numpy.ma.allclose

numpy.ma.allclose(a, b, masked_equal=True, rtol=1e-05, atol=1e-08)

Returns True if two arrays are element-wise equal within a tolerance.

This function is equivalent to allclose except that masked values are treated as equal (default) or unequal, depending on the masked_equal argument.

Parameters:

a, b : array_like

Input arrays to compare.

masked_equal : bool, optional

Whether masked values in a and b are considered equal (True) or not (False). They are considered equal by default.

rtol : float, optional

Relative tolerance. The relative difference is equal to rtol * b. Default is 1e-5.

atol : float, optional

Absolute tolerance. The absolute difference is equal to atol. Default is 1e-8.

Returns:

y : bool

Returns True if the two arrays are equal within the given tolerance, False otherwise. If either array contains NaN, then False is returned.

See also

all, any

numpy.allclose

the non-masked allclose.

Notes

If the following equation is element-wise True, then allclose returns True:

absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`))

Return True if all elements of a and b are equal subject to given tolerances.

Examples

>>> a = np.ma.array([1e10, 1e-7, 42.0], mask=[0, 0, 1])
>>> a
masked_array(data=[10000000000.0, 1e-07, --],
             mask=[False, False,  True],
       fill_value=1e+20)
>>> b = np.ma.array([1e10, 1e-8, -42.0], mask=[0, 0, 1])
>>> np.ma.allclose(a, b)
False

>>> a = np.ma.array([1e10, 1e-8, 42.0], mask=[0, 0, 1])
>>> b = np.ma.array([1.00001e10, 1e-9, -42.0], mask=[0, 0, 1])
>>> np.ma.allclose(a, b)
True
>>> np.ma.allclose(a, b, masked_equal=False)
False

Masked values are not compared directly.

>>> a = np.ma.array([1e10, 1e-8, 42.0], mask=[0, 0, 1])
>>> b = np.ma.array([1.00001e10, 1e-9, 42.0], mask=[0, 0, 1])
>>> np.ma.allclose(a, b)
True
>>> np.ma.allclose(a, b, masked_equal=False)
False