{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Converting STPT mosaic datasets from Zarr to TIFF format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the module that we are going to use. The class is `STPTDataset` defined in `imaxt_image.datasets`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imaxt_image.datasets import STPTDataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define here the location (`path`) and name (`name`) of the dataset. Additionaly set the scale that we want to use: 1, 2, 4, 8, 16 or 32 where 1 is the original resolution, 2 is downsampled 2x2 pixels, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"/data/meds1_c/storage/processed/stpt/\"\n",
    "name = \"20200311_balbc_8020_3dIMC_20x15_2x8umOS_set1\"\n",
    "scale = 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We read the data as shown below, instantiating the `STPTDataset` class wih the location of the dataset and the scale (resolution) that we want to read."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (channel: 4, x: 2340, y: 1820, z: 2)\n",
       "Coordinates:\n",
       "  * channel  (channel) int64 1 2 3 4\n",
       "    type     <U6 'mosaic'\n",
       "  * x        (x) int64 0 1 2 3 4 5 6 7 ... 2333 2334 2335 2336 2337 2338 2339\n",
       "  * y        (y) int64 0 1 2 3 4 5 6 7 ... 1813 1814 1815 1816 1817 1818 1819\n",
       "  * z        (z) int64 0 1\n",
       "Data variables:\n",
       "    S001     (z, channel, y, x) float64 dask.array<chunksize=(1, 1, 1040, 1040), meta=np.ndarray>\n",
       "    S002     (z, channel, y, x) float64 dask.array<chunksize=(1, 1, 1040, 1040), meta=np.ndarray>\n",
       "    S003     (z, channel, y, x) float64 dask.array<chunksize=(1, 1, 1040, 1040), meta=np.ndarray>\n",
       "    S004     (z, channel, y, x) float64 dask.array<chunksize=(1, 1, 1040, 1040), meta=np.ndarray>\n",
       "    S005     (z, channel, y, x) float64 dask.array<chunksize=(1, 1, 1040, 1040), meta=np.ndarray>\n",
       "    S006     (z, channel, y, x) float64 dask.array<chunksize=(1, 1, 1040, 1040), meta=np.ndarray>\n",
       "    S007     (z, channel, y, x) float64 dask.array<chunksize=(1, 1, 1040, 1040), meta=np.ndarray>\n",
       "    S008     (z, channel, y, x) float64 dask.array<chunksize=(1, 1, 1040, 1040), meta=np.ndarray>\n",
       "    S009     (z, channel, y, x) float64 dask.array<chunksize=(1, 1, 1040, 1040), meta=np.ndarray>\n",
       "    S010     (z, channel, y, x) float64 dask.array<chunksize=(1, 1, 1040, 1040), meta=np.ndarray>\n",
       "    S011     (z, channel, y, x) float64 dask.array<chunksize=(1, 1, 1040, 1040), meta=np.ndarray>\n",
       "    S012     (z, channel, y, x) float64 dask.array<chunksize=(1, 1, 1040, 1040), meta=np.ndarray>\n",
       "    S013     (z, channel, y, x) float64 dask.array<chunksize=(1, 1, 1040, 1040), meta=np.ndarray>\n",
       "    S014     (z, channel, y, x) float64 dask.array<chunksize=(1, 1, 1040, 1040), meta=np.ndarray>\n",
       "    S015     (z, channel, y, x) float64 dask.array<chunksize=(1, 1, 1040, 1040), meta=np.ndarray>\n",
       "    S016     (z, channel, y, x) float64 dask.array<chunksize=(1, 1, 1040, 1040), meta=np.ndarray>\n",
       "    S017     (z, channel, y, x) float64 dask.array<chunksize=(1, 1, 1040, 1040), meta=np.ndarray>\n",
       "    S018     (z, channel, y, x) float64 dask.array<chunksize=(1, 1, 1040, 1040), meta=np.ndarray>\n",
       "    S019     (z, channel, y, x) float64 dask.array<chunksize=(1, 1, 1040, 1040), meta=np.ndarray>\n",
       "    S020     (z, channel, y, x) float64 dask.array<chunksize=(1, 1, 1040, 1040), meta=np.ndarray>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = STPTDataset(name, path, scale=scale)\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can access e.g. the first slide as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'S001' (z: 2, channel: 4, y: 1820, x: 2340)>\n",
       "dask.array<astype, shape=(2, 4, 1820, 2340), dtype=uint16, chunksize=(1, 1, 1040, 1040), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * channel  (channel) int64 1 2 3 4\n",
       "    type     <U6 'mosaic'\n",
       "  * x        (x) int64 0 1 2 3 4 5 6 7 ... 2333 2334 2335 2336 2337 2338 2339\n",
       "  * y        (y) int64 0 1 2 3 4 5 6 7 ... 1813 1814 1815 1816 1817 1818 1819\n",
       "  * z        (z) int64 0 1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds[\"S001\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to save the slide to a OME.TIFF file use the `.to_tiff` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Written ./20200311_balbc_8020_3dIMC_20x15_2x8umOS_set1_S001_l.8.ome.tiff\n"
     ]
    }
   ],
   "source": [
    "ds[\"S001\"].to_tiff(name, dir=\".\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where the `dir` keyword is the directory where the file is to be saved."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the same way we can define just a region to save:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Written ./20200311_balbc_8020_3dIMC_20x15_2x8umOS_set1_cutout_S001_l.8.ome.tiff\n"
     ]
    }
   ],
   "source": [
    "ds[\"S001\"][5000:7000,6000:9000].to_tiff(f\"{name}_cutout\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finnally combined with a `for` loop the above can be done for all sections or for a range of sections:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Written ./20200311_balbc_8020_3dIMC_20x15_2x8umOS_set1_S001_l.8.ome.tiff\n",
      "Written ./20200311_balbc_8020_3dIMC_20x15_2x8umOS_set1_S002_l.8.ome.tiff\n",
      "Written ./20200311_balbc_8020_3dIMC_20x15_2x8umOS_set1_S003_l.8.ome.tiff\n",
      "Written ./20200311_balbc_8020_3dIMC_20x15_2x8umOS_set1_S004_l.8.ome.tiff\n",
      "Written ./20200311_balbc_8020_3dIMC_20x15_2x8umOS_set1_S005_l.8.ome.tiff\n",
      "Written ./20200311_balbc_8020_3dIMC_20x15_2x8umOS_set1_S006_l.8.ome.tiff\n",
      "Written ./20200311_balbc_8020_3dIMC_20x15_2x8umOS_set1_S007_l.8.ome.tiff\n",
      "Written ./20200311_balbc_8020_3dIMC_20x15_2x8umOS_set1_S008_l.8.ome.tiff\n",
      "Written ./20200311_balbc_8020_3dIMC_20x15_2x8umOS_set1_S009_l.8.ome.tiff\n",
      "Written ./20200311_balbc_8020_3dIMC_20x15_2x8umOS_set1_S010_l.8.ome.tiff\n",
      "Written ./20200311_balbc_8020_3dIMC_20x15_2x8umOS_set1_S011_l.8.ome.tiff\n",
      "Written ./20200311_balbc_8020_3dIMC_20x15_2x8umOS_set1_S012_l.8.ome.tiff\n",
      "Written ./20200311_balbc_8020_3dIMC_20x15_2x8umOS_set1_S013_l.8.ome.tiff\n",
      "Written ./20200311_balbc_8020_3dIMC_20x15_2x8umOS_set1_S014_l.8.ome.tiff\n",
      "Written ./20200311_balbc_8020_3dIMC_20x15_2x8umOS_set1_S015_l.8.ome.tiff\n",
      "Written ./20200311_balbc_8020_3dIMC_20x15_2x8umOS_set1_S016_l.8.ome.tiff\n",
      "Written ./20200311_balbc_8020_3dIMC_20x15_2x8umOS_set1_S017_l.8.ome.tiff\n",
      "Written ./20200311_balbc_8020_3dIMC_20x15_2x8umOS_set1_S018_l.8.ome.tiff\n",
      "Written ./20200311_balbc_8020_3dIMC_20x15_2x8umOS_set1_S019_l.8.ome.tiff\n",
      "Written ./20200311_balbc_8020_3dIMC_20x15_2x8umOS_set1_S020_l.8.ome.tiff\n"
     ]
    }
   ],
   "source": [
    "for section in ds:\n",
    "    section.to_tiff(name, dir=\".\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}