Add game center, per-model shuffle, audio focus fixes, README, tests
- README.md with build/architecture overview - Game Center screen with at-bat timeline, pitch sequence, spray chart, and strike zone component views - VideoShuffle service: per-model bucketed random selection with no-back-to-back guarantee; replaces flat shuffle-bag approach - Refresh JWT token for authenticated NSFW feed; add josie-hamming-2 and dani-speegle-2 to the user list - MultiStreamView audio focus: remove redundant isMuted writes during startStream and playNextWerkoutClip so audio stops ducking during clip transitions; gate AVAudioSession.setCategory(.playback) behind a one-shot flag - GamesViewModel.attachPlayer: skip mute recalculation when the same player is re-attached (prevents toggle flicker on item replace) - mlbTVOSTests target wired through project.yml with GENERATE_INFOPLIST_FILE; VideoShuffleTests covers groupByModel, pickRandomFromBuckets, real-distribution no-back-to-back invariant, and uniform model distribution over 6000 picks Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Trey t
252
mlbTVOSTests/VideoShuffleTests.swift
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252
mlbTVOSTests/VideoShuffleTests.swift
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import Foundation
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import Testing
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@testable import mlbTVOS
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struct VideoShuffleTests {
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// MARK: - Helpers
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private struct Clip: Equatable, Hashable {
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let model: String
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let id: Int
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}
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private static func makeClips(_ counts: [String: Int]) -> [Clip] {
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var result: [Clip] = []
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var idCounter = 0
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for (model, count) in counts.sorted(by: { $0.key < $1.key }) {
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for _ in 0..<count {
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result.append(Clip(model: model, id: idCounter))
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idCounter += 1
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}
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}
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return result
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}
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/// Returns the length of the longest contiguous run of same-model clips.
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private static func maxRunLength(_ clips: [Clip]) -> Int {
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guard !clips.isEmpty else { return 0 }
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var maxRun = 1
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var currentRun = 1
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for i in 1..<clips.count {
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if clips[i].model == clips[i - 1].model {
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currentRun += 1
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maxRun = max(maxRun, currentRun)
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} else {
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currentRun = 1
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}
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}
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return maxRun
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}
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/// A deterministic RNG so tests that depend on randomness are reproducible.
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private struct SeededRNG: RandomNumberGenerator {
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private var state: UInt64
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init(seed: UInt64) { self.state = seed == 0 ? 0xdeadbeef : seed }
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mutating func next() -> UInt64 {
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state ^= state << 13
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state ^= state >> 7
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state ^= state << 17
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return state
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}
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}
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// MARK: - groupByModel
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@Test func groupByModelBucketsCorrectly() {
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let clips = Self.makeClips(["a": 3, "b": 2, "c": 1])
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var rng = SeededRNG(seed: 1)
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let groups = VideoShuffle.groupByModel(
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clips,
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keyFor: { $0.model },
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using: &rng
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)
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#expect(groups["a"]?.count == 3)
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#expect(groups["b"]?.count == 2)
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#expect(groups["c"]?.count == 1)
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#expect(groups.count == 3)
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}
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@Test func groupByModelHandlesEmptyInput() {
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var rng = SeededRNG(seed: 1)
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let groups = VideoShuffle.groupByModel(
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[Clip](),
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keyFor: { $0.model },
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using: &rng
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)
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#expect(groups.isEmpty)
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}
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@Test func groupByModelNilKeysGoToEmptyStringBucket() {
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struct Item { let key: String?; let id: Int }
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let items = [
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Item(key: "a", id: 0),
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Item(key: nil, id: 1),
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Item(key: nil, id: 2),
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]
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var rng = SeededRNG(seed: 1)
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let groups = VideoShuffle.groupByModel(
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items,
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keyFor: { $0.key },
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using: &rng
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)
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#expect(groups["a"]?.count == 1)
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#expect(groups[""]?.count == 2)
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}
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// MARK: - pickRandomFromBuckets
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@Test func pickRandomFromBucketsReturnsNilWhenAllEmpty() {
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let buckets: [String: [Clip]] = ["a": [], "b": []]
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var rng = SeededRNG(seed: 1)
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let result = VideoShuffle.pickRandomFromBuckets(
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buckets,
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using: &rng
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)
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#expect(result == nil)
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}
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@Test func pickRandomFromBucketsRemovesPickedItem() {
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let buckets: [String: [Clip]] = [
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"a": [Clip(model: "a", id: 0), Clip(model: "a", id: 1)],
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"b": [Clip(model: "b", id: 2)],
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]
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var rng = SeededRNG(seed: 1)
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let result = VideoShuffle.pickRandomFromBuckets(
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buckets,
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using: &rng
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)
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#expect(result != nil)
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guard let pick = result else { return }
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let originalCount = buckets.values.reduce(0) { $0 + $1.count }
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let remainingCount = pick.remaining.values.reduce(0) { $0 + $1.count }
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#expect(remainingCount == originalCount - 1)
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}
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@Test func pickRandomFromBucketsRespectsExclusion() {
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// With two non-empty buckets, excluding "a" should always return "b".
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let buckets: [String: [Clip]] = [
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"a": [Clip(model: "a", id: 0), Clip(model: "a", id: 1)],
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"b": [Clip(model: "b", id: 2), Clip(model: "b", id: 3)],
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]
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var rng = SeededRNG(seed: 1)
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for _ in 0..<20 {
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let result = VideoShuffle.pickRandomFromBuckets(
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buckets,
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excludingKey: "a",
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using: &rng
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)
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#expect(result?.key == "b")
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}
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}
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@Test func pickRandomFromBucketsFallsBackWhenOnlyExcludedIsNonEmpty() {
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let buckets: [String: [Clip]] = [
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"a": [Clip(model: "a", id: 0)],
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"b": [],
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]
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var rng = SeededRNG(seed: 1)
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let result = VideoShuffle.pickRandomFromBuckets(
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buckets,
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excludingKey: "a",
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using: &rng
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)
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#expect(result?.key == "a", "should fall back to 'a' since 'b' is empty")
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}
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// MARK: - End-to-end: never same model back-to-back
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/// Simulates a full playback session on the real NSFW distribution and
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/// verifies that no two consecutive picks share a model. This is the
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/// invariant the user actually cares about.
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@Test func realNSFWDistributionNeverBackToBack() {
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let clips = Self.makeClips([
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"kayla-lauren": 502,
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"tabatachang": 147,
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"werkout": 137,
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"ray-mattos": 108,
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"dani-speegle-2": 49,
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"josie-hamming-2": 13,
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])
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#expect(clips.count == 956)
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var rng = SeededRNG(seed: 42)
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var buckets = VideoShuffle.groupByModel(
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clips,
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keyFor: { $0.model },
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using: &rng
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)
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// Simulate 2000 picks — more than the total clip count so we exercise
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// the refill logic for the small buckets (josie-hamming-2 has only 13).
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var sequence: [Clip] = []
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var lastModel: String? = nil
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for _ in 0..<2000 {
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// Refill any empty buckets by reshuffling the original clips for
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// that model (mirrors the production logic in GamesViewModel).
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for key in Array(buckets.keys) where buckets[key]?.isEmpty ?? true {
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let modelClips = clips.filter { $0.model == key }
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buckets[key] = modelClips.shuffled(using: &rng)
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}
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guard let pick = VideoShuffle.pickRandomFromBuckets(
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buckets,
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excludingKey: lastModel,
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using: &rng
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) else {
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Issue.record("pick returned nil")
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return
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}
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buckets = pick.remaining
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sequence.append(pick.item)
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lastModel = pick.key
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}
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let run = Self.maxRunLength(sequence)
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#expect(run == 1, "expected max run 1, got \(run)")
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}
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@Test func modelDistributionIsRoughlyUniform() {
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// Over a long session, each model should be picked roughly
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// (totalPicks / modelCount) times — not weighted by bucket size.
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let clips = Self.makeClips([
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"kayla-lauren": 502,
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"tabatachang": 147,
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"werkout": 137,
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"ray-mattos": 108,
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"dani-speegle-2": 49,
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"josie-hamming-2": 13,
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])
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var rng = SeededRNG(seed: 99)
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var buckets = VideoShuffle.groupByModel(
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clips,
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keyFor: { $0.model },
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using: &rng
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)
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let totalPicks = 6000
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var modelCounts: [String: Int] = [:]
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var lastModel: String? = nil
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for _ in 0..<totalPicks {
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for key in Array(buckets.keys) where buckets[key]?.isEmpty ?? true {
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let modelClips = clips.filter { $0.model == key }
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buckets[key] = modelClips.shuffled(using: &rng)
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}
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guard let pick = VideoShuffle.pickRandomFromBuckets(
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buckets,
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excludingKey: lastModel,
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using: &rng
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) else { continue }
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buckets = pick.remaining
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modelCounts[pick.key, default: 0] += 1
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lastModel = pick.key
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}
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// With 6 models and 6000 picks, expected per model ≈ 1000.
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// Allow ±25% (750 to 1250) since the exclusion rule introduces
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// slight bias against whichever model was just picked.
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for (model, count) in modelCounts {
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#expect(count >= 750 && count <= 1250, "\(model) got \(count) picks")
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}
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}
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}
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