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June 21, 2017 23:13
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import numpy as np, matplotlib.pyplot as plt\n", | |
| "%matplotlib inline\n", | |
| "plt.style.use('ggplot')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Inspired by Stephen Few, The DataVis Jitterbug: Let’s Improve an Old Dance, *Visual Business Intelligence Newsletter* April/May/June 2017 http://www.perceptualedge.com/articles/visual_business_intelligence/the_datavis_jitterbug.pdf" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "np.random.seed(12345)\n", | |
| "x = np.random.weibull(2, size=100)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWYAAABZCAYAAAANfY7aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAACqJJREFUeJzt211sE9kVB/D/eBwnOLEQjkuAAAJCYQVdpCVpycISQkNZ\nFvEAiNIiAeKBrropogiBCi/ACyCBgihKsqCCWJQVWvrCY/mQSgqIgjAB2gRBGoKqSMCmjsmSJXzE\n9u3DxI7tGXsmwbEv8f/3Apm5d+6ZM2dOnJlEEUIIEBGRNGyZDoCIiGKxMRMRSYaNmYhIMmzMRESS\nYWMmIpIMGzMRkWTYmImIJMPGTEQkGTZmIiLJsDETEUnGPtSJT58+hcfjgc/nS2U8IwLzYox5Mca8\nJDbScjNhwgRL4/iJmYhIMmzMRESSYWMmIpIMGzMRkWTYmImIJMPGTEQkGTZmIiLJsDETEUmGjZmI\nSDJszEREkmFjJiKSDBszEZFk2JiJiCTDxkxEJBk2ZiIiybAxExFJho2ZiEgybMxERJJhYyYikgwb\nMxGRZNiYiYgkw8ZMRCQZNmYiIsnY07VQ6GYjxPkGwO8D3B4oqzbAVl6pH/PdX4BXPdqGfBeU3/4O\ntvLK2Pn5Bdr+Vz8mPFbi9f83sNFmA2b8DOh8potLt17fO+DdW/2Bc/MAe44WS34B8KYX3weDA/sd\nudo8RQGE0J1XwtjCwvOi5xttizumYd7zC4C3b4BA38CA8ZOAZx36dcdPAro6B85ZUYCKZVDXf6W/\nTuE8CBE7XgjA/RMoqzYAADr/egqi5wf9Wqod+OxXwL+9Wg5sNiAUipmrWy86PxXLtP9fu6jNs9mA\nhZ/HbgvH+PZNbN6SyXdp/xrUWbJajexPUO/x1+T7QJ8WVzjGcD05HFrdRcfan5Po6xz89mvduavr\nvzI8pfi48HFZf94T35e6edFxDXI9K/eqjNJ9HooQVipU7+nTp/B4PPD5fKZjQzcbIRrqYhubIxfK\nhj/EFus3x4BgIHayqgKfLQX++XfjxmhwLEvrJ+LIBT79ZfL1UkFVoWz6IwBYj830mHYom7bG5jRV\nxw77aA7wnxYg+puPaVyqdhOHG+RgqHYgFLTWSIdbf50BgPjmz/oc9OcfMLim0XPf95pE1Xvw26+B\nf/xNP2bRF7pmaakeDO4lS/Osrmdyr8az2mOGUyrOI2zChAmWxqXlUYY436C/qO/eatujx8Q3ZUAr\n/msXkxdF3LEsrZ/kWKbrpUIwCHG+YXCxmR4zoM9pqs/j4b8G15QBbfxQmjKg1YQMTRmI1JlWqwY5\n6M9/snpPyTWJrvdrF43HGGy3tLbBvWRpntX1TO5VGWXiPNLzKMOf4Dte9PZEYwBrN3Wy+cn2DXW9\nVBhsXIM95nAcP9uZ5TSVdWhlnUS1arTd6vrx46zMG8x6H1pdZuA80vPyz+0x355oDKA9xxrqGmb7\nhrpeKrg9g4/NyjGN/k+pYXbNku1P5fUOHydRrRptt7p2/Dgr8waz3odWlxk4j7R0IGXVBu3ZbTRH\nbuTFTmSMavABXlW1Fznx85Mcy9L6SY5lul4qqCqUVRsGF5vpMe36nKb6PD6ao12TQcWlDv2bnWrX\nXtbJoL/OtFo1yEF//pPVe0quSXS9h19yxjPYbmltg3vJ0jyr65ncqzLKxHmo+/bt2zeUiT09PXA6\nnejt7TUdq0ycAhSOBf7bBrx+rb1Z/s3mmAfnysQpgKcIaG3W3vgC2pvu9dVQl/86dn6+S0tUX5/h\nsZKvHxWvzQbM/Fj7MSwqLsP1FMX4uWJuHpA3Sosl36V/UeXI1eZFN5f+87KVVyaOLRK8Evtvom1R\nx0yY93yX/iXc+EnAjy/1646fpD1XC5+zomgveH7/J8AzLvY6hfOgqrHjAS2n676E8kk5lLYHxs8q\nVTtQ8TnQ84OWA5tt4Dc61n0J5ZNP9etF52LRF8CUnwId7QO/KVCxLHZbOMZgwHqjT1BnWq2OM6xV\n/TV9rZ8bf01stoH3K9H15MjVPyKIq3fbnJ9DvOzWnbvRb0kYxYVfLOrPu/F9aTgvOq5Brmd2r8az\n2mOGUyrOI8zlcllbMx2/lZFtmBdjzIsx5iWxkZYbqX4rg4iIrGNjJiKSDBszEZFk2JiJiCTDxkxE\nJBk2ZiIiybAxExFJho2ZiEgybMxERJJhYyYikgwbMxGRZNiYiYgkw8ZMRCQZNmYiIsmwMRMRSYaN\nmYhIMmzMRESSYWMmIpIMGzMRkWTYmImIJMPGTEQkGTZmIiLJKEIIkekgiIhowHt9Yt61a1eq4hhR\nmBdjzIsx5iWxbM0NH2UQEUmGjZmISDLv1ZiXLFmSqjhGFObFGPNijHlJLFtzw5d/RESS4aMMIiLJ\n2K0MunfvHk6fPo1QKISqqiqsXLkyZn9fXx9qa2vR3t4Ol8uFbdu2YezYscMSsEzM8tLY2IiGhga4\n3W4AwLJly1BVVZWJUNOmvr4eTU1NGD16NGpqanT7hRA4ffo07t69i9zcXFRXV2PatGkZiDT9zHLT\n0tKCQ4cORe6defPmYc2aNekOM618Ph/q6urQ3d0NRVGwZMkSLF++PGZMVtaMMBEMBsWWLVvE8+fP\nRV9fn9ixY4fo6OiIGXPhwgVx4sQJIYQQ169fF0eOHDE77AfPSl6uXLkiTp48maEIM6OlpUU8fvxY\nbN++3XD/nTt3xP79+0UoFBKPHj0Su3fvTnOEmWOWm+bmZnHw4ME0R5VZfr9fPH78WAghRG9vr9i6\ndavuPsrGmjF9lNHW1oZx48ahqKgIdrsd8+fPx+3bt2PGeL1eVFZWAgDKy8vR3NwMMcIfXVvJSzaa\nNWsWCgoKEu73er2oqKiAoiiYMWMGXr16hRcvXqQxwswxy002GjNmTOTT76hRo1BcXAy/3x8zJhtr\nxrQx+/1+FBYWRr4uLCzUJS56jKqqcDqd6OnpSXGocrGSFwC4desWduzYgZqaGvh8vnSGKCW/3w+P\nxxP5OlHeslVrayt27tyJAwcOoKOjI9PhpFVnZyeePHmC6dOnx2zPxpqx9IyZhqa0tBQLFixATk4O\nLl++jLq6OuzduzfTYZGkpk6divr6euTl5aGpqQmHDx/GsWPHMh1WWrx58wY1NTXYtGkTnE5npsPJ\nONNPzG63G11dXZGvu7q6Ii+zjMYEg0H09vbC5XKlOFS5WMmLy+VCTk4OAKCqqgrt7e1pjVFGbrc7\n5icHo7xlK6fTiby8PADA3LlzEQwG8fLlywxHNfwCgQBqamqwcOFCzJs3T7c/G2vGtDGXlJTg2bNn\n6OzsRCAQwI0bN1BWVhYzprS0FI2NjQCAmzdvYvbs2VAUZVgCloWVvEQ/B/N6vZg4cWK6w5ROWVkZ\nrl69CiEEWltb4XQ6MWbMmEyHJYXu7u7Iu5m2tjaEQqER/wFHCIHjx4+juLgYK1asMByTjTVj6Q9M\nmpqacObMGYRCISxevBirV6/GuXPnUFJSgrKyMrx79w61tbV48uQJCgoKsG3bNhQVFaUj/owyy8vZ\ns2fh9XqhqioKCgqwefNmFBcXZzrsYXX06FE8ePAAPT09GD16NNauXYtAIAAAWLp0KYQQOHXqFO7f\nvw+Hw4Hq6mqUlJRkOOr0MMvNhQsXcOnSJaiqCofDgY0bN2LmzJkZjnp4PXz4EHv27MHkyZMjH+bW\nrVsX+YScrTXDv/wjIpIM//KPiEgybMxERJJhYyYikgwbMxGRZNiYiYgkw8ZMRCQZNmYiIsmwMRMR\nSeb/AuMabhPk4NsAAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x2b02f3b7ca58>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=[6,1])\n", | |
| "plt.plot(x, np.zeros_like(x), 'o')\n", | |
| "plt.yticks([]);" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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FJQwOp7o5duwYe+qppxSXKlhyuRw7efIkY4yxCxcusAcffND0HUWxzTi6MsbHx3HRRRdh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\nYSYIgtAMEmaCIAjN+D/0bKbzBxd+jQAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x2b02f3e06630>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=[6,1])\n", | |
| "plt.plot(x, np.random.normal(size=x.shape), 'o')\n", | |
| "plt.yticks([]);" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "hist, bin_edges = np.histogram(x)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "(10, 11)" | |
| ] | |
| }, | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "len(hist), len(bin_edges)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "array([ 5, 14, 14, 19, 18, 13, 8, 7, 1, 1])" | |
| ] | |
| }, | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "hist" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# make y position based on values of hist\n", | |
| "y = []\n", | |
| "for h_i in hist:\n", | |
| " y += range(h_i)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x2b02f6648b00>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=[6,1])\n", | |
| "plt.plot(sorted(x), y, 'o')\n", | |
| "plt.yticks([]);" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWYAAABZCAYAAAANfY7aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x2b02f66cfb38>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "def wheat_plot(x):\n", | |
| " hist, bin_edges = np.histogram(x)\n", | |
| " \n", | |
| " # make y position based on values of hist\n", | |
| " y = []\n", | |
| " for h_i in hist:\n", | |
| " y += range(h_i)\n", | |
| "\n", | |
| " plt.plot(sorted(x), y, 'o')\n", | |
| " plt.yticks([])\n", | |
| " \n", | |
| "plt.figure(figsize=[6,1])\n", | |
| "wheat_plot(x)\n", | |
| "plt.yticks([]);" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "anaconda-cloud": {}, | |
| "kernelspec": { | |
| "display_name": "Python [conda env:pymc3_env]", | |
| "language": "python", | |
| "name": "conda-env-pymc3_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": "3.6.1" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 1 | |
| } |
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import numpy as np, matplotlib.pyplot as plt\n", | |
| "%matplotlib inline\n", | |
| "plt.style.use('ggplot')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Inspired by Stephen Few, The DataVis Jitterbug: Let’s Improve an Old Dance, *Visual Business Intelligence Newsletter* April/May/June 2017 http://www.perceptualedge.com/articles/visual_business_intelligence/the_datavis_jitterbug.pdf" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "np.random.seed(12345)\n", | |
| "x = np.random.weibull(2, size=100)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWYAAABZCAYAAAANfY7aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSeb/AuMabhPk4NsAAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x2b02f3b7ca58>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=[6,1])\n", | |
| "plt.plot(x, np.zeros_like(x), 'o')\n", | |
| "plt.yticks([]);" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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FJQwOp7o5duwYe+qppxSXKlhyuRw7efIkY4yxCxcusAcffND0HUWxzTi6MsbHx3HRRRdh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\nYSYIgtAMEmaCIAjN+D/0bKbzBxd+jQAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x2b02f3e06630>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=[6,1])\n", | |
| "plt.plot(x, np.random.normal(size=x.shape), 'o')\n", | |
| "plt.yticks([]);" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "hist, bin_edges = np.histogram(x)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "(10, 11)" | |
| ] | |
| }, | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "len(hist), len(bin_edges)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "array([ 5, 14, 14, 19, 18, 13, 8, 7, 1, 1])" | |
| ] | |
| }, | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "hist" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# make y position based on values of hist\n", | |
| "y = []\n", | |
| "for h_i in hist:\n", | |
| " y += range(h_i)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x2b02f6648b00>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=[6,1])\n", | |
| "plt.plot(sorted(x), y, 'o')\n", | |
| "plt.yticks([]);" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x2b02f66cfb38>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "def wheat_plot(x):\n", | |
| " hist, bin_edges = np.histogram(x)\n", | |
| " \n", | |
| " # make y position based on values of hist\n", | |
| " y = []\n", | |
| " for h_i in hist:\n", | |
| " y += range(h_i)\n", | |
| "\n", | |
| " plt.plot(sorted(x), y, 'o')\n", | |
| " plt.yticks([])\n", | |
| " \n", | |
| "plt.figure(figsize=[6,1])\n", | |
| "wheat_plot(x)\n", | |
| "plt.yticks([]);" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "anaconda-cloud": {}, | |
| "kernelspec": { | |
| "display_name": "Python [conda env:pymc3_env]", | |
| "language": "python", | |
| "name": "conda-env-pymc3_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": "3.6.1" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 1 | |
| } |
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