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Add plot functionality to electrochemistry

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rasmusvt 2021-10-13 18:06:56 +02:00
parent 4f255fd9d5
commit 43e6ef27c8
2 changed files with 433 additions and 33 deletions
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91
beamtime/electrochemistry/io.py
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@ -4,6 +4,22 @@ import matplotlib.pyplot as plt
import os
def read_data(path, kind, options=None):
if kind == 'neware':
df = read_neware(path)
cycles = process_neware_data(df, options=options)
elif kind == 'batsmall':
df = read_batsmall(path)
cycles = process_batsmall_data(df=df, options=options)
elif kind == 'biologic':
df = read_biologic(path)
cycles = process_biologic_data(df=df, options=options)
return cycles
def read_batsmall(path):
''' Reads BATSMALL-data into a DataFrame.
@ -77,7 +93,7 @@ def read_biologic(path):
def process_batsmall_data(df, units=None, splice_cycles=None, molecular_weight=None):
def process_batsmall_data(df, options=None):
''' Takes BATSMALL-data in the form of a DataFrame and cleans the data up and converts units into desired units.
Splits up into individual charge and discharge DataFrames per cycle, and outputs a list where each element is a tuple with the Chg and DChg-data. E.g. cycles[10][0] gives the charge data for the 11th cycle.
@ -94,12 +110,24 @@ def process_batsmall_data(df, units=None, splice_cycles=None, molecular_weight=N
cycles: A list with
'''
required_options = ['splice_cycles', 'molecular_weight', 'reverse_discharge', 'units']
default_options = {'splice_cycles': None, 'molecular_weight': None, 'reverse_discharge': False, 'units': None}
if not options:
options = default_options
else:
for option in required_options:
if option not in options.keys():
options[option] = default_options[option]
# 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)
new_units = set_units(units=options['units'])
old_units = get_old_units(df, kind='batsmall')
df = unit_conversion(df=df, new_units=new_units, old_units=old_units, kind='batsmall')
options['units'] = new_units
# Replace NaN with empty string in the Comment-column and then remove all steps where the program changes - this is due to inconsistent values for current
df[["comment"]] = df[["comment"]].fillna(value={'comment': ''})
df = df[df["comment"].str.contains("program")==False]
@ -126,6 +154,18 @@ def process_batsmall_data(df, units=None, splice_cycles=None, molecular_weight=N
if chg_df.empty and dchg_df.empty:
continue
if options['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))
@ -134,7 +174,7 @@ def process_batsmall_data(df, units=None, splice_cycles=None, molecular_weight=N
return cycles
def process_neware_data(df, units=None, splice_cycles=None, active_material_weight=None, molecular_weight=None, reverse_discharge=False):
def process_neware_data(df, options=None):
""" Takes data from NEWARE in a DataFrame as read by read_neware() and converts units, adds columns and splits into cycles.
@ -145,14 +185,27 @@ def process_neware_data(df, units=None, splice_cycles=None, active_material_weig
active_materiale_weight: weight of the active material (in mg) used in the cell.
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 """
required_options = ['units', 'active_material_weight', 'molecular_weight', 'reverse_discharge', 'splice_cycles']
default_options = {'units': None, 'active_material_weight': None, 'molecular_weight': None, 'reverse_discharge': False, 'splice_cycles': None}
if not options:
options = default_options
else:
for option in required_options:
if option not in options.keys():
options[option] = default_options[option]
# 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)
new_units = set_units(units=options['units'])
old_units = get_old_units(df=df, kind='neware')
df = add_columns(df=df, active_material_weight=active_material_weight, molecular_weight=molecular_weight, old_units=old_units, kind='neware')
df = add_columns(df=df, active_material_weight=options['active_material_weight'], molecular_weight=options['molecular_weight'], old_units=old_units, kind='neware')
df = unit_conversion(df=df, new_units=new_units, old_units=old_units, kind='neware')
options['units'] = new_units
# Creates masks for charge and discharge curves
chg_mask = df['status'] == 'CC Chg'
@ -176,7 +229,7 @@ def process_neware_data(df, units=None, splice_cycles=None, active_material_weig
if chg_df.empty and dchg_df.empty:
continue
if reverse_discharge:
if options['reverse_discharge']:
max_capacity = dchg_df['capacity'].max()
dchg_df['capacity'] = np.abs(dchg_df['capacity'] - max_capacity)
@ -195,19 +248,31 @@ def process_neware_data(df, units=None, splice_cycles=None, active_material_weig
return cycles
def process_biologic_data(df, units=None, splice_cycles=None, active_material_weight=None, molecular_weight=None, reverse_discharge=False):
def process_biologic_data(df, options=None):
required_options = ['units', 'active_material_weight', 'molecular_weight', 'reverse_discharge', 'splice_cycles']
default_options = {'units': None, 'active_material_weight': None, 'molecular_weight': None, 'reverse_discharge': False, 'splice_cycles': None}
if not options:
options = default_options
else:
for option in required_options:
if option not in options.keys():
options[option] = default_options[option]
# 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)
new_units = set_units(units=options['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 = add_columns(df=df, active_material_weight=options['active_material_weight'], molecular_weight=options['molecular_weight'], old_units=old_units, kind='biologic')
df = unit_conversion(df=df, new_units=new_units, old_units=old_units, kind='biologic')
options['units'] = new_units
# Creates masks for charge and discharge curves
chg_mask = (df['status'] == 1) & (df['status_change'] != 1)
@ -233,7 +298,7 @@ def process_biologic_data(df, units=None, splice_cycles=None, active_material_we
if chg_df.empty and dchg_df.empty:
continue
if reverse_discharge:
if options['reverse_discharge']:
max_capacity = dchg_df['capacity'].max()
dchg_df['capacity'] = np.abs(dchg_df['capacity'] - max_capacity)
@ -348,8 +413,8 @@ def unit_conversion(df, new_units, old_units, kind):
def set_units(units=None):
# Complete the list of units - if not all are passed, then default value will be used
required_units = ['time', 'current', 'voltage', 'capacity', 'mass', 'energy']
default_units = {'time': 'h', 'current': 'mA', 'voltage': 'V', 'capacity': 'mAh', 'mass': 'g', 'energy': 'mWh'}
required_units = ['time', 'current', 'voltage', 'capacity', 'mass', 'energy', 'specific_capacity']
default_units = {'time': 'h', 'current': 'mA', 'voltage': 'V', 'capacity': 'mAh', 'mass': 'g', 'energy': 'mWh', 'specific_capacity': None}
if not units:
units = default_units
@ -359,6 +424,8 @@ def set_units(units=None):
if unit not in units.keys():
units[unit] = default_units[unit]
units['specific_capacity'] = r'{} {}'.format(units['capacity'], units['mass']) + '$^{-1}$'
return units