mirror of
https://github.com/fjosw/pyerrors.git
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395 lines
40 KiB
Text
395 lines
40 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"
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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": "modern-relay",
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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.: 0.3395164548834892\n",
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"fit parameters [0.98791658 1.00784727 1.56875359]\n",
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"chisquare/expected_chisquare: 0.339844373345418\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,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": "persistent-mathematics",
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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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"Goodness of fit:\n",
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"χ²/d.o.f. = 0.339516\n",
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"χ²/χ²exp = 0.339844\n",
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"p-value = 0.9620\n",
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"Fit parameters:\n",
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"0\t 0.988(35)\n",
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"1\t 1.008(32)\n",
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"2\t 1.569(42)\n",
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"\n"
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]
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}
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],
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"source": [
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"print(output_test)"
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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": 7,
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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()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "3b82d1c6",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"x_const = {'c':[0,1,2,3,4,5,6,7,8,9],'d':list(np.arange(10,20))}\n",
|
|
"y_const = {'c':[pe.Obs([np.random.normal(1, val, 1000)],['ensemble1']) \n",
|
|
" for val in [0.25,0.3,0.01,0.2,0.5,1.3,0.26,0.4,0.1,1.0]],\n",
|
|
" 'd':[pe.Obs([np.random.normal(1, val, 1000)],['ensemble1'])\n",
|
|
" for val in [0.5,1.12,0.26,0.25,0.3,0.01,0.2,1.0,0.38,0.1]]}\n",
|
|
"for key in y_const.keys():\n",
|
|
" [item.gamma_method() for item in y_const[key]]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "7c1f7950",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#needs to be vectorized for expected chi2 to work (jacobian matrix incorrect dim. otherwise)\n",
|
|
"#@anp.vectorize\n",
|
|
"def func_const(a,x):\n",
|
|
" return a[0]#*anp.ones(len(x))\n",
|
|
"\n",
|
|
"funcs_const = {\"c\": func_const,\"d\": func_const}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "82e0cdb6",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Fit with 1 parameter\n",
|
|
"Method: migrad\n",
|
|
"Optimization terminated successfully.\n",
|
|
"chisquare/d.o.f.: 1.444161495357013\n",
|
|
"fit parameters [0.9997047]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"output_const = pe.fits.least_squares(x_const,y_const,funcs_const,method='migrad')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "ab5c5bef",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"output_const.gamma_method()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"id": "d6abfe4f",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
" chisquare: 27.439068411783246\n",
|
|
" chisquare_by_dof: 1.444161495357013\n",
|
|
" dof: 19\n",
|
|
" fit_function: {'c': <function func_const at 0x7fd602cf69d8>, 'd': <function func_const at 0x7fd602cf69d8>}\n",
|
|
" fit_parameters: [Obs[0.99970(22)]]\n",
|
|
" iterations: 15\n",
|
|
" message: 'Optimization terminated successfully.'\n",
|
|
" method: 'migrad'\n",
|
|
" p_value: 0.09483431965197764"
|
|
]
|
|
},
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"output_const"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "50e3de50",
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Fit with 1 parameter\n",
|
|
"Method: migrad\n",
|
|
"Optimization terminated successfully.\n",
|
|
"chisquare/d.o.f.: 1.444161495357013\n",
|
|
"fit parameters [0.9997047]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"output_const = pe.fits.least_squares(x_const,y_const,funcs_const,method='migrad')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "efd3d4d0",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"y_const_ls = []\n",
|
|
"for key in y_const:\n",
|
|
" for item in y_const[key]:\n",
|
|
" y_const_ls.append(item)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"id": "57d65824",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[Obs[1.0101(80)], Obs[0.9908(97)], Obs[0.99919(32)], Obs[0.9962(64)], Obs[0.965(17)], Obs[1.004(42)], Obs[1.0094(82)], Obs[1.004(13)], Obs[0.9974(31)], Obs[0.954(34)], Obs[1.004(16)], Obs[1.058(37)], Obs[0.9893(84)], Obs[0.9895(85)], Obs[0.9914(96)], Obs[1.00028(33)], Obs[1.0005(62)], Obs[0.957(32)], Obs[0.988(13)], Obs[1.0040(32)]]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(y_const_ls)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"id": "731552bc",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Fit with 1 parameter\n",
|
|
"Method: Levenberg-Marquardt\n",
|
|
"`ftol` termination condition is satisfied.\n",
|
|
"chisquare/d.o.f.: 1.4441614953561615\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"output_const2 = pe.fits.least_squares(list(np.arange(0,20)),y_const_ls, func_const)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"id": "019583b5",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"output_const2.gamma_method()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"id": "f28a3478",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
" chisquare: 27.439068411767067\n",
|
|
" chisquare_by_dof: 1.4441614953561615\n",
|
|
" dof: 19\n",
|
|
" fit_function: <function func_const at 0x7fd602cf69d8>\n",
|
|
" fit_parameters: [Obs[0.99970(22)]]\n",
|
|
" iterations: 7\n",
|
|
" message: '`ftol` termination condition is satisfied.'\n",
|
|
" method: 'Levenberg-Marquardt'\n",
|
|
" p_value: 0.0948343196523247"
|
|
]
|
|
},
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"output_const2"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"id": "466cd303",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"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= {'c':'red','d':'black'}\n",
|
|
"plt.figure()\n",
|
|
"for key in funcs_const.keys():\n",
|
|
" plt.errorbar(x_const[key],[o.value for o in y_const[key]],ls='none',marker='*',\n",
|
|
" color=colour[key],yerr=[o.dvalue for o in y_const[key]],capsize=3,label=key)\n",
|
|
"plt.plot(np.arange(0,20),[func_const(output_const.fit_parameters,x_val) for x_val in list(np.arange(0,20))],\n",
|
|
" label='combined fit',color ='lightblue')\n",
|
|
"plt.plot(np.arange(0,20),[func_const(output_const2.fit_parameters,x_val) for x_val in list(np.arange(0,20))],\n",
|
|
" label='one fit',color='black',ls='--')\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"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|