mirror of
https://github.com/fjosw/pyerrors.git
synced 2025-06-29 16:29:27 +02:00
454 lines
130 KiB
Text
454 lines
130 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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"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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"metadata": {},
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"outputs": [],
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"source": [
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"plt.style.use('./base_style.mplstyle')\n",
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"plt.rc('text', usetex=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Read data from the pcac example"
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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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"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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"Data has been written using pyerrors 2.0.0.\n",
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"Format version 0.1\n",
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"Written by fjosw on 2022-01-06 11:27:34 +0100 on host XPS139305, Linux-5.11.0-44-generic-x86_64-with-glibc2.29\n",
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"\n",
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"Description: SF correlation function f_P on a test ensemble\n"
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]
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}
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],
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"source": [
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"fP = pe.Corr(pe.input.json.load_json(\"./data/f_P\"), padding_front=1, padding_back=1)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We can now define a custom fit function, in this case a single exponential. __Here we need to use the autograd wrapped version of numpy__ (imported as anp) to use automatic differentiation."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import autograd.numpy as anp\n",
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"def func_exp(a, x):\n",
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" y = a[1] * anp.exp(-a[0] * x)\n",
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" return y"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Fit single exponential to f_P. The kwarg `resplot` generates a figure which visualizes the fit with residuals."
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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": 33,
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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 2 parameters\n",
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"Method: Levenberg-Marquardt\n",
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"`xtol` termination condition is satisfied.\n",
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"chisquare/d.o.f.: 0.0023324250917749687\n",
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"\n",
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" Goodness of fit:\n",
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"χ²/d.o.f. = 0.002332\n",
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"Fit parameters:\n",
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"0\t 0.2036(92)\n",
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"1\t 16.3(1.3)\n",
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"\n"
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]
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},
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{
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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 800x494.438 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"start_fit = 9\n",
|
|
"stop_fit = 18\n",
|
|
"\n",
|
|
"fit_result = fP.fit(func_exp, [start_fit, stop_fit], resplot=True)\n",
|
|
"print(\"\\n\", fit_result)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"The covariance of the two fit parameters can be computed in the following way"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Covariance: 0.009831165592706342\n",
|
|
"Normalized covariance: 0.8384671239654656\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"cov_01 = pe.fits.covariance(fit_result[0], fit_result[1])\n",
|
|
"print('Covariance: ', cov_01)\n",
|
|
"print('Normalized covariance: ', cov_01 / fit_result[0].dvalue / fit_result[1].dvalue)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Effective mass"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Calculate the effective mass for comparison"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"m_eff_fP = fP.m_eff()\n",
|
|
"m_eff_fP.tag = r\"Effective mass of f_P\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Calculate the corresponding plateau and compare the two results"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 39,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Fit with 1 parameters\n",
|
|
"Method: Levenberg-Marquardt\n",
|
|
"`ftol` termination condition is satisfied.\n",
|
|
"chisquare/d.o.f.: 0.13241808096937788\n",
|
|
"\n",
|
|
"Effective mass:\t 0.2057(68)\n",
|
|
"Fitted mass:\t 0.2036(92)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"m_eff_plateau = m_eff_fP.plateau([start_fit, stop_fit])\n",
|
|
"m_eff_plateau.gamma_method()\n",
|
|
"print()\n",
|
|
"print('Effective mass:\\t', m_eff_plateau)\n",
|
|
"print('Fitted mass:\\t', fit_result[0])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can now visualize the effective mass compared to the result of the fit"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 37,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
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"text/plain": [
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"<Figure size 640x395.55 with 1 Axes>"
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]
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},
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|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"m_eff_fP.show(plateau=fit_result[0])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Fitting with x-errors"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We first generate pseudo data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 40,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"(Obs[-0.37(35)], Obs[0.61(25)])\n",
|
|
"(Obs[1.40(35)], Obs[0.92(25)])\n",
|
|
"(Obs[3.83(35)], Obs[-1.38(25)])\n",
|
|
"(Obs[6.39(35)], Obs[-1.58(25)])\n",
|
|
"(Obs[8.69(35)], Obs[-0.62(25)])\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"ox = []\n",
|
|
"oy = []\n",
|
|
"for i in range(0,10,2):\n",
|
|
" ox.append(pe.pseudo_Obs(i + 0.35 * np.random.normal(), 0.35, str(i)))\n",
|
|
" oy.append(pe.pseudo_Obs(np.sin(i) + 0.25 * np.random.normal() - 0.2 * i + 0.17, 0.25, str(i)))\n",
|
|
"\n",
|
|
"[o.gamma_method() for o in ox + oy]\n",
|
|
"[print(o) for o in zip(ox, oy)];"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"And choose a function to fit"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 41,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def func(a, x):\n",
|
|
" y = a[0] + a[1] * x + a[2] * anp.sin(x)\n",
|
|
" return y"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can then fit this function to the data and get the fit parameter as Obs with the function `odr_fit` which uses orthogonal distance regression."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 43,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Fit with 3 parameters\n",
|
|
"Method: ODR\n",
|
|
"Sum of squares convergence\n",
|
|
"Residual variance: 2.605726458598027\n",
|
|
"Parameter 1 : 0.37(34)\n",
|
|
"Parameter 2 : -0.254(62)\n",
|
|
"Parameter 3 : 1.13(27)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"beta = pe.fits.odr_fit(ox, oy, func)\n",
|
|
"\n",
|
|
"for i, item in enumerate(beta):\n",
|
|
" item.gamma_method()\n",
|
|
" print('Parameter', i + 1, ':', item)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"For the visulization we determine the value of the fit function in a range of x values"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 44,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 640x395.55 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"x_t = np.arange(min(ox).value - 1, max(ox).value + 1, 0.01)\n",
|
|
"y_t = func([o.value for o in beta], x_t)\n",
|
|
"\n",
|
|
"plt.errorbar([e.value for e in ox], [e.value for e in oy], xerr=[e.dvalue for e in ox], yerr=[e.dvalue for e in oy], marker='D', lw=1, ls='none', zorder=10)\n",
|
|
"plt.plot(x_t, y_t, '--')\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can also take a look at how much the inidividual ensembles contribute to the uncetainty of the fit parameters"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 45,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Parameter 0\n",
|
|
"\n",
|
|
"Parameter 1\n",
|
|
"\n",
|
|
"Parameter 2\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
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"text/plain": [
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"<Figure size 640x395.55 with 1 Axes>"
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]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
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{
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"data": {
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"image/png": 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\n",
|
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"text/plain": [
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"<Figure size 640x395.55 with 1 Axes>"
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]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
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"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 640x395.55 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"for i, item in enumerate(beta):\n",
|
|
" print('Parameter', i)\n",
|
|
" item.plot_piechart()\n",
|
|
" print()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"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": "3.8.10"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|