MiniExperiment¶

Summary¶

MiniExperiment is a lightweight subclass of Experiment for a folder of ordinary CSV files. It is useful when you want flat tabular data to behave like an experiment inside a Batch, without the full FLASH/ImageJ marker-folder layout.

It imports each CSV file as an Attribute table, builds a subject-level summary by averaging numeric columns per subject/sample, and can map factor columns to PyFLASH groups.

How To Create It¶

Create a MiniExperiment, then place it in a Batch:

from PyFLASH import Batch, MiniExperiment, GroupBuilder

groups = (
    GroupBuilder("Diagnosis")
    .add("Control", short="Control", color="grey")
    .add("AD", short="AD", color="red")
    .build()
)

experiment = MiniExperiment(
    "human-table",
    "/path/to/csv-folder",
    subject_column="Subject ID",
    factor_mappings={
        "Diagnosis": {
            "Healthy control": "Control",
            "Dementia-AD": "AD",
        },
    },
)

batch = Batch("human-batch", [experiment], groups, "/path/to/output-folder")
batch.processData(import_images=False)

The CSV folder can contain files such as:

/path/to/csv-folder/
  Data.csv
  ExtraMeasurements.csv

Each CSV needs a subject column. If the subject column is not named AnimalName, pass subject_column=....

Important Attributes¶

Attribute	Meaning
`subject_column`	Optional source column used as the subject identifier. If omitted, common names such as `AnimalName`, `Animal Name`, `Animal ID`, `ID`, `Id`, and `id` are tried. Legacy alias: `animal_column`.
`factor_mappings`	Optional mapping that converts source factor values to PyFLASH group names.
`data_layout`	Always set to `"mini"` after construction.
`data`	Dictionary of CSV-derived `Attribute` tables. Each value has a `.df` DataFrame.
`summary`	Subject-level summary table built from the flat CSV data.
`summaries`	Dictionary containing the primary `"SCN"` summary.
`condition_list`, `conditions`, `factor`, `factorDict`	Group metadata attached by `Batch.processData()` or `set_condition_list()`. `conditions` is the legacy/internal attribute name.
`filePath`	Folder containing the CSV files.
`source_root`	Same as `filePath` for mini experiments.
Output paths	Inherited from `Experiment` and set under the mini experiment folder or batch output folder depending on workflow.

Common Methods¶

Method	Use
`importCSVs(progress=True)`	Import all CSV files in the folder except `Condition Labels.csv`.
`createSummary(progress=True)`	Build a subject-level summary from imported CSV tables.
`set_condition_list(condition_list)`	Map group and factor columns using the supplied group list.
`processData(import_images=True, progress=True)`	Import CSVs, build the summary, configure save paths, and optionally import images.
`createSavePaths()`	Inherited from `Experiment`; sets standard output path attributes.
`getDisplaySummary(roi_base=None)`	Inherited display helper for readable summary labels.
`save_csvs()`	Inherited CSV export helper.

Accepted By¶

MiniExperiment can be included in a Batch and can be passed to many summary-first plots and helpers after it has been processed. For most analysis, pass the surrounding Batch.

Returned By¶

MiniExperiment is constructed directly:

from PyFLASH import MiniExperiment

experiment = MiniExperiment("name", "/path/to/csv-folder", subject_column="ID")

Examples¶

Flat CSV with crossed factors:

from PyFLASH import Batch, GroupBuilder, MiniExperiment

diagnosis = (
    GroupBuilder("Diagnosis")
    .add("Control", "Control", color="grey")
    .add("AD", "AD", color="red")
    .build()
)
sex = (
    GroupBuilder("Sex")
    .add("Female", "Female", style="hollow")
    .add("Male", "Male")
    .build()
)
groups = GroupBuilder.cross(diagnosis, sex).build()

experiment = MiniExperiment(
    "human",
    "/path/to/csv-folder",
    subject_column="ID",
    factor_mappings={
        "Diagnosis": {"Healthy control": "Control", "Dementia-AD": "AD"},
        "Sex": {"female": "Female", "male": "Male"},
    },
)

batch = Batch("human", [experiment], groups, "/path/to/output-folder")
batch.processData(import_images=False)

print(batch.summary[["AnimalName", "Diagnosis", "Sex", "Condition"]])

Inspect the imported CSV table:

data = batch.data["Data"].df
print(data.head())

Notes¶

MiniExperiment is flat-table oriented. It does not classify ImageJ Objects, Attributes, ROI Intensities, and ROIs folders the way Experiment.importCSVs() does.
Empty rows are dropped during CSV preparation.
Numeric subject identifiers such as 1.0 are normalized to string form such as "1".
If all factor columns in the group list are present, MiniExperiment derives the combined Condition value from those factors.