{ "cells": [ { "cell_type": "markdown", "id": "01-00", "metadata": {}, "source": [ "# Quickstart: look at a pattern, then fit it with Le Bail\n", "\n", "*Written by Claude Code, Anthropic's coding agent, for the rietx project.*\n", "\n", "We read a laboratory powder pattern, look at it, and fit its cell and peak shapes.\n", "The fit uses no atomic positions.\n", "By the end you can read a fit's summary in the order the package intends, and say whether a cell from it is worth quoting.\n", "\n", "**You need** Python 3.11 or later and rietx 1.7 or later, which the next cell installs.\n", "You should be comfortable running a Jupyter cell.\n", "You do not need to have refined anything before.\n", "\n", "**The data** is fluorapatite, Ca₅(PO₄)₃F, measured on a Bragg-Brentano diffractometer with a copper tube.\n", "It is the `LabData` example from the GSAS-II tutorials and ships with rietx.\n", "Its provenance and licence are in the repository's `tests/data/README.md`.\n", "\n", "**Runtime** is under a minute on a laptop." ] }, { "cell_type": "code", "execution_count": null, "id": "01-01", "metadata": {}, "outputs": [], "source": [ "%pip install rietx" ] }, { "cell_type": "code", "execution_count": 1, "id": "01-02", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PatternData of 5753 points, 15–130.04° 2θ, median step 0.02°\n", " intensity: <5753 floats 0…19693>\n", " sigma: None\n", " excluded_regions: none\n", " metadata: {source_file: 'FAP.XRA', format: 'gsas-std'}" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "\n", "import rietx as rx\n", "from rietx.examples import examples_dir\n", "\n", "data = rx.read_pattern(examples_dir() / \"FAP.XRA\")\n", "data" ] }, { "cell_type": "markdown", "id": "01-03", "metadata": {}, "source": [ "A pattern prints as its size and range.\n", "`sigma: None` means the file carries no uncertainty column.\n", "The fit will weight each point by the square root of its counts instead.\n", "\n", "## Look before fitting\n", "\n", "Plot the pattern before any model touches it." ] }, { "cell_type": "code", "execution_count": 2, "id": "01-04", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ], "text/plain": [ "<_NotebookFigure size 2280x1320 with 1 Axes>" ] }, "execution_count": 2, "metadata": { "image/png": { "width": 672 } }, "output_type": "execute_result" } ], "source": [ "data.plot()" ] }, { "cell_type": "markdown", "id": "01-05", "metadata": {}, "source": [ "The peaks are sharp and the background is low and smooth.\n", "Nothing in the scan looks like a second phase or a broad hump from an amorphous component, so a single-phase model is a fair start.\n", "\n", "`rx.diagnose` measures a few things about the data that decide how it can be fitted.\n", "We print four of its fields.\n", "Its peak counts (`n_peaks`, `peak_fraction`, `peak_density_per_deg`) are estimates and are not quoted here." ] }, { "cell_type": "code", "execution_count": 3, "id": "01-06", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "steps across a peak's FWHM: 4.6\n", "signal over background: 146\n", "contaminating lines: none\n", "dead channels: none\n" ] } ], "source": [ "diag = rx.diagnose(data)\n", "print(f\"steps across a peak's FWHM: {diag.steps_per_fwhm:.1f}\")\n", "print(f\"signal over background: {diag.signal_to_background:.0f}\")\n", "print(f\"contaminating lines: {diag.contamination or 'none'}\")\n", "print(f\"dead channels: {diag.dead_channels or 'none'}\")" ] }, { "cell_type": "markdown", "id": "01-07", "metadata": {}, "source": [ "The step count matters most.\n", "The refinement guidelines (McCusker et al., 1999) ask for at least five measured points across each peak's full width at half maximum.\n", "This scan has fewer, so the integrated intensities are less well measured than they could be.\n", "No refinement choice recovers that, and the fit will say so again.\n", "\n", "## Build a model and check it against the data\n", "\n", "A Le Bail fit needs a cell, a space group and an instrument.\n", "The cell and space group come from a CIF of fluorapatite shipped beside the pattern.\n", "The CIF has atoms too, which a Le Bail fit ignores.\n", "The instrument is a Bragg-Brentano diffractometer with a copper Kα doublet." ] }, { "cell_type": "code", "execution_count": 4, "id": "01-08", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ], "text/plain": [ "<_NotebookFigure size 2280x1320 with 1 Axes>" ] }, "execution_count": 4, "metadata": { "image/png": { "width": 691 } }, "output_type": "execute_result" } ], "source": [ "structure = rx.Structure.from_cif(examples_dir() / \"fluorapatite.cif\")\n", "\n", "instrument = rx.Instrument.bragg_brentano(radiation=\"CuKa\")\n", "instrument.background = rx.BackgroundChebyshev.with_terms(6)\n", "# Start the background's constant term at the floor of the data.\n", "# Left at zero, the first pass hands the whole pedestal to the peaks.\n", "instrument.background.coefficients[0].value = float(np.percentile(data.intensity, 5))\n", "\n", "ref = rx.Refinement(structure, instrument)\n", "data.plot(model=ref, two_theta_range=(25, 35))" ] }, { "cell_type": "markdown", "id": "01-09", "metadata": {}, "source": [ "The ticks under the pattern are where the model puts each reflection before any fitting.\n", "They include the Kα2 line of each reflection, so most peaks have a pair.\n", "Each peak should have a tick under it.\n", "Here each pair sits slightly to the right of its peak.\n", "That is within a fit's reach, and the zero-shift stage takes it up.\n", "If ticks and peaks drift apart across the scan, the starting cell or the wavelength is wrong, and a fit will not walk that far.\n", "Zoom into a few windows like this one before fitting.\n", "\n", "The background is a six-term Chebyshev polynomial.\n", "`rx.auto_background(data)` would size a penalised spline to the pattern instead.\n", "On this pattern that spline currently reports each correlated pair of its coefficients as a separate warning, several hundred of them.\n", "The polynomial keeps the diagnostics below readable.\n", "\n", "## Fit\n", "\n", "`mode=\"lebail\"` extracts each reflection's intensity from the data instead of computing it from atoms.\n", "The `profile_only` plan frees parameters in stages: background, zero shift, cell, then peak widths.\n", "`two_theta_limits` sets the channels the fit uses.\n", "We stop at 130° to match the GSAS-II tutorial, so both programs fit the same channels." ] }, { "cell_type": "code", "execution_count": 5, "id": "01-10", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
RefinementResult: converged (lebail)\n",
       "  stage bkg: converged (2 evals, ftol=1e-06)\n",
       "  stage zero: converged (64 evals, ftol=1e-06)\n",
       "  stage cell: converged (34 evals, ftol=1e-06)\n",
       "  stage profile_w: converged (14 evals, ftol=1e-06)\n",
       "  stage profile: converged (15 evals, ftol=solver default), max|Δθ|/esd=0.000\n",
       "  diagnostics: 3 unresolved\n",
       "    WARNING FLOOR_HIT: instrument.profile.y refined to its floor (ρ=+0.073) — the data push this parameter below zero, where it cannot go. The usual reasons are a phase or term the data do not support, a width the instrument already supplies, or, for a displacement parameter, absorption or a wrong species or occupancy. Fix it at 0\n",
       "    INFO FROZEN_COMPILE_STALE: the reported statistics and curves come from a compile at the returned parameters, chi2 = 17624.1; the last stage's own compile, whose peak windows and FCJ node counts were frozen at the values it started from, gives 17429.8 at the same parameters, 1.1 % apart. The fit is not biased by it; the history node keeps the frozen figure, the result the fresh one, and the esds are the last solve's either way\n",
       "    WARNING PATTERN_UNDERSAMPLED: 4.6 steps across the FWHM (median over 76 fitted peaks) — below the 5 the guidelines ask for — a data-collection limit, not a refinement one: the integrated intensities were not measured finely enough and no choice made here recovers them. Re-collect at a step size near FWHM/5 if the intensities have to be quotable\n",
       "  provenance: rietx 1.10.0.dev0, backend=numpy, solver=lm\n",
       "  Rwp 0.0982 / Rexp 0.0560 (GoF 1.75), Rp 0.0718, χ² 3.1, DW 0.91
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instrument.profile.u0.0011(12)false
instrument.profile.v0.0011(16)false
instrument.profile.w0.00255(52)false
instrument.profile.x0.0356(24)false
instrument.profile.y0.0000(53)true
instrument.background.c0165.9(13)false
instrument.background.c1-168.9(25)false
instrument.background.c2119.8(22)false
instrument.background.c3-71.7(20)false
instrument.background.c431.5(16)false
instrument.background.c5-16.9(15)false
" ], "text/plain": [ "RefinementResult: converged (lebail)\n", " stage bkg: converged (2 evals, ftol=1e-06)\n", " stage zero: converged (64 evals, ftol=1e-06)\n", " stage cell: converged (34 evals, ftol=1e-06)\n", " stage profile_w: converged (14 evals, ftol=1e-06)\n", " stage profile: converged (15 evals, ftol=solver default), max|Δθ|/esd=0.000\n", " diagnostics: 3 unresolved\n", " WARNING FLOOR_HIT: instrument.profile.y refined to its floor (ρ=+0.073) — the data push this parameter below zero, where it cannot go. The usual reasons are a phase or term the data do not support, a width the instrument already supplies, or, for a displacement parameter, absorption or a wrong species or occupancy. Fix it at 0\n", " INFO FROZEN_COMPILE_STALE: the reported statistics and curves come from a compile at the returned parameters, chi2 = 17624.1; the last stage's own compile, whose peak windows and FCJ node counts were frozen at the values it started from, gives 17429.8 at the same parameters, 1.1 % apart. The fit is not biased by it; the history node keeps the frozen figure, the result the fresh one, and the esds are the last solve's either way\n", " WARNING PATTERN_UNDERSAMPLED: 4.6 steps across the FWHM (median over 76 fitted peaks) — below the 5 the guidelines ask for — a data-collection limit, not a refinement one: the integrated intensities were not measured finely enough and no choice made here recovers them. Re-collect at a step size near FWHM/5 if the intensities have to be quotable\n", " provenance: rietx 1.10.0.dev0, backend=numpy, solver=lm\n", " Rwp 0.0982 / Rexp 0.0560 (GoF 1.75), Rp 0.0718, χ² 3.1, DW 0.91" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "LIMITS = (15, 130)\n", "result = ref.fit(data, mode=\"lebail\", plan=\"profile_only\", two_theta_limits=LIMITS)\n", "result" ] }, { "cell_type": "markdown", "id": "01-11", "metadata": {}, "source": [ "Read the summary from the top.\n", "\n", "1. **Status.** Every stage converged.\n", " Each stage line gives the residual evaluations it took and its `ftol`, the relative drop in the cost below which the stage stops.\n", " Every stage but the last stops at `1e-06`, because each later stage refines its parameters again.\n", " The last runs to the solver's default, `1e-9`, and that is the answer the result reports.\n", " `max|Δθ|/esd` on the last line is the largest final step of any parameter, in units of its own esd. McCusker et al. (1999) treat 0.1 or less as converged.\n", "2. **Diagnostics.** These outrank every statistic.\n", " `PATTERN_UNDERSAMPLED` is the step count from `diagnose` again.\n", " `FLOOR_HIT` says the data push `instrument.profile.y`, the Lorentzian tan θ width, below zero, so it ends on its floor of 0.\n", " `FROZEN_COMPILE_STALE` says the peak windows were sized at the values the last stage started from, and the cell has moved since.\n", " Running the plan again re-sizes them.\n", "3. **Agreement indices** come last. Rwp is useful for comparing two fits of the same data over the same channels, and says little on its own.\n", "\n", "## Fit again, and keep the best pass\n", "\n", "A Le Bail fit holds its extracted intensities fixed inside each least-squares run.\n", "The intensities and the profile converge only by alternating, so run the plan more than once.\n", "Later passes are not guaranteed to be better, so compare them and keep the best." ] }, { "cell_type": "code", "execution_count": 6, "id": "01-12", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pass 1: Rwp 0.0982\n", "pass 2: Rwp 0.0875\n", "pass 3: Rwp 0.0882\n", "kept pass 2: a = 9.37090 +/- 0.00007 Å, c = 6.88532 +/- 0.00007 Å\n" ] } ], "source": [ "passes = [result]\n", "for _ in range(2):\n", " passes.append(ref.fit(data, mode=\"lebail\", plan=\"profile_only\", two_theta_limits=LIMITS))\n", "for i, r in enumerate(passes, 1):\n", " print(f\"pass {i}: Rwp {r.statistics.rwp:.4f}\")\n", "\n", "best = min(passes, key=lambda r: r.statistics.rwp)\n", "ref.checkout(best.node_id)\n", "a, c = best.parameter(\"phases.0.cell.a\"), best.parameter(\"phases.0.cell.c\")\n", "print(f\"kept pass {passes.index(best) + 1}: a = {a.value:.5f} +/- {a.stderr:.5f} Å, c = {c.value:.5f} +/- {c.stderr:.5f} Å\")" ] }, { "cell_type": "markdown", "id": "01-13", "metadata": {}, "source": [ "Every pass is a node in the refinement's history, and `ref.checkout` restores the model to the one we kept.\n", "The cell's esd is a counting-statistics figure.\n", "It does not include any error in the wavelength or the zero shift.\n", "\n", "## Look at the fit" ] }, { "cell_type": "code", "execution_count": 7, "id": "01-14", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ], "text/plain": [ "<_NotebookFigure size 2280x1320 with 1 Axes>" ] }, "execution_count": 7, "metadata": { "image/png": { "width": 693 } }, "output_type": "execute_result" } ], "source": [ "best.plot()" ] }, { "cell_type": "markdown", "id": "01-15", "metadata": {}, "source": [ "The lower panel is the difference between the data and the model.\n", "A good fit leaves it as noise of even width across the scan.\n", "Look for structure in it: a wiggle at every peak means the peak shape is wrong, and a peak with no tick under it means a phase the model lacks.\n", "\n", "## Checking an agent's work\n", "\n", "When an agent runs a fit like this for you, check these in order.\n", "The numbers are the rules in the rietx agent skill (`SKILL.md` §4).\n", "\n", "- **Status and diagnostics first** (rule 9). Ask the agent what each diagnostic means for its answer. \"Converged\" means only that the cost stopped falling.\n", "- **The difference curve, region by region** (rule 10). A low Rwp can hide one badly fitted region.\n", "- **Rwp last, and only against another fit of the same channels** (rule 16).\n", "- **A Le Bail cell needs a structural check** (§2). Where reflections overlap, arbitrary intensities can index one pattern more than one way. Notebook 02 refines the same pattern with atoms." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3" }, "rietx": { "version": "1.10.0.dev0" } }, "nbformat": 4, "nbformat_minor": 5 }