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Created September 4, 2012 00:36
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Explanation of Dissertation Research Using IPython Notebook
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{
"metadata": {
"name": "prospectus"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# A Method for Calibrating Probabilistic Forecasts\n",
"\n",
"The fundamental concept behind this method is that, statistically speaking, forecast error consists of two parts: random error and systematic error. The calibration approach that follows determines both components of the statastical forecast error. The systematic error is corrected and knowledge of the random error is exploited to produce a probabilitic forecast.\n",
"\n",
"What follows is an attempt to demonstrate the concepts behind this approach using randomly generated datasets.\n",
"\n",
"-----\n",
"\n",
"## Single Member\n",
"\n",
"First, import the necessary modules to conduct the analysis\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import scipy as sp\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from scipy import ndimage\n",
"from mpl_toolkits.axes_grid1 import ImageGrid\n",
"\n",
"%pylab inline\n",
"\n",
"# Variables needed for analysis\n",
"samples = 1000000\n",
"lim = 40\n",
"cmap = plt.cm.Reds_r"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
"For more information, type 'help(pylab)'.\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, generate a random two-dimensional frequency distribution composite where observations fell relative to a forecast point for a deterministic forecast. The origin, point (0, 0), is considered to be the forecast. For this idealized case, the observation distribution is generated from a Gaussian distribution."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class Member(object):\n",
" def __init__(self, locx=0, locy=0, stdx=1, stdy=1, samples=1000, lim=20, step=1):\n",
" \n",
" # Configure the grid\n",
" drange = range(-lim, lim+1, step)\n",
" self.x, self.y = np.meshgrid(drange, drange)\n",
" self.lim = lim\n",
" self.step = step\n",
"\n",
" # Generate the random \"observations\"\n",
" self.xpts = np.random.normal(locx, stdx, (samples,))\n",
" self.ypts = np.random.normal(locy, stdy, (samples,))\n",
" \n",
" # Grid the observations\n",
" z, xx, yy = np.histogram2d(self.xpts, self.ypts, bins=[drange, drange], \n",
" normed=False)\n",
" self.data = z.T # Frequency Count\n",
" self.pdata = self.data / self.data.sum() # Probability\n",
"\n",
" # Create masked versions for plotting\n",
" self.mdata = np.ma.array(self.data)\n",
" self.mdata[self.data <= 0] = np.ma.masked\n",
" self.pmdata = self.mdata / self.data.sum()\n",
"\n",
" # Center of Mass Calculations\n",
" cxpt, cypt = ndimage.center_of_mass(self.data.T)\n",
" self.cxpt = cxpt - lim\n",
" self.cypt = cypt - lim\n",
" self.cx = int(self.cxpt)\n",
" self.cy = int(self.cypt)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we need to use the class to generate our freqency distribution for our deterministic forecast."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Generate the distribution\n",
"mem1 = Member(locx=5, stdx=10, locy=5, stdy=10, lim=lim, samples=samples)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Set up the figure\n",
"fig = plt.figure(figsize=(12, 6))\n",
"grid = ImageGrid(fig, 111, nrows_ncols = (1, 2), axes_pad = 2, direction='row',\n",
" add_all=True, share_all=True, aspect='equal', label_mode = \"all\",\n",
" cbar_mode='each', cbar_location='right', cbar_pad=0.1, cbar_size='5%')\n",
"ax1 = grid[0]; cax1 = grid.cbar_axes[0]\n",
"ax2 = grid[1]; cax2 = grid.cbar_axes[1]\n",
"\n",
"\n",
"# Plot the frequency distribution\n",
"cbar1 = ax1.pcolormesh(mem1.x, mem1.y, mem1.mdata, cmap=cmap)\n",
"ax1.plot(0, 0, 'wo', markersize=10)\n",
"ax1.plot(mem1.cx, mem1.cy, 'k.', markersize=10)\n",
"ax1.axhline(0, lw=0.75, ls=':', color='k')\n",
"ax1.axvline(0, lw=0.75, ls=':', color='k')\n",
"ax1.set_xlim(-lim, lim)\n",
"ax1.set_ylim(-lim, lim)\n",
"ax1.set_title('Member 1 Frequency Distribution')\n",
"cax1.colorbar(cbar1)\n",
"print '\\n\\n'\n",
"print 'The total number of observations in the domain is:', mem1.data.sum()\n",
"print 'The maximum frequency is %i observations.' % (mem1.data.max())\n",
"print 'The center of mass of the frequency distribution is displaced (%f, %f).' % (mem1.cx, mem1.cy)\n",
"print 'The black dot is the center of mass of the frequency distribution: %i observations.' % (\n",
" mem1.data[int(mem1.cx)+mem1.lim, int(mem1.cy)+mem1.lim])\n",
"print 'The white dot is the representative forecast.'\n",
"\n",
"\n",
"# Plot the probability distribution\n",
"cbar2 = ax2.pcolormesh(mem1.x, mem1.y, mem1.pmdata, cmap=cmap)\n",
"ax2.plot(0, 0, 'wo', markersize=10)\n",
"ax2.plot(mem1.cx, mem1.cy, 'k.', markersize=10)\n",
"ax2.axhline(0, lw=0.75, ls=':', color='k')\n",
"ax2.axvline(0, lw=0.75, ls=':', color='k')\n",
"ax2.set_xlim(-lim, lim)\n",
"ax2.set_ylim(-lim, lim)\n",
"ax2.set_title('Member 1 Probability Distribution')\n",
"cax2.colorbar(cbar2)\n",
"print '\\n\\n'\n",
"print 'The total probability in the domain is:', mem1.pdata.sum()\n",
"print 'The maximum probability is %f%%.' % (mem1.pdata.max() * 100.)\n",
"print 'The center of mass of the probability distribution is displaced (%f, %f).' % (mem1.cx, mem1.cy)\n",
"print 'The black dot is the center of mass of the probability distribution: %f%%.' % (\n",
" mem1.pdata[int(mem1.cx)+mem1.lim, int(mem1.cy)+mem1.lim] * 100.)\n",
"print 'The white dot is the representative forecast.'\n",
"\n",
"\n",
"print '\\n\\n'\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"\n",
"\n",
"The total number of observations in the domain is: 999543.0\n",
"The maximum frequency is 1647 observations.\n",
"The center of mass of the frequency distribution is displaced (4.000000, 4.000000).\n",
"The black dot is the center of mass of the frequency distribution: 1529 observations.\n",
"The white dot is the representative forecast.\n",
"\n",
"\n",
"\n",
"The total probability in the domain is: 1.0\n",
"The maximum probability is 0.164775%.\n",
"The center of mass of the probability distribution is displaced (4.000000, 4.000000).\n",
"The black dot is the center of mass of the probability distribution: 0.152970%.\n",
"The white dot is the representative forecast.\n",
"\n",
"\n",
"\n"
]
},
{
"output_type": "display_data",
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UDGtLx8PaEhj4Id3PiC5Uut4be1JkjbkTOd+uyHQ3rD4ROuIAWshvSyNwBmn9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\nQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x110ca2750>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using this visualization, it is readily apparent that there are random and systematic errors associated with the \"forecast\". The systematic error is given by the center of mass displacement vector. The random error is given by the observation frequency distribution. Using this information in a forecast sense is not extremely difficult. The general idea is to use the systematic error to \"correct\" the forecast by shifting the prediction to remove the systematic error. Once this is done, the determinstic yes/no forecast can be converted into a spatial probabilistic forecast by replacing the yes/no forecast with the spatial probability distribution -- which is based on the historical performace of the model.\n",
"\n",
"For example, the following class generates the frequency and probabily distributions assuming there was no systematic error. This is achieved by taking a forecast grid point -- originally at point (0, 0) -- and \"shifts\" it by the displacement vector, resulting in a new, \"corrected\" forecast point (x, y) -- which is the now located at the center of mass of the previous distribution. (As mentioned before, one could use a displacement vector based on the maximum proability instead of the center of mass.)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Create a class to do the correction of individual members\n",
"class CorrectedMember(object):\n",
" def __init__(self, mem):\n",
" \n",
" # Set up the grid\n",
" self.x = mem.x\n",
" self.y = mem.y\n",
" self.lim = mem.lim\n",
" self.step = mem.step\n",
"\n",
" # Correct the data\n",
" self.data = ndimage.interpolation.shift(mem.data.T, (-mem.cx, -mem.cy), order=0).T\n",
" self.pdata = self.data / self.data.sum()\n",
"\n",
" # Generate masked versions for plotting\n",
" self.mdata = np.ma.array(self.data)\n",
" self.mdata[self.data <= 0] = np.ma.masked\n",
" self.pmdata = np.ma.array(self.pdata)\n",
" self.pmdata[self.pdata <= 0] = np.ma.masked\n",
"\n",
" # Determine the new Center of Mass\n",
" cxpt, cypt = ndimage.center_of_mass(self.data.T)\n",
" self.cxpt = cxpt - self.lim\n",
" self.cypt = cypt - self.lim\n",
" self.cx = int(self.cxpt)\n",
" self.cy = int(self.cypt)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cmem1 = CorrectedMember(mem1)\n",
"\n",
"\n",
"# Set up the figure\n",
"fig = plt.figure(figsize=(12, 6))\n",
"grid = ImageGrid(fig, 111, nrows_ncols = (1, 2), axes_pad = 2, direction='row',\n",
" add_all=True, share_all=True, aspect='equal', label_mode = \"all\",\n",
" cbar_mode='each', cbar_location='right', cbar_pad=0.1, cbar_size='5%')\n",
"ax1 = grid[0]; cax1 = grid.cbar_axes[0]\n",
"ax2 = grid[1]; cax2 = grid.cbar_axes[1]\n",
"\n",
"\n",
"# Plot the frequency distribution\n",
"cbar1 = ax1.pcolormesh(cmem1.x, cmem1.y, cmem1.mdata, cmap=cmap)\n",
"ax1.plot(0, 0, 'wo', markersize=10)\n",
"ax1.plot(cmem1.cx, cmem1.cy, 'k.', markersize=10)\n",
"ax1.axhline(0, lw=0.75, ls=':', color='k')\n",
"ax1.axvline(0, lw=0.75, ls=':', color='k')\n",
"ax1.set_xlim(-lim, lim)\n",
"ax1.set_ylim(-lim, lim)\n",
"ax1.set_title('Member 1 Frequency Distribution')\n",
"cax1.colorbar(cbar1)\n",
"print '\\n\\n'\n",
"print 'The total number of observations in the domain is:', mem1.data.sum()\n",
"print 'The maximum frequency is %i observations.' % (mem1.data.max())\n",
"print 'The center of mass of the frequency distribution is displaced (%f, %f).' % (mem1.cx, mem1.cy)\n",
"print 'The black dot is the center of mass of the frequency distribution: %i observations.' % (\n",
" mem1.data[int(mem1.cx)+mem1.lim, int(mem1.cy)+mem1.lim])\n",
"print 'The white dot is the representative forecast.'\n",
"\n",
"\n",
"# Plot the probability distribution\n",
"cbar2 = ax2.pcolormesh(cmem1.x, cmem1.y, cmem1.pmdata, cmap=cmap)\n",
"ax2.plot(0, 0, 'wo', markersize=10)\n",
"ax2.plot(cmem1.cx, cmem1.cy, 'k.', markersize=10)\n",
"ax2.axhline(0, lw=0.75, ls=':', color='k')\n",
"ax2.axvline(0, lw=0.75, ls=':', color='k')\n",
"ax2.set_xlim(-lim, lim)\n",
"ax2.set_ylim(-lim, lim)\n",
"ax2.set_title('Member 1 Probability Distribution')\n",
"cax2.colorbar(cbar2)\n",
"print '\\n\\n'\n",
"print 'The total probability in the domain is:', mem1.pdata.sum()\n",
"print 'The maximum probability is %f%%.' % (mem1.pdata.max() * 100.)\n",
"print 'The center of mass of the probability distribution is displaced (%f, %f).' % (mem1.cx, mem1.cy)\n",
"print 'The black dot is the center of mass of the probability distribution: %f%%.' % (\n",
" mem1.pdata[int(mem1.cx)+mem1.lim, int(mem1.cy)+mem1.lim] * 100.)\n",
"print 'The white dot is the representative forecast.'\n",
"\n",
"\n",
"print '\\n\\n'\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"\n",
"\n",
"The total number of observations in the domain is: 999543.0\n",
"The maximum frequency is 1647 observations.\n",
"The center of mass of the frequency distribution is displaced (4.000000, 4.000000).\n",
"The black dot is the center of mass of the frequency distribution: 1529 observations.\n",
"The white dot is the representative forecast.\n",
"\n",
"\n",
"\n",
"The total probability in the domain is: 1.0\n",
"The maximum probability is 0.164775%.\n",
"The center of mass of the probability distribution is displaced (4.000000, 4.000000).\n",
"The black dot is the center of mass of the probability distribution: 0.152970%.\n",
"The white dot is the representative forecast.\n",
"\n",
"\n",
"\n"
]
},
{
"output_type": "display_data",
"png": 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MSjCNFfSM0LsxMkxr4ImutFTOFwRKW3ylK24fm6yztrC2BDOtMSMSo1G+hyXQ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\n",
"text": [
"<matplotlib.figure.Figure at 0x110c98ad0>"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In principle the calibration method is simply:\n",
"\n",
" 1. Find the model's systematic and random errors by utilizing a relative frequency distribution \n",
" of forecast points to observations.\n",
" 2. Everytime a model forecasts said event, shift it by the displacement vector. (Correct the \n",
" systematic error.)\n",
" 3. Replace the forecast point with the relative probability distribution. (Use the random error \n",
" to \"smooth\" the forecast.)\n",
" \n",
"Previous results (Marsh et al. 2012) have shown that using this method results in generally reliable probabilitistic forecasts of preciptiation amounts exceeding 1\" in 6 hours from a single model. Comparing probabilistic precipitation forecasts of exceeding 1\" in 6 hours from a deterministic member to those generated from a 15 member ensemble have shown the forecasts from the deterministic member to be more reliable.\n",
"\n",
"-----\n",
"\n",
"## Extending to an Ensemble\n",
"\n",
"How to extend this method from a single member to an ensemble has been a source of debate amongst various particpants in the Hazardous Weather Testbed. Is it better to:\n",
"\n",
" 1. Take the ensemble mean and then apply calibration method to the ensemble mean (Averaging then Correcting); or\n",
" 2. Apply the calibration method to each member of the ensemble and then take the mean of the corrected members.\n",
" (Correcting then Averaging)\n",
"\n",
"My hypotheses are:\n",
"\n",
" 1. Each approach will produce reliable probabilistic forecasts as the underlying calibration\n",
" method is designed to produce reliable forecasts.\n",
" 2. \"Correcting then Averaging\" will produce a \"more narrow forecast\" than \"Averaging then Correcting\"\n",
" (i.e., it is \"smoothed\" over a smaller area). In the context of fitting gaussian functions to \n",
" the underlying probability distribution, a more narrow forecast equates to a sharper forecast.\n",
" 3. Given that both approaches are assumed to produce reliable forecasts, then a sharper forecast \n",
" is better.\n",
"\n",
"The rest of this document provides an idealized justification of hypothesis 2.\n",
"\n",
"-----\n",
"\n",
"### \"Average then Correct\" Approach\n",
"\n",
"To begin, first we must generate the relative frequency distributions for the members of our ensemble. For now we'll begin with a 2 member ensemble. We will also assume that each model has a bias of 1. That means each observation is associated with one and only one forecast point.\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Generate the distributions\n",
"mem1 = Member(locx=10, stdx=7.5, locy=10, stdy=7.5, lim=lim, samples=samples)\n",
"mem2 = Member(locx=-10, stdx=5, locy=0, stdy=5, lim=lim, samples=samples)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The probability distributions for these two members are shown below."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Set up the figure\n",
"fig = plt.figure(figsize=(12, 6))\n",
"grid = ImageGrid(fig, 111, nrows_ncols = (1, 2), axes_pad = 2, direction='row',\n",
" add_all=True, share_all=True, aspect='equal', label_mode = \"all\",\n",
" cbar_mode='each', cbar_location='right', cbar_pad=0.1, cbar_size='5%')\n",
"ax1 = grid[0]; cax1 = grid.cbar_axes[0]\n",
"ax2 = grid[1]; cax2 = grid.cbar_axes[1]\n",
"\n",
"\n",
"# Compute the min/max values to be shaded so that the colors are consistent between figures\n",
"vmin = 0\n",
"vmax = max(mem1.pdata.max(), mem2.pdata.max())\n",
"\n",
"\n",
"# Plot Member 1\n",
"cbar1 = ax1.pcolormesh(mem1.x, mem1.y, mem1.pmdata, cmap=cmap, vmin=vmin, vmax=vmax)\n",
"ax1.plot(0, 0, 'wo', markersize=10)\n",
"ax1.plot(mem1.cx, mem1.cy, 'k.', markersize=10)\n",
"ax1.axhline(0, lw=0.75, ls=':', color='k')\n",
"ax1.axvline(0, lw=0.75, ls=':', color='k')\n",
"ax1.set_xlim(-lim, lim)\n",
"ax1.set_ylim(-lim, lim)\n",
"ax1.set_title('Member 1 Probability Distribution')\n",
"cax1.colorbar(cbar1)\n",
"\n",
"\n",
"# Plot Member 2\n",
"cbar2 = ax2.pcolormesh(mem2.x, mem2.y, mem2.pmdata, cmap=cmap, vmin=vmin, vmax=vmax)\n",
"ax2.plot(0, 0, 'wo', markersize=10)\n",
"ax2.plot(mem2.cx, mem2.cy, 'k.', markersize=10)\n",
"ax2.axhline(0, lw=0.75, ls=':', color='k')\n",
"ax2.axvline(0, lw=0.75, ls=':', color='k')\n",
"ax2.set_xlim(-lim, lim)\n",
"ax2.set_ylim(-lim, lim)\n",
"ax2.set_title('Member 2 Probability Distribution')\n",
"cax2.colorbar(cbar2)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 8,
"text": [
"<mpl_toolkits.axes_grid1.axes_grid.Colorbar instance at 0x114a14248>"
]
},
{
"output_type": "display_data",
"png": 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Q32Q5gktF9O9ZSmK0c1UxTilxkRldmuUDK11+pHtEX63ybwYSH11mGdHGMEtM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\nRHGGg3QiIiIiojjDQToRERERUZz5L0TedfrCT21vAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x114a3b510>"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's create a class to generate the ensemble of these members:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Create a class to generate an ensemble from a group of members\n",
"class Ensemble(object):\n",
" def __init__(self, *args):\n",
" \n",
" # Ensemble the Members\n",
" N = len(args)\n",
" mem1 = args[0]\n",
" combined = mem1.data.copy().astype('float')\n",
" combined += mem1.data\n",
" for arg in args[1:]:\n",
" combined += arg.data\n",
" combined /= N\n",
" self.data = combined # frequency counts\n",
" self.pdata = combined / combined.sum() # probabilities\n",
"\n",
" # Set up the grid\n",
" self.x = mem1.x\n",
" self.y = mem1.y\n",
" self.lim = mem1.lim\n",
" self.step = mem1.step\n",
"\n",
" # Center of Mass Calculations\n",
" cxpt, cypt = ndimage.center_of_mass(self.data.T)\n",
" self.cxpt = (cxpt - self.lim)\n",
" self.cypt = (cypt - self.lim)\n",
" self.cx = int(self.cxpt)\n",
" self.cy = int(self.cypt)\n",
" \n",
" # Create masked versions of the distributions for plotting\n",
" self.mdata = np.ma.array(self.data)\n",
" self.mdata[self.data <= 0] = np.ma.masked\n",
" self.pmdata = np.ma.array(self.pdata)\n",
" self.pmdata[self.pdata <= 0] = np.ma.masked\n",
" \n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Ensemble the forecasts\n",
"ens = Ensemble(mem1, mem2)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following figure shows the probability distributions for each member and the ensemble mean of these two members."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Set up the figure\n",
"fig = plt.figure(figsize=(15, 5))\n",
"grid = ImageGrid(fig, 111, nrows_ncols = (1, 3), axes_pad = 1, direction='row',\n",
" add_all=True, share_all=True, aspect='equal', label_mode = \"all\",\n",
" cbar_mode='each', cbar_location='right', cbar_pad=0.1, cbar_size='5%')\n",
"ax1 = grid[0]; cax1 = grid.cbar_axes[0]\n",
"ax2 = grid[1]; cax2 = grid.cbar_axes[1]\n",
"ax3 = grid[2]; cax3 = grid.cbar_axes[2]\n",
"\n",
"\n",
"# Compute the min/max values to be shaded so that the colors are consistent between figures\n",
"vmin = 0\n",
"vmax = max(mem1.pdata.max(), mem2.pdata.max(), ens.pdata.max())\n",
"\n",
"\n",
"# Plot Member 1\n",
"cbar1 = ax1.pcolormesh(mem1.x, mem1.y, mem1.pmdata, cmap=cmap, vmin=vmin, vmax=vmax)\n",
"ax1.plot(0, 0, 'wo', markersize=10)\n",
"ax1.plot(mem1.cx, mem1.cy, 'k.', markersize=10)\n",
"ax1.axhline(0, lw=0.75, ls=':', color='k')\n",
"ax1.axvline(0, lw=0.75, ls=':', color='k')\n",
"ax1.set_xlim(-lim, lim)\n",
"ax1.set_ylim(-lim, lim)\n",
"ax1.set_title('Member 1 Probability Distribution')\n",
"cax1.colorbar(cbar1)\n",
"\n",
"\n",
"# Plot Member 2\n",
"cbar2 = ax2.pcolormesh(mem2.x, mem2.y, mem2.pmdata, cmap=cmap, vmin=vmin, vmax=vmax)\n",
"ax2.plot(0, 0, 'wo', markersize=10)\n",
"ax2.plot(mem2.cx, mem2.cy, 'k.', markersize=10)\n",
"ax2.axhline(0, lw=0.75, ls=':', color='k')\n",
"ax2.axvline(0, lw=0.75, ls=':', color='k')\n",
"ax2.set_xlim(-lim, lim)\n",
"ax2.set_ylim(-lim, lim)\n",
"ax2.set_title('Member 2 Probability Distribution')\n",
"cax2.colorbar(cbar2)\n",
"\n",
"\n",
"# Plot Ensemble\n",
"cbar3 = ax3.pcolormesh(ens.x, ens.y, ens.pmdata, cmap=cmap, vmin=vmin, vmax=vmax)\n",
"ax3.plot(0, 0, 'wo', markersize=10)\n",
"ax3.plot(ens.cx, ens.cy, 'k.', markersize=10)\n",
"ax3.axhline(0, lw=0.75, ls=':', color='k')\n",
"ax3.axvline(0, lw=0.75, ls=':', color='k')\n",
"ax3.set_xlim(-lim, lim)\n",
"ax3.set_ylim(-lim, lim)\n",
"ax3.set_title('Raw Average Probability Distribution')\n",
"cax3.colorbar(cbar3)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 11,
"text": [
"<mpl_toolkits.axes_grid1.axes_grid.Colorbar instance at 0x115e07a70>"
]
},
{
"output_type": "display_data",
"png": 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/pF1kd0UK2V0NjIm1K6IorGEXKggJOWE0ngjHVz3U6wHgR7hxLuxB18vsBOoJ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\nCCEkKf4/6vVYQEzg6z8AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x114cdbc10>"
]
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's create the corrected ensemble, recalling that the \"correction\" is merely accounting for the systematic error (displacement)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ensc = CorrectedMember(ens) # Average (Ensemble) Then Correct"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Set up the figure\n",
"fig = plt.figure(figsize=(10, 10))\n",
"grid = ImageGrid(fig, 111, nrows_ncols = (2, 2), axes_pad = 1, direction='row',\n",
" add_all=True, share_all=True, aspect='equal', label_mode = \"all\",\n",
" cbar_mode='each', cbar_location='right', cbar_pad=0.1, cbar_size='5%')\n",
"ax1 = grid[0]; cax1 = grid.cbar_axes[0]\n",
"ax2 = grid[1]; cax2 = grid.cbar_axes[1]\n",
"ax3 = grid[2]; cax3 = grid.cbar_axes[2]\n",
"ax4 = grid[3]; cax4 = grid.cbar_axes[3]\n",
"\n",
"\n",
"# Compute the min/max values to be shaded so that the colors are consistent between figures\n",
"vmin = 0\n",
"vmax = max(mem1.pdata.max(), mem2.pdata.max(), ens.pdata.max(), ensc.pdata.max())\n",
"\n",
"\n",
"# Plot Member 1\n",
"cbar1 = ax1.pcolormesh(mem1.x, mem1.y, mem1.pmdata, cmap=cmap, vmin=vmin, vmax=vmax)\n",
"ax1.plot(0, 0, 'wo', markersize=10)\n",
"ax1.plot(mem1.cx, mem1.cy, 'k.', markersize=10)\n",
"ax1.axhline(0, lw=0.75, ls=':', color='k')\n",
"ax1.axvline(0, lw=0.75, ls=':', color='k')\n",
"ax1.set_xlim(-lim, lim)\n",
"ax1.set_ylim(-lim, lim)\n",
"ax1.set_title('Member 1 Probability Distribution')\n",
"cax1.colorbar(cbar1)\n",
"\n",
"\n",
"# Plot Member 2\n",
"cbar2 = ax2.pcolormesh(mem2.x, mem2.y, mem2.pmdata, cmap=cmap, vmin=vmin, vmax=vmax)\n",
"ax2.plot(0, 0, 'wo', markersize=10)\n",
"ax2.plot(mem2.cx, mem2.cy, 'k.', markersize=10)\n",
"ax2.axhline(0, lw=0.75, ls=':', color='k')\n",
"ax2.axvline(0, lw=0.75, ls=':', color='k')\n",
"ax2.set_xlim(-lim, lim)\n",
"ax2.set_ylim(-lim, lim)\n",
"ax2.set_title('Member 2 Probability Distribution')\n",
"cax2.colorbar(cbar2)\n",
"\n",
"\n",
"# Plot Ensemble\n",
"cbar3 = ax3.pcolormesh(ens.x, ens.y, ens.pmdata, cmap=cmap, vmin=vmin, vmax=vmax)\n",
"ax3.plot(0, 0, 'wo', markersize=10)\n",
"ax3.plot(ens.cx, ens.cy, 'k.', markersize=10)\n",
"ax3.axhline(0, lw=0.75, ls=':', color='k')\n",
"ax3.axvline(0, lw=0.75, ls=':', color='k')\n",
"ax3.set_xlim(-lim, lim)\n",
"ax3.set_ylim(-lim, lim)\n",
"ax3.set_title('Raw Average Probability Distribution')\n",
"cax3.colorbar(cbar3)\n",
"\n",
"\n",
"# Plot \"Average Then Correct\" \n",
"cbar4 = ax4.pcolormesh(ensc.x, ensc.y, ensc.pmdata, cmap=cmap, vmin=vmin, vmax=vmax)\n",
"ax4.plot(0, 0, 'wo', markersize=10)\n",
"ax4.plot(ensc.cx, ensc.cy, 'k.', markersize=10)\n",
"ax4.axhline(0, lw=0.75, ls=':', color='k')\n",
"ax4.axvline(0, lw=0.75, ls=':', color='k')\n",
"ax4.set_xlim(-lim, lim)\n",
"ax4.set_ylim(-lim, lim)\n",
"ax4.set_title('\"Average Then Correct\" Probability')\n",
"cax4.colorbar(cbar4)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 13,
"text": [
"<mpl_toolkits.axes_grid1.axes_grid.Colorbar instance at 0x1161668c0>"
]
},
{
"output_type": "display_data",
"png": 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fsWzZMhw+fBhLly5VC5WkhLeDh2I60MsTKbSzcPqzd3px62heOgtFnRtSdWbk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hAAAg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\nCFGQ/w8aJWLCZif+iwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x115fb4750>"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The figure above shows the probability distributions for each member (top two figures) and the probability distributions for the raw ensemble mean (bottom left) and \"average then correct\" method (bottom right). The two ensemble probability distributions (bottom two figures) produce a probability distribution that is larger than either of the two member probabilty distributions. This results is a maximum probability that is lower in the ensemble probabilities than either of the member probabilities.\n",
"\n",
"-----\n",
"\n",
"### \"Correct then Average\" Approach\n",
"\n",
"Now consider the case where each member of the ensemble is corrected before being ensembled. To do this, first we must produce the corrected members. (Remember, again, \"corrected\" here simply means that the systematic error is being removed.) After correcting each member, next we generate the \"Correct then Average\" ensemble."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cmem1 = CorrectedMember(mem1) # Corrected Member 1\n",
"cmem2 = CorrectedMember(mem2) # Corrected Member 2\n",
"cens = Ensemble(cmem1, cmem2) # Correct Then Average"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Set up the figure\n",
"fig = plt.figure(figsize=(15, 10))\n",
"grid = ImageGrid(fig, 111, nrows_ncols = (2, 3), axes_pad = 1, direction='row',\n",
" add_all=True, share_all=True, aspect='equal', label_mode = \"all\",\n",
" cbar_mode='each', cbar_location='right', cbar_pad=0.1, cbar_size='5%')\n",
"ax1 = grid[0]; cax1 = grid.cbar_axes[0]\n",
"ax2 = grid[1]; cax2 = grid.cbar_axes[1]\n",
"ax3 = grid[2]; cax3 = grid.cbar_axes[2]\n",
"ax4 = grid[3]; cax4 = grid.cbar_axes[3]\n",
"ax5 = grid[4]; cax5 = grid.cbar_axes[4]\n",
"ax6 = grid[5]; cax6 = grid.cbar_axes[5]\n",
"\n",
"\n",
"# Compute the min/max values to be shaded so that the colors are consistent between figures\n",
"vmin = 0\n",
"vmax = max(mem1.pdata.max(), mem2.pdata.max(), ens.pdata.max(), cmem1.pdata.max(), cmem2.pdata.max(), cens.pdata.max())\n",
"\n",
"\n",
"# Plot Un-Corrected Member 1\n",
"cbar1 = ax1.pcolormesh(mem1.x, mem1.y, mem1.pmdata, cmap=cmap, vmin=vmin, vmax=vmax)\n",
"ax1.plot(0, 0, 'wo', markersize=10)\n",
"ax1.plot(mem1.cx, mem1.cy, 'k.', markersize=10)\n",
"ax1.axhline(0, lw=0.75, ls=':', color='k')\n",
"ax1.axvline(0, lw=0.75, ls=':', color='k')\n",
"ax1.set_xlim(-lim, lim)\n",
"ax1.set_ylim(-lim, lim)\n",
"ax1.set_title('Member 1 Probability Distribution')\n",
"cax1.colorbar(cbar1)\n",
"\n",
"\n",
"# Plot Un-Corrected Member 2\n",
"cbar2 = ax2.pcolormesh(mem2.x, mem2.y, mem2.pmdata, cmap=cmap, vmin=vmin, vmax=vmax)\n",
"ax2.plot(0, 0, 'wo', markersize=10)\n",
"ax2.plot(mem2.cx, mem2.cy, 'k.', markersize=10)\n",
"ax2.axhline(0, lw=0.75, ls=':', color='k')\n",
"ax2.axvline(0, lw=0.75, ls=':', color='k')\n",
"ax2.set_xlim(-lim, lim)\n",
"ax2.set_ylim(-lim, lim)\n",
"ax2.set_title('Member 2 Probability Distribution')\n",
"cax2.colorbar(cbar2)\n",
"\n",
"\n",
"# Plot Raw Average\n",
"cbar3 = ax3.pcolormesh(ens.x, ens.y, ens.pmdata, cmap=cmap, vmin=vmin, vmax=vmax)\n",
"ax3.plot(0, 0, 'wo', markersize=10)\n",
"ax3.plot(ens.cx, ens.cy, 'k.', markersize=10)\n",
"ax3.axhline(0, lw=0.75, ls=':', color='k')\n",
"ax3.axvline(0, lw=0.75, ls=':', color='k')\n",
"ax3.set_xlim(-lim, lim)\n",
"ax3.set_ylim(-lim, lim)\n",
"ax3.set_title('Raw Average Probability')\n",
"cax3.colorbar(cbar3)\n",
"\n",
"\n",
"# Plot Corrected Member 1\n",
"cbar4 = ax4.pcolormesh(cmem1.x, cmem1.y, cmem1.pmdata, cmap=cmap, vmin=vmin, vmax=vmax)\n",
"ax4.plot(0, 0, 'wo', markersize=10)\n",
"ax4.plot(cmem1.cx, cmem1.cy, 'k.', markersize=10)\n",
"ax4.axhline(0, lw=0.75, ls=':', color='k')\n",
"ax4.axvline(0, lw=0.75, ls=':', color='k')\n",
"ax4.set_xlim(-lim, lim)\n",
"ax4.set_ylim(-lim, lim)\n",
"ax4.set_title('Corrected Member 1 Probability Distribution')\n",
"cax4.colorbar(cbar4)\n",
"\n",
"\n",
"# Plot Corrected Member 2\n",
"cbar5 = ax5.pcolormesh(cmem2.x, cmem2.y, cmem2.pmdata, cmap=cmap, vmin=vmin, vmax=vmax)\n",
"ax5.plot(0, 0, 'wo', markersize=10)\n",
"ax5.plot(cmem2.cx, cmem2.cy, 'k.', markersize=10)\n",
"ax5.axhline(0, lw=0.75, ls=':', color='k')\n",
"ax5.axvline(0, lw=0.75, ls=':', color='k')\n",
"ax5.set_xlim(-lim, lim)\n",
"ax5.set_ylim(-lim, lim)\n",
"ax5.set_title('Corrected Member 2 Probability Distribution')\n",
"cax5.colorbar(cbar5)\n",
"\n",
"\n",
"# Plot Correct Then Average\n",
"cbar6 = ax6.pcolormesh(cens.x, cens.y, cens.pmdata, cmap=cmap, vmin=vmin, vmax=vmax)\n",
"ax6.plot(0, 0, 'wo', markersize=10)\n",
"ax6.plot(cens.cx, cens.cy, 'k.', markersize=10)\n",
"ax6.axhline(0, lw=0.75, ls=':', color='k')\n",
"ax6.axvline(0, lw=0.75, ls=':', color='k')\n",
"ax6.set_xlim(-lim, lim)\n",
"ax6.set_ylim(-lim, lim)\n",
"ax6.set_title('\"Correct Then Average\" Probability')\n",
"cax6.colorbar(cbar6)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 15,
"text": [
"<mpl_toolkits.axes_grid1.axes_grid.Colorbar instance at 0x1166d2b48>"
]
},
{
"output_type": "display_data",
"png": 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Mhg8fjrvvvhv79u2Dz+fDZZddhsmTJ2Pnzp04evSopav5W2+9BZfLhZEjRwIALrvsMpx7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\ndztivDMDCdsCasjZn0BoE7UlR/R8u4p+J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EGsXLkS3/nOd7B792488cQT2L17N3p7e3Hl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\npAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x11648b4d0>"
]
}
],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the figure above, the top row of images (shown for easy comparison) are from the raw members and thus the raw ensemble. The bottom set of figures are from the corrected members and thus the \"correct then average\" ensemble. It is important to note here that the \"correct then average\" approach produces a probability distribution that is essentially the average between the two members that contributed to it's generation. It also produces a probability distribution with a maximum probability greater than that of the raw ensemble, as the entire probabiliy distribution is contrained over a smaller area.\n",
"\n",
"Below is a direct comparison between the \"Average then Correct\" and the \"Correct then Average\" probability distributions."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Set up the figure\n",
"fig = plt.figure(figsize=(15, 10))\n",
"grid = ImageGrid(fig, 111, nrows_ncols = (2, 3), axes_pad = 1, direction='row',\n",
" add_all=True, share_all=True, aspect='equal', label_mode = \"all\",\n",
" cbar_mode='each', cbar_location='right', cbar_pad=0.1, cbar_size='5%')\n",
"ax1 = grid[0]; cax1 = grid.cbar_axes[0]\n",
"ax2 = grid[1]; cax2 = grid.cbar_axes[1]\n",
"ax3 = grid[2]; cax3 = grid.cbar_axes[2]\n",
"ax4 = grid[3]; cax4 = grid.cbar_axes[3]\n",
"ax5 = grid[4]; cax5 = grid.cbar_axes[4]\n",
"ax6 = grid[5]; cax6 = grid.cbar_axes[5]\n",
"\n",
"\n",
"# Compute the min/max values to be shaded so that the colors are consistent between figures\n",
"vmin = 0\n",
"vmax = max(mem1.pdata.max(), mem2.pdata.max(), ensc.pdata.max(), cmem1.pdata.max(), cmem2.pdata.max(), cens.pdata.max())\n",
"\n",
"\n",
"# Plot Un-Corrected Member 1\n",
"cbar1 = ax1.pcolormesh(mem1.x, mem1.y, mem1.pmdata, cmap=cmap, vmin=vmin, vmax=vmax)\n",
"ax1.plot(0, 0, 'wo', markersize=10)\n",
"ax1.plot(mem1.cx, mem1.cy, 'k.', markersize=10)\n",
"ax1.axhline(0, lw=0.75, ls=':', color='k')\n",
"ax1.axvline(0, lw=0.75, ls=':', color='k')\n",
"ax1.set_xlim(-lim, lim)\n",
"ax1.set_ylim(-lim, lim)\n",
"ax1.set_title('Member 1 Probability Distribution')\n",
"cax1.colorbar(cbar1)\n",
"\n",
"\n",
"# Plot Un-Corrected Member 2\n",
"cbar2 = ax2.pcolormesh(mem2.x, mem2.y, mem2.pmdata, cmap=cmap, vmin=vmin, vmax=vmax)\n",
"ax2.plot(0, 0, 'wo', markersize=10)\n",
"ax2.plot(mem2.cx, mem2.cy, 'k.', markersize=10)\n",
"ax2.axhline(0, lw=0.75, ls=':', color='k')\n",
"ax2.axvline(0, lw=0.75, ls=':', color='k')\n",
"ax2.set_xlim(-lim, lim)\n",
"ax2.set_ylim(-lim, lim)\n",
"ax2.set_title('Member 2 Probability Distribution')\n",
"cax2.colorbar(cbar2)\n",
"\n",
"\n",
"# Plot Average then Correct\n",
"cbar3 = ax3.pcolormesh(ensc.x, ensc.y, ensc.pmdata, cmap=cmap, vmin=vmin, vmax=vmax)\n",
"ax3.plot(0, 0, 'wo', markersize=10)\n",
"ax3.plot(ensc.cx, ensc.cy, 'k.', markersize=10)\n",
"ax3.axhline(0, lw=0.75, ls=':', color='k')\n",
"ax3.axvline(0, lw=0.75, ls=':', color='k')\n",
"ax3.set_xlim(-lim, lim)\n",
"ax3.set_ylim(-lim, lim)\n",
"ax3.set_title('\"Average Then Correct\" Probability')\n",
"cax3.colorbar(cbar3)\n",
"\n",
"\n",
"# Plot Corrected Member 1\n",
"cbar4 = ax4.pcolormesh(cmem1.x, cmem1.y, cmem1.pmdata, cmap=cmap, vmin=vmin, vmax=vmax)\n",
"ax4.plot(0, 0, 'wo', markersize=10)\n",
"ax4.plot(cmem1.cx, cmem1.cy, 'k.', markersize=10)\n",
"ax4.axhline(0, lw=0.75, ls=':', color='k')\n",
"ax4.axvline(0, lw=0.75, ls=':', color='k')\n",
"ax4.set_xlim(-lim, lim)\n",
"ax4.set_ylim(-lim, lim)\n",
"ax4.set_title('Corrected Member 1 Probability Distribution')\n",
"cax4.colorbar(cbar4)\n",
"\n",
"\n",
"# Plot Corrected Member 2\n",
"cbar5 = ax5.pcolormesh(cmem2.x, cmem2.y, cmem2.pmdata, cmap=cmap, vmin=vmin, vmax=vmax)\n",
"ax5.plot(0, 0, 'wo', markersize=10)\n",
"ax5.plot(cmem2.cx, cmem2.cy, 'k.', markersize=10)\n",
"ax5.axhline(0, lw=0.75, ls=':', color='k')\n",
"ax5.axvline(0, lw=0.75, ls=':', color='k')\n",
"ax5.set_xlim(-lim, lim)\n",
"ax5.set_ylim(-lim, lim)\n",
"ax5.set_title('Corrected Member 2 Probability Distribution')\n",
"cax5.colorbar(cbar5)\n",
"\n",
"\n",
"# Plot Correct Then Average\n",
"cbar6 = ax6.pcolormesh(cens.x, cens.y, cens.pmdata, cmap=cmap, vmin=vmin, vmax=vmax)\n",
"ax6.plot(0, 0, 'wo', markersize=10)\n",
"ax6.plot(cens.cx, cens.cy, 'k.', markersize=10)\n",
"ax6.axhline(0, lw=0.75, ls=':', color='k')\n",
"ax6.axvline(0, lw=0.75, ls=':', color='k')\n",
"ax6.set_xlim(-lim, lim)\n",
"ax6.set_ylim(-lim, lim)\n",
"ax6.set_title('\"Correct Then Average\" Probability')\n",
"cax6.colorbar(cbar6)\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 16,
"text": [
"<mpl_toolkits.axes_grid1.axes_grid.Colorbar instance at 0x116d8c5a8>"
]
},
{
"output_type": "display_data",
"png": 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qq6+iWbNmEEURffv2hdPprLZJ7BVXXIHU1FQMGTIE9913H+655x4lzalTpyIhIQHx8fF4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\n13TW63SS8eascvkrdBqXfHz+znjtlB+1y8B0Vs43pNfWKGh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PmF0tI5A+w46+g4XKz8pD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XAAAg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\nh6JE21LE7jJmDW3eEgJRMcr/Q3oEC2J1+n2K3pFld9HjVLuMGVcnbwrD0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g7z\n+3nqqafKUtdLL72kzp8/X+3o6FDnzJmj3nfffaqqqmWpi7J9+3Yzeli562I0WLsKh7WrMFi7WLvK\nAWtX4bB2FQZrV21ol6KqeZzkGYZhGIZhGIZhGCaLqqb/YBiGYRiGYRiGYcIHdyQZhmEYhmEYhmGY\nguCOJMMwDMMwDMMwDFMQ3JFkGIZhGIZhGIZhCoI7kgzDMAzDMAzDMExBcEeSYRiGYRiGYRiGKYj/\nHx4so/bymiFcAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1166f7110>"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
}
],
"metadata": {}
}
]
}
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