Function Of Numpy Array With If-statement

I am using Matplotlib and Numpy to produce some plots. I wish to define a function which given an array returns another array with values calculated elementwise, for example: def f

Solution 1:

Use numpy.vectorize to wrap func before applying it to array x:

from numpy import vectorize
vfunc = vectorize(func)
y = vfunc(x)

Solution 2:

I know it is too late for this answer, but I am excited learning NumPy. You can vectorize the function on your own with numpy.where.

def func(x):
    import numpy as np
    x = np.where(x<0, 0., x*10)
    return x   

Examples

Using a scalar as data input:

x = 10
y = func(10)
y = array(100.0)

using an array as data input:

x = np.arange(-1,1,0.1)
y = func(x)
y = array([ -1.00000000e+00,  -9.00000000e-01,  -8.00000000e-01,
    -7.00000000e-01,  -6.00000000e-01,  -5.00000000e-01,
    -4.00000000e-01,  -3.00000000e-01,  -2.00000000e-01,
    -1.00000000e-01,  -2.22044605e-16,   1.00000000e-01,
     2.00000000e-01,   3.00000000e-01,   4.00000000e-01,
     5.00000000e-01,   6.00000000e-01,   7.00000000e-01,
     8.00000000e-01,   9.00000000e-01])

Caveats:

1) If x is a masked array, you need to use np.ma.where instead, since this works for masked arrays.

Solution 3:

This should do what you want:

def func(x):
    small_indices = x < 10
    x[small_indices] = 0
    x[invert(small_indices)] *= 10
    return x

invert is a Numpy-function. Note that this modifies the argument. To prevent this, you'd have to modify and return a copy of x.

Solution 4:

(I realize this is an old question, but ...)

There is one more option which wasn't mentioned here -- using np.choose.

np.choose(
    # the boolean condition
    x < 0,
    [
        # index 0: value if condition is False
        10 * x,
        # index 1: value if condition is True
        0
    ]
)

Though not terribly readable, this is just a single expression (not a series of statements), and does not compromize numpy's inherent speed (as np.vectorize does).

Solution 5:

x = numpy.arrange(-1,1,0.01)
mask = x>=0
y = numpy.zeros(len(x))
y[mask] = x[mask]*10

mask is a boolean array that equates to True are array indices matching the condition and False elsewhere. The last line replaces all values in the original array with that value mulitplied by 10.

Edited to reflect Bjorn's pertinent comment