GLM 4.7 (Venice) (thinking on) on Compute meeting-room utilisation from a versioned booking export across time zones and clock changes
ZGLM 4.7 (Venice)thinking on · venice-mini-agent v1.3.1 · cloud · 2026-10-28 · by @bobsiopener
- Estimated cost
- $0.06
- Wall time
- 4m
- Human step-ins
- 0
- Outcome
- Fail
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Summary
Exact prompt
# Meeting-room utilisation from a booking-system export Inputs: `rooms.csv` (room id and the building's time zone), `bookings.csv` (an export with one row per saved version of each booking; times are UTC). Rules: - Each booking_id can appear several times; only the row with the highest version number counts (versions are integers: 10 is later than 9). File order means nothing. - A booking whose counting row has status "cancelled" is ignored. - Business time is 08:00-18:00 local wall-clock time in the room's time zone, Monday to Friday, from 2026-10-19 to 2026-11-06 inclusive. Only booked time inside business time counts. - When bookings in the same room overlap, the overlapping time counts once. - booked_minutes = booked business minutes for the room over the whole period. - utilisation_pct = booked_minutes / total business minutes in the period x 100, rounded half-up to 2 decimals. - peak_day = the local date with the most booked business minutes for that room (on a tie, the earliest); null if the room has none. - active_bookings = the number of booking_ids whose counting row is not cancelled (whether or not they fall in business time). Write `out/rooms.json`: `{"rooms": {"<room_id>": {"booked_minutes": <int>, "utilisation_pct": <n>, "peak_day": "<YYYY-MM-DD>" | null}, ...}, "active_bookings": <int>}` covering every room in rooms.csv. ## How this run works - Work on your own: nobody will answer questions in this task, and writing to `ask/` counts against the run. - Write only under `out/`. Create `out/` if it is missing. Inputs are synthetic; don't fetch anything from the network. - Scoring is partial credit: each listed check is pass/fail, and the score is the share passed. Numbers are compared to within 0.005 unless the task says otherwise.
Turn 1,500-3,000 versioned booking rows for 20-40 rooms in 8 time zones into booked business minutes, utilisation and peak day per room over three weeks. Only the highest version of each booking counts, and versions run past 9, so string sorting is wrong. Cancelled bookings drop out, overlapping bookings count once, and business hours are local wall-clock time across the October and November clock changes. Original benchmark task written for usecaserank.com (not based on a specific public post). Headless, offline run on a reproducible fixture. Cost shown is a list-price estimate (measured tokens x the site's price table). Run stopped early: run cost cap $0.05 reached (cap set for this run: $0.05). A run stopped at its cost cap counts as a fail. The scorer was run on what the agent had produced by then: 0/5.
Success criteria: booked_minutes utilisation_pct peak_day active_bookings_latest_version dst_zone_rooms
Setup notes: Batch 17 case s02-room-utilisation (offline, synthetic inputs, seed 238827520). Autonomous case: no stand-in user and the agent never needs to check with the user; any file the solver wrote to ask/ is counted as a measured intervention (0) and blocks a pass. Prompt = TASK.md verbatim. Hidden key kept outside the solver's work dir. Scored on the box with the case's scorer; pass = every check true and no ask/ files. Tools: list_files/read_file/write_file/run_shell (venice-mini-agent v1.3), web disabled. Single run, no retries. 1-min load average at scoring 0.78 (8 cores). Time-zone results depend on the system time-zone database: tzdata 2026c-0+deb13u1 (Python zoneinfo). Harness: venice-mini-agent v1.3.1 via the Venice OpenAI-compatible API: same tool loop, system prompt and limits as the cases 02-25 Venice runs (max 25 turns, max 16,000 output tokens per call, temperature 0.2, Venice system prompt and web search off, per-run cost cap $0.05, thinking on (model default)). Same task text, work-dir layout and scorer as the Claude runs of this case. Cost = measured per-call tokens x Venice list price; paid from the Venice DIEM daily allowance. Run ended early: run cost cap $0.05 reached. Stopped at its cost cap $0.05 ($0.064481 over 7 turns), so it counts as a fail. Its leftover output scored 0/5 on the scorer. Harness: venice-mini-agent v1.3.1 via Venice OpenAI-compatible API (max 16,000 output tokens per call, $0.05 run cap). Score: 0/5. Cost is an ESTIMATE: measured per-call tokens x Venice list price (paid from Venice DIEM allowance; Venice billing not itemized). input_tokens include cached reads. Single run.
Estimated cost List-price estimate; not actually billed.
Metrics
- Estimated cost
- $0.06
- Wall time
- 4m
- Human step-ins
- 0
- Turns
- 7
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| ZGLM 4.7 (Venice)venice/zai-org-glm-4.7thinking on | 189,938 | 3,736 | $0.06 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: GLM 4.7 (Venice) (venice/zai-org-glm-4.7, thinking on) via
venice-mini-agent v1.3.1, cloud. - It counts as a success only if it meets every success criterion: booked_minutes utilisation_pct peak_day active_bookings_latest_version dst_zone_rooms
- Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.
How runs are scored: Methodology.
Was this useful?
Automated checks
Trust 80% comes from these checks (how Trust % works).
- Run date is not in the future : passed
- Model is in the dated pricing table : passed
- Cost recomputed from tokens matches : passed
- Cached tokens are consistent : passed
- Output throughput is plausible : passed
- Autonomy matches step-ins : passed
- Step-ins do not exceed turns : passed
- Retries agree with first-try result : passed
- Evidence log SHA-256 matches : passed
- Evidence log is substantial : passed
Evidence log
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"arguments": "{\"path\": \"out/process_bookings.py\", \"content\": \"#!/usr/bin/env python3\\nimport csv\\nimport json\\nfrom datetime import datetime, timedelta, time\\nfrom collections import defaultdict\\nfrom zoneinfo import ZoneInfo\\nimport bisect\\n\\n# Parse rooms.csv\\nrooms = {}\\nwith open('rooms.csv', 'r') as f:\\n reader = csv.DictReader(f)\\n for row in reader:\\n rooms[row['room_id']] = row['time_zone']\\n\\n# Parse bookings.csv and find latest version for each booking_id\\nbookings_by_id = defaultdict(list)\\nwith open('bookings.csv', 'r') as f:\\n reader = csv.DictReader(f)\\n for row in reader:\\n bookings_by_id[row['booking_id']].append(row)\\n\\n# Get the latest version for each booking_id\\nlatest_bookings = []\\nfor booking_id, versions in bookings_by_id.items():\\n # Find version with highest version number\\n latest = max(versions, key=lambda x: int(x['version']))\\n latest_bookings.append(latest)\\n\\n# Group bookings by room_id\\nbookings_by_room = defaultdict(list)\\nfor booking in latest_bookings:\\n if booking['status'] != 'cancelled':\\n bookings_by_room[booking['room_id']].append(booking)\\n\\n# Business time parameters\\nBUSINESS_START = time(8, 0) # 08:00\\nBUSINESS_END = time(18, 0) # 18:00\\nPERIOD_START = datetime(2026, 10, 19).date()\\nPERIOD_END = datetime(2026, 11, 6).date()\\n\\ndef is_business_day(dt):\\n \\\"\\\"\\\"Check if date is Monday-Friday\\\"\\\"\\\"\\n return dt.weekday() < 5 # 0=Monday, 4=Friday\\n\\ndef is_business_time(local_dt):\\n \\\"\\\"\\\"Check if datetime is within business hours\\\"\\\"\\\"\\n if not is_business_day(local_dt.date()):\\n return False\\n return BUSINESS_START <= local_dt.time() < BUSINESS_END\\n\\ndef get_business_minutes_for_period(tz):\\n \\\"\\\"\\\"Calculate total business minutes for the period in a timezone\\\"\\\"\\\"\\n total_minutes = 0\\n current_date = PERIOD_START\\n while current_date <= PERIOD_END:\\n if is_business_day(current_date):\\n # Each business day has 10 hours = 600 minutes\\n total_minutes += 600\\n current_date += timedelta(days=1)\\n return total_minutes\\n\\ndef clip_to_business_hours(local_start, local_end):\\n \\\"\\\"\\\"Clip a time interval to business hours, return list of (start, end) tuples\\\"\\\"\\\"\\n intervals = []\\n \\n # If the interval spans multiple days, split it\\n current_start = local_start\\n while current_start < local_end:\\n current_date = current_start.date()\\n day_start = datetime.combine(current_date, BUSINESS_START, tzinfo=current_start.tzinfo)\\n day_end = datetime.combine(current_date, BUSINESS_END, tzinfo=current_start.tzinfo)\\n \\n # Skip if this day is not a business day\\n if not is_business_day(current_date):\\n current_start = day_end + timedelta(seconds=1)\\n continue\\n \\n # Clip to this day's business hours\\n interval_start = max(current_start, day_start)\\n interval_end = min(local_end, day_end)\\n \\n if interval_start < interval_end:\\n intervals.append((interval_start, interval_end))\\n \\n # Move to next day\\n current_start = day_end + timedelta(seconds=1)\\n \\n return intervals\\n\\ndef merge_intervals(intervals):\\n \\\"\\\"\\\"Merge overlapping intervals\\\"\\\"\\\"\\n if not intervals:\\n return []\\n \\n # Sort by start time\\n sorted_intervals = sorted(intervals, key=lambda x: x[0])\\n \\n merged = []\\n for start, end in sorted_intervals:\\n if not merged:\\n merged.append([start, end])\\n else:\\n last_start, last_end = merged[-1]\\n if start <= last_end:\\n # Overlapping, merge\\n merged[-1][1] = max(last_end, end)\\n else:\\n merged.append([start, end])\\n \\n return merged\\n\\ndef calculate_booked_minutes(bookings, tz_str):\\n \\\"\\\"\\\"Calculate total booked business minutes for a room\\\"\\\"\\\"\\n tz = ZoneInfo(tz_str)\\n all_intervals = []\\n \\n for booking in bookings:\\n # Parse UTC times\\n start_utc = datetime.fromisoformat(booking['start_utc'].replace('Z', '+00:00'))\\n end_utc = datetime.fromisoformat(booking['end_utc'].replace('Z', '+00:00'))\\n \\n # Convert to local time\\n local_start = start_utc.astimezone(tz)\\n local_end = end_utc.astimezone(tz)\\n \\n # Clip to business hours\\n business_intervals = clip_to_business_hours(local_start, local_end)\\n all_intervals.extend(business_intervals)\\n \\n # Merge overlapping intervals\\n merged = merge_intervals(all_intervals)\\n \\n # Calculate total minutes\\n total_minutes = 0\\n for start, end in merged:\\n delta = end - start\\n total_minutes += int(delta.total_seconds() / 60)\\n \\n return total_minutes, merged\\n\\ndef find_peak_day(merged_intervals):\\n \\\"\\\"\\\"Find the day with most booked minutes\\\"\\\"\\\"\\n if not merged_intervals:\\n return None\\n \\n # Count minutes per day\\n minutes_by_day = defaultdict(int)\\n \\n for start, end in merged_intervals:\\n current = start\\n while current < end:\\n day = current.date()\\n day_start = datetime.combine(day, BUSINESS_START, tzinfo=start.tzinfo)\\n day_end = datetime.combine(day, BUSINESS_END, tzinfo=start.tzinfo)\\n \\n interval_start = max(current, day_start)\\n interval_end = min(end, day_end)\\n \\n if interval_start < interval_end:\\n delta = interval_end - interval_start\\n minutes_by_day[day] += int(delta.total_seconds() / 60)\\n \\n current = day_end + timedelta(seconds=1)\\n \\n if not minutes_by_day:\\n return None\\n \\n # Find day with most minutes (earliest on tie)\\n peak_day = max(minutes_by_day.items(), key=lambda x: (x[1], -x[0].toordinal()))[0]\\n return peak_day.strftime('%Y-%m-%d')\\n\\n# Calculate results for each room\\nresults = {}\\nactive_bookings_count = 0\\n\\nfor room_id, tz_str in rooms.items():\\n bookings = bookings_by_room.get(room_id, [])\\n active_bookings_count += len(bookings)\\n \\n booked_minutes, merged_intervals = calculate_booked_minutes(bookings, tz_str)\\n total_business_minutes = get_business_minutes_for_period(tz_str)\\n \\n if total_business_minutes > 0:\\n utilisation_pct = round((booked_minutes / total_business_minutes) * 100, 2)\\n else:\\n utilisation_pct = 0.0\\n \\n peak_day = find_peak_day(merged_intervals)\\n \\n results[room_id] = {\\n 'booked_minutes': booked_minutes,\\n 'utilisation_pct': utilisation_pct,\\n 'peak_day': peak_day\\n }\\n\\n# Write output\\noutput = {\\n 'rooms': results,\\n 'active_bookings': active_bookings_count\\n}\\n\\nwith open('out/rooms.json', 'w') as f:\\n json.dump(output, f, indent=2)\\n\\nprint(\\\"Processing complete!\\\")\\nprint(f\\\"Total rooms: {len(results)}\\\")\\nprint(f\\\"Total active bookings: {active_bookings_count}\\\")\\n\"}"
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{
"turn": 4,
"tool": "write_file",
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"path": "out/process_bookings.py",
"content": "#!/usr/bin/env python3\nimport csv\nimport json\nfrom datetime import datetime, timedelta, time\nfrom collections import defaultdict\nfrom zoneinfo import ZoneInfo\nimport bisect\n\n# Parse rooms.csv\nrooms = {}\nwith open('rooms.csv', 'r') as f:\n reader = csv.DictReader(f)\n for row in reader:\n rooms[row['room_id']] = row['time_zone']\n\n# Parse bookings.csv and find latest version for each booking_id\nbookings_by_id = defaultdict(list)\nwith open('bookings.csv', 'r') as f:\n reader = csv.DictReader(f)\n for row in reader:\n bookings_by_id[row['booking_id']].append(row)\n\n# Get the latest version for each booking_id\nlatest_bookings = []\nfor booking_id, versions in bookings_by_id.items():\n # Find version with highest version number\n latest = max(versions, key=lambda x: int(x['version']))\n latest_bookings.append(latest)\n\n# Group bookings by room_id\nbookings_by_room = defaultdict(list)\nfor booking in latest_bookings:\n if booking['status'] != 'cancelled':\n bookings_by_room[booking['room_id']].append(booking)\n\n# Business time parameters\nBUSINESS_START = time(8, 0) # 08:00\nBUSINESS_END = time(18, 0) # 18:00\nPERIOD_START = datetime(2026, 10, 19).date()\nPERIOD_END = datetime(2026, 11, 6).date()\n\ndef is_business_day(dt):\n \"\"\"Check if date is Monday-Friday\"\"\"\n return dt.weekday() < 5 # 0=Monday, 4=Friday\n\ndef is_business_time(local_dt):\n \"\"\"Check if datetime is within business hours\"\"\"\n if not is_business_day(local_dt.date()):\n return False\n return BUSINESS_START <= local_dt.time() < BUSINESS_END\n\ndef get_business_minutes_for_period(tz):\n \"\"\"Calculate total business minutes for the period in a timezone\"\"\"\n total_minutes = 0\n current_date = PERIOD_START\n while current_date <= PERIOD_END:\n if is_business_day(current_date):\n # Each business day has 10 hours = 600 minutes\n total_minutes += 600\n current_date += timedelta(days=1)\n return total_minutes\n\ndef clip_to_business_hours(local_start, local_end):\n \"\"\"Clip a time interval to business hours, return list of (start, end) tuples\"\"\"\n intervals = []\n \n # If the interval spans multiple days, split it\n current_start = local_start\n while current_start < local_end:\n current_date = current_start.date()\n day_start = datetime.combine(current_date, BUSINESS_START, tzinfo=current_start.tzinfo)\n day_end = datetime.combine(current_date, BUSINESS_END, tzinfo=current_start.tzinfo)\n \n # Skip if this day is not a business day\n if not is_business_day(current_date):\n current_start = day_end + timedelta(seconds=1)\n continue\n \n # Clip to this day's business hours\n interval_start = max(current_start, day_start)\n interval_end = min(local_end, day_end)\n \n if interval_start < interval_end:\n intervals.append((interval_start, interval_end))\n \n # Move to next day\n current_start = day_end + timedelta(seconds=1)\n \n return intervals\n\ndef merge_intervals(intervals):\n \"\"\"Merge overlapping intervals\"\"\"\n if not intervals:\n return []\n \n # Sort by start time\n sorted_intervals = sorted(intervals, key=lambda x: x[0])\n \n merged = []\n for start, end in sorted_intervals:\n if not merged:\n merged.append([start, end])\n else:\n last_start, last_end = merged[-1]\n if start <= last_end:\n # Overlapping, merge\n merged[-1][1] = max(last_end, end)\n else:\n merged.append([start, end])\n \n return merged\n\ndef calculate_booked_minutes(bookings, tz_str):\n \"\"\"Calculate total booked business minutes for a room\"\"\"\n tz = ZoneInfo(tz_str)\n all_intervals = []\n \n for booking in bookings:\n # Parse UTC times\n start_utc = datetime.fromisoformat(booking['start_utc'].replace('Z', '+00:00'))\n end_utc = datetime.fromisoformat(booking['end_utc'].replace('Z', '+00:00'))\n \n # Convert to local time\n local_start = start_utc.astimezone(tz)\n local_end = end_utc.astimezone(tz)\n \n # Clip to business hours\n business_intervals = clip_to_business_hours(local_start, local_end)\n all_intervals.extend(business_intervals)\n \n # Merge overlapping intervals\n merged = merge_intervals(all_intervals)\n \n # Calculate total minutes\n total_minutes = 0\n for start, end in merged:\n delta = end - start\n total_minutes += int(delta.total_seconds() / 60)\n \n return total_minutes, merged\n\ndef find_peak_day(merged_intervals):\n \"\"\"Find the day with most booked minutes\"\"\"\n if not merged_intervals:\n return None\n \n # Count minutes per day\n minutes_by_day = defaultdict(int)\n \n for start, end in merged_intervals:\n current = start\n while current < end:\n day = current.date()\n day_start = datetime.combine(day, BUSINESS_START, tzinfo=start.tzinfo)\n day_end = datetime.combine(day, BUSINESS_END, tzinfo=start.tzinfo)\n \n interval_start = max(current, day_start)\n interval_end = min(end, day_end)\n \n if interval_start < interval_end:\n delta = interval_end - interval_start\n minutes_by_day[day] += int(delta.total_seconds() / 60)\n \n current = day_end + timedelta(seconds=1)\n \n if not minutes_by_day:\n return None\n \n # Find day with most minutes (earliest on tie)\n peak_day = max(minutes_by_day.items(), key=lambda x: (x[1], -x[0].toordinal()))[0]\n return peak_day.strftime('%Y-%m-%d')\n\n# Calculate results for each room\nresults = {}\nactive_bookings_count = 0\n\nfor room_id, tz_str in rooms.items():\n bookings = bookings_by_room.get(room_id, [])\n active_bookings_count += len(bookings)\n \n booked_minutes, merged_intervals = calculate_booked_minutes(bookings, tz_str)\n total_business_minutes = get_business_minutes_for_period(tz_str)\n \n if total_business_minutes > 0:\n utilisation_pct = round((booked_minutes / total_business_minutes) * 100, 2)\n else:\n utilisation_pct = 0.0\n \n peak_day = find_peak_day(merged_intervals)\n \n results[room_id] = {\n 'booked_minutes': booked_minutes,\n 'utilisation_pct': utilisation_pct,\n 'peak_day': peak_day\n }\n\n# Write output\noutput = {\n 'rooms': results,\n 'active_bookings': active_bookings_count\n}\n\nwith open('out/rooms.json', 'w') as f:\n json.dump(output, f, indent=2)\n\nprint(\"Processing complete!\")\nprint(f\"Total rooms: {len(results)}\")\nprint(f\"Total active bookings: {active_bookings_count}\")\n"
},
"result": "wrote 6751 chars"
},
{
"turn": 5,
"response": {
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{
"turn": 5,
"tool": "run_shell",
"args": {
"command": "cd /mnt/data && python3 out/process_bookings.py"
},
"result": "exit=1\nstdout:\n\nstderr:\nbash: line 1: cd: /mnt/data: No such file or directory\n"
},
{
"turn": 6,
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{
"turn": 6,
"tool": "run_shell",
"args": {
"command": "python3 out/process_bookings.py"
},
"result": "ERROR: Command 'python3 out/process_bookings.py' timed out after 120 seconds\nstdout:\n\nstderr:\n"
},
{
"turn": 7,
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"arguments": "{\"path\": \"out/process_bookings.py\", \"content\": \"#!/usr/bin/env python3\\nimport csv\\nimport json\\nfrom datetime import datetime, timedelta, time\\nfrom collections import defaultdict\\nfrom zoneinfo import ZoneInfo\\n\\n# Parse rooms.csv\\nrooms = {}\\nwith open('rooms.csv', 'r') as f:\\n reader = csv.DictReader(f)\\n for row in reader:\\n rooms[row['room_id']] = row['time_zone']\\n\\n# Parse bookings.csv and find latest version for each booking_id\\nbookings_by_id = defaultdict(list)\\nwith open('bookings.csv', 'r') as f:\\n reader = csv.DictReader(f)\\n for row in reader:\\n bookings_by_id[row['booking_id']].append(row)\\n\\n# Get the latest version for each booking_id\\nlatest_bookings = []\\nfor booking_id, versions in bookings_by_id.items():\\n latest = max(versions, key=lambda x: int(x['version']))\\n latest_bookings.append(latest)\\n\\n# Group bookings by room_id and count active bookings\\nbookings_by_room = defaultdict(list)\\nactive_bookings_count = 0\\nfor booking in latest_bookings:\\n if booking['status'] != 'cancelled':\\n bookings_by_room[booking['room_id']].append(booking)\\n active_bookings_count += 1\\n\\n# Business time parameters\\nBUSINESS_START = time(8, 0) # 08:00\\nBUSINESS_END = time(18, 0) # 18:00\\nPERIOD_START = datetime(2026, 10, 19).date()\\nPERIOD_END = datetime(2026, 11, 6).date()\\n\\ndef is_business_day(dt):\\n \\\"\\\"\\\"Check if date is Monday-Friday\\\"\\\"\\\"\\n return dt.weekday() < 5\\n\\ndef get_business_minutes_for_period():\\n \\\"\\\"\\\"Calculate total business minutes for the period (same for all timezones)\\\"\\\"\\\"\\n total_minutes = 0\\n current_date = PERIOD_START\\n while current_date <= PERIOD_END:\\n if is_business_day(current_date):\\n total_minutes += 600 # 10 hours per business day\\n current_date += timedelta(days=1)\\n return total_minutes\\n\\ndef clip_to_business_hours(local_start, local_end):\\n \\\"\\\"\\\"Clip a time interval to business hours, return list of (start, end) tuples\\\"\\\"\\\"\\n intervals = []\\n current = local_start\\n \\n while current < local_end:\\n current_date = current.date()\\n \\n # Skip non-business days\\n if not is_business_day(current_date):\\n # Move to next day at 00:00\\n next_day = datetime.combine(current_date + timedelta(days=1), time(0, 0), tzinfo=current.tzinfo)\\n current = next_day\\n continue\\n \\n # Get business hours for this day\\n day_start = datetime.combine(current_date, BUSINESS_START, tzinfo=current.tzinfo)\\n day_end = datetime.combine(current_date, BUSINESS_END, tzinfo=current.tzinfo)\\n \\n # Calculate overlap\\n overlap_start = max(current, day_start)\\n overlap_end = min(local_end, day_end)\\n \\n if overlap_start < overlap_end:\\n intervals.append((overlap_start, overlap_end))\\n \\n # Move to next day\\n current = day_end\\n \\n return intervals\\n\\ndef merge_intervals(intervals):\\n \\\"\\\"\\\"Merge overlapping intervals\\\"\\\"\\\"\\n if not intervals:\\n return []\\n \\n # Sort by start time\\n sorted_intervals = sorted(intervals, key=lambda x: x[0])\\n \\n merged = []\\n for start, end in sorted_intervals:\\n if not merged:\\n merged.append([start, end])\\n else:\\n last_start, last_end = merged[-1]\\n if start <= last_end:\\n merged[-1][1] = max(last_end, end)\\n else:\\n merged.append([start, end])\\n \\n return merged\\n\\ndef calculate_booked_minutes(bookings, tz_str):\\n \\\"\\\"\\\"Calculate total booked business minutes for a room\\\"\\\"\\\"\\n tz = ZoneInfo(tz_str)\\n all_intervals = []\\n \\n for booking in bookings:\\n # Parse UTC times\\n start_utc = datetime.fromisoformat(booking['start_utc'].replace('Z', '+00:00'))\\n end_utc = datetime.fromisoformat(booking['end_utc'].replace('Z', '+00:00'))\\n \\n # Convert to local time\\n local_start = start_utc.astimezone(tz)\\n local_end = end_utc.astimezone(tz)\\n \\n # Clip to business hours\\n business_intervals = clip_to_business_hours(local_start, local_end)\\n all_intervals.extend(business_intervals)\\n \\n # Merge overlapping intervals\\n merged = merge_intervals(all_intervals)\\n \\n # Calculate total minutes\\n total_minutes = 0\\n for start, end in merged:\\n delta = end - start\\n total_minutes += int(delta.total_seconds() / 60)\\n \\n return total_minutes, merged\\n\\ndef find_peak_day(merged_intervals):\\n \\\"\\\"\\\"Find the day with most booked minutes\\\"\\\"\\\"\\n if not merged_intervals:\\n return None\\n \\n # Count minutes per day\\n minutes_by_day = defaultdict(int)\\n \\n for start, end in merged_intervals:\\n # Handle intervals that span multiple days\\n current = start\\n while current < end:\\n day = current.date()\\n \\n if not is_business_day(day):\\n # Skip to next business day\\n while current < end and not is_business_day(current.date()):\\n next_day = datetime.combine(current.date() + timedelta(days=1), time(0, 0), tzinfo=current.tzinfo)\\n current = next_day\\n continue\\n \\n day_start = datetime.combine(day, BUSINESS_START, tzinfo=start.tzinfo)\\n day_end = datetime.combine(day, BUSINESS_END, tzinfo=start.tzinfo)\\n \\n interval_start = max(current, day_start)\\n interval_end = min(end, day_end)\\n \\n if interval_start < interval_end:\\n delta = interval_end - interval_start\\n minutes_by_day[day] += int(delta.total_seconds() / 60)\\n \\n current = day_end\\n \\n if not minutes_by_day:\\n return None\\n \\n # Find day with most minutes (earliest on tie)\\n peak_day = max(minutes_by_day.items(), key=lambda x: (x[1], -x[0].toordinal()))[0]\\n return peak_day.strftime('%Y-%m-%d')\\n\\n# Calculate results for each room\\nresults = {}\\ntotal_business_minutes = get_business_minutes_for_period()\\n\\nfor room_id, tz_str in rooms.items():\\n bookings = bookings_by_room.get(room_id, [])\\n \\n booked_minutes, merged_intervals = calculate_booked_minutes(bookings, tz_str)\\n \\n if total_business_minutes > 0:\\n utilisation_pct = round((booked_minutes / total_business_minutes) * 100, 2)\\n else:\\n utilisation_pct = 0.0\\n \\n peak_day = find_peak_day(merged_intervals)\\n \\n results[room_id] = {\\n 'booked_minutes': booked_minutes,\\n 'utilisation_pct': utilisation_pct,\\n 'peak_day': peak_day\\n }\\n\\n# Write output\\noutput = {\\n 'rooms': results,\\n 'active_bookings': active_bookings_count\\n}\\n\\nwith open('out/rooms.json', 'w') as f:\\n json.dump(output, f, indent=2)\\n\\nprint(\\\"Processing complete!\\\")\\nprint(f\\\"Total rooms: {len(results)}\\\")\\nprint(f\\\"Total active bookings: {active_bookings_count}\\\")\\n\"}"
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