Object Types¶

This section documents the Python objects users create, inspect, and pass into PyFLASH functions. Start with Batch for folder-backed analysis, or DataFrameExperiment for already-tabular data.

Page	Use It For
Batch	A processed collection of experiments, summary tables, conditions, paths, images, and export methods.
Experiment	One full FLASH/ImageJ experiment folder and its imported marker tables.
MiniExperiment	A folder of simple CSV tables that should behave like an experiment.
DataFrameExperiment	In-memory `pandas.DataFrame` input for plots and pipelines.
Groups	`group`, `groupList`, crossed designs, colors, styles, and comparisons. Classic `condition` names remain supported.
Markers	`Attribute`, `Antibody`, `cellMarker`, and `objectMarker` objects inside `.data`.
Config	Global defaults such as thresholds, colors, saving behavior, montage filenames, and aliases.

Choosing An Input Object¶

Starting Point	Recommended Object
FLASH/ImageJ output folders	Use `create_batch`, which returns a `Batch`.
One folder of flat CSV files	Create a `MiniExperiment`, then place it in a `Batch`.
A `pandas.DataFrame` summary table	Use `from_dataframe`, which returns a `DataFrameExperiment`.
A raw table passed directly to a plot or pipeline	Use `group_col` and `subject_col`; PyFLASH wraps it internally as a `DataFrameExperiment`.
Existing processed pickle	Use `load_state`, which returns the object that was saved.

Stable Vocabulary¶

A subject or animal is stored in the AnimalName column inside PyFLASH objects.
A group or condition is stored in the Condition column.
group, groupList, and GroupBuilder are aliases for the older condition, conditionList, and ConditionBuilder names.
A summary table is subject-level data in .summary or .summaries.
A marker table is row-level measurement data in .data["name"].df.