Add strings_to_ctypes_array to convert a sequence of strings into a ctypes array

[seisman]

Description of proposed changes

To pass a list of strings into a ctypes function, currently, we have codes like the below, which is not readable:

strings_pointer = (ctp.c_char_p * len(strings))()
strings_pointer[:] = np.char.encode(strings)

So better to have a strings_to_ctypes_array function, which can hide the technical details.

The above two lines codes can be shortened into a single line of code:

strings_pointer = (ctp.c_char_p * len(strings))(*np.char.encode(strings))

Actually np.char.encode calls the str.encode element-wise, so it can be further written as:

strings_pointer = (ctp.c_char_p * len(strings))(*[s.encode() for s in strings])

The list comprehension version is faster than the np.char.encode version:

>>> import numpy as np
>>> import ctypes as ctp
>>> strings = ["ABC", "DEFGHI", "ABC123", "ABC1234566"]
>>> %timeit (ctp.c_char_p * 4)(*[s.encode() for s in strings])
1.37 µs ± 3.79 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)

>>> %timeit (ctp.c_char_p * 4)(*np.char.encode(strings))
6.17 µs ± 22.5 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)

This PR adds the strings_to_ctypes_array function to do the conversion work. It just contains one line of code and doesn't check if the strings is an empty list to avoid extra overheads.

PR #3136 is a similar work but for converting a sequence of numbers to a ctypes array. These two functions share similar codes but I prefer not to combine them into a single function to avoid too many if-else clauses in the low-level function. Better to review these two PRs back-to-back.