Qwen3 Coder 480B Turbo (Venice) (thinking on) on Go/no-go departure time from a gridded wind file
QQwen3 Coder 480B Turbo (Venice)thinking on · venice-mini-agent v1.3.1 · cloud · 2026-10-27 · by @bobsiopener
- Estimated cost
- $0.0089
- Wall time
- 41s
- Human step-ins
- 0
- Outcome
- Pass
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Summary
Exact prompt
# Go/no-go departure time from a gridded wind file `input.json` (synthetic; not a forecast) names `grid.json` and gives `from` and `to` [lat, lon], `passage_hours` D (integer), `latest_departure_h`, and `limits`. `grid.json` has `lats`, `lons`, `times_h` (ascending) and fields `u_kt`, `v_kt` (eastward/northward wind components, knots, the direction the air moves TOWARD) and `gust_kt`, each indexed `[time][lat][lon]`. Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `window.svg`. Fresh inputs are used too. ## Rules 1. Interpolation of each field: bilinear in (lat, lon) at the two bracketing grid times, then linear in time. Outside a grid axis, clamp to its edge. 2. Course = atan2((lon_to - lon_from) * cos(mean lat), lat_to - lat_from) in degrees mod 360. 3. For every integer departure hour t0 = 0 .. latest_departure_h: sample k = 0..D at time t0 + k and position from + (to - from) * k / D (straight in lat/lon). At each sample: wind speed = hypot(u, v); wind FROM direction = atan2(-u, -v) mod 360; off = smallest angle between it and the course. The sample fails, checked in this order, with reason `wind@<t>` if speed > max_wind_kt, `gust@<t>` if gust > max_gust_kt, `headwind@<t>` if off < min_wind_angle_deg and speed > headwind_max_kt. Stop at the first failing sample; that departure is not ok and its reason is recorded (t as an integer). Otherwise ok with reason null. 4. `earliest_ok_h`: first ok departure or null. `longest_ok_run`: [first, last] departure hours of the longest run of consecutive ok departures (earliest wins ties), or null. ## Visual output: window.svg Static, self-contained SVG (root `<svg>`, no scripts, images, links or external assets; renders headless) with an element `id="timeline"` (one mark per departure hour) and an element `id="best-departure"`. The drawing itself is not graded. ## answer.json `{"course_deg", "departures": [{"depart_h", "ok", "reason"}, ...], "earliest_ok_h", "longest_ok_run"}` ## Scoring Hidden checks (partial credit; all must pass): `course` (within 0.11), `departure_flags` (ok flags), `failure_reasons` (exact), `best_departure`, `window_svg_static` (window.svg exists, follows the static rules, has the ids above), `fresh_run_1`, `fresh_run_2`. 60-second limit per run.
Space-time interpolation of vector components (not speed and direction), the toward/from convention, ordered failure reasons per sample along a moving position, and run-length selection with ties. Inspired by https://news.ycombinator.com/item?id=34929968 Headless, offline run on a reproducible fixture. Cost shown is a list-price estimate (measured tokens x the site's price table).
Benchmark code for comparing AI models, not navigation or safety software.
Success criteria: 1. course 2. departure_flags 3. failure_reasons 4. best_departure 5. window_svg_static 6. fresh_run_1 7. fresh_run_2
Setup notes: Use Case Scout batch 10 case k10-wind-grid-departure (offline fixture). Prepared with the case's setup script; hidden key/tests kept outside the agent's work dir. Prompt = TASK.md verbatim. Scored automatically with the scout's scorer. Tools: list_files/read_file/write_file/run_shell (venice-mini-agent v1.3), web disabled. Single run, no retries. Visual output (window.svg) rendered offline to PNG with google-chrome headless, network blocked, 1280x800 at ~3 s after load. 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. Harness: venice-mini-agent v1.3.1 via Venice OpenAI-compatible API (16000 max tokens/call, $0.05 run cap). Score: 7/7. Cost is an ESTIMATE: measured per-call tokens x the site's list-price table (paid from Venice DIEM allowance; Venice billing not itemized). input tokens include cached reads. Single run.
Estimated cost List-price estimate; not actually billed.
Output
Metrics
- Estimated cost
- $0.0089
- Wall time
- 41s
- Human step-ins
- 0
- Turns
- 9
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| QQwen3 Coder 480B Turbo (Venice)venice/qwen3-coder-480b-a35b-instruct-turbothinking on | 51,955 | 3,221 | $0.0089 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: Qwen3 Coder 480B Turbo (Venice) (venice/qwen3-coder-480b-a35b-instruct-turbo, thinking on) via
venice-mini-agent v1.3.1, cloud. - It counts as a success only if it meets every success criterion: 1. course 2. departure_flags 3. failure_reasons 4. best_departure 5. window_svg_static 6. fresh_run_1 7. fresh_run_2
- 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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"final_message": "The implementation is working correctly. Let me summarize what was done:\n\n1. Created a Python script (`solve.py`) that:\n - Reads input data from JSON files\n - Calculates the course between start and end positions\n - For each possible departure hour, checks if the journey is\u53ef\u884c by sampling wind conditions at regular intervals\n - Applies the specified rules for wind speed, gusts, and headwind limitations\n - Finds the earliest OK departure and the longest consecutive run of OK departures\n - Outputs results to `answer.json` and creates a visualization in `window.svg`\n\n2. The solution handles:\n - Bilinear interpolation in space and linear interpolation in time for wind data\n - Proper clamping at grid boundaries\n - Accurate calculation of course and wind angles\n - All specified failure conditions with correct formatting of reasons\n - Generation of a static SVG with proper IDs as required\n\nThe program successfully processes the input data and produces the expected outputs with correct results.",
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"result": "{\"lats\": [30.9, 31.4, 31.9], \"lons\": [-42.2, -41.7, -41.2], \"times_h\": [0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45, 48], \"u_kt\": [[[4.1, 4.6, 1.7], [6.1, 1.6, 4.1], [2.8, 2.2, 1.5]], [[0.5, 1.1, 0.1], [3.0, 1.2, -0.8], [0.1, 0.9, 1.2]], [[9.0, 3.9, 6.5], [5.2, 6.2, 4.0], [7.1, 5.6, 5.3]], [[8.9, 9.0, 10.1], [9.0, 11.9, 8.0], [7.9, 8.9, 10.7]], [[17.6, 18.4, 20.4], [17.9, 16.7, 18.2], [17.3, 20.0, 17.6]], [[13.8, 13.2, 15.2], [15.6, 13.5, 15.8], [13.4, 11.2, 14.3]], [[10.9, 12.2, 11.4], [8.0, 7.5, 8.7], [11.9, 10.9, 8.8]], [[11.8, 8.7, 11.6], [8.8, 9.3, 7.2], [9.1, 10.4, 9.3]], [[8.2, 10.0, 8.9], [7.4, 9.6, 9.0], [9.2, 10.4, 6.7]], [[10.0, 10.4, 7.1], [10.8, 10.2, 10.6], [7.1, 9.4, 7.5]], [[7.8, 7.8, 9.2], [6.8, 7.0, 8.8], [6.4, 7.1, 4.1]], [[8.5, 7.0, 8.4], [11.1, 10.9, 8.2], [5.8, 9.1, 10.1]], [[9.9, 13.7, 9.2], [10.6, 10.9, 11.9], [11.0, 10.9, 9.3]], [[4.9, 5.6, 5.1], [4.8, 5.4, 6.6], [4.3, 4.2, 6.7]], [[8.7, 9.6, 10.7], [10.1, 12.7, 10.7], [10.2, 9.5, 9.7]], [[8.8, 10.2, 10.0], [11.5, 11.3, 9.6], [10.5, 12.5, 7.2]], [[9.4, 12.4, 9.3], [7.2, 9.8, 11.5], [10.5, 8.8, 10.1]]], \"v_kt\": [[[9.7, 10.7, 9.0], [9.2, 10.2, 11.0], [11.1, 7.0, 11.0]], [[9.6, 10.6, 10.6], [12.4, 8.2, 11.3], [10.7, 12.1, 11.6]], [[18.5, 17.2, 17.0], [20.0, 18.0, 20.7], [19.3, 17.9, 20.1]], [[20.6, 19.9, 21.9], [19.9, 21.6, 19.9], [22.5, 22.3, 20.4]], [[-8.9, -4.8, -9.5], [-6.7, -6.6, -7.7], [-9.9, -8.3, -5.9]], [[-6.1, -6.5, -4.5], [-3.0, -3.8, -5.6], [-5.9, -4.7, -4.4]], [[-4.1, -3.0, -2.0], [0.4, -0.6, 0.3], [-2.1, -2.2, -0.5]], [[-3.7, -0.9, -2.3], [-1.8, -0.7, -1.5], [-3.8, -2.0, 0.2]], [[-4.0, -2.1, -6.7], [-2.0, -3.7, -3.4], [-5.2, -2.7, -2.3]], [[0.5, -1.5, -1.2], [-2.4, -3.7, -0.6], [-2.8, -4.8, -2.2]], [[-1.3, -3.1, -1.0], [-3.0, -0.3, -2.3], [-1.9, -0.5, -2.5]], [[-1.8, -0.5, -3.2], [-3.3, -1.2, -3.8], [-1.1, -1.6, -6.4]], [[-4.7, -4.4, -5.5], [-2.3, -4.6, -3.0], [-3.4, -3.2, -7.5]], [[-7.1, -2.0, -6.9], [-2.3, -5.3, -7.6], [-4.5, -5.6, -4.8]], [[-6.0, -4.7, -4.6], [-5.8, -9.4, -5.4], [-6.0, -4.2, -9.1]], [[-7.6, -7.2, -6.4], [-6.3, -3.9, -6.7], [-9.5, -4.3, -2.8]], [[0.0, -0.9, -0.4], [1.4, 0.4, -1.5], [-1.6, -2.7, -1.0]]], \"gust_kt\": [[[14.8, 13.4, 14.6], [15.2, 13.7, 14.9], [14.4, 15.0, 14.1]], [[14.5, 14.0, 12.6], [12.3, 14.3, 13.9], [13.6, 13.0, 14.1]], [[26.7, 26.2, 27.6], [26.4, 28.2, 25.6], [25.7, 25.4, 24.5]], [[34.1, 28.6, 29.9], [31.8, 28.6, 33.1], [28.8, 32.1, 29.8]], [[26.1, 25.0, 27.7], [25.2, 28.5, 28.6], [28.4, 24.1, 26.1]], [[21.3, 21.2, 18.7], [19.6, 21.7, 21.2], [19.0, 18.8, 19.7]], [[14.1, 13.4, 15.2], [13.5, 13.7, 14.7], [13.6, 15.1, 13.8]], [[13.7, 13.6, 12.7], [13.7, 14.3, 14.2], [13.4, 12.8, 13.7]], [[14.2, 12.4, 14.1], [12.1, 13.0, 14.1], [13.1, 12.1, 13.6]], [[12.9, 11.9, 13.5], [12.7, 11.5, 12.6], [12.0, 12.5, 13.4]], [[10.2, 10.4, 11.7], [10.9, 10.9, 10.6], [11.2, 12.1, 10.4]], [[12.5, 12.7, 13.1], [12.2, 13.7, 12.6], [12.7, 12.1, 13.3]], [[15.3, 16.9, 15.1], [15.8, 16.3, 16.3], [15.4, 14.4, 17.1]], [[11.4, 11.0, 10.5], ["
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"arguments": "{\"path\": \"solve.py\", \"content\": \"import json\\nimport math\\nimport sys\\n\\ndef read_input(input_file):\\n with open(input_file, 'r') as f:\\n return json.load(f)\\n\\ndef read_grid(grid_file):\\n with open(grid_file, 'r') as f:\\n return json.load(f)\\n\\ndef interpolate_1d(x, x_list, y_list):\\n \\\"\\\"\\\"Linear interpolation in 1D\\\"\\\"\\\"\\n if x <= x_list[0]:\\n return y_list[0]\\n if x >= x_list[-1]:\\n return y_list[-1]\\n \\n # Find the two points to interpolate between\\n for i in range(len(x_list) - 1):\\n if x_list[i] <= x <= x_list[i+1]:\\n x1, x2 = x_list[i], x_list[i+1]\\n y1, y2 = y_list[i], y_list[i+1]\\n return y1 + (y2 - y1) * (x - x1) / (x2 - x1)\\n return y_list[-1]\\n\\ndef interpolate_2d(lat, lon, lats, lons, values):\\n \\\"\\\"\\\"Bilinear interpolation in 2D\\\"\\\"\\\"\\n # Clamp lat/lon to grid bounds\\n lat = max(lats[0], min(lats[-1], lat))\\n lon = max(lons[0], min(lons[-1], lon))\\n \\n # Find surrounding indices\\n lat_i = 0\\n while lat_i < len(lats) - 2 and lats[lat_i+1] <= lat:\\n lat_i += 1\\n \\n lon_i = 0\\n while lon_i < len(lons) - 2 and lons[lon_i+1] <= lon:\\n lon_i += 1\\n \\n # Get the four corners\\n lat1, lat2 = lats[lat_i], lats[lat_i+1]\\n lon1, lon2 = lons[lon_i], lons[lon_i+1]\\n \\n # Values at the four corners\\n v11 = values[lat_i][lon_i]\\n v12 = values[lat_i][lon_i+1]\\n v21 = values[lat_i+1][lon_i]\\n v22 = values[lat_i+1][lon_i+1]\\n \\n # Bilinear interpolation\\n if lat1 == lat2:\\n v1 = v11\\n v2 = v21\\n else:\\n v1 = v11 + (v21 - v11) * (lat - lat1) / (lat2 - lat1)\\n v2 = v12 + (v22 - v12) * (lat - lat1) / (lat2 - lat1)\\n \\n if lon1 == lon2:\\n return v1\\n else:\\n return v1 + (v2 - v1) * (lon - lon1) / (lon2 - lon1)\\n\\ndef interpolate_3d(time, lat, lon, times, lats, lons, values):\\n \\\"\\\"\\\"Trilinear interpolation in space and time\\\"\\\"\\\"\\n # Find bracketing time indices\\n if time <= times[0]:\\n t_idx1, t_idx2 = 0, 0\\n t_factor = 0\\n elif time >= times[-1]:\\n t_idx1, t_idx2 = len(times)-1, len(times)-1\\n t_factor = 0\\n else:\\n t_idx1 = 0\\n while t_idx1 < len(times) - 1 and times[t_idx1+1] <= time:\\n t_idx1 += 1\\n t_idx2 = min(t_idx1 + 1, len(times) - 1)\\n if times[t_idx1] == times[t_idx2]:\\n t_factor = 0\\n else:\\n t_factor = (time - times[t_idx1]) / (times[t_idx2] - times[t_idx1])\\n \\n # Interpolate at both time points\\n if t_idx1 == t_idx2:\\n val1 = interpolate_2d(lat, lon, lats, lons, values[t_idx1])\\n val2 = val1\\n else:\\n val1 = interpolate_2d(lat, lon, lats, lons, values[t_idx1])\\n val2 = interpolate_2d(lat, lon, lats, lons, values[t_idx2])\\n \\n # Linear interpolation in time\\n return val1 + (val2 - val1) * t_factor\\n\\ndef calculate_course(from_pos, to_pos):\\n \\\"\\\"\\\"Calculate course in degrees\\\"\\\"\\\"\\n lat_from, lon_from = from_pos\\n lat_to, lon_to = to_pos\\n \\n # Mean latitude in radians\\n mean_lat = math.radians((lat_from + lat_to) / 2)\\n \\n # Calculate course using atan2\\n delta_lat = lat_to - lat_from\\n delta_lon = lon_to - lon_from\\n y = delta_lon * math.cos(mean_lat)\\n x = delta_lat\\n \\n course = math.degrees(math.atan2(y, x))\\n return course % 360\\n\\ndef get_wind_components(time, lat, lon, grid_data):\\n \\\"\\\"\\\"Get interpolated wind components\\\"\\\"\\\"\\n u = interpolate_3d(time, lat, lon, grid_data['times_h'], grid_data['lats'], \\n grid_data['lons'], grid_data['u_kt'])\\n v = interpolate_3d(time, lat, lon, grid_data['times_h'], grid_data['lats'], \\n grid_data['lons'], grid_data['v_kt'])\\n gust = interpolate_3d(time, lat, lon, grid_data['times_h'], grid_data['lats'], \\n grid_data['lons'], grid_data['gust_kt'])\\n return u, v, gust\\n\\ndef check_departure(t0, from_pos, to_pos, passage_hours, limits, grid_data):\\n \\\"\\\"\\\"Check if a departure at time t0 is valid\\\"\\\"\\\"\\n lat_from, lon_from = from_pos\\n lat_to, lon_to = to_pos\\n \\n for k in range(passage_hours + 1):\\n # Calculate position at step k\\n fraction = k / passage_hours if passage_hours > 0 else 0\\n lat = lat_from + (lat_to - lat_from) * fraction\\n lon = lon_from + (lon_to - lon_from) * fraction\\n time = t0 + k\\n \\n # Get wind data\\n u, v, gust = get_wind_components(time, lat, lon, grid_data)\\n \\n # Calculate wind speed and direction\\n wind_speed = math.hypot(u, v)\\n wind_from_direction = math.degrees(math.atan2(-u, -v)) % 360\\n \\n # Check limits\\n if wind_speed > limits['max_wind_kt']:\\n return False, f'wind@{int(time)}'\\n \\n if gust > limits['max_gust_kt']:\\n return False, f'gust@{int(time)}'\\n \\n # Calculate angle difference\\n course = calculate_course(from_pos, to_pos)\\n angle_diff = abs(wind_from_direction - course)\\n if angle_diff > 180:\\n angle_diff = 360 - angle_diff\\n \\n # Check headwind condition\\n if angle_diff < limits['min_wind_angle_deg'] and wind_speed > limits['headwind_max_kt']:\\n return False, f'headwind@{int(time)}'\\n \\n return True, None\\n\\ndef find_longest_ok_run(departures):\\n \\\"\\\"\\\"Find the longest consecutive run of OK departures\\\"\\\"\\\"\\n if not departures:\\n return None\\n \\n best_start = None\\n best_length = 0\\n current_start = None\\n current_length = 0\\n \\n for dep in departures:\\n if dep['ok']:\\n if current_start is None:\\n current_start = dep['depart_h']\\n current_length = 1\\n else:\\n current_length += 1\\n else:\\n if current_length > best_length:\\n best_start = current_start\\n best_length = current_length\\n current_start = None\\n current_length = 0\\n \\n # Check if the last sequence is the best\\n if current_length > best_length:\\n best_start = current_start\\n best_length = current_length\\n \\n if best_length == 0:\\n return None\\n else:\\n return [best_start, best_start + best_length - 1]\\n\\ndef create_svg(departures, filename):\\n \\\"\\\"\\\"Create SVG visualization\\\"\\\"\\\"\\n width = 800\\n height = 200\\n margin = 50\\n \\n # Calculate positions\\n depart_hours = [dep['depart_h'] for dep in departures]\\n if not depart_hours:\\n max_hour = 1\\n else:\\n max_hour = max(depart_hours)\\n \\n # Create SVG content\\n svg_content = f'''<svg width=\\\"{width}\\\" height=\\\"{height}\\\" xmlns=\\\"http://www.w3.org/2000/svg\\\">\\n <style>\\n .axis {{ stroke: black; stroke-width: 1; }}\\n .tick {{ stroke: black; stroke-width: 1; }}\\n .label {{ font-family: sans-serif; font-size: 10px; }}\\n .ok {{ fill: green; }}\\n .fail {{ fill: red; }}\\n </style>\\n \\n <!-- Timeline -->\\n <g id=\\\"timeline\\\">\\n <!-- Axis line -->\\n <line class=\\\"axis\\\" x1=\\\"{margin}\\\" y1=\\\"{height//2}\\\" x2=\\\"{width-margin}\\\" y2=\\\"{height//2}\\\" />\\n \\n <!-- Ticks and labels -->\\n'''\\n \\n # Add ticks for each departure hour\\n for dep in departures:\\n x = margin + (width - 2*margin) * dep['depart_h'] / max(1, max_hour)\\n y = height // 2\\n color_class = \\\"ok\\\" if dep['ok'] else \\\"fail\\\"\\n svg_content += f' <circle cx=\\\"{x}\\\" cy=\\\"{y}\\\" r=\\\"3\\\" class=\\\"{color_class}\\\" />\\\\n'\\n \\n svg_content += ''' </g>\\n \\n <!-- Best departure marker -->\\n <g id=\\\"best-departure\\\">\\n'''\\n\\n # Mark earliest OK departure\\n earliest_ok = None\\n for dep in departures:\\n if dep['ok']:\\n earliest_ok = dep['depart_h']\\n break\\n \\n if earliest_ok is not None:\\n x = margin + (width - 2*margin) * earliest_ok / max(1, max_hour)\\n svg_content += f' <rect x=\\\"{x-2}\\\" y=\\\"{height//2-15}\\\" width=\\\"4\\\" height=\\\"10\\\" fill=\\\"blue\\\" />\\\\n'\\n \\n svg_content += ''' </g>\\n</svg>'''\\n \\n with open(filename, 'w') as f:\\n f.write(svg_content)\\n\\ndef main(input_file, output_file):\\n # Read input data\\n input_data = read_input(input_file)\\n grid_data = read_grid(input_data['grid'])\\n \\n # Extract parameters\\n from_pos = input_data['from']\\n to_pos = input_data['to']\\n passage_hours = input_data['passage_hours']\\n latest_departure_h = input_data['latest_departure_h']\\n limits = input_data['limits']\\n \\n # Calculate course\\n course = calculate_course(from_pos, to_pos)\\n \\n # Check all departure times\\n departures = []\\n for t0 in range(latest_departure_h + 1):\\n ok, reason = check_departure(t0, from_pos, to_pos, passage_hours, limits, grid_data)\\n departures.append({\\n 'depart_h': t0,\\n 'ok': ok,\\n 'reason': reason\\n })\\n \\n # Find earliest OK departure\\n earliest_ok_h = None\\n for dep in departures:\\n if dep['ok']:\\n earliest_ok_h = dep['depart_h']\\n break\\n \\n # Find longest OK run\\n longest_ok_run = find_longest_ok_run(departures)\\n \\n # Create output JSON\\n result = {\\n 'course_deg': course,\\n 'departures': departures,\\n 'earliest_ok_h': earliest_ok_h,\\n 'longest_ok_run': longest_ok_run\\n }\\n \\n with open(output_file, 'w') as f:\\n json.dump(result, f, indent=2)\\n \\n # Create SVG\\n create_svg(departures, 'window.svg')\\n\\nif __name__ == '__main__':\\n if len(sys.argv) != 3:\\n print('Usage: python solve.py input.json answer.json')\\n sys.exit(1)\\n \\n input_file = sys.argv[1]\\n output_file = sys.argv[2]\\n main(input_file, output_file)\"}"
}
}
]
},
"finish_reason": "tool_calls",
"logprobs": null
}
],
"usage": {
"prompt_tokens": 4093,
"completion_tokens": 2782,
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"cache_read_input_tokens": 1360
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"cost": {
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"diem": 0.00518395
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"venice_parameters": {
"strip_thinking_response": false,
"disable_thinking": false,
"enable_e2ee": true,
"enable_web_search": "off",
"enable_web_scraping": false,
"enable_web_citations": false,
"include_search_results_in_stream": false,
"include_venice_system_prompt": false,
"enable_x_search": false,
"web_search_citations": [],
"return_search_results_as_documents": false
}
}
},
{
"turn": 3,
"tool": "write_file",
"args": {
"path": "solve.py",
"content": "import json\nimport math\nimport sys\n\ndef read_input(input_file):\n with open(input_file, 'r') as f:\n return json.load(f)\n\ndef read_grid(grid_file):\n with open(grid_file, 'r') as f:\n return json.load(f)\n\ndef interpolate_1d(x, x_list, y_list):\n \"\"\"Linear interpolation in 1D\"\"\"\n if x <= x_list[0]:\n return y_list[0]\n if x >= x_list[-1]:\n return y_list[-1]\n \n # Find the two points to interpolate between\n for i in range(len(x_list) - 1):\n if x_list[i] <= x <= x_list[i+1]:\n x1, x2 = x_list[i], x_list[i+1]\n y1, y2 = y_list[i], y_list[i+1]\n return y1 + (y2 - y1) * (x - x1) / (x2 - x1)\n return y_list[-1]\n\ndef interpolate_2d(lat, lon, lats, lons, values):\n \"\"\"Bilinear interpolation in 2D\"\"\"\n # Clamp lat/lon to grid bounds\n lat = max(lats[0], min(lats[-1], lat))\n lon = max(lons[0], min(lons[-1], lon))\n \n # Find surrounding indices\n lat_i = 0\n while lat_i < len(lats) - 2 and lats[lat_i+1] <= lat:\n lat_i += 1\n \n lon_i = 0\n while lon_i < len(lons) - 2 and lons[lon_i+1] <= lon:\n lon_i += 1\n \n # Get the four corners\n lat1, lat2 = lats[lat_i], lats[lat_i+1]\n lon1, lon2 = lons[lon_i], lons[lon_i+1]\n \n # Values at the four corners\n v11 = values[lat_i][lon_i]\n v12 = values[lat_i][lon_i+1]\n v21 = values[lat_i+1][lon_i]\n v22 = values[lat_i+1][lon_i+1]\n \n # Bilinear interpolation\n if lat1 == lat2:\n v1 = v11\n v2 = v21\n else:\n v1 = v11 + (v21 - v11) * (lat - lat1) / (lat2 - lat1)\n v2 = v12 + (v22 - v12) * (lat - lat1) / (lat2 - lat1)\n \n if lon1 == lon2:\n return v1\n else:\n return v1 + (v2 - v1) * (lon - lon1) / (lon2 - lon1)\n\ndef interpolate_3d(time, lat, lon, times, lats, lons, values):\n \"\"\"Trilinear interpolation in space and time\"\"\"\n # Find bracketing time indices\n if time <= times[0]:\n t_idx1, t_idx2 = 0, 0\n t_factor = 0\n elif time >= times[-1]:\n t_idx1, t_idx2 = len(times)-1, len(times)-1\n t_factor = 0\n else:\n t_idx1 = 0\n while t_idx1 < len(times) - 1 and times[t_idx1+1] <= time:\n t_idx1 += 1\n t_idx2 = min(t_idx1 + 1, len(times) - 1)\n if times[t_idx1] == times[t_idx2]:\n t_factor = 0\n else:\n t_factor = (time - times[t_idx1]) / (times[t_idx2] - times[t_idx1])\n \n # Interpolate at both time points\n if t_idx1 == t_idx2:\n val1 = interpolate_2d(lat, lon, lats, lons, values[t_idx1])\n val2 = val1\n else:\n val1 = interpolate_2d(lat, lon, lats, lons, values[t_idx1])\n val2 = interpolate_2d(lat, lon, lats, lons, values[t_idx2])\n \n # Linear interpolation in time\n return val1 + (val2 - val1) * t_factor\n\ndef calculate_course(from_pos, to_pos):\n \"\"\"Calculate course in degrees\"\"\"\n lat_from, lon_from = from_pos\n lat_to, lon_to = to_pos\n \n # Mean latitude in radians\n mean_lat = math.radians((lat_from + lat_to) / 2)\n \n # Calculate course using atan2\n delta_lat = lat_to - lat_from\n delta_lon = lon_to - lon_from\n y = delta_lon * math.cos(mean_lat)\n x = delta_lat\n \n course = math.degrees(math.atan2(y, x))\n return course % 360\n\ndef get_wind_components(time, lat, lon, grid_data):\n \"\"\"Get interpolated wind components\"\"\"\n u = interpolate_3d(time, lat, lon, grid_data['times_h'], grid_data['lats'], \n grid_data['lons'], grid_data['u_kt'])\n v = interpolate_3d(time, lat, lon, grid_data['times_h'], grid_data['lats'], \n grid_data['lons'], grid_data['v_kt'])\n gust = interpolate_3d(time, lat, lon, grid_data['times_h'], grid_data['lats'], \n grid_data['lons'], grid_data['gust_kt'])\n return u, v, gust\n\ndef check_departure(t0, from_pos, to_pos, passage_hours, limits, grid_data):\n \"\"\"Check if a departure at time t0 is valid\"\"\"\n lat_from, lon_from = from_pos\n lat_to, lon_to = to_pos\n \n for k in range(passage_hours + 1):\n # Calculate position at step k\n fraction = k / passage_hours if passage_hours > 0 else 0\n lat = lat_from + (lat_to - lat_from) * fraction\n lon = lon_from + (lon_to - lon_from) * fraction\n time = t0 + k\n \n # Get wind data\n u, v, gust = get_wind_components(time, lat, lon, grid_data)\n \n # Calculate wind speed and direction\n wind_speed = math.hypot(u, v)\n wind_from_direction = math.degrees(math.atan2(-u, -v)) % 360\n \n # Check limits\n if wind_speed > limits['max_wind_kt']:\n return False, f'wind@{int(time)}'\n \n if gust > limits['max_gust_kt']:\n return False, f'gust@{int(time)}'\n \n # Calculate angle difference\n course = calculate_course(from_pos, to_pos)\n angle_diff = abs(wind_from_direction - course)\n if angle_diff > 180:\n angle_diff = 360 - angle_diff\n \n # Check headwind condition\n if angle_diff < limits['min_wind_angle_deg'] and wind_speed > limits['headwind_max_kt']:\n return False, f'headwind@{int(time)}'\n \n return True, None\n\ndef find_longest_ok_run(departures):\n \"\"\"Find the longest consecutive run of OK departures\"\"\"\n if not departures:\n return None\n \n best_start = None\n best_length = 0\n current_start = None\n current_length = 0\n \n for dep in departures:\n if dep['ok']:\n if current_start is None:\n current_start = dep['depart_h']\n current_length = 1\n else:\n current_length += 1\n else:\n if current_length > best_length:\n best_start = current_start\n best_length = current_length\n current_start = None\n current_length = 0\n \n # Check if the last sequence is the best\n if current_length > best_length:\n best_start = current_start\n best_length = current_length\n \n if best_length == 0:\n return None\n else:\n return [best_start, best_start + best_length - 1]\n\ndef create_svg(departures, filename):\n \"\"\"Create SVG visualization\"\"\"\n width = 800\n height = 200\n margin = 50\n \n # Calculate positions\n depart_hours = [dep['depart_h'] for dep in departures]\n if not depart_hours:\n max_hour = 1\n else:\n max_hour = max(depart_hours)\n \n # Create SVG content\n svg_content = f'''<svg width=\"{width}\" height=\"{height}\" xmlns=\"http://www.w3.org/2000/svg\">\n <style>\n .axis {{ stroke: black; stroke-width: 1; }}\n .tick {{ stroke: black; stroke-width: 1; }}\n .label {{ font-family: sans-serif; font-size: 10px; }}\n .ok {{ fill: green; }}\n .fail {{ fill: red; }}\n </style>\n \n <!-- Timeline -->\n <g id=\"timeline\">\n <!-- Axis line -->\n <line class=\"axis\" x1=\"{margin}\" y1=\"{height//2}\" x2=\"{width-margin}\" y2=\"{height//2}\" />\n \n <!-- Ticks and labels -->\n'''\n \n # Add ticks for each departure hour\n for dep in departures:\n x = margin + (width - 2*margin) * dep['depart_h'] / max(1, max_hour)\n y = height // 2\n color_class = \"ok\" if dep['ok'] else \"fail\"\n svg_content += f' <circle cx=\"{x}\" cy=\"{y}\" r=\"3\" class=\"{color_class}\" />\\n'\n \n svg_content += ''' </g>\n \n <!-- Best departure marker -->\n <g id=\"best-departure\">\n'''\n\n # Mark earliest OK departure\n earliest_ok = None\n for dep in departures:\n if dep['ok']:\n earliest_ok = dep['depart_h']\n break\n \n if earliest_ok is not None:\n x = margin + (width - 2*margin) * earliest_ok / max(1, max_hour)\n svg_content += f' <rect x=\"{x-2}\" y=\"{height//2-15}\" width=\"4\" height=\"10\" fill=\"blue\" />\\n'\n \n svg_content += ''' </g>\n</svg>'''\n \n with open(filename, 'w') as f:\n f.write(svg_content)\n\ndef main(input_file, output_file):\n # Read input data\n input_data = read_input(input_file)\n grid_data = read_grid(input_data['grid'])\n \n # Extract parameters\n from_pos = input_data['from']\n to_pos = input_data['to']\n passage_hours = input_data['passage_hours']\n latest_departure_h = input_data['latest_departure_h']\n limits = input_data['limits']\n \n # Calculate course\n course = calculate_course(from_pos, to_pos)\n \n # Check all departure times\n departures = []\n for t0 in range(latest_departure_h + 1):\n ok, reason = check_departure(t0, from_pos, to_pos, passage_hours, limits, grid_data)\n departures.append({\n 'depart_h': t0,\n 'ok': ok,\n 'reason': reason\n })\n \n # Find earliest OK departure\n earliest_ok_h = None\n for dep in departures:\n if dep['ok']:\n earliest_ok_h = dep['depart_h']\n break\n \n # Find longest OK run\n longest_ok_run = find_longest_ok_run(departures)\n \n # Create output JSON\n result = {\n 'course_deg': course,\n 'departures': departures,\n 'earliest_ok_h': earliest_ok_h,\n 'longest_ok_run': longest_ok_run\n }\n \n with open(output_file, 'w') as f:\n json.dump(result, f, indent=2)\n \n # Create SVG\n create_svg(departures, 'window.svg')\n\nif __name__ == '__main__':\n if len(sys.argv) != 3:\n print('Usage: python solve.py input.json answer.json')\n sys.exit(1)\n \n input_file = sys.argv[1]\n output_file = sys.argv[2]\n main(input_file, output_file)"
},
"result": "wrote 9556 chars"
},
{
"turn": 4,
"response": {
"service_tier": "default",
"id": "chatcmpl-R1C6TL1Pp5YDQgj0zCpJ68D4",
"object": "chat.completion",
"created": 1793060867,
"model": "qwen3-coder-480b-a35b-instruct-turbo",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Let me test the implementation with the provided input files.\n\n",
"reasoning_content": null,
"name": null,
"tool_calls": [
{
"id": "call_e00424edf7b09a2b",
"type": "function",
"function": {
"name": "run_shell",
"arguments": "{\"command\": \"python solve.py input.json answer.json\"}"
}
}
]
},
"finish_reason": "tool_calls",
"logprobs": null
}
],
"usage": {
"prompt_tokens": 6897,
"completion_tokens": 40,
"total_tokens": 6937,
"prompt_tokens_details": {
"cached_tokens": 6848
},
"cache_read_input_tokens": 6848
},
"cost": {
"usd": 0,
"diem": 0.00035107
},
"venice_parameters": {
"strip_thinking_response": false,
"disable_thinking": false,
"enable_e2ee": true,
"enable_web_search": "off",
"enable_web_scraping": false,
"enable_web_citations": false,
"include_search_results_in_stream": false,
"include_venice_system_prompt": false,
"enable_x_search": false,
"web_search_citations": [],
"return_search_results_as_documents": false
}
}
},
{
"turn": 4,
"tool": "run_shell",
"args": {
"command": "python solve.py input.json answer.json"
},
"result": "exit=127\nstdout:\n\nstderr:\nbash: line 1: python: command not found\n"
},
{
"turn": 5,
"response": {
"service_tier": "default",
"id": "chatcmpl-RPCB3598cdZ5CqkubQBSAMu2",
"object": "chat.completion",
"created": 1793060868,
"model": "qwen3-coder-480b-a35b-instruct-turbo",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Let me check what Python version is available:\n\n",
"reasoning_content": null,
"name": null,
"tool_calls": [
{
"id": "call_d7e7a78c45cc22b9",
"type": "function",
"function": {
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"arguments": "{\"command\": \"which python3\"}"
}
}
]
},
"finish_reason": "tool_calls",
"logprobs": null
}
],
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"cache_read_input_tokens": 6912
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"cost": {
"usd": 0,
"diem": 0.00034848
},
"venice_parameters": {
"strip_thinking_response": false,
"disable_thinking": false,
"enable_e2ee": true,
"enable_web_search": "off",
"enable_web_scraping": false,
"enable_web_citations": false,
"include_search_results_in_stream": false,
"include_venice_system_prompt": false,
"enable_x_search": false,
"web_search_citations": [],
"return_search_results_as_documents": false
}
}
},
{
"turn": 5,
"tool": "run_shell",
"args": {
"command": "which python3"
},
"result": "exit=0\nstdout:\n/usr/bin/python3\n\nstderr:\n"
},
{
"turn": 6,
"response": {
"service_tier": "default",
"id": "chatcmpl-R584ML62LkXyAt8hOqHGanFk",
"object": "chat.completion",
"created": 1793060868,
"model": "qwen3-coder-480b-a35b-instruct-turbo",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Let me try running with python3:\n\n",
"reasoning_content": null,
"name": null,
"tool_calls": [
{
"id": "call_7668fc287818063d",
"type": "function",
"function": {
"name": "run_shell",
"arguments": "{\"command\": \"python3 solve.py input.json answer.json\"}"
}
}
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{
"turn": 6,
"tool": "run_shell",
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"result": "exit=0\nstdout:\n\nstderr:\n"
},
{
"turn": 7,
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"content": "Great! Let's check the output files:\n\n",
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{
"turn": 7,
"tool": "read_file",
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"result": "{\n \"course_deg\": 39.976776556045984,\n \"departures\": [\n {\n \"depart_h\": 0,\n \"ok\": false,\n \"reason\": \"wind@6\"\n },\n {\n \"depart_h\": 1,\n \"ok\": false,\n \"reason\": \"wind@6\"\n },\n {\n \"depart_h\": 2,\n \"ok\": false,\n \"reason\": \"gust@6\"\n },\n {\n \"depart_h\": 3,\n \"ok\": false,\n \"reason\": \"gust@6\"\n },\n {\n \"depart_h\": 4,\n \"ok\": false,\n \"reason\": \"gust@6\"\n },\n {\n \"depart_h\": 5,\n \"ok\": false,\n \"reason\": \"gust@6\"\n },\n {\n \"depart_h\": 6,\n \"ok\": false,\n \"reason\": \"gust@6\"\n },\n {\n \"depart_h\": 7,\n \"ok\": false,\n \"reason\": \"wind@7\"\n },\n {\n \"depart_h\": 8,\n \"ok\": false,\n \"reason\": \"wind@8\"\n },\n {\n \"depart_h\": 9,\n \"ok\": false,\n \"reason\": \"wind@9\"\n },\n {\n \"depart_h\": 10,\n \"ok\": false,\n \"reason\": \"gust@10\"\n },\n {\n \"depart_h\": 11,\n \"ok\": false,\n \"reason\": \"gust@11\"\n },\n {\n \"depart_h\": 12,\n \"ok\": true,\n \"reason\": null\n },\n {\n \"depart_h\": 13,\n \"ok\": true,\n \"reason\": null\n },\n {\n \"depart_h\": 14,\n \"ok\": true,\n \"reason\": null\n },\n {\n \"depart_h\": 15,\n \"ok\": true,\n \"reason\": null\n },\n {\n \"depart_h\": 16,\n \"ok\": true,\n \"reason\": null\n },\n {\n \"depart_h\": 17,\n \"ok\": true,\n \"reason\": null\n },\n {\n \"depart_h\": 18,\n \"ok\": true,\n \"reason\": null\n },\n {\n \"depart_h\": 19,\n \"ok\": true,\n \"reason\": null\n },\n {\n \"depart_h\": 20,\n \"ok\": true,\n \"reason\": null\n },\n {\n \"depart_h\": 21,\n \"ok\": true,\n \"reason\": null\n },\n {\n \"depart_h\": 22,\n \"ok\": true,\n \"reason\": null\n },\n {\n \"depart_h\": 23,\n \"ok\": true,\n \"reason\": null\n },\n {\n \"depart_h\": 24,\n \"ok\": true,\n \"reason\": null\n },\n {\n \"depart_h\": 25,\n \"ok\": true,\n \"reason\": null\n },\n {\n \"depart_h\": 26,\n \"ok\": true,\n \"reason\": null\n },\n {\n \"depart_h\": 27,\n \"ok\": true,\n \"reason\": null\n },\n {\n \"depart_h\": 28,\n \"ok\": true,\n \"reason\": null\n },\n {\n \"depart_h\": 29,\n \"ok\": true,\n \"reason\": null\n },\n {\n \"depart_h\": 30,\n \"ok\": true,\n \"reason\": null\n }\n ],\n \"earliest_ok_h\": 12,\n \"longest_ok_run\": [\n 12,\n 30\n ]\n}"
},
{
"turn": 8,
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"content": "Let's also check the SVG file:\n\n",
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{
"turn": 8,
"tool": "read_file",
"args": {
"path": "window.svg"
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"result": "<svg width=\"800\" height=\"200\" xmlns=\"http://www.w3.org/2000/svg\">\n <style>\n .axis { stroke: black; stroke-width: 1; }\n .tick { stroke: black; stroke-width: 1; }\n .label { font-family: sans-serif; font-size: 10px; }\n .ok { fill: green; }\n .fail { fill: red; }\n </style>\n \n <!-- Timeline -->\n <g id=\"timeline\">\n <!-- Axis line -->\n <line class=\"axis\" x1=\"50\" y1=\"100\" x2=\"750\" y2=\"100\" />\n \n <!-- Ticks and labels -->\n <circle cx=\"50.0\" cy=\"100\" r=\"3\" class=\"fail\" />\n <circle cx=\"73.33333333333333\" cy=\"100\" r=\"3\" class=\"fail\" />\n <circle cx=\"96.66666666666666\" cy=\"100\" r=\"3\" class=\"fail\" />\n <circle cx=\"120.0\" cy=\"100\" r=\"3\" class=\"fail\" />\n <circle cx=\"143.33333333333331\" cy=\"100\" r=\"3\" class=\"fail\" />\n <circle cx=\"166.66666666666669\" cy=\"100\" r=\"3\" class=\"fail\" />\n <circle cx=\"190.0\" cy=\"100\" r=\"3\" class=\"fail\" />\n <circle cx=\"213.33333333333334\" cy=\"100\" r=\"3\" class=\"fail\" />\n <circle cx=\"236.66666666666666\" cy=\"100\" r=\"3\" class=\"fail\" />\n <circle cx=\"260.0\" cy=\"100\" r=\"3\" class=\"fail\" />\n <circle cx=\"283.33333333333337\" cy=\"100\" r=\"3\" class=\"fail\" />\n <circle cx=\"306.6666666666667\" cy=\"100\" r=\"3\" class=\"fail\" />\n <circle cx=\"330.0\" cy=\"100\" r=\"3\" class=\"ok\" />\n <circle cx=\"353.3333333333333\" cy=\"100\" r=\"3\" class=\"ok\" />\n <circle cx=\"376.6666666666667\" cy=\"100\" r=\"3\" class=\"ok\" />\n <circle cx=\"400.0\" cy=\"100\" r=\"3\" class=\"ok\" />\n <circle cx=\"423.3333333333333\" cy=\"100\" r=\"3\" class=\"ok\" />\n <circle cx=\"446.6666666666667\" cy=\"100\" r=\"3\" class=\"ok\" />\n <circle cx=\"470.0\" cy=\"100\" r=\"3\" class=\"ok\" />\n <circle cx=\"493.3333333333333\" cy=\"100\" r=\"3\" class=\"ok\" />\n <circle cx=\"516.6666666666667\" cy=\"100\" r=\"3\" class=\"ok\" />\n <circle cx=\"540.0\" cy=\"100\" r=\"3\" class=\"ok\" />\n <circle cx=\"563.3333333333334\" cy=\"100\" r=\"3\" class=\"ok\" />\n <circle cx=\"586.6666666666666\" cy=\"100\" r=\"3\" class=\"ok\" />\n <circle cx=\"610.0\" cy=\"100\" r=\"3\" class=\"ok\" />\n <circle cx=\"633.3333333333334\" cy=\"100\" r=\"3\" class=\"ok\" />\n <circle cx=\"656.6666666666666\" cy=\"100\" r=\"3\" class=\"ok\" />\n <circle cx=\"680.0\" cy=\"100\" r=\"3\" class=\"ok\" />\n <circle cx=\"703.3333333333334\" cy=\"100\" r=\"3\" class=\"ok\" />\n <circle cx=\"726.6666666666666\" cy=\"100\" r=\"3\" class=\"ok\" />\n <circle cx=\"750.0\" cy=\"100\" r=\"3\" class=\"ok\" />\n </g>\n \n <!-- Best departure marker -->\n <g id=\"best-departure\">\n <rect x=\"328.0\" y=\"85\" width=\"4\" height=\"10\" fill=\"blue\" />\n </g>\n</svg>"
},
{
"turn": 9,
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{
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"message": {
"role": "assistant",
"content": "The implementation is working correctly. Let me summarize what was done:\n\n1. Created a Python script (`solve.py`) that:\n - Reads input data from JSON files\n - Calculates the course between start and end positions\n - For each possible departure hour, checks if the journey is\u53ef\u884c by sampling wind conditions at regular intervals\n - Applies the specified rules for wind speed, gusts, and headwind limitations\n - Finds the earliest OK departure and the longest consecutive run of OK departures\n - Outputs results to `answer.json` and creates a visualization in `window.svg`\n\n2. The solution handles:\n - Bilinear interpolation in space and linear interpolation in time for wind data\n - Proper clamping at grid boundaries\n - Accurate calculation of course and wind angles\n - All specified failure conditions with correct formatting of reasons\n - Generation of a static SVG with proper IDs as required\n\nThe program successfully processes the input data and produces the expected outputs with correct results.",
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