{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "0", "metadata": { "execution": { "iopub.execute_input": "2026-06-30T22:33:50.237350Z", "iopub.status.busy": "2026-06-30T22:33:50.237134Z", "iopub.status.idle": "2026-06-30T22:33:50.241490Z", "shell.execute_reply": "2026-06-30T22:33:50.240783Z" }, "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==0.19.1" ] }, { "cell_type": "markdown", "id": "1", "metadata": {}, "source": [ "# LBCO — powder neutron CW — preferred orientation\n", "\n", "Verifies the March-Dollase preferred-orientation correction against a\n", "FullProf reference with orientation along [0 0 1]. The page refines\n", "the scale because cryspy's Modified March correction is not\n", "volume-normalised, so it differs from FullProf by a constant per-phase\n", "scale factor (the orientation coefficient itself transfers)." ] }, { "cell_type": "code", "execution_count": 2, "id": "2", "metadata": { "execution": { "iopub.execute_input": "2026-06-30T22:33:50.243410Z", "iopub.status.busy": "2026-06-30T22:33:50.243242Z", "iopub.status.idle": "2026-06-30T22:33:53.091905Z", "shell.execute_reply": "2026-06-30T22:33:53.091046Z" } }, "outputs": [], "source": [ "import easydiffraction as edi\n", "from easydiffraction import ExperimentFactory\n", "from easydiffraction import StructureFactory\n", "from easydiffraction.analysis import verification as verify" ] }, { "cell_type": "markdown", "id": "3", "metadata": {}, "source": [ "## Build the project" ] }, { "cell_type": "code", "execution_count": 3, "id": "4", "metadata": { "execution": { "iopub.execute_input": "2026-06-30T22:33:53.093686Z", "iopub.status.busy": "2026-06-30T22:33:53.093406Z", "iopub.status.idle": "2026-06-30T22:33:53.310245Z", "shell.execute_reply": "2026-06-30T22:33:53.309239Z" } }, "outputs": [], "source": [ "project = edi.Project()" ] }, { "cell_type": "markdown", "id": "5", "metadata": {}, "source": [ "## Define the structure" ] }, { "cell_type": "code", "execution_count": 4, "id": "6", "metadata": { "execution": { "iopub.execute_input": "2026-06-30T22:33:53.311915Z", "iopub.status.busy": "2026-06-30T22:33:53.311758Z", "iopub.status.idle": "2026-06-30T22:33:53.320084Z", "shell.execute_reply": "2026-06-30T22:33:53.319273Z" } }, "outputs": [], "source": [ "structure = StructureFactory.from_scratch(name='lbco')\n", "\n", "structure.space_group.name_h_m = 'P m -3 m' # FullProf Space group symbol\n", "\n", "structure.cell.length_a = 3.890790 # FullProf a\n", "\n", "structure.atom_sites.create(\n", " id='La', # FullProf Atom\n", " type_symbol='La', # FullProf Typ\n", " fract_x=0.0, # FullProf X\n", " fract_y=0.0, # FullProf Y\n", " fract_z=0.0, # FullProf Z\n", " occupancy=0.5, # FullProf Occ\n", " adp_type='Biso', # FullProf Biso\n", " adp_iso=0.57511, # FullProf Biso\n", ")\n", "structure.atom_sites.create(\n", " id='Ba', # FullProf Atom\n", " type_symbol='Ba', # FullProf Typ\n", " fract_x=0.0, # FullProf X\n", " fract_y=0.0, # FullProf Y\n", " fract_z=0.0, # FullProf Z\n", " occupancy=0.5, # FullProf Occ\n", " adp_type='Biso', # FullProf Biso\n", " adp_iso=0.57511, # FullProf Biso\n", ")\n", "structure.atom_sites.create(\n", " id='Co', # FullProf Atom\n", " type_symbol='Co', # FullProf Typ\n", " fract_x=0.5, # FullProf X\n", " fract_y=0.5, # FullProf Y\n", " fract_z=0.5, # FullProf Z\n", " occupancy=1.0, # FullProf Occ\n", " adp_type='Biso', # FullProf Biso\n", " adp_iso=0.26023, # FullProf Biso\n", ")\n", "structure.atom_sites.create(\n", " id='O', # FullProf Atom\n", " type_symbol='O', # FullProf Typ\n", " fract_x=0.0, # FullProf X\n", " fract_y=0.5, # FullProf Y\n", " fract_z=0.5, # FullProf Z\n", " occupancy=0.97856, # FullProf Occ\n", " adp_type='Biso', # FullProf Biso\n", " adp_iso=1.36662, # FullProf Biso\n", ")\n", "\n", "project.structures.add(structure)" ] }, { "cell_type": "markdown", "id": "7", "metadata": {}, "source": [ "## Load the FullProf reference" ] }, { "cell_type": "code", "execution_count": 5, "id": "8", "metadata": { "execution": { "iopub.execute_input": "2026-06-30T22:33:53.321968Z", "iopub.status.busy": "2026-06-30T22:33:53.321822Z", "iopub.status.idle": "2026-06-30T22:33:53.330161Z", "shell.execute_reply": "2026-06-30T22:33:53.329388Z" } }, "outputs": [], "source": [ "FULLPROF_PROJECT_DIR = 'pd-neut-cwl_lbco_preferred-orientation'\n", "FULLPROF_PRF_FILE = 'lbco.prf'\n", "FULLPROF_SUM_FILE = 'lbco.sum'\n", "FULLPROF_BAC_FILE = 'lbco.bac'\n", "FULLPROF_LABEL = verify.fullprof_label(FULLPROF_PROJECT_DIR, FULLPROF_SUM_FILE)\n", "\n", "FULLPROF_ZERO = 0.62040 # FullProf Zero\n", "FULLPROF_SCALE = 9.405870 # FullProf Scale\n", "FULLPROF_WAVELENGTH = 1.494000 # FullProf Lambda\n", "FULLPROF_U = 0.081547 # FullProf U\n", "FULLPROF_V = -0.115345 # FullProf V\n", "FULLPROF_W = 0.121125 # FullProf W\n", "FULLPROF_X = 0.0 # FullProf X\n", "FULLPROF_Y = 0.083038 # FullProf Y\n", "FULLPROF_WDT = 30.0 # FullProf Wdt\n", "FULLPROF_PREF_1 = 1.2 # FullProf Pref1\n", "FULLPROF_PREF_2 = 0.3 # FullProf Pref2\n", "FULLPROF_PR_1 = 0 # FullProf Pr1\n", "FULLPROF_PR_2 = 0 # FullProf Pr2\n", "FULLPROF_PR_3 = 1 # FullProf Pr3\n", "\n", "x, calc_fullprof = verify.load_fullprof_calc_profile(\n", " FULLPROF_PROJECT_DIR,\n", " FULLPROF_PRF_FILE,\n", " FULLPROF_BAC_FILE,\n", " FULLPROF_ZERO,\n", ")" ] }, { "cell_type": "markdown", "id": "9", "metadata": {}, "source": [ "## Create the experiment" ] }, { "cell_type": "code", "execution_count": 6, "id": "10", "metadata": { "execution": { "iopub.execute_input": "2026-06-30T22:33:53.331812Z", "iopub.status.busy": "2026-06-30T22:33:53.331600Z", "iopub.status.idle": "2026-06-30T22:33:53.827605Z", "shell.execute_reply": "2026-06-30T22:33:53.826725Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;36mPeak profile type for experiment \u001b[0m\u001b[32m'lbco'\u001b[0m\u001b[1;36m changed to\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "pseudo-voigt\n" ] } ], "source": [ "experiment = ExperimentFactory.from_scratch(\n", " name='lbco',\n", " sample_form='powder',\n", " beam_mode='constant wavelength',\n", " radiation_probe='neutron',\n", " scattering_type='bragg',\n", ")\n", "verify.set_reference_as_measured(experiment, x, calc_fullprof)\n", "\n", "experiment.linked_structures.create(structure_id='lbco', scale=FULLPROF_SCALE)\n", "\n", "experiment.instrument.setup_wavelength = FULLPROF_WAVELENGTH\n", "experiment.instrument.calib_twotheta_offset = FULLPROF_ZERO\n", "\n", "experiment.peak.type = 'pseudo-voigt'\n", "experiment.peak.broad_gauss_u = FULLPROF_U\n", "experiment.peak.broad_gauss_v = FULLPROF_V\n", "experiment.peak.broad_gauss_w = FULLPROF_W\n", "experiment.peak.broad_lorentz_x = FULLPROF_X\n", "experiment.peak.broad_lorentz_y = FULLPROF_Y\n", "\n", "experiment.preferred_orientation.create(\n", " structure_id='lbco',\n", " march_r=FULLPROF_PREF_1,\n", " march_random_fract=FULLPROF_PREF_2,\n", " index_h=FULLPROF_PR_1,\n", " index_k=FULLPROF_PR_2,\n", " index_l=FULLPROF_PR_3,\n", ")\n", "\n", "experiment.peak.cutoff_fwhm = FULLPROF_WDT\n", "\n", "project.experiments.add(experiment)" ] }, { "cell_type": "markdown", "id": "11", "metadata": {}, "source": [ "## edi-cryspy VS FullProf" ] }, { "cell_type": "code", "execution_count": 7, "id": "12", "metadata": { "execution": { "iopub.execute_input": "2026-06-30T22:33:53.829249Z", "iopub.status.busy": "2026-06-30T22:33:53.829057Z", "iopub.status.idle": "2026-06-30T22:33:54.684361Z", "shell.execute_reply": "2026-06-30T22:33:54.683450Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;36mCalculator for experiment \u001b[0m\u001b[32m'lbco'\u001b[0m\u001b[1;36m already set to\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "cryspy\n" ] }, { "data": { "text/html": [ "
Loading plot…
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "experiment.calculator.type = 'cryspy'\n", "\n", "project.analysis.calculate()\n", "calc_ed_cryspy = experiment.data.intensity_calc\n", "LABEL_ED_CRYSPY = verify.engine_label('cryspy')\n", "\n", "project.display.pattern_comparison(\n", " 'lbco',\n", " reference=calc_fullprof,\n", " candidate=calc_ed_cryspy,\n", " reference_label=FULLPROF_LABEL,\n", " candidate_label=LABEL_ED_CRYSPY,\n", ")" ] }, { "cell_type": "markdown", "id": "13", "metadata": {}, "source": [ "## Fit edi-cryspy to FullProf\n", "\n", "cryspy uses the reciprocal March coefficient (g₁ = 1/r, converted\n", "automatically) and does not volume-normalise the correction, so it\n", "differs from FullProf by a constant per-phase scale factor. Refining\n", "the scale absorbs that factor and recovers the FullProf profile." ] }, { "cell_type": "code", "execution_count": 8, "id": "14", "metadata": { "execution": { "iopub.execute_input": "2026-06-30T22:33:54.687088Z", "iopub.status.busy": "2026-06-30T22:33:54.686868Z", "iopub.status.idle": "2026-06-30T22:33:57.580409Z", "shell.execute_reply": "2026-06-30T22:33:57.579492Z" } }, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function() {\n", " const button = document.getElementById('ed-fit-stop-b9970041122347139da05cb83e40fbf6-button');\n", " const status = document.getElementById('ed-fit-stop-b9970041122347139da05cb83e40fbf6-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;36mStandard fitting\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📋 Using experiment 🔬 \u001b[32m'lbco'\u001b[0m for \u001b[32m'single'\u001b[0m fitting\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "🚀 Starting fit process with \u001b[32m'lmfit \u001b[0m\u001b[32m(\u001b[0m\u001b[32mleastsq\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[33m...\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "📈 Goodness-of-fit progress:\n" ] }, { "data": { "text/html": [ "
iterationtime (s)χ²change / status
110.163816.37
250.994.0799.9% ↓
381.754.07
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "🏆 Best goodness-of-fit \u001b[1m(\u001b[0mreduced χ²\u001b[1m)\u001b[0m is \u001b[1;36m4.07\u001b[0m at iteration \u001b[1;36m7\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "✅ Fitting complete.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "⚙️ Settings used:\n" ] }, { "data": { "text/html": [ "
NameValueDescription
1max_iterations1000Maximum solver iterations.
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "📋 Least-squares fit results:\n" ] }, { "data": { "text/html": [ "
MetricValue
1🧪 Minimizerlmfit (leastsq)
2✅ Overall statussuccess
3⏱️ Fitting time (seconds)1.75
4🔁 Iterations5
5📏 Goodness-of-fit (reduced χ²)4.07
6📏 R-factor (Rf, %)1.42
7📏 R-factor squared (Rf², %)0.72
8📏 Weighted R-factor (wR, %)0.72
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "📈 Refined parameters:\n" ] }, { "data": { "text/html": [ "
datablockcategoryentryparameterunitsstartvalues.u.change
1lbcolinked_structurelbcoscale9.40597.71170.001018.01 % ↓
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
• start = parameter value before refinement
• value = refined value from least-squares minimization
• s.u. = standard uncertainty (one sigma), from the covariance matrix
• change = relative change from start, in %; ↑ = increase, ↓ = decrease
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Loading plot…
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "experiment.linked_structures['lbco'].scale.free = True\n", "\n", "project.analysis.fit()\n", "project.display.fit.results()\n", "\n", "project.analysis.calculate()\n", "calc_ed_cryspy_refined = experiment.data.intensity_calc\n", "LABEL_ED_CRYSPY_REFINED = verify.engine_label('cryspy', note='refined')\n", "\n", "project.display.pattern_comparison(\n", " 'lbco',\n", " reference=calc_fullprof,\n", " candidate=calc_ed_cryspy_refined,\n", " reference_label=FULLPROF_LABEL,\n", " candidate_label=LABEL_ED_CRYSPY_REFINED,\n", ")" ] }, { "cell_type": "markdown", "id": "15", "metadata": {}, "source": [ "## Agreement check" ] }, { "cell_type": "code", "execution_count": 9, "id": "16", "metadata": { "execution": { "iopub.execute_input": "2026-06-30T22:33:57.583341Z", "iopub.status.busy": "2026-06-30T22:33:57.583161Z", "iopub.status.idle": "2026-06-30T22:33:57.590613Z", "shell.execute_reply": "2026-06-30T22:33:57.589813Z" } }, "outputs": [ { "data": { "text/html": [ "
ComparisonMetricExpectedActualOK
1edi 0.19.1 (cryspy 0.12.1, refined) vs FullProf 8.40Profile diff (%)< 2.50.72✅
2Max deviation (%)< 60.58✅
3Area ratio0.99 to 1.010.9918✅
4Shape correlation> 0.9991.0000✅
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "True" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "verify.assert_patterns_agree(\n", " [\n", " (f'{LABEL_ED_CRYSPY_REFINED} vs {FULLPROF_LABEL}', calc_fullprof, calc_ed_cryspy_refined),\n", " ],\n", ")" ] } ], "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 }