Update main.py

main.py

@@ -35,12 +35,13 @@ def geocode_address(address):
         
     return lat, lon
 
-def get_hail_data(address, start_date, end_date, radius_miles, get_max):
+
+def get_data(address, start_date, end_date, radius_miles, get_max):
 
     start_date = pd.Timestamp(str(start_date)).strftime('%Y%m%d%H')
     end_date = pd.Timestamp(str(end_date)).strftime('%Y%m%d%H')
-    date_years = pd.date_range(start=start_date, end=end_date, freq='M')
-    date_range_days = pd.date_range(start_date, end_date)
+    date_years = pd.date_range(start=start_date[:-2], end=end_date[:-2], freq='M')
+    date_range_days = pd.date_range(start_date[:-2], end_date[:-2], freq='H')
     years = list(set([d.year for d in date_years]))
 
     if len(years) == 0:
@@ -49,6 +50,7 @@ def get_hail_data(address, start_date, end_date, radius_miles, get_max):
     # Geocode Address
     lat, lon= geocode_address(address)
     
+    
     # Convert Lat Lon to row & col on Array
     transform = pickle.load(open('Data/hrrr_crs.pkl', 'rb'))
     row, col = rasterio.transform.rowcol(transform['affine'], lon, lat)
@@ -62,19 +64,20 @@ def get_hail_data(address, start_date, end_date, radius_miles, get_max):
 
     files_choosen = [i for i in files if any(i for j in years if str(j) in i)]
 
+
     # Query and Collect H5 Data
     all_data = []
     all_dates = []
     for file in files_choosen:
         with h5py.File(file, 'r') as f:
             # Get Dates from H5
-            dates = f['dates'][:]
+            dates = f['date_time_hr'][:]
             date_idx = np.where((dates >= int(start_date))
                                 & (dates <= int(end_date)))[0]
 
             # Select Data by Date and Radius
             dates = dates[date_idx]
-            data = f['hail'][date_idx, row-radius_miles:row +
+            data = f['APCP'][date_idx, row-radius_miles:row +
                              radius_miles+1, col-radius_miles:col+radius_miles+1]
 
             all_data.append(data)
@@ -95,14 +98,14 @@ def get_hail_data(address, start_date, end_date, radius_miles, get_max):
     if get_max == True:
         data_max = np.max(data_mat, axis=(1, 2))
         df_data = pd.DataFrame({'Date': dates_all,
-                               'Hail_max': data_max})
+                               'APCP_max': data_max})
     # Get all Data
     else:
         data_all = list(data_mat)
         df_data = pd.DataFrame({'Date': dates_all,
-                               'Hail_all': data_all})
+                               'APCP_all': data_all})
 
-    df_data['Date'] = pd.to_datetime(df_data['Date'], format='%Y%m%d')
+    df_data['Date'] = pd.to_datetime(df_data['Date'], format='%Y%m%d%H')
     df_data = df_data.set_index('Date')
 
     df_data = df_data.reindex(date_range_days, fill_value=0).reset_index().rename(
@@ -116,10 +119,10 @@ def get_hail_data(address, start_date, end_date, radius_miles, get_max):
 async def predict(address: str, start_date: str, end_date: str, radius_miles: int, get_max: bool):
 
     try:
-        results = get_hail_data(address, start_date,
+        results = get_data(address, start_date,
                                 end_date, radius_miles, get_max)
     except:
-        results = pd.DataFrame({'Date': ['error'], 'Hail_max': ['error']})
+        results = pd.DataFrame({'Date': ['error'], 'APCP_max': ['error']})
 
     return results.to_json()