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# Source code for astropy.visualization.stretch

    # Licensed under a 3-clause BSD style license - see LICENSE.rst

    """
    Classes that deal with stretching, i.e. mapping a range of [0:1] values onto
    another set of [0:1] values with a transformation
    """

    import numpy as np

    from .transform import BaseTransform, CompositeTransform

    __all__ = [
        "BaseStretch",
        "LinearStretch",
        "SqrtStretch",
        "PowerStretch",
        "PowerDistStretch",
        "SquaredStretch",
        "LogStretch",
        "AsinhStretch",
        "SinhStretch",
        "HistEqStretch",
        "ContrastBiasStretch",
        "CompositeStretch",
    ]

    def _logn(n, x, out=None):
        """Calculate the log base n of x."""
        # We define this because numpy.lib.scimath.logn doesn't support out=
        if out is None:
            return np.log(x) / np.log(n)
        else:
            np.log(x, out=out)
            np.true_divide(out, np.log(n), out=out)
            return out

    def _prepare(values, clip=True, out=None):
        """
        Prepare the data by optionally clipping and copying, and return the
        array that should be subsequently used for in-place calculations.
        """

        if clip:
            return np.clip(values, 0.0, 1.0, out=out)
        else:
            if out is None:
                return np.array(values, copy=True)
            else:
                out[:] = np.asarray(values)
                return out

    class BaseStretch(BaseTransform):
        """
        Base class for the stretch classes, which, when called with an array
        of values in the range [0:1], return an transformed array of values,
        also in the range [0:1].
        """

        @property
        def _supports_invalid_kw(self):
            return False

        def __add__(self, other):
            return CompositeStretch(other, self)

        def __call__(self, values, clip=True, out=None):
            """
            Transform values using this stretch.

            Parameters
            ----------
            values : array-like
                The input values, which should already be normalized to the
                [0:1] range.
            clip : bool, optional
                If `True` (default), values outside the [0:1] range are
                clipped to the [0:1] range.
            out : ndarray, optional
                If specified, the output values will be placed in this array
                (typically used for in-place calculations).

            Returns
            -------
            result : ndarray
                The transformed values.
            """

        @property
        def inverse(self):
            """A stretch object that performs the inverse operation."""

    class LinearStretch(BaseStretch):
        """
        A linear stretch with a slope and offset.

        The stretch is given by:

        .. math::
            y = slope x + intercept

        Parameters
        ----------
        slope : float, optional
            The ``slope`` parameter used in the above formula.  Default is 1.
        intercept : float, optional
            The ``intercept`` parameter used in the above formula.  Default is 0.
        """

        def __init__(self, slope=1, intercept=0):
            super().__init__()
            self.slope = slope
            self.intercept = intercept

        def __call__(self, values, clip=True, out=None):
            values = _prepare(values, clip=clip, out=out)
            if self.slope != 1:
                np.multiply(values, self.slope, out=values)
            if self.intercept != 0:
                np.add(values, self.intercept, out=values)
            return values

        @property
        def inverse(self):
            """A stretch object that performs the inverse operation."""
            return LinearStretch(1.0 / self.slope, -self.intercept / self.slope)

    class SqrtStretch(BaseStretch):
        r"""
        A square root stretch.

        The stretch is given by:

        .. math::
            y = \sqrt{x}
        """

        @property
        def _supports_invalid_kw(self):
            return True

        def __call__(self, values, clip=True, out=None, invalid=None):
            """
            Transform values using this stretch.

            Parameters
            ----------
            values : array-like
                The input values, which should already be normalized to the
                [0:1] range.
            clip : bool, optional
                If `True` (default), values outside the [0:1] range are
                clipped to the [0:1] range.
            out : ndarray, optional
                If specified, the output values will be placed in this array
                (typically used for in-place calculations).
            invalid : None or float, optional
                Value to assign NaN values generated by this class.  NaNs in
                the input ``values`` array are not changed.  This option is
                generally used with matplotlib normalization classes, where
                the ``invalid`` value should map to the matplotlib colormap
                "under" value (i.e., any finite value < 0).  If `None`, then
                NaN values are not replaced.  This keyword has no effect if
                ``clip=True``.

            Returns
            -------
            result : ndarray
                The transformed values.
            """

            values = _prepare(values, clip=clip, out=out)
            replace_invalid = not clip and invalid is not None
            with np.errstate(invalid="ignore"):
                if replace_invalid:
                    idx = values < 0
                np.sqrt(values, out=values)

            if replace_invalid:
                # Assign new NaN (i.e., NaN not in the original input
                # values, but generated by this class) to the invalid value.
                values[idx] = invalid

            return values

        @property
        def inverse(self):
            """A stretch object that performs the inverse operation."""
            return PowerStretch(2)

    class PowerStretch(BaseStretch):
        r"""
        A power stretch.

        The stretch is given by:

        .. math::
            y = x^a

        Parameters
        ----------
        a : float
            The power index (see the above formula).  ``a`` must be greater
            than 0.
        """

        @property
        def _supports_invalid_kw(self):
            return True

        def __init__(self, a):
            super().__init__()
            if a <= 0:
                raise ValueError("a must be > 0")
            self.power = a

        def __call__(self, values, clip=True, out=None, invalid=None):
            """
            Transform values using this stretch.

            Parameters
            ----------
            values : array-like
                The input values, which should already be normalized to the
                [0:1] range.
            clip : bool, optional
                If `True` (default), values outside the [0:1] range are
                clipped to the [0:1] range.
            out : ndarray, optional
                If specified, the output values will be placed in this array
                (typically used for in-place calculations).
            invalid : None or float, optional
                Value to assign NaN values generated by this class.  NaNs in
                the input ``values`` array are not changed.  This option is
                generally used with matplotlib normalization classes, where
                the ``invalid`` value should map to the matplotlib colormap
                "under" value (i.e., any finite value < 0).  If `None`, then
                NaN values are not replaced.  This keyword has no effect if
                ``clip=True``.

            Returns
            -------
            result : ndarray
                The transformed values.
            """

            values = _prepare(values, clip=clip, out=out)
            replace_invalid = (
                not clip
                and invalid is not None
                and ((-1 < self.power < 0) or (0 < self.power < 1))
            )
            with np.errstate(invalid="ignore"):
                if replace_invalid:
                    idx = values < 0
                np.power(values, self.power, out=values)

            if replace_invalid:
                # Assign new NaN (i.e., NaN not in the original input
                # values, but generated by this class) to the invalid value.
                values[idx] = invalid

            return values

        @property
        def inverse(self):
            """A stretch object that performs the inverse operation."""
            return PowerStretch(1.0 / self.power)

    class PowerDistStretch(BaseStretch):
        r"""
        An alternative power stretch.

        The stretch is given by:

        .. math::
            y = \frac{a^x - 1}{a - 1}

        Parameters
        ----------
        a : float, optional
            The ``a`` parameter used in the above formula.  ``a`` must be
            greater than or equal to 0, but cannot be set to 1.  Default is
            1000.
        """

        def __init__(self, a=1000.0):
            if a < 0 or a == 1:  # singularity
                raise ValueError("a must be >= 0, but cannot be set to 1")
            super().__init__()
            self.exp = a

        def __call__(self, values, clip=True, out=None):
            values = _prepare(values, clip=clip, out=out)
            np.power(self.exp, values, out=values)
            np.subtract(values, 1, out=values)
            np.true_divide(values, self.exp - 1.0, out=values)
            return values

        @property
        def inverse(self):
            """A stretch object that performs the inverse operation."""
            return InvertedPowerDistStretch(a=self.exp)

    class InvertedPowerDistStretch(BaseStretch):
        r"""
        Inverse transformation for
        `~astropy.image.scaling.PowerDistStretch`.

        The stretch is given by:

        .. math::
            y = \frac{\log(y (a-1) + 1)}{\log a}

        Parameters
        ----------
        a : float, optional
            The ``a`` parameter used in the above formula.  ``a`` must be
            greater than or equal to 0, but cannot be set to 1.  Default is
            1000.
        """

        def __init__(self, a=1000.0):
            if a < 0 or a == 1:  # singularity
                raise ValueError("a must be >= 0, but cannot be set to 1")
            super().__init__()
            self.exp = a

        def __call__(self, values, clip=True, out=None):
            values = _prepare(values, clip=clip, out=out)
            np.multiply(values, self.exp - 1.0, out=values)
            np.add(values, 1, out=values)
            _logn(self.exp, values, out=values)
            return values

        @property
        def inverse(self):
            """A stretch object that performs the inverse operation."""
            return PowerDistStretch(a=self.exp)

    class SquaredStretch(PowerStretch):
        r"""
        A convenience class for a power stretch of 2.

        The stretch is given by:

        .. math::
            y = x^2
        """

        def __init__(self):
            super().__init__(2)

        @property
        def inverse(self):
            """A stretch object that performs the inverse operation."""
            return SqrtStretch()

    class LogStretch(BaseStretch):
        r"""
        A log stretch.

        The stretch is given by:

        .. math::
            y = \frac{\log{(a x + 1)}}{\log{(a + 1)}}

        Parameters
        ----------
        a : float
            The ``a`` parameter used in the above formula.  ``a`` must be
            greater than 0.  Default is 1000.
        """

        @property
        def _supports_invalid_kw(self):
            return True

        def __init__(self, a=1000.0):
            super().__init__()
            if a <= 0:  # singularity
                raise ValueError("a must be > 0")
            self.exp = a

        def __call__(self, values, clip=True, out=None, invalid=None):
            """
            Transform values using this stretch.

            Parameters
            ----------
            values : array-like
                The input values, which should already be normalized to the
                [0:1] range.
            clip : bool, optional
                If `True` (default), values outside the [0:1] range are
                clipped to the [0:1] range.
            out : ndarray, optional
                If specified, the output values will be placed in this array
                (typically used for in-place calculations).
            invalid : None or float, optional
                Value to assign NaN values generated by this class.  NaNs in
                the input ``values`` array are not changed.  This option is
                generally used with matplotlib normalization classes, where
                the ``invalid`` value should map to the matplotlib colormap
                "under" value (i.e., any finite value < 0).  If `None`, then
                NaN values are not replaced.  This keyword has no effect if
                ``clip=True``.

            Returns
            -------
            result : ndarray
                The transformed values.
            """

            values = _prepare(values, clip=clip, out=out)
            replace_invalid = not clip and invalid is not None
            with np.errstate(invalid="ignore"):
                if replace_invalid:
                    idx = values < 0
                np.multiply(values, self.exp, out=values)
                np.add(values, 1.0, out=values)
                np.log(values, out=values)
                np.true_divide(values, np.log(self.exp + 1.0), out=values)

            if replace_invalid:
                # Assign new NaN (i.e., NaN not in the original input
                # values, but generated by this class) to the invalid value.
                values[idx] = invalid

            return values

        @property
        def inverse(self):
            """A stretch object that performs the inverse operation."""
            return InvertedLogStretch(self.exp)

    class InvertedLogStretch(BaseStretch):
        r"""
        Inverse transformation for `~astropy.image.scaling.LogStretch`.

        The stretch is given by:

        .. math::
            y = \frac{e^{y \log{a + 1}} - 1}{a} \\
            y = \frac{e^{y} (a + 1) - 1}{a}

        Parameters
        ----------
        a : float, optional
            The ``a`` parameter used in the above formula.  ``a`` must be
            greater than 0.  Default is 1000.
        """

        def __init__(self, a):
            super().__init__()
            if a <= 0:  # singularity
                raise ValueError("a must be > 0")
            self.exp = a

        def __call__(self, values, clip=True, out=None):
            values = _prepare(values, clip=clip, out=out)
            np.multiply(values, np.log(self.exp + 1.0), out=values)
            np.exp(values, out=values)
            np.subtract(values, 1.0, out=values)
            np.true_divide(values, self.exp, out=values)
            return values

        @property
        def inverse(self):
            """A stretch object that performs the inverse operation."""
            return LogStretch(self.exp)

    class AsinhStretch(BaseStretch):
        r"""
        An asinh stretch.

        The stretch is given by:

        .. math::
            y = \frac{{\rm asinh}(x / a)}{{\rm asinh}(1 / a)}.

        Parameters
        ----------
        a : float, optional
            The ``a`` parameter used in the above formula.  The value of
            this parameter is where the asinh curve transitions from linear
            to logarithmic behavior, expressed as a fraction of the
            normalized image.  ``a`` must be greater than 0 and less than or
            equal to 1 (0 < a <= 1).  Default is 0.1.
        """

        def __init__(self, a=0.1):
            super().__init__()
            if a <= 0 or a > 1:
                raise ValueError("a must be > 0 and <= 1")
            self.a = a

        def __call__(self, values, clip=True, out=None):
            values = _prepare(values, clip=clip, out=out)
            np.true_divide(values, self.a, out=values)
            np.arcsinh(values, out=values)
            np.true_divide(values, np.arcsinh(1.0 / self.a), out=values)
            return values

        @property
        def inverse(self):
            """A stretch object that performs the inverse operation."""
            return SinhStretch(a=1.0 / np.arcsinh(1.0 / self.a))

    class SinhStretch(BaseStretch):
        r"""
        A sinh stretch.

        The stretch is given by:

        .. math::
            y = \frac{{\rm sinh}(x / a)}{{\rm sinh}(1 / a)}

        Parameters
        ----------
        a : float, optional
            The ``a`` parameter used in the above formula.  ``a`` must be
            greater than 0 and less than or equal to 1 (0 < a <= 1).
            Default is 1/3.
        """

        def __init__(self, a=1.0 / 3.0):
            super().__init__()
            if a <= 0 or a > 1:
                raise ValueError("a must be > 0 and <= 1")
            self.a = a

        def __call__(self, values, clip=True, out=None):
            values = _prepare(values, clip=clip, out=out)
            np.true_divide(values, self.a, out=values)
            np.sinh(values, out=values)
            np.true_divide(values, np.sinh(1.0 / self.a), out=values)
            return values

        @property
        def inverse(self):
            """A stretch object that performs the inverse operation."""
            return AsinhStretch(a=1.0 / np.sinh(1.0 / self.a))

    class HistEqStretch(BaseStretch):
        """
        A histogram equalization stretch.

        Parameters
        ----------
        data : array-like
            The data defining the equalization.
        values : array-like, optional
            The input image values, which should already be normalized to
            the [0:1] range.
        """

        def __init__(self, data, values=None):
            # Assume data is not necessarily normalized at this point
            self.data = np.sort(data.ravel())
            self.data = self.data[np.isfinite(self.data)]
            vmin = self.data.min()
            vmax = self.data.max()
            self.data = (self.data - vmin) / (vmax - vmin)

            # Compute relative position of each pixel
            if values is None:
                self.values = np.linspace(0.0, 1.0, len(self.data))
            else:
                self.values = values

        def __call__(self, values, clip=True, out=None):
            values = _prepare(values, clip=clip, out=out)
            values[:] = np.interp(values, self.data, self.values)
            return values

        @property
        def inverse(self):
            """A stretch object that performs the inverse operation."""
            return InvertedHistEqStretch(self.data, values=self.values)

    class InvertedHistEqStretch(BaseStretch):
        """
        Inverse transformation for `~astropy.image.scaling.HistEqStretch`.

        Parameters
        ----------
        data : array-like
            The data defining the equalization.
        values : array-like, optional
            The input image values, which should already be normalized to
            the [0:1] range.
        """

        def __init__(self, data, values=None):
            self.data = data[np.isfinite(data)]
            if values is None:
                self.values = np.linspace(0.0, 1.0, len(self.data))
            else:
                self.values = values

        def __call__(self, values, clip=True, out=None):
            values = _prepare(values, clip=clip, out=out)
            values[:] = np.interp(values, self.values, self.data)
            return values

        @property
        def inverse(self):
            """A stretch object that performs the inverse operation."""
            return HistEqStretch(self.data, values=self.values)

    class ContrastBiasStretch(BaseStretch):
        r"""
        A stretch that takes into account contrast and bias.

        The stretch is given by:

        .. math::
            y = (x - {\rm bias}) * {\rm contrast} + 0.5

        and the output values are clipped to the [0:1] range.

        Parameters
        ----------
        contrast : float
            The contrast parameter (see the above formula).

        bias : float
            The bias parameter (see the above formula).
        """

        def __init__(self, contrast, bias):
            super().__init__()
            self.contrast = contrast
            self.bias = bias

        def __call__(self, values, clip=True, out=None):
            # As a special case here, we only clip *after* the
            # transformation since it does not map [0:1] to [0:1]
            values = _prepare(values, clip=False, out=out)

            np.subtract(values, self.bias, out=values)
            np.multiply(values, self.contrast, out=values)
            np.add(values, 0.5, out=values)

            if clip:
                np.clip(values, 0, 1, out=values)

            return values

        @property
        def inverse(self):
            """A stretch object that performs the inverse operation."""
            return InvertedContrastBiasStretch(self.contrast, self.bias)

    class InvertedContrastBiasStretch(BaseStretch):
        """
        Inverse transformation for ContrastBiasStretch.

        Parameters
        ----------
        contrast : float
            The contrast parameter (see
            `~astropy.visualization.ConstrastBiasStretch).

        bias : float
            The bias parameter (see
            `~astropy.visualization.ConstrastBiasStretch).
        """

        def __init__(self, contrast, bias):
            super().__init__()
            self.contrast = contrast
            self.bias = bias

        def __call__(self, values, clip=True, out=None):
            # As a special case here, we only clip *after* the
            # transformation since it does not map [0:1] to [0:1]
            values = _prepare(values, clip=False, out=out)
            np.subtract(values, 0.5, out=values)
            np.true_divide(values, self.contrast, out=values)
            np.add(values, self.bias, out=values)

            if clip:
                np.clip(values, 0, 1, out=values)

            return values

        @property
        def inverse(self):
            """A stretch object that performs the inverse operation."""
            return ContrastBiasStretch(self.contrast, self.bias)

    class CompositeStretch(CompositeTransform, BaseStretch):
        """
        A combination of two stretches.

        Parameters
        ----------
        stretch_1 : :class:`astropy.visualization.BaseStretch`
            The first stretch to apply.
        stretch_2 : :class:`astropy.visualization.BaseStretch`
            The second stretch to apply.
        """

        def __call__(self, values, clip=True, out=None):
            return self.transform_2(
                self.transform_1(values, clip=clip, out=out), clip=clip, out=out
            )