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from dataclasses import dataclass |
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from typing import List, Dict, Any, NewType, Optional |
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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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An IndexSelection represents the state of an Altair |
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point selection (as constructed by alt.selection_point()) |
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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: Optional[Dict[str, dict]], 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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A PointSelection represents the state of an Altair |
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point selection (as constructed by alt.selection_point()) |
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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: Optional[Dict[str, dict]], 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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if signal is None: |
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points = [] |
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else: |
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points = 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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An IntervalSelection represents the state of an Altair |
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interval selection (as constructed by 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: Optional[Dict[str, list]], 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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