From 1c29e285fd7d7f0b51048373434b8339b6833b3d Mon Sep 17 00:00:00 2001
From: Lucy Wang <wangl1@uah.edu>
Date: Wed, 7 Aug 2024 10:22:47 -0500
Subject: [PATCH] Added lookup zip creator code asosimpacts.py

---
 .../src/helpers/creators/asosimpacts.py       | 73 +++++++++++++++++++
 1 file changed, 73 insertions(+)
 create mode 100644 mdx/granule_metadata_extractor/src/helpers/creators/asosimpacts.py

diff --git a/mdx/granule_metadata_extractor/src/helpers/creators/asosimpacts.py b/mdx/granule_metadata_extractor/src/helpers/creators/asosimpacts.py
new file mode 100644
index 0000000..0db7dc9
--- /dev/null
+++ b/mdx/granule_metadata_extractor/src/helpers/creators/asosimpacts.py
@@ -0,0 +1,73 @@
+# create lookup zip for asosimpacts
+# 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 = "asosimpacts"
+provider_path = "asosimpacts/fieldCampaigns/impacts/ASOS/data/"
+file_type = "netCDF-4"
+
+
+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-4 files
+        """
+        nc = Dataset("in-mem-file", mode='r', memory=file_obj_stream.read())
+        lat = float(nc.getncattr('site_latitude'))
+        lon = float(nc.getncattr('site_longitude'))
+        sec = nc['time'][:]
+        ref_ymd = nc.variables['time'].getncattr('long_name').split()[2]
+        start_time = datetime.strptime(ref_ymd, "%Y-%m-%d") + timedelta(seconds=float(min(sec)))
+        end_time = datetime.strptime(ref_ymd, "%Y-%m-%d") + timedelta(seconds=float(max(sec)))
+        north, south, east, west  = [round(lat+0.001, 3), round(lat-0.001, 3),
+                                     round(lon+0.001, 3), round(lon-0.001, 3)]
+
+        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')