Merge pull request #14 from rasmusthog/rasmus_electrochemistry
Merge new electrochemistry functionality into main
This commit is contained in:
Rasmus Vester Thøgersen
GitHub
commit
eb2666b85d
3 changed files with 584 additions and 129 deletions
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@ -1,4 +1,3 @@
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from email.policy import default
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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@ -7,6 +6,12 @@ import os
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import nafuma.auxillary as aux
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from sympy import re
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# FIXME This is not good practice, but a temporary fix as I don't have time to understand what causes the SettingWithCopyWarning.
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# Read this: https://www.dataquest.io/blog/settingwithcopywarning/
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pd.set_option('mode.chained_assignment', None)
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def read_data(data, options={}):
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if data['kind'] == 'neware':
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@ -50,10 +55,9 @@ def read_neware(path, options={}):
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else:
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df = pd.read_csv(csv_details)
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elif path.split('.')[-1] == 'csv':
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elif path.endswith('csv'):
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df = pd.read_csv(path)
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return df
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@ -66,7 +70,9 @@ def read_batsmall(path):
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Output:
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df: pandas DataFrame containing the data as-is, but without additional NaN-columns.'''
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df = pd.read_csv(path, skiprows=2, sep='\t')
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# FIXME Now it is hardcoded that the decimal is a comma. It should do a check, as datasets can vary depending on the system settings of the machine that does the data conversion
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df = pd.read_csv(path, skiprows=2, sep='\t', decimal=',')
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df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
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return df
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@ -129,9 +135,11 @@ def process_batsmall_data(df, options=None):
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df = unit_conversion(df=df, options=options)
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if options['splice_cycles']:
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df = splice_cycles(df=df, options=options)
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# 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
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df[["comment"]] = df[["comment"]].fillna(value={'comment': ''})
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df = df[df["comment"].str.contains("program")==False]
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@ -207,6 +215,43 @@ def splice_cycles(df, options: dict) -> pd.DataFrame:
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add = df['specific_capacity'].iloc[i-1]
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df['specific_capacity'].iloc[i:last_chg] = df['specific_capacity'].iloc[i:last_chg] + add
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if options['kind'] == 'neware':
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if options['summary']:
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for i in range(df['cycle'].max()):
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sub_df = df.loc[df['cycle'] == i+1].copy()
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if sub_df['status'].loc[sub_df['status'] == 'CC Chg'].count() > 1:
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indices = sub_df.index[sub_df['status'] == 'CC Chg']
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add_columns = ['capacity', 'specific_capacity', 'ions', 'energy', 'cycle_time']
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for column in add_columns:
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if column in df.columns:
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df[column].iloc[indices[-1]] = df[column].iloc[indices[-1]] + df[column].iloc[indices[0]]
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df.drop(index=indices[0], inplace=True)
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df.reset_index(inplace=True, drop=True)
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else:
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for i in range(df['cycle'].max()):
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sub_df = df.loc[df['cycle'] == i+1].copy()
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sub_chg_df = sub_df.loc[sub_df['status'] == 'CC Chg'].copy()
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steps_indices = sub_chg_df['steps'].unique()
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if len(steps_indices) > 1:
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add_columns = ['capacity', 'specific_capacity', 'ions', 'energy', 'cycle_time']
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for column in add_columns:
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if column in df.columns:
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# Extract the maximum value from the first of the two cycles by accessing the column value of the highest index of the first cycle
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add = df[column].iloc[df.loc[df['steps'] == steps_indices[0]].index.max()]
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df[column].loc[df['steps'] == steps_indices[1]] += add
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return df
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@ -223,70 +268,116 @@ 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', 'delete_datapoints']
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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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'delete_datapoints': None, # list of indices
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}
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aux.update_options(options=options, required_options=required_options, default_options=default_options)
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options['kind'] = 'neware'
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# Complete set of new units and get the units used in the dataset, and convert values in the DataFrame from old to new.
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set_units(options=options) # sets options['units']
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options['old_units'] = get_old_units(df=df, options=options)
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if not options['summary']:
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# Complete set of new units and get the units used in the dataset, and convert values in the DataFrame from old to new.
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set_units(options=options) # sets options['units']
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options['old_units'] = get_old_units(df=df, options=options)
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df = add_columns(df=df, options=options) # adds columns to the DataFrame if active material weight and/or molecular weight has been passed in options
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df = add_columns(df=df, options=options) # adds columns to the DataFrame if active material weight and/or molecular weight has been passed in 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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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['delete_datapoints']:
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for datapoint in options['delete_datapoints']:
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df.drop(index=datapoint, inplace=True)
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if options['splice_cycles']:
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df = splice_cycles(df=df, options=options)
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# Creates masks for charge and discharge curves
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chg_mask = df['status'] == 'CC Chg'
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dchg_mask = df['status'] == 'CC DChg'
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# Creates masks for charge and discharge curves
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chg_mask = df['status'] == 'CC Chg'
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dchg_mask = df['status'] == 'CC DChg'
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# Initiate cycles list
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cycles = []
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# Loop through all the cycling steps, change the current and capacities in the
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for i in range(df["cycle"].max()):
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sub_df = df.loc[df['cycle'] == i+1].copy()
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#sub_df.loc[dchg_mask, 'current'] *= -1
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#sub_df.loc[dchg_mask, 'capacity'] *= -1
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chg_df = sub_df.loc[chg_mask]
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dchg_df = sub_df.loc[dchg_mask]
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# Continue to next iteration if the charge and discharge DataFrames are empty (i.e. no current)
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if chg_df.empty and dchg_df.empty:
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continue
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# Reverses the discharge curve if specified
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if options['reverse_discharge']:
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max_capacity = dchg_df['capacity'].max()
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dchg_df['capacity'] = np.abs(dchg_df['capacity'] - max_capacity)
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if 'specific_capacity' in df.columns:
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max_capacity = dchg_df['specific_capacity'].max()
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dchg_df['specific_capacity'] = np.abs(dchg_df['specific_capacity'] - max_capacity)
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if 'ions' in df.columns:
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max_capacity = dchg_df['ions'].max()
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dchg_df['ions'] = np.abs(dchg_df['ions'] - max_capacity)
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cycles.append((chg_df, dchg_df))
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# Initiate cycles list
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cycles = []
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return cycles
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# Loop through all the cycling steps, change the current and capacities in the
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for i in range(df["cycle"].max()):
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sub_df = df.loc[df['cycle'] == i+1].copy()
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chg_df = sub_df.loc[chg_mask]
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dchg_df = sub_df.loc[dchg_mask]
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# Continue to next iteration if the charge and discharge DataFrames are empty (i.e. no current)
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if chg_df.empty and dchg_df.empty:
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continue
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# Reverses the discharge curve if specified
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if options['reverse_discharge']:
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max_capacity = dchg_df['capacity'].max()
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dchg_df['capacity'] = np.abs(dchg_df['capacity'] - max_capacity)
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if 'specific_capacity' in df.columns:
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max_capacity = dchg_df['specific_capacity'].max()
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dchg_df['specific_capacity'] = np.abs(dchg_df['specific_capacity'] - max_capacity)
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if 'ions' in df.columns:
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max_capacity = dchg_df['ions'].max()
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dchg_df['ions'] = np.abs(dchg_df['ions'] - max_capacity)
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cycles.append((chg_df, dchg_df))
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return cycles
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elif options['summary']:
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set_units(options=options)
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options['old_units'] = get_old_units(df=df, options=options)
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df = add_columns(df=df, options=options)
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df = unit_conversion(df=df, options=options)
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if options['splice_cycles']:
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df = splice_cycles(df=df, options=options)
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chg_df = df.loc[df['status'] == 'CC Chg']
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chg_df.reset_index(inplace=True)
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dchg_df = df.loc[df['status'] == 'CC DChg']
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dchg_df.reset_index(inplace=True)
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# Construct new DataFrame
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new_df = pd.DataFrame(chg_df["cycle"])
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new_df.insert(1,'charge_capacity',chg_df['capacity'])
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new_df.insert(1,'charge_specific_capacity',chg_df['specific_capacity'])
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new_df.insert(1,'discharge_capacity',dchg_df['capacity'])
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new_df.insert(1,'discharge_specific_capacity',dchg_df['specific_capacity'])
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new_df.insert(1,'charge_energy',chg_df['energy'])
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new_df.insert(1,'charge_specific_energy',chg_df['specific_energy'])
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new_df.insert(1,'discharge_energy',dchg_df['energy'])
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new_df.insert(1,'discharge_specific_energy',dchg_df['specific_energy'])
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new_df = calculate_efficiency(df=new_df, options=options)
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return new_df
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def process_biologic_data(df, options=None):
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@ -301,11 +392,22 @@ def process_biologic_data(df, options=None):
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'splice_cycles': None}
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# Check if the DataFrame contains GC or CV data.
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# 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
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if not 'mode' in options.keys():
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options['mode'] = 'GC' if int(df['mode'].mean()) == 1 else 'CV'
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aux.update_options(options=options, required_options=required_options, default_options=default_options)
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options['kind'] = 'biologic'
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# Pick out necessary columns
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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()
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headers = [
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'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 [
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'ox/red', 'time/s', 'control/V', 'Ewe/V', '<I>/mA', 'cycle number', '(Q-Qo)/C', 'P/W'
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]
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df = df[headers].copy()
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# Complete set of new units and get the units used in the dataset, and convert values in the DataFrame from old to new.
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set_units(options)
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@ -315,10 +417,15 @@ def process_biologic_data(df, options=None):
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df = unit_conversion(df=df, options=options)
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# Creates masks for charge and discharge curves
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chg_mask = (df['status'] == 1) & (df['status_change'] != 1)
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dchg_mask = (df['status'] == 2) & (df['status_change'] != 1)
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# Creates masks for charge and discharge curves
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if options['mode'] == 'GC':
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chg_mask = (df['status'] == 1) & (df['status_change'] != 1)
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dchg_mask = (df['status'] == 2) & (df['status_change'] != 1)
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elif options['mode'] == 'CV':
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chg_mask = (df['status'] == 1) # oxidation
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dchg_mask = (df['status'] == 0) # reduction
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# Initiate cycles list
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cycles = []
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@ -338,7 +445,7 @@ def process_biologic_data(df, options=None):
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if chg_df.empty and dchg_df.empty:
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continue
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if options['reverse_discharge']:
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if options['mode'] == 'GC' and options['reverse_discharge']:
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max_capacity = dchg_df['capacity'].max()
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dchg_df['capacity'] = np.abs(dchg_df['capacity'] - max_capacity)
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@ -350,8 +457,12 @@ def process_biologic_data(df, options=None):
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max_capacity = dchg_df['ions'].max()
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dchg_df['ions'] = np.abs(dchg_df['ions'] - max_capacity)
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cycles.append((chg_df, dchg_df))
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if options['mode'] == 'CV':
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chg_df = chg_df.sort_values(by='voltage').reset_index(drop=True)
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dchg_df = dchg_df.sort_values(by='voltage', ascending=False).reset_index(drop=True)
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cycles.append((chg_df, dchg_df))
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return cycles
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@ -360,8 +471,13 @@ def process_biologic_data(df, options=None):
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def add_columns(df, options):
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if options['kind'] == 'neware':
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if options['summary']:
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df[f'Energy({options["old_units"]["energy"]})'] = np.abs(df[f'Net discharge energy({options["old_units"]["energy"]})'])
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if options['active_material_weight']:
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df["SpecificCapacity({}/mg)".format(options['old_units']["capacity"])] = df["Capacity({})".format(options['old_units']['capacity'])] / (options['active_material_weight'])
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df[f"SpecificCapacity({options['old_units']['capacity']}/mg)"] = df["Capacity({})".format(options['old_units']['capacity'])] / (options['active_material_weight'])
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df[f"SpecificEnergy({options['old_units']['energy']}/mg)"] = df["Energy({})".format(options['old_units']['energy'])] / (options['active_material_weight'])
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if options['molecular_weight']:
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faradays_constant = 96485.3365 # [F] = C mol^-1 = As mol^-1
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@ -389,6 +505,25 @@ def add_columns(df, options):
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return df
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def calculate_efficiency(df: pd.DataFrame, options: dict) -> pd.DataFrame:
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default_options = {
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'reference_index': 0
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}
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options = aux.update_options(options=options, required_options=default_options.keys(), default_options=default_options)
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df['charge_capacity_fade'] = (df['charge_capacity'] / df['charge_capacity'].iloc[options['reference_index']])*100
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df['discharge_capacity_fade'] = (df['discharge_capacity'] / df['discharge_capacity'].iloc[options['reference_index']])*100
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df['coulombic_efficiency'] = (df['discharge_capacity'] / df['charge_capacity'])*100
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df['energy_efficiency'] = (df['discharge_energy'] / df['charge_energy'])*100
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return df
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def unit_conversion(df, options):
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from . import unit_tables
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@ -403,47 +538,131 @@ def unit_conversion(df, options):
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if options['kind'] == 'neware':
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df['Current({})'.format(options['old_units']['current'])] = df['Current({})'.format(options['old_units']['current'])] * unit_tables.current()[options['old_units']['current']].loc[options['units']['current']]
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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], 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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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"]]
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columns.append('specific_capacity')
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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']:
|
||||
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'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'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'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'CycleTime({options["units"]["time"]})'] = df.apply(lambda row : convert_time_string(row[relative_time], unit=options['units']['time']), axis=1)
|
||||
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)
|
||||
|
||||
|
||||
|
||||
droplist = [
|
||||
'Chnl',
|
||||
'Original step',
|
||||
f'End Volt({options["old_units"]["voltage"]})',
|
||||
f'Termination current({options["old_units"]["current"]})',
|
||||
relative_time,
|
||||
real_time,
|
||||
continuous_time,
|
||||
f'Net discharge capacity({options["old_units"]["capacity"]})',
|
||||
f'Chg Cap({options["old_units"]["capacity"]})',
|
||||
f'DChg Cap({options["old_units"]["capacity"]})',
|
||||
f'Net discharge energy({options["old_units"]["energy"]})',
|
||||
f'Chg Eng({options["old_units"]["energy"]})',
|
||||
f'DChg Eng({options["old_units"]["energy"]})'
|
||||
]
|
||||
|
||||
# Drop all undesireable columns
|
||||
for drop in droplist:
|
||||
if drop in df.columns:
|
||||
df.drop(drop, axis=1, inplace=True)
|
||||
|
||||
columns = ['cycle', 'steps', 'status', 'voltage', 'current', 'capacity', 'energy']
|
||||
|
||||
|
||||
# Add column labels for specific capacity and ions if they exist
|
||||
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 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:
|
||||
columns.append('ions')
|
||||
|
||||
# Append energy column label here as it was the last column to be generated
|
||||
columns.append('cycle_time')
|
||||
columns.append('runtime')
|
||||
|
||||
# Apply new column labels
|
||||
df.columns = columns
|
||||
|
||||
|
||||
columns.append('cycle_time')
|
||||
columns.append('time')
|
||||
else:
|
||||
df['Current({})'.format(options['old_units']['current'])] = df['Current({})'.format(options['old_units']['current'])] * unit_tables.current()[options['old_units']['current']].loc[options['units']['current']]
|
||||
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']]
|
||||
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']]
|
||||
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']]
|
||||
df['CycleTime({})'.format(options['units']['time'])] = df.apply(lambda row : convert_time_string(row[relative_time], unit=options['units']['time']), axis=1)
|
||||
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)
|
||||
columns = ['status', 'jump', 'cycle', 'steps', 'current', 'voltage', 'capacity', 'energy']
|
||||
|
||||
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 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')
|
||||
|
||||
|
||||
df.drop(['Record number', 'Relative Time(h:min:s.ms)', 'Real Time(h:min:s.ms)'], axis=1, inplace=True)
|
||||
if 'IonsExtracted' in df.columns:
|
||||
columns.append('ions')
|
||||
|
||||
df.columns = columns
|
||||
columns.append('cycle_time')
|
||||
columns.append('time')
|
||||
|
||||
|
||||
droplist = [record_number, relative_time, real_time]
|
||||
|
||||
for drop in droplist:
|
||||
if drop in df.columns:
|
||||
df.drop(drop, axis=1, inplace=True)
|
||||
|
||||
df.columns = columns
|
||||
|
||||
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']]
|
||||
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']]
|
||||
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']]
|
||||
for column in df.columns:
|
||||
if 'time' in column:
|
||||
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']]
|
||||
|
||||
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"]])
|
||||
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']]
|
||||
|
||||
columns = ['status_change', 'status', 'time', 'voltage', 'energy_charge', 'energy_discharge', 'current', 'capacity', 'cycle']
|
||||
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']]
|
||||
|
||||
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 'Capacity' in column:
|
||||
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"]])
|
||||
|
||||
if 'IonsExtracted' in df.columns:
|
||||
columns.append('ions')
|
||||
|
||||
|
||||
columns = [
|
||||
'status_change', 'status', 'time', 'voltage', 'energy_charge', 'energy_discharge', 'current', 'capacity', 'cycle'] if options['mode'] == 'GC' else [ # GC headers
|
||||
'status', 'time', 'control_voltage', 'voltage', 'current', 'cycle', 'charge', 'power' # CV headers
|
||||
]
|
||||
|
||||
if options['mode'] == 'GC':
|
||||
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
|
||||
|
||||
|
|
@ -479,22 +698,34 @@ def get_old_units(df: pd.DataFrame, options: dict) -> dict:
|
|||
|
||||
if options['kind'] == 'batsmall':
|
||||
|
||||
time = df.columns[0].split()[-1].strip('[]')
|
||||
voltage = df.columns[1].split()[-1].strip('[]')
|
||||
current = df.columns[2].split()[-1].strip('[]')
|
||||
capacity, mass = df.columns[4].split()[-1].strip('[]').split('/')
|
||||
old_units = {'time': time, 'current': current, 'voltage': voltage, 'capacity': capacity, 'mass': mass}
|
||||
old_units = {}
|
||||
|
||||
for column in df.columns:
|
||||
if 'TT [' in column:
|
||||
old_units['time'] = column.split()[-1].strip('[]')
|
||||
elif 'U [' in column:
|
||||
old_units['voltage'] = column.split()[-1].strip('[]')
|
||||
elif 'I [' in column:
|
||||
old_units['current'] = column.split()[-1].strip('[]')
|
||||
elif 'C [' in column:
|
||||
old_units['capacity'], old_units['mass'] = column.split()[-1].strip('[]').split('/')
|
||||
|
||||
# time = df.columns[0].split()[-1].strip('[]')
|
||||
# voltage = df.columns[1].split()[-1].strip('[]')
|
||||
# current = df.columns[2].split()[-1].strip('[]')
|
||||
# capacity, mass = df.columns[4].split()[-1].strip('[]').split('/')
|
||||
# old_units = {'time': time, 'current': current, 'voltage': voltage, 'capacity': capacity, 'mass': mass}
|
||||
|
||||
if options['kind']=='neware':
|
||||
|
||||
for column in df.columns:
|
||||
if 'Voltage' in column:
|
||||
if 'Voltage' in column or 'Start Volt' in column:
|
||||
voltage = column.split('(')[-1].strip(')')
|
||||
elif 'Current' in column:
|
||||
elif 'Current' in column or 'Starting current' in column:
|
||||
current = column.split('(')[-1].strip(')')
|
||||
elif 'Capacity' in column:
|
||||
capacity = column.split('(')[-1].strip(')')
|
||||
elif 'Energy' in column:
|
||||
elif 'Energy' in column or 'Eng' in column:
|
||||
energy = column.split('(')[-1].strip(')')
|
||||
|
||||
old_units = {'voltage': voltage, 'current': current, 'capacity': capacity, 'energy': energy}
|
||||
|
|
@ -502,19 +733,18 @@ def get_old_units(df: pd.DataFrame, options: dict) -> dict:
|
|||
|
||||
if options['kind'] == 'biologic':
|
||||
|
||||
old_units = {}
|
||||
for column in df.columns:
|
||||
if 'time' in column:
|
||||
time = column.split('/')[-1]
|
||||
old_units['time'] = column.split('/')[-1]
|
||||
elif 'Ewe' in column:
|
||||
voltage = column.split('/')[-1]
|
||||
old_units['voltage'] = column.split('/')[-1]
|
||||
elif 'Capacity' in column:
|
||||
capacity = column.split('/')[-1].replace('.', '')
|
||||
old_units['capacity'] = column.split('/')[-1].replace('.', '')
|
||||
elif 'Energy' in column:
|
||||
energy = column.split('/')[-1].replace('.', '')
|
||||
old_units['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}
|
||||
old_units['current'] = column.split('/')[-1]
|
||||
|
||||
return old_units
|
||||
|
||||
|
|
@ -532,7 +762,7 @@ def convert_time_string(time_string, unit='ms'):
|
|||
|
||||
|
||||
|
||||
def convert_datetime_string(datetime_string, reference, unit='s'):
|
||||
def convert_datetime_string(datetime_string, reference, ref_time, unit='s'):
|
||||
''' Convert time string from Neware-data with the format yyy-mm-dd hh:mm:ss to any given unit'''
|
||||
|
||||
from datetime import datetime
|
||||
|
|
@ -555,7 +785,7 @@ def convert_datetime_string(datetime_string, reference, unit='s'):
|
|||
|
||||
factors = {'ms': 1000, 's': 1, 'min': 1/(60), 'h': 1/(60*60)}
|
||||
|
||||
time = s * factors[unit]
|
||||
time = s * factors[unit] + ref_time
|
||||
|
||||
return time
|
||||
|
||||
|
|
|
|||
|
|
@ -1,9 +1,13 @@
|
|||
from pickle import MARK
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,AutoMinorLocator)
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import math
|
||||
import os
|
||||
import shutil
|
||||
from PIL import Image
|
||||
|
||||
import ipywidgets as widgets
|
||||
from IPython.display import display
|
||||
|
|
@ -19,10 +23,15 @@ def plot_gc(data, options=None):
|
|||
|
||||
|
||||
# Update options
|
||||
required_options = ['x_vals', 'y_vals', 'which_cycles', 'charge', 'discharge', 'colours', 'differentiate_charge_discharge', 'gradient', 'interactive', 'interactive_session_active', 'rc_params', 'format_params']
|
||||
required_options = ['force_reload', 'x_vals', 'y_vals', 'which_cycles', 'limit', 'exclude_cycles', 'show_plot', 'summary', 'charge', 'discharge', 'colours', 'differentiate_charge_discharge', 'gradient', 'interactive', 'interactive_session_active', 'rc_params', 'format_params', 'save_gif', 'save_path', 'fps']
|
||||
default_options = {
|
||||
'force_reload': False,
|
||||
'x_vals': 'capacity', 'y_vals': 'voltage',
|
||||
'which_cycles': 'all',
|
||||
'limit': None, # Limit line to be drawn
|
||||
'exclude_cycles': [],
|
||||
'show_plot': True,
|
||||
'summary': False,
|
||||
'charge': True, 'discharge': True,
|
||||
'colours': None,
|
||||
'differentiate_charge_discharge': True,
|
||||
|
|
@ -30,20 +39,22 @@ def plot_gc(data, options=None):
|
|||
'interactive': False,
|
||||
'interactive_session_active': False,
|
||||
'rc_params': {},
|
||||
'format_params': {}}
|
||||
'format_params': {},
|
||||
'save_gif': False,
|
||||
'save_path': 'animation.gif',
|
||||
'fps': 1
|
||||
}
|
||||
|
||||
options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
|
||||
|
||||
|
||||
|
||||
|
||||
if not 'cycles' in data.keys():
|
||||
# Read data if not already loaded
|
||||
if not 'cycles' in data.keys() or options['force_reload']:
|
||||
data['cycles'] = ec.io.read_data(data=data, options=options)
|
||||
|
||||
# Update list of cycles to correct indices
|
||||
update_cycles_list(cycles=data['cycles'], options=options)
|
||||
|
||||
colours = generate_colours(cycles=data['cycles'], options=options)
|
||||
# Update list of cycles to correct indices
|
||||
update_cycles_list(data=data, options=options)
|
||||
|
||||
if options['interactive']:
|
||||
options['interactive'], options['interactive_session_active'] = False, True
|
||||
|
|
@ -51,30 +62,121 @@ def plot_gc(data, options=None):
|
|||
return
|
||||
|
||||
|
||||
# Prepare plot, and read and process data
|
||||
|
||||
fig, ax = btp.prepare_plot(options=options)
|
||||
colours = generate_colours(cycles=data['cycles'], options=options)
|
||||
if not options['summary']:
|
||||
|
||||
for i, cycle in enumerate(data['cycles']):
|
||||
if i in options['which_cycles']:
|
||||
if options['show_plot']:
|
||||
# Prepare plot
|
||||
fig, ax = btp.prepare_plot(options=options)
|
||||
for i, cycle in enumerate(data['cycles']):
|
||||
if i in options['which_cycles']:
|
||||
if options['charge']:
|
||||
cycle[0].plot(x=options['x_vals'], y=options['y_vals'], ax=ax, c=colours[i][0])
|
||||
|
||||
if options['discharge']:
|
||||
cycle[1].plot(x=options['x_vals'], y=options['y_vals'], ax=ax, c=colours[i][1])
|
||||
|
||||
|
||||
if options['interactive_session_active']:
|
||||
update_labels(options, force=True)
|
||||
else:
|
||||
update_labels(options)
|
||||
|
||||
|
||||
|
||||
if options['save_gif'] and not options['interactive_session_active']:
|
||||
if not os.path.isdir('tmp'):
|
||||
os.makedirs('tmp')
|
||||
|
||||
# Scale image to make GIF smaller
|
||||
options['format_params']['width'] = 7.5
|
||||
options['format_params']['height'] = 3
|
||||
|
||||
options['format_params']['dpi'] = 200
|
||||
|
||||
for i, cycle in enumerate(data['cycles']):
|
||||
if i in options['which_cycles']:
|
||||
|
||||
giffig, gifax = btp.prepare_plot(options=options)
|
||||
|
||||
if options['charge']:
|
||||
cycle[0].plot(x=options['x_vals'], y=options['y_vals'], ax=gifax, c=colours[i][0])
|
||||
if options['discharge']:
|
||||
cycle[1].plot(x=options['x_vals'], y=options['y_vals'], ax=gifax, c=colours[i][1])
|
||||
|
||||
gifax.text(x=gifax.get_xlim()[1]*0.8, y=3, s=f'{i+1}')
|
||||
update_labels(options)
|
||||
|
||||
giffig, gifax = btp.adjust_plot(fig=giffig, ax=gifax, options=options)
|
||||
|
||||
plt.savefig(os.path.join('tmp', str(i+1).zfill(4)+'.png'))
|
||||
plt.close()
|
||||
|
||||
|
||||
img_paths = [os.path.join('tmp', path) for path in os.listdir('tmp') if path.endswith('png')]
|
||||
frames = []
|
||||
for path in img_paths:
|
||||
frame = Image.open(path)
|
||||
frames.append(frame)
|
||||
|
||||
frames[0].save(options['save_path'], format='GIF', append_images=frames[1:], save_all=True, duration=(1/options['fps'])*1000, loop=0)
|
||||
|
||||
shutil.rmtree('tmp')
|
||||
|
||||
|
||||
|
||||
|
||||
elif options['summary'] and options['show_plot']:
|
||||
# Prepare plot
|
||||
fig, ax = btp.prepare_plot(options=options)
|
||||
|
||||
mask = []
|
||||
for i in range(data['cycles'].shape[0]):
|
||||
if i+1 in options['which_cycles']:
|
||||
mask.append(True)
|
||||
else:
|
||||
mask.append(False)
|
||||
|
||||
|
||||
# Drop the last row if it is midway through a charge in order to avoid mismatch of length of mask and dataset.
|
||||
if len(mask) > data['cycles'].shape[0]:
|
||||
del mask[-1]
|
||||
data['cycles'].drop(data['cycles'].tail(1).index, inplace=True)
|
||||
|
||||
|
||||
# FIXME To begin, the default is that y-values correspond to x-values. This should probably be implemented in more logical and consistent manner in the future.
|
||||
if options['x_vals'] in ['coulombic_efficiency', 'energy_efficiency']:
|
||||
data['cycles'].loc[mask].plot(x='cycle', y=options['x_vals'], ax=ax, color=colours[0][0], kind='scatter', marker="$\u25EF$", s=plt.rcParams['lines.markersize'])
|
||||
if options['limit']:
|
||||
ax.axhline(y=options['limit'], ls='--', c='black')
|
||||
|
||||
else:
|
||||
if options['charge']:
|
||||
cycle[0].plot(x=options['x_vals'], y=options['y_vals'], ax=ax, c=colours[i][0])
|
||||
yval = 'charge_' + options['x_vals']
|
||||
data['cycles'].loc[mask].plot(x='cycle', y=yval, ax=ax, color=colours[0][0], kind='scatter', marker="$\u25EF$", s=plt.rcParams['lines.markersize'])
|
||||
|
||||
if options['discharge']:
|
||||
cycle[1].plot(x=options['x_vals'], y=options['y_vals'], ax=ax, c=colours[i][1])
|
||||
yval = 'discharge_' + options['x_vals']
|
||||
data['cycles'].loc[mask].plot(x='cycle', y=yval, ax=ax, color=colours[0][1], kind='scatter', marker="$\u25EF$", s=plt.rcParams['lines.markersize'])
|
||||
|
||||
|
||||
if options['interactive_session_active']:
|
||||
update_labels(options, force=True)
|
||||
if options['limit']:
|
||||
ax.axhline(y=options['limit'], ls='--', c='black')
|
||||
|
||||
|
||||
if options['interactive_session_active']:
|
||||
update_labels(options, force=True)
|
||||
else:
|
||||
update_labels(options)
|
||||
|
||||
|
||||
|
||||
if options['show_plot']:
|
||||
fig, ax = btp.adjust_plot(fig=fig, ax=ax, options=options)
|
||||
return data['cycles'], fig, ax
|
||||
else:
|
||||
update_labels(options)
|
||||
|
||||
fig, ax = btp.adjust_plot(fig=fig, ax=ax, options=options)
|
||||
|
||||
#if options['interactive_session_active']:
|
||||
|
||||
|
||||
return data['cycles'], fig, ax
|
||||
return data['cycles'], None, None
|
||||
|
||||
|
||||
def plot_gc_interactive(data, options):
|
||||
|
|
@ -90,6 +192,107 @@ def plot_gc_interactive(data, options):
|
|||
display(w)
|
||||
|
||||
|
||||
|
||||
|
||||
def plot_cv(data, options):
|
||||
|
||||
# Update options
|
||||
required_options = ['force_reload', 'x_vals', 'y_vals', 'which_cycles', 'limit', 'exclude_cycles', 'show_plot', 'charge', 'discharge', 'colours', 'differentiate_charge_discharge', 'gradient', 'interactive', 'interactive_session_active', 'rc_params', 'format_params', 'save_gif', 'save_path', 'fps']
|
||||
default_options = {
|
||||
'force_reload': False,
|
||||
'x_vals': 'voltage', 'y_vals': 'current',
|
||||
'which_cycles': 'all',
|
||||
'limit': None, # Limit line to be drawn
|
||||
'exclude_cycles': [],
|
||||
'show_plot': True,
|
||||
'charge': True, 'discharge': True,
|
||||
'colours': None,
|
||||
'differentiate_charge_discharge': True,
|
||||
'gradient': False,
|
||||
'interactive': False,
|
||||
'interactive_session_active': False,
|
||||
'rc_params': {},
|
||||
'format_params': {},
|
||||
'save_gif': False,
|
||||
'save_path': 'animation.gif',
|
||||
'fps': 1
|
||||
}
|
||||
|
||||
options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
|
||||
|
||||
|
||||
# Read data if not already loaded
|
||||
if not 'cycles' in data.keys() or options['force_reload']:
|
||||
data['cycles'] = ec.io.read_data(data=data, options=options)
|
||||
|
||||
|
||||
# Update list of cycles to correct indices
|
||||
update_cycles_list(data=data, options=options)
|
||||
|
||||
colours = generate_colours(cycles=data['cycles'], options=options)
|
||||
|
||||
if options['show_plot']:
|
||||
# Prepare plot
|
||||
fig, ax = btp.prepare_plot(options=options)
|
||||
for i, cycle in enumerate(data['cycles']):
|
||||
if i in options['which_cycles']:
|
||||
if options['charge']:
|
||||
cycle[0].plot(x=options['x_vals'], y=options['y_vals'], ax=ax, c=colours[i][0])
|
||||
|
||||
if options['discharge']:
|
||||
cycle[1].plot(x=options['x_vals'], y=options['y_vals'], ax=ax, c=colours[i][1])
|
||||
|
||||
update_labels(options)
|
||||
|
||||
|
||||
|
||||
if options['save_gif'] and not options['interactive_session_active']:
|
||||
if not os.path.isdir('tmp'):
|
||||
os.makedirs('tmp')
|
||||
|
||||
# Scale image to make GIF smaller
|
||||
options['format_params']['width'] = 7.5
|
||||
options['format_params']['height'] = 3
|
||||
|
||||
options['format_params']['dpi'] = 200
|
||||
|
||||
for i, cycle in enumerate(data['cycles']):
|
||||
if i in options['which_cycles']:
|
||||
|
||||
giffig, gifax = btp.prepare_plot(options=options)
|
||||
|
||||
if options['charge']:
|
||||
cycle[0].plot(x=options['x_vals'], y=options['y_vals'], ax=gifax, c=colours[i][0])
|
||||
if options['discharge']:
|
||||
cycle[1].plot(x=options['x_vals'], y=options['y_vals'], ax=gifax, c=colours[i][1])
|
||||
|
||||
gifax.text(x=gifax.get_xlim()[1]*0.8, y=3, s=f'{i+1}')
|
||||
update_labels(options)
|
||||
|
||||
giffig, gifax = btp.adjust_plot(fig=giffig, ax=gifax, options=options)
|
||||
|
||||
plt.savefig(os.path.join('tmp', str(i+1).zfill(4)+'.png'))
|
||||
plt.close()
|
||||
|
||||
|
||||
img_paths = [os.path.join('tmp', path) for path in os.listdir('tmp') if path.endswith('png')]
|
||||
frames = []
|
||||
for path in img_paths:
|
||||
frame = Image.open(path)
|
||||
frames.append(frame)
|
||||
|
||||
frames[0].save(options['save_path'], format='GIF', append_images=frames[1:], save_all=True, duration=(1/options['fps'])*1000, loop=0)
|
||||
|
||||
shutil.rmtree('tmp')
|
||||
|
||||
|
||||
|
||||
if options['show_plot']:
|
||||
fig, ax = btp.adjust_plot(fig=fig, ax=ax, options=options)
|
||||
return data['cycles'], fig, ax
|
||||
else:
|
||||
return data['cycles'], None, None
|
||||
|
||||
def update_labels(options, force=False):
|
||||
|
||||
if 'xlabel' not in options.keys() or force:
|
||||
|
|
@ -118,30 +321,47 @@ def update_labels(options, force=False):
|
|||
|
||||
|
||||
|
||||
def update_cycles_list(cycles, options: dict) -> None:
|
||||
def update_cycles_list(data, options: dict) -> None:
|
||||
|
||||
if options['which_cycles'] == 'all':
|
||||
options['which_cycles'] = [i for i in range(len(cycles))]
|
||||
options['which_cycles'] = [i for i in range(len(data['cycles']))]
|
||||
|
||||
|
||||
elif type(options['which_cycles']) == list:
|
||||
options['which_cycles'] = [i-1 for i in options['which_cycles']]
|
||||
elif isinstance(options['which_cycles'], list):
|
||||
|
||||
cycles =[]
|
||||
|
||||
for cycle in options['which_cycles']:
|
||||
if isinstance(cycle, int):
|
||||
cycles.append(cycle-1)
|
||||
|
||||
elif isinstance(cycle, tuple):
|
||||
interval = [i-1 for i in range(cycle[0], cycle[1]+1)]
|
||||
cycles.extend(interval)
|
||||
|
||||
|
||||
options['which_cycles'] = cycles
|
||||
|
||||
|
||||
# Tuple is used to define an interval - as elements tuples can't be assigned, I convert it to a list here.
|
||||
elif type(options['which_cycles']) == tuple:
|
||||
elif isinstance(options['which_cycles'], tuple):
|
||||
which_cycles = list(options['which_cycles'])
|
||||
|
||||
if which_cycles[0] <= 0:
|
||||
which_cycles[0] = 1
|
||||
|
||||
elif which_cycles[1] < 0:
|
||||
which_cycles[1] = len(cycles)
|
||||
which_cycles[1] = len(options['which_cycles'])
|
||||
|
||||
|
||||
options['which_cycles'] = [i-1 for i in range(which_cycles[0], which_cycles[1]+1)]
|
||||
|
||||
|
||||
for i, cycle in enumerate(options['which_cycles']):
|
||||
if cycle in options['exclude_cycles']:
|
||||
del options['which_cycles'][i]
|
||||
|
||||
|
||||
|
||||
|
||||
def prettify_gc_plot(fig, ax, options=None):
|
||||
|
|
|
|||
|
|
@ -182,8 +182,13 @@ def adjust_plot(fig, ax, options):
|
|||
# Hide x- and y-ticks:
|
||||
if options['hide_y_ticks']:
|
||||
ax.tick_params(axis='y', direction='in', which='both', left=False, right=False)
|
||||
else:
|
||||
ax.tick_params(axis='y', direction='in', which='both', left=True, right=True)
|
||||
|
||||
if options['hide_x_ticks']:
|
||||
ax.tick_params(axis='x', direction='in', which='both', bottom=False, top=False)
|
||||
else:
|
||||
ax.tick_params(axis='x', direction='in', which='both', bottom=True, top=True)
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue