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For performance reporting: timing wrappers, throughput calculations, profiling summaries.
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Basierend auf der SOC-Berufsklassifikation
Conducts iterative deep research on any topic using web search, progressive exploration, and structured synthesis. Use when asked for comprehensive research, deep investigation, thorough analysis, or multi-source exploration of any topic. Triggers: research, investigate, deep dive, comprehensive analysis, explore thoroughly, find everything about.
For cross-cutting concerns: add behavior without modifying functions, caching, timing, logging, validation wrappers.
For performance work: measure before changing, profile to find bottlenecks, compare before and after.
For symbolic computation: ASTs, mathematical expressions, code that manipulates code structure, expression transformations.
For ordered processing: A* search, Dijkstra, event simulation, task scheduling. Efficient min/max extraction with heap-based queue.
For dynamic programming: overlapping subproblems, recursive solutions with repeated computations, memoization to avoid redundant work.
| name | time-and-report |
| description | For performance reporting: timing wrappers, throughput calculations, profiling summaries. |
Wrap execution with timing, report statistics.
import time
def timed(func):
"""Decorator to time function calls."""
def wrapper(*args, **kwargs):
start = time.process_time()
result = func(*args, **kwargs)
elapsed = time.process_time() - start
print(f"{func.__name__}: {elapsed:.3f}s")
return result
return wrapper
def time_many(func, inputs, name=""):
"""Time function on multiple inputs, report statistics."""
times = []
for inp in inputs:
start = time.process_time()
func(inp)
times.append(time.process_time() - start)
print(f"{name}: n={len(times)}, "
f"avg={sum(times)/len(times):.4f}s, "
f"max={max(times):.4f}s, "
f"rate={len(times)/sum(times):.0f}/s")
# sudoku.py - comprehensive timing
import time
def time_solve(grid):
"""Time solving a single grid."""
start = time.process_time()
values = solve(grid)
t = time.process_time() - start
return (t, solved(values))
def solve_all(grids, name=''):
"""Solve grids and report timing statistics."""
times, results = zip(*[time_solve(grid) for grid in grids])
N = len(results)
if N > 1:
print("Solved %d of %d %s puzzles "
"(avg %.2f secs (%d Hz), max %.2f secs)." % (
sum(results), N, name,
sum(times)/N, N/sum(times), max(times)))
# Usage
if __name__ == '__main__':
test()
solve_all(open("sudoku-easy50.txt"), "easy")
solve_all(open("sudoku-top95.txt"), "hard")
solve_all(open("sudoku-hardest.txt"), "hardest")
# Output:
# Solved 50 of 50 easy puzzles (avg 0.01 secs (141 Hz), max 0.02 secs).
# Solved 95 of 95 hard puzzles (avg 0.03 secs (30 Hz), max 0.19 secs).
# Solved 11 of 11 hardest puzzles (avg 0.01 secs (100 Hz), max 0.02 secs).
# spell.py - throughput
def spelltest(tests, verbose=False):
import time
start = time.process_time()
good = sum(correction(wrong) == right for right, wrong in tests)
dt = time.process_time() - start
n = len(tests)
print(f'{good/n:.0%} of {n} correct at {n/dt:.0f} words per second')