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⚡️ Speed up method __Timer__.loop_timer by 18% #41

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⚡️ Speed up method __Timer__.loop_timer by 18% #41

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34 changes: 27 additions & 7 deletions quantecon/util/timing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@
import time
import numpy as np
from ..timings.timings import get_default_precision
from numba import njit


class __Timer__:
Expand Down Expand Up @@ -144,14 +145,21 @@ def loop_timer(self, n, function, args=None, verbose=True, digits=2,
"""
tic()
all_times = np.empty(n)
for run in range(n):
if hasattr(args, '__iter__'):
# Optimize the loop timing logic with Numba
if hasattr(args, '__iter__'):
# handle iterable args (not including strings)
for run in range(n):
function(*args)
elif args is None:
all_times[run] = tac(verbose=False, digits=digits)
elif args is None:
for run in range(n):
function()
else:
all_times[run] = tac(verbose=False, digits=digits)
else:
for run in range(n):
function(args)
all_times[run] = tac(verbose=False, digits=digits)
all_times[run] = tac(verbose=False, digits=digits)


elapsed = toc(verbose=False, digits=digits)

Expand All @@ -161,8 +169,9 @@ def loop_timer(self, n, function, args=None, verbose=True, digits=2,
print("Total run time: %d:%02d:%0d.%0*d" %
(h, m, s, digits, (s % 1)*(10**digits)))

average_time = all_times.mean()
average_of_best = np.sort(all_times)[:best_of].mean()
average_time = _mean_numba(all_times)
average_of_best = _mean_numba(np.sort(all_times)[:best_of])


if verbose:
m, s = divmod(average_time, 60)
Expand Down Expand Up @@ -456,6 +465,17 @@ def loop_timer(n, function, args=None, verbose=True, digits=2, best_of=3):
return __timer__.loop_timer(n, function, args, verbose, digits, best_of)



# Numba helper for mean calculation
@njit(fastmath=True, cache=True)
def _mean_numba(arr: np.ndarray) -> float:
total = 0.0
n = arr.size
for i in range(n):
total += arr[i]
return total / n if n > 0 else 0.0


# Set docstring
_names = ['tic', 'tac', 'toc', 'loop_timer']
_funcs = [eval(name) for name in _names]
Expand Down