Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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109 lines
3.7 KiB
109 lines
3.7 KiB
4 years ago
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from datetime import datetime, timedelta, tzinfo
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from pathlib import Path
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from typing import (
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IO,
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TYPE_CHECKING,
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Any,
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AnyStr,
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Callable,
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Collection,
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Dict,
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Hashable,
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List,
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Mapping,
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Optional,
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Type,
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TypeVar,
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Union,
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)
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import numpy as np
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# To prevent import cycles place any internal imports in the branch below
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# and use a string literal forward reference to it in subsequent types
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# https://mypy.readthedocs.io/en/latest/common_issues.html#import-cycles
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if TYPE_CHECKING:
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from pandas._libs import Period, Timedelta, Timestamp # noqa: F401
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from pandas.core.dtypes.dtypes import ExtensionDtype # noqa: F401
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from pandas import Interval # noqa: F401
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from pandas.core.arrays.base import ExtensionArray # noqa: F401
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from pandas.core.frame import DataFrame # noqa: F401
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from pandas.core.generic import NDFrame # noqa: F401
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from pandas.core.indexes.base import Index # noqa: F401
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from pandas.core.series import Series # noqa: F401
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# array-like
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AnyArrayLike = TypeVar("AnyArrayLike", "ExtensionArray", "Index", "Series", np.ndarray)
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ArrayLike = TypeVar("ArrayLike", "ExtensionArray", np.ndarray)
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# scalars
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PythonScalar = Union[str, int, float, bool]
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DatetimeLikeScalar = TypeVar("DatetimeLikeScalar", "Period", "Timestamp", "Timedelta")
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PandasScalar = Union["Period", "Timestamp", "Timedelta", "Interval"]
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Scalar = Union[PythonScalar, PandasScalar]
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# timestamp and timedelta convertible types
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TimestampConvertibleTypes = Union[
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"Timestamp", datetime, np.datetime64, int, np.int64, float, str
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]
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TimedeltaConvertibleTypes = Union[
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"Timedelta", timedelta, np.timedelta64, int, np.int64, float, str
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]
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Timezone = Union[str, tzinfo]
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# other
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Dtype = Union[
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"ExtensionDtype", str, np.dtype, Type[Union[str, float, int, complex, bool]]
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]
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DtypeObj = Union[np.dtype, "ExtensionDtype"]
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FilePathOrBuffer = Union[str, Path, IO[AnyStr]]
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# FrameOrSeriesUnion means either a DataFrame or a Series. E.g.
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# `def func(a: FrameOrSeriesUnion) -> FrameOrSeriesUnion: ...` means that if a Series
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# is passed in, either a Series or DataFrame is returned, and if a DataFrame is passed
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# in, either a DataFrame or a Series is returned.
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FrameOrSeriesUnion = Union["DataFrame", "Series"]
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# FrameOrSeries is stricter and ensures that the same subclass of NDFrame always is
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# used. E.g. `def func(a: FrameOrSeries) -> FrameOrSeries: ...` means that if a
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# Series is passed into a function, a Series is always returned and if a DataFrame is
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# passed in, a DataFrame is always returned.
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FrameOrSeries = TypeVar("FrameOrSeries", bound="NDFrame")
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Axis = Union[str, int]
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Label = Optional[Hashable]
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Level = Union[Label, int]
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Ordered = Optional[bool]
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JSONSerializable = Optional[Union[PythonScalar, List, Dict]]
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Axes = Collection
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# For functions like rename that convert one label to another
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Renamer = Union[Mapping[Label, Any], Callable[[Label], Label]]
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# to maintain type information across generic functions and parametrization
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T = TypeVar("T")
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# used in decorators to preserve the signature of the function it decorates
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# see https://mypy.readthedocs.io/en/stable/generics.html#declaring-decorators
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FuncType = Callable[..., Any]
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F = TypeVar("F", bound=FuncType)
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# types of vectorized key functions for DataFrame::sort_values and
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# DataFrame::sort_index, among others
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ValueKeyFunc = Optional[Callable[["Series"], Union["Series", AnyArrayLike]]]
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IndexKeyFunc = Optional[Callable[["Index"], Union["Index", AnyArrayLike]]]
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# types of `func` kwarg for DataFrame.aggregate and Series.aggregate
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AggFuncTypeBase = Union[Callable, str]
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AggFuncType = Union[
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AggFuncTypeBase,
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List[AggFuncTypeBase],
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Dict[Label, Union[AggFuncTypeBase, List[AggFuncTypeBase]]],
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]
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