File Formats and Data Structures
This part of the documentation is currently being updated. It is 99.9% trustable, but in need of better logical structure and formatting.
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.
CellpyData - 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:
CellpyData.tester = "arbin_res" CellpyData.auto_dirs = True print(CellpyData.cellpy_datadir)
The data for the experiment(s)/runs(s) are stored in the class attribute
This attribute is just a list of runs (each run is a
This implies that you can store many runs in one
Sometimes this can be necessary, but it is recommended to only store one
run in one instance. Most of the functions (the class methods) automatically
selects the 0-th item in
CellpyData.cells if the
test_number is not
You may already have figured it out: in cellpy, data for a given cell
is usually named a run. And each run is a
Here is a list of other important class attributes in
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
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(BaseSettings): 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" 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(BaseSettings): 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" discharge_capacity: str = "discharge_capacity_u_mAh_g" charge_capacity: str = "charge_capacity_u_mAh_g" cumulated_charge_capacity: str = "cumulated_charge_capacity_u_mAh_g" cumulated_discharge_capacity: str = "cumulated_discharge_capacity_u_mAh_g" coulombic_efficiency: str = "coulombic_efficiency_u_percentage" cumulated_coulombic_efficiency: str = "cumulated_coulombic_efficiency_u_percentage" coulombic_difference: str = "coulombic_difference_u_mAh_g" cumulated_coulombic_difference: str = "cumulated_coulombic_difference_u_mAh_g" discharge_capacity_loss: str = "discharge_capacity_loss_u_mAh_g" charge_capacity_loss: str = "charge_capacity_loss_u_mAh_g" cumulated_discharge_capacity_loss: str = "cumulated_discharge_capacity_loss_u_mAh_g" cumulated_charge_capacity_loss: str = "cumulated_charge_capacity_loss_u_mAh_g" ir_discharge: str = "ir_discharge_u_Ohms" ir_charge: str = "ir_charge_u_Ohms" ocv_first_min: str = "ocv_first_min_u_V" ocv_second_min: str = "ocv_second_min_u_V" ocv_first_max: str = "ocv_first_max_u_V" ocv_second_max: str = "ocv_second_max_u_V" end_voltage_discharge: str = "end_voltage_discharge_u_V" end_voltage_charge: str = "end_voltage_charge_u_V" cumulated_ric_disconnect: str = "cumulated_ric_disconnect_u_none" cumulated_ric_sei: str = "cumulated_ric_sei_u_none" cumulated_ric: str = "cumulated_ric_u_none" normalized_cycle_index: str = "normalized_cycle_index" normalized_charge_capacity: str = "normalized_charge_capacity" normalized_discharge_capacity: str = "normalized_discharge_capacity" low_level: str = "low_level_u_percentage" high_level: str = "high_level_u_percentage" shifted_charge_capacity: str = "shifted_charge_capacity_u_mAh_g" shifted_discharge_capacity: str = "shifted_discharge_capacity_u_mAh_g" temperature_last: str = "temperature_last_u_C" temperature_mean: str = "temperature_mean_u_C" areal_charge_capacity: str = "areal_charge_capacity_u_mAh_cm2" areal_discharge_capacity: str = "areal_discharge_capacity_u_mAh_cm2" 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(BaseSettings): 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(BaseSettings): filename: str = "filename" mass: str = "mass" total_mass: str = "total_mass" loading: str = "loading" 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" CellpyData.keys_journal_session = ["starred", "bad_cells", "bad_cycles", "notes"]
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']
For each type of testers that are supported by
a set of column headings and
other different settings/attributes must be provided. These definitions stored in the
cellpy.parameters.internal_settings module and are also injected into
the CellpyData class upon initiation.
Supported testers are:
arbin (.res type files)
Testers that are partly supported (but not tested very well) are:
pec (txt files)
arbin (ms sql-server and .csv and .xlsx exports)
maccor (txt files)
Testers that is planned supported:
cellpy can load custom csv-ish files by providing a file description (using the
Tester dependent attributes
Three tables are read from the .res file:
normal table: contains measurement data.
global table: contains overall parametres for the test.
stats table: contains statistics (for each cycle).
tablename_normal = "Channel_Normal_Table" tablename_global = "Global_Table" tablename_statistic = "Channel_Statistic_Table"
column headings - global table
applications_path_txt = 'Applications_Path' channel_index_txt = 'Channel_Index' channel_nuer_txt = 'Channel_Number' channel_type_txt = 'Channel_Type' comments_txt = 'Comments' creator_txt = 'Creator' daq_index_txt = 'DAQ_Index' item_id_txt = 'Item_ID' log_aux_data_flag_txt = 'Log_Aux_Data_Flag' log_chanstat_data_flag_txt = 'Log_ChanStat_Data_Flag' log_event_data_flag_txt = 'Log_Event_Data_Flag' log_smart_battery_data_flag_txt = 'Log_Smart_Battery_Data_Flag' mapped_aux_conc_cnumber_txt = 'Mapped_Aux_Conc_CNumber' mapped_aux_di_cnumber_txt = 'Mapped_Aux_DI_CNumber' mapped_aux_do_cnumber_txt = 'Mapped_Aux_DO_CNumber' mapped_aux_flow_rate_cnumber_txt = 'Mapped_Aux_Flow_Rate_CNumber' mapped_aux_ph_number_txt = 'Mapped_Aux_PH_Number' mapped_aux_pressure_number_txt = 'Mapped_Aux_Pressure_Number' mapped_aux_temperature_number_txt = 'Mapped_Aux_Temperature_Number' mapped_aux_voltage_number_txt = 'Mapped_Aux_Voltage_Number' schedule_file_name_txt = 'Schedule_File_Name' start_datetime_txt = 'Start_DateTime' test_id_txt = 'Test_ID' test_name_txt = 'Test_Name'
column headings - normal table
aci_phase_angle_txt = 'ACI_Phase_Angle' ac_impedance_txt = 'AC_Impedance' charge_capacity_txt = 'Charge_Capacity' charge_energy_txt = 'Charge_Energy' current_txt = 'Current' cycle_index_txt = 'Cycle_Index' data_point_txt = 'Data_Point' datetime_txt = 'DateTime' discharge_capacity_txt = 'Discharge_Capacity' discharge_energy_txt = 'Discharge_Energy' internal_resistance_txt = 'Internal_Resistance' is_fc_data_txt = 'Is_FC_Data' step_index_txt = 'Step_Index' step_time_txt = 'Step_Time' test_id_txt = 'Test_ID' test_time_txt = 'Test_Time' voltage_txt = 'Voltage' dv_dt_txt = 'dV/dt'
arbin MS SQL SERVER
CellpyData - methods
CellpyData 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
CellpyData object when running
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.CellpyData) for more info.
Each run is a
The instance contain general information about
the run-settings (such as mass etc.).
The measurement data, information, and summary is stored
raw: raw data from the run.
steps: stats from each step (and step type), created using the
summary: summary data vs. cycle number (e.g. coulombic coulombic efficiency), created using the
The headers (columns) for the different DataFrames were given earlier in this chapter.
As mentioned above, the
Cell object also contains metadata for the run.
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
Cell object can also take custom metadata if provieded as keyword arguments (for developers).
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).
FileID will contain a list of file identification parameters if the run is from several raw files.