Add correct unit conversion of Neware summaries
This commit is contained in:
rasmusvt
parent
c67629bef6
commit
574f633db0
1 changed files with 120 additions and 57 deletions
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@ -1,4 +1,3 @@
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from email.policy import default
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import pandas as pd
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import pandas as pd
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import numpy as np
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.pyplot as plt
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@ -50,7 +49,7 @@ def read_neware(path, options={}):
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else:
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else:
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df = pd.read_csv(csv_details)
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df = pd.read_csv(csv_details)
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elif path.split('.')[-1] == 'csv':
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elif path.endswith('csv'):
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df = pd.read_csv(path)
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df = pd.read_csv(path)
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@ -237,56 +236,66 @@ def process_neware_data(df, options={}):
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options['kind'] = 'neware'
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options['kind'] = 'neware'
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# Complete set of new units and get the units used in the dataset, and convert values in the DataFrame from old to new.
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if not options['summary']:
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set_units(options=options) # sets options['units']
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# Complete set of new units and get the units used in the dataset, and convert values in the DataFrame from old to new.
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options['old_units'] = get_old_units(df=df, options=options)
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set_units(options=options) # sets options['units']
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options['old_units'] = get_old_units(df=df, options=options)
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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
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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
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df = unit_conversion(df=df, options=options) # converts all units from the old units to the desired units
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df = unit_conversion(df=df, options=options) # converts all units from the old units to the desired units
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# Creates masks for charge and discharge curves
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# Creates masks for charge and discharge curves
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chg_mask = df['status'] == 'CC Chg'
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chg_mask = df['status'] == 'CC Chg'
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dchg_mask = df['status'] == 'CC DChg'
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dchg_mask = df['status'] == 'CC DChg'
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# Initiate cycles list
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# Initiate cycles list
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cycles = []
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cycles = []
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# Loop through all the cycling steps, change the current and capacities in the
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# Loop through all the cycling steps, change the current and capacities in the
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for i in range(df["cycle"].max()):
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for i in range(df["cycle"].max()):
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sub_df = df.loc[df['cycle'] == i+1].copy()
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sub_df = df.loc[df['cycle'] == i+1].copy()
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#sub_df.loc[dchg_mask, 'current'] *= -1
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#sub_df.loc[dchg_mask, 'current'] *= -1
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#sub_df.loc[dchg_mask, 'capacity'] *= -1
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#sub_df.loc[dchg_mask, 'capacity'] *= -1
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chg_df = sub_df.loc[chg_mask]
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chg_df = sub_df.loc[chg_mask]
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dchg_df = sub_df.loc[dchg_mask]
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dchg_df = sub_df.loc[dchg_mask]
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# Continue to next iteration if the charge and discharge DataFrames are empty (i.e. no current)
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# Continue to next iteration if the charge and discharge DataFrames are empty (i.e. no current)
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if chg_df.empty and dchg_df.empty:
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if chg_df.empty and dchg_df.empty:
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continue
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continue
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# Reverses the discharge curve if specified
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# Reverses the discharge curve if specified
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if options['reverse_discharge']:
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if options['reverse_discharge']:
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max_capacity = dchg_df['capacity'].max()
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max_capacity = dchg_df['capacity'].max()
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dchg_df['capacity'] = np.abs(dchg_df['capacity'] - max_capacity)
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dchg_df['capacity'] = np.abs(dchg_df['capacity'] - max_capacity)
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if 'specific_capacity' in df.columns:
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if 'specific_capacity' in df.columns:
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max_capacity = dchg_df['specific_capacity'].max()
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max_capacity = dchg_df['specific_capacity'].max()
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dchg_df['specific_capacity'] = np.abs(dchg_df['specific_capacity'] - max_capacity)
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dchg_df['specific_capacity'] = np.abs(dchg_df['specific_capacity'] - max_capacity)
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if 'ions' in df.columns:
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if 'ions' in df.columns:
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max_capacity = dchg_df['ions'].max()
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max_capacity = dchg_df['ions'].max()
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dchg_df['ions'] = np.abs(dchg_df['ions'] - max_capacity)
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dchg_df['ions'] = np.abs(dchg_df['ions'] - max_capacity)
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cycles.append((chg_df, dchg_df))
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cycles.append((chg_df, dchg_df))
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return cycles
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elif options['summary']:
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set_units(options=options)
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options['old_units'] = get_old_units(df=df, options=options)
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df = add_columns(df=df, options=options)
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df = unit_conversion(df=df, options=options)
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return df
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return cycles
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def process_biologic_data(df, options=None):
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def process_biologic_data(df, options=None):
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@ -403,30 +412,84 @@ def unit_conversion(df, options):
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if options['kind'] == 'neware':
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if options['kind'] == 'neware':
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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']]
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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']]
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if options['summary']:
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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']]
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# Add the charge and discharge energy columns to get a single energy column
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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']]
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df[f'Energy({options["old_units"]["energy"]})'] = df[f'Chg Eng({options["old_units"]["energy"]})'] + df[f'DChg Eng({options["old_units"]["energy"]})']
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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)
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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)
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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']]
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columns = ['status', 'jump', 'cycle', 'steps', 'current', 'voltage', 'capacity', 'energy']
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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']]
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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']]
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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']]
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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)
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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)
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if 'SpecificCapacity({}/mg)'.format(options['old_units']['capacity']) in df.columns:
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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"]]
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# Drop all undesireable columns
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columns.append('specific_capacity')
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df.drop(
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[
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'Chnl',
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'Original step',
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f'End Volt({options["old_units"]["voltage"]})',
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f'Termination current({options["old_units"]["current"]})',
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'Relative Time(h:min:s.ms)',
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'Real Time',
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'Continuous Time(h:min:s.ms)',
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f'Net discharge capacity({options["old_units"]["capacity"]})',
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f'Chg Cap({options["old_units"]["capacity"]})',
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f'DChg Cap({options["old_units"]["capacity"]})',
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f'Net discharge energy({options["old_units"]["energy"]})',
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f'Chg Eng({options["old_units"]["energy"]})',
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f'DChg Eng({options["old_units"]["energy"]})'
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],
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axis=1, inplace=True
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)
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columns = ['cycle', 'steps', 'status', 'voltage', 'current', 'capacity']
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# Add column labels for specific capacity and ions if they exist
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if 'SpecificCapacity({}/mg)'.format(options['old_units']['capacity']) in df.columns:
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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"]]
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columns.append('specific_capacity')
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if 'IonsExtracted' in df.columns:
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if 'IonsExtracted' in df.columns:
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columns.append('ions')
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columns.append('ions')
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# Append energy column label here as it was the last column to be generated
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columns.append('energy')
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columns.append('cycle_time')
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columns.append('runtime')
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# Apply new column labels
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df.columns = columns
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else:
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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']]
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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']]
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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']]
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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']]
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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)
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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)
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columns = ['status', 'jump', 'cycle', 'steps', 'current', 'voltage', 'capacity', 'energy']
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if 'SpecificCapacity({}/mg)'.format(options['old_units']['capacity']) in df.columns:
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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"]]
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columns.append('specific_capacity')
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if 'IonsExtracted' in df.columns:
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columns.append('ions')
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columns.append('cycle_time')
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columns.append('time')
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columns.append('cycle_time')
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df.drop(['Record number', 'Relative Time(h:min:s.ms)', 'Real Time(h:min:s.ms)'], axis=1, inplace=True)
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columns.append('time')
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df.columns = columns
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df.drop(['Record number', 'Relative Time(h:min:s.ms)', 'Real Time(h:min:s.ms)'], axis=1, inplace=True)
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df.columns = columns
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if options['kind'] == 'biologic':
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if options['kind'] == 'biologic':
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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']]
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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']]
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@ -488,13 +551,13 @@ def get_old_units(df: pd.DataFrame, options: dict) -> dict:
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if options['kind']=='neware':
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if options['kind']=='neware':
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for column in df.columns:
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for column in df.columns:
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if 'Voltage' in column:
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if 'Voltage' in column or 'Start Volt' in column:
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voltage = column.split('(')[-1].strip(')')
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voltage = column.split('(')[-1].strip(')')
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elif 'Current' in column:
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elif 'Current' in column or 'Starting current' in column:
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current = column.split('(')[-1].strip(')')
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current = column.split('(')[-1].strip(')')
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elif 'Capacity' in column:
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elif 'Capacity' in column:
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capacity = column.split('(')[-1].strip(')')
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capacity = column.split('(')[-1].strip(')')
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elif 'Energy' in column:
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elif 'Energy' in column or 'Eng' in column:
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energy = column.split('(')[-1].strip(')')
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energy = column.split('(')[-1].strip(')')
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old_units = {'voltage': voltage, 'current': current, 'capacity': capacity, 'energy': energy}
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old_units = {'voltage': voltage, 'current': current, 'capacity': capacity, 'energy': energy}
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@ -521,7 +584,7 @@ def get_old_units(df: pd.DataFrame, options: dict) -> dict:
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def convert_time_string(time_string, unit='ms'):
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def convert_time_string(time_string, unit='ms'):
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''' Convert time string from Neware-data with the format hh:mm:ss.xx to any given unit'''
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''' Convert time string from Neware-data with the format hh:mm:ss.xx to any given unit'''
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h, m, s = time_string.split(':')
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h, m, s = time_string.split(':')
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ms = float(s)*1000 + int(m)*1000*60 + int(h)*1000*60*60
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ms = float(s)*1000 + int(m)*1000*60 + int(h)*1000*60*60
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factors = {'ms': 1, 's': 1/1000, 'min': 1/(1000*60), 'h': 1/(1000*60*60)}
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factors = {'ms': 1, 's': 1/1000, 'min': 1/(1000*60), 'h': 1/(1000*60*60)}
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@ -532,7 +595,7 @@ def convert_time_string(time_string, unit='ms'):
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def convert_datetime_string(datetime_string, reference, unit='s'):
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def convert_datetime_string(datetime_string, reference, ref_time, unit='s'):
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''' Convert time string from Neware-data with the format yyy-mm-dd hh:mm:ss to any given unit'''
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''' Convert time string from Neware-data with the format yyy-mm-dd hh:mm:ss to any given unit'''
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from datetime import datetime
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from datetime import datetime
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@ -555,7 +618,7 @@ def convert_datetime_string(datetime_string, reference, unit='s'):
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factors = {'ms': 1000, 's': 1, 'min': 1/(60), 'h': 1/(60*60)}
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factors = {'ms': 1000, 's': 1, 'min': 1/(60), 'h': 1/(60*60)}
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time = s * factors[unit]
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time = s * factors[unit] + ref_time
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return time
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return time
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