Scale

This pages contains classes defined in hakowan.scale module.

ScaleLike: TypeAlias = float | Scale module-attribute

Type alias for scale-like objects.

• A scalar value will be converted to Uniform scale with the scalar value as the factor.
• A Scale object will be unchanged.

Affine dataclass

Bases: Scale

Scale the data using an affine transformation.

Attributes:

Name	Type	Description
matrix	NDArray	The affine transformation matrix.

Source code in hakowan/grammar/scale/scale.py

@dataclass(slots=True)
class Affine(Scale):
    """Scale the data using an affine transformation.

    Attributes:
        matrix: The affine transformation matrix.
    """

    matrix: npt.NDArray

Clip dataclass

Bases: Scale

Clip the data to the range [min, max].

Attributes:

Name	Type	Description
domain	tuple[float, float]	The clip minimum and maximum values.

Source code in hakowan/grammar/scale/scale.py

@dataclass(slots=True)
class Clip(Scale):
    """Clip the data to the range [min, max].

    Attributes:
        domain: The clip minimum and maximum values.
    """

    domain: tuple[float, float]

Custom dataclass

Bases: Scale

Scale the data using a custom function.

Attributes:

Name	Type	Description
function	Callable	The scaling function. E.g. lambda x: x ** 2 for squaring the data.

Source code in hakowan/grammar/scale/scale.py

@dataclass(slots=True)
class Custom(Scale):
    """Scale the data using a custom function.

    Attributes:
        function: The scaling function. E.g. `lambda x: x ** 2` for squaring the data.
    """

    function: Callable

Log dataclass

Bases: Scale

Logarithmic scale.

Attributes:

Name	Type	Description
base	float	The base of the logarithm.

Source code in hakowan/grammar/scale/scale.py

@dataclass(slots=True)
class Log(Scale):
    """Logarithmic scale.

    Attributes:
        base: The base of the logarithm.
    """

    base: float = 10.0

Normalize dataclass

Bases: Scale

Normalize the data so that the box defined by domain_min and domain_max is scaled to the box defined by range_min and range_max.

Attributes:

Name	Type	Description
range_min	ArrayLike	The minimum value of the output range.
range_max	ArrayLike	The maximum value of the output range.
domain_min	ArrayLike | None	The minimum value of the input range. If not specified, the minimum value of the input data will be used.
domain_max	ArrayLike | None	The maximum value of the input range. If not specified, the maximum value of the input data will be used.

Source code in hakowan/grammar/scale/scale.py

@dataclass(slots=True)
class Normalize(Scale):
    """Normalize the data so that the box defined by `domain_min` and `domain_max` is scaled to the box
    defined by `range_min` and `range_max`.

    Attributes:
        range_min: The minimum value of the output range.
        range_max: The maximum value of the output range.
        domain_min: The minimum value of the input range. If not specified, the minimum value of the
            input data will be used.
        domain_max: The maximum value of the input range. If not specified, the maximum value of the
            input data will be used.
    """

    range_min: npt.ArrayLike
    range_max: npt.ArrayLike
    domain_min: npt.ArrayLike | None = None
    domain_max: npt.ArrayLike | None = None

Scale dataclass

Base class for all scales.

Source code in hakowan/grammar/scale/scale.py

@dataclass(kw_only=True, slots=True)
class Scale:
    """Base class for all scales."""

    _child: Optional["Scale"] = None

    def __imul__(self, other: "Scale") -> "Scale":
        """Combine the current scale with the `other` scale in place. The current scale will be applied
        before the `other` scale.
        """
        s = self
        while s._child is not None:
            s = s._child
        s._child = other
        return self

    def __mul__(self, other: "Scale") -> "Scale":
        """Combine the current scale with the `other` scale in a new scale. Both the current and
        the `other` scale is not modified. In the new scale, the current scale will be applied
        before the `other` scale.
        """
        r = copy.deepcopy(self)
        r *= other
        return r

__imul__(other)

Combine the current scale with the other scale in place. The current scale will be applied before the other scale.

Source code in hakowan/grammar/scale/scale.py

def __imul__(self, other: "Scale") -> "Scale":
    """Combine the current scale with the `other` scale in place. The current scale will be applied
    before the `other` scale.
    """
    s = self
    while s._child is not None:
        s = s._child
    s._child = other
    return self

__mul__(other)

Combine the current scale with the other scale in a new scale. Both the current and the other scale is not modified. In the new scale, the current scale will be applied before the other scale.

Source code in hakowan/grammar/scale/scale.py

def __mul__(self, other: "Scale") -> "Scale":
    """Combine the current scale with the `other` scale in a new scale. Both the current and
    the `other` scale is not modified. In the new scale, the current scale will be applied
    before the `other` scale.
    """
    r = copy.deepcopy(self)
    r *= other
    return r

Uniform dataclass

Bases: Scale

Scale the data uniformly by multiplying it with a factor.

Attributes:

Name	Type	Description
factor	float	The scaling factor.

Source code in hakowan/grammar/scale/scale.py

@dataclass(slots=True)
class Uniform(Scale):
    """Scale the data uniformly by multiplying it with a factor.

    Attributes:
        factor: The scaling factor.
    """

    factor: float

Offset dataclass

Bases: Scale

Offset the data by a constant.

Attributes:

Name	Type	Description
offset	Attribute	The offset to apply to the data.

Source code in hakowan/grammar/scale/offset.py

@dataclass(slots=True)
class Offset(Scale):
    """Offset the data by a constant.

    Attributes:
        offset (Attribute): The offset to apply to the data.
    """

    offset: Attribute