Hi Eric,

visually it will be hardly noticeable in most cases. However, I'd expect the histogram of normalized intensity data to be the same as the histogram of a linear grayscale image of that data (neglecting gamma correction, image scaling/interpolation for now). Consider this code for example:

import numpy as np

a = np.random.rand(1024*1024)

a[0], a[-1] = 0.0, 1.0

h0 = np.histogram(a, bins=256, range=(0, 1))[0]

h1 = np.bincount(np.uint8(a * 255))

h2 = np.bincount(np.uint8(a * 255.9999999999999))

print (h0 - h1)

print (h0 - h2)

Christoph

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On 9/18/2011 2:30 PM, Eric Firing wrote:

On 09/18/2011 09:30 AM, Christoph Gohlke wrote:

Hello,

matplotlib uses int(x*255) or np.array(x*255, np.uint8) to quantize

normalized floating point numbers x in the range [0.0 to 1.0] to

integers in the range [0 to 255]. This way only 1.0 is mapped to 255,

not for example 0.999. Is this really intended or would not the largest

floating point number below 256.0 be a better scale factor than 255? The

exact factor depends on the floating point precision (~255.999992 for

np.float32, ~255.93 for np.float16).

Christoph

Christoph,

It's a reasonable question; but do you have use cases in mind where it

actually makes a difference?

The simple scaling with truncation is used in many places, both in the

python and the c++ code.

Eric