Add BioLogic-functions to io.py
This commit is contained in:
rasmusvt
parent
26bd7d8a15
commit
4f255fd9d5
1 changed files with 143 additions and 11 deletions
|
|
@ -21,7 +21,7 @@ def read_batsmall(path):
|
|||
|
||||
|
||||
|
||||
def read_neware(path, summary=False, active_material_weight=None, molecular_weight=None):
|
||||
def read_neware(path, summary=False):
|
||||
''' Reads electrochemistry data, currently only from the Neware battery cycler. Will convert to .csv if the filetype is .xlsx,
|
||||
which is the file format the Neware provides for the backup data. In this case it matters if summary is False or not. If file
|
||||
type is .csv, it will just open the datafile and it does not matter if summary is False or not.'''
|
||||
|
|
@ -51,6 +51,29 @@ def read_neware(path, summary=False, active_material_weight=None, molecular_weig
|
|||
|
||||
|
||||
|
||||
def read_biologic(path):
|
||||
''' Reads Bio-Logic-data into a DataFrame.
|
||||
|
||||
Input:
|
||||
path (required): string with path to datafile
|
||||
|
||||
Output:
|
||||
df: pandas DataFrame containing the data as-is, but without additional NaN-columns.'''
|
||||
|
||||
with open(path, 'r') as f:
|
||||
lines = f.readlines()
|
||||
|
||||
header_lines = int(lines[1].split()[-1]) - 1
|
||||
|
||||
|
||||
df = pd.read_csv(path, sep='\t', skiprows=header_lines)
|
||||
df.dropna(inplace=True, axis=1)
|
||||
|
||||
return df
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -94,7 +117,7 @@ def process_batsmall_data(df, units=None, splice_cycles=None, molecular_weight=N
|
|||
sub_df = df.loc[df['count'] == i].copy()
|
||||
|
||||
sub_df.loc[dchg_mask, 'current'] *= -1
|
||||
sub_df.loc[dchg_mask, 'capacity'] *= -1
|
||||
sub_df.loc[dchg_mask, 'specific_capacity'] *= -1
|
||||
|
||||
chg_df = sub_df.loc[chg_mask]
|
||||
dchg_df = sub_df.loc[dchg_mask]
|
||||
|
|
@ -126,7 +149,7 @@ def process_neware_data(df, units=None, splice_cycles=None, active_material_weig
|
|||
new_units = set_units(units=units)
|
||||
old_units = get_old_units(df=df, kind='neware')
|
||||
|
||||
df = add_columns_neware(df=df, active_material_weight=active_material_weight, molecular_weight=molecular_weight, old_units=old_units)
|
||||
df = add_columns(df=df, active_material_weight=active_material_weight, molecular_weight=molecular_weight, old_units=old_units, kind='neware')
|
||||
|
||||
df = unit_conversion(df=df, new_units=new_units, old_units=old_units, kind='neware')
|
||||
|
||||
|
|
@ -172,9 +195,66 @@ def process_neware_data(df, units=None, splice_cycles=None, active_material_weig
|
|||
return cycles
|
||||
|
||||
|
||||
def add_columns_neware(df, active_material_weight, molecular_weight, old_units):
|
||||
def process_biologic_data(df, units=None, splice_cycles=None, active_material_weight=None, molecular_weight=None, reverse_discharge=False):
|
||||
|
||||
# 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()
|
||||
|
||||
# Complete set of new units and get the units used in the dataset, and convert values in the DataFrame from old to new.
|
||||
new_units = set_units(units=units)
|
||||
old_units = get_old_units(df=df, kind='biologic')
|
||||
|
||||
df = add_columns(df=df, active_material_weight=active_material_weight, molecular_weight=molecular_weight, old_units=old_units, kind='biologic')
|
||||
|
||||
df = unit_conversion(df=df, new_units=new_units, old_units=old_units, kind='biologic')
|
||||
|
||||
|
||||
# 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)
|
||||
|
||||
|
||||
|
||||
# Initiate cycles list
|
||||
cycles = []
|
||||
|
||||
# Loop through all the cycling steps, change the current and capacities in the
|
||||
for i in range(int(df["cycle"].max())):
|
||||
|
||||
sub_df = df.loc[df['cycle'] == i].copy()
|
||||
|
||||
#sub_df.loc[dchg_mask, 'current'] *= -1
|
||||
#sub_df.loc[dchg_mask, 'capacity'] *= -1
|
||||
|
||||
chg_df = sub_df.loc[chg_mask]
|
||||
dchg_df = sub_df.loc[dchg_mask]
|
||||
|
||||
# Continue to next iteration if the charge and discharge DataFrames are empty (i.e. no current)
|
||||
if chg_df.empty and dchg_df.empty:
|
||||
continue
|
||||
|
||||
if reverse_discharge:
|
||||
max_capacity = dchg_df['capacity'].max()
|
||||
dchg_df['capacity'] = np.abs(dchg_df['capacity'] - max_capacity)
|
||||
|
||||
if 'specific_capacity' in df.columns:
|
||||
max_capacity = dchg_df['specific_capacity'].max()
|
||||
dchg_df['specific_capacity'] = np.abs(dchg_df['specific_capacity'] - max_capacity)
|
||||
|
||||
if 'ions' in df.columns:
|
||||
max_capacity = dchg_df['ions'].max()
|
||||
dchg_df['ions'] = np.abs(dchg_df['ions'] - max_capacity)
|
||||
|
||||
cycles.append((chg_df, dchg_df))
|
||||
|
||||
|
||||
|
||||
return cycles
|
||||
|
||||
|
||||
def add_columns(df, active_material_weight, molecular_weight, old_units, kind):
|
||||
|
||||
if kind == 'neware':
|
||||
if active_material_weight:
|
||||
df["SpecificCapacity({}/mg)".format(old_units["capacity"])] = df["Capacity({})".format(old_units['capacity'])] / (active_material_weight)
|
||||
|
||||
|
|
@ -186,6 +266,21 @@ def add_columns_neware(df, active_material_weight, molecular_weight, old_units):
|
|||
df["IonsExtracted"] = (df["SpecificCapacity({}/mg)".format(old_units['capacity'])]*molecular_weight)*1000/f
|
||||
|
||||
|
||||
if kind == 'biologic':
|
||||
if active_material_weight:
|
||||
|
||||
capacity = old_units['capacity'].split('h')[0] + '.h'
|
||||
|
||||
|
||||
df["SpecificCapacity({}/mg)".format(old_units["capacity"])] = df["Capacity/{}".format(capacity)] / (active_material_weight)
|
||||
|
||||
if molecular_weight:
|
||||
faradays_constant = 96485.3365 # [F] = C mol^-1 = As mol^-1
|
||||
seconds_per_hour = 3600 # s h^-1
|
||||
f = faradays_constant / seconds_per_hour * 1000.0 # [f] = mAh mol^-1
|
||||
|
||||
df["IonsExtracted"] = (df["SpecificCapacity({}/mg)".format(old_units['capacity'])]*molecular_weight)*1000/f
|
||||
|
||||
return df
|
||||
|
||||
|
||||
|
|
@ -228,6 +323,25 @@ def unit_conversion(df, new_units, old_units, kind):
|
|||
|
||||
df.columns = columns
|
||||
|
||||
if kind == 'biologic':
|
||||
df['time/{}'.format(old_units['time'])] = df["time/{}".format(old_units["time"])] * unit_tables.time()[old_units["time"]].loc[new_units['time']]
|
||||
df["Ewe/{}".format(old_units["voltage"])] = df["Ewe/{}".format(old_units["voltage"])] * unit_tables.voltage()[old_units["voltage"]].loc[new_units['voltage']]
|
||||
df["<I>/{}".format(old_units["current"])] = df["<I>/{}".format(old_units["current"])] * unit_tables.current()[old_units["current"]].loc[new_units['current']]
|
||||
|
||||
capacity = old_units['capacity'].split('h')[0] + '.h'
|
||||
df["Capacity/{}".format(capacity)] = df["Capacity/{}".format(capacity)] * (unit_tables.capacity()[old_units["capacity"]].loc[new_units["capacity"]])
|
||||
|
||||
columns = ['status_change', 'status', 'time', 'voltage', 'energy_charge', 'energy_discharge', 'current', 'capacity', 'cycle']
|
||||
|
||||
if 'SpecificCapacity({}/mg)'.format(old_units['capacity']) in df.columns:
|
||||
df['SpecificCapacity({}/mg)'.format(old_units['capacity'])] = df['SpecificCapacity({}/mg)'.format(old_units['capacity'])] * unit_tables.capacity()[old_units['capacity']].loc[new_units['capacity']] / unit_tables.mass()['mg'].loc[new_units["mass"]]
|
||||
columns.append('specific_capacity')
|
||||
|
||||
if 'IonsExtracted' in df.columns:
|
||||
columns.append('ions')
|
||||
|
||||
df.columns = columns
|
||||
|
||||
return df
|
||||
|
||||
|
||||
|
|
@ -274,6 +388,24 @@ def get_old_units(df, kind):
|
|||
old_units = {'voltage': voltage, 'current': current, 'capacity': capacity, 'energy': energy}
|
||||
|
||||
|
||||
if kind=='biologic':
|
||||
|
||||
for column in df.columns:
|
||||
if 'time' in column:
|
||||
time = column.split('/')[-1]
|
||||
elif 'Ewe' in column:
|
||||
voltage = column.split('/')[-1]
|
||||
elif 'Capacity' in column:
|
||||
capacity = column.split('/')[-1].replace('.', '')
|
||||
elif 'Energy' in column:
|
||||
energy = column.split('/')[-1].replace('.', '')
|
||||
elif '<I>' in column:
|
||||
current = column.split('/')[-1]
|
||||
|
||||
old_units = {'voltage': voltage, 'current': current, 'capacity': capacity, 'energy': energy, 'time': time}
|
||||
|
||||
|
||||
|
||||
return old_units
|
||||
|
||||
def convert_time_string(time_string, unit='ms'):
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue