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"\n",
" \n",
"Disclaimer: \n",
" \n",
"The main objective of the Jupyter notebooks is to show how to use the models of the QENS library by\n",
" \n",
"- building a fitting model: composition of models, convolution with a resolution function \n",
"- setting and running the fit \n",
"- extracting and displaying information about the results \n",
"\n",
"These steps have a minimizer-dependent syntax. That's one of the reasons why different minimizers have been used in the notebooks provided as examples. \n",
"But, note that the initial guessed parameters might not be optimal, resulting in a poor fit of the reference data.\n",
" \n",
"
\n",
"\n",
"\n",
"# Equivalent sites circle with lmfit\n",
"\n",
"## Introduction\n",
"\n",
"\n",
" \n",
"The objective of this notebook is to show how to use the
Equivalent Sites Circle model to perform some \n",
"fits using
lmfit .\n",
"
"
]
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"source": [
"fig2 = plt.figure()\n",
"plt.plot(xx, equiv_sites_circle_noisy, 'b-', label='reference data')\n",
"plt.plot(xx, result.best_fit, 'r', label='fitting result')\n",
"plt.legend()\n",
"plt.xlabel('x')\n",
"plt.title('Fit result and reference data')\n",
"plt.grid();"
]
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"Print values and errors of refined parameters:"
]
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"text": [
"resTime: 3.0223323418811363 +/- 0.17278081176482926 ps\n",
"radius: 4.367424188198108 +/- 0.39816818761927975 Angstrom\n",
"center: 0.29066458538309625 +/- 0.004683646032517251 1/ps\n",
"scale: 1.312415165006191 +/- 0.01108276952841706 unit_of_signal/ps\n"
]
}
],
"source": [
"for item in ['resTime', 'radius', 'center', 'scale']:\n",
" print(f\"{item}: {result.params[item].value} +/- {result.params[item].stderr} {dict_physical_units[item]}\")"
]
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