Update main.py

main.py

@@ -56,66 +56,66 @@ def get_data(address, start_date, end_date, radius_miles, get_max):
     row, col = rasterio.transform.rowcol(transform['affine'], lon, lat)
 
 
-    files = [
-        # 'Data/APCP_2024_hrrr_v2.h5',
-        'Data/APCP_2020_hrrr_v3.h5',
-        'Data/APCP_2021_hrrr_3.h5',
-        'Data/APCP_2022_hrrr_v2.h5',
-        # 'Data/APCP_2023_hrrr_v2c.h5'
-    ]
-
-    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['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['APCP'][date_idx, row-radius_miles:row +
-                             radius_miles+1, col-radius_miles:col+radius_miles+1]
-
-            all_data.append(data)
-            all_dates.append(dates)
-
-    data_all = np.vstack(all_data)
-    dates_all = np.concatenate(all_dates)
-
-    # Convert to Inches
-    data_mat = np.where(data_all < 0, 0, data_all)*0.0393701
-
-    # Get Radius of Data
-    disk_mask = np.where(disk(radius_miles) == 1, True, False)
-    data_mat = np.where(disk_mask, data_mat, -1).round(3)
-
-    # Process to DataFrame
-    # Find Max of Data
-    if get_max == True:
-        data_max = np.max(data_mat, axis=(1, 2))
-        df_data = pd.DataFrame({'Date': dates_all,
-                               'APCP_max': data_max})
-    # Get all Data
-    else:
-        data_all = list(data_mat)
-        df_data = pd.DataFrame({'Date': dates_all,
-                               'APCP_all': data_all})
-
-    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(
-        columns={'index': 'Date'})
-    df_data['Date'] = df_data['Date'].dt.strftime('%Y-%m-%d:%H')
-
-    return df_data
-
+    # files = [
+    #     # 'Data/APCP_2024_hrrr_v2.h5',
+    #     'Data/APCP_2020_hrrr_v3.h5',
+    #     'Data/APCP_2021_hrrr_3.h5',
+    #     'Data/APCP_2022_hrrr_v2.h5',
+    #     # 'Data/APCP_2023_hrrr_v2c.h5'
+    # ]
+
+    # 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['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['APCP'][date_idx, row-radius_miles:row +
+    #                          radius_miles+1, col-radius_miles:col+radius_miles+1]
+
+    #         all_data.append(data)
+    #         all_dates.append(dates)
+
+    # data_all = np.vstack(all_data)
+    # dates_all = np.concatenate(all_dates)
+
+    # # Convert to Inches
+    # data_mat = np.where(data_all < 0, 0, data_all)*0.0393701
+
+    # # Get Radius of Data
+    # disk_mask = np.where(disk(radius_miles) == 1, True, False)
+    # data_mat = np.where(disk_mask, data_mat, -1).round(3)
+
+    # # Process to DataFrame
+    # # Find Max of Data
+    # if get_max == True:
+    #     data_max = np.max(data_mat, axis=(1, 2))
+    #     df_data = pd.DataFrame({'Date': dates_all,
+    #                            'APCP_max': data_max})
+    # # Get all Data
+    # else:
+    #     data_all = list(data_mat)
+    #     df_data = pd.DataFrame({'Date': dates_all,
+    #                            'APCP_all': data_all})
+
+    # 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(
+    #     columns={'index': 'Date'})
+    # df_data['Date'] = df_data['Date'].dt.strftime('%Y-%m-%d:%H')
+
+    # return df_data
+    return lat, lon, transform, row, col
 
 @app.get('/APCP_Docker_Data')
 async def predict(address: str, start_date: str, end_date: str, radius_miles: int, get_max: bool):