# Source code for PIL.ImageStat

    #
    # The Python Imaging Library.
    # $Id$
    #
    # global image statistics
    #
    # History:
    # 1996-04-05 fl   Created
    # 1997-05-21 fl   Added mask; added rms, var, stddev attributes
    # 1997-08-05 fl   Added median
    # 1998-07-05 hk   Fixed integer overflow error
    #
    # Notes:
    # This class shows how to implement delayed evaluation of attributes.
    # To get a certain value, simply access the corresponding attribute.
    # The __getattr__ dispatcher takes care of the rest.
    #
    # Copyright (c) Secret Labs AB 1997.
    # Copyright (c) Fredrik Lundh 1996-97.
    #
    # See the README file for information on usage and redistribution.
    #

    import functools
    import math
    import operator

    class Stat:
        def __init__(self, image_or_list, mask=None):
            try:
                if mask:
                    self.h = image_or_list.histogram(mask)
                else:
                    self.h = image_or_list.histogram()
            except AttributeError:
                self.h = image_or_list  # assume it to be a histogram list
            if not isinstance(self.h, list):
                raise TypeError("first argument must be image or list")
            self.bands = list(range(len(self.h) // 256))

        def __getattr__(self, id):
            """Calculate missing attribute"""
            if id[:4] == "_get":
                raise AttributeError(id)
            # calculate missing attribute
            v = getattr(self, "_get" + id)()
            setattr(self, id, v)
            return v

        def _getextrema(self):
            """Get min/max values for each band in the image"""

            def minmax(histogram):
                n = 255
                x = 0
                for i in range(256):
                    if histogram[i]:
                        n = min(n, i)
                        x = max(x, i)
                return n, x  # returns (255, 0) if there's no data in the histogram

            v = []
            for i in range(0, len(self.h), 256):
                v.append(minmax(self.h[i:]))
            return v

        def _getcount(self):
            """Get total number of pixels in each layer"""

            v = []
            for i in range(0, len(self.h), 256):
                v.append(functools.reduce(operator.add, self.h[i : i + 256]))
            return v

        def _getsum(self):
            """Get sum of all pixels in each layer"""

            v = []
            for i in range(0, len(self.h), 256):
                layerSum = 0.0
                for j in range(256):
                    layerSum += j * self.h[i + j]
                v.append(layerSum)
            return v

        def _getsum2(self):
            """Get squared sum of all pixels in each layer"""

            v = []
            for i in range(0, len(self.h), 256):
                sum2 = 0.0
                for j in range(256):
                    sum2 += (j ** 2) * float(self.h[i + j])
                v.append(sum2)
            return v

        def _getmean(self):
            """Get average pixel level for each layer"""

            v = []
            for i in self.bands:
                v.append(self.sum[i] / self.count[i])
            return v

        def _getmedian(self):
            """Get median pixel level for each layer"""

            v = []
            for i in self.bands:
                s = 0
                half = self.count[i] // 2
                b = i * 256
                for j in range(256):
                    s = s + self.h[b + j]
                    if s > half:
                        break
                v.append(j)
            return v

        def _getrms(self):
            """Get RMS for each layer"""

            v = []
            for i in self.bands:
                v.append(math.sqrt(self.sum2[i] / self.count[i]))
            return v

        def _getvar(self):
            """Get variance for each layer"""

            v = []
            for i in self.bands:
                n = self.count[i]
                v.append((self.sum2[i] - (self.sum[i] ** 2.0) / n) / n)
            return v

        def _getstddev(self):
            """Get standard deviation for each layer"""

            v = []
            for i in self.bands:
                v.append(math.sqrt(self.var[i]))
            return v

    Global = Stat  # compatibility