from __future__ import annotations from dataclasses import dataclass from typing import Any, Dict, List, NewType # Type representing the "{selection}_store" dataset that corresponds to a # Vega-Lite selection Store = NewType("Store", List[Dict[str, Any]]) @dataclass(frozen=True, eq=True) class IndexSelection: """ Represents the state of an alt.selection_point() when neither the fields nor encodings arguments are specified. The value field is a list of zero-based indices into the selected dataset. Note: These indices only apply to the input DataFrame for charts that do not include aggregations (e.g. a scatter chart). """ name: str value: list[int] store: Store @staticmethod def from_vega(name: str, signal: dict[str, dict] | None, store: Store): """ Construct an IndexSelection from the raw Vega signal and dataset values. Parameters ---------- name: str The selection's name signal: dict or None The value of the Vega signal corresponding to the selection store: list The value of the Vega dataset corresponding to the selection. This dataset is named "{name}_store" in the Vega view. Returns ------- IndexSelection """ if signal is None: indices = [] else: points = signal.get("vlPoint", {}).get("or", []) indices = [p["_vgsid_"] - 1 for p in points] return IndexSelection(name=name, value=indices, store=store) @dataclass(frozen=True, eq=True) class PointSelection: """ Represents the state of an alt.selection_point() when the fields or encodings arguments are specified. The value field is a list of dicts of the form: [{"dim1": 1, "dim2": "A"}, {"dim1": 2, "dim2": "BB"}] where "dim1" and "dim2" are dataset columns and the dict values correspond to the specific selected values. """ name: str value: list[dict[str, Any]] store: Store @staticmethod def from_vega(name: str, signal: dict[str, dict] | None, store: Store): """ Construct a PointSelection from the raw Vega signal and dataset values. Parameters ---------- name: str The selection's name signal: dict or None The value of the Vega signal corresponding to the selection store: list The value of the Vega dataset corresponding to the selection. This dataset is named "{name}_store" in the Vega view. Returns ------- PointSelection """ points = [] if signal is None else signal.get("vlPoint", {}).get("or", []) return PointSelection(name=name, value=points, store=store) @dataclass(frozen=True, eq=True) class IntervalSelection: """ Represents the state of an alt.selection_interval(). The value field is a dict of the form: {"dim1": [0, 10], "dim2": ["A", "BB", "CCC"]} where "dim1" and "dim2" are dataset columns and the dict values correspond to the selected range. """ name: str value: dict[str, list] store: Store @staticmethod def from_vega(name: str, signal: dict[str, list] | None, store: Store): """ Construct an IntervalSelection from the raw Vega signal and dataset values. Parameters ---------- name: str The selection's name signal: dict or None The value of the Vega signal corresponding to the selection store: list The value of the Vega dataset corresponding to the selection. This dataset is named "{name}_store" in the Vega view. Returns ------- PointSelection """ if signal is None: signal = {} return IntervalSelection(name=name, value=signal, store=store)