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@yoavram
Created April 28, 2015 13:15
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"sns.set_style(\"ticks\")\n",
"sns.set_context('notebook')\n",
"from lmfit import Model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Model growth with the following function:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def br_function(t, y0, r, K, nu, q0, v): \n",
" At = t + (1./v) * np.log((np.exp(-v * t) + q0)/(1 + q0))\n",
" return K / ((1 - (1 - (K/y0)**nu) * np.exp( -r * nu * At ))**(1./nu))\n",
"br_model = Model(br_function)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read the data:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>Time [s]</th>\n",
" <th>OD</th>\n",
" <th>Time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>180</th>\n",
" <td>0</td>\n",
" <td>0.109</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>181</th>\n",
" <td>3640</td>\n",
" <td>0.111</td>\n",
" <td>1.011111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>182</th>\n",
" <td>7279</td>\n",
" <td>0.115</td>\n",
" <td>2.021944</td>\n",
" </tr>\n",
" <tr>\n",
" <th>183</th>\n",
" <td>10919</td>\n",
" <td>0.121</td>\n",
" <td>3.033056</td>\n",
" </tr>\n",
" <tr>\n",
" <th>184</th>\n",
" <td>14559</td>\n",
" <td>0.131</td>\n",
" <td>4.044167</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Time [s] OD Time\n",
"180 0 0.109 0.000000\n",
"181 3640 0.111 1.011111\n",
"182 7279 0.115 2.021944\n",
"183 10919 0.121 3.033056\n",
"184 14559 0.131 4.044167"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"weights_bug.csv\", index_col=0)\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot the data - OD as function of Time:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xc1e4780>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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FtUiSNC8tGBkZqeSFI2Ir8OHM3N3efgpYmpnHO57zOuDdwEeBX+o8Ns7rbQRuG+/Yli1b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\nXYffAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xc1e46a0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot('Time', 'OD', kind='scatter')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have two replicates for each time point so we group by time:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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>Time</th>\n",
" <th>OD</th>\n",
" <th>OD_std</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.000000</td>\n",
" <td>0.1095</td>\n",
" <td>0.000707</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.011111</td>\n",
" <td>0.1110</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2.021944</td>\n",
" <td>0.1145</td>\n",
" <td>0.000707</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3.033056</td>\n",
" <td>0.1200</td>\n",
" <td>0.001414</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4.044167</td>\n",
" <td>0.1295</td>\n",
" <td>0.002121</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Time OD OD_std\n",
"0 0.000000 0.1095 0.000707\n",
"1 1.011111 0.1110 0.000000\n",
"2 2.021944 0.1145 0.000707\n",
"3 3.033056 0.1200 0.001414\n",
"4 4.044167 0.1295 0.002121"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = df.groupby(by='Time').OD.agg([np.mean, np.std]).reset_index().rename(columns={'mean':'OD', 'std':'OD_std'})\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot aggregated data:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<Container object of 3 artists>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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P4EszjduRRCSLqKxF0uxoQ5B7f7aUXfsb+NzFw7lp9tluRxKRLKOyFkmjusYW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\nlbWIiEiG+//CcPEBTzeKYQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xc4447b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.errorbar(df.Time, df.OD, yerr=df.OD_std)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Try to fit - we get a poor fit."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"BIC -91.5018576338\n"
]
},
{
"data": {
"image/png": 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hKehFpMVtLsxh7tr3SYrqxI+HX+bvckQ6NAW9iLSoGncNjyx5Fo/jYeroa4gK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\nG+Ue2B4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xc659828>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"params = br_model.make_params(K=1.0, y0=0.1, r=0.1, nu=1.0, q0=1.0, v=1.0)\n",
"fit = br_model.fit(df.OD, t=df.Time, params=params)\n",
"fit.plot_fit()\n",
"print \"BIC\", fit.bic"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can get a better fit by changing our guess, but this is heuristic:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"BIC -187.949078516\n"
]
},
{
"data": {
"image/png": 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AQs+N/9///V+CwSDV1dU88MADLF++nIMHD/L973+fhx56iLvvvpvi4mJKSkq46KKL+N73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\nXsAzJQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xc4220f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"params = br_model.make_params(K=0.8, y0=0.1, r=0.1, nu=1.0, q0=1.0, v=1.0)\n",
"fit = br_model.fit(df.OD, t=df.Time, params=params)\n",
"fit.plot_fit()\n",
"print \"BIC\", fit.bic"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We might expect we can get a better fit by giving weights, but we have a problem:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(df.OD_std==0).any()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A zero standard deviation gives an `inf` weight which causes very longer fit time and a poor fit:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"BIC nan\n"
]
},
{
"data": {
"image/png": 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X1/D3BSuZ++JqtlXvYMpnTuT4I/s3ajti0EF5qFDtjUEvJcioyZOYf+3X66d1\nYP32iZe5/9FIzfZaAA4u78q7Th7CwL7e5678MeilBCkbPozSPr3rp3Vg9epRSt+DujBh7AAmjB3I\n8EN6NvmIWulAMuglqZk2bKrmn4tWsWXbdi48+/BG6888dhBnHTfIcFebYtBLCePLbFrXug3b+Oei\nVfx9wSr+/co66lLQpbQjHzh9OJ067noRnU+pU1tk0EvSbtRsr+Wz33m8/pz7yMG9mDB2ICePGdAo\n5KW2yqCX1O7tqK0jlaJReJd06sCFZ42gZ7cSThozgN49u+SpQqnlDHpJ7dLWqu08t3gN//z3Kp5b\nvJrPf3Qcp407pFG7j/u6VxU4g15SuzJvyRoe+stSFi5bx47aFAB9e3Vh+47aPFcm5YZBL6ld2bJt\nBy8sWcvwQ3ty4pH9OXH0AIYO7OGV8kosg15SouyorWPRsnWsWreFd08Y2mj9caP6cfc3z6dvL8+3\nq30w6CUVvC3btvP8S++cb99StYNOHYs587hBjd7t3rmkI51L/NWn9iNnP+0hhGLgJ8BYoBq4Isa4\nLGv9xcCXgR3AQuBzMcZUruqRlEy1dSk++53HqNxcA0C/Xl04+/jDOPGo/pR4C5yU0xH9BUBJjHFC\nCOFEYEZmGSGELsC3gdExxqoQwv3A+4A/5rAeSQWsti5FXV2q0S1wHYqLeNfJQ+hQXMxJo/szZIDn\n26VsuQz6U4BHAGKMz4QQxmetqwJOjjFWZdWxLYe1SCpAm7bW8EJcw9zFq3n+pTV8+r1Hct6Jgxu1\nu/Rdo/JQnVQYchn0PYCNWfO1IYTiGGNd5hD9WoAQwheBbjHGx3JYi6QC8nxcwwOPRuJr66nLnNAr\n79GZHbV1+S1MKkC5DPqNQPes+eIYY/2/0sw5/O8BI4ALc1iHpAJTV5civraeMLic8aMOZvyog70F\nTmqhXAb9HOD9wIMhhJOABQ3W30n6EP6HmnMRXghhKnB9axcp6cBKpVKsWLOZZxevZl3lNiZ+cEyj\nNkcf3pdZ099N964leahQKhjLQwgNl02LMU7NXlCUSuXmQvcQQhHvXHUPcBlwHFAGPJv576msj9wS\nY/zdPu5jCLD88ccf59BDD93vmiXlRm1diudfWs2zi1fz7EtrWLN+K5C+kO7+b7+brp075blCqXCs\nWLGCc845B2BojPHVvbXP2Yg+M0q/usHiJVnTHXK1b0ltSxFw66/nsWFTNd06d+TUowcyftTBHDuy\nnyEv5Zj8j+KdAAAPXklEQVRPjZDUKrbvqOXFV9ZzWP/u9OrReZd1xcVFfPZDYziorJSRQ8rp2MH7\n26UDxaCX1GJvVWzhhSVref6l1cx/eS3bqmv57IfG8L5ThzVqe+rRjd8MJyn3DHpJLfKbJ17m5w+/\nWD9/SN9uHDfqYEYOLs9jVZIaMuilHHt24lUAjJ95R54r2Xe1tXWs31jd5Atgjhxazkmj+zPuiH4c\nE/oysE9ZHiqUtDcGvZRDi6ZMo3rN2vrp0dPb/h2iq9Zt4YUla3ghrmHB0nUM6NONm79yZqN2Rw7t\nzZFDex/4AiXtE4NeypFFU6ZROf+dx0dUzl/A3MsnMmryJMqGNz6HnW8bt9Rwzc1/ZXXm1jeAAb27\nEQ7rRV1diuJiH1YjFSKDXsqRygULGy2rqVjP4htv4vi7Z+ahorQdtXV0KC5q9JS57l070a1zJ04e\nM4BjQj+OOaIv/Xt3y1OVklqLQS8lXCqVYuW6LbwQ1zBvyVoWLF3Hd79wKkMH9tylXVFRETdfc4aP\nmZUSxqCXcqTn2DG7HLoHKOldzqjJkw5YDQ/9ZSkPz3mFNW+/83LIgX26Ubm5usn2hryUPAa9lCOj\np1/P3MsnUlOxHkiHfK4O2adSqSZDurpmB1urdnDK0QM55oi+jDuiHweXd81JDZLaJoNeyqFRkycx\n/9qv10+3lqqaHbz06noWLF3HwqXrGDmknM98YHSjdh86cwT/+7xABy+kk9otg17KobLhwzhl9oOt\ntr3lKyu5c/ZC4mtv17+bvbi4iP59mr5ornOp/8Sl9s7fAlIbtLvb2bp16cTi5RUMO6QnY0b0ZeyI\nPhw5tNwXw0jaLYNeagNqa+tYumJD/aH411dv4q5vnt/okHu/Xl25/9vvoVsXg11S8xj0Uh6lUim+\n+4tneT6uYVv1jvrlgw7uztsbq+hzUONHzxrykvaFQS8dAHV1KVKpFB0avJ61qKiIzdtq6NW9lNOP\nOYSjR/Rl9Ije9OreeTdbkqR9Y9BLOVBbl2L5ykpefKWCfy+vYNGyCq6+cGyTr2r91mdOorRThzxU\nKak9MOilVvaHp5cx639e2uVQfJ+enamuqW2yvSEvKZcMeqkFNm2tYfPW7Qxo4ra27l1LKO/RmSOH\nlnPUsPQb3vr37upT5yTlhUEvNcPat7fx7+UVvLi8ghdfqeC1tzZx7Mh+TJt4cqO2Zx57KGcdNygP\nVUpSYwa92o1FU6bVv1Gu59gxzX43/MtvvM01Nz9VP19a0oGxI/pw9Ig+TbZ35C6pLTHo1S7s6d3w\npYOHsOzNDSx/s5J3Txja6LNDBvRkwtgBjBxczpFDyxl+6EF0bHD1vCS1VQa92oXdvRv+n5Omcfuw\nj1KzPX2h3EmjB9Crx663tnXqWMykT51wQOqUpNZm0Ktd21Fbx8A+3eovnPPZ8JKSxt9qSpTNW2tY\n8voG4mvreen1t7n4vMDIIeVNvhueHgdx7HVf411HhfwUK0kHgEGvRHj4b6/wpznLWbFm8y7LTziy\nPyOHlB/Qd8NLUlti0KtgbNhUTc2OWvr16tpoXVVNLRWV2zj68D6EweWEwb0Ih/WiZ1lpfZtRkyex\n+Mab6qclqT0w6NUmbd9Rx/KVlcTX3k7/9/p63qrYyrnHH8aXP3ZMo/bvO20YF5w5otHb3rKVDR/m\nKF5Su2PQq0167qXV3Pizf9XPd+/aifGjDiYM7tVkex8jK0lNM+h1wG3YVM3SFRtYumID26p2cNn7\nj2rUZuTgct4zYQhhcDkjB/diQJ9uPohGklrAoNcBsbVqOz+8/3mWrdjAusoqAC56888cvm0Vc+4q\navSkuoO6l3L1hUfnq1xJSgwf76VWs2FTNc+9tJq6ulSjdV1KO7Jo2Tpq61KMH3UwX66aw9BtqygC\nSKXqn1S3edkrB7xuSUoyR/RqsXlL1hBfezt9GP6Nd0bqd153DgP7lu3StqioiJmTz6N71xIA5lzw\ng0bbq6lYz+Ibb/KCOUlqRQa99iqVSjV5fnzWIy8RX3sbgF7dSxk/6mAOH3QQpSVNXxi3M+QlSQeO\nQa96qVSKDZuqWfZmJctWbODlNzawbMUGPvvhsZw0ekCj9h87L1BXl2L4oT3p3bPLPu2rqSfVlfQu\n9/52SWplBr3qPfq5r9FtZfoc+fYuA3jmkPPo1b2UrVU7mmw/ftTBLd6XT6qTpAPDoE+42roUqyu2\n8OqqjfX/HRP68e6Th+zSbtGUaZStfOdCuKHbVvHNij9y1Oe+QdnwQTmpzSfVSVLuGfRt2KIp0+pf\nr9rw9rPmePL5Fdz263n1r2DdqUtpx0ZB39RrXHe8/XZOL47zSXWSlHsGfRu1aMq0Xc5h77z9bNTk\nSZQcNpjXV2/itcwIvUe3Ej56zhGNttG7Z2cO6duNwQN6MHRADwYP6MGQAT0ob/C+dUlSchn0+2B/\nR9j7oqkRdk3Fev5+3VT+a/CFZN+qflj/7k0G/Zjhfbj1q2c1a39eHCdJyZSIoF/yo1s4dMb3c7qP\nPY2wy4YPa/F26+pSVFRWsXLdZlau3czKdVuo3l7L7p4Jl6pLMXJI+S6j9MH9e7R4/zt5cZwkJVMi\ngn7Tiy+1Sujuye5G2M05h727+9A3b63hU9MfbXQOvWOHIk4fM7rRPkt6lzPhG9fxv0YMb0EP9s6L\n4yQpeRIR9NA2nqqWSqVY8vrbrFy3hTfXbmbV2i2sXLeZ1eu38oup76Jjh12fONytSycOH3QQ5T06\nM7BPNwb2LWNg324M7FNGj24fOOAjbC+Ok6TkSUzQ51pzzmEXFRUx/a5n2Lilpn5Zp47FDOzTjY1b\nahpdBFdUVMR3Pn/qbvfpCFuStL9yHvQhhGLgJ8BYoBq4Isa4rEGbrsCfgctjjLEl+8nVhWM/f/hF\nlq7YQEXZaXyw41LKdmwFoMNBvZoc/V507hF07FjMIX3KGNC3G316dqG4uGWvV3WELUnaXwdiRH8B\nUBJjnBBCOBGYkVkGQAhhPHAHMBBo/NqzZujU66BmB+Kzi1fz6qqNVFRuo6KyivWVVVRUbmPSp0/g\niMN6NWq/+NX1/PuVCsq6dOJvo9/HmS/9X4qLixj6pa80uf0PnJ6b8+eSJLXEgQj6U4BHAGKMz2SC\nPVsJ6eC/t6U76PqxT/DPRauoyIR2RWUVF5wxnKEDezZq+8enX+H5uKZ+vmOHInr16Ex1TW2jtgDX\nffJ4Opd2oHPJzj+qi1papiRJB9yBCPoewMas+doQQnGMsQ4gxvh3gBBCS7bdAeDbD/2bTl3e3GXF\noF61dKpr/CKW00eXccIRnelVVspB3TvTvWunzBXxVaxYsaLJnWxuSWWSJOXAW2+9tXOy6VeFNnAg\ngn4j0D1rvj7k90UIYSrQ5BNqVvzjjkbLJj+xr3uQJKmgLG1ikDwtxjg1e8GBCPo5wPuBB0MIJwEL\n9tK+SZnCp2YvCyGUAlXACKDpY++FbzkwNN9F5JD9K2xJ7l+S+wb2r5B1AJYCnWOM1XtrXJRKtej6\nt2YLIRTxzlX3AJcBxwFlMcaZWe3+Anw2xrhkH7efijG27LL2AmD/Cpv9K1xJ7hvYv0K3L/3L+Yg+\nxpgCrm6wuFGYxxib91B2SZLUbMV7byJJkgqVQS9JUoIlIein5buAHLN/hc3+Fa4k9w3sX6Frdv9y\nfjGeJEnKnySM6CVJ0m4Y9JIkJZhBL0lSghn0kiQlmEEvSVKCHYhn3edECKGYdx6tWw1cEWNclt+q\nWk8IoRNwNzAYKAVuiDH+Mb9Vta4QQj/gOeCcfX30cVsXQphE+h0PJcBPYox357mkVpP52fw56Z/N\nWmBijDHmt6rWEUI4EfhOjPGsEMII4B6gDlgEfD7zpM+C1KBv44BbSf/9VQOfjDHr/d0FKLt/Wcs+\nDnwhxjghf5W1jgZ/f/2AmcBBpJ97/8kY4yu7+2whj+gvAEoyf4HXATPyXE9ruwRYG2M8HXgX8OM8\n19OqMmFxJ7Al37W0thDCmcDJmZ/NM4BB+a2o1b0H6BBjPAWYDtyY53paRQjha6R/eZZmFv0Q+Ebm\n32AR8MF81ba/mujbzaQD8CzgIeDr+aqtNTTRP0IIxwCX562oVtRE/74H3BtjPAP4JjByT58v5KA/\nBXgEIMb4DDA+v+W0ugeBKZnpYmBHHmvJhe8DtwOr8l1IDpwPLAwh/A74I/CnPNfT2iLQMfPCqp5A\nTZ7raS1LgQ+TDnWAY2OMT2Wm/wc4Ny9VtY6GfftYjHHnm0Q7AdvyUlXr2aV/IYTepL+A/gfv9LmQ\nNfz7mwAMCiH8mfSg8Mk9fbiQg74H6Xfd71SbOZyfCDHGLTHGzSGE7qRDf3K+a2otIYRPkz5a8Whm\nURL+IWbrS/oNjR8BrgLuy285rW4LMAR4CfgpcFteq2klMcaH2PULdfbP5WbSX2oKUsO+xRjfAggh\nTAA+D/woT6W1iuz+ZXLgLuAa0n9vBa+Jn80hwPoY43nA6+zliEwhB+NGoHvWfHGMsS5fxeRCCGEQ\n8ATwixjjA/mupxVdBpyXeTXxOODnIYSD81xTa1oHPBpj3JG59qAqhNAn30W1oq8Aj8QYA3A06b+/\nkjzXlAvZv0+6AxvyVUguhBAuIn1U7T0xxop819OKjgNGkO7bL4EjQwg/zG9Jra4C+ENm+o/s5Yh2\nIQf9HNLnCgkhnAQs2HPzwpIJvkeBr8UY78lzOa0qxnhGjPHMzPnBeaQvJFmd77pa0d9IX1dBCGEg\n0I30P8ykWM87R9PeJn3ot0P+ysmZF0IIZ2Sm3w08tafGhSSEcCnpkfyZMcZX81xOq4oxzo0xjs78\nfvkY8GKM8Zp819XK/ga8NzN9BumLRXerYK+6B2aTHhXOycxfls9icuAbpA8VTgkh7DxX/+4YY1Ue\na1IzxBgfDiGcHkL4F+kv058r5Ku1m/Aj4O4QwlOk7yqYFGMs9HO82Xb+XX0VmJk5WvEi8Jv8ldRq\nUplD27cArwEPhRAA/hpjnJrPwlpJw39nRU0sK2TZP5v/HUK4mvSRpo/v6UO+1EaSpAQr5EP3kiRp\nLwx6SZISzKCXJCnBDHpJkhLMoJckKcEMekmSEsygl7TPQgg9Qwiz812HpL0z6CW1RC/Sjy+W1MYZ\n9JJa4lZgYAjht/kuRNKeGfSSWuKLwMoY44X5LkTSnhn0kloiaa8WlhLLoJckKcEMekktsYPCfvul\n1G4Y9JJa4i3g9RDC4/kuRNKe+ZpaSZISzBG9JEkJZtBLkpRgBr0kSQlm0EuSlGAGvSRJCWbQS5KU\nYAa9JEkJZtBLkpRg/x8acqQs7XEnJQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xc662080>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"params = br_model.make_params(K=1.0, y0=0.1, r=0.1, nu=1.0, q0=1.0, v=1.0)\n",
"fit = br_model.fit(df.OD, t=df.Time, params=params, weights=1./df.OD_std)\n",
"fit.plot_fit()\n",
"print \"BIC\", fit.bic\n",
"## This runs for a long time..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I would have expected a warning or error if I give a weight of infinity - I get none.\n",
"\n",
"We can clean these zero deviations:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.OD_std[df.OD_std==0] = df.OD_std[df.OD_std!=0].min()\n",
"(df.OD_std==0).any()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And then the fit works:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"BIC -44.9564462975\n"
]
},
{
"data": {
"image/png": 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ewhMJqtPeo7fWnm+M6QN8BXjdGLMbeBZ41Vpbe4pNS4HERtNea60/8PkaIB34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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xc0ed2b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"params = br_model.make_params(K=1.0, y0=0.1, r=0.1, nu=1.0, q0=1.0, v=1.0)\n",
"fit = br_model.fit(df.OD, t=df.Time, params=params, weights=1./df.OD_std)\n",
"fit.plot_fit()\n",
"print \"BIC\", fit.bic"
]
}
],
"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": "2.7.8"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
Time [s] OD Time
180 0 0.109 0.0
181 3640 0.111 1.011111111111111
182 7279 0.115 2.0219444444444443
183 10919 0.121 3.0330555555555554
184 14559 0.131 4.0441666666666665
185 18198 0.151 5.055
186 21838 0.18600000000000003 6.066111111111111
187 25478 0.24600000000000002 7.077222222222222
188 29117 0.34600000000000003 8.088055555555556
189 32757 0.488 9.099166666666667
190 36397 0.599 10.110277777777778
191 40036 0.637 11.12111111111111
192 43676 0.649 12.132222222222222
193 47316 0.65 13.143333333333333
194 50955 0.652 14.154166666666667
270 0 0.11 0.0
271 3640 0.111 1.011111111111111
272 7279 0.114 2.0219444444444443
273 10919 0.11900000000000001 3.0330555555555554
274 14559 0.128 4.0441666666666665
275 18198 0.145 5.055
276 21838 0.175 6.066111111111111
277 25478 0.228 7.077222222222222
278 29117 0.319 8.088055555555556
279 32757 0.451 9.099166666666667
280 36397 0.564 10.110277777777778
281 40036 0.614 11.12111111111111
282 43676 0.632 12.132222222222222
283 47316 0.637 13.143333333333333
284 50955 0.639 14.154166666666667
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