{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "0", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:15:55.478644Z", "iopub.status.busy": "2026-06-04T16:15:55.478456Z", "iopub.status.idle": "2026-06-04T16:15:55.482467Z", "shell.execute_reply": "2026-06-04T16:15:55.481741Z" }, "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==0.18.0" ] }, { "cell_type": "markdown", "id": "1", "metadata": {}, "source": [ "# Structure Refinement: LBCO, HRPT\n", "\n", "This minimalistic example is designed to show how Rietveld refinement\n", "can be performed when both the crystal structure and experiment are\n", "defined directly in code. Only the experimentally measured data is\n", "loaded from an external file. It also shows how to switch calculation\n", "engine.\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.\n", "Default values are used for all parameters if not specified. Only\n", "essential and self-explanatory code is provided.\n", "\n", "The example is intended for users who are already familiar with the\n", "EasyDiffraction library and want to quickly get started with a simple\n", "refinement. It is also useful for those who want to see what a\n", "refinement might look like in code. For a more detailed explanation of\n", "the code, please refer to 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": "2026-06-04T16:15:55.484093Z", "iopub.status.busy": "2026-06-04T16:15:55.483938Z", "iopub.status.idle": "2026-06-04T16:15:58.134250Z", "shell.execute_reply": "2026-06-04T16:15:58.133421Z" } }, "outputs": [], "source": [ "import easydiffraction as ed" ] }, { "cell_type": "markdown", "id": "4", "metadata": {}, "source": [ "## πŸ“¦ Define Project" ] }, { "cell_type": "code", "execution_count": 3, "id": "5", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:15:58.136521Z", "iopub.status.busy": "2026-06-04T16:15:58.136227Z", "iopub.status.idle": "2026-06-04T16:15:58.622848Z", "shell.execute_reply": "2026-06-04T16:15:58.621956Z" } }, "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 = ed.Project(name='lbco_hrpt')" ] }, { "cell_type": "markdown", "id": "6", "metadata": {}, "source": [ "## 🧩 Define Structure" ] }, { "cell_type": "code", "execution_count": 4, "id": "7", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:15:58.624777Z", "iopub.status.busy": "2026-06-04T16:15:58.624609Z", "iopub.status.idle": "2026-06-04T16:15:58.627904Z", "shell.execute_reply": "2026-06-04T16:15:58.627098Z" } }, "outputs": [], "source": [ "project.structures.create(name='lbco')" ] }, { "cell_type": "code", "execution_count": 5, "id": "8", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:15:58.629346Z", "iopub.status.busy": "2026-06-04T16:15:58.629192Z", "iopub.status.idle": "2026-06-04T16:15:58.632417Z", "shell.execute_reply": "2026-06-04T16:15:58.631220Z" } }, "outputs": [], "source": [ "structure = project.structures['lbco']" ] }, { "cell_type": "code", "execution_count": 6, "id": "9", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:15:58.633803Z", "iopub.status.busy": "2026-06-04T16:15:58.633655Z", "iopub.status.idle": "2026-06-04T16:15:58.636796Z", "shell.execute_reply": "2026-06-04T16:15:58.635985Z" } }, "outputs": [], "source": [ "structure.space_group.name_h_m = 'P m -3 m'\n", "structure.space_group.it_coordinate_system_code = '1'" ] }, { "cell_type": "code", "execution_count": 7, "id": "10", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:15:58.638331Z", "iopub.status.busy": "2026-06-04T16:15:58.638114Z", "iopub.status.idle": "2026-06-04T16:15:58.641336Z", "shell.execute_reply": "2026-06-04T16:15:58.640451Z" } }, "outputs": [], "source": [ "structure.cell.length_a = 3.88" ] }, { "cell_type": "code", "execution_count": 8, "id": "11", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:15:58.642881Z", "iopub.status.busy": "2026-06-04T16:15:58.642719Z", "iopub.status.idle": "2026-06-04T16:15:58.649182Z", "shell.execute_reply": "2026-06-04T16:15:58.648397Z" } }, "outputs": [], "source": [ "structure.atom_sites.create(\n", " label='La',\n", " type_symbol='La',\n", " fract_x=0,\n", " fract_y=0,\n", " fract_z=0,\n", " adp_iso=0.5,\n", " occupancy=0.5,\n", ")\n", "structure.atom_sites.create(\n", " label='Ba',\n", " type_symbol='Ba',\n", " fract_x=0,\n", " fract_y=0,\n", " fract_z=0,\n", " adp_iso=0.5,\n", " occupancy=0.5,\n", ")\n", "structure.atom_sites.create(\n", " label='Co',\n", " type_symbol='Co',\n", " fract_x=0.5,\n", " fract_y=0.5,\n", " fract_z=0.5,\n", " adp_iso=0.5,\n", ")\n", "structure.atom_sites.create(\n", " label='O',\n", " type_symbol='O',\n", " fract_x=0,\n", " fract_y=0.5,\n", " fract_z=0.5,\n", " adp_iso=0.5,\n", ")" ] }, { "cell_type": "code", "execution_count": 9, "id": "12", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:15:58.650892Z", "iopub.status.busy": "2026-06-04T16:15:58.650725Z", "iopub.status.idle": "2026-06-04T16:15:59.270491Z", "shell.execute_reply": "2026-06-04T16:15:59.269685Z" } }, "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" }, { "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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Number of data points: \u001b[1;36m3098\u001b[0m.\n" ] } ], "source": [ "project.experiments.add_from_data_path(\n", " name='hrpt',\n", " data_path=data_path,\n", " sample_form='powder',\n", " beam_mode='constant wavelength',\n", " radiation_probe='neutron',\n", ")" ] }, { "cell_type": "code", "execution_count": 12, "id": "16", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:15:59.987048Z", "iopub.status.busy": "2026-06-04T16:15:59.986874Z", "iopub.status.idle": "2026-06-04T16:15:59.989904Z", "shell.execute_reply": "2026-06-04T16:15:59.988895Z" } }, "outputs": [], "source": [ "experiment = project.experiments['hrpt']" ] }, { "cell_type": "code", "execution_count": 13, "id": "17", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:15:59.991488Z", "iopub.status.busy": "2026-06-04T16:15:59.991274Z", "iopub.status.idle": "2026-06-04T16:15:59.994636Z", "shell.execute_reply": "2026-06-04T16:15:59.993960Z" } }, "outputs": [], "source": [ "experiment.instrument.setup_wavelength = 1.494\n", "experiment.instrument.calib_twotheta_offset = 0.6" ] }, { "cell_type": "code", "execution_count": 14, "id": "18", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:15:59.996661Z", "iopub.status.busy": "2026-06-04T16:15:59.996427Z", "iopub.status.idle": "2026-06-04T16:16:00.000257Z", "shell.execute_reply": "2026-06-04T16:15:59.999546Z" } }, "outputs": [], "source": [ "experiment.peak.broad_gauss_u = 0.1\n", "experiment.peak.broad_gauss_v = -0.1\n", "experiment.peak.broad_gauss_w = 0.1\n", "experiment.peak.broad_lorentz_y = 0.1" ] }, { "cell_type": "code", "execution_count": 15, "id": "19", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:00.002296Z", "iopub.status.busy": "2026-06-04T16:16:00.002139Z", "iopub.status.idle": "2026-06-04T16:16:00.007402Z", "shell.execute_reply": "2026-06-04T16:16:00.006584Z" } }, "outputs": [], "source": [ "experiment.background.create(id='1', x=10, y=170)\n", "experiment.background.create(id='2', x=30, y=170)\n", "experiment.background.create(id='3', x=50, y=170)\n", "experiment.background.create(id='4', x=110, y=170)\n", "experiment.background.create(id='5', x=165, y=170)" ] }, { "cell_type": "code", "execution_count": 16, "id": "20", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:00.008899Z", "iopub.status.busy": "2026-06-04T16:16:00.008734Z", "iopub.status.idle": "2026-06-04T16:16:00.011950Z", "shell.execute_reply": "2026-06-04T16:16:00.011150Z" } }, "outputs": [], "source": [ "experiment.excluded_regions.create(id='1', start=0, end=5)\n", "experiment.excluded_regions.create(id='2', start=165, end=180)" ] }, { "cell_type": "code", "execution_count": 17, "id": "21", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:00.013382Z", "iopub.status.busy": "2026-06-04T16:16:00.013235Z", "iopub.status.idle": "2026-06-04T16:16:00.016444Z", "shell.execute_reply": "2026-06-04T16:16:00.015505Z" } }, "outputs": [], "source": [ "experiment.linked_phases.create(id='lbco', scale=10.0)" ] }, { "cell_type": "markdown", "id": "22", "metadata": {}, "source": [ "## πŸš€ Perform Analysis" ] }, { "cell_type": "markdown", "id": "23", "metadata": {}, "source": [ "### Without Constraints" ] }, { "cell_type": "code", "execution_count": 18, "id": "24", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:00.017860Z", "iopub.status.busy": "2026-06-04T16:16:00.017711Z", "iopub.status.idle": "2026-06-04T16:16:00.021138Z", "shell.execute_reply": "2026-06-04T16:16:00.020291Z" } }, "outputs": [], "source": [ "structure.cell.length_a.free = True\n", "\n", "structure.atom_sites['La'].adp_iso.free = True\n", "structure.atom_sites['Ba'].adp_iso.free = True\n", "structure.atom_sites['Co'].adp_iso.free = True\n", "structure.atom_sites['O'].adp_iso.free = True" ] }, { "cell_type": "code", "execution_count": 19, "id": "25", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:00.022541Z", "iopub.status.busy": "2026-06-04T16:16:00.022390Z", "iopub.status.idle": "2026-06-04T16:16:00.026345Z", "shell.execute_reply": "2026-06-04T16:16:00.025593Z" } }, "outputs": [], "source": [ "experiment.instrument.calib_twotheta_offset.free = True\n", "\n", "experiment.peak.broad_gauss_u.free = True\n", "experiment.peak.broad_gauss_v.free = True\n", "experiment.peak.broad_gauss_w.free = True\n", "experiment.peak.broad_lorentz_y.free = True\n", "\n", "experiment.background['1'].y.free = True\n", "experiment.background['2'].y.free = True\n", "experiment.background['3'].y.free = True\n", "experiment.background['4'].y.free = True\n", "experiment.background['5'].y.free = True\n", "\n", "experiment.linked_phases['lbco'].scale.free = True" ] }, { "cell_type": "code", "execution_count": 20, "id": "26", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:00.027834Z", "iopub.status.busy": "2026-06-04T16:16:00.027663Z", "iopub.status.idle": "2026-06-04T16:16:13.249412Z", "shell.execute_reply": "2026-06-04T16:16:13.248654Z" } }, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function() {\n", " const button = document.getElementById('ed-fit-stop-615addae03544aa8af5e0d7e6097fcd3-button');\n", " const status = document.getElementById('ed-fit-stop-615addae03544aa8af5e0d7e6097fcd3-status');\n", " const kernelId = '';\n", " if (!button) {\n", " return;\n", " }\n", "\n", " function setStatus(text) {\n", " if (status) {\n", " status.textContent = text;\n", " }\n", " }\n", "\n", " function pageConfig() {\n", " const element = document.getElementById('jupyter-config-data');\n", " if (!element || !element.textContent) {\n", " return {};\n", " }\n", " try {\n", " return JSON.parse(element.textContent);\n", " } catch (error) {\n", " return {};\n", " }\n", " }\n", "\n", " function baseUrl(config) {\n", " const configured = 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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
110.29165.02
2281.5233.5879.7% ↓
3452.2810.8267.8% ↓
4633.106.4940.0% ↓
5813.953.3548.4% ↓
6984.722.2433.1% ↓
71165.771.9114.7% ↓
81336.551.5021.3% ↓
91507.321.453.6% ↓
101678.091.347.8% ↓
111858.921.293.4% ↓
1227613.171.29
" ], "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;36m1.29\u001b[0m at iteration \u001b[1;36m275\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "βœ… Fitting complete.\n" ] } ], "source": [ "project.analysis.fit()" ] }, { "cell_type": "code", "execution_count": 21, "id": "27", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:13.251318Z", "iopub.status.busy": "2026-06-04T16:16:13.251140Z", "iopub.status.idle": "2026-06-04T16:16:13.581091Z", "shell.execute_reply": "2026-06-04T16:16:13.580173Z" } }, "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 (leastsq)
2βœ… Overall statussuccess
3⏱️ Fitting time (seconds)13.17
4πŸ” Iterations273
5πŸ“ Goodness-of-fit (reduced χ²)1.29
6πŸ“ R-factor (Rf, %)5.63
7πŸ“ R-factor squared (RfΒ², %)5.27
8πŸ“ Weighted R-factor (wR, %)4.41
" ], "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…3.88003.89090.00000.28 % ↑
2lbcoatom_siteLaadp_isoΓ…Β²0.50000.50523231.72971.04 % ↑
3lbcoatom_siteBaadp_isoΓ…Β²0.50000.50495252.55940.97 % ↑
4lbcoatom_siteCoadp_isoΓ…Β²0.50000.23700.061152.60 % ↓
5lbcoatom_siteOadp_isoΓ…Β²0.50001.39350.0167178.71 % ↑
6hrptlinked_phaseslbcoscale10.00009.13510.06428.65 % ↓
7hrptpeakbroad_gauss_udegΒ²0.10000.08160.003118.43 % ↓
8hrptpeakbroad_gauss_vdegΒ²-0.1000-0.11590.006715.93 % ↑
9hrptpeakbroad_gauss_wdegΒ²0.10000.12040.003320.45 % ↑
10hrptpeakbroad_lorentz_ydeg0.10000.08440.002115.55 % ↓
11hrptinstrumenttwotheta_offsetdeg0.60000.62260.00103.76 % ↑
12hrptbackground1y170.0000168.55851.36710.85 % ↓
13hrptbackground2y170.0000164.33570.99923.33 % ↓
14hrptbackground3y170.0000166.88810.73881.83 % ↓
15hrptbackground4y170.0000175.40060.65783.18 % ↑
16hrptbackground5y170.0000174.28130.91052.52 % ↑
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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
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⚠️ Red s.u.: exceeds the refined value (consider adding constraints)
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Loading plot…
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.display.pattern(expt_name='hrpt')" ] }, { "cell_type": "markdown", "id": "30", "metadata": {}, "source": [ "### With Constraints" ] }, { "cell_type": "code", "execution_count": 24, "id": "31", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:15.329663Z", "iopub.status.busy": "2026-06-04T16:16:15.329406Z", "iopub.status.idle": "2026-06-04T16:16:15.335036Z", "shell.execute_reply": "2026-06-04T16:16:15.333883Z" } }, "outputs": [], "source": [ "# As can be seen from the parameter-correlation plot, the isotropic\n", "# displacement parameters of La and Ba are highly correlated. Because\n", "# La and Ba share the same mixed-occupancy site, their contributions to\n", "# the neutron diffraction pattern are difficult to separate, especially\n", "# since their coherent scattering lengths are not very different.\n", "# Therefore, it is necessary to constrain them to be equal. First we\n", "# define aliases and then use them to create a constraint.\n", "project.analysis.aliases.create(\n", " label='biso_La',\n", " param=project.structures['lbco'].atom_sites['La'].adp_iso,\n", ")\n", "project.analysis.aliases.create(\n", " label='biso_Ba',\n", " param=project.structures['lbco'].atom_sites['Ba'].adp_iso,\n", ")\n", "project.analysis.constraints.create(expression='biso_Ba = biso_La')" ] }, { "cell_type": "code", "execution_count": 25, "id": "32", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:15.336647Z", "iopub.status.busy": "2026-06-04T16:16:15.336446Z", "iopub.status.idle": "2026-06-04T16:16:15.346908Z", "shell.execute_reply": "2026-06-04T16:16:15.346134Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;36mMinimizer types\u001b[0m\n" ] }, { "data": { "text/html": [ "
TypeDescription
1bumpsBUMPS library using the default Levenberg-Marquardt method
2bumps (amoeba)BUMPS library with Nelder-Mead simplex method
3bumps (de)BUMPS library with differential evolution method
4bumps (dream)BUMPS library with DREAM Bayesian sampling
5bumps (lm)BUMPS library with Levenberg-Marquardt method
6dfolsDFO-LS library for derivative-free least-squares optimization
7emceeemcee affine-invariant ensemble Bayesian sampling
8lmfitLMFIT library using the default Levenberg-Marquardt method
9lmfit (least_squares)LMFIT library with SciPy's trust region reflective algorithm
10*lmfit (leastsq)LMFIT library with Levenberg-Marquardt least squares method
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;36mCurrent minimizer changed to\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "lmfit\n" ] } ], "source": [ "project.analysis.minimizer.show_supported()\n", "project.analysis.minimizer.type = 'lmfit'" ] }, { "cell_type": "code", "execution_count": 26, "id": "33", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:15.348897Z", "iopub.status.busy": "2026-06-04T16:16:15.348666Z", "iopub.status.idle": "2026-06-04T16:16:17.416230Z", "shell.execute_reply": "2026-06-04T16:16:17.415446Z" } }, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function() {\n", " const button = document.getElementById('ed-fit-stop-a6801f7d44b643a087e237c4050c6f1f-button');\n", " const status = document.getElementById('ed-fit-stop-a6801f7d44b643a087e237c4050c6f1f-status');\n", " const kernelId = '';\n", " if (!button) {\n", " return;\n", " }\n", "\n", " function setStatus(text) {\n", " if (status) {\n", " status.textContent = text;\n", " }\n", " }\n", "\n", " function pageConfig() {\n", " const element = document.getElementById('jupyter-config-data');\n", " if (!element || !element.textContent) {\n", " return {};\n", " }\n", " try {\n", " return JSON.parse(element.textContent);\n", " } catch (error) {\n", " return {};\n", " }\n", " }\n", "\n", " function baseUrl(config) {\n", " const configured = config.baseUrl || config.base_url ||\n", " (window.Jupyter && Jupyter.notebook && Jupyter.notebook.base_url);\n", " if (configured) {\n", " return configured.endsWith('/') ? 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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
110.051.29
2362.001.29
" ], "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;36m1.29\u001b[0m at iteration \u001b[1;36m35\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "βœ… Fitting complete.\n" ] } ], "source": [ "project.analysis.fit()" ] }, { "cell_type": "code", "execution_count": 27, "id": "34", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:17.418057Z", "iopub.status.busy": "2026-06-04T16:16:17.417896Z", "iopub.status.idle": "2026-06-04T16:16:17.740405Z", "shell.execute_reply": "2026-06-04T16:16:17.739599Z" } }, "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)2.00
4πŸ” Iterations33
5πŸ“ Goodness-of-fit (reduced χ²)1.29
6πŸ“ R-factor (Rf, %)5.63
7πŸ“ R-factor squared (RfΒ², %)5.27
8πŸ“ Weighted R-factor (wR, %)4.41
" ], "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…3.89093.89090.00000.00 % ↑
2lbcoatom_siteLaadp_isoΓ…Β²0.50520.50510.02780.03 % ↓
3lbcoatom_siteCoadp_isoΓ…Β²0.23700.23700.05640.00 % ↑
4lbcoatom_siteOadp_isoΓ…Β²1.39351.39350.01600.00 % ↓
5hrptlinked_phaseslbcoscale9.13519.13510.05380.00 % ↓
6hrptpeakbroad_gauss_udegΒ²0.08160.08160.00310.00 % ↑
7hrptpeakbroad_gauss_vdegΒ²-0.1159-0.11590.00660.00 % ↑
8hrptpeakbroad_gauss_wdegΒ²0.12040.12040.00320.00 % ↑
9hrptpeakbroad_lorentz_ydeg0.08440.08440.00210.00 % ↓
10hrptinstrumenttwotheta_offsetdeg0.62260.62260.00100.00 % ↑
11hrptbackground1y168.5585168.55851.36690.00 % ↓
12hrptbackground2y164.3357164.33570.99900.00 % ↑
13hrptbackground3y166.8881166.88810.73860.00 % ↑
14hrptbackground4y175.4006175.40060.64880.00 % ↑
15hrptbackground5y174.2813174.28130.89440.00 % ↓
" ], "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": 28, "id": "35", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:17.742047Z", "iopub.status.busy": "2026-06-04T16:16:17.741887Z", "iopub.status.idle": "2026-06-04T16:16:18.581614Z", "shell.execute_reply": "2026-06-04T16:16:18.580701Z" } }, "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 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Loading plot…
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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.display.fit.correlations()" ] }, { "cell_type": "code", "execution_count": 29, "id": "36", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:18.583470Z", "iopub.status.busy": "2026-06-04T16:16:18.583294Z", "iopub.status.idle": "2026-06-04T16:16:19.480280Z", "shell.execute_reply": "2026-06-04T16:16:19.478661Z" } }, "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 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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') 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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 = 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Loading plot…
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.display.pattern(expt_name='hrpt')" ] }, { "cell_type": "markdown", "id": "37", "metadata": {}, "source": [ "### Switch Calculator" ] }, { "cell_type": "code", "execution_count": 30, "id": "38", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:19.485070Z", "iopub.status.busy": "2026-06-04T16:16:19.484835Z", "iopub.status.idle": "2026-06-04T16:16:19.493051Z", "shell.execute_reply": "2026-06-04T16:16:19.492049Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;36mCalculator types\u001b[0m\n" ] }, { "data": { "text/html": [ "
TypeDescription
1crysfmlCrysFML library for crystallographic calculations
2*cryspyCrysPy library for crystallographic calculations
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "experiment.calculator.show_supported()" ] }, { "cell_type": "code", "execution_count": 31, "id": "39", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:19.494830Z", "iopub.status.busy": "2026-06-04T16:16:19.494606Z", "iopub.status.idle": "2026-06-04T16:16:19.501634Z", "shell.execute_reply": "2026-06-04T16:16:19.500836Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;36mCalculator for experiment \u001b[0m\u001b[32m'hrpt'\u001b[0m\u001b[1;36m changed to\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "crysfml\n" ] } ], "source": [ "experiment.calculator.type = 'crysfml'" ] }, { "cell_type": "code", "execution_count": 32, "id": "40", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:19.503313Z", "iopub.status.busy": "2026-06-04T16:16:19.503167Z", "iopub.status.idle": "2026-06-04T16:16:24.912805Z", "shell.execute_reply": "2026-06-04T16:16:24.911721Z" } }, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function() {\n", " const button = document.getElementById('ed-fit-stop-a43c30ef7ffb4ab3ba80b4a9c0534aea-button');\n", " const status = document.getElementById('ed-fit-stop-a43c30ef7ffb4ab3ba80b4a9c0534aea-status');\n", " const kernelId = '';\n", " if (!button) {\n", " return;\n", " }\n", "\n", " function setStatus(text) {\n", " if (status) {\n", " status.textContent = text;\n", " }\n", " }\n", "\n", " function pageConfig() {\n", " const element = document.getElementById('jupyter-config-data');\n", " if (!element || !element.textContent) {\n", " return {};\n", " }\n", " try {\n", " return JSON.parse(element.textContent);\n", " } catch (error) {\n", " return {};\n", " }\n", " }\n", "\n", " function baseUrl(config) {\n", " const configured = config.baseUrl || config.base_url ||\n", " (window.Jupyter && Jupyter.notebook && Jupyter.notebook.base_url);\n", " if (configured) {\n", " return configured.endsWith('/') ? 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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
110.051.29
21175.071.29
31225.361.29
" ], "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;36m1.29\u001b[0m at iteration \u001b[1;36m112\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "βœ… Fitting complete.\n" ] } ], "source": [ "project.analysis.fit()" ] }, { "cell_type": "code", "execution_count": 33, "id": "41", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:24.914656Z", "iopub.status.busy": "2026-06-04T16:16:24.914394Z", "iopub.status.idle": "2026-06-04T16:16:24.984375Z", "shell.execute_reply": "2026-06-04T16:16:24.983265Z" } }, "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)5.36
4πŸ” Iterations119
5πŸ“ Goodness-of-fit (reduced χ²)1.29
6πŸ“ R-factor (Rf, %)5.62
7πŸ“ R-factor squared (RfΒ², %)5.27
8πŸ“ Weighted R-factor (wR, %)4.44
" ], "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…3.89093.89080.00000.00 % ↓
2lbcoatom_siteLaadp_isoΓ…Β²0.50510.51920.02552.79 % ↑
3lbcoatom_siteCoadp_isoΓ…Β²0.23700.21630.05508.74 % ↓
4lbcoatom_siteOadp_isoΓ…Β²1.39351.39550.01550.14 % ↑
5hrptlinked_phaseslbcoscale9.13519.15610.05160.23 % ↑
6hrptpeakbroad_gauss_udegΒ²0.08160.08150.00290.10 % ↓
7hrptpeakbroad_gauss_vdegΒ²-0.1159-0.11590.00630.06 % ↓
8hrptpeakbroad_gauss_wdegΒ²0.12040.12040.00300.05 % ↓
9hrptpeakbroad_lorentz_ydeg0.08440.08460.00210.13 % ↑
10hrptinstrumenttwotheta_offsetdeg0.62260.62000.00070.41 % ↓
11hrptbackground1y168.5585169.69101.36550.67 % ↑
12hrptbackground2y164.3357164.78580.99570.27 % ↑
13hrptbackground3y166.8881167.02040.73650.08 % ↑
14hrptbackground4y175.4006175.28160.65060.07 % ↓
15hrptbackground5y174.2813175.12320.88510.48 % ↑
" ], "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": 34, "id": "42", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:24.986012Z", "iopub.status.busy": "2026-06-04T16:16:24.985836Z", "iopub.status.idle": "2026-06-04T16:16:25.819346Z", "shell.execute_reply": "2026-06-04T16:16:25.818587Z" } }, "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 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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 = 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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.display.fit.correlations()" ] }, { "cell_type": "code", "execution_count": 35, "id": "43", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:25.820955Z", "iopub.status.busy": "2026-06-04T16:16:25.820792Z", "iopub.status.idle": "2026-06-04T16:16:26.707902Z", "shell.execute_reply": "2026-06-04T16:16:26.706893Z" } }, "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", " 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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') 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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": [ "" ] }, 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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.display.pattern(expt_name='hrpt')" ] }, { "cell_type": "markdown", "id": "44", "metadata": {}, "source": [ "## πŸ’Ύ Save Project" ] }, { "cell_type": "code", "execution_count": 36, "id": "45", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:26.712059Z", "iopub.status.busy": "2026-06-04T16:16:26.711845Z", "iopub.status.idle": "2026-06-04T16:16:27.617551Z", "shell.execute_reply": "2026-06-04T16:16:27.616942Z" } }, "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/ed_2_lbco_hrpt'\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”œβ”€β”€ πŸ“„ project.cif\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”œβ”€β”€ πŸ“ structures/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”‚ └── πŸ“„ lbco.cif\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”œβ”€β”€ πŸ“ experiments/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”‚ └── πŸ“„ hrpt.cif\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”œβ”€β”€ πŸ“ analysis/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”‚ └── πŸ“„ analysis.cif\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) {\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" }, { "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": [ "└── πŸ“ reports/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " └── πŸ“„ lbco_hrpt.html\n" ] } ], "source": [ "project.save_as(dir_path='projects/ed_2_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 }