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    "slug": "magnetometer-calibration",
    "name": "Pywayne Calibration Magnetometer Calibration",
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    "sourceUrl": "https://clawhub.ai/wangyendt/magnetometer-calibration",
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    "prompts": [
      {
        "label": "New install",
        "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete."
      },
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        "label": "Upgrade existing",
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  "documentation": {
    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "Pywayne Calibration",
        "body": "pywayne.calibration.MagnetometerCalibrator provides magnetometer calibration using sensor data (accelerometer, gyroscope, magnetometer)."
      },
      {
        "title": "Quick Start",
        "body": "from pywayne.calibration import MagnetometerCalibrator\nimport numpy as np\n\n# Sensor data: ts (N,), acc (N,3), gyro (N,3), mag (N,3)\ncalibrator = MagnetometerCalibrator(method='close_form')\nSm, h = calibrator.process(ts, acc, gyro, mag)\n\n# Sm: Soft-iron matrix (3x3)\n# h: Hard-iron offset vector (3,)"
      },
      {
        "title": "Input Data Format",
        "body": "Sensor data must be numpy arrays with matching sample counts:\n\nts   # (N,)     - Timestamps (seconds)\nacc  # (N, 3)   - Accelerometer [ax, ay, az]\ngyro # (N, 3)   - Gyroscope [gx, gy, gz]\nmag  # (N, 3)   - Magnetometer [mx, my, mz]\n\nData requirements:\n\nSensor data should cover various orientations for effective calibration\nMinimum data points required (exact number depends on calibration stability)\nArrays must be C-contiguous (auto-converted internally)"
      },
      {
        "title": "Calibration Parameters",
        "body": "process() returns:\n\nParameterShapeDescriptionSm(3, 3)Soft-iron matrixh(3,)Hard-iron offset vector"
      },
      {
        "title": "Usage in Application",
        "body": "Apply calibration to raw magnetometer readings:\n\n# Calibrated reading\nm_calibrated = Sm @ (m_raw - h)"
      },
      {
        "title": "Temporal Calibration",
        "body": "Temporal calibration module exists but is reserved for future expansion. Currently no functionality is implemented."
      },
      {
        "title": "Important Notes",
        "body": "Dependencies: Requires vqf (VQF quaternion filter) and qmt (quaternion math) modules\nMethod: Currently only supports close_form method\nOrientation: Uses VQF for sensor fusion and orientation estimation during calibration\nOutput: Prints calibration parameters during processing"
      }
    ],
    "body": "Pywayne Calibration\n\npywayne.calibration.MagnetometerCalibrator provides magnetometer calibration using sensor data (accelerometer, gyroscope, magnetometer).\n\nQuick Start\nfrom pywayne.calibration import MagnetometerCalibrator\nimport numpy as np\n\n# Sensor data: ts (N,), acc (N,3), gyro (N,3), mag (N,3)\ncalibrator = MagnetometerCalibrator(method='close_form')\nSm, h = calibrator.process(ts, acc, gyro, mag)\n\n# Sm: Soft-iron matrix (3x3)\n# h: Hard-iron offset vector (3,)\n\nInput Data Format\n\nSensor data must be numpy arrays with matching sample counts:\n\nts   # (N,)     - Timestamps (seconds)\nacc  # (N, 3)   - Accelerometer [ax, ay, az]\ngyro # (N, 3)   - Gyroscope [gx, gy, gz]\nmag  # (N, 3)   - Magnetometer [mx, my, mz]\n\n\nData requirements:\n\nSensor data should cover various orientations for effective calibration\nMinimum data points required (exact number depends on calibration stability)\nArrays must be C-contiguous (auto-converted internally)\nCalibration Parameters\n\nprocess() returns:\n\nParameter\tShape\tDescription\nSm\t(3, 3)\tSoft-iron matrix\nh\t(3,)\tHard-iron offset vector\nUsage in Application\n\nApply calibration to raw magnetometer readings:\n\n# Calibrated reading\nm_calibrated = Sm @ (m_raw - h)\n\nTemporal Calibration\n\nTemporal calibration module exists but is reserved for future expansion. Currently no functionality is implemented.\n\nImportant Notes\nDependencies: Requires vqf (VQF quaternion filter) and qmt (quaternion math) modules\nMethod: Currently only supports close_form method\nOrientation: Uses VQF for sensor fusion and orientation estimation during calibration\nOutput: Prints calibration parameters during processing"
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    "sourceLabel": "tencent",
    "provenanceUrl": "https://clawhub.ai/wangyendt/magnetometer-calibration",
    "publisherUrl": "https://clawhub.ai/wangyendt/magnetometer-calibration",
    "owner": "wangyendt",
    "version": "0.1.0",
    "license": null,
    "verificationStatus": "Indexed source record"
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