Add plot functionality to electrochemistry
This commit is contained in:
rasmusvt
parent
4f255fd9d5
commit
43e6ef27c8
2 changed files with 433 additions and 33 deletions
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@ -4,6 +4,22 @@ import matplotlib.pyplot as plt
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import os
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def read_data(path, kind, options=None):
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if kind == 'neware':
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df = read_neware(path)
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cycles = process_neware_data(df, options=options)
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elif kind == 'batsmall':
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df = read_batsmall(path)
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cycles = process_batsmall_data(df=df, options=options)
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elif kind == 'biologic':
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df = read_biologic(path)
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cycles = process_biologic_data(df=df, options=options)
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return cycles
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def read_batsmall(path):
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''' Reads BATSMALL-data into a DataFrame.
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@ -77,7 +93,7 @@ def read_biologic(path):
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def process_batsmall_data(df, units=None, splice_cycles=None, molecular_weight=None):
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def process_batsmall_data(df, options=None):
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''' Takes BATSMALL-data in the form of a DataFrame and cleans the data up and converts units into desired units.
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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.
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@ -94,12 +110,24 @@ def process_batsmall_data(df, units=None, splice_cycles=None, molecular_weight=N
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cycles: A list with
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'''
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required_options = ['splice_cycles', 'molecular_weight', 'reverse_discharge', 'units']
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default_options = {'splice_cycles': None, 'molecular_weight': None, 'reverse_discharge': False, 'units': None}
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if not options:
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options = default_options
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else:
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for option in required_options:
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if option not in options.keys():
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options[option] = default_options[option]
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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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new_units = set_units(units=units)
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new_units = set_units(units=options['units'])
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old_units = get_old_units(df, kind='batsmall')
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df = unit_conversion(df=df, new_units=new_units, old_units=old_units, kind='batsmall')
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options['units'] = new_units
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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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@ -126,6 +154,18 @@ def process_batsmall_data(df, units=None, splice_cycles=None, molecular_weight=N
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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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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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@ -134,7 +174,7 @@ def process_batsmall_data(df, units=None, splice_cycles=None, molecular_weight=N
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return cycles
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def process_neware_data(df, units=None, splice_cycles=None, active_material_weight=None, molecular_weight=None, reverse_discharge=False):
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def process_neware_data(df, options=None):
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""" Takes data from NEWARE in a DataFrame as read by read_neware() and converts units, adds columns and splits into cycles.
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@ -145,14 +185,27 @@ def process_neware_data(df, units=None, splice_cycles=None, active_material_weig
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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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default_options = {'units': None, 'active_material_weight': None, 'molecular_weight': None, 'reverse_discharge': False, 'splice_cycles': None}
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if not options:
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options = default_options
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else:
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for option in required_options:
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if option not in options.keys():
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options[option] = default_options[option]
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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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new_units = set_units(units=units)
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new_units = set_units(units=options['units'])
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old_units = get_old_units(df=df, kind='neware')
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df = add_columns(df=df, active_material_weight=active_material_weight, molecular_weight=molecular_weight, old_units=old_units, kind='neware')
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df = add_columns(df=df, active_material_weight=options['active_material_weight'], molecular_weight=options['molecular_weight'], old_units=old_units, kind='neware')
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df = unit_conversion(df=df, new_units=new_units, old_units=old_units, kind='neware')
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options['units'] = new_units
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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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@ -176,7 +229,7 @@ def process_neware_data(df, units=None, splice_cycles=None, active_material_weig
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if chg_df.empty and dchg_df.empty:
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continue
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if reverse_discharge:
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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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@ -195,19 +248,31 @@ def process_neware_data(df, units=None, splice_cycles=None, active_material_weig
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return cycles
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def process_biologic_data(df, units=None, splice_cycles=None, active_material_weight=None, molecular_weight=None, reverse_discharge=False):
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def process_biologic_data(df, options=None):
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required_options = ['units', 'active_material_weight', 'molecular_weight', 'reverse_discharge', 'splice_cycles']
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default_options = {'units': None, 'active_material_weight': None, 'molecular_weight': None, 'reverse_discharge': False, 'splice_cycles': None}
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if not options:
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options = default_options
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else:
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for option in required_options:
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if option not in options.keys():
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options[option] = default_options[option]
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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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# 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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new_units = set_units(units=units)
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new_units = set_units(units=options['units'])
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old_units = get_old_units(df=df, kind='biologic')
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df = add_columns(df=df, active_material_weight=active_material_weight, molecular_weight=molecular_weight, old_units=old_units, kind='biologic')
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df = add_columns(df=df, active_material_weight=options['active_material_weight'], molecular_weight=options['molecular_weight'], old_units=old_units, kind='biologic')
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df = unit_conversion(df=df, new_units=new_units, old_units=old_units, kind='biologic')
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options['units'] = new_units
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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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@ -233,7 +298,7 @@ def process_biologic_data(df, units=None, splice_cycles=None, active_material_we
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if chg_df.empty and dchg_df.empty:
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continue
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if reverse_discharge:
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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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@ -348,8 +413,8 @@ def unit_conversion(df, new_units, old_units, kind):
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def set_units(units=None):
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# Complete the list of units - if not all are passed, then default value will be used
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required_units = ['time', 'current', 'voltage', 'capacity', 'mass', 'energy']
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default_units = {'time': 'h', 'current': 'mA', 'voltage': 'V', 'capacity': 'mAh', 'mass': 'g', 'energy': 'mWh'}
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required_units = ['time', 'current', 'voltage', 'capacity', 'mass', 'energy', 'specific_capacity']
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default_units = {'time': 'h', 'current': 'mA', 'voltage': 'V', 'capacity': 'mAh', 'mass': 'g', 'energy': 'mWh', 'specific_capacity': None}
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if not units:
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units = default_units
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@ -359,6 +424,8 @@ def set_units(units=None):
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if unit not in units.keys():
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units[unit] = default_units[unit]
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units['specific_capacity'] = r'{} {}'.format(units['capacity'], units['mass']) + '$^{-1}$'
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return units
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@ -1,40 +1,373 @@
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import matplotlib.pyplot as plt
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from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,AutoMinorLocator)
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import pandas as pd
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import numpy as np
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import math
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import beamtime.electrochemistry as ec
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def plot_gc(cycles, which_cycles='all', chg=True, dchg=True, colours=None, x='C', y='U'):
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def plot_gc(path, kind, options=None):
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fig, ax = prepare_gc_plot()
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# Prepare plot, and read and process data
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fig, ax = prepare_gc_plot(options=options)
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cycles = ec.io.read_data(path=path, kind=kind, options=options)
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if which_cycles == 'all':
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which_cycles = [i for i, c in enumerate(cycles)]
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# Update options
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required_options = ['x_vals', 'y_vals', 'which_cycles', 'chg', 'dchg', 'colours', 'gradient']
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default_options = {'x_vals': 'capacity', 'y_vals': 'voltage', 'which_cycles': 'all', 'chg': True, 'dchg': True, 'colours': None, 'gradient': False}
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if not colours:
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chg_colour = (40/255, 70/255, 75/255) # Dark Slate Gray #28464B
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dchg_colour = (239/255, 160/255, 11/255) # Marigold #EFA00B
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options = update_options(options=options, required_options=required_options, default_options=default_options)
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# Update list of cycles to correct indices
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update_cycles_list(cycles=cycles, options=options)
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colours = generate_colours(cycles=cycles, options=options)
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print(len(options['which_cycles']))
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print(len(colours))
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for i, cycle in enumerate(cycles):
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if i in options['which_cycles']:
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if options['chg']:
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cycle[0].plot(x=options['x_vals'], y=options['y_vals'], ax=ax, c=colours[i][0])
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if options['dchg']:
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cycle[1].plot(x=options['x_vals'], y=options['y_vals'], ax=ax, c=colours[i][1])
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fig, ax = prettify_gc_plot(fig=fig, ax=ax, options=options)
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return cycles, fig, ax
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def update_options(options, required_options, default_options):
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if not options:
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options = default_options
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else:
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for option in required_options:
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if option not in options.keys():
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options[option] = default_options[option]
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return options
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def update_cycles_list(cycles, options):
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if not options:
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options['which_cycles']
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if options['which_cycles'] == 'all':
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options['which_cycles'] = [i for i in range(len(cycles))]
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elif type(options['which_cycles']) == list:
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options['which_cycles'] = [i-1 for i in options['which_cycles']]
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# Tuple is used to define an interval - as elements tuples can't be assigned, I convert it to a list here.
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elif type(options['which_cycles']) == tuple:
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which_cycles = list(options['which_cycles'])
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for i, cycle in cycles:
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if i in which_cycles:
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if chg:
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cycle[0].plot(ax=ax)
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if which_cycles[0] <= 0:
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which_cycles[0] = 1
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elif which_cycles[1] < 0:
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which_cycles[1] = len(cycles)
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options['which_cycles'] = [i-1 for i in range(which_cycles[0], which_cycles[1]+1)]
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return options
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def prepare_gc_plot(options=None):
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# First take care of the options for plotting - set any values not specified to the default values
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required_options = ['columns', 'width', 'height', 'format', 'dpi', 'facecolor']
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default_options = {'columns': 1, 'width': 14, 'format': 'golden_ratio', 'dpi': None, 'facecolor': 'w'}
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# If none are set at all, just pass the default_options
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if not options:
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options = default_options
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options['height'] = options['width'] * (math.sqrt(5) - 1) / 2
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options['figsize'] = (options['width'], options['height'])
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# If options is passed, go through to fill out the rest.
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else:
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# Start by setting the width:
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if 'width' not in options.keys():
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options['width'] = default_options['width']
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# Then set height - check options for format. If not given, set the height to the width scaled by the golden ratio - if the format is square, set the same. This should possibly allow for the tweaking of custom ratios later.
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if 'height' not in options.keys():
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if 'format' not in options.keys():
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options['height'] = options['width'] * (math.sqrt(5) - 1) / 2
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elif options['format'] == 'square':
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options['height'] = options['width']
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options['figsize'] = (options['width'], options['height'])
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# After height and width are set, go through the rest of the options to make sure that all the required options are filled
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for option in required_options:
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if option not in options.keys():
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options[option] = default_options[option]
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fig, ax = plt.subplots(figsize=(options['figsize']), dpi=options['dpi'], facecolor=options['facecolor'])
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linewidth = 1*options['columns']
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axeswidth = 3*options['columns']
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plt.rc('lines', linewidth=linewidth)
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plt.rc('axes', linewidth=axeswidth)
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return fig, ax
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def prettify_gc_plot(fig, ax, options=None):
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##################################################################
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######################### UPDATE OPTIONS #########################
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##################################################################
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# Define the required options
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required_options = [
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'columns',
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'xticks', 'yticks',
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'show_major_ticks',
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'show_minor_ticks',
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'xlim', 'ylim',
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'hide_x_axis', 'hide_y_axis',
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'x_vals', 'y_vals',
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'xlabel', 'ylabel',
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'units', 'sizes',
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'title'
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]
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# Define the default options
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default_options = {
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'columns': 1,
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'xticks': None,
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'yticks': None,
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'show_major_ticks': [True, True, True, True],
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'show_minor_ticks': [True, True, True, True],
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'xlim': None,
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'ylim': None,
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'hide_x_axis': False,
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'hide_y_axis': False,
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'x_vals': 'specific_capacity',
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'y_vals': 'voltage',
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'xlabel': None,
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'ylabel': None,
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'units': None,
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'sizes': None,
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'title': None
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}
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update_options(options, required_options, default_options)
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##################################################################
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########################## DEFINE SIZES ##########################
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##################################################################
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# Define the required sizes
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required_sizes = [
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'labels',
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'legend',
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'title',
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'line', 'axes',
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'tick_labels',
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'major_ticks', 'minor_ticks']
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# Define default sizes
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default_sizes = {
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'labels': 30*options['columns'],
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'legend': 30*options['columns'],
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'title': 30*options['columns'],
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'line': 3*options['columns'],
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'axes': 3*options['columns'],
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'tick_labels': 30*options['columns'],
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'major_ticks': 20*options['columns'],
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'minor_ticks': 10*options['columns']
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}
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# Initialise dictionary if it doesn't exist
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if not options['sizes']:
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options['sizes'] = {}
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# Update dictionary with default values where none is supplied
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for size in required_sizes:
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if size not in options['sizes']:
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options['sizes'][size] = default_sizes[size]
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##################################################################
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########################## AXIS LABELS ###########################
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##################################################################
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if not options['xlabel']:
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print(options['x_vals'])
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print(options['units'])
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options['xlabel'] = prettify_labels(options['x_vals']) + ' [{}]'.format(options['units'][options['x_vals']])
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else:
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options['xlabel'] = options['xlabel'] + ' [{}]'.format(options['units'][options['x_vals']])
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if not options['ylabel']:
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options['ylabel'] = prettify_labels(options['y_vals']) + ' [{}]'.format(options['units'][options['y_vals']])
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else:
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options['ylabel'] = options['ylabel'] + ' [{}]'.format(options['units'][options['y_vals']])
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||||
ax.set_xlabel(options['xlabel'], size=options['sizes']['labels'])
|
||||
ax.set_ylabel(options['ylabel'], size=options['sizes']['labels'])
|
||||
|
||||
##################################################################
|
||||
###################### TICK MARKS & LABELS #######################
|
||||
##################################################################
|
||||
|
||||
ax.tick_params(direction='in', which='major', bottom=options['show_major_ticks'][0], left=options['show_major_ticks'][1], top=options['show_major_ticks'][2], right=options['show_major_ticks'][0], length=options['sizes']['major_ticks'], width=options['sizes']['axes'])
|
||||
ax.tick_params(direction='in', which='minor', bottom=options['show_minor_ticks'][0], left=options['show_minor_ticks'][1], top=options['show_minor_ticks'][2], right=options['show_minor_ticks'][0], length=options['sizes']['minor_ticks'], width=options['sizes']['axes'])
|
||||
|
||||
|
||||
|
||||
# DEFINE AND SET TICK DISTANCES
|
||||
|
||||
default_ticks = {
|
||||
'specific_capacity': [100, 50],
|
||||
'capacity': [0.1, 0.05],
|
||||
'voltage': [0.5, 0.25]
|
||||
}
|
||||
|
||||
|
||||
|
||||
# Set default tick distances for x-axis if not specified
|
||||
if not options['xticks']:
|
||||
|
||||
major_xtick = default_ticks[options['x_vals']][0]
|
||||
minor_xtick = default_ticks[options['x_vals']][1]
|
||||
|
||||
# Otherwise apply user input
|
||||
else:
|
||||
major_xtick = options['xticks'][0]
|
||||
minor_xtick = options['xticks'][1]
|
||||
|
||||
|
||||
# Set default tick distances for x-axis if not specified
|
||||
if not options['yticks']:
|
||||
|
||||
major_ytick = default_ticks[options['y_vals']][0]
|
||||
minor_ytick = default_ticks[options['y_vals']][1]
|
||||
|
||||
# Otherwise apply user input
|
||||
else:
|
||||
major_xtick = options['yticks'][0]
|
||||
minor_xtick = options['yticks'][1]
|
||||
|
||||
|
||||
# Apply values
|
||||
ax.xaxis.set_major_locator(MultipleLocator(major_xtick))
|
||||
ax.xaxis.set_minor_locator(MultipleLocator(minor_xtick))
|
||||
|
||||
ax.yaxis.set_major_locator(MultipleLocator(major_ytick))
|
||||
ax.yaxis.set_minor_locator(MultipleLocator(minor_ytick))
|
||||
|
||||
|
||||
# SET FONTSIZE OF TICK LABELS
|
||||
|
||||
plt.xticks(fontsize=options['sizes']['tick_labels'])
|
||||
plt.yticks(fontsize=options['sizes']['tick_labels'])
|
||||
|
||||
##################################################################
|
||||
############################# TITLE ##############################
|
||||
##################################################################
|
||||
|
||||
if options['title']:
|
||||
ax.set_title(options['title'], size=options['sizes']['title'])
|
||||
|
||||
##################################################################
|
||||
############################# LEGEND #############################
|
||||
##################################################################
|
||||
|
||||
ax.get_legend().remove()
|
||||
|
||||
return fig, ax
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def prepare_gc_plot(figsize=(14,7), dpi=None):
|
||||
|
||||
fig, ax = plt.subplots(figsize=figsize, dpi=dpi)
|
||||
def prettify_labels(label):
|
||||
|
||||
labels_dict = {
|
||||
'capacity': 'Capacity',
|
||||
'specific_capacity': 'Specific capacity',
|
||||
'voltage': 'Voltage',
|
||||
'current': 'Current',
|
||||
'energy': 'Energy',
|
||||
}
|
||||
|
||||
return fig, ax
|
||||
return labels_dict[label]
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def generate_colours(cycles, options):
|
||||
|
||||
# Assign colours from the options dictionary if it is defined, otherwise use standard colours.
|
||||
if options['colours']:
|
||||
charge_colour = options['colours'][0]
|
||||
discharge_colour = options['colours'][1]
|
||||
|
||||
else:
|
||||
charge_colour = (40/255, 70/255, 75/255) # Dark Slate Gray #28464B, coolors.co
|
||||
discharge_colour = (239/255, 160/255, 11/255) # Marigold #EFA00B, coolors.co
|
||||
|
||||
|
||||
|
||||
# If gradient is enabled, find start and end points for each colour
|
||||
if options['gradient']:
|
||||
|
||||
add_charge = min([(1-x)*0.75 for x in charge_colour])
|
||||
add_discharge = min([(1-x)*0.75 for x in discharge_colour])
|
||||
|
||||
charge_colour_start = charge_colour
|
||||
charge_colour_end = [x+add_charge for x in charge_colour]
|
||||
|
||||
discharge_colour_start = discharge_colour
|
||||
discharge_colour_end = [x+add_discharge for x in discharge_colour]
|
||||
|
||||
|
||||
|
||||
# Generate lists of colours
|
||||
colours = []
|
||||
|
||||
for cycle_number in range(0, len(cycles)):
|
||||
if options['gradient']:
|
||||
weight_start = (len(cycles) - cycle_number)/len(cycles)
|
||||
weight_end = cycle_number/len(cycles)
|
||||
|
||||
charge_colour = [weight_start*start_colour + weight_end*end_colour for start_colour, end_colour in zip(charge_colour_start, charge_colour_end)]
|
||||
discharge_colour = [weight_start*start_colour + weight_end*end_colour for start_colour, end_colour in zip(discharge_colour_start, discharge_colour_end)]
|
||||
|
||||
colours.append([charge_colour, discharge_colour])
|
||||
|
||||
return colours
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue