Merge pull request #89 from ghrcdaac/Lucy-GHRCCLOUD-5788-tammsimpacts…

…-CloudOnly

Lucy ghrccloud 5788 tammsimpacts cloud only

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

@@ -0,0 +1,80 @@
+# Create lookup zip for tammsimpacts
+# for all future collections
+from datetime import datetime, timedelta
+from utils.mdx import MDX
+import cProfile
+import time
+import math
+import re
+
+short_name = "tammsimpacts"
+provider_path = "tammsimpacts/fieldCampaigns/impacts/TAMMS/data/"
+file_type = "ASCII"
+
+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.read_metadata_ascii(filename, file_obj_stream)
+
+    def read_metadata_ascii(self,filename, file_obj_stream):
+        """
+        Extract temporal and spatial metadata from ascii files
+        """
+        file_lines = []
+        for encoded_line in file_obj_stream.iter_lines():
+            file_lines.append(encoded_line.decode("utf-8"))
+
+        sec0 = []
+        lat0 = []
+        lon0 = []
+        num_header_lines = int(file_lines[0].split(',')[0])
+        for line in file_lines[num_header_lines:]:
+            tkn = line.split(',')
+            if tkn[1] != '-9999.000000' and tkn[2] != '-9999.000000':
+               sec, lat, lon = [float(tkn[0]), float(tkn[1]), float(tkn[2])]
+               sec0.append(sec)
+               lat0.append(lat)
+               lon0.append(lon)
+
+        utc_date = datetime.strptime(filename.split('/')[-1].split('_')[-2],'%Y%m%d')
+        minTime = utc_date+timedelta(seconds=min(sec0))
+        maxTime = utc_date+timedelta(seconds=max(sec0))
+        north = max(lat0)
+        south = min(lat0)
+        west = min(lon0)
+        east = max(lon0)
+
+        return {
+            "start": minTime,
+            "end": maxTime,
+            "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')