notebooks/dev/goes/1.4-GOES-geoprocess-val.ipynb

notebooks/dev/goes/0.3-GOES-filename-parser.ipynb

@@ -261,617 +261,6 @@
    "source": [
     "GOESFileName.from_filename(filenames[10])"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds = xr.open_dataset(filenames[10])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;band_wavelength&#x27; (band: 1)&gt;\n",
-       "[1 values with dtype=float32]\n",
-       "Coordinates:\n",
-       "    t                datetime64[ns] ...\n",
-       "    y_image          float32 ...\n",
-       "    x_image          float32 ...\n",
-       "    band_id          (band) int8 ...\n",
-       "    band_wavelength  (band) float32 ...\n",
-       "Dimensions without coordinates: band\n",
-       "Attributes:\n",
-       "    long_name:      ABI band central wavelength\n",
-       "    standard_name:  sensor_band_central_radiation_wavelength\n",
-       "    units:          um</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'band_wavelength'</div><ul class='xr-dim-list'><li><span>band</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f731bb08-9211-461b-adb0-62c31c89291a' class='xr-array-in' type='checkbox' checked><label for='section-f731bb08-9211-461b-adb0-62c31c89291a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[1 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-bdac9820-dd2d-4f7d-ba8d-8b7d393b211b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bdac9820-dd2d-4f7d-ba8d-8b7d393b211b' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a6541f93-6c27-43c3-b769-0c77543dfcda' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6541f93-6c27-43c3-b769-0c77543dfcda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-685001ae-2f7a-497f-b93f-2372562fe5c1' class='xr-var-data-in' type='checkbox'><label for='data-685001ae-2f7a-497f-b93f-2372562fe5c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>J2000 epoch mid-point between the start and end image scan in seconds</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_image</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7fb53ca4-97e2-47aa-b152-333e974d27e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7fb53ca4-97e2-47aa-b152-333e974d27e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92360da5-8694-48b4-b13e-3b86d0d2a40a' class='xr-var-data-in' type='checkbox'><label for='data-92360da5-8694-48b4-b13e-3b86d0d2a40a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GOES-R fixed grid projection y-coordinate center of image</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>rad</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_image</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fbdc059e-5ec1-42ec-8e9b-1d78b25a744c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fbdc059e-5ec1-42ec-8e9b-1d78b25a744c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ae26097-9f04-41e9-9b4c-ddd151a0fd2f' class='xr-var-data-in' type='checkbox'><label for='data-2ae26097-9f04-41e9-9b4c-ddd151a0fd2f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>GOES-R fixed grid projection x-coordinate center of image</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>rad</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>band_id</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0408ccc7-dca4-4c5e-acc7-a685891b5cba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0408ccc7-dca4-4c5e-acc7-a685891b5cba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61c826f1-6743-4ef3-975e-a275669a9e06' class='xr-var-data-in' type='checkbox'><label for='data-61c826f1-6743-4ef3-975e-a275669a9e06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ABI band number</dd><dt><span>standard_name :</span></dt><dd>sensor_band_identifier</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>band_wavelength</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-37ee02fa-6885-44f3-b215-174c4a207792' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37ee02fa-6885-44f3-b215-174c4a207792' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8da9520b-303c-4e34-8e17-58be8cbfd5fc' class='xr-var-data-in' type='checkbox'><label for='data-8da9520b-303c-4e34-8e17-58be8cbfd5fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ABI band central wavelength</dd><dt><span>standard_name :</span></dt><dd>sensor_band_central_radiation_wavelength</dd><dt><span>units :</span></dt><dd>um</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-81634e0c-e097-43f4-b4c3-7780c13caa68' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-81634e0c-e097-43f4-b4c3-7780c13caa68' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-5effea18-e3c5-4fbc-98de-1d3f520ac8c1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5effea18-e3c5-4fbc-98de-1d3f520ac8c1' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ABI band central wavelength</dd><dt><span>standard_name :</span></dt><dd>sensor_band_central_radiation_wavelength</dd><dt><span>units :</span></dt><dd>um</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'band_wavelength' (band: 1)>\n",
-       "[1 values with dtype=float32]\n",
-       "Coordinates:\n",
-       "    t                datetime64[ns] ...\n",
-       "    y_image          float32 ...\n",
-       "    x_image          float32 ...\n",
-       "    band_id          (band) int8 ...\n",
-       "    band_wavelength  (band) float32 ...\n",
-       "Dimensions without coordinates: band\n",
-       "Attributes:\n",
-       "    long_name:      ABI band central wavelength\n",
-       "    standard_name:  sensor_band_central_radiation_wavelength\n",
-       "    units:          um"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds.band_wavelength"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/anaconda3/envs/iti-env/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
-   "source": [
-    "from rs_tools._src.utils.io import get_list_filenames\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def parse_goes16_dates_from_file(file: str):\n",
-    "    timestamp = Path(file).name.replace(\"-\",\"_\").split(\"_\")\n",
-    "    return datetime.strptime(timestamp[-2][1:], \"%Y%j%H%M%S%f\").strftime(\"%Y%j%H%M\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def goes_files(read_path):\n",
-    "        \"\"\"\n",
-    "        Returns a list of all GOES files in the read path.\n",
-    "\n",
-    "        Returns:\n",
-    "            List[str]: A list of file paths.\n",
-    "        \"\"\"\n",
-    "        # get a list of all GOES files from specified path\n",
-    "        files = get_list_filenames(read_path, \".nc\")\n",
-    "        return files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path = '/Users/anna.jungbluth/Desktop/git/rs_tools/data/goes16/'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "files_ = goes_files(path)\n",
-    "unique_times = list(set(map(parse_goes16_dates_from_file, files_)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['20202761419',\n",
-       " '20182741420',\n",
-       " '20202761439',\n",
-       " '20182751411',\n",
-       " '20202751409',\n",
-       " '20182741405',\n",
-       " '20202761409']"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "unique_times"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# get files from unique times\n",
-    "files = []\n",
-    "for ut in unique_times:\n",
-    "    files.extend(filter(lambda x: ut in x, files_))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "files = list(filter(lambda x: unique_times[1] in x, files_))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "files = list(filter(lambda x: \"Rad\" in x, files))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['/Users/anna.jungbluth/Desktop/git/rs_tools/data/goes16/L1b/noaa-goes16/ABI-L1b-RadF/2018/274/14/OR_ABI-L1b-RadF-M4C01_G16_s20182741415224_e20182741420026_c20182741420080.nc',\n",
-       " '/Users/anna.jungbluth/Desktop/git/rs_tools/data/goes16/L1b/noaa-goes16/ABI-L1b-RadF/2018/274/14/OR_ABI-L1b-RadF-M4C02_G16_s20182741415224_e20182741420026_c20182741420075.nc',\n",
-       " '/Users/anna.jungbluth/Desktop/git/rs_tools/data/goes16/L1b/noaa-goes16/ABI-L1b-RadF/2018/274/14/OR_ABI-L1b-RadF-M4C03_G16_s20182741415224_e20182741420026_c20182741420081.nc',\n",
-       " '/Users/anna.jungbluth/Desktop/git/rs_tools/data/goes16/L1b/noaa-goes16/ABI-L1b-RadF/2018/274/14/OR_ABI-L1b-RadF-M4C04_G16_s20182741415224_e20182741420027_c20182741420080.nc',\n",
-       " '/Users/anna.jungbluth/Desktop/git/rs_tools/data/goes16/L1b/noaa-goes16/ABI-L1b-RadF/2018/274/14/OR_ABI-L1b-RadF-M4C05_G16_s20182741415224_e20182741420026_c20182741420082.nc',\n",
-       " '/Users/anna.jungbluth/Desktop/git/rs_tools/data/goes16/L1b/noaa-goes16/ABI-L1b-RadF/2018/274/14/OR_ABI-L1b-RadF-M4C06_G16_s20182741415224_e20182741420033_c20182741420067.nc',\n",
-       " '/Users/anna.jungbluth/Desktop/git/rs_tools/data/goes16/L1b/noaa-goes16/ABI-L1b-RadF/2018/274/14/OR_ABI-L1b-RadF-M4C07_G16_s20182741415224_e20182741420039_c20182741420083.nc',\n",
-       " '/Users/anna.jungbluth/Desktop/git/rs_tools/data/goes16/L1b/noaa-goes16/ABI-L1b-RadF/2018/274/14/OR_ABI-L1b-RadF-M4C08_G16_s20182741415224_e20182741420027_c20182741420083.nc',\n",
-       " '/Users/anna.jungbluth/Desktop/git/rs_tools/data/goes16/L1b/noaa-goes16/ABI-L1b-RadF/2018/274/14/OR_ABI-L1b-RadF-M4C09_G16_s20182741415224_e20182741420033_c20182741420101.nc',\n",
-       " '/Users/anna.jungbluth/Desktop/git/rs_tools/data/goes16/L1b/noaa-goes16/ABI-L1b-RadF/2018/274/14/OR_ABI-L1b-RadF-M4C10_G16_s20182741415224_e20182741420039_c20182741420095.nc',\n",
-       " '/Users/anna.jungbluth/Desktop/git/rs_tools/data/goes16/L1b/noaa-goes16/ABI-L1b-RadF/2018/274/14/OR_ABI-L1b-RadF-M4C11_G16_s20182741415224_e20182741420027_c20182741420096.nc',\n",
-       " '/Users/anna.jungbluth/Desktop/git/rs_tools/data/goes16/L1b/noaa-goes16/ABI-L1b-RadF/2018/274/14/OR_ABI-L1b-RadF-M4C12_G16_s20182741415224_e20182741420033_c20182741420094.nc',\n",
-       " '/Users/anna.jungbluth/Desktop/git/rs_tools/data/goes16/L1b/noaa-goes16/ABI-L1b-RadF/2018/274/14/OR_ABI-L1b-RadF-M4C13_G16_s20182741415224_e20182741420039_c20182741420100.nc',\n",
-       " '/Users/anna.jungbluth/Desktop/git/rs_tools/data/goes16/L1b/noaa-goes16/ABI-L1b-RadF/2018/274/14/OR_ABI-L1b-RadF-M4C14_G16_s20182741415224_e20182741420026_c20182741420101.nc',\n",
-       " '/Users/anna.jungbluth/Desktop/git/rs_tools/data/goes16/L1b/noaa-goes16/ABI-L1b-RadF/2018/274/14/OR_ABI-L1b-RadF-M4C15_G16_s20182741415224_e20182741420032_c20182741420102.nc',\n",
-       " '/Users/anna.jungbluth/Desktop/git/rs_tools/data/goes16/L1b/noaa-goes16/ABI-L1b-RadF/2018/274/14/OR_ABI-L1b-RadF-M4C16_G16_s20182741415224_e20182741420038_c20182741420099.nc']"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'long_name': 'ABI L1b Radiances',\n",
-       " 'standard_name': 'toa_outgoing_radiance_per_unit_wavelength',\n",
-       " 'sensor_band_bit_depth': 10,\n",
-       " 'valid_range': array([   0, 1022], dtype=int16),\n",
-       " 'units': 'W m-2 sr-1 um-1',\n",
-       " 'resolution': 'y: 0.000028 rad x: 0.000028 rad',\n",
-       " 'grid_mapping': 'goes_imager_projection',\n",
-       " 'cell_methods': 't: point area: point',\n",
-       " 'ancillary_variables': 'DQF'}"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds.Rad.attrs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

rs_tools/_src/geoprocessing/goes/geoprocessor_goes.py

@@ -15,6 +15,7 @@
 from rs_tools._src.geoprocessing.goes.validation import correct_goes16_bands, correct_goes16_satheight
 from rs_tools._src.geoprocessing.goes.reproject import add_goes16_crs
 from rs_tools._src.geoprocessing.reproject import convert_lat_lon_to_x_y, calc_latlon
+from rs_tools._src.geoprocessing.utils import check_sat_FOV
 import pandas as pd
 from datetime import datetime
 from functools import partial
@@ -53,7 +54,7 @@ class GOES16GeoProcessing:
     resolution: float
     read_path: str
     save_path: str
-    region: Tuple[int, int, int, int]
+    region: Optional[Tuple[int, int, int, int]]
     resample_method: str
 
     @property
@@ -91,17 +92,27 @@ def preprocess_fn(self, ds: xr.Dataset) -> Tuple[xr.Dataset, xr.Dataset]:
         # assign coordinate reference system
         ds = add_goes16_crs(ds)
 
-        if self.region is not None:
+        if self.region != (None, None, None, None):
+            logger.info(f"Subsetting data to region: {self.region}")
             # subset data
             lon_bnds = (self.region[0], self.region[2])
             lat_bnds = (self.region[1], self.region[3])
             # convert lat lon bounds to x y (in meters)
             x_bnds, y_bnds = convert_lat_lon_to_x_y(ds.FOV.crs, lon=lon_bnds, lat=lat_bnds, )
+            # check that region is within the satellite field of view
+            # compile satellite FOV
+            satellite_FOV = (min(ds.x.values), min(ds.y.values), max(ds.x.values), max(ds.y.values))
+            # compile region bounds in x y
+            region_xy = (x_bnds[0], y_bnds[0], x_bnds[1], y_bnds[1])
+            if not check_sat_FOV(region_xy, FOV=satellite_FOV):
+                raise ValueError("Region is not within the satellite field of view")
+
             ds = ds.sortby("x").sortby("y")
             # slice based on x y bounds
             ds = ds.sel(y=slice(y_bnds[0], y_bnds[1]), x=slice(x_bnds[0], x_bnds[1]))
 
         if self.resolution is not None:
+            logger.info(f"Resampling data to resolution: {self.resolution} m")
             # resampling
             ds_subset = resample_rioxarray(ds, resolution=self.resolution, method=self.resample_method)
         else:
@@ -171,46 +182,6 @@ def preprocess_fn_cloudmask(self, ds: xr.Dataset) -> xr.Dataset:
 
         return ds_subset
 
-    def preprocess_files(self):
-        """
-        Preprocesses multiple files in the read path and saves processed files to the save path.
-        """
-        # get unique times from read path
-        unique_times = list(set(map(parse_goes16_dates_from_file, self.goes_files)))
-
-        pbar_time = tqdm(unique_times)
-
-        for itime in pbar_time:
-
-            pbar_time.set_description(f"Processing: {itime}")
-
-            # get files from unique times
-            files = list(filter(lambda x: itime in x, self.goes_files))
-
-            try:
-                # load radiances
-                ds = self.preprocess_radiances(files)
-            except AssertionError:
-                logger.error(f"Skipping {itime} due to missing bands")
-                continue
-            try:
-                # load cloud mask
-                ds_clouds = self.preprocess_cloud_mask(files)["cloud_mask"]
-            except AssertionError:
-                logger.error(f"Skipping {itime} due to missing cloud mask")
-                continue
-
-            # interpolate cloud mask to data
-            ds_clouds = ds_clouds.interp(x=ds.x, y=ds.y)
-            # save cloud mask as data coordinate
-            ds = ds.assign_coords({"cloud_mask": (("y", "x"), ds_clouds.values.squeeze())})
-            ds["cloud_mask"].attrs = ds_clouds.attrs
-
-            if not os.path.exists(self.save_path):
-                os.makedirs(self.save_path)
-
-            ds.to_netcdf(Path(self.save_path).joinpath(f"{itime}_goes16.nc"), engine="netcdf4")
-
     def preprocess_radiances(self, files: List[str]) -> xr.Dataset:
         """
         Preprocesses radiances from the input files.
@@ -282,21 +253,69 @@ def preprocess_cloud_mask(self, files: List[str]) -> xr.Dataset:
 
         return ds
 
+    def preprocess_files(self):
+        """
+        Preprocesses multiple files in the read path and saves processed files to the save path.
+        """
+        # get unique times from read path
+        unique_times = list(set(map(parse_goes16_dates_from_file, self.goes_files)))
+
+        pbar_time = tqdm(unique_times)
+
+        for itime in pbar_time:
+
+            pbar_time.set_description(f"Processing: {itime}")
+
+            # get files from unique times
+            files = list(filter(lambda x: itime in x, self.goes_files))
+
+            try:
+                # load radiances
+                ds = self.preprocess_radiances(files)
+            except AssertionError:
+                logger.error(f"Skipping {itime} due to missing bands")
+                continue
+            try:
+                # load cloud mask
+                ds_clouds = self.preprocess_cloud_mask(files)["cloud_mask"]
+            except AssertionError:
+                logger.error(f"Skipping {itime} due to missing cloud mask")
+                continue
+
+            # interpolate cloud mask to data
+            ds_clouds = ds_clouds.interp(x=ds.x, y=ds.y)
+            # save cloud mask as data coordinate
+            ds = ds.assign_coords({"cloud_mask": (("y", "x"), ds_clouds.values.squeeze())})
+            ds["cloud_mask"].attrs = ds_clouds.attrs
+
+            # check if save path exists, and create if not
+            if not os.path.exists(self.save_path):
+                os.makedirs(self.save_path)
+
+            # remove file if it already exists
+            save_filename = Path(self.save_path).joinpath(f"{itime}_goes16.nc")
+            if os.path.exists(save_filename):
+                logger.info(f"File already exists. Overwriting file: {save_filename}")
+                os.remove(save_filename)
+            # save to netcdf
+            ds.to_netcdf(save_filename, engine="netcdf4")
+
+
 def geoprocess_goes16(
         resolution: float = None, # defined in meters
         read_path: str = "./",
         save_path: str = "./",
-        region: Tuple[int, int, int, int] = (-130, -15, -90, 5),
+        region: Tuple[int, int, int, int] = (None, None, None, None),
         resample_method: str = "bilinear",
 ):
     """
     Downloads MODIS TERRA and GOES 16 files for the specified period, region, and save path.
 
     Args:
-        resolution (float, optional): The resolution of the downloaded files in meters. Defaults to 1_000.
+        resolution (float, optional): The resolution of the downloaded files in meters. Defaults to None.
         read_path (str, optional): The path to read the files from. Defaults to "./".
         save_path (str, optional): The path to save the downloaded files. Defaults to "./".
-        region (Tuple[int, int, int, int], optional): The geographic region to download files for. Defaults to (-130, -15, -90, 5).
+        region (Tuple[int, int, int, int], optional): The geographic region to download files for. Defaults to None.
         resample_method (str, optional): The resampling method to use. Defaults to "bilinear".
 
     Returns:
@@ -319,6 +338,17 @@ def geoprocess_goes16(
 
 if __name__ == '__main__':
     """
+    # =========================
+    # Test Cases
+    # =========================
     python geoprocessor_goes.py --read-path "/home/data" --save-path /home/data/goes/geoprocessed
+    python geoprocessor_goes.py --read-path "/home/data" --save-path /home/data/goes/geoprocessed --resolution 1000
+    python geoprocessor_goes.py --read-path "/home/data" --save-path /home/data/goes/geoprocessed --resolution 5000
+    python geoprocessor_goes.py --read-path "/home/data" --save-path /home/data/goes/geoprocessed --resolution 5000 --region -130 -15 -90 5
+
+    # ====================
+    # FAILURE TEST CASES
+    # ====================
+    python geoprocessor_goes.py --read-path "/home/data" --save-path /home/data/goes/geoprocessed --resolution 5000 --region -200 -15 90 5
     """
     typer.run(geoprocess_goes16)

rs_tools/_src/geoprocessing/utils.py

@@ -0,0 +1,19 @@
+import numpy as np
+from typing import Tuple
+
+def check_sat_FOV(region: Tuple[int, int, int, int], FOV: Tuple[int, int, int, int]) -> bool:
+    """
+    Check if the region is within the Field of View (FOV) of the satellite.
+    
+    Args:
+        region (Tuple[int, int, int, int]): The region (lon_min, lat_min, lon_max, lat_max) to check if it is within the FOV
+        FOV (Tuple[int, int]): The Field of View (FOV) (lon_min, lat_min, lon_max, lat_max) of the satellite.
+    
+    Returns:
+        bool: True if the region is within the FOV, False otherwise.
+    """
+    # Check if the region is within the Field of View (FOV) of the satellite.
+    if abs(region[0]) <= abs(FOV[0]) and abs(region[1]) <= abs(FOV[1]) and abs(region[2]) <= abs(FOV[2]) and abs(region[3]) <= abs(FOV[3]):
+        return True
+    else:
+        return False