{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "0", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:17.734481Z", "iopub.status.busy": "2026-04-14T15:04:17.734253Z", "iopub.status.idle": "2026-04-14T15:04:17.738846Z", "shell.execute_reply": "2026-04-14T15:04:17.738146Z" }, "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": [ "# Structure Refinement: Si, SEPD\n", "\n", "This example demonstrates a Rietveld refinement of Si crystal\n", "structure using time-of-flight neutron powder diffraction data from\n", "SEPD at Argonne." ] }, { "cell_type": "markdown", "id": "2", "metadata": {}, "source": [ "## Import Library" ] }, { "cell_type": "code", "execution_count": 2, "id": "3", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:17.740659Z", "iopub.status.busy": "2026-04-14T15:04:17.740487Z", "iopub.status.idle": "2026-04-14T15:04:20.325908Z", "shell.execute_reply": "2026-04-14T15:04:20.325042Z" } }, "outputs": [], "source": [ "from easydiffraction import ExperimentFactory\n", "from easydiffraction import Project\n", "from easydiffraction import StructureFactory\n", "from easydiffraction import download_data" ] }, { "cell_type": "markdown", "id": "4", "metadata": {}, "source": [ "## Define Structure\n", "\n", "This section shows how to add structures and modify their\n", "parameters.\n", "\n", "#### Create Structure" ] }, { "cell_type": "code", "execution_count": 3, "id": "5", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:20.328055Z", "iopub.status.busy": "2026-04-14T15:04:20.327728Z", "iopub.status.idle": "2026-04-14T15:04:20.332428Z", "shell.execute_reply": "2026-04-14T15:04:20.331218Z" } }, "outputs": [], "source": [ "structure = StructureFactory.from_scratch(name='si')" ] }, { "cell_type": "markdown", "id": "6", "metadata": {}, "source": [ "#### Set Space Group" ] }, { "cell_type": "code", "execution_count": 4, "id": "7", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:20.334075Z", "iopub.status.busy": "2026-04-14T15:04:20.333872Z", "iopub.status.idle": "2026-04-14T15:04:20.337785Z", "shell.execute_reply": "2026-04-14T15:04:20.337032Z" } }, "outputs": [], "source": [ "structure.space_group.name_h_m = 'F d -3 m'\n", "structure.space_group.it_coordinate_system_code = '2'" ] }, { "cell_type": "markdown", "id": "8", "metadata": {}, "source": [ "#### Set Unit Cell" ] }, { "cell_type": "code", "execution_count": 5, "id": "9", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:20.339389Z", "iopub.status.busy": "2026-04-14T15:04:20.339224Z", "iopub.status.idle": "2026-04-14T15:04:20.342130Z", "shell.execute_reply": "2026-04-14T15:04:20.341345Z" } }, "outputs": [], "source": [ "structure.cell.length_a = 5.431" ] }, { "cell_type": "markdown", "id": "10", "metadata": {}, "source": [ "#### Set Atom Sites" ] }, { "cell_type": "code", "execution_count": 6, "id": "11", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:20.343665Z", "iopub.status.busy": "2026-04-14T15:04:20.343488Z", "iopub.status.idle": "2026-04-14T15:04:20.347834Z", "shell.execute_reply": "2026-04-14T15:04:20.347070Z" } }, "outputs": [], "source": [ "structure.atom_sites.create(\n", " label='Si',\n", " type_symbol='Si',\n", " fract_x=0.125,\n", " fract_y=0.125,\n", " fract_z=0.125,\n", " adp_iso=0.5,\n", ")" ] }, { "cell_type": "markdown", "id": "12", "metadata": {}, "source": [ "## Define Experiment\n", "\n", "This section shows how to add experiments, configure their\n", "parameters, and link the structures defined in the previous step.\n", "\n", "#### Download Measured Data" ] }, { "cell_type": "code", "execution_count": 7, "id": "13", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:20.349551Z", "iopub.status.busy": "2026-04-14T15:04:20.349372Z", "iopub.status.idle": "2026-04-14T15:04:20.362038Z", "shell.execute_reply": "2026-04-14T15:04:20.361252Z" } }, "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;36m7\u001b[0m: Si, SEPD \u001b[1m(\u001b[0mArgonne\u001b[1m)\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "✅ Data #\u001b[1;36m7\u001b[0m already present at \u001b[32m'data/ed-7.xye'\u001b[0m. Keeping existing file.\n" ] } ], "source": [ "data_path = download_data(id=7, destination='data')" ] }, { "cell_type": "markdown", "id": "14", "metadata": {}, "source": [ "#### Create Experiment" ] }, { "cell_type": "code", "execution_count": 8, "id": "15", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:20.363883Z", "iopub.status.busy": "2026-04-14T15:04:20.363695Z", "iopub.status.idle": "2026-04-14T15:04:20.795175Z", "shell.execute_reply": "2026-04-14T15:04:20.794240Z" } }, "outputs": [], "source": [ "expt = ExperimentFactory.from_data_path(\n", " name='sepd', data_path=data_path, beam_mode='time-of-flight'\n", ")" ] }, { "cell_type": "markdown", "id": "16", "metadata": {}, "source": [ "#### Set Instrument" ] }, { "cell_type": "code", "execution_count": 9, "id": "17", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:20.796860Z", "iopub.status.busy": "2026-04-14T15:04:20.796677Z", "iopub.status.idle": "2026-04-14T15:04:20.800779Z", "shell.execute_reply": "2026-04-14T15:04:20.799762Z" } }, "outputs": [], "source": [ "expt.instrument.setup_twotheta_bank = 144.845\n", "expt.instrument.calib_d_to_tof_offset = 0.0\n", "expt.instrument.calib_d_to_tof_linear = 7476.91\n", "expt.instrument.calib_d_to_tof_quad = -1.54" ] }, { "cell_type": "markdown", "id": "18", "metadata": {}, "source": [ "#### Set Peak Profile" ] }, { "cell_type": "code", "execution_count": 10, "id": "19", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:20.802291Z", "iopub.status.busy": "2026-04-14T15:04:20.802091Z", "iopub.status.idle": "2026-04-14T15:04:21.094753Z", "shell.execute_reply": "2026-04-14T15:04:21.093705Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;34mSupported types\u001b[0m\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": { "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", "
 TypeDescription
1jorgensenJorgensen BBE ⊗ Gaussian profile
2jorgensen-von-dreeleJorgensen-Von Dreele BBE ⊗ pseudo-Voigt profile
3double-jorgensen-von-dreeleDouble-exp ⊗ pseudo-Voigt profile (Z-Rietveld type0m)
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;34mCurrent peak profile type\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "jorgensen\n" ] } ], "source": [ "expt.show_supported_peak_profile_types()\n", "expt.show_current_peak_profile_type()\n", "expt.peak.broad_gauss_sigma_0 = 3.0\n", "expt.peak.broad_gauss_sigma_1 = 40.0\n", "expt.peak.broad_gauss_sigma_2 = 2.0\n", "expt.peak.exp_decay_beta_0 = 0.04221\n", "expt.peak.exp_decay_beta_1 = 0.00946\n", "expt.peak.exp_rise_alpha_0 = 0.0\n", "expt.peak.exp_rise_alpha_1 = 0.5971" ] }, { "cell_type": "markdown", "id": "20", "metadata": {}, "source": [ "#### Set Background" ] }, { "cell_type": "code", "execution_count": 11, "id": "21", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:21.096691Z", "iopub.status.busy": "2026-04-14T15:04:21.096491Z", "iopub.status.idle": "2026-04-14T15:04:21.103961Z", "shell.execute_reply": "2026-04-14T15:04:21.103083Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;34mBackground type for experiment \u001b[0m\u001b[32m'sepd'\u001b[0m\u001b[1;34m already set to\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "line-segment\n" ] } ], "source": [ "expt.background_type = 'line-segment'\n", "for x in range(0, 35000, 5000):\n", " expt.background.create(id=str(x), x=x, y=200)" ] }, { "cell_type": "markdown", "id": "22", "metadata": {}, "source": [ "#### Set Linked Phases" ] }, { "cell_type": "code", "execution_count": 12, "id": "23", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:21.105727Z", "iopub.status.busy": "2026-04-14T15:04:21.105542Z", "iopub.status.idle": "2026-04-14T15:04:21.108883Z", "shell.execute_reply": "2026-04-14T15:04:21.108053Z" } }, "outputs": [], "source": [ "expt.linked_phases.create(id='si', scale=10.0)" ] }, { "cell_type": "markdown", "id": "24", "metadata": {}, "source": [ "## Define Project\n", "\n", "The project object is used to manage the structure, experiment, and\n", "analysis.\n", "\n", "#### Create Project" ] }, { "cell_type": "code", "execution_count": 13, "id": "25", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:21.110682Z", "iopub.status.busy": "2026-04-14T15:04:21.110525Z", "iopub.status.idle": "2026-04-14T15:04:21.631931Z", "shell.execute_reply": "2026-04-14T15:04:21.631136Z" } }, "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()" ] }, { "cell_type": "markdown", "id": "26", "metadata": {}, "source": [ "#### Add Structure" ] }, { "cell_type": "code", "execution_count": 14, "id": "27", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:21.633610Z", "iopub.status.busy": "2026-04-14T15:04:21.633447Z", "iopub.status.idle": "2026-04-14T15:04:21.636697Z", "shell.execute_reply": "2026-04-14T15:04:21.635946Z" } }, "outputs": [], "source": [ "project.structures.add(structure)" ] }, { "cell_type": "markdown", "id": "28", "metadata": {}, "source": [ "#### Add Experiment" ] }, { "cell_type": "code", "execution_count": 15, "id": "29", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:21.638465Z", "iopub.status.busy": "2026-04-14T15:04:21.638310Z", "iopub.status.idle": "2026-04-14T15:04:21.640972Z", "shell.execute_reply": "2026-04-14T15:04:21.640209Z" } }, "outputs": [], "source": [ "project.experiments.add(expt)" ] }, { "cell_type": "markdown", "id": "30", "metadata": {}, "source": [ "## Perform Analysis\n", "\n", "This section shows the analysis process, including how to set up\n", "calculation and fitting engines.\n", "\n", "#### Plot Measured vs Calculated" ] }, { "cell_type": "code", "execution_count": 16, "id": "31", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:21.642743Z", "iopub.status.busy": "2026-04-14T15:04:21.642589Z", "iopub.status.idle": "2026-04-14T15:04:22.355289Z", "shell.execute_reply": "2026-04-14T15:04:22.354351Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.plotter.plot_meas_vs_calc(expt_name='sepd', show_residual=True)\n", "project.plotter.plot_meas_vs_calc(expt_name='sepd', x_min=23200, x_max=23700, show_residual=True)" ] }, { "cell_type": "markdown", "id": "32", "metadata": {}, "source": [ "### Perform Fit 1/5\n", "\n", "Set parameters to be refined." ] }, { "cell_type": "code", "execution_count": 17, "id": "33", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:22.356889Z", "iopub.status.busy": "2026-04-14T15:04:22.356697Z", "iopub.status.idle": "2026-04-14T15:04:22.360541Z", "shell.execute_reply": "2026-04-14T15:04:22.359520Z" } }, "outputs": [], "source": [ "structure.cell.length_a.free = True\n", "\n", "expt.linked_phases['si'].scale.free = True\n", "expt.instrument.calib_d_to_tof_offset.free = True" ] }, { "cell_type": "markdown", "id": "34", "metadata": {}, "source": [ "Show free parameters after selection." ] }, { "cell_type": "code", "execution_count": 18, "id": "35", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:22.362493Z", "iopub.status.busy": "2026-04-14T15:04:22.362235Z", "iopub.status.idle": "2026-04-14T15:04:22.618266Z", "shell.execute_reply": "2026-04-14T15:04:22.617429Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;34mFree parameters for both structures \u001b[0m\u001b[1;34m(\u001b[0m\u001b[1;34m🧩 data blocks\u001b[0m\u001b[1;34m)\u001b[0m\u001b[1;34m and experiments \u001b[0m\u001b[1;34m(\u001b[0m\u001b[1;34m🔬 data blocks\u001b[0m\u001b[1;34m)\u001b[0m\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": { "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", "
 datablockcategoryentryparametervalueuncertaintyminmaxunits
1sicelllength_a5.43100-infinfÅ
2sepdlinked_phasessiscale10.00000-infinf
3sepdinstrumentd_to_tof_offset0.00000-infinfμs
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.analysis.display.free_params()" ] }, { "cell_type": "markdown", "id": "36", "metadata": {}, "source": [ "#### Run Fitting" ] }, { "cell_type": "code", "execution_count": 19, "id": "37", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:22.620256Z", "iopub.status.busy": "2026-04-14T15:04:22.620048Z", "iopub.status.idle": "2026-04-14T15:04:34.382390Z", "shell.execute_reply": "2026-04-14T15:04:34.381426Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;34mStandard fitting\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📋 Using experiment 🔬 \u001b[32m'sepd'\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", "
 iterationχ²improvement [%]
11113.06
2772.2036.1% ↓
31166.767.5% ↓
43066.72
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// 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" }, { "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;36m66.72\u001b[0m at iteration \u001b[1;36m26\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "✅ Fitting complete.\n" ] }, { "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;36m10.35\u001b[0m seconds\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📏 Goodness-of-fit \u001b[1m(\u001b[0mreduced χ²\u001b[1m)\u001b[0m: \u001b[1;36m66.72\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📏 R-factor \u001b[1m(\u001b[0mRf\u001b[1m)\u001b[0m: \u001b[1;36m23.08\u001b[0m%\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📏 R-factor squared \u001b[1m(\u001b[0mRf²\u001b[1m)\u001b[0m: \u001b[1;36m12.55\u001b[0m%\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📏 Weighted R-factor \u001b[1m(\u001b[0mwR\u001b[1m)\u001b[0m: \u001b[1;36m12.51\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') || 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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": { "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", " 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 datablockcategoryentryparameterstartfitteduncertaintyunitschange
1sicelllength_a5.43105.43140.0002Å0.01 % ↑
2sepdlinked_phasessiscale10.000013.36190.115333.62 % ↑
3sepdinstrumentd_to_tof_offset0.0000-9.25430.2503μsN/A
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.analysis.fit()\n", "project.analysis.display.fit_results()" ] }, { "cell_type": "markdown", "id": "38", "metadata": {}, "source": [ "#### Plot Measured vs Calculated" ] }, { "cell_type": "code", "execution_count": 20, "id": "39", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:34.384709Z", "iopub.status.busy": "2026-04-14T15:04:34.384509Z", "iopub.status.idle": "2026-04-14T15:04:34.437248Z", "shell.execute_reply": "2026-04-14T15:04:34.436170Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.plotter.plot_meas_vs_calc(expt_name='sepd', show_residual=True)" ] }, { "cell_type": "code", "execution_count": 21, "id": "40", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:34.440178Z", "iopub.status.busy": "2026-04-14T15:04:34.439975Z", "iopub.status.idle": "2026-04-14T15:04:34.470761Z", "shell.execute_reply": "2026-04-14T15:04:34.469856Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.plotter.plot_meas_vs_calc(expt_name='sepd', x_min=23200, x_max=23700, show_residual=True)" ] }, { "cell_type": "markdown", "id": "41", "metadata": {}, "source": [ "### Perform Fit 2/5\n", "\n", "Set more parameters to be refined." ] }, { "cell_type": "code", "execution_count": 22, "id": "42", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:34.472379Z", "iopub.status.busy": "2026-04-14T15:04:34.472211Z", "iopub.status.idle": "2026-04-14T15:04:34.475542Z", "shell.execute_reply": "2026-04-14T15:04:34.474698Z" } }, "outputs": [], "source": [ "for point in expt.background:\n", " point.y.free = True" ] }, { "cell_type": "markdown", "id": "43", "metadata": {}, "source": [ "Show free parameters after selection." ] }, { "cell_type": "code", "execution_count": 23, "id": "44", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:34.476985Z", "iopub.status.busy": "2026-04-14T15:04:34.476825Z", "iopub.status.idle": "2026-04-14T15:04:34.723990Z", "shell.execute_reply": "2026-04-14T15:04:34.723088Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;34mFree parameters for both structures \u001b[0m\u001b[1;34m(\u001b[0m\u001b[1;34m🧩 data blocks\u001b[0m\u001b[1;34m)\u001b[0m\u001b[1;34m and experiments \u001b[0m\u001b[1;34m(\u001b[0m\u001b[1;34m🔬 data blocks\u001b[0m\u001b[1;34m)\u001b[0m\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": { "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", " \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", "
 datablockcategoryentryparametervalueuncertaintyminmaxunits
1sicelllength_a5.431350.00018-infinfÅ
2sepdlinked_phasessiscale13.361870.11531-infinf
3sepdinstrumentd_to_tof_offset-9.254320.25033-infinfμs
4sepdbackground0y200.00000-infinf
5sepdbackground5000y200.00000-infinf
6sepdbackground10000y200.00000-infinf
7sepdbackground15000y200.00000-infinf
8sepdbackground20000y200.00000-infinf
9sepdbackground25000y200.00000-infinf
10sepdbackground30000y200.00000-infinf
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.analysis.display.free_params()" ] }, { "cell_type": "markdown", "id": "45", "metadata": {}, "source": [ "#### Run Fitting" ] }, { "cell_type": "code", "execution_count": 24, "id": "46", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:34.725690Z", "iopub.status.busy": "2026-04-14T15:04:34.725501Z", "iopub.status.idle": "2026-04-14T15:04:51.807211Z", "shell.execute_reply": "2026-04-14T15:04:51.806200Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;34mStandard fitting\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📋 Using experiment 🔬 \u001b[32m'sepd'\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", "
 iterationχ²improvement [%]
1166.80
2143.3894.9% ↓
3483.38
\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" }, { "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;36m3.38\u001b[0m at iteration \u001b[1;36m47\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "✅ Fitting complete.\n" ] }, { "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;36m15.76\u001b[0m seconds\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📏 Goodness-of-fit \u001b[1m(\u001b[0mreduced χ²\u001b[1m)\u001b[0m: \u001b[1;36m3.38\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📏 R-factor \u001b[1m(\u001b[0mRf\u001b[1m)\u001b[0m: \u001b[1;36m9.29\u001b[0m%\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📏 R-factor squared \u001b[1m(\u001b[0mRf²\u001b[1m)\u001b[0m: \u001b[1;36m6.33\u001b[0m%\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📏 Weighted R-factor \u001b[1m(\u001b[0mwR\u001b[1m)\u001b[0m: \u001b[1;36m5.95\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) {\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": { "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", " 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 datablockcategoryentryparameterstartfitteduncertaintyunitschange
1sicelllength_a5.43145.43140.0000Å0.00 % ↑
2sepdlinked_phasessiscale13.361914.63170.02659.50 % ↑
3sepdinstrumentd_to_tof_offset-9.2543-9.25340.0515μs0.01 % ↓
4sepdbackground0y200.0000268.60020.974534.30 % ↑
5sepdbackground5000y200.0000144.75890.407127.62 % ↓
6sepdbackground10000y200.0000120.02470.428239.99 % ↓
7sepdbackground15000y200.0000135.84940.816932.08 % ↓
8sepdbackground20000y200.0000132.68871.431733.66 % ↓
9sepdbackground25000y200.0000175.17752.875512.41 % ↓
10sepdbackground30000y200.0000180.45565.85259.77 % ↓
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.analysis.fit()\n", "project.analysis.display.fit_results()" ] }, { "cell_type": "markdown", "id": "47", "metadata": {}, "source": [ "#### Plot Measured vs Calculated" ] }, { "cell_type": "code", "execution_count": 25, "id": "48", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:51.808987Z", "iopub.status.busy": "2026-04-14T15:04:51.808804Z", "iopub.status.idle": "2026-04-14T15:04:51.847213Z", "shell.execute_reply": "2026-04-14T15:04:51.846416Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.plotter.plot_meas_vs_calc(expt_name='sepd', show_residual=True)" ] }, { "cell_type": "code", "execution_count": 26, "id": "49", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:51.850432Z", "iopub.status.busy": "2026-04-14T15:04:51.850240Z", "iopub.status.idle": "2026-04-14T15:04:51.881866Z", "shell.execute_reply": "2026-04-14T15:04:51.880922Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.plotter.plot_meas_vs_calc(expt_name='sepd', x_min=23200, x_max=23700, show_residual=True)" ] }, { "cell_type": "markdown", "id": "50", "metadata": {}, "source": [ "### Perform Fit 3/5\n", "\n", "Fix background points." ] }, { "cell_type": "code", "execution_count": 27, "id": "51", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:51.883546Z", "iopub.status.busy": "2026-04-14T15:04:51.883360Z", "iopub.status.idle": "2026-04-14T15:04:51.886663Z", "shell.execute_reply": "2026-04-14T15:04:51.885893Z" } }, "outputs": [], "source": [ "for point in expt.background:\n", " point.y.free = False" ] }, { "cell_type": "markdown", "id": "52", "metadata": {}, "source": [ "Set more parameters to be refined." ] }, { "cell_type": "code", "execution_count": 28, "id": "53", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:51.888479Z", "iopub.status.busy": "2026-04-14T15:04:51.888322Z", "iopub.status.idle": "2026-04-14T15:04:51.891400Z", "shell.execute_reply": "2026-04-14T15:04:51.890607Z" } }, "outputs": [], "source": [ "expt.peak.broad_gauss_sigma_0.free = True\n", "expt.peak.broad_gauss_sigma_1.free = True\n", "expt.peak.broad_gauss_sigma_2.free = True" ] }, { "cell_type": "markdown", "id": "54", "metadata": {}, "source": [ "Show free parameters after selection." ] }, { "cell_type": "code", "execution_count": 29, "id": "55", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:51.892876Z", "iopub.status.busy": "2026-04-14T15:04:51.892682Z", "iopub.status.idle": "2026-04-14T15:04:52.137002Z", "shell.execute_reply": "2026-04-14T15:04:52.135928Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;34mFree parameters for both structures \u001b[0m\u001b[1;34m(\u001b[0m\u001b[1;34m🧩 data blocks\u001b[0m\u001b[1;34m)\u001b[0m\u001b[1;34m and experiments \u001b[0m\u001b[1;34m(\u001b[0m\u001b[1;34m🔬 data blocks\u001b[0m\u001b[1;34m)\u001b[0m\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": { "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", "
 datablockcategoryentryparametervalueuncertaintyminmaxunits
1sicelllength_a5.431370.00004-infinfÅ
2sepdlinked_phasessiscale14.631670.02651-infinf
3sepdpeakgauss_sigma_03.00000-infinfμs²
4sepdpeakgauss_sigma_140.00000-infinfμs/Å
5sepdpeakgauss_sigma_22.00000-infinfμs²/Ų
6sepdinstrumentd_to_tof_offset-9.253360.05153-infinfμs
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.analysis.display.free_params()" ] }, { "cell_type": "markdown", "id": "56", "metadata": {}, "source": [ "#### Run Fitting" ] }, { "cell_type": "code", "execution_count": 30, "id": "57", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:04:52.138983Z", "iopub.status.busy": "2026-04-14T15:04:52.138787Z", "iopub.status.idle": "2026-04-14T15:05:06.388279Z", "shell.execute_reply": "2026-04-14T15:05:06.387490Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;34mStandard fitting\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📋 Using experiment 🔬 \u001b[32m'sepd'\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", "
 iterationχ²improvement [%]
113.38
2103.215.0% ↓
3393.21
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(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": [ "🏆 Best goodness-of-fit \u001b[1m(\u001b[0mreduced χ²\u001b[1m)\u001b[0m is \u001b[1;36m3.21\u001b[0m at iteration \u001b[1;36m38\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "✅ Fitting complete.\n" ] }, { "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;36m12.93\u001b[0m seconds\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📏 Goodness-of-fit \u001b[1m(\u001b[0mreduced χ²\u001b[1m)\u001b[0m: \u001b[1;36m3.21\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📏 R-factor \u001b[1m(\u001b[0mRf\u001b[1m)\u001b[0m: \u001b[1;36m8.99\u001b[0m%\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📏 R-factor squared \u001b[1m(\u001b[0mRf²\u001b[1m)\u001b[0m: \u001b[1;36m5.52\u001b[0m%\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📏 Weighted R-factor \u001b[1m(\u001b[0mwR\u001b[1m)\u001b[0m: \u001b[1;36m4.88\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) {\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 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 datablockcategoryentryparameterstartfitteduncertaintyunitschange
1sicelllength_a5.43145.43140.0000Å0.00 % ↑
2sepdlinked_phasessiscale14.631714.70570.02570.51 % ↑
3sepdpeakgauss_sigma_03.00005.77270.4206μs²92.42 % ↑
4sepdpeakgauss_sigma_140.000044.28270.7966μs/Å10.71 % ↑
5sepdpeakgauss_sigma_22.00001.29620.1680μs²/Ų35.19 % ↓
6sepdinstrumentd_to_tof_offset-9.2534-9.25060.0546μs0.03 % ↓
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.analysis.fit()\n", "project.analysis.display.fit_results()" ] }, { "cell_type": "markdown", "id": "58", "metadata": {}, "source": [ "#### Plot Measured vs Calculated" ] }, { "cell_type": "code", "execution_count": 31, "id": "59", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:05:06.390092Z", "iopub.status.busy": "2026-04-14T15:05:06.389925Z", "iopub.status.idle": "2026-04-14T15:05:06.427725Z", "shell.execute_reply": "2026-04-14T15:05:06.426893Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.plotter.plot_meas_vs_calc(expt_name='sepd', show_residual=True)" ] }, { "cell_type": "code", "execution_count": 32, "id": "60", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:05:06.430893Z", "iopub.status.busy": "2026-04-14T15:05:06.430689Z", "iopub.status.idle": "2026-04-14T15:05:06.462687Z", "shell.execute_reply": "2026-04-14T15:05:06.461470Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.plotter.plot_meas_vs_calc(expt_name='sepd', x_min=23200, x_max=23700, show_residual=True)" ] }, { "cell_type": "markdown", "id": "61", "metadata": {}, "source": [ "### Perform Fit 4/5\n", "\n", "Set more parameters to be refined." ] }, { "cell_type": "code", "execution_count": 33, "id": "62", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:05:06.464383Z", "iopub.status.busy": "2026-04-14T15:05:06.464198Z", "iopub.status.idle": "2026-04-14T15:05:06.467606Z", "shell.execute_reply": "2026-04-14T15:05:06.466781Z" } }, "outputs": [], "source": [ "structure.atom_sites['Si'].adp_iso.free = True\n", "\n", "expt.peak.exp_decay_beta_0.free = True\n", "expt.peak.exp_decay_beta_1.free = True\n", "expt.peak.exp_rise_alpha_1.free = True" ] }, { "cell_type": "markdown", "id": "63", "metadata": {}, "source": [ "Show free parameters after selection." ] }, { "cell_type": "code", "execution_count": 34, "id": "64", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:05:06.469682Z", "iopub.status.busy": "2026-04-14T15:05:06.469463Z", "iopub.status.idle": "2026-04-14T15:05:06.721339Z", "shell.execute_reply": "2026-04-14T15:05:06.720676Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;34mFree parameters for both structures \u001b[0m\u001b[1;34m(\u001b[0m\u001b[1;34m🧩 data blocks\u001b[0m\u001b[1;34m)\u001b[0m\u001b[1;34m and experiments \u001b[0m\u001b[1;34m(\u001b[0m\u001b[1;34m🔬 data blocks\u001b[0m\u001b[1;34m)\u001b[0m\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": { "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", " \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", "
 datablockcategoryentryparametervalueuncertaintyminmaxunits
1sicelllength_a5.431430.00004-infinfÅ
2siatom_siteSiadp_iso0.50000-infinfŲ
3sepdlinked_phasessiscale14.705680.02567-infinf
4sepdpeakrise_alpha_10.59710-infinfμs/Å
5sepdpeakdecay_beta_00.04221-infinfμs
6sepdpeakdecay_beta_10.00946-infinfμs/Å
7sepdpeakgauss_sigma_05.772740.42055-infinfμs²
8sepdpeakgauss_sigma_144.282650.79664-infinfμs/Å
9sepdpeakgauss_sigma_21.296210.16795-infinfμs²/Ų
10sepdinstrumentd_to_tof_offset-9.250650.05458-infinfμs
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.analysis.display.free_params()" ] }, { "cell_type": "markdown", "id": "65", "metadata": {}, "source": [ "#### Run Fitting" ] }, { "cell_type": "code", "execution_count": 35, "id": "66", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:05:06.723283Z", "iopub.status.busy": "2026-04-14T15:05:06.723086Z", "iopub.status.idle": "2026-04-14T15:05:43.230306Z", "shell.execute_reply": "2026-04-14T15:05:43.229057Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;34mStandard fitting\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📋 Using experiment 🔬 \u001b[32m'sepd'\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", "
 iterationχ²improvement [%]
113.21
2153.171.2% ↓
3163.131.5% ↓
4263.013.7% ↓
5382.961.7% ↓
6602.931.1% ↓
71052.93
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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;36m2.93\u001b[0m at iteration \u001b[1;36m104\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "✅ Fitting complete.\n" ] }, { "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;36m35.20\u001b[0m seconds\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📏 Goodness-of-fit \u001b[1m(\u001b[0mreduced χ²\u001b[1m)\u001b[0m: \u001b[1;36m2.93\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📏 R-factor \u001b[1m(\u001b[0mRf\u001b[1m)\u001b[0m: \u001b[1;36m8.39\u001b[0m%\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📏 R-factor squared \u001b[1m(\u001b[0mRf²\u001b[1m)\u001b[0m: \u001b[1;36m4.16\u001b[0m%\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📏 Weighted R-factor \u001b[1m(\u001b[0mwR\u001b[1m)\u001b[0m: \u001b[1;36m2.48\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", " 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 datablockcategoryentryparameterstartfitteduncertaintyunitschange
1sicelllength_a5.43145.43250.0001Å0.02 % ↑
2siatom_siteSiadp_iso0.50000.52400.0033Ų4.80 % ↑
3sepdlinked_phasessiscale14.705714.96930.03551.79 % ↑
4sepdpeakrise_alpha_10.59710.23700.0043μs/Å60.31 % ↓
5sepdpeakdecay_beta_00.04220.03860.0002μs8.65 % ↓
6sepdpeakdecay_beta_10.00950.01060.0002μs/Å11.67 % ↑
7sepdpeakgauss_sigma_05.77276.96570.4577μs²20.67 % ↑
8sepdpeakgauss_sigma_144.282725.65091.0250μs/Å42.07 % ↓
9sepdpeakgauss_sigma_21.29621.10010.1584μs²/Ų15.13 % ↓
10sepdinstrumentd_to_tof_offset-9.2506-8.72480.0740μs5.68 % ↓
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.analysis.fit()\n", "project.analysis.display.fit_results()" ] }, { "cell_type": "markdown", "id": "67", "metadata": {}, "source": [ "#### Show parameter correlations" ] }, { "cell_type": "code", "execution_count": 36, "id": "68", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:05:43.231957Z", "iopub.status.busy": "2026-04-14T15:05:43.231761Z", "iopub.status.idle": "2026-04-14T15:05:43.236966Z", "shell.execute_reply": "2026-04-14T15:05:43.235974Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "⚠️ No parameter pairs with |correlation| >= 0.70 were found. \n" ] } ], "source": [ "project.plotter.plot_param_correlations()" ] }, { "cell_type": "markdown", "id": "69", "metadata": {}, "source": [ "#### Plot Measured vs Calculated" ] }, { "cell_type": "code", "execution_count": 37, "id": "70", "metadata": { "execution": { "iopub.execute_input": "2026-04-14T15:05:43.238578Z", "iopub.status.busy": "2026-04-14T15:05:43.238398Z", "iopub.status.idle": "2026-04-14T15:05:43.282057Z", "shell.execute_reply": "2026-04-14T15:05:43.281009Z" } }, "outputs": [ { "data": { "text/html": [ "
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