Numpy Error: Operands could not be broadcast together with shapes (2,) (0,)-CodePudding

I am trying to create a 2d-array with values taken from the array's previous value, added to values computed from a function. I am however getting the error :

ValueError: operands could not be broadcast together with shapes (2,) (0,)

Now while I know what the error means, I can't seem to find where the problem is exactly or how to fix it.

The program's use is to create a custom euler solver which adds previous values of the array with the function

Code:

import numpy as np
from scipy.integrate import odeint


def func(): 
    d1 = #equation
    d2 = #equation
    return [[d1], [d2]]

l = 1
m = 1
g = 1
mu = 1

max = 1

arr_in = np.array(1, 1)

arr_2 = np.linspace(1, 4, maxx) 
dt = 1

arr_out = np.zeros((max, 2))
arr_out[0] = arr_in[0]
for i in range(len(arr_2)):
    arr_out[i 1] = arr_out[i]   func(arr_out[i-1], arr_2[i-1], l, m, g, mu) * dt

Traceback:

Traceback (most recent call last):
  File "task3.py", line 31, in <module>
    res_arr[i] = solver(res_arr, i)
ValueError: could not broadcast input array from shape (2,2) into shape (2,)

CodePudding user response：

You're either missing a pair of parentheses:

for i in range(len(time_arr)):
    #              v                                                                v
    res_arr[i 1] = (res_arr[i]   pend_func(res_arr[i-1], time_arr[i-1], l, m, g, mu)) * dt
    #              ^                                                                ^

Or you need to convert the return type of pend_func to np.array:

for i in range(len(time_arr)):
    #                           vvvvvvvvv                                                   v
    res_arr[i 1] = res_arr[i]   np.array(pend_func(res_arr[i-1], time_arr[i-1], l, m, g, mu)) * dt
    #                           ^^^^^^^^^                                                   ^

Both will solve the problem, but they'll produce different results, so I'm not sure which one is appropriate for your purpose.

CodePudding user response：

The issue was that the pend_func function was returning an array of a different shape than the rest of the arrays. This was fixed by changing

return [[d1], [d2]]

return [d1, d2]