示例#1
0
文件: term.py 项目: zmskei/zipline
    def _validate_dtype(cls, passed_dtype):
        """
        Validate a `dtype` passed to Term.__new__.

        If passed_dtype is NotSpecified, then we try to fall back to a
        class-level attribute.  If a value is found at that point, we pass it
        to np.dtype so that users can pass `float` or `bool` and have them
        coerce to the appropriate numpy types.

        Returns
        -------
        validated : np.dtype
            The dtype to use for the new term.

        Raises
        ------
        DTypeNotSpecified
            When no dtype was passed to the instance, and the class doesn't
            provide a default.
        InvalidDType
            When either the class or the instance provides a value not
            coercible to a numpy dtype.
        """
        dtype = passed_dtype
        if dtype is NotSpecified:
            dtype = cls.dtype
        if dtype is NotSpecified:
            raise DTypeNotSpecified(termname=cls.__name__)
        try:
            dtype = dtype_class(dtype)
        except TypeError:
            raise InvalidDType(dtype=dtype, termname=cls.__name__)
        return dtype
示例#2
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文件: term.py 项目: oliverjo/zipline
    def _validate_dtype(cls, passed_dtype):
        """
        Validate a `dtype` passed to Term.__new__.

        If passed_dtype is NotSpecified, then we try to fall back to a
        class-level attribute.  If a value is found at that point, we pass it
        to np.dtype so that users can pass `float` or `bool` and have them
        coerce to the appropriate numpy types.

        Returns
        -------
        validated : np.dtype
            The dtype to use for the new term.

        Raises
        ------
        DTypeNotSpecified
            When no dtype was passed to the instance, and the class doesn't
            provide a default.
        InvalidDType
            When either the class or the instance provides a value not
            coercible to a numpy dtype.
        """
        dtype = passed_dtype
        if dtype is NotSpecified:
            dtype = cls.dtype
        if dtype is NotSpecified:
            raise DTypeNotSpecified(termname=cls.__name__)
        try:
            dtype = dtype_class(dtype)
        except TypeError:
            raise InvalidDType(dtype=dtype, termname=cls.__name__)
        return dtype
示例#3
0
文件: term.py 项目: Giruvegan/zipline
def validate_dtype(termname, dtype, missing_value):
    """
    Validate a `dtype` and `missing_value` passed to Term.__new__.

    Ensures that we know how to represent ``dtype``, and that missing_value
    is specified for types without default missing values.

    Returns
    -------
    validated_dtype, validated_missing_value : np.dtype, any
        The dtype and missing_value to use for the new term.

    Raises
    ------
    DTypeNotSpecified
        When no dtype was passed to the instance, and the class doesn't
        provide a default.
    NotDType
        When either the class or the instance provides a value not
        coercible to a numpy dtype.
    NoDefaultMissingValue
        When dtype requires an explicit missing_value, but
        ``missing_value`` is NotSpecified.
    """
    if dtype is NotSpecified:
        raise DTypeNotSpecified(termname=termname)

    try:
        dtype = dtype_class(dtype)
    except TypeError:
        raise NotDType(dtype=dtype, termname=termname)

    if not can_represent_dtype(dtype):
        raise UnsupportedDType(dtype=dtype, termname=termname)

    if missing_value is NotSpecified:
        missing_value = default_missing_value_for_dtype(dtype)

    try:
        if dtype == categorical_dtype:
            # This check is necessary because we use object dtype for
            # categoricals, and numpy will allow us to promote numerical
            # values to object even though we don't support them.
            _assert_valid_categorical_missing_value(missing_value)

        # For any other type, we can check if the missing_value is safe by
        # making an array of that value and trying to safely convert it to
        # the desired type.
        # 'same_kind' allows casting between things like float32 and
        # float64, but not str and int.
        array([missing_value]).astype(dtype=dtype, casting="same_kind")
    except TypeError as e:
        raise TypeError(
            "Missing value {value!r} is not a valid choice "
            "for term {termname} with dtype {dtype}.\n\n"
            "Coercion attempt failed with: {error}".format(termname=termname, value=missing_value, dtype=dtype, error=e)
        )

    return dtype, missing_value
示例#4
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def validate_dtype(termname, dtype, missing_value):
    """
    Validate a `dtype` and `missing_value` passed to Term.__new__.

    Ensures that we know how to represent ``dtype``, and that missing_value
    is specified for types without default missing values.

    Returns
    -------
    validated_dtype, validated_missing_value : np.dtype, any
        The dtype and missing_value to use for the new term.

    Raises
    ------
    DTypeNotSpecified
        When no dtype was passed to the instance, and the class doesn't
        provide a default.
    NotDType
        When either the class or the instance provides a value not
        coercible to a numpy dtype.
    NoDefaultMissingValue
        When dtype requires an explicit missing_value, but
        ``missing_value`` is NotSpecified.
    """
    if dtype is NotSpecified:
        raise DTypeNotSpecified(termname=termname)

    try:
        dtype = dtype_class(dtype)
    except TypeError:
        raise NotDType(dtype=dtype, termname=termname)

    if not can_represent_dtype(dtype):
        raise UnsupportedDType(dtype=dtype, termname=termname)

    if missing_value is NotSpecified:
        missing_value = default_missing_value_for_dtype(dtype)

    try:
        _coerce_to_dtype(missing_value, dtype)
    except TypeError as e:
        raise TypeError(
            "Missing value {value!r} is not a valid choice "
            "for term {termname} with dtype {dtype}.\n\n"
            "Coercion attempt failed with: {error}".format(
                termname=termname,
                value=missing_value,
                dtype=dtype,
                error=e,
            )
        )

    return dtype, missing_value
示例#5
0
    def validate_dtype(termname, dtype, missing_value):
        """
        Validate a `dtype` and `missing_value` passed to Term.__new__.

        Ensures that we know how to represent ``dtype``, and that missing_value
        is specified for types without default missing values.

        Returns
        -------
        validated_dtype, validated_missing_value : np.dtype, any
            The dtype and missing_value to use for the new term.

        Raises
        ------
        DTypeNotSpecified
            When no dtype was passed to the instance, and the class doesn't
            provide a default.
        NotDType
            When either the class or the instance provides a value not
            coercible to a numpy dtype.
        NoDefaultMissingValue
            When dtype requires an explicit missing_value, but
            ``missing_value`` is NotSpecified.
        """
        if dtype is NotSpecified:
            raise DTypeNotSpecified(termname=termname)
        try:
            dtype = dtype_class(dtype)
        except TypeError:
            raise NotDType(dtype=dtype, termname=termname)

        if not can_represent_dtype(dtype):
            raise UnsupportedDType(dtype=dtype, termname=termname)

        if missing_value is NotSpecified:
            missing_value = default_missing_value_for_dtype(dtype)

        return dtype, missing_value
示例#6
0
    def validate_dtype(termname, dtype, missing_value):
        """
        Validate a `dtype` and `missing_value` passed to Term.__new__.

        Ensures that we know how to represent ``dtype``, and that missing_value
        is specified for types without default missing values.

        Returns
        -------
        validated_dtype, validated_missing_value : np.dtype, any
            The dtype and missing_value to use for the new term.

        Raises
        ------
        DTypeNotSpecified
            When no dtype was passed to the instance, and the class doesn't
            provide a default.
        NotDType
            When either the class or the instance provides a value not
            coercible to a numpy dtype.
        NoDefaultMissingValue
            When dtype requires an explicit missing_value, but
            ``missing_value`` is NotSpecified.
        """
        if dtype is NotSpecified:
            raise DTypeNotSpecified(termname=termname)
        try:
            dtype = dtype_class(dtype)
        except TypeError:
            raise NotDType(dtype=dtype, termname=termname)

        if not can_represent_dtype(dtype):
            raise UnsupportedDType(dtype=dtype, termname=termname)

        if missing_value is NotSpecified:
            missing_value = default_missing_value_for_dtype(dtype)

        return dtype, missing_value
示例#7
0
def validate_dtype(termname, dtype, missing_value):
    """
    Validate a `dtype` and `missing_value` passed to Term.__new__.

    Ensures that we know how to represent ``dtype``, and that missing_value
    is specified for types without default missing values.

    Returns
    -------
    validated_dtype, validated_missing_value : np.dtype, any
        The dtype and missing_value to use for the new term.

    Raises
    ------
    DTypeNotSpecified
        When no dtype was passed to the instance, and the class doesn't
        provide a default.
    NotDType
        When either the class or the instance provides a value not
        coercible to a numpy dtype.
    NoDefaultMissingValue
        When dtype requires an explicit missing_value, but
        ``missing_value`` is NotSpecified.
    """
    if dtype is NotSpecified:
        raise DTypeNotSpecified(termname=termname)

    try:
        dtype = dtype_class(dtype)
    except TypeError:
        raise NotDType(dtype=dtype, termname=termname)

    if not can_represent_dtype(dtype):
        raise UnsupportedDType(dtype=dtype, termname=termname)

    if missing_value is NotSpecified:
        missing_value = default_missing_value_for_dtype(dtype)

    try:
        if (dtype == categorical_dtype):
            # This check is necessary because we use object dtype for
            # categoricals, and numpy will allow us to promote numerical
            # values to object even though we don't support them.
            _assert_valid_categorical_missing_value(missing_value)

        # For any other type, we can check if the missing_value is safe by
        # making an array of that value and trying to safely convert it to
        # the desired type.
        # 'same_kind' allows casting between things like float32 and
        # float64, but not str and int.
        array([missing_value]).astype(dtype=dtype, casting='same_kind')
    except TypeError as e:
        raise TypeError(
            "Missing value {value!r} is not a valid choice "
            "for term {termname} with dtype {dtype}.\n\n"
            "Coercion attempt failed with: {error}".format(
                termname=termname,
                value=missing_value,
                dtype=dtype,
                error=e,
            )
        )

    return dtype, missing_value