mirror of
https://github.com/fjosw/pyerrors.git
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137 lines
21 KiB
Text
137 lines
21 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "ethical-frontier",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pyerrors as pe\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from packaging import version\n",
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"if version.parse(pe.__version__) < version.parse(\"2.6.0\"):\n",
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" raise Exception(f\"v2.6.0 or newer is required for this example, you are using {pe.__version__}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "incredible-posting",
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"metadata": {},
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"outputs": [],
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"source": [
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"x_test = {'a':[0,1,2,3,4,5],'b':[0,1,2,3,4,5]}\n",
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"y_test = {'a':[pe.Obs([np.random.normal(i, i*1.5, 1000)],['ensemble1']) for i in range(1,7)],\n",
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" 'b':[pe.Obs([np.random.normal(val, val*1.5, 1000)],['ensemble1']) for val in [1.0,2.5,4.0,5.5,7.0,8.5]]}\n",
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"for key in y_test.keys():\n",
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" [item.gamma_method() for item in y_test[key]]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "subtle-malaysia",
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"metadata": {},
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"outputs": [],
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"source": [
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"def func_a(a, x):\n",
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" return a[1] * x + a[0]\n",
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"\n",
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"def func_b(a, x):\n",
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" return a[2] * x + a[0]\n",
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"\n",
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"funcs_test = {\"a\": func_a,\"b\": func_b}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "45f67973",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Fit with 3 parameters\n",
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"Method: migrad\n",
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"Optimization terminated successfully.\n",
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"chisquare/d.o.f.: 0.8085703524653507\n",
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"fit parameters [0.97737577 1.01063624 1.47900852]\n",
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"chisquare/expected_chisquare: 0.8121288230401409\n"
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]
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}
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],
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"source": [
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"output_test = pe.fits.least_squares(x_test,y_test,funcs_test,method='migrad',expected_chisquare=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "technological-rolling",
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"metadata": {},
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"outputs": [],
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"source": [
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"output_test.gamma_method()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "wooden-potential",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"colour= {'a':'red','b':'black'}\n",
|
|
"plt.figure()\n",
|
|
"for key in funcs_test.keys():\n",
|
|
" plt.errorbar(x_test[key],[o.value for o in y_test[key]],ls='none',marker='*',color=colour[key],yerr=[o.dvalue for o in y_test[key]],capsize=3,label=key)\n",
|
|
" plt.plot([x_val for x_val in x_test[key]],[funcs_test[key](output_test.fit_parameters,x_val) for x_val in x_test[key]],color=colour[key],label='func_'+key)\n",
|
|
"plt.legend()\n",
|
|
"plt.show()"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.7.3"
|
|
},
|
|
"vscode": {
|
|
"interpreter": {
|
|
"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|