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@jasonmhite
Last active December 18, 2015 10:29
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Results from QUESO calibration
{
"metadata": {
"name": "data processing"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Load data"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cal_dat = pd.read_table('dakota_tabular.dat', index_col=0, sep='\\s+', header=0)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cnames = list(cal_dat.keys()) + ['residual']\n",
"queso_dat = pd.read_table('QuesoOutput.txt', header=False, sep='\\s+', names=cnames)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"The basic statistical moments using ALL points from the MCMC chain"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"queso_dat[cnames[:4]].describe()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"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>inlet_mass_flow</th>\n",
" <th>inlet_enthalpy</th>\n",
" <th>outlet_enthalpy</th>\n",
" <th>outlet_pressure</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td> 3320685.000000</td>\n",
" <td> 3320685.000000</td>\n",
" <td> 3320685.000000</td>\n",
" <td> 3320685.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td> 85.090131</td>\n",
" <td> 1214.167710</td>\n",
" <td> 1486.602890</td>\n",
" <td> 156.068589</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td> 2.154878</td>\n",
" <td> 49.263173</td>\n",
" <td> 53.219891</td>\n",
" <td> 2.433027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td> 80.000000</td>\n",
" <td> 1100.000000</td>\n",
" <td> 1350.000000</td>\n",
" <td> 148.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td> 83.381600</td>\n",
" <td> 1177.210000</td>\n",
" <td> 1450.510000</td>\n",
" <td> 154.559000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td> 85.189800</td>\n",
" <td> 1210.690000</td>\n",
" <td> 1489.540000</td>\n",
" <td> 156.085000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td> 86.787700</td>\n",
" <td> 1248.230000</td>\n",
" <td> 1526.130000</td>\n",
" <td> 157.668000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td> 90.000000</td>\n",
" <td> 1350.000000</td>\n",
" <td> 1600.000000</td>\n",
" <td> 164.998000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 4,
"text": [
" inlet_mass_flow inlet_enthalpy outlet_enthalpy outlet_pressure\n",
"count 3320685.000000 3320685.000000 3320685.000000 3320685.000000\n",
"mean 85.090131 1214.167710 1486.602890 156.068589\n",
"std 2.154878 49.263173 53.219891 2.433027\n",
"min 80.000000 1100.000000 1350.000000 148.000000\n",
"25% 83.381600 1177.210000 1450.510000 154.559000\n",
"50% 85.189800 1210.690000 1489.540000 156.085000\n",
"75% 86.787700 1248.230000 1526.130000 157.668000\n",
"max 90.000000 1350.000000 1600.000000 164.998000"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**NOTE**: From here, we will only look at the last 1000000 sample points because of memory limits"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"chunk = 1000000"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Nominal values for calibration variables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These are the \"true\" parameter values that were used to generate the calibration data.\n",
"\n",
"The process was to generate experimental data using a given set of input parameters. Then, the calibration process was started with an initial point that was a small perturbation of the parameter values used to generate the calibration data.\n",
"\n",
"**NOTE**: I would not expect that the calibration experiment will be able to converge to the \"true\" values. What I hope is that the residual error should be at least be lower at the mean of the posterior than it is at the initial point. See the end of the notebook for a comparison."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from ipy_table import *"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tab = [['variable', 'true value', 'starting value']]\n",
"nom = [85.979, 1281.97, 1496.80, 155.13]\n",
"init = [85.0, 1200.0, 1500., 155.]\n",
"tab += zip(cnames[:4], nom, init)\n",
"make_table(tab)\n",
"apply_theme('basic')\n",
"HTML(render()._repr_html_())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table border=\"1\" cellpadding=\"3\" cellspacing=\"0\" style=\"border:1px solid black;border-collapse:collapse;\"><tr><td style=\"background-color:LightGray;\"><b>variable</b></td><td style=\"background-color:LightGray;\"><b>true&nbspvalue</b></td><td style=\"background-color:LightGray;\"><b>starting&nbspvalue</b></td></tr><tr><td style=\"background-color:Ivory;\">inlet_mass_flow</td><td style=\"background-color:Ivory;\">85.9790</td><td style=\"background-color:Ivory;\">85.0000</td></tr><tr><td style=\"background-color:AliceBlue;\">inlet_enthalpy</td><td style=\"background-color:AliceBlue;\">1281.9700</td><td style=\"background-color:AliceBlue;\">1200.0000</td></tr><tr><td style=\"background-color:Ivory;\">outlet_enthalpy</td><td style=\"background-color:Ivory;\">1496.8000</td><td style=\"background-color:Ivory;\">1500.0000</td></tr><tr><td style=\"background-color:AliceBlue;\">outlet_pressure</td><td style=\"background-color:AliceBlue;\">155.1300</td><td style=\"background-color:AliceBlue;\">155.0000</td></tr>"
],
"output_type": "pyout",
"prompt_number": 7,
"text": [
"<IPython.core.display.HTML at 0x2c90f90>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Trace plots for calibration variables"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for key in queso_dat.keys()[:4]:\n",
" queso_dat[key][-chunk:].plot()\n",
" title(key)\n",
" show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
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736TGnGv/diSsOS4QphPUARQS1AFc8t//bXRNYWIKc5j7xU/CzMZ8kKAOYDlcIBhjPkH1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\nhcKGq+n8sX/19fXIyspCjx4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},
{
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x2QEEY7IDGA4VDkIIIV6hMQ6HHLITEEKMisY4CCGE9EhUOHTDZAcQiMkOIBiT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\nhBASFDQ76mYymXDU5thBfX0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},
{
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},
{
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dPGUT7mt7EMEPOQjTUWQLEIgiW4BgFACBHHehzbp1ZqeomJ1gkKHIFhB2kIMg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\n0MoAAAAASUVORK5CYII=\n"
}
],
"prompt_number": 8
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Histograms for calibration variables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**NOTE**: some of the distributions are clipped at the edges, this is because of the variable ranges I set in DAKOTA."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bins = dict()\n",
"for key in queso_dat.keys()[:4]:\n",
" fig = hist(queso_dat[key][-chunk:], bins=75)\n",
" bins[key] = fig[:2]\n",
" title(key)\n",
" show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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},
{
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},
{
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},
{
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EipggiIhIERMEEREpYoIgIiJFTBBE81bwsG/k0+n4iBryL36jHNG8NfwBfnx4H/kbexBE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\niIgUMUEQEZGi/wewD4XDxPMazAAAAABJRU5ErkJggg==\n"
}
],
"prompt_number": 9
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Approximate statistical modes for calibration variables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is an approximation of the statistical mode for each calibration variable. These are calculated based on the histograms in the previous section and the mode here corresponds to the tallest bar in the histograms."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tab = [['Calibration variable', 'Mode']]\n",
"for key in bins.keys():\n",
" tab.append([key, bins[key][1][bins[key][0].argmax()]])\n",
" \n",
"make_table(tab)\n",
"apply_theme('basic')\n",
"HTML(render()._repr_html_())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table border=\"1\" cellpadding=\"3\" cellspacing=\"0\" style=\"border:1px solid black;border-collapse:collapse;\"><tr><td style=\"background-color:LightGray;\"><b>Calibration&nbspvariable</b></td><td style=\"background-color:LightGray;\"><b>Mode</b></td></tr><tr><td style=\"background-color:Ivory;\">outlet_pressure</td><td style=\"background-color:Ivory;\">155.4760</td></tr><tr><td style=\"background-color:AliceBlue;\">outlet_enthalpy</td><td style=\"background-color:AliceBlue;\">1500.0000</td></tr><tr><td style=\"background-color:Ivory;\">inlet_mass_flow</td><td style=\"background-color:Ivory;\">86.6665</td></tr><tr><td style=\"background-color:AliceBlue;\">inlet_enthalpy</td><td style=\"background-color:AliceBlue;\">1180.0000</td></tr>"
],
"output_type": "pyout",
"prompt_number": 10,
"text": [
"<IPython.core.display.HTML at 0x38d2210>"
]
}
],
"prompt_number": 10
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Running mean for calibration variables"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for key in queso_dat.keys()[:4]:\n",
" pd.expanding_mean(queso_dat[key][-chunk:]).plot()\n",
" title(key)\n",
" show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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},
{
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GDT2uXhXH69EDmDtXj3PngAce0OHZZ4GuXfVo3x4IC2vb+ynX36cjbev1eqSn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\nOTxOHHai5HZWJccGcHzOTun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SkLLCwkLpsbS0NAoICKCAgABKT0+XymsPKZs8eXKrh8yZ\ni6+2Pn361Bky5yzxNRbbtm3bqLKykqZNm0YhISE0aNAg2rlzp9PFZi6+rVu30t69e6VhkMOHD6f8\n/HynjO/QoUMUHh5OoaGhpNFo6K233iIicVJ44IEHGh2Oq4T4lHJuMRdfbc5wbnGYNccZY4w5B14B\nkDHGWItw4mCMMdYinDgYY4y1CCcOxhhjLcKJgzHGWItw4mCMMdYi/x8QciNABYYiTAAAAABJRU5E\nrkJggg==\n"
},
{
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},
{
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Bt9wCeHkBN94IDBjQ/FLnl2r/fjl765ZbZGE+KUneTKlbN3kBxIkTgV/NqCYiZxNdTIWP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\nSUVORK5CYII=\n"
}
],
"prompt_number": 11
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Running standard deviation for calibration variables"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for key in queso_dat.keys()[:4]:\n",
" pd.expanding_std(queso_dat[key][-chunk:]).plot()\n",
" title(key)\n",
" show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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BlbMJwXx6p3o+LfaddjuLKSAgAIWFhZbloqIiBAQE2GvzumF+JaAilbMBzKd3\nqufTgt0KRGpqKtauXQsAyMnJgY+Pj9XpJSIi0gebC8SMGTMwYsQIHD9+HEFBQVi9ejUyMjKQkZEB\nALjvvvsQGhqK8PBwpKen45133nHYoJ1Z83lQ1aicDWA+vVM9nxZsblJnZmZed52VK1fe1GCIiMh5\nuOR7MZWXA6Gh8jsRkR449XUQRETkWlgg7EzleVCVswHMp3eq59MCCwQREVnFHgQRkQ6wB0FERE6D\nBcLOVJ4HVTkbwHx6p3o+LbBAEBGRVexBEBHpAHsQRETkNFgg7EzleVCVswHMp3eq59MCCwQREVnF\nHgQRkQ6wB0FERE6DBcLOVJ4HVTkbwHx6p3o+LbBAEBGRVexBEBHpAHsQRETkNFgg7EzleVCVswHM\np3eq59MCCwQREVnFHgQRkQ6wB0FERE6DBcLOVJ4HVTkbwHx6p3o+LbBAEBGRVexBEBHpAHsQRETk\nNFgg7EzleVCVswHMp3eq59MCCwQREVnFHgQRkQ6wB0FERE6DBcLOVJ4HVTkbwHx6p3o+LbBAEBGR\nVexBEBHpAHsQRETkNFgg7EzleVCVswHMp3eq59NChwrE1q1bERkZiYiICCxbtqzV/UajEd7e3khI\nSEBCQgJee+01uw2UiIg6l809iKamJgwYMADbt29HQEAAhg4diszMTERFRVnWMRqN+OMf/4jNmze3\n/YDsQRARdZhT9yDy8vIQHh6OkJAQeHh4IC0tDZs2bWq1ntY7fyIisg93W1csLi5GUFCQZTkwMBC5\nubkt1jEYDNi7dy/i4uIQEBCAt956C9HR0a22NWfOHISEhAAAfHx8EB8fj+TkZABX5xEduVxdDQCO\n2f6f/vSnTs/TWcvN53idYTzMx3wq5zMajVizZg0AWPaXnU7YaOPGjeLRRx+1LP/1r38V8+bNa7FO\nVVWVqK2tFUIIkZWVJSIiIlptpwMP6TBlZUL4+Dhm29nZ2Y7ZsBNQOZsQzKd3qufTYt9p8xRTQEAA\nCgsLLcuFhYUIDAxssY6Xlxd69OgBABg/fjwaGhpQVlZml0KmF+ZXAipSORvAfHqnej4t2FwghgwZ\nghMnTiA/Px9XrlzBhg0bkJqa2mKd0tJSSw8iLy8PQgj07t3bviMmIqJOYXOBcHd3x8qVK5GSkoLo\n6GhMnz4dUVFRyMjIQEZGBgBg48aNiI2NRXx8PJ5++mmsX7/eYQN3Vs3nQVWjcjaA+fRO9XxasLlJ\nDchpo/FhkVpSAAAKGElEQVTjx7e4LT093fLzk08+iSeffNI+IyMiIk3xvZiIiHTAqa+DICIi18IC\nYWcqz4OqnA1gPr1TPZ8WWCCIiMgq9iCIiHSAPQgiInIaLBB2pvI8qMrZAObTO9XzaYEFgoiIrGIP\ngohIB9iDICIip8ECYWcqz4OqnA1gPr1TPZ8WWCCIiMgq9iCIiHSAPQgiInIaLBB2pvI8qMrZAObT\nO9XzaYEFgoiIrGIPgohIB9iDICIip8ECYWcqz4OqnA1gPr1TPZ8WWCCIiMgq9iCIiHSAPQgiInIa\nLBB2pvI8qMrZAObTO9XzaYEFgoiIrGIPgohIB9iDICIip8ECYWcqz4OqnA1gPr1TPZ8WWCCIiMgq\n9iCIiHSAPQgiInIaLBB2pvI8qMrZAObTO9XzaYEFgoiIrGIPgohIB9iDICIip8ECYWcqz4OqnA1g\nPr1TPZ8WWCDs7JtvvtF6CA6jcjaA+fRO9Xxa6FCB2Lp1KyIjIxEREYFly5ZZXWf+/PmIiIhAXFwc\nDh06ZJdB2punJ/C3vzlm2xUVFY7ZsBNQORvAfHqnej4t2FwgmpqaMG/ePGzduhXHjh1DZmYmvv/+\n+xbrZGVl4eTJkzhx4gT+8pe/4PHHH7f7gO3BwwMYO1brURAROTebC0ReXh7Cw8MREhICDw8PpKWl\nYdOmTS3W2bx5M2bPng0ASExMREVFBUpLS+07YieXn5+v9RAcRuVsAPPpner5tOBu64rFxcUICgqy\nLAcGBiI3N/e66xQVFcHPz6/FegaD4UbHqwsffPCB1kNwGJWzAcynd6rn62w2Fwhbd+rXnqd77e9p\nfQ0EERHZxuYppoCAABQWFlqWCwsLERgY2O46RUVFCAgIsMMwiYios9lcIIYMGYITJ04gPz8fV65c\nwYYNG5CamtpindTUVKxduxYAkJOTAx8fn1bTS0REpA82TzG5u7tj5cqVSElJQVNTEx555BFERUUh\nIyMDAJCeno777rsPWVlZCA8PR8+ePfH+++87bOBERORY7R5BFBYWYvTo0Rg4cCBiYmJw4sQJHD9+\nHJMmTcLatWsRFxeHL774AmlpaZbfCQgIgMFgQH19PS5evGi5/cCBA4iNjUVERAQWLFhgub2+vh7T\np09HREQEhg8fjh9//NFy3wcffID+/fujf//+liMTADhz5gwSExMRERGBtLQ0NDQ03FD4a/OtWLGi\nxf3/+Z//CTc3N5SVlVlue+ONNxAREYHIyEhs27bNafO1l+2//uu/EBUVhZiYGDz33HO6y9Zevry8\nPAwbNgwJCQkYOnQo9u3bp8t8dXV1SExMRHx8PKKjo/H8888DAMrKyjB27Fj0798f48aNa3Huvwr5\nFi5ciKioKMTFxeH+++9HZWWl7vK1lc1MV/sV0Y6zZ8+KQ4cOCSGEqK6uFv379xfHjh0T27ZtE01N\nTUIIIZ577jnx3HPPCSGEOHr0qIiLixNXrlwRZ86cEWFhYcJkMgkhhBg6dKjIzc0VQggxfvx4sWXL\nFiGEEG+//bZ4/PHHhRBCrF+/XkyfPl0IIcSlS5dEaGioKC8vF+Xl5SI0NFRUVFQIIYSYOnWq2LBh\ngxBCiLlz54pVq1a1F6PD+YQQoqCgQKSkpIiQkBBx6dIl3eVrK9s///lPMWbMGHHlyhUhhBDnz5/X\nXbb28iUlJYmtW7cKIYTIysoSycnJuswnhBC1tbVCCCEaGhpEYmKi2LVrl1i4cKFYtmyZEEKIpUuX\n6vb/Xlv5VNm3WMsmhP72K+0WiGtNmDBBbN++vcVtH3/8sfjtb38rhBBiyZIlYunSpZb7UlJSxNdf\nfy1KSkpEZGSk5fbMzEyRnp5uWScnJ0cIIZ/Mvn37CiGEWLdunZg7d67ld9LT00VmZqYwmUyib9++\nln9EX3/9tUhJSelIDJvyTZkyRXz77bct/pB6zjdhwgTx5ZdfimnTpomvvvqq1f16ztY8X1pamuU/\nwbp165T4t1lbWyuGDBkivvvuOzFgwABx7tw5IYQskgMGDFAm39GjR1vcrsK+5dpsetuv2Nykzs/P\nx6FDh5CYmNji9tWrV+O+++4DAJSUlLQ4sykwMBDFxcWtbg8ICEBxcTGAltdOuLu7w9vbG5cuXWpz\nW2VlZfDx8YGbm1urbd2M5vk2bdqEwMBADBo0qMU6es3XPNsPP/yAnTt3Yvjw4UhOTsb+/ft1na15\nvuHDh2Pp0qX4t3/7NwQHB2PhwoV44403dJvPZDIhPj4efn5+lum00tJSy4kffn5+lgtRVcgXHR3d\n4n4971usZdPjfsWmJnVNTQ2mTJmCP//5z/D09LTc/vrrr6Nr16544IEHbNnMTXPUBXbN87m5uWHJ\nkiX48ssvLfeLTrp2wxH5mmfz8vJCY2MjysvLkZOTg3379mHatGk4ffq03R/3Wp3xt/P09MTEiROx\nYsUKTJo0CX//+9/x8MMPt/hbOooj8rm5ueGbb75BZWUlUlJSkJ2d3eoxO+ui087IZzQakZycDED/\n+5Zrs2VlZeGNN95o0V/Qw37lukcQDQ0NmDx5MmbOnImJEydabl+zZg2ysrLw0UcfWW6zdh1EYGAg\nAgICUFRU1Op28+8UFBQAABobG1FZWYk+ffpYve4iICAAvXv3RkVFBUwmk2VbN3OtxbX5Tp06hfz8\nfMTFxaFfv34oKirC4MGDUVpaqrt81v52gYGBuP/++wEAQ4cOhZubGy5evKi7bG3ly8vLw6RJkwAA\nU6ZMQV5enmWsestn5u3tjV/96lc4cOAA/Pz8cO7cOQDA2bNnceuttyqTz3w0q8q+pXm2gwcP4syZ\nM/rbr7Q3/2QymcSDDz4onn766Ra3b9myRURHR4sLFy60uN3cbKmvrxenT58WoaGhlmbLsGHDRE5O\njjCZTK2aLeY5s8zMzBbNln79+ony8nJRVlZm+VkI2WxZv369ZY7tRhtJbeVrzlozSQ/52sr27rvv\nisWLFwshhDh+/LgICgrSXbb28iUkJAij0SiEEGL79u1iyJAhusx34cIFyzYvX74sRo0aJbZv3y4W\nLlxoma9+4403WjVx9Z5PhX1LW9ma08t+pd0CsWvXLmEwGERcXJyIj48X8fHxIisrS4SHh4vg4GDL\nbeZuuhBCvP766yIsLEwMGDDAcjaJEELs379fxMTEiLCwMPHUU09Zbq+rqxNTp04V4eHhIjExUZw5\nc8Zy3+rVq0V4eLgIDw8Xa9assdx++vRpMWzYMBEeHi6mTZtmOSOno9rK11y/fv0sf0g95bOWbcuW\nLeLKlSti5syZIiYmRtx+++0iOztbd9naypeVlSX27dsnhg0bJuLi4sTw4cPFwYMHdZnv8OHDIiEh\nQcTFxYnY2Fjx5ptvCiHkDuCee+4RERERYuzYsZb//KrkU2Hf0la25vSyX+n0z6QmIiJ94CfKERGR\nVSwQRERkFQsEERFZxQJBRERWsUAQEZFVLBBERGTV/wefiHw8iDC75QAAAABJRU5ErkJggg==\n"
},
{
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i4+MBwH6+dLU6z4PIz8/H0KFDERcXh6CgIHv/w8svv4zMzEysXLmy/AHdqInp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\nkpeXJ507d5aLFy+KiMiYMWP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},
{
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gXaGoCKjD4ykiIiuHC8SZM2cQGxuLSZMmWavaxo0bMX78eADA+PHjsWHDBudE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\n69YAgDp16qBx48a4dOlShdv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},
{
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NVZVaqn7/fnWfkz171LDewkJVYP/lF3VecXeC8OkriG+++QYffPABevfujaSk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\ngiAiIoeYIIiIyCEmCCIicuj/AT1OVwt5r+MtAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 12
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Comparison of residuals"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here I compare the calibrated residuals to the \"true\" values. The hope is that the calibrated residual should be lower.\n",
"\n",
"I calculated the residual at both the mean *and* the (approximate) mode. Note that the mean is supposed to be the best estimator, but the mode should be the most likely. The mode is worse, but the mean behaves as expected and shows a decrease in the residual.\n",
"\n",
"I would *guess* (\"hypothesize\" would be the more scientist-like word) that residual is higher at the mode because the distributions aren't truly independent.\n",
"\n",
"**NOTE**: these residuals are all calculated in the 2-norm."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"exper = asarray([815.1, 812.75, 815.5])\n",
"initial = asarray([813.6, 805.1, 810.45])\n",
"atmode = asarray([810.95, 816.65, 805.9])\n",
"atmean = asarray([816.725, 813.15, 808.15])\n",
"\n",
"ires = norm(exper - initial)\n",
"pres = norm(exper - atmode)\n",
"mres = norm(exper - atmean)\n",
"\n",
"tab = [['Initial residual', 'Posterior residual (mode)', 'Posterior residual (mean)']]\n",
"tab.append([ires, pres, mres])\n",
"make_table(tab)\n",
"apply_theme('basic')\n",
"HTML(render()._repr_html_())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table border=\"1\" cellpadding=\"3\" cellspacing=\"0\" style=\"border:1px solid black;border-collapse:collapse;\"><tr><td style=\"background-color:LightGray;\"><b>Initial&nbspresidual</b></td><td style=\"background-color:LightGray;\"><b>Posterior&nbspresidual&nbsp(mode)</b></td><td style=\"background-color:LightGray;\"><b>Posterior&nbspresidual&nbsp(mean)</b></td></tr><tr><td style=\"background-color:Ivory;\">9.2884</td><td style=\"background-color:Ivory;\">11.1621</td><td style=\"background-color:Ivory;\">7.5381</td></tr>"
],
"output_type": "pyout",
"prompt_number": 13,
"text": [
"<IPython.core.display.HTML at 0x2c84fd0>"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
}
],
"metadata": {}
}
]
}
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