{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import pathlib\n",
    "from tqdm.notebook import tqdm\n",
    "import napari\n",
    "import dask.array as da\n",
    "from coda_visual.datasets.raw_hamamatsu_file import RawHamamatsuFile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "RAW_IMAGE_DIR = pathlib.Path().resolve().parent.parent / \"sample_dataset\" / \"20x files\"\n",
    "raw_image_list = sorted(list(RAW_IMAGE_DIR.glob(\"*.ndpi\")), key=lambda x: x.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "LOW_RES_LEVEL = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "largest_layer_width = 0.\n",
    "largest_layer_height = 0.\n",
    "for fp in raw_image_list:\n",
    "    hamamatsu_file = RawHamamatsuFile(fp)\n",
    "    level_width, level_height = hamamatsu_file.openslide_slide.level_dimensions[LOW_RES_LEVEL]\n",
    "    if level_width>largest_layer_width:\n",
    "        largest_layer_width = level_width\n",
    "    if level_height>largest_layer_height:\n",
    "        largest_layer_height = level_height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ad5d890426524863ac49fbb21140a8bb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading images...:   0%|          | 0/150 [00:00<?, ?step/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "progress_bar = tqdm(total=len(raw_image_list), desc=\"Loading images...\", unit=\"step\")\n",
    "layer_images = []\n",
    "for raw_image_fp in raw_image_list:\n",
    "    hamamatsu_file = RawHamamatsuFile(raw_image_fp)\n",
    "    layer_image = hamamatsu_file.get_dask_image_level(LOW_RES_LEVEL, pad_to=(largest_layer_width, largest_layer_height))\n",
    "    layer_images.append(layer_image)\n",
    "    progress_bar.update(1)\n",
    "layer_images = da.stack(layer_images, axis=0)\n",
    "progress_bar.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Image layer 'dataset' at 0x11fdccd30>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viewer = napari.Viewer()\n",
    "viewer.add_image(\n",
    "    layer_images,\n",
    "    name=\"dataset\",\n",
    "    contrast_limits=[0, 255],\n",
    "    rgb=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "coda-visual",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}