====== Performance Comparison of libdsepio_read and read_trk Functions ======

This script benchmarks the performance of two functions, libdsepio_read and read_trk, used for reading tracking data from file paths. It measures the execution time for reading the same track file multiple times using both Python loops and leveraging a library possibly written in C++. Additionally, it employs the pandas library to organize the timing results into a dataframe and outputs these results into a CSV file. Finally, it prints the execution timings for each function to the console, providing insight into which method is more efficient for reading track data files.

<code python>
from pysep.io.trk.read import libdsepio_read
from pysep.io.trk import read_trk
from datetime import datetime
import numpy as np
import pandas as pd

path = 'example.track'

# Total = np.linspace(1, 100, 25)
#
# print("========= Python Loop Test ==========")
# PythonTimes = list()
# for t in Total:
#     start = datetime.now()
#     for i in range(int(t)):
#         n  = libdsepio_read([path], 60)
#     end = datetime.now()
#     PythonTimes.append((end-start).total_seconds())
#
# print("========= C++ Loop Test ===========")
# Ctimes = list()
# for t in Total:
#     start = datetime.now()
#     n  = libdsepio_read([path]*int(t), 60)
#     end = datetime.now()
#     Ctimes.append((end-start).total_seconds())
#
# ActualTotal = list()
# for t in Total:
#     ActualTotal.append(int(t))
#
# payload = {'files':ActualTotal, 'PyTime':PythonTimes, 'CTime':Ctimes}
# df = pd.DataFrame(payload)
# df.to_csv('res.csv', index=False)
print('=========== libdsepio timeing ==============')
start = datetime.now()
n = libdsepio_read([path]*50, 60)
end = datetime.now()
print(f"Took: {(end-start).total_seconds()} s")
print('=========== read_trk timeing ==============')
start = datetime.now()
n,m = read_trk([path]*50)
end = datetime.now()
print(f"Took: {(end-start).total_seconds()} s")
# print(type(n))

</code>