{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MTSAT-1R/2 HRIT Reader Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supported Files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MTSAT-1R JAMI and MTSAT-2 IMAGER data in [JMA HRIT format](https://www.jma.go.jp/jma/jma-eng/satellite/introduction/4_2HRIT.pdf). Both __segmented__ (data split into multiple files) and __non-segmented__ (data in one file) images, as well as __full disk__ and __half disk__ (northern/southern hemisphere) images are supported.\n",
    "\n",
    "Example filenames:\n",
    "\n",
    "- Segmented: `IMG_DK01IR1_201006150332_001`\n",
    "- Non-Segmented: `IMG_DK02IR1_20140524` or `HRIT_MTSAT2_20140524_2201_DK02IR1`\n",
    "\n",
    "Full disk scans are identified by ``DK01`` , half disk scans by ``DK02/03`` (northern/southern hemisphere). \n",
    "\n",
    "Sample data can be found here: https://www.data.jma.go.jp/mscweb/en/operation/sample/hrit.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can either read the raw detector counts, or calibrate to Reflectance (VIS channel) or Brightness Temperature (IR channels). Radiance is not supported."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ``SatPy`` reader names are ``jami_hrit`` for JAMI@MTSAT-1R and ``mtsat2-imager_hrit`` for IMAGER@MTSAT-2. The following examples are for MTSAT-2, but you only have to change the reader name to adapt them to MTSAT-1R."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mosaic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import satpy\n",
    "\n",
    "channels = ['VIS', 'IR4', 'IR3', 'IR1', 'IR2']  # sorted by increasing wavelength\n",
    "\n",
    "# Create full disk scene of segmented files\n",
    "filenames = glob.glob('IMG_DK01???_201006150332_???')\n",
    "scene = satpy.Scene(filenames=filenames, reader='mtsat2-imager_hrit')\n",
    "\n",
    "# Save quicklook of each channel to file\n",
    "for ch in channels:\n",
    "    scene.load([ch])\n",
    "    scene.save_dataset(ch, filename=ch+'.png')\n",
    "    del scene[ch]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then run the following command to create a mosaic from the `.png` files:\n",
    "\n",
    "    montage VIS.png IR4.png IR3.png IR1.png IR2.png -geometry 256x256 -background black mtsat2_mosaic.jpg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"mtsat2_mosaic.jpg?1\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Half Disk Images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Half disk images are supported, too! Information about the covered area are provided in the dataset's area definition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Area ID: NH\n",
       "Description: Northern Hemisphere\n",
       "Projection ID: geosmsg\n",
       "Projection: {'a': '6378169.0', 'b': '6356583.8', 'h': '35785831.0', 'lon_0': '145.0', 'proj': 'geos', 'units': 'm'}\n",
       "Number of columns: 2750\n",
       "Number of rows: 1375\n",
       "Area extent: (-5498000.088960204, -198000.00320373234, 5502000.089024927, 5302000.085788833)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scene_nh = satpy.Scene(filenames=['HRIT_MTSAT2_20140524_0101_DK02IR1'], reader='mtsat2-imger_hrit')\n",
    "scene_nh.load(['IR1'], calibration='brightness_temperature')\n",
    "scene_nh['IR1'].area"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quicklook using cartopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "crs = scene_nh['IR1'].area.to_cartopy_crs()\n",
    "ax = plt.axes(projection=crs)\n",
    "ax.coastlines()\n",
    "ax.gridlines()\n",
    "im = ax.imshow(scene_nh['IR1'], origin='upper', transform=crs, extent=crs.bounds)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Half disk images can easily be resampled to the full disk:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/cmsaf/nfshome/routcm/Modules_CentOS/python/2.7.14/lib/python2.7/site-packages/cartopy/mpl/feature_artist.py:136: UserWarning: Unable to determine extent. Defaulting to global.\n",
      "  warnings.warn('Unable to determine extent. Defaulting to global.')\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pyresample.geometry\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "full_disk = pyresample.geometry.AreaDefinition(\n",
    "    area_id='FLDK',\n",
    "    name='Full Disk',\n",
    "    proj_id='geosmsg',\n",
    "    proj_dict={'a': '6378169.0', \n",
    "               'b': '6356583.8', \n",
    "               'h': '35785831.0', \n",
    "               'lon_0': '145.0', \n",
    "               'proj': 'geos',\n",
    "               'units': 'm'},\n",
    "    y_size=2750, x_size=2750,\n",
    "    area_extent=(-5498000.088960204, -5498000.088960204, \n",
    "                 5502000.089024927, 5502000.089024927)\n",
    ")  # copied from a full disk scene\n",
    "\n",
    "# Resample to full disk\n",
    "resampled = scene_nh.resample(full_disk)\n",
    "\n",
    "# Plot\n",
    "crs = full_disk.to_cartopy_crs()\n",
    "fig, ax = plt.subplots(subplot_kw=dict(projection=crs))\n",
    "ax.gridlines()\n",
    "ax.coastlines()\n",
    "im = ax.imshow(resampled['IR1'], origin='upper', transform=crs, extent=crs.bounds)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}