API Reference#

This page documents the public top-level PyDPEET API.

The functions are grouped according to a typical PyDPEET workflow:

Read and write battery test data
Process and structure sequences and primitive segments
Add derived quantities such as SOC, capacity, or resistance
Extract reduced representations and analysis data

Additional utilities and citation helpers are listed separately.

Read and write#

Functions for reading, converting, and exporting battery test data in the unified PyDPEET format.

`pydpeet.convert`
`pydpeet.merge_into_series`	Run a list of DataFrames as a single aging test series.
`pydpeet.read`	Read and convert battery test data into the unified PyDPEET format.
`pydpeet.write`	Export the given DataFrame to the given output path.

Sequence and primitive processing#

Functions for detecting, correcting, filtering, and visualizing test sequences and primitive segments.

`pydpeet.add_primitive_segments`	Function to perform step analysis to create a segmentation of a dataframe into primitive Segments.
`pydpeet.df_primitives_correction`	Corrects the primitives in the dataframe based on the given configuration.
`pydpeet.extract_sequence_overview`	Create a DataFrame of segments and sequences from a DataFrame of primitives.
`pydpeet.filter_and_split_df_by_blocks`
`pydpeet.generate_instructions`	Generate PyBaMM instructions based on the given primitives dataframe and end condition map.
`pydpeet.visualize_phases`

Add derived quantities#

Functions that add derived quantities such as SOC, capacity, or resistance to existing datasets.

`pydpeet.add_capacity`	Compute the capacity of a battery cell from its discharge data.
`pydpeet.add_resistance_internal`	Calculate the internal resistance of a battery from given test data.
`pydpeet.add_soc`	Computes the Soc (State of Charge) for a battery cell, from the given dataframe.

Extract data#

Functions for extracting OCV points, sequence summaries, and other reduced representations from datasets.

pydpeet.extract_ocv_iocv

Compute iOCV blocks from given DataFrames.

Citation utilities#

Utilities for handling references, citations, and BibTeX export.

`pydpeet.print_references`
`pydpeet.write_to_bibtex`

Other#

Additional public functions, classes, configurations, and utilities that do not belong to one of the main API categories.

`pydpeet.BatteryConfig`
`pydpeet.SocMethod`
`pydpeet.battery_config_default`	str = 'Default', c_ref: Optional[float] = None, soc_start: float = 0, max_voltage: float = 4.2, min_voltage: float = 2.5, threshold_current: float = 0.075, voltage_intervall: float = 0.01, minimal_current_for_capacity: float = -1.2, maximal_current_for_capacity: float = -0.8, min_current_diff: float = 1, max_time_diff: float = 0.5, min_voltage_diff: float = 0, ignore_negative_resistance_values: bool = False)
`pydpeet.hakadi_nmc_1500`	str = 'Default', c_ref: Optional[float] = None, soc_start: float = 0, max_voltage: float = 4.2, min_voltage: float = 2.5, threshold_current: float = 0.075, voltage_intervall: float = 0.01, minimal_current_for_capacity: float = -1.2, maximal_current_for_capacity: float = -0.8, min_current_diff: float = 1, max_time_diff: float = 0.5, min_voltage_diff: float = 0, ignore_negative_resistance_values: bool = False)
`pydpeet.lgm50lt_nmc_4800`	str = 'Default', c_ref: Optional[float] = None, soc_start: float = 0, max_voltage: float = 4.2, min_voltage: float = 2.5, threshold_current: float = 0.075, voltage_intervall: float = 0.01, minimal_current_for_capacity: float = -1.2, maximal_current_for_capacity: float = -0.8, min_current_diff: float = 1, max_time_diff: float = 0.5, min_voltage_diff: float = 0, ignore_negative_resistance_values: bool = False)
`pydpeet.mapping`	Renames and maps specific columns in the DataFrame to standardized names.
`pydpeet.set_logging_style`	Sets up on import the logging configuration to use the specified level and a custom format.