diff --git a/.gitignore b/.gitignore index c9da832..872d41c 100644 --- a/.gitignore +++ b/.gitignore @@ -28,10 +28,19 @@ ros_ws openai_api_key.sh +.DS_Store + .cursor +# Claude Code: personal settings and runtime state (worktrees, locks, caches). +# Un-ignore a specific path if you ever want to share project config with the +# team, e.g. `!.claude/settings.json` — note `.claude/*` form is required for +# that, since git will not descend into a directory ignored as `.claude/`. +.claude/ docs/ui-previews +# Internal design docs: local-only, never published. docs/ is also the mkdocs +# docs_dir, so these are excluded from the site build in mkdocs.yml too. docs/specs/ -docs/superpowers +docs/plans/ test-results playwright-report web/dist diff --git a/mkdocs.yml b/mkdocs.yml index eb16c84..c283313 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1,4 +1,12 @@ site_name: SwarmGPT - Learning Systems Lab + +# docs/ doubles as the mkdocs source and the home of local-only design docs +# (docs/specs, docs/plans -- both gitignored). Exclude them so a local build or +# `mkdocs gh-deploy` from a dirty checkout cannot publish them. +exclude_docs: | + specs/ + plans/ + nav: - Home: index.md - Getting Started and Installation: getting_started.md diff --git a/pyproject.toml b/pyproject.toml index faea947..5cd44a3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -108,6 +108,7 @@ maturin = ">=1.13.3,<2" [tool.pixi.tasks] api = { cmd = "python swarm_gpt/launch.py", description = "Serve the SwarmGPT browser API and built frontend", depends-on = ["web-build"] } +render = { cmd = "python swarm_gpt/render.py", description = "Render a saved preset to a video (--help for flags, --list for presets)" } 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" } diff --git a/swarm_gpt/api/server.py b/swarm_gpt/api/server.py index c6f7825..fd3d8b9 100644 --- a/swarm_gpt/api/server.py +++ b/swarm_gpt/api/server.py @@ -21,7 +21,6 @@ from pydantic import BaseModel, Field from swarm_gpt.core import AppBackend -from swarm_gpt.utils import generate_default_colors from swarm_gpt.utils.llm_providers import ( DEFAULT_OPENAI_MODEL_CHOICES, PROVIDER_LABEL_OLLAMA, @@ -239,13 +238,12 @@ def normalize_playback(sim_data: dict[str, Any], backend: AppBackend) -> dict[st if states.shape[1] != num_drones: raise ValueError(f"State drone count mismatch: {states.shape[1]} != {num_drones}") - colors = generate_default_colors(num_drones, limit=1.0) bounds = backend.settings["axswarm"] sample_rate = 0.0 if len(timestamps) > 1: sample_rate = float(1.0 / np.median(np.diff(timestamps))) return { - "schemaVersion": 1, + "schemaVersion": 2, "audioUrl": _audio_url(backend.music_manager.song), "audioOffset": backend.crop_window(backend.music_manager.song)[0], "song": backend.music_manager.song, @@ -254,7 +252,7 @@ def normalize_playback(sim_data: dict[str, Any], backend: AppBackend) -> dict[st "states": states.tolist(), "fields": {"pos": [0, 3], "quat": [3, 7], "vel": [7, 10], "angVel": [10, 13]}, "bounds": {"min": bounds["pos_min"], "max": bounds["pos_max"]}, - "colors": colors.tolist(), + "lighting": backend.browser_cues(), "sampleRate": sample_rate, } @@ -265,11 +263,20 @@ def _start_thread(job: Job, target: Any) -> None: thread.start() +def _watch_backend(store: JobStore, job: Job) -> None: + """Forward the backend's exchange events onto the job's socket as they happen. + + The panel is a live transcript, so nothing is held back to the end: a rejected response and the + retry it triggers both reach the browser while the run is still going. + """ + job.backend.on_event = lambda event_type, payload: store.emit(job, event_type, payload) + + def _run_initial_job(store: JobStore, job: Job, selection: str) -> None: try: + _watch_backend(store, job) store.emit(job, "thinking_started", {"selection": selection}, status="thinking") - messages = job.backend.initial_prompt(selection) - store.emit(job, "conversation", {"messages": messages}) + job.backend.initial_prompt(selection) store.emit(job, "safety_started", {}, status="filtering") sim_data = _run_simulation_with_events(job.backend, store, job) playback = normalize_playback(sim_data, job.backend) @@ -295,9 +302,9 @@ def _run_refine_job( if provider is not None and model_id: job.backend.choreographer.configure_llm(provider, model_id) store.emit(job, "llm_configured", {"provider": provider, "modelId": model_id}) + _watch_backend(store, job) store.emit(job, "thinking_started", {"refine": True}, status="thinking") - messages = job.backend.reprompt(message) - store.emit(job, "conversation", {"messages": messages}) + job.backend.reprompt(message) store.emit(job, "safety_started", {"refine": True}, status="filtering") sim_data = _run_simulation_with_events(job.backend, store, job) playback = normalize_playback(sim_data, job.backend) diff --git a/swarm_gpt/core/__init__.py b/swarm_gpt/core/__init__.py index 7827482..24f93a2 100644 --- a/swarm_gpt/core/__init__.py +++ b/swarm_gpt/core/__init__.py @@ -1,8 +1,4 @@ -"""Core package for the swarm_gpt package. - -This submodule contains the backend code for interfacing with the LLMs, AMSwarm, pybullet-drones, -and the crazyflies. -""" +"""Backend code for interfacing with the LLMs, AMSwarm, pybullet-drones, and the crazyflies.""" from typing import TYPE_CHECKING, Any @@ -16,20 +12,8 @@ def __getattr__(name: str) -> Any: """Resolve the package's two entry points on first access (PEP 562). - Importing them eagerly made *any* ``swarm_gpt.core.`` import pull in the choreographer, - which imports ``swarm_gpt.utils.music_analyzer``, which imports - ``swarm_gpt.core.structured_output_schema`` -- a cycle that broke ``tools/analyze_songs.py`` - with a partially-initialized-module ImportError. Nothing in that chain needs the choreographer; - only this convenience re-export did, so it is deferred until something asks for it. - - Args: - name: Attribute being looked up on the package. - - Returns: - The requested class. - - Raises: - AttributeError: If the package has no such attribute. + Importing them eagerly made any ``swarm_gpt.core.`` import pull in the choreographer + and reach ``structured_output_schema`` via ``music_analyzer`` -- a cycle. """ if name == "AppBackend": from swarm_gpt.core.backend import AppBackend diff --git a/swarm_gpt/core/backend.py b/swarm_gpt/core/backend.py index 67dd0cb..775abdd 100644 --- a/swarm_gpt/core/backend.py +++ b/swarm_gpt/core/backend.py @@ -15,31 +15,39 @@ from scipy.interpolate import make_smoothing_spline from swarm_gpt.core import Choreographer +from swarm_gpt.core.lighting import compile_cues, load_lighting_config 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 import MusicManager from swarm_gpt.utils.music_analyzer import SongStructure if TYPE_CHECKING: from numpy.typing import NDArray as Array from swarm_gpt.core.drone_swarm import DroneSwarm + from swarm_gpt.core.lighting import LightingTimeline from swarm_gpt.utils.llm_providers import LLMProvider logging.basicConfig(level=logging.WARNING) logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) -P = ParamSpec("P") # Represents arbitrary parameters -R = TypeVar("R") # Represents the return type +P = ParamSpec("P") +R = TypeVar("R") +# What an observer is told, as it happens rather than once the run settles: the messages leaving for +# the model, its account of its own thinking, the answer that came back, and the checker's verdict +# on that answer. A rejected answer is followed by another `prompt_sent` from the retry, so the +# stream reads as the exchange it was. +BackendEvent = Callable[[str, dict[str, Any]], None] -def self_correct(n_retries: int) -> Callable[[Callable[P, R]], Callable[P, R]]: - """Create a decorator that retries a function n times if it fails. - Args: - n_retries: Number of times to retry the function - """ +def _ignore_event(event_type: str, payload: dict[str, Any]) -> None: + """Drop a backend event, for the callers that are not watching one.""" + + +def self_correct(n_retries: int) -> Callable[[Callable[P, R]], Callable[P, R]]: + """Create a decorator that retries a function ``n_retries`` times if it fails.""" def decorator(fn: Callable[P, R]) -> Callable[P, R]: """Decorator that retries a function n times if it fails.""" @@ -50,6 +58,7 @@ def wrapper(self: AppBackend, *args: P.args, **kwargs: P.kwargs) -> R: try: return fn(self, *args, **kwargs) except LLMException as e: + self._emit("response_rejected", {"message": str(e)}) error_message = str(e) for i in range(n_retries): try: @@ -61,6 +70,7 @@ def wrapper(self: AppBackend, *args: P.args, **kwargs: P.kwargs) -> R: # recursion. return self.reprompt.__wrapped__(self, message) except LLMException as inner_e: + self._emit("response_rejected", {"message": str(inner_e)}) if i == n_retries - 1: raise inner_e error_message = str(inner_e) @@ -72,6 +82,17 @@ def wrapper(self: AppBackend, *args: P.args, **kwargs: P.kwargs) -> R: return decorator +def _fold_cues_to_rgb(cues: dict[float, Array]) -> dict[str, list]: + """Fold a ``{time: (4,) WRGB}`` cue dict into JSON-serializable ``{"times", "rgb"}`` arrays. + + Three.js has no white channel, so W folds into all three as ``clip(rgb + w, 0, 255)``; without + that clip a near-white cue overflows. The truncation matches ``_apply_drone_color``. + """ + wrgb = np.stack(list(cues.values())) + rgb = np.clip(wrgb[:, 1:] + wrgb[:, :1], 0, 255).astype(int) + return {"times": list(cues), "rgb": rgb.tolist()} + + class AppBackend: """Backend for choreography generation, filtering, preset storage, and deployment.""" @@ -89,26 +110,16 @@ def __init__( ): """Initialize the backend by loading the music files and initializing the choreographer. - Args: - config_file: Path to the config file. - music_dir: Path to the music directory. - preset_dir: Path to the preset directory. - strict_processing: Flag to raise an error on waypoint collisions. - strict_drone_match: Flag to raise an error when preset drones do not match the current - swarm. - model_id: The OpenAI or Ollama model name (see LLM selector in the UI). - use_motion_primitives: If we want LLM to use motion primitives for choreography - llm_provider: ``openai`` or ``ollama`` for the choreographer backend. + The ``strict_*`` flags raise rather than warn on, respectively, waypoint collisions and a + preset whose drone count does not match the current swarm. """ self.root_path = Path(__file__).resolve().parents[2] self.preset_dir = preset_dir or self.root_path / "swarm_gpt/data/presets" with open(self.root_path / "swarm_gpt/data/settings.yaml", "r") as f: self.settings = yaml.safe_load(f) - # Initialize drone control elements self.waypoints: Array | None = None # High-level LLM commands self.splines = {} # Low-level optimized commands from axswarm self.drone_controller = None # TODO Controller for the Crazyflie drones - # Initialize chat elements self.choreographer = Choreographer( config_file=config_file, model_id=model_id, @@ -121,6 +132,7 @@ def __init__( self._strict_processing = strict_processing self._strict_drone_match = strict_drone_match self._active_swarm: DroneSwarm | None = None + self.on_event: BackendEvent = _ignore_event if set(self.songs) & set(self.presets): raise ValueError("Songs and presets must have unique names") @@ -173,16 +185,34 @@ def preset_metadata(self, preset_id: str) -> dict[str, Any]: raise FileNotFoundError(f"Preset not found: {preset_id}") return self.parse_preset_id(preset_id) + def _emit(self, event_type: str, payload: dict[str, Any]) -> None: + """Report one step of the exchange to whoever is watching this backend.""" + self.on_event(event_type, payload) + + def _generate(self, prompt: list[dict[str, str]], structure: SongStructure) -> str: + """Send one prompt to the model, reporting it and the answer at the moment each happens. + + Only the messages this turn adds are reported: the observer is building a transcript and + already holds everything before them. + """ + self._emit("prompt_sent", {"messages": prompt}) + response = self.choreographer.generate_choreography(prompt, structure=structure) + if summary := self.choreographer.last_reasoning_summary: + self._emit("reasoning_summary", {"text": summary}) + self._emit("llm_response", {"text": response}) + return response + + def _emit_replayed(self, sent: list[dict[str, str]], response: str) -> None: + """Report an exchange that never reached a model -- a preset, or a fixed test response.""" + self._emit("prompt_sent", {"messages": sent}) + self._emit("llm_response", {"text": response}) + @self_correct(n_retries=2) def initial_prompt(self, song: str, *, response: str | None = None) -> list[dict[str, str]]: - """Set the song and generate the choreography. + """Set the song and generate the choreography, returning the chat history. - Args: - song: Name of the song or preset to use. - response: Optional, predefined response. Used for testing. - - Returns: - The chat history as a list of dictionaries with the role and content. + ``response`` supplies a predefined response instead of calling the LLM; used for testing. + Every attempt reports itself through :attr:`on_event` as it happens, retries included. """ logger.info(f"Generating initial choreography for song: {song}") song_name = self._load_song(song) @@ -191,24 +221,30 @@ def initial_prompt(self, song: str, *, response: str | None = None) -> list[dict prompt = self.choreographer.format_initial_prompt(song_name, structure) fixed_response = response is not None - if preset := song in self.presets: # Preset was provided + if preset := song in self.presets: logger.debug(f"Loading preset: {song}") response = self.load_preset(song) - elif fixed_response: # Response was provided, do not use LLM + # The preset carries the whole original exchange; its last message is the response. + self._emit_replayed(self.choreographer.messages[:-1], response) + elif fixed_response: logger.debug(f"Using predefined response: {response}") self.choreographer.messages.append({"role": "assistant", "content": response}) - else: # Use LLM to generate the choreography + self._emit_replayed(prompt, response) + else: logger.debug(f"Using LLM to generate choreography for song: {song_name}") - response = self.choreographer.generate_choreography(prompt, structure=structure) + response = self._generate(prompt, structure) try: self.waypoints = self.choreographer.response2waypoints( response, structure, strict=self._strict_processing ) + # The lighting cannot be compiled yet -- a look freezes a position snapshot, and + # positions exist only after the axswarm pass. Checking the half that needs no + # positions here is what lets a malformed lighting track reprompt. + self.choreographer.validate_lighting(response) except LLMException as e: - # We do not want to retry if we are using a preset or a fixed response. This - # would use the LLM. We raise an error type that is not caught by - # self_correct to exit immediately. + # Retrying a preset or fixed response would call the LLM, so raise an error type + # `self_correct` does not catch and exit immediately. if preset or fixed_response: raise RuntimeError("Initial prompt failed") from e raise e @@ -217,38 +253,26 @@ def initial_prompt(self, song: str, *, response: str | None = None) -> list[dict @self_correct(n_retries=3) def reprompt(self, message: str) -> list[dict[str, str]]: - """Reprompt the LLM to generate new waypoints based on the previous choreography. - - Args: - message: The reprompt. - - Returns: - The chat history as a list of dictionaries with the role and content. - """ + """Reprompt the LLM for new waypoints, returning the chat history.""" logger.info(f"Reprompting with message: {message}") if message == "": logger.warning("No message provided, returning current history") return self.choreographer.messages prompt = self.choreographer.format_reprompt(message) structure = self._load_structure(self.music_manager.song) - response = self.choreographer.generate_choreography(prompt, structure=structure) + response = self._generate(prompt, structure) self.waypoints = self.choreographer.response2waypoints( response, structure, strict=self._strict_processing ) + self.choreographer.validate_lighting(response) logger.info("Successfully generated choreography") return self.choreographer.messages def simulate(self, gui: bool = False) -> dict[str, Any]: """Run the simulation with waypoints generated by the choreographer. - Before the simulation is run, the waypoints are interpolated by axswarm to ensure that the - trajectories are collision-free. - - Args: - gui: Whether to show the MuJoCo debug replay after filtering. Use for debugging only. - - Returns: - A collection of data from the simulation. + Waypoints are first interpolated by axswarm so the trajectories are collision-free. + ``gui`` shows the MuJoCo debug replay afterwards; for debugging only. """ logger.info("Simulating trajectories with axswarm") assert self.waypoints is not None, "Please generate a choreography first" @@ -266,19 +290,54 @@ def simulate(self, gui: bool = False) -> dict[str, Any]: controls = sim_data["controls"][:, i, :3] self.splines[drone] = make_smoothing_spline(t, controls, lam=lam) if gui: - replay_sim_states(sim_data, self.settings, self.music_manager) + replay_sim_states(sim_data, self.settings, self.lighting_timeline(), self.music_manager) logger.info("Simulation successful") return sim_data - def deploy(self, drone_ids: list[int] | None = None) -> bool: - """Run the Crazyflie drones with waypoints generated by the choreographer. + def lighting_timeline(self) -> LightingTimeline: + """Compile the current response's lighting track into a timeline covering the flight. - We call the waypoint_helpers.py script from the Crazyflie ROS package to run the drones. + Each look freezes a snapshot from the axswarm splines, so this needs :meth:`simulate` to + have run. A response with no lighting compiles to the default hue wheel rather than skipping. + """ + assert self.splines, "Please run the simulation first!" + assert self.waypoints is not None, "Please generate a choreography first" + structure = self._load_structure(self.music_manager.song) + + def position_at(t: float) -> Array: + return np.array([self.splines[i](t) for i in sorted(self.splines)]) + + return self.choreographer.response2lighting( + self._response_text(), structure, position_at, float(self.waypoints["time"][0, -1]) + ) + + def browser_cues(self) -> dict[str, list[dict[str, list]]]: + """Adapt the compiled lighting cues into ``{"top", "bot"}`` lists, one entry per drone. - Returns: - The chat history as a list of prompts and answers. + The browser replays the hardware's baked cue list, so the preview shows the quantization + that will fly. Gotcha: editing `lighting.toml` mid-job desyncs the two until it is re-run. """ - # Check if even in deploy environment + cfg = load_lighting_config() + # Index keys, not radio URIs: `compile_cues` keys its output by whatever it is handed, and + # a browser payload has no business carrying radio addresses. + keys = [str(i) for i in range(self.choreographer.num_drones)] + decks = compile_cues( + self.lighting_timeline(), keys, cfg.col_freq, float(self.waypoints["time"][0, -1]) + ) + return { + deck: [_fold_cues_to_rgb(cues[key]) for key in keys] + for deck, cues in zip(("top", "bot"), decks, strict=True) + } + + def _response_text(self) -> str: + """The assistant response the current waypoints and splines were generated from.""" + assert self.choreographer.messages, "Please generate a choreography first" + message = self.choreographer.messages[-1] + assert message["role"] == "assistant", "Last message in history is not a response" + return message["content"] + + def deploy(self, drone_ids: list[int] | None = None) -> bool: + """Run the Crazyflie drones with waypoints generated by the choreographer.""" if not self.settings["lighthouse"]: try: import rclpy @@ -300,21 +359,26 @@ def deploy(self, drone_ids: list[int] | None = None) -> bool: logger.error("VLC/libvlc is not available. Install VLC (see README) before deploying.") return False - swarm = DroneSwarm(self.choreographer.drones, lighthouse=self.settings["lighthouse"]) + # Bake the lighting before connecting any radio, so a malformed track fails cheaply. + # `cfg.col_freq` reaches both the cue consumer and the cue compiler from the same config + # field, so the Nyquist clamp in `build_look` can never disagree with the rate the cues + # are actually drained at. + cfg = load_lighting_config() + t_end = float(self.waypoints["time"][0, -1]) + uris = [d["uri"] for d in self.choreographer.drones.values()] + color_top, color_bot = compile_cues(self.lighting_timeline(), uris, cfg.col_freq, t_end) + + swarm = DroneSwarm( + self.choreographer.drones, col_freq=cfg.col_freq, lighthouse=self.settings["lighthouse"] + ) self._active_swarm = swarm logger.info("Swarm connected...") - # generate references correct_positions = True init_pos_dict = {} final_pos_dict = {} landing_pos_dict = {} choreography_dict = {} - color_top = {} - color_bot = {} - colors_array = np.zeros((self.choreographer.num_drones, 4)) - colors_array[:, 1:] = generate_default_colors(self.choreographer.num_drones, limit=255) - colors_array[:, 3] *= 0.8 # Dim blue channel since that LED is brighter for i, d in enumerate(self.choreographer.drones.values()): uri = d["uri"] init_pos = np.array(self.splines[i](0)) @@ -330,20 +394,11 @@ def deploy(self, drone_ids: list[int] | None = None) -> bool: final_pos_dict[uri] = np.array([*landing_pos + np.array([0.0, 0.0, 0.5]), 0.0]) landing_pos_dict[uri] = np.array([*landing_pos - np.array([0.0, 0.0, 0.2]), 0.0]) choreography_dict[uri] = self.splines[i] - color_top[uri] = { - 0.0: colors_array[i], - self.waypoints["time"][0, -1] - 0.1: np.zeros(4), - } - color_bot[uri] = { - 0.0: colors_array[i], - self.waypoints["time"][0, -1] - 0.1: np.zeros(4), - } try: if not correct_positions: raise RuntimeError("Some drone(s) are not in the expected initial positions.") swarm.goto(init_pos_dict) # takeoff - # Check active drones after the initial climb. taken_off = True for d in self.choreographer.drones.values(): uri = d["uri"] @@ -357,7 +412,7 @@ def deploy(self, drone_ids: list[int] | None = None) -> bool: logger.debug(f"got obs for {uri}") z = obs["pos"][2] qw = np.abs(obs["quat"][-1]) - # Demo fix: If the drone is disconnected, we cannot get its position. We assume it has not taken off. + # A disconnected drone has no position, so assume it has not taken off. # TODO: Replace the general exception catch with the specific cflib2 exception. except Exception as e: logger.warning(f"Could not get position for drone {uri} after takeoff: {e}") @@ -383,7 +438,7 @@ def deploy(self, drone_ids: list[int] | None = None) -> bool: swarm.goto(final_pos_dict, duration=2.0) # Transition from ideal point to hover pos if self.settings["land_on_docks"]: swarm.goto(final_pos_dict, duration=3.0) # Hovering - swarm.land(duration=1.5) # Landing + swarm.land(duration=1.5) finally: self._active_swarm = None swarm.close() @@ -399,11 +454,7 @@ def emergency_stop_active_swarm(self) -> None: self.music_manager.stop() def load_preset(self, preset_id: str) -> str: - """Load a preset response. - - Args: - preset_id: Name of the preset. - """ + """Load a preset response.""" assert preset_id, "Please select a valid preset" assert preset_id in self.presets, "No preset for this song" preset_path = self.preset_dir / preset_id @@ -505,32 +556,18 @@ def _load_song(self, song: str) -> str: 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). + """Return the ``(start_s, end_s)`` crop window in seconds for an MP3 stem. - Returns: - The ``(start_s, end_s)`` window the song is cropped to. + Reads ``song_crops`` from settings, falling back to ``song_crops.default``. """ 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). + """Load the cached SongStructure JSON for an MP3 stem, cropped to its window. - Raises: - FileNotFoundError: If no analysis JSON exists yet for the song. + Only the :meth:`crop_window` window is choreographed. """ json_path = self.root_path / "music" / "analyzed" / f"{song_name}.json" if not json_path.exists(): diff --git a/swarm_gpt/core/choreographer.py b/swarm_gpt/core/choreographer.py index 3bc7b3d..c49e3c7 100644 --- a/swarm_gpt/core/choreographer.py +++ b/swarm_gpt/core/choreographer.py @@ -7,42 +7,53 @@ import logging import re from pathlib import Path -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, Any import einops # pyright: ignore[reportMissingImports] import numpy as np import toml import yaml +from swarm_gpt.core.lighting import LightingTimeline, build_look, load_lighting_config +from swarm_gpt.core.motion_primitives import _sanitize_drone_ids, primitive_by_name 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, + LIGHTING_PRIMITIVE_ARG_ORDER, build_motion_primitive_response_schema, decode_key, encode_key, structured_payload_to_choreography, + structured_payload_to_lighting, ) from swarm_gpt.exception import LLMFormatError, LLMPlanError, LLMResponseProcessingError from swarm_gpt.utils.llm_providers import ( - RESPONSES_MAX_OUTPUT_TOKENS, RESPONSES_TEMPERATURE, cancellable_ollama_chat, openai_client_for_provider, prepare_responses_messages, register_ollama_client, + responses_model_kwargs, ) from swarm_gpt.utils.music_analyzer import dynamics_window_keys if TYPE_CHECKING: + from collections.abc import Callable + from numpy.typing import NDArray from openai import OpenAI + from swarm_gpt.core.lighting import LightingConfig, Look from swarm_gpt.utils.llm_providers import LLMProvider from swarm_gpt.utils.music_analyzer import SongStructure logger = logging.getLogger(__name__) +# Tempo the generation-time lighting dry run converts `period_beats` with. Slow enough that no +# emitted period can trip the cue-rate clamp and log a spurious warning: the dry run is only +# checking names, and `response2lighting` does the real conversion with the song's own tempo. +_DRY_RUN_BPM = 1.0 + _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"} @@ -50,22 +61,14 @@ 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. + """Return the 0-indexed drone IDs the ``{fn_name: args}`` action touches. - 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. + Subsets go through the same `_sanitize_drone_ids` the primitives use, so a compact range spec + and an explicit id list agree on which drones an action covers. """ 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]) + return frozenset(_sanitize_drone_ids(args[0], num_drones)) if fn_name == "swap": return frozenset({args[0] - 1, args[1] - 1}) if fn_name == "move": @@ -76,17 +79,7 @@ def _overlapping_drone_set(action: dict[str, tuple], num_drones: int) -> frozens 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. - """ + """Return True if a motion primitive on overlapping drones follows ``form_idx`` in the list.""" form_drones = _overlapping_drone_set(action_list[form_idx], num_drones) for later in action_list[form_idx + 1 :]: fn_name = next(iter(later)) @@ -96,19 +89,30 @@ def _form_should_drop_holds( return False -# OLLAMA_CONTEXT_LENGTH = None # Set None to use Ollama's VRAM-based default. +# None uses Ollama's VRAM-based default. OLLAMA_CONTEXT_LENGTH = None -# Investigate and improve error message for the case when func = "", and we get key error, during sanitize llm output -# Also improve error message when there is an issue with function output, so that we can re-prompt with super specific messag -# Need to imorove parsing -# Add a log everytime some waypoint is clamped. -class Choreographer: - """The choreographer handles the interaction with the language model. +def _reasoning_summary(response: Any) -> str | None: + """Join the reasoning summaries on a Responses result, or None if the model emitted none. - It formats the prompts and parses the output of the language model into the desired format. + Only reasoning models asked for a summary carry these, and they sit in their own output + items -- ``output_text`` holds the answer alone. """ + parts = [ + text + for item in getattr(response, "output", None) or [] + if getattr(item, "type", None) == "reasoning" + for summary in getattr(item, "summary", None) or [] + if (text := getattr(summary, "text", None)) + ] + return "\n\n".join(parts) or None + + +# TODO: improve the error messages for an empty func name and for bad function output, so reprompts +# can be specific. Log every time a waypoint is clamped. +class Choreographer: + """Formats the prompts for the language model and parses its output.""" def __init__( self, @@ -118,14 +122,7 @@ def __init__( llm_provider: LLMProvider = "openai", use_motion_primitives: bool = False, ): - """Initialize the choreographer. - - Args: - config_file: Path to the drone configuration file that is used for crazyswarm. - model_id: Model name passed to ``responses.create`` (OpenAI id or Ollama tag). - llm_provider: ``openai`` for the cloud API, ``ollama`` for local via OpenAI-compatible URL. - use_motion_primitives: Whether to use motion primitives or raw waypoints. - """ + """Initialize the choreographer against a crazyswarm drone config and an LLM provider.""" self.settings = None self.llm_provider: LLMProvider = llm_provider self._model_id = model_id @@ -137,18 +134,16 @@ def __init__( self.starting_pos = {} self.num_drones = 0 self.messages = [] - # Load prompts from file + self.last_reasoning_summary: str | None = None prompt = "motion_primitive_prompts" if self.use_motion_primitives else "prompts" with open(Path(__file__).resolve().parents[1] / f"data/{prompt}.yaml", "r") as f: self.prompts = yaml.safe_load(f) self.load_drone_config(config_file) - # Limits define boundaries of permissible flying area + # Boundaries of the permissible flying area. 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). + # Ellipsoidal (x, y, z) envelope in meters that axswarm enforces as a hard MPC constraint. 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. @@ -182,15 +177,7 @@ def _chat_client_for_call(self) -> OpenAI: return self._chat_client 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. - structure: Hierarchical music structure (segments / bars / beats). - - Returns: - The formatted initial prompt as a list of role/content message dicts. - """ + """Format the initial prompt for the LLM as a list of role/content message dicts.""" logger.debug("Formatting initial prompt") msgs = [] user_prompt = self._format_initial_user_prompt(song, structure) @@ -225,19 +212,13 @@ def _uses_structured_outputs(self) -> bool: def generate_choreography( self, prompt: list[dict[str, str]], structure: SongStructure | None = None ) -> str: - """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). - """ + """Generate the initial choreography, returning YAML-shaped response text.""" logger.debug( "Generating choreography with provider=%s model=%s", self.llm_provider, self._model_id ) self.messages.extend(prompt) + # Ollama's native path never sets one, so clear it rather than show the last model's. + self.last_reasoning_summary = None if self._uses_structured_outputs(): if structure is None: raise ValueError("structure is required for structured output generation") @@ -253,16 +234,10 @@ def reset_history(self): self.messages.clear() def load_drone_config(self, config_file: Path | None = None) -> None: - """Load the drone configuration from the config file. + """Load the drone configuration, defaulting to ``swarm_gpt/data/drones.toml``. - 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), ``channel`` (radio channel), and ``pos`` (initial xyz - position). The URI is derived at load time; it is not stored in the file. - - Args: - config_file: Path to the TOML config file. Defaults to - ``swarm_gpt/data/drones.toml``. + The TOML holds an ``active`` list of cf-names and one ``[cfXX]`` table per drone with + ``addr``, ``channel`` and ``pos``. The URI is derived at load time, not stored in the file. """ with open(Path(__file__).resolve().parents[1] / "data/settings.yaml", "r") as f: self.settings = yaml.safe_load(f) @@ -300,13 +275,8 @@ def load_drone_config(self, config_file: Path | None = None) -> None: assert self.num_drones > 0, "No drones detected in config file" 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. - structure: Hierarchical song structure used to render segments/keys. - """ - # Convert starting positions to cm for the LLM (integer tokens). + """Format the initial user prompt for the LLM.""" + # Positions go to the LLM in cm, so they render as integer tokens. starting_pos = [(pos * 100).astype(int).tolist() for pos in self.starting_pos.values()] segments_table = _render_segments_table(structure) required_keys_csv = ", ".join( @@ -351,8 +321,7 @@ def _call_responses(self, messages: list[dict[str, str]]) -> str: model=self._model_id, input=input_messages, instructions=instructions, - max_output_tokens=RESPONSES_MAX_OUTPUT_TOKENS, - temperature=RESPONSES_TEMPERATURE, + **responses_model_kwargs(self._model_id), ) except Exception as e: hint = ( @@ -364,6 +333,7 @@ def _call_responses(self, messages: list[dict[str, str]]) -> str: f"Responses API call failed for provider={self.llm_provider!r} " f"model={self._model_id!r}. {hint} ({e})" ) from e + self.last_reasoning_summary = _reasoning_summary(response) if response.error is not None: raise LLMPlanError( f"Model {self._model_id!r} returned an error: {response.error.message}" @@ -387,33 +357,17 @@ def _collision_check( 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. - 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: - LLMPlanError: If two drones violate the collision envelope at the same time. + """Check that no two drones in the (n_drones, T, 3) ``pos`` violate the MPC's envelope. + + Separations scale by ``self.collision_envelope``, so a pair conflicts below 1. An isotropic + sphere lets stacked formations slip through; ``margin`` above 1.0 rejects close ones too. """ - differences = pos[:, None, :, :] - pos[None, :, :, :] # Reshape for broadcasting - # 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. + differences = pos[:, None, :, :] - pos[None, :, :, :] + # Scaling per axis makes the norm <1 exactly inside the ellipsoid, however 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 + # Push the diagonal out of range so a drone is never compared against itself. 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 not np.any(min_distance < 1.0): @@ -477,8 +431,7 @@ def _call_responses_structured( model=self._model_id, input=input_messages, instructions=instructions, - max_output_tokens=RESPONSES_MAX_OUTPUT_TOKENS, - temperature=RESPONSES_TEMPERATURE, + **responses_model_kwargs(self._model_id), text={ "format": { "type": "json_schema", @@ -498,6 +451,7 @@ def _call_responses_structured( f"Structured output call failed for provider={self.llm_provider!r} " f"model={self._model_id!r}. {hint} ({e})" ) from e + self.last_reasoning_summary = _reasoning_summary(response) if response.error is not None: raise LLMPlanError( f"Model {self._model_id!r} returned an error: {response.error.message}" @@ -584,7 +538,11 @@ def _structured_payload_to_choreography(self, payload: dict) -> dict[tuple[int, 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.""" + """Convert structured payload to legacy YAML-like text for downstream parsing/history. + + ``lighting`` is rendered as a second block in the same `` s#b#t#: call; call`` idiom, so + one text parser serves both the structured and the free-text path. + """ required_fields = ["song_mood", "choreography_plan", "choreography"] missing = [field for field in required_fields if field not in payload] if missing: @@ -592,15 +550,19 @@ def _structured_payload_to_text(self, payload: dict) -> str: "Structured output is missing required keys: " + ", ".join(sorted(missing)) ) choreography = self._structured_payload_to_choreography(payload) + lighting = structured_payload_to_lighting(payload) lines = [ f"song_mood: {json.dumps(payload['song_mood'])}", f"choreography_plan: {json.dumps(payload['choreography_plan'])}", "choreography:", ] - # 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") + lines.append("lighting:") + for addr in sorted(lighting): + lines.append(f" {encode_key(*addr)}: {lighting[addr]}") + lines.append(" END") return "\n".join(lines) def response2waypoints( @@ -608,15 +570,8 @@ def response2waypoints( ) -> dict[str, NDArray]: """Translate the LLM output into waypoints. - Args: - 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. - - Returns: - The waypoints as a dictionary of "time", "pos", "vel", "acc". "time" has shape - (n_drones, T), and "pos", "vel", "acc" have shape (n_drones, T, 3). + Returns "time" of shape (n_drones, T) plus "pos", "vel" and "acc" of shape (n_drones, T, 3). + ``strict`` enables the proximity checks; ``t_rth`` is the return-to-home time. """ logger.debug("Converting LLM output into waypoints") if self.use_motion_primitives: @@ -627,7 +582,6 @@ def response2waypoints( # 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"], time=waypoints["time"][0], structure=structure) @@ -648,6 +602,97 @@ def response2waypoints( return waypoints + def validate_lighting(self, text: str) -> None: + """Check a response's lighting track for names and arities the engine would reject. + + Positions do not exist until the axswarm pass, so this rebuilds the looks against a dry-run + snapshot -- a diagonal, not zeros, or every `sweep` would warn about the fixture. + """ + cfg = load_lighting_config() + positions = np.tile(np.arange(self.num_drones, dtype=float)[:, None], (1, 3)) + for addr, action_str in self.lighting_from_text(text).items(): + actions = self._parse_lighting_actions(action_str, addr) + self._build_look(actions, addr, 0.0, positions, cfg, _DRY_RUN_BPM) + + def _build_look( + self, + actions: list[dict], + addr: tuple[int, int, int], + t_start: float, + positions: NDArray, + cfg: LightingConfig, + bpm: float, + ) -> Look: + """Compile one key's actions, reporting the engine's bare name errors as format errors. + + Shared by the generation-time dry run and the real compile so both report a malformed + emission identically, and so neither can drift into swallowing an error the other raises. + """ + try: + return build_look(actions, t_start, positions, self.num_drones, cfg, bpm) + except (KeyError, ValueError, IndexError) as e: + raise LLMFormatError( + f"Cannot compile the lighting at {encode_key(*addr)}: {e.__class__.__name__}: {e}" + ) from e + + def response2lighting( + self, + text: str, + structure: SongStructure, + position_at: Callable[[float], NDArray], + t_end: float, + ) -> LightingTimeline: + """Translate the LLM output's lighting track into an evaluable timeline. + + Snapshots are taken at `_settle_time`, not at each look's start, so selectors resolve + against the formation the look was written for. ``t_end`` is the *flight*, not the song. + """ + logger.debug("Converting LLM output into a lighting timeline") + cfg = load_lighting_config() + emitted = self.lighting_from_text(text) + boundaries = self._motion_boundaries(text, structure) if emitted else [] + starts = sorted(structure.time_of(*addr) for addr in emitted) + looks = [] + for addr, action_str in emitted.items(): + actions = self._parse_lighting_actions(action_str, addr) + t_start = structure.time_of(*addr) + t_next_look = next((t for t in starts if t > t_start), np.inf) + # One snapshot per look, frozen here, which keeps the timeline a pure function of t. + t_sample = self._settle_time(t_start, t_next_look, boundaries) + positions = np.asarray(position_at(t_sample), dtype=float) + looks.append( + self._build_look(actions, addr, t_start, positions, cfg, float(structure.bpm)) + ) + return LightingTimeline(looks, self.num_drones, t_end, cfg) + + def _motion_boundaries(self, text: str, structure: SongStructure) -> list[float]: + """Ascending show times at which the motion track hands one primitive over to the next. + + A motion primitive plays forward until the next key, so these are the instants a formation + has arrived. No parsable motion track yields none, and looks sample at their own start. + """ + try: + choreography = self._slice_choreography_from_text(text, structure) + except LLMFormatError: + return [] + times = sorted(structure.time_of(*addr) for addr in choreography) + # The last primitive plays until the song ends, with the same zero-length guard + # `_choreo2waypoints` applies, so both passes agree on where it finishes. + song_end = structure.segments[-1].end_s + if song_end <= times[-1]: + song_end = times[-1] + 1.0 + return [*times, song_end] + + @staticmethod + def _settle_time(t_start: float, t_next_look: float, boundaries: list[float]) -> float: + """Pick the show time a look's position snapshot is taken at, never before ``t_start``. + + Not ``t_start`` itself: a look sharing an address with a formation lands where that + formation *begins*. One expiring before the primitive finishes is sampled at its own end. + """ + settled = next((t for t in boundaries if t > t_start), t_start) + return max(t_start, min(settled, t_next_look)) + def _response2choreo( self, text: str, structure: SongStructure | None = None ) -> dict[tuple[int, int, int], str]: @@ -669,18 +714,10 @@ def _response2choreo( def _choreo2waypoints( 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. + """Translate a (seq, bar, beat)-keyed choreography into ``time``/``pos``/``vel``/``acc``. - 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. + Actions are sorted by their resolved time and renumbered 1..N as synthetic indices, which is + what the time-based primitive execution pipeline expects. """ required = set(structure.required_keys(self._bars_per_required)) emitted = set(choreography) @@ -691,7 +728,6 @@ def _choreo2waypoints( 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) @@ -749,7 +785,7 @@ def _raw_response2waypoints( for i, positions in choreography.items(): try: - # literal_eval is safe because it only supports a restricted subset of python + # literal_eval is safe: it only supports a restricted subset of Python. positions = ast.literal_eval(positions) except (SyntaxError, ValueError): raise LLMFormatError(f"Cannot interpret waypoint {i} as a list (got {positions})") @@ -768,19 +804,10 @@ def _raw_response2waypoints( 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 may not be valid YAML (formatting, quotes, dashes). We slice the - ``choreography`` block manually and parse hierarchical keys of the form - ``sbt``. + """Extract the choreography from the LLM output as ``(seq, bar, beat)`` -> action strings. - 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: - Dict mapping ``(seq, bar, beat)`` tuples to action strings. + The LLM output may not be valid YAML (formatting, quotes, dashes), so the ``choreography`` + block is sliced manually. ``structure`` only annotates the debug print with resolved times. """ yaml_text = re.findall(r"```yaml\n(.*?)(?:```)", text, re.DOTALL) try: @@ -806,7 +833,6 @@ def _annotate(match: re.Match[str]) -> str: 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|$)", yaml_text, re.DOTALL) if not match: raise LLMFormatError( @@ -814,11 +840,7 @@ def _annotate(match: re.Match[str]) -> str: "choreography plan with the 'choreography' keyword." ) choreography = match.group(1).strip() - - # Step 2: Strip line comments (everything after `#`). choreography = "\n".join(line.split("#")[0].strip() for line in choreography.splitlines()) - - # Step 3: Parse `sbt: ` entries. entry_re = re.compile(rf"({KEY_PATTERN}):\s*(.*?)\s*(?={KEY_PATTERN}:|$)", re.DOTALL) choreography_steps: dict[tuple[int, int, int], str] = {} for entry in entry_re.findall(choreography): @@ -837,23 +859,74 @@ def _annotate(match: re.Match[str]) -> str: return dict(sorted(choreography_steps.items())) + @staticmethod + def lighting_from_text(text: str) -> dict[tuple[int, int, int], str]: + """Extract the ``lighting:`` block as ``(seq, bar, beat)`` -> action strings, in key order. + + More forgiving than :meth:`_slice_choreography_from_text`: an absent block yields an empty + dict. The header is line-anchored, or it would match "lighting:" in the plan prose. + """ + yaml_text = re.findall(r"```yaml\n(.*?)(?:```)", text, re.DOTALL) + yaml_text = yaml_text[0] if yaml_text else text + match = re.search( + r"^[ \t]*lighting:[ \t]*$(.*?)(?:^[ \t]*END[ \t]*$|\Z)", + yaml_text, + re.DOTALL | re.MULTILINE, + ) + if match is None: + return {} + # Strip line comments (everything after `#`), as the choreography slice does. + block = "\n".join(line.split("#")[0].strip() for line in match.group(1).splitlines()) + entry_re = re.compile(rf"({KEY_PATTERN}):\s*(.*?)\s*(?={KEY_PATTERN}:|$)", re.DOTALL) + entries = {decode_key(key): action.strip() for key, action in entry_re.findall(block)} + return dict(sorted(entries.items())) + + @staticmethod + def _parse_lighting_actions(action_str: str, addr: tuple[int, int, int]) -> list[dict]: + """Parse one lighting key's ``primitive(args); primitive(args)`` string into actions. + + Produces the ``{"primitive", "params"}`` shape `build_look` consumes, in the emission order + that resolves overlapping colours. Arity is checked before the zip onto argument names. + """ + actions: list[dict] = [] + for raw_move in action_str.strip(" ;").split(";"): + move = raw_move.strip() + if not move: + continue + name = move.split("(")[0].strip(" -\n") + if name not in LIGHTING_PRIMITIVE_ARG_ORDER: + raise LLMFormatError(f"Unknown lighting primitive '{name}' at {encode_key(*addr)}") + # Parse the `args` portion the way the motion path does: `ast.literal_eval` on a + # re-wrapped tuple expression, splitting on the first `(`. The selector is rendered as + # a list rather than a tuple precisely so that split stays valid. + try: + args = ast.literal_eval("(" + move.split("(")[1].split("#")[0][:-1] + ",)") + except (SyntaxError, ValueError, IndexError) as e: + raise LLMFormatError( + f"Cannot interpret arguments of '{move}' at {encode_key(*addr)}. " + f"Failed with {e.__class__.__name__}: {e}" + ) from e + arg_names = LIGHTING_PRIMITIVE_ARG_ORDER[name] + if len(args) != len(arg_names): + raise LLMFormatError( + f"{name} at {encode_key(*addr)} must have {len(arg_names)} arguments " + f"({arg_names}), got {list(args)}" + ) + actions.append({"primitive": name, "params": dict(zip(arg_names, args))}) + return actions + def _motion_primitives2time_and_pos( self, motion_primitives: dict, timestamps: NDArray, t_end: float ) -> tuple[NDArray, NDArray]: - """Convert motion primitives to waypoints over forward-looking intervals. + """Convert motion primitives to waypoint timings and positions over forward 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. + primitive runs until ``t_end``. Drones hold their start positions until the first action. """ waypoints = {} # 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)} - # 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) @@ -883,14 +956,13 @@ def _motion_primitives2time_and_pos( def _fill_missing_waypoints( self, waypoints: dict[float, dict[int, NDArray]] ) -> dict[float, dict[int, NDArray]]: - """Fill in missing waypoints. + """Fill in missing waypoints by copying the previous timestep. - Some motion primitives operate on a subset of drones. Therefore, some drones will not have a - waypoint at every timestep. We fill in the missing ones by copying over the previous - timestep. + Motion primitives may operate on a subset of drones, so not every drone has a waypoint at + every timestep. """ for i, waypoint in enumerate(waypoints.values()): - # First time step must have all drones because we added the start positions at time 0 + # The first timestep must have all drones: the start positions were added at time 0. if i == 0: assert all(d in waypoint for d in range(self.num_drones)), "Missing start positions" continue @@ -900,14 +972,12 @@ def _fill_missing_waypoints( return waypoints def _merge_motion_primitives(self, motion_primitives: dict, timesteps: NDArray) -> dict: - """Merge and annotate motion primitives. + """Merge the motion primitives sharing a timestep and annotate them with time information. - Merge multiple motion primitives for a single timestep, add time information and add the - time from PLAN motion_primitives to the previous function. + A PLAN primitive contributes its time to the preceding function rather than to itself. """ merged_motion_primitives = [] - # Filter out any PLAN motion_primitives that are at the end of the list. Make sure to not cut off - # any other motion_primitives. + # Trailing PLAN primitives are dropped; anything else past the end is an error. if max(motion_primitives.keys()) >= len(timesteps): excess_primitives = [ [list(d.keys())[0] for d in motion_primitives[i]] @@ -940,7 +1010,6 @@ def _merge_motion_primitives(self, motion_primitives: dict, timesteps: NDArray) } ) motion_primitives = {primitive["key"]: primitive for primitive in merged_motion_primitives} - # Check that the motion primitives do not exceed the number of waypoints for motion_primitive in motion_primitives.values(): if motion_primitive["key"] + motion_primitive["steps"] > len(timesteps): raise LLMFormatError( @@ -961,10 +1030,9 @@ def _primitive2waypoints( if motion_primitives_collection[fn_name]["n_args"] != len(args): raise LLMFormatError(f"Wrong number of arguments for {fn_name}") limits = {"lower": self.lim_lower, "upper": self.lim_upper} - # We need to pass waypoints and swarm_pos because some motion primitives operate on a subset - # of drones. Therefore, waypoints could contain positions for only some of the drones. - # swarm_pos always tracks the current position of all drones. We also need the dictionary - # instead of a list of positions in waypoints to track which drones have been moved. + # `waypoints` may cover only a subset of drones, so `swarm_pos` is passed alongside it to + # track every drone's current position. Both are dicts so it stays visible which drones a + # primitive actually moved. swarm_pos, waypoints = fn(args, swarm_pos, tstart, tend, limits) return swarm_pos, waypoints @@ -985,17 +1053,7 @@ def dicts2arrays(dict_of_dicts: dict[float, dict[int, NDArray]]) -> dict[float, 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 - """ + """Render a SongStructure as the prompt's segment block, one indented line per segment.""" lines: list[str] = [] for seg in structure.segments: n_bars = len(seg.bars) diff --git a/swarm_gpt/core/drone_swarm.py b/swarm_gpt/core/drone_swarm.py index b2fa6b2..6910775 100644 --- a/swarm_gpt/core/drone_swarm.py +++ b/swarm_gpt/core/drone_swarm.py @@ -48,14 +48,9 @@ def __init__( col_freq: float = 10, lighthouse: bool = True, ): - """Create and connect a Crazyflie swarm. - - Args: - drones: Dictionary of drones.toml entries including id, pos, and uri. - ctrl_freq: Control frequency (Hz). Defaults to 50. - update_freq: Frequency (Hz) of position updates sent to the drone. Defaults to 10. - col_freq: Maximum frequency (Hz) of color updates. Defaults to 10. - lighthouse: Whether to use lighthouse or mocap for localization. Defaults to True. + """Create and connect a Crazyflie swarm from ``drones.toml`` entries. + + Frequencies are in Hz; ``lighthouse`` selects lighthouse over mocap for localization. """ self.drones = drones self.ctrl_freq = ctrl_freq @@ -148,12 +143,7 @@ async def _land(uri: str) -> None: self._run(self._parallel_by_uri("Landing", self.uris, _land, timeout=duration + 1.0)) def goto(self, target: dict[str, list], duration: float = 3.0): - """Execute a high-level goto command for all drones. - - Args: - target: Position+Yaw references in the form {'uri1': [target], ...}. - duration: Duration of the motion in seconds. - """ + """Execute a high-level goto to ``{uri: [x, y, z, yaw]}`` over ``duration`` seconds.""" self._validate_required_uris("pos", target) for uri, setpoint in target.items(): if len(setpoint) != 4: @@ -170,11 +160,7 @@ async def _goto(uri: str) -> None: self._run(self._parallel_by_uri("Goto", self.uris, _goto, timeout=duration + 1.0)) def setpoint(self, target: dict[str, list]): - """Send one position+yaw setpoint to all drones and return. - - Args: - target: Position+Yaw references in the form {'uri1': [target], ...}. - """ + """Send one ``{uri: [x, y, z, yaw]}`` setpoint to all drones and return.""" self._validate_required_uris("pos", target) for uri, setpoint in target.items(): if len(setpoint) != 4: @@ -194,13 +180,9 @@ def execute_choreography( color_top: dict[str, dict[float, Array]] = {}, color_bot: dict[str, dict[float, Array]] = {}, ): - """Execute a choreography with position, orientation, and light commands. + """Execute a choreography of 3D splines with per-deck light cues. - Args: - choreography: Reference in the form of a 3d spline. - t_end: End time of the choreography. - color_top: Top deck color cues in the form {uri: {time: wrgb}}. - color_bot: Bottom deck color cues in the form {uri: {time: wrgb}}. + Colour cues take the form ``{uri: {time: wrgb}}``. """ self._validate_required_uris("choreography", choreography) if not color_top and not color_bot: @@ -225,12 +207,7 @@ async def _execute(uri: str) -> None: ) def apply_colors(self, color_top: dict[str, Array] | None, color_bot: dict[str, Array] | None): - """Apply colors to the drones. - - Args: - color_top: Top deck colors in the form {uri: wrgb}. - color_bot: Bottom deck colors in the form {uri: wrgb}. - """ + """Apply ``{uri: wrgb}`` colors to each deck; ``None`` blacks that deck out.""" if color_top is None: color_top = dict.fromkeys(self.uris, np.zeros(4)) if color_bot is None: @@ -247,12 +224,7 @@ async def _apply_colors(uri: str) -> None: self._run(self._parallel_by_uri("Applying colors", self.uris, _apply_colors, timeout=0.5)) def set_param(self, param: str, value: float): - """Set a Crazyflie parameter on all active drones. - - Args: - param: Parameter name in ``group.name`` format. - value: Value to set. - """ + """Set a ``group.name`` Crazyflie parameter on all active drones.""" async def _set_param(uri: str) -> None: await self._cf(uri).param().set(param, value) @@ -348,9 +320,8 @@ async def _close() -> None: def _run(self, coroutine: Awaitable[Any]) -> Any: """Run a cflib2 coroutine on the swarm event loop. - Dispatches cross-thread when the loop runs in another thread or is already running - (e.g. a deployment driving it), so an emergency stop from the request thread that - handles the frontend button still reaches the swarm mid-performance. + Dispatches cross-thread when the loop is elsewhere or already running, so an emergency stop + from the frontend's request thread still reaches the swarm mid-performance. """ if self._loop_thread is not None or self._loop.is_running(): return asyncio.run_coroutine_threadsafe(coroutine, self._loop).result() diff --git a/swarm_gpt/core/lighting.py b/swarm_gpt/core/lighting.py new file mode 100644 index 0000000..0596aa8 --- /dev/null +++ b/swarm_gpt/core/lighting.py @@ -0,0 +1,912 @@ +"""The lighting engine: config, selectors, waveforms, spreads, colour sources, primitives, cues. + +Colour and brightness are independent layers that multiply at the read-out, so any effect composes +with any colour source without needing a primitive per combination. The two stacks reduce +differently because the quantities do: two colours on one drone have no meaningful blend, so the +later one wins outright, where two brightness effects have an obvious one -- the brighter, as with +real light. + +Where this sits: `Choreographer` parses the LLM's `lighting:` track into ``{"primitive", "params"}`` +actions and calls `build_look` once per emitted key, handing in a position snapshot sampled off the +axswarm splines. The resulting `LightingTimeline` has two consumers that must agree -- the sim and +render viewers evaluate it per frame, and `compile_cues` bakes it into the cue lists `DroneSwarm` +streams over the radio. Everything here is a pure function of ``t`` and those frozen snapshots, +which is what lets the preview show what will actually fly. +""" + +from __future__ import annotations + +import logging +import tomllib +from dataclasses import dataclass +from pathlib import Path +from typing import TYPE_CHECKING + +import numpy as np + +from swarm_gpt.core.motion_primitives import _sanitize_drone_ids + +if TYPE_CHECKING: + from collections.abc import Callable + + from numpy.typing import NDArray + + _Builder = Callable[[dict, "_BuildContext"], "ColourLayer | BrightnessLayer | None"] + +logger = logging.getLogger(__name__) + +# World axis index and sign that point to the audience's right, keyed by `stage_axis`. +_STAGE_AXES = {"+x": (0, 1.0), "-x": (0, -1.0), "+y": (1, 1.0), "-y": (1, -1.0)} + +_SPREAD_AXES = {"x": 0, "y": 1, "z": 2} + +# `duty` is clamped to (0, 1]; the open lower bound needs a positive floor. +_DUTY_MIN = 1e-6 + +_DECKS = ("top", "bot") + +# How far before the end of the show the blackout lands, so drones never land lit. +_BLACKOUT_LEAD_S = 0.1 + +# A selector is a name plus its arguments, e.g. ("all", ()), ("ids", (1, 3, 5)), ("first", (4,)). +Selector = tuple[str, tuple] + +# Arguments each fixed-arity selector takes. `ids` is variadic and so absent. +_SELECTOR_ARITY = { + "all": 0, + "even": 0, + "odd": 0, + "left": 0, + "right": 0, + "upper": 0, + "lower": 0, + "first": 1, +} + +# The spreads that rank the selection, and so the only ones `group_size` can bucket. +RANKED_SPREADS = ("neighbour", "index") + +# Fraction of the coordinate magnitude below which a span counts as no extent. Relative rather than +# exact-zero: a cos/sin ring is degenerate only to ~1e-16, so an equality test would divide by that. +_SPAN_REL_TOL = 1e-9 + +_DECK_CHOICES = {"top": ("top",), "bot": ("bot",), "both": _DECKS} + +# `alternate_blink`'s `by`, mapped onto the spread that splits the group into antiphase halves. +_ALTERNATE_SPREADS = {"parity": "alternate_parity", "side": "alternate_side"} + +_DEFAULT_DUTY = 0.5 + +# Spreads that measure geometry, so a formation can leave them nothing to run along. +_SPATIAL_SPREADS = ("radius", *_SPREAD_AXES) + + +@dataclass(frozen=True) +class LightingConfig: + """Palette and calibration constants loaded from ``swarm_gpt/data/lighting.toml``. + + `col_freq` is the maximum colour-cue rate in Hz and must match `DroneSwarm`'s. + """ + + palette: dict[str, NDArray] + gamma: float + b_min: float + hue_steps: int + brightness_steps: int + channel_gain: NDArray + stage_axis: str + col_freq: float + + +def load_lighting_config(path: Path | None = None) -> LightingConfig: + """Load the lighting palette and calibration constants, defaulting to ``data/lighting.toml``.""" + if path is None: + path = Path(__file__).resolve().parents[1] / "data/lighting.toml" + with open(path, "rb") as f: + raw = tomllib.load(f) + return LightingConfig( + palette={name: np.asarray(v, dtype=float) for name, v in raw["palette"].items()}, + gamma=float(raw["gamma"]), + b_min=float(raw["b_min"]), + hue_steps=int(raw["hue_steps"]), + brightness_steps=int(raw["brightness_steps"]), + channel_gain=np.asarray(raw["channel_gain"], dtype=float), + stage_axis=raw["stage_axis"], + col_freq=float(raw["col_freq"]), + ) + + +def _mean_split(coord: NDArray, axis: str, half: str) -> NDArray: + """Mark the drones above the mean of ``coord``; the complement is the other half. + + A formation with no extent along the axis lands wholly in that complement and warns, rather + than being dealt into halves by ``coord > coord.mean()`` on float noise. + """ + if coord.size > 1 and coord.max() - coord.min() <= _SPAN_REL_TOL * np.abs(coord).max(): + logger.warning( + "Lighting split has no extent along %s: every drone falls outside `%s`, which selects " + "nobody, so the effect covers the whole swarm or none of it.", + axis, + half, + ) + return np.zeros(coord.size, dtype=bool) + return coord > coord.mean() + + +def _right_mask(positions: NDArray, cfg: LightingConfig) -> NDArray: + """Mark the drones stage right of the swarm centroid; the complement is stage left.""" + axis, sign = _STAGE_AXES[cfg.stage_axis] + return _mean_split(sign * positions[:, axis], f"stage_axis {cfg.stage_axis}", "right") + + +def _upper_mask(positions: NDArray) -> NDArray: + """Mark the drones above the swarm's mean height; the complement is the lower half. + + Split on the mean rather than on rank, as left/right is: two stacked formations of unequal size + then part along the gap between them instead of being cut at the halfway drone. + """ + return _mean_split(positions[:, _SPREAD_AXES["z"]], "z", "upper") + + +def select(sel: Selector, n: int, positions: NDArray, cfg: LightingConfig) -> NDArray: + """Resolve a selector into an (n,) boolean mask of the drones a lighting layer covers. + + Bounds are checked here, not in the output schema, because presets and hand-written blocks + bypass it: ``ids(0)`` shifts to -1 and selects the last drone, ``first(99)`` selects all. + """ + kind, args = sel + if kind in _SELECTOR_ARITY and len(args) != _SELECTOR_ARITY[kind]: + raise IndexError( + f"Lighting selector {kind} takes {_SELECTOR_ARITY[kind]} arguments, got {len(args)}" + ) + if kind == "all": + return np.ones(n, dtype=bool) + if kind == "left": + return ~_right_mask(positions, cfg) + if kind == "right": + return _right_mask(positions, cfg) + if kind == "lower": + return ~_upper_mask(positions) + if kind == "upper": + return _upper_mask(positions) + mask = np.zeros(n, dtype=bool) + if kind == "ids": + if not args: + raise IndexError("Lighting selector ids() names no drones") + # `ids` is 1-indexed on the LLM side; _sanitize_drone_ids shifts it and validates the shape. + ids = _sanitize_drone_ids(list(args), n) + if out_of_range := sorted(i + 1 for i in ids if not 0 <= i < n): + raise IndexError(f"Lighting drone ids {out_of_range} are outside the 1..{n} swarm") + mask[ids] = True + elif kind == "even": + # Parity of the 1-indexed id the LLM writes, so `even` and `ids([2, 4, 6])` agree. + mask[1::2] = True + elif kind == "odd": + mask[0::2] = True + elif kind == "first": + count = int(args[0]) + if not 1 <= count <= n: + raise IndexError(f"Lighting first({count}) is outside the 1..{n} swarm") + mask[:count] = True + else: + raise KeyError(f"Unknown lighting selector {kind}") + return mask + + +def waveform(kind: str, phase: NDArray, duty: float = 0.5) -> NDArray: + """Evaluate a "sine", "square" or "ramp" effect waveform onto [0, 1]. + + All three peak at ``phase = 0``, and the phase (in turns) wraps. + """ + frac = np.mod(phase, 1.0) + if kind == "sine": + return 0.5 * (1.0 + np.cos(2.0 * np.pi * frac)) + if kind == "square": + return (frac < np.clip(duty, _DUTY_MIN, 1.0)).astype(float) + if kind == "ramp": + return 1.0 - frac + raise KeyError(f"Unknown lighting waveform {kind}") + + +def _normalize_span(values: NDArray) -> NDArray: + """Normalize spatial values into the half-open [0, 1), as the `index` spread does. + + The ``(n - 1) / n`` scaling keeps the far drone off 1.0, which is the near drone's phase. + """ + span = values.max() - values.min() + if span <= _SPAN_REL_TOL * np.abs(values).max(): + return np.zeros_like(values) + n_sel = values.size + return (values - values.min()) / span * (n_sel - 1) / n_sel + + +def _neighbour_ranks(points: NDArray) -> NDArray: + """Rank (m, 3) points along a greedy nearest-neighbour walk over them. + + Drone id order is not spatial order: formations assign slots by cheapest permutation. The walk + runs over lexicographically sorted points, so that permutation cannot break a ring's first tie. + """ + # A walk rather than a closed-form ordering because the formation is arbitrary. Sorting by + # angle would order a ring and scramble a line; by arc length needs a curve nobody fits. A + # greedy hop to the nearest unvisited point needs no model of the shape at all, and "adjacent + # in the walk" is exactly what an author means by a light running *along* the formation. + # Its price is that greedy nearest-neighbour is not an optimal tour: strand a point and the + # walk crosses the formation to collect it, which reads as the chase teleporting once a loop. + # Acceptable because a formation dense enough to chase along rarely strands one. + # Sorting x first then fixes where the walk visibly begins: rank 0 is the selection's + # smallest-x drone, which with the audience at +x is the one furthest upstage, so a chase + # enters from the back and comes forward. Deliberately world-frame and not stage-relative -- + # `stage_axis` cannot reach here -- so re-rigging the room silently moves the entry point. + lex = np.lexsort(points.T[::-1]) + walk = points[lex] + order = [0] + unvisited = np.ones(walk.shape[0], dtype=bool) + unvisited[0] = False + while unvisited.any(): + distance = np.linalg.norm(walk - walk[order[-1]], axis=1) + distance[~unvisited] = np.inf + nearest = int(np.argmin(distance)) + order.append(nearest) + unvisited[nearest] = False + ranks = np.empty(walk.shape[0], dtype=int) + ranks[lex[order]] = np.arange(walk.shape[0]) + return ranks + + +# One waveform plus one offset per drone is the whole effect model. Every time-varying primitive +# reads `phase = t / period_s - offset`, and all three waveforms peak at phase 0, so a drone peaks +# at `t = offset * period_s`. Offsets rising with rank is what makes a pattern *travel* toward +# increasing rank rather than merely flash out of sync, and is why the rank-0 drone -- whichever +# the spread puts there -- is the one an author sees the effect start from. + + +def spread_offsets( + kind: str, mask: NDArray, positions: NDArray, group_size: int, cfg: LightingConfig +) -> NDArray: + """Compute the (n,) per-drone phase offsets that turn one waveform into a family of effects. + + Offsets are relative to the selected subset, except "alternate_side", which splits against the + centroid. ``group_size`` above 1 needs a ranked spread: bucketing a coordinate changes meaning. + """ + if group_size < 1: + raise ValueError(f"group_size must be >= 1, got {group_size}") + if group_size > 1 and kind not in RANKED_SPREADS: + raise ValueError( + f"group_size={group_size} needs a ranked spread ({' or '.join(RANKED_SPREADS)}), " + f"got {kind}" + ) + offsets = np.zeros(mask.shape[0]) + idx = np.flatnonzero(mask) + if kind == "none" or idx.size == 0: + return offsets + if kind in RANKED_SPREADS: + ranks = _neighbour_ranks(positions[idx]) if kind == "neighbour" else np.arange(idx.size) + n_groups = int(np.ceil(idx.size / group_size)) + offsets[idx] = (ranks // group_size) / n_groups + # Both `alternate_*` spreads read a property of the whole swarm -- id parity, and the stage side + # measured against every drone's centroid rather than the selection's. That is deliberate: + # "stage left" has to mean the same half whichever subset an author lights, or two effects + # written against the same side would disagree. The cost is that either collapses to a single + # phase when the selector has already isolated one value of that property: `alternate_blink` on + # an `even`/`odd` or `left`/`right` selection is a plain synchronised `blink`, silently. Unlike + # the spatial spreads there is nothing to warn on -- the offsets are legitimately uniform. + elif kind == "alternate_parity": + offsets[idx] = 0.5 * (idx % 2) + elif kind == "alternate_side": + offsets[idx] = 0.5 * _right_mask(positions, cfg)[idx] + elif kind == "radius": + centroid = positions[idx].mean(axis=0) + offsets[idx] = _normalize_span(np.linalg.norm(positions[idx] - centroid, axis=1)) + elif kind in _SPREAD_AXES: + offsets[idx] = _normalize_span(positions[idx, _SPREAD_AXES[kind]]) + else: + raise KeyError(f"Unknown lighting spread {kind}") + return offsets + + +def hue_to_wrgb(hue: NDArray, cfg: LightingConfig) -> NDArray: + """Convert hues in turns to calibrated full-brightness WRGB, shaped ``hue.shape + (4,)``. + + The order is load-bearing: normalize to a constant channel sum first, then apply + ``channel_gain``. The other way divides the gain back out for any hue on a single channel. + """ + # A triangular ramp normalized to a constant channel sum, not textbook HSV. HSV holds + # *saturation* constant, which drives one, two or three LEDs depending on the hue and makes a + # cycling `rainbow` visibly throb as it passes the primaries. Holding the sum constant instead + # trades exact hue fidelity for equal apparent brightness right around the wheel, which is what + # a swarm of point sources needs. The W channel stays 0 because the dedicated white LED is off + # the hue circle entirely -- `white` is reachable only as a named palette entry. + h6 = 6.0 * np.mod(np.asarray(hue, dtype=float), 1.0) + rgb = np.stack( + [ + np.clip(np.abs(h6 - 3.0) - 1.0, 0.0, 1.0), + np.clip(2.0 - np.abs(h6 - 2.0), 0.0, 1.0), + np.clip(2.0 - np.abs(h6 - 4.0), 0.0, 1.0), + ], + axis=-1, + ) + rgb = 255.0 * rgb / rgb.sum(axis=-1, keepdims=True) + rgb = rgb * cfg.channel_gain[1:] + return np.concatenate([np.zeros(rgb.shape[:-1] + (1,)), rgb], axis=-1) + + +@dataclass(frozen=True) +class ColourLayer: + """One colour source ("named", "gradient", "cycled" or "fade") covering a subset of the swarm. + + Within a look, later colour layers overwrite earlier ones over the rows their mask covers. + ``params`` is ``{"color"}``, ``{"color_a", "color_b", "s"}``, ``{"period_s", "offsets"}`` or + ``{"color_a", "color_b", "t_start", "duration_s"}``. + + "gradient" interpolates across the swarm and is frozen in time; "fade" interpolates in time and + is uniform across the swarm. They are the two halves of the same operation. + """ + + mask: NDArray + decks: tuple[str, ...] + kind: str + params: dict + + def evaluate(self, t: float, cfg: LightingConfig) -> NDArray: + """Evaluate the layer at show time ``t`` into (n, 4) WRGB; rows outside the mask are 0.""" + colours = np.zeros((self.mask.shape[0], 4)) + idx = np.flatnonzero(self.mask) + if idx.size == 0: + return colours + if self.kind == "named": + colours[idx] = cfg.palette[self.params["color"]] + elif self.kind == "gradient": + s = np.asarray(self.params["s"], dtype=float)[idx, None] + colours[idx] = (1.0 - s) * cfg.palette[self.params["color_a"]] + s * cfg.palette[ + self.params["color_b"] + ] + elif self.kind == "fade": + # Held at color_b past the end rather than looping: a fade is a one-shot, and the look + # persists until the next key, which may be far later. + span = (t - self.params["t_start"]) / self.params["duration_s"] + s = float(np.clip(span, 0.0, 1.0)) + colours[idx] = (1.0 - s) * cfg.palette[self.params["color_a"]] + s * cfg.palette[ + self.params["color_b"] + ] + elif self.kind == "cycled": + offsets = np.asarray(self.params["offsets"], dtype=float)[idx] + hue = np.mod(t / self.params["period_s"] - offsets, 1.0) + colours[idx] = hue_to_wrgb(np.floor(hue * cfg.hue_steps) / cfg.hue_steps, cfg) + else: + raise KeyError(f"Unknown colour layer kind {self.kind}") + return colours + + +@dataclass(frozen=True) +class BrightnessLayer: + """One brightness effect ("constant" or a waveform name) covering a subset of the swarm. + + Within a look, brightness layers reduce with ``max``, and ``light_on`` is a "constant" layer at + 1.0. ``light_off`` is a post-reduction kill mask: a layer contributing 0 would be a no-op. + """ + + mask: NDArray + decks: tuple[str, ...] + kind: str + period_s: float + duty: float + offsets: NDArray + + def evaluate(self, t: float) -> NDArray: + """Evaluate the layer at show time ``t`` into (n,) brightness; masked-out rows are 0.""" + out = np.zeros(self.mask.shape[0]) + idx = np.flatnonzero(self.mask) + if idx.size == 0: + return out + if self.kind == "constant": + out[idx] = 1.0 + return out + phase = t / self.period_s - np.asarray(self.offsets, dtype=float)[idx] + out[idx] = waveform(self.kind, phase, self.duty) + return out + + +@dataclass(frozen=True) +class Look: + """The complete lighting state from one emitted key until the next. + + The next look replaces this one rather than layering onto it, so a persisting colour must be + restated. ``off_mask`` is the (n, 2) kill mask applied after the brightness reduction. + """ + + t_start: float + colour_layers: tuple[ColourLayer, ...] + brightness_layers: tuple[BrightnessLayer, ...] + off_mask: NDArray + positions: NDArray | None = None + + +class LightingTimeline: + """An ordered list of looks, evaluable at any show time. + + A pure function of ``t`` plus the snapshots already frozen into its layers, so the per-frame sim + read-out and the baked hardware cues see exactly the same thing. + """ + + def __init__(self, looks: list[Look], n: int, t_end: float, cfg: LightingConfig) -> None: + """Assemble the timeline; ``looks`` sort stably by ``t_start``, so may arrive unordered.""" + self._n = n + self._cfg = cfg + self._t_blackout = t_end - _BLACKOUT_LEAD_S + ordered = sorted(looks, key=lambda look: look.t_start) + # A layerless look covering everything before the first emitted key, so the lookup never has + # to special-case "no look yet". It borrows the first look's snapshot so the hue order does + # not change when that look takes over. + snapshot = ordered[0].positions if ordered else None + base = Look(-np.inf, (), (), np.zeros((n, 2), dtype=bool), snapshot) + self._looks = [base, *ordered] + self._starts = np.array([look.t_start for look in self._looks]) + self._base_colours = [self._base_colour(look) for look in self._looks] + + # Together with the full-on fallback in `_merge_brightness`, this is why an unlit drone carries + # a distinct hue instead of going dark: the LLM is told to light the moments the music asks for + # rather than every key, so the common case is a sparse track over a swarm that must already + # look like a show. Dark-by-default would make an empty lighting track a blackout, and would + # punish a good sparse track more than a bad dense one. Laid out in `neighbour` order so the + # wheel reads as a gradient around the formation rather than scattered by drone id. + def _base_colour(self, look: Look) -> NDArray: + """Assign the default hue wheel across the swarm in one look's `neighbour` order.""" + ranks = np.arange(self._n) if look.positions is None else _neighbour_ranks(look.positions) + return hue_to_wrgb(ranks / self._n, self._cfg) + + def _look_index_at(self, t: float) -> int: + """Index the look covering ``t``, which is 0 -- the base look -- before the first one.""" + return int(np.searchsorted(self._starts, t, side="right")) - 1 + + def _merge_colour(self, look: Look, base: NDArray, t: float, deck: str) -> NDArray: + """Merge one deck's colour layers over ``base``, later layers overwriting earlier ones. + + The overwrite is driven by each layer's mask, not by whether its output is non-zero: an + unselected row and a legitimately dark drone both read as zeros. + """ + colours = base.copy() + for layer in look.colour_layers: + if deck in layer.decks: + colours[layer.mask] = layer.evaluate(t, self._cfg)[layer.mask] + return colours + + def _merge_brightness(self, look: Look, t: float, deck: str, deck_idx: int) -> NDArray: + """Reduce one deck's brightness layers with ``max``, then apply the kill mask. + + Coverage comes from the layer masks, not the merged values: a `square` layer in its off + phase legitimately contributes 0, and reading the base state off the value would invert it. + """ + brightness = np.zeros(self._n) + covered = np.zeros(self._n, dtype=bool) + for layer in look.brightness_layers: + if deck in layer.decks: + brightness = np.maximum(brightness, layer.evaluate(t)) + covered |= layer.mask + # Full-on is a fallback, not a participant in the max: it applies only where nothing else + # does. + brightness[~covered] = 1.0 + brightness[look.off_mask[:, deck_idx]] = 0.0 + return brightness + + def evaluate(self, t: float) -> NDArray: + """Evaluate both decks at show time ``t`` into (n, 2, 4) WRGB, deck axis ordered (top, bot). + + Brightness floors into ``brightness_steps`` buckets before the multiply, making the + waveforms piecewise-constant so `compile_cues` dedups. ``b_min`` precedes that, or it is inert. + """ + if t >= self._t_blackout: + return np.zeros((self._n, 2, 4)) + index = self._look_index_at(t) + look = self._looks[index] + steps = self._cfg.brightness_steps + out = np.empty((self._n, 2, 4)) + for deck_idx, deck in enumerate(_DECKS): + colours = self._merge_colour(look, self._base_colours[index], t, deck) + merged = self._merge_brightness(look, t, deck, deck_idx) + merged[merged < self._cfg.b_min] = 0.0 + brightness = (np.floor(merged * steps) / steps)[:, None] + out[:, deck_idx] = np.round(colours * brightness**self._cfg.gamma) + return out + + def evaluate_rgb01(self, t: float, deck: str = "top") -> NDArray: + """Evaluate one deck as (n, 3) RGB in [0, 1] for the 3D viewer, folding W into all three.""" + wrgb = self.evaluate(t)[:, _DECKS.index(deck)] + return np.clip((wrgb[:, 1:] + wrgb[:, :1]) / 255.0, 0.0, 1.0) + + +# The mapping of the primitives below onto the engine above is thin: `chase` and `sweep` are both +# "square wave plus a spread", and `rainbow` and `chase` differ only in whether the spread drives +# hue. They exist as twelve names anyway because the LLM picks from a musical description, and +# "ripple out from the centre" is a name it can reach for where "sine wave, radius spread" is a +# parameter space it has to derive. Only the three whose spread is a real authoring choice -- +# `rainbow`, `chase`, `sweep` -- expose one; the rest hardcode theirs, and that is the only thing +# separating `ripple_light` from `pulse`. + + +@dataclass(frozen=True) +class _BuildContext: + """Everything a primitive builder reads besides its own parameters. + + ``primitive`` is the name the action was emitted under, for diagnostics; ``bpm`` converts every + `period_beats` into seconds. ``t_start`` is the look's own start, which only a time-anchored + effect needs -- layers are evaluated at absolute show time, so a one-shot cannot locate itself + without it. + """ + + primitive: str + mask: NDArray + decks: tuple[str, ...] + positions: NDArray + cfg: LightingConfig + bpm: float + t_start: float = 0.0 + + +def _period_seconds( + period_beats: float, ctx: _BuildContext, lit_fraction: float = _DEFAULT_DUTY +) -> float: + """Convert an emitted beat period into seconds, held off the cue-rate aliasing floor. + + The clamped quantity is the lit window, not the period: under one ``1 / col_freq`` tick it can + fall between two grid ticks and the drone is never lit at all. Over-fast effects clamp. + """ + tick_s = 1.0 / ctx.cfg.col_freq + min_period_s = max(2.0 * tick_s, tick_s / lit_fraction) + period_s = float(period_beats) * 60.0 / ctx.bpm + if period_s >= min_period_s: + return period_s + logger.warning( + "Lighting period_beats=%g is %.3f s at %.1f BPM, leaving each drone lit for %.3f s — under " + "the %.3f s cue tick, which drops the effect from some drones entirely rather than merely " + "coarsening it. Clamping that window to %.3f s, stretching the period to %.3f s.", + period_beats, + period_s, + ctx.bpm, + period_s * lit_fraction, + tick_s, + min_period_s * lit_fraction, + min_period_s, + ) + return min_period_s + + +def _min_lit_duty(period_s: float, duty: float, ctx: _BuildContext) -> float: + """Widen a duty whose lit window falls under one cue tick, leaving the period alone. + + A window shorter than a tick lands between two grid samples for every period that is not a + whole multiple of one, so the flash drops out of the cue list rather than merely coarsening. + """ + tick_s = 1.0 / ctx.cfg.col_freq + if duty * period_s >= tick_s: + return duty + logger.warning( + "Lighting %s has a duty of %g over %.3f s, leaving each flash lit for %.3f s — under the " + "%.3f s cue tick, so most flashes fall between two cues and vanish. Widening the duty to " + "%.3f, which is one tick lit and keeps the effect on the beat it was written for.", + ctx.primitive, + duty, + period_s, + duty * period_s, + tick_s, + tick_s / period_s, + ) + # `_period_seconds` already floors the period at two ticks, so this can never exceed 0.5. + return tick_s / period_s + + +def _gradient_s(by: str, mask: NDArray, positions: NDArray) -> NDArray: + """Compute `gradient`'s (n,) interpolation parameter along ``by``. + + Normalized onto the inclusive [0, 1] so the far drone reproduces ``color_b`` exactly, which is + why this is not `spread_offsets` -- that range is half-open and leaves ``color_b`` unreachable. + """ + s = np.zeros(mask.shape[0]) + idx = np.flatnonzero(mask) + if idx.size == 0: + return s + if by == "index": + values = np.arange(idx.size, dtype=float) + elif by == "radius": + values = np.linalg.norm(positions[idx] - positions[idx].mean(axis=0), axis=1) + elif by in _SPREAD_AXES: + values = positions[idx, _SPREAD_AXES[by]].astype(float) + else: + raise KeyError(f"Unknown gradient axis {by}") + span = values.max() - values.min() + if span > _SPAN_REL_TOL * np.abs(values).max(): + s[idx] = (values - values.min()) / span + return s + + +def _spread(ctx: _BuildContext, kind: str, group_size: int = 1) -> NDArray: + """Resolve a phase spread against the frozen snapshot, reporting one it collapses on. + + A selection with no extent along a spatial spread's axis gets every offset 0 and the effect + degrades into a synchronised blink, which is legal but indistinguishable from a working one. + """ + offsets = spread_offsets(kind, ctx.mask, ctx.positions, group_size, ctx.cfg) + if kind in _SPATIAL_SPREADS and ctx.mask.sum() > 1 and not offsets[ctx.mask].any(): + logger.warning( + "Lighting %s covers drones with no extent along %s, so every phase offset is 0 and it " + "fires as one synchronised flash instead of travelling. It needs a formation spread " + "out along that axis.", + ctx.primitive, + kind, + ) + return offsets + + +def _brightness( + ctx: _BuildContext, kind: str, period_s: float, duty: float, spread: str, group_size: int +) -> BrightnessLayer: + """Assemble a brightness layer, resolving its phase spread against the frozen snapshot.""" + offsets = _spread(ctx, spread, group_size) + return BrightnessLayer(ctx.mask, ctx.decks, kind, period_s, duty, offsets) + + +def _palette_colour(name: str, cfg: LightingConfig) -> str: + """Check a colour name against the palette and return it unchanged. + + `ColourLayer` resolves palette names lazily, so an unchecked one would surface as a bare + ``KeyError`` mid-render or mid-deploy rather than as a reprompt. + """ + if name not in cfg.palette: + raise KeyError(f"Unknown lighting colour {name}") + return name + + +def _light_color(params: dict, ctx: _BuildContext) -> ColourLayer: + """`light_color(sel, color, deck)`: assign a calibrated palette colour to a subset.""" + return ColourLayer( + ctx.mask, ctx.decks, "named", {"color": _palette_colour(params["color"], ctx.cfg)} + ) + + +def _gradient(params: dict, ctx: _BuildContext) -> ColourLayer: + """`gradient(sel, color_a, color_b, by, deck)`: interpolate two palette colours across it. + + A `by` axis with no extent lands every drone on ``color_a``, so it is reported for the reason + `_spread` reports a collapsed spread. + """ + s = _gradient_s(params["by"], ctx.mask, ctx.positions) + if params["by"] in _SPATIAL_SPREADS and ctx.mask.sum() > 1 and not s[ctx.mask].any(): + logger.warning( + "Lighting gradient covers drones with no extent along %s, so every drone lands on " + "color_a and it paints one flat colour instead of interpolating. It needs a formation " + "spread out along that axis.", + params["by"], + ) + return ColourLayer( + ctx.mask, + ctx.decks, + "gradient", + { + "color_a": _palette_colour(params["color_a"], ctx.cfg), + "color_b": _palette_colour(params["color_b"], ctx.cfg), + "s": s, + }, + ) + + +def _rainbow(params: dict, ctx: _BuildContext) -> ColourLayer: + """`rainbow(sel, period_beats, spread, deck)`: a spectrum cycle along the chosen spread.""" + return ColourLayer( + ctx.mask, + ctx.decks, + "cycled", + { + "period_s": _period_seconds(params["period_beats"], ctx), + "offsets": _spread(ctx, params["spread"]), + }, + ) + + +def _light_on(params: dict, ctx: _BuildContext) -> BrightnessLayer: + """`light_on(sel, deck)`: force full on. Contributes 1.0 to the max, so it dominates.""" + return _brightness(ctx, "constant", 0.0, _DEFAULT_DUTY, "none", 1) + + +def _light_off(params: dict, ctx: _BuildContext) -> None: + """`light_off(sel, deck)`: force dark, via `Look.off_mask` rather than a layer. + + A layer contributing 0 is a no-op under the ``max`` reduction the instant anything else covers + the same drone, so `build_look` reads this ``None`` and sets the mask bits instead. + """ + return None + + +def _pulse(params: dict, ctx: _BuildContext) -> BrightnessLayer: + """`pulse(sel, period_beats, deck)`: the whole group breathes together.""" + period_s = _period_seconds(params["period_beats"], ctx) + return _brightness(ctx, "sine", period_s, _DEFAULT_DUTY, "none", 1) + + +def _blink(params: dict, ctx: _BuildContext) -> BrightnessLayer: + """`blink(sel, period_beats, duty, deck)`: hard on/off flash, the group in sync. + + A short `duty` is held off the cue grid by widening the window, not by stretching the period: + `blink` runs at one shared phase, so no drone is skipped relative to another and slowing it + would move a legal 0.1-duty stab five bars off the beat it was written for. + """ + period_s = _period_seconds(params["period_beats"], ctx) + duty = _min_lit_duty(period_s, float(params["duty"]), ctx) + return _brightness(ctx, "square", period_s, duty, "none", 1) + + +def _strobe_decay(params: dict, ctx: _BuildContext) -> BrightnessLayer: + """`strobe_decay(sel, period_beats, deck)`: flash on the beat, decay out.""" + period_s = _period_seconds(params["period_beats"], ctx) + return _brightness(ctx, "ramp", period_s, _DEFAULT_DUTY, "none", 1) + + +def _chase(params: dict, ctx: _BuildContext) -> BrightnessLayer: + """`chase(sel, period_beats, length, group_size, spread, deck)`: a running light along `spread`. + + ``length`` is how many drones are lit at once, i.e. ``duty = length / n_sel``. That makes it the + one primitive whose lit window is narrower than half a period, hence the ``duty`` passed on. + """ + # Gotcha: `duty` counts drones while `group_size` buckets them, and blocks light whole. A + # `length` under `group_size` therefore buys a lit window narrower than one block's slot, and + # the chase gaps out to darkness between blocks instead of running. Keep it a multiple. + # The `n_sel` floor is only here because `chase` is the one primitive that would divide by zero + # on an empty selection, where the duty is irrelevant anyway. + n_sel = max(int(ctx.mask.sum()), 1) + duty = float(np.clip(int(params["length"]) / n_sel, 1.0 / n_sel, 1.0)) + period_s = _period_seconds(params["period_beats"], ctx, duty) + return _brightness(ctx, "square", period_s, duty, params["spread"], int(params["group_size"])) + + +def _sweep(params: dict, ctx: _BuildContext) -> BrightnessLayer: + """`sweep(sel, period_beats, axis, deck)`: a directional sweep across the stage.""" + period_s = _period_seconds(params["period_beats"], ctx) + return _brightness(ctx, "square", period_s, _DEFAULT_DUTY, params["axis"], 1) + + +def _ripple_light(params: dict, ctx: _BuildContext) -> BrightnessLayer: + """`ripple_light(sel, period_beats, deck)`: a wave out from the swarm centre.""" + period_s = _period_seconds(params["period_beats"], ctx) + return _brightness(ctx, "sine", period_s, _DEFAULT_DUTY, "radius", 1) + + +def _alternate_blink(params: dict, ctx: _BuildContext) -> BrightnessLayer: + """`alternate_blink(sel, period_beats, by, deck)`: ping-pong between two halves. + + Not two `blink` calls: `blink` has no phase parameter, so the half-period offset that makes the + ping-pong read can only come from a spread. + """ + period_s = _period_seconds(params["period_beats"], ctx) + spread = _ALTERNATE_SPREADS[params["by"]] + return _brightness(ctx, "square", period_s, _DEFAULT_DUTY, spread, 1) + + +# The catalogue the prompt documents and the LLM output schema enumerates, in prompt order. +# Adding or resignaturing one means four edits, and fewer than four ships a primitive the LLM +# cannot emit or the parser cannot read: the builder here, `_LIGHTING_PRIMITIVE_ARG_ORDER` in +# structured_output_schema.py (which the JSON schema and the text parser both read, so those two +# cannot drift apart), any new parameter in `_lighting_param_schemas` beside it, and the +# `` prose in data/motion_primitive_prompts.yaml. The prompt is the one with no import +# tying it back here, so it is the one that rots -- tests in test_structured_output.py parse that +# prose and diff it against the schema to keep the pair honest. Selector and spread names are the +# same story, minus the builder. +def _fade(params: dict, ctx: _BuildContext) -> ColourLayer: + """`fade(sel, color_a, color_b, duration_beats, deck)`: cross-fade the subset over time. + + The temporal counterpart of `gradient`. Clamped to a whole cue tick, below which the fade + resolves to a single step and reads as a cut. + """ + duration_s = float(params["duration_beats"]) * 60.0 / ctx.bpm + tick_s = 1.0 / ctx.cfg.col_freq + if duration_s < tick_s: + logger.warning( + "Lighting fade duration_beats=%g is %.3f s at %.1f BPM, under the %.3f s cue tick, so " + "it would quantize to a cut rather than a fade. Clamping to one tick.", + params["duration_beats"], + duration_s, + ctx.bpm, + tick_s, + ) + duration_s = tick_s + return ColourLayer( + ctx.mask, + ctx.decks, + "fade", + { + "color_a": _palette_colour(params["color_a"], ctx.cfg), + "color_b": _palette_colour(params["color_b"], ctx.cfg), + "t_start": ctx.t_start, + "duration_s": duration_s, + }, + ) + + +LIGHTING_PRIMITIVES: dict[str, _Builder] = { + "light_color": _light_color, + "gradient": _gradient, + "fade": _fade, + "rainbow": _rainbow, + "light_on": _light_on, + "light_off": _light_off, + "pulse": _pulse, + "blink": _blink, + "strobe_decay": _strobe_decay, + "chase": _chase, + "sweep": _sweep, + "ripple_light": _ripple_light, + "alternate_blink": _alternate_blink, +} + + +def build_look( + actions: list[dict], t_start: float, positions: NDArray, n: int, cfg: LightingConfig, bpm: float +) -> Look: + """Compile one emitted lighting key's actions into a `Look`. + + Colour layers keep their order in ``actions``: a later colour overwrites by position, not kind. + The caller picks when ``positions`` was sampled -- not ``t_start``, where nothing has arrived. + """ + colour_layers: list[ColourLayer] = [] + brightness_layers: list[BrightnessLayer] = [] + off_mask = np.zeros((n, 2), dtype=bool) + for action in actions: + name = action["primitive"] + if name not in LIGHTING_PRIMITIVES: + raise KeyError(f"Unknown lighting primitive {name}") + params = action["params"] + decks = _DECK_CHOICES[params["deck"]] + mask = select(params["sel"], n, positions, cfg) + ctx = _BuildContext(name, mask, decks, positions, cfg, bpm, t_start) + layer = LIGHTING_PRIMITIVES[name](params, ctx) + if layer is None: + for deck in decks: + off_mask[mask, _DECKS.index(deck)] = True + elif isinstance(layer, ColourLayer): + colour_layers.append(layer) + else: + brightness_layers.append(layer) + return Look(t_start, tuple(colour_layers), tuple(brightness_layers), off_mask, positions) + + +# `DroneSwarm` drains at most one cue per deck per `1 / col_freq` tick and never drops, so a denser +# cue list plays back slowed and drifts out of sync with the music permanently. Sampling on a +# uniform `col_freq` grid and dropping consecutive duplicates rules that out structurally. + + +def _sample_times(col_freq: float, t_end: float) -> NDArray: + """Build the cue sample grid, opening at 0 and terminated by the blackout instant. + + The blackout is appended explicitly, since a grid anchored at 0 lands on it only by luck; ticks + it would crowd are dropped first, so it cannot itself break the minimum spacing between cues. + """ + period = 1.0 / col_freq + t_blackout = t_end - _BLACKOUT_LEAD_S + if t_blackout < period: + raise ValueError( + f"A {t_end} s show is too short to compile lighting cues: it leaves {t_blackout} s " + f"before the blackout, under the {period} s cue period at {col_freq} Hz" + ) + ticks = np.arange(int(np.floor(t_blackout * col_freq)) + 1) / col_freq + return np.append(ticks[t_blackout - ticks >= period], t_blackout) + + +def compile_cues( + timeline: LightingTimeline, uris: list[str], col_freq: float, t_end: float +) -> tuple[dict[str, dict[float, NDArray]], dict[str, dict[float, NDArray]]]: + """Bake a lighting timeline into per-deck colour cues for `DroneSwarm`. + + Returns ``(color_top, color_bot)``, each ``{uri: {time: (4,) WRGB}}``, ready for + ``execute_choreography``. ``uris`` is in the timeline's drone-index order. + """ + times = _sample_times(col_freq, t_end) + frames = np.stack([timeline.evaluate(float(t)) for t in times]) # (n_samples, n, 2, 4) + n = frames.shape[1] + if len(uris) != n: + raise ValueError(f"Got {len(uris)} URIs for a {n}-drone lighting timeline") + top: dict[str, dict[float, NDArray]] = {} + bot: dict[str, dict[float, NDArray]] = {} + for deck_idx, cues in enumerate((top, bot)): # the deck axis is ordered (top, bot) throughout + for i, uri in enumerate(uris): + track = frames[:, i, deck_idx] + changed = np.ones(times.size, dtype=bool) + changed[1:] = np.any(track[1:] != track[:-1], axis=1) + cues[uri] = {float(times[k]): track[k] for k in np.flatnonzero(changed)} + return top, bot diff --git a/swarm_gpt/core/motion_primitives.py b/swarm_gpt/core/motion_primitives.py index dcdd7f3..fa6c17a 100644 --- a/swarm_gpt/core/motion_primitives.py +++ b/swarm_gpt/core/motion_primitives.py @@ -1,5 +1,6 @@ """Motion primitive library.""" +import re import sys from types import EllipsisType from typing import Callable @@ -52,8 +53,7 @@ def rotate( """Rotate all drones by angle theta.""" angle, axis = params angle = np.deg2rad(float(angle)) - steps = max(1, min(int(tend - tstart), 2)) # Number of steps to rotate - # override rotation to be around z axis atm + steps = max(1, min(int(tend - tstart), 2)) if "z" in axis: axis = np.array([0, 0, 1]) elif "y" in axis: @@ -63,12 +63,11 @@ def rotate( else: raise LLMFormatError("Invalid axis for rotation") max_radius = np.max(np.linalg.norm(swarm_pos[..., :2], axis=-1)) - vmax = 1.0 # Maximum velocity in m/s + vmax = 1.0 # m/s max_angle = (vmax * 100) / max_radius * (tend - tstart) angle = np.clip(angle, -max_angle, max_angle) r = R.identity() if steps == 0 else R.from_rotvec(axis * angle / steps) - # Apply the rotation to the vector waypoints = {} for t in np.linspace(tstart, tend, steps + 1)[1:]: swarm_pos = r.apply(swarm_pos) @@ -86,14 +85,12 @@ def spiral( """Spiral primitive.""" n_drones = swarm_pos.shape[0] steps, height = params - # steps = 4 - min_spacing = 60 # Minimum distance between drones in cm + min_spacing = 60 # cm - # Calculate the circumference needed to place all drones with at least the minimum spacing + # Chord formula: the radius whose circumference seats every drone min_spacing apart. start_radius = min_spacing / (2 * np.sin(np.pi / n_drones)) end_radius = min(2 * start_radius, limits["upper"][0] * 100) angles = np.linspace(0, 2 * np.pi, n_drones, endpoint=False) - # Match start positions to drones x = start_radius * np.cos(angles) y = start_radius * np.sin(angles) # TODO: Vary height over time? @@ -104,8 +101,7 @@ def spiral( waypoints = {} for t in np.linspace(tstart, tend, steps + 1)[1:]: radius = start_radius + (end_radius - start_radius) * ((t - tstart) / (tend - tstart)) - # Either full rotation around the circle or max angular velocity with 100cm/s linear - # velocity hard-coded as drone limit + # Whichever is slower: a full revolution, or the 100 cm/s linear velocity drone limit. rot_rate = min(100 / radius, 2 * np.pi / (tend - tstart)) angles += rot_rate * dt swarm_pos = np.array( @@ -125,14 +121,13 @@ def spiral_speed( """Spiral primitive with speed control.""" steps, height, degrees, increase = params n_drones = swarm_pos.shape[0] - min_spacing = 60 # Minimum distance between drones in cm + min_spacing = 60 # cm steps = max(1, min(int(tend - tstart), 2)) - # Calculate the circumference needed to place all drones with at least the minimum spacing + # Chord formula: the radius whose circumference seats every drone min_spacing apart. start_radius = min_spacing / (2 * np.sin(np.pi / n_drones)) end_radius = min(increase * start_radius, limits["upper"][0] * 100) angles = np.linspace(0, 2 * np.pi, n_drones, endpoint=False) - # Match start positions to drones x = start_radius * np.cos(angles) y = start_radius * np.sin(angles) des_pos = np.array([x, y, [height] * n_drones]).T @@ -142,8 +137,7 @@ def spiral_speed( waypoints = {} for t in np.linspace(tstart, tend, steps + 1)[1:]: radius = start_radius + (end_radius - start_radius) * ((t - tstart) / (tend - tstart)) - # Either full rotation around the circle or max angular velocity with 100cm/s linear - # velocity hard-coded as drone limit + # Whichever is slower: the requested sweep, or the 100 cm/s linear velocity drone limit. rot_rate = min(100 / radius, np.deg2rad(degrees) / (tend - tstart)) angles += rot_rate * dt des_pos = np.array( @@ -161,16 +155,12 @@ def zig_zag( tend: float, limits: dict[str, NDArray], ) -> tuple[NDArray, dict[float, dict[int, NDArray]]]: - """Moves drones in a zigzag pattern. + """Move drones in a zigzag pattern, with ``params`` of ``[steps, delta, delta_h]``. - Params: - params: [steps, delta, delta_h] - steps: Number of steps (an integer). - delta: Horizontal displacement per step (an integer). - delta_h: Vertical displacement per step (an integer). + ``delta`` is the horizontal displacement per step and ``delta_h`` the vertical one. """ steps, delta, delta_h = params - delta = abs(delta) # Ensure delta is positive for displacement + delta = abs(delta) delta_xy = np.abs(np.array([delta, delta, 0])) delta_z = np.array([0, 0, delta_h]) @@ -181,7 +171,7 @@ def zig_zag( pos = _form_grid(swarm_pos, limits=limits) waypoints[t] = {i: p.copy() for i, p in enumerate(pos)} continue - displacement_factor = (-1) ** i # Alternates between 1 and -1 + displacement_factor = (-1) ** i pos += displacement_factor * delta_xy + delta_z waypoints[t] = {i: p.copy() for i, p in enumerate(pos)} @@ -195,22 +185,18 @@ def helix( tend: float, limits: dict[str, NDArray], ) -> tuple[NDArray, dict[float, dict[int, NDArray]]]: - """Helix primitive. - - Drones rise up and circle around the center at the same time. - """ + """Rise the drones up while they circle around the center.""" steps, delta_h, height = params n_drones = swarm_pos.shape[0] - min_spacing = 60 # Minimum distance between drones in cm - # Calculate the circumference needed to place all drones with at least the minimum spacing + min_spacing = 60 # cm + # Chord formula: the radius whose circumference seats every drone min_spacing apart. radius = min_spacing / (2 * np.sin(np.pi / n_drones)) angles = np.linspace(0, 2 * np.pi, n_drones, endpoint=False) - # Match start positions to drones x = radius * np.cos(angles) y = radius * np.sin(angles) des_pos = np.array([x, y, [height] * n_drones]).T assignment = _assign_positions(swarm_pos, des_pos) - vmax = 100 # Maximum velocity in cm/s + vmax = 100 # cm/s rot_rate = min(vmax / radius, 2 * np.pi / (tend - tstart)) dt = (tend - tstart) / steps @@ -234,39 +220,28 @@ def wave( tend: float, limits: dict[str, NDArray], ) -> tuple[NDArray, dict[float, dict[int, NDArray]]]: - """Specific wave pattern. - - Args: - 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. - """ + """Run a standing-wave pattern over a grid, with ``params`` of ``[steps, height_cm]``.""" steps, height = params steps = int(steps) # TODO: Tune default values a = 100.0 # Rectangle length - b = 100.0 # Rectangle Width + b = 100.0 # Rectangle width c = np.pi # Speed of wave propagation - a_mu = np.array([[0.0, 0.0, 0.25]]) # Shape: (N, 3) - b_mu = np.array([[0.0, 0.0, 0.25]]) # Shape: (N, 3) - mu1_mu2 = np.array([[0.4, 0.4]]) # Shape: (N, 2) - height = max(height, 150) # Restrict to 75cm for ground effect avoidance + a_mu = np.array([[0.0, 0.0, 0.25]]) # (N, 3) + b_mu = np.array([[0.0, 0.0, 0.25]]) # (N, 3) + mu1_mu2 = np.array([[0.4, 0.4]]) # (N, 2) + height = max(height, 150) # Floored to stay out of ground effect. # Frequencies dictated by dispersion relation omega = c * np.pi * np.sqrt((mu1_mu2[:, 0] ** 2) / a**2 + (mu1_mu2[:, 1] ** 2) / b**2) - # Arrange all drones in a grid like formation grid_time = np.linspace(tstart, tend, steps + 1)[1] - # First step is to form a grid waypoints = {} swarm_pos = _form_grid(swarm_pos, limits=limits, height=height, spacing=50) waypoints[grid_time] = {i: p.copy() for i, p in enumerate(swarm_pos)} start_pos = swarm_pos.copy() for t in np.linspace(tstart, tend, steps + 1)[2:]: - # Calculate all sum terms vectorized sin_mu1 = np.sin(mu1_mu2[None, :, 0] / a * np.pi * start_pos[:, [0]]) # (n_drones, N) sin_mu2 = np.sin(mu1_mu2[None, :, 1] / b * np.pi * start_pos[:, [1]]) # (n_drones, N) sin2_term = sin_mu1 * sin_mu2 # (n_drones, N) @@ -298,7 +273,7 @@ def form_star( drones_per_circle = n_drones // 2 height = int(height) - # Calculate the circumference needed to place all drones with at least the minimum spacing + # Chord formula: the radius whose circumference seats every drone min_spacing apart. radius = min_spacing / (2 * np.sin(np.pi / drones_per_circle)) radii = [radius, radius + delta_radius] @@ -313,7 +288,7 @@ def form_star( des_pos = np.array([x, y, [height] * drones_per_circle]).T else: des_pos = np.vstack([des_pos, np.array([x, y, [height] * drones_per_circle]).T]) - # If odd number of drones, put the drone at the center + # An odd swarm leaves one drone over; it goes in the centre. if n_drones != drones_per_circle * 2: des_pos = np.vstack([des_pos, np.array([0, 0, height]).T]) @@ -334,14 +309,12 @@ def form_cone( delta_height, spacing, is_inverted, time_to_finish_s = params n_drones = swarm_pos.shape[0] - # Define limits start_height = (limits["lower"][2] if is_inverted else limits["upper"][2]) * 100 delta_height = delta_height * (1 if is_inverted else -1) drones_left = n_drones drone_increase_per_layer = 4 - # Place first drone radius = 0 z = start_height des_pos = np.array([0, 0, z]).T @@ -382,20 +355,32 @@ def twister( # LLM will output omega that is 10x to avoid decimals. TODO: Change this omega = omega / 10 max_omega = 2 - omega = min(omega, max_omega) # Restrict angular velocity + omega = min(omega, max_omega) lim_lower, lim_upper = limits["lower"], limits["upper"] - max_radius = min(np.min(lim_upper[:2] - lim_lower[:2] * 100) / 2, 400) - min_radius = 30 + min_spacing = 60 # cm + turns = 2 # Full revolutions the helix winds through + # The drones are spread evenly along the winding, so consecutive ones sit + # 2*pi*turns/(n_drones - 1) apart in angle and the innermost pair is the tightest. Size the + # inner radius from that step the way the ring primitives do, rather than from a fixed 30cm + # that only cleared the collision envelope for a handful of drones. Capped at a half turn so + # a swarm small enough to put neighbours on opposite sides does not inflate the cone. + half_step = min(np.pi * turns / max(n_drones - 1, 1), np.pi / 2) + min_radius = min_spacing / (2 * np.sin(half_step)) + # The cone opens out to fill the arena. Small swarms lean on that taper for their spacing: + # at n_drones - 1 <= turns the angular step is a whole revolution, so every drone lands on + # one ray and only the radial step keeps them apart. Floored at the inner radius so a swarm + # too large for the arena keeps its spacing and reaches past the limits, as helix does. + arena_radius = np.min(lim_upper[:2] - lim_lower[:2]) * 100 / 2 + max_radius = max(float(arena_radius), min_radius) z_center = 100 * (lim_lower[2] + (lim_upper[2] - lim_lower[2]) / 2) max_height = min(z_center + z_spacing * n_drones / 2, lim_upper[2] * 100) min_height = max(z_center - z_spacing * n_drones / 2, lim_lower[2] * 100) - # Calculate the radius and height for each drone radius = np.linspace(min_radius, max_radius, n_drones) z = np.linspace(min_height, max_height, n_drones) - angles = np.linspace(0, 4 * np.pi, n_drones) + angles = np.linspace(0, 2 * np.pi * turns, n_drones) x = radius * np.cos(angles) y = radius * np.sin(angles) des_pos = np.array([x, y, z]).T @@ -413,7 +398,7 @@ def twister( def center( - params: tuple[list[int]], + params: tuple[str | list[int]], swarm_pos: NDArray, tstart: float, tend: float, @@ -423,8 +408,8 @@ def center( drone_ids = _sanitize_drone_ids(params[0], swarm_pos.shape[0]) n_drones = len(drone_ids) centroid = np.mean(swarm_pos, axis=0) - min_spacing = 60 # Minimum distance between drones in cm - # Calculate the circumference needed to place all drones with at least the minimum spacing + min_spacing = 60 # cm + # Chord formula: the radius whose circumference seats every drone min_spacing apart. radius = min_spacing / (2 * np.sin(np.pi / n_drones)) angles = np.linspace(0, 2 * np.pi, n_drones, endpoint=False) x = radius * np.cos(angles) @@ -439,7 +424,7 @@ def center( def form_circle( - params: tuple[list[int], int, int, float], + params: tuple[str | list[int], int, int, float], swarm_pos: NDArray, tstart: float, tend: float, @@ -450,7 +435,7 @@ def form_circle( drone_ids = _sanitize_drone_ids(drone_ids, swarm_pos.shape[0]) n_drones = len(drone_ids) z_coord = int(z_coord_cm) - min_spacing = 80 # Minimum distance between drones in cm + min_spacing = 80 # cm 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) @@ -506,7 +491,7 @@ def swap( def move_z( - params: tuple[list[int], int], + params: tuple[str | list[int], int], swarm_pos: NDArray, tstart: float, tend: float, @@ -548,11 +533,9 @@ def _form_grid( spacing: int | None = None, ) -> NDArray: """Form a grid of drones at the current position.""" - # Get the number of rows and columns n_drones = swarm_pos.shape[0] rows = int(np.sqrt(n_drones)) cols = int(np.ceil(n_drones / rows)) - # Get the spacing between the drones min_spacing = 50 spacing = min_spacing if spacing is None else max(spacing, min_spacing) x, y = np.meshgrid(np.arange(cols) * spacing, np.arange(rows) * spacing) @@ -577,9 +560,69 @@ def _form_grid( return des_pos[assignment] -def _sanitize_drone_ids(drone_ids: list[int], n_drones: int) -> list[int]: +# Comma-separated 1-indexed ids and inclusive ranges, e.g. "1-50" or "1-20,31,45-60". Also the +# `pattern` the structured-output schema puts on `drone_ids`, so the syntax the model is +# constrained to and the syntax the backend accepts are one string. `expand_drone_id_spec` +# additionally tolerates whitespace around the tokens, so a preset is not rejected over a space. +DRONE_ID_SPEC_PATTERN = r"^\d+(-\d+)?(,\d+(-\d+)?)*$" +_DRONE_ID_TOKEN_RE = re.compile(r"^(\d+)(?:-(\d+))?$") + + +def expand_drone_id_spec(spec: str) -> list[int]: + """Expand a compact drone selection into the explicit 1-indexed ids it names, in order. + + BOTH ENDPOINTS OF A RANGE ARE INCLUSIVE: ``"1-50"`` contains drone 50, so the next block starts + at 51. A drone named twice is rejected rather than flown to two targets at once. + """ + if not isinstance(spec, str): + raise LLMFormatError(f"Drone IDs must be a range string like '1-50', got {spec}") + ids: list[int] = [] + seen: set[int] = set() + for token in spec.split(","): + token = token.strip() + match = _DRONE_ID_TOKEN_RE.match(token) + if match is None: + raise LLMFormatError( + f"Drone ID selection '{spec}' is malformed at '{token}'. Write comma-separated " + "ids and inclusive ranges, e.g. '7', '1-50' or '1-20,31,45-60'" + ) + start = int(match.group(1)) + end = int(match.group(2)) if match.group(2) is not None else start + if start > end: + raise LLMFormatError( + f"Drone ID range '{token}' in '{spec}' runs backwards. Write it as '{end}-{start}'" + ) + if start < 1: + raise LLMFormatError(f"Drone IDs are 1-indexed, but '{spec}' names drone 0") + block = range(start, end + 1) + if repeated := sorted(seen.intersection(block)): + raise LLMFormatError( + f"Drone ID selection '{spec}' names drone(s) {repeated} more than once. Range " + "endpoints are inclusive, so consecutive blocks must not share one: split the " + "swarm as '1-50' then '51-100', never '1-50' then '50-100'" + ) + seen.update(block) + ids.extend(block) + return ids + + +def _sanitize_drone_ids(drone_ids: str | list[int], n_drones: int) -> list[int]: + """Resolve a 1-indexed drone selection into 0-indexed swarm indices, in selection order. + + Accepts the compact spec the LLM emits (``"1-50"``), a plain 1-indexed list, or a list holding + ``...`` for the whole swarm. Bounds on the list form are left to callers (`lighting.select`). + """ + if isinstance(drone_ids, str): + ids = expand_drone_id_spec(drone_ids) + if out_of_range := sorted({i for i in ids if i > n_drones}): + raise LLMFormatError( + f"Drone IDs {out_of_range} in '{drone_ids}' are outside the 1..{n_drones} swarm" + ) + return [i - 1 for i in ids] if not isinstance(drone_ids, list): - raise LLMFormatError(f"Drone IDs must be a list of integers, got {drone_ids}") + raise LLMFormatError( + f"Drone IDs must be a range string like '1-50' or a list of integers, got {drone_ids}" + ) if any(isinstance(i, EllipsisType) for i in drone_ids): return list(range(n_drones)) if not all(isinstance(id, int) for id in drone_ids): @@ -588,47 +631,32 @@ def _sanitize_drone_ids(drone_ids: list[int], n_drones: int) -> list[int]: def _assign_positions(pos: NDArray, des_pos: NDArray) -> NDArray: - """Assign drones to the closest desired positions. - - Returns: - The assigned IDs as a numpy array. - """ - # Get the distance matrix + """Assign drones to the closest desired positions, returning the assigned IDs.""" dist = np.linalg.norm(pos[:, None, :] - des_pos[None, :, :], axis=-1) - # Use the Hungarian algorithm to find the optimal assignment + # The Hungarian algorithm gives the globally 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 +# Effective speed cap used when scheduling formation arrivals. Held below axswarm's vel_max=1.73 +# m/s to leave the MPC headroom. 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 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]``. + """Estimate the earliest feasible arrival time, in ``[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]``. + Sizes the interval to the bottleneck drone's displacement at an effective max velocity, with + headroom for MPC smoothness. Positions are in cm. """ 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) @@ -643,26 +671,10 @@ def _formation_waypoints( 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]``. + """Schedule a formation arrival at the LLM-chosen time, clamped to physics and the interval. + + The physics floor is ``_formation_arrival_time``'s duration. Holds are emitted only when the + remaining interval exceeds ``_MPC_HORIZON_S``, i.e. when axswarm's lookahead would be empty. """ 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 @@ -670,7 +682,6 @@ def _formation_waypoints( 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)} diff --git a/swarm_gpt/core/sim.py b/swarm_gpt/core/sim.py index 74e8b07..5c9eb65 100644 --- a/swarm_gpt/core/sim.py +++ b/swarm_gpt/core/sim.py @@ -1,9 +1,7 @@ """Simulation module for swarm_gpt. -Before we deploy the choreography to the drones, we run a simulation to check if the modified paths -from AMSwarm are collision-free and can be executed. While there is no guarantee that the -trajectories work in reality, it is a good sanity check to ensure that the drones do not crash into -each other or have to perform infeasible maneuvers. +A pre-deploy sanity check that the AMSwarm paths are collision-free and flyable. It guarantees +nothing about reality, but it does catch crashes and infeasible maneuvers. """ from __future__ import annotations @@ -22,31 +20,28 @@ from crazyflow.sim.visualize import change_material, draw_line from tqdm import tqdm -from swarm_gpt.utils import MusicManager, generate_default_colors +from swarm_gpt.utils import MusicManager logger = logging.getLogger(__name__) if TYPE_CHECKING: from numpy.typing import NDArray + from swarm_gpt.core.lighting import LightingTimeline from swarm_gpt.utils import MusicManager +# Trail colour, deliberately decoupled from the lighting so a trail never reads as an LED. Alpha 0 +# is the current setting and means no trail at all; every viewer skips the geometry while it holds. +TRAIL_RGBA = np.array([0.5, 0.5, 0.5, 0.0]) + def simulate_axswarm( waypoints: dict[str, NDArray], settings: dict, gui: bool = False ) -> dict[int, NDArray]: - """Run the crazyflow simulation from waypoints. - - Args: - waypoints: The waypoints to fly to. Dictionary of drone IDs to waypoints. Each waypoint - consists of [time, x, y, z, vx, vy, vz]. - settings: Settings for the simulation and AMSwarm. - gui: Flag to render the simulation. + """Run the crazyflow simulation from waypoints, yielding progress then the sim log. - Returns: - A collection of data from the simulation. + ``waypoints`` maps drone IDs to ``[time, x, y, z, vx, vy, vz]`` entries. """ - # Set up the simulation sim = Sim( n_worlds=1, n_drones=waypoints["pos"].shape[0], @@ -62,13 +57,11 @@ def simulate_axswarm( fps = 60 sim.max_visual_geom = 100_000 - # JIT compile the simulation sim.reset() sim.state_control(np.random.random((sim.n_worlds, sim.n_drones, 13))) sim.step(sim.freq // sim.control_freq) sim.reset() - # Set up solver solver_settings = { k: v if not isinstance(v, list) else np.asarray(v) for k, v in settings["axswarm"].items() } @@ -103,14 +96,12 @@ def simulate_axswarm( "control freq {sim.control_freq} must be divisible by amswarm freq {solver_settings.freq}" ) - # Set up initial states control = np.zeros((sim.n_worlds, sim.n_drones, 13), dtype=np.float32) pos = sim.data.states.pos.at[0, ...].set(waypoints["pos"][:, 0]) sim.data = sim.data.replace(states=sim.data.states.replace(pos=pos)) pos, vel = np.asarray(sim.data.states.pos[0]), np.asarray(sim.data.states.vel[0]) - states, controls, solve_times = [], [], [] # logging variables + states, controls, solve_times = [], [], [] - # Set up colours for tracking lines rng = np.random.default_rng(0) rgbas = rng.random((sim.n_drones, 4)) rgbas[..., 3] = 1 @@ -137,14 +128,11 @@ def simulate_axswarm( control[0, :, :3] = solver_data.u_pos[:, 0] control[0, :, 3:6] = solver_data.u_vel[:, 0] - # Log inputs controls.append(control[0, :, :6].copy()) - # Run the simulation sim.state_control(control) sim.step(sim.freq // sim.control_freq) - # Store the state states.append( np.concatenate( ( @@ -157,7 +145,6 @@ def simulate_axswarm( ) ) - # Render simulation with visualizations of the planned trajectories if ((step * fps) % sim.control_freq) < fps and gui: for i in range(sim.n_drones): draw_line(sim, solver_data.u_pos[i, :], rgba=rgbas[i % len(rgbas)]) @@ -190,16 +177,42 @@ def simulate_axswarm( "solve_times": np.array(solve_times), } yield "result", sim_log, "placeholder" - # return sim_log + + +def paint_lighting( + sim: Sim, lighting: LightingTimeline, t: float, emission_gain: float = 1.0 +) -> None: + """Evaluate the lighting timeline at ``t`` and paint both LED rings, shared by both viewers. + + Each ring gets its own deck of one `evaluate` call, or ``deck="bot"`` actions are invisible. + ``emission_gain`` is kneed against the brightest channel, so it cannot clip or move a hue. + """ + wrgb = lighting.evaluate(t) + rgba = np.ones((sim.n_drones, 2, 4)) + rgba[..., :3] = np.clip((wrgb[..., 1:] + wrgb[..., :1]) / 255.0, 0.0, 1.0) + drone_ids = np.arange(sim.n_drones) + peak = rgba[..., :3].max(axis=-1) + emission = emission_gain / (1.0 + (emission_gain - 1.0) * peak) + for mat_name, deck_idx in (("led_top", 0), ("led_bot", 1)): + change_material( + sim, + mat_name=mat_name, + drone_ids=drone_ids, + rgba=rgba[:, deck_idx], + emission=emission[:, deck_idx], + ) def replay_sim_states( - sim_data: dict[str, NDArray], settings: dict, music_manager: MusicManager | None = None + sim_data: dict[str, NDArray], + settings: dict, + lighting: LightingTimeline, + music_manager: MusicManager | None = None, ) -> None: - """Replay a previously recorded Crazyflow state log in MuJoCo. + """Replay a recorded Crazyflow state log in MuJoCo as a debug viewer. - This is a debug viewer for the exact states produced by ``simulate_axswarm``. Unlike - ``simulate_spline``, it does not run another controller/physics pass. + Unlike ``simulate_spline``, this runs no further controller/physics pass. ``lighting`` drives + the LED colours per frame; the trails are `TRAIL_RGBA` and do not follow it. """ timestamps = np.asarray(sim_data["timestamps"], dtype=float) states = np.asarray(sim_data["states"], dtype=np.float32) @@ -226,8 +239,6 @@ def replay_sim_states( ) sim.max_visual_geom = 100_000 - rgbas = np.ones((sim.n_drones, 4)) - rgbas[:, :3] = generate_default_colors(sim.n_drones, limit=1.0) swarm_pos = [deque(maxlen=100) for _ in range(sim.n_drones)] def sample_state(t: float) -> NDArray: @@ -280,30 +291,16 @@ def set_state(frame: NDArray) -> None: t = float(np.clip(t_playback, timestamps[0], timestamps[-1])) frame = sample_state(t) + # Per frame, not once before the loop: lighting is a function of time. + paint_lighting(sim, lighting, t) progress.update(max(0.0, t - last_progress_time)) last_progress_time = t set_state(frame) - for j, dq in enumerate(swarm_pos): - dq.append(frame[j, 0:3]) - draw_line( - sim, np.array(dq), rgba=rgbas[j % len(rgbas)], start_size=2, end_size=5 - ) - - change_material( - sim, - mat_name="led_top", - drone_ids=np.arange(sim.n_drones), - rgba=rgbas[np.arange(sim.n_drones) % len(rgbas)], - emission=np.ones((sim.n_drones,)), - ) - change_material( - sim, - mat_name="led_bot", - drone_ids=np.arange(sim.n_drones), - rgba=rgbas[np.arange(sim.n_drones) % len(rgbas)], - emission=np.ones((sim.n_drones,)), - ) + if TRAIL_RGBA[3] > 0.0: + for j, dq in enumerate(swarm_pos): + dq.append(frame[j, 0:3]) + draw_line(sim, np.array(dq), rgba=TRAIL_RGBA, start_size=2, end_size=5) sim.render(cam_config=default_cam_config) if t_playback >= timestamps[-1]: diff --git a/swarm_gpt/core/structured_output_schema.py b/swarm_gpt/core/structured_output_schema.py index da5fd73..28ba72f 100644 --- a/swarm_gpt/core/structured_output_schema.py +++ b/swarm_gpt/core/structured_output_schema.py @@ -1,10 +1,7 @@ """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. +Keys take the form ``"s{seq}b{bar}t{beat}"`` (``"s2b4t1"`` = segment 2, bar 4, beat 1). +``lighting`` is a second array over the same space, unconstrained: LEDs have no continuity constraint. """ from __future__ import annotations @@ -13,39 +10,57 @@ import re from typing import Any +from swarm_gpt.core.lighting import LIGHTING_PRIMITIVES, RANKED_SPREADS, load_lighting_config +from swarm_gpt.core.motion_primitives import DRONE_ID_SPEC_PATTERN, expand_drone_id_spec 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+)$") +# Each list is exactly what the engine resolves: offering a name `lighting.select` or +# `spread_offsets` would reject turns a schema-valid emission into a `KeyError` at compile time. +_DECK_ENUM = ["top", "bot", "both"] +_SELECTOR_KINDS = ["all", "ids", "even", "odd", "first", "left", "right", "upper", "lower"] +_SPREAD_ENUM = [ + "none", + "neighbour", + "index", + "alternate_parity", + "alternate_side", + "radius", + "x", + "y", + "z", +] +# `chase` is the one primitive whose parameters interact: `group_size` buckets a ranking, so +# `spread_offsets` rejects it above 1 under any spread that does not rank the drones. Splitting the +# primitive into two variants keeps that pairing out of the emissions, rather than letting a +# schema-valid one fail at compile time and burn a reprompt. +_UNRANKED_SPREAD_ENUM = [kind for kind in _SPREAD_ENUM if kind not in RANKED_SPREADS] +_CHASE_VARIANTS = ( + {"spread": {"type": "string", "enum": list(RANKED_SPREADS)}}, + { + "spread": {"type": "string", "enum": _UNRANKED_SPREAD_ENUM}, + "group_size": {"type": "integer", "enum": [1]}, + }, +) + +# `by` is the one parameter name two primitives disagree on: `gradient` interpolates along a +# spatial axis, `alternate_blink` splits the swarm two ways. Resolved per primitive. +_LIGHTING_BY_ENUM = { + "gradient": ["index", "x", "y", "z", "radius"], + "alternate_blink": ["parity", "side"], +} -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}"``. - """ +def encode_key(seq: int, bar: int, beat: int) -> str: + """Encode a 1-indexed ``(segment, bar, beat)`` address as ``"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. - """ + """Decode an ``sbt`` key into 1-indexed ``(seq, bar, beat)`` integers.""" m = _KEY_RE.match(key) if m is None: raise LLMFormatError(f"Choreography key {key!r} is not in the form 'sbt'") @@ -65,8 +80,12 @@ def _number_schema() -> dict[str, Any]: return {"type": "number"} -def _drone_ids_schema(num_drones: int) -> dict[str, Any]: - return {"type": "array", "minItems": 1, "items": _int_schema(minimum=1, maximum=num_drones)} +def _drone_ids_schema() -> dict[str, Any]: + # A compact 1-indexed spec ("1-50", "1-20,31,45-60") rather than an explicit array: at 100 + # drones the array form costs several hundred tokens per call. The pattern pins the syntax, + # which is all JSON Schema can express here -- the swarm bound and the no-overlap rule need + # the expansion, and `_sanitize_drone_ids` raises a reprompt-able LLMFormatError for both. + return {"type": "string", "pattern": DRONE_ID_SPEC_PATTERN} def _array_schema(item_schema: dict[str, Any]) -> dict[str, Any]: @@ -81,7 +100,7 @@ def _param_schemas(num_drones: int) -> dict[str, dict[str, Any]]: "drone_id": _int_schema(minimum=1, maximum=num_drones), "angle_deg": _number_schema(), "axis": {"type": "string", "enum": _AXIS_ENUM}, - "drone_ids": _drone_ids_schema(num_drones), + "drone_ids": _drone_ids_schema(), "drone_id_1": _int_schema(minimum=1, maximum=num_drones), "drone_id_2": _int_schema(minimum=1, maximum=num_drones), "delta_cm": _number_schema(), @@ -135,6 +154,104 @@ def _action_schema(num_drones: int) -> dict[str, Any]: } +def _selector_schema(num_drones: int) -> dict[str, Any]: + # Strict mode cannot express a variant by omission -- every declared property is required -- so + # a selector carries all three fields and the unused ones are ignored: `ids` is read only when + # `kind` is "ids", `count` only when it is "first". `ids` therefore takes no `minItems`, + # so an empty list is the natural filler everywhere else. + return { + "type": "object", + "additionalProperties": False, + "properties": { + "kind": {"type": "string", "enum": _SELECTOR_KINDS}, + "ids": _array_schema(_int_schema(minimum=1, maximum=num_drones)), + "count": _int_schema(minimum=1, maximum=num_drones), + }, + "required": ["kind", "ids", "count"], + } + + +def _lighting_param_schemas(num_drones: int) -> dict[str, dict[str, Any]]: + # The colour vocabulary is exactly the shipped palette, so an invented colour cannot be + # expressed. Resolved here rather than at import: this module is imported widely, and + # reading `lighting.toml` from disk at import time turns a malformed calibration file into an + # import error, surfacing far from its cause and taking every importer down with it. + palette = list(load_lighting_config().palette) + return { + "sel": _selector_schema(num_drones), + "deck": {"type": "string", "enum": _DECK_ENUM}, + "color": {"type": "string", "enum": palette}, + "color_a": {"type": "string", "enum": palette}, + "color_b": {"type": "string", "enum": palette}, + # Periods are in beats, not seconds; `build_look` converts them via the song BPM. + "period_beats": {"type": "number", "exclusiveMinimum": 0}, + # Like `period_beats`, in beats rather than seconds; `_fade` converts it with the song BPM. + "duration_beats": {"type": "number", "exclusiveMinimum": 0}, + "duty": {"type": "number", "exclusiveMinimum": 0, "maximum": 1}, + "length": _int_schema(minimum=1, maximum=num_drones), + "group_size": _int_schema(minimum=1, maximum=num_drones), + "spread": {"type": "string", "enum": _SPREAD_ENUM}, + "axis": {"type": "string", "enum": _AXIS_ENUM}, + } + + +def _lighting_params_schema( + primitive: str, num_drones: int, overrides: dict[str, Any] +) -> dict[str, Any]: + param_names = _LIGHTING_PRIMITIVE_ARG_ORDER[primitive] + param_schemas = _lighting_param_schemas(num_drones) + if "by" in param_names: + param_schemas["by"] = {"type": "string", "enum": _LIGHTING_BY_ENUM[primitive]} + param_schemas.update(overrides) + return { + "type": "object", + "additionalProperties": False, + "properties": {name: param_schemas[name] for name in param_names}, + "required": param_names, + } + + +def _lighting_action_variant_schema( + primitive: str, num_drones: int, overrides: dict[str, Any] +) -> dict[str, Any]: + return { + "type": "object", + "additionalProperties": False, + "properties": { + "primitive": {"type": "string", "enum": [primitive]}, + "params": _lighting_params_schema(primitive, num_drones, overrides), + }, + "required": ["primitive", "params"], + } + + +def _lighting_action_schema(num_drones: int) -> dict[str, Any]: + # Enumerated from the engine's own table so the schema cannot drift from it: a primitive added + # to `LIGHTING_PRIMITIVES` without an entry here fails loudly at schema-build time. + return { + "anyOf": [ + _lighting_action_variant_schema(primitive, num_drones, overrides) + for primitive in LIGHTING_PRIMITIVES + for overrides in (_CHASE_VARIANTS if primitive == "chase" else ({},)) + ] + } + + +def _key_track_schema(encoded_all: list[str], action_list_ref: str) -> dict[str, Any]: + return { + "type": "array", + "items": { + "type": "object", + "additionalProperties": False, + "properties": { + "key": {"type": "string", "enum": encoded_all}, + "actions": {"$ref": action_list_ref}, + }, + "required": ["key", "actions"], + }, + } + + def build_motion_primitive_response_schema( *, all_keys: list[tuple[int, int, int]], @@ -143,22 +260,8 @@ def build_motion_primitive_response_schema( ) -> dict[str, Any]: """Build a strict response schema keyed by hierarchical ``(seq, bar, beat)`` addresses. - ``choreography`` is an array of ``{"key", "actions"}`` entries; ``key`` is constrained to - an enum of every beat in ``all_keys``. OpenAI strict mode requires all object properties be - required, so per-entry both fields are required; the LLM controls sparseness by emitting - only the entries it wants. Presence of ``required_keys`` is validated downstream, not here. - - Args: - all_keys: Every addressable ``(seq, bar, beat)`` tuple in the song, in time order. - required_keys: Subset of ``all_keys`` that the LLM must emit (segment openings). - num_drones: Number of drones in the swarm (constrains drone-id ranges). - - Returns: - A JSON-Schema dict suitable for OpenAI Responses API ``response_format``. - - Raises: - ValueError: If ``all_keys`` or ``num_drones`` is empty / non-positive, or if any - entry in ``required_keys`` is missing from ``all_keys``. + Strict mode requires every property, so sparseness comes from which entries the LLM emits and + ``required_keys`` is validated downstream. It does not constrain ``lighting`` at all. """ if not all_keys: raise ValueError("all_keys must contain at least one (seq, bar, beat) entry") @@ -176,23 +279,21 @@ def build_motion_primitive_response_schema( "properties": { "song_mood": {"type": "string"}, "choreography_plan": {"type": "string"}, - "choreography": { - "type": "array", - "items": { - "type": "object", - "additionalProperties": False, - "properties": { - "key": {"type": "string", "enum": encoded_all}, - "actions": {"$ref": "#/$defs/action_list"}, - }, - "required": ["key", "actions"], - }, - }, + "choreography": _key_track_schema(encoded_all, "#/$defs/action_list"), + "lighting": _key_track_schema(encoded_all, "#/$defs/lighting_action_list"), }, - "required": ["song_mood", "choreography_plan", "choreography"], + # Strict mode requires every declared property, so `lighting` must be emitted -- but an + # empty array satisfies it, which is what keeps lighting genuinely optional. + "required": ["song_mood", "choreography_plan", "choreography", "lighting"], "$defs": { "action": _action_schema(num_drones), "action_list": {"type": "array", "minItems": 1, "items": {"$ref": "#/$defs/action"}}, + "lighting_action": _lighting_action_schema(num_drones), + "lighting_action_list": { + "type": "array", + "minItems": 1, + "items": {"$ref": "#/$defs/lighting_action"}, + }, }, } @@ -214,6 +315,24 @@ def build_motion_primitive_response_schema( "form_cone": ["delta_height_cm", "spacing_cm", "is_inverted", "time_to_finish_s"], } +# The catalogue's parameter order, which is also the rendered call's argument order: every +# lighting primitive takes `sel` first and `deck` last. +_LIGHTING_PRIMITIVE_ARG_ORDER: dict[str, list[str]] = { + "light_color": ["sel", "color", "deck"], + "gradient": ["sel", "color_a", "color_b", "by", "deck"], + "fade": ["sel", "color_a", "color_b", "duration_beats", "deck"], + "rainbow": ["sel", "period_beats", "spread", "deck"], + "light_on": ["sel", "deck"], + "light_off": ["sel", "deck"], + "pulse": ["sel", "period_beats", "deck"], + "blink": ["sel", "period_beats", "duty", "deck"], + "strobe_decay": ["sel", "period_beats", "deck"], + "chase": ["sel", "period_beats", "length", "group_size", "spread", "deck"], + "sweep": ["sel", "period_beats", "axis", "deck"], + "ripple_light": ["sel", "period_beats", "deck"], + "alternate_blink": ["sel", "period_beats", "by", "deck"], +} + def _python_literal(value: Any) -> str: if isinstance(value, bool): @@ -223,12 +342,12 @@ def _python_literal(value: Any) -> str: return json.dumps(value) -def _args_from_params(primitive: str, params: Any) -> list[Any]: +def _args_from_params(primitive: str, params: Any, arg_order: dict[str, list[str]]) -> list[Any]: if not isinstance(params, dict): raise LLMFormatError( f"Params for primitive '{primitive}' must be an object, got {type(params).__name__}" ) - ordered_arg_names = _PRIMITIVE_ARG_ORDER[primitive] + ordered_arg_names = arg_order[primitive] missing = [name for name in ordered_arg_names if name not in params] extras = [name for name in params if name not in ordered_arg_names] if missing or extras: @@ -253,9 +372,9 @@ def action_to_motion_primitive(action: dict[str, Any]) -> str: primitive = action.get("primitive") 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 + ordered_arg_names = _PRIMITIVE_ARG_ORDER[primitive] if "params" in action: - args = _args_from_params(primitive, action["params"]) + args = _args_from_params(primitive, action["params"], _PRIMITIVE_ARG_ORDER) else: args = action.get("args", []) if not isinstance(args, list): @@ -266,16 +385,17 @@ def action_to_motion_primitive(action: dict[str, Any]) -> str: f"({ordered_arg_names}), got {len(args)} args: {args}" ) if primitive in {"center", "move_z", "form_circle"}: + # Neither uniqueness nor -- for the compact spec -- the syntax itself is expressible in + # the strict schema, so both are checked on the way out. The swarm bound is not: it needs + # `num_drones`, and `_sanitize_drone_ids` raises for it when the primitive runs. drone_ids = args[0] - if not isinstance(drone_ids, list): - raise LLMFormatError( - f"Args for primitive '{primitive}' require 'drone_ids' to be a list, got " - f"{type(drone_ids).__name__}" - ) - if len(set(drone_ids)) != len(drone_ids): - raise LLMFormatError( - f"Args for primitive '{primitive}' must have unique drone_ids, got {drone_ids}" - ) + if isinstance(drone_ids, list): # Plain id lists, as saved presets carry them. + if len(set(drone_ids)) != len(drone_ids): + raise LLMFormatError( + f"Args for primitive '{primitive}' must have unique drone_ids, got {drone_ids}" + ) + else: + expand_drone_id_spec(drone_ids) try: rendered_args = ", ".join(_python_literal(arg) for arg in args) except Exception as e: @@ -283,20 +403,51 @@ 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[tuple[int, int, int], str]: - """Convert a structured OpenAI payload to a ``(seq, bar, beat)``-keyed choreography dict. +def _selector_literal(sel: Any) -> str: + """Render a ``{"kind", "ids", "count"}`` selector as the ``[kind, args]`` `select` consumes. - Args: - payload: The structured-output payload from the LLM. + A list rather than a tuple on purpose: the text form splits on the first ``(``, which a + parenthesised argument would break. ``ids`` is read only for "ids", ``count`` only for "first". + """ + if not isinstance(sel, dict): + raise LLMFormatError(f"Lighting 'sel' must be an object, got {type(sel).__name__}") + # Every field is checked rather than indexed: strict mode guarantees all three, but presets and + # hand-written payloads reach here too, and a bare KeyError escapes the reprompt path. + missing = [name for name in ("kind", "ids", "count") if name not in sel] + if missing: + raise LLMFormatError(f"Lighting 'sel' is missing {', '.join(missing)}") + kind = sel["kind"] + if kind == "ids": + args = list(sel["ids"]) + elif kind == "first": + args = [sel["count"]] + elif kind in _SELECTOR_KINDS: + args = [] + else: + raise LLMFormatError(f"Unknown lighting selector kind '{kind}' in structured output") + return repr([kind, args]) - 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.). +def action_to_lighting_primitive(action: dict[str, Any]) -> str: + """Convert one structured lighting action to ``primitive(args)`` syntax. + + The counterpart of `action_to_motion_primitive`. Arguments are rendered in the catalogue's + order -- ``sel`` first, ``deck`` last -- e.g. ``"pulse(['ids', [1, 3, 5]], 2, 'both')"``. """ + primitive = action.get("primitive") + if primitive not in _LIGHTING_PRIMITIVE_ARG_ORDER: + raise LLMFormatError(f"Unknown lighting primitive '{primitive}' in structured output") + arg_names = _LIGHTING_PRIMITIVE_ARG_ORDER[primitive] + args = _args_from_params(primitive, action.get("params"), _LIGHTING_PRIMITIVE_ARG_ORDER) + rendered_args = ", ".join( + _selector_literal(arg) if name == "sel" else _python_literal(arg) + for name, arg in zip(arg_names, args) + ) + return f"{primitive}({rendered_args})" + + +def structured_payload_to_choreography(payload: dict[str, Any]) -> dict[tuple[int, int, int], str]: + """Convert a structured payload to ``(seq, bar, beat)`` -> ``"; "``-joined primitive calls.""" choreography = payload.get("choreography", []) if not isinstance(choreography, list): raise LLMFormatError("Structured output field 'choreography' must be an array") @@ -321,5 +472,35 @@ def structured_payload_to_choreography(payload: dict[str, Any]) -> dict[tuple[in return converted +def structured_payload_to_lighting(payload: dict[str, Any]) -> dict[tuple[int, int, int], str]: + """Convert a payload's lighting track to ``(seq, bar, beat)`` -> ``"; "``-joined calls. + + A *missing* ``lighting`` key is not an error: payloads predating this feature, including this + repo's own preset fixtures, carry no key at all and must still load. + """ + lighting = payload.get("lighting", []) + if not isinstance(lighting, list): + raise LLMFormatError("Structured output field 'lighting' must be an array") + converted: dict[tuple[int, int, int], str] = {} + for entry in lighting: + if not isinstance(entry, dict): + raise LLMFormatError("Each lighting entry must be an object with 'key' and 'actions'") + key = entry.get("key") + actions = entry.get("actions") + if not isinstance(key, str): + raise LLMFormatError("Lighting entry 'key' must be a string") + addr = decode_key(key) + if addr in converted: + raise LLMFormatError(f"Duplicate lighting key {key!r}") + if not isinstance(actions, list) or len(actions) == 0: + raise LLMFormatError(f"Lighting key {key!r} must include a non-empty action list") + converted[addr] = "; ".join(action_to_lighting_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 + +# Re-exported for the lighting text parser, which zips a parsed `primitive(args)` call back into +# the named `params` dict `build_look` consumes. +LIGHTING_PRIMITIVE_ARG_ORDER = _LIGHTING_PRIMITIVE_ARG_ORDER diff --git a/swarm_gpt/data/drones.toml b/swarm_gpt/data/drones.toml index c15d409..563b449 100644 --- a/swarm_gpt/data/drones.toml +++ b/swarm_gpt/data/drones.toml @@ -1,4 +1,25 @@ -active = ["cf11", "cf12", "cf13", "cf14", "cf15", "cf21", "cf22", "cf23", "cf24", "cf25"] +active = [ + "cf11", + "cf12", + "cf13", + "cf14", + "cf15", + "cf21", + "cf22", + "cf23", + "cf24", + "cf25", + "cf31", + "cf32", + "cf33", + "cf34", + "cf35", + "cf41", + "cf42", + "cf43", + "cf44", + "cf45", +] [cf11] addr = 11 diff --git a/swarm_gpt/data/lighting.toml b/swarm_gpt/data/lighting.toml new file mode 100644 index 0000000..bd16c85 --- /dev/null +++ b/swarm_gpt/data/lighting.toml @@ -0,0 +1,49 @@ +# Lighting palette and calibration. Retuning an LED or flipping the show's orientation is a +# change to this file, never to lighting.py. + +gamma = 2.2 # Perceived-brightness exponent. UNMEASURED; tune by eye on hardware. +# Merged brightness below which the LED goes fully dark. Inert while it sits under one brightness +# step, which floors the same values anyway; it bites only once those steps get much finer. +b_min = 0.02 + +# Must match the col_freq handed to DroneSwarm, which drains one cue per deck per tick: a denser +# cue list plays back slowed and drifts out of sync with the music permanently. +col_freq = 10.0 + +hue_steps = 24 # Hue steps per `rainbow`; keeps cue dedup working. Divisible by 6. +brightness_steps = 16 # Brightness buckets, same dedup device. Lower for radio headroom. + +# [W, R, G, B] trim for generated hues only -- `rainbow` and the default wheel, never the named +# palette below. Applied after the constant-sum normalization; reversing that order divides the +# gain straight back out for any hue landing on one channel. UNMEASURED. +channel_gain = [1.0, 1.0, 1.0, 0.8] + +# World axis pointing audience-right. The audience views from +x with the show centred on x = 0, +# so their right hand points along +y. Only `left`/`right` and `alternate_side` read this; `sweep` +# and `gradient` take world axes and do not follow it. +stage_axis = "+y" + +# [W, R, G, B] in 0-255 at full brightness, on a constant channel sum so every hue reads alike. +# `white` is the only entry that drives the dedicated white LED; the hue wheel cannot reach it. +[palette] +red = [0.0, 255.00, 0.00, 0.00] # hue 0 +orange = [0.0, 170.00, 85.00, 0.00] # hue 30 +amber = [0.0, 145.71, 109.29, 0.00] # hue 45 +yellow = [0.0, 127.50, 127.50, 0.00] # hue 60 +green = [0.0, 0.00, 255.00, 0.00] # hue 120 +teal = [0.0, 0.00, 145.71, 87.43] # hue 165 +cyan = [0.0, 0.00, 127.50, 102.00] # hue 180 +azure = [0.0, 0.00, 85.00, 136.00] # hue 210 +blue = [0.0, 0.00, 0.00, 204.00] # hue 240 +indigo = [0.0, 75.00, 0.00, 144.00] # hue 265 +magenta = [0.0, 127.50, 0.00, 102.00] # hue 300 +pink = [0.0, 170.00, 0.00, 68.00] # hue 330 +white = [255.0, 0.00, 0.00, 0.00] # the white LED, not a hue +violet = [0.0, 102.00, 0.00, 122.40] # hue 280 +# gold and silver are the only entries that mix the white LED with a hue. `rainbow` cannot reach +# them and `gradient` interpolates through the W channel, which reads as a wash rather than a hue +# sweep. Both hold the same pre-gain channel sum as the pure hues, so they match them in apparent +# brightness. The mix fractions are chosen, not measured: run tools/calibrate_lighting.py before +# trusting either on hardware. UNMEASURED. +gold = [89.25, 94.71, 71.04, 0.00] # 35% white into hue 45 +silver = [198.90, 0.00, 18.70, 29.92] # 78% white into hue 210 diff --git a/swarm_gpt/data/motion_primitive_prompts.yaml b/swarm_gpt/data/motion_primitive_prompts.yaml index 2ad94b6..0a45131 100644 --- a/swarm_gpt/data/motion_primitive_prompts.yaml +++ b/swarm_gpt/data/motion_primitive_prompts.yaml @@ -88,11 +88,22 @@ user_initial: | + drone_ids — which drones a primitive covers, written as ONE compact string of 1-indexed ids + and ranges separated by commas. Never write out an explicit list of every id. + "1-{num_drones}" the whole swarm + "7" a single drone + "1-8" drones 1 through 8 + "1-4,7,10-12" several blocks in one string + BOTH RANGE ENDPOINTS ARE INCLUSIVE. "1-10" ALREADY CONTAINS drone 10, so the next block starts + at 11: split a 20-drone swarm as "1-10" and "11-20", NEVER as "1-10" and "10-20". Naming the + same drone twice — within one string, or across two actions at the same key — is rejected, + because a drone cannot fly to two targets at once. Every id must be in 1..{num_drones}. + 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; 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) + - center(drone_ids) — listed drones to formation around their centroid + - move_z(drone_ids, delta_cm) — vertical shift; aim for |delta_cm| ~{move_z_typical_cm} so the motion is visibly noticeable + - form_circle(drone_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. @@ -116,10 +127,98 @@ user_initial: | - wave(steps, height_cm) — 2D standing-wave surface; drones first form a grid then oscillate. + + The LEDs are a SECOND track, independent of motion. Lighting actions go in the "lighting" array + under the same sbt keys. + + Each lighting key defines a COMPLETE look that holds until the next lighting key REPLACES it + outright. It is not a delta, so a colour you want to keep must be restated. + + Three things the names do not tell you: + 1. period_beats is measured in BEATS, not seconds. 1 is one beat, 4 is one bar, 0.5 is a + half-beat flicker; the tempo is applied for you, so effects stay locked to the music. + 2. Lighting keys are NOT required keys and carry no ordering or alternation constraint. Put a + lighting entry on any address, with or without a choreography action there, and leave the + array empty if the song wants no lighting at all. + 3. light_on DOMINATES every other brightness effect on the same drones: light_on(all) over + pulse(all) is solid, and the pulse underneath is silently swallowed. Use it to override + deliberately, never as a "make sure they are lit" safety net — drones with no brightness + effect on them are already fully on. + + sel — which drones an action covers: + all every drone + ids an explicit 1-indexed list of drone ids + even / odd by drone index parity + first the first N drones by index + left / right by position about the swarm centre, from the audience's point of view + upper / lower by height: the drones above the swarm's mean altitude, and the rest. Needs a + formation with vertical extent — a flat ring has no upper half — so reach for + it after a helix, a cone, stacked rings, or two formations at different heights. + + deck — which of the two LED rings ON EACH DRONE the action drives: top, bot, or both. This is a + different axis from upper/lower: upper/lower picks which DRONES, deck picks which ring on them. + Driving the two rings differently makes a single drone read as two-tone, which is an effect + worth using: light_color(all, red, top) with light_color(all, blue, bot) glows red upward and + blue downward on every drone at once. + + colors (exactly these, no others): red, orange, amber, yellow, green, teal, cyan, azure, blue, + indigo, violet, magenta, pink, white, gold, silver + gold and silver are white-mixed, so they read as metallic rather than as a hue; rainbow never + produces them and a gradient into one washes out rather than sweeping. + + Colour primitives — which hue each drone carries: + - light_color(sel, color, deck) — one palette colour across the subset. + - gradient(sel, color_a, color_b, by, deck) — blend two palette colours across the subset; + by is index, x, y, z or radius. Static in time. + - fade(sel, color_a, color_b, duration_beats, deck) — cross-fade the subset from color_a to + color_b over duration_beats, then hold color_b. The time counterpart of gradient: uniform + across the swarm, moving in time. Use it for a colour that arrives with a phrase rather than + snapping on the beat. + - rainbow(sel, period_beats, spread, deck) — spectrum cycle through the whole wheel. spread + none cycles the group in sync, neighbour runs the rainbow around the formation in spatial + order, index runs it along drone id order instead, x/y/z sweeps it across the stage, radius + ripples it out from the centre, alternate_parity or alternate_side splits the group into two + opposed halves. Prefer neighbour over index: drone ids are handed out by whichever hand-off + is cheapest to fly and end up scattered around the stage. + + Brightness primitives — how bright each drone is over time: + - light_on(sel, deck) — force full on; dominates everything else, see above. + - light_off(sel, deck) — force dark; beats every brightness effect on those drones. + - pulse(sel, period_beats, deck) — the whole group breathes together, smoothly. + - blink(sel, period_beats, duty, deck) — hard on/off. duty is the lit fraction of each period: + 0.5 is even, 0.1 is a short stab. + - strobe_decay(sel, period_beats, deck) — flash on the beat and decay out. The most musical + accent there is; period_beats 1 locks it to the pulse of a driving section. + - chase(sel, period_beats, length, group_size, spread, deck) — a running light. length is how + many drones are lit at once; group_size advances the pattern in blocks of that many drones + instead of one at a time, and needs spread neighbour or index — those are the two that rank + the drones, so leave group_size at 1 with any other spread. spread is the same vocabulary + rainbow takes and sets what the light runs along: use neighbour, which walks the formation + itself, unless you specifically want drone id order. + - sweep(sel, period_beats, axis, deck) — a directional sweep across the stage; axis x, y or z. + The drones must be spread out along that axis or there is no sweep, just a flash: a flat + formation has no extent in z, so axis z only travels after a helix, a cone or stacked rings. + - ripple_light(sel, period_beats, deck) — a wave travelling out from the swarm centre. It runs + on distance from that centre, so it needs drones at different radius: on a plain circle they + are all the same distance out and the ripple collapses into one synchronised flash. + - alternate_blink(sel, period_beats, by, deck) — two halves ping-pong in antiphase; by is + parity for even-vs-odd or side for stage-left-vs-right. + + Colour and brightness MULTIPLY, so stack them freely: rainbow under pulse is a breathing + spectrum, chase under light_color is a running light in one hue. Where two brightness effects + overlap, the brighter one wins at every instant, so a dim accent under a bright base is + invisible — write accents at full strength. + + Drones with no lighting action at all are fully lit, each carrying its own distinct hue, so an + empty lighting track already looks like a normal show. Light the moments the music asks for + rather than every key. + + 1) song_mood — brief mood for "{song}". 2) choreography_plan — sections, primitives chosen, and intensity rationale per segment. 3) choreography — sparse map of action keys to action lists. + 4) lighting — sparse map of lighting keys to the look each one puts on stage. example: | @@ -135,7 +234,7 @@ example: | sweeps through a helix at the start of bar 2. choreography: s1b1t1: spiral(3, 100) - s2b1t1: form_circle([1, 2, 3], 120, 100, 1.5) + s2b1t1: form_circle('1-3', 120, 100, 1.5) s2b1t3: rotate(60, 'z') s2b2t1: helix(2, 40, 90) END @@ -165,11 +264,13 @@ output_format_structured: | 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}} + {"primitive":"form_circle","params":{"drone_ids":"1-5","radius_cm":100,"z_coord_cm":80,"time_to_finish_s":2.0}}, + {"primitive":"form_circle","params":{"drone_ids":"6-10","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. + The subsets MUST be disjoint — range endpoints are inclusive, so "1-5" is followed by + "6-10", never "5-10". They MUST also 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}}, @@ -177,17 +278,48 @@ output_format_structured: | ] The system suppresses form_* hold waypoints automatically — no extra timing needed. Do NOT stack two formations on overlapping drones. + - "lighting" is a SEPARATE array of entries of the same shape: + {"key":"sbt","actions":[...]}. It has NO required keys — return [] for a show + with no lighting, or as few entries as the music needs. A lighting key may share an address + with a choreography key or stand alone, and lighting entries need not be dense. + - Each lighting action is {"primitive":"name","params":{...}} carrying EVERY parameter of that + primitive. "sel" is always an object with all three fields present: + {"kind":"all|ids|even|odd|first|left|right|upper|lower","ids":[...],"count":N}. "ids" is read + only when kind is "ids" and "count" only when kind is "first"; fill the unused ones with [] + and 1. + - "drone_ids" is a STRING, not an array: one compact spec of 1-indexed ids and inclusive + ranges, e.g. "1-8", "7" or "1-4,7,10-12". Endpoints are inclusive, so consecutive blocks + must not share one ("1-10" then "11-20"), and no drone may be named twice. 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}} + - center(drone_ids) -> {"primitive":"center","params":{"drone_ids":"1-4"}} + - move_z(drone_ids, delta_cm) -> {"primitive":"move_z","params":{"drone_ids":"1-4","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,"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) -> {"primitive":"wave","params":{"steps":2,"height_cm":80}} + Correct lighting JSON examples: + - every drone blue on both rings -> + {"primitive":"light_color","params":{"sel":{"kind":"all","ids":[],"count":1},"color":"blue","deck":"both"}} + - drones 1, 3 and 5 breathing once every 2 beats -> + {"primitive":"pulse","params":{"sel":{"kind":"ids","ids":[1,3,5],"count":1},"period_beats":2,"deck":"both"}} + - a running light across the first 6 drones, 2 lit at a time -> + {"primitive":"chase","params":{"sel":{"kind":"first","ids":[],"count":6},"period_beats":4,"length":2,"group_size":1,"spread":"neighbour","deck":"both"}} + - stage left and right flashing against each other every beat -> + {"primitive":"alternate_blink","params":{"sel":{"kind":"all","ids":[],"count":1},"period_beats":1,"by":"side","deck":"both"}} + - a whole look at one key — a travelling spectrum with a flash on every beat -> + {"key":"s2b1t1","actions":[{"primitive":"rainbow","params":{"sel":{"kind":"all","ids":[],"count":1},"period_beats":8,"spread":"neighbour","deck":"both"}},{"primitive":"strobe_decay","params":{"sel":{"kind":"all","ids":[],"count":1},"period_beats":1,"deck":"both"}}]} + - the high half of a helix amber over a blue base, the two halves pulsing against each other -> + {"key":"s3b1t1","actions":[{"primitive":"light_color","params":{"sel":{"kind":"lower","ids":[],"count":1},"color":"blue","deck":"both"}},{"primitive":"light_color","params":{"sel":{"kind":"upper","ids":[],"count":1},"color":"amber","deck":"both"}},{"primitive":"alternate_blink","params":{"sel":{"kind":"all","ids":[],"count":1},"period_beats":2,"by":"parity","deck":"both"}}]} + - every drone two-tone, warm above and cool below -> + {"key":"s4b1t1","actions":[{"primitive":"light_color","params":{"sel":{"kind":"all","ids":[],"count":1},"color":"amber","deck":"top"}},{"primitive":"light_color","params":{"sel":{"kind":"all","ids":[],"count":1},"color":"azure","deck":"bot"}}]} + Common mistakes that will fail syntax checks: - - Wrong: {"primitive":"form_cone","params":{"drone_ids":[1,2,3],...}} because form_cone has no drone_ids param. + - Wrong: {"primitive":"form_cone","params":{"drone_ids":"1-3",...}} because form_cone has no drone_ids param. + - Wrong: {"drone_ids":[1,2,3]} because drone_ids is the string "1-3", never an array. + - Wrong: two actions at one key with "drone_ids":"1-10" and "drone_ids":"10-20" because + endpoints are inclusive and drone 10 is named twice; write "1-10" and "11-20". - 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/Fearless2 | 10 | 20260805_150000/history.json b/swarm_gpt/data/presets/Fearless2 | 10 | 20260805_150000/history.json new file mode 100644 index 0000000..5595312 --- /dev/null +++ b/swarm_gpt/data/presets/Fearless2 | 10 | 20260805_150000/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\nlighting:\n s1b1t1: rainbow(['all', []], 8, 'neighbour', 'both'); pulse(['all', []], 1, 'both')\n s1b5t1: light_color(['all', []], 'amber', 'both'); chase(['all', []], 4, 3, 1, 'neighbour', 'both')\n s2b1t1: light_color(['all', []], 'white', 'top'); light_color(['all', []], 'magenta', 'bot'); strobe_decay(['all', []], 1, 'both')\n s2b5t1: gradient(['all', []], 'blue', 'pink', 'z', 'both'); alternate_blink(['all', []], 2, 'side', 'both')\n s3b1t1: rainbow(['all', []], 4, 'radius', 'both'); ripple_light(['all', []], 2, 'both')\n END"}] \ No newline at end of file diff --git a/swarm_gpt/data/presets/Fearless2 | 10 | 20260805_150000/meta.json b/swarm_gpt/data/presets/Fearless2 | 10 | 20260805_150000/meta.json new file mode 100644 index 0000000..2f0965b --- /dev/null +++ b/swarm_gpt/data/presets/Fearless2 | 10 | 20260805_150000/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/On & On | 10 | 20260806_173602/history.json b/swarm_gpt/data/presets/On & On | 10 | 20260806_173602/history.json new file mode 100644 index 0000000..1fe7a30 --- /dev/null +++ b/swarm_gpt/data/presets/On & On | 10 | 20260806_173602/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: \"chorus\" (0.00s - 3.44s) \u2014 1 bars \u00d7 4 beats\n segment 2: \"chorus\" (3.44s - 30.00s) \u2014 10 bars \u00d7 4 beats\nTotal addressable beats: 43.\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: 0.92 / 0.82\ns2b1t1: 0.95 / 0.69\ns2b3t1: 0.86 / 0.74\ns2b5t1: 0.95 / 0.68\ns2b7t1: 0.63 / 0.70\ns2b9t1: 0.74 / 0.84\n\n\n\ncount: 10\nstart_cm: [[150, -100, 100], [75, -100, 100], [0, -100, 100], [-75, -100, 100], [-150, -100, 100], [150, 100, 100], [75, 100, 100], [0, 100, 100], [-75, 100, 100], [-150, 100, 100]]\nbounds_cm: lower=[-200, -200, 25] upper=[200, 200, 170]\n\n\n\n- REQUIRED keys (must emit one action list at each): s1b1t1, s2b1t1, s2b5t1, s2b9t1\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\nThe LEDs are a SECOND track, independent of motion. Lighting actions go in the \"lighting\" array\nunder the same sbt keys.\n\nEach lighting key defines a COMPLETE look that holds until the next lighting key REPLACES it\noutright. It is not a delta, so a colour you want to keep must be restated.\n\nThree things the names do not tell you:\n1. period_beats is measured in BEATS, not seconds. 1 is one beat, 4 is one bar, 0.5 is a\n half-beat flicker; the tempo is applied for you, so effects stay locked to the music.\n2. Lighting keys are NOT required keys and carry no ordering or alternation constraint. Put a\n lighting entry on any address, with or without a choreography action there, and leave the\n array empty if the song wants no lighting at all.\n3. light_on DOMINATES every other brightness effect on the same drones: light_on(all) over\n pulse(all) is solid, and the pulse underneath is silently swallowed. Use it to override\n deliberately, never as a \"make sure they are lit\" safety net \u2014 drones with no brightness\n effect on them are already fully on.\n\nsel \u2014 which drones an action covers:\n all every drone\n ids an explicit 1-indexed list of drone ids\n even / odd by drone index parity\n first the first N drones by index\n left / right by position about the swarm centre, from the audience's point of view\n\ndeck \u2014 which LED ring the action drives: top, bot, or both. Use both unless you specifically\nwant the two rings to differ.\n\ncolors (exactly these, no others): red, orange, amber, yellow, green, teal, cyan, azure, blue,\nindigo, magenta, pink, white\n\nColour primitives \u2014 which hue each drone carries:\n- light_color(sel, color, deck) \u2014 one palette colour across the subset.\n- gradient(sel, color_a, color_b, by, deck) \u2014 blend two palette colours across the subset;\n by is index, x, y, z or radius. Static in time.\n- rainbow(sel, period_beats, spread, deck) \u2014 spectrum cycle through the whole wheel. spread\n none cycles the group in sync, neighbour runs the rainbow around the formation in spatial\n order, index runs it along drone id order instead, x/y/z sweeps it across the stage, radius\n ripples it out from the centre, alternate_parity or alternate_side splits the group into two\n opposed halves. Prefer neighbour over index: drone ids are handed out by whichever hand-off\n is cheapest to fly and end up scattered around the stage.\n\nBrightness primitives \u2014 how bright each drone is over time:\n- light_on(sel, deck) \u2014 force full on; dominates everything else, see above.\n- light_off(sel, deck) \u2014 force dark; beats every brightness effect on those drones.\n- pulse(sel, period_beats, deck) \u2014 the whole group breathes together, smoothly.\n- blink(sel, period_beats, duty, deck) \u2014 hard on/off. duty is the lit fraction of each period:\n 0.5 is even, 0.1 is a short stab.\n- strobe_decay(sel, period_beats, deck) \u2014 flash on the beat and decay out. The most musical\n accent there is; period_beats 1 locks it to the pulse of a driving section.\n- chase(sel, period_beats, length, group_size, spread, deck) \u2014 a running light. length is how\n many drones are lit at once; group_size advances the pattern in blocks of that many drones\n instead of one at a time, and needs spread neighbour or index \u2014 those are the two that rank\n the drones, so leave group_size at 1 with any other spread. spread is the same vocabulary\n rainbow takes and sets what the light runs along: use neighbour, which walks the formation\n itself, unless you specifically want drone id order.\n- sweep(sel, period_beats, axis, deck) \u2014 a directional sweep across the stage; axis x, y or z.\n The drones must be spread out along that axis or there is no sweep, just a flash: a flat\n formation has no extent in z, so axis z only travels after a helix, a cone or stacked rings.\n- ripple_light(sel, period_beats, deck) \u2014 a wave travelling out from the swarm centre. It runs\n on distance from that centre, so it needs drones at different radius: on a plain circle they\n are all the same distance out and the ripple collapses into one synchronised flash.\n- alternate_blink(sel, period_beats, by, deck) \u2014 two halves ping-pong in antiphase; by is\n parity for even-vs-odd or side for stage-left-vs-right.\n\nColour and brightness MULTIPLY, so stack them freely: rainbow under pulse is a breathing\nspectrum, chase under light_color is a running light in one hue. Where two brightness effects\noverlap, the brighter one wins at every instant, so a dim accent under a bright base is\ninvisible \u2014 write accents at full strength.\n\nDrones with no lighting action at all are fully lit, each carrying its own distinct hue, so an\nempty lighting track already looks like a normal show. Light the moments the music asks for\nrather than every key.\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.\n4) lighting \u2014 sparse map of lighting keys to the look each one puts on stage.\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.\n- \"lighting\" is a SEPARATE array of entries of the same shape:\n {\"key\":\"sbt\",\"actions\":[...]}. It has NO required keys \u2014 return [] for a show\n with no lighting, or as few entries as the music needs. A lighting key may share an address\n with a choreography key or stand alone, and lighting entries need not be dense.\n- Each lighting action is {\"primitive\":\"name\",\"params\":{...}} carrying EVERY parameter of that\n primitive. \"sel\" is always an object with all three fields present:\n {\"kind\":\"all|ids|even|odd|first|left|right\",\"ids\":[...],\"count\":N}. \"ids\" is read only when\n kind is \"ids\" and \"count\" only when kind is \"first\"; fill the unused ones with [] and 1.\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\nCorrect lighting JSON examples:\n- every drone blue on both rings ->\n {\"primitive\":\"light_color\",\"params\":{\"sel\":{\"kind\":\"all\",\"ids\":[],\"count\":1},\"color\":\"blue\",\"deck\":\"both\"}}\n- drones 1, 3 and 5 breathing once every 2 beats ->\n {\"primitive\":\"pulse\",\"params\":{\"sel\":{\"kind\":\"ids\",\"ids\":[1,3,5],\"count\":1},\"period_beats\":2,\"deck\":\"both\"}}\n- a running light across the first 6 drones, 2 lit at a time ->\n {\"primitive\":\"chase\",\"params\":{\"sel\":{\"kind\":\"first\",\"ids\":[],\"count\":6},\"period_beats\":4,\"length\":2,\"group_size\":1,\"spread\":\"neighbour\",\"deck\":\"both\"}}\n- stage left and right flashing against each other every beat ->\n {\"primitive\":\"alternate_blink\",\"params\":{\"sel\":{\"kind\":\"all\",\"ids\":[],\"count\":1},\"period_beats\":1,\"by\":\"side\",\"deck\":\"both\"}}\n- a whole look at one key \u2014 a travelling spectrum with a flash on every beat ->\n {\"key\":\"s2b1t1\",\"actions\":[{\"primitive\":\"rainbow\",\"params\":{\"sel\":{\"kind\":\"all\",\"ids\":[],\"count\":1},\"period_beats\":8,\"spread\":\"neighbour\",\"deck\":\"both\"}},{\"primitive\":\"strobe_decay\",\"params\":{\"sel\":{\"kind\":\"all\",\"ids\":[],\"count\":1},\"period_beats\":1,\"deck\":\"both\"}}]}\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: \"Endless, hypnotic chorus with bright treble energy\\u2014movement blooms on the big downbeats, then keeps flowing in spirals and helixes.\"\nchoreography_plan: \"Segment 1 (s1b1t1) is the chorus entry: snap into a compact circle at mid-height, then immediately start a gentle upward spiral so the swarm feels \\u201calready moving.\\u201d Segment 2 is sustained and energetic: use periodic full-swarm motion accents on the required anchors (s2b1t1, s2b5t1, s2b9t1) with fast spiral_speed/twister/helix so the downbeat moments are visibly explosive. Between anchors, keep phrasing by alternating between slower helix/spiral and brief rotate-only breathes, avoiding a rigid every-beat cadence. Lighting mirrors the same accents: strobe/decay on the downbeat anchors, then breathing rainbow/pulse through the rest of the chorus.\"\nchoreography:\n s1b1t1: form_circle([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 95, 95, 1.0); spiral_speed(3, 70, 180, 2)\n s2b1t1: spiral_speed(3, 95, 330, 3); twister(2, 18, 18)\n s2b2t2: helix(3, 55, 105)\n s2b3t1: rotate(75, 'z')\n s2b4t3: spiral(3, 85)\n s2b5t1: twister(3, 20, 22); move_z([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 72)\n s2b6t2: zig_zag(2, 55, 35)\n s2b7t4: rotate(-90, 'z')\n s2b8t2: helix(2, 45, 95)\n s2b9t1: spiral_speed(3, 110, 360, 3); move_z([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], -72)\n s2b10t3: form_star(105, 55, 85, 1.2); rotate(60, 'z')\n END\nlighting:\n s1b1t1: rainbow(['all', []], 8, 'neighbour', 'both'); pulse(['all', []], 2, 'both')\n s2b1t1: strobe_decay(['all', []], 1, 'both'); rainbow(['all', []], 4, 'alternate_parity', 'both')\n s2b3t1: pulse(['all', []], 2, 'both'); gradient(['all', []], 'cyan', 'magenta', 'radius', 'both')\n s2b5t1: strobe_decay(['all', []], 1, 'both'); alternate_blink(['all', []], 1, 'side', 'both')\n s2b7t4: rainbow(['all', []], 6, 'neighbour', 'both'); pulse(['all', []], 2, 'both')\n s2b9t1: strobe_decay(['all', []], 1, 'both'); light_color(['all', []], 'amber', 'both')\n s2b10t3: chase(['first', [10]], 4, 2, 1, 'neighbour', 'both'); pulse(['all', []], 2, 'both')\n END"}] \ No newline at end of file diff --git a/swarm_gpt/data/presets/On & On | 10 | 20260806_173602/meta.json b/swarm_gpt/data/presets/On & On | 10 | 20260806_173602/meta.json new file mode 100644 index 0000000..a6687ab --- /dev/null +++ b/swarm_gpt/data/presets/On & On | 10 | 20260806_173602/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 | 20260806_173602/trajectory.csv b/swarm_gpt/data/presets/On & On | 10 | 20260806_173602/trajectory.csv new file mode 100644 index 0000000..6bcbe47 --- /dev/null +++ b/swarm_gpt/data/presets/On & On | 10 | 20260806_173602/trajectory.csv @@ -0,0 +1,1701 @@ 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+33.980000,1.545016,-1.021299,1.038118,0.009256,-0.148387,-0.010595,0.768183,-1.024613,1.038475,0.096599,-0.119447,-0.005389,0.004141,-1.030359,1.030741,-0.118925,-0.009596,-0.003360,-0.768988,-1.029930,1.031422,-0.049602,-0.173144,0.026900,-1.534145,-1.051046,1.046065,-0.110307,0.088717,-0.036373,1.550518,1.012657,1.014228,0.148959,-0.051624,-0.002540,0.790281,1.035924,1.017362,0.163195,0.073898,-0.013056,0.002706,1.034088,1.029811,0.075161,0.105656,-0.009158,-0.828691,1.090335,1.031662,-0.071562,0.130444,0.056461,-1.567525,1.021996,1.020537,-0.160256,0.019348,0.008501 diff --git a/swarm_gpt/data/presets/On & On | 10 | 20260806_173602/waypoints.npy b/swarm_gpt/data/presets/On & On | 10 | 20260806_173602/waypoints.npy new file mode 100644 index 0000000..6339060 Binary files /dev/null and b/swarm_gpt/data/presets/On & On | 10 | 20260806_173602/waypoints.npy differ diff --git a/swarm_gpt/data/settings.yaml b/swarm_gpt/data/settings.yaml index 2d5c2c9..379ef79 100644 --- a/swarm_gpt/data/settings.yaml +++ b/swarm_gpt/data/settings.yaml @@ -55,7 +55,8 @@ axswarm: input_smoothness_weight: 10.0 # Weight of the input smoothness objective in the cost input_continuity_weight: 1.0 # Weight of the input continuity objective in the cost - # Limits + # Limits, sized for the lab volume. The primitives scale their formation radius as + # min_spacing / (2 sin(pi/n)), so a 100-drone sim swarm needs roughly [-10, -10] / [10, 10, 6]. 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 diff --git a/swarm_gpt/launch.py b/swarm_gpt/launch.py index 155ef7e..ee94705 100644 --- a/swarm_gpt/launch.py +++ b/swarm_gpt/launch.py @@ -28,7 +28,7 @@ def main( ): """Launch the SwarmGPT browser app API.""" logging.basicConfig(level=logging.WARNING) - logging.getLogger("httpx").setLevel(logging.WARNING) # Suppress httpx info messages + logging.getLogger("httpx").setLevel(logging.WARNING) logging.getLogger("jax").setLevel(logging.WARNING) # logging.getLogger("swarm_gpt").setLevel(logging.DEBUG) diff --git a/swarm_gpt/render.py b/swarm_gpt/render.py index 585a107..51ca643 100644 --- a/swarm_gpt/render.py +++ b/swarm_gpt/render.py @@ -2,10 +2,12 @@ from __future__ import annotations +import argparse import json import logging import math import os +import re import shutil import subprocess import sys @@ -13,6 +15,7 @@ from collections import deque from fractions import Fraction from pathlib import Path +from typing import TYPE_CHECKING from drone_models.core import load_params from drone_models.transform import motor_force2rotor_vel @@ -32,19 +35,23 @@ import numpy as np from crazyflow.control import Control from crazyflow.sim import Physics, Sim -from crazyflow.sim.visualize import change_material, draw_line +from crazyflow.sim.visualize import draw_line from scipy.spatial.transform import Rotation from tqdm import tqdm from swarm_gpt.core import AppBackend -from swarm_gpt.utils import generate_default_colors +from swarm_gpt.core.sim import TRAIL_RGBA, paint_lighting + +if TYPE_CHECKING: + from scipy.interpolate import BSpline ROOT = Path(__file__).resolve().parents[1] 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. -PRESET_PATH = ROOT / "swarm_gpt/data/presets/The Blue Danube - Op. 314 | 20 | 20260601_004157" +PRESET_DIR = ROOT / "swarm_gpt/data/presets" +PRESET_PATH = PRESET_DIR / "The Blue Danube - Op. 314 | 20 | 20260601_004157" OUTPUT_PATH = ROOT / "renders/the_blue_danube.mp4" RENDER_MODE = "rgb_array" @@ -53,14 +60,33 @@ CAMERA_MOVE_START_TIME = 0.0 CAMERA_MOVE_END_TIME = 30.0 +# The shape of the move, not its placement: the offsets from CAMERA_LOOKAT set the start and end +# azimuth and elevation and the ratio between the two distances, and the push-in that ratio +# encodes is preserved. Where the camera actually sits comes from the swarm's own extent, so one +# set of constants frames a 20-drone lab show in a 4m box and a 100-drone show in a 20m one. +# The audience views from +x, so the move stays inside that arc: it opens dead on their eyeline +# and swings 45 degrees, never past the wings. Going further is what the -y end pose used to do, +# and there the audience's right (+y) points straight into frame, so `left`/`right` lighting reads +# as depth and a stage-left/right effect looks like nothing at all. CAMERA_START_POS = np.array([6.0, 0.0, 6.0], dtype=float) -CAMERA_END_POS = np.array([0.0, -6.00, 3.00], dtype=float) +CAMERA_END_POS = np.array([4.24, -4.24, 3.00], dtype=float) CAMERA_LOOKAT = np.array([0.0, 0.0, 1.1], dtype=float) CAMERA_UP = np.array([0.0, 0.0, 1.0], dtype=float) +CAMERA_FIT_MARGIN = 1.15 # Headroom on the exact frame fit, at the move's closest approach. + +# Render-only gain on the LEDs' emission, because `lighting.toml` normalizes every hue to a +# constant channel *sum* -- right for the flown LEDs, but it leaves two-channel hues peaking at 0.5 +# of the display's range while pure red sits at 1.0. See `paint_lighting` for why it can neither +# blow out nor shift a hue. +RENDER_EMISSION_GAIN = 2.5 WIDTH = 3840 HEIGHT = 2160 FPS = 60 +# `--preview` resolution: fast enough to check a change, coarse enough not to be worth keeping. +PREVIEW_WIDTH = 1280 +PREVIEW_HEIGHT = 720 +PREVIEW_FPS = 30 TRAIL_LENGTH = 120 logger = logging.getLogger(__name__) @@ -78,8 +104,12 @@ def preset_audio_path(preset_meta: dict[str, object]) -> Path: return audio_path -def mux_audio(video_path: Path, audio_path: Path, duration: float) -> Path: - """Mux a song into an existing video, replacing the original file on success.""" +def mux_audio(video_path: Path, audio_path: Path, duration: float, audio_start: float) -> Path: + """Mux a song into an existing video, replacing the original file on success. + + ``audio_start`` is the `song_crops` window start: the trajectory is rebased to 0 while the mp3 + is not, so without the seek the render is 35 s out of sync for `Fearless2`. + """ ffmpeg = shutil.which("ffmpeg") if ffmpeg is None: raise RuntimeError("ffmpeg is required to add audio to rendered videos") @@ -100,6 +130,9 @@ def mux_audio(video_path: Path, audio_path: Path, duration: float) -> Path: "-y", "-i", str(video_path), + # Before `-i`, so ffmpeg seeks the input rather than decoding and discarding the lead-in. + "-ss", + f"{audio_start:.6f}", "-i", str(audio_path), "-map", @@ -163,8 +196,60 @@ def close(self) -> None: self._writer.close = lambda: None -def camera_position_at(t: float) -> np.ndarray: - """Orbit the camera around the look-at point and land at the configured end pose.""" +def swarm_points( + pos_splines: list[BSpline], t_end: float, samples: int = 512 +) -> tuple[np.ndarray, np.ndarray]: + """Sample where every drone is across the whole flight. + + Returns the centre of the swarm's bounding box and every sampled position as an ``(n, 3)`` + array, both in metres. + """ + if not pos_splines: + raise ValueError("At least one position spline is required to frame the camera") + times = np.linspace(0.0, t_end, samples) + pos = np.array([spline(times) for spline in pos_splines], dtype=float).reshape(-1, 3) + centre = (pos.min(axis=0) + pos.max(axis=0)) / 2 + return centre, pos + + +def camera_fit_distance( + sim: Sim, + camera_id: int, + centre: np.ndarray, + points: np.ndarray, + width: int, + height: int, + samples: int = 64, +) -> float: + """Find the move's closest approach to ``centre`` that keeps every point inside the frame. + + The frame is a rectangle, so the half-angles solve separately: a fit satisfying only ``fovy`` + throws away width. Only depth scales with ``distance``, so each point names a fit; take the max. + """ + fovy = math.radians(float(sim.mj_model.cam_fovy[camera_id])) + # Half-angles of the actual frame: vertical is `fovy / 2`, horizontal widens it by the aspect. + half_tans = (math.tan(fovy / 2) * width / height, math.tan(fovy / 2)) + offsets = np.asarray(points, dtype=float) - centre + distance = 0.0 + for t in np.linspace(CAMERA_MOVE_START_TIME, CAMERA_MOVE_END_TIME, samples): + # The same move at a fitted distance of one metre, so `span` is the depth each fitted + # metre buys at this moment -- more than one, wherever the move is not at its closest. + offset = camera_position_at(float(t), np.zeros(3), 1.0) + span = float(np.linalg.norm(offset)) + forward = -offset / span + depth = offsets @ forward + for axis, half_tan in zip(camera_basis(forward, CAMERA_UP), half_tans, strict=True): + required = (np.abs(offsets @ axis) / half_tan - depth).max() / span + distance = max(distance, float(required)) + return distance * CAMERA_FIT_MARGIN + + +def camera_position_at(t: float, centre: np.ndarray, distance: float) -> np.ndarray: + """Orbit the camera around the swarm and land at the configured end pose. + + ``distance`` is the move's closest approach to ``centre``; the configured poses rescale around + it, so the push-in they encode survives at any swarm size. + """ if CAMERA_MOVE_END_TIME <= CAMERA_MOVE_START_TIME: raise ValueError("CAMERA_MOVE_END_TIME must be larger than CAMERA_MOVE_START_TIME") alpha = (t - CAMERA_MOVE_START_TIME) / (CAMERA_MOVE_END_TIME - CAMERA_MOVE_START_TIME) @@ -183,7 +268,11 @@ def camera_position_at(t: float) -> np.ndarray: start_elevation = math.atan2(start_offset[2], np.linalg.norm(start_offset[:2])) end_elevation = math.atan2(end_offset[2], np.linalg.norm(end_offset[:2])) - radius = (1.0 - alpha) * start_radius + alpha * end_radius + # Rescale the configured distances so the move keeps its ratio and its closest approach is + # `distance`. Pinning the closest point rather than the first one means the swarm still fits + # after the push-in, which is where a move framed only at its start crops. + scale = distance / min(start_radius, end_radius) + radius = ((1.0 - alpha) * start_radius + alpha * end_radius) * scale azimuth = start_azimuth + alpha * azimuth_delta elevation = (1.0 - alpha) * start_elevation + alpha * end_elevation planar_radius = radius * math.cos(elevation) @@ -195,17 +284,15 @@ def camera_position_at(t: float) -> np.ndarray: ], dtype=float, ) - return CAMERA_LOOKAT + orbit_offset + return centre + orbit_offset -def look_at_quat(position: np.ndarray, target: np.ndarray, up_hint: np.ndarray) -> np.ndarray: - """Build a MuJoCo quaternion so the camera points at a fixed target.""" - forward = target - position - forward_norm = np.linalg.norm(forward) - if forward_norm == 0.0: - raise ValueError("Camera position and look-at point must differ") - forward /= forward_norm +def camera_basis(forward: np.ndarray, up_hint: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + """Build the right and up axes of a camera looking along ``forward``. + Held apart from `look_at_quat` so the framing fit projects onto exactly the axes the renderer + uses, fallback included; two copies would disagree about up and crop the swarm sideways. + """ up = up_hint / np.linalg.norm(up_hint) if abs(np.dot(forward, up)) > 0.99: up = np.array([0.0, 1.0, 0.0], dtype=float) @@ -213,15 +300,25 @@ def look_at_quat(position: np.ndarray, target: np.ndarray, up_hint: np.ndarray) right = np.cross(forward, up) right /= np.linalg.norm(right) true_up = np.cross(right, forward) - true_up /= np.linalg.norm(true_up) + return right, true_up / np.linalg.norm(true_up) + +def look_at_quat(position: np.ndarray, target: np.ndarray, up_hint: np.ndarray) -> np.ndarray: + """Build a MuJoCo quaternion so the camera points at a fixed target.""" + forward = target - position + forward_norm = np.linalg.norm(forward) + if forward_norm == 0.0: + raise ValueError("Camera position and look-at point must differ") + forward /= forward_norm + + right, true_up = camera_basis(forward, up_hint) rotation = np.column_stack((right, true_up, -forward)) quat_xyzw = Rotation.from_matrix(rotation).as_quat() return np.roll(quat_xyzw, 1) -def get_camera_mocap_id(sim: Sim) -> int: - """Resolve the mocap slot that drives the camera rig.""" +def get_camera_ids(sim: Sim) -> tuple[int, int]: + """Resolve the ``(mocap id, camera id)`` of the camera rig and the camera riding on it.""" body_id = mujoco.mj_name2id(sim.mj_model, mujoco.mjtObj.mjOBJ_BODY, CAMERA_BODY_NAME) if body_id < 0: raise ValueError(f"Body {CAMERA_BODY_NAME!r} not found in {SCENE_XML}") @@ -231,13 +328,13 @@ def get_camera_mocap_id(sim: Sim) -> int: camera_id = mujoco.mj_name2id(sim.mj_model, mujoco.mjtObj.mjOBJ_CAMERA, CAMERA_NAME) if camera_id < 0: raise ValueError(f"Camera {CAMERA_NAME!r} not found in {SCENE_XML}") - return mocap_id + return mocap_id, camera_id -def set_camera_pose(sim: Sim, mocap_id: int, t: float) -> None: - """Move the mocap camera rig and keep the camera aimed at the target.""" - position = camera_position_at(t) - quat_wxyz = look_at_quat(position, CAMERA_LOOKAT, CAMERA_UP) +def set_camera_pose(sim: Sim, mocap_id: int, t: float, centre: np.ndarray, distance: float) -> None: + """Move the mocap camera rig and keep the camera aimed at the swarm.""" + position = camera_position_at(t, centre, distance) + quat_wxyz = look_at_quat(position, centre, CAMERA_UP) sim.mjx_data = sim.mjx_data.replace( mocap_pos=sim.mjx_data.mocap_pos.at[0, mocap_id].set(jnp.asarray(position)), mocap_quat=sim.mjx_data.mocap_quat.at[0, mocap_id].set(jnp.asarray(quat_wxyz)), @@ -314,18 +411,26 @@ def render_preset( raise RuntimeError("No splines were generated by the simulation pipeline") sim = build_sim(backend) - mocap_id = get_camera_mocap_id(sim) + mocap_id, camera_id = get_camera_ids(sim) spline_ids = sorted(backend.splines) pos_splines = [backend.splines[i] for i in spline_ids] vel_splines = [spline.derivative() for spline in pos_splines] - rgbas = np.ones((sim.n_drones, 4), dtype=float) - rgbas[:, :3] = generate_default_colors(sim.n_drones, limit=1.0) - drone_ids = np.arange(sim.n_drones) + lighting = backend.lighting_timeline() trails = [deque(maxlen=TRAIL_LENGTH) for _ in range(sim.n_drones)] t_end = float(backend.waypoints["time"][0, -1]) if render_end_time is not None: t_end = min(t_end, float(render_end_time)) + centre, points = swarm_points(pos_splines, t_end) + camera_distance = camera_fit_distance(sim, camera_id, centre, points, width, height) + logger.info( + "Framing %d drones in %dx%d: centre (%.2f, %.2f, %.2f) m, camera %.2f m out", + sim.n_drones, + width, + height, + *centre, + camera_distance, + ) if fps <= 0: raise ValueError("fps must be positive") if sim.freq % sim.control_freq != 0: @@ -347,33 +452,20 @@ def apply_control(current_time: float) -> None: sim.state_control(controls) def render_frame(frame_time: float) -> None: - positions = np.asarray(sim.data.states.pos[0]) - for i, trail in enumerate(trails): - trail.append(positions[i]) - if len(trail) > 1: - draw_line(sim, np.array(trail), rgba=rgbas[i], start_size=2, end_size=5) - - set_camera_pose(sim, mocap_id, frame_time) + # Per frame, not once before the loop: the lighting timeline is a function of time. + paint_lighting(sim, lighting, frame_time, emission_gain=RENDER_EMISSION_GAIN) + if TRAIL_RGBA[3] > 0.0: + positions = np.asarray(sim.data.states.pos[0]) + for i, trail in enumerate(trails): + trail.append(positions[i]) + if len(trail) > 1: + draw_line(sim, np.array(trail), rgba=TRAIL_RGBA, start_size=2, end_size=5) + set_camera_pose(sim, mocap_id, frame_time, centre, camera_distance) frame = sim.render(mode=RENDER_MODE, camera=CAMERA_NAME, width=width, height=height) if frame is None: raise RuntimeError("Crazyflow returned no frame in rgb_array mode") frame_sink.append_data(frame) - change_material( - sim, - mat_name="led_top", - drone_ids=drone_ids, - rgba=rgbas[drone_ids], - emission=np.ones((sim.n_drones,)), - ) - change_material( - sim, - mat_name="led_bot", - drone_ids=drone_ids, - rgba=rgbas[drone_ids], - emission=np.ones((sim.n_drones,)), - ) - frame_sink = FrameSink(output_path, fps=fps) try: apply_control(0.0) @@ -409,30 +501,88 @@ def render_frame(frame_time: float) -> None: sim.close() if audio_path is not None: - mux_audio(frame_sink.result_path, audio_path, duration=total_frames / fps) + # Same source `normalize_playback` reads for the web player's `audioOffset`. + crop_start, _crop_end = backend.crop_window(backend.music_manager.song) + mux_audio( + frame_sink.result_path, audio_path, duration=total_frames / fps, audio_start=crop_start + ) logger.info("Saved render to %s", frame_sink.result_path) return frame_sink.result_path -def main( - preset_path: Path = PRESET_PATH, - output_path: Path = OUTPUT_PATH, - render_end_time: float | None = None, - width: int = WIDTH, - height: int = HEIGHT, - fps: int = FPS, - include_audio: bool = True, -) -> Path: - """Entrypoint for local rendering.""" +def _resolve_preset(name: str) -> Path: + """Find a preset directory by exact name, or by a case-insensitive substring matching only one. + + Preset names carry the song, drone count and timestamp, so they are long to type in full. + """ + if (exact := PRESET_DIR / name).is_dir(): + return exact + matches = sorted( + d for d in PRESET_DIR.iterdir() if d.is_dir() and name.lower() in d.name.lower() + ) + if not matches: + raise SystemExit(f"No preset matches {name!r}. Use --list to see them.") + if len(matches) > 1: + listed = "\n ".join(d.name for d in matches) + raise SystemExit(f"{name!r} matches {len(matches)} presets, pick one:\n {listed}") + return matches[0] + + +def _default_output(preset_path: Path) -> Path: + """Derive the ``renders/.mp4`` written when ``--out`` is not given.""" + slug = re.sub(r"[^a-z0-9]+", "_", preset_path.name.lower()).strip("_") + return ROOT / "renders" / f"{slug}.mp4" + + +def main(argv: list[str] | None = None) -> Path: + """Render a saved preset from the command line, returning the path written to.""" + parser = argparse.ArgumentParser(description="Render a saved preset to a video.") + parser.add_argument( + "preset", + nargs="?", + default=PRESET_PATH.name, + help="preset name, or a substring matching exactly one (default: %(default)s)", + ) + parser.add_argument( + "-o", "--out", type=Path, help="output file (default: renders/.mp4)" + ) + parser.add_argument("-s", "--seconds", type=float, help="stop after this many seconds of show") + parser.add_argument( + "--preview", + action="store_true", + help=f"quick pass at {PREVIEW_WIDTH}x{PREVIEW_HEIGHT} @ {PREVIEW_FPS}fps", + ) + parser.add_argument("--width", type=int, help=f"frame width (default: {WIDTH})") + parser.add_argument("--height", type=int, help=f"frame height (default: {HEIGHT})") + parser.add_argument("--fps", type=int, help=f"frames per second (default: {FPS})") + parser.add_argument("--no-audio", dest="audio", action="store_false", help="skip audio muxing") + parser.add_argument("--list", action="store_true", help="list available presets and exit") + args = parser.parse_args(argv) + + if args.list: + for name in sorted(d.name for d in PRESET_DIR.iterdir() if d.is_dir()): + print(name) # stdout is this flag's output, not a diagnostic + raise SystemExit(0) + + # An explicit --width/--height/--fps wins over --preview, so the two compose. + preset_path = _resolve_preset(args.preset) + defaults = ( + (PREVIEW_WIDTH, PREVIEW_HEIGHT, PREVIEW_FPS) if args.preview else (WIDTH, HEIGHT, FPS) + ) + width, height, fps = ( + args.width or defaults[0], + args.height or defaults[1], + args.fps or defaults[2], + ) return render_preset( preset_path=preset_path, - output_path=output_path, - render_end_time=render_end_time, + output_path=args.out or _default_output(preset_path), + render_end_time=args.seconds, width=width, height=height, fps=fps, - include_audio=include_audio, + include_audio=args.audio, ) diff --git a/swarm_gpt/utils/llm_providers.py b/swarm_gpt/utils/llm_providers.py index 1f93616..23e8d57 100644 --- a/swarm_gpt/utils/llm_providers.py +++ b/swarm_gpt/utils/llm_providers.py @@ -21,6 +21,14 @@ RESPONSES_MAX_OUTPUT_TOKENS: Final = 4096 RESPONSES_TEMPERATURE: Final = 0.0 +# Reasoning models reject `temperature` with a 400; effort replaces it as the creativity knob. +# Medium buys plan-space exploration on a whole-song choreography without max's latency. +REASONING_MODEL_PREFIXES: Final = ("gpt-5", "o1", "o3", "o4") +REASONING_EFFORT: Final = "medium" +# Reasoning tokens are billed against max_output_tokens, so the choreography JSON is only part of +# the budget. Too low truncates mid-think and surfaces as an empty response, not a token error. +REASONING_MAX_OUTPUT_TOKENS: Final = 32768 + LLMProvider = Literal["openai", "ollama"] PROVIDER_LABEL_OPENAI: Final = "ChatGPT / OpenAI" @@ -51,14 +59,24 @@ def default_openai_model() -> str: return DEFAULT_OPENAI_MODEL_CHOICES[0] +def responses_model_kwargs(model_id: str) -> dict[str, Any]: + """Per-model ``responses.create`` kwargs: reasoning effort, or temperature for older models.""" + if model_id.lower().startswith(REASONING_MODEL_PREFIXES): + return { + "max_output_tokens": REASONING_MAX_OUTPUT_TOKENS, + # The raw reasoning tokens are never exposed; "auto" asks for the summary of them. + "reasoning": {"effort": REASONING_EFFORT, "summary": "auto"}, + } + return {"max_output_tokens": RESPONSES_MAX_OUTPUT_TOKENS, "temperature": RESPONSES_TEMPERATURE} + + def prepare_responses_messages( messages: list[dict[str, str]], ) -> tuple[list[dict[str, str]], str | None]: """Split chat-style messages for ``responses.create``. - Ollama's ``/v1/responses`` returns empty ``output_text`` when multiple ``system`` - messages are interleaved with ``user``/``assistant`` turns. Hoist all system content - into ``instructions`` and keep only dialogue roles in ``input``. + Ollama's ``/v1/responses`` returns empty ``output_text`` when ``system`` messages interleave + with dialogue turns, so system content is hoisted into ``instructions``. """ system_parts: list[str] = [] input_messages: list[dict[str, str]] = [] diff --git a/swarm_gpt/utils/music_analyzer.py b/swarm_gpt/utils/music_analyzer.py index d88cdd0..3469f06 100644 --- a/swarm_gpt/utils/music_analyzer.py +++ b/swarm_gpt/utils/music_analyzer.py @@ -1,12 +1,7 @@ """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. +``allin1.analyze`` itself, so ``allin1`` is an optional import. """ from __future__ import annotations @@ -43,13 +38,7 @@ @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). - """ + """One beat within a bar; ``position_in_bar`` is 1 for the downbeat.""" id: int time_s: float @@ -58,13 +47,7 @@ class Beat: @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. - """ + """One bar (measure) within a segment, holding its beats in time order.""" id: int start_s: float @@ -73,15 +56,7 @@ class Bar: @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. - """ + """One functional segment of the song, labelled by all-in-one (e.g. "intro", "chorus").""" id: int label: str @@ -94,13 +69,7 @@ class Segment: 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. + ``song_sha256`` detects MP3 changes; ``source_path`` is relative to the project root. """ schema_version: int @@ -116,21 +85,10 @@ class SongStructure: def from_allin1( cls, result: Any, source_path: str, song_sha256: str, analyzer: str ) -> SongStructure: - """Build a SongStructure from an ``allin1.AnalysisResult``. + """Build a SongStructure from a duck-typed ``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. + 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. """ bars_flat = _group_beats_into_bars(list(result.beats), list(result.beat_positions)) segments_out = _drop_empty_segments( @@ -156,17 +114,7 @@ def from_allin1( @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. - """ + """Load a SongStructure from its JSON serialization.""" data = json.loads(path.read_text()) version = data["schema_version"] if version != SCHEMA_VERSION: @@ -211,27 +159,11 @@ def from_json(cls, path: Path) -> SongStructure: ) def to_json(self, path: Path) -> None: - """Serialize this SongStructure to JSON. - - Args: - path: Where to write the JSON file. Parent directory must exist. - """ + """Serialize this SongStructure to JSON; the 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. - """ + """Look up the seconds-since-song-start of a 1-indexed ``(segment, bar, beat)``.""" for segment in self.segments: if segment.id != seq: continue @@ -244,22 +176,10 @@ def time_of(self, seq: int, bar: int, beat: int) -> float: 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). + """Return the ``(seq, bar, beat)`` tuples the LLM must emit actions at, in time order. - Returns: - List of ``(seq, bar, beat)`` tuples, in time order. - - Raises: - ValueError: If ``bars_per_required`` is less than 1. + The first bar of every segment is always required and ``bars_per_required`` only thins the + bars between; unreturned beats stay addressable as optional accents. """ if bars_per_required < 1: raise ValueError(f"bars_per_required must be >= 1, got {bars_per_required}") @@ -271,11 +191,7 @@ def required_keys(self, bars_per_required: int = 1) -> list[tuple[int, int, int] ] 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 every addressable ``(seq, bar, beat)`` tuple in the song, in time order.""" return [ (segment.id, bar.id, beat.id) for segment in self.segments @@ -284,22 +200,10 @@ def all_keys(self) -> list[tuple[int, int, int]]: ] 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). + """Return a copy restricted to the song-absolute window ``[start_s, end_s]``, rebased to 0. - Returns: - A new SongStructure spanning ``[0, end_s - start_s]``. - - Raises: - ValueError: If ``end_s`` is not greater than ``start_s``. + Empty bars and segments are dropped and ids renumbered contiguously. Both consumers expect + a 0-based timeline, so this runs at load time and leaves the on-disk JSON untouched. """ if end_s <= start_s: raise ValueError(f"crop end ({end_s}) must be greater than start ({start_s})") @@ -360,18 +264,8 @@ def compute_dynamics_per_2bar( ) -> 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 reset at segment boundaries, and both features normalize by the song-wide maximum so + they are scale-invariant. ``structure`` must be pre-crop: its bars define the windows. """ windows: list[tuple[float, float]] = [] for seg in structure.segments: @@ -410,14 +304,7 @@ def compute_dynamics_per_2bar( 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. + A pure function of the bar layout, not persisted. Length matches ``structure.rms_per_2bar``. """ keys: list[str] = [] for seg in structure.segments: @@ -434,16 +321,8 @@ def _group_beats_into_bars( ) -> 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. + A new bar starts whenever ``position_in_bar`` does not strictly increase, which handles + variable meters and anacrusis implicitly. """ bars: list[list[tuple[float, int]]] = [] current: list[tuple[float, int]] = [] @@ -460,16 +339,7 @@ def _group_beats_into_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``. - """ + """Assign each flat bar to the segment containing its first beat, preserving segment order.""" segments_out: list[Segment] = [] bar_cursor = 0 for seg_id, raw_seg in enumerate(segments_in, start=1): @@ -507,15 +377,8 @@ def _assign_bars_to_segments( 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. + allin1 can emit segment boundaries no beat falls within; they carry nothing to choreograph + and would only mislead the LLM. """ kept = [seg for seg in segments if seg.bars] for new_id, seg in enumerate(kept, start=1): @@ -524,14 +387,7 @@ def _drop_empty_segments(segments: list[Segment]) -> list[Segment]: 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. - """ + """Compute the SHA-256 hex digest of a file.""" h = hashlib.sha256() with path.open("rb") as f: for chunk in iter(lambda: f.read(1 << 20), b""): @@ -542,16 +398,7 @@ def sha256_of(path: Path) -> str: 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. + Renders the same figure as ``allin1.visualize``, which only writes PDFs. """ fig = allin1.visualize(result, out_dir=None, multiprocess=False) viz_dir.mkdir(parents=True, exist_ok=True) @@ -566,18 +413,8 @@ def analyze_song( ) -> 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. + An existing ``cache_dir/.json`` is returned as-is, without re-running analysis or + regenerating the optional ``viz_dir`` visualization. """ cache_path = cache_dir / f"{mp3_path.stem}.json" if cache_path.exists(): diff --git a/swarm_gpt/utils/music_manager.py b/swarm_gpt/utils/music_manager.py index dc45efd..51fefb4 100644 --- a/swarm_gpt/utils/music_manager.py +++ b/swarm_gpt/utils/music_manager.py @@ -28,11 +28,7 @@ class MusicManager: min_beat_time: float = 2.0 # Minimum time between beats in seconds def __init__(self, music_dir: Path): - """Read in all available songs from the music directory. - - Args: - music_dir: Path to the music directory. - """ + """Read in all available songs from the music directory.""" self.music_dir = music_dir self.songs = [f.stem for f in music_dir.glob("*.mp3") if not f.stem.endswith("[deploy]")] assert not any("|" in s for s in self.songs), "Songs cannot contain |" @@ -76,11 +72,7 @@ def song(self) -> str: @song.setter def song(self, song: str): - """Set the song to choreograph. - - Args: - song: The song to choreograph. This song needs to be present in the music directory. - """ + """Set the song to choreograph; it must be present in the music directory.""" assert (self.music_dir / (song + ".mp3")).is_file(), "Song not found in music dir" self._song = song @@ -93,8 +85,7 @@ def song_length(self) -> float: def verify_libvlc(self) -> bool: """Return True if the native VLC library can be initialized. - ``import vlc`` only checks the ``python-vlc`` package; libvlc must be installed - separately (e.g. ``sudo apt install vlc`` on Linux, VLC.app on macOS). + ``import vlc`` only checks the ``python-vlc`` package; libvlc is installed separately. """ try: self._get_vlc_instance() @@ -111,16 +102,9 @@ def play( start_s: float = 0.0, end_s: float | None = None, ) -> bool: - """Play the song with VLC. + """Play the song with VLC over ``[start_s, end_s]``, returning True if it accepted. - 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. + ``wait`` blocks up to ``timeout`` seconds until VLC reports active playback. """ assert self.song, "Song not set" media_path = str(self.music_dir / (self.song + ".mp3")) @@ -197,37 +181,20 @@ def extract_song_info(self) -> dict: return music_info def dbfs(self) -> np.ndarray: - """Compute the dBFS of the song. - - Returns: - The dBFS of the song. - """ - # RMS energy + """Compute the dBFS of the song from its RMS energy.""" path = self.music_dir / (self.song + ".mp3") assert path.exists(), "Could not find the song in the music directory" wav, sr = librosa.load(path) N = max(int(0.2 * sr), 1) # 200ms window size H = max(int(0.02 * sr), 1) # 20ms hop size rms = librosa.feature.rms(y=wav, frame_length=N, hop_length=H)[0] - # Convert to dBFS return 20 * np.log10(np.abs(rms) + np.finfo(float).eps) def spectral_novelty(self, song: str) -> tuple[np.ndarray, int]: - """Compute the novelty of the song using a spectral approach. - - See https://www.audiolabs-erlangen.de/resources/MIR/FMP/C6/C6S1_OnsetDetection.html for more - information. + """Compute the song's spectral novelty, returning it with its sample rate. - Note: - This function calls the libfmp library, which uses numba to jit compile its code. This - may cause the first call to this function to take longer for each session. Subsequent - calls in the same session are faster. - - Args: - song: The song to compute the novelty for. - - Returns: - The novelty of the song and the sample rate. + See https://www.audiolabs-erlangen.de/resources/MIR/FMP/C6/C6S1_OnsetDetection.html. The + first call per session is slow: libfmp jit-compiles through numba. """ path = self.music_dir / (song + ".mp3") assert path.exists(), "Could not find the song in the music directory" @@ -244,17 +211,9 @@ def spectral_novelty(self, song: str) -> tuple[np.ndarray, int]: return nov, fs_nov def _peak_detection(self, nov: np.ndarray, fs_nov: int) -> np.ndarray: - """Find the peaks in the novelty of the song for musical onset detection. - - See https://www.audiolabs-erlangen.de/resources/MIR/FMP/C6/C6S1_PeakPicking.html for more - information. - - Args: - nov: The novelty function of the song. - fs_nov: The sample rate of the novelty function. + """Find the novelty-function peak indices for musical onset detection. - Returns: - The indices of the peaks in the novelty function. + See https://www.audiolabs-erlangen.de/resources/MIR/FMP/C6/C6S1_PeakPicking.html. """ distance = self.min_beat_time * fs_nov # minimum distance between peaks peak_idx, _ = find_peaks( @@ -263,26 +222,14 @@ def _peak_detection(self, nov: np.ndarray, fs_nov: int) -> np.ndarray: return peak_idx def chord_analysis(self, song: str, plot: bool = False) -> list[str]: - """Perform chord analysis on the song. - - See https://www.audiolabs-erlangen.de/resources/MIR/FMP/C5/C5S3_ChordRec_HMM.html - - Note: - This function calls the libfmp library, which uses numba to jit compile its code. This - may cause the first call to this function to take longer for each session. Subsequent - calls in the same session are faster. - - Args: - song: The song to perform the chord analysis on. - plot: Whether to plot the chords or not. + """Perform chord analysis on the song, optionally plotting the chromagram. - Returns: - The chords of the song. + See https://www.audiolabs-erlangen.de/resources/MIR/FMP/C5/C5S3_ChordRec_HMM.html. The + first call per session is slow: libfmp jit-compiles through numba. """ path = self.music_dir / (song + ".mp3") assert path.exists(), "Could not find the song in the music directory" wav, sr = librosa.load(path) - # Create chroma Short-Time Fourier Transform features N = max(int(0.2 * sr), 1) # 0.2 seconds H = max(int(0.02 * sr), 1) chords = librosa.feature.chroma_stft( @@ -293,7 +240,7 @@ def chord_analysis(self, song: str, plot: bool = False) -> list[str]: C = 1 / 24 * np.ones((1, 24)) chord_HMM, _, _, _ = libfmp.c5.viterbi_log_likelihood(A, C, chord_sim) chord_labels = libfmp.c5.get_chord_labels() - if plot: # Plot the chromagram + if plot: librosa.display.specshow( 10 * np.log10(chords + np.finfo(float).eps), x_axis="time", @@ -308,10 +255,8 @@ def chord_analysis(self, song: str, plot: bool = False) -> list[str]: def animate_peaks(self): """Play the song, plot its novelty peaks and animate the current time as a moving line.""" assert self.song, "Song not set" - # Detect peaks nov, fs_nov = self.spectral_novelty(self.song) peak_idx = self._peak_detection(nov, fs_nov) - # Create plot plt.ion() fig, ax = plt.subplots(1, 1, figsize=(13, 5)) t_nov = np.linspace(0, self.song_length, len(nov)) @@ -324,7 +269,6 @@ def animate_peaks(self): ax.scatter(t_nov[peak_idx], nov[peak_idx], c="r", label="Novelty peaks") ax.legend() t_bar = ax.plot([0, 0], [0, 1], c="b") - # Play the song, animate current time as blue line on the novelty plot self.play() while not self.is_playing: time.sleep(0.001) # Wait for the player to start @@ -334,5 +278,4 @@ def animate_peaks(self): t_bar[0].set_xdata([dt, dt]) fig.canvas.draw() fig.canvas.flush_events() - # Reduce loop iterations - time.sleep(0.001) + time.sleep(0.001) # Keep the redraw loop off a busy spin diff --git a/swarm_gpt/utils/utils.py b/swarm_gpt/utils/utils.py index 7f6f3c6..f12c42f 100644 --- a/swarm_gpt/utils/utils.py +++ b/swarm_gpt/utils/utils.py @@ -37,7 +37,7 @@ def generate_default_colors( """Generates a default color sequence for the given number of drones.""" colors = [] for i in range(num_drones): - hue = i / num_drones # evenly spaced in [0,1) + hue = i / num_drones rgb = colorsys.hsv_to_rgb(hue, saturation, value) rgb /= np.sum(rgb) rgb *= limit diff --git a/tests/conftest.py b/tests/conftest.py index 4913395..9788170 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -3,6 +3,18 @@ import numpy as np +def with_ulp_noise(points: np.ndarray, col: int = 0) -> np.ndarray: + """Return ``points`` with its largest ``col`` coordinate nudged up two ULPs. + + How much noise ``cos``/``sin`` leave in a ring is platform-dependent, so pin it and keep the + fixtures degenerate-but-not-exact. Nudging the largest row avoids a denormal at zero. + """ + out = np.asarray(points, dtype=float).copy() + row = int(np.argmax(np.abs(out[:, col]))) + out[row, col] = np.nextafter(np.nextafter(out[row, col], np.inf), np.inf) + return out + + def virtual_crazyswarm_config(n_drones: int) -> Path: """Create a virtual crazyswarm config file for testing.""" n_cols = int(np.ceil(np.sqrt(n_drones))) diff --git a/tests/unit/test_api.py b/tests/unit/test_api.py index edddb5a..c2c0ca1 100644 --- a/tests/unit/test_api.py +++ b/tests/unit/test_api.py @@ -14,23 +14,36 @@ from swarm_gpt.utils.llm_providers import DEFAULT_OPENAI_MODEL_CHOICES +def _lighting_stub(num_drones: int) -> dict[str, list[dict[str, list]]]: + """A stand-in for `AppBackend.browser_cues`; the real adapter is covered in test_backend.py.""" + return { + deck: [{"times": [0.0, 1.0], "rgb": [[255, 0, 0], [0, 0, 0]]} for _ in range(num_drones)] + for deck in ("top", "bot") + } + + 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), + browser_cues=lambda: _lighting_stub(3), ) states = np.zeros((2, 3, 13)) states[:, :, 3:7] = [0, 0, 0, 1] payload = normalize_playback( {"timestamps": np.array([0.0, 0.02]), "states": states, "num_drones": 3}, backend ) - assert payload["schemaVersion"] == 1 + assert payload["schemaVersion"] == 2 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"]) + # The timeline is the single colour source, so the static `colors` array is gone and the + # payload carries one cue list per deck per drone instead. + assert "colors" not in payload + assert payload["lighting"] == _lighting_stub(3) def test_normalize_playback_rejects_mismatched_states(): @@ -99,7 +112,7 @@ def __init__(self) -> None: self.stop_requested = threading.Event() self.emergency_stop_calls = 0 - def initial_prompt(self, selection: str) -> list[dict[str, str]]: + def initial_prompt(self, selection: str, **_kwargs: object) -> list[dict[str, str]]: return [] def simulate(self) -> Generator[None, None, dict[str, object]]: @@ -153,3 +166,74 @@ def backend_from_config(config: ApiConfig, provider: str, model_id: str) -> Depl assert stop_response.json() == {"jobId": job_id, "emergencyStopped": True} assert backend.emergency_stop_calls == 1 + + +def test_prompt_sent_reaches_the_socket_with_its_messages_while_the_model_thinks( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + """The details panel is driven by the event payload, not the event's arrival. + + A `prompt_sent` row with no `messages` renders an empty panel that looks identical to + having sent nothing, which is the whole failure this event exists to rule out. + """ + prompt = [{"role": "system", "content": "you choreograph"}, {"role": "user", "content": "go"}] + + class ThinkingBackend: + def __init__(self) -> None: + self.songs = ["Test Song"] + self.presets: list[str] = [] + self.settings = {"axswarm": {"pos_min": [-1, -1, 0], "pos_max": [1, 1, 2]}} + self.music_manager = SimpleNamespace(song="Test Song") + self.splines: dict[int, object] = {} + self.may_answer = threading.Event() + self.on_event: object = None + + def initial_prompt(self, selection: str, **_kw: object) -> list: + self.on_event("prompt_sent", {"messages": prompt}) + assert self.may_answer.wait(timeout=5.0), "model was released before the assertion" + return [*prompt, {"role": "assistant", "content": "done"}] + + def simulate(self) -> Generator[None, None, dict[str, object]]: + self.splines[0] = object() + states = np.zeros((1, 1, 13)) + states[:, :, 3:7] = [0, 0, 0, 1] + if False: + yield None + return {"timestamps": np.array([0.0]), "states": states, "num_drones": 1} + + def crop_window(self, song: str) -> tuple[float, float]: + return (0.0, 60.0) + + def browser_cues(self) -> dict[str, list]: + return _lighting_stub(1) + + backends: list[ThinkingBackend] = [] + + def backend_from_config(config: ApiConfig, provider: str, model_id: str) -> ThinkingBackend: + backend = ThinkingBackend() + backends.append(backend) + return backend + + (tmp_path / "Test Song.mp3").write_bytes(b"") + monkeypatch.setattr(server, "_backend_from_config", backend_from_config) + client = TestClient(create_app(ApiConfig(music_dir=tmp_path))) + + created = client.post( + "/api/jobs", json={"selection": "Test Song", "provider": "openai", "modelId": "gpt"} + ) + created.raise_for_status() + job_id = created.json()["jobId"] + + try: + with client.websocket_connect(f"/api/jobs/{job_id}/events") as socket: + seen = [] + for _ in range(20): + event = socket.receive_json() + seen.append(event) + if event["type"] == "prompt_sent": + break + assert seen[-1]["type"] == "prompt_sent", f"never arrived: {[e['type'] for e in seen]}" + assert seen[-1]["payload"]["messages"] == prompt + assert not any(event["type"] == "conversation" for event in seen) + finally: + backends[0].may_answer.set() diff --git a/tests/unit/test_backend.py b/tests/unit/test_backend.py index a4e9458..ba742ba 100644 --- a/tests/unit/test_backend.py +++ b/tests/unit/test_backend.py @@ -1,8 +1,16 @@ +import dataclasses +import json from pathlib import Path +import numpy as np +import pytest from conftest import virtual_crazyswarm_config -from swarm_gpt.core.backend import AppBackend +from swarm_gpt.core.backend import AppBackend, _fold_cues_to_rgb +from swarm_gpt.core.lighting import compile_cues, hue_to_wrgb, load_lighting_config +from swarm_gpt.exception import LLMFormatError +from swarm_gpt.utils import generate_default_colors +from swarm_gpt.utils.music_analyzer import Bar, Beat, Segment, SongStructure def test_backend_init(): @@ -67,3 +75,561 @@ def emergency_stop(self) -> None: assert swarm.calls == ["emergency_stop"] assert music_calls == ["stop"] + + +LIGHTING_CFG = load_lighting_config() +LIGHTING_N = 4 +BPM = 120 +# The flight ends 4s after the music: `response2waypoints` appends the return-to-home legs. +SONG_END_S = 16.0 +FLIGHT_END_S = SONG_END_S + 4.0 + +# A look at s1b1t1 (t = 0s): the swarm blue, blinking once a beat. +LIGHTING_RESPONSE = ( + 'song_mood: "x"\n' + "choreography:\n" + " s1b1t1: spiral(3, 100)\n" + " END\n" + "lighting:\n" + " s1b1t1: light_color(['all', []], 'blue', 'both'); blink(['all', []], 1, 0.5, 'both')\n" + " END" +) + +# A look that is **deck-asymmetric and spatially varying**: the top deck runs red -> blue along x +# and blinks, the bottom holds a steady green -> amber. Both properties are load-bearing wherever a +# test claims the payload is not permuted. `LIGHTING_RESPONSE` is uniform across drones and +# identical across decks, so against it a reversed drone order and a top/bot swap are both +# byte-identical no-ops -- the assertion passes without constraining anything. The fixture's +# splines put drone i at x = i (`_deploy_backend`), which is what makes `by="x"` vary. +DECK_ASYMMETRIC_RESPONSE = ( + 'song_mood: "x"\n' + "choreography:\n" + " s1b1t1: spiral(3, 100)\n" + " END\n" + "lighting:\n" + " s1b1t1: gradient(['all', []], 'red', 'blue', 'x', 'top'); " + "gradient(['all', []], 'green', 'amber', 'x', 'bot'); " + "blink(['all', []], 1, 0.5, 'top')\n" + " END" +) + + +def _lighting_structure() -> SongStructure: + """One segment of eight bars at 120 BPM, so s1b1t1 is t = 0s and the song ends at 16s.""" + bars = [ + Bar( + id=bar + 1, + start_s=bar * 2.0, + beats=[ + Beat(id=j + 1, time_s=bar * 2.0 + j * 0.5, position_in_bar=j + 1) for j in range(4) + ], + ) + for bar in range(8) + ] + segment = Segment(id=1, label="seg", start_s=0.0, end_s=SONG_END_S, bars=bars) + return SongStructure( + schema_version=2, + source_path="t.mp3", + song_sha256="a", + analyzer="t", + bpm=BPM, + segments=[segment], + ) + + +class _FakeSwarm: + """A `DroneSwarm` stand-in that records what `deploy` hands it.""" + + instances: list["_FakeSwarm"] = [] + + def __init__(self, drones: dict, **kwargs: object) -> None: + self.drones = drones + self.kwargs = kwargs + self.executed: dict | None = None + _FakeSwarm.instances.append(self) + + def get_obs(self, uri: str) -> dict: + pos = np.array(next(d["pos"] for d in self.drones.values() if d["uri"] == uri), float) + # Within the 0.3m pre-flight tolerance of the configured position, and high enough that + # the post-takeoff check counts the drone as airborne. + return {"pos": pos + np.array([0.0, 0.0, 0.25]), "quat": np.array([0.0, 0.0, 0.0, 1.0])} + + def is_active(self, uri: str) -> bool: + return True + + def goto(self, positions: dict, duration: float | None = None) -> None: + return None + + def execute_choreography( + self, choreography: dict, t_end: float, color_top: dict, color_bot: dict + ) -> None: + self.executed = {"t_end": t_end, "color_top": color_top, "color_bot": color_bot} + + def land(self, duration: float | None = None) -> None: + return None + + def close(self) -> None: + return None + + +def _deploy_backend(monkeypatch: pytest.MonkeyPatch, response: str) -> AppBackend: + """A backend with the simulation already run, wired to a fake swarm and a fake song.""" + app = AppBackend(config_file=virtual_crazyswarm_config(n_drones=LIGHTING_N)) + app.settings["lighthouse"] = True # Skips the rclpy import branch. + app.choreographer.messages = [{"role": "assistant", "content": response}] + app.waypoints = {"time": np.tile([0.0, FLIGHT_END_S], (LIGHTING_N, 1))} + app.splines = {i: (lambda t, i=i: np.array([float(i), 0.0, 1.0])) for i in range(LIGHTING_N)} + monkeypatch.setattr(app, "_load_structure", lambda _song: _lighting_structure()) + monkeypatch.setattr(app.music_manager, "verify_libvlc", lambda: True) + monkeypatch.setattr(app.music_manager, "play", lambda **_kwargs: True) + monkeypatch.setattr(app.music_manager, "stop", lambda: None) + monkeypatch.setattr("swarm_gpt.core.drone_swarm.DroneSwarm", _FakeSwarm) + _FakeSwarm.instances.clear() + return app + + +def test_deploy_builds_colour_cues_from_the_compiled_timeline(monkeypatch: pytest.MonkeyPatch): + """Deploy's colour dicts come from `compile_cues`, not from a two-cue stub.""" + app = _deploy_backend(monkeypatch, LIGHTING_RESPONSE) + + assert app.deploy() is True + + swarm = _FakeSwarm.instances[-1] + uris = {d["uri"] for d in app.choreographer.drones.values()} + assert swarm.executed["t_end"] == FLIGHT_END_S + for deck in ("color_top", "color_bot"): + cues = swarm.executed[deck] + assert set(cues) == uris + for track in cues.values(): + times = sorted(track) + # The stub emitted exactly two cues per deck; a blinking look cannot compile to that. + assert len(times) > 2 + # Never denser than the consumer drains. + assert min(np.diff(times)) >= 1.0 / LIGHTING_CFG.col_freq - 1e-9 + assert all(np.all(v >= 0) and np.all(v <= 255) for v in track.values()) + + +def test_deploy_passes_one_col_freq_to_the_swarm_and_the_compiler(monkeypatch: pytest.MonkeyPatch): + """The cue consumer and the cue compiler read the same config field, never a literal. + + `col_freq` is patched off the shipped 10 Hz because a hardcoded 10.0 is otherwise + indistinguishable from `cfg.col_freq`, and the two diverging is permanent desync. + """ + cfg = dataclasses.replace(LIGHTING_CFG, col_freq=4.0) + monkeypatch.setattr("swarm_gpt.core.backend.load_lighting_config", lambda: cfg) + app = _deploy_backend(monkeypatch, LIGHTING_RESPONSE) + + app.deploy() + + swarm = _FakeSwarm.instances[-1] + assert swarm.kwargs["col_freq"] == cfg.col_freq + for deck in ("color_top", "color_bot"): + for track in swarm.executed[deck].values(): + times = sorted(track) + assert len(times) > 2 + # The compiler's sample grid follows the same field: never denser than the consumer + # drains, and every cue but the terminal blackout lands on a tick of that grid. + assert min(np.diff(times)) >= 1.0 / cfg.col_freq - 1e-9 + ticks = [t * cfg.col_freq for t in times[:-1]] + assert ticks == pytest.approx([round(tick) for tick in ticks]) + + +def test_deploy_ends_every_drone_and_deck_black(monkeypatch: pytest.MonkeyPatch): + """The terminal blackout is unconditional, so the drones never land lit.""" + app = _deploy_backend(monkeypatch, LIGHTING_RESPONSE) + + app.deploy() + + swarm = _FakeSwarm.instances[-1] + for deck in ("color_top", "color_bot"): + for track in swarm.executed[deck].values(): + last = max(track) + # Exactly at the end of the flight, not the end of the music: the drones stay lit + # through the return-to-home legs, as they do today (`backend.py:332`). + assert last == pytest.approx(FLIGHT_END_S - 0.1) + assert np.allclose(track[last], 0.0) + + +def test_deploy_without_a_lighting_track_keeps_todays_static_colours( + monkeypatch: pytest.MonkeyPatch, +): + """A preset predating the feature compiles to one colour, then black.""" + response = 'song_mood: "x"\nchoreography:\n s1b1t1: spiral(3, 100)\n END' + app = _deploy_backend(monkeypatch, response) + + app.deploy() + + swarm = _FakeSwarm.instances[-1] + base = np.round(hue_to_wrgb(np.arange(LIGHTING_N) / LIGHTING_N, LIGHTING_CFG)) + for deck in ("color_top", "color_bot"): + for i, uri in enumerate(d["uri"] for d in app.choreographer.drones.values()): + track = swarm.executed[deck][uri] + times = sorted(track) + assert len(times) == 2 + assert np.allclose(track[times[0]], base[i]) + assert np.allclose(track[times[1]], 0.0) + + +def test_the_position_snapshot_is_ordered_by_drone_index(monkeypatch: pytest.MonkeyPatch): + """The snapshot's row order *is* the drone index, and this is the seam it crosses. + + Nothing position-free notices a wrong order, so this needs a position-dependent primitive: + `left`, `sweep` and friends would otherwise address the mirror image of the swarm. + """ + response = ( + 'song_mood: "x"\n' + "choreography:\n s1b1t1: spiral(3, 100)\n END\n" + "lighting:\n s1b1t1: gradient(['all', []], 'red', 'blue', 'x', 'both')\n END" + ) + app = _deploy_backend(monkeypatch, response) + + top = app.lighting_timeline().evaluate(0.0)[:, 0] + + # The fixture's splines put drone i at x = i, so the gradient runs from color_a on drone 0 to + # color_b on the last one. Reversed, the two ends swap. + assert np.allclose(top[0], np.round(LIGHTING_CFG.palette["red"])) + assert np.allclose(top[-1], np.round(LIGHTING_CFG.palette["blue"])) + + +def test_lighting_compiles_from_the_latest_response_in_the_history(monkeypatch: pytest.MonkeyPatch): + """After a self-correct round the history holds several responses, not one. + + The lights must come from the last, or the show flies a superseded response's lighting. + """ + superseded = ( + 'song_mood: "x"\n' + "choreography:\n s1b1t1: spiral(3, 100)\n END\n" + "lighting:\n s1b1t1: light_color(['all', []], 'green', 'both')\n END" + ) + app = _deploy_backend(monkeypatch, LIGHTING_RESPONSE) + app.choreographer.messages = [ + {"role": "user", "content": "choreograph this"}, + {"role": "assistant", "content": superseded}, + {"role": "user", "content": "the lighting was wrong, try again"}, + {"role": "assistant", "content": LIGHTING_RESPONSE}, + ] + + top = app.lighting_timeline().evaluate(0.0)[:, 0] + + assert np.allclose(top, np.round(LIGHTING_CFG.palette["blue"])) + + +def test_lighting_refuses_a_history_that_does_not_end_in_a_response( + monkeypatch: pytest.MonkeyPatch, +): + """A history ending on a prompt has no response to compile, and must say so rather than + silently reading the prompt text — which parses as a lighting-less response and compiles to + the default hue wheel, a plausible-looking show built from the wrong message.""" + app = _deploy_backend(monkeypatch, LIGHTING_RESPONSE) + app.choreographer.messages.append({"role": "user", "content": "make it bluer"}) + + with pytest.raises(AssertionError, match="not a response"): + app.lighting_timeline() + + +def test_reprompt_rejects_a_malformed_lighting_emission(monkeypatch: pytest.MonkeyPatch): + """A reprompt's own lighting track has to be inside the retry loop, not just the first one. + + Otherwise a malformed emission produced while correcting something else surfaces only at + compile time -- after the axswarm pass, past every retry, with the show about to deploy. + """ + app = AppBackend(config_file=virtual_crazyswarm_config(n_drones=LIGHTING_N)) + monkeypatch.setattr(app, "_load_structure", lambda _song: _lighting_structure()) + monkeypatch.setattr( + app.choreographer, + "response2waypoints", + lambda *_args, **_kwargs: {"time": np.tile([0.0, FLIGHT_END_S], (LIGHTING_N, 1))}, + ) + bad = ( + 'song_mood: "x"\nchoreography:\n s1b1t1: spiral(3, 100)\n END\n' + "lighting:\n s1b1t1: disco_ball(['all', []], 'both')\n END" + ) + attempts = [] + + def generate(prompt: list[dict[str, str]], **_kwargs: object) -> str: + attempts.append(prompt) + return bad + + monkeypatch.setattr(app.choreographer, "generate_choreography", generate) + + with pytest.raises(LLMFormatError, match="disco_ball"): + app.reprompt("make it bluer") + + assert len(attempts) > 1, "and the rejection must go back round the self-correct loop" + + +def test_sim_colours_change_over_a_blink(monkeypatch: pytest.MonkeyPatch): + """The colour `render.py` and `sim.py` draw is a function of time, sampled per frame. + + A render path keeping its one-shot ``rgbas[:, :3]`` assignment draws these two identically. + """ + app = _deploy_backend(monkeypatch, LIGHTING_RESPONSE) + + timeline = app.lighting_timeline() + + # One beat is ~0.55s at 120 BPM, duty 0.5, so the swarm is lit on the beat and dark after it. + lit = timeline.evaluate_rgb01(0.0) + dark = timeline.evaluate_rgb01(0.4) + assert not np.allclose(lit, dark) + assert np.any(lit > 0.0) + assert np.allclose(dark, 0.0) + + +def test_sim_colours_without_a_lighting_track_are_the_base_hue_wheel( + monkeypatch: pytest.MonkeyPatch, +): + """No lighting is full on, each drone in its own hue — today's colouring, calibrated. + + The one visible change for presets predating the feature: the sim now carries the same + ``channel_gain`` blue dim the deploy path always has, so the preview matches what flies. + """ + response = 'song_mood: "x"\nchoreography:\n s1b1t1: spiral(3, 100)\n END' + app = _deploy_backend(monkeypatch, response) + + timeline = app.lighting_timeline() + + base = hue_to_wrgb(np.arange(LIGHTING_N) / LIGHTING_N, LIGHTING_CFG) + expected = np.round(base)[:, 1:] / 255.0 + for t in (0.0, 0.4, 7.5): + assert np.allclose(timeline.evaluate_rgb01(t), expected) + # Same hue wheel as today, and dimmer only in blue -- that difference is the calibration. + uncalibrated = generate_default_colors(LIGHTING_N, limit=1.0) + assert not np.allclose(timeline.evaluate_rgb01(0.0), uncalibrated) + assert np.allclose(timeline.evaluate_rgb01(0.0)[:, :2], uncalibrated[:, :2], atol=2e-3) + + +def test_browser_cues_are_drone_indexed_and_json_ready(monkeypatch: pytest.MonkeyPatch): + """`compile_cues` output is not browser-ready, and this is the whole of the adaptation. + + URI keys become drone indices, `{time: NDArray}` dicts become parallel JSON-safe lists, and + 4-channel WRGB becomes 3-channel RGB. + """ + app = _deploy_backend(monkeypatch, LIGHTING_RESPONSE) + + cues = app.browser_cues() + + assert set(cues) == {"top", "bot"} + for deck in ("top", "bot"): + assert len(cues[deck]) == LIGHTING_N + for entry in cues[deck]: + assert set(entry) == {"times", "rgb"} + assert len(entry["times"]) == len(entry["rgb"]) + # "Initial colour from the first cue" is only defined if every list opens at 0. + assert entry["times"][0] == 0.0 + assert all(b > a for a, b in zip(entry["times"], entry["times"][1:], strict=False)) + # `type(...) is float`, not `isinstance`: `np.float64` subclasses `float`, so an + # isinstance check passes on exactly the NumPy scalar this is meant to exclude -- + # and `json.dumps` accepts it too, so the round-trip below does not catch it either. + assert all(type(t) is float for t in entry["times"]) + for rgb in entry["rgb"]: + assert len(rgb) == 3 + assert all(isinstance(channel, int) for channel in rgb) + assert all(0 <= channel <= 255 for channel in rgb) + + # Serializable at all -- an NDArray raises here -- and carrying no radio address, which has no + # business reaching a browser. `np.float64` would survive this step; the `type` check above is + # what excludes it. + payload = json.dumps(cues) + assert all(d["uri"] not in payload for d in app.choreographer.drones.values()) + + +def test_browser_cues_are_the_same_baked_list_the_hardware_gets(monkeypatch: pytest.MonkeyPatch): + """Browser == hardware, so the preview shows the `col_freq` quantization that will fly. + + Also pins index-vs-URI and deck keying, which need `DECK_ASYMMETRIC_RESPONSE`: under a uniform + look a reversed order or a deck swap is byte-identical, and so is comparing only `times`. + """ + app = _deploy_backend(monkeypatch, DECK_ASYMMETRIC_RESPONSE) + uris = [d["uri"] for d in app.choreographer.drones.values()] + + browser = app.browser_cues() + hardware = dict( + zip( + ("top", "bot"), + compile_cues(app.lighting_timeline(), uris, LIGHTING_CFG.col_freq, FLIGHT_END_S), + strict=True, + ) + ) + + for deck, cues in hardware.items(): + for i, uri in enumerate(uris): + times = sorted(cues[uri]) + # The W fold recomputed from the hardware cues rather than run back through + # `_fold_cues_to_rgb`, so this compares two independent derivations. + folded = [ + np.clip(cues[uri][t][1:] + cues[uri][t][0], 0, 255).astype(int).tolist() + for t in times + ] + assert browser[deck][i]["times"] == times + assert browser[deck][i]["rgb"] == folded + # The terminal blackout is the last cue, and the browser's zero-order-hold + # lookup has to be able to reach it. + assert browser[deck][i]["times"][-1] == pytest.approx(FLIGHT_END_S - 0.1) + assert browser[deck][i]["rgb"][-1] == [0, 0, 0] + + # The permutation and deck claims above are vacuous unless the fixture actually distinguishes + # the things being keyed, so pin that here rather than trusting the response string. + for deck in ("top", "bot"): + assert len({tuple(entry["rgb"][0]) for entry in browser[deck]}) == LIGHTING_N, deck + assert [e["rgb"][0] for e in browser["top"]] != [e["rgb"][0] for e in browser["bot"]] + + +def test_browser_cues_fold_the_white_channel_into_rgb(monkeypatch: pytest.MonkeyPatch): + """Three.js has no white channel, so W folds into all three, as `evaluate_rgb01` does.""" + response = ( + 'song_mood: "x"\n' + "choreography:\n s1b1t1: spiral(3, 100)\n END\n" + "lighting:\n s1b1t1: light_color(['all', []], 'white', 'both')\n END" + ) + app = _deploy_backend(monkeypatch, response) + + cues = app.browser_cues() + + # `white` is WRGB (255, 0, 0, 0) -- the dedicated white LED. Dropping W renders it black. + for deck in ("top", "bot"): + for entry in cues[deck]: + assert entry["rgb"][0] == [255, 255, 255] + + +def test_the_white_fold_clips_rather_than_overflowing(): + """The fold is `clip(rgb + w, 0, 255)`, and the clip is not optional. + + Asserted on the fold directly because the shipped palette sums to 255 and cannot reach it. A + retuned `lighting.toml` can, and an unclipped fold hands three.js a channel above 1.0. + """ + folded = _fold_cues_to_rgb({0.0: np.array([200.0, 100.0, 0.0, 60.0])}) + + assert folded == {"times": [0.0], "rgb": [[255, 200, 255]]} + + +def test_initial_prompt_rejects_a_malformed_lighting_emission(monkeypatch: pytest.MonkeyPatch): + """A malformed lighting track must fail at generation time, where `self_correct` can reprompt. + + Otherwise it surfaces only at compile time, long past the retry loop. + """ + app = AppBackend(config_file=virtual_crazyswarm_config(n_drones=LIGHTING_N)) + monkeypatch.setattr(app, "_load_structure", lambda _song: _lighting_structure()) + monkeypatch.setattr( + app.choreographer, + "response2waypoints", + lambda *_args, **_kwargs: {"time": np.tile([0.0, FLIGHT_END_S], (LIGHTING_N, 1))}, + ) + bad = ( + 'song_mood: "x"\nchoreography:\n s1b1t1: spiral(3, 100)\n END\n' + "lighting:\n s1b1t1: disco_ball(['all', []], 'both')\n END" + ) + + with pytest.raises(RuntimeError, match="Initial prompt failed") as excinfo: + app.initial_prompt("Fearless2", response=bad) + + assert isinstance(excinfo.value.__cause__, LLMFormatError) + assert "disco_ball" in str(excinfo.value.__cause__) + + +def test_initial_prompt_accepts_a_well_formed_lighting_emission(monkeypatch: pytest.MonkeyPatch): + """The positive control: a valid track passes the same gate untouched.""" + app = AppBackend(config_file=virtual_crazyswarm_config(n_drones=LIGHTING_N)) + monkeypatch.setattr(app, "_load_structure", lambda _song: _lighting_structure()) + monkeypatch.setattr( + app.choreographer, + "response2waypoints", + lambda *_args, **_kwargs: {"time": np.tile([0.0, FLIGHT_END_S], (LIGHTING_N, 1))}, + ) + + app.initial_prompt("Fearless2", response=LIGHTING_RESPONSE) + + assert app.choreographer.messages[-1]["content"] == LIGHTING_RESPONSE + + +def _watched_backend(monkeypatch: pytest.MonkeyPatch) -> tuple[AppBackend, list[tuple[str, dict]]]: + """A backend whose waypoint pass is stubbed out, plus the events it reports, in order.""" + app = AppBackend(config_file=virtual_crazyswarm_config(n_drones=LIGHTING_N)) + monkeypatch.setattr(app, "_load_structure", lambda _song: _lighting_structure()) + monkeypatch.setattr( + app.choreographer, + "response2waypoints", + lambda *_args, **_kwargs: {"time": np.tile([0.0, FLIGHT_END_S], (LIGHTING_N, 1))}, + ) + events: list[tuple[str, dict]] = [] + app.on_event = lambda event_type, payload: events.append((event_type, payload)) + return app, events + + +def test_initial_prompt_reports_the_prompt_before_the_model_answers( + monkeypatch: pytest.MonkeyPatch, +): + """The panel has nothing to show for the whole of a reasoning model's think unless the prompt + is reported up front, so `prompt_sent` has to fire ahead of the call, not with its result. + """ + app, events = _watched_backend(monkeypatch) + + def generate(_prompt: list[dict[str, str]], **_kwargs: object) -> str: + assert [name for name, _ in events] == ["prompt_sent"], "the prompt arrives first" + return LIGHTING_RESPONSE + + monkeypatch.setattr(app.choreographer, "generate_choreography", generate) + + app.initial_prompt("Fearless2") + + assert [name for name, _ in events] == ["prompt_sent", "llm_response"] + sent = events[0][1]["messages"] + assert any(message["role"] == "user" for message in sent) + assert any("Fearless2" in message["content"] for message in sent) + assert events[1][1]["text"] == LIGHTING_RESPONSE, "the raw answer, not the parsed history" + + +def test_a_rejected_response_is_reported_with_the_retry_it_triggers( + monkeypatch: pytest.MonkeyPatch, +): + """A response the checker throws out is the one the panel most needs to show. + + It used to be invisible: only the attempt that survived every check was ever surfaced, so a + run that reprompted twice looked identical to one that answered correctly first time. + """ + app, events = _watched_backend(monkeypatch) + attempts: list[str] = [] + + def generate(_prompt: list[dict[str, str]], **_kwargs: object) -> str: + attempts.append("call") + return LIGHTING_RESPONSE + + def validate(_response: str) -> None: + if len(attempts) == 1: + raise LLMFormatError("gradient at s1b1t1 names no such colour") + + monkeypatch.setattr(app.choreographer, "generate_choreography", generate) + monkeypatch.setattr(app.choreographer, "validate_lighting", validate) + + app.initial_prompt("Fearless2") + + assert [name for name, _ in events] == [ + "prompt_sent", + "llm_response", + "response_rejected", + "prompt_sent", + "llm_response", + ] + assert "names no such colour" in events[2][1]["message"] + retry = events[3][1]["messages"] + assert any("failed with the following error" in m["content"] for m in retry), ( + "the retry's own prompt must reach the panel too, not just the rejection" + ) + + +def test_a_preset_reports_the_exchange_it_replays(monkeypatch: pytest.MonkeyPatch): + """A preset never reaches a model, but the panel still shows what produced it.""" + app, events = _watched_backend(monkeypatch) + monkeypatch.setattr(type(app), "presets", property(lambda _self: ["Fearless2"])) + monkeypatch.setattr( + app, + "load_preset", + lambda _preset: ( + app.choreographer.messages.append({"role": "assistant", "content": LIGHTING_RESPONSE}) + or LIGHTING_RESPONSE + ), + ) + + app.initial_prompt("Fearless2") + + assert [name for name, _ in events] == ["prompt_sent", "llm_response"] + assert events[1][1]["text"] == LIGHTING_RESPONSE diff --git a/tests/unit/test_choreographer.py b/tests/unit/test_choreographer.py index b9678b4..4f2ab99 100644 --- a/tests/unit/test_choreographer.py +++ b/tests/unit/test_choreographer.py @@ -1,8 +1,11 @@ """Tests for choreographer orchestration helpers (F3) and form_* motion primitives (F1).""" +import logging +from collections.abc import Callable from pathlib import Path import numpy as np +import pytest from conftest import virtual_crazyswarm_config from swarm_gpt.core.choreographer import ( @@ -10,6 +13,23 @@ _form_should_drop_holds, _overlapping_drone_set, ) +from swarm_gpt.core.lighting import hue_to_wrgb, load_lighting_config +from swarm_gpt.exception import LLMFormatError +from swarm_gpt.utils.music_analyzer import Bar, Beat, Segment, SongStructure + + +def _single_bar_structure() -> SongStructure: + """One 4-beat bar at 120 BPM, running to 20s.""" + beats = [Beat(id=j + 1, time_s=j * 0.5, position_in_bar=j + 1) for j in range(4)] + bar = Bar(id=1, start_s=0.0, beats=beats) + return SongStructure( + schema_version=2, + source_path="test.mp3", + song_sha256="abc", + analyzer="test", + bpm=120, + segments=[Segment(id=1, label="chorus", start_s=0.0, end_s=20.0, bars=[bar])], + ) def test_form_should_drop_holds_when_overlapping_motion_follows(): @@ -67,6 +87,31 @@ def test_overlapping_drone_set_move(): assert _overlapping_drone_set({"move": (100, 0, 150, 5)}, num_drones=10) == frozenset({4}) +def test_overlapping_drone_set_compact_range(): + """A range spec must resolve to the same drones as the list it replaces.""" + compact = _overlapping_drone_set({"form_circle": ("1-5", 150, 100, 1.0)}, num_drones=10) + explicit = _overlapping_drone_set( + {"form_circle": ([1, 2, 3, 4, 5], 150, 100, 1.0)}, num_drones=10 + ) + assert compact == explicit == frozenset({0, 1, 2, 3, 4}) + assert _overlapping_drone_set({"move_z": ("2,4", 50)}, num_drones=10) == frozenset({1, 3}) + assert _overlapping_drone_set({"center": ("1-3",)}, num_drones=10) == frozenset({0, 1, 2}) + + +def test_overlapping_drone_set_ellipsis_is_the_whole_swarm(): + """`[...]` means every drone; the check must agree with the primitive that flies it.""" + assert _overlapping_drone_set({"center": ([...],)}, num_drones=4) == frozenset({0, 1, 2, 3}) + + +def test_form_should_drop_holds_reads_range_specs(): + """The disjoint/overlap decision must survive the compact form, both ways.""" + disjoint = [{"form_circle": ("1-5", 150, 100, 1.0)}, {"move_z": ("6-10", 50)}] + assert _form_should_drop_holds(disjoint, 0, num_drones=10) is False + # Inclusive endpoints: "5-10" shares drone 5 with "1-5", so the holds must drop. + overlapping = [{"form_circle": ("1-5", 150, 100, 1.0)}, {"move_z": ("5-10", 50)}] + assert _form_should_drop_holds(overlapping, 0, num_drones=10) is True + + 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 @@ -89,8 +134,6 @@ def test_form_star_hold_pruning_in_pipeline(): 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) @@ -114,6 +157,38 @@ def test_form_star_hold_pruning_in_pipeline(): assert waypoints["pos"].shape[1] > 2 +def test_range_and_list_forms_produce_identical_waypoints(): + """End-to-end through the text pipeline: the encoding changes, the choreography does not. + + Saved presets store explicit id lists, so both spellings reach `_choreo2waypoints`. + """ + config_path = virtual_crazyswarm_config(n_drones=10) + structure = _single_bar_structure() + + def _waypoints(drone_ids: str) -> np.ndarray: + choreographer = Choreographer( + config_file=config_path, llm_provider="openai", use_motion_primitives=True + ) + for i in choreographer.starting_pos: + choreographer.starting_pos[i] = np.array([(i - 5) * 0.3, 0.0, 1.0]) + choreography = {(1, 1, 1): f"form_circle({drone_ids}, 120, 100, 1.0)"} + return choreographer._choreo2waypoints(choreography, structure)["pos"] + + np.testing.assert_allclose(_waypoints("'1-5'"), _waypoints("[1, 2, 3, 4, 5]")) + + +def test_out_of_bounds_range_reprompts_rather_than_crashing(): + """An id above the swarm must raise a format error the self-correct loop can act on.""" + config_path = virtual_crazyswarm_config(n_drones=10) + choreographer = Choreographer( + config_file=config_path, llm_provider="openai", use_motion_primitives=True + ) + with pytest.raises(LLMFormatError, match=r"outside the 1\.\.10 swarm"): + choreographer._choreo2waypoints( + {(1, 1, 1): "form_circle('1-50', 120, 100, 1.0)"}, _single_bar_structure() + ) + + def test_load_drone_config_uses_active_list(tmp_path: Path) -> None: """Loader must respect active list order and build uri from addr and channel.""" cfg = tmp_path / "drones.toml" @@ -137,3 +212,468 @@ def test_load_drone_config_uses_active_list(tmp_path: Path) -> None: 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 + + +LIGHTING_CFG = load_lighting_config() +LIGHTING_N = 6 +# Six drones spread along +x at distinct heights, so the spatial selectors and spreads all resolve +# unambiguously against the frozen snapshot. +LIGHTING_POSITIONS = np.stack([np.arange(6.0), np.zeros(6), np.linspace(1.0, 2.0, 6)], axis=1) +# The music ends at 8s; the flight runs 4s longer, because `response2waypoints` appends the +# Return-to-home legs. The blackout belongs at the end of the *flight*. +SONG_END_S = 8.0 +FLIGHT_END_S = 12.0 +# The strict-mode selector object: every field present, the unused ones ignored. +ALL_SEL = {"kind": "all", "ids": [], "count": 1} + + +def _lighting_structure(bpm: int = 120) -> SongStructure: + """Two one-bar segments of four beats, half a second apart: s1b1t1 = 0s, s2b1t1 = 4s.""" + segments = [] + for seq in (1, 2): + start = (seq - 1) * 4.0 + beats = [Beat(id=j + 1, time_s=start + j * 0.5, position_in_bar=j + 1) for j in range(4)] + segments.append( + Segment( + id=seq, + label="seg", + start_s=start, + end_s=start + 4.0, + bars=[Bar(id=1, start_s=start, beats=beats)], + ) + ) + return SongStructure( + schema_version=2, + source_path="t.mp3", + song_sha256="a", + analyzer="t", + bpm=bpm, + segments=segments, + ) + + +def _lighting_choreographer() -> Choreographer: + return Choreographer( + config_file=virtual_crazyswarm_config(n_drones=LIGHTING_N), + llm_provider="openai", + use_motion_primitives=True, + ) + + +def _payload(lighting: list | None) -> dict: + """A structured payload with one motion key, plus the given lighting track.""" + payload = { + "song_mood": "steady", + "choreography_plan": "one spiral", + "choreography": [ + { + "key": "s1b1t1", + "actions": [{"primitive": "spiral", "params": {"steps": 3, "height_cm": 100}}], + } + ], + } + if lighting is not None: + payload["lighting"] = lighting + return payload + + +def _blue_then_blink() -> list[dict]: + """Two lighting keys: the swarm blue from s1b1t1, red and blinking from s2b1t1.""" + return [ + { + "key": "s1b1t1", + "actions": [ + { + "primitive": "light_color", + "params": {"sel": ALL_SEL, "color": "blue", "deck": "both"}, + } + ], + }, + { + "key": "s2b1t1", + "actions": [ + { + "primitive": "light_color", + "params": {"sel": ALL_SEL, "color": "red", "deck": "both"}, + }, + { + "primitive": "blink", + "params": {"sel": ALL_SEL, "period_beats": 1, "duty": 0.5, "deck": "both"}, + }, + ], + }, + ] + + +def _spatial_lighting() -> list[dict]: + """Two keys of `sweep`, whose phase spread reads the frozen position snapshot.""" + return [ + { + "key": key, + "actions": [ + { + "primitive": "sweep", + "params": {"sel": ALL_SEL, "period_beats": 4, "axis": "x", "deck": "both"}, + } + ], + } + for key in ("s1b1t1", "s2b1t1") + ] + + +def _recording_position_at(calls: list[float]) -> Callable[[float], np.ndarray]: + """A `position_at` that records every time it is asked for a snapshot.""" + + def position_at(t: float) -> np.ndarray: + calls.append(t) + return LIGHTING_POSITIONS + + return position_at + + +def test_structured_payload_to_text_emits_a_lighting_block(): + """The lighting track renders in the same idiom as `choreography:`, ended by END.""" + text = _lighting_choreographer()._structured_payload_to_text(_payload(_blue_then_blink())) + + assert "\nlighting:\n" in text + assert "\n s1b1t1: light_color(['all', []], 'blue', 'both')\n" in text + assert ( + "\n s2b1t1: light_color(['all', []], 'red', 'both'); " + "blink(['all', []], 1, 0.5, 'both')\n" in text + ) + # One END per block, and the lighting block comes last. + assert text.count("END") == 2 + assert text.rstrip().endswith("END") + assert text.index("choreography:") < text.index("\nlighting:") + + +def test_lighting_block_stays_out_of_the_choreography_slice(): + """The two tracks share an address space; slicing must not mix them.""" + choreographer = _lighting_choreographer() + text = choreographer._structured_payload_to_text(_payload(_blue_then_blink())) + + assert choreographer._response2choreo(text) == {(1, 1, 1): "spiral(3, 100)"} + + +def test_lighting_slice_ignores_the_word_lighting_inside_a_multi_line_plan(): + """The ``lighting:`` header is anchored to a line of its own, so prose cannot claim it. + + A free-text response can wrap the plan onto several lines. An unanchored header would match + inside that prose and hand the motion track's actions to the lighting parser. + """ + text = ( + 'song_mood: "steady"\n' + "choreography_plan: |\n" + " The drop lands at s2b1t1.\n" + " lighting: hold blue until then, then blink.\n" + "choreography:\n" + " s1b1t1: spiral(3, 100)\n" + " END\n" + "lighting:\n" + " s1b1t1: light_color(['all', []], 'blue', 'both')\n" + " END" + ) + choreographer = _lighting_choreographer() + + assert choreographer.lighting_from_text(text) == { + (1, 1, 1): "light_color(['all', []], 'blue', 'both')" + } + assert choreographer._response2choreo(text) == {(1, 1, 1): "spiral(3, 100)"} + + +def test_response2lighting_puts_each_look_at_its_resolved_time(): + """Each emitted key becomes a look at `structure.time_of` of that address.""" + choreographer = _lighting_choreographer() + structure = _lighting_structure() + text = choreographer._structured_payload_to_text(_payload(_blue_then_blink())) + + timeline = choreographer.response2lighting( + text, structure, _recording_position_at([]), FLIGHT_END_S + ) + + t_switch = structure.time_of(2, 1, 1) + assert t_switch == 4.0 + # The first look holds right up to the second one's start, which replaces it outright. + assert np.allclose( + timeline.evaluate(t_switch - 0.01)[:, 0], np.round(LIGHTING_CFG.palette["blue"]) + ) + # `blink` is on at phase 0, so the second look reads as its colour at its own start time. + assert np.allclose(timeline.evaluate(t_switch)[:, 0], np.round(LIGHTING_CFG.palette["red"])) + # ... and off half a beat later: 1 beat is 0.5s at 120 BPM, duty 0.5. + assert np.allclose(timeline.evaluate(t_switch + 0.3)[:, 0], 0.0) + + +def test_response2lighting_converts_period_beats_with_the_songs_own_tempo(): + """`period_beats` is beats, so the song's tempo has to reach every effect it builds. + + Every other fixture here is 120 BPM, where a hardcoded 120.0 is indistinguishable from the + forwarded `structure.bpm`; a tempo that never arrives mistimes the whole show. + """ + choreographer = _lighting_choreographer() + text = ( + "lighting:\n" + " s1b1t1: light_color(['all', []], 'red', 'both'); blink(['all', []], 1, 0.5, 'both')\n" + " END" + ) + + timeline = choreographer.response2lighting( + text, _lighting_structure(bpm=90), _recording_position_at([]), FLIGHT_END_S + ) + + # One beat is 2/3 s at 90 BPM, so duty 0.5 holds the swarm lit to t = 1/3 and dark to 2/3. At + # 120 BPM the beat is 0.5 s and both samples read the other way round. + assert np.allclose(timeline.evaluate(0.3)[:, 0], np.round(LIGHTING_CFG.palette["red"])) + assert np.allclose(timeline.evaluate(0.55)[:, 0], 0.0) + + +def test_response2lighting_snapshots_once_per_look_and_at_no_other_time(): + """One frozen snapshot per look is what keeps the timeline a pure function of t.""" + choreographer = _lighting_choreographer() + structure = _lighting_structure() + text = choreographer._structured_payload_to_text(_payload(_spatial_lighting())) + calls: list[float] = [] + + timeline = choreographer.response2lighting( + text, structure, _recording_position_at(calls), FLIGHT_END_S + ) + + assert len(calls) == 2, "one per look" + # Evaluating the timeline must never reach for a position again. + for t in (0.0, 1.7, 4.0, 6.5): + timeline.evaluate(t) + assert len(calls) == 2 + + +def test_response2lighting_snapshots_where_the_formation_has_arrived_not_where_it_starts(): + """The bug this exists for: a look emitted alongside a formation must not read the old one. + + A lighting key sharing a motion key's address lands where the formation *begins*, so sampling + there froze the outgoing formation for the whole look. + """ + choreographer = _lighting_choreographer() + structure = _lighting_structure() + # Motion at s1b1t1 and s2b1t1, so the first primitive runs 0s -> 4s and the second 4s -> 8s. + payload = _payload(_spatial_lighting()) + payload["choreography"].append( + { + "key": "s2b1t1", + "actions": [{"primitive": "spiral", "params": {"steps": 3, "height_cm": 60}}], + } + ) + calls: list[float] = [] + + choreographer.response2lighting( + choreographer._structured_payload_to_text(payload), + structure, + _recording_position_at(calls), + FLIGHT_END_S, + ) + + # Not [0.0, 4.0]: each look reads the end of the motion primitive it was emitted alongside. + assert calls == [4.0, 8.0] + + +def test_response2lighting_samples_a_short_look_at_its_own_end(): + """A look that expires mid-primitive has no settled pose, so it takes the latest one it sees.""" + choreographer = _lighting_choreographer() + structure = _lighting_structure() + # One motion key at s1b1t1 running to the song end, but two looks inside that one primitive. + payload = _payload(_spatial_lighting()) + calls: list[float] = [] + + choreographer.response2lighting( + choreographer._structured_payload_to_text(payload), + structure, + _recording_position_at(calls), + FLIGHT_END_S, + ) + + # The first look is replaced at 4.0, well before the primitive ends at the 8.0 song end. + assert calls == [structure.time_of(2, 1, 1), 8.0] + + +def test_response2lighting_falls_back_to_the_look_start_without_a_motion_track(): + """A hand-written lighting-only block has no primitive to settle against.""" + choreographer = _lighting_choreographer() + text = "lighting:\n s2b1t1: light_color(['all', []], 'green', 'both')\n END" + calls: list[float] = [] + + choreographer.response2lighting( + text, _lighting_structure(), _recording_position_at(calls), FLIGHT_END_S + ) + + assert calls == [4.0] + + +@pytest.mark.parametrize("lighting", [None, []]) +def test_a_payload_without_lighting_yields_a_full_on_timeline(lighting: list | None): + """An absent key and an empty array both mean 'no lighting', never an error. + + ``None`` is a payload predating the feature; ``[]`` is what strict mode forces. + """ + choreographer = _lighting_choreographer() + calls: list[float] = [] + text = choreographer._structured_payload_to_text(_payload(lighting)) + + timeline = choreographer.response2lighting( + text, _lighting_structure(), _recording_position_at(calls), FLIGHT_END_S + ) + + assert calls == [] + # Brightness 1.0 everywhere and the base hue wheel, which is today's colouring exactly. + base = np.round(hue_to_wrgb(np.arange(LIGHTING_N) / LIGHTING_N, LIGHTING_CFG)) + for deck in range(2): + assert np.allclose(timeline.evaluate(1.0)[:, deck], base) + + +def test_a_response_with_no_lighting_block_at_all_yields_a_full_on_timeline(): + """A free-text or preset response that never mentions lighting is not an error.""" + text = 'song_mood: "x"\nchoreography:\n s1b1t1: spiral(3, 100)\n END' + + timeline = _lighting_choreographer().response2lighting( + text, _lighting_structure(), _recording_position_at([]), FLIGHT_END_S + ) + + base = np.round(hue_to_wrgb(np.arange(LIGHTING_N) / LIGHTING_N, LIGHTING_CFG)) + assert np.allclose(timeline.evaluate(1.0)[:, 0], base) + + +def test_response2lighting_parses_a_hand_written_lighting_block(): + """The free-text path emits the same idiom, so one text parser serves both modes.""" + text = "lighting:\n s2b1t1: light_color(['ids', [1, 3]], 'green', 'top')\n END" + + timeline = _lighting_choreographer().response2lighting( + text, _lighting_structure(), _recording_position_at([]), FLIGHT_END_S + ) + + top = timeline.evaluate(4.0)[:, 0] + green = np.round(LIGHTING_CFG.palette["green"]) + assert np.allclose(top[[0, 2]], green) + # `ids` is 1-indexed, and the deck stacks resolve independently. + assert not np.allclose(top[1], green) + assert np.allclose( + timeline.evaluate(4.0)[:, 1], + np.round(hue_to_wrgb(np.arange(LIGHTING_N) / LIGHTING_N, LIGHTING_CFG)), + ) + + +def test_response2lighting_reports_an_unknown_primitive_as_a_format_error(): + """A malformed lighting emission is reported the way a malformed motion one is.""" + text = "lighting:\n s1b1t1: disco_ball(['all', []], 'both')\n END" + + with pytest.raises(LLMFormatError, match="Unknown lighting primitive 'disco_ball' at s1b1t1"): + _lighting_choreographer().response2lighting( + text, _lighting_structure(), _recording_position_at([]), FLIGHT_END_S + ) + + +def test_response2lighting_reports_an_unknown_selector_as_a_format_error(): + """`build_look` raises a bare KeyError for these; the choreographer must name the key.""" + text = "lighting:\n s1b1t1: light_on(['nobody', []], 'both')\n END" + + with pytest.raises(LLMFormatError, match="s1b1t1"): + _lighting_choreographer().response2lighting( + text, _lighting_structure(), _recording_position_at([]), FLIGHT_END_S + ) + + +def test_response2lighting_reports_a_wrong_argument_count_as_a_format_error(): + """Zipping args onto names would silently drop a missing one, so arity is checked first.""" + text = "lighting:\n s1b1t1: pulse(['all', []], 'both')\n END" + + with pytest.raises(LLMFormatError, match="pulse at s1b1t1 must have 3 arguments"): + _lighting_choreographer().response2lighting( + text, _lighting_structure(), _recording_position_at([]), FLIGHT_END_S + ) + + +def test_response2lighting_blacks_out_at_the_end_of_the_flight_not_the_music(): + """The blackout is `t_end - 0.1` where `t_end` is the flight, not the music. + + Deriving it from the song structure would fly the whole return-to-home leg dark. + """ + choreographer = _lighting_choreographer() + text = choreographer._structured_payload_to_text(_payload(_blue_then_blink())) + + timeline = choreographer.response2lighting( + text, _lighting_structure(), _recording_position_at([]), FLIGHT_END_S + ) + + # Still lit through the return-to-home legs, which start after the music ends. + assert np.any(timeline.evaluate(SONG_END_S + 1.0) > 0.0) + assert np.allclose(timeline.evaluate(FLIGHT_END_S - 0.05), 0.0) + + +def test_validate_lighting_rejects_a_malformed_emission_without_positions(): + """A malformed lighting track must reprompt, and cannot wait for the axswarm pass. + + Positions do not exist yet, so the full compile cannot run at generation time -- but the + vocabulary and arity can, which catches a hallucinated primitive in time for `self_correct`. + """ + choreographer = _lighting_choreographer() + + with pytest.raises(LLMFormatError, match="Unknown lighting primitive 'disco_ball'"): + choreographer.validate_lighting( + "lighting:\n s1b1t1: disco_ball(['all', []], 'both')\n END" + ) + with pytest.raises(LLMFormatError, match="must have 3 arguments"): + choreographer.validate_lighting("lighting:\n s1b1t1: pulse(['all', []], 'both')\n END") + + +@pytest.mark.parametrize( + ("emission", "match"), + [ + ("light_color(['all', []], 'chartreuse', 'both')", "chartreuse"), + ("gradient(['all', []], 'red', 'chartreuse', 'z', 'both')", "chartreuse"), + ("light_on(['nobody', []], 'both')", "nobody"), + ("light_on(['all', []], 'middle')", "middle"), + ("rainbow(['all', []], 4, 'sideways', 'both')", "sideways"), + ("gradient(['all', []], 'red', 'blue', 'sideways', 'both')", "sideways"), + ("alternate_blink(['all', []], 2, 'diagonal', 'both')", "diagonal"), + # The bounds are not the schema's alone: presets and hand-written blocks skip it entirely. + ("light_on(['ids', [0]], 'both')", r"1\.\.6"), + ("light_on(['ids', [99]], 'both')", r"1\.\.6"), + ("light_on(['first', [99]], 'both')", r"1\.\.6"), + ], +) +def test_validate_lighting_rejects_every_name_the_engine_would_reject(emission: str, match: str): + """Names, not just primitives: none of these checks needs a position, so all belong here. + + A bad colour, deck, selector, spread or axis used to escape and raise a bare `KeyError` at + deploy or render time. Colour was worst: palette names resolve lazily at read-out. + """ + choreographer = _lighting_choreographer() + + with pytest.raises(LLMFormatError, match=match): + choreographer.validate_lighting(f"lighting:\n s1b1t1: {emission}\n END") + + +def test_validate_lighting_does_not_warn_about_its_own_dry_run_snapshot( + caplog: pytest.LogCaptureFixture, monkeypatch: pytest.MonkeyPatch +): + """The dry run discards every position-dependent result, so it must not diagnose one. + + A synthetic snapshot of zeros has no extent along any axis, which is exactly what the collapse + warnings report -- noise about the fixture that trains the reader to ignore the real case. + """ + name = "swarm_gpt.core.lighting" + monkeypatch.setattr(logging.getLogger(name), "propagate", True) + caplog.set_level(logging.WARNING, logger=name) + emission = ( + "sweep(['all', []], 4, 'z', 'both'); ripple_light(['all', []], 4, 'both'); " + "light_color(['right', []], 'red', 'both'); light_color(['upper', []], 'blue', 'both')" + ) + + _lighting_choreographer().validate_lighting(f"lighting:\n s1b1t1: {emission}\n END") + + assert not [r for r in caplog.records if r.name.startswith("swarm_gpt.core.lighting")] + + +def test_validate_lighting_accepts_a_good_track_and_a_missing_one(): + """Lighting is optional, so a response without a block must pass validation untouched.""" + choreographer = _lighting_choreographer() + + choreographer.validate_lighting(choreographer._structured_payload_to_text(_payload(None))) + choreographer.validate_lighting( + choreographer._structured_payload_to_text(_payload(_blue_then_blink())) + ) diff --git a/tests/unit/test_lighting.py b/tests/unit/test_lighting.py new file mode 100644 index 0000000..4d30588 --- /dev/null +++ b/tests/unit/test_lighting.py @@ -0,0 +1,1458 @@ +"""Unit tests for the lighting engine: selectors, waveforms, spreads, layers and the timeline.""" + +import dataclasses +import logging +from pathlib import Path + +import numpy as np +import pytest +from conftest import with_ulp_noise + +from swarm_gpt.core.lighting import ( + BrightnessLayer, + ColourLayer, + LightingConfig, + LightingTimeline, + Look, + hue_to_wrgb, + load_lighting_config, + select, + spread_offsets, + waveform, +) + + +@pytest.fixture +def lighting_log( + caplog: pytest.LogCaptureFixture, monkeypatch: pytest.MonkeyPatch +) -> pytest.LogCaptureFixture: + """`caplog`, wired so it actually sees this module's records. + + The ROS `launch` pytest plugin calls `logging.setLoggerClass` with ``propagate = False``, so + every logger built after it bypasses the root logger `caplog` listens on. + """ + logger = logging.getLogger("swarm_gpt.core.lighting") + monkeypatch.setattr(logger, "propagate", True) + caplog.set_level(logging.WARNING, logger=logger.name) + return caplog + + +# The names the prompt offers the LLM as the `color` enum. +PALETTE_NAMES = ( + "red", + "orange", + "amber", + "yellow", + "green", + "teal", + "cyan", + "azure", + "blue", + "indigo", + "magenta", + "pink", + "white", +) + +# Six drones spread along +x, no two sharing an x and none sitting on the x centroid (0.5), so the +# left/right split is unambiguous and flipping `stage_axis` swaps the two halves exactly. +POSITIONS_6 = np.array( + [ + [-2.0, 0.0, 1.0], + [-1.0, 1.0, 1.2], + [0.0, -1.0, 1.4], + [1.0, 0.5, 1.6], + [2.0, -0.5, 1.8], + [3.0, 1.0, 2.0], + ] +) + +# Six drones whose height split crosses their stage split: z climbs with the index while x and y +# both alternate about their means, so `upper` cannot coincide with `right` on either stage axis. +# `POSITIONS_6` rises in x and z together, which makes `upper` there `right` by coincidence. +POSITIONS_CROSSED_6 = np.array( + [ + [2.0, 0.0, 0.5], + [-2.0, 1.0, 0.6], + [2.0, -1.0, 0.7], + [-2.0, 0.5, 2.3], + [2.0, -0.5, 2.4], + [-2.0, 1.0, 2.5], + ] +) + +# Two drones parked well above four, the unequal stack the mean split exists for: the z mean is +# 1.33, so `upper` takes the high two, where a rank split would take three. +POSITIONS_TOP_HEAVY_6 = np.array( + [ + [-1.0, 0.0, 0.5], + [0.0, 1.0, 0.5], + [1.0, -1.0, 0.5], + [2.0, 0.5, 0.5], + [-0.5, -0.5, 3.0], + [0.5, 1.0, 3.0], + ] +) + + +def _staged(axis: str) -> LightingConfig: + """The shipped config with `stage_axis` pinned, for tests whose fixture picks the axis. + + Which axis faces the audience is a property of the room, checked once in + `test_shipped_stage_axis_matches_the_lab_geometry`. Tests of the split mechanism state the axis + their fixture is laid out along, so re-rigging the room cannot fail them. + """ + return dataclasses.replace(load_lighting_config(), stage_axis=axis) + + +def test_shipped_stage_axis_matches_the_lab_geometry(): + """The audience views from +x with the show on x = 0, so their right hand points along +y. + + Facing -x with z up puts right at ``(-1,0,0) x (0,0,1) = (0,1,0)``. Getting this wrong is not a + crash: `left`/`right` silently split near/far from the audience, which reads as no split at all. + """ + assert load_lighting_config().stage_axis == "+y" + + +def test_palette_entries_are_wrgb_vectors_in_range(): + cfg = load_lighting_config() + assert len(cfg.palette) >= 12 + for name, entry in cfg.palette.items(): + assert entry.shape == (4,), f"{name} is not a WRGB 4-vector" + assert np.all(entry >= 0.0) and np.all(entry <= 255.0), f"{name} outside [0, 255]" + + +def test_palette_holds_every_prompt_colour(): + cfg = load_lighting_config() + missing = [name for name in PALETTE_NAMES if name not in cfg.palette] + assert not missing, f"palette is missing prompt colours: {missing}" + + +def test_calibration_constants_present_and_typed(): + cfg = load_lighting_config() + assert isinstance(cfg.gamma, float) and cfg.gamma > 0.0 + assert isinstance(cfg.b_min, float) and 0.0 <= cfg.b_min < 1.0 + assert isinstance(cfg.hue_steps, int) and cfg.hue_steps > 0 + # Quantization on both axes, not just hue: the unquantized brightness waveforms were the + # expensive ones to compile. + assert isinstance(cfg.brightness_steps, int) and cfg.brightness_steps > 0 + assert cfg.channel_gain.shape == (4,) + assert np.all(cfg.channel_gain > 0.0) + assert cfg.stage_axis in ("+x", "-x", "+y", "-y") + # The cue rate is data, not a constant duplicated next to `DroneSwarm.col_freq`: it sets both + # the compile grid and the Nyquist floor the primitives clamp against. + assert isinstance(cfg.col_freq, float) and cfg.col_freq > 0.0 + + +def test_blue_is_dimmed_relative_to_constant_channel_sum(): + """The backend.py:314 blue dim survives as a per-colour value, not a global multiply.""" + cfg = load_lighting_config() + assert cfg.palette["blue"][3] == pytest.approx(255.0 * 0.8) + assert cfg.palette["red"].sum() == pytest.approx(255.0) + + +def test_every_palette_entry_carries_the_same_gain_corrected_output(): + """One invariant over the whole palette, which is what says the thirteen are calibrated alike. + + The constant quantity is the *gain-corrected* channel sum, the one `hue_to_wrgb` holds across + the wheel. Spot-checking two entries lets a miscalibrated `indigo` through. + """ + cfg = load_lighting_config() + for name, entry in cfg.palette.items(): + assert (entry / cfg.channel_gain).sum() == pytest.approx(255.0, abs=0.01), name + + +# Every key `load_lighting_config` indexes directly, and the fragment of TOML that supplies it. The +# palette is a table, so it is rendered last whatever the omitted key is. +_CONFIG_LINES = { + "gamma": "gamma = 2.2", + "b_min": "b_min = 0.02", + "col_freq": "col_freq = 10.0", + "hue_steps": "hue_steps = 24", + "brightness_steps": "brightness_steps = 16", + "channel_gain": "channel_gain = [1.0, 1.0, 1.0, 0.8]", + "stage_axis": 'stage_axis = "+y"', + "palette": "[palette]\nred = [0.0, 255.0, 0.0, 0.0]", +} + + +def _config_toml(omit: str = "") -> str: + """The shipped config's shape, with one required key left out.""" + return "\n".join(line for key, line in _CONFIG_LINES.items() if key != omit) + "\n" + + +def test_the_synthetic_config_fixture_loads_when_it_is_complete(tmp_path: Path): + """The positive control for the parametrization below, which would otherwise pass vacuously.""" + path = tmp_path / "lighting.toml" + path.write_text(_config_toml()) + assert load_lighting_config(path).stage_axis == "+y" + + +@pytest.mark.parametrize("key", sorted(_CONFIG_LINES)) +def test_every_required_key_is_indexed_directly(key: str, tmp_path: Path): + """The loader indexes required keys directly, so a truncated file fails loudly (CLAUDE.md 6.2). + + One case per key: a fixture missing two is satisfied by whichever the loader reads first, so + seven of the eight could become `.get(key, default)` and still fly on a silent default. + """ + path = tmp_path / "lighting.toml" + path.write_text(_config_toml(omit=key)) + with pytest.raises(KeyError, match=key): + load_lighting_config(path) + + +def test_select_all_covers_every_drone(): + cfg = load_lighting_config() + assert np.all(select(("all", ()), 6, POSITIONS_6, cfg)) + + +def test_select_ids_is_one_indexed(): + """`ids` is LLM-facing and 1-indexed like move_z/form_circle; the mask is 0-indexed.""" + cfg = load_lighting_config() + mask = select(("ids", (1, 3, 5)), 6, POSITIONS_6, cfg) + assert list(mask) == [True, False, True, False, True, False] + + +@pytest.mark.parametrize("sel", [("ids", (7,)), ("ids", (0,)), ("ids", (1, -2))]) +def test_select_ids_outside_the_swarm_raise(sel: tuple): + """Both ends, not just the top one — the schema is not the only path into `select`. + + An id of 0 shifts to -1 and quietly selects the *last* drone. The schema's ``minimum: 1`` + blocks that, but a preset or hand-written `lighting:` block reaches `build_look` without one. + """ + cfg = load_lighting_config() + with pytest.raises(IndexError, match="1..6"): + select(sel, 6, POSITIONS_6, cfg) + + +@pytest.mark.parametrize("count", [0, -1, 7]) +def test_select_first_outside_the_swarm_raises(count: int): + """`first(k)` clamped silently against the slice: `first(99)` selected the whole swarm.""" + cfg = load_lighting_config() + with pytest.raises(IndexError, match="1..6"): + select(("first", (count,)), 6, POSITIONS_6, cfg) + + +def test_select_even_and_odd_split_on_the_parity_of_the_1_indexed_id(): + """`even` must mean the drones the LLM calls 2, 4, 6, not the array slots at 0, 2, 4. + + Every other way of naming a drone -- `ids`, `first`, the motion primitives -- is 1-indexed, so + an array-parity `even` is the exact complement of what an author writing `ids([2, 4, 6])` gets. + """ + cfg = load_lighting_config() + even = select(("even", ()), 6, POSITIONS_6, cfg) + odd = select(("odd", ()), 6, POSITIONS_6, cfg) + assert list(even) == [False, True, False, True, False, True] + assert list(odd) == [True, False, True, False, True, False] + assert np.all(even ^ odd) + assert list(even) == list(select(("ids", (2, 4, 6)), 6, POSITIONS_6, cfg)) + + +def test_select_first_n_takes_the_lowest_indices(): + cfg = load_lighting_config() + mask = select(("first", (4,)), 6, POSITIONS_6, cfg) + assert list(mask) == [True, True, True, True, False, False] + + +def test_select_left_and_right_partition_about_the_centroid(): + """x centroid of the fixture is 0.5, so drones 3-5 are stage right when the axis is "+x".""" + cfg = _staged("+x") + right = select(("right", ()), 6, POSITIONS_6, cfg) + left = select(("left", ()), 6, POSITIONS_6, cfg) + assert list(right) == [False, False, False, True, True, True] + assert list(left) == [True, True, True, False, False, False] + assert np.all(left ^ right), "left and right must be exact complements" + + +def test_flipping_stage_axis_swaps_left_and_right(): + """Reorienting the show is a one-line data change, never a code change (CLAUDE.md 6.6).""" + cfg = _staged("+x") + flipped = _staged("-x") + assert list(select(("right", ()), 6, POSITIONS_6, flipped)) == list( + select(("left", ()), 6, POSITIONS_6, cfg) + ) + assert list(select(("left", ()), 6, POSITIONS_6, flipped)) == list( + select(("right", ()), 6, POSITIONS_6, cfg) + ) + + +def test_select_upper_and_lower_partition_about_the_mean_height(): + """The fixture's z mean is 1.5, and its stage split crosses its height split. + + `POSITIONS_6` rises monotonically in both x and z, so `upper` there is `right` by coincidence + and a test on it passes just as well when `upper` reads the wrong column. + """ + cfg = load_lighting_config() + upper = select(("upper", ()), 6, POSITIONS_CROSSED_6, cfg) + lower = select(("lower", ()), 6, POSITIONS_CROSSED_6, cfg) + assert list(upper) == [False, False, False, True, True, True] + assert list(lower) == [True, True, True, False, False, False] + assert np.all(upper ^ lower), "upper and lower must be exact complements" + assert list(select(("right", ()), 6, POSITIONS_CROSSED_6, cfg)) != list(upper), ( + "the fixture must separate the height split from the stage split" + ) + + +def test_upper_splits_unequal_stacks_along_the_gap_between_them(): + """Two formations at different heights part where they actually part, whatever their sizes. + + This is the mean rule's reason for being: a rank split cuts at the halfway drone, so a small + high formation over a large low one drags the low one's top drones up with it. + """ + cfg = load_lighting_config() + upper = select(("upper", ()), 6, POSITIONS_TOP_HEAVY_6, cfg) + assert list(upper) == [False, False, False, False, True, True] + assert upper.sum() == 2, "a rank split would take three, one of them from the lower stack" + + +def test_upper_and_lower_are_independent_of_stage_axis(): + """Height needs no calibration, so unlike left/right it cannot be flipped by the config.""" + cfg = _staged("+x") + flipped = _staged("-y") + assert list(select(("upper", ()), 6, POSITIONS_CROSSED_6, flipped)) == list( + select(("upper", ()), 6, POSITIONS_CROSSED_6, cfg) + ) + + +def test_select_ids_naming_no_drones_raises(): + """`ids([])` yielded an all-False mask and a layer that did nothing at all. + + `first(0)` -- the other way to ask for nothing -- already raises, and the two must not + disagree. `IndexError` is one of the three `_build_look` turns into an `LLMFormatError`. + """ + cfg = load_lighting_config() + with pytest.raises(IndexError, match="no drones"): + select(("ids", ()), 6, POSITIONS_6, cfg) + + +@pytest.mark.parametrize( + "sel", [("all", (1, 2, 3)), ("even", (2,)), ("left", (1,)), ("first", ()), ("first", (2, 3))] +) +def test_select_with_the_wrong_argument_count_raises(sel: tuple): + """Arity was never checked: `all` dropped extra arguments silently and `first` had none to drop. + + A hand-written `("all", (1, 2, 3))` reads as "drones 1-3" and selected all of them, while + `("first", ())` raised a bare tuple-index `IndexError` naming neither selector nor problem. + """ + cfg = load_lighting_config() + with pytest.raises(IndexError, match="takes"): + select(sel, 6, POSITIONS_6, cfg) + + +def test_unknown_selector_raises(): + cfg = load_lighting_config() + with pytest.raises(KeyError): + select(("middle", ()), 6, POSITIONS_6, cfg) + + +QUARTER_PHASES = np.array([0.0, 0.25, 0.5, 0.75]) + + +def test_waveform_sine_at_quarter_phases(): + """0.5 * (1 + cos(2*pi*phi)).""" + assert waveform("sine", QUARTER_PHASES) == pytest.approx([1.0, 0.5, 0.0, 0.5]) + + +def test_waveform_square_at_quarter_phases(): + """1 while frac(phi) < duty, else 0.""" + assert waveform("square", QUARTER_PHASES) == pytest.approx([1.0, 1.0, 0.0, 0.0]) + + +def test_waveform_ramp_at_quarter_phases(): + """1 - frac(phi): full on the beat, decaying out.""" + assert waveform("ramp", QUARTER_PHASES) == pytest.approx([1.0, 0.75, 0.5, 0.25]) + + +def test_every_waveform_peaks_on_the_beat(): + """Effects land *on* the beat, not between beats -- including at wrapped phases.""" + on_beat = np.array([-2.0, -1.0, 0.0, 1.0, 3.0]) + for kind in ("sine", "square", "ramp"): + assert waveform(kind, on_beat) == pytest.approx(np.ones(5)), kind + + +def test_waveform_duty_defaults_to_half(): + assert waveform("square", np.array([0.49])) == pytest.approx([1.0]) + assert waveform("square", np.array([0.51])) == pytest.approx([0.0]) + + +def test_waveform_duty_is_clamped_to_the_open_unit_interval(): + """duty <= 0 still lights the beat instant; duty > 1 is solid on.""" + pinched = waveform("square", np.array([0.0, 0.01, 0.5]), duty=0.0) + assert pinched == pytest.approx([1.0, 0.0, 0.0]) + solid = waveform("square", np.array([0.0, 0.5, 0.99]), duty=5.0) + assert solid == pytest.approx([1.0, 1.0, 1.0]) + + +def test_waveform_outputs_stay_within_the_unit_interval(): + phases = np.linspace(-3.0, 3.0, 401) + for kind in ("sine", "square", "ramp"): + out = waveform(kind, phases) + assert np.all(out >= 0.0) and np.all(out <= 1.0), kind + + +def test_unknown_waveform_raises(): + with pytest.raises(KeyError): + waveform("sawtooth", QUARTER_PHASES) + + +# Ten drones evenly spaced along +x, so the x centroid is 4.5 and the spatial spreads have +# hand-computable normalizations. +POSITIONS_10 = np.stack([np.arange(10.0), np.zeros(10), np.ones(10)], axis=1) +ALL_10 = np.ones(10, dtype=bool) + + +def test_spread_none_is_all_zeros(): + cfg = load_lighting_config() + assert spread_offsets("none", ALL_10, POSITIONS_10, 1, cfg) == pytest.approx(np.zeros(10)) + + +def test_spread_index_ranks_within_the_selected_subset(): + """A chase over ids([2, 5, 9]) runs across three drones, not across gaps in the swarm.""" + cfg = load_lighting_config() + mask = select(("ids", (2, 5, 9)), 10, POSITIONS_10, cfg) + offsets = spread_offsets("index", mask, POSITIONS_10, 1, cfg) + assert offsets[[1, 4, 8]] == pytest.approx([0.0, 1 / 3, 2 / 3]) + # The full-swarm ranking would have given these rows 0.1, 0.4 and 0.8 instead. + assert offsets[4] != pytest.approx(0.4) + + +def test_spread_index_over_the_whole_swarm_is_rank_over_n(): + cfg = load_lighting_config() + offsets = spread_offsets("index", ALL_10, POSITIONS_10, 1, cfg) + assert offsets == pytest.approx(np.arange(10) / 10) + + +# +# Every formation primitive routes through `_assign_positions` (motion_primitives.py:590), a +# Hungarian assignment that returns whichever drone -> slot permutation is cheapest to fly. Drone 6 +# is therefore as likely to sit beside drone 1 as beside drone 5, and the fixtures below all model +# that: a formation laid out slot by slot, plus a permutation saying which drone flies which slot. +# A fixture in id order could not tell `neighbour` from `index` at all. + +_RING_ANGLES = 2.0 * np.pi * np.arange(10) / 10 +# Ten ring slots in ring order, radius 2 at a constant height. +RING_SLOTS = np.stack( + [2.0 * np.cos(_RING_ANGLES), 2.0 * np.sin(_RING_ANGLES), np.full(10, 1.5)], axis=1 +) +# Drone i flies ring slot SCRAMBLE[i]. Deliberately not a rotation: no cyclic shift of the ids +# reproduces it, so `index` cannot pass the ring assertions by luck. +SCRAMBLE = np.array([3, 7, 0, 9, 4, 1, 8, 2, 6, 5]) +RING_10 = RING_SLOTS[SCRAMBLE] + +# Eight unevenly spaced slots along +x -- uneven because even spacing makes a nearest-neighbour +# walk trivially correct in both directions -- and again a scrambled assignment. +LINE_SLOTS = np.stack( + [np.array([0.0, 0.7, 1.1, 2.4, 2.9, 4.2, 5.0, 6.3]), np.zeros(8), np.full(8, 1.2)], axis=1 +) +LINE_SCRAMBLE = np.array([5, 2, 7, 0, 4, 1, 6, 3]) +LINE_8 = LINE_SLOTS[LINE_SCRAMBLE] + +# A 3x3 grid at 0.5 m pitch, walked as a snake, with the ids scrambled across it. +GRID_SLOTS = np.array([[0.5 * i, 0.5 * j, 1.0] for i in range(3) for j in range(3)]) +GRID_SCRAMBLE = np.array([4, 0, 8, 2, 6, 1, 7, 3, 5]) +GRID_9 = GRID_SLOTS[GRID_SCRAMBLE] + + +def _ranks(offsets: np.ndarray, mask: np.ndarray) -> np.ndarray: + """Recover integer walk ranks from per-drone offsets, which are ``rank / n_sel``.""" + return np.round(offsets[mask] * int(mask.sum())).astype(int) + + +def test_spread_neighbour_recovers_ring_order_from_a_scrambled_id_assignment(): + """The bug `neighbour` exists for, on the formation that shows it worst. + + Under an arbitrary id rotation an `index` chase jumps clean across the stage. So the assertion + is on *ring* order: consecutive ranks land on adjacent slots, all the way round, one direction. + """ + cfg = load_lighting_config() + offsets = spread_offsets("neighbour", ALL_10, RING_10, 1, cfg) + assert np.all(offsets >= 0.0) and np.all(offsets < 1.0) + ranks = _ranks(offsets, ALL_10) + assert sorted(ranks) == list(range(10)), "every drone takes a distinct rank" + steps = set(np.diff(SCRAMBLE[np.argsort(ranks)]) % 10) + assert steps in ({1}, {9}), f"the walk must run slot by slot around the ring, got {steps}" + # And the fixture really is scrambled, so id order does not run round the ring by accident. + assert set(np.diff(SCRAMBLE) % 10) not in ({1}, {9}) + + +def test_spread_neighbour_recovers_line_order_from_a_scrambled_id_assignment(): + """On a line the walk is unambiguous, so the ranks are pinned exactly rather than up to sign.""" + cfg = load_lighting_config() + mask = np.ones(8, dtype=bool) + offsets = spread_offsets("neighbour", mask, LINE_8, 1, cfg) + assert list(_ranks(offsets, mask)) == list(LINE_SCRAMBLE), "rank is the slot along the line" + assert offsets != pytest.approx(spread_offsets("index", mask, LINE_8, 1, cfg)) + + +def test_spread_neighbour_on_a_grid_keeps_adjacent_ranks_spatially_adjacent(): + """The requirement in general form: neighbouring ranks are neighbouring drones, on any shape.""" + cfg = load_lighting_config() + mask = np.ones(9, dtype=bool) + ranks = _ranks(spread_offsets("neighbour", mask, GRID_9, 1, cfg), mask) + walk = GRID_9[np.argsort(ranks)] + hops = np.linalg.norm(np.diff(walk, axis=0), axis=1) + assert hops == pytest.approx(np.full(8, 0.5)), "a 3x3 grid snakes with no long jump" + + +def test_spread_neighbour_ranks_the_same_positions_however_the_ids_are_permuted(): + """Determinism: the ranking is a function of the positions alone, never of the id assignment. + + A ring is hardest: the start's two neighbours are equidistant, so breaking that tie by array + order would let the permutation choose which way round the walk runs. + """ + cfg = load_lighting_config() + slot_ranks = _ranks(spread_offsets("neighbour", ALL_10, RING_SLOTS, 1, cfg), ALL_10) + for perm in (SCRAMBLE, np.arange(10)[::-1], np.roll(np.arange(10), 4)): + ranks = _ranks(spread_offsets("neighbour", ALL_10, RING_SLOTS[perm], 1, cfg), ALL_10) + assert list(ranks) == list(slot_ranks[perm]), perm + + +def test_spread_neighbour_ranks_within_the_selected_subset(): + """A chase over three drones walks those three, not the gaps in the full swarm.""" + cfg = load_lighting_config() + # `ids(1, 2, 6)`, unevenly spread through the swarm. + mask = select(("ids", (1, 2, 6)), 8, LINE_8, cfg) + offsets = spread_offsets("neighbour", mask, LINE_8, 1, cfg) + # Those three sit at line slots 5, 2 and 1, so along the line the order is drone 6, 2, 1. + assert offsets[[0, 1, 5]] == pytest.approx([2 / 3, 1 / 3, 0.0]) + # Ranking over the whole swarm instead would have given these rows 5/8, 2/8 and 1/8. + assert offsets[0] != pytest.approx(5 / 8) + assert np.all(offsets[[2, 3, 4, 6, 7]] == 0.0), "unselected rows stay 0, as for every spread" + + +def test_spread_neighbour_takes_the_same_group_quantization_as_index(): + """`group_size` buckets the walk ranks exactly as it buckets the id ranks.""" + cfg = load_lighting_config() + ranks = _ranks(spread_offsets("neighbour", ALL_10, RING_10, 1, cfg), ALL_10) + by_three = spread_offsets("neighbour", ALL_10, RING_10, 3, cfg) + assert by_three == pytest.approx((ranks // 3) / 4) + assert len(set(by_three)) == 4 # ceil(10 / 3) + + +def test_spread_neighbour_of_a_single_drone_is_a_zero_offset(): + """A one-drone walk has no step to take; `n_sel = 1` must not divide by zero or wrap to 1.0.""" + cfg = load_lighting_config() + mask = select(("ids", (4,)), 10, RING_10, cfg) + assert spread_offsets("neighbour", mask, RING_10, 1, cfg) == pytest.approx(np.zeros(10)) + + +def test_spread_alternate_parity_gives_two_offsets_half_a_turn_apart(): + cfg = load_lighting_config() + offsets = spread_offsets("alternate_parity", ALL_10, POSITIONS_10, 1, cfg) + assert sorted(set(offsets)) == pytest.approx([0.0, 0.5]) + assert offsets[0::2] == pytest.approx(np.zeros(5)) + assert offsets[1::2] == pytest.approx(np.full(5, 0.5)) + + +def test_spread_alternate_side_gives_two_offsets_half_a_turn_apart(): + """Stage right (x > 4.5) is half a period behind stage left.""" + cfg = _staged("+x") + offsets = spread_offsets("alternate_side", ALL_10, POSITIONS_10, 1, cfg) + assert sorted(set(offsets)) == pytest.approx([0.0, 0.5]) + assert offsets[:5] == pytest.approx(np.zeros(5)) + assert offsets[5:] == pytest.approx(np.full(5, 0.5)) + + +def test_spatial_spreads_normalize_into_the_half_open_unit_interval(): + """Offsets must stay in [0, 1) so no two ends of a sweep sit at the same phase.""" + cfg = load_lighting_config() + for kind in ("x", "y", "z", "radius"): + offsets = spread_offsets(kind, ALL_10, POSITIONS_10, 1, cfg) + assert np.all(offsets >= 0.0) and np.all(offsets < 1.0), kind + + +def test_spread_x_matches_index_for_evenly_spaced_drones(): + """The spatial normalization uses the same (n_sel - 1) / n_sel convention as `index`.""" + cfg = load_lighting_config() + assert spread_offsets("x", ALL_10, POSITIONS_10, 1, cfg) == pytest.approx( + spread_offsets("index", ALL_10, POSITIONS_10, 1, cfg) + ) + + +def test_spread_radius_ripples_out_from_the_centre(): + """Distances from the x centroid (4.5) are 0.5 .. 4.5, normalized over that span.""" + cfg = load_lighting_config() + offsets = spread_offsets("radius", ALL_10, POSITIONS_10, 1, cfg) + expected = [0.9, 0.675, 0.45, 0.225, 0.0, 0.0, 0.225, 0.45, 0.675, 0.9] + assert offsets == pytest.approx(expected) + + +def test_spread_radius_on_a_cos_sin_ring_collapses_rather_than_amplifying_float_noise(): + """A ring's radii are equal only to ~1e-16, so an exact-zero span test is the wrong test. + + Against exact zero a `cos`/`sin` ring takes the non-degenerate branch and divides by that noise, + giving a random per-drone phase. Equal literals cannot catch it, so the fixture is a real ring. + """ + cfg = load_lighting_config() + ring = with_ulp_noise(RING_10) + radii = np.linalg.norm(ring - ring.mean(axis=0), axis=1) + span = float(radii.max() - radii.min()) + assert 0.0 < span < 1e-12, ( + f"the fixture must be degenerate only to within float noise, got a span of {span}; " + "an exactly-equal ring passes this test without the tolerance and pins nothing" + ) + assert spread_offsets("radius", ALL_10, ring, 1, cfg) == pytest.approx(np.zeros(10)) + + +def test_spread_group_size_quantizes_into_ceil_buckets(): + """group_size = k advances the pattern group-by-group over ceil(n_sel / k) buckets.""" + cfg = load_lighting_config() + by_three = spread_offsets("index", ALL_10, POSITIONS_10, 3, cfg) + assert by_three == pytest.approx([0.0, 0.0, 0.0, 0.25, 0.25, 0.25, 0.5, 0.5, 0.5, 0.75]) + assert len(set(by_three)) == 4 # ceil(10 / 3) + by_four = spread_offsets("index", ALL_10, POSITIONS_10, 4, cfg) + assert len(set(by_four)) == 3 # ceil(10 / 4) + per_drone = spread_offsets("index", ALL_10, POSITIONS_10, 1, cfg) + assert len(set(per_drone)) == 10 + + +@pytest.mark.parametrize( + "kind", ["neighbour", "index", "alternate_parity", "alternate_side", "radius", "x", "y", "z"] +) +def test_spread_offsets_of_an_empty_selection_are_all_zero(kind: str): + """An empty selection is reachable: `right` on a formation with no extent along the stage axis. + + The layers are then no-ops, but the spatial spreads reduce over the selected rows -- + `values.max()` raises on a zero-size array -- so the offsets are short-circuited first. + """ + cfg = load_lighting_config() + empty = np.zeros(10, dtype=bool) + assert spread_offsets(kind, empty, POSITIONS_10, 1, cfg) == pytest.approx(np.zeros(10)) + + +@pytest.mark.parametrize("kind", ["neighbour", "index", "none", "radius", "x", "alternate_side"]) +def test_spread_group_size_below_one_raises_whatever_the_spread(kind: str): + """The check used to sit inside the ranked-spread branch, so only ranked spreads saw it. + + `group_size=0` with `spread="x"` then succeeded silently while `spread="neighbour"` raised, and + the catalogue lists `group_size` as a plain parameter with no spread restriction. + """ + cfg = load_lighting_config() + with pytest.raises(ValueError, match="group_size"): + spread_offsets(kind, ALL_10, POSITIONS_10, 0, cfg) + + +@pytest.mark.parametrize("kind", ["none", "radius", "x", "y", "z", "alternate_parity"]) +def test_spread_group_size_above_one_is_rejected_by_the_spreads_that_cannot_honour_it(kind: str): + """Bucketing is defined over `rank_i`, which only `neighbour` and `index` produce. + + A spatial spread carries a normalized coordinate, so an evenly bucketed `x` is a different + effect from a proportional one. Rejected rather than accepted-and-ignored, so it reprompts. + """ + cfg = load_lighting_config() + with pytest.raises(ValueError, match="group_size"): + spread_offsets(kind, ALL_10, POSITIONS_10, 2, cfg) + + +@pytest.mark.parametrize( + "kind", ["none", "neighbour", "index", "alternate_parity", "alternate_side", "radius", "x"] +) +def test_the_default_group_size_stays_legal_on_every_spread(kind: str): + """`group_size = 1` is per-drone and means "no bucketing", so no spread can object to it.""" + cfg = load_lighting_config() + assert spread_offsets(kind, ALL_10, POSITIONS_10, 1, cfg).shape == (10,) + + +def test_a_stage_axis_with_no_extent_warns_that_the_split_collapsed( + lighting_log: pytest.LogCaptureFixture, +): + """`left`/`right` on a planar formation is a silent no-op, and must not stay silent. + + A formation with no extent along the stage axis goes entirely left, so `light_color(right,...)` + paints nobody. The show still runs, hence a warning -- but it has to name the axis. + """ + cfg = _staged("+x") + flat = np.stack([np.zeros(6), np.arange(6.0), np.ones(6)], axis=1) + assert not select(("right", ()), 6, flat, cfg).any() + assert select(("left", ()), 6, flat, cfg).all() + assert lighting_log.records, "a silent no-op is the failure mode being fixed" + assert lighting_log.records[0].levelno == logging.WARNING + assert cfg.stage_axis in lighting_log.records[0].getMessage(), "name the axis" + + +def test_a_stage_axis_degenerate_only_to_float_noise_collapses_left_rather_than_splitting( + lighting_log: pytest.LogCaptureFixture, +): + """The stage axis collapses on edge-on formations, whose coordinate is equal only to ~1e-16. + + A heading perpendicular to the stage axis has a `cos` of 6.1e-17, not 0, so `coord > + coord.mean()` deals the swarm into arbitrary halves on noise and the warning stays silent. + """ + cfg = _staged("+x") + angles = np.linspace(0.0, 2.0 * np.pi, 10, endpoint=False) + # A radius-2.5 ring standing in the plane spanned by z and the horizontal heading at pi/2, + # which is edge-on to the "+x" stage axis. `np.cos(np.pi / 2)` is 6.1e-17, not 0, and that is + # the whole point of the fixture: writing the x column as the literal 0.5 pins nothing. + edge_on = with_ulp_noise( + np.stack( + [ + 0.5 + 2.5 * np.cos(angles) * np.cos(np.pi / 2), + 2.5 * np.cos(angles) * np.sin(np.pi / 2), + 3.0 + 2.5 * np.sin(angles), + ], + axis=1, + ) + ) + span = float(edge_on[:, 0].max() - edge_on[:, 0].min()) + assert 0.0 < span < 1e-12, ( + f"the fixture must be degenerate only to within float noise, got a span of {span}; " + "an exactly-equal x column passes this test without the tolerance and pins nothing" + ) + assert not select(("right", ()), 10, edge_on, cfg).any() + assert select(("left", ()), 10, edge_on, cfg).all() + assert spread_offsets("alternate_side", ALL_10, edge_on, 1, cfg) == pytest.approx(np.zeros(10)) + assert lighting_log.records, "the collapse must be reported, not decided by rounding" + assert lighting_log.records[0].levelno == logging.WARNING + assert cfg.stage_axis in lighting_log.records[0].getMessage(), "name the axis" + + +def test_a_stage_axis_with_extent_does_not_warn(lighting_log: pytest.LogCaptureFixture): + cfg = _staged("+x") + assert select(("right", ()), 6, POSITIONS_6, cfg).any() + assert not lighting_log.records + + +def test_a_flat_formation_warns_that_the_height_split_collapsed( + lighting_log: pytest.LogCaptureFixture, +): + """The failure mode `upper`/`lower` invites: every formation that is not vertical is flat. + + A ring or a grid at one altitude has no upper half, so `upper` paints nobody while `lower` + covers the swarm. The show still runs, hence a warning -- but it has to name the axis. + """ + cfg = load_lighting_config() + flat = np.stack([np.arange(6.0), np.arange(6.0), np.full(6, 1.2)], axis=1) + assert not select(("upper", ()), 6, flat, cfg).any() + assert select(("lower", ()), 6, flat, cfg).all() + assert lighting_log.records, "a silent no-op is the failure mode being fixed" + assert lighting_log.records[0].levelno == logging.WARNING + assert "z" in lighting_log.records[0].getMessage(), "name the axis" + + +def test_a_formation_with_height_does_not_warn(lighting_log: pytest.LogCaptureFixture): + cfg = load_lighting_config() + assert select(("upper", ()), 6, POSITIONS_CROSSED_6, cfg).any() + assert not lighting_log.records + + +def test_unknown_spread_raises(): + cfg = load_lighting_config() + with pytest.raises(KeyError): + spread_offsets("spiral", ALL_10, POSITIONS_10, 1, cfg) + + +# Hand-computed from HSV at full saturation and value, normalized to a constant channel sum of 255, +# and only then multiplied by channel_gain [1, 1, 1, 0.8]. +PRIMARY_WRGB = { + 0 / 6: [0.0, 255.0, 0.0, 0.0], + 1 / 6: [0.0, 127.5, 127.5, 0.0], + 2 / 6: [0.0, 0.0, 255.0, 0.0], + 3 / 6: [0.0, 0.0, 127.5, 0.8 * 127.5], + 4 / 6: [0.0, 0.0, 0.0, 0.8 * 255.0], + 5 / 6: [0.0, 127.5, 0.0, 0.8 * 127.5], +} + + +def test_hue_wheel_closes(): + cfg = load_lighting_config() + assert hue_to_wrgb(np.array(1.0), cfg) == pytest.approx(hue_to_wrgb(np.array(0.0), cfg)) + + +def test_hue_primaries_land_where_expected(): + cfg = load_lighting_config() + for hue, expected in PRIMARY_WRGB.items(): + assert hue_to_wrgb(np.array(hue), cfg) == pytest.approx(expected), hue + + +def test_hue_output_shape_follows_the_input(): + cfg = load_lighting_config() + assert hue_to_wrgb(np.array(0.3), cfg).shape == (4,) + assert hue_to_wrgb(np.zeros(7), cfg).shape == (7, 4) + assert hue_to_wrgb(np.zeros((2, 3)), cfg).shape == (2, 3, 4) + + +def test_generated_hues_never_drive_the_white_led(): + cfg = load_lighting_config() + out = hue_to_wrgb(np.linspace(0.0, 1.0, 101), cfg) + assert np.all(out[:, 0] == 0.0) + + +def test_every_hue_stays_within_the_addressable_range(): + cfg = load_lighting_config() + out = hue_to_wrgb(np.linspace(0.0, 1.0, 101), cfg) + assert np.all(out >= 0.0) and np.all(out <= 255.0) + + +def test_hue_gain_holds_perceived_output_constant_across_the_wheel(): + """A rainbow that visibly throbs as it sweeps would show up here as a varying output. + + The constant quantity is the *gain-corrected* sum; asserting on the raw sum would pass even + with the gain applied before the normalization, which is the ordering bug this pins. + """ + cfg = load_lighting_config() + out = hue_to_wrgb(np.arange(cfg.hue_steps) / cfg.hue_steps, cfg) + corrected = (out / cfg.channel_gain).sum(axis=-1) + assert np.ptp(corrected) < 0.01 * corrected.mean() + + +def test_generated_pure_blue_matches_the_palette_entry(): + """The gain must land *after* the normalization, or pure blue comes out at 255 instead of 204.""" + cfg = load_lighting_config() + assert hue_to_wrgb(np.array(4 / 6), cfg) == pytest.approx(cfg.palette["blue"], abs=0.5) + + +def test_colour_layer_named_returns_the_palette_entry_on_masked_rows(): + cfg = load_lighting_config() + mask = np.array([True, False, True]) + layer = ColourLayer(mask, ("top", "bot"), "named", {"color": "amber"}) + out = layer.evaluate(0.0, cfg) + assert out[0] == pytest.approx(cfg.palette["amber"]) + assert out[2] == pytest.approx(cfg.palette["amber"]) + assert out[1] == pytest.approx(np.zeros(4)), "unselected rows are left dark for the LTP merge" + + +def test_colour_layer_named_is_static_in_time(): + cfg = load_lighting_config() + layer = ColourLayer(np.ones(3, dtype=bool), ("top",), "named", {"color": "teal"}) + assert layer.evaluate(0.0, cfg) == pytest.approx(layer.evaluate(97.3, cfg)) + + +def test_colour_layer_named_rejects_an_unknown_colour(): + cfg = load_lighting_config() + layer = ColourLayer(np.ones(2, dtype=bool), ("top",), "named", {"color": "chartreuse"}) + with pytest.raises(KeyError): + layer.evaluate(0.0, cfg) + + +def test_colour_layer_gradient_hits_both_endpoints_and_their_average(): + cfg = load_lighting_config() + params = {"color_a": "red", "color_b": "blue", "s": np.array([0.0, 0.5, 1.0])} + out = ColourLayer(np.ones(3, dtype=bool), ("top",), "gradient", params).evaluate(0.0, cfg) + assert out[0] == pytest.approx(cfg.palette["red"]) + assert out[2] == pytest.approx(cfg.palette["blue"]) + assert out[1] == pytest.approx(0.5 * (cfg.palette["red"] + cfg.palette["blue"])) + + +def test_colour_layer_gradient_is_static_in_time(): + """`gradient` costs the same as a named colour to compile because it never changes.""" + cfg = load_lighting_config() + params = {"color_a": "green", "color_b": "pink", "s": np.array([0.0, 0.25, 1.0])} + layer = ColourLayer(np.ones(3, dtype=bool), ("top",), "gradient", params) + assert layer.evaluate(0.0, cfg) == pytest.approx(layer.evaluate(42.0, cfg)) + + +def test_colour_layer_cycled_advances_hue_with_time(): + """A quarter of the way through the period the hue is a quarter of the way round the wheel.""" + cfg = load_lighting_config() + params = {"period_s": 4.0, "offsets": np.zeros(2)} + layer = ColourLayer(np.ones(2, dtype=bool), ("top",), "cycled", params) + assert layer.evaluate(0.0, cfg)[0] == pytest.approx(hue_to_wrgb(np.array(0.0), cfg)) + assert layer.evaluate(1.0, cfg)[0] == pytest.approx(hue_to_wrgb(np.array(0.25), cfg)) + + +def test_colour_layer_cycled_applies_the_spread_offsets(): + """The same offsets that make a chase run along drone order make a rainbow travel along it.""" + cfg = load_lighting_config() + params = {"period_s": 4.0, "offsets": np.array([0.0, 0.5])} + out = ColourLayer(np.ones(2, dtype=bool), ("top",), "cycled", params).evaluate(0.0, cfg) + assert out[0] == pytest.approx(hue_to_wrgb(np.array(0.0), cfg)) + assert out[1] == pytest.approx(hue_to_wrgb(np.array(0.5), cfg)) + + +def test_colour_layer_cycled_travels_forward_along_the_offsets(): + """The hue a drone carries is handed on to the *next* drone in the spread, never the previous. + + Offsets of 0 and 0.5 cannot see the direction `phase = t / period - offset` sets: 0.5 is its + own negative mod 1. Quarter-turn offsets break that symmetry. + """ + cfg = load_lighting_config() + offsets = np.array([0.0, 0.25, 0.5, 0.75]) + params = {"period_s": 4.0, "offsets": offsets} + layer = ColourLayer(np.ones(4, dtype=bool), ("top",), "cycled", params) + # Quarter-turn offsets land on exact `hue_steps` boundaries, so quantization is the identity. + assert layer.evaluate(0.0, cfg) == pytest.approx( + hue_to_wrgb(np.array([0.0, 0.75, 0.5, 0.25]), cfg) + ) + # A quarter period later, drone 1 carries what drone 0 carried -- the spectrum travelled along + # drone order. Reversed, it would have gone to drone 3. + assert layer.evaluate(1.0, cfg)[1] == pytest.approx(layer.evaluate(0.0, cfg)[0]) + + +def test_colour_layer_cycled_is_quantized_to_hue_steps_per_period(): + """Quantization is what restores cue dedup for the one primitive that defeats it.""" + cfg = load_lighting_config() + period = 4.0 + params = {"period_s": period, "offsets": np.zeros(1)} + layer = ColourLayer(np.ones(1, dtype=bool), ("top",), "cycled", params) + seen = { + tuple(layer.evaluate(t, cfg)[0]) + for t in np.linspace(0.0, period, 100 * cfg.hue_steps, endpoint=False) + } + assert len(seen) == cfg.hue_steps + + +def test_colour_layer_rejects_an_unknown_kind(): + cfg = load_lighting_config() + with pytest.raises(KeyError): + ColourLayer(np.ones(2, dtype=bool), ("top",), "strobe", {}).evaluate(0.0, cfg) + + +# Six drones, so even/odd split them three and three. The merge rules are position-free -- they see +# the swarm only through the masks the caller hands them -- so these tests never need positions. +N6 = 6 +ALL_6 = np.ones(N6, dtype=bool) +EVEN_6 = np.array([True, False, True, False, True, False]) +ODD_6 = ~EVEN_6 +BOTH_DECKS = ("top", "bot") + +# Deck indices into an (n, 2, 4) evaluate() result. +TOP, BOT = 0, 1 + +# Rounded palette entries, which is what a full-brightness read-out must produce. +RED = np.array([0.0, 255.0, 0.0, 0.0]) +GREEN = np.array([0.0, 0.0, 255.0, 0.0]) +WHITE = np.array([255.0, 0.0, 0.0, 0.0]) +AMBER = np.array([0.0, 146.0, 109.0, 0.0]) + + +def _linear_cfg() -> LightingConfig: + """Shipped config with gamma = 1, so a merged brightness reads straight off the WRGB output. + + Otherwise retuning gamma by eye on hardware breaks tests that are not about gamma. + """ + return dataclasses.replace(load_lighting_config(), gamma=1.0) + + +def _ramp( + mask: np.ndarray, + period_s: float = 4.0, + offsets: np.ndarray | None = None, + decks: tuple[str, ...] = BOTH_DECKS, +) -> BrightnessLayer: + """A ramp brightness layer. At period 4 the quarter phases give exact 0.75/0.5/0.25 values.""" + return BrightnessLayer( + mask, decks, "ramp", period_s, 0.5, np.zeros(N6) if offsets is None else offsets + ) + + +def _on(mask: np.ndarray, decks: tuple[str, ...] = BOTH_DECKS) -> BrightnessLayer: + """A `light_on` layer: an HTP participant pinned at 1.0.""" + return BrightnessLayer(mask, decks, "constant", 0.0, 0.5, np.zeros(N6)) + + +def _named(mask: np.ndarray, color: str, decks: tuple[str, ...] = BOTH_DECKS) -> ColourLayer: + return ColourLayer(mask, decks, "named", {"color": color}) + + +def _look( + t_start: float, + colours: tuple[ColourLayer, ...] = (), + brightnesses: tuple[BrightnessLayer, ...] = (), + off: np.ndarray | None = None, + positions: np.ndarray | None = None, +) -> Look: + return Look( + t_start, + tuple(colours), + tuple(brightnesses), + np.zeros((N6, 2), dtype=bool) if off is None else off, + positions, + ) + + +def _both_decks(mask: np.ndarray) -> np.ndarray: + """Lift an (n,) selection into the (n, 2) per-deck shape `Look.off_mask` carries.""" + return np.stack([mask, mask], axis=1) + + +def _base_colours() -> np.ndarray: + """The base colour of a look-less timeline: drone i carries hue i / n, in id order. + + Six drones land on the six primaries, so drone 0 is red, 2 green and 4 blue. + """ + return np.round(hue_to_wrgb(np.arange(N6) / N6, load_lighting_config())) + + +# Six unevenly spaced slots along +x, with the ids scrambled across them the way `_assign_positions` +# scrambles them. Drone i sits at slot SLOT_OF[i], counting from the -x end, so its +# base hue is SLOT_OF[i] / 6 rather than i / 6. A line rather than a ring here because the +# walk over it is unambiguous, which makes the expected hue per drone hand-computable. +LINE_SLOTS_6 = np.stack( + [np.array([-2.0, -1.4, -0.2, 0.9, 1.5, 2.8]), np.zeros(N6), np.full(N6, 1.3)], axis=1 +) +SLOT_OF = np.array([2, 5, 0, 3, 1, 4]) +LINE_6 = LINE_SLOTS_6[SLOT_OF] + + +def test_brightness_layer_constant_is_one_on_the_masked_rows(): + assert _on(EVEN_6).evaluate(3.7) == pytest.approx([1.0, 0.0, 1.0, 0.0, 1.0, 0.0]) + + +def test_brightness_layer_leaves_unselected_rows_at_zero(): + """Unselected rows are zero so the HTP merge can reduce with a plain `max`.""" + assert _ramp(EVEN_6).evaluate(0.0) == pytest.approx([1.0, 0.0, 1.0, 0.0, 1.0, 0.0]) + + +def test_brightness_layer_applies_its_phase_offsets(): + """The same offsets that make a chase run along drone order phase-shift its brightness.""" + offsets = np.array([0.0, 0.5, 0.0, 0.5, 0.0, 0.5]) + out = _ramp(ALL_6, period_s=4.0, offsets=offsets).evaluate(1.0) + assert out == pytest.approx([0.75, 0.25, 0.75, 0.25, 0.75, 0.25]) + + +def test_brightness_layer_travels_forward_along_the_offsets(): + """A chase runs from low drone index to high, and `phase = t / period - offset` says so. + + At offsets of 0 or 0.5 the sign of the offset term is invisible and a backwards chase reads + identically, so this is pinned on quarter-turn offsets, where the two differ. + """ + offsets = np.array([0.0, 0.25, 0.5, 0.75]) + layer = BrightnessLayer(np.ones(4, dtype=bool), BOTH_DECKS, "square", 4.0, 0.25, offsets) + lit = [] + for t in (0.0, 1.0, 2.0, 3.0): + on = np.flatnonzero(layer.evaluate(t)) + assert on.size == 1, f"a quarter duty over four evenly spread phases lights one, at t={t}" + lit.append(int(on[0])) + assert lit == [0, 1, 2, 3], "the lit drone must advance with time, not retreat" + + +def test_brightness_layer_rejects_an_unknown_kind(): + with pytest.raises(KeyError): + BrightnessLayer(ALL_6, BOTH_DECKS, "sawtooth", 4.0, 0.5, np.zeros(N6)).evaluate(0.0) + + +def test_final_wrgb_is_colour_times_brightness(): + """At t = 1 a period-4 ramp is exactly 0.75, so 255 * 0.75 = 191.25 rounds to 191.""" + cfg = _linear_cfg() + timeline = LightingTimeline( + [_look(0.0, (_named(ALL_6, "red"),), (_ramp(ALL_6),))], N6, 100.0, cfg + ) + assert timeline.evaluate(1.0)[0, TOP] == pytest.approx([0.0, 191.0, 0.0, 0.0]) + + +def test_gamma_applies_to_the_brightness_and_not_to_the_colour(): + """gamma = 2 turns b = 0.75 into 0.5625, so 255 * 0.5625 = 143.4 rounds to 143. + + At full brightness the two gammas must agree, which says gamma never touches the colour. + """ + squared = dataclasses.replace(load_lighting_config(), gamma=2.0) + dimmed = LightingTimeline( + [_look(0.0, (_named(ALL_6, "red"),), (_ramp(ALL_6),))], N6, 100.0, squared + ) + assert dimmed.evaluate(1.0)[0, TOP] == pytest.approx([0.0, 143.0, 0.0, 0.0]) + for cfg in (squared, _linear_cfg()): + full = LightingTimeline( + [_look(0.0, (_named(ALL_6, "red"),), (_on(ALL_6),))], N6, 100.0, cfg + ) + assert full.evaluate(1.0)[0, TOP] == pytest.approx(RED) + + +def test_brightness_never_changes_the_hue(): + """Dimming scales all four channels uniformly, so the channel ratio survives it.""" + cfg = _linear_cfg() + full = LightingTimeline([_look(0.0, (_named(ALL_6, "amber"),), (_on(ALL_6),))], N6, 100.0, cfg) + dim = LightingTimeline([_look(0.0, (_named(ALL_6, "amber"),), (_ramp(ALL_6),))], N6, 100.0, cfg) + bright, faded = full.evaluate(1.0)[0, TOP], dim.evaluate(1.0)[0, TOP] + assert bright == pytest.approx(AMBER) + assert faded == pytest.approx([0.0, 109.0, 82.0, 0.0]) + assert faded[1] / faded[2] == pytest.approx(bright[1] / bright[2], rel=0.02) + + +def test_a_colour_layer_never_changes_the_brightness(): + """Two different colours under the same brightness layer are both scaled by the same factor.""" + cfg = _linear_cfg() + for color, expected in (("red", [0.0, 191.0, 0.0, 0.0]), ("amber", [0.0, 109.0, 82.0, 0.0])): + timeline = LightingTimeline( + [_look(0.0, (_named(ALL_6, color),), (_ramp(ALL_6),))], N6, 100.0, cfg + ) + assert timeline.evaluate(1.0)[0, TOP] == pytest.approx(expected), color + + +def test_brightness_below_b_min_goes_fully_dark(): + """Below b_min the LED is quantization noise and coloured fringing, so it is cut. + + The floor precedes quantization, or it is inert -- the smallest non-zero bucket exceeds any + b_min. Hence the finer dimmer: at 16 steps the bottom bucket answers for every such value. + """ + cfg = dataclasses.replace(_linear_cfg(), brightness_steps=255) + timeline = LightingTimeline( + [_look(0.0, (_named(ALL_6, "red"),), (_ramp(ALL_6, period_s=1.0),))], N6, 100.0, cfg + ) + assert cfg.b_min == 0.02 + assert timeline.evaluate(0.99)[0, TOP] == pytest.approx(np.zeros(4)) # b = 0.01 < b_min + # b = 0.021 is above b_min and stays lit, even though its bucket floor (5/255 = 0.0196) is + # below it: the comparison reads the continuous value, not the quantized one. + assert timeline.evaluate(0.979)[0, TOP] == pytest.approx([0.0, 5.0, 0.0, 0.0]) + # Clear of the floor, quantization is the only thing acting: b = 0.25 -> bucket 63/255. + assert timeline.evaluate(0.75)[0, TOP] == pytest.approx([0.0, 63.0, 0.0, 0.0]) + + +def test_brightness_is_quantized_to_brightness_steps_before_the_multiply(): + """`brightness_steps` buckets the merged brightness, which is what lets dedup collapse a ramp. + + Sixteen samples over a period-4 ramp land on four bucket floors plus the exact-1.0 peak. + """ + cfg = dataclasses.replace(_linear_cfg(), brightness_steps=4) + timeline = LightingTimeline( + [_look(0.0, (_named(ALL_6, "red"),), (_ramp(ALL_6),))], N6, 100.0, cfg + ) + reds = [timeline.evaluate(0.25 * k)[0, TOP][1] for k in range(16)] + assert sorted(set(reds)) == [0.0, 64.0, 128.0, 191.0, 255.0] + + +def test_brightness_quantization_leaves_a_ramp_monotone(): + """The visual result is still a recognisable ramp: piecewise constant, but never non-monotone.""" + cfg = dataclasses.replace(_linear_cfg(), brightness_steps=8) + timeline = LightingTimeline( + [_look(0.0, (_named(ALL_6, "red"),), (_ramp(ALL_6),))], N6, 100.0, cfg + ) + # One descending ramp segment, sampled far finer than the quantizer's own resolution. + reds = np.array([timeline.evaluate(0.02 * k)[0, TOP][1] for k in range(200)]) + assert np.all(np.diff(reds) <= 0.0) + assert len(set(reds)) >= 6 # still a ramp, not a single hold + + +def test_quantization_floors_rather_than_rounds_so_it_can_only_darken(): + """`floor` and `round` differ only at the bottom of the range, and `floor` is the darker one. + + A brightness in ``[1/(2*steps), 1/steps)`` rounds *up* into the first lit bucket but floors to + zero, and only darkening is safe. `brightness_steps` is explicit: the shipped value is a lever. + """ + cfg = dataclasses.replace(_linear_cfg(), brightness_steps=16) + timeline = LightingTimeline( + [_look(0.0, (_named(ALL_6, "red"),), (_ramp(ALL_6, period_s=1.0),))], N6, 100.0, cfg + ) + # b = 0.05, inside [1/32, 1/16) and well above b_min: floor gives 0, round gives 1/16 and a + # visibly lit LED at 16/255. + assert timeline.evaluate(0.95)[0, TOP] == pytest.approx(np.zeros(4)) + + +def test_quantization_never_dims_a_fully_lit_drone(): + """A brightness of exactly 1.0 is the top bucket, so `light_on` and the base state are exact.""" + for steps in (4, 8, 16, 64): + cfg = dataclasses.replace(_linear_cfg(), brightness_steps=steps) + timeline = LightingTimeline( + [_look(0.0, (_named(ALL_6, "red"),), (_on(ALL_6),))], N6, 100.0, cfg + ) + assert timeline.evaluate(1.0)[0, TOP] == pytest.approx(RED), steps + + +def test_colour_ltp_later_layer_wins_on_its_subset(): + """light_color(all, blue) then light_color(even, amber) is two colours from two primitives.""" + cfg = _linear_cfg() + look = _look(0.0, (_named(ALL_6, "blue"), _named(EVEN_6, "amber"))) + out = LightingTimeline([look], N6, 100.0, cfg).evaluate(0.0)[:, TOP] + assert out[EVEN_6] == pytest.approx(np.tile(AMBER, (3, 1))) + assert out[ODD_6] == pytest.approx(np.tile([0.0, 0.0, 0.0, 204.0], (3, 1))) + + +def test_colour_ltp_overwrites_by_mask_not_by_non_zero_channels(): + """A merge that keeps whichever channel is non-zero blends a layer into whatever it covers. + + Drone 2's base green and `red` share no non-zero channel, so a "non-zero wins" merge yields + yellow. The stacked case pins the same trap between two layers rather than against the base. + """ + cfg = _linear_cfg() + over_base = LightingTimeline([_look(0.0, (_named(EVEN_6, "red"),))], N6, 100.0, cfg) + out = over_base.evaluate(0.0)[:, TOP] + assert out[EVEN_6] == pytest.approx(np.tile(RED, (3, 1))) + assert out[ODD_6] == pytest.approx(_base_colours()[ODD_6]) + stacked = LightingTimeline( + [_look(0.0, (_named(ALL_6, "white"), _named(EVEN_6, "red")))], N6, 100.0, cfg + ) + stacked_out = stacked.evaluate(0.0)[:, TOP] + assert stacked_out[EVEN_6] == pytest.approx(np.tile(RED, (3, 1))) + assert stacked_out[ODD_6] == pytest.approx(np.tile(WHITE, (3, 1))) + + +def test_colour_ltp_precedence_is_by_position_not_by_kind(): + """A named layer after a cycled one freezes its drones while the rest keep cycling.""" + cfg = _linear_cfg() + cycled = ColourLayer(ALL_6, BOTH_DECKS, "cycled", {"period_s": 4.0, "offsets": np.zeros(N6)}) + named_last = LightingTimeline([_look(0.0, (cycled, _named(EVEN_6, "amber")))], N6, 100.0, cfg) + frozen = named_last.evaluate(0.0)[:, TOP], named_last.evaluate(1.0)[:, TOP] + assert frozen[0][EVEN_6] == pytest.approx(np.tile(AMBER, (3, 1))) + assert frozen[1][EVEN_6] == pytest.approx(np.tile(AMBER, (3, 1))) + assert frozen[0][ODD_6] != pytest.approx(frozen[1][ODD_6]), "odd drones must keep cycling" + cycled_last = LightingTimeline([_look(0.0, (_named(EVEN_6, "amber"), cycled))], N6, 100.0, cfg) + reversed_out = cycled_last.evaluate(0.0)[:, TOP] + assert reversed_out == pytest.approx( + np.tile(np.round(hue_to_wrgb(np.array(0.0), cfg)), (N6, 1)) + ) + + +def test_brightness_htp_takes_the_max_not_the_sum(): + """Two ramps half a turn apart read 0.75 and 0.25 at t = 1: max is 191, a sum would be 255.""" + cfg = _linear_cfg() + ahead = _ramp(ALL_6) + behind = _ramp(ALL_6, offsets=np.full(N6, 0.5)) + look = _look(0.0, (_named(ALL_6, "red"),), (ahead, behind)) + assert LightingTimeline([look], N6, 100.0, cfg).evaluate(1.0)[0, TOP] == pytest.approx( + [0.0, 191.0, 0.0, 0.0] + ) + + +def test_light_off_kills_drones_that_a_competing_layer_lights(): + """light_off is a post-reduction kill mask, not an HTP participant. + + The ramp runs over *every* drone deliberately: as an HTP participant light_off contributes 0 + and `max(0, ramp)` is just `ramp`. Sample times stay clear of the ramp's dark last bucket. + """ + cfg = _linear_cfg() + look = _look(0.0, (_named(ALL_6, "red"),), (_ramp(ALL_6),), off=_both_decks(EVEN_6)) + timeline = LightingTimeline([look], N6, 100.0, cfg) + for t in (0.0, 1.0, 2.0, 3.0, 3.5): + out = timeline.evaluate(t) + assert np.all(out[EVEN_6] == 0.0), f"light_off drones must be dark at t={t}" + assert np.all(out[ODD_6, :, 1] > 0.0), f"the competing layer must still light odd at t={t}" + + +def test_light_on_dominates_a_pulse_underneath_it(): + """light_on is an HTP participant at 1.0, so it swallows every other layer.""" + cfg = _linear_cfg() + look = _look(0.0, (_named(ALL_6, "red"),), (_ramp(ALL_6), _on(ALL_6))) + timeline = LightingTimeline([look], N6, 100.0, cfg) + for t in (0.0, 1.0, 2.5, 3.9): + assert timeline.evaluate(t)[0, TOP] == pytest.approx(RED), t + + +def test_a_layer_reading_zero_still_suppresses_the_base(): + """Base suppression is decided by the layer's mask, never by the value it happens to return. + + A square wave off-phase returns exactly 0, so deciding from the value inverts the blink. + """ + cfg = _linear_cfg() + blink = BrightnessLayer( + np.array([True, False, False, False, False, False]), + BOTH_DECKS, + "square", + 4.0, + 0.5, + np.zeros(N6), + ) + timeline = LightingTimeline([_look(0.0, (_named(ALL_6, "red"),), (blink,))], N6, 100.0, cfg) + out = timeline.evaluate(3.0) # phase 0.75, past the duty, so the layer reads 0 + assert out[0, TOP] == pytest.approx(np.zeros(4)), "covered drone stays dark in the off phase" + assert out[1, TOP] == pytest.approx(RED), "an uncovered drone still sits on the base" + + +def test_a_look_does_not_inherit_the_previous_looks_layers(): + """Each key defines a complete look; the next key replaces it, deltas included.""" + cfg = _linear_cfg() + first = _look(0.0, (_named(ALL_6, "red"),), (_ramp(ALL_6),)) + second = _look(10.0, (_named(ALL_6, "green"),)) + timeline = LightingTimeline([first, second], N6, 100.0, cfg) + assert timeline.evaluate(1.0)[0, TOP] == pytest.approx([0.0, 191.0, 0.0, 0.0]) + # Inheriting the ramp would put t = 11 at phase 0.75, i.e. green at 0.25 -> 64, not 255. + assert timeline.evaluate(11.0)[0, TOP] == pytest.approx(GREEN) + + +def test_look_dispatch_is_by_start_time_and_independent_of_emission_order(): + """Lighting keys carry no ordering guarantee, so dispatch sorts by t_start.""" + cfg = _linear_cfg() + late = _look(10.0, (_named(ALL_6, "green"),)) + early = _look(2.0, (_named(ALL_6, "red"),)) + timeline = LightingTimeline([late, early], N6, 100.0, cfg) + # Drone 1's base hue is neither look colour, so all three states are distinguishable on it. + base = _base_colours()[1] + assert timeline.evaluate(0.0)[1, TOP] == pytest.approx(base), "before any look, the base state" + assert timeline.evaluate(2.0)[1, TOP] == pytest.approx(RED), "a look owns its own start instant" + assert timeline.evaluate(9.99)[1, TOP] == pytest.approx(RED) + assert timeline.evaluate(10.0)[1, TOP] == pytest.approx(GREEN) + # Re-entered last: a merge that painted over the stored base would have destroyed it by now. + assert timeline.evaluate(0.0)[1, TOP] == pytest.approx(base), "the base survives a look" + + +def test_an_empty_timeline_is_full_on(): + """One forgetful emission must not black out the show, so the base is a fallback at 1.0.""" + cfg = _linear_cfg() + empty = LightingTimeline([], N6, 100.0, cfg).evaluate(5.0) + forced = LightingTimeline([_look(0.0, (), (_on(ALL_6),))], N6, 100.0, cfg).evaluate(5.0) + assert empty.shape == (N6, 2, 4) + assert empty == pytest.approx(forced), "an empty stack must match an explicit light_on(all)" + assert empty.max() == 255.0, "and be undimmed, not merely non-zero" + + +def test_the_base_colour_is_each_drones_own_hue_off_the_wheel(): + """A colour-less emission reproduces today's per-drone colouring exactly. + + A swarm flattened to one hue is unidentifiable in the viewer and in flight. Same wheel as + `generate_default_colors`, with the blue dim now carried by `channel_gain`. + """ + cfg = _linear_cfg() + out = LightingTimeline([], N6, 100.0, cfg).evaluate(0.0)[:, TOP] + for i in range(N6): + assert out[i] == pytest.approx(np.round(hue_to_wrgb(np.array(i / N6), cfg))), i + assert len({tuple(row) for row in out}) == N6, "no two drones may share a base colour" + assert out[4] == pytest.approx([0.0, 0.0, 0.0, 204.0]), "and blue still carries its 0.8 dim" + + +def test_the_base_colour_within_a_look_follows_neighbour_order_not_id_order(): + """The default wheel must read as a wheel *around the formation*, not around the id list. + + `_assign_positions` hands ids out by cheapest assignment, so an id-keyed wheel puts unrelated + hues side by side. Within a look the wheel follows that look's nearest-neighbour walk. + """ + cfg = _linear_cfg() + out = LightingTimeline([_look(0.0, positions=LINE_6)], N6, 100.0, cfg).evaluate(3.0)[:, TOP] + for drone, slot in enumerate(SLOT_OF): + expected = np.round(hue_to_wrgb(np.array(slot / N6), cfg)) + assert out[drone] == pytest.approx(expected), drone + assert len({tuple(row) for row in out}) == N6, "still one distinct hue per drone" + assert not np.allclose(out, _base_colours()), "ring order and id order must actually differ" + + +def test_no_drone_changes_colour_when_the_first_look_takes_over(): + """A timeline whose first key resolves to t > 0 must not re-colour the whole swarm at it. + + Without a snapshot the pre-show look falls back to the id-order scramble the walk exists to + remove, and the swarm snaps out of it in one frame -- measured, 10 of 10 drones changed. + """ + cfg = _linear_cfg() + timeline = LightingTimeline([_look(20.0, positions=LINE_6)], N6, 100.0, cfg) + before, after = timeline.evaluate(19.9)[:, TOP], timeline.evaluate(20.0)[:, TOP] + assert before == pytest.approx(after), "the first look must not repaint the swarm" + for drone, slot in enumerate(SLOT_OF): + expected = np.round(hue_to_wrgb(np.array(slot / N6), cfg)) + assert before[drone] == pytest.approx(expected), drone + assert not np.allclose(before, _base_colours()), "id order and walk order must actually differ" + + +def test_the_pre_show_base_takes_the_earliest_looks_snapshot_whatever_the_emission_order(): + """The looks arrive in emission order, so "first" means earliest in time, not first in the list.""" + cfg = _linear_cfg() + reformed = LINE_SLOTS_6[(N6 - 1) - SLOT_OF] + timeline = LightingTimeline( + [_look(40.0, positions=reformed), _look(20.0, positions=LINE_6)], N6, 100.0, cfg + ) + for drone, slot in enumerate(SLOT_OF): + expected = np.round(hue_to_wrgb(np.array(slot / N6), cfg)) + assert timeline.evaluate(5.0)[drone, TOP] == pytest.approx(expected), drone + + +def test_a_timeline_with_no_looks_keeps_the_id_ordered_base(): + """Nothing was authored and there is no snapshot to order against, so id order stands. + + A show carrying no lighting reproduces `generate_default_colors` drone for drone. + """ + cfg = _linear_cfg() + out = LightingTimeline([], N6, 100.0, cfg).evaluate(3.0)[:, TOP] + assert out == pytest.approx(_base_colours()) + + +def test_each_look_orders_the_base_colour_against_its_own_snapshot(): + """The wheel re-sorts as formations change, which is why the snapshot rides on the look. + + The second look mirrors the assignment end to end, so every base hue must mirror with it -- + which a single wheel computed once for the timeline could not do. + """ + cfg = _linear_cfg() + reformed = LINE_SLOTS_6[(N6 - 1) - SLOT_OF] + timeline = LightingTimeline( + [_look(0.0, positions=LINE_6), _look(20.0, positions=reformed)], N6, 100.0, cfg + ) + before, after = timeline.evaluate(5.0)[:, TOP], timeline.evaluate(25.0)[:, TOP] + for drone, slot in enumerate(SLOT_OF): + held = np.round(hue_to_wrgb(np.array(slot / N6), cfg)) + assert before[drone] == pytest.approx(held), drone + mirrored = np.round(hue_to_wrgb(np.array((N6 - 1 - slot) / N6), cfg)) + assert after[drone] == pytest.approx(mirrored), drone + + +def test_the_base_colour_of_a_one_drone_swarm_is_the_top_of_the_wheel(): + """The walk over a single point has no step to take, and `rank / n` must stay 0, not wrap.""" + cfg = _linear_cfg() + solo = Look(0.0, (), (), np.zeros((1, 2), dtype=bool), np.array([[0.4, -1.0, 1.1]])) + out = LightingTimeline([solo], 1, 100.0, cfg).evaluate(3.0) + assert out.shape == (1, 2, 4) + assert out[0, TOP] == pytest.approx(np.round(hue_to_wrgb(np.array(0.0), cfg))) + + +def test_a_layer_on_one_drone_leaves_the_others_on_the_base(): + """The base is suppressed per drone, not for the whole swarm.""" + cfg = _linear_cfg() + covered = np.zeros(N6, dtype=bool) + covered[3] = True + timeline = LightingTimeline( + [_look(0.0, (_named(ALL_6, "red"),), (_ramp(covered),))], N6, 100.0, cfg + ) + out = timeline.evaluate(1.0)[:, TOP] + assert out[3] == pytest.approx([0.0, 191.0, 0.0, 0.0]), "drone 3 is driven by the layer" + assert out[5] == pytest.approx(RED), "drone 5 has no layer, so it sits at brightness 1.0" + + +def test_a_colour_only_look_is_full_brightness(): + """The failure-safe property: colour primitives alone reproduce today's lights-on behaviour.""" + cfg = _linear_cfg() + timeline = LightingTimeline([_look(0.0, (_named(ALL_6, "teal"),))], N6, 100.0, cfg) + for t in (0.0, 12.5, 60.0): + assert timeline.evaluate(t) == pytest.approx(np.tile([0.0, 0.0, 146.0, 87.0], (N6, 2, 1))) + + +def test_a_top_only_brightness_effect_leaves_bot_on_the_base(): + cfg = _linear_cfg() + look = _look(0.0, (_named(ALL_6, "red"),), (_ramp(ALL_6, decks=("top",)),)) + out = LightingTimeline([look], N6, 100.0, cfg).evaluate(1.0) + assert out[0, TOP] == pytest.approx([0.0, 191.0, 0.0, 0.0]) + assert out[0, BOT] == pytest.approx(RED) + + +def test_colour_stacks_resolve_independently_per_deck(): + """A slow wash on bot under a different colour on top is free -- the hardware has two decks.""" + cfg = _linear_cfg() + look = _look( + 0.0, (_named(ALL_6, "red", decks=("top",)), _named(ALL_6, "green", decks=("bot",))) + ) + out = LightingTimeline([look], N6, 100.0, cfg).evaluate(0.0) + assert out[0, TOP] == pytest.approx(RED) + assert out[0, BOT] == pytest.approx(GREEN) + + +def test_light_off_is_per_deck(): + cfg = _linear_cfg() + off = np.zeros((N6, 2), dtype=bool) + off[:, TOP] = True + look = _look(0.0, (_named(ALL_6, "red"),), (_on(ALL_6),), off=off) + out = LightingTimeline([look], N6, 100.0, cfg).evaluate(0.0) + assert np.all(out[:, TOP] == 0.0) + assert out[:, BOT] == pytest.approx(np.tile(RED, (N6, 1))) + + +def test_the_terminal_blackout_is_present_whatever_was_emitted(): + """The drones never land lit, and this is not the LLM's to override.""" + cfg = _linear_cfg() + look = _look(0.0, (_named(ALL_6, "white"),), (_on(ALL_6),)) + timeline = LightingTimeline([look], N6, 10.0, cfg) + assert timeline.evaluate(9.8)[0, TOP] == pytest.approx(WHITE) + for t in (9.9, 9.95, 10.0, 50.0): + assert np.all(timeline.evaluate(t) == 0.0), f"the swarm must be dark at t={t}" + + +def test_a_look_after_the_blackout_cannot_relight_the_swarm(): + cfg = _linear_cfg() + late = _look(9.95, (_named(ALL_6, "white"),), (_on(ALL_6),)) + assert np.all(LightingTimeline([late], N6, 10.0, cfg).evaluate(9.96) == 0.0) + + +def test_evaluate_returns_integral_wrgb_per_drone_and_deck(): + """`_apply_drone_color` asserts integral 0-255 values, so the read-out rounds.""" + cfg = load_lighting_config() + look = _look(0.0, (_named(ALL_6, "amber"),), (_ramp(ALL_6),)) + out = LightingTimeline([look], N6, 100.0, cfg).evaluate(1.3) + assert out.shape == (N6, 2, 4) + assert np.all(out == np.round(out)), "every emitted WRGB must be integral" + assert np.all(out >= 0.0) and np.all(out <= 255.0) + + +def test_evaluate_rgb01_folds_white_into_rgb_and_selects_the_deck(): + """The viewer has no white channel, and a marker shows the top deck.""" + cfg = _linear_cfg() + look = _look( + 0.0, (_named(ALL_6, "red", decks=("top",)), _named(ALL_6, "green", decks=("bot",))) + ) + timeline = LightingTimeline([look], N6, 100.0, cfg) + assert timeline.evaluate_rgb01(0.0).shape == (N6, 3) + assert timeline.evaluate_rgb01(0.0)[0] == pytest.approx([1.0, 0.0, 0.0]) + assert timeline.evaluate_rgb01(0.0, "bot")[0] == pytest.approx([0.0, 1.0, 0.0]) + white = LightingTimeline([_look(0.0, (_named(ALL_6, "white"),))], N6, 100.0, cfg) + assert white.evaluate_rgb01(0.0)[0] == pytest.approx([1.0, 1.0, 1.0]), "W folds into all three" diff --git a/tests/unit/test_lighting_compile.py b/tests/unit/test_lighting_compile.py new file mode 100644 index 0000000..2f0cafb --- /dev/null +++ b/tests/unit/test_lighting_compile.py @@ -0,0 +1,336 @@ +"""Unit tests for the hardware cue read-out.""" + +import dataclasses + +import numpy as np +import pytest + +from swarm_gpt.core.lighting import ( + LightingConfig, + LightingTimeline, + build_look, + compile_cues, + hue_to_wrgb, + load_lighting_config, +) + +CFG = load_lighting_config() + +BPM = 120.0 +N6 = 6 +POSITIONS_6 = np.stack([np.arange(6.0), np.zeros(6), np.ones(6)], axis=1) +URIS_6 = [f"radio://0/80/2M/E7E7E7E70{i}" for i in range(N6)] + +N10 = 10 +# Ten drones, which is the swarm the chase measurements were taken on. The ids are scrambled +# across the line the way `_assign_positions` scrambles them, so the `neighbour` walk that orders +# the chase disagrees with id order and a per-drone assertion cannot pass by accident. +LINE_10 = np.stack([np.arange(10.0), np.zeros(10), np.ones(10)], axis=1) +POSITIONS_10 = LINE_10[[4, 9, 0, 6, 2, 8, 1, 7, 3, 5]] +URIS_10 = [f"radio://0/80/2M/E7E7E7E7{i:02d}" for i in range(N10)] + +# `DroneSwarm.col_freq` defaults to 10 Hz (drone_swarm.py:48) and caps cue consumption. +COL_FREQ = 10.0 + +# How far before the end of the show the unconditional blackout lands. +BLACKOUT_LEAD_S = 0.1 + +ALL = ("all", ()) + +# Float slack for comparing a decimal cue grid: 0.1 is not representable in binary, so consecutive +# differences of k / 10.0 land ~4e-16 below 0.1. Eight orders of magnitude below the tolerance used +# here, and eight above it lies the period itself -- this is float noise, not semantic slack. +GRID_SLACK = 1e-9 + + +def _action(primitive: str, **params: object) -> dict: + """One entry of the emitted actions array.""" + return {"primitive": primitive, "params": params} + + +def _timeline(actions: list[dict], t_end: float, cfg: LightingConfig = CFG) -> LightingTimeline: + """A one-look timeline over the six-drone fixture, the look starting at t = 0.""" + return LightingTimeline([build_look(actions, 0.0, POSITIONS_6, N6, cfg, BPM)], N6, t_end, cfg) + + +def _lit_grid_samples(cues: dict[float, np.ndarray], t_end: float) -> int: + """Count the grid ticks on which a drone-deck's compiled cue stream is not dark. + + Cues are step events under zero-order hold, so a tick shows the last cue at or before it. The + grid is rebuilt as ``k / col_freq``, bit-identical to the times that were compiled. + """ + times = np.arange(int(round((t_end - BLACKOUT_LEAD_S) * COL_FREQ))) / COL_FREQ + cue_times = np.array(sorted(cues)) + holding = np.searchsorted(cue_times, times, "right") - 1 + held = np.stack([cues[float(cue_times[k])] for k in holding]) + return int(np.count_nonzero(held.any(axis=1))) + + +def test_minimum_cue_spacing_is_never_denser_than_col_freq(): + """The cue-drift regression test, and the sharpest constraint in the design. + + `_stream_reference` drains one cue per deck per tick and never drops, so a denser list plays + back slowed. An off-grid `t_end` is where an unguarded compile crowds the blackout. + """ + # `period_beats = 0.05` is 0.025 s at 120 BPM, so `rainbow` and `blink` are both clamped to the + # 0.2 s Nyquist floor: the show does contain the fastest legal effect. `sweep` sits just above + # it on purpose, and `chase` is held at 0.3 s by the lit-window floor its `length = 2` of + # six implies. A floor-period square wave lands on exactly two samples per period, + # which aliases into a *static* lit/unlit pattern — a legitimate compile result, but one that + # leaves some drone-deck permanently dark and so makes its spacing assertion vacuous. + fastest = [ + _action("rainbow", sel=ALL, period_beats=0.05, spread="index", deck="both"), + _action( + "chase", + sel=ALL, + period_beats=0.5, + length=2, + group_size=1, + spread="neighbour", + deck="both", + ), + _action("blink", sel=("odd", ()), period_beats=0.05, duty=0.5, deck="top"), + _action("sweep", sel=ALL, period_beats=0.7, axis="x", deck="bot"), + ] + period = 1.0 / COL_FREQ + for t_end in (30.0, 30.05, 27.37): + top, bot = compile_cues(_timeline(fastest, t_end), URIS_6, COL_FREQ, t_end) + for deck, cues in (("top", top), ("bot", bot)): + for uri, deck_cues in cues.items(): + assert len(deck_cues) > 150, f"{deck} {uri} must be dense, not trivially deduped" + gaps = np.diff(sorted(deck_cues)) + assert gaps.min() >= period - GRID_SLACK, f"{deck} {uri} at t_end={t_end}" + + +@pytest.mark.parametrize("bpm", [120.0, 120.0000001]) +def test_a_chase_lights_every_drone_on_the_compile_grid(bpm: float): + """The quantity that has to survive the sampling grid is each drone's lit window. + + Clamping the *period* leaves a ten-drone chase's window at half a tick, so a drone is never lit + while the preview looks right. The second tempo flipped which five dropped under the old clamp. + """ + t_end = 60.1 # so the grid is exactly 0.0 .. 59.9 plus the blackout at 60.0 + action = _action( + "chase", sel=ALL, period_beats=1.0, length=1, group_size=1, spread="neighbour", deck="both" + ) + look = build_look([action], 0.0, POSITIONS_10, N10, CFG, bpm) + timeline = LightingTimeline([look], N10, t_end, CFG) + top, bot = compile_cues(timeline, URIS_10, COL_FREQ, t_end) + for deck, cues in (("top", top), ("bot", bot)): + lit = np.array([_lit_grid_samples(cues[uri], t_end) for uri in URIS_10]) + assert lit.min() > 0, f"{deck} leaves drones {np.flatnonzero(lit == 0)} dark all show" + assert lit.max() - lit.min() <= 0.5 * lit.mean(), f"{deck} on-time is uneven: {lit}" + + +def test_a_chase_period_the_clamp_stretches_still_lights_every_drone(): + """The case where the old clamp fired and still produced a broken show. + + The period-only clamp logged "clamping to 0.200 s" as though the effect were representable. + Measured over ten drones, six never lit at all. + """ + t_end = 60.1 + action = _action( + "chase", sel=ALL, period_beats=0.25, length=1, group_size=1, spread="neighbour", deck="both" + ) + look = build_look([action], 0.0, POSITIONS_10, N10, CFG, BPM) + top, _ = compile_cues(LightingTimeline([look], N10, t_end, CFG), URIS_10, COL_FREQ, t_end) + lit = np.array([_lit_grid_samples(top[uri], t_end) for uri in URIS_10]) + assert lit.min() > 0, f"drones {np.flatnonzero(lit == 0)} are dark all show" + assert lit.max() - lit.min() <= 0.5 * lit.mean(), f"uneven on-time: {lit}" + + +def test_a_static_look_dedups_to_one_cue_plus_the_terminal_blackout(): + """Dedup means a static look costs ~1 cue, not `10 x duration`.""" + t_end = 60.0 + action = _action("light_color", sel=ALL, color="teal", deck="both") + top, bot = compile_cues(_timeline([action], t_end), URIS_6, COL_FREQ, t_end) + for cues in (top, bot): + for uri in URIS_6: + times = sorted(cues[uri]) + assert len(times) == 2, "one content cue, then the blackout" + assert times[0] == 0.0 + assert times[1] == pytest.approx(t_end - BLACKOUT_LEAD_S) + assert cues[uri][times[0]] == pytest.approx(np.round(CFG.palette["teal"])) + assert cues[uri][times[1]] == pytest.approx(np.zeros(4)) + + +def test_a_gradient_look_dedups_like_a_static_one(): + """`gradient` is time-invariant, so it costs exactly what a named colour costs.""" + t_end = 60.0 + action = _action("gradient", sel=ALL, color_a="red", color_b="blue", by="index", deck="both") + top, _ = compile_cues(_timeline([action], t_end), URIS_6, COL_FREQ, t_end) + for uri in URIS_6: + assert len(top[uri]) == 2 + near, far = top[URIS_6[0]][0.0], top[URIS_6[5]][0.0] + assert near == pytest.approx(np.round(CFG.palette["red"])), "the near end is exactly color_a" + assert far == pytest.approx(np.round(CFG.palette["blue"])), "the far end is exactly color_b" + + +def test_rainbow_cue_count_tracks_hue_steps_over_the_period(): + """`hue_steps` is what keeps a continuously advancing hue from defeating dedup. + + Quantizing makes the hue piecewise-constant and brings the rate to `min(col_freq, hue_steps / + period)` -- 2 Hz at 24 steps over a 12 s cycle, a fifth of the ceiling. + """ + period_s = 12.0 + t_end = period_s + BLACKOUT_LEAD_S # the sample grid then covers exactly one cycle + action = _action( + "rainbow", sel=ALL, period_beats=period_s * BPM / 60.0, spread="none", deck="both" + ) + top, bot = compile_cues(_timeline([action], t_end), URIS_6, COL_FREQ, t_end) + rate = min(COL_FREQ, CFG.hue_steps / period_s) + expected = int(rate * period_s) + 1 # one cue per hue step, plus the blackout + for cues in (top, bot): + for uri in URIS_6: + assert len(cues[uri]) == expected + assert expected < COL_FREQ * period_s / 4, "and far below the undeduped ceiling" + + +def test_brightness_steps_collapses_a_slow_brightness_waveform(): + """`brightness_steps` is to `sine`/`ramp` what `hue_steps` is to `rainbow`. + + Unquantized, a 16 s breathe changes on nearly every tick. Bucketing lets dedup collapse the + runs to roughly two buckets' worth of edges per period. + """ + t_end = 60.0 + action = _action("pulse", sel=ALL, period_beats=32.0, deck="both") + # 255 buckets is one per addressable output level, i.e. the unquantized behaviour. + unquantized = dataclasses.replace(CFG, brightness_steps=255) + raw, _ = compile_cues(_timeline([action], t_end, unquantized), URIS_6, COL_FREQ, t_end) + quantized, _ = compile_cues(_timeline([action], t_end), URIS_6, COL_FREQ, t_end) + for uri in URIS_6: + assert len(raw[uri]) > 0.5 * COL_FREQ * t_end, "the cost this is mitigating" + assert len(quantized[uri]) < len(raw[uri]) / 3 + + +def test_square_wave_primitives_are_unaffected_by_brightness_quantization(): + """`blink`, `chase` and `sweep` never paid the cost: `square` is already piecewise-constant.""" + t_end = 60.0 + cases = [ + _action("blink", sel=ALL, period_beats=2.0, duty=0.5, deck="both"), + _action( + "chase", + sel=ALL, + period_beats=4.0, + length=3, + group_size=1, + spread="neighbour", + deck="both", + ), + _action("sweep", sel=ALL, period_beats=4.0, axis="x", deck="both"), + ] + unquantized = dataclasses.replace(CFG, brightness_steps=255) + for action in cases: + raw, _ = compile_cues(_timeline([action], t_end, unquantized), URIS_6, COL_FREQ, t_end) + quantized, _ = compile_cues(_timeline([action], t_end), URIS_6, COL_FREQ, t_end) + for uri in URIS_6: + assert sorted(raw[uri]) == sorted(quantized[uri]), action["primitive"] + for t, wrgb in raw[uri].items(): + assert quantized[uri][t] == pytest.approx(wrgb), action["primitive"] + + +def test_decks_compile_independently(): + """A top-only effect leaves bot deduped down to its base colour plus the blackout.""" + t_end = 20.0 + actions = [ + _action("light_color", sel=ALL, color="red", deck="both"), + _action("blink", sel=ALL, period_beats=1.0, duty=0.5, deck="top"), + ] + top, bot = compile_cues(_timeline(actions, t_end), URIS_6, COL_FREQ, t_end) + assert len(bot[URIS_6[0]]) == 2 + assert len(top[URIS_6[0]]) > 20 + + +def test_the_terminal_blackout_cue_is_emitted_explicitly(): + """The timeline implements the blackout as an early return, which guarantees zeros *from* + `t_end - 0.1` but does not put a sample there: a uniform grid anchored at 0 lands on that + instant only by luck, so `compile_cues` must emit it itself.""" + t_end = 12.34 # deliberately off the 10 Hz grid + action = _action("light_on", sel=ALL, deck="both") + top, bot = compile_cues(_timeline([action], t_end), URIS_6, COL_FREQ, t_end) + blackout = t_end - BLACKOUT_LEAD_S + for cues in (top, bot): + for uri in URIS_6: + assert blackout in cues[uri], "the drones must never land lit" + assert cues[uri][blackout] == pytest.approx(np.zeros(4)) + assert max(cues[uri]) == blackout, "and nothing may be emitted after it" + + +def test_cue_dicts_are_keyed_by_uri_for_both_decks(): + """The two returned dicts drop straight into `execute_choreography(color_top=, color_bot=)`.""" + t_end = 10.0 + top, bot = compile_cues(LightingTimeline([], N6, t_end, CFG), URIS_6, COL_FREQ, t_end) + assert list(top) == URIS_6 + assert list(bot) == URIS_6 + + +def test_every_compiled_wrgb_is_integral_and_in_range(): + """`_apply_drone_color` asserts 0-255 and packs the channels with `int()` (drone_swarm.py:553).""" + t_end = 20.0 + actions = [ + _action("rainbow", sel=ALL, period_beats=4.0, spread="index", deck="both"), + _action("pulse", sel=ALL, period_beats=2.0, deck="both"), + ] + top, bot = compile_cues(_timeline(actions, t_end), URIS_6, COL_FREQ, t_end) + for cues in (top, bot): + for deck_cues in cues.values(): + for wrgb in deck_cues.values(): + assert wrgb.shape == (4,) + assert np.all(wrgb == np.round(wrgb)), f"non-integral WRGB {wrgb}" + assert np.all(wrgb >= 0.0) and np.all(wrgb <= 255.0) + + +def test_a_lighting_less_show_compiles_to_todays_static_cue_structure(): + """The failure-safe property, end to end: an emission carrying no lighting at all + reproduces the two-cue-per-drone stub the deploy path uses today (backend.py:330-337).""" + t_end = 45.0 + top, bot = compile_cues(LightingTimeline([], N6, t_end, CFG), URIS_6, COL_FREQ, t_end) + for cues in (top, bot): + for i, uri in enumerate(URIS_6): + times = sorted(cues[uri]) + assert len(times) == 2 + assert times[0] == 0.0 + assert times[1] == pytest.approx(t_end - BLACKOUT_LEAD_S) + assert cues[uri][times[0]] == pytest.approx( + np.round(hue_to_wrgb(np.array(i / N6), CFG)) + ) + assert cues[uri][times[1]] == pytest.approx(np.zeros(4)) + + +@pytest.mark.parametrize("t_end", [0.0, 0.05, 0.15, 0.19]) +def test_a_show_too_short_for_the_cue_grid_raises(t_end: float): + """Below `1 / col_freq + 0.1` the grid cannot open at 0 and can open before it. + + A show shorter than one tick plus the blackout lead loses every tick: `t_end = 0.05` compiles to + a single cue at **-0.05**. Unreachable via `deploy`, but `compile_cues` is a public entry point. + """ + with pytest.raises(ValueError, match="too short"): + compile_cues(LightingTimeline([], N6, t_end, CFG), URIS_6, COL_FREQ, t_end) + + +def test_the_shortest_compilable_show_still_opens_at_zero(): + """One tick plus the blackout lead is the boundary, and it must be on the legal side of it.""" + t_end = 1.0 / COL_FREQ + BLACKOUT_LEAD_S + top, bot = compile_cues(LightingTimeline([], N6, t_end, CFG), URIS_6, COL_FREQ, t_end) + for cues in (top, bot): + for uri in URIS_6: + times = sorted(cues[uri]) + assert times[0] == 0.0, "the payload contract's initial colour has to be defined" + assert times[-1] == pytest.approx(t_end - BLACKOUT_LEAD_S) + + +def test_the_short_show_guard_tracks_the_configured_cue_rate(): + """The boundary is one cue tick, not a hardcoded 0.2 s: `col_freq` is the source of truth.""" + t_end = 0.16 + faster = dataclasses.replace(CFG, col_freq=20.0) + top, _ = compile_cues(LightingTimeline([], N6, t_end, faster), URIS_6, 20.0, t_end) + assert sorted(top[URIS_6[0]])[0] == 0.0, "legal at 20 Hz, where a tick is 0.05 s" + with pytest.raises(ValueError, match="too short"): + compile_cues(LightingTimeline([], N6, t_end, CFG), URIS_6, COL_FREQ, t_end) + + +def test_a_uri_list_that_does_not_cover_the_swarm_raises(): + """Silently zipping short would leave the uncovered drones dark for a whole show.""" + t_end = 10.0 + with pytest.raises(ValueError): + compile_cues(LightingTimeline([], N6, t_end, CFG), URIS_6[:3], COL_FREQ, t_end) diff --git a/tests/unit/test_lighting_primitives.py b/tests/unit/test_lighting_primitives.py new file mode 100644 index 0000000..005f0c1 --- /dev/null +++ b/tests/unit/test_lighting_primitives.py @@ -0,0 +1,840 @@ +"""Unit tests for the LLM-facing lighting primitive vocabulary. + +Companion to ``test_lighting.py``, which covers the engine underneath. This file covers only the +twelve catalogued primitives and the ``build_look`` dispatch that turns actions into a ``Look``. +""" + +import dataclasses +import logging + +import numpy as np +import pytest +from conftest import with_ulp_noise + +from swarm_gpt.core.lighting import ( + LIGHTING_PRIMITIVES, + LightingTimeline, + Look, + build_look, + hue_to_wrgb, + load_lighting_config, +) + +CFG = load_lighting_config() + +# Which axis faces the audience is a property of the room, checked in test_lighting.py. The two +# assertions below that turn on the left/right split state the axis their fixture is laid out +# along, so re-rigging the room cannot fail them. +CFG_STAGE_X = dataclasses.replace(CFG, stage_axis="+x") + +# The aliasing floor: an effect faster than `col_freq / 2` cannot be represented by the cue +# stream. Derived here the same way the clamp derives it, from the one configured cue rate. +MIN_PERIOD_S = 2.0 / CFG.col_freq + +# 120 BPM makes one beat exactly 0.5 s, so every `period_beats` below converts to a round number. +BPM = 120.0 + +N6 = 6 +# Six drones spread along +x, no two sharing a coordinate and none on the x centroid (0.5), so the +# left/right split is unambiguous. z runs 1.0 .. 2.0 evenly, which makes the `z` spread exact. +POSITIONS_6 = np.array( + [ + [-2.0, 0.0, 1.0], + [-1.0, 1.0, 1.2], + [0.0, -1.0, 1.4], + [1.0, 0.5, 1.6], + [2.0, -0.5, 1.8], + [3.0, 1.0, 2.0], + ] +) + +N8 = 8 +# Eight drones evenly spaced along +x, so the centroid is 3.5 and `radius` is hand-computable. +POSITIONS_8 = np.stack([np.arange(8.0), np.zeros(8), np.ones(8)], axis=1) + +ALL = ("all", ()) + + +def _action(primitive: str, **params: object) -> dict: + """One entry of the emitted actions array.""" + return {"primitive": primitive, "params": params} + + +def _build( + action: dict, positions: np.ndarray = POSITIONS_6, n: int = N6, bpm: float = BPM +) -> Look: + """Build a single-action look against the six-drone fixture.""" + return build_look([action], 0.0, positions, n, CFG, bpm) + + +@pytest.fixture +def clamp_log( + caplog: pytest.LogCaptureFixture, monkeypatch: pytest.MonkeyPatch +) -> pytest.LogCaptureFixture: + """`caplog`, wired so it actually sees this module's records. + + The ROS `launch` pytest plugin calls `logging.setLoggerClass` with ``propagate = False``, so + every logger built after it bypasses the root logger `caplog` listens on. + """ + logger = logging.getLogger("swarm_gpt.core.lighting") + monkeypatch.setattr(logger, "propagate", True) + caplog.set_level(logging.WARNING, logger=logger.name) + return caplog + + +def test_the_catalogue_holds_exactly_the_spec_primitives(): + assert set(LIGHTING_PRIMITIVES) == { + "light_color", + "gradient", + "fade", + "rainbow", + "light_on", + "light_off", + "pulse", + "blink", + "strobe_decay", + "chase", + "sweep", + "ripple_light", + "alternate_blink", + } + + +def test_unknown_primitive_raises(): + with pytest.raises(KeyError): + _build(_action("laser_show", sel=ALL, deck="both")) + + +def test_build_look_carries_its_start_time(): + look = build_look( + [_action("light_color", sel=ALL, color="red", deck="both")], 12.5, POSITIONS_6, N6, CFG, BPM + ) + assert look.t_start == 12.5 + + +def test_deck_choice_maps_onto_the_deck_tuple(): + for deck, expected in (("top", ("top",)), ("bot", ("bot",)), ("both", ("top", "bot"))): + look = _build(_action("light_color", sel=ALL, color="red", deck=deck)) + assert look.colour_layers[0].decks == expected, deck + + +def test_unknown_deck_raises(): + with pytest.raises(KeyError): + _build(_action("light_color", sel=ALL, color="red", deck="middle")) + + +def test_colour_layers_keep_their_order_in_the_actions_array(): + """Colour resolves by position in the actions array, so build_look must not reorder them.""" + look = build_look( + [ + _action("light_color", sel=ALL, color="blue", deck="both"), + _action("pulse", sel=ALL, period_beats=4.0, deck="both"), + _action("light_color", sel=("even", ()), color="amber", deck="both"), + ], + 0.0, + POSITIONS_6, + N6, + CFG, + BPM, + ) + assert [layer.params["color"] for layer in look.colour_layers] == ["blue", "amber"] + assert len(look.brightness_layers) == 1 + + +def test_light_color_builds_a_named_colour_layer(): + look = _build(_action("light_color", sel=("even", ()), color="amber", deck="both")) + assert look.brightness_layers == () + (layer,) = look.colour_layers + assert layer.kind == "named" + assert layer.params == {"color": "amber"} + assert list(layer.mask) == [False, True, False, True, False, True], "even is 1-indexed" + assert layer.decks == ("top", "bot") + + +@pytest.mark.parametrize( + "params", + [ + {"primitive": "light_color", "color": "chartreuse"}, + {"primitive": "gradient", "color_a": "chartreuse", "color_b": "red", "by": "x"}, + {"primitive": "gradient", "color_a": "red", "color_b": "chartreuse", "by": "x"}, + ], +) +def test_an_off_palette_colour_is_rejected_when_the_layer_is_built(params: dict): + """The palette is resolved lazily at read-out, so an unchecked name fails far too late. + + `ColourLayer.evaluate` indexes `cfg.palette` per frame, putting a bare `KeyError` mid-render + or mid-deploy -- long past the reprompt loop. + """ + params = dict(params) + with pytest.raises(KeyError, match="chartreuse"): + _build(_action(params.pop("primitive"), sel=("all", ()), deck="both", **params)) + + +def test_gradient_interpolation_parameter_is_inclusive_so_color_b_is_reached(): + """`s` must hit 1.0 exactly at the far end. + + ``spread_offsets`` is half-open so a sweep's two ends do not share a phase, which would leave + `color_b` unreachable -- the far drone sitting at `lerp(a, b, 5/6)`. + """ + look = _build( + _action("gradient", sel=ALL, color_a="red", color_b="blue", by="index", deck="both") + ) + (layer,) = look.colour_layers + assert layer.kind == "gradient" + assert layer.params["color_a"] == "red" + assert layer.params["color_b"] == "blue" + s = layer.params["s"] + assert s.shape == (N6,) + assert s == pytest.approx([0.0, 0.2, 0.4, 0.6, 0.8, 1.0]) + out = layer.evaluate(0.0, CFG) + assert out[0] == pytest.approx(CFG.palette["red"]), "the near end is exactly color_a" + assert out[5] == pytest.approx(CFG.palette["blue"]), "the far end is exactly color_b" + + +def test_gradient_by_index_ranks_within_the_selected_subset(): + """The subset is unevenly spaced on purpose: rank-in-subset and absolute index differ on it. + + `ids(2, 4, 6)` normalizes to [0, 0.5, 1] either way and so cannot tell them apart. + `ids(1, 2, 6)` ranks to [0, 0.5, 1] but normalizes absolutely to [0, 0.2, 1]. + """ + look = _build( + _action( + "gradient", + sel=("ids", (1, 2, 6)), + color_a="red", + color_b="blue", + by="index", + deck="top", + ) + ) + s = look.colour_layers[0].params["s"] + assert s[[0, 1, 5]] == pytest.approx([0.0, 0.5, 1.0]) + assert s[1] != pytest.approx(0.2), "the absolute drone index would have put drone 2 here" + assert s[[2, 3, 4]] == pytest.approx([0.0, 0.0, 0.0]), "unselected rows stay at 0" + + +def test_gradient_by_axis_min_max_normalizes_the_coordinate(): + """x runs -2 .. 3 over the fixture, a span of 5.""" + look = _build(_action("gradient", sel=ALL, color_a="red", color_b="green", by="x", deck="both")) + assert look.colour_layers[0].params["s"] == pytest.approx([0.0, 0.2, 0.4, 0.6, 0.8, 1.0]) + + +def test_gradient_by_radius_normalizes_distance_from_the_subset_centroid(): + """Distances from the x centroid (3.5) are 0.5 .. 3.5, so the span is 3.0.""" + look = _build( + _action("gradient", sel=ALL, color_a="red", color_b="blue", by="radius", deck="both"), + positions=POSITIONS_8, + n=N8, + ) + assert look.colour_layers[0].params["s"] == pytest.approx( + [1.0, 2 / 3, 1 / 3, 0.0, 0.0, 1 / 3, 2 / 3, 1.0] + ) + + +def test_gradient_over_a_formation_with_no_extent_stays_finite(): + """A flat formation gives `gradient(by="z")` a zero span, which unguarded is 0 / 0. + + Most formations are planar, and the resulting `nan` propagates to `_apply_drone_color`, whose + `int()` packing is the last thing between it and the radio. + """ + flat = np.stack([np.arange(6.0), np.zeros(6), np.full(6, 1.5)], axis=1) + look = _build( + _action("gradient", sel=ALL, color_a="red", color_b="blue", by="z", deck="both"), + positions=flat, + ) + (layer,) = look.colour_layers + assert layer.params["s"] == pytest.approx(np.zeros(N6)) + out = layer.evaluate(0.0, CFG) + assert np.all(np.isfinite(out)), "a span-free gradient must not emit nan" + assert out == pytest.approx(np.tile(CFG.palette["red"], (N6, 1))), "it collapses onto color_a" + + +def test_gradient_rejects_an_unknown_by_axis(): + with pytest.raises(KeyError): + _build( + _action("gradient", sel=ALL, color_a="red", color_b="blue", by="spiral", deck="both") + ) + + +def test_rainbow_builds_a_cycled_layer_carrying_the_spread_offsets(): + look = _build(_action("rainbow", sel=ALL, period_beats=8.0, spread="index", deck="both")) + assert look.brightness_layers == () + (layer,) = look.colour_layers + assert layer.kind == "cycled" + assert layer.params["period_s"] == pytest.approx(4.0), "8 beats at 120 BPM" + assert layer.params["offsets"] == pytest.approx(np.arange(N6) / N6) + + +def test_rainbow_takes_the_whole_spread_vocabulary(): + """`none` cycles the swarm in sync; `x` sweeps the spectrum across the stage.""" + synced = _build(_action("rainbow", sel=ALL, period_beats=8.0, spread="none", deck="both")) + assert synced.colour_layers[0].params["offsets"] == pytest.approx(np.zeros(N6)) + swept = _build(_action("rainbow", sel=ALL, period_beats=8.0, spread="x", deck="both")) + assert swept.colour_layers[0].params["offsets"] == pytest.approx(np.arange(N6) / N6) + + +def test_light_on_builds_a_constant_brightness_layer(): + look = _build(_action("light_on", sel=("first", (2,)), deck="top")) + assert look.colour_layers == () + (layer,) = look.brightness_layers + assert layer.kind == "constant" + assert layer.decks == ("top",) + assert list(layer.mask) == [True, True, False, False, False, False] + assert layer.offsets.shape == (N6,) + assert layer.evaluate(7.3) == pytest.approx([1.0, 1.0, 0.0, 0.0, 0.0, 0.0]) + assert not look.off_mask.any(), "light_on is a layer, not a mask" + + +def test_light_off_becomes_an_off_mask_bit_not_a_brightness_layer(): + """Under a plain `max` a layer contributing 0 is a no-op, so light_off is a kill mask.""" + look = _build(_action("light_off", sel=("odd", ()), deck="both")) + assert look.brightness_layers == () + assert look.colour_layers == () + assert look.off_mask.shape == (N6, 2) + assert list(look.off_mask[:, 0]) == [True, False, True, False, True, False], "odd is 1-indexed" + assert list(look.off_mask[:, 1]) == [True, False, True, False, True, False] + + +def test_light_off_marks_only_the_named_deck(): + look = _build(_action("light_off", sel=ALL, deck="bot")) + assert not look.off_mask[:, 0].any(), "top is untouched" + assert look.off_mask[:, 1].all() + + +def test_pulse_is_a_sine_with_the_whole_group_in_sync(): + look = _build(_action("pulse", sel=ALL, period_beats=2.0, deck="both")) + (layer,) = look.brightness_layers + assert layer.kind == "sine" + assert layer.period_s == pytest.approx(1.0) + assert layer.offsets == pytest.approx(np.zeros(N6)) + + +def test_blink_is_a_square_carrying_its_own_duty(): + look = _build(_action("blink", sel=ALL, period_beats=1.0, duty=0.25, deck="both")) + (layer,) = look.brightness_layers + assert layer.kind == "square" + assert layer.period_s == pytest.approx(0.5) + assert layer.duty == pytest.approx(0.25) + assert layer.offsets == pytest.approx(np.zeros(N6)) + + +def test_strobe_decay_is_a_ramp_flashing_on_the_beat(): + look = _build(_action("strobe_decay", sel=ALL, period_beats=1.0, deck="both")) + (layer,) = look.brightness_layers + assert layer.kind == "ramp" + assert layer.period_s == pytest.approx(0.5) + assert layer.evaluate(0.0) == pytest.approx(np.ones(N6)), "full on the beat" + assert layer.evaluate(0.25) == pytest.approx(np.full(N6, 0.5)), "decayed halfway" + + +def _chase_action(**overrides: object) -> dict: + """A `chase` action, `spread` included -- the catalogue gives it one and the schema needs it.""" + params = dict( + sel=ALL, period_beats=4.0, length=2, group_size=1, spread="neighbour", deck="both" + ) + return _action("chase", **(params | overrides)) + + +def test_chase_is_a_square_running_along_the_neighbour_spread(): + """`chase` used to hardcode `index`, which is spatially scrambled by construction. + + POSITIONS_6 is not a straight line, so the walk picks drone 2 up last off a long jump back -- + the walk's accepted seam. An evenly spaced line would rank identically to `index`. + """ + (layer,) = _build(_chase_action()).brightness_layers + assert layer.kind == "square" + assert layer.period_s == pytest.approx(2.0) + assert layer.duty == pytest.approx(2 / N6), "duty = length / n_sel" + assert layer.offsets == pytest.approx(np.array([0, 1, 5, 2, 3, 4]) / N6) + assert layer.offsets != pytest.approx(np.arange(N6) / N6), "and not drone id order" + + +def test_chase_honours_whichever_spread_it_is_given(): + """`index` stays reachable for a choreography that deliberately addresses drones by id. + + A `chase` that ignored the emitted `spread` would look right on every fixture whose walk + happens to agree with id order. + """ + by_walk = _build(_chase_action()).brightness_layers[0] + by_id = _build(_chase_action(spread="index")).brightness_layers[0] + assert by_walk.offsets == pytest.approx(np.array([0, 1, 5, 2, 3, 4]) / N6) + assert by_id.offsets == pytest.approx(np.arange(N6) / N6) + assert by_id.offsets != pytest.approx(by_walk.offsets) + + +def test_chase_without_a_spread_fails_loudly(): + """`spread` is required, so a short emission raises rather than flying on a silent default. + + Neither production path can reach here missing it, so a `.get(default)` would be unreachable + code masking a required field. `KeyError` becomes an `LLMFormatError`, so it reprompts. + """ + params = {"sel": ALL, "period_beats": 4.0, "length": 2, "group_size": 1, "deck": "both"} + with pytest.raises(KeyError): + _build({"primitive": "chase", "params": params}) + + +def test_a_built_look_colours_the_swarm_along_its_own_snapshot(): + """The default hue wheel end to end, through `build_look` rather than a hand-assembled `Look`. + + The base colour ranks against the look's snapshot at read-out, so it has to ride on the `Look`. + POSITIONS_6 walks as 0, 1, 3, 4, 5, 2, so walk order and id order genuinely disagree here. + """ + look = _build(_action("light_on", sel=("first", (4,)), deck="both")) + out = LightingTimeline([look], N6, 100.0, CFG).evaluate(3.0)[:, 0] + for drone, rank in enumerate([0, 1, 5, 2, 3, 4]): + assert out[drone] == pytest.approx(np.round(hue_to_wrgb(np.array(rank / N6), CFG))), drone + + +def test_chase_lights_exactly_length_drones_at_any_instant(): + """`duty = length / n_sel` is what makes the running light a window of fixed width. + + Period and sample times keep every phase an exact binary fraction: 16 beats is 8 s at 120 BPM, + and 0.25 s steps put `t / period` on multiples of 1/32 against offsets of 1/8. + """ + look = _build(_chase_action(period_beats=16.0, length=3), positions=POSITIONS_8, n=N8) + (layer,) = look.brightness_layers + assert layer.period_s == pytest.approx(8.0) + assert layer.duty == pytest.approx(3 / N8) + for t in np.arange(0.0, 8.0, 0.25): + assert int(layer.evaluate(float(t)).sum()) == 3, t + + +def test_chase_duty_is_computed_over_the_selection_not_the_whole_swarm(): + """`duty = length / n_sel` is over the *selected* subset, which `sel=all` cannot distinguish. + + On the full swarm the mask's population and its length agree. Over `first(4)` of eight they + differ by two, and `chase(first(4), length=2)` must light 2 drones at a time, not 1. + """ + look = _build( + _chase_action(sel=("first", (4,)), period_beats=16.0), positions=POSITIONS_8, n=N8 + ) + (layer,) = look.brightness_layers + assert layer.duty == pytest.approx(2 / 4), "over the four selected, not the eight in the swarm" + # 16 beats is 8 s at 120 BPM, so 0.25 s steps keep every phase an exact binary fraction. + for t in np.arange(0.0, 8.0, 0.25): + assert int(layer.evaluate(float(t)).sum()) == 2, t + + +def test_chase_over_an_empty_selection_builds_instead_of_dividing_by_zero(): + """`ids([])` now raises, but an empty selection is still reachable through `right`. + + Every layer is then a no-op and the duty irrelevant; the floor exists only because `chase` would + divide by zero, and `ZeroDivisionError` escapes the three `_build_look` catches. + """ + no_stage_extent = np.stack([np.zeros(N6), np.arange(6.0), np.ones(N6)], axis=1) + look = build_look( + [_chase_action(sel=("right", ()))], 0.0, no_stage_extent, N6, CFG_STAGE_X, BPM + ) + (layer,) = look.brightness_layers + assert not layer.mask.any() + assert layer.evaluate(1.3) == pytest.approx(np.zeros(N6)) + + +def test_chase_group_size_quantizes_whichever_spread_it_runs_along(): + """The supervisor's "blinking with different group size": the window advances group-by-group. + + POSITIONS_8 is an evenly spaced line, so the walk *is* id order and the buckets are + hand-writable. The fixture pins the bucketing, not which order it buckets. + """ + look = _build(_chase_action(group_size=2), positions=POSITIONS_8, n=N8) + (layer,) = look.brightness_layers + assert layer.offsets == pytest.approx([0.0, 0.0, 0.25, 0.25, 0.5, 0.5, 0.75, 0.75]) + + +@pytest.mark.parametrize("group_size", [0, 2]) +def test_chase_group_size_is_never_silently_inert(group_size: int): + """`group_size` is a plain `chase` parameter in the catalogue, carrying no spread restriction. + + Validation used to sit in the ranked-spread branch, so `group_size=0` with `spread="x"` built + happily while `spread="neighbour"` raised, and a `group_size` of 2 was accepted then ignored. + """ + with pytest.raises(ValueError, match="group_size"): + _build(_chase_action(group_size=group_size, spread="x")) + + +def test_sweep_uses_the_named_axis_spread(): + look = _build(_action("sweep", sel=ALL, period_beats=4.0, axis="z", deck="both")) + (layer,) = look.brightness_layers + assert layer.kind == "square" + assert layer.duty == pytest.approx(0.5), "sweep takes the default duty" + # z runs 1.0 .. 2.0 evenly, so the half-open spatial normalization gives exactly rank / n. + assert layer.offsets == pytest.approx(np.arange(N6) / N6) + across = _build(_action("sweep", sel=ALL, period_beats=4.0, axis="y", deck="both")) + assert across.brightness_layers[0].offsets != pytest.approx(layer.offsets) + + +def test_ripple_light_is_a_sine_over_the_radius_spread(): + """Distances from the centroid are 0.5 .. 3.5; the half-open normalization scales by 7/8.""" + look = _build( + _action("ripple_light", sel=ALL, period_beats=4.0, deck="both"), positions=POSITIONS_8, n=N8 + ) + (layer,) = look.brightness_layers + assert layer.kind == "sine" + assert layer.offsets == pytest.approx([7 / 8, 7 / 12, 7 / 24, 0.0, 0.0, 7 / 24, 7 / 12, 7 / 8]) + + +def test_alternate_blink_maps_by_onto_the_two_alternate_spreads(): + parity = _build( + _action("alternate_blink", sel=ALL, period_beats=2.0, by="parity", deck="both") + ).brightness_layers[0] + side = build_look( + [_action("alternate_blink", sel=ALL, period_beats=2.0, by="side", deck="both")], + 0.0, + POSITIONS_6, + N6, + CFG_STAGE_X, + BPM, + ).brightness_layers[0] + assert parity.kind == "square" and side.kind == "square" + assert parity.offsets == pytest.approx([0.0, 0.5, 0.0, 0.5, 0.0, 0.5]) + # The fixture's x centroid is 0.5, so drones 3-5 are stage right when the axis is "+x". + assert side.offsets == pytest.approx([0.0, 0.0, 0.0, 0.5, 0.5, 0.5]) + + +def test_alternate_blink_puts_its_two_halves_in_antiphase(): + """This is why the primitive exists: `blink` has no phase parameter, so two `blink` calls + cannot express a ping-pong — the half-period offset can only come from a spread.""" + (layer,) = _build( + _action("alternate_blink", sel=ALL, period_beats=2.0, by="parity", deck="both") + ).brightness_layers + assert layer.period_s == pytest.approx(1.0) + assert layer.evaluate(0.0) == pytest.approx([1.0, 0.0, 1.0, 0.0, 1.0, 0.0]) + assert layer.evaluate(0.5) == pytest.approx([0.0, 1.0, 0.0, 1.0, 0.0, 1.0]) + for t in np.linspace(0.0, 1.0, 21): + out = layer.evaluate(float(t)) + assert out[0::2] == pytest.approx(1.0 - out[1::2]), f"halves must never coincide at t={t}" + + +def test_rainbow_and_chase_share_the_spread_offsets(): + """They differ only in the attribute driven — hue versus intensity.""" + rainbow = _build( + _action("rainbow", sel=ALL, period_beats=8.0, spread="radius", deck="both") + ).colour_layers[0] + chase = _build(_chase_action(period_beats=8.0, length=1, spread="radius")).brightness_layers[0] + assert rainbow.params["offsets"] == pytest.approx(chase.offsets) + assert rainbow.params["period_s"] == pytest.approx(chase.period_s) + + +def test_offsets_are_full_swarm_shaped_even_for_a_subset(): + """`BrightnessLayer.evaluate` indexes offsets by absolute drone index, so a subset-shaped + array would silently phase-shift the wrong drones.""" + look = _build(_chase_action(sel=("ids", (4, 5, 6)), length=1)) + (layer,) = look.brightness_layers + assert layer.offsets.shape == (N6,) + assert layer.offsets[[3, 4, 5]] == pytest.approx([0.0, 1 / 3, 2 / 3]) + assert layer.offsets[[0, 1, 2]] == pytest.approx([0.0, 0.0, 0.0]) + + +# A horizontal ring, built exactly the way `form_circle` builds one (`motion_primitives.py:473`): +# no extent in z at all, and every drone the same distance from the centre. `cos`/`sin` rather than +# hand-written axis coordinates, because that leaves the radii equal only to within a couple of +# ulps — the case `_normalize_span`'s tolerance exists for, and the one an exactly-equal fixture +# cannot reach. +_RING_ANGLES = np.linspace(0.0, 2.0 * np.pi, N6, endpoint=False) +RING_6 = np.stack( + [2.0 * np.cos(_RING_ANGLES), 2.0 * np.sin(_RING_ANGLES), np.full(N6, 1.4)], axis=1 +) + + +def _spread_offsets_of(look: Look) -> np.ndarray: + """The phase offsets of a single-action look's layer, whichever attribute it drives.""" + if look.brightness_layers: + return look.brightness_layers[0].offsets + return look.colour_layers[0].params["offsets"] + + +@pytest.mark.parametrize( + ("action", "axis"), + [ + (_action("sweep", sel=ALL, period_beats=4.0, axis="z", deck="both"), "z"), + (_action("ripple_light", sel=ALL, period_beats=4.0, deck="both"), "radius"), + (_action("rainbow", sel=ALL, period_beats=4.0, spread="z", deck="both"), "z"), + ], +) +def test_a_spread_with_no_extent_to_run_along_warns( + action: dict, axis: str, clamp_log: pytest.LogCaptureFixture +): + """`sweep(axis="z")` on a planar formation degrades into a synchronised blink, silently. + + Every offset comes out 0, and `ripple_light` on a ring is the same. Both are natural to author + over a `form_circle` and both are legal, so this warns -- but it must warn. + """ + look = _build(action, positions=RING_6, n=N6) + assert _spread_offsets_of(look) == pytest.approx(np.zeros(N6)), "the collapse being reported" + records = [r for r in clamp_log.records if r.name == "swarm_gpt.core.lighting"] + assert len(records) == 1 + assert records[0].levelno == logging.WARNING + message = records[0].getMessage() + assert action["primitive"] in message, "name the primitive" + assert axis in message, "and the axis it had nothing to run along" + + +def test_a_spread_with_extent_does_not_warn(clamp_log: pytest.LogCaptureFixture): + """POSITIONS_6 runs 1.0 .. 2.0 in z, so the same `sweep` has a real gradient to follow.""" + _build(_action("sweep", sel=ALL, period_beats=4.0, axis="z", deck="both")) + assert not clamp_log.records + + +def test_a_single_drone_selection_does_not_warn(clamp_log: pytest.LogCaptureFixture): + """One drone has nothing to spread against by definition, which is not a degenerate formation.""" + _build(_action("sweep", sel=("first", (1,)), period_beats=4.0, axis="z", deck="both")) + assert not clamp_log.records + + +def test_gradient_by_radius_on_a_cos_sin_ring_collapses_rather_than_amplifying_float_noise(): + """The colour-axis twin of `_normalize_span`'s tolerance, and the same fixture forces it. + + Against exact zero a `cos`/`sin` ring takes the non-degenerate branch and divides by rounding + noise, giving per-drone random colours. Equal literals cannot catch it, so this is a real ring. + """ + ring = with_ulp_noise(RING_6) + radii = np.linalg.norm(ring - ring.mean(axis=0), axis=1) + span = float(radii.max() - radii.min()) + assert 0.0 < span < 1e-12, ( + f"the fixture must be degenerate only to within float noise, got a span of {span}; " + "an exactly-equal ring passes this test without the tolerance and pins nothing" + ) + look = _build( + _action("gradient", sel=ALL, color_a="red", color_b="blue", by="radius", deck="both"), + positions=ring, + ) + (layer,) = look.colour_layers + assert layer.params["s"] == pytest.approx(np.zeros(N6)) + assert layer.evaluate(0.0, CFG) == pytest.approx(np.tile(CFG.palette["red"], (N6, 1))), ( + "it collapses onto color_a" + ) + + +def test_a_gradient_with_no_extent_to_run_along_warns(clamp_log: pytest.LogCaptureFixture): + """A collapsed gradient paints one flat colour, which no emission distinguishes from intent. + + `gradient(by="radius")` over a ring puts every drone on `color_a`, which is `light_color`. + """ + _build( + _action("gradient", sel=ALL, color_a="red", color_b="blue", by="radius", deck="both"), + positions=RING_6, + ) + records = [r for r in clamp_log.records if r.name == "swarm_gpt.core.lighting"] + assert len(records) == 1 + assert records[0].levelno == logging.WARNING + message = records[0].getMessage() + assert "gradient" in message, "name the primitive" + assert "radius" in message, "and the axis it had nothing to run along" + + +def test_a_gradient_with_extent_does_not_warn(clamp_log: pytest.LogCaptureFixture): + """POSITIONS_6 runs 1.0 .. 2.0 in z, so the same `gradient` has a real span to interpolate.""" + _build(_action("gradient", sel=ALL, color_a="red", color_b="blue", by="z", deck="both")) + assert not clamp_log.records + + +def test_period_beats_converts_through_the_song_bpm(): + """`period_beats` is beats, not seconds — the same emission is faster at a faster tempo.""" + for bpm, expected in ((120.0, 2.0), (60.0, 4.0), (90.0, 8.0 / 3.0)): + look = _build(_action("pulse", sel=ALL, period_beats=4.0, deck="both"), bpm=bpm) + assert look.brightness_layers[0].period_s == pytest.approx(expected), bpm + + +def test_nyquist_clamp_warns_and_clamps_rather_than_rejecting(clamp_log: pytest.LogCaptureFixture): + """An effect faster than col_freq / 2 aliases, so it is slowed — never rejected, because one + over-eager LLM parameter must not fail a whole show.""" + look = _build(_action("blink", sel=ALL, period_beats=0.1, duty=0.5, deck="both")) + assert look.brightness_layers[0].period_s == pytest.approx(MIN_PERIOD_S), ( + "clamped, not rejected" + ) + records = [r for r in clamp_log.records if r.name == "swarm_gpt.core.lighting"] + assert len(records) == 1 + assert records[0].levelno == logging.WARNING + message = records[0].getMessage() + assert "0.050" in message, "the requested period must be reported" + assert "0.200" in message, "and so must the applied one" + + +def test_the_clamp_guards_each_drones_lit_window_not_the_period( + clamp_log: pytest.LogCaptureFixture, +): + """`chase` divides the period into `length / n_sel`, so the period is the wrong quantity. + + A 0.5 s period clears the 0.2 s Nyquist floor while each drone is lit for 0.0625 s, under one + cue tick, so its on-interval fits between two ticks: `period_s >= n_sel / (length x col_freq)`. + """ + look = _build(_chase_action(period_beats=1.0, length=1), positions=POSITIONS_8, n=N8) + assert look.brightness_layers[0].period_s == pytest.approx(N8 / CFG.col_freq) + assert look.brightness_layers[0].period_s > MIN_PERIOD_S, "the Nyquist floor alone lets it pass" + message = clamp_log.records[0].getMessage() + assert "0.062" in message, "the requested lit window, which is the quantity being clamped" + assert "0.100" in message, "and the applied one, which is one cue tick" + + +def test_the_clamp_never_returns_a_period_below_the_nyquist_floor(): + """A `chase` wide enough to want a sub-Nyquist period still cannot have one. + + A duty of 1.0 has a single-tick lit-window floor, but a waveform sampled under twice per period + aliases whatever its duty -- so the two floors are a `max`, not a replacement. + """ + look = _build( + _chase_action(period_beats=0.1, length=N8), positions=POSITIONS_8, n=N8 + ).brightness_layers[0] + assert look.period_s == pytest.approx(MIN_PERIOD_S) + + +def test_a_short_blink_duty_widens_its_window_and_keeps_its_period( + clamp_log: pytest.LogCaptureFixture, +): + """A sub-tick lit window is widened to one tick; the period is left where the author put it. + + `blink` runs at `spread="none"`, so no drone is skipped relative to another and stretching the + period would move a legal 0.1-duty stab off the beat entirely. Leaving the window under a tick + is not an option either: it falls between two cue samples for every period that is not a whole + multiple of one, and the flash disappears from the cue list rather than merely coarsening. + """ + look = _build(_action("blink", sel=ALL, period_beats=1.0, duty=0.1, deck="both")) + layer = look.brightness_layers[0] + assert layer.period_s == pytest.approx(0.5), "the beat the stab was written against" + assert layer.duty * layer.period_s == pytest.approx(1.0 / CFG.col_freq), "one tick lit" + assert len(clamp_log.records) == 1 + + +def test_a_blink_whose_window_already_clears_a_tick_is_left_alone( + clamp_log: pytest.LogCaptureFixture, +): + look = _build(_action("blink", sel=ALL, period_beats=1.0, duty=0.5, deck="both")) + assert look.brightness_layers[0].duty == pytest.approx(0.5) + assert not clamp_log.records + + +def test_a_short_blink_stays_lit_on_a_period_that_does_not_divide_the_cue_tick(): + """The failure the widening exists to stop: flashes dropping out of the compiled cue list. + + At 0.375 s the period is no multiple of the 0.1 s tick, so an un-widened 0.1 duty is lit on + barely a third of its periods, at irregular times. Every period must now carry a lit sample. + """ + look = _build(_action("blink", sel=ALL, period_beats=0.75, duty=0.1, deck="both")) + layer = look.brightness_layers[0] + ticks = np.arange(0.0, 3.0, 1.0 / CFG.col_freq) + lit = ticks[np.array([layer.evaluate(float(t))[0] for t in ticks]) > 0.0] + for start in np.arange(0.0, 3.0 - layer.period_s, layer.period_s): + assert np.any((lit >= start) & (lit < start + layer.period_s)), f"dark period at {start}" + + +def test_the_nyquist_clamp_also_covers_rainbow(): + """`rainbow` drives hue off the same period, so it aliases the same way.""" + look = _build(_action("rainbow", sel=ALL, period_beats=0.01, spread="none", deck="both")) + assert look.colour_layers[0].params["period_s"] == pytest.approx(MIN_PERIOD_S) + + +def test_a_legal_period_is_left_alone(clamp_log: pytest.LogCaptureFixture): + look = _build(_action("pulse", sel=ALL, period_beats=1.0, deck="both")) + assert look.brightness_layers[0].period_s == pytest.approx(0.5) + assert [r for r in clamp_log.records if r.name == "swarm_gpt.core.lighting"] == [] + + +def test_the_clamp_floor_tracks_the_configured_cue_rate(clamp_log: pytest.LogCaptureFixture): + """The floor is `2 / col_freq`, read from the one place the cue rate is written down. + + A second hardcoded copy mis-tunes the moment `DroneSwarm.col_freq` moves: too high throttles + effects, too low stops guarding cue drift. So one emission survives 20 Hz and clamps at 10. + """ + # 0.3 beats is 0.15 s at 120 BPM: above the 0.1 s floor at 20 Hz, below the 0.2 s floor at 10. + action = _action("blink", sel=ALL, period_beats=0.3, duty=0.5, deck="both") + faster = dataclasses.replace(CFG, col_freq=20.0) + at_20hz = build_look([action], 0.0, POSITIONS_6, N6, faster, BPM) + assert at_20hz.brightness_layers[0].period_s == pytest.approx(0.15), "legal at 20 Hz" + assert not clamp_log.records, "and so must not warn" + at_10hz = build_look([action], 0.0, POSITIONS_6, N6, CFG, BPM) + assert at_10hz.brightness_layers[0].period_s == pytest.approx(0.2), "clamped at 10 Hz" + assert len(clamp_log.records) == 1 + + +def test_upper_and_lower_reach_build_look_as_selectors(): + """The height split is a `sel`, so it must work under every primitive, not just colour ones. + + `POSITIONS_6` climbs 1.0 .. 2.0 in z with a mean of 1.5, so drones 4-6 are the upper half. + """ + upper = _build(_action("light_color", sel=("upper", ()), color="amber", deck="both")) + lower = _build(_action("pulse", sel=("lower", ()), period_beats=2.0, deck="both")) + assert list(upper.colour_layers[0].mask) == [False, False, False, True, True, True] + assert list(lower.brightness_layers[0].mask) == [True, True, True, False, False, False] + + +def test_two_colour_actions_on_opposite_decks_make_one_drone_two_tone(): + """The prompt now offers split decks as an effect, so the emitted pair must survive the compile. + + Both actions cover every drone, so a look that merged the decks would leave only the second. + """ + look = build_look( + [ + _action("light_color", sel=ALL, color="amber", deck="top"), + _action("light_color", sel=ALL, color="azure", deck="bot"), + ], + 0.0, + POSITIONS_6, + N6, + CFG, + BPM, + ) + wrgb = LightingTimeline([look], N6, 60.0, CFG).evaluate(0.0) + assert wrgb[0, 0] == pytest.approx(CFG.palette["amber"], abs=1.0), "top ring amber" + assert wrgb[0, 1] == pytest.approx(CFG.palette["azure"], abs=1.0), "bot ring azure" + + +def test_the_new_palette_entries_hold_the_constant_channel_sum(): + """A hue that breaks the sum reads brighter or dimmer than the rest of the wheel.""" + cfg = CFG + gain_b = cfg.channel_gain[3] + for name in ("violet", "gold", "silver"): + w, r, g, b = cfg.palette[name] + assert w + r + g + b / gain_b == pytest.approx(255.0, abs=0.5), name + + +def test_violet_lands_between_indigo_and_magenta(): + cfg = CFG + assert cfg.palette["indigo"][1] < cfg.palette["violet"][1] < cfg.palette["magenta"][1] + assert cfg.palette["magenta"][3] < cfg.palette["violet"][3] < cfg.palette["indigo"][3] + + +def test_gold_and_silver_are_the_only_hue_plus_white_entries(): + """`rainbow` cannot reach a W-mixed entry, so which ones exist is worth pinning.""" + cfg = CFG + mixed = {n for n, v in cfg.palette.items() if v[0] > 0 and v[1:].sum() > 0} + assert mixed == {"gold", "silver"} + + +def test_fade_interpolates_in_time_and_holds_the_far_end(): + action = _action( + "fade", sel=["all", []], color_a="red", color_b="blue", duration_beats=4.0, deck="both" + ) + look = build_look([action], 10.0, POSITIONS_6, N6, CFG, BPM) + cfg = CFG + layer = look.colour_layers[0] + red, blue = cfg.palette["red"], cfg.palette["blue"] + # 4 beats at 120 BPM is 2 s, so the fade runs over t = 10..12. + assert np.allclose(layer.evaluate(10.0, cfg)[0], red) + assert np.allclose(layer.evaluate(12.0, cfg)[0], blue) + assert np.allclose(layer.evaluate(11.0, cfg)[0], 0.5 * red + 0.5 * blue) + # A one-shot, so it holds rather than looping once the look outlives its duration. + assert np.allclose(layer.evaluate(40.0, cfg)[0], blue) + + +def test_fade_before_its_look_starts_is_pinned_to_color_a(): + action = _action( + "fade", sel=["all", []], color_a="red", color_b="blue", duration_beats=4.0, deck="both" + ) + look = build_look([action], 10.0, POSITIONS_6, N6, CFG, BPM) + assert np.allclose(look.colour_layers[0].evaluate(0.0, CFG)[0], CFG.palette["red"]) + + +def test_fade_shorter_than_a_cue_tick_is_clamped(clamp_log: pytest.LogCaptureFixture): + tick_s = 1.0 / CFG.col_freq + look = _build( + _action( + "fade", + sel=["all", []], + color_a="red", + color_b="blue", + duration_beats=0.001, + deck="both", + ) + ) + assert look.colour_layers[0].params["duration_s"] == pytest.approx(tick_s) + assert "quantize to a cut" in clamp_log.text diff --git a/tests/unit/test_motion_primitives.py b/tests/unit/test_motion_primitives.py index e977361..bc98682 100644 --- a/tests/unit/test_motion_primitives.py +++ b/tests/unit/test_motion_primitives.py @@ -1,8 +1,16 @@ -"""Tests for F1: time_to_finish_s on formation primitives.""" +"""Tests for time_to_finish_s on formation primitives and compact drone-id addressing.""" import numpy as np +import pytest -from swarm_gpt.core.motion_primitives import form_circle, form_cone, form_star +from swarm_gpt.core.motion_primitives import ( + _sanitize_drone_ids, + expand_drone_id_spec, + form_circle, + form_cone, + form_star, +) +from swarm_gpt.exception import LLMFormatError def _limits() -> dict: @@ -60,3 +68,129 @@ def test_form_cone_respects_time_to_finish(): _, 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]}" + + +def test_range_endpoints_are_inclusive_at_both_ends(): + """ "1-50" over 100 drones is exactly 0..49 -- it already contains drone 50, not 51.""" + ids = _sanitize_drone_ids("1-50", 100) + assert len(ids) == 50 + assert ids[0] == 0 # drone 1 + assert ids[-1] == 49 # drone 50 + assert 50 not in ids # drone 51 is NOT in "1-50" + + +def test_consecutive_blocks_partition_the_swarm(): + """The split the prompt mandates: "1-50" then "51-100" is disjoint and covers everything.""" + lower = _sanitize_drone_ids("1-50", 100) + upper = _sanitize_drone_ids("51-100", 100) + assert set(lower).isdisjoint(upper) + assert sorted(lower + upper) == list(range(100)) + assert upper[0] == 50 # drone 51 + assert upper[-1] == 99 # drone 100 + + +def test_single_id_needs_no_range(): + assert _sanitize_drone_ids("7", 10) == [6] + + +def test_mixed_singles_and_ranges(): + assert _sanitize_drone_ids("1-3,7,10-11", 20) == [0, 1, 2, 6, 9, 10] + + +def test_spec_preserves_the_order_it_is_written_in(): + """Order is not normalised: the explicit list form never sorted either.""" + assert _sanitize_drone_ids("5,1-2", 10) == [4, 0, 1] + + +def test_spec_matches_the_explicit_list_it_replaces(): + """The whole point: the compact form must select exactly what the old list selected.""" + assert _sanitize_drone_ids("1-5", 10) == _sanitize_drone_ids([1, 2, 3, 4, 5], 10) + assert _sanitize_drone_ids("1-100", 100) == _sanitize_drone_ids(list(range(1, 101)), 100) + + +def test_whitespace_around_tokens_is_tolerated(): + """Hand-written presets and `lighting:` blocks are not rejected over a space.""" + assert _sanitize_drone_ids(" 1-3 , 7 ", 10) == [0, 1, 2, 6] + + +def test_plain_integer_lists_still_work(): + """Saved presets store the raw response text, so the old list form must keep loading.""" + assert _sanitize_drone_ids([1, 3, 5], 10) == [0, 2, 4] + + +def test_ellipsis_still_means_the_whole_swarm(): + assert _sanitize_drone_ids([...], 6) == [0, 1, 2, 3, 4, 5] + + +@pytest.mark.parametrize("spec", ["1-50,50-100", "1-5,3", "3,3", "1-5,5", "10-20,15-25", "5,1-10"]) +def test_overlapping_selections_are_rejected(spec: str): + """A drone named twice has no defined target -- reject rather than double-assign.""" + with pytest.raises(LLMFormatError, match="more than once"): + _sanitize_drone_ids(spec, 100) + + +def test_overlap_error_names_the_shared_drone(): + """The message must be actionable for the reprompt loop.""" + with pytest.raises(LLMFormatError, match=r"\[50\]"): + _sanitize_drone_ids("1-50,50-100", 100) + + +def test_reversed_range_is_rejected(): + with pytest.raises(LLMFormatError, match="runs backwards"): + _sanitize_drone_ids("50-1", 100) + + +@pytest.mark.parametrize("spec", ["0", "0-5", "1-3,0"]) +def test_drone_zero_is_rejected(spec: str): + """Ids are 1-indexed; drone 0 would shift to -1 and silently select the last drone.""" + with pytest.raises(LLMFormatError, match="1-indexed"): + _sanitize_drone_ids(spec, 10) + + +def test_lowest_id_is_in_bounds(): + """The other side of the drone-0 boundary: drone 1 is valid.""" + assert _sanitize_drone_ids("1", 10) == [0] + + +def test_highest_id_is_in_bounds(): + assert _sanitize_drone_ids("10", 10) == [9] + + +@pytest.mark.parametrize("spec", ["11", "1-11", "1-5,11", "101"]) +def test_ids_above_the_swarm_are_rejected(spec: str): + with pytest.raises(LLMFormatError, match=r"outside the 1\.\.10 swarm"): + _sanitize_drone_ids(spec, 10) + + +@pytest.mark.parametrize("spec", ["", "1-", "-5", "a-b", "1--5", "1,,2", "1 - 5", "1;2", "1-2-3"]) +def test_malformed_specs_are_rejected(spec: str): + with pytest.raises(LLMFormatError, match="malformed"): + _sanitize_drone_ids(spec, 10) + + +def test_non_string_non_list_is_rejected(): + with pytest.raises(LLMFormatError, match="range string"): + _sanitize_drone_ids(5, 10) + + +def test_expand_returns_one_indexed_ids(): + """`expand_drone_id_spec` stays 1-indexed; only `_sanitize_drone_ids` shifts to 0.""" + assert expand_drone_id_spec("1-3,9") == [1, 2, 3, 9] + + +def test_form_circle_selects_the_same_drones_as_the_explicit_list(): + """End-to-end at the primitive: the range form must not change which drones move.""" + limits = _limits() + by_range, wps_range = form_circle(("1-5", 100, 100, 1.0), _swarm_10(), 0.0, 10.0, limits) + by_list, wps_list = form_circle( + ([1, 2, 3, 4, 5], 100, 100, 1.0), _swarm_10(), 0.0, 10.0, limits + ) + np.testing.assert_allclose(by_range, by_list) + assert sorted(wps_range[min(wps_range)]) == [0, 1, 2, 3, 4] + assert sorted(wps_range) == sorted(wps_list) + + +def test_form_circle_leaves_unselected_drones_where_they_were(): + swarm = _swarm_10() + pos, _ = form_circle(("1-5", 100, 100, 1.0), swarm.copy(), 0.0, 10.0, _limits()) + np.testing.assert_allclose(pos[5:], swarm[5:]) diff --git a/tests/unit/test_music_analyzer.py b/tests/unit/test_music_analyzer.py index 7d695e4..9832bd2 100644 --- a/tests/unit/test_music_analyzer.py +++ b/tests/unit/test_music_analyzer.py @@ -1,7 +1,4 @@ -"""Unit tests for :mod:`swarm_gpt.utils.music_analyzer`. - -Tests use duck-typed synthetic ``AnalysisResult`` objects so allin1 is never imported. -""" +"""Unit tests for :mod:`swarm_gpt.utils.music_analyzer`, on synthetic ``AnalysisResult`` objects.""" from __future__ import annotations @@ -181,9 +178,7 @@ def test_crop_rejects_non_positive_window() -> None: structure.crop(4.0, 2.0) -# --------------------------------------------------------------------------- # F2 helpers -# --------------------------------------------------------------------------- def _make_synthetic_structure(n_bars: int, bar_dur: float) -> SongStructure: diff --git a/tests/unit/test_render.py b/tests/unit/test_render.py new file mode 100644 index 0000000..7405c03 --- /dev/null +++ b/tests/unit/test_render.py @@ -0,0 +1,351 @@ +"""Unit tests for the offline renderer's audio muxing, camera framing and LED brightness. + +`render_preset` needs a full backend, the axswarm pass and an offscreen MuJoCo context, so the +wiring stays unpinned. The ffmpeg command and the camera geometry are reachable without it. +""" + +import math +import subprocess +from pathlib import Path + +import numpy as np +import pytest +from scipy.interpolate import BSpline, make_interp_spline +from scipy.spatial.transform import Rotation + +import swarm_gpt.render as render +from swarm_gpt.core import sim as sim_module + +FOVY_DEG = 39.2 # The `cinema_cam` fovy in swarm_gpt/data/scene.xml. +# Lab scale (a 4 m box) and sim scale (the 20 m box the 100-drone config needs). +SWARM_SCALES = [(20, 2.0, 0.25, 1.7), (100, 10.0, 0.25, 6.0)] + + +class _StubSim: + """Stands in for `Sim`, which only `cam_fovy` is read off during framing.""" + + def __init__(self, fovy_deg: float = FOVY_DEG): + self.mj_model = type("_Model", (), {"cam_fovy": np.array([fovy_deg])})() + + +def _orbit_splines(n_drones: int, radius: float, z_low: float, z_high: float) -> list[BSpline]: + """Build position splines for a swarm circling `radius` out while climbing z_low -> z_high.""" + times = np.linspace(0.0, 60.0, 64) + splines = [] + for i in range(n_drones): + phase = 2 * np.pi * i / n_drones + angle = phase + np.linspace(0.0, 2 * np.pi, times.size) + pos = np.stack( + [ + radius * np.cos(angle), + radius * np.sin(angle), + np.linspace(z_low, z_high, times.size), + ], + axis=-1, + ) + splines.append(make_interp_spline(times, pos, k=3)) + return splines + + +def _worst_frame_fill( + splines: list[BSpline], + centre: np.ndarray, + distance: float, + width: int = render.WIDTH, + height: int = render.HEIGHT, +) -> float: + """How far across the frame the swarm ever reaches, as a fraction of the frame's half-extent. + + Above 1 something was cropped, well below 1 the camera is parked too far out. Axes come from + `look_at_quat`, not the fit's own basis, which is what catches the two disagreeing. + """ + tan_v = math.tan(math.radians(FOVY_DEG) / 2) + tan_h = tan_v * width / height + worst = 0.0 + for t in np.linspace(render.CAMERA_MOVE_START_TIME, render.CAMERA_MOVE_END_TIME, 127): + position = render.camera_position_at(float(t), centre, distance) + quat_wxyz = render.look_at_quat(position, centre, render.CAMERA_UP) + rotation = Rotation.from_quat(np.roll(quat_wxyz, -1)).as_matrix() + right, up, forward = rotation[:, 0], rotation[:, 1], -rotation[:, 2] + for spline in splines: + rays = np.atleast_2d(spline(np.linspace(0.0, 60.0, 512))) - position + depth = rays @ forward + assert np.all(depth > 0.0) # nothing behind the camera, where the ratios go nonsense + worst = max(worst, float(np.max(np.abs(rays @ right) / (depth * tan_h)))) + worst = max(worst, float(np.max(np.abs(rays @ up) / (depth * tan_v)))) + return worst + + +@pytest.mark.parametrize(("n_drones", "radius", "z_low", "z_high"), SWARM_SCALES) +def test_camera_keeps_the_whole_swarm_on_screen( + n_drones: int, radius: float, z_low: float, z_high: float +): + """Every drone stays inside the frame for the whole move, at lab scale and at sim scale. + + The camera used to orbit a hardcoded 7.75 m tuned for a 4 m box, so a 20 m show flew out of + frame. The lower bound catches the opposite: the old bounding sphere filled 0.64 of the frame. + """ + splines = _orbit_splines(n_drones, radius, z_low, z_high) + centre, points = render.swarm_points(splines, t_end=60.0) + distance = render.camera_fit_distance( + _StubSim(), 0, centre, points, render.WIDTH, render.HEIGHT + ) + + fill = _worst_frame_fill(splines, centre, distance) + assert fill <= 1.0 + assert fill >= 0.8 + + +@pytest.mark.parametrize(("n_drones", "radius", "z_low", "z_high"), SWARM_SCALES) +def test_camera_fit_is_exact_up_to_the_margin( + n_drones: int, radius: float, z_low: float, z_high: float, monkeypatch: pytest.MonkeyPatch +): + """Strip the margin and the outermost drone lands exactly on the frame edge. + + At margin 1.0 the fit is exact, so the shipped margin is the only headroom there is. + """ + splines = _orbit_splines(n_drones, radius, z_low, z_high) + centre, points = render.swarm_points(splines, t_end=60.0) + monkeypatch.setattr(render, "CAMERA_FIT_MARGIN", 1.0) + distance = render.camera_fit_distance( + _StubSim(), 0, centre, points, render.WIDTH, render.HEIGHT + ) + + assert _worst_frame_fill(splines, centre, distance) == pytest.approx(1.0, rel=1e-3) + + +def test_camera_fit_reads_the_frame_aspect_not_just_the_fovy(): + """The horizontal half-angle is the fovy widened by the render's own aspect ratio. + + Two frames of the same shape must agree whatever their pixel count, and a square frame -- + narrower in the axis that was binding -- must push the camera back. + """ + splines = _orbit_splines(*SWARM_SCALES[1]) + centre, points = render.swarm_points(splines, t_end=60.0) + + def fit(width: int, height: int) -> float: + return render.camera_fit_distance(_StubSim(), 0, centre, points, width, height) + + wide = fit(render.WIDTH, render.HEIGHT) + assert fit(render.PREVIEW_WIDTH, render.PREVIEW_HEIGHT) == pytest.approx(wide, rel=1e-9) + + square = fit(render.HEIGHT, render.HEIGHT) + assert square > wide + assert _worst_frame_fill(splines, centre, square, render.HEIGHT, render.HEIGHT) <= 1.0 + + +def test_camera_scales_with_the_swarm_but_keeps_its_move(): + """Framing tracks swarm size; the shape of the move does not. + + Scale the show by five and the camera moves out by five, but azimuth, elevation and the + push-in ratio stay as configured -- a camera that merely backed off would throw the move away. + """ + scale = 5.0 + small = _orbit_splines(20, 2.0, 0.25, 1.7) + large = _orbit_splines(20, 2.0 * scale, 0.25 * scale, 1.7 * scale) + + frames = [] + for splines in (small, large): + centre, points = render.swarm_points(splines, t_end=60.0) + distance = render.camera_fit_distance( + _StubSim(), 0, centre, points, render.WIDTH, render.HEIGHT + ) + start = render.camera_position_at(render.CAMERA_MOVE_START_TIME, centre, distance) + end = render.camera_position_at(render.CAMERA_MOVE_END_TIME, centre, distance) + frames.append((start - centre, end - centre)) + + (small_start, small_end), (large_start, large_end) = frames + assert np.linalg.norm(large_start) == pytest.approx( + np.linalg.norm(small_start) * scale, rel=1e-9 + ) + + # Same directions, and the same closing ratio, at both scales. + for small_off, large_off in ((small_start, large_start), (small_end, large_end)): + cosine = np.dot(small_off, large_off) / ( + np.linalg.norm(small_off) * np.linalg.norm(large_off) + ) + assert cosine == pytest.approx(1.0, abs=1e-9) + small_ratio = np.linalg.norm(small_end) / np.linalg.norm(small_start) + large_ratio = np.linalg.norm(large_end) / np.linalg.norm(large_start) + assert small_ratio == pytest.approx(large_ratio, rel=1e-9) + assert small_ratio < 1.0 # the configured move pushes in, and still does + + +def test_camera_fits_the_swarm_at_its_closest_approach(monkeypatch: pytest.MonkeyPatch): + """The whole move has to fit, and it is the push-in that binds. + + Fitting the opening frame leaves the tightest part cropped. `samples=1` is exactly that + mistake, and the margin is stripped so the crop it causes is not absorbed. + """ + splines = _orbit_splines(*SWARM_SCALES[1]) + centre, points = render.swarm_points(splines, t_end=60.0) + monkeypatch.setattr(render, "CAMERA_FIT_MARGIN", 1.0) + distance = render.camera_fit_distance( + _StubSim(), 0, centre, points, render.WIDTH, render.HEIGHT + ) + + offsets = [ + np.linalg.norm(render.camera_position_at(float(t), centre, distance) - centre) + for t in np.linspace(render.CAMERA_MOVE_START_TIME, render.CAMERA_MOVE_END_TIME, 31) + ] + assert min(offsets) == pytest.approx(distance, rel=1e-9) + assert max(offsets) > distance # the move really does travel, so one moment binds and not all + + start_only = render.camera_fit_distance( + _StubSim(), 0, centre, points, render.WIDTH, render.HEIGHT, samples=1 + ) + assert start_only < distance + assert _worst_frame_fill(splines, centre, start_only) > 1.0 + + +def _paint_leds( + monkeypatch: pytest.MonkeyPatch, wrgb: np.ndarray, **kwargs: float +) -> dict[str, tuple[np.ndarray, np.ndarray]]: + """Paint one frame of fixed WRGB and return the rgba and emission each LED ring was handed. + + The timeline is stubbed to the one call `paint_lighting` makes, keeping the compile path out. + """ + painted: dict[str, tuple[np.ndarray, np.ndarray]] = {} + + def record( + _sim: object, mat_name: str, drone_ids: np.ndarray, rgba: np.ndarray, emission: np.ndarray + ) -> None: + painted[mat_name] = ( + np.asarray(rgba, dtype=float).copy(), + np.asarray(emission, dtype=float).copy(), + ) + + monkeypatch.setattr(sim_module, "change_material", record) + wrgb = np.asarray(wrgb, dtype=float) + stub_sim = type("_Sim", (), {"n_drones": len(wrgb)})() + timeline = type("_Timeline", (), {"evaluate": lambda _self, _t: wrgb})() + render.paint_lighting(stub_sim, timeline, 0.0, **kwargs) + return painted + + +def test_render_emission_gain_brightens_the_leds_without_moving_their_hue( + monkeypatch: pytest.MonkeyPatch, +): + """The render's LED boost is a scalar on emission, so it can neither clip nor wash out. + + The obvious fix -- multiplying the colour and letting channels clip -- drags mixed hues towards + white. The three drones are a hue with headroom, one with none, and one that must stay dimmer. + """ + wrgb = np.array( + [ + [[0.0, 127.5, 127.5, 0.0]] * 2, # palette yellow, peaking at half the range + [[0.0, 255.0, 0.0, 0.0]] * 2, # palette red, already at the top of it + [[0.0, 76.5, 0.0, 0.0]] * 2, # the same red, dimmed to 30% + ] + ) + plain = _paint_leds(monkeypatch, wrgb) + boosted = _paint_leds(monkeypatch, wrgb, emission_gain=render.RENDER_EMISSION_GAIN) + + assert render.RENDER_EMISSION_GAIN > 1.0 + for ring in ("led_top", "led_bot"): + rgba, emission = plain[ring] + # The default is the viewer's call, and it paints the show's own brightness, unamplified. + assert np.allclose(emission, 1.0), ring + + boosted_rgba, boosted_emission = boosted[ring] + # Only the emission moved. The rgba -- colour and alpha both -- is handed over untouched, + # so what each LED emits stays a scalar multiple of its own colour and the hue is fixed. + assert np.allclose(boosted_rgba, rgba), ring + emitted = boosted_emission[:, None] * boosted_rgba[:, :3] + assert np.all(emitted <= 1.0), ring # nothing is driven into saturation + assert np.allclose(emitted[0, 0], emitted[0, 1]), ring # yellow's channels stay equal + + # Yellow spends its headroom, red has none to spend and is left exactly alone, and the + # dimmed drone gains without ever catching up to the one at full brightness. + assert emitted[0].max() > rgba[0, :3].max() + assert boosted_emission[1] == pytest.approx(1.0) + assert rgba[2, :3].max() < emitted[2].max() < emitted[1].max() + + +def _fake_ffmpeg(monkeypatch: pytest.MonkeyPatch) -> list[list[str]]: + """Capture the ffmpeg command instead of running it, and report success.""" + commands: list[list[str]] = [] + + def run(command: list[str], **_kwargs: object) -> subprocess.CompletedProcess: + commands.append(list(command)) + return subprocess.CompletedProcess(command, 0, "", "") + + monkeypatch.setattr(render.shutil, "which", lambda _name: "/usr/bin/ffmpeg") + monkeypatch.setattr(render.subprocess, "run", run) + return commands + + +def test_mux_audio_seeks_the_song_to_the_crop_start( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +): + """The render's audio starts at the crop, not at 0:00. + + The choreography's timeline is rebased to 0 while the mp3 is not -- 35 s out of sync for + `Fearless2`. `-ss` must sit *before* `-i