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# Source code for fabio.bruker100image

    import logging
    logger = logging.getLogger("bruker100image")
    try:
        from PIL import Image
    except ImportError:
        logger.warning("PIL is not installed ... trying to do without")
        Image = None

    from brukerimage import brukerimage
    from readbytestream import readbytestream

    class bruker100image(brukerimage):

        def toPIL16(self, filename=None):
            if not Image:
                raise RuntimeError("PIL is not installed !!! ")

            if filename:
                self.read(filename)
            PILimage = Image.frombuffer("F",
                                            (self.dim1, self.dim2),
                                            self.data,
                                            "raw",
                                            "F;16", 0, -1)
            return PILimage

        def read(self, fname, frame=None):
            f = open(fname, "rb")
            try:
                self._readheader(f)
            except:
                raise

            rows = int(self.header['NROWS'])
            cols = int(self.header['NCOLS'])
            npixelb = int(self.header['NPIXELB'][0])
            # you had to read the Bruker docs to know this!

            # We are now at the start of the image - assuming
            #   readbrukerheader worked
            # size = rows * cols * npixelb
            self.data = readbytestream(f, f.tell(), rows, cols, npixelb,
                                        datatype="int", signed='n', swap='n')

            noverfl = self.header['NOVERFL'].split() # now process the overflows
            #read the set of "underflow pixels" - these will be completely
            # disregarded for now
            data = self.data
            k = 0

            while k < 2:#for the time being things - are done in 16 bits
                datatype = {'1' : numpy.uint8,
                            '2' : numpy.uint16,
                            '4' : numpy.uint32 }[("%d" % 2 ** k)]
                ar = numpy.array(numpy.fromstring(f.read(int(noverfl[k]) * (2 ** k)),
                                            datatype), numpy.uint16)
                #insert the the overflow pixels in the image array:
                #this is probably a memory intensive way of doing this -
                # might be done in a more clever way
                lim = 2 ** (8 * k) - 1
                #generate an array comprising of the indices into data.ravel()
                # where its value equals lim.
                M = numpy.compress(numpy.equal(data.ravel(), lim), numpy.arange(rows * cols))
                #now put values from ar into those indices
                numpy.put(data.ravel(), M, ar)
                padding = 16 * int(math.ceil(int(noverfl[k]) * (2 ** k) / 16.)) - \
                             int(noverfl[k]) * (2 ** k)
                f.seek(padding, 1)
                print noverfl[k] + " bytes read + %d bytes padding" % padding
                k = k + 1

            f.close()

            (self.dim1, self.dim2) = (rows, cols)
            print self.dim1, self.dim2
            self.resetvals()
            return self

    if __name__ == '__main__':
        import sys, time
        I = bruker100image()
        b = time.clock()
        while (sys.argv[1:]):
            I.read(sys.argv[1])
            r = I.toPIL16()
            I.rebin(2, 2)
            print sys.argv[1] + (": max=%d, min=%d, mean=%.2e, stddev=%.2e") % (
                I.getmax(), I.getmin(), I.getmean(), I.getstddev())
            print 'integrated intensity (%d %d %d %d) =%.3f' % (
                10, 20, 20, 40, I.integrate_area((10, 20, 20, 40)))
            sys.argv[1:] = sys.argv[2:]
        e = time.clock()
        print (e - b)

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