How do you add create multiple of the same item and add them to an array?

I'm trying to create and an array of shape (1, inter) [i.e. 1 row, inter Columns], where inter is user input;

If you look at the code below,

l_o_s, Inter, n_o_s, L, d_o_s are all from user inputs

The n_o_s represents the number of sections across the total length of the shaft that have lengths corresponding to the values in l_o_s and diameters corresponding to the values in d_o_s.

So

Section 1 has a length of 1.5 and diameter 3.75

Section 2 = length of 4.5-1.5 = 3 and diameter 3.5

Section 3 = length of 7.5-4.5 = 3 and diameter 3.75

and so forth...

Here's an image of the shaft arrangement: This is a shaft of length = 36, with 13 sections that have different size diameters

Inter is the number of intervals I require in the analysis, in this case inter is 3600, so I require a (1,3600) array.

si is an array that is a function (mathematical) of the length of the individual section in l_o_s, the total length (L) of the system and the interval (Inter).

Here's the question

So if you take every value in si = [ 150. 450. 750. 1050. 1350. 1650. 1950. 2250. 2550. 2850. 3150. 3450. 3600.]

I require an array of shape (1,3600) whose first 150 elements are all equal to the diameter of section 1 - (3.75), and the elements between 150 and 450 i need them to equal the diameter of the second section (3.5) and so forth...

So i need the first 150 element corresponding to index 0 in d_o_s and the next 300 elements corresponding to index 1 in d_o_s, etc...

Here's a code I began with, but I don't think it's worth talking about. I was creating an array of zeros with inner inner shapes corresponding to each of the 150,300,300,300 elements.

import numpy as np
import math 

L = 36
Inter = 3600
n_o_s = 13
l_o_s = np.asarray([1.5,4.5,7.5,10.5,13.5,16.5,19.5,22.5,25.5,28.5,31.5,34.5,36])
d_o_s = np.asarray([3.75,3.5,3.75,3.5,3.75,3.5,3.75,3.5,3.75,3.5,3.75,3.5,3.75])
si = np.asarray((l_o_s/L)*Inter)
print(si)





z = (si.size) 
def f(x):
for i in si:
    zz = np.zeros((x,1,int(i)))
    for j in range(int(z)):
        for p in range(int(d_o_s[j])):
            zz[j][0][p] = np.full((1,int(i)),(math.pi*d_o_s**4)/64)
return zz


print(f(z))

Any ideas, Dallan

This is what I ended up with but I'm only receiving 3599 values instead of the required 3600 any ideas? I used the diameter to output another variable (basically swapped the diameters in d_o_s for values in i_o_s)

L = 36
Inter = 3600
n_o_s = 13
l_o_s = np.asarray([0,1.5,4.5,7.5,10.5,13.5,16.5,19.5,22.5,25.5,28.5,31.5,34.5,36])
d_o_s = np.asarray([3.75,3.5,3.75,3.5,3.75,3.5,3.75,3.5,3.75,3.5,3.75,3.5,3.75])
i_o_s = (math.pi*d_o_s**4)/64
si = np.asarray((l_o_s/L)*Inter)

lengths = si[1:] - si[:-1]

Iu = np.asarray(sum([[value]*(int(length)) for value, length in zip(i_o_s, lengths)], []))
print(Iu,Iu.shape)