Input Objects¶

Summary¶

Input-object parameters say where PyFLASH should read data from. The common case is a PyFLASH object with a subject-level .summary table. Some functions also accept a raw pandas.DataFrame and wrap it as a DataFrameExperiment before running.

Used By¶

Summary plots such as plot_mean_bars, plot_matrices, plot_regressions, plot_radar, and group-summary plots.
Pipelines such as correlation, adjusted_correlation, data_overview, group_comparison, linear_model, and rhythm.
Modelling helpers such as iterative_best_fit and iterative_model_sweep.
Image, location, and colocalisation plots that need marker tables or images.
Exclusion helpers such as exclusions, which expect an experiment-like object rather than a bare summary table.

Accepted Values¶

Parameter	Accepted values	Notes
`experiment`	`Batch`, `Experiment`, `MiniExperiment`, `DataFrameExperiment`, or another object exposing the attributes the function needs.	Most plots and pipelines read `.summary`; ROI-aware calls may read `.summaries`; image/location calls may also read `.data`, `.images`, or region dictionaries.
`batch`	Usually a `Batch` or batch-like object with `.summary` and output paths.	Several statistical and modelling helpers use this name even when a `DataFrameExperiment` also works.
`source`	Function-specific source object, often an experiment/batch or a marker-level `DataFrame`.	Used by colocalisation and image-related functions. Check the function page because `source` is not one universal type.
`batch_or_df`	A batch-like object or a raw `pandas.DataFrame`.	Used by `iterative_model_sweep`.
Raw `DataFrame` first argument	A subject-level summary table, when the function calls PyFLASH's DataFrame adapter.	Supply `group_col`/`subject_col`, or `conditions`/`groups` plus factor columns when the table is not already named `Condition` and `AnimalName`.

For raw DataFrame input, the important aliases are:

Public alias	Internal name	Meaning
`group_col`	`condition_col`	Column containing group labels such as `Control` or `AD`.
`group_cols`	`factor_cols`	Columns that define crossed groups, such as `["Diagnosis", "Sex"]`.
`subject_col`	`animal_col`	Column containing subject or animal identifiers.
`groups` / `group_list`	`conditions`	A group list that defines order, colors, and comparisons.

Examples¶

Use an existing PyFLASH object:

from PyFLASH.plotting import plot_matrices

plot_matrices(batch, data_cols=["GFAP_Count", "Iba1_Count"], save=False)

Use a plain table:

from PyFLASH.plotting import plot_mean_bars

plot_mean_bars(
    df,
    data_cols=["GFAP_VolumeTotal"],
    group_col="Diagnosis",
    subject_col="Mouse ID",
    save=False,
)

Use crossed group columns from a table:

from PyFLASH import data_overview

result = data_overview(
    df,
    data_cols=["GFAP_Count", "Iba1_Count"],
    group_cols=["Diagnosis", "Sex"],
    subject_col="AnimalName",
    split_by=["Condition", "Sex"],
    save=False,
)

Interactions¶

Raw DataFrame support depends on the function. DataFrame-aware plots and pipelines call the adapter and create a .summary, .summaries, condition list, and default output paths. Lower-level helpers that never call the adapter need a PyFLASH-like object directly.

group_col creates or maps the canonical Condition column. It is different from split_by, which chooses how an already prepared table is panelled or grouped for analysis.

If group_cols/factor_cols are supplied, PyFLASH can build crossed groups from multiple columns and derive Condition from the component levels.

Common Errors¶

Passing a raw DataFrame to a helper that expects an object with .summary, .summaries, .data, or output paths.
Using split_by="Diagnosis" without also telling the DataFrame adapter which column defines the primary group, usually group_col="Diagnosis".
Supplying a table without any usable subject column. If subject_col or AnimalName is missing, the adapter falls back to the DataFrame index.
Supplying only a subject-level summary table to an image, location, or marker-level plot that needs raw marker tables or image metadata.