Bug correction in samir model : fixed parameter rasterization.

source/modspa_samir.py

@@ -10,7 +10,7 @@ Usage of the SAMIR model in the Modspa framework.
 import os  # os management
 import xarray as xr  # to manage datasets
 import rioxarray  # to better manage dataset projections
-from numba import njit, float32, int8, boolean, set_num_threads  # to compile functions in nopython mode for faster calculation
+from numba import njit, float32, boolean, set_num_threads  # to compile functions in nopython mode for faster calculation
 import numpy as np  # for vectorized maths
 import pandas as pd  # to manage dataframes
 from typing import List, Tuple, Dict  # to declare argument types
@@ -195,7 +195,7 @@ def rasterize_samir_parameters(csv_param_file: str, land_cover_raster: str, irri
 
             # Parameter values are multiplied by the scale factor in order to store all values as int16 types
             # These values are then rounded to make sure there isn't any decimal point issues when casting the values to int16
-            value = np.where(value == class_val, round(param_value * scale_factor), value).astype(dtype)
+            value = np.where(land_cover == class_val, round(param_value * scale_factor), value).astype(dtype)
         
         # Assign parameter value
         # Parameters have an underscore (_) behind their name for recognition
@@ -1164,7 +1164,7 @@ def samir_daily(NDVI: np.ndarray, ET0: np.ndarray, Rain: np.ndarray, Wfc: np.nda
     frac_Kcb_stop_irrig_ = params['frac_Kcb_stop_irrig_']
     Lame_max_ = params['Lame_max_']
     Lame_min_ = params['Lame_min_']
-    REW_ = params['REW_']  # used in eval function to calculate Kri and Krp
+    REW_ = params['REW_']
     Ze_ = params['Ze_']
     Zsoil_ = params['Zsoil_']
     maxZr_ = params['maxZr_']
@@ -1441,17 +1441,17 @@ def run_samir(csv_param_file: str, ndvi_cube_path: str, weather_path: str, soil_
     else:
         nb_outputs = 6  # DP, E, Irr, SWCe, SWCr, Tr
     nb_variables = 38  # calculation variables (e.g:  Dd, Diff_rei, FCov, etc.)
-    np_params = (len(params) - 1)/2  # One scalar parameter
+    nb_params = (len(params))/2 - 1  # One scalar parameter
     
     # Get total memory requirement
-    total_memory_requirement = calculate_memory_requirement(x_size, y_size, time_size, nb_inputs, nb_outputs, nb_variables, np_params, nb_bytes)
+    total_memory_requirement = calculate_memory_requirement(x_size, y_size, time_size, nb_inputs, nb_outputs, nb_variables, nb_params, nb_bytes)
     
     # Determine how many time slices can be loaded in memory at once
     # This will allow faster I/O operations and a faster runtime
-    time_slice, remainder_to_load, already_loaded = calculate_time_slices_to_load(x_size, y_size, time_size, nb_inputs, nb_outputs, nb_variables, np_params, nb_bytes, available_ram)
+    time_slice, remainder_to_load, already_loaded = calculate_time_slices_to_load(x_size, y_size, time_size, nb_inputs, nb_outputs, nb_variables, nb_params, nb_bytes, available_ram)
     
     # Get actual memory requirement
-    actual_memory_requirement = calculate_memory_requirement(x_size, y_size, time_slice, nb_inputs, nb_outputs, nb_variables, np_params, nb_bytes)
+    actual_memory_requirement = calculate_memory_requirement(x_size, y_size, time_slice, nb_inputs, nb_outputs, nb_variables, nb_params, nb_bytes)
     
     print_size1, print_unit1 = format_Byte_size(total_memory_requirement, decimals = 1)
     print_size2, print_unit2, = format_Byte_size(actual_memory_requirement, decimals = 1)