{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "66907835", "metadata": { "execution": { "iopub.execute_input": "2026-01-06T13:53:56.040215Z", "iopub.status.busy": "2026-01-06T13:53:56.036749Z", "iopub.status.idle": "2026-01-06T13:53:56.067580Z", "shell.execute_reply": "2026-01-06T13:53:56.051267Z" }, "tags": [ "hide-in-docs" ] }, "outputs": [], "source": [ "# Check if the easydiffraction library is installed.\n", "# If not, install it with the 'visualization' extras.\n", "# Needed when running remotely (e.g. Colab) where the lib is absent.\n", "import builtins\n", "import importlib.util\n", "\n", "if (hasattr(builtins, '__IPYTHON__') and\n", " importlib.util.find_spec('easydiffraction') is None):\n", " !pip install 'easydiffraction[visualization]==0.10.1'" ] }, { "cell_type": "markdown", "id": "0", "metadata": {}, "source": [ "# Structure Refinement: LBCO, HRPT\n", "\n", "This minimalistic example is designed to show how Rietveld refinement\n", "of a crystal structure can be performed when both the sample model and\n", "experiment are defined directly in code. Only the experimentally\n", "measured data is loaded from an external file.\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 explanationsβ€”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": "1", "metadata": {}, "source": [ "## Import Library" ] }, { "cell_type": "code", "execution_count": 2, "id": "2", "metadata": { "execution": { "iopub.execute_input": "2026-01-06T13:53:56.110246Z", "iopub.status.busy": "2026-01-06T13:53:56.108415Z", "iopub.status.idle": "2026-01-06T13:53:59.270568Z", "shell.execute_reply": "2026-01-06T13:53:59.269398Z" } }, "outputs": [], "source": [ "import easydiffraction as ed" ] }, { "cell_type": "markdown", "id": "3", "metadata": {}, "source": [ "## Step 1: Define Project" ] }, { "cell_type": "code", "execution_count": 3, "id": "4", "metadata": { "execution": { "iopub.execute_input": "2026-01-06T13:53:59.274617Z", "iopub.status.busy": "2026-01-06T13:53:59.273353Z", "iopub.status.idle": "2026-01-06T13:53:59.453863Z", "shell.execute_reply": "2026-01-06T13:53:59.452776Z" } }, "outputs": [], "source": [ "project = ed.Project()" ] }, { "cell_type": "markdown", "id": "5", "metadata": {}, "source": [ "## Step 2: Define Sample Model" ] }, { "cell_type": "code", "execution_count": 4, "id": "6", "metadata": { "execution": { "iopub.execute_input": "2026-01-06T13:53:59.458284Z", "iopub.status.busy": "2026-01-06T13:53:59.457569Z", "iopub.status.idle": "2026-01-06T13:53:59.468590Z", "shell.execute_reply": "2026-01-06T13:53:59.467318Z" } }, "outputs": [], "source": [ "project.sample_models.add(name='lbco')" ] }, { "cell_type": "code", "execution_count": 5, "id": "7", "metadata": { "execution": { "iopub.execute_input": "2026-01-06T13:53:59.471790Z", "iopub.status.busy": "2026-01-06T13:53:59.470997Z", "iopub.status.idle": "2026-01-06T13:53:59.479299Z", "shell.execute_reply": "2026-01-06T13:53:59.477668Z" } }, "outputs": [], "source": [ "sample_model = project.sample_models['lbco']" ] }, { "cell_type": "code", "execution_count": 6, "id": "8", "metadata": { "execution": { "iopub.execute_input": "2026-01-06T13:53:59.483829Z", "iopub.status.busy": "2026-01-06T13:53:59.483006Z", "iopub.status.idle": "2026-01-06T13:53:59.487638Z", "shell.execute_reply": "2026-01-06T13:53:59.486472Z" } }, "outputs": [], "source": [ "sample_model.space_group.name_h_m = 'P m -3 m'\n", "sample_model.space_group.it_coordinate_system_code = '1'" ] }, { "cell_type": "code", "execution_count": 7, "id": "9", "metadata": { "execution": { "iopub.execute_input": "2026-01-06T13:53:59.491210Z", "iopub.status.busy": "2026-01-06T13:53:59.490482Z", "iopub.status.idle": "2026-01-06T13:53:59.493565Z", "shell.execute_reply": "2026-01-06T13:53:59.493163Z" } }, "outputs": [], "source": [ "sample_model.cell.length_a = 3.88" ] }, { "cell_type": "code", "execution_count": 8, "id": "10", "metadata": { "execution": { "iopub.execute_input": "2026-01-06T13:53:59.495384Z", "iopub.status.busy": "2026-01-06T13:53:59.495211Z", "iopub.status.idle": "2026-01-06T13:53:59.503026Z", "shell.execute_reply": "2026-01-06T13:53:59.502162Z" } }, "outputs": [], "source": [ "sample_model.atom_sites.add(\n", " label='La',\n", " type_symbol='La',\n", " fract_x=0,\n", " fract_y=0,\n", " fract_z=0,\n", " wyckoff_letter='a',\n", " b_iso=0.5,\n", " occupancy=0.5,\n", ")\n", "sample_model.atom_sites.add(\n", " label='Ba',\n", " type_symbol='Ba',\n", " fract_x=0,\n", " fract_y=0,\n", " fract_z=0,\n", " wyckoff_letter='a',\n", " b_iso=0.5,\n", " occupancy=0.5,\n", ")\n", "sample_model.atom_sites.add(\n", " label='Co',\n", " type_symbol='Co',\n", " fract_x=0.5,\n", " fract_y=0.5,\n", " fract_z=0.5,\n", " wyckoff_letter='b',\n", " b_iso=0.5,\n", ")\n", "sample_model.atom_sites.add(\n", " label='O',\n", " type_symbol='O',\n", " fract_x=0,\n", " fract_y=0.5,\n", " fract_z=0.5,\n", " wyckoff_letter='c',\n", " b_iso=0.5,\n", ")" ] }, { "cell_type": "markdown", "id": "11", "metadata": {}, "source": [ "## Step 3: Define Experiment" ] }, { "cell_type": "code", "execution_count": 9, "id": "12", "metadata": { "execution": { "iopub.execute_input": "2026-01-06T13:53:59.505279Z", "iopub.status.busy": "2026-01-06T13:53:59.505116Z", "iopub.status.idle": "2026-01-06T13:53:59.512682Z", "shell.execute_reply": "2026-01-06T13:53:59.512240Z" } }, "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;36m3\u001b[0m: La0.5Ba0.5CoO3, HRPT \u001b[1m(\u001b[0mPSI\u001b[1m)\u001b[0m, \u001b[1;36m300\u001b[0m K\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "βœ… Data #\u001b[1;36m3\u001b[0m already present at \u001b[32m'data/ed-3.xye'\u001b[0m. Keeping existing file.\n" ] } ], "source": [ "data_path = ed.download_data(id=3, destination='data')" ] }, { "cell_type": "code", "execution_count": 10, "id": "13", "metadata": { "execution": { "iopub.execute_input": "2026-01-06T13:53:59.514648Z", "iopub.status.busy": "2026-01-06T13:53:59.514274Z", "iopub.status.idle": "2026-01-06T13:53:59.997400Z", "shell.execute_reply": "2026-01-06T13:53:59.996441Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;34mData loaded successfully\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Experiment πŸ”¬ \u001b[32m'hrpt'\u001b[0m. Number of data points: \u001b[1;36m3098\u001b[0m\n" ] } ], "source": [ "project.experiments.add(\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": 11, "id": "14", "metadata": { "execution": { "iopub.execute_input": "2026-01-06T13:54:00.000414Z", "iopub.status.busy": "2026-01-06T13:54:00.000239Z", "iopub.status.idle": "2026-01-06T13:54:00.002618Z", "shell.execute_reply": "2026-01-06T13:54:00.002026Z" } }, "outputs": [], "source": [ "experiment = project.experiments['hrpt']" ] }, { "cell_type": "code", "execution_count": 12, "id": "15", "metadata": { "execution": { "iopub.execute_input": "2026-01-06T13:54:00.004944Z", "iopub.status.busy": "2026-01-06T13:54:00.004804Z", "iopub.status.idle": "2026-01-06T13:54:00.008177Z", "shell.execute_reply": "2026-01-06T13:54:00.006731Z" } }, "outputs": [], "source": [ "experiment.instrument.setup_wavelength = 1.494\n", "experiment.instrument.calib_twotheta_offset = 0.6" ] }, { "cell_type": "code", "execution_count": 13, "id": "16", "metadata": { "execution": { "iopub.execute_input": "2026-01-06T13:54:00.010949Z", "iopub.status.busy": "2026-01-06T13:54:00.010810Z", "iopub.status.idle": "2026-01-06T13:54:00.013410Z", "shell.execute_reply": "2026-01-06T13:54:00.013037Z" } }, "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": 14, "id": "17", "metadata": { "execution": { "iopub.execute_input": "2026-01-06T13:54:00.015117Z", "iopub.status.busy": "2026-01-06T13:54:00.014998Z", "iopub.status.idle": "2026-01-06T13:54:00.018617Z", "shell.execute_reply": "2026-01-06T13:54:00.017492Z" } }, "outputs": [], "source": [ "experiment.background.add(id='1', x=10, y=170)\n", "experiment.background.add(id='2', x=30, y=170)\n", "experiment.background.add(id='3', x=50, y=170)\n", "experiment.background.add(id='4', x=110, y=170)\n", "experiment.background.add(id='5', x=165, y=170)" ] }, { "cell_type": "code", "execution_count": 15, "id": "18", "metadata": { "execution": { "iopub.execute_input": "2026-01-06T13:54:00.020454Z", "iopub.status.busy": "2026-01-06T13:54:00.020354Z", "iopub.status.idle": "2026-01-06T13:54:00.022693Z", "shell.execute_reply": "2026-01-06T13:54:00.022219Z" } }, "outputs": [], "source": [ "experiment.excluded_regions.add(id='1', start=0, end=5)\n", "experiment.excluded_regions.add(id='2', start=165, end=180)" ] }, { "cell_type": "code", "execution_count": 16, "id": "19", "metadata": { "execution": { "iopub.execute_input": "2026-01-06T13:54:00.024353Z", "iopub.status.busy": "2026-01-06T13:54:00.024252Z", "iopub.status.idle": "2026-01-06T13:54:00.026403Z", "shell.execute_reply": "2026-01-06T13:54:00.025853Z" } }, "outputs": [], "source": [ "experiment.linked_phases.add(id='lbco', scale=10.0)" ] }, { "cell_type": "markdown", "id": "20", "metadata": {}, "source": [ "## Step 4: Perform Analysis" ] }, { "cell_type": "code", "execution_count": 17, "id": "21", "metadata": { "execution": { "iopub.execute_input": "2026-01-06T13:54:00.028019Z", "iopub.status.busy": "2026-01-06T13:54:00.027927Z", "iopub.status.idle": "2026-01-06T13:54:00.045753Z", "shell.execute_reply": "2026-01-06T13:54:00.034257Z" } }, "outputs": [], "source": [ "sample_model.cell.length_a.free = True\n", "\n", "sample_model.atom_sites['La'].b_iso.free = True\n", "sample_model.atom_sites['Ba'].b_iso.free = True\n", "sample_model.atom_sites['Co'].b_iso.free = True\n", "sample_model.atom_sites['O'].b_iso.free = True" ] }, { "cell_type": "code", "execution_count": 18, "id": "22", "metadata": { "execution": { "iopub.execute_input": "2026-01-06T13:54:00.051725Z", "iopub.status.busy": "2026-01-06T13:54:00.051441Z", "iopub.status.idle": "2026-01-06T13:54:00.059076Z", "shell.execute_reply": "2026-01-06T13:54:00.058608Z" }, "lines_to_next_cell": 2 }, "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": 19, "id": "23", "metadata": { "execution": { "iopub.execute_input": "2026-01-06T13:54:00.060968Z", "iopub.status.busy": "2026-01-06T13:54:00.060864Z", "iopub.status.idle": "2026-01-06T13:54:16.756417Z", "shell.execute_reply": "2026-01-06T13:54:16.755180Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;34mUsing experiment πŸ”¬ \u001b[0m\u001b[32m'hrpt'\u001b[0m\u001b[1;34m for \u001b[0m\u001b[32m'single'\u001b[0m\u001b[1;34m fitting\u001b[0m\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 \u001b[1m(\u001b[0mreduced χ²\u001b[1m)\u001b[0m change:\n" ] }, { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 iterationχ²improvement [%]
11165.02
22833.5879.7% ↓
34510.8267.8% ↓
4636.4940.0% ↓
5813.3548.4% ↓
6982.2433.1% ↓
71161.9114.7% ↓
81331.5021.3% ↓
91501.453.6% ↓
101671.347.8% ↓
111851.293.4% ↓
122761.29
\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" }, { "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" }, { "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" }, { "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† 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" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;34mFit results\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "βœ… Success: \u001b[3;92mTrue\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "⏱️ Fitting time: \u001b[1;36m15.04\u001b[0m seconds\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "πŸ“ Goodness-of-fit \u001b[1m(\u001b[0mreduced χ²\u001b[1m)\u001b[0m: \u001b[1;36m1.29\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "πŸ“ R-factor \u001b[1m(\u001b[0mRf\u001b[1m)\u001b[0m: \u001b[1;36m5.65\u001b[0m%\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "πŸ“ R-factor squared \u001b[1m(\u001b[0mRfΒ²\u001b[1m)\u001b[0m: \u001b[1;36m5.30\u001b[0m%\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "πŸ“ Weighted R-factor \u001b[1m(\u001b[0mwR\u001b[1m)\u001b[0m: \u001b[1;36m4.47\u001b[0m%\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "πŸ“ˆ 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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 datablockcategoryentryparameterstartfitteduncertaintyunitschange
1lbcocelllength_a3.88003.89090.0000Γ…0.28 % ↑
2lbcoatom_siteLab_iso0.50000.50523231.4177Γ…Β²1.04 % ↑
3lbcoatom_siteBab_iso0.50000.50495252.0523Γ…Β²0.97 % ↑
4lbcoatom_siteCob_iso0.50000.23700.0611Γ…Β²52.60 % ↓
5lbcoatom_siteOb_iso0.50001.39350.0167Γ…Β²178.71 % ↑
6hrptlinked_phaseslbcoscale10.00009.13510.06428.65 % ↓
7hrptpeakbroad_gauss_u0.10000.08160.0031degΒ²18.43 % ↓
8hrptpeakbroad_gauss_v-0.1000-0.11590.0067degΒ²15.93 % ↑
9hrptpeakbroad_gauss_w0.10000.12040.0033degΒ²20.45 % ↑
10hrptpeakbroad_lorentz_y0.10000.08440.0021deg15.55 % ↓
11hrptinstrumenttwotheta_offset0.60000.62260.0010deg3.76 % ↑
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 % ↑
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.analysis.fit()\n", "project.analysis.show_fit_results()" ] }, { "cell_type": "code", "execution_count": 20, "id": "24", "metadata": { "execution": { "iopub.execute_input": "2026-01-06T13:54:16.759684Z", "iopub.status.busy": "2026-01-06T13:54:16.759116Z", "iopub.status.idle": "2026-01-06T13:54:17.130662Z", "shell.execute_reply": "2026-01-06T13:54:17.130233Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.plot_meas_vs_calc(expt_name='hrpt', show_residual=True)" ] } ], "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": "3.13.11" } }, "nbformat": 4, "nbformat_minor": 5 }