{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "0", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:02:05.865680Z", "iopub.status.busy": "2026-04-14T15:02:05.865298Z", "iopub.status.idle": "2026-04-14T15:02:05.870968Z", "shell.execute_reply": "2026-04-14T15:02:05.869866Z" }, "tags": [ "hide-in-docs" ] }, "outputs": [], "source": [ "# Check whether easydiffraction is installed; install it if needed.\n", "# Required for remote environments such as Google Colab.\n", "import importlib.util\n", "\n", "if importlib.util.find_spec('easydiffraction') is None:\n", " %pip install easydiffraction==0.13.1" ] }, { "cell_type": "markdown", "id": "1", "metadata": {}, "source": [ "# Load Project and Fit: LBCO, HRPT\n", "\n", "This is the most minimal example of using EasyDiffraction. It shows\n", "how to load a previously saved project from a directory and run\n", "refinement — all in just a few lines of code.\n", "\n", "For this example, constant-wavelength neutron powder diffraction data\n", "for La0.5Ba0.5CoO3 from HRPT at PSI is used.\n", "\n", "It does not contain any advanced features or options, and includes no\n", "comments or explanations — these can be found in the other tutorials." ] }, { "cell_type": "markdown", "id": "2", "metadata": {}, "source": [ "## Import Modules" ] }, { "cell_type": "code", "execution_count": 2, "id": "3", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:02:05.872633Z", "iopub.status.busy": "2026-04-14T15:02:05.872442Z", "iopub.status.idle": "2026-04-14T15:02:08.417705Z", "shell.execute_reply": "2026-04-14T15:02:08.416695Z" } }, "outputs": [], "source": [ "from easydiffraction import Project\n", "from easydiffraction import download_data\n", "from easydiffraction import extract_project_from_zip" ] }, { "cell_type": "markdown", "id": "4", "metadata": {}, "source": [ "## Download Project Archive" ] }, { "cell_type": "code", "execution_count": 3, "id": "5", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:02:08.420505Z", "iopub.status.busy": "2026-04-14T15:02:08.420113Z", "iopub.status.idle": "2026-04-14T15:02:08.625587Z", "shell.execute_reply": "2026-04-14T15:02:08.624731Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;34mGetting data\u001b[0m\u001b[1;34m...\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Data #\u001b[1;36m30\u001b[0m: La0.5Ba0.5CoO3, HRPT \u001b[1m(\u001b[0mPSI\u001b[1m)\u001b[0m, \u001b[1;36m300\u001b[0m K - Updated #\u001b[1;36m28\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "✅ Data #\u001b[1;36m30\u001b[0m downloaded to \u001b[32m'data/ed-30.zip'\u001b[0m\n" ] } ], "source": [ "zip_path = download_data(id=30, destination='data')" ] }, { "cell_type": "markdown", "id": "6", "metadata": {}, "source": [ "## Extract Project" ] }, { "cell_type": "code", "execution_count": 4, "id": "7", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:02:08.627110Z", "iopub.status.busy": "2026-04-14T15:02:08.626939Z", "iopub.status.idle": "2026-04-14T15:02:08.632523Z", "shell.execute_reply": "2026-04-14T15:02:08.631810Z" } }, "outputs": [], "source": [ "project_dir = extract_project_from_zip(zip_path, destination='data')" ] }, { "cell_type": "markdown", "id": "8", "metadata": {}, "source": [ "## Load Project" ] }, { "cell_type": "code", "execution_count": 5, "id": "9", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:02:08.634024Z", "iopub.status.busy": "2026-04-14T15:02:08.633867Z", "iopub.status.idle": "2026-04-14T15:02:10.415049Z", "shell.execute_reply": "2026-04-14T15:02:10.413526Z" } }, "outputs": [ { "data": { "application/javascript": [ "\n", " (function() {\n", " var isDark = false;\n", "\n", " // Check JupyterLab theme\n", " if (document.body.classList.contains('jp-mod-dark') || \n", " document.body.classList.contains('theme-dark') ||\n", " document.body.classList.contains('vscode-dark')) {\n", " isDark = true;\n", " }\n", "\n", " // Check theme attribute\n", " var themeAttr = document.body.getAttribute('data-jp-theme-name');\n", " if (themeAttr && themeAttr.includes('dark')) {\n", " isDark = true;\n", " }\n", "\n", " // Check computed background color\n", " var notebookEl = document.querySelector('.jp-Notebook') || \n", " document.querySelector('.notebook_app') ||\n", " document.body;\n", " if (notebookEl) {\n", " var bgColor = window.getComputedStyle(notebookEl).backgroundColor;\n", " var rgb = bgColor.match(/\\d+/g);\n", " if (rgb && rgb.length >= 3) {\n", " var brightness = (parseInt(rgb[0]) + parseInt(rgb[1]) + parseInt(rgb[2])) / 3;\n", " if (brightness < 128) {\n", " isDark = true;\n", " }\n", " }\n", " }\n", "\n", " // Store result\n", " if (typeof IPython !== 'undefined' && IPython.notebook && IPython.notebook.kernel) {\n", " IPython.notebook.kernel.execute('_jupyter_dark_detect_result = ' + isDark);\n", " }\n", " })();\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", " if (typeof IPython !== 'undefined' && IPython.notebook) {\n", " IPython.notebook.kernel.execute(\"_jupyter_dark_detect_result = \" + \n", " (document.body.classList.contains('theme-dark') || \n", " document.body.classList.contains('jp-mod-dark') ||\n", " (document.body.getAttribute('data-jp-theme-name') && \n", " document.body.getAttribute('data-jp-theme-name').includes('dark'))));\n", " }\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project = Project.load(project_dir)" ] }, { "cell_type": "markdown", "id": "10", "metadata": {}, "source": [ "## Perform Analysis" ] }, { "cell_type": "code", "execution_count": 6, "id": "11", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:02:10.416764Z", "iopub.status.busy": "2026-04-14T15:02:10.416564Z", "iopub.status.idle": "2026-04-14T15:02:29.225336Z", "shell.execute_reply": "2026-04-14T15:02:29.224453Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;34mStandard fitting\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📋 Using experiment 🔬 \u001b[32m'hrpt'\u001b[0m for \u001b[32m'single'\u001b[0m fitting\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "🚀 Starting fit process with \u001b[32m'lmfit \u001b[0m\u001b[32m(\u001b[0m\u001b[32mleastsq\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[33m...\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📈 Goodness-of-fit \u001b[1m(\u001b[0mreduced χ²\u001b[1m)\u001b[0m change:\n" ] }, { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 iterationχ²improvement [%]
11254.47
22143.0483.1% ↓
33936.1716.0% ↓
45734.883.6% ↓
57622.7634.8% ↓
69417.5922.7% ↓
71128.3852.4% ↓
81304.5745.5% ↓
91482.1852.2% ↓
101661.8814.1% ↓
111841.5318.5% ↓
122021.427.3% ↓
132201.2511.5% ↓
143291.25
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" })();\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", " if (typeof IPython !== 'undefined' && IPython.notebook) {\n", " IPython.notebook.kernel.execute(\"_jupyter_dark_detect_result = \" + \n", " (document.body.classList.contains('theme-dark') || \n", " document.body.classList.contains('jp-mod-dark') ||\n", " (document.body.getAttribute('data-jp-theme-name') && \n", " document.body.getAttribute('data-jp-theme-name').includes('dark'))));\n", " }\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", " (function() {\n", " var isDark = false;\n", "\n", " // Check JupyterLab theme\n", " if (document.body.classList.contains('jp-mod-dark') || \n", " document.body.classList.contains('theme-dark') ||\n", " document.body.classList.contains('vscode-dark')) {\n", " isDark = true;\n", " }\n", "\n", " // Check theme attribute\n", " var themeAttr = document.body.getAttribute('data-jp-theme-name');\n", " if (themeAttr && themeAttr.includes('dark')) {\n", " isDark = true;\n", " }\n", "\n", " // Check computed background color\n", " var notebookEl = document.querySelector('.jp-Notebook') || \n", " document.querySelector('.notebook_app') ||\n", " document.body;\n", " if (notebookEl) {\n", " var bgColor = window.getComputedStyle(notebookEl).backgroundColor;\n", " var rgb = bgColor.match(/\\d+/g);\n", " if (rgb && rgb.length >= 3) {\n", " var brightness = (parseInt(rgb[0]) + parseInt(rgb[1]) + parseInt(rgb[2])) / 3;\n", " if (brightness < 128) {\n", " isDark = true;\n", " }\n", " }\n", " }\n", "\n", " // Store result\n", " if (typeof IPython !== 'undefined' && IPython.notebook && IPython.notebook.kernel) {\n", " IPython.notebook.kernel.execute('_jupyter_dark_detect_result = ' + isDark);\n", " }\n", " })();\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", " if (typeof IPython !== 'undefined' && IPython.notebook) {\n", " IPython.notebook.kernel.execute(\"_jupyter_dark_detect_result = \" + \n", " (document.body.classList.contains('theme-dark') || \n", " document.body.classList.contains('jp-mod-dark') ||\n", " (document.body.getAttribute('data-jp-theme-name') && \n", " document.body.getAttribute('data-jp-theme-name').includes('dark'))));\n", " }\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", " (function() {\n", " var isDark = false;\n", "\n", " // Check JupyterLab theme\n", " if (document.body.classList.contains('jp-mod-dark') || \n", " document.body.classList.contains('theme-dark') ||\n", " document.body.classList.contains('vscode-dark')) {\n", " isDark = true;\n", " }\n", "\n", " // Check theme attribute\n", " var themeAttr = document.body.getAttribute('data-jp-theme-name');\n", " if (themeAttr && themeAttr.includes('dark')) {\n", " isDark = true;\n", " }\n", "\n", " // Check computed background color\n", " var notebookEl = document.querySelector('.jp-Notebook') || \n", " document.querySelector('.notebook_app') ||\n", " document.body;\n", " if (notebookEl) {\n", " var bgColor = window.getComputedStyle(notebookEl).backgroundColor;\n", " var rgb = bgColor.match(/\\d+/g);\n", " if (rgb && rgb.length >= 3) {\n", " var brightness = (parseInt(rgb[0]) + parseInt(rgb[1]) + parseInt(rgb[2])) / 3;\n", " if (brightness < 128) {\n", " isDark = true;\n", " }\n", " }\n", " }\n", "\n", " // Store result\n", " if (typeof IPython !== 'undefined' && IPython.notebook && IPython.notebook.kernel) {\n", " IPython.notebook.kernel.execute('_jupyter_dark_detect_result = ' + isDark);\n", " }\n", " })();\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", " if (typeof IPython !== 'undefined' && IPython.notebook) {\n", " IPython.notebook.kernel.execute(\"_jupyter_dark_detect_result = \" + \n", " (document.body.classList.contains('theme-dark') || \n", " document.body.classList.contains('jp-mod-dark') ||\n", " (document.body.getAttribute('data-jp-theme-name') && \n", " document.body.getAttribute('data-jp-theme-name').includes('dark'))));\n", " }\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "🏆 Best goodness-of-fit \u001b[1m(\u001b[0mreduced χ²\u001b[1m)\u001b[0m is \u001b[1;36m1.25\u001b[0m at iteration \u001b[1;36m328\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "✅ Fitting complete.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;34mSaving project 📦 \u001b[0m\u001b[32m'lbco_hrpt'\u001b[0m\u001b[1;34m to\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[35m/home/runner/work/diffraction-lib/diffraction-lib/docs/docs/tutorials/data/\u001b[0m\u001b[95mlbco_project\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "├── 📄 project.cif\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "├── 📁 structures/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "│ └── 📄 lbco.cif\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "├── 📁 experiments/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "│ └── 📄 hrpt.cif\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "├── 📁 analysis/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "│ └── 📄 analysis.cif\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "└── 📄 summary.cif\n" ] } ], "source": [ "project.analysis.fit()" ] }, { "cell_type": "markdown", "id": "12", "metadata": {}, "source": [ "## Show Results" ] }, { "cell_type": "code", "execution_count": 7, "id": "13", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:02:29.227021Z", "iopub.status.busy": "2026-04-14T15:02:29.226824Z", "iopub.status.idle": "2026-04-14T15:02:29.771984Z", "shell.execute_reply": "2026-04-14T15:02:29.770979Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;34mFit results\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "✅ Success: \u001b[3;92mTrue\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "⏱️ Fitting time: \u001b[1;36m18.24\u001b[0m seconds\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📏 Goodness-of-fit \u001b[1m(\u001b[0mreduced χ²\u001b[1m)\u001b[0m: \u001b[1;36m1.25\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📏 R-factor \u001b[1m(\u001b[0mRf\u001b[1m)\u001b[0m: \u001b[1;36m5.48\u001b[0m%\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📏 R-factor squared \u001b[1m(\u001b[0mRf²\u001b[1m)\u001b[0m: \u001b[1;36m4.94\u001b[0m%\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📏 Weighted R-factor \u001b[1m(\u001b[0mwR\u001b[1m)\u001b[0m: \u001b[1;36m3.97\u001b[0m%\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📈 Fitted parameters:\n" ] }, { "data": { "application/javascript": [ "\n", " (function() {\n", " var isDark = false;\n", "\n", " // Check JupyterLab theme\n", " if (document.body.classList.contains('jp-mod-dark') || \n", " document.body.classList.contains('theme-dark') ||\n", " document.body.classList.contains('vscode-dark')) {\n", " isDark = true;\n", " }\n", "\n", " // Check theme attribute\n", " var themeAttr = document.body.getAttribute('data-jp-theme-name');\n", " if (themeAttr && themeAttr.includes('dark')) {\n", " isDark = true;\n", " }\n", "\n", " // Check computed background color\n", " var notebookEl = document.querySelector('.jp-Notebook') || \n", " document.querySelector('.notebook_app') ||\n", " document.body;\n", " if (notebookEl) 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 datablockcategoryentryparameterstartfitteduncertaintyunitschange
1lbcocelllength_a3.88003.89140.0001Å0.29 % ↑
2lbcoatom_siteLaadp_iso0.50000.50520.0273Ų1.05 % ↑
3lbcoatom_siteCoadp_iso0.50000.24690.0558Ų50.62 % ↓
4lbcoatom_siteOadp_iso1.00001.39210.0158Ų39.21 % ↑
5hrptlinked_phaseslbcoscale7.00009.12340.053030.33 % ↑
6hrptpeakasym_empir_20.0000-0.01770.0019N/A
7hrptpeakasym_empir_40.00000.03180.0041N/A
8hrptpeakbroad_gauss_u0.10000.08590.0032deg²14.11 % ↓
9hrptpeakbroad_gauss_v-0.1000-0.12060.0067deg²20.57 % ↑
10hrptpeakbroad_gauss_w0.10000.12250.0032deg²22.49 % ↑
11hrptpeakbroad_lorentz_y0.00000.08300.0021degN/A
12hrptinstrumenttwotheta_offset0.00000.62640.0026degN/A
13hrptbackground1y168.4237168.40681.37610.01 % ↓
14hrptbackground2y164.3727164.45070.98710.05 % ↑
15hrptbackground3y166.8885167.01880.72790.08 % ↑
16hrptbackground4y175.3971175.45570.63940.03 % ↑
17hrptbackground5y174.3046174.41620.88150.06 % ↑
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