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profiling.py
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profiling.py
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# Demo: using profiling tools time and pyinstrument
# running with pdb: python3 -m pdb profiling.py
import time
import numpy as np
import multiprocessing as mp
import pandas as pd
#from timeit import timeit
#import cProfile
from pyinstrument import Profiler
a = np.random.normal(size=(2000, 1000)).astype('float32')
b = np.random.normal(size=(1000, 200)).astype('float32')
def read_file(filename):
df = pd.read_csv(filename)
#print(df.to_string())
def iterate(N):
for i in range(N):
result = np.matmul(a, b)
def calculate_pi(darts):
"""approximate pi by throwing darts at a board"""
np.random.seed() # we need to set the random seed... see what happens if you comment this line out
x = np.random.uniform(-1, 1, darts)
y = np.random.uniform(-1, 1, darts)
r_sq = x**2 + y**2
return 4*np.sum(r_sq<1)/darts
if __name__=="__main__":
start = time.time()
read_file('iris.csv')
end = time.time()
print('Read a file (seconds): ', end - start)
# Total number of darts
# TODO: try different values of N for elapsed times
N = 10000000
start = time.time()
a = calculate_pi(N)
end = time.time()
print('Compute pi (seconds): ', end - start)
# using timeit
#t = timeit(lambda: calculate_pi(N), number=10)
#print(f'Average elapsed time in seconds {t}')
# using cProfile
#prof = cProfile.Profile()
#prof.enable()
#a = calculate_pi(N)
#prof.disable()
#prof.print_stats()
# using pyinstrument
profiler = Profiler()
profiler.start()
a = calculate_pi(N)
print(a)
profiler.stop()
profiler.print()
# TODO: try iterate() instead of calculate_pi()