cellpy.utils.plotutils

Utilities for helping to plot cellpy-data.

Module Contents

Functions

create_col_info(c)	Create column information for summary plots.
create_colormarkerlist(groups, sub_groups[, ...])	Fetch lists with color names and marker types of correct length.
create_colormarkerlist_for_journal(journal[, ...])	Fetch lists with color names and marker types of correct length for a journal.
create_label_dict(c)	Create label dictionary for summary plots.
cycle_info_plot(cell[, cycle, get_axes, interactive, ...])	Show raw data together with step and cycle information.
partition_summary_cv_steps(c, x, column_set[, split, ...])	Partition the summary data into CV and non-CV steps.
raw_plot(cell[, y, y_label, x, x_label, title, ...])	Plot raw data.
summary_plot(c[, x, y, height, markers, title, ...])	Create a summary plot. Currently only supports plotly.

Attributes

COLOR_DICT
SYMBOL_DICT
plotly_available
seaborn_available

create_col_info(c)

Create column information for summary plots.

Parameters:

c – cellpy object

Returns:

x_columns (tuple), y_cols (dict)

create_colormarkerlist(groups, sub_groups, symbol_label='all', color_style_label='seaborn-colorblind')

Fetch lists with color names and marker types of correct length.

Parameters:

• groups – list of group numbers (used to generate the list of colors)

• sub_groups – list of sub-group numbers (used to generate the list of markers).

• symbol_label – sub-set of markers to use

• color_style_label – cmap to use for colors

Returns:

colors (list), markers (list)

create_colormarkerlist_for_journal(journal, symbol_label='all', color_style_label='seaborn-colorblind')

Fetch lists with color names and marker types of correct length for a journal.

Parameters:

• journal – cellpy journal

• symbol_label – sub-set of markers to use

• color_style_label – cmap to use for colors

Returns:

colors (list), markers (list)

create_label_dict(c)

Create label dictionary for summary plots.

Parameters:

c – cellpy object

Returns:

x_axis_labels (dict), y_axis_label (dict)

cycle_info_plot(cell, cycle=None, get_axes=False, interactive=True, t_unit='hours', v_unit='V', i_unit='mA', **kwargs)

Show raw data together with step and cycle information.

Parameters:

• cell – cellpy object

• cycle (int or list or tuple) – cycle(s) to select (must be int for matplotlib)

• get_axes (bool) – return axes (for matplotlib) or figure (for plotly)

• interactive (bool) – use interactive plotting (if available)

• t_unit (str) – unit for x-axis (default: “hours”)

• v_unit (str) – unit for y-axis (default: “V”)

• i_unit (str) – unit for current (default: “mA”)

• **kwargs – parameters specific to plotting backend.

Returns:

matplotlib.axes or None

partition_summary_cv_steps(c, x: str, column_set: list, split: bool = False, var_name: str = 'variable', value_name: str = 'value')

Partition the summary data into CV and non-CV steps.

Parameters:

• c – cellpy object

• x – x-axis column name

• column_set – names of columns to include

• split – add additional column that can be used to split the data when plotting.

• var_name – name of the variable column after melting

• value_name – name of the value column after melting

Returns:

pandas.DataFrame (melted with columns x, var_name, value_name, and optionally “row” if split is True)

raw_plot(cell, y=None, y_label=None, x=None, x_label=None, title=None, interactive=True, plot_type='voltage-current', double_y=True, return_matplotlib_figure=False, **kwargs)

Plot raw data.

Parameters:

• cell – cellpy object

• y (str or list) – y-axis column

• y_label (str or list) – label for y-axis

• x (str) – x-axis column

• x_label (str) – label for x-axis

• title (str) – title of the plot

• interactive (bool) – use interactive plotting

• plot_type (str) – type of plot (defaults to “voltage-current”) (overrides given y if y is not None), currently only “voltage-current” is supported.

• double_y (bool) – use double y-axis (only for matplotlib and when plot_type is used)

• return_matplotlib_figure (bool) – return a matplotlib figure (only for matplotlib)

• **kwargs – additional parameters for the plotting backend

Returns:

matplotlib figure or plotly figure

summary_plot(c, x: str = None, y: str = 'capacities_gravimetric', height: int = 600, markers: bool = True, title=None, x_range: list = None, y_range: list = None, split: bool = False, interactive: bool = True, share_y: bool = False, rangeslider: bool = False, **kwargs)

Create a summary plot. Currently only supports plotly.

Parameters:

• c – cellpy object

• x – x-axis column (default: ‘cycle_index’)

• y –

  y-axis column or column set. Currently, the following predefined sets exists:

  • ”voltages”, “capacities_gravimetric”, “capacities_areal”, “capacities”, “capacities_gravimetric_split_constant_voltage”, “capacities_areal_split_constant_voltage”

• height – height of the plot

• markers – use markers

• title – title of the plot

• x_range – limits for x-axis

• y_range – limits for y-axis

• split – split the plot

• interactive – use interactive plotting

• rangeslider – add a range slider to the x-axis (only for plotly)

• share_y (bool) – share y-axis

• **kwargs – additional parameters for the plotting backend

Returns:

plotly figure or None

COLOR_DICT

SYMBOL_DICT

plotly_available

seaborn_available