{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "0", "metadata": { "execution": { "iopub.execute_input": "2026-06-30T22:32:04.776197Z", "iopub.status.busy": "2026-06-30T22:32:04.775981Z", "iopub.status.idle": "2026-06-30T22:32:04.780869Z", "shell.execute_reply": "2026-06-30T22:32:04.780125Z" }, "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==0.19.1" ] }, { "cell_type": "markdown", "id": "1", "metadata": {}, "source": [ "# LBCO Resume\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 β€” 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 β€” these can be found in the other tutorials." ] }, { "cell_type": "markdown", "id": "2", "metadata": {}, "source": [ "## πŸ› οΈ Import Library" ] }, { "cell_type": "code", "execution_count": 2, "id": "3", "metadata": { "execution": { "iopub.execute_input": "2026-06-30T22:32:04.783474Z", "iopub.status.busy": "2026-06-30T22:32:04.783269Z", "iopub.status.idle": "2026-06-30T22:32:08.993946Z", "shell.execute_reply": "2026-06-30T22:32:08.993001Z" } }, "outputs": [], "source": [ "import easydiffraction as edi" ] }, { "cell_type": "markdown", "id": "4", "metadata": {}, "source": [ "## πŸ“‚ Load Project" ] }, { "cell_type": "markdown", "id": "5", "metadata": {}, "source": [ "### Locate Project\n", "\n", "Download and extract the saved project from the EasyDiffraction data\n", "repository." ] }, { "cell_type": "code", "execution_count": 3, "id": "6", "metadata": { "execution": { "iopub.execute_input": "2026-06-30T22:32:08.996735Z", "iopub.status.busy": "2026-06-30T22:32:08.996353Z", "iopub.status.idle": "2026-06-30T22:32:09.223550Z", "shell.execute_reply": "2026-06-30T22:32:09.222396Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;36mGetting data\u001b[0m\u001b[1;36m...\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Data \u001b[32m'proj-lbco-hrpt'\u001b[0m: La0.5Ba0.5CoO3, HRPT \u001b[1m(\u001b[0mPSI\u001b[1m)\u001b[0m, \u001b[1;36m300\u001b[0m K\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "βœ… Data \u001b[32m'proj-lbco-hrpt'\u001b[0m downloaded and extracted to \u001b[32m'../../../projects/proj-lbco-hrpt-69567a192590'\u001b[0m\n" ] } ], "source": [ "project_dir = edi.download_data('proj-lbco-hrpt', destination='projects')" ] }, { "cell_type": "markdown", "id": "7", "metadata": {}, "source": [ "### Load Project" ] }, { "cell_type": "code", "execution_count": 4, "id": "8", "metadata": { "execution": { "iopub.execute_input": "2026-06-30T22:32:09.225591Z", "iopub.status.busy": "2026-06-30T22:32:09.225359Z", "iopub.status.idle": "2026-06-30T22:32:12.678514Z", "shell.execute_reply": "2026-06-30T22:32:12.676976Z" } }, "outputs": [], "source": [ "project = edi.Project.load(project_dir)" ] }, { "cell_type": "markdown", "id": "9", "metadata": {}, "source": [ "Re-save the project to a fresh working directory so fitting below\n", "writes there instead of the bundled read-only copy." ] }, { "cell_type": "code", "execution_count": 5, "id": "10", "metadata": { "execution": { "iopub.execute_input": "2026-06-30T22:32:12.680731Z", "iopub.status.busy": "2026-06-30T22:32:12.680487Z", "iopub.status.idle": "2026-06-30T22:32:13.330117Z", "shell.execute_reply": "2026-06-30T22:32:13.328850Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;36mSaving project πŸ“¦ \u001b[0m\u001b[32m'lbco_hrpt'\u001b[0m\u001b[1;36m to \u001b[0m\u001b[32m'../../../projects/load-and-fit-lbco-hrpt'\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”œβ”€β”€ πŸ“„ project.edi\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”œβ”€β”€ πŸ“ structures/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”‚ └── πŸ“„ lbco.edi\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”œβ”€β”€ πŸ“ experiments/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”‚ └── πŸ“„ hrpt.edi\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”œβ”€β”€ πŸ“ analysis/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”‚ └── πŸ“„ analysis.edi\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "└── πŸ“ reports/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " └── πŸ“„ lbco_hrpt.html\n" ] } ], "source": [ "project.save_as(dir_path='projects/load-and-fit-lbco-hrpt')" ] }, { "cell_type": "markdown", "id": "11", "metadata": {}, "source": [ "## πŸš€ Perform Analysis" ] }, { "cell_type": "markdown", "id": "12", "metadata": {}, "source": [ "### Display Structure" ] }, { "cell_type": "code", "execution_count": 6, "id": "13", "metadata": { "execution": { "iopub.execute_input": "2026-06-30T22:32:13.332107Z", "iopub.status.busy": "2026-06-30T22:32:13.331872Z", "iopub.status.idle": "2026-06-30T22:32:13.395335Z", "shell.execute_reply": "2026-06-30T22:32:13.394279Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;36mStructure 🧩 \u001b[0m\u001b[32m'lbco'\u001b[0m\u001b[1;36m \u001b[0m\u001b[1;36m(\u001b[0m\u001b[1;36mAtom view type: \u001b[0m\u001b[32m'covalent'\u001b[0m\u001b[1;36m)\u001b[0m\n" ] }, { "data": { "text/html": [ "
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configured : configured + '/';\n", " }\n", " const markers = ['/lab/', '/notebooks/', '/tree/'];\n", " for (const marker of markers) {\n", " const index = window.location.pathname.indexOf(marker);\n", " if (index >= 0) {\n", " return window.location.pathname.slice(0, index + 1);\n", " }\n", " }\n", " return '/';\n", " }\n", "\n", " function token(config) {\n", " return config.token || new URLSearchParams(window.location.search).get('token') || '';\n", " }\n", "\n", " function cookie(name) {\n", " const prefix = name + '=';\n", " for (const part of document.cookie.split(';')) {\n", " const trimmed = part.trim();\n", " if (trimmed.startsWith(prefix)) {\n", " return decodeURIComponent(trimmed.slice(prefix.length));\n", " }\n", " }\n", " return '';\n", " }\n", "\n", " function notebookPath() {\n", " const decoded = decodeURIComponent(window.location.pathname);\n", " const markers = ['/lab/tree/', '/notebooks/', '/tree/'];\n", " for (const marker of markers) {\n", " const index = decoded.indexOf(marker);\n", " if (index >= 0) {\n", " return decoded.slice(index + marker.length);\n", " }\n", " }\n", " return '';\n", " }\n", "\n", " async function kernelFromSessions(config) {\n", " const url = new URL(baseUrl(config) + 'api/sessions', window.location.origin);\n", " const authToken = token(config);\n", " if (authToken) {\n", " url.searchParams.set('token', authToken);\n", " }\n", " const response = await fetch(url, {credentials: 'same-origin'});\n", " if (!response.ok) {\n", " return '';\n", " }\n", " const sessions = await response.json();\n", " const path = notebookPath();\n", " const session = sessions.find((item) => item.path === path) || sessions[0];\n", " return session && session.kernel ? session.kernel.id : '';\n", " }\n", "\n", " async function interruptKernel(config, resolvedKernelId) {\n", " const url = new URL(\n", " baseUrl(config) + 'api/kernels/' + resolvedKernelId + '/interrupt',\n", " window.location.origin\n", " );\n", " const authToken = token(config);\n", " if (authToken) {\n", " url.searchParams.set('token', authToken);\n", " }\n", " const xsrfToken = cookie('_xsrf');\n", " const headers = {};\n", " if (xsrfToken) {\n", " headers['X-XSRFToken'] = xsrfToken;\n", " }\n", " const response = await fetch(url, {\n", " method: 'POST',\n", " credentials: 'same-origin',\n", " headers: headers\n", " });\n", " return response.ok;\n", " }\n", "\n", " button.addEventListener('click', async function() {\n", " button.disabled = true;\n", " setStatus('Stopping...');\n", " const config = pageConfig();\n", " try {\n", " const resolvedKernelId = kernelId || await kernelFromSessions(config);\n", " if (!resolvedKernelId) {\n", " throw new Error('Could not resolve the current kernel id.');\n", " }\n", " const interrupted = await interruptKernel(config, resolvedKernelId);\n", " if (!interrupted) {\n", " throw new Error('Jupyter Server rejected the interrupt request.');\n", " }\n", " setStatus('Interrupt sent...');\n", " } catch (error) {\n", " button.disabled = false;\n", " setStatus('Use Kernel > Interrupt to stop this fit.');\n", " }\n", " });\n", "})();\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;36mStandard fitting\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "πŸ“‹ Using experiment πŸ”¬ \u001b[32m'hrpt'\u001b[0m for \u001b[32m'single'\u001b[0m fitting\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "πŸš€ 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": [ "πŸ“ˆ Goodness-of-fit progress:\n" ] }, { "data": { "text/html": [ "
iterationtime (s)χ²change / status
110.081.27
2452.491.26
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "πŸ† Best goodness-of-fit \u001b[1m(\u001b[0mreduced χ²\u001b[1m)\u001b[0m is \u001b[1;36m1.26\u001b[0m at iteration \u001b[1;36m24\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "βœ… Fitting complete.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;36mSaving project πŸ“¦ \u001b[0m\u001b[32m'lbco_hrpt'\u001b[0m\u001b[1;36m to \u001b[0m\u001b[32m'../../../projects/load-and-fit-lbco-hrpt'\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”œβ”€β”€ πŸ“„ project.edi\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”œβ”€β”€ πŸ“ structures/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”‚ └── πŸ“„ lbco.edi\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”œβ”€β”€ πŸ“ experiments/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”‚ └── πŸ“„ hrpt.edi\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”œβ”€β”€ πŸ“ analysis/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”‚ └── πŸ“„ analysis.edi\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "└── πŸ“ reports/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " └── πŸ“„ lbco_hrpt.html\n" ] } ], "source": [ "project.analysis.fit()" ] }, { "cell_type": "markdown", "id": "16", "metadata": {}, "source": [ "### Display Fit Results" ] }, { "cell_type": "code", "execution_count": 8, "id": "17", "metadata": { "execution": { "iopub.execute_input": "2026-06-30T22:32:16.475364Z", "iopub.status.busy": "2026-06-30T22:32:16.475138Z", "iopub.status.idle": "2026-06-30T22:32:16.558321Z", "shell.execute_reply": "2026-06-30T22:32:16.557224Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "βš™οΈ Settings used:\n" ] }, { "data": { "text/html": [ "
NameValueDescription
1max_iterations1000Maximum solver iterations.
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "πŸ“‹ Least-squares fit results:\n" ] }, { "data": { "text/html": [ "
MetricValue
1πŸ§ͺ Minimizerlmfit
2βœ… Overall statussuccess
3⏱️ Fitting time (seconds)2.49
4πŸ” Iterations42
5πŸ“ Goodness-of-fit (reduced χ²)1.26
6πŸ“ R-factor (Rf, %)5.57
7πŸ“ R-factor squared (RfΒ², %)5.26
8πŸ“ Weighted R-factor (wR, %)4.46
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "πŸ“ˆ Refined parameters:\n" ] }, { "data": { "text/html": [ "
datablockcategoryentryparameterunitsstartvalues.u.change
1lbcocelllength_aΓ…3.89083.89080.00000.00 % ↓
2lbcoatom_siteLaadp_isoΓ…Β²0.49200.46340.02685.82 % ↓
3lbcoatom_siteCoadp_isoΓ…Β²0.24900.29240.048217.41 % ↑
4lbcoatom_siteOadp_isoΓ…Β²1.40401.40630.01590.17 % ↑
5hrptlinked_structurelbcoscale9.12809.10760.05480.22 % ↓
6hrptpeakbroad_gauss_udegΒ²0.08030.08070.00310.44 % ↑
7hrptpeakbroad_gauss_vdegΒ²-0.1125-0.11380.00671.13 % ↑
8hrptpeakbroad_gauss_wdegΒ²0.11950.11970.00320.17 % ↑
9hrptpeakbroad_lorentz_ydeg0.08360.08420.00210.75 % ↑
10hrptinstrumenttwotheta_offsetdeg0.62050.61940.00080.17 % ↓
11hrptbackground1intensity166.9200166.91370.89220.00 % ↓
12hrptbackground2intensity167.6100167.67640.68990.04 % ↑
13hrptbackground3intensity172.8800172.66390.71140.13 % ↓
14hrptbackground4intensity180.9200181.05530.87460.07 % ↑
15hrptbackground5intensity168.7000162.63111.24413.60 % ↓
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
β€’ start = parameter value before refinement
β€’ value = refined value from least-squares minimization
β€’ s.u. = standard uncertainty (one sigma), from the covariance matrix
β€’ change = relative change from start, in %; ↑ = increase, ↓ = decrease
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.display.fit.results()" ] }, { "cell_type": "code", "execution_count": 9, "id": "18", "metadata": { "execution": { "iopub.execute_input": "2026-06-30T22:32:16.560177Z", "iopub.status.busy": "2026-06-30T22:32:16.559934Z", "iopub.status.idle": "2026-06-30T22:32:16.592773Z", "shell.execute_reply": "2026-06-30T22:32:16.591971Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.display.fit.correlations()" ] }, { "cell_type": "code", "execution_count": 10, "id": "19", "metadata": { "execution": { "iopub.execute_input": "2026-06-30T22:32:16.594428Z", "iopub.status.busy": "2026-06-30T22:32:16.594261Z", "iopub.status.idle": "2026-06-30T22:32:16.664262Z", "shell.execute_reply": "2026-06-30T22:32:16.663400Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.display.pattern(expt_name='hrpt')" ] }, { "cell_type": "markdown", "id": "20", "metadata": {}, "source": [ "## πŸ’Ύ Save Project" ] }, { "cell_type": "code", "execution_count": 11, "id": "21", "metadata": { "execution": { "iopub.execute_input": "2026-06-30T22:32:16.668161Z", "iopub.status.busy": "2026-06-30T22:32:16.667974Z", "iopub.status.idle": "2026-06-30T22:32:17.135525Z", "shell.execute_reply": "2026-06-30T22:32:17.134870Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;36mSaving project πŸ“¦ \u001b[0m\u001b[32m'lbco_hrpt'\u001b[0m\u001b[1;36m to \u001b[0m\u001b[32m'../../../projects/load-and-fit-lbco-hrpt'\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”œβ”€β”€ πŸ“„ project.edi\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”œβ”€β”€ πŸ“ structures/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”‚ └── πŸ“„ lbco.edi\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”œβ”€β”€ πŸ“ experiments/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”‚ └── πŸ“„ hrpt.edi\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”œβ”€β”€ πŸ“ analysis/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”‚ └── πŸ“„ analysis.edi\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "└── πŸ“ reports/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " └── πŸ“„ lbco_hrpt.html\n" ] } ], "source": [ "project.save_as(dir_path='projects/load-and-fit-lbco-hrpt')" ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "-all", "main_language": "python", "notebook_metadata_filter": "-all" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.14.5" } }, "nbformat": 4, "nbformat_minor": 5 }