mirror of
https://github.com/fjosw/pyerrors.git
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467 lines
46 KiB
Text
467 lines
46 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.: 1.1407448193242595\n",
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"fit parameters [0.98418071 0.95797691 1.52431702]\n",
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"chisquare/expected_chisquare: 1.1485431097238927\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": 6,
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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": 7,
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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. = 1.140745\n",
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"χ²/χ²exp = 1.148543\n",
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"p-value = 0.3293\n",
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"Fit parameters:\n",
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"0\t 0.984(33)\n",
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"1\t 0.958(32)\n",
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"2\t 1.524(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": 8,
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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": 9,
|
|
"id": "3b82d1c6",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"x_const = {'c':list(np.arange(0,10)),'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": 10,
|
|
"id": "7c1f7950",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def func_const(a, x):\n",
|
|
" return a[0]\n",
|
|
"\n",
|
|
"funcs_const = {\"c\": func_const,\"d\": func_const}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"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.: 0.7268201670950173\n",
|
|
"fit parameters [0.99968989]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"output_const = pe.combined_fits.combined_fit(x_const,y_const,funcs_const,method='migrad')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"id": "53021f73",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"13.80958317480533"
|
|
]
|
|
},
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"output_const.chisquare"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "ab5c5bef",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"output_const.gamma_method()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "d6abfe4f",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
" chisquare: 13.80958317480533\n",
|
|
" chisquare_by_dof: 0.7268201670950173\n",
|
|
" dof: 19\n",
|
|
" fit_function: {'c': <function func_const at 0x7f3742783378>, 'd': <function func_const at 0x7f3742783378>}\n",
|
|
" fit_parameters: [Obs[0.99969(22)]]\n",
|
|
" iterations: 15\n",
|
|
" message: 'Optimization terminated successfully.'\n",
|
|
" method: 'migrad'\n",
|
|
" p_value: 0.7946762502119166"
|
|
]
|
|
},
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"output_const"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"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": 16,
|
|
"id": "57d65824",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[Obs[0.9905(78)], Obs[1.0090(96)], Obs[0.99960(32)], Obs[1.0032(62)], Obs[1.018(18)], Obs[0.988(49)], Obs[1.0084(83)], Obs[1.000(13)], Obs[0.9960(32)], Obs[1.009(34)], Obs[0.990(16)], Obs[0.970(35)], Obs[0.9865(91)], Obs[0.9981(80)], Obs[1.0065(97)], Obs[0.99983(31)], Obs[0.9985(61)], Obs[1.040(32)], Obs[1.011(12)], Obs[0.9966(31)]]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(y_const_ls)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"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.: 0.7268201670947627\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"output_const2 = pe.fits.least_squares(list(np.arange(0,20)),y_const_ls, func_const)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"id": "019583b5",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"output_const2.gamma_method()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"id": "f28a3478",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
" chisquare: 13.809583174800492\n",
|
|
" chisquare_by_dof: 0.7268201670947627\n",
|
|
" dof: 19\n",
|
|
" fit_function: <function func_const at 0x7f3742783378>\n",
|
|
" fit_parameters: [Obs[0.99969(22)]]\n",
|
|
" iterations: 7\n",
|
|
" message: '`ftol` termination condition is satisfied.'\n",
|
|
" method: 'Levenberg-Marquardt'\n",
|
|
" p_value: 0.7946762502121925"
|
|
]
|
|
},
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"output_const2"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"id": "466cd303",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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0sMuEsWpi76Rd3MG/fybEn4iuGP0znl43l7t7kbv3dPeB7v5zd/9pkOQJvjVc7+4nu/vp7r42Zt0l7v43wdT8SoxENXwjefbZ6JRtT5hKN91ZnLBExqrJhAd3ZPNYO13xjUTj0WeChm8k0jnbtsHs2bB8efSms/z8aNPZokXpjixrlJWVMT74RtXRM+pIJJL24QYSiT/duuIbSUb0uhFJSDe/s1iDemW/VH8j0Rm9hEM3fkJUwxl1wxnt6nRco5KEpPobiRK9hIMeBSnSKjXdiIikUVdczA5XotegWN1ThvRaqquro7y8PKt6fEj6NYyn33RSom+NBsXqnhpumGo6dXGiT+UNL9K6TOjemenCkejVj1rSKC8vDzOjMjj+GgbkytPx1yW64ow424Uj0esJTZJGYbkFX8IrHIm+m/ejThpd4+iUbL8FX00f4Ree7pXduB910mismE5ruOGlqKiIcePGNTbjZINMuLNVUis8iV79qDsvL+/wsfeDB3+QmwtJGlQp7DLhFvy6ujq2bNnCrl27subbhHSNcDTdSGJ0jSMU1OtHWqNEL+m/xpEh/eCzlXr9SHuU6CUqkQd/JCpD+sFnK/X6kfaEp41eEqNrHFkr23v9SOop0YuEQDb3+pHUU6IXCYFM6PUjmUtt9CIiIReORK9eGyIirYor0ZvZ+Wb2hpltNbObWyj/tJn9wcw2mNlqMxsYU/bvZrbJzLaY2V1mZsn8BQD12hARaUO7id7MegCLgYnAUOByMxvapNoiYJm7DwcWAN8L1v088LfAcOA04AygJGnRi4hIu+I5ox8LbHX3be7+EbAcmNykzlBgVfD6mZhyB3KBXsCngJ5AVaJBi4hI/OJJ9AOAd2LmK4JlsdYDFwWvpwBHmdlx7v4S0cRfGUxPuvuWxEIWEZGOSNbF2NlAiZm9RrRpZifwsZn9DXAqMJDoh8MEMzun6cpmNsPM1prZ2urq6iSFJCIiEF+i3wmcGDM/MFjWyN3/4u4XufsoYE6wbB/Rs/s17v6Bu38APAGc1XQD7n6Puxe7e3Hfvn0795uIiEiL4kn0rwBDzGywmfUCLgNWxFYws0Iza3ivW4Alwes/Ez3TP8LMehI921fTjYhIF2o30bv7IeAG4EmiSfo37r7JzBaY2aSg2njgDTP7E3ACsDBY/jDwFvA60Xb89e7+eHJ/BZHuTU+IkvbENQSCu68EVjZZNjfm9cNEk3rT9T4G/jnBGEWkDXpClLQnHHfGiohIqzSomUgkAvPnfzLfcPP2vHm6u7qJgwcPUlFRQW3soyelS+Xm5jJw4EB69uwZ9zpK9CKRiBJ6nCoqKjjqqKMYNGgQqRjNRNrm7uzZs4eKigoGDx4c93pquhFJUHe6GFpbW8txxx2nJJ8mZsZxxx3X4W9UOqMXSVB3uxiqJJ9endn/OqMXyRB1dXWUl5ezqyuf1yvdghK9SIbYsWMHNTU1LFiwIN2hdDu9e/ducfncuXP5/e9/n5RtjB8/nrVr1zZb/vzzzzNs2DBGjhzJzp07ufjiiwEoLy9n5cqVzep3hhK9SJrl5eVhZo3PeS0tLcXMyMvLS3NksmDBAs4777yUbuOBBx7glltuoby8nAEDBvDww9FbkpKZ6NVGL5Jm27ZtY/bs2Sxfvpz6+nry8/OZMmUKixYtSndobVq/u4aa2kNJfc+jc49gxPFHt1ln2bJlLFq0CDNj+PDh/OIXv2D79u189atf5d1336Vv377cf//9nHTSSUyfPp28vDxee+01du/ezZIlS1i2bBkvvfQS48aNY+nSpY3ve9NNN/HUU0/Rr18/li9fTt++fZk+fToXXnghF198MYMGDWLatGk8/vjjHDx4kIceeohTTjmFDz/8kBtvvJGNGzdy8OBBIpEIkydP5sCBA1xzzTWsX7+eU045hQMHDjT7Xe677z5+85vf8OSTT/LEE0+wcOFCLrzwQl599VXmzp3LgQMHeOGFF7jlllu49NJLO71fdUYvkmZFRUUUFBRQX19PTk4OtbW1FBQU0K9fv3SHlnE2bdrE7bffzqpVq1i/fj133nknADfeeCPTpk1jw4YNTJ06la9//euN6/z1r3/lpZde4sc//jGTJk3ipptuYtOmTbz++uuUl5cD8OGHH1JcXMymTZsoKSlhfux9FTEKCwt59dVXue666xo/iBcuXMiECRN4+eWXeeaZZ/j2t7/Nhx9+SGlpKfn5+WzZsoX58+ezbt26Zu937bXXMmnSJO644w4eeOCBxuW9evViwYIFXHrppZSXlyeU5EFn9CIZoaqqiv79+1NUVMS4ceMam3EyWXtn3qmwatUqLrnkEgoLCwE49thjAXjppZcoKysD4KqrruI73/lO4zpf/vKXMTNOP/10TjjhBE4//XQAhg0bxvbt2xk5ciQ5OTmNyfTKK6/koosuoiUNy8eMGdO4vaeeeooVK1Y0Jv7a2lr+/Oc/89xzzzV+4AwfPpzhw4cndV90hBK9SAYoKytj/PjxACxevDi9wYTMpz71KQBycnIaXzfMHzrUctNTa10YG9bv0aNH47ruziOPPMLnPve5ZIadVGq6EZGsMWHCBB566CH27NkDwN69ewH4/Oc/z/Lly4Hoxc1zzmn2fKM21dfXN14E/dWvfsXZZ58d97pf+tKXuPvuu3F3AF577TUAzj33XH71q18BsHHjRjZs2NChmI466ijef//9Dq3TGiV6Eckaw4YNY86cOZSUlDBixAi++c1vAnD33Xdz//33N16cbWi7j9eRRx7Jyy+/zGmnncaqVauYO3du+ysFbr31Vg4ePMjw4cMZNmwYt956KwDXXXcdH3zwAaeeeipz585lzJgxHYrpC1/4Aps3b2bkyJE8+OCDHVq3KWv4FMoUxcXF3lJfU+kCQdMBq1enM4puq6HpZnUG7/8tW7Zw6qmnpjuMbq+lv4OZrXP34pbq64xeRCTklOhFREJOiV5EJOSU6EVEQk6JXkQk5OJK9GZ2vpm9YWZbzezmFso/bWZ/MLMNZrbazAbGlJ1kZk+Z2RYz22xmg5IYv4iItKPdRG9mPYDFwERgKHC5mQ1tUm0RsMzdhwMLgO/FlC0D7nD3U4GxwO5kBC4ikix1dXWcd955jX3Wr732WjZv3gzAd7/73TRHl7h4zujHAlvdfZu7fwQsByY3qTMUWBW8fqahPPhAOMLdnwZw9w/cfX9SIhcRSZKGu1kbBhC77777GDo0ej4bhkQfz1g3A4B3YuYrgHFN6qwHLgLuBKYAR5nZccBngX1mVgYMBn4P3OzuH8eubGYzgBkAJ510Uid+DRFJh4abvGJ95StfYdasWezfv58LLrigWfn06dOZPn067777buNDNhrEc7PYj370I5YsWQJER3/8xje+wfbt25k4cSJnn302L774IgMGDOC3v/0teXl5vPXWW1x//fVUV1eTn5/PvffeyymnnNL4frt37+bKK6+kurqakSNH8sgjj/C1r32NRYsW8fDDD3PgwAFGjhzJsGHDDhthMpsk62LsbKDEzF4DSoCdwMdEP0jOCcrPAD4DTG+6srvf4+7F7l7ct2/fJIUkImGzbt067r//fv74xz+yZs0a7r333saz8TfffJPrr7+eTZs20adPHx555BEAZsyYwd133826detYtGgRs2bNOuw9jz/+eO677z7OOeccysvLOfnkkxvLvv/975OXl0d5eXnWJnmI74x+J3BizPzAYFkjd/8L0TN6zKw38I/uvs/MKoByd98WlD0GnAn8PPHQRSTd2joDz8/Pb7O8sLCww8M9vPDCC0yZMoUjjzwSiA4b/PzzzzNp0iQGDx7MyJEjgegwwtu3b+eDDz7gxRdf5JJLLml8j7q6ug5tMwziSfSvAEPMbDDRBH8ZcEVsBTMrBPa6ez1wC7AkZt0+ZtbX3auBCYAGshGRpIsdgrhHjx4cOHCA+vp6+vTp0/iAke6q3aYbdz8E3AA8CWwBfuPum8xsgZlNCqqNB94wsz8BJwALg3U/Jtps8wczex0w4N6k/xYi0i2cc845PPbYY+zfv58PP/yQRx99tM0hiQsKChg8eDAPPfQQEB07fv369R3aZs+ePTl48GBCcadbXA8ecfeVwMomy+bGvH4YeLiVdZ8G0vdoFREJjdGjRzN9+nTGjh0LRC/Gjho1iu3bt7e6zgMPPMB1113H7bffzsGDB7nssssYMWJE3NucMWMGw4cPZ/To0VnbTq9hiuUTGqY4rTRMscRLwxSLiMhhlOhFREJOiV5EJOSU6EVEQk6JXkRSIxIBs+ZTJJLuyLqduLpXioh0WCQSndSbK+10Ri8iqVVXB+XlsGtXuiPptpToRSS1duyAmhpYsCDdkXRbSvQikhp5edE2+crK6HxpaXQ+Ly+ht122bBnDhw9nxIgRXHXVVUkINPzURi8iqbFtG8yeDcuXQ3095OfDlCmwaFGn33LTpk3cfvvtvPjiixQWFrJ3794kBhxeOqMXkdQoKoKCgmiSz8mB2trofL9+nX7LVatWcckll1BYWAjAsccem6xoQ01n9CKSOlVV0L9/NOmPG/dJM450KZ3Ri0jqlJXBkCHQuzcsXhydT8CECRN46KGH2LNnD4CabuKkM3oRyRrDhg1jzpw5lJSU0KNHD0aNGsXSpUvTHVbGU6IXkdSIRGD+/E/mzaI/581L6O7YadOmMW3atIRC626U6EUkNRrujJW0Uxu9iEjIKdGLiIScEr2ISMjFlejN7Hwze8PMtprZzS2Uf9rM/mBmG8xstZkNbFJeYGYVZvaTZAUuIpktEolgZs2miNrtu1y7id7MegCLgYnAUOByMxvapNoiYJm7DwcWAN9rUn4b8Fzi4YpItohEIrg7JSUllJSU4O64uxJ9GsRzRj8W2Oru29z9I2A5MLlJnaHAquD1M7HlZjYGOAF4KvFwRSTb1NXVUV5ezq4UDFMciURYlMDYOd1FPIl+APBOzHxFsCzWeuCi4PUU4CgzO87McoAfArPb2oCZzTCztWa2trq6Or7IRSQr7Nixg5qaGhZomOK0SdbF2NlAiZm9BpQAO4GPgVnASnevaGtld7/H3Yvdvbhv375JCklE0ikvLw8zozIY36a0tBQzIy/BYYoXLlzIZz/7Wc4++2zeeOONZIQaevEk+p3AiTHzA4Nljdz9L+5+kbuPAuYEy/YBZwE3mNl2ou34V5vZ95MQtyRTw7M9n302OunZnpIE27Zt44orriAnJ5pm8vPzmTp1Km+//Xan33PdunUsX76c8vJyVq5cySuvvJKscEMtnjtjXwGGmNlgogn+MuCK2ApmVgjsdfd64BZgCYC7T42pMx0odvdmvXYkzXQHo6RAUVERBQUF1NfXk5OTQ21tLQUFBfRLYJji559/nilTppCfnw/ApEmTkhVuqLWb6N39kJndADwJ9ACWuPsmM1sArHX3FcB44Htm5kR711yfwphFJEtUVVXRv39/ioqKGDduXGMzjnStuNro3X2lu3/W3U9294XBsrlBksfdH3b3IUGda929roX3WOruNyQ3fBHJZGVlZQwZMoTevXuzePFiyhIcpvjcc8/lscce48CBA7z//vs8/vjjSYo03DSomYhkjdGjR3PppZcyYsQIjj/+eM4444x0h5QdGm5iyJRpzJgxLtKdzJs3z4Fm07x589IdWjObN2+Ou242/V7ZpqW/A9Gm9BbzqkXLM0dxcbGvXbs23WGISAu2bNnCqaeemu4wur2W/g5mts7di1uqr0HNRERCToleRDok01oBupvO7H8lehGJW25uLnv27FGyTxN3Z8+ePeTm5nZoPfW6EZG4DRw4kIqKCjQmVfrk5uYycODA9ivGUKIXkbj17NmTwYMHpzsM6SA13YiIhJwSvYhIyCnRi4iEXMbdMGVm1cCOBN6iEHg3SeGkguJLjOJLjOJLTCbH92l3b/GBHhmX6BNlZmtbuzssEyi+xCi+xCi+xGR6fK1R042ISMgp0YuIhFwYE/096Q6gHYovMYovMYovMZkeX4tC10YvIiKHC+MZvYiIxFCiFxEJuaxM9GZ2vpm9YWZbzezmFso/ZWYPBuV/NLNBXRjbiWb2jJltNrNNZvYvLdQZb2Y1ZlYeTHO7Kr6YGLab2evB9ps96cWi7gr24QYzG92FsX0uZt+Um9l7ZvaNJnW6dB+a2RIz221mG2OWHWtmT5vZm8HPY1pZd1pQ500zm9aF8d1hZv8b/P0eNbM+razb5rGQwvgiZrYz5m94QSvrtvn/nsL4HoyJbbuZlbeybsr3X8Jae/RUpk5AD+At4DNAL2A9MLRJnVnAT4PXlwEPdmF8RcDo4PVRwJ9aiG888P/TvB+3A4VtlF8APAEYcCbwxzT+vXcRvRkkbfsQOBcYDWyMWfbvwM3B65uBH7Sw3rHAtuDnMcHrY7oovi8CRwSvf9BSfPEcCymMLwLMjuPv3+b/e6ria1L+Q2BuuvZfolM2ntGPBba6+zZ3/whYDkxuUmcy8F/B64eBvzMz64rg3L3S3V8NXr8PbAEGdMW2k2wysMyj1gB9zKwoDXH8HfCWuydyt3TC3P05YG+TxbHH2X8B/9DCql8Cnnb3ve7+V+Bp4PyuiM/dn3L3Q8HsGqBjY9smUSv7Lx7x/L8nrK34gtzxFeDXyd5uV8nGRD8AeCdmvoLmibSxTnCg1wDHdUl0MYImo1HAH1soPsvM1pvZE2Y2rGsjA6IPan7KzNaZ2YwWyuPZz13hMlr/B0v3PjzB3SuD17uAE1qokyn78atEv6G1pL1jIZVuCJqWlrTS9JUJ++8coMrd32ylPJ37Ly7ZmOizgpn1Bh4BvuHu7zUpfpVoU8QI4G7gsS4OD+Bsdx8NTASuN7Nz0xBDm8ysFzAJeKiF4kzYh408+h0+I/sqm9kc4BDwQCtV0nUslAInAyOBSqLNI5nocto+m8/4/6VsTPQ7gRNj5gcGy1qsY2ZHAEcDe7okuug2exJN8g+4e1nTcnd/z90/CF6vBHqaWWFXxRdsd2fwczfwKNGvyLHi2c+pNhF41d2rmhZkwj4Eqhqas4Kfu1uok9b9aGbTgQuBqcGHUTNxHAsp4e5V7v6xu9cD97ay3XTvvyOAi4AHW6uTrv3XEdmY6F8BhpjZ4OCM7zJgRZM6K4CG3g0XA6taO8iTLWjP+zmwxd1/1Eqdfg3XDMxsLNG/Q1d+EB1pZkc1vCZ60W5jk2orgKuD3jdnAjUxzRRdpdUzqXTvw0DscTYN+G0LdZ4EvmhmxwRNE18MlqWcmZ0PfAeY5O77W6kTz7GQqvhir/lMaWW78fy/p9J5wP+6e0VLhencfx2S7qvBnZmI9gj5E9Gr8XOCZQuIHtAAuUS/7m8FXgY+04WxnU30K/wGoDyYLgBmAjODOjcAm4j2IFgDfL6L999ngm2vD+Jo2IexMRqwONjHrwPFXRzjkUQT99Exy9K2D4l+4FQCB4m2E3+N6HWfPwBvAr8Hjg3qFgP3xaz71eBY3Apc04XxbSXavt1wHDb0ROsPrGzrWOii+H4RHFsbiCbvoqbxBfPN/t+7Ir5g+dKGYy6mbpfvv0QnDYEgIhJy2dh0IyIiHaBELyISckr0IiIhp0QvIhJySvQiIiGnRC8iEnJK9CIiIfd/BJeklr6HMykAAAAASUVORK5CYII=\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()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"id": "dd14c5dc",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def func_const_wrong():\n",
|
|
" a=x"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"id": "d4e8adbc",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"funcs_const_wrong = {\"c\": 4,\"d\": func_const_wrong}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"id": "27f8d77c",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"ename": "TypeError",
|
|
"evalue": "func (key=c) is not a function.",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
|
|
"\u001b[0;32m/tmp/ipykernel_55611/20019894.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0moutput_const2_wrong\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcombined_fits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcombined_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_const\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_const\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfuncs_const_wrong\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'migrad'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
|
"\u001b[0;32m~/phd/develop_pyerrors/piapyerrors/pyerrors/combined_fits.py\u001b[0m in \u001b[0;36mcombined_fit\u001b[0;34m(x, y, funcs, silent, **kwargs)\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfuncs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfuncs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'func (key='\u001b[0m\u001b[0;34m+\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m') is not a function.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 74\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'x and y input (key='\u001b[0m\u001b[0;34m+\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m') do not have the same length'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;31mTypeError\u001b[0m: func (key=c) is not a function."
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"output_const2_wrong = pe.combined_fits.combined_fit(x_const,y_const,funcs_const_wrong,method='migrad')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"id": "e7110837",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"x_const_wrong = {'c':list(np.arange(0,11)),'d':list(np.arange(10,20))}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"id": "2dae0db9",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"ename": "Exception",
|
|
"evalue": "x and y input (key=c) do not have the same length",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mException\u001b[0m Traceback (most recent call last)",
|
|
"\u001b[0;32m/tmp/ipykernel_55611/2795677260.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcombined_fits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcombined_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_const_wrong\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_const\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfuncs_const\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'migrad'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
|
"\u001b[0;32m~/phd/develop_pyerrors/piapyerrors/pyerrors/combined_fits.py\u001b[0m in \u001b[0;36mcombined_fit\u001b[0;34m(x, y, funcs, silent, **kwargs)\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'func (key='\u001b[0m\u001b[0;34m+\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m') is not a function.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 75\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'x and y input (key='\u001b[0m\u001b[0;34m+\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m') do not have the same length'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 76\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;31mException\u001b[0m: x and y input (key=c) do not have the same length"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"pe.combined_fits.combined_fit(x_const_wrong,y_const,funcs_const,method='migrad')"
|
|
]
|
|
}
|
|
],
|
|
"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
|
|
}
|