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PyFLASH

Plot Spec Batch

Jay2owe/PyFLASH

Plot Spec Batch¶

Goal¶

Run two registered plot entries from a JSON plot spec against one table.

Code¶

import json
from pathlib import Path
from tempfile import TemporaryDirectory

import pandas as pd
from PyFLASH import run_spec

df = pd.DataFrame({
    "Subject": ["C1", "C2", "C3", "A1", "A2", "A3"],
    "Diagnosis": ["Control", "Control", "Control", "AD", "AD", "AD"],
    "GFAP Volume": [1.0, 1.1, 0.9, 2.0, 2.2, 2.1],
    "Iba1 Volume": [0.8, 0.7, 0.9, 1.4, 1.5, 1.3],
})

spec = {
    "plots": [
        {
            "type": "mean_bars",
            "data_cols": ["GFAP Volume"],
            "group_col": "Diagnosis",
            "subject_col": "Subject",
            "save": False,
            "save_normality": False,
        },
        {
            "type": "matrices",
            "data_cols": ["GFAP Volume", "Iba1 Volume"],
            "group_col": "Diagnosis",
            "subject_col": "Subject",
            "split_by": "all",
            "save": False,
        },
    ]
}

with TemporaryDirectory() as tmp:
    spec_path = Path(tmp) / "plots.json"
    spec_path.write_text(json.dumps(spec, indent=2), encoding="utf-8")
    results = run_spec(df, spec_path)

print([type(item).__name__ if item is not None else None for item in results])

Result¶

run_spec returns a list with one item per plots entry. The first item is the mean-bars result and the second is the matrix result. Each entry controls its own saving behavior.

Notes¶

JSON avoids an optional YAML dependency for this minimal example. In normal projects, keep the spec file beside the analysis script so the exact plot batch can be rerun.

See Also¶