notebooks/04_geosat_AMI_GK2A.ipynb

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   "source": [
    "<img src=\"images/logos/ncar_pythia.png\" alt=\"pythia ncar\" />\n",
    "\n",
    "**This notebook is developed during the pythia cook-off at NCAR Mesa-Lab Boulder Colorado, June 12-14, 2024**\n",
    "\n",
    "Participants in the workshop event have the chance to practice collaborative problem-solving and hands-on learning in the field of Python programming.\n",
    "\n",
    "This notebook is part of the **Breakout Topic: Geostationary on AWS**, lead by **Jorge Humberto Bravo Mendez** jbravo2@stevens.edu, from Stevens Institute of Technology "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advance Meteorological Imager (AMI) data with Satpy\n",
    "\n",
    "\n",
    "Using Satpy to read and  Advanced Baseline Imager (ABI) data from GOES-R satellites. Here's a step-by-step guide:"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.simplefilter('ignore', SyntaxWarning)\n",
    "\n",
    "from satpy.scene import Scene\n",
    "from satpy.utils import debug_on\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "from glob import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Starting to create satpy scenes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sat_files = glob(\"input/GK2A_AMI_L1B_LA/*\")\n",
    "sat_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scn = Scene(filenames = sat_files, reader='ami_l1b')\n",
    "\n",
    "dataset_names = scn.all_dataset_names()\n",
    "\n",
    "print(dataset_names)"
   ]
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   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "scn.load(dataset_names)"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(scn.available_composite_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rgb_im = 'airmass'\n",
    "\n",
    "scn.load([rgb_im])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scn.show(rgb_im)"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = scn[rgb_im]\n",
    "\n",
    "result"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "keys = scn.keys()\n",
    "\n",
    "keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "area_info = scn['IR133'].area\n",
    "\n",
    "area_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scn.load([\"natural_color\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rs = scn['IR133'].area\n",
    "\n",
    "lscn = scn.resample(rs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lscn.load([\"natural_color\"])\n",
    "\n",
    "lscn.show(\"natural_color\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lscn.load(['true_color'])\n",
    "\n",
    "lscn.show('true_color')"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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