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Add correct unit conversion of Neware summaries

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rasmusvt 2022-08-01 14:50:10 +02:00
parent c67629bef6
commit 574f633db0
1 changed files with 120 additions and 57 deletions
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169
nafuma/electrochemistry/io.py
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@ -1,4 +1,3 @@
from email.policy import default
import pandas as pd import pandas as pd
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
@ -50,7 +49,7 @@ def read_neware(path, options={}):
else: else:
df = pd.read_csv(csv_details) df = pd.read_csv(csv_details)
elif path.split('.')[-1] == 'csv': elif path.endswith('csv'):
df = pd.read_csv(path) df = pd.read_csv(path)
@ -237,56 +236,66 @@ def process_neware_data(df, options={}):
options['kind'] = 'neware' options['kind'] = 'neware'
# Complete set of new units and get the units used in the dataset, and convert values in the DataFrame from old to new. if not options['summary']:
set_units(options=options) # sets options['units'] # Complete set of new units and get the units used in the dataset, and convert values in the DataFrame from old to new.
options['old_units'] = get_old_units(df=df, options=options) set_units(options=options) # sets options['units']
options['old_units'] = get_old_units(df=df, options=options)
df = add_columns(df=df, options=options) # adds columns to the DataFrame if active material weight and/or molecular weight has been passed in options df = add_columns(df=df, options=options) # adds columns to the DataFrame if active material weight and/or molecular weight has been passed in options
df = unit_conversion(df=df, options=options) # converts all units from the old units to the desired units df = unit_conversion(df=df, options=options) # converts all units from the old units to the desired units
# Creates masks for charge and discharge curves # Creates masks for charge and discharge curves
chg_mask = df['status'] == 'CC Chg' chg_mask = df['status'] == 'CC Chg'
dchg_mask = df['status'] == 'CC DChg' dchg_mask = df['status'] == 'CC DChg'
# Initiate cycles list # Initiate cycles list
cycles = [] cycles = []
# Loop through all the cycling steps, change the current and capacities in the # Loop through all the cycling steps, change the current and capacities in the
for i in range(df["cycle"].max()): for i in range(df["cycle"].max()):
sub_df = df.loc[df['cycle'] == i+1].copy() sub_df = df.loc[df['cycle'] == i+1].copy()
#sub_df.loc[dchg_mask, 'current'] *= -1 #sub_df.loc[dchg_mask, 'current'] *= -1
#sub_df.loc[dchg_mask, 'capacity'] *= -1 #sub_df.loc[dchg_mask, 'capacity'] *= -1
chg_df = sub_df.loc[chg_mask] chg_df = sub_df.loc[chg_mask]
dchg_df = sub_df.loc[dchg_mask] dchg_df = sub_df.loc[dchg_mask]
# Continue to next iteration if the charge and discharge DataFrames are empty (i.e. no current) # Continue to next iteration if the charge and discharge DataFrames are empty (i.e. no current)
if chg_df.empty and dchg_df.empty: if chg_df.empty and dchg_df.empty:
continue continue
# Reverses the discharge curve if specified # Reverses the discharge curve if specified
if options['reverse_discharge']: if options['reverse_discharge']:
max_capacity = dchg_df['capacity'].max() max_capacity = dchg_df['capacity'].max()
dchg_df['capacity'] = np.abs(dchg_df['capacity'] - max_capacity) dchg_df['capacity'] = np.abs(dchg_df['capacity'] - max_capacity)
if 'specific_capacity' in df.columns: if 'specific_capacity' in df.columns:
max_capacity = dchg_df['specific_capacity'].max() max_capacity = dchg_df['specific_capacity'].max()
dchg_df['specific_capacity'] = np.abs(dchg_df['specific_capacity'] - max_capacity) dchg_df['specific_capacity'] = np.abs(dchg_df['specific_capacity'] - max_capacity)
if 'ions' in df.columns: if 'ions' in df.columns:
max_capacity = dchg_df['ions'].max() max_capacity = dchg_df['ions'].max()
dchg_df['ions'] = np.abs(dchg_df['ions'] - max_capacity) dchg_df['ions'] = np.abs(dchg_df['ions'] - max_capacity)
cycles.append((chg_df, dchg_df)) 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
return cycles
def process_biologic_data(df, options=None): def process_biologic_data(df, options=None):
@ -403,30 +412,84 @@ def unit_conversion(df, options):
if options['kind'] == 'neware': if options['kind'] == 'neware':
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)
columns = ['status', 'jump', 'cycle', 'steps', 'current', 'voltage', 'capacity', 'energy']
if 'SpecificCapacity({}/mg)'.format(options['old_units']['capacity']) in df.columns: if options['summary']:
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"]] # Add the charge and discharge energy columns to get a single energy column
columns.append('specific_capacity') 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: if 'IonsExtracted' in df.columns:
columns.append('ions') 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
columns.append('cycle_time') else:
columns.append('time') 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], 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:
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')
columns.append('cycle_time')
columns.append('time')
df.drop(['Record number', 'Relative Time(h:min:s.ms)', 'Real Time(h:min:s.ms)'], axis=1, inplace=True) df.drop(['Record number', 'Relative Time(h:min:s.ms)', 'Real Time(h:min:s.ms)'], axis=1, inplace=True)
df.columns = columns df.columns = columns
if options['kind'] == 'biologic': if options['kind'] == 'biologic':
df['time/{}'.format(options['old_units']['time'])] = df["time/{}".format(options['old_units']["time"])] * unit_tables.time()[options['old_units']["time"]].loc[options['units']['time']] df['time/{}'.format(options['old_units']['time'])] = df["time/{}".format(options['old_units']["time"])] * unit_tables.time()[options['old_units']["time"]].loc[options['units']['time']]
@ -488,13 +551,13 @@ def get_old_units(df: pd.DataFrame, options: dict) -> dict:
if options['kind']=='neware': if options['kind']=='neware':
for column in df.columns: for column in df.columns:
if 'Voltage' in column: if 'Voltage' in column or 'Start Volt' in column:
voltage = column.split('(')[-1].strip(')') voltage = column.split('(')[-1].strip(')')
elif 'Current' in column: elif 'Current' in column or 'Starting current' in column:
current = column.split('(')[-1].strip(')') current = column.split('(')[-1].strip(')')
elif 'Capacity' in column: elif 'Capacity' in column:
capacity = column.split('(')[-1].strip(')') capacity = column.split('(')[-1].strip(')')
elif 'Energy' in column: elif 'Energy' in column or 'Eng' in column:
energy = column.split('(')[-1].strip(')') energy = column.split('(')[-1].strip(')')
old_units = {'voltage': voltage, 'current': current, 'capacity': capacity, 'energy': energy} 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''' ''' Convert time string from Neware-data with the format yyy-mm-dd hh:mm:ss to any given unit'''
from datetime import datetime 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)} factors = {'ms': 1000, 's': 1, 'min': 1/(60), 'h': 1/(60*60)}
time = s * factors[unit] time = s * factors[unit] + ref_time
return time return time