Initial data inspection and plotting¶
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 unitsc.data.raw_units) - Summary:
c.data.summarywith cycle-based summaries - Steps:
c.data.stepswith Stats from each step (and step type), created using thec.make_step_tablemethod
test_id data_point test_time step_time date_time \
data_point
1 1 1 5.008961 5.008961 2021-05-10 10:14:45
2 1 2 10.019319 10.019319 2021-05-10 10:14:50
step_index cycle_index is_fc_data current voltage \
data_point
1 1 1 0 0.0 3.051165
2 1 1 0 0.0 3.051165
charge_capacity discharge_capacity charge_energy \
data_point
1 0.0 0.0 0.0
2 0.0 0.0 0.0
discharge_energy dv_dt internal_resistance ac_impedance \
data_point
1 0.0 -0.000061 0.0 0.0
2 0.0 0.000000 0.0 0.0
aci_phase_angle
data_point
1 0.0
2 0.0
data_point test_time date_time \
cycle_index
1 5797 174328.601353 2021-05-12 10:40:11
2 7188 317161.773416 2021-05-14 02:20:47
end_voltage_charge end_voltage_discharge charge_capacity \
cycle_index
1 4.200052 3.129170 0.003819
2 4.200052 3.188442 0.003422
discharge_capacity coulombic_efficiency \
cycle_index
1 0.003324 87.049469
2 0.003234 94.510786
cumulated_coulombic_efficiency cumulated_charge_capacity ... \
cycle_index ...
1 87.049469 0.003819 ...
2 181.560255 0.007241 ...
cumulated_charge_capacity_areal \
cycle_index
1 3.818560
2 7.240795
cumulated_discharge_capacity_areal coulombic_difference_areal \
cycle_index
1 3.324036 0.494524
2 6.558417 0.187854
cumulated_coulombic_difference_areal \
cycle_index
1 0.494524
2 0.682378
discharge_capacity_loss_areal charge_capacity_loss_areal \
cycle_index
1 NaN NaN
2 0.089654 0.396324
cumulated_discharge_capacity_loss_areal \
cycle_index
1 NaN
2 0.089654
cumulated_charge_capacity_loss_areal \
cycle_index
1 NaN
2 0.396324
shifted_charge_capacity_areal shifted_discharge_capacity_areal
cycle_index
1 0.494524 4.313083
2 0.682378 4.104613
[2 rows x 49 columns]
index cycle step sub_step point_avr point_std point_min point_max \
0 0 1 1 1 2157.5 1245.48886 1 4314
1 1 1 2 1 4315.0 NaN 4315 4315
point_first point_last ... ir_std ir_min ir_max ir_first \
0 1 4314 ... 0.0 0.000000 0.000000 0.000000
1 4315 4315 ... NaN 6.650723 6.650723 6.650723
ir_last ir_delta rate_avr type sub_type info
0 0.000000 0.0 0.00000 rest NaN
1 6.650723 0.0 1.75791 ir NaN
[2 rows x 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:
Cycle info plots¶
The cycle_info_plot function plots the raw data together with step and cycle info:
These plot functions offer some flexibility. You can, e.g. select specific cycles to look at, or adjust the units of the plot variables:
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.
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.
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"