{ "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/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| path | value | at_bound |
|---|---|---|
| phases.0.cell.a | 9.37071(12) | false |
| phases.0.cell.b | 9.37071(12) | |
| phases.0.cell.c | 6.88529(12) | false |
| phases.0.atoms.0.z | 0.001913 | |
| phases.0.atoms.1.x | 0.241976 | |
| phases.0.atoms.1.y | 0.992603 | |
| phases.0.atoms.2.x | 0.397416 | |
| phases.0.atoms.2.y | 0.367704 | |
| phases.0.atoms.4.x | 0.325053 | |
| phases.0.atoms.4.y | 0.484763 | |
| phases.0.atoms.5.x | 0.591494 | |
| phases.0.atoms.5.y | 0.469954 | |
| phases.0.atoms.6.x | 0.33951 | |
| phases.0.atoms.6.y | 0.258126 | |
| phases.0.atoms.6.z | 0.070641 | |
| instrument.zero_shift | -0.05275(69) | false |
| instrument.profile.u | 0.0011(12) | false |
| instrument.profile.v | 0.0011(16) | false |
| instrument.profile.w | 0.00255(52) | false |
| instrument.profile.x | 0.0356(24) | false |
| instrument.profile.y | 0.0000(53) | true |
| instrument.background.c0 | 165.9(13) | false |
| instrument.background.c1 | -168.9(25) | false |
| instrument.background.c2 | 119.8(22) | false |
| instrument.background.c3 | -71.7(20) | false |
| instrument.background.c4 | 31.5(16) | false |
| instrument.background.c5 | -16.9(15) | false |