`cellpy.utils.ica`#

ica contains routines for creating and working with incremental capacity analysis data

Module Contents#

Classes#

Converter

Class for dq-dv handling.

Functions#

`dqdv`(cell[, split, tidy, label_direction])	Calculates dq-dv data for all cycles contained in
`dqdv_cycle`(cycle_df[, splitter, label_direction])	Convenience function for creating dq-dv data from a given capacity and
`dqdv_cycles`(cycles_df[, not_merged, label_direction])	Convenience function for creating dq-dv data from several given capacity and
`dqdv_np`(voltage, capacity[, voltage_resolution, ...])	Convenience functions for creating dq-dv data from given arrays
`index_bounds`(x)	Returns tuple with first and last item.
`value_bounds`(x)	Returns tuple with min and max in x.

class Converter(capacity=None, voltage=None, points_pr_split=10, max_points=None, voltage_resolution=None, capacity_resolution=None, minimum_splits=3, interpolation_method='linear', increment_method='diff', pre_smoothing=False, smoothing=False, post_smoothing=True, normalize=True, normalizing_factor=None, normalizing_roof=None, savgol_filter_window_divisor_default=50, savgol_filter_window_order=3, voltage_fwhm=0.01, gaussian_order=0, gaussian_mode='reflect', gaussian_cval=0.0, gaussian_truncate=4.0)[source]#

Class for dq-dv handling.

Typical usage is to (1) set the data, (2) inspect the data, (3) pre-process the data, (4) perform the dq-dv transform, and finally (5) post-process the data.

A short note about normalization:

If normalization is set to False, then no normalization will be done.

If normalization is True, and normalization_factor is None, the total capacity of the half cycle will be used for normalization, else the normalization_factor will be used.

If normalization is True, and normalization_roof is not None, the capacity divided by normalization_roof will be used for normalization.

increment_data()[source]#: Perform the dq-dv transform.

inspect_data(capacity=None, voltage=None, err_est=False, diff_est=False)[source]#: Check and inspect the data.

post_process_data(voltage=None, incremental_capacity=None, voltage_step=None)[source]#: Perform post-processing (smoothing, normalisation, interpolation) of the data.

pre_process_data()[source]#: Perform some pre-processing of the data (i.e. interpolation).

set_data(capacity, voltage=None, capacity_label='q', voltage_label='v')[source]#: Set the data.

dqdv(cell, split=False, tidy=True, label_direction=False, **kwargs)[source]#

Calculates dq-dv data for all cycles contained in the given CellpyCell object, returns data as pandas.DataFrame(s)

Parameters:

cell (CellpyCell-object)
split (bool) – return one frame for charge and one for discharge if True (defaults to False).
tidy (bool) – returns the split frames in wide format (defaults to True. Remark that this option is currently not available for non-split frames).

Returns:

cycle: cycle number (if split is set to True). voltage: voltage dq: the incremental capacity

Return type:

one or two pandas.DataFrame with the following columns

Additional key-word arguments are sent to Converter:

Keyword Arguments:

cycle (int or list of ints (cycle numbers)) – will process all (or up to max_cycle_number) if not given or equal to None.
points_pr_split (int) – only used when investigating data using splits, defaults to 10.
max_points – None
voltage_resolution (float) – used for interpolating voltage data (e.g. 0.005)
capacity_resolution – used for interpolating capacity data
minimum_splits (int) – defaults to 3.
interpolation_method – scipy interpolation method
increment_method (str) – defaults to “diff”
pre_smoothing (bool) – set to True for pre-smoothing (window)
smoothing (bool) – set to True for smoothing during differentiation (window)
post_smoothing (bool) – set to True for post-smoothing (gaussian)
normalize (bool) – set to True for normalizing to capacity
normalizing_factor (float)
normalizing_roof (float)
savgol_filter_window_divisor_default (int) – used for window smoothing, defaults to 50
savgol_filter_window_order – used for window smoothing
voltage_fwhm (float) – used for setting the post-processing gaussian sigma, defaults to 0.01
gaussian_order (int) – defaults to 0
gaussian_mode (str) – defaults to “reflect”
gaussian_cval (float) – defaults to 0.0
gaussian_truncate (float) – defaults to 4.0

Example

>>> from cellpy.utils import ica
>>> charge_df, dcharge_df = ica.dqdv(my_cell, split=True)
>>> charge_df.plot(x="voltage",y="dq")

dqdv_cycle(cycle_df, splitter=True, label_direction=False, **kwargs)[source]#

Convenience function for creating dq-dv data from a given capacity and voltage cycle.

Returns a tuple of numpy arrays with ‘voltage’ and ‘incremental_capacity’.

Parameters:

cycle_df (pandas.DataFrame) – the cycle data (‘voltage’, ‘capacity’, ‘direction’ (1 or -1)).
splitter (bool) – insert a np.NaN row between charge and discharge.
label_direction (bool) – include ‘direction’ (1 or -1).

Returns:

Returns a tuple of numpy arrays with ‘voltage’ and ‘incremental_capacity’.

Additional key-word arguments are sent to Converter:

Keyword Arguments:

points_pr_split (int) – only used when investigating data using splits, defaults to 10.
max_points – None
voltage_resolution (float) – used for interpolating voltage data (e.g. 0.005)
capacity_resolution – used for interpolating capacity data
minimum_splits (int) – defaults to 3.
interpolation_method – scipy interpolation method
increment_method (str) – defaults to “diff”
pre_smoothing (bool) – set to True for pre-smoothing (window)
smoothing (bool) – set to True for smoothing during differentiation (window)
post_smoothing (bool) – set to True for post-smoothing (gaussian)
normalize (bool) – set to True for normalizing to capacity
normalizing_factor (float)
normalizing_roof (float)
savgol_filter_window_divisor_default (int) – used for window smoothing, defaults to 50
savgol_filter_window_order – used for window smoothing
voltage_fwhm (float) – used for setting the post-processing gaussian sigma, defaults to 0.01
gaussian_order (int) – defaults to 0
gaussian_mode (str) – defaults to “reflect”
gaussian_cval (float) – defaults to 0.0
gaussian_truncate (float) – defaults to 4.0

Example

>>> cycle_df = my_data.get_cap(
>>> ...   1,
>>> ...   categorical_column=True,
>>> ...   method = "forth-and-forth"
>>> ...   insert_nan=False,
>>> ... )
>>> voltage, incremental = ica.dqdv_cycle(cycle_df)

dqdv_cycles(cycles_df, not_merged=False, label_direction=False, **kwargs)[source]#

Convenience function for creating dq-dv data from several given capacity and voltage cycles.

Returns a DataFrame with a ‘voltage’ and a ‘incremental_capacity’ column.

Parameters:

cycles_df (pandas.DataFrame) – the cycle data (‘cycle’, ‘voltage’, ‘capacity’, ‘direction’ (1 or -1)).
not_merged (bool) – return list of frames instead of concatenating ( defaults to False).
label_direction (bool) – include ‘direction’ (1 or -1).

Returns:

pandas.DataFrame with columns ‘cycle’, ‘voltage’, ‘dq’ (and ‘direction’ if label_direction is True).

Additional key-word arguments are sent to Converter:

Keyword Arguments:

points_pr_split (int) – only used when investigating data using splits, defaults to 10.
max_points – None
voltage_resolution (float) – used for interpolating voltage data (e.g. 0.005)
capacity_resolution – used for interpolating capacity data
minimum_splits (int) – defaults to 3.
interpolation_method – scipy interpolation method
increment_method (str) – defaults to “diff”
pre_smoothing (bool) – set to True for pre-smoothing (window)
smoothing (bool) – set to True for smoothing during differentiation (window)
post_smoothing (bool) – set to True for post-smoothing (gaussian)
normalize (bool) – set to True for normalizing to capacity
normalizing_factor (float)
normalizing_roof (float)
savgol_filter_window_divisor_default (int) – used for window smoothing, defaults to 50
savgol_filter_window_order – used for window smoothing
voltage_fwhm (float) – used for setting the post-processing gaussian sigma, defaults to 0.01
gaussian_order (int) – defaults to 0
gaussian_mode (str) – defaults to “reflect”
gaussian_cval (float) – defaults to 0.0
gaussian_truncate (float) – defaults to 4.0

Example

>>> cycles_df = my_data.get_cap(
>>> ...   categorical_column=True,
>>> ...   method = "forth-and-forth",
>>> ...   label_cycle_number=True,
>>> ...   insert_nan=False,
>>> ... )
>>> ica_df = ica.dqdv_cycles(cycles_df)

dqdv_np(voltage, capacity, voltage_resolution=None, capacity_resolution=None, voltage_fwhm=0.01, pre_smoothing=True, diff_smoothing=False, post_smoothing=True, post_normalization=True, interpolation_method=None, gaussian_order=None, gaussian_mode=None, gaussian_cval=None, gaussian_truncate=None, points_pr_split=None, savgol_filter_window_divisor_default=None, savgol_filter_window_order=None, max_points=None, **kwargs)[source]#

Convenience functions for creating dq-dv data from given arrays of capacity and voltage data.

Parameters:

voltage – nd.array or pd.Series
capacity – nd.array or pd.Series
voltage_resolution – used for interpolating voltage data (e.g. 0.005)
capacity_resolution – used for interpolating capacity data
voltage_fwhm – used for setting the post-processing gaussian sigma
pre_smoothing – set to True for pre-smoothing (window)
diff_smoothing – set to True for smoothing during differentiation (window)
post_smoothing – set to True for post-smoothing (gaussian)
post_normalization – set to True for normalizing to capacity
interpolation_method – scipy interpolation method
gaussian_order – int
gaussian_mode – mode
gaussian_cval
gaussian_truncate
points_pr_split – only used when investigating data using splits
savgol_filter_window_divisor_default – used for window smoothing
savgol_filter_window_order – used for window smoothing
max_points – restricting to max points in vector (capacity-selected)

Returns:

(voltage, dqdv)

index_bounds(x)[source]#: Returns tuple with first and last item.

value_bounds(x)[source]#: Returns tuple with min and max in x.

cellpy.utils.ica

Contents

cellpy.utils.ica#

Module Contents#

Classes#

Functions#

`cellpy.utils.ica`#