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Allow reading CV data from BioLogic

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rasmusvt 2022-08-08 15:44:55 +02:00
parent b69a4b9521
commit 9a73f57a82
1 changed files with 67 additions and 25 deletions
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92
nafuma/electrochemistry/io.py
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@ -6,6 +6,12 @@ import os
import nafuma.auxillary as aux import nafuma.auxillary as aux
from sympy import re from sympy import re
# FIXME This is not good practice, but a temporary fix as I don't have time to understand what causes the SettingWithCopyWarning.
# Read this: https://www.dataquest.io/blog/settingwithcopywarning/
pd.set_option('mode.chained_assignment', None)
def read_data(data, options={}): def read_data(data, options={}):
if data['kind'] == 'neware': if data['kind'] == 'neware':
@ -382,11 +388,22 @@ def process_biologic_data(df, options=None):
'splice_cycles': None} 'splice_cycles': None}
# Check if the DataFrame contains GC or CV data.
# FIXME This might not be a very rigorous method of checking. E.g. Rest has mode == 3, so if loading a short GC with many Rest-datapoints, the mean will be 2 and it will be treated as CV. For now manual override is sufficient
if not 'mode' in options.keys():
options['mode'] = 'GC' if int(df['mode'].mean()) == 1 else 'CV'
aux.update_options(options=options, required_options=required_options, default_options=default_options) aux.update_options(options=options, required_options=required_options, default_options=default_options)
options['kind'] = 'biologic' options['kind'] = 'biologic'
# Pick out necessary columns # Pick out necessary columns
df = df[['Ns changes', 'Ns', 'time/s', 'Ewe/V', 'Energy charge/W.h', 'Energy discharge/W.h', '<I>/mA', 'Capacity/mA.h', 'cycle number']].copy() headers = [
'Ns changes', 'Ns', 'time/s', 'Ewe/V', 'Energy charge/W.h', 'Energy discharge/W.h', '<I>/mA', 'Capacity/mA.h', 'cycle number' ] if options['mode'] == 'GC' else [
'ox/red', 'time/s', 'control/V', 'Ewe/V', '<I>/mA', 'cycle number', '(Q-Qo)/C', 'P/W'
]
df = df[headers].copy()
# Complete set of new units and get the units used in the dataset, and convert values in the DataFrame from old to new. # 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) set_units(options)
@ -396,10 +413,15 @@ def process_biologic_data(df, options=None):
df = unit_conversion(df=df, options=options) df = unit_conversion(df=df, options=options)
# Creates masks for charge and discharge curves
chg_mask = (df['status'] == 1) & (df['status_change'] != 1)
dchg_mask = (df['status'] == 2) & (df['status_change'] != 1)
# Creates masks for charge and discharge curves
if options['mode'] == 'GC':
chg_mask = (df['status'] == 1) & (df['status_change'] != 1)
dchg_mask = (df['status'] == 2) & (df['status_change'] != 1)
elif options['mode'] == 'CV':
chg_mask = (df['status'] == 1) # oxidation
dchg_mask = (df['status'] == 0) # reduction
# Initiate cycles list # Initiate cycles list
cycles = [] cycles = []
@ -419,7 +441,7 @@ def process_biologic_data(df, options=None):
if chg_df.empty and dchg_df.empty: if chg_df.empty and dchg_df.empty:
continue continue
if options['reverse_discharge']: if options['mode'] == 'GC' and 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)
@ -431,10 +453,14 @@ def process_biologic_data(df, options=None):
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)
if options['mode'] == 'CV':
chg_df = chg_df.sort_values(by='voltage').reset_index(drop=True)
dchg_df = dchg_df.sort_values(by='voltage', ascending=False).reset_index(drop=True)
cycles.append((chg_df, dchg_df)) cycles.append((chg_df, dchg_df))
return cycles return cycles
@ -568,7 +594,6 @@ def unit_conversion(df, options):
columns.append('runtime') columns.append('runtime')
# Apply new column labels # Apply new column labels
print(df.columns, columns)
df.columns = columns df.columns = columns
@ -585,6 +610,11 @@ 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')
@ -601,21 +631,34 @@ def unit_conversion(df, options):
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']] for column in df.columns:
df["Ewe/{}".format(options['old_units']["voltage"])] = df["Ewe/{}".format(options['old_units']["voltage"])] * unit_tables.voltage()[options['old_units']["voltage"]].loc[options['units']['voltage']] if 'time' in column:
df["<I>/{}".format(options['old_units']["current"])] = df["<I>/{}".format(options['old_units']["current"])] * unit_tables.current()[options['old_units']["current"]].loc[options['units']['current']] 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']]
if 'Ewe' in column:
df["Ewe/{}".format(options['old_units']["voltage"])] = df["Ewe/{}".format(options['old_units']["voltage"])] * unit_tables.voltage()[options['old_units']["voltage"]].loc[options['units']['voltage']]
if '<I>' in column:
df["<I>/{}".format(options['old_units']["current"])] = df["<I>/{}".format(options['old_units']["current"])] * unit_tables.current()[options['old_units']["current"]].loc[options['units']['current']]
capacity = options['old_units']['capacity'].split('h')[0] + '.h' if 'Capacity' in column:
df["Capacity/{}".format(capacity)] = df["Capacity/{}".format(capacity)] * (unit_tables.capacity()[options['old_units']["capacity"]].loc[options['units']["capacity"]]) capacity = options['old_units']['capacity'].split('h')[0] + '.h'
df["Capacity/{}".format(capacity)] = df["Capacity/{}".format(capacity)] * (unit_tables.capacity()[options['old_units']["capacity"]].loc[options['units']["capacity"]])
columns = ['status_change', 'status', 'time', 'voltage', 'energy_charge', 'energy_discharge', 'current', 'capacity', 'cycle']
if 'SpecificCapacity({}/mg)'.format(options['old_units']['capacity']) in df.columns: 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"]] 'status_change', 'status', 'time', 'voltage', 'energy_charge', 'energy_discharge', 'current', 'capacity', 'cycle'] if options['mode'] == 'GC' else [ # GC headers
columns.append('specific_capacity') 'status', 'time', 'control_voltage', 'voltage', 'current', 'cycle', 'charge', 'power' # CV headers
]
if 'IonsExtracted' in df.columns: if options['mode'] == 'GC':
columns.append('ions') 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')
df.columns = columns df.columns = columns
@ -674,19 +717,18 @@ def get_old_units(df: pd.DataFrame, options: dict) -> dict:
if options['kind'] == 'biologic': if options['kind'] == 'biologic':
old_units = {}
for column in df.columns: for column in df.columns:
if 'time' in column: if 'time' in column:
time = column.split('/')[-1] old_units['time'] = column.split('/')[-1]
elif 'Ewe' in column: elif 'Ewe' in column:
voltage = column.split('/')[-1] old_units['voltage'] = column.split('/')[-1]
elif 'Capacity' in column: elif 'Capacity' in column:
capacity = column.split('/')[-1].replace('.', '') old_units['capacity'] = column.split('/')[-1].replace('.', '')
elif 'Energy' in column: elif 'Energy' in column:
energy = column.split('/')[-1].replace('.', '') old_units['energy'] = column.split('/')[-1].replace('.', '')
elif '<I>' in column: elif '<I>' in column:
current = column.split('/')[-1] old_units['current'] = column.split('/')[-1]
old_units = {'voltage': voltage, 'current': current, 'capacity': capacity, 'energy': energy, 'time': time}
return old_units return old_units