CAP.caps2corr#

CAP.caps2corr(output_dir=None, suffix_title=None, suffix_filename=None, show_figs=True, save_plots=True, return_df=False, save_df=False, as_pickle=False, **kwargs)[source]#

Generate Pearson Correlation Matrix for CAPs.

Produces a correlation matrix of all CAPs. Separate correlation matrices are created for each group.

Parameters:

  • output_dir (str or None, default=None) – Directory to save plots (if save_plots is True) and correlation matrices DataFrames (if save_df is True). The directory will be created if it does not exist. If None, plots and dataFrame will not be saved.

  • suffix_title (str or None, default=None) – Appended to the title of each plot.

  • suffix_filename (str or None, default=None) – Appended to the filename of each saved plot if output_dir is provided.

  • show_figs (bool, default=True) – Display figures.

  • save_plots (bool, default=True) – If True, plots are saves as png images. For this to be used, output_dir must be specified.

  • return_df (bool, default=False) – If True, returns a dictionary with a correlation matrix for each group.

  • save_df (bool, default=False,) – If True, saves the correlation matrix contained in the DataFrames as csv files. For this to be used, output_dir must be specified.

  • as_pickle (bool, default=False) –

    When output_dir is specified, plots are saved as pickle files, which can be further modified, instead of png images.

    Added in version 0.26.5.

  • **kwargs –

    Keyword arguments used when modifying figures. Valid keywords include:

    • dpi: int, default=300 – Dots per inch for the figure.

    • figsize: tuple, default=(8, 6) – Size of the figure in inches.

    • fontsize: int, default=14 – Font size for the title each plot.

    • xticklabels_size: int, default=8 – Font size for x-axis tick labels.

    • yticklabels_size: int, default=8 – Font size for y-axis tick labels.

    • shrink: float, default=0.8 – Fraction by which to shrink the colorbar.

    • cbarlabels_size: int, default=8 – Font size for the colorbar labels.

    • xlabel_rotation: int, default=0 – Rotation angle for x-axis labels.

    • ylabel_rotation: int, default=0 – Rotation angle for y-axis labels.

    • annot: bool, default=False – Add values to each cell.

    • annot_kws: dict, default=None – Customize the annotations.

    • fmt: str, default=”.2g” – Modify how the annotated vales are presented.

    • linewidths: float, default=0 – Padding between each cell in the plot.

    • borderwidths: float, default=0 – Width of the border around the plot.

    • linecolor: str, default=”black” – Color of the line that seperates each cell.

    • edgecolors: str or None, default=None – Color of the edges.

    • alpha: float or None, default=None – Controls transparency and ranges from 0 (transparent) to 1 (opaque).

    • bbox_inches: str or None, default=”tight” – Alters size of the whitespace in the saved image.

    • cmap: str, callable default=”coolwarm” – Color map for the plot cells. Options include strings to call seaborn’s pre-made palettes, seaborn.diverging_palette function to generate custom palettes, and matplotlib.color.LinearSegmentedColormap to generate custom palettes.

    • vmin: float or None, default=None – The minimum value to display in colormap.

    • vmax: float or None, default=None – The maximum value to display in colormap.

Returns:

dict[str, pd.DataFrame] – A dictionary mapping an instance of a pandas DataFrame for each group. Only returned if return_df is True.

Note

Color Palettes: Refer to seaborn’s Color Palettes for valid pre-made palettes.

Significance Values: If return_df is True, each element will contain its uncorrected p-value in parenthesis with a single asterisk if < 0.05, a double asterisk if < 0.01, and a triple asterisk < 0.001.

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