File Formats and Data Structures

Warning

This part of the documentation is currently being updated.

The most important file formats and data structures for cellpy is summarized here. It is also possible to look into the source-code at the repository https://github.com/jepegit/cellpy.

Data Structures

CellpyCell - main structure

This class is the main work-horse for cellpy where all the functions for reading, selecting, and tweaking your data is located. It also contains the header definitions, both for the cellpy hdf5 format, and for the various cell-tester file-formats that can be read. The class can contain several tests and each test is stored in a list.

The class contains several attributes that can be assigned directly:

CellpyCell.tester = "arbin_res"  # TODO - update this
CellpyCell.auto_dirs = True
print(CellpyCell.cellpy_datadir)

CellpyCell - methods

The CellpyCell object contains lots of methods for manipulating, extracting and summarising the data from the run(s). Two methods are typically automatically run when you create your CellpyCell object when running cellpy.get(filename):

• make_step_table: creates a statistical summary of all the steps in the run(s) and categorizes the step type from that. It is also possible to give the step types directly (step_specifications).

• make_summary: create a summary based on cycle number.

Other methods worth mentioning are (based on what I typically use):

• load: load a cellpy file.

• load_raw: load raw data file(s) (merges automatically if several filenames are given as a list).

• get_cap: get the capacity-voltage graph from one or more cycles in three different formats as well as optionally interpolated, normalized and/or scaled.

• get_cycle_numbers: get the cycle numbers for your run.

• get_ocv: get the rest steps after each charge and discharge step.

Take a look at API section (Module index, cellpy.readers.cellreader.CellpyCell) for more info.

Data

The data for the experiment are stored in the class attribute CellpyCell.data (a cellpy.cellreader.Data instance).

The instance contain general information about the run-settings (such as mass etc.). The measurement data, information, and summary is stored in three pandas.DataFrames:

• raw: raw data from the run.

• steps: stats from each step (and step type), created using the CellpyCell.make_step_table method.

• summary: summary data vs. cycle number (e.g. coulombic coulombic efficiency), created using the CellpyCell.make_summary method.

The headers (columns) for the different DataFrames were given earlier in this chapter. As mentioned above, the Data object also contains metadata for the run.

metadata

cell_no = None
mass = prms.Materials.default_mass  # active material (in mg)
tot_mass = prms.Materials.default_mass  # total material (in mg)
no_cycles = 0.0
charge_steps = None
discharge_steps = None
ir_steps = None
ocv_steps = None
nom_cap = prms.DataSet.nom_cap  # mAh/g (for finding c-rates)
mass_given = False
material = prms.Materials.default_material
merged = False
file_errors = None  # not in use at the moment
loaded_from = None  # loaded from (can be list if merged)
channel_index = None
channel_number = None
creator = None
item_ID = None
schedule_file_name = None
start_datetime = None
test_ID = None
name = None
cycle_mode = prms.Reader.cycle_mode
active_electrode_area = None  # [cm2]
active_electrode_thickness = None  # [micron]
electrolyte_type = None  #
electrolyte_volume = None  # [micro-liter]
active_electrode_type = None
counter_electrode_type = None
reference_electrode_type = None
experiment_type = None
cell_type = None
separator_type = None
active_electrode_current_collector = None
reference_electrode_current_collector = None
comment = None

The Data object can also take custom metadata if provided as keyword arguments (for developers).

FileID

The FileID object contains information about the raw file(s) and is used when comparing the cellpy-file with the raw file(s) (for example to check if it has been updated compared to the cellpy-file). Notice that FileID will contain a list of file identification parameters if the run is from several raw files.

Column headings

cellpy uses pandas.DataFrame objects internally. The column headers of the dataframes are defined in corresponding dataclass objects that can be accessed using both dot-notation and through normal dictionary look-up.

All the headers are set internally in cellpy and you can get them directly by e.g.

from cellpy.parameters.internal_settings import headers_normal

cycle_column_header = headers_normal.cycle_index_txt

column headings - raw data (or “normal” data)

@dataclass
class HeadersNormal(BaseHeaders):
    aci_phase_angle_txt: str = "aci_phase_angle"
    ref_aci_phase_angle_txt: str = "ref_aci_phase_angle"
    ac_impedance_txt: str = "ac_impedance"
    ref_ac_impedance_txt: str = "ref_ac_impedance"
    charge_capacity_txt: str = "charge_capacity"
    charge_energy_txt: str = "charge_energy"
    current_txt: str = "current"
    cycle_index_txt: str = "cycle_index"
    data_point_txt: str = "data_point"
    datetime_txt: str = "date_time"
    discharge_capacity_txt: str = "discharge_capacity"
    discharge_energy_txt: str = "discharge_energy"
    internal_resistance_txt: str = "internal_resistance"
    power_txt: str = "power"
    is_fc_data_txt: str = "is_fc_data"
    step_index_txt: str = "step_index"
    sub_step_index_txt: str = "sub_step_index"
    step_time_txt: str = "step_time"
    sub_step_time_txt: str = "sub_step_time"
    test_id_txt: str = "test_id"
    test_time_txt: str = "test_time"
    voltage_txt: str = "voltage"
    ref_voltage_txt: str = "reference_voltage"
    dv_dt_txt: str = "dv_dt"
    frequency_txt: str = "frequency"
    amplitude_txt: str = "amplitude"
    channel_id_txt: str = "channel_id"
    data_flag_txt: str = "data_flag"
    test_name_txt: str = "test_name"

column headings - summary data

@dataclass
class HeadersSummary(BaseHeaders):
    """In addition to the headers defined here, the summary might also contain
    specific headers (ending in _gravimetric or _areal).
    """

    postfixes = ["gravimetric", "areal"]

    cycle_index: str = "cycle_index"
    data_point: str = "data_point"
    test_time: str = "test_time"
    datetime: str = "date_time"
    discharge_capacity_raw: str = "discharge_capacity"
    charge_capacity_raw: str = "charge_capacity"
    test_name: str = "test_name"
    data_flag: str = "data_flag"
    channel_id: str = "channel_id"

    coulombic_efficiency: str = "coulombic_efficiency"
    cumulated_coulombic_efficiency: str = "cumulated_coulombic_efficiency"

    discharge_capacity: str = "discharge_capacity"
    charge_capacity: str = "charge_capacity"
    cumulated_charge_capacity: str = "cumulated_charge_capacity"
    cumulated_discharge_capacity: str = "cumulated_discharge_capacity"

    coulombic_difference: str = "coulombic_difference"
    cumulated_coulombic_difference: str = "cumulated_coulombic_difference"
    discharge_capacity_loss: str = "discharge_capacity_loss"
    charge_capacity_loss: str = "charge_capacity_loss"
    cumulated_discharge_capacity_loss: str = "cumulated_discharge_capacity_loss"
    cumulated_charge_capacity_loss: str = "cumulated_charge_capacity_loss"

    normalized_charge_capacity: str = "normalized_charge_capacity"
    normalized_discharge_capacity: str = "normalized_discharge_capacity"

    shifted_charge_capacity: str = "shifted_charge_capacity"
    shifted_discharge_capacity: str = "shifted_discharge_capacity"

    ir_discharge: str = "ir_discharge"
    ir_charge: str = "ir_charge"
    ocv_first_min: str = "ocv_first_min"
    ocv_second_min: str = "ocv_second_min"
    ocv_first_max: str = "ocv_first_max"
    ocv_second_max: str = "ocv_second_max"
    end_voltage_discharge: str = "end_voltage_discharge"
    end_voltage_charge: str = "end_voltage_charge"
    cumulated_ric_disconnect: str = "cumulated_ric_disconnect"
    cumulated_ric_sei: str = "cumulated_ric_sei"
    cumulated_ric: str = "cumulated_ric"
    normalized_cycle_index: str = "normalized_cycle_index"
    low_level: str = "low_level"
    high_level: str = "high_level"

    temperature_last: str = "temperature_last"
    temperature_mean: str = "temperature_mean"

    charge_c_rate: str = "charge_c_rate"
    discharge_c_rate: str = "discharge_c_rate"
    pre_aux: str = "aux_"

column headings - step table

@dataclass
class HeadersStepTable(BaseHeaders):
    test: str = "test"
    ustep: str = "ustep"
    cycle: str = "cycle"
    step: str = "step"
    test_time: str = "test_time"
    step_time: str = "step_time"
    sub_step: str = "sub_step"
    type: str = "type"
    sub_type: str = "sub_type"
    info: str = "info"
    voltage: str = "voltage"
    current: str = "current"
    charge: str = "charge"
    discharge: str = "discharge"
    point: str = "point"
    internal_resistance: str = "ir"
    internal_resistance_change: str = "ir_pct_change"
    rate_avr: str = "rate_avr"

column headings - journal pages

@dataclass
class HeadersJournal(BaseHeaders):
    filename: str = "filename"
    mass: str = "mass"
    total_mass: str = "total_mass"
    loading: str = "loading"
    area: str = "area"
    nom_cap: str = "nom_cap"
    experiment: str = "experiment"
    fixed: str = "fixed"
    label: str = "label"
    cell_type: str = "cell_type"
    instrument: str = "instrument"
    raw_file_names: str = "raw_file_names"
    cellpy_file_name: str = "cellpy_file_name"
    group: str = "group"
    sub_group: str = "sub_group"
    comment: str = "comment"
    argument: str = "argument"


CellpyCell.keys_journal_session = ["starred", "bad_cells", "bad_cycles", "notes"]

step types

Identifiers for the different steps have pre-defined names given in the class attribute list list_of_step_types and is written to the “step” column.

list_of_step_types = ['charge', 'discharge',
                      'cv_charge', 'cv_discharge',
                      'charge_cv', 'discharge_cv',
                      'ocvrlx_up', 'ocvrlx_down', 'ir',
                      'rest', 'not_known']

Tester dependent attributes

For each type of testers that are supported by cellpy, a set of column headings and other different settings/attributes might also exist. These definitions stored in the cellpy.parameters.internal_settings module and are also injected into the CellpyCell class upon initiation.