Initial data inspection and plotting

import pathlib

from rich import print

import cellpy
from cellpy.utils import plotutils

Hint

If you have plotly installed, some of the functions will produce interactive plots. If not, the output will be simpler matplotlib figures. If you have not installed plotly, you can do so by running pip install plotly.

Either load raw data or your saved cellpy files:

filedir = pathlib.Path("data")  # foldername within the same directory
c = cellpy.get(filedir / "out" / "20210210_FC.h5")

Looking at the data

Your CellpyCell object (here called c) contains all your raw data as well as some additional elements, in the format of pandas DataFrames:

• Raw data: c.data.raw, raw data from the run (with units c.data.raw_units)

• Summary: c.data.summary with cycle-based summaries

• Steps: c.data.steps with Stats from each step (and step type), created using the c.make_step_table method

c.data.raw.head(2)

	test_id	data_point	test_time	step_time	date_time	step_index	cycle_index	is_fc_data	current	voltage	charge_capacity	discharge_capacity	charge_energy	discharge_energy	dv_dt	internal_resistance	ac_impedance	aci_phase_angle
data_point
1	1	1	5.008961	5.008961	2021-05-10 10:14:45	1	1	0	0.0	3.051165	0.0	0.0	0.0	0.0	-0.000061	0.0	0.0	0.0
2	1	2	10.019319	10.019319	2021-05-10 10:14:50	1	1	0	0.0	3.051165	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0

c.data.summary.head(2)

	data_point	test_time	date_time	end_voltage_charge	end_voltage_discharge	charge_capacity	discharge_capacity	coulombic_efficiency	cumulated_coulombic_efficiency	cumulated_charge_capacity	...	cumulated_charge_capacity_areal	cumulated_discharge_capacity_areal	coulombic_difference_areal	cumulated_coulombic_difference_areal	discharge_capacity_loss_areal	charge_capacity_loss_areal	cumulated_discharge_capacity_loss_areal	cumulated_charge_capacity_loss_areal	shifted_charge_capacity_areal	shifted_discharge_capacity_areal
cycle_index
1	5797	174328.601353	2021-05-12 10:40:11	4.200052	3.129170	0.003819	0.003324	87.049469	87.049469	0.003819	...	3.818560	3.324036	0.494524	0.494524	NaN	NaN	NaN	NaN	0.494524	4.313083
2	7188	317161.773416	2021-05-14 02:20:47	4.200052	3.188442	0.003422	0.003234	94.510786	181.560255	0.007241	...	7.240795	6.558417	0.187854	0.682378	0.089654	0.396324	0.089654	0.396324	0.682378	4.104613

2 rows × 49 columns

c.data.steps.head(2)

	index	cycle	step	sub_step	point_avr	point_std	point_min	point_max	point_first	point_last	...	ir_std	ir_min	ir_max	ir_first	ir_last	ir_delta	rate_avr	type	sub_type	info
0	0	1	1	1	2157.5	1245.48886	1	4314	1	4314	...	0.0	0.000000	0.000000	0.000000	0.000000	0.0	0.00000	rest	NaN
1	1	1	2	1	4315.0	NaN	4315	4315	4315	4315	...	NaN	6.650723	6.650723	6.650723	6.650723	0.0	1.75791	ir	NaN

2 rows × 64 columns

Simple plotting

The plotutils module contains several convenient plot functions:

Raw plots

The raw_plot gives an overview of your datacollection, plotting voltage vs time:

plotutils.raw_plot(c, title="Voltage vs time")

Cycle info plots

The cycle_info_plot function plots the raw data together with step and cycle info:

plotutils.cycle_info_plot(c, title="Cycle info plot:")

These plot functions offer some flexibility. You can, e.g. select specific cycles to look at, or adjust the units of the plot variables:

plotutils.cycle_info_plot(c, cycle=[7, 8, 9], title="Cycle info plot:", t_unit="days")

Summary plots

summary_plots allows you to plot different summary variables. You can inspect the columns of c.data.summary to check what variables are available.

print(c.data.summary.columns)

Index(['data_point', 'test_time', 'date_time', 'end_voltage_charge',
       'end_voltage_discharge', 'charge_capacity', 'discharge_capacity',
       'coulombic_efficiency', 'cumulated_coulombic_efficiency',
       'cumulated_charge_capacity', 'cumulated_discharge_capacity',
       'discharge_capacity_loss', 'charge_capacity_loss',
       'coulombic_difference', 'cumulated_coulombic_difference',
       'cumulated_discharge_capacity_loss', 'cumulated_charge_capacity_loss',
       'shifted_charge_capacity', 'shifted_discharge_capacity',
       'cumulated_ric', 'cumulated_ric_sei', 'cumulated_ric_disconnect',
       'normalized_cycle_index', 'charge_c_rate', 'discharge_c_rate',
       'discharge_capacity_gravimetric', 'charge_capacity_gravimetric',
       'cumulated_charge_capacity_gravimetric',
       'cumulated_discharge_capacity_gravimetric',
       'coulombic_difference_gravimetric',
       'cumulated_coulombic_difference_gravimetric',
       'discharge_capacity_loss_gravimetric',
       'charge_capacity_loss_gravimetric',
       'cumulated_discharge_capacity_loss_gravimetric',
       'cumulated_charge_capacity_loss_gravimetric',
       'shifted_charge_capacity_gravimetric',
       'shifted_discharge_capacity_gravimetric', 'discharge_capacity_areal',
       'charge_capacity_areal', 'cumulated_charge_capacity_areal',
       'cumulated_discharge_capacity_areal', 'coulombic_difference_areal',
       'cumulated_coulombic_difference_areal', 'discharge_capacity_loss_areal',
       'charge_capacity_loss_areal', 'cumulated_discharge_capacity_loss_areal',
       'cumulated_charge_capacity_loss_areal', 'shifted_charge_capacity_areal',
       'shifted_discharge_capacity_areal'],
      dtype='object')

Here is one example:

plotutils.summary_plot(
    c,
    y="shifted_discharge_capacity_gravimetric",
    title="<b>Shifted Charge Capacity</b>",
)

The summary_plot function also have some pre-defined sets of variables for plotting the most common variables.

plotutils.summary_plot(
    c, y="capacities_gravimetric", title="<b>Gravimetric Capacities</b>"
)

plotutils.summary_plot(c, y="voltages", title="<b>End Voltages</b>")

The pre-defined variable sets for the summary plots are: - “voltages” - “capacities_gravimetric” - “capacities_areal” - “capacities_gravimetric_split_constant_voltage” - “capacities_areal_split_constant_voltage”