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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": "iVBORw0KGgoAAAANSUhEUgAAAWYAAABZCAYAAAANfY7aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAWYAAABZCAYAAAANfY7aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
"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\nAAALEgAACxIB0t1+/AAADatJREFUeJztnV1onFUax//vTDNJp8mGTtNGTBE1olBhL2ygQdGNJLoi\nsrUi7haJW2kFt0rNdivWGy0LVlaJFGmCoiW4WUWvhL2qeuEoIhFjVGhESz8uCippOpbGxH5k5uxF\nttNMMud9z8ycr5n5/+76vm/nPO+TM8+c85znIxBCCBBCCPGGmGsBCCGEFELDTAghnkHDTAghnkHD\nTAghnkHDTAghnkHDTAghnkHDTAghnkHDTAghnkHDTAghnkHDTAghnrGi3P/4448/oq2tDdPT0zrl\nqQnqUS+5sTTE6BBw8cKVi4lGBP1PINbdA6A+9aIC9SKn1nRz9dVXKz3HFTPRgnh/tNAoA8DFCwvX\nCSElUfaKmZACMqcl1+2sdnJj6YUfgcw0kGpDsKU/v1InpNqgYSYVkxtLy2+m2qyMX+BGyZyGGB1C\nDqBxJlUJDTMpmyurVMlqGUCwpd+4HKFuFBpmUoXQMJOyKHrYVwQrK1apG0X+g0GIz/Dwj5RF0VXq\nUlJr7QgTk0xj2XVCPIcrZlIeUavRRKMxN8bSgz7kcpIHJdcJ8RwaZlIyoYd9AJBaaywqothBX5gc\nhFQjNMykZMJik4Ptu436lZVcKIDRFTshpqETjpROyCrV+GFfWFx0ai2AYGHFvijjkJBqgytmUhLh\nMcsWXAermoHZmSLXWxD/1yHz4xNiAa6YSUmEujHoOiBEC1wxayY3lsbp/76N3Okp46nBptOQi31+\nmCvBiuug2GoZAGZ/NT82IZagYdbI5YgBYSE12HQasuzzpa4EC24M16nfhNiCrgyN2KywJt59w+hY\nsncBACQaC69bioAQ774hvefCjZIbSyP7zHZkH9uM7DPbo8MICVGEhlknllKDc2Np+ZZeVzU3mcyz\nMwj6n7AeARH6zrBfrCi/o8icBiCu7FhonIkG6MrQSSxWPNtMc2pw6KpY15Y+5F1i3T3WiwOFv7P9\nRBIWTiIm4YpZJxZSg3NjaTvV3HxLcw7ZCTiJBnFcf5rUNjTMmrAR35vfPstY1aLt4E+KqzTnVc3F\nrycanbgxpPAQkmiAhlkTNuJ7Q9ORE40I/vKYvnEkeBer3JCwPmRV6YdUJfQxV4hKsXhtq9gwF0aF\nB3AFMcsQ0uecpTk7iF+Wxom7juUmNQ8NcwUoFYvXsPWPdGGk1lZulBWK3rtyY7hwHYTFiSPVVvxH\nktXsiCboyqiAyEpnmuJ7I10YFY6hVLHNYbU2F66DsKiLYEu/s1huUh9wxVwGKu4LrTWJDbowFj4/\nLJIgcN912mI1u8i/bWYase4e5AB25SbGoGEuEZVtf2xtO4L98iy1UseTUqELI09YxbYDb1f++RVg\nM0JEzTW14DpxEctN6gca5hIpmgq9mEQjmh9+HLMaxsqNpSFGDkjv1+LWeVnbqAvnpc/qfP+8rsPi\ntOmuIJagYS6BqLTgy+6LlX/4I2anK0s0yK/eQgyFtq2zNOIh5F0NUFLbKOh7fxVdm2yXRchSaJhL\nICotWGeh9sgDOZ3beEup5FEot40CtL6/iq5ZhJ/YhIa5FGykQiM6Zln7ltqD9OvId16M7vcPO/z0\nwH1huu428Q8aZkVCD6E0pUJfHic0ZjkW01rNzYf0a5VUczQ2mTNMssPPIHDeO9B03W3iJzTMioTG\n0mpKhc6PExazrNlQ+JBerJJq7sQIJZudGz9WsatPaJhVsRVLazpmedl4btKLVVPATa5Y8zJ42q4q\n1L3jsIodXSvmoWFWwNZ230rM8lJCYphNUUoKuFGjrBiz7ILoNHw3stG1YgemZCtga7vvg1vBBj6k\ngNtKpy8X02n45WKzfVo9wxWzCh64MYytRlxs40OjLyylgIe5AnyIWQ5rDODyQFImFxsEaIWGOQJf\n3BgmcFW1TT6mxXjhsDR0H2KWQ+Rz+oMhlUvSyICURVUaZhuHDyqFiqrdjVEvYy4lMoPTIZEHko7w\nVa5apeoMs43DB9XDqap3Y9TLmIuIPFRzGImhNO8cyOerXLVM1R3+2Th8UDqc0u1ekKU/m0yLrpcx\nFxGdfu0uEkNt3tmXz1e5apmqWTGr1Mmt7HOvuEUiP0vzqXhuLG0tLVopfthkKrbr9G+bqe6K/PbJ\nB8j+ezg6Jd2AfEpuQcvfB1IlhtlUzKnMLSI94AC0n9irtI3ShRctpFa1WI+bvkzowaPmVHdVcmNp\nnPvPEHAh+m+i+yxF2S0oa6VlSC5SJYbZVMypzC2ChsRC66DF9wykQ0tlWDym7gNGj1tImSb04PHR\nASfGRbw/Gm6UDc27/NgK6d7Blv7lP+gG5SIeGuaSXQsl/mIrbeVnf0Ww/e9aIz/K6bisfeI7biH1\n2ycfOEt/jqpe52PbLGOr5Kj5v2SesJWWfbwyzCW7FkqMe1XfyrdpbR1Ubsdl7RPfYQup/JZdhsHD\nI5vuolKwHdNdyvxfCltp2cWrqAypawHQ0pXY1VaeHZcjtuwu069dpzdLMCFTvbuyqgmvVszSbZ0u\n14KrrbwvHZddVlGzXTUPiklCjg78ouQqRSblhCvfu6GTPN4Y5qj0YC1bKQdbeZW0ZxvbRBfp12pj\nm6kgpxbJY656nQxVucr+vLCEqxC3mRdp6CSPNcMc9atuelvnIg3Xhy7XtlPLC8e02+l6Ma6rx8nm\nu265SimkL42ucKCHasP2e1gxzEq/6gYLtrtIw7Xa5TpKBoup5a46XS/DcrTDYkIPe3VXtZO6yZZf\ntx1dUSu1m128hxXDrPSrHrLNMjJ+wRj6t/JWu1yXK4MBOVx1ul5GSOdv09v20Pmu251QYodzm9EV\ntdIWy8V72InKUKjhajQ6wUUXZB86L7tIpVVNjTetA5ep3yHzveg8b6xAF65T3MOoldrNDt7DjmGW\nrUgXXY919yDof+L/q6hgYVun68RcNr7JNFwXY6rKAOjVr8qYq1oQW9sO7X9bqRyS1biNmOWQ+V5s\nnv/ub3vL14XL94xC4XtfFTh4j/i+ffv2lfMfZ2ZmkEwmMTc3F/1wSyswOQFks1euJRoR/HkHgvXX\n5i8F669F7K7NiP1pK2J3bS64VxGy8f+6y4hxSCaTmIs3WB2zKLL33vYU4o8+pU+/KmM+/DjW/eOf\nON+3We/ftlQ5lsw5F2MvneetG36v9j0qYyynaJBN2caYRKOOW1rUasIEQgh5i2JCCCHWqciVsXfv\nXl1y1BTUS3Gol+JQL3LqVTdepWQTQgihYSaEEO+oyDD39fXpkqOmoF6KQ70Uh3qRU6+64eEfIYR4\nBl0ZhBDiGUop2d988w1GRkaQy+XQ29uL+++/v+D+pUuXcPDgQZw4cQItLS0YGBjAunXrjAjsE1F6\nSafTGB0dRSqVAgDcc8896O3tdSGqNYaHhzExMYHW1lYMDg4uuy+EwMjICL7++ms0NjZi586duP76\n6x1Iap8o3UxOTuKll17Kf3c2bdqEBx980LaYVpmensbQ0BDOnj2LIAjQ19eHe++9t+CZupwzIoJs\nNiuefPJJ8fPPP4tLly6JPXv2iFOnThU8c/jwYfH6668LIYT47LPPxCuvvBL1sVWPil4+/vhj8eab\nbzqS0A2Tk5Pi+PHjYvfu3UXvf/XVV+KFF14QuVxO/PDDD+LZZ5+1LKE7onRz5MgR8eKLL1qWyi2Z\nTEYcP35cCCHE3Nyc2LVr17LvUT3OmUhXxrFjx3DVVVehvb0dK1aswK233oovv/yy4Jnx8XH09PQA\nALq7u3HkyBGIGnddq+ilHtmwYQOam5ul98fHx3HHHXcgCALceOONmJ2dxS+//GJRQndE6aYeWb16\ndX71u3LlSnR0dCCTyRQ8U49zJtIwZzIZrFmzJv/vNWvWLFPc4mfi8TiSySRmZuzWPraNil4A4Isv\nvsCePXswODiI6ekqK95igEwmg7a2KzUGZHqrV44ePYqnn34a+/fvx6lTp1yLY5WpqSmcPHkSN9xw\nQ8H1epwz3nQwqUU2btyI2267DQ0NDfjoo48wNDSE559/3rVYxFOuu+46DA8Po6mpCRMTE3j55Zfx\n6quvuhbLCufPn8fg4CC2bduGZDLpWhznRK6YU6kUzpw5k//3mTNn8odZxZ7JZrOYm5tTLtZRrajo\npaWlBQ0NDQCA3t5enDhxwqqMPpJKpQp2DsX0Vq8kk0k0NTUBAG655RZks1mcO3fOsVTmmZ+fx+Dg\nIG6//XZs2rRp2f16nDORhrmzsxM//fQTpqamMD8/j88//xxdXV0Fz2zcuBHpdBoAMDY2hptvvhlB\nEBgR2BdU9LLYDzY+Po7169fbFtM7urq68Omnn0IIgaNHjyKZTGL16tWuxfKCs2fP5s9mjh07hlwu\nV/MLHCEEXnvtNXR0dOC+++4r+kw9zhmlBJOJiQm89dZbyOVyuPPOO/HAAw/gvffeQ2dnJ7q6unDx\n4kUcPHgQJ0+eRHNzMwYGBtDe3m5DfqdE6eWdd97B+Pg44vE4mpubsWPHDnR0dLgW2ygHDhzAd999\nh5mZGbS2tuKhhx7C/Pw8AODuu++GEAKHDh3Ct99+i0QigZ07d6Kzs9Ox1HaI0s3hw4fx4YcfIh6P\nI5FI4JFHHsFNN93kWGqzfP/993juuedwzTXX5BdzW7duza+Q63XOMPOPEEI8g5l/hBDiGTTMhBDi\nGTTMhBDiGTTMhBDiGTTMhBDiGTTMhBDiGTTMhBDiGTTMhBDiGf8DdasVLb2tIa0AAAAASUVORK5C\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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