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For result reporting: tabular output, aligned columns, statistics summaries, human-readable reports.
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Baseado na classificação ocupacional SOC
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 | 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