Am looking to pass numpy arrays as arguments to a function? How is this done? Can i simply pass it like variables as shown below?
def force(x,y,z):
for i in range(N):
for j in range(i+1,N,1):
xij=x[i]-x[j]
yij=y[i]-y[j]
zij=z[i]-z[j]
Python is a dynamic language, meaning there is no type checking done during compilation. You can pass anything into a function.
I believe in newer version( I use 1.13) of numpy you can simply call the function by passing the numpy array to the fuction that you wrote for scalar type, it will automatically apply the function call to each element over the numpy array and return you another numpy array
>>> import numpy as np
>>> squarer = lambda t: t ** 2
>>> x = np.array([1, 2, 3, 4, 5])
>>> squarer(x)
array([ 1, 4, 9, 16, 25])
In Python 3.11.6 you can pass a numpy array to a user-defined function. The function as applied across the array and the returned object is a numpy array:
>>> import numpy as np
>>> def f(x):
... return 3*x**2 - 4*x + 5
...
>>> f(3.0)
20.0
>>> xs = np.arange(-5,5,0.25)
>>> ys = f(xs)
>>> ys
array([100. , 91.6875, 83.75 , 76.1875, 69. , 62.1875,
55.75 , 49.6875, 44. , 38.6875, 33.75 , 29.1875,
25. , 21.1875, 17.75 , 14.6875, 12. , 9.6875,
7.75 , 6.1875, 5. , 4.1875, 3.75 , 3.6875,
4. , 4.6875, 5.75 , 7.1875, 9. , 11.1875,
13.75 , 16.6875, 20. , 23.6875, 27.75 , 32.1875,
37. , 42.1875, 47.75 , 53.6875])
>>> type(ys)
<class 'numpy.ndarray'>
# lifted from
# https://github.com/karpathy/nn-zero-to-hero/blob/master/lectures/micrograd/micrograd_lecture_first_half_roughly.ipynb
