diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml index 4231448..e5ad485 100644 --- a/.github/workflows/docs.yaml +++ b/.github/workflows/docs.yaml @@ -39,7 +39,7 @@ jobs: - name: Setup pixi uses: prefix-dev/setup-pixi@v0.8.1 with: - pixi-version: v0.69.0 + pixi-version: v0.70.2 # enable caching of installed envs cache: true # only write new caches on main pushes (TODO: Enable) diff --git a/.github/workflows/tests.yaml b/.github/workflows/tests.yaml index fa78572..d408a50 100644 --- a/.github/workflows/tests.yaml +++ b/.github/workflows/tests.yaml @@ -14,7 +14,7 @@ jobs: - name: Setup Pixi (installs pixi + caches envs) # https://github.com/marketplace/actions/setup-pixi uses: prefix-dev/setup-pixi@v0.9.3 # pin the action version with: - pixi-version: v0.69.0 # pin the pixi binary version (optional) + pixi-version: v0.70.2 # pin the pixi binary version (optional) cache: true # enable caching of installed envs # only write new caches on main pushes (TODO: Enable) # cache-write: ${{ github.event_name == 'push' && github.ref_name == 'main' }} diff --git a/.gitignore b/.gitignore index 4d35a22..c9da832 100644 --- a/.gitignore +++ b/.gitignore @@ -30,6 +30,8 @@ openai_api_key.sh .cursor docs/ui-previews +docs/specs/ +docs/superpowers test-results playwright-report web/dist @@ -38,3 +40,10 @@ web/tsconfig.tsbuildinfo /log/ /install/ + +# all-in-one analysis byproducts +/demix/ +/spec/ + +CLAUDE.md +memory diff --git a/README.md b/README.md index 18abee1..1c6ccae 100644 --- a/README.md +++ b/README.md @@ -123,6 +123,23 @@ The system will automatically: - Ensure all movements stay within the configured flight boundaries - Synchronize drone movements with the musical timeline +### Adding your own music + +The bundled tracks in `music/songs/` are already analyzed — their structure data lives in +`music/analyzed/`, so they work out of the box. To add a song of your own: + +1. Drop the `.mp3` into `music/songs/`. +2. Analyze it: + ```bash + pixi run -e music analyze + ``` + This scans `music/songs/` and analyzes any track that doesn't yet have a cached result, + writing its structure to `music/analyzed/` and a visualization to `music/viz/`. Songs that + are already analyzed are skipped, so the command is safe to re-run. A CUDA GPU is strongly + recommended; analysis still runs on CPU but is much slower. + +The new song then shows up in the music library the next time you launch the interface. + ### Ready for Deployment Once you're happy with your generated choreography, you can proceed to deploy it on your physical drone swarm. @@ -154,3 +171,18 @@ us as follows: publisher={IEEE} } ``` + +### Music analysis + +Song structure (beats, downbeats, and functional segments) is analyzed with the +[All-In-One Music Structure Analyzer](https://github.com/mir-aidj/all-in-one) by Taejun Kim +and Juhan Nam. If you use this project's music analysis, please also cite their work: + +```bibtex +@inproceedings{taejun2023allinone, + title={All-In-One Metrical And Functional Structure Analysis With Neighborhood Attentions on Demixed Audio}, + author={Kim, Taejun and Nam, Juhan}, + booktitle={IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)}, + year={2023} +} +``` diff --git a/music/Chopin Noctune Op9No2 [deploy].mp3 b/music/Chopin Noctune Op9No2 [deploy].mp3 deleted file mode 100644 index 1da25c9..0000000 Binary files a/music/Chopin Noctune Op9No2 [deploy].mp3 and /dev/null differ diff --git a/music/Chopin Noctune Op9No2.mp3 b/music/Chopin Noctune Op9No2.mp3 deleted file mode 100644 index 1da25c9..0000000 Binary files a/music/Chopin Noctune Op9No2.mp3 and /dev/null differ diff --git a/music/Clair de Lune [deploy].mp3 b/music/Clair de Lune [deploy].mp3 deleted file mode 100644 index 42c55f5..0000000 Binary files a/music/Clair de Lune [deploy].mp3 and /dev/null differ diff --git a/music/Clair de Lune.mp3 b/music/Clair de Lune.mp3 deleted file mode 100644 index 1098f9d..0000000 Binary files a/music/Clair de Lune.mp3 and /dev/null differ diff --git a/music/Dance of the Flying Machines [deploy].mp3 b/music/Dance of the Flying Machines [deploy].mp3 deleted file mode 100644 index abbd676..0000000 Binary files a/music/Dance of the Flying Machines [deploy].mp3 and /dev/null differ diff --git a/music/Ed Sheeran - Perfect.mp3 b/music/Ed Sheeran - Perfect.mp3 deleted file mode 100644 index 935a976..0000000 Binary files a/music/Ed Sheeran - Perfect.mp3 and /dev/null differ diff --git a/music/Mozart Serenade in G, Eine kleine Nachtmusik.mp3 b/music/Mozart Serenade in G, Eine kleine Nachtmusik.mp3 deleted file mode 100644 index 21f2e36..0000000 Binary files a/music/Mozart Serenade in G, Eine kleine Nachtmusik.mp3 and /dev/null differ diff --git a/music/On & On.mp3 b/music/On & On.mp3 deleted file mode 100644 index 51c4e57..0000000 Binary files a/music/On & On.mp3 and /dev/null differ diff --git a/music/OutKast - Hey Ya!.mp3 b/music/OutKast - Hey Ya!.mp3 deleted file mode 100644 index a1d60fe..0000000 Binary files a/music/OutKast - Hey Ya!.mp3 and /dev/null differ diff --git a/music/Radetzky March.mp3 b/music/Radetzky March.mp3 deleted file mode 100644 index 2f8859e..0000000 Binary files a/music/Radetzky March.mp3 and /dev/null differ diff --git a/music/Rap God.mp3 b/music/Rap God.mp3 deleted file mode 100644 index 1ea59d0..0000000 Binary files a/music/Rap God.mp3 and /dev/null differ diff --git a/music/Royalty [deploy].mp3 b/music/Royalty [deploy].mp3 deleted file mode 100644 index cbfe42a..0000000 Binary files a/music/Royalty [deploy].mp3 and /dev/null differ diff --git a/music/Still D.R.E.mp3 b/music/Still D.R.E.mp3 deleted file mode 100644 index f0798e3..0000000 Binary files a/music/Still D.R.E.mp3 and /dev/null differ diff --git a/music/The Blue Danube - Op. 314.mp3 b/music/The Blue Danube - Op. 314.mp3 deleted file mode 100644 index 770e9a1..0000000 Binary files a/music/The Blue Danube - Op. 314.mp3 and /dev/null differ diff --git a/music/Vivaldi Summer [deploy].mp3 b/music/Vivaldi Summer [deploy].mp3 deleted file mode 100644 index 5f78f00..0000000 Binary files a/music/Vivaldi Summer [deploy].mp3 and /dev/null differ diff --git a/music/Vivaldi Summer.mp3 b/music/Vivaldi Summer.mp3 deleted file mode 100644 index 0a3043f..0000000 Binary files a/music/Vivaldi Summer.mp3 and /dev/null differ diff --git a/music/Walking on Sunshine.mp3 b/music/Walking on Sunshine.mp3 deleted file mode 100644 index e53c628..0000000 Binary files a/music/Walking on Sunshine.mp3 and /dev/null differ diff --git a/music/analyzed/.gitkeep b/music/analyzed/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/music/analyzed/Chopin - Nocturne Op. 9 No. 2.json b/music/analyzed/Chopin - Nocturne Op. 9 No. 2.json new file mode 100644 index 0000000..fcf6280 --- /dev/null +++ b/music/analyzed/Chopin - Nocturne Op. 9 No. 2.json @@ -0,0 +1,1782 @@ +{ + "schema_version": 2, + "source_path": "music/songs/Chopin - Nocturne Op. 9 No. 2.mp3", + "song_sha256": "f49e8cd3d533409289a3fae796e112a58d34bf8d2ef31705afcb9e6bd448d649", + "analyzer": "allin1@unknown", + "bpm": 65, + "segments": [ + { + "id": 1, + "label": "intro", + "start_s": 0.01, + "end_s": 28.76, + "bars": [ + { + "id": 1, + "start_s": 3.8, + "beats": [ + { + "id": 1, + "time_s": 3.8, + "position_in_bar": 2 + }, + { + "id": 2, + "time_s": 4.81, + "position_in_bar": 3 + }, + { + "id": 3, + "time_s": 5.73, + "position_in_bar": 4 + } + ] + }, + { + "id": 2, + "start_s": 6.67, + "beats": [ + { + "id": 1, + "time_s": 6.67, + "position_in_bar": 1 + }, + { + "id": 2, + "time_s": 7.61, + "position_in_bar": 2 + }, + { + "id": 3, + "time_s": 8.61, + "position_in_bar": 3 + }, + { + "id": 4, + "time_s": 9.58, + "position_in_bar": 4 + } + ] + }, + { + "id": 3, + "start_s": 10.5, + "beats": [ + { + "id": 1, + "time_s": 10.5, + "position_in_bar": 1 + }, + { + "id": 2, + 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BY-NC-ND 3.0 DEED - -https://creativecommons.org/licenses/by-nc-nd/3.0/ - -Performance by A Far Cry - -https://musopen.org/music/28074-serenade-no-13-a-little-night-music-eine-kleine-nachtmusik-k-525/ diff --git a/music/licenses.toml b/music/licenses.toml new file mode 100644 index 0000000..1d19b48 --- /dev/null +++ b/music/licenses.toml @@ -0,0 +1,64 @@ +# Provenance and attribution for every track in this directory. +# +# Keyed by the song's filename stem (the .mp3 name without extension), which is +# how the app identifies songs (see swarm_gpt/utils/music_manager.py). + +[songs."Chopin - Nocturne Op. 9 No. 2"] +license = "CC BY-SA 2.0 (recording); composition is public domain" +license_url = "https://creativecommons.org/licenses/by-sa/2.0/" +attribution = "Performance by Martha Goldstein (rec. 2006), from the Al Goldstein collection, ibiblio.org" +source = "https://upload.wikimedia.org/wikipedia/commons/5/5c/Frederic_Chopin_-_Nocturne_Eb_major_Opus_9%2C_number_2.ogg" + +[songs."Debussy - Clair de Lune"] +license = "CC BY-NC 3.0" +license_url = "https://creativecommons.org/licenses/by-nc/3.0/" +attribution = "Performance by Simone Renzi" +source = "https://musopen.org/music/2504-suite-bergamasque/" + +[songs."Fearless2"] +license = "NCS (NoCopyrightSounds) Free Download/Stream license" +license_url = "https://ncs.io/Fearless2" +attribution = "TULE - Fearless pt.II (feat. Chris Linton) [NCS Release]. Music provided by NoCopyrightSounds. Watch: http://youtu.be/S19UcWdOA-I" +source = "https://ncs.io/Fearless2" + +[songs."Mortals"] +license = "NCS (NoCopyrightSounds) Free Download/Stream license" +license_url = "https://ncs.io/Mortals" +attribution = "Warriyo - Mortals (feat. Laura Brehm) [NCS Release]. Music provided by NoCopyrightSounds. Watch: http://youtu.be/yJg-Y5byMMw" +source = "https://ncs.io/Fearless2" + +[songs."Mozart - Eine kleine Nachtmusik"] +license = "CC BY-NC-ND 3.0" +license_url = "https://creativecommons.org/licenses/by-nc-nd/3.0/" +attribution = "Performance by A Far Cry" +source = "https://musopen.org/music/28074-serenade-no-13-a-little-night-music-eine-kleine-nachtmusik-k-525/" + +[songs."J. Strauss I - Radetzky March"] +license = "Public Domain (US Government Work)" +license_url = "https://creativecommons.org/publicdomain/mark/1.0/" +attribution = "Recording by \"The President's Own\" United States Marine Band, from Sound Off!, recorded May 18-21, 1992 at Center for the Arts, George Mason University; conducted by John R. Bourgeois" +source = "https://www.marineband.marines.mil/Portals/175/Docs/Audio/Educational_Series/sound_off/radetzky_march.mp3" + +[songs."J. Strauss II - The Blue Danube Op. 314"] +license = "CC BY-NC 4.0" +license_url = "https://creativecommons.org/licenses/by-nc/4.0/" +attribution = "Live concert recording by Das Orchester Tsumugi / Takashi Inoue (Recorded 10 Mar. 2024, Fukuoka Symphony Hall, Japan); Source: IMSLP" +source = "https://www.classicals.de/strauss-2-blue-danube" + +[songs."On & On"] +license = "NCS (NoCopyrightSounds) Free Download/Stream license" +license_url = "http://ncs.io/onandon" +attribution = "Cartoon, Jeja - On & On (feat. Daniel Levi) [NCS Release]. Music provided by NoCopyrightSounds. Watch: http://youtu.be/K4DyBUG242c" +source = "http://ncs.io/onandon" + +[songs."Vivaldi - Summer"] +license = "CC BY-SA 3.0" +license_url = "https://creativecommons.org/licenses/by-sa/3.0/" +attribution = "Performance by John Harrison with the Wichita State University Chamber Players" +source = "https://freemusicarchive.org/music/John_Harrison_with_the_Wichita_State_University_Chamber_Players/The_Four_Seasons_Vivaldi" + +[songs."We Wish You a Merry Christmas"] +license = "Pixabay Content License" +license_url = "https://pixabay.com/service/terms/" +attribution = "mintmusic (https://pixabay.com/users/mintmusic-41154881/), \"Merry Christmas\", audio ID 270190" +source = "https://pixabay.com/music/christmas-merry-christmas-270190/" diff --git a/music/radetzky_march.LICENSE b/music/radetzky_march.LICENSE deleted file mode 100644 index 871f0c8..0000000 --- a/music/radetzky_march.LICENSE +++ /dev/null @@ -1,7 +0,0 @@ -Public Domain (US Government Work) - -https://creativecommons.org/publicdomain/mark/1.0/ - -Recording by "The President's Own" United States Marine Band, from Sound Off!, recorded May 18–21, 1992 at Center for the Arts, George Mason University; conducted by John R. Bourgeois - -https://www.marineband.marines.mil/Portals/175/Docs/Audio/Educational_Series/sound_off/radetzky_march.mp3 diff --git a/music/songs/Chopin - Nocturne Op. 9 No. 2.mp3 b/music/songs/Chopin - Nocturne Op. 9 No. 2.mp3 new file mode 100644 index 0000000..41194cf Binary files /dev/null and b/music/songs/Chopin - Nocturne Op. 9 No. 2.mp3 differ diff --git a/music/Crazyflie Drones Theme (short).mp3 b/music/songs/Crazyflie Drones Theme (short).mp3 similarity index 100% rename from music/Crazyflie Drones Theme (short).mp3 rename to music/songs/Crazyflie Drones Theme (short).mp3 diff --git a/music/Crazyflie Drones Theme.mp3 b/music/songs/Crazyflie Drones Theme.mp3 similarity index 100% rename from music/Crazyflie Drones Theme.mp3 rename to music/songs/Crazyflie Drones Theme.mp3 diff --git a/music/Dance of the Flying Machines.mp3 b/music/songs/Dance of the Flying Machines.mp3 similarity index 100% rename from music/Dance of the Flying Machines.mp3 rename to music/songs/Dance of the Flying Machines.mp3 diff --git a/music/songs/Debussy - Clair de Lune.mp3 b/music/songs/Debussy - Clair de Lune.mp3 new file mode 100644 index 0000000..81cf523 Binary files /dev/null and b/music/songs/Debussy - Clair de Lune.mp3 differ diff --git a/music/songs/Fearless2.mp3 b/music/songs/Fearless2.mp3 new file mode 100644 index 0000000..f166e56 Binary files /dev/null and b/music/songs/Fearless2.mp3 differ diff --git a/music/songs/J. 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The above-named Licensor is responsible for this audio file. Pixabay monitors uploaded audio files only to a reasonable extent. 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envwrap ; extra == 'discord' + - slack-sdk ; extra == 'slack' + - envwrap ; extra == 'slack' + - requests ; extra == 'telegram' + - envwrap ; extra == 'telegram' + - ipywidgets>=6 ; extra == 'notebook' + requires_python: '>=3.7' - pypi: https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl name: python-dateutil version: 2.9.0.post0 @@ -18379,6 +32822,11 @@ packages: requires_dist: - nvidia-nvjitlink requires_python: '>=3' +- pypi: https://files.pythonhosted.org/packages/fa/48/4b7fe0e34c169fa2f12532916133e0b219d2823b540733651b34fdac509a/llvmlite-0.47.0-cp312-cp312-macosx_11_0_arm64.whl + name: llvmlite + version: 0.47.0 + sha256: 306a265f408c259067257a732c8e159284334018b4083a9e35f67d19792b164f + requires_python: '>=3.10' - pypi: https://files.pythonhosted.org/packages/fd/28/45deb15c11859d2f10702b32e71de9328a9fa494f989626916db39a9617f/mido-1.3.3-py3-none-any.whl name: mido version: 1.3.3 @@ -18418,3 +32866,100 @@ packages: - 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nvidia-cublas-cu12 + - nvidia-nvjitlink-cu12 + - nvidia-cusparse-cu12 + requires_python: '>=3' +- pypi: https://pypi.nvidia.com/nvidia-cusparse-cu12/nvidia_cusparse_cu12-12.5.4.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl + name: nvidia-cusparse-cu12 + version: 12.5.4.2 + sha256: 7556d9eca156e18184b94947ade0fba5bb47d69cec46bf8660fd2c71a4b48b73 + index: https://download.pytorch.org/whl/cu126 + requires_dist: + - nvidia-nvjitlink-cu12 + requires_python: '>=3' +- pypi: https://pypi.nvidia.com/nvidia-cusparselt-cu12/nvidia_cusparselt_cu12-0.6.3-py3-none-manylinux2014_x86_64.whl + name: nvidia-cusparselt-cu12 + version: 0.6.3 + sha256: e5c8a26c36445dd2e6812f1177978a24e2d37cacce7e090f297a688d1ec44f46 + index: https://download.pytorch.org/whl/cu126 +- pypi: https://pypi.nvidia.com/nvidia-nccl-cu12/nvidia_nccl_cu12-2.21.5-py3-none-manylinux2014_x86_64.whl + name: nvidia-nccl-cu12 + version: 2.21.5 + sha256: 8579076d30a8c24988834445f8d633c697d42397e92ffc3f63fa26766d25e0a0 + index: https://download.pytorch.org/whl/cu126 + requires_python: '>=3' +- pypi: https://pypi.nvidia.com/nvidia-nvjitlink-cu12/nvidia_nvjitlink_cu12-12.6.85-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl + name: nvidia-nvjitlink-cu12 + version: 12.6.85 + sha256: eedc36df9e88b682efe4309aa16b5b4e78c2407eac59e8c10a6a47535164369a + index: https://download.pytorch.org/whl/cu126 + requires_python: '>=3' +- pypi: https://pypi.nvidia.com/nvidia-nvtx-cu12/nvidia_nvtx_cu12-12.6.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl + name: nvidia-nvtx-cu12 + version: 12.6.77 + sha256: b90bed3df379fa79afbd21be8e04a0314336b8ae16768b58f2d34cb1d04cd7d2 + index: https://download.pytorch.org/whl/cu126 + requires_python: '>=3' diff --git a/pyproject.toml b/pyproject.toml index 0878a2e..faea947 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -77,7 +77,10 @@ markers = ["unit", "integration"] [tool.pixi.workspace] channels = ["conda-forge"] -platforms = ["linux-64"] +platforms = ["linux-64", "osx-arm64"] + +[tool.pixi.system-requirements] +cuda = "12" [tool.pixi.activation] scripts = ["openai_api_key.sh", "tools/frontend_install.sh"] @@ -88,6 +91,9 @@ default = { features = ["sim"], solve-group = "default" } deploy = { features = ["sim", "kilted", "deploy"] } docs = { features = ["docs"] } tests = { features = ["tests", "sim", "kilted", "deploy"] } +# Music analysis env — isolated from sim/ROS so allin1's deps don't conflict +# with ros-kilted's fmt/spdlog pins. Run analyzer with: pixi run -e music +music = { features = ["music"] } [tool.pixi.dependencies] # Environment packages @@ -105,6 +111,9 @@ api = { cmd = "python swarm_gpt/launch.py", description = "Serve the SwarmGPT br web-dev = { cmd = "npm --prefix web run dev", description = "Serve the browser frontend with Vite" } web-build = { cmd = "npm --prefix web run build", description = "Build the browser frontend" } +[tool.pixi.feature.music.tasks] +analyze = { cmd = "python tools/analyze_songs.py", description = "Analyze every un-analyzed song in music/ (use --test for a console-only smoke test)" } + [tool.pixi.pypi-dependencies] swarmgpt = { path = ".", editable = true } @@ -160,6 +169,27 @@ mkdocs-section-index = "*" docs-serve = { cmd = "mkdocs serve", description = "Serve docs locally" } docs-build = { cmd = "mkdocs build", description = "Build docs" } + +[tool.pixi.feature.music] + +platforms = ["linux-64"] + +[tool.pixi.feature.music.dependencies] +ffmpeg = "*" + +[tool.pixi.feature.music.pypi-options] + +extra-index-urls = ["https://download.pytorch.org/whl/cu126"] +find-links = [{ url = "https://whl.natten.org" }] + +[tool.pixi.feature.music.pypi-dependencies] +torch = "==2.6.0+cu126" +torchaudio = "==2.6.0+cu126" +allin1 = { git = "https://github.com/govi218/all-in-one.git", branch = "gov/update-deprecated" } +madmom = { git = "https://github.com/CPJKU/madmom.git" } + +natten = "==0.17.5+torch260cu126" + ######################## # ruff Settings ######################## diff --git a/swarm_gpt/api/server.py b/swarm_gpt/api/server.py index ebdb3a6..568cc9a 100644 --- a/swarm_gpt/api/server.py +++ b/swarm_gpt/api/server.py @@ -35,7 +35,7 @@ logger = logging.getLogger(__name__) ROOT = Path(__file__).resolve().parents[2] -MUSIC_DIR = ROOT / "music" +MUSIC_DIR = ROOT / "music" / "songs" WEB_DIST_DIR = ROOT / "web" / "dist" SWARM_BACKGROUND = ROOT / "swarm_gpt" / "ui" / "swarm.png" DRONE_ASSET_DIR = Path(drone_models.__file__).resolve().parent / "data" / "assets" @@ -51,7 +51,7 @@ class ApiConfig: preset_dir: Path | None = None strict_processing: bool = True strict_drone_match: bool = True - model_id: str = "gpt-4o" + model_id: str = "gpt-5.4-nano" llm_provider: LLMProvider = "openai" use_motion_primitives: bool = True @@ -247,6 +247,7 @@ def normalize_playback(sim_data: dict[str, Any], backend: AppBackend) -> dict[st return { "schemaVersion": 1, "audioUrl": _audio_url(backend.music_manager.song), + "audioOffset": backend.crop_window(backend.music_manager.song)[0], "song": backend.music_manager.song, "numDrones": num_drones, "timestamps": timestamps.tolist(), @@ -389,7 +390,13 @@ def drone_asset(asset_path: str) -> FileResponse: @app.get("/api/media/music/{song}") def music(song: str) -> FileResponse: - return FileResponse(_safe_music_path(config, song), media_type="audio/mpeg") + # no-cache forces the browser to revalidate (cheap 304 via ETag/Last-Modified) so a + # swapped-out MP3 at the same URL is never served stale from the browser cache. + return FileResponse( + _safe_music_path(config, song), + media_type="audio/mpeg", + headers={"Cache-Control": "no-cache"}, + ) @app.post("/api/jobs", status_code=202) def create_job(request: JobRequest) -> dict[str, Any]: diff --git a/swarm_gpt/core/backend.py b/swarm_gpt/core/backend.py index 864157a..4c9116a 100644 --- a/swarm_gpt/core/backend.py +++ b/swarm_gpt/core/backend.py @@ -18,6 +18,7 @@ from swarm_gpt.core.sim import replay_sim_states, simulate_axswarm from swarm_gpt.exception import LLMException from swarm_gpt.utils import MusicManager, generate_default_colors +from swarm_gpt.utils.music_analyzer import SongStructure if TYPE_CHECKING: from numpy.typing import NDArray as Array @@ -85,12 +86,12 @@ class AppBackend: def __init__( self, *, - music_dir: Path = Path(__file__).parents[2] / "music", + music_dir: Path = Path(__file__).parents[2] / "music" / "songs", preset_dir: Path | None = None, config_file: Path | None = None, strict_processing: bool = True, strict_drone_match: bool = True, - model_id: str = "gpt-4o", + model_id: str = "gpt-5.4-nano", use_motion_primitives: bool = True, llm_provider: LLMProvider = "openai", ): @@ -192,9 +193,9 @@ def initial_prompt(self, song: str, *, response: str | None = None) -> list[dict """ logger.info(f"Generating initial choreography for song: {song}") song_name = self._load_song(song) - music_info = self.music_manager.extract_song_info() + structure = self._load_structure(song_name) self.choreographer.reset_history() - prompt = self.choreographer.format_initial_prompt(song_name, music_info) + prompt = self.choreographer.format_initial_prompt(song_name, structure) fixed_response = response is not None if preset := song in self.presets: # Preset was provided @@ -205,13 +206,11 @@ def initial_prompt(self, song: str, *, response: str | None = None) -> list[dict self.choreographer.messages.append({"role": "assistant", "content": response}) else: # Use LLM to generate the choreography logger.debug(f"Using LLM to generate choreography for song: {song_name}") - response = self.choreographer.generate_choreography( - prompt, num_beats=len(music_info["beat_times"]) - ) + response = self.choreographer.generate_choreography(prompt, structure=structure) try: self.waypoints = self.choreographer.response2waypoints( - response, music_info=music_info, strict=self._strict_processing + response, structure, strict=self._strict_processing ) except LLMException as e: # We do not want to retry if we are using a preset or a fixed response. This @@ -238,12 +237,10 @@ def reprompt(self, message: str) -> list[dict[str, str]]: logger.warning("No message provided, returning current history") return self.choreographer.messages prompt = self.choreographer.format_reprompt(message) - music_info = self.music_manager.extract_song_info() - response = self.choreographer.generate_choreography( - prompt, num_beats=len(music_info["beat_times"]) - ) + structure = self._load_structure(self.music_manager.song) + response = self.choreographer.generate_choreography(prompt, structure=structure) self.waypoints = self.choreographer.response2waypoints( - response, music_info=music_info, strict=self._strict_processing + response, structure, strict=self._strict_processing ) logger.info("Successfully generated choreography") return self.choreographer.messages @@ -304,12 +301,7 @@ def deploy(self, drone_ids: list[int] | None = None) -> bool: logger.info("Deploying drones") assert self.splines, "Please run the simulation first!" - # If a deploy version of the song is present, play it - original_song = self.music_manager.song - try: - self.music_manager.song = original_song + "[deploy]" - except AssertionError: - ... + play_start_s, play_end_s = self.crop_window(self.music_manager.song) if not self.music_manager.verify_libvlc(): logger.error("VLC/libvlc is not available. Install VLC (see README) before deploying.") @@ -377,7 +369,7 @@ def deploy(self, drone_ids: list[int] | None = None) -> bool: taken_off = False logger.warning(f"Drone {uri} has not taken off yet: z={z:.2f}m") if taken_off: - if not self.music_manager.play(wait=True): + if not self.music_manager.play(wait=True, start_s=play_start_s, end_s=play_end_s): logger.error( "VLC could not start playback; skipping choreography (drones will land)." ) @@ -388,13 +380,13 @@ def deploy(self, drone_ids: list[int] | None = None) -> bool: color_top=color_top, color_bot=color_bot, ) + self.music_manager.stop() swarm.goto(final_pos_dict, duration=2.0) # Transition from ideal point to hover pos # if self.settings["land_on_docks"]: # Commented out for demo # swarm.goto(final_pos_dict, duration=5.0) # Hovering swarm.goto(landing_pos_dict, duration=1.5) # Landing finally: swarm.close() - self.music_manager.song = original_song logger.info("Deployment successful") return True @@ -503,3 +495,39 @@ def _load_song(self, song: str) -> str: song = self.parse_preset_id(song)["song"] self.music_manager.song = song return song + + def crop_window(self, song_name: str) -> tuple[float, float]: + """Return the ``(start_s, end_s)`` crop window for a song, in seconds. + + Reads ``song_crops`` from settings, falling back to ``song_crops.default`` for any song + without an explicit entry. + + Args: + song_name: Stem of the MP3 file (no extension). + + Returns: + The ``(start_s, end_s)`` window the song is cropped to. + """ + crops = self.settings["song_crops"] + window = crops.get(song_name, crops["default"]) + return float(window[0]), float(window[1]) + + def _load_structure(self, song_name: str) -> SongStructure: + """Load the cached SongStructure JSON for a song, cropped to its window. + + The full-song analysis is loaded from disk and then cropped to the song's + ``song_crops`` window (see :meth:`crop_window`); only that window is choreographed. + + Args: + song_name: Stem of the MP3 file (no extension). + + Raises: + FileNotFoundError: If no analysis JSON exists yet for the song. + """ + json_path = self.root_path / "music" / "analyzed" / f"{song_name}.json" + if not json_path.exists(): + raise FileNotFoundError( + f"No analysis found for '{song_name}'. Run `pixi run -e music analyze` first." + ) + start_s, end_s = self.crop_window(song_name) + return SongStructure.from_json(json_path).crop(start_s, end_s) diff --git a/swarm_gpt/core/choreographer.py b/swarm_gpt/core/choreographer.py index 57907f1..a23cd34 100644 --- a/swarm_gpt/core/choreographer.py +++ b/swarm_gpt/core/choreographer.py @@ -17,7 +17,10 @@ from swarm_gpt.core.motion_primitives import motion_primitives as motion_primitives_collection from swarm_gpt.core.motion_primitives import primitive_by_name from swarm_gpt.core.structured_output_schema import ( + KEY_PATTERN, build_motion_primitive_response_schema, + decode_key, + encode_key, structured_payload_to_choreography, ) from swarm_gpt.exception import LLMFormatError, LLMPlanError, LLMResponseProcessingError @@ -29,17 +32,70 @@ prepare_responses_messages, register_ollama_client, ) +from swarm_gpt.utils.music_analyzer import dynamics_window_keys if TYPE_CHECKING: from numpy.typing import NDArray from openai import OpenAI from swarm_gpt.utils.llm_providers import LLMProvider + from swarm_gpt.utils.music_analyzer import SongStructure logger = logging.getLogger(__name__) -# Set to True to see raw LLM outputs in terminal -DEBUG_LLM_OUTPUT = True +_FORMATION_PRIMITIVES: frozenset[str] = frozenset({"form_circle", "form_star", "form_cone"}) +_MOTION_PRIMITIVES_FOR_COMPOSITION: frozenset[str] = frozenset( + {"rotate", "spiral", "spiral_speed", "twister", "helix", "wave", "zig_zag", "move", "move_z"} +) + + +def _overlapping_drone_set(action: dict[str, tuple], num_drones: int) -> frozenset[int]: + """Return the 0-indexed drone IDs this action touches. + + ``form_circle``, ``move_z``, ``center`` take an explicit drone subset as their first arg. + ``swap`` takes two individual drone IDs. ``move`` takes a single drone ID as the fourth arg. + All other primitives operate on the full swarm. + + Args: + action: Single-entry dict ``{fn_name: args_tuple}``. + num_drones: Total number of drones in the swarm. + + Returns: + Frozenset of 0-indexed drone IDs the action applies to. + """ + fn_name, args = next(iter(action.items())) + if fn_name in {"form_circle", "move_z", "center"}: + return frozenset(d - 1 for d in args[0]) + if fn_name == "swap": + return frozenset({args[0] - 1, args[1] - 1}) + if fn_name == "move": + return frozenset({args[3] - 1}) + return frozenset(range(num_drones)) + + +def _form_should_drop_holds( + action_list: list[dict[str, tuple]], form_idx: int, num_drones: int +) -> bool: + """Return True if a motion primitive on overlapping drones follows ``form_idx`` in the list. + + Args: + action_list: Ordered list of ``{fn_name: args}`` dicts for one choreography key. + form_idx: Index of the formation primitive to check. + num_drones: Total number of drones in the swarm. + + Returns: + True when any later entry is in ``_MOTION_PRIMITIVES_FOR_COMPOSITION`` and shares at + least one drone with the formation. + """ + form_drones = _overlapping_drone_set(action_list[form_idx], num_drones) + for later in action_list[form_idx + 1 :]: + fn_name = next(iter(later)) + if fn_name in _MOTION_PRIMITIVES_FOR_COMPOSITION: + if form_drones & _overlapping_drone_set(later, num_drones): + return True + return False + + # OLLAMA_CONTEXT_LENGTH = None # Set None to use Ollama's VRAM-based default. OLLAMA_CONTEXT_LENGTH = None @@ -90,6 +146,13 @@ def __init__( self.lim_lower = np.array(self.settings["axswarm"]["pos_min"]) self.lim_upper = np.array(self.settings["axswarm"]["pos_max"]) assert len(self.lim_lower) == 3 and len(self.lim_upper) == 3, "Limits must be 3D" + # Ellipsoidal collision envelope (x, y, z) in meters that axswarm enforces as a hard + # MPC constraint. The offline collision check scales separations by this so it rejects + # the same layouts the solver cannot hold (e.g. circles stacked < 0.6m apart in z). + self.collision_envelope = np.array(self.settings["axswarm"]["collision_envelope"]) + assert len(self.collision_envelope) == 3, "Collision envelope must be 3D" + # Stride (in bars) between required downbeats; beats in between are optional accents. + self._bars_per_required = int(self.settings["choreography"]["bars_per_required"]) def configure_llm(self, provider: LLMProvider, model_id: str) -> None: """Switch provider and model (used by the web UI). @@ -118,19 +181,19 @@ def _chat_client_for_call(self) -> OpenAI: register_ollama_client(self._chat_client) return self._chat_client - def format_initial_prompt(self, song: str, music_info: dict) -> list[dict[str, str]]: + def format_initial_prompt(self, song: str, structure: SongStructure) -> list[dict[str, str]]: """Format the initial prompt for the LLM. Args: song: The name of the song. - music_info: The beat times, amplitude and frequency of the song. + structure: Hierarchical music structure (segments / bars / beats). Returns: - The formatted initial prompt. + The formatted initial prompt as a list of role/content message dicts. """ logger.debug("Formatting initial prompt") msgs = [] - user_prompt = self._format_initial_user_prompt(song, music_info) + user_prompt = self._format_initial_user_prompt(song, structure) msgs.append({"role": "system", "content": self.prompts["system_initial"]}) msgs.append({"role": "user", "content": user_prompt}) msgs.append({"role": "system", "content": self.prompts["example"]}) @@ -160,17 +223,25 @@ def _uses_structured_outputs(self) -> bool: return self.use_motion_primitives def generate_choreography( - self, prompt: list[dict[str, str]], num_beats: int | None = None + self, prompt: list[dict[str, str]], structure: SongStructure | None = None ) -> str: - """Generate the initial choreography for the LLM.""" + """Generate the initial choreography for the LLM. + + Args: + prompt: The message list returned by :meth:`format_initial_prompt`. + structure: Hierarchical song structure; required when using structured outputs. + + Returns: + The assistant's response text (YAML-shaped for legacy parsing). + """ logger.debug( "Generating choreography with provider=%s model=%s", self.llm_provider, self._model_id ) self.messages.extend(prompt) if self._uses_structured_outputs(): - if num_beats is None: - raise ValueError("num_beats is required for structured output generation") - payload = self._call_responses_structured(self.messages, num_beats=num_beats) + if structure is None: + raise ValueError("structure is required for structured output generation") + payload = self._call_responses_structured(self.messages, structure=structure) response = self._structured_payload_to_text(payload) else: response = self._call_responses(self.messages) @@ -181,11 +252,17 @@ def reset_history(self): """Reset the LLM history to ensure a clean slate.""" self.messages.clear() - def load_drone_config(self, config_file: Path | None = None): + def load_drone_config(self, config_file: Path | None = None) -> None: """Load the drone configuration from the config file. - The configuration file is a yaml file that contains the drone IDs and their initial - positions. + The configuration file is a TOML file containing an ``active`` list of + cf-names and one ``[cfXX]`` table per drone with ``addr`` (last radio + address byte) and ``pos`` (initial xyz position). URI and channel are + derived at load time; they are not stored in the file. + + Args: + config_file: Path to the TOML config file. Defaults to + ``swarm_gpt/data/drones.toml``. """ with open(Path(__file__).resolve().parents[1] / "data/settings.yaml", "r") as f: self.settings = yaml.safe_load(f) @@ -193,64 +270,75 @@ def load_drone_config(self, config_file: Path | None = None): if config_file is None: config_file = Path(__file__).resolve().parents[1] / "data/drones.toml" with open(config_file) as f: - self.drones = toml.load(f) + raw = toml.load(f) - for drone_name, data in self.drones.items(): - i = int(drone_name[2:]) + uri_base: str = self.settings["radio"]["uri_base"] + active: list[str] = raw["active"] + registry: dict[str, dict] = {k: v for k, v in raw.items() if k != "active"} + + missing = [name for name in active if name not in registry] + if missing: + raise ValueError(f"Drones in 'active' not found in drone table: {missing}") + + addrs = [registry[name]["addr"] for name in active] + if len(addrs) != len(set(addrs)): + raise ValueError(f"Duplicate addr values in active drones: {addrs}") + + self.drones = {} + for i, name in enumerate(active): + entry = registry[name] + addr: int = entry["addr"] + channel: int = (addr // 10) * 10 + uri: str = uri_base.format(channel=channel, addr=addr) self.agents[i] = i - self.starting_pos[i] = np.array(data["pos"]) + self.starting_pos[i] = np.array(entry["pos"]) self.starting_pos[i][2] = self.settings["starting_height"] - self.uris[i] = data["uri"] - self.num_drones = len(self.agents.values()) + self.uris[i] = uri + self.drones[name] = {"addr": addr, "uri": uri, "pos": entry["pos"]} + + self.num_drones = len(self.agents) assert self.num_drones > 0, "No drones detected in config file" - def _format_initial_user_prompt(self, song: str, music_info: dict) -> str: + def _format_initial_user_prompt(self, song: str, structure: SongStructure) -> str: """Format the initial user prompt for the LLM. Args: song: The name of the song. - music_info: The beat times, amplitude and frequency of the song. + structure: Hierarchical song structure used to render segments/keys. """ - # Convert to cm for LLM compatibility + # Convert starting positions to cm for the LLM (integer tokens). starting_pos = [(pos * 100).astype(int).tolist() for pos in self.starting_pos.values()] - beat_times = {i + 1: round(10 * x) for i, x in enumerate(music_info["beat_times"])} - novelty = {i + 1: int(x * 100) for i, x in enumerate(music_info["novelty"])} - chords = {i + 1: v for i, v in enumerate(music_info["chords"])} - dt = np.diff(music_info["beat_times"]) - dt[1:] = dt[:-1] # Shift to the right to align with the beat times - dt[0] = music_info["beat_times"][0] # Set the first value to the first beat time - beat_intervals = {i + 1: round(10 * x) for i, x in enumerate(dt)} - # We calculate a maximum distance each drone is allowed to travel at the given beat to limit - # excessive movements - max_vel = self.settings["axswarm"]["vel_max"] - # Divide by sqrt(3) to get the maximum distance in 3D space for one axis assuming all axes - # change in the worst case - max_distances = dict() - for i in range(len(music_info["beat_times"])): - dt = music_info["beat_times"][i] - (music_info["beat_times"][i - 1] if i > 0 else 0) - max_distances[i + 1] = int(max_vel * dt / np.sqrt(3) * 100) - dbfs = {i + 1: int(x) for i, x in enumerate(music_info["dBFS"])} + segments_table = _render_segments_table(structure) + required_keys_csv = ", ".join( + encode_key(*k) for k in structure.required_keys(self._bars_per_required) + ) + n_total_beats = len(structure.all_keys()) if self.use_motion_primitives: - # Load the YAML file latex_file = Path(__file__).resolve().parents[1] / "data/latex_eqn.yaml" with open(latex_file, "r") as file: data = yaml.safe_load(file) - + if self.use_motion_primitives and structure.rms_per_2bar: + keys = dynamics_window_keys(structure) + dynamics_lines = "\n".join( + f"{k}: {r:.2f} / {c:.2f}" + for k, r, c in zip(keys, structure.rms_per_2bar, structure.centroid_per_2bar) + ) + dynamics_table = dynamics_lines + else: + dynamics_table = "(not available)" prompt_kwargs = { "song": song, + "bpm": structure.bpm, "num_drones": self.num_drones, - "beat_times": beat_times, - "num_beats": len(music_info["beat_times"]), "starting_pos": starting_pos, - "num_waypoints": len(music_info["beat_times"]), - "beat_novelty": novelty, - "chords": chords, - "max_distances": max_distances, - "beat_intervals": beat_intervals, - "dbfs": dbfs, - "lim_lower": self.lim_lower * 100, - "lim_upper": self.lim_upper * 100, + "segments_table": segments_table, + "required_keys_csv": required_keys_csv, + "n_total_beats": n_total_beats, + "lim_lower": (self.lim_lower * 100).astype(int).tolist(), + "lim_upper": (self.lim_upper * 100).astype(int).tolist(), + "move_z_typical_cm": int((self.lim_upper[2] - self.lim_lower[2]) * 100 / 2), "wave_eqn": data["wave"] if self.use_motion_primitives else None, + "dynamics_table": dynamics_table, } return self.prompts["user_initial"].format(**prompt_kwargs) @@ -285,41 +373,103 @@ def _call_responses(self, messages: list[dict[str, str]]) -> str: raise LLMPlanError( f"Model {self._model_id!r} returned empty content. Try another model or reprompt." ) - if DEBUG_LLM_OUTPUT: - print("\n" + "=" * 80) - print("RAW LLM OUTPUT:") - print(content) - print("=" * 80 + "\n") + + logger.debug("\n" + "=" * 80) + logger.debug("RAW LLM OUTPUT:") + logger.debug(content) + logger.debug("=" * 80 + "\n") return content - def _collision_check(self, pos: NDArray, min_dist: float = 0.1): - """Check that no two drones are too close to each other at the same time. + def _collision_check( + self, + pos: NDArray, + margin: float = 1.0, + time: NDArray | None = None, + structure: SongStructure | None = None, + ): + """Check that no two drones violate the MPC's collision envelope at the same time. + + Separations are scaled by ``self.collision_envelope`` (the ellipsoid axswarm enforces), + so a pair is in conflict when the envelope-scaled distance drops below 1. This matches + the solver's constraint — e.g. two drones must be >=0.6m apart in z but only >=0.35m in + x/y — instead of the old isotropic sphere, which let vertically stacked formations slip + through and then jitter under the MPC. Args: pos: The positions of the drones as a (n_drones, T, 3) array. - min_dist: The minimum allowed distance between any two drones at the same time. + margin: Multiplicative inflation of the envelope. ``1.0`` matches the MPC exactly; + values >1 reject layouts that are merely close to the constraint. + time: Optional 1-D array of length ``T`` giving the seconds of each waypoint column. + When provided together with ``structure``, offending columns are reported as the + nearest ``s#b#t#`` key instead of opaque waypoint indices. + structure: Optional song structure used to resolve waypoint times to the nearest + hierarchical key for a choreographer-friendly error message. Raises: - ValueError: If two drones are too close together at the same time. + LLMPlanError: If two drones violate the collision envelope at the same time. """ differences = pos[:, None, :, :] - pos[None, :, :, :] # Reshape for broadcasting - distance = np.linalg.norm(differences, axis=-1) + # Scale each axis by the envelope so the norm is <1 exactly when the pair is inside the + # ellipsoidal collision constraint, regardless of how the separation splits across axes. + scaled = differences / (self.collision_envelope * margin) + distance = np.linalg.norm(scaled, axis=-1) # Set the diagonal to a large number to avoid comparing the same drone distance += np.eye(self.num_drones).reshape(self.num_drones, self.num_drones, 1) * 1000 min_distance = np.min(distance, axis=1) # (n_drones, T). Closest encounter for each time - if np.any(min_distance < min_dist): - drones, times = np.nonzero(min_distance < min_dist) - drones, times = drones.tolist(), times.tolist() - raise LLMPlanError(f"Drones {set(drones)} get too close at waypoints {set(times)}") + if not np.any(min_distance < 1.0): + return + drones, times = np.nonzero(min_distance < 1.0) + if time is None or structure is None: + raise LLMPlanError( + f"Drones {set((d + 1) for d in drones.tolist())} get too close " + f"at waypoints {set(times.tolist())}" + ) + # Group offending drones by the nearest s#b#t# key so the LLM can act on a reprompt. + time_to_key = self._time_to_key_lookup(structure) + by_key: dict[str, set[int]] = {} + key_time: dict[str, float] = {} + for drone_idx, time_idx in zip(drones.tolist(), times.tolist()): + key, key_t = self._nearest_key(float(time[time_idx]), time_to_key) + by_key.setdefault(key, set()).add(drone_idx + 1) # 1-indexed for the LLM + key_time[key] = key_t + locations = "; ".join( + f"{key} (t≈{key_time[key]:.1f}s): drones {sorted(by_key[key])}" + for key in sorted(by_key, key=lambda k: key_time[k]) + ) + raise LLMPlanError( + "Drones get too close to each other near these moments: " + f"{locations}. Separate the colliding drones there by height (z), radius, or x/y " + "center, move them to different keys. Try moving some drones lower in height (z) if they are colliding with other drones near the height limit." + ) + + @staticmethod + def _time_to_key_lookup(structure: SongStructure) -> list[tuple[float, str]]: + """Build a time-sorted ``(time_s, s#b#t#)`` table for every addressable beat.""" + table = [ + (structure.time_of(seq, bar, beat), encode_key(seq, bar, beat)) + for seq, bar, beat in structure.all_keys() + ] + table.sort(key=lambda pair: pair[0]) + return table - def _call_responses_structured(self, messages: list[dict[str, str]], num_beats: int) -> dict: + @staticmethod + def _nearest_key(t_sec: float, time_to_key: list[tuple[float, str]]) -> tuple[str, float]: + """Return the ``(key, beat_time_s)`` whose beat time is closest to ``t_sec``.""" + beat_t, key = min(time_to_key, key=lambda pair: abs(pair[0] - t_sec)) + return key, beat_t + + def _call_responses_structured( + self, messages: list[dict[str, str]], structure: SongStructure + ) -> dict: """Call Responses API with strict json_schema and parse JSON output.""" if self.llm_provider == "ollama": - return self._call_ollama_structured(messages, num_beats) + return self._call_ollama_structured(messages, structure) client = self._chat_client_for_call() schema = build_motion_primitive_response_schema( - num_beats=num_beats, num_drones=self.num_drones + all_keys=structure.all_keys(), + required_keys=structure.required_keys(self._bars_per_required), + num_drones=self.num_drones, ) input_messages, instructions = prepare_responses_messages(messages) try: @@ -360,10 +510,14 @@ def _call_responses_structured(self, messages: list[dict[str, str]], num_beats: except json.JSONDecodeError as e: raise LLMFormatError(f"Structured output was not valid JSON: {e}") from e - def _call_ollama_structured(self, messages: list[dict[str, str]], num_beats: int) -> dict: + def _call_ollama_structured( + self, messages: list[dict[str, str]], structure: SongStructure + ) -> dict: """Call Ollama's native chat structured output path.""" schema = build_motion_primitive_response_schema( - num_beats=num_beats, num_drones=self.num_drones + all_keys=structure.all_keys(), + required_keys=structure.required_keys(self._bars_per_required), + num_drones=self.num_drones, ) grounded_messages = [ *messages, @@ -425,13 +579,13 @@ def _extract_ollama_content(response: object) -> str: return json.dumps(content) return "" - def _structured_payload_to_choreography(self, payload: dict) -> dict[int, str]: - """Convert structured payload to the existing beat -> primitive string format.""" + def _structured_payload_to_choreography(self, payload: dict) -> dict[tuple[int, int, int], str]: + """Convert structured payload to a (seq, bar, beat) -> action-string dict.""" return structured_payload_to_choreography(payload) def _structured_payload_to_text(self, payload: dict) -> str: """Convert structured payload to legacy YAML-like text for downstream parsing/history.""" - required_fields = ["song_mood", "cord_analysis", "choreography_plan", "choreography"] + required_fields = ["song_mood", "choreography_plan", "choreography"] missing = [field for field in required_fields if field not in payload] if missing: raise LLMFormatError( @@ -440,26 +594,25 @@ def _structured_payload_to_text(self, payload: dict) -> str: choreography = self._structured_payload_to_choreography(payload) lines = [ f"song_mood: {json.dumps(payload['song_mood'])}", - f"cord_analysis: {json.dumps(payload['cord_analysis'])}", f"choreography_plan: {json.dumps(payload['choreography_plan'])}", "choreography:", ] - for beat in sorted(choreography): - lines.append(f" {beat}: {choreography[beat]}") + # Sort by (seq, bar, beat) tuple for deterministic, time-ordered output. + for addr in sorted(choreography): + lines.append(f" {encode_key(*addr)}: {choreography[addr]}") lines.append(" END") return "\n".join(lines) def response2waypoints( - self, text: str, music_info: dict, strict: bool = True, t_rth: float = 3.0 + self, text: str, structure: SongStructure, strict: bool = True, t_rth: float = 3.0 ) -> dict[str, NDArray]: """Translate the LLM output into waypoints. Args: - text: The output of the LLM. Is expected to follow the format specified in the - format instructions of the output parser. - music_info: The beat times, amplitude and frequency of the song. + text: The output of the LLM, in the YAML-like form produced by the prompt. + structure: Hierarchical song structure used to resolve action keys to seconds. strict: Enable/disable waypoint proximity and distance checks. - t_rth: Time for the drones to return to their starting position + t_rth: Time for the drones to return to their starting position. Returns: The waypoints as a dictionary of "time", "pos", "vel", "acc". "time" has shape @@ -467,14 +620,17 @@ def response2waypoints( """ logger.debug("Converting LLM output into waypoints") if self.use_motion_primitives: - choreo = self._response2choreo(text) - waypoints = self._choreo2waypoints(choreo, music_info["beat_times"]) + choreo = self._response2choreo(text, structure) + waypoints = self._choreo2waypoints(choreo, structure) else: - waypoints = self._raw_response2waypoints(text, music_info["beat_times"]) + # Raw-waypoint mode predates the structure rewrite and still expects a flat + # beat_times list. Pull it from the structure for the time being. + flat_times = [b.time_s for s in structure.segments for bar in s.bars for b in bar.beats] + waypoints = self._raw_response2waypoints(text, np.asarray(flat_times), structure) # Clip waypoint values to the physical limits waypoints["pos"] = np.clip(waypoints["pos"], self.lim_lower, self.lim_upper) if strict: - self._collision_check(waypoints["pos"], 0.25) + self._collision_check(waypoints["pos"], time=waypoints["time"][0], structure=structure) # Add home position (TODO make cleaner) home = np.zeros((len(self.agents.values()), 1, 3)) @@ -492,66 +648,102 @@ def response2waypoints( return waypoints - def _response2choreo(self, text: str) -> dict[int, list[str]]: - """Translate the LLM output into a choreography.""" + def _response2choreo( + self, text: str, structure: SongStructure | None = None + ) -> dict[tuple[int, int, int], str]: + """Translate the LLM output into a (seq, bar, beat) -> action-string choreography.""" assert self.use_motion_primitives, "Motion primitives not set in _response2choreo" - choreography = self._slice_choreography_from_text(text) - # Filter out unnecessary PLAN commands - for i, moves in choreography.items(): - if any(k in moves for k in ["helix", "spiral", "zig_zag", "wave"]) and ( - moves.endswith("PLAN") + choreography = self._slice_choreography_from_text(text, structure) + # PLAN is not in the new schema but tolerated for legacy / preset payloads. + for addr, moves in list(choreography.items()): + if any(k in moves for k in ["helix", "spiral", "zig_zag", "wave"]) and moves.endswith( + "PLAN" ): moves = moves.replace("PLAN", "").strip() moves = moves.replace("-", "").strip() - # Count no of PLAN in moves elif moves.count("PLAN") > 1: moves = "PLAN" - choreography[i] = moves + choreography[addr] = moves return choreography def _choreo2waypoints( - self, choreography: dict[int, list[str]], timestamps: list[float] - ) -> dict[int, np.ndarray]: - """Translate the choreography into waypoints.""" - if missing := set(range(1, len(timestamps) + 1)) - set(choreography.keys()): - raise LLMResponseProcessingError(f"Choreography plan is missing primitive at {missing}") + self, choreography: dict[tuple[int, int, int], str], structure: SongStructure + ) -> dict[str, np.ndarray]: + """Translate a (seq, bar, beat)-keyed choreography into time-based waypoints. - motion_primitives = {} - for i in choreography: - motion_primitives[i] = [] - moves = choreography[i].strip(" ;").split(";") + Resolves each hierarchical key to its absolute time via :meth:`SongStructure.time_of`, + sorts actions by time, and renumbers them 1..N as synthetic indices for the existing + time-based primitive execution pipeline. + + Args: + choreography: Action strings keyed by ``(seq, bar, beat)``. + structure: Song structure providing ``time_of`` and ``required_keys``. + + Returns: + Waypoints dict with ``time``, ``pos``, ``vel``, ``acc`` arrays. + """ + required = set(structure.required_keys(self._bars_per_required)) + emitted = set(choreography) + if missing := required - emitted: + raise LLMResponseProcessingError( + f"Choreography is missing required keys at {sorted(missing)}" + ) + if not choreography: + raise LLMResponseProcessingError("Choreography is empty") + + # Sort emitted actions by their resolved time. + ordered = sorted( + ( + (structure.time_of(seq, bar, beat), (seq, bar, beat), action_str) + for (seq, bar, beat), action_str in choreography.items() + ), + key=lambda triple: triple[0], + ) + + motion_primitives: dict[int, list[dict[str, tuple]]] = {} + for synth_idx, (_time, addr, action_str) in enumerate(ordered, start=1): + motion_primitives[synth_idx] = [] + moves = action_str.strip(" ;").split(";") for move in moves: fn_name = move.split("(")[0].strip(" -\n") if fn_name == "PLAN": - motion_primitives[i].append({fn_name: ()}) + motion_primitives[synth_idx].append({fn_name: ()}) continue if fn_name not in motion_primitives_collection: raise LLMResponseProcessingError( - f"Unknown motion primitive '{fn_name}' at timestep {i}" + f"Unknown motion primitive '{fn_name}' at {encode_key(*addr)}" ) - # Get the arguments after "(", remove comments, and add a , before the closing ) to - # enforce that one argument functions are length 1 tuples. + # Parse `args` portion: ast.literal_eval on a re-wrapped tuple expression. The + # trailing comma forces single-arg functions to parse as length-1 tuples. try: fn_args = ast.literal_eval("(" + move.split("(")[1].split("#")[0][:-1] + ",)") except (SyntaxError, ValueError) as e: raise LLMFormatError( - f"Cannot interpret arguments of '{move}' at timestep {i}. Failed with " - f"{e.__class__.__name__}: {e}" + f"Cannot interpret arguments of '{move}' at {encode_key(*addr)}. " + f"Failed with {e.__class__.__name__}: {e}" ) n_args = motion_primitives_collection[fn_name.lower()]["n_args"] if len(fn_args) != n_args: raise LLMFormatError( - f"{fn_name} at timestep {i} must have {n_args} arguments, got {fn_args}" + f"{fn_name} at {encode_key(*addr)} must have {n_args} arguments, " + f"got {fn_args}" ) - motion_primitives[i].append({fn_name: fn_args}) - - t, pos = self._motion_primitives2time_and_pos(motion_primitives, timestamps) + motion_primitives[synth_idx].append({fn_name: fn_args}) + + timestamps = np.array([t for t, _, _ in ordered]) + # Forward-looking semantics: the last emitted primitive plays until the song ends. + t_end = structure.segments[-1].end_s + if t_end <= timestamps[-1]: + t_end = float(timestamps[-1]) + 1.0 # guard against a zero-length final interval + t, pos = self._motion_primitives2time_and_pos(motion_primitives, timestamps, t_end) return {"time": t, "pos": pos, "vel": np.zeros_like(pos), "acc": np.zeros_like(pos)} - def _raw_response2waypoints(self, text: str, timestamps: NDArray) -> dict[int, np.ndarray]: + def _raw_response2waypoints( + self, text: str, timestamps: NDArray, structure: SongStructure | None = None + ) -> dict[int, np.ndarray]: """Translate the raw LLM output into waypoints.""" assert not self.use_motion_primitives, "Motion primitives set in raw response processing" - choreography = self._slice_choreography_from_text(text) + choreography = self._slice_choreography_from_text(text, structure) if missing := set(range(1, len(timestamps) + 1)) - set(choreography.keys()): raise LLMResponseProcessingError(f"Choreography plan is missing waypoints {missing}") @@ -573,18 +765,22 @@ def _raw_response2waypoints(self, text: str, timestamps: NDArray) -> dict[int, n return {"time": t, "pos": pos, "vel": np.zeros_like(pos), "acc": np.zeros_like(pos)} @staticmethod - def _slice_choreography_from_text(text: str) -> dict[int, str]: + def _slice_choreography_from_text( + text: str, structure: SongStructure | None = None + ) -> dict[tuple[int, int, int], str]: """Extract the choreography from the YAML output of the LLM. - The LLM output might not be a valid YAML because of its formatting, use of quotes and - dashes. To reduce formatting issues, we slice out the waypoints manually before attempting - to parse the YAML. + The LLM output may not be valid YAML (formatting, quotes, dashes). We slice the + ``choreography`` block manually and parse hierarchical keys of the form + ``sbt``. Args: text: The YAML output of the LLM. + structure: Optional song structure used to annotate each key with its resolved + ``time_of`` value in the debug print. Has no effect on parsing. Returns: - The sliced YAML output. Not guaranteed to be valid YAML. + Dict mapping ``(seq, bar, beat)`` tuples to action strings. """ yaml_text = re.findall(r"```yaml\n(.*?)(?:```)", text, re.DOTALL) try: @@ -592,47 +788,63 @@ def _slice_choreography_from_text(text: str) -> dict[int, str]: except IndexError: yaml_text = text - if DEBUG_LLM_OUTPUT: - print("\n" + "=" * 80) - print("EXTRACTED YAML TEXT (after slicing):") - print(yaml_text) - print("=" * 80 + "\n") + debug_text = yaml_text + if structure is not None: - # Step 1: Extract the chunk between `choreography:` and `END` or end of file + def _annotate(match: re.Match[str]) -> str: + key = match.group(1) + try: + seq, bar, beat = decode_key(key) + t = structure.time_of(seq, bar, beat) + return f"{key} [t={t:.2f}s]:" + except (LLMFormatError, KeyError): + return f"{key} [t=?]:" + + debug_text = re.sub(rf"({KEY_PATTERN}):", _annotate, yaml_text) + logger.debug("\n" + "=" * 80) + logger.debug("EXTRACTED YAML TEXT (after slicing):") + logger.debug(debug_text) + logger.debug("=" * 80 + "\n") + + # Step 1: Extract the chunk between `choreography:` and `END` or end of file. match = re.search(r"choreography:\s*(.*?)(?:\s*END|$)", yaml_text, re.DOTALL) if not match: raise LLMFormatError( "Could not find a valid choreography in the YAML text. Make sure to start the " "choreography plan with the 'choreography' keyword." ) - choreography = match.group(1).strip() # Extract and trim whitespace + choreography = match.group(1).strip() - # Step 2: Remove everything after # up to newline + # Step 2: Strip line comments (everything after `#`). choreography = "\n".join(line.split("#")[0].strip() for line in choreography.splitlines()) - # Step 3: Parse the extracted chunk into a dictionary - choreography_steps = {} - # Find all entries that start with a number and are followed by a colon - entries = re.findall(r"(\d+):\s*(.*?)\s*(?=\d+:|$)", choreography, re.DOTALL) - for i, entry in enumerate(entries): + # Step 3: Parse `sbt: ` entries. + entry_re = re.compile(rf"({KEY_PATTERN}):\s*(.*?)\s*(?={KEY_PATTERN}:|$)", re.DOTALL) + choreography_steps: dict[tuple[int, int, int], str] = {} + for entry in entry_re.findall(choreography): + key_str, action_str = entry try: - step = int(entry[0]) - except (IndexError, ValueError, TypeError) as e: - # We do not raise from because all information has to be included in the message of - # the LLM exception. - raise LLMFormatError( - f"Planning step {i} does not have a valid timestep number. Make sure to start " - "every planning step with the timestep number. E.g. 1: rotate(90, z). This " - f"error originated from the following error: {e}" - ) - choreography_steps[step] = entry[1].strip() + addr = decode_key(key_str) + except LLMFormatError: + raise + choreography_steps[addr] = action_str.strip() + + if not choreography_steps: + raise LLMFormatError( + "No choreography entries parsed. Keys must be in sbt form, " + "e.g. s1b1t1." + ) return dict(sorted(choreography_steps.items())) def _motion_primitives2time_and_pos( - self, motion_primitives: dict, timestamps: NDArray + self, motion_primitives: dict, timestamps: NDArray, t_end: float ) -> tuple[NDArray, NDArray]: - """Convert motion primitives to waypoint. + """Convert motion primitives to waypoints over forward-looking intervals. + + Each primitive plays from its own action time until the next action's time; the final + primitive runs until ``t_end`` (the song's end). Drones hold their start positions + until the first action. Returns: The motion primitive waypoint timings and positions. @@ -641,15 +853,23 @@ def _motion_primitives2time_and_pos( # TODO: Remove all conversions into cm swarm_pos = np.array(list(self.starting_pos.values())) * 100 waypoints[0] = {i: p.copy() for i, p in enumerate(swarm_pos)} - # Add time information to the motion_primitives, filter out PLAN motion_primitives, add - # additional time to the function before plan - timestamps = np.concatenate(([0], timestamps)) # Add 0 start time - motion_primitives = self._merge_motion_primitives(motion_primitives, timestamps) + # Forward-looking intervals: primitive i plays [T_i, T_{i+1}], the last one to t_end. + # _merge_motion_primitives reads tstart=timesteps[i-1], tend=timesteps[i] for key i. + timesteps = np.concatenate((timestamps, [t_end])) + motion_primitives = self._merge_motion_primitives(motion_primitives, timesteps) for motion_primitive in motion_primitives.values(): - for fn, args in zip(motion_primitive["fn"], motion_primitive["args"]): + action_list = [ + {fn: args} for fn, args in zip(motion_primitive["fn"], motion_primitive["args"]) + ] + for i, (fn, args) in enumerate(zip(motion_primitive["fn"], motion_primitive["args"])): swarm_pos, _waypoints = self._primitive2waypoints( fn, args, swarm_pos, motion_primitive["tstart"], motion_primitive["tend"] ) + if fn in _FORMATION_PRIMITIVES and _form_should_drop_holds( + action_list, i, self.num_drones + ): + arrival = min(_waypoints.keys()) + _waypoints = {arrival: _waypoints[arrival]} for k, v in _waypoints.items(): waypoints[k] = v if k not in waypoints else waypoints[k] | v @@ -762,3 +982,27 @@ def dicts2arrays(dict_of_dicts: dict[float, dict[int, NDArray]]) -> dict[float, if not all(len(v) == homogeneous_len for v in dict_of_lists.values()): raise RuntimeError("Expected all lists to have the same length") return {k: np.array(v) for k, v in dict_of_lists.items()} + + +def _render_segments_table(structure: SongStructure) -> str: + """Render a SongStructure as the multi-line block injected into the prompt. + + Args: + structure: Song structure to describe. + + Returns: + A newline-joined string with one indented line per segment, e.g.:: + + segment 1: "intro" (0.00s - 12.30s) — 6 bars × 4 beats + segment 2: "verse" (12.30s - 32.10s) — 10 bars × 4 beats + """ + lines: list[str] = [] + for seg in structure.segments: + n_bars = len(seg.bars) + beats_per_bar = max((len(bar.beats) for bar in seg.bars), default=0) + lines.append( + f' segment {seg.id}: "{seg.label}" ' + f"({seg.start_s:.2f}s - {seg.end_s:.2f}s) — " + f"{n_bars} bars × {beats_per_bar} beats" + ) + return "\n".join(lines) diff --git a/swarm_gpt/core/drone_swarm.py b/swarm_gpt/core/drone_swarm.py index 5c1c220..717a952 100644 --- a/swarm_gpt/core/drone_swarm.py +++ b/swarm_gpt/core/drone_swarm.py @@ -183,8 +183,8 @@ def execute_choreography( choreography: dict[str, BSpline], t_end: float, *, - color_top: dict[str, dict[float, Array]] | None = None, - color_bot: dict[str, dict[float, Array]] | None = None, + color_top: dict[str, dict[float, Array]] = {}, + color_bot: dict[str, dict[float, Array]] = {}, ): """Execute a choreography with position, orientation, and light commands. @@ -195,10 +195,10 @@ def execute_choreography( color_bot: Bottom deck color cues in the form {uri: {time: wrgb}}. """ self._validate_required_uris("choreography", choreography) - if color_top is None and color_bot is None: + if color_top.empty() and color_bot.empty(): logger.warning("No colors provided for choreography.") - self._validate_known_uris("color_top", color_top or {}) - self._validate_known_uris("color_bot", color_bot or {}) + self._validate_known_uris("color_top", color_top.keys()) + self._validate_known_uris("color_bot", color_bot.keys()) async def _execute(uri: str) -> None: await self._change_commander_level(uri, "low") @@ -206,8 +206,8 @@ async def _execute(uri: str) -> None: uri, t_end, lambda t: np.asarray([*choreography[uri](t), 0.0], dtype=float), - (color_top or {}).get(uri, {}), - (color_bot or {}).get(uri, {}), + color_top.get(uri, {}), + color_bot.get(uri, {}), ) self._run(self._parallel_by_uri("Choreography execution", self.uris, _execute)) @@ -397,7 +397,7 @@ async def _check_lighthouse_decks(self) -> None: if failures: raise RuntimeError( - "Lighthouse deck check failed. Expected " + "Lighthouse deck check failed. Are you using lighthouse? Expected " f"{_LIGHTHOUSE_DECK_PARAM}=1 for every drone: {'; '.join(failures)}" ) diff --git a/swarm_gpt/core/motion_primitives.py b/swarm_gpt/core/motion_primitives.py index 3829670..dcdd7f3 100644 --- a/swarm_gpt/core/motion_primitives.py +++ b/swarm_gpt/core/motion_primitives.py @@ -21,12 +21,12 @@ "spiral_speed": {"n_args": 4}, "helix": {"n_args": 3}, "plan": {"n_args": 1}, - "form_circle": {"n_args": 2}, + "form_circle": {"n_args": 4}, "zig_zag": {"n_args": 3}, - "wave": {"n_args": 5}, + "wave": {"n_args": 2}, "twister": {"n_args": 3}, - "form_star": {"n_args": 3}, - "form_cone": {"n_args": 3}, + "form_star": {"n_args": 4}, + "form_cone": {"n_args": 4}, } @@ -52,7 +52,7 @@ def rotate( """Rotate all drones by angle theta.""" angle, axis = params angle = np.deg2rad(float(angle)) - steps = int(tend - tstart) # Number of steps to rotate + steps = max(1, min(int(tend - tstart), 2)) # Number of steps to rotate # override rotation to be around z axis atm if "z" in axis: axis = np.array([0, 0, 1]) @@ -126,7 +126,7 @@ def spiral_speed( steps, height, degrees, increase = params n_drones = swarm_pos.shape[0] min_spacing = 60 # Minimum distance between drones in cm - steps = int(tend - tstart) + steps = max(1, min(int(tend - tstart), 2)) # Calculate the circumference needed to place all drones with at least the minimum spacing start_radius = min_spacing / (2 * np.sin(np.pi / n_drones)) @@ -228,7 +228,7 @@ def helix( def wave( - params: tuple[int, int, list[tuple[float, float]], list[float], list[float]], + params: tuple[int, int], swarm_pos: NDArray, tstart: float, tend: float, @@ -237,13 +237,13 @@ def wave( """Specific wave pattern. Args: - params: [steps, height, µ_pairs, aµ1µ2, bµ1µ2] + params: [steps, height_cm] swarm_pos: Current positions of the drones. tstart: Start time of the primitive. tend: End time of the primitive. limits: Spatial limits for the drones. """ - steps, height, mu_pairs, a_mu, b_mu = params + steps, height = params steps = int(steps) # TODO: Tune default values a = 100.0 # Rectangle length @@ -284,14 +284,14 @@ def wave( def form_star( - params: tuple[int, int, int], + params: tuple[int, int, int, float], swarm_pos: NDArray, tstart: float, tend: float, limits: dict[str, NDArray], ) -> tuple[NDArray, dict[float, dict[int, NDArray]]]: """Form a star shape with the drones with {n_drones}//2 spokes.""" - height, min_spacing, delta_radius = params + height, min_spacing, delta_radius, time_to_finish_s = params min_spacing = max(min_spacing, 40) delta_radius = max(delta_radius, 40) n_drones = swarm_pos.shape[0] @@ -318,21 +318,20 @@ def form_star( des_pos = np.vstack([des_pos, np.array([0, 0, height]).T]) assignment = _assign_positions(swarm_pos, des_pos) - - waypoints = {} - waypoints[tend] = {i: p.copy() for i, p in enumerate(des_pos[assignment])} - return des_pos[assignment], waypoints + target = des_pos[assignment] + waypoints = _formation_waypoints(target, swarm_pos, tstart, tend, time_to_finish_s) + return target, waypoints def form_cone( - params: tuple[int, int, bool], + params: tuple[int, int, bool, float], swarm_pos: NDArray, tstart: float, tend: float, limits: dict[str, NDArray], ) -> tuple[NDArray, dict[float, dict[int, NDArray]]]: """Form a cone with the drones.""" - delta_height, spacing, is_inverted = params + delta_height, spacing, is_inverted, time_to_finish_s = params n_drones = swarm_pos.shape[0] # Define limits @@ -365,10 +364,9 @@ def form_cone( des_pos = np.vstack([des_pos, np.array([x, y, [z] * drones_in_layer]).T]) assignment = _assign_positions(swarm_pos, des_pos) - - waypoints = {} - waypoints[tend] = {i: p.copy() for i, p in enumerate(des_pos[assignment])} - return des_pos[assignment], waypoints + target = des_pos[assignment] + waypoints = _formation_waypoints(target, swarm_pos, tstart, tend, time_to_finish_s) + return target, waypoints def twister( @@ -441,21 +439,21 @@ def center( def form_circle( - params: tuple[list[int], int], + params: tuple[list[int], int, int, float], swarm_pos: NDArray, tstart: float, tend: float, limits: dict[str, NDArray], ) -> tuple[NDArray, dict[float, dict[int, NDArray]]]: - """Position drones around the circumference of a circle at height z with at least {min_spacing} cm apart.""" - drone_ids, z_coord = params + """Position drones around the circumference of a circle at a given radius and height.""" + drone_ids, radius_cm, z_coord_cm, time_to_finish_s = params drone_ids = _sanitize_drone_ids(drone_ids, swarm_pos.shape[0]) n_drones = len(drone_ids) - z_coord = int(z_coord) # z coordinate in cm + z_coord = int(z_coord_cm) min_spacing = 80 # Minimum distance between drones in cm - # Calculate the circumference needed to place all drones with at least the minimum spacing - # If radius is bigger than the limits, make concentric circles - radius = min_spacing / (2 * np.sin(np.pi / n_drones)) + min_radius = min_spacing / (2 * np.sin(np.pi / n_drones)) + # Respect LLM-specified radius but enforce minimum safe spacing + radius = max(float(radius_cm), min_radius) lim_upper, lim_lower = limits["upper"], limits["lower"] max_diameter = min(lim_upper[0] - lim_lower[0], lim_upper[1] - lim_lower[1]) max_radius = max_diameter * 100 / 2 @@ -481,10 +479,12 @@ def form_circle( des_pos = np.vstack([des_pos, np.array([x, y, [z_coord] * n]).T]) assignment = _assign_positions(swarm_pos[drone_ids], des_pos) - waypoints = {} - waypoints[tend] = {i: p.copy() for i, p in enumerate(des_pos[assignment])} + target = des_pos[assignment] + waypoints = _formation_waypoints( + target, swarm_pos[drone_ids], tstart, tend, time_to_finish_s, drone_ids=drone_ids + ) pos = swarm_pos.copy() - pos[drone_ids] = des_pos[assignment] + pos[drone_ids] = target return pos, waypoints @@ -515,11 +515,13 @@ def move_z( """Move the drones along the z-axis.""" drone_ids, distance = params drone_ids = _sanitize_drone_ids(drone_ids, swarm_pos.shape[0]) - steps = int(tend - tstart) + steps = max(1, min(int(tend - tstart), 2)) + z_min = limits["lower"][2] * 100 + z_max = limits["upper"][2] * 100 waypoints = {} for t in np.linspace(tstart, tend, steps + 1)[1:]: - swarm_pos[drone_ids, 2] = np.clip(swarm_pos[drone_ids, 2] + distance / steps, 100, 200) + swarm_pos[drone_ids, 2] = np.clip(swarm_pos[drone_ids, 2] + distance / steps, z_min, z_max) waypoints[t] = {i: swarm_pos[i].copy() for i in drone_ids} return swarm_pos, waypoints @@ -595,3 +597,84 @@ def _assign_positions(pos: NDArray, des_pos: NDArray) -> NDArray: dist = np.linalg.norm(pos[:, None, :] - des_pos[None, :, :], axis=-1) # Use the Hungarian algorithm to find the optimal assignment return linear_sum_assignment(dist)[1] + + +# Effective speed cap used when scheduling formation arrivals. Held below axswarm's +# vel_max=1.73 m/s to leave the MPC headroom; matches the convention used by `rotate` +# and `spiral`. HEADROOM is a multiplicative buffer for the solver's smoothness and +# input-continuity penalties; T_MIN prevents trivially small moves from scheduling +# zero-duration arrivals that the MPC can't track cleanly. +_FORMATION_V_EFF_MPS = 1.0 +_FORMATION_HEADROOM = 1.3 +_FORMATION_T_MIN_S = 0.5 +# MPC lookahead window length (K=50 timesteps at freq=10 Hz). Hold waypoints are +# only emitted when the remaining interval exceeds this, so the axswarm lookahead +# is never empty without flooding the waypoints array for normal-length intervals. +_MPC_HORIZON_S = 5.0 + + +def _formation_arrival_time( + target_pos: NDArray, current_pos: NDArray, tstart: float, tend: float +) -> float: + """Estimate the earliest feasible arrival time for a formation primitive. + + Sizes the interval to the bottleneck drone's displacement at an effective max + velocity (with headroom for MPC smoothness), then clamps to ``[tstart, tend]``. + + Args: + target_pos: Desired post-assignment drone positions in cm, shape (n, 3). + current_pos: Current positions of the same drones in cm, shape (n, 3). + tstart: Interval start time in seconds. + tend: Interval end time in seconds. + + Returns: + Arrival time in seconds, in ``[tstart, tend]``. + """ + max_travel_m = float(np.linalg.norm(target_pos - current_pos, axis=-1).max()) / 100 + travel_time = max(max_travel_m / _FORMATION_V_EFF_MPS * _FORMATION_HEADROOM, _FORMATION_T_MIN_S) + return min(tstart + travel_time, tend) + + +def _formation_waypoints( + target_pos: NDArray, + current_pos: NDArray, + tstart: float, + tend: float, + time_to_finish_s: float, + drone_ids: list[int] | None = None, +) -> dict[float, dict[int, NDArray]]: + """Schedule a formation arrival at the LLM-chosen time (clamped to physics + interval). + + The physics floor is the existing ``_formation_arrival_time`` duration; ``time_to_finish_s`` + is clamped between that floor and the full interval. Hold waypoints are emitted only when + the remaining interval exceeds ``_MPC_HORIZON_S`` — i.e. only when axswarm's lookahead + would otherwise be empty — spaced ``_MPC_HORIZON_S`` apart. For typical inter-beat + intervals (< 5s) no hold waypoints are emitted, keeping the waypoints array small. + + Args: + target_pos: Desired post-assignment drone positions in cm, shape (n, 3). + current_pos: Current positions of the same drones in cm, shape (n, 3). + tstart: Interval start time in seconds. + tend: Interval end time in seconds. + time_to_finish_s: LLM-requested arrival duration in seconds. Clamped to + ``[physics_min_duration, tend - tstart]``. + drone_ids: Global 0-indexed drone IDs corresponding to rows of ``target_pos``. + When ``None``, indices 0..n-1 are used (full-swarm primitives). + + Returns: + ``{time: {drone_id: pos}}`` waypoints covering ``[arrival, tend]``. + """ + ids = drone_ids if drone_ids is not None else list(range(len(target_pos))) + physics_min_duration = _formation_arrival_time(target_pos, current_pos, tstart, tend) - tstart + duration = float(np.clip(time_to_finish_s, physics_min_duration, tend - tstart)) + arrival = tstart + duration + entry = {d: p.copy() for d, p in zip(ids, target_pos)} + waypoints: dict[float, dict[int, NDArray]] = {arrival: entry} + # Only emit holds when the lookahead window would otherwise be empty. + t = arrival + _MPC_HORIZON_S + while t < tend: + waypoints[t] = {d: p.copy() for d, p in zip(ids, target_pos)} + t += _MPC_HORIZON_S + if arrival < tend: + waypoints[tend] = {d: p.copy() for d, p in zip(ids, target_pos)} + return waypoints diff --git a/swarm_gpt/core/sim.py b/swarm_gpt/core/sim.py index 5a9a50d..74e8b07 100644 --- a/swarm_gpt/core/sim.py +++ b/swarm_gpt/core/sim.py @@ -91,6 +91,11 @@ def simulate_axswarm( ) n_steps = int(waypoints["time"][0, -1] * sim.control_freq) solve_every_n_steps = sim.control_freq // solver_settings.freq + logger.debug( + f"[sim] waypoints T={waypoints['time'].shape[1]} columns, " + f"n_drones={waypoints['pos'].shape[0]}, " + f"duration={waypoints['time'][0, -1]:.1f}s" + ) assert sim.freq % sim.control_freq == 0, ( "control freq {sim.control_freq} must be divisible by sim.freq {sim.freq}" ) @@ -122,6 +127,10 @@ def simulate_axswarm( solve_times.append(time.perf_counter() - t_solve) if not all(success): logger.info("Solve failed") + if len(solve_times) == 20: + logger.debug( + f"[sim] mean solve time (first 20 calls): {sum(solve_times) / 20 * 1000:.1f}ms" + ) solver_data = solver_data.step(solver_data) pos, vel = solver_data.u_pos[:, 0], solver_data.u_vel[:, 0] diff --git a/swarm_gpt/core/structured_output_schema.py b/swarm_gpt/core/structured_output_schema.py index f64dc9c..da5fd73 100644 --- a/swarm_gpt/core/structured_output_schema.py +++ b/swarm_gpt/core/structured_output_schema.py @@ -1,13 +1,55 @@ -"""Structured output schema helpers for OpenAI Responses API.""" +"""Structured output schema helpers for OpenAI Responses API. + +Keys take the hierarchical form ``"s{seq}b{bar}t{beat}"`` (e.g. ``"s2b4t1"`` = segment 2, +bar 4, beat 1). The choreographer addresses moments at this granularity; the schema models +``choreography`` as an array of ``{"key", "actions"}`` entries, with ``key`` constrained to +an enum of every addressable beat. The LLM emits only the entries it wants; presence of the +required segment-opening keys is validated downstream. +""" from __future__ import annotations import json +import re from typing import Any from swarm_gpt.exception import LLMFormatError _AXIS_ENUM = ["x", "y", "z"] +_KEY_PATTERN = r"s\d+b\d+t\d+" +_KEY_RE = re.compile(r"^s(\d+)b(\d+)t(\d+)$") + + +def encode_key(seq: int, bar: int, beat: int) -> str: + """Encode a ``(segment, bar, beat)`` address as a structured-output key string. + + Args: + seq: 1-indexed segment id. + bar: 1-indexed bar id within the segment. + beat: 1-indexed beat id within the bar. + + Returns: + Key string in the form ``"s{seq}b{bar}t{beat}"``. + """ + return f"s{seq}b{bar}t{beat}" + + +def decode_key(key: str) -> tuple[int, int, int]: + """Decode a structured-output key string into ``(seq, bar, beat)``. + + Args: + key: A string matching ``sbt``. + + Returns: + ``(seq, bar, beat)`` as 1-indexed integers. + + Raises: + LLMFormatError: If ``key`` does not match the expected pattern. + """ + m = _KEY_RE.match(key) + if m is None: + raise LLMFormatError(f"Choreography key {key!r} is not in the form 'sbt'") + return int(m.group(1)), int(m.group(2)), int(m.group(3)) def _int_schema(*, minimum: int | None = None, maximum: int | None = None) -> dict[str, Any]: @@ -49,17 +91,16 @@ def _param_schemas(num_drones: int) -> dict[str, dict[str, Any]]: "radius_increase": _number_schema(), "delta_height_cm": _number_schema(), "radius_cm": _number_schema(), + "z_coord_cm": _number_schema(), "delta_xy_cm": _number_schema(), "delta_z_cm": _number_schema(), - "mu_pairs": _array_schema(_array_schema(_number_schema())), - "a_mu": _array_schema(_number_schema()), - "b_mu": _array_schema(_number_schema()), "omega_times_ten": _number_schema(), "z_spacing_cm": _number_schema(), "min_spacing_cm": _number_schema(), "delta_radius_cm": _number_schema(), "spacing_cm": _number_schema(), "is_inverted": _int_schema(minimum=0, maximum=1), + "time_to_finish_s": _number_schema(), } @@ -74,15 +115,12 @@ def _params_schema(num_drones: int, param_names: list[str]) -> dict[str, Any]: def _action_variant_schema(primitive: str, num_drones: int) -> dict[str, Any]: - param_names = [] if primitive == "PLAN" else _PRIMITIVE_ARG_ORDER[primitive] + param_names = _PRIMITIVE_ARG_ORDER[primitive] return { "type": "object", "additionalProperties": False, "properties": { - "primitive": { - "type": "string", - "enum": [primitive], - }, + "primitive": {"type": "string", "enum": [primitive]}, "params": _params_schema(num_drones, param_names), }, "required": ["primitive", "params"], @@ -92,43 +130,69 @@ def _action_variant_schema(primitive: str, num_drones: int) -> dict[str, Any]: def _action_schema(num_drones: int) -> dict[str, Any]: return { "anyOf": [ - _action_variant_schema(primitive, num_drones) - for primitive in ["PLAN", *_PRIMITIVE_ARG_ORDER.keys()] + _action_variant_schema(primitive, num_drones) for primitive in _PRIMITIVE_ARG_ORDER ] } -def build_motion_primitive_response_schema(*, num_beats: int, num_drones: int) -> dict[str, Any]: - """Build a strict schema that enforces beat-exact motion-primitive outputs.""" - if num_beats < 1: - raise ValueError("num_beats must be >= 1") +def build_motion_primitive_response_schema( + *, + all_keys: list[tuple[int, int, int]], + required_keys: list[tuple[int, int, int]], + num_drones: int, +) -> dict[str, Any]: + """Build a strict response schema keyed by hierarchical ``(seq, bar, beat)`` addresses. + + ``choreography`` is an array of ``{"key", "actions"}`` entries; ``key`` is constrained to + an enum of every beat in ``all_keys``. OpenAI strict mode requires all object properties be + required, so per-entry both fields are required; the LLM controls sparseness by emitting + only the entries it wants. Presence of ``required_keys`` is validated downstream, not here. + + Args: + all_keys: Every addressable ``(seq, bar, beat)`` tuple in the song, in time order. + required_keys: Subset of ``all_keys`` that the LLM must emit (segment openings). + num_drones: Number of drones in the swarm (constrains drone-id ranges). + + Returns: + A JSON-Schema dict suitable for OpenAI Responses API ``response_format``. + + Raises: + ValueError: If ``all_keys`` or ``num_drones`` is empty / non-positive, or if any + entry in ``required_keys`` is missing from ``all_keys``. + """ + if not all_keys: + raise ValueError("all_keys must contain at least one (seq, bar, beat) entry") if num_drones < 1: raise ValueError("num_drones must be >= 1") - beat_keys = [str(i) for i in range(1, num_beats + 1)] + encoded_all = [encode_key(*addr) for addr in all_keys] + encoded_required = [encode_key(*addr) for addr in required_keys] + all_set = set(encoded_all) + missing = [k for k in encoded_required if k not in all_set] + if missing: + raise ValueError(f"required_keys not present in all_keys: {missing}") return { "type": "object", "additionalProperties": False, "properties": { "song_mood": {"type": "string"}, - "cord_analysis": {"type": "string"}, "choreography_plan": {"type": "string"}, "choreography": { - "type": "object", - "additionalProperties": False, - "properties": { - beat_key: {"$ref": "#/$defs/action_list"} for beat_key in beat_keys + "type": "array", + "items": { + "type": "object", + "additionalProperties": False, + "properties": { + "key": {"type": "string", "enum": encoded_all}, + "actions": {"$ref": "#/$defs/action_list"}, + }, + "required": ["key", "actions"], }, - "required": beat_keys, }, }, - "required": ["song_mood", "cord_analysis", "choreography_plan", "choreography"], + "required": ["song_mood", "choreography_plan", "choreography"], "$defs": { "action": _action_schema(num_drones), - "action_list": { - "type": "array", - "minItems": 1, - "items": {"$ref": "#/$defs/action"}, - }, + "action_list": {"type": "array", "minItems": 1, "items": {"$ref": "#/$defs/action"}}, }, } @@ -142,12 +206,12 @@ def build_motion_primitive_response_schema(*, num_beats: int, num_drones: int) - "spiral": ["steps", "height_cm"], "spiral_speed": ["steps", "height_cm", "degrees", "radius_increase"], "helix": ["steps", "delta_height_cm", "height_cm"], - "form_circle": ["drone_ids", "radius_cm"], + "form_circle": ["drone_ids", "radius_cm", "z_coord_cm", "time_to_finish_s"], "zig_zag": ["steps", "delta_xy_cm", "delta_z_cm"], - "wave": ["steps", "height_cm", "mu_pairs", "a_mu", "b_mu"], + "wave": ["steps", "height_cm"], "twister": ["steps", "omega_times_ten", "z_spacing_cm"], - "form_star": ["height_cm", "min_spacing_cm", "delta_radius_cm"], - "form_cone": ["delta_height_cm", "spacing_cm", "is_inverted"], + "form_star": ["height_cm", "min_spacing_cm", "delta_radius_cm", "time_to_finish_s"], + "form_cone": ["delta_height_cm", "spacing_cm", "is_inverted", "time_to_finish_s"], } @@ -181,18 +245,12 @@ def _args_from_params(primitive: str, params: Any) -> list[Any]: def action_to_motion_primitive(action: dict[str, Any]) -> str: - """Convert one structured action object to legacy `primitive(args)` syntax.""" + """Convert one structured action object to legacy ``primitive(args)`` syntax.""" if not isinstance(action, dict): raise LLMFormatError( f"Structured choreography action must be an object, got {type(action).__name__}" ) primitive = action.get("primitive") - if primitive == "PLAN": - params = action.get("params", {}) - args = action.get("args", []) - if params or args: - raise LLMFormatError("PLAN does not accept params or args") - return "PLAN" if primitive not in _PRIMITIVE_ARG_ORDER: raise LLMFormatError(f"Unknown motion primitive '{primitive}' in structured output") ordered_arg_names = _PRIMITIVE_ARG_ORDER[primitive] # used for expected arity messaging @@ -225,20 +283,43 @@ def action_to_motion_primitive(action: dict[str, Any]) -> str: return f"{primitive}({rendered_args})" -def structured_payload_to_choreography(payload: dict[str, Any]) -> dict[int, str]: - """Convert structured OpenAI payload to the existing choreography dictionary format.""" - choreography = payload.get("choreography", {}) - if not isinstance(choreography, dict): - raise LLMFormatError("Structured output field 'choreography' must be an object") - converted: dict[int, str] = {} - for beat_text, actions in choreography.items(): - try: - beat = int(beat_text) - except (TypeError, ValueError) as e: +def structured_payload_to_choreography(payload: dict[str, Any]) -> dict[tuple[int, int, int], str]: + """Convert a structured OpenAI payload to a ``(seq, bar, beat)``-keyed choreography dict. + + Args: + payload: The structured-output payload from the LLM. + + Returns: + Dict mapping ``(seq, bar, beat)`` tuples to action strings (one or more primitive + calls separated by ``"; "``). + + Raises: + LLMFormatError: If the payload is malformed (wrong field types, unknown keys, + empty action lists, duplicate keys, etc.). + """ + choreography = payload.get("choreography", []) + if not isinstance(choreography, list): + raise LLMFormatError("Structured output field 'choreography' must be an array") + converted: dict[tuple[int, int, int], str] = {} + for entry in choreography: + if not isinstance(entry, dict): raise LLMFormatError( - f"Structured output beat key {beat_text!r} is not an integer" - ) from e + "Each choreography entry must be an object with 'key' and 'actions'" + ) + key = entry.get("key") + actions = entry.get("actions") + if not isinstance(key, str): + raise LLMFormatError("Choreography entry 'key' must be a string") + addr = decode_key(key) + if addr in converted: + raise LLMFormatError(f"Duplicate choreography key {key!r}") if not isinstance(actions, list) or len(actions) == 0: - raise LLMFormatError(f"Structured output beat {beat} must include non-empty 'actions'") - converted[beat] = "; ".join(action_to_motion_primitive(action) for action in actions) + raise LLMFormatError( + f"Structured output beat {key!r} must include a non-empty action list" + ) + converted[addr] = "; ".join(action_to_motion_primitive(action) for action in actions) return converted + + +# Re-exported for callers that want to validate raw key strings without decoding. +KEY_PATTERN = _KEY_PATTERN diff --git a/swarm_gpt/data/drones.toml b/swarm_gpt/data/drones.toml index 29080da..84dc69f 100644 --- a/swarm_gpt/data/drones.toml +++ b/swarm_gpt/data/drones.toml @@ -1,24 +1,81 @@ -[cf0] -name = "cf31" -uri = "radio://0/73/2M/ABAD1DEA11" -pos = [0.0, 0.0, 0.0] - -[cf1] -name = "cf32" -uri = "radio://0/73/2M/ABAD1DEA13" -pos = [0.94, 0.54, 0.0] - -[cf2] -name = "cf33" -uri = "radio://0/73/2M/ABAD1DEA12" -pos = [0.9, -0.3, 0.0] - -[cf3] -name = "cf34" -uri = "radio://0/73/2M/ABAD1DEA14" -pos = [-0.87, 0.55, 0.0] - -[cf4] -name = "cf35" -uri = "radio://0/73/2M/ABAD1DEA15" -pos = [-0.83, -0.5, 0.0] \ No newline at end of file +active = ["cf11", "cf12"] + +[cf11] +addr = 11 +pos = [0.0, 1.0, 0.0] + +[cf12] +addr = 12 +pos = [0.0, -1.0, 0.0] + +[cf13] +addr = 13 +pos = [1.1, 0.0, 0.0] + +[cf14] +addr = 14 +pos = [1.1, 1.0, 0.0] + +[cf15] +addr = 15 +pos = [1.1, 2.0, 0.0] + +[cf21] +addr = 21 +pos = [-1.1, -2.0, 0.0] + +[cf22] +addr = 22 +pos = [-1.1, -1.0, 0.0] + +[cf23] +addr = 23 +pos = [-1.1, 0.0, 0.0] + +[cf24] +addr = 24 +pos = [-1.1, 1.0, 0.0] + +[cf25] +addr = 25 +pos = [-1.1, 2.0, 0.0] + +[cf31] +addr = 31 +pos = [1.1, -2.0, 0.0] + +[cf32] +addr = 32 +pos = [1.1, -1.0, 0.0] + +[cf33] +addr = 33 +pos = [1.1, 0.0, 0.0] + +[cf34] +addr = 34 +pos = [1.1, 1.0, 0.0] + +[cf35] +addr = 35 +pos = [1.1, 2.0, 0.0] + +[cf41] +addr = 41 +pos = [-1.1, -2.0, 0.0] + +[cf42] +addr = 42 +pos = [-1.1, -1.0, 0.0] + +[cf43] +addr = 43 +pos = [-1.1, 0.0, 0.0] + +[cf44] +addr = 44 +pos = [-1.1, 1.0, 0.0] + +[cf45] +addr = 45 +pos = [-1.1, 2.0, 0.0] diff --git a/swarm_gpt/data/motion_primitive_prompts.yaml b/swarm_gpt/data/motion_primitive_prompts.yaml index 92a5e74..2ad94b6 100644 --- a/swarm_gpt/data/motion_primitive_prompts.yaml +++ b/swarm_gpt/data/motion_primitive_prompts.yaml @@ -1,132 +1,193 @@ -# Prompts for motion-primitive choreography. user_initial is .format()-expanded in choreographer. +# Prompts for motion-primitive choreography keyed by hierarchical (segment, bar, beat). +# user_initial is .format()-expanded in choreographer._format_initial_user_prompt. # Coordinates use integer cm (z=100 is 1 m) for fewer tokenizer splits than floats. system_initial: | - You choreograph drone swarms using motion primitives. Choose primitives that match the music; - respect timing, collision, and step limits. Coordinates are integer cm. + You choreograph drone swarms using motion primitives. Choose primitives that match the song's + structural sections; respect timing, collision, and bounds. Coordinates are integer cm. -user_initial: > - Plan a beat-by-beat choreography for {num_drones} drones to "{song}". + You have creative freedom over WHEN the swarm moves. You do NOT need to choreograph every bar + or beat — that produces busy, repetitive motion. Instead vary the rhythm of your choreography: + cluster moves where the music is active, let the swarm hold a pose and breathe through calmer + passages, and avoid a predictable "something every bar" cadence. Uneven, musically-motivated + spacing reads as intentional and expressive; mechanical regularity does not. - - beats: {num_beats} - beat_times_100ms: {beat_times} - beat_intervals_100ms: {beat_intervals} - amplitude_dbfs: {dbfs} - novelty_pct: {beat_novelty} - chords: {chords} - +user_initial: | + Plan a choreography for {num_drones} drones to "{song}" at {bpm} BPM. + + + {segments_table} + Total addressable beats: {n_total_beats}. + Keys take the form sbt (1-indexed): segment, then bar within that segment, + then beat within that bar. Any specific keys shown in the example below illustrate FORMAT + ONLY — choose keys from THIS song's actual segment/bar/beat counts above. + + + + Per-2-bar audio features. Each line is "key: rms / centroid", both normalized to [0, 1]. + The key is the FIRST beat of the 2-bar window in s#b#t# notation. + - rms ~ loudness (1.0 = song's loudest moment) + - centroid ~ brightness (1.0 = brightest; high = treble cymbals/vocals/leads, low = bass/kicks) + Use the contrast to choose primitives that match the moment. Examples: + - rising rms across adjacent windows → a build-up; favor accumulating motion + - high rms + low centroid → bass-driven; bigger vertical moves, slow rotations + - high rms + high centroid → treble-driven; fast rotations, helices, waves + - low rms regardless of centroid → hold, breathe, minimal new primitives + - BEAT DROP → the first downbeat of a NEW segment (a sb1t1 key) whose rms AND centroid + are high — usually a jump up from the bars just before it. The segment boundary matters more + than the raw peak: a build-up can spike rms/centroid too, but the real drop lands on the + downbeat where the new section begins. Treat these as the biggest moments in the show. + {dynamics_table} + count: {num_drones} start_cm: {starting_pos} - max_step_cm_per_beat: {max_distances} bounds_cm: lower={lim_lower} upper={lim_upper} - - Exactly {num_waypoints} beats (keys 1..{num_beats}); one choreography line per beat; end with END. - - No two drones share the same position at the same beat. - - Respect max_step_cm_per_beat and bounds_cm; z > 0. - - Tie motion to music: louder/novel beats -> bolder primitives; all drones move on loud+novel beats when possible. - - Prefer symmetric formations and varied 3D motion (altitude changes, geometric shapes). - - At most 4 multi-beat primitives per song; prefer spiral, helix, zig_zag, wave over trivial moves. - - Use enough PLAN beats for readable motion: after a formation/rotate/height change, insert PLAN before the next different primitive unless beats are very scarce. - - Multi-beat primitives are exclusive: a primitive with steps=S must be followed immediately by S-1 PLAN beats before any other primitive. - - One beat = one line. Multiple calls on one beat: separate with "; " (e.g. center([1,2]); move_z([3], 10)). - - Each drone touched at most once per beat (except separate move() per drone). + - REQUIRED keys (must emit one action list at each): {required_keys_csv} + These are a SPARSE skeleton — roughly one anchor every few bars plus each segment's + opening — not every bar. They are the floor, not the target. + - OPTIONAL keys: every other sbt in the song's address space. Add them only + where the music genuinely calls for an accent — a build, a hit, a section change. It is good + and expected to leave many bars with no action: do NOT try to cover every bar. Vary the + spacing between actions so the motion feels phrased, not metronomic. + - Drones hold or continue the previous primitive between emitted actions. Holding a pose + through a calm passage is a valid, expressive choice — not a gap to be filled. + - Formation primitives (form_circle, form_star, form_cone) snap into shape quickly and then + sit still. A long hold after a formation reads as lifeless on energetic passages — prefer + to follow up on a later bar (often the next bar or two later) with a continuation that + keeps the swarm alive: rotate preserves the formation while turning it, move_z lifts or + lowers it, twister/helix/spiral_speed evolve it into something new. Omit the follow-up + only when the music genuinely wants stillness (intro/outro tails, breakdowns). + - No two drones occupy the same position at the same moment. + - Stay within bounds_cm; z > 0. + - Match motion to music: dense, energetic sections (chorus, drop, bridge) get bolder + primitives; intros and outros stay sparse and gentle. + - AT A BEAT DROP (see song_dynamics), do NOT answer with a static formation — a drop that + resolves into the swarm quietly gathering into a circle is the most underwhelming thing you + can do. The drop is the payoff: hit it with big, FAST, full-swarm MOTION. Favor high-speed + multi-step primitives — spiral_speed with a large degrees (270-360) and radius_increase 2-3, + a fast twister (omega_times_ten 14-20), a tall helix, or a large-angle rotate (90+ deg) — + so the swarm visibly explodes into movement on the downbeat. If the section needs a shape, + snap it in a bar or two BEFORE the drop so the drop itself is spent on motion, not arriving. + - One entry's "actions" array can hold ONE or MORE actions. Two valid stacking patterns: + 1. Disjoint drone subsets — different drones, different shapes, coexisting. + IMPORTANT: the two subsets MUST end up at different points in space or they will + collide. Separating by height alone needs a LARGE gap: the vertical collision + envelope is 60cm, so stacked formations must differ in z by at least 70cm, preferably larger to allow for movement. + A smaller gap (or same x/y footprint) makes the drones jitter. Otherwise separate + by radius or x/y center. + 2. Formation + motion on overlapping drones — formation snaps in, motion takes over. + The system handles the handoff automatically. + Do NOT stack two formations on overlapping drones (e.g. form_star + form_circle on the + same drones); the result is undefined. + Do NOT stack two formations in the same point in space on different subsets + (e.g. form_circle with different radii but same center); the result is a collision. - Single-beat (usually followed by PLAN before switching primitive): + Single-shot (executes once over the interval until the next emitted action): - rotate(angle_deg, axis) — all drones; axis usually 'z' - center([ids]) — listed drones to formation around their centroid - - move_z([ids], delta_cm) — vertical shift - - form_circle([ids], height_cm) — circle at height; radius from count - - form_star(height_cm, spacing 40-80, delta_radius 50-100) - - form_cone(delta_height 30-80, spacing 40-80, is_inverted 0|1) - - swap(id1, id2) — swap two drones (only one swap pair per beat) + - move_z([ids], delta_cm) — vertical shift; aim for |delta_cm| ~{move_z_typical_cm} so the motion is visibly noticeable + - form_circle([ids], radius_cm, z_coord_cm, time_to_finish_s) + radius_cm: circle radius; the system enforces a minimum based on drone count and spacing. + z_coord_cm: height of the circle in cm (e.g. 80, 100, 120). + time_to_finish_s: seconds to arrive; pick ~1s for snappy hits, ≥half the interval for slow blooms. + The system clamps to physics minimum if your value is too small. + When stacking two disjoint form_circle calls vertically, their z_coord_cm values MUST differ + by at least 60 (e.g. 80 and 140): a smaller gap puts the drones inside each other's collision + envelope and they will jitter instead of holding the rings. + - form_star(height_cm, spacing 40-80, delta_radius 50-100, time_to_finish_s) + time_to_finish_s: seconds to arrive in the star; same guidance as form_circle. + - form_cone(delta_height 30-80, spacing 40-80, is_inverted 0|1, time_to_finish_s) + time_to_finish_s: seconds to arrive in the cone; same guidance as form_circle. + - swap(id1, id2) - move(x_cm, y_cm, z_cm, drone_id) — absolute position for one drone - Multi-beat (steps in [2,3]; follow immediately with steps-1 PLAN lines; start by beat s-steps+1 so it finishes before END): + Multi-step (plays out across the interval; pick steps in [2, 3]): - spiral(steps, height_cm) - - spiral_speed(steps, height_cm, angle_deg 0-360, radius_increase 1-3) - - helix(steps, delta_h_cm 0-100, height_cm 50-100) - - twister(steps, omega_x10 2-20, z_spacing_cm 5-30) + - spiral_speed(steps, height_cm, degrees 0-360, radius_increase 1-3) + - helix(steps, delta_height_cm 0-100, height_cm 50-100) + - twister(steps, omega_times_ten 2-20, z_spacing_cm 5-30) - zig_zag(steps, delta_xy_cm, delta_z_cm) - - wave(steps, height, mu_pairs, a_coeffs, b_coeffs) — 2D wave surface; equation: - {wave_eqn} - - PLAN — hold positions for one beat, add breathing room, and fill multi-beat tails. Never end the song on a multi-beat primitive without trailing PLANs. + - wave(steps, height_cm) — 2D standing-wave surface; drones first form a grid then oscillate. 1) song_mood — brief mood for "{song}". - 2) cord_analysis — chords, major/minor, emotional effect. - 3) choreography_plan — sections, primitives, and why; scale intensity to dbfs/novelty/intervals. - 4) choreography — full beat list 1..{num_beats}, no "..." placeholders. + 2) choreography_plan — sections, primitives chosen, and intensity rationale per segment. + 3) choreography — sparse map of action keys to action lists. example: | - Input: song=rêverie, drones=3, beats=22 (quiet dbfs ~-50..-30; novelty peak ~98 at beat 6). + Input: song=Sample, drones=3, bpm=120, structure: + segment 1: "intro" (0.00s - 4.00s) — 2 bars x 4 beats + segment 2: "chorus" (4.00s - 12.00s) — 4 bars x 4 beats + Required keys: s1b1t1, s2b1t1. Output: - song_mood: Dreamy, contemplative Debussy; gentle fluid motion. - cord_analysis: Cm, A#m, F, Gm mix — wistful, not dramatic. - choreography_plan: Beats 1-5: slow spiral inward (low novelty). Beat 6+: rotate and move_z on peak. Mid song: second spiral + zig_zag. Use PLAN between primitive changes; keep steps small for quiet dynamics. + song_mood: Steady, building from a soft intro to a confident chorus. + choreography_plan: Intro begins with a gentle inward spiral. Chorus opens with a bold + form_circle on the downbeat, accents the third beat of bar 1 with a rotation, then + sweeps through a helix at the start of bar 2. choreography: - 1: spiral(5, 100) - 2: PLAN - 3: PLAN - 4: PLAN - 5: PLAN - 6: PLAN - 7: rotate(60, 'z') - 8: PLAN - 9: rotate(-90, 'z') - 10: PLAN - 11: move_z([1, 2, 3], 30) - 12: PLAN - 13: spiral(4, 100) - 14: PLAN - 15: PLAN - 16: PLAN - 17: PLAN - 18: zig_zag(5, 20, 20) - 19: PLAN - 20: PLAN - 21: PLAN - 22: PLAN + s1b1t1: spiral(3, 100) + s2b1t1: form_circle([1, 2, 3], 120, 100, 1.5) + s2b1t3: rotate(60, 'z') + s2b2t1: helix(2, 40, 90) END output_format: | - Return one ```yaml``` block: + Return one ```yaml``` block. song_mood: - cord_analysis: choreography_plan: choreography: - 1: + sbt: ... - N: END - Every beat 1..N present. Use "; " for multiple calls on one beat. No ellipsis rows. Multi-beat primitives must include trailing PLAN lines. + All REQUIRED keys must be present. Add OPTIONAL keys sparingly, only where the music calls + for an accent — leaving bars unchoreographed is expected. Do not invent keys that are not in + the song's address space. output_format_structured: | - Return JSON only (no markdown). Schema enforces exactly num_beats beats and named primitive params. - Rules still apply: musically coherent choices, immediate PLAN tails for multi-beat primitives, transition PLAN between different primitives, at most 4 multi-beat primitives, respect bounds and step limits. - Each choreography action must be an object: {"primitive":"name","params":{...}}. - Use exactly the parameter names from the primitive signature. PLAN uses empty params: {"primitive":"PLAN","params":{}}. + Return JSON only (no markdown). The schema enforces: + - "choreography" is an array of entries, each {"key":"sbt","actions":[...]}. + - Every key matches the pattern sbt and is one of the song's valid addresses. + - All REQUIRED keys are present (as entries); add OPTIONAL keys only where musically warranted, + leaving many bars empty. Do not repeat a key. + - "actions" is a list of one or more action objects: {"primitive":"name","params":{...}}. + Two valid multi-action patterns (both in a single entry's "actions" array): + 1. Disjoint drone subsets — different drones at different positions in space: + "actions": [ + {"primitive":"form_circle","params":{"drone_ids":[1,2,3,4,5],"radius_cm":100,"z_coord_cm":80,"time_to_finish_s":2.0}}, + {"primitive":"form_circle","params":{"drone_ids":[6,7,8,9,10],"radius_cm":100,"z_coord_cm":150,"time_to_finish_s":2.0}} + ] + Subsets MUST occupy different regions to avoid collision. Stacking by z requires a gap of + at least 60cm (vertical collision envelope); otherwise separate by radius or x/y center. + 2. Formation + motion on overlapping drones — formation arrives, then motion takes over: + "actions": [ + {"primitive":"form_star","params":{"height_cm":100,"min_spacing_cm":60,"delta_radius_cm":80,"time_to_finish_s":2.0}}, + {"primitive":"rotate","params":{"angle_deg":60,"axis":"z"}} + ] + The system suppresses form_* hold waypoints automatically — no extra timing needed. + Do NOT stack two formations on overlapping drones. + Use exactly the parameter names from the primitive signature. Correct JSON param examples: - center([ids]) -> {"primitive":"center","params":{"drone_ids":[1,2,3,4]}} - move_z([ids], delta_cm) -> {"primitive":"move_z","params":{"drone_ids":[1,2,3,4],"delta_cm":30}} - - form_cone(delta_height_cm, spacing_cm, is_inverted) -> {"primitive":"form_cone","params":{"delta_height_cm":60,"spacing_cm":60,"is_inverted":0}} + - form_cone(delta_height_cm, spacing_cm, is_inverted) -> {"primitive":"form_cone","params":{"delta_height_cm":60,"spacing_cm":60,"is_inverted":0,"time_to_finish_s":2.0}} - spiral_speed(steps, height_cm, degrees, radius_increase) -> {"primitive":"spiral_speed","params":{"steps":3,"height_cm":100,"degrees":180,"radius_increase":1}} - - wave(steps, height_cm, mu_pairs, a_mu, b_mu) -> {"primitive":"wave","params":{"steps":2,"height_cm":80,"mu_pairs":[[0,0],[1,0]],"a_mu":[0.5,0.25],"b_mu":[0.2,0.1]}} + - wave(steps, height_cm) -> {"primitive":"wave","params":{"steps":2,"height_cm":80}} Common mistakes that will fail syntax checks: - - Wrong: {"primitive":"form_cone","params":{"drone_ids":[1,2,3],"delta_height_cm":60,"spacing_cm":60,"is_inverted":0}} because form_cone has no drone_ids param. - - Wrong: {"primitive":"spiral_speed","params":{"steps":3,"height_cm":100,"angle_deg":90,"degrees":180,"radius_increase":1}} because spiral_speed has no angle_deg param. + - Wrong: {"primitive":"form_cone","params":{"drone_ids":[1,2,3],...}} because form_cone has no drone_ids param. + - Wrong: {"primitive":"spiral_speed","params":{"steps":3,"angle_deg":90,...}} because spiral_speed uses 'degrees', not 'angle_deg'. - Wrong: {"primitive":"move_z","params":{"delta_cm":30}} because move_z also needs drone_ids. + - Wrong: {"primitive":"form_star","params":{"height_cm":100,"min_spacing_cm":60,"delta_radius_cm":80}} because form_star also needs time_to_finish_s. diff --git a/swarm_gpt/data/presets/.gitkeep b/swarm_gpt/data/presets/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/swarm_gpt/data/presets/Crazyflie Drones Theme (short) | 9 | 20251202_183056/history.json b/swarm_gpt/data/presets/Crazyflie Drones Theme (short) | 9 | 20251202_183056/history.json deleted file mode 100644 index 1904510..0000000 --- a/swarm_gpt/data/presets/Crazyflie Drones Theme (short) | 9 | 20251202_183056/history.json +++ /dev/null @@ -1 +0,0 @@ -[{"role": "system", "content": "You're a helpful assistant that will help a human choreograph a dance for a swarm of small drones."}, {"role": "user", "content": "Generate the trajectories for a choreography for 9 drones dancing to the music of the song Crazyflie Drones Theme (short). The trajectories are defined by coordinate waypoints that the drones fly to. Each movement can be defined by multiple template functions, which are explained below.\n# Information on the song: - Name: Crazyflie Drones Theme (short) - Number of beats: 12 - Beat times in 100ms: {1: 5, 2: 37, 3: 65, 4: 93, 5: 130, 6: 153, 7: 185, 8: 208, 9: 231, 10: 255, 11: 287, 12: 315} - Beat intervals in 100ms: {1: 5, 2: 32, 3: 28, 4: 28, 5: 37, 6: 23, 7: 32, 8: 23, 9: 23, 10: 23, 11: 32} - Beat amplitude in dBFS: {1: -23, 2: -26, 3: -25, 4: -26, 5: -17, 6: -18, 7: -17, 8: -18, 9: -18, 10: -16, 11: -17, 12: -17} - Beat novelty in %: {1: 92, 2: 83, 3: 73, 4: 95, 5: 55, 6: 78, 7: 99, 8: 67, 9: 69, 10: 94, 11: 90, 12: 100} - Beat chords: {1: 'E', 2: 'E', 3: 'A', 4: 'A', 5: 'E', 6: 'E', 7: 'A', 8: 'E', 9: 'E', 10: 'E', 11: 'A', 12: 'E'}\n# Information on the drones: - Number: 9 - Drones must not collide with each other. No two drones must be at the same coordinates at the same beat. - Starting positions: [[100, -100, 100], [100, 0, 100], [100, 100, 100], [0, -100, 100], [0, 0, 100], [0, 100, 100], [-100, -100, 100], [-100, 0, 100], [-100, 100, 100]] - Maximum step distance between waypoints in cm: {1: 52, 2: 320, 3: 278, 4: 276, 5: 368, 6: 232, 7: 324, 8: 230, 9: 230, 10: 232, 11: 324, 12: 276} - Lower limit for generated waypoints: [-200. -150. 10.] - Upper Limit for generated waypoints: [200. 150. 200.]\n# Information on the environment: - The drones are limited to [-200, 200] in x, [-200, 200] in y, [0, 200] in z - The drones must not touch the ground, i.e. their z coordinate must always be greater than 0 \n# Information on the choreography: - The drone choreography must have 12 waypoints, one for each beat. - Each drone must move at least once during the song. - Movements must depend on the beats of the song. If the beats are loud and novel, all drones must move. - If possible, coordinates for the drones should be symmetrical at each beat. - Successive waypoints should form patterns for single drones and for the whole swarm - Coordinates are given in cm. A drone flying at 1m height will have a z coordinate of 100. - The difference between two waypoints must be less than the maximum step distance in each dimension.\n E.g. if the maximum step distance between beat 3 and 4 is 50, the difference between the x, y,\n z coordinates of the waypoints at beat 3 and 4 must be less than 50 in each dimension.\n\n# Information on template functions: Make sure to limit actions to each drone once per time step. So if you choose swap for (1, 2) then you can only choose swap(3, 4) or two seperate move functions for each drone 2: time dependent template functions ( For such functions we need only one function call for one time step) - rotate:\n takes 2 parameters ( angle, axis ), and rotates all drones by 'angle' along 'axis'\n #angle is a float between [0, 360]\n #axis is one of 'x', 'y', or 'z', but for now always rotate around 'z'\n eg\n rotate(30, 'z')\n4: variadic functions ( such function takes a list with drone ids as arguments ) - center:\n Takes a list of drone IDs as input. It calculates the geometric center of the current positions of these specified drones and then moves all the drones with the matching IDs to this calculated center position.\n eg\n center([1, 2, 3])\n- move_z:\n Takes a list of drone IDs as input, and distance. It moves all the drones with the matching IDs along z axis by xcm. if distance is positive, it moves them up, otherwise down\n eg\n move_z([1, 2, 3], 10)\n- form_circle\n Takes a list of drone IDs as input, and z coordinate ( height ) for the circle formation. Radius of circle is decided by number of drones in list argument. So bigger circle for more drones.\n # height has to be greater than zero\n eg \n # Cone formation as 4 drones are at 150cm height, and 3 drones are at 50cm height with smaller radius as less drones.\n form_circle( [1, 2, 3, 4], 150 ), form_circle( [5, 6, 7], 50 )\n- form_star\n Takes 3 parameters ( height, spacing, delta_radius ) and creates a star in the z-plane, which is defined by 2 concentric circles in staggered angles with different radii. \n Height corresponds to the height of the star. Spacing corresponds to the minimum spacing between drones, where a higher spacing means larger star. \n Delta_radius corresponds to the difference of the 2 radii, where a larger delta_radius means a more spread out star\n #height has to be greater than zero\n #spacing is an integer between [40, 80]\n #delta_radius is an integer between [50 100]\n eg \n form_star( 120, 60, 50 )\n- form_cone\n Takes 3 parameters ( delta_height, spacing, is_inverted ) and creates a cone using concentric circles of different radii. Each layer has 4 additional drones.\n Delta_height is the difference in height between layers. A larger delta_height corresponds to a taller cone.\n Spacing is the minimum_spacing between drones of a layer. A larger spacing means a larger circle radius and wider cone.\n Is_inverted is a boolean value (an integer of 0 or 1) that determines if the cone is inverted or not. Inverted cones are more expressive and dynamic. \n #delta_height is an integer between [30, 80]\n #spacing is an integer between [40, 80]\n #is_inverted is an integer with the values 0 or 1\n eg \n form_star( 120, 60, 50 )\n\n\n5: multi time dependent functions ( For such functions we need only one function call for multiple time steps)\n Any multi-time dependent function needs to be followed by the 'PLAN' keyword for the number of steps minus 1. This means, for functions that span over `step` time steps, the function call must be followed by `step - 1` occurrences of 'PLAN'.\n These functions must be initiated `s - step + 1` beats before the last beat, where `s` is the total number of beats. This ensures that the multi-time function completes before the song ends.\n\n**Important Timing Constraint**: If you have a multi-time function that takes 3 steps, it must be called at least 2 steps before the last beat to allow for the necessary 'PLAN' steps. For example, with a 22-beat song, a 3-step function should be called by the 20th beat, followed by 'PLAN' for the 21st and 22nd beats. - spiral:\n takes 2 parameters ( steps, height ), and makes all drones move in a outward spiral pattern with the calculate radius ( from no of drones ), and number of steps at height h\n\n #steps is an integer between [2, 3]\n #height is an integer between [0, 200] that determines the height of the spiral\n #Since spiral is a multi time dependent function, it should be falled by 'PLAN' keyword for the number of steps -1 times, so if steps is 3, then spiral is called once, and PLAN follows twice in output\n # spiral needs to be called atleast 's' steps before last timestep as 's' PLAN follow the spiral call\n eg:\n 1: spiral(3, 50 )\n 2: PLAN\n 3: PLAN\n\n- spiral_speed:\n Takes 4 parameters (steps, height, speed, radius_increase), and has the same effect as spiral. The difference is that spiral_speed increases the radius of the spiral by radius_increase over the number of steps. In addition,\n speed defines the angle by which the drones rotate around the center. Lower speeds results in slower drone motions.\n # Steps is an integer between [2, 3]\n # height is an integer between [0, 200] that determines the height of the spiral\n # speeds is an integer between [0, 360] that defines the angle by which the drones rotate around the center. Very low speeds are in the range of 0 to 90, fast speeds are in the range of 100 to 360\n # radius_increase is a float between [1, 3] that increases the radius of the spiral over the number of steps\n eg:\n 1: spiral_speed(3, 100, 200, 1.5)\n 2: PLAN\n 3: PLAN\n\n- helix:\n takes 2 parameters ( steps, delta_h, height ), and makes all drones move in a helix pattern with the calculate radius ( from no of drones ), and number of steps.\n\n #steps is an integer between [2, 3]\n #delta_h is an integer between [0, 100], that changes the height of drones in the helix pattern per step\n #height is between [50, 100] as helix rises up with steps, lets be safe.\n #Since helix is a multi time dependent function, it should be falled by 'PLAN' keyword for the number of steps -1 times, so if steps is 3, then helix is called once, and PLAN follows twice in output\n eg:\n 1: helix(3, 20, 50)\n 2: PLAN\n 3: PLAN\n\n- twister:\n takes 3 parameter ( steps, omega, z_spacing ), and makes all drones move into a upside down cone pattern and rotate at the speed specified by omega, for the number of steps specified.\n Z_spacing corresponds to the height difference between neighbouring drones, where a smaller z_spacing corresponds to a more compact cone.\n\n #steps is an integer between [2, 3]\n #omega is an integer between [2, 20] with a higher omega corresponding to a more dynamic performance.\n #z_spacing is an integer between [5, 30]\n #Since twister is a multi time dependent function, it should be falled by 'PLAN' keyword for the number of steps -1 times, so if steps is 3, then conic_rotate is called once, and PLAN follows twice in output\n eg:\n 1: twister(3, 20, 10)\n 2: PLAN\n 3: PLAN\n\n- zig_zag:\n Moves all drones in a zigzag pattern. Takes 3 parameters\n Parameters: `steps` (int), `delta` (int) - the horizontal displacement per step, `delta_h` (int) - the vertical displacement per step.\n eg:\n 4: zig_zag(3, 20, 10)\n 5: PLAN\n 6: PLAN\n \n- wave:\n Moves all drones in a three-dimensional wave propagating through a two-dimensional rectangular elastic surface with bounded edges. The disturbance at any point on the surface is defined by its parameters in space and time, and the wave propagation is governed by the wave equation with a speed `c > 0`. Takes 5 parameters.\n Parameters:\n - `steps` (int): The number of discretization points.\n - `height` (float): The desired height of drones above the surface.\n - `mu_pairs` (list of list): List of \\((\u03bc1, \u03bc2)\\) pairs that define the spatial frequencies.\n - `a\u00b51\u00b52` (list of floats): List of amplitude coefficients corresponding to each \\((\u03bc1, \u03bc2)\\) pair for the sine component.\n - `b\u00b51\u00b52` (list of floats): List of amplitude coefficients corresponding to each \\((\u03bc1, \u03bc2)\\) pair for the cosine component.\n eg:\n 4: wave( 4, 10, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4] )\n 5: PLAN\n 6: PLAN\n 7: PLAN\n latex equn for the concerned wave equation:\n The disturbance \\(u(s, t)\\) at any point \\(s = (s_1, s_2) \\in [0, a] \\times [0, b]\\) \nat time \\(t\\) is governed by:\n\\[\nc^2 \\nabla^2 u(s, t) = \\frac{\\partial^2 u(s, t)}{\\partial t^2},\n\\]\nwhere \\( \\nabla^2 \\) is the spatial Laplacian and \\(c > 0\\) is the wave speed. \nThe solution:\n\\[\nu(s, t) = \\sum_{(\\mu_1, \\mu_2)} a_{\\mu_1 \\mu_2} \\sin \\left( \\frac{\\mu_1 \\pi s_1}{a} \\right) \n\\sin \\left( \\frac{\\mu_2 pi s_2}{b} \\right) \\sin(\\omega_{\\mu_1 \\mu_2} t) \n+ b_{\\mu_1 \\mu_2} \\sin \\left( \\frac{\\mu_1 \\pi s_1}{a} \\right) \n\\sin \\left( \\frac{\\mu_2 \\pi s_2}{b} \\right) \\cos(\\omega_{\\mu_1 \\mu_2} t),\n\\]\n\nwith frequencies:\n\n\\[\n\\omega_{\\mu_1 \\mu_2}^2 = c^2 \\pi^2 \\left( \\frac{\\mu_1^2}{a^2} + \\frac{\\mu_2^2}{b^2} \\right).\n\\]\n\nPLAN can also be used to indicate that the drones should maintain their current position for the next beat, so after forming cone formation, you can use PLAN to maintain the formation for next beat. Using `PLAN` also counts as part of the waypoints for the drones, so you can't have multi-time dependent functions as the last waypoint as we need to have `PLAN` after them in future steps.\n# Patterns: - Drones must fly in geometric patterns and shapes - Drones must fly in tight formations - The formations must be symmetrical if possible - Drones should move up and down in altitude multiple times for a 3D effect - Example patterns include spirals, triangles, squares, circles, lines\n# Response structure First, describe the emotions and mood associated with the song Crazyflie Drones Theme (short). Then, give a description of the dominant chords of the song. Is it a major or minor cord? What does that mean for the mood? Next, give a specific description of the choreography you want to create. What patterns do you want the drones to fly? Why do you use these particular patterns? Last, fill out the individual waypoints for each drone. Adapt the distance and position of waypoints to the amplitude of the song's beats. Loud beats lead to larger movements, while soft beats lead to smaller movements. Furthermore, take into account the dominant chords of the song and the emotions and mood associated with these chords. Try to use a variety of functional options to design an amazing geometrical choreography. Make sure to only do motion planning per beat, so in otherwords motion planning steps should be limited to 12,and end yaml output with END like in examples below. You can use multiple beat functions for more than 3 timestep as well like spiral(), zig_zag(), wave(), helix(). Only do complex primtives. Adjust change in height accordingly like do them for 5 steps if you have to, and make height increments small. Limit to at max 4 primitives. Between any two primitives, there must be a extra PLAN which consumes a timestep so that there is enough time for transition.\n"}, {"role": "system", "content": "# Example prompts and responses - Example 1:\n Prompt:\n # Information on the song:\n - Name: r\u00eaverie\n - Number of beats: 22\n - Beat times in 100ms: {{1: 7, 2: 20, 3: 29, 4: 41, 5: 55, 6: 66, 7: 80, 8: 96, 9: 104, 10: 112, 11: 119, 12: 128, 13: 137, 14: 147, 15: 157, 16: 167, 17: 187, 18: 196, 19: 210, 20: 221, 21: 232, 22: 242}}\n - Beat intervals in 100ms: {{1: 7, 2: 13, 3: 9, 4: 12, 5: 14, 6: 11, 7: 13, 8: 16, 9: 9, 10: 7, 11: 8, 12: 8, 13: 9, 14: 10, 15: 10, 16: 10, 17: 20, 18: 8, 19: 14, 20: 12, 21: 10}}\n - Beat amplitude in dBFS: {{1: -53, 2: -40, 3: -40, 4: -39, 5: -44, 6: -44, 7: -36, 8: -43, 9: -34, 10: -35, 11: -37, 12: -37, 13: -39, 14: -44, 15: -38, 16: -32, 17: -35, 18: -34, 19: -33, 20: -37, 21: -35, 22: -38}}\n - Beat novelty in %: {{1: 37, 2: 52, 3: 24, 4: 31, 5: 40, 6: 98, 7: 45, 8: 21, 9: 76, 10: 72, 11: 52, 12: 29, 13: 10, 14: 15, 15: 81, 16: 100, 17: 27, 18: 65, 19: 64, 20: 28, 21: 19, 22: 30}}\n - Beat chords: {{: 'A#m', 2: 'Cm', 3: 'Cm', 4: 'Cm', 5: 'Cm', 6: 'Cm', 7: 'Cm', 8: 'A#m', 9: 'C', 10: 'C', 11: 'C', 12: 'C', 13: 'Cm', 14: 'Cm', 15: 'Gm', 16: 'A#m', 17: 'Gm', 18: 'Gm', 19: 'Gm', 20: 'F', 21: 'F', 22: 'F'}}\n \n # Information on the drones:\n - Number: 3\n - Drones must not collide with each other. No two drones must be at the same coordinates at the same beat.\n - Starting positions: [[100, 100, 100], [-100, 100, 100], [0, -100, 100]]\n - Maximum step distance between waypoints in cm: 100cm\n\n Response:\n song_mood: Debussy's R\u00e9verie is a short and expressive piano piece that captures the essence of daydreaming and contemplation.\n It is characteristic of Debussy's impressionistic style, known for its use of unconventional harmonies, tonal colors, and fluidity.\n cord_analysis: The song is in the key of F major, with a few chords in the song being A#m, Cm, G, F, and Gm. The mix of major and minor chords indicates that the music is neither elated nor gloomy. It remains in a state of contemplation and daydreaming, with a touch of melancholy.\n choreography_plan: The main intention of the choreography is to reflect the dreamy and contemplative mood of the song. Therefore, drones must not move too fast, and waypoints must not be too far from each other. \n The beat amplitude in dBFS roughly remains within the -50 to -30 interval, indicating that the song is very quiet. Consequently, the drones should move slowly and gently. Across all beats, the intervals are evenly spaced around 7 to 13, indicating that the drones should move at a steady pace.\n so we have no pause that we have to accentuate in the choreography. We will therefore focus on the beat amplitude.\n For beats 1 to 5, the three drones will spiral to a center point around [0, 0, 100]]. The novelty of the beats is low to moderate at around 25 to 50, and the beat amplitude is also low at around -50 to -40. Thus, we plan the first waypoints closer to each other.\n For the following beats 6 to 11, the novelty of the beats reaches a peak at 98 and a slightly raised beat amplitude of up to -35. To account for the peak, one drone will rise slowly in the center while the other two drones begin to slowly rotate in the other direction and then move outwards. We let the drones move slowly because the amplitude has not yet reached full force. We change the direction of the drones to reflect the dreamy and swirling mood of the song.\n For the next beats 12 to 17, the novelty and amplitudes of the beats are reaching the peak. Accordingly, the drones will move into the triangle formation again, while oscillating in altitude. Although we now move faster, we adhere to the maximum step distance of approximately 80 to 100cm.\n Beats 18 to 22 are the final beats of the song, with decreasing novelty and amplitude. The drones will slowly spiral outwards and downwards to mark the decline in tension and the end of the dance.\n choreography:\n 1: spiral(5, 100)\n 2: PLAN\n 3: PLAN\n 4: PLAN\n 5: PLAN\n 6: PLAN # extra PLAN for primitive switch\n 7: rotate(60, 'z')\n 8: PLAN # extra\n 9: rotate(-90, 'z')\n 10: PLAN # extra\n 11: move_z( [1,2,3], 30)\n 12: PLAN # extra\n 13: spiral(4, 100)\n 14: PLAN\n 15: PLAN\n 16: PLAN\n 17: PLAN # extra\n 18: zig_zag(5, 20, 20)\n 19: PLAN\n 20: PLAN\n 21: PLAN \n 22: PLAN\n END\n"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations, such as circles, spirals, and other geometric shapes. The movements will emphasize the strong beats of the music, with all drones participating during louder and more novel beats.\nchoreography:\n 1: form_circle([1, 2, 3, 4, 5, 6, 7, 8, 9], 100)\n 2: PLAN # extra PLAN for switch\n 3: form_star(150, 60, 50)\n 4: PLAN # extra PLAN for switch\n 5: rotate(60, 'z')\n 6: spiral(3, 120)\n 7: PLAN\n 8: PLAN\n 9: twister(3, 10, 15)\n 10: PLAN\n 11: PLAN\n 12: helix(3, 20, 70)\n 13: PLAN\n 14: PLAN\n 15: center([1, 2, 3, 4, 5, 6, 7, 8, 9])\n 16: move_z([1, 2, 3, 4, 5, 6, 7, 8, 9], -20)\n 17: wave(4, 30, [[1, 1], [2, 2], [3, 3]], [0.5, 0.5, 0.5], [0.4, 0.4, 0.4])\n 18: PLAN\n 19: PLAN\n 20: PLAN\n 21: spiral_speed(3, 180, 100, 2.0)\n 22: PLAN\n 23: PLAN\n 24: zig_zag(3, 50, 10)\n 25: PLAN\n 26: PLAN\n 27: move_z([1, 2, 3, 4, 5, 6, 7, 8, 9], 30)\n 28: move_z([1, 2, 3, 4, 5, 6, 7, 8, 9], -30)\n 29: form_cone(50, 40, 0)\n 30: PLAN\n 31: PLAN\n 32: PLAN\n 33: PLAN\n 34: PLAN # extra PLAN for switch\n 35: center([1, 2, 3, 4, 5, 6, 7, 8, 9])\n 36: rotate(120, 'z')\n END\n```"}, {"role": "user", "content": "The provided response failed with the following error:\nNumber of timesteps in output doesn't match the number of beats.\n\nAnalyze the error, re-read the instructions and try again."}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations, such as circles, stars, and other geometric shapes. The movements will emphasize the strong beats of the music, with all drones participating during louder and more novel beats. The patterns will be chosen to ensure no collisions and will involve vertical movements to enhance the 3D effect.\nchoreography:\n 1: form_circle([1, 2, 3, 4, 5, 6, 7, 8, 9], 100)\n 2: PLAN # extra PLAN for switch\n 3: form_star(100, 60, 50)\n 4: PLAN # extra PLAN for switch\n 5: rotate(360, 'z')\n 6: spiral_speed(3, 120, 180, 1.5)\n 7: PLAN\n 8: PLAN\n 9: form_cone(40, 50, 0)\n 10: PLAN\n 11: PLAN\n 12: center([1, 2, 3, 4, 5, 6, 7, 8, 9])\n END\n```"}, {"role": "user", "content": "Do a star with a 360 roation, then a cone with a -120 rotation and last a wave"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(40, 50, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: wave(3, 100, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 8: PLAN\n 9: PLAN\n 10: PLAN\n 11: PLAN\n 12: center([1, 2, 3, 4, 5, 6, 7, 8, 9])\n END\n```"}, {"role": "user", "content": "Make the wave more impressive"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(40, 50, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: wave(5, 100, [[1, 1], [2, 2], [3, 3]], [0.5, 0.5, 0.5], [0.4, 0.4, 0.4])\n 8: PLAN\n 9: PLAN\n 10: PLAN\n 11: PLAN\n 12: PLAN\n END\n```"}, {"role": "user", "content": "The wave looks really bad. Rather do a twister in the end"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(40, 50, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: twister(3, 20, 15)\n 8: PLAN\n 9: PLAN\n 10: PLAN\n 11: PLAN\n 12: center([1, 2, 3, 4, 5, 6, 7, 8, 9])\n END\n```"}, {"role": "user", "content": "How about in the end forming a wave quickly and then switching places with swap to make it interesting"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(40, 50, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: wave(3, 120, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 8: PLAN\n 9: PLAN\n 10: swap(1, 9); swap(2, 8); swap(3, 7); swap(4, 6)\n 11: swap(1, 5); swap(2, 4); swap(3, 8)\n 12: center([1, 2, 3, 4, 5, 6, 7, 8, 9])\n END\n```"}, {"role": "user", "content": "Do one of the swaps earlier. Also, make the cone higher (80,70)."}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(80, 70, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: swap(1, 9); swap(2, 8); swap(3, 7); swap(4, 6)\n 8: wave(3, 120, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 9: PLAN\n 10: PLAN\n 11: swap(1, 5); swap(2, 4); swap(3, 8)\n 12: center([1, 2, 3, 4, 5, 6, 7, 8, 9])\n END\n```"}, {"role": "user", "content": "This is too cluttered. Remove the first swap. Only do the second one, but do it right after the wave"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(80, 70, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: wave(3, 120, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 8: PLAN\n 9: swap(1, 5); swap(2, 4); swap(3, 8)\n 10: PLAN\n 11: center([1, 2, 3, 4, 5, 6, 7, 8, 9])\n 12: PLAN\n END\n```"}, {"role": "user", "content": "Only swap the corner drones. This is still too cluttered. The rest looks good"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(80, 70, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: wave(3, 120, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 8: PLAN\n 9: swap(1, 9); swap(3, 7)\n 10: PLAN\n 11: center([1, 2, 3, 4, 5, 6, 7, 8, 9])\n 12: PLAN\n END\n```"}, {"role": "user", "content": "Something is weird. Lets test only swapping drone 1 and 9 for now. Remove the center primitive"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(80, 70, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: wave(3, 120, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 8: PLAN\n 9: swap(1, 9)\n 10: PLAN\n 11: PLAN # no additional primitive, just to maintain waypoint length\n 12: PLAN\n END\n```"}, {"role": "user", "content": "Move those drones in z before swapping them"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(80, 70, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: wave(3, 120, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 8: move_z([1, 9], 30)\n 9: swap(1, 9)\n 10: PLAN\n 11: PLAN # no additional primitive, just to maintain waypoint length\n 12: PLAN\n END\n```"}, {"role": "user", "content": "Move and swap 2 and 8 instead"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(80, 70, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: wave(3, 120, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 8: move_z([2, 8], 30)\n 9: swap(2, 8)\n 10: PLAN\n 11: PLAN # no additional primitive, just to maintain waypoint length\n 12: PLAN\n END\n```"}, {"role": "user", "content": "Okay, this worked now. Now do the following. Execute the move and swap in the same tick. Also add another move -30 and swap for 4 and 6"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(80, 70, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: wave(3, 120, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 8: move_z([2, 8], 30); swap(2, 8)\n 9: move_z([4, 6], -30); swap(4, 6)\n 10: PLAN\n 11: PLAN # no additional primitive, just to maintain waypoint length\n 12: PLAN\n END\n```"}, {"role": "user", "content": "Make all move and swaps into the same tick. ALso, add one PLAN after the cone rotate. Its a bit too short"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(80, 70, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: wave(3, 120, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 8: move_z([2, 8], 30); swap(2, 8)\n 9: move_z([4, 6], -30); swap(4, 6)\n 10: PLAN\n 11: PLAN # no additional primitive, just to maintain waypoint length\n 12: PLAN\n END\n```"}, {"role": "user", "content": "Put all move and swaps to \"9:\" Add a PLAN before the wave and remove the last PLAN"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(80, 70, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: PLAN\n 8: wave(3, 120, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 9: move_z([2, 8], 30); swap(2, 8); move_z([4, 6], -30); swap(4, 6)\n 10: PLAN\n 11: PLAN # no additional primitive, just to maintain waypoint length\n 12: END\n```"}, {"role": "user", "content": "The provided response failed with the following error:\nUnknown motion primitive '' at timestep 12\n\nAnalyze the error, re-read the instructions and try again."}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(80, 70, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: PLAN\n 8: wave(3, 120, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 9: move_z([2, 8], 30); swap(2, 8); move_z([4, 6], -30); swap(4, 6)\n 10: PLAN\n 11: PLAN\n 12: PLAN\n END\n```"}, {"role": "user", "content": "Remove the move_z entirely. Keep the rest the same"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(80, 70, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: PLAN\n 8: wave(3, 120, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 9: swap(2, 8); swap(4, 6)\n 10: PLAN\n 11: PLAN # no additional primitive, just to maintain waypoint length\n 12: PLAN\n END\n```"}] \ No newline at end of file diff --git a/swarm_gpt/data/presets/Crazyflie Drones Theme (short) | 9 | 20251202_183056/meta.json b/swarm_gpt/data/presets/Crazyflie Drones Theme (short) | 9 | 20251202_183056/meta.json deleted file mode 100644 index adbf59c..0000000 --- a/swarm_gpt/data/presets/Crazyflie Drones Theme (short) | 9 | 20251202_183056/meta.json +++ /dev/null @@ -1 +0,0 @@ -{"n_drones": 9, "song": "Crazyflie Drones Theme (short)", "use_motion_primitives": true} \ No newline at end of file diff --git a/swarm_gpt/data/presets/Crazyflie Drones Theme (short) | 9 | 20251202_183056/trajectory.csv b/swarm_gpt/data/presets/Crazyflie Drones Theme (short) | 9 | 20251202_183056/trajectory.csv deleted file mode 100644 index b91cb4a..0000000 --- a/swarm_gpt/data/presets/Crazyflie Drones Theme (short) | 9 | 20251202_183056/trajectory.csv +++ /dev/null @@ -1,3548 +0,0 @@ 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-35.460000,0.997501,-0.997959,1.055745,0.095474,-0.100744,0.015494,0.995855,0.022188,1.068441,0.099082,-0.032113,-0.003103,1.003108,0.965862,1.077414,0.033426,0.056393,-0.010316,0.067364,-0.988290,1.087877,-0.021082,-0.028191,-0.002410,0.001533,0.011955,1.075082,-0.003048,-0.022002,-0.033729,-0.064515,1.060727,1.055794,0.019292,0.006201,0.013445,-0.973934,-0.965728,1.047129,-0.051749,-0.054701,0.012179,-1.022613,-0.020471,1.076652,-0.064519,0.038728,-0.037937,-0.994031,1.019272,1.071805,-0.104751,0.063710,-0.019668 diff --git a/swarm_gpt/data/presets/Crazyflie Drones Theme (short) | 9 | 20251202_183056/waypoints.npy b/swarm_gpt/data/presets/Crazyflie Drones Theme (short) | 9 | 20251202_183056/waypoints.npy deleted file mode 100644 index 14d8a43..0000000 Binary files a/swarm_gpt/data/presets/Crazyflie Drones Theme (short) | 9 | 20251202_183056/waypoints.npy and /dev/null differ diff --git a/swarm_gpt/data/presets/Crazyflie Drones Theme (short) | 9 | 20251204_182045/history.json b/swarm_gpt/data/presets/Crazyflie Drones Theme (short) | 9 | 20251204_182045/history.json deleted file mode 100644 index c2e1920..0000000 --- a/swarm_gpt/data/presets/Crazyflie Drones Theme (short) | 9 | 20251204_182045/history.json +++ /dev/null @@ -1 +0,0 @@ -[{"role": "system", "content": "You're a helpful assistant that will help a human choreograph a dance for a swarm of small drones."}, {"role": "user", "content": "Generate the trajectories for a choreography for 9 drones dancing to the music of the song Crazyflie Drones Theme (short). The trajectories are defined by coordinate waypoints that the drones fly to. Each movement can be defined by multiple template functions, which are explained below.\n# Information on the song: - Name: Crazyflie Drones Theme (short) - Number of beats: 12 - Beat times in 100ms: {1: 5, 2: 37, 3: 65, 4: 93, 5: 130, 6: 153, 7: 185, 8: 208, 9: 231, 10: 255, 11: 287, 12: 315} - Beat intervals in 100ms: {1: 5, 2: 32, 3: 28, 4: 28, 5: 37, 6: 23, 7: 32, 8: 23, 9: 23, 10: 23, 11: 32} - Beat amplitude in dBFS: {1: -23, 2: -26, 3: -25, 4: -26, 5: -17, 6: -18, 7: -17, 8: -18, 9: -18, 10: -16, 11: -17, 12: -17} - Beat novelty in %: {1: 92, 2: 83, 3: 73, 4: 95, 5: 55, 6: 78, 7: 99, 8: 67, 9: 69, 10: 94, 11: 90, 12: 100} - Beat chords: {1: 'E', 2: 'E', 3: 'A', 4: 'A', 5: 'E', 6: 'E', 7: 'A', 8: 'E', 9: 'E', 10: 'E', 11: 'A', 12: 'E'}\n# Information on the drones: - Number: 9 - Drones must not collide with each other. No two drones must be at the same coordinates at the same beat. - Starting positions: [[100, -100, 100], [100, 0, 100], [100, 100, 100], [0, -100, 100], [0, 0, 100], [0, 100, 100], [-100, -100, 100], [-100, 0, 100], [-100, 100, 100]] - Maximum step distance between waypoints in cm: {1: 52, 2: 320, 3: 278, 4: 276, 5: 368, 6: 232, 7: 324, 8: 230, 9: 230, 10: 232, 11: 324, 12: 276} - Lower limit for generated waypoints: [-200. -150. 10.] - Upper Limit for generated waypoints: [200. 150. 200.]\n# Information on the environment: - The drones are limited to [-200, 200] in x, [-200, 200] in y, [0, 200] in z - The drones must not touch the ground, i.e. their z coordinate must always be greater than 0 \n# Information on the choreography: - The drone choreography must have 12 waypoints, one for each beat. - Each drone must move at least once during the song. - Movements must depend on the beats of the song. If the beats are loud and novel, all drones must move. - If possible, coordinates for the drones should be symmetrical at each beat. - Successive waypoints should form patterns for single drones and for the whole swarm - Coordinates are given in cm. A drone flying at 1m height will have a z coordinate of 100. - The difference between two waypoints must be less than the maximum step distance in each dimension.\n E.g. if the maximum step distance between beat 3 and 4 is 50, the difference between the x, y,\n z coordinates of the waypoints at beat 3 and 4 must be less than 50 in each dimension.\n\n# Information on template functions: Make sure to limit actions to each drone once per time step. So if you choose swap for (1, 2) then you can only choose swap(3, 4) or two seperate move functions for each drone 2: time dependent template functions ( For such functions we need only one function call for one time step) - rotate:\n takes 2 parameters ( angle, axis ), and rotates all drones by 'angle' along 'axis'\n #angle is a float between [0, 360]\n #axis is one of 'x', 'y', or 'z', but for now always rotate around 'z'\n eg\n rotate(30, 'z')\n4: variadic functions ( such function takes a list with drone ids as arguments ) - center:\n Takes a list of drone IDs as input. It calculates the geometric center of the current positions of these specified drones and then moves all the drones with the matching IDs to this calculated center position.\n eg\n center([1, 2, 3])\n- move_z:\n Takes a list of drone IDs as input, and distance. It moves all the drones with the matching IDs along z axis by xcm. if distance is positive, it moves them up, otherwise down\n eg\n move_z([1, 2, 3], 10)\n- form_circle\n Takes a list of drone IDs as input, and z coordinate ( height ) for the circle formation. Radius of circle is decided by number of drones in list argument. So bigger circle for more drones.\n # height has to be greater than zero\n eg \n # Cone formation as 4 drones are at 150cm height, and 3 drones are at 50cm height with smaller radius as less drones.\n form_circle( [1, 2, 3, 4], 150 ), form_circle( [5, 6, 7], 50 )\n- form_star\n Takes 3 parameters ( height, spacing, delta_radius ) and creates a star in the z-plane, which is defined by 2 concentric circles in staggered angles with different radii. \n Height corresponds to the height of the star. Spacing corresponds to the minimum spacing between drones, where a higher spacing means larger star. \n Delta_radius corresponds to the difference of the 2 radii, where a larger delta_radius means a more spread out star\n #height has to be greater than zero\n #spacing is an integer between [40, 80]\n #delta_radius is an integer between [50 100]\n eg \n form_star( 120, 60, 50 )\n- form_cone\n Takes 3 parameters ( delta_height, spacing, is_inverted ) and creates a cone using concentric circles of different radii. Each layer has 4 additional drones.\n Delta_height is the difference in height between layers. A larger delta_height corresponds to a taller cone.\n Spacing is the minimum_spacing between drones of a layer. A larger spacing means a larger circle radius and wider cone.\n Is_inverted is a boolean value (an integer of 0 or 1) that determines if the cone is inverted or not. Inverted cones are more expressive and dynamic. \n #delta_height is an integer between [30, 80]\n #spacing is an integer between [40, 80]\n #is_inverted is an integer with the values 0 or 1\n eg \n form_star( 120, 60, 50 )\n\n\n5: multi time dependent functions ( For such functions we need only one function call for multiple time steps)\n Any multi-time dependent function needs to be followed by the 'PLAN' keyword for the number of steps minus 1. This means, for functions that span over `step` time steps, the function call must be followed by `step - 1` occurrences of 'PLAN'.\n These functions must be initiated `s - step + 1` beats before the last beat, where `s` is the total number of beats. This ensures that the multi-time function completes before the song ends.\n\n**Important Timing Constraint**: If you have a multi-time function that takes 3 steps, it must be called at least 2 steps before the last beat to allow for the necessary 'PLAN' steps. For example, with a 22-beat song, a 3-step function should be called by the 20th beat, followed by 'PLAN' for the 21st and 22nd beats. - spiral:\n takes 2 parameters ( steps, height ), and makes all drones move in a outward spiral pattern with the calculate radius ( from no of drones ), and number of steps at height h\n\n #steps is an integer between [2, 3]\n #height is an integer between [0, 200] that determines the height of the spiral\n #Since spiral is a multi time dependent function, it should be falled by 'PLAN' keyword for the number of steps -1 times, so if steps is 3, then spiral is called once, and PLAN follows twice in output\n # spiral needs to be called atleast 's' steps before last timestep as 's' PLAN follow the spiral call\n eg:\n 1: spiral(3, 50 )\n 2: PLAN\n 3: PLAN\n\n- spiral_speed:\n Takes 4 parameters (steps, height, speed, radius_increase), and has the same effect as spiral. The difference is that spiral_speed increases the radius of the spiral by radius_increase over the number of steps. In addition,\n speed defines the angle by which the drones rotate around the center. Lower speeds results in slower drone motions.\n # Steps is an integer between [2, 3]\n # height is an integer between [0, 200] that determines the height of the spiral\n # speeds is an integer between [0, 360] that defines the angle by which the drones rotate around the center. Very low speeds are in the range of 0 to 90, fast speeds are in the range of 100 to 360\n # radius_increase is a float between [1, 3] that increases the radius of the spiral over the number of steps\n eg:\n 1: spiral_speed(3, 100, 200, 1.5)\n 2: PLAN\n 3: PLAN\n\n- helix:\n takes 2 parameters ( steps, delta_h, height ), and makes all drones move in a helix pattern with the calculate radius ( from no of drones ), and number of steps.\n\n #steps is an integer between [2, 3]\n #delta_h is an integer between [0, 100], that changes the height of drones in the helix pattern per step\n #height is between [50, 100] as helix rises up with steps, lets be safe.\n #Since helix is a multi time dependent function, it should be falled by 'PLAN' keyword for the number of steps -1 times, so if steps is 3, then helix is called once, and PLAN follows twice in output\n eg:\n 1: helix(3, 20, 50)\n 2: PLAN\n 3: PLAN\n\n- twister:\n takes 3 parameter ( steps, omega, z_spacing ), and makes all drones move into a upside down cone pattern and rotate at the speed specified by omega, for the number of steps specified.\n Z_spacing corresponds to the height difference between neighbouring drones, where a smaller z_spacing corresponds to a more compact cone.\n\n #steps is an integer between [2, 3]\n #omega is an integer between [2, 20] with a higher omega corresponding to a more dynamic performance.\n #z_spacing is an integer between [5, 30]\n #Since twister is a multi time dependent function, it should be falled by 'PLAN' keyword for the number of steps -1 times, so if steps is 3, then conic_rotate is called once, and PLAN follows twice in output\n eg:\n 1: twister(3, 20, 10)\n 2: PLAN\n 3: PLAN\n\n- zig_zag:\n Moves all drones in a zigzag pattern. Takes 3 parameters\n Parameters: `steps` (int), `delta` (int) - the horizontal displacement per step, `delta_h` (int) - the vertical displacement per step.\n eg:\n 4: zig_zag(3, 20, 10)\n 5: PLAN\n 6: PLAN\n \n- wave:\n Moves all drones in a three-dimensional wave propagating through a two-dimensional rectangular elastic surface with bounded edges. The disturbance at any point on the surface is defined by its parameters in space and time, and the wave propagation is governed by the wave equation with a speed `c > 0`. Takes 5 parameters.\n Parameters:\n - `steps` (int): The number of discretization points.\n - `height` (float): The desired height of drones above the surface.\n - `mu_pairs` (list of list): List of \\((\u03bc1, \u03bc2)\\) pairs that define the spatial frequencies.\n - `a\u00b51\u00b52` (list of floats): List of amplitude coefficients corresponding to each \\((\u03bc1, \u03bc2)\\) pair for the sine component.\n - `b\u00b51\u00b52` (list of floats): List of amplitude coefficients corresponding to each \\((\u03bc1, \u03bc2)\\) pair for the cosine component.\n eg:\n 4: wave( 4, 10, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4] )\n 5: PLAN\n 6: PLAN\n 7: PLAN\n latex equn for the concerned wave equation:\n The disturbance \\(u(s, t)\\) at any point \\(s = (s_1, s_2) \\in [0, a] \\times [0, b]\\) \nat time \\(t\\) is governed by:\n\\[\nc^2 \\nabla^2 u(s, t) = \\frac{\\partial^2 u(s, t)}{\\partial t^2},\n\\]\nwhere \\( \\nabla^2 \\) is the spatial Laplacian and \\(c > 0\\) is the wave speed. \nThe solution:\n\\[\nu(s, t) = \\sum_{(\\mu_1, \\mu_2)} a_{\\mu_1 \\mu_2} \\sin \\left( \\frac{\\mu_1 \\pi s_1}{a} \\right) \n\\sin \\left( \\frac{\\mu_2 pi s_2}{b} \\right) \\sin(\\omega_{\\mu_1 \\mu_2} t) \n+ b_{\\mu_1 \\mu_2} \\sin \\left( \\frac{\\mu_1 \\pi s_1}{a} \\right) \n\\sin \\left( \\frac{\\mu_2 \\pi s_2}{b} \\right) \\cos(\\omega_{\\mu_1 \\mu_2} t),\n\\]\n\nwith frequencies:\n\n\\[\n\\omega_{\\mu_1 \\mu_2}^2 = c^2 \\pi^2 \\left( \\frac{\\mu_1^2}{a^2} + \\frac{\\mu_2^2}{b^2} \\right).\n\\]\n\nPLAN can also be used to indicate that the drones should maintain their current position for the next beat, so after forming cone formation, you can use PLAN to maintain the formation for next beat. Using `PLAN` also counts as part of the waypoints for the drones, so you can't have multi-time dependent functions as the last waypoint as we need to have `PLAN` after them in future steps.\n# Patterns: - Drones must fly in geometric patterns and shapes - Drones must fly in tight formations - The formations must be symmetrical if possible - Drones should move up and down in altitude multiple times for a 3D effect - Example patterns include spirals, triangles, squares, circles, lines\n# Response structure First, describe the emotions and mood associated with the song Crazyflie Drones Theme (short). Then, give a description of the dominant chords of the song. Is it a major or minor cord? What does that mean for the mood? Next, give a specific description of the choreography you want to create. What patterns do you want the drones to fly? Why do you use these particular patterns? Last, fill out the individual waypoints for each drone. Adapt the distance and position of waypoints to the amplitude of the song's beats. Loud beats lead to larger movements, while soft beats lead to smaller movements. Furthermore, take into account the dominant chords of the song and the emotions and mood associated with these chords. Try to use a variety of functional options to design an amazing geometrical choreography. Make sure to only do motion planning per beat, so in otherwords motion planning steps should be limited to 12,and end yaml output with END like in examples below. You can use multiple beat functions for more than 3 timestep as well like spiral(), zig_zag(), wave(), helix(). Only do complex primtives. Adjust change in height accordingly like do them for 5 steps if you have to, and make height increments small. Limit to at max 4 primitives. Between any two primitives, there must be a extra PLAN which consumes a timestep so that there is enough time for transition.\n"}, {"role": "system", "content": "# Example prompts and responses - Example 1:\n Prompt:\n # Information on the song:\n - Name: r\u00eaverie\n - Number of beats: 22\n - Beat times in 100ms: {{1: 7, 2: 20, 3: 29, 4: 41, 5: 55, 6: 66, 7: 80, 8: 96, 9: 104, 10: 112, 11: 119, 12: 128, 13: 137, 14: 147, 15: 157, 16: 167, 17: 187, 18: 196, 19: 210, 20: 221, 21: 232, 22: 242}}\n - Beat intervals in 100ms: {{1: 7, 2: 13, 3: 9, 4: 12, 5: 14, 6: 11, 7: 13, 8: 16, 9: 9, 10: 7, 11: 8, 12: 8, 13: 9, 14: 10, 15: 10, 16: 10, 17: 20, 18: 8, 19: 14, 20: 12, 21: 10}}\n - Beat amplitude in dBFS: {{1: -53, 2: -40, 3: -40, 4: -39, 5: -44, 6: -44, 7: -36, 8: -43, 9: -34, 10: -35, 11: -37, 12: -37, 13: -39, 14: -44, 15: -38, 16: -32, 17: -35, 18: -34, 19: -33, 20: -37, 21: -35, 22: -38}}\n - Beat novelty in %: {{1: 37, 2: 52, 3: 24, 4: 31, 5: 40, 6: 98, 7: 45, 8: 21, 9: 76, 10: 72, 11: 52, 12: 29, 13: 10, 14: 15, 15: 81, 16: 100, 17: 27, 18: 65, 19: 64, 20: 28, 21: 19, 22: 30}}\n - Beat chords: {{: 'A#m', 2: 'Cm', 3: 'Cm', 4: 'Cm', 5: 'Cm', 6: 'Cm', 7: 'Cm', 8: 'A#m', 9: 'C', 10: 'C', 11: 'C', 12: 'C', 13: 'Cm', 14: 'Cm', 15: 'Gm', 16: 'A#m', 17: 'Gm', 18: 'Gm', 19: 'Gm', 20: 'F', 21: 'F', 22: 'F'}}\n \n # Information on the drones:\n - Number: 3\n - Drones must not collide with each other. No two drones must be at the same coordinates at the same beat.\n - Starting positions: [[100, 100, 100], [-100, 100, 100], [0, -100, 100]]\n - Maximum step distance between waypoints in cm: 100cm\n\n Response:\n song_mood: Debussy's R\u00e9verie is a short and expressive piano piece that captures the essence of daydreaming and contemplation.\n It is characteristic of Debussy's impressionistic style, known for its use of unconventional harmonies, tonal colors, and fluidity.\n cord_analysis: The song is in the key of F major, with a few chords in the song being A#m, Cm, G, F, and Gm. The mix of major and minor chords indicates that the music is neither elated nor gloomy. It remains in a state of contemplation and daydreaming, with a touch of melancholy.\n choreography_plan: The main intention of the choreography is to reflect the dreamy and contemplative mood of the song. Therefore, drones must not move too fast, and waypoints must not be too far from each other. \n The beat amplitude in dBFS roughly remains within the -50 to -30 interval, indicating that the song is very quiet. Consequently, the drones should move slowly and gently. Across all beats, the intervals are evenly spaced around 7 to 13, indicating that the drones should move at a steady pace.\n so we have no pause that we have to accentuate in the choreography. We will therefore focus on the beat amplitude.\n For beats 1 to 5, the three drones will spiral to a center point around [0, 0, 100]]. The novelty of the beats is low to moderate at around 25 to 50, and the beat amplitude is also low at around -50 to -40. Thus, we plan the first waypoints closer to each other.\n For the following beats 6 to 11, the novelty of the beats reaches a peak at 98 and a slightly raised beat amplitude of up to -35. To account for the peak, one drone will rise slowly in the center while the other two drones begin to slowly rotate in the other direction and then move outwards. We let the drones move slowly because the amplitude has not yet reached full force. We change the direction of the drones to reflect the dreamy and swirling mood of the song.\n For the next beats 12 to 17, the novelty and amplitudes of the beats are reaching the peak. Accordingly, the drones will move into the triangle formation again, while oscillating in altitude. Although we now move faster, we adhere to the maximum step distance of approximately 80 to 100cm.\n Beats 18 to 22 are the final beats of the song, with decreasing novelty and amplitude. The drones will slowly spiral outwards and downwards to mark the decline in tension and the end of the dance.\n choreography:\n 1: spiral(5, 100)\n 2: PLAN\n 3: PLAN\n 4: PLAN\n 5: PLAN\n 6: PLAN # extra PLAN for primitive switch\n 7: rotate(60, 'z')\n 8: PLAN # extra\n 9: rotate(-90, 'z')\n 10: PLAN # extra\n 11: move_z( [1,2,3], 30)\n 12: PLAN # extra\n 13: spiral(4, 100)\n 14: PLAN\n 15: PLAN\n 16: PLAN\n 17: PLAN # extra\n 18: zig_zag(5, 20, 20)\n 19: PLAN\n 20: PLAN\n 21: PLAN \n 22: PLAN\n END\n"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations, such as circles, spirals, and other geometric shapes. The movements will emphasize the strong beats of the music, with all drones participating during louder and more novel beats.\nchoreography:\n 1: form_circle([1, 2, 3, 4, 5, 6, 7, 8, 9], 100)\n 2: PLAN # extra PLAN for switch\n 3: form_star(150, 60, 50)\n 4: PLAN # extra PLAN for switch\n 5: rotate(60, 'z')\n 6: spiral(3, 120)\n 7: PLAN\n 8: PLAN\n 9: twister(3, 10, 15)\n 10: PLAN\n 11: PLAN\n 12: helix(3, 20, 70)\n 13: PLAN\n 14: PLAN\n 15: center([1, 2, 3, 4, 5, 6, 7, 8, 9])\n 16: move_z([1, 2, 3, 4, 5, 6, 7, 8, 9], -20)\n 17: wave(4, 30, [[1, 1], [2, 2], [3, 3]], [0.5, 0.5, 0.5], [0.4, 0.4, 0.4])\n 18: PLAN\n 19: PLAN\n 20: PLAN\n 21: spiral_speed(3, 180, 100, 2.0)\n 22: PLAN\n 23: PLAN\n 24: zig_zag(3, 50, 10)\n 25: PLAN\n 26: PLAN\n 27: move_z([1, 2, 3, 4, 5, 6, 7, 8, 9], 30)\n 28: move_z([1, 2, 3, 4, 5, 6, 7, 8, 9], -30)\n 29: form_cone(50, 40, 0)\n 30: PLAN\n 31: PLAN\n 32: PLAN\n 33: PLAN\n 34: PLAN # extra PLAN for switch\n 35: center([1, 2, 3, 4, 5, 6, 7, 8, 9])\n 36: rotate(120, 'z')\n END\n```"}, {"role": "user", "content": "The provided response failed with the following error:\nNumber of timesteps in output doesn't match the number of beats.\n\nAnalyze the error, re-read the instructions and try again."}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations, such as circles, stars, and other geometric shapes. The movements will emphasize the strong beats of the music, with all drones participating during louder and more novel beats. The patterns will be chosen to ensure no collisions and will involve vertical movements to enhance the 3D effect.\nchoreography:\n 1: form_circle([1, 2, 3, 4, 5, 6, 7, 8, 9], 100)\n 2: PLAN # extra PLAN for switch\n 3: form_star(100, 60, 50)\n 4: PLAN # extra PLAN for switch\n 5: rotate(360, 'z')\n 6: spiral_speed(3, 120, 180, 1.5)\n 7: PLAN\n 8: PLAN\n 9: form_cone(40, 50, 0)\n 10: PLAN\n 11: PLAN\n 12: center([1, 2, 3, 4, 5, 6, 7, 8, 9])\n END\n```"}, {"role": "user", "content": "Do a star with a 360 roation, then a cone with a -120 rotation and last a wave"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(40, 50, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: wave(3, 100, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 8: PLAN\n 9: PLAN\n 10: PLAN\n 11: PLAN\n 12: center([1, 2, 3, 4, 5, 6, 7, 8, 9])\n END\n```"}, {"role": "user", "content": "Make the wave more impressive"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(40, 50, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: wave(5, 100, [[1, 1], [2, 2], [3, 3]], [0.5, 0.5, 0.5], [0.4, 0.4, 0.4])\n 8: PLAN\n 9: PLAN\n 10: PLAN\n 11: PLAN\n 12: PLAN\n END\n```"}, {"role": "user", "content": "The wave looks really bad. Rather do a twister in the end"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(40, 50, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: twister(3, 20, 15)\n 8: PLAN\n 9: PLAN\n 10: PLAN\n 11: PLAN\n 12: center([1, 2, 3, 4, 5, 6, 7, 8, 9])\n END\n```"}, {"role": "user", "content": "How about in the end forming a wave quickly and then switching places with swap to make it interesting"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(40, 50, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: wave(3, 120, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 8: PLAN\n 9: PLAN\n 10: swap(1, 9); swap(2, 8); swap(3, 7); swap(4, 6)\n 11: swap(1, 5); swap(2, 4); swap(3, 8)\n 12: center([1, 2, 3, 4, 5, 6, 7, 8, 9])\n END\n```"}, {"role": "user", "content": "Do one of the swaps earlier. Also, make the cone higher (80,70)."}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(80, 70, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: swap(1, 9); swap(2, 8); swap(3, 7); swap(4, 6)\n 8: wave(3, 120, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 9: PLAN\n 10: PLAN\n 11: swap(1, 5); swap(2, 4); swap(3, 8)\n 12: center([1, 2, 3, 4, 5, 6, 7, 8, 9])\n END\n```"}, {"role": "user", "content": "This is too cluttered. Remove the first swap. Only do the second one, but do it right after the wave"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(80, 70, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: wave(3, 120, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 8: PLAN\n 9: swap(1, 5); swap(2, 4); swap(3, 8)\n 10: PLAN\n 11: center([1, 2, 3, 4, 5, 6, 7, 8, 9])\n 12: PLAN\n END\n```"}, {"role": "user", "content": "Only swap the corner drones. This is still too cluttered. The rest looks good"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(80, 70, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: wave(3, 120, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 8: PLAN\n 9: swap(1, 9); swap(3, 7)\n 10: PLAN\n 11: center([1, 2, 3, 4, 5, 6, 7, 8, 9])\n 12: PLAN\n END\n```"}, {"role": "user", "content": "Something is weird. Lets test only swapping drone 1 and 9 for now. Remove the center primitive"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(80, 70, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: wave(3, 120, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 8: PLAN\n 9: swap(1, 9)\n 10: PLAN\n 11: PLAN # no additional primitive, just to maintain waypoint length\n 12: PLAN\n END\n```"}, {"role": "user", "content": "Move those drones in z before swapping them"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(80, 70, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: wave(3, 120, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 8: move_z([1, 9], 30)\n 9: swap(1, 9)\n 10: PLAN\n 11: PLAN # no additional primitive, just to maintain waypoint length\n 12: PLAN\n END\n```"}, {"role": "user", "content": "Move and swap 2 and 8 instead"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(80, 70, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: wave(3, 120, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 8: move_z([2, 8], 30)\n 9: swap(2, 8)\n 10: PLAN\n 11: PLAN # no additional primitive, just to maintain waypoint length\n 12: PLAN\n END\n```"}, {"role": "user", "content": "Okay, this worked now. Now do the following. Execute the move and swap in the same tick. Also add another move -30 and swap for 4 and 6"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(80, 70, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: wave(3, 120, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 8: move_z([2, 8], 30); swap(2, 8)\n 9: move_z([4, 6], -30); swap(4, 6)\n 10: PLAN\n 11: PLAN # no additional primitive, just to maintain waypoint length\n 12: PLAN\n END\n```"}, {"role": "user", "content": "Make all move and swaps into the same tick. ALso, add one PLAN after the cone rotate. Its a bit too short"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(80, 70, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: wave(3, 120, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 8: move_z([2, 8], 30); swap(2, 8)\n 9: move_z([4, 6], -30); swap(4, 6)\n 10: PLAN\n 11: PLAN # no additional primitive, just to maintain waypoint length\n 12: PLAN\n END\n```"}, {"role": "user", "content": "Put all move and swaps to \"9:\" Add a PLAN before the wave and remove the last PLAN"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (Major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(80, 70, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: PLAN\n 8: wave(3, 120, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 9: move_z([2, 8], 30); swap(2, 8); move_z([4, 6], -30); swap(4, 6)\n 10: PLAN\n 11: PLAN # no additional primitive, just to maintain waypoint length\n 12: END\n```"}, {"role": "user", "content": "The provided response failed with the following error:\nUnknown motion primitive '' at timestep 12\n\nAnalyze the error, re-read the instructions and try again."}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(80, 70, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: PLAN\n 8: wave(3, 120, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 9: move_z([2, 8], 30); swap(2, 8); move_z([4, 6], -30); swap(4, 6)\n 10: PLAN\n 11: PLAN\n 12: PLAN\n END\n```"}, {"role": "user", "content": "Remove the move_z entirely. Keep the rest the same"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(100, 60, 50)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(80, 70, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: PLAN\n 8: wave(3, 120, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 9: swap(2, 8); swap(4, 6)\n 10: PLAN\n 11: PLAN # no additional primitive, just to maintain waypoint length\n 12: PLAN\n END\n```"}, {"role": "user", "content": "Make the wave on tick 9 and make it larger. Also, make the star in the beginning larger"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: The Crazyflie Drones Theme (short) presents an energetic yet somewhat robotic and dynamic mood, which suits a high-tech, futuristic dance performance. The music is structured with prominent beats and has a slightly mechanical feel, indicative of a strong, precise, and coordinated effort.\ncord_analysis: The song is based on the chords E and A, alternating primarily between these two. The E chord (major) generally conveys a bright, triumphant feel, while the A chord (major) brings an additional sense of harmony and transition. The song's mood is optimistic and driven, making it suitable for showcasing intricate patterns and movements.\nchoreography_plan: We'll create a dynamic flight choreography that mirrors the energy and upbeat nature of the music. The choreography will incorporate symmetrical patterns and formations to reflect the strong beats and the mechanical nature of the song. We'll design the patterns to be visually striking, include vertical movements for a 3D effect, and ensure no collisions. The patterns will emphasize strong geometric shapes and synchronized movements.\n\nchoreography:\n 1: form_star(150, 80, 80)\n 2: rotate(360, 'z')\n 3: PLAN\n 4: form_cone(80, 70, 0)\n 5: rotate(-120, 'z')\n 6: PLAN\n 7: PLAN\n 8: PLAN\n 9: wave(4, 140, [[1, 1], [2, 2], [3, 3]], [0.5, 0.3, 0.2], [0.4, 0.4, 0.3])\n 10: PLAN\n 11: swap(2, 8); swap(4, 6)\n 12: PLAN\n END\n```"}] \ No newline at end of file diff --git a/swarm_gpt/data/presets/Crazyflie Drones Theme (short) | 9 | 20251204_182045/meta.json b/swarm_gpt/data/presets/Crazyflie Drones Theme (short) | 9 | 20251204_182045/meta.json deleted file mode 100644 index adbf59c..0000000 --- a/swarm_gpt/data/presets/Crazyflie Drones Theme (short) | 9 | 20251204_182045/meta.json +++ /dev/null @@ -1 +0,0 @@ -{"n_drones": 9, "song": "Crazyflie Drones Theme (short)", "use_motion_primitives": true} \ No newline at end of file diff --git a/swarm_gpt/data/presets/Crazyflie Drones Theme (short) | 9 | 20251204_182045/trajectory.csv b/swarm_gpt/data/presets/Crazyflie Drones Theme (short) | 9 | 20251204_182045/trajectory.csv deleted file mode 100644 index 0196a58..0000000 --- a/swarm_gpt/data/presets/Crazyflie Drones Theme (short) | 9 | 20251204_182045/trajectory.csv +++ /dev/null @@ -1,3548 +0,0 @@ -time[s],drone0_posx[m],drone0_posy[m],drone0_posz[m],drone0_velx[m/s],drone0_vely[m/s],drone0_velz[m/s],drone1_posx[m],drone1_posy[m],drone1_posz[m],drone1_velx[m/s],drone1_vely[m/s],drone1_velz[m/s],drone2_posx[m],drone2_posy[m],drone2_posz[m],drone2_velx[m/s],drone2_vely[m/s],drone2_velz[m/s],drone3_posx[m],drone3_posy[m],drone3_posz[m],drone3_velx[m/s],drone3_vely[m/s],drone3_velz[m/s],drone4_posx[m],drone4_posy[m],drone4_posz[m],drone4_velx[m/s],drone4_vely[m/s],drone4_velz[m/s],drone5_posx[m],drone5_posy[m],drone5_posz[m],drone5_velx[m/s],drone5_vely[m/s],drone5_velz[m/s],drone6_posx[m],drone6_posy[m],drone6_posz[m],drone6_velx[m/s],drone6_vely[m/s],drone6_velz[m/s],drone7_posx[m],drone7_posy[m],drone7_posz[m],drone7_velx[m/s],drone7_vely[m/s],drone7_velz[m/s],drone8_posx[m],drone8_posy[m],drone8_posz[m],drone8_velx[m/s],drone8_vely[m/s],drone8_velz[m/s] 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-35.460000,0.993331,-1.014501,1.052571,0.106434,-0.068612,0.008932,1.030404,-0.017343,1.073143,0.050356,0.032907,-0.029820,1.017187,0.989251,1.053137,0.062901,0.105746,0.010471,-0.022757,-1.024313,1.075786,0.042799,-0.060929,-0.036010,0.001902,-0.003703,1.076665,-0.003465,0.006749,-0.030455,0.022736,1.023874,1.075830,-0.042631,0.062324,-0.035712,-1.007935,-0.992106,1.057710,-0.078347,-0.105490,0.001762,-0.997225,0.044703,1.091589,-0.025364,-0.014030,-0.003587,-0.991885,1.007394,1.055976,-0.103205,0.078114,0.005503 diff --git a/swarm_gpt/data/presets/Crazyflie Drones Theme (short) | 9 | 20251204_182045/waypoints.npy b/swarm_gpt/data/presets/Crazyflie Drones Theme (short) | 9 | 20251204_182045/waypoints.npy deleted file mode 100644 index 1caf649..0000000 Binary files a/swarm_gpt/data/presets/Crazyflie Drones Theme (short) | 9 | 20251204_182045/waypoints.npy and /dev/null differ diff --git a/swarm_gpt/data/presets/Fearless2 | 10 | 20260610_115735/history.json b/swarm_gpt/data/presets/Fearless2 | 10 | 20260610_115735/history.json new file mode 100644 index 0000000..a69f1fb --- /dev/null +++ b/swarm_gpt/data/presets/Fearless2 | 10 | 20260610_115735/history.json @@ -0,0 +1 @@ +[{"role": "system", "content": "You choreograph drone swarms using motion primitives. Choose primitives that match the song's\nstructural sections; respect timing, collision, and bounds. Coordinates are integer cm.\n\nYou have creative freedom over WHEN the swarm moves. You do NOT need to choreograph every bar\nor beat \u2014 that produces busy, repetitive motion. Instead vary the rhythm of your choreography:\ncluster moves where the music is active, let the swarm hold a pose and breathe through calmer\npassages, and avoid a predictable \"something every bar\" cadence. Uneven, musically-motivated\nspacing reads as intentional and expressive; mechanical regularity does not.\n"}, {"role": "user", "content": "Plan a choreography for 10 drones to \"Fearless2\" at 109 BPM.\n\n\n segment 1: \"verse\" (0.00s - 17.35s) \u2014 8 bars \u00d7 4 beats\n segment 2: \"chorus\" (17.35s - 34.81s) \u2014 8 bars \u00d7 4 beats\n segment 3: \"chorus\" (34.81s - 35.00s) \u2014 1 bars \u00d7 1 beats\nTotal addressable beats: 64.\nKeys take the form sbt (1-indexed): segment, then bar within that segment,\nthen beat within that bar. Any specific keys shown in the example below illustrate FORMAT\nONLY \u2014 choose keys from THIS song's actual segment/bar/beat counts above.\n\n\n\nPer-2-bar audio features. Each line is \"key: rms / centroid\", both normalized to [0, 1].\nThe key is the FIRST beat of the 2-bar window in s#b#t# notation.\n- rms ~ loudness (1.0 = song's loudest moment)\n- centroid ~ brightness (1.0 = brightest; high = treble cymbals/vocals/leads, low = bass/kicks)\nUse the contrast to choose primitives that match the moment. Examples:\n- rising rms across adjacent windows \u2192 a build-up; favor accumulating motion\n- high rms + low centroid \u2192 bass-driven; bigger vertical moves, slow rotations\n- high rms + high centroid \u2192 treble-driven; fast rotations, helices, waves\n- low rms regardless of centroid \u2192 hold, breathe, minimal new primitives\ns1b1t1: 0.49 / 0.84\ns1b3t1: 0.47 / 1.00\ns1b5t1: 0.44 / 0.90\ns1b7t1: 1.00 / 0.74\ns2b1t1: 0.99 / 0.71\ns2b3t1: 0.98 / 0.78\ns2b5t1: 0.98 / 0.76\ns2b7t1: 0.99 / 0.67\ns3b1t1: 0.99 / 0.66\n\n\n\ncount: 10\nstart_cm: [[110, -200, 100], [110, -100, 100], [110, 0, 100], [110, 100, 100], [110, 200, 100], [-110, -200, 100], [-110, -100, 100], [-110, 0, 100], [-110, 100, 100], [-110, 200, 100]]\nbounds_cm: lower=[-220, -270, 25] upper=[220, 270, 170]\n\n\n\n- REQUIRED keys (must emit one action list at each): s1b1t1, s1b5t1, s2b1t1, s2b5t1, s3b1t1\n These are a SPARSE skeleton \u2014 roughly one anchor every few bars plus each segment's\n opening \u2014 not every bar. They are the floor, not the target.\n- OPTIONAL keys: every other sbt in the song's address space. Add them only\n where the music genuinely calls for an accent \u2014 a build, a hit, a section change. It is good\n and expected to leave many bars with no action: do NOT try to cover every bar. Vary the\n spacing between actions so the motion feels phrased, not metronomic.\n- Drones hold or continue the previous primitive between emitted actions. Holding a pose\n through a calm passage is a valid, expressive choice \u2014 not a gap to be filled.\n- Formation primitives (form_circle, form_star, form_cone) snap into shape quickly and then\n sit still. A long hold after a formation reads as lifeless on energetic passages \u2014 prefer\n to follow up on a later bar (often the next bar or two later) with a continuation that\n keeps the swarm alive: rotate preserves the formation while turning it, move_z lifts or\n lowers it, twister/helix/spiral_speed evolve it into something new. Omit the follow-up\n only when the music genuinely wants stillness (intro/outro tails, breakdowns).\n- No two drones occupy the same position at the same moment.\n- Stay within bounds_cm; z > 0.\n- Match motion to music: dense, energetic sections (chorus, drop, bridge) get bolder\n primitives; intros and outros stay sparse and gentle.\n- One entry's \"actions\" array can hold ONE or MORE actions. Two valid stacking patterns:\n 1. Disjoint drone subsets \u2014 different drones, different shapes, coexisting.\n IMPORTANT: the two subsets MUST end up at different points in space or they will\n collide. Separate by z (height), radius, or x/y center.\n 2. Formation + motion on overlapping drones \u2014 formation snaps in, motion takes over.\n The system handles the handoff automatically.\n Do NOT stack two formations on overlapping drones (e.g. form_star + form_circle on the\n same drones); the result is undefined.\n Do NOT stack two formations in the same point in space on different subsets \n (e.g. form_circle with different radii but same center); the result is a collision.\n\n\n\nSingle-shot (executes once over the interval until the next emitted action):\n- rotate(angle_deg, axis) \u2014 all drones; axis usually 'z'\n- center([ids]) \u2014 listed drones to formation around their centroid\n- move_z([ids], delta_cm) \u2014 vertical shift; aim for |delta_cm| ~72 so the motion is visibly noticeable\n- form_circle([ids], radius_cm, time_to_finish_s)\n time_to_finish_s: seconds to arrive; pick ~1s for snappy hits, \u2265half the interval for slow blooms.\n The system clamps to physics minimum if your value is too small.\n- form_star(height_cm, spacing 40-80, delta_radius 50-100, time_to_finish_s)\n time_to_finish_s: seconds to arrive in the star; same guidance as form_circle.\n- form_cone(delta_height 30-80, spacing 40-80, is_inverted 0|1, time_to_finish_s)\n time_to_finish_s: seconds to arrive in the cone; same guidance as form_circle.\n- swap(id1, id2)\n- move(x_cm, y_cm, z_cm, drone_id) \u2014 absolute position for one drone\n\nMulti-step (plays out across the interval; pick steps in [2, 3]):\n- spiral(steps, height_cm)\n- spiral_speed(steps, height_cm, degrees 0-360, radius_increase 1-3)\n- helix(steps, delta_height_cm 0-100, height_cm 50-100)\n- twister(steps, omega_times_ten 2-20, z_spacing_cm 5-30)\n- zig_zag(steps, delta_xy_cm, delta_z_cm)\n- wave(steps, height_cm, mu_pairs, a_mu, b_mu) \u2014 2D wave surface; equation:\nThe disturbance \\(u(s, t)\\) at any point \\(s = (s_1, s_2) \\in [0, a] \\times [0, b]\\) \nat time \\(t\\) is governed by:\n\\[\nc^2 \\nabla^2 u(s, t) = \\frac{\\partial^2 u(s, t)}{\\partial t^2},\n\\]\nwhere \\( \\nabla^2 \\) is the spatial Laplacian and \\(c > 0\\) is the wave speed. \nThe solution:\n\\[\nu(s, t) = \\sum_{(\\mu_1, \\mu_2)} a_{\\mu_1 \\mu_2} \\sin \\left( \\frac{\\mu_1 \\pi s_1}{a} \\right) \n\\sin \\left( \\frac{\\mu_2 pi s_2}{b} \\right) \\sin(\\omega_{\\mu_1 \\mu_2} t) \n+ b_{\\mu_1 \\mu_2} \\sin \\left( \\frac{\\mu_1 \\pi s_1}{a} \\right) \n\\sin \\left( \\frac{\\mu_2 \\pi s_2}{b} \\right) \\cos(\\omega_{\\mu_1 \\mu_2} t),\n\\]\n\nwith frequencies:\n\n\\[\n\\omega_{\\mu_1 \\mu_2}^2 = c^2 \\pi^2 \\left( \\frac{\\mu_1^2}{a^2} + \\frac{\\mu_2^2}{b^2} \\right).\n\\]\n\n\n\n1) song_mood \u2014 brief mood for \"Fearless2\".\n2) choreography_plan \u2014 sections, primitives chosen, and intensity rationale per segment.\n3) choreography \u2014 sparse map of action keys to action lists.\n\n"}, {"role": "system", "content": "\nInput: song=Sample, drones=3, bpm=120, structure:\n segment 1: \"intro\" (0.00s - 4.00s) \u2014 2 bars x 4 beats\n segment 2: \"chorus\" (4.00s - 12.00s) \u2014 4 bars x 4 beats\nRequired keys: s1b1t1, s2b1t1.\nOutput:\nsong_mood: Steady, building from a soft intro to a confident chorus.\nchoreography_plan: Intro begins with a gentle inward spiral. Chorus opens with a bold\n form_circle on the downbeat, accents the third beat of bar 1 with a rotation, then\n sweeps through a helix at the start of bar 2.\nchoreography:\n s1b1t1: spiral(3, 100)\n s2b1t1: form_circle([1, 2, 3], 120, 1.5)\n s2b1t3: rotate(60, 'z')\n s2b2t1: helix(2, 40, 90)\n END\n\n"}, {"role": "system", "content": "Return JSON only (no markdown). The schema enforces:\n- \"choreography\" is an array of entries, each {\"key\":\"sbt\",\"actions\":[...]}.\n- Every key matches the pattern sbt and is one of the song's valid addresses.\n- All REQUIRED keys are present (as entries); add OPTIONAL keys only where musically warranted,\n leaving many bars empty. Do not repeat a key.\n- \"actions\" is a list of one or more action objects: {\"primitive\":\"name\",\"params\":{...}}.\n Two valid multi-action patterns (both in a single entry's \"actions\" array):\n 1. Disjoint drone subsets \u2014 different drones at different positions in space:\n \"actions\": [\n {\"primitive\":\"form_circle\",\"params\":{\"drone_ids\":[1,2,3,4,5],\"radius_cm\":100,\"time_to_finish_s\":2.0}},\n {\"primitive\":\"form_circle\",\"params\":{\"drone_ids\":[6,7,8,9,10],\"radius_cm\":180,\"time_to_finish_s\":2.0}}\n ]\n Subsets MUST occupy different regions (different z, different radii, etc.) to avoid collision.\n 2. Formation + motion on overlapping drones \u2014 formation arrives, then motion takes over:\n \"actions\": [\n {\"primitive\":\"form_star\",\"params\":{\"height_cm\":100,\"min_spacing_cm\":60,\"delta_radius_cm\":80,\"time_to_finish_s\":2.0}},\n {\"primitive\":\"rotate\",\"params\":{\"angle_deg\":60,\"axis\":\"z\"}}\n ]\n The system suppresses form_* hold waypoints automatically \u2014 no extra timing needed.\n Do NOT stack two formations on overlapping drones.\nUse exactly the parameter names from the primitive signature.\n\nCorrect JSON param examples:\n- center([ids]) -> {\"primitive\":\"center\",\"params\":{\"drone_ids\":[1,2,3,4]}}\n- move_z([ids], delta_cm) -> {\"primitive\":\"move_z\",\"params\":{\"drone_ids\":[1,2,3,4],\"delta_cm\":30}}\n- form_cone(delta_height_cm, spacing_cm, is_inverted) -> {\"primitive\":\"form_cone\",\"params\":{\"delta_height_cm\":60,\"spacing_cm\":60,\"is_inverted\":0,\"time_to_finish_s\":2.0}}\n- spiral_speed(steps, height_cm, degrees, radius_increase) -> {\"primitive\":\"spiral_speed\",\"params\":{\"steps\":3,\"height_cm\":100,\"degrees\":180,\"radius_increase\":1}}\n- wave(steps, height_cm, mu_pairs, a_mu, b_mu) -> {\"primitive\":\"wave\",\"params\":{\"steps\":2,\"height_cm\":80,\"mu_pairs\":[[0,0],[1,0]],\"a_mu\":[0.5,0.25],\"b_mu\":[0.2,0.1]}}\n\nCommon mistakes that will fail syntax checks:\n- Wrong: {\"primitive\":\"form_cone\",\"params\":{\"drone_ids\":[1,2,3],...}} because form_cone has no drone_ids param.\n- Wrong: {\"primitive\":\"spiral_speed\",\"params\":{\"steps\":3,\"angle_deg\":90,...}} because spiral_speed uses 'degrees', not 'angle_deg'.\n- Wrong: {\"primitive\":\"move_z\",\"params\":{\"delta_cm\":30}} because move_z also needs drone_ids.\n- Wrong: {\"primitive\":\"form_star\",\"params\":{\"height_cm\":100,\"min_spacing_cm\":60,\"delta_radius_cm\":80}} because form_star also needs time_to_finish_s.\n"}, {"role": "assistant", "content": "song_mood: \"Confident, high-energy build into a bright, chorus-driven surge\\u2014bold formations with quick rotational accents and vertical lifts timed to the loudest, mid-to-high brightness moments.\"\nchoreography_plan: \"Segment 1 (verse, 8 bars): start with a grounded, mid-height circle to establish the swarm, then add a sharper star and a gentle vertical lift as the verse grows. Keep motion phrased\\u2014formation hits on the required anchors, with a couple of extra accents near the loudest/brightest windows (not every bar).\\n\\nSegment 2 (chorus, 8 bars): switch to more kinetic geometry\\u2014circle-to-helix evolution and faster rotational accents. Use vertical moves and twister/helix-style multi-step motion to match the chorus intensity, with additional hits around the high-rms windows.\\n\\nSegment 3 (final 1 beat): a short, punchy closure\\u2014tight circle snap and a final rotation to land the phrase cleanly without lingering.\"\nchoreography:\n s1b1t1: form_circle([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 120, 1.2); move_z([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 36)\n s1b3t1: rotate(35, 'z'); helix(2, 18, 92)\n s1b5t1: form_star(110, 60, 70, 1.0); rotate(-55, 'z')\n s1b6t3: move_z([1, 2, 3, 4, 5], 72); move_z([6, 7, 8, 9, 10], -36)\n s1b8t2: spiral_speed(3, 105, 180, 2)\n s2b1t1: form_circle([1, 2, 3, 4, 5], 150, 1.0); form_circle([6, 7, 8, 9, 10], 95, 1.0); rotate(70, 'z')\n s2b2t2: twister(2, 14, 18)\n s2b3t1: helix(3, 28, 120); rotate(-40, 'z')\n s2b4t3: zig_zag(2, 55, -24)\n s2b5t1: form_cone(70, 60, 0, 1.1); rotate(95, 'z')\n s2b6t2: wave(2, 85, [[0, 0], [1, 0], [0, 1]], [0.55, 0.25, 0.18], [0.22, 0.12, 0.1])\n s2b7t4: move_z([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], -72)\n s3b1t1: form_circle([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 105, 0.9); rotate(120, 'z')\n END"}] \ No newline at end of file diff --git a/swarm_gpt/data/presets/Fearless2 | 10 | 20260610_115735/meta.json b/swarm_gpt/data/presets/Fearless2 | 10 | 20260610_115735/meta.json new file mode 100644 index 0000000..2f0965b --- /dev/null +++ b/swarm_gpt/data/presets/Fearless2 | 10 | 20260610_115735/meta.json @@ -0,0 +1 @@ +{"n_drones": 10, "song": "Fearless2", "use_motion_primitives": true} \ No newline at end of file diff --git a/swarm_gpt/data/presets/Fearless2 | 10 | 20260610_115735/trajectory.csv b/swarm_gpt/data/presets/Fearless2 | 10 | 20260610_115735/trajectory.csv new file mode 100644 index 0000000..16635b3 --- /dev/null +++ b/swarm_gpt/data/presets/Fearless2 | 10 | 20260610_115735/trajectory.csv @@ -0,0 +1,1951 @@ 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+38.980000,1.130017,-2.034543,1.018194,0.144192,0.002613,0.014605,1.171094,-1.020190,1.047894,-0.037973,-0.068759,0.009249,1.128662,-0.001025,1.033077,0.157486,-0.015435,0.017177,1.135473,1.035763,1.025802,-0.038106,-0.069523,0.034559,1.123436,2.056700,1.031805,0.144419,0.089949,0.030028,-1.160787,-2.114384,0.984955,-0.118833,-0.235574,-0.049559,-1.125712,-1.020844,1.023787,0.055137,0.077647,0.015672,-1.123192,-0.007131,1.033168,-0.141177,0.066460,0.043709,-1.057019,1.014581,1.037460,0.039255,-0.076114,-0.019083,-1.134419,2.127693,0.994844,-0.068337,0.281205,-0.018869 diff --git a/swarm_gpt/data/presets/Fearless2 | 10 | 20260610_115735/waypoints.npy b/swarm_gpt/data/presets/Fearless2 | 10 | 20260610_115735/waypoints.npy new file mode 100644 index 0000000..3df379b Binary files /dev/null and b/swarm_gpt/data/presets/Fearless2 | 10 | 20260610_115735/waypoints.npy differ diff --git a/swarm_gpt/data/presets/Fearless2 | 10 | 20260610_161527/history.json b/swarm_gpt/data/presets/Fearless2 | 10 | 20260610_161527/history.json new file mode 100644 index 0000000..1614704 --- /dev/null +++ b/swarm_gpt/data/presets/Fearless2 | 10 | 20260610_161527/history.json @@ -0,0 +1 @@ +[{"role": "system", "content": "You choreograph drone swarms using motion primitives. Choose primitives that match the song's\nstructural sections; respect timing, collision, and bounds. Coordinates are integer cm.\n\nYou have creative freedom over WHEN the swarm moves. You do NOT need to choreograph every bar\nor beat \u2014 that produces busy, repetitive motion. Instead vary the rhythm of your choreography:\ncluster moves where the music is active, let the swarm hold a pose and breathe through calmer\npassages, and avoid a predictable \"something every bar\" cadence. Uneven, musically-motivated\nspacing reads as intentional and expressive; mechanical regularity does not.\n"}, {"role": "user", "content": "Plan a choreography for 10 drones to \"Fearless2\" at 109 BPM.\n\n\n segment 1: \"verse\" (0.00s - 17.35s) \u2014 8 bars \u00d7 4 beats\n segment 2: \"chorus\" (17.35s - 34.81s) \u2014 8 bars \u00d7 4 beats\n segment 3: \"chorus\" (34.81s - 35.00s) \u2014 1 bars \u00d7 1 beats\nTotal addressable beats: 64.\nKeys take the form sbt (1-indexed): segment, then bar within that segment,\nthen beat within that bar. Any specific keys shown in the example below illustrate FORMAT\nONLY \u2014 choose keys from THIS song's actual segment/bar/beat counts above.\n\n\n\nPer-2-bar audio features. Each line is \"key: rms / centroid\", both normalized to [0, 1].\nThe key is the FIRST beat of the 2-bar window in s#b#t# notation.\n- rms ~ loudness (1.0 = song's loudest moment)\n- centroid ~ brightness (1.0 = brightest; high = treble cymbals/vocals/leads, low = bass/kicks)\nUse the contrast to choose primitives that match the moment. Examples:\n- rising rms across adjacent windows \u2192 a build-up; favor accumulating motion\n- high rms + low centroid \u2192 bass-driven; bigger vertical moves, slow rotations\n- high rms + high centroid \u2192 treble-driven; fast rotations, helices, waves\n- low rms regardless of centroid \u2192 hold, breathe, minimal new primitives\n- BEAT DROP \u2192 the first downbeat of a NEW segment (a sb1t1 key) whose rms AND centroid\n are high \u2014 usually a jump up from the bars just before it. The segment boundary matters more\n than the raw peak: a build-up can spike rms/centroid too, but the real drop lands on the\n downbeat where the new section begins. Treat these as the biggest moments in the show.\ns1b1t1: 0.49 / 0.84\ns1b3t1: 0.47 / 1.00\ns1b5t1: 0.44 / 0.90\ns1b7t1: 1.00 / 0.74\ns2b1t1: 0.99 / 0.71\ns2b3t1: 0.98 / 0.78\ns2b5t1: 0.98 / 0.76\ns2b7t1: 0.99 / 0.67\ns3b1t1: 0.99 / 0.66\n\n\n\ncount: 10\nstart_cm: [[110, -200, 100], [110, -100, 100], [110, 0, 100], [110, 100, 100], [110, 200, 100], [-110, -200, 100], [-110, -100, 100], [-110, 0, 100], [-110, 100, 100], [-110, 200, 100]]\nbounds_cm: lower=[-220, -270, 25] upper=[220, 270, 170]\n\n\n\n- REQUIRED keys (must emit one action list at each): s1b1t1, s1b5t1, s2b1t1, s2b5t1, s3b1t1\n These are a SPARSE skeleton \u2014 roughly one anchor every few bars plus each segment's\n opening \u2014 not every bar. They are the floor, not the target.\n- OPTIONAL keys: every other sbt in the song's address space. Add them only\n where the music genuinely calls for an accent \u2014 a build, a hit, a section change. It is good\n and expected to leave many bars with no action: do NOT try to cover every bar. Vary the\n spacing between actions so the motion feels phrased, not metronomic.\n- Drones hold or continue the previous primitive between emitted actions. Holding a pose\n through a calm passage is a valid, expressive choice \u2014 not a gap to be filled.\n- Formation primitives (form_circle, form_star, form_cone) snap into shape quickly and then\n sit still. A long hold after a formation reads as lifeless on energetic passages \u2014 prefer\n to follow up on a later bar (often the next bar or two later) with a continuation that\n keeps the swarm alive: rotate preserves the formation while turning it, move_z lifts or\n lowers it, twister/helix/spiral_speed evolve it into something new. Omit the follow-up\n only when the music genuinely wants stillness (intro/outro tails, breakdowns).\n- No two drones occupy the same position at the same moment.\n- Stay within bounds_cm; z > 0.\n- Match motion to music: dense, energetic sections (chorus, drop, bridge) get bolder\n primitives; intros and outros stay sparse and gentle.\n- AT A BEAT DROP (see song_dynamics), do NOT answer with a static formation \u2014 a drop that\n resolves into the swarm quietly gathering into a circle is the most underwhelming thing you\n can do. The drop is the payoff: hit it with big, FAST, full-swarm MOTION. Favor high-speed\n multi-step primitives \u2014 spiral_speed with a large degrees (270-360) and radius_increase 2-3,\n a fast twister (omega_times_ten 14-20), a tall helix, or a large-angle rotate (90+ deg) \u2014\n so the swarm visibly explodes into movement on the downbeat. If the section needs a shape,\n snap it in a bar or two BEFORE the drop so the drop itself is spent on motion, not arriving.\n- One entry's \"actions\" array can hold ONE or MORE actions. Two valid stacking patterns:\n 1. Disjoint drone subsets \u2014 different drones, different shapes, coexisting.\n IMPORTANT: the two subsets MUST end up at different points in space or they will\n collide. Separating by height alone needs a LARGE gap: the vertical collision\n envelope is 60cm, so stacked formations must differ in z by at least 70cm, preferably larger to allow for movement.\n A smaller gap (or same x/y footprint) makes the drones jitter. Otherwise separate\n by radius or x/y center.\n 2. Formation + motion on overlapping drones \u2014 formation snaps in, motion takes over.\n The system handles the handoff automatically.\n Do NOT stack two formations on overlapping drones (e.g. form_star + form_circle on the\n same drones); the result is undefined.\n Do NOT stack two formations in the same point in space on different subsets \n (e.g. form_circle with different radii but same center); the result is a collision.\n\n\n\nSingle-shot (executes once over the interval until the next emitted action):\n- rotate(angle_deg, axis) \u2014 all drones; axis usually 'z'\n- center([ids]) \u2014 listed drones to formation around their centroid\n- move_z([ids], delta_cm) \u2014 vertical shift; aim for |delta_cm| ~72 so the motion is visibly noticeable\n- form_circle([ids], radius_cm, z_coord_cm, time_to_finish_s)\n radius_cm: circle radius; the system enforces a minimum based on drone count and spacing.\n z_coord_cm: height of the circle in cm (e.g. 80, 100, 120).\n time_to_finish_s: seconds to arrive; pick ~1s for snappy hits, \u2265half the interval for slow blooms.\n The system clamps to physics minimum if your value is too small.\n When stacking two disjoint form_circle calls vertically, their z_coord_cm values MUST differ\n by at least 60 (e.g. 80 and 140): a smaller gap puts the drones inside each other's collision\n envelope and they will jitter instead of holding the rings.\n- form_star(height_cm, spacing 40-80, delta_radius 50-100, time_to_finish_s)\n time_to_finish_s: seconds to arrive in the star; same guidance as form_circle.\n- form_cone(delta_height 30-80, spacing 40-80, is_inverted 0|1, time_to_finish_s)\n time_to_finish_s: seconds to arrive in the cone; same guidance as form_circle.\n- swap(id1, id2)\n- move(x_cm, y_cm, z_cm, drone_id) \u2014 absolute position for one drone\n\nMulti-step (plays out across the interval; pick steps in [2, 3]):\n- spiral(steps, height_cm)\n- spiral_speed(steps, height_cm, degrees 0-360, radius_increase 1-3)\n- helix(steps, delta_height_cm 0-100, height_cm 50-100)\n- twister(steps, omega_times_ten 2-20, z_spacing_cm 5-30)\n- zig_zag(steps, delta_xy_cm, delta_z_cm)\n- wave(steps, height_cm) \u2014 2D standing-wave surface; drones first form a grid then oscillate.\n\n\n\n1) song_mood \u2014 brief mood for \"Fearless2\".\n2) choreography_plan \u2014 sections, primitives chosen, and intensity rationale per segment.\n3) choreography \u2014 sparse map of action keys to action lists.\n\n"}, {"role": "system", "content": "\nInput: song=Sample, drones=3, bpm=120, structure:\n segment 1: \"intro\" (0.00s - 4.00s) \u2014 2 bars x 4 beats\n segment 2: \"chorus\" (4.00s - 12.00s) \u2014 4 bars x 4 beats\nRequired keys: s1b1t1, s2b1t1.\nOutput:\nsong_mood: Steady, building from a soft intro to a confident chorus.\nchoreography_plan: Intro begins with a gentle inward spiral. Chorus opens with a bold\n form_circle on the downbeat, accents the third beat of bar 1 with a rotation, then\n sweeps through a helix at the start of bar 2.\nchoreography:\n s1b1t1: spiral(3, 100)\n s2b1t1: form_circle([1, 2, 3], 120, 100, 1.5)\n s2b1t3: rotate(60, 'z')\n s2b2t1: helix(2, 40, 90)\n END\n\n"}, {"role": "system", "content": "Return JSON only (no markdown). The schema enforces:\n- \"choreography\" is an array of entries, each {\"key\":\"sbt\",\"actions\":[...]}.\n- Every key matches the pattern sbt and is one of the song's valid addresses.\n- All REQUIRED keys are present (as entries); add OPTIONAL keys only where musically warranted,\n leaving many bars empty. Do not repeat a key.\n- \"actions\" is a list of one or more action objects: {\"primitive\":\"name\",\"params\":{...}}.\n Two valid multi-action patterns (both in a single entry's \"actions\" array):\n 1. Disjoint drone subsets \u2014 different drones at different positions in space:\n \"actions\": [\n {\"primitive\":\"form_circle\",\"params\":{\"drone_ids\":[1,2,3,4,5],\"radius_cm\":100,\"z_coord_cm\":80,\"time_to_finish_s\":2.0}},\n {\"primitive\":\"form_circle\",\"params\":{\"drone_ids\":[6,7,8,9,10],\"radius_cm\":100,\"z_coord_cm\":150,\"time_to_finish_s\":2.0}}\n ]\n Subsets MUST occupy different regions to avoid collision. Stacking by z requires a gap of\n at least 60cm (vertical collision envelope); otherwise separate by radius or x/y center.\n 2. Formation + motion on overlapping drones \u2014 formation arrives, then motion takes over:\n \"actions\": [\n {\"primitive\":\"form_star\",\"params\":{\"height_cm\":100,\"min_spacing_cm\":60,\"delta_radius_cm\":80,\"time_to_finish_s\":2.0}},\n {\"primitive\":\"rotate\",\"params\":{\"angle_deg\":60,\"axis\":\"z\"}}\n ]\n The system suppresses form_* hold waypoints automatically \u2014 no extra timing needed.\n Do NOT stack two formations on overlapping drones.\nUse exactly the parameter names from the primitive signature.\n\nCorrect JSON param examples:\n- center([ids]) -> {\"primitive\":\"center\",\"params\":{\"drone_ids\":[1,2,3,4]}}\n- move_z([ids], delta_cm) -> {\"primitive\":\"move_z\",\"params\":{\"drone_ids\":[1,2,3,4],\"delta_cm\":30}}\n- form_cone(delta_height_cm, spacing_cm, is_inverted) -> {\"primitive\":\"form_cone\",\"params\":{\"delta_height_cm\":60,\"spacing_cm\":60,\"is_inverted\":0,\"time_to_finish_s\":2.0}}\n- spiral_speed(steps, height_cm, degrees, radius_increase) -> {\"primitive\":\"spiral_speed\",\"params\":{\"steps\":3,\"height_cm\":100,\"degrees\":180,\"radius_increase\":1}}\n- wave(steps, height_cm) -> {\"primitive\":\"wave\",\"params\":{\"steps\":2,\"height_cm\":80}}\n\nCommon mistakes that will fail syntax checks:\n- Wrong: {\"primitive\":\"form_cone\",\"params\":{\"drone_ids\":[1,2,3],...}} because form_cone has no drone_ids param.\n- Wrong: {\"primitive\":\"spiral_speed\",\"params\":{\"steps\":3,\"angle_deg\":90,...}} because spiral_speed uses 'degrees', not 'angle_deg'.\n- Wrong: {\"primitive\":\"move_z\",\"params\":{\"delta_cm\":30}} because move_z also needs drone_ids.\n- Wrong: {\"primitive\":\"form_star\",\"params\":{\"height_cm\":100,\"min_spacing_cm\":60,\"delta_radius_cm\":80}} because form_star also needs time_to_finish_s.\n"}, {"role": "assistant", "content": "song_mood: \"Bright, confident build into a high-energy chorus drop\\u2014fast spirals and twisting motion on the downbeats, with brief breathing holds between phrases.\"\nchoreography_plan: \"Segment 1 (verse): start with a grounded, mid-height circle, then gradually energize with a star-to-spiral transition. Anchor moments at s1b1t1 and s1b5t1; keep motion phrased and avoid constant movement.\\n\\nSegment 2 (chorus): treat s2b1t1 as the big section payoff\\u2014explode into fast full-swarm motion (spiral_speed + twister) rather than a static shape. Use s2b5t1 as a second high-energy anchor with a helix/twister continuation, then let the swarm breathe briefly before the final chorus hit.\\n\\nSegment 3 (final chorus hit): s3b1t1 is the last big downbeat\\u2014snap into a circle quickly, then immediately drive it into a fast helix/spiral_speed so the payoff is motion-forward, not static.\"\nchoreography:\n s1b1t1: form_circle([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 120, 90, 1.2)\n s1b3t1: rotate(35, 'z'); move_z([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 36)\n s1b5t1: form_star(110, 55, 70, 1.4); rotate(70, 'z')\n s1b6t3: spiral_speed(3, 95, 240, 2)\n s2b1t1: spiral_speed(3, 120, 330, 3); twister(3, 18, 18)\n s2b2t2: helix(3, 55, 95)\n s2b4t4: zig_zag(2, 70, -30)\n s2b5t1: form_circle([1, 2, 3, 4, 5], 95, 110, 1.0); form_circle([6, 7, 8, 9, 10], 95, 170, 1.0); rotate(110, 'z')\n s2b6t3: spiral(3, 105)\n s2b7t4: move_z([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], -54)\n s3b1t1: form_circle([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 130, 100, 0.9); spiral_speed(3, 130, 360, 2); rotate(140, 'z')\n END"}, {"role": "user", "content": "The provided response failed with the following error:\nDrones get too close to each other near these moments: s2b5t3 (t\u224827.2s): drones [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]; s2b6t1 (t\u224828.3s): drones [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]; s2b6t2 (t\u224828.8s): drones [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]; s2b6t3 (t\u224829.3s): drones [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]. Separate the colliding drones there by height (z), radius, or x/y center, move them to different keys. Try moving some drones lower in height (z) if they are colliding with other drones near the height limit.\n\nAnalyze the error, re-read the instructions and try again."}, {"role": "system", "content": "Return JSON only (no markdown). The schema enforces:\n- \"choreography\" is an array of entries, each {\"key\":\"sbt\",\"actions\":[...]}.\n- Every key matches the pattern sbt and is one of the song's valid addresses.\n- All REQUIRED keys are present (as entries); add OPTIONAL keys only where musically warranted,\n leaving many bars empty. Do not repeat a key.\n- \"actions\" is a list of one or more action objects: {\"primitive\":\"name\",\"params\":{...}}.\n Two valid multi-action patterns (both in a single entry's \"actions\" array):\n 1. Disjoint drone subsets \u2014 different drones at different positions in space:\n \"actions\": [\n {\"primitive\":\"form_circle\",\"params\":{\"drone_ids\":[1,2,3,4,5],\"radius_cm\":100,\"z_coord_cm\":80,\"time_to_finish_s\":2.0}},\n {\"primitive\":\"form_circle\",\"params\":{\"drone_ids\":[6,7,8,9,10],\"radius_cm\":100,\"z_coord_cm\":150,\"time_to_finish_s\":2.0}}\n ]\n Subsets MUST occupy different regions to avoid collision. Stacking by z requires a gap of\n at least 60cm (vertical collision envelope); otherwise separate by radius or x/y center.\n 2. Formation + motion on overlapping drones \u2014 formation arrives, then motion takes over:\n \"actions\": [\n {\"primitive\":\"form_star\",\"params\":{\"height_cm\":100,\"min_spacing_cm\":60,\"delta_radius_cm\":80,\"time_to_finish_s\":2.0}},\n {\"primitive\":\"rotate\",\"params\":{\"angle_deg\":60,\"axis\":\"z\"}}\n ]\n The system suppresses form_* hold waypoints automatically \u2014 no extra timing needed.\n Do NOT stack two formations on overlapping drones.\nUse exactly the parameter names from the primitive signature.\n\nCorrect JSON param examples:\n- center([ids]) -> {\"primitive\":\"center\",\"params\":{\"drone_ids\":[1,2,3,4]}}\n- move_z([ids], delta_cm) -> {\"primitive\":\"move_z\",\"params\":{\"drone_ids\":[1,2,3,4],\"delta_cm\":30}}\n- form_cone(delta_height_cm, spacing_cm, is_inverted) -> {\"primitive\":\"form_cone\",\"params\":{\"delta_height_cm\":60,\"spacing_cm\":60,\"is_inverted\":0,\"time_to_finish_s\":2.0}}\n- spiral_speed(steps, height_cm, degrees, radius_increase) -> {\"primitive\":\"spiral_speed\",\"params\":{\"steps\":3,\"height_cm\":100,\"degrees\":180,\"radius_increase\":1}}\n- wave(steps, height_cm) -> {\"primitive\":\"wave\",\"params\":{\"steps\":2,\"height_cm\":80}}\n\nCommon mistakes that will fail syntax checks:\n- Wrong: {\"primitive\":\"form_cone\",\"params\":{\"drone_ids\":[1,2,3],...}} because form_cone has no drone_ids param.\n- Wrong: {\"primitive\":\"spiral_speed\",\"params\":{\"steps\":3,\"angle_deg\":90,...}} because spiral_speed uses 'degrees', not 'angle_deg'.\n- Wrong: {\"primitive\":\"move_z\",\"params\":{\"delta_cm\":30}} because move_z also needs drone_ids.\n- Wrong: {\"primitive\":\"form_star\",\"params\":{\"height_cm\":100,\"min_spacing_cm\":60,\"delta_radius_cm\":80}} because form_star also needs time_to_finish_s.\n"}, {"role": "assistant", "content": "song_mood: \"Bright, confident build into a high-energy chorus drop\\u2014fast spirals and twisting motion on the downbeats, with brief breathing holds between phrases.\"\nchoreography_plan: \"Segment 1 (verse): establish a mid-height circle, then add energy with a star and a short spiral-speed burst. Keep motion sparse to avoid crowding.\\n\\nSegment 2 (chorus): treat s2b1t1 as the main payoff\\u2014use fast full-swarm motion (spiral_speed + twister) to avoid static formations at the drop. After that, avoid any full-swarm formation primitives that could bring drones too close; instead use global rotations and gentle vertical shifts, and only use disjoint formations at s2b5t1 with a large z separation.\\n\\nSegment 3 (final chorus hit): s3b1t1 snaps into a circle quickly, then immediately drives into fast motion (spiral_speed + rotate) so the payoff is motion-forward, not static.\"\nchoreography:\n s1b1t1: form_circle([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 120, 90, 1.2)\n s1b3t1: rotate(35, 'z'); move_z([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 36)\n s1b5t1: form_star(110, 55, 90, 1.4); rotate(70, 'z')\n s1b6t3: spiral_speed(3, 95, 240, 2)\n s2b1t1: spiral_speed(3, 120, 330, 3); twister(3, 18, 18)\n s2b2t2: helix(3, 55, 95)\n s2b4t4: zig_zag(2, 70, -30)\n s2b5t1: form_circle([1, 2, 3, 4, 5], 95, 70, 1.0); form_circle([6, 7, 8, 9, 10], 95, 150, 1.0); rotate(110, 'z')\n s2b6t2: move_z([1, 2, 3, 4, 5], -54); move_z([6, 7, 8, 9, 10], 36)\n s2b7t4: rotate(45, 'z'); spiral(3, 105)\n s3b1t1: form_circle([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 130, 100, 0.9); spiral_speed(3, 130, 360, 3); rotate(140, 'z')\n END"}] \ No newline at end of file diff --git a/swarm_gpt/data/presets/Fearless2 | 10 | 20260610_161527/meta.json b/swarm_gpt/data/presets/Fearless2 | 10 | 20260610_161527/meta.json new file mode 100644 index 0000000..2f0965b --- /dev/null +++ b/swarm_gpt/data/presets/Fearless2 | 10 | 20260610_161527/meta.json @@ -0,0 +1 @@ +{"n_drones": 10, "song": "Fearless2", "use_motion_primitives": true} \ No newline at end of file diff --git a/swarm_gpt/data/presets/Fearless2 | 10 | 20260610_161527/trajectory.csv b/swarm_gpt/data/presets/Fearless2 | 10 | 20260610_161527/trajectory.csv new file mode 100644 index 0000000..f9c1731 --- /dev/null +++ b/swarm_gpt/data/presets/Fearless2 | 10 | 20260610_161527/trajectory.csv @@ -0,0 +1,1951 @@ 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Summer | 20 | 20260430_062211/waypoints.npy and b/swarm_gpt/data/presets/Fearless2 | 10 | 20260610_161527/waypoints.npy differ diff --git a/swarm_gpt/data/presets/Fearless2 | 9 | 20260610_192332/history.json b/swarm_gpt/data/presets/Fearless2 | 9 | 20260610_192332/history.json new file mode 100644 index 0000000..0f8c665 --- /dev/null +++ b/swarm_gpt/data/presets/Fearless2 | 9 | 20260610_192332/history.json @@ -0,0 +1 @@ +[{"role": "system", "content": "You choreograph drone swarms using motion primitives. Choose primitives that match the song's\nstructural sections; respect timing, collision, and bounds. Coordinates are integer cm.\n\nYou have creative freedom over WHEN the swarm moves. You do NOT need to choreograph every bar\nor beat \u2014 that produces busy, repetitive motion. Instead vary the rhythm of your choreography:\ncluster moves where the music is active, let the swarm hold a pose and breathe through calmer\npassages, and avoid a predictable \"something every bar\" cadence. Uneven, musically-motivated\nspacing reads as intentional and expressive; mechanical regularity does not.\n"}, {"role": "user", "content": "Plan a choreography for 9 drones to \"Fearless2\" at 109 BPM.\n\n\n segment 1: \"verse\" (0.00s - 17.35s) \u2014 8 bars \u00d7 4 beats\n segment 2: \"chorus\" (17.35s - 34.81s) \u2014 8 bars \u00d7 4 beats\n segment 3: \"chorus\" (34.81s - 35.00s) \u2014 1 bars \u00d7 1 beats\nTotal addressable beats: 64.\nKeys take the form sbt (1-indexed): segment, then bar within that segment,\nthen beat within that bar. Any specific keys shown in the example below illustrate FORMAT\nONLY \u2014 choose keys from THIS song's actual segment/bar/beat counts above.\n\n\n\nPer-2-bar audio features. Each line is \"key: rms / centroid\", both normalized to [0, 1].\nThe key is the FIRST beat of the 2-bar window in s#b#t# notation.\n- rms ~ loudness (1.0 = song's loudest moment)\n- centroid ~ brightness (1.0 = brightest; high = treble cymbals/vocals/leads, low = bass/kicks)\nUse the contrast to choose primitives that match the moment. Examples:\n- rising rms across adjacent windows \u2192 a build-up; favor accumulating motion\n- high rms + low centroid \u2192 bass-driven; bigger vertical moves, slow rotations\n- high rms + high centroid \u2192 treble-driven; fast rotations, helices, waves\n- low rms regardless of centroid \u2192 hold, breathe, minimal new primitives\n- BEAT DROP \u2192 the first downbeat of a NEW segment (a sb1t1 key) whose rms AND centroid\n are high \u2014 usually a jump up from the bars just before it. The segment boundary matters more\n than the raw peak: a build-up can spike rms/centroid too, but the real drop lands on the\n downbeat where the new section begins. Treat these as the biggest moments in the show.\ns1b1t1: 0.49 / 0.84\ns1b3t1: 0.47 / 1.00\ns1b5t1: 0.44 / 0.90\ns1b7t1: 1.00 / 0.74\ns2b1t1: 0.99 / 0.71\ns2b3t1: 0.98 / 0.78\ns2b5t1: 0.98 / 0.76\ns2b7t1: 0.99 / 0.67\ns3b1t1: 0.99 / 0.66\n\n\n\ncount: 9\nstart_cm: [[110, -200, 100], [110, -100, 100], [110, 0, 100], [110, 100, 100], [110, 200, 100], [-110, -200, 100], [-110, -100, 100], [-110, 0, 100], [-110, 100, 100]]\nbounds_cm: lower=[-220, -270, 25] upper=[220, 270, 170]\n\n\n\n- REQUIRED keys (must emit one action list at each): s1b1t1, s1b5t1, s2b1t1, s2b5t1, s3b1t1\n These are a SPARSE skeleton \u2014 roughly one anchor every few bars plus each segment's\n opening \u2014 not every bar. They are the floor, not the target.\n- OPTIONAL keys: every other sbt in the song's address space. Add them only\n where the music genuinely calls for an accent \u2014 a build, a hit, a section change. It is good\n and expected to leave many bars with no action: do NOT try to cover every bar. Vary the\n spacing between actions so the motion feels phrased, not metronomic.\n- Drones hold or continue the previous primitive between emitted actions. Holding a pose\n through a calm passage is a valid, expressive choice \u2014 not a gap to be filled.\n- Formation primitives (form_circle, form_star, form_cone) snap into shape quickly and then\n sit still. A long hold after a formation reads as lifeless on energetic passages \u2014 prefer\n to follow up on a later bar (often the next bar or two later) with a continuation that\n keeps the swarm alive: rotate preserves the formation while turning it, move_z lifts or\n lowers it, twister/helix/spiral_speed evolve it into something new. Omit the follow-up\n only when the music genuinely wants stillness (intro/outro tails, breakdowns).\n- No two drones occupy the same position at the same moment.\n- Stay within bounds_cm; z > 0.\n- Match motion to music: dense, energetic sections (chorus, drop, bridge) get bolder\n primitives; intros and outros stay sparse and gentle.\n- AT A BEAT DROP (see song_dynamics), do NOT answer with a static formation \u2014 a drop that\n resolves into the swarm quietly gathering into a circle is the most underwhelming thing you\n can do. The drop is the payoff: hit it with big, FAST, full-swarm MOTION. Favor high-speed\n multi-step primitives \u2014 spiral_speed with a large degrees (270-360) and radius_increase 2-3,\n a fast twister (omega_times_ten 14-20), a tall helix, or a large-angle rotate (90+ deg) \u2014\n so the swarm visibly explodes into movement on the downbeat. If the section needs a shape,\n snap it in a bar or two BEFORE the drop so the drop itself is spent on motion, not arriving.\n- One entry's \"actions\" array can hold ONE or MORE actions. Two valid stacking patterns:\n 1. Disjoint drone subsets \u2014 different drones, different shapes, coexisting.\n IMPORTANT: the two subsets MUST end up at different points in space or they will\n collide. Separating by height alone needs a LARGE gap: the vertical collision\n envelope is 60cm, so stacked formations must differ in z by at least 70cm, preferably larger to allow for movement.\n A smaller gap (or same x/y footprint) makes the drones jitter. Otherwise separate\n by radius or x/y center.\n 2. Formation + motion on overlapping drones \u2014 formation snaps in, motion takes over.\n The system handles the handoff automatically.\n Do NOT stack two formations on overlapping drones (e.g. form_star + form_circle on the\n same drones); the result is undefined.\n Do NOT stack two formations in the same point in space on different subsets \n (e.g. form_circle with different radii but same center); the result is a collision.\n\n\n\nSingle-shot (executes once over the interval until the next emitted action):\n- rotate(angle_deg, axis) \u2014 all drones; axis usually 'z'\n- center([ids]) \u2014 listed drones to formation around their centroid\n- move_z([ids], delta_cm) \u2014 vertical shift; aim for |delta_cm| ~72 so the motion is visibly noticeable\n- form_circle([ids], radius_cm, z_coord_cm, time_to_finish_s)\n radius_cm: circle radius; the system enforces a minimum based on drone count and spacing.\n z_coord_cm: height of the circle in cm (e.g. 80, 100, 120).\n time_to_finish_s: seconds to arrive; pick ~1s for snappy hits, \u2265half the interval for slow blooms.\n The system clamps to physics minimum if your value is too small.\n When stacking two disjoint form_circle calls vertically, their z_coord_cm values MUST differ\n by at least 60 (e.g. 80 and 140): a smaller gap puts the drones inside each other's collision\n envelope and they will jitter instead of holding the rings.\n- form_star(height_cm, spacing 40-80, delta_radius 50-100, time_to_finish_s)\n time_to_finish_s: seconds to arrive in the star; same guidance as form_circle.\n- form_cone(delta_height 30-80, spacing 40-80, is_inverted 0|1, time_to_finish_s)\n time_to_finish_s: seconds to arrive in the cone; same guidance as form_circle.\n- swap(id1, id2)\n- move(x_cm, y_cm, z_cm, drone_id) \u2014 absolute position for one drone\n\nMulti-step (plays out across the interval; pick steps in [2, 3]):\n- spiral(steps, height_cm)\n- spiral_speed(steps, height_cm, degrees 0-360, radius_increase 1-3)\n- helix(steps, delta_height_cm 0-100, height_cm 50-100)\n- twister(steps, omega_times_ten 2-20, z_spacing_cm 5-30)\n- zig_zag(steps, delta_xy_cm, delta_z_cm)\n- wave(steps, height_cm) \u2014 2D standing-wave surface; drones first form a grid then oscillate.\n\n\n\n1) song_mood \u2014 brief mood for \"Fearless2\".\n2) choreography_plan \u2014 sections, primitives chosen, and intensity rationale per segment.\n3) choreography \u2014 sparse map of action keys to action lists.\n\n"}, {"role": "system", "content": "\nInput: song=Sample, drones=3, bpm=120, structure:\n segment 1: \"intro\" (0.00s - 4.00s) \u2014 2 bars x 4 beats\n segment 2: \"chorus\" (4.00s - 12.00s) \u2014 4 bars x 4 beats\nRequired keys: s1b1t1, s2b1t1.\nOutput:\nsong_mood: Steady, building from a soft intro to a confident chorus.\nchoreography_plan: Intro begins with a gentle inward spiral. Chorus opens with a bold\n form_circle on the downbeat, accents the third beat of bar 1 with a rotation, then\n sweeps through a helix at the start of bar 2.\nchoreography:\n s1b1t1: spiral(3, 100)\n s2b1t1: form_circle([1, 2, 3], 120, 100, 1.5)\n s2b1t3: rotate(60, 'z')\n s2b2t1: helix(2, 40, 90)\n END\n\n"}, {"role": "system", "content": "Return JSON only (no markdown). The schema enforces:\n- \"choreography\" is an array of entries, each {\"key\":\"sbt\",\"actions\":[...]}.\n- Every key matches the pattern sbt and is one of the song's valid addresses.\n- All REQUIRED keys are present (as entries); add OPTIONAL keys only where musically warranted,\n leaving many bars empty. Do not repeat a key.\n- \"actions\" is a list of one or more action objects: {\"primitive\":\"name\",\"params\":{...}}.\n Two valid multi-action patterns (both in a single entry's \"actions\" array):\n 1. Disjoint drone subsets \u2014 different drones at different positions in space:\n \"actions\": [\n {\"primitive\":\"form_circle\",\"params\":{\"drone_ids\":[1,2,3,4,5],\"radius_cm\":100,\"z_coord_cm\":80,\"time_to_finish_s\":2.0}},\n {\"primitive\":\"form_circle\",\"params\":{\"drone_ids\":[6,7,8,9,10],\"radius_cm\":100,\"z_coord_cm\":150,\"time_to_finish_s\":2.0}}\n ]\n Subsets MUST occupy different regions to avoid collision. Stacking by z requires a gap of\n at least 60cm (vertical collision envelope); otherwise separate by radius or x/y center.\n 2. Formation + motion on overlapping drones \u2014 formation arrives, then motion takes over:\n \"actions\": [\n {\"primitive\":\"form_star\",\"params\":{\"height_cm\":100,\"min_spacing_cm\":60,\"delta_radius_cm\":80,\"time_to_finish_s\":2.0}},\n {\"primitive\":\"rotate\",\"params\":{\"angle_deg\":60,\"axis\":\"z\"}}\n ]\n The system suppresses form_* hold waypoints automatically \u2014 no extra timing needed.\n Do NOT stack two formations on overlapping drones.\nUse exactly the parameter names from the primitive signature.\n\nCorrect JSON param examples:\n- center([ids]) -> {\"primitive\":\"center\",\"params\":{\"drone_ids\":[1,2,3,4]}}\n- move_z([ids], delta_cm) -> {\"primitive\":\"move_z\",\"params\":{\"drone_ids\":[1,2,3,4],\"delta_cm\":30}}\n- form_cone(delta_height_cm, spacing_cm, is_inverted) -> {\"primitive\":\"form_cone\",\"params\":{\"delta_height_cm\":60,\"spacing_cm\":60,\"is_inverted\":0,\"time_to_finish_s\":2.0}}\n- spiral_speed(steps, height_cm, degrees, radius_increase) -> {\"primitive\":\"spiral_speed\",\"params\":{\"steps\":3,\"height_cm\":100,\"degrees\":180,\"radius_increase\":1}}\n- wave(steps, height_cm) -> {\"primitive\":\"wave\",\"params\":{\"steps\":2,\"height_cm\":80}}\n\nCommon mistakes that will fail syntax checks:\n- Wrong: {\"primitive\":\"form_cone\",\"params\":{\"drone_ids\":[1,2,3],...}} because form_cone has no drone_ids param.\n- Wrong: {\"primitive\":\"spiral_speed\",\"params\":{\"steps\":3,\"angle_deg\":90,...}} because spiral_speed uses 'degrees', not 'angle_deg'.\n- Wrong: {\"primitive\":\"move_z\",\"params\":{\"delta_cm\":30}} because move_z also needs drone_ids.\n- Wrong: {\"primitive\":\"form_star\",\"params\":{\"height_cm\":100,\"min_spacing_cm\":60,\"delta_radius_cm\":80}} because form_star also needs time_to_finish_s.\n"}, {"role": "assistant", "content": "song_mood: \"Confident and bright\\u2014verse builds with controlled spirals, chorus explodes into fast rotational motion, and the final chorus tag sustains a fearless, high-energy swirl.\"\nchoreography_plan: \"Verse: start grounded and cohesive with a gentle circle at the verse downbeat, then lift into a tighter, brighter spiral as the verse energy rises. Chorus: hit the chorus downbeat with a full-swarm fast spiral_speed (motion-first, not static), then transition into a helix/twister blend to keep the formation evolving. Final 1-beat chorus tag: finish with a quick, high-altitude twister burst to feel like a fearless punch-in. Drones are kept within bounds by using formation radii/heights that fit the provided x/y spread and by avoiding overlapping formations on the same drones; motion primitives (spiral_speed/helix/twister/rotate) evolve the same swarm without collision risk.\"\nchoreography:\n s1b1t1: form_circle([1, 2, 3, 4, 5, 6, 7, 8, 9], 120, 90, 1.2)\n s1b3t1: spiral_speed(3, 110, 270, 2)\n s1b5t1: helix(3, 35, 120)\n s1b6t3: rotate(35, 'z')\n s1b7t1: spiral_speed(3, 130, 315, 3)\n s2b1t1: twister(3, 18, 18)\n s2b2t2: rotate(90, 'z')\n s2b3t1: spiral_speed(3, 140, 360, 2)\n s2b5t1: helix(3, -25, 105)\n s2b6t4: zig_zag(3, 55, 18)\n s2b7t1: rotate(-70, 'z')\n s3b1t1: twister(2, 20, 22)\n END"}] \ No newline at end of file diff --git a/swarm_gpt/data/presets/Fearless2 | 9 | 20260610_192332/meta.json b/swarm_gpt/data/presets/Fearless2 | 9 | 20260610_192332/meta.json new file mode 100644 index 0000000..9673374 --- /dev/null +++ b/swarm_gpt/data/presets/Fearless2 | 9 | 20260610_192332/meta.json @@ -0,0 +1 @@ +{"n_drones": 9, "song": "Fearless2", "use_motion_primitives": true} \ No newline at end of file diff --git a/swarm_gpt/data/presets/Fearless2 | 9 | 20260610_192332/trajectory.csv b/swarm_gpt/data/presets/Fearless2 | 9 | 20260610_192332/trajectory.csv new file mode 100644 index 0000000..4ade265 --- /dev/null +++ b/swarm_gpt/data/presets/Fearless2 | 9 | 20260610_192332/trajectory.csv @@ -0,0 +1,1951 @@ 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0000000..44b46d1 --- /dev/null +++ b/swarm_gpt/data/presets/On & On | 10 | 20260610_101328/history.json @@ -0,0 +1 @@ +[{"role": "system", "content": "You choreograph drone swarms using motion primitives. Choose primitives that match the song's\nstructural sections; respect timing, collision, and bounds. Coordinates are integer cm.\n\nYou have creative freedom over WHEN the swarm moves. You do NOT need to choreograph every bar\nor beat \u2014 that produces busy, repetitive motion. Instead vary the rhythm of your choreography:\ncluster moves where the music is active, let the swarm hold a pose and breathe through calmer\npassages, and avoid a predictable \"something every bar\" cadence. Uneven, musically-motivated\nspacing reads as intentional and expressive; mechanical regularity does not.\n"}, {"role": "user", "content": "Plan a choreography for 10 drones to \"On & On\" at 87 BPM.\n\n\n segment 1: \"verse\" (0.25s - 33.37s) \u2014 12 bars \u00d7 4 beats\n segment 2: \"chorus\" (33.37s - 55.44s) \u2014 8 bars \u00d7 4 beats\n segment 3: \"chorus\" (55.44s - 60.00s) \u2014 2 bars \u00d7 4 beats\nTotal addressable beats: 87.\nKeys take the form sbt (1-indexed): segment, then bar within that segment,\nthen beat within that bar. Any specific keys shown in the example below illustrate FORMAT\nONLY \u2014 choose keys from THIS song's actual segment/bar/beat counts above.\n\n\n\nPer-2-bar audio features. Each line is \"key: rms / centroid\", both normalized to [0, 1].\nThe key is the FIRST beat of the 2-bar window in s#b#t# notation.\n- rms ~ loudness (1.0 = song's loudest moment)\n- centroid ~ brightness (1.0 = brightest; high = treble cymbals/vocals/leads, low = bass/kicks)\nUse the contrast to choose primitives that match the moment. Examples:\n- rising rms across adjacent windows \u2192 a build-up; favor accumulating motion\n- high rms + low centroid \u2192 bass-driven; bigger vertical moves, slow rotations\n- high rms + high centroid \u2192 treble-driven; fast rotations, helices, waves\n- low rms regardless of centroid \u2192 hold, breathe, minimal new primitives\ns1b1t1: 0.23 / 0.15\ns1b3t1: 0.34 / 0.29\ns1b5t1: 0.53 / 1.00\ns1b7t1: 0.50 / 0.67\ns1b9t1: 0.55 / 0.91\ns1b11t1: 0.52 / 0.77\ns2b1t1: 0.68 / 0.77\ns2b3t1: 0.61 / 0.97\ns2b5t1: 0.67 / 0.58\ns2b7t1: 0.60 / 0.84\ns3b1t1: 0.92 / 0.82\n\n\n\ncount: 10\nstart_cm: [[60, -200, 100], [60, -100, 100], [60, 0, 100], [60, 100, 100], [60, 200, 100], [-60, -200, 100], [-60, -100, 100], [-60, 0, 100], [-60, 100, 100], [-60, 200, 100]]\nbounds_cm: lower=[-220, -270, 25] upper=[220, 270, 170]\n\n\n\n- REQUIRED keys (must emit one action list at each): s1b1t1, s1b5t1, s1b9t1, s2b1t1, s2b5t1, s3b1t1\n These are a SPARSE skeleton \u2014 roughly one anchor every few bars plus each segment's\n opening \u2014 not every bar. They are the floor, not the target.\n- OPTIONAL keys: every other sbt in the song's address space. Add them only\n where the music genuinely calls for an accent \u2014 a build, a hit, a section change. It is good\n and expected to leave many bars with no action: do NOT try to cover every bar. Vary the\n spacing between actions so the motion feels phrased, not metronomic.\n- Drones hold or continue the previous primitive between emitted actions. Holding a pose\n through a calm passage is a valid, expressive choice \u2014 not a gap to be filled.\n- Formation primitives (form_circle, form_star, form_cone) snap into shape quickly and then\n sit still. A long hold after a formation reads as lifeless on energetic passages \u2014 prefer\n to follow up on a later bar (often the next bar or two later) with a continuation that\n keeps the swarm alive: rotate preserves the formation while turning it, move_z lifts or\n lowers it, twister/helix/spiral_speed evolve it into something new. Omit the follow-up\n only when the music genuinely wants stillness (intro/outro tails, breakdowns).\n- No two drones occupy the same position at the same moment.\n- Stay within bounds_cm; z > 0.\n- Match motion to music: dense, energetic sections (chorus, drop, bridge) get bolder\n primitives; intros and outros stay sparse and gentle.\n- One entry's \"actions\" array can hold ONE or MORE actions. Two valid stacking patterns:\n 1. Disjoint drone subsets \u2014 different drones, different shapes, coexisting.\n IMPORTANT: the two subsets MUST end up at different points in space or they will\n collide. Separate by z (height), radius, or x/y center.\n 2. Formation + motion on overlapping drones \u2014 formation snaps in, motion takes over.\n The system handles the handoff automatically.\n Do NOT stack two formations on overlapping drones (e.g. form_star + form_circle on the\n same drones); the result is undefined.\n Do NOT stack two formations in the same point in space on different subsets \n (e.g. form_circle with different radii but same center); the result is a collision.\n\n\n\nSingle-shot (executes once over the interval until the next emitted action):\n- rotate(angle_deg, axis) \u2014 all drones; axis usually 'z'\n- center([ids]) \u2014 listed drones to formation around their centroid\n- move_z([ids], delta_cm) \u2014 vertical shift; aim for |delta_cm| ~72 so the motion is visibly noticeable\n- form_circle([ids], radius_cm, time_to_finish_s)\n time_to_finish_s: seconds to arrive; pick ~1s for snappy hits, \u2265half the interval for slow blooms.\n The system clamps to physics minimum if your value is too small.\n- form_star(height_cm, spacing 40-80, delta_radius 50-100, time_to_finish_s)\n time_to_finish_s: seconds to arrive in the star; same guidance as form_circle.\n- form_cone(delta_height 30-80, spacing 40-80, is_inverted 0|1, time_to_finish_s)\n time_to_finish_s: seconds to arrive in the cone; same guidance as form_circle.\n- swap(id1, id2)\n- move(x_cm, y_cm, z_cm, drone_id) \u2014 absolute position for one drone\n\nMulti-step (plays out across the interval; pick steps in [2, 3]):\n- spiral(steps, height_cm)\n- spiral_speed(steps, height_cm, degrees 0-360, radius_increase 1-3)\n- helix(steps, delta_height_cm 0-100, height_cm 50-100)\n- twister(steps, omega_times_ten 2-20, z_spacing_cm 5-30)\n- zig_zag(steps, delta_xy_cm, delta_z_cm)\n- wave(steps, height_cm, mu_pairs, a_mu, b_mu) \u2014 2D wave surface; equation:\nThe disturbance \\(u(s, t)\\) at any point \\(s = (s_1, s_2) \\in [0, a] \\times [0, b]\\) \nat time \\(t\\) is governed by:\n\\[\nc^2 \\nabla^2 u(s, t) = \\frac{\\partial^2 u(s, t)}{\\partial t^2},\n\\]\nwhere \\( \\nabla^2 \\) is the spatial Laplacian and \\(c > 0\\) is the wave speed. \nThe solution:\n\\[\nu(s, t) = \\sum_{(\\mu_1, \\mu_2)} a_{\\mu_1 \\mu_2} \\sin \\left( \\frac{\\mu_1 \\pi s_1}{a} \\right) \n\\sin \\left( \\frac{\\mu_2 pi s_2}{b} \\right) \\sin(\\omega_{\\mu_1 \\mu_2} t) \n+ b_{\\mu_1 \\mu_2} \\sin \\left( \\frac{\\mu_1 \\pi s_1}{a} \\right) \n\\sin \\left( \\frac{\\mu_2 \\pi s_2}{b} \\right) \\cos(\\omega_{\\mu_1 \\mu_2} t),\n\\]\n\nwith frequencies:\n\n\\[\n\\omega_{\\mu_1 \\mu_2}^2 = c^2 \\pi^2 \\left( \\frac{\\mu_1^2}{a^2} + \\frac{\\mu_2^2}{b^2} \\right).\n\\]\n\n\n\n1) song_mood \u2014 brief mood for \"On & On\".\n2) choreography_plan \u2014 sections, primitives chosen, and intensity rationale per segment.\n3) choreography \u2014 sparse map of action keys to action lists.\n\n"}, {"role": "system", "content": "\nInput: song=Sample, drones=3, bpm=120, structure:\n segment 1: \"intro\" (0.00s - 4.00s) \u2014 2 bars x 4 beats\n segment 2: \"chorus\" (4.00s - 12.00s) \u2014 4 bars x 4 beats\nRequired keys: s1b1t1, s2b1t1.\nOutput:\nsong_mood: Steady, building from a soft intro to a confident chorus.\nchoreography_plan: Intro begins with a gentle inward spiral. Chorus opens with a bold\n form_circle on the downbeat, accents the third beat of bar 1 with a rotation, then\n sweeps through a helix at the start of bar 2.\nchoreography:\n s1b1t1: spiral(3, 100)\n s2b1t1: form_circle([1, 2, 3], 120, 1.5)\n s2b1t3: rotate(60, 'z')\n s2b2t1: helix(2, 40, 90)\n END\n\n"}, {"role": "system", "content": "Return JSON only (no markdown). The schema enforces:\n- \"choreography\" is an array of entries, each {\"key\":\"sbt\",\"actions\":[...]}.\n- Every key matches the pattern sbt and is one of the song's valid addresses.\n- All REQUIRED keys are present (as entries); add OPTIONAL keys only where musically warranted,\n leaving many bars empty. Do not repeat a key.\n- \"actions\" is a list of one or more action objects: {\"primitive\":\"name\",\"params\":{...}}.\n Two valid multi-action patterns (both in a single entry's \"actions\" array):\n 1. Disjoint drone subsets \u2014 different drones at different positions in space:\n \"actions\": [\n {\"primitive\":\"form_circle\",\"params\":{\"drone_ids\":[1,2,3,4,5],\"radius_cm\":100,\"time_to_finish_s\":2.0}},\n {\"primitive\":\"form_circle\",\"params\":{\"drone_ids\":[6,7,8,9,10],\"radius_cm\":180,\"time_to_finish_s\":2.0}}\n ]\n Subsets MUST occupy different regions (different z, different radii, etc.) to avoid collision.\n 2. Formation + motion on overlapping drones \u2014 formation arrives, then motion takes over:\n \"actions\": [\n {\"primitive\":\"form_star\",\"params\":{\"height_cm\":100,\"min_spacing_cm\":60,\"delta_radius_cm\":80,\"time_to_finish_s\":2.0}},\n {\"primitive\":\"rotate\",\"params\":{\"angle_deg\":60,\"axis\":\"z\"}}\n ]\n The system suppresses form_* hold waypoints automatically \u2014 no extra timing needed.\n Do NOT stack two formations on overlapping drones.\nUse exactly the parameter names from the primitive signature.\n\nCorrect JSON param examples:\n- center([ids]) -> {\"primitive\":\"center\",\"params\":{\"drone_ids\":[1,2,3,4]}}\n- move_z([ids], delta_cm) -> {\"primitive\":\"move_z\",\"params\":{\"drone_ids\":[1,2,3,4],\"delta_cm\":30}}\n- form_cone(delta_height_cm, spacing_cm, is_inverted) -> {\"primitive\":\"form_cone\",\"params\":{\"delta_height_cm\":60,\"spacing_cm\":60,\"is_inverted\":0,\"time_to_finish_s\":2.0}}\n- spiral_speed(steps, height_cm, degrees, radius_increase) -> {\"primitive\":\"spiral_speed\",\"params\":{\"steps\":3,\"height_cm\":100,\"degrees\":180,\"radius_increase\":1}}\n- wave(steps, height_cm, mu_pairs, a_mu, b_mu) -> {\"primitive\":\"wave\",\"params\":{\"steps\":2,\"height_cm\":80,\"mu_pairs\":[[0,0],[1,0]],\"a_mu\":[0.5,0.25],\"b_mu\":[0.2,0.1]}}\n\nCommon mistakes that will fail syntax checks:\n- Wrong: {\"primitive\":\"form_cone\",\"params\":{\"drone_ids\":[1,2,3],...}} because form_cone has no drone_ids param.\n- Wrong: {\"primitive\":\"spiral_speed\",\"params\":{\"steps\":3,\"angle_deg\":90,...}} because spiral_speed uses 'degrees', not 'angle_deg'.\n- Wrong: {\"primitive\":\"move_z\",\"params\":{\"delta_cm\":30}} because move_z also needs drone_ids.\n- Wrong: {\"primitive\":\"form_star\",\"params\":{\"height_cm\":100,\"min_spacing_cm\":60,\"delta_radius_cm\":80}} because form_star also needs time_to_finish_s.\n"}, {"role": "assistant", "content": "song_mood: \"Persistent, hypnotic groove that builds from a bright, rising verse into a more kinetic, treble-driven chorus\\u2014then sustains momentum into the final chorus with tighter, faster motion.\"\nchoreography_plan: \"Segment 1 (verse, 12 bars): Start with a bright, mid-height circle to establish the loop. As rms rises and centroid stays high around later verse windows, shift into a star and then a gentle helix/spiral evolution to keep the swarm \\u201con\\u201d without becoming chaotic. Use a vertical lift near the verse peak to emphasize the build into the chorus.\\n\\nSegment 2 (chorus, 8 bars): Higher rms with mixed centroid drives bolder, more treble-leaning motion: snap into a larger circle, then rotate and evolve with a helix/spiral_speed so the formation keeps moving. Keep motion phrased by alternating between formation hits and short rotational/vertical accents.\\n\\nSegment 3 (chorus tail, 2 bars): Very high rms near the end: finish with a compact, energetic star and a final rotation to lock into the track\\u2019s repeating hook. Maintain bounds and avoid collisions by using formation primitives on the full set and keeping subsequent motions as rotations/evolutions rather than new absolute placements.\"\nchoreography:\n s1b1t1: form_circle([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 120, 1.2); move_z([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 36)\n s1b5t1: form_star(120, 60, 70, 1.1); rotate(35, 'z')\n s1b7t1: helix(3, 30, 120)\n s1b9t1: spiral_speed(3, 140, 180, 2)\n s1b11t1: move_z([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], -54); rotate(-25, 'z')\n s2b1t1: form_circle([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 160, 1.0); twister(3, 14, 18)\n s2b3t1: rotate(60, 'z')\n s2b5t1: form_cone(70, 55, 0, 1.1); move_z([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 72)\n s2b7t1: zig_zag(3, 55, -18)\n s3b1t1: form_star(150, 65, 85, 1.0); rotate(-70, 'z')\n END"}] \ No newline at end of file diff --git a/swarm_gpt/data/presets/On & On | 10 | 20260610_101328/meta.json b/swarm_gpt/data/presets/On & On | 10 | 20260610_101328/meta.json new file mode 100644 index 0000000..a6687ab --- /dev/null +++ b/swarm_gpt/data/presets/On & On | 10 | 20260610_101328/meta.json @@ -0,0 +1 @@ +{"n_drones": 10, "song": "On & On", "use_motion_primitives": true} \ No newline at end of file diff --git a/swarm_gpt/data/presets/On & On | 10 | 20260610_101328/trajectory.csv b/swarm_gpt/data/presets/On & On | 10 | 20260610_101328/trajectory.csv new file mode 100644 index 0000000..2fa8449 --- /dev/null +++ b/swarm_gpt/data/presets/On & On | 10 | 20260610_101328/trajectory.csv @@ -0,0 +1,3201 @@ 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Ya! | 6 | 20250724_114032/history.json deleted file mode 100644 index 14eb646..0000000 --- a/swarm_gpt/data/presets/OutKast - Hey Ya! | 6 | 20250724_114032/history.json +++ /dev/null @@ -1 +0,0 @@ -[{"role": "system", "content": "You're a helpful assistant that will help a human choreograph a dance for a swarm of small drones."}, {"role": "user", "content": "Generate the trajectories for a choreography for 6 drones dancing to the music of the song OutKast - Hey Ya!. The trajectories are defined by coordinate waypoints that the drones fly to. Each movement can be defined by multiple template functions, which are explained below.\n# Information on the song: - Name: OutKast - Hey Ya! - Number of beats: 12 - Beat times in 100ms: {1: 2, 2: 25, 3: 51, 4: 78, 5: 108, 6: 131, 7: 168, 8: 191, 9: 214, 10: 251, 11: 289, 12: 327} - Beat intervals in 100ms: {1: 2, 2: 23, 3: 26, 4: 26, 5: 30, 6: 23, 7: 38, 8: 23, 9: 23, 10: 38, 11: 38} - Beat amplitude in dBFS: {1: -8, 2: -10, 3: -10, 4: -11, 5: -10, 6: -8, 7: -8, 8: -10, 9: -8, 10: -7, 11: -11, 12: -10} - Beat novelty in %: {1: 89, 2: 54, 3: 69, 4: 89, 5: 53, 6: 79, 7: 90, 8: 50, 9: 77, 10: 85, 11: 80, 12: 100} - Beat chords: {1: 'C', 2: 'C', 3: 'G', 4: 'Em', 5: 'C', 6: 'Em', 7: 'Em', 8: 'C', 9: 'C', 10: 'Em', 11: 'C', 12: 'Em'}\n# Information on the drones: - Number: 6 - Drones must not collide with each other. No two drones must be at the same coordinates at the same beat. - Starting positions: [[-50, -50, 100], [-100, 0, 100], [-50, 50, 100], [50, -50, 100], [100, 0, 100], [50, 50, 100]] - Maximum step distance between waypoints in cm: {1: 20, 2: 226, 3: 264, 4: 264, 5: 302, 6: 228, 7: 376, 8: 228, 9: 226, 10: 376, 11: 378, 12: 378} - Lower limit for generated waypoints: [-2000. -2000. -2010.] - Upper Limit for generated waypoints: [2000 2000 2000]\n# Information on the environment: - The drones are limited to [-200, 200] in x, [-200, 200] in y, [0, 200] in z - The drones must not touch the ground, i.e. their z coordinate must always be greater than 0 \n# Information on the choreography: - The drone choreography must have 12 waypoints, one for each beat. - Each drone must move at least once during the song. - Movements must depend on the beats of the song. If the beats are loud and novel, all drones must move. - If possible, coordinates for the drones should be symmetrical at each beat. - Successive waypoints should form patterns for single drones and for the whole swarm - Coordinates are given in cm. A drone flying at 1m height will have a z coordinate of 100. - The difference between two waypoints must be less than the maximum step distance in each dimension.\n E.g. if the maximum step distance between beat 3 and 4 is 50, the difference between the x, y,\n z coordinates of the waypoints at beat 3 and 4 must be less than 50 in each dimension.\n\n# Information on template functions: Make sure to limit actions to each drone once per time step. So if you choose swap for (1, 2) then you can only choose swap(3, 4) or two seperate move functions for each drone 2: time dependent template functions ( For such functions we need only one function call for one time step) - rotate:\n takes 2 parameters ( angle, axis ), and rotates all drones by 'angle' along 'axis'\n #angle is a float between [0, 360]\n #axis is one of 'x', 'y', or 'z', but for now always rotate around 'z'\n eg\n rotate(30, z)\n4: variadic functions ( such function takes a list with drone ids as arguments ) - center:\n Takes a list of drone IDs as input. It calculates the geometric center of the current positions of these specified drones and then moves all the drones with the matching IDs to this calculated center position.\n eg\n center([1, 2, 3])\n- move_z:\n Takes a list of drone IDs as input, and distance. It moves all the drones with the matching IDs along z axis by xcm. if distance is positive, it moves them up, otherwise down\n eg\n move_z([1, 2, 3], 10)\n- form_circle\n Takes a list of drone IDs as input, and z coordinate ( height ) for the circle formation. Radius of circle is decided by number of drones in list argument. So bigger circle for more drones.\n # height has to be greater than zero\n eg \n # Cone formation as 4 drones are at 150cm height, and 3 drones are at 50cm height with smaller radius as less drones.\n form_circle( [1, 2, 3, 4], 150 ), form_circle( [5, 6, 7], 50 )\n- form_star\n Takes 3 parameters ( height, spacing, delta_radius ) and creates a star in the z-plane, which is defined by 2 concentric circles in staggered angles with different radii. \n Height corresponds to the height of the star. Spacing corresponds to the minimum spacing between drones, where a higher spacing means larger star. \n Delta_radius corresponds to the difference of the 2 radii, where a larger delta_radius means a more spread out star\n #height has to be greater than zero\n #spacing is an integer between [40, 80]\n #delta_radius is an integer between [50 100]\n eg \n form_star( 120, 60, 50 )\n- form_cone\n Takes 3 parameters ( delta_height, spacing, is_inverted ) and creates a cone using concentric circles of different radii. Each layer has 4 additional drones.\n Delta_height is the difference in height between layers. A larger delta_height corresponds to a taller cone.\n Spacing is the minimum_spacing between drones of a layer. A larger spacing means a larger circle radius and wider cone.\n Is_inverted is a boolean value (an integer of 0 or 1) that determines if the cone is inverted or not. Inverted cones are more expressive and dynamic. \n #delta_height is an integer between [30, 80]\n #spacing is an integer between [40, 80]\n #is_inverted is an integer with the values 0 or 1\n eg \n form_star( 120, 60, 50 )\n\n\n5: multi time dependent functions ( For such functions we need only one function call for multiple time steps)\n Any multi-time dependent function needs to be followed by the 'PLAN' keyword for the number of steps minus 1. This means, for functions that span over `step` time steps, the function call must be followed by `step - 1` occurrences of 'PLAN'.\n These functions must be initiated `s - step + 1` beats before the last beat, where `s` is the total number of beats. This ensures that the multi-time function completes before the song ends.\n\n**Important Timing Constraint**: If you have a multi-time function that takes 3 steps, it must be called at least 2 steps before the last beat to allow for the necessary 'PLAN' steps. For example, with a 22-beat song, a 3-step function should be called by the 20th beat, followed by 'PLAN' for the 21st and 22nd beats. - spiral:\n takes 2 parameters ( steps, height ), and makes all drones move in a outward spiral pattern with the calculate radius ( from no of drones ), and number of steps at height h\n\n #steps is an integer between [2, 3]\n #height is an integer between [0, 200] that determines the height of the spiral\n #Since spiral is a multi time dependent function, it should be falled by 'PLAN' keyword for the number of steps -1 times, so if steps is 3, then spiral is called once, and PLAN follows twice in output\n # spiral needs to be called atleast 's' steps before last timestep as 's' PLAN follow the spiral call\n eg:\n 1: spiral(3, 50 )\n 2: PLAN\n 3: PLAN\n\n- spiral_speed:\n Takes 4 parameters (steps, height, speed, radius_increase), and has the same effect as spiral. The difference is that spiral_speed increases the radius of the spiral by radius_increase over the number of steps. In addition,\n speed defines the angle by which the drones rotate around the center. Lower speeds results in slower drone motions.\n # Steps is an integer between [2, 3]\n # height is an integer between [0, 200] that determines the height of the spiral\n # speeds is an integer between [0, 360] that defines the angle by which the drones rotate around the center. Very low speeds are in the range of 0 to 90, fast speeds are in the range of 100 to 360\n # radius_increase is a float between [1, 3] that increases the radius of the spiral over the number of steps\n\n- helix:\n takes 2 parameters ( steps, delta_h, height ), and makes all drones move in a helix pattern with the calculate radius ( from no of drones ), and number of steps.\n\n #steps is an integer between [2, 3]\n #delta_h is an integer between [0, 100], that changes the height of drones in the helix pattern per step\n #height is between [50, 100] as helix rises up with steps, lets be safe.\n #Since helix is a multi time dependent function, it should be falled by 'PLAN' keyword for the number of steps -1 times, so if steps is 3, then helix is called once, and PLAN follows twice in output\n eg:\n 1: helix(3, 20, 50)\n 2: PLAN\n 3: PLAN\n\n- twister:\n takes 3 parameter ( steps, omega, z_spacing ), and makes all drones move into a upside down cone pattern and rotate at the speed specified by omega, for the number of steps specified.\n Z_spacing corresponds to the height difference between neighbouring drones, where a smaller z_spacing corresponds to a more compact cone.\n\n #steps is an integer between [2, 3]\n #omega is an integer between [2, 20] with a higher omega corresponding to a more dynamic performance.\n #z_spacing is an integer between [5, 30]\n #Since twister is a multi time dependent function, it should be falled by 'PLAN' keyword for the number of steps -1 times, so if steps is 3, then conic_rotate is called once, and PLAN follows twice in output\n eg:\n 1: twister(3, 20, 10)\n 2: PLAN\n 3: PLAN\n\n- zig_zag:\n Moves all drones in a zigzag pattern. Takes 3 parameters\n Parameters: `steps` (int), `delta` (int) - the horizontal displacement per step, `delta_h` (int) - the vertical displacement per step.\n eg:\n 4: zig_zag(3, 20, 10)\n 5: PLAN\n 6: PLAN\n \n- wave:\n Moves all drones in a three-dimensional wave propagating through a two-dimensional rectangular elastic surface with bounded edges. The disturbance at any point on the surface is defined by its parameters in space and time, and the wave propagation is governed by the wave equation with a speed `c > 0`. Takes 5 parameters.\n Parameters:\n - `steps` (int): The number of discretization points.\n - `height` (float): The desired height of drones above the surface.\n - `mu_pairs` (list of list): List of \\((\u03bc1, \u03bc2)\\) pairs that define the spatial frequencies.\n - `a\u00b51\u00b52` (list of floats): List of amplitude coefficients corresponding to each \\((\u03bc1, \u03bc2)\\) pair for the sine component.\n - `b\u00b51\u00b52` (list of floats): List of amplitude coefficients corresponding to each \\((\u03bc1, \u03bc2)\\) pair for the cosine component.\n eg:\n 4: wave( 4, 10, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4] )\n 5: PLAN\n 6: PLAN\n 7: PLAN\n latex equn for the concerned wave equation:\n The disturbance \\(u(s, t)\\) at any point \\(s = (s_1, s_2) \\in [0, a] \\times [0, b]\\) \nat time \\(t\\) is governed by:\n\\[\nc^2 \\nabla^2 u(s, t) = \\frac{\\partial^2 u(s, t)}{\\partial t^2},\n\\]\nwhere \\( \\nabla^2 \\) is the spatial Laplacian and \\(c > 0\\) is the wave speed. \nThe solution:\n\\[\nu(s, t) = \\sum_{(\\mu_1, \\mu_2)} a_{\\mu_1 \\mu_2} \\sin \\left( \\frac{\\mu_1 \\pi s_1}{a} \\right) \n\\sin \\left( \\frac{\\mu_2 pi s_2}{b} \\right) \\sin(\\omega_{\\mu_1 \\mu_2} t) \n+ b_{\\mu_1 \\mu_2} \\sin \\left( \\frac{\\mu_1 \\pi s_1}{a} \\right) \n\\sin \\left( \\frac{\\mu_2 \\pi s_2}{b} \\right) \\cos(\\omega_{\\mu_1 \\mu_2} t),\n\\]\n\nwith frequencies:\n\n\\[\n\\omega_{\\mu_1 \\mu_2}^2 = c^2 \\pi^2 \\left( \\frac{\\mu_1^2}{a^2} + \\frac{\\mu_2^2}{b^2} \\right).\n\\]\n\nPLAN can also be used to indicate that the drones should maintain their current position for the next beat, so after forming cone formation, you can use PLAN to maintain the formation for next beat. Using `PLAN` also counts as part of the waypoints for the drones, so you can't have multi-time dependent functions as the last waypoint as we need to have `PLAN` after them in future steps.\n# Patterns: - Drones must fly in geometric patterns and shapes - Drones must fly in tight formations - The formations must be symmetrical if possible - Drones should move up and down in altitude multiple times for a 3D effect - Example patterns include spirals, triangles, squares, circles, lines\n# Response structure First, describe the emotions and mood associated with the song OutKast - Hey Ya!. Then, give a description of the dominant chords of the song. Is it a major or minor cord? What does that mean for the mood? Next, give a specific description of the choreography you want to create. What patterns do you want the drones to fly? Why do you use these particular patterns? Last, fill out the individual waypoints for each drone. Adapt the distance and position of waypoints to the amplitude of the song's beats. Loud beats lead to larger movements, while soft beats lead to smaller movements. Furthermore, take into account the dominant chords of the song and the emotions and mood associated with these chords. Try to use a variety of functional options to design an amazing geometrical choreography. Make sure to only do motion planning per beat, so in otherwords motion planning steps should be limited to 12,and end yaml output with END like in examples below. You can use multiple beat functions for more than 3 timestep as well like spiral(), zig_zag(), wave(), helix(). Only do complex primtives. Adjust change in height accordingly like do them for 5 steps if you have to, and make height increments small. Limit to at max 4 primitives. Between any two primitives, there must be a extra PLAN which consumes a timestep so that there is enough time for transition.\n"}, {"role": "system", "content": "# Example prompts and responses - Example 1:\n Prompt:\n # Information on the song:\n - Name: r\u00eaverie\n - Number of beats: 22\n - Beat times in 100ms: {{1: 7, 2: 20, 3: 29, 4: 41, 5: 55, 6: 66, 7: 80, 8: 96, 9: 104, 10: 112, 11: 119, 12: 128, 13: 137, 14: 147, 15: 157, 16: 167, 17: 187, 18: 196, 19: 210, 20: 221, 21: 232, 22: 242}}\n - Beat intervals in 100ms: {{1: 7, 2: 13, 3: 9, 4: 12, 5: 14, 6: 11, 7: 13, 8: 16, 9: 9, 10: 7, 11: 8, 12: 8, 13: 9, 14: 10, 15: 10, 16: 10, 17: 20, 18: 8, 19: 14, 20: 12, 21: 10}}\n - Beat amplitude in dBFS: {{1: -53, 2: -40, 3: -40, 4: -39, 5: -44, 6: -44, 7: -36, 8: -43, 9: -34, 10: -35, 11: -37, 12: -37, 13: -39, 14: -44, 15: -38, 16: -32, 17: -35, 18: -34, 19: -33, 20: -37, 21: -35, 22: -38}}\n - Beat novelty in %: {{1: 37, 2: 52, 3: 24, 4: 31, 5: 40, 6: 98, 7: 45, 8: 21, 9: 76, 10: 72, 11: 52, 12: 29, 13: 10, 14: 15, 15: 81, 16: 100, 17: 27, 18: 65, 19: 64, 20: 28, 21: 19, 22: 30}}\n - Beat chords: {{: 'A#m', 2: 'Cm', 3: 'Cm', 4: 'Cm', 5: 'Cm', 6: 'Cm', 7: 'Cm', 8: 'A#m', 9: 'C', 10: 'C', 11: 'C', 12: 'C', 13: 'Cm', 14: 'Cm', 15: 'Gm', 16: 'A#m', 17: 'Gm', 18: 'Gm', 19: 'Gm', 20: 'F', 21: 'F', 22: 'F'}}\n \n # Information on the drones:\n - Number: 3\n - Drones must not collide with each other. No two drones must be at the same coordinates at the same beat.\n - Starting positions: [[100, 100, 100], [-100, 100, 100], [0, -100, 100]]\n - Maximum step distance between waypoints in cm: 100cm\n\n Response:\n song_mood: Debussy's R\u00e9verie is a short and expressive piano piece that captures the essence of daydreaming and contemplation.\n It is characteristic of Debussy's impressionistic style, known for its use of unconventional harmonies, tonal colors, and fluidity.\n cord_analysis: The song is in the key of F major, with a few chords in the song being A#m, Cm, G, F, and Gm. The mix of major and minor chords indicates that the music is neither elated nor gloomy. It remains in a state of contemplation and daydreaming, with a touch of melancholy.\n choreography_plan: The main intention of the choreography is to reflect the dreamy and contemplative mood of the song. Therefore, drones must not move too fast, and waypoints must not be too far from each other. \n The beat amplitude in dBFS roughly remains within the -50 to -30 interval, indicating that the song is very quiet. Consequently, the drones should move slowly and gently. Across all beats, the intervals are evenly spaced around 7 to 13, indicating that the drones should move at a steady pace.\n so we have no pause that we have to accentuate in the choreography. We will therefore focus on the beat amplitude.\n For beats 1 to 5, the three drones will spiral to a center point around [0, 0, 100]]. The novelty of the beats is low to moderate at around 25 to 50, and the beat amplitude is also low at around -50 to -40. Thus, we plan the first waypoints closer to each other.\n For the following beats 6 to 11, the novelty of the beats reaches a peak at 98 and a slightly raised beat amplitude of up to -35. To account for the peak, one drone will rise slowly in the center while the other two drones begin to slowly rotate in the other direction and then move outwards. We let the drones move slowly because the amplitude has not yet reached full force. We change the direction of the drones to reflect the dreamy and swirling mood of the song.\n For the next beats 12 to 17, the novelty and amplitudes of the beats are reaching the peak. Accordingly, the drones will move into the triangle formation again, while oscillating in altitude. Although we now move faster, we adhere to the maximum step distance of approximately 80 to 100cm.\n Beats 18 to 22 are the final beats of the song, with decreasing novelty and amplitude. The drones will slowly spiral outwards and downwards to mark the decline in tension and the end of the dance.\n choreography:\n 1: spiral(5, 100)\n 2: PLAN\n 3: PLAN\n 4: PLAN\n 5: PLAN\n 6: PLAN # extra PLAN for primitive switch\n 7: rotate(60, z)\n 8: PLAN # extra\n 9: rotate(-90, z)\n 10: PLAN # extra\n 11: move_z( [1,2,3], 30)\n 12: PLAN # extra\n 13: spiral(4, 100)\n 14: PLAN\n 15: PLAN\n 16: PLAN\n 17: PLAN # extra\n 18: zig_zag(5, 20, 20)\n 19: PLAN\n 20: PLAN\n 21: PLAN \n 22: PLAN\n END\n"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: \"OutKast's 'Hey Ya!' is an energetic, upbeat, and funky song. It's known for its lively rhythm, catchy beats, and infectious energy. The song conveys a fun and carefree vibe, encouraging listeners to dance and enjoy the moment.\"\ncord_analysis: \"'Hey Ya!' primarily revolves around the chords C, G, and Em, which are major and minor chords. These chords contribute to a mix of happy and slightly melancholic tones, adding depth to the overall lively and upbeat feel of the song.\"\nchoreography_plan: \"The choreography for the drones will reflect the energetic and lively mood of the song. Drones will perform dynamic and symmetrical movements, creating geometric patterns and shapes. The use of multi-time dependent functions like spiral and wave will add to the complexity and beauty of the performance. Movements will be larger and more expressive on louder and novel beats to match the song's intensity.\"\nchoreography:\n 1: form_circle([1, 2, 3, 4, 5, 6], 100)\n 2: PLAN # extra PLAN for switch\n 3: form_cone(3, 60, 0)\n 4: PLAN # extra PLAN for switch\n 5: rotate(90, 'z')\n 6: PLAN # extra PLAN for switch\n 7: spiral(3, 120)\n 8: PLAN\n 9: PLAN\n 10: PLAN # extra PLAN for switch\n 11: wave(3, 150, [[1,1],[2,2]], [0.5, 0.4], [0.3, 0.4])\n 12: PLAN\n 13: PLAN\n END\n```"}] \ No newline at end of file diff --git a/swarm_gpt/data/presets/OutKast - Hey Ya! | 6 | 20250724_114032/meta.json b/swarm_gpt/data/presets/OutKast - Hey Ya! | 6 | 20250724_114032/meta.json deleted file mode 100644 index 28952d5..0000000 --- a/swarm_gpt/data/presets/OutKast - Hey Ya! | 6 | 20250724_114032/meta.json +++ /dev/null @@ -1 +0,0 @@ -{"n_drones": 6, "song": "OutKast - Hey Ya!", "use_motion_primitives": true} \ No newline at end of file diff --git a/swarm_gpt/data/presets/Still D.R.E | 10 | 20260521_125534/history.json b/swarm_gpt/data/presets/Still D.R.E | 10 | 20260521_125534/history.json deleted file mode 100644 index 78cbe76..0000000 --- a/swarm_gpt/data/presets/Still D.R.E | 10 | 20260521_125534/history.json +++ /dev/null @@ -1 +0,0 @@ -[{"role": "system", "content": "You're a helpful assistant that will help a human choreograph a dance for a swarm of small drones."}, {"role": "user", "content": "Generate the trajectories for a choreography for 10 drones dancing to the music of the song Still D.R.E. The trajectories are defined by coordinate waypoints that the drones fly to. Each movement can be defined by multiple template functions, which are explained below.\n# Information on the song: - Name: Still D.R.E - Number of beats: 8 - Beat times in 100ms: {1: 4, 2: 29, 3: 58, 4: 94, 5: 119, 6: 145, 7: 171, 8: 196} - Beat intervals in 100ms: {1: 4, 2: 26, 3: 29, 4: 36, 5: 25, 6: 26, 7: 26} - Beat amplitude in dBFS: {1: -14, 2: -14, 3: -13, 4: -20, 5: -17, 6: -9, 7: -11, 8: -12} - Beat novelty in %: {1: 20, 2: 44, 3: 57, 4: 70, 5: 98, 6: 31, 7: 63, 8: 100} - Beat chords: {1: 'Fm', 2: 'F#', 3: 'A#m', 4: 'E', 5: 'A#m', 6: 'Fm', 7: 'A#m', 8: 'E'}\n# Information on the drones: - Number: 10 - Drones must not collide with each other. No two drones must be at the same coordinates at the same beat. - Starting positions: [[90, -100, 100], [90, -50, 100], [90, 0, 100], [90, 50, 100], [90, 100, 100], [30, -100, 100], [30, -50, 100], [30, 0, 100], [30, 50, 100], [30, 100, 100]] - Maximum step distance between waypoints in cm: {1: 38, 2: 256, 3: 288, 4: 356, 5: 250, 6: 260, 7: 256, 8: 256} - Lower limit for generated waypoints: [-300. -300. 25.] - Upper Limit for generated waypoints: [300. 300. 175.]\n# Information on the environment: - The drones are limited to [-200, 200] in x, [-200, 200] in y, [0, 200] in z - The drones must not touch the ground, i.e. their z coordinate must always be greater than 0 \n# Information on the choreography: - The drone choreography must have 8 waypoints, one for each beat. - Each drone must move at least once during the song. - Movements must depend on the beats of the song. If the beats are loud and novel, all drones must move. - If possible, coordinates for the drones should be symmetrical at each beat. - Successive waypoints should form patterns for single drones and for the whole swarm - Coordinates are given in cm. A drone flying at 1m height will have a z coordinate of 100. - The difference between two waypoints must be less than the maximum step distance in each dimension.\n E.g. if the maximum step distance between beat 3 and 4 is 50, the difference between the x, y,\n z coordinates of the waypoints at beat 3 and 4 must be less than 50 in each dimension.\n\n# Information on template functions: Make sure to limit actions to each drone once per time step. So if you choose swap for (1, 2) then you can only choose swap(3, 4) or two seperate move functions for each drone 2: time dependent template functions ( For such functions we need only one function call for one time step) - rotate:\n takes 2 parameters ( angle, axis ), and rotates all drones by 'angle' along 'axis'\n #angle is a float between [0, 360]\n #axis is one of 'x', 'y', or 'z', but for now always rotate around 'z'\n eg\n rotate(30, 'z')\n4: variadic functions ( such function takes a list with drone ids as arguments ) - center:\n Takes a list of drone IDs as input. It calculates the geometric center of the current positions of these specified drones and then moves all the drones with the matching IDs to this calculated center position.\n eg\n center([1, 2, 3])\n- move_z:\n Takes a list of drone IDs as input, and distance. It moves all the drones with the matching IDs along z axis by xcm. if distance is positive, it moves them up, otherwise down\n eg\n move_z([1, 2, 3], 10)\n- form_circle\n Takes a list of drone IDs as input, and z coordinate ( height ) for the circle formation. Radius of circle is decided by number of drones in list argument. So bigger circle for more drones.\n # height has to be greater than zero\n eg \n # Cone formation as 4 drones are at 150cm height, and 3 drones are at 50cm height with smaller radius as less drones.\n form_circle( [1, 2, 3, 4], 150 ), form_circle( [5, 6, 7], 50 )\n- form_star\n Takes 3 parameters ( height, spacing, delta_radius ) and creates a star in the z-plane, which is defined by 2 concentric circles in staggered angles with different radii. \n Height corresponds to the height of the star. Spacing corresponds to the minimum spacing between drones, where a higher spacing means larger star. \n Delta_radius corresponds to the difference of the 2 radii, where a larger delta_radius means a more spread out star\n #height has to be greater than zero\n #spacing is an integer between [40, 80]\n #delta_radius is an integer between [50 100]\n eg \n form_star( 120, 60, 50 )\n- form_cone\n Takes 3 parameters ( delta_height, spacing, is_inverted ) and creates a cone using concentric circles of different radii. Each layer has 4 additional drones.\n Delta_height is the difference in height between layers. A larger delta_height corresponds to a taller cone.\n Spacing is the minimum_spacing between drones of a layer. A larger spacing means a larger circle radius and wider cone.\n Is_inverted is a boolean value (an integer of 0 or 1) that determines if the cone is inverted or not. Inverted cones are more expressive and dynamic. \n #delta_height is an integer between [30, 80]\n #spacing is an integer between [40, 80]\n #is_inverted is an integer with the values 0 or 1\n eg \n form_star( 120, 60, 50 )\n\n\n5: multi time dependent functions ( For such functions we need only one function call for multiple time steps)\n Any multi-time dependent function needs to be followed by the 'PLAN' keyword for the number of steps minus 1. This means, for functions that span over `step` time steps, the function call must be followed by `step - 1` occurrences of 'PLAN'.\n These functions must be initiated `s - step + 1` beats before the last beat, where `s` is the total number of beats. This ensures that the multi-time function completes before the song ends.\n\n**Important Timing Constraint**: If you have a multi-time function that takes 3 steps, it must be called at least 2 steps before the last beat to allow for the necessary 'PLAN' steps. For example, with a 22-beat song, a 3-step function should be called by the 20th beat, followed by 'PLAN' for the 21st and 22nd beats. - spiral:\n takes 2 parameters ( steps, height ), and makes all drones move in a outward spiral pattern with the calculate radius ( from no of drones ), and number of steps at height h\n\n #steps is an integer between [2, 3]\n #height is an integer between [0, 200] that determines the height of the spiral\n #Since spiral is a multi time dependent function, it should be falled by 'PLAN' keyword for the number of steps -1 times, so if steps is 3, then spiral is called once, and PLAN follows twice in output\n # spiral needs to be called atleast 's' steps before last timestep as 's' PLAN follow the spiral call\n eg:\n 1: spiral(3, 50 )\n 2: PLAN\n 3: PLAN\n\n- spiral_speed:\n Takes 4 parameters (steps, height, speed, radius_increase), and has the same effect as spiral. The difference is that spiral_speed increases the radius of the spiral by radius_increase over the number of steps. In addition,\n speed defines the angle by which the drones rotate around the center. Lower speeds results in slower drone motions.\n # Steps is an integer between [2, 3]\n # height is an integer between [0, 200] that determines the height of the spiral\n # speeds is an integer between [0, 360] that defines the angle by which the drones rotate around the center. Very low speeds are in the range of 0 to 90, fast speeds are in the range of 100 to 360\n # radius_increase is a float between [1, 3] that increases the radius of the spiral over the number of steps\n eg:\n 1: spiral_speed(3, 100, 200, 1.5)\n 2: PLAN\n 3: PLAN\n\n- helix:\n takes 2 parameters ( steps, delta_h, height ), and makes all drones move in a helix pattern with the calculate radius ( from no of drones ), and number of steps.\n\n #steps is an integer between [2, 3]\n #delta_h is an integer between [0, 100], that changes the height of drones in the helix pattern per step\n #height is between [50, 100] as helix rises up with steps, lets be safe.\n #Since helix is a multi time dependent function, it should be falled by 'PLAN' keyword for the number of steps -1 times, so if steps is 3, then helix is called once, and PLAN follows twice in output\n eg:\n 1: helix(3, 20, 50)\n 2: PLAN\n 3: PLAN\n\n- twister:\n takes 3 parameter ( steps, omega, z_spacing ), and makes all drones move into a upside down cone pattern and rotate at the speed specified by omega, for the number of steps specified.\n Z_spacing corresponds to the height difference between neighbouring drones, where a smaller z_spacing corresponds to a more compact cone.\n\n #steps is an integer between [2, 3]\n #omega is an integer between [2, 20] with a higher omega corresponding to a more dynamic performance.\n #z_spacing is an integer between [5, 30]\n #Since twister is a multi time dependent function, it should be falled by 'PLAN' keyword for the number of steps -1 times, so if steps is 3, then conic_rotate is called once, and PLAN follows twice in output\n eg:\n 1: twister(3, 20, 10)\n 2: PLAN\n 3: PLAN\n\n- zig_zag:\n Moves all drones in a zigzag pattern. Takes 3 parameters\n Parameters: `steps` (int), `delta` (int) - the horizontal displacement per step, `delta_h` (int) - the vertical displacement per step.\n eg:\n 4: zig_zag(3, 20, 10)\n 5: PLAN\n 6: PLAN\n \n- wave:\n Moves all drones in a three-dimensional wave propagating through a two-dimensional rectangular elastic surface with bounded edges. The disturbance at any point on the surface is defined by its parameters in space and time, and the wave propagation is governed by the wave equation with a speed `c > 0`. Takes 5 parameters.\n Parameters:\n - `steps` (int): The number of discretization points.\n - `height` (float): The desired height of drones above the surface.\n - `mu_pairs` (list of list): List of \\((\u03bc1, \u03bc2)\\) pairs that define the spatial frequencies.\n - `a\u00b51\u00b52` (list of floats): List of amplitude coefficients corresponding to each \\((\u03bc1, \u03bc2)\\) pair for the sine component.\n - `b\u00b51\u00b52` (list of floats): List of amplitude coefficients corresponding to each \\((\u03bc1, \u03bc2)\\) pair for the cosine component.\n eg:\n 4: wave( 4, 10, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4] )\n 5: PLAN\n 6: PLAN\n 7: PLAN\n latex equn for the concerned wave equation:\n The disturbance \\(u(s, t)\\) at any point \\(s = (s_1, s_2) \\in [0, a] \\times [0, b]\\) \nat time \\(t\\) is governed by:\n\\[\nc^2 \\nabla^2 u(s, t) = \\frac{\\partial^2 u(s, t)}{\\partial t^2},\n\\]\nwhere \\( \\nabla^2 \\) is the spatial Laplacian and \\(c > 0\\) is the wave speed. \nThe solution:\n\\[\nu(s, t) = \\sum_{(\\mu_1, \\mu_2)} a_{\\mu_1 \\mu_2} \\sin \\left( \\frac{\\mu_1 \\pi s_1}{a} \\right) \n\\sin \\left( \\frac{\\mu_2 pi s_2}{b} \\right) \\sin(\\omega_{\\mu_1 \\mu_2} t) \n+ b_{\\mu_1 \\mu_2} \\sin \\left( \\frac{\\mu_1 \\pi s_1}{a} \\right) \n\\sin \\left( \\frac{\\mu_2 \\pi s_2}{b} \\right) \\cos(\\omega_{\\mu_1 \\mu_2} t),\n\\]\n\nwith frequencies:\n\n\\[\n\\omega_{\\mu_1 \\mu_2}^2 = c^2 \\pi^2 \\left( \\frac{\\mu_1^2}{a^2} + \\frac{\\mu_2^2}{b^2} \\right).\n\\]\n\nPLAN can also be used to indicate that the drones should maintain their current position for the next beat, so after forming cone formation, you can use PLAN to maintain the formation for next beat. Using `PLAN` also counts as part of the waypoints for the drones, so you can't have multi-time dependent functions as the last waypoint as we need to have `PLAN` after them in future steps.\n# Patterns: - Drones must fly in geometric patterns and shapes - Drones must fly in tight formations - The formations must be symmetrical if possible - Drones should move up and down in altitude multiple times for a 3D effect - Example patterns include spirals, triangles, squares, circles, lines\n# Response structure First, describe the emotions and mood associated with the song Still D.R.E. Then, give a description of the dominant chords of the song. Is it a major or minor cord? What does that mean for the mood? Next, give a specific description of the choreography you want to create. What patterns do you want the drones to fly? Why do you use these particular patterns? Last, fill out the individual waypoints for each drone. Adapt the distance and position of waypoints to the amplitude of the song's beats. Loud beats lead to larger movements, while soft beats lead to smaller movements. Furthermore, take into account the dominant chords of the song and the emotions and mood associated with these chords. Try to use a variety of functional options to design an amazing geometrical choreography. Make sure to only do motion planning per beat, so in otherwords motion planning steps should be limited to 8,and end yaml output with END like in examples below. You can use multiple beat functions for more than 3 timestep as well like spiral(), zig_zag(), wave(), helix(). Only do complex primtives. Adjust change in height accordingly like do them for 5 steps if you have to, and make height increments small. Limit to at max 4 primitives. Between any two primitives, there must be a extra PLAN which consumes a timestep so that there is enough time for transition.\n"}, {"role": "system", "content": "# Example prompts and responses - Example 1:\n Prompt:\n # Information on the song:\n - Name: r\u00eaverie\n - Number of beats: 22\n - Beat times in 100ms: {{1: 7, 2: 20, 3: 29, 4: 41, 5: 55, 6: 66, 7: 80, 8: 96, 9: 104, 10: 112, 11: 119, 12: 128, 13: 137, 14: 147, 15: 157, 16: 167, 17: 187, 18: 196, 19: 210, 20: 221, 21: 232, 22: 242}}\n - Beat intervals in 100ms: {{1: 7, 2: 13, 3: 9, 4: 12, 5: 14, 6: 11, 7: 13, 8: 16, 9: 9, 10: 7, 11: 8, 12: 8, 13: 9, 14: 10, 15: 10, 16: 10, 17: 20, 18: 8, 19: 14, 20: 12, 21: 10}}\n - Beat amplitude in dBFS: {{1: -53, 2: -40, 3: -40, 4: -39, 5: -44, 6: -44, 7: -36, 8: -43, 9: -34, 10: -35, 11: -37, 12: -37, 13: -39, 14: -44, 15: -38, 16: -32, 17: -35, 18: -34, 19: -33, 20: -37, 21: -35, 22: -38}}\n - Beat novelty in %: {{1: 37, 2: 52, 3: 24, 4: 31, 5: 40, 6: 98, 7: 45, 8: 21, 9: 76, 10: 72, 11: 52, 12: 29, 13: 10, 14: 15, 15: 81, 16: 100, 17: 27, 18: 65, 19: 64, 20: 28, 21: 19, 22: 30}}\n - Beat chords: {{: 'A#m', 2: 'Cm', 3: 'Cm', 4: 'Cm', 5: 'Cm', 6: 'Cm', 7: 'Cm', 8: 'A#m', 9: 'C', 10: 'C', 11: 'C', 12: 'C', 13: 'Cm', 14: 'Cm', 15: 'Gm', 16: 'A#m', 17: 'Gm', 18: 'Gm', 19: 'Gm', 20: 'F', 21: 'F', 22: 'F'}}\n \n # Information on the drones:\n - Number: 3\n - Drones must not collide with each other. No two drones must be at the same coordinates at the same beat.\n - Starting positions: [[100, 100, 100], [-100, 100, 100], [0, -100, 100]]\n - Maximum step distance between waypoints in cm: 100cm\n\n Response:\n song_mood: Debussy's R\u00e9verie is a short and expressive piano piece that captures the essence of daydreaming and contemplation.\n It is characteristic of Debussy's impressionistic style, known for its use of unconventional harmonies, tonal colors, and fluidity.\n cord_analysis: The song is in the key of F major, with a few chords in the song being A#m, Cm, G, F, and Gm. The mix of major and minor chords indicates that the music is neither elated nor gloomy. It remains in a state of contemplation and daydreaming, with a touch of melancholy.\n choreography_plan: The main intention of the choreography is to reflect the dreamy and contemplative mood of the song. Therefore, drones must not move too fast, and waypoints must not be too far from each other. \n The beat amplitude in dBFS roughly remains within the -50 to -30 interval, indicating that the song is very quiet. Consequently, the drones should move slowly and gently. Across all beats, the intervals are evenly spaced around 7 to 13, indicating that the drones should move at a steady pace.\n so we have no pause that we have to accentuate in the choreography. We will therefore focus on the beat amplitude.\n For beats 1 to 5, the three drones will spiral to a center point around [0, 0, 100]]. The novelty of the beats is low to moderate at around 25 to 50, and the beat amplitude is also low at around -50 to -40. Thus, we plan the first waypoints closer to each other.\n For the following beats 6 to 11, the novelty of the beats reaches a peak at 98 and a slightly raised beat amplitude of up to -35. To account for the peak, one drone will rise slowly in the center while the other two drones begin to slowly rotate in the other direction and then move outwards. We let the drones move slowly because the amplitude has not yet reached full force. We change the direction of the drones to reflect the dreamy and swirling mood of the song.\n For the next beats 12 to 17, the novelty and amplitudes of the beats are reaching the peak. Accordingly, the drones will move into the triangle formation again, while oscillating in altitude. Although we now move faster, we adhere to the maximum step distance of approximately 80 to 100cm.\n Beats 18 to 22 are the final beats of the song, with decreasing novelty and amplitude. The drones will slowly spiral outwards and downwards to mark the decline in tension and the end of the dance.\n choreography:\n 1: spiral(5, 100)\n 2: PLAN\n 3: PLAN\n 4: PLAN\n 5: PLAN\n 6: PLAN # extra PLAN for primitive switch\n 7: rotate(60, 'z')\n 8: PLAN # extra\n 9: rotate(-90, 'z')\n 10: PLAN # extra\n 11: move_z( [1,2,3], 30)\n 12: PLAN # extra\n 13: spiral(4, 100)\n 14: PLAN\n 15: PLAN\n 16: PLAN\n 17: PLAN # extra\n 18: zig_zag(5, 20, 20)\n 19: PLAN\n 20: PLAN\n 21: PLAN \n 22: PLAN\n END\n"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: \"Still D.R.E. is a confident and assertive hip-hop track with a steady, driving beat. It embodies themes of resilience and triumph, characterized by its iconic piano riff and rhythmic flow.\"\ncord_analysis: \"The song features a mix of minor chords like Fm and A#m, which add a sense of seriousness and intensity. The presence of major chords like F# and E introduces a sense of resolution and power, creating a balanced yet assertive mood.\"\nchoreography_plan: \"The choreography will reflect the song's confident and rhythmic nature. We'll use symmetrical and dynamic patterns to mirror the song's structure. The drones will form circles, stars, and cones, moving in sync with the beat's amplitude and novelty. The choreography will emphasize height changes and geometric formations to create a visually striking performance.\"\nchoreography:\n 1: form_circle([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 100)\n 2: PLAN\n 3: form_star(120, 60, 50)\n 4: PLAN\n 5: form_cone(40, 70, 0)\n 6: PLAN\n 7: spiral(3, 150)\n 8: PLAN\n END\n```"}] \ No newline at end of file diff --git a/swarm_gpt/data/presets/Still D.R.E | 10 | 20260521_125534/meta.json b/swarm_gpt/data/presets/Still D.R.E | 10 | 20260521_125534/meta.json deleted file mode 100644 index dadb837..0000000 --- a/swarm_gpt/data/presets/Still D.R.E | 10 | 20260521_125534/meta.json +++ /dev/null @@ -1 +0,0 @@ -{"n_drones": 10, "song": "Still D.R.E", "use_motion_primitives": true} \ No newline at end of file diff --git a/swarm_gpt/data/presets/Still D.R.E | 10 | 20260521_125534/trajectory.csv b/swarm_gpt/data/presets/Still D.R.E | 10 | 20260521_125534/trajectory.csv deleted file mode 100644 index 69ab7f4..0000000 --- a/swarm_gpt/data/presets/Still D.R.E | 10 | 20260521_125534/trajectory.csv +++ /dev/null @@ -1,1183 +0,0 @@ -time[s],drone0_posx[m],drone0_posy[m],drone0_posz[m],drone0_velx[m/s],drone0_vely[m/s],drone0_velz[m/s],drone1_posx[m],drone1_posy[m],drone1_posz[m],drone1_velx[m/s],drone1_vely[m/s],drone1_velz[m/s],drone2_posx[m],drone2_posy[m],drone2_posz[m],drone2_velx[m/s],drone2_vely[m/s],drone2_velz[m/s],drone3_posx[m],drone3_posy[m],drone3_posz[m],drone3_velx[m/s],drone3_vely[m/s],drone3_velz[m/s],drone4_posx[m],drone4_posy[m],drone4_posz[m],drone4_velx[m/s],drone4_vely[m/s],drone4_velz[m/s],drone5_posx[m],drone5_posy[m],drone5_posz[m],drone5_velx[m/s],drone5_vely[m/s],drone5_velz[m/s],drone6_posx[m],drone6_posy[m],drone6_posz[m],drone6_velx[m/s],drone6_vely[m/s],drone6_velz[m/s],drone7_posx[m],drone7_posy[m],drone7_posz[m],drone7_velx[m/s],drone7_vely[m/s],drone7_velz[m/s],drone8_posx[m],drone8_posy[m],drone8_posz[m],drone8_velx[m/s],drone8_vely[m/s],drone8_velz[m/s],drone9_posx[m],drone9_posy[m],drone9_posz[m],drone9_velx[m/s],drone9_vely[m/s],drone9_velz[m/s] 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b/swarm_gpt/data/presets/Vivaldi Summer | 20 | 20260430_062211/history.json deleted file mode 100644 index 4b7b763..0000000 --- a/swarm_gpt/data/presets/Vivaldi Summer | 20 | 20260430_062211/history.json +++ /dev/null @@ -1 +0,0 @@ -[{"role": "system", "content": "You're a helpful assistant that will help a human choreograph a dance for a swarm of small drones."}, {"role": "user", "content": "Generate the trajectories for a choreography for 20 drones dancing to the music of the song Vivaldi Summer. The trajectories are defined by coordinate waypoints that the drones fly to. Each movement can be defined by multiple template functions, which are explained below.\n# Information on the song: - Name: Vivaldi Summer - Number of beats: 14 - Beat times in 100ms: {1: 11, 2: 45, 3: 71, 4: 93, 5: 116, 6: 149, 7: 172, 8: 199, 9: 233, 10: 253, 11: 276, 12: 300, 13: 333, 14: 357} - Beat intervals in 100ms: {1: 11, 2: 33, 3: 26, 4: 22, 5: 23, 6: 33, 7: 23, 8: 27, 9: 34, 10: 20, 11: 23, 12: 24, 13: 33} - Beat amplitude in dBFS: {1: -31, 2: -20, 3: -35, 4: -19, 5: -26, 6: -17, 7: -20, 8: -23, 9: -21, 10: -19, 11: -22, 12: -23, 13: -24, 14: -27} - Beat novelty in %: {1: 66, 2: 25, 3: 100, 4: 18, 5: 32, 6: 44, 7: 15, 8: 14, 9: 26, 10: 13, 11: 21, 12: 22, 13: 29, 14: 21} - Beat chords: {1: 'G', 2: 'G', 3: 'D', 4: 'D', 5: 'D', 6: 'Gm', 7: 'F', 8: 'G', 9: 'Gm', 10: 'D', 11: 'D', 12: 'D', 13: 'Gm', 14: 'Gm'}\n# Information on the drones: - Number: 20 - Drones must not collide with each other. No two drones must be at the same coordinates at the same beat. - Starting positions: [[90, -100, 100], [90, -50, 100], [90, 0, 100], [90, 50, 100], [90, 100, 100], [30, -100, 100], [30, -50, 100], [30, 0, 100], [30, 50, 100], [30, 100, 100], [-30, -100, 100], [-30, -50, 100], [-30, 0, 100], [-30, 50, 100], [-30, 100, 100], [-90, -100, 100], [-90, -50, 100], [-90, 0, 100], [-90, 50, 100], [-90, 100, 100]] - Maximum step distance between waypoints in cm: {1: 113, 2: 331, 3: 263, 4: 223, 5: 227, 6: 327, 7: 225, 8: 267, 9: 339, 10: 203, 11: 231, 12: 237, 13: 333, 14: 237} - Lower limit for generated waypoints: [-190. -130. 25.] - Upper Limit for generated waypoints: [190. 130. 250.]\n# Information on the environment: - The drones are limited to [-200, 200] in x, [-200, 200] in y, [0, 200] in z - The drones must not touch the ground, i.e. their z coordinate must always be greater than 0 \n# Information on the choreography: - The drone choreography must have 14 waypoints, one for each beat. - Each drone must move at least once during the song. - Movements must depend on the beats of the song. If the beats are loud and novel, all drones must move. - If possible, coordinates for the drones should be symmetrical at each beat. - Successive waypoints should form patterns for single drones and for the whole swarm - Coordinates are given in cm. A drone flying at 1m height will have a z coordinate of 100. - The difference between two waypoints must be less than the maximum step distance in each dimension.\n E.g. if the maximum step distance between beat 3 and 4 is 50, the difference between the x, y,\n z coordinates of the waypoints at beat 3 and 4 must be less than 50 in each dimension.\n\n# Information on template functions: Make sure to limit actions to each drone once per time step. So if you choose swap for (1, 2) then you can only choose swap(3, 4) or two seperate move functions for each drone 2: time dependent template functions ( For such functions we need only one function call for one time step) - rotate:\n takes 2 parameters ( angle, axis ), and rotates all drones by 'angle' along 'axis'\n #angle is a float between [0, 360]\n #axis is one of 'x', 'y', or 'z', but for now always rotate around 'z'\n eg\n rotate(30, 'z')\n4: variadic functions ( such function takes a list with drone ids as arguments ) - center:\n Takes a list of drone IDs as input. It calculates the geometric center of the current positions of these specified drones and then moves all the drones with the matching IDs to this calculated center position.\n eg\n center([1, 2, 3])\n- move_z:\n Takes a list of drone IDs as input, and distance. It moves all the drones with the matching IDs along z axis by xcm. if distance is positive, it moves them up, otherwise down\n eg\n move_z([1, 2, 3], 10)\n- form_circle\n Takes a list of drone IDs as input, and z coordinate ( height ) for the circle formation. Radius of circle is decided by number of drones in list argument. So bigger circle for more drones.\n # height has to be greater than zero\n eg \n # Cone formation as 4 drones are at 150cm height, and 3 drones are at 50cm height with smaller radius as less drones.\n form_circle( [1, 2, 3, 4], 150 ), form_circle( [5, 6, 7], 50 )\n- form_star\n Takes 3 parameters ( height, spacing, delta_radius ) and creates a star in the z-plane, which is defined by 2 concentric circles in staggered angles with different radii. \n Height corresponds to the height of the star. Spacing corresponds to the minimum spacing between drones, where a higher spacing means larger star. \n Delta_radius corresponds to the difference of the 2 radii, where a larger delta_radius means a more spread out star\n #height has to be greater than zero\n #spacing is an integer between [40, 80]\n #delta_radius is an integer between [50 100]\n eg \n form_star( 120, 60, 50 )\n- form_cone\n Takes 3 parameters ( delta_height, spacing, is_inverted ) and creates a cone using concentric circles of different radii. Each layer has 4 additional drones.\n Delta_height is the difference in height between layers. A larger delta_height corresponds to a taller cone.\n Spacing is the minimum_spacing between drones of a layer. A larger spacing means a larger circle radius and wider cone.\n Is_inverted is a boolean value (an integer of 0 or 1) that determines if the cone is inverted or not. Inverted cones are more expressive and dynamic. \n #delta_height is an integer between [30, 80]\n #spacing is an integer between [40, 80]\n #is_inverted is an integer with the values 0 or 1\n eg \n form_star( 120, 60, 50 )\n\n\n5: multi time dependent functions ( For such functions we need only one function call for multiple time steps)\n Any multi-time dependent function needs to be followed by the 'PLAN' keyword for the number of steps minus 1. This means, for functions that span over `step` time steps, the function call must be followed by `step - 1` occurrences of 'PLAN'.\n These functions must be initiated `s - step + 1` beats before the last beat, where `s` is the total number of beats. This ensures that the multi-time function completes before the song ends.\n\n**Important Timing Constraint**: If you have a multi-time function that takes 3 steps, it must be called at least 2 steps before the last beat to allow for the necessary 'PLAN' steps. For example, with a 22-beat song, a 3-step function should be called by the 20th beat, followed by 'PLAN' for the 21st and 22nd beats. - spiral:\n takes 2 parameters ( steps, height ), and makes all drones move in a outward spiral pattern with the calculate radius ( from no of drones ), and number of steps at height h\n\n #steps is an integer between [2, 3]\n #height is an integer between [0, 200] that determines the height of the spiral\n #Since spiral is a multi time dependent function, it should be falled by 'PLAN' keyword for the number of steps -1 times, so if steps is 3, then spiral is called once, and PLAN follows twice in output\n # spiral needs to be called atleast 's' steps before last timestep as 's' PLAN follow the spiral call\n eg:\n 1: spiral(3, 50 )\n 2: PLAN\n 3: PLAN\n\n- spiral_speed:\n Takes 4 parameters (steps, height, speed, radius_increase), and has the same effect as spiral. The difference is that spiral_speed increases the radius of the spiral by radius_increase over the number of steps. In addition,\n speed defines the angle by which the drones rotate around the center. Lower speeds results in slower drone motions.\n # Steps is an integer between [2, 3]\n # height is an integer between [0, 200] that determines the height of the spiral\n # speeds is an integer between [0, 360] that defines the angle by which the drones rotate around the center. Very low speeds are in the range of 0 to 90, fast speeds are in the range of 100 to 360\n # radius_increase is a float between [1, 3] that increases the radius of the spiral over the number of steps\n eg:\n 1: spiral_speed(3, 100, 200, 1.5)\n 2: PLAN\n 3: PLAN\n\n- helix:\n takes 2 parameters ( steps, delta_h, height ), and makes all drones move in a helix pattern with the calculate radius ( from no of drones ), and number of steps.\n\n #steps is an integer between [2, 3]\n #delta_h is an integer between [0, 100], that changes the height of drones in the helix pattern per step\n #height is between [50, 100] as helix rises up with steps, lets be safe.\n #Since helix is a multi time dependent function, it should be falled by 'PLAN' keyword for the number of steps -1 times, so if steps is 3, then helix is called once, and PLAN follows twice in output\n eg:\n 1: helix(3, 20, 50)\n 2: PLAN\n 3: PLAN\n\n- twister:\n takes 3 parameter ( steps, omega, z_spacing ), and makes all drones move into a upside down cone pattern and rotate at the speed specified by omega, for the number of steps specified.\n Z_spacing corresponds to the height difference between neighbouring drones, where a smaller z_spacing corresponds to a more compact cone.\n\n #steps is an integer between [2, 3]\n #omega is an integer between [2, 20] with a higher omega corresponding to a more dynamic performance.\n #z_spacing is an integer between [5, 30]\n #Since twister is a multi time dependent function, it should be falled by 'PLAN' keyword for the number of steps -1 times, so if steps is 3, then conic_rotate is called once, and PLAN follows twice in output\n eg:\n 1: twister(3, 20, 10)\n 2: PLAN\n 3: PLAN\n\n- zig_zag:\n Moves all drones in a zigzag pattern. Takes 3 parameters\n Parameters: `steps` (int), `delta` (int) - the horizontal displacement per step, `delta_h` (int) - the vertical displacement per step.\n eg:\n 4: zig_zag(3, 20, 10)\n 5: PLAN\n 6: PLAN\n \n- wave:\n Moves all drones in a three-dimensional wave propagating through a two-dimensional rectangular elastic surface with bounded edges. The disturbance at any point on the surface is defined by its parameters in space and time, and the wave propagation is governed by the wave equation with a speed `c > 0`. Takes 5 parameters.\n Parameters:\n - `steps` (int): The number of discretization points.\n - `height` (float): The desired height of drones above the surface.\n - `mu_pairs` (list of list): List of \\((\u03bc1, \u03bc2)\\) pairs that define the spatial frequencies.\n - `a\u00b51\u00b52` (list of floats): List of amplitude coefficients corresponding to each \\((\u03bc1, \u03bc2)\\) pair for the sine component.\n - `b\u00b51\u00b52` (list of floats): List of amplitude coefficients corresponding to each \\((\u03bc1, \u03bc2)\\) pair for the cosine component.\n eg:\n 4: wave( 4, 10, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4] )\n 5: PLAN\n 6: PLAN\n 7: PLAN\n latex equn for the concerned wave equation:\n The disturbance \\(u(s, t)\\) at any point \\(s = (s_1, s_2) \\in [0, a] \\times [0, b]\\) \nat time \\(t\\) is governed by:\n\\[\nc^2 \\nabla^2 u(s, t) = \\frac{\\partial^2 u(s, t)}{\\partial t^2},\n\\]\nwhere \\( \\nabla^2 \\) is the spatial Laplacian and \\(c > 0\\) is the wave speed. \nThe solution:\n\\[\nu(s, t) = \\sum_{(\\mu_1, \\mu_2)} a_{\\mu_1 \\mu_2} \\sin \\left( \\frac{\\mu_1 \\pi s_1}{a} \\right) \n\\sin \\left( \\frac{\\mu_2 pi s_2}{b} \\right) \\sin(\\omega_{\\mu_1 \\mu_2} t) \n+ b_{\\mu_1 \\mu_2} \\sin \\left( \\frac{\\mu_1 \\pi s_1}{a} \\right) \n\\sin \\left( \\frac{\\mu_2 \\pi s_2}{b} \\right) \\cos(\\omega_{\\mu_1 \\mu_2} t),\n\\]\n\nwith frequencies:\n\n\\[\n\\omega_{\\mu_1 \\mu_2}^2 = c^2 \\pi^2 \\left( \\frac{\\mu_1^2}{a^2} + \\frac{\\mu_2^2}{b^2} \\right).\n\\]\n\nPLAN can also be used to indicate that the drones should maintain their current position for the next beat, so after forming cone formation, you can use PLAN to maintain the formation for next beat. Using `PLAN` also counts as part of the waypoints for the drones, so you can't have multi-time dependent functions as the last waypoint as we need to have `PLAN` after them in future steps.\n# Patterns: - Drones must fly in geometric patterns and shapes - Drones must fly in tight formations - The formations must be symmetrical if possible - Drones should move up and down in altitude multiple times for a 3D effect - Example patterns include spirals, triangles, squares, circles, lines\n# Response structure First, describe the emotions and mood associated with the song Vivaldi Summer. Then, give a description of the dominant chords of the song. Is it a major or minor cord? What does that mean for the mood? Next, give a specific description of the choreography you want to create. What patterns do you want the drones to fly? Why do you use these particular patterns? Last, fill out the individual waypoints for each drone. Adapt the distance and position of waypoints to the amplitude of the song's beats. Loud beats lead to larger movements, while soft beats lead to smaller movements. Furthermore, take into account the dominant chords of the song and the emotions and mood associated with these chords. Try to use a variety of functional options to design an amazing geometrical choreography. Make sure to only do motion planning per beat, so in otherwords motion planning steps should be limited to 14,and end yaml output with END like in examples below. You can use multiple beat functions for more than 3 timestep as well like spiral(), zig_zag(), wave(), helix(). Only do complex primtives. Adjust change in height accordingly like do them for 5 steps if you have to, and make height increments small. Limit to at max 4 primitives. Between any two primitives, there must be a extra PLAN which consumes a timestep so that there is enough time for transition.\n"}, {"role": "system", "content": "# Example prompts and responses - Example 1:\n Prompt:\n # Information on the song:\n - Name: r\u00eaverie\n - Number of beats: 22\n - Beat times in 100ms: {{1: 7, 2: 20, 3: 29, 4: 41, 5: 55, 6: 66, 7: 80, 8: 96, 9: 104, 10: 112, 11: 119, 12: 128, 13: 137, 14: 147, 15: 157, 16: 167, 17: 187, 18: 196, 19: 210, 20: 221, 21: 232, 22: 242}}\n - Beat intervals in 100ms: {{1: 7, 2: 13, 3: 9, 4: 12, 5: 14, 6: 11, 7: 13, 8: 16, 9: 9, 10: 7, 11: 8, 12: 8, 13: 9, 14: 10, 15: 10, 16: 10, 17: 20, 18: 8, 19: 14, 20: 12, 21: 10}}\n - Beat amplitude in dBFS: {{1: -53, 2: -40, 3: -40, 4: -39, 5: -44, 6: -44, 7: -36, 8: -43, 9: -34, 10: -35, 11: -37, 12: -37, 13: -39, 14: -44, 15: -38, 16: -32, 17: -35, 18: -34, 19: -33, 20: -37, 21: -35, 22: -38}}\n - Beat novelty in %: {{1: 37, 2: 52, 3: 24, 4: 31, 5: 40, 6: 98, 7: 45, 8: 21, 9: 76, 10: 72, 11: 52, 12: 29, 13: 10, 14: 15, 15: 81, 16: 100, 17: 27, 18: 65, 19: 64, 20: 28, 21: 19, 22: 30}}\n - Beat chords: {{: 'A#m', 2: 'Cm', 3: 'Cm', 4: 'Cm', 5: 'Cm', 6: 'Cm', 7: 'Cm', 8: 'A#m', 9: 'C', 10: 'C', 11: 'C', 12: 'C', 13: 'Cm', 14: 'Cm', 15: 'Gm', 16: 'A#m', 17: 'Gm', 18: 'Gm', 19: 'Gm', 20: 'F', 21: 'F', 22: 'F'}}\n \n # Information on the drones:\n - Number: 3\n - Drones must not collide with each other. No two drones must be at the same coordinates at the same beat.\n - Starting positions: [[100, 100, 100], [-100, 100, 100], [0, -100, 100]]\n - Maximum step distance between waypoints in cm: 100cm\n\n Response:\n song_mood: Debussy's R\u00e9verie is a short and expressive piano piece that captures the essence of daydreaming and contemplation.\n It is characteristic of Debussy's impressionistic style, known for its use of unconventional harmonies, tonal colors, and fluidity.\n cord_analysis: The song is in the key of F major, with a few chords in the song being A#m, Cm, G, F, and Gm. The mix of major and minor chords indicates that the music is neither elated nor gloomy. It remains in a state of contemplation and daydreaming, with a touch of melancholy.\n choreography_plan: The main intention of the choreography is to reflect the dreamy and contemplative mood of the song. Therefore, drones must not move too fast, and waypoints must not be too far from each other. \n The beat amplitude in dBFS roughly remains within the -50 to -30 interval, indicating that the song is very quiet. Consequently, the drones should move slowly and gently. Across all beats, the intervals are evenly spaced around 7 to 13, indicating that the drones should move at a steady pace.\n so we have no pause that we have to accentuate in the choreography. We will therefore focus on the beat amplitude.\n For beats 1 to 5, the three drones will spiral to a center point around [0, 0, 100]]. The novelty of the beats is low to moderate at around 25 to 50, and the beat amplitude is also low at around -50 to -40. Thus, we plan the first waypoints closer to each other.\n For the following beats 6 to 11, the novelty of the beats reaches a peak at 98 and a slightly raised beat amplitude of up to -35. To account for the peak, one drone will rise slowly in the center while the other two drones begin to slowly rotate in the other direction and then move outwards. We let the drones move slowly because the amplitude has not yet reached full force. We change the direction of the drones to reflect the dreamy and swirling mood of the song.\n For the next beats 12 to 17, the novelty and amplitudes of the beats are reaching the peak. Accordingly, the drones will move into the triangle formation again, while oscillating in altitude. Although we now move faster, we adhere to the maximum step distance of approximately 80 to 100cm.\n Beats 18 to 22 are the final beats of the song, with decreasing novelty and amplitude. The drones will slowly spiral outwards and downwards to mark the decline in tension and the end of the dance.\n choreography:\n 1: spiral(5, 100)\n 2: PLAN\n 3: PLAN\n 4: PLAN\n 5: PLAN\n 6: PLAN # extra PLAN for primitive switch\n 7: rotate(60, 'z')\n 8: PLAN # extra\n 9: rotate(-90, 'z')\n 10: PLAN # extra\n 11: move_z( [1,2,3], 30)\n 12: PLAN # extra\n 13: spiral(4, 100)\n 14: PLAN\n 15: PLAN\n 16: PLAN\n 17: PLAN # extra\n 18: zig_zag(5, 20, 20)\n 19: PLAN\n 20: PLAN\n 21: PLAN \n 22: PLAN\n END\n"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: Vivaldi's \"Summer\" is a dynamic and intense composition that embodies the full energy of summer. It is filled with dramatic contrasts, quick passages, and sudden shifts, painting a vivid picture of summer storms and the extreme heat of the season. The mood is passionate, lively, and at times chaotic, reflecting both the beauty and the fury of nature.\ncord_analysis: The piece is in the key of G minor, with prominent chords including G, D, F, and Gm, which contribute to a somber and intense atmosphere. The minor chords in particular evoke a sense of urgency and tension, ideal for dramatic and energetic choreography.\nchoreography_plan: The choreography aims to capture the vibrant and dramatic nature of \"Summer.\" With the high novelty and amplitude peaks in the music, the drones will engage in large, expressive movements in synchronized patterns, making use of symmetrical formations to reflect the balanced and structured aspects of Vivaldi's composition. The use of structures like circles, spirals, and cones will showcase the dynamic range of the music, with ascending and descending motions to mirror the intense shifts in mood and atmosphere. \nchoreography:\n 1: form_circle([1, 2, 3, ..., 20], 120)\n 2: PLAN\n 3: rotate(90, 'z')\n 4: PLAN\n 5: form_star(150, 70, 60)\n 6: PLAN\n 7: zig_zag(5, 30, 10)\n 8: PLAN\n 9: PLAN\n 10: PLAN\n 11: PLAN\n 12: move_z([1, 2, 3, ..., 20], -20) # Descending slightly for added drama\n 13: PLAN\n 14: form_cone(60, 50, 1) # Inverted cone to convey dynamic tension\n END\n```"}, {"role": "user", "content": "The provided response failed with the following error:\nDrones {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19} get too close at waypoints {1, 2, 3, 4, 5}\n\nAnalyze the error, re-read the instructions and try again."}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: Vivaldi's \"Summer\" is filled with dramatic intensity and vivid imagery that evokes the power and fury of summer storms. It transitions from serene calmness to energetic chaos, reflecting both the beauty and the tumult of summer.\ncord_analysis: With dominant chords like G minor, D, and F, the composition features a somber and intense atmosphere typical of a minor key. This creates a mood of urgency and tension, which complements the rapid tempo and dynamic contrasts in the music.\nchoreography_plan: The choreography is designed to capture the dramatic energy and tension of \"Summer.\" Geometric patterns such as spirals and zig-zags will showcase the intensity and fluidity of the piece, with dynamic altitude changes mirroring the song's changing moods. Each structure reflects the song's emotional journey, from calm to intense, utilizing large, expressive formations for moments of high amplitude and novelty.\nchoreography:\n 1: form_circle([1, 4, 7, 10, 13, 16, 19], 150); form_circle([2, 5, 8, 11, 14, 17, 20], 150); form_circle([3, 6, 9, 12, 15, 18], 150)\n 2: PLAN\n 3: rotate(45, 'z')\n 4: PLAN\n 5: move_z([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 10); move_z([11, 12, 13, 14, 15, 16, 17, 18, 19, 20], -10)\n 6: PLAN\n 7: spiral(3, 100)\n 8: PLAN\n 9: PLAN\n 10: PLAN # pause before transition\n 11: form_star(140, 60, 70)\n 12: PLAN\n 13: zig_zag(4, 40, 20)\n 14: PLAN\n 15: PLAN\n 16: PLAN\n 17: PLAN # extra for transition\n 18: form_cone(60, 50, 0)\n 19: PLAN\n 20: spiral_speed(3, 120, 180, 2.5)\n 21: PLAN\n 22: PLAN\n END\n```"}, {"role": "user", "content": "The provided response failed with the following error:\nNumber of timesteps in output doesn't match the number of beats.\n\nAnalyze the error, re-read the instructions and try again."}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: Vivaldi's \"Summer\" exudes dramatic energy and intensity, capturing the dynamic essence of summer storms. It juxtaposes calm serenity with moments of intense chaos, exemplifying the beauty and fury of nature during the summer.\ncord_analysis: The piece is predominantly in G minor, with chords like G, D, and F creating a somber yet intense atmosphere. The use of minor chords heightens the sense of urgency and tension, enhancing the composition's vibrant and dynamic character.\nchoreography_plan: The choreography reflects the drama and energy of \"Summer,\" using geometric patterns like spirals and zig-zags to mirror the song's fluidity and tension. The choreography includes dynamic altitude changes to reflect the song's emotional shifts. Large formations and expressive movements coincide with high amplitude and novelty peaks, illustrating the piece's intensity and passion.\nchoreography:\n 1: form_circle([1, 5, 9, 13, 17], 120)\n 2: PLAN # Ensuring the circle formation transition completes\n 3: form_circle([2, 6, 10, 14, 18], 120)\n 4: PLAN\n 5: form_circle([3, 7, 11, 15, 19], 120)\n 6: PLAN\n 7: form_circle([4, 8, 12, 16, 20], 120) # Circles staggered to start the dynamic sequence\n 8: PLAN\n 9: spiral(3, 100)\n 10: PLAN\n 11: PLAN\n 12: move_z([1, 2, 3, 8, 9, 10, 11, 18, 19, 20], 20); move_z([4, 5, 6, 7, 12, 13, 14, 15, 16, 17], -20)\n 13: PLAN\n 14: zig_zag(3, 30, 15)\n END\n```"}, {"role": "user", "content": "Create the circle faster (no Plan), then use spiral."}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: Vivaldi's \"Summer\" captures the intensity and dynamic energy of the season, richly portraying summer's vibrant life and tumultuous storms. The music's quick transitions and powerful crescendos create an atmosphere of excitement and drama.\ncord_analysis: Predominantly in G minor, the piece features chords like G, D, and F. The minor key adds a somber and intense touch, evoking tension and urgency throughout the composition, perfectly complementing the stormy and passionate mood.\nchoreography_plan: The choreography mirrors the intensity and structure of Vivaldi's \"Summer\" through dynamic geometric patterns. The formation of circles reflects the fluidity and circular motion found in nature, while spirals and zig-zags illustrate the chaotic energy of summer storms. Altitude changes enhance the 3D effect, capturing the emotional highs and lows.\nchoreography:\n 1: form_circle([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 120); form_circle([11, 12, 13, 14, 15, 16, 17, 18, 19, 20], 120)\n 2: spiral(3, 100)\n 3: PLAN\n 4: PLAN\n 5: move_z([1, 3, 5, 7, 9, 11, 13, 15, 17, 19], 20); move_z([2, 4, 6, 8, 10, 12, 14, 16, 18, 20], -20)\n 6: PLAN\n 7: zig_zag(3, 30, 15)\n 8: PLAN\n 9: PLAN\n 10: form_star(140, 60, 70)\n 11: PLAN\n 12: wave(4, 110, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4] )\n 13: PLAN\n 14: PLAN\n END\n```"}, {"role": "user", "content": "Inverse the order of things. Do the circle with spiral towards the end. Start with the wave"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: Vivaldi's \"Summer\" epitomizes the dynamic intensity and power of the season. It ingeniously depicts summer's vibrant life and fierce storms through rapid transitions and powerful crescendos, generating a rich, dramatic atmosphere.\ncord_analysis: The piece, predominantly in G minor, features chords such as G, D, and F. The minor key imparts a somber yet intense quality, enhancing the music's urgency and tension, which aligns with the stormy and passionate theme.\nchoreography_plan: The choreography starts with a dynamic wave pattern to express the fluidity of the season, then transitions through zig-zags to illustrate the unpredictability of nature. The plan culminates in a structured spiral and circle formation, reflecting the intense and harmonious closure of the musical storm. Altitude variations are used to enhance the dramatic effect and add a 3D perspective to the choreography.\nchoreography:\n 1: wave(4, 110, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 2: PLAN\n 3: PLAN\n 4: PLAN\n 5: zig_zag(3, 30, 15)\n 6: PLAN\n 7: PLAN\n 8: form_star(140, 60, 70)\n 9: PLAN\n 10: move_z([1, 3, 5, 7, 9, 11, 13, 15, 17, 19], 20); move_z([2, 4, 6, 8, 10, 12, 14, 16, 18, 20], -20)\n 11: PLAN\n 12: spiral(3, 100)\n 13: PLAN\n 14: form_circle([1, 2, 3, ..., 20], 120)\n END\n```"}, {"role": "user", "content": "The provided response failed with the following error:\nDrones {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19} get too close at waypoints {16}\n\nAnalyze the error, re-read the instructions and try again."}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: Vivaldi's \"Summer\" is characterized by its vibrant and intense portrayal of the season's energy, capturing the tumultuous yet beautiful essence of summer storms with rapid transitions and dramatic crescendos.\ncord_analysis: The piece mainly in G minor with chords like G, D, and F, adds a nuanced layer of urgency and passion, as the minor key offers a sense of tension and intensity that aligns perfectly with the dramatic theme of the music.\nchoreography_plan: The choreography uses dynamic patterns to reflect the music's intensity and flow. Beginning with a wave formation to convey fluidity, it transitions into zig-zags to portray nature's chaos, and culminates in a structured spiral and circle to embody the harmony after the storm. Altitude changes add a 3D effect, enhancing the visual drama.\nchoreography:\n 1: wave(4, 110, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 2: PLAN\n 3: PLAN\n 4: PLAN\n 5: zig_zag(3, 30, 15)\n 6: PLAN\n 7: PLAN # transition betwen zig_zag and star\n 8: form_star(140, 60, 70)\n 9: PLAN\n 10: spiral(3, 120)\n 11: PLAN\n 12: PLAN\n 13: move_z([1, 3, 5, 7, 9, 11, 13, 15, 17, 19], 30); move_z([2, 4, 6, 8, 10, 12, 14, 16, 18, 20], -30)\n 14: PLAN\n END\n```"}, {"role": "user", "content": "Keep everything as is, but remove 2 of the 3 PLAN before the zigzag. It's too boring. Rather move the star eaelier and let it rotate"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: Vivaldi's \"Summer\" is characterized by its vibrant and intense portrayal of the season's energy, capturing the tumultuous yet beautiful essence of summer storms with rapid transitions and dramatic crescendos.\ncord_analysis: The piece mainly in G minor with chords like G, D, and F, adds a nuanced layer of urgency and passion, as the minor key offers a sense of tension and intensity that aligns perfectly with the dramatic theme of the music.\nchoreography_plan: The choreography utilizes dynamic patterns to reflect the music's intensity and flow. Beginning with a wave formation to convey fluidity, it transitions into zig-zags to portray nature's chaos. A star formation early in the sequence adds visual variety and rotates for dynamic effect. The dance culminates in a structured spiral and circle to embody harmony after the storm. Altitude changes add a dramatic 3D effect to the choreography.\nchoreography:\n 1: wave(4, 110, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 2: PLAN\n 3: PLAN\n 4: form_star(140, 60, 70)\n 5: rotate(30, 'z')\n 6: zig_zag(3, 30, 15)\n 7: PLAN # transition between zig_zag and spiral\n 8: spiral(3, 120)\n 9: PLAN\n 10: PLAN\n 11: move_z([1, 3, 5, 7, 9, 11, 13, 15, 17, 19], 30); move_z([2, 4, 6, 8, 10, 12, 14, 16, 18, 20], -30)\n 12: PLAN\n END\n```"}, {"role": "user", "content": "The provided response failed with the following error:\nChoreography plan is missing primitive at {13, 14}\n\nAnalyze the error, re-read the instructions and try again."}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: Vivaldi's \"Summer\" is characterized by its vibrant and intense portrayal of the season's energy, capturing the tumultuous yet beautiful essence of summer storms with rapid transitions and dramatic crescendos.\ncord_analysis: The piece mainly in G minor with chords like G, D, and F, adds a nuanced layer of urgency and passion, as the minor key offers a sense of tension and intensity that aligns perfectly with the dramatic theme of the music.\nchoreography_plan: The choreography utilizes dynamic patterns to reflect the music's intensity and flow. Beginning with a wave formation to convey fluidity, it transitions into zig-zags to portray nature's chaos. A star formation early in the sequence adds visual variety and rotates for dynamic effect. The dance culminates in a structured spiral and circle to embody harmony after the storm. Altitude changes add a dramatic 3D effect to the choreography.\nchoreography:\n 1: wave(4, 110, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4])\n 2: PLAN\n 3: PLAN\n 4: form_star(140, 60, 70)\n 5: rotate(30, 'z')\n 6: zig_zag(3, 30, 15)\n 7: PLAN # transition between zig_zag and spiral\n 8: spiral(3, 120)\n 9: PLAN\n 10: PLAN\n 11: PLAN # transition between spiral and the final formations\n 12: move_z([1, 3, 5, 7, 9, 11, 13, 15, 17, 19], 30); move_z([2, 4, 6, 8, 10, 12, 14, 16, 18, 20], -30)\n 13: PLAN # extra beat to finalize altitudes\n 14: form_circle([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 120); form_circle([11, 12, 13, 14, 15, 16, 17, 18, 19, 20], 120)\n END\n```"}] \ No newline at end of file diff --git a/swarm_gpt/data/presets/Vivaldi Summer | 20 | 20260430_062211/meta.json b/swarm_gpt/data/presets/Vivaldi Summer | 20 | 20260430_062211/meta.json deleted file mode 100644 index 8421a7a..0000000 --- a/swarm_gpt/data/presets/Vivaldi Summer | 20 | 20260430_062211/meta.json +++ /dev/null @@ -1 +0,0 @@ -{"n_drones": 20, "song": "Vivaldi Summer", "use_motion_primitives": true} \ No newline at end of file diff --git a/swarm_gpt/data/presets/Vivaldi Summer | 20 | 20260430_062211/trajectory.csv b/swarm_gpt/data/presets/Vivaldi Summer | 20 | 20260430_062211/trajectory.csv deleted file mode 100644 index 7c6ab36..0000000 --- a/swarm_gpt/data/presets/Vivaldi Summer | 20 | 20260430_062211/trajectory.csv +++ /dev/null @@ -1,3973 +0,0 @@ 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diff --git a/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20251205_104245/history.json b/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20251205_104245/history.json deleted file mode 100644 index 6f739fd..0000000 --- a/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20251205_104245/history.json +++ /dev/null @@ -1 +0,0 @@ -[{"role": "system", "content": "You're a helpful assistant that will help a human choreograph a dance for a swarm of small drones."}, {"role": "user", "content": "Generate the trajectories for a choreography for 9 drones dancing to the music of the song Vivaldi Summer. The trajectories are defined by coordinate waypoints that the drones fly to. Each movement can be defined by multiple template functions, which are explained below.\n# Information on the song: - Name: Vivaldi Summer - Number of beats: 14 - Beat times in 100ms: {1: 11, 2: 45, 3: 71, 4: 93, 5: 116, 6: 149, 7: 172, 8: 199, 9: 233, 10: 253, 11: 276, 12: 300, 13: 333, 14: 357} - Beat intervals in 100ms: {1: 11, 2: 33, 3: 26, 4: 22, 5: 23, 6: 33, 7: 23, 8: 27, 9: 34, 10: 20, 11: 23, 12: 24, 13: 33} - Beat amplitude in dBFS: {1: -31, 2: -20, 3: -35, 4: -19, 5: -26, 6: -17, 7: -20, 8: -23, 9: -21, 10: -19, 11: -22, 12: -23, 13: -24, 14: -27} - Beat novelty in %: {1: 66, 2: 25, 3: 100, 4: 18, 5: 32, 6: 44, 7: 15, 8: 14, 9: 26, 10: 13, 11: 21, 12: 22, 13: 29, 14: 21} - Beat chords: {1: 'G', 2: 'G', 3: 'D', 4: 'D', 5: 'D', 6: 'Gm', 7: 'F', 8: 'G', 9: 'Gm', 10: 'D', 11: 'D', 12: 'D', 13: 'Gm', 14: 'Gm'}\n# Information on the drones: - Number: 9 - Drones must not collide with each other. No two drones must be at the same coordinates at the same beat. - Starting positions: [[100, -100, 100], [100, 0, 100], [100, 100, 100], [0, -100, 100], [0, 0, 100], [0, 100, 100], [-100, -100, 100], [-100, 0, 100], [-100, 100, 100]] - Maximum step distance between waypoints in cm: {1: 113, 2: 331, 3: 263, 4: 223, 5: 227, 6: 327, 7: 225, 8: 267, 9: 339, 10: 203, 11: 231, 12: 237, 13: 333, 14: 237} - Lower limit for generated waypoints: [-200. -150. 10.] - Upper Limit for generated waypoints: [200. 150. 200.]\n# Information on the environment: - The drones are limited to [-200, 200] in x, [-200, 200] in y, [0, 200] in z - The drones must not touch the ground, i.e. their z coordinate must always be greater than 0 \n# Information on the choreography: - The drone choreography must have 14 waypoints, one for each beat. - Each drone must move at least once during the song. - Movements must depend on the beats of the song. If the beats are loud and novel, all drones must move. - If possible, coordinates for the drones should be symmetrical at each beat. - Successive waypoints should form patterns for single drones and for the whole swarm - Coordinates are given in cm. A drone flying at 1m height will have a z coordinate of 100. - The difference between two waypoints must be less than the maximum step distance in each dimension.\n E.g. if the maximum step distance between beat 3 and 4 is 50, the difference between the x, y,\n z coordinates of the waypoints at beat 3 and 4 must be less than 50 in each dimension.\n\n# Information on template functions: Make sure to limit actions to each drone once per time step. So if you choose swap for (1, 2) then you can only choose swap(3, 4) or two seperate move functions for each drone 2: time dependent template functions ( For such functions we need only one function call for one time step) - rotate:\n takes 2 parameters ( angle, axis ), and rotates all drones by 'angle' along 'axis'\n #angle is a float between [0, 360]\n #axis is one of 'x', 'y', or 'z', but for now always rotate around 'z'\n eg\n rotate(30, 'z')\n4: variadic functions ( such function takes a list with drone ids as arguments ) - center:\n Takes a list of drone IDs as input. It calculates the geometric center of the current positions of these specified drones and then moves all the drones with the matching IDs to this calculated center position.\n eg\n center([1, 2, 3])\n- move_z:\n Takes a list of drone IDs as input, and distance. It moves all the drones with the matching IDs along z axis by xcm. if distance is positive, it moves them up, otherwise down\n eg\n move_z([1, 2, 3], 10)\n- form_circle\n Takes a list of drone IDs as input, and z coordinate ( height ) for the circle formation. Radius of circle is decided by number of drones in list argument. So bigger circle for more drones.\n # height has to be greater than zero\n eg \n # Cone formation as 4 drones are at 150cm height, and 3 drones are at 50cm height with smaller radius as less drones.\n form_circle( [1, 2, 3, 4], 150 ), form_circle( [5, 6, 7], 50 )\n- form_star\n Takes 3 parameters ( height, spacing, delta_radius ) and creates a star in the z-plane, which is defined by 2 concentric circles in staggered angles with different radii. \n Height corresponds to the height of the star. Spacing corresponds to the minimum spacing between drones, where a higher spacing means larger star. \n Delta_radius corresponds to the difference of the 2 radii, where a larger delta_radius means a more spread out star\n #height has to be greater than zero\n #spacing is an integer between [40, 80]\n #delta_radius is an integer between [50 100]\n eg \n form_star( 120, 60, 50 )\n- form_cone\n Takes 3 parameters ( delta_height, spacing, is_inverted ) and creates a cone using concentric circles of different radii. Each layer has 4 additional drones.\n Delta_height is the difference in height between layers. A larger delta_height corresponds to a taller cone.\n Spacing is the minimum_spacing between drones of a layer. A larger spacing means a larger circle radius and wider cone.\n Is_inverted is a boolean value (an integer of 0 or 1) that determines if the cone is inverted or not. Inverted cones are more expressive and dynamic. \n #delta_height is an integer between [30, 80]\n #spacing is an integer between [40, 80]\n #is_inverted is an integer with the values 0 or 1\n eg \n form_star( 120, 60, 50 )\n\n\n5: multi time dependent functions ( For such functions we need only one function call for multiple time steps)\n Any multi-time dependent function needs to be followed by the 'PLAN' keyword for the number of steps minus 1. This means, for functions that span over `step` time steps, the function call must be followed by `step - 1` occurrences of 'PLAN'.\n These functions must be initiated `s - step + 1` beats before the last beat, where `s` is the total number of beats. This ensures that the multi-time function completes before the song ends.\n\n**Important Timing Constraint**: If you have a multi-time function that takes 3 steps, it must be called at least 2 steps before the last beat to allow for the necessary 'PLAN' steps. For example, with a 22-beat song, a 3-step function should be called by the 20th beat, followed by 'PLAN' for the 21st and 22nd beats. - spiral:\n takes 2 parameters ( steps, height ), and makes all drones move in a outward spiral pattern with the calculate radius ( from no of drones ), and number of steps at height h\n\n #steps is an integer between [2, 3]\n #height is an integer between [0, 200] that determines the height of the spiral\n #Since spiral is a multi time dependent function, it should be falled by 'PLAN' keyword for the number of steps -1 times, so if steps is 3, then spiral is called once, and PLAN follows twice in output\n # spiral needs to be called atleast 's' steps before last timestep as 's' PLAN follow the spiral call\n eg:\n 1: spiral(3, 50 )\n 2: PLAN\n 3: PLAN\n\n- spiral_speed:\n Takes 4 parameters (steps, height, speed, radius_increase), and has the same effect as spiral. The difference is that spiral_speed increases the radius of the spiral by radius_increase over the number of steps. In addition,\n speed defines the angle by which the drones rotate around the center. Lower speeds results in slower drone motions.\n # Steps is an integer between [2, 3]\n # height is an integer between [0, 200] that determines the height of the spiral\n # speeds is an integer between [0, 360] that defines the angle by which the drones rotate around the center. Very low speeds are in the range of 0 to 90, fast speeds are in the range of 100 to 360\n # radius_increase is a float between [1, 3] that increases the radius of the spiral over the number of steps\n eg:\n 1: spiral_speed(3, 100, 200, 1.5)\n 2: PLAN\n 3: PLAN\n\n- helix:\n takes 2 parameters ( steps, delta_h, height ), and makes all drones move in a helix pattern with the calculate radius ( from no of drones ), and number of steps.\n\n #steps is an integer between [2, 3]\n #delta_h is an integer between [0, 100], that changes the height of drones in the helix pattern per step\n #height is between [50, 100] as helix rises up with steps, lets be safe.\n #Since helix is a multi time dependent function, it should be falled by 'PLAN' keyword for the number of steps -1 times, so if steps is 3, then helix is called once, and PLAN follows twice in output\n eg:\n 1: helix(3, 20, 50)\n 2: PLAN\n 3: PLAN\n\n- twister:\n takes 3 parameter ( steps, omega, z_spacing ), and makes all drones move into a upside down cone pattern and rotate at the speed specified by omega, for the number of steps specified.\n Z_spacing corresponds to the height difference between neighbouring drones, where a smaller z_spacing corresponds to a more compact cone.\n\n #steps is an integer between [2, 3]\n #omega is an integer between [2, 20] with a higher omega corresponding to a more dynamic performance.\n #z_spacing is an integer between [5, 30]\n #Since twister is a multi time dependent function, it should be falled by 'PLAN' keyword for the number of steps -1 times, so if steps is 3, then conic_rotate is called once, and PLAN follows twice in output\n eg:\n 1: twister(3, 20, 10)\n 2: PLAN\n 3: PLAN\n\n- zig_zag:\n Moves all drones in a zigzag pattern. Takes 3 parameters\n Parameters: `steps` (int), `delta` (int) - the horizontal displacement per step, `delta_h` (int) - the vertical displacement per step.\n eg:\n 4: zig_zag(3, 20, 10)\n 5: PLAN\n 6: PLAN\n \n- wave:\n Moves all drones in a three-dimensional wave propagating through a two-dimensional rectangular elastic surface with bounded edges. The disturbance at any point on the surface is defined by its parameters in space and time, and the wave propagation is governed by the wave equation with a speed `c > 0`. Takes 5 parameters.\n Parameters:\n - `steps` (int): The number of discretization points.\n - `height` (float): The desired height of drones above the surface.\n - `mu_pairs` (list of list): List of \\((\u03bc1, \u03bc2)\\) pairs that define the spatial frequencies.\n - `a\u00b51\u00b52` (list of floats): List of amplitude coefficients corresponding to each \\((\u03bc1, \u03bc2)\\) pair for the sine component.\n - `b\u00b51\u00b52` (list of floats): List of amplitude coefficients corresponding to each \\((\u03bc1, \u03bc2)\\) pair for the cosine component.\n eg:\n 4: wave( 4, 10, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4] )\n 5: PLAN\n 6: PLAN\n 7: PLAN\n latex equn for the concerned wave equation:\n The disturbance \\(u(s, t)\\) at any point \\(s = (s_1, s_2) \\in [0, a] \\times [0, b]\\) \nat time \\(t\\) is governed by:\n\\[\nc^2 \\nabla^2 u(s, t) = \\frac{\\partial^2 u(s, t)}{\\partial t^2},\n\\]\nwhere \\( \\nabla^2 \\) is the spatial Laplacian and \\(c > 0\\) is the wave speed. \nThe solution:\n\\[\nu(s, t) = \\sum_{(\\mu_1, \\mu_2)} a_{\\mu_1 \\mu_2} \\sin \\left( \\frac{\\mu_1 \\pi s_1}{a} \\right) \n\\sin \\left( \\frac{\\mu_2 pi s_2}{b} \\right) \\sin(\\omega_{\\mu_1 \\mu_2} t) \n+ b_{\\mu_1 \\mu_2} \\sin \\left( \\frac{\\mu_1 \\pi s_1}{a} \\right) \n\\sin \\left( \\frac{\\mu_2 \\pi s_2}{b} \\right) \\cos(\\omega_{\\mu_1 \\mu_2} t),\n\\]\n\nwith frequencies:\n\n\\[\n\\omega_{\\mu_1 \\mu_2}^2 = c^2 \\pi^2 \\left( \\frac{\\mu_1^2}{a^2} + \\frac{\\mu_2^2}{b^2} \\right).\n\\]\n\nPLAN can also be used to indicate that the drones should maintain their current position for the next beat, so after forming cone formation, you can use PLAN to maintain the formation for next beat. Using `PLAN` also counts as part of the waypoints for the drones, so you can't have multi-time dependent functions as the last waypoint as we need to have `PLAN` after them in future steps.\n# Patterns: - Drones must fly in geometric patterns and shapes - Drones must fly in tight formations - The formations must be symmetrical if possible - Drones should move up and down in altitude multiple times for a 3D effect - Example patterns include spirals, triangles, squares, circles, lines\n# Response structure First, describe the emotions and mood associated with the song Vivaldi Summer. Then, give a description of the dominant chords of the song. Is it a major or minor cord? What does that mean for the mood? Next, give a specific description of the choreography you want to create. What patterns do you want the drones to fly? Why do you use these particular patterns? Last, fill out the individual waypoints for each drone. Adapt the distance and position of waypoints to the amplitude of the song's beats. Loud beats lead to larger movements, while soft beats lead to smaller movements. Furthermore, take into account the dominant chords of the song and the emotions and mood associated with these chords. Try to use a variety of functional options to design an amazing geometrical choreography. Make sure to only do motion planning per beat, so in otherwords motion planning steps should be limited to 14,and end yaml output with END like in examples below. You can use multiple beat functions for more than 3 timestep as well like spiral(), zig_zag(), wave(), helix(). Only do complex primtives. Adjust change in height accordingly like do them for 5 steps if you have to, and make height increments small. Limit to at max 4 primitives. Between any two primitives, there must be a extra PLAN which consumes a timestep so that there is enough time for transition.\n"}, {"role": "system", "content": "# Example prompts and responses - Example 1:\n Prompt:\n # Information on the song:\n - Name: r\u00eaverie\n - Number of beats: 22\n - Beat times in 100ms: {{1: 7, 2: 20, 3: 29, 4: 41, 5: 55, 6: 66, 7: 80, 8: 96, 9: 104, 10: 112, 11: 119, 12: 128, 13: 137, 14: 147, 15: 157, 16: 167, 17: 187, 18: 196, 19: 210, 20: 221, 21: 232, 22: 242}}\n - Beat intervals in 100ms: {{1: 7, 2: 13, 3: 9, 4: 12, 5: 14, 6: 11, 7: 13, 8: 16, 9: 9, 10: 7, 11: 8, 12: 8, 13: 9, 14: 10, 15: 10, 16: 10, 17: 20, 18: 8, 19: 14, 20: 12, 21: 10}}\n - Beat amplitude in dBFS: {{1: -53, 2: -40, 3: -40, 4: -39, 5: -44, 6: -44, 7: -36, 8: -43, 9: -34, 10: -35, 11: -37, 12: -37, 13: -39, 14: -44, 15: -38, 16: -32, 17: -35, 18: -34, 19: -33, 20: -37, 21: -35, 22: -38}}\n - Beat novelty in %: {{1: 37, 2: 52, 3: 24, 4: 31, 5: 40, 6: 98, 7: 45, 8: 21, 9: 76, 10: 72, 11: 52, 12: 29, 13: 10, 14: 15, 15: 81, 16: 100, 17: 27, 18: 65, 19: 64, 20: 28, 21: 19, 22: 30}}\n - Beat chords: {{: 'A#m', 2: 'Cm', 3: 'Cm', 4: 'Cm', 5: 'Cm', 6: 'Cm', 7: 'Cm', 8: 'A#m', 9: 'C', 10: 'C', 11: 'C', 12: 'C', 13: 'Cm', 14: 'Cm', 15: 'Gm', 16: 'A#m', 17: 'Gm', 18: 'Gm', 19: 'Gm', 20: 'F', 21: 'F', 22: 'F'}}\n \n # Information on the drones:\n - Number: 3\n - Drones must not collide with each other. No two drones must be at the same coordinates at the same beat.\n - Starting positions: [[100, 100, 100], [-100, 100, 100], [0, -100, 100]]\n - Maximum step distance between waypoints in cm: 100cm\n\n Response:\n song_mood: Debussy's R\u00e9verie is a short and expressive piano piece that captures the essence of daydreaming and contemplation.\n It is characteristic of Debussy's impressionistic style, known for its use of unconventional harmonies, tonal colors, and fluidity.\n cord_analysis: The song is in the key of F major, with a few chords in the song being A#m, Cm, G, F, and Gm. The mix of major and minor chords indicates that the music is neither elated nor gloomy. It remains in a state of contemplation and daydreaming, with a touch of melancholy.\n choreography_plan: The main intention of the choreography is to reflect the dreamy and contemplative mood of the song. Therefore, drones must not move too fast, and waypoints must not be too far from each other. \n The beat amplitude in dBFS roughly remains within the -50 to -30 interval, indicating that the song is very quiet. Consequently, the drones should move slowly and gently. Across all beats, the intervals are evenly spaced around 7 to 13, indicating that the drones should move at a steady pace.\n so we have no pause that we have to accentuate in the choreography. We will therefore focus on the beat amplitude.\n For beats 1 to 5, the three drones will spiral to a center point around [0, 0, 100]]. The novelty of the beats is low to moderate at around 25 to 50, and the beat amplitude is also low at around -50 to -40. Thus, we plan the first waypoints closer to each other.\n For the following beats 6 to 11, the novelty of the beats reaches a peak at 98 and a slightly raised beat amplitude of up to -35. To account for the peak, one drone will rise slowly in the center while the other two drones begin to slowly rotate in the other direction and then move outwards. We let the drones move slowly because the amplitude has not yet reached full force. We change the direction of the drones to reflect the dreamy and swirling mood of the song.\n For the next beats 12 to 17, the novelty and amplitudes of the beats are reaching the peak. Accordingly, the drones will move into the triangle formation again, while oscillating in altitude. Although we now move faster, we adhere to the maximum step distance of approximately 80 to 100cm.\n Beats 18 to 22 are the final beats of the song, with decreasing novelty and amplitude. The drones will slowly spiral outwards and downwards to mark the decline in tension and the end of the dance.\n choreography:\n 1: spiral(5, 100)\n 2: PLAN\n 3: PLAN\n 4: PLAN\n 5: PLAN\n 6: PLAN # extra PLAN for primitive switch\n 7: rotate(60, 'z')\n 8: PLAN # extra\n 9: rotate(-90, 'z')\n 10: PLAN # extra\n 11: move_z( [1,2,3], 30)\n 12: PLAN # extra\n 13: spiral(4, 100)\n 14: PLAN\n 15: PLAN\n 16: PLAN\n 17: PLAN # extra\n 18: zig_zag(5, 20, 20)\n 19: PLAN\n 20: PLAN\n 21: PLAN \n 22: PLAN\n END\n"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: Vivaldi's \"Summer\" from \"The Four Seasons\" is an energetic and sometimes frantic composition that embodies the feeling of a hot, intense summer. The music alternates between calm, peaceful moments and sudden bursts of storm-like intensity.\ncord_analysis: The dominant chords in the song include G, D, Gm, F, and D. The use of both major and minor chords gives the piece a dynamic range of emotions. G major and D major chords often signify brightness and strength, while G minor and D minor signify tension and darkness, reflecting a stormy and unpredictable summer.\nchoreography_plan: The choreography for the drones will mirror the dynamics of the song, with fluid, swirling motions during the calmer sections and sudden, energetic movements during the intense sections. Symmetry and geometric patterns will enhance the visual spectacle. We'll employ spirals, circles, and rotations to reflect the swirling nature of a summer storm. Movements will be synchronized with the beat amplitude and novelty, with larger movements during louder and novel beats.\nchoreography:\n 1: form_circle([1, 2, 3, 4, 5, 6, 7, 8, 9], 120)\n 2: PLAN # transition\n 3: helix(3, 20, 100)\n 4: PLAN\n 5: PLAN\n 6: form_star(150, 50, 60)\n 7: PLAN # transition\n 8: form_cone(60, 50, 0)\n 9: PLAN # transition\n 10: move_z([1, 2, 3, 4, 5, 6, 7, 8, 9], -30)\n 11: PLAN # transition\n 12: move_z([1, 2, 3, 4, 5, 6, 7, 8, 9], 20)\n 13: PLAN # transition\n 14: spiral_speed(3, 150, 200, 1.5)\n END\n```"}] \ No newline at end of file diff --git a/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20251205_104245/meta.json b/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20251205_104245/meta.json deleted file mode 100644 index b1af26d..0000000 --- a/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20251205_104245/meta.json +++ /dev/null @@ -1 +0,0 @@ -{"n_drones": 9, "song": "Vivaldi Summer", "use_motion_primitives": true} \ No newline at end of file diff --git a/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20251205_104245/trajectory.csv b/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20251205_104245/trajectory.csv deleted file mode 100644 index 829fc61..0000000 --- a/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20251205_104245/trajectory.csv +++ /dev/null @@ -1,3973 +0,0 @@ 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deleted file mode 100644 index 32238bb..0000000 --- a/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20251205_105804/history.json +++ /dev/null @@ -1 +0,0 @@ -[{"role": "system", "content": "You're a helpful assistant that will help a human choreograph a dance for a swarm of small drones."}, {"role": "user", "content": "Generate the trajectories for a choreography for 9 drones dancing to the music of the song Vivaldi Summer. The trajectories are defined by coordinate waypoints that the drones fly to. Each movement can be defined by multiple template functions, which are explained below.\n# Information on the song: - Name: Vivaldi Summer - Number of beats: 14 - Beat times in 100ms: {1: 11, 2: 45, 3: 71, 4: 93, 5: 116, 6: 149, 7: 172, 8: 199, 9: 233, 10: 253, 11: 276, 12: 300, 13: 333, 14: 357} - Beat intervals in 100ms: {1: 11, 2: 33, 3: 26, 4: 22, 5: 23, 6: 33, 7: 23, 8: 27, 9: 34, 10: 20, 11: 23, 12: 24, 13: 33} - Beat amplitude in dBFS: {1: -31, 2: -20, 3: -35, 4: -19, 5: -26, 6: -17, 7: -20, 8: -23, 9: -21, 10: -19, 11: -22, 12: -23, 13: -24, 14: -27} - Beat novelty in %: {1: 66, 2: 25, 3: 100, 4: 18, 5: 32, 6: 44, 7: 15, 8: 14, 9: 26, 10: 13, 11: 21, 12: 22, 13: 29, 14: 21} - Beat chords: {1: 'G', 2: 'G', 3: 'D', 4: 'D', 5: 'D', 6: 'Gm', 7: 'F', 8: 'G', 9: 'Gm', 10: 'D', 11: 'D', 12: 'D', 13: 'Gm', 14: 'Gm'}\n# Information on the drones: - Number: 9 - Drones must not collide with each other. No two drones must be at the same coordinates at the same beat. - Starting positions: [[100, -100, 100], [100, 0, 100], [100, 100, 100], [0, -100, 100], [0, 0, 100], [0, 100, 100], [-100, -100, 100], [-100, 0, 100], [-100, 100, 100]] - Maximum step distance between waypoints in cm: {1: 113, 2: 331, 3: 263, 4: 223, 5: 227, 6: 327, 7: 225, 8: 267, 9: 339, 10: 203, 11: 231, 12: 237, 13: 333, 14: 237} - Lower limit for generated waypoints: [-200. -150. 10.] - Upper Limit for generated waypoints: [200. 150. 200.]\n# Information on the environment: - The drones are limited to [-200, 200] in x, [-200, 200] in y, [0, 200] in z - The drones must not touch the ground, i.e. their z coordinate must always be greater than 0 \n# Information on the choreography: - The drone choreography must have 14 waypoints, one for each beat. - Each drone must move at least once during the song. - Movements must depend on the beats of the song. If the beats are loud and novel, all drones must move. - If possible, coordinates for the drones should be symmetrical at each beat. - Successive waypoints should form patterns for single drones and for the whole swarm - Coordinates are given in cm. A drone flying at 1m height will have a z coordinate of 100. - The difference between two waypoints must be less than the maximum step distance in each dimension.\n E.g. if the maximum step distance between beat 3 and 4 is 50, the difference between the x, y,\n z coordinates of the waypoints at beat 3 and 4 must be less than 50 in each dimension.\n\n# Information on template functions: Make sure to limit actions to each drone once per time step. So if you choose swap for (1, 2) then you can only choose swap(3, 4) or two seperate move functions for each drone 2: time dependent template functions ( For such functions we need only one function call for one time step) - rotate:\n takes 2 parameters ( angle, axis ), and rotates all drones by 'angle' along 'axis'\n #angle is a float between [0, 360]\n #axis is one of 'x', 'y', or 'z', but for now always rotate around 'z'\n eg\n rotate(30, 'z')\n4: variadic functions ( such function takes a list with drone ids as arguments ) - center:\n Takes a list of drone IDs as input. It calculates the geometric center of the current positions of these specified drones and then moves all the drones with the matching IDs to this calculated center position.\n eg\n center([1, 2, 3])\n- move_z:\n Takes a list of drone IDs as input, and distance. It moves all the drones with the matching IDs along z axis by xcm. if distance is positive, it moves them up, otherwise down\n eg\n move_z([1, 2, 3], 10)\n- form_circle\n Takes a list of drone IDs as input, and z coordinate ( height ) for the circle formation. Radius of circle is decided by number of drones in list argument. So bigger circle for more drones.\n # height has to be greater than zero\n eg \n # Cone formation as 4 drones are at 150cm height, and 3 drones are at 50cm height with smaller radius as less drones.\n form_circle( [1, 2, 3, 4], 150 ), form_circle( [5, 6, 7], 50 )\n- form_star\n Takes 3 parameters ( height, spacing, delta_radius ) and creates a star in the z-plane, which is defined by 2 concentric circles in staggered angles with different radii. \n Height corresponds to the height of the star. Spacing corresponds to the minimum spacing between drones, where a higher spacing means larger star. \n Delta_radius corresponds to the difference of the 2 radii, where a larger delta_radius means a more spread out star\n #height has to be greater than zero\n #spacing is an integer between [40, 80]\n #delta_radius is an integer between [50 100]\n eg \n form_star( 120, 60, 50 )\n- form_cone\n Takes 3 parameters ( delta_height, spacing, is_inverted ) and creates a cone using concentric circles of different radii. Each layer has 4 additional drones.\n Delta_height is the difference in height between layers. A larger delta_height corresponds to a taller cone.\n Spacing is the minimum_spacing between drones of a layer. A larger spacing means a larger circle radius and wider cone.\n Is_inverted is a boolean value (an integer of 0 or 1) that determines if the cone is inverted or not. Inverted cones are more expressive and dynamic. \n #delta_height is an integer between [30, 80]\n #spacing is an integer between [40, 80]\n #is_inverted is an integer with the values 0 or 1\n eg \n form_star( 120, 60, 50 )\n\n\n5: multi time dependent functions ( For such functions we need only one function call for multiple time steps)\n Any multi-time dependent function needs to be followed by the 'PLAN' keyword for the number of steps minus 1. This means, for functions that span over `step` time steps, the function call must be followed by `step - 1` occurrences of 'PLAN'.\n These functions must be initiated `s - step + 1` beats before the last beat, where `s` is the total number of beats. This ensures that the multi-time function completes before the song ends.\n\n**Important Timing Constraint**: If you have a multi-time function that takes 3 steps, it must be called at least 2 steps before the last beat to allow for the necessary 'PLAN' steps. For example, with a 22-beat song, a 3-step function should be called by the 20th beat, followed by 'PLAN' for the 21st and 22nd beats. - spiral:\n takes 2 parameters ( steps, height ), and makes all drones move in a outward spiral pattern with the calculate radius ( from no of drones ), and number of steps at height h\n\n #steps is an integer between [2, 3]\n #height is an integer between [0, 200] that determines the height of the spiral\n #Since spiral is a multi time dependent function, it should be falled by 'PLAN' keyword for the number of steps -1 times, so if steps is 3, then spiral is called once, and PLAN follows twice in output\n # spiral needs to be called atleast 's' steps before last timestep as 's' PLAN follow the spiral call\n eg:\n 1: spiral(3, 50 )\n 2: PLAN\n 3: PLAN\n\n- spiral_speed:\n Takes 4 parameters (steps, height, speed, radius_increase), and has the same effect as spiral. The difference is that spiral_speed increases the radius of the spiral by radius_increase over the number of steps. In addition,\n speed defines the angle by which the drones rotate around the center. Lower speeds results in slower drone motions.\n # Steps is an integer between [2, 3]\n # height is an integer between [0, 200] that determines the height of the spiral\n # speeds is an integer between [0, 360] that defines the angle by which the drones rotate around the center. Very low speeds are in the range of 0 to 90, fast speeds are in the range of 100 to 360\n # radius_increase is a float between [1, 3] that increases the radius of the spiral over the number of steps\n eg:\n 1: spiral_speed(3, 100, 200, 1.5)\n 2: PLAN\n 3: PLAN\n\n- helix:\n takes 2 parameters ( steps, delta_h, height ), and makes all drones move in a helix pattern with the calculate radius ( from no of drones ), and number of steps.\n\n #steps is an integer between [2, 3]\n #delta_h is an integer between [0, 100], that changes the height of drones in the helix pattern per step\n #height is between [50, 100] as helix rises up with steps, lets be safe.\n #Since helix is a multi time dependent function, it should be falled by 'PLAN' keyword for the number of steps -1 times, so if steps is 3, then helix is called once, and PLAN follows twice in output\n eg:\n 1: helix(3, 20, 50)\n 2: PLAN\n 3: PLAN\n\n- twister:\n takes 3 parameter ( steps, omega, z_spacing ), and makes all drones move into a upside down cone pattern and rotate at the speed specified by omega, for the number of steps specified.\n Z_spacing corresponds to the height difference between neighbouring drones, where a smaller z_spacing corresponds to a more compact cone.\n\n #steps is an integer between [2, 3]\n #omega is an integer between [2, 20] with a higher omega corresponding to a more dynamic performance.\n #z_spacing is an integer between [5, 30]\n #Since twister is a multi time dependent function, it should be falled by 'PLAN' keyword for the number of steps -1 times, so if steps is 3, then conic_rotate is called once, and PLAN follows twice in output\n eg:\n 1: twister(3, 20, 10)\n 2: PLAN\n 3: PLAN\n\n- zig_zag:\n Moves all drones in a zigzag pattern. Takes 3 parameters\n Parameters: `steps` (int), `delta` (int) - the horizontal displacement per step, `delta_h` (int) - the vertical displacement per step.\n eg:\n 4: zig_zag(3, 20, 10)\n 5: PLAN\n 6: PLAN\n \n- wave:\n Moves all drones in a three-dimensional wave propagating through a two-dimensional rectangular elastic surface with bounded edges. The disturbance at any point on the surface is defined by its parameters in space and time, and the wave propagation is governed by the wave equation with a speed `c > 0`. Takes 5 parameters.\n Parameters:\n - `steps` (int): The number of discretization points.\n - `height` (float): The desired height of drones above the surface.\n - `mu_pairs` (list of list): List of \\((\u03bc1, \u03bc2)\\) pairs that define the spatial frequencies.\n - `a\u00b51\u00b52` (list of floats): List of amplitude coefficients corresponding to each \\((\u03bc1, \u03bc2)\\) pair for the sine component.\n - `b\u00b51\u00b52` (list of floats): List of amplitude coefficients corresponding to each \\((\u03bc1, \u03bc2)\\) pair for the cosine component.\n eg:\n 4: wave( 4, 10, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4] )\n 5: PLAN\n 6: PLAN\n 7: PLAN\n latex equn for the concerned wave equation:\n The disturbance \\(u(s, t)\\) at any point \\(s = (s_1, s_2) \\in [0, a] \\times [0, b]\\) \nat time \\(t\\) is governed by:\n\\[\nc^2 \\nabla^2 u(s, t) = \\frac{\\partial^2 u(s, t)}{\\partial t^2},\n\\]\nwhere \\( \\nabla^2 \\) is the spatial Laplacian and \\(c > 0\\) is the wave speed. \nThe solution:\n\\[\nu(s, t) = \\sum_{(\\mu_1, \\mu_2)} a_{\\mu_1 \\mu_2} \\sin \\left( \\frac{\\mu_1 \\pi s_1}{a} \\right) \n\\sin \\left( \\frac{\\mu_2 pi s_2}{b} \\right) \\sin(\\omega_{\\mu_1 \\mu_2} t) \n+ b_{\\mu_1 \\mu_2} \\sin \\left( \\frac{\\mu_1 \\pi s_1}{a} \\right) \n\\sin \\left( \\frac{\\mu_2 \\pi s_2}{b} \\right) \\cos(\\omega_{\\mu_1 \\mu_2} t),\n\\]\n\nwith frequencies:\n\n\\[\n\\omega_{\\mu_1 \\mu_2}^2 = c^2 \\pi^2 \\left( \\frac{\\mu_1^2}{a^2} + \\frac{\\mu_2^2}{b^2} \\right).\n\\]\n\nPLAN can also be used to indicate that the drones should maintain their current position for the next beat, so after forming cone formation, you can use PLAN to maintain the formation for next beat. Using `PLAN` also counts as part of the waypoints for the drones, so you can't have multi-time dependent functions as the last waypoint as we need to have `PLAN` after them in future steps.\n# Patterns: - Drones must fly in geometric patterns and shapes - Drones must fly in tight formations - The formations must be symmetrical if possible - Drones should move up and down in altitude multiple times for a 3D effect - Example patterns include spirals, triangles, squares, circles, lines\n# Response structure First, describe the emotions and mood associated with the song Vivaldi Summer. Then, give a description of the dominant chords of the song. Is it a major or minor cord? What does that mean for the mood? Next, give a specific description of the choreography you want to create. What patterns do you want the drones to fly? Why do you use these particular patterns? Last, fill out the individual waypoints for each drone. Adapt the distance and position of waypoints to the amplitude of the song's beats. Loud beats lead to larger movements, while soft beats lead to smaller movements. Furthermore, take into account the dominant chords of the song and the emotions and mood associated with these chords. Try to use a variety of functional options to design an amazing geometrical choreography. Make sure to only do motion planning per beat, so in otherwords motion planning steps should be limited to 14,and end yaml output with END like in examples below. You can use multiple beat functions for more than 3 timestep as well like spiral(), zig_zag(), wave(), helix(). Only do complex primtives. Adjust change in height accordingly like do them for 5 steps if you have to, and make height increments small. Limit to at max 4 primitives. Between any two primitives, there must be a extra PLAN which consumes a timestep so that there is enough time for transition.\n"}, {"role": "system", "content": "# Example prompts and responses - Example 1:\n Prompt:\n # Information on the song:\n - Name: r\u00eaverie\n - Number of beats: 22\n - Beat times in 100ms: {{1: 7, 2: 20, 3: 29, 4: 41, 5: 55, 6: 66, 7: 80, 8: 96, 9: 104, 10: 112, 11: 119, 12: 128, 13: 137, 14: 147, 15: 157, 16: 167, 17: 187, 18: 196, 19: 210, 20: 221, 21: 232, 22: 242}}\n - Beat intervals in 100ms: {{1: 7, 2: 13, 3: 9, 4: 12, 5: 14, 6: 11, 7: 13, 8: 16, 9: 9, 10: 7, 11: 8, 12: 8, 13: 9, 14: 10, 15: 10, 16: 10, 17: 20, 18: 8, 19: 14, 20: 12, 21: 10}}\n - Beat amplitude in dBFS: {{1: -53, 2: -40, 3: -40, 4: -39, 5: -44, 6: -44, 7: -36, 8: -43, 9: -34, 10: -35, 11: -37, 12: -37, 13: -39, 14: -44, 15: -38, 16: -32, 17: -35, 18: -34, 19: -33, 20: -37, 21: -35, 22: -38}}\n - Beat novelty in %: {{1: 37, 2: 52, 3: 24, 4: 31, 5: 40, 6: 98, 7: 45, 8: 21, 9: 76, 10: 72, 11: 52, 12: 29, 13: 10, 14: 15, 15: 81, 16: 100, 17: 27, 18: 65, 19: 64, 20: 28, 21: 19, 22: 30}}\n - Beat chords: {{: 'A#m', 2: 'Cm', 3: 'Cm', 4: 'Cm', 5: 'Cm', 6: 'Cm', 7: 'Cm', 8: 'A#m', 9: 'C', 10: 'C', 11: 'C', 12: 'C', 13: 'Cm', 14: 'Cm', 15: 'Gm', 16: 'A#m', 17: 'Gm', 18: 'Gm', 19: 'Gm', 20: 'F', 21: 'F', 22: 'F'}}\n \n # Information on the drones:\n - Number: 3\n - Drones must not collide with each other. No two drones must be at the same coordinates at the same beat.\n - Starting positions: [[100, 100, 100], [-100, 100, 100], [0, -100, 100]]\n - Maximum step distance between waypoints in cm: 100cm\n\n Response:\n song_mood: Debussy's R\u00e9verie is a short and expressive piano piece that captures the essence of daydreaming and contemplation.\n It is characteristic of Debussy's impressionistic style, known for its use of unconventional harmonies, tonal colors, and fluidity.\n cord_analysis: The song is in the key of F major, with a few chords in the song being A#m, Cm, G, F, and Gm. The mix of major and minor chords indicates that the music is neither elated nor gloomy. It remains in a state of contemplation and daydreaming, with a touch of melancholy.\n choreography_plan: The main intention of the choreography is to reflect the dreamy and contemplative mood of the song. Therefore, drones must not move too fast, and waypoints must not be too far from each other. \n The beat amplitude in dBFS roughly remains within the -50 to -30 interval, indicating that the song is very quiet. Consequently, the drones should move slowly and gently. Across all beats, the intervals are evenly spaced around 7 to 13, indicating that the drones should move at a steady pace.\n so we have no pause that we have to accentuate in the choreography. We will therefore focus on the beat amplitude.\n For beats 1 to 5, the three drones will spiral to a center point around [0, 0, 100]]. The novelty of the beats is low to moderate at around 25 to 50, and the beat amplitude is also low at around -50 to -40. Thus, we plan the first waypoints closer to each other.\n For the following beats 6 to 11, the novelty of the beats reaches a peak at 98 and a slightly raised beat amplitude of up to -35. To account for the peak, one drone will rise slowly in the center while the other two drones begin to slowly rotate in the other direction and then move outwards. We let the drones move slowly because the amplitude has not yet reached full force. We change the direction of the drones to reflect the dreamy and swirling mood of the song.\n For the next beats 12 to 17, the novelty and amplitudes of the beats are reaching the peak. Accordingly, the drones will move into the triangle formation again, while oscillating in altitude. Although we now move faster, we adhere to the maximum step distance of approximately 80 to 100cm.\n Beats 18 to 22 are the final beats of the song, with decreasing novelty and amplitude. The drones will slowly spiral outwards and downwards to mark the decline in tension and the end of the dance.\n choreography:\n 1: spiral(5, 100)\n 2: PLAN\n 3: PLAN\n 4: PLAN\n 5: PLAN\n 6: PLAN # extra PLAN for primitive switch\n 7: rotate(60, 'z')\n 8: PLAN # extra\n 9: rotate(-90, 'z')\n 10: PLAN # extra\n 11: move_z( [1,2,3], 30)\n 12: PLAN # extra\n 13: spiral(4, 100)\n 14: PLAN\n 15: PLAN\n 16: PLAN\n 17: PLAN # extra\n 18: zig_zag(5, 20, 20)\n 19: PLAN\n 20: PLAN\n 21: PLAN \n 22: PLAN\n END\n"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: Vivaldi's \"Summer\" from \"The Four Seasons\" embodies the intensity and passion of the summer season. The music is dynamic, energetic, and dramatic with sudden bursts of energy and briskness, depicting the thunderstorms and heat of summer.\ncord_analysis: The piece is predominantly in G minor, which adds to its dramatic and intense feel. Minor chords typically evoke feelings of tension, melancholy, and a sense of urgency, which is fitting for the turbulent and powerful movements in this piece.\nchoreography_plan: The choreography plan will reflect the intensity and turbulence of summer storms, moving between calm and chaos. The drones will form various geometric patterns that evolve with the music, emphasizing symmetry and dynamic movements. The movements will take advantage of the changes in amplitude and novelty to create a spectacle that synchronizes with the music's dramatic shifts.\n\nchoreography:\n 1: form_circle([1, 2, 3, 4, 5, 6, 7, 8, 9], 100)\n 2: PLAN # extra PLAN for primitive switch\n 3: rotate(45, 'z')\n 4: PLAN # extra PLAN for primitive switch\n 5: form_cone(50, 60, 0)\n 6: PLAN\n 7: PLAN # extra PLAN for primitive switch\n 8: form_star(120, 60, 60)\n 9: PLAN\n 10: PLAN # extra PLAN for primitive switch\n 11: helix(3, 20, 100)\n 12: PLAN\n 13: PLAN\n 14: PLAN # extra PLAN for ending formation\n END\n```"}] \ No newline at end of file diff --git a/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20251205_105804/meta.json b/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20251205_105804/meta.json deleted file mode 100644 index b1af26d..0000000 --- a/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20251205_105804/meta.json +++ /dev/null @@ -1 +0,0 @@ -{"n_drones": 9, "song": "Vivaldi Summer", "use_motion_primitives": true} \ No newline at end of file diff --git a/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20251205_105804/trajectory.csv b/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20251205_105804/trajectory.csv deleted file mode 100644 index 91c782d..0000000 --- a/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20251205_105804/trajectory.csv +++ /dev/null @@ -1,3973 +0,0 @@ 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-39.710000,1.043128,-0.989209,1.042831,0.009030,-0.068996,0.012331,1.004966,-0.064903,1.079598,0.014646,0.011266,0.002460,0.996539,1.045338,1.073649,0.083941,0.026416,-0.004643,0.013073,-1.006984,1.042646,-0.026798,-0.088215,0.032713,-0.047988,-0.017406,1.063352,0.055554,0.020132,0.005309,-0.047839,1.023124,1.078430,0.007837,0.011378,0.003004,-0.987241,-1.039634,1.053503,-0.088929,-0.023521,0.011986,-1.018583,-0.045777,1.063528,-0.052762,0.053685,0.005138,-1.022644,0.972982,1.079183,-0.011577,0.020006,0.002787 diff --git a/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20251205_105804/waypoints.npy b/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20251205_105804/waypoints.npy deleted file mode 100644 index de186be..0000000 Binary files a/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20251205_105804/waypoints.npy and /dev/null differ diff --git a/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20260331_171655/history.json b/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20260331_171655/history.json deleted file mode 100644 index 9441ad2..0000000 --- a/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20260331_171655/history.json +++ /dev/null @@ -1 +0,0 @@ -[{"role": "system", "content": "You're a helpful assistant that will help a human choreograph a dance for a swarm of small drones."}, {"role": "user", "content": "Generate the trajectories for a choreography for 9 drones dancing to the music of the song Vivaldi Summer. The trajectories are defined by coordinate waypoints that the drones fly to. Each movement can be defined by multiple template functions, which are explained below.\n# Information on the song: - Name: Vivaldi Summer - Number of beats: 14 - Beat times in 100ms: {1: 11, 2: 45, 3: 71, 4: 93, 5: 116, 6: 149, 7: 172, 8: 199, 9: 233, 10: 253, 11: 276, 12: 300, 13: 333, 14: 357} - Beat intervals in 100ms: {1: 11, 2: 33, 3: 26, 4: 22, 5: 23, 6: 33, 7: 23, 8: 27, 9: 34, 10: 20, 11: 23, 12: 24, 13: 33} - Beat amplitude in dBFS: {1: -31, 2: -20, 3: -35, 4: -19, 5: -26, 6: -17, 7: -20, 8: -23, 9: -21, 10: -19, 11: -22, 12: -23, 13: -24, 14: -27} - Beat novelty in %: {1: 66, 2: 25, 3: 100, 4: 18, 5: 32, 6: 44, 7: 15, 8: 14, 9: 26, 10: 13, 11: 21, 12: 22, 13: 29, 14: 21} - Beat chords: {1: 'G', 2: 'G', 3: 'D', 4: 'D', 5: 'D', 6: 'Gm', 7: 'F', 8: 'G', 9: 'Gm', 10: 'D', 11: 'D', 12: 'D', 13: 'Gm', 14: 'Gm'}\n# Information on the drones: - Number: 9 - Drones must not collide with each other. No two drones must be at the same coordinates at the same beat. - Starting positions: [[-50, -50, 100], [-50, 0, 100], [-50, 50, 100], [0, -50, 100], [0, 0, 100], [0, 50, 100], [50, -50, 100], [50, 0, 100], [50, 50, 100]] - Maximum step distance between waypoints in cm: {1: 113, 2: 331, 3: 263, 4: 223, 5: 227, 6: 327, 7: 225, 8: 267, 9: 339, 10: 203, 11: 231, 12: 237, 13: 333, 14: 237} - Lower limit for generated waypoints: [-140. -90. 10.] - Upper Limit for generated waypoints: [140. 90. 200.]\n# Information on the environment: - The drones are limited to [-200, 200] in x, [-200, 200] in y, [0, 200] in z - The drones must not touch the ground, i.e. their z coordinate must always be greater than 0 \n# Information on the choreography: - The drone choreography must have 14 waypoints, one for each beat. - Each drone must move at least once during the song. - Movements must depend on the beats of the song. If the beats are loud and novel, all drones must move. - If possible, coordinates for the drones should be symmetrical at each beat. - Successive waypoints should form patterns for single drones and for the whole swarm - Coordinates are given in cm. A drone flying at 1m height will have a z coordinate of 100. - The difference between two waypoints must be less than the maximum step distance in each dimension.\n E.g. if the maximum step distance between beat 3 and 4 is 50, the difference between the x, y,\n z coordinates of the waypoints at beat 3 and 4 must be less than 50 in each dimension.\n\n# Information on template functions: Make sure to limit actions to each drone once per time step. So if you choose swap for (1, 2) then you can only choose swap(3, 4) or two seperate move functions for each drone 2: time dependent template functions ( For such functions we need only one function call for one time step) - rotate:\n takes 2 parameters ( angle, axis ), and rotates all drones by 'angle' along 'axis'\n #angle is a float between [0, 360]\n #axis is one of 'x', 'y', or 'z', but for now always rotate around 'z'\n eg\n rotate(30, 'z')\n4: variadic functions ( such function takes a list with drone ids as arguments ) - center:\n Takes a list of drone IDs as input. It calculates the geometric center of the current positions of these specified drones and then moves all the drones with the matching IDs to this calculated center position.\n eg\n center([1, 2, 3])\n- move_z:\n Takes a list of drone IDs as input, and distance. It moves all the drones with the matching IDs along z axis by xcm. if distance is positive, it moves them up, otherwise down\n eg\n move_z([1, 2, 3], 10)\n- form_circle\n Takes a list of drone IDs as input, and z coordinate ( height ) for the circle formation. Radius of circle is decided by number of drones in list argument. So bigger circle for more drones.\n # height has to be greater than zero\n eg \n # Cone formation as 4 drones are at 150cm height, and 3 drones are at 50cm height with smaller radius as less drones.\n form_circle( [1, 2, 3, 4], 150 ), form_circle( [5, 6, 7], 50 )\n- form_star\n Takes 3 parameters ( height, spacing, delta_radius ) and creates a star in the z-plane, which is defined by 2 concentric circles in staggered angles with different radii. \n Height corresponds to the height of the star. Spacing corresponds to the minimum spacing between drones, where a higher spacing means larger star. \n Delta_radius corresponds to the difference of the 2 radii, where a larger delta_radius means a more spread out star\n #height has to be greater than zero\n #spacing is an integer between [40, 80]\n #delta_radius is an integer between [50 100]\n eg \n form_star( 120, 60, 50 )\n- form_cone\n Takes 3 parameters ( delta_height, spacing, is_inverted ) and creates a cone using concentric circles of different radii. Each layer has 4 additional drones.\n Delta_height is the difference in height between layers. A larger delta_height corresponds to a taller cone.\n Spacing is the minimum_spacing between drones of a layer. A larger spacing means a larger circle radius and wider cone.\n Is_inverted is a boolean value (an integer of 0 or 1) that determines if the cone is inverted or not. Inverted cones are more expressive and dynamic. \n #delta_height is an integer between [30, 80]\n #spacing is an integer between [40, 80]\n #is_inverted is an integer with the values 0 or 1\n eg \n form_star( 120, 60, 50 )\n\n\n5: multi time dependent functions ( For such functions we need only one function call for multiple time steps)\n Any multi-time dependent function needs to be followed by the 'PLAN' keyword for the number of steps minus 1. This means, for functions that span over `step` time steps, the function call must be followed by `step - 1` occurrences of 'PLAN'.\n These functions must be initiated `s - step + 1` beats before the last beat, where `s` is the total number of beats. This ensures that the multi-time function completes before the song ends.\n\n**Important Timing Constraint**: If you have a multi-time function that takes 3 steps, it must be called at least 2 steps before the last beat to allow for the necessary 'PLAN' steps. For example, with a 22-beat song, a 3-step function should be called by the 20th beat, followed by 'PLAN' for the 21st and 22nd beats. - spiral:\n takes 2 parameters ( steps, height ), and makes all drones move in a outward spiral pattern with the calculate radius ( from no of drones ), and number of steps at height h\n\n #steps is an integer between [2, 3]\n #height is an integer between [0, 200] that determines the height of the spiral\n #Since spiral is a multi time dependent function, it should be falled by 'PLAN' keyword for the number of steps -1 times, so if steps is 3, then spiral is called once, and PLAN follows twice in output\n # spiral needs to be called atleast 's' steps before last timestep as 's' PLAN follow the spiral call\n eg:\n 1: spiral(3, 50 )\n 2: PLAN\n 3: PLAN\n\n- spiral_speed:\n Takes 4 parameters (steps, height, speed, radius_increase), and has the same effect as spiral. The difference is that spiral_speed increases the radius of the spiral by radius_increase over the number of steps. In addition,\n speed defines the angle by which the drones rotate around the center. Lower speeds results in slower drone motions.\n # Steps is an integer between [2, 3]\n # height is an integer between [0, 200] that determines the height of the spiral\n # speeds is an integer between [0, 360] that defines the angle by which the drones rotate around the center. Very low speeds are in the range of 0 to 90, fast speeds are in the range of 100 to 360\n # radius_increase is a float between [1, 3] that increases the radius of the spiral over the number of steps\n eg:\n 1: spiral_speed(3, 100, 200, 1.5)\n 2: PLAN\n 3: PLAN\n\n- helix:\n takes 2 parameters ( steps, delta_h, height ), and makes all drones move in a helix pattern with the calculate radius ( from no of drones ), and number of steps.\n\n #steps is an integer between [2, 3]\n #delta_h is an integer between [0, 100], that changes the height of drones in the helix pattern per step\n #height is between [50, 100] as helix rises up with steps, lets be safe.\n #Since helix is a multi time dependent function, it should be falled by 'PLAN' keyword for the number of steps -1 times, so if steps is 3, then helix is called once, and PLAN follows twice in output\n eg:\n 1: helix(3, 20, 50)\n 2: PLAN\n 3: PLAN\n\n- twister:\n takes 3 parameter ( steps, omega, z_spacing ), and makes all drones move into a upside down cone pattern and rotate at the speed specified by omega, for the number of steps specified.\n Z_spacing corresponds to the height difference between neighbouring drones, where a smaller z_spacing corresponds to a more compact cone.\n\n #steps is an integer between [2, 3]\n #omega is an integer between [2, 20] with a higher omega corresponding to a more dynamic performance.\n #z_spacing is an integer between [5, 30]\n #Since twister is a multi time dependent function, it should be falled by 'PLAN' keyword for the number of steps -1 times, so if steps is 3, then conic_rotate is called once, and PLAN follows twice in output\n eg:\n 1: twister(3, 20, 10)\n 2: PLAN\n 3: PLAN\n\n- zig_zag:\n Moves all drones in a zigzag pattern. Takes 3 parameters\n Parameters: `steps` (int), `delta` (int) - the horizontal displacement per step, `delta_h` (int) - the vertical displacement per step.\n eg:\n 4: zig_zag(3, 20, 10)\n 5: PLAN\n 6: PLAN\n \n- wave:\n Moves all drones in a three-dimensional wave propagating through a two-dimensional rectangular elastic surface with bounded edges. The disturbance at any point on the surface is defined by its parameters in space and time, and the wave propagation is governed by the wave equation with a speed `c > 0`. Takes 5 parameters.\n Parameters:\n - `steps` (int): The number of discretization points.\n - `height` (float): The desired height of drones above the surface.\n - `mu_pairs` (list of list): List of \\((\u03bc1, \u03bc2)\\) pairs that define the spatial frequencies.\n - `a\u00b51\u00b52` (list of floats): List of amplitude coefficients corresponding to each \\((\u03bc1, \u03bc2)\\) pair for the sine component.\n - `b\u00b51\u00b52` (list of floats): List of amplitude coefficients corresponding to each \\((\u03bc1, \u03bc2)\\) pair for the cosine component.\n eg:\n 4: wave( 4, 10, [[1, 1], [2, 2]], [0.5, 0.3], [0.2, 0.4] )\n 5: PLAN\n 6: PLAN\n 7: PLAN\n latex equn for the concerned wave equation:\n The disturbance \\(u(s, t)\\) at any point \\(s = (s_1, s_2) \\in [0, a] \\times [0, b]\\) \nat time \\(t\\) is governed by:\n\\[\nc^2 \\nabla^2 u(s, t) = \\frac{\\partial^2 u(s, t)}{\\partial t^2},\n\\]\nwhere \\( \\nabla^2 \\) is the spatial Laplacian and \\(c > 0\\) is the wave speed. \nThe solution:\n\\[\nu(s, t) = \\sum_{(\\mu_1, \\mu_2)} a_{\\mu_1 \\mu_2} \\sin \\left( \\frac{\\mu_1 \\pi s_1}{a} \\right) \n\\sin \\left( \\frac{\\mu_2 pi s_2}{b} \\right) \\sin(\\omega_{\\mu_1 \\mu_2} t) \n+ b_{\\mu_1 \\mu_2} \\sin \\left( \\frac{\\mu_1 \\pi s_1}{a} \\right) \n\\sin \\left( \\frac{\\mu_2 \\pi s_2}{b} \\right) \\cos(\\omega_{\\mu_1 \\mu_2} t),\n\\]\n\nwith frequencies:\n\n\\[\n\\omega_{\\mu_1 \\mu_2}^2 = c^2 \\pi^2 \\left( \\frac{\\mu_1^2}{a^2} + \\frac{\\mu_2^2}{b^2} \\right).\n\\]\n\nPLAN can also be used to indicate that the drones should maintain their current position for the next beat, so after forming cone formation, you can use PLAN to maintain the formation for next beat. Using `PLAN` also counts as part of the waypoints for the drones, so you can't have multi-time dependent functions as the last waypoint as we need to have `PLAN` after them in future steps.\n# Patterns: - Drones must fly in geometric patterns and shapes - Drones must fly in tight formations - The formations must be symmetrical if possible - Drones should move up and down in altitude multiple times for a 3D effect - Example patterns include spirals, triangles, squares, circles, lines\n# Response structure First, describe the emotions and mood associated with the song Vivaldi Summer. Then, give a description of the dominant chords of the song. Is it a major or minor cord? What does that mean for the mood? Next, give a specific description of the choreography you want to create. What patterns do you want the drones to fly? Why do you use these particular patterns? Last, fill out the individual waypoints for each drone. Adapt the distance and position of waypoints to the amplitude of the song's beats. Loud beats lead to larger movements, while soft beats lead to smaller movements. Furthermore, take into account the dominant chords of the song and the emotions and mood associated with these chords. Try to use a variety of functional options to design an amazing geometrical choreography. Make sure to only do motion planning per beat, so in otherwords motion planning steps should be limited to 14,and end yaml output with END like in examples below. You can use multiple beat functions for more than 3 timestep as well like spiral(), zig_zag(), wave(), helix(). Only do complex primtives. Adjust change in height accordingly like do them for 5 steps if you have to, and make height increments small. Limit to at max 4 primitives. Between any two primitives, there must be a extra PLAN which consumes a timestep so that there is enough time for transition.\n"}, {"role": "system", "content": "# Example prompts and responses - Example 1:\n Prompt:\n # Information on the song:\n - Name: r\u00eaverie\n - Number of beats: 22\n - Beat times in 100ms: {{1: 7, 2: 20, 3: 29, 4: 41, 5: 55, 6: 66, 7: 80, 8: 96, 9: 104, 10: 112, 11: 119, 12: 128, 13: 137, 14: 147, 15: 157, 16: 167, 17: 187, 18: 196, 19: 210, 20: 221, 21: 232, 22: 242}}\n - Beat intervals in 100ms: {{1: 7, 2: 13, 3: 9, 4: 12, 5: 14, 6: 11, 7: 13, 8: 16, 9: 9, 10: 7, 11: 8, 12: 8, 13: 9, 14: 10, 15: 10, 16: 10, 17: 20, 18: 8, 19: 14, 20: 12, 21: 10}}\n - Beat amplitude in dBFS: {{1: -53, 2: -40, 3: -40, 4: -39, 5: -44, 6: -44, 7: -36, 8: -43, 9: -34, 10: -35, 11: -37, 12: -37, 13: -39, 14: -44, 15: -38, 16: -32, 17: -35, 18: -34, 19: -33, 20: -37, 21: -35, 22: -38}}\n - Beat novelty in %: {{1: 37, 2: 52, 3: 24, 4: 31, 5: 40, 6: 98, 7: 45, 8: 21, 9: 76, 10: 72, 11: 52, 12: 29, 13: 10, 14: 15, 15: 81, 16: 100, 17: 27, 18: 65, 19: 64, 20: 28, 21: 19, 22: 30}}\n - Beat chords: {{: 'A#m', 2: 'Cm', 3: 'Cm', 4: 'Cm', 5: 'Cm', 6: 'Cm', 7: 'Cm', 8: 'A#m', 9: 'C', 10: 'C', 11: 'C', 12: 'C', 13: 'Cm', 14: 'Cm', 15: 'Gm', 16: 'A#m', 17: 'Gm', 18: 'Gm', 19: 'Gm', 20: 'F', 21: 'F', 22: 'F'}}\n \n # Information on the drones:\n - Number: 3\n - Drones must not collide with each other. No two drones must be at the same coordinates at the same beat.\n - Starting positions: [[100, 100, 100], [-100, 100, 100], [0, -100, 100]]\n - Maximum step distance between waypoints in cm: 100cm\n\n Response:\n song_mood: Debussy's R\u00e9verie is a short and expressive piano piece that captures the essence of daydreaming and contemplation.\n It is characteristic of Debussy's impressionistic style, known for its use of unconventional harmonies, tonal colors, and fluidity.\n cord_analysis: The song is in the key of F major, with a few chords in the song being A#m, Cm, G, F, and Gm. The mix of major and minor chords indicates that the music is neither elated nor gloomy. It remains in a state of contemplation and daydreaming, with a touch of melancholy.\n choreography_plan: The main intention of the choreography is to reflect the dreamy and contemplative mood of the song. Therefore, drones must not move too fast, and waypoints must not be too far from each other. \n The beat amplitude in dBFS roughly remains within the -50 to -30 interval, indicating that the song is very quiet. Consequently, the drones should move slowly and gently. Across all beats, the intervals are evenly spaced around 7 to 13, indicating that the drones should move at a steady pace.\n so we have no pause that we have to accentuate in the choreography. We will therefore focus on the beat amplitude.\n For beats 1 to 5, the three drones will spiral to a center point around [0, 0, 100]]. The novelty of the beats is low to moderate at around 25 to 50, and the beat amplitude is also low at around -50 to -40. Thus, we plan the first waypoints closer to each other.\n For the following beats 6 to 11, the novelty of the beats reaches a peak at 98 and a slightly raised beat amplitude of up to -35. To account for the peak, one drone will rise slowly in the center while the other two drones begin to slowly rotate in the other direction and then move outwards. We let the drones move slowly because the amplitude has not yet reached full force. We change the direction of the drones to reflect the dreamy and swirling mood of the song.\n For the next beats 12 to 17, the novelty and amplitudes of the beats are reaching the peak. Accordingly, the drones will move into the triangle formation again, while oscillating in altitude. Although we now move faster, we adhere to the maximum step distance of approximately 80 to 100cm.\n Beats 18 to 22 are the final beats of the song, with decreasing novelty and amplitude. The drones will slowly spiral outwards and downwards to mark the decline in tension and the end of the dance.\n choreography:\n 1: spiral(5, 100)\n 2: PLAN\n 3: PLAN\n 4: PLAN\n 5: PLAN\n 6: PLAN # extra PLAN for primitive switch\n 7: rotate(60, 'z')\n 8: PLAN # extra\n 9: rotate(-90, 'z')\n 10: PLAN # extra\n 11: move_z( [1,2,3], 30)\n 12: PLAN # extra\n 13: spiral(4, 100)\n 14: PLAN\n 15: PLAN\n 16: PLAN\n 17: PLAN # extra\n 18: zig_zag(5, 20, 20)\n 19: PLAN\n 20: PLAN\n 21: PLAN \n 22: PLAN\n END\n"}, {"role": "system", "content": "The output should be a YAML string in the following format: \n```yaml\n song_mood: str # Description of the emotions and mood associated with the song\n cord_analysis: str # Description of the dominant chords and how they relate to the song\n choreography_plan: str # Description of the planned choreography and how it relates to the song\n choreography: # Waypoints for the drones at each beat in the song. Drones are indexed from x_1 to x_n\n 1: form_circle( [1, 2, 3 ..., n], 100 )\n 2: PLAN # extra PLAN for switch\n 3: center([1, 2, 3]); ... ; center([n-1, n] )\n ... \n num_beats: move(drone_1_x, drone_1_y, drone_1_z, 1); move(...); move(drone_n_x, drone_n_y, drone_n_z, n) # m'th waypoint planning\n END \n```\n, where x_n is the number of drones and the waypoint list length is equal to the number of beats num_beats in the song. It is important you never skip beats and always provide the full list of waypoints for each drone! Never skip lines with ... !\n"}, {"role": "assistant", "content": "```yaml\nsong_mood: \"Vivaldi's Summer is a dynamic and intense piece that evokes the feeling of a powerful summer storm. The music is highly expressive, full of rapid movements, sudden dynamics, and emotional outbursts.\"\ncord_analysis: \"The dominant chords include G, D, G minor, F, G, and D, alternating between major and minor. These chords add to the intensity and dramatic feel of the music, enhancing its storm-like qualities.\"\nchoreography_plan: \"The choreography for the drones will reflect the increasing intensity of the summer storm depicted in the music. We'll use symmetrical patterns and 3D maneuvers to match the energy. The drones will start with simple rotations, moving to more complex structures like spirals and cones as the music builds up. Movements will be synchronized with the intensity of the beats, with larger movements for louder and more novel beats.\"\nchoreography:\n 1: form_circle([1, 2, 3, 4, 5, 6, 7, 8, 9], 100)\n 2: rotate(45, 'z')\n 3: PLAN\n 4: rotate(-45, 'z')\n 5: form_star(120, 50, 70)\n 6: PLAN\n 7: form_cone(40, 60, 0)\n 8: PLAN\n 9: rotate(90, 'z')\n 10: PLAN\n 11: spiral(3, 150)\n 12: PLAN\n 13: PLAN\n 14: form_circle([1, 2, 3, 4, 5, 6, 7, 8, 9], 150)\n END\n```"}] \ No newline at end of file diff --git a/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20260331_171655/meta.json b/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20260331_171655/meta.json deleted file mode 100644 index b1af26d..0000000 --- a/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20260331_171655/meta.json +++ /dev/null @@ -1 +0,0 @@ -{"n_drones": 9, "song": "Vivaldi Summer", "use_motion_primitives": true} \ No newline at end of file diff --git a/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20260331_171655/trajectory.csv b/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20260331_171655/trajectory.csv deleted file mode 100644 index 1efba4a..0000000 --- a/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20260331_171655/trajectory.csv +++ /dev/null @@ -1,3973 +0,0 @@ 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-39.710000,-0.487262,-0.489252,1.056017,-0.023326,-0.033909,0.009611,-0.543240,-0.049302,1.068750,0.003330,0.008486,0.008634,-0.576318,0.504980,1.083417,0.007339,0.001774,0.005858,-0.023654,-0.522587,1.038944,0.012771,-0.002023,0.018446,-0.041996,0.031615,1.066918,0.051908,-0.033746,0.002635,0.043425,0.439067,1.084632,-0.003330,0.019690,0.005614,0.513460,-0.502227,1.078065,0.008564,-0.002859,0.003989,0.531975,0.052457,1.092690,0.000901,-0.003808,0.003054,0.458438,0.518333,1.091677,0.010436,0.003395,0.003728 diff --git a/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20260331_171655/waypoints.npy b/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20260331_171655/waypoints.npy deleted file mode 100644 index ea56e70..0000000 Binary files a/swarm_gpt/data/presets/Vivaldi Summer | 9 | 20260331_171655/waypoints.npy and /dev/null differ diff --git a/swarm_gpt/data/settings.yaml b/swarm_gpt/data/settings.yaml index 7770a87..2d5c2c9 100644 --- a/swarm_gpt/data/settings.yaml +++ b/swarm_gpt/data/settings.yaml @@ -1,7 +1,35 @@ +radio: + uri_base: "radio://0/{channel}/2M/E7E7E7E7{addr:02X}" + starting_height: 1.0 # Starting height for all drones -lighthouse: true # Whether to use lighthouse or mocap for localization +lighthouse: false # Whether to use lighthouse or mocap for localization land_on_docks: true +# Choreography +choreography: + bars_per_required: 4 # Stride (in bars) between REQUIRED downbeats the LLM must choreograph. + # 1 = every bar; 4 = first bar of each segment + every 4th bar after. + # Beats in between stay addressable as encouraged-but-optional accents. + +# Per-song crop window [start_s, end_s] in seconds. Songs are analyzed in full, but only this +# window is choreographed and played back (the snippet). Times are rebased so the window starts +# at 0:00. Falls back to `default` for any song without an explicit entry. +song_crops: + default: [0, 60] + "Chopin - Nocturne Op. 9 No. 2": [0, 60] + "Crazyflie Drones Theme": [0, 60] + "Crazyflie Drones Theme (short)": [0, 60] + "Dance of the Flying Machines": [0, 60] + "Debussy - Clair de Lune": [0, 60] + "J. Strauss I - Radetzky March": [0, 60] + "J. Strauss II - The Blue Danube Op. 314": [64, 124] + "Mozart - Eine kleine Nachtmusik": [0, 60] + "On & On": [52, 82] + "Vivaldi - Summer": [0, 60] + "We Wish You a Merry Christmas": [0, 60] + "Fearless2": [35, 70] + "Mortals": [0, 60] + # Simulation sim_freq: 500 # Frequency for the crazyflow simulation attitude_freq: 500 # Frequency for the crazyflow simulation for the drone attitudes @@ -28,11 +56,11 @@ axswarm: input_continuity_weight: 1.0 # Weight of the input continuity objective in the cost # Limits - pos_min: [-1.2, -0.7, 0.25] # Minimum position of the AMSwarm drones - pos_max: [1.2, 0.7, 1.7] # Maximum position of the AMSwarm drones + pos_min: [-2.0, -2.0, 0.25] # Minimum position of the AMSwarm drones + pos_max: [2.0, 2.0, 1.7] # Maximum position of the AMSwarm drones vel_max: 1.73 # Maximum velocity of the AMSwarm drones acc_max: 1.0 # Maximum force of the AMSwarm drones (0.75 gs) - collision_envelope: [0.35, 0.35, 0.6] # Collision envelope width in x, y, z + collision_envelope: [0.25, 0.25, 0.6] # Collision envelope width in x, y, z # MPC K: 50 # Number of timesteps in the optimization horizon diff --git a/swarm_gpt/launch.py b/swarm_gpt/launch.py index 29ec4cd..b67c421 100644 --- a/swarm_gpt/launch.py +++ b/swarm_gpt/launch.py @@ -18,10 +18,10 @@ os.environ.setdefault("JAX_PLATFORMS", "cpu") -# models: gpt-4o-2024-05-13, o3-mini +# models: gpt-5.4-nano, gpt-4o, gpt-4o-2024-05-13, o3-mini def main( strict: bool = True, - model_id: str = "gpt-4o", + model_id: str = "gpt-5.4-nano", llm_provider: LLMProvider = "openai", use_motion_primitives: bool = True, host: str = "127.0.0.1", @@ -41,7 +41,7 @@ def main( "or switch the UI to Ollama (local)." ) - music_dir = Path(__file__).resolve().parents[1] / "music" + music_dir = Path(__file__).resolve().parents[1] / "music" / "songs" app = create_app( ApiConfig( diff --git a/swarm_gpt/render.py b/swarm_gpt/render.py index 40de716..585a107 100644 --- a/swarm_gpt/render.py +++ b/swarm_gpt/render.py @@ -40,7 +40,7 @@ from swarm_gpt.utils import generate_default_colors ROOT = Path(__file__).resolve().parents[1] -MUSIC_DIR = ROOT / "music" +MUSIC_DIR = ROOT / "music" / "songs" SCENE_XML = ROOT / "swarm_gpt/data/scene.xml" # Pick a preset that matches the drone count in swarm_gpt/data/drones.toml. @@ -68,7 +68,7 @@ def preset_audio_path(preset_meta: dict[str, object]) -> Path: """Resolve the audio file declared by a preset's metadata.""" - song = preset_meta.get("song") + song = preset_meta["song"] if not isinstance(song, str) or not song: raise ValueError("Preset metadata must contain a non-empty 'song' field") diff --git a/swarm_gpt/utils/llm_providers.py b/swarm_gpt/utils/llm_providers.py index fb779b5..1b257a8 100644 --- a/swarm_gpt/utils/llm_providers.py +++ b/swarm_gpt/utils/llm_providers.py @@ -28,6 +28,7 @@ # Shown in the browser UI; users can type another OpenAI id if needed. DEFAULT_OPENAI_MODEL_CHOICES: tuple[str, ...] = ( + "gpt-5.4-nano", "gpt-4o", "gpt-4o-mini", "gpt-4-turbo", diff --git a/swarm_gpt/utils/music_analyzer.py b/swarm_gpt/utils/music_analyzer.py new file mode 100644 index 0000000..82eca5d --- /dev/null +++ b/swarm_gpt/utils/music_analyzer.py @@ -0,0 +1,627 @@ +"""SongStructure data model and per-song analysis orchestration. + +Provides the hierarchical music-structure types the choreographer addresses moments by +(``segment``, ``bar``, ``beat``), plus :func:`analyze_song` which runs all-in-one on an +MP3 and caches the result as JSON. + +At runtime the choreographer reads JSONs from ``music/analyzed/`` and never invokes +``allin1.analyze`` itself. ``allin1`` is an optional import so this module can still be +imported in environments (e.g. ``tests``) that do not have allin1. +""" + +from __future__ import annotations + +import hashlib +import json +import logging +from dataclasses import asdict, dataclass, field +from typing import TYPE_CHECKING, Any + +import librosa +import matplotlib.pyplot as plt +import numpy as np + +from swarm_gpt.core.structured_output_schema import encode_key + +logger = logging.getLogger(__name__) + +try: + import allin1 +except ImportError as e: + allin1 = None + _ALLIN1_IMPORT_ERROR = e + logger.error(f"{e} - please use the music env to analyze songs") +else: + _ALLIN1_IMPORT_ERROR = None + +if TYPE_CHECKING: + from pathlib import Path + +SCHEMA_VERSION = 2 +"""On-disk JSON schema version. Bump on incompatible shape changes.""" + +# Match allin1.visualize's librosa params for consistency with its RMS trace. +_FRAME_LENGTH = 4096 +_HOP_LENGTH = 1024 + + +@dataclass +class Beat: + """One beat within a bar. + + Attributes: + id: 1-indexed beat number within the bar. + time_s: Time in seconds since song start. + position_in_bar: Metric position (1 = downbeat, 2/3/4 = off-beats). + """ + + id: int + time_s: float + position_in_bar: int + + +@dataclass +class Bar: + """One bar (measure) within a segment. + + Attributes: + id: 1-indexed bar number within the segment. + start_s: Time in seconds when this bar starts. + beats: Beats within this bar, in time order. + """ + + id: int + start_s: float + beats: list[Beat] + + +@dataclass +class Segment: + """One functional segment (intro / verse / chorus / etc.) of the song. + + Attributes: + id: 1-indexed segment number within the song. + label: Functional label from all-in-one (e.g. ``"intro"``, ``"chorus"``). + start_s: Time in seconds when this segment starts. + end_s: Time in seconds when this segment ends. + bars: Bars within this segment, in time order. + """ + + id: int + label: str + start_s: float + end_s: float + bars: list[Bar] + + +@dataclass +class SongStructure: + """Hierarchical music structure for a single song. + + Attributes: + schema_version: Format version of the JSON serialization. + source_path: Path to the source audio file, as a string relative to project root. + song_sha256: SHA-256 of the source audio file, used to detect MP3 changes. + analyzer: Identifier of the analysis engine that produced this structure. + bpm: Tempo in beats per minute. + segments: Functional segments of the song, in time order. + """ + + schema_version: int + source_path: str + song_sha256: str + analyzer: str + bpm: int + segments: list[Segment] + rms_per_2bar: tuple[float, ...] = field(default_factory=tuple) + centroid_per_2bar: tuple[float, ...] = field(default_factory=tuple) + + @classmethod + def from_allin1( + cls, result: Any, source_path: str, song_sha256: str, analyzer: str + ) -> SongStructure: + """Build a SongStructure from an ``allin1.AnalysisResult``. + + Groups flat ``beats`` / ``beat_positions`` into bars by detecting position + resets, then assigns bars to segments by the start time of the bar's first beat. + + Args: + result: An ``allin1.AnalysisResult`` (duck-typed: needs ``bpm``, ``beats``, + ``beat_positions``, and ``segments`` whose entries expose ``start``, + ``end``, ``label``). + source_path: Source audio file path, as a string relative to project root. + song_sha256: SHA-256 hex digest of the source audio file. + analyzer: Identifier of the analyzer used (e.g. ``"allin1@1.1.0"``). + + Returns: + A populated SongStructure. + """ + bars_flat = _group_beats_into_bars(list(result.beats), list(result.beat_positions)) + segments_out = _drop_empty_segments( + _assign_bars_to_segments(bars_flat, list(result.segments)) + ) + raw_bpm = result.bpm + if raw_bpm is None: + beats_list = list(result.beats) + if len(beats_list) >= 2: + intervals = [beats_list[i + 1] - beats_list[i] for i in range(len(beats_list) - 1)] + median_interval = sorted(intervals)[len(intervals) // 2] + raw_bpm = 60.0 / median_interval + else: + raw_bpm = 0 + return cls( + schema_version=SCHEMA_VERSION, + source_path=source_path, + song_sha256=song_sha256, + analyzer=analyzer, + bpm=int(raw_bpm), + segments=segments_out, + ) + + @classmethod + def from_json(cls, path: Path) -> SongStructure: + """Load a SongStructure from its JSON serialization. + + Args: + path: Path to the JSON file. + + Returns: + A populated SongStructure. + + Raises: + ValueError: If the JSON's ``schema_version`` is missing or unsupported. + """ + data = json.loads(path.read_text()) + version = data["schema_version"] + if version != SCHEMA_VERSION: + raise ValueError( + f"Unsupported schema_version {version!r} in {path}; " + f"this code expects {SCHEMA_VERSION}." + ) + segments = [ + Segment( + id=int(s["id"]), + label=str(s["label"]), + start_s=float(s["start_s"]), + end_s=float(s["end_s"]), + bars=[ + Bar( + id=int(b["id"]), + start_s=float(b["start_s"]), + beats=[ + Beat( + id=int(beat["id"]), + time_s=float(beat["time_s"]), + position_in_bar=int(beat["position_in_bar"]), + ) + for beat in b["beats"] + ], + ) + for b in s["bars"] + ], + ) + for s in data["segments"] + ] + segments = _drop_empty_segments(segments) + return cls( + schema_version=int(data["schema_version"]), + source_path=str(data["source_path"]), + song_sha256=str(data["song_sha256"]), + analyzer=str(data["analyzer"]), + bpm=int(data["bpm"]), + segments=segments, + rms_per_2bar=tuple(float(v) for v in data["rms_per_2bar"]), + centroid_per_2bar=tuple(float(v) for v in data["centroid_per_2bar"]), + ) + + def to_json(self, path: Path) -> None: + """Serialize this SongStructure to JSON. + + Args: + path: Where to write the JSON file. Parent directory must exist. + """ + path.write_text(json.dumps(asdict(self), indent=2)) + + def time_of(self, seq: int, bar: int, beat: int) -> float: + """Look up the absolute time of a ``(segment, bar, beat)`` address. + + Args: + seq: 1-indexed segment id. + bar: 1-indexed bar id within the segment. + beat: 1-indexed beat id within the bar. + + Returns: + Time in seconds since song start. + + Raises: + KeyError: If the ``(seq, bar, beat)`` tuple does not exist. + """ + for segment in self.segments: + if segment.id != seq: + continue + for bar_obj in segment.bars: + if bar_obj.id != bar: + continue + for beat_obj in bar_obj.beats: + if beat_obj.id == beat: + return beat_obj.time_s + raise KeyError(f"No beat at (seq={seq}, bar={bar}, beat={beat})") + + def required_keys(self, bars_per_required: int = 1) -> list[tuple[int, int, int]]: + """Return the ``(seq, bar, beat)`` tuples the LLM must emit actions at. + + The downbeat (first beat) of every ``bars_per_required``-th bar, counted from the start + of each segment. The first bar of every segment is always required (segment openings are + musically load-bearing); the stride only thins the bars in between. A stride of 1 + requires every bar's downbeat; a stride of 4 requires bars 1, 5, 9, ... within each + segment. Beats not returned here remain addressable as optional accents. + + Args: + bars_per_required: Stride between required downbeats within a segment (>= 1). + + Returns: + List of ``(seq, bar, beat)`` tuples, in time order. + + Raises: + ValueError: If ``bars_per_required`` is less than 1. + """ + if bars_per_required < 1: + raise ValueError(f"bars_per_required must be >= 1, got {bars_per_required}") + return [ + (segment.id, bar.id, bar.beats[0].id) + for segment in self.segments + for i, bar in enumerate(segment.bars) + if bar.beats and i % bars_per_required == 0 + ] + + def all_keys(self) -> list[tuple[int, int, int]]: + """Return every addressable ``(seq, bar, beat)`` tuple in the song. + + Returns: + Tuples in time order. + """ + return [ + (segment.id, bar.id, beat.id) + for segment in self.segments + for bar in segment.bars + for beat in bar.beats + ] + + def crop(self, start_s: float, end_s: float) -> SongStructure: + """Return a copy restricted to the window ``[start_s, end_s]``, rebased to 0. + + Keeps only beats whose time falls within the window, drops bars and segments left + empty, renumbers all ids contiguously from 1, and shifts every time so the window + starts at 0:00. The simulator and player both expect a 0-based timeline, so this is + applied at load time while the on-disk full-song JSON is left untouched. + + Args: + start_s: Window start in seconds (song-absolute). + end_s: Window end in seconds (song-absolute). + + Returns: + A new SongStructure spanning ``[0, end_s - start_s]``. + + Raises: + ValueError: If ``end_s`` is not greater than ``start_s``. + """ + if end_s <= start_s: + raise ValueError(f"crop end ({end_s}) must be greater than start ({start_s})") + segments_out: list[Segment] = [] + for seg in self.segments: + bars_out: list[Bar] = [] + for bar in seg.bars: + beats_in = [b for b in bar.beats if start_s <= b.time_s <= end_s] + if not beats_in: + continue + bars_out.append( + Bar( + id=len(bars_out) + 1, + start_s=beats_in[0].time_s - start_s, + beats=[ + Beat(id=i, time_s=b.time_s - start_s, position_in_bar=b.position_in_bar) + for i, b in enumerate(beats_in, start=1) + ], + ) + ) + if not bars_out: + continue + segments_out.append( + Segment( + id=len(segments_out) + 1, + label=seg.label, + start_s=max(seg.start_s, start_s) - start_s, + end_s=min(seg.end_s, end_s) - start_s, + bars=bars_out, + ) + ) + # Slice dynamics to the windows that survive. A window survives when its first bar has + # at least one beat in [start_s, end_s]. Count how many original windows are skipped + # (their first bar's first beat is before start_s), then take surviving_count from there. + skip = sum( + 1 + for seg in self.segments + for i, bar in enumerate(seg.bars) + if i % 2 == 0 and bar.beats and bar.beats[0].time_s < start_s + ) + surviving_window_count = sum(1 for seg in segments_out for i in range(0, len(seg.bars), 2)) + rms_cropped = self.rms_per_2bar[skip : skip + surviving_window_count] + centroid_cropped = self.centroid_per_2bar[skip : skip + surviving_window_count] + return SongStructure( + schema_version=self.schema_version, + source_path=self.source_path, + song_sha256=self.song_sha256, + analyzer=self.analyzer, + bpm=self.bpm, + segments=segments_out, + rms_per_2bar=rms_cropped, + centroid_per_2bar=centroid_cropped, + ) + + +def compute_dynamics_per_2bar( + structure: SongStructure, mp3_path: Path +) -> tuple[tuple[float, ...], tuple[float, ...]]: + """Per-2-bar RMS amplitude and spectral centroid, normalized to [0, 1]. + + Computes two audio features for each 2-bar window in the song. Windows reset at segment + boundaries so each window cleanly belongs to one segment. Both features are normalized by + the song-wide maximum so values are scale-invariant across songs. + + Args: + structure: The full-song SongStructure (pre-crop). Bars in ``structure.segments`` + define the window boundaries. + mp3_path: Path to the source audio file. + + Returns: + ``(rms_tuple, centroid_tuple)`` of equal length + ``sum(ceil(len(seg.bars) / 2) for seg in structure.segments)``. + """ + windows: list[tuple[float, float]] = [] + for seg in structure.segments: + bars = seg.bars + for i in range(0, len(bars), 2): + start_bar = bars[i] + end_bar = bars[i + 1] if i + 1 < len(bars) else bars[i] + t_start = start_bar.beats[0].time_s if start_bar.beats else start_bar.start_s + t_end = end_bar.beats[-1].time_s if end_bar.beats else end_bar.start_s + windows.append((t_start, t_end)) + if not windows: + return ((), ()) + + y, sr = librosa.load(str(mp3_path), sr=None, mono=True) + rms = librosa.feature.rms(y=y, frame_length=_FRAME_LENGTH, hop_length=_HOP_LENGTH)[0] + centroid = librosa.feature.spectral_centroid( + y=y, sr=sr, n_fft=_FRAME_LENGTH, hop_length=_HOP_LENGTH + )[0] + frame_times = librosa.frames_to_time(np.arange(len(rms)), sr=sr, hop_length=_HOP_LENGTH) + + rms_vals: list[float] = [] + centroid_vals: list[float] = [] + for t_start, t_end in windows: + mask = (frame_times >= t_start) & (frame_times < t_end) + rms_vals.append(float(rms[mask].mean()) if mask.any() else 0.0) + centroid_vals.append(float(centroid[mask].mean()) if mask.any() else 0.0) + + rms_peak = max(rms_vals) or 1.0 + centroid_peak = max(centroid_vals) or 1.0 + return ( + tuple(round(v / rms_peak, 2) for v in rms_vals), + tuple(round(v / centroid_peak, 2) for v in centroid_vals), + ) + + +def dynamics_window_keys(structure: SongStructure) -> tuple[str, ...]: + """Generate the s#b#t# key for the start of each 2-bar window, in order. + + Pure function of the bar layout — not persisted; regenerated whenever the prompt is + built. Length matches ``structure.rms_per_2bar``. + + Args: + structure: The SongStructure whose bar layout defines the windows. + + Returns: + Tuple of key strings, one per 2-bar window. + """ + keys: list[str] = [] + for seg in structure.segments: + bars = seg.bars + for i in range(0, len(bars), 2): + start_bar = bars[i] + if start_bar.beats: + keys.append(encode_key(seg.id, start_bar.id, start_bar.beats[0].id)) + return tuple(keys) + + +def _group_beats_into_bars( + beats: list[float], positions: list[int] +) -> list[list[tuple[float, int]]]: + """Group flat ``(beat_time, position_in_bar)`` pairs into bars. + + A new bar starts whenever ``position_in_bar`` does not strictly increase relative to + the previous beat. Handles variable meters (3/4, 4/4, etc.) and anacrusis (a partial + first bar) implicitly. + + Args: + beats: Beat times in seconds, in time order. + positions: Position-in-bar for each beat (1-indexed), parallel to ``beats``. + + Returns: + List of bars, where each bar is a list of ``(time, position)`` pairs. + """ + bars: list[list[tuple[float, int]]] = [] + current: list[tuple[float, int]] = [] + for time, pos in zip(beats, positions, strict=True): + if current and pos <= current[-1][1]: + bars.append(current) + current = [] + current.append((float(time), int(pos))) + if current: + bars.append(current) + return bars + + +def _assign_bars_to_segments( + bars_flat: list[list[tuple[float, int]]], segments_in: list[Any] +) -> list[Segment]: + """Assign each flat bar to the segment containing its first beat. + + Args: + bars_flat: Bars as produced by :func:`_group_beats_into_bars`. + segments_in: Segments from an ``allin1.AnalysisResult`` (duck-typed: ``start``, + ``end``, ``label``). + + Returns: + Segments populated with their child bars, in the same order as ``segments_in``. + """ + segments_out: list[Segment] = [] + bar_cursor = 0 + for seg_id, raw_seg in enumerate(segments_in, start=1): + seg_bars: list[Bar] = [] + bar_id = 0 + while bar_cursor < len(bars_flat): + bar_beats = bars_flat[bar_cursor] + bar_start_time = bar_beats[0][0] + if bar_start_time >= float(raw_seg.end): + break + bar_id += 1 + seg_bars.append( + Bar( + id=bar_id, + start_s=float(bar_beats[0][0]), + beats=[ + Beat(id=beat_id, time_s=t, position_in_bar=p) + for beat_id, (t, p) in enumerate(bar_beats, start=1) + ], + ) + ) + bar_cursor += 1 + segments_out.append( + Segment( + id=seg_id, + label=str(raw_seg.label), + start_s=float(raw_seg.start), + end_s=float(raw_seg.end), + bars=seg_bars, + ) + ) + return segments_out + + +def _drop_empty_segments(segments: list[Segment]) -> list[Segment]: + """Drop segments that contain no beats and renumber the survivors from 1. + + allin1 can emit segment boundaries that no beat falls within (e.g. very short + sections). Such segments carry nothing to choreograph and would only mislead the LLM, + so they are removed and the remaining segment ids are made contiguous. + + Args: + segments: Segments in time order, possibly including empty ones. + + Returns: + The non-empty segments with ``id`` renumbered to be contiguous from 1. + """ + kept = [seg for seg in segments if seg.bars] + for new_id, seg in enumerate(kept, start=1): + seg.id = new_id + return kept + + +def sha256_of(path: Path) -> str: + """Compute the SHA-256 hex digest of a file. + + Args: + path: File to hash. + + Returns: + Hex-encoded SHA-256 digest. + """ + h = hashlib.sha256() + with path.open("rb") as f: + for chunk in iter(lambda: f.read(1 << 20), b""): + h.update(chunk) + return h.hexdigest() + + +def save_visualization(result: Any, viz_dir: Path, stem: str) -> Path: + """Save all-in-one's RMS-over-segments visualization as a PNG. + + Renders the same figure as ``allin1.visualize`` (which only writes PDFs) but saves it + as a raster image instead. + + Args: + result: An ``allin1.AnalysisResult``. + viz_dir: Directory to write the PNG into. Created if missing. + stem: Output file stem (the song's MP3 stem, no extension). + + Returns: + Path to the written PNG. + """ + if allin1 is None: + raise ImportError("allin1 is required to analyze songs; please use the music env") from ( + _ALLIN1_IMPORT_ERROR + ) + fig = allin1.visualize(result, out_dir=None, multiprocess=False) + viz_dir.mkdir(parents=True, exist_ok=True) + out_path = viz_dir / f"{stem}.png" + fig.savefig(out_path, dpi=200, bbox_inches="tight") + plt.close(fig) + return out_path + + +def analyze_song( + mp3_path: Path, cache_dir: Path, device: str = "cuda", viz_dir: Path | None = None +) -> SongStructure: + """Analyze a single MP3 and cache the result as JSON. + + If ``cache_dir/.json`` already exists, loads and returns it without + re-running analysis (and without regenerating any visualization). + + Args: + mp3_path: Path to the MP3 file. + cache_dir: Directory to read/write JSON caches in. Created if missing. + device: Torch device for analysis (``"cuda"`` or ``"cpu"``). + viz_dir: If given, save a PNG visualization of the analysis here when the song is + analyzed (skipped on a cache hit). Created if missing. + + Returns: + The SongStructure for the song. + """ + cache_path = cache_dir / f"{mp3_path.stem}.json" + if cache_path.exists(): + return SongStructure.from_json(cache_path) + + if allin1 is None: + raise ImportError("allin1 is required to analyze songs; please use the music env") from ( + _ALLIN1_IMPORT_ERROR + ) + result = allin1.analyze(str(mp3_path), device=device) + if isinstance(result, list): + result = result[0] + + source_path = _project_relative(mp3_path) + structure = SongStructure.from_allin1( + result=result, + source_path=source_path, + song_sha256=sha256_of(mp3_path), + analyzer=f"allin1@{getattr(allin1, '__version__', 'unknown')}", + ) + rms_per_2bar, centroid_per_2bar = compute_dynamics_per_2bar(structure, mp3_path) + structure.rms_per_2bar = rms_per_2bar + structure.centroid_per_2bar = centroid_per_2bar + cache_dir.mkdir(parents=True, exist_ok=True) + structure.to_json(cache_path) + if viz_dir is not None: + save_visualization(result, viz_dir, mp3_path.stem) + return structure + + +def _project_relative(path: Path) -> str: + """Format a path as a string relative to the project root when possible.""" + resolved = path.resolve() + # music_analyzer.py lives at /swarm_gpt/utils/music_analyzer.py; go up 3. + project_root = type(path)(__file__).resolve().parents[2] + try: + return str(resolved.relative_to(project_root)) + except ValueError: + return str(resolved) diff --git a/swarm_gpt/utils/music_manager.py b/swarm_gpt/utils/music_manager.py index b964a95..dc45efd 100644 --- a/swarm_gpt/utils/music_manager.py +++ b/swarm_gpt/utils/music_manager.py @@ -103,12 +103,21 @@ def verify_libvlc(self) -> bool: return False return True - def play(self, *, wait: bool = False, timeout: float = 2.0) -> bool: + def play( + self, + *, + wait: bool = False, + timeout: float = 2.0, + start_s: float = 0.0, + end_s: float | None = None, + ) -> bool: """Play the song with VLC. Args: wait: If True, block briefly until VLC reports active playback. timeout: Maximum time to wait for playback to start. + start_s: Seek to this offset in seconds before playback begins. + end_s: Stop playback at this offset in seconds. ``None`` plays to the end of the file. Returns: True if VLC accepted the play request and, when ``wait`` is set, started playback. @@ -120,22 +129,33 @@ def play(self, *, wait: bool = False, timeout: float = 2.0) -> bool: self._music_player.stop() self._music_player.release() self._music_player = None + media = inst.media_new(media_path) + if start_s > 0: + media.add_option(f":start-time={start_s:g}") + if end_s is not None: + media.add_option(f":stop-time={end_s:g}") self._music_player = inst.media_player_new() - self._music_player.set_media(inst.media_new(media_path)) + self._music_player.set_media(media) self._music_player.audio_set_volume(100) result = self._music_player.play() if result == -1: logger.error("VLC failed to start playback for %s", media_path) return False if not wait: + if start_s > 0: + self._music_player.set_time(int(start_s * 1000)) return True deadline = time.perf_counter() + timeout while time.perf_counter() < deadline: if self.is_playing: + if start_s > 0: + self._music_player.set_time(int(start_s * 1000)) return True time.sleep(0.01) logger.warning("Timed out waiting for VLC playback to start for %s", media_path) + if self.is_playing and start_s > 0: + self._music_player.set_time(int(start_s * 1000)) return self.is_playing def stop(self): diff --git a/tests/conftest.py b/tests/conftest.py index 1c9d403..35c9bf3 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,47 +1,35 @@ from pathlib import Path import numpy as np -import toml def virtual_crazyswarm_config(n_drones: int) -> Path: """Create a virtual crazyswarm config file for testing.""" - # Create grid positions like in llm_success_rates.py n_cols = int(np.ceil(np.sqrt(n_drones))) n_rows = int(np.ceil(n_drones / n_cols)) spacing = 1.0 - # Create meshgrid x = np.linspace(0, (n_cols - 1) * spacing, n_cols) y = np.linspace(0, (n_rows - 1) * spacing, n_rows) X, Y = np.meshgrid(x, y) - # Flatten and combine into positions array positions = np.zeros((n_drones, 3)) positions[:, 0] = X.flatten()[:n_drones] positions[:, 1] = Y.flatten()[:n_drones] - - # Center the grid around origin positions[:, 0] -= np.mean(positions[:, 0]) positions[:, 1] -= np.mean(positions[:, 1]) - - # Round to integers positions = np.round(positions).astype(int) - config = {} - for i, pos in enumerate(positions): - config[f"cf{i}"] = { - "name": f"cf{i}", - "uri": f"radio://0/80/2M/E7E7E7E7{i:02X}", - "pos": pos.astype(float).tolist(), - } + # Assign fake cf-names cf00..cfNN and addresses 0x00..0xNN. + # Channel = (addr // 10) * 10 = 0 for all (fine for unit tests). + names = [f"cf{i:02d}" for i in range(n_drones)] + active_line = "active = [" + ", ".join(f'"{n}"' for n in names) + "]\n\n" + drone_entries = "" + for i, (name, pos) in enumerate(zip(names, positions)): + drone_entries += f"[{name}]\naddr = {i}\npos = {pos.astype(float).tolist()}\n\n" - # Create temporary config file tmp_dir = Path("/tmp/swarm_gpt_test") tmp_dir.mkdir(exist_ok=True) config_path = tmp_dir / "drones.toml" - - with open(config_path, "w") as f: - toml.dump(config, f) - + config_path.write_text(active_line + drone_entries) return config_path diff --git a/tests/unit/test_api.py b/tests/unit/test_api.py index f058058..efeb997 100644 --- a/tests/unit/test_api.py +++ b/tests/unit/test_api.py @@ -14,6 +14,7 @@ def test_normalize_playback_schema(): backend = SimpleNamespace( settings={"axswarm": {"pos_min": [-1, -1, 0], "pos_max": [1, 1, 2]}}, music_manager=SimpleNamespace(song="Example Song"), + crop_window=lambda song: (0.0, 60.0), ) states = np.zeros((2, 3, 13)) states[:, :, 3:7] = [0, 0, 0, 1] @@ -22,6 +23,7 @@ def test_normalize_playback_schema(): ) assert payload["schemaVersion"] == 1 assert payload["audioUrl"] == "/api/media/music/Example%20Song" + assert payload["audioOffset"] == 0.0 assert payload["numDrones"] == 3 assert payload["fields"]["pos"] == [0, 3] assert len(payload["states"]) == len(payload["timestamps"]) @@ -42,11 +44,11 @@ def test_normalize_playback_rejects_mismatched_states(): def test_app_and_library_metadata_build(tmp_path: Path): (tmp_path / "Test Song.mp3").write_bytes(b"") app = create_app(ApiConfig(music_dir=tmp_path)) - backend = _backend_from_config(ApiConfig(music_dir=tmp_path), "openai", "gpt-4o") + backend = _backend_from_config(ApiConfig(music_dir=tmp_path), "openai", "gpt-5.4-nano") assert app.title == "SwarmGPT Browser API" assert backend.songs == ["Test Song"] - assert DEFAULT_OPENAI_MODEL_CHOICES[0] == "gpt-4o" + assert DEFAULT_OPENAI_MODEL_CHOICES[0] == "gpt-5.4-nano" def test_library_returns_preset_display_metadata_and_delete(tmp_path: Path): diff --git a/tests/unit/test_choreographer.py b/tests/unit/test_choreographer.py new file mode 100644 index 0000000..f442c38 --- /dev/null +++ b/tests/unit/test_choreographer.py @@ -0,0 +1,139 @@ +"""Tests for choreographer orchestration helpers (F3) and form_* motion primitives (F1).""" + +from pathlib import Path + +import numpy as np +from conftest import virtual_crazyswarm_config + +from swarm_gpt.core.choreographer import ( + Choreographer, + _form_should_drop_holds, + _overlapping_drone_set, +) + + +def test_form_should_drop_holds_when_overlapping_motion_follows(): + """form_star (full swarm) + rotate (full swarm) → overlap, drop holds.""" + action_list = [{"form_star": (100, 60, 80, 1.0)}, {"rotate": (45, "z")}] + assert _form_should_drop_holds(action_list, 0, num_drones=10) is True + + +def test_form_should_not_drop_holds_when_motion_targets_disjoint_drones(): + """form_circle drones 1-5 + move_z drones 6-10 → no overlap, keep holds.""" + action_list = [{"form_circle": ([1, 2, 3, 4, 5], 150, 1.0)}, {"move_z": ([6, 7, 8, 9, 10], 50)}] + assert _form_should_drop_holds(action_list, 0, num_drones=10) is False + + +def test_form_should_not_drop_holds_when_followed_by_another_form(): + """form_star followed only by form_circle → no motion primitive, keep holds.""" + action_list = [{"form_star": (100, 60, 80, 1.0)}, {"form_circle": ([1, 2, 3, 4, 5], 150, 1.0)}] + assert _form_should_drop_holds(action_list, 0, num_drones=10) is False + + +def test_form_should_drop_holds_with_three_deep_stack(): + """form_star; rotate; move_z — both later entries are motion, still drops holds.""" + action_list = [ + {"form_star": (100, 60, 80, 1.0)}, + {"rotate": (45, "z")}, + {"move_z": ([1, 2, 3], 30)}, + ] + assert _form_should_drop_holds(action_list, 0, num_drones=10) is True + + +def test_overlapping_drone_set_full_swarm(): + """Primitives without drone subset args touch the full swarm.""" + assert _overlapping_drone_set({"form_star": (100, 60, 80, 1.0)}, num_drones=5) == frozenset( + {0, 1, 2, 3, 4} + ) + assert _overlapping_drone_set({"rotate": (45, "z")}, num_drones=3) == frozenset({0, 1, 2}) + + +def test_overlapping_drone_set_subset(): + """form_circle / move_z / center return 0-indexed drone IDs from their first arg.""" + assert _overlapping_drone_set( + {"form_circle": ([1, 3, 5], 150, 1.0)}, num_drones=10 + ) == frozenset({0, 2, 4}) + assert _overlapping_drone_set({"move_z": ([2, 4], 50)}, num_drones=10) == frozenset({1, 3}) + assert _overlapping_drone_set({"center": ([1, 2, 3],)}, num_drones=10) == frozenset({0, 1, 2}) + + +def test_overlapping_drone_set_swap(): + """swap returns exactly the two drone IDs (0-indexed).""" + assert _overlapping_drone_set({"swap": (3, 7)}, num_drones=10) == frozenset({2, 6}) + + +def test_overlapping_drone_set_move(): + """move returns the single target drone ID (0-indexed, 4th arg).""" + assert _overlapping_drone_set({"move": (100, 0, 150, 5)}, num_drones=10) == frozenset({4}) + + +def test_schema_allows_multiple_actions_per_entry(): + """After F3 rollback, action_list must not have maxItems: 1.""" + from swarm_gpt.core.structured_output_schema import build_motion_primitive_response_schema + + schema = build_motion_primitive_response_schema( + all_keys=[(1, 1, 1)], required_keys=[(1, 1, 1)], num_drones=10 + ) + action_list_schema = schema["$defs"]["action_list"] + assert "maxItems" not in action_list_schema or action_list_schema["maxItems"] > 1 + assert action_list_schema["minItems"] == 1 + + +def test_form_star_hold_pruning_in_pipeline(): + """form_star + rotate: arrival waypoint kept, holds stripped before merge.""" + config_path = virtual_crazyswarm_config(n_drones=10) + choreographer = Choreographer( + config_file=config_path, llm_provider="openai", use_motion_primitives=True + ) + # 10 drones arranged in a line at z=100 cm + for i in choreographer.starting_pos: + choreographer.starting_pos[i] = np.array([(i - 5) * 0.3, 0.0, 1.0]) + + from swarm_gpt.utils.music_analyzer import Bar, Beat, Segment, SongStructure + + t = 0.0 + beats = [Beat(id=j + 1, time_s=t + j * 0.5, position_in_bar=j + 1) for j in range(4)] + bar = Bar(id=1, start_s=0.0, beats=beats) + seg = Segment(id=1, label="chorus", start_s=0.0, end_s=20.0, bars=[bar]) + structure = SongStructure( + schema_version=2, + source_path="test.mp3", + song_sha256="abc", + analyzer="test", + bpm=120, + segments=[seg], + ) + + # Build a choreography with form_star followed by rotate at the same key + choreography = {(1, 1, 1): "form_star(100, 60, 80, 1.0); rotate(45, 'z')"} + waypoints = choreographer._choreo2waypoints(choreography, structure) + + # The position array must have shape (n_drones, T, 3) + assert waypoints["pos"].shape[0] == 10 + # Must have more than one timestep (rotate emits dense waypoints) + assert waypoints["pos"].shape[1] > 2 + + +def test_load_drone_config_uses_active_list(tmp_path: Path) -> None: + """Loader must respect active list order and derive uri from addr.""" + cfg = tmp_path / "drones.toml" + cfg.write_text( + 'active = ["cf41", "cf31"]\n' + "[cf31]\naddr = 0x1F\npos = [0.0, 0.0, 0.0]\n" + "[cf41]\naddr = 0x29\npos = [1.0, 0.0, 0.0]\n" + ) + import yaml + + c = Choreographer.__new__(Choreographer) + c.agents = {} + c.uris = {} + c.starting_pos = {} + c.num_drones = 0 + settings_path = Path(__file__).resolve().parents[2] / "swarm_gpt/data/settings.yaml" + c.settings = yaml.safe_load(settings_path.read_text()) + c.load_drone_config(config_file=cfg) + + # active = ["cf41", "cf31"] → swarm index 0 = cf41, 1 = cf31 + assert c.num_drones == 2 + assert c.uris[0] == "radio://0/40/2M/E7E7E7E729" # cf41, channel=40, addr=0x29 + assert c.uris[1] == "radio://0/30/2M/E7E7E7E71F" # cf31, channel=30, addr=0x1F diff --git a/tests/unit/test_frontend_setup.py b/tests/unit/test_frontend_setup.py index 6961303..7826dd2 100644 --- a/tests/unit/test_frontend_setup.py +++ b/tests/unit/test_frontend_setup.py @@ -33,7 +33,7 @@ def test_frontend_install_skips_cleanly_when_web_package_is_missing(tmp_path: Pa bin_dir = tmp_path / "bin" bin_dir.mkdir() npm = bin_dir / "npm" - npm.write_text(f"#!/usr/bin/env bash\necho \"$@\" >> {npm_log}\n") + npm.write_text(f'#!/usr/bin/env bash\necho "$@" >> {npm_log}\n') npm.chmod(0o755) env = os.environ | {"PATH": f"{bin_dir}:{os.environ['PATH']}"} diff --git a/tests/unit/test_motion_primitives.py b/tests/unit/test_motion_primitives.py new file mode 100644 index 0000000..e977361 --- /dev/null +++ b/tests/unit/test_motion_primitives.py @@ -0,0 +1,62 @@ +"""Tests for F1: time_to_finish_s on formation primitives.""" + +import numpy as np + +from swarm_gpt.core.motion_primitives import form_circle, form_cone, form_star + + +def _limits() -> dict: + return {"lower": np.array([-2.2, -2.7, 0.25]), "upper": np.array([2.2, 2.7, 1.7])} + + +def _swarm_10() -> np.ndarray: + return np.array( + [[x, y, 100] for x in (-200, -100, 0, 100, 200) for y in (-100, 100)], dtype=float + ) + + +def test_form_star_respects_time_to_finish(): + """Large time_to_finish_s → arrival should be ~5s into a 10s interval, not at physics min.""" + swarm = _swarm_10() + limits = _limits() + _, wps = form_star((100, 60, 80, 5.0), swarm, 0.0, 10.0, limits) + times = sorted(wps.keys()) + assert 4.5 <= times[0] <= 5.5, f"expected arrival ~5s, got {times[0]}" + + +def test_form_star_clamps_below_physics_min(): + """Tiny time_to_finish_s → should clamp UP to the physics floor (>= T_MIN = 0.5s).""" + swarm = _swarm_10() + limits = _limits() + _, wps = form_star((100, 60, 80, 0.05), swarm, 0.0, 10.0, limits) + times = sorted(wps.keys()) + assert times[0] >= 0.5, f"expected clamp to physics floor, got {times[0]}" + + +def test_form_star_clamps_above_interval(): + """time_to_finish_s larger than the interval → arrival should be clamped to tend.""" + swarm = _swarm_10() + limits = _limits() + _, wps = form_star((100, 60, 80, 999.0), swarm, 0.0, 5.0, limits) + times = sorted(wps.keys()) + # Arrival must not exceed tend + assert times[0] <= 5.0, f"arrival {times[0]} exceeds tend=5.0" + + +def test_form_circle_respects_time_to_finish(): + """form_circle with large time_to_finish_s should arrive late in the interval.""" + swarm = _swarm_10() + limits = _limits() + drone_ids = list(range(1, 6)) # drones 1-5 + _, wps = form_circle((drone_ids, 100, 100, 8.0), swarm, 0.0, 10.0, limits) + times = sorted(wps.keys()) + assert times[0] >= 7.0, f"expected late arrival, got {times[0]}" + + +def test_form_cone_respects_time_to_finish(): + """form_cone with time_to_finish_s close to tend → arrival near end of interval.""" + swarm = _swarm_10() + limits = _limits() + _, wps = form_cone((50, 60, 0, 8.0), swarm, 0.0, 10.0, limits) + times = sorted(wps.keys()) + assert times[0] >= 7.0, f"expected late arrival, got {times[0]}" diff --git a/tests/unit/test_music_analyzer.py b/tests/unit/test_music_analyzer.py new file mode 100644 index 0000000..7d695e4 --- /dev/null +++ b/tests/unit/test_music_analyzer.py @@ -0,0 +1,299 @@ +"""Unit tests for :mod:`swarm_gpt.utils.music_analyzer`. + +Tests use duck-typed synthetic ``AnalysisResult`` objects so allin1 is never imported. +""" + +from __future__ import annotations + +import json +from dataclasses import dataclass +from typing import TYPE_CHECKING + +import numpy as np +import pytest + +from swarm_gpt.utils.music_analyzer import ( + SCHEMA_VERSION, + Bar, + Beat, + Segment, + SongStructure, + compute_dynamics_per_2bar, + dynamics_window_keys, +) + +if TYPE_CHECKING: + from pathlib import Path + + +@dataclass +class FakeAllin1Segment: + start: float + end: float + label: str + + +@dataclass +class FakeAllin1Result: + bpm: int + beats: list[float] + beat_positions: list[int] + segments: list[FakeAllin1Segment] + + +def _three_segment_song_4_4() -> FakeAllin1Result: + """120 BPM, 4/4 throughout: 3 segments, 1 bar each, 4 beats each.""" + return FakeAllin1Result( + bpm=120, + beats=[0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5], + beat_positions=[1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4], + segments=[ + FakeAllin1Segment(start=0.0, end=2.0, label="intro"), + FakeAllin1Segment(start=2.0, end=4.0, label="verse"), + FakeAllin1Segment(start=4.0, end=6.0, label="outro"), + ], + ) + + +def _waltz_song_3_4() -> FakeAllin1Result: + """3/4 (waltz): one segment, two bars, three beats each.""" + return FakeAllin1Result( + bpm=120, + beats=[0.0, 0.5, 1.0, 1.5, 2.0, 2.5], + beat_positions=[1, 2, 3, 1, 2, 3], + segments=[FakeAllin1Segment(start=0.0, end=3.0, label="whole")], + ) + + +def _build(result: FakeAllin1Result) -> SongStructure: + return SongStructure.from_allin1( + result=result, source_path="music/Test.mp3", song_sha256="deadbeef", analyzer="allin1@test" + ) + + +def test_from_allin1_groups_beats_into_4_4_bars() -> None: + structure = _build(_three_segment_song_4_4()) + assert len(structure.segments) == 3 + for seg in structure.segments: + assert len(seg.bars) == 1 + assert [b.position_in_bar for b in seg.bars[0].beats] == [1, 2, 3, 4] + + +def test_segment_labels_and_ids_preserved() -> None: + structure = _build(_three_segment_song_4_4()) + assert [s.label for s in structure.segments] == ["intro", "verse", "outro"] + assert [s.id for s in structure.segments] == [1, 2, 3] + + +def test_time_of_returns_beat_time() -> None: + structure = _build(_three_segment_song_4_4()) + assert structure.time_of(1, 1, 1) == 0.0 + assert structure.time_of(2, 1, 1) == 2.0 + assert structure.time_of(3, 1, 4) == 5.5 + + +def test_time_of_raises_for_missing_key() -> None: + structure = _build(_three_segment_song_4_4()) + with pytest.raises(KeyError): + structure.time_of(99, 1, 1) + + +def test_required_keys_are_bar_downbeats() -> None: + # One bar per segment here, so the downbeats coincide with the segment openings. + structure = _build(_three_segment_song_4_4()) + assert structure.required_keys() == [(1, 1, 1), (2, 1, 1), (3, 1, 1)] + + +def test_required_keys_cover_every_bar() -> None: + # Two bars in one segment: both bar downbeats are required, not just the segment opening. + structure = _build(_waltz_song_3_4()) + assert structure.required_keys() == [(1, 1, 1), (1, 2, 1)] + + +def test_all_keys_covers_every_beat() -> None: + structure = _build(_three_segment_song_4_4()) + # 3 segments x 1 bar x 4 beats = 12 + assert len(structure.all_keys()) == 12 + + +def test_variable_meter_3_4() -> None: + structure = _build(_waltz_song_3_4()) + assert len(structure.segments) == 1 + assert len(structure.segments[0].bars) == 2 + assert all(len(bar.beats) == 3 for bar in structure.segments[0].bars) + + +def test_json_round_trip(tmp_path: Path) -> None: + original = _build(_three_segment_song_4_4()) + path = tmp_path / "test.json" + original.to_json(path) + loaded = SongStructure.from_json(path) + assert loaded == original + + +def test_schema_version_mismatch_raises(tmp_path: Path) -> None: + path = tmp_path / "wrong.json" + path.write_text(json.dumps({"schema_version": 999})) + with pytest.raises(ValueError, match="schema_version"): + SongStructure.from_json(path) + + +def test_schema_version_constant_is_current() -> None: + # Guards against accidentally bumping SCHEMA_VERSION without updating tests/JSONs. + assert SCHEMA_VERSION == 2 + + +def test_crop_selects_window_and_rebases_to_zero() -> None: + structure = _build(_three_segment_song_4_4()) # 3 segments, beats 0.0..5.5 + cropped = structure.crop(2.0, 3.9) # only the "verse" segment's beats fall inside + assert len(cropped.segments) == 1 + seg = cropped.segments[0] + assert seg.label == "verse" + assert seg.id == 1 + assert seg.start_s == 0.0 # rebased: window start -> 0 + assert len(seg.bars) == 1 + beats = seg.bars[0].beats + assert [b.time_s for b in beats] == [0.0, 0.5, 1.0, 1.5] # 2.0..3.5 shifted by -2.0 + assert [b.position_in_bar for b in beats] == [1, 2, 3, 4] + assert [b.id for b in beats] == [1, 2, 3, 4] + + +def test_crop_renumbers_ids_contiguously() -> None: + structure = _build(_three_segment_song_4_4()) + cropped = structure.crop(2.0, 6.0) # drops the intro, keeps verse + outro + assert [seg.id for seg in cropped.segments] == [1, 2] + assert [seg.label for seg in cropped.segments] == ["verse", "outro"] + assert cropped.segments[0].bars[0].beats[0].time_s == 0.0 + + +def test_crop_full_window_preserves_keys() -> None: + structure = _build(_three_segment_song_4_4()) + cropped = structure.crop(0.0, 6.0) + assert cropped.all_keys() == structure.all_keys() + assert cropped.segments[0].bars[0].beats[0].time_s == 0.0 + + +def test_crop_rejects_non_positive_window() -> None: + structure = _build(_three_segment_song_4_4()) + with pytest.raises(ValueError, match="must be greater"): + structure.crop(3.0, 3.0) + with pytest.raises(ValueError, match="must be greater"): + structure.crop(4.0, 2.0) + + +# --------------------------------------------------------------------------- +# F2 helpers +# --------------------------------------------------------------------------- + + +def _make_synthetic_structure(n_bars: int, bar_dur: float) -> SongStructure: + """Single-segment structure with ``n_bars`` bars of ``bar_dur`` seconds each.""" + bars: list[Bar] = [] + t = 0.0 + for bar_id in range(1, n_bars + 1): + beats = [Beat(id=1, time_s=t, position_in_bar=1)] + bars.append(Bar(id=bar_id, start_s=t, beats=beats)) + t += bar_dur + seg = Segment(id=1, label="test", start_s=0.0, end_s=t, bars=bars) + return SongStructure( + schema_version=SCHEMA_VERSION, + source_path="test.mp3", + song_sha256="abc", + analyzer="test", + bpm=120, + segments=[seg], + ) + + +def _make_synthetic_structure_two_segments(bars_per_seg: int) -> SongStructure: + """Two-segment structure, each with ``bars_per_seg`` bars at 1s each.""" + bar_dur = 1.0 + segments: list[Segment] = [] + t = 0.0 + for seg_id in range(1, 3): + bars: list[Bar] = [] + seg_start = t + for bar_id in range(1, bars_per_seg + 1): + beats = [Beat(id=1, time_s=t, position_in_bar=1)] + bars.append(Bar(id=bar_id, start_s=t, beats=beats)) + t += bar_dur + segments.append( + Segment(id=seg_id, label=f"seg{seg_id}", start_s=seg_start, end_s=t, bars=bars) + ) + return SongStructure( + schema_version=SCHEMA_VERSION, + source_path="test.mp3", + song_sha256="abc", + analyzer="test", + bpm=120, + segments=segments, + ) + + +def test_rms_per_2bar_aligns_with_bars(tmp_path: "Path") -> None: + """Bars 1-2 silent, bars 3-4 tone → first window quiet, second loud.""" + import soundfile as sf + + sr = 22050 + bar_dur = 1.0 + n_bars = 4 + y = np.zeros(int(n_bars * bar_dur * sr)) + y[int(2 * bar_dur * sr) :] = 0.5 * np.sin( + 2 * np.pi * 440 * np.arange(len(y) - int(2 * bar_dur * sr)) / sr + ) + wav_path = tmp_path / "test.wav" + sf.write(wav_path, y, sr) + structure = _make_synthetic_structure(n_bars=4, bar_dur=bar_dur) + rms, _centroid = compute_dynamics_per_2bar(structure, wav_path) + assert len(rms) == 2 + assert rms[0] < 0.1, f"silent window should be ~0, got {rms[0]}" + assert rms[1] > 0.5, f"tone window should be loud, got {rms[1]}" + + +def test_centroid_distinguishes_low_vs_high_frequencies(tmp_path: "Path") -> None: + """Bars 1-2 low tone (110 Hz), bars 3-4 high tone (4400 Hz) → centroid rises.""" + import soundfile as sf + + sr = 22050 + bar_dur = 1.0 + n_samples = int(2 * bar_dur * sr) + t = np.arange(n_samples) / sr + y_low = 0.5 * np.sin(2 * np.pi * 110 * t) + y_high = 0.5 * np.sin(2 * np.pi * 4400 * t) + y = np.concatenate([y_low, y_high]) + wav_path = tmp_path / "test.wav" + sf.write(wav_path, y, sr) + structure = _make_synthetic_structure(n_bars=4, bar_dur=bar_dur) + _rms, centroid = compute_dynamics_per_2bar(structure, wav_path) + assert centroid[1] > centroid[0] * 2, ( + f"high-frequency window should have ~2x+ centroid, got {centroid}" + ) + + +def test_dynamics_window_keys_do_not_cross_segment_boundary() -> None: + """A 3-bar segment 1 + 3-bar segment 2 → 4 keys, each segment's keys stay in that segment.""" + structure = _make_synthetic_structure_two_segments(bars_per_seg=3) + keys = dynamics_window_keys(structure) + # Each 3-bar segment: 1 full + 1 truncated window = 2 windows. Total = 4. + assert len(keys) == 4 + assert keys[0].startswith("s1b") and keys[1].startswith("s1b") + assert keys[2].startswith("s2b") and keys[3].startswith("s2b") + + +def test_crop_slices_dynamics_arrays() -> None: + """crop() trims rms_per_2bar / centroid_per_2bar to windows that survive.""" + structure = _make_synthetic_structure(n_bars=8, bar_dur=1.0) + structure.rms_per_2bar = (0.1, 0.2, 0.3, 0.4) + structure.centroid_per_2bar = (0.5, 0.6, 0.7, 0.8) + cropped = structure.crop(start_s=2.0, end_s=8.0) + assert len(cropped.rms_per_2bar) == 3 + assert cropped.rms_per_2bar == (0.2, 0.3, 0.4) + assert cropped.centroid_per_2bar == (0.6, 0.7, 0.8) + + +def test_from_json_rejects_v1_cache(tmp_path: "Path") -> None: + """Loading a schema-v1 JSON raises ValueError after the version bump.""" + v1_path = tmp_path / "old.json" + v1_path.write_text('{"schema_version": 1, "bpm": 120, "segments": []}') + with pytest.raises(ValueError, match="schema_version"): + SongStructure.from_json(v1_path) diff --git a/tests/unit/test_music_manager.py b/tests/unit/test_music_manager.py new file mode 100644 index 0000000..1275530 --- /dev/null +++ b/tests/unit/test_music_manager.py @@ -0,0 +1,52 @@ +from pathlib import Path +from unittest.mock import MagicMock, patch + +import pytest + +from swarm_gpt.utils.music_manager import MusicManager + + +@pytest.fixture +def music_dir(tmp_path: Path) -> Path: + song_path = tmp_path / "Example Song.mp3" + song_path.write_bytes(b"fake mp3") + return tmp_path + + +def test_play_applies_crop_window(music_dir: Path) -> None: + manager = MusicManager(music_dir) + manager.song = "Example Song" + + media = MagicMock() + player = MagicMock() + player.play.return_value = 0 + player.is_playing.return_value = True + instance = MagicMock() + instance.media_new.return_value = media + instance.media_player_new.return_value = player + + with patch.object(manager, "_get_vlc_instance", return_value=instance): + started = manager.play(wait=True, start_s=12.5, end_s=72.5) + + assert started is True + media.add_option.assert_any_call(":start-time=12.5") + media.add_option.assert_any_call(":stop-time=72.5") + player.set_time.assert_called_once_with(12500) + + +def test_play_without_crop_window_skips_media_options(music_dir: Path) -> None: + manager = MusicManager(music_dir) + manager.song = "Example Song" + + media = MagicMock() + player = MagicMock() + player.play.return_value = 0 + instance = MagicMock() + instance.media_new.return_value = media + instance.media_player_new.return_value = player + + with patch.object(manager, "_get_vlc_instance", return_value=instance): + manager.play(wait=False) + + media.add_option.assert_not_called() + player.set_time.assert_not_called() diff --git a/tests/unit/test_structured_output.py b/tests/unit/test_structured_output.py index f958b98..d4293e0 100644 --- a/tests/unit/test_structured_output.py +++ b/tests/unit/test_structured_output.py @@ -6,9 +6,41 @@ from conftest import virtual_crazyswarm_config from swarm_gpt.core.choreographer import Choreographer -from swarm_gpt.core.structured_output_schema import build_motion_primitive_response_schema +from swarm_gpt.core.structured_output_schema import ( + build_motion_primitive_response_schema, + decode_key, + encode_key, +) from swarm_gpt.exception import LLMFormatError from swarm_gpt.utils.llm_providers import RESPONSES_TEMPERATURE +from swarm_gpt.utils.music_analyzer import SCHEMA_VERSION, Bar, Beat, Segment, SongStructure + + +def _simple_structure(n_segments: int = 2, n_bars: int = 1, n_beats: int = 4) -> SongStructure: + """Synthetic SongStructure: ``n_segments`` × ``n_bars`` × ``n_beats`` at 0.5s spacing.""" + segments: list[Segment] = [] + t = 0.0 + for seg_id in range(1, n_segments + 1): + bars: list[Bar] = [] + seg_start = t + for bar_id in range(1, n_bars + 1): + bar_start = t + beats: list[Beat] = [] + for beat_id in range(1, n_beats + 1): + beats.append(Beat(id=beat_id, time_s=t, position_in_bar=beat_id)) + t += 0.5 + bars.append(Bar(id=bar_id, start_s=bar_start, beats=beats)) + segments.append( + Segment(id=seg_id, label=f"seg{seg_id}", start_s=seg_start, end_s=t, bars=bars) + ) + return SongStructure( + schema_version=SCHEMA_VERSION, + source_path="music/Test.mp3", + song_sha256="deadbeef", + analyzer="allin1@test", + bpm=120, + segments=segments, + ) def _contains_one_of(node: Any) -> bool: @@ -21,78 +53,114 @@ def _contains_one_of(node: Any) -> bool: return False -def test_build_motion_primitive_response_schema_enforces_exact_num_beats(): - schema = build_motion_primitive_response_schema(num_beats=3, num_drones=5) +def test_encode_decode_key_round_trip(): + assert encode_key(2, 4, 1) == "s2b4t1" + assert decode_key("s2b4t1") == (2, 4, 1) + + +def test_decode_key_rejects_malformed(): + with pytest.raises(LLMFormatError): + decode_key("1") + + +def test_schema_lists_all_keys_in_enum_as_array_of_entries(): + structure = _simple_structure(n_segments=2, n_bars=1, n_beats=4) + schema = build_motion_primitive_response_schema( + all_keys=structure.all_keys(), required_keys=structure.required_keys(), num_drones=5 + ) choreography = schema["properties"]["choreography"] + # choreography is an array of {key, actions} entries. + assert choreography["type"] == "array" + item = choreography["items"] + # 2 segments × 1 bar × 4 beats = 8 addressable keys, all offered via the key enum. + assert set(item["properties"]["key"]["enum"]) == { + "s1b1t1", + "s1b1t2", + "s1b1t3", + "s1b1t4", + "s2b1t1", + "s2b1t2", + "s2b1t3", + "s2b1t4", + } + # Strict mode: both fields of an entry are required. + assert item["required"] == ["key", "actions"] + assert item["additionalProperties"] is False - assert choreography["type"] == "object" - assert choreography["required"] == ["1", "2", "3"] - assert set(choreography["properties"]) == {"1", "2", "3"} - assert choreography["properties"]["1"] == {"$ref": "#/$defs/action_list"} - assert choreography["additionalProperties"] is False - assert schema["$defs"]["action_list"]["items"] == {"$ref": "#/$defs/action"} - assert not _contains_one_of(schema) +def test_schema_rejects_required_keys_not_in_all_keys(): + with pytest.raises(ValueError, match="required_keys not present"): + build_motion_primitive_response_schema( + all_keys=[(1, 1, 1)], required_keys=[(1, 1, 2)], num_drones=4 + ) -def test_structured_payload_to_choreography_preserves_plan_and_action_order(): + +def test_structured_payload_to_choreography_uses_hierarchical_keys(): config_path = virtual_crazyswarm_config(n_drones=4) choreographer = Choreographer( config_file=config_path, llm_provider="openai", use_motion_primitives=True ) payload = { "song_mood": "calm", - "cord_analysis": "minor", "choreography_plan": "simple", - "choreography": { - "1": [ - { - "primitive": "form_circle", - "params": {"drone_ids": [1, 2], "radius_cm": 100}, - }, - ], - "2": [{"primitive": "PLAN", "params": {}}], - "3": [ - {"primitive": "rotate", "params": {"angle_deg": 90, "axis": "z"}}, - {"primitive": "move_z", "params": {"drone_ids": [1], "delta_cm": 10}}, - ], - }, + "choreography": [ + { + "key": "s1b1t1", + "actions": [ + { + "primitive": "form_circle", + "params": { + "drone_ids": [1, 2], + "radius_cm": 100, + "z_coord_cm": 100, + "time_to_finish_s": 1.5, + }, + } + ], + }, + { + "key": "s2b1t1", + "actions": [ + {"primitive": "rotate", "params": {"angle_deg": 90, "axis": "z"}}, + {"primitive": "move_z", "params": {"drone_ids": [1], "delta_cm": 10}}, + ], + }, + ], } choreography = choreographer._structured_payload_to_choreography(payload) assert choreography == { - 1: "form_circle([1, 2], 100)", - 2: "PLAN", - 3: "rotate(90, 'z'); move_z([1], 10)", + (1, 1, 1): "form_circle([1, 2], 100, 100, 1.5)", + (2, 1, 1): "rotate(90, 'z'); move_z([1], 10)", } def test_structured_payload_to_choreography_rejects_unexpected_named_params(): config_path = virtual_crazyswarm_config(n_drones=4) choreographer = Choreographer( - config_file=config_path, - llm_provider="openai", - use_motion_primitives=True, + config_file=config_path, llm_provider="openai", use_motion_primitives=True ) payload = { "song_mood": "calm", - "cord_analysis": "minor", "choreography_plan": "simple", - "choreography": { - "1": [ - { - "primitive": "form_cone", - "params": { - "drone_ids": [1, 2, 3], - "delta_height_cm": 60, - "spacing_cm": 60, - "is_inverted": 0, - }, - }, - ], - }, + "choreography": [ + { + "key": "s1b1t1", + "actions": [ + { + "primitive": "form_cone", + "params": { + "drone_ids": [1, 2, 3], + "delta_height_cm": 60, + "spacing_cm": 60, + "is_inverted": 0, + }, + } + ], + } + ], } - with pytest.raises(LLMFormatError, match="unexpected \\['drone_ids'\\]"): choreographer._structured_payload_to_choreography(payload) @@ -102,6 +170,7 @@ def test_call_responses_structured_includes_json_schema_format(): choreographer = Choreographer( config_file=config_path, llm_provider="openai", use_motion_primitives=True ) + structure = _simple_structure(n_segments=1, n_bars=1, n_beats=1) captured: dict[str, Any] = {} class FakeResponses: @@ -109,9 +178,15 @@ def create(self, **kwargs: Any) -> SimpleNamespace: captured.update(kwargs) payload = { "song_mood": "energetic", - "cord_analysis": "major", "choreography_plan": "test", - "choreography": {"1": [{"primitive": "PLAN", "params": {}}]}, + "choreography": [ + { + "key": "s1b1t1", + "actions": [ + {"primitive": "rotate", "params": {"angle_deg": 0, "axis": "z"}} + ], + } + ], } return SimpleNamespace(error=None, output_text=json.dumps(payload)) @@ -121,17 +196,16 @@ class FakeClient: choreographer._chat_client_for_call = lambda: FakeClient() # noqa: E731 messages = [{"role": "user", "content": "hello"}] - parsed = choreographer._call_responses_structured(messages, num_beats=1) + parsed = choreographer._call_responses_structured(messages, structure=structure) - assert "1" in parsed["choreography"] + assert parsed["choreography"][0]["key"] == "s1b1t1" assert captured["text"]["format"]["type"] == "json_schema" assert captured["text"]["format"]["name"] == "swarmgpt_choreography" assert captured["text"]["format"]["strict"] is True - assert captured["text"]["format"]["schema"]["properties"]["choreography"]["required"] == ["1"] schema = captured["text"]["format"]["schema"] - assert schema["properties"]["choreography"]["properties"]["1"] == { - "$ref": "#/$defs/action_list" - } + item = schema["properties"]["choreography"]["items"] + assert item["properties"]["key"]["enum"] == ["s1b1t1"] + assert item["properties"]["actions"] == {"$ref": "#/$defs/action_list"} action_schema = schema["$defs"]["action"] variants = action_schema["anyOf"] assert any( @@ -144,7 +218,10 @@ class FakeClient: def test_schema_contains_no_openai_unsupported_keywords(): - schema = build_motion_primitive_response_schema(num_beats=2, num_drones=4) + structure = _simple_structure(n_segments=2, n_bars=2, n_beats=4) + schema = build_motion_primitive_response_schema( + all_keys=structure.all_keys(), required_keys=structure.required_keys(), num_drones=4 + ) schema_text = json.dumps(schema) assert "oneOf" not in schema_text assert "uniqueItems" not in schema_text @@ -165,53 +242,56 @@ def test_call_responses_structured_ollama_uses_native_chat(monkeypatch: pytest.M choreographer = Choreographer( config_file=config_path, llm_provider="ollama", use_motion_primitives=True ) + structure = _simple_structure(n_segments=1, n_bars=1, n_beats=1) captured: dict[str, Any] = {} def fake_ollama_chat(**kwargs: Any) -> dict[str, Any]: captured.update(kwargs) payload = { "song_mood": "energetic", - "cord_analysis": "major", "choreography_plan": "test", - "choreography": {"1": [{"primitive": "PLAN", "params": {}}]}, + "choreography": [ + { + "key": "s1b1t1", + "actions": [{"primitive": "rotate", "params": {"angle_deg": 0, "axis": "z"}}], + } + ], } return {"message": {"content": json.dumps(payload)}} - monkeypatch.setattr( - "swarm_gpt.core.choreographer.cancellable_ollama_chat", fake_ollama_chat + monkeypatch.setattr("swarm_gpt.core.choreographer.cancellable_ollama_chat", fake_ollama_chat) + parsed = choreographer._call_responses_structured( + [{"role": "user", "content": "hello"}], structure=structure ) - parsed = choreographer._call_responses_structured([{"role": "user", "content": "hello"}], 1) - assert parsed["choreography"]["1"][0]["primitive"] == "PLAN" + assert parsed["choreography"][0]["actions"][0]["primitive"] == "rotate" assert captured["model"] == choreographer.model_id - assert captured["format"]["properties"]["choreography"]["required"] == ["1"] + assert captured["format"]["properties"]["choreography"]["items"]["properties"]["key"][ + "enum" + ] == ["s1b1t1"] tail = captured["messages"][-1]["content"] assert "Return valid JSON only. Match the provided response format exactly." in tail assert "never positional args arrays" in tail - assert '"required":["1"]' not in tail assert captured["options"]["temperature"] == RESPONSES_TEMPERATURE -def test_structured_initial_prompt_uses_legacy_example_for_ollama(): +def test_structured_initial_prompt_renders_hierarchical_keys(): config_path = virtual_crazyswarm_config(n_drones=4) choreographer = Choreographer( - config_file=config_path, - llm_provider="ollama", - use_motion_primitives=True, - ) - music_info = { - "beat_times": [0.5, 1.0], - "novelty": [0.1, 0.9], - "chords": ["C", "G"], - "dBFS": [-40, -30], - } + config_file=config_path, llm_provider="ollama", use_motion_primitives=True + ) + structure = _simple_structure(n_segments=2, n_bars=1, n_beats=4) - messages = choreographer.format_initial_prompt("test song", music_info) + messages = choreographer.format_initial_prompt("test song", structure) - example = messages[2]["content"] - assert "choreography:" in example - assert "move_z([1, 2, 3], 30)" in example - assert '"primitive": "PLAN"' not in example + user_content = messages[1]["content"] + assert "test song" in user_content + assert "120 BPM" in user_content + # Required keys block must include the segment-opening keys. + assert "s1b1t1" in user_content + assert "s2b1t1" in user_content + # Segment label is rendered. + assert "seg1" in user_content and "seg2" in user_content def test_generate_choreography_ollama_raises_on_structured_errors(monkeypatch: pytest.MonkeyPatch): @@ -219,6 +299,7 @@ def test_generate_choreography_ollama_raises_on_structured_errors(monkeypatch: p choreographer = Choreographer( config_file=config_path, llm_provider="ollama", use_motion_primitives=True ) + structure = _simple_structure(n_segments=1, n_bars=1, n_beats=1) monkeypatch.setattr( choreographer, "_call_responses_structured", @@ -231,7 +312,7 @@ def test_generate_choreography_ollama_raises_on_structured_errors(monkeypatch: p ) with pytest.raises(LLMFormatError, match="bad json"): choreographer.generate_choreography( - prompt=[{"role": "user", "content": "hello"}], num_beats=1 + prompt=[{"role": "user", "content": "hello"}], structure=structure ) @@ -242,12 +323,57 @@ def test_generate_choreography_ollama_raises_when_structured_payload_incomplete( choreographer = Choreographer( config_file=config_path, llm_provider="ollama", use_motion_primitives=True ) + structure = _simple_structure(n_segments=1, n_bars=1, n_beats=1) monkeypatch.setattr( choreographer, "_call_responses_structured", - lambda *_args, **_kwargs: {"choreography": {"1": [{"primitive": "PLAN", "params": {}}]}}, + lambda *_args, **_kwargs: { + "choreography": [ + { + "key": "s1b1t1", + "actions": [{"primitive": "rotate", "params": {"angle_deg": 0, "axis": "z"}}], + } + ] + }, ) with pytest.raises(LLMFormatError, match="missing required keys"): choreographer.generate_choreography( - prompt=[{"role": "user", "content": "hello"}], num_beats=1 + prompt=[{"role": "user", "content": "hello"}], structure=structure ) + + +def test_form_star_schema_includes_time_to_finish_s(): + """F1: form_star schema must include time_to_finish_s as a required param.""" + schema = build_motion_primitive_response_schema( + all_keys=[(1, 1, 1)], required_keys=[(1, 1, 1)], num_drones=5 + ) + variants = schema["$defs"]["action"]["anyOf"] + form_star_variant = next( + v for v in variants if v["properties"]["primitive"]["enum"] == ["form_star"] + ) + assert "time_to_finish_s" in form_star_variant["properties"]["params"]["required"] + assert "time_to_finish_s" in form_star_variant["properties"]["params"]["properties"] + + +def test_form_circle_schema_includes_time_to_finish_s(): + """F1: form_circle schema must include time_to_finish_s as a required param.""" + schema = build_motion_primitive_response_schema( + all_keys=[(1, 1, 1)], required_keys=[(1, 1, 1)], num_drones=5 + ) + variants = schema["$defs"]["action"]["anyOf"] + form_circle_variant = next( + v for v in variants if v["properties"]["primitive"]["enum"] == ["form_circle"] + ) + assert "time_to_finish_s" in form_circle_variant["properties"]["params"]["required"] + + +def test_form_cone_schema_includes_time_to_finish_s(): + """F1: form_cone schema must include time_to_finish_s as a required param.""" + schema = build_motion_primitive_response_schema( + all_keys=[(1, 1, 1)], required_keys=[(1, 1, 1)], num_drones=5 + ) + variants = schema["$defs"]["action"]["anyOf"] + form_cone_variant = next( + v for v in variants if v["properties"]["primitive"]["enum"] == ["form_cone"] + ) + assert "time_to_finish_s" in form_cone_variant["properties"]["params"]["required"] diff --git a/tools/analyze_songs.py b/tools/analyze_songs.py new file mode 100644 index 0000000..a29e25e --- /dev/null +++ b/tools/analyze_songs.py @@ -0,0 +1,199 @@ +"""Batch-analyze every un-analyzed song in ``music/`` via all-in-one. + +Run with:: + + pixi run -e music analyze + pixi run -e music analyze --test + +Scans ``music/songs/*.mp3`` (skipping any ``[deploy]`` variants), and for each song that +doesn't already have a JSON under ``music/analyzed/``, runs ``allin1.analyze``, caches the +result, and writes a PNG visualization under ``music/viz/``. Existing JSONs are left +untouched (their visualizations are not regenerated). + +With ``--test``, analyzes only the default ``On & On`` song as a smoke test: nothing is +saved (no JSON cache, no PNG); the result is printed to the console. +""" + +from __future__ import annotations + +import argparse +import re +import sys +import tempfile +import time +from pathlib import Path + +import torch +import yaml + +from swarm_gpt.utils.music_analyzer import analyze_song + +PROJECT_ROOT = Path(__file__).resolve().parent.parent +MUSIC_DIR = PROJECT_ROOT / "music" +SONGS_DIR = MUSIC_DIR / "songs" +ANALYZED_DIR = MUSIC_DIR / "analyzed" +VIZ_DIR = MUSIC_DIR / "viz" +SETTINGS_PATH = PROJECT_ROOT / "swarm_gpt" / "data" / "settings.yaml" +TEST_SONG = "On & On" + + +def _ensure_song_crop(song_stem: str) -> None: + """Add ``song_stem`` to ``song_crops`` in settings.yaml if it is not already present. + + Uses a line-oriented insertion so comments and formatting are fully preserved. + The new entry is appended after the last existing line in the ``song_crops:`` block. + + Args: + song_stem: MP3 filename without extension, used as the settings key. + """ + text = SETTINGS_PATH.read_text() + + # Quick check via YAML parse — avoids a text-insert if the key already exists. + settings = yaml.safe_load(text) + if song_stem in settings["song_crops"]: + return + + # Find the song_crops: block and locate its last indented line. + lines = text.splitlines(keepends=True) + in_block = False + last_entry_idx = -1 + for i, line in enumerate(lines): + if re.match(r"^song_crops\s*:", line): + in_block = True + continue + if in_block: + if re.match(r"^\S", line): + # A non-indented line marks the end of the block. + break + if line.strip() and not line.strip().startswith("#"): + last_entry_idx = i + + if last_entry_idx == -1: + # Fallback: couldn't find the block — print a warning and skip. + print( + f" WARNING: could not locate song_crops block in {SETTINGS_PATH}; " + f"add '{song_stem}: [0, 60]' manually." + ) + return + + new_line = f' "{song_stem}": [0, 60]\n' + lines.insert(last_entry_idx + 1, new_line) + SETTINGS_PATH.write_text("".join(lines)) + print(f" Added '{song_stem}' to song_crops in settings.yaml with default crop [0, 60].") + + +def _run_test(device: str) -> None: + """Analyze the default song without persisting anything; print to console. + + Runs a fresh analysis of :data:`TEST_SONG` into a throwaway temp directory (so no JSON + cache and no PNG are written) and prints a summary. Used as a smoke test for the music + pixi env. + + Args: + device: Torch device for analysis (``"cuda"`` or ``"cpu"``). + """ + song_path = SONGS_DIR / f"{TEST_SONG}.mp3" + if not song_path.exists(): + print(f"ERROR: test song not found at {song_path}") + sys.exit(1) + + print(f"Test mode: analyzing '{TEST_SONG}' (nothing will be saved)") + print() + t0 = time.perf_counter() + with tempfile.TemporaryDirectory() as tmp: + structure = analyze_song(song_path, Path(tmp), device=device) + elapsed = time.perf_counter() - t0 + + beats = sum(len(bar.beats) for seg in structure.segments for bar in seg.bars) + print( + f"done in {elapsed:5.1f}s -- " + f"{structure.bpm} BPM, {len(structure.segments)} segments, {beats} beats" + ) + print() + print("Segments:") + for seg in structure.segments: + print(f" {seg.start_s:6.2f}s - {seg.end_s:6.2f}s : {seg.label} ({len(seg.bars)} bars)") + + +def main() -> None: + """Scan ``music/``, analyze every song that is missing a cached JSON.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--test", + action="store_true", + help=f"Analyze only '{TEST_SONG}' without saving; print the result to the console.", + ) + args = parser.parse_args() + + device = "cuda" if torch.cuda.is_available() else "cpu" + print(f"Device: {device}") + if device == "cpu": + print("WARNING: no CUDA detected. Analysis on CPU is ~10x+ slower.") + print(f"PyTorch: {torch.__version__}") + print() + + if args.test: + _run_test(device) + return + + print(f"Songs dir: {SONGS_DIR}") + print(f"Analyzed dir: {ANALYZED_DIR}") + print(f"Viz dir: {VIZ_DIR}") + print() + + mp3s = sorted(p for p in SONGS_DIR.glob("*.mp3") if not p.stem.endswith("[deploy]")) + if not mp3s: + print(f"No MP3s found in {SONGS_DIR}.") + sys.exit(1) + + print(f"Found {len(mp3s)} songs:") + pending: list[Path] = [] + for mp3 in mp3s: + cache_path = ANALYZED_DIR / f"{mp3.stem}.json" + marker = "[cached]" if cache_path.exists() else "[pending]" + print(f" {marker} {mp3.stem}") + if not cache_path.exists(): + pending.append(mp3) + print() + + if not pending: + print("All songs already analyzed. Nothing to do.") + return + + print(f"Analyzing {len(pending)} song(s)...") + print() + + failures: list[tuple[str, str]] = [] + total_start = time.perf_counter() + for i, mp3 in enumerate(pending, start=1): + print(f"[{i}/{len(pending)}] {mp3.stem}") + t0 = time.perf_counter() + try: + structure = analyze_song(mp3, ANALYZED_DIR, device=device, viz_dir=VIZ_DIR) + except Exception as e: + print(f" FAILED: {e.__class__.__name__}: {e}") + failures.append((mp3.stem, f"{e.__class__.__name__}: {e}")) + continue + elapsed = time.perf_counter() - t0 + beats = sum(len(bar.beats) for seg in structure.segments for bar in seg.bars) + print( + f" done in {elapsed:5.1f}s -- " + f"{structure.bpm} BPM, " + f"{len(structure.segments)} segments, " + f"{beats} beats" + ) + _ensure_song_crop(mp3.stem) + + total_elapsed = time.perf_counter() - total_start + print() + succeeded = len(pending) - len(failures) + print(f"Done. {succeeded}/{len(pending)} succeeded in {total_elapsed:.1f}s total.") + if failures: + print(f"{len(failures)} failure(s):") + for stem, err in failures: + print(f" - {stem}: {err}") + sys.exit(1) + + +if __name__ == "__main__": + main() diff --git a/web/index.html b/web/index.html index 1f78ee3..9d53634 100644 --- a/web/index.html +++ b/web/index.html @@ -3,6 +3,7 @@ + SwarmGPT diff --git a/web/public/favicon.png b/web/public/favicon.png new file mode 100644 index 0000000..30c7be4 Binary files /dev/null and b/web/public/favicon.png differ diff --git a/web/src/App.tsx b/web/src/App.tsx index 4870880..dc48d4c 100644 --- a/web/src/App.tsx +++ b/web/src/App.tsx @@ -51,7 +51,7 @@ export function App() { const [library, setLibrary] = useState({ songs: [], presets: [] }); const [llm, setLlm] = useState(null); const [provider, setProvider] = useState<"openai" | "ollama">("openai"); - const [modelId, setModelId] = useState("gpt-4o"); + const [modelId, setModelId] = useState("gpt-5.4-nano"); const [selected, setSelected] = useState(null); const [previewing, setPreviewing] = useState(null); const [jobId, setJobId] = useState(null); diff --git a/web/src/Player.tsx b/web/src/Player.tsx index aa2cef9..0ac805b 100644 --- a/web/src/Player.tsx +++ b/web/src/Player.tsx @@ -215,6 +215,9 @@ export function Player({ playback, onClose }: PlayerProps) { () => playback.timestamps[playback.timestamps.length - 1] ?? 0, [playback.timestamps] ); + // Songs are cropped to a window of the full MP3; the trajectory timeline is rebased to 0, + // so audio playback is shifted by this offset (seconds into the full file). + const audioOffset = playback.audioOffset ?? 0; useEffect(() => { const mount = mountRef.current; @@ -334,7 +337,16 @@ export function Player({ playback, onClose }: PlayerProps) { if (!active) { return; } - const time = audio && !audio.paused ? Math.min(audio.currentTime, duration) : playheadRef.current; + let time = playheadRef.current; + if (audio && !audio.paused) { + time = audio.currentTime - audioOffset; + if (time >= duration) { + // Reached the end of the crop window: stop here instead of letting the full song play on. + time = duration; + audio.pause(); + setPlaying(false); + } + } playheadRef.current = time; renderAt(time); const now = performance.now(); @@ -363,7 +375,7 @@ export function Player({ playback, onClose }: PlayerProps) { playheadRef.current = nextTime; setPlayhead(nextTime); if (audioRef.current) { - audioRef.current.currentTime = nextTime; + audioRef.current.currentTime = nextTime + audioOffset; } }; @@ -373,7 +385,11 @@ export function Player({ playback, onClose }: PlayerProps) { return; } if (audio.paused) { - audio.currentTime = playheadRef.current; + // Restart from the window start if we're sitting at the end. + if (playheadRef.current >= duration) { + setTime(0); + } + audio.currentTime = playheadRef.current + audioOffset; await audio.play(); setPlaying(true); } else { @@ -387,13 +403,13 @@ export function Player({ playback, onClose }: PlayerProps) { if (!audio) { return; } - audio.currentTime = 0; + audio.currentTime = audioOffset; audio.play().then(() => { setPlaying(true); }).catch(() => { setPlaying(false); }); - }, [playback.audioUrl]); + }, [playback.audioUrl, audioOffset]); const restart = () => { setTime(0); diff --git a/web/src/types.ts b/web/src/types.ts index b2740bf..33cdbbe 100644 --- a/web/src/types.ts +++ b/web/src/types.ts @@ -42,6 +42,7 @@ export type ChatMessage = { export type Playback = { schemaVersion: number; audioUrl: string; + audioOffset: number; song: string; numDrones: number; timestamps: number[];