Getting Started

Goal

Get from an installed PyFLASH package to a loaded data object, one plot, and a clear idea of where saved files go.

Before You Start

• Use Python 3.9 or newer.
• Decide which data path you have: raw FLASH/ImageJ experiment folders, or an already-clean pandas.DataFrame.
• Keep local paths as placeholders while learning, then replace them with your own paths.

Steps

Step	Page	Use it when
1	Installation	You need to install the package or the optional UI extra.
2A	First batch	You have FLASH/ImageJ export folders.
2B	First table-backed batch	You already have a clean table.
3	First plot	You want one Python plot call.
4	First plot spec	You want a reusable YAML, TOML, or JSON plot recipe.
5	Launch the UI	You want the optional point-and-click interface.
6	Where results go	You need to find figures, workbooks, pickles, or pipeline runs.

Check It Worked

After the first three pages, this kind of script should run without import errors:

from PyFLASH import GroupBuilder, create_batch
from PyFLASH.plotting import plot_mean_bars

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

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

plot_mean_bars(batch, data_cols=["GFAP Volume"], save=False)

Next

• API reference
• Object model
• create_batch
• from_dataframe
• plot_mean_bars
• run_spec