is there a parameter to set the precision for numpy.linspace?

Question

I am trying to check if a numpy array contains a specific value:

>>> x = np.linspace(-5,5,101)
>>> x
array([-5. , -4.9, -4.8, -4.7, -4.6, -4.5, -4.4, -4.3, -4.2, -4.1, -4. ,
       -3.9, -3.8, -3.7, -3.6, -3.5, -3.4, -3.3, -3.2, -3.1, -3. , -2.9,
       -2.8, -2.7, -2.6, -2.5, -2.4, -2.3, -2.2, -2.1, -2. , -1.9, -1.8,
       -1.7, -1.6, -1.5, -1.4, -1.3, -1.2, -1.1, -1. , -0.9, -0.8, -0.7,
       -0.6, -0.5, -0.4, -0.3, -0.2, -0.1,  0. ,  0.1,  0.2,  0.3,  0.4,
        0.5,  0.6,  0.7,  0.8,  0.9,  1. ,  1.1,  1.2,  1.3,  1.4,  1.5,
        1.6,  1.7,  1.8,  1.9,  2. ,  2.1,  2.2,  2.3,  2.4,  2.5,  2.6,
        2.7,  2.8,  2.9,  3. ,  3.1,  3.2,  3.3,  3.4,  3.5,  3.6,  3.7,
        3.8,  3.9,  4. ,  4.1,  4.2,  4.3,  4.4,  4.5,  4.6,  4.7,  4.8,
        4.9,  5. ])
>>> -5. in x
True
>>> a = 0.2
>>> a
0.2
>>> a in x
False

I assigned a constant to variable a . It seems that the precision of a is not compatible with the elements in the numpy array generated by np.linspace() .

I've searched the docs , but didn't find anything about this.

Answer 1

This is not a question of the precision of np.linspace , but rather of the type of the elements in the generated array .

np.linspace generates elements which, conceptually, equally divide the input range between them. However, these elements are then stored as floating point numbers with limited precision, which makes the generation process itself appear to lack precision.

By passing the dtype argument to np.linspace , you can specify the precision of the floating point type used to store its result, which can increase the apparent precision of the generation process.

Nevertheless, you should not use the equality operator to compare floating point numbers . Instead, use np.isclose in conjunction with np.ndarray.any , or some equivalent:

>>> floats_64 = np.linspace(-5, 5, 101, dtype='float64')
>>> floats_128 = np.linspace(-5, 5, 101, dtype='float128')
>>> print(0.2 in floats_64)
False
>>> print(floats_64[52])
0.20000000000000018
>>> print(np.isclose(0.2, floats_64).any())  # check if any element in floats_64 is close to 0.2
True
>>> print(0.2 in floats_128)
False
>>> print(floats_128[52])
0.20000000000000017764
>>> print(np.isclose(0.2, floats_128).any())  # check if any element in floats_128 is close to 0.2
True