{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4cbb1f83",
   "metadata": {},
   "source": [
    "# Performing data and compatibility analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21417e82",
   "metadata": {},
   "source": [
    "**pyddt** main goal is to establish a set of systematic procedures for indexing dynamical diffraction peaks and reading their line profile asymmetries, so this approach of using X-ray dynamical diffraction becomes reproducible and transferable.\n",
    "\n",
    "This last tutorial aims to show the standard procedures implemented into **pyddt** and analyze two data sets using them."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "844f48bd",
   "metadata": {},
   "source": [
    "### 0. Importing packages"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "739c07de",
   "metadata": {},
   "source": [
    "In this tutorial, it's necessary to use the magic function `%matplotlib qt`. Using it, matplotlib plots will be opened on an external window from the notebook.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a2b6b193",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "62596a4099c349e9beb924e39c1ff60d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib qt  \n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.size'] = '10'\n",
    "\n",
    "import sys\n",
    "sys.path.append('path/to/pyddt')\n",
    "\n",
    "import pyddt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "554e4126",
   "metadata": {},
   "source": [
    "## 1. Analyzing Renninger scans"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9c54c24",
   "metadata": {},
   "source": [
    "### 1.1 CeFe$_4$P$_{12}$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f32f989",
   "metadata": {},
   "source": [
    "Let's start by analyzing the scan of the reflection 002, filled skutterudite CeFe$_{4}$P$_{12}$.\n",
    "\n",
    "X-ray data acquisition was carried out at the Brazilian Synchrotron Light Laboratory (LNLS), bending magnetic\n",
    "beamline XRD2. X-rays of 7105.8 eV.\n",
    "\n",
    "See [Phonon scattering mechanism in thermoelectric materials revised via resonant x-ray dynamical diffraction](https://link.springer.com/article/10.1557/mrc.2020.37) for a detailed reference."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b420adac",
   "metadata": {},
   "source": [
    "#### 1.1.1 ExpData object"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ffe6f30",
   "metadata": {},
   "source": [
    "Creating an `expdata` object is the first step of data analysis. The necessary inputs are energy value, primary reflection indices, and the filename containing the data.\n",
    "\n",
    "The data must include the azimuth position and corresponding intensity. For the filled skutterudite, the filename is `data_CFP.dat`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "541a0b8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "exp = pyddt.ExpData(7105.8, [0, 0, 2], 'data_CFP.dat')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c60674f",
   "metadata": {},
   "source": [
    "Once this object was generated, we can visualize the scan. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fbbaf916",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50674d2a",
   "metadata": {},
   "source": [
    "#### 1.1.2 Selecting the MD peaks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1f08b26",
   "metadata": {},
   "source": [
    "Now, we proceed to find the multiple-diffraction (MD) peaks. You can inform the minimum height to be considered (provide a percentage of the maximum intensity). \n",
    "\n",
    "For this dataset, 60% is a good choice. Feel free to test other values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "58434913",
   "metadata": {},
   "outputs": [],
   "source": [
    "exp.peak_finder(0.6) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62c1bc60",
   "metadata": {},
   "source": [
    "Visualize the scan again to check the selected peaks. You can access the peak positions by using the `phi` and `peaks` attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7d224c78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "48c98bf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([25.164, 25.344, 25.568])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp.phi[exp.peaks]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5db85e8",
   "metadata": {},
   "source": [
    "Our scan is very narrow, so it's perfectly possible to select the peaks manually. Use the function `pyddt.peak_picker()` for it. \n",
    "\n",
    "Also, you can delete peaks by using the function `pyddt.review()`: \n",
    "\n",
    "* To select a peak, stop the mouse over the maximum and press SPACE.\n",
    "\n",
    "* To unselect, stop the mouse over the maximum and press DEL.\n",
    "\n",
    "* Feel free to zoom in or out.\n",
    "\n",
    "* After finishing the selection, press ENTER. Then, close the figure (mouse or press q).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d860f9e",
   "metadata": {},
   "source": [
    "#### 1.1.3 Defining the fitting regions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db3f778c",
   "metadata": {},
   "source": [
    "For reading the asymmetries, we fit a superposition of a symmetric peak with a linear slope. This way, it's necessary to define the fitting region for each selected peak. \n",
    "\n",
    "The function `pyddt.region_of_fit()` manually calculates the *full width at half maximum* $f$ for each peak, then defines the region of fit as \n",
    "\n",
    "$$\\phi_{0} \\pm nf$$\n",
    "\n",
    "where $\\phi_{0}$ is the center position and $n\\in \\mathcal{Z}$. By default, $n = 15$.\n",
    "\n",
    "The calculated regions are exhibited by using `flag = 1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3770f8c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "exp.region_of_fit(flag=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8345a21",
   "metadata": {},
   "source": [
    "![pyddt_t3_1](https://user-images.githubusercontent.com/106104347/187980762-c1e598c3-6f47-400f-b570-1c151c76f63b.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e7fd756",
   "metadata": {},
   "source": [
    "#### 1.1.4 Indexing the multiple-diffraction cases"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb06642a",
   "metadata": {},
   "source": [
    "Now, let's index the MD peaks. To do this, use the `pyddt.indexer()` function. The secondary reflections are exhibited by using `flag = 1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c0e56bbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "exp.indexer('ce3.in', [1, 1, 0], 50, flag=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3116dc4e",
   "metadata": {},
   "source": [
    "![pyddt_t3_2](https://user-images.githubusercontent.com/106104347/187985771-3f126b90-6c8a-46ff-8315-3396608281c8.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "462eb83f",
   "metadata": {},
   "source": [
    "`exp.indexer()` arguments:\n",
    "\n",
    "* Structural model (generated in the last tutorial). \n",
    "* Reference direction for the azimuthal rotation\n",
    "* Minimum cutoff for the strength of excitation. \n",
    "\n",
    "The red color of the 4-beam peaks indicates the *in-out* geometry of excitation, while blue stands for out-in. \n",
    "\n",
    "It's also possible directly index the MD peaks by visualizing $\\omega \\times \\phi$ graphs. The secondary reflections are shown as hover text by mouse motions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2da621ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "exp.BC_plot('ce3.in', [1, 1, 0], 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b91be2ca",
   "metadata": {},
   "source": [
    "You can also access the indexation by calling the `index` attribute, where $\\pm 1$ stands for the excitation geometry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5e2bbe60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['1', '4 1 3/ 4 1 -1'],\n",
       "       ['-1', '-3 4 1'],\n",
       "       ['1', '4 2 4/ 4 2 -2']], dtype='<U21')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp.index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50b9689a",
   "metadata": {},
   "source": [
    "#### 1.1.5 Asymmetry reading"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5cc3c94",
   "metadata": {},
   "source": [
    "Finally, let's fit the MD peaks and read their asymmetry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3a803a06",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████| 3/3 [00:12<00:00,  4.11s/it]\n"
     ]
    }
   ],
   "source": [
    "exp.fitter(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7889aa2",
   "metadata": {},
   "source": [
    "The slope error is estimated using [bootstrap resampling](https://onlinelibrary.wiley.com/doi/full/10.1002/9780470057339.vab028), and the number of resamples is the only argument of `exp.fitter()` function.\n",
    "\n",
    "Let's check the estimated slopes and their errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bc5f4040",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.19067251,  0.05101513],\n",
       "       [ 0.10344877,  0.00676736],\n",
       "       [-0.20538606,  0.10659999]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp.slope_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4bd1771",
   "metadata": {},
   "source": [
    "It's easy to relate the slope sign with the asymmetry type: if negative, the asymmetry is HL (the MD intensity profile has higher/lower shoulders); if positive, it's LH.\n",
    "\n",
    "So, the MD peaks observed at 25.16º and 25.57º are HL, while the remaining is LH. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a62cb85d",
   "metadata": {},
   "outputs": [],
   "source": [
    "exp.asymmetry_assigner(0, 10, flag=1)  # flag = 1 to exhibits the asymmetries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dde77ee",
   "metadata": {},
   "source": [
    "![pyddt_t3_4](https://user-images.githubusercontent.com/106104347/187992200-5db7adc9-9d0e-4627-8fbe-5e9cd15705ed.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "573532a0",
   "metadata": {},
   "source": [
    "The `exp.asymmetry_assigner` function realizes the asymmetry reading based on the slope sign. Take a look at the [API documentation](../api.rst) for exploiting the first arguments (cutoff values of $\\bar{S}$ and $\\tau$). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "756fb78f",
   "metadata": {},
   "source": [
    "#### 1.1.6 Saving the analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3958df23",
   "metadata": {},
   "source": [
    "We have indexed the multiple-diffraction cases and read their asymmetries, so the analysis is finished. Let's save the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9b3f62a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'_E7105.8_G002'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp.save()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39f94e6d",
   "metadata": {},
   "source": [
    "In the current folder, two new files are available: `_E7105.8_G002.ext` and `_E7105.8_G002.red`. Take a look at them.\n",
    "\n",
    "* *.ext* $\\rightarrow$ peak position, hkl, slope, slope error, statistical properties, asymmetry type, and excitation geometry.\n",
    "\n",
    "* *.red* $\\rightarrow$  hkl, asymmetry, and diffraction geometry (primary indices and energy in 1st line)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f227b791",
   "metadata": {},
   "source": [
    "### 1.2 Fe-doped L-asparagine monohydrated"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83442fcc",
   "metadata": {},
   "source": [
    "It may seem too much effort to analyze this data using **pyddt**, since a visual analysis quickly leads to the same conclusions. However, a visual inspection is unworkable as the data set increases. In this situation, **pyddt** is very helpful.\n",
    "\n",
    "Another advantage of using **pyddt** is the analysis of biological materials or X-ray data acquisition on laboratory setups, in which asymmetric profiles aren't so well-marked.\n",
    "\n",
    "For illustrating these situations, let's quickly analyze the scan of the reflection 026, Fe-doped L-asparagine monohydrated.\n",
    "\n",
    "X-ray data acquisition was carried out at the Sirius, beamline EMA. X-rays of 8048 eV."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f08bb36",
   "metadata": {},
   "source": [
    "#### 1.2.1 Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4044afee",
   "metadata": {},
   "outputs": [],
   "source": [
    "exp2 = pyddt.ExpData(8048, [0, 2, 6], 'data_lasp.dat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1281f6a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp2.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d34b7bd",
   "metadata": {},
   "source": [
    "Please, note how the asymmetries are subtle, and a visual asymmetry reading is tricky."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bc320dd",
   "metadata": {},
   "source": [
    "#### 1.2.2 Selecting the MD peaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9df7a661",
   "metadata": {},
   "outputs": [],
   "source": [
    "exp2.peak_finder(0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "203b55e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp2.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "913dbf8f",
   "metadata": {},
   "source": [
    "#### 1.2.3 Defining the fitting regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "91873ac1",
   "metadata": {},
   "outputs": [],
   "source": [
    "exp2.region_of_fit(5, flag=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "472351b0",
   "metadata": {},
   "source": [
    "![pyddt_t3_5](https://user-images.githubusercontent.com/106104347/187998792-4fcb6b13-ebae-47e0-b539-fcec5b5d186d.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4647902",
   "metadata": {},
   "source": [
    "#### 1.2.4 Asymmetry reading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4fba0430",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████| 4/4 [00:05<00:00,  1.28s/it]\n"
     ]
    }
   ],
   "source": [
    "exp2.fitter(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "30c8100d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.17184866,  0.10397331],\n",
       "       [ 0.15763806,  0.05487308],\n",
       "       [-0.12616801,  0.08049835],\n",
       "       [-0.95195789,  0.11601925]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp2.slope_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eaf914d",
   "metadata": {},
   "source": [
    "Compare these values with the estimated for the skutterudite. The ratio slope error/slope is higher now, which indicates low-resolution asymmetries. In this context, **pyddt** is very helpful."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ccaf8a7",
   "metadata": {},
   "source": [
    "## 2. Compatibility analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ebc29e6",
   "metadata": {},
   "source": [
    "Returning to the filled skutterudite data, we will show how to compare the experimental asymmetries with the predicted structural models.\n",
    "\n",
    "Within **pyddt**, the compatibility analysis is performed by the `AMD` class. The required inputs are the *.red* file saved after the data analysis and the structural models (created in the last tutorial)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6a82247b",
   "metadata": {},
   "outputs": [],
   "source": [
    "amd = pyddt.AMD('_E7105.8_G002.red', ['ce3.in', 'ce4.in'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13dceeea",
   "metadata": {},
   "source": [
    "Let's check the experimental asymmetries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "7038a8aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['HL', 'LH', 'HL'], dtype='<U2')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amd.asy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c8d14af",
   "metadata": {},
   "source": [
    "Now, we will use the `amd.theoretical_asymmetries()` function to calculate the asymmetries predicted by each structural model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "11e22587",
   "metadata": {},
   "outputs": [],
   "source": [
    "amd.theoretical_asymmetries()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5c4278e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 1.],\n",
       "       [1., 0., 1.]], dtype=float128)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amd.theo_asy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d86cd446",
   "metadata": {},
   "source": [
    "The first line of `amd.theo_asy` presents the asymmetries calculated by the model `ce3.in`, where 1 stands for HL and 0 for LH. The asymmetries in the second line were calculated by the model `ce4.in`.\n",
    "\n",
    "The multiple-diffraction cases under evaluation don't allow differentiation of the structural models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4dff12bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4.,  1.,  3.],\n",
       "       [-3.,  4.,  1.],\n",
       "       [ 4.,  2.,  4.]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amd.hkl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd100ecc",
   "metadata": {},
   "source": [
    "**pyddt** has other advanced tools for compatibility analysis, which will not be covered in this tutorial. Please, check the [API documentation](../api.rst) for more details."
   ]
  }
 ],
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