numpy.ma.allclose#

ma.allclose(a, b, masked_equal=True, rtol=1e-05, atol=1e-08)[source]#

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, barray_like: Input arrays to compare.
masked_equalbool, optional: Whether masked values in a and b are considered equal (True) or not (False). They are considered equal by default.
rtolfloat, optional: Relative tolerance. The relative difference is equal to rtol * b. Default is 1e-5.
atolfloat, optional: Absolute tolerance. The absolute difference is equal to atol. Default is 1e-8.

Returns:

ybool: 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

>>> import numpy as np
>>> 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