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from dataclasses import dataclass
from typing import List, Dict, Any, NewType, Optional
# 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:
"""
An IndexSelection represents the state of an Altair
point selection (as constructed by 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: Optional[Dict[str, dict]], 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:
"""
A PointSelection represents the state of an Altair
point selection (as constructed by 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: Optional[Dict[str, dict]], 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
"""
if signal is None:
points = []
else:
points = signal.get("vlPoint", {}).get("or", [])
return PointSelection(name=name, value=points, store=store)
@dataclass(frozen=True, eq=True)
class IntervalSelection:
"""
An IntervalSelection represents the state of an Altair
interval selection (as constructed by 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: Optional[Dict[str, list]], 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)
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