Small config file change, added possibility to compress output file.

config/config.py

@@ -22,22 +22,24 @@ class config:
     - end_date: ``str``
     - mode: ``str``
     - run_name: ``str``
-    - path_to_config_file: ``str``
+    - path_to_eodag_config_file: ``str``
     - preferred_provider: ``str``
     - cloud_cover_limit: ``int``
-    - minimum_overlap: ``str``
     - data_path: ``str``
     - output_path: ``str``
     - soil_path: ``str``
     - landcover_path: ``str``
+    - irrigation_mask_path: ``str``
     - shapefile_path: ``str``
     - param_csv_file: ``str``
+    - init_RU_path: ``str``
     - resolution: ``int``
     - ndvi_overwrite: ``bool``
     - weather_overwrite: ``bool``
     - open_browser: ``bool``
     - max_cpu: ``int``
     - max_ram: ``int``
+    - compress_outputs: ``bool``
     - additional_output: ``dict``
     - parcel_shapefile_vars: ``list``
     """

config/config_modspa.example.json

@@ -62,6 +62,9 @@
     "_comment": "Max amount of RAM memory (in GiB) to use for calculations",
     "max_ram": 16,
 
+    "_comment": "Choice to compress output netCDF file (takes less space but longer to write)",
+    "compress_outputs": false,
+
     "_comment": "Dictionnary containing the name of additionnal variables to write in output and the integer scaling factor. Equal to null if no additionnal output is required.",
     "additional_output": null,
 

main_run_samir.py

@@ -82,7 +82,7 @@ if __name__ == '__main__':
     output_shapefile_dir = os.path.join(data_path, 'SHP', run_name)
     
     # Run SAMIR
-    run_samir(param_csv_file, ndvi_path, weather_path, soil_path, land_cover_path, irrigation_mask_path, init_RU_path, Kcb_max_obs_path, output_save_path, additional_outputs = config_params.additional_output, available_ram = max_ram, available_cpu = max_cpu)
+    run_samir(param_csv_file, ndvi_path, weather_path, soil_path, land_cover_path, irrigation_mask_path, init_RU_path, Kcb_max_obs_path, output_save_path, additional_outputs = config_params.additional_output, available_ram = max_ram, available_cpu = max_cpu, compress_outputs = config_params.compress_outputs)
     
     # Convert output dataset to dataframe and shapefile for parcel mode
     if mode == 'parcel':

source/modspa_samir.py

@@ -1289,7 +1289,7 @@ def samir_daily(NDVI: np.ndarray, ET0: np.ndarray, Rain: np.ndarray, Wfc: np.nda
     return DP, Dd, Dei, Dep, Dr, E, Irrig, Irrig_test, SWCe, SWCr, Tr, Zr
 
 
-def run_samir(csv_param_file: str, ndvi_cube_path: str, weather_path: str, soil_params_path: str, land_cover_path: str, irrigation_raster: str, init_RU_path: str, Kcb_max_obs_path: str, save_path: str, scaling_dict: Dict[str, int] = {'E': 1000, 'Tr': 1000, 'SWCe': 1000, 'SWCr': 1000, 'DP': 100, 'Irr': 100}, additional_outputs: Dict[str, int] = None, available_ram: int = 8, available_cpu: int = 4) -> None:
+def run_samir(csv_param_file: str, ndvi_cube_path: str, weather_path: str, soil_params_path: str, land_cover_path: str, irrigation_raster: str, init_RU_path: str, Kcb_max_obs_path: str, save_path: str, scaling_dict: Dict[str, int] = {'E': 1000, 'Tr': 1000, 'SWCe': 1000, 'SWCr': 1000, 'DP': 100, 'Irr': 100}, additional_outputs: Dict[str, int] = None, available_ram: int = 8, available_cpu: int = 4, compress_outputs: bool = False) -> None:
     """
     Run the *SAMIR* model on the prepared inputs. Calls the ``samir_daily()`` in a time loop.
     Maximizes memory usage with given limits to run faster.
@@ -1326,6 +1326,8 @@ def run_samir(csv_param_file: str, ndvi_cube_path: str, weather_path: str, soil_
         available RAM in **GiB** for the model
     12. available_cpu: ``int`` ``default = 4``
         number of available **physical** CPU cores
+    12. compress_outputs: ``bool`` ``default = False``
+        choice to compress output file (takes longer)
 
     Returns
     =======
@@ -1407,6 +1409,9 @@ def run_samir(csv_param_file: str, ndvi_cube_path: str, weather_path: str, soil_
             encod['dtype'] = 'u2'
         # TODO: figure out optimal file chunk size
         encod['chunksizes'] = (1, y_size, x_size)
+        if compress_outputs:
+            encod['zlib'] = True
+            encod['complevel'] = 1
         encoding_dict[variable] = encod
 
     # Save empty output