Skip to content

create_batch

Summary

create_batch creates a processed Batch from one or more FLASH/ImageJ experiment folders, or loads a previously saved batch from a pickle cache.

Use it at the start of most analyses.

Signature

create_batch(
    name,
    conditions,
    batch_path,
    experiments=None,
    threshold=None,
    pickle_path=None,
    rerun=False,
    import_images=True,
    reimport_images=False,
    progress=True,
)

Input Object Types

Object type Accepted? Notes
groupList / conditionList Yes Required by the current signature as conditions. Build it with GroupBuilder; ConditionBuilder remains a legacy alias.
Path-like folder Yes Used for batch_path, pickle_path, and experiment paths.
Experiment list Yes Pass as experiments when experiments are already constructed.
pandas.DataFrame No create_batch imports experiment folders; it does not create a batch directly from a table.

Parameters

Parameter Type Default Meaning
name str required Batch name. Also used as the pickle filename when pickle_path is set.
conditions groupList / conditionList required Experimental groups, labels, colors, factors, and comparisons. The parameter name is retained by the current create_batch signature.
batch_path Path-like required Output folder for the batch. Also used for experiment discovery when experiments=None.
experiments dict, list, path-like, or None None Source experiments. A dict maps experiment names to paths; a list contains prebuilt experiments; a folder is scanned; None scans batch_path.
threshold int or None None Colocalisation threshold. None uses Config.THRESHOLD.
pickle_path Path-like or None None Folder for saved pickle cache files.
rerun bool False If False, return an existing pickle when available. If True, reprocess from source data.
import_images bool True Import image paths and metadata during processing.
reimport_images bool False When rerun=True, force image import even if import_images=False.
progress bool True Show progress output while creating and processing the batch.

Returns

Return value Type Meaning
batch Batch Processed batch with summary, condition_list, fig_path, experiments, aliases, and export methods.

Saved Outputs

create_batch itself returns the in-memory Batch. When pickle_path is set, it can also load from or later save to <pickle_path>/<name>.pkl through the batch workflow.

The returned batch writes plots and analysis outputs under batch.fig_path.

Examples

Basic batch creation

from PyFLASH import GroupBuilder, create_batch

groups = (
    GroupBuilder("Diagnosis")
    .add("Control", short="Control", color="blue")
    .add("MCI", short="MCI", color="orange")
    .add("AD", short="AD", color="red")
    .compare("Control", "MCI")
    .compare("Control", "AD")
    .build()
)

batch = create_batch(
    "SCN_Diagnosis",
    groups,
    batch_path=r"C:\path\to\batch-output",
    experiments={
        "Cohort_1": r"C:\path\to\Cohort_1",
        "Cohort_2": r"C:\path\to\Cohort_2",
    },
    pickle_path=r"C:\path\to\pickles",
)

Force a rebuild

batch = create_batch(
    "SCN_Diagnosis",
    groups,
    batch_path=r"C:\path\to\batch-output",
    experiments=r"C:\path\to\experiment-parent",
    pickle_path=r"C:\path\to\pickles",
    rerun=True,
)

Skip image import for faster table-only work

batch = create_batch(
    "SCN_Diagnosis",
    groups,
    batch_path=r"C:\path\to\batch-output",
    experiments=r"C:\path\to\experiment-parent",
    import_images=False,
)

Notes

  • If a matching pickle exists and rerun=False, the function returns the cached batch instead of reprocessing folders.
  • If experiments is a folder, PyFLASH scans immediate subfolders and keeps the ones that look like experiment folders.
  • threshold=None means "use the project default", not "disable thresholding".
  • Use rerun=True after changing source data, import settings, or condition definitions.

See Also