Precast Module
daspi.plotlib.precast
¶
Ready-to-use composite chart classes (precast charts).
Each class in this module wraps a JointChart (or SingleChart)
together with a specific combination of Plotter calls that are useful
for a well-defined analytical task. Pass the relevant model or DataFrame
and call plot(), stripes(), and label() to produce a
publication-ready multi-panel figure in a few lines.
| CLASS | DESCRIPTION |
|---|---|
``ParameterRelevanceCharts`` |
Two-panel Pareto chart showing standardised effects and Sum of
Squares for each term of a fitted |
``ResidualsCharts`` |
Four-panel residual diagnostic panel for a fitted |
``PairComparisonCharts`` |
Side-by-side comparison of two samples or groups using box plots, jitter, and optional confidence intervals for the difference in means or medians. |
``PairwiseMatrixCharts`` |
Scatter-plot matrix (SPLOM) for exploring all pairwise relationships between a set of continuous variables, optionally coloured by a grouping variable. |
``BivariateUnivariateCharts`` |
Joint distribution chart combining a central bivariate scatter (or KDE / contour) with marginal univariate density strips. |
``ProcessCapabilityAnalysisCharts`` |
Process capability analysis panel: histogram with fitted distribution and specification limits, plus a capability indices summary (Cp, Cpk, Cpm, Pp, PpK). |
``GageStudyCharts`` |
MSA Type-1 gage study panel: scatter of measurements vs. reference values overlaid with reference-line slopes plus a capability summary (Cg, Cgk, Q_MS). |
``GageRnRCharts`` |
Crossed Gage R&R panel: individual measurements by part and operator, R-chart, X-bar chart by operator, and variance-component bar chart. |
Notes
All classes inherit from JointChart (or SingleChart) and
therefore expose the same plot() / label() / stripes()
fluent interface. The plot() method of each subclass is
overridden to hard-wire the appropriate plotter sequence.