Fixing poor implementation of phase integration for AMI

converters/csv-ami2glm-player.py

@@ -46,6 +46,13 @@ def string_clean(input_str):
 network = False
 ami_key = False
 
+def write_player(file, obj, node_ID, phase) : 
+	file.write('object player {\n')
+	file.write('\tparent "' + obj + '";\n')
+	file.write('\tfile "' + os.path.join(folder_name,str(node_ID)) + '.csv";\n')
+	file.write(f'\tproperty constant_power_{phase};\n')
+	file.write('}\n')
+
 def convert(input_files, output_file, options={}):
 
 	if type(input_files) is dict:
@@ -98,22 +105,20 @@ def convert(input_files, output_file, options={}):
 	if os.path.splitext(output_file)[1]=='.csv' : 
 		df.to_csv(os.path.join(folder_name,os.path.basename(output_file)), index=False)
 	elif os.path.splitext(output_file)[1]=='.glm' :
+		phase_dict = {}
 		with open(output_file, mode='w') as file :  
 			file.write('module tape;\n')
 
 			for node_ID in node_ID_set : 
 				if isinstance(node_ID, float) and math.isnan(node_ID) :
 					continue 
-				file.write('object player {\n')
 				for obj,val in network["objects"].items() : 
 					if "load" in val["class"] and node_ID in obj: 
-						node_phase = val["phases"]
+						node_phase = val["phases"].replace("N", '').replace("S",'')
 						parent = val["parent"]
-				file.write('\tparent "' + str(parent) + '";\n')
-				file.write('\tfile "' + os.path.join(folder_name,str(node_ID)) + '.csv";\n')
-				file.write('\tphases "' + node_phase  + '";\n')
-				file.write('}\n')
-
+						phase_dict[node_ID]=node_phase
+				for p in node_phase : 
+					write_player(file, obj, node_ID, p)
 
 	new_column_names = {
 		'reading_dttm': 'timestamp',
@@ -123,12 +128,14 @@ def convert(input_files, output_file, options={}):
 	df_ami.rename(columns=new_column_names,inplace=True)
 	df_ami.drop(['interval_pcfc_date','interval_pcfc_hour'],axis=1,inplace=True)
 	df_ami.sort_index(inplace=True)
-
 	# Iterate over unique customer IDs
 	for customer_id in df_ami['customer_id'].unique():
 	    # Create a new DataFrame for each customer ID
+		if isinstance(customer_id, float) and math.isnan(customer_id) : 
+			continue
 		customer_df = df_ami[df_ami['customer_id'] == customer_id].drop(columns='customer_id')
 		customer_df = customer_df.sort_values(by='timestamp')
+		customer_df['power[kW]'] = customer_df['power[kW]']/len(phase_dict[customer_id])*1000
 	    # Save the DataFrame to a CSV file
 		output_file = f"{folder_name}/{customer_id}.csv"
 		customer_df.to_csv(output_file, index=False, header=False)