[SciPy-user] "Approximately equal to" operator?

[Martin Spacek]

Hello,

I'm comparing two numpy 1d arrays of the same length. One has ints, the 
other floats. From what I can tell, using == isn't very reliable when 
mixing ints and floats. Sometimes 1.0 == 1, and sometimes it doesn't. 
For example:

[mspacek]|71> arange(10, 13)
          <71> array([10, 11, 12])
[mspacek]|72> arange(10, 13, 0.1)[[0, 10, 20]]
          <72> array([ 10.,  11.,  12.])
[mspacek]|73> arange(10, 13, 0.1)[[0, 10, 20]] == arange(10,13)
          <73> array([True, False, False], dtype=bool)

In this case, 10 == 10.0, but 11 != 11.0 and 12 != 12.0

I guess comparing ints to floats is a touchy thing, and depends on the 
system's C libraries. Maybe minuscule errors accumulate when building up 
a float array using arange(). Anyways, rounding and then converting the 
float array into an int array doesn't seem very Pythonic. I've noticed 
there's a numpy function allclose(), but this doesn't return a boolean 
array, just a single True if all the elements are equal.

Is there a "~=" or "approximately equal to" operator that does an 
elementwise comparison of two arrays, and given a certain tolerance, 
returns a boolean array?

Cheers,

Martin