simon
bc25226a31
Includes platform steering via calibrated accel axes, ball physics, calibration overlay with axis detection, and runtime tuning sliders. Co-authored-by: Cursor <cursoragent@cursor.com>
111 lines
2.3 KiB
GDScript
111 lines
2.3 KiB
GDScript
class_name AccelCalibration
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extends RefCounted
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enum Axis { X, Y, Z }
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var axis: Axis = Axis.X
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var sign: float = 1.0
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var center: float = 0.0
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var suggested_threshold: float = 3000.0
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var _samples_right: Array[Vector3] = []
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var _samples_left: Array[Vector3] = []
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func reset() -> void:
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_samples_right.clear()
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_samples_left.clear()
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func record_right(accel: Vector3i) -> void:
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_samples_right.append(Vector3(accel))
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func record_left(accel: Vector3i) -> void:
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_samples_left.append(Vector3(accel))
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func is_ready() -> bool:
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return _samples_right.size() >= 20 and _samples_left.size() >= 20
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func analyze() -> bool:
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if not is_ready():
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return false
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var mean_r := _mean(_samples_right)
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var mean_l := _mean(_samples_left)
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var diff := mean_r - mean_l
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var diffs := [absf(diff.x), absf(diff.y), absf(diff.z)]
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axis = Axis.X
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var best: float = diffs[0]
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if diffs[1] > best:
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best = diffs[1]
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axis = Axis.Y
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if diffs[2] > best:
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best = diffs[2]
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axis = Axis.Z
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var delta := _component(diff, axis)
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sign = 1.0 if delta >= 0.0 else -1.0
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var all_samples: Array[Vector3] = []
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all_samples.append_array(_samples_right)
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all_samples.append_array(_samples_left)
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var projections: Array[float] = []
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for sample in all_samples:
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projections.append(_component(sample, axis) * sign)
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projections.sort()
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center = projections[projections.size() / 2]
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var deviations: Array[float] = []
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for value in projections:
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deviations.append(absf(value - center))
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deviations.sort()
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var median_idx := deviations.size() / 2
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var spread := deviations[median_idx] if not deviations.is_empty() else 500.0
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suggested_threshold = clampf(spread * 1.35, 200.0, 12000.0)
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return true
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func project(accel: Vector3i) -> float:
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return _component(Vector3(accel), axis) * sign
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func axis_name() -> String:
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match axis:
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Axis.X:
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return "x"
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Axis.Y:
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return "y"
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Axis.Z:
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return "z"
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return "?"
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func _mean(samples: Array[Vector3]) -> Vector3:
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if samples.is_empty():
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return Vector3.ZERO
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var sum := Vector3.ZERO
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for s in samples:
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sum += s
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return sum / float(samples.size())
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func _mean_scalar(samples: Array[Vector3], ax: Axis) -> float:
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return _component(_mean(samples), ax)
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func _component(v: Vector3, ax: Axis) -> float:
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match ax:
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Axis.X:
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return v.x
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Axis.Y:
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return v.y
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Axis.Z:
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return v.z
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return 0.0
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