misc_tools

Convenience functions that don’t directly deal with plotting or bootstrap computations are placed here.

prepare_bars_for_plot


def prepare_bars_for_plot(
    bar_type, bar_kwargs, horizontal, plot_palette_raw, color_col, show_pairs, bootstraps_color_by_group,
    plot_data:NoneType=None, xvar:NoneType=None, yvar:NoneType=None, # Raw data
    results:NoneType=None, ticks_to_plot:NoneType=None, extra_delta:NoneType=None, # Contrast data
    reference_band:NoneType=None, summary_axes:NoneType=None, ci_type:NoneType=None, # Summary data
):

Call self as a function.

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color_picker


def color_picker(
    color_type:str, kwargs:dict, elements:list, color_col:str, show_pairs:bool, color_palette:dict,
    bootstraps_color_by_group:bool
)->list:

Call self as a function.

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extract_group_summaries


def extract_group_summaries(
    proportional:bool, # A boolean flag to determine if the plot is for proportional data.
    rawdata_axes:axes.Axes, # The raw data axes.
    asymmetric_side:str, # The side of the asymmetric error bars.
    horizontal:bool, # A boolean flag to determine if the plot is for horizontal plotting.
    bootstraps_color_by_group:bool, # A boolean flag to determine if the bootstraps are colored by group.
    plot_palette_raw:list, # A list of the plot palette colors.
    all_plot_groups:list, # A list of all the plot groups.
    n_groups:int, # The number of groups.
    color_col, # The name of the color column.
    ytick_color, # The color of the y-ticks.
    group_summaries_kwargs:dict, # Kwargs passed to the group summaries.
):

Extract the group summaries for the plotter function.

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redraw_dependent_spines


def redraw_dependent_spines(
    rawdata_axes:axes.Axes, # The raw data axes.
    contrast_axes:axes.Axes, # The contrast axes.
    redraw_axes_kwargs:dict, # Kwargs passed to the redraw axes.
    float_contrast:bool, # A boolean flag to determine if the plot is GA or Cum
    horizontal:bool, # A boolean flag to determine if the plot is for horizontal plotting.
    show_delta2:bool, # A boolean flag to determine if the plot will have a delta-delta effect size.
    delta2_axes:axes.Axes, # The delta2 axes.
):

Draw the dependent axis spine lines.

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redraw_independent_spines


def redraw_independent_spines(
    rawdata_axes:axes.Axes, # The raw data axes.
    contrast_axes:axes.Axes, # The contrast axes.
    horizontal:bool, # A boolean flag to determine if the plot is for horizontal plotting.
    two_col_sankey:bool, # A boolean flag to determine if the plot is for two-col sankey.
    ticks_to_start_twocol_sankey:list, # A list of ticks to start for sankey plot.
    idx:list, # A list of indices.
    is_paired:str, # A boolean flag to determine if the data is paired.
    show_pairs:bool, # A boolean flag to determine if pairs should be shown.
    proportional:bool, # A boolean flag to determine if the plot is proportional/binary.
    ticks_to_skip:list, # A list of ticks to be skipped in the raw data axes.
    temp_idx:list, # A temporary list of indices to be used for skipping ticks in the raw data axes.
    ticks_to_skip_contrast:list, # A list of ticks to be skipped in the contrast axes.
    redraw_axes_kwargs:dict, # Kwargs passed to the redraw axes.
):

Draw the independent axis spine lines.

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draw_zeroline


def draw_zeroline(
    ax:axes.Axes, # The contrast data axes.
    horizontal:bool, # A boolean flag to determine if the plot is for horizontal plotting.
    reflines_kwargs:dict, # Additional keyword arguments to be passed to the zeroline.
    extra_delta:bool, # A boolean flag to determine if the plot includes an extra delta (delta-delta or mini-meta).
):

Draw the independent axis spine lines.

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gardner_altman_adjustments


def gardner_altman_adjustments(
    effect_size_type:str, # The type of effect size.
    plot_data:pd.DataFrame, # A dataframe of plot data.
    xvar:str, # The name of the x-axis variable.
    yvar:str, # The name of the y-axis variable.
    current_control:str, # The name of the current control group.
    current_group:str, # The name of the current test group.
    rawdata_axes:axes.Axes, # The raw data axes.
    contrast_axes:axes.Axes, # The contrast axes.
    results:pd.DataFrame, # A dataframe of the results.
    current_effsize:float, # The current effect size.
    is_paired:bool, # A boolean flag to determine if the plot is for paired data.
    one_sankey:bool, # A boolean flag to determine if the plot is for a single sankey diagram.
    reflines_kwargs:dict, # Kwargs passed to the reference lines.
    redraw_axes_kwargs:dict, # Kwargs passed to the redraw axes.
):

Aesthetic adjustments specific to Gardner-Altman plots (float_contrast=True).

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show_legend


def show_legend(
    legend_labels:list, # A list of legend labels.
    legend_handles:list, # A list of legend handles.
    rawdata_axes:axes.Axes, # The raw data axes.
    contrast_axes:axes.Axes, # The contrast axes.
    table_axes:axes.Axes, # The table axes.
    float_contrast:bool, # A boolean flag to determine if the plot is GA or Cumming format.
    show_pairs:bool, # A boolean flag to determine if the plot will show the paired data.
    horizontal:bool, # A boolean flag to determine if the plot is for horizontal plotting.
    legend_kwargs:dict, # Kwargs passed to the legend function.
    table_kwargs:dict
):

Show the legend for the plotter function.

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set_xaxis_ticks_and_lims


def set_xaxis_ticks_and_lims(
    show_delta2:bool, # A boolean flag to determine if the plot will have a delta-delta effect size.
    show_mini_meta:bool, # A boolean flag to determine if the plot will have a mini-meta effect size.
    rawdata_axes:axes.Axes, # The raw data axes.
    contrast_axes:axes.Axes, # The contrast axes.
    show_pairs:bool, # A boolean flag to determine if the plot will show the paired data.
    float_contrast:bool, # A boolean flag to determine if the plot is a GA or Cumming design.
    ticks_to_skip:list, # A list of ticks to skip.
    contrast_xtick_labels:list, # A list of contrast xtick labels.
    plot_kwargs:dict, # Kwargs passed to the plot function.
    proportional:bool, horizontal:bool, # A boolean flag to determine if the plot is for horizontal plotting.
):

Set the x-axis/yaxis ticks and limits for the plotter function.

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extract_contrast_plotting_ticks


def extract_contrast_plotting_ticks(
    is_paired:bool, # A boolean flag to determine if the plot is for paired data.
    show_pairs:bool, # A boolean flag to determine if the plot will show the paired data.
    two_col_sankey:bool, # A boolean flag to determine if the plot will show a two-column sankey diagram.
    plot_groups:list, # A list of the plot groups.
    idx:list, # A list of tuples containing the group names.
    sankey_control_group:list, # A list of the control group names.
):

Extract the contrast plotting ticks from the idx object for use in the plotter function.

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add_counts_to_ticks


def add_counts_to_ticks(
    plot_data:pd.DataFrame, # A dataframe of plot data.
    xvar:str, # The name of the x-axis variable.
    yvar:str, # The name of the y-axis variable.
    rawdata_axes:axes.Axes, # The raw data axes.
    plot_kwargs:dict, # Kwargs passed to the plot function.
    flow:bool, # Whether sankey flow is enabled or not.
    horizontal:bool, # A boolean flag to determine if the plot is for horizontal plotting.
):

Add the counts to the raw data axes labels.

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get_plot_groups


def get_plot_groups(
    is_paired:bool, # A boolean flag to determine if the plot is for paired data.
    idx:list, # A list of tuples containing the group names.
    proportional:bool, # A boolean flag to determine if the plot is for proportional data.
    all_plot_groups:list, # A list of all the group names.
):

Extract the plot groups from the idx object for use in the plotter function.

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initialize_fig


def initialize_fig(
    plot_kwargs:dict, # Kwargs passed to the plot function.
    dabest_obj:object, # A `dabest` EffectSizeDataFrame object.
    show_delta2:bool, # A boolean flag to determine if the plot will have a delta-delta effect size.
    show_mini_meta:bool, # A boolean flag to determine if the plot will have a mini-meta effect size.
    is_paired:bool, # A boolean flag to determine if the plot is for paired data.
    show_pairs:bool, # A boolean flag to determine if the plot will show the paired data.
    proportional:bool, # A boolean flag to determine if the plot is for proportional data.
    float_contrast:bool, # A boolean flag to determine if the plot is for floating contrast data.
    effect_size_type:str, # The type of effect size to be plotted.
    yvar:str, # The name of the y-axis variable.
    horizontal:bool, # A boolean flag to determine if the plot is for horizontal plotting.
    show_table:bool, # A boolean flag to determine if the table will be shown in horizontal plot.
    color_col:str, # The column name for coloring the data points.
):

Initialize the figure and axes for the plotter function.

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get_color_palette


def get_color_palette(
    plot_kwargs:dict, # Kwargs passed to the plot function.
    plot_data:pd.DataFrame, # A dataframe of plot data.
    xvar:str, # The name of the x-axis variable.
    show_pairs:bool, # A boolean flag to determine if the plot is for paired data.
    idx:list, # A list of tuples containing the group names.
    all_plot_groups:list, # A list of all the group names.
    delta2:bool, # A boolean flag to determine if the plot will have a delta-delta effect size.
    proportional:bool, # A boolean flag to determine if the plot is for a proportional plot.
):

Create the color palette to be used in the plotter function.

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get_kwargs


def get_kwargs(
    plot_kwargs:dict, # Kwargs passed to the plot function.
    ytick_color, # Color of the yticks.
    is_paired:bool=False, # A boolean flag to determine if the plot is for paired data. Default is False.
):

Extracts the kwargs from the plot_kwargs object for use in the plotter function.

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get_params


def get_params(
    effectsize_df:object, # A `dabest` EffectSizeDataFrame object.
    plot_kwargs:dict, # Kwargs passed to the plot function.
    sankey_kwargs:dict, barplot_kwargs:dict, # Kwargs relating to the barplot
):

Extracts parameters from the effectsize_df and plot_kwargs objects for use in the plotter function.

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get_unique_categories


def get_unique_categories(
    names
):

Extract unique categories from various input types.

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get_varname


def get_varname(
    obj
):

Call self as a function.

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print_greeting


def print_greeting(
    
):

Generates a greeting message based on the current time, along with the version information of DABEST.

This function dynamically generates a greeting (‘Good morning’, ‘Good afternoon’, ‘Good evening’) based on the current system time. It also retrieves and displays the version of DABEST (Data Analysis using Bootstrap-Coupled ESTimation). The message includes a header with the DABEST version and the current time formatted in a user-friendly manner.

Returns: str: A formatted string containing the greeting message, DABEST version, and current time.

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unpack_and_add


def unpack_and_add(
    l, c
):

Convenience function to allow me to add to an existing list without altering that list.

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merge_two_dicts


def merge_two_dicts(
    x:dict, y:dict
)->dict: # A dictionary containing a union of all keys in both original dicts.

Given two dicts, merge them into a new dict as a shallow copy. Any overlapping keys in y will override the values in x.

Taken from here