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Calculate specific energy in Neware data

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rasmusvt 2022-08-05 09:51:28 +02:00
parent 642f166d71
commit 745522ed82
1 changed files with 19 additions and 8 deletions
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27
nafuma/electrochemistry/io.py
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@ -342,6 +342,7 @@ def process_neware_data(df, options={}):
df = add_columns(df=df, options=options) df = add_columns(df=df, options=options)
df = unit_conversion(df=df, options=options) df = unit_conversion(df=df, options=options)
#df = calculate_efficiency(df=df, options=options)
if options['splice_cycles']: if options['splice_cycles']:
df = splice_cycles(df=df, options=options) df = splice_cycles(df=df, options=options)
@ -427,8 +428,13 @@ def process_biologic_data(df, options=None):
def add_columns(df, options): def add_columns(df, options):
if options['kind'] == 'neware': if options['kind'] == 'neware':
if options['summary']:
df[f'Energy({options["old_units"]["energy"]})'] = np.abs(df[f'Net discharge energy({options["old_units"]["energy"]})'])
if options['active_material_weight']: if options['active_material_weight']:
df["SpecificCapacity({}/mg)".format(options['old_units']["capacity"])] = df["Capacity({})".format(options['old_units']['capacity'])] / (options['active_material_weight']) df[f"SpecificCapacity({options['old_units']['capacity']}/mg)"] = df["Capacity({})".format(options['old_units']['capacity'])] / (options['active_material_weight'])
df[f"SpecificEnergy({options['old_units']['energy']}/mg)"] = df["Energy({})".format(options['old_units']['energy'])] / (options['active_material_weight'])
if options['molecular_weight']: if options['molecular_weight']:
faradays_constant = 96485.3365 # [F] = C mol^-1 = As mol^-1 faradays_constant = 96485.3365 # [F] = C mol^-1 = As mol^-1
@ -456,6 +462,11 @@ def add_columns(df, options):
return df return df
#def calculate_efficiency(df, options):
#
# df['coulombic_efficiency'] =
def unit_conversion(df, options): def unit_conversion(df, options):
from . import unit_tables from . import unit_tables
@ -479,9 +490,7 @@ def unit_conversion(df, options):
if options['summary']: 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'Energy({options["old_units"]["energy"]})'] * unit_tables.energy()[options['old_units']['energy']].loc[options['units']['energy']]
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'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'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'Capacity({options["old_units"]["capacity"]})'] = df[f'Capacity({options["old_units"]["capacity"]})'] * unit_tables.capacity()[options['old_units']['capacity']].loc[options['units']['capacity']]
@ -512,9 +521,7 @@ def unit_conversion(df, options):
if drop in df.columns: if drop in df.columns:
df.drop(drop, axis=1, inplace=True) df.drop(drop, axis=1, inplace=True)
columns = ['cycle', 'steps', 'status', 'voltage', 'current', 'capacity'] columns = ['cycle', 'steps', 'status', 'voltage', 'current', 'capacity', 'energy']
# Add column labels for specific capacity and ions if they exist # Add column labels for specific capacity and ions if they exist
@ -522,15 +529,19 @@ def unit_conversion(df, options):
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"]] 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') columns.append('specific_capacity')
if f'SpecificEnergy({options["old_units"]["energy"]}/mg)' in df.columns:
df[f'SpecificEnergy({options["old_units"]["energy"]}/mg)'] = df[f'SpecificEnergy({options["old_units"]["energy"]}/mg)'] * unit_tables.energy()[options['old_units']['energy']].loc[options['units']['energy']] / unit_tables.mass()['mg'].loc[options['units']["mass"]]
columns.append('specific_energy')
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 # Append energy column label here as it was the last column to be generated
columns.append('energy')
columns.append('cycle_time') columns.append('cycle_time')
columns.append('runtime') columns.append('runtime')
# Apply new column labels # Apply new column labels
print(df.columns, columns)
df.columns = columns df.columns = columns