{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
\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, the initial guessed parameters might not be optimal, resulting in a poor fit of the reference data.\n", "\n", "
\n", "\n", "# Jump sites log norm diffusion with scipy\n", "\n", "## Introduction\n", "\n", "
\n", " \n", "The objective of this notebook is to show how to use one of the models of \n", "the QENSlibrary, sqwJumpSitesLogNormDist, to perform some fits. \n", "\n", "scipy.optimize.curve_fit is used for fitting.\n", "
\n", "\n", "### Physical units\n", "\n", "For information about unit conversion, please refer to the jupyter notebook called `Convert_units.ipynb` in the `tools` folder.\n", "\n", "The dictionary of units defined in the cell below specify the units of the refined parameters adapted to the convention used in the experimental datafile." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Units of parameters for selected QENS model and experimental data\n", "\n", "dict_physical_units = {'scale': \"unit_of_signal.ps\", \n", " 'center': \"1/ps\", \n", " 'radius': 'Angstrom', \n", " 'resTime': 'ps'}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import libraries" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.optimize import curve_fit\n", "import ipywidgets\n", "import QENSmodels" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot fitting model\n", "\n", "The widget below shows the peak shape function imported from QENSmodels where the function's parameters can be varied." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4f174bcd4ea94f3bac8fae7fa20006f7", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(VBox(children=(FloatSlider(value=1.0, continuous_update=False, description='q', max=10.0, min=0…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1d8e96c652d84d45a4a4de9bcb022b5b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d16bf73e3dfa435da5eb910a515c721d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Button(description='Reset', style=ButtonStyle())" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Dictionary of initial values\n", "ini_parameters = {'q': 1., 'scale': 5., 'center': 5., 'Nsites': 3, 'radius': 1., 'resTime':1., 'sigma': 1.}\n", "\n", "def interactive_fct(q, scale, center, Nsites, radius, resTime, sigma):\n", " \"\"\"\n", " Plot to be updated when ipywidgets sliders are modified\n", " \"\"\"\n", " xs = np.linspace(-10, 10, 100)\n", " fig0, ax0 = plt.subplots()\n", " ax0.plot(xs, QENSmodels.sqwJumpSitesLogNormDist(xs, q, scale, center, Nsites, radius, resTime, sigma))\n", " ax0.set_xlabel('x')\n", " ax0.grid()\n", "\n", "# Define sliders for modifiable parameters and their range of variations\n", "q_slider = ipywidgets.FloatSlider(value=ini_parameters['q'],\n", " min=0.1, max=10., step=0.1,\n", " description='q', \n", " continuous_update=False) \n", "\n", "scale_slider = ipywidgets.FloatSlider(value=ini_parameters['scale'],\n", " min=0.1, max=10, step=0.1,\n", " description='scale',\n", " continuous_update=False) \n", "\n", "center_slider = ipywidgets.IntSlider(value=ini_parameters['center'],\n", " min=-10, max=10, step=1,\n", " description='center', \n", " continuous_update=False) \n", "\n", "Nsites_slider = ipywidgets.IntSlider(value=ini_parameters['Nsites'],\n", " min=2, max=10, step=1,\n", " description='Nsites',\n", " continuous_update=False)\n", "\n", "radius_slider = ipywidgets.FloatSlider(value=ini_parameters['radius'],\n", " min=0.1, max=10, step=0.1,\n", " description='radius',\n", " continuous_update=False)\n", "\n", "resTime_slider = ipywidgets.FloatSlider(value=ini_parameters['resTime'],\n", " min=0.1, max=10, step=0.1,\n", " description='resTime', \n", " continuous_update=False)\n", "\n", "sigma_slider = ipywidgets.FloatSlider(value=ini_parameters['sigma'],\n", " min=0.1, max=10, step=0.1,\n", " description='sigma', \n", " continuous_update=False)\n", "\n", "grid_sliders = ipywidgets.HBox([ipywidgets.VBox([q_slider, scale_slider, center_slider, Nsites_slider])\n", " ,ipywidgets.VBox([radius_slider, resTime_slider, sigma_slider])])\n", " \n", "# Define function to reset all parameters' values to the initial ones\n", "def reset_values(b):\n", " \"\"\"\n", " Reset the interactive plots to inital values\n", " \"\"\"\n", " q_slider.value = ini_parameters['q'] \n", " scale_slider.value = ini_parameters['scale'] \n", " center_slider.value = ini_parameters['center'] \n", " Nsites_slider.value = ini_parameters['Nsites'] \n", " radius_slider.value = ini_parameters['radius'] \n", " resTime_slider.value = ini_parameters['resTime']\n", " sigma_slider.value = ini_parameters['slider']\n", "\n", "# Define reset button and occurring action when clicking on it\n", "reset_button = ipywidgets.Button(description = \"Reset\")\n", "reset_button.on_click(reset_values)\n", "\n", "# Display the interactive plot\n", "interactive_plot = ipywidgets.interactive_output(\n", " interactive_fct,\n", " {'q': q_slider, \n", " 'scale': scale_slider,\n", " 'center': center_slider,\n", " 'Nsites': Nsites_slider,\n", " 'radius': radius_slider,\n", " 'resTime': resTime_slider,\n", " 'sigma': sigma_slider}\n", ") \n", " \n", "display(grid_sliders, interactive_plot, reset_button)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating reference data\n", "\n", "**Input:** the reference data for this simple example correspond to sqwJumpSitesLogNormDist with added noise.\n", "\n", "The fit is performed using `scipy.optimize.curve_fit`.
The example is based on implementations from https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Creation of reference data\n", "nb_points = 100\n", "xx = np.linspace(-10, 10, nb_points)\n", "added_noise = np.random.normal(0, 1, nb_points)\n", "sqw_jump_sites_noisy = QENSmodels.sqwJumpSitesLogNormDist(\n", " xx, \n", " q=0.89, \n", " scale=1, \n", " center=0.3, \n", " Nsites=5, \n", " radius=2, \n", " resTime=0.45, \n", " sigma=0.25\n", ") * (1. + 0.04 * added_noise) + 0.02 * added_noise\n", "\n", "fig1, ax1 = plt.subplots()\n", "ax1.plot(xx, sqw_jump_sites_noisy, label='reference data')\n", "ax1.set_xlabel('x')\n", "ax1.grid()\n", "ax1.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setting and fitting\n", "From https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html \n", "Perform fit varying `scale`, `center`, `radius`, `resTime` and `sigma`. \n", "`Nsites` and `q` are fixed to 5 and 0.89, respectively." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def func_to_fit(xx, scale, center, radius, resTime, sigma):\n", " return QENSmodels.sqwJumpSitesLogNormDist(\n", " xx, \n", " 0.89, \n", " scale, \n", " center, \n", " 5, \n", " radius, \n", " resTime, \n", " sigma)\n", "\n", "fig2, ax2 = plt.subplots()\n", "ax2.plot(xx, sqw_jump_sites_noisy, 'b-', label='reference data')\n", "ax2.plot(xx, \n", " QENSmodels.sqwJumpSitesLogNormDist(\n", " xx, \n", " 0.89, \n", " scale=0.95, \n", " center=0.2, \n", " Nsites=5, \n", " radius=2, \n", " resTime=0.45, \n", " sigma=0.25\n", " ), \n", " 'r-', \n", " label='model with initial guesses')\n", "ax2.set_xlabel('x')\n", "ax2.grid()\n", "ax2.legend(bbox_to_anchor=(0.6, 1), loc=2, borderaxespad=0.);" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "success_fit = True\n", "\n", "try:\n", " popt, pcov = curve_fit(\n", " func_to_fit, \n", " xx, \n", " sqw_jump_sites_noisy, \n", " p0=[0.95, 0.2, 2, 0.45, 0.25], \n", " bounds=((0.1, -2, 0.1, 0.1, 0.1), \n", " (5., 2., 5., 11., 1.))\n", " )\n", " \n", "except RuntimeError:\n", " success_fit = False\n", " print(\"Error - curve_fit failed\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting the results\n", "\n", "Calculation of the errors on the refined parameters" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Values of refined parameters:\n", "scale: 1.9096072979837913 +/- 0.2648608568483786 unit_of_signal.ps\n", "center 0.342073883505371 +/- 0.2573380929761853 1/ps\n", "radius: 4.999999999999999 +/- 33.59669925356897 Angstrom\n", "resTime: 0.14626722132107697 +/- 0.7559034928398951 ps\n", "sigma: 0.9999999999999999 +/- 0.26792555744643887\n" ] } ], "source": [ "if success_fit: \n", " perr = np.sqrt(np.diag(pcov))\n", " print(\n", " \"Values of refined parameters:\\n\"\n", " f\"scale: {popt[0]} +/- {perr[0]} {dict_physical_units['scale']}\\n\"\n", " f\"center {popt[1]} +/- {perr[1]} {dict_physical_units['center']}\\n\"\n", " f\"radius: {popt[2]} +/- {perr[2]} {dict_physical_units['radius']}\\n\"\n", " f\"resTime: {popt[3]} +/- {perr[3]} {dict_physical_units['resTime']}\\n\"\n", " f\"sigma: {popt[4]} +/- {perr[4]}\"\n", " )" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Comparison of reference data with fitting result\n", "if success_fit: \n", " fig3, ax3 = plt.subplots()\n", " ax3.plot(xx, sqw_jump_sites_noisy, 'b-', label='reference data')\n", " ax3.plot(xx, func_to_fit(xx, *popt), 'g--', label='fit: %5.3f, %5.3f, %5.3f, %5.3f, %5.3f' % tuple(popt))\n", " ax3.legend(bbox_to_anchor=(0., 1.15), loc='upper left', borderaxespad=0.)\n", " ax3.set_xlabel('x')\n", " ax3.grid();" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "hide_input": false, "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": "3.8.9" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }