|
from __future__ import annotations |
|
|
|
from dataclasses import dataclass |
|
from typing import Any, Dict, List, NewType |
|
|
|
|
|
|
|
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) |
|
|
