{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d91893e9",
   "metadata": {
    "tags": [
     "hide-in-docs"
    ]
   },
   "outputs": [],
   "source": [
    "# Check if the easydiffraction library is installed.\n",
    "# If not, install it including the 'visualization' extras.\n",
    "# This is needed, e.g., when running this as a notebook via Google Colab or\n",
    "# Jupyter Notebook in an environment where the library is not pre-installed.\n",
    "import builtins\n",
    "import importlib.util\n",
    "\n",
    "if hasattr(builtins, '__IPYTHON__'):\n",
    "    if importlib.util.find_spec('easydiffraction') is None:\n",
    "        !pip install 'easydiffraction[visualization]'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {},
   "source": [
    "# Structure Refinement: NCAF, WISH\n",
    "\n",
    "This example demonstrates a Rietveld refinement of Na2Ca3Al2F14 crystal\n",
    "structure using time-of-flight neutron powder diffraction data from WISH at\n",
    "ISIS.\n",
    "\n",
    "Two datasets from detector banks 5+6 and 4+7 are used for joint fitting."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1",
   "metadata": {},
   "source": [
    "## Import Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from easydiffraction import Experiment\n",
    "from easydiffraction import Project\n",
    "from easydiffraction import SampleModel\n",
    "from easydiffraction import download_from_repository"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3",
   "metadata": {},
   "source": [
    "## Define Sample Model\n",
    "\n",
    "This section covers how to add sample models and modify their parameters.\n",
    "\n",
    "#### Create Sample Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SampleModel('ncaf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5",
   "metadata": {},
   "source": [
    "#### Set Space Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.space_group.name_h_m = 'I 21 3'\n",
    "model.space_group.it_coordinate_system_code = '1'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7",
   "metadata": {},
   "source": [
    "#### Set Unit Cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.cell.length_a = 10.250256"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9",
   "metadata": {},
   "source": [
    "#### Set Atom Sites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.atom_sites.add('Ca', 'Ca', 0.4663, 0.0, 0.25, wyckoff_letter='b', b_iso=0.92)\n",
    "model.atom_sites.add('Al', 'Al', 0.2521, 0.2521, 0.2521, wyckoff_letter='a', b_iso=0.73)\n",
    "model.atom_sites.add('Na', 'Na', 0.0851, 0.0851, 0.0851, wyckoff_letter='a', b_iso=2.08)\n",
    "model.atom_sites.add('F1', 'F', 0.1377, 0.3054, 0.1195, wyckoff_letter='c', b_iso=0.90)\n",
    "model.atom_sites.add('F2', 'F', 0.3625, 0.3633, 0.1867, wyckoff_letter='c', b_iso=1.37)\n",
    "model.atom_sites.add('F3', 'F', 0.4612, 0.4612, 0.4612, wyckoff_letter='a', b_iso=0.88)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11",
   "metadata": {},
   "source": [
    "## Define Experiment\n",
    "\n",
    "This section shows how to add experiments, configure their parameters, and\n",
    "link the sample models defined in the previous step.\n",
    "\n",
    "#### Download Measured Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12",
   "metadata": {},
   "outputs": [],
   "source": [
    "download_from_repository(\n",
    "    'wish_ncaf_5_6.xys',\n",
    "    destination='data',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13",
   "metadata": {},
   "outputs": [],
   "source": [
    "download_from_repository(\n",
    "    'wish_ncaf_4_7.xys',\n",
    "    destination='data',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14",
   "metadata": {},
   "source": [
    "#### Create Experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15",
   "metadata": {},
   "outputs": [],
   "source": [
    "expt56 = Experiment(\n",
    "    'wish_5_6',\n",
    "    beam_mode='time-of-flight',\n",
    "    data_path='data/wish_ncaf_5_6.xys',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16",
   "metadata": {},
   "outputs": [],
   "source": [
    "expt47 = Experiment(\n",
    "    'wish_4_7',\n",
    "    beam_mode='time-of-flight',\n",
    "    data_path='data/wish_ncaf_4_7.xys',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17",
   "metadata": {},
   "source": [
    "#### Set Instrument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18",
   "metadata": {},
   "outputs": [],
   "source": [
    "expt56.instrument.setup_twotheta_bank = 152.827\n",
    "expt56.instrument.calib_d_to_tof_offset = -13.5\n",
    "expt56.instrument.calib_d_to_tof_linear = 20773.0\n",
    "expt56.instrument.calib_d_to_tof_quad = -1.08308"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19",
   "metadata": {},
   "outputs": [],
   "source": [
    "expt47.instrument.setup_twotheta_bank = 121.660\n",
    "expt47.instrument.calib_d_to_tof_offset = -15.0\n",
    "expt47.instrument.calib_d_to_tof_linear = 18660.0\n",
    "expt47.instrument.calib_d_to_tof_quad = -0.47488"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20",
   "metadata": {},
   "source": [
    "#### Set Peak Profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21",
   "metadata": {},
   "outputs": [],
   "source": [
    "expt56.peak.broad_gauss_sigma_0 = 0.0\n",
    "expt56.peak.broad_gauss_sigma_1 = 0.0\n",
    "expt56.peak.broad_gauss_sigma_2 = 15.5\n",
    "expt56.peak.broad_mix_beta_0 = 0.007\n",
    "expt56.peak.broad_mix_beta_1 = 0.01\n",
    "expt56.peak.asym_alpha_0 = -0.0094\n",
    "expt56.peak.asym_alpha_1 = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22",
   "metadata": {},
   "outputs": [],
   "source": [
    "expt47.peak.broad_gauss_sigma_0 = 0.0\n",
    "expt47.peak.broad_gauss_sigma_1 = 29.8\n",
    "expt47.peak.broad_gauss_sigma_2 = 18.0\n",
    "expt47.peak.broad_mix_beta_0 = 0.006\n",
    "expt47.peak.broad_mix_beta_1 = 0.015\n",
    "expt47.peak.asym_alpha_0 = -0.0115\n",
    "expt47.peak.asym_alpha_1 = 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23",
   "metadata": {},
   "source": [
    "#### Set Background"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24",
   "metadata": {},
   "outputs": [],
   "source": [
    "expt56.background_type = 'line-segment'\n",
    "for x, y in [\n",
    "    (9162, 465),\n",
    "    (11136, 593),\n",
    "    (13313, 497),\n",
    "    (14906, 546),\n",
    "    (16454, 533),\n",
    "    (17352, 496),\n",
    "    (18743, 428),\n",
    "    (20179, 452),\n",
    "    (21368, 397),\n",
    "    (22176, 468),\n",
    "    (22827, 477),\n",
    "    (24644, 380),\n",
    "    (26439, 381),\n",
    "    (28257, 378),\n",
    "    (31196, 343),\n",
    "    (34034, 328),\n",
    "    (37265, 310),\n",
    "    (41214, 323),\n",
    "    (44827, 283),\n",
    "    (49830, 273),\n",
    "    (52905, 257),\n",
    "    (58204, 260),\n",
    "    (62916, 261),\n",
    "    (70186, 262),\n",
    "    (74204, 262),\n",
    "    (82103, 268),\n",
    "    (91958, 268),\n",
    "    (102712, 262),\n",
    "]:\n",
    "    expt56.background.add(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25",
   "metadata": {},
   "outputs": [],
   "source": [
    "expt47.background_type = 'line-segment'\n",
    "for x, y in [\n",
    "    (9090, 488),\n",
    "    (10672, 566),\n",
    "    (12287, 494),\n",
    "    (14037, 559),\n",
    "    (15451, 529),\n",
    "    (16764, 445),\n",
    "    (18076, 460),\n",
    "    (19456, 413),\n",
    "    (20466, 511),\n",
    "    (21880, 396),\n",
    "    (23798, 391),\n",
    "    (25447, 385),\n",
    "    (28073, 349),\n",
    "    (30058, 332),\n",
    "    (32583, 309),\n",
    "    (34804, 355),\n",
    "    (37160, 318),\n",
    "    (40324, 290),\n",
    "    (46895, 260),\n",
    "    (50631, 256),\n",
    "    (54602, 246),\n",
    "    (58439, 264),\n",
    "    (66520, 250),\n",
    "    (75002, 258),\n",
    "    (83649, 257),\n",
    "    (92770, 255),\n",
    "    (101524, 260),\n",
    "]:\n",
    "    expt47.background.add(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26",
   "metadata": {},
   "source": [
    "#### Set Linked Phases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27",
   "metadata": {},
   "outputs": [],
   "source": [
    "expt56.linked_phases.add('ncaf', scale=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28",
   "metadata": {},
   "outputs": [],
   "source": [
    "expt47.linked_phases.add('ncaf', scale=2.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29",
   "metadata": {},
   "source": [
    "#### Set Excluded Regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30",
   "metadata": {},
   "outputs": [],
   "source": [
    "expt56.excluded_regions.add(minimum=0, maximum=10010)\n",
    "expt56.excluded_regions.add(minimum=100010, maximum=200000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31",
   "metadata": {},
   "outputs": [],
   "source": [
    "expt47.excluded_regions.add(minimum=0, maximum=10006)\n",
    "expt47.excluded_regions.add(minimum=100004, maximum=200000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32",
   "metadata": {},
   "source": [
    "## Define Project\n",
    "\n",
    "The project object is used to manage the sample model, experiments, and\n",
    "analysis\n",
    "\n",
    "#### Create Project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33",
   "metadata": {},
   "outputs": [],
   "source": [
    "project = Project()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34",
   "metadata": {},
   "source": [
    "#### Set Plotting Engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35",
   "metadata": {},
   "outputs": [],
   "source": [
    "project.plotter.engine = 'plotly'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36",
   "metadata": {},
   "source": [
    "#### Add Sample Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37",
   "metadata": {},
   "outputs": [],
   "source": [
    "project.sample_models.add(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38",
   "metadata": {},
   "source": [
    "#### Add Experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39",
   "metadata": {},
   "outputs": [],
   "source": [
    "project.experiments.add(expt56)\n",
    "project.experiments.add(expt47)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40",
   "metadata": {},
   "source": [
    "## Perform Analysis\n",
    "\n",
    "This section shows the analysis process, including how to set up\n",
    "calculation and fitting engines.\n",
    "\n",
    "#### Set Calculator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41",
   "metadata": {},
   "outputs": [],
   "source": [
    "project.analysis.current_calculator = 'cryspy'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42",
   "metadata": {},
   "source": [
    "#### Set Minimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43",
   "metadata": {},
   "outputs": [],
   "source": [
    "project.analysis.current_minimizer = 'lmfit (leastsq)'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44",
   "metadata": {},
   "source": [
    "#### Set Fit Mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45",
   "metadata": {},
   "outputs": [],
   "source": [
    "project.analysis.fit_mode = 'joint'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46",
   "metadata": {},
   "source": [
    "#### Set Free Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.atom_sites['Ca'].b_iso.free = True\n",
    "model.atom_sites['Al'].b_iso.free = True\n",
    "model.atom_sites['Na'].b_iso.free = True\n",
    "model.atom_sites['F1'].b_iso.free = True\n",
    "model.atom_sites['F2'].b_iso.free = True\n",
    "model.atom_sites['F3'].b_iso.free = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48",
   "metadata": {},
   "outputs": [],
   "source": [
    "expt56.linked_phases['ncaf'].scale.free = True\n",
    "expt56.instrument.calib_d_to_tof_offset.free = True\n",
    "expt56.instrument.calib_d_to_tof_linear.free = True\n",
    "expt56.peak.broad_gauss_sigma_2.free = True\n",
    "expt56.peak.broad_mix_beta_0.free = True\n",
    "expt56.peak.broad_mix_beta_1.free = True\n",
    "expt56.peak.asym_alpha_1.free = True\n",
    "\n",
    "expt47.linked_phases['ncaf'].scale.free = True\n",
    "expt47.instrument.calib_d_to_tof_linear.free = True\n",
    "expt47.instrument.calib_d_to_tof_offset.free = True\n",
    "expt47.peak.broad_gauss_sigma_2.free = True\n",
    "expt47.peak.broad_mix_beta_0.free = True\n",
    "expt47.peak.broad_mix_beta_1.free = True\n",
    "expt47.peak.asym_alpha_1.free = True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49",
   "metadata": {},
   "source": [
    "#### Plot Measured vs Calculated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50",
   "metadata": {},
   "outputs": [],
   "source": [
    "project.plot_meas_vs_calc(expt_name='wish_5_6', show_residual=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51",
   "metadata": {},
   "outputs": [],
   "source": [
    "project.plot_meas_vs_calc(expt_name='wish_4_7', show_residual=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52",
   "metadata": {},
   "source": [
    "#### Run Fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53",
   "metadata": {},
   "outputs": [],
   "source": [
    "project.analysis.fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54",
   "metadata": {},
   "source": [
    "#### Plot Measured vs Calculated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55",
   "metadata": {},
   "outputs": [],
   "source": [
    "project.plot_meas_vs_calc(expt_name='wish_5_6', show_residual=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56",
   "metadata": {},
   "outputs": [],
   "source": [
    "project.plot_meas_vs_calc(expt_name='wish_4_7', show_residual=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "This final section shows how to review the results of the analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58",
   "metadata": {},
   "source": [
    "#### Show Project Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59",
   "metadata": {},
   "outputs": [],
   "source": [
    "project.summary.show_report()"
   ]
  }
 ],
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