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For result reporting: tabular output, aligned columns, statistics summaries, human-readable reports.
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Basado en la clasificación ocupacional SOC
| name | format-statistics-table |
| description | For result reporting: tabular output, aligned columns, statistics summaries, human-readable reports. |
Format data as aligned columns with headers and separators.
def print_table(headers, rows, widths=None):
"""Print formatted table."""
if widths is None:
widths = [max(len(str(row[i])) for row in [headers] + rows)
for i in range(len(headers))]
# Header
print(' | '.join(h.ljust(w) for h, w in zip(headers, widths)))
print('-+-'.join('-' * w for w in widths))
# Rows
for row in rows:
print(' | '.join(str(v).ljust(w) for v, w in zip(row, widths)))
# SET.py - game statistics
def show(tallies, label):
"""Print out the counts."""
print()
print('Size | Sets | NoSets | Set:NoSet ratio for', label)
print('-----+--------+--------+----------------')
for size in sorted(tallies):
y, n = tallies[size][True], tallies[size][False]
ratio = ('inft' if n == 0 else int(round(float(y) / n)))
print('{:4d} |{:7,d} |{:7,d} | {:4}:1'
.format(size, y, n, ratio))
# Output:
# Size | Sets | NoSets | Set:NoSet ratio for random
# -----+--------+--------+----------------
# 3 | 100 | 0 | inft:1
# 4 | 200 | 50 | 4:1
# 5 | 400 | 100 | 4:1
# sudoku.py - solve statistics
def solve_all(grids, name=''):
"""Report solution 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)))
# Output:
# Solved 50 of 50 easy puzzles (avg 0.01 secs (100 Hz), max 0.03 secs).
# spell.py - accuracy reporting
print('{:.0%} of {} correct ({:.0%} unknown) at {:.0f} words per second'
.format(good/n, n, unknown/n, n/dt))
# Output:
# 74% of 270 correct (6% unknown) at 41 words per second
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.