## classification_plot()


Create a classification plot with flexible display options.


Usage

``` python
classification_plot(
    df,
    classes,
    conditions,
    condition_col="condition",
    class_col="Class",
    selector_col="cell_line",
    selector_val=None,
    show_triplicates=False,
    fig_size=(7, 7),
    size_units="cm",
    y_lim=(0, 100),
    title=None,
    colors=COLORS,
    axes=None,
    bar_width=0.75,
    show_legend=True,
    legend_bbox=(0.98, 1.0),
    group_size=1,
    repeat_offset=0.18,
    within_group_spacing=0.2,
    between_group_gap=0.4,
    show_boxes=True,
    show_significance=True,
    stats_class=None,
    paired=True,
    show_ns=True,
    min_effect=None,
    save=False,
    save_stats=False,
    path=None,
    file_format="pdf",
    tight_layout=True,
    dpi=300
)
```


The plot behavior changes based on parameters: - show_triplicates="stacked": Shows stacked bars with error bars - show_triplicates="triplicates": Shows individual repeat bars - group_size \> 1: Groups conditions together (works with triplicates mode)


Data Filtering df : pd.DataFrame DataFrame containing classification data classes : list\[str\] List of class names to plot conditions : list\[str\] List of condition names to plot condition_col : str, default="condition" Column name for experimental condition class_col : str, default="Class" Column name for class/category (dynamic based on model) selector_col : str \| None, default="cell_line" Column name for selector (e.g., cell line) selector_val : str \| None, default=None Value to filter selector_col by

Display Options ^ show_triplicates : bool, default=False If False, shows stacked bars with error bars (mean ± SD). If True, shows individual replicate bars side-by-side. y_lim : tuple\[int, int\], default=(0, 100) Y-axis limits

Styling & Colors fig_size : tuple\[float, float\], default=(7, 7) Figure size as (width, height) size_units : str, default="cm" Units for fig_size ("cm" or "inches") title : str \| None, default=None Plot title colors : list\[str\], default=COLORS List of colors for plotting axes : Axes \| None, default=None Existing axes to plot on

Stacked Mode Settings ^ bar_width : float, default=0.75 Width of bars in stacked mode show_legend : bool, default=True Whether to show legend legend_bbox : tuple\[float, float\], default=(0.98, 1.0) Legend position as (x, y)

Triplicates Mode Settings ^ group_size : int, default=1 Number of conditions to group (1 = no grouping) repeat_offset : float, default=0.18 Offset for repeat bars within_group_spacing : float, default=0.2 Spacing within groups between_group_gap : float, default=0.4 Gap between groups show_boxes : bool, default=True Whether to draw a box around each condition's triplicate bars (only applies when show_triplicates=True). Set False for a cleaner look when the bars do not sum to 100% (e.g. a single class selected). show_significance : bool, default=True Whether to compute significance (condition vs the first in the group, paired by plate). Exports a p-value and a medians CSV when save=True. stats_class : str \| None, default=None Which class's comparison to annotate on the plot (default = the first class in `classes`). Both CSV tables always cover every class. paired : bool, default=True Use a paired t-test (ttest_rel, matched by plate_id); set False for the unpaired ttest_ind.

Save Options save : bool, default=False Whether to save the plot save_stats : bool, default=False Write {title}\_stats.csv and {title}\_medians.csv to `path` (independent of `save`; works when plotting onto a provided/composed `axes`). path : Path \| None, default=None Path to save the plot file_format : str, default="pdf" File format for saving tight_layout : bool, default=True Whether to use tight layout dpi : int, default=300 Resolution for saving


## Returns


`tuple[Figure, Axes]`  
Tuple of (figure, axes)


## Examples

Stacked plot with error bars (default)

fig, ax = classification_plot( df, \["G1", "S", "G2M"\], \["Control", "Treatment"\] )

Individual triplicates without grouping

fig, ax = classification_plot( df, \["G1", "S", "G2M"\], \["Control", "Treatment"\], show_triplicates=True )

Individual triplicates with grouping

fig, ax = classification_plot( df, \["G1", "S", "G2M"\], \["Control", "Treat1", "Treat2", "Treat3"\], show_triplicates=True, group_size=2 \# Groups conditions in pairs )