{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "0", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:25:40.538445Z", "iopub.status.busy": "2026-06-04T16:25:40.538234Z", "iopub.status.idle": "2026-06-04T16:25:40.542628Z", "shell.execute_reply": "2026-06-04T16:25:40.541848Z" }, "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": [ "# Bayesian Analysis Display (`bumps-dream`): LBCO, HRPT\n", "\n", "This tutorial shows how to reopen the Bayesian project created in\n", "`ed-21.py` and inspect the saved fit results without rerunning DREAM.\n", "\n", "The project already contains posterior samples together with cached\n", "posterior density, pair, and predictive data, so the plots below are\n", "restored directly from disk." ] }, { "cell_type": "markdown", "id": "2", "metadata": {}, "source": [ "## πŸ› οΈ Import Library" ] }, { "cell_type": "code", "execution_count": 2, "id": "3", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:25:40.544228Z", "iopub.status.busy": "2026-06-04T16:25:40.543926Z", "iopub.status.idle": "2026-06-04T16:25:43.149664Z", "shell.execute_reply": "2026-06-04T16:25:43.148885Z" } }, "outputs": [], "source": [ "import easydiffraction as ed" ] }, { "cell_type": "markdown", "id": "4", "metadata": {}, "source": [ "## πŸ“‚ Load Project" ] }, { "cell_type": "markdown", "id": "5", "metadata": {}, "source": [ "### Download Project\n", "\n", "The returned path points directly to the saved project directory with\n", "the completed Bayesian fit and persisted posterior samples and plot\n", "caches." ] }, { "cell_type": "code", "execution_count": 3, "id": "6", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:25:43.152065Z", "iopub.status.busy": "2026-06-04T16:25:43.151790Z", "iopub.status.idle": "2026-06-04T16:25:43.458120Z", "shell.execute_reply": "2026-06-04T16:25:43.457307Z" } }, "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;36m39\u001b[0m: Bayesian Analysis \u001b[1m(\u001b[0mbumps-dream\u001b[1m)\u001b[0m: LBCO, HRPT\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "βœ… Data #\u001b[1;36m39\u001b[0m downloaded and extracted to \u001b[32m'../../../projects/ed-39/lbco_hrpt_bumps-dream'\u001b[0m\n" ] } ], "source": [ "project_dir = ed.download_data(id=39, destination='projects')" ] }, { "cell_type": "markdown", "id": "7", "metadata": {}, "source": [ "### Load Project\n", "\n", "Loading restores the persisted fit state, posterior samples, and plot\n", "caches. No new fit is launched in this tutorial." ] }, { "cell_type": "code", "execution_count": 4, "id": "8", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:25:43.459979Z", "iopub.status.busy": "2026-06-04T16:25:43.459651Z", "iopub.status.idle": "2026-06-04T16:25:45.193630Z", "shell.execute_reply": "2026-06-04T16:25:45.192821Z" } }, "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": [ "⚠️ Switching minimizer type removes these settings: \n", " β€’ max_iterations \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "⚠️ Switching minimizer type adds these settings with defaults: \n", " β€’ burn_in_steps=600 \n", " β€’ initialization_method='latin_hypercube' \n", " β€’ parallel_workers=0 \n", " β€’ population_size=4 \n", " β€’ random_seed=None \n", " β€’ sampling_steps=3000 \n", " β€’ thinning_interval=1 \n" ] } ], "source": [ "project = ed.Project.load(project_dir)" ] }, { "cell_type": "markdown", "id": "9", "metadata": {}, "source": [ "## πŸ“Š Inspect Results" ] }, { "cell_type": "markdown", "id": "10", "metadata": {}, "source": [ "### Display Structure\n", "\n", "Render the La0.5Ba0.5CoO3 structure restored from the saved project." ] }, { "cell_type": "code", "execution_count": 5, "id": "11", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:25:45.195798Z", "iopub.status.busy": "2026-06-04T16:25:45.195612Z", "iopub.status.idle": "2026-06-04T16:25:45.491078Z", "shell.execute_reply": "2026-06-04T16:25:45.488503Z" } }, "outputs": [ { "data": { "application/javascript": [ "\n", " (function() {\n", " var isDark = false;\n", "\n", " // Check JupyterLab theme\n", " if (document.body.classList.contains('jp-mod-dark') || \n", " document.body.classList.contains('theme-dark') ||\n", " document.body.classList.contains('vscode-dark')) {\n", " isDark = true;\n", " }\n", "\n", " // Check theme attribute\n", " var themeAttr = document.body.getAttribute('data-jp-theme-name');\n", " if (themeAttr && themeAttr.includes('dark')) {\n", " isDark = true;\n", " }\n", "\n", " // Check computed background color\n", " var notebookEl = document.querySelector('.jp-Notebook') || \n", " document.querySelector('.notebook_app') ||\n", " document.body;\n", " if (notebookEl) {\n", " var bgColor = window.getComputedStyle(notebookEl).backgroundColor;\n", " var rgb = bgColor.match(/\\d+/g);\n", " if (rgb && rgb.length >= 3) {\n", " var brightness = (parseInt(rgb[0]) + parseInt(rgb[1]) + parseInt(rgb[2])) / 3;\n", " if (brightness < 128) {\n", " isDark = true;\n", " }\n", " }\n", " }\n", "\n", " // Store result\n", " if (typeof IPython !== 'undefined' && IPython.notebook && IPython.notebook.kernel) {\n", " IPython.notebook.kernel.execute('_jupyter_dark_detect_result = ' + isDark);\n", " }\n", " })();\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", " if (typeof IPython !== 'undefined' && IPython.notebook) {\n", " IPython.notebook.kernel.execute(\"_jupyter_dark_detect_result = \" + \n", " (document.body.classList.contains('theme-dark') || \n", " document.body.classList.contains('jp-mod-dark') ||\n", " (document.body.getAttribute('data-jp-theme-name') && \n", " document.body.getAttribute('data-jp-theme-name').includes('dark'))));\n", " }\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;36mStructure 🧩 \u001b[0m\u001b[32m'lbco'\u001b[0m\u001b[1;36m \u001b[0m\u001b[1;36m(\u001b[0m\u001b[1;36mAtom view type: \u001b[0m\u001b[32m'covalent'\u001b[0m\u001b[1;36m)\u001b[0m\n" ] }, { "data": { "text/html": [ "
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NameValueDescription
1sampling_steps10000Total sampler iterations per chain.
2burn_in_steps2000Sampler iterations discarded as warm-up.
3thinning_interval1Sampler thinning interval.
4population_size4Number of chains or walkers.
5parallel_workers0Worker count; 0 uses all available CPUs.
6initialization_methodlatin_hypercubeSampler initialization method.
7random_seedNoneRandom seed; None uses a system-derived seed.
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "πŸ“‹ Bayesian fit results:\n" ] }, { "data": { "text/html": [ "
MetricValue
1πŸ§ͺ Samplerdream
2βœ… Overall statussuccess
3πŸ’¬ Engine messageDREAM sampling completed
4⏱️ Fitting time (seconds)1637.83
5πŸ“ Goodness-of-fit (reduced χ²)1.29
6πŸ“ R-factor (Rf, %)5.65
7πŸ“ R-factor squared (RfΒ², %)4.91
8πŸ“ Weighted R-factor (wR, %)4.08
9πŸ“‰ Best log-posterior-1157.00
10πŸ“Š Convergence statuspassed
11πŸ“Š Max r-hat1.004
12πŸ“Š Min ess bulk7391.625
13πŸ“Š Draws per chain10000
14πŸ“Š Chains20
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "πŸ“ˆ Committed parameters:\n" ] }, { "data": { "text/html": [ "
datablockcategoryentryparameterunitsstartvalues.u.change
1lbcocelllength_aΓ…3.89133.89130.00010.00 % ↓
2hrptlinked_phaseslbcoscale9.13299.13600.02910.03 % ↑
3hrptpeakbroad_gauss_udegΒ²0.08170.08110.00660.70 % ↓
4hrptpeakbroad_gauss_vdegΒ²-0.1169-0.11650.00480.35 % ↓
5hrptinstrumenttwotheta_offsetdeg0.63060.63040.00170.03 % ↓
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
β€’ start = parameter value before sampling
β€’ value = estimate written back to the project (best posterior sample)
β€’ s.u. = standard uncertainty (one sigma), posterior standard deviation
β€’ change = relative change from start, in %; ↑ = increase, ↓ = decrease
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "πŸ“Š Posterior distribution:\n" ] }, { "data": { "text/html": [ "
datablockcategoryentryparameterunitsmedian95% CIr-hatess bulk
1lbcocelllength_aΓ…3.8913[3.8911, 3.8915]1.0047458.0
2hrptlinked_phaseslbcoscale9.1329[9.0759, 9.1903]1.0038472.0
3hrptpeakbroad_gauss_udegΒ²0.0815[0.0687, 0.0945]1.0047537.0
4hrptpeakbroad_gauss_vdegΒ²-0.1168[-0.1261, -0.1075]1.0047582.8
5hrptinstrumenttwotheta_offsetdeg0.6303[0.6269, 0.6336]1.0047391.6
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
β€’ median = 50th percentile of the marginal posterior
β€’ 95% CI = 95% credible interval (2.5%-97.5%, asymmetric)
β€’ r-hat = Gelman-Rubin diagnostic (good convergence: r-hat <= 1.01)
β€’ ess bulk = bulk effective sample size (typically >= 400)
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.display.fit.results()" ] }, { "cell_type": "markdown", "id": "14", "metadata": {}, "source": [ "### Display Correlations\n", "\n", "The correlation matrix is restored from the saved project state." ] }, { "cell_type": "code", "execution_count": 7, "id": "15", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:25:46.004441Z", "iopub.status.busy": "2026-06-04T16:25:46.004270Z", "iopub.status.idle": "2026-06-04T16:25:46.937126Z", "shell.execute_reply": "2026-06-04T16:25:46.936530Z" } }, "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" }, { "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": [ "\n", "\n", "
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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.display.fit.correlations()" ] }, { "cell_type": "markdown", "id": "16", "metadata": {}, "source": [ "### Display Posterior Densities\n", "\n", "The pair plot and one-dimensional posterior distributions now load\n", "from the persisted caches generated when the Bayesian fit was saved." ] }, { "cell_type": "code", "execution_count": 8, "id": "17", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:25:46.940714Z", "iopub.status.busy": "2026-06-04T16:25:46.939176Z", "iopub.status.idle": "2026-06-04T16:25:47.981922Z", "shell.execute_reply": "2026-06-04T16:25:47.981377Z" } }, "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", " 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Loading plot…
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Loading plot…
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Loading plot…
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(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 && 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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": { 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Loading plot…
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Loading plot…
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.display.posterior.distribution()" ] }, { "cell_type": "markdown", "id": "19", "metadata": {}, "source": [ "### Display Posterior Predictive\n", "\n", "The posterior predictive view reuses the cached predictive summary\n", "stored in the project rather than recalculating it on first display.\n", "It overlays the 95% credible interval propagated from the posterior\n", "samples." ] }, { "cell_type": "code", "execution_count": 10, "id": "20", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:25:58.612910Z", "iopub.status.busy": "2026-06-04T16:25:58.612440Z", "iopub.status.idle": "2026-06-04T16:25:59.600363Z", "shell.execute_reply": "2026-06-04T16:25:59.599784Z" } }, "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", " 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Loading plot…
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.display.posterior.predictive(expt_name='hrpt')" ] }, { "cell_type": "markdown", "id": "21", "metadata": {}, "source": [ "A zoomed view is useful for checking the propagated uncertainty in a\n", "narrow region of the diffraction pattern." ] }, { "cell_type": "code", "execution_count": 11, "id": "22", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:25:59.604232Z", "iopub.status.busy": "2026-06-04T16:25:59.604008Z", "iopub.status.idle": "2026-06-04T16:26:00.544093Z", "shell.execute_reply": "2026-06-04T16:26:00.543521Z" } }, "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", 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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.posterior.predictive(expt_name='hrpt', x_min=92, x_max=93)" ] }, { "cell_type": "markdown", "id": "23", "metadata": {}, "source": [ "## πŸ’Ύ Save Project" ] }, { "cell_type": "code", "execution_count": 12, "id": "24", "metadata": { "execution": { "iopub.execute_input": "2026-06-04T16:26:00.546193Z", "iopub.status.busy": "2026-06-04T16:26:00.545994Z", "iopub.status.idle": "2026-06-04T16:26:01.690648Z", "shell.execute_reply": "2026-06-04T16:26:01.689999Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;36mSaving project πŸ“¦ \u001b[0m\u001b[32m'lbco_hrpt_bumps-dream'\u001b[0m\u001b[1;36m to \u001b[0m\u001b[32m'../../../projects/ed_24_lbco_hrpt_bumps_dream'\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”œβ”€β”€ πŸ“„ project.cif\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”œβ”€β”€ πŸ“ structures/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”‚ └── πŸ“„ lbco.cif\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”œβ”€β”€ πŸ“ experiments/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”‚ └── πŸ“„ hrpt.cif\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”œβ”€β”€ πŸ“ analysis/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”‚ β”œβ”€β”€ πŸ“„ analysis.cif\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "β”‚ └── πŸ“„ results.h5\n" ] }, { "data": { "application/javascript": [ "\n", " (function() {\n", " var isDark = false;\n", "\n", " // Check JupyterLab theme\n", " if (document.body.classList.contains('jp-mod-dark') || \n", " document.body.classList.contains('theme-dark') ||\n", " document.body.classList.contains('vscode-dark')) {\n", " isDark = true;\n", " }\n", "\n", " // Check theme attribute\n", " var themeAttr = document.body.getAttribute('data-jp-theme-name');\n", " if (themeAttr && themeAttr.includes('dark')) {\n", " isDark = true;\n", " }\n", "\n", " // Check computed background color\n", " var notebookEl = document.querySelector('.jp-Notebook') || \n", " document.querySelector('.notebook_app') ||\n", " document.body;\n", " if (notebookEl) {\n", " var bgColor = window.getComputedStyle(notebookEl).backgroundColor;\n", " var rgb = bgColor.match(/\\d+/g);\n", " if (rgb && rgb.length >= 3) {\n", " var brightness = (parseInt(rgb[0]) + parseInt(rgb[1]) + parseInt(rgb[2])) / 3;\n", " if (brightness < 128) {\n", " isDark = true;\n", " }\n", " }\n", " }\n", "\n", " // Store result\n", " if (typeof IPython !== 'undefined' && IPython.notebook && IPython.notebook.kernel) {\n", " IPython.notebook.kernel.execute('_jupyter_dark_detect_result = ' + isDark);\n", " }\n", " })();\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", " if (typeof IPython !== 'undefined' && IPython.notebook) {\n", " IPython.notebook.kernel.execute(\"_jupyter_dark_detect_result = \" + \n", " (document.body.classList.contains('theme-dark') || \n", " document.body.classList.contains('jp-mod-dark') ||\n", " (document.body.getAttribute('data-jp-theme-name') && \n", " document.body.getAttribute('data-jp-theme-name').includes('dark'))));\n", " }\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", " (function() {\n", " var isDark = false;\n", "\n", " // Check JupyterLab theme\n", " if (document.body.classList.contains('jp-mod-dark') || \n", " document.body.classList.contains('theme-dark') ||\n", " document.body.classList.contains('vscode-dark')) {\n", " isDark = true;\n", " }\n", "\n", " // Check theme attribute\n", " var themeAttr = document.body.getAttribute('data-jp-theme-name');\n", " if (themeAttr && themeAttr.includes('dark')) {\n", " isDark = true;\n", " }\n", "\n", " // Check computed background color\n", " var notebookEl = document.querySelector('.jp-Notebook') || \n", " document.querySelector('.notebook_app') ||\n", " document.body;\n", " if (notebookEl) {\n", " var bgColor = window.getComputedStyle(notebookEl).backgroundColor;\n", " var rgb = bgColor.match(/\\d+/g);\n", " if (rgb && rgb.length >= 3) {\n", " var brightness = (parseInt(rgb[0]) + parseInt(rgb[1]) + parseInt(rgb[2])) / 3;\n", " if (brightness < 128) {\n", " isDark = true;\n", " }\n", " }\n", " }\n", "\n", " // Store result\n", " if (typeof IPython !== 'undefined' && IPython.notebook && IPython.notebook.kernel) {\n", " IPython.notebook.kernel.execute('_jupyter_dark_detect_result = ' + isDark);\n", " }\n", " })();\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", " if (typeof IPython !== 'undefined' && IPython.notebook) {\n", " IPython.notebook.kernel.execute(\"_jupyter_dark_detect_result = \" + \n", " (document.body.classList.contains('theme-dark') || \n", " document.body.classList.contains('jp-mod-dark') ||\n", " (document.body.getAttribute('data-jp-theme-name') && \n", " document.body.getAttribute('data-jp-theme-name').includes('dark'))));\n", " }\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", " (function() {\n", " var isDark = false;\n", "\n", " // Check JupyterLab theme\n", " if (document.body.classList.contains('jp-mod-dark') || \n", " document.body.classList.contains('theme-dark') ||\n", " document.body.classList.contains('vscode-dark')) {\n", " isDark = true;\n", " }\n", "\n", " // Check theme attribute\n", " var themeAttr = document.body.getAttribute('data-jp-theme-name');\n", " if (themeAttr && themeAttr.includes('dark')) {\n", " isDark = true;\n", " }\n", "\n", " // Check computed background color\n", " var notebookEl = document.querySelector('.jp-Notebook') || \n", " document.querySelector('.notebook_app') ||\n", " document.body;\n", " if (notebookEl) {\n", " var bgColor = window.getComputedStyle(notebookEl).backgroundColor;\n", " var rgb = bgColor.match(/\\d+/g);\n", " if (rgb && rgb.length >= 3) {\n", " var brightness = (parseInt(rgb[0]) + parseInt(rgb[1]) + parseInt(rgb[2])) / 3;\n", " if (brightness < 128) {\n", " isDark = true;\n", " }\n", " }\n", " }\n", "\n", " // Store result\n", " if (typeof IPython !== 'undefined' && IPython.notebook && IPython.notebook.kernel) {\n", " IPython.notebook.kernel.execute('_jupyter_dark_detect_result = ' + isDark);\n", " }\n", " })();\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", " if (typeof IPython !== 'undefined' && IPython.notebook) {\n", " IPython.notebook.kernel.execute(\"_jupyter_dark_detect_result = \" + \n", " (document.body.classList.contains('theme-dark') || \n", " document.body.classList.contains('jp-mod-dark') ||\n", " (document.body.getAttribute('data-jp-theme-name') && \n", " document.body.getAttribute('data-jp-theme-name').includes('dark'))));\n", " }\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "└── πŸ“ reports/\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " └── πŸ“„ lbco_hrpt_bumps-dream.html\n" ] } ], "source": [ "project.save_as(dir_path='projects/ed_24_lbco_hrpt_bumps_dream')" ] } ], "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 }