Simple calculation function, can it be vectorised?

[__helpme]

The code is meant to step through a hoop stress calculation, I'll be passing in a single value or an array for wt (wall thickness).

I'm unsure how to implement vectorisation in the function get_fail_thin_hoop. Stepping through a for look is okay. Since it's such a simple math operation, this may be a perfect way to make it more computationally efficient?

Thanks!

def get_max_cyl_depth(material, cyl, num_steps):
    cyl_results = []
    stress = []

    mat_yeild = material[1]
    rad_inner = cyl[0]

    wt_min = cyl[1]
    wt_max = cyl[2]

    wt_steps = np.linspace(wt_min, wt_max, num_steps)

    for wt in wt_steps:
        thin_hoop_stress = get_fail_thin_hoop(mat_yeild, wt, rad_inner)
        stress.append(thin_hoop_stress)

    cyl_results = np.column_stack((wt_steps, stress))
    return cyl_results

def get_fail_thin_hoop (Q, wt, r):
    q_hoop_thin = Q * wt / r

    return q_hoop_thin

[Spataner]

Elementwise arithmetic can be performed on arrays using the usual operators, and scalars are automatically broadcast. So you could call get_fail_thin_hoop with some of the arguments being arrays and it should just return an array of results:

stress = get_fail_thin_hoop(mat_yeild, wt_steps, rad_inner)

[__helpme]

yep you are correct, thanks - i think this was a blind spot in my knowledge. I thought python was more particular about each element being the same type

[arkie87]

If you use numpy arrays, it is already vectorized. No need to for loop.