From 75ba3eeb48fd12134276491b85459f0a2c22d3c5 Mon Sep 17 00:00:00 2001
From: Lucy Wang <wangl1@uah.edu>
Date: Thu, 2 May 2024 14:36:27 -0500
Subject: [PATCH] Added lookup creator code pmhailclim.py

---
 .../src/helpers/creators/pmhailclim.py        | 88 +++++++++++++++++++
 1 file changed, 88 insertions(+)
 create mode 100644 mdx/granule_metadata_extractor/src/helpers/creators/pmhailclim.py

diff --git a/mdx/granule_metadata_extractor/src/helpers/creators/pmhailclim.py b/mdx/granule_metadata_extractor/src/helpers/creators/pmhailclim.py
new file mode 100644
index 0000000..2fd789d
--- /dev/null
+++ b/mdx/granule_metadata_extractor/src/helpers/creators/pmhailclim.py
@@ -0,0 +1,88 @@
+# create lookup zip for pmhailclim 
+# 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
+
+short_name = "pmhailclim"
+provider_path = "pmhailclim/"
+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
+        """
+        nc = Dataset("in-mem-file", mode='r', memory=file_obj_stream.read())
+        lat = nc.variables['latitude'][:]
+        lon = nc.variables['longitude'][:]
+        north, south, east, west = [lat.max(),lat.min(),lon.max(),lon.min()]
+
+        sat_obs = filename.split('/')[-1].split('_')[0] #TRMM,COMBO,GPM
+
+        #TRMM: Jan 1998 – Sept 2014
+        #GPM: Apr 2014-March 2022
+        #Combo: Jan 1998 – March 2022
+
+        #Updated in Apr. 2024:
+        #TRMM: Jan 1998 – Sept 2014
+        #GPM: Apr 2014-March 2023
+        #Combo: Jan 1998 – March 2023
+
+        if sat_obs == 'TRMM':
+           start_time = datetime(1998,1,1)
+           end_time = datetime.strptime('09/30/2014 23:59:59', '%m/%d/%Y %H:%M:%S')
+        elif sat_obs == 'GPM':
+           start_time = datetime(2014,4,1)
+           end_time = datetime.strptime('03/31/2023 23:59:59', '%m/%d/%Y %H:%M:%S')
+        elif sat_obs == 'COMBO':
+           start_time = datetime(1998,1,1)
+           end_time = datetime.strptime('03/31/2023 23:59:59', '%m/%d/%Y %H:%M:%S')
+
+        nc.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')