Add split-screen Space Shooter with pod steering and boost.
Two-player mode with client assignment, 3D vector calibration for cylindrical pods, lives, tap/double-tap boost with low-intensity LED cooldown feedback, and Kenney sprite assets. Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
Cursor
@@ -3,18 +3,33 @@ extends RefCounted
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enum Axis { X, Y, Z }
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const MIN_SAMPLES := 25
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const TAIL_FRACTION := 0.55
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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 direction: Vector3 = Vector3.RIGHT
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var neutral: Vector3 = Vector3.ZERO
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var uses_vector: bool = false
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var _samples_neutral: Array[Vector3] = []
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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_neutral.clear()
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_samples_right.clear()
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_samples_left.clear()
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uses_vector = false
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direction = Vector3.RIGHT
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neutral = Vector3.ZERO
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func record_neutral(accel: Vector3i) -> void:
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_samples_neutral.append(Vector3(accel))
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func record_right(accel: Vector3i) -> void:
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@@ -26,56 +41,74 @@ func record_left(accel: Vector3i) -> void:
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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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return (
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_samples_neutral.size() >= MIN_SAMPLES
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and _samples_right.size() >= MIN_SAMPLES
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and _samples_left.size() >= MIN_SAMPLES
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)
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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 mean_n := _tail_mean(_samples_neutral)
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var mean_r := _tail_mean(_samples_right)
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var mean_l := _tail_mean(_samples_left)
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var delta := 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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if delta.length_squared() < 150.0 * 150.0:
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return false
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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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direction = delta.normalized()
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neutral = mean_n
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uses_vector = true
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sign = 1.0
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_set_axis_from_direction()
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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 neutral_proj: Array[float] = []
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for sample in _samples_neutral:
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neutral_proj.append(_project_vec(sample))
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neutral_proj.sort()
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center = neutral_proj[neutral_proj.size() / 2]
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var deviations: Array[float] = []
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for value in projections:
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for value in neutral_proj:
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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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var spread := deviations[deviations.size() / 2] if not deviations.is_empty() else 500.0
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suggested_threshold = clampf(spread * 2.2 + 180.0, 250.0, 8000.0)
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var right_proj := _project_vec(mean_r) - center
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var left_proj := _project_vec(mean_l) - center
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var separation := absf(right_proj - left_proj)
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if separation < suggested_threshold * 2.0:
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suggested_threshold = clampf(separation * 0.18, 250.0, 8000.0)
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return true
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func project(accel: Vector3i) -> float:
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if uses_vector:
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return _project_vec(Vector3(accel))
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return _component(Vector3(accel), axis) * sign
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func duplicate() -> AccelCalibration:
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var copy := AccelCalibration.new()
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copy.axis = axis
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copy.sign = sign
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copy.center = center
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copy.suggested_threshold = suggested_threshold
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copy.direction = direction
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copy.neutral = neutral
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copy.uses_vector = uses_vector
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return copy
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func axis_name() -> String:
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if uses_vector:
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return "3D-Schwank (%+.1f,%+.1f,%+.1f)" % [direction.x, direction.y, direction.z]
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match axis:
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Axis.X:
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return "x"
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@@ -86,6 +119,26 @@ func axis_name() -> String:
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return "?"
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func _project_vec(v: Vector3) -> float:
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return (v - neutral).dot(direction)
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func _tail_mean(samples: Array[Vector3]) -> Vector3:
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var start_idx := int(samples.size() * TAIL_FRACTION)
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return _mean(samples.slice(start_idx))
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func _set_axis_from_direction() -> void:
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var abs_dir := direction.abs()
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axis = Axis.X
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var best := abs_dir.x
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if abs_dir.y > best:
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best = abs_dir.y
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axis = Axis.Y
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if abs_dir.z > best:
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axis = Axis.Z
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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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@@ -95,10 +148,6 @@ func _mean(samples: Array[Vector3]) -> Vector3:
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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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