cplots 0.0.4

C-Plots: A unified plotting library for Cosmology.

A backend-agnostic cosmological plotting library.
Write your analysis once; render with matplotlib, plotly, or altair — no lock-in.

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How it works

Every plot flows through three clean layers:

Raw arrays / DataFrames / xarray Datasets
        ↓
    PlotData      immutable input containers
        ↓
    PlotSpec      backend-agnostic IR — the decoupling boundary
        ↓
    Backend       matplotlib · plotly · altair

Plot subclasses never import backends. Backends never import plot subclasses. Swap the backend at call time, via a context manager, or globally — the plot code stays unchanged.

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Features

• Six plot types — XYPlot, PKSpectrum, ClSpectrum, BackgroundEvolution, TrianglePlot, XYZColored
• Three backends — publication PNG via matplotlib, interactive HTML via plotly, Vega-Lite JSON via altair
• Multi-panel figures — compose plots into grids with colspan/rowspan, shared axes, and custom ratios
• LaTeX labels — pass raw LaTeX strings through to plotly's MathJax renderer
• Pluggable adapters — accepts NumPy arrays, pandas DataFrames, xarray Datasets, and GetDist MCSamples
• Zero lock-in — third-party backends and adapters register via pyproject.toml entry points

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Quick example

import numpy as np
import cplots

k  = np.logspace(-3, 0, 200)
pk = k ** -1.5

# Display with default backend (matplotlib)
cplots.PKSpectrum(k, pk, label="z=0").show()

# Interactive HTML with LaTeX labels
cplots.PKSpectrum(
    k, pk,
    x_label=r"$k\;[h\,\mathrm{Mpc}^{-1}]$",
    y_label=r"$P(k)\;[h^{-3}\,\mathrm{Mpc}^3]$",
    latex_labels=True,
).save("pk.html", backend="plotly")

# Multi-panel figure
z  = np.linspace(0, 3)
Hz = 67.34 * np.sqrt(0.3 * (1 + z)**3 + 0.7)

fig = cplots.Figure.grid([
    [cplots.PKSpectrum(k, pk), cplots.BackgroundEvolution(z, {"H": Hz})],
])
fig.show()

Switching backends

# Per call
plot.show(backend="plotly")

# Context manager
with cplots.backend("altair"):
    plot.show()

# Global default
cplots.set_backend("plotly")

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Installation

pip install cplots[matplotlib]              # matplotlib only
pip install cplots[matplotlib,plotly]       # two backends
pip install cplots[all]                     # all backends + getdist

Development install:

git clone https://github.com/rcalderonb6/cplots
pip install -e ".[matplotlib,dev]"

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Documentation

Full documentation, including the API reference, user guide, and interactive examples, is available at rcalderonb6.github.io/cplots.