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https://forge.fsky.io/oneflux/omegafox.git
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82 lines
2.6 KiB
Python
82 lines
2.6 KiB
Python
import sqlite3
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from pathlib import Path
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from typing import Dict, Optional
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import numpy as np
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import orjson
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def sample_webgl(
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os: str, vendor: Optional[str] = None, renderer: Optional[str] = None
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) -> Dict[str, str]:
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"""
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Sample a random WebGL vendor/renderer combination and its data based on OS probabilities.
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Optionally use a specific vendor/renderer pair.
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Args:
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os: Operating system ('win', 'mac', or 'lin')
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vendor: Optional specific vendor to use
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renderer: Optional specific renderer to use (requires vendor to be set)
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Returns:
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Dict containing WebGL data including vendor, renderer and additional parameters
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Raises:
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ValueError: If invalid OS provided or no data found for OS/vendor/renderer
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"""
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# Map OS to probability column
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os_map = {'win': 'windows', 'mac': 'macos', 'lin': 'linux'}
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if os not in os_map:
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raise ValueError(f'Invalid OS: {os}. Must be one of: {", ".join(os_map)}')
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os = os_map[os]
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# Get database path relative to this file
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db_path = Path(__file__).parent / 'webgl_data.db'
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# Connect to database
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conn = sqlite3.connect(db_path)
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cursor = conn.cursor()
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if vendor and renderer:
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# Get specific vendor/renderer pair and verify it exists for this OS
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cursor.execute(
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f'SELECT vendor, renderer, data, {os} FROM webgl_fingerprints WHERE vendor = ? AND renderer = ?',
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(vendor, renderer),
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)
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result = cursor.fetchone()
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conn.close()
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if not result:
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raise ValueError(f'No WebGL data found for vendor "{vendor}" and renderer "{renderer}"')
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if result[3] <= 0: # Check OS-specific probability
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raise ValueError(
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f'Vendor "{vendor}" and renderer "{renderer}" combination not valid for {os}'
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)
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return {
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'vendor': result[0],
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'renderer': result[1],
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**orjson.loads(result[2]),
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}
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# Get all vendor/renderer pairs and their probabilities for this OS
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cursor.execute(f'SELECT vendor, renderer, data, {os} FROM webgl_fingerprints WHERE {os} > 0')
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results = cursor.fetchall()
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conn.close()
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if not results:
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raise ValueError(f'No WebGL data found for OS: {os}')
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# Split into separate arrays
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_, _, data_strs, probs = map(list, zip(*results))
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# Convert probabilities to numpy array and normalize
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probs_array = np.array(probs, dtype=np.float64)
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probs_array = probs_array / probs_array.sum()
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# Sample based on probabilities
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idx = np.random.choice(len(probs_array), p=probs_array)
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# Parse the JSON data string
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return orjson.loads(data_strs[idx])
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