Plotting Library

daspi.plotlib

Plotting library for data analysis, statistics, and process improvement.

This package provides a composable system for building publication-ready charts from raw pandas DataFrames. Four layers of abstraction stack cleanly on top of each other:

appearance Style management (Style), colormap registration, and axis utility helpers (get_shared_axes, transpose_xy_axes_params).

classify Category-label helpers that map data labels to visual properties: HueLabel (colours), ShapeLabel (markers), SizeLabel (marker sizes), and Dodger (categorical axis dodging).

plotter Low-level Plotter classes. Each class is responsible for a single mark type (e.g. Scatter, Line, GaussianKDE, Probability, ErrorBar). Plotters are designed to be instantiated and then called on an Axes object. They can be freely combined inside a Chart.

facets Layout and annotation helpers: AxesFacets (subplot grid + mosaic), LabelFacets (axis and legend labelling), and StripesFacets (reference lines and bands).

chart High-level Chart classes. SingleChart, JointChart, and MultivariateChart each accept a source DataFrame and expose a fluent plot() / label() / stripes() interface that wires together the lower layers automatically.

precast Ready-to-use composite charts built on top of the chart layer. Pass a LinearModel or a DataFrame and get a complete multi-panel figure in one call — examples: ResidualsCharts, ParameterRelevanceCharts, ProcessCapabilityAnalysisCharts, GageRnRCharts.

All public names from each submodule are re-exported at the package level, so from daspi.plotlib import Scatter works without knowing which submodule it lives in.

Module reference

Module	Contents
Precast Charts	Ready-to-use composite charts (ResidualsCharts, GageRnRCharts, …)
Chart	SingleChart, JointChart, MultivariateChart
Plotter	Individual mark classes (Scatter, GaussianKDE, ErrorBar, …)
Facets	AxesFacets, LabelFacets, StripesFacets