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# Source code for astropy.visualization.lupton_rgb

    # Licensed under a 3-clause BSD style license - see LICENSE.rst
    """
    Combine 3 images to produce a properly-scaled RGB image following Lupton et al. (2004).

    The three images must be aligned and have the same pixel scale and size.

    For details, see : https://ui.adsabs.harvard.edu/abs/2004PASP..116..133L
    """

    import numpy as np

    from . import ZScaleInterval

    __all__ = ["make_lupton_rgb"]

    def compute_intensity(image_r, image_g=None, image_b=None):
        """
        Return a naive total intensity from the red, blue, and green intensities.

        Parameters
        ----------
        image_r : ndarray
            Intensity of image to be mapped to red; or total intensity if ``image_g``
            and ``image_b`` are None.
        image_g : ndarray, optional
            Intensity of image to be mapped to green.
        image_b : ndarray, optional
            Intensity of image to be mapped to blue.

        Returns
        -------
        intensity : ndarray
            Total intensity from the red, blue and green intensities, or ``image_r``
            if green and blue images are not provided.
        """
        if image_g is None or image_b is None:
            if not (image_g is None and image_b is None):
                raise ValueError(
                    "please specify either a single image or red, green, and blue images."
                )
            return image_r

        intensity = (image_r + image_g + image_b) / 3.0

        # Repack into whatever type was passed to us
        return np.asarray(intensity, dtype=image_r.dtype)

    class Mapping:
        """
        Baseclass to map red, blue, green intensities into uint8 values.

        Parameters
        ----------
        minimum : float or sequence(3)
            Intensity that should be mapped to black (a scalar or array for R, G, B).
        image : ndarray, optional
            An image used to calculate some parameters of some mappings.
        """

        def __init__(self, minimum=None, image=None):
            self._uint8Max = float(np.iinfo(np.uint8).max)

            try:
                len(minimum)
            except TypeError:
                minimum = 3 * [minimum]
            if len(minimum) != 3:
                raise ValueError("please provide 1 or 3 values for minimum.")

            self.minimum = minimum
            self._image = np.asarray(image)

        def make_rgb_image(self, image_r, image_g, image_b):
            """
            Convert 3 arrays, image_r, image_g, and image_b into an 8-bit RGB image.

            Parameters
            ----------
            image_r : ndarray
                Image to map to red.
            image_g : ndarray
                Image to map to green.
            image_b : ndarray
                Image to map to blue.

            Returns
            -------
            RGBimage : ndarray
                RGB (integer, 8-bits per channel) color image as an NxNx3 numpy array.
            """
            image_r = np.asarray(image_r)
            image_g = np.asarray(image_g)
            image_b = np.asarray(image_b)

            if (image_r.shape != image_g.shape) or (image_g.shape != image_b.shape):
                msg = "The image shapes must match. r: {}, g: {} b: {}"
                raise ValueError(msg.format(image_r.shape, image_g.shape, image_b.shape))

            return np.dstack(
                self._convert_images_to_uint8(image_r, image_g, image_b)
            ).astype(np.uint8)

        def intensity(self, image_r, image_g, image_b):
            """
            Return the total intensity from the red, blue, and green intensities.
            This is a naive computation, and may be overridden by subclasses.

            Parameters
            ----------
            image_r : ndarray
                Intensity of image to be mapped to red; or total intensity if
                ``image_g`` and ``image_b`` are None.
            image_g : ndarray, optional
                Intensity of image to be mapped to green.
            image_b : ndarray, optional
                Intensity of image to be mapped to blue.

            Returns
            -------
            intensity : ndarray
                Total intensity from the red, blue and green intensities, or
                ``image_r`` if green and blue images are not provided.
            """
            return compute_intensity(image_r, image_g, image_b)

        def map_intensity_to_uint8(self, I):
            """
            Return an array which, when multiplied by an image, returns that image
            mapped to the range of a uint8, [0, 255] (but not converted to uint8).

            The intensity is assumed to have had minimum subtracted (as that can be
            done per-band).

            Parameters
            ----------
            I : ndarray
                Intensity to be mapped.

            Returns
            -------
            mapped_I : ndarray
                ``I`` mapped to uint8
            """
            with np.errstate(invalid="ignore", divide="ignore"):
                return np.clip(I, 0, self._uint8Max)

        def _convert_images_to_uint8(self, image_r, image_g, image_b):
            """
            Use the mapping to convert images image_r, image_g, and image_b to a triplet of uint8 images
            """
            image_r = image_r - self.minimum[0]  # n.b. makes copy
            image_g = image_g - self.minimum[1]
            image_b = image_b - self.minimum[2]

            fac = self.map_intensity_to_uint8(self.intensity(image_r, image_g, image_b))

            image_rgb = [image_r, image_g, image_b]
            for c in image_rgb:
                c *= fac
                with np.errstate(invalid="ignore"):
                    c[c < 0] = 0  # individual bands can still be < 0, even if fac isn't

            pixmax = self._uint8Max
            # copies -- could work row by row to minimise memory usage
            r0, g0, b0 = image_rgb

            # n.b. np.where can't and doesn't short-circuit
            with np.errstate(invalid="ignore", divide="ignore"):
                for i, c in enumerate(image_rgb):
                    c = np.where(
                        r0 > g0,
                        np.where(
                            r0 > b0,
                            np.where(r0 >= pixmax, c * pixmax / r0, c),
                            np.where(b0 >= pixmax, c * pixmax / b0, c),
                        ),
                        np.where(
                            g0 > b0,
                            np.where(g0 >= pixmax, c * pixmax / g0, c),
                            np.where(b0 >= pixmax, c * pixmax / b0, c),
                        ),
                    ).astype(np.uint8)
                    c[c > pixmax] = pixmax

                    image_rgb[i] = c

            return image_rgb

    class LinearMapping(Mapping):
        """
        A linear map map of red, blue, green intensities into uint8 values.

        A linear stretch from [minimum, maximum].
        If one or both are omitted use image min and/or max to set them.

        Parameters
        ----------
        minimum : float
            Intensity that should be mapped to black (a scalar or array for R, G, B).
        maximum : float
            Intensity that should be mapped to white (a scalar).
        """

        def __init__(self, minimum=None, maximum=None, image=None):
            if minimum is None or maximum is None:
                if image is None:
                    raise ValueError(
                        "you must provide an image if you don't "
                        "set both minimum and maximum"
                    )
                if minimum is None:
                    minimum = image.min()
                if maximum is None:
                    maximum = image.max()

            Mapping.__init__(self, minimum=minimum, image=image)
            self.maximum = maximum

            if maximum is None:
                self._range = None
            else:
                if maximum == minimum:
                    raise ValueError("minimum and maximum values must not be equal")
                self._range = float(maximum - minimum)

        def map_intensity_to_uint8(self, I):
            # n.b. np.where can't and doesn't short-circuit
            with np.errstate(invalid="ignore", divide="ignore"):
                return np.where(
                    I <= 0,
                    0,
                    np.where(
                        I >= self._range, self._uint8Max / I, self._uint8Max / self._range
                    ),
                )

    class AsinhMapping(Mapping):
        """
        A mapping for an asinh stretch (preserving colours independent of brightness)

        x = asinh(Q (I - minimum)/stretch)/Q

        This reduces to a linear stretch if Q == 0

        See https://ui.adsabs.harvard.edu/abs/2004PASP..116..133L

        Parameters
        ----------

        minimum : float
            Intensity that should be mapped to black (a scalar or array for R, G, B).
        stretch : float
            The linear stretch of the image.
        Q : float
            The asinh softening parameter.
        """

        def __init__(self, minimum, stretch, Q=8):
            Mapping.__init__(self, minimum)

            # 32bit floating point machine epsilon; sys.float_info.epsilon is 64bit
            epsilon = 1.0 / 2**23
            if abs(Q) < epsilon:
                Q = 0.1
            else:
                Qmax = 1e10
                if Q > Qmax:
                    Q = Qmax

            frac = 0.1  # gradient estimated using frac*stretch is _slope
            self._slope = frac * self._uint8Max / np.arcsinh(frac * Q)

            self._soften = Q / float(stretch)

        def map_intensity_to_uint8(self, I):
            # n.b. np.where can't and doesn't short-circuit
            with np.errstate(invalid="ignore", divide="ignore"):
                return np.where(I <= 0, 0, np.arcsinh(I * self._soften) * self._slope / I)

    class AsinhZScaleMapping(AsinhMapping):
        """
        A mapping for an asinh stretch, estimating the linear stretch by zscale.

        x = asinh(Q (I - z1)/(z2 - z1))/Q

        Parameters
        ----------
        image1 : ndarray or a list of arrays
            The image to analyse, or a list of 3 images to be converted to
            an intensity image.
        image2 : ndarray, optional
            the second image to analyse (must be specified with image3).
        image3 : ndarray, optional
            the third image to analyse (must be specified with image2).
        Q : float, optional
            The asinh softening parameter. Default is 8.
        pedestal : float or sequence(3), optional
            The value, or array of 3 values, to subtract from the images; or None.

        Notes
        -----
        pedestal, if not None, is removed from the images when calculating the
        zscale stretch, and added back into Mapping.minimum[]
        """

        def __init__(self, image1, image2=None, image3=None, Q=8, pedestal=None):
            if image2 is None or image3 is None:
                if not (image2 is None and image3 is None):
                    raise ValueError(
                        "please specify either a single image or three images."
                    )
                image = [image1]
            else:
                image = [image1, image2, image3]

            if pedestal is not None:
                try:
                    len(pedestal)
                except TypeError:
                    pedestal = 3 * [pedestal]

                if len(pedestal) != 3:
                    raise ValueError("please provide 1 or 3 pedestals.")

                image = list(image)  # needs to be mutable
                for i, im in enumerate(image):
                    if pedestal[i] != 0.0:
                        image[i] = im - pedestal[i]  # n.b. a copy
            else:
                pedestal = len(image) * [0.0]

            image = compute_intensity(*image)

            zscale_limits = ZScaleInterval().get_limits(image)
            zscale = LinearMapping(*zscale_limits, image=image)
            # zscale.minimum is always a triple
            stretch = zscale.maximum - zscale.minimum[0]
            minimum = zscale.minimum

            for i, level in enumerate(pedestal):
                minimum[i] += level

            AsinhMapping.__init__(self, minimum, stretch, Q)
            self._image = image

    def make_lupton_rgb(
        image_r, image_g, image_b, minimum=0, stretch=5, Q=8, filename=None
    ):
        """
        Return a Red/Green/Blue color image from up to 3 images using an asinh stretch.
        The input images can be int or float, and in any range or bit-depth.

        For a more detailed look at the use of this method, see the document
        :ref:`astropy:astropy-visualization-rgb`.

        Parameters
        ----------
        image_r : ndarray
            Image to map to red.
        image_g : ndarray
            Image to map to green.
        image_b : ndarray
            Image to map to blue.
        minimum : float
            Intensity that should be mapped to black (a scalar or array for R, G, B).
        stretch : float
            The linear stretch of the image.
        Q : float
            The asinh softening parameter.
        filename : str
            Write the resulting RGB image to a file (file type determined
            from extension).

        Returns
        -------
        rgb : ndarray
            RGB (integer, 8-bits per channel) color image as an NxNx3 numpy array.
        """
        asinhMap = AsinhMapping(minimum, stretch, Q)
        rgb = asinhMap.make_rgb_image(image_r, image_g, image_b)

        if filename:
            import matplotlib.image

            matplotlib.image.imsave(filename, rgb, origin="lower")

        return rgb