Add correct unit conversion of Neware summaries

nafuma/electrochemistry/io.py

@@ -1,4 +1,3 @@
-from email.policy import default
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
@@ -50,7 +49,7 @@ def read_neware(path, options={}):
 		else:
 			df = pd.read_csv(csv_details)
 
-	elif path.split('.')[-1] == 'csv':
+	elif path.endswith('csv'):
 		df = pd.read_csv(path)
 
 
@@ -237,6 +236,7 @@ def process_neware_data(df, options={}):
 	options['kind'] = 'neware'
 
 
+	if not options['summary']:
 		# Complete set of new units and get the units used in the dataset, and convert values in the DataFrame from old to new.
 		set_units(options=options) # sets options['units']
 		options['old_units'] = get_old_units(df=df, options=options)
@@ -284,11 +284,20 @@ def process_neware_data(df, options={}):
 
 			cycles.append((chg_df, dchg_df))
 
-
-
 			return cycles
 
 
+	elif options['summary']:
+		set_units(options=options)
+		options['old_units'] = get_old_units(df=df, options=options)
+
+		df = add_columns(df=df, options=options)
+		df = unit_conversion(df=df, options=options)
+
+		return df
+
+
+
 def process_biologic_data(df, options=None):
 
 	required_options = ['units', 'active_material_weight', 'molecular_weight', 'reverse_discharge', 'splice_cycles']
@@ -403,12 +412,68 @@ def unit_conversion(df, options):
 
 
 	if options['kind'] == 'neware':
+		
+		if options['summary']:
+			# Add the charge and discharge energy columns to get a single energy column 
+			df[f'Energy({options["old_units"]["energy"]})'] = df[f'Chg Eng({options["old_units"]["energy"]})'] + df[f'DChg Eng({options["old_units"]["energy"]})']
+			
+			df[f'Starting current({options["old_units"]["current"]})'] 	= df[f'Starting current({options["old_units"]["current"]})'] 	* unit_tables.current()[options['old_units']['current']].loc[options['units']['current']]
+			df[f'Start Volt({options["old_units"]["voltage"]})'] 		= df[f'Start Volt({options["old_units"]["voltage"]})'] 			* unit_tables.voltage()[options['old_units']['voltage']].loc[options['units']['voltage']]
+			df[f'Capacity({options["old_units"]["capacity"]})'] 		= df[f'Capacity({options["old_units"]["capacity"]})'] 			* unit_tables.capacity()[options['old_units']['capacity']].loc[options['units']['capacity']]
+			df[f'Energy({options["old_units"]["energy"]})'] 			= df[f'Energy({options["old_units"]["energy"]})'] 				* unit_tables.energy()[options['old_units']['energy']].loc[options['units']['energy']]
+			df[f'CycleTime({options["units"]["time"]})'] 				= df.apply(lambda row : convert_time_string(row['Relative Time(h:min:s.ms)'], unit=options['units']['time']), axis=1)
+			df[f'RunTime({options["units"]["time"]})'] 					= df.apply(lambda row : convert_datetime_string(row['Real Time'], reference=df['Real Time'].iloc[0], ref_time=df[f'CycleTime({options["units"]["time"]})'].iloc[0],unit=options['units']['time']), axis=1)
+
+
+			# Drop all undesireable columns
+			df.drop(
+				[
+					'Chnl',
+					'Original step',
+					f'End Volt({options["old_units"]["voltage"]})',
+					f'Termination current({options["old_units"]["current"]})',
+					'Relative Time(h:min:s.ms)',
+					'Real Time',
+					'Continuous Time(h:min:s.ms)',
+					f'Net discharge capacity({options["old_units"]["capacity"]})',
+					f'Chg Cap({options["old_units"]["capacity"]})',
+					f'DChg Cap({options["old_units"]["capacity"]})',
+					f'Net discharge energy({options["old_units"]["energy"]})',
+					f'Chg Eng({options["old_units"]["energy"]})', 
+					f'DChg Eng({options["old_units"]["energy"]})'
+				],
+				axis=1, inplace=True
+			)
+
+			columns = ['cycle', 'steps', 'status', 'voltage', 'current', 'capacity']
+
+
+
+
+			# Add column labels for specific capacity and ions if they exist
+			if 'SpecificCapacity({}/mg)'.format(options['old_units']['capacity']) in df.columns:
+				df['SpecificCapacity({}/mg)'.format(options['old_units']['capacity'])] = df['SpecificCapacity({}/mg)'.format(options['old_units']['capacity'])] * unit_tables.capacity()[options['old_units']['capacity']].loc[options['units']['capacity']] / unit_tables.mass()['mg'].loc[options['units']["mass"]]
+				columns.append('specific_capacity')
+
+			if 'IonsExtracted' in df.columns:
+				columns.append('ions')
+
+			# Append energy column label here as it was the last column to be generated
+			columns.append('energy')
+			columns.append('cycle_time')
+			columns.append('runtime')
+
+			# Apply new column labels 
+			df.columns = columns
+	
+
+		else:
 			df['Current({})'.format(options['old_units']['current'])] = df['Current({})'.format(options['old_units']['current'])] * unit_tables.current()[options['old_units']['current']].loc[options['units']['current']]
 			df['Voltage({})'.format(options['old_units']['voltage'])] = df['Voltage({})'.format(options['old_units']['voltage'])] * unit_tables.voltage()[options['old_units']['voltage']].loc[options['units']['voltage']]
 			df['Capacity({})'.format(options['old_units']['capacity'])] = df['Capacity({})'.format(options['old_units']['capacity'])] * unit_tables.capacity()[options['old_units']['capacity']].loc[options['units']['capacity']]
 			df['Energy({})'.format(options['old_units']['energy'])] = df['Energy({})'.format(options['old_units']['energy'])] * unit_tables.energy()[options['old_units']['energy']].loc[options['units']['energy']]
 			df['CycleTime({})'.format(options['units']['time'])] = df.apply(lambda row : convert_time_string(row['Relative Time(h:min:s.ms)'], unit=options['units']['time']), axis=1)
-		df['RunTime({})'.format(options['units']['time'])] = df.apply(lambda row : convert_datetime_string(row['Real Time(h:min:s.ms)'], reference=df['Real Time(h:min:s.ms)'].iloc[0], unit=options['units']['time']), axis=1)
+			df['RunTime({})'.format(options['units']['time'])] = df.apply(lambda row : convert_datetime_string(row['Real Time(h:min:s.ms)'], reference=df['Real Time(h:min:s.ms)'].iloc[0],  ref_time=df[f'CycleTime({options["units"]["time"]})'].iloc[0], unit=options['units']['time']), axis=1)
 			columns = ['status', 'jump', 'cycle', 'steps', 'current', 'voltage', 'capacity', 'energy']
 
 			if 'SpecificCapacity({}/mg)'.format(options['old_units']['capacity']) in df.columns:
@@ -418,8 +483,6 @@ def unit_conversion(df, options):
 			if 'IonsExtracted' in df.columns:
 				columns.append('ions')
 
-
-
 			columns.append('cycle_time')
 			columns.append('time')
 
@@ -488,13 +551,13 @@ def get_old_units(df: pd.DataFrame, options: dict) -> dict:
 	if options['kind']=='neware':
 
 		for column in df.columns:
-			if 'Voltage' in column:
+			if 'Voltage' in column or 'Start Volt' in column:
 				voltage = column.split('(')[-1].strip(')')
-			elif 'Current' in column:
+			elif 'Current' in column or 'Starting current' in column:
 				current = column.split('(')[-1].strip(')')
 			elif 'Capacity' in column:
 				capacity = column.split('(')[-1].strip(')')
-			elif 'Energy' in column:
+			elif 'Energy' in column or 'Eng' in column:
 				energy = column.split('(')[-1].strip(')')
 
 		old_units = {'voltage': voltage, 'current': current, 'capacity': capacity, 'energy': energy}
@@ -532,7 +595,7 @@ def convert_time_string(time_string, unit='ms'):
 
 
 
-def convert_datetime_string(datetime_string, reference, unit='s'):
+def convert_datetime_string(datetime_string, reference, ref_time, unit='s'):
 	''' Convert time string from Neware-data with the format yyy-mm-dd hh:mm:ss to any given unit'''
 
 	from datetime import datetime
@@ -555,7 +618,7 @@ def convert_datetime_string(datetime_string, reference, unit='s'):
 
 	factors = {'ms': 1000, 's': 1, 'min': 1/(60), 'h': 1/(60*60)}
 
-	time = s * factors[unit]
+	time = s * factors[unit] + ref_time
 
 	return time