Initial commit: Godot Pong game with WebSocket accelerometer control.

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>

scripts/accel_calibration.gd

@@ -0,0 +1,110 @@
+class_name AccelCalibration
+extends RefCounted
+
+enum Axis { X, Y, Z }
+
+var axis: Axis = Axis.X
+var sign: float = 1.0
+var center: float = 0.0
+var suggested_threshold: float = 3000.0
+
+var _samples_right: Array[Vector3] = []
+var _samples_left: Array[Vector3] = []
+
+
+func reset() -> void:
+	_samples_right.clear()
+	_samples_left.clear()
+
+
+func record_right(accel: Vector3i) -> void:
+	_samples_right.append(Vector3(accel))
+
+
+func record_left(accel: Vector3i) -> void:
+	_samples_left.append(Vector3(accel))
+
+
+func is_ready() -> bool:
+	return _samples_right.size() >= 20 and _samples_left.size() >= 20
+
+
+func analyze() -> bool:
+	if not is_ready():
+		return false
+
+	var mean_r := _mean(_samples_right)
+	var mean_l := _mean(_samples_left)
+	var diff := mean_r - mean_l
+
+	var diffs := [absf(diff.x), absf(diff.y), absf(diff.z)]
+	axis = Axis.X
+	var best: float = diffs[0]
+	if diffs[1] > best:
+		best = diffs[1]
+		axis = Axis.Y
+	if diffs[2] > best:
+		best = diffs[2]
+		axis = Axis.Z
+
+	var delta := _component(diff, axis)
+	sign = 1.0 if delta >= 0.0 else -1.0
+
+	var all_samples: Array[Vector3] = []
+	all_samples.append_array(_samples_right)
+	all_samples.append_array(_samples_left)
+
+	var projections: Array[float] = []
+	for sample in all_samples:
+		projections.append(_component(sample, axis) * sign)
+	projections.sort()
+	center = projections[projections.size() / 2]
+
+	var deviations: Array[float] = []
+	for value in projections:
+		deviations.append(absf(value - center))
+
+	deviations.sort()
+	var median_idx := deviations.size() / 2
+	var spread := deviations[median_idx] if not deviations.is_empty() else 500.0
+	suggested_threshold = clampf(spread * 1.35, 200.0, 12000.0)
+	return true
+
+
+func project(accel: Vector3i) -> float:
+	return _component(Vector3(accel), axis) * sign
+
+
+func axis_name() -> String:
+	match axis:
+		Axis.X:
+			return "x"
+		Axis.Y:
+			return "y"
+		Axis.Z:
+			return "z"
+	return "?"
+
+
+func _mean(samples: Array[Vector3]) -> Vector3:
+	if samples.is_empty():
+		return Vector3.ZERO
+	var sum := Vector3.ZERO
+	for s in samples:
+		sum += s
+	return sum / float(samples.size())
+
+
+func _mean_scalar(samples: Array[Vector3], ax: Axis) -> float:
+	return _component(_mean(samples), ax)
+
+
+func _component(v: Vector3, ax: Axis) -> float:
+	match ax:
+		Axis.X:
+			return v.x
+		Axis.Y:
+			return v.y
+		Axis.Z:
+			return v.z
+	return 0.0