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# Source code for astropy.visualization.interval

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

    """
    Classes that deal with computing intervals from arrays of values based on
    various criteria.
    """

    import abc

    import numpy as np

    from astropy.utils.decorators import deprecated_attribute, deprecated_renamed_argument

    from .transform import BaseTransform

    __all__ = [
        "BaseInterval",
        "ManualInterval",
        "MinMaxInterval",
        "AsymmetricPercentileInterval",
        "PercentileInterval",
        "ZScaleInterval",
    ]

    class BaseInterval(BaseTransform):
        """
        Base class for the interval classes, which, when called with an
        array of values, return an interval computed following different
        algorithms.
        """

        @abc.abstractmethod
        def get_limits(self, values):
            """
            Return the minimum and maximum value in the interval based on
            the values provided.

            Parameters
            ----------
            values : ndarray
                The image values.

            Returns
            -------
            vmin, vmax : float
                The mininium and maximum image value in the interval.
            """

            raise NotImplementedError("Needs to be implemented in a subclass.")

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

            Parameters
            ----------
            values : array-like
                The input values.
            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.
            """

            vmin, vmax = self.get_limits(values)

            if out is None:
                values = np.subtract(values, float(vmin))
            else:
                if out.dtype.kind != "f":
                    raise TypeError(
                        "Can only do in-place scaling for floating-point arrays"
                    )
                values = np.subtract(values, float(vmin), out=out)

            if (vmax - vmin) != 0:
                np.true_divide(values, vmax - vmin, out=values)

            if clip:
                np.clip(values, 0.0, 1.0, out=values)

            return values

    class ManualInterval(BaseInterval):
        """
        Interval based on user-specified values.

        Parameters
        ----------
        vmin : float, optional
            The minimum value in the scaling.  Defaults to the image
            minimum (ignoring NaNs)
        vmax : float, optional
            The maximum value in the scaling.  Defaults to the image
            maximum (ignoring NaNs)
        """

        def __init__(self, vmin=None, vmax=None):
            self.vmin = vmin
            self.vmax = vmax

        def get_limits(self, values):
            # Avoid overhead of preparing array if both limits have been specified
            # manually, for performance.
            if self.vmin is not None and self.vmax is not None:
                return self.vmin, self.vmax

            # Make sure values is a Numpy array
            values = np.asarray(values).ravel()

            # Filter out invalid values (inf, nan)
            values = values[np.isfinite(values)]

            vmin = np.min(values) if self.vmin is None else self.vmin
            vmax = np.max(values) if self.vmax is None else self.vmax

            return vmin, vmax

    class MinMaxInterval(BaseInterval):
        """
        Interval based on the minimum and maximum values in the data.
        """

        def get_limits(self, values):
            # Make sure values is a Numpy array
            values = np.asarray(values).ravel()

            # Filter out invalid values (inf, nan)
            values = values[np.isfinite(values)]

            return np.min(values), np.max(values)

    class AsymmetricPercentileInterval(BaseInterval):
        """
        Interval based on a keeping a specified fraction of pixels (can be
        asymmetric).

        Parameters
        ----------
        lower_percentile : float
            The lower percentile below which to ignore pixels.
        upper_percentile : float
            The upper percentile above which to ignore pixels.
        n_samples : int, optional
            Maximum number of values to use. If this is specified, and there
            are more values in the dataset as this, then values are randomly
            sampled from the array (with replacement).
        """

        def __init__(self, lower_percentile, upper_percentile, n_samples=None):
            self.lower_percentile = lower_percentile
            self.upper_percentile = upper_percentile
            self.n_samples = n_samples

        def get_limits(self, values):
            # Make sure values is a Numpy array
            values = np.asarray(values).ravel()

            # If needed, limit the number of samples. We sample with replacement
            # since this is much faster.
            if self.n_samples is not None and values.size > self.n_samples:
                values = np.random.choice(values, self.n_samples)

            # Filter out invalid values (inf, nan)
            values = values[np.isfinite(values)]

            # Determine values at percentiles
            vmin, vmax = np.percentile(
                values, (self.lower_percentile, self.upper_percentile)
            )

            return vmin, vmax

    class PercentileInterval(AsymmetricPercentileInterval):
        """
        Interval based on a keeping a specified fraction of pixels.

        Parameters
        ----------
        percentile : float
            The fraction of pixels to keep. The same fraction of pixels is
            eliminated from both ends.
        n_samples : int, optional
            Maximum number of values to use. If this is specified, and there
            are more values in the dataset as this, then values are randomly
            sampled from the array (with replacement).
        """

        def __init__(self, percentile, n_samples=None):
            lower_percentile = (100 - percentile) * 0.5
            upper_percentile = 100 - lower_percentile
            super().__init__(lower_percentile, upper_percentile, n_samples=n_samples)

    class ZScaleInterval(BaseInterval):
        """
        Interval based on IRAF's zscale.

        https://iraf.net/forum/viewtopic.php?showtopic=134139

        Original implementation:
        https://github.com/spacetelescope/stsci.numdisplay/blob/master/lib/stsci/numdisplay/zscale.py

        Licensed under a 3-clause BSD style license (see AURA_LICENSE.rst).

        Parameters
        ----------
        n_samples : int, optional
            The number of points in the array to sample for determining
            scaling factors.  Defaults to 1000.

            .. versionchanged:: 5.2
                ``n_samples`` replaces the deprecated ``nsamples`` argument,
                which will be removed in the future.

        contrast : float, optional
            The scaling factor (between 0 and 1) for determining the minimum
            and maximum value.  Larger values increase the difference
            between the minimum and maximum values used for display.
            Defaults to 0.25.
        max_reject : float, optional
            If more than ``max_reject * npixels`` pixels are rejected, then
            the returned values are the minimum and maximum of the data.
            Defaults to 0.5.
        min_npixels : int, optional
            If there are less than ``min_npixels`` pixels remaining after
            the pixel rejection, then the returned values are the minimum
            and maximum of the data.  Defaults to 5.
        krej : float, optional
            The number of sigma used for the rejection. Defaults to 2.5.
        max_iterations : int, optional
            The maximum number of iterations for the rejection. Defaults to
            5.
        """

        @deprecated_renamed_argument("nsamples", "n_samples", "5.2")
        def __init__(
            self,
            n_samples=1000,
            contrast=0.25,
            max_reject=0.5,
            min_npixels=5,
            krej=2.5,
            max_iterations=5,
        ):
            self.n_samples = n_samples
            self.contrast = contrast
            self.max_reject = max_reject
            self.min_npixels = min_npixels
            self.krej = krej
            self.max_iterations = max_iterations

        # Mark `nsamples` as deprecated
        nsamples = deprecated_attribute("nsamples", "5.2", alternative="n_samples")

        def get_limits(self, values):
            # Sample the image
            values = np.asarray(values)
            values = values[np.isfinite(values)]
            stride = int(max(1.0, values.size / self.n_samples))
            samples = values[::stride][: self.n_samples]
            samples.sort()

            npix = len(samples)
            vmin = samples[0]
            vmax = samples[-1]

            # Fit a line to the sorted array of samples
            minpix = max(self.min_npixels, int(npix * self.max_reject))
            x = np.arange(npix)
            ngoodpix = npix
            last_ngoodpix = npix + 1

            # Bad pixels mask used in k-sigma clipping
            badpix = np.zeros(npix, dtype=bool)

            # Kernel used to dilate the bad pixels mask
            ngrow = max(1, int(npix * 0.01))
            kernel = np.ones(ngrow, dtype=bool)

            for _ in range(self.max_iterations):
                if ngoodpix >= last_ngoodpix or ngoodpix < minpix:
                    break

                fit = np.polyfit(x, samples, deg=1, w=(~badpix).astype(int))
                fitted = np.poly1d(fit)(x)

                # Subtract fitted line from the data array
                flat = samples - fitted

                # Compute the k-sigma rejection threshold
                threshold = self.krej * flat[~badpix].std()

                # Detect and reject pixels further than k*sigma from the
                # fitted line
                badpix[(flat < -threshold) | (flat > threshold)] = True

                # Convolve with a kernel of length ngrow
                badpix = np.convolve(badpix, kernel, mode="same")

                last_ngoodpix = ngoodpix
                ngoodpix = np.sum(~badpix)

            if ngoodpix >= minpix:
                slope, _ = fit

                if self.contrast > 0:
                    slope = slope / self.contrast
                center_pixel = (npix - 1) // 2
                median = np.median(samples)
                vmin = max(vmin, median - (center_pixel - 1) * slope)
                vmax = min(vmax, median + (npix - center_pixel) * slope)

            return vmin, vmax