Numpy for loop gives a different result each time

Question

First time publishing in here, here it goes:

I have two sets of data(v and t), each one has 46 values. The data is imported with "pandas" module and coverted to a numpy array in order to do the calculation.

I need to set ml_min1[45], ml_min2[45], and so on to the value "0". The problem is that each time I ran the script, the values corresponding to the position 45 of ml_min1 and ml_min2 are different. This is the piece of code that I have:

t1 = fil_copy.t1.as_matrix()
t2 = fil_copy.t2.as_matrix()
v1 = fil_copy.v1.as_matrix()
v2 = fil_copy.v2.as_matrix()

ml_min1 = np.empty(len(t1))
l_h1 = np.empty(len(t1))

ml_min2 = np.empty(len(t2))
l_h2 = np.empty(len(t2))

for i in range(0, (len(v1) - 1)):

    if (i != (len(v1) - 1)) and (v1[i+1] > v1[i]):
        ml_min1[i] = v1[i+1] - v1[i]
        l_h1[i] = ml_min1[i] * (60/1000)
    elif i == (len(v1)-1):
        ml_min1[i] = 0
        l_h1[i] = 0
        print(i, ml_min1[i])
    else:
        ml_min1[i] = 0
        l_h1[i] = 0
        print(i, ml_min1[i])

for i in range(0, (len(v2) - 1)):

    if (i != (len(v2) - 1)) and (v2[i+1] > v2[i]):
        ml_min2[i] = v2[i+1] - v2[i]
        l_h2[i] = ml_min2[i] * (60/1000)
    elif i == (len(v2)-1):
        ml_min2[i] = 0
        l_h2[i] = 0
        print(i, ml_min2[i])
    else:
        ml_min2[i] = 0
        l_h2[i] = 0
        print(i, ml_min2[i])

Answer 1

Your code as it is currently written doesn't work because the elif blocks are never hit, since range(0, x) does not include x (it stops just before getting there). The easiest way to solve this is probably just to initialize your output arrays with numpy.zeros rather than numpy.empty, since then you don't need to do anything in the elif and else blocks (you can just delete them).

That said, it's generally a design error to use loops like yours in numpy code. Instead, you should use numpy's broadcasting features to perform your mathematical operations to a whole array (or a slice of one) at once.

If I understand correctly, the following should be equivalent to what you wanted your code to do (just for one of the arrays, the other should work the same):

ml_min1 = np.zeros(len(t1)) # use zeros rather than empty, so we don't need to assign any 0s
diff = v1[1:] - v1[:-1]     # find the differences between all adjacent values (using slices)
mask = diff > 0             # check which ones are positive (creates a Boolean array)
ml_min1[:-1][mask] = diff[mask]   # assign with mask to a slice of the ml_min1 array
l_h1 = ml_min1 * (60/1000)  # create l_h1 array with a broadcast scalar multiplication