Allow older formats and manual incrementing of cycles
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rasmusvt
parent
130e120690
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3a6a000b14
1 changed files with 42 additions and 25 deletions
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@ -258,14 +258,15 @@ def process_neware_data(df, options={}):
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active_materiale_weight: weight of the active material (in mg) used in the cell.
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molecular_weight: the molar mass (in g mol^-1) of the active material, to calculate the number of ions extracted. Assumes one electron per Li+/Na+-ion """
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required_options = ['units', 'active_material_weight', 'molecular_weight', 'reverse_discharge', 'splice_cycles']
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required_options = ['units', 'active_material_weight', 'molecular_weight', 'reverse_discharge', 'splice_cycles', 'increment_cycles_from']
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default_options = {
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'units': None,
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'active_material_weight': None,
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'molecular_weight': None,
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'reverse_discharge': False,
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'splice_cycles': None}
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'splice_cycles': None,
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'increment_cycles_from': None} # index
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aux.update_options(options=options, required_options=required_options, default_options=default_options)
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@ -281,6 +282,9 @@ def process_neware_data(df, 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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if options['increment_cycles_from']:
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df['cycle'].iloc[options['increment_cycles_from']:] += 1
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if options['splice_cycles']:
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df = splice_cycles(df=df, options=options)
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@ -461,6 +465,13 @@ def unit_conversion(df, options):
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if options['kind'] == 'neware':
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record_number = 'Data serial number' if 'Data serial number' in df.columns else 'Record number'
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relative_time = 'Relative Time(h:min:s.ms)' if 'Relative Time(h:min:s.ms)' in df.columns else 'Relative Time'
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continuous_time = 'Continuous Time(h:min:s.ms)' if 'Continuous Time(h:min:s.ms)' in df.columns else 'Continuous Time'
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real_time = 'Real Time(h:min:s:ms)' if 'Real Time(h:min:s:ms)' in df.columns else 'Real Time'
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if options['summary']:
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# Add the charge and discharge energy columns to get a single energy column
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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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@ -469,29 +480,31 @@ def unit_conversion(df, options):
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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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df[f'CycleTime({options["units"]["time"]})'] = df.apply(lambda row : convert_time_string(row[relative_time], 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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# Drop all undesireable columns
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df.drop(
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[
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droplist = [
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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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relative_time,
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real_time,
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continuous_time,
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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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]
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# Drop all undesireable columns
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for drop in droplist:
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if drop in df.columns:
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df.drop(drop, axis=1, inplace=True)
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columns = ['cycle', 'steps', 'status', 'voltage', 'current', 'capacity']
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@ -520,8 +533,8 @@ def unit_conversion(df, options):
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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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df['CycleTime({})'.format(options['units']['time'])] = df.apply(lambda row : convert_time_string(row[relative_time], 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], 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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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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@ -535,7 +548,11 @@ def unit_conversion(df, options):
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columns.append('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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droplist = [record_number, relative_time, real_time]
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for drop in droplist:
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if drop in df.columns:
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df.drop(drop, axis=1, inplace=True)
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df.columns = columns
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