{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phase/Orientation Mapping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial demonstrates how to achieve phase and orientation mapping via scanning electron diffraction using both pattern and vector matching."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data was acquired from a GaAs nanowire displaying polymorphism between zinc blende and wurtzite structures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This functionaility has been checked to run in pyxem-0.13.0 (Feb 2021). Bugs are always possible, do not trust the code blindly, and if you experience any issues please report them here: https://github.com/pyxem/pyxem-demos/issues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. <a href='#loa'> Load & Inspect Data</a>\n",
    "2. <a href='#pre'> Pre-processing</a>\n",
    "3. <a href='#tem'> Template matching</a>\n",
    "    1. <a href='#tema'> [Build Template Library]</a>\n",
    "    2. <a href='#temb'>[Indexing]</a>\n",
    "4. <a href='#vec'> Vector Matching</a>\n",
    "    1. <a href='#veca'> [Build Vector Library]</a>\n",
    "    2. <a href='#vecb'>[Indexing Vectors]</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import pyxem and other required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:silx.opencl.common:Unable to import pyOpenCl. Please install it from: https://pypi.org/project/pyopencl\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import diffpy.structure\n",
    "import pyxem as pxm\n",
    "import hyperspy.api as hs\n",
    "\n",
    "accelarating_voltage = 200  # kV\n",
    "camera_length = 0.2  # m\n",
    "diffraction_calibration = 0.032  # px / Angstrom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='loa'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  1. Loading and Inspection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the demo data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/carterfrancis/mambaforge/envs/pyxem-dev/lib/python3.11/site-packages/hyperspy/misc/utils.py:471: VisibleDeprecationWarning: Use of the `binned` attribute in metadata is going to be deprecated in v2.0. Set the `axis.is_binned` attribute instead. \n",
      "  warnings.warn(\n",
      "/Users/carterfrancis/mambaforge/envs/pyxem-dev/lib/python3.11/site-packages/hyperspy/io.py:572: VisibleDeprecationWarning: Loading old file version. The binned attribute has been moved from metadata.Signal to axis.is_binned. Setting this attribute for all signal axes instead.\n",
      "  warnings.warn('Loading old file version. The binned attribute '\n"
     ]
    },
    {
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       "                        <th><p style=\"text-align:left;\">Navigation Axes</p>  </th>\n",
       "                        <th> <p style=\"text-align:left;\">Signal Axes</p> </th>\n",
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      "text/plain": [
       "<LazyElectronDiffraction2D, title: GaAs NW (110 zone), dimensions: (20, 45|144, 144)>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dp = hs.load('./data/02/polymorphic_nanowire.hdf5', lazy=True)\n",
    "dp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set data type, scale intensity range and set calibration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dp.data = dp.data.astype('float64')\n",
    "dp.data *= 1 / dp.data.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ul style=\"margin: 0px; list-style-position: outside;\">\n",
       "                    <details open>\n",
       "                    <summary style=\"display: list-item;\">\n",
       "                    <li style=\"display: inline;\">\n",
       "                    Acquisition_instrument\n",
       "                    </li></summary>\n",
       "                    <ul style=\"margin: 0px; list-style-position: outside;\">\n",
       "                    <details closed>\n",
       "                    <summary style=\"display: list-item;\">\n",
       "                    <li style=\"display: inline;\">\n",
       "                    TEM\n",
       "                    </li></summary>\n",
       "                    <ul style=\"margin: 0px; list-style-position: outside;\">\n",
       "                    <details closed>\n",
       "                    <summary style=\"display: list-item;\">\n",
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       "                    Detector\n",
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       "                    <summary style=\"display: list-item;\">\n",
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       "                    Diffraction\n",
       "                    </li></summary>\n",
       "                    \n",
       "    <ul style=\"margin: 0px; list-style-position: outside;\">\n",
       "    <li style='margin-left:1em; padding-left: 0.5em'>camera_length = 10.0</li></ul>\n",
       "    \n",
       "    <ul style=\"margin: 0px; list-style-position: outside;\">\n",
       "    <li style='margin-left:1em; padding-left: 0.5em'>exposure_time = 10.0</li></ul>\n",
       "    </details></ul></details></ul>\n",
       "    <ul style=\"margin: 0px; list-style-position: outside;\">\n",
       "    <li style='margin-left:1em; padding-left: 0.5em'>rocking_angle = 12.217304763960307</li></ul>\n",
       "    \n",
       "    <ul style=\"margin: 0px; list-style-position: outside;\">\n",
       "    <li style='margin-left:1em; padding-left: 0.5em'>rocking_frequency = 100.0</li></ul>\n",
       "    \n",
       "    <ul style=\"margin: 0px; list-style-position: outside;\">\n",
       "    <li style='margin-left:1em; padding-left: 0.5em'>scan_rotation = 1.0</li></ul>\n",
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       "                    <details open>\n",
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       "                    FileIO\n",
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       "                    0\n",
       "                    </li></summary>\n",
       "                    \n",
       "    <ul style=\"margin: 0px; list-style-position: outside;\">\n",
       "    <li style='margin-left:1em; padding-left: 0.5em'>hyperspy_version = 1.7.5</li></ul>\n",
       "    \n",
       "    <ul style=\"margin: 0px; list-style-position: outside;\">\n",
       "    <li style='margin-left:1em; padding-left: 0.5em'>io_plugin = hyperspy.io_plugins.hspy</li></ul>\n",
       "    \n",
       "    <ul style=\"margin: 0px; list-style-position: outside;\">\n",
       "    <li style='margin-left:1em; padding-left: 0.5em'>operation = load</li></ul>\n",
       "    \n",
       "    <ul style=\"margin: 0px; list-style-position: outside;\">\n",
       "    <li style='margin-left:1em; padding-left: 0.5em'>timestamp = 2023-11-06T13:07:24.894429-06:00</li></ul>\n",
       "    </details></ul></details></ul>\n",
       "    <ul style=\"margin: 0px; list-style-position: outside;\">\n",
       "    <li style='margin-left:1em; padding-left: 0.5em'>title = GaAs NW (110 zone)</li></ul>\n",
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       "                    <details open>\n",
       "                    <summary style=\"display: list-item;\">\n",
       "                    <li style=\"display: inline;\">\n",
       "                    Signal\n",
       "                    </li></summary>\n",
       "                    \n",
       "    <ul style=\"margin: 0px; list-style-position: outside;\">\n",
       "    <li style='margin-left:1em; padding-left: 0.5em'>signal_type = electron_diffraction</li></ul>\n",
       "    </details></ul>"
      ],
      "text/plain": [
       "├── Acquisition_instrument\n",
       "│   └── TEM\n",
       "│       ├── Detector\n",
       "│       │   └── Diffraction\n",
       "│       │       ├── camera_length = 10.0\n",
       "│       │       └── exposure_time = 10.0\n",
       "│       ├── rocking_angle = 12.217304763960307\n",
       "│       ├── rocking_frequency = 100.0\n",
       "│       └── scan_rotation = 1.0\n",
       "├── General\n",
       "│   ├── FileIO\n",
       "│   │   └── 0\n",
       "│   │       ├── hyperspy_version = 1.7.5\n",
       "│   │       ├── io_plugin = hyperspy.io_plugins.hspy\n",
       "│   │       ├── operation = load\n",
       "│   │       └── timestamp = 2023-11-06T13:07:24.894429-06:00\n",
       "│   └── title = GaAs NW (110 zone)\n",
       "└── Signal\n",
       "    └── signal_type = electron_diffraction"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dp.metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot an interactive virtual image to inspect data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 317.33 ms\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 312.889x640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x581.818 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 312.889x640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "roi = hs.roi.CircleROI(cx=72, cy=72, r_inner=0, r=2)\n",
    "dp.plot_integrated_intensity(roi=roi, cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='pre'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  2. Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply affine transformation to correct for off axis camera geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale_x = 0.995\n",
    "scale_y = 1.031\n",
    "offset_x = 0.631\n",
    "offset_y = -0.351\n",
    "dp.apply_affine_transformation(np.array([[scale_x, 0, offset_x],\n",
    "                                         [0, scale_y, offset_y],\n",
    "                                         [0, 0, 1]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform difference of gaussian background subtraction with various parameters on one selected diffraction pattern and plot to identify good parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyxem.utils.expt_utils import investigate_dog_background_removal_interactive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x581.818 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dp_test_area = dp.inav[0, 0]\n",
    "\n",
    "gauss_stddev_maxs = np.arange(2, 12, 0.2) # min, max, step\n",
    "gauss_stddev_mins = np.arange(1, 4, 0.2) # min, max, step\n",
    "\n",
    "investigate_dog_background_removal_interactive(dp_test_area,\n",
    "                                               gauss_stddev_maxs,\n",
    "                                               gauss_stddev_mins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove background using difference of gaussians method with parameters identified above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[                                        ] | 0% Completed | 204.29 us"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/carterfrancis/mambaforge/envs/pyxem-dev/lib/python3.11/site-packages/pyxem/signals/diffraction2d.py:392: VisibleDeprecationWarning: Argument `lazy_result` is deprecated and will be removed in version 1.0.0. To avoid this warning, please do not use `lazy_result`. Use `lazy_output` instead. See the documentation of `subtract_diffraction_background()` for more details.\n",
      "  name=\"lazy_result\", alternative=\"lazy_output\", since=\"0.15.0\", removal=\"1.0.0\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 954.47 ms\n"
     ]
    }
   ],
   "source": [
    "dp = dp.subtract_diffraction_background('difference of gaussians',\n",
    "                          min_sigma=2, max_sigma=8,\n",
    "                          lazy_result=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform further adjustments to the data ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dp.data -= dp.data.min()\n",
    "dp.data *= 1 / dp.data.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set diffraction calibration and scan calibration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dp = pxm.signals.ElectronDiffraction2D(dp) #this is needed because of a bug in the code\n",
    "dp.set_diffraction_calibration(diffraction_calibration)\n",
    "dp.set_scan_calibration(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='tem'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  3. Pattern Matching"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pattern matching generates a database of simulated diffraction patterns and then compares all simulated patterns against each experimental pattern to find the best match"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import generators required for simulation and indexation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from diffsims.libraries.structure_library import StructureLibrary\n",
    "from diffsims.generators.diffraction_generator import DiffractionGenerator\n",
    "from diffsims.generators.library_generator import DiffractionLibraryGenerator\n",
    "\n",
    "from diffsims.generators.zap_map_generator import get_rotation_from_z_to_direction\n",
    "from diffsims.generators.rotation_list_generators import get_grid_around_beam_direction\n",
    "\n",
    "from pyxem.generators.indexation_generator import AcceleratedIndexationGenerator\n",
    "from pyxem.utils.indexation_utils import results_dict_to_crystal_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1. Define Library of Structures & Orientations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the crystal phases to be included in the simulated library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "structure_zb = diffpy.structure.loadStructure('./data/02/GaAs_mp-2534_conventional_standard.cif')\n",
    "structure_wz = diffpy.structure.loadStructure('./data/02/GaAs_mp-8883_conventional_standard.cif')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a basic rotations list.    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct a StructureLibrary defining crystal structures and orientations for which diffraction will be simulated "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rot_list_cubic = np.array([[0.0, 90.0, 225.0],])\n",
    "rot_list_hex = np.array([[0.0, 90.0, 240.0],])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "struc_lib = StructureLibrary(['ZB','WZ'],\n",
    "                             [structure_zb,structure_wz],\n",
    "                             [rot_list_cubic,rot_list_hex])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='temb'></a>\n",
    "###  3.2. Simulate Diffraction for all Structures & Orientations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a diffsims DiffractionGenerator with diffraction simulation parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "diff_gen = DiffractionGenerator(accelerating_voltage=accelarating_voltage)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize a diffsims DiffractionLibraryGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lib_gen = DiffractionLibraryGenerator(diff_gen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calulate library of diffraction patterns for all phases and unique orientations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "target_pattern_dimension_pixels = dp.axes_manager.signal_shape[0]\n",
    "half_size = target_pattern_dimension_pixels // 2\n",
    "reciprocal_radius = diffraction_calibration*(half_size - 1)\n",
    "\n",
    "diff_lib = lib_gen.get_diffraction_library(struc_lib,\n",
    "                                           calibration=diffraction_calibration,\n",
    "                                           reciprocal_radius=reciprocal_radius,\n",
    "                                           half_shape=(half_size, half_size),\n",
    "                                           max_excitation_error=1/10,\n",
    "                                           with_direct_beam=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optionally, save the library for later use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "diff_lib.pickle_library('./GaAs_cubic_hex.pickle')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If saved, the library can be loaded as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from diffsims.libraries.diffraction_library import load_DiffractionLibrary\n",
    "diff_lib = load_DiffractionLibrary('./GaAs_cubic_hex.pickle', safety=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='temb'></a>\n",
    "###  3.3. Pattern Matching Indexation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize `TemplateIndexationGenerator` with the experimental data and diffraction library and perform correlation, returning the `n_largest` matches with highest correlation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\"><b>Note:</b> Try using the workflow from 11 Accelerated Orientation Mapping</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[                                        ] | 0% Completed | 1.68 s ms"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed | 3.98 s\n"
     ]
    }
   ],
   "source": [
    "indexer = AcceleratedIndexationGenerator(dp, diff_lib)\n",
    "indexation_results,phase_dict  = indexer.correlate(n_largest=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the solutions via a plotting (can be slow, so we don't run by default)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#if False:\n",
    "#    indexation_results.plot_best_matching_results_on_signal(dp, diff_lib)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get crystallographic map from indexation results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "crystal_map = results_dict_to_crystal_map(indexation_results, phase_key_dict=phase_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "crystal_map is now a CrystalMap object, which comes from orix, see their documentation for details. Below we lift their code to plot a phase map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from orix import plot\n",
    "\n",
    "fig, ax = plt.subplots(subplot_kw=dict(projection=\"plot_map\"))\n",
    "im = ax.plot_map(crystal_map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='vec'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  4. Vector Matching"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\"><b>Note:</b> This workflow is less well developed than the template matching one, and may well be broken</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vector matching generates a database of vector pairs (magnitues and inter-vector angles) and then compares all theoretical values against each measured diffraction vector pair to find the best match"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import generators required for simulation and indexation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from diffsims.generators.library_generator import VectorLibraryGenerator\n",
    "#from diffsims.libraries.structure_library import StructureLibrary\n",
    "#from diffsims.libraries.vector_library import load_VectorLibrary\n",
    "\n",
    "#from pyxem.generators.indexation_generator import VectorIndexationGenerator\n",
    "\n",
    "#from pyxem.generators.subpixelrefinement_generator import SubpixelrefinementGenerator\n",
    "#from pyxem.signals.diffraction_vectors import DiffractionVectors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='veca'></a>\n",
    "###  4.1. Define Library of Structures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define crystal structure for which to determine theoretical vector pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#structure_zb = diffpy.structure.loadStructure('./data/02/GaAs_mp-2534_conventional_standard.cif')\n",
    "#structure_wz = diffpy.structure.loadStructure('./data/02/GaAs_mp-8883_conventional_standard.cif')\n",
    "\n",
    "#structure_library = StructureLibrary(['ZB', 'WZ'],\n",
    "#                                     [structure_zb, structure_wz],\n",
    "#                                     [[], []])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize VectorLibraryGenerator with structures to be considered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#vlib_gen = VectorLibraryGenerator(structure_library)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Determine VectorLibrary with all vectors within given reciprocal radius"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reciprocal_radius = diffraction_calibration*(half_size - 1)\n",
    "\n",
    "#vec_lib = vlib_gen.get_vector_library(reciprocal_radius)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optionally, save the library for later use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#vec_lib.pickle_library('./GaAs_cubic_hex_vectors.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#vec_lib = load_VectorLibrary('./GaAs_cubic_hex_vectors.pickle',safety=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2. Find Diffraction Peaks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tune peak finding parameters interactively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dp.find_peaks(interactive=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform peak finding on the data with parameters from above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#peaks = dp.find_peaks(method='difference_of_gaussian',\n",
    "#                      min_sigma=0.005,\n",
    "#                      max_sigma=5.0,\n",
    "#                      sigma_ratio=2.0,\n",
    "#                      threshold=0.06,\n",
    "#                      overlap=0.8,\n",
    "#                      interactive=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "coaxing peaks back into a DiffractionVectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#peaks = DiffractionVectors(peaks).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`peaks` now contain the 2D positions of the diffraction spots on the detector. The vector matching method works in 3D coordinates, which are found by projecting the detector positions back onto the Ewald sphere. Because the methods that follow are slow, we constrain ourselves to looking at a smaller subset of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#peaks = peaks.inav[:2,:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#peaks.calculate_cartesian_coordinates?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#peaks.calculate_cartesian_coordinates(accelerating_voltage=accelarating_voltage,\n",
    "#                                      camera_length=camera_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='vecb'></a>\n",
    "###  4.3. Vector Matching Indexation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize `VectorIndexationGenerator` with the experimental data and vector library and perform indexation using `n_peaks_to_index` and returning the `n_best` indexation results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\"><b>Alert: This code no longer works on this example, and may even be completely broken. Caution is advised.</b> </div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#indexation_generator = VectorIndexationGenerator(peaks, vec_lib)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#indexation_results = indexation_generator.index_vectors(mag_tol=3*diffraction_calibration,\n",
    "#                                                        angle_tol=4,  # degree\n",
    "#                                                        index_error_tol=0.2,\n",
    "#                                                        n_peaks_to_index=7,\n",
    "#                                                        n_best=5,\n",
    "#                                                        show_progressbar=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#indexation_results.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Refine all crystal orientations for improved phase reliability and orientation reliability maps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#refined_results = indexation_generator.refine_n_best_orientations(indexation_results,\n",
    "#                                                                  accelarating_voltage=accelarating_voltage,\n",
    "#                                                                  camera_length=camera_length,\n",
    "#                                                                  index_error_tol=0.2,\n",
    "#                                                                  vary_angles=True,\n",
    "#                                                                  vary_scale=True,\n",
    "#                                                                  method=\"leastsq\")\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get crystallographic map from optimized indexation results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#crystal_map = refined_results.get_crystallographic_map()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See the objections documentation for further details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#crystal_map?"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pyxem-dev",
   "language": "python",
   "name": "pyxem-dev"
  },
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