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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pyerrors as pe\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"plt.style.use('./base_style.mplstyle')\n",
"plt.rc('text', usetex=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read data from the pcac example"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data has been written using pyerrors 2.0.0.\n",
"Format version 0.1\n",
"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",
"\n",
"Description: SF correlation function f_P on a test ensemble\n"
]
}
],
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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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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import autograd.numpy as anp\n",
"def func_exp(a, x):\n",
" y = a[1] * anp.exp(-a[0] * x)\n",
" return y"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fit single exponential to f_P. The kwarg `resplot` generates a figure which visualizes the fit with residuals."
]
},
{
"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fit with 2 parameters\n",
"Method: Levenberg-Marquardt\n",
"`xtol` termination condition is satisfied.\n",
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"chisquare/d.o.f.: 0.0023324250917749687\n",
"\n",
" Goodness of fit:\n",
"χ²/d.o.f. = 0.002332\n",
"Fit parameters:\n",
"0\t 0.2036(92)\n",
"1\t 16.3(1.3)\n",
"\n"
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]
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 800x494.438 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
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"start_fit = 9\n",
"stop_fit = 18\n",
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"\n",
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"fit_result = fP.fit(func_exp, [start_fit, stop_fit], resplot=True)\n",
"print(\"\\n\", fit_result)"
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The covariance of the two fit parameters can be computed in the following way"
]
},
{
"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Covariance: 0.009831165592706342\n",
"Normalized covariance: 0.8384671239654656\n"
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]
}
],
"source": [
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"cov_01 = pe.fits.covariance(fit_result[0], fit_result[1])\n",
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"print('Covariance: ', cov_01)\n",
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"print('Normalized covariance: ', cov_01 / fit_result[0].dvalue / fit_result[1].dvalue)"
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Effective mass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculate the effective mass for comparison"
]
},
{
"cell_type": "code",
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"execution_count": 34,
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"metadata": {},
"outputs": [],
"source": [
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"m_eff_fP = fP.m_eff()\n",
"m_eff_fP.tag = r\"Effective mass of f_P\""
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculate the corresponding plateau and compare the two results"
]
},
{
"cell_type": "code",
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"execution_count": 39,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"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"
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]
}
],
"source": [
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"m_eff_plateau = m_eff_fP.plateau([start_fit, stop_fit])\n",
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"m_eff_plateau.gamma_method()\n",
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"print()\n",
"print('Effective mass:\\t', m_eff_plateau)\n",
"print('Fitted mass:\\t', fit_result[0])"
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
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"We can now visualize the effective mass compared to the result of the fit"
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]
},
{
"cell_type": "code",
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"execution_count": 37,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 640x395.55 with 1 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
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"m_eff_fP.show(plateau=fit_result[0])"
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fitting with x-errors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We first generate pseudo data"
]
},
{
"cell_type": "code",
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"execution_count": 40,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"(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"
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]
}
],
"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",
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"execution_count": 41,
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"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",
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"execution_count": 43,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fit with 3 parameters\n",
"Method: ODR\n",
"Sum of squares convergence\n",
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"Residual variance: 2.605726458598027\n",
"Parameter 1 : 0.37(34)\n",
"Parameter 2 : -0.254(62)\n",
"Parameter 3 : 1.13(27)\n"
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]
}
],
"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",
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"execution_count": 44,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"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",
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"execution_count": 45,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Parameter 0\n",
"\n",
"Parameter 1\n",
"\n",
"Parameter 2\n",
"\n"
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]
},
{
"data": {
2022-01-06 12:07:19 +01:00
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2021-10-11 18:31:02 +01:00
"text/plain": [
"<Figure size 640x395.55 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
2022-01-06 12:07:19 +01:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAiQAAAFcCAYAAAD4aqVBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAvx0lEQVR4nO3deXxcZaH/8e9zzmTpnjZpC2Jh2rI0ZS+bLIIOiwKyF/WK1w2F/ByXKyiM652LegV3vUYH1OvFDRdUUEYE6kDL0pXS0tKUpaW0LNJC24GuSWbO749JSppmmWSW58ycz/v1mlfJLOd8gfTkm+c85znG8zwBAADY5NgOAAAAQCEBAADWUUgAAIB1FBIAAGAdhQQAAFhHIQEAANZRSAAAgHUUEgAAYB2FBAAAWEchAQAA1lFIAACAdRQSAABgHYUEAABYRyEBAADWUUgAAIB1FBIAAGAdhQQAAFhHIQEAANZRSAAAgHUUEgAAYB2FBAAAWEchAQAA1oVsBwBQOcKxpCtpnKTRyh0/HEnukbtd7507ayWpQ1Jnr0eHpO3RRMSzEhpARTCexzECCKpwLFkj6QBJUyQd2PXnFEmTJI1Vrnz0fIzsazsn7wo9fNqumlMH2FWnpJclvdj1eKmfPzdFE5Fswf9iACoOhQSocuFY0pE0XdKRko6QNFNSWLnisZ+KcOo2j0KSr+7i0l1Qnpe0XNKjklZEE5H2IuwDgA9RSIAqEo4lJ0g6Qbny0V1AmiWNKOV+i1hIBtIuaYWkJcoVlCWSVkYTkY4S7xdAGVBIgAoWjiWnSzpV0mldfzZLMuXOUaZC0pfd6rukdFrIAqAAFBKggoRjySMlnak3Csh+dhPlWCwkfdkl6XFJKUl/lbSACbWA/1FIAB8Lx5IjlCsg50s6T7mJp77js0LS28uS7pJ0p6Q50URkp+U8APpAIQF8JhxLHqRcATlf0ttV4vkfxeDzQtLTDklzlCsnd0UTkY2W8wDoQiEBfCAcS+4v6d8kXSFpluU4Q1ZBhaSnrKQFyp3WuTOaiKy2nAcINAoJYEk4lhwr6TLlSsjbVcErJ1doIentKeXKyW+iicgyy1mAwKGQAGXUtRDZecqVkAsk1dtNVBxVUkh6WiLpFkm3RRORbbbDAEFAIQHKoOuUzNVdD19cGVNMVVhIur0u6beSbo4mIo/ZDgNUMwoJUELhWPI0SZ+QdKmkGstxSqaKC0lPSyT9SLlRE1aMBYqMQgIUWdeluldIiko6xm6a8ghIIen2L0k/kfSTaCKyyXYYoFpQSIAiCceSjZI+rVwRmWA5TlkFrJB02y3pN5K+H01EVtgOA1Q6CglQoK75IdcqNz9ktOU4VgS0kPR0n6QvRhORxbaDAJWKQgIMU1cRiUm6SlVytcxwUUgkSZ6k2yV9IZqIPGM7DFBpKCTAEIVjycnKFZEWBbyIdKOQ7KVD0k8l/RcrwQL5o5AAeeqarPo5SddJGmU5jq9QSPq0TdK3JX2HtUyAwVFIgEGEY0mj3FUz35D0ZstxfIlCMqCXJd0g6ZZoItJpOwzgVxW7VDVQDuFY8lRJCyX9SpQRDM9kSa2SVrW2pC63HQbwK0ZIgD6EY8mpkm6SxA+QPDBCMiSLJF0fTUQesB0E8BMKCdBD171mrpf0JUl1luNUDArJsPxVUks0EXnJdhDADzhlA3QJx5InSnpU0ldFGUHpXShpZWtL6n22gwB+wAgJAi8cS46S9DVJnxIlfVgYISnYn5UbLWEpegQWB18EWjiWPEfSSkn/If4+wJ5LJT3R2pK61HYQwBZGSBBI4ViyQdIPJH3AcpSqwAhJUf1W0ieiicgW20GAcuI3QgROOJY8RdIyUUbgT+9Tbm7JebaDAOXECAkCIxxLOpK+KOk/JbmW41QVRkhK5n8lfSaaiLxmOwhQaoyQIBDCseQBklLKrZhJGUGl+IikFa0tqTNtBwFKjUKCqheOJS+StFzSGbazAMNwoKT7WltSP2xtSdXYDgOUCoUEVSscS9aGY8kfSbpDUqPlOEAhjKRPSrq3tSXF9zKqEoUEVSkcS05S7hRN1HYWoIjeJmlha0uq2XYQoNgoJKg64VhylqQlkphkiWo0XdKC1pbUO20HAYqJQoKqEo4l3yPpIUlTbGcBSmispLtaW1L/YTsIUCxc9ouqEI4ljaSvS/q87SxBxGW/Vv1M0sejiUiH7SBAIRghQcULx5JjlJu4ShlBEH1UuatwmOyKikYhQUULx5KTJc1T7s6pQFCdodxk15m2gwDDRSFBxQrHklOVmy9yjOUogB9MlzS/tSV1ru0gwHBQSFCRwrHkUZIelnSw7SyAj4yV9LfWltRnbAcBhopCgooTjiVPkzRX0v62swA+5Er6bmtL6mu2gwBDQSFBRQnHku+SdK+kBstRAL/7YmtL6ibbIYB8UUhQMcKx5Psl/UXSCNtZgApxXWtL6ju2QwD5oJCgIoRjySsk3SopZDsLUGGuaW1J/cB2CGAwLIyGITHGXCdpa9eXDZ7nfbPU+wzHkpdLuk25c+PwIRZGqwg/lvSJaCLCQR++xAgJ8tZVRuR53i2e590iaakx5uZS7jMcS14k6beijACF+rik/7EdAugPIyTImzFmi6Spnudt7fGc53meKcX+wrHkecrNGaktxfZRPIyQVJQbo4kIqxrDdxghQV6MMdOUO0WztY/Xzir2/sKx5NmS/iTKCFBssdaW1BdthwB6o5AgX9P6eX6rinwJbjiWPF25e9PUF3O7APb4WmtL6tO2QwA9UUhQqM2SJhRrY+FYcqakOyWNLNY2AfTpe60tqStthwC6UUhQqGKWkcmS/i4WPQPKwUi6pbUldantIIBEIUH+1vbzfMMAr+UtHEuOlPQ3SQcVui0AeXMk3drakjrCdhCAQoK8eJ63VtLWrsmtvV+bU8i2w7Gko9ylvScUsh0AwzJa0h2tLanxtoMg2CgkGIpvSNpzRY0xZrakW4qw3e9JuqgI2wEwPNMl/a61JcV6P7CGQoK8da3K2mCMmd1VRk7wPO/qQrYZjiU/LelTRQkIoBDnSLrRdggEFwujwZpwLPkO5SaxUowrHAujVZX3RROR22yHQPDwgwBWhGPJqcrNG+F7EPCXn7e2pI61HQLBww8DlF04lhwh6c8q4iXDAIpmhKS/tLakmmwHQbBQSGDDTyQdYzsEgH4dJOkPrS2pkO0gCA4KCcoqHEteKemDtnMAGNTbJX3HdggEB4UEZROOJY8Stz8HKsmnWltSH7IdAsFAIUFZhGPJ0ZL+qNz5aQCVI9HakmLRQpQchQTl8n1Jh9oOAWDI6iT9urUlxd23UVIUEpRcOJZ8lyTuKgpUrkMlxW2HQHWjkKCkwrFko6Sf2s4BoGCfbW1JHW87BKoXhQSl9mNJ+9kOAaBgrnKLptXYDoLqRCFByYRjyfdKerftHACK5ihJn7cdAtWJQoKSCMeS+0tqtZ0DQNF9sbUldYTtEKg+FBKUys/E0vBANaqV9L+tLSnXdhBUFwoJiq7rVM15tnMAKJkTJH3GdghUFwoJiiocS44Ry00DQXBDa0vqENshUD0oJCi2uKQ32Q4BoORGSPpZa0vK2A6C6kAhQdGEY8nDJX3Kdg4AZXO6pP9nOwSqA4UExdQqiduVA8FyY2tL6kDbIVD5KCQoinAs+X5JZ9jOAaDsmDeGoqCQoGDhWHKspG/ZzgHAmstaW1LH2g6BykYhQTF8QSwPDwSZkfQ12yFQ2SgkKEjXiqxMZAVwXmtL6lTbIVC5KCQo1JeVu/wPAL5uOwAqF4UEwxaOJadJ+qjtHAB844zWltTZtkOgMlFIUIgbJHErcgA9MUqCYaGQYFjCseSRkv7Ndg4AvnNCa0vqYtshUHkoJBiur4vvHwB9+2prS4rjA4aEbxgMWTiWPEnSBbZzAPCtIyS913YIVBYKCYYjZjsAAN/7r9aWFLeSQN4oJBiScCx5qKQLbecA4HsHS/qw7RCoHBQSDNW14vsGQH6+3NqSqrMdApWBHyzIWziWnCTpA7ZzAKgYU8QxA3mikGAoPimp3nYIABUlajsAKgOFBHkJx5KjJH3cdg4AFef
2021-10-11 18:31:02 +01:00
"text/plain": [
"<Figure size 640x395.55 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
2022-01-06 12:07:19 +01:00
"image/png": "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"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": {
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"display_name": "Python 3 (ipykernel)",
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"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",
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"version": "3.8.10"
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}
},
"nbformat": 4,
"nbformat_minor": 4
}
