Reports#

The coco_pipe.report module assembles single-file HTML reports from decoding results, dim-reduction runs, raw data containers, and any combination thereof. Reports are self-contained (everything in one .html), interactive (Plotly-driven plots, search, dark mode), and lineage-aware (automatic environment + git provenance).

Design Philosophy

A report is a tree of Section containers holding Element primitives. Heavy data (Plotly figures, interactive tables) is collected into a single gzip+base64 payload embedded in the HTML, then decompressed in the browser on demand. The same HTML opens identically online or offline when assets are inlined.

Key Features

  • One-shot factories (from_experiment_result, from_reductions, from_container, from_bids, …) for the common cases.

  • 19 reusable HTML primitives in elements: images, Plotly figures, tables, code blocks, tabs, accordions, download assets, etc.

  • 9 decoding section adders + 11 dim-reduction section adders, attached as fluent methods on Report.

  • Automatic provenance: git hash, package versions, Python/OS, command, timestamp.

  • Strict pydantic configs (ReportConfig, ProvenanceConfig).

  • Data-quality checks (missingness, flatline, outliers, constant columns) run inline when a DataContainer is added.

  • Three asset modes: CDN (default), self-hosted URLs, or inline (downloads bundles once, embeds them in the HTML — fully offline).

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Quickstart

The fastest path: pass a result to one of the factory functions.

from coco_pipe.report import from_experiment_result

report = from_experiment_result(result, output_path="decoding.html")

Build a report incrementally with the fluent API:

from coco_pipe.report import Report

report = (
    Report(title="My Analysis")
    .add_container(container)
    .add_decoding_overview(result)
    .add_decoding_performance(result)
    .add_decoding_temporal(result)
)
report.save("report.html")

Make the rendered HTML offline-openable (downloads and inlines the JS bundles on first run):

report = Report(title="Air-gapped", asset_urls="inline")
report.add_reduction(pca_reducer, X_emb=embedding)
report.save("offline.html")

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How to Choose an Entry Point

You have…

Reach for…

An ExperimentResult

from_experiment_result() or make_decoding_report()

A list of scored DimReduction objects

from_reductions() or make_reduction_report()

A DataContainer

from_container()

A BIDS dataset directory

from_bids()

A tabular file (CSV/parquet/…)

from_tabular()

A raw embedding array

from_embeddings()

Multiple reports to combine

merge_reports()

Hand-built sections

Report + elements

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User Guide