pyfunkyheatmap 0.1.8

Pure-Python port of the R funkyheatmap package — generate publication-ready dataframe heatmaps with funky rectangles, pies, bars and more.

py-funkyheatmap

Pure-Python port of the R package funkyheatmap, built under the omicverse-rebuildr reference-driven porting protocol.

Generate publication-ready, dynbenchmark-style data-frame heatmaps with funky rectangles, circles, bars, pies, and text — directly from pandas.DataFrame inputs, rendered with matplotlib, no R / ggplot dependency.

Upstream	funkyheatmap 0.5.2 (R, MIT)
Algorithm class	Visualisation / layout — adapted Class 1 (deterministic geometry)
Parity gate	max|Δ| over funkyrect_data / circle_data ≤ 1e-6 on canonical fixture
Achieved parity	max|Δ| = 0.0 on xmin / xmax / ymin / ymax / corner_size / color_value
License	MIT (matches upstream)

Install

pip install -e .            # editable, from this directory

pyfunkyheatmap needs only numpy, pandas, matplotlib — no R, no rpy2, no system-level dependencies.

Quick start

import pandas as pd
from pyfunkyheatmap import funky_heatmap

df = pd.DataFrame({
    "id":  ["A", "B", "C", "D"],
    "x":   [0.10, 0.55, 0.80, 0.95],
    "y":   [0.50, 0.25, 0.75, 0.60],
    "tag": ["alpha", "beta", "gamma", "delta"],
})
fh = funky_heatmap(df)
fh.save("out.png", dpi=150, bbox_inches="tight")

See:

• examples/tutorial.ipynb — five-example tour (default heatmap → custom column info → pies → multi-group dynbenchmark layout → R⇄Python parity diff).
• examples/scIB.ipynb — Python equivalent of the R scIB vignette. Same fixture, same column_info, same legends.
• examples/dynbenchmark.ipynb — Python equivalent of the R dynbenchmark vignette. Reproduces the Saelens et al. (2019) figure off the bundled dynbenchmark_data object.

All three notebooks ship pre-executed so GitHub renders them inline.

Public API

funky_heatmap(data, column_info, row_info, column_groups, row_groups, palettes, legends, position_args, scale_column, add_abc, *, fig=None, ax=None, fig_scale=0.25, dpi=100)

Mirrors the R funky_heatmap signature. Returns a FunkyHeatmap object with:

• fh.figure — the matplotlib Figure
• fh.geom_positions — dict of per-glyph DataFrames (row_pos, column_pos, rect_data, circle_data, funkyrect_data, pie_data, img_data, text_data, segment_data)
• fh.bounds, fh.width, fh.height — layout extents
• fh.save(path, **kwargs) — convenience wrapper around Figure.savefig

Side-API: verify_data, verify_column_info, verify_row_info, verify_column_groups, verify_row_groups, verify_palettes, verify_legends, scale_minmax, position_arguments. Each matches its R counterpart.

Refactoring notes

The port is not a literal R-to-Python transliteration:

1. One dataclass for layout knobs. position_arguments(...) returns a typed dataclass that supports pa.row_height and pa["row_height"] — both styles work, so R-style mapping access and Python attribute access stay open.
2. Geometry tables are first-class. The R package builds them internally and emits ggplot layers; we keep them on the result so downstream code (e.g. custom annotations, alternative renderers) can subclass without forking.
3. No cowplot / patchwork / ggforce. Rounded rectangles are drawn from a hand-rolled cubic-Bézier path; pies use matplotlib.patches.Wedge; layout is bare matplotlib.
4. Default-palette parity. The RColorBrewer colour stops for the Blues / Reds / YlOrBr / Greens / Greys and Set1 / Set2 / Set3 / Dark2 palettes are copied verbatim and interpolated with the same linear RGB ramp that colorRampPalette uses, so palettes="Blues" produces the same hex strings as in R.

Parity gate

$ Rscript tests/r_reference_driver.R data        # produces data/r_basic_*.csv + data/r_basic.png
$ pytest tests/                                  # 8 / 8 pass
$ python examples/build_tutorial.py              # rebuilds and re-executes the notebook

The pre-registered gate is max|Δ| over the funkyrect / circle geometry columns on the canonical fixture (tests/r_reference_driver.R). Achieved: max|Δ| = 0.0 — every coordinate matches R bit-for-bit.

See RECONSTRUCTION_REPORT.md for the full audit trail.

Layout

pyfunkyheatmap/
├── __init__.py          public surface
├── api.py               funky_heatmap entry point
├── verify.py            verify_* validators (one per R counterpart)
├── position.py          PositionArguments dataclass
├── positions.py         calculate_geom_positions + row/column layout
├── scale.py             scale_minmax
├── palettes.py          DEFAULT_PALETTES + brewer_pal + smear
└── figure.py            compose_figure + FunkyHeatmap + matplotlib draw

examples/
├── build_tutorial.py    nbformat script that builds + executes tutorial.ipynb
└── tutorial.ipynb       pre-executed tutorial (5 examples + R parity diff)

tests/
├── r_reference_driver.R Rscript that produces data/r_basic_*.csv
└── test_basic.py        pytest, 8 tests

funkyheatmap-ref/        upstream R source vendored for reference
data/                    canonical fixture (R + Python outputs)