{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Batchfitting of multiple datasets\n", "\n", "This notebook is a demonstration of how to batch fit multiple datasets using `refnx`. Batch fitting is essential for bulk analysis of hundreds or thousands of datasets. Such situations are becoming more common as faster instruments (such as those being built at the ESS) come online.\n", "This example is based on a deuterated polymer film that is gradually being swollen by an hydrogenous solvent. 314 datasets were acquired over a period of a couple of hours." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# some initial imports\n", "%matplotlib inline\n", "import time\n", "from multiprocessing import Pool\n", "from copy import deepcopy\n", "import glob\n", "from tqdm import tqdm\n", "\n", "import matplotlib.pyplot as plt\n", "from natsort import natsorted\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.1.45\n" ] } ], "source": [ "# the refnx imports\n", "import refnx\n", "from refnx.dataset import Data1D\n", "from refnx.analysis import Objective, CurveFitter\n", "from refnx.reflect import SLD, Slab, ReflectModel, Structure\n", "from refnx.dataset import ReflectDataset\n", "\n", "# we print out the version so that others can repeat our analysis\n", "print(refnx.version.version)\n", "\n", "# natsort is used for alphanumeric sort, but it's not essential\n", "# An alphanumeric sort typically ensures that the datasets are loaded in\n", "# the order in which they were run.\n", "files = natsorted(list(glob.iglob('./*.dat')))\n", "\n", "\"\"\"\n", "initial model setup\n", "\"\"\"\n", "# set up the SLD objects for each layer\n", "air = SLD(0.0 + 0.0j, name='air_sld')\n", "polymer = SLD(6.14127029941648 + 0.0j, name='d-polymer')\n", "sio2 = SLD(3.47 + 0.0j, name='native SiO2')\n", "si = SLD(2.07 + 0.0j, name='Si')\n", "\n", "# set up Slabs corresponding to each layer\n", "polymer_l = Slab(520.5277261491321, polymer, 8.762992087388763, name='polymer layer')\n", "sio2_l = Slab(15.305814908968332, sio2, 5.020239736927396, name='sio2 layer')\n", "si_l = Slab(0.0, si, 3.0, name='Si')\n", "\n", "# SLD limits for polymer film\n", "polymer.real.setp(vary=True, bounds=(0, 7.0))\n", "\n", "# limits for thickness and roughness of the layers\n", "polymer_l.thick.setp(vary=True, bounds=(200, 1020.0))\n", "polymer_l.rough.setp(vary=True, bounds=(2.0, 20.0))\n", "sio2_l.thick.setp(vary=True, bounds=(5.0, 10.0))\n", "sio2_l.rough.setp(vary=True, bounds=(2.0, 10.0))\n", "\n", "# set up the Structure object from the Slabs\n", "structure = air | polymer_l | sio2_l | si_l\n", "\n", "# make the reflectometry model\n", "model = ReflectModel(structure, scale=1.0, bkg=1e-10, dq=8.6, dq_type='constant')\n", "model.scale.setp(vary=True, bounds=(0.5, 1.5))\n", "model.bkg.setp(vary=True, bounds=(1e-10, 1e-5))\n", "\n", "\"\"\"\n", "Create lists of all the objectives to fit\n", "\"\"\"\n", "filenames = []\n", "models = []\n", "objectives = []\n", "\n", "for idx, file in enumerate(files):\n", " data = Data1D(data=file)\n", "\n", " # make the objective for each dataset. Deepcopy is used so that all the\n", " # models are independent of each other\n", " objective = Objective(deepcopy(model), data)\n", "\n", " filenames.append(data.filename)\n", " models.append(objective.model)\n", " objectives.append(objective)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since all the objectives and curvefits are independent we can fit the datasets in parallel. To do this in Python one can use a [`multiprocessing.Pool`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool) object to map over all the datasets. Use of a `Pool` object requires us to create a `fit_an_objective` function in a separate Python file to fit each objective (this is because the function needs to be pickleable). The `tqdm` package can be used to display a progress bar." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 314/314 [00:46<00:00, 6.77it/s]\n" ] } ], "source": [ "from parallel_curvefitter import fit_an_objective\n", "\"\"\"\n", "# parallel_curvefitter.py\n", "\n", "def fit_an_objective(objective):\n", " # make the curvefitter and do the fit\n", " fitter = CurveFitter(objective)\n", " fitter.fit('differential_evolution', verbose=False, tol=0.05)\n", " return objective\n", "\"\"\"\n", "\n", "with Pool() as p:\n", " obj_out = list(p.map(fit_an_objective, tqdm(objectives)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We fitted all 314 datasets in 46\"\" (using an M3 Macbook Pro), which is 0.15 seconds per fit. If we had fitted in a serial manner this would have taken a lot longer." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# now process the output\n", "thickness = []\n", "sld = []\n", "for objective in obj_out:\n", " model = objective.model\n", " thickness.append(model.structure[1].thick.value)\n", " sld.append(model.structure[1].sld.real.value)\n", "\n", "fig, ax1 = plt.subplots()\n", "ax1.set_xlabel('dataset #')\n", "ax1.set_ylabel('thickness /$\\\\AA$')\n", "l_0 = ax1.plot(thickness, 'b', label='thickness')\n", "ax2 = ax1.twinx()\n", "l_1 = ax2.plot(sld, 'r', label='sld')\n", "ax2.set_ylabel('SLD /$10^{-6}\\\\AA^{-2}$');\n", "\n", "# setup the legend\n", "lines = l_0 + l_1\n", "labels = [line.get_label() for line in lines]\n", "ax1.legend(lines, labels, loc='right');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we see an induction period during which the film thickness and SLD remains constant (even a possible loss of solvent?). After this period the the thickness grows rapidly before with the rate than slowing down. The SLD has a continuous decrease, but the decrease is not significantly faster during the period in which the thickness of the film grows rapidly." ] } ], "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": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 4 }