Lucy ghrccloud 6352 wbandimpacts cloud only

mdx/granule_metadata_extractor/src/helpers/creators/wbandimpacts.py

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+# create lookup zip for wbandimpacts 
+# for all future collections
+from datetime import datetime, timedelta
+from utils.mdx import MDX
+import cProfile
+import time
+import math
+import re
+
+from netCDF4 import Dataset
+import numpy as np
+from math import radians, degrees, sin, cos, asin, acos, sqrt
+
+short_name = "wbandimpacts"
+provider_path = "wbandimpacts__1/"
+file_type = "netCDF-3"
+
+class MDXProcessing(MDX):
+
+    def __init__(self):
+        super().__init__()
+
+    def process(self, filename, file_obj_stream) -> dict:
+        """
+        Individual collection processing logic for spatial and temporal 
+        metadata extraction
+        :param filename: name of file to process
+        :type filename: str
+        :param file_obj_stream: file object stream to be processed
+        :type file_obj_stream: botocore.response.StreamingBody
+        """
+        return self.get_nc_metadata(filename, file_obj_stream)
+
+
+    def get_nc_metadata(self, filename, file_obj_stream):
+        """
+        Extract temporal and spatial metadata from netCDF-3 files
+        """
+        #if 'IMPACTS_ACHV_WBAND_RHI_UCONN20230217_014003Z_to_20230217_014205Z_dR50m.nc' in filename:
+        #    start_time = datetime(2100,1,1)
+        #    end_time = datetime(1900,1,1)
+        #    north = -90.
+        #    south = 90.
+        #    east = -180.
+        #    west = 180.
+        #    return {
+        #    "start": start_time,
+        #    "end": end_time,
+        #    "north": north,
+        #    "south": south,
+        #    "east": east,
+        #    "west": west,
+        #    "format": file_type
+        #     }
+
+        data = Dataset("in-mem-file", mode='r', memory=file_obj_stream.read())
+
+        #Raxpol radar range ~ 50 km
+        radar_range = 50.
+        #Earth radius ~ 6371 km
+        radar_lat = float(data['latitude'][0])
+        radar_lon = float(data['longitude'][0])
+        mlat = radians(radar_lat)
+        mlon = radians(radar_lon)
+        plat = mlat
+        dlon = degrees(acos((cos(radar_range/6371)-sin(mlat)*sin(plat))/cos(mlat)/cos(plat)))
+        dlat = degrees(radar_range/6371.)
+        north, south, east, west = [radar_lat+dlat, radar_lat-dlat,
+                                    radar_lon+dlon, radar_lon-dlon]
+
+        tkn=data['time_coverage_start'][:]
+        tkn=tkn[tkn.mask==False]
+        t0=[x.decode("utf-8") for x in tkn]
+        t0_str = ''.join(t0) #'2023-01-25T11:46:08Z'
+        start_time = datetime.strptime(t0_str,'%Y-%m-%dT%H:%M:%SZ') 
+
+
+        tkn=data['time_coverage_end'][:]
+        tkn=tkn[tkn.mask==False]
+        t1=[x.decode("utf-8") for x in tkn]
+        t1_str = ''.join(t1) #'2023-01-25T11:46:08Z'
+        end_time = datetime.strptime(t1_str,'%Y-%m-%dT%H:%M:%SZ') 
+
+        data.close()
+        return {
+            "start": start_time,
+            "end": end_time,
+            "north": north,
+            "south": south,
+            "east": east,
+            "west": west,
+            "format": file_type
+        }
+
+
+    def main(self):
+        # start_time = time.time()
+        self.process_collection(short_name, provider_path)
+        # elapsed_time = time.time() - start_time
+        # print(f"Elapsed time in seconds: {elapsed_time}")
+        self.shutdown_ec2()
+
+
+if __name__ == '__main__':
+    MDXProcessing().main()
+    # The below can be use to run a profiler and see which functions are
+    # taking the most time to process
+    # cProfile.run('MDXProcessing().main()', sort='tottime')