Mistral Small 3.2 24B Instruct (Venice) (thinking on) on Best sailing angle and leg times from a polar table
MMistral Small 3.2 24B Instruct (Venice)thinking on · venice-mini-agent v1.3.1 · cloud · 2026-10-27 · by @bobsiopener
- Estimated cost
- $0.0023
- Wall time
- 46s
- Human step-ins
- 0
- Outcome
- Pass
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Superseded. This run was rescored on 2026-10-28 and replaced by a newer run. It no longer counts toward rankings, use-case tables or the data export; it is kept here for the record.
Summary
Exact prompt
# Best sailing angle from a polar table `input.json` (synthetic) has a boat polar (`polar.tws` wind speeds in knots, `polar.twa` true wind angles in degrees ascending, `polar.speed[j][k]` = boat speed at `twa[j]`, `tws[k]`), the current true wind speed `tws`, and a list of `legs` each with `bearing` (degrees true to the mark), `distance_nm` and `twd` (true wind direction, degrees the wind blows FROM). Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `polar.svg`. Fresh inputs are used too, so no hard-coding. ## Rules 1. Boat speed at (tws, a): for each table row, interpolate linearly in tws (clamped to the first/last column outside the range); then interpolate linearly in twa between rows (clamped to 180 at the end). For a < `twa[0]` (no-go zone) speed is 0. 2. `upwind`: over integer a in 0..90, maximise VMG = speed*cos(a); `downwind`: over integer a in 90..180, maximise -speed*cos(a). Compare VMG rounded to 4 decimals; on a tie take the smaller angle. Report `{"twa": a, "vmg": value rounded to 3}`. 3. Each leg: over integer courses c = 0..359, a = smallest angle between c and `twd` (0..180), s = speed(tws, a), VMC = s*cos(c - bearing). Take the c with the greatest VMC compared at 6 decimals; ties keep the smaller c. Report `course`, `twa` (a), `boat_speed` (3 dp), `vmc` (3 dp), `hours` = distance / VMC (3 dp) or null if VMC <= 0. 4. `total_hours` = sum of non-null leg hours (3 dp). ## Visual output: polar.svg Static, self-contained SVG (root `<svg>`, no scripts, images, links, fonts or other external assets; renders headless), containing an element `id="polar-curve"` (the polar curve at the given tws) and `<line id="best-upwind">` and `<line id="best-downwind">` drawn from the origin along the best angles. The drawing itself is not graded. ## answer.json `{"upwind": {"twa", "vmg"}, "downwind": {"twa", "vmg"}, "legs": [{"course", "twa", "boat_speed", "vmc", "hours"}, ...], "total_hours"}` ## Scoring Hidden checks (partial credit = fraction passed; all must pass): `best_upwind_downwind`, `upwind_and_downwind_legs` (legs 0-1), `reaching_legs_and_total` (legs 2-3 and total), `polar_svg_static` (polar.svg exists, follows the static rules, has the ids/tags above), `fresh_run_1`, `fresh_run_2`. Numbers within 0.002; integers exact. 60-second limit per run.
Bilinear polar lookup with clamping and a no-go zone, integer angle searches with stated rounding and tie rules, and course-vs-mark VMC that forces tacking or gybing angles on beats and runs. Inspired by https://github.com/fpugliano/oroboro-dashboard Headless, offline run on a reproducible fixture. Cost shown is a list-price estimate (measured tokens x pricing.json list price).
Benchmark code for comparing AI models, not navigation or safety software.
Success criteria: 1. best_upwind_downwind 2. upwind_and_downwind_legs 3. reaching_legs_and_total 4. polar_svg_static 5. fresh_run_1 6. fresh_run_2
Setup notes: Use Case Scout batch 10 case k02-polar-vmg-optimizer (offline fixture). Prepared with batch10/prepare.sh; hidden key/tests kept outside the agent's work dir. Prompt = TASK.md verbatim. Scored with the scout's scorer: `python score.py WORKDIR KEYDIR`. Tools: list_files/read_file/write_file/run_shell (venice-mini-agent v1.3), web disabled. Single run, no retries. Visual output (polar.svg) rendered offline to PNG with google-chrome headless, network blocked, 1280x800 at ~3 s after load. Harness: venice-mini-agent v1.3.1 (/workspace/usecase-runner/venice_agent2.py) via the Venice OpenAI-compatible API: same tool loop, system prompt and limits as the cases 02-25 Venice runs (max 25 turns, max_tokens 16000/call, temperature 0.2, Venice system prompt and web search off, per-run cost cap $0.0055, 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_prices.json 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, $0.0055 run cap). Score: 6/6. Cost is an ESTIMATE: measured per-call tokens x pricing.json 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.
Output
Metrics
- Estimated cost
- $0.0023
- Wall time
- 46s
- Human step-ins
- 0
- Turns
- 6
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| MMistral Small 3.2 24B Instruct (Venice)venice/mistral-small-3-2-24b-instructthinking on | 17,425 | 2,791 | $0.0023 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: Mistral Small 3.2 24B Instruct (Venice) (venice/mistral-small-3-2-24b-instruct, thinking on) via
venice-mini-agent v1.3.1, cloud. - It counts as a success only if it meets every success criterion: 1. best_upwind_downwind 2. upwind_and_downwind_legs 3. reaching_legs_and_total 4. polar_svg_static 5. fresh_run_1 6. 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.
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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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"result": "{\n \"polar\": {\n \"tws\": [\n 6,\n 8,\n 10,\n 12,\n 16,\n 20\n ],\n \"twa\": [\n 38,\n 45,\n 52,\n 60,\n 75,\n 90,\n 110,\n 120,\n 135,\n 150,\n 165,\n 180\n ],\n \"speed\": [\n [\n 2.22,\n 3.19,\n 4.01,\n 4.57,\n 6.33,\n 6.82\n ],\n [\n 2.68,\n 3.51,\n 4.7,\n 5.58,\n 7.12,\n 6.99\n ],\n [\n 3.17,\n 4.14,\n 5.34,\n 6.44,\n 7.4,\n 7.25\n ],\n [\n 3.57,\n 4.84,\n 6.07,\n 7.08,\n 7.63,\n 7.59\n ],\n [\n 4.28,\n 5.8,\n 7.1,\n 7.97,\n 7.98,\n 7.89\n ],\n [\n 4.95,\n 6.44,\n 7.87,\n 7.99,\n 7.96,\n 8.08\n ],\n [\n 4.48,\n 6.04,\n 7.72,\n 7.96,\n 7.92,\n 7.66\n ],\n [\n 4.21,\n 5.66,\n 7.22,\n 7.45,\n 7.51,\n 7.65\n ],\n [\n 4.1,\n 5.24,\n 6.71,\n 7.51,\n 7.63,\n 7.72\n ],\n [\n 3.62,\n 4.67,\n 5.8,\n 7.06,\n 7.66,\n 7.62\n ],\n [\n 3.14,\n 4.18,\n 5.16,\n 5.99,\n 7.67,\n 7.57\n ],\n [\n 2.54,\n 3.44,\n 4.31,\n 5.05,\n 6.91,\n 7.51\n ]\n ]\n },\n \"tws\": 17.0,\n \"legs\": [\n {\n \"bearing\": 11,\n \"distance_nm\": 8.4,\n \"twd\": 10\n },\n {\n \"bearing\": 50,\n \"distance_nm\": 8.3,\n \"twd\": 226\n },\n {\n \"bearing\": 27,\n \"distance_nm\": 5.3,\n \"twd\": 121\n },\n {\n \"bearing\": 265,\n \"distance_nm\": 3.5,\n \"twd\": 41\n }\n ]\n}"
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"arguments": "{\"path\": \"./solve.py\", \"content\": \"import json\\r\\nimport math\\r\\n\\r\\ndef interpolate_speed(polar, tws, twa):\\r\\n # Interpolate boat speed based on given tws and twa\\r\\n if twa < polar['twa'][0]:\\r\\n return 0.0 # No-go zone\\r\\n \\r\\n # Find the closest tws values\\r\\n tws_values = polar['tws']\\r\\n if tws <= tws_values[0]:\\r\\n tws1, tws2 = tws_values[0], tws_values[0]\\r\\n ratio = 0.0\\r\\n elif tws >= tws_values[-1]:\\r\\n tws1, tws2 = tws_values[-1], tws_values[-1]\\r\\n ratio = 1.0\\r\\n else:\\r\\n for i in range(len(tws_values) - 1):\\r\\n if tws_values[i] <= tws <= tws_values[i + 1]:\\r\\n tws1, tws2 = tws_values[i], tws_values[i + 1]\\r\\n ratio = (tws - tws1) / (tws2 - tws1)\\r\\n break\\r\\n \\r\\n # Interpolate speed for each twa and then interpolate between them\\r\\n speeds = []\\r\\n for i in range(len(polar['twa'])):\\r\\n speed1 = polar['speed'][i][tws_values.index(tws1)]\\r\\n speed2 = polar['speed'][i][tws_values.index(tws2)]\\r\\n interpolated_speed = speed1 + ratio * (speed2 - speed1)\\r\\n speeds.append(interpolated_speed)\\r\\n \\r\\n # Interpolate between the speeds based on twa\\r\\n if twa >= polar['twa'][-1]:\\r\\n twa1, twa2 = polar['twa'][-1], polar['twa'][-1]\\r\\n ratio_twa = 1.0\\r\\n else:\\r\\n for i in range(len(polar['twa']) - 1):\\r\\n if polar['twa'][i] <= twa <= polar['twa'][i + 1]:\\r\\n twa1, twa2 = polar['twa'][i], polar['twa'][i + 1]\\r\\n ratio_twa = (twa - twa1) / (twa2 - twa1)\\r\\n break\\r\\n \\r\\n speed1 = speeds[polar['twa'].index(twa1)]\\r\\n speed2 = speeds[polar['twa'].index(twa2)]\\r\\n final_speed = speed1 + ratio_twa * (speed2 - speed1)\\r\\n \\r\\n return final_speed\\r\\n\\r\\ndef best_upwind_downwind(polar, tws):\\r\\n best_upwind = {'twa': 0, 'vmg': -1}\\r\\n best_downwind = {'twa': 0, 'vmg': -1}\\r\\n \\r\\n for a in range(0, 91):\\r\\n speed = interpolate_speed(polar, tws, a)\\r\\n vmg = speed * math.cos(math.radians(a))\\r\\n if vmg > best_upwind['vmg']:\\r\\n best_upwind['twa'] = a\\r\\n best_upwind['vmg'] = vmg\\r\\n \\r\\n for a in range(90, 181):\\r\\n speed = interpolate_speed(polar, tws, a)\\r\\n vmg = -speed * math.cos(math.radians(a))\\r\\n if vmg > best_downwind['vmg']:\\r\\n best_downwind['twa'] = a\\r\\n best_downwind['vmg'] = vmg\\r\\n \\r\\n return best_upwind, best_downwind\\r\\n\\r\\ndef solve_legs(polar, tws, legs):\\r\\n results = []\\r\\n for leg in legs:\\r\\n bearing = leg['bearing']\\r\\n distance_nm = leg['distance_nm']\\r\\n twd = leg['twd']\\r\\n \\r\\n best_course = {'course': 0, 'vmc': -1}\\r\\n \\r\\n for c in range(0, 360):\\r\\n a = min(abs(c - twd), abs(c - twd - 360), abs(c - twd + 360))\\r\\n a = a % 360\\r\\n speed = interpolate_speed(polar, tws, a)\\r\\n vmc = speed * math.cos(math.radians(c - bearing))\\r\\n \\r\\n if vmc > best_course['vmc']:\\r\\n best_course['course'] = c\\r\\n best_course['vmc'] = vmc\\r\\n elif vmc == best_course['vmc'] and c < best_course['course']:\\r\\n best_course['course'] = c\\r\\n \\r\\n twa = min(abs(best_course['course'] - twd), abs(best_course['course'] - twd - 360), abs(best_course['course'] - twd + 360)) % 360\\r\\n boat_speed = interpolate_speed(polar, tws, twa)\\r\\n hours = distance_nm / best_course['vmc'] if best_course['vmc'] > 0 else None\\r\\n \\r\\n results.append({\\r\\n 'course': best_course['course'],\\r\\n 'twa': twa,\\r\\n 'boat_speed': round(boat_speed, 3),\\r\\n 'vmc': round(best_course['vmc'], 3),\\r\\n 'hours': round(hours, 3) if hours is not None else None\\r\\n })\\r\\n \\r\\n total_hours = sum(leg['hours'] for leg in results if leg['hours'] is not None)\\r\\n return results, round(total_hours, 3)\\r\\n\\r\\ndef generate_polar_svg(polar, tws, best_upwind, best_downwind):\\r\\n svg_content = \\\"\\\"\\\"\\r\\n <svg width=\\\"500\\\" height=\\\"500\\\" viewBox=\\\"0 0 500 500\\\" xmlns=\\\"http://www.w3.org/2000/svg\\\">\\r\\n <circle cx=\\\"250\\\" cy=\\\"250\\\" r=\\\"200\\\" fill=\\\"none\\\" stroke=\\\"black\\\"/>\\r\\n <path id=\\\"polar-curve\\\" d=\\\"\\\" fill=\\\"none\\\" stroke=\\\"blue\\\"/>\\r\\n <line id=\\\"best-upwind\\\" x1=\\\"250\\\" y1=\\\"250\\\" x2=\\\"250\\\" y2=\\\"50\\\" stroke=\\\"red\\\" stroke-width=\\\"2\\\"/>\\r\\n <line id=\\\"best-downwind\\\" x1=\\\"250\\\" y1=\\\"250\\\" x2=\\\"250\\\" y2=\\\"450\\\" stroke=\\\"green\\\" stroke-width=\\\"2\\\"/>\\r\\n </svg>\\r\\n \\\"\\\"\\\"\\r\\n \\r\\n # Generate polar curve data\\r\\n polar_curve = []\\r\\n for twa in range(0, 360):\\r\\n speed = interpolate_speed(polar, tws, twa)\\r\\n x = 250 + 200 * math.cos(math.radians(twa)) * (speed / 10)\\r\\n y = 250 - 200 * math.sin(math.radians(twa)) * (speed / 10)\\r\\n polar_curve.append(f\\\"L{x},{y}\\\")\\r\\n \\r\\n polar_curve_path = \\\"M250,250 \\\" + \\\" \\\".join(polar_curve) + \\\" Z\\\"\\r\\n svg_content = svg_content.replace(\\\"<path id=\\\\\\\"polar-curve\\\\\\\" d=\\\\\\\"\\\\\\\"\\\", f\\\"<path id=\\\\\\\"polar-curve\\\\\\\" d=\\\\\\\"{polar_curve_path}\\\")\\r\\n \\r\\n # Adjust best upwind and downwind lines\\r\\n best_upwind_angle = best_upwind['twa']\\r\\n best_downwind_angle = best_downwind['twa']\\r\\n \\r\\n x_upwind = 250 + 200 * math.cos(math.radians(best_upwind_angle))\\r\\n y_upwind = 250 - 200 * math.sin(math.radians(best_upwind_angle))\\r\\n svg_content = svg_content.replace(\\r\\n \\\"<line id=\\\\\\\"best-upwind\\\\\\\" x1=\\\\\\\"250\\\\\\\" y1=\\\\\\\"250\\\\\\\" x2=\\\\\\\"250\\\\\\\" y2=\\\\\\\"50\\\\\\\"\\\",\\r\\n f\\\"<line id=\\\\\\\"best-upwind\\\\\\\" x1=\\\\\\\"250\\\\\\\" y1=\\\\\\\"250\\\\\\\" x2=\\\\\\\"{x_upwind}\\\\\\\" y2=\\\\\\\"{y_upwind}\\\\\\\"\\\")\\r\\n \\r\\n x_downwind = 250 + 200 * math.cos(math.radians(best_downwind_angle))\\r\\n y_downwind = 250 - 200 * math.sin(math.radians(best_downwind_angle))\\r\\n svg_content = svg_content.replace(\\r\\n \\\"<line id=\\\\\\\"best-downwind\\\\\\\" x1=\\\\\\\"250\\\\\\\" y1=\\\\\\\"250\\\\\\\" x2=\\\\\\\"250\\\\\\\" y2=\\\\\\\"450\\\\\\\"\\\",\\r\\n f\\\"<line id=\\\\\\\"best-downwind\\\\\\\" x1=\\\\\\\"250\\\\\\\" y1=\\\\\\\"250\\\\\\\" x2=\\\\\\\"{x_downwind}\\\\\\\" y2=\\\\\\\"{y_downwind}\\\\\\\"\\\")\\r\\n \\r\\n return svg_content\\r\\n\\r\\ndef main():\\r\\n with open('input.json', 'r') as f:\\r\\n data = json.load(f)\\r\\n \\r\\n polar = data['polar']\\r\\n tws = data['tws']\\r\\n legs = data['legs']\\r\\n \\r\\n best_upwind, best_downwind = best_upwind_downwind(polar, tws)\\r\\n legs_results, total_hours = solve_legs(polar, tws, legs)\\r\\n \\r\\n answer = {\\r\\n 'upwind': {\\r\\n 'twa': best_upwind['twa'],\\r\\n 'vmg': round(best_upwind['vmg'], 3)\\r\\n },\\r\\n 'downwind': {\\r\\n 'twa': best_downwind['twa'],\\r\\n 'vmg': round(best_downwind['vmg'], 3)\\r\\n },\\r\\n 'legs': legs_results,\\r\\n 'total_hours': total_hours\\r\\n }\\r\\n \\r\\n with open('answer.json', 'w') as f:\\r\\n json.dump(answer, f, indent=4)\\r\\n \\r\\n svg_content = generate_polar_svg(polar, tws, best_upwind, best_downwind)\\r\\n with open('polar.svg', 'w') as f:\\r\\n f.write(svg_content)\\r\\n\\r\\nif __name__ == \\\"__main__\\\":\\r\\n main()\"}"
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"content": "import json\r\nimport math\r\n\r\ndef interpolate_speed(polar, tws, twa):\r\n # Interpolate boat speed based on given tws and twa\r\n if twa < polar['twa'][0]:\r\n return 0.0 # No-go zone\r\n \r\n # Find the closest tws values\r\n tws_values = polar['tws']\r\n if tws <= tws_values[0]:\r\n tws1, tws2 = tws_values[0], tws_values[0]\r\n ratio = 0.0\r\n elif tws >= tws_values[-1]:\r\n tws1, tws2 = tws_values[-1], tws_values[-1]\r\n ratio = 1.0\r\n else:\r\n for i in range(len(tws_values) - 1):\r\n if tws_values[i] <= tws <= tws_values[i + 1]:\r\n tws1, tws2 = tws_values[i], tws_values[i + 1]\r\n ratio = (tws - tws1) / (tws2 - tws1)\r\n break\r\n \r\n # Interpolate speed for each twa and then interpolate between them\r\n speeds = []\r\n for i in range(len(polar['twa'])):\r\n speed1 = polar['speed'][i][tws_values.index(tws1)]\r\n speed2 = polar['speed'][i][tws_values.index(tws2)]\r\n interpolated_speed = speed1 + ratio * (speed2 - speed1)\r\n speeds.append(interpolated_speed)\r\n \r\n # Interpolate between the speeds based on twa\r\n if twa >= polar['twa'][-1]:\r\n twa1, twa2 = polar['twa'][-1], polar['twa'][-1]\r\n ratio_twa = 1.0\r\n else:\r\n for i in range(len(polar['twa']) - 1):\r\n if polar['twa'][i] <= twa <= polar['twa'][i + 1]:\r\n twa1, twa2 = polar['twa'][i], polar['twa'][i + 1]\r\n ratio_twa = (twa - twa1) / (twa2 - twa1)\r\n break\r\n \r\n speed1 = speeds[polar['twa'].index(twa1)]\r\n speed2 = speeds[polar['twa'].index(twa2)]\r\n final_speed = speed1 + ratio_twa * (speed2 - speed1)\r\n \r\n return final_speed\r\n\r\ndef best_upwind_downwind(polar, tws):\r\n best_upwind = {'twa': 0, 'vmg': -1}\r\n best_downwind = {'twa': 0, 'vmg': -1}\r\n \r\n for a in range(0, 91):\r\n speed = interpolate_speed(polar, tws, a)\r\n vmg = speed * math.cos(math.radians(a))\r\n if vmg > best_upwind['vmg']:\r\n best_upwind['twa'] = a\r\n best_upwind['vmg'] = vmg\r\n \r\n for a in range(90, 181):\r\n speed = interpolate_speed(polar, tws, a)\r\n vmg = -speed * math.cos(math.radians(a))\r\n if vmg > best_downwind['vmg']:\r\n best_downwind['twa'] = a\r\n best_downwind['vmg'] = vmg\r\n \r\n return best_upwind, best_downwind\r\n\r\ndef solve_legs(polar, tws, legs):\r\n results = []\r\n for leg in legs:\r\n bearing = leg['bearing']\r\n distance_nm = leg['distance_nm']\r\n twd = leg['twd']\r\n \r\n best_course = {'course': 0, 'vmc': -1}\r\n \r\n for c in range(0, 360):\r\n a = min(abs(c - twd), abs(c - twd - 360), abs(c - twd + 360))\r\n a = a % 360\r\n speed = interpolate_speed(polar, tws, a)\r\n vmc = speed * math.cos(math.radians(c - bearing))\r\n \r\n if vmc > best_course['vmc']:\r\n best_course['course'] = c\r\n best_course['vmc'] = vmc\r\n elif vmc == best_course['vmc'] and c < best_course['course']:\r\n best_course['course'] = c\r\n \r\n twa = min(abs(best_course['course'] - twd), abs(best_course['course'] - twd - 360), abs(best_course['course'] - twd + 360)) % 360\r\n boat_speed = interpolate_speed(polar, tws, twa)\r\n hours = distance_nm / best_course['vmc'] if best_course['vmc'] > 0 else None\r\n \r\n results.append({\r\n 'course': best_course['course'],\r\n 'twa': twa,\r\n 'boat_speed': round(boat_speed, 3),\r\n 'vmc': round(best_course['vmc'], 3),\r\n 'hours': round(hours, 3) if hours is not None else None\r\n })\r\n \r\n total_hours = sum(leg['hours'] for leg in results if leg['hours'] is not None)\r\n return results, round(total_hours, 3)\r\n\r\ndef generate_polar_svg(polar, tws, best_upwind, best_downwind):\r\n svg_content = \"\"\"\r\n <svg width=\"500\" height=\"500\" viewBox=\"0 0 500 500\" xmlns=\"http://www.w3.org/2000/svg\">\r\n <circle cx=\"250\" cy=\"250\" r=\"200\" fill=\"none\" stroke=\"black\"/>\r\n <path id=\"polar-curve\" d=\"\" fill=\"none\" stroke=\"blue\"/>\r\n <line id=\"best-upwind\" x1=\"250\" y1=\"250\" x2=\"250\" y2=\"50\" stroke=\"red\" stroke-width=\"2\"/>\r\n <line id=\"best-downwind\" x1=\"250\" y1=\"250\" x2=\"250\" y2=\"450\" stroke=\"green\" stroke-width=\"2\"/>\r\n </svg>\r\n \"\"\"\r\n \r\n # Generate polar curve data\r\n polar_curve = []\r\n for twa in range(0, 360):\r\n speed = interpolate_speed(polar, tws, twa)\r\n x = 250 + 200 * math.cos(math.radians(twa)) * (speed / 10)\r\n y = 250 - 200 * math.sin(math.radians(twa)) * (speed / 10)\r\n polar_curve.append(f\"L{x},{y}\")\r\n \r\n polar_curve_path = \"M250,250 \" + \" \".join(polar_curve) + \" Z\"\r\n svg_content = svg_content.replace(\"<path id=\\\"polar-curve\\\" d=\\\"\\\"\", f\"<path id=\\\"polar-curve\\\" d=\\\"{polar_curve_path}\")\r\n \r\n # Adjust best upwind and downwind lines\r\n best_upwind_angle = best_upwind['twa']\r\n best_downwind_angle = best_downwind['twa']\r\n \r\n x_upwind = 250 + 200 * math.cos(math.radians(best_upwind_angle))\r\n y_upwind = 250 - 200 * math.sin(math.radians(best_upwind_angle))\r\n svg_content = svg_content.replace(\r\n \"<line id=\\\"best-upwind\\\" x1=\\\"250\\\" y1=\\\"250\\\" x2=\\\"250\\\" y2=\\\"50\\\"\",\r\n f\"<line id=\\\"best-upwind\\\" x1=\\\"250\\\" y1=\\\"250\\\" x2=\\\"{x_upwind}\\\" y2=\\\"{y_upwind}\\\"\")\r\n \r\n x_downwind = 250 + 200 * math.cos(math.radians(best_downwind_angle))\r\n y_downwind = 250 - 200 * math.sin(math.radians(best_downwind_angle))\r\n svg_content = svg_content.replace(\r\n \"<line id=\\\"best-downwind\\\" x1=\\\"250\\\" y1=\\\"250\\\" x2=\\\"250\\\" y2=\\\"450\\\"\",\r\n f\"<line id=\\\"best-downwind\\\" x1=\\\"250\\\" y1=\\\"250\\\" x2=\\\"{x_downwind}\\\" y2=\\\"{y_downwind}\\\"\")\r\n \r\n return svg_content\r\n\r\ndef main():\r\n with open('input.json', 'r') as f:\r\n data = json.load(f)\r\n \r\n polar = data['polar']\r\n tws = data['tws']\r\n legs = data['legs']\r\n \r\n best_upwind, best_downwind = best_upwind_downwind(polar, tws)\r\n legs_results, total_hours = solve_legs(polar, tws, legs)\r\n \r\n answer = {\r\n 'upwind': {\r\n 'twa': best_upwind['twa'],\r\n 'vmg': round(best_upwind['vmg'], 3)\r\n },\r\n 'downwind': {\r\n 'twa': best_downwind['twa'],\r\n 'vmg': round(best_downwind['vmg'], 3)\r\n },\r\n 'legs': legs_results,\r\n 'total_hours': total_hours\r\n }\r\n \r\n with open('answer.json', 'w') as f:\r\n json.dump(answer, f, indent=4)\r\n \r\n svg_content = generate_polar_svg(polar, tws, best_upwind, best_downwind)\r\n with open('polar.svg', 'w') as f:\r\n f.write(svg_content)\r\n\r\nif __name__ == \"__main__\":\r\n main()"
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