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@kerel-fs
Last active January 4, 2019 16:34
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from scipy.optimize import curve_fit"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"data = [[0 ,0 ,0],\n",
" [0 ,0 ,0],\n",
" [0 ,0 ,0],\n",
" [0 ,0 ,0],\n",
" [0 ,0 ,0],\n",
" [0 ,0 ,0],\n",
" [1 ,0 ,1],\n",
" [1 ,1 ,1],\n",
" [1 ,1 ,1],\n",
" [1 ,1 ,1],\n",
" [1 ,1 ,1],\n",
" [3 ,1 ,1],\n",
" [5 ,1 ,1],\n",
" [7 ,1 ,1],\n",
" [8 ,1 ,2],\n",
" [8 ,2 ,6],\n",
" [8 ,2 ,7],\n",
" [9 ,2 ,8],\n",
" [9 ,2 ,9],\n",
" [9 ,2 ,7]]\n",
"df = pd.DataFrame(data, columns=['a', 'b','c'])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def fit_to_dataframe(df, function, parameter_names):\n",
" popts = {}\n",
" pcovs = {}\n",
"\n",
" for c in df.columns:\n",
" popts[c], pcovs[c] = curve_fit(function, df.index, df[c])\n",
" \n",
" fit_parameters = pd.DataFrame.from_dict(popts, orient='index', columns=parameter_names)\n",
" return fit_parameters\n",
"\n",
"def plot_fit_results(df, function, fit_parameters):\n",
" NUM_POINTS = 50\n",
" t = np.linspace(df.index.values.min(), df.index.values.max(), NUM_POINTS)\n",
" df.plot(style='.')\n",
" for idx, column in enumerate(df.columns):\n",
" plt.plot(t, function(t, *fit_parameters.loc[column]), color='C{}'.format(idx))\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>x0</th>\n",
" <th>k</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>8.869996</td>\n",
" <td>11.714575</td>\n",
" <td>0.844969</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>2.366496</td>\n",
" <td>12.006009</td>\n",
" <td>0.282359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td>8.041970</td>\n",
" <td>14.487101</td>\n",
" <td>1.516682</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a x0 k\n",
"a 8.869996 11.714575 0.844969\n",
"b 2.366496 12.006009 0.282359\n",
"c 8.041970 14.487101 1.516682"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def sigmoid(x, a, x0, k):\n",
" y = a / (1 + np.exp(-k*(x-x0)))\n",
" return y\n",
"\n",
"fit_parameters = fit_to_dataframe(df, sigmoid, parameter_names=['a', 'x0', 'k'])\n",
"fit_parameters"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_fit_results(df, sigmoid, fit_parameters)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "notebooks",
"language": "python",
"name": "notebooks"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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