Quick Start¶

Python workflow (recommended)¶

from gitter_py import gitter, plot_results

df = gitter(
    image_file="examples/extdata/sample.jpg",
    plate_format=1536,
    verbose="n",
)

print(df[["row", "col", "size", "circularity", "flags"]].head())

fig = plot_results(df, kind="heatmap", title="Sample plate")
fig.savefig("sample-heatmap.png", dpi=200)

gitter(...) returns one row per colony position with the key columns:

• row, col: colony grid position
• size: quantified colony size
• circularity: morphology metric
• flags: quality/edge warnings

gitter(...) is for single-plate images. For multi-plate images, split first with PlateSplitter, then quantify each extracted plate crop separately. It accepts either an image path or a numpy.ndarray.

Optional CLI workflow¶

gitter run examples/extdata/sample.jpg --plate-format 1536 --out sample.csv
gitter read sample.csv
gitter plot sample.csv --plot-type heatmap --out sample-heatmap.png

If gitter is not on your PATH, use uv run gitter ... instead.