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