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Common Parameters

Summary

These pages explain parameter names that appear across many PyFLASH plots, pipelines, modelling helpers, and exclusion helpers. Function pages still list the exact signature for each callable; this section explains the reusable meaning of shared options.

Used By

Use this section when a function page mentions one of these shared option families:

Page Covers
Input objects experiment, batch, source, batch_or_df, raw DataFrame input, and object-like requirements.
Column selection data_cols, data_col_contains, data_col_regex, data_col_exclude, legacy column selectors, and predictor-selection variants.
Filter By and row filters filter_by, legacy specificity, mapping filters, row-filter queues, and filename tags.
ROI selection roi, ROI bases, ROI queues, and how ROI selection differs from row filters such as Region.
Groups and factors by, factor, split_by, comparisons, multiple_comparison, and crossed designs.
Saving and run labels save, save_path, output_dir, run_label, if_exists, dpi, write_manifest, and resume.
Statistics options force_nonparametric, posthoc, posthoc_correction, ns, alpha, gate, and correction names.
Model options cv, scoring, model_preset, model_families, search_strategy, n_jobs, and random_state.

Accepted Values

Accepted values are documented in each page. The most important rule is that newer public aliases are usually accepted alongside older internal names:

Newer public name Older/internal name
data_cols filtered_columns
data_col_contains column_strings
data_col_regex regex_string
data_col_exclude exclude
filter_by specificity
split_by by or factor, depending on the value
group_col condition_col
group_cols factor_cols
subject_col animal_col

Examples

from PyFLASH.plotting import plot_mean_bars

plot_mean_bars(
    df,
    data_cols=["GFAP_VolumeTotal"],
    group_col="Diagnosis",
    subject_col="Animal ID",
    filter_by={"Region": "SCN"},
    split_by="Diagnosis",
    save=False,
)

The same call can still be written with older names:

plot_mean_bars(
    df,
    filtered_columns=["GFAP_VolumeTotal"],
    condition_col="Diagnosis",
    animal_col="Animal ID",
    specificity=("Region", "SCN"),
    factor="Diagnosis",
    save=False,
)

Interactions

PyFLASH resolves aliases before analysis. If both names in an alias pair are given with different non-default values, it raises a ValueError instead of choosing one.

Function support is not universal. For example, many summary plots accept raw pandas.DataFrame input, but manual exclusion helpers expect an experiment-like object. Check the function page when in doubt.

Common Errors

  • Passing a display label from a figure or Excel export instead of the real column name in the summary table.
  • Supplying both an old name and a new alias with different values, such as filtered_columns=["A"] and data_cols=["B"].
  • Expecting a row filter, ROI filter, and group split to do the same thing. They act at different layers.

See Also