Labeled Samples object for less verbose plotting

[janfb]

Context

• pairplot refactoring in Refactor Plotting functions #1631 introduces typed options and upper/lower/diag API; offdiag is deprecated.
• Pain point remains: users still pass many repeated args (labels, limits, ticks) in tutorials (see docs/advanced_tutorials/17_plotting_functionality.ipynb).
• Proposal below reduces boilerplate and error-prone dimension bookkeeping by encapsulating per-dataset metadata.

Proposal

LabeledSamples dataclass (or NamedTuple) in sbi.analysis

• Fields:
  • data: np.ndarray | torch.Tensor, shape (N, D)
  • dim_labels: list[str] length D (optional; defaults to ["θ1", "θ2", ...])
  • Optional: ticks: Optional[List[Tuple[float, float]]], limits: Optional[List[Tuple[float, float]]]
• Contract:
  • pairplot and marginal_plot accept either raw arrays or LabeledSamples. When provided:
    • Use dim_labels if labels arg not supplied.
    • Use limits inline if limits arg not supplied.
    • Use ticks inline if ticks arg not supplied.
  • Support list of LabeledSamples to overlay multiple sources; FigOptions.samples_labels defaults to the .name or generated labels if present.
• Interop:
  • A from_xarray(dataarray) constructor could leverage xarray’s named dimensions and coordinates (optional; follow-up).

Migration

• Backward compatible: continue supporting raw arrays; adopt container to reduce passing repeated labels/ticks in every call.
• Precedence: explicit function args override container metadata.

Benefits

• Encapsulates dimension labeling and axis metadata in one place.
• Simplifies notebook and pipeline code; fewer chances for label/order mismatches.
• Autocomplete-friendly, aligns with stronger typing direction from this branch.

Minimal sketch

from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch

ArrayLike = Union[np.ndarray, torch.Tensor]

@dataclass(frozen=True)
class LabeledSamples:
    name: Optional[str] = None
    data: ArrayLike = None  # shape (N, D)
    dim_labels: Optional[List[str]] = None
    limits: Optional[List[Tuple[float, float]]] = None
    ticks: Optional[List[Tuple[float, float]]] = None

# prepare_for_plot() detects LabeledSamples, unpacks metadata,
# and prefers explicit function args over container fields.

More context:

• open questions

  • Defaults for samples-level labels/colors when mixing raw arrays and LabeledSamples.
  • Whether limits are per-sample-set or global; if mixed, choose intersection/union or prefer first.
  • Validation surface: check D consistency across overlaid datasets and metadata lengths.
  • Immutability (frozen=True) and torch/numpy conversion policy at boundaries.
  • Export location and public API: sbi.analysis export for discoverability.

• Impact on docs/tests

  • Update 17_plotting_functionality.ipynb to show both raw arrays and LabeledSamples flows.
  • Add smoke tests for container + precedence rules; type tests for dim_labels length and limits shape.
  • Re-export LabeledSamples from sbi.analysis in init.py.

• Suggested steps (one or several PRs depending on size)

  • Introduce LabeledSamples, wire into prepare_for_plot, add precedence + validation.
  • Update tutorials and add unit tests; add from_xarray helper (optional).
  • Consider deprecation notice in docs encouraging container for repeated plotting scenarios.