{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "0", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:32.830282Z", "iopub.status.busy": "2026-06-04T16:16:32.830124Z", "iopub.status.idle": "2026-06-04T16:16:32.833867Z", "shell.execute_reply": "2026-06-04T16:16:32.833093Z" }, "tags": [ "hide-in-docs" ] }, "outputs": [], "source": [ "# Check whether easydiffraction is installed; install it if needed.\n", "# Required for remote environments such as Google Colab.\n", "import importlib.util\n", "\n", "if importlib.util.find_spec('easydiffraction') is None:\n", " %pip install easydiffraction==0.18.0" ] }, { "cell_type": "markdown", "id": "1", "metadata": {}, "source": [ "# Joint Refinement: Si, Bragg + PDF\n", "\n", "This example demonstrates a joint refinement of the Si crystal\n", "structure combining Bragg diffraction and pair distribution function\n", "(PDF) analysis. The Bragg experiment uses time-of-flight neutron\n", "powder diffraction data from SEPD at Argonne, while the PDF\n", "experiment uses data from NOMAD at SNS. A single shared Si structure\n", "is refined simultaneously against both datasets." ] }, { "cell_type": "markdown", "id": "2", "metadata": {}, "source": [ "## πŸ› οΈ Import Library" ] }, { "cell_type": "code", "execution_count": 2, "id": "3", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:32.835353Z", "iopub.status.busy": "2026-06-04T16:16:32.835203Z", "iopub.status.idle": "2026-06-04T16:16:35.582589Z", "shell.execute_reply": "2026-06-04T16:16:35.581689Z" } }, "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", "A single Si structure is shared between the Bragg and PDF\n", "experiments. Structural parameters refined against both datasets\n", "simultaneously.\n", "\n", "### Create Structure" ] }, { "cell_type": "code", "execution_count": 3, "id": "5", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:35.584481Z", "iopub.status.busy": "2026-06-04T16:16:35.584186Z", "iopub.status.idle": "2026-06-04T16:16:35.589213Z", "shell.execute_reply": "2026-06-04T16:16:35.588203Z" } }, "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": "2026-06-04T16:16:35.590815Z", "iopub.status.busy": "2026-06-04T16:16:35.590646Z", "iopub.status.idle": "2026-06-04T16:16:35.593711Z", "shell.execute_reply": "2026-06-04T16:16:35.592903Z" } }, "outputs": [], "source": [ "structure.space_group.name_h_m = 'F d -3 m'\n", "structure.space_group.it_coordinate_system_code = '1'" ] }, { "cell_type": "markdown", "id": "8", "metadata": {}, "source": [ "### Set Unit Cell" ] }, { "cell_type": "code", "execution_count": 5, "id": "9", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:35.595176Z", "iopub.status.busy": "2026-06-04T16:16:35.595018Z", "iopub.status.idle": "2026-06-04T16:16:35.597813Z", "shell.execute_reply": "2026-06-04T16:16:35.597015Z" } }, "outputs": [], "source": [ "structure.cell.length_a = 5.42" ] }, { "cell_type": "markdown", "id": "10", "metadata": {}, "source": [ "### Set Atom Sites" ] }, { "cell_type": "code", "execution_count": 6, "id": "11", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:35.599355Z", "iopub.status.busy": "2026-06-04T16:16:35.599189Z", "iopub.status.idle": "2026-06-04T16:16:35.603075Z", "shell.execute_reply": "2026-06-04T16:16:35.602317Z" } }, "outputs": [], "source": [ "structure.atom_sites.create(\n", " label='Si',\n", " type_symbol='Si',\n", " fract_x=0,\n", " fract_y=0,\n", " fract_z=0,\n", " adp_iso=0.2,\n", ")" ] }, { "cell_type": "markdown", "id": "12", "metadata": {}, "source": [ "## πŸ”¬ Define Experiments\n", "\n", "Two experiments are defined: one for Bragg diffraction and one for\n", "PDF analysis. Both are linked to the same Si structure.\n", "\n", "### Experiment 1: Bragg (SEPD, TOF)\n", "\n", "#### Download Data" ] }, { "cell_type": "code", "execution_count": 7, "id": "13", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:35.604665Z", "iopub.status.busy": "2026-06-04T16:16:35.604486Z", "iopub.status.idle": "2026-06-04T16:16:35.744282Z", "shell.execute_reply": "2026-06-04T16:16:35.743469Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;36mGetting data\u001b[0m\u001b[1;36m...\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Data #\u001b[1;36m7\u001b[0m: Si, SEPD \u001b[1m(\u001b[0mArgonne\u001b[1m)\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "βœ… Data #\u001b[1;36m7\u001b[0m downloaded to \u001b[32m'../../../data/ed-7.xye'\u001b[0m\n" ] } ], "source": [ "bragg_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": "2026-06-04T16:16:35.745892Z", "iopub.status.busy": "2026-06-04T16:16:35.745734Z", "iopub.status.idle": "2026-06-04T16:16:36.400066Z", "shell.execute_reply": "2026-06-04T16:16:36.399179Z" } }, "outputs": [], "source": [ "bragg_expt = ExperimentFactory.from_data_path(\n", " name='sepd', data_path=bragg_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": "2026-06-04T16:16:36.401910Z", "iopub.status.busy": "2026-06-04T16:16:36.401620Z", "iopub.status.idle": "2026-06-04T16:16:36.405452Z", "shell.execute_reply": "2026-06-04T16:16:36.404546Z" } }, "outputs": [], "source": [ "bragg_expt.instrument.setup_twotheta_bank = 144.845\n", "bragg_expt.instrument.calib_d_to_tof_offset = -9.2\n", "bragg_expt.instrument.calib_d_to_tof_linear = 7476.91\n", "bragg_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": "2026-06-04T16:16:36.407042Z", "iopub.status.busy": "2026-06-04T16:16:36.406834Z", "iopub.status.idle": "2026-06-04T16:16:36.414922Z", "shell.execute_reply": "2026-06-04T16:16:36.414095Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;36mPeak profile type for experiment \u001b[0m\u001b[32m'sepd'\u001b[0m\u001b[1;36m changed to\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "jorgensen\n" ] } ], "source": [ "bragg_expt.peak.type = 'jorgensen'\n", "bragg_expt.peak.broad_gauss_sigma_0 = 5.0\n", "bragg_expt.peak.broad_gauss_sigma_1 = 45.0\n", "bragg_expt.peak.broad_gauss_sigma_2 = 1.0\n", "bragg_expt.peak.exp_decay_beta_0 = 0.04221\n", "bragg_expt.peak.exp_decay_beta_1 = 0.00946\n", "bragg_expt.peak.exp_rise_alpha_0 = 0.0\n", "bragg_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": "2026-06-04T16:16:36.416606Z", "iopub.status.busy": "2026-06-04T16:16:36.416374Z", "iopub.status.idle": "2026-06-04T16:16:36.422718Z", "shell.execute_reply": "2026-06-04T16:16:36.422038Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;36mBackground type for experiment \u001b[0m\u001b[32m'sepd'\u001b[0m\u001b[1;36m already set to\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "line-segment\n" ] } ], "source": [ "bragg_expt.background.type = 'line-segment'\n", "for x in range(0, 35000, 5000):\n", " bragg_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": "2026-06-04T16:16:36.424398Z", "iopub.status.busy": "2026-06-04T16:16:36.424229Z", "iopub.status.idle": "2026-06-04T16:16:36.427228Z", "shell.execute_reply": "2026-06-04T16:16:36.426467Z" } }, "outputs": [], "source": [ "bragg_expt.linked_phases.create(id='si', scale=13.0)" ] }, { "cell_type": "markdown", "id": "24", "metadata": {}, "source": [ "### Experiment 2: PDF (NOMAD, TOF)\n", "\n", "#### Download Data" ] }, { "cell_type": "code", "execution_count": 13, "id": "25", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:36.428744Z", "iopub.status.busy": "2026-06-04T16:16:36.428584Z", "iopub.status.idle": "2026-06-04T16:16:36.435211Z", "shell.execute_reply": "2026-06-04T16:16:36.434108Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;36mGetting data\u001b[0m\u001b[1;36m...\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Data #\u001b[1;36m5\u001b[0m: NOM_9999_Si_640g_PAC_50_ff_ftfrgr_up-to-\u001b[1;36m50.\u001b[0mgr\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "βœ… Data #\u001b[1;36m5\u001b[0m already present at \u001b[32m'../../../data/ed-5.gr'\u001b[0m. Keeping existing.\n" ] } ], "source": [ "pdf_data_path = download_data(id=5, destination='data')" ] }, { "cell_type": "markdown", "id": "26", "metadata": {}, "source": [ "#### Create Experiment" ] }, { "cell_type": "code", "execution_count": 14, "id": "27", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:36.436723Z", "iopub.status.busy": "2026-06-04T16:16:36.436530Z", "iopub.status.idle": "2026-06-04T16:16:36.957249Z", "shell.execute_reply": "2026-06-04T16:16:36.956526Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "⚠️ No uncertainty (sy) column provided. Defaulting to 0.03. \n" ] } ], "source": [ "pdf_expt = ExperimentFactory.from_data_path(\n", " name='nomad',\n", " data_path=pdf_data_path,\n", " beam_mode='time-of-flight',\n", " scattering_type='total',\n", ")" ] }, { "cell_type": "markdown", "id": "28", "metadata": {}, "source": [ "#### Set Peak Profile (PDF Parameters)" ] }, { "cell_type": "code", "execution_count": 15, "id": "29", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:36.959172Z", "iopub.status.busy": "2026-06-04T16:16:36.959018Z", "iopub.status.idle": "2026-06-04T16:16:36.962968Z", "shell.execute_reply": "2026-06-04T16:16:36.962300Z" } }, "outputs": [], "source": [ "pdf_expt.peak.damp_q = 0.02\n", "pdf_expt.peak.broad_q = 0.02\n", "pdf_expt.peak.cutoff_q = 35.0\n", "pdf_expt.peak.sharp_delta_1 = 0.001\n", "pdf_expt.peak.sharp_delta_2 = 4.0\n", "pdf_expt.peak.damp_particle_diameter = 0" ] }, { "cell_type": "markdown", "id": "30", "metadata": {}, "source": [ "#### Set Linked Phases" ] }, { "cell_type": "code", "execution_count": 16, "id": "31", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:36.964398Z", "iopub.status.busy": "2026-06-04T16:16:36.964246Z", "iopub.status.idle": "2026-06-04T16:16:36.967103Z", "shell.execute_reply": "2026-06-04T16:16:36.966382Z" } }, "outputs": [], "source": [ "pdf_expt.linked_phases.create(id='si', scale=1.0)" ] }, { "cell_type": "markdown", "id": "32", "metadata": {}, "source": [ "## πŸ“¦ Define Project\n", "\n", "The project object manages the shared structure, both experiments,\n", "and the analysis.\n", "\n", "### Create Project" ] }, { "cell_type": "code", "execution_count": 17, "id": "33", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:36.968416Z", "iopub.status.busy": "2026-06-04T16:16:36.968274Z", "iopub.status.idle": "2026-06-04T16:16:37.457205Z", "shell.execute_reply": "2026-06-04T16:16:37.456434Z" } }, "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(name='si_bragg_pdf')" ] }, { "cell_type": "markdown", "id": "34", "metadata": {}, "source": [ "### Add Structure" ] }, { "cell_type": "code", "execution_count": 18, "id": "35", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:37.458837Z", "iopub.status.busy": "2026-06-04T16:16:37.458679Z", "iopub.status.idle": "2026-06-04T16:16:37.461353Z", "shell.execute_reply": "2026-06-04T16:16:37.460628Z" } }, "outputs": [], "source": [ "project.structures.add(structure)" ] }, { "cell_type": "markdown", "id": "36", "metadata": {}, "source": [ "### Add Experiments" ] }, { "cell_type": "code", "execution_count": 19, "id": "37", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:37.462701Z", "iopub.status.busy": "2026-06-04T16:16:37.462520Z", "iopub.status.idle": "2026-06-04T16:16:37.465140Z", "shell.execute_reply": "2026-06-04T16:16:37.464447Z" } }, "outputs": [], "source": [ "project.experiments.add(bragg_expt)\n", "project.experiments.add(pdf_expt)" ] }, { "cell_type": "markdown", "id": "38", "metadata": {}, "source": [ "## πŸš€ Perform Analysis\n", "\n", "This section shows the joint analysis process. The calculator is\n", "auto-resolved per experiment: CrysPy for Bragg, PDFfit for PDF.\n", "\n", "### Set Fit Mode and Weights" ] }, { "cell_type": "code", "execution_count": 20, "id": "39", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:37.466466Z", "iopub.status.busy": "2026-06-04T16:16:37.466317Z", "iopub.status.idle": "2026-06-04T16:16:37.471410Z", "shell.execute_reply": "2026-06-04T16:16:37.470761Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;36mFitting mode changed to\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "joint\n" ] } ], "source": [ "project.analysis.fitting_mode.type = 'joint'\n", "project.analysis.joint_fit.create(experiment_id='sepd', weight=0.7)\n", "project.analysis.joint_fit.create(experiment_id='nomad', weight=0.3)" ] }, { "cell_type": "markdown", "id": "40", "metadata": {}, "source": [ "### Display Structure" ] }, { "cell_type": "code", "execution_count": 21, "id": "41", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:37.472818Z", "iopub.status.busy": "2026-06-04T16:16:37.472644Z", "iopub.status.idle": "2026-06-04T16:16:37.975029Z", "shell.execute_reply": "2026-06-04T16:16:37.973449Z" } }, "outputs": [ { "data": { "application/javascript": [ "\n", " (function() {\n", " var isDark = false;\n", "\n", " // Check JupyterLab theme\n", " if (document.body.classList.contains('jp-mod-dark') || \n", " document.body.classList.contains('theme-dark') ||\n", " document.body.classList.contains('vscode-dark')) {\n", " isDark = true;\n", " }\n", "\n", " // Check theme attribute\n", " var themeAttr = document.body.getAttribute('data-jp-theme-name');\n", " if (themeAttr && themeAttr.includes('dark')) {\n", " isDark = true;\n", " }\n", "\n", " // Check computed background color\n", " var notebookEl = document.querySelector('.jp-Notebook') || \n", " document.querySelector('.notebook_app') ||\n", " document.body;\n", " if (notebookEl) {\n", " var bgColor = window.getComputedStyle(notebookEl).backgroundColor;\n", " var rgb = bgColor.match(/\\d+/g);\n", " if (rgb && rgb.length >= 3) {\n", " var brightness = (parseInt(rgb[0]) + parseInt(rgb[1]) + parseInt(rgb[2])) / 3;\n", " if (brightness < 128) {\n", " isDark = true;\n", " }\n", " }\n", " }\n", "\n", " // Store result\n", " if (typeof IPython !== 'undefined' && IPython.notebook && IPython.notebook.kernel) {\n", " IPython.notebook.kernel.execute('_jupyter_dark_detect_result = ' + isDark);\n", " }\n", " })();\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", " if (typeof IPython !== 'undefined' && IPython.notebook) {\n", " IPython.notebook.kernel.execute(\"_jupyter_dark_detect_result = \" + \n", " (document.body.classList.contains('theme-dark') || \n", " document.body.classList.contains('jp-mod-dark') ||\n", " (document.body.getAttribute('data-jp-theme-name') && \n", " document.body.getAttribute('data-jp-theme-name').includes('dark'))));\n", " }\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;36mStructure 🧩 \u001b[0m\u001b[32m'si'\u001b[0m\u001b[1;36m \u001b[0m\u001b[1;36m(\u001b[0m\u001b[1;36mAtom view type: \u001b[0m\u001b[32m'covalent'\u001b[0m\u001b[1;36m)\u001b[0m\n" ] }, { "data": { "text/html": [ "
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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": { "text/html": [ "
Loading plot…
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.display.pattern(expt_name='nomad')" ] }, { "cell_type": "markdown", "id": "45", "metadata": {}, "source": [ "### Set Free Parameters\n", "\n", "Shared structural parameters are refined against both datasets\n", "simultaneously." ] }, { "cell_type": "code", "execution_count": 24, "id": "46", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:42.073323Z", "iopub.status.busy": "2026-06-04T16:16:42.073145Z", "iopub.status.idle": "2026-06-04T16:16:42.076480Z", "shell.execute_reply": "2026-06-04T16:16:42.075881Z" } }, "outputs": [], "source": [ "structure.cell.length_a.free = True\n", "structure.atom_sites['Si'].adp_iso.free = True" ] }, { "cell_type": "markdown", "id": "47", "metadata": {}, "source": [ "Bragg experiment parameters." ] }, { "cell_type": "code", "execution_count": 25, "id": "48", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:42.078422Z", "iopub.status.busy": "2026-06-04T16:16:42.078253Z", "iopub.status.idle": "2026-06-04T16:16:42.082057Z", "shell.execute_reply": "2026-06-04T16:16:42.081219Z" } }, "outputs": [], "source": [ "bragg_expt.linked_phases['si'].scale.free = True\n", "bragg_expt.instrument.calib_d_to_tof_offset.free = True\n", "bragg_expt.peak.broad_gauss_sigma_0.free = True\n", "bragg_expt.peak.broad_gauss_sigma_1.free = True\n", "bragg_expt.peak.broad_gauss_sigma_2.free = True\n", "for point in bragg_expt.background:\n", " point.y.free = True" ] }, { "cell_type": "markdown", "id": "49", "metadata": {}, "source": [ "PDF experiment parameters." ] }, { "cell_type": "code", "execution_count": 26, "id": "50", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:42.083448Z", "iopub.status.busy": "2026-06-04T16:16:42.083289Z", "iopub.status.idle": "2026-06-04T16:16:42.087236Z", "shell.execute_reply": "2026-06-04T16:16:42.086523Z" } }, "outputs": [], "source": [ "pdf_expt.linked_phases['si'].scale.free = True\n", "pdf_expt.peak.damp_q.free = True\n", "pdf_expt.peak.broad_q.free = True\n", "pdf_expt.peak.sharp_delta_1.free = True\n", "pdf_expt.peak.sharp_delta_2.free = True" ] }, { "cell_type": "markdown", "id": "51", "metadata": {}, "source": [ "### Display Free Parameters" ] }, { "cell_type": "code", "execution_count": 27, "id": "52", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:42.088696Z", "iopub.status.busy": "2026-06-04T16:16:42.088511Z", "iopub.status.idle": "2026-06-04T16:16:42.147589Z", "shell.execute_reply": "2026-06-04T16:16:42.146706Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;36mFree parameters for both structures \u001b[0m\u001b[1;36m(\u001b[0m\u001b[1;36m🧩 data blocks\u001b[0m\u001b[1;36m)\u001b[0m\u001b[1;36m and experiments \u001b[0m\u001b[1;36m(\u001b[0m\u001b[1;36mπŸ”¬ data blocks\u001b[0m\u001b[1;36m)\u001b[0m\n" ] }, { "data": { "text/html": [ "
datablockcategoryentryparametervalueuncertaintyminmaxunits
1sicelllength_a5.42000-infinfΓ…
2siatom_siteSiadp_iso0.20000-infinfΓ…Β²
3sepdlinked_phasessiscale13.00000-infinf
4sepdpeakgauss_sigma_05.00000-infinfΞΌsΒ²
5sepdpeakgauss_sigma_145.00000-infinfΞΌs/Γ…
6sepdpeakgauss_sigma_21.00000-infinfΞΌsΒ²/Γ…Β²
7sepdinstrumentd_to_tof_offset-9.20000-infinfΞΌs
8sepdbackground0y200.00000-infinf
9sepdbackground5000y200.00000-infinf
10sepdbackground10000y200.00000-infinf
11sepdbackground15000y200.00000-infinf
12sepdbackground20000y200.00000-infinf
13sepdbackground25000y200.00000-infinf
14sepdbackground30000y200.00000-infinf
15nomadlinked_phasessiscale1.00000-infinf
16nomadpeakdamp_q0.02000-infinfÅ⁻¹
17nomadpeakbroad_q0.02000-infinfÅ⁻²
18nomadpeaksharp_delta_10.00100-infinfΓ…
19nomadpeaksharp_delta_24.00000-infinfΓ…Β²
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.display.parameters.free()" ] }, { "cell_type": "markdown", "id": "53", "metadata": {}, "source": [ "### Run Fitting" ] }, { "cell_type": "code", "execution_count": 28, "id": "54", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:16:42.149325Z", "iopub.status.busy": "2026-06-04T16:16:42.149111Z", "iopub.status.idle": "2026-06-04T16:18:39.066864Z", "shell.execute_reply": "2026-06-04T16:18:39.064988Z" } }, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function() {\n", " const button = document.getElementById('ed-fit-stop-5c8aed0ed998407099f1339def142beb-button');\n", " const status = document.getElementById('ed-fit-stop-5c8aed0ed998407099f1339def142beb-status');\n", " const kernelId = '';\n", " if (!button) {\n", " return;\n", " }\n", "\n", " function setStatus(text) {\n", " if (status) {\n", " status.textContent = text;\n", " }\n", " }\n", "\n", " function pageConfig() {\n", " const element = document.getElementById('jupyter-config-data');\n", " if (!element || !element.textContent) {\n", " return {};\n", " }\n", " try {\n", " return JSON.parse(element.textContent);\n", " } catch (error) {\n", " return {};\n", " }\n", " }\n", "\n", " function baseUrl(config) {\n", " const configured = config.baseUrl || config.base_url ||\n", " (window.Jupyter && Jupyter.notebook && Jupyter.notebook.base_url);\n", " if (configured) {\n", " return configured.endsWith('/') ? configured : configured + '/';\n", " }\n", " const markers = ['/lab/', '/notebooks/', '/tree/'];\n", " for (const marker of markers) {\n", " const index = window.location.pathname.indexOf(marker);\n", " if (index >= 0) {\n", " return window.location.pathname.slice(0, index + 1);\n", " }\n", " }\n", " return '/';\n", " }\n", "\n", " function token(config) {\n", " return config.token || new URLSearchParams(window.location.search).get('token') || '';\n", " }\n", "\n", " function cookie(name) {\n", " const prefix = name + '=';\n", " for (const part of document.cookie.split(';')) {\n", " const trimmed = part.trim();\n", " if (trimmed.startsWith(prefix)) {\n", " return decodeURIComponent(trimmed.slice(prefix.length));\n", " }\n", " }\n", " return '';\n", " }\n", "\n", " function notebookPath() {\n", " const decoded = decodeURIComponent(window.location.pathname);\n", " const markers = ['/lab/tree/', '/notebooks/', '/tree/'];\n", " for (const marker of markers) {\n", " const index = decoded.indexOf(marker);\n", " if (index >= 0) {\n", " return decoded.slice(index + marker.length);\n", " }\n", " }\n", " return '';\n", " }\n", "\n", " async function kernelFromSessions(config) {\n", " const url = new URL(baseUrl(config) + 'api/sessions', window.location.origin);\n", " const authToken = token(config);\n", " if (authToken) {\n", " url.searchParams.set('token', authToken);\n", " }\n", " const response = await fetch(url, {credentials: 'same-origin'});\n", " if (!response.ok) {\n", " return '';\n", " }\n", " const sessions = await response.json();\n", " const path = notebookPath();\n", " const session = sessions.find((item) => item.path === path) || sessions[0];\n", " return session && session.kernel ? session.kernel.id : '';\n", " }\n", "\n", " async function interruptKernel(config, resolvedKernelId) {\n", " const url = new URL(\n", " baseUrl(config) + 'api/kernels/' + resolvedKernelId + '/interrupt',\n", " window.location.origin\n", " );\n", " const authToken = token(config);\n", " if (authToken) {\n", " url.searchParams.set('token', authToken);\n", " }\n", " const xsrfToken = cookie('_xsrf');\n", " const headers = {};\n", " if (xsrfToken) {\n", " headers['X-XSRFToken'] = xsrfToken;\n", " }\n", " const response = await fetch(url, {\n", " method: 'POST',\n", " credentials: 'same-origin',\n", " headers: headers\n", " });\n", " return response.ok;\n", " }\n", "\n", " button.addEventListener('click', async function() {\n", " button.disabled = true;\n", " setStatus('Stopping...');\n", " const config = pageConfig();\n", " try {\n", " const resolvedKernelId = kernelId || await kernelFromSessions(config);\n", " if (!resolvedKernelId) {\n", " throw new Error('Could not resolve the current kernel id.');\n", " }\n", " const interrupted = await interruptKernel(config, resolvedKernelId);\n", " if (!interrupted) {\n", " throw new Error('Jupyter Server rejected the interrupt request.');\n", " }\n", " setStatus('Interrupt sent...');\n", " } catch (error) {\n", " button.disabled = false;\n", " setStatus('Use Kernel > Interrupt to stop this fit.');\n", " }\n", " });\n", "})();\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;36mUsing all experiments πŸ”¬ \u001b[0m\u001b[1;36m[\u001b[0m\u001b[32m'sepd'\u001b[0m\u001b[1;36m, \u001b[0m\u001b[32m'nomad'\u001b[0m\u001b[1;36m]\u001b[0m\u001b[1;36m for \u001b[0m\u001b[32m'joint'\u001b[0m\u001b[1;36m 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 progress:\n" ] }, { "data": { "text/html": [ "
iterationtime (s)χ²change / status
110.513378.13
2116.013378.13
32011.043378.11
42312.58844.3375.0% ↓
53217.77844.33
64122.87844.33
74323.99267.0168.4% ↓
85129.09267.01
96034.83267.02
106336.5759.9877.5% ↓
117141.6659.98
128046.7359.98
138348.9052.5812.3% ↓
149153.9552.58
1510059.0452.58
1610361.2251.891.3% ↓
1711166.2651.89
1812071.4151.89
1912976.9751.87
2013582.2851.87
2114187.9151.87
2214793.0051.87
2315398.7251.87
24159103.8251.87
25165109.5551.87
26170114.0251.87
" ], "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;36m51.87\u001b[0m at iteration \u001b[1;36m153\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "βœ… Fitting complete.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "βš™οΈ Settings used:\n" ] }, { "data": { "text/html": [ "
NameValueDescription
1max_iterations1000Maximum solver iterations.
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "πŸ“‹ Least-squares fit results:\n" ] }, { "data": { "text/html": [ "
MetricValue
1πŸ§ͺ Minimizerlmfit (leastsq)
2βœ… Overall statussuccess
3⏱️ Fitting time (seconds)114.02
4πŸ” Iterations167
5πŸ“ Goodness-of-fit (reduced χ²)51.87
6πŸ“ R-factor (Rf, %)10.50
7πŸ“ R-factor squared (RfΒ², %)9.32
8πŸ“ Weighted R-factor (wR, %)8.30
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "πŸ“ˆ Refined parameters:\n" ] }, { "data": { "text/html": [ "
datablockcategoryentryparameterunitsstartvalues.u.change
1sicelllength_aΓ…5.42005.43060.00000.20 % ↑
2siatom_siteSiadp_isoΓ…Β²0.20000.70410.0037252.05 % ↑
3sepdlinked_phasessiscale13.000016.05690.100823.51 % ↑
4sepdpeakgauss_sigma_0ΞΌsΒ²5.0000-1.96301.2495139.26 % ↓
5sepdpeakgauss_sigma_1ΞΌs/Γ…45.000050.39132.460111.98 % ↑
6sepdpeakgauss_sigma_2ΞΌsΒ²/Γ…Β²1.00000.21550.497478.45 % ↓
7sepdinstrumentd_to_tof_offsetΞΌs-9.2000-8.24240.095010.41 % ↓
8sepdbackground0y200.0000280.60643.229140.30 % ↑
9sepdbackground5000y200.0000148.72361.352525.64 % ↓
10sepdbackground10000y200.0000118.29951.419140.85 % ↓
11sepdbackground15000y200.0000135.85292.703632.07 % ↓
12sepdbackground20000y200.0000132.68494.736933.66 % ↓
13sepdbackground25000y200.0000174.98509.520112.51 % ↓
14sepdbackground30000y200.0000180.582119.36489.71 % ↓
15nomadlinked_phasessiscale1.00001.27130.001027.13 % ↑
16nomadpeakdamp_qÅ⁻¹0.02000.02500.000125.16 % ↑
17nomadpeakbroad_qÅ⁻²0.02000.01880.00025.92 % ↓
18nomadpeaksharp_delta_1Γ…0.00102.43990.0382243887.89 % ↑
19nomadpeaksharp_delta_2Γ…Β²4.0000-1.52820.0898138.21 % ↓
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
β€’ start = parameter value before refinement
β€’ value = refined value from least-squares minimization
β€’ s.u. = standard uncertainty (one sigma), from the covariance matrix
β€’ change = relative change from start, in %; ↑ = increase, ↓ = decrease
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
⚠️ Red s.u.: exceeds the refined value (consider adding constraints)
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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.analysis.fit()\n", "project.display.fit.results()\n", "project.display.fit.correlations()" ] }, { "cell_type": "markdown", "id": "55", "metadata": {}, "source": [ "### Display Pattern (After Fit)" ] }, { "cell_type": "code", "execution_count": 29, "id": "56", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:18:39.068872Z", "iopub.status.busy": "2026-06-04T16:18:39.068667Z", "iopub.status.idle": "2026-06-04T16:18:40.067586Z", "shell.execute_reply": "2026-06-04T16:18:40.066684Z" } }, "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 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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') 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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.display.pattern(expt_name='sepd')" ] }, { "cell_type": "code", "execution_count": 30, "id": "57", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:18:40.082452Z", "iopub.status.busy": "2026-06-04T16:18:40.082197Z", "iopub.status.idle": "2026-06-04T16:18:42.185523Z", "shell.execute_reply": "2026-06-04T16:18:42.184654Z" } }, "outputs": [ { "data": { "application/javascript": [ "\n", " (function() {\n", " var isDark = false;\n", "\n", " // Check JupyterLab theme\n", " if (document.body.classList.contains('jp-mod-dark') || \n", " document.body.classList.contains('theme-dark') ||\n", " document.body.classList.contains('vscode-dark')) {\n", " isDark = true;\n", " }\n", "\n", " // Check theme attribute\n", " var themeAttr = document.body.getAttribute('data-jp-theme-name');\n", " if (themeAttr && themeAttr.includes('dark')) {\n", " isDark = true;\n", " }\n", "\n", " // Check computed background color\n", " var notebookEl = document.querySelector('.jp-Notebook') || \n", " document.querySelector('.notebook_app') ||\n", " document.body;\n", " if (notebookEl) {\n", " var bgColor = window.getComputedStyle(notebookEl).backgroundColor;\n", " var rgb = bgColor.match(/\\d+/g);\n", " if (rgb && rgb.length >= 3) {\n", " var brightness = (parseInt(rgb[0]) + parseInt(rgb[1]) + parseInt(rgb[2])) / 3;\n", " if (brightness < 128) {\n", " isDark = true;\n", " }\n", " }\n", " }\n", "\n", " // Store result\n", " if (typeof IPython !== 'undefined' && IPython.notebook && IPython.notebook.kernel) {\n", " IPython.notebook.kernel.execute('_jupyter_dark_detect_result = ' + isDark);\n", " }\n", " })();\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", " if (typeof IPython !== 'undefined' && IPython.notebook) {\n", " IPython.notebook.kernel.execute(\"_jupyter_dark_detect_result = \" + \n", " 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||\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 = 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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": { "text/html": [ "
Loading plot…
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.display.pattern(expt_name='nomad')" ] }, { "cell_type": "markdown", "id": "58", "metadata": {}, "source": [ "## πŸ’Ύ Save Project" ] }, { "cell_type": "code", "execution_count": 31, "id": "59", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:18:42.187830Z", "iopub.status.busy": "2026-06-04T16:18:42.187665Z", "iopub.status.idle": "2026-06-04T16:18:44.640581Z", "shell.execute_reply": "2026-06-04T16:18:44.639832Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;36mSaving project πŸ“¦ \u001b[0m\u001b[32m'si_bragg_pdf'\u001b[0m\u001b[1;36m to \u001b[0m\u001b[32m'../../../projects/ed_16_si_bragg_pdf'\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”œβ”€β”€ πŸ“„ project.cif\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”œβ”€β”€ πŸ“ structures/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”‚ └── πŸ“„ si.cif\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”œβ”€β”€ πŸ“ experiments/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”‚ └── πŸ“„ sepd.cif\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”‚ └── πŸ“„ nomad.cif\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”œβ”€β”€ πŸ“ analysis/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”‚ └── πŸ“„ analysis.cif\n" ] }, { "data": { "application/javascript": [ "\n", " (function() {\n", " var isDark = false;\n", "\n", " // Check JupyterLab theme\n", " if (document.body.classList.contains('jp-mod-dark') || \n", " document.body.classList.contains('theme-dark') ||\n", " document.body.classList.contains('vscode-dark')) {\n", " isDark = true;\n", " }\n", "\n", " // Check theme attribute\n", " var themeAttr = document.body.getAttribute('data-jp-theme-name');\n", " if (themeAttr && themeAttr.includes('dark')) {\n", " isDark = true;\n", 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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“ reports/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " └── πŸ“„ si_bragg_pdf.html\n" ] } ], "source": [ "project.save_as(dir_path='projects/ed_16_si_bragg_pdf')" ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "-all", "main_language": "python", "notebook_metadata_filter": "-all" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.14.5" } }, "nbformat": 4, "nbformat_minor": 5 }