's\", 'constitution', \"Stephan's\", 'prolongation', 'Rover', 'roping', 'chiropractors', 'recites', \"settle's\", 'applicable', 'coding', 'Joaquin', 'sledgehammers', 'toxicity', 'daze', \"gobbledygook's\", 'unfit', 'forbids', \"ragout's\", 'fathomed', 'ringers', 'spared', 'durability', 'checklist', 'remedying', \"presence's\", \"Cr's\", \"Yuletide's\", 'disillusioning', 'automatons', 'compelled', 'passenger', \"roughneck's\", 'Aurora', 'expelling', 'crave', 'Sumerian', \"Walmart's\", 'canoeing', 'sealer', 'petioles', 'sinkholes', 'uneconomical', 'painted', \"corespondent's\", \"tidbit's\", 'increments', \"willpower's\", 'converses', 'schematically', 'Freud', 'despoiling', \"Mackinac's\", 'turd', 'Gorey'}\n"
]
}
],
"source": [
"print(str(b))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'armlet', \"figurehead's\", \"deviance's\", 'palisades', \"monstrance's\", 'nightclothes', 'packet', 'miniscule', 'evinces', 'orphan', \"torpedo's\", 'drank', 'denounces', \"leapfrog's\", 'Mancini', 'intending', 'yield', \"classification's\", 'gougers', 'Ponce', 'circularizing', 'pulse', \"delinquent's\", 'errs', 'quickened', 'operations', 'sublimating', 'exact', 'gantries', \"sweetbrier's\", 'exercising', 'midyear', 'dislocating', 'phantasmagorias', 'classes', 'resigned', 'slewing', \"knitwear's\", 'reasonableness', 'Yellowstone', \"euphemism's\", 'floggings', 'respect', 'underlain', \"split's\", 'aureolas', 'destructive', 'nincompoops', 'wobblier', 'daughter', 'piranha', 'lamed', 'torts', 'overbore', 'plague', 'ferocity', \"dying's\", 'blinding', 'labels', 'plenary', \"browse's\", 'feds', 'extenuate', 'purpose', 'flabbergasting', 'rewriting', 'annoyances', 'appreciate', 'babysat', \"troublemaker's\", 'spreadsheets', 'insurer', \"clime's\", \"Dunkirk's\", 'classmate', 'consummate', 'utterance', 'lapwing', 'confiscating', \"melon's\", \"venue's\", \"spleen's\", 'salve', 'handled', 'snored', 'preaching', 'inventions', 'devilries', 'Polanski', \"gosling's\", 'westerns', 'bold', \"cigarillo's\", 'protective', 'discounts', \"phallus's\", 'marches', \"thong's\", \"Knesset's\", 'palsied', \"presumption's\", \"Persia's\", 'ducats', 'sermons', \"enlightenment's\", 'greasier', \"turbojet's\", 'whites', \"gravity's\", \"backstroke's\", 'incorporate', 'customized', \"Cupid's\", 'Mitterrand', 'decentralizing', \"distillery's\", \"Oise's\", 'strait', 'abscessing', 'retrieves', 'haw', 'eclipsed', \"collation's\", \"blotter's\", 'narcissistic', 'possibilities', 'spireas', 'vulcanizing', 'bourgeois', 'tomboys', 'Cheetos', 'manifest', \"lassie's\", \"ramble's\", \"wordiness's\", \"Felice's\", 'dire', 'disfigured', 'damages', 'provides', 'regenerate', 'parentheses', 'reddish', 'jejune', \"nuke's\", \"tanner's\", 'puttied', 'smelliest', \"plow's\", 'metamorphism', 'liter', 'deformities', 'sidelight', 'initials', 'Cormack', \"mahatma's\", 'yuletide', \"jet's\", 'Zamora', 'instills', 'griped', 'sesame', \"skim's\", 'misdone', 'fireman', 'materialistic', 'rawest', \"tare's\", 'Melville', 'county', 'floodlight', \"clothespin's\", 'humbugged', \"edit's\", 'swooned', 'Lawson', 'ecstatically', 'eddying', 'phial', 'mirrors', \"mistake's\", 'fumigation', 'trenches', 'different', 'suspenseful', 'orgasm', 'corpses', 'never', 'prettier', 'submission', 'indoctrinating', 'pumped', 'pedestals', 'pellucid', 'bailiff', 'devises', \"Snyder's\", \"footman's\", \"confine's\", \"militiaman's\", 'civics', \"profession's\", 'aches', \"lichee's\", 'Ahriman', \"sirup's\", 'superscript', 'Mohammedans', 'antiquaries', 'rigidness', 'knacker', 'skylines', 'keels', 'defiled', \"mommy's\", \"easel's\", 'tap', \"urge's\", 'Mather', \"pertness's\", 'distinction', 'sensuously', 'catalogers', 'Long', 'telecommuted', 'enmeshed', \"flounder's\", 'island', 'anorexic', \"spreadsheet's\", \"trial's\", 'clapboard', 'Croats', \"simplification's\", \"linoleum's\", 'Lela', 'Hangzhou', \"potluck's\", 'impressionistic', 'prolonging', 'dedications', 'tameness', 'transceivers', \"physiotherapist's\", 'Sakhalin', 'fate', 'Iroquois', 'unimaginative', 'blindfolding', 'destabilize', \"dick's\", 'accessing', 'institutionalize', 'Adela', 'staggers', \"disposable's\", \"cherry's\", \"Mark's\", 'impinge', \"Descartes's\", 'decriminalized', \"gallantry's\", 'larboard', 'weapon', 'dwarfed', 'excruciatingly', 'Godiva', \"mishmash's\", 'slue', 'adjurations', 'indict', 'trustworthiness', 'Gantry', 'gooier', \"elf's\", 'inhabit', 'disadvantageously', 'deceived', \"sled's\", \"Bundesbank's\", 'ventriloquist', \"Hitler's\", \"gamekeeper's\", 'inveigled', 'motormouth', \"monologue's\", 'undersell', 'dominoes', 'slovenliness', 'bounds', 'stoning', 'tankers', 'blazed', 'athletically', 'semipermeable', 'Fafnir', \"sparkler's\", \"madder's\", 'advertisement', 'detraction', 'leak', 'musketeers', \"phobia's\", 'occludes', 'knobbier', 'umping', 'dashboards', 'sensitives', 'flouting', 'intrudes', 'pragmatists', 'led', 'Stamford', 'physiognomy', 'Chou', 'pauses', 'pluralistic', \"Eocene's\", 'Brits', 'preeminently', 'funniest', 'miasmata', 'rowdiness', \"summer's\", 'spans', 'eyeliners', 'umpired', 'Gandhian', 'laburnums', 'lariat', 'Xanthippe', 'racetrack', \"Merriam's\", 'dynastic', 'recall', 'lees', 'reapportionment', \"puberty's\", 'permissible', 'inducements', 'pediatrics', 'biassed', 'capacitance', 'chillers', 'Alyce', 'maligned', 'healers', \"goalpost's\", \"spokesperson's\", 'barbarity', 'embolden', 'nerving', 'have', 'cemeteries', 'oftener', 'moth', 'born', 'Kurosawa', 'competences', \"housetop's\", 'Trujillo', 'petting', 'Tubman', 'mysterious', 'relishing', 'appertain', 'cedes', 'vibration', 'gibbeting', 'anxiously', 'intelligible', \"idea's\", 'industrialists', \"quadruped's\", \"furze's\", 'participating', 'proletariat', \"landlubber's\", \"Allison's\", 'addle', 'dress', \"lightning's\", 'barrener', 'sailors', 'multitasking', 'Carlene', 'collectable', \"amiability's\", 'floras', \"twitch's\", 'vitality', \"disaster's\", 'Alicia', 'Ingres', 'hydrangeas', 'bills', \"manufacture's\", 'celibates', 'commitments', 'Luann', 'unloosing', 'cooky', 'clothespins', 'perturb', 'trochee', \"walkway's\", 'Leeds', 'victims', 'Westinghouse', 'springtime', 'platooning', 'benign', 'emporium', 'hardy', 'pudgy', 'dithers', 'heckled', 'rescind', 'umber', 'turnabouts', \"HP's\", \"stream's\", 'Casals', 'extendable', 'Prut', \"hermitage's\", 'leafier', 'consider', 'exorcize', \"weaver's\", 'execrating', 'heckling', 'elks', 'sermonize', \"chromosome's\", 'kinking', 'philologists', \"brat's\", 'lopsidedness', 'communicator', 'chagrining', 'ingenuous', 'pediatrician', \"nickel's\", 'Mississippian', 'anonymity', 'clitoral', 'professorship', 'caucused', \"mutilation's\", \"riddle's\", \"pepsin's\", 'bookend', 'mailer', 'sovereign', 'explorers', \"retardant's\", 'drudged', 'wicker', 'sniffed', \"falconer's\", 'findings', 'Christina', 'researches', 'objectors', 'valises', 'humiliated', 'pompadour', 'homeopathy', 'toughened', 'Blucher', 'necklaces', 'close', 'siesta', 'analogues', 'exorcised', 'remake', 'minuter', 'Duncan', 'dis', 'repeals', 'infielders', \"emulsion's\", \"showgirl's\", 'slashed', 'duelling', 'trimly', 'technocracy', \"overachiever's\", 'outstripping', \"dentist's\", \"Judaism's\", 'insincerely', 'pleads', \"Rostov's\", 'privateers', 'boroughs', 'Barclay', 'slithering', 'lamentations', 'Delgado', 'incurable', 'obstructive', \"Adolf's\", 'avows', \"mush's\", 'tier', \"chock's\", 'district', 'enfolded', 'soda', 'big', 'cinema', \"Morpheus's\", 'junking', 'subterranean', \"turner's\", 'hayseeds', \"desecration's\", 'fallowing', 'clarified', 'ratcheted', 'negligee', 'Perseid', 'crunchier', 'slavered', \"birch's\", 'suitable', 'miscellaneous', 'ladybirds', \"hastiness's\", \"turquoise's\", \"CPA's\", 'refit', 'servility', 'Scripture', 'hunger', \"phenomenon's\", \"Yamagata's\", \"Xingu's\", 'Anubis', \"consumerism's\", 'dumbness', \"Liege's\", \"sidewall's\", 'kinky', 'cognac', 'luxurious', \"milkmaid's\", 'stormy', \"frog's\", \"panderer's\", 'brainy', 'incremental', 'Ginny', 'prosperous', 'instigate', 'scripts', 'joiners', 'furors', 'policies', 'elapsed', 'authorship', 'bushwhackers', \"reconnaissance's\", 'quid', \"inmate's\", 'messiah', 'misdoings', \"Psalter's\", \"teacher's\", 'Cyrano', 'bib', 'thirds', \"yen's\", 'berates', \"undesirable's\", 'Delphic', 'bulkier', \"bronchitis's\", 'commissariats', 'underrated', 'armistice', \"washable's\", 'astrophysicist', 'syllabication', \"ambrosia's\", \"colonialism's\", 'placidity', 'Gillette', \"municipal's\", 'Lucile', \"spear's\", 'Gadsden', 'fora', \"Naphtali's\", \"chemical's\", 'dwelling', 'invaders', 'debarment', 'qua', 'drown', 'hydroplaning', 'Armando', 'waking', 'gamekeepers', 'Arcadia', \"jeopardy's\", 'situated', 'propagandizes', \"armlet's\", 'promptest', \"Kara's\", 'Otto', \"Naziism's\", 'fasted', 'scrotums', 'bolted', 'chokers', 'prophylactics', 'interments', 'Maracaibo', 'opaques', \"referee's\", 'invincibility', \"Podhoretz's\", 'earthward', \"tiptop's\", 'gritting', 'Subaru', 'respell', 'daiquiris', 'lustily', \"decompression's\", \"bleach's\", 'postmark', 'commendable', 'couriers', 'advents', 'railroaded', 'mortal', 'creamed', 'iffiest', 'seventeens', \"analogy's\", \"refreshment's\", 'visions', 'hangmen', \"Nobelist's\", 'revues', \"Cancun's\", 'halitosis', 'herded', 'dormice', 'snivels', \"projectionist's\", \"wisher's\", 'blowtorch', \"packet's\", 'reckons', 'cosmically', 'genetic', 'spider', 'cookouts', 'indulged', 'tinnier', 'barber', \"scratchiness's\", \"dateline's\", 'plaice', 'strumpet', 'Joplin', 'tuckers', 'joggles', \"Maidenform's\", \"Trojan's\", \"empire's\", 'flatted', \"unhappiness's\", \"Valentine's\", \"plenary's\", \"Mohorovicic's\", 'cheapening', 'electrocution', 'modelings', 'refueled', 'betrothals', 'uprising', 'restfulness', \"teacup's\", 'disease', 'dryads', 'incarnation', 'tango', \"file's\", \"cantaloup's\", \"tautology's\", 'nonprofessional', \"killdeer's\", 'hawkish', 'devaluations', 'grout', 'bussing', \"slack's\", \"fondness's\", \"tetrahedron's\", 'crisps', 'vivaciousness', 'transmissions', 'sorely', 'telekinesis', 'homesteader', 'parading', \"tillage's\", 'frizzle', 'comediennes', \"tweet's\", 'gabbier', 'grotto', 'flyspecked', \"hooter's\", 'footbridge', 'boundless', \"Danielle's\", 'mutilated', 'regrettable', \"pine's\", 'zoologists', \"easterner's\", 'bounces', 'girdling', 'circlets', 'otiose', \"poncho's\", 'maneuvered', 'Knickerbocker', 'Innocent', \"Eniwetok's\", 'graves', 'hyphening', 'outtakes', 'broth', \"pictograph's\", 'griddlecakes', 'sancta', 'anaesthetic', 'corroded', 'piston', \"synchronization's\", 'transmigrated', 'Roosevelt', \"neglig's\", 'expanding', \"shirtwaist's\", 'variables', 'Sq', \"Vulcan's\", 'respires', 'soy', 'Teri', 'orthopaedists', \"competitiveness's\", \"Trey's\", 'castigation', 'gazing', \"Earle's\", \"inertia's\", 'herbivorous', 'obtainable', 'battlements', 'sanded', 'June', \"stipend's\", 'refill', 'quarantining', 'storms', 'commencing', \"Emacs's\", \"frustration's\", 'fluids', \"leitmotif's\", \"hush's\", 'Yaobang', \"golf's\", 'Turner', \"intransigence's\", 'opposes', \"parabola's\", 'crypt', 'depressingly', 'unexceptionable', \"vowel's\", 'vantages', 'Brahmans', 'drams', 'reformulating', 'boxing', 'vocalize', \"Eurasia's\", \"Laocoon's\", \"aneurism's\", 'kilter', \"something's\", 'flatulence', 'entrails', 'likelier', 'moan', \"slanderer's\", 'investigation', 'shrinks', \"snip's\", 'loosened', \"petrel's\", 'swiping', \"Patterson's\", 'advertisers', 'remakes', 'Dexter', 'pelt', 'Francisco', 'Paula', 'alleluia', \"mukluk's\", 'gulped', \"anaemia's\", 'tarring', 'searing', 'slides', \"Abe's\", \"shiver's\", 'coordinating', 'Getty', 'constable', \"Rosalyn's\", 'Rosie', 'luxuries', 'whizzed', 'disestablishing', \"counselor's\", \"native's\", 'sloshes', 'phosphors', 'wormwood', \"chill's\", 'avenge', 'mobilized', 'encores', 'kleptomaniacs', 'totters', 'chlorine', 'clasping', 'into', 'manageable', 'type', 'Drano', 'peculiar', 'rickshas', 'deadest', \"pornography's\", 'yews', 'Siberia', \"AB's\", 'nonwhites', \"Byers's\", 'hose', \"Starr's\", 'perceptive', 'Popocatepetl', \"Juanita's\", \"sonnet's\", 'emolument', \"incantation's\", 'failure', 'Sterno', \"Lew's\", 'payroll', 'peony', 'embedding', 'unkindness', 'tamper', \"reprint's\", 'chorale', \"paring's\", 'trisects', 'uncompromisingly', 'buttoned', 'peritoneums', 'freedoms', 'eighteen', 'Ringo', \"Khwarizmi's\", 'exalts', 'wish', 'resenting', 'vindicated', 'invokes', 'mewls', 'eyestrain', 'decencies', 'preteen', 'Piaget', 'alms', 'caliphate', 'excess', 'transferal', 'plunge', 'reprobates', 'coeducational', 'eighth', \"rifleman's\", 'transposes', 'spanners', 'awoke', 'pelvis', 'disillusioned', \"deism's\", 'trifle', 'Confucians', 'Petersen', 'inadvertent', 'sexiest', \"farewell's\", \"fetus's\", 'northbound', 'loges', \"hyperbola's\", 'ration', 'ridiculously', 'sailcloth', 'Niger', 'Midwest', 'Avalon', \"Gertrude's\", 'mountaineers', \"scholarship's\", \"lube's\", 'secedes', 'editorializes', 'swankest', 'lepers', 'airbrushing', 'spillages', \"Tibetan's\", \"powerlessness's\", \"secret's\", 'constitution', \"Stephan's\", 'prolongation', 'Rover', 'roping', 'chiropractors', 'recites', \"settle's\", 'applicable', 'coding', 'Joaquin', 'sledgehammers', 'toxicity', 'daze', \"gobbledygook's\", 'unfit', 'forbids', \"ragout's\", 'fathomed', 'ringers', 'spared', 'durability', 'checklist', 'remedying', \"presence's\", \"Cr's\", \"Yuletide's\", 'disillusioning', 'automatons', 'compelled', 'passenger', \"roughneck's\", 'Aurora', 'expelling', 'crave', 'Sumerian', \"Walmart's\", 'canoeing', 'sealer', 'petioles', 'sinkholes', 'uneconomical', 'painted', \"corespondent's\", \"tidbit's\", 'increments', \"willpower's\", 'converses', 'schematically', 'Freud', 'despoiling', \"Mackinac's\", 'turd', 'Gorey'}\n"
]
}
],
"source": [
"print(repr(b))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"'armlet'\n",
"\"figurehead's\"\n",
"\"deviance's\"\n",
"'palisades'\n",
"\"monstrance's\"\n",
"'nightclothes'\n",
"'packet'\n",
"'miniscule'\n",
"'evinces'\n",
"'orphan'\n",
"\"torpedo's\"\n",
"'drank'\n",
"'denounces'\n",
"\"leapfrog's\"\n",
"'Mancini'\n",
"'intending'\n",
"'yield'\n",
"\"classification's\"\n",
"'gougers'\n",
"'Ponce'\n",
"'circularizing'\n",
"'pulse'\n",
"\"delinquent's\"\n",
"'errs'\n",
"'quickened'\n",
"'operations'\n",
"'sublimating'\n",
"'exact'\n",
"'gantries'\n",
"\"sweetbrier's\"\n",
"'exercising'\n",
"'midyear'\n",
"'dislocating'\n",
"'phantasmagorias'\n",
"'classes'\n",
"'resigned'\n",
"'slewing'\n",
"\"knitwear's\"\n",
"'reasonableness'\n",
"'Yellowstone'\n",
"\"euphemism's\"\n",
"'floggings'\n",
"'respect'\n",
"'underlain'\n",
"\"split's\"\n",
"'aureolas'\n",
"'destructive'\n",
"'nincompoops'\n",
"'wobblier'\n",
"'daughter'\n",
"'piranha'\n",
"'lamed'\n",
"'torts'\n",
"'overbore'\n",
"'plague'\n",
"'ferocity'\n",
"\"dying's\"\n",
"'blinding'\n",
"'labels'\n",
"'plenary'\n",
"\"browse's\"\n",
"'feds'\n",
"'extenuate'\n",
"'purpose'\n",
"'flabbergasting'\n",
"'rewriting'\n",
"'annoyances'\n",
"'appreciate'\n",
"'babysat'\n",
"\"troublemaker's\"\n",
"'spreadsheets'\n",
"'insurer'\n",
"\"clime's\"\n",
"\"Dunkirk's\"\n",
"'classmate'\n",
"'consummate'\n",
"'utterance'\n",
"'lapwing'\n",
"'confiscating'\n",
"\"melon's\"\n",
"\"venue's\"\n",
"\"spleen's\"\n",
"'salve'\n",
"'handled'\n",
"'snored'\n",
"'preaching'\n",
"'inventions'\n",
"'devilries'\n",
"'Polanski'\n",
"\"gosling's\"\n",
"'westerns'\n",
"'bold'\n",
"\"cigarillo's\"\n",
"'protective'\n",
"'discounts'\n",
"\"phallus's\"\n",
"'marches'\n",
"\"thong's\"\n",
"\"Knesset's\"\n",
"'palsied'\n",
"\"presumption's\"\n",
"\"Persia's\"\n",
"'ducats'\n",
"'sermons'\n",
"\"enlightenment's\"\n",
"'greasier'\n",
"\"turbojet's\"\n",
"'whites'\n",
"\"gravity's\"\n",
"\"backstroke's\"\n",
"'incorporate'\n",
"'customized'\n",
"\"Cupid's\"\n",
"'Mitterrand'\n",
"'decentralizing'\n",
"\"distillery's\"\n",
"\"Oise's\"\n",
"'strait'\n",
"'abscessing'\n",
"'retrieves'\n",
"'haw'\n",
"'eclipsed'\n",
"\"collation's\"\n",
"\"blotter's\"\n",
"'narcissistic'\n",
"'possibilities'\n",
"'spireas'\n",
"'vulcanizing'\n",
"'bourgeois'\n",
"'tomboys'\n",
"'Cheetos'\n",
"'manifest'\n",
"\"lassie's\"\n",
"\"ramble's\"\n",
"\"wordiness's\"\n",
"\"Felice's\"\n",
"'dire'\n",
"'disfigured'\n",
"'damages'\n",
"'provides'\n",
"'regenerate'\n",
"'parentheses'\n",
"'reddish'\n",
"'jejune'\n",
"\"nuke's\"\n",
"\"tanner's\"\n",
"'puttied'\n",
"'smelliest'\n",
"\"plow's\"\n",
"'metamorphism'\n",
"'liter'\n",
"'deformities'\n",
"'sidelight'\n",
"'initials'\n",
"'Cormack'\n",
"\"mahatma's\"\n",
"'yuletide'\n",
"\"jet's\"\n",
"'Zamora'\n",
"'instills'\n",
"'griped'\n",
"'sesame'\n",
"\"skim's\"\n",
"'misdone'\n",
"'fireman'\n",
"'materialistic'\n",
"'rawest'\n",
"\"tare's\"\n",
"'Melville'\n",
"'county'\n",
"'floodlight'\n",
"\"clothespin's\"\n",
"'humbugged'\n",
"\"edit's\"\n",
"'swooned'\n",
"'Lawson'\n",
"'ecstatically'\n",
"'eddying'\n",
"'phial'\n",
"'mirrors'\n",
"\"mistake's\"\n",
"'fumigation'\n",
"'trenches'\n",
"'different'\n",
"'suspenseful'\n",
"'orgasm'\n",
"'corpses'\n",
"'never'\n",
"'prettier'\n",
"'submission'\n",
"'indoctrinating'\n",
"'pumped'\n",
"'pedestals'\n",
"'pellucid'\n",
"'bailiff'\n",
"'devises'\n",
"\"Snyder's\"\n",
"\"footman's\"\n",
"\"confine's\"\n",
"\"militiaman's\"\n",
"'civics'\n",
"\"profession's\"\n",
"'aches'\n",
"\"lichee's\"\n",
"'Ahriman'\n",
"\"sirup's\"\n",
"'superscript'\n",
"'Mohammedans'\n",
"'antiquaries'\n",
"'rigidness'\n",
"'knacker'\n",
"'skylines'\n",
"'keels'\n",
"'defiled'\n",
"\"mommy's\"\n",
"\"easel's\"\n",
"'tap'\n",
"\"urge's\"\n",
"'Mather'\n",
"\"pertness's\"\n",
"'distinction'\n",
"'sensuously'\n",
"'catalogers'\n",
"'Long'\n",
"'telecommuted'\n",
"'enmeshed'\n",
"\"flounder's\"\n",
"'island'\n",
"'anorexic'\n",
"\"spreadsheet's\"\n",
"\"trial's\"\n",
"'clapboard'\n",
"'Croats'\n",
"\"simplification's\"\n",
"\"linoleum's\"\n",
"'Lela'\n",
"'Hangzhou'\n",
"\"potluck's\"\n",
"'impressionistic'\n",
"'prolonging'\n",
"'dedications'\n",
"'tameness'\n",
"'transceivers'\n",
"\"physiotherapist's\"\n",
"'Sakhalin'\n",
"'fate'\n",
"'Iroquois'\n",
"'unimaginative'\n",
"'blindfolding'\n",
"'destabilize'\n",
"\"dick's\"\n",
"'accessing'\n",
"'institutionalize'\n",
"'Adela'\n",
"'staggers'\n",
"\"disposable's\"\n",
"\"cherry's\"\n",
"\"Mark's\"\n",
"'impinge'\n",
"\"Descartes's\"\n",
"'decriminalized'\n",
"\"gallantry's\"\n",
"'larboard'\n",
"'weapon'\n",
"'dwarfed'\n",
"'excruciatingly'\n",
"'Godiva'\n",
"\"mishmash's\"\n",
"'slue'\n",
"'adjurations'\n",
"'indict'\n",
"'trustworthiness'\n",
"'Gantry'\n",
"'gooier'\n",
"\"elf's\"\n",
"'inhabit'\n",
"'disadvantageously'\n",
"'deceived'\n",
"\"sled's\"\n",
"\"Bundesbank's\"\n",
"'ventriloquist'\n",
"\"Hitler's\"\n",
"\"gamekeeper's\"\n",
"'inveigled'\n",
"'motormouth'\n",
"\"monologue's\"\n",
"'undersell'\n",
"'dominoes'\n",
"'slovenliness'\n",
"'bounds'\n",
"'stoning'\n",
"'tankers'\n",
"'blazed'\n",
"'athletically'\n",
"'semipermeable'\n",
"'Fafnir'\n",
"\"sparkler's\"\n",
"\"madder's\"\n",
"'advertisement'\n",
"'detraction'\n",
"'leak'\n",
"'musketeers'\n",
"\"phobia's\"\n",
"'occludes'\n",
"'knobbier'\n",
"'umping'\n",
"'dashboards'\n",
"'sensitives'\n",
"'flouting'\n",
"'intrudes'\n",
"'pragmatists'\n",
"'led'\n",
"'Stamford'\n",
"'physiognomy'\n",
"'Chou'\n",
"'pauses'\n",
"'pluralistic'\n",
"\"Eocene's\"\n",
"'Brits'\n",
"'preeminently'\n",
"'funniest'\n",
"'miasmata'\n",
"'rowdiness'\n",
"\"summer's\"\n",
"'spans'\n",
"'eyeliners'\n",
"'umpired'\n",
"'Gandhian'\n",
"'laburnums'\n",
"'lariat'\n",
"'Xanthippe'\n",
"'racetrack'\n",
"\"Merriam's\"\n",
"'dynastic'\n",
"'recall'\n",
"'lees'\n",
"'reapportionment'\n",
"\"puberty's\"\n",
"'permissible'\n",
"'inducements'\n",
"'pediatrics'\n",
"'biassed'\n",
"'capacitance'\n",
"'chillers'\n",
"'Alyce'\n",
"'maligned'\n",
"'healers'\n",
"\"goalpost's\"\n",
"\"spokesperson's\"\n",
"'barbarity'\n",
"'embolden'\n",
"'nerving'\n",
"'have'\n",
"'cemeteries'\n",
"'oftener'\n",
"'moth'\n",
"'born'\n",
"'Kurosawa'\n",
"'competences'\n",
"\"housetop's\"\n",
"'Trujillo'\n",
"'petting'\n",
"'Tubman'\n",
"'mysterious'\n",
"'relishing'\n",
"'appertain'\n",
"'cedes'\n",
"'vibration'\n",
"'gibbeting'\n",
"'anxiously'\n",
"'intelligible'\n",
"\"idea's\"\n",
"'industrialists'\n",
"\"quadruped's\"\n",
"\"furze's\"\n",
"'participating'\n",
"'proletariat'\n",
"\"landlubber's\"\n",
"\"Allison's\"\n",
"'addle'\n",
"'dress'\n",
"\"lightning's\"\n",
"'barrener'\n",
"'sailors'\n",
"'multitasking'\n",
"'Carlene'\n",
"'collectable'\n",
"\"amiability's\"\n",
"'floras'\n",
"\"twitch's\"\n",
"'vitality'\n",
"\"disaster's\"\n",
"'Alicia'\n",
"'Ingres'\n",
"'hydrangeas'\n",
"'bills'\n",
"\"manufacture's\"\n",
"'celibates'\n",
"'commitments'\n",
"'Luann'\n",
"'unloosing'\n",
"'cooky'\n",
"'clothespins'\n",
"'perturb'\n",
"'trochee'\n",
"\"walkway's\"\n",
"'Leeds'\n",
"'victims'\n",
"'Westinghouse'\n",
"'springtime'\n",
"'platooning'\n",
"'benign'\n",
"'emporium'\n",
"'hardy'\n",
"'pudgy'\n",
"'dithers'\n",
"'heckled'\n",
"'rescind'\n",
"'umber'\n",
"'turnabouts'\n",
"\"HP's\"\n",
"\"stream's\"\n",
"'Casals'\n",
"'extendable'\n",
"'Prut'\n",
"\"hermitage's\"\n",
"'leafier'\n",
"'consider'\n",
"'exorcize'\n",
"\"weaver's\"\n",
"'execrating'\n",
"'heckling'\n",
"'elks'\n",
"'sermonize'\n",
"\"chromosome's\"\n",
"'kinking'\n",
"'philologists'\n",
"\"brat's\"\n",
"'lopsidedness'\n",
"'communicator'\n",
"'chagrining'\n",
"'ingenuous'\n",
"'pediatrician'\n",
"\"nickel's\"\n",
"'Mississippian'\n",
"'anonymity'\n",
"'clitoral'\n",
"'professorship'\n",
"'caucused'\n",
"\"mutilation's\"\n",
"\"riddle's\"\n",
"\"pepsin's\"\n",
"'bookend'\n",
"'mailer'\n",
"'sovereign'\n",
"'explorers'\n",
"\"retardant's\"\n",
"'drudged'\n",
"'wicker'\n",
"'sniffed'\n",
"\"falconer's\"\n",
"'findings'\n",
"'Christina'\n",
"'researches'\n",
"'objectors'\n",
"'valises'\n",
"'humiliated'\n",
"'pompadour'\n",
"'homeopathy'\n",
"'toughened'\n",
"'Blucher'\n",
"'necklaces'\n",
"'close'\n",
"'siesta'\n",
"'analogues'\n",
"'exorcised'\n",
"'remake'\n",
"'minuter'\n",
"'Duncan'\n",
"'dis'\n",
"'repeals'\n",
"'infielders'\n",
"\"emulsion's\"\n",
"\"showgirl's\"\n",
"'slashed'\n",
"'duelling'\n",
"'trimly'\n",
"'technocracy'\n",
"\"overachiever's\"\n",
"'outstripping'\n",
"\"dentist's\"\n",
"\"Judaism's\"\n",
"'insincerely'\n",
"'pleads'\n",
"\"Rostov's\"\n",
"'privateers'\n",
"'boroughs'\n",
"'Barclay'\n",
"'slithering'\n",
"'lamentations'\n",
"'Delgado'\n",
"'incurable'\n",
"'obstructive'\n",
"\"Adolf's\"\n",
"'avows'\n",
"\"mush's\"\n",
"'tier'\n",
"\"chock's\"\n",
"'district'\n",
"'enfolded'\n",
"'soda'\n",
"'big'\n",
"'cinema'\n",
"\"Morpheus's\"\n",
"'junking'\n",
"'subterranean'\n",
"\"turner's\"\n",
"'hayseeds'\n",
"\"desecration's\"\n",
"'fallowing'\n",
"'clarified'\n",
"'ratcheted'\n",
"'negligee'\n",
"'Perseid'\n",
"'crunchier'\n",
"'slavered'\n",
"\"birch's\"\n",
"'suitable'\n",
"'miscellaneous'\n",
"'ladybirds'\n",
"\"hastiness's\"\n",
"\"turquoise's\"\n",
"\"CPA's\"\n",
"'refit'\n",
"'servility'\n",
"'Scripture'\n",
"'hunger'\n",
"\"phenomenon's\"\n",
"\"Yamagata's\"\n",
"\"Xingu's\"\n",
"'Anubis'\n",
"\"consumerism's\"\n",
"'dumbness'\n",
"\"Liege's\"\n",
"\"sidewall's\"\n",
"'kinky'\n",
"'cognac'\n",
"'luxurious'\n",
"\"milkmaid's\"\n",
"'stormy'\n",
"\"frog's\"\n",
"\"panderer's\"\n",
"'brainy'\n",
"'incremental'\n",
"'Ginny'\n",
"'prosperous'\n",
"'instigate'\n",
"'scripts'\n",
"'joiners'\n",
"'furors'\n",
"'policies'\n",
"'elapsed'\n",
"'authorship'\n",
"'bushwhackers'\n",
"\"reconnaissance's\"\n",
"'quid'\n",
"\"inmate's\"\n",
"'messiah'\n",
"'misdoings'\n",
"\"Psalter's\"\n",
"\"teacher's\"\n",
"'Cyrano'\n",
"'bib'\n",
"'thirds'\n",
"\"yen's\"\n",
"'berates'\n",
"\"undesirable's\"\n",
"'Delphic'\n",
"'bulkier'\n",
"\"bronchitis's\"\n",
"'commissariats'\n",
"'underrated'\n",
"'armistice'\n",
"\"washable's\"\n",
"'astrophysicist'\n",
"'syllabication'\n",
"\"ambrosia's\"\n",
"\"colonialism's\"\n",
"'placidity'\n",
"'Gillette'\n",
"\"municipal's\"\n",
"'Lucile'\n",
"\"spear's\"\n",
"'Gadsden'\n",
"'fora'\n",
"\"Naphtali's\"\n",
"\"chemical's\"\n",
"'dwelling'\n",
"'invaders'\n",
"'debarment'\n",
"'qua'\n",
"'drown'\n",
"'hydroplaning'\n",
"'Armando'\n",
"'waking'\n",
"'gamekeepers'\n",
"'Arcadia'\n",
"\"jeopardy's\"\n",
"'situated'\n",
"'propagandizes'\n",
"\"armlet's\"\n",
"'promptest'\n",
"\"Kara's\"\n",
"'Otto'\n",
"\"Naziism's\"\n",
"'fasted'\n",
"'scrotums'\n",
"'bolted'\n",
"'chokers'\n",
"'prophylactics'\n",
"'interments'\n",
"'Maracaibo'\n",
"'opaques'\n",
"\"referee's\"\n",
"'invincibility'\n",
"\"Podhoretz's\"\n",
"'earthward'\n",
"\"tiptop's\"\n",
"'gritting'\n",
"'Subaru'\n",
"'respell'\n",
"'daiquiris'\n",
"'lustily'\n",
"\"decompression's\"\n",
"\"bleach's\"\n",
"'postmark'\n",
"'commendable'\n",
"'couriers'\n",
"'advents'\n",
"'railroaded'\n",
"'mortal'\n",
"'creamed'\n",
"'iffiest'\n",
"'seventeens'\n",
"\"analogy's\"\n",
"\"refreshment's\"\n",
"'visions'\n",
"'hangmen'\n",
"\"Nobelist's\"\n",
"'revues'\n",
"\"Cancun's\"\n",
"'halitosis'\n",
"'herded'\n",
"'dormice'\n",
"'snivels'\n",
"\"projectionist's\"\n",
"\"wisher's\"\n",
"'blowtorch'\n",
"\"packet's\"\n",
"'reckons'\n",
"'cosmically'\n",
"'genetic'\n",
"'spider'\n",
"'cookouts'\n",
"'indulged'\n",
"'tinnier'\n",
"'barber'\n",
"\"scratchiness's\"\n",
"\"dateline's\"\n",
"'plaice'\n",
"'strumpet'\n",
"'Joplin'\n",
"'tuckers'\n",
"'joggles'\n",
"\"Maidenform's\"\n",
"\"Trojan's\"\n",
"\"empire's\"\n",
"'flatted'\n",
"\"unhappiness's\"\n",
"\"Valentine's\"\n",
"\"plenary's\"\n",
"\"Mohorovicic's\"\n",
"'cheapening'\n",
"'electrocution'\n",
"'modelings'\n",
"'refueled'\n",
"'betrothals'\n",
"'uprising'\n",
"'restfulness'\n",
"\"teacup's\"\n",
"'disease'\n",
"'dryads'\n",
"'incarnation'\n",
"'tango'\n",
"\"file's\"\n",
"\"cantaloup's\"\n",
"\"tautology's\"\n",
"'nonprofessional'\n",
"\"killdeer's\"\n",
"'hawkish'\n",
"'devaluations'\n",
"'grout'\n",
"'bussing'\n",
"\"slack's\"\n",
"\"fondness's\"\n",
"\"tetrahedron's\"\n",
"'crisps'\n",
"'vivaciousness'\n",
"'transmissions'\n",
"'sorely'\n",
"'telekinesis'\n",
"'homesteader'\n",
"'parading'\n",
"\"tillage's\"\n",
"'frizzle'\n",
"'comediennes'\n",
"\"tweet's\"\n",
"'gabbier'\n",
"'grotto'\n",
"'flyspecked'\n",
"\"hooter's\"\n",
"'footbridge'\n",
"'boundless'\n",
"\"Danielle's\"\n",
"'mutilated'\n",
"'regrettable'\n",
"\"pine's\"\n",
"'zoologists'\n",
"\"easterner's\"\n",
"'bounces'\n",
"'girdling'\n",
"'circlets'\n",
"'otiose'\n",
"\"poncho's\"\n",
"'maneuvered'\n",
"'Knickerbocker'\n",
"'Innocent'\n",
"\"Eniwetok's\"\n",
"'graves'\n",
"'hyphening'\n",
"'outtakes'\n",
"'broth'\n",
"\"pictograph's\"\n",
"'griddlecakes'\n",
"'sancta'\n",
"'anaesthetic'\n",
"'corroded'\n",
"'piston'\n",
"\"synchronization's\"\n",
"'transmigrated'\n",
"'Roosevelt'\n",
"\"neglig's\"\n",
"'expanding'\n",
"\"shirtwaist's\"\n",
"'variables'\n",
"'Sq'\n",
"\"Vulcan's\"\n",
"'respires'\n",
"'soy'\n",
"'Teri'\n",
"'orthopaedists'\n",
"\"competitiveness's\"\n",
"\"Trey's\"\n",
"'castigation'\n",
"'gazing'\n",
"\"Earle's\"\n",
"\"inertia's\"\n",
"'herbivorous'\n",
"'obtainable'\n",
"'battlements'\n",
"'sanded'\n",
"'June'\n",
"\"stipend's\"\n",
"'refill'\n",
"'quarantining'\n",
"'storms'\n",
"'commencing'\n",
"\"Emacs's\"\n",
"\"frustration's\"\n",
"'fluids'\n",
"\"leitmotif's\"\n",
"\"hush's\"\n",
"'Yaobang'\n",
"\"golf's\"\n",
"'Turner'\n",
"\"intransigence's\"\n",
"'opposes'\n",
"\"parabola's\"\n",
"'crypt'\n",
"'depressingly'\n",
"'unexceptionable'\n",
"\"vowel's\"\n",
"'vantages'\n",
"'Brahmans'\n",
"'drams'\n",
"'reformulating'\n",
"'boxing'\n",
"'vocalize'\n",
"\"Eurasia's\"\n",
"\"Laocoon's\"\n",
"\"aneurism's\"\n",
"'kilter'\n",
"\"something's\"\n",
"'flatulence'\n",
"'entrails'\n",
"'likelier'\n",
"'moan'\n",
"\"slanderer's\"\n",
"'investigation'\n",
"'shrinks'\n",
"\"snip's\"\n",
"'loosened'\n",
"\"petrel's\"\n",
"'swiping'\n",
"\"Patterson's\"\n",
"'advertisers'\n",
"'remakes'\n",
"'Dexter'\n",
"'pelt'\n",
"'Francisco'\n",
"'Paula'\n",
"'alleluia'\n",
"\"mukluk's\"\n",
"'gulped'\n",
"\"anaemia's\"\n",
"'tarring'\n",
"'searing'\n",
"'slides'\n",
"\"Abe's\"\n",
"\"shiver's\"\n",
"'coordinating'\n",
"'Getty'\n",
"'constable'\n",
"\"Rosalyn's\"\n",
"'Rosie'\n",
"'luxuries'\n",
"'whizzed'\n",
"'disestablishing'\n",
"\"counselor's\"\n",
"\"native's\"\n",
"'sloshes'\n",
"'phosphors'\n",
"'wormwood'\n",
"\"chill's\"\n",
"'avenge'\n",
"'mobilized'\n",
"'encores'\n",
"'kleptomaniacs'\n",
"'totters'\n",
"'chlorine'\n",
"'clasping'\n",
"'into'\n",
"'manageable'\n",
"'type'\n",
"'Drano'\n",
"'peculiar'\n",
"'rickshas'\n",
"'deadest'\n",
"\"pornography's\"\n",
"'yews'\n",
"'Siberia'\n",
"\"AB's\"\n",
"'nonwhites'\n",
"\"Byers's\"\n",
"'hose'\n",
"\"Starr's\"\n",
"'perceptive'\n",
"'Popocatepetl'\n",
"\"Juanita's\"\n",
"\"sonnet's\"\n",
"'emolument'\n",
"\"incantation's\"\n",
"'failure'\n",
"'Sterno'\n",
"\"Lew's\"\n",
"'payroll'\n",
"'peony'\n",
"'embedding'\n",
"'unkindness'\n",
"'tamper'\n",
"\"reprint's\"\n",
"'chorale'\n",
"\"paring's\"\n",
"'trisects'\n",
"'uncompromisingly'\n",
"'buttoned'\n",
"'peritoneums'\n",
"'freedoms'\n",
"'eighteen'\n",
"'Ringo'\n",
"\"Khwarizmi's\"\n",
"'exalts'\n",
"'wish'\n",
"'resenting'\n",
"'vindicated'\n",
"'invokes'\n",
"'mewls'\n",
"'eyestrain'\n",
"'decencies'\n",
"'preteen'\n",
"'Piaget'\n",
"'alms'\n",
"'caliphate'\n",
"'excess'\n",
"'transferal'\n",
"'plunge'\n",
"'reprobates'\n",
"'coeducational'\n",
"'eighth'\n",
"\"rifleman's\"\n",
"'transposes'\n",
"'spanners'\n",
"'awoke'\n",
"'pelvis'\n",
"'disillusioned'\n",
"\"deism's\"\n",
"'trifle'\n",
"'Confucians'\n",
"'Petersen'\n",
"'inadvertent'\n",
"'sexiest'\n",
"\"farewell's\"\n",
"\"fetus's\"\n",
"'northbound'\n",
"'loges'\n",
"\"hyperbola's\"\n",
"'ration'\n",
"'ridiculously'\n",
"'sailcloth'\n",
"'Niger'\n",
"'Midwest'\n",
"'Avalon'\n",
"\"Gertrude's\"\n",
"'mountaineers'\n",
"\"scholarship's\"\n",
"\"lube's\"\n",
"'secedes'\n",
"'editorializes'\n",
"'swankest'\n",
"'lepers'\n",
"'airbrushing'\n",
"'spillages'\n",
"\"Tibetan's\"\n",
"\"powerlessness's\"\n",
"\"secret's\"\n",
"'constitution'\n",
"\"Stephan's\"\n",
"'prolongation'\n",
"'Rover'\n",
"'roping'\n",
"'chiropractors'\n",
"'recites'\n",
"\"settle's\"\n",
"'applicable'\n",
"'coding'\n",
"'Joaquin'\n",
"'sledgehammers'\n",
"'toxicity'\n",
"'daze'\n",
"\"gobbledygook's\"\n",
"'unfit'\n",
"'forbids'\n",
"\"ragout's\"\n",
"'fathomed'\n",
"'ringers'\n",
"'spared'\n",
"'durability'\n",
"'checklist'\n",
"'remedying'\n",
"\"presence's\"\n",
"\"Cr's\"\n",
"\"Yuletide's\"\n",
"'disillusioning'\n",
"'automatons'\n",
"'compelled'\n",
"'passenger'\n",
"\"roughneck's\"\n",
"'Aurora'\n",
"'expelling'\n",
"'crave'\n",
"'Sumerian'\n",
"\"Walmart's\"\n",
"'canoeing'\n",
"'sealer'\n",
"'petioles'\n",
"'sinkholes'\n",
"'uneconomical'\n",
"'painted'\n",
"\"corespondent's\"\n",
"\"tidbit's\"\n",
"'increments'\n",
"\"willpower's\"\n",
"'converses'\n",
"'schematically'\n",
"'Freud'\n",
"'despoiling'\n",
"\"Mackinac's\"\n",
"'turd'\n",
"'Gorey'\n"
]
}
],
"source": [
"for word in b:\n",
" print(repr(word))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'armlet',\n",
" \"figurehead's\",\n",
" \"deviance's\",\n",
" 'palisades',\n",
" \"monstrance's\",\n",
" 'nightclothes',\n",
" 'packet',\n",
" 'miniscule',\n",
" 'evinces',\n",
" 'orphan',\n",
" \"torpedo's\",\n",
" 'drank',\n",
" 'denounces',\n",
" \"leapfrog's\",\n",
" 'Mancini',\n",
" 'intending',\n",
" 'yield',\n",
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" 'pedestals',\n",
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" 'keels',\n",
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" 'trajectory',\n",
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" 'benign',\n",
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" 'hardy',\n",
" 'pudgy',\n",
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" 'heckled',\n",
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" 'umber',\n",
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" 'researches',\n",
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" 'valises',\n",
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" 'homeopathy',\n",
" 'toughened',\n",
" 'Blucher',\n",
" 'necklaces',\n",
" 'close',\n",
" 'siesta',\n",
" 'analogues',\n",
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" 'remake',\n",
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" 'dis',\n",
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" 'lamentations',\n",
" 'Delgado',\n",
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" 'cognac',\n",
" 'luxurious',\n",
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" 'instigate',\n",
" 'scripts',\n",
" 'joiners',\n",
" 'furors',\n",
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" 'waking',\n",
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" 'refueled',\n",
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" \"Vulcan's\",\n",
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" 'orthopaedists',\n",
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" \"Earle's\",\n",
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" 'fluids',\n",
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" 'Turner',\n",
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" \"petrel's\",\n",
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" 'Paula',\n",
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" \"mukluk's\",\n",
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" \"Abe's\",\n",
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" 'coordinating',\n",
" 'Getty',\n",
" 'constable',\n",
" \"Rosalyn's\",\n",
" 'Rosie',\n",
" 'luxuries',\n",
" 'whizzed',\n",
" 'disestablishing',\n",
" \"counselor's\",\n",
" \"native's\",\n",
" 'sloshes',\n",
" 'phosphors',\n",
" 'wormwood',\n",
" \"chill's\",\n",
" 'avenge',\n",
" 'mobilized',\n",
" 'encores',\n",
" 'kleptomaniacs',\n",
" 'totters',\n",
" 'chlorine',\n",
" 'clasping',\n",
" 'into',\n",
" 'manageable',\n",
" 'type',\n",
" 'Drano',\n",
" 'peculiar',\n",
" 'rickshas',\n",
" 'deadest',\n",
" \"pornography's\",\n",
" 'yews',\n",
" 'Siberia',\n",
" \"AB's\",\n",
" 'nonwhites',\n",
" \"Byers's\",\n",
" 'hose',\n",
" \"Starr's\",\n",
" 'perceptive',\n",
" 'Popocatepetl',\n",
" \"Juanita's\",\n",
" \"sonnet's\",\n",
" 'emolument',\n",
" \"incantation's\",\n",
" 'failure',\n",
" 'Sterno',\n",
" \"Lew's\",\n",
" 'payroll',\n",
" 'peony',\n",
" 'embedding',\n",
" 'unkindness',\n",
" 'tamper',\n",
" \"reprint's\",\n",
" 'chorale',\n",
" \"paring's\",\n",
" 'trisects',\n",
" 'uncompromisingly',\n",
" 'buttoned',\n",
" 'peritoneums',\n",
" 'freedoms',\n",
" 'eighteen',\n",
" 'Ringo',\n",
" \"Khwarizmi's\",\n",
" 'exalts',\n",
" 'wish',\n",
" 'resenting',\n",
" 'vindicated',\n",
" 'invokes',\n",
" 'mewls',\n",
" 'eyestrain',\n",
" 'decencies',\n",
" 'preteen',\n",
" 'Piaget',\n",
" 'alms',\n",
" 'caliphate',\n",
" 'excess',\n",
" 'transferal',\n",
" 'plunge',\n",
" 'reprobates',\n",
" 'coeducational',\n",
" 'eighth',\n",
" \"rifleman's\",\n",
" 'transposes',\n",
" 'spanners',\n",
" 'awoke',\n",
" 'pelvis',\n",
" 'disillusioned',\n",
" \"deism's\",\n",
" 'trifle',\n",
" 'Confucians',\n",
" 'Petersen',\n",
" 'inadvertent',\n",
" 'sexiest',\n",
" \"farewell's\",\n",
" \"fetus's\",\n",
" 'northbound',\n",
" 'loges',\n",
" \"hyperbola's\",\n",
" 'ration',\n",
" 'ridiculously',\n",
" 'sailcloth',\n",
" 'Niger',\n",
" 'Midwest',\n",
" 'Avalon',\n",
" \"Gertrude's\",\n",
" 'mountaineers',\n",
" \"scholarship's\",\n",
" \"lube's\",\n",
" 'secedes',\n",
" 'editorializes',\n",
" 'swankest',\n",
" 'lepers',\n",
" 'airbrushing',\n",
" 'spillages',\n",
" \"Tibetan's\",\n",
" \"powerlessness's\",\n",
" \"secret's\",\n",
" 'constitution',\n",
" \"Stephan's\",\n",
" 'prolongation',\n",
" 'Rover',\n",
" 'roping',\n",
" 'chiropractors',\n",
" 'recites',\n",
" \"settle's\",\n",
" 'applicable',\n",
" 'coding',\n",
" 'Joaquin',\n",
" 'sledgehammers',\n",
" 'toxicity',\n",
" 'daze',\n",
" \"gobbledygook's\",\n",
" 'unfit',\n",
" 'forbids',\n",
" \"ragout's\",\n",
" 'fathomed',\n",
" 'ringers',\n",
" 'spared',\n",
" 'durability',\n",
" 'checklist',\n",
" 'remedying',\n",
" \"presence's\",\n",
" \"Cr's\",\n",
" \"Yuletide's\",\n",
" 'disillusioning',\n",
" 'automatons',\n",
" 'compelled',\n",
" 'passenger',\n",
" \"roughneck's\",\n",
" 'Aurora',\n",
" 'expelling',\n",
" 'crave',\n",
" 'Sumerian',\n",
" \"Walmart's\",\n",
" 'canoeing',\n",
" 'sealer',\n",
" 'petioles',\n",
" 'sinkholes',\n",
" 'uneconomical',\n",
" 'painted',\n",
" \"corespondent's\",\n",
" \"tidbit's\",\n",
" 'increments',\n",
" \"willpower's\",\n",
" 'converses',\n",
" 'schematically',\n",
" 'Freud',\n",
" 'despoiling',\n",
" \"Mackinac's\",\n",
" 'turd',\n",
" 'Gorey'}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b = set()\n",
"for word in islice(words, 1000):\n",
" b |= set([word])\n",
" # print(word)\n",
" \n",
"b"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
queirozfcom/python-sandbox | python3/notebooks/apply.ipynb | 1 | 21823 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'3.6.9'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from platform import python_version\n",
"\n",
"python_version()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('1.0.5', '1.19.0')"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"pd.__version__, np.__version__"
]
},
{
"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>name</th>\n",
" <th>age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>alice</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>bob</td>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>charlie</td>\n",
" <td>27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>david</td>\n",
" <td>22</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name age\n",
"0 alice 25\n",
"1 bob 26\n",
"2 charlie 27\n",
"3 david 22"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame({\n",
" 'name': ['alice','bob','charlie','david'],\n",
" 'age': [25,26,27,22],\n",
"})[['name', 'age']]\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## apply example"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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>name</th>\n",
" <th>age</th>\n",
" <th>name_uppercase</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>alice</td>\n",
" <td>25</td>\n",
" <td>ALICE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>bob</td>\n",
" <td>26</td>\n",
" <td>BOB</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>charlie</td>\n",
" <td>27</td>\n",
" <td>CHARLIE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>david</td>\n",
" <td>22</td>\n",
" <td>DAVID</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name age name_uppercase\n",
"0 alice 25 ALICE\n",
"1 bob 26 BOB\n",
"2 charlie 27 CHARLIE\n",
"3 david 22 DAVID"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"df = pd.DataFrame({\n",
" 'name': ['alice','bob','charlie','david'],\n",
" 'age': [25,26,27,22],\n",
"})[['name', 'age']]\n",
"\n",
"# each element of the age column is a string\n",
"# so you can call .upper() on it\n",
"df['name_uppercase'] = df['name'].apply(lambda element: element.upper())\n",
"\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## custom function"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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>name</th>\n",
" <th>age</th>\n",
" <th>first_letter</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>alice</td>\n",
" <td>25</td>\n",
" <td>a</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>bob</td>\n",
" <td>26</td>\n",
" <td>b</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>charlie</td>\n",
" <td>27</td>\n",
" <td>c</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>david</td>\n",
" <td>22</td>\n",
" <td>d</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name age first_letter\n",
"0 alice 25 a\n",
"1 bob 26 b\n",
"2 charlie 27 c\n",
"3 david 22 d"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"df = pd.DataFrame({\n",
" 'name': ['alice','bob','charlie','david'],\n",
" 'age': [25,26,27,22],\n",
"})[['name', 'age']]\n",
"\n",
"\n",
"def first_letter(input_str):\n",
" return input_str[:1]\n",
"\n",
"# each element of the age column is a string\n",
"# so you can call .upper() on it\n",
"df['first_letter'] = df['name'].apply(first_letter)\n",
"\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Take multiple columns as parameters"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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>name</th>\n",
" <th>age</th>\n",
" <th>concatenated</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>alice</td>\n",
" <td>25</td>\n",
" <td>alice--25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>bob</td>\n",
" <td>26</td>\n",
" <td>bob--26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>charlie</td>\n",
" <td>27</td>\n",
" <td>charlie--27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>david</td>\n",
" <td>22</td>\n",
" <td>david--22</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name age concatenated\n",
"0 alice 25 alice--25\n",
"1 bob 26 bob--26\n",
"2 charlie 27 charlie--27\n",
"3 david 22 david--22"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"df = pd.DataFrame({\n",
" 'name': ['alice','bob','charlie','david'],\n",
" 'age': [25,26,27,22],\n",
"})[['name', 'age']]\n",
"\n",
"\n",
"def concatenate(value_1, value_2):\n",
" return str(value_1)+ \"--\" + str(value_2) \n",
"\n",
"# note the use of DOUBLE SQUARE BRACKETS!\n",
"df['concatenated'] = df[['name','age']].apply(lambda row: concatenate(row['name'], row['age']) , axis=1)\n",
"\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Apply function to row"
]
},
{
"cell_type": "code",
"execution_count": 16,
"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>value1</th>\n",
" <th>value2</th>\n",
" <th>value3</th>\n",
" <th>value4</th>\n",
" <th>sum_all</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>10</td>\n",
" <td>99.0</td>\n",
" <td>115.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>20</td>\n",
" <td>99.0</td>\n",
" <td>125.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>30</td>\n",
" <td>99.0</td>\n",
" <td>135.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>40</td>\n",
" <td>99.0</td>\n",
" <td>145.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>50</td>\n",
" <td>NaN</td>\n",
" <td>56.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" value1 value2 value3 value4 sum_all\n",
"0 1 5 10 99.0 115.0\n",
"1 2 4 20 99.0 125.0\n",
"2 3 3 30 99.0 135.0\n",
"3 4 2 40 99.0 145.0\n",
"4 5 1 50 NaN 56.0"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"df = pd.DataFrame({\n",
" 'value1': [1,2,3,4,5],\n",
" 'value2': [5,4,3,2,1],\n",
" 'value3': [10,20,30,40,50],\n",
" 'value4': [99,99,99,99,np.nan],\n",
"})\n",
"\n",
"def sum_all(row):\n",
" return np.sum(row)\n",
"\n",
"# note that apply was called on the dataframe itself, not on columns\n",
"df['sum_all'] = df.apply(lambda row: sum_all(row), axis=1)\n",
"\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## return multiple values"
]
},
{
"cell_type": "code",
"execution_count": 48,
"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>name</th>\n",
" <th>age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>alice</td>\n",
" <td>25.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>bob</td>\n",
" <td>26.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>charlie</td>\n",
" <td>27.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>david</td>\n",
" <td>22.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>edward</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name age\n",
"0 alice 25.0\n",
"1 bob 26.0\n",
"2 charlie 27.0\n",
"3 david 22.0\n",
"4 edward NaN"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame({\n",
" 'name': ['alice','bob','charlie','david','edward'],\n",
" 'age': [25,26,27,22,np.nan],\n",
"})[['name', 'age']]\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 45,
"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>name</th>\n",
" <th>age</th>\n",
" <th>times_2</th>\n",
" <th>times_3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>alice</td>\n",
" <td>25.0</td>\n",
" <td>50.0</td>\n",
" <td>75.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>bob</td>\n",
" <td>26.0</td>\n",
" <td>52.0</td>\n",
" <td>78.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>charlie</td>\n",
" <td>27.0</td>\n",
" <td>54.0</td>\n",
" <td>81.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>david</td>\n",
" <td>22.0</td>\n",
" <td>44.0</td>\n",
" <td>66.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>edward</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name age times_2 times_3\n",
"0 alice 25.0 50.0 75.0\n",
"1 bob 26.0 52.0 78.0\n",
"2 charlie 27.0 54.0 81.0\n",
"3 david 22.0 44.0 66.0\n",
"4 edward NaN NaN NaN"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"df = pd.DataFrame({\n",
" 'name': ['alice','bob','charlie','david','edward'],\n",
" 'age': [25,26,27,22,np.nan],\n",
"})[['name', 'age']]\n",
"\n",
"def times_two_times_three(value):\n",
" value_times_2 = value*2\n",
" value_times_3 = value*3\n",
"\n",
" return pd.Series([value_times_2,value_times_3])\n",
"\n",
"# note that apply was called on age column\n",
"df[['times_2','times_3']]= df['age'].apply(times_two_times_three)\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" 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>name</th>\n",
" <th>age</th>\n",
" <th>times_2</th>\n",
" <th>times_3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>alice</td>\n",
" <td>25.0</td>\n",
" <td>50.0</td>\n",
" <td>75.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>bob</td>\n",
" <td>26.0</td>\n",
" <td>52.0</td>\n",
" <td>78.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>charlie</td>\n",
" <td>27.0</td>\n",
" <td>54.0</td>\n",
" <td>81.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>david</td>\n",
" <td>22.0</td>\n",
" <td>44.0</td>\n",
" <td>66.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>edward</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name age times_2 times_3\n",
"0 alice 25.0 50.0 75.0\n",
"1 bob 26.0 52.0 78.0\n",
"2 charlie 27.0 54.0 81.0\n",
"3 david 22.0 44.0 66.0\n",
"4 edward NaN NaN NaN"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.9"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
geoffbacon/semrep | semrep/data/training/phonological/phonological.ipynb | 1 | 1263 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Phonological training corpus\n",
"\n",
"This notebook prepares the phonological training corpus for learning phonological representations. For the time being, I'm just including preprocessed corpora."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import glob\n",
"\n",
"for f in glob.glob('*.txt'):\n",
" with open(f, 'r') as g:\n",
" contents = g.read()\n",
" contents = ''.join([c.lower() for c in contents])\n",
" with open(f, 'w') as g:\n",
" g.write(contents)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
ES-DOC/esdoc-jupyterhub | notebooks/awi/cmip6/models/awi-cm-1-0-hr/atmos.ipynb | 1 | 209003 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"source": [
"# ES-DOC CMIP6 Model Properties - Atmos \n",
"**MIP Era**: CMIP6 \n",
"**Institute**: AWI \n",
"**Source ID**: AWI-CM-1-0-HR \n",
"**Topic**: Atmos \n",
"**Sub-Topics**: Dynamical Core, Radiation, Turbulence Convection, Microphysics Precipitation, Cloud Scheme, Observation Simulation, Gravity Waves, Solar, Volcanos. \n",
"**Properties**: 156 (127 required) \n",
"**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/atmos?client=jupyter-notebook) \n",
"**Initialized From**: -- \n",
"\n",
"**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n",
"**Notebook Initialised**: 2018-02-15 16:53:37"
],
"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', 'awi', 'awi-cm-1-0-hr', 'atmos')"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Authors \n",
"*Set document authors*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set as follows: DOC.set_author(\"name\", \"email\") \n",
"# TODO - please enter value(s)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Contributors \n",
"*Specify document contributors* "
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set as follows: DOC.set_contributor(\"name\", \"email\") \n",
"# TODO - please enter value(s)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Publication \n",
"*Specify document publication status* "
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set publication status: \n",
"# 0=do not publish, 1=publish. \n",
"DOC.set_publication_status(0)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Table of Contents \n",
"[1. Key Properties --> Overview](#1.-Key-Properties--->-Overview) \n",
"[2. Key Properties --> Resolution](#2.-Key-Properties--->-Resolution) \n",
"[3. Key Properties --> Timestepping](#3.-Key-Properties--->-Timestepping) \n",
"[4. Key Properties --> Orography](#4.-Key-Properties--->-Orography) \n",
"[5. Grid --> Discretisation](#5.-Grid--->-Discretisation) \n",
"[6. Grid --> Discretisation --> Horizontal](#6.-Grid--->-Discretisation--->-Horizontal) \n",
"[7. Grid --> Discretisation --> Vertical](#7.-Grid--->-Discretisation--->-Vertical) \n",
"[8. Dynamical Core](#8.-Dynamical-Core) \n",
"[9. Dynamical Core --> Top Boundary](#9.-Dynamical-Core--->-Top-Boundary) \n",
"[10. Dynamical Core --> Lateral Boundary](#10.-Dynamical-Core--->-Lateral-Boundary) \n",
"[11. Dynamical Core --> Diffusion Horizontal](#11.-Dynamical-Core--->-Diffusion-Horizontal) \n",
"[12. Dynamical Core --> Advection Tracers](#12.-Dynamical-Core--->-Advection-Tracers) \n",
"[13. Dynamical Core --> Advection Momentum](#13.-Dynamical-Core--->-Advection-Momentum) \n",
"[14. Radiation](#14.-Radiation) \n",
"[15. Radiation --> Shortwave Radiation](#15.-Radiation--->-Shortwave-Radiation) \n",
"[16. Radiation --> Shortwave GHG](#16.-Radiation--->-Shortwave-GHG) \n",
"[17. Radiation --> Shortwave Cloud Ice](#17.-Radiation--->-Shortwave-Cloud-Ice) \n",
"[18. Radiation --> Shortwave Cloud Liquid](#18.-Radiation--->-Shortwave-Cloud-Liquid) \n",
"[19. Radiation --> Shortwave Cloud Inhomogeneity](#19.-Radiation--->-Shortwave-Cloud-Inhomogeneity) \n",
"[20. Radiation --> Shortwave Aerosols](#20.-Radiation--->-Shortwave-Aerosols) \n",
"[21. Radiation --> Shortwave Gases](#21.-Radiation--->-Shortwave-Gases) \n",
"[22. Radiation --> Longwave Radiation](#22.-Radiation--->-Longwave-Radiation) \n",
"[23. Radiation --> Longwave GHG](#23.-Radiation--->-Longwave-GHG) \n",
"[24. Radiation --> Longwave Cloud Ice](#24.-Radiation--->-Longwave-Cloud-Ice) \n",
"[25. Radiation --> Longwave Cloud Liquid](#25.-Radiation--->-Longwave-Cloud-Liquid) \n",
"[26. Radiation --> Longwave Cloud Inhomogeneity](#26.-Radiation--->-Longwave-Cloud-Inhomogeneity) \n",
"[27. Radiation --> Longwave Aerosols](#27.-Radiation--->-Longwave-Aerosols) \n",
"[28. Radiation --> Longwave Gases](#28.-Radiation--->-Longwave-Gases) \n",
"[29. Turbulence Convection](#29.-Turbulence-Convection) \n",
"[30. Turbulence Convection --> Boundary Layer Turbulence](#30.-Turbulence-Convection--->-Boundary-Layer-Turbulence) \n",
"[31. Turbulence Convection --> Deep Convection](#31.-Turbulence-Convection--->-Deep-Convection) \n",
"[32. Turbulence Convection --> Shallow Convection](#32.-Turbulence-Convection--->-Shallow-Convection) \n",
"[33. Microphysics Precipitation](#33.-Microphysics-Precipitation) \n",
"[34. Microphysics Precipitation --> Large Scale Precipitation](#34.-Microphysics-Precipitation--->-Large-Scale-Precipitation) \n",
"[35. Microphysics Precipitation --> Large Scale Cloud Microphysics](#35.-Microphysics-Precipitation--->-Large-Scale-Cloud-Microphysics) \n",
"[36. Cloud Scheme](#36.-Cloud-Scheme) \n",
"[37. Cloud Scheme --> Optical Cloud Properties](#37.-Cloud-Scheme--->-Optical-Cloud-Properties) \n",
"[38. Cloud Scheme --> Sub Grid Scale Water Distribution](#38.-Cloud-Scheme--->-Sub-Grid-Scale-Water-Distribution) \n",
"[39. Cloud Scheme --> Sub Grid Scale Ice Distribution](#39.-Cloud-Scheme--->-Sub-Grid-Scale-Ice-Distribution) \n",
"[40. Observation Simulation](#40.-Observation-Simulation) \n",
"[41. Observation Simulation --> Isscp Attributes](#41.-Observation-Simulation--->-Isscp-Attributes) \n",
"[42. Observation Simulation --> Cosp Attributes](#42.-Observation-Simulation--->-Cosp-Attributes) \n",
"[43. Observation Simulation --> Radar Inputs](#43.-Observation-Simulation--->-Radar-Inputs) \n",
"[44. Observation Simulation --> Lidar Inputs](#44.-Observation-Simulation--->-Lidar-Inputs) \n",
"[45. Gravity Waves](#45.-Gravity-Waves) \n",
"[46. Gravity Waves --> Orographic Gravity Waves](#46.-Gravity-Waves--->-Orographic-Gravity-Waves) \n",
"[47. Gravity Waves --> Non Orographic Gravity Waves](#47.-Gravity-Waves--->-Non-Orographic-Gravity-Waves) \n",
"[48. Solar](#48.-Solar) \n",
"[49. Solar --> Solar Pathways](#49.-Solar--->-Solar-Pathways) \n",
"[50. Solar --> Solar Constant](#50.-Solar--->-Solar-Constant) \n",
"[51. Solar --> Orbital Parameters](#51.-Solar--->-Orbital-Parameters) \n",
"[52. Solar --> Insolation Ozone](#52.-Solar--->-Insolation-Ozone) \n",
"[53. Volcanos](#53.-Volcanos) \n",
"[54. Volcanos --> Volcanoes Treatment](#54.-Volcanos--->-Volcanoes-Treatment) \n",
"\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"# 1. Key Properties --> Overview \n",
"*Top level key properties*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 1.1. Model Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of atmosphere model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.overview.model_overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.2. Model Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Name of atmosphere model code (CAM 4.0, ARPEGE 3.2,...)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.overview.model_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.3. Model Family\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of atmospheric model.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.overview.model_family') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"AGCM\" \n",
"# \"ARCM\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.4. Basic Approximations\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Basic approximations made in the atmosphere.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.overview.basic_approximations') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"primitive equations\" \n",
"# \"non-hydrostatic\" \n",
"# \"anelastic\" \n",
"# \"Boussinesq\" \n",
"# \"hydrostatic\" \n",
"# \"quasi-hydrostatic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 2. Key Properties --> Resolution \n",
"*Characteristics of the model resolution*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 2.1. Horizontal Resolution Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *This is a string usually used by the modelling group to describe the resolution of the model grid, e.g. T42, N48.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.resolution.horizontal_resolution_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.2. Canonical Horizontal Resolution\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Expression quoted for gross comparisons of resolution, e.g. 2.5 x 3.75 degrees lat-lon.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.resolution.canonical_horizontal_resolution') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.3. Range Horizontal Resolution\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Range of horizontal resolution with spatial details, eg. 1 deg (Equator) - 0.5 deg*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.resolution.range_horizontal_resolution') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.4. Number Of Vertical Levels\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Number of vertical levels resolved on the computational grid.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.resolution.number_of_vertical_levels') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.5. High Top\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Does the atmosphere have a high-top? High-Top atmospheres have a fully resolved stratosphere with a model top above the stratopause.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.resolution.high_top') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 3. Key Properties --> Timestepping \n",
"*Characteristics of the atmosphere model time stepping*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 3.1. Timestep Dynamics\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Timestep for the dynamics, e.g. 30 min.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_dynamics') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.2. Timestep Shortwave Radiative Transfer\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Timestep for the shortwave radiative transfer, e.g. 1.5 hours.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_shortwave_radiative_transfer') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.3. Timestep Longwave Radiative Transfer\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Timestep for the longwave radiative transfer, e.g. 3 hours.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_longwave_radiative_transfer') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 4. Key Properties --> Orography \n",
"*Characteristics of the model orography*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 4.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Time adaptation of the orography.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.orography.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"present day\" \n",
"# \"modified\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.2. Changes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *If the orography type is modified describe the time adaptation changes.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.key_properties.orography.changes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"related to ice sheets\" \n",
"# \"related to tectonics\" \n",
"# \"modified mean\" \n",
"# \"modified variance if taken into account in model (cf gravity waves)\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 5. Grid --> Discretisation \n",
"*Atmosphere grid discretisation*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 5.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of grid discretisation in the atmosphere*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.grid.discretisation.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 6. Grid --> Discretisation --> Horizontal \n",
"*Atmosphere discretisation in the horizontal*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 6.1. Scheme Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Horizontal discretisation type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"spectral\" \n",
"# \"fixed grid\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.2. Scheme Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Horizontal discretisation method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"finite elements\" \n",
"# \"finite volumes\" \n",
"# \"finite difference\" \n",
"# \"centered finite difference\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.3. Scheme Order\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Horizontal discretisation function order*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_order') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"second\" \n",
"# \"third\" \n",
"# \"fourth\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.4. Horizontal Pole\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Horizontal discretisation pole singularity treatment*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.horizontal_pole') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"filter\" \n",
"# \"pole rotation\" \n",
"# \"artificial island\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.5. Grid Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Horizontal grid type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.grid_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Gaussian\" \n",
"# \"Latitude-Longitude\" \n",
"# \"Cubed-Sphere\" \n",
"# \"Icosahedral\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 7. Grid --> Discretisation --> Vertical \n",
"*Atmosphere discretisation in the vertical*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 7.1. Coordinate Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Type of vertical coordinate system*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.grid.discretisation.vertical.coordinate_type') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"isobaric\" \n",
"# \"sigma\" \n",
"# \"hybrid sigma-pressure\" \n",
"# \"hybrid pressure\" \n",
"# \"vertically lagrangian\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 8. Dynamical Core \n",
"*Characteristics of the dynamical core*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 8.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of atmosphere dynamical core*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.2. Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Commonly used name for the dynamical core of the model.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.3. Timestepping Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Timestepping framework type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.timestepping_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Adams-Bashforth\" \n",
"# \"explicit\" \n",
"# \"implicit\" \n",
"# \"semi-implicit\" \n",
"# \"leap frog\" \n",
"# \"multi-step\" \n",
"# \"Runge Kutta fifth order\" \n",
"# \"Runge Kutta second order\" \n",
"# \"Runge Kutta third order\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.4. Prognostic Variables\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *List of the model prognostic variables*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.prognostic_variables') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"surface pressure\" \n",
"# \"wind components\" \n",
"# \"divergence/curl\" \n",
"# \"temperature\" \n",
"# \"potential temperature\" \n",
"# \"total water\" \n",
"# \"water vapour\" \n",
"# \"water liquid\" \n",
"# \"water ice\" \n",
"# \"total water moments\" \n",
"# \"clouds\" \n",
"# \"radiation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 9. Dynamical Core --> Top Boundary \n",
"*Type of boundary layer at the top of the model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 9.1. Top Boundary Condition\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Top boundary condition*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_boundary_condition') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"sponge layer\" \n",
"# \"radiation boundary condition\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 9.2. Top Heat\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Top boundary heat treatment*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_heat') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 9.3. Top Wind\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Top boundary wind treatment*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_wind') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 10. Dynamical Core --> Lateral Boundary \n",
"*Type of lateral boundary condition (if the model is a regional model)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 10.1. Condition\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Type of lateral boundary condition*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.lateral_boundary.condition') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"sponge layer\" \n",
"# \"radiation boundary condition\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 11. Dynamical Core --> Diffusion Horizontal \n",
"*Horizontal diffusion scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 11.1. Scheme Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Horizontal diffusion scheme name*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.diffusion_horizontal.scheme_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 11.2. Scheme Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Horizontal diffusion scheme method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.diffusion_horizontal.scheme_method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"iterated Laplacian\" \n",
"# \"bi-harmonic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 12. Dynamical Core --> Advection Tracers \n",
"*Tracer advection scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 12.1. Scheme Name\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Tracer advection scheme name*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.scheme_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Heun\" \n",
"# \"Roe and VanLeer\" \n",
"# \"Roe and Superbee\" \n",
"# \"Prather\" \n",
"# \"UTOPIA\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 12.2. Scheme Characteristics\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Tracer advection scheme characteristics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.scheme_characteristics') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Eulerian\" \n",
"# \"modified Euler\" \n",
"# \"Lagrangian\" \n",
"# \"semi-Lagrangian\" \n",
"# \"cubic semi-Lagrangian\" \n",
"# \"quintic semi-Lagrangian\" \n",
"# \"mass-conserving\" \n",
"# \"finite volume\" \n",
"# \"flux-corrected\" \n",
"# \"linear\" \n",
"# \"quadratic\" \n",
"# \"quartic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 12.3. Conserved Quantities\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Tracer advection scheme conserved quantities*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.conserved_quantities') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"dry mass\" \n",
"# \"tracer mass\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 12.4. Conservation Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Tracer advection scheme conservation method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.conservation_method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"conservation fixer\" \n",
"# \"Priestley algorithm\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 13. Dynamical Core --> Advection Momentum \n",
"*Momentum advection scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 13.1. Scheme Name\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Momentum advection schemes name*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"VanLeer\" \n",
"# \"Janjic\" \n",
"# \"SUPG (Streamline Upwind Petrov-Galerkin)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.2. Scheme Characteristics\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Momentum advection scheme characteristics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_characteristics') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"2nd order\" \n",
"# \"4th order\" \n",
"# \"cell-centred\" \n",
"# \"staggered grid\" \n",
"# \"semi-staggered grid\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.3. Scheme Staggering Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Momentum advection scheme staggering type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_staggering_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Arakawa B-grid\" \n",
"# \"Arakawa C-grid\" \n",
"# \"Arakawa D-grid\" \n",
"# \"Arakawa E-grid\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.4. Conserved Quantities\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Momentum advection scheme conserved quantities*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.conserved_quantities') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Angular momentum\" \n",
"# \"Horizontal momentum\" \n",
"# \"Enstrophy\" \n",
"# \"Mass\" \n",
"# \"Total energy\" \n",
"# \"Vorticity\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.5. Conservation Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Momentum advection scheme conservation method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.conservation_method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"conservation fixer\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 14. Radiation \n",
"*Characteristics of the atmosphere radiation process*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 14.1. Aerosols\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Aerosols whose radiative effect is taken into account in the atmosphere model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.aerosols') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"sulphate\" \n",
"# \"nitrate\" \n",
"# \"sea salt\" \n",
"# \"dust\" \n",
"# \"ice\" \n",
"# \"organic\" \n",
"# \"BC (black carbon / soot)\" \n",
"# \"SOA (secondary organic aerosols)\" \n",
"# \"POM (particulate organic matter)\" \n",
"# \"polar stratospheric ice\" \n",
"# \"NAT (nitric acid trihydrate)\" \n",
"# \"NAD (nitric acid dihydrate)\" \n",
"# \"STS (supercooled ternary solution aerosol particle)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 15. Radiation --> Shortwave Radiation \n",
"*Properties of the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 15.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of shortwave radiation in the atmosphere*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.2. Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Commonly used name for the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.3. Spectral Integration\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Shortwave radiation scheme spectral integration*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.spectral_integration') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"wide-band model\" \n",
"# \"correlated-k\" \n",
"# \"exponential sum fitting\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.4. Transport Calculation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Shortwave radiation transport calculation methods*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.transport_calculation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"two-stream\" \n",
"# \"layer interaction\" \n",
"# \"bulk\" \n",
"# \"adaptive\" \n",
"# \"multi-stream\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.5. Spectral Intervals\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Shortwave radiation scheme number of spectral intervals*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.spectral_intervals') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 16. Radiation --> Shortwave GHG \n",
"*Representation of greenhouse gases in the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 16.1. Greenhouse Gas Complexity\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Complexity of greenhouse gases whose shortwave radiative effects are taken into account in the atmosphere model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.greenhouse_gas_complexity') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"CO2\" \n",
"# \"CH4\" \n",
"# \"N2O\" \n",
"# \"CFC-11 eq\" \n",
"# \"CFC-12 eq\" \n",
"# \"HFC-134a eq\" \n",
"# \"Explicit ODSs\" \n",
"# \"Explicit other fluorinated gases\" \n",
"# \"O3\" \n",
"# \"H2O\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 16.2. ODS\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Ozone depleting substances whose shortwave radiative effects are explicitly taken into account in the atmosphere model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.ODS') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"CFC-12\" \n",
"# \"CFC-11\" \n",
"# \"CFC-113\" \n",
"# \"CFC-114\" \n",
"# \"CFC-115\" \n",
"# \"HCFC-22\" \n",
"# \"HCFC-141b\" \n",
"# \"HCFC-142b\" \n",
"# \"Halon-1211\" \n",
"# \"Halon-1301\" \n",
"# \"Halon-2402\" \n",
"# \"methyl chloroform\" \n",
"# \"carbon tetrachloride\" \n",
"# \"methyl chloride\" \n",
"# \"methylene chloride\" \n",
"# \"chloroform\" \n",
"# \"methyl bromide\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 16.3. Other Flourinated Gases\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Other flourinated gases whose shortwave radiative effects are explicitly taken into account in the atmosphere model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.other_flourinated_gases') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"HFC-134a\" \n",
"# \"HFC-23\" \n",
"# \"HFC-32\" \n",
"# \"HFC-125\" \n",
"# \"HFC-143a\" \n",
"# \"HFC-152a\" \n",
"# \"HFC-227ea\" \n",
"# \"HFC-236fa\" \n",
"# \"HFC-245fa\" \n",
"# \"HFC-365mfc\" \n",
"# \"HFC-43-10mee\" \n",
"# \"CF4\" \n",
"# \"C2F6\" \n",
"# \"C3F8\" \n",
"# \"C4F10\" \n",
"# \"C5F12\" \n",
"# \"C6F14\" \n",
"# \"C7F16\" \n",
"# \"C8F18\" \n",
"# \"c-C4F8\" \n",
"# \"NF3\" \n",
"# \"SF6\" \n",
"# \"SO2F2\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 17. Radiation --> Shortwave Cloud Ice \n",
"*Shortwave radiative properties of ice crystals in clouds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 17.1. General Interactions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *General shortwave radiative interactions with cloud ice crystals*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.general_interactions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"scattering\" \n",
"# \"emission/absorption\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.2. Physical Representation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Physical representation of cloud ice crystals in the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.physical_representation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"bi-modal size distribution\" \n",
"# \"ensemble of ice crystals\" \n",
"# \"mean projected area\" \n",
"# \"ice water path\" \n",
"# \"crystal asymmetry\" \n",
"# \"crystal aspect ratio\" \n",
"# \"effective crystal radius\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.3. Optical Methods\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Optical methods applicable to cloud ice crystals in the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.optical_methods') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"T-matrix\" \n",
"# \"geometric optics\" \n",
"# \"finite difference time domain (FDTD)\" \n",
"# \"Mie theory\" \n",
"# \"anomalous diffraction approximation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 18. Radiation --> Shortwave Cloud Liquid \n",
"*Shortwave radiative properties of liquid droplets in clouds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 18.1. General Interactions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *General shortwave radiative interactions with cloud liquid droplets*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.general_interactions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"scattering\" \n",
"# \"emission/absorption\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 18.2. Physical Representation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Physical representation of cloud liquid droplets in the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.physical_representation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"cloud droplet number concentration\" \n",
"# \"effective cloud droplet radii\" \n",
"# \"droplet size distribution\" \n",
"# \"liquid water path\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 18.3. Optical Methods\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Optical methods applicable to cloud liquid droplets in the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.optical_methods') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"geometric optics\" \n",
"# \"Mie theory\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 19. Radiation --> Shortwave Cloud Inhomogeneity \n",
"*Cloud inhomogeneity in the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 19.1. Cloud Inhomogeneity\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Method for taking into account horizontal cloud inhomogeneity*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_inhomogeneity.cloud_inhomogeneity') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Monte Carlo Independent Column Approximation\" \n",
"# \"Triplecloud\" \n",
"# \"analytic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 20. Radiation --> Shortwave Aerosols \n",
"*Shortwave radiative properties of aerosols*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 20.1. General Interactions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *General shortwave radiative interactions with aerosols*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.general_interactions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"scattering\" \n",
"# \"emission/absorption\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 20.2. Physical Representation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Physical representation of aerosols in the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.physical_representation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"number concentration\" \n",
"# \"effective radii\" \n",
"# \"size distribution\" \n",
"# \"asymmetry\" \n",
"# \"aspect ratio\" \n",
"# \"mixing state\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 20.3. Optical Methods\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Optical methods applicable to aerosols in the shortwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.optical_methods') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"T-matrix\" \n",
"# \"geometric optics\" \n",
"# \"finite difference time domain (FDTD)\" \n",
"# \"Mie theory\" \n",
"# \"anomalous diffraction approximation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 21. Radiation --> Shortwave Gases \n",
"*Shortwave radiative properties of gases*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 21.1. General Interactions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *General shortwave radiative interactions with gases*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.shortwave_gases.general_interactions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"scattering\" \n",
"# \"emission/absorption\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 22. Radiation --> Longwave Radiation \n",
"*Properties of the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 22.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of longwave radiation in the atmosphere*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_radiation.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 22.2. Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Commonly used name for the longwave radiation scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_radiation.name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 22.3. Spectral Integration\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Longwave radiation scheme spectral integration*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_radiation.spectral_integration') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"wide-band model\" \n",
"# \"correlated-k\" \n",
"# \"exponential sum fitting\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 22.4. Transport Calculation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Longwave radiation transport calculation methods*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_radiation.transport_calculation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"two-stream\" \n",
"# \"layer interaction\" \n",
"# \"bulk\" \n",
"# \"adaptive\" \n",
"# \"multi-stream\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 22.5. Spectral Intervals\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Longwave radiation scheme number of spectral intervals*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_radiation.spectral_intervals') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 23. Radiation --> Longwave GHG \n",
"*Representation of greenhouse gases in the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 23.1. Greenhouse Gas Complexity\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Complexity of greenhouse gases whose longwave radiative effects are taken into account in the atmosphere model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_GHG.greenhouse_gas_complexity') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"CO2\" \n",
"# \"CH4\" \n",
"# \"N2O\" \n",
"# \"CFC-11 eq\" \n",
"# \"CFC-12 eq\" \n",
"# \"HFC-134a eq\" \n",
"# \"Explicit ODSs\" \n",
"# \"Explicit other fluorinated gases\" \n",
"# \"O3\" \n",
"# \"H2O\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 23.2. ODS\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Ozone depleting substances whose longwave radiative effects are explicitly taken into account in the atmosphere model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_GHG.ODS') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"CFC-12\" \n",
"# \"CFC-11\" \n",
"# \"CFC-113\" \n",
"# \"CFC-114\" \n",
"# \"CFC-115\" \n",
"# \"HCFC-22\" \n",
"# \"HCFC-141b\" \n",
"# \"HCFC-142b\" \n",
"# \"Halon-1211\" \n",
"# \"Halon-1301\" \n",
"# \"Halon-2402\" \n",
"# \"methyl chloroform\" \n",
"# \"carbon tetrachloride\" \n",
"# \"methyl chloride\" \n",
"# \"methylene chloride\" \n",
"# \"chloroform\" \n",
"# \"methyl bromide\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 23.3. Other Flourinated Gases\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Other flourinated gases whose longwave radiative effects are explicitly taken into account in the atmosphere model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_GHG.other_flourinated_gases') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"HFC-134a\" \n",
"# \"HFC-23\" \n",
"# \"HFC-32\" \n",
"# \"HFC-125\" \n",
"# \"HFC-143a\" \n",
"# \"HFC-152a\" \n",
"# \"HFC-227ea\" \n",
"# \"HFC-236fa\" \n",
"# \"HFC-245fa\" \n",
"# \"HFC-365mfc\" \n",
"# \"HFC-43-10mee\" \n",
"# \"CF4\" \n",
"# \"C2F6\" \n",
"# \"C3F8\" \n",
"# \"C4F10\" \n",
"# \"C5F12\" \n",
"# \"C6F14\" \n",
"# \"C7F16\" \n",
"# \"C8F18\" \n",
"# \"c-C4F8\" \n",
"# \"NF3\" \n",
"# \"SF6\" \n",
"# \"SO2F2\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 24. Radiation --> Longwave Cloud Ice \n",
"*Longwave radiative properties of ice crystals in clouds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 24.1. General Interactions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *General longwave radiative interactions with cloud ice crystals*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.general_interactions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"scattering\" \n",
"# \"emission/absorption\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 24.2. Physical Reprenstation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Physical representation of cloud ice crystals in the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.physical_reprenstation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"bi-modal size distribution\" \n",
"# \"ensemble of ice crystals\" \n",
"# \"mean projected area\" \n",
"# \"ice water path\" \n",
"# \"crystal asymmetry\" \n",
"# \"crystal aspect ratio\" \n",
"# \"effective crystal radius\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 24.3. Optical Methods\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Optical methods applicable to cloud ice crystals in the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.optical_methods') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"T-matrix\" \n",
"# \"geometric optics\" \n",
"# \"finite difference time domain (FDTD)\" \n",
"# \"Mie theory\" \n",
"# \"anomalous diffraction approximation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 25. Radiation --> Longwave Cloud Liquid \n",
"*Longwave radiative properties of liquid droplets in clouds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 25.1. General Interactions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *General longwave radiative interactions with cloud liquid droplets*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.general_interactions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"scattering\" \n",
"# \"emission/absorption\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 25.2. Physical Representation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Physical representation of cloud liquid droplets in the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.physical_representation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"cloud droplet number concentration\" \n",
"# \"effective cloud droplet radii\" \n",
"# \"droplet size distribution\" \n",
"# \"liquid water path\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 25.3. Optical Methods\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Optical methods applicable to cloud liquid droplets in the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.optical_methods') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"geometric optics\" \n",
"# \"Mie theory\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 26. Radiation --> Longwave Cloud Inhomogeneity \n",
"*Cloud inhomogeneity in the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 26.1. Cloud Inhomogeneity\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Method for taking into account horizontal cloud inhomogeneity*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_cloud_inhomogeneity.cloud_inhomogeneity') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Monte Carlo Independent Column Approximation\" \n",
"# \"Triplecloud\" \n",
"# \"analytic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 27. Radiation --> Longwave Aerosols \n",
"*Longwave radiative properties of aerosols*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 27.1. General Interactions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *General longwave radiative interactions with aerosols*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.general_interactions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"scattering\" \n",
"# \"emission/absorption\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 27.2. Physical Representation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Physical representation of aerosols in the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.physical_representation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"number concentration\" \n",
"# \"effective radii\" \n",
"# \"size distribution\" \n",
"# \"asymmetry\" \n",
"# \"aspect ratio\" \n",
"# \"mixing state\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 27.3. Optical Methods\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Optical methods applicable to aerosols in the longwave radiation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.optical_methods') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"T-matrix\" \n",
"# \"geometric optics\" \n",
"# \"finite difference time domain (FDTD)\" \n",
"# \"Mie theory\" \n",
"# \"anomalous diffraction approximation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 28. Radiation --> Longwave Gases \n",
"*Longwave radiative properties of gases*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 28.1. General Interactions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *General longwave radiative interactions with gases*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.radiation.longwave_gases.general_interactions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"scattering\" \n",
"# \"emission/absorption\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 29. Turbulence Convection \n",
"*Atmosphere Convective Turbulence and Clouds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 29.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of atmosphere convection and turbulence*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 30. Turbulence Convection --> Boundary Layer Turbulence \n",
"*Properties of the boundary layer turbulence scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 30.1. Scheme Name\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Boundary layer turbulence scheme name*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.scheme_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Mellor-Yamada\" \n",
"# \"Holtslag-Boville\" \n",
"# \"EDMF\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.2. Scheme Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Boundary layer turbulence scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.scheme_type') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"TKE prognostic\" \n",
"# \"TKE diagnostic\" \n",
"# \"TKE coupled with water\" \n",
"# \"vertical profile of Kz\" \n",
"# \"non-local diffusion\" \n",
"# \"Monin-Obukhov similarity\" \n",
"# \"Coastal Buddy Scheme\" \n",
"# \"Coupled with convection\" \n",
"# \"Coupled with gravity waves\" \n",
"# \"Depth capped at cloud base\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.3. Closure Order\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Boundary layer turbulence scheme closure order*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.closure_order') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.4. Counter Gradient\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Uses boundary layer turbulence scheme counter gradient*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.counter_gradient') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 31. Turbulence Convection --> Deep Convection \n",
"*Properties of the deep convection scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 31.1. Scheme Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Deep convection scheme name*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 31.2. Scheme Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Deep convection scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_type') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"mass-flux\" \n",
"# \"adjustment\" \n",
"# \"plume ensemble\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 31.3. Scheme Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Deep convection scheme method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_method') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"CAPE\" \n",
"# \"bulk\" \n",
"# \"ensemble\" \n",
"# \"CAPE/WFN based\" \n",
"# \"TKE/CIN based\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 31.4. Processes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Physical processes taken into account in the parameterisation of deep convection*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.processes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"vertical momentum transport\" \n",
"# \"convective momentum transport\" \n",
"# \"entrainment\" \n",
"# \"detrainment\" \n",
"# \"penetrative convection\" \n",
"# \"updrafts\" \n",
"# \"downdrafts\" \n",
"# \"radiative effect of anvils\" \n",
"# \"re-evaporation of convective precipitation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 31.5. Microphysics\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Microphysics scheme for deep convection. Microphysical processes directly control the amount of detrainment of cloud hydrometeor and water vapor from updrafts*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.microphysics') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"tuning parameter based\" \n",
"# \"single moment\" \n",
"# \"two moment\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 32. Turbulence Convection --> Shallow Convection \n",
"*Properties of the shallow convection scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 32.1. Scheme Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Shallow convection scheme name*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 32.2. Scheme Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *shallow convection scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_type') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"mass-flux\" \n",
"# \"cumulus-capped boundary layer\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 32.3. Scheme Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *shallow convection scheme method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"same as deep (unified)\" \n",
"# \"included in boundary layer turbulence\" \n",
"# \"separate diagnosis\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 32.4. Processes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Physical processes taken into account in the parameterisation of shallow convection*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.processes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"convective momentum transport\" \n",
"# \"entrainment\" \n",
"# \"detrainment\" \n",
"# \"penetrative convection\" \n",
"# \"re-evaporation of convective precipitation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 32.5. Microphysics\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Microphysics scheme for shallow convection*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.microphysics') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"tuning parameter based\" \n",
"# \"single moment\" \n",
"# \"two moment\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 33. Microphysics Precipitation \n",
"*Large Scale Cloud Microphysics and Precipitation*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 33.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of large scale cloud microphysics and precipitation*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.microphysics_precipitation.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 34. Microphysics Precipitation --> Large Scale Precipitation \n",
"*Properties of the large scale precipitation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 34.1. Scheme Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Commonly used name of the large scale precipitation parameterisation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_precipitation.scheme_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 34.2. Hydrometeors\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Precipitating hydrometeors taken into account in the large scale precipitation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_precipitation.hydrometeors') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"liquid rain\" \n",
"# \"snow\" \n",
"# \"hail\" \n",
"# \"graupel\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 35. Microphysics Precipitation --> Large Scale Cloud Microphysics \n",
"*Properties of the large scale cloud microphysics scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 35.1. Scheme Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Commonly used name of the microphysics parameterisation scheme used for large scale clouds.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_cloud_microphysics.scheme_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 35.2. Processes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Large scale cloud microphysics processes*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_cloud_microphysics.processes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"mixed phase\" \n",
"# \"cloud droplets\" \n",
"# \"cloud ice\" \n",
"# \"ice nucleation\" \n",
"# \"water vapour deposition\" \n",
"# \"effect of raindrops\" \n",
"# \"effect of snow\" \n",
"# \"effect of graupel\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 36. Cloud Scheme \n",
"*Characteristics of the cloud scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 36.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of the atmosphere cloud scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 36.2. Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Commonly used name for the cloud scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 36.3. Atmos Coupling\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Atmosphere components that are linked to the cloud scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.atmos_coupling') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"atmosphere_radiation\" \n",
"# \"atmosphere_microphysics_precipitation\" \n",
"# \"atmosphere_turbulence_convection\" \n",
"# \"atmosphere_gravity_waves\" \n",
"# \"atmosphere_solar\" \n",
"# \"atmosphere_volcano\" \n",
"# \"atmosphere_cloud_simulator\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 36.4. Uses Separate Treatment\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Different cloud schemes for the different types of clouds (convective, stratiform and boundary layer)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.uses_separate_treatment') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 36.5. Processes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Processes included in the cloud scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.processes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"entrainment\" \n",
"# \"detrainment\" \n",
"# \"bulk cloud\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 36.6. Prognostic Scheme\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is the cloud scheme a prognostic scheme?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.prognostic_scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 36.7. Diagnostic Scheme\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is the cloud scheme a diagnostic scheme?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.diagnostic_scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 36.8. Prognostic Variables\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *List the prognostic variables used by the cloud scheme, if applicable.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.prognostic_variables') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"cloud amount\" \n",
"# \"liquid\" \n",
"# \"ice\" \n",
"# \"rain\" \n",
"# \"snow\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 37. Cloud Scheme --> Optical Cloud Properties \n",
"*Optical cloud properties*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 37.1. Cloud Overlap Method\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Method for taking into account overlapping of cloud layers*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.optical_cloud_properties.cloud_overlap_method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"random\" \n",
"# \"maximum\" \n",
"# \"maximum-random\" \n",
"# \"exponential\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 37.2. Cloud Inhomogeneity\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Method for taking into account cloud inhomogeneity*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.optical_cloud_properties.cloud_inhomogeneity') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 38. Cloud Scheme --> Sub Grid Scale Water Distribution \n",
"*Sub-grid scale water distribution*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 38.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Sub-grid scale water distribution type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prognostic\" \n",
"# \"diagnostic\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 38.2. Function Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Sub-grid scale water distribution function name*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.function_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 38.3. Function Order\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Sub-grid scale water distribution function type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.function_order') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 38.4. Convection Coupling\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Sub-grid scale water distribution coupling with convection*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.convection_coupling') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"coupled with deep\" \n",
"# \"coupled with shallow\" \n",
"# \"not coupled with convection\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 39. Cloud Scheme --> Sub Grid Scale Ice Distribution \n",
"*Sub-grid scale ice distribution*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 39.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Sub-grid scale ice distribution type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prognostic\" \n",
"# \"diagnostic\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 39.2. Function Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Sub-grid scale ice distribution function name*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.function_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 39.3. Function Order\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Sub-grid scale ice distribution function type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.function_order') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 39.4. Convection Coupling\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Sub-grid scale ice distribution coupling with convection*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.convection_coupling') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"coupled with deep\" \n",
"# \"coupled with shallow\" \n",
"# \"not coupled with convection\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 40. Observation Simulation \n",
"*Characteristics of observation simulation*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 40.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of observation simulator characteristics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 41. Observation Simulation --> Isscp Attributes \n",
"*ISSCP Characteristics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 41.1. Top Height Estimation Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Cloud simulator ISSCP top height estimation methodUo*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.isscp_attributes.top_height_estimation_method') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"no adjustment\" \n",
"# \"IR brightness\" \n",
"# \"visible optical depth\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 41.2. Top Height Direction\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Cloud simulator ISSCP top height direction*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.isscp_attributes.top_height_direction') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"lowest altitude level\" \n",
"# \"highest altitude level\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 42. Observation Simulation --> Cosp Attributes \n",
"*CFMIP Observational Simulator Package attributes*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 42.1. Run Configuration\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Cloud simulator COSP run configuration*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.run_configuration') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Inline\" \n",
"# \"Offline\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 42.2. Number Of Grid Points\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Cloud simulator COSP number of grid points*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_grid_points') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 42.3. Number Of Sub Columns\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Cloud simulator COSP number of sub-cloumns used to simulate sub-grid variability*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_sub_columns') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 42.4. Number Of Levels\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Cloud simulator COSP number of levels*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_levels') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 43. Observation Simulation --> Radar Inputs \n",
"*Characteristics of the cloud radar simulator*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 43.1. Frequency\n",
"**Is Required:** TRUE **Type:** FLOAT **Cardinality:** 1.1\n",
"### *Cloud simulator radar frequency (Hz)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.frequency') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 43.2. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Cloud simulator radar type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"surface\" \n",
"# \"space borne\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 43.3. Gas Absorption\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Cloud simulator radar uses gas absorption*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.gas_absorption') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 43.4. Effective Radius\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Cloud simulator radar uses effective radius*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.effective_radius') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 44. Observation Simulation --> Lidar Inputs \n",
"*Characteristics of the cloud lidar simulator*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 44.1. Ice Types\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Cloud simulator lidar ice type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.lidar_inputs.ice_types') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"ice spheres\" \n",
"# \"ice non-spherical\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 44.2. Overlap\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Cloud simulator lidar overlap*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.observation_simulation.lidar_inputs.overlap') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"max\" \n",
"# \"random\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 45. Gravity Waves \n",
"*Characteristics of the parameterised gravity waves in the atmosphere, whether from orography or other sources.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 45.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of gravity wave parameterisation in the atmosphere*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 45.2. Sponge Layer\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Sponge layer in the upper levels in order to avoid gravity wave reflection at the top.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.sponge_layer') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Rayleigh friction\" \n",
"# \"Diffusive sponge layer\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 45.3. Background\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Background wave distribution*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.background') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"continuous spectrum\" \n",
"# \"discrete spectrum\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 45.4. Subgrid Scale Orography\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Subgrid scale orography effects taken into account.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.subgrid_scale_orography') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"effect on drag\" \n",
"# \"effect on lifting\" \n",
"# \"enhanced topography\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 46. Gravity Waves --> Orographic Gravity Waves \n",
"*Gravity waves generated due to the presence of orography*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 46.1. Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Commonly used name for the orographic gravity wave scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 46.2. Source Mechanisms\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Orographic gravity wave source mechanisms*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.source_mechanisms') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"linear mountain waves\" \n",
"# \"hydraulic jump\" \n",
"# \"envelope orography\" \n",
"# \"low level flow blocking\" \n",
"# \"statistical sub-grid scale variance\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 46.3. Calculation Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Orographic gravity wave calculation method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.calculation_method') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"non-linear calculation\" \n",
"# \"more than two cardinal directions\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 46.4. Propagation Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Orographic gravity wave propogation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.propagation_scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"linear theory\" \n",
"# \"non-linear theory\" \n",
"# \"includes boundary layer ducting\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 46.5. Dissipation Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Orographic gravity wave dissipation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.dissipation_scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"total wave\" \n",
"# \"single wave\" \n",
"# \"spectral\" \n",
"# \"linear\" \n",
"# \"wave saturation vs Richardson number\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 47. Gravity Waves --> Non Orographic Gravity Waves \n",
"*Gravity waves generated by non-orographic processes.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 47.1. Name\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Commonly used name for the non-orographic gravity wave scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 47.2. Source Mechanisms\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Non-orographic gravity wave source mechanisms*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.source_mechanisms') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"convection\" \n",
"# \"precipitation\" \n",
"# \"background spectrum\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 47.3. Calculation Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Non-orographic gravity wave calculation method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.calculation_method') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"spatially dependent\" \n",
"# \"temporally dependent\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 47.4. Propagation Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Non-orographic gravity wave propogation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.propagation_scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"linear theory\" \n",
"# \"non-linear theory\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 47.5. Dissipation Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Non-orographic gravity wave dissipation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.dissipation_scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"total wave\" \n",
"# \"single wave\" \n",
"# \"spectral\" \n",
"# \"linear\" \n",
"# \"wave saturation vs Richardson number\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 48. Solar \n",
"*Top of atmosphere solar insolation characteristics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 48.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of solar insolation of the atmosphere*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 49. Solar --> Solar Pathways \n",
"*Pathways for solar forcing of the atmosphere*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 49.1. Pathways\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Pathways for the solar forcing of the atmosphere model domain*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.solar_pathways.pathways') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"SW radiation\" \n",
"# \"precipitating energetic particles\" \n",
"# \"cosmic rays\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 50. Solar --> Solar Constant \n",
"*Solar constant and top of atmosphere insolation characteristics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 50.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Time adaptation of the solar constant.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.solar_constant.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"fixed\" \n",
"# \"transient\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 50.2. Fixed Value\n",
"**Is Required:** FALSE **Type:** FLOAT **Cardinality:** 0.1\n",
"### *If the solar constant is fixed, enter the value of the solar constant (W m-2).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.solar_constant.fixed_value') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 50.3. Transient Characteristics\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *solar constant transient characteristics (W m-2)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.solar_constant.transient_characteristics') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 51. Solar --> Orbital Parameters \n",
"*Orbital parameters and top of atmosphere insolation characteristics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 51.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Time adaptation of orbital parameters*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.orbital_parameters.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"fixed\" \n",
"# \"transient\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 51.2. Fixed Reference Date\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Reference date for fixed orbital parameters (yyyy)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.orbital_parameters.fixed_reference_date') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 51.3. Transient Method\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Description of transient orbital parameters*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.orbital_parameters.transient_method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 51.4. Computation Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Method used for computing orbital parameters.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.orbital_parameters.computation_method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Berger 1978\" \n",
"# \"Laskar 2004\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 52. Solar --> Insolation Ozone \n",
"*Impact of solar insolation on stratospheric ozone*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 52.1. Solar Ozone Impact\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Does top of atmosphere insolation impact on stratospheric ozone?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.solar.insolation_ozone.solar_ozone_impact') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 53. Volcanos \n",
"*Characteristics of the implementation of volcanoes*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 53.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview description of the implementation of volcanic effects in the atmosphere*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.volcanos.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 54. Volcanos --> Volcanoes Treatment \n",
"*Treatment of volcanoes in the atmosphere*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 54.1. Volcanoes Implementation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *How volcanic effects are modeled in the atmosphere.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmos.volcanos.volcanoes_treatment.volcanoes_implementation') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"high frequency solar constant anomaly\" \n",
"# \"stratospheric aerosols optical thickness\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### \u00a92017 [ES-DOC](https://es-doc.org) \n"
],
"cell_type": "markdown",
"metadata": {}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"name": "python2",
"language": "python"
},
"language_info": {
"mimetype": "text/x-python",
"nbconvert_exporter": "python",
"name": "python",
"file_extension": ".py",
"version": "2.7.10",
"pygments_lexer": "ipython2",
"codemirror_mode": {
"version": 2,
"name": "ipython"
}
}
}
} | gpl-3.0 |
timzhangau/ml_nano | practice_projects/cnn/cifar10-classification/cifar10_cnn.ipynb | 1 | 393595 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"\n",
"\n",
"## Convolutional Neural Networks\n",
"\n",
"---\n",
"\n",
"In this notebook, we train a CNN to classify images from the CIFAR-10 database.\n",
"\n",
"### 1. Load CIFAR-10 Database"
]
},
{
"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": [
"import keras\n",
"from keras.datasets import cifar10\n",
"\n",
"# load the pre-shuffled train and test data\n",
"(x_train, y_train), (x_test, y_test) = cifar10.load_data()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### 2. Visualize the First 24 Training Images"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": 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eUoguzTLwwnTCk6cGR5ZI8fiI7i+EeOkZQ5EuOqLL1Mo8NgfkRe+LdKlBqtLU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\nBCtFiuQh9U9HzLluQOnExfM45FEfONyfDnl21++xEPn2AD9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IXUEGVepK6sLqnF5vaXWO\nguB4e2We51FjapZyEFPqy6lvDruJo9HhE+5Idv9GkFovkTRyMQhUG/HUfl/7ti+CaAnsEjZmJf31\nFLfTdAOEH2WHMYXdR5KdTUxB3xA21O4d/d5Q0tZnmY4FR9IgZqk+Y7PBO7BnTisrYNDn581BoGyq\noX1wVHi+R1OzUxRD97WF0fHmK8o02brGp2x4UlgVtlHo6q5rHkXyPT1dOCnMr9kGiGyJUN35s5qi\n9kbKu8GtPT5lTUt6crMlIsp9SHvc2uPda0ynORSh1lZfBV1dSSOfeVBPORXvAyvLpB6shTrP61Mi\nDAwnW1l+fAFAIqLAD2h5fplKMHeqIkxYqWr7JTLHAxADa5Z5HFw4oWNkWuzT6qK2W13SjzZrICro\nSorxTNujLeyIsqSND6o6D80p7S1Iw/zOFW77zTt6+tA+EHHOWE9pHnmY04bWy3q91IgHw8mf2Zkv\ng+heKvbHgaOlJE3G2W1HgOd6NF2pUWdXmRkbMv/nl7Xdp+S+tYa2JwmTwXPUrkiGTZqqK8shlzmR\nAHvgrTf5VGZhQYWnq1VmMpnTpifPqu3+7NMsVDyAk2NzePXAKR2DW7s8l9Y39SRqU0TRb0Kq26Ew\niyrTyuabfozXp6ce0pPgE9eYZfXqC39alG1vmlN2TR9/t8jTmKL2Ol3Z1VOngTA1pk+qjX0y4LZt\n+GrrzomQd7Ou6+NI5sUITg/DgNtlmEOZ9EUY6fUGe9xWrgiAZpC7d0vGxf5bbxZlVRFy7JTh5FhE\nuEfQ74ZZVZ3Xeu7JCV0nAfZmLOy2TZ0rbpnteRvGTK2tbInjoFSt0cUnnqE8UDtgWEu+B8zDlD93\nKjo3+69zvW/fUibj3pD/bkCK4GRTTsNLWrY4y6eOc031dbp9kzqb2yUGJmK3xeNrCOucK2ttd6iM\nz6583s6UzeKITxY4ahPfvMLrwNS8fm/f5/4KajqHu8Ie2t3X/ji39DSNEm2boyDLM+qNOlSb1XbP\niO+LDBtH1pdkhKeVZl2A5AjCnJhe0jH3G7/3ZSIi+hebXyvK+i1zHe3HXWGPzy9yX3QhscZI0n77\nkMa9IiLdiwvans9+4hEiIvr4Fz+qdZ/meq6eQzF+tuNXrig75zf+OjPbHnpopSh76acs6Lt2XQVA\nz1xcJcc5pj/pulSrVcmDeb0np+P9CBJQGPsIzB9NMQ6MbaEqpDAuP3KS14XPPADPLafiB/DWkZo0\nwpIoog5z4cmPPk1ERE9//FNFWV0YNhGwHovMy8CENi5WCCKvxt9du64M/e+8yGKkL27oHHirxc9z\nEB1OsnEcpFlG7cGQqmBn2pIS3Adh46r8Dbr/NJJkAfWq1mkoCTdy8O1jse05iOybg/0UTvgNk940\nFI6p92MCfFCWAH7PMMfGkpukH8xPzLLjsf0KuC555QZ1+8rIc0Px6yrQ0OLnhtD4qbTNAGzx3j7P\nFyeA5BXCCj9oq6+xssis0wceXC3KXn+JP+93+F5DEF2PE0nsA357R/yfJNT5ZVj/PXhfMyLQTqZ9\nGQj7JEZRZOlzz9V7GNcySrB/782Yb/W6dHNzvSi7ts7zb6er/tLaFvsVPggbX3iA3893dpTh60m7\nwCsHlQNul6c/rIzFT32abfDaTWXRbOzxHD8QJmBpCPsYst4mIFRvSDmzTa1TJO9wpVTHZVnePfba\nWs+OjKkDmJtGxLhW1zls7CnOh/wDzg0Dy8SxsLCwsLCwsLCwsLCwsLCwuA9gN3EsLCwsLCwsLCws\nLCwsLCws7gPcnbBxltFoMKRqSX/mCJU6cJXqlUsOdyPmR0T0m3/rN4mI6Lkvf6Eoa84zFXXrqort\neXKdVkfprNvXmVa63lGa0bf/6I+ISHPED0dK9V1eYkpmE0Jqrq0x3TiCes6uniUiogcfV/orpUy/\n3Gsp5bIvIWP7A6SzcRsMB5AHXuiCeVfDKB6eVsHho6LVatMffe3PKA1U/HZfBDi7Byr0KIzpIqyK\niGhri7+XQiVmF5jGPTOvApMlCY/o7WlY2qXL3C/trrbtqXNniEhFopoNvca5cxz+cPKUin2dOy/h\nQiWl5jUkfCQTQSq+II+VOAUhKp/3GD347dJZDi0og2BeLNRriAii2Vm49hHheR7VZ6fIh3EU7TK1\nceeS0tdP1Xm8ORA61REh1SGMN0dosiUQFt3eYmrlSz/6WVG21GA6+C4KAIq4a1eG22AHQze4fXxo\ngErA/T2MQPBSqLsp0CirfkXqrvu5btl8DlTWnGmBvZ6KwLXb/PfMHITUZPdGiC53iHLXpXJFKeyB\njIegpHUddphmHIMg21SD+/6ppzQMxbRHgOJwvgn/g+d0ud9Kodq4uggmhjIO80w/C6Td3nz7naKs\nJwK7lGq4gQlnDCEk0hXRuRzE+zIRm2wPtJ07fa4T9m8k4S8JCNdGo9GYQNpREHgercxOkQP0270t\npon+7NUrRdnLr/PzLp1QofpPf5bFOU8sKCV+uM+UVc/XMDySeeL72o6nVzk0rwKhZaWQ27YZyhho\n6PPHKX+/A4LJAxGMfOvy9aJsf8T06Y+c1zCt7iLf99qGhoy9dYPDuX52VZ+xIyGL800dg48ssT17\n+jO/WpS9/IO/JCKidkvptHeLZsmlL52p0faehtv85Bq33V9e17Wwcp5tUbWu9q/hcf1iECJPHR5H\nPRgfZbHxKXKRhSqewfzf67G9z4c8xsKeXiNuCV3/3ZtFWVXOgqKq2tzXEp6X14EKXZahGWY6tgMJ\nc3ZiEOxssU3s5RoW48scTIG+fmYG7M4x4HoeVaemKAEaeqE9Gqj9znLujzL4NXGPx9fWZQ0vy4Uu\nvbD8aFF25R2mkw8cnQdOj9vIP4ECovz3hgg49vpq5/uSbMEDurWTi40p61qRy9p8a1PXqJkprtOp\n0xouOBpxXQaRru+R+FGNWZ2HQwljiiB8rUTvUpxomPNRkOcZJUlEGR4lylrux9rGifGrwF3Nxf+K\nExDfd7meSaD1OvXEWSIiqizr2Dx4i8PiHUjGcepZFjn/zb/5JSIi2tjSEKY7d7htOz0ISReu/YkV\nXWNOn2a/KvL1e/sDDq07eUbDinyX++LqJQ3Pr/0+1/3pj6g4+cs/ZeHuQU/X8DTOjq03mmYptdtt\nvpYgMmK2YAfCCW8HJlwXu8yT5B4Xl9RP+tuf5bF/ALZj/0CEV+H94XaXx9QTj3Eo2rOf+pXis5lZ\nWROgn0rih8xAiENZKhqCr7W7w/PyDViXv/uDHxIR0fe/+32tk882ZPa5rxRl/UT8UwfCGiBU7KjI\n8pwGUUweSAvs7bBdWFhSn/nEKo+jcknXu71d9vN3tjVsM0slRNPV8RaKb7e4qtfb3OE23gfR6MPh\nVIf9tkkCw0cJpzL+lTshLA/DSNwJgt33StiYyKHU9agEsh/lmkiBQMj+/rq0UQz9LVWEHDJFWM2o\no/a5IrIcCbx/9WQtnQLR7XKF+9UR/zkB8V1XwqRrU+pzbEuo31RdbdhA/PAYwqADGS+dnoYGViUE\nNIHQqEzW/BzmdygJKhJ416PYnaxkfjfIifI0pxCEiIMq16kDIX9DCTXa39P2dMXPXpoGyQ4ZLxVP\nbfxai8OC07ras4UFfsaXXtTrDUT7xIij456BScaTJdpOe+JP+TX1QxZX2I/cg3puiw86iCDEVPz4\nQaJllZBtVhNCIh0JWR3FOF+ssLGFhYWFhYWFhYWFhYWFhYXFv3e4OyYO5ZTlkeZcIyJHdpqSHIST\nZGe+XNKdw6c+ymyXUqA7km++wqm999dVaHUkJ4cdSIl26wqfcnVz3UUNUv5eXbZHm2Xd3VqY4ZPg\njS09aU1E1Kzf0d23W9fMaeIbRVlXUnqWfRABK/HO+G6iz1MRVkW1oXWqSDrVDpyeJVly7ERt7U6X\n/vJbL9D0SRW6zVN+jpdf+FZRduYkn7LNzyk75rakjk2gz0xa4sjVXcItYSp94WMq4vnUE3ya0ofT\nXFcEv67d5FScly5r3732Ovfn9JSeJv/e3/gdIiL65KMPFmWhiBKeXNFT/EiYOI4LO8ayCxxjynKf\n/y5N60lMxQinebqzq6Ps6Mgdoix0KYeUkCadZgDMj9NNSbsODJeOMCm8praFKzuxgy0QTWzx6W5n\nV3fPd+RosjVS4a2zH3mCiIg25SSmta/XqMvJ77CvzI9YxPOGsMs/iM2JiD5PWeqUgxhtKgwcD5gS\nrgieodDcnW0+WQNNUvLDe8TEyYiiOKFOD4TbGryDP2hpW8VF+kzdLfeE6dHahXYWJs5BV1kAhs2R\nQxsFImAYQF/2jbC2PGc00FMAw0rc3NST21HObTry4IRMWD9eGa4rQtQJsKVKIg5+MNR6bu6yOFsO\nQpxGxNGB08JKySc65snVoN+jV1/+CeW7N4qyqTk+fXjpDU0J+bawXT75eWVW/uE//2dERPQbX1Ah\nypmyrAXQP34g/QjCcgtzbGOzktrx/dH4Sb8DDJJYzh8cEIm8coPZk3//f/77RdnOHV5HngVxzK/8\n/n9MRESLy8rOqUma0VU4iXlDBFAzOOG9I3bvARBWP/8QnyJfeu1HdFSUA4ceXPXp71Y1Lf2pEp/U\nf/MdXbO+cZ3H1FNnVCix+y4LpLbgTMaTedqKoI1FvDnNgUmS8fW2Qah0p8o2ayingg1II18T8eoM\nTgBJUqaWoO/WZPzugnj0sqz91ZraxEaNf5NDCvidiH/re1p3T4TDH8v1dLre0fl1XLieMoiJVOg/\nSbVeWcjjMYP7Ol22x0lXBalnFpjVMYLUqL07vL4mwFSMRdgRU6h6IrY+GHTkX/UlOn2+l+eC2+Zx\n/U6e07LFFfZTqnr4WZyM92L1ic6d5bHmpyoY3o/YF3J9ZSJHKfs4tbqyeLL4+Ie0LLHpFL4ZEZEv\n/hwSCvsi9p7j0bGsUWmivw2ExRfB0WRlmq9XX1XW1qacVE8BE3jxAq8FU2d5bJZXzxSfXXT47xhy\nOXdFaDaDMWNOXx2YSyWPO2F+QX2yhrBIwkDnS1USeTz5MRXnnPkqp9jOYJhXSv6x2Ql5nlOUpmMp\n4n1Z9xxPr220pROwK6FhZMCiv1TnOfk7HztflJ2U9Ol9YH8sTbPtmCmp/Zmvsb/58EMPExFRc0oZ\nS5Gk7C2B2Kgr7xl7d3S9vXGdfdAfv/jTouwnP2Vm85V3rxZlHZlvKayjM8/+NhERDVJdRxxhCATI\nWMyPf96dZyklgwPK8Ow8lXUs13Hk+9wvyyvKplmUqIU/e1cF9VdXeA2ogLPbF/HdHviniZzs431d\noe1PmsNmfE0aZ9mEVMjj38vhv+O/mcS0wTLz9wdNRX5XcIj8wKFBF5j+4tCVgOlVk/dIF1gV5n3X\nDXTcNIR1GkCyiZKMl/lpHcPVMvs6/aGuIz1JXOLLfYG4R9Uqz5s5YDO3JNFADgwuM0+jFPtDIhLA\nJ3REKD6D9+5Y/H/D+uZri9gxCHhnSXoP2j8nh1IiSEgQCMu7CiyVacnB3oP07Dv7zKYrV3Qh68u6\n2Buqb39pl+e/O9TrZbIuxiC+397lazsZ25UGMN2E5E8dYA6OpH6Nmr7jnznFwvOjBU2F/upr7Bf7\nDV1PVlbZt2y9c7koqwkDZ7YJUSKTRIwhUuODwDJxLCwsLCwsLCwsLCwsLCwsLO4D2E0cCwsLCwsL\nCwsLCwsLCwsLi/sAdxVOxSS5jDIQJDLU+BTolZHQvpamZoqyP//anxAR0eyShi4tSjhN1FdqVCAC\nSPWaUo58CW2oASVseZHpqYMOhxpUPKVc7W6zCFgcaZ0aZaZERSDcdPnlF4mIaOPtS0XZSGj1BNQ5\nIwRbO6n0V6pxG7glEI4U8bMZUvrVw4+eo0pZKZ1HwczsHP3+f/ifUGlRqbb9DtOiL7+mgrgry9ye\nSFGslLkdIxCTfPAxvs7MilLC+vPcV1/58heLMhMqhuKYhg2eCBV3CMKCdyR04ca1db2G0A4311SQ\n7fobTDFzgWJ4dZMFMD/2paeLsjNnmS6KYsduWShwAVAGjegc0AhD53gCr0REaZpRq9WhUV/Hey3i\nsbCwrOEMuze47leuawjKdszPNjur1EpXxmAv29d7iKBn0tfQkeFIwmwcpTJub/KY7nU5rCAHIaxq\niedgBCEJTonnQzLU64YmdAEomEMRYctcvV4k87sEIsBhWeZlVUMhKvJ3DHWZRJk9CpI0oZ39Fq0u\nKg3dhFYlmT7n7By3b6etoRdJwn+PIEzJaIW9feWa1lXGSAi06dMy5lwQjh32uD9SuV4S6VwyFFoM\nb7t0m8fBuYUVrafQ5X0Q3O6JUOZ+or/1RaSxA325L39nQOl2xHQHjs6NXn9ESTKBnnkXiNOMtlt9\nejvYLsq8Ozx3b24ohf0zX/gcERH9vf/+vyvK/uE/+sdERPT1P/5aUfahE9x/Qaj2tCa0UxQ1nBUa\n/cKshikZ4eNQQsxcCOvpik2IfG2T/+1//wMiInrz7deKMjOGv/q1/7coO/nQ40RE9PgDGuJZKYno\nHNDaV2WoJ3CPnoRW5pHOqzMnNATqqMjyjEZRn2bLSmf+xIMs6LfT0/n60m0eK29tqQ15QEKXIlAi\nzSUkswPzPx9xWxgxYf5ebipQlJm26OQ87toQOjb36IeIiMgD8/ran3PYxym418kZCVUDkceyhAkc\nxDp/ers8V5fBrqyK4H4IYUPBHj/3GQiHPjV9b4SN8zynQTSkaKDjcShzPM21rknC61tC+kz9A6Z3\nuyC+79e43i0Qn9/Z4PCkKNd5naT87PVptRPJUMKJJAyuP9B5OEx5nXGAwu9LmOj8Sb3GxQc5nGtz\nV8O0QjE7jqtlUY+fZ3nm8aKMXLZ/eV3r/s7bPNZWFnQc1EpV8t0f03HAQq85eWB/Q5nzGITelzE0\nGGoYra4z+r2ax2ModTBEg9t7ekV90UTE5V0Q25wVEV3ja0SkMQ6uCDg7UEYSihBBmIAjIa451CkU\nMfp6U9exmXm+/8oJ9SFSETueO62/PX2Bf4Ph3L7j0PGCqaSulBNheEZuxIF1zk1JaMcI7pgk/BsP\nwnVO1rm9H4I2HkhYj5OqTTChKmfOaaiaK4kvSiH3RQpra2eHfdyXrqjY/Btv8PvDyz9Tv/fdq+xf\nd0BkNpV6ZrDGeNK05Tkdx40Fvn+egIC5+JNj4ct0fH8y9F06PV+luVkVrZ2e4boEIAo/TLnttkEU\n/syJC0REdArWmoV5tn9JquNy/Q1ORrIDIecmMsgZExY24+znh8tMCqUZD50yYVd0uGysvX5+eBb6\ni55IKiTQF/cKTp6TlwypDLYhafPYGMIcTkRyoOLBGilthJ6V8UmaTQ0TN+8fM9Pal6Fcpw+JejJ5\ndzL+jQ/i+ams2+0DsD+SAGNhUcO/TVKO9b2Xi7JApBE8iK+LxD+sgaxDTcSOo1h95n6H/y6Vwe/t\nH3/MOy6RX3GoPK3XbUfSFh6EEDalnaCVt1KWa3Ac7Z/1lOf4fKZ1v9zmkKmNq+qfuiPul/MPa6hw\n/BqHXW1sigwE2LXZelnKtE7TIstyGsIaqzJvPv2JZ4qyuoTFfe+HGk5fLXHocbWsYZpL4tesQD96\nZiwcw6hbJo6FhYWFhYWFhYWFhYWFhYXFfYC7Y+LkDmWZQyHkWjOnawRiqbmkWssgTe2O7Kp3t1VY\nrxLzrloGO96zM7xbNb2qu1WJ7ObfXtffFqkO5eQggt1bz+GdsVpZd7yNhpIHYkoku2pppLukrlBN\n2n096YxKfDrQWNVThV5F0k5mkCatx3tic00VeJtfnCM/uEvC03vgOJxu99Lbrxdl7QNuC9wtj4Up\n0O324Lf8POUSCFv1eZf+YFt/u3WThRf/7M//rCjb78j3uto+DRFlmprhk/MapPpeW2MGzuK87n6W\nm8z2+e7X9bp7l18lIqIUxseVTT4hXIP0eA88zIyhKUjxOyW7o5Wq7nBO1fjZAhCNraKq41GROUSD\ngAj0VROHd8B7cEizIUJUGzC2uub4A8R1vUBOV1EcTsbbAMavESgLgQlzW9hlJjUknsdt78tYhZOO\nXE6ggoqywppyeoCsOTN+PGAbVEQW2oUTUpOa2wm1TiadNQrOIlviOIjimG6tr1MAjDjDgDkFKex7\nwmBqd5GJI8+E4sTCLnrrirLiDMNv/Zbu4M/LiezUlJ7yX77MJ4HG5vzmX1fx71LO82FmWk9kKm0e\n17stTfubyXjA52l3eVz3Rjpf+/KMbggnIrFpZ21bIxa4D3NzHkTWj4qwVKITZy9SSiAeLayyEARp\nV07JCSawxU6t8unDX/2bf1WUdTa5PasgTlcqxqSOVyMuiEyvqqSXN/OgHOrz5XJitD3Qer7xFgvg\nf/GLKrb85FNPEhHRP/mnf1CU/eA7bIvOL2sfh1Xul51NXWN+dpkZmgEI2y01+TcpsDYq4fHPQhxy\nyPF8ciBt84qItz93TkUO2xH3xfUWnKKJyOHiKRWK9yQt+zDR/hmKPffhFD0M+Nn0DkTJFrM/msJK\nGAHLbU/G4vSMnrpPy+lmAELVJ4T1F8I5kVPjPnNA0NXt8rxc8tXGGzKSO9J69qXuUyB2fOG0rgHH\nQU5EaeYgGYnKIc/nGOZm1GI7sRfrvK7O8Xj47Jc+XZSti+9wa09TSC9c4GfP4CQ4lZPQiJRdVGsy\nO+OO2KRhpEycB54SVmdFK7p7wCy56UWY++L/DLpw0rgg6W9z9Wvml7jXTTpWIiLXZfZXa6D9sTAt\nqVk9LbuzPihOrY+KPCcaxkQurIexsJziWOeBYQ2EIERp1rAMOs2wSocgShqLyWxAsgVPWIFBWdus\nFPBzj+T0OXH1/pkkGPAztd0mT0SOLBVJS9wf6Bgdicj+3p6Oo4GwrKpgV3aEaZbA3KwJe7PXg3nQ\nj8ee+ShwHIdKXkBINHlQ0lpfWFG/+8wsz68W+JMH8ncIDOxGzGMqGmo9R5IsoNHQMWMYw0iSrtX4\nHvv7zDr51re+W3z2wgt8sv3W25o8Y0dE/iOwkyaFNaXYLuIDwJppbGIwp2wWR8pc8OPNOovCz3l+\nfHZIKfTpwql5qjZ0LAY1th831neKsl1hFPUhtfz2aWHNnVDG3ba8S129fqsou725bR5C6y5/5zBu\n7lYc2/iJmBSjYJzB/C0uC9fPxJ/Nx8ShTV2gHpOqdK8yjGcp5YMuuSCibhjpPUhU4Yl/WwHbkMo4\naI+UJWbe6VDoORMG1R4wwqaFleNCexjWXxRxG0T6deoOeZy1IYV2RVJyt9q67qTGb6+oTXSFgTOi\nw2PVz+D9Qt41HPDd6nV+3v3dIfzq+I3vhz7Nrc7TXqDP86Nt9qcTWLLSc9wmLkRd3Ep4zQ8DSHUv\na+/uuxrRc/k2z52rVyC5gM/P8dlnPlOUrS6yf/r//Ev2/zARjenFZySBDBHRudPMGFwC5gwN2P5d\nXNK059VnPkxERD984YWi7OoVsVnwfr6ywNeZn1G/05N5EMC8ouzu2t0ycSwsLCwsLCwsLCwsLCws\nLCzuA9hNHAsLCwsLCwsLCwsLCwsLC4v7AHcZ++CQ65SoXAJau1C3ahWlTdYaTDXqx0rNmmuEckOl\nS0UHTH/KXKWE9QMmNi0tnSvKMgkTeuiJk0XZC9/6Bl8jZ2pqAHS1gYRWNCFvuxHM84DL2RVh3Wsb\nSjFutYRC7iiFdOFB3us6Ma3PHeVc5/0dpc6GQwnjOqEidoN+imzDIyFLYursbtI3/83Xi7JbmyyU\n6IJI5KuvCi8P2qIQCYPn/ss/+SbXF4T9nvrwR4iIKAo1LKQtNOKrN1VgbXeXhdOiIV/Mi4amAAAg\nAElEQVRvffN68dm16/zZ0x/+aFH2X/2X/w0REf34hz/QOgkFvD1Sit1AaGVXX1Rq6HdfYkp5zVcK\npBFI9Upa94aEU508c7Yo+63f+w/ouHAch3wnoBgocV2hXu61lQO5JyKnCYTN5QnXc4hiw0L3joGm\n6xrR7Ckdq0bgzfPherLdWoQ/ed6h7yPV1ejFZSAc5xbX1funwgvP8bfF9VAITz6HcIBMfos6dPdK\nlC4noiTPafdAw4WaEkKHoVOmjTAksyd0dtRYzkXYu1HR793Z4++98poKUtcqTEceDUHEUsiWoYTr\nvXVZv79UZVtnxiAR0fIyl+3e0NAcR5TL7mxreMTJk2wnUqBPjiT8pQ9hhYl8noI4eUOE6iKgSPei\nbJxRfgTklFNCKaVw3VBo8KA1X/TB1h19np09tqNrmypinifcjrhmxBIygFUtydypQdinJ2G7FRGH\nK0N4bCYhRDe3lUJLIiz627/zO0XRc889R0REt26tFWVf/dofExHRyz9Tgc1UhDj3t3S8RbscDuOn\nahP7CYe+XN1XO1WFMI+jIieiPHcozyDUKWO78sis2oHtFe73HtjORGzM/JzSfst1DsVoQT/GEr6a\nQBjryOPfukC/b8q8McFKUVvbhGTNzDd1TTgptOsAhAobA/7Noqf9vi8hYKWGhmJlMd8s6StV3Kw7\nEE1FmYQ1rTyiYvznTgPN+RjIs5yiKCvEwomIHBGYpBTElSWErwyhk/Ue/925quPh6Ue5XhcehXgV\nlwVMo4EapZ98h3+zs6NjvtLg6/UHPM6mZvWzJ57h8Xrtzjt63Qa3/eppDTGdmeGQi3pNhXMHCc+T\nDgjoZzlfe21Hw7Rnp01YkQbYTVVE9BdCCEfDEWUThE/vBmlG1IsSSkBY1A9EkLuj46EhITcLc+pX\n5SLojOHkRkx30Fc7mYoCdwrhBG7Ibdbq6hp+4xrbrpkVbn+voiFuuQjHZrH2Z0fExIcgcG7qEkPI\nRiL1vAkhuwcSbuEGOhbaknDDzdWWDIb828tXNCzvoB2P2eajoFEp0WefeICmq3qdCwts3GsgBDzl\nc5vFIJ8wkHUu6al/POrLc+CCK6EaVQg1DSR5QndHE19017ktvvEjFmj9w3+pPu6OrC3oP2dy7pyB\nvXJzbu8cxHQd8W3N2kVEFIoguL+o4f7ki5UDu5tJ/PxYyFF+vIQBRESe51JtqkZuScMp+qk8D4Sk\n+xKyXynBeOuxPe2BGO3V65ykYW9Px3GSHQ5TMqH340LF7lgZfjYx1Er6M4ePfPEZM1jJTYh9hvcX\nnxETlJgQJYwiccn4cofD4o6NPCdKRmNhK7Uq2zXQDadRzjakP1AbEsi4qdUgDFh8ZBQxr0gI/EJT\nHaWyhJHv7ek7pie+S1WEw0+COPLbkhylDHIR8YhtwwBEvws/D54nc4yMAEGZ9AeM3+JzbHt5nlJZ\n15tedzBR3PpuUC6V6aEHHqRLrZtFWUdscjilz704zaHCYyHUA+4LD8eSjP/r76o/NxIR6KlI14dK\nxs/hwfvXyRle25bn2Ie4fUd9x4Um3/+xs7rHMCcyHg1P28A3IbAd7c8FeS/4wsc1Kc+f/fAn/DVI\n7NAQGYGoj+G2Zr6A/bnL5DCWiWNhYWFhYWFhYWFhYWFhYWFxH+CumDiuw2ny+nAK6EnawAxSfPeF\nHeIFuoNVElHKAEQNwyqf9kw1tWxTTlb7J3RHbPHURSIiun1Hxb8efeaTRETU3eZd/auXVOio1+VT\nHN/TncspYTpg6ruN2/zbmzdA2LjEdWkugbCfpAV2ICW2s8ffm9nXJjyxyLt5J6e17lfe3KTRAE/1\n7x5BENLK0go9cFbZSebUwXf1eTzZQUdBWiNmFpYhPXrAu7yrq3oi8blf+zUiImpUQUS4zDvVb76u\n6RwviWDT8omzREQ0BLEyT9hYr196uyh78xKLg1bPPlyUra/zdWem9UR2UQTFqnU9ud3b5F3p3dua\nYnJ7h8fHECgHsTAVNlraF8994fiiXFmaUrfTpXZbT516XR5TvR6MBblVE1ILliqHhZVNiseKr6dt\ngezeI7MmEFYCMnGMeJ/ujMPph/zpTTgJwzTOhiUzJoYtZSlczzAgfGQCyW/KkDLPsCeQPVAq3QNB\naSLyPZ9m5uapCbahLPfbaytLpSJjLo60DpGIXvpw0mlEMSNIx3lnj68zTPR7sw0+JTt5XoXLYhGs\nbMvp8PU1ZZ+ECyICDcKHdTlhcRZ1fDcrPDa6LT01u37jOhERXXhQhRYjOeqKUhCYkymO7JzTYpMq\nZR1Lo0FExxWjS5KUdlq7FINwpS/jKgdB7Jdf5dP7x5/8KJRxau8YzgYiX04f4BR7Y4Pt+HCk9zBM\nSdB9Lp7EnIQFwHQzJ3ndodr42XlmO8zDiX1HGHPLkCZyb5/77y/+4k+LsqEIdu7u6gl8T04QfZjL\nnvTPzJKyQBaX9NpHh0OZ41KKaqPCYpqC/JMfPiUsr85eURZt8Sl/DKfjoZwYDYE5F4utdjOdA6mw\nohw4jkzkN1EhKAipiGUMpB6wj+Q0MEVxdlkry6me7OXCuNgsK8silnmZgdkI5LS/39dTrFD6ewEY\nJ2X/+AwoIrafaZRSCuu777O9c3xkv3GbpgOt/+2bzD69/LquUY0yp2EfzioTbyDPPlfRue5mfL+F\nGU11X6qwvRuJaPDUvJ7axyLO3umoH3TiJI9DB+zF899kQdigqr7B4mkRywc/bXOd50GUKnNur8vs\nndmy+gZTdbY1CYjfJ1k2JoZ/FGRZSp1ud0zA3wichyDsbsTyHRDNj0Tguw+nmobhh4f35s8Y7LNX\n5nq3Wnqa+vU//SsiImrO/ToREZ09r+KzqaQWT4BJ0BdGbqer9sKsrwGkgHflRHhjS9u4WJ9K/qGy\nFJg9iaz56zeVubK726UkOR4rZKZWor/5zDkKS9pQNzZ4LLzwvAoLPypi2Q70TyR+xbvvKHvr4gM8\nfl2wE63b7Cf29tW33txg9t7ld1Wo+NYOt0tS5Xk9ewJ8XM+kHdfrmqV6hGmhJVFHBQRQXWEeDPtq\nE9My287KjLL5DMsqAR8ml1NxZKSk6fEZxp4f0NT8Mt3c0HXctHsK94oGfK/hQJ+xJf6mA2vgSMY7\nErOMz5aBPTfiu2MRAc54eAD6hOZvJOT48k6RodizvD46wOg3YsEeChuLD5qkeA9JOw7vD04xzzH5\nzPEZUHy/nOI4oVoTGcE81zIQuB2JOHEFmF7Gh05ByH0k46ZZVf90Shg1JbheLr4jzlnjIxtfugMi\n7rGwrZ1Qx1tT3skiYFH222x3miAcHogv6JVgLKUm2Y3OwxOLPNe6wH6NZO0Lw3uzphrkSUbZ3oDO\n1dRfqknblhN4lxDzWEp0fJeEAepDeyYj9ueSqtrYTNrKnVd/uywMQGekz21+8aEVZqp2QSj6uace\nJyKiR06pcLgrQtYV2CVxPL5KBd4tzPj/3Cc/VpT9TBhVnevKGJoWQfMBrBmO2EwXfLzcv6ttGcvE\nsbCwsLCwsLCwsLCwsLCwsLgfYDdxLCwsLCwsLCwsLCwsLCwsLO4D3BVvx/cdWlpwKd5VauhA6HPA\n5KbcFboo0IKaTaa4h4HSoAY9pkZVgCJIEf/9IuRcP/8Qh9CsrSk92Yi4VkUI0wOacEUoySb0hYho\nIEJVSaIUxbrQ5J/7sNKZyyKGnIBIYypiSoNbSll2O0wFW6yqONOHH3yUy6aXirKXNq5REh+Pipkk\nCe1t79HHn32uKHvus58lIqISiJ8ZyiMK0hr6owc0fRN6MoiUiry7JiJpIOi6t8OU/SLnPRGt3+E+\nqC+KaGJJKXFOKLS/RGl/f/n894iI6MyFx4uyU7NM1S672u9VoaSNhko1vdrmELk6CFSnQove3FdK\n2vz8WSIi6gPd8ZvP/5iOiyRJaGd3dyxUZyjiiVGk4ygQMbAAQlvMeMPQNiNiTEi3FFopUrVdoalX\nqjqmTSiWiZ1KJ6hlI/3XmRBWY6jnGGLlm5AoEEgz98LrKd0Wxf74n3JZKar3KpwqzTLq9PuUQejH\n6hLToEMQUe+LcFitqmPE8YUODYJkQcjt5UDoVF+EOsOKjuH6HFMeYxco3CLwWJ7m+2a+2rCOCPw+\ncF5FcpNNHptJT+3PQZfn0gMXHyjK1m5d5nsB1daIq3bbOjeNmGMdQh1NyFavByLP1cbY2DoKcien\n1MnIgXCZrowbpIFubvMa8L/8w39UlN24whTSLsyXK7eZLp4D59uMvzgFIcqUbYYH5wpmDDvST7kD\nYT1FhUFYsMbX2IX1qST04PaBhrGNRnyd60B1NWFCYEIoFyFllPYzoR+1koZb9HvHp3w7rkthpUYe\niDdHLW5vE/JERLQqY/DxA12L3mrx+ri5ruKB7QE/bxfsxFDmdQB9kUjYgZurLe7JvO+LbfKhT7KR\nUPMhFM4xtgOuO5Q5mEGIVU8+H5Z0fSDxFcpAyc+EAl7L9HsXl3idnQn1Hv1dpUMfB46TUxDEFINg\nui8C+sNUQ5fWt14lIqK3X3ytKGt4PA5qsdqQt779ChERlc6qrdwVunr1goZHnT3Jfbm2pc9pQkd8\nGbdLp0HwMOfxkPV1blZdbrdr71wuyl74EY/rk49on2YN6ftEQw2TNl9ndkG/d/0ar/VvH2i43pc+\n/2kiIlo+qXa+l+yS4x5eg+4GruNQpRRSGdbNUGjq5RkVVi5J2NwARCoPWgdSpn1Wl7AvDO8twq3g\nuLI2xe3+4Wc+UpRdF1v8T/7Xf0ZERJ/9jFLjP/TEKSIimlqCsJFcQo498H8kDCeB8J/tAx6jV969\nDg/O/6QQ4mXE7QfgV1Tq0mcdmJuDiLJjChvnuUOD3Kc9CAl/W0J8vv/6m0XZmoTjzUGI+1TAdW42\n1O+tNLiv1jZ0rly+wTb4pVd+qmVrHBbWGcK48blNf+XDjxAR0a8/fL74qGwE1iG07vYdDslaA2mF\ntvj5l97QEK93XuL3hwx8nXCF116Ufkj7Ms5RKFls/Hg41fFtfEZEo4RobV1F4dc2JaQR+1RE1XEc\nVUVU108gKUUsYsPwWyOWDVFPRTgVjhqHDr8r6PcPh1M55tf54XXcA5/D+I4hTLjcOyysXIR4gQ+Q\nSSihi4k/vOONda2YQ+QHlLmw9mVGEFv9OV9kDUII1Y1i4/PrbyPx1wMIV/Zn2Lan8L5npAlK+J7k\n8n1rdS5r7eo7z6mzHHaEz10TXw87dXiH7Vq9iXZSEpJAmGtZ3g+TEoQmSwhzOdM6mfeusXcD358s\ncn0X8LOcZvspOTE8j9yj6kDyDHk/DWDc1BsiTg6+Qdzn+pRDDZ0K61zmOeqTmSGJoUmOw3bsRYnZ\nD+HdbGmW+24RJDG8mL/veTo+UtPfoPDty57GxbNap/NnJAnBmgranzvN60izriF4Tsq2C8W8R7AG\nfBBYJo6FhYWFhYWFhYWFhYWFhYXFfYC7YuKEoUOnT4U05egO3pVbkmp2G3cpeeesXocThD6fnKSZ\nnuaaU9e9bT057XRF1CtWQSIv578bdd3p2trkHfQ1OU3IYGdsaYFPmxw4xd8XEbtSTXf1piXFGe7I\njcwpMpy290Yi9NjVsprsll88pUKLq8t831trmrpsd7tPSXLMEyvXoVq1RLttPTl5+dWXiIhoEcRT\nlxZZuA1TXO7vy2klijZKu5w4pylIT81wW9y+pDuHvS7vjKNwZ3WOdyy9Mu9Y9uF0bGWFRRs31/WE\ne2eX+25lValajuzId0cg+CwnMjGK5AqjqoSib7siKutqXyyJyHIE6dyOmRmPiIiyPKc4jjS/NxH5\nMi6QcFKS1HFIUjEajChYbA5M0vzwCQ+eZnhyCowpSEO5rznNwB3zSWkATTPiScv0NPcdjg+z65vC\nrrjZfcfrGtHGJIE+K0SCD5/OHBeu51K1VqUUmHMjqbcfoAi0iLl5yECRUyYdIuQHh+fgSOaBAylU\nq1N8vU4HxZO5f7e32eb4vp5CzlT4XlXYwa+XeXd9aUFPSXZytj9VEGRbXGR70YF09cb8YOrN5hT3\nWwNE+dpywruzoyeSuVs/dop33/dpdm6WCJh7AxH9HdXgpENOJFr7yoaYW2Cm1NSsitiZlKdZDkKU\nIuSHQrhGlDSLD4+lkczrsZTGJkUpnEO0pB2//8L3i7LPf/7zRET0xptvwXX5Xzz9NEzFDIWA5ZQw\nRTslp3G3bmhKaa+k4+FYcD1yHDgVlO4eunr/QJgop1eUsXNtTU4KRyDimXFZC+bPjhilBswVpxCx\n1AF3IFNlUwajC23i5YdP5synAYyZLZlbB5A6syvXPQGDe1r63dvT+bYkaX8/CmvrhVPcGNWB+g9G\nhPK4SPOI9uNbFI2UOWcIblstZd2s7z9PREQ7mzrmlwNm387BSX5bhI+DTbUJoYiVrqWXirKHfoVP\n63Yzvd7+OvfRwgq3yxPPwKmqpNre2VFxZGOTanUdgw8/zIkVmieVpZLLiV8aq0+2eZvHS28PBHaF\nYdUCIczbD7NfUWuoIOzGzqsUJ9peR4FDRAGl5EI/liUlfT4pbTEIo5qTbRTiNAzsTkfHSJpyG2DK\n3kTEJC88pOzJBx9n9vTX/2/u46/+X2pDvtRjxs7TX9DvZ8IiToAlZ1Ip5zBH7txh37bTVT/p1JnT\nUqZjflPSafvATp6a47/dQNu92+sVLIajohsn9MP1fRoNlQG2scV1AbIn7Ylg8LVNZY6sijjn7/72\np4uyRx5/koiIwoqOwbkVPnVe/NBDRdnnhVmyOKvr4rSohk4Ju7YEiRNq8ncAPkxXkqrsgej5Rovb\n9jsLmoxgILZ9HViZubAb+nsqFG30fytVXdty8cUmM5GPjizNaNDrj/lfxramMdoySVriod3l+/tQ\njVAczgyc0ahg9aKdzsf+IVKWjYlomDSkXGRnk2H063h35YIuiKp78psKMCB837QnCNTGRiwcb2za\nBZK1eMdPUELEjx5lRK6HrHFea/G9oSxtWQF2dkeSHTjgd5ZNu0FihUQiEDzw22MRYJ8Ghu2+pArv\niYhxY1HHXjASoX9w40YSMWEiXIiI5iSJTgx1Ny8Y8UDndVAWNjMIRAfCHBntI+348FaA5+d0TCIO\nha5LZ8LS2HuBGSMB2LrAMKDQj0/4uT0P3glrRvxav2ZSciPz3jjQHvjqroy/TBikQ2C8GOHyxpTa\nJkolsUVJx0zBogT2lmgokxfCu5bsLdSq+ttFYZfikO6Kn4S+bX6X+wWWiWNhYWFhYWFhYWFhYWFh\nYWFxH8Bu4lhYWFhYWFhYWFhYWFhYWFjcB7ircCrPd6g5E9BgW2m6M4tCf6opXWxHhPqQruSHTC1G\nzZ4sNgKXSv86GHDYQa2iFMFhnylpg6GGDkTyWyP6aITmiFQUtAnhB00RgEIhvJ1dvlcdhYYMNSsB\n0SXhtYM2FYUS8nL24tmibNDn33znOyoO9+qlOzQYHi/EwXWISkFGo6HSrl944RtERJTHSmVsCnUr\nBmGtoQjsojjlmbNMdX3s448UZRdOc2hV65aGQm3uc3uH0BcX5pjivr3NFMPHH3qs+OzRx5k6+y/+\n8P8synxiemAMInqRUAxzEHSlMtfZA2ro2XMscnfn1jv6PaG6ViAs7uGHWZh62Fca9akVpSAfFb7v\n09zcHLlAnUuF0h2jwJyEIg2BWul4hsYIoqDCWY2AQuplh4VoTWhQlmv7mPtNEiw21EIUPEykbZGC\nbkTWMOQmlr9j4G+63s+nE2PYkqHTIlXyuFTv4tqOQ+VKSK4DYtEifFeCNquISJtDWv/Q0F6Bt9ic\nYvrpsK1hApHPxsgvaZ0HMjY9EPaV6B+KBvy8G2CHZk+wSHe8oZTzioyHckPruTDF43FnV8VnZ6ck\n3ALivrpCyX1oRUMdM7Ft/b7SsPu9WK6hQqlxolTVoyKnnFLKxvrRlzYuldTGG9H6mRmlsJMZcyi0\nKOMlARF1I1yboqihEV8ERqwRhO/2eF6PRrpOxEI/T8GGmM//5OtfL8pef5Nt8YsvqcCmI+2dwlxK\nTJgiiAcaWmsGouPmLxfCH8s5hFsdGQ5R5tII1icTuuRAn+YR36te0zVrvsltsbetY7AjIRAHQMl/\nQai7M9DGTQnfqsFcj0X8sS1r4BAo9OZbHoQ4GAp0dcw2ifCro+1ZletmsD5FEs9QgXtM1eXzWMMM\nu0L9bjd1rjjJvWh3Frjc725Qr62JE9IBhxq1uirqb6jzU1UQVz64QkREtVmwiyKwG5SVJt+M2f9w\nl3QOzSywQ9Gc0na7+Q6v8Y60394WhHonbHeWlk8WZbdu83jZ3dFQujzg8bAI/kqpZNYjvddIRKo3\nLmk71wL+0YNPnSvKuhJatbMPQvGllBzneCEmeZ5REg0pAcFQE9laBRp6ETIL9HsjMI5hLiY8KANh\ndTfl8ZKMYC0Vg763r6E2n/jMw0RE9OynniYioh8+/0bx2bUb7BMt31Kfo1Tnvp2SdYVIBVDbbe2L\nQvj+kQtF2fQ0+1DNGR0zLRFex7Dq0w/w2jLsgxh/dPxwqjRNaX9vnzDy1pHQ6BDW20hEs5dntY1P\nXnyKiIjOP/lMUdaY5tABDN1uitjo0pyGU4UmhAdsrBHMNX5NiguAvBdE4GuZkIhqCOH0Uzwunn36\n6aKsVOd18U+++Y2i7OY6C++nGYTAyHh3QbzU+Kxo448r8ErEgtvDboeSAfiJqQlTwjDiRO4JCTBk\nzPq4vsufObyYJLlpM+3cfILPaBJjmDCOSdFiGBaYyTXw1L/q8zWqAfhZkoyjCuGLxgfARDdmrOQ4\nFuQyGEYWSIjK2zfVbzsKsjynUZaQ62u9fFnNMSTJhBfH0H5hWeY9tL0JZavAOPSk3jmEU3UPOCQx\nQF9HBM1vbrI9n1lVHyoaSgg5JKwwiTpS8OWLELVM72X8+ghCqE3CktFIrzeQkGQffHnzvhCE2j5Z\n3hsLaz0KHHIo8DxyYQwWcwn62QgQZ5D4KJS2LVfV1wkkfNaDZEiZCanFcWNkL8pqs827y7ktHkun\ntveLz/yQ15upWe2LWN7bPWwT8VOGCfhk2WE/JBM9i3pDfYCyhIdmIBniGjFqaB/PuattGcvEsbCw\nsLCwsLCwsLCwsLCwsLgfcFdbPo7jkF/2qdzU3fpZSYPoo5hSRU7N9uHyIhJUKStDIg2McKQyTMIq\n/yaAFG+ex6dXI9i1NaceZrcYD4VyOU0Hva1iZ44gXWFrn3fiBpHupE2JQKkPpwqu1KUPp/1bO7zD\nut/Vsk6Pd/j+6ttv6/f6RMPoeEycLMuoP+gTQZ1+7ctf4c8iPfXx5IQT0/blsvvoQXuWhTW12dIT\ngU6LBRf3BpDGV3YO33nlalG2+wMW4Dt/jk9YnoGUyZGIHFegjXMRMEMBZNeTXVc4IBjIyYAPp95n\nTjITZ9jVE7NHmrwr++OXXi7K1m8wU2cAee7zvu6yHhWe51Gz2aQshYqKyPEIxkxbGEAouOvJ32NC\nv/InCvUlhWgjppKVv4HF45hTkQnpRc1pyli/y/5shnNmwHMGhfWK1HZ42m8+y/B6XFoF4cHQ7CLD\nKRWethwHjuNQ6LlUBaXFQgQaTu2N8F2a6jMlchKRw858p8N1HYCIsLlOuQzCnjKHYpgH/QO2bYaR\n15hV9ouxJ3Ff55In4rMmlSMRUS4nByhObFJCToMQcN5moVIHROyGHR7Xg76WlaVdxk4I87w4DToq\nHHLIcTwK4DTJsMoI5oERx8ODmlzqUkJxOikLYVg4xGMoAUaGORnEI0Fzgjc3LyJ+8H1zgjfO5uH2\nwbTrm1ssM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/O8GLpkKMM5UEhjuR6GaRjKrAM0T0NPNaJzSL8rxOkwpEjmBYZdTQqxMsK0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bi7z+6XZ1LujLN0Tgw3o7Gc1IFxwLnkdeVKERtL0nrr0ovN+S/lcFV29P1glD+Iaa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\naKCjxIHzImJkm7BXsdEkSSAMfQgEuqRew4hsQaRmnaG0oT2Pyy4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CQqheOMPCygWCF15Y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\nOwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b76c940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"fig = plt.figure(figsize=(20,5))\n",
"for i in range(36):\n",
" ax = fig.add_subplot(3, 12, i + 1, xticks=[], yticks=[])\n",
" ax.imshow(np.squeeze(x_train[i]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### 3. Rescale the Images by Dividing Every Pixel in Every Image by 255"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# rescale [0,255] --> [0,1]\n",
"x_train = x_train.astype('float32')/255\n",
"x_test = x_test.astype('float32')/255"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### 4. Break Dataset into Training, Testing, and Validation Sets"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x_train shape: (45000, 32, 32, 3)\n",
"45000 train samples\n",
"10000 test samples\n",
"5000 validation samples\n"
]
}
],
"source": [
"from keras.utils import np_utils\n",
"\n",
"# one-hot encode the labels\n",
"num_classes = len(np.unique(y_train))\n",
"y_train = keras.utils.to_categorical(y_train, num_classes)\n",
"y_test = keras.utils.to_categorical(y_test, num_classes)\n",
"\n",
"# break training set into training and validation sets\n",
"(x_train, x_valid) = x_train[5000:], x_train[:5000]\n",
"(y_train, y_valid) = y_train[5000:], y_train[:5000]\n",
"\n",
"# print shape of training set\n",
"print('x_train shape:', x_train.shape)\n",
"\n",
"# print number of training, validation, and test images\n",
"print(x_train.shape[0], 'train samples')\n",
"print(x_test.shape[0], 'test samples')\n",
"print(x_valid.shape[0], 'validation samples')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### 5. Define the Model Architecture "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"conv2d_1 (Conv2D) (None, 32, 32, 16) 208 \n",
"_________________________________________________________________\n",
"max_pooling2d_1 (MaxPooling2 (None, 16, 16, 16) 0 \n",
"_________________________________________________________________\n",
"conv2d_2 (Conv2D) (None, 16, 16, 32) 2080 \n",
"_________________________________________________________________\n",
"max_pooling2d_2 (MaxPooling2 (None, 8, 8, 32) 0 \n",
"_________________________________________________________________\n",
"conv2d_3 (Conv2D) (None, 8, 8, 64) 8256 \n",
"_________________________________________________________________\n",
"max_pooling2d_3 (MaxPooling2 (None, 4, 4, 64) 0 \n",
"_________________________________________________________________\n",
"dropout_1 (Dropout) (None, 4, 4, 64) 0 \n",
"_________________________________________________________________\n",
"flatten_1 (Flatten) (None, 1024) 0 \n",
"_________________________________________________________________\n",
"dense_1 (Dense) (None, 500) 512500 \n",
"_________________________________________________________________\n",
"dropout_2 (Dropout) (None, 500) 0 \n",
"_________________________________________________________________\n",
"dense_2 (Dense) (None, 10) 5010 \n",
"=================================================================\n",
"Total params: 528,054\n",
"Trainable params: 528,054\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"from keras.models import Sequential\n",
"from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
"\n",
"model = Sequential()\n",
"model.add(Conv2D(filters=16, kernel_size=2, padding='same', activation='relu', \n",
" input_shape=(32, 32, 3)))\n",
"model.add(MaxPooling2D(pool_size=2))\n",
"model.add(Conv2D(filters=32, kernel_size=2, padding='same', activation='relu'))\n",
"model.add(MaxPooling2D(pool_size=2))\n",
"model.add(Conv2D(filters=64, kernel_size=2, padding='same', activation='relu'))\n",
"model.add(MaxPooling2D(pool_size=2))\n",
"model.add(Dropout(0.3))\n",
"model.add(Flatten())\n",
"model.add(Dense(500, activation='relu'))\n",
"model.add(Dropout(0.4))\n",
"model.add(Dense(10, activation='softmax'))\n",
"\n",
"model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### 6. Compile the Model "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# compile the model\n",
"model.compile(loss='categorical_crossentropy', optimizer='rmsprop', \n",
" metrics=['accuracy'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### 7. Train the Model "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 45000 samples, validate on 5000 samples\n",
"Epoch 1/100\n",
"Epoch 00000: val_loss improved from inf to 1.35820, saving model to model.weights.best.hdf5\n",
"46s - loss: 1.6192 - acc: 0.4140 - val_loss: 1.3582 - val_acc: 0.5166\n",
"Epoch 2/100\n",
"Epoch 00001: val_loss improved from 1.35820 to 1.22245, saving model to model.weights.best.hdf5\n",
"53s - loss: 1.2881 - acc: 0.5402 - val_loss: 1.2224 - val_acc: 0.5644\n",
"Epoch 3/100\n",
"Epoch 00002: val_loss improved from 1.22245 to 1.12096, saving model to model.weights.best.hdf5\n",
"49s - loss: 1.1630 - acc: 0.5879 - val_loss: 1.1210 - val_acc: 0.6046\n",
"Epoch 4/100\n",
"Epoch 00003: val_loss improved from 1.12096 to 1.10724, saving model to model.weights.best.hdf5\n",
"56s - loss: 1.0928 - acc: 0.6160 - val_loss: 1.1072 - val_acc: 0.6134\n",
"Epoch 5/100\n",
"Epoch 00004: val_loss improved from 1.10724 to 0.97377, saving model to model.weights.best.hdf5\n",
"52s - loss: 1.0413 - acc: 0.6382 - val_loss: 0.9738 - val_acc: 0.6596\n",
"Epoch 6/100\n",
"Epoch 00005: val_loss improved from 0.97377 to 0.95501, saving model to model.weights.best.hdf5\n",
"50s - loss: 1.0090 - acc: 0.6484 - val_loss: 0.9550 - val_acc: 0.6768\n",
"Epoch 7/100\n",
"Epoch 00006: val_loss improved from 0.95501 to 0.94448, saving model to model.weights.best.hdf5\n",
"49s - loss: 0.9967 - acc: 0.6561 - val_loss: 0.9445 - val_acc: 0.6828\n",
"Epoch 8/100\n",
"Epoch 00007: val_loss did not improve\n",
"61s - loss: 0.9934 - acc: 0.6604 - val_loss: 1.1300 - val_acc: 0.6376\n",
"Epoch 9/100\n",
"Epoch 00008: val_loss improved from 0.94448 to 0.91779, saving model to model.weights.best.hdf5\n",
"49s - loss: 0.9858 - acc: 0.6672 - val_loss: 0.9178 - val_acc: 0.6882\n",
"Epoch 10/100\n",
"Epoch 00009: val_loss did not improve\n",
"50s - loss: 0.9839 - acc: 0.6658 - val_loss: 0.9669 - val_acc: 0.6748\n",
"Epoch 11/100\n",
"Epoch 00010: val_loss improved from 0.91779 to 0.91570, saving model to model.weights.best.hdf5\n",
"49s - loss: 1.0002 - acc: 0.6624 - val_loss: 0.9157 - val_acc: 0.6936\n",
"Epoch 12/100\n",
"Epoch 00011: val_loss did not improve\n",
"54s - loss: 1.0001 - acc: 0.6659 - val_loss: 1.1442 - val_acc: 0.6646\n",
"Epoch 13/100\n",
"Epoch 00012: val_loss did not improve\n",
"56s - loss: 1.0161 - acc: 0.6633 - val_loss: 0.9702 - val_acc: 0.6788\n",
"Epoch 14/100\n",
"Epoch 00013: val_loss did not improve\n",
"46s - loss: 1.0316 - acc: 0.6568 - val_loss: 0.9937 - val_acc: 0.6766\n",
"Epoch 15/100\n",
"Epoch 00014: val_loss did not improve\n",
"54s - loss: 1.0412 - acc: 0.6525 - val_loss: 1.1574 - val_acc: 0.6190\n",
"Epoch 16/100\n",
"Epoch 00015: val_loss did not improve\n",
"55s - loss: 1.0726 - acc: 0.6462 - val_loss: 1.0492 - val_acc: 0.6790\n",
"Epoch 17/100\n",
"Epoch 00016: val_loss did not improve\n",
"48s - loss: 1.0891 - acc: 0.6387 - val_loss: 1.0739 - val_acc: 0.6528\n",
"Epoch 18/100\n",
"Epoch 00017: val_loss did not improve\n",
"46s - loss: 1.1152 - acc: 0.6337 - val_loss: 1.0672 - val_acc: 0.6610\n",
"Epoch 19/100\n",
"Epoch 00018: val_loss did not improve\n",
"47s - loss: 1.1392 - acc: 0.6258 - val_loss: 1.5400 - val_acc: 0.5742\n",
"Epoch 20/100\n",
"Epoch 00019: val_loss did not improve\n",
"47s - loss: 1.1565 - acc: 0.6207 - val_loss: 1.0309 - val_acc: 0.6636\n",
"Epoch 21/100\n",
"Epoch 00020: val_loss did not improve\n",
"44s - loss: 1.1711 - acc: 0.6159 - val_loss: 1.4559 - val_acc: 0.5736\n",
"Epoch 22/100\n",
"Epoch 00021: val_loss did not improve\n",
"44s - loss: 1.1802 - acc: 0.6132 - val_loss: 1.1716 - val_acc: 0.6288\n",
"Epoch 23/100\n",
"Epoch 00022: val_loss did not improve\n",
"44s - loss: 1.2012 - acc: 0.6033 - val_loss: 1.3916 - val_acc: 0.6222\n",
"Epoch 24/100\n",
"Epoch 00023: val_loss did not improve\n",
"47s - loss: 1.2319 - acc: 0.5964 - val_loss: 1.5698 - val_acc: 0.5688\n",
"Epoch 25/100\n",
"Epoch 00024: val_loss did not improve\n",
"50s - loss: 1.2479 - acc: 0.5914 - val_loss: 1.2740 - val_acc: 0.6038\n",
"Epoch 26/100\n",
"Epoch 00025: val_loss did not improve\n",
"58s - loss: 1.2616 - acc: 0.5870 - val_loss: 1.2803 - val_acc: 0.5496\n",
"Epoch 27/100\n",
"Epoch 00026: val_loss did not improve\n",
"57s - loss: 1.2908 - acc: 0.5792 - val_loss: 1.0756 - val_acc: 0.6432\n",
"Epoch 28/100\n",
"Epoch 00027: val_loss did not improve\n",
"55s - loss: 1.3248 - acc: 0.5667 - val_loss: 1.2289 - val_acc: 0.5800\n",
"Epoch 29/100\n",
"Epoch 00028: val_loss did not improve\n",
"57s - loss: 1.3258 - acc: 0.5633 - val_loss: 1.3088 - val_acc: 0.5756\n",
"Epoch 30/100\n",
"Epoch 00029: val_loss did not improve\n",
"46s - loss: 1.3381 - acc: 0.5586 - val_loss: 1.2569 - val_acc: 0.6044\n",
"Epoch 31/100\n",
"Epoch 00030: val_loss did not improve\n",
"55s - loss: 1.3507 - acc: 0.5545 - val_loss: 1.3436 - val_acc: 0.5562\n",
"Epoch 32/100\n",
"Epoch 00031: val_loss did not improve\n",
"61s - loss: 1.3643 - acc: 0.5513 - val_loss: 1.2951 - val_acc: 0.5646\n",
"Epoch 33/100\n",
"Epoch 00032: val_loss did not improve\n",
"69s - loss: 1.3873 - acc: 0.5426 - val_loss: 1.4049 - val_acc: 0.6066\n",
"Epoch 34/100\n",
"Epoch 00033: val_loss did not improve\n",
"53s - loss: 1.3842 - acc: 0.5415 - val_loss: 1.8164 - val_acc: 0.5640\n",
"Epoch 35/100\n",
"Epoch 00034: val_loss did not improve\n",
"48s - loss: 1.4187 - acc: 0.5303 - val_loss: 1.7554 - val_acc: 0.5616\n",
"Epoch 36/100\n",
"Epoch 00035: val_loss did not improve\n",
"57s - loss: 1.4278 - acc: 0.5268 - val_loss: 1.9956 - val_acc: 0.5072\n",
"Epoch 37/100\n",
"Epoch 00036: val_loss did not improve\n",
"58s - loss: 1.4365 - acc: 0.5216 - val_loss: 1.8344 - val_acc: 0.4748\n",
"Epoch 38/100\n",
"Epoch 00037: val_loss did not improve\n",
"64s - loss: 1.4529 - acc: 0.5205 - val_loss: 1.2752 - val_acc: 0.5690\n",
"Epoch 39/100\n",
"Epoch 00038: val_loss did not improve\n",
"62s - loss: 1.4726 - acc: 0.5111 - val_loss: 1.7092 - val_acc: 0.5600\n",
"Epoch 40/100\n",
"Epoch 00039: val_loss did not improve\n",
"70s - loss: 1.4673 - acc: 0.5107 - val_loss: 1.2288 - val_acc: 0.5698\n",
"Epoch 41/100\n",
"Epoch 00040: val_loss did not improve\n",
"68s - loss: 1.4872 - acc: 0.5083 - val_loss: 1.4082 - val_acc: 0.5162\n",
"Epoch 42/100\n",
"Epoch 00041: val_loss did not improve\n",
"69s - loss: 1.4983 - acc: 0.5003 - val_loss: 1.5808 - val_acc: 0.4818\n",
"Epoch 43/100\n",
"Epoch 00042: val_loss did not improve\n",
"79s - loss: 1.5211 - acc: 0.4957 - val_loss: 1.2271 - val_acc: 0.5882\n",
"Epoch 44/100\n",
"Epoch 00043: val_loss did not improve\n",
"95s - loss: 1.5474 - acc: 0.4867 - val_loss: 3.7681 - val_acc: 0.3394\n",
"Epoch 45/100\n",
"Epoch 00044: val_loss did not improve\n",
"80s - loss: 1.5432 - acc: 0.4854 - val_loss: 1.3349 - val_acc: 0.5830\n",
"Epoch 46/100\n",
"Epoch 00045: val_loss did not improve\n",
"63s - loss: 1.5615 - acc: 0.4785 - val_loss: 1.4494 - val_acc: 0.5332\n",
"Epoch 47/100\n",
"Epoch 00046: val_loss did not improve\n",
"47s - loss: 1.5731 - acc: 0.4752 - val_loss: 1.4689 - val_acc: 0.4648\n",
"Epoch 48/100\n",
"Epoch 00047: val_loss did not improve\n",
"49s - loss: 1.5832 - acc: 0.4694 - val_loss: 1.6045 - val_acc: 0.3992\n",
"Epoch 49/100\n",
"Epoch 00048: val_loss did not improve\n",
"50s - loss: 1.6000 - acc: 0.4670 - val_loss: 3.0627 - val_acc: 0.3648\n",
"Epoch 50/100\n",
"Epoch 00049: val_loss did not improve\n",
"73s - loss: 1.5988 - acc: 0.4655 - val_loss: 1.4299 - val_acc: 0.5020\n",
"Epoch 51/100\n",
"Epoch 00050: val_loss did not improve\n",
"52s - loss: 1.6025 - acc: 0.4623 - val_loss: 1.6269 - val_acc: 0.4766\n",
"Epoch 52/100\n",
"Epoch 00051: val_loss did not improve\n",
"53s - loss: 1.6104 - acc: 0.4601 - val_loss: 1.4260 - val_acc: 0.5390\n",
"Epoch 53/100\n",
"Epoch 00052: val_loss did not improve\n",
"51s - loss: 1.6203 - acc: 0.4569 - val_loss: 1.3396 - val_acc: 0.5366\n",
"Epoch 54/100\n",
"Epoch 00053: val_loss did not improve\n",
"50s - loss: 1.6354 - acc: 0.4500 - val_loss: 1.6159 - val_acc: 0.4512\n",
"Epoch 55/100\n",
"Epoch 00054: val_loss did not improve\n",
"53s - loss: 1.6552 - acc: 0.4433 - val_loss: 1.7258 - val_acc: 0.4468\n",
"Epoch 56/100\n",
"Epoch 00055: val_loss did not improve\n",
"47s - loss: 1.6696 - acc: 0.4363 - val_loss: 1.4365 - val_acc: 0.4938\n",
"Epoch 57/100\n",
"Epoch 00056: val_loss did not improve\n",
"46s - loss: 1.6605 - acc: 0.4368 - val_loss: 2.5907 - val_acc: 0.3732\n",
"Epoch 58/100\n",
"Epoch 00057: val_loss did not improve\n",
"50s - loss: 1.6720 - acc: 0.4336 - val_loss: 1.5503 - val_acc: 0.4274\n",
"Epoch 59/100\n",
"Epoch 00058: val_loss did not improve\n",
"68s - loss: 1.6897 - acc: 0.4281 - val_loss: 1.5233 - val_acc: 0.4362\n",
"Epoch 60/100\n",
"Epoch 00059: val_loss did not improve\n",
"73s - loss: 1.7099 - acc: 0.4201 - val_loss: 1.4141 - val_acc: 0.5124\n",
"Epoch 61/100\n",
"Epoch 00060: val_loss did not improve\n",
"71s - loss: 1.7182 - acc: 0.4182 - val_loss: 1.5190 - val_acc: 0.4486\n",
"Epoch 62/100\n",
"Epoch 00061: val_loss did not improve\n",
"72s - loss: 1.7177 - acc: 0.4179 - val_loss: 1.4966 - val_acc: 0.4860\n",
"Epoch 63/100\n",
"Epoch 00062: val_loss did not improve\n",
"59s - loss: 1.7079 - acc: 0.4228 - val_loss: 1.6089 - val_acc: 0.4384\n",
"Epoch 64/100\n",
"Epoch 00063: val_loss did not improve\n",
"50s - loss: 1.7101 - acc: 0.4147 - val_loss: 1.6014 - val_acc: 0.4430\n",
"Epoch 65/100\n",
"Epoch 00064: val_loss did not improve\n",
"49s - loss: 1.7180 - acc: 0.4144 - val_loss: 2.2502 - val_acc: 0.3712\n",
"Epoch 66/100\n",
"Epoch 00065: val_loss did not improve\n",
"50s - loss: 1.7190 - acc: 0.4140 - val_loss: 1.3967 - val_acc: 0.4964\n",
"Epoch 67/100\n",
"Epoch 00066: val_loss did not improve\n",
"50s - loss: 1.7262 - acc: 0.4082 - val_loss: 1.5334 - val_acc: 0.4650\n",
"Epoch 68/100\n",
"Epoch 00067: val_loss did not improve\n",
"50s - loss: 1.7432 - acc: 0.4032 - val_loss: 1.7911 - val_acc: 0.3588\n",
"Epoch 69/100\n",
"Epoch 00068: val_loss did not improve\n",
"50s - loss: 1.7309 - acc: 0.4054 - val_loss: 1.6592 - val_acc: 0.3892\n",
"Epoch 70/100\n",
"Epoch 00069: val_loss did not improve\n",
"50s - loss: 1.7581 - acc: 0.3977 - val_loss: 1.6551 - val_acc: 0.4056\n",
"Epoch 71/100\n",
"Epoch 00070: val_loss did not improve\n",
"50s - loss: 1.7619 - acc: 0.3930 - val_loss: 1.5855 - val_acc: 0.4670\n",
"Epoch 72/100\n",
"Epoch 00071: val_loss did not improve\n",
"55s - loss: 1.7690 - acc: 0.3918 - val_loss: 1.5534 - val_acc: 0.4350\n",
"Epoch 73/100\n",
"Epoch 00072: val_loss did not improve\n",
"77s - loss: 1.7910 - acc: 0.3890 - val_loss: 1.5390 - val_acc: 0.4692\n",
"Epoch 74/100\n",
"Epoch 00073: val_loss did not improve\n",
"68s - loss: 1.7941 - acc: 0.3853 - val_loss: 1.4875 - val_acc: 0.4764\n",
"Epoch 75/100\n",
"Epoch 00074: val_loss did not improve\n",
"71s - loss: 1.8069 - acc: 0.3816 - val_loss: 1.6594 - val_acc: 0.3990\n",
"Epoch 76/100\n",
"Epoch 00075: val_loss did not improve\n",
"63s - loss: 1.8160 - acc: 0.3776 - val_loss: 1.6119 - val_acc: 0.3804\n",
"Epoch 77/100\n",
"Epoch 00076: val_loss did not improve\n",
"52s - loss: 1.8073 - acc: 0.3793 - val_loss: 1.5836 - val_acc: 0.4578\n",
"Epoch 78/100\n",
"Epoch 00077: val_loss did not improve\n",
"72s - loss: 1.8185 - acc: 0.3731 - val_loss: 1.6415 - val_acc: 0.4004\n",
"Epoch 79/100\n",
"Epoch 00078: val_loss did not improve\n",
"78s - loss: 1.8229 - acc: 0.3724 - val_loss: 1.7005 - val_acc: 0.3834\n",
"Epoch 80/100\n",
"Epoch 00079: val_loss did not improve\n",
"61s - loss: 1.8316 - acc: 0.3664 - val_loss: 1.8900 - val_acc: 0.2996\n",
"Epoch 81/100\n",
"Epoch 00080: val_loss did not improve\n",
"50s - loss: 1.8274 - acc: 0.3656 - val_loss: 1.6902 - val_acc: 0.3794\n",
"Epoch 82/100\n",
"Epoch 00081: val_loss did not improve\n",
"50s - loss: 1.8448 - acc: 0.3609 - val_loss: 1.9591 - val_acc: 0.3094\n",
"Epoch 83/100\n",
"Epoch 00082: val_loss did not improve\n",
"48s - loss: 1.8468 - acc: 0.3566 - val_loss: 1.6827 - val_acc: 0.4108\n",
"Epoch 84/100\n",
"Epoch 00083: val_loss did not improve\n",
"48s - loss: 1.9039 - acc: 0.3516 - val_loss: 1.5814 - val_acc: 0.4456\n",
"Epoch 85/100\n",
"Epoch 00084: val_loss did not improve\n",
"68s - loss: 1.8499 - acc: 0.3550 - val_loss: 1.8199 - val_acc: 0.3736\n",
"Epoch 86/100\n",
"Epoch 00085: val_loss did not improve\n",
"77s - loss: 1.8404 - acc: 0.3556 - val_loss: 1.7326 - val_acc: 0.3518\n",
"Epoch 87/100\n",
"Epoch 00086: val_loss did not improve\n",
"59s - loss: 1.8509 - acc: 0.3513 - val_loss: 1.6321 - val_acc: 0.4042\n",
"Epoch 88/100\n",
"Epoch 00087: val_loss did not improve\n",
"51s - loss: 1.8580 - acc: 0.3502 - val_loss: 2.8168 - val_acc: 0.3208\n",
"Epoch 89/100\n",
"Epoch 00088: val_loss did not improve\n",
"60s - loss: 1.8760 - acc: 0.3392 - val_loss: 1.6616 - val_acc: 0.4156\n",
"Epoch 90/100\n",
"Epoch 00089: val_loss did not improve\n",
"61s - loss: 1.8682 - acc: 0.3462 - val_loss: 1.6725 - val_acc: 0.3900\n",
"Epoch 91/100\n",
"Epoch 00090: val_loss did not improve\n",
"57s - loss: 1.8900 - acc: 0.3312 - val_loss: 1.6851 - val_acc: 0.3424\n",
"Epoch 92/100\n",
"Epoch 00091: val_loss did not improve\n",
"54s - loss: 1.8889 - acc: 0.3363 - val_loss: 1.6296 - val_acc: 0.4230\n",
"Epoch 93/100\n",
"Epoch 00092: val_loss did not improve\n",
"56s - loss: 1.9040 - acc: 0.3343 - val_loss: 1.7510 - val_acc: 0.3306\n",
"Epoch 94/100\n",
"Epoch 00093: val_loss did not improve\n",
"50s - loss: 1.9041 - acc: 0.3266 - val_loss: 1.7218 - val_acc: 0.3582\n",
"Epoch 95/100\n",
"Epoch 00094: val_loss did not improve\n",
"48s - loss: 1.8978 - acc: 0.3224 - val_loss: 1.6739 - val_acc: 0.3970\n",
"Epoch 96/100\n",
"Epoch 00095: val_loss did not improve\n",
"48s - loss: 1.9173 - acc: 0.3180 - val_loss: 1.7337 - val_acc: 0.3526\n",
"Epoch 97/100\n",
"Epoch 00096: val_loss did not improve\n",
"48s - loss: 1.9016 - acc: 0.3204 - val_loss: 1.7351 - val_acc: 0.3452\n",
"Epoch 98/100\n",
"Epoch 00097: val_loss did not improve\n",
"48s - loss: 1.9117 - acc: 0.3170 - val_loss: 2.2827 - val_acc: 0.2592\n",
"Epoch 99/100\n",
"Epoch 00098: val_loss did not improve\n",
"48s - loss: 1.9319 - acc: 0.3049 - val_loss: 2.9560 - val_acc: 0.3060\n",
"Epoch 100/100\n",
"Epoch 00099: val_loss did not improve\n",
"48s - loss: 1.9390 - acc: 0.3070 - val_loss: 1.9106 - val_acc: 0.3102\n"
]
}
],
"source": [
"from keras.callbacks import ModelCheckpoint \n",
"\n",
"# train the model\n",
"checkpointer = ModelCheckpoint(filepath='model.weights.best.hdf5', verbose=1, \n",
" save_best_only=True)\n",
"hist = model.fit(x_train, y_train, batch_size=32, epochs=100,\n",
" validation_data=(x_valid, y_valid), callbacks=[checkpointer], \n",
" verbose=2, shuffle=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### 8. Load the Model with the Best Validation Accuracy"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# load the weights that yielded the best validation accuracy\n",
"model.load_weights('model.weights.best.hdf5')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### 9. Calculate Classification Accuracy on Test Set"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Test accuracy: 0.68\n"
]
}
],
"source": [
"# evaluate and print test accuracy\n",
"score = model.evaluate(x_test, y_test, verbose=0)\n",
"print('\\n', 'Test accuracy:', score[1])"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### 10. Visualize Some Predictions\n",
"\n",
"This may give you some insight into why the network is misclassifying certain objects."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# get predictions on the test set\n",
"y_hat = model.predict(x_test)\n",
"\n",
"# define text labels (source: https://www.cs.toronto.edu/~kriz/cifar.html)\n",
"cifar10_labels = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": 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6fsuPEjOwG+F5Btm0e/igyX0c/y7cQiIQ329iFFWWiDBDiijgDWkrZqbUPfpi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ZnnurC4yPJ3ke77uYcfSuK2SK+mxiNpy/VYcO35hPcezVjrW5D1UkqyeK0TWqpKbkoFXrvm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\n/+Ccc+4bLzyavf7EMaFrHlP6PCJLw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hXdk/Yd3Yfb7e59mysIY0tiJXyVE82VHz+utuUW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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x123907eb8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot a random sample of test images, their predicted labels, and ground truth\n",
"fig = plt.figure(figsize=(20, 8))\n",
"for i, idx in enumerate(np.random.choice(x_test.shape[0], size=32, replace=False)):\n",
" ax = fig.add_subplot(4, 8, i + 1, xticks=[], yticks=[])\n",
" ax.imshow(np.squeeze(x_test[idx]))\n",
" pred_idx = np.argmax(y_hat[idx])\n",
" true_idx = np.argmax(y_test[idx])\n",
" ax.set_title(\"{} ({})\".format(cifar10_labels[pred_idx], cifar10_labels[true_idx]),\n",
" color=(\"green\" if pred_idx == true_idx else \"red\"))"
]
}
],
"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 |
chengts95/homeworkOfPowerSystem | power_system/第4题-三绕组变压器.ipynb | 1 | 2628 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"4．\t三绕组变压器型号为$SFPS_{7}-120000/220$ ，容量比为100/100/100 \n",
"1）\t作变压器等值电路图，并将参数（归算至高压侧）注在图上。 \n",
"2）\t计算额定电压下变压器的空载损耗。（有功和无功） \n",
"3）\t设变压器低压绕组开路，高压及中压绕组承担额定负荷，计算变压器的有功功率损耗和无功功率损耗。\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"960.0\n",
"0.8066666666666666 54.45 34.28333333333333 -6.05 2.747933884297521e-06 1.9834710743801653e-05\n"
]
}
],
"source": [
"import math\n",
"Un=220#kv\n",
"Sn=120#MVA\n",
"I0p=0.8#%\n",
"P0=133#kw\n",
"Pkmax=480#kw\n",
"Uk12=22#%\n",
"Uk13=12\n",
"Uk23=7\n",
"Rt100=Pkmax*Un**2/(2000*Sn**2)\n",
"Uk1=0.5*(Uk12+Uk13-Uk23)\n",
"Uk2=0.5*(Uk12+Uk23-Uk13)\n",
"Uk3=0.5*(Uk13+Uk23-Uk12)\n",
"Xt1=Uk1*(Un**2)/(100*Sn)\n",
"Xt2=Uk2*Un**2/(100*Sn)\n",
"Xt3=Uk3*Un**2/(100*Sn)\n",
"Gt=P0/(1000*Un**2)#空载损耗是有功，认为由电导Gt消耗\n",
"Bt=I0p*Sn/(100*Un**2)#空载时认为电流主要通过Bt,Ib约等于I0\n",
"R0=1/Gt\n",
"X0=1/Bt\n",
"Z0=complex(R0,X0)\n",
"Q0=10*I0p*Sn #kw\n",
"print(Q0)\n",
"print(Rt100,Xt1,Xt2,Xt3,Gt,Bt)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"613 3600.0 0.97\n"
]
}
],
"source": [
"#低压空载，高中压额定运行，求有功和无功损耗\n",
"\n",
"P=Pkmax+P0\n",
"\n",
"Q=Q0+(Uk1+Uk2)*Sn\n",
"PF=(120000-Q)/120000\n",
"print(P,Q,PF)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-2.0 |
akihirosasaki/MyMatchingA.jl | src/Untitled.ipynb | 1 | 7505 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"deferred_acceptance (generic function with 2 methods)"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"function deferred_acceptance(prop_prefs,resp_prefs,caps)\n",
"\n",
" m = size(prop_prefs,2)\n",
" n = size(resp_prefs,2)\n",
" prop_matches = zeros(Int64,m)\n",
" L = sum(caps)\n",
" resp_matches = zeros(Int64,L)\n",
" unchanged_counter = 0\n",
" next_m_approach = ones(Int64,m)\n",
" \n",
" indptr = Array(Int,n+1)\n",
" indptr[1] = 1\n",
" for i in 1:n\n",
" indptr[i+1] = indptr[i] + caps[i]\n",
" end\n",
" \n",
" while unchanged_counter < m\n",
" unchanged_counter = 0\n",
" for i in 1:m\n",
" if prop_matches[i] == 0\n",
" j = prop_prefs[next_m_approach[i],i]\n",
" if j == 0\n",
" prop_matches[i] = 0\n",
" unchanged_counter += 1\n",
" \n",
" else\n",
" a=resp_matches[indptr[j]:indptr[j+1]-1]\n",
" b=zeros(Int64,caps[j])\n",
" for k in 1:caps[j]\n",
" b[k] = findfirst(resp_prefs[:,j],a[k])\n",
" end\n",
" c = maximum(b)\n",
" if c > findfirst(resp_prefs[:,j],i)\n",
" prop_matches[i] = j\n",
" r = findfirst(a,resp_prefs[c,d])\n",
" if resp_matches[indptr[j]-1+r] != 0\n",
" prop_matches[resp_prefs[c,d]] = 0\n",
" next_m_approach[resp_prefs[c,d]] += 1\n",
" end\n",
" resp_matches[indptr[j]-1+r] = i\n",
" else #Žó‚¯“ü‚ê‚È‚¢‚Æ‚«\n",
" next_m_approach[i] += 1\n",
" end\n",
" end\n",
" else unchanged_counter += 1 \n",
" end\n",
" end\n",
" end\n",
" return prop_matches,resp_matches,indptr\n",
"end\n",
"\n",
"function deferred_acceptance(prop_prefs,resp_prefs)\n",
" caps = ones(Int, size(resp_prefs, 2))\n",
" prop_matches, resp_matches, indptr =\n",
" deferred_acceptance(prop_prefs, resp_prefs, caps)\n",
" return prop_matches, resp_matches\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"module MyMatchingA\n",
"export deferred_acceptanceA\n",
"\n",
"# 多対一のケース\n",
"function deferred_acceptanceA(prop_prefs::Array{Int64,2}, resp_prefs::Array{Int64,2}, caps::Array{Int64,1})\n",
" m = size(prop_prefs, 1)\n",
" n = size(resp_prefs, 1)\n",
" \n",
" prop_matches = fill(-1, m)\n",
" \n",
" prop_next_to_propose = ones(Int64, m)\n",
" L = sum(caps)\n",
" resp_matches = zeros(Int64, L)\n",
" \n",
" indptr = Array(Int,n+1)\n",
" indptr[1] = 1\n",
" for i in 1:n\n",
" indptr[i+1] = indptr[i] + caps[i]\n",
" end \n",
" \n",
" resp_rankings = (m+1)*ones(Int, m, n) \n",
" \n",
" for j in 1:n \n",
" for i in 1:length(resp_prefs[j,:])\n",
" @show b = length(resp_prefs[j,:]) \n",
" @show a = resp_prefs[j,i]\n",
" resp_rankings[a,j] = i\n",
" end\n",
" end\n",
" \n",
" @show q = resp_rankings\n",
" \n",
" while true\n",
" @show i = get_single(prop_matches)\n",
" if i == 0\n",
" break\n",
" end\n",
" if prop_next_to_propose[i] > length(prop_prefs[i])\n",
" prop_matches[i] = 0\n",
" continue\n",
" end \n",
" \n",
"\n",
" #学生iが入りたい大学jを探す．\n",
" @show j = prop_prefs[i][prop_next_to_propose[i]]\n",
" if j == 0\n",
" prop_matches[i] = 0\n",
" continue\n",
" end\n",
" \n",
" #大学jのリストを探す\n",
" #要修正？\n",
" @show p = resp_matches[indptr[j]:indptr[j+1]-1]\n",
" \n",
" if resp_rankings[i,j] >= m+1\n",
" prop_next_to_propose[i] += 1\n",
" continue\n",
" end\n",
"\n",
" #大学jが受け入れ可能なら受け入れる．\n",
" @show a = findfirst(p,0)\n",
" if a != 0\n",
" resp_matches[indptr[j]-1+a] = i\n",
" prop_matches[i] = j\n",
"\n",
" #大学jが定員オーバーしている場合\n",
" else\n",
" #大学jがiの方を選好していれば，受け入れる．\n",
" list_comp = Array(Int, length(p))\n",
" for k in 1:length(p)\n",
" b = resp_rankings[p[k],j]\n",
" list_comp[k] = b\n",
" end\n",
" b_max = maximum(list_comp)\n",
" c = findfirst(resp_rankings[:,j], b_max)\n",
" \n",
" if resp_rankings[i,j] < resp_rankings[c,j]\n",
" d = findfirst(resp_matches, c)\n",
" resp_matches[d] = i\n",
" prop_matches[i] = j\n",
" prop_matches[c] = -1\n",
" end\n",
" end\n",
" prop_next_to_propose[i] += 1\n",
" end\n",
" \n",
" return prop_matches, resp_matches, indptr \n",
"end\n",
"\n",
"#どこにも決まってない学生を返す．(存在しなければ0)\n",
"function get_single(partners)\n",
" m = size(partners, 1)\n",
" for i in 1:m\n",
" if partners[i] == -1\n",
" return i\n",
" end \n",
" end\n",
" return 0 \n",
"end\n",
"\n",
"\n",
"end\n",
"\n",
"\n",
"\n",
"# 一対一のケース\n",
"function deferred_acceptanceB(prop_prefs::Array{Int64,2},resp_prefs::Array{Int64,2})\n",
" caps = ones(Int, size(resp_prefs, 1))\n",
" prop_matches, resp_matches, indptr =\n",
" my_deferred_acceptance(prop_prefs, resp_prefs, caps)\n",
" return prop_matches, resp_matches\n",
"end"
]
},
{
"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": "Julia 0.5.1",
"language": "julia",
"name": "julia-0.5"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "0.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
RaoUmer/python-fundamentals | cheat-sheets/10-Comprehensions.ipynb | 1 | 3968 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bonus material: List comprehensions\n",
"\n",
"Materials by: [John Blischak](https://github.com/jdblischak \"GitHub\") and other Software Carpentry instructors (Joshua R. Smith, Milad Fatenejad, Katy Huff, Tommy Guy and many more)\n",
"\n",
"Note - this stuff confuses most people. If you are feeling confident, please proceed! Otherwise, this is really \"bonus\" material, so please focus on the core material."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introducing comprehensions\n",
"\n",
"Python has another way to perform iteration called list comprehensions. First, let's look at how we would create a \"transformed\" version of a list with loops. (If you don't understand loops already, you should probably review that material!)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Multiply every number in a list by 2 using a for loop\n",
"nums1 = [5, 1, 3, 10]\n",
"nums2 = []\n",
"for i in range(len(nums1)):\n",
" nums2.append(nums1[i] * 2)\n",
" \n",
"print nums2"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can see that doing the same thing with a list comprehension is very clear and compact (as long as it makes sense ;)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Multiply every number in a list by 2 using a list comprehension\n",
"nums2 = [x * 2 for x in nums1]\n",
"\n",
"print nums2"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What if we also have some conditional logic?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Multiply every number in a list by 2, but only if the number is greater than 4\n",
"nums1 = [5, 1, 3, 10]\n",
"nums2 = []\n",
"for i in range(len(nums1)):\n",
" if nums1[i] > 4:\n",
" nums2.append(nums1[i] * 2)\n",
" \n",
"print nums2"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# And using a list comprehension\n",
"nums2 = [x * 2 for x in nums1 if x > 4]\n",
"\n",
"print nums2"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Repeated Exercise: Convert genotypes\n",
"This is the same material from 08-Loops. See if you can use list comprehensions to do the following exercises with more compact code.\n",
"\n",
"Again, create a new list which has the converted genotype for each subject ('AA' -> 0, 'AG' -> 1, 'GG' -> 2). Use the Dictionary provided below as a lookup table to do the conversion."
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"converted = {'AA': 0, 'AG': 1, 'GG': 2}\n",
"\n",
"genos = ['AA', 'GG', 'AG', 'AG', 'GG']\n",
"genos_new = [] # Use a comprehension here"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check your work:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"genos_new == [0, 2, 1, 1, 2]"
],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | apache-2.0 |
legacysurvey/obiwan | doc/nb/GaussianMixtures.ipynb | 1 | 2096031 | null | bsd-3-clause |
guillermodeandajauregui/enrichmentator | lib/infomap/examples/python/infomap-examples.ipynb | 3 | 62768 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Infomap\n",
"Multi-level network clustering based on the [Map equation](http://www.mapequation.org/publications.html#Rosvall-Axelsson-Bergstrom-2009-Map-equation).\n",
"\n",
"\n",
"### The Map Equation\n",
"\n",
"\\begin{equation*}\n",
" L(M) = q_\\curvearrowright H(\\mathcal{Q}) + \\sum_{i = 1}^{m}{p_{\\circlearrowright}^i H(\\mathcal{P}^i)}\n",
"\\end{equation*}\n",
"\n",
"$L(M)$ measures the amount of information it takes to describe a random walk on a network given a partition of the network into modules $M$. It is a sum of the amount of information needed to describe the movements _between_ and _within_ the modules, which balances the goodness of fit with the complexity of the model. For more information, see [www.mapequation.org](http://www.mapequation.org)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import Infomap\n",
"Infomap includes an `examples/python` folder with some examples, including this notebook. Run `make` in that directory to build the python interface to a local folder.\n",
"\n",
"The `infomap` package exposes two classes, `Infomap` and `MemInfomap`, that wraps an input `network`, an output `tree`, and a `run` method to run Infomap on the input network. The classes takes a string of [options](http://www.mapequation.org/code.html#Options) as input."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from infomap import infomap"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simple example"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 2 modules with codelength: 2.320730\n",
"\n",
"#node module\n",
"0 0\n",
"1 0\n",
"2 0\n",
"3 1\n",
"4 1\n",
"5 1\n"
]
}
],
"source": [
"infomapWrapper = infomap.Infomap(\"--two-level\")\n",
"\n",
"# Add link weight as an optional third argument\n",
"infomapWrapper.addLink(0, 1)\n",
"infomapWrapper.addLink(0, 2)\n",
"infomapWrapper.addLink(0, 3)\n",
"infomapWrapper.addLink(1, 0)\n",
"infomapWrapper.addLink(1, 2)\n",
"infomapWrapper.addLink(2, 1)\n",
"infomapWrapper.addLink(2, 0)\n",
"infomapWrapper.addLink(3, 0)\n",
"infomapWrapper.addLink(3, 4)\n",
"infomapWrapper.addLink(3, 5)\n",
"infomapWrapper.addLink(4, 3)\n",
"infomapWrapper.addLink(4, 5)\n",
"infomapWrapper.addLink(5, 4)\n",
"infomapWrapper.addLink(5, 3)\n",
"\n",
"infomapWrapper.run()\n",
"\n",
"tree = infomapWrapper.tree\n",
"\n",
"print(\"Found %d modules with codelength: %f\" % (tree.numTopModules(), tree.codelength()))\n",
"\n",
"print(\"\\n#node module\")\n",
"for node in tree.leafIter():\n",
" print(\"%d %d\" % (node.physIndex, node.moduleIndex()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Memory networks\n",
"\n",
"With memory networks, the flow between two nodes depends on how you arrived at the first node. This higher-order relationships can be described by trigrams as in the example below:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Trigrams"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 3 modules with codelength: 1.227078\n",
"\n",
"#node module\n",
"4 0\n",
"3 0\n",
"2 0\n",
"1 1\n",
"2 1\n",
"0 2\n"
]
}
],
"source": [
"infomapWrapper = infomap.MemInfomap(\"--two-level\")\n",
"\n",
"# Trigrams represents a path from node A through B to C.\n",
"# Add link weight as an optional fourth argument\n",
"infomapWrapper.addTrigram(0, 2, 0)\n",
"infomapWrapper.addTrigram(0, 2, 1)\n",
"infomapWrapper.addTrigram(1, 2, 1)\n",
"infomapWrapper.addTrigram(1, 2, 0)\n",
"infomapWrapper.addTrigram(1, 2, 3)\n",
"infomapWrapper.addTrigram(3, 2, 3)\n",
"infomapWrapper.addTrigram(2, 3, 4)\n",
"infomapWrapper.addTrigram(3, 2, 4)\n",
"infomapWrapper.addTrigram(4, 2, 4)\n",
"infomapWrapper.addTrigram(4, 2, 3)\n",
"infomapWrapper.addTrigram(4, 3, 3)\n",
"\n",
"infomapWrapper.run()\n",
"\n",
"tree = infomapWrapper.tree\n",
"\n",
"print(\"Found %d modules with codelength: %f\" % (tree.numTopModules(), tree.codelength()))\n",
"\n",
"print(\"\\n#node module\")\n",
"for node in tree.leafIter():\n",
" print(\"%d %d\" % (node.physIndex, node.moduleIndex()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Overlapping modules\n",
"\n",
"Notice that node `2` in the example below exists in both module `0` and `1`. This is because `MemInfomap` partitions the higher-order state network which can include multiple state nodes for each physical node. For trigrams, a state node is a pair of `previousNode node`. To keep the state network in the output tree, add the `--expanded` flag to configure `MemInfomap`:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 3 modules with codelength: 1.227078\n",
"\n",
"#previousNode node module\n",
"2 3 0\n",
"3 4 0\n",
"2 4 0\n",
"3 2 0\n",
"4 2 0\n",
"2 1 1\n",
"0 2 1\n",
"1 2 1\n",
"2 0 2\n"
]
}
],
"source": [
"# Store expanded state network\n",
"infomapWrapper = infomap.MemInfomap(\"--two-level --expanded\")\n",
"\n",
"infomapWrapper.addTrigram(0, 2, 0)\n",
"infomapWrapper.addTrigram(0, 2, 1)\n",
"infomapWrapper.addTrigram(1, 2, 1)\n",
"infomapWrapper.addTrigram(1, 2, 0)\n",
"infomapWrapper.addTrigram(1, 2, 3)\n",
"infomapWrapper.addTrigram(3, 2, 3)\n",
"infomapWrapper.addTrigram(2, 3, 4)\n",
"infomapWrapper.addTrigram(3, 2, 4)\n",
"infomapWrapper.addTrigram(4, 2, 4)\n",
"infomapWrapper.addTrigram(4, 2, 3)\n",
"infomapWrapper.addTrigram(4, 3, 3)\n",
"\n",
"infomapWrapper.run()\n",
"\n",
"tree = infomapWrapper.tree\n",
"\n",
"print(\"Found %d modules with codelength: %f\" % (tree.numTopModules(), tree.codelength()))\n",
"\n",
"print(\"\\n#previousNode node module\")\n",
"for node in tree.leafIter():\n",
" print(\"%d %d %d\" % (node.stateIndex, node.physIndex, node.moduleIndex()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As seen in the expanded output above, node `2` is represented by four state nodes partitioned into two modules depending on where you come from; if you go to node `2` from node `0` or `1` you are still considered to be in module `1`, but if you go to node `2` from node `3` and `4` you are still considered to be in module `0`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Multi-layer networks"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 3 modules with codelength: 0.865437\n",
"\n",
"#layer node module:\n",
"2 1 0\n",
"1 2 0\n",
"2 3 1\n",
"3 2 1\n",
"0 0 2\n"
]
}
],
"source": [
"infomapWrapper = infomap.MemInfomap(\"--two-level --expanded\")\n",
"\n",
"# from (layer, node) to (layer, node) weight\n",
"infomapWrapper.addMultiplexLink(2, 1, 1, 2, 1.0)\n",
"infomapWrapper.addMultiplexLink(1, 2, 2, 1, 1.0)\n",
"infomapWrapper.addMultiplexLink(3, 2, 2, 3, 1.0)\n",
"\n",
"infomapWrapper.run()\n",
"\n",
"tree = infomapWrapper.tree\n",
"\n",
"print(\"Found %d modules with codelength: %f\" % (tree.numTopModules(), tree.codelength()))\n",
"\n",
"print(\"\\n#layer node module:\")\n",
"for node in tree.leafIter():\n",
" print(\"%d %d %d\" % (node.stateIndex, node.physIndex, node.moduleIndex()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Infomap + NetworkX\n",
"Generate and draw a network with NetworkX, colored\n",
"according to the community structure found by Infomap."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import networkx as nx\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.colors as colors\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def findCommunities(G):\n",
" \"\"\"\n",
" Partition network with the Infomap algorithm.\n",
" Annotates nodes with 'community' id and return number of communities found.\n",
" \"\"\"\n",
" infomapWrapper = infomap.Infomap(\"--two-level --silent\")\n",
"\n",
" print(\"Building Infomap network from a NetworkX graph...\")\n",
" for e in G.edges_iter():\n",
" infomapWrapper.addLink(*e)\n",
"\n",
" print(\"Find communities with Infomap...\")\n",
" infomapWrapper.run();\n",
"\n",
" tree = infomapWrapper.tree\n",
"\n",
" print(\"Found %d modules with codelength: %f\" % (tree.numTopModules(), tree.codelength()))\n",
"\n",
" communities = {}\n",
" for node in tree.leafIter():\n",
" communities[node.originalLeafIndex] = node.moduleIndex()\n",
"\n",
" nx.set_node_attributes(G, 'community', communities)\n",
" return tree.numTopModules()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def drawNetwork(G):\n",
" # position map\n",
" pos = nx.spring_layout(G)\n",
" # community ids\n",
" communities = [v for k,v in nx.get_node_attributes(G, 'community').items()]\n",
" numCommunities = max(communities) + 1\n",
" # color map from http://colorbrewer2.org/\n",
" cmapLight = colors.ListedColormap(['#a6cee3', '#b2df8a', '#fb9a99', '#fdbf6f', '#cab2d6'], 'indexed', numCommunities)\n",
" cmapDark = colors.ListedColormap(['#1f78b4', '#33a02c', '#e31a1c', '#ff7f00', '#6a3d9a'], 'indexed', numCommunities)\n",
"\n",
" # Draw edges\n",
" nx.draw_networkx_edges(G, pos)\n",
"\n",
" # Draw nodes\n",
" nodeCollection = nx.draw_networkx_nodes(G,\n",
" pos = pos,\n",
" node_color = communities,\n",
" cmap = cmapLight\n",
" )\n",
" # Set node border color to the darker shade\n",
" darkColors = [cmapDark(v) for v in communities]\n",
" nodeCollection.set_edgecolor(darkColors)\n",
"\n",
" # Draw node labels\n",
" for n in G.nodes_iter():\n",
" plt.annotate(n,\n",
" xy = pos[n],\n",
" textcoords = 'offset points',\n",
" horizontalalignment = 'center',\n",
" verticalalignment = 'center',\n",
" xytext = [0, 0],\n",
" color = cmapDark(communities[n])\n",
" )\n",
"\n",
" plt.axis('off')\n",
" # plt.savefig(\"karate.png\")\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Building Infomap network from a NetworkX graph...\n",
"Find communities with Infomap...\n",
"Found 3 modules with codelength: 4.311793\n"
]
},
{
"data": {
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ypdtJ3PmMnOCcFlwEOCclIyc9XiOId0uCeFOMXyhLCYnx04taOBUZ1W0NtgO1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"text/plain": [
"<matplotlib.figure.Figure at 0x10e8d6790>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"G=nx.karate_club_graph()\n",
"\n",
"findCommunities(G)\n",
"\n",
"drawNetwork(G)"
]
},
{
"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-2.0 |
openp2pdesign/Labs-Survey---Analysis | 000-Export-Final-Anonymized-Data.ipynb | 1 | 9512 | {
"metadata": {
"name": "",
"signature": "sha256:67fc0cae934bb53011c544c74a0cf8206ca4cba9858be6803be790d6675e92f3"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"000 - Export final anonymized data"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# -*- coding: UTF-8 -*-\n",
"\n",
"import pandas as pd\n",
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Load csv file first\n",
"data = pd.read_csv(\"data/lab-survey.csv\", encoding=\"utf-8\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Check data\n",
"#data[0:4] # Equals to data.head()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#\u00a0Data to be exported with the name:\n",
"\n",
"data_full_codes = [ \"D1\", \n",
" \"D4[SQ001]\",\"D4[SQ002]\",\n",
" \"D8\", \"D9\", \"D12\", \"D13\", \"D15\", \n",
" \"D16[SQ001]\",\"D16[SQ002]\",\"D16[SQ004]\",\"D16[SQ012]\",\"D16[SQ013]\",\"D16[SQ003]\",\"D16[SQ005]\",\n",
" \"D16[SQ006]\",\"D16[SQ007]\",\"D16[SQ008]\",\"D16[SQ009]\",\"D16[SQ010]\",\"D16[SQ011]\",\"D16[other]\",\n",
" \"D17\", \"D18\", \"D19\"]\n",
"\n",
"#\u00a0Data to be exported in an anonymized version:\n",
"\n",
"data_anonymized_codes = [\"D2[SQ001]\",\"D2[SQ002]\",\"D2[SQ003]\",\"D2[SQ004]\",\"D2[SQ005]\",\"D2[SQ006]\",\"D2[SQ007]\",\"D2[SQ008]\",\n",
" \"D2[SQ009]\",\"D2[SQ010]\",\"D2[SQ011]\",\"D2[other]\",\n",
" \"D3\",\"D3[other]\", \n",
" \"D5\", \n",
" \"D6\",\"D6[other]\",\n",
" \"D7\",\"D7[other]\",\n",
" \"D10[SQ001]\",\"D10[SQ002]\",\"D10[SQ003]\",\"D10[SQ004]\",\"D10[SQ005]\",\"D10[SQ006]\",\"D10[SQ007]\",\n",
" \"D10[SQ008]\",\"D10[other]\",\n",
" \"D11[SQ001]\",\"D11[SQ002]\",\"D11[SQ003]\",\"D11[SQ004]\",\"D11[SQ005]\",\"D11[other]\",\n",
" \"D14[SQ001]\",\"D14[SQ002]\",\"D14[SQ004]\",\"D14[SQ010]\",\"D14[SQ011]\",\"D14[SQ003]\",\"D14[SQ005]\",\n",
" \"D14[SQ006]\",\"D14[SQ007]\",\"D14[SQ008]\",\"D14[SQ009]\",\"D14[SQ012]\",\"D14[other]\",\n",
" \"D20[SQ001]\", \n",
" \"D21[SQ001]\",\"D21[SQ002]\",\"D21[SQ003]\",\"D21[SQ004]\",\"D21[SQ005]\",\"D21[SQ006]\",\"D21[SQ007]\",\n",
" \"D21[SQ008]\",\"D21[other]\",\n",
" \"D22\", \"D23\", \"D24\", \"D25\",\n",
" \"D26[SQ001]\",\"D26[SQ002]\",\"D26[SQ003]\",\"D26[SQ004]\",\"D26[SQ005]\",\"D26[SQ006]\",\"D26[SQ007]\",\n",
" \"D26[SQ008]\",\"D26[other]\",\n",
" \"D27[SQ001_SQ001]\",\"D27[SQ001_SQ002]\",\"D27[SQ001_SQ003]\",\"D27[SQ001_SQ004]\",\"D27[SQ001_SQ005]\",\n",
" \"D27[SQ001_SQ006]\",\"D27[SQ001_SQ007]\",\"D27[SQ002_SQ001]\",\"D27[SQ002_SQ002]\",\"D27[SQ002_SQ003]\",\n",
" \"D27[SQ002_SQ004]\",\"D27[SQ002_SQ005]\",\"D27[SQ002_SQ006]\",\"D27[SQ002_SQ007]\",\n",
" \"D28[SQ001]\",\"D28[SQ002]\",\"D28[SQ003]\",\"D28[SQ004]\",\"D28[SQ005]\",\"D28[SQ006]\",\"D28[SQ007]\",\n",
" \"D28[SQ008]\",\"D28[SQ009]\",\"D28[SQ010]\",\"D28[SQ011]\",\"D28[SQ012]\",\"D28[SQ013]\",\"D28[SQ016]\",\n",
" \"D28[SQ014]\",\"D28[SQ015]\",\n",
" \"D29[SQ001_SQ002]\",\"D29[SQ001_SQ001]\",\"D29[SQ002_SQ002]\",\"D29[SQ002_SQ001]\",\"D29[SQ003_SQ002]\",\n",
" \"D29[SQ003_SQ001]\",\"D29[SQ004_SQ002]\",\"D29[SQ004_SQ001]\",\"D29[SQ005_SQ002]\",\"D29[SQ005_SQ001]\",\n",
" \"D29[SQ006_SQ002]\",\"D29[SQ006_SQ001]\",\"D29[SQ007_SQ002]\",\"D29[SQ007_SQ001]\",\"D29[SQ008_SQ002]\",\n",
" \"D29[SQ008_SQ001]\",\"D29[SQ009_SQ002]\",\"D29[SQ009_SQ001]\",\"D29[SQ010_SQ002]\",\"D29[SQ010_SQ001]\",\n",
" \"D30[SQ002]\",\"D30[SQ003]\",\"D30[SQ004]\",\"D30[SQ005]\",\"D30[SQ006]\",\"D30[SQ007]\",\"D30[SQ008]\",\n",
" \"D30[SQ009]\",\"D30[SQ010]\",\"D30[SQ011]\",\"D30[SQ012]\",\"D30[SQ013]\",\"D30[SQ014]\",\n",
" \"D31[SQ001_SQ001]\",\"D31[SQ001_SQ002]\",\"D31[SQ002_SQ001]\",\"D31[SQ002_SQ002]\",\"D31[SQ003_SQ001]\",\n",
" \"D31[SQ003_SQ002]\",\"D31[SQ004_SQ001]\",\"D31[SQ004_SQ002]\",\"D31[SQ005_SQ001]\",\"D31[SQ005_SQ002]\",\n",
" \"D31[SQ006_SQ001]\",\"D31[SQ006_SQ002]\",\"D31[SQ007_SQ001]\",\"D31[SQ007_SQ002]\",\"D31[SQ008_SQ001]\",\n",
" \"D31[SQ008_SQ002]\",\"D31[SQ009_SQ001]\",\"D31[SQ009_SQ002]\",\"D31[SQ010_SQ001]\",\"D31[SQ010_SQ002]\",\n",
" \"D32[SQ001]\",\"D32[SQ002]\",\"D32[SQ003]\",\"D32[SQ004]\",\"D32[SQ005]\",\n",
" \"D33\", \n",
" \"D34[SQ001]\",\"D34[SQ002]\",\n",
" \"D35\", \"D36\", \"D37\", \n",
" \"D38[SQ001]\",\"D38[SQ002]\",\"D38[SQ003]\",\"D38[SQ004]\",\"D38[SQ005]\",\"D38[SQ006]\",\"D38[SQ007]\",\n",
" \"D39[SQ001]\",\"D39[SQ002]\",\"D39[SQ003]\",\"D39[SQ004]\",\n",
" \"D40[SQ001]\",\"D40[SQ002]\",\"D40[SQ003]\",\"D40[SQ004]\",\"D40[SQ005]\",\"D40[SQ006]\",\"D40[SQ007]\",\n",
" \"D41[SQ001]\",\"D41[SQ002]\",\"D41[SQ003]\",\"D41[SQ004]\",\"D41[SQ005]\",\"D41[SQ006]\",\"D41[SQ007]\",\n",
" \"D41[SQ008]\",\"D41[SQ009]\",\"D41[SQ010]\",\n",
" ]\n",
"\n",
"#\u00a0Load data from these selections\n",
"\n",
"data_full = data[data_full_codes]\n",
"data_anonymized = data[data_anonymized_codes]\n",
"data_business_models = data[\"D42\"]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Shuffle the anonymized data in order to change the order of the rows\n",
"#\u00a0Learnt here: http://stackoverflow.com/a/15772330/2237113\n",
"sorted_data_anonymized = data_anonymized.reindex(np.random.permutation(data_anonymized.index))\n",
"sorted_data_business_models = data_business_models.reindex(np.random.permutation(data_business_models.index))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#\u00a0Rename the index, for more anonymization... for all the anonymized data except business models data\n",
"new_index = {}\n",
"for k,i in enumerate(sorted_data_anonymized.index):\n",
" new_index[i] = k\n",
"sorted_data_anonymized_final = sorted_data_anonymized.rename(index=new_index)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#\u00a0Rename the index, for more anonymization... for the business models data\n",
"new_index = {}\n",
"for k,i in enumerate(sorted_data_business_models.index):\n",
" new_index[i] = k\n",
"sorted_data_business_models_final = sorted_data_business_models.rename(index=new_index)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#\u00a0Debug\n",
"#data_full"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#\u00a0Debug\n",
"#data_anonymized"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#\u00a0Export datasets\n",
"data_full.to_csv('data/italian-labs_final_data_with_names.csv', encoding='utf-8')\n",
"sorted_data_anonymized_final.to_csv('data/italian-labs_final_data_anonymized.csv', encoding='utf-8')\n",
"sorted_data_business_models_final.to_csv('data/italian-labs_final_data_business_models_anonymized.csv', encoding='utf-8')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
}
],
"metadata": {}
}
]
} | gpl-3.0 |
GoogleCloudPlatform/ml-on-gcp | tutorials/sklearn/hpsearch/gke_bayes_search.ipynb | 1 | 19444 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train locally"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import training data\n",
"\n",
"For illustration purposes we will use the MNIST dataset. The following code downloads the dataset and puts it in `./mnist_data`.\n",
"\n",
"The first 60000 images and targets are the original training set, while the last 10000 are the testing set. The training set is ordered by the labels so we shuffle them since we will use a very small portion of the data to shorten training time."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.datasets import fetch_mldata\n",
"from sklearn.utils import shuffle\n",
"\n",
"mnist = fetch_mldata('MNIST original', data_home='./mnist_data')\n",
"X, y = shuffle(mnist.data[:60000], mnist.target[:60000])\n",
"\n",
"X_small = X[:100]\n",
"y_small = y[:100]\n",
"\n",
"# Note: using only 10% of the training data\n",
"X_large = X[:6000]\n",
"y_large = y[:6000]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Instantiate the estimator and the SearchCV objects\n",
"\n",
"For illustration purposes we will use the `RandomForestClassifier` with `scikit-optimize`'s `BayesSearchCV`:\n",
"\n",
"http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html\n",
"\n",
"https://scikit-optimize.github.io/#skopt.BayesSearchCV"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"from skopt import BayesSearchCV\n",
"from skopt.space import Integer, Real\n",
"\n",
"rfc = RandomForestClassifier(n_jobs=-1)\n",
"search_spaces = {\n",
" 'max_features': Real(0.5, 1.0),\n",
" 'n_estimators': Integer(10, 200),\n",
" 'max_depth': Integer(5, 45),\n",
" 'min_samples_split': Real(0.01, 0.1)\n",
"}\n",
"search = BayesSearchCV(estimator=rfc, search_spaces=search_spaces, n_jobs=-1, verbose=3, n_iter=100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fit the BayesSearchCV object locally\n",
"\n",
"After fitting we can examine the best score (accuracy) and the best parameters that achieve that score."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%time search.fit(X_small, y_small)\n",
"\n",
"print(search.best_score_, search.best_params_)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Everything up to this point is what you would do when training locally. With larger amount of data it would take much longer."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train on Google Container Engine"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Set up for training on Google Container Engine\n",
"\n",
"Before we can start training on the Container Engine we need to:\n",
"\n",
"- Build the Docker image which will be handling the workloads.\n",
"- Create a cluster.\n",
"\n",
"For these we will first set up some configuration variables."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Your Google Cloud Platform project id."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"project_id = 'YOUR-PROJECT-ID'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A Google Cloud Storage bucket belonging to your project created through either:\n",
"- gsutil mb gs://YOUR-BUCKET-NAME; or\n",
"- https://console.cloud.google.com/storage\n",
"\n",
"This bucket will be used for storing temporary data during Docker image building, for storing training data, and for storing trained models.\n",
"\n",
"This can be an existing bucket, but we recommend you create a new one."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"bucket_name = 'YOUR-BUCKET-NAME'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pick a cluster id for the cluster on Google Container Engine we will create. Preferably not an existing cluster to avoid affecting its workload."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cluster_id = 'YOUR-CLUSTER-ID'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Choose a name for the image that will be running on the container."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"image_name = 'YOUR-IMAGE-NAME'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Choose a zone to host the cluster.\n",
"\n",
"List of zones: https://cloud.google.com/compute/docs/regions-zones/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"zone = 'us-central1-b'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Change this only if you have customized the source."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"source_dir = 'source'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Build a Docker image\n",
"\n",
"This step builds a Docker image using the content in the `source/` folder. The image will be tagged with the provided `image_name` so the workers can pull it. The main script `source/worker.py` would retrieve a pickled `BayesSearchCV` object from Cloud Storage and fit it to data on GCS.\n",
"\n",
"Note: This step only needs to be run once the first time you follow these steps,\n",
"and each time you modify the codes in `source/`. If you have not modified `source/` then\n",
"you can just re-use the same image.\n",
"\n",
"Note: This could take a couple minutes.\n",
"To monitor the build process: https://console.cloud.google.com/gcr/builds"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from helpers.cloudbuild_helper import build\n",
"\n",
"build(project_id, source_dir, bucket_name, image_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a cluster\n",
"\n",
"This step creates a cluster on the Container Engine.\n",
"\n",
"You can alternatively create the cluster with the gcloud command line tool or through the console, but\n",
"you must add the additional scope of write access to Google Clous Storage: `'https://www.googleapis.com/auth/devstorage.read_write'`\n",
"\n",
"Note: This could take several minutes.\n",
"To monitor the cluster creation process: https://console.cloud.google.com/kubernetes/list\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from helpers.gke_helper import create_cluster\n",
"\n",
"create_cluster(project_id, zone, cluster_id, n_nodes=1, machine_type='n1-standard-64')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For GCE instance pricing: https://cloud.google.com/compute/pricing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Instantiate the GKEParallel object\n",
"\n",
"The `GKEParallel` class is a helper wrapper around a `BayesSearchCV` object that manages deploying fitting jobs to the Container Engine cluster created above.\n",
"\n",
"We pass in the `BayesSearchCV` object, which will be pickled and stored on Cloud Storage with\n",
"uri of the form: \n",
"\n",
"```gs://YOUR-BUCKET-NAME/YOUR-CLUSTER-ID.YOUR-IMAGE-NAME.UNIX-TIME/search.pkl```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"from skopt import BayesSearchCV\n",
"from skopt.space import Integer, Real\n",
"\n",
"rfc = RandomForestClassifier(n_jobs=-1)\n",
"search_spaces = {\n",
" 'max_features': Real(0.5, 1.0),\n",
" 'n_estimators': Integer(10, 200),\n",
" 'max_depth': Integer(5, 45),\n",
" 'min_samples_split': Real(0.01, 0.1)\n",
"}\n",
"search = BayesSearchCV(estimator=rfc, search_spaces=search_spaces, n_jobs=-1, verbose=3, n_iter=100)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from gke_parallel import GKEParallel\n",
"\n",
"gke_search = GKEParallel(search, project_id, zone, cluster_id, bucket_name, image_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Refresh access token to the cluster\n",
"\n",
"To make it easy to gain access to the cluster through the [Kubernetes client library](https://github.com/kubernetes-incubator/client-python), included in this sample is a script that retrieves credentials for the cluster with gcloud\n",
"and refreshes access token with kubectl."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"! bash get_cluster_credentials.sh $cluster_id $zone"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Deploy the fitting task\n",
"\n",
"`GKEParallel` instances implement a similar (but different!) interface as `BayesSearchCV`.\n",
"\n",
"Calling `fit(X, y)` first uploads the training data to Cloud Storage as:\n",
"\n",
"```\n",
"gs://YOUR-BUCKET-NAME/YOUR-CLUSTER-ID.YOUR-IMAGE-NAME.UNIX-TIME/X.pkl\n",
"gs://YOUR-BUCKET-NAME/YOUR-CLUSTER-ID.YOUR-IMAGE-NAME.UNIX-TIME/y.pkl\n",
"```\n",
"\n",
"This allows reusing the same uploaded datasets for future training tasks.\n",
"\n",
"For instance, if you already have pickled data on Cloud Storage:\n",
"\n",
"```\n",
"gs://DATA-BUCKET/X.pkl\n",
"gs://DATA-BUCKET/y.pkl\n",
"```\n",
"\n",
"then you can deploy the fitting task with:\n",
"\n",
"```\n",
"gke_search.fit(X='gs://DATA-BUCKET/X.pkl', y='gs://DATA-BUCKET/y.pkl')\n",
"```\n",
"\n",
"Calling `fit(X, y)` also pickles the wrapped `search` and `gke_search`, stores them on Cloud Storage as:\n",
"\n",
"```\n",
"gs://YOUR-BUCKET-NAME/YOUR-CLUSTER-ID.YOUR-IMAGE-NAME.UNIX-TIME/search.pkl\n",
"gs://YOUR-BUCKET-NAME/YOUR-CLUSTER-ID.YOUR-IMAGE-NAME.UNIX-TIME/gke_search.pkl\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"gke_search.fit(X_large, y_large)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Inspect the GKEParallel object\n",
"\n",
"In the background, the `GKEParallel` instance splits the `search_spaces` into smaller `search_spaces`\n",
"\n",
"Each smaller `search_spaces` is pickled and stored on GCS within each worker's workspace:\n",
"\n",
"```gs://YOUR-BUCKET-NAME/YOUR-CLUSTER-ID.YOUR-IMAGE-NAME.UNIX-TIME/WORKER-ID/search_spaces.pkl```\n",
"\n",
"The `search_spaces` can be accessed as follows, showing how they are assigned to each worker.\n",
"\n",
"The keys of this dictionary are the `worker_ids`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"gke_search.search_spaces"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"You could optionally specify a `task_name` when creating a `GKEParallel` instance.\n",
"\n",
"If you did not specify a `task_name`, when you call `fit(X, y)` the task_name will be set to:\n",
"\n",
"```YOUR-CLUSTER-ID.YOUR-IMAGE-NAME.UNIX-TIME``` "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"gke_search.task_name"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similarly, each job is given a `job_name`. The dictionary of `job_names` can be accessed as follows. Each worker pod handles one job processing one of the smaller `search_spaces`. \n",
"\n",
"To monitor the jobs: https://console.cloud.google.com/kubernetes/workload"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"gke_search.job_names"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cancel the task\n",
"\n",
"To cancel the task, run `cancel()`. This will delete all the deployed worker pods and jobs,\n",
"but will NOT delete the cluster, nor delete any data already persisted to Cloud Storage."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#gke_search.cancel()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Monitor the progress\n",
"\n",
"\n",
"`GKEParallel` instances implement a similar (but different!) interface as Future instances.\n",
"Calling `done()` checks whether each worker has completed the job and persisted its outcome\n",
"on GCS with uri:\n",
"\n",
"```gs://YOUR-BUCKET-NAME/YOUR-CLUSTER-ID.YOUR-IMAGE-NAME.UNIX-TIME/WORKER-ID/fitted_search.pkl```\n",
"\n",
"To monitor the jobs: https://console.cloud.google.com/kubernetes/workload\n",
"\n",
"To access the persisted data directly: https://console.cloud.google.com/storage/browser/YOUR-BUCKET-NAME/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"gke_search.done(), gke_search.dones"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When all the jobs are finished, the pods will stop running (but the cluster will remain), and we can retrieve the fitted model.\n",
"\n",
"Calling `result()` will populate the `gke_search.results` attribute which is returned.\n",
"This attribute records all the fitted `BayesSearchCV` from the jobs. The fitted model is downloaded only if the `download` argument is set to `True`.\n",
"\n",
"Calling `result()` also updates the pickled `gke_search` object on Cloud Storage:\n",
"\n",
"`gs://YOUR-BUCKET-NAME/YOUR-CLUSTER-ID.YOUR-IMAGE-NAME.UNIX-TIME/gke_search.pkl`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"result = gke_search.result(download=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also get the logs from the pods:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from helpers.kubernetes_helper import get_pod_logs\n",
"\n",
"for pod_name, log in get_pod_logs().items():\n",
" print('=' * 20)\n",
" print('\\t{}\\n'.format(pod_name))\n",
" print(log)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once the jobs are finished, the cluster can be deleted. All the fitted models are stored on GCS.\n",
"\n",
"The cluster can also be deleted from the console: https://console.cloud.google.com/kubernetes/list"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from helpers.gke_helper import delete_cluster\n",
"\n",
"#delete_cluster(project_id, zone, cluster_id)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The next cell continues to poll the jobs until they are all finished, downloads the results, and deletes the cluster."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"from helpers.gke_helper import delete_cluster\n",
"\n",
"while not gke_search.done():\n",
" n_done = len([d for d in gke_search.dones.values() if d])\n",
" print('{}/{} finished'.format(n_done, len(gke_search.job_names)))\n",
" time.sleep(60)\n",
"\n",
"delete_cluster(project_id, zone, cluster_id)\n",
"result = gke_search.result(download=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Restore the GKEParallel object\n",
"\n",
"To restore the fitted `gke_search object` (for example from a different notebook), you can use the helper function included in this sample."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from helpers.gcs_helper import download_uri_and_unpickle\n",
"gcs_uri = 'gs://YOUR-BUCKET-NAME/YOUR-CLUSTER-ID.YOUR-IMAGE-NAME.UNIX-TIME/gke_search.pkl'\n",
"gke_search_restored = download_uri_and_unpickle(gcs_uri)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Inspect the result\n",
"\n",
"`GKEParallel` also implements part of the interface of `BayesSearchCV` to allow easy access to `best_score+`, `best_param_`, and `beat_estimator_`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"gke_search.best_score_, gke_search.best_params_, gke_search.best_estimator_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also call `predict()`, which deligates the call to the `best_estimator_`.\n",
"\n",
"Below we calculate the accuracy on the 10000 test images."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"predicted = gke_search.predict(mnist.data[60000:])\n",
"\n",
"print(len([p for i, p in enumerate(predicted) if p == mnist.target[60000:][i]]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Clean up\n",
"\n",
"To clean up, delete the cluster so your project will no longer be charged for VM instance usage. The simplest way to delete the cluster is through the console:\n",
"https://console.cloud.google.com/kubernetes/list\n",
"\n",
"This will not delete any data persisted on Cloud Storage."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
james-prior/euler | euler-003-largest-prime-factor-20150227.ipynb | 1 | 12511 | {
"metadata": {
"name": "euler-003-largest-prime-factor-20150227"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"!factor 600851475143 | rev | awk '{print $1}' | rev"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"6857\r\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!factor 600851475143 | awk '{print $NF}'"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"6857\r\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import math\n",
"\n",
"def get_factors(x):\n",
" '''Returns of prime factors of x in ascending order.'''\n",
" factors = []\n",
" factor = 2\n",
" while factor <= math.sqrt(x):\n",
" while x % factor == 0:\n",
" factors.append(factor)\n",
" x /= factor\n",
" factor += 1\n",
" if x != 1:\n",
" factors.append(x)\n",
" return factors"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"n = 600851475143"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"get_factors(n)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 5,
"text": [
"[71, 839, 1471, 6857L]"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def foo(x):\n",
" return get_factors(x)[-1]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%timeit foo(n)\n",
"foo(n)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1000 loops, best of 3: 1.34 ms per loop\n"
]
},
{
"output_type": "pyout",
"prompt_number": 7,
"text": [
"6857L"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def get_factors(x):\n",
" '''Returns of prime factors of x in ascending order.'''\n",
" factors = []\n",
" factor = 2\n",
" while factor * factor <= x:\n",
" while x % factor == 0:\n",
" factors.append(factor)\n",
" x /= factor\n",
" factor += 1\n",
" if x != 1:\n",
" factors.append(x)\n",
" return factors"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%timeit foo(n)\n",
"foo(n)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1000 loops, best of 3: 973 us per loop\n"
]
},
{
"output_type": "pyout",
"prompt_number": 9,
"text": [
"6857L"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"get_factors(1000)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 10,
"text": [
"[2, 2, 2, 5, 5, 5]"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"get_factors(600851475143)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 11,
"text": [
"[71, 839, 1471, 6857L]"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"get_factors(600851475143)[-1]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 12,
"text": [
"6857L"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def get_factors(x):\n",
" '''Returns of prime factors of x in ascending order.'''\n",
" factors = []\n",
" factor = 2\n",
" while factor * factor <= x:\n",
" while x % factor == 0:\n",
" factors.append(factor)\n",
" x /= factor\n",
" factor += 1\n",
" if x != 1:\n",
" factors.append(x)\n",
" return factors"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from euler import get_factors2\n",
"get_factors = get_factors2"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%timeit foo(n)\n",
"foo(n)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"10000 loops, best of 3: 188 us per loop\n"
]
},
{
"output_type": "pyout",
"prompt_number": 15,
"text": [
"6857L"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from euler import get_factors3\n",
"get_factors = get_factors3"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%timeit foo(n)\n",
"foo(n)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1000 loops, best of 3: 229 us per loop\n"
]
},
{
"output_type": "pyout",
"prompt_number": 17,
"text": [
"6857L"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from euler import get_factors4\n",
"get_factors = get_factors4"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%timeit foo(n)\n",
"foo(n)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1000000 loops, best of 3: 661 ns per loop\n"
]
},
{
"output_type": "pyout",
"prompt_number": 19,
"text": [
"6857L"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from euler import get_factors"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%timeit foo(n)\n",
"foo(n)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1000000 loops, best of 3: 661 ns per loop\n"
]
},
{
"output_type": "pyout",
"prompt_number": 21,
"text": [
"6857L"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from euler import get_factors5\n",
"get_factors = get_factors5"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%timeit foo(n)\n",
"foo(n)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1000000 loops, best of 3: 661 ns per loop\n"
]
},
{
"output_type": "pyout",
"prompt_number": 23,
"text": [
"6857L"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from euler import canonical_decomposition2\n",
"foo = canonical_decomposition2\n",
"%timeit foo(n)\n",
"foo(n)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"10000 loops, best of 3: 197 us per loop\n"
]
},
{
"output_type": "pyout",
"prompt_number": 24,
"text": [
"{71: 1, 839: 1, 1471: 1, 6857L: 1}"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from euler import canonical_decomposition3\n",
"foo = canonical_decomposition3\n",
"%timeit foo(n)\n",
"foo(n)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"10000 loops, best of 3: 192 us per loop\n"
]
},
{
"output_type": "pyout",
"prompt_number": 25,
"text": [
"{71: 1, 839: 1, 1471: 1, 6857L: 1}"
]
}
],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from euler import canonical_decomposition4\n",
"foo = canonical_decomposition4\n",
"%timeit foo(n)\n",
"foo(n)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"10000 loops, best of 3: 197 us per loop\n"
]
},
{
"output_type": "pyout",
"prompt_number": 26,
"text": [
"Counter({6857L: 1, 839: 1, 1471: 1, 71: 1})"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from euler import canonical_decomposition5\n",
"foo = canonical_decomposition5\n",
"%timeit foo(n)\n",
"foo(n)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1000 loops, best of 3: 235 us per loop\n"
]
},
{
"output_type": "pyout",
"prompt_number": 27,
"text": [
"Counter({6857L: 1, 839: 1, 1471: 1, 71: 1})"
]
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from euler import canonical_decomposition\n",
"foo = canonical_decomposition\n",
"%timeit foo(n)\n",
"foo(n)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1000 loops, best of 3: 230 us per loop\n"
]
},
{
"output_type": "pyout",
"prompt_number": 28,
"text": [
"{71: 1, 839: 1, 1471: 1, 6857L: 1}"
]
}
],
"prompt_number": 28
}
],
"metadata": {}
}
]
} | mit |
XENON1T/pax | examples/parse_s1_corrmap/Parse S1 xyz correction map.ipynb | 1 | 573125 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"from numpy.lib.recfunctions import rec_append_fields\n",
"import csv\n",
"from pax import core, units"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Load the map text file\n",
"with open('s1xyzmap-20111215.dat') as lymapfile:\n",
" \n",
" tuples = []\n",
" for i, row in enumerate(lymapfile):\n",
" if i == 0:\n",
" continue\n",
" tuples.append(tuple(map(float, row.split())))\n",
" \n",
" data = np.array(tuples, dtype=[('z', int), \n",
" ('t', int),\n",
" ('r', int),\n",
" ('zmid', float),\n",
" ('tmid', float),\n",
" ('rmid', float),\n",
" ('ly', float),\n",
" ('errly', float),])\n",
"\n",
"# Convert to pax units\n",
"for field in ('z', 'r', 'zmid', 'rmid'):\n",
" data[field] = data[field].astype(np.float) * units.mm\n",
" \n",
"# Convert (r, theta) to (x, y)\n",
"data = rec_append_fields(data, 'xmid', data['rmid'] * np.cos(data['tmid']))\n",
"data = rec_append_fields(data, 'ymid', data['rmid'] * np.sin(data['tmid']))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Normalize the average correction to 0\n",
"data['ly'] /= data['ly'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Write the map to JSON\n",
"import textwrap\n",
"desc = textwrap.dedent(\"\"\"\n",
" The XENON100 S1 mean light yield map used in Xerawdp 0.4.5.\n",
" Extracted from data by Cecilia in 2011,\n",
" see xenon:xenon100:analysis:cecilia:s13dcorrectionmap.\n",
" Ripped from s1xyzmap-20111215.dat by Jelle, October 2015.\n",
" \"\"\"[1:])\n",
"import time\n",
"mapd = dict(name='XENON100 S1(x,y,z) relative light yield map',\n",
" description=desc,\n",
" coordinate_system=np.vstack((data['xmid'], data['ymid'], data['zmid'])).T.tolist(),\n",
" map=(data['ly']).tolist(),\n",
" irregular=True,\n",
" timestamp=time.time())\n",
"\n",
"import json\n",
"with open('s1_xyz_XENON100_xerawdp045.json', mode='w') as outfile:\n",
" json.dump(mapd, outfile)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3d Scatter"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda3\\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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6af+fwrjNlkkwqKGqalR4GTx4MHfe+XvWrFmDzWbjkEMOiTtS2NUcccTP6dGj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uQJ9u7d5rQ16dSpE8eOJbJp0wxEUUtkpIpHHx2NIAh/ZNQ2A/607jEYDM6qN927d6/Sr/z8fCZN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\nInPLer6Yv4AfP3qPo8ePU2W1EhUUxJzff6UsOw+/qHAcdjvW/GIS4pqxNf0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oCI2mJoPm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mvLXoToZogrt9MlSE2xM4jg1jVVUrSB8ez6ZRxmsxmlUtlgOzf07MOXi+Zx7FQe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HoVC4iJhGoyEmJuac7TocDoRG9jve4QEoqgsp3/4HVv9IZJk7iFUaGNdBw/qlC7hh+kNI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\n+qrNAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1554e9e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"%matplotlib inline\n",
"fig = plt.figure(figsize=(12, 12))\n",
"ax = fig.add_subplot(111, projection='3d')\n",
"sc = ax.scatter(data['xmid'], data['ymid'], data['zmid'],\n",
" c=data['ly'])\n",
"plt.colorbar(sc, label='Relative light yield')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we'll plot the map using pax, to verify everything went correctly. Note this picks up the version of the map we just placed in the current directory, rather than the versionin pax/data!"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mypax = core.Processor(config_names='XENON100', config_dict=dict(pax=dict(output='Dummy.DummyOutput')))\n",
"s1map = mypax.simulator.s1_light_yield_map"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda3\\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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6af+fwrjNlkkwqKGqalR4GTx4MHfe+XvWrFmDzWbjkEMOiTtS2NUcccTP6dGj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uQJ9u7d5rQ16dSpE8eOJbJp0wxEUUtkpIpHHx2NIAh/ZNQ2A/607jEYDM6qN927d6/Sr/z8fCZN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\nInPLer6Yv4AfP3qPo8ePU2W1EhUUxJzff6UsOw+/qHAcdjvW/GIS4pqxNf0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oCI2mJoPm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mvLXoToZogrt9MlSE2xM4jg1jVVUrSB8ez6ZRxmsxmlUtlgOzf07MOXi+Zx7FQe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HoVC4iJhGoyEmJuac7TocDoRG9jve4QEoqgsp3/4HVv9IZJk7iFUaGNdBw/qlC7hh+kNI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\n+qrNAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x146a7b70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from tqdm import tqdm\n",
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"%matplotlib inline\n",
"#%matplotlib qt\n",
"fig = plt.figure(figsize=(12, 12))\n",
"ax = fig.add_subplot(111, projection='3d')\n",
"points = []\n",
"values = []\n",
"for i in tqdm(range(len(data['xmid']))):\n",
" points.append((data[i]['xmid'], data[i]['ymid'], data[i]['zmid']))\n",
" values.append(s1map.get_value(*points[-1]))\n",
"points = np.array(points)\n",
"values = np.array(values)\n",
" \n",
"sc = ax.scatter(*points.T, c=values)\n",
"plt.colorbar(sc, label='Relative light yield')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Z-slices"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Z-slices of map (compare with gallery 5 in Cecilia's note)\n",
"points = {}\n",
"for z in np.unique(data['zmid']):\n",
" plt.figure(figsize=(12, 10))\n",
" for r in np.unique(data['rmid']): \n",
" this_data = data[(data['rmid'] == r) & (data['zmid'] == z)]\n",
" plt.errorbar(this_data['tmid'], this_data['ly'], yerr=this_data['errly'], \n",
" label='r=%s cm' % (r/10))\n",
" plt.xlabel('Theta (degrees)')\n",
" plt.ylabel('Light yield')\n",
" plt.legend()\n",
" plt.title('z = %s cm' % (z/10))\n",
" plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.4"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-3-clause |
hainm/scikit-xray-examples | demos/1_time_correlation/Multi_tau_one_time_correlation_example.ipynb | 1 | 466607 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# One Time Correlation Example for NIPA_GEL 250K¶"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
":0: FutureWarning: IPython widgets are experimental and may change in the future.\n"
]
}
],
"source": [
"import numpy as np\n",
"from matplotlib.ticker import MaxNLocator\n",
"from matplotlib.colors import LogNorm\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.patches as mp\n",
"%matplotlib notebook\n",
"\n",
"import skxray.core.correlation as corr\n",
"import skxray.core.roi as roi"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## One Time Correlation¶"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Multi-tau Scheme"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" fig.waiting = false;\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.send_message('closing', {});\n",
" fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# it would be great to have a link to what this multi-tau scheme is!\n",
"num_levels = 7\n",
"num_bufs = 8\n",
"\n",
"# load the data\n",
"img_stack = np.load(\"100_500_NIPA_GEL.npy\")\n",
"\n",
"# plot the first image to make sure the data loaded correctly\n",
"plt.imshow(img_stack[0])\n",
"plt.title(\"NIPA_GEL_250K\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Get the Reqiured ROI's \n",
"### Call skxray.diff_roi_choice.roi_rings_step"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# define the ROIs\n",
"roi_start = 65 # in pixels\n",
"roi_width = 9 # in pixels\n",
"roi_spacing = (5.0, 4.0)\n",
"x_center = 7. # in pixels\n",
"y_center = (129.) # in pixels\n",
"num_rings = 3\n",
"\n",
"# get the edges of the rings\n",
"edges = roi.ring_edges(roi_start, width=roi_width, \n",
" spacing=roi_spacing, num_rings=num_rings)\n",
"\n",
"# get the label array from the ring shaped 3 region of interests(ROI's)\n",
"labeled_roi_array = roi.rings(\n",
" edges, (y_center, x_center), img_stack.shape[1:])\n",
"\n",
"# extarct the ROI's lables and pixel indices corresponding to those labels\n",
"roi_indices, pixel_list = corr.extract_label_indices(labeled_roi_array)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Plot the Reqiured ROI's"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" fig.waiting = false;\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.send_message('closing', {});\n",
" fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def overlay_rois(ax, inds, pix_list, img_dim, image):\n",
" \"\"\"\n",
" This will plot the reqiured roi's on the image\n",
" \"\"\"\n",
" tt = np.zeros(img_dim).ravel() * np.nan\n",
" tt[pix_list] = inds\n",
"\n",
" im = ax.imshow(image, interpolation='none', norm=LogNorm())\n",
" im = ax.imshow(tt.reshape(*img_dim), cmap='Paired', \n",
" interpolation='nearest')\n",
" \n",
"roi_names = ['gray', 'orange', 'brown']\n",
"tt = np.zeros(img_stack.shape[1:]).ravel()\n",
"tt[pixel_list] = roi_indices\n",
"\n",
"fig, ax = plt.subplots()\n",
"plt.title(\"NIPA_GEL_250K\")\n",
"overlay_rois(ax, roi_indices, pixel_list, \n",
" img_stack.shape[1:], img_stack[0])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use the 1-time correlation function in scikit-xray"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# g2 one time correlation results for 3 ROI's\n",
"g2, lag_steps = corr.multi_tau_auto_corr(\n",
" num_levels, num_bufs, labeled_roi_array, img_stack)\n",
"# lag_staps are delays for multiple tau analysis\n",
"lag_time = 0.001\n",
"lag_step = lag_steps[:g2.shape[0]]\n",
"lags = lag_step*lag_time"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot the one time correlation functions"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
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" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
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" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
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" }\n",
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"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
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"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
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"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
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" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
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"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
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" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
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"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
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" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
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" }\n",
"\n",
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"\n",
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" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.send_message('closing', {});\n",
" fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(num_rings, sharex=True, figsize=(5,10))\n",
"axes[num_rings-1].set_xlabel(\"lags\")\n",
"for i, roi_color in zip(range(num_rings), roi_names):\n",
" axes[i].set_ylabel(\"g2\") \n",
" axes[i].semilogx(lags, g2[:, i], 'o', markerfacecolor=roi_color, markersize=10)\n",
" axes[i].set_ylim(bottom=1, top=np.max(g2[1:, i]))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import skxray"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'0.0.4+95.gb764d5c'"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"skxray.__version__"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-3-clause |
awsteiner/o2sclpy | doc/static/examples/nucmass.ipynb | 1 | 3096 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "d22847a1",
"metadata": {},
"outputs": [],
"source": [
"import o2sclpy\n",
"import numpy"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d8bfc0af",
"metadata": {},
"outputs": [],
"source": [
"link=o2sclpy.linker()\n",
"link.link_o2scl()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "4b2bf486",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hbarc = 1.973270e+02\n"
]
}
],
"source": [
"fc=o2sclpy.find_constants(link)\n",
"hc=fc.find_unique('hbarc','MeV*fm')\n",
"print('hbarc = %7.6e' % (hc))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "6932b5bc",
"metadata": {},
"outputs": [],
"source": [
"# Instantiate and load the Atomic Mass Evaluation\n",
"ame=o2sclpy.nucmass_ame(link)\n",
"o2sclpy.ame_load(link,ame,'16',False)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "3ecc90a3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of isotopes in the AME list: 3436\n"
]
}
],
"source": [
"# Print out the number of entries\n",
"print('Number of isotopes in the AME list:',ame.get_nentries())"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "9e0805fb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get lead-208\n",
"nuc=o2sclpy.nucleus(link)\n",
"ame.get_nucleus(82,126,nuc)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "b8bdd0ac",
"metadata": {
"lines_to_next_cell": 1
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Binding energy per nucleon in Pb-208 = -7.867459e+00 \n"
]
}
],
"source": [
"# Output the binding energy per nucleon in MeV\n",
"print('Binding energy per nucleon in Pb-208 = %7.6e ' % (nuc.be/208*hc))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "acfea247",
"metadata": {},
"outputs": [],
"source": [
"def test_fun():\n",
" assert numpy.allclose(nuc.be/208*hc,-7.867,rtol=1.0e-3)\n",
" return"
]
}
],
"metadata": {
"jupytext": {
"cell_metadata_filter": "-all",
"main_language": "python",
"notebook_metadata_filter": "-all"
},
"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": "3.10.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| gpl-3.0 |
mojolab/cowmesh | cowbots/.ipynb_checkpoints/Articles-checkpoint.ipynb | 1 | 1583 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from bs4 import BeautifulSoup\n",
"import urllib2,urlparse\n",
"import sys,os\n",
"sys.path.append(\"/opt/livingdata/lib\")\n",
"from livdatcsvlib import *\n",
"from cowbotfunctions import *\n",
"\n",
"\n",
"baseurl=\"http://environicsindia.in/index.php/en/?option=com_content&view=article&layout=&id=1481\"\n",
"baseurl2=\"http://environicsindia.in/index.php/en/?option=com_content&view=article&layout=&id=1322\"\n",
"gettextandlinks(baseurl)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
kit-cel/wt | qc/quantization/Uniform_Quantization_Sine.ipynb | 1 | 32356 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Illustration of Uniform Quantization\n",
"\n",
"This code is provided as supplementary material of the lecture Quellencodierung.\n",
"\n",
"This code illustrates\n",
"* Uniform scalar quantization with midrise characteristic"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import librosa\n",
"import librosa.display\n",
"import IPython.display as ipd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate artificial signal\n",
"$$\n",
"x[k] = \\sin\\left(2\\pi\\frac{2k}{f_s}\\right),\\qquad k = 0,\\ldots,f_s\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"sr = 22050 # sample rate\n",
"T = 1.0 # seconds\n",
"t = np.linspace(0, T, int(T*sr), endpoint=False) # time variable\n",
"x = np.sin(2*np.pi*2*t) # pure sine wave at 2 Hz"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Uniform Quantization"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Sample to 4 bit ... 16 quantization levels\n",
"w = 4\n",
"\n",
"# fix x_max based on the current signal, leave some tiny room\n",
"x_max = np.max(x) + 1e-10\n",
"Delta_x = x_max / (2**(w-1))\n",
"\n",
"xh_max = (2**w-1)*Delta_x/2\n",
"\n",
"# Quantize\n",
"xh_uniform_midrise = np.sign(x)*Delta_x*(np.floor(np.abs(x)/Delta_x)+0.5) "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x432 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"font = {'size' : 12}\n",
"plt.rc('font', **font)\n",
"plt.rc('text', usetex=True)\n",
"\n",
"plt.figure(figsize=(6, 6))\n",
"plt.subplot(3,1,1)\n",
"plt.plot(range(len(t)),x, c=(0,0.59,0.51))\n",
"plt.autoscale(enable=True, axis='x', tight=True)\n",
"#plt.title('Original')\n",
"plt.xlabel('Sample index $k$', fontsize=14)\n",
"plt.ylabel('$x[k]$', fontsize=14)\n",
"plt.ylim((-1.1,+1.1))\n",
"\n",
"plt.subplot(3,1,2)\n",
"plt.plot(range(len(t)),xh_uniform_midrise, c=(0,0.59,0.51))\n",
"plt.autoscale(enable=True, axis='x', tight=True)\n",
"#plt.title('Quantized')\n",
"plt.xlabel('Sample index $k$', fontsize=14)\n",
"plt.ylabel('$\\hat{x}[k]$', fontsize=14)\n",
"plt.ylim((-1.1,+1.1))\n",
"\n",
"plt.subplot(3,1,3)\n",
"plt.plot(range(len(t)),x-xh_uniform_midrise,c=(0,0.59,0.51))\n",
"plt.autoscale(enable=True, axis='x', tight=True)\n",
"#plt.title('Quantization error signal')\n",
"plt.xlabel('Sample index $k$', fontsize=14)\n",
"plt.ylabel('$e[k]$', fontsize=14)\n",
"plt.ylim((-1.1,+1.1))\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig('figure_DST_7.2c.pdf',bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| gpl-2.0 |
garth-wells/fenics-x3dom | DOLFIN_X3DOM.ipynb | 1 | 251997 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# X3DOM examples\n",
"\n",
"This notebook demonstrates experimental DOLFIN features (<http://fenicsproject.org>) for X3DOM (<http://www.x3dom.org/>) and HTML output. The objective is to support the interactive display of meshes and simulation outputs in web browsers, including in Jupyter notebooks.\n",
"\n",
"These examples use a development version of DOLFIN at <https://bitbucket.org/garth-wells/dolfin-coding-days>, and the branch `x3dom`."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from IPython.core.display import HTML\n",
"from dolfin import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create mesh in 2D\n",
"mesh = UnitSquareMesh(32, 32)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<!DOCTYPE html>\n",
"<html>\n",
" <head>\n",
" <meta http-equiv=\"content-type\" content=\"text/html;charset=UTF-8\" />\n",
" <meta name=\"generator\" content=\"FEniCS/DOLFIN (http://fenicsproject.org)\" />\n",
" <title>FEniCS/DOLFIN X3DOM plot</title>\n",
" <script type=\"text/javascript\" src=\"http://www.x3dom.org/download/x3dom.js\"></script>\n",
" <link rel=\"stylesheet\" type=\"text/css\" href=\"http://www.x3dom.org/download/x3dom.css\" />\n",
" </head>\n",
" <body>\n",
" <x3d showStat=\"false\" xmlns=\"http://www.web3d.org/specifications/x3d-namespace\" width=\"500.000000px\" height=\"400.000000px\">\n",
" <scene>\n",
" <shape>\n",
" <appearance>\n",
" <material diffuseColor=\"1.000000 1.000000 1.000000\" emissiveColor=\"0.000000 0.000000 0.000000\" specularColor=\"0.000000 0.000000 0.000000\" ambientIntensity=\"0\" shininess=\"0.5\" transparency=\"0\"></material>\n",
" </appearance>\n",
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" </indexedLineSet>\n",
" </shape>\n",
" <viewpoint id=\"default\" position=\"1.914214 1.914214 1.414214\" orientation=\"-0.7071067812 0.7071067812 0 1\" fieldOfView=\"0.785398\" centerOfRotation=\"0.500000 0.500000 0.000000\" zNear=\"-1\" zFar=\"-1\"></viewpoint>\n",
" <background skyColor=\"0.950000 0.950000 0.950000\"></background>\n",
" <directionalLight ambientIntensity=\"0\" intensity=\"1\"></directionalLight>\n",
" </scene>\n",
" </x3d>\n",
" <div style=\"position: absolute; margin: 1%; text-align: left; font-size: 12px; color: black;\">Number of vertices: 1089, number of cells: 2048</div>\n",
" </body>\n",
"</html>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Display mesh\n",
"HTML(X3DOM.html(mesh))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<!DOCTYPE html>\n",
"<html>\n",
" <head>\n",
" <meta http-equiv=\"content-type\" content=\"text/html;charset=UTF-8\" />\n",
" <meta name=\"generator\" content=\"FEniCS/DOLFIN (http://fenicsproject.org)\" />\n",
" <title>FEniCS/DOLFIN X3DOM plot</title>\n",
" <script type=\"text/javascript\" src=\"http://www.x3dom.org/download/x3dom.js\"></script>\n",
" <link rel=\"stylesheet\" type=\"text/css\" href=\"http://www.x3dom.org/download/x3dom.css\" />\n",
" </head>\n",
" <body>\n",
" <x3d showStat=\"false\" xmlns=\"http://www.web3d.org/specifications/x3d-namespace\" width=\"500.000000px\" height=\"400.000000px\">\n",
" <scene>\n",
" <shape>\n",
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" </appearance>\n",
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" </shape>\n",
" <viewpoint id=\"default\" position=\"2.267767 2.267767 2.267767\" orientation=\"-0.7071067812 0.7071067812 0 1\" fieldOfView=\"0.785398\" centerOfRotation=\"0.500000 0.500000 0.500000\" zNear=\"-1\" zFar=\"-1\"></viewpoint>\n",
" <background skyColor=\"0.950000 0.950000 0.950000\"></background>\n",
" <directionalLight ambientIntensity=\"0\" intensity=\"1\"></directionalLight>\n",
" </scene>\n",
" </x3d>\n",
" <div style=\"position: absolute; margin: 1%; text-align: left; font-size: 12px; color: black;\">Number of vertices: 4913, number of cells: 24576</div>\n",
" </body>\n",
"</html>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create mesh in 3D and display\n",
"mesh = UnitCubeMesh(16, 16, 16)\n",
"HTML(X3DOM.html(mesh))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create a contour plot\n",
"e = Expression(\"sin(x[0])*sin(x[1])\", degree=1)\n",
"V = FunctionSpace(mesh, \"Lagrange\", 1)\n",
"u = Function(V)\n",
"u.interpolate(e)\n",
"\n",
"HTML(X3DOM.html(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": "2.7.11+"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-2-clause |
wiheto/sentiment-timelines | notebooks/League_06_Leicester.ipynb | 1 | 108608 | {
"metadata": {
"language": "python",
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Sentiment analysis of match thread"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## To run the code\n",
"\n",
"Firstly, I write for Python 3.x. Python 2 may work but I don't consciously try and correct for any Python 2 differences. Apart from installing the necessary packages (pandas, numpy, matplotlib and so on). One additional thing is needed:\n",
"\n",
"1. Get a client_id / client_secret set up with PRAW / Reddit. In this code it is assumed that there is a file called: *praw.json* which contains client_id, client_secret, password, user_agent, and username.\n",
"\n",
"Tweaks will need to be made before the match events are fully automatic.\n",
"\n",
"Notebooks are run in the main directory of the repository (and just archived in the notebook folder). So paths will have to be modified if you run the notebook in the notebook folder"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Notes for this mathch.\n",
"\n",
"Premier league games will be automatic in getting match events. But Europe and cup games will probabaly be manually entering events."
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Import packages"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import praw\n",
"import datetime\n",
"import pandas as pd\n",
"import nltk.sentiment.vader\n",
"import matplotlib.pyplot as plt\n",
"from bs4 import BeautifulSoup\n",
"# from selenium import webdriver\n",
"import numpy as np\n",
"import os\n",
"from urllib.request import urlopen, urlretrieve"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Some parameters\n",
"\n",
"These need to be changed every match"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"url = 'https://www.premierleague.com/match/22396'\n",
"thread_id = '71z1i2'\n",
"opposition = 'Leicester'\n",
"competition = 'League'\n",
"hometeam = 'Leicester'\n",
"\n",
"#analysis_name = 'League_1_' + opposition\n",
"analysis_name = competition + '_06_' + opposition\n",
"\n",
"# Hopefully this can be fixed somehow without having to specify it\n",
"kickoff = datetime.time(17,30)\n",
"firsthalfend = datetime.time(18,19)\n",
"secondhalfbegin = datetime.time(18,34)\n",
"secondhalfend = datetime.time(19,23)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### More parameters\n",
"\n",
"These parameters and definitions that don't need to change each game"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Define some objects to be used later\n",
"# set up driver for scraping\n",
"# driver = webdriver.PhantomJS()\n",
"# Define NLTK object\n",
"vader = nltk.sentiment.vader.SentimentIntensityAnalyzer()\n",
"# set matplotlib style\n",
"plt.style.use('ggplot')\n",
"# Change this to 0 if you have downloaded the data and want to redownload\n",
"use_saved_data = 1"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Function definitions\n",
"\n",
"Funcitons that do most of the work"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Functions for match events"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def get_match_report(url,kickoff,secondhalfbegin):\n",
" \"\"\"\n",
" Function gets all times and titles of telegraph match report\n",
"\n",
" \"\"\"\n",
" #Open page and make a soup object\n",
" #driver.get(url)\n",
" #r = driver.page_source\n",
" r = urlopen(url).read()\n",
" soup = BeautifulSoup(r, 'lxml')\n",
" # This gets the titles and timeing of match events\n",
" home_events_html = soup.findAll('div', class_='event home')\n",
" away_events_html = soup.findAll('div', class_='event away')\n",
" home_events = parse_match_events_html(home_events_html,kickoff,secondhalfbegin)\n",
" away_events = parse_match_events_html(away_events_html,kickoff,secondhalfbegin)\n",
" return home_events,away_events\n",
"\n",
"def parse_match_events_html(events_html,kickoff,secondhalfbegin):\n",
" event_list = []\n",
" for e in events_html:\n",
" time = e.find('time').text\n",
" evtype = e.find('time').nextSibling.lower()\n",
" evtype = evtype.replace(' ','')\n",
" evtype = evtype.replace('\\n','')\n",
" time = time.replace(' ','')\n",
" time = time.replace(\"'\",'')\n",
" # Adds time together (e.g. 90+2) if it exists\n",
" time_split = time.split('+')\n",
" time = np.sum(list(map(int,time_split)))\n",
"\n",
" if (len(time_split)>1 and time < 90) or time < 45:\n",
" time_real = add_times(kickoff,time)\n",
" else:\n",
" time_real = add_times(secondhalfbegin,time-45)\n",
"\n",
" event_list.append([time_real,evtype])\n",
" return event_list\n",
"\n",
"\n",
"def parse_match_events(liverpool_events,opposition_events):\n",
"\n",
" match_events = {}\n",
" # Preallocate\n",
" match_events['liverpool_goal'] = []\n",
" match_events['opponenet_goal'] = []\n",
" #match_events['liverpool_dis_goal'] = []\n",
" #match_events['opponent_dis_goal'] = []\n",
" match_events['liverpool_yellowcard'] = []\n",
" match_events['opponenet_yellowcard'] = []\n",
" match_events['liverpool_redcard'] = []\n",
" match_events['opponenet_redcard'] = []\n",
" match_events['liverpool_substitution'] = []\n",
" match_events['opponenet_substitution'] = []\n",
"\n",
" for e in liverpool_events:\n",
" match_events['liverpool_' + e[1]] += [e[0]]\n",
" for e in opposition_events:\n",
" match_events['opponenet_' + e[1]] += [e[0]]\n",
"\n",
" return match_events"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "heading",
"level": 3,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Get comment data and sentiment funcitons"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def get_comments(thread_id,praw_info):\n",
" reddit = praw.Reddit(client_id=praw_info['client_id'][0],\n",
" client_secret=praw_info['client_secret'][0],\n",
" password=praw_info['password'][0],\n",
" user_agent=praw_info['user_agent'][0],\n",
" username=praw_info['username'][0])\n",
" submission = reddit.submission(id=thread_id)\n",
" submission.comments.replace_more(limit=None, threshold = 0)\n",
" return submission\n",
"\n",
"def comment_time_and_sentiment(submission):\n",
" time = []\n",
" sentiment = []\n",
" score = []\n",
" # Loop through top comments and add to time and sentiment list\n",
" for top_level_comment in submission.comments:\n",
" time.append((datetime.datetime.fromtimestamp(top_level_comment.created_utc) - datetime.timedelta(hours=1)))\n",
" sentiment.append(vader.polarity_scores(top_level_comment.body)['compound'])\n",
" score.append(top_level_comment.score)\n",
" # Make time format\n",
" pd_time = pd.to_datetime(time)\n",
" # Make to dateframe\n",
" df = pd.DataFrame(data={'sentiment': sentiment,'score':score}, index = pd_time)\n",
" return df\n",
"\n",
"def posneg_sentiment_difference(df,bins='1min'):\n",
" # Find comments with positive > 0 and negative < 0 sentiment\n",
" pdf = df[df['sentiment'] > 0]\n",
" ndf = df[df['sentiment'] < 0]\n",
"\n",
" # Bin\n",
" pgdf = pdf.groupby(pd.TimeGrouper(freq=bins)).count()\n",
" ngdf = ndf.groupby(pd.TimeGrouper(freq=bins)).count()\n",
" diff_df = (pgdf['sentiment']-ngdf['sentiment']).dropna()\n",
" return diff_df\n",
"\n",
"\n",
"def weighted_posneg_sentiment_difference(df,bins='1min'):\n",
" # Find comments with positive > 0 and negative < 0 sentiment\n",
" df = pd.DataFrame(df[df['score']>0])\n",
" pdf = df[df['sentiment'] > 0]\n",
" ndf = df[df['sentiment'] < 0]\n",
" # Bin\n",
" pgdf = pdf.groupby(pd.TimeGrouper(freq=bins)).count()\n",
" ngdf = ndf.groupby(pd.TimeGrouper(freq=bins)).count()\n",
" # Take the difference\n",
" diff_df = (pgdf['sentiment']*pgdf['score']-ngdf['sentiment']*ngdf['score']).dropna()\n",
" return diff_df"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "heading",
"level": 3,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Plotting and misc functions"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def add_times(base_time, match_time):\n",
" time = datetime.datetime.combine(datetime.date.today(),base_time)+datetime.timedelta(minutes=int(match_time))\n",
" return time.time()\n",
"\n",
"def plot_sentiment_figure(df,match_events,opposition):\n",
"\n",
" fig = plt.figure(figsize=(6,8))\n",
" ax = plt.subplot2grid((7, 1), (1, 0), rowspan=6)\n",
" ax_me = plt.subplot2grid((7, 1), (0, 0),sharex=ax)\n",
" # Main line\n",
" ax.plot(df.index.time,df,linewidth=2,color='firebrick')\n",
" # Scale y axis (make even -/+ directions)\n",
" ax.set_ylim([-np.max(np.abs(ax.get_ylim())),np.max(np.abs(ax.get_ylim()))])\n",
" # Make axis ticks and labels correct\n",
" start_xaxis=datetime.datetime.combine(datetime.date.today(),match_events['kickoff'])-datetime.timedelta(minutes=10)\n",
" end_xaxis=datetime.datetime.combine(datetime.date.today(),match_events['secondhalfend'])+datetime.timedelta(minutes=10)\n",
" ax.set_xticks([match_events['kickoff'].hour*3600+m*60 for m in range(0,180,30)])\n",
" ax.set_xlim([start_xaxis.time(),end_xaxis.time()])\n",
" ax.set_xlabel('Time (GMT/BST)')\n",
" # Get y axis lims to place events\n",
" scatter_y_min, scatter_y_max = ax.get_ylim()\n",
" # Define first and second half\n",
" ax.fill_between([match_events['kickoff'],match_events['firsthalfend']],scatter_y_min,scatter_y_max+np.abs(scatter_y_max*0.05),facecolor='dimgray',alpha=0.25,zorder=0)\n",
" ax.fill_between([match_events['secondhalfbegin'],match_events['secondhalfend']],scatter_y_min,scatter_y_max+np.abs(scatter_y_max*0.05),facecolor='dimgray',alpha=0.25,zorder=0)\n",
" ax.text(datetime.time(match_events['kickoff'].hour,match_events['kickoff'].minute+3),scatter_y_min+np.abs(scatter_y_min*0.05),'First Half')\n",
" ax.text(datetime.time(match_events['secondhalfbegin'].hour,match_events['secondhalfbegin'].minute+3),scatter_y_min+np.abs(scatter_y_min*0.05),'Second Half')\n",
"\n",
" # MATCH EVENTS (BELOW HERE) MIGHT HAVE TO CHANGE\n",
" # Place match events\n",
" axlabs = []\n",
" if match_events['liverpool_goal']:\n",
" axlabs += [ax_me.scatter(match_events['liverpool_goal'],np.tile(2,len(match_events['liverpool_goal'])),color='black',s=50,label='goal')]\n",
" if match_events['opponenet_goal']:\n",
" axlabs += [ax_me.scatter(match_events['opponenet_goal'],np.tile(1,len(match_events['opponenet_goal'])),color='black',s=50,label='goal')]\n",
" if match_events['liverpool_dis_goal']:\n",
" ax_me.scatter(match_events['liverpool_dis_goal'],np.tile(2,len(match_events['liverpool_dis_goal'])),color='black',s=50)\n",
" ax_me.scatter(match_events['liverpool_dis_goal'],np.tile(2,len(match_events['liverpool_dis_goal'])),marker='x',color='red',s=40)\n",
" if match_events['opponent_dis_goal']:\n",
" ax_me.scatter(match_events['opponent_dis_goal'],np.tile(1,len(match_events['opponent_dis_goal'])),color='black',s=50)\n",
" ax_me.scatter(match_events['opponent_dis_goal'],np.tile(1,len(match_events['opponent_dis_goal'])),marker='x',color='red',s=40)\n",
" if match_events['liverpool_yellowcard']:\n",
" axlabs += [ax_me.scatter(match_events['liverpool_yellowcard'],np.tile(2,len(match_events['liverpool_yellowcard'])),marker='s',color='y',s=40,label='yellow')]\n",
" if match_events['opponenet_yellowcard']:\n",
" axlabs += [ax_me.scatter(match_events['opponenet_yellowcard'],np.tile(1,len(match_events['opponenet_yellowcard'])),marker='s',color='y',s=40,label='yellow')]\n",
" if match_events['liverpool_redcard']:\n",
" axlabs += [ax_me.scatter(match_events['liverpool_redcard'],np.tile(2,len(match_events['liverpool_redcard'])),marker='s',color='r',s=40,label='red')]\n",
" if match_events['opponenet_redcard']:\n",
" axlabs += [ax_me.scatter(match_events['opponenet_redcard'],np.tile(1,len(match_events['opponenet_redcard'])),marker='s',color='r',s=40,label='red')]\n",
" if match_events['liverpool_substitution']:\n",
" axlabs += [ax_me.scatter(match_events['liverpool_substitution'],np.tile(2,len(match_events['liverpool_substitution'])),marker='s',color='g',s=10,label='sub')]\n",
" if match_events['opponenet_substitution']:\n",
" axlabs += [ax_me.scatter(match_events['opponenet_substitution'],np.tile(1,len(match_events['opponenet_substitution'])),marker='s',color='g',s=10,label='sub')]\n",
"\n",
" # Filter out any duplicate labels\n",
" l = []\n",
" lax = []\n",
" for n in axlabs:\n",
" lt = n.get_label()\n",
" if lt not in l:\n",
" l += [lt]\n",
" lax.append(n)\n",
" fig.legend(lax,l,ncol=len(lax),loc=9,fontsize='small')\n",
"\n",
" ax_me.set_ylim(0.5,2.5)\n",
" ax_me.set_yticks([1,2])\n",
" ax_me.set_yticklabels([opposition,'Liverpool'])\n",
" ax_me.set_xlabel('')\n",
" plt.setp(ax_me.get_xticklabels(), visible=False)\n",
"\n",
" return fig,ax,ax_me"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Get comments and sentiment score\n",
"\n",
"If the data already exists, it loads that (remember to run in main directory, not notebook directory)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# If data doesn't exist, download it. If data exists, load it.\n",
"if use_saved_data == 1 and os.path.exists('./data/' + analysis_name + '.csv'):\n",
" df = pd.read_csv('./data/' + analysis_name + '.csv', index_col=0, parse_dates=[0])\n",
"else:\n",
" # read in reddit api info\n",
" praw_info = pd.read_json('praw.json')\n",
" # do the sentiment analysis\n",
" submission = get_comments(thread_id,praw_info)\n",
" df = comment_time_and_sentiment(submission)\n",
" df.to_csv('./data/' + analysis_name + '.csv')\n",
" # Delete reddit api info\n",
" praw_info = {}"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "heading",
"level": 3,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Get match report"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"if hometeam.lower() == 'liverpool':\n",
" liverpool_events,opposition_events = get_match_report(url,kickoff,secondhalfbegin)\n",
"else:\n",
" opposition_events,liverpool_events = get_match_report(url,kickoff,secondhalfbegin)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "heading",
"level": 3,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Parse match report"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"match_events = {}\n",
"\n",
"# Manual entry\n",
"\n",
"\n",
"#match_events['liverpool_goal'] = []\n",
"#match_events['opponenet_goal'] = [datetime.time(21,9),datetime.time(21,22)]\n",
"#match_events['liverpool_yellowcard'] = [datetime.time(21,0),datetime.time(21,18)]\n",
"#match_events['opponenet_yellowcard'] = [datetime.time(21,34)]\n",
"#match_events['liverpool_redcard'] = []\n",
"#match_events['opponenet_redcard'] = []\n",
"#match_events['liverpool_substitution'] = [datetime.time(20,48),datetime.time(21,19)]\n",
"#match_events['opponenet_substitution'] = [datetime.time(20,54),datetime.time(21,29)]\n",
"\n",
"\n",
"match_events = parse_match_events(liverpool_events,opposition_events)\n",
"\n",
"match_events['liverpool_dis_goal'] = []\n",
"match_events['opponent_dis_goal'] = [datetime.time(19,3)]\n",
"\n",
"# match_events = parse_match_events(liverpool_events,opposition_events)\n",
"match_events['kickoff'] = kickoff\n",
"match_events['firsthalfend'] = firsthalfend\n",
"match_events['secondhalfbegin'] = secondhalfbegin\n",
"match_events['secondhalfend'] = secondhalfend"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Get positive/negative difference\n",
"\n",
"Sort into number of positive and number of negative comments"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"posneg_df = posneg_sentiment_difference(df,bins='2min')\n",
"weighted_posneg_df = weighted_posneg_sentiment_difference(df,bins='1min')"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "heading",
"level": 3,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Plot figure (unweighted)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Plot weighted figure\n",
"fig,ax,axtop = plot_sentiment_figure(posneg_df,match_events,opposition)\n",
"ax.set_ylabel('# Pos - Neg Comments')\n",
"fig.tight_layout()\n",
"# Save\n",
"fig.savefig('./figures/' + analysis_name + '.png',dpi=300)\n",
"fig.savefig('./figures/' + analysis_name + '.pdf',dpi=300)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"output_type": "display_data",
"png": 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Pjh/v9G+q5nsgp6AC1J49e3DixAn59+bmZlgsljav2bt3L+655x4AgE6nQ2xsLL788ksU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\nMRGpQbz3mJiYiD9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AIObyy2GaNAkAYN29228tR5IkxRl8boIgyLUtNvN1n9ZvvpH/H8h5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\nIiISEgMUEREJiQGKiIiE9P9TA/vJ+1+yuwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7fa659034240>"
]
}
],
"prompt_number": 11
},
{
"cell_type": "heading",
"level": 3,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Plot figure (weighted)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Plot weighted figure\n",
"fig,ax,axtop = plot_sentiment_figure(weighted_posneg_df,match_events,opposition)\n",
"ax.set_ylabel('# Pos - Neg Comments (weighted by upvotes)')\n",
"fig.tight_layout()\n",
"# Save\n",
"fig.savefig('./figures/weighted_' + analysis_name + '.png',dpi=300)\n",
"fig.savefig('./figures/weighted_' + analysis_name + '.pdf',dpi=300)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"output_type": "display_data",
"png": 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gAQDz58/niSee8EtAQgghBHhYJPHEE09gt9s5ePAgZrOZqKgoLr/8cvesEkII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\nCCGELkmAEkIIoUv/H0TN6ZbuzK81AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7fa6597cfe48>"
]
}
],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Number of comments\n",
"\n",
"# Get number of comments per minute\n",
"commentnumber = df.groupby(pd.TimeGrouper(freq='1min')).count()\n",
"# Plot weighted figure\n",
"fig,ax,axtop = plot_sentiment_figure(commentnumber,match_events,opposition)\n",
"ax.set_ylabel('Comments per minute')\n",
"ax.set_ylim([0,ax.get_ylim()[1]])\n",
"fig.tight_layout()\n",
"# Save\n",
"fig.savefig('./figures/numberofcomments_' + analysis_name + '.png',dpi=300)\n",
"fig.savefig('./figures/numberofcomments_' + analysis_name + '.pdf',dpi=300)"
]
}
],
"metadata": {}
}
]
}
| mit |
vnpy/vnpy | examples/cta_backtesting/backtesting_demo.ipynb | 1 | 2421 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#%%\n",
"from vnpy.trader.optimize import OptimizationSetting\n",
"from vnpy_ctastrategy.backtesting import BacktestingEngine\n",
"from vnpy_ctastrategy.strategies.atr_rsi_strategy import (\n",
" AtrRsiStrategy,\n",
")\n",
"from datetime import datetime"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#%%\n",
"engine = BacktestingEngine()\n",
"engine.set_parameters(\n",
" vt_symbol=\"IF888.CFFEX\",\n",
" interval=\"1m\",\n",
" start=datetime(2019, 1, 1),\n",
" end=datetime(2019, 4, 30),\n",
" rate=0.3/10000,\n",
" slippage=0.2,\n",
" size=300,\n",
" pricetick=0.2,\n",
" capital=1_000_000,\n",
")\n",
"engine.add_strategy(AtrRsiStrategy, {})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"#%%\n",
"engine.load_data()\n",
"engine.run_backtesting()\n",
"df = engine.calculate_result()\n",
"engine.calculate_statistics()\n",
"engine.show_chart()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"setting = OptimizationSetting()\n",
"setting.set_target(\"sharpe_ratio\")\n",
"setting.add_parameter(\"atr_length\", 25, 27, 1)\n",
"setting.add_parameter(\"atr_ma_length\", 10, 30, 10)\n",
"\n",
"engine.run_ga_optimization(setting)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"engine.run_bf_optimization(setting)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
ledell/useR-machine-learning-tutorial | generalized-linear-models.ipynb | 1 | 218499 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Generalized Linear Models\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* * *\n",
"![Alt text](./images/linear_regression.png \"Linear Regression Model\")\n",
"* * *\n",
"Image Source: Wikipedia"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n",
"[Linear Models](https://en.wikipedia.org/wiki/Linear_regression) are one of the oldest and most well known statistical prediction algorithms which nowadays is often categorized as a \"machine learning algorithm.\" [Generalized Linear Models](https://en.wikipedia.org/wiki/Generalized_linear_model) (GLMs) are are a framework for modeling a response variable $y$ that is bounded or discrete. Generalized linear models allow for an arbitrary link function $g$ that relates the mean of the response variable to the predictors, i.e. $E(y) = g(β′x)$. The link function is often related to the distribution of the response, and in particular it typically has the effect of transforming between, $(-\\infty ,\\infty )$, the range of the linear predictor, and the range of the response variable (e.g. $[0,1]$). [1]\n",
"\n",
"Therefore, GLMs allow for response variables that have error distribution models other than a normal distribution. Some common examples of GLMs are:\n",
"- [Poisson regression](https://en.wikipedia.org/wiki/Poisson_regression) for count data.\n",
"- [Logistic regression](https://en.wikipedia.org/wiki/Logistic_regression) and [probit regression](https://en.wikipedia.org/wiki/Probit_regression) for binary data.\n",
"- [Multinomial logistic regression](https://en.wikipedia.org/wiki/Multinomial_logistic_regression) and [multinomial probit](https://en.wikipedia.org/wiki/Multinomial_probit) regression for categorical data.\n",
"- [Ordered probit](https://en.wikipedia.org/wiki/Ordered_probit) regression for ordinal data.\n",
"\n",
"\n",
"\n",
"## Linear Models\n",
"\n",
"In a linear model, given a vector of inputs, $X^T = (X_1, X_2, ..., X_p)$, we predict the output $Y$ via the model:\n",
"\n",
"$$\\hat{Y} = \\hat{\\beta}_0 + \\sum_{j=1}^p X_j \\hat{\\beta}_j$$\n",
"\n",
"The term $\\hat{\\beta}_0$ is the intercept, also known as the *bias* in machine learning. Often it is convenient to include the constant variable $1$ in $X$, include $\\beta_0$ in the vector of coefficients $\\hat{\\beta}$, and then write the linear model in vector form as an inner product, \n",
"\n",
"$$\\hat{Y} = X^T\\hat{\\beta},$$\n",
"\n",
"where $X^T$ denotes the transpose of the design matrix. We will review the case where $Y$ is a scalar, however, in general $Y$ can have more than one dimension. Viewed as a function over the $p$-dimensional input space, $f(X) = X^T\\beta$ is linear, and the [gradient](https://en.wikipedia.org/wiki/Gradient), $f′(X) = \\beta$, is a vector in input space that points in the steepest uphill direction.\n",
"\n",
"### Ordinary Least Squares (OLS)\n",
"\n",
"There are many different methods to fitting a linear model, but the most simple and popular method is [Ordinary Least Squares](https://en.wikipedia.org/wiki/Ordinary_least_squares) (OLS). The OLS method minimizes the [residual sum of squares](https://en.wikipedia.org/wiki/Residual_sum_of_squares) (RSS), and leads to a closed-form expression for the estimated value of the unknown parameter $\\beta$.\n",
"\n",
"$$RSS(\\beta) = \\sum_{i=1}^n (y_i - x_i^T\\beta)^2$$\n",
"\n",
"$RSS(\\beta)$ is a quadratic function of the parameters, and hence its minimum always exists, but may not be unique. The solution is easiest to characterize in matrix notation:\n",
"\n",
"$$RSS(\\beta) = (\\boldsymbol{y} - \\boldsymbol{X}\\beta)^T(\\boldsymbol{y} - \\boldsymbol{X}\\beta)$$\n",
"\n",
"where $\\boldsymbol{X}$ is an $n \\times p$ matrix with each row an input vector, and $\\boldsymbol{y}$ is a vector of length $n$ representing the response in the training set. Differentiating with respect to $\\beta$, we get the *normal equations*, \n",
"\n",
"$$\\boldsymbol{X}^T(\\boldsymbol{y} - \\boldsymbol{X}\\beta) = 0$$\n",
"\n",
"If $\\boldsymbol{X}^T\\boldsymbol{X}$ is [nonsingular](https://en.wikipedia.org/wiki/Invertible_matrix), then the unique solution is given by:\n",
"\n",
"$$\\hat{\\beta} = (\\boldsymbol{X}^T\\boldsymbol{X})^{-1}\\boldsymbol{X}^T\\boldsymbol{y}$$\n",
"\n",
"The fitted value at the $i^{th}$ input, $x_i$ is $\\hat{y}_i = \\hat{y}(x_i) = x_i^T\\hat{\\beta}$. To solve this equation for $\\beta$, we must invert a matrix, $\\boldsymbol{X}^T\\boldsymbol{X}$, however it can be computationally expensive to invert this matrix directly. There are computational shortcuts for solving the normal equations available via [QR](https://en.wikipedia.org/wiki/QR_decomposition) or [Cholesky](https://en.wikipedia.org/wiki/Cholesky_decomposition) decomposition. When dealing with large training sets, it is useful to have an understanding of the underlying computational methods in the software that you are using. Some GLM software implementations may not utilize all available computational shortcuts, costing you extra time to train your GLMs, or require you to upgrade the memory on your machine."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Regularization\n",
"\n",
"http://web.stanford.edu/~hastie/Papers/glmpath.pdf\n",
"\n",
"\n",
"### Ridge Regression\n",
"\n",
"\n",
"Consider a sample consisting of $n$ cases, each of which consists of $p$ covariates and a single outcome. Let $y_i$ be the outcome and $X_i := ( x_1 , x_2 , … , x_p)^T$. \n",
"\n",
"Then the objective of Ridge is to solve:\n",
"\n",
"$${\\displaystyle \\min _{\\beta }\\left\\{{\\frac {1}{N}}\\sum _{i=1}^{N}\\left(y_{i}-\\beta_0 - \\sum_{j=1}^p x_{ij}\\beta_j \\right)^{2}\\right\\}{\\text{ subject to }}\\sum _{j=1}^{p}\\beta _{j}^2 \\leq t.}$$\n",
"\n",
"\n",
"Here $t$ is a prespecified free parameter that determines the amount of regularization. Ridge is also called $\\ell_2$ regularization.\n",
"\n",
"### Lasso Regression\n",
"\n",
"[Lasso](https://en.wikipedia.org/wiki/Lasso_(statistics) (least absolute shrinkage and selection operator) (also Lasso or LASSO) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces. \n",
"\n",
"- It was [introduced by Robert Tibshirani in 1996](http://www-stat.stanford.edu/%7Etibs/lasso/lasso.pdf) based on Leo Breiman’s Nonnegative Garrote.\n",
"- Lasso conveniently performs coefficient shrinkage comparable to the ridge regression as well as variable selection by reducing coefficients to zero. \n",
"- By sacrificing a small amount of bias in the predicted response variable in order to decrease variance, the lasso achieves improved predictive accuracy compared with ordinary least squares (OLS) models, particularly with data containing highly correlated predictor variables or in over determined data where $p>n$.\n",
"\n",
"Then the objective of Lasso is to solve:\n",
"\n",
"$${\\displaystyle \\min _{\\beta }\\left\\{{\\frac {1}{N}}\\sum _{i=1}^{N}\\left(y_{i}-\\beta_0 - \\sum_{j=1}^p x_{ij}\\beta_j \\right)^{2}\\right\\}{\\text{ subject to }}\\sum _{j=1}^{p}|\\beta _{j}| \\leq t.}$$\n",
"\n",
"Here $t$ is a prespecified free parameter that determines the amount of regularization.\n",
"Lasso is also called $\\ell_1$ regularization.\n",
"\n",
"\n",
"### Elastic Net\n",
"\n",
"[Elastic Net regularization](https://en.wikipedia.org/wiki/Elastic_net_regularization) is a simple blend of Lasso and Ridge regularization. In software, this is typically controlled by an `alpha` parameter in between 0 and 1, where:\n",
"- `alpha = 0.0` is Ridge regression\n",
"- `alpha = 0.5` is a 50/50 blend of Ridge/Lasso regression\n",
"- `alpha = 1.0` is Lasso regression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Other Solvers\n",
"\n",
"GLM models are trained by finding the set of parameters that maximizes the likelihood of the data. For the Gaussian family, maximum likelihood consists of minimizing the mean squared error. This has an analytical solution and can be\n",
"solved with a standard method of least squares. This is also applicable when the $\\ell_2$ penalty is added to the optimization. For all other families and when the $\\ell_1$ penalty is included, the maximum likelihood\n",
"problem has no analytical solution. Therefore an iterative method such as IRLSM, L-BFGS, the Newton method, or gradient descent, must be used.\n",
"\n",
"\n",
"### Iteratively Re-weighted Least Squares (IRLS)\n",
"\n",
"The [IRLS](https://en.wikipedia.org/wiki/Iteratively_reweighted_least_squares) method is used to solve certain optimization problems with objective functions of the form:\n",
"\n",
"$${\\underset {{\\boldsymbol \\beta }}{\\operatorname {arg\\,min}}}\\sum _{{i=1}}^{n}{\\big |}y_{i}-f_{i}({\\boldsymbol \\beta }){\\big |}^{p},$$\n",
"\n",
"by an iterative method in which each step involves solving a weighted least squares problem of the form:\n",
"\n",
"$${\\boldsymbol \\beta }^{{(t+1)}}={\\underset {{\\boldsymbol \\beta }}{\\operatorname {arg\\,min}}}\\sum _{{i=1}}^{n}w_{i}({\\boldsymbol \\beta }^{{(t)}}){\\big |}y_{i}-f_{i}({\\boldsymbol \\beta }){\\big |}^{2}.$$\n",
"\n",
"IRLS is used to find the [maximum likelihood](https://en.wikipedia.org/wiki/Maximum_likelihood) estimates of a generalized linear model as a way of mitigating the influence of outliers in an otherwise normally-distributed data set. For example, by minimizing the least absolute error rather than the least square error.\n",
"\n",
"One of the advantages of IRLS over [linear programming](https://en.wikipedia.org/wiki/Linear_programming) and [convex programming](https://en.wikipedia.org/wiki/Convex_programming) is that it can be used with [Gauss-Newton](https://en.wikipedia.org/wiki/Gauss%E2%80%93Newton) and [Levenberg-Marquardt](https://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt) numerical algorithms.\n",
"\n",
"The IRL1 algorithm solves a sequence of non-smooth weighted $\\ell_1$-minimization problems, and hence can be seen as the non-smooth counterpart to the IRLS algorithm. \n",
"\n",
"\n",
"### Iteratively Re-weighted Least Squares with ADMM\n",
"\n",
"The IRLS method with [alternating direction method of multipliers](http://web.stanford.edu/~boyd/admm.html) (ADMM) inner solver as described in [Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers](http://web.stanford.edu/~boyd/papers/admm_distr_stats.html) by Boyd et. al to deal with the $\\ell_1$ penalty. ADMM is an algorithm that solves convex optimization problems by breaking them into smaller pieces, each of which are then easier to handle. Every iteration of the algorithm consists of following steps:\n",
"\n",
"1. Generate weighted least squares problem based on previous solution, i.e. vector of weights w and response z.\n",
"2. Compute the weighted [Gram matrix](https://en.wikipedia.org/wiki/Gramian_matrix) XT WX and XT z vector\n",
"3. Decompose the Gram matrix ([Cholesky decomposition](https://en.wikipedia.org/wiki/Cholesky_decomposition)) and apply ADMM solver to solve the $\\ell_1$ penalized least squares problem.\n",
"\n",
"In the [H2O GLM](http://docs.h2o.ai/h2o/latest-stable/h2o-docs/booklets/GLMBooklet.pdf) implementation, steps 1 and 2 are performed distributively, and Step 3 is computed in parallel on a single node. The Gram matrix approach is very efficient for tall and narrow datasets when running lambda search with a sparse solution. \n",
"\n",
"\n",
"### Cyclical Coordinate Descent\n",
"\n",
"The IRLS method can also use cyclical coordinate descent in its inner loop (as opposed to ADMM). The [glmnet](http://web.stanford.edu/~hastie/glmnet/glmnet_beta.html) package uses [cyclical coordinate descent](http://web.stanford.edu/~hastie/Papers/glmnet.pdf) which successively optimizes the objective function over each parameter with others fixed, and cycles repeatedly until convergence.\n",
"\n",
"Cyclical coordinate descent methods are a natural approach for solving\n",
"convex problems with $\\ell_1$ or $\\ell_2$ constraints, or mixtures of the two (elastic net). Each coordinate-descent step is fast, with an explicit formula for each coordinate-wise minimization. The method also exploits the sparsity of the model, spending much of its time evaluating only inner products for variables with non-zero coefficients.\n",
"\n",
"\n",
"### L-BFGS \n",
"\n",
"[Limited-memory BFGS](https://en.wikipedia.org/wiki/Limited-memory_BFGS) (L-BFGS) is an optimization algorithm in the family of [quasi-Newton methods](https://en.wikipedia.org/wiki/Quasi-Newton_method) that approximates the [Broyden–Fletcher–Goldfarb–Shanno](https://en.wikipedia.org/wiki/BFGS_method) (BFGS) algorithm using a limited amount of computer memory. Due to its resulting linear memory requirement, the L-BFGS method is particularly well suited for optimization problems with a large number of variables. The method is popular among \"big data\" GLM implementations such as [h2o::h2o.glm()](http://www.rdocumentation.org/packages/h2o/functions/h2o.glm) (one of two available solvers) and [SparkR::glm()](https://spark.apache.org/docs/latest/api/R/index.html). The [L-BFGS-B algorithm](http://sepwww.stanford.edu/data/media/public/docs/sep117/antoine1/paper_html/node12.html#lbfgsb) is an extension of the L-BFGS algorithm to handle simple bounds on the model. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Preprocessing\n",
"\n",
"In order for the coefficients to be easily interpretable, the features must be centered and scaled (aka \"normalized\"). Many software packages will allow the direct input of categorical/factor columns in the training frame, however internally any categorical columns will be expanded into binary indicator variables. The caret package offers a handy utility function, [caret::dummyVars()](http://www.rdocumentation.org/packages/caret/functions/dummyVars), for dummy/indicator expansion if you need to do this manually.\n",
"\n",
"Missing data will need to be imputed, otherwise in many GLM packages, those rows will simply be omitted from the training set at train time. For example, in the `stats::glm()` function there is an `na.action` argument which allows the user to do one of the three options:\n",
"\n",
"- na.omit and na.exclude: observations are removed if they contain any missing values; if na.exclude is used some functions will pad residuals and predictions to the correct length by inserting NAs for omitted cases.\n",
"- na.pass: keep all data, including NAs\n",
"- na.fail: returns the object only if it contains no missing values\n",
"\n",
"Other GLM implementations such as `h2o::glm()` will impute the mean automatically (in both training and test data), unless specified by the user."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# GLM Software in R\n",
"\n",
"There is an implementation of the standard GLM (no regularization) in the built-in \"stats\" package in R called [glm](http://www.rdocumentation.org/packages/stats/functions/glm).\n",
"\n",
"## glm\n",
"\n",
"Authors: The original R implementation of glm was written by Simon Davies working for Ross Ihaka at the University of Auckland, but has since been extensively re-written by members of the R Core team. The design was inspired by the S function of the same name described in Hastie & Pregibon (1992).\n",
"\n",
"Backend: Fortran\n",
"\n",
"### Example Linear Regression with glm()"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading required package: lattice\n",
"Loading required package: ggplot2\n"
]
}
],
"source": [
"#install.packages(\"caret\")\n",
"library(caret)\n",
"data(\"Sacramento\")\n",
"\n",
"# Split the data into a 70/25% train/test sets\n",
"set.seed(1)\n",
"idxs <- caret::createDataPartition(y = Sacramento$price, p = 0.75)[[1]]\n",
"train <- Sacramento[idxs,]\n",
"test <- Sacramento[-idxs,]"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\n",
"Call:\n",
"glm(formula = price ~ ., family = gaussian(), data = train)\n",
"\n",
"Deviance Residuals: \n",
" Min 1Q Median 3Q Max \n",
"-269404 -39233 -6677 27418 279476 \n",
"\n",
"Coefficients: (32 not defined because of singularities)\n",
" Estimate Std. Error t value Pr(>|t|) \n",
"(Intercept) 7.434e+06 1.931e+07 0.385 0.700365 \n",
"cityAUBURN 1.450e+05 7.163e+04 2.024 0.043348 * \n",
"cityCAMERON_PARK 3.497e+04 6.341e+04 0.551 0.581536 \n",
"cityCARMICHAEL 8.019e+04 2.699e+04 2.971 0.003078 ** \n",
"cityCITRUS_HEIGHTS -8.352e+03 2.222e+04 -0.376 0.707161 \n",
"cityCOOL 1.300e+05 1.015e+05 1.281 0.200691 \n",
"cityEL_DORADO 3.534e+04 1.061e+05 0.333 0.739141 \n",
"cityEL_DORADO_HILLS 1.243e+05 5.417e+04 2.295 0.022047 * \n",
"cityELK_GROVE -6.302e+04 5.984e+04 -1.053 0.292688 \n",
"cityELVERTA -5.559e+04 5.066e+04 -1.097 0.272929 \n",
"cityFAIR_OAKS 6.136e+04 3.307e+04 1.855 0.064008 . \n",
"cityFOLSOM 1.056e+05 4.168e+04 2.533 0.011540 * \n",
"cityFORESTHILL 7.605e+04 1.283e+05 0.593 0.553518 \n",
"cityGALT -9.711e+04 8.547e+04 -1.136 0.256354 \n",
"cityGOLD_RIVER 3.459e+04 6.240e+04 0.554 0.579555 \n",
"cityGRANITE_BAY 3.217e+05 7.939e+04 4.053 5.70e-05 ***\n",
"cityGREENWOOD 7.445e+04 1.119e+05 0.665 0.506249 \n",
"cityLINCOLN 3.605e+04 3.989e+04 0.904 0.366484 \n",
"cityLOOMIS 3.754e+05 6.367e+04 5.896 6.08e-09 ***\n",
"cityMATHER -8.306e+04 7.679e+04 -1.082 0.279856 \n",
"cityMEADOW_VISTA 1.221e+05 1.060e+05 1.153 0.249537 \n",
"cityNORTH_HIGHLANDS -3.655e+04 2.425e+04 -1.507 0.132220 \n",
"cityORANGEVALE 5.875e+04 3.518e+04 1.670 0.095465 . \n",
"cityPLACERVILLE 1.271e+05 9.188e+04 1.384 0.166998 \n",
"cityPOLLOCK_PINES 3.648e+04 1.291e+05 0.283 0.777580 \n",
"cityRANCHO_CORDOVA -6.894e+04 4.502e+04 -1.531 0.126222 \n",
"cityRANCHO_MURIETA -1.675e+05 8.849e+04 -1.893 0.058780 . \n",
"cityRIO_LINDA -2.221e+04 3.001e+04 -0.740 0.459428 \n",
"cityROCKLIN 9.179e+04 3.562e+04 2.577 0.010205 * \n",
"cityROSEVILLE 9.536e+04 2.678e+04 3.560 0.000398 ***\n",
"citySACRAMENTO 1.203e+05 4.113e+04 2.926 0.003561 ** \n",
"cityWALNUT_GROVE 4.904e+04 1.173e+05 0.418 0.676124 \n",
"cityWEST_SACRAMENTO -7.501e+04 6.405e+04 -1.171 0.242019 \n",
"cityWILTON 1.265e+05 7.234e+04 1.749 0.080744 . \n",
"zipz95608 NA NA NA NA \n",
"zipz95610 7.732e+03 3.056e+04 0.253 0.800363 \n",
"zipz95614 NA NA NA NA \n",
"zipz95621 NA NA NA NA \n",
"zipz95623 NA NA NA NA \n",
"zipz95624 -1.452e+03 2.038e+04 -0.071 0.943237 \n",
"zipz95626 NA NA NA NA \n",
"zipz95628 NA NA NA NA \n",
"zipz95630 NA NA NA NA \n",
"zipz95631 NA NA NA NA \n",
"zipz95632 NA NA NA NA \n",
"zipz95635 NA NA NA NA \n",
"zipz95648 NA NA NA NA \n",
"zipz95650 NA NA NA NA \n",
"zipz95655 NA NA NA NA \n",
"zipz95660 NA NA NA NA \n",
"zipz95661 2.926e+04 3.994e+04 0.733 0.464062 \n",
"zipz95662 NA NA NA NA \n",
"zipz95667 NA NA NA NA \n",
"zipz95670 5.343e+04 3.267e+04 1.635 0.102495 \n",
"zipz95673 NA NA NA NA \n",
"zipz95677 1.607e+04 4.510e+04 0.356 0.721739 \n",
"zipz95678 -4.077e+04 2.838e+04 -1.437 0.151329 \n",
"zipz95682 NA NA NA NA \n",
"zipz95683 NA NA NA NA \n",
"zipz95690 NA NA NA NA \n",
"zipz95691 NA NA NA NA \n",
"zipz95693 NA NA NA NA \n",
"zipz95722 NA NA NA NA \n",
"zipz95726 NA NA NA NA \n",
"zipz95742 NA NA NA NA \n",
"zipz95746 NA NA NA NA \n",
"zipz95747 NA NA NA NA \n",
"zipz95757 1.339e+04 1.874e+04 0.715 0.475149 \n",
"zipz95758 NA NA NA NA \n",
"zipz95762 NA NA NA NA \n",
"zipz95765 NA NA NA NA \n",
"zipz95811 1.067e+05 7.624e+04 1.400 0.162053 \n",
"zipz95814 -7.275e+04 6.049e+04 -1.203 0.229549 \n",
"zipz95815 -1.940e+05 3.691e+04 -5.256 2.02e-07 ***\n",
"zipz95816 -2.631e+03 4.872e+04 -0.054 0.956956 \n",
"zipz95817 -1.662e+05 4.612e+04 -3.605 0.000337 ***\n",
"zipz95818 -5.284e+04 4.452e+04 -1.187 0.235783 \n",
"zipz95819 1.074e+05 5.104e+04 2.104 0.035800 * \n",
"zipz95820 -1.712e+05 3.770e+04 -4.540 6.73e-06 ***\n",
"zipz95821 -9.943e+04 4.344e+04 -2.289 0.022420 * \n",
"zipz95822 -1.762e+05 4.333e+04 -4.066 5.39e-05 ***\n",
"zipz95823 -2.001e+05 4.157e+04 -4.813 1.86e-06 ***\n",
"zipz95824 -2.056e+05 4.097e+04 -5.019 6.77e-07 ***\n",
"zipz95825 -1.386e+05 3.719e+04 -3.727 0.000212 ***\n",
"zipz95826 -1.435e+05 3.811e+04 -3.765 0.000182 ***\n",
"zipz95827 -1.880e+05 4.005e+04 -4.694 3.30e-06 ***\n",
"zipz95828 -2.005e+05 3.935e+04 -5.096 4.60e-07 ***\n",
"zipz95829 -1.802e+05 4.471e+04 -4.030 6.25e-05 ***\n",
"zipz95831 -1.137e+05 5.317e+04 -2.138 0.032914 * \n",
"zipz95832 -2.242e+05 4.891e+04 -4.584 5.52e-06 ***\n",
"zipz95833 -1.549e+05 4.048e+04 -3.827 0.000143 ***\n",
"zipz95834 -1.510e+05 4.150e+04 -3.639 0.000297 ***\n",
"zipz95835 -1.202e+05 4.195e+04 -2.865 0.004309 ** \n",
"zipz95838 -1.729e+05 3.648e+04 -4.741 2.64e-06 ***\n",
"zipz95841 -7.757e+04 5.190e+04 -1.495 0.135538 \n",
"zipz95842 -1.651e+05 3.921e+04 -4.210 2.93e-05 ***\n",
"zipz95843 NA NA NA NA \n",
"zipz95864 NA NA NA NA \n",
"beds -1.336e+04 5.016e+03 -2.664 0.007926 ** \n",
"baths 1.314e+04 6.323e+03 2.078 0.038099 * \n",
"sqft 1.148e+02 7.457e+00 15.388 < 2e-16 ***\n",
"typeMulti_Family 3.034e+04 2.731e+04 1.111 0.266933 \n",
"typeResidential 4.665e+04 1.298e+04 3.594 0.000351 ***\n",
"latitude -2.121e+05 1.895e+05 -1.120 0.263319 \n",
"longitude -6.519e+03 1.547e+05 -0.042 0.966399 \n",
"---\n",
"Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
"\n",
"(Dispersion parameter for gaussian family taken to be 4622177625)\n",
"\n",
" Null deviance: 1.2350e+13 on 699 degrees of freedom\n",
"Residual deviance: 2.8981e+12 on 627 degrees of freedom\n",
"AIC: 17635\n",
"\n",
"Number of Fisher Scoring iterations: 2\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Fit the GLM\n",
"fit <- glm(price ~ ., \n",
" data = train, \n",
" family = gaussian())\n",
"summary(fit)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ERROR",
"evalue": "Error in model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels): factor city has new levels DIAMOND_SPRINGS, GARDEN_VALLEY, PENRYN\n",
"output_type": "error",
"traceback": [
"Error in model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels): factor city has new levels DIAMOND_SPRINGS, GARDEN_VALLEY, PENRYN\nTraceback:\n",
"1. predict(fit, newdata = test)",
"2. predict.glm(fit, newdata = test)",
"3. predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == \n . \"link\", \"response\", type), terms = terms, na.action = na.action)",
"4. model.frame(Terms, newdata, na.action = na.action, xlev = object$xlevels)",
"5. model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels)",
"6. stop(sprintf(ngettext(length(m), \"factor %s has new level %s\", \n . \"factor %s has new levels %s\"), nm, paste(nxl[m], collapse = \", \")), \n . domain = NA)"
]
}
],
"source": [
"# Predict on the test set\n",
"pred <- predict(fit, newdata = test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Above we have a slight issue. The `city` column has new factor levels in the test set that were not present in the training set. Even though the `train` and `test` data frames originated from a single data frame, `Sacramento`, and therefore have identical factor levels, we still run into this problem. Let's take a closer look at the factor levels to see what's going on:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"'data.frame':\t700 obs. of 9 variables:\n",
" $ city : Factor w/ 37 levels \"ANTELOPE\",\"AUBURN\",..: 34 34 34 34 34 29 31 34 34 34 ...\n",
" $ zip : Factor w/ 68 levels \"z95603\",\"z95608\",..: 52 44 44 53 65 24 25 44 51 66 ...\n",
" $ beds : int 3 2 2 2 3 2 3 1 3 2 ...\n",
" $ baths : num 1 1 1 1 1 2 2 1 1 2 ...\n",
" $ sqft : int 1167 796 852 797 1122 941 1146 871 1020 1022 ...\n",
" $ type : Factor w/ 3 levels \"Condo\",\"Multi_Family\",..: 3 3 3 3 1 1 3 3 3 3 ...\n",
" $ price : int 68212 68880 69307 81900 89921 94905 98937 106852 107502 108750 ...\n",
" $ latitude : num 38.5 38.6 38.6 38.5 38.7 ...\n",
" $ longitude: num -121 -121 -121 -121 -121 ...\n"
]
}
],
"source": [
"str(train)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"'data.frame':\t232 obs. of 9 variables:\n",
" $ city : Factor w/ 37 levels \"ANTELOPE\",\"AUBURN\",..: 34 34 34 34 34 1 34 24 11 10 ...\n",
" $ zip : Factor w/ 68 levels \"z95603\",\"z95608\",..: 64 66 49 64 52 67 57 19 9 8 ...\n",
" $ beds : int 2 3 3 3 3 3 3 3 3 3 ...\n",
" $ baths : num 1 2 1 2 2 2 2 2 2 2 ...\n",
" $ sqft : int 836 1104 1177 909 1289 1088 1248 1152 1116 1056 ...\n",
" $ type : Factor w/ 3 levels \"Condo\",\"Multi_Family\",..: 3 3 3 3 3 3 3 3 3 3 ...\n",
" $ price : int 59222 90895 91002 100309 106250 126640 132000 134555 138750 156896 ...\n",
" $ latitude : num 38.6 38.7 38.5 38.6 38.5 ...\n",
" $ longitude: num -121 -121 -121 -121 -121 ...\n"
]
}
],
"source": [
"str(test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Although `train` and `test` have identical structure, not all the levels are represented in the training data. To validate this, let's take a look at the actual unique levels that were used in the model:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1] 34\n",
"[1] 65\n",
"[1] 3\n"
]
},
{
"data": {
"text/html": [
"<dl class=dl-horizontal>\n",
"\t<dt>city</dt>\n",
"\t\t<dd>34</dd>\n",
"\t<dt>zip</dt>\n",
"\t\t<dd>65</dd>\n",
"\t<dt>type</dt>\n",
"\t\t<dd>3</dd>\n",
"</dl>\n"
],
"text/latex": [
"\\begin{description*}\n",
"\\item[city] 34\n",
"\\item[zip] 65\n",
"\\item[type] 3\n",
"\\end{description*}\n"
],
"text/markdown": [
"city\n",
": 34zip\n",
": 65type\n",
": 3\n",
"\n"
],
"text/plain": [
"city zip type \n",
" 34 65 3 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Check the number of levels in the model features\n",
"sapply(fit$xlevels, function(x) print(length(x)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can manually fix this by updating the `xlevels` element of the model. We have the same issue with `zip`, so we should go ahead and manually update that as well."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Update factor levels so that prediction works\n",
"fit$xlevels[[\"city\"]] <- union(fit$xlevels[[\"city\"]], levels(test$city))\n",
"fit$xlevels[[\"zip\"]] <- union(fit$xlevels[[\"zip\"]], levels(test$zip))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning message:\n",
"In predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == : prediction from a rank-deficient fit may be misleading"
]
},
{
"data": {
"text/plain": [
"\n",
"Call:\n",
"glm(formula = price ~ ., family = gaussian(), data = train)\n",
"\n",
"Deviance Residuals: \n",
" Min 1Q Median 3Q Max \n",
"-269404 -39233 -6677 27418 279476 \n",
"\n",
"Coefficients: (32 not defined because of singularities)\n",
" Estimate Std. Error t value Pr(>|t|) \n",
"(Intercept) 7.434e+06 1.931e+07 0.385 0.700365 \n",
"cityAUBURN 1.450e+05 7.163e+04 2.024 0.043348 * \n",
"cityCAMERON_PARK 3.497e+04 6.341e+04 0.551 0.581536 \n",
"cityCARMICHAEL 8.019e+04 2.699e+04 2.971 0.003078 ** \n",
"cityCITRUS_HEIGHTS -8.352e+03 2.222e+04 -0.376 0.707161 \n",
"cityCOOL 1.300e+05 1.015e+05 1.281 0.200691 \n",
"cityEL_DORADO 3.534e+04 1.061e+05 0.333 0.739141 \n",
"cityEL_DORADO_HILLS 1.243e+05 5.417e+04 2.295 0.022047 * \n",
"cityELK_GROVE -6.302e+04 5.984e+04 -1.053 0.292688 \n",
"cityELVERTA -5.559e+04 5.066e+04 -1.097 0.272929 \n",
"cityFAIR_OAKS 6.136e+04 3.307e+04 1.855 0.064008 . \n",
"cityFOLSOM 1.056e+05 4.168e+04 2.533 0.011540 * \n",
"cityFORESTHILL 7.605e+04 1.283e+05 0.593 0.553518 \n",
"cityGALT -9.711e+04 8.547e+04 -1.136 0.256354 \n",
"cityGOLD_RIVER 3.459e+04 6.240e+04 0.554 0.579555 \n",
"cityGRANITE_BAY 3.217e+05 7.939e+04 4.053 5.70e-05 ***\n",
"cityGREENWOOD 7.445e+04 1.119e+05 0.665 0.506249 \n",
"cityLINCOLN 3.605e+04 3.989e+04 0.904 0.366484 \n",
"cityLOOMIS 3.754e+05 6.367e+04 5.896 6.08e-09 ***\n",
"cityMATHER -8.306e+04 7.679e+04 -1.082 0.279856 \n",
"cityMEADOW_VISTA 1.221e+05 1.060e+05 1.153 0.249537 \n",
"cityNORTH_HIGHLANDS -3.655e+04 2.425e+04 -1.507 0.132220 \n",
"cityORANGEVALE 5.875e+04 3.518e+04 1.670 0.095465 . \n",
"cityPLACERVILLE 1.271e+05 9.188e+04 1.384 0.166998 \n",
"cityPOLLOCK_PINES 3.648e+04 1.291e+05 0.283 0.777580 \n",
"cityRANCHO_CORDOVA -6.894e+04 4.502e+04 -1.531 0.126222 \n",
"cityRANCHO_MURIETA -1.675e+05 8.849e+04 -1.893 0.058780 . \n",
"cityRIO_LINDA -2.221e+04 3.001e+04 -0.740 0.459428 \n",
"cityROCKLIN 9.179e+04 3.562e+04 2.577 0.010205 * \n",
"cityROSEVILLE 9.536e+04 2.678e+04 3.560 0.000398 ***\n",
"citySACRAMENTO 1.203e+05 4.113e+04 2.926 0.003561 ** \n",
"cityWALNUT_GROVE 4.904e+04 1.173e+05 0.418 0.676124 \n",
"cityWEST_SACRAMENTO -7.501e+04 6.405e+04 -1.171 0.242019 \n",
"cityWILTON 1.265e+05 7.234e+04 1.749 0.080744 . \n",
"zipz95608 NA NA NA NA \n",
"zipz95610 7.732e+03 3.056e+04 0.253 0.800363 \n",
"zipz95614 NA NA NA NA \n",
"zipz95621 NA NA NA NA \n",
"zipz95623 NA NA NA NA \n",
"zipz95624 -1.452e+03 2.038e+04 -0.071 0.943237 \n",
"zipz95626 NA NA NA NA \n",
"zipz95628 NA NA NA NA \n",
"zipz95630 NA NA NA NA \n",
"zipz95631 NA NA NA NA \n",
"zipz95632 NA NA NA NA \n",
"zipz95635 NA NA NA NA \n",
"zipz95648 NA NA NA NA \n",
"zipz95650 NA NA NA NA \n",
"zipz95655 NA NA NA NA \n",
"zipz95660 NA NA NA NA \n",
"zipz95661 2.926e+04 3.994e+04 0.733 0.464062 \n",
"zipz95662 NA NA NA NA \n",
"zipz95667 NA NA NA NA \n",
"zipz95670 5.343e+04 3.267e+04 1.635 0.102495 \n",
"zipz95673 NA NA NA NA \n",
"zipz95677 1.607e+04 4.510e+04 0.356 0.721739 \n",
"zipz95678 -4.077e+04 2.838e+04 -1.437 0.151329 \n",
"zipz95682 NA NA NA NA \n",
"zipz95683 NA NA NA NA \n",
"zipz95690 NA NA NA NA \n",
"zipz95691 NA NA NA NA \n",
"zipz95693 NA NA NA NA \n",
"zipz95722 NA NA NA NA \n",
"zipz95726 NA NA NA NA \n",
"zipz95742 NA NA NA NA \n",
"zipz95746 NA NA NA NA \n",
"zipz95747 NA NA NA NA \n",
"zipz95757 1.339e+04 1.874e+04 0.715 0.475149 \n",
"zipz95758 NA NA NA NA \n",
"zipz95762 NA NA NA NA \n",
"zipz95765 NA NA NA NA \n",
"zipz95811 1.067e+05 7.624e+04 1.400 0.162053 \n",
"zipz95814 -7.275e+04 6.049e+04 -1.203 0.229549 \n",
"zipz95815 -1.940e+05 3.691e+04 -5.256 2.02e-07 ***\n",
"zipz95816 -2.631e+03 4.872e+04 -0.054 0.956956 \n",
"zipz95817 -1.662e+05 4.612e+04 -3.605 0.000337 ***\n",
"zipz95818 -5.284e+04 4.452e+04 -1.187 0.235783 \n",
"zipz95819 1.074e+05 5.104e+04 2.104 0.035800 * \n",
"zipz95820 -1.712e+05 3.770e+04 -4.540 6.73e-06 ***\n",
"zipz95821 -9.943e+04 4.344e+04 -2.289 0.022420 * \n",
"zipz95822 -1.762e+05 4.333e+04 -4.066 5.39e-05 ***\n",
"zipz95823 -2.001e+05 4.157e+04 -4.813 1.86e-06 ***\n",
"zipz95824 -2.056e+05 4.097e+04 -5.019 6.77e-07 ***\n",
"zipz95825 -1.386e+05 3.719e+04 -3.727 0.000212 ***\n",
"zipz95826 -1.435e+05 3.811e+04 -3.765 0.000182 ***\n",
"zipz95827 -1.880e+05 4.005e+04 -4.694 3.30e-06 ***\n",
"zipz95828 -2.005e+05 3.935e+04 -5.096 4.60e-07 ***\n",
"zipz95829 -1.802e+05 4.471e+04 -4.030 6.25e-05 ***\n",
"zipz95831 -1.137e+05 5.317e+04 -2.138 0.032914 * \n",
"zipz95832 -2.242e+05 4.891e+04 -4.584 5.52e-06 ***\n",
"zipz95833 -1.549e+05 4.048e+04 -3.827 0.000143 ***\n",
"zipz95834 -1.510e+05 4.150e+04 -3.639 0.000297 ***\n",
"zipz95835 -1.202e+05 4.195e+04 -2.865 0.004309 ** \n",
"zipz95838 -1.729e+05 3.648e+04 -4.741 2.64e-06 ***\n",
"zipz95841 -7.757e+04 5.190e+04 -1.495 0.135538 \n",
"zipz95842 -1.651e+05 3.921e+04 -4.210 2.93e-05 ***\n",
"zipz95843 NA NA NA NA \n",
"zipz95864 NA NA NA NA \n",
"beds -1.336e+04 5.016e+03 -2.664 0.007926 ** \n",
"baths 1.314e+04 6.323e+03 2.078 0.038099 * \n",
"sqft 1.148e+02 7.457e+00 15.388 < 2e-16 ***\n",
"typeMulti_Family 3.034e+04 2.731e+04 1.111 0.266933 \n",
"typeResidential 4.665e+04 1.298e+04 3.594 0.000351 ***\n",
"latitude -2.121e+05 1.895e+05 -1.120 0.263319 \n",
"longitude -6.519e+03 1.547e+05 -0.042 0.966399 \n",
"---\n",
"Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
"\n",
"(Dispersion parameter for gaussian family taken to be 4622177625)\n",
"\n",
" Null deviance: 1.2350e+13 on 699 degrees of freedom\n",
"Residual deviance: 2.8981e+12 on 627 degrees of freedom\n",
"AIC: 17635\n",
"\n",
"Number of Fisher Scoring iterations: 2\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Predict on the test set\n",
"pred <- predict(fit, newdata = test)\n",
"summary(fit)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"0.0371512375879591"
],
"text/latex": [
"0.0371512375879591"
],
"text/markdown": [
"0.0371512375879591"
],
"text/plain": [
"[1] 0.03715124"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"7173981.7581349"
],
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],
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],
"text/plain": [
"[1] 7173982"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Compute model performance on the test set\n",
"\n",
"caret::R2(pred = pred, obs = test$price)\n",
"caret::RMSE(pred = pred, obs = test$price)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### GLM in caret\n",
"\n",
"Now let's run the same model using caret's glm method to get a sense of how much easier it is to use."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\n",
"Call:\n",
"NULL\n",
"\n",
"Deviance Residuals: \n",
" Min 1Q Median 3Q Max \n",
"-269404 -39233 -6677 27418 279476 \n",
"\n",
"Coefficients: (38 not defined because of singularities)\n",
" Estimate Std. Error t value Pr(>|t|) \n",
"(Intercept) 7.434e+06 1.931e+07 0.385 0.700365 \n",
"cityAUBURN 1.450e+05 7.163e+04 2.024 0.043348 * \n",
"cityCAMERON_PARK 3.497e+04 6.341e+04 0.551 0.581536 \n",
"cityCARMICHAEL 8.019e+04 2.699e+04 2.971 0.003078 ** \n",
"cityCITRUS_HEIGHTS -8.352e+03 2.222e+04 -0.376 0.707161 \n",
"cityCOOL 1.300e+05 1.015e+05 1.281 0.200691 \n",
"cityDIAMOND_SPRINGS NA NA NA NA \n",
"cityEL_DORADO 3.534e+04 1.061e+05 0.333 0.739141 \n",
"cityEL_DORADO_HILLS 1.243e+05 5.417e+04 2.295 0.022047 * \n",
"cityELK_GROVE -6.302e+04 5.984e+04 -1.053 0.292688 \n",
"cityELVERTA -5.559e+04 5.066e+04 -1.097 0.272929 \n",
"cityFAIR_OAKS 6.136e+04 3.307e+04 1.855 0.064008 . \n",
"cityFOLSOM 1.056e+05 4.168e+04 2.533 0.011540 * \n",
"cityFORESTHILL 7.605e+04 1.283e+05 0.593 0.553518 \n",
"cityGALT -9.711e+04 8.547e+04 -1.136 0.256354 \n",
"cityGARDEN_VALLEY NA NA NA NA \n",
"cityGOLD_RIVER 3.459e+04 6.240e+04 0.554 0.579555 \n",
"cityGRANITE_BAY 3.217e+05 7.939e+04 4.053 5.70e-05 ***\n",
"cityGREENWOOD 7.445e+04 1.119e+05 0.665 0.506249 \n",
"cityLINCOLN 3.605e+04 3.989e+04 0.904 0.366484 \n",
"cityLOOMIS 3.754e+05 6.367e+04 5.896 6.08e-09 ***\n",
"cityMATHER -8.306e+04 7.679e+04 -1.082 0.279856 \n",
"cityMEADOW_VISTA 1.221e+05 1.060e+05 1.153 0.249537 \n",
"cityNORTH_HIGHLANDS -3.655e+04 2.425e+04 -1.507 0.132220 \n",
"cityORANGEVALE 5.875e+04 3.518e+04 1.670 0.095465 . \n",
"cityPENRYN NA NA NA NA \n",
"cityPLACERVILLE 1.271e+05 9.188e+04 1.384 0.166998 \n",
"cityPOLLOCK_PINES 3.648e+04 1.291e+05 0.283 0.777580 \n",
"cityRANCHO_CORDOVA -6.894e+04 4.502e+04 -1.531 0.126222 \n",
"cityRANCHO_MURIETA -1.675e+05 8.849e+04 -1.893 0.058780 . \n",
"cityRIO_LINDA -2.221e+04 3.001e+04 -0.740 0.459428 \n",
"cityROCKLIN 9.179e+04 3.562e+04 2.577 0.010205 * \n",
"cityROSEVILLE 9.536e+04 2.678e+04 3.560 0.000398 ***\n",
"citySACRAMENTO 1.203e+05 4.113e+04 2.926 0.003561 ** \n",
"cityWALNUT_GROVE 4.904e+04 1.173e+05 0.418 0.676124 \n",
"cityWEST_SACRAMENTO -7.501e+04 6.405e+04 -1.171 0.242019 \n",
"cityWILTON 1.265e+05 7.234e+04 1.749 0.080744 . \n",
"zipz95608 NA NA NA NA \n",
"zipz95610 7.732e+03 3.056e+04 0.253 0.800363 \n",
"zipz95614 NA NA NA NA \n",
"zipz95619 NA NA NA NA \n",
"zipz95621 NA NA NA NA \n",
"zipz95623 NA NA NA NA \n",
"zipz95624 -1.452e+03 2.038e+04 -0.071 0.943237 \n",
"zipz95626 NA NA NA NA \n",
"zipz95628 NA NA NA NA \n",
"zipz95630 NA NA NA NA \n",
"zipz95631 NA NA NA NA \n",
"zipz95632 NA NA NA NA \n",
"zipz95633 NA NA NA NA \n",
"zipz95635 NA NA NA NA \n",
"zipz95648 NA NA NA NA \n",
"zipz95650 NA NA NA NA \n",
"zipz95655 NA NA NA NA \n",
"zipz95660 NA NA NA NA \n",
"zipz95661 2.926e+04 3.994e+04 0.733 0.464062 \n",
"zipz95662 NA NA NA NA \n",
"zipz95663 NA NA NA NA \n",
"zipz95667 NA NA NA NA \n",
"zipz95670 5.343e+04 3.267e+04 1.635 0.102495 \n",
"zipz95673 NA NA NA NA \n",
"zipz95677 1.607e+04 4.510e+04 0.356 0.721739 \n",
"zipz95678 -4.077e+04 2.838e+04 -1.437 0.151329 \n",
"zipz95682 NA NA NA NA \n",
"zipz95683 NA NA NA NA \n",
"zipz95690 NA NA NA NA \n",
"zipz95691 NA NA NA NA \n",
"zipz95693 NA NA NA NA \n",
"zipz95722 NA NA NA NA \n",
"zipz95726 NA NA NA NA \n",
"zipz95742 NA NA NA NA \n",
"zipz95746 NA NA NA NA \n",
"zipz95747 NA NA NA NA \n",
"zipz95757 1.339e+04 1.874e+04 0.715 0.475149 \n",
"zipz95758 NA NA NA NA \n",
"zipz95762 NA NA NA NA \n",
"zipz95765 NA NA NA NA \n",
"zipz95811 1.067e+05 7.624e+04 1.400 0.162053 \n",
"zipz95814 -7.275e+04 6.049e+04 -1.203 0.229549 \n",
"zipz95815 -1.940e+05 3.691e+04 -5.256 2.02e-07 ***\n",
"zipz95816 -2.631e+03 4.872e+04 -0.054 0.956956 \n",
"zipz95817 -1.662e+05 4.612e+04 -3.605 0.000337 ***\n",
"zipz95818 -5.284e+04 4.452e+04 -1.187 0.235783 \n",
"zipz95819 1.074e+05 5.104e+04 2.104 0.035800 * \n",
"zipz95820 -1.712e+05 3.770e+04 -4.540 6.73e-06 ***\n",
"zipz95821 -9.943e+04 4.344e+04 -2.289 0.022420 * \n",
"zipz95822 -1.762e+05 4.333e+04 -4.066 5.39e-05 ***\n",
"zipz95823 -2.001e+05 4.157e+04 -4.813 1.86e-06 ***\n",
"zipz95824 -2.056e+05 4.097e+04 -5.019 6.77e-07 ***\n",
"zipz95825 -1.386e+05 3.719e+04 -3.727 0.000212 ***\n",
"zipz95826 -1.435e+05 3.811e+04 -3.765 0.000182 ***\n",
"zipz95827 -1.880e+05 4.005e+04 -4.694 3.30e-06 ***\n",
"zipz95828 -2.005e+05 3.935e+04 -5.096 4.60e-07 ***\n",
"zipz95829 -1.802e+05 4.471e+04 -4.030 6.25e-05 ***\n",
"zipz95831 -1.137e+05 5.317e+04 -2.138 0.032914 * \n",
"zipz95832 -2.242e+05 4.891e+04 -4.584 5.52e-06 ***\n",
"zipz95833 -1.549e+05 4.048e+04 -3.827 0.000143 ***\n",
"zipz95834 -1.510e+05 4.150e+04 -3.639 0.000297 ***\n",
"zipz95835 -1.202e+05 4.195e+04 -2.865 0.004309 ** \n",
"zipz95838 -1.729e+05 3.648e+04 -4.741 2.64e-06 ***\n",
"zipz95841 -7.757e+04 5.190e+04 -1.495 0.135538 \n",
"zipz95842 -1.651e+05 3.921e+04 -4.210 2.93e-05 ***\n",
"zipz95843 NA NA NA NA \n",
"zipz95864 NA NA NA NA \n",
"beds -1.336e+04 5.016e+03 -2.664 0.007926 ** \n",
"baths 1.314e+04 6.323e+03 2.078 0.038099 * \n",
"sqft 1.148e+02 7.457e+00 15.388 < 2e-16 ***\n",
"typeMulti_Family 3.034e+04 2.731e+04 1.111 0.266933 \n",
"typeResidential 4.665e+04 1.298e+04 3.594 0.000351 ***\n",
"latitude -2.121e+05 1.895e+05 -1.120 0.263319 \n",
"longitude -6.519e+03 1.547e+05 -0.042 0.966399 \n",
"---\n",
"Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
"\n",
"(Dispersion parameter for gaussian family taken to be 4622177625)\n",
"\n",
" Null deviance: 1.2350e+13 on 699 degrees of freedom\n",
"Residual deviance: 2.8981e+12 on 627 degrees of freedom\n",
"AIC: 17635\n",
"\n",
"Number of Fisher Scoring iterations: 2\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Train a caret glm model\n",
"fit <- caret::train(form = price ~ ., \n",
" data = train, \n",
" trControl = trainControl(method = \"none\"), \n",
" method = \"glm\", \n",
" family = gaussian())\n",
"summary(fit$finalModel)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning message:\n",
"In predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == : prediction from a rank-deficient fit may be misleading"
]
}
],
"source": [
"# Predict on the test set\n",
"pred <- predict(fit, newdata = test)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"source": [
"# Compute model performance on the test set\n",
"\n",
"caret::R2(pred = pred, obs = test$price)\n",
"caret::RMSE(pred = pred, obs = test$price)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OK, this looks much better. And we didn't have to deal with the missing factor levels! :-)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### h2o\n",
"\n",
"Authors: Tomas Nykodym, H2O.ai contributors\n",
"\n",
"Backend: Java\n",
"\n",
"The [h2o](https://cran.r-project.org/web/packages/h2o/index.html) package offers a data-distributed implementation of Generalized Linear Models. A \"data-distributed\" version uses distributed data frames, so that the whole design matrix does not need to fit into memory at once. The h2o package fits both regularized and non-regularized GLMs. The implementation details are documented [here](http://docs.h2o.ai/h2o/latest-stable/h2o-docs/booklets/GLMBooklet.pdf)."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ERROR",
"evalue": "Error in eval(expr, envir, enclos): could not find function \"h2o.shutdown\"\n",
"output_type": "error",
"traceback": [
"Error in eval(expr, envir, enclos): could not find function \"h2o.shutdown\"\nTraceback:\n"
]
}
],
"source": [
"h2o.shutdown(prompt = FALSE)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading required package: statmod\n",
"\n",
"----------------------------------------------------------------------\n",
"\n",
"Your next step is to start H2O:\n",
" > h2o.init()\n",
"\n",
"For H2O package documentation, ask for help:\n",
" > ??h2o\n",
"\n",
"After starting H2O, you can use the Web UI at http://localhost:54321\n",
"For more information visit http://docs.h2o.ai\n",
"\n",
"----------------------------------------------------------------------\n",
"\n",
"\n",
"Attaching package: ‘h2o’\n",
"\n",
"The following objects are masked from ‘package:stats’:\n",
"\n",
" sd, var\n",
"\n",
"The following objects are masked from ‘package:base’:\n",
"\n",
" &&, %*%, %in%, ||, apply, as.factor, as.numeric, colnames,\n",
" colnames<-, ifelse, is.character, is.factor, is.numeric, log,\n",
" log10, log1p, log2, round, signif, trunc\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"H2O is not running yet, starting it now...\n",
"\n",
"Note: In case of errors look at the following log files:\n",
" /var/folders/2j/jg4sl53d5q53tc2_nzm9fz5h0000gn/T//RtmpN5WlEN/h2o_me_started_from_r.out\n",
" /var/folders/2j/jg4sl53d5q53tc2_nzm9fz5h0000gn/T//RtmpN5WlEN/h2o_me_started_from_r.err\n",
"\n",
"\n",
"Starting H2O JVM and connecting: . Connection successful!\n",
"\n",
"R is connected to the H2O cluster: \n",
" H2O cluster uptime: 1 seconds 202 milliseconds \n",
" H2O cluster version: 3.8.2.6 \n",
" H2O cluster name: H2O_started_from_R_me_sot072 \n",
" H2O cluster total nodes: 1 \n",
" H2O cluster total memory: 3.56 GB \n",
" H2O cluster total cores: 8 \n",
" H2O cluster allowed cores: 8 \n",
" H2O cluster healthy: TRUE \n",
" H2O Connection ip: localhost \n",
" H2O Connection port: 54321 \n",
" H2O Connection proxy: NA \n",
" R Version: R version 3.3.0 (2016-05-03) \n",
"\n"
]
}
],
"source": [
"# h2o.glm example\n",
"#install.packages(\"h2o\")\n",
"library(h2o)\n",
"h2o.init(nthreads = -1) #Start a local H2O cluster using nthreads = num available cores"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Typically one would load a dataset in parallel from disk using the `h2o.importFile()` function, however for the purposes of this tutorial, we are going to use a tiny built-in R dataset, so we can send that data to the H2O cluster (from R memory) using the `as.h2o()` function. We would also use the `h2o.splitFrame()` function to split the data instead of the `caret::createDataPartition()`, but for an apples-to-apples comparison with the methods above, it's good to use the same exact train and test split, generated the same way as above."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" | \r",
" | | 0%\r",
" | \r",
" |======================================================================| 100%\n"
]
},
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>700</li>\n",
"\t<li>9</li>\n",
"</ol>\n"
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"\\begin{enumerate*}\n",
"\\item 700\n",
"\\item 9\n",
"\\end{enumerate*}\n"
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"1. 700\n",
"2. 9\n",
"\n",
"\n"
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"[1] 700 9"
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"1. 232\n",
"2. 9\n",
"\n",
"\n"
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"[1] 232 9"
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"metadata": {},
"output_type": "display_data"
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{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>'city'</li>\n",
"\t<li>'zip'</li>\n",
"\t<li>'beds'</li>\n",
"\t<li>'baths'</li>\n",
"\t<li>'sqft'</li>\n",
"\t<li>'type'</li>\n",
"\t<li>'price'</li>\n",
"\t<li>'latitude'</li>\n",
"\t<li>'longitude'</li>\n",
"</ol>\n"
],
"text/latex": [
"\\begin{enumerate*}\n",
"\\item 'city'\n",
"\\item 'zip'\n",
"\\item 'beds'\n",
"\\item 'baths'\n",
"\\item 'sqft'\n",
"\\item 'type'\n",
"\\item 'price'\n",
"\\item 'latitude'\n",
"\\item 'longitude'\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 'city'\n",
"2. 'zip'\n",
"3. 'beds'\n",
"4. 'baths'\n",
"5. 'sqft'\n",
"6. 'type'\n",
"7. 'price'\n",
"8. 'latitude'\n",
"9. 'longitude'\n",
"\n",
"\n"
],
"text/plain": [
"[1] \"city\" \"zip\" \"beds\" \"baths\" \"sqft\" \"type\" \n",
"[7] \"price\" \"latitude\" \"longitude\""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Load Sacramento dataset\n",
"library(caret)\n",
"data(\"Sacramento\")\n",
"\n",
"# Convert the data into an H2OFrame\n",
"sac <- as.h2o(Sacramento)\n",
"\n",
"# Split the data into a 70/25% train/test sets\n",
"set.seed(1)\n",
"idxs <- caret::createDataPartition(y = Sacramento$price, p = 0.75)[[1]]\n",
"train <- sac[idxs,]\n",
"test <- sac[-idxs,]\n",
"\n",
"# Dimensions\n",
"dim(train)\n",
"dim(test)\n",
"\n",
"# Columns\n",
"names(train)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" | \r",
" | | 0%\r",
" | \r",
" |======================================================================| 100%\n"
]
},
{
"data": {
"text/plain": [
" user system elapsed \n",
" 0.152 0.005 1.298 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Identify the predictor columns\n",
"xcols <- setdiff(names(train), \"price\")\n",
"\n",
"# Train a default GLM model with no regularization\n",
"system.time(fit <- h2o.glm(x = xcols,\n",
" y = \"price\",\n",
" training_frame = train,\n",
" family = \"gaussian\",\n",
" lambda = 0)) #lambda = 0 means no regularization"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model Details:\n",
"==============\n",
"\n",
"H2ORegressionModel: glm\n",
"Model Key: GLM_model_R_1467015677812_1 \n",
"GLM Model: summary\n",
" family link regularization number_of_predictors_total\n",
"1 gaussian identity None 113\n",
" number_of_active_predictors number_of_iterations training_frame\n",
"1 104 0 RTMP_sid_8211_2\n",
"\n",
"H2ORegressionMetrics: glm\n",
"** Reported on training data. **\n",
"\n",
"MSE: 4140201839\n",
"R2 : 0.765328\n",
"Mean Residual Deviance : 4140201839\n",
"Null Deviance :1.234975e+13\n",
"Null D.o.F. :699\n",
"Residual Deviance :2.898141e+12\n",
"Residual D.o.F. :595\n",
"AIC :17699.32\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"Scoring History: \n",
"data frame with 0 columns and 0 rows\n",
"\n",
"Variable Importances: (Extract with `h2o.varimp`) \n",
"=================================================\n",
"\n",
"Standardized Coefficient Magnitudes: standardized coefficient magnitudes\n",
" names coefficients sign\n",
"1 zip.z95819 227041.126260 POS\n",
"2 zip.z95811 225456.089371 POS\n",
"3 zip.z95650 168023.485796 POS\n",
"4 city.LOOMIS 168023.485796 POS\n",
"5 zip.z95746 141127.252069 POS\n",
"\n",
"---\n",
" names coefficients sign\n",
"108 zip.missing(NA) 0.000000 POS\n",
"109 city.DIAMOND_SPRINGS 0.000000 POS\n",
"110 city.GARDEN_VALLEY 0.000000 POS\n",
"111 city.PENRYN 0.000000 POS\n",
"112 city.missing(NA) 0.000000 POS\n",
"113 type.missing(NA) 0.000000 POS\n"
]
}
],
"source": [
"summary(fit)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
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],
"source": [
"# H2O computes many model performance metrics automatically, accessible by utility functions\n",
"\n",
"perf <- h2o.performance(model = fit, newdata = test)\n",
"h2o.r2(perf)\n",
"sqrt(h2o.mse(perf))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### speedglm\n",
"\n",
"Also worth mentioning is the [speedglm](https://cran.r-project.org/web/packages/speedglm/index.html) package, which fits Linear and Generalized Linear Models to large data sets. This is particularly useful if R is linked against an optimized [BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms). For data sets of size greater of R memory, the fitting is performed by an iterative algorithm."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Regularized GLM in R\n",
"\n",
"OK, so let's assume that we have wide, sparse, collinear or big data. If your training set falls into any of those categories, it might be a good idea to use a regularized GLM.\n",
"\n",
"### glmnet\n",
"\n",
"Authors: [Jerome Friedman](https://statweb.stanford.edu/~jhf/), [Trevor Hastie](http://web.stanford.edu/~hastie/), [Noah Simon](http://faculty.washington.edu/nrsimon/), [Rob Tibshirani](http://statweb.stanford.edu/~tibs/)\n",
"\n",
"Backend: [Mortran](https://en.wikipedia.org/wiki/Mortran) (extension of Fortran used for scientific computation)\n",
"\n",
"[glmnet](http://web.stanford.edu/~hastie/glmnet/glmnet_beta.html) is a package that fits a generalized linear model via penalized maximum likelihood. The regularization path is computed for the lasso or elastic-net penalty at a grid of values for the regularization parameter lambda. The algorithm is extremely fast, and can exploit sparsity in the input matrix $\\boldsymbol{X}$.\n",
"\n",
"Features:\n",
"\n",
"- The code can handle sparse input-matrix formats, as well as range constraints on coefficients. \n",
"- Glmnet also makes use of the strong rules for efficient restriction of the active set. \n",
"- The core of Glmnet is a set of FORTRAN subroutines, which make for very fast execution. \n",
"- The algorithms use coordinate descent with warm starts and active set iterations. \n",
"- Supports the following distributions: `\"gaussian\",\"binomial\",\"poisson\",\"multinomial\",\"cox\",\"mgaussian\"`\n",
"- Supports standardization and offsets.\n",
"\n",
"The Glmnet package is a fast implementation, but it requires some extra processing up-front to your data if it's not already represented as a numeric matrix. For example, if you have categorical data or missing data, you need to deal with that yourself."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading required package: Matrix\n",
"Loading required package: foreach\n",
"Loaded glmnet 2.0-5\n",
"\n"
]
}
],
"source": [
"#install.packages(\"glmnet\")\n",
"#install.packages(\"Cairo\") #for plotting lasso coefficients in Jupyter notebook\n",
"library(glmnet)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" num [1:100, 1:20] 0.274 2.245 -0.125 -0.544 -1.459 ...\n"
]
},
{
"data": {
"text/html": [
"'matrix'"
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"'matrix'"
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"[1] \"matrix\""
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},
"metadata": {},
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}
],
"source": [
"data(\"QuickStartExample\") #loads 'x' and 'y'\n",
"str(x)\n",
"class(x)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fit <- glmnet(x, y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can visualize the coefficients by executing the `plot` function. Each curve corresponds to a variable. It shows the path of its coefficient against the $\\ell_1$-norm of the whole coefficient vector at as $\\lambda$ varies. The axis above indicates the number of nonzero coefficients at the current $\\lambda$, which is the effective degrees of freedom for the lasso."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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kRERH/t9u3bKSkpycnJ2jKXlZWlvW5r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"text/plain": [
"plot without title"
]
},
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"isolated": true
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],
"source": [
"plot(fit)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# TO DO: Add caret::twoClassSim example for comparison instead of \"QuickStartExample\""
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>100000</li>\n",
"\t<li>116</li>\n",
"</ol>\n"
],
"text/latex": [
"\\begin{enumerate*}\n",
"\\item 100000\n",
"\\item 116\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 100000\n",
"2. 116\n",
"\n",
"\n"
],
"text/plain": [
"[1] 100000 116"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Simulate a binary response dataset\n",
"library(caret)\n",
"set.seed(1)\n",
"df <- caret::twoClassSim(n = 100000,\n",
" linearVars = 10, \n",
" noiseVars = 50, \n",
" corrVars = 50)\n",
"dim(df)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
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],
"source": [
"# Identify the response & predictor columns\n",
"ycol <- \"Class\"\n",
"xcols <- setdiff(names(df), ycol)\n",
"df[,ycol] <- ifelse(df[,ycol]==\"Class1\", 0, 1)\n",
"\n",
"# Split the data into a 70/25% train/test sets\n",
"set.seed(1)\n",
"idxs <- caret::createDataPartition(y = df[,ycol], p = 0.75)[[1]]\n",
"train <- df[idxs,]\n",
"test <- df[-idxs,]\n",
"train_y <- df[idxs, ycol]\n",
"test_y <- df[-idxs, ycol]\n",
"train_x <- model.matrix(~-1 + ., train[, xcols])\n",
"test_x <- model.matrix(~-1 + ., test[, xcols])\n",
"\n",
"\n",
"# Dimensions\n",
"dim(train_x)\n",
"length(train_y)\n",
"dim(test_x)\n",
"length(test_y)"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>0</li>\n",
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"1. 0\n",
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],
"text/plain": [
"[1] 0 1 1 0 0 0"
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},
"metadata": {},
"output_type": "display_data"
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],
"source": [
"head(test_y)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" user system elapsed \n",
" 38.178 0.998 39.187 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Train a Lasso GLM\n",
"system.time(cvfit <- cv.glmnet(x = train_x,\n",
" y = train_y,\n",
" family = \"binomial\",\n",
" alpha = 1.0)) # alpha = 1 means lasso by default"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>1</th></tr></thead>\n",
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"1. 0.313623573108128\n",
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"3. 0.92693141202768\n",
"4. 0.102776418399302\n",
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"\n",
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"2 0.3136236\n",
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"29 0.6640567\n",
"30 0.5079524"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"preds <- predict(cvfit$glmnet.fit, \n",
" newx = test_x, \n",
" s = cvfit$lambda.min, \n",
" type = \"response\")\n",
"head(preds)"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"0.908288759734547"
],
"text/latex": [
"0.908288759734547"
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"text/markdown": [
"0.908288759734547"
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"[1] 0.9082888"
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#install.packages(\"cvAUC\")\n",
"library(cvAUC)\n",
"\n",
"cvAUC::AUC(predictions = preds, labels = test_y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plot(cvfit)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"coef(cvfit, s = \"lambda.min\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### h2o\n",
"\n",
"Introduced in the previous section, the h2o package can perform unregularized or regularized regression. By default, `h2o.glm` will perform an Elastic Net regression. Similar to the `glmnet` function, you can adjust the Elastic Net penalty through the `alpha` parameter (`alpha = 1.0` is Lasso and `alpha = 0.0` is Ridge)."
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<ol class=list-inline>\n",
"\t<li>100000</li>\n",
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"</ol>\n"
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"\\item 100000\n",
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"source": [
"# Simulate a binary response dataset\n",
"library(caret)\n",
"set.seed(1)\n",
"df <- caret::twoClassSim(n = 100000,\n",
" linearVars = 10, \n",
" noiseVars = 50, \n",
" corrVars = 50)\n",
"dim(df)"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Connection successful!\n",
"\n",
"R is connected to the H2O cluster: \n",
" H2O cluster uptime: 40 minutes 25 seconds \n",
" H2O cluster version: 3.8.2.6 \n",
" H2O cluster name: H2O_started_from_R_me_sot072 \n",
" H2O cluster total nodes: 1 \n",
" H2O cluster total memory: 2.84 GB \n",
" H2O cluster total cores: 8 \n",
" H2O cluster allowed cores: 8 \n",
" H2O cluster healthy: TRUE \n",
" H2O Connection ip: localhost \n",
" H2O Connection port: 54321 \n",
" H2O Connection proxy: NA \n",
" R Version: R version 3.3.0 (2016-05-03) \n",
"\n",
" |======================================================================| 100%\n"
]
}
],
"source": [
"# Convert the data into an H2OFrame\n",
"library(h2o)\n",
"h2o.init(nthreads = -1)\n",
"hf <- as.h2o(df)"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Identify the response & predictor columns\n",
"ycol <- \"Class\"\n",
"xcols <- setdiff(names(hf), ycol)\n",
"\n",
"# Convert the 0/1 binary response to a factor \n",
"hf[,ycol] <- as.factor(hf[,ycol])"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<ol class=list-inline>\n",
"\t<li>100000</li>\n",
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"\n",
"\n"
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"source": [
"dim(df)"
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{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"\n"
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],
"source": [
"# Split the data into a 70/25% train/test sets\n",
"set.seed(1)\n",
"idxs <- caret::createDataPartition(y = df[,ycol], p = 0.75)[[1]]\n",
"train <- hf[idxs,]\n",
"test <- hf[-idxs,]\n",
"\n",
"# Dimensions\n",
"dim(train)\n",
"dim(test)"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" | \r",
" | | 0%\r",
" | \r",
" |============= | 18%\r",
" | \r",
" |======================== | 34%\r",
" | \r",
" |================================== | 49%\r",
" | \r",
" |======================================================================| 100%\n"
]
},
{
"data": {
"text/plain": [
" user system elapsed \n",
" 0.196 0.008 4.297 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Train a Lasso GLM\n",
"system.time(fit <- h2o.glm(x = xcols,\n",
" y = ycol,\n",
" training_frame = train,\n",
" family = \"binomial\",\n",
" lambda_search = TRUE, # compute lasso path\n",
" alpha = 1)) # alpha = 1 means lasso, same as glmnet above"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"0.91198390500988"
],
"text/latex": [
"0.91198390500988"
],
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"text/plain": [
"[1] 0.9119839"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Compute AUC on test dataset\n",
"# H2O computes many model performance metrics automatically, including AUC\n",
"\n",
"perf <- h2o.performance(model = fit,\n",
" newdata = test)\n",
"h2o.auc(perf)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# References\n",
"\n",
"[1] [https://en.wikipedia.org/wiki/Linear_regression#Generalized\\_linear\\_models](https://en.wikipedia.org/wiki/Linear_regression#Generalized_linear_models)\n",
"\n",
"[2] [https://en.wikipedia.org/wiki/Generalized\\_linear\\_model](https://en.wikipedia.org/wiki/Generalized_linear_model)\n",
"\n",
"[3] [Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. J. Royal. Statist. Soc B., Vol. 58, No. 1, pages 267-288). ](http://www-stat.stanford.edu/%7Etibs/lasso/lasso.pdf)\n"
]
}
],
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"kernelspec": {
"display_name": "R",
"language": "R",
"name": "ir"
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"language_info": {
"codemirror_mode": "r",
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"mimetype": "text/x-r-source",
"name": "R",
"pygments_lexer": "r",
"version": "3.3.0"
}
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| apache-2.0 |
batfish/pybatfish | docs/source/notebooks/resolvingSpecifiers.ipynb | 1 | 47925 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"nbsphinx": "hidden"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"from pybatfish.client.session import Session\n",
"from pybatfish.datamodel import *\n",
"\n",
"pd.set_option(\"display.width\", 300) \n",
"pd.set_option(\"display.max_columns\", 20) \n",
"pd.set_option(\"display.max_rows\", 1000) \n",
"pd.set_option(\"display.max_colwidth\", None)\n",
"\n",
"# Configure all pybatfish loggers to use WARN level\n",
"import logging\n",
"logging.getLogger('pybatfish').setLevel(logging.WARN)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"nbsphinx": "hidden"
},
"outputs": [],
"source": [
"bf = Session(host=\"localhost\")\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Resolving Specifiers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Specifier grammars allow you to specify complex inputs for Batfish questions.\n",
"This category of questions reveals how specifier inputs are resolved\n",
"by Batfish.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* [Resolve Location Specifier](#Resolve-Location-Specifier)\n",
"* [Resolve Filter Specifier](#Resolve-Filter-Specifier)\n",
"* [Resolve Node Specifier](#Resolve-Node-Specifier)\n",
"* [Resolve Interface Specifier](#Resolve-Interface-Specifier)\n",
"* [Resolve IPs from Location Specifier](#Resolve-IPs-from-Location-Specifier)\n",
"* [Resolve IP Specifier](#Resolve-IP-Specifier)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"nbsphinx": "hidden"
},
"outputs": [
{
"data": {
"text/plain": [
"'generate_questions'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bf.set_network('generate_questions')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"nbsphinx": "hidden"
},
"outputs": [
{
"data": {
"text/plain": [
"'generate_questions'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bf.set_snapshot('generate_questions')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Resolve Location Specifier"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Returns the set of locations corresponding to a locationSpec value."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Helper question that shows how specified locationSpec values resolve to the locations in the network."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### **Inputs**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Name | Description | Type | Optional | Default Value\n",
"--- | --- | --- | --- | --- \n",
"locations | Input to the LocationSpecifier. | [LocationSpec](../specifiers.md#location-specifier) | False | \n",
"grammarVersion | Version of grammar to use for resolution. | str | True | "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### **Invocation**"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"result = bf.q.resolveLocationSpecifier(locations='@enter(as2border1[GigabitEthernet2/0])').answer().frame()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### **Return Value**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Name | Description | Type\n",
"--- | --- | ---\n",
"Location | Location | str"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print the first 5 rows of the returned Dataframe"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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>Location</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>InterfaceLinkLocation{nodeName=as2border1, interfaceName=GigabitEthernet2/0}</td>\n",
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"text/plain": [
" Location\n",
"0 InterfaceLinkLocation{nodeName=as2border1, interfaceName=GigabitEthernet2/0}"
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print the first row of the returned Dataframe"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Location InterfaceLinkLocation{nodeName=as2border1, interfaceName=GigabitEthernet2/0}\n",
"Name: 0, dtype: object"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result.iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"nbsphinx": "hidden"
},
"outputs": [
{
"data": {
"text/plain": [
"'generate_questions'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bf.set_network('generate_questions')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"nbsphinx": "hidden"
},
"outputs": [
{
"data": {
"text/plain": [
"'generate_questions'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bf.set_snapshot('generate_questions')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Resolve Filter Specifier"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Returns the set of filters corresponding to a filterSpec value."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Helper question that shows how specified filterSpec values resolve to the filters in the network."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### **Inputs**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Name | Description | Type | Optional | Default Value\n",
"--- | --- | --- | --- | --- \n",
"filters | Input to the FilterSpecifier. | [FilterSpec](../specifiers.md#filter-specifier) | False | \n",
"grammarVersion | Version of grammar to use for resolution. | str | True | \n",
"nodes | Input to the NodeSpecifier that specifies the set of nodes that should be considered. | [NodeSpec](../specifiers.md#node-specifier) | True | /.*/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### **Invocation**"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"result = bf.q.resolveFilterSpecifier(filters='@in(as2border1[GigabitEthernet0/0])').answer().frame()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### **Return Value**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Name | Description | Type\n",
"--- | --- | ---\n",
"Node | Node | str\n",
"Filter_Name | Filter name | str"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print the first 5 rows of the returned Dataframe"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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>Node</th>\n",
" <th>Filter_Name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>as2border1</td>\n",
" <td>OUTSIDE_TO_INSIDE</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Node Filter_Name\n",
"0 as2border1 OUTSIDE_TO_INSIDE"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print the first row of the returned Dataframe"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Node as2border1\n",
"Filter_Name OUTSIDE_TO_INSIDE\n",
"Name: 0, dtype: object"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result.iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"nbsphinx": "hidden"
},
"outputs": [
{
"data": {
"text/plain": [
"'generate_questions'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bf.set_network('generate_questions')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"nbsphinx": "hidden"
},
"outputs": [
{
"data": {
"text/plain": [
"'generate_questions'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bf.set_snapshot('generate_questions')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Resolve Node Specifier"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Returns the set of nodes corresponding to a nodeSpec value."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Helper question that shows how specified nodeSpec values resolve to the nodes in the network."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### **Inputs**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Name | Description | Type | Optional | Default Value\n",
"--- | --- | --- | --- | --- \n",
"nodes | Input to the NodeSpecifier. | [NodeSpec](../specifiers.md#node-specifier) | False | \n",
"grammarVersion | Version of grammar to use for resolution. | str | True | "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### **Invocation**"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"result = bf.q.resolveNodeSpecifier(nodes='/border/').answer().frame()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### **Return Value**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Name | Description | Type\n",
"--- | --- | ---\n",
"Node | Node | str"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print the first 5 rows of the returned Dataframe"
]
},
{
"cell_type": "code",
"execution_count": 16,
"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>Node</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>as1border1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>as1border2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>as2border1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>as2border2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>as3border1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Node\n",
"0 as1border1\n",
"1 as1border2\n",
"2 as2border1\n",
"3 as2border2\n",
"4 as3border1"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print the first row of the returned Dataframe"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Node as1border1\n",
"Name: 0, dtype: object"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result.iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"nbsphinx": "hidden"
},
"outputs": [
{
"data": {
"text/plain": [
"'generate_questions'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bf.set_network('generate_questions')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"nbsphinx": "hidden"
},
"outputs": [
{
"data": {
"text/plain": [
"'generate_questions'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bf.set_snapshot('generate_questions')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Resolve Interface Specifier"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Returns the set of interfaces corresponding to an interfaceSpec value."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Helper question that shows how specified interfaceSpec values resolve to the interfaces in the network."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### **Inputs**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Name | Description | Type | Optional | Default Value\n",
"--- | --- | --- | --- | --- \n",
"interfaces | Input to the interfaceSpecifier. | [InterfaceSpec](../specifiers.md#interface-specifier) | False | \n",
"grammarVersion | Version of grammar to use for resolution. | str | True | \n",
"nodes | Input to the NodeSpecifier that specifies the set of nodes that should be considered. | [NodeSpec](../specifiers.md#node-specifier) | True | /.*/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### **Invocation**"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"result = bf.q.resolveInterfaceSpecifier(interfaces='/border/[.*Ethernet]').answer().frame()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### **Return Value**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Name | Description | Type\n",
"--- | --- | ---\n",
"Interface | Interface | [Interface](../datamodel.rst#pybatfish.datamodel.primitives.Interface)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print the first 5 rows of the returned Dataframe"
]
},
{
"cell_type": "code",
"execution_count": 21,
"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>Interface</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>as1border1[Ethernet0/0]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>as1border1[GigabitEthernet0/0]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>as1border1[GigabitEthernet1/0]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>as1border2[Ethernet0/0]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>as1border2[GigabitEthernet0/0]</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Interface\n",
"0 as1border1[Ethernet0/0]\n",
"1 as1border1[GigabitEthernet0/0]\n",
"2 as1border1[GigabitEthernet1/0]\n",
"3 as1border2[Ethernet0/0]\n",
"4 as1border2[GigabitEthernet0/0]"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print the first row of the returned Dataframe"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Interface as1border1[Ethernet0/0]\n",
"Name: 0, dtype: object"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result.iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"nbsphinx": "hidden"
},
"outputs": [
{
"data": {
"text/plain": [
"'generate_questions'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bf.set_network('generate_questions')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"nbsphinx": "hidden"
},
"outputs": [
{
"data": {
"text/plain": [
"'generate_questions'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bf.set_snapshot('generate_questions')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Resolve IPs from Location Specifier"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Returns IPs that are auto-assigned to locations."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Helper question that shows IPs that will be assigned to specified locationSpec values by questions are automatically pick IPs based on locations."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### **Inputs**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Name | Description | Type | Optional | Default Value\n",
"--- | --- | --- | --- | --- \n",
"locations | Input to the LocationSpecifier. | [LocationSpec](../specifiers.md#location-specifier) | False | \n",
"grammarVersion | Version of grammar to use for resolution. | str | True | "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### **Invocation**"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"result = bf.q.resolveIpsOfLocationSpecifier(locations='@enter(as2border1[GigabitEthernet2/0])').answer().frame()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### **Return Value**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Name | Description | Type\n",
"--- | --- | ---\n",
"Locations | Resolution | str\n",
"IP_Space | IP space | str"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print the first 5 rows of the returned Dataframe"
]
},
{
"cell_type": "code",
"execution_count": 26,
"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>Locations</th>\n",
" <th>IP_Space</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>[InterfaceLinkLocation{nodeName=as2border1, interfaceName=GigabitEthernet2/0}]</td>\n",
" <td>AclIpSpace{lines=[AclIpSpaceLine{action=DENY, ipSpace=AclIpSpace{lines=[AclIpSpaceLine{ipSpace=IpIpSpace{ip=3.2.2.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=3.1.1.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.1.3.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.1.2.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.1.1.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=1.2.2.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=1.1.1.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=3.0.2.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=3.0.1.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.128.1.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.128.0.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=1.0.2.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=1.0.1.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=3.0.1.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=3.0.2.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=1.0.1.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=1.0.2.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.1.3.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.1.2.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.1.1.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=3.10.1.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=1.10.1.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=10.13.22.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.12.21.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.12.22.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.12.12.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.12.11.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=10.12.11.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=10.13.22.3}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.12.12.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.12.22.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.12.21.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.12.11.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=10.12.11.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=10.14.22.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=10.23.21.3}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.23.12.3}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.23.22.3}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.23.21.3}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.23.11.3}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.23.21.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.23.22.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.23.12.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.23.11.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=10.23.21.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.34.201.3}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.34.101.3}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.34.201.4}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.34.101.4}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=90.90.90.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=90.90.90.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.128.1.101}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.128.0.101}}]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[2.12.12.0, 2.12.12.255], whitelist=[2.12.12.0/24]}}]}</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Locations \\\n",
"0 [InterfaceLinkLocation{nodeName=as2border1, interfaceName=GigabitEthernet2/0}] \n",
"\n",
" IP_Space \n",
"0 AclIpSpace{lines=[AclIpSpaceLine{action=DENY, ipSpace=AclIpSpace{lines=[AclIpSpaceLine{ipSpace=IpIpSpace{ip=3.2.2.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=3.1.1.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.1.3.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.1.2.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.1.1.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=1.2.2.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=1.1.1.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=3.0.2.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=3.0.1.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.128.1.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.128.0.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=1.0.2.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=1.0.1.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=3.0.1.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=3.0.2.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=1.0.1.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=1.0.2.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.1.3.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.1.2.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.1.1.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=3.10.1.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=1.10.1.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=10.13.22.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.12.21.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.12.22.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.12.12.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.12.11.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=10.12.11.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=10.13.22.3}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.12.12.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.12.22.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.12.21.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.12.11.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=10.12.11.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=10.14.22.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=10.23.21.3}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.23.12.3}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.23.22.3}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.23.21.3}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.23.11.3}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.23.21.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.23.22.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.23.12.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.23.11.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=10.23.21.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.34.201.3}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.34.101.3}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.34.201.4}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.34.101.4}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=90.90.90.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=90.90.90.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.128.1.101}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.128.0.101}}]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[2.12.12.0, 2.12.12.255], whitelist=[2.12.12.0/24]}}]} "
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print the first row of the returned Dataframe"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Locations [InterfaceLinkLocation{nodeName=as2border1, interfaceName=GigabitEthernet2/0}]\n",
"IP_Space AclIpSpace{lines=[AclIpSpaceLine{action=DENY, ipSpace=AclIpSpace{lines=[AclIpSpaceLine{ipSpace=IpIpSpace{ip=3.2.2.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=3.1.1.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.1.3.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.1.2.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.1.1.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=1.2.2.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=1.1.1.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=3.0.2.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=3.0.1.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.128.1.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.128.0.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=1.0.2.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=1.0.1.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=3.0.1.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=3.0.2.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=1.0.1.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=1.0.2.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.1.3.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.1.2.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.1.1.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=3.10.1.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=1.10.1.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=10.13.22.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.12.21.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.12.22.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.12.12.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.12.11.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=10.12.11.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=10.13.22.3}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.12.12.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.12.22.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.12.21.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.12.11.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=10.12.11.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=10.14.22.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=10.23.21.3}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.23.12.3}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.23.22.3}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.23.21.3}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.23.11.3}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.23.21.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.23.22.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.23.12.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.23.11.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=10.23.21.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.34.201.3}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.34.101.3}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.34.201.4}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.34.101.4}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=90.90.90.2}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=90.90.90.1}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.128.1.101}}, AclIpSpaceLine{ipSpace=IpIpSpace{ip=2.128.0.101}}]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[2.12.12.0, 2.12.12.255], whitelist=[2.12.12.0/24]}}]}\n",
"Name: 0, dtype: object"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result.iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"nbsphinx": "hidden"
},
"outputs": [
{
"data": {
"text/plain": [
"'generate_questions'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bf.set_network('generate_questions')"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"nbsphinx": "hidden"
},
"outputs": [
{
"data": {
"text/plain": [
"'generate_questions'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bf.set_snapshot('generate_questions')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Resolve IP Specifier"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Returns the IP address space corresponding to an ipSpec value."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Helper question that shows how specified ipSpec values resolve to IPs."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### **Inputs**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Name | Description | Type | Optional | Default Value\n",
"--- | --- | --- | --- | --- \n",
"ips | Input to the IP space specifier. | [IpSpec](../specifiers.md#ip-specifier) | False | \n",
"grammarVersion | Version of grammar to use for resolution. | str | True | "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### **Invocation**"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"result = bf.q.resolveIpSpecifier(ips='/border/[.*Ethernet]').answer().frame()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### **Return Value**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Name | Description | Type\n",
"--- | --- | ---\n",
"IP_Space | IP space | str"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print the first 5 rows of the returned Dataframe"
]
},
{
"cell_type": "code",
"execution_count": 31,
"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>IP_Space</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>AclIpSpace{lines=[AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[1.0.1.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[10.12.11.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[10.13.22.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[1.0.2.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[10.14.22.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[10.12.11.2]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[2.12.11.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[2.12.12.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[10.23.21.2]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[2.12.22.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[2.12.21.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[3.0.1.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[10.23.21.3]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[10.13.22.3]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[3.0.2.1]}}]}</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" IP_Space\n",
"0 AclIpSpace{lines=[AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[1.0.1.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[10.12.11.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[10.13.22.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[1.0.2.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[10.14.22.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[10.12.11.2]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[2.12.11.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[2.12.12.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[10.23.21.2]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[2.12.22.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[2.12.21.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[3.0.1.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[10.23.21.3]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[10.13.22.3]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[3.0.2.1]}}]}"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print the first row of the returned Dataframe"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"IP_Space AclIpSpace{lines=[AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[1.0.1.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[10.12.11.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[10.13.22.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[1.0.2.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[10.14.22.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[10.12.11.2]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[2.12.11.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[2.12.12.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[10.23.21.2]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[2.12.22.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[2.12.21.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[3.0.1.1]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[10.23.21.3]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[10.13.22.3]}}, AclIpSpaceLine{ipSpace=IpWildcardSetIpSpace{blacklist=[], whitelist=[3.0.2.1]}}]}\n",
"Name: 0, dtype: object"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result.iloc[0]"
]
}
],
"metadata": {
"celltoolbar": "Edit Metadata",
"hide_input": 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": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
developerQuinnZ/this_will_work | lessons/09-Pandas/data-wrangling-with-Pandas.ipynb | 1 | 8797831 | null | mit |
tensorflow/docs-l10n | site/zh-cn/tutorials/keras/save_and_load.ipynb | 1 | 27175 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "g_nWetWWd_ns"
},
"source": [
"##### Copyright 2019 The TensorFlow Authors."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "2pHVBk_seED1"
},
"outputs": [],
"source": [
"#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "N_fMsQ-N8I7j"
},
"outputs": [],
"source": [
"#@title MIT License\n",
"#\n",
"# Copyright (c) 2017 François Chollet\n",
"#\n",
"# Permission is hereby granted, free of charge, to any person obtaining a\n",
"# copy of this software and associated documentation files (the \"Software\"),\n",
"# to deal in the Software without restriction, including without limitation\n",
"# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
"# and/or sell copies of the Software, and to permit persons to whom the\n",
"# Software is furnished to do so, subject to the following conditions:\n",
"#\n",
"# The above copyright notice and this permission notice shall be included in\n",
"# all copies or substantial portions of the Software.\n",
"#\n",
"# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
"# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
"# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
"# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
"# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
"# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
"# DEALINGS IN THE SOFTWARE."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pZJ3uY9O17VN"
},
"source": [
"# 保存和恢复模型"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "M4Ata7_wMul1"
},
"source": [
"<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
" <td> <a target=\"_blank\" href=\"https://tensorflow.google.cn/tutorials/keras/save_and_load\"><img src=\"https://tensorflow.google.cn/images/tf_logo_32px.png\">在 tensorflow.google.cn 上查看</a> </td>\n",
" <td> <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/keras/save_and_load.ipynb\"><img src=\"https://tensorflow.google.cn/images/colab_logo_32px.png\">在 Google Colab 运行</a> </td>\n",
" <td> <a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/keras/save_and_load.ipynb\"><img src=\"https://tensorflow.google.cn/images/GitHub-Mark-32px.png\">在 Github 上查看源代码</a> </td>\n",
" <td> <a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/zh-cn/tutorials/keras/save_and_load.ipynb\" class=\"_active_edit_href\"><img src=\"https://tensorflow.google.cn/images/download_logo_32px.png\">下载此 notebook</a> </td>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mBdde4YJeJKF"
},
"source": [
"可以在训练期间和之后保存模型进度。这意味着模型可以从停止的地方恢复，避免长时间的训练。此外，保存还意味着您可以分享您的模型，其他人可以重现您的工作。在发布研究模型和技术时，大多数机器学习从业者会分享：\n",
"\n",
"- 用于创建模型的代码\n",
"- 模型训练的权重 (weight) 和参数 (parameters) 。\n",
"\n",
"共享数据有助于其他人了解模型的工作原理，并使用新数据自行尝试。\n",
"\n",
"小心：TensorFlow 模型是代码，对于不受信任的代码，一定要小心。请参阅 [安全使用 TensorFlow](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) 以了解详情。\n",
"\n",
"### 选项\n",
"\n",
"根据您使用的 API，可以通过多种方式保存 TensorFlow 模型。本指南使用 [tf.keras](https://tensorflow.google.cn/guide/keras)，这是一种在 TensorFlow 中构建和训练模型的高级 API。对于其他方式，请参阅 TensorFlow [保存和恢复](https://tensorflow.google.cn/guide/saved_model)指南或[在 Eager 中保存](https://tensorflow.google.cn/guide/eager#object-based_saving)。"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xCUREq7WXgvg"
},
"source": [
"## 配置\n",
"\n",
"### 安装并导入"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7l0MiTOrXtNv"
},
"source": [
"安装并导入Tensorflow和依赖项："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "RzIOVSdnMYyO"
},
"outputs": [],
"source": [
"!pip install pyyaml h5py # Required to save models in HDF5 format"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7Nm7Tyb-gRt-"
},
"outputs": [],
"source": [
"import os\n",
"\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"\n",
"print(tf.version.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SbGsznErXWt6"
},
"source": [
"### 获取示例数据集\n",
"\n",
"为了演示如何保存和加载权重，您将使用 [MNIST 数据集](http://yann.lecun.com/exdb/mnist/)。为了加快运行速度，请使用前 1000 个样本："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9rGfFwE9XVwz"
},
"outputs": [],
"source": [
"(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()\n",
"\n",
"train_labels = train_labels[:1000]\n",
"test_labels = test_labels[:1000]\n",
"\n",
"train_images = train_images[:1000].reshape(-1, 28 * 28) / 255.0\n",
"test_images = test_images[:1000].reshape(-1, 28 * 28) / 255.0"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "anG3iVoXyZGI"
},
"source": [
"### 定义模型"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wynsOBfby0Pa"
},
"source": [
"首先构建一个简单的序列（sequential）模型："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0HZbJIjxyX1S"
},
"outputs": [],
"source": [
"# Define a simple sequential model\n",
"def create_model():\n",
" model = tf.keras.models.Sequential([\n",
" keras.layers.Dense(512, activation='relu', input_shape=(784,)),\n",
" keras.layers.Dropout(0.2),\n",
" keras.layers.Dense(10)\n",
" ])\n",
"\n",
" model.compile(optimizer='adam',\n",
" loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
" metrics=[tf.metrics.SparseCategoricalAccuracy()])\n",
"\n",
" return model\n",
"\n",
"# Create a basic model instance\n",
"model = create_model()\n",
"\n",
"# Display the model's architecture\n",
"model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "soDE0W_KH8rG"
},
"source": [
"## 在训练期间保存模型（以 checkpoints 形式保存）"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mRyd5qQQIXZm"
},
"source": [
"您可以使用经过训练的模型而无需重新训练，或者在训练过程中断的情况下从离开处继续训练。`tf.keras.callbacks.ModelCheckpoint` 回调允许您在训练*期间*和*结束*时持续保存模型。\n",
"\n",
"### Checkpoint 回调用法\n",
"\n",
"创建一个只在训练期间保存权重的 `tf.keras.callbacks.ModelCheckpoint` 回调："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "IFPuhwntH8VH"
},
"outputs": [],
"source": [
"checkpoint_path = \"training_1/cp.ckpt\"\n",
"checkpoint_dir = os.path.dirname(checkpoint_path)\n",
"\n",
"# Create a callback that saves the model's weights\n",
"cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n",
" save_weights_only=True,\n",
" verbose=1)\n",
"\n",
"# Train the model with the new callback\n",
"model.fit(train_images, \n",
" train_labels, \n",
" epochs=10,\n",
" validation_data=(test_images, test_labels),\n",
" callbacks=[cp_callback]) # Pass callback to training\n",
"\n",
"# This may generate warnings related to saving the state of the optimizer.\n",
"# These warnings (and similar warnings throughout this notebook)\n",
"# are in place to discourage outdated usage, and can be ignored."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rlM-sgyJO084"
},
"source": [
"这将创建一个 TensorFlow checkpoint 文件集合，这些文件在每个 epoch 结束时更新："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "gXG5FVKFOVQ3"
},
"outputs": [],
"source": [
"os.listdir(checkpoint_dir)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wlRN_f56Pqa9"
},
"source": [
"只要两个模型共享相同的架构，您就可以在它们之间共享权重。因此，当从仅权重恢复模型时，创建一个与原始模型具有相同架构的模型，然后设置其权重。\n",
"\n",
"现在，重新构建一个未经训练的全新模型并基于测试集对其进行评估。未经训练的模型将以机会水平执行（约 10% 的准确率）："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Fp5gbuiaPqCT"
},
"outputs": [],
"source": [
"# Create a basic model instance\n",
"model = create_model()\n",
"\n",
"# Evaluate the model\n",
"loss, acc = model.evaluate(test_images, test_labels, verbose=2)\n",
"print(\"Untrained model, accuracy: {:5.2f}%\".format(100 * acc))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1DTKpZssRSo3"
},
"source": [
"然后从 checkpoint 加载权重并重新评估："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2IZxbwiRRSD2"
},
"outputs": [],
"source": [
"# Loads the weights\n",
"model.load_weights(checkpoint_path)\n",
"\n",
"# Re-evaluate the model\n",
"loss, acc = model.evaluate(test_images, test_labels, verbose=2)\n",
"print(\"Restored model, accuracy: {:5.2f}%\".format(100 * acc))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bpAbKkAyVPV8"
},
"source": [
"### checkpoint 回调选项\n",
"\n",
"回调提供了几个选项，为 checkpoint 提供唯一名称并调整 checkpoint 频率。\n",
"\n",
"训练一个新模型，每五个 epochs 保存一次唯一命名的 checkpoint ："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mQF_dlgIVOvq"
},
"outputs": [],
"source": [
"# Include the epoch in the file name (uses `str.format`)\n",
"checkpoint_path = \"training_2/cp-{epoch:04d}.ckpt\"\n",
"checkpoint_dir = os.path.dirname(checkpoint_path)\n",
"\n",
"batch_size = 32\n",
"\n",
"# Create a callback that saves the model's weights every 5 epochs\n",
"cp_callback = tf.keras.callbacks.ModelCheckpoint(\n",
" filepath=checkpoint_path, \n",
" verbose=1, \n",
" save_weights_only=True,\n",
" save_freq=5*batch_size)\n",
"\n",
"# Create a new model instance\n",
"model = create_model()\n",
"\n",
"# Save the weights using the `checkpoint_path` format\n",
"model.save_weights(checkpoint_path.format(epoch=0))\n",
"\n",
"# Train the model with the new callback\n",
"model.fit(train_images, \n",
" train_labels,\n",
" epochs=50, \n",
" batch_size=batch_size, \n",
" callbacks=[cp_callback],\n",
" validation_data=(test_images, test_labels),\n",
" verbose=0)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1zFrKTjjavWI"
},
"source": [
"现在查看生成的 checkpoint 并选择最新的 checkpoint ："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "p64q3-V4sXt0"
},
"outputs": [],
"source": [
"os.listdir(checkpoint_dir)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "1AN_fnuyR41H"
},
"outputs": [],
"source": [
"latest = tf.train.latest_checkpoint(checkpoint_dir)\n",
"latest"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Zk2ciGbKg561"
},
"source": [
"注：默认 TensorFlow 格式只保存最近的 5 个检查点。\n",
"\n",
"如果要进行测试，请重置模型并加载最新的 checkpoint ："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3M04jyK-H3QK"
},
"outputs": [],
"source": [
"# Create a new model instance\n",
"model = create_model()\n",
"\n",
"# Load the previously saved weights\n",
"model.load_weights(latest)\n",
"\n",
"# Re-evaluate the model\n",
"loss, acc = model.evaluate(test_images, test_labels, verbose=2)\n",
"print(\"Restored model, accuracy: {:5.2f}%\".format(100 * acc))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "c2OxsJOTHxia"
},
"source": [
"## 这些文件是什么？"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JtdYhvWnH2ib"
},
"source": [
"上述代码将权重存储到 [checkpoint](https://tensorflow.google.cn/guide/saved_model#save_and_restore_variables)—— 格式化文件的集合中，这些文件仅包含二进制格式的训练权重。 Checkpoints 包含：\n",
"\n",
"- 一个或多个包含模型权重的分片。\n",
"- 一个索引文件，指示哪些权重存储在哪个分片中。\n",
"\n",
"如果您在一台计算机上训练模型，您将获得一个具有如下后缀的分片：`.data-00000-of-00001`"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "S_FA-ZvxuXQV"
},
"source": [
"## 手动保存权重\n",
"\n",
"使用 `Model.save_weights` 方法手动保存权重。默认情况下，`tf.keras`（尤其是 `save_weights`）使用扩展名为 `.ckpt` 的 TensorFlow [检查点](../../guide/checkpoint.ipynb)格式（保存在扩展名为 `.h5` 的 [HDF5](https://js.tensorflow.org/tutorials/import-keras.html) 中，[保存和序列化模型](../../guide/keras/save_and_serialize#weights-only_saving_in_savedmodel_format)指南中会讲到这一点）："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "R7W5plyZ-u9X"
},
"outputs": [],
"source": [
"# Save the weights\n",
"model.save_weights('./checkpoints/my_checkpoint')\n",
"\n",
"# Create a new model instance\n",
"model = create_model()\n",
"\n",
"# Restore the weights\n",
"model.load_weights('./checkpoints/my_checkpoint')\n",
"\n",
"# Evaluate the model\n",
"loss, acc = model.evaluate(test_images, test_labels, verbose=2)\n",
"print(\"Restored model, accuracy: {:5.2f}%\".format(100 * acc))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kOGlxPRBEvV1"
},
"source": [
"## 保存整个模型\n",
"\n",
"调用 [`model.save`](https://tensorflow.google.cn/api_docs/python/tf/keras/Model#save) 将保存模型的结构，权重和训练配置保存在单个文件/文件夹中。这可以让您导出模型，以便在不访问原始 Python 代码*的情况下使用它。因为优化器状态（optimizer-state）已经恢复，您可以从中断的位置恢复训练。\n",
"\n",
"整个模型可以保存为两种不同的文件格式（`SavedModel` 和 `HDF5`）。TensorFlow `SavedModel` 格式是 TF2.x 中的默认文件格式。但是，模型能够以 `HDF5` 格式保存。下面详细介绍了如何以两种文件格式保存整个模型。\n",
"\n",
"保存完整模型会非常有用——您可以在 TensorFlow.js（[Saved Model](https://tensorflow.google.cn/js/tutorials/conversion/import_saved_model), [HDF5](https://tensorflow.google.cn/js/tutorials/conversion/import_keras)）加载它们，然后在 web 浏览器中训练和运行它们，或者使用 TensorFlow Lite 将它们转换为在移动设备上运行（[Saved Model](https://tensorflow.google.cn/lite/convert/python_api#converting_a_savedmodel_), [HDF5](https://tensorflow.google.cn/lite/convert/python_api#converting_a_keras_model_)）\n",
"\n",
"*自定义对象（例如，子类化模型或层）在保存和加载时需要特别注意。请参阅下面的**保存自定义对象**部分 "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kPyhgcoVzqUB"
},
"source": [
"### SavedModel 格式"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LtcN4VIb7JkK"
},
"source": [
"SavedModel 格式是另一种序列化模型的方式。以这种格式保存的模型可以使用 `tf.keras.models.load_model` 恢复，并且与 TensorFlow Serving 兼容。[SavedModel 指南](https://tensorflow.google.cn/guide/saved_model)详细介绍了如何应用/检查 SavedModel。以下部分说明了保存和恢复模型的步骤。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "sI1YvCDFzpl3"
},
"outputs": [],
"source": [
"# Create and train a new model instance.\n",
"model = create_model()\n",
"model.fit(train_images, train_labels, epochs=5)\n",
"\n",
"# Save the entire model as a SavedModel.\n",
"!mkdir -p saved_model\n",
"model.save('saved_model/my_model') "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iUvT_3qE8hV5"
},
"source": [
"SavedModel 格式是一个包含 protobuf 二进制文件和 TensorFlow 检查点的目录。检查保存的模型目录："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "sq8fPglI1RWA"
},
"outputs": [],
"source": [
"# my_model directory\n",
"!ls saved_model\n",
"\n",
"# Contains an assets folder, saved_model.pb, and variables folder.\n",
"!ls saved_model/my_model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "B7qfpvpY9HCe"
},
"source": [
"从保存的模型重新加载一个新的 Keras 模型："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0YofwHdN0pxa"
},
"outputs": [],
"source": [
"new_model = tf.keras.models.load_model('saved_model/my_model')\n",
"\n",
"# Check its architecture\n",
"new_model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uWwgNaz19TH2"
},
"source": [
"使用与原始模型相同的参数编译恢复的模型。尝试使用加载的模型运行评估和预测："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Yh5Mu0yOgE5J"
},
"outputs": [],
"source": [
"# Evaluate the restored model\n",
"loss, acc = new_model.evaluate(test_images, test_labels, verbose=2)\n",
"print('Restored model, accuracy: {:5.2f}%'.format(100 * acc))\n",
"\n",
"print(new_model.predict(test_images).shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SkGwf-50zLNn"
},
"source": [
"### HDF5 格式\n",
"\n",
"Keras使用 [HDF5](https://en.wikipedia.org/wiki/Hierarchical_Data_Format) 标准提供了一种基本的保存格式。 "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "m2dkmJVCGUia"
},
"outputs": [],
"source": [
"# Create and train a new model instance.\n",
"model = create_model()\n",
"model.fit(train_images, train_labels, epochs=5)\n",
"\n",
"# Save the entire model to a HDF5 file.\n",
"# The '.h5' extension indicates that the model should be saved to HDF5.\n",
"model.save('my_model.h5') "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GWmttMOqS68S"
},
"source": [
"现在，从该文件重新创建模型："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "5NDMO_7kS6Do"
},
"outputs": [],
"source": [
"# Recreate the exact same model, including its weights and the optimizer\n",
"new_model = tf.keras.models.load_model('my_model.h5')\n",
"\n",
"# Show the model architecture\n",
"new_model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JXQpbTicTBwt"
},
"source": [
"检查其准确率（accuracy）："
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jwEaj9DnTCVA"
},
"outputs": [],
"source": [
"loss, acc = new_model.evaluate(test_images, test_labels, verbose=2)\n",
"print('Restored model, accuracy: {:5.2f}%'.format(100 * acc))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dGXqd4wWJl8O"
},
"source": [
"Keras 通过检查模型的架构来保存这些模型。这种技术可以保存所有内容：\n",
"\n",
"- 权重值\n",
"- 模型的架构\n",
"- 模型的训练配置（您传递给 `.compile()` 方法的内容）\n",
"- 优化器及其状态（如果有）（这样，您便可从中断的地方重新启动训练）\n",
"\n",
"Keras 无法保存 `v1.x` 优化器（来自 `tf.compat.v1.train`），因为它们与检查点不兼容。对于 v1.x 优化器，您需要在加载-失去优化器的状态后，重新编译模型。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kAUKJQyGqTNH"
},
"source": [
"### 保存自定义对象\n",
"\n",
"如果您正在使用 SavedModel 格式，则可以跳过此部分。HDF5 和 SavedModel 之间的主要区别在于，HDF5 使用对象配置来保存模型架构，而 SavedModel 则保存执行计算图。因此，SavedModel 能够在不需要原始代码的情况下保存自定义对象，如子类模型和自定义层。\n",
"\n",
"要将自定义对象保存到 HDF5，您必须执行以下操作：\n",
"\n",
"1. 在您的对象中定义一个 `get_config` 方法，并且可以选择定义一个 `from_config` 类方法。\n",
" - `get_config(self)` 返回重新创建对象所需的参数的 JSON 可序列化字典。\n",
" - `from_config(cls, config)` 使用从 `get_config` 返回的配置来创建一个新对象。默认情况下，此函数将使用配置作为初始化 kwarg (`return cls(**config)`)。\n",
"2. 加载模型时将对象传递给 `custom_objects` 参数。参数必须是将字符串类名映射到 Python 类的字典。例如 `tf.keras.models.load_model(path, custom_objects={'CustomLayer': CustomLayer})`\n",
"\n",
"有关自定义对象和 `get_config` 的示例，请参阅[从头开始编写层和模型](https://tensorflow.google.cn/guide/keras/custom_layers_and_models)教程。\n"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"name": "save_and_load.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
colour-science/colour-ipython | notebooks/colorimetry/photometry.ipynb | 1 | 745 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# !!! D . R . A . F . T !!!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Photometry"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bibliography"
]
}
],
"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 |
hawkw/breadplan | Breadplan Science.ipynb | 1 | 6164 | {
"metadata": {
"name": "",
"signature": "sha256:b8554cf229ad6e20983d575938d5c0aa4def4378a69bc30a550cae78c17810bd"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# BreadPlan Science!\n",
"\n",
"This is where I'm doing the calculations that go into BreadPlan\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Densities for flour types in grams/cup according to different sources\n",
"# USDA and Gold Medal from http://www.weekendbakery.com/cooking-conversions/\n",
"# King Arthur from http://www.kingarthurflour.com/recipe/master-weight-chart.html \n",
"# (rounded to nearest gram)\n",
"whole_wheat = {\n",
" \"USDA\": 125,\n",
" \"Gold Medal\": 128,\n",
" \"King Arthur\": 113,\n",
" }\n",
"all_purpose = {\n",
" \"USDA\": 125,\n",
" \"Gold Medal\": 130,\n",
" \"King Arthur\": 120\n",
" }\n",
"bread_flour = {\n",
" \"USDA\": 127,\n",
" \"Gold Medal\": 135,\n",
" \"King Arthur\": 120\n",
" }\n",
"rye = {\n",
" \"USDA\": 102,\n",
" # couldn't find Gold Medal's reported density for rye flour (do they make it?)\n",
" \"King Arthur Medium Rye\": 103,\n",
" \"King Arthur White Rye\": 106\n",
" }"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"values = lambda flour: [grams for key, grams in flour.iteritems() if key not in [\"average\", \"std\"]]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Compute average density\n",
"whole_wheat[\"average\"] = numpy.mean(values(whole_wheat))\n",
"all_purpose[\"average\"] = numpy.mean(values(all_purpose))\n",
"bread_flour[\"average\"] = numpy.mean(values(bread_flour))\n",
"rye[\"average\"] = numpy.mean(values(rye))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Compute standard deviations\n",
"whole_wheat[\"std\"] = numpy.std(values(whole_wheat))\n",
"all_purpose[\"std\"] = numpy.std(values(all_purpose))\n",
"bread_flour[\"std\"] = numpy.std(values(bread_flour))\n",
"rye[\"std\"] = numpy.std(values(rye))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"whole_wheat"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 27,
"text": [
"{'Gold Medal': 128,\n",
" 'King Arthur': 113,\n",
" 'USDA': 125,\n",
" 'average': 122.0,\n",
" 'std': 6.4807406984078604}"
]
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"all_purpose"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 28,
"text": [
"{'Gold Medal': 130,\n",
" 'King Arthur': 120,\n",
" 'USDA': 125,\n",
" 'average': 125.0,\n",
" 'std': 4.0824829046386304}"
]
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bread_flour"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 29,
"text": [
"{'Gold Medal': 135,\n",
" 'King Arthur': 120,\n",
" 'USDA': 127,\n",
" 'average': 127.33333333333333,\n",
" 'std': 6.1282587702834119}"
]
}
],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rye"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 30,
"text": [
"{'King Arthur Medium Rye': 103,\n",
" 'King Arthur White Rye': 106,\n",
" 'USDA': 102,\n",
" 'average': 103.66666666666667,\n",
" 'std': 1.699673171197595}"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# A nice simple percentage function\n",
"percentage = lambda part, whole: (part * whole) / 100.0"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Eggs!\n",
"large_egg = 54.4 # USDA large egg (this is American)\n",
"shell = percentage(13, large_egg) #"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"7.071999999999999"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
datascience-practice/data-quest | python_introduction/intermediate/Classes.ipynb | 2 | 8755 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3: Class syntax\n",
"### Instructions\n",
"Create a class called Team.\n",
"\n",
"Inside the class, create a name property. Assign the value \"Tampa Bay Buccaneers\" to this property.\n",
"\n",
"Create an instance of the Team class, and assign it to the variable bucs.\n",
"\n",
"Print the name property of the bucs instance."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"black\n"
]
}
],
"source": [
"class Car():\n",
" def __init__(self):\n",
" self.color = \"black\"\n",
" self.make = \"honda\"\n",
" self.model = \"accord\"\n",
"\n",
"black_honda_accord = Car()\n",
"\n",
"print(black_honda_accord.color)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Answer\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tampa Bay Buccaneers\n"
]
}
],
"source": [
"class Team():\n",
" def __init__(self):\n",
" self.name = \"Tampa Bay Buccaneers\"\n",
" \n",
"bucs = Team()\n",
"\n",
"print(bucs.name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4: Instance methods and __init__\n",
"### Instrutions\n",
"Add a name parameter to the __init__ method, and set the self.name property to the name argument.\n",
"\n",
"Make an instance of the class, passing in the value \"New York Giants\" to the __init__ function (when you write Team()).\n",
"\n",
"Assign the result to the variable giants.\n",
"### Answer"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"New York Giants\n"
]
}
],
"source": [
"class Team():\n",
" def __init__(self, name):\n",
" self.name = name\n",
" \n",
"giants = Team(\"New York Giants\")\n",
"\n",
"print(giants.name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6: More instance methods\n",
"### Instructions\n",
"Add an instance method called count_total_wins to the Team class. The method should take no arguments (except self), and should return the number of games the team won from 2009-2013.\n",
"\n",
"Use the instance method to assign the number of wins by the \"Denver Broncos\" to broncos_wins.\n",
"\n",
"Use the instance method to assign the number of wins by the \"Kansas City Chiefs\" to chiefs_wins."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tampa Bay Buccaneers\n"
]
}
],
"source": [
"import csv\n",
"\n",
"f = open(\"nfl.csv\", 'r')\n",
"nfl = list(csv.reader(f))\n",
"\n",
"# The nfl data is loaded into the nfl variable.\n",
"class Team():\n",
" def __init__(self, name):\n",
" self.name = name\n",
"\n",
" def print_name(self):\n",
" print(self.name)\n",
" \n",
" # Your method goes here\n",
" \n",
" \n",
"bucs = Team(\"Tampa Bay Buccaneers\")\n",
"bucs.print_name()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Answer"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tampa Bay Buccaneers\n",
"8 3\n"
]
}
],
"source": [
"import csv\n",
"\n",
"f = open(\"nfl.csv\", 'r')\n",
"nfl = list(csv.reader(f))\n",
"\n",
"# The nfl data is loaded into the nfl variable.\n",
"class Team():\n",
" def __init__(self, name):\n",
" self.name = name\n",
"\n",
" def print_name(self):\n",
" print(self.name)\n",
" \n",
" # Your method goes here\n",
" def count_total_wins(self):\n",
" wins = 0\n",
" for row in nfl:\n",
" if row[2] == self.name:\n",
" wins += 1\n",
" return wins\n",
" \n",
" \n",
"bucs = Team(\"Tampa Bay Buccaneers\")\n",
"bucs.print_name()\n",
"\n",
"broncos_wins = Team(\"Denver Broncos\").count_total_wins()\n",
"chiefs_wins = Team(\"Kansas City Chiefs\").count_total_wins()\n",
"\n",
"print(broncos_wins, chiefs_wins)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7: Adding to the init function\n",
"### Instructions\n",
"Add code in the __init__ method to read and store the nfl data in the self.nfl property. The data is stored in \"nfl.csv\".\n",
"\n",
"Then alter the code in the count_total_wins method to use the self.nfl property instead of the nfl variable (which no longer exists).\n",
"\n",
"Use the instance method to assign the number of wins by the \"Jacksonville Jaguars\" to jaguars_wins."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import csv\n",
"class Team():\n",
" def __init__(self, name):\n",
" self.name = name\n",
" f = open(\"nfl.csv\", 'r')\n",
" self.nfl = list(csv.reader(f))\n",
" \n",
" def count_total_wins(self):\n",
" count = 0\n",
" for row in self.nfl:\n",
" if row[2] == self.name:\n",
" count = count + 1\n",
" return count\n",
"jaguars_wins = Team(\"Jacksonville Jaguars\").count_total_wins()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"7\n"
]
}
],
"source": [
"print(jaguars_wins)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8: Wins in a year\n",
"### Instructions\n",
"Add a method to the Team class that computes how many wins the team had in a given year.\n",
"\n",
"The count_wins_in_year method should take a year string as an argument (e.g. \"2011\"), and return the number of wins the team had in that year.\n",
"\n",
"Use the instance method to assign the number of wins in \"2013\" by the \"San Francisco 49ers\" to niners_wins_2013."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n"
]
}
],
"source": [
"import csv\n",
"class Team():\n",
" def __init__(self, name):\n",
" self.name = name\n",
" f = open(\"nfl.csv\", 'r')\n",
" csvreader = csv.reader(f)\n",
" self.nfl = list(csvreader)\n",
"\n",
" def count_total_wins(self):\n",
" count = 0\n",
" for row in self.nfl:\n",
" if row[2] == self.name:\n",
" count = count + 1\n",
" return count\n",
" \n",
" def count_wins_in_a_year(self, year):\n",
" count = 0\n",
" for row in self.nfl:\n",
" if row[0] == year and row[2] == self.name:\n",
" count = count + 1\n",
" return count \n",
" \n",
"niners_wins_2013 = Team(\"San Francisco 49ers\").count_wins_in_a_year(\"2013\")\n",
"print(niners_wins_2013)"
]
},
{
"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 |
awitney/2017 | hic_workshop_2017/WD/Basic_HiC_analysis.ipynb | 2 | 28147 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"navigation\"></a>\n",
"# Hi-C data analysis\n",
"\n",
"Welcome to the [Jupyter notebook](http://jupyter.org/) dedicated to Hi-C data analysis. Here we will be working in interactive Python environment with some mixture of bash command line tools. \n",
"\n",
"Here is the outline of what we are going to do:\n",
"\n",
"0. [Notebook basics](#basics)\n",
"1. [Reads maping](#mapping)\n",
"2. [Data filtering](#filtering)\n",
"3. [Binning](#binning)\n",
"4. [Hi-C data visualisation](#visualisation)\n",
"5. [Iterative correction](#correction)\n",
"6. [Compartments and TADs](#meta)\n",
"\n",
"If you have any questions, please, contact Aleksandra Galitsyna ([email protected])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"basics\"></a>\n",
"## 0. Notebook basics\n",
"\n",
"If you are new to Python and Jupyter notebook, please, take a quick look through this small list of tips.\n",
"\n",
"- First of all, __Jupyter notebook is organised in cells__, which may contain text, comments and code blocks of any size."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# This is regular Python comment inside Jupyter \"Code\" cell.\n",
"# You can easily run \"Hello world\" in the \"Code\" cell (focus on the cell and press Shift+Enter):\n",
"print(\"Hello world!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- There are also other types of cells, for example, \"Markdown\". Double click this cell to view raw Markdown markup content.\n",
"[comment]: <> (Wow, can you see it? This is Markdown commented line. Please, click Shift+Enter to render Markdown output again.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- You can define functions, classes, run pipelines and visualisations, run thousands of code lines inside a Jupyter cell.\n",
"But usually, it is convenient to write simple and clean blocks of code.\n",
"\n",
"\n",
"- Note that behind this interactive notebook you have __regular Python session running__. Thus Python variables are accessible only throughout your history of actions in the notebook. To create a variable, you have to execute the corresponding block of code. All your variables will be lost when you restart the kernel of the notebook. \n",
"\n",
"- You can pause or stop the kernel, save notebook (.ipynb) file, copy and insert cells via __buttons in the toolbar__. Please, take a look at these useful buttons.\n",
"\n",
"- Also, try pressing 'Esc' and then 'h'. You will see __shortcuts help__. \n",
"\n",
"- Jupyter notebook allows you to create __[\"magical\" cells](http://ipython.readthedocs.io/en/stable/interactive/magics.html )__. We will use %%bash, %%capture, %matplotlib. For example, %%bash magic makes it easier to access bash commands:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"%%bash \n",
"echo \"Current directory is: \"; pwd\n",
"echo \"List of files in the current directory is: \"; ls"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- If you are not sure about the function, class or variable then use its name with '?' at the end to get available documentation. Here is an example for common module numpy:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Module import under custom name\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# You've started asking questions about it\n",
"np?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OK, it seems that now we are ready to start our Hi-C data analysis! I've placed [Go top](#navigation) shortcut for you in each section so that you can navigate quickly throughout the notebook."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"mapping\"></a>\n",
"## 1. Reads mapping\n",
"[Go top](#navigation)\n",
"\n",
"#### 1.1 Input raw data \n",
"Hi-C results in paired-end sequencing, where each pair represents one possible contact. The analysis starts with raw sequencing data (.fastq files). \n",
"\n",
"I've downloaded raw files from [Flyamer et al. 2017](http://www.nature.com/nature/journal/v544/n7648/full/nature21711.html?foxtrotcallback=true) (GEO ID [GSE80006](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE80006)) and placed them in the DATA/FASTQ/ directory. \n",
"\n",
"We can view these files easily with bash help. Forward and reverse reads, correspondingly:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%bash \n",
"head -n 8 '../DATA/FASTQ/K562_B-bulk_R1.fastq'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%bash \n",
"head -n 8 '../DATA/FASTQ/K562_B-bulk_R2.fastq'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1.2 Genome\n",
"\n",
"Now we have to map these reads to the genome of interest (*Homo sapiens* hg19 downloaded from [UCSC](ftp://hgdownload.cse.ucsc.edu/goldenPath/hg19/chromosomes/) in this case). \n",
"We are going to use only chromosome 1 to minimise computational time. \n",
"\n",
"The genome is also pre-downloaded:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%bash \n",
"ls ../GENOMES/HG19_FASTA"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For Hi-C data mapping we will use [hiclib](https://bitbucket.org/mirnylab/hiclib). It utilizes [bowtie 2](http://bowtie-bio.sourceforge.net/bowtie2/manual.shtml) read mapping software. Bowtie 2 indexes the genome prior to reads mapping in order to reduce memory usage. Usually, you have to run genome indexing, but I've already done this time-consuming step. That's why code for this step is included but commented."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#%%bash\n",
"#bowtie2-build /home/jovyan/GENOMES/HG19_FASTA/chr1.fa /home/jovyan/GENOMES/HG19_IND/hg19_chr1\n",
"#Time consuming step"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%bash \n",
"ls ../GENOMES/HG19_IND"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1.3 Iterative mapping\n",
"\n",
"First of all, we need to import useful Python packages:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os\n",
"\n",
"from hiclib import mapping\n",
"from mirnylib import h5dict, genome"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we need to set some parameters and prepare our environment:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%bash \n",
"which bowtie2\n",
"# Bowtie 2 path"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%bash\n",
"pwd\n",
"# Current working directory path"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Setting parameters and environmental variables\n",
"bowtie_path = '/opt/conda/bin/bowtie2'\n",
"\n",
"enzyme = 'DpnII'\n",
"\n",
"bowtie_index_path = '/home/jovyan/GENOMES/HG19_IND/hg19_chr1'\n",
"fasta_path = '/home/jovyan/GENOMES/HG19_FASTA/'\n",
"chrms = ['1']\n",
"\n",
"# Reading the genome\n",
"genome_db = genome.Genome(fasta_path, readChrms=chrms)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Creating directories for further data processing\n",
"\n",
"if not os.path.exists('tmp/'):\n",
" os.mkdir('tmp/', exists_)\n",
"if not os.path.exists('../DATA/SAM/'):\n",
" os.mkdir('../DATA/SAM/')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Set parameters for iterative mapping\n",
"\n",
"min_seq_len = 25\n",
"len_step = 5\n",
"nthreads = 2\n",
"temp_dir = 'tmp'\n",
"bowtie_flags = '--very-sensitive'\n",
"\n",
"infile1 = '/home/jovyan/DATA/FASTQ1/K562_B-bulk_R1.fastq'\n",
"infile2 = '/home/jovyan/DATA/FASTQ1/K562_B-bulk_R2.fastq'\n",
"out1 = '/home/jovyan/DATA/SAM/K562_B-bulk_R1.chr1.sam'\n",
"out2 = '/home/jovyan/DATA/SAM/K562_B-bulk_R2.chr1.sam'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"# Iterative mapping itself. Time consuming step!\n",
"\n",
"mapping.iterative_mapping(\n",
" bowtie_path = bowtie_path,\n",
" bowtie_index_path = bowtie_index_path,\n",
" fastq_path = infile1,\n",
" out_sam_path = out1,\n",
" min_seq_len = min_seq_len,\n",
" len_step = len_step,\n",
" nthreads = nthreads,\n",
" temp_dir = temp_dir, \n",
" bowtie_flags = bowtie_flags)\n",
"\n",
"mapping.iterative_mapping(\n",
" bowtie_path = bowtie_path,\n",
" bowtie_index_path = bowtie_index_path,\n",
" fastq_path = infile2,\n",
" out_sam_path = out2,\n",
" min_seq_len = min_seq_len,\n",
" len_step = len_step,\n",
" nthreads = nthreads,\n",
" temp_dir = temp_dir, \n",
" bowtie_flags = bowtie_flags)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's take a look at .sam files that were created during iterative mapping:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%bash\n",
"ls /home/jovyan/DATA/SAM/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%bash\n",
"head -n 10 /home/jovyan/DATA/SAM/K562_B-bulk_R1.chr1.sam.25"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1.4 Making sense of mapping output \n",
"For each read length and orientation, we have a file. Now we need to merge them into the single dataset ([.hdf5](https://en.wikipedia.org/wiki/Hierarchical_Data_Format) file):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create the directory for output\n",
"if not os.path.exists('../DATA/HDF5/'):\n",
" os.mkdir('../DATA/HDF5/')\n",
"\n",
"# Define file name for output\n",
"out = '/home/jovyan/DATA/HDF5/K562_B-bulk.fragments.hdf5'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Open output file\n",
"mapped_reads = h5dict.h5dict(out)\n",
"\n",
"# Parse mapping data and write to output file\n",
"mapping.parse_sam(\n",
" sam_basename1 = out1,\n",
" sam_basename2 = out2,\n",
" out_dict = mapped_reads,\n",
" genome_db = genome_db,\n",
" enzyme_name = enzyme, \n",
" save_seqs = False,\n",
" keep_ids = False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's take a look at the created file:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%bash\n",
"ls /home/jovyan/DATA/HDF5/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import h5py\n",
"\n",
"# Reading the file\n",
"a = h5py.File('/home/jovyan/DATA/HDF5/K562_B-bulk.fragments.hdf5')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# \"a\" variable has dictionary-like structure, we can view its keys, for example:\n",
"list( a.keys() )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Mapping positions for forward reads are stored under 'cuts1' key:\n",
"a['cuts1'].value"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"filtering\"></a>\n",
"## 2. Data filtering\n",
"[Go top](#navigation)\n",
"\n",
"The raw Hi-C data is mapped and interpreted, the next step is to filter out possible methodological artefacts:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from hiclib import fragmentHiC"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"inp = '/home/jovyan/DATA/HDF5/K562_B-bulk.fragments.hdf5'\n",
"out = '/home/jovyan/DATA/HDF5/K562_B-bulk.fragments_filtered.hdf5'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create output file\n",
"fragments = fragmentHiC.HiCdataset(\n",
" filename = out,\n",
" genome = genome_db,\n",
" maximumMoleculeLength= 500,\n",
" mode = 'w')\n",
"\n",
"# Parse input data\n",
"fragments.parseInputData(\n",
" dictLike=inp)\n",
"\n",
"# Filtering\n",
"fragments.filterRsiteStart(offset=5) # reads map too close to restriction site\n",
"fragments.filterDuplicates() # remove PCR duplicates\n",
"fragments.filterLarge() # remove too large restriction fragments\n",
"fragments.filterExtreme(cutH=0.005, cutL=0) # remove fragments with too high and low counts"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Some hidden filteres were also applied, we can check them all:\n",
"fragments.printMetadata()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nice visualisation of the data:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"df_stat = pd.DataFrame(list(fragments.metadata.items()), columns=['Feature', 'Count'])\n",
"df_stat"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df_stat['Ratio of total'] = 100*df_stat['Count']/df_stat.loc[2,'Count']\n",
"df_stat"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"binning\"></a>\n",
"## 3. Data binning\n",
"[Go top](#navigation)\n",
"\n",
"The previous analysis involved interactions of restriction fragments, now we would like to work with interactions of genomic bins."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Define file name for binned data. Note \"{}\" prepared for string formatting\n",
"out_bin = '/home/jovyan/DATA/HDF5/K562_B-bulk.binned_{}.hdf5'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"res_kb = [100, 20] # Several resolutions in Kb\n",
"\n",
"for res in res_kb:\n",
" print(res)\n",
" \n",
" outmap = out_bin.format(str(res)+'kb') # String formatting\n",
"\n",
" fragments.saveHeatmap(outmap, res*1000) # Save heatmap"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"del fragments # delete unwanted object"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"visualisation\"></a>\n",
"## 4. Hi-C data visualisation\n",
"[Go top](#navigation)\n",
"\n",
"Let's take a look at the resulting heat maps."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Importing visualisation modules\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"sns.set_style('ticks')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from hiclib.binnedData import binnedDataAnalysis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"res = 100 # Resolution in Kb"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# prepare to read the data\n",
"data_hic = binnedDataAnalysis(resolution=res*1000, genome=genome_db)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# read the data\n",
"data_hic.simpleLoad('/home/jovyan/DATA/HDF5/K562_B-bulk.binned_{}.hdf5'.format(str(res)+'kb'),'hic')\n",
"mtx = data_hic.dataDict['hic']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"# show heatmap\n",
"plt.figure(figsize=[15,15])\n",
"plt.imshow(mtx[0:200, 0:200], cmap='jet', interpolation='None')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"correction\"></a>\n",
"## 5. Iterative correction\n",
"[Go top](#navigation)\n",
"\n",
"The next typical step is data correction for unequal amplification and accessibility of genomic regions. \n",
"We will use iterative correction. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Additional data filtering \n",
"data_hic.removeDiagonal()\n",
"data_hic.removePoorRegions()\n",
"data_hic.removeZeros()\n",
"data_hic.iterativeCorrectWithoutSS(force=True)\n",
"data_hic.restoreZeros()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mtx = data_hic.dataDict['hic']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.figure(figsize=[15,15])\n",
"plt.imshow(mtx[200:500, 200:500], cmap='jet', interpolation='None')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"meta\"></a>\n",
"## 7. Compartmanets and TADs\n",
"[Go top](#navigation)\n",
"\n",
"#### 7.1 Comparison with compartments\n",
"Compartments usually can be found at whole-genome datasets, but we have only chromosome 1. Still, we can try to find some visual signs of compartments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Load compartments computed previously based on K562 dataset from Rao et al. 2014\n",
"eig = np.loadtxt('/home/jovyan/DATA/ANNOT/comp_K562_100Kb_chr1.tsv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"eig"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from matplotlib import gridspec"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bgn = 0\n",
"end = 500\n",
"\n",
"fig = plt.figure(figsize=(10,10))\n",
"\n",
"gs = gridspec.GridSpec(2, 1, height_ratios=[20,2]) \n",
"gs.update(wspace=0.0, hspace=0.0)\n",
"\n",
"ax = plt.subplot(gs[0,0])\n",
"\n",
"ax.matshow(mtx[bgn:end, bgn:end], cmap='jet', origin='lower', aspect='auto')\n",
"ax.set_xticks([])\n",
"ax.set_yticks([])\n",
"\n",
"axl = plt.subplot(gs[1,0])\n",
"plt.plot(range(end-bgn), eig[bgn:end] )\n",
"plt.xlim(0, end-bgn)\n",
"plt.xlabel('Eigenvector values')\n",
"\n",
"ticks = range(bgn, end+1, 100)\n",
"ticklabels = ['{} Kb'.format(x) for x in ticks]\n",
"plt.xticks(ticks, ticklabels)\n",
"\n",
"print('')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Seems to be nothing special with compartments. What if we had much better coverage by reads? Let's take a look at the dataset from [Rao et al. 2014](https://linkinghub.elsevier.com/retrieve/pii/S0092-8674(14)01497-4), GEO [ GSE63525, HIC069](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63525):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mtx_Rao = np.genfromtxt('../DATA/ANNOT/Rao_K562_chr1.csv', delimiter=',')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bgn = 0\n",
"end = 500\n",
"\n",
"fig = plt.figure(figsize=(10,10))\n",
"\n",
"gs = gridspec.GridSpec(2, 1, height_ratios=[20,2])\n",
"gs.update(wspace=0.0, hspace=0.0)\n",
"\n",
"ax = plt.subplot(gs[0,0])\n",
"\n",
"ax.matshow(mtx_Rao[bgn:end, bgn:end], cmap='jet', origin='lower', aspect='auto', vmax=1000)\n",
"ax.set_xticks([])\n",
"ax.set_yticks([])\n",
"\n",
"axl = plt.subplot(gs[1,0])\n",
"plt.plot(range(end-bgn), eig[bgn:end] )\n",
"plt.xlim(0, end-bgn)\n",
"plt.xlabel('Eigenvector values')\n",
"\n",
"ticks = range(bgn, end+1, 100)\n",
"ticklabels = ['{} Kb'.format(x) for x in ticks]\n",
"plt.xticks(ticks, ticklabels)\n",
"\n",
"print('')"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"#### 7.2 Topologically associating domains (TADs)\n",
"\n",
"For TADs calling we will use [lavaburst](https://github.com/nezar-compbio/lavaburst) package. The code below is based on [this example](http://nbviewer.jupyter.org/github/nezar-compbio/lavaburst/blob/master/example/example.ipynb)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Import Python package \n",
"import lavaburst"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"good_bins = mtx.astype(bool).sum(axis=0) > 1 # We have to mask rows/cols if data is missing\n",
"\n",
"gam=[0.15, 0.25, 0.5, 0.75, 1.0] # set of parameters gamma for TADs calling\n",
"\n",
"segments_dict = {}\n",
"\n",
"for gam_current in gam:\n",
" print(gam_current)\n",
" \n",
" S = lavaburst.scoring.armatus_score(mtx, gamma=gam_current, binmask=good_bins)\n",
" model = lavaburst.model.SegModel(S)\n",
" segments = model.optimal_segmentation() # Positions of TADs for input matrix\n",
"\n",
" segments_dict[gam_current] = segments.copy()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"A = mtx.copy()\n",
"\n",
"good_bins = A.astype(bool).sum(axis=0) > 0\n",
"\n",
"At = lavaburst.utils.tilt_heatmap(mtx, n_diags=100)\n",
"\n",
"start_tmp = 0\n",
"end_tmp = 500\n",
"\n",
"f = plt.figure(figsize=(20, 6))\n",
"\n",
"ax = f.add_subplot(111)\n",
"blues = sns.cubehelix_palette(0.4, gamma=0.5, rot=-0.3, dark=0.1, light=0.9, as_cmap=True)\n",
"ax.matshow(np.log(At[start_tmp: end_tmp]), cmap=blues)\n",
"\n",
"cmap = mpl.cm.get_cmap('brg')\n",
"\n",
"gammas = segments_dict.keys()\n",
"for n, gamma in enumerate(gammas):\n",
"\n",
" segments = segments_dict[gamma]\n",
"\n",
" for a in segments[:-1]:\n",
" if a[1]<start_tmp or a[0]>end_tmp:\n",
" continue\n",
" ax.plot([a[0]-start_tmp, a[0]+(a[1]-a[0])/2-start_tmp], [0, -(a[1]-a[0])], c=cmap(n/len(gammas)), alpha=0.5)\n",
" ax.plot([a[0]+(a[1]-a[0])/2-start_tmp, a[1]-start_tmp], [-(a[1]-a[0]), 0], c=cmap(n/len(gammas)), alpha=0.5)\n",
"\n",
" a = segments[-1]\n",
" ax.plot([a[0]-start_tmp, a[0]+(a[1]-a[0])/2-start_tmp], [0, -(a[1]-a[0])], c=cmap(n/len(gammas)), alpha=0.5, label=gamma)\n",
" ax.plot([a[0]+(a[1]-a[0])/2-start_tmp, a[1]-start_tmp], [-(a[1]-a[0]), 0], c=cmap(n/len(gammas)), alpha=0.5)\n",
" \n",
"ax.set_xlim([0,end_tmp-start_tmp])\n",
"ax.set_ylim([100,-100])\n",
" \n",
"ax.legend(bbox_to_anchor=(1.1, 1.05))\n",
"ax.set_aspect(0.5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Let's check what are median TAD sized with different parameters:\n",
"\n",
"for gam_current in gam:\n",
" segments = segments_dict[gam_current]\n",
" tad_lens = segments[:,1]-segments[:,0]\n",
" good_lens = (tad_lens>=200/res)&(tad_lens<100)\n",
" print(res*1000*np.mean(tad_lens[good_lens]))"
]
}
],
"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 |