Automating the Spreadhseet Processing

SEBAL/Run_py3.py

@@ -12,11 +12,16 @@
 #inputExcel = r"/mnt/d/PySEBAL_dev/test_data/InputEXCEL_v3_3_7_LIN.xlsx"
 ##### For Windows SET THE PATH TO INPUT EXCEL SHEET #####
 inputExcel = r"D:\PySEBAL_dev\test_data\InputEXCEL_v3_3_7_WIN.xlsx"
+dataPath = # Data folder location here
+imgType = 1 # Landsat
 st = 2 # starting row number
 en = 2 # ending row number
 
 ####### USER INPUTS FINISH HERE
 
+# Fill in the spreadhseet
+pysebal_xl_py3(dataPath, inputExcel, imgType).fill_xl()
+
 for number in range(st, en + 1):
     try:
         print ('starting line num: %d' % number)

SEBAL/pysebal_xl_py3.py

@@ -0,0 +1,145 @@
+# The input locations can be any directory or file path. However, the data directory must have a certain format.
+# DEM file inside ../DEM, climate files inside ../Meteo
+# Satellite files inside ../Satellite-data and soil files inside ../Soil
+# The script can be altered to have different names of course.
+# The climate files must include the date in YYYYMMDD format, much like the satellite files downloaded.
+
+import os
+import pandas as pd
+from openpyxl import load_workbook
+
+class pysebal_xl:
+    def __init__(self, data_dir, xl_path, img_type):
+        self.data_dir = data_dir
+        self.xl_path = xl_path
+        self.workbook = load_workbook(xl_path)
+        self.img_type = img_type
+    def find_location(self, folder):
+        templist1 = []
+        folder_path = os.path.join(self.data_dir, folder)
+        for folder_obj in os.listdir(folder_path):
+            data_folder_path = os.path.join(folder_path, folder_obj)
+            templist1.append(data_folder_path)
+        return templist1
+    def sort_meteo(self, meteo_var, date_list, meteo_list):
+        templist2 = []
+        for date in date_list:
+            for meteo_file in meteo_list:
+                strings_to_search = [date, meteo_var]
+                meteo_file_path = os.path.join(self.data_dir, "Meteo", meteo_file)
+                if all(search_string in meteo_file for search_string in strings_to_search):
+                    templist2.append(meteo_file_path)
+        return templist2
+    def write_xl(self, sheet_name, column_number, listname):
+        worksheet = self.workbook[sheet_name]
+        column_index = int(column_number)
+        for index, value in enumerate(listname, start=2):
+            worksheet.cell(row=index, column=column_index).value = value
+        self.workbook.save(self.xl_path)
+    def fill_xl(self):
+        # Input locations
+        satellite_data_list = self.find_location("Satellite_data")
+        list_length = len(satellite_data_list)
+        meteo_list = self.find_location("Meteo")
+        soil_base_list = self.find_location("Soil")
+        soil_list = [[obj] * list_length for obj in soil_base_list]
+        dem_list = self.find_location("DEM") * list_length
+
+        # List of output folder names to create separate output folders for each image and the output locations
+        output_folder_names = [foldername.split('\\')[-1] for foldername in satellite_data_list]
+
+        # Making output folders
+        os.makedirs(os.path.join(self.data_dir, "SEBAL_Out"), exist_ok=True)
+        for folder_name in output_folder_names:
+            output_folder_path = os.path.join(self.data_dir, "SEBAL_Out", folder_name)
+            os.makedirs(output_folder_path, exist_ok=True)
+        output_list = self.find_location("SEBAL_Out")
+        # Necessary input lists of meteo data and satellite names
+        date_list = [obj[17:25] for obj in output_folder_names]
+        sat_list  = [obj[0:4] for obj in output_folder_names]
+
+        # List of meteo data locations
+        Tair_inst_list = self.sort_meteo("Tair_inst", date_list, meteo_list)
+        Tair_24_list = self.sort_meteo("Tair_24", date_list, meteo_list)
+        Rh_inst_list = self.sort_meteo("Rh_inst", date_list, meteo_list)
+        Rh_24_list = self.sort_meteo("Rh_24", date_list, meteo_list)
+        Wind_inst_list = self.sort_meteo("Wind_inst", date_list, meteo_list)
+        Wind_24_list = self.sort_meteo("Wind_24", date_list, meteo_list)
+        SWdown_inst_list = self.sort_meteo("SWdown_inst", date_list, meteo_list)
+        SWdown_24_list = self.sort_meteo("SWdown_24", date_list, meteo_list)
+
+        # List of other parameters
+        image_type_list = [self.img_type] * list_length
+        zx_list = [2] * list_length
+        method_radiation_list = [1] * list_length
+        method_radiation_inst_list = [1] * list_length
+        transm_24_list = [0.7] * list_length
+        transm_inst_cloud_free_list = [0.75] * list_length
+        obstacle_height_list = [0.4] * list_length
+        depletion_factor_list = [0.5] * list_length     # 0.5 average value (0.2-0.3 for shallow and 0.4-0.6 for deep roots)
+        luemax_list = [2.5] * list_length
+
+        # Landsat 5, 7 and 8 parameters
+        LT05 = [5, 1, 2, 5, 1, 3, 0.0065]
+        LE07 = [7, 1, 2, 5, 1, 3, 0.0065]
+        LC08 = [8, 2, 2, 5, 1, 3, 0.0065]
+        LC09 = [9, 2, 2, 5, 1, 3, 0.0065]
+        sat_dict = {'LT05': LT05, 'LE07': LE07, 'LC08': LC08}
+
+        # Writing the locations and parameters
+        # General input
+        self.write_xl("General_Input", 2, satellite_data_list)
+        self.write_xl("General_Input", 3, output_list)
+        self.write_xl("General_Input", 4, image_type_list)
+        self.write_xl("General_Input", 5, dem_list)
+
+        # Meteo input
+        self.write_xl("Meteo_Input", 2, Tair_inst_list)
+        self.write_xl("Meteo_Input", 3, Tair_24_list)
+        self.write_xl("Meteo_Input", 4, Rh_inst_list)
+        self.write_xl("Meteo_Input", 5, Rh_24_list)
+        self.write_xl("Meteo_Input", 6, zx_list)
+        self.write_xl("Meteo_Input", 7, Wind_inst_list)
+        self.write_xl("Meteo_Input", 8, Wind_24_list)
+        self.write_xl("Meteo_Input", 9, method_radiation_list)
+        self.write_xl("Meteo_Input", 10, SWdown_24_list)
+        self.write_xl("Meteo_Input", 11, transm_24_list)
+        self.write_xl("Meteo_Input", 12, method_radiation_inst_list)
+        self.write_xl("Meteo_Input", 13, SWdown_inst_list)
+        self.write_xl("Meteo_Input", 14, transm_inst_cloud_free_list)
+        self.write_xl("Meteo_Input", 15, obstacle_height_list)
+
+        # Soil input
+        self.write_xl("Soil_Input", 2, soil_list[5])
+        self.write_xl("Soil_Input", 3, soil_list[4])
+        self.write_xl("Soil_Input", 4, soil_list[3])
+        self.write_xl("Soil_Input", 5, soil_list[2])
+        self.write_xl("Soil_Input", 6, soil_list[1])
+        self.write_xl("Soil_Input", 7, soil_list[0])
+        self.write_xl("Soil_Input", 8, depletion_factor_list)
+        self.write_xl("Soil_Input", 9, luemax_list)     # C3/C4 crops
+
+        if self.img_type == 1:
+            # Landsat input
+            self.write_xl("Landsat_Input", 2, output_folder_names)
+            sheet = self.workbook["Landsat_Input"]
+            for row in sheet.iter_rows(min_row=2, max_row=list_length+1, min_col=2, max_col=2):
+                for cell in row:
+                    sat_name = str(cell.value)[0:4]
+                    if sat_name in sat_dict:
+                        sat_data_list = sat_dict[sat_name]
+
+                        # Start from column C
+                        column_index = 3
+
+                        # Write the data from the list to the corresponding columns
+                        for sat_data in sat_data_list:
+                            sheet.cell(row = cell.row, column = column_index, value = sat_data)
+                            column_index += 1
+                self.workbook.save(self.xl_path)
+
+        else:
+            # PROBAV and VIIRS input
+            print("Support unavailable")
+
+        print("Processing PySEBAL spreadsheet completed.")