[Numpy-discussion] Floating point "close" function?

[Ralf Gommers]

On Thu, Mar 1, 2012 at 11:44 PM, Joe Kington <jkington at wisc.edu> wrote:

> Is there a numpy function for testing floating point equality that returns
> a boolean array?
>
> I'm aware of np.allclose, but I need a boolean array.  Properly handling
> NaN's and Inf's (as allclose does) would be a nice bonus.
>
> I wrote the function below to do this, but I suspect there's a method in
> numpy that I missed.
>

I don't think such a function exists, would be nice to have. How about just
adding a keyword "return_array" to allclose to do so?

Ralf


> import numpy as np
>
> def close(a, b, rtol=1.e-5, atol=1.e-8, check_invalid=True):
>     """Similar to numpy.allclose, but returns a boolean array.
>     See numpy.allclose for an explanation of *rtol* and *atol*."""
>     def within_tol(x, y, atol, rtol):
>         return np.less_equal(np.abs(x-y), atol + rtol * np.abs(y))
>     x = np.array(a, copy=False)
>     y = np.array(b, copy=False)
>     if not check_invalid:
>         return within_tol(x, y, atol, rtol)
>     xfin = np.isfinite(x)
>     yfin = np.isfinite(y)
>     if np.all(xfin) and np.all(yfin):
>         return within_tol(x, y, atol, rtol)
>     else:
>         # Avoid subtraction with infinite/nan values...
>         cond = np.zeros(np.broadcast(x, y).shape, dtype=np.bool)
>         mask = xfin & yfin
>         cond[mask] = within_tol(x[mask], y[mask], atol, rtol)
>         # Inf and -Inf equality...
>         cond[~mask] = (x[~mask] == y[~mask])
>         # NaN equality...
>         cond[np.isnan(x) & np.isnan(y)] = True
>         return cond
>
> # A few quick tests...
> assert np.any(close(0.300001, np.array([0.1, 0.2, 0.3, 0.4])))
>
> x = np.array([0.1, np.nan, np.inf, -np.inf])
> y = np.array([0.1000001, np.nan, np.inf, -np.inf])
> assert np.all(close(x, y))
>
> x = np.array([0.1, 0.2, np.inf])
> y = np.array([0.101, np.nan, 0.2])
> assert not np.all(close(x, y))
>
>
> Thanks,
> -Joe
>
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