{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Tutorial 5: Analysis from memory",
   "id": "bd0f8378fe6c4303"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Instead of loading images and peaks from a file and converting them to polar coordinates at each step, we developed an approach that works directly from a `pygid.Conversion` instance. After the analysis, all results can be saved to a NeXus file for further use.\n",
    "\n",
    "---\n",
    "### 1. `pygid.Conversion` initialization\n",
    "\n",
    "Create a `pygid.Conversion` class instance based on the experimental parameters and load metadata using `pygid`. The detailed description can be found in [`pygid` documentation](https://pygid.readthedocs.io/en/latest/tutorials_toctree.html)."
   ],
   "id": "25b3fd41c4c0ed19"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-27T12:21:55.299536379Z",
     "start_time": "2026-03-27T12:21:55.288173017Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pygid\n",
    "\n",
    "# Experimental metadata\n",
    "exp_metadata = pygid.ExpMetadata(\n",
    "    start_time = r\"2025-09-09T20:36:23.076828\",\n",
    "    end_time    = r\"2025-09-09T20:37:24.076828\",\n",
    "    source_type = \"synchrotron\",\n",
    "    source_name = \"ESRF ID10\",\n",
    "    detector = \"eiger4m\",\n",
    "    monitor = 294302)\n",
    "\n",
    "# Sample metadata from YAML file\n",
    "smpl_metadata = pygid.SampleMetadata(path_to_load=\"../../example/sample.yaml\")"
   ],
   "id": "f5cc2a7e4042b94d",
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-27T12:21:55.323345856Z",
     "start_time": "2026-03-27T12:21:55.300591604Z"
    }
   },
   "cell_type": "code",
   "source": [
    "poni_path = '../../example/laB6_2025_09_05.poni'  # path to the PONI file\n",
    "mask_path = '../../example/mask.npy'              # path to the mask file\n",
    "filename = '../../example/eiger4m_0000.h5'        # path to the raw data\n",
    "dataset = '/entry/data0/image'                    # image root in the raw data file\n",
    "frame_num = None                                  # frame number to process\n",
    "\n",
    "# Set the experimental parameters\n",
    "params = pygid.ExpParams(\n",
    "\tponi_path = poni_path,\n",
    "    mask_path = mask_path,\n",
    "    fliplr = True,\n",
    "    flipud = True,\n",
    "    # transp = False,\n",
    "    ai = 0.075\n",
    ")\n",
    "\n",
    "# Set the range, resolution and intensity corrections\n",
    "matrix = pygid.CoordMaps(\n",
    "    params,\n",
    "    vert_positive = True, hor_positive = True,\n",
    "    q_xy_range = (0, 3.5), q_z_range = (0, 3.5), dq = 0.002,\n",
    ")\n",
    "\n",
    "# Load the data\n",
    "conversion = pygid.Conversion(\n",
    "    matrix = matrix,\n",
    "    path = filename,\n",
    "    dataset = dataset,\n",
    "    frame_num = frame_num\n",
    ")"
   ],
   "id": "c2dde4a74433fd2",
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "NOTE: the choice of the frame number will be ignored during the analysis. All images loaded at this step (`pygid.Conversion`) will be processed.",
   "id": "de6f193e6d882028"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "---\n",
    "\n",
    "### 2. `mlgidBASE` initialization\n",
    "The initialization of `mlgidBASE` should be performed before the conversion."
   ],
   "id": "3c279af3986b29b5"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-27T12:21:55.751925508Z",
     "start_time": "2026-03-27T12:21:55.323807995Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from mlgidbase import mlgidBASE\n",
    "analysis = mlgidBASE(pygid_conversion=conversion)"
   ],
   "id": "3a911dc23c676f41",
   "outputs": [],
   "execution_count": 9
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "---\n",
    "\n",
    "### 3. Run analysis methods\n",
    "\n",
    "Run analysis functions as described in previous tutorials:"
   ],
   "id": "b2e056b7ab3a6b85"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-27T12:22:01.491424637Z",
     "start_time": "2026-03-27T12:21:55.755121183Z"
    }
   },
   "cell_type": "code",
   "source": [
    "analysis.run_detection()\n",
    "analysis.run_fitting()\n",
    "analysis.run_matching(\n",
    "    cif_prepr = r'../../example/prepr_cifs.pickle',\n",
    "    peaks_type='segments')"
   ],
   "id": "977a20912c6cbbae",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - Loading model\n",
      "INFO - Using the GPU for inference\n"
     ]
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "---\n",
    "### 4. Save result"
   ],
   "id": "7092b2add763de46"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-03-27T12:22:01.535824738Z",
     "start_time": "2026-03-27T12:22:01.496427778Z"
    }
   },
   "cell_type": "code",
   "source": [
    "analysis.save_result(\n",
    "    path_to_save='../../example/BA2PbI4.h5',\n",
    "    smpl_metadata=smpl_metadata,\n",
    "    exp_metadata=exp_metadata,\n",
    ")"
   ],
   "id": "c558a9b762ba1c1b",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - Saved in /home/ainurabukaev/data/Programming/mlgid/mlgidBASE/examples/BA2PbI4.h5 in group entry_0000\n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "NOTE: The converted image is not available in memory after saving",
   "id": "1162f76ffaffb79e"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "---\n",
    "\n",
    "### Parameters\n",
    "\n",
    "- `path_to_save` (str) – output path for the HDF5 result file.\n",
    "- `h5_group` (str) – name of the NXentry dataset within the HDF5 file.\n",
    "- `overwrite_file` (bool) – if `True`, overwrites existing HDF5 file. Default is `True`.\n",
    "- `overwrite_group` (bool) – if `True`, overwrites existing NXentry group. Default is `True`.\n",
    "- `exp_metadata` (`ExpMetadata` )–  instance containing experimental information\n",
    "- `smpl_metadata` (`SampleMetadata` ) – instance containing sample information"
   ],
   "id": "6f16700a8dfcc5a5"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "---\n",
    "### Batch processing\n",
    "The analysis is not implemented for batch processing. For usage with huge amount of images use an external loop for data conversion and analysis:\n",
    "\n",
    "\n",
    "```python\n",
    "for frame_num in range(100):\n",
    "    # load a single image\n",
    "    conversion = pygid.Conversion(\n",
    "        matrix = matrix,\n",
    "        path = filename,\n",
    "        dataset = dataset,\n",
    "        frame_num = frame_num)\n",
    "\n",
    "    # run analysis\n",
    "    analysis = mlgidBASE(pygid_conversion=conversion)\n",
    "    analysis.run_detection()\n",
    "    analysis.run_fitting()\n",
    "    analysis.run_matching(\n",
    "        cif_prepr = r'../../example/prepr_cifs.pickle',\n",
    "        peaks_type='segments')\n",
    "\n",
    "    # save result\n",
    "    analysis.save_result(\n",
    "        path_to_save='result_from_conversion.h5',\n",
    "        smpl_metadata = smpl_metadata,\n",
    "        exp_metadata=exp_metadata,\n",
    "        overwrite_file=False,\n",
    "        overwrite_group=False)\n",
    "```"
   ],
   "id": "a42678b1f777cdc7"
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   "source": "",
   "id": "9cc68fc3d3f748ea",
   "outputs": [],
   "execution_count": 11
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