Vectorization Of Multiple Return Of A Complex Function In A Dataframe

I am trying to plot various data including complex vectors.Thanks to contributors see answers https://stackoverflow.com/a/64480659/13953414, i managed to generate the dataframes bu

Solution 1:

np.vectorize can return multiple "columns" as a tuple of arrays. Here I showcase how to add a "column" to the vectorized function and how to rearrange them.

def f_u(omega_elem):
    val1 = (-j / (math.pi * omega_elem)) * meijerg([[1, 3 / 2], []], [[1, 1], [0.5, 0]], j * omega_elem)
    asympt = (4 / (math.pi)) * (-(1 / 2) * np.log(omega_elem) + 3 / 2 - gamma - j * ((math.pi) / 4))
    return val1, asympt

# return a tuple of array. Remember to assign two otypes.
f_u_vec = np.vectorize(f_u, otypes=[np.complex128, np.complex128])

tup = f_u_vec(omega)  # tuple of arrays: (val1, asympt)
df["Re"] = np.real(tup[0])  # val1
df["Im"] = np.imag(tup[0])
df["asympt_R"] = np.real(tup[1])  # asympt
df["asympt_Im"] = np.imag(tup[1])

# result
df
Out[94]: 
        bh  frequency        Re        Im  asympt_R  asympt_Im
0  0.00001          1  5.868486 -0.999374  5.868401       -1.0
1  0.00001         11  4.342982 -0.994876  4.341854       -1.0
2  0.00001         21  3.932365 -0.991121  3.930198       -1.0
3  0.00001         31  3.685457 -0.987696  3.682257       -1.0
4  0.00001         41  3.508498 -0.984488  3.504268       -1.0
5  0.00002          1  4.986257 -0.997867  4.985859       -1.0
6  0.00002         11  3.463849 -0.983559  3.459311       -1.0
7  0.00002         21  3.056269 -0.972212  3.047656       -1.0
8  0.00002         31  2.812349 -0.962168  2.799715       -1.0
9  0.00002         41  2.638332 -0.952979  2.621726       -1.0