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

[Joe Kington]

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.

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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