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# Copyright (c) 2012 - 2022, Anaconda, Inc., and Bokeh Contributors.
# All rights reserved.
#
# The full license is in the file LICENSE.txt, distributed with this software.
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'''

'''

#-----------------------------------------------------------------------------
# Boilerplate
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from __future__ import annotations

import logging # isort:skip
log = logging.getLogger(__name__)

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# Imports
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# Bokeh imports
from ..core.has_props import abstract
from .transforms import Transform

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# Globals and constants
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__all__ = (
    'CategoricalScale',
    'LinearScale',
    'LogScale',
    'Scale',
)

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# General API
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@abstract
class Scale(Transform):
    ''' Base class for ``Scale`` models that represent an invertible
    computation to be carried out on the client-side.

    JavaScript implementations should implement the following methods:

    .. code-block

        compute(x: number): number {
            # compute and return the transform of a single value
        }

        v_compute(xs: Arrayable<number>): Arrayable<number> {
            # compute and return the transform of an array of values
        }

        invert(sx: number): number {
            # compute and return the inverse transform of a single value
        }

        v_invert(sxs: Arrayable<number>): Arrayable<number> {
            # compute and return the inverse transform of an array of values
        }

    '''

    # explicit __init__ to support Init signatures
    def __init__(self, *args, **kwargs) -> None:
        super().__init__(*args, **kwargs)


class ContinuousScale(Scale):
    ''' Represent a scale transformation between continuous ranges.

    '''

    # explicit __init__ to support Init signatures
    def __init__(self, *args, **kwargs) -> None:
        super().__init__(*args, **kwargs)


class LinearScale(ContinuousScale):
    ''' Represent a linear scale transformation between continuous ranges.

    '''

    # explicit __init__ to support Init signatures
    def __init__(self, *args, **kwargs) -> None:
        super().__init__(*args, **kwargs)

class LogScale(ContinuousScale):
    ''' Represent a log scale transformation between continuous ranges.

    '''

    # explicit __init__ to support Init signatures
    def __init__(self, *args, **kwargs) -> None:
        super().__init__(*args, **kwargs)

class CategoricalScale(Scale):
    ''' Represent a scale transformation between a categorical source range and
    continuous target range.

    '''

    # explicit __init__ to support Init signatures
    def __init__(self, *args, **kwargs) -> None:
        super().__init__(*args, **kwargs)


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# Dev API
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# Private API
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#-----------------------------------------------------------------------------
# Code
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