Custom pipeline plot#

Plot classes are ordinary serializable models that opt into a lifecycle from phenotypic.abc_.plotting. Add the same configured object to its normal pipeline slot and to ImagePipeline(plots=[...]); the serialized plot binding preserves that shared identity.

import plotly.graph_objects as go
from pydantic import BaseModel, ConfigDict

from phenotypic.abc_.plotting import PlotMeas


class PlotColonyArea(BaseModel, PlotMeas):
    model_config = ConfigDict(extra="forbid")

    area_column: str = "Size_Area"

    def inspect(self, subject=None, *, for_save=False, **overrides):
        del for_save, overrides
        if subject is None:
            raise TypeError("PlotColonyArea requires measurements")
        return go.Figure(
            go.Histogram(x=subject[self.area_column], name=self.area_column)
        )

    def report(self, subject=None, **overrides):
        return self.inspect(subject, **overrides)

Use PlotImage for per-image output, PlotMeas for the post-applied measurement mirror, PlotAnalysis for a named analysis table, and PlotQc for QC-aware output. inspect() returns the primary saveable figure. report() returns the complete interactive report. Plotly and Matplotlib figures are both accepted by the CLI publisher.

The CLI writes plots below deliverables/plots/<ClassName>/. A multi-page plot may return PlotOutput with deterministically keyed PlotPage entries. Import both output contracts from phenotypic.abc_.plotting; phenotypic.plotting contains only the ready-to-use plot models.

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