Create version 4.3.3 (#5)

Signed-off-by: David P. Chassin <dchassin@slac.stanford.edu>
Signed-off-by: David P. Chassin <david.chassin@me.com>
Signed-off-by: Mitchell Victoriano <mitchell.victoriano@gmail.com>
Signed-off-by: Mitchell Victoriano <47313912+MitchellAV@users.noreply.github.com>
Signed-off-by: Duncan Ragsdale <88173870+Thistleman@users.noreply.github.com>
Co-authored-by: Mitchell Victoriano <47313912+MitchellAV@users.noreply.github.com>
Co-authored-by: aivanova5 <ivanova.alyona5@gmail.com>
Co-authored-by: Duncan Ragsdale <88173870+Thistleman@users.noreply.github.com>
Signed-off-by: David P. Chassin <david.chassin@me.com>

converters/csv-ami2glm-player.py

@@ -4,7 +4,7 @@
 
 	Shell: 
 		$ gridlabd convert -i ami:AMI.csv,ami_key:AMI_KEYS.csv, network:NETWORK.csv 
-			-o PLAYERS.csv -f csv-ami -t glm-player [OPTIONS ...]
+			-o PLAYERS.csv -f xlsx-spida -t csv-geodata [OPTIONS ...]
 
 	GLM: 
 		#convert ami:AMI.csv,ami_key:AMI_KEYS.csv 
@@ -29,11 +29,10 @@
 import re
 import numpy as np
 import os
-import csv
 
 
 default_options = {
-	"folder_name" : "./player/",
+	# "include_network" : None,
 }
 
 def string_clean(input_str):
@@ -46,6 +45,7 @@ def string_clean(input_str):
 ami_key = False
 
 def convert(input_files, output_file, options={}):
+	print('test')
 
 	if type(input_files) is dict:
 		for key in input_files:
@@ -88,37 +88,18 @@ def convert(input_files, output_file, options={}):
 
 	node_ID_set = set(df_ami['transformer_structure'])
 
-	df = pd.DataFrame({'class': ['player']*len(node_ID_set), 'parent' : list(node_ID_set), 'file' : ['player_' + str(node) + '.csv' for node in node_ID_set]})
-
-	if not os.path.exists(folder_name):
-		os.makedirs(folder_name)
-
-	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' :
-		with open(output_file, mode='w') as file :  
-			file.write('module tape;\n')
-
-			for node_ID in node_ID_set : 
-				file.write('object player {\n')
-				file.write('\tparent "' + str(node_ID) + '";\n')
-				file.write('\tfile "' + os.path.join(folder_name,str(node_ID)) + '.csv";\n')
-				file.write('}\n')
-
-	new_column_names = {
-		'reading_dttm': 'timestamp',
-		'net_usage': 'power[kW]',
-		'transformer_structure': 'customer_id'
-	}
-	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
-		customer_df = df_ami[df_ami['customer_id'] == customer_id].drop(columns='customer_id')
-		customer_df = customer_df.sort_values(by='timestamp')
-	    # 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)
+	with open(output_file, mode='w', newline='') as file :  
+		writer = csv.writer(file)
+		writer.writerow(['module tape;'])
+
+		for node_ID in node_ID_set : 
+			writer.writerow(['\n'])
+			writer.writerow(['object player {\n'])
+			writer.writerow(['\tproperty measured_real_energy;\n'])
+			writer.writerow(['\tparent ' + node_ID + '\n'])
+			writer.writerow(['\tfile ./player/' + node_ID + '.csv\n'])
+			writer.writerow(['}\n'])
+
+
+
+

converters/xlsx-spida2csv-geodata.py

@@ -61,7 +61,6 @@
 	"include_dummy_network" : None,
 	"include_weather" : None,
 	"include_mount" : None,
-	"include_network" : None
 }
 
 def string_clean(input_str):
@@ -74,6 +73,7 @@ def string_clean(input_str):
 extract_equipment = False
 
 def convert(input_files, output_file, options={}):
+
 	if type(input_files) is dict:
 		for key in input_files:
 			if not key in ["poles","equipment","network"]:
@@ -92,10 +92,11 @@ def convert(input_files, output_file, options={}):
 			extract_equipment = True
 
 		if "network" in input_files:
-			global network_name
-			network_name = input_files["network"]
+			global include_network
+			include_network = input_files["network"]
 			global include_mount
 			include_mount = True
+
 	elif type(input_files) is str:
 		input_pole_file = input_files
 	else:
@@ -134,7 +135,7 @@ def convert(input_files, output_file, options={}):
 			'AS-IS Effective Stress Adjustment', 'AS-IS GPS Point']) 
 
 	# Read the overhead lines 
-	df_lines = pd.read_csv(network_name) if include_network else pd.DataFrame()
+	df_lines = pd.read_csv(include_network) if include_network else pd.DataFrame()
 	overheadline_names = []
 	for index, row in df_lines.iterrows():