mirror of
https://github.com/fjosw/pyerrors.git
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782 lines
315 KiB
Text
782 lines
315 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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"source": [
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"p_obs = {}\n",
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"p_obs['f_P'] = pe.load_object('./data/B1k2_f_P.p')\n",
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"\n",
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"# f_A can be accesed via p_obs['f_A']\n",
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"\n",
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"[o.gamma_method() for o in p_obs['f_P']];"
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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": 5,
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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.: 0.00287692704517733\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"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Mass, Matrix element:\n",
|
|
"[Obs[0.2102(63)], Obs[14.24(66)]]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Specify fit range for single exponential fit\n",
|
|
"start_se = 8\n",
|
|
"stop_se = 19\n",
|
|
"\n",
|
|
"a = pe.fits.standard_fit(np.arange(start_se, stop_se), p_obs['f_P'][start_se:stop_se], func_exp, resplot=True)\n",
|
|
"[o.gamma_method() for o in a]\n",
|
|
"print('Mass, Matrix element:')\n",
|
|
"print(a)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"The covariance of the two fit parameters can be computed in the following way"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Covariance: 0.003465486601483565\n",
|
|
"Normalized covariance: 0.8360758153764554\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"cov_01 = pe.fits.covariance(a[0], a[1])\n",
|
|
"print('Covariance: ', cov_01)\n",
|
|
"print('Normalized covariance: ', cov_01 / a[0].dvalue / a[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": 7,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"m_eff_f_P = []\n",
|
|
"for i in range(len(p_obs['f_P']) - 1):\n",
|
|
" m_eff_f_P.append(np.log(p_obs['f_P'][i] / p_obs['f_P'][i+1]))\n",
|
|
" m_eff_f_P[i].gamma_method()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Calculate the corresponding plateau and compare the two results"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Effective mass:\n",
|
|
"Obs[0.2114(52)]\n",
|
|
"Fitted mass:\n",
|
|
"Obs[0.2102(63)]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"m_eff_plateau = np.mean(m_eff_f_P[start_se: stop_se]) # Plateau from 8 to 16\n",
|
|
"m_eff_plateau.gamma_method()\n",
|
|
"print('Effective mass:')\n",
|
|
"m_eff_plateau.print(0)\n",
|
|
"print('Fitted mass:')\n",
|
|
"a[0].print(0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"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": [
|
|
"pe.plot_corrs([m_eff_f_P], plateau=[a[0], m_eff_plateau], xrange=[3.5, 19.5], prange=[start_se, stop_se], label=['Effective mass'])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Fitting two exponentials"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can also fit the data with two exponentials where the second term describes the cutoff effects imposed by the boundary."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def func_2exp(a, x):\n",
|
|
" y = a[1] * anp.exp(-a[0] * x) + a[3] * anp.exp(-a[2] * x)\n",
|
|
" return y"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can trigger the computation of $\\chi^2/\\chi^2_\\text{exp}$ with the kwarg `expected_chisquare` which takes into account correlations in the data and non-linearities in the fit function and should give a more reliable measure for goodness of fit than $\\chi^2/\\text{d.o.f.}$."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Fit with 4 parameters\n",
|
|
"Method: Levenberg-Marquardt\n",
|
|
"`xtol` termination condition is satisfied.\n",
|
|
"chisquare/d.o.f.: 0.05399877210985092\n",
|
|
"chisquare/expected_chisquare: 0.7915235152330492\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 800x494.438 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Fit result:\n",
|
|
"[Obs[0.2146(65)], Obs[15.15(88)], Obs[0.623(60)], Obs[-9.64(74)]]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Specify fit range for double exponential fit\n",
|
|
"start_de = 2\n",
|
|
"stop_de = 21\n",
|
|
"\n",
|
|
"a = pe.fits.standard_fit(np.arange(start_de, stop_de), p_obs['f_P'][start_de:stop_de], func_2exp, initial_guess=[0.21, 14.0, 0.6, -10], resplot=True, expected_chisquare=True)\n",
|
|
"[o.gamma_method() for o in a]\n",
|
|
"print('Fit result:')\n",
|
|
"print(a)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Fitting with x-errors"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We first generate pseudo data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"(Obs[-0.08(35)], Obs[0.19(25)])\n",
|
|
"(Obs[1.85(35)], Obs[0.34(25)])\n",
|
|
"(Obs[4.01(35)], Obs[-1.39(25)])\n",
|
|
"(Obs[6.10(35)], Obs[-1.30(25)])\n",
|
|
"(Obs[8.08(35)], Obs[-0.37(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": 13,
|
|
"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": 14,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Fit with 3 parameters\n",
|
|
"Method: ODR\n",
|
|
"Sum of squares convergence\n",
|
|
"Residual variance: 0.5988333933914471\n",
|
|
"Parameter 1 : Obs[-0.01(29)]\n",
|
|
"Parameter 2 : Obs[-0.165(55)]\n",
|
|
"Parameter 3 : Obs[0.89(23)]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"beta = pe.fits.odr_fit(ox, oy, func)\n",
|
|
"\n",
|
|
"pe.Obs.e_tag_global = 1 # Makes sure that the different samples with name length 1 are treated as ensembles and not as replica\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": 15,
|
|
"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": 16,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Parameter 0\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 640x395.55 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"Parameter 1\n"
|
|
]
|
|
},
|
|
{
|
|
"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"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"Parameter 2\n"
|
|
]
|
|
},
|
|
{
|
|
"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"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"for i, item in enumerate(beta):\n",
|
|
" print('Parameter', i)\n",
|
|
" item.plot_piechart()\n",
|
|
" print()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Fitting with priors"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"When extracting energy levels and matrix elements from correlation functions one is interested in using as much data is possible in order to decrease the final error estimate and also have better control over systematic effects from higher states. This can in principle be achieved by fitting a tower of exponentials to the data. However, in practice it can be very difficult to fit a function with 6 or more parameters to noisy data. One way around this is to cnostrain the fit parameters with Bayesian priors. The principle idea is that any parameter which is determined by the data is almost independent of the priors while the additional parameters which would let a standard fit collapse are essentially constrained by the priors."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We first generate fake data as a tower of three exponentials with noise which increases with temporal separation."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"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": [
|
|
"m1 = 0.18\n",
|
|
"m2 = 0.5\n",
|
|
"m3 = 0.8\n",
|
|
"\n",
|
|
"A1 = 180\n",
|
|
"A2 = 300\n",
|
|
"A3 = 500\n",
|
|
"\n",
|
|
"px = []\n",
|
|
"py = []\n",
|
|
"for i in range(40):\n",
|
|
" px.append(i)\n",
|
|
" val = (A1 * np.exp(-m1 * i) + A2 * np.exp(-m2 * i) + A3 * np.exp(-m3 * i))\n",
|
|
" err = 0.03 * np.sqrt(i + 1)\n",
|
|
" tmp = pe.pseudo_Obs(val * (1 + err * np.random.normal()), val * err, 'e1')\n",
|
|
" py.append(tmp)\n",
|
|
" \n",
|
|
"[o.gamma_method() for o in py];\n",
|
|
"\n",
|
|
"pe.plot_corrs([py], logscale=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"As fit function we choose the sum of three exponentials"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def func_3exp(a, x):\n",
|
|
" y = a[1] * anp.exp(-a[0] * x) + a[3] * anp.exp(-a[2] * x) + a[5] * anp.exp(-a[4] * x)\n",
|
|
" return y"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can specify the priors in a string format or alternatively input `Obs` from a previous analysis. It is important to choose the priors wide enough, otherwise they can influence the final result."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"priors = ['0.2(4)', '200(500)', \n",
|
|
" '0.6(1.2)', '300(550)',\n",
|
|
" '0.9(1.8)', '400(700)']"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"It is important to chose a sufficiently large value of `Obs.e_tag_global`, as every prior is given an ensemble id."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"pe.Obs.e_tag_global = 5"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"The fit can then be performed by calling `prior_fit` which in comparison to the standard fit requires the priors as additional input."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Fit with 6 parameters\n",
|
|
"Method: migrad\n",
|
|
"chisquare/d.o.f.: 1.100354109100944\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 800x494.438 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[Obs[0.1795(32)], Obs[186(14)], Obs[0.578(70)], Obs[597(170)], Obs[1.42(83)], Obs[239(173)]]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"beta_p = pe.fits.prior_fit(px, py, func_3exp, priors, resplot=True)\n",
|
|
"[o.gamma_method() for o in beta_p]\n",
|
|
"print(beta_p)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can now observe how far the individual fit parameters are constrained by the data or the priors"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"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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]
|
|
},
|
|
"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"
|
|
},
|
|
{
|
|
"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": [
|
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"<Figure size 640x395.55 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"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>"
|
|
]
|
|
},
|
|
"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": [
|
|
"[o.plot_piechart() for o in beta_p];"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"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": "3.6.9"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|