Performance and Caching

All readers support automatic caching for improved performance.

import os
import time

import matplotlib.pyplot as plt

from pylimer_tools.io.extract_thermo_data import extract_thermo_params

# Read complete log file
log_file = os.path.join(
    os.getcwd(),
    "../..",
    "tests/pylimer_tools/fixtures/log.lammps",
)

time_start = time.time()

extract_thermo_params() is a more elaborate version of read_log_file()

thermo_data = extract_thermo_params(log_file, use_cache=False)
time_end = time.time()

time_no_cache = time_end - time_start

time_start = time.time()
thermo_data = extract_thermo_params(log_file, use_cache=True)
time_end = time.time()

time_cache = time_end - time_start

plt.figure()
plt.bar(["Without Cache", "With Cache"], [time_no_cache, time_cache])
plt.ylabel("Time [s]")
plt.show()