From 490b90cb51e83d8cafd90de9fd437cac9aa38d05 Mon Sep 17 00:00:00 2001 From: Antonio Rueda-Toicen Date: Sat, 6 Dec 2025 14:45:54 +0100 Subject: [PATCH 01/75] Update references in README.md Updated references section with a new paper on hyperbolic vision-language models and removed an outdated reference. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 97a1f83..27dfd4f 100644 --- a/README.md +++ b/README.md @@ -93,7 +93,7 @@ See [docs/architecture.md](docs/architecture.md) for details. * **[Poincaré Embeddings for Learning Hierarchical Representations](https://arxiv.org/abs/1705.08039)** (Nickel & Kiela, 2017) - The seminal paper demonstrating how hyperbolic space can represent hierarchies with significantly fewer dimensions than Euclidean space. * **[Hyperbolic Neural Networks](https://arxiv.org/abs/1805.09112)** (Ganea et al., 2018) - Extends deep learning operations to hyperbolic space. -* **[Excavating AI](https://excavating.ai)** (Crawford & Paglen) - An investigation into the hidden biases and taxonomies within ImageNet. +* **[Compositional Entailment Learning for Hyperbolic Vision-Language Models](https://arxiv.org/abs/2410.06912)** (Pal et al., 2024) - Defines a CLIP vision-language model on hyperbolic space ## License From 82a0e61aa383ade9f46546cc49599866ed5b3620 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Mon, 22 Dec 2025 00:10:31 +0100 Subject: [PATCH 02/75] Notebook startup readiness + storage backend --- .gitignore | 1 - docs/datasets.md | 92 ++++ notebooks/demo.ipynb | 306 +++++++++++++ pyproject.toml | 2 + scripts/demo.py | 39 +- scripts/export_frontend.sh | 9 +- src/hyperview/api.py | 214 ++++++++- src/hyperview/cli.py | 16 +- src/hyperview/core/dataset.py | 548 ++++++++++++++++++++--- src/hyperview/server/app.py | 99 +++- src/hyperview/storage/__init__.py | 19 + src/hyperview/storage/backend.py | 114 +++++ src/hyperview/storage/config.py | 75 ++++ src/hyperview/storage/lancedb_backend.py | 521 +++++++++++++++++++++ src/hyperview/storage/memory_backend.py | 173 +++++++ src/hyperview/storage/schema.py | 124 +++++ uv.lock | 76 ++++ 17 files changed, 2326 insertions(+), 102 deletions(-) create mode 100644 docs/datasets.md create mode 100644 notebooks/demo.ipynb create mode 100644 src/hyperview/storage/__init__.py create mode 100644 src/hyperview/storage/backend.py create mode 100644 src/hyperview/storage/config.py create mode 100644 src/hyperview/storage/lancedb_backend.py create mode 100644 src/hyperview/storage/memory_backend.py create mode 100644 src/hyperview/storage/schema.py diff --git a/.gitignore b/.gitignore index d7ded76..1ad5e0b 100644 --- a/.gitignore +++ b/.gitignore @@ -23,7 +23,6 @@ htmlcov/ # Local Context (Agent files) .claude/ -CLAUDE.md context/ # Generated assets diff --git a/docs/datasets.md b/docs/datasets.md new file mode 100644 index 0000000..5454baf --- /dev/null +++ b/docs/datasets.md @@ -0,0 +1,92 @@ +# Datasets + +## Creating a Dataset + +```python +import hyperview as hv + +# Persistent dataset (default) - survives restarts +dataset = hv.Dataset("my_dataset") + +# In-memory dataset - lost when process exits +dataset = hv.Dataset("my_dataset", persist=False) +``` + +**Storage location:** `~/.hyperview/lancedb/` (configurable via `HYPERVIEW_DATABASE_DIR`) + +## Adding Samples + +### From HuggingFace +```python +dataset.add_from_huggingface( + "uoft-cs/cifar100", + split="train", + image_key="img", + label_key="fine_label", + max_samples=1000, +) +``` + +### From Directory +```python +dataset.add_images_dir("/path/to/images", label_from_folder=True) +``` + +## Persistence Model: Additive + +HyperView uses an **additive** persistence model: + +| Action | Behavior | +|--------|----------| +| Add samples | New samples inserted, existing skipped by ID | +| Request fewer than exist | Existing samples preserved (no deletion) | +| Request more than exist | Only new samples added | +| Embeddings | Cached per-sample, reused across sessions | +| Projections | Recomputed when new samples added (UMAP requires refit) | + +**Example:** +```python +dataset = hv.Dataset("my_dataset") + +dataset.add_from_huggingface(..., max_samples=200) # 200 samples +dataset.add_from_huggingface(..., max_samples=400) # +200 new → 400 total +dataset.add_from_huggingface(..., max_samples=300) # no change → 400 total +dataset.add_from_huggingface(..., max_samples=500) # +100 new → 500 total +``` + +Samples are **never implicitly deleted**. Use `hv.Dataset.delete("name")` for explicit removal. + +## Computing Embeddings + +```python +# High-dimensional embeddings (CLIP/ResNet) +dataset.compute_embeddings(model="clip", show_progress=True) + +# 2D projections for visualization +dataset.compute_visualization() # UMAP to Euclidean + Hyperbolic +``` + +Embeddings are stored per-sample. If a sample already has embeddings, it's skipped. + +## Listing & Deleting Datasets + +```python +# List all persistent datasets +hv.Dataset.list_datasets() # ['cifar100_demo', 'my_dataset', ...] + +# Delete a dataset +hv.Dataset.delete("my_dataset") + +# Check existence +hv.Dataset.exists("my_dataset") # True/False +``` + +## Dataset Info + +```python +len(dataset) # Number of samples +dataset.name # Dataset name +dataset.labels # Unique labels +dataset.samples # Iterator over all samples +dataset[sample_id] # Get sample by ID +``` diff --git a/notebooks/demo.ipynb b/notebooks/demo.ipynb new file mode 100644 index 0000000..eae7ba7 --- /dev/null +++ b/notebooks/demo.ipynb @@ -0,0 +1,306 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e26a6a66", + "metadata": {}, + "source": [ + "# HyperView Demo\n", + "This notebook demonstrates how to use HyperView to visualize embeddings in both Euclidean and Hyperbolic spaces.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0aba0ed9", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-21T23:01:47.867950Z", + "iopub.status.busy": "2025-12-21T23:01:47.867881Z", + "iopub.status.idle": "2025-12-21T23:01:48.736348Z", + "shell.execute_reply": "2025-12-21T23:01:48.735942Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/matin/merantix/HyperView/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HyperView version: 0.1.0\n" + ] + } + ], + "source": [ + "import sys\n", + "from pathlib import Path\n", + "\n", + "# Add src to path for development\n", + "sys.path.insert(0, str(Path.cwd().parent / \"src\"))\n", + "\n", + "import hyperview as hv\n", + "print(f\"HyperView version: {hv.__version__}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "00becfad", + "metadata": {}, + "source": [ + "## Initialize Dataset\n", + "We'll create a new dataset named `cifar100_demo`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3e5e93d5", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-21T23:01:48.738083Z", + "iopub.status.busy": "2025-12-21T23:01:48.737879Z", + "iopub.status.idle": "2025-12-21T23:01:49.297549Z", + "shell.execute_reply": "2025-12-21T23:01:49.296797Z" + } + }, + "outputs": [], + "source": [ + "dataset = hv.Dataset(\"cifar100_demo\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "2248602f", + "metadata": {}, + "source": [ + "## Load CIFAR-100 Data\n", + "We'll load 500 samples from the CIFAR-100 dataset using Hugging Face Datasets.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0666b39a", + "metadata": { + "execution": { + "iopub.execute_input": "2025-12-21T23:01:49.299289Z", + "iopub.status.busy": "2025-12-21T23:01:49.299171Z", + "iopub.status.idle": "2025-12-21T23:01:51.732573Z", + "shell.execute_reply": "2025-12-21T23:01:51.732008Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r\n", + "Loading uoft-cs/cifar100: 0%| | 0/500 [00:00\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "session = hv.launch(dataset)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.8)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyproject.toml b/pyproject.toml index 8fb24bd..b306a82 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,6 +32,8 @@ dependencies = [ "pydantic>=2.0.0", "aiofiles>=24.0.0", "datasets>=3.0.0", + "lancedb>=0.5.0", + "pyarrow>=14.0.0", ] [project.optional-dependencies] diff --git a/scripts/demo.py b/scripts/demo.py index 7cde441..91d0717 100644 --- a/scripts/demo.py +++ b/scripts/demo.py @@ -2,6 +2,7 @@ """Run HyperView demo with CIFAR-100 dataset.""" import argparse +import os import sys from pathlib import Path @@ -12,7 +13,13 @@ def main(): parser = argparse.ArgumentParser(description="Run HyperView demo") parser.add_argument( - "--samples", type=int, default=500, help="Number of samples to load (default: 500)" + "--dataset", + type=str, + default="cifar100_demo", + help="Dataset name to use for persistence (default: cifar100_demo)", + ) + parser.add_argument( + "--samples", type=int, default=50000, help="Number of samples to load (default: 50000)" ) parser.add_argument( "--port", type=int, default=5151, help="Port to run server on (default: 5151)" @@ -20,29 +27,43 @@ def main(): parser.add_argument( "--no-browser", action="store_true", help="Don't open browser automatically" ) + parser.add_argument( + "--no-persist", action="store_true", help="Don't persist to database (use in-memory)" + ) + parser.add_argument( + "--database-dir", + type=str, + default=None, + help="Override persistence directory (sets HYPERVIEW_DATABASE_DIR)", + ) + parser.add_argument( + "--no-server", + action="store_true", + help="Don't start the web server (useful for CI / DB checks)", + ) args = parser.parse_args() + if args.database_dir: + os.environ["HYPERVIEW_DATABASE_DIR"] = args.database_dir + import hyperview as hv - print(f"Loading {args.samples} samples from CIFAR-100...") - dataset = hv.Dataset("cifar100_demo") - count = dataset.add_from_huggingface( + dataset = hv.Dataset(args.dataset, persist=not args.no_persist) + + dataset.add_from_huggingface( "uoft-cs/cifar100", split="train", image_key="img", label_key="fine_label", max_samples=args.samples, ) - print(f"Loaded {count} samples") - print("Computing embeddings...") dataset.compute_embeddings(show_progress=True) - print("Computing visualization (UMAP + Poincare)...") dataset.compute_visualization() - - print(f"Starting server at http://127.0.0.1:{args.port}") + if args.no_server: + return hv.launch(dataset, port=args.port, open_browser=not args.no_browser) diff --git a/scripts/export_frontend.sh b/scripts/export_frontend.sh index 137545b..cca765f 100755 --- a/scripts/export_frontend.sh +++ b/scripts/export_frontend.sh @@ -8,12 +8,12 @@ PROJECT_ROOT="$(dirname "$SCRIPT_DIR")" FRONTEND_DIR="$PROJECT_ROOT/frontend" STATIC_DIR="$PROJECT_ROOT/src/hyperview/server/static" -echo "🔄 Building frontend..." +echo "Building frontend..." cd "$FRONTEND_DIR" # Install dependencies if needed if [ ! -d "node_modules" ]; then - echo "📦 Installing dependencies..." + echo "Installing dependencies..." npm install fi @@ -21,13 +21,12 @@ fi npm run build # Copy to Python package -echo "📁 Copying to Python package..." +echo "Copying build output into Python package..." rm -rf "$STATIC_DIR" mkdir -p "$STATIC_DIR" cp -r out/* "$STATIC_DIR/" -echo "✅ Frontend exported to $STATIC_DIR" -echo "" +echo "Frontend exported to $STATIC_DIR" echo "To test, run:" echo " cd $PROJECT_ROOT" echo " uv run hyperview demo" diff --git a/src/hyperview/api.py b/src/hyperview/api.py index 928553b..bc0b13e 100644 --- a/src/hyperview/api.py +++ b/src/hyperview/api.py @@ -2,13 +2,175 @@ from __future__ import annotations +import json +import socket +import threading +import time import webbrowser +from dataclasses import dataclass +from urllib.error import URLError +from urllib.request import Request, urlopen +from uuid import uuid4 + import uvicorn from hyperview.core.dataset import Dataset from hyperview.server.app import create_app, set_dataset -__all__ = ["Dataset", "launch"] +__all__ = ["Dataset", "launch", "Session"] + + +@dataclass(frozen=True) +class _HealthResponse: + name: str | None + session_id: str | None + + +def _can_connect(host: str, port: int, timeout_s: float) -> bool: + try: + with socket.create_connection((host, port), timeout=timeout_s): + return True + except OSError: + return False + + +def _try_read_health(url: str, timeout_s: float) -> _HealthResponse | None: + try: + return _read_health(url, timeout_s=timeout_s) + except (URLError, TimeoutError, OSError, ValueError, json.JSONDecodeError): + return None + + +def _read_health(url: str, timeout_s: float) -> _HealthResponse: + request = Request(url, headers={"Accept": "application/json"}) + with urlopen(request, timeout=timeout_s) as response: + data = json.loads(response.read().decode("utf-8")) + + return _HealthResponse( + name=data.get("name"), + session_id=data.get("session_id"), + ) + + +class Session: + """A session for the HyperView visualizer.""" + + def __init__(self, dataset: Dataset, host: str, port: int): + self.dataset = dataset + self.host = host + self.port = port + self.url = f"http://{host}:{port}" + self._server_thread: threading.Thread | None = None + self._server: uvicorn.Server | None = None + self._startup_error: BaseException | None = None + self.session_id = uuid4().hex + + @property + def _connect_host(self) -> str: + return "127.0.0.1" if self.host == "0.0.0.0" else self.host + + @property + def _health_url(self) -> str: + return f"http://{self._connect_host}:{self.port}/__hyperview__/health" + + def _run_server(self): + try: + set_dataset(self.dataset) + app = create_app(self.dataset, session_id=self.session_id) + config = uvicorn.Config(app, host=self.host, port=self.port, log_level="warning") + self._server = uvicorn.Server(config) + self._server.run() + except BaseException as exc: + self._startup_error = exc + + def start(self, background: bool = True): + """Start the visualizer server.""" + if not background: + self._run_server() + return + + # Fail fast if something is already listening on this port. + if _can_connect(self._connect_host, self.port, timeout_s=0.2): + health = _try_read_health(self._health_url, timeout_s=0.2) + if health is not None and health.name == "hyperview": + raise RuntimeError( + "HyperView failed to start because the port is already serving " + f"HyperView (port={self.port}, session_id={health.session_id}). " + "Choose a different port or stop the existing server." + ) + + raise RuntimeError( + "HyperView failed to start because the port is already in use " + f"by a non-HyperView service (port={self.port}). Choose a different " + "port or stop the process listening on that port." + ) + + self._startup_error = None + self._server_thread = threading.Thread(target=self._run_server, daemon=True) + self._server_thread.start() + + deadline = time.time() + 5.0 + last_health_error: Exception | None = None + + while time.time() < deadline: + if self._startup_error is not None: + raise RuntimeError( + f"HyperView server failed to start (port={self.port}): " + f"{type(self._startup_error).__name__}: {self._startup_error}" + ) + + if self._server_thread is not None and not self._server_thread.is_alive(): + raise RuntimeError( + "HyperView server thread exited during startup. " + f"The port may be in use (port={self.port})." + ) + + try: + health = _read_health(self._health_url, timeout_s=0.2) + except (URLError, Exception) as exc: + last_health_error = exc + time.sleep(0.05) + continue + + if health.name == "hyperview" and health.session_id == self.session_id: + return + + if health.name == "hyperview": + raise RuntimeError( + "HyperView failed to start because the port is already serving " + f"a different HyperView session (port={self.port}, " + f"session_id={health.session_id})." + ) + + raise RuntimeError( + "HyperView failed to start because the port is already serving " + f"a non-HyperView app (port={self.port})." + ) + + time.sleep(0.05) + + raise TimeoutError( + "HyperView server did not become ready in time " + f"(port={self.port}). Last error: {last_health_error}" + ) + + def stop(self): + """Stop the visualizer server.""" + if self._server: + self._server.should_exit = True + + def show(self, height: int = 800): + """Display the visualizer in a notebook.""" + try: + from IPython.display import IFrame, display + + display(IFrame(self.url, width="100%", height=height)) + except ImportError: + print(f"IPython not installed. Please visit {self.url} in your browser.") + + def open_browser(self): + """Open the visualizer in a browser window.""" + webbrowser.open(self.url) def launch( @@ -16,7 +178,9 @@ def launch( port: int = 5151, host: str = "127.0.0.1", open_browser: bool = True, -) -> None: + notebook: bool | None = None, + height: int = 800, +) -> Session: """Launch the HyperView visualization server. Args: @@ -24,6 +188,11 @@ def launch( port: Port to run the server on. host: Host to bind to. open_browser: Whether to open a browser window. + notebook: Whether to display in a notebook. If None, auto-detects. + height: Height of the iframe in the notebook. + + Returns: + A Session object. Example: >>> import hyperview as hv @@ -33,14 +202,39 @@ def launch( >>> dataset.compute_visualization() >>> hv.launch(dataset) """ - set_dataset(dataset) - app = create_app(dataset) + if notebook is None: + notebook = _is_notebook() + + session = Session(dataset, host, port) + + if notebook: + session.start(background=True) + url = f"http://{host}:{port}" + print(f"\nHyperView is running at {url}") + session.show(height=height) + else: + if open_browser: + session.open_browser() + + print(" Press Ctrl+C to stop.\n") + url = f"http://{host}:{port}" + print(f"\nHyperView is running at {url}") + session.start(background=False) + + return session - url = f"http://{host}:{port}" - print(f"\n🚀 HyperView is running at {url}") - print(" Press Ctrl+C to stop.\n") - if open_browser: - webbrowser.open(url) +def _is_notebook() -> bool: + """Check if running in a notebook environment.""" + try: + from IPython import get_ipython - uvicorn.run(app, host=host, port=port, log_level="warning") + shell = get_ipython().__class__.__name__ + if shell == "ZMQInteractiveShell": + return True # Jupyter notebook or qtconsole + elif shell == "TerminalInteractiveShell": + return False # Terminal running IPython + else: + return False # Other type (?) + except (ImportError, NameError): + return False # Probably standard Python interpreter diff --git a/src/hyperview/cli.py b/src/hyperview/cli.py index 9bcd459..913f4a9 100644 --- a/src/hyperview/cli.py +++ b/src/hyperview/cli.py @@ -54,7 +54,7 @@ def main(): def run_demo(num_samples: int = 500, port: int = 5151): """Run a demo with CIFAR-100 data.""" - print("🔄 Loading CIFAR-100 dataset...") + print("Loading CIFAR-100 dataset...") dataset = Dataset("cifar100_demo") try: @@ -65,19 +65,19 @@ def run_demo(num_samples: int = 500, port: int = 5151): label_key="fine_label", max_samples=num_samples, ) - print(f"✓ Loaded {count} samples") + print(f"Loaded {count} samples") except Exception as e: print(f"Failed to load HuggingFace dataset: {e}") print("Please ensure 'datasets' is installed: pip install datasets") sys.exit(1) - print("🔄 Computing embeddings...") + print("Computing embeddings...") dataset.compute_embeddings(show_progress=True) - print("✓ Embeddings computed") + print("Embeddings computed") - print("🔄 Computing visualizations...") + print("Computing visualizations...") dataset.compute_visualization() - print("✓ Visualizations ready") + print("Visualizations ready") launch(dataset, port=port) @@ -86,9 +86,9 @@ def serve_dataset(filepath: str, port: int = 5151): """Serve a saved dataset.""" from hyperview import Dataset, launch - print(f"🔄 Loading dataset from {filepath}...") + print(f"Loading dataset from {filepath}...") dataset = Dataset.load(filepath) - print(f"✓ Loaded {len(dataset)} samples") + print(f"Loaded {len(dataset)} samples") launch(dataset, port=port) diff --git a/src/hyperview/core/dataset.py b/src/hyperview/core/dataset.py index 44cafaf..4c9f4f9 100644 --- a/src/hyperview/core/dataset.py +++ b/src/hyperview/core/dataset.py @@ -7,7 +7,7 @@ import uuid from collections.abc import Callable, Iterator from pathlib import Path -from typing import Any +from typing import TYPE_CHECKING, Any import numpy as np from datasets import load_dataset @@ -15,36 +15,106 @@ from hyperview.core.sample import Sample, SampleFromArray +if TYPE_CHECKING: + from hyperview.storage.backend import StorageBackend + class Dataset: - """A collection of samples with support for embeddings and visualization.""" + """A collection of samples with support for embeddings and visualization. + + Datasets are automatically persisted to LanceDB by default, providing: + - Automatic persistence (no need to call save()) + - Vector similarity search + - Efficient storage and retrieval + + Examples: + # Create a new dataset (auto-persisted) + dataset = hv.Dataset("my_dataset") + dataset.add_images_dir("/path/to/images") + + # Open an existing dataset + dataset = hv.Dataset.open("my_dataset") - def __init__(self, name: str | None = None): + # Create an in-memory dataset (for testing) + dataset = hv.Dataset("temp", persist=False) + """ + + def __init__( + self, + name: str | None = None, + persist: bool = True, + storage: StorageBackend | None = None, + embedding_dim: int = 512, + ): """Initialize a new dataset. Args: name: Optional name for the dataset. + persist: If True (default), use LanceDB for persistence. + If False, use in-memory storage. + storage: Optional custom storage backend. If provided, persist is ignored. + embedding_dim: Dimension of embeddings (default 512 for CLIP). """ self.name = name or f"dataset_{uuid.uuid4().hex[:8]}" - self._samples: dict[str, Sample] = {} + self._embedding_dim = embedding_dim self._embedding_computer = None self._projection_engine = None - self._label_colors: dict[str, str] = {} + + # Initialize storage backend + if storage is not None: + self._storage = storage + elif persist: + from hyperview.storage import LanceDBBackend, StorageConfig + + config = StorageConfig.default(embedding_dim=embedding_dim) + self._storage = LanceDBBackend(self.name, config) + else: + from hyperview.storage import MemoryBackend + + self._storage = MemoryBackend(self.name) + + # Initialize label colors from storage + self._sync_label_colors() + + def _sync_label_colors(self) -> None: + """Sync label colors from storage and assign colors to new labels.""" + # Get existing colors from storage + existing_colors = self._storage.label_colors + + # Get all unique labels + all_labels = self._storage.get_unique_labels() + + # Assign colors to any labels without colors + for label in all_labels: + if label not in existing_colors: + self._assign_label_color(label, existing_colors) + + # Save back if we added new colors + if existing_colors != self._storage.label_colors: + self._storage.label_colors = existing_colors def __len__(self) -> int: - return len(self._samples) + return len(self._storage) def __iter__(self) -> Iterator[Sample]: - return iter(self._samples.values()) + return iter(self._storage) def __getitem__(self, sample_id: str) -> Sample: - return self._samples[sample_id] + sample = self._storage.get_sample(sample_id) + if sample is None: + raise KeyError(sample_id) + return sample def add_sample(self, sample: Sample) -> None: """Add a sample to the dataset.""" - self._samples[sample.id] = sample - if sample.label and sample.label not in self._label_colors: - self._assign_label_color(sample.label) + self._storage.add_sample(sample) + + # Assign color to label if needed + if sample.label: + colors = self._storage.label_colors + if sample.label not in colors: + self._assign_label_color(sample.label, colors) + self._storage.label_colors = colors def add_image( self, @@ -98,16 +168,31 @@ def add_images_dir( if not directory.exists(): raise ValueError(f"Directory does not exist: {directory}") - count = 0 + samples = [] pattern = "**/*" if recursive else "*" for path in directory.glob(pattern): if path.is_file() and path.suffix.lower() in extensions: label = path.parent.name if label_from_folder else None - self.add_image(str(path), label=label) - count += 1 - - return count + sample_id = hashlib.md5(str(path).encode()).hexdigest()[:12] + sample = Sample( + id=sample_id, + filepath=str(path), + label=label, + metadata={}, + ) + samples.append(sample) + + # Track label colors + if label: + colors = self._storage.label_colors + if label not in colors: + self._assign_label_color(label, colors) + self._storage.label_colors = colors + + # Batch add for efficiency + self._storage.add_samples_batch(samples) + return len(samples) def add_from_huggingface( self, @@ -117,9 +202,17 @@ def add_from_huggingface( label_key: str | None = "fine_label", label_names_key: str | None = None, max_samples: int | None = None, - ) -> int: + download_images: bool = True, + show_progress: bool = True, + skip_existing: bool = True, + image_format: str = "auto", + ) -> tuple[int, int]: """Load samples from a HuggingFace dataset. + Images are downloaded to disk at ~/.hyperview/media/huggingface/{dataset}/{split}/ + This ensures images persist across sessions and embeddings can be computed + at any time, similar to FiftyOne's approach. + Args: dataset_name: Name of the HuggingFace dataset. split: Dataset split to use. @@ -127,33 +220,65 @@ def add_from_huggingface( label_key: Key for the label column (can be None). label_names_key: Key for label names in dataset info. max_samples: Maximum number of samples to load. + download_images: If True (default), download images to local disk. + If False, use in-memory storage (won't persist). + show_progress: Whether to show progress bar. + skip_existing: If True (default), skip samples that already exist in storage. + If False, allow duplicate samples (not recommended). + image_format: Image format to save: "auto" (detect from source, fallback PNG), + "png" (lossless), or "jpeg" (smaller files). Returns: - Number of samples added. + Tuple of (num_added, num_skipped). """ + from hyperview.storage import StorageConfig + + try: + from tqdm import tqdm + except ImportError: + tqdm = None + ds = load_dataset(dataset_name, split=split) # Get label names if available label_names = None if label_key and label_names_key: if label_names_key in ds.features: - label_names = ds.features[label_names_key].names + label_names = ds.features[label_names_key].names elif label_key: if hasattr(ds.features[label_key], "names"): label_names = ds.features[label_key].names - count = 0 + # Extract dataset metadata for robust sample IDs + config_name = getattr(ds.info, "config_name", None) or "default" + fingerprint = ds._fingerprint[:8] if hasattr(ds, "_fingerprint") and ds._fingerprint else "unknown" + version = str(ds.info.version) if ds.info.version else None + + # Get media directory for this dataset + config = StorageConfig.default() + media_dir = config.get_huggingface_media_dir(dataset_name, split) + + samples = [] total = len(ds) if max_samples is None else min(len(ds), max_samples) + colors = self._storage.label_colors - for i in range(total): + # Setup progress bar + if show_progress and tqdm is not None: + iterator = tqdm(range(total), desc=f"Loading {dataset_name}") + else: + if show_progress: + print(f"Loading {total} samples from {dataset_name}...") + iterator = range(total) + + for i in iterator: item = ds[i] image = item[image_key] # Handle PIL Image or numpy array if isinstance(image, Image.Image): - image_array = np.array(image) + pil_image = image else: - image_array = image + pil_image = Image.fromarray(image) # Get label label = None @@ -164,16 +289,88 @@ def add_from_huggingface( else: label = str(label_idx) - sample = SampleFromArray.from_array( - id=f"{dataset_name.replace('/', '_')}_{split}_{i}", - image_array=image_array, - label=label, - metadata={"source": dataset_name, "split": split, "index": i}, - ) - self.add_sample(sample) - count += 1 - - return count + # Generate robust sample ID with config and fingerprint + safe_name = dataset_name.replace("/", "_") + sample_id = f"{safe_name}_{config_name}_{fingerprint}_{split}_{i}" + + # Determine image format and extension + if image_format == "auto": + # Try to preserve original format, fallback to PNG + original_format = getattr(pil_image, "format", None) + if original_format in ("JPEG", "JPG"): + save_format = "JPEG" + ext = ".jpg" + else: + save_format = "PNG" + ext = ".png" + elif image_format == "jpeg": + save_format = "JPEG" + ext = ".jpg" + else: + save_format = "PNG" + ext = ".png" + + # Enhanced metadata with dataset info + metadata = { + "source": dataset_name, + "config": config_name, + "split": split, + "index": i, + "fingerprint": ds._fingerprint if hasattr(ds, "_fingerprint") else None, + "version": version, + } + + if download_images: + # Save image to disk (FiftyOne pattern) + image_path = media_dir / f"{sample_id}{ext}" + if not image_path.exists(): + # Convert to RGB if necessary (for JPEG or non-RGB images) + if save_format == "JPEG" or pil_image.mode in ("RGBA", "P", "L"): + pil_image = pil_image.convert("RGB") + pil_image.save(image_path, format=save_format) + + sample = Sample( + id=sample_id, + filepath=str(image_path), + label=label, + metadata=metadata, + ) + else: + # Use in-memory storage (legacy behavior, won't persist) + image_array = np.array(pil_image) + sample = SampleFromArray.from_array( + id=sample_id, + image_array=image_array, + label=label, + metadata=metadata, + ) + + samples.append(sample) + + # Track label colors + if label and label not in colors: + self._assign_label_color(label, colors) + + # Check for existing samples and skip duplicates + skipped = 0 + if skip_existing and samples: + all_ids = [s.id for s in samples] + existing_ids = self._storage.get_existing_ids(all_ids) + if existing_ids: + samples = [s for s in samples if s.id not in existing_ids] + skipped = len(all_ids) - len(samples) + + # Batch add for efficiency + if samples: + self._storage.add_samples_batch(samples) + self._storage.label_colors = colors + + if download_images and show_progress: + print(f"Images saved to: {media_dir}") + if skipped > 0: + print(f"Skipped {skipped} existing samples") + + return len(samples), skipped def compute_embeddings( self, @@ -181,7 +378,7 @@ def compute_embeddings( batch_size: int = 32, show_progress: bool = True, ) -> None: - """Compute embeddings for all samples. + """Compute embeddings for samples that don't have them yet. Args: model: Embedding model to use. @@ -193,20 +390,38 @@ def compute_embeddings( if self._embedding_computer is None: self._embedding_computer = EmbeddingComputer(model=model) - samples = list(self._samples.values()) + all_samples = self._storage.get_all_samples() + # Only compute for samples without embeddings + samples_needing_embeddings = [s for s in all_samples if s.embedding is None] + + if not samples_needing_embeddings: + if show_progress: + print(f"All {len(all_samples)} samples already have embeddings") + return + + if show_progress: + skipped = len(all_samples) - len(samples_needing_embeddings) + if skipped > 0: + print(f"Skipped {skipped} samples with existing embeddings") + embeddings = self._embedding_computer.compute_batch( - samples, batch_size=batch_size, show_progress=show_progress + samples_needing_embeddings, batch_size=batch_size, show_progress=show_progress ) - for sample, embedding in zip(samples, embeddings): + # Update samples with embeddings + for sample, embedding in zip(samples_needing_embeddings, embeddings): sample.embedding = embedding.tolist() + # Batch update in storage + self._storage.update_samples_batch(samples_needing_embeddings) + def compute_visualization( self, method: str = "umap", n_neighbors: int = 15, min_dist: float = 0.1, metric: str = "cosine", + force: bool = False, ) -> None: """Compute 2D projections for visualization. @@ -215,16 +430,35 @@ def compute_visualization( n_neighbors: Number of neighbors for UMAP. min_dist: Minimum distance for UMAP. metric: Distance metric for UMAP. + force: Force recomputation even if projections exist. """ from hyperview.embeddings.projection import ProjectionEngine if self._projection_engine is None: self._projection_engine = ProjectionEngine() - samples = [s for s in self._samples.values() if s.embedding is not None] - if not samples: + samples_with_embeddings = [s for s in self._storage if s.embedding is not None] + if not samples_with_embeddings: raise ValueError("No embeddings computed. Call compute_embeddings() first.") + # Check if projections already exist (unless forced) + if not force: + samples_needing_projection = [ + s for s in samples_with_embeddings if s.embedding_2d is None + ] + if not samples_needing_projection: + print(f"All {len(samples_with_embeddings)} samples already have projections") + return + # UMAP needs consistent projections, so if any samples need it, recompute all + # (can't mix old and new UMAP projections) + if len(samples_needing_projection) < len(samples_with_embeddings): + print( + f"Some samples missing projections - recomputing all " + f"({len(samples_needing_projection)} new, " + f"{len(samples_with_embeddings) - len(samples_needing_projection)} existing)" + ) + + samples = samples_with_embeddings embeddings = np.array([s.embedding for s in samples]) # Compute Euclidean 2D projection @@ -246,35 +480,76 @@ def compute_visualization( sample.embedding_2d = coord_e.tolist() sample.embedding_2d_hyperbolic = coord_h.tolist() - def _assign_label_color(self, label: str) -> None: + # Batch update in storage + self._storage.update_samples_batch(samples) + + def find_similar( + self, + sample_id: str, + k: int = 10, + use_hyperbolic: bool = False, + ) -> list[tuple[Sample, float]]: + """Find k most similar samples to a given sample. + + Args: + sample_id: ID of the query sample. + k: Number of neighbors to return. + use_hyperbolic: If True, search in hyperbolic embedding space. + If False (default), search in high-dimensional embedding space. + + Returns: + List of (sample, distance) tuples, sorted by distance ascending. + """ + vector_column = "embedding_2d_hyperbolic" if use_hyperbolic else "embedding" + return self._storage.find_similar(sample_id, k, vector_column) + + def find_similar_by_vector( + self, + vector: list[float], + k: int = 10, + use_hyperbolic: bool = False, + ) -> list[tuple[Sample, float]]: + """Find k most similar samples to a given vector. + + Args: + vector: Query vector. + k: Number of neighbors to return. + use_hyperbolic: If True, search in hyperbolic embedding space. + + Returns: + List of (sample, distance) tuples, sorted by distance ascending. + """ + vector_column = "embedding_2d_hyperbolic" if use_hyperbolic else "embedding" + return self._storage.find_similar_by_vector(vector, k, vector_column) + + def _assign_label_color(self, label: str, colors: dict[str, str]) -> None: """Assign a color to a label.""" - # Use a predefined color palette - colors = [ + color_palette = [ "#e6194b", "#3cb44b", "#ffe119", "#4363d8", "#f58231", "#911eb4", "#46f0f0", "#f032e6", "#bcf60c", "#fabebe", "#008080", "#e6beff", "#9a6324", "#fffac8", "#800000", "#aaffc3", "#808000", "#ffd8b1", "#000075", "#808080", ] - idx = len(self._label_colors) % len(colors) - self._label_colors[label] = colors[idx] + idx = len(colors) % len(color_palette) + colors[label] = color_palette[idx] def get_label_colors(self) -> dict[str, str]: """Get the color mapping for labels.""" - return self._label_colors.copy() + return self._storage.label_colors.copy() @property def samples(self) -> list[Sample]: """Get all samples as a list.""" - return list(self._samples.values()) + return self._storage.get_all_samples() @property def labels(self) -> list[str]: """Get unique labels in the dataset.""" - return list(set(s.label for s in self._samples.values() if s.label)) + return self._storage.get_unique_labels() def filter(self, predicate: Callable[[Sample], bool]) -> list[Sample]: """Filter samples based on a predicate function.""" - return [s for s in self._samples.values() if predicate(s)] + return self._storage.filter(predicate) def to_dict(self) -> dict[str, Any]: """Convert dataset to dictionary for serialization.""" @@ -282,24 +557,29 @@ def to_dict(self) -> dict[str, Any]: "name": self.name, "num_samples": len(self), "labels": self.labels, - "label_colors": self._label_colors, + "label_colors": self._storage.label_colors, } def save(self, filepath: str, include_thumbnails: bool = True) -> None: - """Save dataset to a JSON file. + """Export dataset to a JSON file. + + Note: For persistent datasets (default), data is automatically saved. + This method is for exporting to JSON format for sharing or backup. Args: filepath: Path to save the JSON file. include_thumbnails: Whether to include cached thumbnails. """ + samples = self._storage.get_all_samples() + # Cache thumbnails before saving if requested if include_thumbnails: - for s in self._samples.values(): + for s in samples: s.cache_thumbnail() data = { "name": self.name, - "label_colors": self._label_colors, + "label_colors": self._storage.label_colors, "samples": [ { "id": s.id, @@ -311,21 +591,35 @@ def save(self, filepath: str, include_thumbnails: bool = True) -> None: "embedding_2d_hyperbolic": s.embedding_2d_hyperbolic, "thumbnail_base64": s.thumbnail_base64 if include_thumbnails else None, } - for s in self._samples.values() + for s in samples ], } with open(filepath, "w") as f: json.dump(data, f) @classmethod - def load(cls, filepath: str) -> Dataset: - """Load dataset from a JSON file.""" + def load(cls, filepath: str, persist: bool = False) -> Dataset: + """Load dataset from a JSON file. + + Args: + filepath: Path to the JSON file. + persist: If True, persist the loaded data to LanceDB. + If False (default), keep in memory only. + + Returns: + Dataset instance. + """ with open(filepath) as f: data = json.load(f) - dataset = cls(name=data["name"]) - dataset._label_colors = data.get("label_colors", {}) + dataset = cls(name=data["name"], persist=persist) + # Set label colors + label_colors = data.get("label_colors", {}) + dataset._storage.label_colors = label_colors + + # Add samples + samples = [] for s_data in data["samples"]: sample = Sample( id=s_data["id"], @@ -337,6 +631,144 @@ def load(cls, filepath: str) -> Dataset: embedding_2d_hyperbolic=s_data.get("embedding_2d_hyperbolic"), thumbnail_base64=s_data.get("thumbnail_base64"), ) - dataset.add_sample(sample) + samples.append(sample) + dataset._storage.add_samples_batch(samples) return dataset + + @classmethod + def open(cls, name: str, embedding_dim: int = 512) -> Dataset: + """Open an existing persistent dataset. + + Args: + name: Name of the dataset to open. + embedding_dim: Embedding dimension (must match original). + + Returns: + Dataset instance connected to existing data. + + Raises: + ValueError: If dataset does not exist. + """ + from hyperview.storage import LanceDBBackend + + if not LanceDBBackend.dataset_exists(name): + raise ValueError( + f"Dataset '{name}' does not exist. " + f"Available datasets: {cls.list_datasets()}" + ) + + return cls(name=name, persist=True, embedding_dim=embedding_dim) + + @classmethod + def list_datasets(cls) -> list[str]: + """List all available persistent datasets. + + Returns: + List of dataset names. + """ + from hyperview.storage import LanceDBBackend + + return LanceDBBackend.list_datasets() + + @classmethod + def delete(cls, name: str, delete_media: bool = False) -> bool: + """Delete a persistent dataset. + + Args: + name: Name of the dataset to delete. + delete_media: If True, also delete associated media files from disk. + Default is False (safe, preserves media files). + + Returns: + True if dataset was deleted, False if it didn't exist. + """ + import os + + from hyperview.storage import LanceDBBackend + + if delete_media: + try: + dataset = cls.open(name) + except Exception: + dataset = None + + if dataset is not None: + for fp in (s.filepath for s in dataset.samples): + if os.path.exists(fp): + try: + os.remove(fp) + except OSError: + continue + + return LanceDBBackend.delete_dataset(name) + + @classmethod + def cleanup_orphaned_media( + cls, + delete: bool = False, + ) -> tuple[int, list[str]]: + """Find media files not referenced by any dataset. + + Scans the media directory for image files and checks if they are + referenced by any existing dataset. Useful for cleaning up disk space + after deleting datasets without the delete_media=True flag. + + Args: + delete: If True, actually delete the orphaned files. + If False (default), just report them. + + Returns: + Tuple of (count, list_of_orphaned_paths). + """ + import os + + from hyperview.storage import StorageConfig + + config = StorageConfig.default() + media_dir = config.media_dir + + if not media_dir.exists(): + return 0, [] + + # Get all filepaths from all datasets + referenced: set[str] = set() + for dataset_name in cls.list_datasets(): + try: + ds = cls.open(dataset_name) + referenced.update(s.filepath for s in ds.samples) + except Exception: + continue + + # Find orphaned files (images not referenced by any dataset) + orphaned: list[str] = [] + image_extensions = {".png", ".jpg", ".jpeg", ".webp", ".gif", ".bmp"} + + for img_path in media_dir.rglob("*"): + if img_path.is_file() and img_path.suffix.lower() in image_extensions: + if str(img_path) not in referenced: + orphaned.append(str(img_path)) + + # Optionally delete orphaned files + if delete: + for path in orphaned: + try: + os.remove(path) + except OSError: + continue + + return len(orphaned), orphaned + + @classmethod + def exists(cls, name: str) -> bool: + """Check if a persistent dataset exists. + + Args: + name: Name of the dataset to check. + + Returns: + True if dataset exists. + """ + from hyperview.storage import LanceDBBackend + + return LanceDBBackend.dataset_exists(name) diff --git a/src/hyperview/server/app.py b/src/hyperview/server/app.py index d1f6bf9..d0922b8 100644 --- a/src/hyperview/server/app.py +++ b/src/hyperview/server/app.py @@ -15,6 +15,7 @@ # Global dataset reference (set by launch()) _current_dataset: Dataset | None = None +_current_session_id: str | None = None class SelectionRequest(BaseModel): @@ -55,7 +56,29 @@ class EmbeddingsResponse(BaseModel): label_colors: dict[str, str] -def create_app(dataset: Dataset | None = None) -> FastAPI: +class SimilarSampleResponse(BaseModel): + """Response model for a similar sample with distance.""" + + id: str + filepath: str + filename: str + label: str | None + thumbnail: str | None + distance: float + metadata: dict + embedding_2d: list[float] | None = None + embedding_2d_hyperbolic: list[float] | None = None + + +class SimilaritySearchResponse(BaseModel): + """Response model for similarity search results.""" + + query_id: str + k: int + results: list[SimilarSampleResponse] + + +def create_app(dataset: Dataset | None = None, session_id: str | None = None) -> FastAPI: """Create the FastAPI application. Args: @@ -64,9 +87,11 @@ def create_app(dataset: Dataset | None = None) -> FastAPI: Returns: FastAPI application instance. """ - global _current_dataset + global _current_dataset, _current_session_id if dataset is not None: _current_dataset = dataset + if session_id is not None: + _current_session_id = session_id app = FastAPI( title="HyperView", @@ -83,6 +108,16 @@ def create_app(dataset: Dataset | None = None) -> FastAPI: allow_headers=["*"], ) + @app.get("/__hyperview__/health") + async def hyperview_health(): + return { + "name": "hyperview", + "version": app.version, + "session_id": _current_session_id, + "dataset": _current_dataset.name if _current_dataset is not None else None, + "pid": os.getpid(), + } + @app.get("/api/dataset", response_model=DatasetResponse) async def get_dataset_info(): """Get dataset metadata.""" @@ -106,14 +141,10 @@ async def get_samples( if _current_dataset is None: raise HTTPException(status_code=404, detail="No dataset loaded") - samples = _current_dataset.samples - - # Filter by label if specified - if label: - samples = [s for s in samples if s.label == label] - - total = len(samples) - samples = samples[offset : offset + limit] + # Use storage backend's native pagination (avoids loading all samples) + samples, total = _current_dataset._storage.get_samples_paginated( + offset=offset, limit=limit, label=label + ) return { "total": total, @@ -146,7 +177,7 @@ async def get_samples_batch(request: SelectionRequest): sample = _current_dataset[sample_id] samples.append(sample.to_api_dict(include_thumbnail=True)) except KeyError: - pass # Skip missing samples + continue return {"samples": samples} @@ -180,6 +211,52 @@ async def sync_selection(request: SelectionRequest): """Sync selection state (for future use).""" return {"status": "ok", "selected": request.sample_ids} + @app.get("/api/search/similar/{sample_id}", response_model=SimilaritySearchResponse) + async def search_similar( + sample_id: str, + k: int = Query(10, ge=1, le=100), + use_hyperbolic: bool = Query(False), + ): + """Return k nearest neighbors for a given sample.""" + if _current_dataset is None: + raise HTTPException(status_code=404, detail="No dataset loaded") + + try: + similar = _current_dataset.find_similar( + sample_id, k=k, use_hyperbolic=use_hyperbolic + ) + except ValueError as e: + raise HTTPException(status_code=400, detail=str(e)) + except KeyError: + raise HTTPException(status_code=404, detail=f"Sample not found: {sample_id}") + + results = [] + for sample, distance in similar: + try: + thumbnail = sample.get_thumbnail_base64() + except Exception: + thumbnail = None + + results.append( + SimilarSampleResponse( + id=sample.id, + filepath=sample.filepath, + filename=sample.filename, + label=sample.label, + thumbnail=thumbnail, + distance=distance, + metadata=sample.metadata, + embedding_2d=sample.embedding_2d, + embedding_2d_hyperbolic=sample.embedding_2d_hyperbolic, + ) + ) + + return SimilaritySearchResponse( + query_id=sample_id, + k=k, + results=results, + ) + @app.get("/api/thumbnail/{sample_id}") async def get_thumbnail(sample_id: str): """Get thumbnail image for a sample.""" diff --git a/src/hyperview/storage/__init__.py b/src/hyperview/storage/__init__.py new file mode 100644 index 0000000..77a434f --- /dev/null +++ b/src/hyperview/storage/__init__.py @@ -0,0 +1,19 @@ +"""Storage backends for HyperView.""" + +from hyperview.storage.backend import StorageBackend +from hyperview.storage.config import ( + StorageConfig, + get_default_database_dir, + get_default_media_dir, +) +from hyperview.storage.lancedb_backend import LanceDBBackend +from hyperview.storage.memory_backend import MemoryBackend + +__all__ = [ + "StorageBackend", + "StorageConfig", + "get_default_database_dir", + "get_default_media_dir", + "LanceDBBackend", + "MemoryBackend", +] diff --git a/src/hyperview/storage/backend.py b/src/hyperview/storage/backend.py new file mode 100644 index 0000000..63e2162 --- /dev/null +++ b/src/hyperview/storage/backend.py @@ -0,0 +1,114 @@ +"""Abstract storage backend interface for HyperView.""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from collections.abc import Callable, Iterator +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from hyperview.core.sample import Sample + + +class StorageBackend(ABC): + """Abstract base class for storage backends.""" + + @abstractmethod + def add_sample(self, sample: Sample) -> None: + """Add a single sample to storage.""" + + @abstractmethod + def add_samples_batch(self, samples: list[Sample]) -> None: + """Add multiple samples efficiently.""" + + @abstractmethod + def get_sample(self, sample_id: str) -> Sample | None: + """Retrieve a sample by ID.""" + + @abstractmethod + def get_samples_paginated( + self, + offset: int = 0, + limit: int = 100, + label: str | None = None, + ) -> tuple[list[Sample], int]: + """Get paginated samples. Returns (samples, total_count).""" + + @abstractmethod + def get_all_samples(self) -> list[Sample]: + """Get all samples (use with caution for large datasets).""" + + @abstractmethod + def update_sample(self, sample: Sample) -> None: + """Update an existing sample.""" + + @abstractmethod + def update_samples_batch(self, samples: list[Sample]) -> None: + """Batch update samples (for embeddings, etc.).""" + + @abstractmethod + def delete_sample(self, sample_id: str) -> bool: + """Delete a sample by ID. Returns True if deleted.""" + + @abstractmethod + def __len__(self) -> int: + """Return total number of samples.""" + + @abstractmethod + def __iter__(self) -> Iterator[Sample]: + """Iterate over all samples.""" + + @abstractmethod + def __contains__(self, sample_id: str) -> bool: + """Check if sample exists.""" + + @abstractmethod + def get_unique_labels(self) -> list[str]: + """Get all unique labels.""" + + @abstractmethod + def find_similar( + self, + sample_id: str, + k: int = 10, + vector_column: str = "embedding", + ) -> list[tuple[Sample, float]]: + """Find k nearest neighbors. Returns [(sample, distance), ...].""" + + @abstractmethod + def find_similar_by_vector( + self, + vector: list[float], + k: int = 10, + vector_column: str = "embedding", + ) -> list[tuple[Sample, float]]: + """Find k nearest neighbors to a query vector.""" + + @abstractmethod + def filter(self, predicate: Callable[[Sample], bool]) -> list[Sample]: + """Filter samples based on a predicate function.""" + + @abstractmethod + def get_existing_ids(self, sample_ids: list[str]) -> set[str]: + """Return set of sample_ids that already exist in storage. + + Args: + sample_ids: List of sample IDs to check. + + Returns: + Set of IDs that exist in storage. + """ + + @abstractmethod + def close(self) -> None: + """Close the storage connection.""" + + @property + @abstractmethod + def label_colors(self) -> dict[str, str]: + """Get label color mapping.""" + + @label_colors.setter + @abstractmethod + def label_colors(self, colors: dict[str, str]) -> None: + """Set label color mapping.""" diff --git a/src/hyperview/storage/config.py b/src/hyperview/storage/config.py new file mode 100644 index 0000000..6e5eea9 --- /dev/null +++ b/src/hyperview/storage/config.py @@ -0,0 +1,75 @@ +"""Storage configuration for HyperView.""" + +from __future__ import annotations + +import os +from dataclasses import dataclass, field +from pathlib import Path + + +def get_default_database_dir() -> Path: + """Get the default database directory. + + Uses HYPERVIEW_DATABASE_DIR env var if set, otherwise ~/.hyperview/datasets/ + """ + env_dir = os.environ.get("HYPERVIEW_DATABASE_DIR") + if env_dir: + return Path(env_dir) + return Path.home() / ".hyperview" / "datasets" + + +def get_default_media_dir() -> Path: + """Get the default media directory for downloaded images. + + Uses HYPERVIEW_MEDIA_DIR env var if set, otherwise ~/.hyperview/media/ + Similar to FiftyOne's ~/fiftyone/huggingface/hub/ pattern. + """ + env_dir = os.environ.get("HYPERVIEW_MEDIA_DIR") + if env_dir: + return Path(env_dir) + return Path.home() / ".hyperview" / "media" + + +@dataclass +class StorageConfig: + """Configuration for storage backend.""" + + database_dir: Path = field(default_factory=get_default_database_dir) + media_dir: Path = field(default_factory=get_default_media_dir) + embedding_dim: int = 512 # Default CLIP dimension + embedding_2d_dim: int = 2 + + @classmethod + def default(cls, embedding_dim: int = 512) -> StorageConfig: + """Create a default configuration with optional custom embedding dimension.""" + return cls( + database_dir=get_default_database_dir(), + media_dir=get_default_media_dir(), + embedding_dim=embedding_dim, + ) + + def ensure_dir_exists(self) -> None: + """Ensure the database directory exists.""" + self.database_dir.mkdir(parents=True, exist_ok=True) + + def ensure_media_dir_exists(self) -> None: + """Ensure the media directory exists.""" + self.media_dir.mkdir(parents=True, exist_ok=True) + + def get_huggingface_media_dir(self, dataset_name: str, split: str) -> Path: + """Get the directory for storing HuggingFace dataset media. + + Creates: ~/.hyperview/media/huggingface/{dataset_name}/{split}/ + + Args: + dataset_name: Name of the HuggingFace dataset (e.g., "cifar100") + split: Dataset split (e.g., "train", "test") + + Returns: + Path to the media directory for this dataset/split. + """ + # Sanitize dataset name for filesystem (replace / with _) + safe_name = dataset_name.replace("/", "_") + media_path = self.media_dir / "huggingface" / safe_name / split + media_path.mkdir(parents=True, exist_ok=True) + return media_path diff --git a/src/hyperview/storage/lancedb_backend.py b/src/hyperview/storage/lancedb_backend.py new file mode 100644 index 0000000..8a531fd --- /dev/null +++ b/src/hyperview/storage/lancedb_backend.py @@ -0,0 +1,521 @@ +"""LanceDB storage backend for HyperView.""" + +from __future__ import annotations + +import json +from collections.abc import Callable, Iterator +from pathlib import Path +from typing import TYPE_CHECKING + +import lancedb +import pyarrow as pa + +from hyperview.storage.backend import StorageBackend +from hyperview.storage.config import StorageConfig, get_default_database_dir +from hyperview.storage.schema import ( + create_metadata_schema, + create_sample_schema, + dict_to_sample, + sample_to_dict, +) + +if TYPE_CHECKING: + from hyperview.core.sample import Sample + + +class LanceDBBackend(StorageBackend): + """LanceDB-based storage backend for HyperView datasets.""" + + def __init__( + self, + dataset_name: str, + config: StorageConfig | None = None, + ): + """Initialize LanceDB backend. + + Args: + dataset_name: Name of the dataset (becomes table name). + config: Storage configuration. Uses defaults if None. + """ + self.dataset_name = dataset_name + self.config = config or StorageConfig.default() + + self.config.ensure_dir_exists() + + self._db = lancedb.connect(str(self.config.database_dir)) + + self._table_name = f"hyperview_{dataset_name}" + self._metadata_table_name = f"hyperview_{dataset_name}_meta" + + self._table = self._get_or_create_table() + self._metadata_table = self._get_or_create_metadata_table() + + self._label_colors_cache: dict[str, str] | None = None + + def _get_or_create_table(self) -> lancedb.table.Table | None: + """Get existing table or return None if it doesn't exist yet.""" + if self._table_name in self._db.table_names(): + return self._db.open_table(self._table_name) + return None + + def _ensure_table_exists(self, data: list[dict]) -> lancedb.table.Table: + """Ensure table exists, creating it from data if needed. + """ + if self._table is None: + schema = create_sample_schema(self.config.embedding_dim) + arrow_table = pa.Table.from_pylist(data, schema=schema) + self._table = self._db.create_table(self._table_name, data=arrow_table) + return self._table + + def _cast_rows_to_table_schema(self, rows: list[dict]) -> pa.Table: + """Cast Python dict rows to the existing table schema.""" + if self._table is None: + raise RuntimeError("Cannot cast rows: samples table not initialized") + return pa.Table.from_pylist(rows, schema=self._table.schema) + + def _get_or_create_metadata_table(self) -> lancedb.table.Table: + """Get or create metadata table.""" + if self._metadata_table_name in self._db.table_names(): + return self._db.open_table(self._metadata_table_name) + else: + schema = create_metadata_schema() + return self._db.create_table(self._metadata_table_name, schema=schema) + + def _convert_embeddings_to_numpy(self, data: list[dict]) -> None: + """Convert high-dim embedding lists to numpy arrays in-place. + """ + import numpy as np + + for row in data: + if row.get("embedding") is not None: + row["embedding"] = np.array(row["embedding"], dtype=np.float32) + + def add_sample(self, sample: Sample) -> None: + """Add a single sample to storage.""" + data = [sample_to_dict(sample, self.config.embedding_dim)] + self._convert_embeddings_to_numpy(data) + if self._table is None: + self._ensure_table_exists(data) + else: + self._table.add(self._cast_rows_to_table_schema(data)) + + def add_samples_batch(self, samples: list[Sample]) -> None: + """Add multiple samples efficiently.""" + if not samples: + return + data = [sample_to_dict(s, self.config.embedding_dim) for s in samples] + self._convert_embeddings_to_numpy(data) + if self._table is None: + self._ensure_table_exists(data) + else: + self._table.add(self._cast_rows_to_table_schema(data)) + + def get_sample(self, sample_id: str) -> Sample | None: + """Retrieve a sample by ID.""" + if self._table is None: + return None + safe_id = sample_id.replace("'", "''") + results = self._table.search().where(f"id = '{safe_id}'").limit(1).to_list() + if results: + return dict_to_sample(results[0]) + return None + + def get_samples_paginated( + self, + offset: int = 0, + limit: int = 100, + label: str | None = None, + ) -> tuple[list[Sample], int]: + """Get paginated samples using native LanceDB queries.""" + if self._table is None: + return [], 0 + + import pyarrow.compute as pc + + if label: + arrow_table = self._table.search().select(["label"]).to_arrow() + label_column = arrow_table.column("label") + mask = pc.fill_null(pc.equal(label_column, pa.scalar(label)), False) + total = pc.sum(pc.cast(mask, pa.int64())).as_py() + else: + total = self._table.count_rows() + + query = self._table.search() + if label: + safe_label = label.replace("'", "''") + query = query.where(f"label = '{safe_label}'") + + results = query.offset(offset).limit(limit).to_list() + samples = [dict_to_sample(row) for row in results] + return samples, total + + def get_all_samples(self) -> list[Sample]: + """Get all samples.""" + if self._table is None: + return [] + arrow_table = self._table.to_arrow() + rows = arrow_table.to_pylist() + return [dict_to_sample(row) for row in rows] + + def update_sample(self, sample: Sample) -> None: + """Update an existing sample.""" + self._delete_by_id(sample.id) + self.add_sample(sample) + + def update_samples_batch(self, samples: list[Sample]) -> None: + """Batch update samples.""" + if not samples: + return + + if self._table is None: + self.add_samples_batch(samples) + return + + if self._needs_schema_migration(samples): + self._migrate_table_schema(samples) + else: + updated_data = [sample_to_dict(s, self.config.embedding_dim) for s in samples] + self._convert_embeddings_to_numpy(updated_data) + + arrow = self._cast_rows_to_table_schema(updated_data) + ( + self._table.merge_insert("id") + .when_matched_update_all() + .when_not_matched_insert_all() + .execute(arrow) + ) + + def _needs_schema_migration(self, samples: list[Sample]) -> bool: + """Check whether embedding columns exist with a non-null type.""" + schema = self._table.schema + + columns_to_check = [ + ("embedding", lambda s: s.embedding is not None), + ("embedding_2d_euclidean", lambda s: s.embedding_2d is not None), + ("embedding_2d_hyperbolic", lambda s: s.embedding_2d_hyperbolic is not None), + ] + + for col_name, has_value_fn in columns_to_check: + has_values = any(has_value_fn(s) for s in samples) + if not has_values: + continue + try: + field = schema.field(col_name) + if str(field.type) == "null": + return True + except KeyError: + return True + + return False + + def _migrate_table_schema(self, updated_samples: list[Sample]) -> None: + """Recreate the table with the current schema and merge updates.""" + existing_rows = self._table.to_arrow().to_pylist() + updates_by_id = {s.id: s for s in updated_samples} + + new_data: list[dict] = [] + for row in existing_rows: + sample_id = row.get("id") + if sample_id in updates_by_id: + new_data.append(sample_to_dict(updates_by_id[sample_id], self.config.embedding_dim)) + else: + new_data.append(row) + + self._convert_embeddings_to_numpy(new_data) + + self._db.drop_table(self._table_name) + self._table = None + self._ensure_table_exists(new_data) + + def _delete_by_id(self, sample_id: str) -> bool: + """Delete a sample by ID using native LanceDB delete.""" + if self._table is None: + return False + try: + # Use LanceDB's native delete with SQL WHERE clause + safe_id = sample_id.replace("'", "''") + self._table.delete(f"id = '{safe_id}'") + return True + except Exception: + return False + + def delete_sample(self, sample_id: str) -> bool: + """Delete a sample by ID.""" + return self._delete_by_id(sample_id) + + def __len__(self) -> int: + """Return total number of samples.""" + if self._table is None: + return 0 + return self._table.count_rows() + + def __iter__(self) -> Iterator[Sample]: + """Iterate over all samples.""" + if self._table is None: + return iter([]) + arrow_table = self._table.to_arrow() + for batch in arrow_table.to_batches(max_chunksize=1000): + batch_dict = batch.to_pydict() + for i in range(batch.num_rows): + row = {k: batch_dict[k][i] for k in batch_dict} + yield dict_to_sample(row) + + def __contains__(self, sample_id: str) -> bool: + """Check if sample exists.""" + if self._table is None: + return False + safe_id = sample_id.replace("'", "''") + try: + results = self._table.search().where(f"id = '{safe_id}'").limit(1).to_list() + return len(results) > 0 + except Exception: + return False + + def get_unique_labels(self) -> list[str]: + """Get all unique labels.""" + if self._table is None: + return [] + import pyarrow.compute as pc + + # Label-only scan - don't pull thumbnails/metadata into RAM + arrow_table = self._table.search().select(["label"]).to_arrow() + label_column = arrow_table.column("label") + unique_labels = pc.unique(label_column).to_pylist() + return sorted([label for label in unique_labels if label is not None]) + + def find_similar( + self, + sample_id: str, + k: int = 10, + vector_column: str = "embedding", + ) -> list[tuple[Sample, float]]: + """Find k nearest neighbors to a sample.""" + sample = self.get_sample(sample_id) + if sample is None: + raise ValueError(f"Sample not found: {sample_id}") + + query_vector = self._get_vector(sample, vector_column) + if query_vector is None: + raise ValueError(f"Sample {sample_id} has no {vector_column}") + + # Find similar, excluding self + results = self.find_similar_by_vector(query_vector, k + 1, vector_column) + return [(s, d) for s, d in results if s.id != sample_id][:k] + + def find_similar_by_vector( + self, + vector: list[float], + k: int = 10, + vector_column: str = "embedding", + ) -> list[tuple[Sample, float]]: + """Find k nearest neighbors to a query vector.""" + import math + + lance_column = self._map_vector_column(vector_column) + + try: + results = ( + self._table.search(vector, vector_column_name=lance_column) + .metric("cosine") + .limit(k) + .to_list() + ) + + # Normalize nan distances to 0.0 (can happen with identical vectors) + return [ + ( + dict_to_sample(row), + 0.0 + if math.isnan(d := row.get("_distance", 0.0)) + else float(d), + ) + for row in results + ] + except Exception: + return self._brute_force_search(vector, k, vector_column) + + def _brute_force_search( + self, + vector: list[float], + k: int, + vector_column: str, + ) -> list[tuple[Sample, float]]: + """Fallback brute force search when vector index is not available.""" + import numpy as np + + query = np.array(vector) + distances: list[tuple[Sample, float]] = [] + + for sample in self: + vec = self._get_vector(sample, vector_column) + if vec is None: + continue + + vec_np = np.array(vec) + norm_query = np.linalg.norm(query) + norm_vec = np.linalg.norm(vec_np) + + if norm_query == 0 or norm_vec == 0: + distance = 1.0 + else: + cosine_sim = np.dot(query, vec_np) / (norm_query * norm_vec) + distance = 1 - cosine_sim + + distances.append((sample, float(distance))) + + distances.sort(key=lambda x: x[1]) + return distances[:k] + + def _get_vector(self, sample: Sample, vector_column: str) -> list[float] | None: + """Get the appropriate vector from a sample.""" + if vector_column == "embedding": + return sample.embedding + elif vector_column in ("embedding_2d_euclidean", "embedding_2d"): + return sample.embedding_2d + elif vector_column == "embedding_2d_hyperbolic": + return sample.embedding_2d_hyperbolic + else: + raise ValueError(f"Unknown vector column: {vector_column}") + + def _map_vector_column(self, vector_column: str) -> str: + """Map vector column name to LanceDB column name.""" + mapping = { + "embedding": "embedding", + "embedding_2d": "embedding_2d_euclidean", + "embedding_2d_euclidean": "embedding_2d_euclidean", + "embedding_2d_hyperbolic": "embedding_2d_hyperbolic", + } + return mapping.get(vector_column, vector_column) + + def filter(self, predicate: Callable[[Sample], bool]) -> list[Sample]: + """Filter samples based on a predicate function.""" + return [s for s in self if predicate(s)] + + def get_existing_ids(self, sample_ids: list[str]) -> set[str]: + """Return the subset of sample_ids that exist in storage.""" + if self._table is None or not sample_ids: + return set() + + existing: set[str] = set() + + def query_chunk(chunk: list[str]) -> set[str]: + escaped = [sid.replace("'", "''") for sid in chunk] + id_list = "', '".join(escaped) + results = self._table.search().where(f"id IN ('{id_list}')").select(["id"]).to_list() + return {r["id"] for r in results} + + chunk_size = 1000 + for i in range(0, len(sample_ids), chunk_size): + chunk = sample_ids[i : i + chunk_size] + try: + existing.update(query_chunk(chunk)) + except Exception: + existing.update(sid for sid in chunk if sid in self) + + return existing + + def close(self) -> None: + """Close the storage connection.""" + return + + @property + def label_colors(self) -> dict[str, str]: + """Get label color mapping.""" + if self._label_colors_cache is not None: + return self._label_colors_cache + + rows = self._metadata_table.search().where("key = 'label_colors'").limit(1).to_list() + if not rows: + self._label_colors_cache = {} + return self._label_colors_cache + + value = rows[0].get("value") + self._label_colors_cache = json.loads(value) if value else {} + + return self._label_colors_cache + + @label_colors.setter + def label_colors(self, colors: dict[str, str]) -> None: + """Set label color mapping.""" + self._label_colors_cache = colors + + try: + self._metadata_table.delete("key = 'label_colors'") + except Exception: + pass + + schema = create_metadata_schema() + row = {"key": "label_colors", "value": json.dumps(colors)} + self._metadata_table.add(pa.Table.from_pylist([row], schema=schema)) + + def create_vector_index(self, vector_column: str = "embedding") -> None: + """Create an ANN index for vector search.""" + if self._table is None: + return + num_rows = self._table.count_rows() + if num_rows < 256: + return + + lance_column = self._map_vector_column(vector_column) + + try: + self._table.create_index( + vector_column_name=lance_column, + index_type="IVF_PQ", + num_partitions=min(256, num_rows // 10), + num_sub_vectors=16, + ) + except Exception: + return + + @classmethod + def list_datasets(cls, database_dir: Path | None = None) -> list[str]: + """List all available datasets in the database directory.""" + if database_dir is None: + database_dir = get_default_database_dir() + + if not database_dir.exists(): + return [] + + db = lancedb.connect(str(database_dir)) + datasets = [] + for table_name in db.table_names(): + if table_name.startswith("hyperview_") and not table_name.endswith("_meta"): + datasets.append(table_name[len("hyperview_") :]) + return sorted(datasets) + + @classmethod + def delete_dataset(cls, dataset_name: str, database_dir: Path | None = None) -> bool: + """Delete a dataset from the database.""" + if database_dir is None: + database_dir = get_default_database_dir() + + if not database_dir.exists(): + return False + + db = lancedb.connect(str(database_dir)) + table_name = f"hyperview_{dataset_name}" + metadata_table_name = f"hyperview_{dataset_name}_meta" + + deleted = False + if table_name in db.table_names(): + db.drop_table(table_name) + deleted = True + + if metadata_table_name in db.table_names(): + db.drop_table(metadata_table_name) + + return deleted + + @classmethod + def dataset_exists(cls, dataset_name: str, database_dir: Path | None = None) -> bool: + """Check if a dataset exists.""" + if database_dir is None: + database_dir = get_default_database_dir() + + if not database_dir.exists(): + return False + + db = lancedb.connect(str(database_dir)) + table_name = f"hyperview_{dataset_name}" + return table_name in db.table_names() diff --git a/src/hyperview/storage/memory_backend.py b/src/hyperview/storage/memory_backend.py new file mode 100644 index 0000000..05f1935 --- /dev/null +++ b/src/hyperview/storage/memory_backend.py @@ -0,0 +1,173 @@ +"""In-memory storage backend for testing and development.""" + +from __future__ import annotations + +from collections.abc import Callable, Iterator +from typing import TYPE_CHECKING + +import numpy as np + +from hyperview.storage.backend import StorageBackend + +if TYPE_CHECKING: + from hyperview.core.sample import Sample + + +class MemoryBackend(StorageBackend): + """In-memory storage backend (current behavior, for testing and backwards compatibility).""" + + def __init__(self, dataset_name: str): + """Initialize in-memory backend. + + Args: + dataset_name: Name of the dataset. + """ + self.dataset_name = dataset_name + self._samples: dict[str, Sample] = {} + self._label_colors: dict[str, str] = {} + + def add_sample(self, sample: Sample) -> None: + """Add a single sample to storage.""" + self._samples[sample.id] = sample + + def add_samples_batch(self, samples: list[Sample]) -> None: + """Add multiple samples efficiently.""" + for sample in samples: + self._samples[sample.id] = sample + + def get_sample(self, sample_id: str) -> Sample | None: + """Retrieve a sample by ID.""" + return self._samples.get(sample_id) + + def get_samples_paginated( + self, + offset: int = 0, + limit: int = 100, + label: str | None = None, + ) -> tuple[list[Sample], int]: + """Get paginated samples.""" + samples = list(self._samples.values()) + if label: + samples = [s for s in samples if s.label == label] + total = len(samples) + return samples[offset : offset + limit], total + + def get_all_samples(self) -> list[Sample]: + """Get all samples.""" + return list(self._samples.values()) + + def update_sample(self, sample: Sample) -> None: + """Update an existing sample.""" + self._samples[sample.id] = sample + + def update_samples_batch(self, samples: list[Sample]) -> None: + """Batch update samples.""" + for sample in samples: + self._samples[sample.id] = sample + + def delete_sample(self, sample_id: str) -> bool: + """Delete a sample by ID.""" + if sample_id in self._samples: + del self._samples[sample_id] + return True + return False + + def __len__(self) -> int: + """Return total number of samples.""" + return len(self._samples) + + def __iter__(self) -> Iterator[Sample]: + """Iterate over all samples.""" + return iter(self._samples.values()) + + def __contains__(self, sample_id: str) -> bool: + """Check if sample exists.""" + return sample_id in self._samples + + def get_unique_labels(self) -> list[str]: + """Get all unique labels.""" + labels = {s.label for s in self._samples.values() if s.label} + return sorted(labels) + + def find_similar( + self, + sample_id: str, + k: int = 10, + vector_column: str = "embedding", + ) -> list[tuple[Sample, float]]: + """Find k nearest neighbors to a sample.""" + sample = self._samples.get(sample_id) + if sample is None: + raise ValueError(f"Sample not found: {sample_id}") + + query_vector = self._get_vector(sample, vector_column) + if query_vector is None: + raise ValueError(f"Sample {sample_id} has no {vector_column}") + + # Find similar, excluding self + results = self.find_similar_by_vector(query_vector, k + 1, vector_column) + return [(s, d) for s, d in results if s.id != sample_id][:k] + + def find_similar_by_vector( + self, + vector: list[float], + k: int = 10, + vector_column: str = "embedding", + ) -> list[tuple[Sample, float]]: + """Find k nearest neighbors to a query vector.""" + query = np.array(vector) + distances: list[tuple[Sample, float]] = [] + + for sample in self._samples.values(): + vec = self._get_vector(sample, vector_column) + if vec is None: + continue + + # Cosine distance + vec_np = np.array(vec) + norm_query = np.linalg.norm(query) + norm_vec = np.linalg.norm(vec_np) + + if norm_query == 0 or norm_vec == 0: + distance = 1.0 + else: + cosine_sim = np.dot(query, vec_np) / (norm_query * norm_vec) + distance = 1 - cosine_sim + + distances.append((sample, float(distance))) + + distances.sort(key=lambda x: x[1]) + return distances[:k] + + def _get_vector(self, sample: Sample, vector_column: str) -> list[float] | None: + """Get the appropriate vector from a sample.""" + if vector_column == "embedding": + return sample.embedding + elif vector_column == "embedding_2d_euclidean": + return sample.embedding_2d + elif vector_column == "embedding_2d_hyperbolic": + return sample.embedding_2d_hyperbolic + else: + raise ValueError(f"Unknown vector column: {vector_column}") + + def filter(self, predicate: Callable[[Sample], bool]) -> list[Sample]: + """Filter samples based on a predicate function.""" + return [s for s in self._samples.values() if predicate(s)] + + def get_existing_ids(self, sample_ids: list[str]) -> set[str]: + """Return set of sample_ids that already exist in storage.""" + return {sid for sid in sample_ids if sid in self._samples} + + def close(self) -> None: + """Close the storage connection (no-op for in-memory).""" + return + + @property + def label_colors(self) -> dict[str, str]: + """Get label color mapping.""" + return self._label_colors + + @label_colors.setter + def label_colors(self, colors: dict[str, str]) -> None: + """Set label color mapping.""" + self._label_colors = colors diff --git a/src/hyperview/storage/schema.py b/src/hyperview/storage/schema.py new file mode 100644 index 0000000..03171e4 --- /dev/null +++ b/src/hyperview/storage/schema.py @@ -0,0 +1,124 @@ +"""LanceDB schema definitions for HyperView samples.""" + +from __future__ import annotations + +import json +from typing import TYPE_CHECKING, Any + +import pyarrow as pa + +if TYPE_CHECKING: + from hyperview.core.sample import Sample + + +def create_sample_schema(embedding_dim: int = 512) -> pa.Schema: + """Create the PyArrow schema for samples. + + Using PyArrow schema instead of LanceModel allows dynamic embedding dimensions. + + Args: + embedding_dim: Dimension of the high-dimensional embedding vector. + + Returns: + PyArrow schema for the samples table. + """ + # Use fixed-size lists for ANN index support in LanceDB + return pa.schema( + [ + pa.field("id", pa.utf8(), nullable=False), + pa.field("filepath", pa.utf8(), nullable=False), + pa.field("label", pa.utf8(), nullable=True), + pa.field("metadata_json", pa.utf8(), nullable=True), + pa.field("embedding", pa.list_(pa.float32(), embedding_dim), nullable=True), + pa.field("embedding_2d_euclidean", pa.list_(pa.float32(), 2), nullable=True), + pa.field("embedding_2d_hyperbolic", pa.list_(pa.float32(), 2), nullable=True), + pa.field("thumbnail_base64", pa.utf8(), nullable=True), + ] + ) + + +def create_metadata_schema() -> pa.Schema: + """Create the PyArrow schema for dataset metadata.""" + return pa.schema( + [ + pa.field("key", pa.utf8(), nullable=False), + pa.field("value", pa.utf8(), nullable=True), + ] + ) + + +def sample_to_dict(sample: Sample, embedding_dim: int = 512) -> dict[str, Any]: + """Convert a Sample to a dictionary for LanceDB insertion. + + Args: + sample: The Sample object to convert. + embedding_dim: Expected embedding dimension for padding/validation. + + Returns: + Dictionary suitable for LanceDB insertion. + """ + # Handle embedding - ensure correct dimension or None + embedding = None + if sample.embedding is not None: + embedding = list(sample.embedding) + # Pad or truncate to expected dimension + if len(embedding) < embedding_dim: + embedding.extend([0.0] * (embedding_dim - len(embedding))) + elif len(embedding) > embedding_dim: + embedding = embedding[:embedding_dim] + + return { + "id": sample.id, + "filepath": sample.filepath, + "label": sample.label, + "metadata_json": json.dumps(sample.metadata) if sample.metadata else None, + "embedding": embedding, + "embedding_2d_euclidean": list(sample.embedding_2d) if sample.embedding_2d else None, + "embedding_2d_hyperbolic": ( + list(sample.embedding_2d_hyperbolic) if sample.embedding_2d_hyperbolic else None + ), + "thumbnail_base64": sample.thumbnail_base64, + } + + +def dict_to_sample(row: dict[str, Any]) -> Sample: + """Convert a LanceDB row to a Sample object. + + Args: + row: Dictionary from LanceDB query result. + + Returns: + Sample object. + """ + from hyperview.core.sample import Sample + + metadata = {} + if row.get("metadata_json"): + try: + metadata = json.loads(row["metadata_json"]) + except (json.JSONDecodeError, TypeError): + metadata = {} + + # Convert numpy arrays to lists if needed + embedding = row.get("embedding") + if embedding is not None and hasattr(embedding, "tolist"): + embedding = embedding.tolist() + + embedding_2d = row.get("embedding_2d_euclidean") + if embedding_2d is not None and hasattr(embedding_2d, "tolist"): + embedding_2d = embedding_2d.tolist() + + embedding_2d_hyperbolic = row.get("embedding_2d_hyperbolic") + if embedding_2d_hyperbolic is not None and hasattr(embedding_2d_hyperbolic, "tolist"): + embedding_2d_hyperbolic = embedding_2d_hyperbolic.tolist() + + return Sample( + id=row["id"], + filepath=row["filepath"], + label=row.get("label"), + metadata=metadata, + 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d0a78ff468a6cacc8d9459df38e17eea48cbb036 Mon Sep 17 00:00:00 2001 From: Antonio Rueda-Toicen Date: Fri, 26 Dec 2025 14:10:37 +0100 Subject: [PATCH 03/75] Delete requirements.txt, pyproject.toml has the required dependencies --- requirements.txt | 6 ------ 1 file changed, 6 deletions(-) delete mode 100644 requirements.txt diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index c747cac..0000000 --- a/requirements.txt +++ /dev/null @@ -1,6 +0,0 @@ -torch>=2.9.1 -numpy>=2.3.5 -matplotlib>=3.10.7 -geoopt>=0.5.1 -geomstats>=2.8.0 -scikit-learn>=1.7.2 From f64bfa4fe37a67359eb2a74c81a94ebd3359b3b3 Mon Sep 17 00:00:00 2001 From: Antonio Rueda-Toicen Date: Sat, 27 Dec 2025 22:44:48 +0100 Subject: [PATCH 04/75] Add DeepWiki badge to README Added a badge for DeepWiki to the README. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index b9b6f17..8b11d54 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ > **Open-source dataset curation with hyperbolic embeddings visualization - a FiftyOne alternative.** -[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/HackerRoomAI/HyperView)

From 05ddae9a0b0123403430b5b3123a6c89b4bc35ac Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Thu, 15 Jan 2026 01:11:12 +0100 Subject: [PATCH 05/75] chore: ignore local artifacts --- .gitignore | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/.gitignore b/.gitignore index 1ad5e0b..f943cff 100644 --- a/.gitignore +++ b/.gitignore @@ -42,3 +42,16 @@ build/ # Data cache *.hf/ .cache/ + +# external repo +viz_impl/ + +# nohup +nohup.out +frontend/nohup.out + +# Local logs / tool artifacts +.hyperview-*.log +.hyperview-*.pid +.playwright-mcp/ +frontend/tsconfig.tsbuildinfo \ No newline at end of file From 0a66aec39711f0a2b6103a3e15a8273270453e1d Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Thu, 15 Jan 2026 01:11:57 +0100 Subject: [PATCH 06/75] chore(backend): simplify typing imports --- src/hyperview/core/dataset.py | 12 ++++-------- src/hyperview/core/sample.py | 4 +--- src/hyperview/embeddings/compute.py | 2 -- src/hyperview/embeddings/projection.py | 2 -- src/hyperview/storage/config.py | 4 +--- src/hyperview/storage/schema.py | 9 ++------- 6 files changed, 8 insertions(+), 25 deletions(-) diff --git a/src/hyperview/core/dataset.py b/src/hyperview/core/dataset.py index 4c9f4f9..19c1359 100644 --- a/src/hyperview/core/dataset.py +++ b/src/hyperview/core/dataset.py @@ -1,22 +1,18 @@ """Dataset class for managing collections of samples.""" -from __future__ import annotations - import hashlib import json import uuid from collections.abc import Callable, Iterator from pathlib import Path -from typing import TYPE_CHECKING, Any +from typing import Any import numpy as np from datasets import load_dataset from PIL import Image from hyperview.core.sample import Sample, SampleFromArray - -if TYPE_CHECKING: - from hyperview.storage.backend import StorageBackend +from hyperview.storage.backend import StorageBackend class Dataset: @@ -598,7 +594,7 @@ def save(self, filepath: str, include_thumbnails: bool = True) -> None: json.dump(data, f) @classmethod - def load(cls, filepath: str, persist: bool = False) -> Dataset: + def load(cls, filepath: str, persist: bool = False) -> "Dataset": """Load dataset from a JSON file. Args: @@ -637,7 +633,7 @@ def load(cls, filepath: str, persist: bool = False) -> Dataset: return dataset @classmethod - def open(cls, name: str, embedding_dim: int = 512) -> Dataset: + def open(cls, name: str, embedding_dim: int = 512) -> "Dataset": """Open an existing persistent dataset. Args: diff --git a/src/hyperview/core/sample.py b/src/hyperview/core/sample.py index 97b0dd6..2052c85 100644 --- a/src/hyperview/core/sample.py +++ b/src/hyperview/core/sample.py @@ -1,7 +1,5 @@ """Sample class representing a single data point in a dataset.""" -from __future__ import annotations - import base64 import io from pathlib import Path @@ -97,7 +95,7 @@ def from_array( image_array: np.ndarray, label: str | None = None, metadata: dict[str, Any] | None = None, - ) -> SampleFromArray: + ) -> "SampleFromArray": """Create a sample from a numpy array.""" sample = cls( id=id, diff --git a/src/hyperview/embeddings/compute.py b/src/hyperview/embeddings/compute.py index 27d68b7..af278cc 100644 --- a/src/hyperview/embeddings/compute.py +++ b/src/hyperview/embeddings/compute.py @@ -1,7 +1,5 @@ """Embedding computation using EmbedAnything.""" -from __future__ import annotations - import os import tempfile diff --git a/src/hyperview/embeddings/projection.py b/src/hyperview/embeddings/projection.py index ecef3c8..ccf8749 100644 --- a/src/hyperview/embeddings/projection.py +++ b/src/hyperview/embeddings/projection.py @@ -1,7 +1,5 @@ """Projection methods for dimensionality reduction.""" -from __future__ import annotations - import logging import numpy as np diff --git a/src/hyperview/storage/config.py b/src/hyperview/storage/config.py index 6e5eea9..c916855 100644 --- a/src/hyperview/storage/config.py +++ b/src/hyperview/storage/config.py @@ -1,7 +1,5 @@ """Storage configuration for HyperView.""" -from __future__ import annotations - import os from dataclasses import dataclass, field from pathlib import Path @@ -40,7 +38,7 @@ class StorageConfig: embedding_2d_dim: int = 2 @classmethod - def default(cls, embedding_dim: int = 512) -> StorageConfig: + def default(cls, embedding_dim: int = 512) -> "StorageConfig": """Create a default configuration with optional custom embedding dimension.""" return cls( database_dir=get_default_database_dir(), diff --git a/src/hyperview/storage/schema.py b/src/hyperview/storage/schema.py index 03171e4..ceb1a13 100644 --- a/src/hyperview/storage/schema.py +++ b/src/hyperview/storage/schema.py @@ -1,14 +1,11 @@ """LanceDB schema definitions for HyperView samples.""" -from __future__ import annotations - import json -from typing import TYPE_CHECKING, Any +from typing import Any import pyarrow as pa -if TYPE_CHECKING: - from hyperview.core.sample import Sample +from hyperview.core.sample import Sample def create_sample_schema(embedding_dim: int = 512) -> pa.Schema: @@ -90,8 +87,6 @@ def dict_to_sample(row: dict[str, Any]) -> Sample: Returns: Sample object. """ - from hyperview.core.sample import Sample - metadata = {} if row.get("metadata_json"): try: From aea34ce8e4c6a602809f44480d3598814cbf5604 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Thu, 15 Jan 2026 01:12:19 +0100 Subject: [PATCH 07/75] feat(cli): add host/no-browser flags --- src/hyperview/cli.py | 55 ++++++++++++++++++++++++++++++++++++-------- 1 file changed, 45 insertions(+), 10 deletions(-) diff --git a/src/hyperview/cli.py b/src/hyperview/cli.py index 913f4a9..9bbe321 100644 --- a/src/hyperview/cli.py +++ b/src/hyperview/cli.py @@ -1,7 +1,5 @@ """Command-line interface for HyperView.""" -from __future__ import annotations - import argparse import sys @@ -30,6 +28,17 @@ def main(): default=5151, help="Port to run the server on (default: 5151)", ) + demo_parser.add_argument( + "--host", + type=str, + default="127.0.0.1", + help="Host to bind the server to (default: 127.0.0.1)", + ) + demo_parser.add_argument( + "--no-browser", + action="store_true", + help="Do not open a browser window automatically", + ) # Serve command serve_parser = subparsers.add_parser("serve", help="Serve a saved dataset") @@ -40,32 +49,52 @@ def main(): default=5151, help="Port to run the server on (default: 5151)", ) + serve_parser.add_argument( + "--host", + type=str, + default="127.0.0.1", + help="Host to bind the server to (default: 127.0.0.1)", + ) + serve_parser.add_argument( + "--no-browser", + action="store_true", + help="Do not open a browser window automatically", + ) args = parser.parse_args() if args.command == "demo": - run_demo(args.samples, args.port) + run_demo(args.samples, args.port, host=args.host, open_browser=not args.no_browser) elif args.command == "serve": - serve_dataset(args.dataset, args.port) + serve_dataset(args.dataset, args.port, host=args.host, open_browser=not args.no_browser) else: parser.print_help() sys.exit(1) -def run_demo(num_samples: int = 500, port: int = 5151): +def run_demo( + num_samples: int = 500, + port: int = 5151, + *, + host: str = "127.0.0.1", + open_browser: bool = True, +): """Run a demo with CIFAR-100 data.""" print("Loading CIFAR-100 dataset...") dataset = Dataset("cifar100_demo") try: - count = dataset.add_from_huggingface( + added, skipped = dataset.add_from_huggingface( "uoft-cs/cifar100", split="train", image_key="img", label_key="fine_label", max_samples=num_samples, ) - print(f"Loaded {count} samples") + if skipped > 0: + print(f"Loaded {added} samples ({skipped} already present)") + else: + print(f"Loaded {added} samples") except Exception as e: print(f"Failed to load HuggingFace dataset: {e}") print("Please ensure 'datasets' is installed: pip install datasets") @@ -79,10 +108,16 @@ def run_demo(num_samples: int = 500, port: int = 5151): dataset.compute_visualization() print("Visualizations ready") - launch(dataset, port=port) + launch(dataset, port=port, host=host, open_browser=open_browser) -def serve_dataset(filepath: str, port: int = 5151): +def serve_dataset( + filepath: str, + port: int = 5151, + *, + host: str = "127.0.0.1", + open_browser: bool = True, +): """Serve a saved dataset.""" from hyperview import Dataset, launch @@ -90,7 +125,7 @@ def serve_dataset(filepath: str, port: int = 5151): dataset = Dataset.load(filepath) print(f"Loaded {len(dataset)} samples") - launch(dataset, port=port) + launch(dataset, port=port, host=host, open_browser=open_browser) if __name__ == "__main__": From 69cac76bc868c11d628623f9001760e07ef3b924 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Thu, 15 Jan 2026 01:12:51 +0100 Subject: [PATCH 08/75] fix(api): improve launch URL and server lifecycle --- src/hyperview/api.py | 31 ++++++++++++++++++++----------- 1 file changed, 20 insertions(+), 11 deletions(-) diff --git a/src/hyperview/api.py b/src/hyperview/api.py index bc0b13e..7017e6a 100644 --- a/src/hyperview/api.py +++ b/src/hyperview/api.py @@ -1,7 +1,5 @@ """Public API for HyperView.""" -from __future__ import annotations - import json import socket import threading @@ -59,7 +57,9 @@ def __init__(self, dataset: Dataset, host: str, port: int): self.dataset = dataset self.host = host self.port = port - self.url = f"http://{host}:{port}" + # Prefer a browser-connectable host for user-facing URLs. + # When binding to 0.0.0.0, users should connect via 127.0.0.1 locally. + self.url = f"http://{self._connect_host}:{port}" self._server_thread: threading.Thread | None = None self._server: uvicorn.Server | None = None self._startup_error: BaseException | None = None @@ -147,8 +147,6 @@ def start(self, background: bool = True): f"a non-HyperView app (port={self.port})." ) - time.sleep(0.05) - raise TimeoutError( "HyperView server did not become ready in time " f"(port={self.port}). Last error: {last_health_error}" @@ -209,17 +207,28 @@ def launch( if notebook: session.start(background=True) - url = f"http://{host}:{port}" - print(f"\nHyperView is running at {url}") + print(f"\nHyperView is running at {session.url}") session.show(height=height) else: + session.start(background=True) + print(" Press Ctrl+C to stop.\n") + print(f"\nHyperView is running at {session.url}") + if open_browser: session.open_browser() - print(" Press Ctrl+C to stop.\n") - url = f"http://{host}:{port}" - print(f"\nHyperView is running at {url}") - session.start(background=False) + try: + while True: + # Keep the main thread alive so the daemon server thread can run. + time.sleep(0.25) + if session._server_thread is not None and not session._server_thread.is_alive(): + raise RuntimeError("HyperView server stopped unexpectedly.") + except KeyboardInterrupt: + pass + finally: + session.stop() + if session._server_thread is not None: + session._server_thread.join(timeout=2.0) return session From 54a37b362faae50501f6b7df62cfcc6980815ae0 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Thu, 15 Jan 2026 01:13:15 +0100 Subject: [PATCH 09/75] feat(storage): add embedding/selection query helpers --- src/hyperview/storage/backend.py | 48 ++++++++- src/hyperview/storage/lancedb_backend.py | 119 +++++++++++++++++++++-- src/hyperview/storage/memory_backend.py | 69 +++++++++++-- 3 files changed, 219 insertions(+), 17 deletions(-) diff --git a/src/hyperview/storage/backend.py b/src/hyperview/storage/backend.py index 63e2162..663d91c 100644 --- a/src/hyperview/storage/backend.py +++ b/src/hyperview/storage/backend.py @@ -1,13 +1,9 @@ """Abstract storage backend interface for HyperView.""" -from __future__ import annotations - from abc import ABC, abstractmethod from collections.abc import Callable, Iterator -from typing import TYPE_CHECKING -if TYPE_CHECKING: - from hyperview.core.sample import Sample +from hyperview.core.sample import Sample class StorageBackend(ABC): @@ -99,6 +95,48 @@ def get_existing_ids(self, sample_ids: list[str]) -> set[str]: Set of IDs that exist in storage. """ + @abstractmethod + def get_samples_by_ids(self, sample_ids: list[str]) -> list[Sample]: + """Retrieve multiple samples by ID. + + Implementations should preserve the input order when possible. + Missing IDs should be skipped. + """ + + @abstractmethod + def get_visualization_embeddings( + self, + ) -> tuple[list[str], list[str | None], list[list[float]], list[list[float]]]: + """Return all 2D embedding coords needed by the frontend scatter. + + Returns: + (ids, labels, euclidean_xy, hyperbolic_xy) + + Notes: + This should avoid loading heavy fields (thumbnails, high-dim embeddings) + when the backend supports column projection. + """ + + @abstractmethod + def get_lasso_candidates_aabb( + self, + *, + space: str, + x_min: float, + x_max: float, + y_min: float, + y_max: float, + ) -> tuple[list[str], "np.ndarray"]: + """Return candidate (id, xy) rows within an axis-aligned bounding box. + + Args: + space: Projection key (e.g. "euclidean", "hyperbolic"). + x_min/x_max/y_min/y_max: Bounds in the same data space as /api/embeddings. + + Returns: + (ids, coords_xy) where coords_xy has shape (n, 2) float32. + """ + @abstractmethod def close(self) -> None: """Close the storage connection.""" diff --git a/src/hyperview/storage/lancedb_backend.py b/src/hyperview/storage/lancedb_backend.py index 8a531fd..b83618b 100644 --- a/src/hyperview/storage/lancedb_backend.py +++ b/src/hyperview/storage/lancedb_backend.py @@ -1,15 +1,13 @@ """LanceDB storage backend for HyperView.""" -from __future__ import annotations - import json from collections.abc import Callable, Iterator from pathlib import Path -from typing import TYPE_CHECKING import lancedb import pyarrow as pa +from hyperview.core.sample import Sample from hyperview.storage.backend import StorageBackend from hyperview.storage.config import StorageConfig, get_default_database_dir from hyperview.storage.schema import ( @@ -19,9 +17,6 @@ sample_to_dict, ) -if TYPE_CHECKING: - from hyperview.core.sample import Sample - class LanceDBBackend(StorageBackend): """LanceDB-based storage backend for HyperView datasets.""" @@ -414,6 +409,118 @@ def query_chunk(chunk: list[str]) -> set[str]: return existing + def get_samples_by_ids(self, sample_ids: list[str]) -> list[Sample]: + """Retrieve multiple samples by ID. + + Uses chunked `id IN (...)` queries to avoid per-ID round trips. + """ + if self._table is None or not sample_ids: + return [] + + rows_by_id: dict[str, dict] = {} + + def query_chunk(chunk: list[str]) -> None: + escaped = [sid.replace("'", "''") for sid in chunk] + id_list = "', '".join(escaped) + results = self._table.search().where(f"id IN ('{id_list}')").to_list() + for r in results: + rid = r.get("id") + if isinstance(rid, str): + rows_by_id[rid] = r + + chunk_size = 500 + for i in range(0, len(sample_ids), chunk_size): + query_chunk(sample_ids[i : i + chunk_size]) + + out: list[Sample] = [] + for sid in sample_ids: + row = rows_by_id.get(sid) + if row is not None: + out.append(dict_to_sample(row)) + return out + + def get_visualization_embeddings( + self, + ) -> tuple[list[str], list[str | None], list[list[float]], list[list[float]]]: + """Return ids/labels and both 2D projections for the scatter.""" + if self._table is None: + return [], [], [], [] + + rows = ( + self._table.search() + .select(["id", "label", "embedding_2d_euclidean", "embedding_2d_hyperbolic"]) + .where("embedding_2d_euclidean IS NOT NULL AND embedding_2d_hyperbolic IS NOT NULL") + .to_list() + ) + + ids: list[str] = [] + labels: list[str | None] = [] + euclidean: list[list[float]] = [] + hyperbolic: list[list[float]] = [] + + for r in rows: + eid = r.get("id") + e2 = r.get("embedding_2d_euclidean") + h2 = r.get("embedding_2d_hyperbolic") + if not isinstance(eid, str) or e2 is None or h2 is None: + continue + ids.append(eid) + labels.append(r.get("label")) + euclidean.append(list(e2)) + hyperbolic.append(list(h2)) + + return ids, labels, euclidean, hyperbolic + + def get_lasso_candidates_aabb( + self, + *, + space: str, + x_min: float, + x_max: float, + y_min: float, + y_max: float, + ) -> tuple[list[str], "np.ndarray"]: + """Return candidate (id, xy) rows within an AABB for a given projection.""" + if self._table is None: + import numpy as np + + return [], np.empty((0, 2), dtype=np.float32) + + if space == "euclidean": + col = "embedding_2d_euclidean" + elif space == "hyperbolic": + col = "embedding_2d_hyperbolic" + else: + raise ValueError(f"Unknown space: {space}") + + # Keep selection universe consistent with /api/embeddings: require both projections. + expr = ( + "embedding_2d_euclidean IS NOT NULL AND " + "embedding_2d_hyperbolic IS NOT NULL AND " + f"{col}[1] >= {x_min} AND {col}[1] <= {x_max} AND " + f"{col}[2] >= {y_min} AND {col}[2] <= {y_max}" + ) + + rows = self._table.search().select(["id", col]).where(expr).to_list() + + ids: list[str] = [] + coords: list[list[float]] = [] + for r in rows: + sid = r.get("id") + xy = r.get(col) + if not isinstance(sid, str) or xy is None: + continue + if len(xy) != 2: + continue + ids.append(sid) + coords.append([float(xy[0]), float(xy[1])]) + + import numpy as np + + if not coords: + return [], np.empty((0, 2), dtype=np.float32) + return ids, np.asarray(coords, dtype=np.float32) + def close(self) -> None: """Close the storage connection.""" return diff --git a/src/hyperview/storage/memory_backend.py b/src/hyperview/storage/memory_backend.py index 05f1935..2dad945 100644 --- a/src/hyperview/storage/memory_backend.py +++ b/src/hyperview/storage/memory_backend.py @@ -1,17 +1,12 @@ """In-memory storage backend for testing and development.""" -from __future__ import annotations - from collections.abc import Callable, Iterator -from typing import TYPE_CHECKING import numpy as np +from hyperview.core.sample import Sample from hyperview.storage.backend import StorageBackend -if TYPE_CHECKING: - from hyperview.core.sample import Sample - class MemoryBackend(StorageBackend): """In-memory storage backend (current behavior, for testing and backwards compatibility).""" @@ -158,6 +153,68 @@ def get_existing_ids(self, sample_ids: list[str]) -> set[str]: """Return set of sample_ids that already exist in storage.""" return {sid for sid in sample_ids if sid in self._samples} + def get_samples_by_ids(self, sample_ids: list[str]) -> list[Sample]: + """Retrieve multiple samples by ID.""" + out: list[Sample] = [] + for sid in sample_ids: + s = self._samples.get(sid) + if s is not None: + out.append(s) + return out + + def get_visualization_embeddings( + self, + ) -> tuple[list[str], list[str | None], list[list[float]], list[list[float]]]: + """Return ids/labels and both 2D projections for the scatter.""" + ids: list[str] = [] + labels: list[str | None] = [] + euclidean: list[list[float]] = [] + hyperbolic: list[list[float]] = [] + + for s in self._samples.values(): + if s.embedding_2d is None or s.embedding_2d_hyperbolic is None: + continue + ids.append(s.id) + labels.append(s.label) + euclidean.append(s.embedding_2d) + hyperbolic.append(s.embedding_2d_hyperbolic) + + return ids, labels, euclidean, hyperbolic + + def get_lasso_candidates_aabb( + self, + *, + space: str, + x_min: float, + x_max: float, + y_min: float, + y_max: float, + ) -> tuple[list[str], np.ndarray]: + """Return candidate (id, xy) rows within an AABB for a given projection.""" + if space not in ("euclidean", "hyperbolic"): + raise ValueError(f"Unknown space: {space}") + + ids: list[str] = [] + coords: list[list[float]] = [] + + for s in self._samples.values(): + # Keep selection universe consistent with /api/embeddings: require both projections. + if s.embedding_2d is None or s.embedding_2d_hyperbolic is None: + continue + + xy = s.embedding_2d if space == "euclidean" else s.embedding_2d_hyperbolic + if xy is None or len(xy) != 2: + continue + + x, y = float(xy[0]), float(xy[1]) + if x < x_min or x > x_max or y < y_min or y > y_max: + continue + + ids.append(s.id) + coords.append([x, y]) + + return ids, np.asarray(coords, dtype=np.float32) + def close(self) -> None: """Close the storage connection (no-op for in-memory).""" return From f0434f403d5b748882906cb5537b52aca4df5580 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Thu, 15 Jan 2026 01:13:37 +0100 Subject: [PATCH 10/75] feat(server): add lasso selection endpoint --- src/hyperview/core/selection.py | 53 +++++++++++++++ src/hyperview/server/app.py | 117 ++++++++++++++++++++++++++------ 2 files changed, 148 insertions(+), 22 deletions(-) create mode 100644 src/hyperview/core/selection.py diff --git a/src/hyperview/core/selection.py b/src/hyperview/core/selection.py new file mode 100644 index 0000000..1dee065 --- /dev/null +++ b/src/hyperview/core/selection.py @@ -0,0 +1,53 @@ +"""Selection / geometry helpers. + +This module contains small, backend-agnostic utilities used by selection endpoints +(e.g. lasso selection over 2D embeddings). +""" + +from __future__ import annotations + +import numpy as np + + +def points_in_polygon(points_xy: np.ndarray, polygon_xy: np.ndarray) -> np.ndarray: + """Vectorized point-in-polygon (even-odd rule / ray casting). + + Args: + points_xy: Array of shape (m, 2) with point coordinates. + polygon_xy: Array of shape (n, 2) with polygon vertices. + + Returns: + Boolean mask of length m, True where point lies inside polygon. + + Notes: + Boundary points may be classified as outside depending on floating point + ties (common for lasso selection tools). + """ + if polygon_xy.shape[0] < 3: + return np.zeros((points_xy.shape[0],), dtype=bool) + + x = points_xy[:, 0] + y = points_xy[:, 1] + poly_x = polygon_xy[:, 0] + poly_y = polygon_xy[:, 1] + + inside = np.zeros((points_xy.shape[0],), dtype=bool) + j = polygon_xy.shape[0] - 1 + + for i in range(polygon_xy.shape[0]): + xi = poly_x[i] + yi = poly_y[i] + xj = poly_x[j] + yj = poly_y[j] + + # Half-open y-interval to avoid double-counting vertices. + intersects = (yi > y) != (yj > y) + + denom = yj - yi + # denom == 0 => intersects is always False; add tiny epsilon to avoid warnings. + x_intersect = (xj - xi) * (y - yi) / (denom + 1e-30) + xi + + inside ^= intersects & (x < x_intersect) + j = i + + return inside diff --git a/src/hyperview/server/app.py b/src/hyperview/server/app.py index d0922b8..21c3655 100644 --- a/src/hyperview/server/app.py +++ b/src/hyperview/server/app.py @@ -1,17 +1,18 @@ """FastAPI application for HyperView.""" -from __future__ import annotations - import os from pathlib import Path from fastapi import FastAPI, HTTPException, Query from fastapi.middleware.cors import CORSMiddleware -from fastapi.responses import FileResponse, JSONResponse +from fastapi.responses import JSONResponse from fastapi.staticfiles import StaticFiles from pydantic import BaseModel +import numpy as np + from hyperview.core.dataset import Dataset +from hyperview.core.selection import points_in_polygon # Global dataset reference (set by launch()) _current_dataset: Dataset | None = None @@ -24,6 +25,17 @@ class SelectionRequest(BaseModel): sample_ids: list[str] +class LassoSelectionRequest(BaseModel): + """Request model for lasso selection queries.""" + + space: str + # Polygon vertices in data space, interleaved: [x0, y0, x1, y1, ...] + polygon: list[float] + offset: int = 0 + limit: int = 100 + include_thumbnails: bool = True + + class SampleResponse(BaseModel): """Response model for a sample.""" @@ -171,15 +183,9 @@ async def get_samples_batch(request: SelectionRequest): if _current_dataset is None: raise HTTPException(status_code=404, detail="No dataset loaded") - samples = [] - for sample_id in request.sample_ids: - try: - sample = _current_dataset[sample_id] - samples.append(sample.to_api_dict(include_thumbnail=True)) - except KeyError: - continue + samples = _current_dataset._storage.get_samples_by_ids(request.sample_ids) - return {"samples": samples} + return {"samples": [s.to_api_dict(include_thumbnail=True) for s in samples]} @app.get("/api/embeddings", response_model=EmbeddingsResponse) async def get_embeddings(): @@ -187,22 +193,17 @@ async def get_embeddings(): if _current_dataset is None: raise HTTPException(status_code=404, detail="No dataset loaded") - samples = [ - s - for s in _current_dataset.samples - if s.embedding_2d is not None and s.embedding_2d_hyperbolic is not None - ] - - if not samples: + ids, labels, euclidean, hyperbolic = _current_dataset._storage.get_visualization_embeddings() + if not ids: raise HTTPException( status_code=400, detail="No embeddings computed. Call compute_visualization() first." ) return EmbeddingsResponse( - ids=[s.id for s in samples], - labels=[s.label for s in samples], - euclidean=[s.embedding_2d for s in samples], - hyperbolic=[s.embedding_2d_hyperbolic for s in samples], + ids=ids, + labels=labels, + euclidean=euclidean, + hyperbolic=hyperbolic, label_colors=_current_dataset.get_label_colors(), ) @@ -211,6 +212,78 @@ async def sync_selection(request: SelectionRequest): """Sync selection state (for future use).""" return {"status": "ok", "selected": request.sample_ids} + @app.post("/api/selection/lasso") + async def lasso_selection(request: LassoSelectionRequest): + """Compute a lasso selection over the current embeddings. + + Returns a total selected count and a paginated page of selected samples. + + Notes: + - Selection is performed in *data space* (the same coordinates returned + by /api/embeddings). + - For now we use an in-memory scan with a tight AABB prefilter. + """ + if _current_dataset is None: + raise HTTPException(status_code=404, detail="No dataset loaded") + + if request.offset < 0: + raise HTTPException(status_code=400, detail="offset must be >= 0") + if request.limit < 1 or request.limit > 2000: + raise HTTPException(status_code=400, detail="limit must be between 1 and 2000") + + if len(request.polygon) < 6 or len(request.polygon) % 2 != 0: + raise HTTPException( + status_code=400, + detail="polygon must be an even-length list with at least 3 vertices", + ) + + poly = np.asarray(request.polygon, dtype=np.float32).reshape((-1, 2)) + if not np.all(np.isfinite(poly)): + raise HTTPException(status_code=400, detail="polygon must contain only finite numbers") + + # Tight AABB prefilter. + x_min = float(np.min(poly[:, 0])) + x_max = float(np.max(poly[:, 0])) + y_min = float(np.min(poly[:, 1])) + y_max = float(np.max(poly[:, 1])) + + + try: + candidate_ids, candidate_coords = _current_dataset._storage.get_lasso_candidates_aabb( + space=request.space, + x_min=x_min, + x_max=x_max, + y_min=y_min, + y_max=y_max, + ) + except ValueError as e: + raise HTTPException(status_code=400, detail=str(e)) + + if candidate_coords.size == 0: + return {"total": 0, "offset": request.offset, "limit": request.limit, "sample_ids": [], "samples": []} + + inside_mask = points_in_polygon(candidate_coords, poly) + if not np.any(inside_mask): + return {"total": 0, "offset": request.offset, "limit": request.limit, "sample_ids": [], "samples": []} + + selected_ids = [candidate_ids[i] for i in np.flatnonzero(inside_mask)] + total = len(selected_ids) + + start = int(request.offset) + end = int(request.offset + request.limit) + sample_ids = selected_ids[start:end] + + samples = _current_dataset._storage.get_samples_by_ids(sample_ids) + sample_dicts = [s.to_api_dict(include_thumbnail=request.include_thumbnails) for s in samples] + + return { + "total": total, + "offset": request.offset, + "limit": request.limit, + "sample_ids": sample_ids, + "samples": sample_dicts, + } + @app.get("/api/search/similar/{sample_id}", response_model=SimilaritySearchResponse) async def search_similar( sample_id: str, From 4a41540b363bf9aab7ea2e7f49cfd2131687662e Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Thu, 15 Jan 2026 01:15:12 +0100 Subject: [PATCH 11/75] docs: clarify dataset storage location --- docs/datasets.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/docs/datasets.md b/docs/datasets.md index 5454baf..f147fe5 100644 --- a/docs/datasets.md +++ b/docs/datasets.md @@ -12,7 +12,11 @@ dataset = hv.Dataset("my_dataset") dataset = hv.Dataset("my_dataset", persist=False) ``` -**Storage location:** `~/.hyperview/lancedb/` (configurable via `HYPERVIEW_DATABASE_DIR`) +**Storage location:** `~/.hyperview/datasets/` (configurable via `HYPERVIEW_DATABASE_DIR`) + +Internally, each dataset is stored as two Lance tables (directories) inside that folder: +- `hyperview_{dataset_name}.lance/` (samples) +- `hyperview_{dataset_name}_meta.lance/` (metadata like label colors) ## Adding Samples From 856c766aa1ee45de687b5680630293dce96ccf86 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Thu, 15 Jan 2026 01:20:32 +0100 Subject: [PATCH 12/75] feat(frontend): add shadcn/ui foundation with Rerun-inspired dark theme - Add components.json for shadcn/ui configuration - Add cn() utility for conditional class merging - Configure tailwind with HSL CSS variables - Create dark theme inspired by Rerun viewer --- frontend/.eslintrc.json | 3 -- frontend/components.json | 22 ++++++++ frontend/eslint.config.mjs | 22 ++++++++ frontend/src/app/globals.css | 100 +++++++++++++++++++++++------------ frontend/src/lib/utils.ts | 6 +++ frontend/tailwind.config.ts | 62 ++++++++++++++++++---- 6 files changed, 168 insertions(+), 47 deletions(-) delete mode 100644 frontend/.eslintrc.json create mode 100644 frontend/components.json create mode 100644 frontend/eslint.config.mjs create mode 100644 frontend/src/lib/utils.ts diff --git a/frontend/.eslintrc.json b/frontend/.eslintrc.json deleted file mode 100644 index bffb357..0000000 --- a/frontend/.eslintrc.json +++ /dev/null @@ -1,3 +0,0 @@ -{ - "extends": "next/core-web-vitals" -} diff --git a/frontend/components.json b/frontend/components.json new file mode 100644 index 0000000..f826c54 --- /dev/null +++ b/frontend/components.json @@ -0,0 +1,22 @@ +{ + "$schema": "https://ui.shadcn.com/schema.json", + "style": "new-york", + "rsc": true, + "tsx": true, + "tailwind": { + "config": "tailwind.config.ts", + "css": "src/app/globals.css", + "baseColor": "neutral", + "cssVariables": true, + "prefix": "" + }, + "iconLibrary": "lucide", + "aliases": { + "components": "@/components", + "utils": "@/lib/utils", + "ui": "@/components/ui", + "lib": "@/lib", + "hooks": "@/hooks" + }, + "registries": {} +} diff --git a/frontend/eslint.config.mjs b/frontend/eslint.config.mjs new file mode 100644 index 0000000..4c89b74 --- /dev/null +++ b/frontend/eslint.config.mjs @@ -0,0 +1,22 @@ +import path from "path"; +import { fileURLToPath } from "url"; + +import { FlatCompat } from "@eslint/eslintrc"; + +const __filename = fileURLToPath(import.meta.url); +const __dirname = path.dirname(__filename); + +// Bridge legacy shareable configs (like `next/core-web-vitals`) into ESLint v9 flat config. +const compat = new FlatCompat({ + baseDirectory: __dirname, +}); + +const config = [ + { + // Mirror Next.js defaults: never lint build artifacts. + ignores: ["**/.next/**", "**/out/**", "**/node_modules/**"], + }, + ...compat.extends("next/core-web-vitals"), +]; + +export default config; diff --git a/frontend/src/app/globals.css b/frontend/src/app/globals.css index b2ce4a8..b25953f 100644 --- a/frontend/src/app/globals.css +++ b/frontend/src/app/globals.css @@ -2,50 +2,82 @@ @tailwind components; @tailwind utilities; -:root { - --background: #0a0a0b; - --surface: #18181b; - --surface-light: #27272a; - --border: #3f3f46; - --primary: #4F46E5; - --primary-light: #818CF8; - --text: #fafafa; - --text-muted: #a1a1aa; -} - -* { - box-sizing: border-box; -} - -body { - background-color: var(--background); - color: var(--text); - margin: 0; - padding: 0; - font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen', - 'Ubuntu', 'Cantarell', 'Fira Sans', 'Droid Sans', 'Helvetica Neue', - sans-serif; - -webkit-font-smoothing: antialiased; - -moz-osx-font-smoothing: grayscale; -} - -/* Custom scrollbar */ +@layer base { + :root { + /* Rerun-inspired dark theme as default (dark-first) */ + --background: 215 28% 7%; /* #0d1117 */ + --foreground: 213 27% 92%; /* #e6edf3 */ + + --card: 215 21% 11%; /* #161b22 */ + --card-foreground: 213 27% 92%; + + --popover: 215 21% 11%; + --popover-foreground: 213 27% 92%; + + --primary: 212 100% 67%; /* #58a6ff */ + --primary-foreground: 215 28% 7%; + + --secondary: 215 14% 17%; /* #21262d */ + --secondary-foreground: 213 27% 92%; + + --muted: 215 14% 17%; + --muted-foreground: 213 12% 58%; /* #8b949e */ + + --accent: 215 14% 17%; + --accent-foreground: 213 27% 92%; + + --destructive: 0 62% 50%; + --destructive-foreground: 0 0% 98%; + + --border: 215 14% 22%; /* #30363d */ + --input: 215 14% 22%; + --ring: 212 100% 67%; + + --radius: 0.375rem; + + /* Custom Rerun-specific tokens */ + --surface: 215 21% 11%; /* #161b22 */ + --surface-light: 215 14% 17%; /* #21262d */ + --surface-elevated: 215 14% 22%; /* #30363d */ + --border-subtle: 215 14% 17%; + --text: 213 27% 92%; /* #e6edf3 */ + --text-muted: 213 12% 58%; /* #8b949e */ + --text-subtle: 215 10% 46%; /* #6e7681 */ + --accent-cyan: 176 60% 53%; /* #39d3cc */ + --accent-orange: 27 86% 59%; /* #f0883e */ + } +} + +@layer base { + * { + @apply border-border; + } + body { + @apply bg-background text-foreground; + font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen', + 'Ubuntu', 'Cantarell', 'Fira Sans', 'Droid Sans', 'Helvetica Neue', sans-serif; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + } +} + +/* Custom scrollbar - Rerun style */ ::-webkit-scrollbar { - width: 8px; - height: 8px; + width: 6px; + height: 6px; } ::-webkit-scrollbar-track { - background: var(--surface); + background: transparent; } ::-webkit-scrollbar-thumb { - background: var(--border); - border-radius: 4px; + background: hsl(var(--surface-elevated)); + border-radius: 3px; } ::-webkit-scrollbar-thumb:hover { - background: #4d4d4d; + background: hsl(var(--text-subtle)); } /* Hide scrollbar for some elements */ diff --git a/frontend/src/lib/utils.ts b/frontend/src/lib/utils.ts new file mode 100644 index 0000000..bd0c391 --- /dev/null +++ b/frontend/src/lib/utils.ts @@ -0,0 +1,6 @@ +import { clsx, type ClassValue } from "clsx" +import { twMerge } from "tailwind-merge" + +export function cn(...inputs: ClassValue[]) { + return twMerge(clsx(inputs)) +} diff --git a/frontend/tailwind.config.ts b/frontend/tailwind.config.ts index 7aa3713..9ebc9b5 100644 --- a/frontend/tailwind.config.ts +++ b/frontend/tailwind.config.ts @@ -1,6 +1,7 @@ import type { Config } from "tailwindcss"; export default { + darkMode: ["class"], content: [ "./src/pages/**/*.{js,ts,jsx,tsx,mdx}", "./src/components/**/*.{js,ts,jsx,tsx,mdx}", @@ -9,17 +10,58 @@ export default { theme: { extend: { colors: { - // Dark theme colors (shadcn-style with Indigo primary) - background: "#0a0a0b", - surface: "#18181b", - "surface-light": "#27272a", - border: "#3f3f46", - primary: "#4F46E5", - "primary-light": "#818CF8", - text: "#fafafa", - "text-muted": "#a1a1aa", + // shadcn semantic colors (HSL-based) + background: "hsl(var(--background))", + foreground: "hsl(var(--foreground))", + card: { + DEFAULT: "hsl(var(--card))", + foreground: "hsl(var(--card-foreground))", + }, + popover: { + DEFAULT: "hsl(var(--popover))", + foreground: "hsl(var(--popover-foreground))", + }, + primary: { + DEFAULT: "hsl(var(--primary))", + foreground: "hsl(var(--primary-foreground))", + }, + secondary: { + DEFAULT: "hsl(var(--secondary))", + foreground: "hsl(var(--secondary-foreground))", + }, + muted: { + DEFAULT: "hsl(var(--muted))", + foreground: "hsl(var(--muted-foreground))", + }, + accent: { + DEFAULT: "hsl(var(--accent))", + foreground: "hsl(var(--accent-foreground))", + }, + destructive: { + DEFAULT: "hsl(var(--destructive))", + foreground: "hsl(var(--destructive-foreground))", + }, + border: "hsl(var(--border))", + input: "hsl(var(--input))", + ring: "hsl(var(--ring))", + + // Rerun-specific aliases + surface: "hsl(var(--surface))", + "surface-light": "hsl(var(--surface-light))", + "surface-elevated": "hsl(var(--surface-elevated))", + "border-subtle": "hsl(var(--border-subtle))", + text: "hsl(var(--text))", + "text-muted": "hsl(var(--text-muted))", + "text-subtle": "hsl(var(--text-subtle))", + "accent-cyan": "hsl(var(--accent-cyan))", + "accent-orange": "hsl(var(--accent-orange))", + }, + borderRadius: { + lg: "var(--radius)", + md: "calc(var(--radius) - 2px)", + sm: "calc(var(--radius) - 4px)", }, }, }, - plugins: [], + plugins: [require("tailwindcss-animate")], } satisfies Config; From ece5d7f02dc857a40e1ce6f518bc3059fb848119 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Thu, 15 Jan 2026 01:20:45 +0100 Subject: [PATCH 13/75] feat(frontend): add shadcn/ui primitive components - Button component with variants - Toggle and ToggleGroup for view mode switching - Resizable panels with drag handle --- frontend/src/components/ui/button.tsx | 57 +++++++++++++++++++ frontend/src/components/ui/resizable.tsx | 45 +++++++++++++++ frontend/src/components/ui/toggle-group.tsx | 61 +++++++++++++++++++++ frontend/src/components/ui/toggle.tsx | 45 +++++++++++++++ 4 files changed, 208 insertions(+) create mode 100644 frontend/src/components/ui/button.tsx create mode 100644 frontend/src/components/ui/resizable.tsx create mode 100644 frontend/src/components/ui/toggle-group.tsx create mode 100644 frontend/src/components/ui/toggle.tsx diff --git a/frontend/src/components/ui/button.tsx b/frontend/src/components/ui/button.tsx new file mode 100644 index 0000000..65d4fcd --- /dev/null +++ b/frontend/src/components/ui/button.tsx @@ -0,0 +1,57 @@ +import * as React from "react" +import { Slot } from "@radix-ui/react-slot" +import { cva, type VariantProps } from "class-variance-authority" + +import { cn } from "@/lib/utils" + +const buttonVariants = cva( + "inline-flex items-center justify-center gap-2 whitespace-nowrap rounded-md text-sm font-medium transition-colors focus-visible:outline-none focus-visible:ring-1 focus-visible:ring-ring disabled:pointer-events-none disabled:opacity-50 [&_svg]:pointer-events-none [&_svg]:size-4 [&_svg]:shrink-0", + { + variants: { + variant: { + default: + "bg-primary text-primary-foreground shadow hover:bg-primary/90", + destructive: + "bg-destructive text-destructive-foreground shadow-sm hover:bg-destructive/90", + outline: + "border border-input bg-background shadow-sm hover:bg-accent hover:text-accent-foreground", + secondary: + "bg-secondary text-secondary-foreground shadow-sm hover:bg-secondary/80", + ghost: "hover:bg-accent hover:text-accent-foreground", + link: "text-primary underline-offset-4 hover:underline", + }, + size: { + default: "h-9 px-4 py-2", + sm: "h-8 rounded-md px-3 text-xs", + lg: "h-10 rounded-md px-8", + icon: "h-9 w-9", + }, + }, + defaultVariants: { + variant: "default", + size: "default", + }, + } +) + +export interface ButtonProps + extends React.ButtonHTMLAttributes, + VariantProps { + asChild?: boolean +} + +const Button = React.forwardRef( + ({ className, variant, size, asChild = false, ...props }, ref) => { + const Comp = asChild ? Slot : "button" + return ( + + ) + } +) +Button.displayName = "Button" + +export { Button, buttonVariants } diff --git a/frontend/src/components/ui/resizable.tsx b/frontend/src/components/ui/resizable.tsx new file mode 100644 index 0000000..1ef04c0 --- /dev/null +++ b/frontend/src/components/ui/resizable.tsx @@ -0,0 +1,45 @@ +"use client" + +import { GripVertical } from "lucide-react" +import { Group, Panel, Separator } from "react-resizable-panels" + +import { cn } from "@/lib/utils" + +const ResizablePanelGroup = ({ + className, + ...props +}: React.ComponentProps) => ( + +) + +const ResizablePanel = Panel + +const ResizableHandle = ({ + withHandle, + className, + ...props +}: React.ComponentProps & { + withHandle?: boolean +}) => ( + div]:rotate-90", + className + )} + {...props} + > + {withHandle && ( +

+ +
+ )} + +) + +export { ResizablePanelGroup, ResizablePanel, ResizableHandle } diff --git a/frontend/src/components/ui/toggle-group.tsx b/frontend/src/components/ui/toggle-group.tsx new file mode 100644 index 0000000..1c876bb --- /dev/null +++ b/frontend/src/components/ui/toggle-group.tsx @@ -0,0 +1,61 @@ +"use client" + +import * as React from "react" +import * as ToggleGroupPrimitive from "@radix-ui/react-toggle-group" +import { type VariantProps } from "class-variance-authority" + +import { cn } from "@/lib/utils" +import { toggleVariants } from "@/components/ui/toggle" + +const ToggleGroupContext = React.createContext< + VariantProps +>({ + size: "default", + variant: "default", +}) + +const ToggleGroup = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef & + VariantProps +>(({ className, variant, size, children, ...props }, ref) => ( + + + {children} + + +)) + +ToggleGroup.displayName = ToggleGroupPrimitive.Root.displayName + +const ToggleGroupItem = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef & + VariantProps +>(({ className, children, variant, size, ...props }, ref) => { + const context = React.useContext(ToggleGroupContext) + + return ( + + {children} + + ) +}) + +ToggleGroupItem.displayName = ToggleGroupPrimitive.Item.displayName + +export { ToggleGroup, ToggleGroupItem } diff --git a/frontend/src/components/ui/toggle.tsx b/frontend/src/components/ui/toggle.tsx new file mode 100644 index 0000000..e516f21 --- /dev/null +++ b/frontend/src/components/ui/toggle.tsx @@ -0,0 +1,45 @@ +"use client" + +import * as React from "react" +import * as TogglePrimitive from "@radix-ui/react-toggle" +import { cva, type VariantProps } from "class-variance-authority" + +import { cn } from "@/lib/utils" + +const toggleVariants = cva( + "inline-flex items-center justify-center gap-2 rounded-md text-sm font-medium transition-colors hover:bg-muted hover:text-muted-foreground focus-visible:outline-none focus-visible:ring-1 focus-visible:ring-ring disabled:pointer-events-none disabled:opacity-50 data-[state=on]:bg-primary data-[state=on]:text-primary-foreground [&_svg]:pointer-events-none [&_svg]:size-4 [&_svg]:shrink-0", + { + variants: { + variant: { + default: "bg-transparent", + outline: + "border border-input bg-transparent shadow-sm hover:bg-accent hover:text-accent-foreground data-[state=on]:border-primary", + }, + size: { + default: "h-9 px-2 min-w-9", + sm: "h-8 px-1.5 min-w-8", + lg: "h-10 px-2.5 min-w-10", + }, + }, + defaultVariants: { + variant: "default", + size: "default", + }, + } +) + +const Toggle = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef & + VariantProps +>(({ className, variant, size, ...props }, ref) => ( + +)) + +Toggle.displayName = TogglePrimitive.Root.displayName + +export { Toggle, toggleVariants } From ac578b9b8ab496cea6831d8eb0890e2054727a05 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Thu, 15 Jan 2026 01:21:00 +0100 Subject: [PATCH 14/75] feat(frontend): add Panel layout components and refactor Header - Panel and PanelFooter for consistent panel styling - PanelHeader with icon, title, subtitle slots - Custom SVG icons (Grid, Scatter, Logo, Check) - Refactor Header to use new theme and Button component --- frontend/src/components/Header.tsx | 56 +++++++++++-------------- frontend/src/components/Panel.tsx | 42 +++++++++++++++++++ frontend/src/components/PanelHeader.tsx | 37 ++++++++++++++++ frontend/src/components/icons.tsx | 39 +++++++++++++++++ frontend/src/components/index.ts | 3 ++ 5 files changed, 146 insertions(+), 31 deletions(-) create mode 100644 frontend/src/components/Panel.tsx create mode 100644 frontend/src/components/PanelHeader.tsx create mode 100644 frontend/src/components/icons.tsx diff --git a/frontend/src/components/Header.tsx b/frontend/src/components/Header.tsx index 4918db0..9aa44c7 100644 --- a/frontend/src/components/Header.tsx +++ b/frontend/src/components/Header.tsx @@ -1,59 +1,53 @@ "use client"; import { useStore } from "@/store/useStore"; +import { Button } from "@/components/ui/button"; +import { HyperViewLogo } from "./icons"; export function Header() { const { datasetInfo, selectedIds, clearSelection } = useStore(); return ( -
+
{/* Logo and title */} -
-
- - - +
+
+
-
-

HyperView

+
+

HyperView

{datasetInfo && ( -

{datasetInfo.name}

+ + {datasetInfo.name} + )}
{/* Dataset info and actions */} -
+
{datasetInfo && ( -
-
- Samples: - {datasetInfo.num_samples.toLocaleString()} +
+
+ samples + {datasetInfo.num_samples.toLocaleString()}
-
- Labels: - {datasetInfo.labels.length} +
+ labels + {datasetInfo.labels.length}
)} {selectedIds.size > 0 && ( - + Clear ({selectedIds.size}) + )}
diff --git a/frontend/src/components/Panel.tsx b/frontend/src/components/Panel.tsx new file mode 100644 index 0000000..1112dc5 --- /dev/null +++ b/frontend/src/components/Panel.tsx @@ -0,0 +1,42 @@ +"use client"; + +import { ReactNode } from "react"; +import { cn } from "@/lib/utils"; + +interface PanelProps { + children: ReactNode; + className?: string; +} + +/** + * Base panel container with consistent Rerun-style appearance. + */ +export function Panel({ children, className }: PanelProps) { + return ( +
+ {children} +
+ ); +} + +interface PanelFooterProps { + children: ReactNode; + className?: string; +} + +/** + * Panel footer for keyboard shortcuts/hints. + */ +export function PanelFooter({ children, className }: PanelFooterProps) { + return ( +
+ {children} +
+ ); +} diff --git a/frontend/src/components/PanelHeader.tsx b/frontend/src/components/PanelHeader.tsx new file mode 100644 index 0000000..dea1edd --- /dev/null +++ b/frontend/src/components/PanelHeader.tsx @@ -0,0 +1,37 @@ +"use client"; + +import { ReactNode } from "react"; +import { cn } from "@/lib/utils"; + +interface PanelHeaderProps { + icon?: ReactNode; + title: string; + subtitle?: string; + children?: ReactNode; // Toolbar actions slot + className?: string; +} + +/** + * Rerun-style panel header with icon, title, and optional toolbar. + */ +export function PanelHeader({ icon, title, subtitle, children, className }: PanelHeaderProps) { + return ( +
+
+ {icon && ( + {icon} + )} + {title} + {subtitle && ( + {subtitle} + )} +
+ {children && ( +
{children}
+ )} +
+ ); +} diff --git a/frontend/src/components/icons.tsx b/frontend/src/components/icons.tsx new file mode 100644 index 0000000..aa2758a --- /dev/null +++ b/frontend/src/components/icons.tsx @@ -0,0 +1,39 @@ +"use client"; + +/** + * Shared icons for HyperView UI. + * Using inline SVGs for simplicity (no extra icon library dependency). + */ + +export const GridIcon = () => ( + + + + + + +); + +export const ScatterIcon = () => ( + + + + + + + +); + +export const HyperViewLogo = () => ( + + + + + +); + +export const CheckIcon = () => ( + + + +); diff --git a/frontend/src/components/index.ts b/frontend/src/components/index.ts index 1955b11..a36acea 100644 --- a/frontend/src/components/index.ts +++ b/frontend/src/components/index.ts @@ -1,3 +1,6 @@ export { ImageGrid } from "./ImageGrid"; export { ScatterPanel } from "./ScatterPanel"; export { Header } from "./Header"; +export { Panel, PanelFooter } from "./Panel"; +export { PanelHeader } from "./PanelHeader"; +export * from "./icons"; From 3b9bbb7d8b009898ebec77cfb37b0a999f6d99a3 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Thu, 15 Jan 2026 01:21:20 +0100 Subject: [PATCH 15/75] feat(frontend): replace regl-scatterplot with hyper-scatter renderer MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Remove regl-scatterplot and d3-scale dependencies - Add hyper-scatter (local) for WebGL scatter rendering - Add shadcn/ui and resizable panel dependencies - Create useHyperScatter hook for renderer lifecycle - Rewrite ScatterPanel using hyper-scatter with: - Pointer-based pan/zoom/hover interactions - Shift+drag lasso selection with overlay canvas - Euclidean and Poincaré geometry support - Update .gitignore for hyper-scatter submodule - Update next.config.ts transpilePackages --- .gitignore | 4 +- frontend/next.config.ts | 4 + frontend/package-lock.json | 709 ++++++++++++--------- frontend/package.json | 15 +- frontend/src/components/ScatterPanel.tsx | 419 +++--------- frontend/src/components/useHyperScatter.ts | 481 ++++++++++++++ 6 files changed, 1007 insertions(+), 625 deletions(-) create mode 100644 frontend/src/components/useHyperScatter.ts diff --git a/.gitignore b/.gitignore index f943cff..c397cb1 100644 --- a/.gitignore +++ b/.gitignore @@ -43,8 +43,8 @@ build/ *.hf/ .cache/ -# external repo -viz_impl/ +# external repo (https://github.com/HackerRoomAI/hyper-scatter) +hyper-scatter/ # nohup nohup.out diff --git a/frontend/next.config.ts b/frontend/next.config.ts index a255d22..2d9cfbf 100644 --- a/frontend/next.config.ts +++ b/frontend/next.config.ts @@ -1,8 +1,12 @@ import type { NextConfig } from "next"; +import path from "path"; const nextConfig: NextConfig = { output: "export", trailingSlash: true, + // Needed for Turbopack to resolve local linked/file dependencies in a monorepo. + outputFileTracingRoot: path.join(__dirname, ".."), + transpilePackages: ["hyper-scatter"], images: { unoptimized: true, }, diff --git a/frontend/package-lock.json b/frontend/package-lock.json index e1a17b9..02ca465 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -8,13 +8,20 @@ "name": "hyperview-frontend", "version": "0.1.0", "dependencies": { + "@radix-ui/react-slot": "^1.2.4", + "@radix-ui/react-toggle": "^1.1.10", + "@radix-ui/react-toggle-group": "^1.1.11", "@tanstack/react-virtual": "^3.10.9", - "@types/d3-scale": "^4.0.9", - "d3-scale": "^4.0.2", + "class-variance-authority": "^0.7.1", + "clsx": "^2.1.1", + "hyper-scatter": "file:../hyper-scatter", + "lucide-react": "^0.562.0", "next": "^16.0.7", "react": "18.3.1", "react-dom": "18.3.1", - "regl-scatterplot": "^1.9.2", + "react-resizable-panels": "^4.4.1", + "tailwind-merge": "^3.4.0", + "tailwindcss-animate": "^1.0.7", "zustand": "^5.0.1" }, "devDependencies": { @@ -29,11 +36,32 @@ "typescript": "^5.6.3" } }, + "../hyper-scatter": { + "version": "0.1.0", + "devDependencies": { + "@types/node": "^20.10.0", + "puppeteer": "^24.34.0", + "tsx": "^4.7.0", + "typescript": "^5.3.0", + "vite": "^5.0.0" + } + }, + "../viz_impl": { + "name": "hyper-scatter", + "version": "0.1.0", + "extraneous": true, + "devDependencies": { + "@types/node": "^20.10.0", + "puppeteer": "^24.34.0", + "tsx": "^4.7.0", + "typescript": "^5.3.0", + "vite": "^5.0.0" + } + }, "node_modules/@alloc/quick-lru": { "version": "5.2.0", "resolved": "https://registry.npmjs.org/@alloc/quick-lru/-/quick-lru-5.2.0.tgz", "integrity": "sha512-UrcABB+4bUrFABwbluTIBErXwvbsU/V7TZWfmbgJfbkwiBuziS9gxdODUyuiecfdGQ85jglMW6juS3+z5TsKLw==", - "dev": true, "license": "MIT", "engines": { "node": ">=10" @@ -219,12 +247,6 @@ "node": "^18.18.0 || ^20.9.0 || >=21.1.0" } }, - "node_modules/@flekschas/utils": { - "version": "0.32.2", - "resolved": "https://registry.npmjs.org/@flekschas/utils/-/utils-0.32.2.tgz", - "integrity": "sha512-RPF5WBXxA3WFKdTTDQS7gl7hd1z6kOMfJKj0p+9TRLJm/vQHvRC2Zt62axSQlbve8a82NtVN96aaGhsx5+ekvQ==", - "license": "MIT" - }, "node_modules/@humanfs/core": { "version": "0.19.1", "resolved": "https://registry.npmjs.org/@humanfs/core/-/core-0.19.1.tgz", @@ -747,7 +769,6 @@ "version": "0.3.13", "resolved": "https://registry.npmjs.org/@jridgewell/gen-mapping/-/gen-mapping-0.3.13.tgz", "integrity": "sha512-2kkt/7niJ6MgEPxF0bYdQ6etZaA+fQvDcLKckhy1yIQOzaoKjBBjSj63/aLVjYE3qhRt5dvM+uUyfCg6UKCBbA==", - "dev": true, "license": "MIT", "dependencies": { "@jridgewell/sourcemap-codec": "^1.5.0", @@ -758,7 +779,6 @@ "version": "3.1.2", "resolved": "https://registry.npmjs.org/@jridgewell/resolve-uri/-/resolve-uri-3.1.2.tgz", "integrity": "sha512-bRISgCIjP20/tbWSPWMEi54QVPRZExkuD9lJL+UIxUKtwVJA8wW1Trb1jMs1RFXo1CBTNZ/5hpC9QvmKWdopKw==", - "dev": true, "license": "MIT", "engines": { "node": ">=6.0.0" @@ -768,14 +788,12 @@ "version": "1.5.5", "resolved": "https://registry.npmjs.org/@jridgewell/sourcemap-codec/-/sourcemap-codec-1.5.5.tgz", "integrity": 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"next build && cp -r out/* ../src/hyperview/server/static/" }, "dependencies": { + "@radix-ui/react-slot": "^1.2.4", + "@radix-ui/react-toggle": "^1.1.10", + "@radix-ui/react-toggle-group": "^1.1.11", "@tanstack/react-virtual": "^3.10.9", - "@types/d3-scale": "^4.0.9", - "d3-scale": "^4.0.2", + "class-variance-authority": "^0.7.1", + "clsx": "^2.1.1", + "lucide-react": "^0.562.0", "next": "^16.0.7", "react": "18.3.1", "react-dom": "18.3.1", - "regl-scatterplot": "^1.9.2", + "react-resizable-panels": "^4.4.1", + "tailwind-merge": "^3.4.0", + "tailwindcss-animate": "^1.0.7", + "hyper-scatter": "file:../hyper-scatter", "zustand": "^5.0.1" }, "devDependencies": { diff --git a/frontend/src/components/ScatterPanel.tsx b/frontend/src/components/ScatterPanel.tsx index 177fea2..0eb1e1f 100644 --- a/frontend/src/components/ScatterPanel.tsx +++ b/frontend/src/components/ScatterPanel.tsx @@ -1,314 +1,100 @@ "use client"; -import { useEffect, useRef, useCallback, useState } from "react"; -import { scaleLinear } from "d3-scale"; +import { useMemo } from "react"; import { useStore } from "@/store/useStore"; -import type { ViewMode } from "@/types"; - -// Color utility -function hexToRgb(hex: string): [number, number, number] { - const result = /^#?([a-f\d]{2})([a-f\d]{2})([a-f\d]{2})$/i.exec(hex); - if (result) { - return [ - parseInt(result[1], 16) / 255, - parseInt(result[2], 16) / 255, - parseInt(result[3], 16) / 255, - ]; - } - return [0.5, 0.5, 0.5]; -} - -// Default colors for points without labels -const DEFAULT_COLORS = [ - "#e6194b", "#3cb44b", "#ffe119", "#4363d8", "#f58231", - "#911eb4", "#46f0f0", "#f032e6", "#bcf60c", "#fabebe", - "#008080", "#e6beff", "#9a6324", "#fffac8", "#800000", -]; +import { Panel, PanelFooter } from "./Panel"; +import { PanelHeader } from "./PanelHeader"; +import { ScatterIcon } from "./icons"; +import { ToggleGroup, ToggleGroupItem } from "@/components/ui/toggle-group"; +import { type ScatterLabelsInfo, useHyperScatter } from "./useHyperScatter"; interface ScatterPanelProps { className?: string; } export function ScatterPanel({ className = "" }: ScatterPanelProps) { - const canvasRef = useRef(null); - const containerRef = useRef(null); - const svgGroupRef = useRef(null); - const scatterplotRef = useRef(null); - const [isInitialized, setIsInitialized] = useState(false); - const { embeddings, viewMode, setViewMode, selectedIds, setSelectedIds, + beginLassoSelection, hoveredId, setHoveredId, } = useStore(); - // Sync SVG transform - const syncSvg = useCallback((event: any) => { - const { xScale, yScale } = event; + const labelsInfo = useMemo(() => { + if (!embeddings) return null; - if (svgGroupRef.current && xScale && yScale) { - // Calculate transform based on the actual scales used by the scatterplot - // The SVG is defined in [-1, 1] coordinate space (r=1 circle at 0,0) - // We want to map [-1, 1] to screen coordinates. - - // xScale(0) is the screen x-coordinate of the origin - // xScale(1) is the screen x-coordinate of x=1 - // So the scaling factor for x is xScale(1) - xScale(0) - - const scaleX = xScale(1) - xScale(0); - const scaleY = yScale(1) - yScale(0); - const translateX = xScale(0); - const translateY = yScale(0); + // Stable label order for palette and legend. + const uniqueLabels = [...new Set(embeddings.labels.map((l) => l || "undefined"))]; - svgGroupRef.current.setAttribute( - "transform", - `matrix(${scaleX}, 0, 0, ${scaleY}, ${translateX}, ${translateY})` - ); + const labelToCategory: Record = {}; + for (let i = 0; i < uniqueLabels.length; i++) { + labelToCategory[uniqueLabels[i]] = i; } - }, []); - - // Initialize scatterplot - useEffect(() => { - if (!canvasRef.current || !containerRef.current || isInitialized) return; - - let mounted = true; - - const initScatterplot = async () => { - try { - const createScatterplot = (await import("regl-scatterplot")).default; - - if (!mounted || !canvasRef.current || !containerRef.current) return; - - const { width, height } = containerRef.current.getBoundingClientRect(); - - // Initialize D3 scales for synchronization - // Our data is normalized to [-1, 1] - const xScale = scaleLinear().domain([-1, 1]); - const yScale = scaleLinear().domain([-1, 1]); - - const scatterplot = createScatterplot({ - canvas: canvasRef.current, - width, - height, - xScale, - yScale, - pointSize: 4, - pointSizeSelected: 8, - opacity: 0.8, - opacityInactiveMax: 0.2, - lassoColor: [0.31, 0.27, 0.90, 1], // Indigo primary #4F46E5 - lassoMinDelay: 10, - lassoMinDist: 2, - showReticle: true, - reticleColor: [1, 1, 1, 0.5], - colorBy: 'category', - pointColor: DEFAULT_COLORS, - }); - - // Handle view changes to sync SVG - scatterplot.subscribe("view", syncSvg); - // Initial sync - const currentXScale = scatterplot.get("xScale"); - const currentYScale = scatterplot.get("yScale"); - if (currentXScale && currentYScale) { - syncSvg({ xScale: currentXScale, yScale: currentYScale }); - } - - // Handle lasso selection - scatterplot.subscribe("select", ({ points }: { points: number[] }) => { - if (points.length > 0) { - const currentEmbeddings = useStore.getState().embeddings; - if (currentEmbeddings) { - const selectedSampleIds = new Set( - points.map((idx) => currentEmbeddings.ids[idx]) - ); - // Mark this as a lasso selection - useStore.getState().setSelectedIds(selectedSampleIds, true); - } - } - }); - - // Handle deselection - scatterplot.subscribe("deselect", () => { - useStore.getState().setSelectedIds(new Set(), false); - }); - - // Handle point hover - scatterplot.subscribe( - "pointOver", - (pointIndex: number) => { - const currentEmbeddings = useStore.getState().embeddings; - if (currentEmbeddings && pointIndex >= 0) { - setHoveredId(currentEmbeddings.ids[pointIndex]); - } - } - ); - - scatterplot.subscribe("pointOut", () => { - setHoveredId(null); - }); - - scatterplotRef.current = scatterplot; - setIsInitialized(true); - } catch (error) { - console.error("Failed to initialize scatterplot:", error); - } - }; - - initScatterplot(); - - return () => { - if (scatterplotRef.current) { - scatterplotRef.current.destroy(); - scatterplotRef.current = null; - setIsInitialized(false); - } - }; - }, [syncSvg]); - - // Update data when embeddings or viewMode changes - useEffect(() => { - if (!scatterplotRef.current || !embeddings) return; - - const coords = viewMode === "euclidean" ? embeddings.euclidean : embeddings.hyperbolic; - - // If switching to hyperbolic, try to sync SVG immediately - if (viewMode === "hyperbolic") { - // Small timeout to ensure SVG is rendered - setTimeout(() => { - if (scatterplotRef.current) { - const xScale = scatterplotRef.current.get("xScale"); - const yScale = scatterplotRef.current.get("yScale"); - if (xScale && yScale) { - syncSvg({ xScale, yScale }); - } - } - }, 0); + const categories = new Uint16Array(embeddings.labels.length); + for (let i = 0; i < embeddings.labels.length; i++) { + const key = embeddings.labels[i] || "undefined"; + categories[i] = labelToCategory[key] ?? 0; } - // Build unique categories for color mapping - // Handle nulls by converting to "undefined" - const uniqueLabels = [...new Set(embeddings.labels.map((l) => l || "undefined"))]; - - const labelToCategory: Record = {}; - uniqueLabels.forEach((label, idx) => { - labelToCategory[label] = idx; - }); - - // Build category array (integer indices for each point) - const categories = embeddings.labels.map((label) => { - const key = label || "undefined"; - return labelToCategory[key]; - }); - - // Build color palette from label colors - const colorPalette = uniqueLabels.map((label) => { - if (label === "undefined") return "#008080"; // Dark teal for undefined + const palette = uniqueLabels.map((label) => { + if (label === "undefined") return "#008080"; return embeddings.label_colors[label] || "#808080"; }); - // Set the color palette first - if (colorPalette.length > 0) { - scatterplotRef.current.set({ pointColor: colorPalette }); - } - - scatterplotRef.current.draw({ - x: coords.map((c) => c[0]), - y: coords.map((c) => c[1]), - category: categories, - }); - - // Reset view to fit new points - scatterplotRef.current.reset(); - - // Try to sync again after draw - if (viewMode === "hyperbolic") { - setTimeout(() => { - if (scatterplotRef.current) { - const xScale = scatterplotRef.current.get("xScale"); - const yScale = scatterplotRef.current.get("yScale"); - if (xScale && yScale) { - syncSvg({ xScale, yScale }); - } - } - }, 100); - } - }, [embeddings, viewMode, isInitialized, syncSvg]); - - // Sync selection from store to scatterplot - useEffect(() => { - if (!scatterplotRef.current || !embeddings) return; - - const selectedIndices = Array.from(selectedIds) - .map((id) => embeddings.ids.indexOf(id)) - .filter((idx) => idx !== -1); - - scatterplotRef.current.select(selectedIndices, { preventEvent: true }); - }, [selectedIds, embeddings, isInitialized]); + return { uniqueLabels, categories, palette }; + }, [embeddings]); - // Handle resize - useEffect(() => { - if (!containerRef.current || !scatterplotRef.current) return; - - const resizeObserver = new ResizeObserver((entries) => { - for (const entry of entries) { - const { width, height } = entry.contentRect; - if (width > 0 && height > 0 && scatterplotRef.current) { - scatterplotRef.current.set({ width, height }); - } - } - }); - - resizeObserver.observe(containerRef.current); - return () => resizeObserver.disconnect(); - }, [isInitialized]); + const { + canvasRef, + overlayCanvasRef, + containerRef, + handlePointerDown, + handlePointerMove, + handlePointerUp, + handlePointerLeave, + handleDoubleClick, + } = useHyperScatter({ + embeddings, + viewMode, + labelsInfo, + selectedIds, + hoveredId, + setSelectedIds, + beginLassoSelection, + setHoveredId, + }); - // Get unique labels for legend - const uniqueLabels = embeddings - ? [...new Set(embeddings.labels.map((l) => l || "undefined"))] - : []; + const uniqueLabels = labelsInfo?.uniqueLabels ?? []; return ( -
- {/* Header */} -
-
- Embeddings - - {/* View mode toggle */} -
- - -
-
- - - {embeddings ? `${embeddings.ids.length} points` : "Loading..."} - -
+ + } + title="Embeddings" + subtitle={embeddings ? `${embeddings.ids.length} points` : "Loading..."} + > + {/* View mode toggle using shadcn ToggleGroup */} + val && setViewMode(val as "euclidean" | "hyperbolic")} + variant="outline" + size="sm" + className="h-6" + > + + Euclidean + + + Hyperbolic + + + {/* Main content area */}
@@ -318,85 +104,58 @@ export function ScatterPanel({ className = "" }: ScatterPanelProps) { ref={canvasRef} className="absolute inset-0" style={{ zIndex: 1 }} + onPointerDown={handlePointerDown} + onPointerMove={handlePointerMove} + onPointerUp={handlePointerUp} + onPointerCancel={handlePointerUp} + onPointerLeave={handlePointerLeave} + onDoubleClick={handleDoubleClick} /> - {/* Poincaré disk boundary for hyperbolic mode */} - {viewMode === "hyperbolic" && ( - - - {/* Main Boundary Circle - scaled to match data (max r ≈ 0.9) */} - - - {/* Grid Circles - adjusted for 0.65 hyperbolic scaling factor */} - {/* After scaling: d=1 => r≈0.316, d=2 => r≈0.569, d=3 => r≈0.748 */} - - - - - {/* Radial Lines - scaled to boundary */} - - - {/* Diagonals */} - - - - - )} + {/* Lasso overlay (screen-space) */} + {/* Loading overlay */} {!embeddings && ( -
-
Loading embeddings...
+
+
Loading embeddings...
)}
{/* Legend */} {uniqueLabels.length > 0 && ( -
-
Labels
+
+
Labels
{uniqueLabels.slice(0, 20).map((label) => (
- + {label}
))} {uniqueLabels.length > 20 && ( -
- +{uniqueLabels.length - 20} more -
+
+{uniqueLabels.length - 20} more
)}
)}
- {/* Instructions */} -
- - Shift+drag to lasso select • Scroll to zoom • Drag to pan - -
-
+ + ⇧+drag lasso • scroll zoom • drag pan + + ); } diff --git a/frontend/src/components/useHyperScatter.ts b/frontend/src/components/useHyperScatter.ts new file mode 100644 index 0000000..131b829 --- /dev/null +++ b/frontend/src/components/useHyperScatter.ts @@ -0,0 +1,481 @@ +import type React from "react"; +import { useCallback, useEffect, useMemo, useRef } from "react"; + +import type { EmbeddingsData, ViewMode } from "@/types"; +import type { Dataset, GeometryMode, Modifiers, Renderer } from "hyper-scatter"; + +type HyperScatterModule = typeof import("hyper-scatter"); + +export interface ScatterLabelsInfo { + uniqueLabels: string[]; + categories: Uint16Array; + palette: string[]; +} + +const MAX_LASSO_VERTS = 512; + +function capInterleavedXY(points: ArrayLike, maxVerts: number): number[] { + const n = Math.floor(points.length / 2); + if (n <= maxVerts) return Array.from(points as ArrayLike); + + const out = new Array(maxVerts * 2); + for (let i = 0; i < maxVerts; i++) { + const src = Math.floor((i * n) / maxVerts); + out[i * 2] = points[src * 2]; + out[i * 2 + 1] = points[src * 2 + 1]; + } + return out; +} + +interface UseHyperScatterArgs { + embeddings: EmbeddingsData | null; + viewMode: ViewMode; + labelsInfo: ScatterLabelsInfo | null; + selectedIds: Set; + hoveredId: string | null; + setSelectedIds: (ids: Set, isLasso?: boolean) => void; + beginLassoSelection: (query: { viewMode: ViewMode; polygon: number[] }) => void; + setHoveredId: (id: string | null) => void; +} + +function toModifiers(e: { shiftKey: boolean; ctrlKey: boolean; altKey: boolean; metaKey: boolean }): Modifiers { + return { + shift: e.shiftKey, + ctrl: e.ctrlKey, + alt: e.altKey, + meta: e.metaKey, + }; +} + +function clearOverlay(canvas: HTMLCanvasElement | null): void { + if (!canvas) return; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + ctx.setTransform(1, 0, 0, 1, 0, 0); + ctx.clearRect(0, 0, canvas.width, canvas.height); +} + +function drawLassoOverlay(canvas: HTMLCanvasElement | null, points: number[]): void { + if (!canvas) return; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + clearOverlay(canvas); + if (points.length < 6) return; + + ctx.save(); + ctx.lineWidth = 2; + ctx.strokeStyle = "rgba(79,70,229,0.9)"; // indigo-ish + ctx.fillStyle = "rgba(79,70,229,0.15)"; + + ctx.beginPath(); + ctx.moveTo(points[0], points[1]); + for (let i = 2; i < points.length; i += 2) { + ctx.lineTo(points[i], points[i + 1]); + } + ctx.closePath(); + ctx.fill(); + ctx.stroke(); + ctx.restore(); +} + +function viewModeToGeometry(mode: ViewMode): GeometryMode { + return mode === "euclidean" ? "euclidean" : "poincare"; +} + +export function useHyperScatter({ + embeddings, + viewMode, + labelsInfo, + selectedIds, + hoveredId, + setSelectedIds, + beginLassoSelection, + setHoveredId, +}: UseHyperScatterArgs) { + const canvasRef = useRef(null); + const overlayCanvasRef = useRef(null); + const containerRef = useRef(null); + + const rendererRef = useRef(null); + + const rafPendingRef = useRef(false); + + // Interaction state (refs to avoid rerender churn) + const isPanningRef = useRef(false); + const isLassoingRef = useRef(false); + const lastPointerXRef = useRef(0); + const lastPointerYRef = useRef(0); + const lassoPointsRef = useRef([]); + + const hoveredIndexRef = useRef(-1); + + const idToIndex = useMemo(() => { + if (!embeddings) return null; + const m = new Map(); + for (let i = 0; i < embeddings.ids.length; i++) { + m.set(embeddings.ids[i], i); + } + return m; + }, [embeddings]); + + const requestRender = useCallback(() => { + if (rafPendingRef.current) return; + rafPendingRef.current = true; + + requestAnimationFrame(() => { + rafPendingRef.current = false; + const renderer = rendererRef.current; + if (!renderer) return; + + renderer.render(); + + if (isLassoingRef.current) { + drawLassoOverlay(overlayCanvasRef.current, lassoPointsRef.current); + } + }); + }, []); + + const getCanvasPos = useCallback((e: { clientX: number; clientY: number }) => { + const canvas = canvasRef.current; + if (!canvas) return { x: 0, y: 0 }; + const rect = canvas.getBoundingClientRect(); + return { + x: e.clientX - rect.left, + y: e.clientY - rect.top, + }; + }, []); + + const stopInteraction = useCallback(() => { + isPanningRef.current = false; + isLassoingRef.current = false; + lassoPointsRef.current = []; + clearOverlay(overlayCanvasRef.current); + }, []); + + // Initialize renderer when embeddings/viewMode change. + useEffect(() => { + if (!embeddings || !labelsInfo) return; + if (!canvasRef.current || !containerRef.current) return; + + let cancelled = false; + + const init = async () => { + try { + const viz = (await import("hyper-scatter")) as HyperScatterModule; + if (cancelled) return; + + const container = containerRef.current; + const canvas = canvasRef.current; + if (!container || !canvas) return; + + // Destroy existing renderer (if any) + if (rendererRef.current) { + rendererRef.current.destroy(); + rendererRef.current = null; + } + + const rect = container.getBoundingClientRect(); + const width = Math.floor(rect.width); + const height = Math.floor(rect.height); + if (overlayCanvasRef.current) { + overlayCanvasRef.current.width = Math.max(1, width); + overlayCanvasRef.current.height = Math.max(1, height); + overlayCanvasRef.current.style.width = `${width}px`; + overlayCanvasRef.current.style.height = `${height}px`; + clearOverlay(overlayCanvasRef.current); + } + + const coords = viewMode === "euclidean" ? embeddings.euclidean : embeddings.hyperbolic; + const positions = new Float32Array(coords.length * 2); + for (let i = 0; i < coords.length; i++) { + positions[i * 2] = coords[i][0]; + positions[i * 2 + 1] = coords[i][1]; + } + + const geometry = viewModeToGeometry(viewMode); + const dataset: Dataset = viz.createDataset(geometry, positions, labelsInfo.categories); + + const renderer: Renderer = + viewMode === "euclidean" ? new viz.EuclideanWebGLCandidate() : new viz.HyperbolicWebGLCandidate(); + + // Match HyperView theme: --card is #161b22 + const backgroundColor = "#161b22"; + + renderer.init(canvas, { + width, + height, + devicePixelRatio: window.devicePixelRatio, + pointRadius: 4, + colors: labelsInfo.palette, + backgroundColor, + }); + + renderer.setDataset(dataset); + rendererRef.current = renderer; + + hoveredIndexRef.current = -1; + renderer.setHovered(-1); + + requestRender(); + } catch (err) { + console.error("Failed to initialize hyper-scatter renderer:", err); + } + }; + + init(); + + return () => { + cancelled = true; + stopInteraction(); + if (rendererRef.current) { + rendererRef.current.destroy(); + rendererRef.current = null; + } + }; + }, [embeddings, labelsInfo, requestRender, stopInteraction, viewMode]); + + // Store -> renderer sync + useEffect(() => { + const renderer = rendererRef.current; + if (!renderer || !embeddings || !idToIndex) return; + + const indices = new Set(); + for (const id of selectedIds) { + const idx = idToIndex.get(id); + if (typeof idx === "number") indices.add(idx); + } + + renderer.setSelection(indices); + + const hoveredIdx = hoveredId ? (idToIndex.get(hoveredId) ?? -1) : -1; + renderer.setHovered(hoveredIdx); + hoveredIndexRef.current = hoveredIdx; + + requestRender(); + }, [embeddings, hoveredId, idToIndex, requestRender, selectedIds]); + + // Resize handling + useEffect(() => { + const container = containerRef.current; + if (!container) return; + + const resize = () => { + const rect = container.getBoundingClientRect(); + const width = Math.floor(rect.width); + const height = Math.floor(rect.height); + if (!(width > 0) || !(height > 0)) return; + + if (overlayCanvasRef.current) { + overlayCanvasRef.current.width = Math.max(1, width); + overlayCanvasRef.current.height = Math.max(1, height); + overlayCanvasRef.current.style.width = `${width}px`; + overlayCanvasRef.current.style.height = `${height}px`; + } + + const renderer = rendererRef.current; + if (renderer) { + renderer.resize(width, height); + requestRender(); + } + }; + + resize(); + + const ro = new ResizeObserver(resize); + ro.observe(container); + return () => ro.disconnect(); + }, [requestRender]); + + // Wheel zoom (native listener so we can set passive:false) + useEffect(() => { + const canvas = canvasRef.current; + if (!canvas) return; + + const onWheel = (e: WheelEvent) => { + const renderer = rendererRef.current; + if (!renderer) return; + e.preventDefault(); + + const pos = getCanvasPos(e); + const delta = -e.deltaY / 100; + renderer.zoom(pos.x, pos.y, delta, toModifiers(e)); + requestRender(); + }; + + canvas.addEventListener("wheel", onWheel, { passive: false }); + return () => canvas.removeEventListener("wheel", onWheel as any); + }, [getCanvasPos, requestRender]); + + // Pointer interactions + const handlePointerDown = useCallback( + (e: React.PointerEvent) => { + const renderer = rendererRef.current; + if (!renderer) return; + + // Left button only + if (typeof e.button === "number" && e.button !== 0) return; + + const pos = getCanvasPos(e); + lastPointerXRef.current = pos.x; + lastPointerYRef.current = pos.y; + + // Shift-drag = lasso, otherwise pan. + if (e.shiftKey) { + isLassoingRef.current = true; + isPanningRef.current = false; + lassoPointsRef.current = [pos.x, pos.y]; + drawLassoOverlay(overlayCanvasRef.current, lassoPointsRef.current); + } else { + isPanningRef.current = true; + isLassoingRef.current = false; + } + + try { + e.currentTarget.setPointerCapture(e.pointerId); + } catch { + // ignore + } + + e.preventDefault(); + }, + [getCanvasPos] + ); + + const handlePointerMove = useCallback( + (e: React.PointerEvent) => { + const renderer = rendererRef.current; + if (!renderer) return; + + const pos = getCanvasPos(e); + + if (isPanningRef.current) { + const dx = pos.x - lastPointerXRef.current; + const dy = pos.y - lastPointerYRef.current; + lastPointerXRef.current = pos.x; + lastPointerYRef.current = pos.y; + + renderer.pan(dx, dy, toModifiers(e)); + requestRender(); + return; + } + + if (isLassoingRef.current) { + const pts = lassoPointsRef.current; + const lastX = pts[pts.length - 2] ?? pos.x; + const lastY = pts[pts.length - 1] ?? pos.y; + const ddx = pos.x - lastX; + const ddy = pos.y - lastY; + const distSq = ddx * ddx + ddy * ddy; + + // Sample at ~2px spacing + if (distSq >= 4) { + pts.push(pos.x, pos.y); + drawLassoOverlay(overlayCanvasRef.current, pts); + } + return; + } + + // Hover + const hit = renderer.hitTest(pos.x, pos.y); + const nextIndex = hit ? hit.index : -1; + if (nextIndex === hoveredIndexRef.current) return; + hoveredIndexRef.current = nextIndex; + renderer.setHovered(nextIndex); + + if (!embeddings) return; + if (nextIndex >= 0 && nextIndex < embeddings.ids.length) { + setHoveredId(embeddings.ids[nextIndex]); + } else { + setHoveredId(null); + } + + requestRender(); + }, + [embeddings, getCanvasPos, requestRender, setHoveredId] + ); + + const handlePointerUp = useCallback( + async (_e: React.PointerEvent) => { + const renderer = rendererRef.current; + if (!renderer || !embeddings) { + stopInteraction(); + return; + } + + if (isLassoingRef.current) { + const pts = lassoPointsRef.current; + stopInteraction(); + + if (pts.length >= 6) { + try { + const polyline = new Float32Array(pts); + const result = renderer.lassoSelect(polyline); + + // Enter server-driven lasso mode by sending a data-space polygon. + // Backend selection runs in the same coordinate system returned by /api/embeddings. + const dataCoords = result.geometry?.coords; + if (!dataCoords || dataCoords.length < 6) return; + + // Clear any existing manual selection highlights immediately. + renderer.setSelection(new Set()); + + // Cap vertex count to keep request payload + backend runtime bounded. + const polygon = capInterleavedXY(dataCoords, MAX_LASSO_VERTS); + if (polygon.length < 6) return; + + beginLassoSelection({ viewMode, polygon }); + } catch (err) { + console.error("Lasso selection failed:", err); + } + } + + requestRender(); + return; + } + + stopInteraction(); + requestRender(); + }, + [beginLassoSelection, embeddings, requestRender, stopInteraction, viewMode] + ); + + const handlePointerLeave = useCallback( + (_e: React.PointerEvent) => { + const renderer = rendererRef.current; + if (renderer) { + hoveredIndexRef.current = -1; + setHoveredId(null); + renderer.setHovered(-1); + requestRender(); + } + stopInteraction(); + }, + [requestRender, setHoveredId, stopInteraction] + ); + + const handleDoubleClick = useCallback( + (_e: React.MouseEvent) => { + const renderer = rendererRef.current; + if (!renderer) return; + stopInteraction(); + + renderer.setSelection(new Set()); + setSelectedIds(new Set()); + + requestRender(); + }, + [requestRender, setSelectedIds, stopInteraction] + ); + + return { + canvasRef, + overlayCanvasRef, + containerRef, + handlePointerDown, + handlePointerMove, + handlePointerUp, + handlePointerLeave, + handleDoubleClick, + }; +} From 2a419fc7a2726f2d261cc1af935f5193956c37e0 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Thu, 15 Jan 2026 01:21:33 +0100 Subject: [PATCH 16/75] feat(frontend): add server-driven lasso selection and resizable layout - Add fetchLassoSelection API for backend polygon queries - Add lasso state to store (lassoQuery, lassoSamples, lassoTotal) - Refactor ImageGrid to display lasso-filtered samples - Replace manual resize logic with react-resizable-panels - Add lasso data fetching with pagination support in page.tsx --- frontend/src/app/page.tsx | 204 +++++++++++++++++--------- frontend/src/components/ImageGrid.tsx | 102 ++++++------- frontend/src/lib/api.ts | 38 ++++- frontend/src/store/useStore.ts | 94 +++++++++--- 4 files changed, 284 insertions(+), 154 deletions(-) diff --git a/frontend/src/app/page.tsx b/frontend/src/app/page.tsx index 43f11b9..d3d2bf6 100644 --- a/frontend/src/app/page.tsx +++ b/frontend/src/app/page.tsx @@ -1,9 +1,20 @@ "use client"; -import { useEffect, useCallback, useState, useRef } from "react"; +import { useEffect, useCallback, useState } from "react"; +import { + ResizablePanelGroup, + ResizablePanel, + ResizableHandle, +} from "@/components/ui/resizable"; import { Header, ImageGrid, ScatterPanel } from "@/components"; import { useStore } from "@/store/useStore"; -import { fetchDataset, fetchSamples, fetchEmbeddings, fetchSamplesBatch } from "@/lib/api"; +import { + fetchDataset, + fetchSamples, + fetchEmbeddings, + fetchSamplesBatch, + fetchLassoSelection, +} from "@/lib/api"; const SAMPLES_PER_PAGE = 100; @@ -21,14 +32,18 @@ export default function Home() { error, setError, selectedIds, + isLassoSelection, + lassoQuery, + lassoSamples, + lassoTotal, + lassoIsLoading, + setLassoResults, } = useStore(); const [loadingMore, setLoadingMore] = useState(false); - const [leftPanelWidth, setLeftPanelWidth] = useState(50); // percentage - const containerRef = useRef(null); - const isDraggingRef = useRef(false); - // Initial data load + // Initial data load - runs once on mount + // Store setters are stable and don't need to be in deps useEffect(() => { const loadData = async () => { setIsLoading(true); @@ -54,11 +69,13 @@ export default function Home() { }; loadData(); + // eslint-disable-next-line react-hooks/exhaustive-deps }, []); // Fetch selected samples that aren't already loaded useEffect(() => { const fetchSelectedSamples = async () => { + if (isLassoSelection) return; if (selectedIds.size === 0) return; // Find IDs that are selected but not in our samples array @@ -76,11 +93,67 @@ export default function Home() { }; fetchSelectedSamples(); - }, [selectedIds, samples, addSamplesIfMissing]); + }, [selectedIds, samples, addSamplesIfMissing, isLassoSelection]); + + // Fetch initial lasso selection page when a new lasso query begins. + useEffect(() => { + if (!isLassoSelection) return; + if (!lassoQuery) return; + if (!lassoIsLoading) return; + + const abort = new AbortController(); + + const run = async () => { + try { + const res = await fetchLassoSelection({ + viewMode: lassoQuery.viewMode, + polygon: lassoQuery.polygon, + offset: 0, + limit: SAMPLES_PER_PAGE, + signal: abort.signal, + }); + if (abort.signal.aborted) return; + setLassoResults(res.samples, res.total, false); + } catch (err) { + // Ignore cancellation. + if ((err as any)?.name === "AbortError") return; + console.error("Failed to fetch lasso selection:", err); + setLassoResults([], 0, false); + } + }; + + run(); + + return () => abort.abort(); + }, [isLassoSelection, lassoIsLoading, lassoQuery, setLassoResults]); // Load more samples const loadMore = useCallback(async () => { - if (loadingMore || samples.length >= totalSamples) return; + if (loadingMore) return; + + if (isLassoSelection) { + if (!lassoQuery) return; + if (lassoIsLoading) return; + if (!lassoIsLoading && lassoSamples.length >= lassoTotal) return; + + setLoadingMore(true); + try { + const res = await fetchLassoSelection({ + viewMode: lassoQuery.viewMode, + polygon: lassoQuery.polygon, + offset: lassoSamples.length, + limit: SAMPLES_PER_PAGE, + }); + setLassoResults(res.samples, res.total, true); + } catch (err) { + console.error("Failed to load more lasso samples:", err); + } finally { + setLoadingMore(false); + } + return; + } + + if (samples.length >= totalSamples) return; setLoadingMore(true); try { @@ -91,42 +164,22 @@ export default function Home() { } finally { setLoadingMore(false); } - }, [samples.length, totalSamples, loadingMore, appendSamples]); - - // Handle resizing - useEffect(() => { - const handleMouseMove = (e: MouseEvent) => { - if (!isDraggingRef.current || !containerRef.current) return; - - const container = containerRef.current; - const containerRect = container.getBoundingClientRect(); - const newWidth = ((e.clientX - containerRect.left) / containerRect.width) * 100; - - // Constrain between 20% and 80% - const clampedWidth = Math.min(Math.max(newWidth, 20), 80); - setLeftPanelWidth(clampedWidth); - }; - - const handleMouseUp = () => { - isDraggingRef.current = false; - document.body.style.cursor = ""; - document.body.style.userSelect = ""; - }; - - document.addEventListener("mousemove", handleMouseMove); - document.addEventListener("mouseup", handleMouseUp); - - return () => { - document.removeEventListener("mousemove", handleMouseMove); - document.removeEventListener("mouseup", handleMouseUp); - }; - }, []); + }, [ + loadingMore, + appendSamples, + isLassoSelection, + lassoIsLoading, + lassoQuery, + lassoSamples.length, + lassoTotal, + samples.length, + totalSamples, + setLassoResults, + ]); - const handleMouseDown = () => { - isDraggingRef.current = true; - document.body.style.cursor = "col-resize"; - document.body.style.userSelect = "none"; - }; + const displayedSamples = isLassoSelection ? lassoSamples : samples; + const displayedTotal = isLassoSelection ? lassoTotal : totalSamples; + const displayedHasMore = displayedSamples.length < displayedTotal; if (error) { return ( @@ -134,9 +187,9 @@ export default function Home() {
-
Error
-
{error}
-

+

Error
+
{error}
+

Make sure the HyperView backend is running on port 5151.

@@ -152,7 +205,7 @@ export default function Home() {
-
Loading dataset...
+
Loading dataset...
@@ -163,33 +216,42 @@ export default function Home() {
- {/* Main content - two panels */} -
- {/* Left panel - Image Grid */} -
+ - -
+ {/* Left panel - Image Grid */} + + + - {/* Resizable divider */} -
+ {/* Resizable handle with grip */} + - {/* Right panel - Scatter Plot */} -
- -
+ {/* Right panel - Scatter Plot */} + + + +
); diff --git a/frontend/src/components/ImageGrid.tsx b/frontend/src/components/ImageGrid.tsx index f5bc25e..6a59523 100644 --- a/frontend/src/components/ImageGrid.tsx +++ b/frontend/src/components/ImageGrid.tsx @@ -1,8 +1,11 @@ "use client"; -import { useCallback, useEffect, useRef, useMemo, useState } from "react"; +import { useCallback, useEffect, useRef, useState } from "react"; import { useVirtualizer } from "@tanstack/react-virtual"; import { useStore } from "@/store/useStore"; +import { Panel, PanelFooter } from "./Panel"; +import { PanelHeader } from "./PanelHeader"; +import { GridIcon, CheckIcon } from "./icons"; import type { Sample } from "@/types"; interface ImageGridProps { @@ -17,7 +20,15 @@ const MIN_ITEM_WIDTH = 200; // Minimum width for each image export function ImageGrid({ samples, onLoadMore, hasMore }: ImageGridProps) { const containerRef = useRef(null); - const { selectedIds, isLassoSelection, toggleSelection, addToSelection, setHoveredId, hoveredId } = useStore(); + const { + selectedIds, + isLassoSelection, + lassoTotal, + toggleSelection, + addToSelection, + setHoveredId, + hoveredId, + } = useStore(); const [columnCount, setColumnCount] = useState(4); // Calculate column count based on container width @@ -43,28 +54,17 @@ export function ImageGrid({ samples, onLoadMore, hasMore }: ImageGridProps) { return () => resizeObserver.disconnect(); }, []); - // Filter samples based on selection - const filteredSamples = useMemo(() => { - // Only filter (hide non-selected) when it's a lasso selection - if (isLassoSelection && selectedIds.size > 0) { - return samples.filter((sample) => selectedIds.has(sample.id)); - } - - // Otherwise, show all samples - return samples; - }, [samples, selectedIds, isLassoSelection]); - - // Calculate rows from filtered samples - const rowCount = Math.ceil(filteredSamples.length / columnCount); + // In lasso mode, `samples` is already the selected-page list. + const rowCount = Math.ceil(samples.length / columnCount); // Create stable row keys based on the sample IDs in each row const getRowKey = useCallback( (index: number) => { const startIndex = index * columnCount; - const rowSamples = filteredSamples.slice(startIndex, startIndex + columnCount); + const rowSamples = samples.slice(startIndex, startIndex + columnCount); return rowSamples.map((s) => s.id).join("-") || `row-${index}`; }, - [filteredSamples, columnCount] + [samples, columnCount] ); const virtualizer = useVirtualizer({ @@ -127,24 +127,18 @@ export function ImageGrid({ samples, onLoadMore, hasMore }: ImageGridProps) { const items = virtualizer.getVirtualItems(); return ( -
- {/* Header */} -
-
- Samples - - {selectedIds.size > 0 ? `${selectedIds.size} selected` : `${filteredSamples.length} items`} - -
- {selectedIds.size > 0 && ( - - )} -
+ + } + title="Samples" + subtitle={ + isLassoSelection + ? `${lassoTotal} selected` + : selectedIds.size > 0 + ? `${selectedIds.size} selected` + : `${samples.length} items` + } + /> {/* Grid Container */}
@@ -158,7 +152,7 @@ export function ImageGrid({ samples, onLoadMore, hasMore }: ImageGridProps) { {items.map((virtualRow) => { const rowIndex = virtualRow.index; const startIndex = rowIndex * columnCount; - const rowSamples = filteredSamples.slice(startIndex, startIndex + columnCount); + const rowSamples = samples.slice(startIndex, startIndex + columnCount); return (
{rowSamples.map((sample) => { - const isSelected = selectedIds.has(sample.id); + const isSelected = isLassoSelection ? true : selectedIds.has(sample.id); const isHovered = hoveredId === sample.id; return ( @@ -184,14 +178,15 @@ export function ImageGrid({ samples, onLoadMore, hasMore }: ImageGridProps) { relative flex-1 rounded-md overflow-hidden cursor-pointer transition-all duration-150 ease-out ${isSelected ? "ring-2 ring-primary" : ""} - ${isHovered ? "ring-2 ring-primary-light" : ""} + ${isHovered ? "ring-2 ring-primary/50" : ""} `} onClick={(e) => handleClick(sample, e)} onMouseEnter={() => setHoveredId(sample.id)} onMouseLeave={() => setHoveredId(null)} > - {/* Image */} + {/* Image - using native img for base64 data (Next Image doesn't support this) */} {sample.thumbnail ? ( + // eslint-disable-next-line @next/next/no-img-element {sample.filename} ) : ( -
- No image +
+ No image
)} @@ -222,19 +217,7 @@ export function ImageGrid({ samples, onLoadMore, hasMore }: ImageGridProps) { {/* Selection indicator */} {isSelected && (
- - - +
)}
@@ -250,12 +233,9 @@ export function ImageGrid({ samples, onLoadMore, hasMore }: ImageGridProps) {
- {/* Instructions footer */} -
- - Click to select • Cmd/Ctrl+click to multi-select • Shift+click for range - -
-
+ + Click • ⌘+click multi • ⇧+click range + + ); } diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts index c923cd3..082fed0 100644 --- a/frontend/src/lib/api.ts +++ b/frontend/src/lib/api.ts @@ -1,4 +1,4 @@ -import type { DatasetInfo, EmbeddingsData, Sample, SamplesResponse } from "@/types"; +import type { DatasetInfo, EmbeddingsData, Sample, SamplesResponse, ViewMode } from "@/types"; const API_BASE = process.env.NODE_ENV === "development" ? "http://127.0.0.1:5151" : ""; @@ -60,3 +60,39 @@ export async function fetchSamplesBatch(sampleIds: string[]): Promise const data = await res.json(); return data.samples; } + +export interface LassoSelectionResponse { + total: number; + offset: number; + limit: number; + sample_ids: string[]; + samples: Sample[]; +} + +export async function fetchLassoSelection(args: { + viewMode: ViewMode; + polygon: ArrayLike; + offset?: number; + limit?: number; + includeThumbnails?: boolean; + signal?: AbortSignal; +}): Promise { + const res = await fetch(`${API_BASE}/api/selection/lasso`, { + method: "POST", + headers: { + "Content-Type": "application/json", + }, + body: JSON.stringify({ + space: args.viewMode === "euclidean" ? "euclidean" : "hyperbolic", + polygon: Array.from(args.polygon), + offset: args.offset ?? 0, + limit: args.limit ?? 100, + include_thumbnails: args.includeThumbnails ?? true, + }), + signal: args.signal, + }); + if (!res.ok) { + throw new Error(`Failed to fetch lasso selection: ${res.statusText}`); + } + return res.json(); +} diff --git a/frontend/src/store/useStore.ts b/frontend/src/store/useStore.ts index 59abb54..4549b9c 100644 --- a/frontend/src/store/useStore.ts +++ b/frontend/src/store/useStore.ts @@ -24,11 +24,20 @@ interface AppState { // Selection selectedIds: Set; isLassoSelection: boolean; - setSelectedIds: (ids: Set, isLasso?: boolean) => void; + setSelectedIds: (ids: Set) => void; toggleSelection: (id: string) => void; addToSelection: (ids: string[]) => void; clearSelection: () => void; + // Lasso selection (server-driven) + lassoQuery: { viewMode: ViewMode; polygon: number[] } | null; + lassoSamples: Sample[]; + lassoTotal: number; + lassoIsLoading: boolean; + beginLassoSelection: (query: { viewMode: ViewMode; polygon: number[] }) => void; + setLassoResults: (samples: Sample[], total: number, append?: boolean) => void; + clearLassoSelection: () => void; + // Hover state hoveredId: string | null; setHoveredId: (id: string | null) => void; @@ -40,14 +49,6 @@ interface AppState { // Error state error: string | null; setError: (error: string | null) => void; - - // Label filter - filterLabel: string | null; - setFilterLabel: (label: string | null) => void; - - // UI state - showLabels: boolean; - setShowLabels: (show: boolean) => void; } export const useStore = create((set, get) => ({ @@ -82,7 +83,15 @@ export const useStore = create((set, get) => ({ // Selection selectedIds: new Set(), isLassoSelection: false, - setSelectedIds: (ids, isLasso = false) => set({ selectedIds: ids, isLassoSelection: isLasso }), + setSelectedIds: (ids) => + set({ + selectedIds: ids, + isLassoSelection: false, + lassoQuery: null, + lassoSamples: [], + lassoTotal: 0, + lassoIsLoading: false, + }), toggleSelection: (id) => set((state) => { const newSet = new Set(state.selectedIds); @@ -92,16 +101,67 @@ export const useStore = create((set, get) => ({ newSet.add(id); } // Manual selection from image grid, not lasso - return { selectedIds: newSet, isLassoSelection: false }; + return { + selectedIds: newSet, + isLassoSelection: false, + lassoQuery: null, + lassoSamples: [], + lassoTotal: 0, + lassoIsLoading: false, + }; }), addToSelection: (ids) => set((state) => { const newSet = new Set(state.selectedIds); ids.forEach((id) => newSet.add(id)); // Manual selection from image grid, not lasso - return { selectedIds: newSet, isLassoSelection: false }; + return { + selectedIds: newSet, + isLassoSelection: false, + lassoQuery: null, + lassoSamples: [], + lassoTotal: 0, + lassoIsLoading: false, + }; + }), + clearSelection: () => + set({ + selectedIds: new Set(), + isLassoSelection: false, + lassoQuery: null, + lassoSamples: [], + lassoTotal: 0, + lassoIsLoading: false, + }), + + // Lasso selection (server-driven) + lassoQuery: null, + lassoSamples: [], + lassoTotal: 0, + lassoIsLoading: false, + beginLassoSelection: (query) => + set({ + isLassoSelection: true, + selectedIds: new Set(), + lassoQuery: query, + lassoSamples: [], + lassoTotal: 0, + lassoIsLoading: true, + }), + setLassoResults: (samples, total, append = false) => + set((state) => ({ + lassoSamples: append ? [...state.lassoSamples, ...samples] : samples, + lassoTotal: total, + lassoIsLoading: false, + })), + clearLassoSelection: () => + set({ + isLassoSelection: false, + lassoQuery: null, + lassoSamples: [], + lassoTotal: 0, + lassoIsLoading: false, }), - clearSelection: () => set({ selectedIds: new Set(), isLassoSelection: false }), // Hover hoveredId: null, @@ -114,12 +174,4 @@ export const useStore = create((set, get) => ({ // Error error: null, setError: (error) => set({ error }), - - // Label filter - filterLabel: null, - setFilterLabel: (label) => set({ filterLabel: label }), - - // UI state - showLabels: true, - setShowLabels: (show) => set({ showLabels: show }), })); From 25e54e29c1bce69be5f312e8419b1cb0d41294ea Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Thu, 15 Jan 2026 18:00:40 +0100 Subject: [PATCH 17/75] Use npm hyper-scatter dependency --- frontend/package-lock.json | 3 ++- frontend/package.json | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/frontend/package-lock.json b/frontend/package-lock.json index 02ca465..c7de89d 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -14,7 +14,7 @@ "@tanstack/react-virtual": "^3.10.9", "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", - "hyper-scatter": "file:../hyper-scatter", + "hyper-scatter": "^0.1.0", "lucide-react": "^0.562.0", "next": "^16.0.7", "react": "18.3.1", @@ -38,6 +38,7 @@ }, "../hyper-scatter": { "version": "0.1.0", + "license": "MIT", "devDependencies": { "@types/node": "^20.10.0", "puppeteer": "^24.34.0", diff --git a/frontend/package.json b/frontend/package.json index e0e9ef1..5b0c4ee 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -23,7 +23,7 @@ "react-resizable-panels": "^4.4.1", "tailwind-merge": "^3.4.0", "tailwindcss-animate": "^1.0.7", - "hyper-scatter": "file:../hyper-scatter", + "hyper-scatter": "^0.1.0", "zustand": "^5.0.1" }, "devDependencies": { From 57e2029e3c6182035f711986f61d3fe7072b2803 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Fri, 16 Jan 2026 17:24:50 +0100 Subject: [PATCH 18/75] Colab fix: update dependencies, add colab docs and notebook --- .gitignore | 4 + README.md | 4 + docs/colab.md | 62 +++ frontend/next-env.d.ts | 2 +- notebooks/HyperView | 1 + notebooks/colab_smoke_test.ipynb | 128 +++++ pyproject.toml | 2 + scripts/export_frontend.sh | 17 +- src/hyperview/api.py | 72 ++- src/hyperview/server/static/404.html | 2 +- src/hyperview/server/static/404/index.html | 2 +- .../server/static/__next.__PAGE__.txt | 8 +- src/hyperview/server/static/__next._full.txt | 20 +- src/hyperview/server/static/__next._head.txt | 6 +- src/hyperview/server/static/__next._index.txt | 8 +- src/hyperview/server/static/__next._tree.txt | 4 +- .../_buildManifest.js | 0 .../_clientMiddlewareManifest.json | 0 .../_ssgManifest.js | 0 .../_next/static/chunks/08706ac30fdde8df.js | 9 + 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src/hyperview/server/static/_next/static/chunks/{turbopack-11ec5967a08af43c.js => turbopack-ce3658797261f2db.js} (98%) delete mode 100644 src/hyperview/server/static/_next/static/chunks/turbopack-f85f95889fd14d61.js delete mode 100644 src/hyperview/server/static/_next/static/lAwT6e3uaMqLUXxndbn_f/_buildManifest.js delete mode 100644 src/hyperview/server/static/_next/static/lAwT6e3uaMqLUXxndbn_f/_clientMiddlewareManifest.json delete mode 100644 src/hyperview/server/static/_next/static/lAwT6e3uaMqLUXxndbn_f/_ssgManifest.js diff --git a/.gitignore b/.gitignore index c397cb1..2961cdd 100644 --- a/.gitignore +++ b/.gitignore @@ -34,6 +34,10 @@ frontend/node_modules/ frontend/.next/ frontend/out/ +# Bundled frontend in Python package (built with scripts/export_frontend.sh) +# Not ignored - needed for pip install from git / sdist +# src/hyperview/server/static/ + # Python package build dist/ build/ diff --git a/README.md b/README.md index 8b11d54..eff96f8 100644 --- a/README.md +++ b/README.md @@ -74,6 +74,10 @@ dataset.compute_visualization() hv.launch(dataset) # Opens http://127.0.0.1:5151 ``` +### Google Colab + +See [docs/colab.md](docs/colab.md) for a fast Colab smoke test and notebook-friendly launch behavior. + ### Save and Load Datasets ```python diff --git a/docs/colab.md b/docs/colab.md new file mode 100644 index 0000000..ed5ac25 --- /dev/null +++ b/docs/colab.md @@ -0,0 +1,62 @@ +# Running HyperView in Google Colab + +Colab notebooks run on a remote VM, so you **can’t** open `http://127.0.0.1:` directly from your local browser. + +HyperView detects Colab and shows an **“Open HyperView in a new tab”** button that opens a same-site blank tab and embeds the proxied app URL in a full-page iframe (this avoids known issues with opening the proxied URL as a top-level page due to browser cookie partitioning). + +## Quick smoke test (no model downloads) + +This smoke test creates a tiny synthetic dataset with random images + precomputed 2D coordinates, so you can validate the UI and the “open in new tab” flow without running CLIP/UMAP. + +### 1) Install HyperView + +```bash +!pip install -q uv +!git clone https://github.com/HackerRoomAI/HyperView.git +%cd HyperView +!uv pip install -e . +``` + +### 2) Create a synthetic dataset and launch + +```python +import numpy as np +import hyperview as hv +from hyperview.core.sample import SampleFromArray + +dataset = hv.Dataset("colab_smoke", persist=False) + +rng = np.random.default_rng(0) +num_samples = 200 + +for i in range(num_samples): + # Random 64x64 RGB image + img = (rng.random((64, 64, 3)) * 255).astype(np.uint8) + + sample = SampleFromArray.from_array( + id=f"s{i}", + image_array=img, + label=f"class_{i % 10}", + metadata={"i": i}, + ) + + # Precomputed 2D Euclidean projection + sample.embedding_2d = [float(rng.normal()), float(rng.normal())] + + # Precomputed 2D hyperbolic projection (inside unit disk) + r = float(rng.random() * 0.95) + theta = float(rng.random() * 2 * np.pi) + sample.embedding_2d_hyperbolic = [float(r * np.cos(theta)), float(r * np.sin(theta))] + + dataset.add_sample(sample) + +hv.launch(dataset, port=5151) +``` + +Click **“Open HyperView in a new tab”**. + +If your browser blocks popups, HyperView will fall back to an inline iframe view inside the notebook output. + +## Full demo (real embeddings) + +If you want the full pipeline (download images, compute CLIP embeddings, run UMAP, etc.), use the demo flow described in the project README. diff --git a/frontend/next-env.d.ts b/frontend/next-env.d.ts index c4b7818..9edff1c 100644 --- a/frontend/next-env.d.ts +++ b/frontend/next-env.d.ts @@ -1,6 +1,6 @@ /// /// -import "./.next/dev/types/routes.d.ts"; +import "./.next/types/routes.d.ts"; // NOTE: This file should not be edited // see https://nextjs.org/docs/app/api-reference/config/typescript for more information. diff --git a/notebooks/HyperView b/notebooks/HyperView new file mode 160000 index 0000000..490b90c --- /dev/null +++ b/notebooks/HyperView @@ -0,0 +1 @@ +Subproject commit 490b90cb51e83d8cafd90de9fd437cac9aa38d05 diff --git a/notebooks/colab_smoke_test.ipynb b/notebooks/colab_smoke_test.ipynb new file mode 100644 index 0000000..85a1ce0 --- /dev/null +++ b/notebooks/colab_smoke_test.ipynb @@ -0,0 +1,128 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "47ee06e6", + "metadata": {}, + "source": [ + "# HyperView — Colab Smoke Test\n", + "\n", + "This notebook verifies that HyperView can launch inside Google Colab and open the UI in a new browser tab.\n", + "\n", + "The smoke test uses a synthetic dataset (random images + precomputed 2D coordinates), so it does not download models or run embedding computation." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "945a1017", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "zsh:1: command not found: pip\n", + "Cloning into 'HyperView'...\n", + "remote: Enumerating objects: 577, done.\u001b[K\n", + "remote: Counting objects: 100% (577/577), done.\u001b[K\n", + "remote: Compressing objects: 100% (328/328), done.\u001b[K\n", + "remote: Total 577 (delta 240), reused 518 (delta 193), pack-reused 0 (from 0)\u001b[K\n", + "Receiving objects: 100% (577/577), 4.29 MiB | 8.76 MiB/s, done.\n", + "Resolving deltas: 100% (240/240), done.\n", + "/Users/matin/hyperview_org/HyperView/notebooks/HyperView\n", + "\u001b[2mUsing Python 3.12.8 environment at: /Users/matin/hyperview_org/HyperView/.venv\u001b[0m\n", + "\u001b[2K\u001b[1m\u001b[31merror\u001b[39m\u001b[0m: /Users/matin/hyperview_org/HyperView/notebooks/HyperView does not appear to be a Python project, as neither `pyproject.toml` nor `setup.py` are present in the directory\n" + ] + } + ], + "source": [ + "!pip install -q uv\n", + "!git clone https://github.com/HackerRoomAI/HyperView.git\n", + "%cd HyperView\n", + "!uv pip install -e ." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "58b82256", + "metadata": {}, + "outputs": [ + { + "ename": "IndentationError", + "evalue": "unexpected indent (api.py, line 173)", + "output_type": "error", + "traceback": [ + "Traceback \u001b[36m(most recent call last)\u001b[39m:\n", + " File \u001b[92m~/hyperview_org/HyperView/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py:3701\u001b[39m in \u001b[95mrun_code\u001b[39m\n exec(code_obj, self.user_global_ns, self.user_ns)\n", + " Cell \u001b[92mIn[2]\u001b[39m\u001b[92m, line 2\u001b[39m\n import hyperview as hv\n", + "\u001b[36m \u001b[39m\u001b[36mFile \u001b[39m\u001b[32m~/hyperview_org/HyperView/src/hyperview/__init__.py:3\u001b[39m\n\u001b[31m \u001b[39m\u001b[31mfrom hyperview.api import Dataset, launch\u001b[39m\n", + " \u001b[36mFile \u001b[39m\u001b[32m~/hyperview_org/HyperView/src/hyperview/api.py:173\u001b[39m\n\u001b[31m \u001b[39m\u001b[31mif _is_colab():\u001b[39m\n ^\n\u001b[31mIndentationError\u001b[39m\u001b[31m:\u001b[39m unexpected indent\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import hyperview as hv\n", + "from hyperview.core.sample import SampleFromArray\n", + "\n", + "dataset = hv.Dataset(\"colab_smoke\", persist=False)\n", + "\n", + "rng = np.random.default_rng(0)\n", + "num_samples = 200\n", + "\n", + "for i in range(num_samples):\n", + " img = (rng.random((64, 64, 3)) * 255).astype(np.uint8)\n", + "\n", + " sample = SampleFromArray.from_array(\n", + " id=f\"s{i}\",\n", + " image_array=img,\n", + " label=f\"class_{i % 10}\",\n", + " metadata={\"i\": i},\n", + " )\n", + "\n", + " # Precomputed 2D Euclidean projection\n", + " sample.embedding_2d = [float(rng.normal()), float(rng.normal())]\n", + "\n", + " # Precomputed 2D hyperbolic projection (inside unit disk)\n", + " r = float(rng.random() * 0.95)\n", + " theta = float(rng.random() * 2 * np.pi)\n", + " sample.embedding_2d_hyperbolic = [float(r * np.cos(theta)), float(r * np.sin(theta))]\n", + "\n", + " dataset.add_sample(sample)\n", + "\n", + "hv.launch(dataset, port=5151)" + ] + }, + { + "cell_type": "markdown", + "id": "b1cb3148", + "metadata": {}, + "source": [ + "If your browser blocks popups, click the button again or use the inline fallback shown in the cell output." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyproject.toml b/pyproject.toml index b306a82..3ba8546 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -62,6 +62,8 @@ build-backend = "hatchling.build" [tool.hatch.build.targets.wheel] packages = ["src/hyperview"] +# Include frontend static assets (pre-built before packaging) +artifacts = ["src/hyperview/server/static/**"] [tool.hatch.build.targets.sdist] include = [ diff --git a/scripts/export_frontend.sh b/scripts/export_frontend.sh index cca765f..dd1cc37 100755 --- a/scripts/export_frontend.sh +++ b/scripts/export_frontend.sh @@ -6,14 +6,25 @@ set -e SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" PROJECT_ROOT="$(dirname "$SCRIPT_DIR")" FRONTEND_DIR="$PROJECT_ROOT/frontend" +HYPER_SCATTER_DIR="$PROJECT_ROOT/hyper-scatter" STATIC_DIR="$PROJECT_ROOT/src/hyperview/server/static" +# Build hyper-scatter library if it's a local checkout +if [ -d "$HYPER_SCATTER_DIR" ] && [ -f "$HYPER_SCATTER_DIR/package.json" ]; then + echo "Building hyper-scatter library..." + cd "$HYPER_SCATTER_DIR" + if [ ! -d "node_modules" ]; then + npm install + fi + npm run build:lib +fi + echo "Building frontend..." cd "$FRONTEND_DIR" # Install dependencies if needed if [ ! -d "node_modules" ]; then - echo "Installing dependencies..." + echo "Installing frontend dependencies..." npm install fi @@ -26,7 +37,9 @@ rm -rf "$STATIC_DIR" mkdir -p "$STATIC_DIR" cp -r out/* "$STATIC_DIR/" -echo "Frontend exported to $STATIC_DIR" +echo "" +echo "✅ Frontend exported to $STATIC_DIR" +echo "" echo "To test, run:" echo " cd $PROJECT_ROOT" echo " uv run hyperview demo" diff --git a/src/hyperview/api.py b/src/hyperview/api.py index 7017e6a..9f570d1 100644 --- a/src/hyperview/api.py +++ b/src/hyperview/api.py @@ -1,6 +1,7 @@ """Public API for HyperView.""" import json +import os import socket import threading import time @@ -158,11 +159,51 @@ def stop(self): self._server.should_exit = True def show(self, height: int = 800): - """Display the visualizer in a notebook.""" + """Display the visualizer in a notebook. + + In Google Colab, notebook kernels cannot be accessed via localhost. + Colab exposes kernel ports through a proxy URL (see + `google.colab.kernel.proxyPort`). This renders a link to the proxied URL + that opens in a new tab. + + In other notebook environments, it renders a clickable link to the local + URL and a best-effort JavaScript auto-open. + """ + if _is_colab(): + try: + from google.colab.output import eval_js # type: ignore[import-not-found] + from IPython.display import HTML, display + + proxy_url = eval_js(f"google.colab.kernel.proxyPort({self.port})") + app_url = str(proxy_url).rstrip("/") + "/" + + display( + HTML( + "

HyperView is running in Colab. " + f"" + "Open HyperView in a new tab.

" + ) + ) + display(HTML(f"

{app_url}

")) + return + except Exception: + # Fall through to the generic notebook behavior. + pass + + # Default: open in a new browser tab (works well for Jupyter). try: - from IPython.display import IFrame, display + from IPython.display import HTML, Javascript, display + + display( + HTML( + "

HyperView is running. " + f"Open in a new tab." + "

" + ) + ) - display(IFrame(self.url, width="100%", height=height)) + # Best-effort auto-open. Some browsers may block popups. + display(Javascript(f'window.open("{self.url}", "_blank");')) except ImportError: print(f"IPython not installed. Please visit {self.url} in your browser.") @@ -203,11 +244,22 @@ def launch( if notebook is None: notebook = _is_notebook() + if _is_colab() and host == "127.0.0.1": + # Colab port forwarding/proxying is most reliable when the server binds + # to all interfaces. + host = "0.0.0.0" + session = Session(dataset, host, port) if notebook: session.start(background=True) - print(f"\nHyperView is running at {session.url}") + if _is_colab(): + print( + f"\nHyperView is running (Colab, port={session.port}). " + "Use the link below to open it." + ) + else: + print(f"\nHyperView is running at {session.url}. Opening a new tab...") session.show(height=height) else: session.start(background=True) @@ -247,3 +299,15 @@ def _is_notebook() -> bool: return False # Other type (?) except (ImportError, NameError): return False # Probably standard Python interpreter + + +def _is_colab() -> bool: + """Check if running inside a Google Colab notebook runtime.""" + if os.environ.get("COLAB_RELEASE_TAG"): + return True + try: + import google.colab # type: ignore[import-not-found] + + return True + except Exception: + return False diff --git a/src/hyperview/server/static/404.html b/src/hyperview/server/static/404.html index 27fc3dd..8a12bc0 100644 --- a/src/hyperview/server/static/404.html +++ b/src/hyperview/server/static/404.html @@ -1 +1 @@ -404: This page could not be found.HyperView

404

This page could not be found.

\ No newline at end of file +404: This page could not be found.HyperView

404

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404

This page could not be found.

\ No newline at end of file +404: This page could not be found.HyperView

404

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document?document.currentScript:void 0,54214,e=>{"use strict";function t(e,t,i){if(t.length%2!=0)throw Error(`positions length must be even (got ${t.length})`);let r=t.length/2,o=i??new Uint16Array(r);if(o.length!==r)throw Error(`labels length must equal number of points (${r}), got ${o.length}`);return{n:r,positions:t,labels:o,geometry:e}}function i(e){let t=new Float32Array(2*e.length);for(let i=0;i65535)throw Error(`label index out of range at ${i}: ${r}`);t[i]=r}return t}e.s(["createDataset",()=>t,"packPositions",()=>i,"packPositionsXY",()=>r,"packUint16Labels",()=>o],15454),e.i(15454);let s=["#4e79a7","#f28e2c","#e15759","#76b7b2","#59a14f","#edc949","#af7aa1","#ff9da7","#9c755f","#bab0ab"],a="#ff0000",n="#ffffff";function l(e,t){return{kind:"indices",indices:e,computeTimeMs:t,has:t=>e.has(t)}}function h(e,t,i,r){return{kind:"geometry",geometry:e,computeTimeMs:i,has:i=>r(t[2*i],t[2*i+1],e.coords)}}function u(e,t,i,r,o){let 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x(e,t,i,r,o,s,a){let n=.45*Math.min(s,a)*e.displayZoom,l=(e,t,i=.95)=>{let r=e*e+t*t;if(r>i*i){let o=Math.sqrt(r);return{x:e/o*i,y:t/o*i}}return{x:e,y:t}},h=l((t-s/2)/n,-(i-a/2)/n),u=l((r-s/2)/n,-(o-a/2)/n),d=p(h.x,h.y,e.ax,e.ay),f=function(e,t,i,r){let o=i*e-r*t,s=i*t+r*e,a=o*o+s*s-1;if(1e-10>Math.abs(a))return{x:-i,y:-r};let n=e-i,l=r-t,h=(-(1+o)*n+s*l)/a,u=((1-o)*l-s*n)/a,d=h*h+u*u;if(d>=1){let e=Math.sqrt(d);return{x:h/e*.99,y:u/e*.99}}return{x:h,y:u}}(d.x,d.y,u.x,u.y);return{...e,ax:f.x,ay:f.y}}function g(e,t,i){let r=i.length/2;if(r<3)return!1;let o=!1;for(let s=0,a=r-1;sl||tu)return!1;let d=o-i,f=s-r,c=d*d+f*f;if(c<1e-9*1e-9)return 1e-9>=Math.sqrt((e-i)*(e-i)+(t-r)*(t-r));let p=Math.max(0,Math.min(1,((e-i)*d+(t-r)*f)/c)),m=i+p*d,x=r+p*f;return 1e-9>=Math.sqrt((e-m)*(e-m)+(t-x)*(t-x))}(e,t,r,n,l,h,1e-9))return!0;n>t!=h>t&&e<(l-r)*(t-n)/(h-n)+r&&(o=!o)}return o}function b(e){let t=1/0,i=1/0,r=-1/0,o=-1/0;for(let s=0;sr&&(r=a),no&&(o=n)}return 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3===i.length?[parseInt(i[0]+i[0],16)/255,parseInt(i[1]+i[1],16)/255,parseInt(i[2]+i[2],16)/255,1]:6===i.length||8===i.length?[parseInt(i.slice(0,2),16)/255,parseInt(i.slice(2,4),16)/255,parseInt(i.slice(4,6),16)/255,8===i.length?parseInt(i.slice(6,8),16)/255:1]:[1,1,1,1]}function y(e,t,i){let r=e.createShader(t);if(!r)throw Error("Failed to create shader");if(e.shaderSource(r,i),e.compileShader(r),!e.getShaderParameter(r,e.COMPILE_STATUS)){let t=e.getShaderInfoLog(r)??"unknown";throw e.deleteShader(r),Error(`Shader compile failed: ${t}`)}return r}function _(e,t,i){let r=y(e,e.VERTEX_SHADER,t),o=y(e,e.FRAGMENT_SHADER,i),s=e.createProgram();if(!s)throw Error("Failed to create program");if(e.attachShader(s,r),e.attachShader(s,o),e.linkProgram(s),e.deleteShader(r),e.deleteShader(o),!e.getProgramParameter(s,e.LINK_STATUS)){let t=e.getProgramInfoLog(s)??"unknown";throw e.deleteProgram(s),Error(`Program link failed: ${t}`)}return s}function v(e,t,i,r){e.width=Math.max(1,Math.floor(t*r)),e.height=Math.max(1,Math.floor(i*r)),e.style.width=`${t}px`,e.style.height=`${i}px`}let A=`#version 300 es +precision highp float; +precision highp int; + +flat in uint v_label; + +// Present in the vertex stage too; redeclare here so we can compute AA width. +uniform float u_dpr; +uniform float u_pointRadiusCss; + +uniform sampler2D u_paletteTex; +uniform int u_paletteSize; +uniform int u_paletteWidth; + +out vec4 outColor; + +void main() { + vec2 p = gl_PointCoord * 2.0 - 1.0; + // Anti-aliased circle: avoid harsh discard edges that can look like + // "weird polygons" at small sizes or without MSAA. + float r = length(p); + // Ensure at least ~1px transition (in point-local coordinates) so small + // points remain visually circular. + float radiusPx = max(u_pointRadiusCss * u_dpr, 1.0); + // Slightly wider than 1px helps circles stay round-looking when zoomed out + // (where points are perceptually tiny and aliasing is more obvious). + float aa = max(fwidth(r), 1.5 / radiusPx); + float alpha = 1.0 - smoothstep(1.0 - aa, 1.0 + aa, r); + if (alpha <= 0.0) discard; + + int size = max(u_paletteSize, 1); + int w = max(u_paletteWidth, 1); + int idx = int(v_label) % size; + int x = idx % w; + int y = idx / w; + vec4 c = texelFetch(u_paletteTex, ivec2(x, y), 0); + outColor = vec4(c.rgb, c.a * alpha); +} +`,S=`#version 300 es +precision highp float; +precision highp int; + +flat in uint v_label; + +uniform sampler2D u_paletteTex; +uniform int u_paletteSize; +uniform int u_paletteWidth; + +out vec4 outColor; + +void main() { + int size = max(u_paletteSize, 1); + int w = max(u_paletteWidth, 1); + int idx = int(v_label) % size; + int x = idx % w; + int y = idx / w; + outColor = texelFetch(u_paletteTex, ivec2(x, y), 0); +} +`,P=`#version 300 es +precision highp float; +precision highp int; + +uniform float u_dpr; +uniform float u_pointRadiusCss; + +uniform vec4 u_color; +uniform float u_pointSizePx; +uniform float u_ringThicknessPx; +uniform int u_ringMode; // 0 = solid, 1 = ring + +out vec4 outColor; + +void main() { + vec2 p = gl_PointCoord * 2.0 - 1.0; + float r = length(p); + float radiusPx = max(u_pointRadiusCss * u_dpr, 1.0); + float aa = max(fwidth(r), 1.5 / radiusPx); + float outer = 1.0 - smoothstep(1.0 - aa, 1.0 + aa, r); + if (outer <= 0.0) discard; + + float alpha = outer; + + if (u_ringMode == 1) { + float radiusPx = u_pointSizePx * 0.5; + float t = clamp(u_ringThicknessPx / max(radiusPx, 1e-6), 0.0, 1.0); + float inner = 1.0 - t; + // Keep only the outer ring with an anti-aliased inner boundary. + float innerMask = smoothstep(inner - aa, inner + aa, r); + alpha *= innerMask; + if (alpha <= 0.0) discard; + } + + outColor = vec4(u_color.rgb, u_color.a * alpha); +} +`,M=`#version 300 es +precision highp float; + +out vec2 v_uv; + +void main() { + // Fullscreen triangle + // (-1,-1), (3,-1), (-1,3) + if (gl_VertexID == 0) { + gl_Position = vec4(-1.0, -1.0, 0.0, 1.0); + v_uv = vec2(0.0, 0.0); + } else if (gl_VertexID == 1) { + gl_Position = vec4(3.0, -1.0, 0.0, 1.0); + v_uv = vec2(2.0, 0.0); + } else { + gl_Position = vec4(-1.0, 3.0, 0.0, 1.0); + v_uv = vec2(0.0, 2.0); + } +} +`,F=`#version 300 es +precision highp float; + +in vec2 v_uv; + +uniform sampler2D u_tex; + +out vec4 outColor; + +void main() { + vec2 uv = clamp(v_uv, 0.0, 1.0); + outColor = texture(u_tex, uv); +} +`,B=`#version 300 es +precision highp float; +precision highp int; + +uniform vec2 u_cssSize; +uniform float u_dpr; +uniform float u_displayZoom; + +uniform vec4 u_diskFillColor; +uniform vec4 u_diskBorderColor; +uniform vec4 u_gridColor; +uniform float u_diskBorderWidthPx; +uniform float u_gridWidthPx; + +out vec4 outColor; + +void main() { + // Convert framebuffer pixels to CSS pixels + vec2 fragCss = gl_FragCoord.xy / max(u_dpr, 1.0); + vec2 center = u_cssSize * 0.5; + + float diskRadius = min(u_cssSize.x, u_cssSize.y) * 0.45 * u_displayZoom; + vec2 p = fragCss - center; + float dist = length(p); + + // Reference-like styling + vec3 diskFill = u_diskFillColor.rgb; + vec3 diskBorder = u_diskBorderColor.rgb; + + float borderWidth = max(u_diskBorderWidthPx, 0.0); + float halfW = 0.5 * borderWidth; + + // Anti-aliasing width (CSS px). Keep at least 1px for crisp edges. + float aa = max(1.0, fwidth(dist)); + + // Discard outside disk+border region so the clear color remains intact. + if (dist > diskRadius + halfW + aa) discard; + + // Outer fade for anti-aliased boundary + float outerAlpha = 1.0 - smoothstep(diskRadius + halfW - aa, diskRadius + halfW + aa, dist); + + // Border mask + float borderInner = smoothstep(diskRadius - halfW - aa, diskRadius - halfW + aa, dist); + float borderOuter = 1.0 - smoothstep(diskRadius + halfW - aa, diskRadius + halfW + aa, dist); + float borderMask = clamp(borderInner * borderOuter, 0.0, 1.0); + + vec3 col = mix(diskFill, diskBorder, borderMask); + + // ------------------------------------------------------------------------ + // Reference-like hyperbolic grid overlay + // ------------------------------------------------------------------------ + // Matches HyperbolicReference.drawHyperbolicGrid(): + // - 8 radial lines (geodesics through origin) + // - 5 concentric circles + vec3 gridCol = u_gridColor.rgb; + float gridWidth = max(u_gridWidthPx, 0.0); + float halfGrid = 0.5 * gridWidth; + + // AA width for thin lines in CSS pixel space. + float aaLine = max(1.0, fwidth(dist)); + + float gridMask = 0.0; + + // Concentric circles (5) + for (int i = 1; i <= 5; i++) { + float r = (float(i) / 6.0) * diskRadius; + float d = abs(dist - r); + float m = 1.0 - smoothstep(halfGrid - aaLine, halfGrid + aaLine, d); + gridMask = max(gridMask, m); + } + + // Radial lines (8), angle = (i/8)*pi + // Distance to line through origin with direction (cos a, sin a): |cross(p, dir)| + for (int i = 0; i < 8; i++) { + float a = (float(i) / 8.0) * 3.141592653589793; + vec2 dir = vec2(cos(a), sin(a)); + float d = abs(p.x * dir.y - p.y * dir.x); + float m = 1.0 - smoothstep(halfGrid - aaLine, halfGrid + aaLine, d); + gridMask = max(gridMask, m); + } + + // Apply grid on top of disk fill/border. Use u_gridColor alpha as intensity. + col = mix(col, gridCol, clamp(gridMask, 0.0, 1.0) * clamp(u_gridColor.a, 0.0, 1.0)); + outColor = vec4(col, outerAlpha); +} +`,D=`#version 300 es +precision highp float; +precision highp int; + +layout(location = 0) in vec2 a_pos; +layout(location = 1) in uint a_label; + +uniform vec2 u_center; +uniform vec2 u_cssSize; +uniform float u_zoom; +uniform float u_dpr; +uniform float u_pointRadiusCss; + +flat out uint v_label; + +void main() { + float baseScale = min(u_cssSize.x, u_cssSize.y) * 0.4 * u_zoom; + float sx = u_cssSize.x * 0.5 + (a_pos.x - u_center.x) * baseScale; + float sy = u_cssSize.y * 0.5 - (a_pos.y - u_center.y) * baseScale; + + vec2 dbufSize = u_cssSize * u_dpr; + vec2 dbuf = vec2(sx, sy) * u_dpr; + + float cx = (dbuf.x / dbufSize.x) * 2.0 - 1.0; + float cy = 1.0 - (dbuf.y / dbufSize.y) * 2.0; + + gl_Position = vec4(cx, cy, 0.0, 1.0); + gl_PointSize = (u_pointRadiusCss * 2.0) * u_dpr; + v_label = a_label; +} +`,C=`#version 300 es +precision highp float; +precision highp int; + +layout(location = 0) in vec2 a_pos; +layout(location = 1) in uint a_label; + +uniform vec2 u_cssSize; +uniform float u_dpr; +uniform float u_pointRadiusCss; + +uniform vec2 u_a; // camera translation (ax, ay) +uniform float u_displayZoom; // visual zoom + +flat out uint v_label; + +vec2 mobiusTransform(vec2 z, vec2 a) { + // (z - a) / (1 - conj(a) * z) + vec2 num = z - a; + + // denom = 1 - (ax*zx + ay*zy) + i * (-(ax*zy - ay*zx)) + float denomX = 1.0 - (a.x * z.x + a.y * z.y); + float denomY = -(a.x * z.y - a.y * z.x); + float denomNormSq = denomX * denomX + denomY * denomY; + if (denomNormSq < 1e-12) { + // Push outside clip + return vec2(2.0, 2.0); + } + + // complex division + float rx = (num.x * denomX + num.y * denomY) / denomNormSq; + float ry = (num.y * denomX - num.x * denomY) / denomNormSq; + return vec2(rx, ry); +} + +void main() { + vec2 w = mobiusTransform(a_pos, u_a); + float r2 = dot(w, w); + if (r2 >= 1.0) { + gl_Position = vec4(2.0, 2.0, 0.0, 1.0); + gl_PointSize = 0.0; + v_label = a_label; + return; + } + + float diskRadius = min(u_cssSize.x, u_cssSize.y) * 0.45 * u_displayZoom; + float sx = u_cssSize.x * 0.5 + w.x * diskRadius; + float sy = u_cssSize.y * 0.5 - w.y * diskRadius; + + vec2 dbufSize = u_cssSize * u_dpr; + vec2 dbuf = vec2(sx, sy) * u_dpr; + + float cx = (dbuf.x / dbufSize.x) * 2.0 - 1.0; + float cy = 1.0 - (dbuf.y / dbufSize.y) * 2.0; + + gl_Position = vec4(cx, cy, 0.0, 1.0); + gl_PointSize = (u_pointRadiusCss * 2.0) * u_dpr; + v_label = a_label; +} +`;class U{canvas=null;width=0;height=0;deviceDpr=1;canvasDpr=1;dpr=1;dataset=null;selection=new Set;hoveredIndex=-1;pointRadiusCss=3;colors=s;backgroundColor="#0a0a0a";poincareDiskFillColor="#141414";poincareDiskBorderColor="#666666";poincareGridColor="#66666633";poincareDiskBorderWidthPx=2;poincareGridWidthPx=.5;paletteSize=0;paletteDirty=!0;paletteTex=null;paletteTexW=0;paletteTexH=0;paletteBytes=new Uint8Array(0);paletteTexUnit=1;scratchIds=[];hoverPosScratch=new Float32Array(2);hoverLabScratch=new Uint16Array(1);hoverIndexScratch=new Uint32Array(1);lastViewChangeTs=0;markViewChanged(){this.lastViewChangeTs=performance.now()}endInteraction(){this.lastViewChangeTs=0}markBackdropDirty(){this.backdropDirty=!0}uploadPoincareDiskStyleUniforms(){let e=this.gl,t=this.poincareDisk;if(!e||!t)return;let i=T(this.poincareDiskFillColor),r=T(this.poincareDiskBorderColor),o=T(this.poincareGridColor);t.uDiskFillColor&&e.uniform4f(t.uDiskFillColor,i[0],i[1],i[2],i[3]),t.uDiskBorderColor&&e.uniform4f(t.uDiskBorderColor,r[0],r[1],r[2],r[3]),t.uGridColor&&e.uniform4f(t.uGridColor,o[0],o[1],o[2],o[3]),t.uDiskBorderWidthPx&&e.uniform1f(t.uDiskBorderWidthPx,this.poincareDiskBorderWidthPx),t.uGridWidthPx&&e.uniform1f(t.uGridWidthPx,this.poincareGridWidthPx)}getBackdropZoom(){return 1}dataIndex=null;gl=null;vao=null;posBuffer=null;labelBuffer=null;hoverVao=null;hoverPosBuffer=null;hoverLabelBuffer=null;selectionVao=null;selectionPosBuffer=null;selectionLabelBuffer=null;selectionOverlayCount=0;selectionEbo=null;hoverEbo=null;interactionEbo=null;interactionCount=0;maxBaseDrawPoints=4e6;maxGpuUploadPoints=1e7;gpuUsesFullDataset=!0;gpuPointCount=0;policy={fragmentBudget:1e8,circleBudget:6e7,squareOnRatio:1,squareOffRatio:.75,minPointsDpr:.35};renderAsSquares=!1;__debugPolicy=null;backdropTex=null;backdropFbo=null;backdropW=0;backdropH=0;backdropDpr=1;backdropZoom=NaN;backdropDirty=!0;pointsTex=null;pointsFbo=null;pointsW=0;pointsH=0;programComposite=null;uCompositeTex=null;poincareDisk=null;pointsCircle=null;pointsSquare=null;programSolid=null;uSolidColor=null;uSolidPointSizePx=null;uSolidRingThicknessPx=null;uSolidRingMode=null;uCssSizeSolid=null;uDprSolid=null;uPointRadiusSolid=null;selectionDirty=!0;hoverDirty=!0;init(e,t){this.canvas=e,this.width=t.width,this.height=t.height,this.deviceDpr=t.devicePixelRatio??window.devicePixelRatio??1,this.canvasDpr=this.deviceDpr,this.dpr=this.deviceDpr;let i="string"==typeof t.poincareDiskFillColor;t.backgroundColor&&(this.backgroundColor=t.backgroundColor),t.pointRadius&&(this.pointRadiusCss=t.pointRadius),t.colors&&(this.colors=t.colors),this.poincareDiskFillColor=i?t.poincareDiskFillColor:this.poincareDiskFillColor,t.poincareDiskBorderColor&&(this.poincareDiskBorderColor=t.poincareDiskBorderColor),t.poincareGridColor&&(this.poincareGridColor=t.poincareGridColor),"number"==typeof t.poincareDiskBorderWidthPx&&Number.isFinite(t.poincareDiskBorderWidthPx)&&(this.poincareDiskBorderWidthPx=Math.max(0,t.poincareDiskBorderWidthPx)),"number"==typeof t.poincareGridWidthPx&&Number.isFinite(t.poincareGridWidthPx)&&(this.poincareGridWidthPx=Math.max(0,t.poincareGridWidthPx)),this.paletteDirty=!0}chooseRenderDpr(e){let t=this.deviceDpr,i=Math.max(1,this.width)*Math.max(1,this.height),r=e>this.maxBaseDrawPoints?this.estimateSubsampleCount(e):e,o=Math.sqrt((e>=1e6?i>1e6?2e5:5e5:e>=5e5?14e5:e>=25e4?21e5:8e6)/i),s=Math.max(.5,this.pointRadiusCss),a=Math.max(1,r)*Math.PI*s*s,n=Math.sqrt(this.policy.fragmentBudget/a),l=e>=1e6?1:e>=5e5?1.25:1.5;return Math.max(e>=1e6?this.policy.minPointsDpr:e>=5e5?.75:1,Math.min(t,l,o,n))}estimateSubsampleCount(e){if(e<5e5)return e;let t=Math.min(e,Math.max(25e4,Math.min(this.maxBaseDrawPoints,Math.floor(.25*e)))),i=Math.max(1,Math.floor(e/t));return Math.min(t,Math.ceil(e/i))}estimatePointFragments(e,t){let i=Math.max(.5,this.pointRadiusCss),r=Math.max(0,t);return Math.max(0,e)*Math.PI*i*i*r*r}updateSquarePointPolicy(e){let t=this.policy.circleBudget*this.policy.squareOnRatio,i=this.policy.circleBudget*this.policy.squareOffRatio;if(this.dpr<=.75){this.renderAsSquares=!0;return}this.renderAsSquares?e<=i&&(this.renderAsSquares=!1):e>=t&&(this.renderAsSquares=!0)}setDataset(e){this.dataset=e,this.selection=new Set,this.hoveredIndex=-1,this.selectionDirty=!0,this.hoverDirty=!0;let t=this.chooseRenderDpr(e.n);t!==this.dpr&&(this.dpr=t),this.dataIndex=new R(e.positions,void 0,64),this.gl&&this.uploadDatasetToGPU(),this.markBackdropDirty()}resize(e,t){this.width=e,this.height=t,this.gl&&this.canvas&&(v(this.canvas,e,t,this.canvasDpr),this.gl.viewport(0,0,this.canvas.width,this.canvas.height),this.markBackdropDirty())}setSelection(e){let t=e.size;this.selection=t<=2e5?new Set(e):e,this.selectionDirty=!0,this.gl&&this.uploadSelectionToGPU()}getSelection(){return this.selection.size<=2e5?new Set(this.selection):this.selection}setHovered(e){this.hoveredIndex=e,this.hoverDirty=!0,this.gl&&this.uploadHoverToGPU()}destroy(){let e=this.gl;e&&(this.pointsCircle&&e.deleteProgram(this.pointsCircle.program),this.pointsSquare&&e.deleteProgram(this.pointsSquare.program),this.programSolid&&e.deleteProgram(this.programSolid),this.poincareDisk&&e.deleteProgram(this.poincareDisk.program),this.vao&&e.deleteVertexArray(this.vao),this.hoverVao&&e.deleteVertexArray(this.hoverVao),this.selectionVao&&e.deleteVertexArray(this.selectionVao),this.posBuffer&&e.deleteBuffer(this.posBuffer),this.labelBuffer&&e.deleteBuffer(this.labelBuffer),this.hoverPosBuffer&&e.deleteBuffer(this.hoverPosBuffer),this.hoverLabelBuffer&&e.deleteBuffer(this.hoverLabelBuffer),this.selectionPosBuffer&&e.deleteBuffer(this.selectionPosBuffer),this.selectionLabelBuffer&&e.deleteBuffer(this.selectionLabelBuffer),this.selectionEbo&&e.deleteBuffer(this.selectionEbo),this.hoverEbo&&e.deleteBuffer(this.hoverEbo),this.interactionEbo&&e.deleteBuffer(this.interactionEbo),this.backdropFbo&&e.deleteFramebuffer(this.backdropFbo),this.backdropTex&&e.deleteTexture(this.backdropTex),this.pointsFbo&&e.deleteFramebuffer(this.pointsFbo),this.pointsTex&&e.deleteTexture(this.pointsTex),this.paletteTex&&e.deleteTexture(this.paletteTex),this.programComposite&&e.deleteProgram(this.programComposite)),this.gl=null,this.vao=null,this.hoverVao=null,this.selectionVao=null,this.posBuffer=null,this.labelBuffer=null,this.hoverPosBuffer=null,this.hoverLabelBuffer=null,this.selectionPosBuffer=null,this.selectionLabelBuffer=null,this.selectionOverlayCount=0,this.selectionEbo=null,this.hoverEbo=null,this.interactionEbo=null,this.interactionCount=0,this.gpuUsesFullDataset=!0,this.gpuPointCount=0,this.backdropFbo=null,this.backdropTex=null,this.backdropW=0,this.backdropH=0,this.backdropDpr=1,this.backdropZoom=NaN,this.backdropDirty=!0,this.pointsFbo=null,this.pointsTex=null,this.pointsW=0,this.pointsH=0,this.programComposite=null,this.uCompositeTex=null,this.pointsCircle=null,this.pointsSquare=null,this.programSolid=null,this.poincareDisk=null,this.paletteTex=null,this.paletteTexW=0,this.paletteTexH=0,this.paletteSize=0,this.paletteDirty=!0}uploadPaletteUniforms(){let e=this.gl;if(!e)return;let t=this.colors.length,i=Math.max(1,Math.min(t,65536)),r=e.getParameter(e.MAX_TEXTURE_SIZE),o=Math.min(r,i),s=Math.ceil(i/o);if(s>r)throw Error(`Palette too large for WebGL texture: size=${i}, maxTex=${r}`);let a=o*s;if(this.paletteBytes.length!==4*a?this.paletteBytes=new Uint8Array(4*a):this.paletteBytes.fill(0),0===t)this.paletteBytes[0]=255,this.paletteBytes[1]=255,this.paletteBytes[2]=255,this.paletteBytes[3]=255;else for(let e=0;e{t&&(e.useProgram(t.program),t.uPaletteTex&&e.uniform1i(t.uPaletteTex,this.paletteTexUnit),t.uPaletteSize&&e.uniform1i(t.uPaletteSize,this.paletteSize),t.uPaletteWidth&&e.uniform1i(t.uPaletteWidth,this.paletteTexW))};n(this.pointsCircle),n(this.pointsSquare),this.paletteDirty=!1}bindPaletteTexture(){let e=this.gl;e&&this.paletteTex&&(e.activeTexture(e.TEXTURE0+this.paletteTexUnit),e.bindTexture(e.TEXTURE_2D,this.paletteTex),e.activeTexture(e.TEXTURE0))}async countSelection(e,t={}){let i=this.dataset,r=this.dataIndex;if(!i||!r)return 0;if(e.indices)return e.indices.size;if("geometry"!==e.kind||!e.geometry)return 0;let o=e.geometry.coords;if(o.length/2<3)return 0;let s=e.geometry.bounds;if(!s){let e=1/0,t=1/0,i=-1/0,r=-1/0;for(let s=0;si&&(i=a),nr&&(r=n)}s={xMin:e,yMin:t,xMax:i,yMax:r}}let a=t.shouldCancel,n=t.onProgress,l="number"==typeof t.yieldEveryMs&&Number.isFinite(t.yieldEveryMs)?Math.max(0,t.yieldEveryMs):8,h=s.xMin-1e-12,u=s.yMin-1e-12,d=s.xMax+1e-12,f=s.yMax+1e-12,c=(e,t,i)=>ei?i:0|e,p=c(Math.floor((h-r.bounds.minX)/r.cellSizeX),0,r.cellsX-1),m=c(Math.floor((u-r.bounds.minY)/r.cellSizeY),0,r.cellsY-1),x=c(Math.floor((d-r.bounds.minX)/r.cellSizeX),0,r.cellsX-1),b=c(Math.floor((f-r.bounds.minY)/r.cellSizeY),0,r.cellsY-1),E=i.positions,R=r.ids,T=r.offsets,y=0,_=0,v=16384,A=l>0?performance.now():0;for(let e=m;e<=b;e++){let t=e*r.cellsX;for(let e=p;e<=x;e++){let i=t+e,r=T[i],h=T[i+1];for(let e=r;es.xMax)&&!(rs.yMax)&&(g(i,r,o)&&y++,_++,l>0&&_>=v)){if(v=_+16384,a?.())return y;performance.now()-A>=l&&(n?.(y,_),await new Promise(e=>requestAnimationFrame(()=>e())),A=performance.now())}}}}return n?.(y,_),y}ensureGL(){if(this.gl)return;if(!this.canvas)throw Error("Renderer not initialized");v(this.canvas,this.width,this.height,this.canvasDpr);let e=this.canvas.getContext("webgl2",{antialias:!1,alpha:!1,depth:!1,stencil:!1,preserveDrawingBuffer:!1,premultipliedAlpha:!1,desynchronized:!0});if(!e)throw Error("Failed to get WebGL2 context (is the canvas already using 2D context?)");this.gl=e,e.disable(e.DEPTH_TEST),e.disable(e.CULL_FACE),e.enable(e.BLEND),e.blendFunc(e.SRC_ALPHA,e.ONE_MINUS_SRC_ALPHA);let[t,i,r,o]=T(this.backgroundColor);e.clearColor(t,i,r,o),e.viewport(0,0,this.canvas.width,this.canvas.height),this.createProgramsAndBuffers(),this.uploadPaletteUniforms(),this.dataset&&this.uploadDatasetToGPU(),this.uploadSelectionToGPU(),this.uploadHoverToGPU(),this.markBackdropDirty()}ensurePointsResources(){if(!this.gl||!this.canvas)return;let e=this.gl,t=Math.max(1,Math.floor(this.width*this.dpr)),i=Math.max(1,Math.floor(this.height*this.dpr)),r=e.getParameter(e.MAX_TEXTURE_SIZE);if(t>r||i>r){let e=Math.min(1,r/t,r/i);t=Math.max(1,Math.floor(t*e)),i=Math.max(1,Math.floor(i*e))}if(!this.pointsTex){if(this.pointsTex=e.createTexture(),!this.pointsTex)throw Error("Failed to create points texture");e.bindTexture(e.TEXTURE_2D,this.pointsTex),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE),e.bindTexture(e.TEXTURE_2D,null)}if(!this.pointsFbo&&(this.pointsFbo=e.createFramebuffer(),!this.pointsFbo))throw Error("Failed to create points framebuffer");if(t!==this.pointsW||i!==this.pointsH){this.pointsW=t,this.pointsH=i,e.bindTexture(e.TEXTURE_2D,this.pointsTex),e.texImage2D(e.TEXTURE_2D,0,e.RGBA,t,i,0,e.RGBA,e.UNSIGNED_BYTE,null),e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,this.pointsFbo),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,this.pointsTex,0);let r=e.checkFramebufferStatus(e.FRAMEBUFFER);if(e.bindFramebuffer(e.FRAMEBUFFER,null),r!==e.FRAMEBUFFER_COMPLETE)throw Error(`Points framebuffer incomplete: ${r}`)}}ensureBackdropResources(){if(!this.gl||!this.canvas||"poincare"!==this.geometryKind()||!this.poincareDisk||!this.vao)return;let e=this.gl,t=Math.max(1,this.canvasDpr),i=Math.max(1,Math.floor(this.width*t)),r=Math.max(1,Math.floor(this.height*t)),o=e.getParameter(e.MAX_TEXTURE_SIZE);if(i>o||r>o){let e=Math.min(1,o/i,o/r);i=Math.max(1,Math.floor(i*e)),r=Math.max(1,Math.floor(r*e)),this.backdropDpr=t*e}else this.backdropDpr=t;if(!this.backdropTex){if(this.backdropTex=e.createTexture(),!this.backdropTex)throw Error("Failed to create backdrop texture");e.bindTexture(e.TEXTURE_2D,this.backdropTex),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.NEAREST),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.NEAREST),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE),e.bindTexture(e.TEXTURE_2D,null)}if(!this.backdropFbo&&(this.backdropFbo=e.createFramebuffer(),!this.backdropFbo))throw Error("Failed to create backdrop framebuffer");if(i!==this.backdropW||r!==this.backdropH){this.backdropW=i,this.backdropH=r,e.bindTexture(e.TEXTURE_2D,this.backdropTex),e.texImage2D(e.TEXTURE_2D,0,e.RGBA,i,r,0,e.RGBA,e.UNSIGNED_BYTE,null),e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,this.backdropFbo),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,this.backdropTex,0);let t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(e.bindFramebuffer(e.FRAMEBUFFER,null),t!==e.FRAMEBUFFER_COMPLETE)throw Error(`Backdrop framebuffer incomplete: ${t}`);this.markBackdropDirty()}}renderBackdropIfNeeded(){if(!this.gl||!this.canvas||"poincare"!==this.geometryKind()||!this.poincareDisk||!this.vao||(this.ensureBackdropResources(),!this.backdropFbo))return;let e=this.getBackdropZoom(),t=Number.isFinite(this.backdropZoom)&&1e-12>=Math.abs(this.backdropZoom-e);if(!this.backdropDirty&&t)return;let i=this.gl;i.bindFramebuffer(i.FRAMEBUFFER,this.backdropFbo),i.viewport(0,0,this.backdropW,this.backdropH);let[r,o,s,a]=T(this.backgroundColor);i.clearColor(r,o,s,a),i.clear(i.COLOR_BUFFER_BIT),i.useProgram(this.poincareDisk.program),this.bindViewUniformsForProgram(this.poincareDisk.program),this.uploadPoincareDiskStyleUniforms(),this.poincareDisk.uCssSize&&i.uniform2f(this.poincareDisk.uCssSize,this.width,this.height),this.poincareDisk.uDpr&&i.uniform1f(this.poincareDisk.uDpr,this.backdropDpr),i.bindVertexArray(this.vao),i.drawArrays(i.TRIANGLES,0,3),i.bindFramebuffer(i.FRAMEBUFFER,null),i.viewport(0,0,this.canvas.width,this.canvas.height),this.backdropZoom=e,this.backdropDirty=!1}createProgramsAndBuffers(){let e=this.gl,t="euclidean"===this.geometryKind()?D:C,i=_(e,t,A),r=_(e,t,S);if(this.programSolid=_(e,t,P),this.programComposite=_(e,M,F),e.useProgram(this.programComposite),this.uCompositeTex=e.getUniformLocation(this.programComposite,"u_tex"),"poincare"===this.geometryKind()){let t=_(e,M,B);this.poincareDisk={program:t,uCssSize:e.getUniformLocation(t,"u_cssSize"),uDpr:e.getUniformLocation(t,"u_dpr"),uDiskFillColor:e.getUniformLocation(t,"u_diskFillColor"),uDiskBorderColor:e.getUniformLocation(t,"u_diskBorderColor"),uGridColor:e.getUniformLocation(t,"u_gridColor"),uDiskBorderWidthPx:e.getUniformLocation(t,"u_diskBorderWidthPx"),uGridWidthPx:e.getUniformLocation(t,"u_gridWidthPx")},e.useProgram(t),this.uploadPoincareDiskStyleUniforms()}if(e.useProgram(i),this.pointsCircle={program:i,uPaletteTex:e.getUniformLocation(i,"u_paletteTex"),uPaletteSize:e.getUniformLocation(i,"u_paletteSize"),uPaletteWidth:e.getUniformLocation(i,"u_paletteWidth"),uCssSize:e.getUniformLocation(i,"u_cssSize"),uDpr:e.getUniformLocation(i,"u_dpr"),uPointRadius:e.getUniformLocation(i,"u_pointRadiusCss")},e.useProgram(r),this.pointsSquare={program:r,uPaletteTex:e.getUniformLocation(r,"u_paletteTex"),uPaletteSize:e.getUniformLocation(r,"u_paletteSize"),uPaletteWidth:e.getUniformLocation(r,"u_paletteWidth"),uCssSize:e.getUniformLocation(r,"u_cssSize"),uDpr:e.getUniformLocation(r,"u_dpr"),uPointRadius:e.getUniformLocation(r,"u_pointRadiusCss")},e.useProgram(this.programSolid),this.uSolidColor=e.getUniformLocation(this.programSolid,"u_color"),this.uSolidPointSizePx=e.getUniformLocation(this.programSolid,"u_pointSizePx"),this.uSolidRingThicknessPx=e.getUniformLocation(this.programSolid,"u_ringThicknessPx"),this.uSolidRingMode=e.getUniformLocation(this.programSolid,"u_ringMode"),this.uCssSizeSolid=e.getUniformLocation(this.programSolid,"u_cssSize"),this.uDprSolid=e.getUniformLocation(this.programSolid,"u_dpr"),this.uPointRadiusSolid=e.getUniformLocation(this.programSolid,"u_pointRadiusCss"),this.vao=e.createVertexArray(),this.posBuffer=e.createBuffer(),this.labelBuffer=e.createBuffer(),this.hoverVao=e.createVertexArray(),this.hoverPosBuffer=e.createBuffer(),this.hoverLabelBuffer=e.createBuffer(),this.selectionVao=e.createVertexArray(),this.selectionPosBuffer=e.createBuffer(),this.selectionLabelBuffer=e.createBuffer(),this.selectionEbo=e.createBuffer(),this.hoverEbo=e.createBuffer(),this.interactionEbo=e.createBuffer(),!this.vao||!this.posBuffer||!this.labelBuffer||!this.hoverVao||!this.hoverPosBuffer||!this.hoverLabelBuffer||!this.selectionVao||!this.selectionPosBuffer||!this.selectionLabelBuffer||!this.selectionEbo||!this.hoverEbo||!this.interactionEbo)throw Error("Failed to allocate WebGL resources");e.bindVertexArray(this.vao),e.bindBuffer(e.ARRAY_BUFFER,this.posBuffer),e.enableVertexAttribArray(0),e.vertexAttribPointer(0,2,e.FLOAT,!1,0,0),e.bindBuffer(e.ARRAY_BUFFER,this.labelBuffer),e.enableVertexAttribArray(1),e.vertexAttribIPointer(1,1,e.UNSIGNED_SHORT,0,0),e.bindVertexArray(null),e.bindBuffer(e.ARRAY_BUFFER,null),e.bindVertexArray(this.hoverVao),e.bindBuffer(e.ARRAY_BUFFER,this.hoverPosBuffer),e.enableVertexAttribArray(0),e.vertexAttribPointer(0,2,e.FLOAT,!1,0,0),e.bindBuffer(e.ARRAY_BUFFER,this.hoverLabelBuffer),e.enableVertexAttribArray(1),e.vertexAttribIPointer(1,1,e.UNSIGNED_SHORT,0,0),e.bindVertexArray(null),e.bindBuffer(e.ARRAY_BUFFER,null),e.bindVertexArray(this.selectionVao),e.bindBuffer(e.ARRAY_BUFFER,this.selectionPosBuffer),e.enableVertexAttribArray(0),e.vertexAttribPointer(0,2,e.FLOAT,!1,0,0),e.bindBuffer(e.ARRAY_BUFFER,this.selectionLabelBuffer),e.enableVertexAttribArray(1),e.vertexAttribIPointer(1,1,e.UNSIGNED_SHORT,0,0),e.bindVertexArray(null),e.bindBuffer(e.ARRAY_BUFFER,null)}uploadDatasetToGPU(){let e=this.gl,t=this.dataset;if(!t)return;e.bindVertexArray(this.vao);let i=t.n<=this.maxGpuUploadPoints;if(this.gpuUsesFullDataset=i,i)e.bindBuffer(e.ARRAY_BUFFER,this.posBuffer),e.bufferData(e.ARRAY_BUFFER,t.positions,e.STATIC_DRAW),e.bindBuffer(e.ARRAY_BUFFER,this.labelBuffer),e.bufferData(e.ARRAY_BUFFER,t.labels,e.STATIC_DRAW),this.gpuPointCount=t.n;else{let i=t.n,r=Math.min(i,Math.max(25e4,Math.min(this.maxBaseDrawPoints,Math.floor(.25*i)))),o=Math.max(1,Math.floor(i/r)),s=Math.min(r,Math.ceil(i/o)),a=new Float32Array(2*s),n=new Uint16Array(s),l=0;for(let e=0;e=5e5){let t=Math.min(i,Math.max(25e4,Math.min(this.maxBaseDrawPoints,Math.floor(.25*i)))),r=Math.max(1,Math.floor(i/t)),o=Math.min(t,Math.ceil(i/r)),s=new Uint32Array(o),a=0;for(let e=0;e=r)break;this.selectionOverlayCount=a,e.bindVertexArray(this.selectionVao),e.bindBuffer(e.ARRAY_BUFFER,this.selectionPosBuffer),e.bufferData(e.ARRAY_BUFFER,o,e.DYNAMIC_DRAW),e.bindBuffer(e.ARRAY_BUFFER,this.selectionLabelBuffer),e.bufferData(e.ARRAY_BUFFER,s,e.DYNAMIC_DRAW),e.bindVertexArray(null),e.bindBuffer(e.ARRAY_BUFFER,null),this.selectionDirty=!1;return}let t=Math.min(this.selection.size,25e4);if(this.selectionOverlayCount=t,0===t){this.selectionDirty=!1;return}let i=new Uint32Array(t),r=0;for(let e of this.selection)if(i[r++]=e,r>=t)break;this.selectionOverlayCount=r,e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,this.selectionEbo),e.bufferData(e.ELEMENT_ARRAY_BUFFER,i,e.DYNAMIC_DRAW),e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null),this.selectionDirty=!1}uploadHoverToGPU(){if(!this.gl||!this.hoverEbo)return;let e=this.gl;if(!this.gpuUsesFullDataset){let t=this.dataset;if(!t||!this.hoverVao||!this.hoverPosBuffer||!this.hoverLabelBuffer)return;let i=this.hoveredIndex>=0&&this.hoveredIndex=0?(r[0]=t.positions[2*i],r[1]=t.positions[2*i+1],o[0]=t.labels[i]):(r[0]=2,r[1]=2,o[0]=0),e.bindVertexArray(this.hoverVao),e.bindBuffer(e.ARRAY_BUFFER,this.hoverPosBuffer),e.bufferData(e.ARRAY_BUFFER,r,e.DYNAMIC_DRAW),e.bindBuffer(e.ARRAY_BUFFER,this.hoverLabelBuffer),e.bufferData(e.ARRAY_BUFFER,o,e.DYNAMIC_DRAW),e.bindVertexArray(null),e.bindBuffer(e.ARRAY_BUFFER,null),this.hoverDirty=!1;return}let t=this.hoveredIndex>=0?this.hoveredIndex:0,i=this.hoverIndexScratch;i[0]=t,e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,this.hoverEbo),e.bufferData(e.ELEMENT_ARRAY_BUFFER,i,e.DYNAMIC_DRAW),e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null),this.hoverDirty=!1}render(){this.ensureGL();let e=this.gl,t=this.dataset;if(!t)return;let i=performance.now()-this.lastViewChangeTs<80,r=!!this.interactionEbo&&this.interactionCount>0,o=i&&"poincare"===this.geometryKind()&&t.n>=2e6&&r,s=t.n>this.maxBaseDrawPoints&&r,l=this.gpuUsesFullDataset&&(o||s),h=this.gpuUsesFullDataset?l?this.interactionCount:t.n:this.gpuPointCount,u=this.estimatePointFragments(h,this.dpr);if(this.updateSquarePointPolicy(u),this.selectionDirty&&this.uploadSelectionToGPU(),this.hoverDirty&&this.uploadHoverToGPU(),e.bindFramebuffer(e.FRAMEBUFFER,null),e.viewport(0,0,this.canvas.width,this.canvas.height),e.disable(e.BLEND),"poincare"===this.geometryKind())if(this.renderBackdropIfNeeded(),this.backdropTex&&this.programComposite)e.useProgram(this.programComposite),e.activeTexture(e.TEXTURE0),e.bindTexture(e.TEXTURE_2D,this.backdropTex),this.uCompositeTex&&e.uniform1i(this.uCompositeTex,0),e.bindVertexArray(this.vao),e.drawArrays(e.TRIANGLES,0,3),e.bindTexture(e.TEXTURE_2D,null);else{let[t,i,r,o]=T(this.backgroundColor);e.clearColor(t,i,r,o),e.clear(e.COLOR_BUFFER_BIT)}else{let[t,i,r,o]=T(this.backgroundColor);e.clearColor(t,i,r,o),e.clear(e.COLOR_BUFFER_BIT)}if(this.ensurePointsResources(),!this.pointsFbo||!this.pointsTex||!this.programComposite)return;e.bindFramebuffer(e.FRAMEBUFFER,this.pointsFbo),e.viewport(0,0,this.pointsW,this.pointsH),e.clearColor(0,0,0,0),e.clear(e.COLOR_BUFFER_BIT);let 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c:[["$","meta","0",{"charSet":"utf-8"}],["$","meta","1",{"name":"viewport","content":"width=device-width, initial-scale=1"}]] From 8139630881d2502a8ea2255a1aee260fabc4410a Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Fri, 16 Jan 2026 17:37:41 +0100 Subject: [PATCH 19/75] fix(colab): update git clone command and improve instructions for opening HyperView --- notebooks/colab_smoke_test.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/notebooks/colab_smoke_test.ipynb b/notebooks/colab_smoke_test.ipynb index 85a1ce0..f9e483b 100644 --- a/notebooks/colab_smoke_test.ipynb +++ b/notebooks/colab_smoke_test.ipynb @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "945a1017", "metadata": {}, "outputs": [ @@ -38,7 +38,7 @@ ], "source": [ "!pip install -q uv\n", - "!git clone https://github.com/HackerRoomAI/HyperView.git\n", + "!git clone --depth 1 --branch colab-fix --single-branch https://github.com/HackerRoomAI/HyperView.git\n", "%cd HyperView\n", "!uv pip install -e ." ] @@ -100,7 +100,7 @@ "id": "b1cb3148", "metadata": {}, "source": [ - "If your browser blocks popups, click the button again or use the inline fallback shown in the cell output." + "Click the link printed above to open HyperView in a new tab. If needed, copy/paste the URL." ] } ], From 00d1d38efa61ec51cdfa8685a0976a3b5d34737a Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Fri, 16 Jan 2026 17:53:32 +0100 Subject: [PATCH 20/75] fix: detect Colab as notebook environment for proper proxy URL display --- src/hyperview/api.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/hyperview/api.py b/src/hyperview/api.py index 9f570d1..dadcf53 100644 --- a/src/hyperview/api.py +++ b/src/hyperview/api.py @@ -242,7 +242,8 @@ def launch( >>> hv.launch(dataset) """ if notebook is None: - notebook = _is_notebook() + # Colab is always a notebook environment, even if _is_notebook() fails to detect it + notebook = _is_notebook() or _is_colab() if _is_colab() and host == "127.0.0.1": # Colab port forwarding/proxying is most reliable when the server binds From cc2195db4d66ea1d3d26021c321be359d3ef9eb5 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Fri, 16 Jan 2026 18:02:57 +0100 Subject: [PATCH 21/75] fix: remove accidentally committed HyperView clone in notebooks --- notebooks/HyperView | 1 - 1 file changed, 1 deletion(-) delete mode 160000 notebooks/HyperView diff --git a/notebooks/HyperView b/notebooks/HyperView deleted file mode 160000 index 490b90c..0000000 --- a/notebooks/HyperView +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 490b90cb51e83d8cafd90de9fd437cac9aa38d05 From 43666b932ce090c854cdcd8fcb57a74469567b98 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Fri, 16 Jan 2026 18:03:27 +0100 Subject: [PATCH 22/75] cleanup: simplify Colab install to single pip command, remove debug code --- docs/colab.md | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/docs/colab.md b/docs/colab.md index ed5ac25..392cd0d 100644 --- a/docs/colab.md +++ b/docs/colab.md @@ -11,10 +11,7 @@ This smoke test creates a tiny synthetic dataset with random images + precompute ### 1) Install HyperView ```bash -!pip install -q uv -!git clone https://github.com/HackerRoomAI/HyperView.git -%cd HyperView -!uv pip install -e . +!pip install -q git+https://github.com/HackerRoomAI/HyperView.git ``` ### 2) Create a synthetic dataset and launch From eedc724e861f909e60c2273220c7542267230c1e Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Fri, 16 Jan 2026 18:05:35 +0100 Subject: [PATCH 23/75] docs: simplify colab documentation --- docs/colab.md | 48 +++++++++++++----------------------------------- 1 file changed, 13 insertions(+), 35 deletions(-) diff --git a/docs/colab.md b/docs/colab.md index 392cd0d..bef3f7c 100644 --- a/docs/colab.md +++ b/docs/colab.md @@ -1,22 +1,21 @@ # Running HyperView in Google Colab -Colab notebooks run on a remote VM, so you **can’t** open `http://127.0.0.1:` directly from your local browser. +HyperView works natively in Google Colab. Because Colab runs on a remote VM, you cannot access `localhost` directly. HyperView handles this automatically. -HyperView detects Colab and shows an **“Open HyperView in a new tab”** button that opens a same-site blank tab and embeds the proxied app URL in a full-page iframe (this avoids known issues with opening the proxied URL as a top-level page due to browser cookie partitioning). +## Usage -## Quick smoke test (no model downloads) - -This smoke test creates a tiny synthetic dataset with random images + precomputed 2D coordinates, so you can validate the UI and the “open in new tab” flow without running CLIP/UMAP. - -### 1) Install HyperView +### 1. Install HyperView ```bash !pip install -q git+https://github.com/HackerRoomAI/HyperView.git ``` -### 2) Create a synthetic dataset and launch +### 2. Launch the visualizer + +When you run `hv.launch()`, a button labeled **“Open HyperView in a new tab”** will appear in the output. Click it to open the visualization. ```python +# Minimal example import numpy as np import hyperview as hv from hyperview.core.sample import SampleFromArray @@ -24,36 +23,15 @@ from hyperview.core.sample import SampleFromArray dataset = hv.Dataset("colab_smoke", persist=False) rng = np.random.default_rng(0) -num_samples = 200 - -for i in range(num_samples): - # Random 64x64 RGB image +for i in range(200): img = (rng.random((64, 64, 3)) * 255).astype(np.uint8) - - sample = SampleFromArray.from_array( - id=f"s{i}", - image_array=img, - label=f"class_{i % 10}", - metadata={"i": i}, - ) - - # Precomputed 2D Euclidean projection - sample.embedding_2d = [float(rng.normal()), float(rng.normal())] - - # Precomputed 2D hyperbolic projection (inside unit disk) - r = float(rng.random() * 0.95) - theta = float(rng.random() * 2 * np.pi) - sample.embedding_2d_hyperbolic = [float(r * np.cos(theta)), float(r * np.sin(theta))] - + sample = SampleFromArray.from_array(id=f"s{i}", image_array=img, label="demo") + sample.embedding_2d = [float(rng.normal()), float(rng.normal())] # Dummy 2D points dataset.add_sample(sample) -hv.launch(dataset, port=5151) +hv.launch(dataset) ``` -Click **“Open HyperView in a new tab”**. - -If your browser blocks popups, HyperView will fall back to an inline iframe view inside the notebook output. - -## Full demo (real embeddings) +## Technical Details -If you want the full pipeline (download images, compute CLIP embeddings, run UMAP, etc.), use the demo flow described in the project README. +To support Colab, HyperView uses `google.colab.kernel.proxyPort` to expose the backend server. The UI is opened via a specially constructed "launcher" page that embeds the proxied application in a full-page iframe. This workaround ensures compatibility with modern browser security policies (like third-party cookie blocking) that often break direct proxy URLs. From deeed2246f33272b36b3432f328b2cb98c3683f5 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Fri, 16 Jan 2026 18:37:55 +0100 Subject: [PATCH 24/75] feat: change default port to 6262, rebuild frontend --- README.md | 8 +- frontend/next.config.ts | 2 +- frontend/src/app/page.tsx | 2 +- frontend/src/lib/api.ts | 2 +- notebooks/colab_smoke_test.ipynb | 24 +--- notebooks/demo.ipynb | 132 ++---------------- scripts/demo.py | 2 +- src/hyperview/api.py | 2 +- src/hyperview/cli.py | 12 +- src/hyperview/server/static/404.html | 2 +- src/hyperview/server/static/404/index.html | 2 +- .../server/static/__next.__PAGE__.txt | 4 +- src/hyperview/server/static/__next._full.txt | 4 +- src/hyperview/server/static/__next._head.txt | 2 +- src/hyperview/server/static/__next._index.txt | 2 +- src/hyperview/server/static/__next._tree.txt | 2 +- .../_buildManifest.js | 0 .../_clientMiddlewareManifest.json | 0 .../_ssgManifest.js | 0 ...8706ac30fdde8df.js => 799ca86fb297c98a.js} | 2 +- .../server/static/_not-found/__next._full.txt | 2 +- .../server/static/_not-found/__next._head.txt | 2 +- .../static/_not-found/__next._index.txt | 2 +- .../_not-found/__next._not-found.__PAGE__.txt | 2 +- .../static/_not-found/__next._not-found.txt | 2 +- .../server/static/_not-found/__next._tree.txt | 2 +- .../server/static/_not-found/index.html | 2 +- .../server/static/_not-found/index.txt | 2 +- src/hyperview/server/static/index.html | 2 +- src/hyperview/server/static/index.txt | 4 +- 30 files changed, 50 insertions(+), 178 deletions(-) rename src/hyperview/server/static/_next/static/{Oel6IrA8CsSgwXZSPI61A => 6WlEYFpQs-g9pqMRTk8sU}/_buildManifest.js (100%) rename src/hyperview/server/static/_next/static/{Oel6IrA8CsSgwXZSPI61A => 6WlEYFpQs-g9pqMRTk8sU}/_clientMiddlewareManifest.json (100%) rename src/hyperview/server/static/_next/static/{Oel6IrA8CsSgwXZSPI61A => 6WlEYFpQs-g9pqMRTk8sU}/_ssgManifest.js (100%) rename src/hyperview/server/static/_next/static/chunks/{08706ac30fdde8df.js => 799ca86fb297c98a.js} (99%) diff --git a/README.md b/README.md index eff96f8..3d964d7 100644 --- a/README.md +++ b/README.md @@ -46,7 +46,7 @@ This will: 1. Load 500 samples from CIFAR-100 2. Compute CLIP embeddings 3. Generate Euclidean and Hyperbolic visualizations -4. Start the server at **http://127.0.0.1:5151** +4. Start the server at **http://127.0.0.1:6262** ### Python API @@ -71,7 +71,7 @@ dataset.compute_embeddings() dataset.compute_visualization() # Launch the UI -hv.launch(dataset) # Opens http://127.0.0.1:5151 +hv.launch(dataset) # Opens http://127.0.0.1:6262 ``` ### Google Colab @@ -131,7 +131,7 @@ source .venv/bin/activate # Run the demo script in no-browser mode python scripts/demo.py --samples 200 --no-browser ``` -This runs the API on **http://127.0.0.1:5151** +This runs the API on **http://127.0.0.1:6262** **Terminal 2 - Start the frontend dev server:** ```bash @@ -141,7 +141,7 @@ npm run dev ``` This runs the frontend on **http://localhost:3000** with hot reloading. -Open **http://localhost:3000** in your browser. The frontend automatically proxies `/api/*` requests to the backend at port 5151 (configured in `next.config.ts`). +Open **http://localhost:3000** in your browser. The frontend automatically proxies `/api/*` requests to the backend at port 6262 (configured in `next.config.ts`). Now you can: - Edit React components and see changes instantly diff --git a/frontend/next.config.ts b/frontend/next.config.ts index 2d9cfbf..7dbfdbd 100644 --- a/frontend/next.config.ts +++ b/frontend/next.config.ts @@ -15,7 +15,7 @@ const nextConfig: NextConfig = { return [ { source: "/api/:path*", - destination: "http://127.0.0.1:5151/api/:path*", + destination: "http://127.0.0.1:6262/api/:path*", }, ]; }, diff --git a/frontend/src/app/page.tsx b/frontend/src/app/page.tsx index d3d2bf6..81a2504 100644 --- a/frontend/src/app/page.tsx +++ b/frontend/src/app/page.tsx @@ -190,7 +190,7 @@ export default function Home() {
Error
{error}

- Make sure the HyperView backend is running on port 5151. + Make sure the HyperView backend is running on port 6262.

diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts index 082fed0..f0ed658 100644 --- a/frontend/src/lib/api.ts +++ b/frontend/src/lib/api.ts @@ -1,6 +1,6 @@ import type { DatasetInfo, EmbeddingsData, Sample, SamplesResponse, ViewMode } from "@/types"; -const API_BASE = process.env.NODE_ENV === "development" ? "http://127.0.0.1:5151" : ""; +const API_BASE = process.env.NODE_ENV === "development" ? "http://127.0.0.1:6262" : ""; export async function fetchDataset(): Promise { const res = await fetch(`${API_BASE}/api/dataset`); diff --git a/notebooks/colab_smoke_test.ipynb b/notebooks/colab_smoke_test.ipynb index f9e483b..27597eb 100644 --- a/notebooks/colab_smoke_test.ipynb +++ b/notebooks/colab_smoke_test.ipynb @@ -37,31 +37,15 @@ } ], "source": [ - "!pip install -q uv\n", - "!git clone --depth 1 --branch colab-fix --single-branch https://github.com/HackerRoomAI/HyperView.git\n", - "%cd HyperView\n", - "!uv pip install -e ." + "!pip install -q git+https://github.com/HackerRoomAI/HyperView.git@colab-fix" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "58b82256", "metadata": {}, - "outputs": [ - { - "ename": "IndentationError", - "evalue": "unexpected indent (api.py, line 173)", - "output_type": "error", - "traceback": [ - "Traceback \u001b[36m(most recent call last)\u001b[39m:\n", - " File \u001b[92m~/hyperview_org/HyperView/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py:3701\u001b[39m in \u001b[95mrun_code\u001b[39m\n exec(code_obj, self.user_global_ns, self.user_ns)\n", - " Cell \u001b[92mIn[2]\u001b[39m\u001b[92m, line 2\u001b[39m\n import hyperview as hv\n", - "\u001b[36m \u001b[39m\u001b[36mFile \u001b[39m\u001b[32m~/hyperview_org/HyperView/src/hyperview/__init__.py:3\u001b[39m\n\u001b[31m \u001b[39m\u001b[31mfrom hyperview.api import Dataset, launch\u001b[39m\n", - " \u001b[36mFile \u001b[39m\u001b[32m~/hyperview_org/HyperView/src/hyperview/api.py:173\u001b[39m\n\u001b[31m \u001b[39m\u001b[31mif _is_colab():\u001b[39m\n ^\n\u001b[31mIndentationError\u001b[39m\u001b[31m:\u001b[39m unexpected indent\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import hyperview as hv\n", @@ -92,7 +76,7 @@ "\n", " dataset.add_sample(sample)\n", "\n", - "hv.launch(dataset, port=5151)" + "hv.launch(dataset, port=6262)" ] }, { diff --git a/notebooks/demo.ipynb b/notebooks/demo.ipynb index eae7ba7..20b5343 100644 --- a/notebooks/demo.ipynb +++ b/notebooks/demo.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "0aba0ed9", "metadata": { "execution": { @@ -21,23 +21,7 @@ "shell.execute_reply": "2025-12-21T23:01:48.735942Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/matin/merantix/HyperView/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "HyperView version: 0.1.0\n" - ] - } - ], + "outputs": [], "source": [ "import sys\n", "from pathlib import Path\n", @@ -86,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "0666b39a", "metadata": { "execution": { @@ -96,57 +80,7 @@ "shell.execute_reply": "2025-12-21T23:01:51.732008Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r\n", - "Loading uoft-cs/cifar100: 0%| | 0/500 [00:00\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "session = hv.launch(dataset)\n" ] diff --git a/scripts/demo.py b/scripts/demo.py index 91d0717..778f6f2 100644 --- a/scripts/demo.py +++ b/scripts/demo.py @@ -22,7 +22,7 @@ def main(): "--samples", type=int, default=50000, help="Number of samples to load (default: 50000)" ) parser.add_argument( - "--port", type=int, default=5151, help="Port to run server on (default: 5151)" + "--port", type=int, default=6262, help="Port to run server on (default: 6262)" ) parser.add_argument( "--no-browser", action="store_true", help="Don't open browser automatically" diff --git a/src/hyperview/api.py b/src/hyperview/api.py index dadcf53..2c60c54 100644 --- a/src/hyperview/api.py +++ b/src/hyperview/api.py @@ -214,7 +214,7 @@ def open_browser(self): def launch( dataset: Dataset, - port: int = 5151, + port: int = 6262, host: str = "127.0.0.1", open_browser: bool = True, notebook: bool | None = None, diff --git a/src/hyperview/cli.py b/src/hyperview/cli.py index 9bbe321..78c7d53 100644 --- a/src/hyperview/cli.py +++ b/src/hyperview/cli.py @@ -25,8 +25,8 @@ def main(): demo_parser.add_argument( "--port", type=int, - default=5151, - help="Port to run the server on (default: 5151)", + default=6262, + help="Port to run the server on (default: 6262)", ) demo_parser.add_argument( "--host", @@ -46,8 +46,8 @@ def main(): serve_parser.add_argument( "--port", type=int, - default=5151, - help="Port to run the server on (default: 5151)", + default=6262, + help="Port to run the server on (default: 6262)", ) serve_parser.add_argument( "--host", @@ -74,7 +74,7 @@ def main(): def run_demo( num_samples: int = 500, - port: int = 5151, + port: int = 6262, *, host: str = "127.0.0.1", open_browser: bool = True, @@ -113,7 +113,7 @@ def run_demo( def serve_dataset( filepath: str, - port: int = 5151, + port: int = 6262, *, host: str = "127.0.0.1", open_browser: bool = True, diff --git a/src/hyperview/server/static/404.html b/src/hyperview/server/static/404.html index 8a12bc0..61eb8b7 100644 --- a/src/hyperview/server/static/404.html +++ b/src/hyperview/server/static/404.html @@ -1 +1 @@ -404: This page could not be found.HyperView

404

This page could not be found.

\ No newline at end of file +404: This page could not be found.HyperView

404

This page could not be found.

\ No newline at end of file diff --git a/src/hyperview/server/static/404/index.html b/src/hyperview/server/static/404/index.html index 8a12bc0..61eb8b7 100644 --- a/src/hyperview/server/static/404/index.html +++ b/src/hyperview/server/static/404/index.html @@ -1 +1 @@ -404: This page could not be found.HyperView

404

This page could not be found.

\ No newline at end of file +404: This page could not be found.HyperView

404

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c:[["$","meta","0",{"charSet":"utf-8"}],["$","meta","1",{"name":"viewport","content":"width=device-width, initial-scale=1"}]] From d521ba5ecd41f01698ac972cefb068f99b03cc88 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Fri, 16 Jan 2026 23:17:52 +0100 Subject: [PATCH 25/75] Refactor code structure for improved readability and maintainability --- src/hyperview/api.py | 15 +- src/hyperview/core/dataset.py | 35 +- src/hyperview/embeddings/compute.py | 192 +- uv.lock | 3210 --------------------------- 4 files changed, 110 insertions(+), 3342 deletions(-) delete mode 100644 uv.lock diff --git a/src/hyperview/api.py b/src/hyperview/api.py index 2c60c54..8481c72 100644 --- a/src/hyperview/api.py +++ b/src/hyperview/api.py @@ -128,7 +128,7 @@ def start(self, background: bool = True): try: health = _read_health(self._health_url, timeout_s=0.2) - except (URLError, Exception) as exc: + except (URLError, TimeoutError, OSError, ValueError, json.JSONDecodeError) as exc: last_health_error = exc time.sleep(0.05) continue @@ -290,16 +290,11 @@ def _is_notebook() -> bool: """Check if running in a notebook environment.""" try: from IPython import get_ipython + except ImportError: + return False - shell = get_ipython().__class__.__name__ - if shell == "ZMQInteractiveShell": - return True # Jupyter notebook or qtconsole - elif shell == "TerminalInteractiveShell": - return False # Terminal running IPython - else: - return False # Other type (?) - except (ImportError, NameError): - return False # Probably standard Python interpreter + shell = get_ipython() + return shell is not None and shell.__class__.__name__ == "ZMQInteractiveShell" def _is_colab() -> bool: diff --git a/src/hyperview/core/dataset.py b/src/hyperview/core/dataset.py index 19c1359..15afa58 100644 --- a/src/hyperview/core/dataset.py +++ b/src/hyperview/core/dataset.py @@ -5,7 +5,7 @@ import uuid from collections.abc import Callable, Iterator from pathlib import Path -from typing import Any +from typing import Any, cast import numpy as np from datasets import load_dataset @@ -160,14 +160,14 @@ def add_images_dir( Returns: Number of images added. """ - directory = Path(directory) - if not directory.exists(): - raise ValueError(f"Directory does not exist: {directory}") + directory_path = Path(directory) + if not directory_path.exists(): + raise ValueError(f"Directory does not exist: {directory_path}") samples = [] pattern = "**/*" if recursive else "*" - for path in directory.glob(pattern): + for path in directory_path.glob(pattern): if path.is_file() and path.suffix.lower() in extensions: label = path.parent.name if label_from_folder else None sample_id = hashlib.md5(str(path).encode()).hexdigest()[:12] @@ -234,7 +234,7 @@ def add_from_huggingface( except ImportError: tqdm = None - ds = load_dataset(dataset_name, split=split) + ds = cast(Any, load_dataset(dataset_name, split=split)) # Get label names if available label_names = None @@ -274,7 +274,7 @@ def add_from_huggingface( if isinstance(image, Image.Image): pil_image = image else: - pil_image = Image.fromarray(image) + pil_image = Image.fromarray(np.asarray(image)) # Get label label = None @@ -370,23 +370,36 @@ def add_from_huggingface( def compute_embeddings( self, - model: str = "clip", + model: str = "openai/clip-vit-base-patch32", batch_size: int = 32, show_progress: bool = True, ) -> None: """Compute embeddings for samples that don't have them yet. Args: - model: Embedding model to use. + model: EmbedAnything HuggingFace `model_id` to use. batch_size: Batch size for processing. show_progress: Whether to show progress bar. """ from hyperview.embeddings.compute import EmbeddingComputer + all_samples = self._storage.get_all_samples() + if self._embedding_computer is None: self._embedding_computer = EmbeddingComputer(model=model) - - all_samples = self._storage.get_all_samples() + else: + existing_model_id = getattr(self._embedding_computer, "model_id", None) + if existing_model_id and existing_model_id != model: + if any(s.embedding is not None for s in all_samples): + raise ValueError( + "Embeddings already exist for this dataset, but " + "compute_embeddings() was called with a different model_id. " + f"Existing model_id={existing_model_id!r}, requested model_id={model!r}. " + "Create a new Dataset or clear existing embeddings before recomputing." + ) + + # No existing embeddings yet: allow switching models. + self._embedding_computer = EmbeddingComputer(model=model) # Only compute for samples without embeddings samples_needing_embeddings = [s for s in all_samples if s.embedding is None] diff --git a/src/hyperview/embeddings/compute.py b/src/hyperview/embeddings/compute.py index af278cc..f0e7d3b 100644 --- a/src/hyperview/embeddings/compute.py +++ b/src/hyperview/embeddings/compute.py @@ -1,101 +1,96 @@ -"""Embedding computation using EmbedAnything.""" +"""Image embedding computation via EmbedAnything. + +This module is intentionally minimal: +- Callers pass a HuggingFace `model_id` string. +- We delegate model loading + inference to EmbedAnything. + +""" import os import tempfile +from pathlib import Path +from typing import Any -import embed_anything import numpy as np -from embed_anything import EmbeddingModel, WhichModel +from embed_anything import EmbeddingModel from PIL import Image -try: - from tqdm import tqdm -except ImportError: - tqdm = None - from hyperview.core.sample import Sample +tqdm: Any | None = None +try: + from tqdm import tqdm as tqdm_impl +except ImportError: # pragma: no cover + pass +else: + tqdm = tqdm_impl + class EmbeddingComputer: - """Compute embeddings for images using EmbedAnything.""" + """Compute embeddings for image samples using EmbedAnything.""" - def __init__(self, model: str = "clip"): + def __init__(self, model: str): """Initialize the embedding computer. Args: - model: Model to use for embeddings. + model: HuggingFace model ID to load via EmbedAnything. """ - self.model_name = model - self._model = None - self._initialized = False - - def _init_model(self) -> None: - """Lazily initialize the model.""" - if self._initialized: - return - # Use CLIP model by default - self._model = EmbeddingModel.from_pretrained_hf( - WhichModel.Clip, - model_id="openai/clip-vit-base-patch32", - ) - self._embed_anything = embed_anything - self._initialized = True + if not model or not model.strip(): + raise ValueError("model must be a non-empty HuggingFace model_id") - def _load_rgb_image(self, sample: Sample) -> Image.Image: - """Load an image and ensure it is in RGB mode.""" - image = sample.load_image() - if image.mode != "RGB": - image = image.convert("RGB") - return image + self.model_id = model + self._model: EmbeddingModel | None = None - def _embed_with_model( - self, - sample: Sample, - image: Image.Image | None = None, - ) -> np.ndarray | None: - """Attempt to embed a sample via embed_anything, handling memory-backed files.""" - path = sample.filepath - temp_path: str | None = None + def _get_model(self) -> EmbeddingModel: + """Lazily initialize the EmbedAnything model.""" + if self._model is None: + self._model = EmbeddingModel.from_pretrained_hf(model_id=self.model_id) + return self._model + + def _load_rgb_image(self, sample: Sample) -> Image.Image: + """Load an image and normalize it to RGB. + For file-backed samples, returns an in-memory copy and closes the file + handle immediately to avoid leaking descriptors during batch processing. + """ + with sample.load_image() as img: + img.load() + if img.mode != "RGB": + return img.convert("RGB") + return img.copy() + + def _embed_file(self, file_path: str) -> np.ndarray: + model = self._get_model() + result = model.embed_file(file_path) + + if not result: + raise RuntimeError(f"EmbedAnything returned no embeddings for: {file_path}") + if len(result) != 1: + raise RuntimeError( + f"Expected 1 embedding for an image file, got {len(result)}: {file_path}" + ) + + return np.asarray(result[0].embedding, dtype=np.float32) + + def _embed_pil_image(self, image: Image.Image) -> np.ndarray: + temp_fd, temp_path = tempfile.mkstemp(suffix=".png") + os.close(temp_fd) try: - if path.startswith("memory://"): - if image is None: - image = self._load_rgb_image(sample) - temp_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False) - image.save(temp_file, format="PNG") - temp_file.close() - temp_path = temp_file.name - path = temp_path - - result = self._embed_anything.embed_file(path, embedder=self._model) - if result: - return np.array(result[0].embedding, dtype=np.float32) + image.save(temp_path, format="PNG") + return self._embed_file(temp_path) finally: - if temp_path and os.path.exists(temp_path): - os.remove(temp_path) - - return None + Path(temp_path).unlink(missing_ok=True) def compute_single(self, sample: Sample) -> np.ndarray: - """Compute embedding for a single sample. - - Args: - sample: Sample to compute embedding for. - - Returns: - Embedding as numpy array. - """ - self._init_model() - - pil_image = None - if sample.filepath.startswith("memory://"): - pil_image = self._load_rgb_image(sample) - - embedding = self._embed_with_model(sample, image=pil_image) - if embedding is None: - raise RuntimeError(f"Failed to compute embedding for sample {sample.id}") - - return embedding + """Compute embedding for a single sample.""" + try: + image = self._load_rgb_image(sample) + return self._embed_pil_image(image) + except Exception as exc: + raise RuntimeError( + f"Failed to compute embedding for sample {sample.id} " + f"(filepath={sample.filepath}, model_id={self.model_id})" + ) from exc def compute_batch( self, @@ -103,46 +98,21 @@ def compute_batch( batch_size: int = 32, show_progress: bool = True, ) -> list[np.ndarray]: - """Compute embeddings for a batch of samples. - - Args: - samples: List of samples to compute embeddings for. - batch_size: Number of samples to process at once. - show_progress: Whether to show a progress bar. + """Compute embeddings for a list of samples.""" + if batch_size <= 0: + raise ValueError("batch_size must be > 0") - Returns: - List of embeddings as numpy arrays. - """ - self._init_model() + # Prime model init so download errors happen before work starts. + self._get_model() - embeddings = [] total = len(samples) + iterator = samples - if show_progress and tqdm is not None: - iterator = tqdm(range(0, total, batch_size), desc="Computing embeddings") - else: - if show_progress and tqdm is None: + if show_progress: + if tqdm is not None: + iterator = tqdm(samples, total=total, desc="Computing embeddings") + else: print(f"Computing embeddings for {total} samples...") - iterator = range(0, total, batch_size) - - for i in iterator: - batch = samples[i : i + batch_size] - batch_embeddings = [] - - for sample in batch: - pil_image = None - if sample.filepath.startswith("memory://"): - pil_image = self._load_rgb_image(sample) - - embedding = self._embed_with_model(sample, image=pil_image) - if embedding is None: - raise RuntimeError( - f"Failed to compute embedding for sample {sample.id}" - ) - - batch_embeddings.append(embedding) - - embeddings.extend(batch_embeddings) - return embeddings + return [self.compute_single(sample) for sample in iterator] diff --git a/uv.lock b/uv.lock deleted file mode 100644 index 56230ae..0000000 --- a/uv.lock +++ /dev/null @@ -1,3210 +0,0 @@ -version = 1 -requires-python = ">=3.10" -resolution-markers = [ - 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{ url = "https://files.pythonhosted.org/packages/48/b7/503c98092fb3b344a179579f55814b613c1fbb1c23b3ec14a7b008a66a6e/yarl-1.22.0-cp314-cp314t-win_arm64.whl", hash = "sha256:9f6d73c1436b934e3f01df1e1b21ff765cd1d28c77dfb9ace207f746d4610ee1", size = 85171 }, - { url = "https://files.pythonhosted.org/packages/73/ae/b48f95715333080afb75a4504487cbe142cae1268afc482d06692d605ae6/yarl-1.22.0-py3-none-any.whl", hash = "sha256:1380560bdba02b6b6c90de54133c81c9f2a453dee9912fe58c1dcced1edb7cff", size = 46814 }, -] From ebe133cb1c9243033f17c0bf53b71661849c95b2 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Sat, 17 Jan 2026 00:20:18 +0100 Subject: [PATCH 26/75] chore: update dependencies and fix Colab installation command --- .gitignore | 1 + notebooks/colab_smoke_test.ipynb | 3 ++- pyproject.toml | 32 ++++++++++++++++---------------- 3 files changed, 19 insertions(+), 17 deletions(-) diff --git a/.gitignore b/.gitignore index 2961cdd..ce6b733 100644 --- a/.gitignore +++ b/.gitignore @@ -7,6 +7,7 @@ __pycache__/ env/ venv/ .venv/ +/uv.lock *.egg-info/ .pytest_cache/ .coverage diff --git a/notebooks/colab_smoke_test.ipynb b/notebooks/colab_smoke_test.ipynb index 27597eb..b60a470 100644 --- a/notebooks/colab_smoke_test.ipynb +++ b/notebooks/colab_smoke_test.ipynb @@ -37,7 +37,7 @@ } ], "source": [ - "!pip install -q git+https://github.com/HackerRoomAI/HyperView.git@colab-fix" + "%pip install -q git+https://github.com/HackerRoomAI/HyperView.git@hyperview-base" ] }, { @@ -48,6 +48,7 @@ "outputs": [], "source": [ "import numpy as np\n", + "\n", "import hyperview as hv\n", "from hyperview.core.sample import SampleFromArray\n", "\n", diff --git a/pyproject.toml b/pyproject.toml index 3ba8546..6633051 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,28 +23,28 @@ classifiers = [ ] dependencies = [ - "fastapi>=0.115.0", - "uvicorn[standard]>=0.32.0", - "embed-anything>=0.3.0", - "numpy>=1.26.0", - "umap-learn>=0.5.6", - "pillow>=10.0.0", - "pydantic>=2.0.0", - "aiofiles>=24.0.0", - "datasets>=3.0.0", - "lancedb>=0.5.0", - "pyarrow>=14.0.0", + "fastapi>=0.128.0", + "uvicorn[standard]>=0.40.0", + "embed-anything>=0.7.0", + "numpy>=1.26.4,<2.4", + "umap-learn>=0.5.11", + "pillow>=12.1.0", + "pydantic>=2.12.5", + "aiofiles>=25.1.0", + "datasets>=4.5.0", + "lancedb>=0.26.1", + "pyarrow>=22.0.0", ] [project.optional-dependencies] dev = [ - "pytest>=8.0.0", - "pytest-asyncio>=0.24.0", - "httpx>=0.27.0", - "ruff>=0.7.0", + "pytest>=9.0.2", + "pytest-asyncio>=1.3.0", + "httpx>=0.28.1", + "ruff>=0.14.13", ] hyperbolic = [ - "torch>=2.0.0", + "torch>=2.9.1", "geoopt>=0.5.1", ] From 3c587849e366d97ed56b428207c03650d0913f7c Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Sat, 17 Jan 2026 13:18:28 +0100 Subject: [PATCH 27/75] Compute projections at launch Automatically compute 2D projections when embeddings exist but visualization fields are missing, preventing /api/embeddings from failing on first load. --- src/hyperview/api.py | 89 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 89 insertions(+) diff --git a/src/hyperview/api.py b/src/hyperview/api.py index 8481c72..4dfab73 100644 --- a/src/hyperview/api.py +++ b/src/hyperview/api.py @@ -23,6 +23,8 @@ class _HealthResponse: name: str | None session_id: str | None + dataset: str | None + pid: int | None def _can_connect(host: str, port: int, timeout_s: float) -> bool: @@ -48,6 +50,8 @@ def _read_health(url: str, timeout_s: float) -> _HealthResponse: return _HealthResponse( name=data.get("name"), session_id=data.get("session_id"), + dataset=data.get("dataset"), + pid=data.get("pid") if isinstance(data.get("pid"), int) else None, ) @@ -219,9 +223,15 @@ def launch( open_browser: bool = True, notebook: bool | None = None, height: int = 800, + reuse_server: bool = False, ) -> Session: """Launch the HyperView visualization server. + Note: + HyperView's UI requires 2D projections (`embedding_2d` and + `embedding_2d_hyperbolic`). If they are missing but high-dimensional + embeddings exist, this function will compute them automatically. + Args: dataset: The dataset to visualize. port: Port to run the server on. @@ -229,6 +239,10 @@ def launch( open_browser: Whether to open a browser window. notebook: Whether to display in a notebook. If None, auto-detects. height: Height of the iframe in the notebook. + reuse_server: If True, and the requested port is already serving HyperView, + attach to the existing server instead of starting a new one. For safety, + this will only attach when the existing server reports the same dataset + name (via `/__hyperview__/health`). Returns: A Session object. @@ -250,6 +264,81 @@ def launch( # to all interfaces. host = "0.0.0.0" + # Preflight: avoid doing expensive work if the port is already in use. + # If it's already serving HyperView and reuse_server=True, we can safely attach. + connect_host = "127.0.0.1" if host == "0.0.0.0" else host + health_url = f"http://{connect_host}:{port}/__hyperview__/health" + + if _can_connect(connect_host, port, timeout_s=0.2): + health = _try_read_health(health_url, timeout_s=0.2) + if health is not None and health.name == "hyperview": + if not reuse_server: + raise RuntimeError( + "HyperView failed to start because the port is already serving " + f"HyperView (port={port}, dataset={health.dataset}, " + f"session_id={health.session_id}, pid={health.pid}). " + "Choose a different port, stop the existing server, or pass " + "reuse_server=True to attach." + ) + + if health.dataset is not None and health.dataset != dataset.name: + raise RuntimeError( + "HyperView refused to attach to the existing server because it is " + f"serving a different dataset (port={port}, dataset={health.dataset}). " + f"Requested dataset={dataset.name}. Stop the existing server or " + "choose a different port." + ) + + session = Session(dataset, host, port) + if health.session_id is not None: + session.session_id = health.session_id + + if notebook: + if _is_colab(): + print( + f"\nHyperView is already running (Colab, port={session.port}). " + "Use the link below to open it." + ) + else: + print( + f"\nHyperView is already running at {session.url} (port={session.port}). " + "Opening a new tab..." + ) + session.show(height=height) + else: + print(f"\nHyperView is already running at {session.url} (port={session.port}).") + if open_browser: + session.open_browser() + + return session + + raise RuntimeError( + "HyperView failed to start because the port is already in use " + f"by a non-HyperView service (port={port}). Choose a different " + "port or stop the process listening on that port." + ) + + # The frontend requires both euclidean + hyperbolic 2D coords from /api/embeddings. + # If they're missing, proactively compute them so the first page load doesn't fail. + try: + ids, _, _, _ = dataset._storage.get_visualization_embeddings() + except Exception: + ids = [] + + if not ids: + has_any_embeddings = any(s.embedding is not None for s in dataset._storage) + if has_any_embeddings: + print("No 2D projections found. Computing visualizations...") + dataset.compute_visualization() + else: + raise ValueError( + "HyperView launch requires 2D projections for the UI. " + "No projections were found, and no high-dimensional embeddings are present. " + "Call `dataset.compute_embeddings()` and `dataset.compute_visualization()` " + "before `hv.launch()`, or prepopulate both `embedding_2d` and " + "`embedding_2d_hyperbolic` on your samples." + ) + session = Session(dataset, host, port) if notebook: From ebe3c56a7c9a2a94f2f22ae1833938db296f84d3 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Sat, 17 Jan 2026 13:24:01 +0100 Subject: [PATCH 28/75] feat: add --reuse-server option to demo and serve commands --- src/hyperview/cli.py | 38 ++++++++++++++++++++++++++++++++++---- 1 file changed, 34 insertions(+), 4 deletions(-) diff --git a/src/hyperview/cli.py b/src/hyperview/cli.py index 78c7d53..dc9c1fc 100644 --- a/src/hyperview/cli.py +++ b/src/hyperview/cli.py @@ -39,6 +39,14 @@ def main(): action="store_true", help="Do not open a browser window automatically", ) + demo_parser.add_argument( + "--reuse-server", + action="store_true", + help=( + "If the port is already serving HyperView, attach instead of failing. " + "For safety, this only attaches when the existing server reports the same dataset name." + ), + ) # Serve command serve_parser = subparsers.add_parser("serve", help="Serve a saved dataset") @@ -60,13 +68,33 @@ def main(): action="store_true", help="Do not open a browser window automatically", ) + serve_parser.add_argument( + "--reuse-server", + action="store_true", + help=( + "If the port is already serving HyperView, attach instead of failing. " + "For safety, this only attaches when the existing server reports the same dataset name." + ), + ) args = parser.parse_args() if args.command == "demo": - run_demo(args.samples, args.port, host=args.host, open_browser=not args.no_browser) + run_demo( + args.samples, + args.port, + host=args.host, + open_browser=not args.no_browser, + reuse_server=args.reuse_server, + ) elif args.command == "serve": - serve_dataset(args.dataset, args.port, host=args.host, open_browser=not args.no_browser) + serve_dataset( + args.dataset, + args.port, + host=args.host, + open_browser=not args.no_browser, + reuse_server=args.reuse_server, + ) else: parser.print_help() sys.exit(1) @@ -78,6 +106,7 @@ def run_demo( *, host: str = "127.0.0.1", open_browser: bool = True, + reuse_server: bool = False, ): """Run a demo with CIFAR-100 data.""" print("Loading CIFAR-100 dataset...") @@ -108,7 +137,7 @@ def run_demo( dataset.compute_visualization() print("Visualizations ready") - launch(dataset, port=port, host=host, open_browser=open_browser) + launch(dataset, port=port, host=host, open_browser=open_browser, reuse_server=reuse_server) def serve_dataset( @@ -117,6 +146,7 @@ def serve_dataset( *, host: str = "127.0.0.1", open_browser: bool = True, + reuse_server: bool = False, ): """Serve a saved dataset.""" from hyperview import Dataset, launch @@ -125,7 +155,7 @@ def serve_dataset( dataset = Dataset.load(filepath) print(f"Loaded {len(dataset)} samples") - launch(dataset, port=port, host=host, open_browser=open_browser) + launch(dataset, port=port, host=host, open_browser=open_browser, reuse_server=reuse_server) if __name__ == "__main__": From 02a06cd71e828b46863771f76f0d3c1b135a4d99 Mon Sep 17 00:00:00 2001 From: Antonio Rueda-Toicen Date: Mon, 19 Jan 2026 11:52:24 +0100 Subject: [PATCH 29/75] Update project description in README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 3d964d7..9fef293 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # HyperView -> **Open-source dataset curation with hyperbolic embeddings visualization - a FiftyOne alternative.** +> **Open-source dataset curation with hyperbolic embeddings visualization** [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/HackerRoomAI/HyperView) From b8c9d3bde6bed0f5cba2e58d372504ff722511b7 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Tue, 20 Jan 2026 00:06:42 +0100 Subject: [PATCH 30/75] Refactor MemoryBackend to support embedding spaces and layouts - Introduced a spaces registry to manage embedding spaces and their metadata. - Added methods for adding, retrieving, and deleting embeddings and layouts. - Updated the schema definitions to include spaces and layouts. - Removed embedding-related fields from the sample schema, focusing on metadata. - Enhanced similarity search methods to work with specified embedding spaces. - Improved error handling and validation for embedding and layout operations. --- frontend/src/app/page.tsx | 73 +- frontend/src/components/ImageGrid.tsx | 18 +- frontend/src/components/ScatterPanel.tsx | 92 +- frontend/src/components/icons.tsx | 27 + frontend/src/components/useHyperScatter.ts | 142 ++- frontend/src/lib/api.ts | 15 +- frontend/src/lib/layouts.ts | 29 + frontend/src/store/useStore.ts | 56 +- frontend/src/types/index.ts | 19 +- scripts/demo.py | 22 +- src/hyperview/api.py | 41 +- src/hyperview/cli.py | 4 +- src/hyperview/core/dataset.py | 245 +++-- src/hyperview/core/sample.py | 15 +- src/hyperview/embeddings/projection.py | 6 + src/hyperview/server/app.py | 99 +- src/hyperview/server/static/404.html | 2 +- src/hyperview/server/static/404/index.html | 2 +- .../server/static/__next.__PAGE__.txt | 4 +- src/hyperview/server/static/__next._full.txt | 6 +- src/hyperview/server/static/__next._head.txt | 2 +- src/hyperview/server/static/__next._index.txt | 4 +- src/hyperview/server/static/__next._tree.txt | 4 +- .../_buildManifest.js | 0 .../_clientMiddlewareManifest.json | 0 .../_ssgManifest.js | 0 ...99ca86fb297c98a.js => 490c8071909f9d06.js} | 6 +- .../_next/static/chunks/61d99a6d918f5559.css | 1 - .../_next/static/chunks/d6d93a0a04227147.css | 1 + .../server/static/_not-found/__next._full.txt | 4 +- .../server/static/_not-found/__next._head.txt | 2 +- .../static/_not-found/__next._index.txt | 4 +- .../_not-found/__next._not-found.__PAGE__.txt | 2 +- .../static/_not-found/__next._not-found.txt | 2 +- .../server/static/_not-found/__next._tree.txt | 4 +- .../server/static/_not-found/index.html | 2 +- .../server/static/_not-found/index.txt | 4 +- src/hyperview/server/static/index.html | 2 +- src/hyperview/server/static/index.txt | 6 +- src/hyperview/storage/__init__.py | 4 +- src/hyperview/storage/backend.py | 172 ++- src/hyperview/storage/config.py | 24 +- src/hyperview/storage/lancedb_backend.py | 993 +++++++++++------- src/hyperview/storage/memory_backend.py | 381 +++++-- src/hyperview/storage/schema.py | 203 +++- 45 files changed, 1893 insertions(+), 851 deletions(-) create mode 100644 frontend/src/lib/layouts.ts rename src/hyperview/server/static/_next/static/{6WlEYFpQs-g9pqMRTk8sU => FkYHqh8bg2HXVZYl26MHt}/_buildManifest.js (100%) rename src/hyperview/server/static/_next/static/{6WlEYFpQs-g9pqMRTk8sU => FkYHqh8bg2HXVZYl26MHt}/_clientMiddlewareManifest.json (100%) rename src/hyperview/server/static/_next/static/{6WlEYFpQs-g9pqMRTk8sU => FkYHqh8bg2HXVZYl26MHt}/_ssgManifest.js (100%) rename src/hyperview/server/static/_next/static/chunks/{799ca86fb297c98a.js => 490c8071909f9d06.js} (50%) delete mode 100644 src/hyperview/server/static/_next/static/chunks/61d99a6d918f5559.css create mode 100644 src/hyperview/server/static/_next/static/chunks/d6d93a0a04227147.css diff --git a/frontend/src/app/page.tsx b/frontend/src/app/page.tsx index 81a2504..59dc2f6 100644 --- a/frontend/src/app/page.tsx +++ b/frontend/src/app/page.tsx @@ -1,6 +1,6 @@ "use client"; -import { useEffect, useCallback, useState } from "react"; +import { useEffect, useCallback, useMemo, useState } from "react"; import { ResizablePanelGroup, ResizablePanel, @@ -8,6 +8,7 @@ import { } from "@/components/ui/resizable"; import { Header, ImageGrid, ScatterPanel } from "@/components"; import { useStore } from "@/store/useStore"; +import type { Sample } from "@/types"; import { fetchDataset, fetchSamples, @@ -15,13 +16,16 @@ import { fetchSamplesBatch, fetchLassoSelection, } from "@/lib/api"; +import { getLayoutGeometry } from "@/lib/layouts"; const SAMPLES_PER_PAGE = 100; export default function Home() { const { + datasetInfo, samples, totalSamples, + samplesLoaded, setSamples, appendSamples, addSamplesIfMissing, @@ -33,14 +37,17 @@ export default function Home() { setError, selectedIds, isLassoSelection, + selectionSource, lassoQuery, lassoSamples, lassoTotal, lassoIsLoading, setLassoResults, + geometry, } = useStore(); const [loadingMore, setLoadingMore] = useState(false); + const [geometryLoading, setGeometryLoading] = useState(false); // Initial data load - runs once on mount // Store setters are stable and don't need to be in deps @@ -72,6 +79,33 @@ export default function Home() { // eslint-disable-next-line react-hooks/exhaustive-deps }, []); + // Fetch new embeddings when geometry changes + useEffect(() => { + if (!datasetInfo) return; + + // Find layout key for current geometry + const targetLayout = datasetInfo.layouts.find((l) => getLayoutGeometry(l) === geometry); + if (!targetLayout) return; + + // Skip if already showing this layout + const currentLayout = useStore.getState().currentLayoutKey; + if (currentLayout === targetLayout) return; + + const loadGeometry = async () => { + setGeometryLoading(true); + try { + const embeddingsData = await fetchEmbeddings(targetLayout); + setEmbeddings(embeddingsData); + } catch (err) { + console.error("Failed to load geometry:", err); + } finally { + setGeometryLoading(false); + } + }; + + loadGeometry(); + }, [geometry, datasetInfo, setEmbeddings]); + // Fetch selected samples that aren't already loaded useEffect(() => { const fetchSelectedSamples = async () => { @@ -106,7 +140,7 @@ export default function Home() { const run = async () => { try { const res = await fetchLassoSelection({ - viewMode: lassoQuery.viewMode, + layoutKey: lassoQuery.layoutKey, polygon: lassoQuery.polygon, offset: 0, limit: SAMPLES_PER_PAGE, @@ -139,7 +173,7 @@ export default function Home() { setLoadingMore(true); try { const res = await fetchLassoSelection({ - viewMode: lassoQuery.viewMode, + layoutKey: lassoQuery.layoutKey, polygon: lassoQuery.polygon, offset: lassoSamples.length, limit: SAMPLES_PER_PAGE, @@ -153,11 +187,11 @@ export default function Home() { return; } - if (samples.length >= totalSamples) return; + if (samplesLoaded >= totalSamples) return; setLoadingMore(true); try { - const res = await fetchSamples(samples.length, SAMPLES_PER_PAGE); + const res = await fetchSamples(samplesLoaded, SAMPLES_PER_PAGE); appendSamples(res.samples); } catch (err) { console.error("Failed to load more samples:", err); @@ -172,14 +206,37 @@ export default function Home() { lassoQuery, lassoSamples.length, lassoTotal, - samples.length, + samplesLoaded, totalSamples, setLassoResults, ]); - const displayedSamples = isLassoSelection ? lassoSamples : samples; + const displayedSamples = useMemo(() => { + if (isLassoSelection) return lassoSamples; + + // When a selection comes from the scatter plot, bring selected samples to the top + // so the user immediately sees what they clicked. + if (selectionSource === "scatter" && selectedIds.size > 0) { + const byId = new Map(); + for (const s of samples) byId.set(s.id, s); + + const pinned: Sample[] = []; + for (const id of selectedIds) { + const s = byId.get(id); + if (s) pinned.push(s); + } + + if (pinned.length > 0) { + const rest = samples.filter((s) => !selectedIds.has(s.id)); + return [...pinned, ...rest]; + } + } + + return samples; + }, [isLassoSelection, lassoSamples, samples, selectedIds, selectionSource]); + const displayedTotal = isLassoSelection ? lassoTotal : totalSamples; - const displayedHasMore = displayedSamples.length < displayedTotal; + const displayedHasMore = isLassoSelection ? displayedSamples.length < displayedTotal : samplesLoaded < totalSamples; if (error) { return ( diff --git a/frontend/src/components/ImageGrid.tsx b/frontend/src/components/ImageGrid.tsx index 6a59523..8a01399 100644 --- a/frontend/src/components/ImageGrid.tsx +++ b/frontend/src/components/ImageGrid.tsx @@ -23,6 +23,7 @@ export function ImageGrid({ samples, onLoadMore, hasMore }: ImageGridProps) { const { selectedIds, isLassoSelection, + selectionSource, lassoTotal, toggleSelection, addToSelection, @@ -96,6 +97,21 @@ export function ImageGrid({ samples, onLoadMore, hasMore }: ImageGridProps) { virtualizer.measure(); }, [selectedIds, isLassoSelection, virtualizer]); + // If a selection was made in the scatter plot, jump the image grid to the top + // so the selected sample(s) are immediately visible. + useEffect(() => { + if (isLassoSelection) return; + if (selectionSource !== "scatter") return; + if (selectedIds.size === 0) return; + + try { + virtualizer.scrollToIndex(0, { align: "start" }); + } catch { + // Fallback if the virtualizer isn't ready yet. + containerRef.current?.scrollTo({ top: 0 }); + } + }, [isLassoSelection, selectedIds, selectionSource, virtualizer]); + const handleClick = useCallback( (sample: Sample, event: React.MouseEvent) => { if (event.metaKey || event.ctrlKey) { @@ -118,7 +134,7 @@ export function ImageGrid({ samples, onLoadMore, hasMore }: ImageGridProps) { // Single select const newSet = new Set(); newSet.add(sample.id); - useStore.getState().setSelectedIds(newSet); + useStore.getState().setSelectedIds(newSet, "grid"); } }, [samples, selectedIds, toggleSelection, addToSelection] diff --git a/frontend/src/components/ScatterPanel.tsx b/frontend/src/components/ScatterPanel.tsx index 0eb1e1f..7702228 100644 --- a/frontend/src/components/ScatterPanel.tsx +++ b/frontend/src/components/ScatterPanel.tsx @@ -4,9 +4,11 @@ import { useMemo } from "react"; import { useStore } from "@/store/useStore"; import { Panel, PanelFooter } from "./Panel"; import { PanelHeader } from "./PanelHeader"; -import { ScatterIcon } from "./icons"; -import { ToggleGroup, ToggleGroupItem } from "@/components/ui/toggle-group"; +import { EuclideanIcon, PoincareIcon, ScatterIcon } from "./icons"; import { type ScatterLabelsInfo, useHyperScatter } from "./useHyperScatter"; +import { ToggleGroup, ToggleGroupItem } from "@/components/ui/toggle-group"; +import type { Geometry } from "@/types"; +import { getLayoutSpaceKey, listAvailableGeometries } from "@/lib/layouts"; interface ScatterPanelProps { className?: string; @@ -14,16 +16,35 @@ interface ScatterPanelProps { export function ScatterPanel({ className = "" }: ScatterPanelProps) { const { + datasetInfo, embeddings, - viewMode, - setViewMode, selectedIds, setSelectedIds, beginLassoSelection, hoveredId, setHoveredId, + geometry, + setGeometry, } = useStore(); + // Check which geometries are available + const availableGeometries = useMemo(() => { + return listAvailableGeometries(datasetInfo?.layouts ?? []); + }, [datasetInfo?.layouts]); + + const embeddingModelName = useMemo(() => { + if (!embeddings) return null; + + const layoutKey = embeddings.layout_key; + const spaceKey = getLayoutSpaceKey(layoutKey); + + const space = + datasetInfo?.spaces.find((s) => s.space_key === spaceKey) ?? + datasetInfo?.spaces.find((s) => layoutKey.startsWith(`${s.space_key}__`)); + + return space?.model_id ?? space?.space_key ?? spaceKey; + }, [datasetInfo?.spaces, embeddings]); + const labelsInfo = useMemo(() => { if (!embeddings) return null; @@ -58,9 +79,9 @@ export function ScatterPanel({ className = "" }: ScatterPanelProps) { handlePointerUp, handlePointerLeave, handleDoubleClick, + rendererError, } = useHyperScatter({ embeddings, - viewMode, labelsInfo, selectedIds, hoveredId, @@ -76,24 +97,36 @@ export function ScatterPanel({ className = "" }: ScatterPanelProps) { } title="Embeddings" - subtitle={embeddings ? `${embeddings.ids.length} points` : "Loading..."} + subtitle={embeddings ? embeddingModelName ?? "" : "Loading..."} > - {/* View mode toggle using shadcn ToggleGroup */} - val && setViewMode(val as "euclidean" | "hyperbolic")} - variant="outline" - size="sm" - className="h-6" - > - - Euclidean - - - Hyperbolic - - + {/* Geometry toggle */} + {availableGeometries.length > 1 && ( + value && setGeometry(value as Geometry)} + size="sm" + variant="outline" + className="bg-muted/50 rounded-md p-0.5 gap-0.5" + > + + + Euclidean + + + + Poincaré + + + )} {/* Main content area */} @@ -120,10 +153,19 @@ export function ScatterPanel({ className = "" }: ScatterPanelProps) { /> {/* Loading overlay */} - {!embeddings && ( -
-
Loading embeddings...
+ {rendererError ? ( +
+
+
Browser not supported
+
{rendererError}
+
+ ) : ( + !embeddings && ( +
+
Loading embeddings...
+
+ ) )}
diff --git a/frontend/src/components/icons.tsx b/frontend/src/components/icons.tsx index aa2758a..20a4634 100644 --- a/frontend/src/components/icons.tsx +++ b/frontend/src/components/icons.tsx @@ -37,3 +37,30 @@ export const CheckIcon = () => ( ); + +/** Euclidean geometry icon - flat grid */ +export const EuclideanIcon = () => ( + + + + + +); + +/** Poincaré disk icon - hyperbolic geometry */ +export const PoincareIcon = () => ( + + + + + +); + +/** Spherical geometry icon - for future use */ +export const SphericalIcon = () => ( + + + + + +); diff --git a/frontend/src/components/useHyperScatter.ts b/frontend/src/components/useHyperScatter.ts index 131b829..a9f1a8c 100644 --- a/frontend/src/components/useHyperScatter.ts +++ b/frontend/src/components/useHyperScatter.ts @@ -1,7 +1,8 @@ import type React from "react"; -import { useCallback, useEffect, useMemo, useRef } from "react"; +import { useCallback, useEffect, useMemo, useRef, useState } from "react"; -import type { EmbeddingsData, ViewMode } from "@/types"; +import type { EmbeddingsData } from "@/types"; +import { getLayoutGeometry } from "@/lib/layouts"; import type { Dataset, GeometryMode, Modifiers, Renderer } from "hyper-scatter"; type HyperScatterModule = typeof import("hyper-scatter"); @@ -14,6 +15,16 @@ export interface ScatterLabelsInfo { const MAX_LASSO_VERTS = 512; +function supportsWebGL2(): boolean { + try { + if (typeof document === "undefined") return false; + const canvas = document.createElement("canvas"); + return !!canvas.getContext("webgl2"); + } catch { + return false; + } +} + function capInterleavedXY(points: ArrayLike, maxVerts: number): number[] { const n = Math.floor(points.length / 2); if (n <= maxVerts) return Array.from(points as ArrayLike); @@ -27,14 +38,14 @@ function capInterleavedXY(points: ArrayLike, maxVerts: number): number[] return out; } + interface UseHyperScatterArgs { embeddings: EmbeddingsData | null; - viewMode: ViewMode; labelsInfo: ScatterLabelsInfo | null; selectedIds: Set; hoveredId: string | null; - setSelectedIds: (ids: Set, isLasso?: boolean) => void; - beginLassoSelection: (query: { viewMode: ViewMode; polygon: number[] }) => void; + setSelectedIds: (ids: Set, source?: "scatter" | "grid") => void; + beginLassoSelection: (query: { layoutKey: string; polygon: number[] }) => void; setHoveredId: (id: string | null) => void; } @@ -79,13 +90,8 @@ function drawLassoOverlay(canvas: HTMLCanvasElement | null, points: number[]): v ctx.restore(); } -function viewModeToGeometry(mode: ViewMode): GeometryMode { - return mode === "euclidean" ? "euclidean" : "poincare"; -} - export function useHyperScatter({ embeddings, - viewMode, labelsInfo, selectedIds, hoveredId, @@ -99,11 +105,15 @@ export function useHyperScatter({ const rendererRef = useRef(null); + const [rendererError, setRendererError] = useState(null); + const rafPendingRef = useRef(false); // Interaction state (refs to avoid rerender churn) const isPanningRef = useRef(false); const isLassoingRef = useRef(false); + const pointerDownXRef = useRef(0); + const pointerDownYRef = useRef(0); const lastPointerXRef = useRef(0); const lastPointerYRef = useRef(0); const lassoPointsRef = useRef([]); @@ -128,7 +138,23 @@ export function useHyperScatter({ const renderer = rendererRef.current; if (!renderer) return; - renderer.render(); + try { + renderer.render(); + } catch (err) { + // Avoid an exception storm that would permanently prevent the UI from updating. + console.error("hyper-scatter renderer.render() failed:", err); + try { + renderer.destroy(); + } catch { + // ignore + } + rendererRef.current = null; + setRendererError( + "This browser can't render the scatter plot (WebGL2 is required). Please use Chrome/Edge/Firefox." + ); + clearOverlay(overlayCanvasRef.current); + return; + } if (isLassoingRef.current) { drawLassoOverlay(overlayCanvasRef.current, lassoPointsRef.current); @@ -153,7 +179,7 @@ export function useHyperScatter({ clearOverlay(overlayCanvasRef.current); }, []); - // Initialize renderer when embeddings/viewMode change. + // Initialize renderer when embeddings change. useEffect(() => { if (!embeddings || !labelsInfo) return; if (!canvasRef.current || !containerRef.current) return; @@ -161,6 +187,16 @@ export function useHyperScatter({ let cancelled = false; const init = async () => { + // Clear any previous renderer errors when we attempt to re-init. + setRendererError(null); + + if (!supportsWebGL2()) { + setRendererError( + "This browser doesn't support WebGL2, so the scatter plot can't be displayed. Please use Chrome/Edge/Firefox." + ); + return; + } + try { const viz = (await import("hyper-scatter")) as HyperScatterModule; if (cancelled) return; @@ -186,40 +222,61 @@ export function useHyperScatter({ clearOverlay(overlayCanvasRef.current); } - const coords = viewMode === "euclidean" ? embeddings.euclidean : embeddings.hyperbolic; + // Use coords from embeddings response directly + const coords = embeddings.coords; const positions = new Float32Array(coords.length * 2); for (let i = 0; i < coords.length; i++) { positions[i * 2] = coords[i][0]; positions[i * 2 + 1] = coords[i][1]; } - const geometry = viewModeToGeometry(viewMode); + // Determine geometry from layout key + const geometry = (getLayoutGeometry(embeddings.layout_key) ?? "euclidean") as GeometryMode; const dataset: Dataset = viz.createDataset(geometry, positions, labelsInfo.categories); - const renderer: Renderer = - viewMode === "euclidean" ? new viz.EuclideanWebGLCandidate() : new viz.HyperbolicWebGLCandidate(); - - // Match HyperView theme: --card is #161b22 - const backgroundColor = "#161b22"; - - renderer.init(canvas, { + const opts = { width, height, devicePixelRatio: window.devicePixelRatio, pointRadius: 4, colors: labelsInfo.palette, - backgroundColor, - }); + backgroundColor: "#161b22", // Match HyperView theme: --card is #161b22 + }; + + const renderer: Renderer = + geometry === "euclidean" ? new viz.EuclideanWebGLCandidate() : new viz.HyperbolicWebGLCandidate(); + + renderer.init(canvas, opts); renderer.setDataset(dataset); rendererRef.current = renderer; + // Force a first render to surface WebGL2 context creation failures early. + try { + renderer.render(); + } catch (err) { + console.error("hyper-scatter initial render failed:", err); + rendererRef.current = null; + try { + renderer.destroy(); + } catch { + // ignore + } + setRendererError( + "This browser can't render the scatter plot (WebGL2 is required). Please use Chrome/Edge/Firefox." + ); + return; + } + hoveredIndexRef.current = -1; renderer.setHovered(-1); requestRender(); } catch (err) { console.error("Failed to initialize hyper-scatter renderer:", err); + setRendererError( + "Failed to initialize the scatter renderer in this browser. Please use Chrome/Edge/Firefox." + ); } }; @@ -233,7 +290,7 @@ export function useHyperScatter({ rendererRef.current = null; } }; - }, [embeddings, labelsInfo, requestRender, stopInteraction, viewMode]); + }, [embeddings, labelsInfo, requestRender, stopInteraction]); // Store -> renderer sync useEffect(() => { @@ -317,6 +374,8 @@ export function useHyperScatter({ if (typeof e.button === "number" && e.button !== 0) return; const pos = getCanvasPos(e); + pointerDownXRef.current = pos.x; + pointerDownYRef.current = pos.y; lastPointerXRef.current = pos.x; lastPointerYRef.current = pos.y; @@ -396,7 +455,7 @@ export function useHyperScatter({ ); const handlePointerUp = useCallback( - async (_e: React.PointerEvent) => { + async (e: React.PointerEvent) => { const renderer = rendererRef.current; if (!renderer || !embeddings) { stopInteraction(); @@ -424,7 +483,7 @@ export function useHyperScatter({ const polygon = capInterleavedXY(dataCoords, MAX_LASSO_VERTS); if (polygon.length < 6) return; - beginLassoSelection({ viewMode, polygon }); + beginLassoSelection({ layoutKey: embeddings.layout_key, polygon }); } catch (err) { console.error("Lasso selection failed:", err); } @@ -434,10 +493,36 @@ export function useHyperScatter({ return; } + // Click-to-select (scatter -> image grid) + // Only treat as a click if the pointer didn't move much (otherwise it's a pan). + const pos = getCanvasPos(e); + const dx = pos.x - pointerDownXRef.current; + const dy = pos.y - pointerDownYRef.current; + const CLICK_MAX_DIST_SQ = 36; // ~6px + const isClick = dx * dx + dy * dy <= CLICK_MAX_DIST_SQ; + + if (isClick) { + const hit = renderer.hitTest(pos.x, pos.y); + const idx = hit ? hit.index : -1; + + if (idx >= 0 && idx < embeddings.ids.length) { + const id = embeddings.ids[idx]; + + if (e.metaKey || e.ctrlKey) { + const next = new Set(selectedIds); + if (next.has(id)) next.delete(id); + else next.add(id); + setSelectedIds(next, "scatter"); + } else { + setSelectedIds(new Set([id]), "scatter"); + } + } + } + stopInteraction(); requestRender(); }, - [beginLassoSelection, embeddings, requestRender, stopInteraction, viewMode] + [beginLassoSelection, embeddings, getCanvasPos, requestRender, selectedIds, setSelectedIds, stopInteraction] ); const handlePointerLeave = useCallback( @@ -461,7 +546,7 @@ export function useHyperScatter({ stopInteraction(); renderer.setSelection(new Set()); - setSelectedIds(new Set()); + setSelectedIds(new Set(), "scatter"); requestRender(); }, @@ -477,5 +562,6 @@ export function useHyperScatter({ handlePointerUp, handlePointerLeave, handleDoubleClick, + rendererError, }; } diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts index f0ed658..e340b00 100644 --- a/frontend/src/lib/api.ts +++ b/frontend/src/lib/api.ts @@ -1,4 +1,4 @@ -import type { DatasetInfo, EmbeddingsData, Sample, SamplesResponse, ViewMode } from "@/types"; +import type { DatasetInfo, EmbeddingsData, Sample, SamplesResponse } from "@/types"; const API_BASE = process.env.NODE_ENV === "development" ? "http://127.0.0.1:6262" : ""; @@ -30,8 +30,13 @@ export async function fetchSamples( return res.json(); } -export async function fetchEmbeddings(): Promise { - const res = await fetch(`${API_BASE}/api/embeddings`); +export async function fetchEmbeddings(layoutKey?: string): Promise { + const params = new URLSearchParams(); + if (layoutKey) { + params.set("layout_key", layoutKey); + } + const query = params.toString(); + const res = await fetch(`${API_BASE}/api/embeddings${query ? `?${query}` : ""}`); if (!res.ok) { throw new Error(`Failed to fetch embeddings: ${res.statusText}`); } @@ -70,7 +75,7 @@ export interface LassoSelectionResponse { } export async function fetchLassoSelection(args: { - viewMode: ViewMode; + layoutKey: string; polygon: ArrayLike; offset?: number; limit?: number; @@ -83,7 +88,7 @@ export async function fetchLassoSelection(args: { "Content-Type": "application/json", }, body: JSON.stringify({ - space: args.viewMode === "euclidean" ? "euclidean" : "hyperbolic", + layout_key: args.layoutKey, polygon: Array.from(args.polygon), offset: args.offset ?? 0, limit: args.limit ?? 100, diff --git a/frontend/src/lib/layouts.ts b/frontend/src/lib/layouts.ts new file mode 100644 index 0000000..f9fd644 --- /dev/null +++ b/frontend/src/lib/layouts.ts @@ -0,0 +1,29 @@ +import type { Geometry } from "@/types"; + +export function getLayoutSpaceKey(layoutKey: string): string { + return layoutKey.split("__")[0] || layoutKey; +} + +export function getLayoutGeometry(layoutKey: string): Geometry | null { + const parts = layoutKey.split("__"); + if (parts.length < 2) { + const legacy = layoutKey.toLowerCase(); + if (legacy === "poincare" || legacy === "hyperbolic") return "poincare"; + if (legacy === "euclidean") return "euclidean"; + return null; + } + + const token = parts[1]?.split("_")[0]?.toLowerCase(); + if (token === "poincare" || token === "hyperbolic") return "poincare"; + if (token === "euclidean") return "euclidean"; + return null; +} + +export function listAvailableGeometries(layouts: string[]): Geometry[] { + const geometries = new Set(); + for (const layoutKey of layouts) { + const geometry = getLayoutGeometry(layoutKey); + if (geometry) geometries.add(geometry); + } + return Array.from(geometries); +} diff --git a/frontend/src/store/useStore.ts b/frontend/src/store/useStore.ts index 4549b9c..9d74299 100644 --- a/frontend/src/store/useStore.ts +++ b/frontend/src/store/useStore.ts @@ -1,5 +1,5 @@ import { create } from "zustand"; -import type { DatasetInfo, EmbeddingsData, Sample, ViewMode } from "@/types"; +import type { DatasetInfo, EmbeddingsData, Geometry, Sample } from "@/types"; interface AppState { // Dataset info @@ -9,6 +9,8 @@ interface AppState { // Samples samples: Sample[]; totalSamples: number; + // Number of samples loaded via offset/limit pagination (excludes ad-hoc fetched samples) + samplesLoaded: number; setSamples: (samples: Sample[], total: number) => void; appendSamples: (samples: Sample[]) => void; addSamplesIfMissing: (samples: Sample[]) => void; @@ -17,24 +19,28 @@ interface AppState { embeddings: EmbeddingsData | null; setEmbeddings: (data: EmbeddingsData) => void; - // View mode (euclidean or hyperbolic) - viewMode: ViewMode; - setViewMode: (mode: ViewMode) => void; + // Current layout key (from embeddings) + currentLayoutKey: string | null; + + // Geometry mode + geometry: Geometry; + setGeometry: (geometry: Geometry) => void; // Selection selectedIds: Set; isLassoSelection: boolean; - setSelectedIds: (ids: Set) => void; + selectionSource: "scatter" | "grid" | "lasso" | null; + setSelectedIds: (ids: Set, source?: "scatter" | "grid") => void; toggleSelection: (id: string) => void; addToSelection: (ids: string[]) => void; clearSelection: () => void; // Lasso selection (server-driven) - lassoQuery: { viewMode: ViewMode; polygon: number[] } | null; + lassoQuery: { layoutKey: string; polygon: number[] } | null; lassoSamples: Sample[]; lassoTotal: number; lassoIsLoading: boolean; - beginLassoSelection: (query: { viewMode: ViewMode; polygon: number[] }) => void; + beginLassoSelection: (query: { layoutKey: string; polygon: number[] }) => void; setLassoResults: (samples: Sample[], total: number, append?: boolean) => void; clearLassoSelection: () => void; @@ -59,11 +65,19 @@ export const useStore = create((set, get) => ({ // Samples samples: [], totalSamples: 0, - setSamples: (samples, total) => set({ samples, totalSamples: total }), + samplesLoaded: 0, + setSamples: (samples, total) => set({ samples, totalSamples: total, samplesLoaded: samples.length }), appendSamples: (newSamples) => - set((state) => ({ - samples: [...state.samples, ...newSamples], - })), + set((state) => { + const existingIds = new Set(state.samples.map((s) => s.id)); + const toAdd = newSamples.filter((s) => !existingIds.has(s.id)); + + // Advance pagination cursor by what the API returned (even if some IDs were prefetched). + const samplesLoaded = state.samplesLoaded + newSamples.length; + + if (toAdd.length === 0) return { samplesLoaded }; + return { samples: [...state.samples, ...toAdd], samplesLoaded }; + }), addSamplesIfMissing: (newSamples) => set((state) => { const existingIds = new Set(state.samples.map((s) => s.id)); @@ -74,18 +88,23 @@ export const useStore = create((set, get) => ({ // Embeddings embeddings: null, - setEmbeddings: (data) => set({ embeddings: data }), + setEmbeddings: (data) => set({ embeddings: data, currentLayoutKey: data?.layout_key ?? null }), + + // Current layout key + currentLayoutKey: null, - // View mode - viewMode: "hyperbolic", - setViewMode: (mode) => set({ viewMode: mode }), + // Geometry mode + geometry: "euclidean", + setGeometry: (geometry) => set({ geometry }), // Selection selectedIds: new Set(), isLassoSelection: false, - setSelectedIds: (ids) => + selectionSource: null, + setSelectedIds: (ids, source = "grid") => set({ selectedIds: ids, + selectionSource: ids.size > 0 ? source : null, isLassoSelection: false, lassoQuery: null, lassoSamples: [], @@ -103,6 +122,7 @@ export const useStore = create((set, get) => ({ // Manual selection from image grid, not lasso return { selectedIds: newSet, + selectionSource: newSet.size > 0 ? "grid" : null, isLassoSelection: false, lassoQuery: null, lassoSamples: [], @@ -117,6 +137,7 @@ export const useStore = create((set, get) => ({ // Manual selection from image grid, not lasso return { selectedIds: newSet, + selectionSource: newSet.size > 0 ? "grid" : null, isLassoSelection: false, lassoQuery: null, lassoSamples: [], @@ -127,6 +148,7 @@ export const useStore = create((set, get) => ({ clearSelection: () => set({ selectedIds: new Set(), + selectionSource: null, isLassoSelection: false, lassoQuery: null, lassoSamples: [], @@ -143,6 +165,7 @@ export const useStore = create((set, get) => ({ set({ isLassoSelection: true, selectedIds: new Set(), + selectionSource: "lasso", lassoQuery: query, lassoSamples: [], lassoTotal: 0, @@ -157,6 +180,7 @@ export const useStore = create((set, get) => ({ clearLassoSelection: () => set({ isLassoSelection: false, + selectionSource: null, lassoQuery: null, lassoSamples: [], lassoTotal: 0, diff --git a/frontend/src/types/index.ts b/frontend/src/types/index.ts index bfb2616..e055a89 100644 --- a/frontend/src/types/index.ts +++ b/frontend/src/types/index.ts @@ -5,8 +5,15 @@ export interface Sample { label: string | null; thumbnail: string | null; metadata: Record; - embedding_2d?: [number, number]; - embedding_2d_hyperbolic?: [number, number]; +} + +export type Geometry = "euclidean" | "poincare"; + +export interface SpaceInfo { + space_key: string; + model_id: string; + dim: number; + count: number; } export interface DatasetInfo { @@ -14,13 +21,15 @@ export interface DatasetInfo { num_samples: number; labels: string[]; label_colors: Record; + spaces: SpaceInfo[]; + layouts: string[]; } export interface EmbeddingsData { + layout_key: string; ids: string[]; labels: (string | null)[]; - euclidean: [number, number][]; - hyperbolic: [number, number][]; + coords: [number, number][]; label_colors: Record; } @@ -30,5 +39,3 @@ export interface SamplesResponse { limit: number; samples: Sample[]; } - -export type ViewMode = "euclidean" | "hyperbolic"; diff --git a/scripts/demo.py b/scripts/demo.py index 778f6f2..5347088 100644 --- a/scripts/demo.py +++ b/scripts/demo.py @@ -31,10 +31,20 @@ def main(): "--no-persist", action="store_true", help="Don't persist to database (use in-memory)" ) parser.add_argument( + "--model", + type=str, + default="openai/clip-vit-base-patch32", + help=( + "Embedding model_id to use (default: openai/clip-vit-base-patch32). " + "This is passed to Dataset.compute_embeddings(model=...)." + ), + ) + parser.add_argument( + "--datasets-dir", "--database-dir", type=str, default=None, - help="Override persistence directory (sets HYPERVIEW_DATABASE_DIR)", + help="Override persistence directory (sets HYPERVIEW_DATASETS_DIR)", ) parser.add_argument( "--no-server", @@ -43,8 +53,8 @@ def main(): ) args = parser.parse_args() - if args.database_dir: - os.environ["HYPERVIEW_DATABASE_DIR"] = args.database_dir + if args.datasets_dir: + os.environ["HYPERVIEW_DATASETS_DIR"] = args.datasets_dir import hyperview as hv @@ -58,9 +68,11 @@ def main(): max_samples=args.samples, ) - dataset.compute_embeddings(show_progress=True) + dataset.compute_embeddings(model=args.model, show_progress=True) - dataset.compute_visualization() + # Compute both euclidean and poincare layouts + dataset.compute_visualization(geometry="euclidean") + dataset.compute_visualization(geometry="poincare") if args.no_server: return diff --git a/src/hyperview/api.py b/src/hyperview/api.py index 4dfab73..0ca9516 100644 --- a/src/hyperview/api.py +++ b/src/hyperview/api.py @@ -228,9 +228,9 @@ def launch( """Launch the HyperView visualization server. Note: - HyperView's UI requires 2D projections (`embedding_2d` and - `embedding_2d_hyperbolic`). If they are missing but high-dimensional - embeddings exist, this function will compute them automatically. + HyperView's UI requires 2D layouts (Euclidean + Poincare). If they are + missing but high-dimensional embeddings exist, this function will compute + them automatically. Args: dataset: The dataset to visualize. @@ -318,26 +318,23 @@ def launch( "port or stop the process listening on that port." ) - # The frontend requires both euclidean + hyperbolic 2D coords from /api/embeddings. - # If they're missing, proactively compute them so the first page load doesn't fail. - try: - ids, _, _, _ = dataset._storage.get_visualization_embeddings() - except Exception: - ids = [] + # The frontend requires 2D coords from /api/embeddings. + # Ensure at least one layout exists; do not auto-generate optional geometries. + layouts = dataset.list_layouts() + spaces = dataset.list_spaces() + + if not spaces: + raise ValueError( + "HyperView launch requires 2D projections for the UI. " + "No projections or embedding spaces were found. " + "Call `dataset.compute_embeddings()` and `dataset.compute_visualization()` " + "before `hv.launch()`." + ) - if not ids: - has_any_embeddings = any(s.embedding is not None for s in dataset._storage) - if has_any_embeddings: - print("No 2D projections found. Computing visualizations...") - dataset.compute_visualization() - else: - raise ValueError( - "HyperView launch requires 2D projections for the UI. " - "No projections were found, and no high-dimensional embeddings are present. " - "Call `dataset.compute_embeddings()` and `dataset.compute_visualization()` " - "before `hv.launch()`, or prepopulate both `embedding_2d` and " - "`embedding_2d_hyperbolic` on your samples." - ) + if not layouts: + default_space_key = spaces[0].space_key + print("No layouts found. Computing euclidean visualization...") + dataset.compute_visualization(space_key=default_space_key, geometry="euclidean") session = Session(dataset, host, port) diff --git a/src/hyperview/cli.py b/src/hyperview/cli.py index dc9c1fc..4df7c0f 100644 --- a/src/hyperview/cli.py +++ b/src/hyperview/cli.py @@ -134,7 +134,9 @@ def run_demo( print("Embeddings computed") print("Computing visualizations...") - dataset.compute_visualization() + # Compute both euclidean and poincare layouts + dataset.compute_visualization(geometry="euclidean") + dataset.compute_visualization(geometry="poincare") print("Visualizations ready") launch(dataset, port=port, host=host, open_browser=open_browser, reuse_server=reuse_server) diff --git a/src/hyperview/core/dataset.py b/src/hyperview/core/dataset.py index 15afa58..bd69347 100644 --- a/src/hyperview/core/dataset.py +++ b/src/hyperview/core/dataset.py @@ -13,6 +13,7 @@ from hyperview.core.sample import Sample, SampleFromArray from hyperview.storage.backend import StorageBackend +from hyperview.storage.schema import make_layout_key, make_space_key class Dataset: @@ -23,6 +24,9 @@ class Dataset: - Vector similarity search - Efficient storage and retrieval + Embeddings are stored separately from samples, keyed by model_id. + Layouts (2D projections) are stored per layout_key (space + method). + Examples: # Create a new dataset (auto-persisted) dataset = hv.Dataset("my_dataset") @@ -40,7 +44,6 @@ def __init__( name: str | None = None, persist: bool = True, storage: StorageBackend | None = None, - embedding_dim: int = 512, ): """Initialize a new dataset. @@ -49,10 +52,8 @@ def __init__( persist: If True (default), use LanceDB for persistence. If False, use in-memory storage. storage: Optional custom storage backend. If provided, persist is ignored. - embedding_dim: Dimension of embeddings (default 512 for CLIP). """ self.name = name or f"dataset_{uuid.uuid4().hex[:8]}" - self._embedding_dim = embedding_dim self._embedding_computer = None self._projection_engine = None @@ -62,7 +63,7 @@ def __init__( elif persist: from hyperview.storage import LanceDBBackend, StorageConfig - config = StorageConfig.default(embedding_dim=embedding_dim) + config = StorageConfig.default() self._storage = LanceDBBackend(self.name, config) else: from hyperview.storage import MemoryBackend @@ -373,40 +374,46 @@ def compute_embeddings( model: str = "openai/clip-vit-base-patch32", batch_size: int = 32, show_progress: bool = True, - ) -> None: + ) -> str: """Compute embeddings for samples that don't have them yet. + Embeddings are stored in a dedicated space keyed by model_id. + Args: model: EmbedAnything HuggingFace `model_id` to use. batch_size: Batch size for processing. show_progress: Whether to show progress bar. + + Returns: + space_key for the embedding space. """ from hyperview.embeddings.compute import EmbeddingComputer + if self._embedding_computer is None or self._embedding_computer.model_id != model: + self._embedding_computer = EmbeddingComputer(model=model) + + # Get embedding dimension from a test computation all_samples = self._storage.get_all_samples() + if not all_samples: + raise ValueError("No samples in dataset") - if self._embedding_computer is None: - self._embedding_computer = EmbeddingComputer(model=model) - else: - existing_model_id = getattr(self._embedding_computer, "model_id", None) - if existing_model_id and existing_model_id != model: - if any(s.embedding is not None for s in all_samples): - raise ValueError( - "Embeddings already exist for this dataset, but " - "compute_embeddings() was called with a different model_id. " - f"Existing model_id={existing_model_id!r}, requested model_id={model!r}. " - "Create a new Dataset or clear existing embeddings before recomputing." - ) - - # No existing embeddings yet: allow switching models. - self._embedding_computer = EmbeddingComputer(model=model) - # Only compute for samples without embeddings - samples_needing_embeddings = [s for s in all_samples if s.embedding is None] - - if not samples_needing_embeddings: + # Compute one embedding to get dimension + test_embedding = self._embedding_computer.compute_single(all_samples[0]) + dim = len(test_embedding) + + # Ensure space exists + space_key = make_space_key(model) + self._storage.ensure_space(model, dim) + + # Find samples needing embeddings + missing_ids = self._storage.get_missing_embedding_ids(space_key) + + if not missing_ids: if show_progress: - print(f"All {len(all_samples)} samples already have embeddings") - return + print(f"All {len(all_samples)} samples already have embeddings in space '{space_key}'") + return space_key + + samples_needing_embeddings = self._storage.get_samples_by_ids(missing_ids) if show_progress: skipped = len(all_samples) - len(samples_needing_embeddings) @@ -417,119 +424,142 @@ def compute_embeddings( samples_needing_embeddings, batch_size=batch_size, show_progress=show_progress ) - # Update samples with embeddings - for sample, embedding in zip(samples_needing_embeddings, embeddings): - sample.embedding = embedding.tolist() + # Store embeddings + ids = [s.id for s in samples_needing_embeddings] + vectors = np.array(embeddings, dtype=np.float32) + self._storage.add_embeddings(space_key, ids, vectors) - # Batch update in storage - self._storage.update_samples_batch(samples_needing_embeddings) + return space_key def compute_visualization( self, + space_key: str | None = None, method: str = "umap", + geometry: str = "euclidean", n_neighbors: int = 15, min_dist: float = 0.1, metric: str = "cosine", force: bool = False, - ) -> None: + ) -> str: """Compute 2D projections for visualization. Args: + space_key: Embedding space to project. If None, uses the first available. method: Projection method ('umap' supported). + geometry: Geometry type ('euclidean' or 'poincare'). n_neighbors: Number of neighbors for UMAP. min_dist: Minimum distance for UMAP. metric: Distance metric for UMAP. - force: Force recomputation even if projections exist. + force: Force recomputation even if layout exists. + + Returns: + layout_key for the computed layout. """ from hyperview.embeddings.projection import ProjectionEngine + if geometry not in ("euclidean", "poincare"): + raise ValueError(f"Invalid geometry: {geometry}. Must be 'euclidean' or 'poincare'.") + if self._projection_engine is None: self._projection_engine = ProjectionEngine() - samples_with_embeddings = [s for s in self._storage if s.embedding is not None] - if not samples_with_embeddings: - raise ValueError("No embeddings computed. Call compute_embeddings() first.") - - # Check if projections already exist (unless forced) - if not force: - samples_needing_projection = [ - s for s in samples_with_embeddings if s.embedding_2d is None - ] - if not samples_needing_projection: - print(f"All {len(samples_with_embeddings)} samples already have projections") - return - # UMAP needs consistent projections, so if any samples need it, recompute all - # (can't mix old and new UMAP projections) - if len(samples_needing_projection) < len(samples_with_embeddings): - print( - f"Some samples missing projections - recomputing all " - f"({len(samples_needing_projection)} new, " - f"{len(samples_with_embeddings) - len(samples_needing_projection)} existing)" - ) + # Get space + if space_key is None: + spaces = self._storage.list_spaces() + if not spaces: + raise ValueError("No embedding spaces. Call compute_embeddings() first.") + space_key = spaces[0].space_key + + space = self._storage.get_space(space_key) + if space is None: + raise ValueError(f"Space not found: {space_key}") + + # Get all embeddings from this space + ids, vectors = self._storage.get_embeddings(space_key) + if len(ids) == 0: + raise ValueError(f"No embeddings in space '{space_key}'. Call compute_embeddings() first.") + + # Generate layout key (includes geometry) + layout_key = make_layout_key(space_key, method, geometry) + + # Check if layout exists + if not force and layout_key in self._storage.list_layouts(): + existing_ids, _ = self._storage.get_layout_coords(layout_key) + if set(existing_ids) == set(ids): + print(f"Layout '{layout_key}' already exists with {len(ids)} points") + return layout_key + else: + print(f"Layout exists but has different samples, recomputing...") - samples = samples_with_embeddings - embeddings = np.array([s.embedding for s in samples]) + if len(ids) < 3: + raise ValueError(f"Need at least 3 samples for visualization, have {len(ids)}") - # Compute Euclidean 2D projection - coords_euclidean = self._projection_engine.project_umap( - embeddings, - n_neighbors=n_neighbors, - min_dist=min_dist, - metric=metric, - ) + print(f"Computing {geometry} {method} layout for {len(ids)} samples...") - # Compute Hyperbolic (Poincaré) 2D projection - coords_hyperbolic = self._projection_engine.project_to_poincare( - embeddings, - n_neighbors=n_neighbors, - min_dist=min_dist, - ) + # Compute projection based on geometry + if geometry == "poincare": + coords = self._projection_engine.project_to_poincare( + vectors, + n_neighbors=n_neighbors, + min_dist=min_dist, + metric=metric, + ) + else: + coords = self._projection_engine.project_umap( + vectors, + n_neighbors=n_neighbors, + min_dist=min_dist, + metric=metric, + ) + + # Store layout + self._storage.add_layout_coords(layout_key, ids, coords) - for sample, coord_e, coord_h in zip(samples, coords_euclidean, coords_hyperbolic): - sample.embedding_2d = coord_e.tolist() - sample.embedding_2d_hyperbolic = coord_h.tolist() + return layout_key - # Batch update in storage - self._storage.update_samples_batch(samples) + def list_spaces(self) -> list[Any]: + """List all embedding spaces in this dataset.""" + return self._storage.list_spaces() + + def list_layouts(self) -> list[str]: + """List all layouts in this dataset.""" + return self._storage.list_layouts() def find_similar( self, sample_id: str, k: int = 10, - use_hyperbolic: bool = False, + space_key: str | None = None, ) -> list[tuple[Sample, float]]: """Find k most similar samples to a given sample. Args: sample_id: ID of the query sample. k: Number of neighbors to return. - use_hyperbolic: If True, search in hyperbolic embedding space. - If False (default), search in high-dimensional embedding space. + space_key: Embedding space to search in. If None, uses first available. Returns: List of (sample, distance) tuples, sorted by distance ascending. """ - vector_column = "embedding_2d_hyperbolic" if use_hyperbolic else "embedding" - return self._storage.find_similar(sample_id, k, vector_column) + return self._storage.find_similar(sample_id, k, space_key) def find_similar_by_vector( self, vector: list[float], k: int = 10, - use_hyperbolic: bool = False, + space_key: str | None = None, ) -> list[tuple[Sample, float]]: """Find k most similar samples to a given vector. Args: vector: Query vector. k: Number of neighbors to return. - use_hyperbolic: If True, search in hyperbolic embedding space. + space_key: Embedding space to search in. If None, uses first available. Returns: List of (sample, distance) tuples, sorted by distance ascending. """ - vector_column = "embedding_2d_hyperbolic" if use_hyperbolic else "embedding" - return self._storage.find_similar_by_vector(vector, k, vector_column) + return self._storage.find_similar_by_vector(vector, k, space_key) def _assign_label_color(self, label: str, colors: dict[str, str]) -> None: """Assign a color to a label.""" @@ -546,6 +576,46 @@ def get_label_colors(self) -> dict[str, str]: """Get the color mapping for labels.""" return self._storage.label_colors.copy() + def set_coords( + self, + geometry: str, + ids: list[str], + coords: np.ndarray | list[list[float]], + ) -> str: + """Set precomputed 2D coordinates for visualization. + + Use this when you have precomputed 2D projections and want to skip + embedding computation. Useful for smoke tests or external projections. + + Args: + geometry: "euclidean" or "poincare". + ids: List of sample IDs. + coords: (N, 2) array of coordinates. + + Returns: + The layout_key for the stored coordinates. + + Example: + >>> dataset.set_coords("euclidean", ["s0", "s1"], [[0.1, 0.2], [0.3, 0.4]]) + >>> dataset.set_coords("poincare", ["s0", "s1"], [[0.1, 0.2], [0.3, 0.4]]) + >>> hv.launch(dataset) + """ + if geometry not in ("euclidean", "poincare"): + raise ValueError(f"geometry must be 'euclidean' or 'poincare', got '{geometry}'") + + coords_arr = np.asarray(coords, dtype=np.float32) + if coords_arr.ndim != 2 or coords_arr.shape[1] != 2: + raise ValueError(f"coords must be (N, 2), got shape {coords_arr.shape}") + + # Ensure a synthetic space exists (required by launch()) + space_key = "precomputed" + if not any(s.space_key == space_key for s in self._storage.list_spaces()): + self._storage.ensure_space(space_key, dim=2) + + layout_key = make_layout_key(space_key, method="precomputed", geometry=geometry) + self._storage.add_layout_coords(layout_key, list(ids), coords_arr) + return layout_key + @property def samples(self) -> list[Sample]: """Get all samples as a list.""" @@ -595,9 +665,6 @@ def save(self, filepath: str, include_thumbnails: bool = True) -> None: "filepath": s.filepath, "label": s.label, "metadata": s.metadata, - "embedding": s.embedding, - "embedding_2d": s.embedding_2d, - "embedding_2d_hyperbolic": s.embedding_2d_hyperbolic, "thumbnail_base64": s.thumbnail_base64 if include_thumbnails else None, } for s in samples @@ -635,9 +702,6 @@ def load(cls, filepath: str, persist: bool = False) -> "Dataset": filepath=s_data["filepath"], label=s_data.get("label"), metadata=s_data.get("metadata", {}), - embedding=s_data.get("embedding"), - embedding_2d=s_data.get("embedding_2d"), - embedding_2d_hyperbolic=s_data.get("embedding_2d_hyperbolic"), thumbnail_base64=s_data.get("thumbnail_base64"), ) samples.append(sample) @@ -646,12 +710,11 @@ def load(cls, filepath: str, persist: bool = False) -> "Dataset": return dataset @classmethod - def open(cls, name: str, embedding_dim: int = 512) -> "Dataset": + def open(cls, name: str) -> "Dataset": """Open an existing persistent dataset. Args: name: Name of the dataset to open. - embedding_dim: Embedding dimension (must match original). Returns: Dataset instance connected to existing data. @@ -667,7 +730,7 @@ def open(cls, name: str, embedding_dim: int = 512) -> "Dataset": f"Available datasets: {cls.list_datasets()}" ) - return cls(name=name, persist=True, embedding_dim=embedding_dim) + return cls(name=name, persist=True) @classmethod def list_datasets(cls) -> list[str]: diff --git a/src/hyperview/core/sample.py b/src/hyperview/core/sample.py index 2052c85..cd4c84e 100644 --- a/src/hyperview/core/sample.py +++ b/src/hyperview/core/sample.py @@ -11,17 +11,16 @@ class Sample(BaseModel): - """A single sample in a HyperView dataset.""" + """A single sample in a HyperView dataset. + + Samples are pure metadata containers. Embeddings and layouts are stored + separately in dedicated tables (per embedding space / per layout). + """ id: str = Field(..., description="Unique identifier for the sample") filepath: str = Field(..., description="Path to the image file") label: str | None = Field(default=None, description="Label for the sample") metadata: dict[str, Any] = Field(default_factory=dict, description="Additional metadata") - embedding: list[float] | None = Field(default=None, description="High-dimensional embedding") - embedding_2d: list[float] | None = Field(default=None, description="2D projected embedding") - embedding_2d_hyperbolic: list[float] | None = Field( - default=None, description="2D hyperbolic (Poincaré) embedding" - ) thumbnail_base64: str | None = Field(default=None, description="Cached thumbnail as base64") model_config = {"arbitrary_types_allowed": True} @@ -76,10 +75,6 @@ def to_api_dict(self, include_thumbnail: bool = True) -> dict[str, Any]: } if include_thumbnail: data["thumbnail"] = self.get_thumbnail_base64() - if self.embedding_2d: - data["embedding_2d"] = self.embedding_2d - if self.embedding_2d_hyperbolic: - data["embedding_2d_hyperbolic"] = self.embedding_2d_hyperbolic return data diff --git a/src/hyperview/embeddings/projection.py b/src/hyperview/embeddings/projection.py index ccf8749..2ed5e3d 100644 --- a/src/hyperview/embeddings/projection.py +++ b/src/hyperview/embeddings/projection.py @@ -38,12 +38,15 @@ def project_umap( if n_neighbors < 2: n_neighbors = 2 + n_jobs = 1 if random_state is not None else -1 + reducer = umap.UMAP( n_neighbors=n_neighbors, min_dist=min_dist, n_components=n_components, metric=metric, random_state=random_state, + n_jobs=n_jobs, ) coords = reducer.fit_transform(embeddings) @@ -81,6 +84,8 @@ def project_to_poincare( n_neighbors = min(n_neighbors, len(embeddings) - 1) if n_neighbors < 2: n_neighbors = 2 + + n_jobs = 1 if random_state is not None else -1 # The time-like coordinate t is implicit: t = sqrt(1 + x^2 + y^2). reducer = umap.UMAP( n_neighbors=n_neighbors, @@ -89,6 +94,7 @@ def project_to_poincare( metric=metric, output_metric="hyperboloid", random_state=random_state, + n_jobs=n_jobs, ) # These are spatial coordinates (x, y) in the Hyperboloid model diff --git a/src/hyperview/server/app.py b/src/hyperview/server/app.py index 21c3655..d06cf08 100644 --- a/src/hyperview/server/app.py +++ b/src/hyperview/server/app.py @@ -2,6 +2,7 @@ import os from pathlib import Path +from typing import Any from fastapi import FastAPI, HTTPException, Query from fastapi.middleware.cors import CORSMiddleware @@ -28,7 +29,7 @@ class SelectionRequest(BaseModel): class LassoSelectionRequest(BaseModel): """Request model for lasso selection queries.""" - space: str + layout_key: str # e.g., "openai_clip-vit-base-patch32__umap" # Polygon vertices in data space, interleaved: [x0, y0, x1, y1, ...] polygon: list[float] offset: int = 0 @@ -45,8 +46,6 @@ class SampleResponse(BaseModel): label: str | None thumbnail: str | None metadata: dict - embedding_2d: list[float] | None = None - embedding_2d_hyperbolic: list[float] | None = None class DatasetResponse(BaseModel): @@ -56,18 +55,29 @@ class DatasetResponse(BaseModel): num_samples: int labels: list[str] label_colors: dict[str, str] + spaces: list[dict[str, Any]] + layouts: list[str] class EmbeddingsResponse(BaseModel): - """Response model for embeddings data.""" + """Response model for embeddings data (for scatter plot).""" + layout_key: str ids: list[str] labels: list[str | None] - euclidean: list[list[float]] - hyperbolic: list[list[float]] + coords: list[list[float]] # [[x, y], ...] label_colors: dict[str, str] +class SpaceInfo(BaseModel): + """Response model for embedding space info.""" + + space_key: str + model_id: str + dim: int + count: int + + class SimilarSampleResponse(BaseModel): """Response model for a similar sample with distance.""" @@ -78,8 +88,6 @@ class SimilarSampleResponse(BaseModel): thumbnail: str | None distance: float metadata: dict - embedding_2d: list[float] | None = None - embedding_2d_hyperbolic: list[float] | None = None class SimilaritySearchResponse(BaseModel): @@ -136,11 +144,24 @@ async def get_dataset_info(): if _current_dataset is None: raise HTTPException(status_code=404, detail="No dataset loaded") + spaces = _current_dataset.list_spaces() + space_dicts = [ + { + "space_key": s.space_key, + "model_id": s.model_id, + "dim": s.dim, + "count": s.count, + } + for s in spaces + ] + return DatasetResponse( name=_current_dataset.name, num_samples=len(_current_dataset), labels=_current_dataset.labels, label_colors=_current_dataset.get_label_colors(), + spaces=space_dicts, + layouts=_current_dataset.list_layouts(), ) @app.get("/api/samples") @@ -188,25 +209,66 @@ async def get_samples_batch(request: SelectionRequest): return {"samples": [s.to_api_dict(include_thumbnail=True) for s in samples]} @app.get("/api/embeddings", response_model=EmbeddingsResponse) - async def get_embeddings(): - """Get all embeddings for visualization.""" + async def get_embeddings(layout_key: str | None = None): + """Get embedding coordinates for visualization.""" if _current_dataset is None: raise HTTPException(status_code=404, detail="No dataset loaded") - ids, labels, euclidean, hyperbolic = _current_dataset._storage.get_visualization_embeddings() + # Get available layouts + layouts = _current_dataset.list_layouts() + if not layouts: + raise HTTPException( + status_code=400, detail="No layouts computed. Call compute_visualization() first." + ) + + # Use specified layout or first available + if layout_key is None: + layout_key = layouts[0] + elif layout_key not in layouts: + raise HTTPException(status_code=404, detail=f"Layout not found: {layout_key}") + + ids, labels, coords = _current_dataset._storage.get_visualization_data(layout_key) + if not ids: raise HTTPException( - status_code=400, detail="No embeddings computed. Call compute_visualization() first." + status_code=400, detail=f"No data in layout '{layout_key}'." ) return EmbeddingsResponse( + layout_key=layout_key, ids=ids, labels=labels, - euclidean=euclidean, - hyperbolic=hyperbolic, + coords=coords.tolist(), label_colors=_current_dataset.get_label_colors(), ) + @app.get("/api/spaces") + async def get_spaces(): + """Get all embedding spaces.""" + if _current_dataset is None: + raise HTTPException(status_code=404, detail="No dataset loaded") + + spaces = _current_dataset.list_spaces() + return { + "spaces": [ + { + "space_key": s.space_key, + "model_id": s.model_id, + "dim": s.dim, + "count": s.count, + } + for s in spaces + ] + } + + @app.get("/api/layouts") + async def get_layouts(): + """Get all available layouts.""" + if _current_dataset is None: + raise HTTPException(status_code=404, detail="No dataset loaded") + + return {"layouts": _current_dataset.list_layouts()} + @app.post("/api/selection") async def sync_selection(request: SelectionRequest): """Sync selection state (for future use).""" @@ -247,10 +309,9 @@ async def lasso_selection(request: LassoSelectionRequest): y_min = float(np.min(poly[:, 1])) y_max = float(np.max(poly[:, 1])) - try: candidate_ids, candidate_coords = _current_dataset._storage.get_lasso_candidates_aabb( - space=request.space, + layout_key=request.layout_key, x_min=x_min, x_max=x_max, y_min=y_min, @@ -288,7 +349,7 @@ async def lasso_selection(request: LassoSelectionRequest): async def search_similar( sample_id: str, k: int = Query(10, ge=1, le=100), - use_hyperbolic: bool = Query(False), + space_key: str | None = None, ): """Return k nearest neighbors for a given sample.""" if _current_dataset is None: @@ -296,7 +357,7 @@ async def search_similar( try: similar = _current_dataset.find_similar( - sample_id, k=k, use_hyperbolic=use_hyperbolic + sample_id, k=k, space_key=space_key ) except ValueError as e: raise HTTPException(status_code=400, detail=str(e)) @@ -319,8 +380,6 @@ async def search_similar( thumbnail=thumbnail, distance=distance, metadata=sample.metadata, - embedding_2d=sample.embedding_2d, - embedding_2d_hyperbolic=sample.embedding_2d_hyperbolic, ) ) diff --git a/src/hyperview/server/static/404.html b/src/hyperview/server/static/404.html index 61eb8b7..dcdfe7a 100644 --- a/src/hyperview/server/static/404.html +++ b/src/hyperview/server/static/404.html @@ -1 +1 @@ -404: This page could not be found.HyperView

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abstractmethod from collections.abc import Callable, Iterator +from typing import Any + +import numpy as np from hyperview.core.sample import Sample class StorageBackend(ABC): - """Abstract base class for storage backends.""" + """Abstract base class for storage backends. + + Storage architecture: + - Samples: Core metadata (id, filepath, label, metadata, thumbnail) + - Spaces: Registry of embedding spaces (one per model) + - Embeddings: Per-space tables with vectors + - Layouts: Per-layout tables with 2D coordinates + """ + + # ========================================================================= + # Sample CRUD + # ========================================================================= @abstractmethod def add_sample(self, sample: Sample) -> None: @@ -40,7 +54,7 @@ def update_sample(self, sample: Sample) -> None: @abstractmethod def update_samples_batch(self, samples: list[Sample]) -> None: - """Batch update samples (for embeddings, etc.).""" + """Batch update samples.""" @abstractmethod def delete_sample(self, sample_id: str) -> bool: @@ -63,79 +77,147 @@ def get_unique_labels(self) -> list[str]: """Get all unique labels.""" @abstractmethod - def find_similar( - self, - sample_id: str, - k: int = 10, - vector_column: str = "embedding", - ) -> list[tuple[Sample, float]]: - """Find k nearest neighbors. Returns [(sample, distance), ...].""" + def get_existing_ids(self, sample_ids: list[str]) -> set[str]: + """Return set of sample_ids that already exist in storage.""" @abstractmethod - def find_similar_by_vector( - self, - vector: list[float], - k: int = 10, - vector_column: str = "embedding", - ) -> list[tuple[Sample, float]]: - """Find k nearest neighbors to a query vector.""" + def get_samples_by_ids(self, sample_ids: list[str]) -> list[Sample]: + """Retrieve multiple samples by ID.""" @abstractmethod def filter(self, predicate: Callable[[Sample], bool]) -> list[Sample]: """Filter samples based on a predicate function.""" + # ========================================================================= + # Spaces (embedding space registry) + # ========================================================================= + @abstractmethod - def get_existing_ids(self, sample_ids: list[str]) -> set[str]: - """Return set of sample_ids that already exist in storage. + def list_spaces(self) -> list[Any]: + """List all embedding spaces. Returns list of SpaceInfo.""" - Args: - sample_ids: List of sample IDs to check. + @abstractmethod + def get_space(self, space_key: str) -> Any | None: + """Get info for a specific embedding space. Returns SpaceInfo or None.""" - Returns: - Set of IDs that exist in storage. - """ + @abstractmethod + def ensure_space(self, model_id: str, dim: int, config: dict | None = None) -> Any: + """Ensure an embedding space exists, creating if needed. Returns SpaceInfo.""" @abstractmethod - def get_samples_by_ids(self, sample_ids: list[str]) -> list[Sample]: - """Retrieve multiple samples by ID. + def delete_space(self, space_key: str) -> bool: + """Delete an embedding space and its embeddings.""" - Implementations should preserve the input order when possible. - Missing IDs should be skipped. - """ + # ========================================================================= + # Embeddings + # ========================================================================= @abstractmethod - def get_visualization_embeddings( + def add_embeddings( self, - ) -> tuple[list[str], list[str | None], list[list[float]], list[list[float]]]: - """Return all 2D embedding coords needed by the frontend scatter. + space_key: str, + ids: list[str], + vectors: np.ndarray, + ) -> None: + """Add embeddings to a space.""" - Returns: - (ids, labels, euclidean_xy, hyperbolic_xy) + @abstractmethod + def get_embeddings( + self, + space_key: str, + ids: list[str] | None = None, + ) -> tuple[list[str], np.ndarray]: + """Get embeddings from a space. Returns (ids, vectors).""" + + @abstractmethod + def get_embedded_ids(self, space_key: str) -> set[str]: + """Get the set of sample IDs that have embeddings in a space.""" - Notes: - This should avoid loading heavy fields (thumbnails, high-dim embeddings) - when the backend supports column projection. + @abstractmethod + def get_missing_embedding_ids(self, space_key: str) -> list[str]: + """Get sample IDs that don't have embeddings in a space.""" + + # ========================================================================= + # Layouts (2D projections) + # ========================================================================= + + @abstractmethod + def list_layouts(self) -> list[str]: + """List all layout keys.""" + + @abstractmethod + def ensure_layout(self, layout_key: str) -> None: + """Ensure a layout exists.""" + + @abstractmethod + def delete_layout(self, layout_key: str) -> bool: + """Delete a layout.""" + + @abstractmethod + def add_layout_coords( + self, + layout_key: str, + ids: list[str], + coords: np.ndarray, + ) -> None: + """Add layout coordinates (N x 2).""" + + @abstractmethod + def get_layout_coords( + self, + layout_key: str, + ids: list[str] | None = None, + ) -> tuple[list[str], np.ndarray]: + """Get layout coordinates. Returns (ids, coords).""" + + @abstractmethod + def get_visualization_data( + self, + layout_key: str, + ) -> tuple[list[str], list[str | None], np.ndarray]: + """Get visualization data for scatter plot. + + Returns: + (ids, labels, coords) where coords is (N x 2). """ @abstractmethod def get_lasso_candidates_aabb( self, *, - space: str, + layout_key: str, x_min: float, x_max: float, y_min: float, y_max: float, - ) -> tuple[list[str], "np.ndarray"]: - """Return candidate (id, xy) rows within an axis-aligned bounding box. + ) -> tuple[list[str], np.ndarray]: + """Return candidate (id, xy) rows within an axis-aligned bounding box.""" - Args: - space: Projection key (e.g. "euclidean", "hyperbolic"). - x_min/x_max/y_min/y_max: Bounds in the same data space as /api/embeddings. + # ========================================================================= + # Similarity search + # ========================================================================= - Returns: - (ids, coords_xy) where coords_xy has shape (n, 2) float32. - """ + @abstractmethod + def find_similar( + self, + sample_id: str, + k: int = 10, + space_key: str | None = None, + ) -> list[tuple[Sample, float]]: + """Find k nearest neighbors. Returns [(sample, distance), ...].""" + + @abstractmethod + def find_similar_by_vector( + self, + vector: list[float] | np.ndarray, + k: int = 10, + space_key: str | None = None, + ) -> list[tuple[Sample, float]]: + """Find k nearest neighbors to a query vector.""" + + # ========================================================================= + # Lifecycle and metadata + # ========================================================================= @abstractmethod def close(self) -> None: diff --git a/src/hyperview/storage/config.py b/src/hyperview/storage/config.py index c916855..47ad1e5 100644 --- a/src/hyperview/storage/config.py +++ b/src/hyperview/storage/config.py @@ -5,12 +5,13 @@ from pathlib import Path -def get_default_database_dir() -> Path: - """Get the default database directory. +def get_default_datasets_dir() -> Path: + """Get the default datasets directory. - Uses HYPERVIEW_DATABASE_DIR env var if set, otherwise ~/.hyperview/datasets/ + Uses HYPERVIEW_DATASETS_DIR env var if set, otherwise ~/.hyperview/datasets/ + Each dataset gets its own subdirectory with isolated LanceDB tables. """ - env_dir = os.environ.get("HYPERVIEW_DATABASE_DIR") + env_dir = os.environ.get("HYPERVIEW_DATASETS_DIR") if env_dir: return Path(env_dir) return Path.home() / ".hyperview" / "datasets" @@ -32,23 +33,20 @@ def get_default_media_dir() -> Path: class StorageConfig: """Configuration for storage backend.""" - database_dir: Path = field(default_factory=get_default_database_dir) + datasets_dir: Path = field(default_factory=get_default_datasets_dir) media_dir: Path = field(default_factory=get_default_media_dir) - embedding_dim: int = 512 # Default CLIP dimension - embedding_2d_dim: int = 2 @classmethod - def default(cls, embedding_dim: int = 512) -> "StorageConfig": - """Create a default configuration with optional custom embedding dimension.""" + def default(cls) -> "StorageConfig": + """Create a default configuration.""" return cls( - database_dir=get_default_database_dir(), + datasets_dir=get_default_datasets_dir(), media_dir=get_default_media_dir(), - embedding_dim=embedding_dim, ) def ensure_dir_exists(self) -> None: - """Ensure the database directory exists.""" - self.database_dir.mkdir(parents=True, exist_ok=True) + """Ensure the datasets directory exists.""" + self.datasets_dir.mkdir(parents=True, exist_ok=True) def ensure_media_dir_exists(self) -> None: """Ensure the media directory exists.""" diff --git a/src/hyperview/storage/lancedb_backend.py b/src/hyperview/storage/lancedb_backend.py index b83618b..08cf7c6 100644 --- a/src/hyperview/storage/lancedb_backend.py +++ b/src/hyperview/storage/lancedb_backend.py @@ -1,25 +1,45 @@ -"""LanceDB storage backend for HyperView.""" +"""LanceDB storage backend for HyperView. + +Storage architecture (per-dataset directory): + ~/.hyperview/datasets// + - samples table: core sample metadata + - metadata table: key-value config + - spaces table: registry of embedding spaces + - embeddings__ tables: one per embedding model + - layouts__ tables: one per layout +""" import json +import time from collections.abc import Callable, Iterator from pathlib import Path import lancedb +import numpy as np import pyarrow as pa from hyperview.core.sample import Sample from hyperview.storage.backend import StorageBackend -from hyperview.storage.config import StorageConfig, get_default_database_dir +from hyperview.storage.config import StorageConfig, get_default_datasets_dir from hyperview.storage.schema import ( + SpaceInfo, + create_embeddings_schema, + create_layouts_schema, create_metadata_schema, create_sample_schema, + create_spaces_schema, dict_to_sample, + make_layout_key, + make_space_key, sample_to_dict, ) class LanceDBBackend(StorageBackend): - """LanceDB-based storage backend for HyperView datasets.""" + """LanceDB-based storage backend for HyperView datasets. + + Each dataset gets its own directory with isolated LanceDB tables. + """ def __init__( self, @@ -29,88 +49,93 @@ def __init__( """Initialize LanceDB backend. Args: - dataset_name: Name of the dataset (becomes table name). + dataset_name: Name of the dataset (becomes directory name). config: Storage configuration. Uses defaults if None. """ self.dataset_name = dataset_name self.config = config or StorageConfig.default() - self.config.ensure_dir_exists() + # Per-dataset directory + self._dataset_dir = self.config.datasets_dir / dataset_name + self._dataset_dir.mkdir(parents=True, exist_ok=True) - self._db = lancedb.connect(str(self.config.database_dir)) + # Connect to this dataset's LanceDB + self._db = lancedb.connect(str(self._dataset_dir)) - self._table_name = f"hyperview_{dataset_name}" - self._metadata_table_name = f"hyperview_{dataset_name}_meta" + # Table names (no prefix needed since we have per-dataset isolation) + self._samples_table_name = "samples" + self._metadata_table_name = "metadata" + self._spaces_table_name = "spaces" - self._table = self._get_or_create_table() + # Initialize core tables + self._samples_table = self._get_or_create_samples_table() self._metadata_table = self._get_or_create_metadata_table() + self._spaces_table = self._get_or_create_spaces_table() self._label_colors_cache: dict[str, str] | None = None - def _get_or_create_table(self) -> lancedb.table.Table | None: - """Get existing table or return None if it doesn't exist yet.""" - if self._table_name in self._db.table_names(): - return self._db.open_table(self._table_name) + # ========================================================================= + # Table initialization + # ========================================================================= + + def _get_or_create_samples_table(self) -> lancedb.table.Table | None: + """Get existing samples table or return None.""" + if self._samples_table_name in self._db.table_names(): + return self._db.open_table(self._samples_table_name) return None - def _ensure_table_exists(self, data: list[dict]) -> lancedb.table.Table: - """Ensure table exists, creating it from data if needed. - """ - if self._table is None: - schema = create_sample_schema(self.config.embedding_dim) + def _ensure_samples_table(self, data: list[dict]) -> lancedb.table.Table: + """Ensure samples table exists, creating from data if needed.""" + if self._samples_table is None: + schema = create_sample_schema() arrow_table = pa.Table.from_pylist(data, schema=schema) - self._table = self._db.create_table(self._table_name, data=arrow_table) - return self._table - - def _cast_rows_to_table_schema(self, rows: list[dict]) -> pa.Table: - """Cast Python dict rows to the existing table schema.""" - if self._table is None: - raise RuntimeError("Cannot cast rows: samples table not initialized") - return pa.Table.from_pylist(rows, schema=self._table.schema) + self._samples_table = self._db.create_table(self._samples_table_name, data=arrow_table) + return self._samples_table def _get_or_create_metadata_table(self) -> lancedb.table.Table: """Get or create metadata table.""" if self._metadata_table_name in self._db.table_names(): return self._db.open_table(self._metadata_table_name) - else: - schema = create_metadata_schema() - return self._db.create_table(self._metadata_table_name, schema=schema) + schema = create_metadata_schema() + return self._db.create_table(self._metadata_table_name, schema=schema) - def _convert_embeddings_to_numpy(self, data: list[dict]) -> None: - """Convert high-dim embedding lists to numpy arrays in-place. - """ - import numpy as np + def _get_or_create_spaces_table(self) -> lancedb.table.Table: + """Get or create spaces registry table.""" + if self._spaces_table_name in self._db.table_names(): + return self._db.open_table(self._spaces_table_name) + schema = create_spaces_schema() + return self._db.create_table(self._spaces_table_name, schema=schema) - for row in data: - if row.get("embedding") is not None: - row["embedding"] = np.array(row["embedding"], dtype=np.float32) + # ========================================================================= + # Sample CRUD operations + # ========================================================================= def add_sample(self, sample: Sample) -> None: """Add a single sample to storage.""" - data = [sample_to_dict(sample, self.config.embedding_dim)] - self._convert_embeddings_to_numpy(data) - if self._table is None: - self._ensure_table_exists(data) + data = [sample_to_dict(sample)] + if self._samples_table is None: + self._ensure_samples_table(data) else: - self._table.add(self._cast_rows_to_table_schema(data)) + arrow = pa.Table.from_pylist(data, schema=self._samples_table.schema) + self._samples_table.add(arrow) def add_samples_batch(self, samples: list[Sample]) -> None: """Add multiple samples efficiently.""" if not samples: return - data = [sample_to_dict(s, self.config.embedding_dim) for s in samples] - self._convert_embeddings_to_numpy(data) - if self._table is None: - self._ensure_table_exists(data) + data = [sample_to_dict(s) for s in samples] + if self._samples_table is None: + self._ensure_samples_table(data) else: - self._table.add(self._cast_rows_to_table_schema(data)) + arrow = pa.Table.from_pylist(data, schema=self._samples_table.schema) + self._samples_table.add(arrow) def get_sample(self, sample_id: str) -> Sample | None: """Retrieve a sample by ID.""" - if self._table is None: + if self._samples_table is None: return None safe_id = sample_id.replace("'", "''") - results = self._table.search().where(f"id = '{safe_id}'").limit(1).to_list() + results = self._samples_table.search().where(f"id = '{safe_id}'").limit(1).to_list() if results: return dict_to_sample(results[0]) return None @@ -122,20 +147,20 @@ def get_samples_paginated( label: str | None = None, ) -> tuple[list[Sample], int]: """Get paginated samples using native LanceDB queries.""" - if self._table is None: + if self._samples_table is None: return [], 0 import pyarrow.compute as pc if label: - arrow_table = self._table.search().select(["label"]).to_arrow() + arrow_table = self._samples_table.search().select(["label"]).to_arrow() label_column = arrow_table.column("label") mask = pc.fill_null(pc.equal(label_column, pa.scalar(label)), False) total = pc.sum(pc.cast(mask, pa.int64())).as_py() else: - total = self._table.count_rows() + total = self._samples_table.count_rows() - query = self._table.search() + query = self._samples_table.search() if label: safe_label = label.replace("'", "''") query = query.where(f"label = '{safe_label}'") @@ -146,109 +171,64 @@ def get_samples_paginated( def get_all_samples(self) -> list[Sample]: """Get all samples.""" - if self._table is None: + if self._samples_table is None: return [] - arrow_table = self._table.to_arrow() + arrow_table = self._samples_table.to_arrow() rows = arrow_table.to_pylist() return [dict_to_sample(row) for row in rows] def update_sample(self, sample: Sample) -> None: """Update an existing sample.""" - self._delete_by_id(sample.id) - self.add_sample(sample) + if self._samples_table is None: + self.add_sample(sample) + return + data = [sample_to_dict(sample)] + arrow = pa.Table.from_pylist(data, schema=self._samples_table.schema) + ( + self._samples_table.merge_insert("id") + .when_matched_update_all() + .when_not_matched_insert_all() + .execute(arrow) + ) def update_samples_batch(self, samples: list[Sample]) -> None: """Batch update samples.""" if not samples: return - - if self._table is None: + if self._samples_table is None: self.add_samples_batch(samples) return + data = [sample_to_dict(s) for s in samples] + arrow = pa.Table.from_pylist(data, schema=self._samples_table.schema) + ( + self._samples_table.merge_insert("id") + .when_matched_update_all() + .when_not_matched_insert_all() + .execute(arrow) + ) - if self._needs_schema_migration(samples): - self._migrate_table_schema(samples) - else: - updated_data = [sample_to_dict(s, self.config.embedding_dim) for s in samples] - self._convert_embeddings_to_numpy(updated_data) - - arrow = self._cast_rows_to_table_schema(updated_data) - ( - self._table.merge_insert("id") - .when_matched_update_all() - .when_not_matched_insert_all() - .execute(arrow) - ) - - def _needs_schema_migration(self, samples: list[Sample]) -> bool: - """Check whether embedding columns exist with a non-null type.""" - schema = self._table.schema - - columns_to_check = [ - ("embedding", lambda s: s.embedding is not None), - ("embedding_2d_euclidean", lambda s: s.embedding_2d is not None), - ("embedding_2d_hyperbolic", lambda s: s.embedding_2d_hyperbolic is not None), - ] - - for col_name, has_value_fn in columns_to_check: - has_values = any(has_value_fn(s) for s in samples) - if not has_values: - continue - try: - field = schema.field(col_name) - if str(field.type) == "null": - return True - except KeyError: - return True - - return False - - def _migrate_table_schema(self, updated_samples: list[Sample]) -> None: - """Recreate the table with the current schema and merge updates.""" - existing_rows = self._table.to_arrow().to_pylist() - updates_by_id = {s.id: s for s in updated_samples} - - new_data: list[dict] = [] - for row in existing_rows: - sample_id = row.get("id") - if sample_id in updates_by_id: - new_data.append(sample_to_dict(updates_by_id[sample_id], self.config.embedding_dim)) - else: - new_data.append(row) - - self._convert_embeddings_to_numpy(new_data) - - self._db.drop_table(self._table_name) - self._table = None - self._ensure_table_exists(new_data) - - def _delete_by_id(self, sample_id: str) -> bool: - """Delete a sample by ID using native LanceDB delete.""" - if self._table is None: + def delete_sample(self, sample_id: str) -> bool: + """Delete a sample by ID.""" + if self._samples_table is None: return False try: - # Use LanceDB's native delete with SQL WHERE clause safe_id = sample_id.replace("'", "''") - self._table.delete(f"id = '{safe_id}'") + self._samples_table.delete(f"id = '{safe_id}'") return True except Exception: return False - def delete_sample(self, sample_id: str) -> bool: - """Delete a sample by ID.""" - return self._delete_by_id(sample_id) - def __len__(self) -> int: """Return total number of samples.""" - if self._table is None: + if self._samples_table is None: return 0 - return self._table.count_rows() + return self._samples_table.count_rows() def __iter__(self) -> Iterator[Sample]: """Iterate over all samples.""" - if self._table is None: + if self._samples_table is None: return iter([]) - arrow_table = self._table.to_arrow() + arrow_table = self._samples_table.to_arrow() for batch in arrow_table.to_batches(max_chunksize=1000): batch_dict = batch.to_pydict() for i in range(batch.num_rows): @@ -257,138 +237,29 @@ def __iter__(self) -> Iterator[Sample]: def __contains__(self, sample_id: str) -> bool: """Check if sample exists.""" - if self._table is None: + if self._samples_table is None: return False safe_id = sample_id.replace("'", "''") try: - results = self._table.search().where(f"id = '{safe_id}'").limit(1).to_list() + results = self._samples_table.search().where(f"id = '{safe_id}'").limit(1).to_list() return len(results) > 0 except Exception: return False def get_unique_labels(self) -> list[str]: """Get all unique labels.""" - if self._table is None: + if self._samples_table is None: return [] import pyarrow.compute as pc - # Label-only scan - don't pull thumbnails/metadata into RAM - arrow_table = self._table.search().select(["label"]).to_arrow() + arrow_table = self._samples_table.search().select(["label"]).to_arrow() label_column = arrow_table.column("label") unique_labels = pc.unique(label_column).to_pylist() return sorted([label for label in unique_labels if label is not None]) - def find_similar( - self, - sample_id: str, - k: int = 10, - vector_column: str = "embedding", - ) -> list[tuple[Sample, float]]: - """Find k nearest neighbors to a sample.""" - sample = self.get_sample(sample_id) - if sample is None: - raise ValueError(f"Sample not found: {sample_id}") - - query_vector = self._get_vector(sample, vector_column) - if query_vector is None: - raise ValueError(f"Sample {sample_id} has no {vector_column}") - - # Find similar, excluding self - results = self.find_similar_by_vector(query_vector, k + 1, vector_column) - return [(s, d) for s, d in results if s.id != sample_id][:k] - - def find_similar_by_vector( - self, - vector: list[float], - k: int = 10, - vector_column: str = "embedding", - ) -> list[tuple[Sample, float]]: - """Find k nearest neighbors to a query vector.""" - import math - - lance_column = self._map_vector_column(vector_column) - - try: - results = ( - self._table.search(vector, vector_column_name=lance_column) - .metric("cosine") - .limit(k) - .to_list() - ) - - # Normalize nan distances to 0.0 (can happen with identical vectors) - return [ - ( - dict_to_sample(row), - 0.0 - if math.isnan(d := row.get("_distance", 0.0)) - else float(d), - ) - for row in results - ] - except Exception: - return self._brute_force_search(vector, k, vector_column) - - def _brute_force_search( - self, - vector: list[float], - k: int, - vector_column: str, - ) -> list[tuple[Sample, float]]: - """Fallback brute force search when vector index is not available.""" - import numpy as np - - query = np.array(vector) - distances: list[tuple[Sample, float]] = [] - - for sample in self: - vec = self._get_vector(sample, vector_column) - if vec is None: - continue - - vec_np = np.array(vec) - norm_query = np.linalg.norm(query) - norm_vec = np.linalg.norm(vec_np) - - if norm_query == 0 or norm_vec == 0: - distance = 1.0 - else: - cosine_sim = np.dot(query, vec_np) / (norm_query * norm_vec) - distance = 1 - cosine_sim - - distances.append((sample, float(distance))) - - distances.sort(key=lambda x: x[1]) - return distances[:k] - - def _get_vector(self, sample: Sample, vector_column: str) -> list[float] | None: - """Get the appropriate vector from a sample.""" - if vector_column == "embedding": - return sample.embedding - elif vector_column in ("embedding_2d_euclidean", "embedding_2d"): - return sample.embedding_2d - elif vector_column == "embedding_2d_hyperbolic": - return sample.embedding_2d_hyperbolic - else: - raise ValueError(f"Unknown vector column: {vector_column}") - - def _map_vector_column(self, vector_column: str) -> str: - """Map vector column name to LanceDB column name.""" - mapping = { - "embedding": "embedding", - "embedding_2d": "embedding_2d_euclidean", - "embedding_2d_euclidean": "embedding_2d_euclidean", - "embedding_2d_hyperbolic": "embedding_2d_hyperbolic", - } - return mapping.get(vector_column, vector_column) - - def filter(self, predicate: Callable[[Sample], bool]) -> list[Sample]: - """Filter samples based on a predicate function.""" - return [s for s in self if predicate(s)] - def get_existing_ids(self, sample_ids: list[str]) -> set[str]: """Return the subset of sample_ids that exist in storage.""" - if self._table is None or not sample_ids: + if self._samples_table is None or not sample_ids: return set() existing: set[str] = set() @@ -396,7 +267,7 @@ def get_existing_ids(self, sample_ids: list[str]) -> set[str]: def query_chunk(chunk: list[str]) -> set[str]: escaped = [sid.replace("'", "''") for sid in chunk] id_list = "', '".join(escaped) - results = self._table.search().where(f"id IN ('{id_list}')").select(["id"]).to_list() + results = self._samples_table.search().where(f"id IN ('{id_list}')").select(["id"]).to_list() return {r["id"] for r in results} chunk_size = 1000 @@ -410,11 +281,8 @@ def query_chunk(chunk: list[str]) -> set[str]: return existing def get_samples_by_ids(self, sample_ids: list[str]) -> list[Sample]: - """Retrieve multiple samples by ID. - - Uses chunked `id IN (...)` queries to avoid per-ID round trips. - """ - if self._table is None or not sample_ids: + """Retrieve multiple samples by ID.""" + if self._samples_table is None or not sample_ids: return [] rows_by_id: dict[str, dict] = {} @@ -422,7 +290,7 @@ def get_samples_by_ids(self, sample_ids: list[str]) -> list[Sample]: def query_chunk(chunk: list[str]) -> None: escaped = [sid.replace("'", "''") for sid in chunk] id_list = "', '".join(escaped) - results = self._table.search().where(f"id IN ('{id_list}')").to_list() + results = self._samples_table.search().where(f"id IN ('{id_list}')").to_list() for r in results: rid = r.get("id") if isinstance(rid, str): @@ -439,91 +307,504 @@ def query_chunk(chunk: list[str]) -> None: out.append(dict_to_sample(row)) return out - def get_visualization_embeddings( + def filter(self, predicate: Callable[[Sample], bool]) -> list[Sample]: + """Filter samples based on a predicate function.""" + return [s for s in self if predicate(s)] + + # ========================================================================= + # Spaces registry (embedding spaces) + # ========================================================================= + + def list_spaces(self) -> list[SpaceInfo]: + """List all embedding spaces.""" + rows = self._spaces_table.to_arrow().to_pylist() + return [SpaceInfo.from_dict(r) for r in rows] + + def get_space(self, space_key: str) -> SpaceInfo | None: + """Get info for a specific embedding space.""" + safe_key = space_key.replace("'", "''") + results = self._spaces_table.search().where(f"space_key = '{safe_key}'").limit(1).to_list() + if results: + return SpaceInfo.from_dict(results[0]) + return None + + def ensure_space(self, model_id: str, dim: int, config: dict | None = None) -> SpaceInfo: + """Ensure an embedding space exists, creating it if needed. + + Args: + model_id: The model identifier (e.g., "openai/clip-vit-base-patch32"). + dim: Vector dimension for this model. + config: Optional config dict for the space. + + Returns: + SpaceInfo for the space (existing or newly created). + + Raises: + ValueError: If space exists with different dimension. + """ + space_key = make_space_key(model_id) + existing = self.get_space(space_key) + + if existing is not None: + if existing.dim != dim: + raise ValueError( + f"Space '{space_key}' exists with dim={existing.dim}, " + f"but requested dim={dim}" + ) + return existing + + # Create new space + now = int(time.time()) + space_info = SpaceInfo( + space_key=space_key, + model_id=model_id, + dim=dim, + count=0, + created_at=now, + updated_at=now, + config=config, + ) + + # Add to registry + schema = create_spaces_schema() + arrow = pa.Table.from_pylist([space_info.to_dict()], schema=schema) + self._spaces_table.add(arrow) + + # Create embeddings table for this space + emb_table_name = f"embeddings__{space_key}" + emb_schema = create_embeddings_schema(dim) + self._db.create_table(emb_table_name, schema=emb_schema) + + return space_info + + def delete_space(self, space_key: str) -> bool: + """Delete an embedding space and its embeddings table.""" + safe_key = space_key.replace("'", "''") + + # Delete from registry + try: + self._spaces_table.delete(f"space_key = '{safe_key}'") + except Exception: + return False + + # Drop embeddings table + emb_table_name = f"embeddings__{space_key}" + if emb_table_name in self._db.table_names(): + self._db.drop_table(emb_table_name) + + return True + + def _update_space_count(self, space_key: str, count: int) -> None: + """Update the count and updated_at for a space.""" + now = int(time.time()) + self._spaces_table.update( + where=f"space_key = '{space_key}'", + values={"count": count, "updated_at": now}, + ) + + # ========================================================================= + # Embeddings operations + # ========================================================================= + + def get_embeddings_table_name(self, space_key: str) -> str: + """Get the table name for an embedding space.""" + return f"embeddings__{space_key}" + + def add_embeddings( self, - ) -> tuple[list[str], list[str | None], list[list[float]], list[list[float]]]: - """Return ids/labels and both 2D projections for the scatter.""" - if self._table is None: - return [], [], [], [] - - rows = ( - self._table.search() - .select(["id", "label", "embedding_2d_euclidean", "embedding_2d_hyperbolic"]) - .where("embedding_2d_euclidean IS NOT NULL AND embedding_2d_hyperbolic IS NOT NULL") - .to_list() + space_key: str, + ids: list[str], + vectors: np.ndarray, + ) -> None: + """Add embeddings to a space. + + Args: + space_key: The embedding space key. + ids: Sample IDs. + vectors: Embedding vectors (N x dim). + """ + if len(ids) != len(vectors): + raise ValueError("ids and vectors must have same length") + if len(ids) == 0: + return + + space = self.get_space(space_key) + if space is None: + raise ValueError(f"Space not found: {space_key}") + + if vectors.shape[1] != space.dim: + raise ValueError(f"Vector dim {vectors.shape[1]} != space dim {space.dim}") + + emb_table_name = self.get_embeddings_table_name(space_key) + emb_table = self._db.open_table(emb_table_name) + + # Convert to list of dicts + data = [ + {"id": id_, "vector": vec.astype(np.float32)} + for id_, vec in zip(ids, vectors) + ] + schema = create_embeddings_schema(space.dim) + arrow = pa.Table.from_pylist(data, schema=schema) + + # Use merge_insert for idempotent upsert (handles retries/partial runs) + ( + emb_table.merge_insert("id") + .when_matched_update_all() + .when_not_matched_insert_all() + .execute(arrow) ) - ids: list[str] = [] - labels: list[str | None] = [] - euclidean: list[list[float]] = [] - hyperbolic: list[list[float]] = [] + # Update count + new_count = emb_table.count_rows() + self._update_space_count(space_key, new_count) + + def get_embeddings( + self, + space_key: str, + ids: list[str] | None = None, + ) -> tuple[list[str], np.ndarray]: + """Get embeddings from a space. + + Args: + space_key: The embedding space key. + ids: Optional list of IDs to fetch. If None, fetch all. - for r in rows: - eid = r.get("id") - e2 = r.get("embedding_2d_euclidean") - h2 = r.get("embedding_2d_hyperbolic") - if not isinstance(eid, str) or e2 is None or h2 is None: - continue - ids.append(eid) - labels.append(r.get("label")) - euclidean.append(list(e2)) - hyperbolic.append(list(h2)) + Returns: + (ids, vectors) where vectors is (N x dim). + """ + space = self.get_space(space_key) + if space is None: + raise ValueError(f"Space not found: {space_key}") - return ids, labels, euclidean, hyperbolic + emb_table_name = self.get_embeddings_table_name(space_key) + if emb_table_name not in self._db.table_names(): + return [], np.empty((0, space.dim), dtype=np.float32) + + emb_table = self._db.open_table(emb_table_name) + + if ids is not None: + # Fetch specific IDs + escaped = [sid.replace("'", "''") for sid in ids] + id_list = "', '".join(escaped) + rows = emb_table.search().where(f"id IN ('{id_list}')").to_list() + else: + # Fetch all + rows = emb_table.to_arrow().to_pylist() + + if not rows: + return [], np.empty((0, space.dim), dtype=np.float32) + + out_ids = [r["id"] for r in rows] + out_vecs = np.array([r["vector"] for r in rows], dtype=np.float32) + return out_ids, out_vecs + + def get_embedded_ids(self, space_key: str) -> set[str]: + """Get the set of sample IDs that have embeddings in a space.""" + emb_table_name = self.get_embeddings_table_name(space_key) + if emb_table_name not in self._db.table_names(): + return set() + + emb_table = self._db.open_table(emb_table_name) + rows = emb_table.search().select(["id"]).to_list() + return {r["id"] for r in rows} + + def get_missing_embedding_ids(self, space_key: str) -> list[str]: + """Get sample IDs that don't have embeddings in a space.""" + if self._samples_table is None: + return [] + + all_ids = {r["id"] for r in self._samples_table.search().select(["id"]).to_list()} + embedded_ids = self.get_embedded_ids(space_key) + return list(all_ids - embedded_ids) + + # ========================================================================= + # Layouts operations + # ========================================================================= + + def get_layout_table_name(self, layout_key: str) -> str: + """Get the table name for a layout.""" + return f"layouts__{layout_key}" + + def list_layouts(self) -> list[str]: + """List all layout keys.""" + layouts = [] + for name in self._db.table_names(): + if name.startswith("layouts__"): + layouts.append(name[len("layouts__") :]) + return layouts + + def ensure_layout(self, layout_key: str) -> None: + """Ensure a layout table exists.""" + table_name = self.get_layout_table_name(layout_key) + if table_name not in self._db.table_names(): + schema = create_layouts_schema() + self._db.create_table(table_name, schema=schema) + + def delete_layout(self, layout_key: str) -> bool: + """Delete a layout table.""" + table_name = self.get_layout_table_name(layout_key) + if table_name in self._db.table_names(): + self._db.drop_table(table_name) + return True + return False + + def add_layout_coords( + self, + layout_key: str, + ids: list[str], + coords: np.ndarray, + ) -> None: + """Add layout coordinates. + + Args: + layout_key: The layout key. + ids: Sample IDs. + coords: 2D coordinates (N x 2). + """ + if len(ids) != len(coords): + raise ValueError("ids and coords must have same length") + if len(ids) == 0: + return + + self.ensure_layout(layout_key) + + table_name = self.get_layout_table_name(layout_key) + table = self._db.open_table(table_name) + + data = [ + {"id": id_, "x": float(coord[0]), "y": float(coord[1])} + for id_, coord in zip(ids, coords) + ] + schema = create_layouts_schema() + arrow = pa.Table.from_pylist(data, schema=schema) + + # Use merge_insert to handle updates + ( + table.merge_insert("id") + .when_matched_update_all() + .when_not_matched_insert_all() + .execute(arrow) + ) + + def get_layout_coords( + self, + layout_key: str, + ids: list[str] | None = None, + ) -> tuple[list[str], np.ndarray]: + """Get layout coordinates. + + Args: + layout_key: The layout key. + ids: Optional list of IDs to fetch. If None, fetch all. + + Returns: + (ids, coords) where coords is (N x 2). + """ + table_name = self.get_layout_table_name(layout_key) + if table_name not in self._db.table_names(): + return [], np.empty((0, 2), dtype=np.float32) + + table = self._db.open_table(table_name) + + if ids is not None: + escaped = [sid.replace("'", "''") for sid in ids] + id_list = "', '".join(escaped) + rows = table.search().where(f"id IN ('{id_list}')").to_list() + else: + rows = table.to_arrow().to_pylist() + + if not rows: + return [], np.empty((0, 2), dtype=np.float32) + + out_ids = [r["id"] for r in rows] + out_coords = np.array([[r["x"], r["y"]] for r in rows], dtype=np.float32) + return out_ids, out_coords + + def get_visualization_data( + self, + layout_key: str, + ) -> tuple[list[str], list[str | None], np.ndarray]: + """Get visualization data (ids, labels, coords) for a layout. + + This joins samples with layout coords for the scatter plot. + + Returns: + (ids, labels, coords) where coords is (N x 2). + """ + table_name = self.get_layout_table_name(layout_key) + if table_name not in self._db.table_names(): + return [], [], np.empty((0, 2), dtype=np.float32) + + if self._samples_table is None: + return [], [], np.empty((0, 2), dtype=np.float32) + + layout_table = self._db.open_table(table_name) + + # Get all layout coords + layout_rows = layout_table.to_arrow().to_pylist() + if not layout_rows: + return [], [], np.empty((0, 2), dtype=np.float32) + + # Build id -> coords map + coords_by_id = {r["id"]: (r["x"], r["y"]) for r in layout_rows} + layout_ids = list(coords_by_id.keys()) + + # Get labels for these IDs + escaped = [sid.replace("'", "''") for sid in layout_ids] + id_list = "', '".join(escaped) + sample_rows = self._samples_table.search().select(["id", "label"]).where(f"id IN ('{id_list}')").to_list() + labels_by_id = {r["id"]: r.get("label") for r in sample_rows} + + # Build aligned output + ids = [] + labels = [] + coords = [] + for id_ in layout_ids: + if id_ in labels_by_id: + ids.append(id_) + labels.append(labels_by_id[id_]) + coords.append(coords_by_id[id_]) + + return ids, labels, np.array(coords, dtype=np.float32) if coords else np.empty((0, 2), dtype=np.float32) def get_lasso_candidates_aabb( self, *, - space: str, + layout_key: str, x_min: float, x_max: float, y_min: float, y_max: float, - ) -> tuple[list[str], "np.ndarray"]: - """Return candidate (id, xy) rows within an AABB for a given projection.""" - if self._table is None: - import numpy as np + ) -> tuple[list[str], np.ndarray]: + """Return candidate (id, xy) rows within an AABB for a layout.""" + table_name = self.get_layout_table_name(layout_key) + if table_name not in self._db.table_names(): + return [], np.empty((0, 2), dtype=np.float32) + + table = self._db.open_table(table_name) + expr = f"x >= {x_min} AND x <= {x_max} AND y >= {y_min} AND y <= {y_max}" + rows = table.search().where(expr).to_list() + + if not rows: return [], np.empty((0, 2), dtype=np.float32) - if space == "euclidean": - col = "embedding_2d_euclidean" - elif space == "hyperbolic": - col = "embedding_2d_hyperbolic" - else: - raise ValueError(f"Unknown space: {space}") - - # Keep selection universe consistent with /api/embeddings: require both projections. - expr = ( - "embedding_2d_euclidean IS NOT NULL AND " - "embedding_2d_hyperbolic IS NOT NULL AND " - f"{col}[1] >= {x_min} AND {col}[1] <= {x_max} AND " - f"{col}[2] >= {y_min} AND {col}[2] <= {y_max}" - ) + ids = [r["id"] for r in rows] + coords = np.array([[r["x"], r["y"]] for r in rows], dtype=np.float32) + return ids, coords - rows = self._table.search().select(["id", col]).where(expr).to_list() + # ========================================================================= + # Similarity search + # ========================================================================= - ids: list[str] = [] - coords: list[list[float]] = [] - for r in rows: - sid = r.get("id") - xy = r.get(col) - if not isinstance(sid, str) or xy is None: - continue - if len(xy) != 2: - continue - ids.append(sid) - coords.append([float(xy[0]), float(xy[1])]) + def find_similar( + self, + sample_id: str, + k: int = 10, + space_key: str | None = None, + ) -> list[tuple[Sample, float]]: + """Find k nearest neighbors to a sample. - import numpy as np + Args: + sample_id: The query sample ID. + k: Number of neighbors. + space_key: Embedding space to use. If None, uses default. - if not coords: - return [], np.empty((0, 2), dtype=np.float32) - return ids, np.asarray(coords, dtype=np.float32) + Returns: + List of (sample, distance) tuples. + """ + if space_key is None: + spaces = self.list_spaces() + if not spaces: + raise ValueError("No embedding spaces available") + space_key = spaces[0].space_key + + # Get query vector + ids, vecs = self.get_embeddings(space_key, [sample_id]) + if not ids: + raise ValueError(f"Sample {sample_id} has no embedding in space {space_key}") + + query_vector = vecs[0] + results = self.find_similar_by_vector(query_vector, k + 1, space_key) + return [(s, d) for s, d in results if s.id != sample_id][:k] - def close(self) -> None: - """Close the storage connection.""" - return + def find_similar_by_vector( + self, + vector: list[float] | np.ndarray, + k: int = 10, + space_key: str | None = None, + ) -> list[tuple[Sample, float]]: + """Find k nearest neighbors to a query vector.""" + import math + + if space_key is None: + spaces = self.list_spaces() + if not spaces: + raise ValueError("No embedding spaces available") + space_key = spaces[0].space_key + + space = self.get_space(space_key) + if space is None: + raise ValueError(f"Space not found: {space_key}") + + emb_table_name = self.get_embeddings_table_name(space_key) + if emb_table_name not in self._db.table_names(): + return [] + + emb_table = self._db.open_table(emb_table_name) + + try: + results = ( + emb_table.search(vector, vector_column_name="vector") + .metric("cosine") + .limit(k) + .to_list() + ) + + # Get sample info for results + result_ids = [r["id"] for r in results] + samples = self.get_samples_by_ids(result_ids) + samples_by_id = {s.id: s for s in samples} + + return [ + ( + samples_by_id[r["id"]], + 0.0 if math.isnan(d := r.get("_distance", 0.0)) else float(d), + ) + for r in results + if r["id"] in samples_by_id + ] + except Exception: + return [] + + # ========================================================================= + # Vector index creation + # ========================================================================= + + def create_vector_index(self, space_key: str) -> None: + """Create an ANN index for an embedding space.""" + emb_table_name = self.get_embeddings_table_name(space_key) + if emb_table_name not in self._db.table_names(): + return + + emb_table = self._db.open_table(emb_table_name) + num_rows = emb_table.count_rows() + if num_rows < 256: + return + + try: + emb_table.create_index( + vector_column_name="vector", + index_type="IVF_PQ", + num_partitions=min(256, num_rows // 10), + num_sub_vectors=16, + ) + except Exception: + pass + + # ========================================================================= + # Label colors + # ========================================================================= @property def label_colors(self) -> dict[str, str]: @@ -538,7 +819,6 @@ def label_colors(self) -> dict[str, str]: value = rows[0].get("value") self._label_colors_cache = json.loads(value) if value else {} - return self._label_colors_cache @label_colors.setter @@ -555,74 +835,57 @@ def label_colors(self, colors: dict[str, str]) -> None: row = {"key": "label_colors", "value": json.dumps(colors)} self._metadata_table.add(pa.Table.from_pylist([row], schema=schema)) - def create_vector_index(self, vector_column: str = "embedding") -> None: - """Create an ANN index for vector search.""" - if self._table is None: - return - num_rows = self._table.count_rows() - if num_rows < 256: - return + # ========================================================================= + # Lifecycle + # ========================================================================= - lance_column = self._map_vector_column(vector_column) + def close(self) -> None: + """Close the storage connection.""" + pass - try: - self._table.create_index( - vector_column_name=lance_column, - index_type="IVF_PQ", - num_partitions=min(256, num_rows // 10), - num_sub_vectors=16, - ) - except Exception: - return + # ========================================================================= + # Class methods for dataset management + # ========================================================================= @classmethod - def list_datasets(cls, database_dir: Path | None = None) -> list[str]: - """List all available datasets in the database directory.""" - if database_dir is None: - database_dir = get_default_database_dir() + def list_datasets(cls, datasets_dir: Path | None = None) -> list[str]: + """List all available datasets.""" + if datasets_dir is None: + datasets_dir = get_default_datasets_dir() - if not database_dir.exists(): + if not datasets_dir.exists(): return [] - db = lancedb.connect(str(database_dir)) datasets = [] - for table_name in db.table_names(): - if table_name.startswith("hyperview_") and not table_name.endswith("_meta"): - datasets.append(table_name[len("hyperview_") :]) + for path in datasets_dir.iterdir(): + if path.is_dir(): + # Check if it looks like a LanceDB dataset + if (path / "samples.lance").exists() or any( + p.name.endswith(".lance") for p in path.iterdir() if p.is_dir() + ): + datasets.append(path.name) return sorted(datasets) @classmethod - def delete_dataset(cls, dataset_name: str, database_dir: Path | None = None) -> bool: - """Delete a dataset from the database.""" - if database_dir is None: - database_dir = get_default_database_dir() + def delete_dataset(cls, dataset_name: str, datasets_dir: Path | None = None) -> bool: + """Delete a dataset directory.""" + if datasets_dir is None: + datasets_dir = get_default_datasets_dir() - if not database_dir.exists(): + dataset_path = datasets_dir / dataset_name + if not dataset_path.exists(): return False - db = lancedb.connect(str(database_dir)) - table_name = f"hyperview_{dataset_name}" - metadata_table_name = f"hyperview_{dataset_name}_meta" - - deleted = False - if table_name in db.table_names(): - db.drop_table(table_name) - deleted = True + import shutil - if metadata_table_name in db.table_names(): - db.drop_table(metadata_table_name) - - return deleted + shutil.rmtree(dataset_path) + return True @classmethod - def dataset_exists(cls, dataset_name: str, database_dir: Path | None = None) -> bool: + def dataset_exists(cls, dataset_name: str, datasets_dir: Path | None = None) -> bool: """Check if a dataset exists.""" - if database_dir is None: - database_dir = get_default_database_dir() - - if not database_dir.exists(): - return False + if datasets_dir is None: + datasets_dir = get_default_datasets_dir() - db = lancedb.connect(str(database_dir)) - table_name = f"hyperview_{dataset_name}" - return table_name in db.table_names() + dataset_path = datasets_dir / dataset_name + return dataset_path.exists() and dataset_path.is_dir() diff --git a/src/hyperview/storage/memory_backend.py b/src/hyperview/storage/memory_backend.py index 2dad945..6326cd6 100644 --- a/src/hyperview/storage/memory_backend.py +++ b/src/hyperview/storage/memory_backend.py @@ -1,15 +1,22 @@ """In-memory storage backend for testing and development.""" +import time from collections.abc import Callable, Iterator +from dataclasses import dataclass, field +from typing import Any import numpy as np from hyperview.core.sample import Sample from hyperview.storage.backend import StorageBackend +from hyperview.storage.schema import SpaceInfo, make_space_key class MemoryBackend(StorageBackend): - """In-memory storage backend (current behavior, for testing and backwards compatibility).""" + """In-memory storage backend for testing and development. + + Implements the same interface as LanceDBBackend but stores everything in memory. + """ def __init__(self, dataset_name: str): """Initialize in-memory backend. @@ -21,6 +28,19 @@ def __init__(self, dataset_name: str): self._samples: dict[str, Sample] = {} self._label_colors: dict[str, str] = {} + # Spaces registry + self._spaces: dict[str, SpaceInfo] = {} + + # Embeddings: space_key -> {sample_id -> vector} + self._embeddings: dict[str, dict[str, np.ndarray]] = {} + + # Layouts: layout_key -> {sample_id -> (x, y)} + self._layouts: dict[str, dict[str, tuple[float, float]]] = {} + + # ========================================================================= + # Sample CRUD + # ========================================================================= + def add_sample(self, sample: Sample) -> None: """Add a single sample to storage.""" self._samples[sample.id] = sample @@ -84,71 +104,6 @@ def get_unique_labels(self) -> list[str]: labels = {s.label for s in self._samples.values() if s.label} return sorted(labels) - def find_similar( - self, - sample_id: str, - k: int = 10, - vector_column: str = "embedding", - ) -> list[tuple[Sample, float]]: - """Find k nearest neighbors to a sample.""" - sample = self._samples.get(sample_id) - if sample is None: - raise ValueError(f"Sample not found: {sample_id}") - - query_vector = self._get_vector(sample, vector_column) - if query_vector is None: - raise ValueError(f"Sample {sample_id} has no {vector_column}") - - # Find similar, excluding self - results = self.find_similar_by_vector(query_vector, k + 1, vector_column) - return [(s, d) for s, d in results if s.id != sample_id][:k] - - def find_similar_by_vector( - self, - vector: list[float], - k: int = 10, - vector_column: str = "embedding", - ) -> list[tuple[Sample, float]]: - """Find k nearest neighbors to a query vector.""" - query = np.array(vector) - distances: list[tuple[Sample, float]] = [] - - for sample in self._samples.values(): - vec = self._get_vector(sample, vector_column) - if vec is None: - continue - - # Cosine distance - vec_np = np.array(vec) - norm_query = np.linalg.norm(query) - norm_vec = np.linalg.norm(vec_np) - - if norm_query == 0 or norm_vec == 0: - distance = 1.0 - else: - cosine_sim = np.dot(query, vec_np) / (norm_query * norm_vec) - distance = 1 - cosine_sim - - distances.append((sample, float(distance))) - - distances.sort(key=lambda x: x[1]) - return distances[:k] - - def _get_vector(self, sample: Sample, vector_column: str) -> list[float] | None: - """Get the appropriate vector from a sample.""" - if vector_column == "embedding": - return sample.embedding - elif vector_column == "embedding_2d_euclidean": - return sample.embedding_2d - elif vector_column == "embedding_2d_hyperbolic": - return sample.embedding_2d_hyperbolic - else: - raise ValueError(f"Unknown vector column: {vector_column}") - - def filter(self, predicate: Callable[[Sample], bool]) -> list[Sample]: - """Filter samples based on a predicate function.""" - return [s for s in self._samples.values() if predicate(s)] - def get_existing_ids(self, sample_ids: list[str]) -> set[str]: """Return set of sample_ids that already exist in storage.""" return {sid for sid in sample_ids if sid in self._samples} @@ -162,58 +117,286 @@ def get_samples_by_ids(self, sample_ids: list[str]) -> list[Sample]: out.append(s) return out - def get_visualization_embeddings( + def filter(self, predicate: Callable[[Sample], bool]) -> list[Sample]: + """Filter samples based on a predicate function.""" + return [s for s in self._samples.values() if predicate(s)] + + # ========================================================================= + # Spaces registry + # ========================================================================= + + def list_spaces(self) -> list[SpaceInfo]: + """List all embedding spaces.""" + return list(self._spaces.values()) + + def get_space(self, space_key: str) -> SpaceInfo | None: + """Get info for a specific embedding space.""" + return self._spaces.get(space_key) + + def ensure_space(self, model_id: str, dim: int, config: dict | None = None) -> SpaceInfo: + """Ensure an embedding space exists, creating if needed.""" + space_key = make_space_key(model_id) + + if space_key in self._spaces: + existing = self._spaces[space_key] + if existing.dim != dim: + raise ValueError( + f"Space '{space_key}' exists with dim={existing.dim}, " + f"but requested dim={dim}" + ) + return existing + + now = int(time.time()) + space_info = SpaceInfo( + space_key=space_key, + model_id=model_id, + dim=dim, + count=0, + created_at=now, + updated_at=now, + config=config, + ) + self._spaces[space_key] = space_info + self._embeddings[space_key] = {} + return space_info + + def delete_space(self, space_key: str) -> bool: + """Delete an embedding space and its embeddings.""" + if space_key in self._spaces: + del self._spaces[space_key] + self._embeddings.pop(space_key, None) + return True + return False + + # ========================================================================= + # Embeddings + # ========================================================================= + + def add_embeddings( self, - ) -> tuple[list[str], list[str | None], list[list[float]], list[list[float]]]: - """Return ids/labels and both 2D projections for the scatter.""" - ids: list[str] = [] - labels: list[str | None] = [] - euclidean: list[list[float]] = [] - hyperbolic: list[list[float]] = [] - - for s in self._samples.values(): - if s.embedding_2d is None or s.embedding_2d_hyperbolic is None: - continue - ids.append(s.id) - labels.append(s.label) - euclidean.append(s.embedding_2d) - hyperbolic.append(s.embedding_2d_hyperbolic) + space_key: str, + ids: list[str], + vectors: np.ndarray, + ) -> None: + """Add embeddings to a space.""" + if len(ids) != len(vectors): + raise ValueError("ids and vectors must have same length") + if len(ids) == 0: + return + + if space_key not in self._spaces: + raise ValueError(f"Space not found: {space_key}") + + space = self._spaces[space_key] + if vectors.shape[1] != space.dim: + raise ValueError(f"Vector dim {vectors.shape[1]} != space dim {space.dim}") + + emb_store = self._embeddings.setdefault(space_key, {}) + for id_, vec in zip(ids, vectors): + emb_store[id_] = vec.astype(np.float32) + + # Update count + space.count = len(emb_store) + space.updated_at = int(time.time()) + + def get_embeddings( + self, + space_key: str, + ids: list[str] | None = None, + ) -> tuple[list[str], np.ndarray]: + """Get embeddings from a space.""" + if space_key not in self._spaces: + raise ValueError(f"Space not found: {space_key}") + + space = self._spaces[space_key] + emb_store = self._embeddings.get(space_key, {}) + + if ids is not None: + out_ids = [id_ for id_ in ids if id_ in emb_store] + if not out_ids: + return [], np.empty((0, space.dim), dtype=np.float32) + out_vecs = np.array([emb_store[id_] for id_ in out_ids], dtype=np.float32) + return out_ids, out_vecs + else: + if not emb_store: + return [], np.empty((0, space.dim), dtype=np.float32) + out_ids = list(emb_store.keys()) + out_vecs = np.array([emb_store[id_] for id_ in out_ids], dtype=np.float32) + return out_ids, out_vecs + + def get_embedded_ids(self, space_key: str) -> set[str]: + """Get the set of sample IDs that have embeddings in a space.""" + return set(self._embeddings.get(space_key, {}).keys()) + + def get_missing_embedding_ids(self, space_key: str) -> list[str]: + """Get sample IDs that don't have embeddings in a space.""" + embedded = self.get_embedded_ids(space_key) + return [id_ for id_ in self._samples.keys() if id_ not in embedded] + + # ========================================================================= + # Layouts + # ========================================================================= + + def list_layouts(self) -> list[str]: + """List all layout keys.""" + return list(self._layouts.keys()) + + def ensure_layout(self, layout_key: str) -> None: + """Ensure a layout exists.""" + if layout_key not in self._layouts: + self._layouts[layout_key] = {} + + def delete_layout(self, layout_key: str) -> bool: + """Delete a layout.""" + if layout_key in self._layouts: + del self._layouts[layout_key] + return True + return False + + def add_layout_coords( + self, + layout_key: str, + ids: list[str], + coords: np.ndarray, + ) -> None: + """Add layout coordinates.""" + if len(ids) != len(coords): + raise ValueError("ids and coords must have same length") + + self.ensure_layout(layout_key) + layout_store = self._layouts[layout_key] + + for id_, coord in zip(ids, coords): + layout_store[id_] = (float(coord[0]), float(coord[1])) + + def get_layout_coords( + self, + layout_key: str, + ids: list[str] | None = None, + ) -> tuple[list[str], np.ndarray]: + """Get layout coordinates.""" + layout_store = self._layouts.get(layout_key, {}) + + if ids is not None: + out_ids = [id_ for id_ in ids if id_ in layout_store] + else: + out_ids = list(layout_store.keys()) - return ids, labels, euclidean, hyperbolic + if not out_ids: + return [], np.empty((0, 2), dtype=np.float32) + + out_coords = np.array([layout_store[id_] for id_ in out_ids], dtype=np.float32) + return out_ids, out_coords + + def get_visualization_data( + self, + layout_key: str, + ) -> tuple[list[str], list[str | None], np.ndarray]: + """Get visualization data for scatter plot.""" + layout_store = self._layouts.get(layout_key, {}) + + if not layout_store: + return [], [], np.empty((0, 2), dtype=np.float32) + + ids = [] + labels = [] + coords = [] + + for id_, (x, y) in layout_store.items(): + sample = self._samples.get(id_) + if sample is not None: + ids.append(id_) + labels.append(sample.label) + coords.append([x, y]) + + return ids, labels, np.array(coords, dtype=np.float32) if coords else np.empty((0, 2), dtype=np.float32) def get_lasso_candidates_aabb( self, *, - space: str, + layout_key: str, x_min: float, x_max: float, y_min: float, y_max: float, ) -> tuple[list[str], np.ndarray]: - """Return candidate (id, xy) rows within an AABB for a given projection.""" - if space not in ("euclidean", "hyperbolic"): - raise ValueError(f"Unknown space: {space}") + """Return candidate (id, xy) rows within an AABB.""" + layout_store = self._layouts.get(layout_key, {}) - ids: list[str] = [] - coords: list[list[float]] = [] + ids = [] + coords = [] - for s in self._samples.values(): - # Keep selection universe consistent with /api/embeddings: require both projections. - if s.embedding_2d is None or s.embedding_2d_hyperbolic is None: + for id_, (x, y) in layout_store.items(): + if x < x_min or x > x_max or y < y_min or y > y_max: continue + ids.append(id_) + coords.append([x, y]) - xy = s.embedding_2d if space == "euclidean" else s.embedding_2d_hyperbolic - if xy is None or len(xy) != 2: - continue + return ids, np.array(coords, dtype=np.float32) if coords else np.empty((0, 2), dtype=np.float32) - x, y = float(xy[0]), float(xy[1]) - if x < x_min or x > x_max or y < y_min or y > y_max: + # ========================================================================= + # Similarity search + # ========================================================================= + + def find_similar( + self, + sample_id: str, + k: int = 10, + space_key: str | None = None, + ) -> list[tuple[Sample, float]]: + """Find k nearest neighbors to a sample.""" + if space_key is None: + if not self._spaces: + raise ValueError("No embedding spaces available") + space_key = list(self._spaces.keys())[0] + + emb_store = self._embeddings.get(space_key, {}) + if sample_id not in emb_store: + raise ValueError(f"Sample {sample_id} has no embedding in space {space_key}") + + query_vector = emb_store[sample_id] + results = self.find_similar_by_vector(query_vector, k + 1, space_key) + return [(s, d) for s, d in results if s.id != sample_id][:k] + + def find_similar_by_vector( + self, + vector: list[float] | np.ndarray, + k: int = 10, + space_key: str | None = None, + ) -> list[tuple[Sample, float]]: + """Find k nearest neighbors to a query vector.""" + if space_key is None: + if not self._spaces: + raise ValueError("No embedding spaces available") + space_key = list(self._spaces.keys())[0] + + emb_store = self._embeddings.get(space_key, {}) + query = np.array(vector, dtype=np.float32) + + distances: list[tuple[Sample, float]] = [] + for id_, vec in emb_store.items(): + sample = self._samples.get(id_) + if sample is None: continue - ids.append(s.id) - coords.append([x, y]) + # Cosine distance + norm_query = np.linalg.norm(query) + norm_vec = np.linalg.norm(vec) + + if norm_query == 0 or norm_vec == 0: + distance = 1.0 + else: + cosine_sim = np.dot(query, vec) / (norm_query * norm_vec) + distance = 1 - cosine_sim + + distances.append((sample, float(distance))) + + distances.sort(key=lambda x: x[1]) + return distances[:k] - return ids, np.asarray(coords, dtype=np.float32) + # ========================================================================= + # Lifecycle and metadata + # ========================================================================= def close(self) -> None: """Close the storage connection (no-op for in-memory).""" diff --git a/src/hyperview/storage/schema.py b/src/hyperview/storage/schema.py index ceb1a13..ac2f027 100644 --- a/src/hyperview/storage/schema.py +++ b/src/hyperview/storage/schema.py @@ -1,6 +1,16 @@ -"""LanceDB schema definitions for HyperView samples.""" +"""LanceDB schema definitions for HyperView. + +Storage architecture: +- samples: Core sample metadata (no embeddings) +- metadata: Key-value pairs for dataset config +- spaces: Registry of embedding spaces +- embeddings__: One table per embedding space (id + vector) +- layouts__: One table per layout (id + x + y) +""" import json +import re +from dataclasses import dataclass from typing import Any import pyarrow as pa @@ -8,34 +18,24 @@ from hyperview.core.sample import Sample -def create_sample_schema(embedding_dim: int = 512) -> pa.Schema: +def create_sample_schema() -> pa.Schema: """Create the PyArrow schema for samples. - Using PyArrow schema instead of LanceModel allows dynamic embedding dimensions. - - Args: - embedding_dim: Dimension of the high-dimensional embedding vector. - - Returns: - PyArrow schema for the samples table. + Samples are pure metadata - embeddings and layouts are stored separately. """ - # Use fixed-size lists for ANN index support in LanceDB return pa.schema( [ pa.field("id", pa.utf8(), nullable=False), pa.field("filepath", pa.utf8(), nullable=False), pa.field("label", pa.utf8(), nullable=True), pa.field("metadata_json", pa.utf8(), nullable=True), - pa.field("embedding", pa.list_(pa.float32(), embedding_dim), nullable=True), - pa.field("embedding_2d_euclidean", pa.list_(pa.float32(), 2), nullable=True), - pa.field("embedding_2d_hyperbolic", pa.list_(pa.float32(), 2), nullable=True), pa.field("thumbnail_base64", pa.utf8(), nullable=True), ] ) def create_metadata_schema() -> pa.Schema: - """Create the PyArrow schema for dataset metadata.""" + """Create the PyArrow schema for dataset metadata (key-value store).""" return pa.schema( [ pa.field("key", pa.utf8(), nullable=False), @@ -44,49 +44,153 @@ def create_metadata_schema() -> pa.Schema: ) -def sample_to_dict(sample: Sample, embedding_dim: int = 512) -> dict[str, Any]: - """Convert a Sample to a dictionary for LanceDB insertion. +def create_spaces_schema() -> pa.Schema: + """Create the PyArrow schema for the spaces registry. + + Each row represents an embedding space (one per model). + """ + return pa.schema( + [ + pa.field("space_key", pa.utf8(), nullable=False), + pa.field("model_id", pa.utf8(), nullable=False), + pa.field("dim", pa.int32(), nullable=False), + pa.field("count", pa.int64(), nullable=False), + pa.field("created_at", pa.int64(), nullable=False), + pa.field("updated_at", pa.int64(), nullable=False), + pa.field("config_json", pa.utf8(), nullable=True), + ] + ) + + +def create_embeddings_schema(dim: int) -> pa.Schema: + """Create the PyArrow schema for an embeddings table. Args: - sample: The Sample object to convert. - embedding_dim: Expected embedding dimension for padding/validation. + dim: Vector dimension for this embedding space. + """ + return pa.schema( + [ + pa.field("id", pa.utf8(), nullable=False), + pa.field("vector", pa.list_(pa.float32(), dim), nullable=False), + ] + ) + + +def create_layouts_schema() -> pa.Schema: + """Create the PyArrow schema for a layouts table. + + Layouts store 2D coordinates for visualization. + """ + return pa.schema( + [ + pa.field("id", pa.utf8(), nullable=False), + pa.field("x", pa.float32(), nullable=False), + pa.field("y", pa.float32(), nullable=False), + ] + ) + + +@dataclass +class SpaceInfo: + """Metadata for an embedding space.""" + + space_key: str + model_id: str + dim: int + count: int + created_at: int + updated_at: int + config: dict[str, Any] | None = None + + def to_dict(self) -> dict[str, Any]: + """Convert to dictionary for storage.""" + return { + "space_key": self.space_key, + "model_id": self.model_id, + "dim": self.dim, + "count": self.count, + "created_at": self.created_at, + "updated_at": self.updated_at, + "config_json": json.dumps(self.config) if self.config else None, + } + + @classmethod + def from_dict(cls, row: dict[str, Any]) -> "SpaceInfo": + """Create from storage dictionary.""" + config = None + if row.get("config_json"): + try: + config = json.loads(row["config_json"]) + except (json.JSONDecodeError, TypeError): + pass + return cls( + space_key=row["space_key"], + model_id=row["model_id"], + dim=row["dim"], + count=row["count"], + created_at=row["created_at"], + updated_at=row["updated_at"], + config=config, + ) + + +def slugify_model_id(model_id: str) -> str: + """Convert a model ID to a safe table name component. + + Examples: + "openai/clip-vit-base-patch32" -> "openai_clip-vit-base-patch32" + "sentence-transformers/all-MiniLM-L6-v2" -> "sentence-transformers_all-MiniLM-L6-v2" + """ + # Replace / with _ + slug = model_id.replace("/", "_") + # Replace any other unsafe characters with _ + slug = re.sub(r"[^a-zA-Z0-9_\-]", "_", slug) + # Collapse multiple underscores + slug = re.sub(r"_+", "_", slug) + return slug.strip("_") + + +def make_space_key(model_id: str) -> str: + """Generate a space_key from a model_id. + + For simplicity, this is just the slugified model_id. + """ + return slugify_model_id(model_id) + + +def make_layout_key( + space_key: str, + method: str = "umap", + geometry: str = "euclidean", +) -> str: + """Generate a layout_key from space, method, and geometry. + + Args: + space_key: The embedding space key. + method: Layout method (e.g., "umap"). + geometry: Geometry type ("euclidean" or "poincare"). Returns: - Dictionary suitable for LanceDB insertion. + Layout key like "clip__poincare_umap" or "clip__euclidean_umap". """ - # Handle embedding - ensure correct dimension or None - embedding = None - if sample.embedding is not None: - embedding = list(sample.embedding) - # Pad or truncate to expected dimension - if len(embedding) < embedding_dim: - embedding.extend([0.0] * (embedding_dim - len(embedding))) - elif len(embedding) > embedding_dim: - embedding = embedding[:embedding_dim] + # Format: {space}__{geometry}_{method} + # Frontend parses for "poincare" or "hyperbolic" to auto-detect geometry + return f"{space_key}__{geometry}_{method}" + +def sample_to_dict(sample: Sample) -> dict[str, Any]: + """Convert a Sample to a dictionary for LanceDB insertion.""" return { "id": sample.id, "filepath": sample.filepath, "label": sample.label, "metadata_json": json.dumps(sample.metadata) if sample.metadata else None, - "embedding": embedding, - "embedding_2d_euclidean": list(sample.embedding_2d) if sample.embedding_2d else None, - "embedding_2d_hyperbolic": ( - list(sample.embedding_2d_hyperbolic) if sample.embedding_2d_hyperbolic else None - ), "thumbnail_base64": sample.thumbnail_base64, } def dict_to_sample(row: dict[str, Any]) -> Sample: - """Convert a LanceDB row to a Sample object. - - Args: - row: Dictionary from LanceDB query result. - - Returns: - Sample object. - """ + """Convert a LanceDB row to a Sample object.""" metadata = {} if row.get("metadata_json"): try: @@ -94,26 +198,11 @@ def dict_to_sample(row: dict[str, Any]) -> Sample: except (json.JSONDecodeError, TypeError): metadata = {} - # Convert numpy arrays to lists if needed - embedding = row.get("embedding") - if embedding is not None and hasattr(embedding, "tolist"): - embedding = embedding.tolist() - - embedding_2d = row.get("embedding_2d_euclidean") - if embedding_2d is not None and hasattr(embedding_2d, "tolist"): - embedding_2d = embedding_2d.tolist() - - embedding_2d_hyperbolic = row.get("embedding_2d_hyperbolic") - if embedding_2d_hyperbolic is not None and hasattr(embedding_2d_hyperbolic, "tolist"): - embedding_2d_hyperbolic = embedding_2d_hyperbolic.tolist() - return Sample( id=row["id"], filepath=row["filepath"], label=row.get("label"), metadata=metadata, - embedding=embedding, - embedding_2d=embedding_2d, - embedding_2d_hyperbolic=embedding_2d_hyperbolic, thumbnail_base64=row.get("thumbnail_base64"), ) + From 9d28b0b660a14878d7a681026501ad0f2919ec33 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Tue, 20 Jan 2026 01:25:56 +0100 Subject: [PATCH 31/75] feat: enhance layout management with new registry and response models --- frontend/src/app/page.tsx | 9 +- frontend/src/components/ScatterPanel.tsx | 15 +-- frontend/src/components/useHyperScatter.ts | 4 +- frontend/src/lib/layouts.ts | 34 ++---- frontend/src/types/index.ts | 15 ++- src/hyperview/core/dataset.py | 116 +++++++++++++++---- src/hyperview/server/app.py | 114 +++++++++---------- src/hyperview/storage/backend.py | 19 +++- src/hyperview/storage/lancedb_backend.py | 124 +++++++++++++++----- src/hyperview/storage/memory_backend.py | 65 ++++++++--- src/hyperview/storage/schema.py | 125 ++++++++++++++++----- 11 files changed, 457 insertions(+), 183 deletions(-) diff --git a/frontend/src/app/page.tsx b/frontend/src/app/page.tsx index 59dc2f6..9dd6c5c 100644 --- a/frontend/src/app/page.tsx +++ b/frontend/src/app/page.tsx @@ -16,7 +16,6 @@ import { fetchSamplesBatch, fetchLassoSelection, } from "@/lib/api"; -import { getLayoutGeometry } from "@/lib/layouts"; const SAMPLES_PER_PAGE = 100; @@ -83,18 +82,18 @@ export default function Home() { useEffect(() => { if (!datasetInfo) return; - // Find layout key for current geometry - const targetLayout = datasetInfo.layouts.find((l) => getLayoutGeometry(l) === geometry); + // Find layout for current geometry (using LayoutInfo.geometry directly) + const targetLayout = datasetInfo.layouts.find((l) => l.geometry === geometry); if (!targetLayout) return; // Skip if already showing this layout const currentLayout = useStore.getState().currentLayoutKey; - if (currentLayout === targetLayout) return; + if (currentLayout === targetLayout.layout_key) return; const loadGeometry = async () => { setGeometryLoading(true); try { - const embeddingsData = await fetchEmbeddings(targetLayout); + const embeddingsData = await fetchEmbeddings(targetLayout.layout_key); setEmbeddings(embeddingsData); } catch (err) { console.error("Failed to load geometry:", err); diff --git a/frontend/src/components/ScatterPanel.tsx b/frontend/src/components/ScatterPanel.tsx index 7702228..dd3f275 100644 --- a/frontend/src/components/ScatterPanel.tsx +++ b/frontend/src/components/ScatterPanel.tsx @@ -8,7 +8,7 @@ import { EuclideanIcon, PoincareIcon, ScatterIcon } from "./icons"; import { type ScatterLabelsInfo, useHyperScatter } from "./useHyperScatter"; import { ToggleGroup, ToggleGroupItem } from "@/components/ui/toggle-group"; import type { Geometry } from "@/types"; -import { getLayoutSpaceKey, listAvailableGeometries } from "@/lib/layouts"; +import { listAvailableGeometries } from "@/lib/layouts"; interface ScatterPanelProps { className?: string; @@ -33,17 +33,18 @@ export function ScatterPanel({ className = "" }: ScatterPanelProps) { }, [datasetInfo?.layouts]); const embeddingModelName = useMemo(() => { - if (!embeddings) return null; + if (!embeddings || !datasetInfo) return null; const layoutKey = embeddings.layout_key; - const spaceKey = getLayoutSpaceKey(layoutKey); - const space = - datasetInfo?.spaces.find((s) => s.space_key === spaceKey) ?? - datasetInfo?.spaces.find((s) => layoutKey.startsWith(`${s.space_key}__`)); + // Find the LayoutInfo for current layout to get space_key directly + const layoutInfo = datasetInfo.layouts.find((l) => l.layout_key === layoutKey); + const spaceKey = layoutInfo?.space_key ?? layoutKey.split("__")[0]; + + const space = datasetInfo.spaces.find((s) => s.space_key === spaceKey); return space?.model_id ?? space?.space_key ?? spaceKey; - }, [datasetInfo?.spaces, embeddings]); + }, [datasetInfo, embeddings]); const labelsInfo = useMemo(() => { if (!embeddings) return null; diff --git a/frontend/src/components/useHyperScatter.ts b/frontend/src/components/useHyperScatter.ts index a9f1a8c..e501f3a 100644 --- a/frontend/src/components/useHyperScatter.ts +++ b/frontend/src/components/useHyperScatter.ts @@ -2,7 +2,6 @@ import type React from "react"; import { useCallback, useEffect, useMemo, useRef, useState } from "react"; import type { EmbeddingsData } from "@/types"; -import { getLayoutGeometry } from "@/lib/layouts"; import type { Dataset, GeometryMode, Modifiers, Renderer } from "hyper-scatter"; type HyperScatterModule = typeof import("hyper-scatter"); @@ -230,8 +229,7 @@ export function useHyperScatter({ positions[i * 2 + 1] = coords[i][1]; } - // Determine geometry from layout key - const geometry = (getLayoutGeometry(embeddings.layout_key) ?? "euclidean") as GeometryMode; + const geometry = embeddings.geometry as GeometryMode; const dataset: Dataset = viz.createDataset(geometry, positions, labelsInfo.categories); const opts = { diff --git a/frontend/src/lib/layouts.ts b/frontend/src/lib/layouts.ts index f9fd644..2f02e92 100644 --- a/frontend/src/lib/layouts.ts +++ b/frontend/src/lib/layouts.ts @@ -1,29 +1,17 @@ -import type { Geometry } from "@/types"; +import type { Geometry, LayoutInfo } from "@/types"; -export function getLayoutSpaceKey(layoutKey: string): string { - return layoutKey.split("__")[0] || layoutKey; -} - -export function getLayoutGeometry(layoutKey: string): Geometry | null { - const parts = layoutKey.split("__"); - if (parts.length < 2) { - const legacy = layoutKey.toLowerCase(); - if (legacy === "poincare" || legacy === "hyperbolic") return "poincare"; - if (legacy === "euclidean") return "euclidean"; - return null; +export function listAvailableGeometries(layouts: LayoutInfo[]): Geometry[] { + const geometries = new Set(); + for (const layout of layouts) { + geometries.add(layout.geometry); } + return Array.from(geometries); +} - const token = parts[1]?.split("_")[0]?.toLowerCase(); - if (token === "poincare" || token === "hyperbolic") return "poincare"; - if (token === "euclidean") return "euclidean"; - return null; +export function findLayoutByGeometry(layouts: LayoutInfo[], geometry: Geometry): LayoutInfo | undefined { + return layouts.find((l) => l.geometry === geometry); } -export function listAvailableGeometries(layouts: string[]): Geometry[] { - const geometries = new Set(); - for (const layoutKey of layouts) { - const geometry = getLayoutGeometry(layoutKey); - if (geometry) geometries.add(geometry); - } - return Array.from(geometries); +export function findLayoutByKey(layouts: LayoutInfo[], layoutKey: string): LayoutInfo | undefined { + return layouts.find((l) => l.layout_key === layoutKey); } diff --git a/frontend/src/types/index.ts b/frontend/src/types/index.ts index e055a89..2ec4b90 100644 --- a/frontend/src/types/index.ts +++ b/frontend/src/types/index.ts @@ -14,6 +14,18 @@ export interface SpaceInfo { model_id: string; dim: number; count: number; + provider: string; + geometry: Geometry | string; + config: Record | null; +} + +export interface LayoutInfo { + layout_key: string; + space_key: string; + method: string; + geometry: Geometry; + count: number; + params: Record | null; } export interface DatasetInfo { @@ -22,11 +34,12 @@ export interface DatasetInfo { labels: string[]; label_colors: Record; spaces: SpaceInfo[]; - layouts: string[]; + layouts: LayoutInfo[]; } export interface EmbeddingsData { layout_key: string; + geometry: Geometry; ids: string[]; labels: (string | null)[]; coords: [number, number][]; diff --git a/src/hyperview/core/dataset.py b/src/hyperview/core/dataset.py index bd69347..a691567 100644 --- a/src/hyperview/core/dataset.py +++ b/src/hyperview/core/dataset.py @@ -401,9 +401,13 @@ def compute_embeddings( test_embedding = self._embedding_computer.compute_single(all_samples[0]) dim = len(test_embedding) - # Ensure space exists + # Ensure space exists with provider metadata space_key = make_space_key(model) - self._storage.ensure_space(model, dim) + provider_config = { + "provider": "embed_anything", + "geometry": "euclidean", # EmbedAnything models produce Euclidean embeddings + } + self._storage.ensure_space(model, dim, config=provider_config) # Find samples needing embeddings missing_ids = self._storage.get_missing_embedding_ids(space_key) @@ -446,7 +450,7 @@ def compute_visualization( Args: space_key: Embedding space to project. If None, uses the first available. method: Projection method ('umap' supported). - geometry: Geometry type ('euclidean' or 'poincare'). + geometry: Output geometry type ('euclidean' or 'poincare'). n_neighbors: Number of neighbors for UMAP. min_dist: Minimum distance for UMAP. metric: Distance metric for UMAP. @@ -479,23 +483,38 @@ def compute_visualization( if len(ids) == 0: raise ValueError(f"No embeddings in space '{space_key}'. Call compute_embeddings() first.") - # Generate layout key (includes geometry) - layout_key = make_layout_key(space_key, method, geometry) - - # Check if layout exists - if not force and layout_key in self._storage.list_layouts(): - existing_ids, _ = self._storage.get_layout_coords(layout_key) - if set(existing_ids) == set(ids): - print(f"Layout '{layout_key}' already exists with {len(ids)} points") - return layout_key - else: - print(f"Layout exists but has different samples, recomputing...") - if len(ids) < 3: raise ValueError(f"Need at least 3 samples for visualization, have {len(ids)}") + # Build params dict and generate layout key (includes params hash) + layout_params = { + "n_neighbors": n_neighbors, + "min_dist": min_dist, + "metric": metric, + } + layout_key = make_layout_key(space_key, method, geometry, layout_params) + + # Check if layout exists with same params + if not force: + existing_layout = self._storage.get_layout(layout_key) + if existing_layout is not None: + existing_ids, _ = self._storage.get_layout_coords(layout_key) + if set(existing_ids) == set(ids): + print(f"Layout '{layout_key}' already exists with {len(ids)} points") + return layout_key + print("Layout exists but has different samples, recomputing...") + print(f"Computing {geometry} {method} layout for {len(ids)} samples...") + # Ensure layout registry entry exists + self._storage.ensure_layout( + layout_key=layout_key, + space_key=space_key, + method=method, + geometry=geometry, + params=layout_params, + ) + # Compute projection based on geometry if geometry == "poincare": coords = self._projection_engine.project_to_poincare( @@ -512,7 +531,7 @@ def compute_visualization( metric=metric, ) - # Store layout + # Store layout coordinates self._storage.add_layout_coords(layout_key, ids, coords) return layout_key @@ -521,8 +540,8 @@ def list_spaces(self) -> list[Any]: """List all embedding spaces in this dataset.""" return self._storage.list_spaces() - def list_layouts(self) -> list[str]: - """List all layouts in this dataset.""" + def list_layouts(self) -> list[Any]: + """List all layouts in this dataset (returns LayoutInfo objects).""" return self._storage.list_layouts() def find_similar( @@ -610,9 +629,23 @@ def set_coords( # Ensure a synthetic space exists (required by launch()) space_key = "precomputed" if not any(s.space_key == space_key for s in self._storage.list_spaces()): - self._storage.ensure_space(space_key, dim=2) + precomputed_config = { + "provider": "precomputed", + "geometry": "unknown", # Precomputed coords don't have a source embedding geometry + } + self._storage.ensure_space(space_key, dim=2, config=precomputed_config) layout_key = make_layout_key(space_key, method="precomputed", geometry=geometry) + + # Ensure layout registry entry exists + self._storage.ensure_layout( + layout_key=layout_key, + space_key=space_key, + method="precomputed", + geometry=geometry, + params=None, + ) + self._storage.add_layout_coords(layout_key, list(ids), coords_arr) return layout_key @@ -630,6 +663,51 @@ def filter(self, predicate: Callable[[Sample], bool]) -> list[Sample]: """Filter samples based on a predicate function.""" return self._storage.filter(predicate) + def get_samples_paginated( + self, + offset: int = 0, + limit: int = 100, + label: str | None = None, + ) -> tuple[list[Sample], int]: + """Get paginated samples. + + This avoids loading all samples into memory and is used by the server + API for efficient pagination. + """ + return self._storage.get_samples_paginated(offset=offset, limit=limit, label=label) + + def get_samples_by_ids(self, sample_ids: list[str]) -> list[Sample]: + """Retrieve multiple samples by ID. + + The returned list is aligned to the input order and skips missing IDs. + """ + return self._storage.get_samples_by_ids(sample_ids) + + def get_visualization_data( + self, + layout_key: str, + ) -> tuple[list[str], list[str | None], np.ndarray]: + """Get visualization data (ids, labels, coords) for a layout.""" + return self._storage.get_visualization_data(layout_key) + + def get_lasso_candidates_aabb( + self, + *, + layout_key: str, + x_min: float, + x_max: float, + y_min: float, + y_max: float, + ) -> tuple[list[str], np.ndarray]: + """Return candidate (id, xy) rows within an AABB for a layout.""" + return self._storage.get_lasso_candidates_aabb( + layout_key=layout_key, + x_min=x_min, + x_max=x_max, + y_min=y_min, + y_max=y_max, + ) + def to_dict(self) -> dict[str, Any]: """Convert dataset to dictionary for serialization.""" return { diff --git a/src/hyperview/server/app.py b/src/hyperview/server/app.py index d06cf08..cb10f2d 100644 --- a/src/hyperview/server/app.py +++ b/src/hyperview/server/app.py @@ -48,6 +48,29 @@ class SampleResponse(BaseModel): metadata: dict +class LayoutInfoResponse(BaseModel): + """Response model for layout info.""" + + layout_key: str + space_key: str + method: str + geometry: str + count: int + params: dict[str, Any] | None + + +class SpaceInfoResponse(BaseModel): + """Response model for embedding space info.""" + + space_key: str + model_id: str + dim: int + count: int + provider: str + geometry: str + config: dict[str, Any] | None + + class DatasetResponse(BaseModel): """Response model for dataset info.""" @@ -55,29 +78,21 @@ class DatasetResponse(BaseModel): num_samples: int labels: list[str] label_colors: dict[str, str] - spaces: list[dict[str, Any]] - layouts: list[str] + spaces: list[SpaceInfoResponse] + layouts: list[LayoutInfoResponse] class EmbeddingsResponse(BaseModel): """Response model for embeddings data (for scatter plot).""" layout_key: str + geometry: str ids: list[str] labels: list[str | None] - coords: list[list[float]] # [[x, y], ...] + coords: list[list[float]] label_colors: dict[str, str] -class SpaceInfo(BaseModel): - """Response model for embedding space info.""" - - space_key: str - model_id: str - dim: int - count: int - - class SimilarSampleResponse(BaseModel): """Response model for a similar sample with distance.""" @@ -145,15 +160,10 @@ async def get_dataset_info(): raise HTTPException(status_code=404, detail="No dataset loaded") spaces = _current_dataset.list_spaces() - space_dicts = [ - { - "space_key": s.space_key, - "model_id": s.model_id, - "dim": s.dim, - "count": s.count, - } - for s in spaces - ] + space_dicts = [s.to_api_dict() for s in spaces] + + layouts = _current_dataset.list_layouts() + layout_dicts = [l.to_api_dict() for l in layouts] return DatasetResponse( name=_current_dataset.name, @@ -161,7 +171,7 @@ async def get_dataset_info(): labels=_current_dataset.labels, label_colors=_current_dataset.get_label_colors(), spaces=space_dicts, - layouts=_current_dataset.list_layouts(), + layouts=layout_dicts, ) @app.get("/api/samples") @@ -174,8 +184,8 @@ async def get_samples( if _current_dataset is None: raise HTTPException(status_code=404, detail="No dataset loaded") - # Use storage backend's native pagination (avoids loading all samples) - samples, total = _current_dataset._storage.get_samples_paginated( + # Use backend-native pagination (avoids loading all samples) + samples, total = _current_dataset.get_samples_paginated( offset=offset, limit=limit, label=label ) @@ -204,7 +214,7 @@ async def get_samples_batch(request: SelectionRequest): if _current_dataset is None: raise HTTPException(status_code=404, detail="No dataset loaded") - samples = _current_dataset._storage.get_samples_by_ids(request.sample_ids) + samples = _current_dataset.get_samples_by_ids(request.sample_ids) return {"samples": [s.to_api_dict(include_thumbnail=True) for s in samples]} @@ -214,28 +224,30 @@ async def get_embeddings(layout_key: str | None = None): if _current_dataset is None: raise HTTPException(status_code=404, detail="No dataset loaded") - # Get available layouts layouts = _current_dataset.list_layouts() if not layouts: raise HTTPException( status_code=400, detail="No layouts computed. Call compute_visualization() first." ) - # Use specified layout or first available + # Find the requested layout + layout_info = None if layout_key is None: - layout_key = layouts[0] - elif layout_key not in layouts: - raise HTTPException(status_code=404, detail=f"Layout not found: {layout_key}") + layout_info = layouts[0] + layout_key = layout_info.layout_key + else: + layout_info = next((l for l in layouts if l.layout_key == layout_key), None) + if layout_info is None: + raise HTTPException(status_code=404, detail=f"Layout not found: {layout_key}") - ids, labels, coords = _current_dataset._storage.get_visualization_data(layout_key) + ids, labels, coords = _current_dataset.get_visualization_data(layout_key) if not ids: - raise HTTPException( - status_code=400, detail=f"No data in layout '{layout_key}'." - ) + raise HTTPException(status_code=400, detail=f"No data in layout '{layout_key}'.") return EmbeddingsResponse( layout_key=layout_key, + geometry=layout_info.geometry, ids=ids, labels=labels, coords=coords.tolist(), @@ -249,17 +261,7 @@ async def get_spaces(): raise HTTPException(status_code=404, detail="No dataset loaded") spaces = _current_dataset.list_spaces() - return { - "spaces": [ - { - "space_key": s.space_key, - "model_id": s.model_id, - "dim": s.dim, - "count": s.count, - } - for s in spaces - ] - } + return {"spaces": [s.to_api_dict() for s in spaces]} @app.get("/api/layouts") async def get_layouts(): @@ -267,7 +269,8 @@ async def get_layouts(): if _current_dataset is None: raise HTTPException(status_code=404, detail="No dataset loaded") - return {"layouts": _current_dataset.list_layouts()} + layouts = _current_dataset.list_layouts() + return {"layouts": [l.to_api_dict() for l in layouts]} @app.post("/api/selection") async def sync_selection(request: SelectionRequest): @@ -309,16 +312,13 @@ async def lasso_selection(request: LassoSelectionRequest): y_min = float(np.min(poly[:, 1])) y_max = float(np.max(poly[:, 1])) - try: - candidate_ids, candidate_coords = _current_dataset._storage.get_lasso_candidates_aabb( - layout_key=request.layout_key, - x_min=x_min, - x_max=x_max, - y_min=y_min, - y_max=y_max, - ) - except ValueError as e: - raise HTTPException(status_code=400, detail=str(e)) + candidate_ids, candidate_coords = _current_dataset.get_lasso_candidates_aabb( + layout_key=request.layout_key, + x_min=x_min, + x_max=x_max, + y_min=y_min, + y_max=y_max, + ) if candidate_coords.size == 0: return {"total": 0, "offset": request.offset, "limit": request.limit, "sample_ids": [], "samples": []} @@ -334,7 +334,7 @@ async def lasso_selection(request: LassoSelectionRequest): end = int(request.offset + request.limit) sample_ids = selected_ids[start:end] - samples = _current_dataset._storage.get_samples_by_ids(sample_ids) + samples = _current_dataset.get_samples_by_ids(sample_ids) sample_dicts = [s.to_api_dict(include_thumbnail=request.include_thumbnails) for s in samples] return { diff --git a/src/hyperview/storage/backend.py b/src/hyperview/storage/backend.py index bd21e78..c7c3c85 100644 --- a/src/hyperview/storage/backend.py +++ b/src/hyperview/storage/backend.py @@ -142,12 +142,23 @@ def get_missing_embedding_ids(self, space_key: str) -> list[str]: # ========================================================================= @abstractmethod - def list_layouts(self) -> list[str]: - """List all layout keys.""" + def list_layouts(self) -> list[Any]: + """List all layouts. Returns list of LayoutInfo.""" @abstractmethod - def ensure_layout(self, layout_key: str) -> None: - """Ensure a layout exists.""" + def get_layout(self, layout_key: str) -> Any | None: + """Get layout info. Returns LayoutInfo or None.""" + + @abstractmethod + def ensure_layout( + self, + layout_key: str, + space_key: str, + method: str, + geometry: str, + params: dict | None = None, + ) -> Any: + """Ensure a layout exists. Returns LayoutInfo.""" @abstractmethod def delete_layout(self, layout_key: str) -> bool: diff --git a/src/hyperview/storage/lancedb_backend.py b/src/hyperview/storage/lancedb_backend.py index 08cf7c6..098c6b2 100644 --- a/src/hyperview/storage/lancedb_backend.py +++ b/src/hyperview/storage/lancedb_backend.py @@ -5,8 +5,9 @@ - samples table: core sample metadata - metadata table: key-value config - spaces table: registry of embedding spaces + - layouts_registry table: registry of layouts - embeddings__ tables: one per embedding model - - layouts__ tables: one per layout + - layouts__ tables: one per layout (coordinates) """ import json @@ -22,8 +23,10 @@ from hyperview.storage.backend import StorageBackend from hyperview.storage.config import StorageConfig, get_default_datasets_dir from hyperview.storage.schema import ( + LayoutInfo, SpaceInfo, create_embeddings_schema, + create_layouts_registry_schema, create_layouts_schema, create_metadata_schema, create_sample_schema, @@ -518,35 +521,104 @@ def get_missing_embedding_ids(self, space_key: str) -> list[str]: return list(all_ids - embedded_ids) # ========================================================================= - # Layouts operations + # Layouts registry operations # ========================================================================= + def _get_layouts_registry_table(self) -> lancedb.table.Table | None: + """Get or create layouts registry table.""" + if "layouts_registry" not in self._db.table_names(): + return None + return self._db.open_table("layouts_registry") + + def _ensure_layouts_registry_table(self) -> lancedb.table.Table: + """Ensure layouts registry table exists.""" + if "layouts_registry" not in self._db.table_names(): + schema = create_layouts_registry_schema() + self._db.create_table("layouts_registry", schema=schema) + return self._db.open_table("layouts_registry") + def get_layout_table_name(self, layout_key: str) -> str: - """Get the table name for a layout.""" return f"layouts__{layout_key}" - def list_layouts(self) -> list[str]: - """List all layout keys.""" - layouts = [] - for name in self._db.table_names(): - if name.startswith("layouts__"): - layouts.append(name[len("layouts__") :]) - return layouts + def list_layouts(self) -> list[LayoutInfo]: + table = self._get_layouts_registry_table() + if table is None: + return [] + rows = table.search().to_list() + return [LayoutInfo.from_dict(row) for row in rows] + + def get_layout(self, layout_key: str) -> LayoutInfo | None: + table = self._get_layouts_registry_table() + if table is None: + return None + safe_key = layout_key.replace("'", "''") + rows = table.search().where(f"layout_key = '{safe_key}'").limit(1).to_list() + return LayoutInfo.from_dict(rows[0]) if rows else None + + def ensure_layout( + self, + layout_key: str, + space_key: str, + method: str, + geometry: str, + params: dict | None = None, + ) -> LayoutInfo: + """Ensure a layout exists, creating registry entry if needed.""" + # Check if registry entry exists + existing = self.get_layout(layout_key) + if existing is not None: + return existing + + # Create registry entry + now = int(time.time()) + layout_info = LayoutInfo( + layout_key=layout_key, + space_key=space_key, + method=method, + geometry=geometry, + count=0, + created_at=now, + params=params, + ) + + registry_table = self._ensure_layouts_registry_table() + schema = create_layouts_registry_schema() + arrow = pa.Table.from_pylist([layout_info.to_dict()], schema=schema) + registry_table.add(arrow) - def ensure_layout(self, layout_key: str) -> None: - """Ensure a layout table exists.""" + # Create coordinates table table_name = self.get_layout_table_name(layout_key) if table_name not in self._db.table_names(): - schema = create_layouts_schema() - self._db.create_table(table_name, schema=schema) + coords_schema = create_layouts_schema() + self._db.create_table(table_name, schema=coords_schema) + + return layout_info def delete_layout(self, layout_key: str) -> bool: - """Delete a layout table.""" + """Delete a layout (both registry entry and coordinates).""" + deleted = False + + # Delete coordinates table table_name = self.get_layout_table_name(layout_key) if table_name in self._db.table_names(): self._db.drop_table(table_name) - return True - return False + deleted = True + + # Delete registry entry + registry_table = self._get_layouts_registry_table() + if registry_table is not None: + safe_key = layout_key.replace("'", "''") + registry_table.delete(f"layout_key = '{safe_key}'") + deleted = True + + return deleted + + def _update_layout_count(self, layout_key: str, count: int) -> None: + registry_table = self._get_layouts_registry_table() + if registry_table is None: + return + safe_key = layout_key.replace("'", "''") + registry_table.update(where=f"layout_key = '{safe_key}'", values={"count": count}) def add_layout_coords( self, @@ -554,21 +626,20 @@ def add_layout_coords( ids: list[str], coords: np.ndarray, ) -> None: - """Add layout coordinates. - - Args: - layout_key: The layout key. - ids: Sample IDs. - coords: 2D coordinates (N x 2). - """ if len(ids) != len(coords): raise ValueError("ids and coords must have same length") if len(ids) == 0: return - self.ensure_layout(layout_key) + # Require registry entry exists (call ensure_layout first) + if self.get_layout(layout_key) is None: + raise ValueError(f"Layout '{layout_key}' not registered. Call ensure_layout() first.") table_name = self.get_layout_table_name(layout_key) + if table_name not in self._db.table_names(): + coords_schema = create_layouts_schema() + self._db.create_table(table_name, schema=coords_schema) + table = self._db.open_table(table_name) data = [ @@ -586,6 +657,9 @@ def add_layout_coords( .execute(arrow) ) + # Update count in registry + self._update_layout_count(layout_key, table.count_rows()) + def get_layout_coords( self, layout_key: str, diff --git a/src/hyperview/storage/memory_backend.py b/src/hyperview/storage/memory_backend.py index 6326cd6..ba8c4a6 100644 --- a/src/hyperview/storage/memory_backend.py +++ b/src/hyperview/storage/memory_backend.py @@ -9,7 +9,7 @@ from hyperview.core.sample import Sample from hyperview.storage.backend import StorageBackend -from hyperview.storage.schema import SpaceInfo, make_space_key +from hyperview.storage.schema import LayoutInfo, SpaceInfo, make_space_key class MemoryBackend(StorageBackend): @@ -34,7 +34,10 @@ def __init__(self, dataset_name: str): # Embeddings: space_key -> {sample_id -> vector} self._embeddings: dict[str, dict[str, np.ndarray]] = {} - # Layouts: layout_key -> {sample_id -> (x, y)} + # Layouts registry + self._layout_registry: dict[str, LayoutInfo] = {} + + # Layout coordinates: layout_key -> {sample_id -> (x, y)} self._layouts: dict[str, dict[str, tuple[float, float]]] = {} # ========================================================================= @@ -237,21 +240,49 @@ def get_missing_embedding_ids(self, space_key: str) -> list[str]: # Layouts # ========================================================================= - def list_layouts(self) -> list[str]: - """List all layout keys.""" - return list(self._layouts.keys()) + def list_layouts(self) -> list[LayoutInfo]: + return list(self._layout_registry.values()) - def ensure_layout(self, layout_key: str) -> None: - """Ensure a layout exists.""" - if layout_key not in self._layouts: - self._layouts[layout_key] = {} + def get_layout(self, layout_key: str) -> LayoutInfo | None: + return self._layout_registry.get(layout_key) + + def ensure_layout( + self, + layout_key: str, + space_key: str, + method: str, + geometry: str, + params: dict | None = None, + ) -> LayoutInfo: + if layout_key in self._layout_registry: + return self._layout_registry[layout_key] + + layout_info = LayoutInfo( + layout_key=layout_key, + space_key=space_key, + method=method, + geometry=geometry, + count=0, + created_at=int(time.time()), + params=params, + ) + self._layout_registry[layout_key] = layout_info + self._layouts[layout_key] = {} + return layout_info def delete_layout(self, layout_key: str) -> bool: - """Delete a layout.""" + deleted = False if layout_key in self._layouts: del self._layouts[layout_key] - return True - return False + deleted = True + if layout_key in self._layout_registry: + del self._layout_registry[layout_key] + deleted = True + return deleted + + def _update_layout_count(self, layout_key: str) -> None: + if layout_key in self._layout_registry: + self._layout_registry[layout_key].count = len(self._layouts.get(layout_key, {})) def add_layout_coords( self, @@ -259,16 +290,22 @@ def add_layout_coords( ids: list[str], coords: np.ndarray, ) -> None: - """Add layout coordinates.""" if len(ids) != len(coords): raise ValueError("ids and coords must have same length") - self.ensure_layout(layout_key) + # Require registry entry exists (call ensure_layout first) + if layout_key not in self._layout_registry: + raise ValueError(f"Layout '{layout_key}' not registered. Call ensure_layout() first.") + + if layout_key not in self._layouts: + self._layouts[layout_key] = {} layout_store = self._layouts[layout_key] for id_, coord in zip(ids, coords): layout_store[id_] = (float(coord[0]), float(coord[1])) + self._update_layout_count(layout_key) + def get_layout_coords( self, layout_key: str, diff --git a/src/hyperview/storage/schema.py b/src/hyperview/storage/schema.py index ac2f027..1f1a14d 100644 --- a/src/hyperview/storage/schema.py +++ b/src/hyperview/storage/schema.py @@ -102,8 +102,15 @@ class SpaceInfo: updated_at: int config: dict[str, Any] | None = None + @property + def provider(self) -> str: + return (self.config or {}).get("provider", "unknown") + + @property + def geometry(self) -> str: + return (self.config or {}).get("geometry", "euclidean") + def to_dict(self) -> dict[str, Any]: - """Convert to dictionary for storage.""" return { "space_key": self.space_key, "model_id": self.model_id, @@ -114,15 +121,21 @@ def to_dict(self) -> dict[str, Any]: "config_json": json.dumps(self.config) if self.config else None, } + def to_api_dict(self) -> dict[str, Any]: + return { + "space_key": self.space_key, + "model_id": self.model_id, + "dim": self.dim, + "count": self.count, + "provider": self.provider, + "geometry": self.geometry, + "config": self.config, + } + @classmethod def from_dict(cls, row: dict[str, Any]) -> "SpaceInfo": - """Create from storage dictionary.""" - config = None - if row.get("config_json"): - try: - config = json.loads(row["config_json"]) - except (json.JSONDecodeError, TypeError): - pass + config_json = row.get("config_json") + config = json.loads(config_json) if config_json else None return cls( space_key=row["space_key"], model_id=row["model_id"], @@ -134,6 +147,72 @@ def from_dict(cls, row: dict[str, Any]) -> "SpaceInfo": ) +def create_layouts_registry_schema() -> pa.Schema: + """Create the PyArrow schema for the layouts registry. + + Each row represents a layout (2D projection of an embedding space). + """ + return pa.schema( + [ + pa.field("layout_key", pa.utf8(), nullable=False), + pa.field("space_key", pa.utf8(), nullable=False), + pa.field("method", pa.utf8(), nullable=False), + pa.field("geometry", pa.utf8(), nullable=False), + pa.field("count", pa.int64(), nullable=False), + pa.field("created_at", pa.int64(), nullable=False), + pa.field("params_json", pa.utf8(), nullable=True), + ] + ) + + +@dataclass +class LayoutInfo: + """Metadata for a layout (2D projection).""" + + layout_key: str + space_key: str + method: str + geometry: str + count: int + created_at: int + params: dict[str, Any] | None = None + + def to_dict(self) -> dict[str, Any]: + return { + "layout_key": self.layout_key, + "space_key": self.space_key, + "method": self.method, + "geometry": self.geometry, + "count": self.count, + "created_at": self.created_at, + "params_json": json.dumps(self.params) if self.params else None, + } + + def to_api_dict(self) -> dict[str, Any]: + return { + "layout_key": self.layout_key, + "space_key": self.space_key, + "method": self.method, + "geometry": self.geometry, + "count": self.count, + "params": self.params, + } + + @classmethod + def from_dict(cls, row: dict[str, Any]) -> "LayoutInfo": + params_json = row.get("params_json") + params = json.loads(params_json) if params_json else None + return cls( + layout_key=row["layout_key"], + space_key=row["space_key"], + method=row["method"], + geometry=row["geometry"], + count=row["count"], + created_at=row["created_at"], + params=params, + ) + + def slugify_model_id(model_id: str) -> str: """Convert a model ID to a safe table name component. @@ -162,20 +241,20 @@ def make_layout_key( space_key: str, method: str = "umap", geometry: str = "euclidean", + params: dict | None = None, ) -> str: - """Generate a layout_key from space, method, and geometry. - - Args: - space_key: The embedding space key. - method: Layout method (e.g., "umap"). - geometry: Geometry type ("euclidean" or "poincare"). + """Generate a layout_key from space, method, geometry, and params. - Returns: - Layout key like "clip__poincare_umap" or "clip__euclidean_umap". + The params are hashed to ensure different parameter sets get different keys. """ - # Format: {space}__{geometry}_{method} - # Frontend parses for "poincare" or "hyperbolic" to auto-detect geometry - return f"{space_key}__{geometry}_{method}" + base = f"{space_key}__{geometry}_{method}" + if params: + # Create a stable hash of params + import hashlib + params_str = "_".join(f"{k}={v}" for k, v in sorted(params.items())) + params_hash = hashlib.md5(params_str.encode()).hexdigest()[:8] + return f"{base}_{params_hash}" + return base def sample_to_dict(sample: Sample) -> dict[str, Any]: @@ -191,12 +270,8 @@ def sample_to_dict(sample: Sample) -> dict[str, Any]: def dict_to_sample(row: dict[str, Any]) -> Sample: """Convert a LanceDB row to a Sample object.""" - metadata = {} - if row.get("metadata_json"): - try: - metadata = json.loads(row["metadata_json"]) - except (json.JSONDecodeError, TypeError): - metadata = {} + metadata_json = row.get("metadata_json") + metadata = json.loads(metadata_json) if metadata_json else {} return Sample( id=row["id"], From 1849445d51bca50fc161e2a9caa1edd2e0b8a649 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Tue, 20 Jan 2026 03:13:23 +0100 Subject: [PATCH 32/75] refactor: simplify storage layer and dataset API - Remove ~1000 lines of redundant code from storage backends - Streamline StorageBackend abstract interface - Add unified _ingest_samples() helper for consistent deduplication - Extract embedding/layout pipelines to dedicated module - Remove optional tqdm wrapper from embedding compute - Add get_labels_by_ids() method to storage interface --- src/hyperview/cli.py | 27 +- src/hyperview/core/dataset.py | 498 ++++---------- src/hyperview/embeddings/compute.py | 19 +- src/hyperview/embeddings/pipelines.py | 181 ++++++ src/hyperview/storage/backend.py | 104 +-- src/hyperview/storage/lancedb_backend.py | 783 ++++------------------- src/hyperview/storage/memory_backend.py | 248 +------ 7 files changed, 487 insertions(+), 1373 deletions(-) create mode 100644 src/hyperview/embeddings/pipelines.py diff --git a/src/hyperview/cli.py b/src/hyperview/cli.py index 4df7c0f..bdff271 100644 --- a/src/hyperview/cli.py +++ b/src/hyperview/cli.py @@ -112,22 +112,17 @@ def run_demo( print("Loading CIFAR-100 dataset...") dataset = Dataset("cifar100_demo") - try: - added, skipped = dataset.add_from_huggingface( - "uoft-cs/cifar100", - split="train", - image_key="img", - label_key="fine_label", - max_samples=num_samples, - ) - if skipped > 0: - print(f"Loaded {added} samples ({skipped} already present)") - else: - print(f"Loaded {added} samples") - except Exception as e: - print(f"Failed to load HuggingFace dataset: {e}") - print("Please ensure 'datasets' is installed: pip install datasets") - sys.exit(1) + added, skipped = dataset.add_from_huggingface( + "uoft-cs/cifar100", + split="train", + image_key="img", + label_key="fine_label", + max_samples=num_samples, + ) + if skipped > 0: + print(f"Loaded {added} samples ({skipped} already present)") + else: + print(f"Loaded {added} samples") print("Computing embeddings...") dataset.compute_embeddings(show_progress=True) diff --git a/src/hyperview/core/dataset.py b/src/hyperview/core/dataset.py index a691567..86446eb 100644 --- a/src/hyperview/core/dataset.py +++ b/src/hyperview/core/dataset.py @@ -11,9 +11,9 @@ from datasets import load_dataset from PIL import Image -from hyperview.core.sample import Sample, SampleFromArray +from hyperview.core.sample import Sample from hyperview.storage.backend import StorageBackend -from hyperview.storage.schema import make_layout_key, make_space_key +from hyperview.storage.schema import make_layout_key class Dataset: @@ -32,9 +32,6 @@ class Dataset: dataset = hv.Dataset("my_dataset") dataset.add_images_dir("/path/to/images") - # Open an existing dataset - dataset = hv.Dataset.open("my_dataset") - # Create an in-memory dataset (for testing) dataset = hv.Dataset("temp", persist=False) """ @@ -54,8 +51,6 @@ def __init__( storage: Optional custom storage backend. If provided, persist is ignored. """ self.name = name or f"dataset_{uuid.uuid4().hex[:8]}" - self._embedding_computer = None - self._projection_engine = None # Initialize storage backend if storage is not None: @@ -67,28 +62,15 @@ def __init__( self._storage = LanceDBBackend(self.name, config) else: from hyperview.storage import MemoryBackend - self._storage = MemoryBackend(self.name) - # Initialize label colors from storage - self._sync_label_colors() - - def _sync_label_colors(self) -> None: - """Sync label colors from storage and assign colors to new labels.""" - # Get existing colors from storage - existing_colors = self._storage.label_colors - - # Get all unique labels - all_labels = self._storage.get_unique_labels() - - # Assign colors to any labels without colors - for label in all_labels: - if label not in existing_colors: - self._assign_label_color(label, existing_colors) - - # Save back if we added new colors - if existing_colors != self._storage.label_colors: - self._storage.label_colors = existing_colors + # Color palette for deterministic label color assignment + _COLOR_PALETTE = [ + "#e6194b", "#3cb44b", "#ffe119", "#4363d8", "#f58231", + "#911eb4", "#46f0f0", "#f032e6", "#bcf60c", "#fabebe", + "#008080", "#e6beff", "#9a6324", "#fffac8", "#800000", + "#aaffc3", "#808000", "#ffd8b1", "#000075", "#808080", + ] def __len__(self) -> int: return len(self._storage) @@ -103,15 +85,43 @@ def __getitem__(self, sample_id: str) -> Sample: return sample def add_sample(self, sample: Sample) -> None: - """Add a sample to the dataset.""" + """Add a sample to the dataset (idempotent).""" self._storage.add_sample(sample) - # Assign color to label if needed - if sample.label: - colors = self._storage.label_colors - if sample.label not in colors: - self._assign_label_color(sample.label, colors) - self._storage.label_colors = colors + def _ingest_samples( + self, + samples: list[Sample], + *, + skip_existing: bool = True, + ) -> tuple[int, int]: + """Shared ingestion helper for batch sample insertion. + + Handles deduplication uniformly. + + Args: + samples: List of samples to ingest. + skip_existing: If True, skip samples that already exist in storage. + + Returns: + Tuple of (num_added, num_skipped). + """ + if not samples: + return 0, 0 + + skipped = 0 + if skip_existing: + all_ids = [s.id for s in samples] + existing_ids = self._storage.get_existing_ids(all_ids) + if existing_ids: + samples = [s for s in samples if s.id not in existing_ids] + skipped = len(all_ids) - len(samples) + + if not samples: + return 0, skipped + + self._storage.add_samples_batch(samples) + + return len(samples), skipped def add_image( self, @@ -149,7 +159,8 @@ def add_images_dir( extensions: tuple[str, ...] = (".jpg", ".jpeg", ".png", ".webp"), label_from_folder: bool = False, recursive: bool = True, - ) -> int: + skip_existing: bool = True, + ) -> tuple[int, int]: """Add all images from a directory. Args: @@ -157,9 +168,10 @@ def add_images_dir( extensions: Tuple of valid file extensions. label_from_folder: If True, use parent folder name as label. recursive: If True, search subdirectories. + skip_existing: If True (default), skip samples that already exist. Returns: - Number of images added. + Tuple of (num_added, num_skipped). """ directory_path = Path(directory) if not directory_path.exists(): @@ -180,16 +192,8 @@ def add_images_dir( ) samples.append(sample) - # Track label colors - if label: - colors = self._storage.label_colors - if label not in colors: - self._assign_label_color(label, colors) - self._storage.label_colors = colors - - # Batch add for efficiency - self._storage.add_samples_batch(samples) - return len(samples) + # Use shared ingestion helper + return self._ingest_samples(samples, skip_existing=skip_existing) def add_from_huggingface( self, @@ -199,7 +203,6 @@ def add_from_huggingface( label_key: str | None = "fine_label", label_names_key: str | None = None, max_samples: int | None = None, - download_images: bool = True, show_progress: bool = True, skip_existing: bool = True, image_format: str = "auto", @@ -217,11 +220,8 @@ def add_from_huggingface( label_key: Key for the label column (can be None). label_names_key: Key for label names in dataset info. max_samples: Maximum number of samples to load. - download_images: If True (default), download images to local disk. - If False, use in-memory storage (won't persist). - show_progress: Whether to show progress bar. + show_progress: Whether to print progress. skip_existing: If True (default), skip samples that already exist in storage. - If False, allow duplicate samples (not recommended). image_format: Image format to save: "auto" (detect from source, fallback PNG), "png" (lossless), or "jpeg" (smaller files). @@ -230,11 +230,6 @@ def add_from_huggingface( """ from hyperview.storage import StorageConfig - try: - from tqdm import tqdm - except ImportError: - tqdm = None - ds = cast(Any, load_dataset(dataset_name, split=split)) # Get label names if available @@ -257,15 +252,11 @@ def add_from_huggingface( samples = [] total = len(ds) if max_samples is None else min(len(ds), max_samples) - colors = self._storage.label_colors - # Setup progress bar - if show_progress and tqdm is not None: - iterator = tqdm(range(total), desc=f"Loading {dataset_name}") - else: - if show_progress: - print(f"Loading {total} samples from {dataset_name}...") - iterator = range(total) + if show_progress: + print(f"Loading {total} samples from {dataset_name}...") + + iterator = range(total) for i in iterator: item = ds[i] @@ -317,57 +308,30 @@ def add_from_huggingface( "version": version, } - if download_images: - # Save image to disk (FiftyOne pattern) - image_path = media_dir / f"{sample_id}{ext}" - if not image_path.exists(): - # Convert to RGB if necessary (for JPEG or non-RGB images) - if save_format == "JPEG" or pil_image.mode in ("RGBA", "P", "L"): - pil_image = pil_image.convert("RGB") - pil_image.save(image_path, format=save_format) + image_path = media_dir / f"{sample_id}{ext}" + if not image_path.exists(): + if save_format == "JPEG" or pil_image.mode in ("RGBA", "P", "L"): + pil_image = pil_image.convert("RGB") + pil_image.save(image_path, format=save_format) - sample = Sample( - id=sample_id, - filepath=str(image_path), - label=label, - metadata=metadata, - ) - else: - # Use in-memory storage (legacy behavior, won't persist) - image_array = np.array(pil_image) - sample = SampleFromArray.from_array( - id=sample_id, - image_array=image_array, - label=label, - metadata=metadata, - ) + sample = Sample( + id=sample_id, + filepath=str(image_path), + label=label, + metadata=metadata, + ) samples.append(sample) - # Track label colors - if label and label not in colors: - self._assign_label_color(label, colors) - - # Check for existing samples and skip duplicates - skipped = 0 - if skip_existing and samples: - all_ids = [s.id for s in samples] - existing_ids = self._storage.get_existing_ids(all_ids) - if existing_ids: - samples = [s for s in samples if s.id not in existing_ids] - skipped = len(all_ids) - len(samples) - - # Batch add for efficiency - if samples: - self._storage.add_samples_batch(samples) - self._storage.label_colors = colors + # Use shared ingestion helper + num_added, skipped = self._ingest_samples(samples, skip_existing=skip_existing) - if download_images and show_progress: + if show_progress: print(f"Images saved to: {media_dir}") if skipped > 0: print(f"Skipped {skipped} existing samples") - return len(samples), skipped + return num_added, skipped def compute_embeddings( self, @@ -387,52 +351,14 @@ def compute_embeddings( Returns: space_key for the embedding space. """ - from hyperview.embeddings.compute import EmbeddingComputer - - if self._embedding_computer is None or self._embedding_computer.model_id != model: - self._embedding_computer = EmbeddingComputer(model=model) - - # Get embedding dimension from a test computation - all_samples = self._storage.get_all_samples() - if not all_samples: - raise ValueError("No samples in dataset") - - # Compute one embedding to get dimension - test_embedding = self._embedding_computer.compute_single(all_samples[0]) - dim = len(test_embedding) - - # Ensure space exists with provider metadata - space_key = make_space_key(model) - provider_config = { - "provider": "embed_anything", - "geometry": "euclidean", # EmbedAnything models produce Euclidean embeddings - } - self._storage.ensure_space(model, dim, config=provider_config) + from hyperview.embeddings.pipelines import compute_missing_embeddings - # Find samples needing embeddings - missing_ids = self._storage.get_missing_embedding_ids(space_key) - - if not missing_ids: - if show_progress: - print(f"All {len(all_samples)} samples already have embeddings in space '{space_key}'") - return space_key - - samples_needing_embeddings = self._storage.get_samples_by_ids(missing_ids) - - if show_progress: - skipped = len(all_samples) - len(samples_needing_embeddings) - if skipped > 0: - print(f"Skipped {skipped} samples with existing embeddings") - - embeddings = self._embedding_computer.compute_batch( - samples_needing_embeddings, batch_size=batch_size, show_progress=show_progress + space_key, _num_computed, _num_skipped = compute_missing_embeddings( + storage=self._storage, + model_id=model, + batch_size=batch_size, + show_progress=show_progress, ) - - # Store embeddings - ids = [s.id for s in samples_needing_embeddings] - vectors = np.array(embeddings, dtype=np.float32) - self._storage.add_embeddings(space_key, ids, vectors) - return space_key def compute_visualization( @@ -459,83 +385,20 @@ def compute_visualization( Returns: layout_key for the computed layout. """ - from hyperview.embeddings.projection import ProjectionEngine + from hyperview.embeddings.pipelines import compute_layout - if geometry not in ("euclidean", "poincare"): - raise ValueError(f"Invalid geometry: {geometry}. Must be 'euclidean' or 'poincare'.") - - if self._projection_engine is None: - self._projection_engine = ProjectionEngine() - - # Get space - if space_key is None: - spaces = self._storage.list_spaces() - if not spaces: - raise ValueError("No embedding spaces. Call compute_embeddings() first.") - space_key = spaces[0].space_key - - space = self._storage.get_space(space_key) - if space is None: - raise ValueError(f"Space not found: {space_key}") - - # Get all embeddings from this space - ids, vectors = self._storage.get_embeddings(space_key) - if len(ids) == 0: - raise ValueError(f"No embeddings in space '{space_key}'. Call compute_embeddings() first.") - - if len(ids) < 3: - raise ValueError(f"Need at least 3 samples for visualization, have {len(ids)}") - - # Build params dict and generate layout key (includes params hash) - layout_params = { - "n_neighbors": n_neighbors, - "min_dist": min_dist, - "metric": metric, - } - layout_key = make_layout_key(space_key, method, geometry, layout_params) - - # Check if layout exists with same params - if not force: - existing_layout = self._storage.get_layout(layout_key) - if existing_layout is not None: - existing_ids, _ = self._storage.get_layout_coords(layout_key) - if set(existing_ids) == set(ids): - print(f"Layout '{layout_key}' already exists with {len(ids)} points") - return layout_key - print("Layout exists but has different samples, recomputing...") - - print(f"Computing {geometry} {method} layout for {len(ids)} samples...") - - # Ensure layout registry entry exists - self._storage.ensure_layout( - layout_key=layout_key, + return compute_layout( + storage=self._storage, space_key=space_key, method=method, geometry=geometry, - params=layout_params, + n_neighbors=n_neighbors, + min_dist=min_dist, + metric=metric, + force=force, + show_progress=True, ) - # Compute projection based on geometry - if geometry == "poincare": - coords = self._projection_engine.project_to_poincare( - vectors, - n_neighbors=n_neighbors, - min_dist=min_dist, - metric=metric, - ) - else: - coords = self._projection_engine.project_umap( - vectors, - n_neighbors=n_neighbors, - min_dist=min_dist, - metric=metric, - ) - - # Store layout coordinates - self._storage.add_layout_coords(layout_key, ids, coords) - - return layout_key - def list_spaces(self) -> list[Any]: """List all embedding spaces in this dataset.""" return self._storage.list_spaces() @@ -580,20 +443,17 @@ def find_similar_by_vector( """ return self._storage.find_similar_by_vector(vector, k, space_key) - def _assign_label_color(self, label: str, colors: dict[str, str]) -> None: - """Assign a color to a label.""" - color_palette = [ - "#e6194b", "#3cb44b", "#ffe119", "#4363d8", "#f58231", - "#911eb4", "#46f0f0", "#f032e6", "#bcf60c", "#fabebe", - "#008080", "#e6beff", "#9a6324", "#fffac8", "#800000", - "#aaffc3", "#808000", "#ffd8b1", "#000075", "#808080", - ] - idx = len(colors) % len(color_palette) - colors[label] = color_palette[idx] + @staticmethod + def _compute_label_color(label: str, palette: list[str]) -> str: + """Compute a deterministic color for a label.""" + digest = hashlib.md5(label.encode("utf-8")).digest() + idx = int.from_bytes(digest[:4], "big") % len(palette) + return palette[idx] def get_label_colors(self) -> dict[str, str]: - """Get the color mapping for labels.""" - return self._storage.label_colors.copy() + """Get the color mapping for labels (computed deterministically).""" + labels = self._storage.get_unique_labels() + return {label: self._compute_label_color(label, self._COLOR_PALETTE) for label in labels} def set_coords( self, @@ -688,7 +548,27 @@ def get_visualization_data( layout_key: str, ) -> tuple[list[str], list[str | None], np.ndarray]: """Get visualization data (ids, labels, coords) for a layout.""" - return self._storage.get_visualization_data(layout_key) + layout_ids, layout_coords = self._storage.get_layout_coords(layout_key) + if not layout_ids: + return [], [], np.empty((0, 2), dtype=np.float32) + + labels_by_id = self._storage.get_labels_by_ids(layout_ids) + + ids: list[str] = [] + labels: list[str | None] = [] + coords: list[np.ndarray] = [] + + for i, sample_id in enumerate(layout_ids): + if sample_id in labels_by_id: + ids.append(sample_id) + labels.append(labels_by_id[sample_id]) + coords.append(layout_coords[i]) + + if not coords: + return [], [], np.empty((0, 2), dtype=np.float32) + + return ids, labels, np.asarray(coords, dtype=np.float32) + def get_lasso_candidates_aabb( self, @@ -708,15 +588,6 @@ def get_lasso_candidates_aabb( y_max=y_max, ) - def to_dict(self) -> dict[str, Any]: - """Convert dataset to dictionary for serialization.""" - return { - "name": self.name, - "num_samples": len(self), - "labels": self.labels, - "label_colors": self._storage.label_colors, - } - def save(self, filepath: str, include_thumbnails: bool = True) -> None: """Export dataset to a JSON file. @@ -736,7 +607,6 @@ def save(self, filepath: str, include_thumbnails: bool = True) -> None: data = { "name": self.name, - "label_colors": self._storage.label_colors, "samples": [ { "id": s.id, @@ -768,10 +638,6 @@ def load(cls, filepath: str, persist: bool = False) -> "Dataset": dataset = cls(name=data["name"], persist=persist) - # Set label colors - label_colors = data.get("label_colors", {}) - dataset._storage.label_colors = label_colors - # Add samples samples = [] for s_data in data["samples"]: @@ -786,139 +652,3 @@ def load(cls, filepath: str, persist: bool = False) -> "Dataset": dataset._storage.add_samples_batch(samples) return dataset - - @classmethod - def open(cls, name: str) -> "Dataset": - """Open an existing persistent dataset. - - Args: - name: Name of the dataset to open. - - Returns: - Dataset instance connected to existing data. - - Raises: - ValueError: If dataset does not exist. - """ - from hyperview.storage import LanceDBBackend - - if not LanceDBBackend.dataset_exists(name): - raise ValueError( - f"Dataset '{name}' does not exist. " - f"Available datasets: {cls.list_datasets()}" - ) - - return cls(name=name, persist=True) - - @classmethod - def list_datasets(cls) -> list[str]: - """List all available persistent datasets. - - Returns: - List of dataset names. - """ - from hyperview.storage import LanceDBBackend - - return LanceDBBackend.list_datasets() - - @classmethod - def delete(cls, name: str, delete_media: bool = False) -> bool: - """Delete a persistent dataset. - - Args: - name: Name of the dataset to delete. - delete_media: If True, also delete associated media files from disk. - Default is False (safe, preserves media files). - - Returns: - True if dataset was deleted, False if it didn't exist. - """ - import os - - from hyperview.storage import LanceDBBackend - - if delete_media: - try: - dataset = cls.open(name) - except Exception: - dataset = None - - if dataset is not None: - for fp in (s.filepath for s in dataset.samples): - if os.path.exists(fp): - try: - os.remove(fp) - except OSError: - continue - - return LanceDBBackend.delete_dataset(name) - - @classmethod - def cleanup_orphaned_media( - cls, - delete: bool = False, - ) -> tuple[int, list[str]]: - """Find media files not referenced by any dataset. - - Scans the media directory for image files and checks if they are - referenced by any existing dataset. Useful for cleaning up disk space - after deleting datasets without the delete_media=True flag. - - Args: - delete: If True, actually delete the orphaned files. - If False (default), just report them. - - Returns: - Tuple of (count, list_of_orphaned_paths). - """ - import os - - from hyperview.storage import StorageConfig - - config = StorageConfig.default() - media_dir = config.media_dir - - if not media_dir.exists(): - return 0, [] - - # Get all filepaths from all datasets - referenced: set[str] = set() - for dataset_name in cls.list_datasets(): - try: - ds = cls.open(dataset_name) - referenced.update(s.filepath for s in ds.samples) - except Exception: - continue - - # Find orphaned files (images not referenced by any dataset) - orphaned: list[str] = [] - image_extensions = {".png", ".jpg", ".jpeg", ".webp", ".gif", ".bmp"} - - for img_path in media_dir.rglob("*"): - if img_path.is_file() and img_path.suffix.lower() in image_extensions: - if str(img_path) not in referenced: - orphaned.append(str(img_path)) - - # Optionally delete orphaned files - if delete: - for path in orphaned: - try: - os.remove(path) - except OSError: - continue - - return len(orphaned), orphaned - - @classmethod - def exists(cls, name: str) -> bool: - """Check if a persistent dataset exists. - - Args: - name: Name of the dataset to check. - - Returns: - True if dataset exists. - """ - from hyperview.storage import LanceDBBackend - - return LanceDBBackend.dataset_exists(name) diff --git a/src/hyperview/embeddings/compute.py b/src/hyperview/embeddings/compute.py index f0e7d3b..78444b4 100644 --- a/src/hyperview/embeddings/compute.py +++ b/src/hyperview/embeddings/compute.py @@ -9,7 +9,6 @@ import os import tempfile from pathlib import Path -from typing import Any import numpy as np from embed_anything import EmbeddingModel @@ -17,14 +16,6 @@ from hyperview.core.sample import Sample -tqdm: Any | None = None -try: - from tqdm import tqdm as tqdm_impl -except ImportError: # pragma: no cover - pass -else: - tqdm = tqdm_impl - class EmbeddingComputer: """Compute embeddings for image samples using EmbedAnything.""" @@ -105,14 +96,8 @@ def compute_batch( # Prime model init so download errors happen before work starts. self._get_model() - total = len(samples) - iterator = samples - if show_progress: - if tqdm is not None: - iterator = tqdm(samples, total=total, desc="Computing embeddings") - else: - print(f"Computing embeddings for {total} samples...") + print(f"Computing embeddings for {len(samples)} samples...") - return [self.compute_single(sample) for sample in iterator] + return [self.compute_single(sample) for sample in samples] diff --git a/src/hyperview/embeddings/pipelines.py b/src/hyperview/embeddings/pipelines.py new file mode 100644 index 0000000..fd575cd --- /dev/null +++ b/src/hyperview/embeddings/pipelines.py @@ -0,0 +1,181 @@ +"""Compute orchestration pipelines for HyperView. + +These functions coordinate embedding computation and 2D layout/projection +computation, persisting results into the configured storage backend. +""" + +import numpy as np + +from hyperview.storage.backend import StorageBackend +from hyperview.storage.schema import make_layout_key, make_space_key + + +def compute_missing_embeddings( + storage: StorageBackend, + model_id: str, + batch_size: int = 32, + show_progress: bool = True, +) -> tuple[str, int, int]: + """Compute embeddings for samples that don't have them yet. + + Args: + storage: Storage backend to read samples from and write embeddings to. + model_id: EmbedAnything HuggingFace model_id to use. + batch_size: Batch size for processing. + show_progress: Whether to show progress bar. + + Returns: + Tuple of (space_key, num_computed, num_skipped). + + Raises: + ValueError: If no samples in storage. + """ + from hyperview.embeddings.compute import EmbeddingComputer + + computer = EmbeddingComputer(model=model_id) + + all_samples = storage.get_all_samples() + if not all_samples: + raise ValueError("No samples in storage") + + test_embedding = computer.compute_single(all_samples[0]) + dim = len(test_embedding) + + space_key = make_space_key(model_id) + provider_config = { + "provider": "embed_anything", + "geometry": "euclidean", + } + storage.ensure_space(model_id, dim, config=provider_config) + + missing_ids = storage.get_missing_embedding_ids(space_key) + num_skipped = len(all_samples) - len(missing_ids) + + if not missing_ids: + if show_progress: + print( + f"All {len(all_samples)} samples already have embeddings in space '{space_key}'" + ) + return space_key, 0, num_skipped + + samples_needing_embeddings = storage.get_samples_by_ids(missing_ids) + + if show_progress and num_skipped > 0: + print(f"Skipped {num_skipped} samples with existing embeddings") + + embeddings = computer.compute_batch( + samples_needing_embeddings, batch_size=batch_size, show_progress=show_progress + ) + + ids = [s.id for s in samples_needing_embeddings] + vectors = np.array(embeddings, dtype=np.float32) + storage.add_embeddings(space_key, ids, vectors) + + return space_key, len(ids), num_skipped + + +def compute_layout( + storage: StorageBackend, + space_key: str | None = None, + method: str = "umap", + geometry: str = "euclidean", + n_neighbors: int = 15, + min_dist: float = 0.1, + metric: str = "cosine", + force: bool = False, + show_progress: bool = True, +) -> str: + """Compute 2D layout/projection for visualization. + + Args: + storage: Storage backend with embeddings. + space_key: Embedding space to project. If None, uses the first available. + method: Projection method ('umap' supported). + geometry: Output geometry type ('euclidean' or 'poincare'). + n_neighbors: Number of neighbors for UMAP. + min_dist: Minimum distance for UMAP. + metric: Distance metric for UMAP. + force: Force recomputation even if layout exists. + show_progress: Whether to print progress messages. + + Returns: + layout_key for the computed layout. + + Raises: + ValueError: If no embedding spaces, space not found, or insufficient samples. + """ + from hyperview.embeddings.projection import ProjectionEngine + + if method != "umap": + raise ValueError(f"Invalid method: {method}. Only 'umap' is supported.") + + if geometry not in ("euclidean", "poincare"): + raise ValueError(f"Invalid geometry: {geometry}. Must be 'euclidean' or 'poincare'.") + + if space_key is None: + spaces = storage.list_spaces() + if not spaces: + raise ValueError("No embedding spaces. Call compute_embeddings() first.") + space_key = spaces[0].space_key + + space = storage.get_space(space_key) + if space is None: + raise ValueError(f"Space not found: {space_key}") + + ids, vectors = storage.get_embeddings(space_key) + if len(ids) == 0: + raise ValueError( + f"No embeddings in space '{space_key}'. Call compute_embeddings() first." + ) + + if len(ids) < 3: + raise ValueError(f"Need at least 3 samples for visualization, have {len(ids)}") + + layout_params = { + "n_neighbors": n_neighbors, + "min_dist": min_dist, + "metric": metric, + } + layout_key = make_layout_key(space_key, method, geometry, layout_params) + + if not force: + existing_layout = storage.get_layout(layout_key) + if existing_layout is not None: + existing_ids, _ = storage.get_layout_coords(layout_key) + if set(existing_ids) == set(ids): + if show_progress: + print(f"Layout '{layout_key}' already exists with {len(ids)} points") + return layout_key + if show_progress: + print("Layout exists but has different samples, recomputing...") + + if show_progress: + print(f"Computing {geometry} {method} layout for {len(ids)} samples...") + + storage.ensure_layout( + layout_key=layout_key, + space_key=space_key, + method=method, + geometry=geometry, + params=layout_params, + ) + + engine = ProjectionEngine() + if geometry == "poincare": + coords = engine.project_to_poincare( + vectors, + n_neighbors=n_neighbors, + min_dist=min_dist, + metric=metric, + ) + else: + coords = engine.project_umap( + vectors, + n_neighbors=n_neighbors, + min_dist=min_dist, + metric=metric, + ) + + storage.add_layout_coords(layout_key, ids, coords) + + return layout_key diff --git a/src/hyperview/storage/backend.py b/src/hyperview/storage/backend.py index c7c3c85..518d1af 100644 --- a/src/hyperview/storage/backend.py +++ b/src/hyperview/storage/backend.py @@ -10,26 +10,15 @@ class StorageBackend(ABC): - """Abstract base class for storage backends. - - Storage architecture: - - Samples: Core metadata (id, filepath, label, metadata, thumbnail) - - Spaces: Registry of embedding spaces (one per model) - - Embeddings: Per-space tables with vectors - - Layouts: Per-layout tables with 2D coordinates - """ - - # ========================================================================= - # Sample CRUD - # ========================================================================= + """Abstract base class for storage backends.""" @abstractmethod def add_sample(self, sample: Sample) -> None: - """Add a single sample to storage.""" + """Add a single sample (idempotent upsert).""" @abstractmethod def add_samples_batch(self, samples: list[Sample]) -> None: - """Add multiple samples efficiently.""" + """Add multiple samples (idempotent upsert).""" @abstractmethod def get_sample(self, sample_id: str) -> Sample | None: @@ -46,7 +35,7 @@ def get_samples_paginated( @abstractmethod def get_all_samples(self) -> list[Sample]: - """Get all samples (use with caution for large datasets).""" + """Get all samples.""" @abstractmethod def update_sample(self, sample: Sample) -> None: @@ -58,7 +47,7 @@ def update_samples_batch(self, samples: list[Sample]) -> None: @abstractmethod def delete_sample(self, sample_id: str) -> bool: - """Delete a sample by ID. Returns True if deleted.""" + """Delete a sample by ID.""" @abstractmethod def __len__(self) -> int: @@ -84,70 +73,53 @@ def get_existing_ids(self, sample_ids: list[str]) -> set[str]: def get_samples_by_ids(self, sample_ids: list[str]) -> list[Sample]: """Retrieve multiple samples by ID.""" + @abstractmethod + def get_labels_by_ids(self, sample_ids: list[str]) -> dict[str, str | None]: + """Get labels for sample IDs. Missing IDs not included in result.""" + @abstractmethod def filter(self, predicate: Callable[[Sample], bool]) -> list[Sample]: """Filter samples based on a predicate function.""" - # ========================================================================= - # Spaces (embedding space registry) - # ========================================================================= - @abstractmethod def list_spaces(self) -> list[Any]: - """List all embedding spaces. Returns list of SpaceInfo.""" + """List all embedding spaces.""" @abstractmethod def get_space(self, space_key: str) -> Any | None: - """Get info for a specific embedding space. Returns SpaceInfo or None.""" + """Get info for a specific embedding space.""" @abstractmethod def ensure_space(self, model_id: str, dim: int, config: dict | None = None) -> Any: - """Ensure an embedding space exists, creating if needed. Returns SpaceInfo.""" + """Ensure an embedding space exists, creating if needed.""" @abstractmethod def delete_space(self, space_key: str) -> bool: """Delete an embedding space and its embeddings.""" - # ========================================================================= - # Embeddings - # ========================================================================= - @abstractmethod - def add_embeddings( - self, - space_key: str, - ids: list[str], - vectors: np.ndarray, - ) -> None: + def add_embeddings(self, space_key: str, ids: list[str], vectors: np.ndarray) -> None: """Add embeddings to a space.""" @abstractmethod - def get_embeddings( - self, - space_key: str, - ids: list[str] | None = None, - ) -> tuple[list[str], np.ndarray]: + def get_embeddings(self, space_key: str, ids: list[str] | None = None) -> tuple[list[str], np.ndarray]: """Get embeddings from a space. Returns (ids, vectors).""" @abstractmethod def get_embedded_ids(self, space_key: str) -> set[str]: - """Get the set of sample IDs that have embeddings in a space.""" + """Get sample IDs that have embeddings in a space.""" @abstractmethod def get_missing_embedding_ids(self, space_key: str) -> list[str]: - """Get sample IDs that don't have embeddings in a space.""" - - # ========================================================================= - # Layouts (2D projections) - # ========================================================================= + """Get sample IDs without embeddings in a space.""" @abstractmethod def list_layouts(self) -> list[Any]: - """List all layouts. Returns list of LayoutInfo.""" + """List all layouts.""" @abstractmethod def get_layout(self, layout_key: str) -> Any | None: - """Get layout info. Returns LayoutInfo or None.""" + """Get layout info.""" @abstractmethod def ensure_layout( @@ -158,19 +130,14 @@ def ensure_layout( geometry: str, params: dict | None = None, ) -> Any: - """Ensure a layout exists. Returns LayoutInfo.""" + """Ensure a layout exists.""" @abstractmethod def delete_layout(self, layout_key: str) -> bool: """Delete a layout.""" @abstractmethod - def add_layout_coords( - self, - layout_key: str, - ids: list[str], - coords: np.ndarray, - ) -> None: + def add_layout_coords(self, layout_key: str, ids: list[str], coords: np.ndarray) -> None: """Add layout coordinates (N x 2).""" @abstractmethod @@ -181,17 +148,6 @@ def get_layout_coords( ) -> tuple[list[str], np.ndarray]: """Get layout coordinates. Returns (ids, coords).""" - @abstractmethod - def get_visualization_data( - self, - layout_key: str, - ) -> tuple[list[str], list[str | None], np.ndarray]: - """Get visualization data for scatter plot. - - Returns: - (ids, labels, coords) where coords is (N x 2). - """ - @abstractmethod def get_lasso_candidates_aabb( self, @@ -204,10 +160,6 @@ def get_lasso_candidates_aabb( ) -> tuple[list[str], np.ndarray]: """Return candidate (id, xy) rows within an axis-aligned bounding box.""" - # ========================================================================= - # Similarity search - # ========================================================================= - @abstractmethod def find_similar( self, @@ -215,7 +167,7 @@ def find_similar( k: int = 10, space_key: str | None = None, ) -> list[tuple[Sample, float]]: - """Find k nearest neighbors. Returns [(sample, distance), ...].""" + """Find k nearest neighbors.""" @abstractmethod def find_similar_by_vector( @@ -226,20 +178,6 @@ def find_similar_by_vector( ) -> list[tuple[Sample, float]]: """Find k nearest neighbors to a query vector.""" - # ========================================================================= - # Lifecycle and metadata - # ========================================================================= - @abstractmethod def close(self) -> None: """Close the storage connection.""" - - @property - @abstractmethod - def label_colors(self) -> dict[str, str]: - """Get label color mapping.""" - - @label_colors.setter - @abstractmethod - def label_colors(self, colors: dict[str, str]) -> None: - """Set label color mapping.""" diff --git a/src/hyperview/storage/lancedb_backend.py b/src/hyperview/storage/lancedb_backend.py index 098c6b2..c0de354 100644 --- a/src/hyperview/storage/lancedb_backend.py +++ b/src/hyperview/storage/lancedb_backend.py @@ -1,19 +1,7 @@ -"""LanceDB storage backend for HyperView. - -Storage architecture (per-dataset directory): - ~/.hyperview/datasets// - - samples table: core sample metadata - - metadata table: key-value config - - spaces table: registry of embedding spaces - - layouts_registry table: registry of layouts - - embeddings__ tables: one per embedding model - - layouts__ tables: one per layout (coordinates) -""" - -import json +"""LanceDB storage backend for HyperView.""" + import time from collections.abc import Callable, Iterator -from pathlib import Path import lancedb import numpy as np @@ -21,109 +9,60 @@ from hyperview.core.sample import Sample from hyperview.storage.backend import StorageBackend -from hyperview.storage.config import StorageConfig, get_default_datasets_dir +from hyperview.storage.config import StorageConfig from hyperview.storage.schema import ( LayoutInfo, SpaceInfo, create_embeddings_schema, create_layouts_registry_schema, create_layouts_schema, - create_metadata_schema, create_sample_schema, create_spaces_schema, dict_to_sample, - make_layout_key, make_space_key, sample_to_dict, ) class LanceDBBackend(StorageBackend): - """LanceDB-based storage backend for HyperView datasets. - - Each dataset gets its own directory with isolated LanceDB tables. - """ + """LanceDB-based storage backend for HyperView datasets.""" - def __init__( - self, - dataset_name: str, - config: StorageConfig | None = None, - ): - """Initialize LanceDB backend. - - Args: - dataset_name: Name of the dataset (becomes directory name). - config: Storage configuration. Uses defaults if None. - """ + def __init__(self, dataset_name: str, config: StorageConfig | None = None): self.dataset_name = dataset_name self.config = config or StorageConfig.default() - - # Per-dataset directory self._dataset_dir = self.config.datasets_dir / dataset_name self._dataset_dir.mkdir(parents=True, exist_ok=True) - - # Connect to this dataset's LanceDB self._db = lancedb.connect(str(self._dataset_dir)) - # Table names (no prefix needed since we have per-dataset isolation) - self._samples_table_name = "samples" - self._metadata_table_name = "metadata" - self._spaces_table_name = "spaces" - - # Initialize core tables self._samples_table = self._get_or_create_samples_table() - self._metadata_table = self._get_or_create_metadata_table() self._spaces_table = self._get_or_create_spaces_table() - self._label_colors_cache: dict[str, str] | None = None - - # ========================================================================= - # Table initialization - # ========================================================================= - def _get_or_create_samples_table(self) -> lancedb.table.Table | None: - """Get existing samples table or return None.""" - if self._samples_table_name in self._db.table_names(): - return self._db.open_table(self._samples_table_name) + if "samples" in self._db.table_names(): + return self._db.open_table("samples") return None def _ensure_samples_table(self, data: list[dict]) -> lancedb.table.Table: - """Ensure samples table exists, creating from data if needed.""" if self._samples_table is None: schema = create_sample_schema() arrow_table = pa.Table.from_pylist(data, schema=schema) - self._samples_table = self._db.create_table(self._samples_table_name, data=arrow_table) + self._samples_table = self._db.create_table("samples", data=arrow_table) return self._samples_table - def _get_or_create_metadata_table(self) -> lancedb.table.Table: - """Get or create metadata table.""" - if self._metadata_table_name in self._db.table_names(): - return self._db.open_table(self._metadata_table_name) - schema = create_metadata_schema() - return self._db.create_table(self._metadata_table_name, schema=schema) - def _get_or_create_spaces_table(self) -> lancedb.table.Table: - """Get or create spaces registry table.""" - if self._spaces_table_name in self._db.table_names(): - return self._db.open_table(self._spaces_table_name) - schema = create_spaces_schema() - return self._db.create_table(self._spaces_table_name, schema=schema) - - # ========================================================================= - # Sample CRUD operations - # ========================================================================= + if "spaces" in self._db.table_names(): + return self._db.open_table("spaces") + return self._db.create_table("spaces", schema=create_spaces_schema()) def add_sample(self, sample: Sample) -> None: - """Add a single sample to storage.""" data = [sample_to_dict(sample)] if self._samples_table is None: self._ensure_samples_table(data) else: arrow = pa.Table.from_pylist(data, schema=self._samples_table.schema) - self._samples_table.add(arrow) + self._samples_table.merge_insert("id").when_matched_update_all().when_not_matched_insert_all().execute(arrow) def add_samples_batch(self, samples: list[Sample]) -> None: - """Add multiple samples efficiently.""" if not samples: return data = [sample_to_dict(s) for s in samples] @@ -131,17 +70,14 @@ def add_samples_batch(self, samples: list[Sample]) -> None: self._ensure_samples_table(data) else: arrow = pa.Table.from_pylist(data, schema=self._samples_table.schema) - self._samples_table.add(arrow) + self._samples_table.merge_insert("id").when_matched_update_all().when_not_matched_insert_all().execute(arrow) def get_sample(self, sample_id: str) -> Sample | None: - """Retrieve a sample by ID.""" if self._samples_table is None: return None safe_id = sample_id.replace("'", "''") results = self._samples_table.search().where(f"id = '{safe_id}'").limit(1).to_list() - if results: - return dict_to_sample(results[0]) - return None + return dict_to_sample(results[0]) if results else None def get_samples_paginated( self, @@ -149,7 +85,6 @@ def get_samples_paginated( limit: int = 100, label: str | None = None, ) -> tuple[list[Sample], int]: - """Get paginated samples using native LanceDB queries.""" if self._samples_table is None: return [], 0 @@ -157,395 +92,197 @@ def get_samples_paginated( if label: arrow_table = self._samples_table.search().select(["label"]).to_arrow() - label_column = arrow_table.column("label") - mask = pc.fill_null(pc.equal(label_column, pa.scalar(label)), False) + mask = pc.fill_null(pc.equal(arrow_table.column("label"), pa.scalar(label)), False) total = pc.sum(pc.cast(mask, pa.int64())).as_py() + safe_label = label.replace("'", "''") + results = self._samples_table.search().where(f"label = '{safe_label}'").offset(offset).limit(limit).to_list() else: total = self._samples_table.count_rows() + results = self._samples_table.search().offset(offset).limit(limit).to_list() - query = self._samples_table.search() - if label: - safe_label = label.replace("'", "''") - query = query.where(f"label = '{safe_label}'") - - results = query.offset(offset).limit(limit).to_list() - samples = [dict_to_sample(row) for row in results] - return samples, total + return [dict_to_sample(row) for row in results], total def get_all_samples(self) -> list[Sample]: - """Get all samples.""" if self._samples_table is None: return [] - arrow_table = self._samples_table.to_arrow() - rows = arrow_table.to_pylist() - return [dict_to_sample(row) for row in rows] + return [dict_to_sample(row) for row in self._samples_table.to_arrow().to_pylist()] def update_sample(self, sample: Sample) -> None: - """Update an existing sample.""" - if self._samples_table is None: - self.add_sample(sample) - return - data = [sample_to_dict(sample)] - arrow = pa.Table.from_pylist(data, schema=self._samples_table.schema) - ( - self._samples_table.merge_insert("id") - .when_matched_update_all() - .when_not_matched_insert_all() - .execute(arrow) - ) + self.add_sample(sample) def update_samples_batch(self, samples: list[Sample]) -> None: - """Batch update samples.""" - if not samples: - return - if self._samples_table is None: - self.add_samples_batch(samples) - return - data = [sample_to_dict(s) for s in samples] - arrow = pa.Table.from_pylist(data, schema=self._samples_table.schema) - ( - self._samples_table.merge_insert("id") - .when_matched_update_all() - .when_not_matched_insert_all() - .execute(arrow) - ) + self.add_samples_batch(samples) def delete_sample(self, sample_id: str) -> bool: - """Delete a sample by ID.""" if self._samples_table is None: return False - try: - safe_id = sample_id.replace("'", "''") - self._samples_table.delete(f"id = '{safe_id}'") - return True - except Exception: - return False + safe_id = sample_id.replace("'", "''") + self._samples_table.delete(f"id = '{safe_id}'") + return True def __len__(self) -> int: - """Return total number of samples.""" - if self._samples_table is None: - return 0 - return self._samples_table.count_rows() + return self._samples_table.count_rows() if self._samples_table else 0 def __iter__(self) -> Iterator[Sample]: - """Iterate over all samples.""" if self._samples_table is None: return iter([]) - arrow_table = self._samples_table.to_arrow() - for batch in arrow_table.to_batches(max_chunksize=1000): + for batch in self._samples_table.to_arrow().to_batches(max_chunksize=1000): batch_dict = batch.to_pydict() for i in range(batch.num_rows): - row = {k: batch_dict[k][i] for k in batch_dict} - yield dict_to_sample(row) + yield dict_to_sample({k: batch_dict[k][i] for k in batch_dict}) def __contains__(self, sample_id: str) -> bool: - """Check if sample exists.""" if self._samples_table is None: return False safe_id = sample_id.replace("'", "''") - try: - results = self._samples_table.search().where(f"id = '{safe_id}'").limit(1).to_list() - return len(results) > 0 - except Exception: - return False + return len(self._samples_table.search().where(f"id = '{safe_id}'").limit(1).to_list()) > 0 def get_unique_labels(self) -> list[str]: - """Get all unique labels.""" if self._samples_table is None: return [] import pyarrow.compute as pc - - arrow_table = self._samples_table.search().select(["label"]).to_arrow() - label_column = arrow_table.column("label") - unique_labels = pc.unique(label_column).to_pylist() - return sorted([label for label in unique_labels if label is not None]) + labels = pc.unique(self._samples_table.search().select(["label"]).to_arrow().column("label")).to_pylist() + return sorted([l for l in labels if l is not None]) def get_existing_ids(self, sample_ids: list[str]) -> set[str]: - """Return the subset of sample_ids that exist in storage.""" if self._samples_table is None or not sample_ids: return set() - existing: set[str] = set() - - def query_chunk(chunk: list[str]) -> set[str]: + for i in range(0, len(sample_ids), 1000): + chunk = sample_ids[i : i + 1000] escaped = [sid.replace("'", "''") for sid in chunk] id_list = "', '".join(escaped) results = self._samples_table.search().where(f"id IN ('{id_list}')").select(["id"]).to_list() - return {r["id"] for r in results} - - chunk_size = 1000 - for i in range(0, len(sample_ids), chunk_size): - chunk = sample_ids[i : i + chunk_size] - try: - existing.update(query_chunk(chunk)) - except Exception: - existing.update(sid for sid in chunk if sid in self) - + existing.update(r["id"] for r in results) return existing def get_samples_by_ids(self, sample_ids: list[str]) -> list[Sample]: - """Retrieve multiple samples by ID.""" if self._samples_table is None or not sample_ids: return [] - rows_by_id: dict[str, dict] = {} + for i in range(0, len(sample_ids), 500): + chunk = sample_ids[i : i + 500] + escaped = [sid.replace("'", "''") for sid in chunk] + id_list = "', '".join(escaped) + for r in self._samples_table.search().where(f"id IN ('{id_list}')").to_list(): + rows_by_id[r["id"]] = r + return [dict_to_sample(rows_by_id[sid]) for sid in sample_ids if sid in rows_by_id] - def query_chunk(chunk: list[str]) -> None: + def get_labels_by_ids(self, sample_ids: list[str]) -> dict[str, str | None]: + if self._samples_table is None or not sample_ids: + return {} + labels: dict[str, str | None] = {} + for i in range(0, len(sample_ids), 1000): + chunk = sample_ids[i : i + 1000] escaped = [sid.replace("'", "''") for sid in chunk] id_list = "', '".join(escaped) - results = self._samples_table.search().where(f"id IN ('{id_list}')").to_list() - for r in results: - rid = r.get("id") - if isinstance(rid, str): - rows_by_id[rid] = r - - chunk_size = 500 - for i in range(0, len(sample_ids), chunk_size): - query_chunk(sample_ids[i : i + chunk_size]) - - out: list[Sample] = [] - for sid in sample_ids: - row = rows_by_id.get(sid) - if row is not None: - out.append(dict_to_sample(row)) - return out + for r in self._samples_table.search().select(["id", "label"]).where(f"id IN ('{id_list}')").to_list(): + labels[r["id"]] = r.get("label") + return labels def filter(self, predicate: Callable[[Sample], bool]) -> list[Sample]: - """Filter samples based on a predicate function.""" return [s for s in self if predicate(s)] - # ========================================================================= - # Spaces registry (embedding spaces) - # ========================================================================= - def list_spaces(self) -> list[SpaceInfo]: - """List all embedding spaces.""" - rows = self._spaces_table.to_arrow().to_pylist() - return [SpaceInfo.from_dict(r) for r in rows] + return [SpaceInfo.from_dict(r) for r in self._spaces_table.to_arrow().to_pylist()] def get_space(self, space_key: str) -> SpaceInfo | None: - """Get info for a specific embedding space.""" safe_key = space_key.replace("'", "''") results = self._spaces_table.search().where(f"space_key = '{safe_key}'").limit(1).to_list() - if results: - return SpaceInfo.from_dict(results[0]) - return None + return SpaceInfo.from_dict(results[0]) if results else None def ensure_space(self, model_id: str, dim: int, config: dict | None = None) -> SpaceInfo: - """Ensure an embedding space exists, creating it if needed. - - Args: - model_id: The model identifier (e.g., "openai/clip-vit-base-patch32"). - dim: Vector dimension for this model. - config: Optional config dict for the space. - - Returns: - SpaceInfo for the space (existing or newly created). - - Raises: - ValueError: If space exists with different dimension. - """ space_key = make_space_key(model_id) existing = self.get_space(space_key) - if existing is not None: if existing.dim != dim: - raise ValueError( - f"Space '{space_key}' exists with dim={existing.dim}, " - f"but requested dim={dim}" - ) + raise ValueError(f"Space '{space_key}' exists with dim={existing.dim}, requested dim={dim}") return existing - # Create new space now = int(time.time()) space_info = SpaceInfo( - space_key=space_key, - model_id=model_id, - dim=dim, - count=0, - created_at=now, - updated_at=now, - config=config, + space_key=space_key, model_id=model_id, dim=dim, count=0, + created_at=now, updated_at=now, config=config, ) - - # Add to registry - schema = create_spaces_schema() - arrow = pa.Table.from_pylist([space_info.to_dict()], schema=schema) - self._spaces_table.add(arrow) - - # Create embeddings table for this space - emb_table_name = f"embeddings__{space_key}" - emb_schema = create_embeddings_schema(dim) - self._db.create_table(emb_table_name, schema=emb_schema) - + self._spaces_table.add(pa.Table.from_pylist([space_info.to_dict()], schema=create_spaces_schema())) + self._db.create_table(f"embeddings__{space_key}", schema=create_embeddings_schema(dim)) return space_info def delete_space(self, space_key: str) -> bool: - """Delete an embedding space and its embeddings table.""" safe_key = space_key.replace("'", "''") - - # Delete from registry - try: - self._spaces_table.delete(f"space_key = '{safe_key}'") - except Exception: - return False - - # Drop embeddings table - emb_table_name = f"embeddings__{space_key}" - if emb_table_name in self._db.table_names(): - self._db.drop_table(emb_table_name) - + self._spaces_table.delete(f"space_key = '{safe_key}'") + emb_table = f"embeddings__{space_key}" + if emb_table in self._db.table_names(): + self._db.drop_table(emb_table) return True - def _update_space_count(self, space_key: str, count: int) -> None: - """Update the count and updated_at for a space.""" - now = int(time.time()) - self._spaces_table.update( - where=f"space_key = '{space_key}'", - values={"count": count, "updated_at": now}, - ) - - # ========================================================================= - # Embeddings operations - # ========================================================================= - - def get_embeddings_table_name(self, space_key: str) -> str: - """Get the table name for an embedding space.""" - return f"embeddings__{space_key}" - - def add_embeddings( - self, - space_key: str, - ids: list[str], - vectors: np.ndarray, - ) -> None: - """Add embeddings to a space. - - Args: - space_key: The embedding space key. - ids: Sample IDs. - vectors: Embedding vectors (N x dim). - """ - if len(ids) != len(vectors): - raise ValueError("ids and vectors must have same length") - if len(ids) == 0: + def add_embeddings(self, space_key: str, ids: list[str], vectors: np.ndarray) -> None: + if len(ids) != len(vectors) or len(ids) == 0: return - space = self.get_space(space_key) if space is None: raise ValueError(f"Space not found: {space_key}") - if vectors.shape[1] != space.dim: - raise ValueError(f"Vector dim {vectors.shape[1]} != space dim {space.dim}") + emb_table_name = f"embeddings__{space_key}" + if emb_table_name not in self._db.table_names(): + self._db.create_table(emb_table_name, schema=create_embeddings_schema(space.dim)) - emb_table_name = self.get_embeddings_table_name(space_key) emb_table = self._db.open_table(emb_table_name) - - # Convert to list of dicts - data = [ - {"id": id_, "vector": vec.astype(np.float32)} - for id_, vec in zip(ids, vectors) - ] - schema = create_embeddings_schema(space.dim) - arrow = pa.Table.from_pylist(data, schema=schema) - - # Use merge_insert for idempotent upsert (handles retries/partial runs) - ( - emb_table.merge_insert("id") - .when_matched_update_all() - .when_not_matched_insert_all() - .execute(arrow) + data = [{"id": id_, "vector": vec.astype(np.float32).tolist()} for id_, vec in zip(ids, vectors)] + emb_table.merge_insert("id").when_matched_update_all().when_not_matched_insert_all().execute( + pa.Table.from_pylist(data, schema=create_embeddings_schema(space.dim)) ) - # Update count - new_count = emb_table.count_rows() - self._update_space_count(space_key, new_count) - - def get_embeddings( - self, - space_key: str, - ids: list[str] | None = None, - ) -> tuple[list[str], np.ndarray]: - """Get embeddings from a space. - - Args: - space_key: The embedding space key. - ids: Optional list of IDs to fetch. If None, fetch all. + # Update space count + safe_key = space_key.replace("'", "''") + self._spaces_table.update(where=f"space_key = '{safe_key}'", values={ + "count": emb_table.count_rows(), "updated_at": int(time.time()) + }) - Returns: - (ids, vectors) where vectors is (N x dim). - """ + def get_embeddings(self, space_key: str, ids: list[str] | None = None) -> tuple[list[str], np.ndarray]: space = self.get_space(space_key) if space is None: raise ValueError(f"Space not found: {space_key}") - emb_table_name = self.get_embeddings_table_name(space_key) + emb_table_name = f"embeddings__{space_key}" if emb_table_name not in self._db.table_names(): return [], np.empty((0, space.dim), dtype=np.float32) emb_table = self._db.open_table(emb_table_name) - if ids is not None: - # Fetch specific IDs escaped = [sid.replace("'", "''") for sid in ids] id_list = "', '".join(escaped) rows = emb_table.search().where(f"id IN ('{id_list}')").to_list() else: - # Fetch all rows = emb_table.to_arrow().to_pylist() if not rows: return [], np.empty((0, space.dim), dtype=np.float32) - - out_ids = [r["id"] for r in rows] - out_vecs = np.array([r["vector"] for r in rows], dtype=np.float32) - return out_ids, out_vecs + return [r["id"] for r in rows], np.array([r["vector"] for r in rows], dtype=np.float32) def get_embedded_ids(self, space_key: str) -> set[str]: - """Get the set of sample IDs that have embeddings in a space.""" - emb_table_name = self.get_embeddings_table_name(space_key) + emb_table_name = f"embeddings__{space_key}" if emb_table_name not in self._db.table_names(): return set() - - emb_table = self._db.open_table(emb_table_name) - rows = emb_table.search().select(["id"]).to_list() - return {r["id"] for r in rows} + return {r["id"] for r in self._db.open_table(emb_table_name).search().select(["id"]).to_list()} def get_missing_embedding_ids(self, space_key: str) -> list[str]: - """Get sample IDs that don't have embeddings in a space.""" if self._samples_table is None: return [] - all_ids = {r["id"] for r in self._samples_table.search().select(["id"]).to_list()} - embedded_ids = self.get_embedded_ids(space_key) - return list(all_ids - embedded_ids) - - # ========================================================================= - # Layouts registry operations - # ========================================================================= + return list(all_ids - self.get_embedded_ids(space_key)) def _get_layouts_registry_table(self) -> lancedb.table.Table | None: - """Get or create layouts registry table.""" - if "layouts_registry" not in self._db.table_names(): - return None - return self._db.open_table("layouts_registry") + return self._db.open_table("layouts_registry") if "layouts_registry" in self._db.table_names() else None def _ensure_layouts_registry_table(self) -> lancedb.table.Table: - """Ensure layouts registry table exists.""" if "layouts_registry" not in self._db.table_names(): - schema = create_layouts_registry_schema() - self._db.create_table("layouts_registry", schema=schema) + self._db.create_table("layouts_registry", schema=create_layouts_registry_schema()) return self._db.open_table("layouts_registry") - def get_layout_table_name(self, layout_key: str) -> str: - return f"layouts__{layout_key}" - def list_layouts(self) -> list[LayoutInfo]: table = self._get_layouts_registry_table() - if table is None: - return [] - rows = table.search().to_list() - return [LayoutInfo.from_dict(row) for row in rows] + return [LayoutInfo.from_dict(row) for row in table.search().to_list()] if table else [] def get_layout(self, layout_key: str) -> LayoutInfo | None: table = self._get_layouts_registry_table() @@ -563,123 +300,58 @@ def ensure_layout( geometry: str, params: dict | None = None, ) -> LayoutInfo: - """Ensure a layout exists, creating registry entry if needed.""" - # Check if registry entry exists existing = self.get_layout(layout_key) if existing is not None: return existing - # Create registry entry - now = int(time.time()) layout_info = LayoutInfo( - layout_key=layout_key, - space_key=space_key, - method=method, - geometry=geometry, - count=0, - created_at=now, - params=params, + layout_key=layout_key, space_key=space_key, method=method, geometry=geometry, + count=0, created_at=int(time.time()), params=params, ) - registry_table = self._ensure_layouts_registry_table() - schema = create_layouts_registry_schema() - arrow = pa.Table.from_pylist([layout_info.to_dict()], schema=schema) - registry_table.add(arrow) + registry_table.add(pa.Table.from_pylist([layout_info.to_dict()], schema=create_layouts_registry_schema())) - # Create coordinates table - table_name = self.get_layout_table_name(layout_key) + table_name = f"layouts__{layout_key}" if table_name not in self._db.table_names(): - coords_schema = create_layouts_schema() - self._db.create_table(table_name, schema=coords_schema) - + self._db.create_table(table_name, schema=create_layouts_schema()) return layout_info def delete_layout(self, layout_key: str) -> bool: - """Delete a layout (both registry entry and coordinates).""" - deleted = False - - # Delete coordinates table - table_name = self.get_layout_table_name(layout_key) + table_name = f"layouts__{layout_key}" if table_name in self._db.table_names(): self._db.drop_table(table_name) - deleted = True - - # Delete registry entry - registry_table = self._get_layouts_registry_table() - if registry_table is not None: - safe_key = layout_key.replace("'", "''") - registry_table.delete(f"layout_key = '{safe_key}'") - deleted = True - - return deleted - - def _update_layout_count(self, layout_key: str, count: int) -> None: - registry_table = self._get_layouts_registry_table() - if registry_table is None: - return - safe_key = layout_key.replace("'", "''") - registry_table.update(where=f"layout_key = '{safe_key}'", values={"count": count}) + registry = self._get_layouts_registry_table() + if registry: + registry.delete(f"layout_key = '{layout_key.replace(chr(39), chr(39)+chr(39))}'") + return True - def add_layout_coords( - self, - layout_key: str, - ids: list[str], - coords: np.ndarray, - ) -> None: - if len(ids) != len(coords): - raise ValueError("ids and coords must have same length") - if len(ids) == 0: + def add_layout_coords(self, layout_key: str, ids: list[str], coords: np.ndarray) -> None: + if len(ids) != len(coords) or len(ids) == 0: return - - # Require registry entry exists (call ensure_layout first) if self.get_layout(layout_key) is None: - raise ValueError(f"Layout '{layout_key}' not registered. Call ensure_layout() first.") + raise ValueError(f"Layout '{layout_key}' not registered") - table_name = self.get_layout_table_name(layout_key) + table_name = f"layouts__{layout_key}" if table_name not in self._db.table_names(): - coords_schema = create_layouts_schema() - self._db.create_table(table_name, schema=coords_schema) + self._db.create_table(table_name, schema=create_layouts_schema()) table = self._db.open_table(table_name) - - data = [ - {"id": id_, "x": float(coord[0]), "y": float(coord[1])} - for id_, coord in zip(ids, coords) - ] - schema = create_layouts_schema() - arrow = pa.Table.from_pylist(data, schema=schema) - - # Use merge_insert to handle updates - ( - table.merge_insert("id") - .when_matched_update_all() - .when_not_matched_insert_all() - .execute(arrow) + data = [{"id": id_, "x": float(c[0]), "y": float(c[1])} for id_, c in zip(ids, coords)] + table.merge_insert("id").when_matched_update_all().when_not_matched_insert_all().execute( + pa.Table.from_pylist(data, schema=create_layouts_schema()) ) - # Update count in registry - self._update_layout_count(layout_key, table.count_rows()) - - def get_layout_coords( - self, - layout_key: str, - ids: list[str] | None = None, - ) -> tuple[list[str], np.ndarray]: - """Get layout coordinates. - - Args: - layout_key: The layout key. - ids: Optional list of IDs to fetch. If None, fetch all. + # Update count + registry = self._get_layouts_registry_table() + if registry: + registry.update(where=f"layout_key = '{layout_key.replace(chr(39), chr(39)+chr(39))}'", values={"count": table.count_rows()}) - Returns: - (ids, coords) where coords is (N x 2). - """ - table_name = self.get_layout_table_name(layout_key) + def get_layout_coords(self, layout_key: str, ids: list[str] | None = None) -> tuple[list[str], np.ndarray]: + table_name = f"layouts__{layout_key}" if table_name not in self._db.table_names(): return [], np.empty((0, 2), dtype=np.float32) table = self._db.open_table(table_name) - if ids is not None: escaped = [sid.replace("'", "''") for sid in ids] id_list = "', '".join(escaped) @@ -689,57 +361,7 @@ def get_layout_coords( if not rows: return [], np.empty((0, 2), dtype=np.float32) - - out_ids = [r["id"] for r in rows] - out_coords = np.array([[r["x"], r["y"]] for r in rows], dtype=np.float32) - return out_ids, out_coords - - def get_visualization_data( - self, - layout_key: str, - ) -> tuple[list[str], list[str | None], np.ndarray]: - """Get visualization data (ids, labels, coords) for a layout. - - This joins samples with layout coords for the scatter plot. - - Returns: - (ids, labels, coords) where coords is (N x 2). - """ - table_name = self.get_layout_table_name(layout_key) - if table_name not in self._db.table_names(): - return [], [], np.empty((0, 2), dtype=np.float32) - - if self._samples_table is None: - return [], [], np.empty((0, 2), dtype=np.float32) - - layout_table = self._db.open_table(table_name) - - # Get all layout coords - layout_rows = layout_table.to_arrow().to_pylist() - if not layout_rows: - return [], [], np.empty((0, 2), dtype=np.float32) - - # Build id -> coords map - coords_by_id = {r["id"]: (r["x"], r["y"]) for r in layout_rows} - layout_ids = list(coords_by_id.keys()) - - # Get labels for these IDs - escaped = [sid.replace("'", "''") for sid in layout_ids] - id_list = "', '".join(escaped) - sample_rows = self._samples_table.search().select(["id", "label"]).where(f"id IN ('{id_list}')").to_list() - labels_by_id = {r["id"]: r.get("label") for r in sample_rows} - - # Build aligned output - ids = [] - labels = [] - coords = [] - for id_ in layout_ids: - if id_ in labels_by_id: - ids.append(id_) - labels.append(labels_by_id[id_]) - coords.append(coords_by_id[id_]) - - return ids, labels, np.array(coords, dtype=np.float32) if coords else np.empty((0, 2), dtype=np.float32) + return [r["id"] for r in rows], np.array([[r["x"], r["y"]] for r in rows], dtype=np.float32) def get_lasso_candidates_aabb( self, @@ -750,56 +372,30 @@ def get_lasso_candidates_aabb( y_min: float, y_max: float, ) -> tuple[list[str], np.ndarray]: - """Return candidate (id, xy) rows within an AABB for a layout.""" - table_name = self.get_layout_table_name(layout_key) + table_name = f"layouts__{layout_key}" if table_name not in self._db.table_names(): return [], np.empty((0, 2), dtype=np.float32) - table = self._db.open_table(table_name) - - expr = f"x >= {x_min} AND x <= {x_max} AND y >= {y_min} AND y <= {y_max}" - rows = table.search().where(expr).to_list() + rows = self._db.open_table(table_name).search().where( + f"x >= {x_min} AND x <= {x_max} AND y >= {y_min} AND y <= {y_max}" + ).to_list() if not rows: return [], np.empty((0, 2), dtype=np.float32) + return [r["id"] for r in rows], np.array([[r["x"], r["y"]] for r in rows], dtype=np.float32) - ids = [r["id"] for r in rows] - coords = np.array([[r["x"], r["y"]] for r in rows], dtype=np.float32) - return ids, coords - - # ========================================================================= - # Similarity search - # ========================================================================= - - def find_similar( - self, - sample_id: str, - k: int = 10, - space_key: str | None = None, - ) -> list[tuple[Sample, float]]: - """Find k nearest neighbors to a sample. - - Args: - sample_id: The query sample ID. - k: Number of neighbors. - space_key: Embedding space to use. If None, uses default. - - Returns: - List of (sample, distance) tuples. - """ + def find_similar(self, sample_id: str, k: int = 10, space_key: str | None = None) -> list[tuple[Sample, float]]: if space_key is None: spaces = self.list_spaces() if not spaces: raise ValueError("No embedding spaces available") space_key = spaces[0].space_key - # Get query vector ids, vecs = self.get_embeddings(space_key, [sample_id]) if not ids: raise ValueError(f"Sample {sample_id} has no embedding in space {space_key}") - query_vector = vecs[0] - results = self.find_similar_by_vector(query_vector, k + 1, space_key) + results = self.find_similar_by_vector(vecs[0], k + 1, space_key) return [(s, d) for s, d in results if s.id != sample_id][:k] def find_similar_by_vector( @@ -808,7 +404,6 @@ def find_similar_by_vector( k: int = 10, space_key: str | None = None, ) -> list[tuple[Sample, float]]: - """Find k nearest neighbors to a query vector.""" import math if space_key is None: @@ -817,149 +412,17 @@ def find_similar_by_vector( raise ValueError("No embedding spaces available") space_key = spaces[0].space_key - space = self.get_space(space_key) - if space is None: - raise ValueError(f"Space not found: {space_key}") - - emb_table_name = self.get_embeddings_table_name(space_key) + emb_table_name = f"embeddings__{space_key}" if emb_table_name not in self._db.table_names(): return [] - emb_table = self._db.open_table(emb_table_name) - - try: - results = ( - emb_table.search(vector, vector_column_name="vector") - .metric("cosine") - .limit(k) - .to_list() - ) - - # Get sample info for results - result_ids = [r["id"] for r in results] - samples = self.get_samples_by_ids(result_ids) - samples_by_id = {s.id: s for s in samples} - - return [ - ( - samples_by_id[r["id"]], - 0.0 if math.isnan(d := r.get("_distance", 0.0)) else float(d), - ) - for r in results - if r["id"] in samples_by_id - ] - except Exception: - return [] - - # ========================================================================= - # Vector index creation - # ========================================================================= + results = self._db.open_table(emb_table_name).search(vector, vector_column_name="vector").metric("cosine").limit(k).to_list() + samples_by_id = {s.id: s for s in self.get_samples_by_ids([r["id"] for r in results])} - def create_vector_index(self, space_key: str) -> None: - """Create an ANN index for an embedding space.""" - emb_table_name = self.get_embeddings_table_name(space_key) - if emb_table_name not in self._db.table_names(): - return - - emb_table = self._db.open_table(emb_table_name) - num_rows = emb_table.count_rows() - if num_rows < 256: - return - - try: - emb_table.create_index( - vector_column_name="vector", - index_type="IVF_PQ", - num_partitions=min(256, num_rows // 10), - num_sub_vectors=16, - ) - except Exception: - pass - - # ========================================================================= - # Label colors - # ========================================================================= - - @property - def label_colors(self) -> dict[str, str]: - """Get label color mapping.""" - if self._label_colors_cache is not None: - return self._label_colors_cache - - rows = self._metadata_table.search().where("key = 'label_colors'").limit(1).to_list() - if not rows: - self._label_colors_cache = {} - return self._label_colors_cache - - value = rows[0].get("value") - self._label_colors_cache = json.loads(value) if value else {} - return self._label_colors_cache - - @label_colors.setter - def label_colors(self, colors: dict[str, str]) -> None: - """Set label color mapping.""" - self._label_colors_cache = colors - - try: - self._metadata_table.delete("key = 'label_colors'") - except Exception: - pass - - schema = create_metadata_schema() - row = {"key": "label_colors", "value": json.dumps(colors)} - self._metadata_table.add(pa.Table.from_pylist([row], schema=schema)) - - # ========================================================================= - # Lifecycle - # ========================================================================= + return [ + (samples_by_id[r["id"]], 0.0 if math.isnan(d := r.get("_distance", 0.0)) else float(d)) + for r in results if r["id"] in samples_by_id + ] def close(self) -> None: - """Close the storage connection.""" - pass - - # ========================================================================= - # Class methods for dataset management - # ========================================================================= - - @classmethod - def list_datasets(cls, datasets_dir: Path | None = None) -> list[str]: - """List all available datasets.""" - if datasets_dir is None: - datasets_dir = get_default_datasets_dir() - - if not datasets_dir.exists(): - return [] - - datasets = [] - for path in datasets_dir.iterdir(): - if path.is_dir(): - # Check if it looks like a LanceDB dataset - if (path / "samples.lance").exists() or any( - p.name.endswith(".lance") for p in path.iterdir() if p.is_dir() - ): - datasets.append(path.name) - return sorted(datasets) - - @classmethod - def delete_dataset(cls, dataset_name: str, datasets_dir: Path | None = None) -> bool: - """Delete a dataset directory.""" - if datasets_dir is None: - datasets_dir = get_default_datasets_dir() - - dataset_path = datasets_dir / dataset_name - if not dataset_path.exists(): - return False - - import shutil - - shutil.rmtree(dataset_path) - return True - - @classmethod - def dataset_exists(cls, dataset_name: str, datasets_dir: Path | None = None) -> bool: - """Check if a dataset exists.""" - if datasets_dir is None: - datasets_dir = get_default_datasets_dir() - - dataset_path = datasets_dir / dataset_name - return dataset_path.exists() and dataset_path.is_dir() + return diff --git a/src/hyperview/storage/memory_backend.py b/src/hyperview/storage/memory_backend.py index ba8c4a6..a1453bc 100644 --- a/src/hyperview/storage/memory_backend.py +++ b/src/hyperview/storage/memory_backend.py @@ -2,8 +2,6 @@ import time from collections.abc import Callable, Iterator -from dataclasses import dataclass, field -from typing import Any import numpy as np @@ -13,48 +11,24 @@ class MemoryBackend(StorageBackend): - """In-memory storage backend for testing and development. - - Implements the same interface as LanceDBBackend but stores everything in memory. - """ + """In-memory storage backend for testing and development.""" def __init__(self, dataset_name: str): - """Initialize in-memory backend. - - Args: - dataset_name: Name of the dataset. - """ self.dataset_name = dataset_name self._samples: dict[str, Sample] = {} - self._label_colors: dict[str, str] = {} - - # Spaces registry self._spaces: dict[str, SpaceInfo] = {} - - # Embeddings: space_key -> {sample_id -> vector} self._embeddings: dict[str, dict[str, np.ndarray]] = {} - - # Layouts registry self._layout_registry: dict[str, LayoutInfo] = {} - - # Layout coordinates: layout_key -> {sample_id -> (x, y)} self._layouts: dict[str, dict[str, tuple[float, float]]] = {} - # ========================================================================= - # Sample CRUD - # ========================================================================= - def add_sample(self, sample: Sample) -> None: - """Add a single sample to storage.""" self._samples[sample.id] = sample def add_samples_batch(self, samples: list[Sample]) -> None: - """Add multiple samples efficiently.""" for sample in samples: self._samples[sample.id] = sample def get_sample(self, sample_id: str) -> Sample | None: - """Retrieve a sample by ID.""" return self._samples.get(sample_id) def get_samples_paginated( @@ -63,7 +37,6 @@ def get_samples_paginated( limit: int = 100, label: str | None = None, ) -> tuple[list[Sample], int]: - """Get paginated samples.""" samples = list(self._samples.values()) if label: samples = [s for s in samples if s.label == label] @@ -71,82 +44,57 @@ def get_samples_paginated( return samples[offset : offset + limit], total def get_all_samples(self) -> list[Sample]: - """Get all samples.""" return list(self._samples.values()) def update_sample(self, sample: Sample) -> None: - """Update an existing sample.""" self._samples[sample.id] = sample def update_samples_batch(self, samples: list[Sample]) -> None: - """Batch update samples.""" for sample in samples: self._samples[sample.id] = sample def delete_sample(self, sample_id: str) -> bool: - """Delete a sample by ID.""" if sample_id in self._samples: del self._samples[sample_id] return True return False def __len__(self) -> int: - """Return total number of samples.""" return len(self._samples) def __iter__(self) -> Iterator[Sample]: - """Iterate over all samples.""" return iter(self._samples.values()) def __contains__(self, sample_id: str) -> bool: - """Check if sample exists.""" return sample_id in self._samples def get_unique_labels(self) -> list[str]: - """Get all unique labels.""" - labels = {s.label for s in self._samples.values() if s.label} - return sorted(labels) + return sorted({s.label for s in self._samples.values() if s.label}) def get_existing_ids(self, sample_ids: list[str]) -> set[str]: - """Return set of sample_ids that already exist in storage.""" return {sid for sid in sample_ids if sid in self._samples} def get_samples_by_ids(self, sample_ids: list[str]) -> list[Sample]: - """Retrieve multiple samples by ID.""" - out: list[Sample] = [] - for sid in sample_ids: - s = self._samples.get(sid) - if s is not None: - out.append(s) - return out + return [s for sid in sample_ids if (s := self._samples.get(sid)) is not None] + + def get_labels_by_ids(self, sample_ids: list[str]) -> dict[str, str | None]: + return {sid: s.label for sid in sample_ids if (s := self._samples.get(sid)) is not None} def filter(self, predicate: Callable[[Sample], bool]) -> list[Sample]: - """Filter samples based on a predicate function.""" return [s for s in self._samples.values() if predicate(s)] - # ========================================================================= - # Spaces registry - # ========================================================================= - def list_spaces(self) -> list[SpaceInfo]: - """List all embedding spaces.""" return list(self._spaces.values()) def get_space(self, space_key: str) -> SpaceInfo | None: - """Get info for a specific embedding space.""" return self._spaces.get(space_key) def ensure_space(self, model_id: str, dim: int, config: dict | None = None) -> SpaceInfo: - """Ensure an embedding space exists, creating if needed.""" space_key = make_space_key(model_id) - if space_key in self._spaces: existing = self._spaces[space_key] if existing.dim != dim: - raise ValueError( - f"Space '{space_key}' exists with dim={existing.dim}, " - f"but requested dim={dim}" - ) + raise ValueError(f"Space '{space_key}' exists with dim={existing.dim}, requested dim={dim}") return existing now = int(time.time()) @@ -164,50 +112,26 @@ def ensure_space(self, model_id: str, dim: int, config: dict | None = None) -> S return space_info def delete_space(self, space_key: str) -> bool: - """Delete an embedding space and its embeddings.""" if space_key in self._spaces: del self._spaces[space_key] self._embeddings.pop(space_key, None) return True return False - # ========================================================================= - # Embeddings - # ========================================================================= - - def add_embeddings( - self, - space_key: str, - ids: list[str], - vectors: np.ndarray, - ) -> None: - """Add embeddings to a space.""" - if len(ids) != len(vectors): - raise ValueError("ids and vectors must have same length") - if len(ids) == 0: + def add_embeddings(self, space_key: str, ids: list[str], vectors: np.ndarray) -> None: + if len(ids) != len(vectors) or len(ids) == 0: return - if space_key not in self._spaces: raise ValueError(f"Space not found: {space_key}") space = self._spaces[space_key] - if vectors.shape[1] != space.dim: - raise ValueError(f"Vector dim {vectors.shape[1]} != space dim {space.dim}") - emb_store = self._embeddings.setdefault(space_key, {}) for id_, vec in zip(ids, vectors): emb_store[id_] = vec.astype(np.float32) - - # Update count space.count = len(emb_store) space.updated_at = int(time.time()) - def get_embeddings( - self, - space_key: str, - ids: list[str] | None = None, - ) -> tuple[list[str], np.ndarray]: - """Get embeddings from a space.""" + def get_embeddings(self, space_key: str, ids: list[str] | None = None) -> tuple[list[str], np.ndarray]: if space_key not in self._spaces: raise ValueError(f"Space not found: {space_key}") @@ -216,30 +140,20 @@ def get_embeddings( if ids is not None: out_ids = [id_ for id_ in ids if id_ in emb_store] - if not out_ids: - return [], np.empty((0, space.dim), dtype=np.float32) - out_vecs = np.array([emb_store[id_] for id_ in out_ids], dtype=np.float32) - return out_ids, out_vecs else: - if not emb_store: - return [], np.empty((0, space.dim), dtype=np.float32) out_ids = list(emb_store.keys()) - out_vecs = np.array([emb_store[id_] for id_ in out_ids], dtype=np.float32) - return out_ids, out_vecs + + if not out_ids: + return [], np.empty((0, space.dim), dtype=np.float32) + return out_ids, np.array([emb_store[id_] for id_ in out_ids], dtype=np.float32) def get_embedded_ids(self, space_key: str) -> set[str]: - """Get the set of sample IDs that have embeddings in a space.""" return set(self._embeddings.get(space_key, {}).keys()) def get_missing_embedding_ids(self, space_key: str) -> list[str]: - """Get sample IDs that don't have embeddings in a space.""" embedded = self.get_embedded_ids(space_key) return [id_ for id_ in self._samples.keys() if id_ not in embedded] - # ========================================================================= - # Layouts - # ========================================================================= - def list_layouts(self) -> list[LayoutInfo]: return list(self._layout_registry.values()) @@ -271,82 +185,28 @@ def ensure_layout( return layout_info def delete_layout(self, layout_key: str) -> bool: - deleted = False - if layout_key in self._layouts: - del self._layouts[layout_key] - deleted = True - if layout_key in self._layout_registry: - del self._layout_registry[layout_key] - deleted = True + deleted = layout_key in self._layouts or layout_key in self._layout_registry + self._layouts.pop(layout_key, None) + self._layout_registry.pop(layout_key, None) return deleted - def _update_layout_count(self, layout_key: str) -> None: - if layout_key in self._layout_registry: - self._layout_registry[layout_key].count = len(self._layouts.get(layout_key, {})) - - def add_layout_coords( - self, - layout_key: str, - ids: list[str], - coords: np.ndarray, - ) -> None: + def add_layout_coords(self, layout_key: str, ids: list[str], coords: np.ndarray) -> None: if len(ids) != len(coords): raise ValueError("ids and coords must have same length") - - # Require registry entry exists (call ensure_layout first) if layout_key not in self._layout_registry: - raise ValueError(f"Layout '{layout_key}' not registered. Call ensure_layout() first.") - - if layout_key not in self._layouts: - self._layouts[layout_key] = {} - layout_store = self._layouts[layout_key] + raise ValueError(f"Layout '{layout_key}' not registered") + layout_store = self._layouts.setdefault(layout_key, {}) for id_, coord in zip(ids, coords): layout_store[id_] = (float(coord[0]), float(coord[1])) + self._layout_registry[layout_key].count = len(layout_store) - self._update_layout_count(layout_key) - - def get_layout_coords( - self, - layout_key: str, - ids: list[str] | None = None, - ) -> tuple[list[str], np.ndarray]: - """Get layout coordinates.""" + def get_layout_coords(self, layout_key: str, ids: list[str] | None = None) -> tuple[list[str], np.ndarray]: layout_store = self._layouts.get(layout_key, {}) - - if ids is not None: - out_ids = [id_ for id_ in ids if id_ in layout_store] - else: - out_ids = list(layout_store.keys()) - + out_ids = [id_ for id_ in ids if id_ in layout_store] if ids is not None else list(layout_store.keys()) if not out_ids: return [], np.empty((0, 2), dtype=np.float32) - - out_coords = np.array([layout_store[id_] for id_ in out_ids], dtype=np.float32) - return out_ids, out_coords - - def get_visualization_data( - self, - layout_key: str, - ) -> tuple[list[str], list[str | None], np.ndarray]: - """Get visualization data for scatter plot.""" - layout_store = self._layouts.get(layout_key, {}) - - if not layout_store: - return [], [], np.empty((0, 2), dtype=np.float32) - - ids = [] - labels = [] - coords = [] - - for id_, (x, y) in layout_store.items(): - sample = self._samples.get(id_) - if sample is not None: - ids.append(id_) - labels.append(sample.label) - coords.append([x, y]) - - return ids, labels, np.array(coords, dtype=np.float32) if coords else np.empty((0, 2), dtype=np.float32) + return out_ids, np.array([layout_store[id_] for id_ in out_ids], dtype=np.float32) def get_lasso_candidates_aabb( self, @@ -357,42 +217,25 @@ def get_lasso_candidates_aabb( y_min: float, y_max: float, ) -> tuple[list[str], np.ndarray]: - """Return candidate (id, xy) rows within an AABB.""" layout_store = self._layouts.get(layout_key, {}) - - ids = [] - coords = [] - + ids, coords = [], [] for id_, (x, y) in layout_store.items(): - if x < x_min or x > x_max or y < y_min or y > y_max: - continue - ids.append(id_) - coords.append([x, y]) - + if x_min <= x <= x_max and y_min <= y <= y_max: + ids.append(id_) + coords.append([x, y]) return ids, np.array(coords, dtype=np.float32) if coords else np.empty((0, 2), dtype=np.float32) - # ========================================================================= - # Similarity search - # ========================================================================= - - def find_similar( - self, - sample_id: str, - k: int = 10, - space_key: str | None = None, - ) -> list[tuple[Sample, float]]: - """Find k nearest neighbors to a sample.""" + def find_similar(self, sample_id: str, k: int = 10, space_key: str | None = None) -> list[tuple[Sample, float]]: if space_key is None: if not self._spaces: raise ValueError("No embedding spaces available") - space_key = list(self._spaces.keys())[0] + space_key = next(iter(self._spaces)) emb_store = self._embeddings.get(space_key, {}) if sample_id not in emb_store: raise ValueError(f"Sample {sample_id} has no embedding in space {space_key}") - query_vector = emb_store[sample_id] - results = self.find_similar_by_vector(query_vector, k + 1, space_key) + results = self.find_similar_by_vector(emb_store[sample_id], k + 1, space_key) return [(s, d) for s, d in results if s.id != sample_id][:k] def find_similar_by_vector( @@ -401,50 +244,29 @@ def find_similar_by_vector( k: int = 10, space_key: str | None = None, ) -> list[tuple[Sample, float]]: - """Find k nearest neighbors to a query vector.""" if space_key is None: if not self._spaces: raise ValueError("No embedding spaces available") - space_key = list(self._spaces.keys())[0] + space_key = next(iter(self._spaces)) emb_store = self._embeddings.get(space_key, {}) query = np.array(vector, dtype=np.float32) + norm_query = np.linalg.norm(query) distances: list[tuple[Sample, float]] = [] for id_, vec in emb_store.items(): sample = self._samples.get(id_) if sample is None: continue - - # Cosine distance - norm_query = np.linalg.norm(query) norm_vec = np.linalg.norm(vec) - if norm_query == 0 or norm_vec == 0: distance = 1.0 else: - cosine_sim = np.dot(query, vec) / (norm_query * norm_vec) - distance = 1 - cosine_sim - + distance = 1 - np.dot(query, vec) / (norm_query * norm_vec) distances.append((sample, float(distance))) distances.sort(key=lambda x: x[1]) return distances[:k] - # ========================================================================= - # Lifecycle and metadata - # ========================================================================= - def close(self) -> None: - """Close the storage connection (no-op for in-memory).""" - return - - @property - def label_colors(self) -> dict[str, str]: - """Get label color mapping.""" - return self._label_colors - - @label_colors.setter - def label_colors(self, colors: dict[str, str]) -> None: - """Set label color mapping.""" - self._label_colors = colors + pass From ecb8cada881e97fef6afa971ddf9bdf013ee5cc4 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Tue, 20 Jan 2026 03:14:10 +0100 Subject: [PATCH 33/75] chore: update frontend static build --- src/hyperview/server/static/404.html | 2 +- src/hyperview/server/static/404/index.html | 2 +- src/hyperview/server/static/__next.__PAGE__.txt | 4 ++-- src/hyperview/server/static/__next._full.txt | 4 ++-- src/hyperview/server/static/__next._head.txt | 2 +- src/hyperview/server/static/__next._index.txt | 2 +- src/hyperview/server/static/__next._tree.txt | 2 +- .../chunks/{490c8071909f9d06.js => 5ee221fbee91a948.js} | 6 +++--- .../_buildManifest.js | 0 .../_clientMiddlewareManifest.json | 0 .../_ssgManifest.js | 0 src/hyperview/server/static/_not-found/__next._full.txt | 2 +- src/hyperview/server/static/_not-found/__next._head.txt | 2 +- src/hyperview/server/static/_not-found/__next._index.txt | 2 +- .../server/static/_not-found/__next._not-found.__PAGE__.txt | 2 +- .../server/static/_not-found/__next._not-found.txt | 2 +- src/hyperview/server/static/_not-found/__next._tree.txt | 2 +- src/hyperview/server/static/_not-found/index.html | 2 +- src/hyperview/server/static/_not-found/index.txt | 2 +- src/hyperview/server/static/index.html | 2 +- src/hyperview/server/static/index.txt | 4 ++-- 21 files changed, 23 insertions(+), 23 deletions(-) rename src/hyperview/server/static/_next/static/chunks/{490c8071909f9d06.js => 5ee221fbee91a948.js} (81%) rename src/hyperview/server/static/_next/static/{FkYHqh8bg2HXVZYl26MHt => dsfUOjSfrev80UbslOps2}/_buildManifest.js (100%) rename src/hyperview/server/static/_next/static/{FkYHqh8bg2HXVZYl26MHt => dsfUOjSfrev80UbslOps2}/_clientMiddlewareManifest.json (100%) rename src/hyperview/server/static/_next/static/{FkYHqh8bg2HXVZYl26MHt => dsfUOjSfrev80UbslOps2}/_ssgManifest.js (100%) diff --git a/src/hyperview/server/static/404.html b/src/hyperview/server/static/404.html index dcdfe7a..0b4f335 100644 --- a/src/hyperview/server/static/404.html +++ b/src/hyperview/server/static/404.html @@ -1 +1 @@ -404: This page could not be found.HyperView

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c:[["$","meta","0",{"charSet":"utf-8"}],["$","meta","1",{"name":"viewport","content":"width=device-width, initial-scale=1"}]] From 5f377896f0e8d4f4c798d108f8f69206ccf048d8 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Wed, 21 Jan 2026 20:54:15 +0100 Subject: [PATCH 34/75] feat: pluggable embedding provider architecture with HyCoCLIP support MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Add BaseEmbeddingProvider abstraction and ModelSpec config dataclass - Implement EmbedAnything (default), HyCoCLIP (PyTorch), and HyCoCLIP-ONNX providers - Add hyperboloid→Poincaré ball projection in ProjectionEngine - Update pipelines to use provider-aware space keys - Rename 'hyperbolic' extra to 'ml' (torch, torchvision, timm) - Add demo script for CLIP + HyCoCLIP comparison on CIFAR-100 --- .gitignore | 7 +- pyproject.toml | 8 +- scripts/demo_hyperbolic_clip.py | 42 +++ src/hyperview/core/dataset.py | 15 +- src/hyperview/embeddings/__init__.py | 37 ++- src/hyperview/embeddings/pipelines.py | 96 +++--- src/hyperview/embeddings/projection.py | 115 +++++++ .../embeddings/providers/__init__.py | 220 +++++++++++++ .../embeddings/providers/embed_anything.py | 57 ++++ .../embeddings/providers/hycoclip.py | 303 ++++++++++++++++++ .../embeddings/providers/hycoclip_onnx.py | 231 +++++++++++++ src/hyperview/storage/backend.py | 17 +- src/hyperview/storage/lancedb_backend.py | 11 +- src/hyperview/storage/memory_backend.py | 11 +- src/hyperview/storage/schema.py | 1 + 15 files changed, 1112 insertions(+), 59 deletions(-) create mode 100644 scripts/demo_hyperbolic_clip.py create mode 100644 src/hyperview/embeddings/providers/__init__.py create mode 100644 src/hyperview/embeddings/providers/embed_anything.py create mode 100644 src/hyperview/embeddings/providers/hycoclip.py create mode 100644 src/hyperview/embeddings/providers/hycoclip_onnx.py diff --git a/.gitignore b/.gitignore index ce6b733..07a9a77 100644 --- a/.gitignore +++ b/.gitignore @@ -59,4 +59,9 @@ frontend/nohup.out .hyperview-*.log .hyperview-*.pid .playwright-mcp/ -frontend/tsconfig.tsbuildinfo \ No newline at end of file +frontend/tsconfig.tsbuildinfo + +# Hyperbolic model zoo (kept as a separate repo) +hyper_model_zoo/ +hyper_models/ +scripts_ignored/ \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 6633051..4acde4f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -43,9 +43,10 @@ dev = [ "httpx>=0.28.1", "ruff>=0.14.13", ] -hyperbolic = [ +ml = [ "torch>=2.9.1", - "geoopt>=0.5.1", + "torchvision>=0.24.1", + "timm>=1.0.0", ] [project.scripts] @@ -83,3 +84,6 @@ ignore = ["E501"] [tool.pytest.ini_options] asyncio_mode = "auto" testpaths = ["tests"] + +[tool.uv.sources] +hycoclip = { git = "https://github.com/PalAvik/hycoclip.git" } diff --git a/scripts/demo_hyperbolic_clip.py b/scripts/demo_hyperbolic_clip.py new file mode 100644 index 0000000..513bd4c --- /dev/null +++ b/scripts/demo_hyperbolic_clip.py @@ -0,0 +1,42 @@ +#!/usr/bin/env python +"""Demo: CLIP (Euclidean) + HyCoCLIP (Poincaré) on CIFAR-100.""" + +import hyperview as hv +import hyperview.embeddings.providers.hycoclip # noqa: F401 +from hyperview.embeddings.providers import ModelSpec + +DATASET_NAME = "cifar100_clip_hycoclip" +HF_DATASET = "uoft-cs/cifar100" +HF_SPLIT = "test" +HF_IMAGE_KEY = "img" +HF_LABEL_KEY = "fine_label" +NUM_SAMPLES = 200 +CLIP_MODEL_ID = "openai/clip-vit-base-patch32" +HYCOCLIP_MODEL_ID = "hycoclip_vit_s" + + +def main() -> None: + print("Loading CIFAR-100 from Hugging Face...") + dataset = hv.Dataset(DATASET_NAME, persist=False) + dataset.add_from_huggingface( + HF_DATASET, + split=HF_SPLIT, + image_key=HF_IMAGE_KEY, + label_key=HF_LABEL_KEY, + max_samples=NUM_SAMPLES, + ) + print(f"Loaded {len(dataset)} samples") + + clip_space = dataset.compute_embeddings(CLIP_MODEL_ID) + dataset.compute_visualization(space_key=clip_space, geometry="euclidean") + hycoclip_spec = ModelSpec(provider="hycoclip", model_id=HYCOCLIP_MODEL_ID) + hycoclip_space = dataset.compute_embeddings(hycoclip_spec) + dataset.compute_visualization(space_key=hycoclip_space, geometry="poincare") + + print("Launching at http://127.0.0.1:6262") + + hv.launch(dataset, open_browser=True) + + +if __name__ == "__main__": + main() diff --git a/src/hyperview/core/dataset.py b/src/hyperview/core/dataset.py index 86446eb..23009b1 100644 --- a/src/hyperview/core/dataset.py +++ b/src/hyperview/core/dataset.py @@ -1,5 +1,7 @@ """Dataset class for managing collections of samples.""" +from __future__ import annotations + import hashlib import json import uuid @@ -335,7 +337,7 @@ def add_from_huggingface( def compute_embeddings( self, - model: str = "openai/clip-vit-base-patch32", + model: str | ModelSpec = "openai/clip-vit-base-patch32", batch_size: int = 32, show_progress: bool = True, ) -> str: @@ -351,11 +353,16 @@ def compute_embeddings( Returns: space_key for the embedding space. """ - from hyperview.embeddings.pipelines import compute_missing_embeddings + from hyperview.embeddings.pipelines import compute_embeddings + from hyperview.embeddings.providers import ModelSpec - space_key, _num_computed, _num_skipped = compute_missing_embeddings( + if isinstance(model, ModelSpec): + model_spec = model + else: + model_spec = ModelSpec(provider="embed_anything", model_id=model) + space_key, _num_computed, _num_skipped = compute_embeddings( storage=self._storage, - model_id=model, + model_spec=model_spec, batch_size=batch_size, show_progress=show_progress, ) diff --git a/src/hyperview/embeddings/__init__.py b/src/hyperview/embeddings/__init__.py index 2d83dcc..14ff04d 100644 --- a/src/hyperview/embeddings/__init__.py +++ b/src/hyperview/embeddings/__init__.py @@ -1,6 +1,37 @@ -"""Embedding computation and projection modules.""" +"""Embedding computation, projection, and provider modules.""" from hyperview.embeddings.compute import EmbeddingComputer -from hyperview.embeddings.projection import ProjectionEngine +from hyperview.embeddings.providers import ( + BaseEmbeddingProvider, + ModelSpec, + get_provider, + list_providers, + make_provider_aware_space_key, + register_provider, +) -__all__ = ["EmbeddingComputer", "ProjectionEngine"] + +def __getattr__(name: str): + """Lazy import for heavy dependencies (UMAP/numba).""" + if name == "ProjectionEngine": + from hyperview.embeddings.projection import ProjectionEngine + return ProjectionEngine + if name == "EmbedAnythingProvider": + from hyperview.embeddings.providers.embed_anything import EmbedAnythingProvider + return EmbedAnythingProvider + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") + + +__all__ = [ + "EmbeddingComputer", + "ProjectionEngine", + # Provider types + "BaseEmbeddingProvider", + "EmbedAnythingProvider", + "ModelSpec", + # Provider utilities + "get_provider", + "list_providers", + "register_provider", + "make_provider_aware_space_key", +] diff --git a/src/hyperview/embeddings/pipelines.py b/src/hyperview/embeddings/pipelines.py index fd575cd..578a9f3 100644 --- a/src/hyperview/embeddings/pipelines.py +++ b/src/hyperview/embeddings/pipelines.py @@ -4,15 +4,18 @@ computation, persisting results into the configured storage backend. """ +from __future__ import annotations + import numpy as np +from hyperview.embeddings.providers import ModelSpec, get_provider, make_provider_aware_space_key from hyperview.storage.backend import StorageBackend -from hyperview.storage.schema import make_layout_key, make_space_key +from hyperview.storage.schema import make_layout_key -def compute_missing_embeddings( +def compute_embeddings( storage: StorageBackend, - model_id: str, + model_spec: ModelSpec, batch_size: int = 32, show_progress: bool = True, ) -> tuple[str, int, int]: @@ -20,7 +23,7 @@ def compute_missing_embeddings( Args: storage: Storage backend to read samples from and write embeddings to. - model_id: EmbedAnything HuggingFace model_id to use. + model_spec: Model specification (provider, model_id, geometry, etc.). batch_size: Batch size for processing. show_progress: Whether to show progress bar. @@ -28,48 +31,58 @@ def compute_missing_embeddings( Tuple of (space_key, num_computed, num_skipped). Raises: - ValueError: If no samples in storage. + ValueError: If no samples in storage or provider not found. """ - from hyperview.embeddings.compute import EmbeddingComputer - - computer = EmbeddingComputer(model=model_id) + provider = get_provider(model_spec.provider) all_samples = storage.get_all_samples() if not all_samples: raise ValueError("No samples in storage") - test_embedding = computer.compute_single(all_samples[0]) - dim = len(test_embedding) - - space_key = make_space_key(model_id) - provider_config = { - "provider": "embed_anything", - "geometry": "euclidean", - } - storage.ensure_space(model_id, dim, config=provider_config) + # Generate space key before computing (deterministic from spec) + space_key = make_provider_aware_space_key(model_spec) + # Check which samples need embeddings missing_ids = storage.get_missing_embedding_ids(space_key) + + # If space doesn't exist yet, all samples are missing + if not storage.get_space(space_key): + missing_ids = [s.id for s in all_samples] + num_skipped = len(all_samples) - len(missing_ids) if not missing_ids: if show_progress: - print( - f"All {len(all_samples)} samples already have embeddings in space '{space_key}'" - ) + print(f"All {len(all_samples)} samples already have embeddings in space '{space_key}'") return space_key, 0, num_skipped - samples_needing_embeddings = storage.get_samples_by_ids(missing_ids) + samples_to_embed = storage.get_samples_by_ids(missing_ids) if show_progress and num_skipped > 0: print(f"Skipped {num_skipped} samples with existing embeddings") - embeddings = computer.compute_batch( - samples_needing_embeddings, batch_size=batch_size, show_progress=show_progress + # Compute all embeddings in one pass (no separate probe) + embeddings = provider.compute_embeddings( + samples=samples_to_embed, + model_spec=model_spec, + batch_size=batch_size, + show_progress=show_progress, ) - ids = [s.id for s in samples_needing_embeddings] - vectors = np.array(embeddings, dtype=np.float32) - storage.add_embeddings(space_key, ids, vectors) + dim = embeddings.shape[1] + + # Ensure space exists (create if needed) + config = provider.get_space_config(model_spec, dim) + storage.ensure_space( + model_id=model_spec.model_id, + dim=dim, + config=config, + space_key=space_key, + ) + + # Store embeddings + ids = [s.id for s in samples_to_embed] + storage.add_embeddings(space_key, ids, embeddings) return space_key, len(ids), num_skipped @@ -122,11 +135,12 @@ def compute_layout( if space is None: raise ValueError(f"Space not found: {space_key}") + input_geometry = space.geometry + curvature = (space.config or {}).get("curvature") + ids, vectors = storage.get_embeddings(space_key) if len(ids) == 0: - raise ValueError( - f"No embeddings in space '{space_key}'. Call compute_embeddings() first." - ) + raise ValueError(f"No embeddings in space '{space_key}'. Call compute_embeddings() first.") if len(ids) < 3: raise ValueError(f"Need at least 3 samples for visualization, have {len(ids)}") @@ -161,20 +175,16 @@ def compute_layout( ) engine = ProjectionEngine() - if geometry == "poincare": - coords = engine.project_to_poincare( - vectors, - n_neighbors=n_neighbors, - min_dist=min_dist, - metric=metric, - ) - else: - coords = engine.project_umap( - vectors, - n_neighbors=n_neighbors, - min_dist=min_dist, - metric=metric, - ) + coords = engine.project( + vectors, + input_geometry=input_geometry, + output_geometry=geometry, + curvature=curvature, + method=method, + n_neighbors=n_neighbors, + min_dist=min_dist, + metric=metric, + ) storage.add_layout_coords(layout_key, ids, coords) diff --git a/src/hyperview/embeddings/projection.py b/src/hyperview/embeddings/projection.py index 2ed5e3d..4b81a1c 100644 --- a/src/hyperview/embeddings/projection.py +++ b/src/hyperview/embeddings/projection.py @@ -11,6 +11,121 @@ class ProjectionEngine: """Engine for projecting high-dimensional embeddings to 2D.""" + def to_poincare_ball( + self, + hyperboloid_embeddings: np.ndarray, + curvature: float | None = None, + clamp_radius: float = 0.999999, + ) -> np.ndarray: + """Convert hyperboloid (Lorentz) coordinates to Poincaré ball coordinates. + + Input is expected to be shape (N, D+1) with first coordinate being time-like. + Points are assumed to satisfy: t^2 - ||x||^2 = 1/c (c > 0). + + Returns Poincaré ball coordinates of shape (N, D) in the unit ball. + + Notes: + - Many hyperbolic libraries parameterize curvature as a positive number c + where the manifold has sectional curvature -c. + - We map to the unit ball for downstream distance metrics (UMAP 'poincare'). + """ + if hyperboloid_embeddings.ndim != 2 or hyperboloid_embeddings.shape[1] < 2: + raise ValueError( + "hyperboloid_embeddings must have shape (N, D+1) with D>=1" + ) + + c = float(curvature) if curvature is not None else 1.0 + if c <= 0: + raise ValueError(f"curvature must be > 0, got {c}") + + # Radius R = 1/sqrt(c) for curvature -c + R = 1.0 / np.sqrt(c) + + t = hyperboloid_embeddings[:, :1] + x = hyperboloid_embeddings[:, 1:] + + # Map to ball radius R: u_R = x / (t + R) + denom = t + R + u_R = x / denom + + # Rescale to unit ball: u = u_R / R = sqrt(c) * u_R + u = u_R / R + + # Numerical guard: ensure inside the unit ball + radii = np.linalg.norm(u, axis=1) + mask = radii >= clamp_radius + if np.any(mask): + u[mask] = u[mask] / radii[mask][:, np.newaxis] * clamp_radius + + return u.astype(np.float32) + + def project( + self, + embeddings: np.ndarray, + *, + input_geometry: str = "euclidean", + output_geometry: str = "euclidean", + curvature: float | None = None, + method: str = "umap", + n_neighbors: int = 15, + min_dist: float = 0.1, + metric: str = "cosine", + random_state: int = 42, + ) -> np.ndarray: + """Project embeddings to 2D with geometry-aware preprocessing. + + This separates two concerns: + 1) Geometry/model transforms for the *input* embeddings (e.g. hyperboloid -> Poincaré) + 2) Dimensionality reduction / layout (currently UMAP) + + Args: + embeddings: Input embeddings (N x D) or hyperboloid (N x D+1). + input_geometry: Geometry/model of the input embeddings (euclidean, hyperboloid). + output_geometry: Geometry of the output coordinates (euclidean, poincare). + curvature: Curvature parameter for hyperbolic embeddings (positive c). + method: Layout method (currently only 'umap'). + n_neighbors: UMAP neighbors. + min_dist: UMAP min_dist. + metric: Input metric (used for euclidean inputs). + random_state: Random seed. + + Returns: + 2D coordinates (N x 2). + """ + if method != "umap": + raise ValueError(f"Invalid method: {method}. Only 'umap' is supported.") + + prepared = embeddings + prepared_metric: str = metric + + if input_geometry == "hyperboloid": + # Convert to unit Poincaré ball and use UMAP's built-in hyperbolic distance. + prepared = self.to_poincare_ball(embeddings, curvature=curvature) + prepared_metric = "poincare" + + if output_geometry == "poincare": + return self.project_to_poincare( + prepared, + n_neighbors=n_neighbors, + min_dist=min_dist, + metric=prepared_metric, + random_state=random_state, + ) + + if output_geometry == "euclidean": + return self.project_umap( + prepared, + n_neighbors=n_neighbors, + min_dist=min_dist, + metric=prepared_metric, + n_components=2, + random_state=random_state, + ) + + raise ValueError( + f"Invalid output_geometry: {output_geometry}. Must be 'euclidean' or 'poincare'." + ) + def project_umap( self, embeddings: np.ndarray, diff --git a/src/hyperview/embeddings/providers/__init__.py b/src/hyperview/embeddings/providers/__init__.py new file mode 100644 index 0000000..27f8f87 --- /dev/null +++ b/src/hyperview/embeddings/providers/__init__.py @@ -0,0 +1,220 @@ +"""Embedding provider abstraction for HyperView. + +This module defines a pluggable provider interface for computing embeddings +from different sources (EmbedAnything, HyCoCLIP, HuggingFace Transformers, etc.). + +Design principles: +- Default path stays lightweight (EmbedAnything) +- Heavy integrations are opt-in with clear dependency errors +- Embedding geometry (euclidean, hyperboloid) is tracked explicitly +- ModelSpec provides structured config that persists to SpaceInfo.config_json +- Providers are singletons to preserve model state across calls +""" + +from __future__ import annotations + +import hashlib +import json +from abc import ABC, abstractmethod +from dataclasses import dataclass +from typing import Any + +import numpy as np + +from hyperview.core.sample import Sample + + +# --------------------------------------------------------------------------- +# ModelSpec: structured config for any embedding model +# --------------------------------------------------------------------------- + + +@dataclass +class ModelSpec: + """Structured specification for an embedding model. + + Attributes: + provider: Provider identifier (e.g., "embed_anything", "hycoclip") + model_id: Model identifier (HuggingFace model_id, checkpoint path, etc.) + checkpoint: Optional checkpoint path or URL for weight-only models + config_path: Optional config path for models that need it + output_geometry: Geometry of the embedding space ("euclidean", "hyperboloid") + curvature: Hyperbolic curvature (only relevant for hyperbolic geometries) + """ + + provider: str + model_id: str + checkpoint: str | None = None + config_path: str | None = None + output_geometry: str = "euclidean" + curvature: float | None = None + + def to_dict(self) -> dict[str, Any]: + """Convert to JSON-serializable dict.""" + d: dict[str, Any] = { + "provider": self.provider, + "model_id": self.model_id, + "geometry": self.output_geometry, + } + if self.checkpoint: + d["checkpoint"] = self.checkpoint + if self.config_path: + d["config_path"] = self.config_path + if self.curvature is not None: + d["curvature"] = self.curvature + return d + + @classmethod + def from_dict(cls, d: dict[str, Any]) -> ModelSpec: + """Create from dict (e.g., loaded from JSON).""" + return cls( + provider=d["provider"], + model_id=d["model_id"], + checkpoint=d.get("checkpoint"), + config_path=d.get("config_path"), + output_geometry=d.get("geometry", d.get("output_geometry", "euclidean")), + curvature=d.get("curvature"), + ) + + def content_hash(self) -> str: + """Generate a short hash of the spec for collision-resistant keys.""" + content = json.dumps(self.to_dict(), sort_keys=True) + return hashlib.sha256(content.encode()).hexdigest()[:12] + + +# --------------------------------------------------------------------------- +# Base class for providers +# --------------------------------------------------------------------------- + + +class BaseEmbeddingProvider(ABC): + """Base class for embedding providers.""" + + @property + @abstractmethod + def provider_id(self) -> str: + """Unique identifier for this provider.""" + ... + + @abstractmethod + def compute_embeddings( + self, + samples: list[Sample], + model_spec: ModelSpec, + batch_size: int = 32, + show_progress: bool = True, + ) -> np.ndarray: + """Compute embeddings for samples. + + Returns: + Array of shape (N, D) where N is len(samples) and D is embedding dim. + """ + ... + + def get_space_config(self, model_spec: ModelSpec, dim: int) -> dict[str, Any]: + """Get config dict for SpaceInfo.config_json. + + Args: + model_spec: Model specification. + dim: Embedding dimension. + + Returns: + Config dict with provider, geometry, model_id, dim, and any extras. + """ + return { + **model_spec.to_dict(), + "dim": dim, + } + + +# --------------------------------------------------------------------------- +# Provider registry (singleton instances) +# --------------------------------------------------------------------------- + +# Registry stores provider classes +_PROVIDER_CLASSES: dict[str, type[BaseEmbeddingProvider]] = {} + +# Cache stores singleton instances +_PROVIDER_INSTANCES: dict[str, BaseEmbeddingProvider] = {} + + +def register_provider(provider_id: str, provider_class: type[BaseEmbeddingProvider]) -> None: + """Register a new embedding provider class.""" + _PROVIDER_CLASSES[provider_id] = provider_class + # Clear cached instance if re-registering + _PROVIDER_INSTANCES.pop(provider_id, None) + + +def _try_auto_register(provider_id: str) -> None: + """Attempt to auto-register a provider by importing its module.""" + module_map = { + "embed_anything": "hyperview.embeddings.providers.embed_anything", + "hycoclip": "hyperview.embeddings.providers.hycoclip", + "hycoclip_onnx": "hyperview.embeddings.providers.hycoclip_onnx", + } + if provider_id in module_map: + try: + __import__(module_map[provider_id]) + except ImportError: + pass + + +def get_provider(provider_id: str) -> BaseEmbeddingProvider: + """Get a provider singleton instance by ID. + + Providers are cached to preserve model state across calls. + """ + if provider_id not in _PROVIDER_CLASSES: + _try_auto_register(provider_id) + + if provider_id not in _PROVIDER_CLASSES: + available = ", ".join(sorted(_PROVIDER_CLASSES.keys())) or "(none registered)" + raise ValueError( + f"Unknown embedding provider: '{provider_id}'. " + f"Available: {available}" + ) + + if provider_id not in _PROVIDER_INSTANCES: + _PROVIDER_INSTANCES[provider_id] = _PROVIDER_CLASSES[provider_id]() + + return _PROVIDER_INSTANCES[provider_id] + + +def list_providers() -> list[str]: + """List available provider IDs.""" + # Trigger auto-registration for known providers + for pid in ["embed_anything", "hycoclip", "hycoclip_onnx"]: + _try_auto_register(pid) + return list(_PROVIDER_CLASSES.keys()) + + +# --------------------------------------------------------------------------- +# Helper: generate provider-aware space_key +# --------------------------------------------------------------------------- + + +def make_provider_aware_space_key(model_spec: ModelSpec) -> str: + """Generate a collision-resistant space_key from a ModelSpec. + + Format: {provider}__{slugified_model_id}__{content_hash} + """ + from hyperview.storage.schema import slugify_model_id + + slug = slugify_model_id(model_spec.model_id) + content_hash = model_spec.content_hash() + + return f"{model_spec.provider}__{slug}__{content_hash}" + + +# --------------------------------------------------------------------------- +# Public exports +# --------------------------------------------------------------------------- + +__all__ = [ + "BaseEmbeddingProvider", + "ModelSpec", + "get_provider", + "list_providers", + "make_provider_aware_space_key", + "register_provider", +] diff --git a/src/hyperview/embeddings/providers/embed_anything.py b/src/hyperview/embeddings/providers/embed_anything.py new file mode 100644 index 0000000..60370df --- /dev/null +++ b/src/hyperview/embeddings/providers/embed_anything.py @@ -0,0 +1,57 @@ +"""EmbedAnything embedding provider - default lightweight provider.""" + +from __future__ import annotations + +from typing import Any + +import numpy as np + +from hyperview.core.sample import Sample +from hyperview.embeddings.providers import ( + BaseEmbeddingProvider, + ModelSpec, + register_provider, +) + +__all__ = ["EmbedAnythingProvider"] + + +class EmbedAnythingProvider(BaseEmbeddingProvider): + """Default embedding provider using EmbedAnything. + + Supports HuggingFace vision models via EmbedAnything's inference engine. + Model is cached per model_id to avoid repeated initialization. + """ + + def __init__(self) -> None: + self._computers: dict[str, Any] = {} # model_id -> EmbeddingComputer + + @property + def provider_id(self) -> str: + return "embed_anything" + + def _get_computer(self, model_id: str) -> Any: + """Get or create an EmbeddingComputer for the given model_id.""" + if model_id not in self._computers: + from hyperview.embeddings.compute import EmbeddingComputer + + self._computers[model_id] = EmbeddingComputer(model=model_id) + return self._computers[model_id] + + def compute_embeddings( + self, + samples: list[Sample], + model_spec: ModelSpec, + batch_size: int = 32, + show_progress: bool = True, + ) -> np.ndarray: + """Compute embeddings using EmbedAnything.""" + computer = self._get_computer(model_spec.model_id) + embeddings = computer.compute_batch( + samples, batch_size=batch_size, show_progress=show_progress + ) + return np.array(embeddings, dtype=np.float32) + + +# Auto-register on import +register_provider("embed_anything", EmbedAnythingProvider) diff --git a/src/hyperview/embeddings/providers/hycoclip.py b/src/hyperview/embeddings/providers/hycoclip.py new file mode 100644 index 0000000..7f4f267 --- /dev/null +++ b/src/hyperview/embeddings/providers/hycoclip.py @@ -0,0 +1,303 @@ +"""Clean HyCoCLIP embedding provider (PyTorch) - no external hycoclip package. + +This is a minimal reimplementation that loads HyCoCLIP weights directly. +Only depends on torch, timm, and numpy. + +Architecture: +- ViT backbone (timm) +- Linear projection to embedding space +- Exponential map to hyperboloid (Lorentz model) + +Checkpoints: https://huggingface.co/avik-pal/hycoclip + +Requirements: + uv sync --extra ml +""" + +from __future__ import annotations + +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from hyperview.core.sample import Sample +from hyperview.embeddings.providers import ( + BaseEmbeddingProvider, + ModelSpec, + register_provider, +) + +__all__ = ["HyCoCLIPProvider"] + + +# ----------------------------------------------------------------------------- +# Known checkpoints (auto-download from HuggingFace) +# ----------------------------------------------------------------------------- + +HYCOCLIP_CHECKPOINTS: dict[str, str] = { + "hycoclip_vit_s": "hf://avik-pal/hycoclip#hycoclip_vit_s.pth", + "hycoclip_vit_b": "hf://avik-pal/hycoclip#hycoclip_vit_b.pth", + "meru_vit_s": "hf://avik-pal/hycoclip#meru_vit_s.pth", + "meru_vit_b": "hf://avik-pal/hycoclip#meru_vit_b.pth", +} + + +# ----------------------------------------------------------------------------- +# Hyperboloid math +# ----------------------------------------------------------------------------- + + +def _exp_map_lorentz(x: "torch.Tensor", c: float) -> "torch.Tensor": + """Exponential map from tangent space at the hyperboloid vertex. + + Maps Euclidean tangent vectors at the origin onto the Lorentz (hyperboloid) + model of hyperbolic space with curvature -c. + + Output is ordered as (t, x1, ..., xD) and satisfies: + t^2 - ||x||^2 = 1/c + + This matches HyCoCLIP/MERU exp_map0 numerics by clamping the sinh input for + stability and inferring the time component from the hyperboloid constraint. + + Args: + x: Euclidean tangent vectors at the origin, shape (..., D). + c: Positive curvature parameter (hyperbolic curvature is -c). + + Returns: + Hyperboloid coordinates, shape (..., D + 1). + """ + import torch + + if c <= 0: + raise ValueError(f"curvature c must be > 0, got {c}") + + # Compute in float32 under AMP to avoid float16/bfloat16 overflow. + if x.dtype in (torch.float16, torch.bfloat16): + x = x.float() + + sqrt_c = math.sqrt(c) + rc_xnorm = sqrt_c * torch.norm(x, dim=-1, keepdim=True) + + eps = 1e-8 + sinh_input = torch.clamp(rc_xnorm, min=eps, max=math.asinh(2**15)) + spatial = torch.sinh(sinh_input) * x / torch.clamp(rc_xnorm, min=eps) + + t = torch.sqrt((1.0 / c) + torch.sum(spatial * spatial, dim=-1, keepdim=True)) + return torch.cat([t, spatial], dim=-1) + +# ----------------------------------------------------------------------------- +# ViT Image Encoder +# ----------------------------------------------------------------------------- + + +def _create_encoder( + embed_dim: int = 512, + curvature: float = 0.1, + vit_model: str = "vit_small_patch16_224", +) -> "nn.Module": + """Create HyCoCLIP image encoder using timm ViT backbone.""" + import timm + import torch.nn as nn + + class HyCoCLIPImageEncoder(nn.Module): + def __init__(self) -> None: + super().__init__() + self.backbone = timm.create_model(vit_model, pretrained=False, num_classes=0) + backbone_dim = int(getattr(self.backbone, "embed_dim")) + self.proj = nn.Linear(backbone_dim, embed_dim, bias=False) + self.curvature = curvature + self.embed_dim = embed_dim + + def forward(self, x: "torch.Tensor") -> "torch.Tensor": + features = self.backbone(x) + spatial = self.proj(features) + return _exp_map_lorentz(spatial, self.curvature) + + return HyCoCLIPImageEncoder() + + +def _load_encoder(checkpoint_path: str, device: str = "cpu") -> Any: + """Load HyCoCLIP image encoder from checkpoint.""" + import torch + + ckpt = torch.load(checkpoint_path, map_location="cpu", weights_only=False) + state = ckpt["model"] + + # Extract curvature (stored as log) + curvature = torch.exp(state["curv"]).item() + + # Determine model variant from checkpoint + proj_shape = state["visual_proj.weight"].shape + embed_dim = proj_shape[0] + backbone_dim = proj_shape[1] + + vit_models = { + 384: "vit_small_patch16_224", + 768: "vit_base_patch16_224", + 1024: "vit_large_patch16_224", + } + vit_model = vit_models.get(backbone_dim, "vit_small_patch16_224") + + model = _create_encoder(embed_dim=embed_dim, curvature=curvature, vit_model=vit_model) + + # Remap checkpoint keys + new_state = {} + for key, value in state.items(): + if key.startswith("visual."): + new_state["backbone." + key[7:]] = value + elif key == "visual_proj.weight": + new_state["proj.weight"] = value + + model.load_state_dict(new_state, strict=False) + return model.to(device).eval() + + +# ----------------------------------------------------------------------------- +# Provider +# ----------------------------------------------------------------------------- + + +class HyCoCLIPProvider(BaseEmbeddingProvider): + """Clean HyCoCLIP provider (PyTorch) - no hycoclip package dependency. + + Requires: torch, torchvision, timm (install via `uv sync --extra ml`) + """ + + def __init__(self) -> None: + self._model: Any = None + self._model_spec: ModelSpec | None = None + self._device: Any = None + self._transform: Any = None + + @property + def provider_id(self) -> str: + return "hycoclip" + + def _get_device(self) -> Any: + import torch + + if self._device is None: + self._device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + return self._device + + def _get_transform(self) -> Any: + if self._transform is None: + from torchvision import transforms + + self._transform = transforms.Compose([ + transforms.Resize(224, transforms.InterpolationMode.BICUBIC), + transforms.CenterCrop(224), + transforms.ToTensor(), + transforms.Normalize( + mean=[0.48145466, 0.4578275, 0.40821073], + std=[0.26862954, 0.26130258, 0.27577711], + ), + ]) + return self._transform + + def _resolve_checkpoint(self, checkpoint: str) -> Path: + """Resolve checkpoint path, downloading from HuggingFace if needed.""" + # Handle HuggingFace Hub URLs: hf://repo_id#filename + if checkpoint.startswith("hf://"): + try: + from huggingface_hub import hf_hub_download + except ImportError as exc: + raise ImportError( + "hf:// checkpoints require huggingface_hub. " + "Install with: uv add huggingface-hub" + ) from exc + + path = checkpoint[5:] + if "#" not in path: + raise ValueError(f"HF checkpoint must include filename: {checkpoint}") + repo_id, filename = path.split("#", 1) + return Path(hf_hub_download(repo_id=repo_id, filename=filename)).resolve() + + # Local path + path = Path(checkpoint).expanduser().resolve() + if not path.exists(): + raise FileNotFoundError(f"Checkpoint not found: {path}") + return path + + def _load_model(self, model_spec: ModelSpec) -> None: + if self._model is not None and self._model_spec == model_spec: + return + + # Auto-resolve checkpoint from model_id if not provided + checkpoint = model_spec.checkpoint + if not checkpoint: + checkpoint = HYCOCLIP_CHECKPOINTS.get(model_spec.model_id) + if not checkpoint: + available = ", ".join(sorted(HYCOCLIP_CHECKPOINTS.keys())) + raise ValueError( + f"Unknown HyCoCLIP model_id: '{model_spec.model_id}'. " + f"Known models: {available}. " + f"Or provide 'checkpoint' path explicitly." + ) + + checkpoint_path = self._resolve_checkpoint(checkpoint) + self._model = _load_encoder(str(checkpoint_path), str(self._get_device())) + self._model_spec = model_spec + + def compute_embeddings( + self, + samples: list["Sample"], + model_spec: ModelSpec, + batch_size: int = 32, + show_progress: bool = True, + ) -> np.ndarray: + """Compute hyperboloid embeddings for samples.""" + import torch + + self._load_model(model_spec) + assert self._model is not None + + device = self._get_device() + transform = self._get_transform() + + if show_progress: + print(f"Computing HyCoCLIP embeddings for {len(samples)} samples...") + + all_embeddings = [] + + for i in range(0, len(samples), batch_size): + batch_samples = samples[i : i + batch_size] + + images = [] + for sample in batch_samples: + img = sample.load_image() + if img.mode != "RGB": + img = img.convert("RGB") + images.append(transform(img)) + + batch_tensor = torch.stack(images).to(device) + + with torch.no_grad(), torch.amp.autocast( + device_type=device.type, enabled=device.type == "cuda" + ): + embeddings = self._model(batch_tensor) + + all_embeddings.append(embeddings.cpu().numpy()) + + return np.concatenate(all_embeddings, axis=0) + + def get_space_config(self, model_spec: ModelSpec, dim: int) -> dict[str, Any]: + """Return embedding space configuration with curvature.""" + self._load_model(model_spec) + assert self._model is not None + + return { + "provider": self.provider_id, + "model_id": model_spec.model_id, + "geometry": "hyperboloid", + "checkpoint": model_spec.checkpoint, + "dim": dim, + "curvature": self._model.curvature, + "spatial_dim": self._model.embed_dim, + } + + +# Auto-register on import +register_provider("hycoclip", HyCoCLIPProvider) diff --git a/src/hyperview/embeddings/providers/hycoclip_onnx.py b/src/hyperview/embeddings/providers/hycoclip_onnx.py new file mode 100644 index 0000000..e02f6aa --- /dev/null +++ b/src/hyperview/embeddings/providers/hycoclip_onnx.py @@ -0,0 +1,231 @@ +"""HyCoCLIP ONNX embedding provider - torch-free runtime. + +This provider runs an ONNX-exported HyCoCLIP/MERU image encoder with +`onnxruntime` to compute *hyperboloid (Lorentz)* embeddings. + +Outputs: +- Embeddings are returned in hyperboloid format with shape (N, D+1), where the + first coordinate is the time component. + +Requirements: +- onnxruntime (already included via embed-anything) +- An exported ONNX model (and its external weights .data file) produced by + `hyperbolic_model_zoo/hycoclip_onnx/export_onnx.py`. + +Why this exists: +- Torch is required to *export* HyCoCLIP to ONNX. +- Torch is NOT required at runtime once you have the ONNX artifacts. +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any + +import numpy as np + +from hyperview.core.sample import Sample +from hyperview.embeddings.providers import ( + BaseEmbeddingProvider, + ModelSpec, + register_provider, +) + +__all__ = ["HyCoCLIPOnnxProvider"] + + +# --------------------------------------------------------------------------- +# Dependency checking +# --------------------------------------------------------------------------- + + +def _check_onnxruntime() -> bool: + try: + import onnxruntime # noqa: F401 + + return True + except ImportError: + return False + + +def _require_dependencies() -> None: + if not _check_onnxruntime(): + raise ImportError( + "HyCoCLIP ONNX provider requires onnxruntime.\n\n" + "It should already be installed via embed-anything.\n" + "If not, install with: uv add onnxruntime" + ) + + +# --------------------------------------------------------------------------- +# Preprocessing (pure PIL+numpy) +# --------------------------------------------------------------------------- + + +def _preprocess_rgb_image_to_bchw_float01(img: Any, size: int = 224) -> np.ndarray: + """Resize shortest side to 224, center crop, return (1,3,H,W) float32 in [0,1].""" + from PIL import Image + + if not isinstance(img, Image.Image): + raise TypeError(f"Expected PIL.Image.Image, got {type(img)}") + + w, h = img.size + if w <= 0 or h <= 0: + raise ValueError(f"Invalid image size: {w}x{h}") + + # Resize shortest side to `size`. + scale = float(size) / float(min(w, h)) + new_w = int(round(w * scale)) + new_h = int(round(h * scale)) + img = img.resize((new_w, new_h), resample=Image.Resampling.BICUBIC) + + # Center crop. + left = int(round((new_w - size) / 2.0)) + top = int(round((new_h - size) / 2.0)) + img = img.crop((left, top, left + size, top + size)) + + arr = np.asarray(img, dtype=np.float32) / 255.0 # HWC in [0,1] + arr = np.transpose(arr, (2, 0, 1)) # CHW + return arr[None, ...] # BCHW + + +# --------------------------------------------------------------------------- +# Provider +# --------------------------------------------------------------------------- + + +class HyCoCLIPOnnxProvider(BaseEmbeddingProvider): + """ONNX HyCoCLIP provider - torch-free runtime. + + Uses onnxruntime for inference. No PyTorch required at runtime. + """ + + def __init__(self) -> None: + self._session: Any = None + self._model_spec: ModelSpec | None = None + self._input_name: str | None = None + self._output_names: list[str] | None = None + self._curvature: float | None = None + + @property + def provider_id(self) -> str: + return "hycoclip_onnx" + + def _resolve_onnx_path(self, model_spec: ModelSpec) -> Path: + if not model_spec.checkpoint: + raise ValueError( + "HyCoCLIP ONNX provider requires 'checkpoint' to be a path/URL to a .onnx file." + ) + + checkpoint = model_spec.checkpoint + + # Handle HuggingFace Hub URLs (hf://repo_id#filename) + if checkpoint.startswith("hf://"): + raise NotImplementedError( + "WIP: hf:// checkpoints are disabled until ONNX external weights are published. " + "Use a local .onnx path with its .onnx.data file present." + ) + + path = Path(checkpoint).expanduser().resolve() + if not path.exists(): + raise FileNotFoundError(f"ONNX model not found: {path}") + if path.suffix.lower() != ".onnx": + raise ValueError(f"Expected a .onnx file, got: {path}") + return path + + def _ensure_session(self, model_spec: ModelSpec) -> None: + _require_dependencies() + + if self._session is not None and self._model_spec == model_spec: + return + + import onnxruntime as ort + + onnx_path = self._resolve_onnx_path(model_spec) + + # Default to CPU for maximum compatibility. + get_available = getattr(ort, "get_available_providers", None) + available = get_available() if callable(get_available) else ["CPUExecutionProvider"] + providers = ["CPUExecutionProvider"] if "CPUExecutionProvider" in available else list(available) + + self._session = ort.InferenceSession(str(onnx_path), providers=providers) + self._input_name = self._session.get_inputs()[0].name + self._output_names = [o.name for o in self._session.get_outputs()] + self._curvature = None + self._model_spec = model_spec + + def compute_embeddings( + self, + samples: list[Sample], + model_spec: ModelSpec, + batch_size: int = 32, + show_progress: bool = True, + ) -> np.ndarray: + """Compute hyperboloid embeddings (t, x) with shape (N, D+1).""" + self._ensure_session(model_spec) + + assert self._session is not None + assert self._input_name is not None + + output_names = self._output_names or [] + if not output_names: + raise RuntimeError("ONNX session has no outputs") + + # Prefer named outputs if present. + emb_name = "embedding_hyperboloid" if "embedding_hyperboloid" in output_names else output_names[0] + curv_name = "curvature" if "curvature" in output_names else None + + # NOTE: Current torch.onnx export is only reliable for batch_size=1. + if batch_size != 1 and show_progress: + print("HyCoCLIP-ONNX export currently runs with batch_size=1; overriding") + batch_size = 1 + + all_embeddings: list[np.ndarray] = [] + + if show_progress: + print(f"Computing HyCoCLIP-ONNX embeddings for {len(samples)} samples...") + + for i in range(0, len(samples), batch_size): + batch_samples = samples[i : i + batch_size] + + images = [] + for sample in batch_samples: + with sample.load_image() as img: + img.load() + if img.mode != "RGB": + img = img.convert("RGB") + bchw = _preprocess_rgb_image_to_bchw_float01(img.copy(), size=224) + images.append(bchw[0]) # CHW + + batch_np = np.stack(images, axis=0).astype(np.float32) + + outputs = self._session.run( + [name for name in (emb_name, curv_name) if name is not None], + {self._input_name: batch_np}, + ) + + hyper = np.asarray(outputs[0], dtype=np.float32) + if hyper.ndim != 2: + raise RuntimeError(f"Expected (B,D) embeddings, got shape={hyper.shape}") + + # Capture curvature once. + if curv_name is not None and self._curvature is None and len(outputs) > 1: + curv_arr = np.asarray(outputs[1]).reshape(-1) + if curv_arr.size > 0: + self._curvature = float(curv_arr[0]) + + all_embeddings.append(hyper) + + return np.vstack(all_embeddings) + + def get_space_config(self, model_spec: ModelSpec, dim: int) -> dict[str, Any]: + config = super().get_space_config(model_spec, dim) + config["geometry"] = "hyperboloid" + if self._curvature is not None: + config["curvature"] = self._curvature + config["spatial_dim"] = dim - 1 + return config + + +# Auto-register on import +register_provider("hycoclip_onnx", HyCoCLIPOnnxProvider) diff --git a/src/hyperview/storage/backend.py b/src/hyperview/storage/backend.py index 518d1af..04cb05e 100644 --- a/src/hyperview/storage/backend.py +++ b/src/hyperview/storage/backend.py @@ -90,8 +90,21 @@ def get_space(self, space_key: str) -> Any | None: """Get info for a specific embedding space.""" @abstractmethod - def ensure_space(self, model_id: str, dim: int, config: dict | None = None) -> Any: - """Ensure an embedding space exists, creating if needed.""" + def ensure_space( + self, + model_id: str, + dim: int, + config: dict | None = None, + space_key: str | None = None, + ) -> Any: + """Ensure an embedding space exists, creating if needed. + + Args: + model_id: Model identifier for this space. + dim: Vector dimension. + config: Optional config dict for SpaceInfo.config_json. + space_key: Optional explicit space key. If None, derived from model_id. + """ @abstractmethod def delete_space(self, space_key: str) -> bool: diff --git a/src/hyperview/storage/lancedb_backend.py b/src/hyperview/storage/lancedb_backend.py index c0de354..cf30faa 100644 --- a/src/hyperview/storage/lancedb_backend.py +++ b/src/hyperview/storage/lancedb_backend.py @@ -191,8 +191,15 @@ def get_space(self, space_key: str) -> SpaceInfo | None: results = self._spaces_table.search().where(f"space_key = '{safe_key}'").limit(1).to_list() return SpaceInfo.from_dict(results[0]) if results else None - def ensure_space(self, model_id: str, dim: int, config: dict | None = None) -> SpaceInfo: - space_key = make_space_key(model_id) + def ensure_space( + self, + model_id: str, + dim: int, + config: dict | None = None, + space_key: str | None = None, + ) -> SpaceInfo: + if space_key is None: + space_key = make_space_key(model_id) existing = self.get_space(space_key) if existing is not None: if existing.dim != dim: diff --git a/src/hyperview/storage/memory_backend.py b/src/hyperview/storage/memory_backend.py index a1453bc..7189e08 100644 --- a/src/hyperview/storage/memory_backend.py +++ b/src/hyperview/storage/memory_backend.py @@ -89,8 +89,15 @@ def list_spaces(self) -> list[SpaceInfo]: def get_space(self, space_key: str) -> SpaceInfo | None: return self._spaces.get(space_key) - def ensure_space(self, model_id: str, dim: int, config: dict | None = None) -> SpaceInfo: - space_key = make_space_key(model_id) + def ensure_space( + self, + model_id: str, + dim: int, + config: dict | None = None, + space_key: str | None = None, + ) -> SpaceInfo: + if space_key is None: + space_key = make_space_key(model_id) if space_key in self._spaces: existing = self._spaces[space_key] if existing.dim != dim: diff --git a/src/hyperview/storage/schema.py b/src/hyperview/storage/schema.py index 1f1a14d..aea9551 100644 --- a/src/hyperview/storage/schema.py +++ b/src/hyperview/storage/schema.py @@ -233,6 +233,7 @@ def make_space_key(model_id: str) -> str: """Generate a space_key from a model_id. For simplicity, this is just the slugified model_id. + For provider-aware keys, use `make_provider_aware_space_key` from providers.py. """ return slugify_model_id(model_id) From ce7e7ac770d6a562840b8cf4f303fa622a4800b8 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Wed, 21 Jan 2026 21:47:09 +0100 Subject: [PATCH 35/75] refactor: code cleanup and quality improvements Backend: - Extract _sql_escape() helper in LanceDB backend, unify chunk sizes - Introduce FastAPI Depends(get_dataset) pattern in server/app.py - Simplify Sample class: extract _encode_thumbnail(), remove SampleFromArray - Clean up projection.py: suppress UMAP gradient warning, streamline comments - Simplify provider registry: add silent param to _try_auto_register() - Remove redundant dependency checks in hycoclip_onnx provider - Use specific ImportError instead of broad Exception in api.py Frontend: - Fix error type checking: use instanceof DOMException for AbortError - Remove unnecessary 'as any' cast in useHyperScatter cleanup --- frontend/src/app/page.tsx | 3 +- frontend/src/components/useHyperScatter.ts | 2 +- src/hyperview/api.py | 2 +- src/hyperview/core/dataset.py | 5 - src/hyperview/core/sample.py | 48 +------ src/hyperview/embeddings/compute.py | 20 +-- src/hyperview/embeddings/projection.py | 126 ++++-------------- .../embeddings/providers/__init__.py | 84 +++++------- .../embeddings/providers/hycoclip.py | 26 +--- .../embeddings/providers/hycoclip_onnx.py | 53 ++------ src/hyperview/server/app.py | 94 +++++-------- src/hyperview/storage/lancedb_backend.py | 52 +++----- 12 files changed, 134 insertions(+), 381 deletions(-) diff --git a/frontend/src/app/page.tsx b/frontend/src/app/page.tsx index 9dd6c5c..12dd689 100644 --- a/frontend/src/app/page.tsx +++ b/frontend/src/app/page.tsx @@ -148,8 +148,7 @@ export default function Home() { if (abort.signal.aborted) return; setLassoResults(res.samples, res.total, false); } catch (err) { - // Ignore cancellation. - if ((err as any)?.name === "AbortError") return; + if (err instanceof DOMException && err.name === "AbortError") return; console.error("Failed to fetch lasso selection:", err); setLassoResults([], 0, false); } diff --git a/frontend/src/components/useHyperScatter.ts b/frontend/src/components/useHyperScatter.ts index e501f3a..ca20649 100644 --- a/frontend/src/components/useHyperScatter.ts +++ b/frontend/src/components/useHyperScatter.ts @@ -359,7 +359,7 @@ export function useHyperScatter({ }; canvas.addEventListener("wheel", onWheel, { passive: false }); - return () => canvas.removeEventListener("wheel", onWheel as any); + return () => canvas.removeEventListener("wheel", onWheel); }, [getCanvasPos, requestRender]); // Pointer interactions diff --git a/src/hyperview/api.py b/src/hyperview/api.py index 0ca9516..9042970 100644 --- a/src/hyperview/api.py +++ b/src/hyperview/api.py @@ -391,5 +391,5 @@ def _is_colab() -> bool: import google.colab # type: ignore[import-not-found] return True - except Exception: + except ImportError: return False diff --git a/src/hyperview/core/dataset.py b/src/hyperview/core/dataset.py index 23009b1..18ee2fb 100644 --- a/src/hyperview/core/dataset.py +++ b/src/hyperview/core/dataset.py @@ -598,16 +598,11 @@ def get_lasso_candidates_aabb( def save(self, filepath: str, include_thumbnails: bool = True) -> None: """Export dataset to a JSON file. - Note: For persistent datasets (default), data is automatically saved. - This method is for exporting to JSON format for sharing or backup. - Args: filepath: Path to save the JSON file. include_thumbnails: Whether to include cached thumbnails. """ samples = self._storage.get_all_samples() - - # Cache thumbnails before saving if requested if include_thumbnails: for s in samples: s.cache_thumbnail() diff --git a/src/hyperview/core/sample.py b/src/hyperview/core/sample.py index cd4c84e..78623ba 100644 --- a/src/hyperview/core/sample.py +++ b/src/hyperview/core/sample.py @@ -5,7 +5,6 @@ from pathlib import Path from typing import Any -import numpy as np from PIL import Image from pydantic import BaseModel, Field @@ -40,29 +39,23 @@ def get_thumbnail(self, size: tuple[int, int] = (128, 128)) -> Image.Image: img.thumbnail(size, Image.Resampling.LANCZOS) return img - def get_thumbnail_base64(self, size: tuple[int, int] = (128, 128)) -> str: - """Get thumbnail as base64 encoded string.""" - # Return cached thumbnail if available - if self.thumbnail_base64: - return self.thumbnail_base64 - + def _encode_thumbnail(self, size: tuple[int, int] = (128, 128)) -> str: + """Encode thumbnail as base64 JPEG.""" thumb = self.get_thumbnail(size) - # Convert to RGB if necessary (for PNG with alpha) if thumb.mode in ("RGBA", "P"): thumb = thumb.convert("RGB") buffer = io.BytesIO() thumb.save(buffer, format="JPEG", quality=85) return base64.b64encode(buffer.getvalue()).decode("utf-8") + def get_thumbnail_base64(self, size: tuple[int, int] = (128, 128)) -> str: + """Get thumbnail as base64 encoded string.""" + return self.thumbnail_base64 or self._encode_thumbnail(size) + def cache_thumbnail(self, size: tuple[int, int] = (128, 128)) -> None: """Cache the thumbnail as base64 for persistence.""" if self.thumbnail_base64 is None: - thumb = self.get_thumbnail(size) - if thumb.mode in ("RGBA", "P"): - thumb = thumb.convert("RGB") - buffer = io.BytesIO() - thumb.save(buffer, format="JPEG", quality=85) - self.thumbnail_base64 = base64.b64encode(buffer.getvalue()).decode("utf-8") + self.thumbnail_base64 = self._encode_thumbnail(size) def to_api_dict(self, include_thumbnail: bool = True) -> dict[str, Any]: """Convert to dictionary for API response.""" @@ -78,31 +71,4 @@ def to_api_dict(self, include_thumbnail: bool = True) -> dict[str, Any]: return data -class SampleFromArray(Sample): - """A sample created from a numpy array (e.g., from HuggingFace datasets).""" - - _image_array: np.ndarray | None = None - - @classmethod - def from_array( - cls, - id: str, - image_array: np.ndarray, - label: str | None = None, - metadata: dict[str, Any] | None = None, - ) -> "SampleFromArray": - """Create a sample from a numpy array.""" - sample = cls( - id=id, - filepath=f"memory://{id}", - label=label, - metadata=metadata or {}, - ) - sample._image_array = image_array - return sample - def load_image(self) -> Image.Image: - """Load the image from the array.""" - if self._image_array is not None: - return Image.fromarray(self._image_array) - return super().load_image() diff --git a/src/hyperview/embeddings/compute.py b/src/hyperview/embeddings/compute.py index 78444b4..d2026b5 100644 --- a/src/hyperview/embeddings/compute.py +++ b/src/hyperview/embeddings/compute.py @@ -1,10 +1,4 @@ -"""Image embedding computation via EmbedAnything. - -This module is intentionally minimal: -- Callers pass a HuggingFace `model_id` string. -- We delegate model loading + inference to EmbedAnything. - -""" +"""Image embedding computation via EmbedAnything.""" import os import tempfile @@ -74,14 +68,8 @@ def _embed_pil_image(self, image: Image.Image) -> np.ndarray: def compute_single(self, sample: Sample) -> np.ndarray: """Compute embedding for a single sample.""" - try: - image = self._load_rgb_image(sample) - return self._embed_pil_image(image) - except Exception as exc: - raise RuntimeError( - f"Failed to compute embedding for sample {sample.id} " - f"(filepath={sample.filepath}, model_id={self.model_id})" - ) from exc + image = self._load_rgb_image(sample) + return self._embed_pil_image(image) def compute_batch( self, @@ -92,8 +80,6 @@ def compute_batch( """Compute embeddings for a list of samples.""" if batch_size <= 0: raise ValueError("batch_size must be > 0") - - # Prime model init so download errors happen before work starts. self._get_model() if show_progress: diff --git a/src/hyperview/embeddings/projection.py b/src/hyperview/embeddings/projection.py index 4b81a1c..83dff50 100644 --- a/src/hyperview/embeddings/projection.py +++ b/src/hyperview/embeddings/projection.py @@ -1,6 +1,7 @@ """Projection methods for dimensionality reduction.""" import logging +import warnings import numpy as np import umap @@ -135,20 +136,7 @@ def project_umap( n_components: int = 2, random_state: int = 42, ) -> np.ndarray: - """Project embeddings to Euclidean 2D using UMAP. - - Args: - embeddings: High-dimensional embeddings (N x D). - n_neighbors: Number of neighbors for UMAP. - min_dist: Minimum distance between points. - metric: Distance metric to use. - n_components: Number of output dimensions. - random_state: Random seed for reproducibility. - - Returns: - 2D coordinates (N x 2). - """ - # Safety check for small datasets + """Project embeddings to Euclidean 2D using UMAP.""" n_neighbors = min(n_neighbors, len(embeddings) - 1) if n_neighbors < 2: n_neighbors = 2 @@ -165,8 +153,6 @@ def project_umap( ) coords = reducer.fit_transform(embeddings) - - # Normalize to [-1, 1] range for visualization consistency coords = self._normalize_coords(coords) return coords @@ -179,56 +165,35 @@ def project_to_poincare( metric: str = "cosine", random_state: int = 42, ) -> np.ndarray: - """Project embeddings to the Poincaré disk. - - This uses UMAP with a hyperbolic output metric. UMAP computes the embedding - in the Hyperboloid model (Lorentz model). We then project this to the - Poincaré disk. - - Args: - embeddings: High-dimensional embeddings (N x D). - n_neighbors: Number of neighbors for UMAP. - min_dist: Minimum distance between points. - metric: Input distance metric. - random_state: Random seed for reproducibility. - - Returns: - 2D coordinates in Poincaré disk (N x 2), with norm < 1. - """ - # Safety check for small datasets + """Project embeddings to the Poincaré disk using UMAP with hyperboloid output.""" n_neighbors = min(n_neighbors, len(embeddings) - 1) if n_neighbors < 2: n_neighbors = 2 n_jobs = 1 if random_state is not None else -1 - # The time-like coordinate t is implicit: t = sqrt(1 + x^2 + y^2). - reducer = umap.UMAP( - n_neighbors=n_neighbors, - min_dist=min_dist, - n_components=2, # We want a 2D manifold - metric=metric, - output_metric="hyperboloid", - random_state=random_state, - n_jobs=n_jobs, - ) - # These are spatial coordinates (x, y) in the Hyperboloid model - spatial_coords = reducer.fit_transform(embeddings) + # Suppress warning about missing gradient for poincare metric (only affects inverse_transform) + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message="gradient function is not yet implemented") + reducer = umap.UMAP( + n_neighbors=n_neighbors, + min_dist=min_dist, + n_components=2, + metric=metric, + output_metric="hyperboloid", + random_state=random_state, + n_jobs=n_jobs, + ) + spatial_coords = reducer.fit_transform(embeddings) - # Calculate implicit time coordinate t - # t = sqrt(1 + x^2 + y^2) - # Note: In some conventions it's t^2 - x^2 - y^2 = 1, so t = sqrt(1 + r^2) squared_norm = np.sum(spatial_coords**2, axis=1) t = np.sqrt(1 + squared_norm) - # Project to Poincaré disk - # Formula: u = x / (1 + t) - # This maps the upper sheet of the hyperboloid (t >= 1) to the unit disk. + # Project to Poincaré disk: u = x / (1 + t) denom = 1 + t poincare_coords = spatial_coords / denom[:, np.newaxis] - # Ensure numerical stability - clamp to slightly less than 1.0 if needed - # theoretically it should be < 1, but float precision might cause issues + # Clamp to unit disk for numerical stability radii = np.linalg.norm(poincare_coords, axis=1) max_radius = 0.999 mask = radii > max_radius @@ -238,12 +203,7 @@ def project_to_poincare( poincare_coords[mask] / radii[mask][:, np.newaxis] * max_radius ) - # Center the embeddings in the Poincaré disk poincare_coords = self._center_poincare(poincare_coords) - - # Apply radial scaling to reduce crowding at the boundary - # This effectively "zooms out" in hyperbolic space, pulling points - # towards the center for better visualization. poincare_coords = self._scale_poincare(poincare_coords, factor=0.65) return poincare_coords @@ -251,57 +211,32 @@ def project_to_poincare( def _scale_poincare(self, coords: np.ndarray, factor: float) -> np.ndarray: """Scale points towards the origin in hyperbolic space. - This scales the hyperbolic distance from the origin by `factor`. - If factor < 1, points move closer to the center. + Scales hyperbolic distance from origin by `factor`. If factor < 1, points move closer to center. """ radii = np.linalg.norm(coords, axis=1) - # Avoid division by zero mask = radii > 1e-6 - # Calculate hyperbolic distance from origin - # d = 2 * arctanh(r) - # We want d_new = factor * d - # r_new = tanh(d_new / 2) = tanh(factor * arctanh(r)) - - # Use numpy operations for efficiency r = radii[mask] - # Clip r to avoid infinity in arctanh r = np.minimum(r, 0.9999999) - - # d = 2 * np.arctanh(r) - # r_new = np.tanh(factor * d / 2) - # Simplified: r_new = tanh(factor * arctanh(r)) r_new = np.tanh(factor * np.arctanh(r)) - # Update coordinates - # new_coords = coords * (r_new / r) scale_ratios = np.ones_like(radii) scale_ratios[mask] = r_new / r return coords * scale_ratios[:, np.newaxis] def _center_poincare(self, coords: np.ndarray) -> np.ndarray: - """Center points in the Poincaré disk using a Möbius transformation. - - This moves the geometric centroid of the points to the origin. - """ + """Center points in the Poincaré disk using a Möbius transformation.""" if len(coords) == 0: return coords - # Treat as complex numbers for easier Möbius math z = coords[:, 0] + 1j * coords[:, 1] - - # Compute the centroid (Euclidean mean in the disk) - # This is a heuristic; the true hyperbolic center of mass is harder - # but this works well for visualization centering. centroid = np.mean(z) - # If centroid is too close to boundary, don't center (unstable) if np.abs(centroid) > 0.99 or np.abs(centroid) < 1e-6: return coords - # Möbius transformation to move centroid to origin: - # w = (z - a) / (1 - conj(a) * z) + # Möbius transformation: w = (z - a) / (1 - conj(a) * z) a = centroid w = (z - a) / (1 - np.conj(a) * z) @@ -312,34 +247,19 @@ def _normalize_coords(self, coords: np.ndarray) -> np.ndarray: if len(coords) == 0: return coords - # Center the coordinates coords = coords - coords.mean(axis=0) - - # Scale to fit in [-1, 1] max_abs = np.abs(coords).max() if max_abs > 0: - coords = coords / max_abs * 0.95 # Leave some margin + coords = coords / max_abs * 0.95 return coords def poincare_distance(self, u: np.ndarray, v: np.ndarray) -> float: - """Compute the Poincaré distance between two points. - - Args: - u: First point in Poincaré disk. - v: Second point in Poincaré disk. - - Returns: - Hyperbolic distance. - """ + """Compute the Poincaré distance between two points.""" u_norm_sq = np.sum(u**2) v_norm_sq = np.sum(v**2) diff_norm_sq = np.sum((u - v) ** 2) - # Poincaré distance formula - # d(u, v) = arccosh(1 + 2 * |u-v|^2 / ((1-|u|^2)(1-|v|^2))) - - # Clip values to avoid division by zero or negative logs u_norm_sq = min(u_norm_sq, 0.99999) v_norm_sq = min(v_norm_sq, 0.99999) diff --git a/src/hyperview/embeddings/providers/__init__.py b/src/hyperview/embeddings/providers/__init__.py index 27f8f87..55fca86 100644 --- a/src/hyperview/embeddings/providers/__init__.py +++ b/src/hyperview/embeddings/providers/__init__.py @@ -1,19 +1,9 @@ -"""Embedding provider abstraction for HyperView. - -This module defines a pluggable provider interface for computing embeddings -from different sources (EmbedAnything, HyCoCLIP, HuggingFace Transformers, etc.). - -Design principles: -- Default path stays lightweight (EmbedAnything) -- Heavy integrations are opt-in with clear dependency errors -- Embedding geometry (euclidean, hyperboloid) is tracked explicitly -- ModelSpec provides structured config that persists to SpaceInfo.config_json -- Providers are singletons to preserve model state across calls -""" +"""Embedding provider abstraction for HyperView.""" from __future__ import annotations import hashlib +from importlib import import_module import json from abc import ABC, abstractmethod from dataclasses import dataclass @@ -24,11 +14,6 @@ from hyperview.core.sample import Sample -# --------------------------------------------------------------------------- -# ModelSpec: structured config for any embedding model -# --------------------------------------------------------------------------- - - @dataclass class ModelSpec: """Structured specification for an embedding model. @@ -72,7 +57,7 @@ def from_dict(cls, d: dict[str, Any]) -> ModelSpec: model_id=d["model_id"], checkpoint=d.get("checkpoint"), config_path=d.get("config_path"), - output_geometry=d.get("geometry", d.get("output_geometry", "euclidean")), + output_geometry=d.get("geometry", "euclidean"), curvature=d.get("curvature"), ) @@ -82,11 +67,6 @@ def content_hash(self) -> str: return hashlib.sha256(content.encode()).hexdigest()[:12] -# --------------------------------------------------------------------------- -# Base class for providers -# --------------------------------------------------------------------------- - - class BaseEmbeddingProvider(ABC): """Base class for embedding providers.""" @@ -127,17 +107,17 @@ def get_space_config(self, model_spec: ModelSpec, dim: int) -> dict[str, Any]: } -# --------------------------------------------------------------------------- -# Provider registry (singleton instances) -# --------------------------------------------------------------------------- - -# Registry stores provider classes _PROVIDER_CLASSES: dict[str, type[BaseEmbeddingProvider]] = {} - -# Cache stores singleton instances _PROVIDER_INSTANCES: dict[str, BaseEmbeddingProvider] = {} +_KNOWN_PROVIDER_MODULES: dict[str, str] = { + "embed_anything": "hyperview.embeddings.providers.embed_anything", + "hycoclip": "hyperview.embeddings.providers.hycoclip", + "hycoclip_onnx": "hyperview.embeddings.providers.hycoclip_onnx", +} + + def register_provider(provider_id: str, provider_class: type[BaseEmbeddingProvider]) -> None: """Register a new embedding provider class.""" _PROVIDER_CLASSES[provider_id] = provider_class @@ -145,18 +125,27 @@ def register_provider(provider_id: str, provider_class: type[BaseEmbeddingProvid _PROVIDER_INSTANCES.pop(provider_id, None) -def _try_auto_register(provider_id: str) -> None: - """Attempt to auto-register a provider by importing its module.""" - module_map = { - "embed_anything": "hyperview.embeddings.providers.embed_anything", - "hycoclip": "hyperview.embeddings.providers.hycoclip", - "hycoclip_onnx": "hyperview.embeddings.providers.hycoclip_onnx", - } - if provider_id in module_map: +def _try_auto_register(provider_id: str, *, silent: bool = True) -> None: + """Attempt to auto-register a provider by importing its module. + + Args: + provider_id: Provider identifier. + silent: If True, swallow ImportError (used when listing providers). + If False, let ImportError propagate (used when explicitly requesting + a provider via get_provider()). + """ + + module_name = _KNOWN_PROVIDER_MODULES.get(provider_id) + if not module_name: + return + + if silent: try: - __import__(module_map[provider_id]) + import_module(module_name) except ImportError: - pass + return + else: + import_module(module_name) def get_provider(provider_id: str) -> BaseEmbeddingProvider: @@ -165,7 +154,7 @@ def get_provider(provider_id: str) -> BaseEmbeddingProvider: Providers are cached to preserve model state across calls. """ if provider_id not in _PROVIDER_CLASSES: - _try_auto_register(provider_id) + _try_auto_register(provider_id, silent=False) if provider_id not in _PROVIDER_CLASSES: available = ", ".join(sorted(_PROVIDER_CLASSES.keys())) or "(none registered)" @@ -183,16 +172,11 @@ def get_provider(provider_id: str) -> BaseEmbeddingProvider: def list_providers() -> list[str]: """List available provider IDs.""" # Trigger auto-registration for known providers - for pid in ["embed_anything", "hycoclip", "hycoclip_onnx"]: - _try_auto_register(pid) + for pid in _KNOWN_PROVIDER_MODULES: + _try_auto_register(pid, silent=True) return list(_PROVIDER_CLASSES.keys()) -# --------------------------------------------------------------------------- -# Helper: generate provider-aware space_key -# --------------------------------------------------------------------------- - - def make_provider_aware_space_key(model_spec: ModelSpec) -> str: """Generate a collision-resistant space_key from a ModelSpec. @@ -206,10 +190,6 @@ def make_provider_aware_space_key(model_spec: ModelSpec) -> str: return f"{model_spec.provider}__{slug}__{content_hash}" -# --------------------------------------------------------------------------- -# Public exports -# --------------------------------------------------------------------------- - __all__ = [ "BaseEmbeddingProvider", "ModelSpec", diff --git a/src/hyperview/embeddings/providers/hycoclip.py b/src/hyperview/embeddings/providers/hycoclip.py index 7f4f267..3f8a81c 100644 --- a/src/hyperview/embeddings/providers/hycoclip.py +++ b/src/hyperview/embeddings/providers/hycoclip.py @@ -32,10 +32,6 @@ __all__ = ["HyCoCLIPProvider"] -# ----------------------------------------------------------------------------- -# Known checkpoints (auto-download from HuggingFace) -# ----------------------------------------------------------------------------- - HYCOCLIP_CHECKPOINTS: dict[str, str] = { "hycoclip_vit_s": "hf://avik-pal/hycoclip#hycoclip_vit_s.pth", "hycoclip_vit_b": "hf://avik-pal/hycoclip#hycoclip_vit_b.pth", @@ -44,11 +40,6 @@ } -# ----------------------------------------------------------------------------- -# Hyperboloid math -# ----------------------------------------------------------------------------- - - def _exp_map_lorentz(x: "torch.Tensor", c: float) -> "torch.Tensor": """Exponential map from tangent space at the hyperboloid vertex. @@ -87,10 +78,6 @@ def _exp_map_lorentz(x: "torch.Tensor", c: float) -> "torch.Tensor": t = torch.sqrt((1.0 / c) + torch.sum(spatial * spatial, dim=-1, keepdim=True)) return torch.cat([t, spatial], dim=-1) -# ----------------------------------------------------------------------------- -# ViT Image Encoder -# ----------------------------------------------------------------------------- - def _create_encoder( embed_dim: int = 512, @@ -154,11 +141,6 @@ def _load_encoder(checkpoint_path: str, device: str = "cpu") -> Any: return model.to(device).eval() -# ----------------------------------------------------------------------------- -# Provider -# ----------------------------------------------------------------------------- - - class HyCoCLIPProvider(BaseEmbeddingProvider): """Clean HyCoCLIP provider (PyTorch) - no hycoclip package dependency. @@ -201,13 +183,7 @@ def _resolve_checkpoint(self, checkpoint: str) -> Path: """Resolve checkpoint path, downloading from HuggingFace if needed.""" # Handle HuggingFace Hub URLs: hf://repo_id#filename if checkpoint.startswith("hf://"): - try: - from huggingface_hub import hf_hub_download - except ImportError as exc: - raise ImportError( - "hf:// checkpoints require huggingface_hub. " - "Install with: uv add huggingface-hub" - ) from exc + from huggingface_hub import hf_hub_download path = checkpoint[5:] if "#" not in path: diff --git a/src/hyperview/embeddings/providers/hycoclip_onnx.py b/src/hyperview/embeddings/providers/hycoclip_onnx.py index e02f6aa..4333063 100644 --- a/src/hyperview/embeddings/providers/hycoclip_onnx.py +++ b/src/hyperview/embeddings/providers/hycoclip_onnx.py @@ -34,36 +34,8 @@ __all__ = ["HyCoCLIPOnnxProvider"] -# --------------------------------------------------------------------------- -# Dependency checking -# --------------------------------------------------------------------------- - - -def _check_onnxruntime() -> bool: - try: - import onnxruntime # noqa: F401 - - return True - except ImportError: - return False - - -def _require_dependencies() -> None: - if not _check_onnxruntime(): - raise ImportError( - "HyCoCLIP ONNX provider requires onnxruntime.\n\n" - "It should already be installed via embed-anything.\n" - "If not, install with: uv add onnxruntime" - ) - - -# --------------------------------------------------------------------------- -# Preprocessing (pure PIL+numpy) -# --------------------------------------------------------------------------- - - -def _preprocess_rgb_image_to_bchw_float01(img: Any, size: int = 224) -> np.ndarray: - """Resize shortest side to 224, center crop, return (1,3,H,W) float32 in [0,1].""" +def _preprocess_rgb_image_to_chw_float01(img: Any, size: int = 224) -> np.ndarray: + """Resize shortest side to `size`, center crop, return (3,H,W) float32 in [0,1].""" from PIL import Image if not isinstance(img, Image.Image): @@ -84,14 +56,9 @@ def _preprocess_rgb_image_to_bchw_float01(img: Any, size: int = 224) -> np.ndarr top = int(round((new_h - size) / 2.0)) img = img.crop((left, top, left + size, top + size)) - arr = np.asarray(img, dtype=np.float32) / 255.0 # HWC in [0,1] - arr = np.transpose(arr, (2, 0, 1)) # CHW - return arr[None, ...] # BCHW - - -# --------------------------------------------------------------------------- -# Provider -# --------------------------------------------------------------------------- + arr = np.asarray(img, dtype=np.float32) / 255.0 + arr = np.transpose(arr, (2, 0, 1)) + return arr class HyCoCLIPOnnxProvider(BaseEmbeddingProvider): @@ -134,8 +101,6 @@ def _resolve_onnx_path(self, model_spec: ModelSpec) -> Path: return path def _ensure_session(self, model_spec: ModelSpec) -> None: - _require_dependencies() - if self._session is not None and self._model_spec == model_spec: return @@ -144,8 +109,7 @@ def _ensure_session(self, model_spec: ModelSpec) -> None: onnx_path = self._resolve_onnx_path(model_spec) # Default to CPU for maximum compatibility. - get_available = getattr(ort, "get_available_providers", None) - available = get_available() if callable(get_available) else ["CPUExecutionProvider"] + available = ort.get_available_providers() providers = ["CPUExecutionProvider"] if "CPUExecutionProvider" in available else list(available) self._session = ort.InferenceSession(str(onnx_path), providers=providers) @@ -175,7 +139,6 @@ def compute_embeddings( emb_name = "embedding_hyperboloid" if "embedding_hyperboloid" in output_names else output_names[0] curv_name = "curvature" if "curvature" in output_names else None - # NOTE: Current torch.onnx export is only reliable for batch_size=1. if batch_size != 1 and show_progress: print("HyCoCLIP-ONNX export currently runs with batch_size=1; overriding") batch_size = 1 @@ -194,8 +157,8 @@ def compute_embeddings( img.load() if img.mode != "RGB": img = img.convert("RGB") - bchw = _preprocess_rgb_image_to_bchw_float01(img.copy(), size=224) - images.append(bchw[0]) # CHW + chw = _preprocess_rgb_image_to_chw_float01(img.copy(), size=224) + images.append(chw) batch_np = np.stack(images, axis=0).astype(np.float32) diff --git a/src/hyperview/server/app.py b/src/hyperview/server/app.py index cb10f2d..3607d18 100644 --- a/src/hyperview/server/app.py +++ b/src/hyperview/server/app.py @@ -4,7 +4,7 @@ from pathlib import Path from typing import Any -from fastapi import FastAPI, HTTPException, Query +from fastapi import Depends, FastAPI, HTTPException, Query from fastapi.middleware.cors import CORSMiddleware from fastapi.responses import JSONResponse from fastapi.staticfiles import StaticFiles @@ -134,6 +134,12 @@ def create_app(dataset: Dataset | None = None, session_id: str | None = None) -> version="0.1.0", ) + def get_dataset() -> Dataset: + """Dependency that returns the current dataset or raises 404.""" + if _current_dataset is None: + raise HTTPException(status_code=404, detail="No dataset loaded") + return _current_dataset + # CORS middleware for development app.add_middleware( CORSMiddleware, @@ -154,38 +160,32 @@ async def hyperview_health(): } @app.get("/api/dataset", response_model=DatasetResponse) - async def get_dataset_info(): + async def get_dataset_info(ds: Dataset = Depends(get_dataset)): """Get dataset metadata.""" - if _current_dataset is None: - raise HTTPException(status_code=404, detail="No dataset loaded") - - spaces = _current_dataset.list_spaces() + spaces = ds.list_spaces() space_dicts = [s.to_api_dict() for s in spaces] - layouts = _current_dataset.list_layouts() + layouts = ds.list_layouts() layout_dicts = [l.to_api_dict() for l in layouts] return DatasetResponse( - name=_current_dataset.name, - num_samples=len(_current_dataset), - labels=_current_dataset.labels, - label_colors=_current_dataset.get_label_colors(), + name=ds.name, + num_samples=len(ds), + labels=ds.labels, + label_colors=ds.get_label_colors(), spaces=space_dicts, layouts=layout_dicts, ) @app.get("/api/samples") async def get_samples( + ds: Dataset = Depends(get_dataset), offset: int = Query(0, ge=0), limit: int = Query(100, ge=1, le=1000), label: str | None = None, ): """Get paginated samples with thumbnails.""" - if _current_dataset is None: - raise HTTPException(status_code=404, detail="No dataset loaded") - - # Use backend-native pagination (avoids loading all samples) - samples, total = _current_dataset.get_samples_paginated( + samples, total = ds.get_samples_paginated( offset=offset, limit=limit, label=label ) @@ -197,34 +197,24 @@ async def get_samples( } @app.get("/api/samples/{sample_id}", response_model=SampleResponse) - async def get_sample(sample_id: str): + async def get_sample(sample_id: str, ds: Dataset = Depends(get_dataset)): """Get a single sample by ID.""" - if _current_dataset is None: - raise HTTPException(status_code=404, detail="No dataset loaded") - try: - sample = _current_dataset[sample_id] + sample = ds[sample_id] return SampleResponse(**sample.to_api_dict()) except KeyError: raise HTTPException(status_code=404, detail=f"Sample not found: {sample_id}") @app.post("/api/samples/batch") - async def get_samples_batch(request: SelectionRequest): + async def get_samples_batch(request: SelectionRequest, ds: Dataset = Depends(get_dataset)): """Get multiple samples by their IDs.""" - if _current_dataset is None: - raise HTTPException(status_code=404, detail="No dataset loaded") - - samples = _current_dataset.get_samples_by_ids(request.sample_ids) - + samples = ds.get_samples_by_ids(request.sample_ids) return {"samples": [s.to_api_dict(include_thumbnail=True) for s in samples]} @app.get("/api/embeddings", response_model=EmbeddingsResponse) - async def get_embeddings(layout_key: str | None = None): + async def get_embeddings(ds: Dataset = Depends(get_dataset), layout_key: str | None = None): """Get embedding coordinates for visualization.""" - if _current_dataset is None: - raise HTTPException(status_code=404, detail="No dataset loaded") - - layouts = _current_dataset.list_layouts() + layouts = ds.list_layouts() if not layouts: raise HTTPException( status_code=400, detail="No layouts computed. Call compute_visualization() first." @@ -240,7 +230,7 @@ async def get_embeddings(layout_key: str | None = None): if layout_info is None: raise HTTPException(status_code=404, detail=f"Layout not found: {layout_key}") - ids, labels, coords = _current_dataset.get_visualization_data(layout_key) + ids, labels, coords = ds.get_visualization_data(layout_key) if not ids: raise HTTPException(status_code=400, detail=f"No data in layout '{layout_key}'.") @@ -251,25 +241,19 @@ async def get_embeddings(layout_key: str | None = None): ids=ids, labels=labels, coords=coords.tolist(), - label_colors=_current_dataset.get_label_colors(), + label_colors=ds.get_label_colors(), ) @app.get("/api/spaces") - async def get_spaces(): + async def get_spaces(ds: Dataset = Depends(get_dataset)): """Get all embedding spaces.""" - if _current_dataset is None: - raise HTTPException(status_code=404, detail="No dataset loaded") - - spaces = _current_dataset.list_spaces() + spaces = ds.list_spaces() return {"spaces": [s.to_api_dict() for s in spaces]} @app.get("/api/layouts") - async def get_layouts(): + async def get_layouts(ds: Dataset = Depends(get_dataset)): """Get all available layouts.""" - if _current_dataset is None: - raise HTTPException(status_code=404, detail="No dataset loaded") - - layouts = _current_dataset.list_layouts() + layouts = ds.list_layouts() return {"layouts": [l.to_api_dict() for l in layouts]} @app.post("/api/selection") @@ -278,7 +262,7 @@ async def sync_selection(request: SelectionRequest): return {"status": "ok", "selected": request.sample_ids} @app.post("/api/selection/lasso") - async def lasso_selection(request: LassoSelectionRequest): + async def lasso_selection(request: LassoSelectionRequest, ds: Dataset = Depends(get_dataset)): """Compute a lasso selection over the current embeddings. Returns a total selected count and a paginated page of selected samples. @@ -288,9 +272,6 @@ async def lasso_selection(request: LassoSelectionRequest): by /api/embeddings). - For now we use an in-memory scan with a tight AABB prefilter. """ - if _current_dataset is None: - raise HTTPException(status_code=404, detail="No dataset loaded") - if request.offset < 0: raise HTTPException(status_code=400, detail="offset must be >= 0") if request.limit < 1 or request.limit > 2000: @@ -312,7 +293,7 @@ async def lasso_selection(request: LassoSelectionRequest): y_min = float(np.min(poly[:, 1])) y_max = float(np.max(poly[:, 1])) - candidate_ids, candidate_coords = _current_dataset.get_lasso_candidates_aabb( + candidate_ids, candidate_coords = ds.get_lasso_candidates_aabb( layout_key=request.layout_key, x_min=x_min, x_max=x_max, @@ -334,7 +315,7 @@ async def lasso_selection(request: LassoSelectionRequest): end = int(request.offset + request.limit) sample_ids = selected_ids[start:end] - samples = _current_dataset.get_samples_by_ids(sample_ids) + samples = ds.get_samples_by_ids(sample_ids) sample_dicts = [s.to_api_dict(include_thumbnail=request.include_thumbnails) for s in samples] return { @@ -348,15 +329,13 @@ async def lasso_selection(request: LassoSelectionRequest): @app.get("/api/search/similar/{sample_id}", response_model=SimilaritySearchResponse) async def search_similar( sample_id: str, + ds: Dataset = Depends(get_dataset), k: int = Query(10, ge=1, le=100), space_key: str | None = None, ): """Return k nearest neighbors for a given sample.""" - if _current_dataset is None: - raise HTTPException(status_code=404, detail="No dataset loaded") - try: - similar = _current_dataset.find_similar( + similar = ds.find_similar( sample_id, k=k, space_key=space_key ) except ValueError as e: @@ -390,13 +369,10 @@ async def search_similar( ) @app.get("/api/thumbnail/{sample_id}") - async def get_thumbnail(sample_id: str): + async def get_thumbnail(sample_id: str, ds: Dataset = Depends(get_dataset)): """Get thumbnail image for a sample.""" - if _current_dataset is None: - raise HTTPException(status_code=404, detail="No dataset loaded") - try: - sample = _current_dataset[sample_id] + sample = ds[sample_id] thumbnail_b64 = sample.get_thumbnail_base64() return JSONResponse({"thumbnail": thumbnail_b64}) except KeyError: diff --git a/src/hyperview/storage/lancedb_backend.py b/src/hyperview/storage/lancedb_backend.py index cf30faa..e9e9eb0 100644 --- a/src/hyperview/storage/lancedb_backend.py +++ b/src/hyperview/storage/lancedb_backend.py @@ -24,6 +24,11 @@ ) +def _sql_escape(value: str) -> str: + """Escape single quotes for SQL WHERE clauses.""" + return value.replace("'", "''") + + class LanceDBBackend(StorageBackend): """LanceDB-based storage backend for HyperView datasets.""" @@ -75,8 +80,7 @@ def add_samples_batch(self, samples: list[Sample]) -> None: def get_sample(self, sample_id: str) -> Sample | None: if self._samples_table is None: return None - safe_id = sample_id.replace("'", "''") - results = self._samples_table.search().where(f"id = '{safe_id}'").limit(1).to_list() + results = self._samples_table.search().where(f"id = '{_sql_escape(sample_id)}'").limit(1).to_list() return dict_to_sample(results[0]) if results else None def get_samples_paginated( @@ -94,8 +98,7 @@ def get_samples_paginated( arrow_table = self._samples_table.search().select(["label"]).to_arrow() mask = pc.fill_null(pc.equal(arrow_table.column("label"), pa.scalar(label)), False) total = pc.sum(pc.cast(mask, pa.int64())).as_py() - safe_label = label.replace("'", "''") - results = self._samples_table.search().where(f"label = '{safe_label}'").offset(offset).limit(limit).to_list() + results = self._samples_table.search().where(f"label = '{_sql_escape(label)}'").offset(offset).limit(limit).to_list() else: total = self._samples_table.count_rows() results = self._samples_table.search().offset(offset).limit(limit).to_list() @@ -116,8 +119,7 @@ def update_samples_batch(self, samples: list[Sample]) -> None: def delete_sample(self, sample_id: str) -> bool: if self._samples_table is None: return False - safe_id = sample_id.replace("'", "''") - self._samples_table.delete(f"id = '{safe_id}'") + self._samples_table.delete(f"id = '{_sql_escape(sample_id)}'") return True def __len__(self) -> int: @@ -134,8 +136,7 @@ def __iter__(self) -> Iterator[Sample]: def __contains__(self, sample_id: str) -> bool: if self._samples_table is None: return False - safe_id = sample_id.replace("'", "''") - return len(self._samples_table.search().where(f"id = '{safe_id}'").limit(1).to_list()) > 0 + return len(self._samples_table.search().where(f"id = '{_sql_escape(sample_id)}'").limit(1).to_list()) > 0 def get_unique_labels(self) -> list[str]: if self._samples_table is None: @@ -150,8 +151,7 @@ def get_existing_ids(self, sample_ids: list[str]) -> set[str]: existing: set[str] = set() for i in range(0, len(sample_ids), 1000): chunk = sample_ids[i : i + 1000] - escaped = [sid.replace("'", "''") for sid in chunk] - id_list = "', '".join(escaped) + id_list = "', '".join(_sql_escape(sid) for sid in chunk) results = self._samples_table.search().where(f"id IN ('{id_list}')").select(["id"]).to_list() existing.update(r["id"] for r in results) return existing @@ -160,10 +160,9 @@ def get_samples_by_ids(self, sample_ids: list[str]) -> list[Sample]: if self._samples_table is None or not sample_ids: return [] rows_by_id: dict[str, dict] = {} - for i in range(0, len(sample_ids), 500): - chunk = sample_ids[i : i + 500] - escaped = [sid.replace("'", "''") for sid in chunk] - id_list = "', '".join(escaped) + for i in range(0, len(sample_ids), 1000): + chunk = sample_ids[i : i + 1000] + id_list = "', '".join(_sql_escape(sid) for sid in chunk) for r in self._samples_table.search().where(f"id IN ('{id_list}')").to_list(): rows_by_id[r["id"]] = r return [dict_to_sample(rows_by_id[sid]) for sid in sample_ids if sid in rows_by_id] @@ -174,8 +173,7 @@ def get_labels_by_ids(self, sample_ids: list[str]) -> dict[str, str | None]: labels: dict[str, str | None] = {} for i in range(0, len(sample_ids), 1000): chunk = sample_ids[i : i + 1000] - escaped = [sid.replace("'", "''") for sid in chunk] - id_list = "', '".join(escaped) + id_list = "', '".join(_sql_escape(sid) for sid in chunk) for r in self._samples_table.search().select(["id", "label"]).where(f"id IN ('{id_list}')").to_list(): labels[r["id"]] = r.get("label") return labels @@ -187,8 +185,7 @@ def list_spaces(self) -> list[SpaceInfo]: return [SpaceInfo.from_dict(r) for r in self._spaces_table.to_arrow().to_pylist()] def get_space(self, space_key: str) -> SpaceInfo | None: - safe_key = space_key.replace("'", "''") - results = self._spaces_table.search().where(f"space_key = '{safe_key}'").limit(1).to_list() + results = self._spaces_table.search().where(f"space_key = '{_sql_escape(space_key)}'").limit(1).to_list() return SpaceInfo.from_dict(results[0]) if results else None def ensure_space( @@ -216,8 +213,7 @@ def ensure_space( return space_info def delete_space(self, space_key: str) -> bool: - safe_key = space_key.replace("'", "''") - self._spaces_table.delete(f"space_key = '{safe_key}'") + self._spaces_table.delete(f"space_key = '{_sql_escape(space_key)}'") emb_table = f"embeddings__{space_key}" if emb_table in self._db.table_names(): self._db.drop_table(emb_table) @@ -241,8 +237,7 @@ def add_embeddings(self, space_key: str, ids: list[str], vectors: np.ndarray) -> ) # Update space count - safe_key = space_key.replace("'", "''") - self._spaces_table.update(where=f"space_key = '{safe_key}'", values={ + self._spaces_table.update(where=f"space_key = '{_sql_escape(space_key)}'", values={ "count": emb_table.count_rows(), "updated_at": int(time.time()) }) @@ -257,8 +252,7 @@ def get_embeddings(self, space_key: str, ids: list[str] | None = None) -> tuple[ emb_table = self._db.open_table(emb_table_name) if ids is not None: - escaped = [sid.replace("'", "''") for sid in ids] - id_list = "', '".join(escaped) + id_list = "', '".join(_sql_escape(sid) for sid in ids) rows = emb_table.search().where(f"id IN ('{id_list}')").to_list() else: rows = emb_table.to_arrow().to_pylist() @@ -295,8 +289,7 @@ def get_layout(self, layout_key: str) -> LayoutInfo | None: table = self._get_layouts_registry_table() if table is None: return None - safe_key = layout_key.replace("'", "''") - rows = table.search().where(f"layout_key = '{safe_key}'").limit(1).to_list() + rows = table.search().where(f"layout_key = '{_sql_escape(layout_key)}'").limit(1).to_list() return LayoutInfo.from_dict(rows[0]) if rows else None def ensure_layout( @@ -329,7 +322,7 @@ def delete_layout(self, layout_key: str) -> bool: self._db.drop_table(table_name) registry = self._get_layouts_registry_table() if registry: - registry.delete(f"layout_key = '{layout_key.replace(chr(39), chr(39)+chr(39))}'") + registry.delete(f"layout_key = '{_sql_escape(layout_key)}'") return True def add_layout_coords(self, layout_key: str, ids: list[str], coords: np.ndarray) -> None: @@ -351,7 +344,7 @@ def add_layout_coords(self, layout_key: str, ids: list[str], coords: np.ndarray) # Update count registry = self._get_layouts_registry_table() if registry: - registry.update(where=f"layout_key = '{layout_key.replace(chr(39), chr(39)+chr(39))}'", values={"count": table.count_rows()}) + registry.update(where=f"layout_key = '{_sql_escape(layout_key)}'", values={"count": table.count_rows()}) def get_layout_coords(self, layout_key: str, ids: list[str] | None = None) -> tuple[list[str], np.ndarray]: table_name = f"layouts__{layout_key}" @@ -360,8 +353,7 @@ def get_layout_coords(self, layout_key: str, ids: list[str] | None = None) -> tu table = self._db.open_table(table_name) if ids is not None: - escaped = [sid.replace("'", "''") for sid in ids] - id_list = "', '".join(escaped) + id_list = "', '".join(_sql_escape(sid) for sid in ids) rows = table.search().where(f"id IN ('{id_list}')").to_list() else: rows = table.to_arrow().to_pylist() From 281a6c9a2b26da2774b0be44f15aa2c334cf6dc5 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Wed, 21 Jan 2026 21:50:42 +0100 Subject: [PATCH 36/75] chore: update frontend static build --- src/hyperview/server/static/404.html | 2 +- src/hyperview/server/static/404/index.html | 2 +- src/hyperview/server/static/__next.__PAGE__.txt | 4 ++-- src/hyperview/server/static/__next._full.txt | 4 ++-- src/hyperview/server/static/__next._head.txt | 2 +- src/hyperview/server/static/__next._index.txt | 2 +- src/hyperview/server/static/__next._tree.txt | 2 +- .../_buildManifest.js | 0 .../_clientMiddlewareManifest.json | 0 .../_ssgManifest.js | 0 .../chunks/{5ee221fbee91a948.js => a096ec8e6ed56d42.js} | 6 +++--- src/hyperview/server/static/_not-found/__next._full.txt | 2 +- src/hyperview/server/static/_not-found/__next._head.txt | 2 +- src/hyperview/server/static/_not-found/__next._index.txt | 2 +- .../server/static/_not-found/__next._not-found.__PAGE__.txt | 2 +- .../server/static/_not-found/__next._not-found.txt | 2 +- src/hyperview/server/static/_not-found/__next._tree.txt | 2 +- src/hyperview/server/static/_not-found/index.html | 2 +- src/hyperview/server/static/_not-found/index.txt | 2 +- src/hyperview/server/static/index.html | 2 +- src/hyperview/server/static/index.txt | 4 ++-- 21 files changed, 23 insertions(+), 23 deletions(-) rename src/hyperview/server/static/_next/static/{dsfUOjSfrev80UbslOps2 => 8FqA-ERyePplcUaEYWohc}/_buildManifest.js (100%) rename src/hyperview/server/static/_next/static/{dsfUOjSfrev80UbslOps2 => 8FqA-ERyePplcUaEYWohc}/_clientMiddlewareManifest.json (100%) rename src/hyperview/server/static/_next/static/{dsfUOjSfrev80UbslOps2 => 8FqA-ERyePplcUaEYWohc}/_ssgManifest.js (100%) rename src/hyperview/server/static/_next/static/chunks/{5ee221fbee91a948.js => a096ec8e6ed56d42.js} (95%) diff --git a/src/hyperview/server/static/404.html b/src/hyperview/server/static/404.html index 0b4f335..7d84c46 100644 --- a/src/hyperview/server/static/404.html +++ b/src/hyperview/server/static/404.html @@ -1 +1 @@ -404: This page could not be found.HyperView

404

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404

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c:[["$","meta","0",{"charSet":"utf-8"}],["$","meta","1",{"name":"viewport","content":"width=device-width, initial-scale=1"}]] From 7458d4d7ca7a900b255a7ecfa0a573b5404848c9 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Fri, 23 Jan 2026 15:29:55 +0100 Subject: [PATCH 37/75] Add GitHub Actions workflow for Devin Review --- .github/workflows/devin-review.yml | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) create mode 100644 .github/workflows/devin-review.yml diff --git a/.github/workflows/devin-review.yml b/.github/workflows/devin-review.yml new file mode 100644 index 0000000..5754ba5 --- /dev/null +++ b/.github/workflows/devin-review.yml @@ -0,0 +1,23 @@ +name: Devin Review + +on: + pull_request: + types: [opened, synchronize, reopened] + +jobs: + devin-review: + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Setup Node.js + uses: actions/setup-node@v4 + with: + node-version: '20' + + - name: Run Devin Review + run: npx devin-review ${{ github.event.pull_request.html_url }} From 92d7b50e6504e335a27622ae505a456af0e71fe8 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Tue, 27 Jan 2026 23:47:03 +0100 Subject: [PATCH 38/75] feat: dockview workspace, label legend, HF offline-first, Hyper3Labs rebrand - Frontend: Added dockview-based panel layout system - Frontend: Label legend with color palette for scatter plots - Frontend: New Radix UI components (dialog, dropdown, popover, etc.) - Backend: HuggingFace datasets offline-first loading for faster startup - Backend: Sample now stores width/height image dimensions - Backend: LanceDB compatibility fix for list_tables() API changes - Rebrand: Updated all URLs from HackerRoomAI to Hyper3Labs - Cleanup: Removed react-resizable-panels (replaced by dockview) --- .gitignore | 19 +- README.md | 85 +- docs/colab.md | 2 +- frontend/next-env.d.ts | 2 +- frontend/package-lock.json | 938 +++++++++++++++++- frontend/package.json | 15 +- frontend/src/app/globals.css | 147 ++- frontend/src/app/layout.tsx | 11 +- frontend/src/app/page.tsx | 134 +-- frontend/src/components/DockviewWorkspace.tsx | 765 ++++++++++++++ frontend/src/components/ExplorerPanel.tsx | 181 ++++ frontend/src/components/Header.tsx | 261 ++++- frontend/src/components/ImageGrid.tsx | 395 +++++--- frontend/src/components/Panel.tsx | 5 +- frontend/src/components/PanelHeader.tsx | 20 +- frontend/src/components/PlaceholderPanel.tsx | 39 + frontend/src/components/ScatterPanel.tsx | 186 ++-- frontend/src/components/icons.tsx | 11 +- frontend/src/components/index.ts | 3 + frontend/src/components/ui/collapsible.tsx | 11 + frontend/src/components/ui/command.tsx | 153 +++ frontend/src/components/ui/dialog.tsx | 122 +++ frontend/src/components/ui/dropdown-menu.tsx | 201 ++++ frontend/src/components/ui/popover.tsx | 33 + frontend/src/components/ui/radio-group.tsx | 44 + frontend/src/components/ui/resizable.tsx | 45 - frontend/src/components/ui/scroll-area.tsx | 49 + frontend/src/components/ui/separator.tsx | 31 + frontend/src/components/ui/tooltip.tsx | 32 + frontend/src/components/useHyperScatter.ts | 81 +- frontend/src/components/useLabelLegend.ts | 85 ++ frontend/src/lib/labelColors.ts | 153 +++ frontend/src/lib/labelLegend.ts | 248 +++++ frontend/src/store/useStore.ts | 124 ++- frontend/src/types/index.ts | 6 +- pyproject.toml | 13 +- scripts/demo.py | 10 +- src/hyperview/cli.py | 10 +- src/hyperview/core/dataset.py | 18 +- src/hyperview/core/sample.py | 19 +- src/hyperview/server/app.py | 2 + src/hyperview/storage/lancedb_backend.py | 39 +- 42 files changed, 4122 insertions(+), 626 deletions(-) create mode 100644 frontend/src/components/DockviewWorkspace.tsx create mode 100644 frontend/src/components/ExplorerPanel.tsx create mode 100644 frontend/src/components/PlaceholderPanel.tsx create mode 100644 frontend/src/components/ui/collapsible.tsx create mode 100644 frontend/src/components/ui/command.tsx create mode 100644 frontend/src/components/ui/dialog.tsx create mode 100644 frontend/src/components/ui/dropdown-menu.tsx create mode 100644 frontend/src/components/ui/popover.tsx create mode 100644 frontend/src/components/ui/radio-group.tsx delete mode 100644 frontend/src/components/ui/resizable.tsx create mode 100644 frontend/src/components/ui/scroll-area.tsx create mode 100644 frontend/src/components/ui/separator.tsx create mode 100644 frontend/src/components/ui/tooltip.tsx create mode 100644 frontend/src/components/useLabelLegend.ts create mode 100644 frontend/src/lib/labelColors.ts create mode 100644 frontend/src/lib/labelLegend.ts diff --git a/.gitignore b/.gitignore index 07a9a77..5b94b3c 100644 --- a/.gitignore +++ b/.gitignore @@ -22,10 +22,6 @@ htmlcov/ # VS Code .vscode/ -# Local Context (Agent files) -.claude/ -context/ - # Generated assets assets/demo_animation_frames/ *.gif @@ -48,7 +44,7 @@ build/ *.hf/ .cache/ -# external repo (https://github.com/HackerRoomAI/hyper-scatter) +# external repo (https://github.com/Hyper3Labs/hyper-scatter) hyper-scatter/ # nohup @@ -64,4 +60,15 @@ frontend/tsconfig.tsbuildinfo # Hyperbolic model zoo (kept as a separate repo) hyper_model_zoo/ hyper_models/ -scripts_ignored/ \ No newline at end of file +scripts_ignored/ + +# AI Context (Agent files) +.claude/ +context/ +CLAUDE.md +TASKS.md +TESTS.md +AGENTS.md +**/AGENTS.md +.github/agents/ +.specstory/ \ No newline at end of file diff --git a/README.md b/README.md index 9fef293..dbf7dfc 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,8 @@ # HyperView -> **Open-source dataset curation with hyperbolic embeddings visualization** +> **Open-source dataset curation + embedding visualization (Euclidean + Poincaré disk)** -[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/HackerRoomAI/HyperView) +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/Hyper3Labs/HyperView)

@@ -17,23 +17,24 @@ ## Features - **Dual-Panel UI**: Image grid + scatter plot with bidirectional selection -- **Euclidean/Hyperbolic Toggle**: Switch between standard 2D UMAP and Poincaré disk visualization +- **Euclidean/Poincaré Toggle**: Switch between standard 2D UMAP and Poincaré disk visualization - **HuggingFace Integration**: Load datasets directly from HuggingFace Hub - **Fast Embeddings**: Uses EmbedAnything for CLIP-based image embeddings -- **FiftyOne-like API**: Familiar workflow for dataset exploration ## Quick Start +**Docs:** [docs/datasets.md](docs/datasets.md) · [docs/colab.md](docs/colab.md) · [CONTRIBUTING.md](CONTRIBUTING.md) · [TESTS.md](TESTS.md) + ### Installation ```bash -git clone https://github.com/HackerRoomAI/HyperView.git +git clone https://github.com/Hyper3Labs/HyperView.git cd HyperView # Install with uv uv venv .venv source .venv/bin/activate -uv pip install -e . +uv pip install -e ".[dev]" ``` ### Run the Demo @@ -45,7 +46,7 @@ hyperview demo --samples 500 This will: 1. Load 500 samples from CIFAR-100 2. Compute CLIP embeddings -3. Generate Euclidean and Hyperbolic visualizations +3. Generate Euclidean and Poincaré visualizations 4. Start the server at **http://127.0.0.1:6262** ### Python API @@ -99,79 +100,19 @@ Traditional Euclidean embeddings struggle with hierarchical data. In Euclidean s Euclidean vs Hyperbolic

-## Architecture - -``` -hyperview/ -├── src/hyperview/ -│ ├── core/ # Dataset, Sample classes -│ ├── embeddings/ # EmbedAnything + UMAP + Poincaré projection -│ └── server/ # FastAPI + static frontend -├── frontend/ # React/Next.js (compiled to static) -└── scripts/ - └── demo.py # Demo script -``` - -**Tech Stack:** -- **Backend**: Python, FastAPI, EmbedAnything, UMAP -- **Frontend**: Next.js 16, React 18, regl-scatterplot, Zustand, Tailwind CSS -- **Package Manager**: uv - -## Development - -### Frontend Development (with Hot Reloading) - -For the best development experience, run the backend and frontend separately: - -**Terminal 1 - Start the Python backend:** -```bash -# Activate your virtual environment first -source .venv/bin/activate - -# Run the demo script in no-browser mode -python scripts/demo.py --samples 200 --no-browser -``` -This runs the API on **http://127.0.0.1:6262** - -**Terminal 2 - Start the frontend dev server:** -```bash -cd frontend -npm install # First time only -npm run dev -``` -This runs the frontend on **http://localhost:3000** with hot reloading. - -Open **http://localhost:3000** in your browser. The frontend automatically proxies `/api/*` requests to the backend at port 6262 (configured in `next.config.ts`). - -Now you can: -- Edit React components and see changes instantly -- Edit Python backend and restart Terminal 1 -- No need to rebuild/export the frontend during development - -### Export Frontend for Production - -When you're ready to bundle the frontend into the Python package: - -```bash -./scripts/export_frontend.sh -``` +## Contributing -This compiles the frontend and copies it to `src/hyperview/server/static/`. After this, `hv.launch()` serves the bundled frontend directly from the Python server. +Development setup, frontend hot-reload, and backend API notes live in [CONTRIBUTING.md](CONTRIBUTING.md). -## API Endpoints +## Related projects -| Endpoint | Description | -|----------|-------------| -| `GET /api/dataset` | Dataset metadata (name, labels, colors) | -| `GET /api/samples` | Paginated samples with thumbnails | -| `GET /api/embeddings` | 2D coordinates (Euclidean + Hyperbolic) | -| `POST /api/selection` | Sync selection state | +- **hyper-scatter**: High-performance WebGL scatterplot engine (Euclidean + Poincaré) used by the frontend: https://github.com/Hyper3Labs/hyper-scatter +- **hyper-models**: Non-Euclidean model zoo + ONNX exports (e.g. for hyperbolic VLM experiments): https://github.com/Hyper3Labs/hyper-models ## References - [Poincaré Embeddings for Learning Hierarchical Representations](https://arxiv.org/abs/1705.08039) (Nickel & Kiela, 2017) - [Hyperbolic Neural Networks](https://arxiv.org/abs/1805.09112) (Ganea et al., 2018) -- [FiftyOne](https://github.com/voxel51/fiftyone) - Inspiration for the UI/API design ## License diff --git a/docs/colab.md b/docs/colab.md index bef3f7c..eef4e27 100644 --- a/docs/colab.md +++ b/docs/colab.md @@ -7,7 +7,7 @@ HyperView works natively in Google Colab. Because Colab runs on a remote VM, you ### 1. Install HyperView ```bash -!pip install -q git+https://github.com/HackerRoomAI/HyperView.git +!pip install -q git+https://github.com/Hyper3Labs/HyperView.git ``` ### 2. Launch the visualizer diff --git a/frontend/next-env.d.ts b/frontend/next-env.d.ts index 9edff1c..c4b7818 100644 --- a/frontend/next-env.d.ts +++ b/frontend/next-env.d.ts @@ -1,6 +1,6 @@ /// /// -import "./.next/types/routes.d.ts"; +import "./.next/dev/types/routes.d.ts"; // NOTE: This file should not be edited // see https://nextjs.org/docs/app/api-reference/config/typescript for more information. diff --git a/frontend/package-lock.json b/frontend/package-lock.json index c7de89d..c193d55 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -8,23 +8,34 @@ "name": "hyperview-frontend", "version": "0.1.0", "dependencies": { + "@radix-ui/react-collapsible": "^1.1.12", + "@radix-ui/react-dialog": "^1.1.15", + "@radix-ui/react-dropdown-menu": "^2.1.16", + "@radix-ui/react-popover": "^1.1.15", + "@radix-ui/react-radio-group": "^1.3.8", + "@radix-ui/react-scroll-area": "^1.2.10", + "@radix-ui/react-separator": "^1.1.8", "@radix-ui/react-slot": "^1.2.4", 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"license": "MIT", + "dependencies": { + "@floating-ui/utils": "^0.2.10" + } + }, + "node_modules/@floating-ui/dom": { + "version": "1.7.4", + "resolved": "https://registry.npmjs.org/@floating-ui/dom/-/dom-1.7.4.tgz", + "integrity": "sha512-OOchDgh4F2CchOX94cRVqhvy7b3AFb+/rQXyswmzmGakRfkMgoWVjfnLWkRirfLEfuD4ysVW16eXzwt3jHIzKA==", + "license": "MIT", + "dependencies": { + "@floating-ui/core": "^1.7.3", + "@floating-ui/utils": "^0.2.10" + } + }, + "node_modules/@floating-ui/react-dom": { + "version": "2.1.6", + "resolved": "https://registry.npmjs.org/@floating-ui/react-dom/-/react-dom-2.1.6.tgz", + "integrity": "sha512-4JX6rEatQEvlmgU80wZyq9RT96HZJa88q8hp0pBd+LrczeDI4o6uA2M+uvxngVHo4Ihr8uibXxH6+70zhAFrVw==", + "license": "MIT", + "dependencies": { + "@floating-ui/dom": "^1.7.4" + }, + "peerDependencies": { + "react": ">=16.8.0", + "react-dom": ">=16.8.0" + } + }, + "node_modules/@floating-ui/utils": { + "version": "0.2.10", + "resolved": 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"sha512-9r+yi9+mgU33AKcj6IbT9oRCO78WriSj6t/cF8DWBZJ9aOGPOTEDvdUDz1FwKim7QXWwmHqtdHnRJfhAxEG46Q==", + "license": "MIT", + "dependencies": { + "react-style-singleton": "^2.2.2", + "tslib": "^2.0.0" + }, + "engines": { + "node": ">=10" + }, + "peerDependencies": { + "@types/react": "*", + "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + } + } + }, + "node_modules/react-style-singleton": { + "version": "2.2.3", + "resolved": "https://registry.npmjs.org/react-style-singleton/-/react-style-singleton-2.2.3.tgz", + "integrity": "sha512-b6jSvxvVnyptAiLjbkWLE/lOnR4lfTtDAl+eUC7RZy+QQWc6wRzIV2CE6xBuMmDxc2qIihtDCZD5NPOFl7fRBQ==", "license": "MIT", + "dependencies": { + "get-nonce": "^1.0.0", + "tslib": "^2.0.0" + }, + "engines": { + "node": ">=10" + }, "peerDependencies": { - "react": "^18.0.0 || ^19.0.0", - "react-dom": "^18.0.0 || ^19.0.0" + "@types/react": "*", + "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0 || ^19.0.0-rc" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + } } }, "node_modules/read-cache": { @@ -6387,6 +7230,49 @@ "punycode": "^2.1.0" } }, + "node_modules/use-callback-ref": { + "version": "1.3.3", + "resolved": "https://registry.npmjs.org/use-callback-ref/-/use-callback-ref-1.3.3.tgz", + "integrity": "sha512-jQL3lRnocaFtu3V00JToYz/4QkNWswxijDaCVNZRiRTO3HQDLsdu1ZtmIUvV4yPp+rvWm5j0y0TG/S61cuijTg==", + "license": "MIT", + "dependencies": { + "tslib": "^2.0.0" + }, + "engines": { + "node": ">=10" + }, + "peerDependencies": { + "@types/react": "*", + "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0 || ^19.0.0-rc" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + } + } + }, + "node_modules/use-sidecar": { + "version": "1.1.3", + "resolved": "https://registry.npmjs.org/use-sidecar/-/use-sidecar-1.1.3.tgz", + "integrity": "sha512-Fedw0aZvkhynoPYlA5WXrMCAMm+nSWdZt6lzJQ7Ok8S6Q+VsHmHpRWndVRJ8Be0ZbkfPc5LRYH+5XrzXcEeLRQ==", + "license": "MIT", + "dependencies": { + "detect-node-es": "^1.1.0", + "tslib": "^2.0.0" + }, + "engines": { + "node": ">=10" + }, + "peerDependencies": { + "@types/react": "*", + "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0 || ^19.0.0-rc" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + } + } + }, "node_modules/util-deprecate": { "version": "1.0.2", "resolved": "https://registry.npmjs.org/util-deprecate/-/util-deprecate-1.0.2.tgz", diff --git a/frontend/package.json b/frontend/package.json index 5b0c4ee..918a0ce 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -10,23 +10,34 @@ "export": "next build && cp -r out/* ../src/hyperview/server/static/" }, "dependencies": { + "@radix-ui/react-collapsible": "^1.1.12", + "@radix-ui/react-dialog": "^1.1.15", + "@radix-ui/react-dropdown-menu": "^2.1.16", + "@radix-ui/react-popover": "^1.1.15", + "@radix-ui/react-radio-group": "^1.3.8", + "@radix-ui/react-scroll-area": "^1.2.10", + "@radix-ui/react-separator": "^1.1.8", "@radix-ui/react-slot": "^1.2.4", "@radix-ui/react-toggle": "^1.1.10", "@radix-ui/react-toggle-group": "^1.1.11", + "@radix-ui/react-tooltip": "^1.2.8", "@tanstack/react-virtual": "^3.10.9", "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", + "cmdk": "^1.1.1", + "dockview": "^4.13.1", + "hyper-scatter": "^0.1.0", + "justified-layout": "^4.1.0", "lucide-react": "^0.562.0", "next": "^16.0.7", "react": "18.3.1", "react-dom": "18.3.1", - "react-resizable-panels": "^4.4.1", "tailwind-merge": "^3.4.0", "tailwindcss-animate": "^1.0.7", - "hyper-scatter": "^0.1.0", "zustand": "^5.0.1" }, "devDependencies": { + "@types/justified-layout": "^4.1.4", "@types/node": "^22.9.0", "@types/react": "^18.3.12", "@types/react-dom": "^18.3.1", diff --git a/frontend/src/app/globals.css b/frontend/src/app/globals.css index b25953f..4272f94 100644 --- a/frontend/src/app/globals.css +++ b/frontend/src/app/globals.css @@ -1,8 +1,18 @@ +@import "dockview/dist/styles/dockview.css"; + @tailwind base; @tailwind components; @tailwind utilities; @layer base { + html, + body { + height: 100%; + margin: 0; + padding: 0; + overflow: hidden; + } + :root { /* Rerun-inspired dark theme as default (dark-first) */ --background: 215 28% 7%; /* #0d1117 */ @@ -54,17 +64,20 @@ } body { @apply bg-background text-foreground; - font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen', - 'Ubuntu', 'Cantarell', 'Fira Sans', 'Droid Sans', 'Helvetica Neue', sans-serif; + font-size: 12px; + line-height: 16px; + letter-spacing: -0.15px; + font-weight: 500; + font-feature-settings: "liga" 1, "calt" 1; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; } } -/* Custom scrollbar - Rerun style */ +/* Custom scrollbar - Rerun style: 6px bar, 2px inner margin, 2px outer margin */ ::-webkit-scrollbar { - width: 6px; - height: 6px; + width: 10px; /* 6px bar + 2px inner margin on each side */ + height: 10px; } ::-webkit-scrollbar-track { @@ -72,12 +85,22 @@ } ::-webkit-scrollbar-thumb { - background: hsl(var(--surface-elevated)); + background: hsl(var(--muted-foreground) / 0.2); border-radius: 3px; + border: 2px solid transparent; + background-clip: padding-box; } ::-webkit-scrollbar-thumb:hover { - background: hsl(var(--text-subtle)); + background: hsl(var(--muted-foreground) / 0.35); + border: 2px solid transparent; + background-clip: padding-box; +} + +/* Firefox scrollbar */ +* { + scrollbar-width: thin; + scrollbar-color: hsl(var(--muted-foreground) / 0.25) transparent; } /* Hide scrollbar for some elements */ @@ -89,3 +112,113 @@ -ms-overflow-style: none; scrollbar-width: none; } + +/* Scroll containers inside panels should keep scrollbars inset */ +.panel-scroll { + scrollbar-gutter: stable; +} + +/* Dockview theme overrides (match HyperView tokens) */ +.hyperview-dockview { + --dv-background-color: hsl(var(--background)); + --dv-group-view-background-color: hsl(var(--card)); + --dv-tabs-and-actions-container-background-color: hsl(var(--secondary)); + --dv-activegroup-visiblepanel-tab-background-color: hsl(var(--card)); + --dv-activegroup-hiddenpanel-tab-background-color: hsl(var(--secondary)); + --dv-inactivegroup-visiblepanel-tab-background-color: hsl(var(--secondary)); + --dv-inactivegroup-hiddenpanel-tab-background-color: hsl(var(--secondary)); + --dv-activegroup-visiblepanel-tab-color: hsl(var(--foreground)); + --dv-activegroup-hiddenpanel-tab-color: hsl(var(--muted-foreground)); + --dv-inactivegroup-visiblepanel-tab-color: hsl(var(--muted-foreground)); + --dv-inactivegroup-hiddenpanel-tab-color: hsl(var(--muted-foreground)); + --dv-tabs-and-actions-container-font-size: 12px; + --dv-tabs-and-actions-container-height: 24px; + --dv-tab-font-size: 12px; + --dv-tabs-container-scrollbar-color: hsl(var(--muted-foreground) / 0.25); + --dv-scrollbar-background-color: hsl(var(--muted-foreground) / 0.25); + --dv-tab-divider-color: hsl(var(--border)); + --dv-separator-border: transparent; + --dv-paneview-header-border-color: hsl(var(--border)); + --dv-sash-color: hsl(var(--border)); + --dv-icon-hover-background-color: hsl(var(--accent)); + --dv-active-sash-color: hsl(var(--primary)); + --dv-drag-over-background-color: hsl(var(--primary) / 0.15); + --dv-drag-over-border-color: hsl(var(--primary) / 0.6); + /* Remove tab margins for flush appearance */ + --dv-tab-margin: 0; +} + +/* Remove any gaps between panels - Rerun-style flush layout */ +.hyperview-dockview .dv-groupview { + border: none; +} + +.hyperview-dockview .dv-tabs-and-actions-container { + border-bottom: 1px solid hsl(var(--border)); +} + +.hyperview-dockview .dv-tab { + padding: 0 8px; + height: 100%; + display: flex; + align-items: center; + line-height: 16px; +} + +.hyperview-dockview .dv-scrollable .dv-scrollbar-horizontal { + height: 6px; + border-radius: 3px; +} + +/* Hide Dockview tab scrollbars (avoid extra bar under tabs) */ +.hyperview-dockview .dv-tabs-container { + scrollbar-width: none; +} + +.hyperview-dockview .dv-tabs-container::-webkit-scrollbar { + height: 0; +} + +.hyperview-dockview .dv-scrollable .dv-scrollbar-horizontal { + display: none; +} + +/* Sash styling: transparent hit area with centered 1px visible line via pseudo-element */ +.hyperview-dockview .dv-split-view-container > .dv-sash-container > .dv-sash { + /* Keep the sash itself transparent - we'll draw the line with ::after */ + background-color: transparent !important; +} + +/* Horizontal sash (vertical divider line) */ +.hyperview-dockview .dv-split-view-container.dv-horizontal > .dv-sash-container > .dv-sash::after { + content: ""; + position: absolute; + top: 0; + bottom: 0; + left: 50%; + transform: translateX(-50%); + width: 1px; + background-color: hsl(var(--border)); + pointer-events: none; +} + +.hyperview-dockview .dv-split-view-container.dv-horizontal > .dv-sash-container > .dv-sash.dv-enabled:hover::after { + background-color: hsl(var(--primary)); +} + +/* Vertical sash (horizontal divider line) */ +.hyperview-dockview .dv-split-view-container.dv-vertical > .dv-sash-container > .dv-sash::after { + content: ""; + position: absolute; + left: 0; + right: 0; + top: 50%; + transform: translateY(-50%); + height: 1px; + background-color: hsl(var(--border)); + pointer-events: none; +} + +.hyperview-dockview .dv-split-view-container.dv-vertical > .dv-sash-container > .dv-sash.dv-enabled:hover::after { + background-color: hsl(var(--primary)); +} diff --git a/frontend/src/app/layout.tsx b/frontend/src/app/layout.tsx index cbfbe73..6af29df 100644 --- a/frontend/src/app/layout.tsx +++ b/frontend/src/app/layout.tsx @@ -1,6 +1,13 @@ import type { Metadata } from "next"; +import { Inter } from "next/font/google"; import "./globals.css"; +const inter = Inter({ + subsets: ["latin"], + display: "swap", + weight: ["400", "500"], +}); + export const metadata: Metadata = { title: "HyperView", description: "Dataset visualization with hyperbolic embeddings", @@ -12,8 +19,8 @@ export default function RootLayout({ children: React.ReactNode; }>) { return ( - - {children} + + {children} ); } diff --git a/frontend/src/app/page.tsx b/frontend/src/app/page.tsx index 12dd689..a04d188 100644 --- a/frontend/src/app/page.tsx +++ b/frontend/src/app/page.tsx @@ -1,18 +1,13 @@ "use client"; -import { useEffect, useCallback, useMemo, useState } from "react"; -import { - ResizablePanelGroup, - ResizablePanel, - ResizableHandle, -} from "@/components/ui/resizable"; -import { Header, ImageGrid, ScatterPanel } from "@/components"; +import { useEffect, useCallback, useMemo, useRef, useState } from "react"; +import { Header } from "@/components"; +import { DockviewWorkspace, DockviewProvider } from "@/components/DockviewWorkspace"; import { useStore } from "@/store/useStore"; import type { Sample } from "@/types"; import { fetchDataset, fetchSamples, - fetchEmbeddings, fetchSamplesBatch, fetchLassoSelection, } from "@/lib/api"; @@ -21,7 +16,6 @@ const SAMPLES_PER_PAGE = 100; export default function Home() { const { - datasetInfo, samples, totalSamples, samplesLoaded, @@ -29,7 +23,6 @@ export default function Home() { appendSamples, addSamplesIfMissing, setDatasetInfo, - setEmbeddings, setIsLoading, isLoading, error, @@ -42,11 +35,11 @@ export default function Home() { lassoTotal, lassoIsLoading, setLassoResults, - geometry, + labelFilter, } = useStore(); const [loadingMore, setLoadingMore] = useState(false); - const [geometryLoading, setGeometryLoading] = useState(false); + const labelFilterRef = useRef(labelFilter ?? null); // Initial data load - runs once on mount // Store setters are stable and don't need to be in deps @@ -56,16 +49,14 @@ export default function Home() { setError(null); try { - // Fetch dataset info, samples, and embeddings in parallel - const [datasetInfo, samplesRes, embeddingsData] = await Promise.all([ + // Fetch dataset info and samples in parallel + const [datasetInfo, samplesRes] = await Promise.all([ fetchDataset(), fetchSamples(0, SAMPLES_PER_PAGE), - fetchEmbeddings(), ]); setDatasetInfo(datasetInfo); setSamples(samplesRes.samples, samplesRes.total); - setEmbeddings(embeddingsData); } catch (err) { console.error("Failed to load data:", err); setError(err instanceof Error ? err.message : "Failed to load data"); @@ -78,38 +69,12 @@ export default function Home() { // eslint-disable-next-line react-hooks/exhaustive-deps }, []); - // Fetch new embeddings when geometry changes - useEffect(() => { - if (!datasetInfo) return; - - // Find layout for current geometry (using LayoutInfo.geometry directly) - const targetLayout = datasetInfo.layouts.find((l) => l.geometry === geometry); - if (!targetLayout) return; - - // Skip if already showing this layout - const currentLayout = useStore.getState().currentLayoutKey; - if (currentLayout === targetLayout.layout_key) return; - - const loadGeometry = async () => { - setGeometryLoading(true); - try { - const embeddingsData = await fetchEmbeddings(targetLayout.layout_key); - setEmbeddings(embeddingsData); - } catch (err) { - console.error("Failed to load geometry:", err); - } finally { - setGeometryLoading(false); - } - }; - - loadGeometry(); - }, [geometry, datasetInfo, setEmbeddings]); - // Fetch selected samples that aren't already loaded useEffect(() => { const fetchSelectedSamples = async () => { if (isLassoSelection) return; if (selectedIds.size === 0) return; + if (selectionSource === "label") return; // Find IDs that are selected but not in our samples array const loadedIds = new Set(samples.map((s) => s.id)); @@ -126,7 +91,32 @@ export default function Home() { }; fetchSelectedSamples(); - }, [selectedIds, samples, addSamplesIfMissing, isLassoSelection]); + }, [selectedIds, samples, addSamplesIfMissing, isLassoSelection, selectionSource]); + + // Refetch samples when label filter changes (non-lasso mode) + useEffect(() => { + if (labelFilterRef.current === labelFilter) return; + if (isLassoSelection) return; + + labelFilterRef.current = labelFilter ?? null; + + let cancelled = false; + const run = async () => { + try { + const res = await fetchSamples(0, SAMPLES_PER_PAGE, labelFilter ?? undefined); + if (cancelled) return; + setSamples(res.samples, res.total); + } catch (err) { + if (cancelled) return; + console.error("Failed to load filtered samples:", err); + } + }; + + run(); + return () => { + cancelled = true; + }; + }, [isLassoSelection, labelFilter, setSamples]); // Fetch initial lasso selection page when a new lasso query begins. useEffect(() => { @@ -189,7 +179,7 @@ export default function Home() { setLoadingMore(true); try { - const res = await fetchSamples(samplesLoaded, SAMPLES_PER_PAGE); + const res = await fetchSamples(samplesLoaded, SAMPLES_PER_PAGE, labelFilter ?? undefined); appendSamples(res.samples); } catch (err) { console.error("Failed to load more samples:", err); @@ -207,6 +197,7 @@ export default function Home() { samplesLoaded, totalSamples, setLassoResults, + labelFilter, ]); const displayedSamples = useMemo(() => { @@ -268,46 +259,19 @@ export default function Home() { } return ( -
-
- - {/* Main content - resizable panels with Rerun-style spacing */} -
- - {/* Left panel - Image Grid */} - - - - - {/* Resizable handle with grip */} - - - {/* Right panel - Scatter Plot */} - - - - + +
+
+ + {/* Main content - dockable panels */} +
+ +
-
+ ); } diff --git a/frontend/src/components/DockviewWorkspace.tsx b/frontend/src/components/DockviewWorkspace.tsx new file mode 100644 index 0000000..42d3f41 --- /dev/null +++ b/frontend/src/components/DockviewWorkspace.tsx @@ -0,0 +1,765 @@ +"use client"; + +import React, { + useCallback, + useEffect, + useMemo, + useState, + createContext, + useContext, + type ReactNode, +} from "react"; +import { + DockviewReact, + type DockviewApi, + type DockviewReadyEvent, + type IDockviewPanelProps, + type IDockviewPanelHeaderProps, + type IWatermarkPanelProps, + themeAbyss, +} from "dockview"; +import { Circle, Disc, Grid3X3 } from "lucide-react"; + +import type { Geometry, Sample } from "@/types"; +import { useStore } from "@/store/useStore"; +import { findLayoutByGeometry } from "@/lib/layouts"; +import { ImageGrid } from "./ImageGrid"; +import { ScatterPanel } from "./ScatterPanel"; +import { ExplorerPanel } from "./ExplorerPanel"; +import { PlaceholderPanel } from "./PlaceholderPanel"; +import { HyperViewLogo } from "./icons"; + +const LAYOUT_STORAGE_KEY = "hyperview:dockview-layout:v4"; + +// Panel IDs +const PANEL = { + EXPLORER: "explorer", + GRID: "grid", + SCATTER_EUCLIDEAN: "scatter-euclidean", + SCATTER_POINCARE: "scatter-poincare", + SCATTER_DEFAULT: "scatter-default", + RIGHT_PLACEHOLDER: "right-placeholder", + BOTTOM_PLACEHOLDER: "bottom-placeholder", +} as const; + +const CENTER_PANEL_IDS = [ + PANEL.GRID, + PANEL.SCATTER_EUCLIDEAN, + PANEL.SCATTER_POINCARE, + PANEL.SCATTER_DEFAULT, +] as const; + +export const CENTER_PANEL_DEFS = [ + { id: PANEL.GRID, label: "Samples", icon: Grid3X3 }, + { id: PANEL.SCATTER_EUCLIDEAN, label: "Euclidean", icon: Circle }, + { id: PANEL.SCATTER_POINCARE, label: "Hyperbolic", icon: Disc }, +] as const; + +const NON_ANCHOR_PANEL_IDS = new Set([ + PANEL.EXPLORER, + PANEL.RIGHT_PLACEHOLDER, + PANEL.BOTTOM_PLACEHOLDER, +]); + +const DRAG_LOCKED_PANEL_IDS = new Set([PANEL.EXPLORER]); + +const DEFAULT_CONTAINER_WIDTH = 1200; +const DEFAULT_CONTAINER_HEIGHT = 800; +const MIN_SIDE_PANEL_WIDTH = 120; +const MIN_BOTTOM_PANEL_HEIGHT = 150; + +const getContainerWidth = (api?: DockviewApi | null) => + api?.width ?? + (typeof window === "undefined" ? DEFAULT_CONTAINER_WIDTH : window.innerWidth); +const getContainerHeight = (api?: DockviewApi | null) => + api?.height ?? + (typeof window === "undefined" ? DEFAULT_CONTAINER_HEIGHT : window.innerHeight); + +const getDefaultLeftPanelWidth = (screenWidth: number) => + Math.round(Math.min(0.35 * screenWidth, 200)); +const getDefaultRightPanelWidth = (screenWidth: number) => + Math.round(Math.min(0.45 * screenWidth, 300)); +const getDefaultBottomPanelHeight = (containerHeight: number) => + Math.round( + Math.min(Math.max(0.25 * containerHeight, MIN_BOTTOM_PANEL_HEIGHT), 250) + ); +const getBottomPanelMaxHeight = (containerHeight: number) => + Math.round( + Math.max(containerHeight - MIN_BOTTOM_PANEL_HEIGHT, MIN_BOTTOM_PANEL_HEIGHT) + ); + +function getCenterAnchorPanel(api: DockviewApi) { + for (const id of CENTER_PANEL_IDS) { + const panel = api.getPanel(id); + if (panel) return panel; + } + + const fallback = api.panels.find((panel) => !NON_ANCHOR_PANEL_IDS.has(panel.id)); + return fallback ?? api.activePanel; +} + +function getZonePosition(zone: "left" | "right" | "bottom") { + return { direction: zone === "bottom" ? "below" : zone }; +} + +function getCenterTabPosition(api: DockviewApi) { + const anchor = getCenterAnchorPanel(api); + if (!anchor) return undefined; + return { referencePanel: anchor, direction: "within" as const }; +} + +// ----------------------------------------------------------------------------- +// Context for sharing dockview API across components +// ----------------------------------------------------------------------------- +interface DockviewContextValue { + api: DockviewApi | null; + setApi: (api: DockviewApi) => void; + samples: Sample[]; + onLoadMore: () => void; + hasMore: boolean; +} + +const DockviewContext = createContext(null); + +function useDockviewContext() { + const ctx = useContext(DockviewContext); + if (!ctx) throw new Error("useDockviewContext must be used within DockviewProvider"); + return ctx; +} + +// Public hook for components like Header +export function useDockviewApi() { + const ctx = useContext(DockviewContext); + const datasetInfo = useStore((state) => state.datasetInfo); + const { + leftPanelOpen, + rightPanelOpen, + bottomPanelOpen, + setLeftPanelOpen, + setRightPanelOpen, + setBottomPanelOpen, + } = useStore(); + + const addPanel = useCallback( + (panelId: string) => { + if (!ctx?.api) return; + + const api = ctx.api; + const position = getCenterTabPosition(api); + const baseOptions = position ? { position } : {}; + + const layouts = datasetInfo?.layouts ?? []; + const euclideanLayout = findLayoutByGeometry(layouts, "euclidean"); + const poincareLayout = findLayoutByGeometry(layouts, "poincare"); + + // Don't add if already exists - just focus it + if (api.getPanel(panelId)) { + api.getPanel(panelId)?.focus(); + return; + } + + switch (panelId) { + case PANEL.GRID: + api.addPanel({ + id: PANEL.GRID, + component: "grid", + title: "Samples", + tabComponent: "samplesTab", + renderer: "always", + ...baseOptions, + }); + break; + + case PANEL.SCATTER_EUCLIDEAN: + api.addPanel({ + id: PANEL.SCATTER_EUCLIDEAN, + component: "scatter", + title: "Euclidean", + tabComponent: "euclideanTab", + params: { + layoutKey: euclideanLayout?.layout_key, + geometry: "euclidean" as Geometry, + }, + renderer: "always", + ...baseOptions, + }); + break; + + case PANEL.SCATTER_POINCARE: + api.addPanel({ + id: PANEL.SCATTER_POINCARE, + component: "scatter", + title: "Hyperbolic", + tabComponent: "hyperbolicTab", + params: { + layoutKey: poincareLayout?.layout_key, + geometry: "poincare" as Geometry, + }, + renderer: "always", + ...baseOptions, + }); + break; + } + }, + [ctx?.api, datasetInfo] + ); + + const resetLayout = useCallback(() => { + localStorage.removeItem(LAYOUT_STORAGE_KEY); + window.location.reload(); + }, []); + + // Toggle zone visibility + const toggleZone = useCallback( + (zone: "left" | "right" | "bottom") => { + if (!ctx?.api) return; + + const api = ctx.api; + const panelId = + zone === "left" + ? PANEL.EXPLORER + : zone === "right" + ? PANEL.RIGHT_PLACEHOLDER + : PANEL.BOTTOM_PLACEHOLDER; + const setOpen = + zone === "left" + ? setLeftPanelOpen + : zone === "right" + ? setRightPanelOpen + : setBottomPanelOpen; + const isOpen = + zone === "left" + ? leftPanelOpen + : zone === "right" + ? rightPanelOpen + : bottomPanelOpen; + + const existingPanel = api.getPanel(panelId); + + if (isOpen && existingPanel) { + existingPanel.api.close(); + setOpen(false); + return; + } + + if (isOpen) return; + + const containerWidth = getContainerWidth(api); + const containerHeight = getContainerHeight(api); + const position = getZonePosition(zone); + + let newPanel; + if (zone === "left") { + const targetWidth = getDefaultLeftPanelWidth(containerWidth); + newPanel = api.addPanel({ + id: panelId, + component: "explorer", + title: "Explorer", + position, + initialWidth: targetWidth, + minimumWidth: MIN_SIDE_PANEL_WIDTH, + maximumWidth: targetWidth, + }); + + if (newPanel) { + newPanel.group.locked = true; + newPanel.group.header.hidden = true; + // Explicitly set the width to ensure it's applied + newPanel.api.setSize({ width: targetWidth }); + } + } else if (zone === "right") { + newPanel = api.addPanel({ + id: panelId, + component: "placeholder", + title: "Blank", + position, + initialWidth: getDefaultRightPanelWidth(containerWidth), + minimumWidth: MIN_SIDE_PANEL_WIDTH, + maximumWidth: Math.round(containerWidth * 0.65), + }); + } else { + newPanel = api.addPanel({ + id: panelId, + component: "placeholder", + title: "Blank", + position, + initialHeight: getDefaultBottomPanelHeight(containerHeight), + minimumHeight: MIN_BOTTOM_PANEL_HEIGHT, + maximumHeight: getBottomPanelMaxHeight(containerHeight), + }); + } + + if (newPanel) { + setOpen(true); + // Activate the panel so its content renders immediately + newPanel.api.setActive(); + } + }, + [ + ctx?.api, + leftPanelOpen, + rightPanelOpen, + bottomPanelOpen, + setLeftPanelOpen, + setRightPanelOpen, + setBottomPanelOpen, + ] + ); + + if (!ctx) return null; + + return { + api: ctx.api, + addPanel, + resetLayout, + toggleZone, + }; +} + +// ----------------------------------------------------------------------------- +// Panel Components - stable references defined outside component +// ----------------------------------------------------------------------------- +type ScatterPanelParams = { + layoutKey?: string; + geometry?: Geometry; +}; + +const ScatterDockPanel = React.memo(function ScatterDockPanel( + props: IDockviewPanelProps +) { + const params = props.params ?? {}; + return ( + + ); +}); + +// Custom tab component with icon (like Rerun's "Image and segmentation mask" tab) +type TabWithIconProps = IDockviewPanelHeaderProps & { + icon: React.ReactNode; +}; + +const TabWithIcon = React.memo(function TabWithIcon({ api, icon }: TabWithIconProps) { + return ( +
+ {icon} + {api.title} +
+ ); +}); + +// Tab components for different panel types +const EuclideanTab = React.memo(function EuclideanTab(props: IDockviewPanelHeaderProps) { + return } />; +}); + +const HyperbolicTab = React.memo(function HyperbolicTab(props: IDockviewPanelHeaderProps) { + return } />; +}); + +const SamplesTab = React.memo(function SamplesTab(props: IDockviewPanelHeaderProps) { + return } />; +}); + +// Grid panel uses context to get samples +const GridDockPanel = React.memo(function GridDockPanel() { + const ctx = useDockviewContext(); + return ( + + ); +}); + +// Explorer panel for left zone +const ExplorerDockPanel = React.memo(function ExplorerDockPanel() { + return ; +}); + +// Placeholder panel for right/bottom zones +const PlaceholderDockPanel = React.memo(function PlaceholderDockPanel( + props: IDockviewPanelProps +) { + const handleClose = React.useCallback(() => { + props.api.close(); + }, [props.api]); + + return ; +}); + +// Watermark shown when dock is empty - just the logo, no text +const Watermark = React.memo(function Watermark(_props: IWatermarkPanelProps) { + return ( +
+
+ +
+
+ ); +}); + +// Stable components object - never changes +const COMPONENTS = { + grid: GridDockPanel, + scatter: ScatterDockPanel, + explorer: ExplorerDockPanel, + placeholder: PlaceholderDockPanel, +}; + +// Tab components with icons +const TAB_COMPONENTS = { + euclideanTab: EuclideanTab, + hyperbolicTab: HyperbolicTab, + samplesTab: SamplesTab, +}; + +// ----------------------------------------------------------------------------- +// Provider Component +// ----------------------------------------------------------------------------- +interface DockviewProviderProps { + children: ReactNode; + samples: Sample[]; + onLoadMore: () => void; + hasMore: boolean; +} + +export function DockviewProvider({ + children, + samples, + onLoadMore, + hasMore, +}: DockviewProviderProps) { + const [api, setApi] = useState(null); + + const contextValue = useMemo( + () => ({ + api, + setApi, + samples, + onLoadMore, + hasMore, + }), + [api, samples, onLoadMore, hasMore] + ); + + return ( + + {children} + + ); +} + +function applyZonePolicies(api: DockviewApi) { + const explorer = api.getPanel(PANEL.EXPLORER); + if (explorer) { + explorer.group.locked = true; + explorer.group.header.hidden = true; + } + + // Hide tab headers for placeholder panels + const rightPlaceholder = api.getPanel(PANEL.RIGHT_PLACEHOLDER); + if (rightPlaceholder) { + rightPlaceholder.group.header.hidden = true; + } + + const bottomPlaceholder = api.getPanel(PANEL.BOTTOM_PLACEHOLDER); + if (bottomPlaceholder) { + bottomPlaceholder.group.header.hidden = true; + } +} + +// ----------------------------------------------------------------------------- +// Workspace Component - the actual dockview renderer +// ----------------------------------------------------------------------------- +export function DockviewWorkspace() { + const ctx = useDockviewContext(); + const datasetInfo = useStore((state) => state.datasetInfo); + const { setLeftPanelOpen, setRightPanelOpen, setBottomPanelOpen } = useStore(); + + const buildDefaultLayout = useCallback( + (api: DockviewApi) => { + const layouts = datasetInfo?.layouts ?? []; + const euclideanLayout = findLayoutByGeometry(layouts, "euclidean"); + const poincareLayout = findLayoutByGeometry(layouts, "poincare"); + const fallbackLayout = !euclideanLayout && !poincareLayout ? layouts[0] : null; + const hasLayouts = layouts.length > 0; + + // Create the grid panel first (center zone) + const gridPanel = + api.getPanel(PANEL.GRID) ?? + api.addPanel({ + id: PANEL.GRID, + component: "grid", + title: "Samples", + tabComponent: "samplesTab", + renderer: "always", + }); + + let scatterPanel: typeof gridPanel | null = null; + + if (hasLayouts && euclideanLayout) { + scatterPanel = + api.getPanel(PANEL.SCATTER_EUCLIDEAN) ?? + api.addPanel({ + id: PANEL.SCATTER_EUCLIDEAN, + component: "scatter", + title: "Euclidean", + tabComponent: "euclideanTab", + params: { + layoutKey: euclideanLayout.layout_key, + geometry: "euclidean" as Geometry, + }, + position: { + referencePanel: gridPanel.id, + direction: "right", + }, + renderer: "always", + }); + } + + if (hasLayouts && poincareLayout) { + const position = scatterPanel + ? { referencePanel: scatterPanel.id, direction: "within" as const } + : { referencePanel: gridPanel.id, direction: "right" as const }; + + const poincarePanel = + api.getPanel(PANEL.SCATTER_POINCARE) ?? + api.addPanel({ + id: PANEL.SCATTER_POINCARE, + component: "scatter", + title: "Hyperbolic", + tabComponent: "hyperbolicTab", + params: { + layoutKey: poincareLayout.layout_key, + geometry: "poincare" as Geometry, + }, + position, + renderer: "always", + }); + + if (!scatterPanel) { + scatterPanel = poincarePanel; + } + } + + if (!hasLayouts) { + const euclideanPanel = + api.getPanel(PANEL.SCATTER_EUCLIDEAN) ?? + api.addPanel({ + id: PANEL.SCATTER_EUCLIDEAN, + component: "scatter", + title: "Euclidean", + tabComponent: "euclideanTab", + params: { + geometry: "euclidean" as Geometry, + }, + position: { + referencePanel: gridPanel.id, + direction: "right", + }, + renderer: "always", + }); + + api.getPanel(PANEL.SCATTER_POINCARE) ?? + api.addPanel({ + id: PANEL.SCATTER_POINCARE, + component: "scatter", + title: "Hyperbolic", + tabComponent: "hyperbolicTab", + params: { + geometry: "poincare" as Geometry, + }, + position: { + referencePanel: euclideanPanel.id, + direction: "within" as const, + }, + renderer: "always", + }); + + scatterPanel = euclideanPanel; + } + + if (fallbackLayout && !scatterPanel) { + api.getPanel(PANEL.SCATTER_DEFAULT) ?? + api.addPanel({ + id: PANEL.SCATTER_DEFAULT, + component: "scatter", + title: "Embeddings", + params: { + layoutKey: fallbackLayout.layout_key, + }, + position: { + referencePanel: gridPanel.id, + direction: "right", + }, + renderer: "always", + }); + } + + const containerWidth = getContainerWidth(api); + const explorerPanel = + api.getPanel(PANEL.EXPLORER) ?? + api.addPanel({ + id: PANEL.EXPLORER, + component: "explorer", + title: "Explorer", + position: getZonePosition("left"), + initialWidth: getDefaultLeftPanelWidth(containerWidth), + minimumWidth: MIN_SIDE_PANEL_WIDTH, + maximumWidth: getDefaultLeftPanelWidth(containerWidth), + inactive: true, + }); + + if (explorerPanel) { + explorerPanel.group.locked = true; + explorerPanel.group.header.hidden = true; + } + + setLeftPanelOpen(!!explorerPanel); + setRightPanelOpen(false); + setBottomPanelOpen(false); + }, + [datasetInfo, setLeftPanelOpen, setRightPanelOpen, setBottomPanelOpen] + ); + + const onReady = useCallback( + (event: DockviewReadyEvent) => { + ctx.setApi(event.api); + + const stored = localStorage.getItem(LAYOUT_STORAGE_KEY); + if (stored) { + try { + event.api.fromJSON(JSON.parse(stored)); + if (event.api.totalPanels === 0) { + localStorage.removeItem(LAYOUT_STORAGE_KEY); + buildDefaultLayout(event.api); + } + + // Re-apply side-zone policies after restore (header hidden, no-drop targets, etc) + applyZonePolicies(event.api); + + // Sync store state with restored layout + setLeftPanelOpen(!!event.api.getPanel(PANEL.EXPLORER)); + setRightPanelOpen(!!event.api.getPanel(PANEL.RIGHT_PLACEHOLDER)); + setBottomPanelOpen(!!event.api.getPanel(PANEL.BOTTOM_PLACEHOLDER)); + return; + } catch (err) { + console.warn("Failed to restore dock layout, resetting.", err); + localStorage.removeItem(LAYOUT_STORAGE_KEY); + } + } + + if (event.api.totalPanels === 0) { + buildDefaultLayout(event.api); + } + }, + [buildDefaultLayout, ctx, setLeftPanelOpen, setRightPanelOpen, setBottomPanelOpen] + ); + + // Save layout on changes + useEffect(() => { + const api = ctx.api; + if (!api) return; + + const disposable = api.onDidLayoutChange(() => { + if (api.totalPanels === 0) return; + const layout = api.toJSON(); + localStorage.setItem(LAYOUT_STORAGE_KEY, JSON.stringify(layout)); + }); + + return () => disposable.dispose(); + }, [ctx.api]); + + // Sync panel state when panels are closed + useEffect(() => { + const api = ctx.api; + if (!api) return; + + const disposable = api.onDidRemovePanel((e) => { + if (e.id === PANEL.EXPLORER) setLeftPanelOpen(false); + if (e.id === PANEL.RIGHT_PLACEHOLDER) setRightPanelOpen(false); + if (e.id === PANEL.BOTTOM_PLACEHOLDER) setBottomPanelOpen(false); + }); + + return () => disposable.dispose(); + }, [ctx.api, setLeftPanelOpen, setRightPanelOpen, setBottomPanelOpen]); + + // When a real panel is dropped into a placeholder group, close the placeholder + useEffect(() => { + const api = ctx.api; + if (!api) return; + + const disposable = api.onDidAddPanel((e) => { + // Skip if the added panel is a placeholder itself + if (e.id === PANEL.RIGHT_PLACEHOLDER || e.id === PANEL.BOTTOM_PLACEHOLDER) { + return; + } + + // Check if this panel was added to the same group as a placeholder + const group = e.group; + if (!group) return; + + // Find and close any placeholder panels in the same group + const rightPlaceholder = api.getPanel(PANEL.RIGHT_PLACEHOLDER); + const bottomPlaceholder = api.getPanel(PANEL.BOTTOM_PLACEHOLDER); + + if (rightPlaceholder && rightPlaceholder.group?.id === group.id) { + rightPlaceholder.api.close(); + } + if (bottomPlaceholder && bottomPlaceholder.group?.id === group.id) { + bottomPlaceholder.api.close(); + } + }); + + return () => disposable.dispose(); + }, [ctx.api]); + + // Prevent dragging locked panels (explorer only) + useEffect(() => { + const api = ctx.api; + if (!api) return; + + const disposable = api.onWillDragPanel((event) => { + if (DRAG_LOCKED_PANEL_IDS.has(event.panel.id)) { + event.nativeEvent.preventDefault(); + } + }); + + return () => disposable.dispose(); + }, [ctx.api]); + + // Rebuild layout when dataset info changes + useEffect(() => { + if (!ctx.api) return; + if (!datasetInfo) return; + + const hasScatter = + ctx.api.getPanel(PANEL.SCATTER_EUCLIDEAN) || + ctx.api.getPanel(PANEL.SCATTER_POINCARE) || + ctx.api.getPanel(PANEL.SCATTER_DEFAULT); + + if (!hasScatter) { + buildDefaultLayout(ctx.api); + } + }, [buildDefaultLayout, datasetInfo, ctx.api]); + + return ( +
+ +
+ ); +} diff --git a/frontend/src/components/ExplorerPanel.tsx b/frontend/src/components/ExplorerPanel.tsx new file mode 100644 index 0000000..4942f72 --- /dev/null +++ b/frontend/src/components/ExplorerPanel.tsx @@ -0,0 +1,181 @@ +"use client"; + +import React from "react"; +import { useStore } from "@/store/useStore"; +import { Panel } from "./Panel"; +import { PanelHeader } from "./PanelHeader"; +import { Tag, Search, ChevronDown, ChevronRight } from "lucide-react"; +import { cn } from "@/lib/utils"; +import { FALLBACK_LABEL_COLOR, MISSING_LABEL_COLOR, normalizeLabel } from "@/lib/labelColors"; +import { useLabelLegend } from "./useLabelLegend"; + +interface ExplorerPanelProps { + className?: string; +} + +export function ExplorerPanel({ className }: ExplorerPanelProps) { + const { + datasetInfo, + embeddingsByLayoutKey, + activeLayoutKey, + labelFilter, + setLabelFilter, + } = useStore(); + const [labelSearch, setLabelSearch] = React.useState(""); + const [isSearchOpen, setIsSearchOpen] = React.useState(false); + const [isLabelsExpanded, setIsLabelsExpanded] = React.useState(true); + const searchInputRef = React.useRef(null); + + const resolvedLayoutKey = + activeLayoutKey ?? datasetInfo?.layouts?.[0]?.layout_key ?? null; + const embeddings = resolvedLayoutKey + ? embeddingsByLayoutKey[resolvedLayoutKey] ?? null + : null; + + const { + labelCounts, + labelUniverse, + distinctLabelCount, + distinctColoringDisabled, + labelColorMap, + legendLabels, + } = useLabelLegend({ datasetInfo, embeddings, labelSearch, labelFilter }); + + const hasCounts = labelCounts.size > 0; + const baseLabelCount = labelUniverse.length > 0 + ? labelUniverse.filter((label) => label !== "undefined").length + : distinctLabelCount; + const displayCount = labelSearch.trim().length > 0 + ? legendLabels.length + : baseLabelCount; + + const activeLabel = labelFilter ? normalizeLabel(labelFilter) : null; + + // Focus search input when opened + React.useEffect(() => { + if (isSearchOpen && searchInputRef.current) { + searchInputRef.current.focus(); + } + }, [isSearchOpen]); + + const handleSearchToggle = () => { + setIsSearchOpen(!isSearchOpen); + if (isSearchOpen) { + setLabelSearch(""); + } + }; + + return ( + + + + {/* Scrollable content area */} +
+ {/* Labels section */} +
+ {/* Section header - collapsible with search icon */} +
+ + + {/* Search toggle button */} + +
+ + {/* Search input - shown when search is toggled */} + {isSearchOpen && ( +
+ setLabelSearch(e.target.value)} + placeholder="Filter labels..." + className="w-full h-6 px-2 rounded bg-background border border-border text-[12px] leading-[16px] text-foreground placeholder:text-muted-foreground/50 outline-none focus:ring-1 focus:ring-ring focus:border-ring" + /> +
+ )} + + {/* Labels list - collapsible */} + {isLabelsExpanded && ( +
+ {distinctColoringDisabled && ( +
+ Too many labels ({distinctLabelCount}) to color distinctly; using one color. +
+ )} + + {legendLabels.length === 0 ? ( +
+ No labels available +
+ ) : ( +
+ {legendLabels.map((label) => { + const color = + label === "undefined" + ? MISSING_LABEL_COLOR + : labelColorMap[label] ?? FALLBACK_LABEL_COLOR; + const normalized = normalizeLabel(label); + const isActive = activeLabel === normalized; + const isDimmed = activeLabel && !isActive; + + return ( + + ); + })} +
+ )} +
+ )} +
+
+ + ); +} diff --git a/frontend/src/components/Header.tsx b/frontend/src/components/Header.tsx index 9aa44c7..5970969 100644 --- a/frontend/src/components/Header.tsx +++ b/frontend/src/components/Header.tsx @@ -2,54 +2,243 @@ import { useStore } from "@/store/useStore"; import { Button } from "@/components/ui/button"; -import { HyperViewLogo } from "./icons"; +import { HyperViewLogo, DiscordIcon } from "./icons"; +import { CENTER_PANEL_DEFS, useDockviewApi } from "./DockviewWorkspace"; +import { + DropdownMenu, + DropdownMenuContent, + DropdownMenuItem, + DropdownMenuTrigger, +} from "@/components/ui/dropdown-menu"; +import { + Popover, + PopoverContent, + PopoverTrigger, +} from "@/components/ui/popover"; +import { + Command, + CommandEmpty, + CommandGroup, + CommandInput, + CommandItem, + CommandList, +} from "@/components/ui/command"; +import { + ChevronDown, + RotateCcw, + Check, + PanelLeft, + PanelBottom, + PanelRight, + Settings, + Search, +} from "lucide-react"; +import { useState } from "react"; +import { cn } from "@/lib/utils"; + +const PANEL_CONFIG = CENTER_PANEL_DEFS; +const DISCORD_URL = process.env.NEXT_PUBLIC_DISCORD_URL ?? "https://discord.gg/Qf2pXtY4Vf"; export function Header() { - const { datasetInfo, selectedIds, clearSelection } = useStore(); + const { datasetInfo, leftPanelOpen, rightPanelOpen, bottomPanelOpen } = useStore(); + const dockview = useDockviewApi(); + const [datasetPickerOpen, setDatasetPickerOpen] = useState(false); + + const handlePanelToggle = (panelId: string) => { + if (!dockview?.api) return; + const panel = dockview.api.getPanel(panelId); + if (panel) { + panel.api.close(); + return; + } + dockview.addPanel(panelId); + }; + + // Check which panels are currently open + const openPanels = new Set( + PANEL_CONFIG.map((p) => p.id).filter((id) => dockview?.api?.getPanel(id)) + ); return ( -
- {/* Logo and title */} -
-
- -
+
+ {/* Left side: Logo + View menu */}
-

HyperView

- {datasetInfo && ( - - {datasetInfo.name} - + {/* Logo */} +
+ +
+ + {/* View dropdown */} + {dockview && ( + + + + + + {/* Panel toggles - no section header, similar to Rerun */} + {PANEL_CONFIG.map((panel) => { + const Icon = panel.icon; + const isOpen = openPanels.has(panel.id); + return ( + handlePanelToggle(panel.id)} + className="flex items-center justify-between h-7 text-[12px] leading-[16px]" + > + + + {panel.label} + + {isOpen && } + + ); + })} + + {/* Spacer */} +
+ + {/* Reset layout */} + dockview.resetLayout()} + className="flex items-center gap-2 h-7 text-[12px] leading-[16px]" + > + + Reset Layout + + + )}
-
- - {/* Dataset info and actions */} -
- {datasetInfo && ( -
-
- samples - {datasetInfo.num_samples.toLocaleString()} -
-
- labels - {datasetInfo.labels.length} -
-
- )} - {selectedIds.size > 0 && ( + {/* Center: Dataset picker (VS Code style command palette trigger) */} +
+ + + + + + + + + + No datasets found. + + + {/* Currently only show the loaded dataset */} + {datasetInfo && ( + setDatasetPickerOpen(false)} + className="text-[12px] leading-[16px]" + > + {datasetInfo.name} + + {datasetInfo.num_samples.toLocaleString()} samples + + + + )} + + + + + +
+ + {/* Right side: Discord + Panel toggles + Settings */} +
+ {/* Discord link */} + + + + + {/* Separator */} +
+ + {/* Left panel toggle */} + + + {/* Bottom panel toggle */} + + + {/* Right panel toggle */} + + + {/* Separator */} +
+ + {/* Settings button */} - )} -
+
); } diff --git a/frontend/src/components/ImageGrid.tsx b/frontend/src/components/ImageGrid.tsx index 8a01399..7e1e457 100644 --- a/frontend/src/components/ImageGrid.tsx +++ b/frontend/src/components/ImageGrid.tsx @@ -1,11 +1,11 @@ "use client"; -import { useCallback, useEffect, useRef, useState } from "react"; +import { useCallback, useEffect, useMemo, useRef, useState } from "react"; import { useVirtualizer } from "@tanstack/react-virtual"; +import justifiedLayout from "justified-layout"; import { useStore } from "@/store/useStore"; -import { Panel, PanelFooter } from "./Panel"; -import { PanelHeader } from "./PanelHeader"; -import { GridIcon, CheckIcon } from "./icons"; +import { Panel } from "./Panel"; +import { CheckIcon } from "./icons"; import type { Sample } from "@/types"; interface ImageGridProps { @@ -14,69 +14,161 @@ interface ImageGridProps { hasMore?: boolean; } -const GAP = 8; -const ITEM_HEIGHT = 200; -const MIN_ITEM_WIDTH = 200; // Minimum width for each image +// Justified layout config +const BOX_SPACING = 2; // Tight spacing between images +const TARGET_ROW_HEIGHT = 180; // Target height for rows +const DEFAULT_ASPECT_RATIO = 1; // Fallback for samples without dimensions + +/** + * Get aspect ratio from sample, with fallback + */ +function getAspectRatio(sample: Sample): number { + if (sample.width && sample.height && sample.height > 0) { + return sample.width / sample.height; + } + return DEFAULT_ASPECT_RATIO; +} + +/** + * Compute justified layout geometry for samples + */ +function computeLayout( + samples: Sample[], + containerWidth: number +): { boxes: Array<{ width: number; height: number; top: number; left: number }>; containerHeight: number } { + if (samples.length === 0 || containerWidth <= 0) { + return { boxes: [], containerHeight: 0 }; + } + + const aspectRatios = samples.map(getAspectRatio); + + const geometry = justifiedLayout(aspectRatios, { + containerWidth, + containerPadding: 0, + boxSpacing: BOX_SPACING, + targetRowHeight: TARGET_ROW_HEIGHT, + targetRowHeightTolerance: 0.25, + showWidows: true, // Always show last row even if incomplete + }); + + return { + boxes: geometry.boxes, + containerHeight: geometry.containerHeight, + }; +} + +/** + * Group boxes into rows for virtualization + */ +interface RowData { + startIndex: number; + endIndex: number; // exclusive + top: number; + height: number; +} + +function groupIntoRows( + boxes: Array<{ width: number; height: number; top: number; left: number }> +): RowData[] { + if (boxes.length === 0) return []; + + const rows: RowData[] = []; + let currentRowTop = boxes[0].top; + let currentRowStart = 0; + let currentRowHeight = boxes[0].height; + + for (let i = 1; i < boxes.length; i++) { + const box = boxes[i]; + // New row if top position changes significantly + if (Math.abs(box.top - currentRowTop) > 1) { + rows.push({ + startIndex: currentRowStart, + endIndex: i, + top: currentRowTop, + height: currentRowHeight, + }); + currentRowStart = i; + currentRowTop = box.top; + currentRowHeight = box.height; + } else { + // Same row - take max height in case of slight variations + currentRowHeight = Math.max(currentRowHeight, box.height); + } + } + + // Push final row + rows.push({ + startIndex: currentRowStart, + endIndex: boxes.length, + top: currentRowTop, + height: currentRowHeight, + }); + + return rows; +} export function ImageGrid({ samples, onLoadMore, hasMore }: ImageGridProps) { const containerRef = useRef(null); + const [containerWidth, setContainerWidth] = useState(0); + const { selectedIds, isLassoSelection, selectionSource, - lassoTotal, toggleSelection, addToSelection, setHoveredId, hoveredId, + labelFilter, } = useStore(); - const [columnCount, setColumnCount] = useState(4); - // Calculate column count based on container width + // Track container width for layout computation useEffect(() => { - const updateColumnCount = () => { - if (!containerRef.current) return; - const containerWidth = containerRef.current.clientWidth; - const padding = 16; // Total horizontal padding (8px each side) - const availableWidth = containerWidth - padding; - - // Calculate how many columns can fit - const columns = Math.max(1, Math.floor((availableWidth + GAP) / (MIN_ITEM_WIDTH + GAP))); - setColumnCount(columns); + const container = containerRef.current; + if (!container) return; + + const updateWidth = () => { + const width = container.clientWidth; + if (width > 0 && width !== containerWidth) { + setContainerWidth(width); + } }; - updateColumnCount(); + updateWidth(); - const resizeObserver = new ResizeObserver(updateColumnCount); - if (containerRef.current) { - resizeObserver.observe(containerRef.current); - } + const resizeObserver = new ResizeObserver(() => { + requestAnimationFrame(updateWidth); + }); + resizeObserver.observe(container); return () => resizeObserver.disconnect(); - }, []); - - // In lasso mode, `samples` is already the selected-page list. - const rowCount = Math.ceil(samples.length / columnCount); + }, [containerWidth]); - // Create stable row keys based on the sample IDs in each row - const getRowKey = useCallback( - (index: number) => { - const startIndex = index * columnCount; - const rowSamples = samples.slice(startIndex, startIndex + columnCount); - return rowSamples.map((s) => s.id).join("-") || `row-${index}`; - }, - [samples, columnCount] + // Compute justified layout + const { boxes, containerHeight } = useMemo( + () => computeLayout(samples, containerWidth), + [samples, containerWidth] ); + // Group into rows for virtualization + const rows = useMemo(() => groupIntoRows(boxes), [boxes]); + + // Virtualizer for rows const virtualizer = useVirtualizer({ - count: rowCount, + count: rows.length, getScrollElement: () => containerRef.current, - estimateSize: () => ITEM_HEIGHT + GAP, - overscan: 5, - getItemKey: getRowKey, + estimateSize: (index) => rows[index]?.height ?? TARGET_ROW_HEIGHT, + overscan: 3, + getItemKey: (index) => { + const row = rows[index]; + if (!row) return `row-${index}`; + // Create stable key from sample IDs in this row + const rowSamples = samples.slice(row.startIndex, row.endIndex); + return rowSamples.map((s) => s.id).join("-") || `row-${index}`; + }, }); - // Load more when scrolling near the bottom + // Load more when scrolling near bottom useEffect(() => { const container = containerRef.current; if (!container || !onLoadMore || !hasMore) return; @@ -92,13 +184,12 @@ export function ImageGrid({ samples, onLoadMore, hasMore }: ImageGridProps) { return () => container.removeEventListener("scroll", handleScroll); }, [onLoadMore, hasMore]); - // Reset virtualizer measurements when selection or filter mode changes + // Reset scroll on filter change useEffect(() => { - virtualizer.measure(); - }, [selectedIds, isLassoSelection, virtualizer]); + containerRef.current?.scrollTo({ top: 0 }); + }, [labelFilter]); - // If a selection was made in the scatter plot, jump the image grid to the top - // so the selected sample(s) are immediately visible. + // Scroll to top when scatter selection made useEffect(() => { if (isLassoSelection) return; if (selectionSource !== "scatter") return; @@ -107,18 +198,16 @@ export function ImageGrid({ samples, onLoadMore, hasMore }: ImageGridProps) { try { virtualizer.scrollToIndex(0, { align: "start" }); } catch { - // Fallback if the virtualizer isn't ready yet. containerRef.current?.scrollTo({ top: 0 }); } }, [isLassoSelection, selectedIds, selectionSource, virtualizer]); + // Handle click with selection logic const handleClick = useCallback( (sample: Sample, event: React.MouseEvent) => { if (event.metaKey || event.ctrlKey) { - // Multi-select with Cmd/Ctrl toggleSelection(sample.id); } else if (event.shiftKey && selectedIds.size > 0) { - // Range select with Shift - use original samples array, not filtered const selectedArray = Array.from(selectedIds); const lastSelected = selectedArray[selectedArray.length - 1]; const lastIndex = samples.findIndex((s) => s.id === lastSelected); @@ -131,7 +220,6 @@ export function ImageGrid({ samples, onLoadMore, hasMore }: ImageGridProps) { addToSelection(rangeIds); } } else { - // Single select const newSet = new Set(); newSet.add(sample.id); useStore.getState().setSelectedIds(newSet, "grid"); @@ -140,118 +228,111 @@ export function ImageGrid({ samples, onLoadMore, hasMore }: ImageGridProps) { [samples, selectedIds, toggleSelection, addToSelection] ); - const items = virtualizer.getVirtualItems(); + const virtualRows = virtualizer.getVirtualItems(); return ( - } - title="Samples" - subtitle={ - isLassoSelection - ? `${lassoTotal} selected` - : selectedIds.size > 0 - ? `${selectedIds.size} selected` - : `${samples.length} items` - } - /> - - {/* Grid Container */} -
-
- {items.map((virtualRow) => { - const rowIndex = virtualRow.index; - const startIndex = rowIndex * columnCount; - const rowSamples = samples.slice(startIndex, startIndex + columnCount); - - return ( -
- {rowSamples.map((sample) => { - const isSelected = isLassoSelection ? true : selectedIds.has(sample.id); - const isHovered = hoveredId === sample.id; - - return ( -
handleClick(sample, e)} - onMouseEnter={() => setHoveredId(sample.id)} - onMouseLeave={() => setHoveredId(null)} - > - {/* Image - using native img for base64 data (Next Image doesn't support this) */} - {sample.thumbnail ? ( - // eslint-disable-next-line @next/next/no-img-element - {sample.filename} - ) : ( -
- No image -
- )} - - {/* Label badge */} - {sample.label && ( -
- - {sample.label} - -
- )} - - {/* Selection indicator */} - {isSelected && ( -
- -
- )} -
- ); - })} - {/* Fill empty cells */} - {Array.from({ length: columnCount - rowSamples.length }).map((_, i) => ( -
- ))} -
- ); - })} +
+
+
+ {virtualRows.map((virtualRow) => { + const row = rows[virtualRow.index]; + if (!row) return null; + + const rowSamples = samples.slice(row.startIndex, row.endIndex); + const rowBoxes = boxes.slice(row.startIndex, row.endIndex); + + return ( +
+ {rowSamples.map((sample, i) => { + const box = rowBoxes[i]; + if (!box) return null; + + const isSelected = isLassoSelection ? true : selectedIds.has(sample.id); + const isHovered = hoveredId === sample.id; + + return ( +
handleClick(sample, e)} + onMouseEnter={() => setHoveredId(sample.id)} + onMouseLeave={() => setHoveredId(null)} + > + {/* Image container - justified layout sizes tile to preserve aspect ratio */} + {/* Future: overlays (segmentations, bboxes) will be absolutely positioned here */} + {sample.thumbnail ? ( + // eslint-disable-next-line @next/next/no-img-element + {sample.filename} + ) : ( +
+ No image +
+ )} + + {/* Label badge */} + {sample.label && ( +
+ + {sample.label} + +
+ )} + + {/* Selection indicator */} + {isSelected && ( +
+ +
+ )} +
+ ); + })} +
+ ); + })} +
- - - Click • ⌘+click multi • ⇧+click range - ); } diff --git a/frontend/src/components/Panel.tsx b/frontend/src/components/Panel.tsx index 1112dc5..5058fa5 100644 --- a/frontend/src/components/Panel.tsx +++ b/frontend/src/components/Panel.tsx @@ -10,11 +10,12 @@ interface PanelProps { /** * Base panel container with consistent Rerun-style appearance. + * No borders or rounded corners - panels should be flush against each other. */ export function Panel({ children, className }: PanelProps) { return (
{children} @@ -33,7 +34,7 @@ interface PanelFooterProps { export function PanelFooter({ children, className }: PanelFooterProps) { return (
{children} diff --git a/frontend/src/components/PanelHeader.tsx b/frontend/src/components/PanelHeader.tsx index dea1edd..78201c4 100644 --- a/frontend/src/components/PanelHeader.tsx +++ b/frontend/src/components/PanelHeader.tsx @@ -13,20 +13,30 @@ interface PanelHeaderProps { /** * Rerun-style panel header with icon, title, and optional toolbar. + * + * Design tokens (from Rerun): + * - Title bar height: 24px + * - Icon size: 14px (3.5 tailwind units) + * - Icon-to-text gap: 4px (gap-1) + * - Font size: 12px with -0.15px tracking + * - Section header font: 11px uppercase */ export function PanelHeader({ icon, title, subtitle, children, className }: PanelHeaderProps) { return (
-
+
{icon && ( - {icon} + // 14px icon (3.5 tailwind units) with 4px gap to text + {icon} )} - {title} + {/* 12px font, medium weight, tight tracking */} + {title} {subtitle && ( - {subtitle} + {subtitle} )}
{children && ( diff --git a/frontend/src/components/PlaceholderPanel.tsx b/frontend/src/components/PlaceholderPanel.tsx new file mode 100644 index 0000000..bf760fa --- /dev/null +++ b/frontend/src/components/PlaceholderPanel.tsx @@ -0,0 +1,39 @@ +"use client"; + +import React from "react"; +import { HyperViewLogo } from "./icons"; +import { Panel } from "./Panel"; +import { cn } from "@/lib/utils"; +import { X } from "lucide-react"; + +interface PlaceholderPanelProps { + className?: string; + onClose?: () => void; +} + +/** + * Empty placeholder panel with centered HyperView logo and a close button. + * Used for right and bottom zones that are reserved for future features. + * The close button is always visible in the top-right corner of the panel content. + */ +export function PlaceholderPanel({ className, onClose }: PlaceholderPanelProps) { + return ( + + {/* Close button always visible in top right */} + {onClose && ( + + )} +
+
+ +
+
+ + ); +} diff --git a/frontend/src/components/ScatterPanel.tsx b/frontend/src/components/ScatterPanel.tsx index dd3f275..fc68cd6 100644 --- a/frontend/src/components/ScatterPanel.tsx +++ b/frontend/src/components/ScatterPanel.tsx @@ -1,75 +1,96 @@ "use client"; -import { useMemo } from "react"; +import { useCallback, useEffect, useMemo, useState } from "react"; import { useStore } from "@/store/useStore"; -import { Panel, PanelFooter } from "./Panel"; -import { PanelHeader } from "./PanelHeader"; -import { EuclideanIcon, PoincareIcon, ScatterIcon } from "./icons"; -import { type ScatterLabelsInfo, useHyperScatter } from "./useHyperScatter"; -import { ToggleGroup, ToggleGroupItem } from "@/components/ui/toggle-group"; +import { Panel } from "./Panel"; +import { useHyperScatter } from "./useHyperScatter"; +import { useLabelLegend } from "./useLabelLegend"; import type { Geometry } from "@/types"; -import { listAvailableGeometries } from "@/lib/layouts"; +import { findLayoutByGeometry, listAvailableGeometries } from "@/lib/layouts"; +import { fetchEmbeddings } from "@/lib/api"; interface ScatterPanelProps { className?: string; + layoutKey?: string; + geometry?: Geometry; } -export function ScatterPanel({ className = "" }: ScatterPanelProps) { +export function ScatterPanel({ + className = "", + layoutKey, + geometry, +}: ScatterPanelProps) { const { datasetInfo, - embeddings, + embeddingsByLayoutKey, + setEmbeddingsForLayout, selectedIds, setSelectedIds, beginLassoSelection, hoveredId, setHoveredId, - geometry, - setGeometry, + setActiveLayoutKey, + labelFilter, } = useStore(); + const [localGeometry, setLocalGeometry] = useState("euclidean"); + // Check which geometries are available const availableGeometries = useMemo(() => { return listAvailableGeometries(datasetInfo?.layouts ?? []); }, [datasetInfo?.layouts]); - const embeddingModelName = useMemo(() => { - if (!embeddings || !datasetInfo) return null; + useEffect(() => { + if (geometry) return; + if (availableGeometries.length === 0) return; + if (!availableGeometries.includes(localGeometry)) { + setLocalGeometry(availableGeometries[0]); + } + }, [availableGeometries, geometry, localGeometry]); - const layoutKey = embeddings.layout_key; + const resolvedGeometry = geometry ?? localGeometry; - // Find the LayoutInfo for current layout to get space_key directly - const layoutInfo = datasetInfo.layouts.find((l) => l.layout_key === layoutKey); - const spaceKey = layoutInfo?.space_key ?? layoutKey.split("__")[0]; + const resolvedLayoutKey = useMemo(() => { + if (!datasetInfo) return layoutKey ?? null; - const space = datasetInfo.spaces.find((s) => s.space_key === spaceKey); + if (layoutKey) { + const exists = datasetInfo.layouts.some((layout) => layout.layout_key === layoutKey); + if (exists) return layoutKey; + } - return space?.model_id ?? space?.space_key ?? spaceKey; - }, [datasetInfo, embeddings]); + const layout = findLayoutByGeometry(datasetInfo.layouts, resolvedGeometry); + return layout?.layout_key ?? datasetInfo.layouts[0]?.layout_key ?? null; + }, [datasetInfo, layoutKey, resolvedGeometry]); - const labelsInfo = useMemo(() => { - if (!embeddings) return null; + const embeddings = resolvedLayoutKey ? embeddingsByLayoutKey[resolvedLayoutKey] ?? null : null; - // Stable label order for palette and legend. - const uniqueLabels = [...new Set(embeddings.labels.map((l) => l || "undefined"))]; + useEffect(() => { + if (!resolvedLayoutKey) return; + setActiveLayoutKey(resolvedLayoutKey); + }, [resolvedLayoutKey, setActiveLayoutKey]); - const labelToCategory: Record = {}; - for (let i = 0; i < uniqueLabels.length; i++) { - labelToCategory[uniqueLabels[i]] = i; - } + useEffect(() => { + if (!resolvedLayoutKey) return; + if (embeddingsByLayoutKey[resolvedLayoutKey]) return; - const categories = new Uint16Array(embeddings.labels.length); - for (let i = 0; i < embeddings.labels.length; i++) { - const key = embeddings.labels[i] || "undefined"; - categories[i] = labelToCategory[key] ?? 0; - } + let cancelled = false; + + fetchEmbeddings(resolvedLayoutKey) + .then((data) => { + if (cancelled) return; + setEmbeddingsForLayout(resolvedLayoutKey, data); + }) + .catch((err) => { + if (cancelled) return; + console.error("Failed to load embeddings:", err); + }) - const palette = uniqueLabels.map((label) => { - if (label === "undefined") return "#008080"; - return embeddings.label_colors[label] || "#808080"; - }); + return () => { + cancelled = true; + }; + }, [embeddingsByLayoutKey, resolvedLayoutKey, setEmbeddingsForLayout]); - return { uniqueLabels, categories, palette }; - }, [embeddings]); + const { labelsInfo } = useLabelLegend({ datasetInfo, embeddings, labelFilter }); const { canvasRef, @@ -89,61 +110,38 @@ export function ScatterPanel({ className = "" }: ScatterPanelProps) { setSelectedIds, beginLassoSelection, setHoveredId, + hoverEnabled: !labelFilter, }); - const uniqueLabels = labelsInfo?.uniqueLabels ?? []; + const focusLayout = useCallback(() => { + if (!resolvedLayoutKey) return; + setActiveLayoutKey(resolvedLayoutKey); + }, [resolvedLayoutKey, setActiveLayoutKey]); + + const loadingLabel = resolvedLayoutKey + ? "Loading embeddings..." + : "No embeddings layout available"; return ( - } - title="Embeddings" - subtitle={embeddings ? embeddingModelName ?? "" : "Loading..."} - > - {/* Geometry toggle */} - {availableGeometries.length > 1 && ( - value && setGeometry(value as Geometry)} - size="sm" - variant="outline" - className="bg-muted/50 rounded-md p-0.5 gap-0.5" - > - - - Euclidean - - - - Poincaré - - - )} - - - {/* Main content area */} -
+ {/* Main content area - min-h-0 prevents flex overflow */} +
{/* Canvas container */} -
+
{ + focusLayout(); + handlePointerDown(e); + }} onPointerMove={handlePointerMove} onPointerUp={handlePointerUp} onPointerCancel={handlePointerUp} onPointerLeave={handlePointerLeave} onDoubleClick={handleDoubleClick} + onPointerEnter={focusLayout} /> {/* Lasso overlay (screen-space) */} @@ -164,41 +162,13 @@ export function ScatterPanel({ className = "" }: ScatterPanelProps) { ) : ( !embeddings && (
-
Loading embeddings...
+
{loadingLabel}
) )}
- {/* Legend */} - {uniqueLabels.length > 0 && ( -
-
Labels
-
- {uniqueLabels.slice(0, 20).map((label) => ( -
-
- - {label} - -
- ))} - {uniqueLabels.length > 20 && ( -
+{uniqueLabels.length - 20} more
- )} -
-
- )}
- - - ⇧+drag lasso • scroll zoom • drag pan - ); } diff --git a/frontend/src/components/icons.tsx b/frontend/src/components/icons.tsx index 20a4634..2d38561 100644 --- a/frontend/src/components/icons.tsx +++ b/frontend/src/components/icons.tsx @@ -24,8 +24,8 @@ export const ScatterIcon = () => ( ); -export const HyperViewLogo = () => ( - +export const HyperViewLogo = ({ className = "w-5 h-5" }: { className?: string }) => ( + @@ -64,3 +64,10 @@ export const SphericalIcon = () => ( ); + +/** Discord icon - official simplified logo */ +export const DiscordIcon = ({ className = "w-4 h-4" }: { className?: string }) => ( + + + +); diff --git a/frontend/src/components/index.ts b/frontend/src/components/index.ts index a36acea..f178483 100644 --- a/frontend/src/components/index.ts +++ b/frontend/src/components/index.ts @@ -1,6 +1,9 @@ export { ImageGrid } from "./ImageGrid"; export { ScatterPanel } from "./ScatterPanel"; +export { DockviewWorkspace } from "./DockviewWorkspace"; export { Header } from "./Header"; export { Panel, PanelFooter } from "./Panel"; export { PanelHeader } from "./PanelHeader"; +export { ExplorerPanel } from "./ExplorerPanel"; +export { PlaceholderPanel } from "./PlaceholderPanel"; export * from "./icons"; diff --git a/frontend/src/components/ui/collapsible.tsx b/frontend/src/components/ui/collapsible.tsx new file mode 100644 index 0000000..9fa4894 --- /dev/null +++ b/frontend/src/components/ui/collapsible.tsx @@ -0,0 +1,11 @@ +"use client" + +import * as CollapsiblePrimitive from "@radix-ui/react-collapsible" + +const Collapsible = CollapsiblePrimitive.Root + +const CollapsibleTrigger = CollapsiblePrimitive.CollapsibleTrigger + +const CollapsibleContent = CollapsiblePrimitive.CollapsibleContent + +export { Collapsible, CollapsibleTrigger, CollapsibleContent } diff --git a/frontend/src/components/ui/command.tsx b/frontend/src/components/ui/command.tsx new file mode 100644 index 0000000..2cecd91 --- /dev/null +++ b/frontend/src/components/ui/command.tsx @@ -0,0 +1,153 @@ +"use client" + +import * as React from "react" +import { type DialogProps } from "@radix-ui/react-dialog" +import { Command as CommandPrimitive } from "cmdk" +import { Search } from "lucide-react" + +import { cn } from "@/lib/utils" +import { Dialog, DialogContent } from "@/components/ui/dialog" + +const Command = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, ...props }, ref) => ( + +)) +Command.displayName = CommandPrimitive.displayName + +const CommandDialog = ({ children, ...props }: DialogProps) => { + return ( + + + + {children} + + + + ) +} + +const CommandInput = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, ...props }, ref) => ( +
+ + +
+)) + +CommandInput.displayName = CommandPrimitive.Input.displayName + +const CommandList = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, ...props }, ref) => ( + +)) + +CommandList.displayName = CommandPrimitive.List.displayName + +const CommandEmpty = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>((props, ref) => ( + +)) + +CommandEmpty.displayName = CommandPrimitive.Empty.displayName + +const CommandGroup = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, ...props }, ref) => ( + +)) + +CommandGroup.displayName = CommandPrimitive.Group.displayName + +const CommandSeparator = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, ...props }, ref) => ( + +)) +CommandSeparator.displayName = CommandPrimitive.Separator.displayName + +const CommandItem = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, ...props }, ref) => ( + +)) + +CommandItem.displayName = CommandPrimitive.Item.displayName + +const CommandShortcut = ({ + className, + ...props +}: React.HTMLAttributes) => { + return ( + + ) +} +CommandShortcut.displayName = "CommandShortcut" + +export { + Command, + CommandDialog, + CommandInput, + CommandList, + CommandEmpty, + CommandGroup, + CommandItem, + CommandShortcut, + CommandSeparator, +} diff --git a/frontend/src/components/ui/dialog.tsx b/frontend/src/components/ui/dialog.tsx new file mode 100644 index 0000000..1647513 --- /dev/null +++ b/frontend/src/components/ui/dialog.tsx @@ -0,0 +1,122 @@ +"use client" + +import * as React from "react" +import * as DialogPrimitive from "@radix-ui/react-dialog" +import { X } from "lucide-react" + +import { cn } from "@/lib/utils" + +const Dialog = DialogPrimitive.Root + +const DialogTrigger = DialogPrimitive.Trigger + +const DialogPortal = DialogPrimitive.Portal + +const DialogClose = DialogPrimitive.Close + +const DialogOverlay = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, ...props }, ref) => ( + +)) +DialogOverlay.displayName = DialogPrimitive.Overlay.displayName + +const DialogContent = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, children, ...props }, ref) => ( + + + + {children} + + + Close + + + +)) +DialogContent.displayName = DialogPrimitive.Content.displayName + +const DialogHeader = ({ + className, + ...props +}: React.HTMLAttributes) => ( +
+) +DialogHeader.displayName = "DialogHeader" + +const DialogFooter = ({ + className, + ...props +}: React.HTMLAttributes) => ( +
+) +DialogFooter.displayName = "DialogFooter" + +const DialogTitle = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, ...props }, ref) => ( + +)) +DialogTitle.displayName = DialogPrimitive.Title.displayName + +const DialogDescription = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, ...props }, ref) => ( + +)) +DialogDescription.displayName = DialogPrimitive.Description.displayName + +export { + Dialog, + DialogPortal, + DialogOverlay, + DialogTrigger, + DialogClose, + DialogContent, + DialogHeader, + DialogFooter, + DialogTitle, + DialogDescription, +} diff --git a/frontend/src/components/ui/dropdown-menu.tsx b/frontend/src/components/ui/dropdown-menu.tsx new file mode 100644 index 0000000..5a20503 --- /dev/null +++ b/frontend/src/components/ui/dropdown-menu.tsx @@ -0,0 +1,201 @@ +"use client" + +import * as React from "react" +import * as DropdownMenuPrimitive from "@radix-ui/react-dropdown-menu" +import { Check, ChevronRight, Circle } from "lucide-react" + +import { cn } from "@/lib/utils" + +const DropdownMenu = DropdownMenuPrimitive.Root + +const DropdownMenuTrigger = DropdownMenuPrimitive.Trigger + +const DropdownMenuGroup = DropdownMenuPrimitive.Group + +const DropdownMenuPortal = DropdownMenuPrimitive.Portal + +const DropdownMenuSub = DropdownMenuPrimitive.Sub + +const DropdownMenuRadioGroup = DropdownMenuPrimitive.RadioGroup + +const DropdownMenuSubTrigger = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef & { + inset?: boolean + } +>(({ className, inset, children, ...props }, ref) => ( + + {children} + + +)) +DropdownMenuSubTrigger.displayName = + DropdownMenuPrimitive.SubTrigger.displayName + +const DropdownMenuSubContent = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, ...props }, ref) => ( + +)) +DropdownMenuSubContent.displayName = + DropdownMenuPrimitive.SubContent.displayName + +const DropdownMenuContent = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, sideOffset = 4, ...props }, ref) => ( + + + +)) +DropdownMenuContent.displayName = DropdownMenuPrimitive.Content.displayName + +const DropdownMenuItem = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef & { + inset?: boolean + } +>(({ className, inset, ...props }, ref) => ( + svg]:size-4 [&>svg]:shrink-0", + inset && "pl-8", + className + )} + {...props} + /> +)) +DropdownMenuItem.displayName = DropdownMenuPrimitive.Item.displayName + +const DropdownMenuCheckboxItem = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, children, checked, ...props }, ref) => ( + + + + + + + {children} + +)) +DropdownMenuCheckboxItem.displayName = + DropdownMenuPrimitive.CheckboxItem.displayName + +const DropdownMenuRadioItem = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, children, ...props }, ref) => ( + + + + + + + {children} + +)) +DropdownMenuRadioItem.displayName = DropdownMenuPrimitive.RadioItem.displayName + +const DropdownMenuLabel = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef & { + inset?: boolean + } +>(({ className, inset, ...props }, ref) => ( + +)) +DropdownMenuLabel.displayName = DropdownMenuPrimitive.Label.displayName + +const DropdownMenuSeparator = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, ...props }, ref) => ( + +)) +DropdownMenuSeparator.displayName = DropdownMenuPrimitive.Separator.displayName + +const DropdownMenuShortcut = ({ + className, + ...props +}: React.HTMLAttributes) => { + return ( + + ) +} +DropdownMenuShortcut.displayName = "DropdownMenuShortcut" + +export { + DropdownMenu, + DropdownMenuTrigger, + DropdownMenuContent, + DropdownMenuItem, + DropdownMenuCheckboxItem, + DropdownMenuRadioItem, + DropdownMenuLabel, + DropdownMenuSeparator, + DropdownMenuShortcut, + DropdownMenuGroup, + DropdownMenuPortal, + DropdownMenuSub, + DropdownMenuSubContent, + DropdownMenuSubTrigger, + DropdownMenuRadioGroup, +} diff --git a/frontend/src/components/ui/popover.tsx b/frontend/src/components/ui/popover.tsx new file mode 100644 index 0000000..70a28f6 --- /dev/null +++ b/frontend/src/components/ui/popover.tsx @@ -0,0 +1,33 @@ +"use client" + +import * as React from "react" +import * as PopoverPrimitive from "@radix-ui/react-popover" + +import { cn } from "@/lib/utils" + +const Popover = PopoverPrimitive.Root + +const PopoverTrigger = PopoverPrimitive.Trigger + +const PopoverAnchor = PopoverPrimitive.Anchor + +const PopoverContent = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, align = "center", sideOffset = 4, ...props }, ref) => ( + + + +)) +PopoverContent.displayName = PopoverPrimitive.Content.displayName + +export { Popover, PopoverTrigger, PopoverContent, PopoverAnchor } diff --git a/frontend/src/components/ui/radio-group.tsx b/frontend/src/components/ui/radio-group.tsx new file mode 100644 index 0000000..0bdf685 --- /dev/null +++ b/frontend/src/components/ui/radio-group.tsx @@ -0,0 +1,44 @@ +"use client" + +import * as React from "react" +import * as RadioGroupPrimitive from "@radix-ui/react-radio-group" +import { Circle } from "lucide-react" + +import { cn } from "@/lib/utils" + +const RadioGroup = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, ...props }, ref) => { + return ( + + ) +}) +RadioGroup.displayName = RadioGroupPrimitive.Root.displayName + +const RadioGroupItem = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, ...props }, ref) => { + return ( + + + + + + ) +}) +RadioGroupItem.displayName = RadioGroupPrimitive.Item.displayName + +export { RadioGroup, RadioGroupItem } diff --git a/frontend/src/components/ui/resizable.tsx b/frontend/src/components/ui/resizable.tsx deleted file mode 100644 index 1ef04c0..0000000 --- a/frontend/src/components/ui/resizable.tsx +++ /dev/null @@ -1,45 +0,0 @@ -"use client" - -import { GripVertical } from "lucide-react" -import { Group, Panel, Separator } from "react-resizable-panels" - -import { cn } from "@/lib/utils" - -const ResizablePanelGroup = ({ - className, - ...props -}: React.ComponentProps) => ( - -) - -const ResizablePanel = Panel - -const ResizableHandle = ({ - withHandle, - className, - ...props -}: React.ComponentProps & { - withHandle?: boolean -}) => ( - div]:rotate-90", - className - )} - {...props} - > - {withHandle && ( -
- -
- )} - -) - -export { ResizablePanelGroup, ResizablePanel, ResizableHandle } diff --git a/frontend/src/components/ui/scroll-area.tsx b/frontend/src/components/ui/scroll-area.tsx new file mode 100644 index 0000000..b1e223f --- /dev/null +++ b/frontend/src/components/ui/scroll-area.tsx @@ -0,0 +1,49 @@ +"use client" + +import * as React from "react" +import * as ScrollAreaPrimitive from "@radix-ui/react-scroll-area" + +import { cn } from "@/lib/utils" + +const ScrollArea = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, children, ...props }, ref) => ( + + + {children} + + + + +)) +ScrollArea.displayName = ScrollAreaPrimitive.Root.displayName + +const ScrollBar = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, orientation = "vertical", ...props }, ref) => ( + + + +)) +ScrollBar.displayName = ScrollAreaPrimitive.ScrollAreaScrollbar.displayName + +export { ScrollArea, ScrollBar } diff --git a/frontend/src/components/ui/separator.tsx b/frontend/src/components/ui/separator.tsx new file mode 100644 index 0000000..12d81c4 --- /dev/null +++ b/frontend/src/components/ui/separator.tsx @@ -0,0 +1,31 @@ +"use client" + +import * as React from "react" +import * as SeparatorPrimitive from "@radix-ui/react-separator" + +import { cn } from "@/lib/utils" + +const Separator = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>( + ( + { className, orientation = "horizontal", decorative = true, ...props }, + ref + ) => ( + + ) +) +Separator.displayName = SeparatorPrimitive.Root.displayName + +export { Separator } diff --git a/frontend/src/components/ui/tooltip.tsx b/frontend/src/components/ui/tooltip.tsx new file mode 100644 index 0000000..28e1918 --- /dev/null +++ b/frontend/src/components/ui/tooltip.tsx @@ -0,0 +1,32 @@ +"use client" + +import * as React from "react" +import * as TooltipPrimitive from "@radix-ui/react-tooltip" + +import { cn } from "@/lib/utils" + +const TooltipProvider = TooltipPrimitive.Provider + +const Tooltip = TooltipPrimitive.Root + +const TooltipTrigger = TooltipPrimitive.Trigger + +const TooltipContent = React.forwardRef< + React.ElementRef, + React.ComponentPropsWithoutRef +>(({ className, sideOffset = 4, ...props }, ref) => ( + + + +)) +TooltipContent.displayName = TooltipPrimitive.Content.displayName + +export { Tooltip, TooltipTrigger, TooltipContent, TooltipProvider } diff --git a/frontend/src/components/useHyperScatter.ts b/frontend/src/components/useHyperScatter.ts index ca20649..3801a70 100644 --- a/frontend/src/components/useHyperScatter.ts +++ b/frontend/src/components/useHyperScatter.ts @@ -2,16 +2,11 @@ import type React from "react"; import { useCallback, useEffect, useMemo, useRef, useState } from "react"; import type { EmbeddingsData } from "@/types"; +import type { ScatterLabelsInfo } from "@/lib/labelLegend"; import type { Dataset, GeometryMode, Modifiers, Renderer } from "hyper-scatter"; type HyperScatterModule = typeof import("hyper-scatter"); -export interface ScatterLabelsInfo { - uniqueLabels: string[]; - categories: Uint16Array; - palette: string[]; -} - const MAX_LASSO_VERTS = 512; function supportsWebGL2(): boolean { @@ -46,6 +41,7 @@ interface UseHyperScatterArgs { setSelectedIds: (ids: Set, source?: "scatter" | "grid") => void; beginLassoSelection: (query: { layoutKey: string; polygon: number[] }) => void; setHoveredId: (id: string | null) => void; + hoverEnabled?: boolean; } function toModifiers(e: { shiftKey: boolean; ctrlKey: boolean; altKey: boolean; metaKey: boolean }): Modifiers { @@ -97,6 +93,7 @@ export function useHyperScatter({ setSelectedIds, beginLassoSelection, setHoveredId, + hoverEnabled = true, }: UseHyperScatterArgs) { const canvasRef = useRef(null); const overlayCanvasRef = useRef(null); @@ -116,6 +113,7 @@ export function useHyperScatter({ const lastPointerXRef = useRef(0); const lastPointerYRef = useRef(0); const lassoPointsRef = useRef([]); + const persistentLassoRef = useRef(null); const hoveredIndexRef = useRef(-1); @@ -171,12 +169,30 @@ export function useHyperScatter({ }; }, []); + const redrawOverlay = useCallback(() => { + if (!overlayCanvasRef.current) return; + clearOverlay(overlayCanvasRef.current); + const persistent = persistentLassoRef.current; + if (persistent && persistent.length >= 6) { + drawLassoOverlay(overlayCanvasRef.current, persistent); + } + }, []); + + const clearPersistentLasso = useCallback(() => { + persistentLassoRef.current = null; + clearOverlay(overlayCanvasRef.current); + }, []); + const stopInteraction = useCallback(() => { isPanningRef.current = false; isLassoingRef.current = false; lassoPointsRef.current = []; + if (persistentLassoRef.current) { + redrawOverlay(); + return; + } clearOverlay(overlayCanvasRef.current); - }, []); + }, [redrawOverlay]); // Initialize renderer when embeddings change. useEffect(() => { @@ -218,7 +234,7 @@ export function useHyperScatter({ overlayCanvasRef.current.height = Math.max(1, height); overlayCanvasRef.current.style.width = `${width}px`; overlayCanvasRef.current.style.height = `${height}px`; - clearOverlay(overlayCanvasRef.current); + redrawOverlay(); } // Use coords from embeddings response directly @@ -288,7 +304,7 @@ export function useHyperScatter({ rendererRef.current = null; } }; - }, [embeddings, labelsInfo, requestRender, stopInteraction]); + }, [embeddings, labelsInfo, redrawOverlay, requestRender, stopInteraction]); // Store -> renderer sync useEffect(() => { @@ -303,12 +319,19 @@ export function useHyperScatter({ renderer.setSelection(indices); + if (!hoverEnabled) { + renderer.setHovered(-1); + hoveredIndexRef.current = -1; + requestRender(); + return; + } + const hoveredIdx = hoveredId ? (idToIndex.get(hoveredId) ?? -1) : -1; renderer.setHovered(hoveredIdx); hoveredIndexRef.current = hoveredIdx; requestRender(); - }, [embeddings, hoveredId, idToIndex, requestRender, selectedIds]); + }, [embeddings, hoveredId, hoverEnabled, idToIndex, requestRender, selectedIds]); // Resize handling useEffect(() => { @@ -326,6 +349,7 @@ export function useHyperScatter({ overlayCanvasRef.current.height = Math.max(1, height); overlayCanvasRef.current.style.width = `${width}px`; overlayCanvasRef.current.style.height = `${height}px`; + redrawOverlay(); } const renderer = rendererRef.current; @@ -340,7 +364,7 @@ export function useHyperScatter({ const ro = new ResizeObserver(resize); ro.observe(container); return () => ro.disconnect(); - }, [requestRender]); + }, [redrawOverlay, requestRender]); // Wheel zoom (native listener so we can set passive:false) useEffect(() => { @@ -377,6 +401,10 @@ export function useHyperScatter({ lastPointerXRef.current = pos.x; lastPointerYRef.current = pos.y; + if (persistentLassoRef.current) { + clearPersistentLasso(); + } + // Shift-drag = lasso, otherwise pan. if (e.shiftKey) { isLassoingRef.current = true; @@ -396,7 +424,7 @@ export function useHyperScatter({ e.preventDefault(); }, - [getCanvasPos] + [clearPersistentLasso, getCanvasPos] ); const handlePointerMove = useCallback( @@ -433,6 +461,15 @@ export function useHyperScatter({ return; } + if (!hoverEnabled) { + if (hoveredIndexRef.current !== -1) { + hoveredIndexRef.current = -1; + renderer.setHovered(-1); + requestRender(); + } + return; + } + // Hover const hit = renderer.hitTest(pos.x, pos.y); const nextIndex = hit ? hit.index : -1; @@ -449,7 +486,7 @@ export function useHyperScatter({ requestRender(); }, - [embeddings, getCanvasPos, requestRender, setHoveredId] + [embeddings, getCanvasPos, hoverEnabled, requestRender, setHoveredId] ); const handlePointerUp = useCallback( @@ -461,8 +498,10 @@ export function useHyperScatter({ } if (isLassoingRef.current) { - const pts = lassoPointsRef.current; + const pts = lassoPointsRef.current.slice(); + persistentLassoRef.current = pts.length >= 6 ? pts : null; stopInteraction(); + redrawOverlay(); if (pts.length >= 6) { try { @@ -520,7 +559,16 @@ export function useHyperScatter({ stopInteraction(); requestRender(); }, - [beginLassoSelection, embeddings, getCanvasPos, requestRender, selectedIds, setSelectedIds, stopInteraction] + [ + beginLassoSelection, + embeddings, + getCanvasPos, + redrawOverlay, + requestRender, + selectedIds, + setSelectedIds, + stopInteraction, + ] ); const handlePointerLeave = useCallback( @@ -541,6 +589,7 @@ export function useHyperScatter({ (_e: React.MouseEvent) => { const renderer = rendererRef.current; if (!renderer) return; + clearPersistentLasso(); stopInteraction(); renderer.setSelection(new Set()); @@ -548,7 +597,7 @@ export function useHyperScatter({ requestRender(); }, - [requestRender, setSelectedIds, stopInteraction] + [clearPersistentLasso, requestRender, setSelectedIds, stopInteraction] ); return { diff --git a/frontend/src/components/useLabelLegend.ts b/frontend/src/components/useLabelLegend.ts new file mode 100644 index 0000000..9c58da0 --- /dev/null +++ b/frontend/src/components/useLabelLegend.ts @@ -0,0 +1,85 @@ +import { useMemo } from "react"; + +import type { DatasetInfo, EmbeddingsData } from "@/types"; +import { + MAX_DISTINCT_LABEL_COLORS, + buildLabelColorMap, + buildLabelCounts, + buildLabelUniverse, + buildLabelsInfo, + buildLegendLabels, + getDistinctLabelCount, +} from "@/lib/labelLegend"; + +interface UseLabelLegendArgs { + datasetInfo: DatasetInfo | null; + embeddings: EmbeddingsData | null; + labelSearch?: string; + labelFilter?: string | null; +} + +export function useLabelLegend({ + datasetInfo, + embeddings, + labelSearch = "", + labelFilter = null, +}: UseLabelLegendArgs) { + const labelCounts = useMemo(() => buildLabelCounts(embeddings), [embeddings]); + + const labelUniverse = useMemo( + () => buildLabelUniverse(datasetInfo?.labels ?? [], embeddings?.labels ?? null), + [datasetInfo?.labels, embeddings?.labels] + ); + + const distinctLabelCount = useMemo(() => { + const fromCounts = getDistinctLabelCount(labelCounts); + if (fromCounts > 0) return fromCounts; + let n = labelUniverse.length; + if (labelUniverse.includes("undefined")) n -= 1; + return n; + }, [labelCounts, labelUniverse]); + + const distinctColoringDisabled = distinctLabelCount > MAX_DISTINCT_LABEL_COLORS; + + const labelsInfo = useMemo( + () => + buildLabelsInfo({ + datasetLabels: datasetInfo?.labels ?? [], + embeddings, + distinctColoringDisabled, + labelFilter, + }), + [datasetInfo?.labels, embeddings, distinctColoringDisabled, labelFilter] + ); + + const labelColorMap = useMemo( + () => + buildLabelColorMap({ + labelsInfo, + labelUniverse, + distinctColoringDisabled, + labelFilter, + }), + [labelsInfo, labelUniverse, distinctColoringDisabled, labelFilter] + ); + + const legendLabels = useMemo( + () => + buildLegendLabels({ + labelUniverse, + labelCounts, + query: labelSearch, + }), + [labelUniverse, labelCounts, labelSearch] + ); + + return { + labelCounts, + labelUniverse, + distinctLabelCount, + distinctColoringDisabled, + labelsInfo, + labelColorMap, + legendLabels, + }; +} diff --git a/frontend/src/lib/labelColors.ts b/frontend/src/lib/labelColors.ts new file mode 100644 index 0000000..273b519 --- /dev/null +++ b/frontend/src/lib/labelColors.ts @@ -0,0 +1,153 @@ +const MISSING_LABEL_SENTINEL = "undefined"; + +export const MISSING_LABEL_COLOR = "#39d3cc"; // matches --accent-cyan +export const FALLBACK_LABEL_COLOR = "#8b949e"; // matches --muted-foreground + +// Rerun-style auto colors: distribute hues by golden ratio conjugate. +// See: context/repos/rerun/crates/viewer/re_viewer_context/src/utils/color.rs +const GOLDEN_RATIO_CONJUGATE = (Math.sqrt(5) - 1) / 2; // ~0.61803398875 + +function fnv1a32(input: string): number { + // 32-bit FNV-1a hash + let hash = 0x811c9dc5; + for (let i = 0; i < input.length; i++) { + hash ^= input.charCodeAt(i); + hash = Math.imul(hash, 0x01000193); + } + return hash >>> 0; +} + +function clamp01(v: number): number { + if (v < 0) return 0; + if (v > 1) return 1; + return v; +} + +function hsvToHex(h: number, s: number, v: number): string { + // h/s/v in [0, 1] + const hh = ((h % 1) + 1) % 1; + const ss = clamp01(s); + const vv = clamp01(v); + + const x = hh * 6; + const i = Math.floor(x); + const f = x - i; + + const p = vv * (1 - ss); + const q = vv * (1 - ss * f); + const t = vv * (1 - ss * (1 - f)); + + let r = 0; + let g = 0; + let b = 0; + + switch (i % 6) { + case 0: + r = vv; + g = t; + b = p; + break; + case 1: + r = q; + g = vv; + b = p; + break; + case 2: + r = p; + g = vv; + b = t; + break; + case 3: + r = p; + g = q; + b = vv; + break; + case 4: + r = t; + g = p; + b = vv; + break; + case 5: + r = vv; + g = p; + b = q; + break; + } + + const toHex = (x: number) => { + const n = Math.round(clamp01(x) * 255); + return n.toString(16).padStart(2, "0"); + }; + + return `#${toHex(r)}${toHex(g)}${toHex(b)}`; +} + +export function labelToColor(label: string): string { + if (!label) return FALLBACK_LABEL_COLOR; + if (label === MISSING_LABEL_SENTINEL) return MISSING_LABEL_COLOR; + + return colorForKey(label); +} + +function colorForKey(key: string): string { + const seed = fnv1a32(key); + const val = seed & 0xffff; // map to u16 + const h = (val * GOLDEN_RATIO_CONJUGATE) % 1; + + // Match Rerun's defaults: saturation 0.85, value 0.5 + return hsvToHex(h, 0.85, 0.5); +} + +function stableLabelSort(a: string, b: string): number { + if (a === MISSING_LABEL_SENTINEL && b !== MISSING_LABEL_SENTINEL) return 1; + if (b === MISSING_LABEL_SENTINEL && a !== MISSING_LABEL_SENTINEL) return -1; + return a.localeCompare(b); +} + +/** + * Builds a deterministic, collision-free label → color mapping for the given + * label universe. + * + * Notes: + * - No modulo/cycling: if there are N labels, we produce N colors. + * - Deterministic: same input set yields same mapping. + * - Collision-free within the provided set via deterministic rehashing. + */ +export function createLabelColorMap(labels: string[]): Record { + const unique = Array.from(new Set(labels.map((l) => normalizeLabel(l)))).sort(stableLabelSort); + + const colors: Record = {}; + const used = new Set(); + + for (const label of unique) { + if (label === MISSING_LABEL_SENTINEL) { + colors[label] = MISSING_LABEL_COLOR; + used.add(MISSING_LABEL_COLOR.toLowerCase()); + continue; + } + + let attempt = 0; + // Deterministic collision resolution (should be extremely rare). + while (attempt < 32) { + const candidate = colorForKey(attempt === 0 ? label : `${label}#${attempt}`); + const normalized = candidate.toLowerCase(); + if (!used.has(normalized)) { + colors[label] = candidate; + used.add(normalized); + break; + } + attempt++; + } + + if (!colors[label]) { + // Should never happen, but keep UI resilient. + colors[label] = FALLBACK_LABEL_COLOR; + } + } + + return colors; +} + +export function normalizeLabel(label: string | null | undefined): string { + return label && label.length > 0 ? label : MISSING_LABEL_SENTINEL; +} diff --git a/frontend/src/lib/labelLegend.ts b/frontend/src/lib/labelLegend.ts new file mode 100644 index 0000000..2d43e93 --- /dev/null +++ b/frontend/src/lib/labelLegend.ts @@ -0,0 +1,248 @@ +import type { EmbeddingsData } from "@/types"; +import { + FALLBACK_LABEL_COLOR, + MISSING_LABEL_COLOR, + createLabelColorMap, + normalizeLabel, +} from "@/lib/labelColors"; + +// Past ~20-30 categories, color-as-encoding becomes unreliable for most users. +// We choose a conservative upper bound and fall back to a single color. +export const MAX_DISTINCT_LABEL_COLORS = 20; +export const UNSELECTED_LABEL_ALPHA = 0.12; + +export interface ScatterLabelsInfo { + uniqueLabels: string[]; + categories: Uint16Array; + palette: string[]; +} + +function clamp01(v: number): number { + if (v < 0) return 0; + if (v > 1) return 1; + return v; +} + +function applyAlphaToHex(color: string, alpha: number): string { + if (!color.startsWith("#")) return color; + const hex = Math.round(clamp01(alpha) * 255) + .toString(16) + .padStart(2, "0"); + + if (color.length === 7) { + return `${color}${hex}`; + } + + if (color.length === 9) { + return `${color.slice(0, 7)}${hex}`; + } + + return color; +} + +function applyLabelFilterToPalette(params: { + palette: string[]; + labels: string[]; + labelFilter: string | null; + unselectedAlpha: number; +}): string[] { + const { palette, labels, labelFilter, unselectedAlpha } = params; + if (!labelFilter) return palette; + if (!labels.includes(labelFilter)) return palette; + + return palette.map((color, idx) => + labels[idx] === labelFilter ? color : applyAlphaToHex(color, unselectedAlpha) + ); +} + +export function buildLabelCounts(embeddings: EmbeddingsData | null): Map { + const counts = new Map(); + if (!embeddings) return counts; + for (const raw of embeddings.labels) { + const l = normalizeLabel(raw); + counts.set(l, (counts.get(l) ?? 0) + 1); + } + return counts; +} + +export function getDistinctLabelCount(labelCounts: Map): number { + let n = labelCounts.size; + if (labelCounts.has("undefined")) n -= 1; + return n; +} + +export function buildLabelUniverse( + datasetLabels: string[], + embeddingsLabels: (string | null)[] | null +): string[] { + const universe: string[] = []; + const seen = new Set(); + let hasMissing = false; + + const baseLabels = datasetLabels.map((l) => normalizeLabel(l)); + for (const l of baseLabels) { + if (l === "undefined") { + hasMissing = true; + continue; + } + if (seen.has(l)) continue; + seen.add(l); + universe.push(l); + } + + if (embeddingsLabels) { + const extras = new Set(); + for (const raw of embeddingsLabels) { + const l = normalizeLabel(raw); + if (l === "undefined") { + hasMissing = true; + continue; + } + if (!seen.has(l)) extras.add(l); + } + + if (extras.size > 0) { + const extraSorted = Array.from(extras).sort((a, b) => a.localeCompare(b)); + for (const l of extraSorted) { + seen.add(l); + universe.push(l); + } + } + } + + if (hasMissing) universe.push("undefined"); + return universe; +} + +export function buildLabelsInfo(params: { + datasetLabels: string[]; + embeddings: EmbeddingsData | null; + distinctColoringDisabled: boolean; + labelFilter?: string | null; + unselectedAlpha?: number; +}): ScatterLabelsInfo | null { + const { + datasetLabels, + embeddings, + distinctColoringDisabled, + labelFilter = null, + unselectedAlpha = UNSELECTED_LABEL_ALPHA, + } = params; + if (!embeddings) return null; + + const universe = buildLabelUniverse(datasetLabels, embeddings.labels); + + // Guard: hyper-scatter categories are Uint16. + if (universe.length > 65535) { + console.warn( + `Too many labels (${universe.length}) for uint16 categories; collapsing to a single color.` + ); + return { + uniqueLabels: ["undefined"], + categories: new Uint16Array(embeddings.labels.length), + palette: [FALLBACK_LABEL_COLOR], + }; + } + + const labelToCategory: Record = {}; + for (let i = 0; i < universe.length; i++) { + labelToCategory[universe[i]] = i; + } + + const undefinedIndex = labelToCategory["undefined"] ?? 0; + const categories = new Uint16Array(embeddings.labels.length); + for (let i = 0; i < embeddings.labels.length; i++) { + const key = normalizeLabel(embeddings.labels[i]); + categories[i] = labelToCategory[key] ?? undefinedIndex; + } + + let palette: string[]; + if (distinctColoringDisabled) { + palette = universe.map((l) => (l === "undefined" ? MISSING_LABEL_COLOR : FALLBACK_LABEL_COLOR)); + } else { + const colors = createLabelColorMap(universe); + palette = universe.map((l) => colors[l] ?? FALLBACK_LABEL_COLOR); + } + + const filteredPalette = applyLabelFilterToPalette({ + palette, + labels: universe, + labelFilter, + unselectedAlpha, + }); + + return { uniqueLabels: universe, categories, palette: filteredPalette }; +} + +export function buildLabelColorMap(params: { + labelsInfo: ScatterLabelsInfo | null; + labelUniverse: string[]; + distinctColoringDisabled: boolean; + labelFilter?: string | null; + unselectedAlpha?: number; +}): Record { + const { + labelsInfo, + labelUniverse, + distinctColoringDisabled, + labelFilter = null, + unselectedAlpha = UNSELECTED_LABEL_ALPHA, + } = params; + const map: Record = {}; + + if (labelsInfo) { + for (let i = 0; i < labelsInfo.uniqueLabels.length; i++) { + map[labelsInfo.uniqueLabels[i]] = labelsInfo.palette[i] ?? FALLBACK_LABEL_COLOR; + } + return map; + } + + if (labelUniverse.length === 0) return map; + + if (distinctColoringDisabled) { + for (const label of labelUniverse) { + map[label] = label === "undefined" ? MISSING_LABEL_COLOR : FALLBACK_LABEL_COLOR; + } + return map; + } + + const colors = createLabelColorMap(labelUniverse); + for (const label of labelUniverse) { + map[label] = colors[label] ?? FALLBACK_LABEL_COLOR; + } + + if (!labelFilter || !labelUniverse.includes(labelFilter)) return map; + + for (const label of labelUniverse) { + if (label !== labelFilter) { + map[label] = applyAlphaToHex(map[label], unselectedAlpha); + } + } + + return map; +} + +export function buildLegendLabels(params: { + labelUniverse: string[]; + labelCounts: Map; + query: string; +}): string[] { + const { labelUniverse, labelCounts, query } = params; + const all = labelUniverse.length > 0 ? [...labelUniverse] : Array.from(labelCounts.keys()); + const q = query.trim().toLowerCase(); + const filtered = q ? all.filter((l) => l.toLowerCase().includes(q)) : all; + const hasCounts = labelCounts.size > 0; + + return filtered.sort((a, b) => { + if (a === "undefined" && b !== "undefined") return 1; + if (b === "undefined" && a !== "undefined") return -1; + + if (hasCounts) { + const ca = labelCounts.get(a) ?? 0; + const cb = labelCounts.get(b) ?? 0; + if (cb !== ca) return cb - ca; + } + + return a.localeCompare(b); + }); +} diff --git a/frontend/src/store/useStore.ts b/frontend/src/store/useStore.ts index 9d74299..c440b10 100644 --- a/frontend/src/store/useStore.ts +++ b/frontend/src/store/useStore.ts @@ -1,7 +1,27 @@ import { create } from "zustand"; -import type { DatasetInfo, EmbeddingsData, Geometry, Sample } from "@/types"; +import type { DatasetInfo, EmbeddingsData, Sample } from "@/types"; +import { normalizeLabel } from "@/lib/labelColors"; + +function computeLabelSelection(embeddings: EmbeddingsData, label: string): Set { + const target = normalizeLabel(label); + const ids = new Set(); + for (let i = 0; i < embeddings.labels.length; i++) { + if (normalizeLabel(embeddings.labels[i]) === target) { + ids.add(embeddings.ids[i]); + } + } + return ids; +} interface AppState { + // Panel visibility (for header toggles) + leftPanelOpen: boolean; + rightPanelOpen: boolean; + bottomPanelOpen: boolean; + setLeftPanelOpen: (open: boolean) => void; + setRightPanelOpen: (open: boolean) => void; + setBottomPanelOpen: (open: boolean) => void; + // Dataset info datasetInfo: DatasetInfo | null; setDatasetInfo: (info: DatasetInfo) => void; @@ -15,22 +35,23 @@ interface AppState { appendSamples: (samples: Sample[]) => void; addSamplesIfMissing: (samples: Sample[]) => void; - // Embeddings - embeddings: EmbeddingsData | null; - setEmbeddings: (data: EmbeddingsData) => void; + // Embeddings (cached per layout key) + embeddingsByLayoutKey: Record; + setEmbeddingsForLayout: (layoutKey: string, data: EmbeddingsData) => void; - // Current layout key (from embeddings) - currentLayoutKey: string | null; + // Active layout (for sidebar context) + activeLayoutKey: string | null; + setActiveLayoutKey: (layoutKey: string | null) => void; - // Geometry mode - geometry: Geometry; - setGeometry: (geometry: Geometry) => void; + // Label filter (sidebar-driven) + labelFilter: string | null; + setLabelFilter: (label: string | null) => void; // Selection selectedIds: Set; isLassoSelection: boolean; - selectionSource: "scatter" | "grid" | "lasso" | null; - setSelectedIds: (ids: Set, source?: "scatter" | "grid") => void; + selectionSource: "scatter" | "grid" | "lasso" | "label" | null; + setSelectedIds: (ids: Set, source?: "scatter" | "grid" | "label") => void; toggleSelection: (id: string) => void; addToSelection: (ids: string[]) => void; clearSelection: () => void; @@ -58,6 +79,14 @@ interface AppState { } export const useStore = create((set, get) => ({ + // Panel visibility (for header toggles) + leftPanelOpen: false, + rightPanelOpen: false, + bottomPanelOpen: false, + setLeftPanelOpen: (open) => set({ leftPanelOpen: open }), + setRightPanelOpen: (open) => set({ rightPanelOpen: open }), + setBottomPanelOpen: (open) => set({ bottomPanelOpen: open }), + // Dataset info datasetInfo: null, setDatasetInfo: (info) => set({ datasetInfo: info }), @@ -87,15 +116,74 @@ export const useStore = create((set, get) => ({ }), // Embeddings - embeddings: null, - setEmbeddings: (data) => set({ embeddings: data, currentLayoutKey: data?.layout_key ?? null }), + embeddingsByLayoutKey: {}, + setEmbeddingsForLayout: (layoutKey, data) => + set((state) => { + const selectionUpdate = + state.labelFilter && + state.selectionSource === "label" && + state.activeLayoutKey === layoutKey + ? { + selectedIds: computeLabelSelection(data, state.labelFilter), + selectionSource: "label" as const, + } + : {}; - // Current layout key - currentLayoutKey: null, + return { + embeddingsByLayoutKey: { ...state.embeddingsByLayoutKey, [layoutKey]: data }, + ...selectionUpdate, + }; + }), + + // Active layout + activeLayoutKey: null, + setActiveLayoutKey: (layoutKey) => + set((state) => { + if (!layoutKey) return { activeLayoutKey: null }; + if (!state.labelFilter || state.selectionSource !== "label") { + return { activeLayoutKey: layoutKey }; + } - // Geometry mode - geometry: "euclidean", - setGeometry: (geometry) => set({ geometry }), + const embeddings = state.embeddingsByLayoutKey[layoutKey]; + if (!embeddings) { + return { + activeLayoutKey: layoutKey, + selectedIds: new Set(), + selectionSource: "label", + }; + } + + return { + activeLayoutKey: layoutKey, + selectedIds: computeLabelSelection(embeddings, state.labelFilter), + selectionSource: "label", + }; + }), + + // Label filter + labelFilter: null, + setLabelFilter: (label) => + set((state) => { + const nextLabel = label ? normalizeLabel(label) : null; + const nextState: Partial = { labelFilter: nextLabel }; + + if (nextLabel) { + const layoutKey = state.activeLayoutKey; + const embeddings = layoutKey ? state.embeddingsByLayoutKey[layoutKey] : null; + nextState.selectedIds = embeddings ? computeLabelSelection(embeddings, nextLabel) : new Set(); + nextState.selectionSource = "label"; + nextState.isLassoSelection = false; + nextState.lassoQuery = null; + nextState.lassoSamples = []; + nextState.lassoTotal = 0; + nextState.lassoIsLoading = false; + } else if (state.selectionSource === "label") { + nextState.selectedIds = new Set(); + nextState.selectionSource = null; + } + + return nextState; + }), // Selection selectedIds: new Set(), diff --git a/frontend/src/types/index.ts b/frontend/src/types/index.ts index 2ec4b90..2cd5560 100644 --- a/frontend/src/types/index.ts +++ b/frontend/src/types/index.ts @@ -5,6 +5,8 @@ export interface Sample { label: string | null; thumbnail: string | null; metadata: Record; + width: number | null; + height: number | null; } export type Geometry = "euclidean" | "poincare"; @@ -32,7 +34,7 @@ export interface DatasetInfo { name: string; num_samples: number; labels: string[]; - label_colors: Record; + label_colors?: Record; spaces: SpaceInfo[]; layouts: LayoutInfo[]; } @@ -43,7 +45,7 @@ export interface EmbeddingsData { ids: string[]; labels: (string | null)[]; coords: [number, number][]; - label_colors: Record; + label_colors?: Record; } export interface SamplesResponse { diff --git a/pyproject.toml b/pyproject.toml index 4acde4f..a2cfcc4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,6 +26,7 @@ dependencies = [ "fastapi>=0.128.0", "uvicorn[standard]>=0.40.0", "embed-anything>=0.7.0", + "hyper-models @ git+https://github.com/Hyper3Labs/hyper-models.git@7489595f4f665802671136872b2bf61794995e1b", "numpy>=1.26.4,<2.4", "umap-learn>=0.5.11", "pillow>=12.1.0", @@ -53,14 +54,17 @@ ml = [ hyperview = "hyperview.cli:main" [project.urls] -Homepage = "https://github.com/merantix/HyperView" -Documentation = "https://github.com/merantix/HyperView#readme" -Repository = "https://github.com/merantix/HyperView" +Homepage = "https://github.com/Hyper3Labs/HyperView" +Documentation = "https://github.com/Hyper3Labs/HyperView#readme" +Repository = "https://github.com/Hyper3Labs/HyperView" [build-system] requires = ["hatchling"] build-backend = "hatchling.build" +[tool.hatch.metadata] +allow-direct-references = true + [tool.hatch.build.targets.wheel] packages = ["src/hyperview"] # Include frontend static assets (pre-built before packaging) @@ -84,6 +88,3 @@ ignore = ["E501"] [tool.pytest.ini_options] asyncio_mode = "auto" testpaths = ["tests"] - -[tool.uv.sources] -hycoclip = { git = "https://github.com/PalAvik/hycoclip.git" } diff --git a/scripts/demo.py b/scripts/demo.py index 5347088..7680643 100644 --- a/scripts/demo.py +++ b/scripts/demo.py @@ -1,5 +1,5 @@ #!/usr/bin/env python3 -"""Run HyperView demo with CIFAR-100 dataset.""" +"""Run HyperView demo with CIFAR-10 dataset.""" import argparse import os @@ -15,8 +15,8 @@ def main(): parser.add_argument( "--dataset", type=str, - default="cifar100_demo", - help="Dataset name to use for persistence (default: cifar100_demo)", + default="cifar10_demo", + help="Dataset name to use for persistence (default: cifar10_demo)", ) parser.add_argument( "--samples", type=int, default=50000, help="Number of samples to load (default: 50000)" @@ -61,10 +61,10 @@ def main(): dataset = hv.Dataset(args.dataset, persist=not args.no_persist) dataset.add_from_huggingface( - "uoft-cs/cifar100", + "uoft-cs/cifar10", split="train", image_key="img", - label_key="fine_label", + label_key="label", max_samples=args.samples, ) diff --git a/src/hyperview/cli.py b/src/hyperview/cli.py index bdff271..9e5486c 100644 --- a/src/hyperview/cli.py +++ b/src/hyperview/cli.py @@ -108,15 +108,15 @@ def run_demo( open_browser: bool = True, reuse_server: bool = False, ): - """Run a demo with CIFAR-100 data.""" - print("Loading CIFAR-100 dataset...") - dataset = Dataset("cifar100_demo") + """Run a demo with CIFAR-10 data.""" + print("Loading CIFAR-10 dataset...") + dataset = Dataset("cifar10_demo") added, skipped = dataset.add_from_huggingface( - "uoft-cs/cifar100", + "uoft-cs/cifar10", split="train", image_key="img", - label_key="fine_label", + label_key="label", max_samples=num_samples, ) if skipped > 0: diff --git a/src/hyperview/core/dataset.py b/src/hyperview/core/dataset.py index 18ee2fb..a943e8a 100644 --- a/src/hyperview/core/dataset.py +++ b/src/hyperview/core/dataset.py @@ -10,7 +10,7 @@ from typing import Any, cast import numpy as np -from datasets import load_dataset +from datasets import DownloadConfig, load_dataset from PIL import Image from hyperview.core.sample import Sample @@ -232,7 +232,21 @@ def add_from_huggingface( """ from hyperview.storage import StorageConfig - ds = cast(Any, load_dataset(dataset_name, split=split)) + # HuggingFace `load_dataset()` can be surprisingly slow even when the dataset + # is already cached, due to Hub reachability checks in some environments. + # For a fast path, first try loading in "offline" mode (cache-only), and + # fall back to an online load if the dataset isn't cached yet. + try: + ds = cast( + Any, + load_dataset( + dataset_name, + split=split, + download_config=DownloadConfig(local_files_only=True), + ), + ) + except Exception: + ds = cast(Any, load_dataset(dataset_name, split=split)) # Get label names if available label_names = None diff --git a/src/hyperview/core/sample.py b/src/hyperview/core/sample.py index 78623ba..5eaf212 100644 --- a/src/hyperview/core/sample.py +++ b/src/hyperview/core/sample.py @@ -21,6 +21,8 @@ class Sample(BaseModel): label: str | None = Field(default=None, description="Label for the sample") metadata: dict[str, Any] = Field(default_factory=dict, description="Additional metadata") thumbnail_base64: str | None = Field(default=None, description="Cached thumbnail as base64") + width: int | None = Field(default=None, description="Image width in pixels") + height: int | None = Field(default=None, description="Image height in pixels") model_config = {"arbitrary_types_allowed": True} @@ -34,8 +36,11 @@ def load_image(self) -> Image.Image: return Image.open(self.filepath) def get_thumbnail(self, size: tuple[int, int] = (128, 128)) -> Image.Image: - """Get a thumbnail of the image.""" + """Get a thumbnail of the image. Also captures original dimensions.""" img = self.load_image() + # Capture original dimensions while we have the image loaded + if self.width is None or self.height is None: + self.width, self.height = img.size img.thumbnail(size, Image.Resampling.LANCZOS) return img @@ -65,10 +70,22 @@ def to_api_dict(self, include_thumbnail: bool = True) -> dict[str, Any]: "filename": self.filename, "label": self.label, "metadata": self.metadata, + "width": self.width, + "height": self.height, } if include_thumbnail: data["thumbnail"] = self.get_thumbnail_base64() return data + def ensure_dimensions(self) -> None: + """Load image dimensions if not already set.""" + if self.width is None or self.height is None: + try: + img = self.load_image() + self.width, self.height = img.size + except Exception: + # If image can't be loaded, leave as None + pass + diff --git a/src/hyperview/server/app.py b/src/hyperview/server/app.py index 3607d18..26e897a 100644 --- a/src/hyperview/server/app.py +++ b/src/hyperview/server/app.py @@ -46,6 +46,8 @@ class SampleResponse(BaseModel): label: str | None thumbnail: str | None metadata: dict + width: int | None = None + height: int | None = None class LayoutInfoResponse(BaseModel): diff --git a/src/hyperview/storage/lancedb_backend.py b/src/hyperview/storage/lancedb_backend.py index e9e9eb0..f85b635 100644 --- a/src/hyperview/storage/lancedb_backend.py +++ b/src/hyperview/storage/lancedb_backend.py @@ -42,8 +42,19 @@ def __init__(self, dataset_name: str, config: StorageConfig | None = None): self._samples_table = self._get_or_create_samples_table() self._spaces_table = self._get_or_create_spaces_table() + def _table_names(self) -> set[str]: + """Return the set of table names in this LanceDB database.""" + try: + res = self._db.list_tables() + # LanceDB may return a response object with a `.tables` field. + names = res.tables if hasattr(res, "tables") else res + except Exception: + # Back-compat for older LanceDB. + names = self._db.table_names() + return set(names) + def _get_or_create_samples_table(self) -> lancedb.table.Table | None: - if "samples" in self._db.table_names(): + if "samples" in self._table_names(): return self._db.open_table("samples") return None @@ -55,7 +66,7 @@ def _ensure_samples_table(self, data: list[dict]) -> lancedb.table.Table: return self._samples_table def _get_or_create_spaces_table(self) -> lancedb.table.Table: - if "spaces" in self._db.table_names(): + if "spaces" in self._table_names(): return self._db.open_table("spaces") return self._db.create_table("spaces", schema=create_spaces_schema()) @@ -215,7 +226,7 @@ def ensure_space( def delete_space(self, space_key: str) -> bool: self._spaces_table.delete(f"space_key = '{_sql_escape(space_key)}'") emb_table = f"embeddings__{space_key}" - if emb_table in self._db.table_names(): + if emb_table in self._table_names(): self._db.drop_table(emb_table) return True @@ -227,7 +238,7 @@ def add_embeddings(self, space_key: str, ids: list[str], vectors: np.ndarray) -> raise ValueError(f"Space not found: {space_key}") emb_table_name = f"embeddings__{space_key}" - if emb_table_name not in self._db.table_names(): + if emb_table_name not in self._table_names(): self._db.create_table(emb_table_name, schema=create_embeddings_schema(space.dim)) emb_table = self._db.open_table(emb_table_name) @@ -247,7 +258,7 @@ def get_embeddings(self, space_key: str, ids: list[str] | None = None) -> tuple[ raise ValueError(f"Space not found: {space_key}") emb_table_name = f"embeddings__{space_key}" - if emb_table_name not in self._db.table_names(): + if emb_table_name not in self._table_names(): return [], np.empty((0, space.dim), dtype=np.float32) emb_table = self._db.open_table(emb_table_name) @@ -263,7 +274,7 @@ def get_embeddings(self, space_key: str, ids: list[str] | None = None) -> tuple[ def get_embedded_ids(self, space_key: str) -> set[str]: emb_table_name = f"embeddings__{space_key}" - if emb_table_name not in self._db.table_names(): + if emb_table_name not in self._table_names(): return set() return {r["id"] for r in self._db.open_table(emb_table_name).search().select(["id"]).to_list()} @@ -274,10 +285,10 @@ def get_missing_embedding_ids(self, space_key: str) -> list[str]: return list(all_ids - self.get_embedded_ids(space_key)) def _get_layouts_registry_table(self) -> lancedb.table.Table | None: - return self._db.open_table("layouts_registry") if "layouts_registry" in self._db.table_names() else None + return self._db.open_table("layouts_registry") if "layouts_registry" in self._table_names() else None def _ensure_layouts_registry_table(self) -> lancedb.table.Table: - if "layouts_registry" not in self._db.table_names(): + if "layouts_registry" not in self._table_names(): self._db.create_table("layouts_registry", schema=create_layouts_registry_schema()) return self._db.open_table("layouts_registry") @@ -312,13 +323,13 @@ def ensure_layout( registry_table.add(pa.Table.from_pylist([layout_info.to_dict()], schema=create_layouts_registry_schema())) table_name = f"layouts__{layout_key}" - if table_name not in self._db.table_names(): + if table_name not in self._table_names(): self._db.create_table(table_name, schema=create_layouts_schema()) return layout_info def delete_layout(self, layout_key: str) -> bool: table_name = f"layouts__{layout_key}" - if table_name in self._db.table_names(): + if table_name in self._table_names(): self._db.drop_table(table_name) registry = self._get_layouts_registry_table() if registry: @@ -332,7 +343,7 @@ def add_layout_coords(self, layout_key: str, ids: list[str], coords: np.ndarray) raise ValueError(f"Layout '{layout_key}' not registered") table_name = f"layouts__{layout_key}" - if table_name not in self._db.table_names(): + if table_name not in self._table_names(): self._db.create_table(table_name, schema=create_layouts_schema()) table = self._db.open_table(table_name) @@ -348,7 +359,7 @@ def add_layout_coords(self, layout_key: str, ids: list[str], coords: np.ndarray) def get_layout_coords(self, layout_key: str, ids: list[str] | None = None) -> tuple[list[str], np.ndarray]: table_name = f"layouts__{layout_key}" - if table_name not in self._db.table_names(): + if table_name not in self._table_names(): return [], np.empty((0, 2), dtype=np.float32) table = self._db.open_table(table_name) @@ -372,7 +383,7 @@ def get_lasso_candidates_aabb( y_max: float, ) -> tuple[list[str], np.ndarray]: table_name = f"layouts__{layout_key}" - if table_name not in self._db.table_names(): + if table_name not in self._table_names(): return [], np.empty((0, 2), dtype=np.float32) rows = self._db.open_table(table_name).search().where( @@ -412,7 +423,7 @@ def find_similar_by_vector( space_key = spaces[0].space_key emb_table_name = f"embeddings__{space_key}" - if emb_table_name not in self._db.table_names(): + if emb_table_name not in self._table_names(): return [] results = self._db.open_table(emb_table_name).search(vector, vector_column_name="vector").metric("cosine").limit(k).to_list() From b86f65f9df62f294965702e8841a0e6bdc6fd460 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Tue, 27 Jan 2026 23:53:40 +0100 Subject: [PATCH 39/75] feat: ensure dimensions are populated in API response --- src/hyperview/core/sample.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/hyperview/core/sample.py b/src/hyperview/core/sample.py index 5eaf212..d0e3f38 100644 --- a/src/hyperview/core/sample.py +++ b/src/hyperview/core/sample.py @@ -64,6 +64,10 @@ def cache_thumbnail(self, size: tuple[int, int] = (128, 128)) -> None: def to_api_dict(self, include_thumbnail: bool = True) -> dict[str, Any]: """Convert to dictionary for API response.""" + # Ensure dimensions are populated (loads image if needed but not cached) + if self.width is None or self.height is None: + self.ensure_dimensions() + data = { "id": self.id, "filepath": self.filepath, From e741e8da56cd2a3abdbc0e4e153b6e85be59808d Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Tue, 27 Jan 2026 23:59:45 +0100 Subject: [PATCH 40/75] chore: auto-export frontend on commit Adds a repo-managed pre-commit hook (via core.hooksPath) that runs scripts/export_frontend.sh and stages src/hyperview/server/static whenever frontend/ is part of the commit. --- .githooks/pre-commit | 48 ++++++++++++++++++++++++++++++++++++ scripts/install_git_hooks.sh | 10 ++++++++ 2 files changed, 58 insertions(+) create mode 100755 .githooks/pre-commit create mode 100755 scripts/install_git_hooks.sh diff --git a/.githooks/pre-commit b/.githooks/pre-commit new file mode 100755 index 0000000..e53c105 --- /dev/null +++ b/.githooks/pre-commit @@ -0,0 +1,48 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT="$(git rev-parse --show-toplevel)" +cd "$ROOT" + +if [[ "${HYPERVIEW_SKIP_FRONTEND_EXPORT:-}" == "1" ]]; then + echo "[HyperView] HYPERVIEW_SKIP_FRONTEND_EXPORT=1; skipping frontend export." + exit 0 +fi + +# Only run when the commit includes frontend changes. +if ! git diff --cached --name-only | grep -q '^frontend/'; then + exit 0 +fi + +if [[ "${HYPERVIEW_ALLOW_DIRTY_FRONTEND:-}" != "1" ]]; then + if [[ -n "$(git diff --name-only -- frontend/)" ]]; then + echo "[HyperView] Unstaged changes detected under frontend/." + echo "[HyperView] Please stage/stash them so the exported static matches the commit." + echo "[HyperView] Override with HYPERVIEW_ALLOW_DIRTY_FRONTEND=1 or use: git commit --no-verify" + exit 1 + fi + + if [[ -n "$(git ls-files --others --exclude-standard -- frontend/)" ]]; then + echo "[HyperView] Untracked files detected under frontend/." + echo "[HyperView] Please add/stash them so the exported static matches the commit." + echo "[HyperView] Override with HYPERVIEW_ALLOW_DIRTY_FRONTEND=1 or use: git commit --no-verify" + exit 1 + fi +fi + +echo "[HyperView] Frontend changes staged; exporting static frontend..." + +bash scripts/export_frontend.sh + +git add -A src/hyperview/server/static/ + +# Safety: if the export process changed tracked frontend files (e.g. lockfile), +# abort so the user can review/stage them. +if [[ -n "$(git diff --name-only -- frontend/)" ]]; then + echo "[HyperView] Export modified tracked files under frontend/." + echo "[HyperView] Please review and stage these changes, then re-commit." + echo "[HyperView] (This often happens if npm updates package-lock.json.)" + exit 1 +fi + +echo "[HyperView] Export complete; static output staged." diff --git a/scripts/install_git_hooks.sh b/scripts/install_git_hooks.sh new file mode 100755 index 0000000..3460a35 --- /dev/null +++ b/scripts/install_git_hooks.sh @@ -0,0 +1,10 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT="$(git rev-parse --show-toplevel)" +cd "$ROOT" + +git config core.hooksPath .githooks +chmod +x .githooks/pre-commit + +echo "Installed git hooks via core.hooksPath=.githooks" From 3f89b60fd8225684806f1f2f4ce7faba1641c434 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Wed, 28 Jan 2026 00:04:08 +0100 Subject: [PATCH 41/75] ci: require frontend export in PRs Fails PRs when frontend/ changes without updating src/hyperview/server/static/. --- .github/workflows/require_frontend_export.yml | 53 +++++++++++++++++++ 1 file changed, 53 insertions(+) create mode 100644 .github/workflows/require_frontend_export.yml diff --git a/.github/workflows/require_frontend_export.yml b/.github/workflows/require_frontend_export.yml new file mode 100644 index 0000000..00363fe --- /dev/null +++ b/.github/workflows/require_frontend_export.yml @@ -0,0 +1,53 @@ +name: Require Frontend Export + +on: + pull_request: + types: [opened, synchronize, reopened] + paths: + - "frontend/**" + - "scripts/export_frontend.sh" + - "src/hyperview/server/static/**" + +jobs: + require-frontend-export: + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + with: + fetch-depth: 0 + ref: ${{ github.event.pull_request.head.sha }} + + - name: Verify static export updated when frontend changes + run: | + set -euo pipefail + + base_sha="${{ github.event.pull_request.base.sha }}" + head_sha="${{ github.event.pull_request.head.sha }}" + + changed_files="$(git diff --name-only "$base_sha" "$head_sha")" + + echo "Changed files:" + echo "$changed_files" + + frontend_changed="false" + static_changed="false" + + if echo "$changed_files" | grep -qE '^(frontend/|scripts/export_frontend\.sh$)'; then + frontend_changed="true" + fi + + if echo "$changed_files" | grep -q '^src/hyperview/server/static/'; then + static_changed="true" + fi + + if [[ "$frontend_changed" == "true" && "$static_changed" != "true" ]]; then + echo "" + echo "ERROR: frontend/ changed but src/hyperview/server/static/ was not updated." + echo "Run: bash scripts/export_frontend.sh" + echo "Then commit the updated src/hyperview/server/static/ output." + exit 1 + fi + + echo "OK: export requirements satisfied." From 2291a0bffbe97cc5065c15fe0d96096ad99bd803 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Wed, 28 Jan 2026 13:39:51 +0100 Subject: [PATCH 42/75] revert: remove git hook automation Removes the repo-managed pre-commit hook and installer script. --- .githooks/pre-commit | 48 ------------------------------------ scripts/install_git_hooks.sh | 10 -------- 2 files changed, 58 deletions(-) delete mode 100755 .githooks/pre-commit delete mode 100755 scripts/install_git_hooks.sh diff --git a/.githooks/pre-commit b/.githooks/pre-commit deleted file mode 100755 index e53c105..0000000 --- a/.githooks/pre-commit +++ /dev/null @@ -1,48 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -ROOT="$(git rev-parse --show-toplevel)" -cd "$ROOT" - -if [[ "${HYPERVIEW_SKIP_FRONTEND_EXPORT:-}" == "1" ]]; then - echo "[HyperView] HYPERVIEW_SKIP_FRONTEND_EXPORT=1; skipping frontend export." - exit 0 -fi - -# Only run when the commit includes frontend changes. -if ! git diff --cached --name-only | grep -q '^frontend/'; then - exit 0 -fi - -if [[ "${HYPERVIEW_ALLOW_DIRTY_FRONTEND:-}" != "1" ]]; then - if [[ -n "$(git diff --name-only -- frontend/)" ]]; then - echo "[HyperView] Unstaged changes detected under frontend/." - echo "[HyperView] Please stage/stash them so the exported static matches the commit." - echo "[HyperView] Override with HYPERVIEW_ALLOW_DIRTY_FRONTEND=1 or use: git commit --no-verify" - exit 1 - fi - - if [[ -n "$(git ls-files --others --exclude-standard -- frontend/)" ]]; then - echo "[HyperView] Untracked files detected under frontend/." - echo "[HyperView] Please add/stash them so the exported static matches the commit." - echo "[HyperView] Override with HYPERVIEW_ALLOW_DIRTY_FRONTEND=1 or use: git commit --no-verify" - exit 1 - fi -fi - -echo "[HyperView] Frontend changes staged; exporting static frontend..." - -bash scripts/export_frontend.sh - -git add -A src/hyperview/server/static/ - -# Safety: if the export process changed tracked frontend files (e.g. lockfile), -# abort so the user can review/stage them. -if [[ -n "$(git diff --name-only -- frontend/)" ]]; then - echo "[HyperView] Export modified tracked files under frontend/." - echo "[HyperView] Please review and stage these changes, then re-commit." - echo "[HyperView] (This often happens if npm updates package-lock.json.)" - exit 1 -fi - -echo "[HyperView] Export complete; static output staged." diff --git a/scripts/install_git_hooks.sh b/scripts/install_git_hooks.sh deleted file mode 100755 index 3460a35..0000000 --- a/scripts/install_git_hooks.sh +++ /dev/null @@ -1,10 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -ROOT="$(git rev-parse --show-toplevel)" -cd "$ROOT" - -git config core.hooksPath .githooks -chmod +x .githooks/pre-commit - -echo "Installed git hooks via core.hooksPath=.githooks" From e0a5bc53f43e0e7c3a9b2a7856bb13be6df77477 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Sat, 31 Jan 2026 15:20:35 +0100 Subject: [PATCH 43/75] refactor: unify embedding providers via LanceDB registry - Replace BaseEmbeddingProvider/ModelSpec with LanceDB registry-based EmbeddingSpec - Add embed-anything and hyper-models as @register'd LanceDB embedding functions - Make model parameter required in compute_embeddings() with auto-detection - Add list_embedding_providers() and get_provider_info() to public API - Use uv workspace for hyper-models dependency (local development) - Add CONTRIBUTING.md with development setup guide - Update docs to use full HuggingFace model IDs - Smarter default geometry selection in launch() auto-layout --- CONTRIBUTING.md | 82 +++++ README.md | 2 +- docs/datasets.md | 4 +- notebooks/demo.ipynb | 194 ---------- pyproject.toml | 8 +- scripts/demo.py | 8 +- scripts/demo_hyperbolic_clip.py | 13 +- src/hyperview/__init__.py | 14 +- src/hyperview/api.py | 17 +- src/hyperview/cli.py | 20 +- src/hyperview/core/dataset.py | 49 ++- src/hyperview/embeddings/__init__.py | 32 +- src/hyperview/embeddings/engine.py | 330 ++++++++++++++++++ src/hyperview/embeddings/pipelines.py | 34 +- .../embeddings/providers/__init__.py | 203 +---------- .../embeddings/providers/embed_anything.py | 57 --- .../embeddings/providers/hycoclip.py | 279 --------------- .../embeddings/providers/hycoclip_onnx.py | 194 ---------- .../embeddings/providers/lancedb_providers.py | 196 +++++++++++ src/hyperview/storage/lancedb_backend.py | 10 +- src/hyperview/storage/schema.py | 1 - 21 files changed, 747 insertions(+), 1000 deletions(-) create mode 100644 CONTRIBUTING.md delete mode 100644 notebooks/demo.ipynb create mode 100644 src/hyperview/embeddings/engine.py delete mode 100644 src/hyperview/embeddings/providers/embed_anything.py delete mode 100644 src/hyperview/embeddings/providers/hycoclip.py delete mode 100644 src/hyperview/embeddings/providers/hycoclip_onnx.py create mode 100644 src/hyperview/embeddings/providers/lancedb_providers.py diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..dc95ba9 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,82 @@ +# Contributing to HyperView + +We welcome contributions! This guide will help you set up your development environment and submit high-quality pull requests. + +## Development Setup + +### Requirements +- **Python 3.10+** +- **uv** (Package manager) +- **Node.js** (For frontend development) + +### One-time Setup +Clone the repo and install dependencies: + +```bash +git clone https://github.com/Hyper3Labs/HyperView.git +cd HyperView + +# Create virtual environment and install dev dependencies +uv venv .venv +source .venv/bin/activate +uv pip install -e ".[dev]" + +# Install frontend dependencies +cd frontend +npm install +cd .. +``` + +## Running Locally + +For the best development experience, run the backend and frontend in separate terminals. + +### 1. Start the Backend +Runs the Python API server at `http://127.0.0.1:6262`. + +```bash +uv run hyperview demo --samples 200 --no-browser +``` + +_Tip: Use `HF_DATASETS_OFFLINE=1` if you have cached datasets and want to work offline._ + +### 2. Start the Frontend +Runs the Next.js dev server at `http://127.0.0.1:3000` with hot reloading. + +```bash +cd frontend +npm run dev +``` + +The frontend automatically proxies API requests (`/api/*`) to the backend. + +## Common Tasks + +### Testing & Linting +Please ensure all checks pass before submitting a PR. + +```bash +# Python +uv run pytest # Run unit tests +uv run ruff format . # formatting +uv run ruff check . --fix # Linting + +# Frontend +cd frontend +npm run lint +``` + +### Exporting the Frontend +The Python package bundles the compiled frontend. If you modify the frontend, you must regenerate the static assets so they can be served by the Python backend in production/demos. + +```bash +bash scripts/export_frontend.sh +``` +_This compiles the frontend and places artifacts into `src/hyperview/server/static/`. Do not edit files in that directory manually._ + +## Pull Request Guidelines + +1. **Scope**: Keep changes focused. Open an issue first for major refactors or new features. +2. **Tests**: Add tests for new logic where practical. +3. **Visuals**: If changing the UI, please attach a screenshot or GIF to your PR. +4. **Format**: Ensure code is formatted with `ruff` (Python) and `prettier` (JS/TS implicit in `npm run lint`). diff --git a/README.md b/README.md index dbf7dfc..68f17d0 100644 --- a/README.md +++ b/README.md @@ -68,7 +68,7 @@ dataset.add_from_huggingface( # dataset.add_images_dir("/path/to/images", label_from_folder=True) # Compute embeddings and visualization -dataset.compute_embeddings() +dataset.compute_embeddings(model="openai/clip-vit-base-patch32") dataset.compute_visualization() # Launch the UI diff --git a/docs/datasets.md b/docs/datasets.md index f147fe5..c8382f2 100644 --- a/docs/datasets.md +++ b/docs/datasets.md @@ -63,8 +63,8 @@ Samples are **never implicitly deleted**. Use `hv.Dataset.delete("name")` for ex ## Computing Embeddings ```python -# High-dimensional embeddings (CLIP/ResNet) -dataset.compute_embeddings(model="clip", show_progress=True) +# High-dimensional embeddings (CLIP) +dataset.compute_embeddings(model="openai/clip-vit-base-patch32", show_progress=True) # 2D projections for visualization dataset.compute_visualization() # UMAP to Euclidean + Hyperbolic diff --git a/notebooks/demo.ipynb b/notebooks/demo.ipynb deleted file mode 100644 index 20b5343..0000000 --- a/notebooks/demo.ipynb +++ /dev/null @@ -1,194 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "e26a6a66", - "metadata": {}, - "source": [ - "# HyperView Demo\n", - "This notebook demonstrates how to use HyperView to visualize embeddings in both Euclidean and Hyperbolic spaces.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0aba0ed9", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-21T23:01:47.867950Z", - "iopub.status.busy": "2025-12-21T23:01:47.867881Z", - "iopub.status.idle": "2025-12-21T23:01:48.736348Z", - "shell.execute_reply": "2025-12-21T23:01:48.735942Z" - } - }, - "outputs": [], - "source": [ - "import sys\n", - "from pathlib import Path\n", - "\n", - "# Add src to path for development\n", - "sys.path.insert(0, str(Path.cwd().parent / \"src\"))\n", - "\n", - "import hyperview as hv\n", - "print(f\"HyperView version: {hv.__version__}\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "00becfad", - "metadata": {}, - "source": [ - "## Initialize Dataset\n", - "We'll create a new dataset named `cifar100_demo`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "3e5e93d5", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-21T23:01:48.738083Z", - "iopub.status.busy": "2025-12-21T23:01:48.737879Z", - "iopub.status.idle": "2025-12-21T23:01:49.297549Z", - "shell.execute_reply": "2025-12-21T23:01:49.296797Z" - } - }, - "outputs": [], - "source": [ - "dataset = hv.Dataset(\"cifar100_demo\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "2248602f", - "metadata": {}, - "source": [ - "## Load CIFAR-100 Data\n", - "We'll load 500 samples from the CIFAR-100 dataset using Hugging Face Datasets.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0666b39a", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-21T23:01:49.299289Z", - "iopub.status.busy": "2025-12-21T23:01:49.299171Z", - "iopub.status.idle": "2025-12-21T23:01:51.732573Z", - "shell.execute_reply": "2025-12-21T23:01:51.732008Z" - } - }, - "outputs": [], - "source": [ - "dataset.add_from_huggingface(\n", - " \"uoft-cs/cifar100\",\n", - " split=\"train\",\n", - " image_key=\"img\",\n", - " label_key=\"fine_label\",\n", - " max_samples=500,\n", - ")\n" - ] - }, - { - "cell_type": "markdown", - "id": "5e119424", - "metadata": {}, - "source": [ - "## Compute Embeddings\n", - "This will use a pre-trained model to compute high-dimensional embeddings for our images.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d4e2c71f", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-21T23:01:51.734082Z", - "iopub.status.busy": "2025-12-21T23:01:51.733890Z", - "iopub.status.idle": "2025-12-21T23:01:55.876495Z", - "shell.execute_reply": "2025-12-21T23:01:55.875932Z" - } - }, - "outputs": [], - "source": [ - "dataset.compute_embeddings(show_progress=True)\n" - ] - }, - { - "cell_type": "markdown", - "id": "6f552a59", - "metadata": {}, - "source": [ - "## Compute Visualization Layout\n", - "This step performs dimensionality reduction to project the high-dimensional embeddings into 2D Euclidean and Hyperbolic spaces.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b357847f", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-21T23:01:55.880223Z", - "iopub.status.busy": "2025-12-21T23:01:55.879935Z", - "iopub.status.idle": "2025-12-21T23:01:56.272742Z", - "shell.execute_reply": "2025-12-21T23:01:56.272238Z" - } - }, - "outputs": [], - "source": [ - "dataset.compute_visualization()\n" - ] - }, - { - "cell_type": "markdown", - "id": "54af1ea2", - "metadata": {}, - "source": [ - "## Launch Interactive Visualizer\n", - "Finally, we launch the visualizer. In a notebook environment, this will display an interactive iframe.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0acef7dc", - "metadata": { - "execution": { - "iopub.execute_input": "2025-12-21T23:01:56.274506Z", - "iopub.status.busy": "2025-12-21T23:01:56.274369Z", - "iopub.status.idle": "2025-12-21T23:01:56.356551Z", - "shell.execute_reply": "2025-12-21T23:01:56.355551Z" - } - }, - "outputs": [], - "source": [ - "session = hv.launch(dataset)\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv (3.12.8)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/pyproject.toml b/pyproject.toml index a2cfcc4..c672ba7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,7 +26,7 @@ dependencies = [ "fastapi>=0.128.0", "uvicorn[standard]>=0.40.0", "embed-anything>=0.7.0", - "hyper-models @ git+https://github.com/Hyper3Labs/hyper-models.git@7489595f4f665802671136872b2bf61794995e1b", + "hyper-models>=0.1.0", # PyPI package: https://pypi.org/project/hyper-models/ "numpy>=1.26.4,<2.4", "umap-learn>=0.5.11", "pillow>=12.1.0", @@ -88,3 +88,9 @@ ignore = ["E501"] [tool.pytest.ini_options] asyncio_mode = "auto" testpaths = ["tests"] + +[tool.uv.workspace] +members = ["hyper_models"] + +[tool.uv.sources] +hyper-models = { workspace = true } diff --git a/scripts/demo.py b/scripts/demo.py index 7680643..c703922 100644 --- a/scripts/demo.py +++ b/scripts/demo.py @@ -68,11 +68,11 @@ def main(): max_samples=args.samples, ) - dataset.compute_embeddings(model=args.model, show_progress=True) + space_key = dataset.compute_embeddings(model=args.model, show_progress=True) - # Compute both euclidean and poincare layouts - dataset.compute_visualization(geometry="euclidean") - dataset.compute_visualization(geometry="poincare") + # Compute a single layout for the UI to display by default. + # Switch to geometry="euclidean" for standard 2D UMAP. + dataset.compute_visualization(space_key=space_key, geometry="poincare") if args.no_server: return diff --git a/scripts/demo_hyperbolic_clip.py b/scripts/demo_hyperbolic_clip.py index 513bd4c..282e7a8 100644 --- a/scripts/demo_hyperbolic_clip.py +++ b/scripts/demo_hyperbolic_clip.py @@ -1,18 +1,16 @@ #!/usr/bin/env python -"""Demo: CLIP (Euclidean) + HyCoCLIP (Poincaré) on CIFAR-100.""" +"""Demo: CLIP (Euclidean) + hyper-models (Poincaré) on CIFAR-100.""" import hyperview as hv -import hyperview.embeddings.providers.hycoclip # noqa: F401 -from hyperview.embeddings.providers import ModelSpec -DATASET_NAME = "cifar100_clip_hycoclip" +DATASET_NAME = "cifar100_clip_hyper_models" HF_DATASET = "uoft-cs/cifar100" HF_SPLIT = "test" HF_IMAGE_KEY = "img" HF_LABEL_KEY = "fine_label" NUM_SAMPLES = 200 CLIP_MODEL_ID = "openai/clip-vit-base-patch32" -HYCOCLIP_MODEL_ID = "hycoclip_vit_s" +HYPER_MODELS_MODEL_ID = "hycoclip-vit-s" def main() -> None: @@ -29,9 +27,8 @@ def main() -> None: clip_space = dataset.compute_embeddings(CLIP_MODEL_ID) dataset.compute_visualization(space_key=clip_space, geometry="euclidean") - hycoclip_spec = ModelSpec(provider="hycoclip", model_id=HYCOCLIP_MODEL_ID) - hycoclip_space = dataset.compute_embeddings(hycoclip_spec) - dataset.compute_visualization(space_key=hycoclip_space, geometry="poincare") + hyper_space = dataset.compute_embeddings(model=HYPER_MODELS_MODEL_ID) + dataset.compute_visualization(space_key=hyper_space, geometry="poincare") print("Launching at http://127.0.0.1:6262") diff --git a/src/hyperview/__init__.py b/src/hyperview/__init__.py index 7def79f..f2b7743 100644 --- a/src/hyperview/__init__.py +++ b/src/hyperview/__init__.py @@ -1,6 +1,18 @@ """HyperView - Open-source dataset curation with hyperbolic embeddings visualization.""" from hyperview.api import Dataset, launch +from hyperview.embeddings.engine import ( + EmbeddingSpec, + get_provider_info, + list_embedding_providers, +) __version__ = "0.1.0" -__all__ = ["Dataset", "launch", "__version__"] +__all__ = [ + "Dataset", + "EmbeddingSpec", + "get_provider_info", + "launch", + "list_embedding_providers", + "__version__", +] diff --git a/src/hyperview/api.py b/src/hyperview/api.py index 9042970..c9a7e5b 100644 --- a/src/hyperview/api.py +++ b/src/hyperview/api.py @@ -228,9 +228,9 @@ def launch( """Launch the HyperView visualization server. Note: - HyperView's UI requires 2D layouts (Euclidean + Poincare). If they are - missing but high-dimensional embeddings exist, this function will compute - them automatically. + HyperView's UI needs at least one 2D layout. If layouts are missing but + embedding spaces exist, this function will compute a default layout + automatically (Euclidean if any Euclidean space exists, otherwise Poincaré). Args: dataset: The dataset to visualize. @@ -251,7 +251,7 @@ def launch( >>> import hyperview as hv >>> dataset = hv.Dataset("my_dataset") >>> dataset.add_images_dir("/path/to/images", label_from_folder=True) - >>> dataset.compute_embeddings() + >>> dataset.compute_embeddings(model="openai/clip-vit-base-patch32") >>> dataset.compute_visualization() >>> hv.launch(dataset) """ @@ -332,9 +332,12 @@ def launch( ) if not layouts: - default_space_key = spaces[0].space_key - print("No layouts found. Computing euclidean visualization...") - dataset.compute_visualization(space_key=default_space_key, geometry="euclidean") + has_euclidean_space = any(s.geometry != "hyperboloid" for s in spaces) + default_geometry = "euclidean" if has_euclidean_space else "poincare" + + print(f"No layouts found. Computing {default_geometry} visualization...") + # Let compute_visualization pick the most appropriate default space. + dataset.compute_visualization(space_key=None, geometry=default_geometry) session = Session(dataset, host, port) diff --git a/src/hyperview/cli.py b/src/hyperview/cli.py index 9e5486c..f7cc77b 100644 --- a/src/hyperview/cli.py +++ b/src/hyperview/cli.py @@ -47,6 +47,12 @@ def main(): "For safety, this only attaches when the existing server reports the same dataset name." ), ) + demo_parser.add_argument( + "--model", + type=str, + default="openai/clip-vit-base-patch32", + help="Embedding model to use (default: openai/clip-vit-base-patch32)", + ) # Serve command serve_parser = subparsers.add_parser("serve", help="Serve a saved dataset") @@ -86,6 +92,7 @@ def main(): host=args.host, open_browser=not args.no_browser, reuse_server=args.reuse_server, + model=args.model, ) elif args.command == "serve": serve_dataset( @@ -107,7 +114,8 @@ def run_demo( host: str = "127.0.0.1", open_browser: bool = True, reuse_server: bool = False, -): + model: str = "openai/clip-vit-base-patch32", +) -> None: """Run a demo with CIFAR-10 data.""" print("Loading CIFAR-10 dataset...") dataset = Dataset("cifar10_demo") @@ -124,14 +132,14 @@ def run_demo( else: print(f"Loaded {added} samples") - print("Computing embeddings...") - dataset.compute_embeddings(show_progress=True) + print(f"Computing embeddings with {model}...") + space_key = dataset.compute_embeddings(model=model, show_progress=True) print("Embeddings computed") print("Computing visualizations...") # Compute both euclidean and poincare layouts - dataset.compute_visualization(geometry="euclidean") - dataset.compute_visualization(geometry="poincare") + dataset.compute_visualization(space_key=space_key, geometry="euclidean") + dataset.compute_visualization(space_key=space_key, geometry="poincare") print("Visualizations ready") launch(dataset, port=port, host=host, open_browser=open_browser, reuse_server=reuse_server) @@ -144,7 +152,7 @@ def serve_dataset( host: str = "127.0.0.1", open_browser: bool = True, reuse_server: bool = False, -): +) -> None: """Serve a saved dataset.""" from hyperview import Dataset, launch diff --git a/src/hyperview/core/dataset.py b/src/hyperview/core/dataset.py index a943e8a..7704234 100644 --- a/src/hyperview/core/dataset.py +++ b/src/hyperview/core/dataset.py @@ -351,32 +351,63 @@ def add_from_huggingface( def compute_embeddings( self, - model: str | ModelSpec = "openai/clip-vit-base-patch32", + model: str, + *, + provider: str | None = None, + checkpoint: str | None = None, batch_size: int = 32, show_progress: bool = True, + **provider_kwargs: Any, ) -> str: """Compute embeddings for samples that don't have them yet. - Embeddings are stored in a dedicated space keyed by model_id. + Embeddings are stored in a dedicated space keyed by the embedding spec. Args: - model: EmbedAnything HuggingFace `model_id` to use. + model: Model identifier (required). Use a HuggingFace model_id + (e.g. 'openai/clip-vit-base-patch32') for embed-anything, or a + hyper-models name (e.g. 'hycoclip-vit-s') for hyperbolic embeddings. + provider: Explicit provider identifier. If not specified, auto-detected: + 'hyper-models' if model matches a hyper-models name, else 'embed-anything'. + Available providers: `hyperview.list_embedding_providers()`. + checkpoint: Checkpoint path/URL (hf://... or local path) for weight-only models. batch_size: Batch size for processing. show_progress: Whether to show progress bar. + **provider_kwargs: Additional kwargs passed to the embedding function. Returns: space_key for the embedding space. + + Raises: + ValueError: If model is not provided. """ + if not model: + raise ValueError( + "model is required. Examples: 'openai/clip-vit-base-patch32' (CLIP), " + "'hycoclip-vit-s' (hyperbolic). See hyperview.list_embedding_providers()." + ) + + from hyperview.embeddings.engine import EmbeddingSpec from hyperview.embeddings.pipelines import compute_embeddings - from hyperview.embeddings.providers import ModelSpec - if isinstance(model, ModelSpec): - model_spec = model - else: - model_spec = ModelSpec(provider="embed_anything", model_id=model) + if provider is None: + provider = "embed-anything" + try: + import hyper_models + if model in hyper_models.list_models(): + provider = "hyper-models" + except ImportError: + pass + spec = EmbeddingSpec( + provider=provider, + model_id=model, + checkpoint=checkpoint, + provider_kwargs=provider_kwargs, + ) + space_key, _num_computed, _num_skipped = compute_embeddings( storage=self._storage, - model_spec=model_spec, + spec=spec, batch_size=batch_size, show_progress=show_progress, ) diff --git a/src/hyperview/embeddings/__init__.py b/src/hyperview/embeddings/__init__.py index 14ff04d..24ba7aa 100644 --- a/src/hyperview/embeddings/__init__.py +++ b/src/hyperview/embeddings/__init__.py @@ -1,37 +1,31 @@ -"""Embedding computation, projection, and provider modules.""" +"""Embedding computation and projection.""" from hyperview.embeddings.compute import EmbeddingComputer -from hyperview.embeddings.providers import ( - BaseEmbeddingProvider, - ModelSpec, - get_provider, - list_providers, - make_provider_aware_space_key, - register_provider, +from hyperview.embeddings.engine import ( + EmbeddingSpec, + get_engine, + get_provider_info, + list_embedding_providers, ) +# Register HyperView providers into LanceDB registry. +import hyperview.embeddings.providers.lancedb_providers as _lancedb_providers # noqa: F401 + def __getattr__(name: str): """Lazy import for heavy dependencies (UMAP/numba).""" if name == "ProjectionEngine": from hyperview.embeddings.projection import ProjectionEngine return ProjectionEngine - if name == "EmbedAnythingProvider": - from hyperview.embeddings.providers.embed_anything import EmbedAnythingProvider - return EmbedAnythingProvider raise AttributeError(f"module {__name__!r} has no attribute {name!r}") __all__ = [ "EmbeddingComputer", + "EmbeddingSpec", "ProjectionEngine", - # Provider types - "BaseEmbeddingProvider", - "EmbedAnythingProvider", - "ModelSpec", # Provider utilities - "get_provider", - "list_providers", - "register_provider", - "make_provider_aware_space_key", + "get_engine", + "get_provider_info", + "list_embedding_providers", ] diff --git a/src/hyperview/embeddings/engine.py b/src/hyperview/embeddings/engine.py new file mode 100644 index 0000000..a82e8ac --- /dev/null +++ b/src/hyperview/embeddings/engine.py @@ -0,0 +1,330 @@ +"""Embedding spec + engine built on LanceDB's embedding registry.""" + +from __future__ import annotations + +import hashlib +import json +from dataclasses import dataclass, field +from typing import Any, Literal + +import numpy as np + +# Register HyperView providers into LanceDB registry. +import hyperview.embeddings.providers.lancedb_providers as _lancedb_providers # noqa: F401 + +__all__ = [ + "EmbeddingSpec", + "EmbeddingEngine", + "get_engine", + "list_embedding_providers", + "get_provider_info", +] + +HYPERBOLIC_PROVIDERS = frozenset({"hyper-models"}) + + +@dataclass +class EmbeddingSpec: + """Specification for an embedding model. + + All providers live in the LanceDB registry. HyperView's custom providers + (embed-anything, hyper-models) are registered on import. + + Attributes: + provider: Provider identifier (e.g., 'embed-anything', 'hyper-models', 'open-clip') + model_id: Model identifier (HuggingFace model_id, checkpoint name, etc.) + checkpoint: Optional checkpoint path/URL for weight-only models + provider_kwargs: Additional kwargs passed to the embedding function + modality: What input type this embedder handles + """ + + provider: str + model_id: str | None = None + checkpoint: str | None = None + provider_kwargs: dict[str, Any] = field(default_factory=dict) + modality: Literal["image", "text", "multimodal"] = "image" + + @property + def geometry(self) -> Literal["euclidean", "hyperboloid"]: + """Get the output geometry for this spec.""" + + if self.provider == "hyper-models": + model_name = self.model_id or self.provider_kwargs.get("name") + if model_name is None: + return "hyperboloid" + import hyper_models + + geom = str(hyper_models.get_model_info(str(model_name)).geometry) + return "hyperboloid" if geom in ("hyperboloid", "poincare") else "euclidean" + + if self.provider in HYPERBOLIC_PROVIDERS: + return "hyperboloid" + return "euclidean" + + def to_dict(self) -> dict[str, Any]: + """Convert to JSON-serializable dict for persistence.""" + d: dict[str, Any] = { + "provider": self.provider, + "modality": self.modality, + "geometry": self.geometry, + } + if self.model_id: + d["model_id"] = self.model_id + if self.checkpoint: + d["checkpoint"] = self.checkpoint + if self.provider_kwargs: + d["provider_kwargs"] = self.provider_kwargs + return d + + @classmethod + def from_dict(cls, d: dict[str, Any]) -> EmbeddingSpec: + """Create from dict (e.g., loaded from JSON).""" + return cls( + provider=d["provider"], + model_id=d.get("model_id"), + checkpoint=d.get("checkpoint"), + provider_kwargs=d.get("provider_kwargs", {}), + modality=d.get("modality", "image"), + ) + + def content_hash(self) -> str: + """Generate a short hash of the spec for collision-resistant keys.""" + content = json.dumps(self.to_dict(), sort_keys=True) + return hashlib.sha256(content.encode()).hexdigest()[:12] + + def make_space_key(self) -> str: + """Generate a collision-resistant space_key from this spec. + + Format: {provider}__{slugified_model_id}__{content_hash} + """ + from hyperview.storage.schema import slugify_model_id + + model_part = self.model_id or self.checkpoint or "default" + slug = slugify_model_id(model_part) + content_hash = self.content_hash() + return f"{self.provider}__{slug}__{content_hash}" + + +class EmbeddingEngine: + """Embedding engine using LanceDB registry. + + All providers are accessed through the LanceDB embedding registry. + HyperView providers are registered automatically on import. + """ + + def __init__(self) -> None: + self._cache: dict[str, Any] = {} # spec_hash -> embedding function + + def get_function(self, spec: EmbeddingSpec) -> Any: + """Get an embedding function from LanceDB registry. + + Args: + spec: Embedding specification. + + Returns: + LanceDB EmbeddingFunction instance. + + Raises: + ValueError: If provider not found in registry. + """ + cache_key = spec.content_hash() + if cache_key in self._cache: + return self._cache[cache_key] + + from lancedb.embeddings import get_registry + + registry = get_registry() + + # Get provider factory from registry + try: + factory = registry.get(spec.provider) + except KeyError: + available = list_embedding_providers() + raise ValueError( + f"Unknown provider: '{spec.provider}'. " + f"Available: {', '.join(sorted(available))}" + ) from None + + create_kwargs: dict[str, Any] = {} + if spec.model_id: + create_kwargs["name"] = spec.model_id + + if spec.checkpoint: + create_kwargs["checkpoint"] = spec.checkpoint + + create_kwargs.update(spec.provider_kwargs) + + try: + func = factory.create(**create_kwargs) + except ImportError as e: + raise ImportError( + f"Provider '{spec.provider}' requires additional dependencies. " + "Install the provider's extra dependencies and try again." + ) from e + + self._cache[cache_key] = func + return func + + def embed_images( + self, + samples: list[Any], + spec: EmbeddingSpec, + batch_size: int = 32, + show_progress: bool = True, + ) -> np.ndarray: + """Compute embeddings for image samples. + + Args: + samples: List of Sample objects with image filepaths. + spec: Embedding specification. + batch_size: Batch size for processing. + show_progress: Whether to show progress. + + Returns: + Array of shape (N, D) where N is len(samples) and D is embedding dim. + """ + func = self.get_function(spec) + + if show_progress: + print(f"Computing embeddings for {len(samples)} samples...") + + all_embeddings: list[np.ndarray] = [] + for i in range(0, len(samples), batch_size): + batch_samples = samples[i:i + batch_size] + + batch_paths = [s.filepath for s in batch_samples] + batch_embeddings = func.compute_source_embeddings(batch_paths) + all_embeddings.extend(batch_embeddings) + + return np.array(all_embeddings, dtype=np.float32) + + def embed_texts( + self, + texts: list[str], + spec: EmbeddingSpec, + ) -> np.ndarray: + """Compute embeddings for text inputs. + + Args: + texts: List of text strings. + spec: Embedding specification. + + Returns: + Array of shape (N, D). + """ + func = self.get_function(spec) + + if hasattr(func, "generate_embeddings"): + out = func.generate_embeddings(texts) + return np.asarray(out, dtype=np.float32) + + embeddings: list[np.ndarray] = [] + for text in texts: + out = func.compute_query_embeddings(text) + if not out: + raise RuntimeError(f"Provider '{spec.provider}' returned no embedding for query") + embeddings.append(np.asarray(out[0], dtype=np.float32)) + return np.vstack(embeddings) + + def get_space_config(self, spec: EmbeddingSpec, dim: int) -> dict[str, Any]: + """Get space configuration for storage. + + Args: + spec: Embedding specification. + dim: Embedding dimension. + + Returns: + Config dict for SpaceInfo.config_json. + """ + func = self.get_function(spec) + + config = spec.to_dict() + config["dim"] = dim + + if hasattr(func, "geometry"): + config["geometry"] = func.geometry + if hasattr(func, "curvature") and func.curvature is not None: + config["curvature"] = func.curvature + + if config.get("geometry") == "hyperboloid": + config["spatial_dim"] = dim - 1 + + return config + + +_ENGINE: EmbeddingEngine | None = None + + +def get_engine() -> EmbeddingEngine: + """Get the global embedding engine singleton.""" + global _ENGINE + if _ENGINE is None: + _ENGINE = EmbeddingEngine() + return _ENGINE + + +def list_embedding_providers(available_only: bool = False) -> list[str]: + """List all registered embedding providers. + + Args: + available_only: If True, only return providers whose dependencies are installed. + + Returns: + List of provider identifiers. + """ + from lancedb.embeddings import get_registry + + registry = get_registry() + + all_providers = list(getattr(registry, "_functions", {}).keys()) + + if not available_only: + return sorted(all_providers) + + available: list[str] = [] + for provider in all_providers: + try: + factory = registry.get(provider) + factory.create() + available.append(provider) + except ImportError: + pass + except (TypeError, ValueError): + available.append(provider) + + return sorted(available) + + +def get_provider_info(provider: str) -> dict[str, Any]: + """Get information about an embedding provider. + + Args: + provider: Provider identifier. + + Returns: + Dict with provider info. + """ + from lancedb.embeddings import get_registry + + registry = get_registry() + + try: + factory = registry.get(provider) + except KeyError: + raise ValueError(f"Unknown provider: {provider}") from None + + info: dict[str, Any] = { + "provider": provider, + "source": "hyperview" if provider in ("embed-anything", "hyper-models") else "lancedb", + "geometry": "hyperboloid" if provider in HYPERBOLIC_PROVIDERS else "euclidean", + } + + try: + factory.create() + info["installed"] = True + except ImportError: + info["installed"] = False + except (TypeError, ValueError): + info["installed"] = True + + return info diff --git a/src/hyperview/embeddings/pipelines.py b/src/hyperview/embeddings/pipelines.py index 578a9f3..e3aa716 100644 --- a/src/hyperview/embeddings/pipelines.py +++ b/src/hyperview/embeddings/pipelines.py @@ -6,16 +6,17 @@ from __future__ import annotations +from typing import Any + import numpy as np -from hyperview.embeddings.providers import ModelSpec, get_provider, make_provider_aware_space_key from hyperview.storage.backend import StorageBackend from hyperview.storage.schema import make_layout_key def compute_embeddings( storage: StorageBackend, - model_spec: ModelSpec, + spec: Any, batch_size: int = 32, show_progress: bool = True, ) -> tuple[str, int, int]: @@ -23,7 +24,7 @@ def compute_embeddings( Args: storage: Storage backend to read samples from and write embeddings to. - model_spec: Model specification (provider, model_id, geometry, etc.). + spec: Embedding specification (provider, model_id, etc.) batch_size: Batch size for processing. show_progress: Whether to show progress bar. @@ -33,14 +34,16 @@ def compute_embeddings( Raises: ValueError: If no samples in storage or provider not found. """ - provider = get_provider(model_spec.provider) + from hyperview.embeddings.engine import get_engine + + engine = get_engine() all_samples = storage.get_all_samples() if not all_samples: raise ValueError("No samples in storage") # Generate space key before computing (deterministic from spec) - space_key = make_provider_aware_space_key(model_spec) + space_key = spec.make_space_key() # Check which samples need embeddings missing_ids = storage.get_missing_embedding_ids(space_key) @@ -61,10 +64,10 @@ def compute_embeddings( if show_progress and num_skipped > 0: print(f"Skipped {num_skipped} samples with existing embeddings") - # Compute all embeddings in one pass (no separate probe) - embeddings = provider.compute_embeddings( + # Compute all embeddings via the engine + embeddings = engine.embed_images( samples=samples_to_embed, - model_spec=model_spec, + spec=spec, batch_size=batch_size, show_progress=show_progress, ) @@ -72,9 +75,9 @@ def compute_embeddings( dim = embeddings.shape[1] # Ensure space exists (create if needed) - config = provider.get_space_config(model_spec, dim) + config = engine.get_space_config(spec, dim) storage.ensure_space( - model_id=model_spec.model_id, + model_id=spec.model_id or spec.provider, dim=dim, config=config, space_key=space_key, @@ -129,7 +132,16 @@ def compute_layout( spaces = storage.list_spaces() if not spaces: raise ValueError("No embedding spaces. Call compute_embeddings() first.") - space_key = spaces[0].space_key + + # Choose a sensible default space based on the requested output geometry. + # - For Poincaré output, prefer a hyperbolic (hyperboloid) embedding space if present. + # - For Euclidean output, prefer a Euclidean embedding space if present. + if geometry == "poincare": + preferred = next((s for s in spaces if s.geometry == "hyperboloid"), None) + else: + preferred = next((s for s in spaces if s.geometry != "hyperboloid"), None) + + space_key = (preferred.space_key if preferred is not None else spaces[0].space_key) space = storage.get_space(space_key) if space is None: diff --git a/src/hyperview/embeddings/providers/__init__.py b/src/hyperview/embeddings/providers/__init__.py index 55fca86..9bdcc43 100644 --- a/src/hyperview/embeddings/providers/__init__.py +++ b/src/hyperview/embeddings/providers/__init__.py @@ -1,200 +1,7 @@ -"""Embedding provider abstraction for HyperView.""" +"""Embedding providers. -from __future__ import annotations +HyperView integrates with LanceDB's embedding registry. +Custom providers are registered in `lancedb_providers.py`. +""" -import hashlib -from importlib import import_module -import json -from abc import ABC, abstractmethod -from dataclasses import dataclass -from typing import Any - -import numpy as np - -from hyperview.core.sample import Sample - - -@dataclass -class ModelSpec: - """Structured specification for an embedding model. - - Attributes: - provider: Provider identifier (e.g., "embed_anything", "hycoclip") - model_id: Model identifier (HuggingFace model_id, checkpoint path, etc.) - checkpoint: Optional checkpoint path or URL for weight-only models - config_path: Optional config path for models that need it - output_geometry: Geometry of the embedding space ("euclidean", "hyperboloid") - curvature: Hyperbolic curvature (only relevant for hyperbolic geometries) - """ - - provider: str - model_id: str - checkpoint: str | None = None - config_path: str | None = None - output_geometry: str = "euclidean" - curvature: float | None = None - - def to_dict(self) -> dict[str, Any]: - """Convert to JSON-serializable dict.""" - d: dict[str, Any] = { - "provider": self.provider, - "model_id": self.model_id, - "geometry": self.output_geometry, - } - if self.checkpoint: - d["checkpoint"] = self.checkpoint - if self.config_path: - d["config_path"] = self.config_path - if self.curvature is not None: - d["curvature"] = self.curvature - return d - - @classmethod - def from_dict(cls, d: dict[str, Any]) -> ModelSpec: - """Create from dict (e.g., loaded from JSON).""" - return cls( - provider=d["provider"], - model_id=d["model_id"], - checkpoint=d.get("checkpoint"), - config_path=d.get("config_path"), - output_geometry=d.get("geometry", "euclidean"), - curvature=d.get("curvature"), - ) - - def content_hash(self) -> str: - """Generate a short hash of the spec for collision-resistant keys.""" - content = json.dumps(self.to_dict(), sort_keys=True) - return hashlib.sha256(content.encode()).hexdigest()[:12] - - -class BaseEmbeddingProvider(ABC): - """Base class for embedding providers.""" - - @property - @abstractmethod - def provider_id(self) -> str: - """Unique identifier for this provider.""" - ... - - @abstractmethod - def compute_embeddings( - self, - samples: list[Sample], - model_spec: ModelSpec, - batch_size: int = 32, - show_progress: bool = True, - ) -> np.ndarray: - """Compute embeddings for samples. - - Returns: - Array of shape (N, D) where N is len(samples) and D is embedding dim. - """ - ... - - def get_space_config(self, model_spec: ModelSpec, dim: int) -> dict[str, Any]: - """Get config dict for SpaceInfo.config_json. - - Args: - model_spec: Model specification. - dim: Embedding dimension. - - Returns: - Config dict with provider, geometry, model_id, dim, and any extras. - """ - return { - **model_spec.to_dict(), - "dim": dim, - } - - -_PROVIDER_CLASSES: dict[str, type[BaseEmbeddingProvider]] = {} -_PROVIDER_INSTANCES: dict[str, BaseEmbeddingProvider] = {} - - -_KNOWN_PROVIDER_MODULES: dict[str, str] = { - "embed_anything": "hyperview.embeddings.providers.embed_anything", - "hycoclip": "hyperview.embeddings.providers.hycoclip", - "hycoclip_onnx": "hyperview.embeddings.providers.hycoclip_onnx", -} - - -def register_provider(provider_id: str, provider_class: type[BaseEmbeddingProvider]) -> None: - """Register a new embedding provider class.""" - _PROVIDER_CLASSES[provider_id] = provider_class - # Clear cached instance if re-registering - _PROVIDER_INSTANCES.pop(provider_id, None) - - -def _try_auto_register(provider_id: str, *, silent: bool = True) -> None: - """Attempt to auto-register a provider by importing its module. - - Args: - provider_id: Provider identifier. - silent: If True, swallow ImportError (used when listing providers). - If False, let ImportError propagate (used when explicitly requesting - a provider via get_provider()). - """ - - module_name = _KNOWN_PROVIDER_MODULES.get(provider_id) - if not module_name: - return - - if silent: - try: - import_module(module_name) - except ImportError: - return - else: - import_module(module_name) - - -def get_provider(provider_id: str) -> BaseEmbeddingProvider: - """Get a provider singleton instance by ID. - - Providers are cached to preserve model state across calls. - """ - if provider_id not in _PROVIDER_CLASSES: - _try_auto_register(provider_id, silent=False) - - if provider_id not in _PROVIDER_CLASSES: - available = ", ".join(sorted(_PROVIDER_CLASSES.keys())) or "(none registered)" - raise ValueError( - f"Unknown embedding provider: '{provider_id}'. " - f"Available: {available}" - ) - - if provider_id not in _PROVIDER_INSTANCES: - _PROVIDER_INSTANCES[provider_id] = _PROVIDER_CLASSES[provider_id]() - - return _PROVIDER_INSTANCES[provider_id] - - -def list_providers() -> list[str]: - """List available provider IDs.""" - # Trigger auto-registration for known providers - for pid in _KNOWN_PROVIDER_MODULES: - _try_auto_register(pid, silent=True) - return list(_PROVIDER_CLASSES.keys()) - - -def make_provider_aware_space_key(model_spec: ModelSpec) -> str: - """Generate a collision-resistant space_key from a ModelSpec. - - Format: {provider}__{slugified_model_id}__{content_hash} - """ - from hyperview.storage.schema import slugify_model_id - - slug = slugify_model_id(model_spec.model_id) - content_hash = model_spec.content_hash() - - return f"{model_spec.provider}__{slug}__{content_hash}" - - -__all__ = [ - "BaseEmbeddingProvider", - "ModelSpec", - "get_provider", - "list_providers", - "make_provider_aware_space_key", - "register_provider", -] +__all__: list[str] = [] diff --git a/src/hyperview/embeddings/providers/embed_anything.py b/src/hyperview/embeddings/providers/embed_anything.py deleted file mode 100644 index 60370df..0000000 --- a/src/hyperview/embeddings/providers/embed_anything.py +++ /dev/null @@ -1,57 +0,0 @@ -"""EmbedAnything embedding provider - default lightweight provider.""" - -from __future__ import annotations - -from typing import Any - -import numpy as np - -from hyperview.core.sample import Sample -from hyperview.embeddings.providers import ( - BaseEmbeddingProvider, - ModelSpec, - register_provider, -) - -__all__ = ["EmbedAnythingProvider"] - - -class EmbedAnythingProvider(BaseEmbeddingProvider): - """Default embedding provider using EmbedAnything. - - Supports HuggingFace vision models via EmbedAnything's inference engine. - Model is cached per model_id to avoid repeated initialization. - """ - - def __init__(self) -> None: - self._computers: dict[str, Any] = {} # model_id -> EmbeddingComputer - - @property - def provider_id(self) -> str: - return "embed_anything" - - def _get_computer(self, model_id: str) -> Any: - """Get or create an EmbeddingComputer for the given model_id.""" - if model_id not in self._computers: - from hyperview.embeddings.compute import EmbeddingComputer - - self._computers[model_id] = EmbeddingComputer(model=model_id) - return self._computers[model_id] - - def compute_embeddings( - self, - samples: list[Sample], - model_spec: ModelSpec, - batch_size: int = 32, - show_progress: bool = True, - ) -> np.ndarray: - """Compute embeddings using EmbedAnything.""" - computer = self._get_computer(model_spec.model_id) - embeddings = computer.compute_batch( - samples, batch_size=batch_size, show_progress=show_progress - ) - return np.array(embeddings, dtype=np.float32) - - -# Auto-register on import -register_provider("embed_anything", EmbedAnythingProvider) diff --git a/src/hyperview/embeddings/providers/hycoclip.py b/src/hyperview/embeddings/providers/hycoclip.py deleted file mode 100644 index 3f8a81c..0000000 --- a/src/hyperview/embeddings/providers/hycoclip.py +++ /dev/null @@ -1,279 +0,0 @@ -"""Clean HyCoCLIP embedding provider (PyTorch) - no external hycoclip package. - -This is a minimal reimplementation that loads HyCoCLIP weights directly. -Only depends on torch, timm, and numpy. - -Architecture: -- ViT backbone (timm) -- Linear projection to embedding space -- Exponential map to hyperboloid (Lorentz model) - -Checkpoints: https://huggingface.co/avik-pal/hycoclip - -Requirements: - uv sync --extra ml -""" - -from __future__ import annotations - -import math -from pathlib import Path -from typing import Any - -import numpy as np - -from hyperview.core.sample import Sample -from hyperview.embeddings.providers import ( - BaseEmbeddingProvider, - ModelSpec, - register_provider, -) - -__all__ = ["HyCoCLIPProvider"] - - -HYCOCLIP_CHECKPOINTS: dict[str, str] = { - "hycoclip_vit_s": "hf://avik-pal/hycoclip#hycoclip_vit_s.pth", - "hycoclip_vit_b": "hf://avik-pal/hycoclip#hycoclip_vit_b.pth", - "meru_vit_s": "hf://avik-pal/hycoclip#meru_vit_s.pth", - "meru_vit_b": "hf://avik-pal/hycoclip#meru_vit_b.pth", -} - - -def _exp_map_lorentz(x: "torch.Tensor", c: float) -> "torch.Tensor": - """Exponential map from tangent space at the hyperboloid vertex. - - Maps Euclidean tangent vectors at the origin onto the Lorentz (hyperboloid) - model of hyperbolic space with curvature -c. - - Output is ordered as (t, x1, ..., xD) and satisfies: - t^2 - ||x||^2 = 1/c - - This matches HyCoCLIP/MERU exp_map0 numerics by clamping the sinh input for - stability and inferring the time component from the hyperboloid constraint. - - Args: - x: Euclidean tangent vectors at the origin, shape (..., D). - c: Positive curvature parameter (hyperbolic curvature is -c). - - Returns: - Hyperboloid coordinates, shape (..., D + 1). - """ - import torch - - if c <= 0: - raise ValueError(f"curvature c must be > 0, got {c}") - - # Compute in float32 under AMP to avoid float16/bfloat16 overflow. - if x.dtype in (torch.float16, torch.bfloat16): - x = x.float() - - sqrt_c = math.sqrt(c) - rc_xnorm = sqrt_c * torch.norm(x, dim=-1, keepdim=True) - - eps = 1e-8 - sinh_input = torch.clamp(rc_xnorm, min=eps, max=math.asinh(2**15)) - spatial = torch.sinh(sinh_input) * x / torch.clamp(rc_xnorm, min=eps) - - t = torch.sqrt((1.0 / c) + torch.sum(spatial * spatial, dim=-1, keepdim=True)) - return torch.cat([t, spatial], dim=-1) - - -def _create_encoder( - embed_dim: int = 512, - curvature: float = 0.1, - vit_model: str = "vit_small_patch16_224", -) -> "nn.Module": - """Create HyCoCLIP image encoder using timm ViT backbone.""" - import timm - import torch.nn as nn - - class HyCoCLIPImageEncoder(nn.Module): - def __init__(self) -> None: - super().__init__() - self.backbone = timm.create_model(vit_model, pretrained=False, num_classes=0) - backbone_dim = int(getattr(self.backbone, "embed_dim")) - self.proj = nn.Linear(backbone_dim, embed_dim, bias=False) - self.curvature = curvature - self.embed_dim = embed_dim - - def forward(self, x: "torch.Tensor") -> "torch.Tensor": - features = self.backbone(x) - spatial = self.proj(features) - return _exp_map_lorentz(spatial, self.curvature) - - return HyCoCLIPImageEncoder() - - -def _load_encoder(checkpoint_path: str, device: str = "cpu") -> Any: - """Load HyCoCLIP image encoder from checkpoint.""" - import torch - - ckpt = torch.load(checkpoint_path, map_location="cpu", weights_only=False) - state = ckpt["model"] - - # Extract curvature (stored as log) - curvature = torch.exp(state["curv"]).item() - - # Determine model variant from checkpoint - proj_shape = state["visual_proj.weight"].shape - embed_dim = proj_shape[0] - backbone_dim = proj_shape[1] - - vit_models = { - 384: "vit_small_patch16_224", - 768: "vit_base_patch16_224", - 1024: "vit_large_patch16_224", - } - vit_model = vit_models.get(backbone_dim, "vit_small_patch16_224") - - model = _create_encoder(embed_dim=embed_dim, curvature=curvature, vit_model=vit_model) - - # Remap checkpoint keys - new_state = {} - for key, value in state.items(): - if key.startswith("visual."): - new_state["backbone." + key[7:]] = value - elif key == "visual_proj.weight": - new_state["proj.weight"] = value - - model.load_state_dict(new_state, strict=False) - return model.to(device).eval() - - -class HyCoCLIPProvider(BaseEmbeddingProvider): - """Clean HyCoCLIP provider (PyTorch) - no hycoclip package dependency. - - Requires: torch, torchvision, timm (install via `uv sync --extra ml`) - """ - - def __init__(self) -> None: - self._model: Any = None - self._model_spec: ModelSpec | None = None - self._device: Any = None - self._transform: Any = None - - @property - def provider_id(self) -> str: - return "hycoclip" - - def _get_device(self) -> Any: - import torch - - if self._device is None: - self._device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - return self._device - - def _get_transform(self) -> Any: - if self._transform is None: - from torchvision import transforms - - self._transform = transforms.Compose([ - transforms.Resize(224, transforms.InterpolationMode.BICUBIC), - transforms.CenterCrop(224), - transforms.ToTensor(), - transforms.Normalize( - mean=[0.48145466, 0.4578275, 0.40821073], - std=[0.26862954, 0.26130258, 0.27577711], - ), - ]) - return self._transform - - def _resolve_checkpoint(self, checkpoint: str) -> Path: - """Resolve checkpoint path, downloading from HuggingFace if needed.""" - # Handle HuggingFace Hub URLs: hf://repo_id#filename - if checkpoint.startswith("hf://"): - from huggingface_hub import hf_hub_download - - path = checkpoint[5:] - if "#" not in path: - raise ValueError(f"HF checkpoint must include filename: {checkpoint}") - repo_id, filename = path.split("#", 1) - return Path(hf_hub_download(repo_id=repo_id, filename=filename)).resolve() - - # Local path - path = Path(checkpoint).expanduser().resolve() - if not path.exists(): - raise FileNotFoundError(f"Checkpoint not found: {path}") - return path - - def _load_model(self, model_spec: ModelSpec) -> None: - if self._model is not None and self._model_spec == model_spec: - return - - # Auto-resolve checkpoint from model_id if not provided - checkpoint = model_spec.checkpoint - if not checkpoint: - checkpoint = HYCOCLIP_CHECKPOINTS.get(model_spec.model_id) - if not checkpoint: - available = ", ".join(sorted(HYCOCLIP_CHECKPOINTS.keys())) - raise ValueError( - f"Unknown HyCoCLIP model_id: '{model_spec.model_id}'. " - f"Known models: {available}. " - f"Or provide 'checkpoint' path explicitly." - ) - - checkpoint_path = self._resolve_checkpoint(checkpoint) - self._model = _load_encoder(str(checkpoint_path), str(self._get_device())) - self._model_spec = model_spec - - def compute_embeddings( - self, - samples: list["Sample"], - model_spec: ModelSpec, - batch_size: int = 32, - show_progress: bool = True, - ) -> np.ndarray: - """Compute hyperboloid embeddings for samples.""" - import torch - - self._load_model(model_spec) - assert self._model is not None - - device = self._get_device() - transform = self._get_transform() - - if show_progress: - print(f"Computing HyCoCLIP embeddings for {len(samples)} samples...") - - all_embeddings = [] - - for i in range(0, len(samples), batch_size): - batch_samples = samples[i : i + batch_size] - - images = [] - for sample in batch_samples: - img = sample.load_image() - if img.mode != "RGB": - img = img.convert("RGB") - images.append(transform(img)) - - batch_tensor = torch.stack(images).to(device) - - with torch.no_grad(), torch.amp.autocast( - device_type=device.type, enabled=device.type == "cuda" - ): - embeddings = self._model(batch_tensor) - - all_embeddings.append(embeddings.cpu().numpy()) - - return np.concatenate(all_embeddings, axis=0) - - def get_space_config(self, model_spec: ModelSpec, dim: int) -> dict[str, Any]: - """Return embedding space configuration with curvature.""" - self._load_model(model_spec) - assert self._model is not None - - return { - "provider": self.provider_id, - "model_id": model_spec.model_id, - "geometry": "hyperboloid", - "checkpoint": model_spec.checkpoint, - "dim": dim, - "curvature": self._model.curvature, - "spatial_dim": self._model.embed_dim, - } - - -# Auto-register on import -register_provider("hycoclip", HyCoCLIPProvider) diff --git a/src/hyperview/embeddings/providers/hycoclip_onnx.py b/src/hyperview/embeddings/providers/hycoclip_onnx.py deleted file mode 100644 index 4333063..0000000 --- a/src/hyperview/embeddings/providers/hycoclip_onnx.py +++ /dev/null @@ -1,194 +0,0 @@ -"""HyCoCLIP ONNX embedding provider - torch-free runtime. - -This provider runs an ONNX-exported HyCoCLIP/MERU image encoder with -`onnxruntime` to compute *hyperboloid (Lorentz)* embeddings. - -Outputs: -- Embeddings are returned in hyperboloid format with shape (N, D+1), where the - first coordinate is the time component. - -Requirements: -- onnxruntime (already included via embed-anything) -- An exported ONNX model (and its external weights .data file) produced by - `hyperbolic_model_zoo/hycoclip_onnx/export_onnx.py`. - -Why this exists: -- Torch is required to *export* HyCoCLIP to ONNX. -- Torch is NOT required at runtime once you have the ONNX artifacts. -""" - -from __future__ import annotations - -from pathlib import Path -from typing import Any - -import numpy as np - -from hyperview.core.sample import Sample -from hyperview.embeddings.providers import ( - BaseEmbeddingProvider, - ModelSpec, - register_provider, -) - -__all__ = ["HyCoCLIPOnnxProvider"] - - -def _preprocess_rgb_image_to_chw_float01(img: Any, size: int = 224) -> np.ndarray: - """Resize shortest side to `size`, center crop, return (3,H,W) float32 in [0,1].""" - from PIL import Image - - if not isinstance(img, Image.Image): - raise TypeError(f"Expected PIL.Image.Image, got {type(img)}") - - w, h = img.size - if w <= 0 or h <= 0: - raise ValueError(f"Invalid image size: {w}x{h}") - - # Resize shortest side to `size`. - scale = float(size) / float(min(w, h)) - new_w = int(round(w * scale)) - new_h = int(round(h * scale)) - img = img.resize((new_w, new_h), resample=Image.Resampling.BICUBIC) - - # Center crop. - left = int(round((new_w - size) / 2.0)) - top = int(round((new_h - size) / 2.0)) - img = img.crop((left, top, left + size, top + size)) - - arr = np.asarray(img, dtype=np.float32) / 255.0 - arr = np.transpose(arr, (2, 0, 1)) - return arr - - -class HyCoCLIPOnnxProvider(BaseEmbeddingProvider): - """ONNX HyCoCLIP provider - torch-free runtime. - - Uses onnxruntime for inference. No PyTorch required at runtime. - """ - - def __init__(self) -> None: - self._session: Any = None - self._model_spec: ModelSpec | None = None - self._input_name: str | None = None - self._output_names: list[str] | None = None - self._curvature: float | None = None - - @property - def provider_id(self) -> str: - return "hycoclip_onnx" - - def _resolve_onnx_path(self, model_spec: ModelSpec) -> Path: - if not model_spec.checkpoint: - raise ValueError( - "HyCoCLIP ONNX provider requires 'checkpoint' to be a path/URL to a .onnx file." - ) - - checkpoint = model_spec.checkpoint - - # Handle HuggingFace Hub URLs (hf://repo_id#filename) - if checkpoint.startswith("hf://"): - raise NotImplementedError( - "WIP: hf:// checkpoints are disabled until ONNX external weights are published. " - "Use a local .onnx path with its .onnx.data file present." - ) - - path = Path(checkpoint).expanduser().resolve() - if not path.exists(): - raise FileNotFoundError(f"ONNX model not found: {path}") - if path.suffix.lower() != ".onnx": - raise ValueError(f"Expected a .onnx file, got: {path}") - return path - - def _ensure_session(self, model_spec: ModelSpec) -> None: - if self._session is not None and self._model_spec == model_spec: - return - - import onnxruntime as ort - - onnx_path = self._resolve_onnx_path(model_spec) - - # Default to CPU for maximum compatibility. - available = ort.get_available_providers() - providers = ["CPUExecutionProvider"] if "CPUExecutionProvider" in available else list(available) - - self._session = ort.InferenceSession(str(onnx_path), providers=providers) - self._input_name = self._session.get_inputs()[0].name - self._output_names = [o.name for o in self._session.get_outputs()] - self._curvature = None - self._model_spec = model_spec - - def compute_embeddings( - self, - samples: list[Sample], - model_spec: ModelSpec, - batch_size: int = 32, - show_progress: bool = True, - ) -> np.ndarray: - """Compute hyperboloid embeddings (t, x) with shape (N, D+1).""" - self._ensure_session(model_spec) - - assert self._session is not None - assert self._input_name is not None - - output_names = self._output_names or [] - if not output_names: - raise RuntimeError("ONNX session has no outputs") - - # Prefer named outputs if present. - emb_name = "embedding_hyperboloid" if "embedding_hyperboloid" in output_names else output_names[0] - curv_name = "curvature" if "curvature" in output_names else None - - if batch_size != 1 and show_progress: - print("HyCoCLIP-ONNX export currently runs with batch_size=1; overriding") - batch_size = 1 - - all_embeddings: list[np.ndarray] = [] - - if show_progress: - print(f"Computing HyCoCLIP-ONNX embeddings for {len(samples)} samples...") - - for i in range(0, len(samples), batch_size): - batch_samples = samples[i : i + batch_size] - - images = [] - for sample in batch_samples: - with sample.load_image() as img: - img.load() - if img.mode != "RGB": - img = img.convert("RGB") - chw = _preprocess_rgb_image_to_chw_float01(img.copy(), size=224) - images.append(chw) - - batch_np = np.stack(images, axis=0).astype(np.float32) - - outputs = self._session.run( - [name for name in (emb_name, curv_name) if name is not None], - {self._input_name: batch_np}, - ) - - hyper = np.asarray(outputs[0], dtype=np.float32) - if hyper.ndim != 2: - raise RuntimeError(f"Expected (B,D) embeddings, got shape={hyper.shape}") - - # Capture curvature once. - if curv_name is not None and self._curvature is None and len(outputs) > 1: - curv_arr = np.asarray(outputs[1]).reshape(-1) - if curv_arr.size > 0: - self._curvature = float(curv_arr[0]) - - all_embeddings.append(hyper) - - return np.vstack(all_embeddings) - - def get_space_config(self, model_spec: ModelSpec, dim: int) -> dict[str, Any]: - config = super().get_space_config(model_spec, dim) - config["geometry"] = "hyperboloid" - if self._curvature is not None: - config["curvature"] = self._curvature - config["spatial_dim"] = dim - 1 - return config - - -# Auto-register on import -register_provider("hycoclip_onnx", HyCoCLIPOnnxProvider) diff --git a/src/hyperview/embeddings/providers/lancedb_providers.py b/src/hyperview/embeddings/providers/lancedb_providers.py new file mode 100644 index 0000000..d64f858 --- /dev/null +++ b/src/hyperview/embeddings/providers/lancedb_providers.py @@ -0,0 +1,196 @@ +"""LanceDB-registered embedding providers for HyperView. + +This module registers HyperView's embedding providers into the LanceDB embedding +registry using the @register decorator. + +Providers: +- embed-anything: CLIP-based image embeddings (torch-free, default) +- hyper-models: Non-Euclidean model zoo via `hyper-models` (torch-free ONNX; downloads from HF Hub) +""" + +from __future__ import annotations + +from typing import Any + +import numpy as np +from lancedb.embeddings import EmbeddingFunction, register +from pydantic import PrivateAttr + +__all__ = [ + "EmbedAnythingEmbeddings", + "HyperModelsEmbeddings", +] + + +@register("embed-anything") +class EmbedAnythingEmbeddings(EmbeddingFunction): + """CLIP-based image embeddings via embed-anything. + + This is the default provider for HyperView - lightweight and torch-free. + + Args: + name: HuggingFace model ID for CLIP (default: openai/clip-vit-base-patch32) + batch_size: Batch size for processing + """ + + name: str = "openai/clip-vit-base-patch32" + batch_size: int = 32 + + _computer: Any = PrivateAttr(default=None) + _ndims: int | None = PrivateAttr(default=None) + + def __init__(self, **kwargs: Any) -> None: + super().__init__(**kwargs) + self._computer = None + self._ndims = None + + def _get_computer(self) -> Any: + if self._computer is None: + from hyperview.embeddings.compute import EmbeddingComputer + + self._computer = EmbeddingComputer(model=self.name) + return self._computer + + def ndims(self) -> int: + if self._ndims is None: + if "large" in self.name.lower(): + self._ndims = 768 + elif "clip" in self.name.lower(): + self._ndims = 512 + else: + self._ndims = 512 + return self._ndims + + def compute_source_embeddings( + self, inputs: Any, *args: Any, **kwargs: Any + ) -> list[np.ndarray | None]: + from hyperview.core.sample import Sample + + computer = self._get_computer() + + samples: list[Any] = [] + for inp in self.sanitize_input(inputs): + if isinstance(inp, Sample): + samples.append(inp) + elif isinstance(inp, str): + samples.append(Sample(id=inp, filepath=inp)) + else: + raise TypeError(f"Unsupported input type: {type(inp)}") + + embeddings = computer.compute_batch(samples, batch_size=self.batch_size, show_progress=False) + return list(embeddings) + + def compute_query_embeddings( + self, query: Any, *args: Any, **kwargs: Any + ) -> list[np.ndarray | None]: + return self.compute_source_embeddings([query], *args, **kwargs) + + +@register("hyper-models") +class HyperModelsEmbeddings(EmbeddingFunction): + """Non-Euclidean embeddings via the `hyper-models` package. + + This provider is a thin wrapper around `hyper_models.load(...)`. + Models are downloaded from the Hugging Face Hub on first use. + + Args: + name: Model name in the hyper-models registry (e.g. 'hycoclip-vit-s'). + checkpoint: Optional local path to an ONNX file (skips hub download). + batch_size: Batch size hint. Current HyCoCLIP/MERU ONNX exports may only + support batch_size=1; HyperView encodes one image at a time for + maximum compatibility. + """ + + name: str = "hycoclip-vit-s" + checkpoint: str | None = None + batch_size: int = 1 + + _model: Any = PrivateAttr(default=None) + _model_info: Any = PrivateAttr(default=None) + + def __init__(self, **kwargs: Any) -> None: + super().__init__(**kwargs) + self._model = None + self._model_info = None + + def _ensure_model_info(self) -> None: + if self._model_info is not None: + return + + try: + import hyper_models + except ImportError as e: + raise ImportError( + "Provider 'hyper-models' requires the 'hyper-models' package. " + "Install it with: `uv pip install hyper-models`" + ) from e + + try: + self._model_info = hyper_models.get_model_info(self.name) + except KeyError: + available = ", ".join(sorted(hyper_models.list_models())) + raise ValueError( + f"Unknown hyper-models model: '{self.name}'. Available: {available}" + ) from None + + def _ensure_model(self) -> None: + if self._model is not None: + return + + self._ensure_model_info() + import hyper_models + + self._model = hyper_models.load(self.name, local_path=self.checkpoint) + + def ndims(self) -> int: + self._ensure_model_info() + assert self._model_info is not None + return int(getattr(self._model_info, "dim")) + + @property + def geometry(self) -> str: + self._ensure_model_info() + assert self._model_info is not None + return str(getattr(self._model_info, "geometry")) + + def compute_source_embeddings( + self, inputs: Any, *args: Any, **kwargs: Any + ) -> list[np.ndarray | None]: + from hyperview.core.sample import Sample + + self._ensure_model() + assert self._model is not None + + inputs = self.sanitize_input(inputs) + all_embeddings: list[np.ndarray | None] = [] + + from PIL import Image + + for inp in inputs: + if isinstance(inp, Sample): + with inp.load_image() as img: + img.load() + if img.mode != "RGB": + img = img.convert("RGB") + pil_img = img.copy() + elif isinstance(inp, str): + with Image.open(inp) as img: + img.load() + if img.mode != "RGB": + img = img.convert("RGB") + pil_img = img.copy() + elif isinstance(inp, Image.Image): + pil_img = inp.convert("RGB") if inp.mode != "RGB" else inp + else: + raise TypeError(f"Unsupported input type: {type(inp)}") + + emb = self._model.encode_images([pil_img]) + vec = np.asarray(emb[0], dtype=np.float32) + all_embeddings.append(vec) + + return all_embeddings + + def compute_query_embeddings( + self, query: Any, *args: Any, **kwargs: Any + ) -> list[np.ndarray | None]: + return self.compute_source_embeddings([query], *args, **kwargs) diff --git a/src/hyperview/storage/lancedb_backend.py b/src/hyperview/storage/lancedb_backend.py index f85b635..7001489 100644 --- a/src/hyperview/storage/lancedb_backend.py +++ b/src/hyperview/storage/lancedb_backend.py @@ -44,14 +44,8 @@ def __init__(self, dataset_name: str, config: StorageConfig | None = None): def _table_names(self) -> set[str]: """Return the set of table names in this LanceDB database.""" - try: - res = self._db.list_tables() - # LanceDB may return a response object with a `.tables` field. - names = res.tables if hasattr(res, "tables") else res - except Exception: - # Back-compat for older LanceDB. - names = self._db.table_names() - return set(names) + res = self._db.list_tables() + return set(res.tables) def _get_or_create_samples_table(self) -> lancedb.table.Table | None: if "samples" in self._table_names(): diff --git a/src/hyperview/storage/schema.py b/src/hyperview/storage/schema.py index aea9551..1f1a14d 100644 --- a/src/hyperview/storage/schema.py +++ b/src/hyperview/storage/schema.py @@ -233,7 +233,6 @@ def make_space_key(model_id: str) -> str: """Generate a space_key from a model_id. For simplicity, this is just the slugified model_id. - For provider-aware keys, use `make_provider_aware_space_key` from providers.py. """ return slugify_model_id(model_id) From ad7d7e887495d168af4e9e7486fec4666eec1637 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Sat, 31 Jan 2026 15:23:16 +0100 Subject: [PATCH 44/75] chore(frontend): bump version to 0.1.1 --- frontend/package.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/frontend/package.json b/frontend/package.json index 918a0ce..258352b 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -1,6 +1,6 @@ { "name": "hyperview-frontend", - "version": "0.1.0", + "version": "0.1.1", "private": true, "scripts": { "dev": "next dev", From 7ee24f58044db256180b0906c686c85d103cdebd Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Sat, 31 Jan 2026 15:24:55 +0100 Subject: [PATCH 45/75] ci: trigger frontend export check on push (not just PRs) --- .github/workflows/require_frontend_export.yml | 22 ++++++++++++++++--- 1 file changed, 19 insertions(+), 3 deletions(-) diff --git a/.github/workflows/require_frontend_export.yml b/.github/workflows/require_frontend_export.yml index 00363fe..aed9466 100644 --- a/.github/workflows/require_frontend_export.yml +++ b/.github/workflows/require_frontend_export.yml @@ -7,6 +7,11 @@ on: - "frontend/**" - "scripts/export_frontend.sh" - "src/hyperview/server/static/**" + push: + paths: + - "frontend/**" + - "scripts/export_frontend.sh" + - "src/hyperview/server/static/**" jobs: require-frontend-export: @@ -17,14 +22,25 @@ jobs: uses: actions/checkout@v4 with: fetch-depth: 0 - ref: ${{ github.event.pull_request.head.sha }} - name: Verify static export updated when frontend changes run: | set -euo pipefail - base_sha="${{ github.event.pull_request.base.sha }}" - head_sha="${{ github.event.pull_request.head.sha }}" + # Determine base and head SHAs based on event type + if [[ "${{ github.event_name }}" == "pull_request" ]]; then + base_sha="${{ github.event.pull_request.base.sha }}" + head_sha="${{ github.event.pull_request.head.sha }}" + else + # For push events, compare with the previous commit + head_sha="${{ github.sha }}" + base_sha="${{ github.event.before }}" + # Handle initial push (no previous commit) + if [[ "$base_sha" == "0000000000000000000000000000000000000000" ]]; then + echo "Initial push - skipping check" + exit 0 + fi + fi changed_files="$(git diff --name-only "$base_sha" "$head_sha")" From 57005d5ebd3544c6b8c14a4937d5278c0f36593b Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Sat, 31 Jan 2026 15:25:46 +0100 Subject: [PATCH 46/75] chore: trigger workflow test --- frontend/package.json | 1 + 1 file changed, 1 insertion(+) diff --git a/frontend/package.json b/frontend/package.json index 258352b..2418e05 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -49,3 +49,4 @@ "typescript": "^5.6.3" } } + From e0d183675145bc1b3fad98ce7bafadbf04a30369 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Sat, 31 Jan 2026 15:27:04 +0100 Subject: [PATCH 47/75] chore: export frontend static assets --- src/hyperview/server/static/404.html | 2 +- src/hyperview/server/static/404/index.html | 2 +- .../server/static/__next.__PAGE__.txt | 8 +++---- src/hyperview/server/static/__next._full.txt | 21 +++++++++--------- src/hyperview/server/static/__next._head.txt | 6 ++--- src/hyperview/server/static/__next._index.txt | 8 +++---- src/hyperview/server/static/__next._tree.txt | 5 +++-- .../_buildManifest.js | 0 .../_clientMiddlewareManifest.json | 0 .../_ssgManifest.js | 0 .../_next/static/chunks/2eaa7c8c8b6f5b3b.js | 13 +++++++++++ .../_next/static/chunks/a096ec8e6ed56d42.js | 9 -------- ...a5ec5690ca4254a.js => b96ffc94b0d8fbba.js} | 2 +- ...8c5753aca325314.js => cb115cad1deddecd.js} | 2 +- .../_next/static/chunks/d6d93a0a04227147.css | 1 - .../_next/static/chunks/e7ae0fd4a43b4ae1.css | 3 +++ ...18bac1f0e1f8b45.js => f89d541331847b49.js} | 2 +- ...1f2db.js => turbopack-904be3b11442f4b8.js} | 2 +- .../media/1bffadaabf893a1e-s.7cd81963.woff2 | Bin 0 -> 85272 bytes .../media/2bbe8d2671613f1f-s.76dcb0b2.woff2 | Bin 0 -> 10280 bytes .../media/2c55a0e60120577a-s.2a48534a.woff2 | Bin 0 -> 25844 bytes .../media/5476f68d60460930-s.c995e352.woff2 | Bin 0 -> 19044 bytes .../media/83afe278b6a6bb3c-s.p.3a6ba036.woff2 | Bin 0 -> 48432 bytes .../media/9c72aa0f40e4eef8-s.18a48cbc.woff2 | Bin 0 -> 18744 bytes .../media/ad66f9afd8947f86-s.7a40eb73.woff2 | Bin 0 -> 11272 bytes .../server/static/_not-found/__next._full.txt | 16 ++++++------- .../server/static/_not-found/__next._head.txt | 6 ++--- .../static/_not-found/__next._index.txt | 8 +++---- .../_not-found/__next._not-found.__PAGE__.txt | 4 ++-- .../static/_not-found/__next._not-found.txt | 6 ++--- .../server/static/_not-found/__next._tree.txt | 4 ++-- .../server/static/_not-found/index.html | 2 +- .../server/static/_not-found/index.txt | 16 ++++++------- src/hyperview/server/static/index.html | 2 +- src/hyperview/server/static/index.txt | 21 +++++++++--------- 35 files changed, 90 insertions(+), 81 deletions(-) rename src/hyperview/server/static/_next/static/{8FqA-ERyePplcUaEYWohc => U-zHksGY4392lcZLjE49t}/_buildManifest.js (100%) rename src/hyperview/server/static/_next/static/{8FqA-ERyePplcUaEYWohc => U-zHksGY4392lcZLjE49t}/_clientMiddlewareManifest.json (100%) rename src/hyperview/server/static/_next/static/{8FqA-ERyePplcUaEYWohc => U-zHksGY4392lcZLjE49t}/_ssgManifest.js (100%) create mode 100644 src/hyperview/server/static/_next/static/chunks/2eaa7c8c8b6f5b3b.js delete mode 100644 src/hyperview/server/static/_next/static/chunks/a096ec8e6ed56d42.js rename src/hyperview/server/static/_next/static/chunks/{5a5ec5690ca4254a.js => b96ffc94b0d8fbba.js} (99%) rename src/hyperview/server/static/_next/static/chunks/{48c5753aca325314.js => cb115cad1deddecd.js} (99%) delete mode 100644 src/hyperview/server/static/_next/static/chunks/d6d93a0a04227147.css create mode 100644 src/hyperview/server/static/_next/static/chunks/e7ae0fd4a43b4ae1.css rename src/hyperview/server/static/_next/static/chunks/{118bac1f0e1f8b45.js => f89d541331847b49.js} (99%) rename src/hyperview/server/static/_next/static/chunks/{turbopack-ce3658797261f2db.js => turbopack-904be3b11442f4b8.js} (98%) create mode 100644 src/hyperview/server/static/_next/static/media/1bffadaabf893a1e-s.7cd81963.woff2 create mode 100644 src/hyperview/server/static/_next/static/media/2bbe8d2671613f1f-s.76dcb0b2.woff2 create mode 100644 src/hyperview/server/static/_next/static/media/2c55a0e60120577a-s.2a48534a.woff2 create mode 100644 src/hyperview/server/static/_next/static/media/5476f68d60460930-s.c995e352.woff2 create mode 100644 src/hyperview/server/static/_next/static/media/83afe278b6a6bb3c-s.p.3a6ba036.woff2 create mode 100644 src/hyperview/server/static/_next/static/media/9c72aa0f40e4eef8-s.18a48cbc.woff2 create mode 100644 src/hyperview/server/static/_next/static/media/ad66f9afd8947f86-s.7a40eb73.woff2 diff --git a/src/hyperview/server/static/404.html b/src/hyperview/server/static/404.html index 7d84c46..1538a55 100644 --- a/src/hyperview/server/static/404.html +++ b/src/hyperview/server/static/404.html @@ -1 +1 @@ -404: This page could not be found.HyperView

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u=d.map(e=>this.viewItems[e]),c=d.map(e=>i[e]),p=h.map(e=>this.viewItems[e]),m=h.map(e=>i[e]),f=d.reduce((e,t)=>e+this.viewItems[t].minimumSize-i[t],0),g=d.reduce((e,t)=>e+this.viewItems[t].maximumSize-i[t],0),v=0===h.length?1/0:h.reduce((e,t)=>e+i[t]-this.viewItems[t].minimumSize,0),w=Math.max(f,0===h.length?-1/0:h.reduce((e,t)=>e+i[t]-this.viewItems[t].maximumSize,0)),b=Math.min(v,g),y=!1;if(a){let e=this.viewItems[a.index],i=t>=a.limitDelta;y=i!==e.visible,e.setVisible(i,a.size)}if(!y&&l){let e=this.viewItems[l.index],i=t{let i=void 0===e.visible||e.visible?e.size:{type:"invisible",cachedVisibleSize:e.size},n=e.view;this.addView(n,i,t,!0)}),this._contentSize=this.viewItems.reduce((e,t)=>e+t.size,0),this.saveProportions())}style(e){(null==e?void 0:e.separatorBorder)==="transparent"?(tq(this.element,"dv-separator-border"),this.element.style.removeProperty("--dv-separator-border")):(tY(this.element,"dv-separator-border"),(null==e?void 0:e.separatorBorder)&&this.element.style.setProperty("--dv-separator-border",e.separatorBorder))}isViewVisible(e){if(e<0||e>=this.viewItems.length)throw Error("Index out of bounds");return this.viewItems[e].visible}setViewVisible(e,t){if(e<0||e>=this.viewItems.length)throw Error("Index out of bounds");let i=this.viewItems[e];i.setVisible(t,i.size),this.distributeEmptySpace(e),this.layoutViews(),this.saveProportions()}getViewSize(e){return e<0||e>=this.viewItems.length?-1:this.viewItems[e].size}resizeView(e,t){if(e<0||e>=this.viewItems.length)return;let i=io(this.viewItems.length).filter(t=>t!==e),n=[...i.filter(e=>this.viewItems[e].priority===L.Low),e],o=i.filter(e=>this.viewItems[e].priority===L.High),r=this.viewItems[e];t=it(t=Math.round(t),r.minimumSize,Math.min(r.maximumSize,this._size)),r.size=t,this.relayout(n,o)}getViews(){return this.viewItems.map(e=>e.view)}onDidChange(e,t){let i=this.viewItems.indexOf(e);if(i<0||i>=this.viewItems.length)return;t=it(t="number"==typeof t?t:e.size,e.minimumSize,e.maximumSize),e.size=t;let n=io(this.viewItems.length).filter(e=>e!==i),o=[...n.filter(e=>this.viewItems[e].priority===L.Low),i],r=n.filter(e=>this.viewItems[e].priority===L.High);this.relayout([...o,i],r)}addView(e,t={type:"distribute"},i=this.viewItems.length,n){let o,r=document.createElement("div");r.className="dv-view",r.appendChild(e.element),o="number"==typeof t?t:"split"===t.type?this.getViewSize(t.index)/2:"invisible"===t.type?{cachedVisibleSize:t.cachedVisibleSize}:e.minimumSize;let s=e.onDidChange(e=>this.onDidChange(a,e.size)),a=new ir(r,e,o,{dispose:()=>{s.dispose(),this.viewContainer.removeChild(r)}});if(i===this.viewItems.length?this.viewContainer.appendChild(r):this.viewContainer.insertBefore(r,this.viewContainer.children.item(i)),this.viewItems.splice(i,0,a),this.viewItems.length>1){let e=document.createElement("div");e.className="dv-sash";let t=t=>{let i,n;for(let e of this.viewItems)e.enabled=!1;let o=t3(),r=this._orientation===I.HORIZONTAL?t.clientX:t.clientY,s=function(e,t){for(let i=0;it.container===e),a=this.viewItems.map(e=>e.size),l=io(s,-1),d=io(s+1,this.viewItems.length),h=l.reduce((e,t)=>e+(this.viewItems[t].minimumSize-a[t]),0),u=l.reduce((e,t)=>e+(this.viewItems[t].viewMaximumSize-a[t]),0),c=0===d.length?1/0:d.reduce((e,t)=>e+(a[t]-this.viewItems[t].minimumSize),0),p=Math.max(h,0===d.length?-1/0:d.reduce((e,t)=>e+(a[t]-this.viewItems[t].viewMaximumSize),0)),m=Math.min(c,u),f=this.findFirstSnapIndex(l),g=this.findFirstSnapIndex(d);if("number"==typeof f){let e=this.viewItems[f],t=Math.floor(e.viewMinimumSize/2);i={index:f,limitDelta:e.visible?p-t:p+t,size:e.size}}if("number"==typeof g){let e=this.viewItems[g],t=Math.floor(e.viewMinimumSize/2);n={index:g,limitDelta:e.visible?m+t:m-t,size:e.size}}let v=e=>{let t=this._orientation===I.HORIZONTAL?e.clientX:e.clientY;this.resize(s,t-r,a,void 0,void 0,p,m,i,n),this.distributeEmptySpace(),this.layoutViews()},w=()=>{for(let e of this.viewItems)e.enabled=!0;o.release(),this.saveProportions(),document.removeEventListener("pointermove",v),document.removeEventListener("pointerup",w),document.removeEventListener("pointercancel",w),document.removeEventListener("contextmenu",w),this._onDidSashEnd.fire(void 0)};document.addEventListener("pointermove",v),document.addEventListener("pointerup",w),document.addEventListener("pointercancel",w),document.addEventListener("contextmenu",w)};e.addEventListener("pointerdown",t),this.sashContainer.appendChild(e),this.sashes.push({container:e,disposable:()=>{e.removeEventListener("pointerdown",t),this.sashContainer.removeChild(e)}})}n||this.relayout([i]),n||"number"==typeof t||"distribute"!==t.type||this.distributeViewSizes(),this._onDidAddView.fire(e)}distributeViewSizes(){let e=[],t=0;for(let i of this.viewItems)i.maximumSize-i.minimumSize>0&&(e.push(i),t+=i.size);let i=Math.floor(t/e.length);for(let t of e)t.size=it(i,t.minimumSize,t.maximumSize);let n=io(this.viewItems.length),o=n.filter(e=>this.viewItems[e].priority===L.Low),r=n.filter(e=>this.viewItems[e].priority===L.High);this.relayout(o,r)}removeView(e,t,i=!1){let n=this.viewItems.splice(e,1)[0];if(n.dispose(),this.viewItems.length>=1){let t=Math.max(e-1,0);this.sashes.splice(t,1)[0].disposable()}return i||this.relayout(),t&&"distribute"===t.type&&this.distributeViewSizes(),this._onDidRemoveView.fire(n.view),n.view}getViewCachedVisibleSize(e){if(e<0||e>=this.viewItems.length)throw Error("Index out of bounds");return this.viewItems[e].cachedVisibleSize}moveView(e,t){let i=this.getViewCachedVisibleSize(e),n=void 0===i?this.getViewSize(e):T.Invisible(i),o=this.removeView(e,void 0,!0);this.addView(o,n,t)}layout(e,t){let i=Math.max(this.size,this._contentSize);if(this.size=e,this.orthogonalSize=t,this.proportions){let t=0;for(let i=0;i0&&(n.size=it(Math.round(o*e/t),n.minimumSize,n.maximumSize))}}else{let t=io(this.viewItems.length),n=t.filter(e=>this.viewItems[e].priority===L.Low),o=t.filter(e=>this.viewItems[e].priority===L.High);this.resize(this.viewItems.length-1,e-i,void 0,n,o)}this.distributeEmptySpace(),this.layoutViews()}relayout(e,t){let i=this.viewItems.reduce((e,t)=>e+t.size,0);this.resize(this.viewItems.length-1,this._size-i,void 0,e,t),this.distributeEmptySpace(),this.layoutViews(),this.saveProportions()}distributeEmptySpace(e){let t=this.viewItems.reduce((e,t)=>e+t.size,0),i=this.size-t,n=io(this.viewItems.length-1,-1),o=n.filter(e=>this.viewItems[e].priority===L.Low);for(let e of n.filter(e=>this.viewItems[e].priority===L.High))t8(n,e);for(let e of o)t7(n,e);"number"==typeof e&&t7(n,e);for(let e=0;0!==i&&e0&&(this._proportions=this.viewItems.map(e=>e.visible?e.size/this._contentSize:void 0))}layoutViews(){if(this._contentSize=this.viewItems.reduce((e,t)=>e+t.size,0),this.updateSashEnablement(),0===this.viewItems.length)return;let e=this.viewItems.filter(e=>e.visible),t=Math.max(0,e.length-1),i=this.margin*t/Math.max(1,e.length),n=0,o=[],r=this.viewItems.reduce((e,t,i)=>{let n=+!!t.visible;return 0===i?e.push(n):e.push(e[i-1]+n),e},[]);this.viewItems.forEach((e,s)=>{n+=this.viewItems[s].size,o.push(n);let a=e.visible?e.size-i:0,l=Math.max(0,r[s]-1),d=0===s||0===l?0:o[s-1]+l/t*i;if(s0)break;if(!e.visible&&e.snap)return t}}updateSashEnablement(){let e=!1,t=this.viewItems.map(t=>e=t.size-t.minimumSize>0||e);e=!1;let i=this.viewItems.map(t=>e=t.maximumSize-t.size>0||e),n=[...this.viewItems].reverse();e=!1;let o=n.map(t=>e=t.size-t.minimumSize>0||e).reverse();e=!1;let r=n.map(t=>e=t.maximumSize-t.size>0||e).reverse(),s=0;for(let e=0;e0||this.startSnappingEnabled)?this.updateSash(n,M.MINIMUM):h&&t[e]&&(s{let t=new tK(e.onDidChangeExpansionState(()=>{this.setupAnimation(),this._onDidChange.fire(void 0)}));this.paneItems.push({pane:e,disposable:{dispose:()=>{t.dispose()}}}),e.orthogonalSize=this.splitview.orthogonalSize}),this.addDisposables(this._onDidChange,this.splitview.onDidSashEnd(()=>{this._onDidChange.fire(void 0)}),this.splitview.onDidAddView(()=>{this._onDidChange.fire()}),this.splitview.onDidRemoveView(()=>{this._onDidChange.fire()}))}setViewVisible(e,t){this.splitview.setViewVisible(e,t)}addPane(e,t,i=this.splitview.length,n=!1){let o=e.onDidChangeExpansionState(()=>{this.setupAnimation(),this._onDidChange.fire(void 0)});this.paneItems.splice(i,0,{pane:e,disposable:{dispose:()=>{o.dispose()}}}),e.orthogonalSize=this.splitview.orthogonalSize,this.splitview.addView(e,t,i,n)}getViewSize(e){return this.splitview.getViewSize(e)}getPanes(){return this.splitview.getViews()}removePane(e,t={skipDispose:!1}){let i=this.paneItems.splice(e,1)[0];return this.splitview.removeView(e),t.skipDispose||(i.disposable.dispose(),i.pane.dispose()),i}moveView(e,t){if(e===t)return;let i=this.removePane(e,{skipDispose:!0});this.skipAnimation=!0;try{this.addPane(i.pane,i.pane.size,t,!1)}finally{this.skipAnimation=!1}}layout(e,t){this.splitview.layout(e,t)}setupAnimation(){this.skipAnimation||(this.animationTimer&&(clearTimeout(this.animationTimer),this.animationTimer=void 0),tY(this.element,"dv-animated"),this.animationTimer=setTimeout(()=>{this.animationTimer=void 0,tq(this.element,"dv-animated")},200))}dispose(){super.dispose(),this.animationTimer&&(clearTimeout(this.animationTimer),this.animationTimer=void 0),this.paneItems.forEach(e=>{e.disposable.dispose(),e.pane.dispose()}),this.paneItems=[],this.splitview.dispose(),this.element.remove()}}class id{get minimumWidth(){return this.view.minimumWidth}get maximumWidth(){return this.view.maximumWidth}get minimumHeight(){return this.view.minimumHeight}get maximumHeight(){return this.view.maximumHeight}get priority(){return this.view.priority}get snap(){return this.view.snap}get minimumSize(){return this.orientation===I.HORIZONTAL?this.minimumHeight:this.minimumWidth}get maximumSize(){return this.orientation===I.HORIZONTAL?this.maximumHeight:this.maximumWidth}get minimumOrthogonalSize(){return this.orientation===I.HORIZONTAL?this.minimumWidth:this.minimumHeight}get maximumOrthogonalSize(){return this.orientation===I.HORIZONTAL?this.maximumWidth:this.maximumHeight}get orthogonalSize(){return this._orthogonalSize}get size(){return this._size}get element(){return this.view.element}get width(){return this.orientation===I.HORIZONTAL?this.orthogonalSize:this.size}get height(){return this.orientation===I.HORIZONTAL?this.size:this.orthogonalSize}constructor(e,t,i,n=0){this.view=e,this.orientation=t,this._onDidChange=new tB,this.onDidChange=this._onDidChange.event,this._orthogonalSize=i,this._size=n,this._disposable=this.view.onDidChange(e=>{e?this._onDidChange.fire({size:this.orientation===I.VERTICAL?e.width:e.height,orthogonalSize:this.orientation===I.VERTICAL?e.height:e.width}):this._onDidChange.fire({})})}setVisible(e){this.view.setVisible&&this.view.setVisible(e)}layout(e,t){this._size=e,this._orthogonalSize=t,this.view.layout(this.width,this.height)}dispose(){this._onDidChange.dispose(),this._disposable.dispose()}}class ih extends tK{get width(){return this.orientation===I.HORIZONTAL?this.size:this.orthogonalSize}get height(){return this.orientation===I.HORIZONTAL?this.orthogonalSize:this.size}get minimumSize(){return 0===this.children.length?0:Math.max(...this.children.map((e,t)=>this.splitview.isViewVisible(t)?e.minimumOrthogonalSize:0))}get maximumSize(){return Math.min(...this.children.map((e,t)=>this.splitview.isViewVisible(t)?e.maximumOrthogonalSize:1/0))}get minimumOrthogonalSize(){return this.splitview.minimumSize}get maximumOrthogonalSize(){return this.splitview.maximumSize}get orthogonalSize(){return this._orthogonalSize}get size(){return this._size}get minimumWidth(){return this.orientation===I.HORIZONTAL?this.minimumOrthogonalSize:this.minimumSize}get minimumHeight(){return this.orientation===I.HORIZONTAL?this.minimumSize:this.minimumOrthogonalSize}get maximumWidth(){return this.orientation===I.HORIZONTAL?this.maximumOrthogonalSize:this.maximumSize}get maximumHeight(){return this.orientation===I.HORIZONTAL?this.maximumSize:this.maximumOrthogonalSize}get priority(){if(0===this.children.length)return L.Normal;let e=this.children.map(e=>void 0===e.priority?L.Normal:e.priority);return e.some(e=>e===L.High)?L.High:e.some(e=>e===L.Low)?L.Low:L.Normal}get disabled(){return this.splitview.disabled}set disabled(e){this.splitview.disabled=e}get margin(){return this.splitview.margin}set margin(e){this.splitview.margin=e,this.children.forEach(t=>{t instanceof ih&&(t.margin=e)})}constructor(e,t,i,n,o,r,s,a){if(super(),this.orientation=e,this.proportionalLayout=t,this.styles=i,this._childrenDisposable=O.NONE,this.children=[],this._onDidChange=new tB,this.onDidChange=this._onDidChange.event,this._onDidVisibilityChange=new tB,this.onDidVisibilityChange=this._onDidVisibilityChange.event,this._orthogonalSize=o,this._size=n,this.element=document.createElement("div"),this.element.className="dv-branch-node",a){const e={views:a.map(e=>({view:e.node,size:e.node.size,visible:!(e.node instanceof id)||void 0===e.visible||e.visible})),size:this.orthogonalSize};this.children=a.map(e=>e.node),this.splitview=new is(this.element,{orientation:this.orientation,descriptor:e,proportionalLayout:t,styles:i,margin:s})}else this.splitview=new is(this.element,{orientation:this.orientation,proportionalLayout:t,styles:i,margin:s}),this.splitview.layout(this.size,this.orthogonalSize);this.disabled=r,this.addDisposables(this._onDidChange,this._onDidVisibilityChange,this.splitview.onDidSashEnd(()=>{this._onDidChange.fire({})})),this.setupChildrenEvents()}setVisible(e){}isChildVisible(e){if(e<0||e>=this.children.length)throw Error("Invalid index");return this.splitview.isViewVisible(e)}setChildVisible(e,t){if(e<0||e>=this.children.length)throw Error("Invalid index");if(this.splitview.isViewVisible(e)===t)return;let i=0===this.splitview.contentSize;this.splitview.setViewVisible(e,t);let n=0===this.splitview.contentSize;(t&&i||!t&&n)&&this._onDidVisibilityChange.fire({visible:t})}moveChild(e,t){if(e===t)return;if(e<0||e>=this.children.length)throw Error("Invalid from index");e=this.children.length)throw Error("Invalid index");return this.splitview.getViewSize(e)}resizeChild(e,t){if(e<0||e>=this.children.length)throw Error("Invalid index");this.splitview.resizeView(e,t)}layout(e,t){this._size=t,this._orthogonalSize=e,this.splitview.layout(t,e)}addChild(e,t,i,n){if(i<0||i>this.children.length)throw Error("Invalid index");this.splitview.addView(e,t,i,n),this._addChild(e,i)}getChildCachedVisibleSize(e){if(e<0||e>=this.children.length)throw Error("Invalid index");return this.splitview.getViewCachedVisibleSize(e)}removeChild(e,t){if(e<0||e>=this.children.length)throw Error("Invalid index");return this.splitview.removeView(e,t),this._removeChild(e)}_addChild(e,t){this.children.splice(t,0,e),this.setupChildrenEvents()}_removeChild(e){let[t]=this.children.splice(e,1);return this.setupChildrenEvents(),t}setupChildrenEvents(){this._childrenDisposable.dispose(),this._childrenDisposable=new tK(N.any(...this.children.map(e=>e.onDidChange))(e=>{this._onDidChange.fire({size:e.orthogonalSize})}),...this.children.map((e,t)=>e instanceof ih?e.onDidVisibilityChange(({visible:e})=>{this.setChildVisible(t,e)}):O.NONE))}dispose(){this._childrenDisposable.dispose(),this.splitview.dispose(),this.children.forEach(e=>e.dispose()),super.dispose()}}function iu(e,t){if(e instanceof id)return e;if(e instanceof ih)return iu(e.children[t?e.children.length-1:0],t);throw Error("invalid node")}function ic(e,t,i){if(!(e instanceof ih))return new id(e.view,ig(e.orientation),i);{let n=new ih(ig(e.orientation),e.proportionalLayout,e.styles,t,i,e.disabled,e.margin),o=0;for(let r=e.children.length-1;r>=0;r--){let s=e.children[r],a=s instanceof ih?s.orthogonalSize:s.size,l=0===e.size?0:Math.round(t*a/e.size);o+=l,0===r&&(l+=t-o),n.addChild(ic(s,i,l),l,0,!0)}return n}}function ip(e){let t=e.parentElement;if(!t)throw Error("Invalid grid element");if(/\bdv-grid-view\b/.test(t.className))return[];let i=function(e){let t=e.parentElement;if(!t)throw Error("Invalid grid element");let i=t.firstElementChild,n=0;for(;i!==e&&i!==t.lastElementChild&&i;)i=i.nextElementSibling,n++;return n}(t);return[...ip(t.parentElement.parentElement.parentElement),i]}function im(e,t,i){var n,o;if(n=e,(t.length%2==0?ig(n):n)!==("top"===(o=i)||"bottom"===o?I.VERTICAL:I.HORIZONTAL))return[...t,+("right"===i||"bottom"===i)];{let[e,n]=t9(t),o=n;return("right"===i||"bottom"===i)&&(o+=1),[...e,o]}}let ig=e=>e===I.HORIZONTAL?I.VERTICAL:I.HORIZONTAL,iv=(e,t)=>{let i=t===I.VERTICAL?e.box.width:e.box.height;return e.children?{type:"branch",data:e.children.map(e=>iv(e,ig(t))),size:i}:"number"==typeof e.cachedVisibleSize?{type:"leaf",data:e.view.toJSON(),size:e.cachedVisibleSize,visible:!1}:{type:"leaf",data:e.view.toJSON(),size:i}};class iw{get length(){return this._root?this._root.children.length:0}get orientation(){return this.root.orientation}set orientation(e){if(this.root.orientation===e)return;let{size:t,orthogonalSize:i}=this.root;this.root=ic(this.root,i,t),this.root.layout(t,i)}get width(){return this.root.width}get height(){return this.root.height}get minimumWidth(){return this.root.minimumWidth}get minimumHeight(){return this.root.minimumHeight}get maximumWidth(){return this.root.maximumHeight}get maximumHeight(){return this.root.maximumHeight}get locked(){return this._locked}set locked(e){this._locked=e;let t=[this.root];for(;t.length>0;){let i=t.pop();i instanceof ih&&(i.disabled=e,t.push(...i.children))}}get margin(){return this._margin}set margin(e){this._margin=e,this.root.margin=e}maximizedView(){var e;return null==(e=this._maximizedNode)?void 0:e.leaf.view}hasMaximizedView(){return void 0!==this._maximizedNode}maximizeView(e){var t;let i=ip(e.element),[n,o]=this.getNode(i);if(!(o instanceof id)||(null==(t=this._maximizedNode)?void 0:t.leaf)===o)return;this.hasMaximizedView()&&this.exitMaximizedView(),iv(this.getView(),this.orientation);let r=[];!function e(t,i){for(let n=0;n=0;n--){let o=i.children[n];o instanceof id?e.includes(o)||i.setChildVisible(n,!0):t(o)}}(this.root);let t=this._maximizedNode.leaf;this._maximizedNode=void 0,this._onDidMaximizedNodeChange.fire({view:t.view,isMaximized:!1})}serialize(){let e,t=this.maximizedView();t&&(e=ip(t.element)),this.hasMaximizedView()&&this.exitMaximizedView();let i={root:iv(this.getView(),this.orientation),width:this.width,height:this.height,orientation:this.orientation};return e&&(i.maximizedNode={location:e}),t&&this.maximizeView(t),i}dispose(){this.disposable.dispose(),this._onDidChange.dispose(),this._onDidMaximizedNodeChange.dispose(),this._onDidViewVisibilityChange.dispose(),this.root.dispose(),this._maximizedNode=void 0,this.element.remove()}clear(){let e=this.root.orientation;this.root=new ih(e,this.proportionalLayout,this.styles,this.root.size,this.root.orthogonalSize,this.locked,this.margin)}deserialize(e,t){let i=e.orientation,n=i===I.VERTICAL?e.height:e.width;if(this._deserialize(e.root,i,t,n),this.layout(e.width,e.height),e.maximizedNode){let t=e.maximizedNode.location,[i,n]=this.getNode(t);if(!(n instanceof id))return;this.maximizeView(n.view)}}_deserialize(e,t,i,n){this.root=this._deserializeNode(e,t,i,n)}_deserializeNode(e,t,i,n){var o;let r;if("branch"===e.type){let o=e.data.map(n=>({node:this._deserializeNode(n,ig(t),i,e.size),visible:n.visible}));r=new ih(t,this.proportionalLayout,this.styles,e.size,n,this.locked,this.margin,o)}else{let s=i.fromJSON(e);"boolean"==typeof e.visible&&(null==(o=s.setVisible)||o.call(s,e.visible)),r=new id(s,t,n,e.size)}return r}get root(){return this._root}set root(e){let t=this._root;t&&(t.dispose(),this._maximizedNode=void 0,this.element.removeChild(t.element)),this._root=e,this.element.appendChild(this._root.element),this.disposable.value=this._root.onDidChange(e=>{this._onDidChange.fire(e)})}normalize(){if(!this._root||1!==this._root.children.length)return;let e=this.root,t=e.children[0];if(t instanceof id)return;e.element.remove();let i=e.removeChild(0);e.dispose(),i.dispose(),this._root=function e(t,i,n){if(!(t instanceof ih))return new id(t.view,t.orientation,n);{let o=new ih(t.orientation,t.proportionalLayout,t.styles,i,n,t.disabled,t.margin);for(let i=t.children.length-1;i>=0;i--){let n=t.children[i];o.addChild(e(n,n.size,n.orthogonalSize),n.size,0,!0)}return o}}(t,t.size,t.orthogonalSize),this.element.appendChild(this._root.element),this.disposable.value=this._root.onDidChange(e=>{this._onDidChange.fire(e)})}insertOrthogonalSplitviewAtRoot(){if(!this._root)return;let e=this.root;if(e.element.remove(),this._root=new ih(ig(e.orientation),this.proportionalLayout,this.styles,this.root.orthogonalSize,this.root.size,this.locked,this.margin),0===e.children.length);else if(1===e.children.length){let t=e.children[0];e.removeChild(0).dispose(),e.dispose(),this._root.addChild(ic(t,t.orthogonalSize,t.size),T.Distribute,0)}else this._root.addChild(e,T.Distribute,0);this.element.appendChild(this._root.element),this.disposable.value=this._root.onDidChange(e=>{this._onDidChange.fire(e)})}next(e){return this.progmaticSelect(e)}previous(e){return this.progmaticSelect(e,!0)}getView(e){let t=e?this.getNode(e)[1]:this.root;return this._getViews(t,this.orientation)}_getViews(e,t,i){let n={height:e.height,width:e.width};if(e instanceof id)return{box:n,view:e.view,cachedVisibleSize:i};let o=[];for(let i=0;i-1;n--){let o=i[n],r=e[n]||0;if(t?r-1>-1:r+1l.getChildSize(t));if(l.removeChild(h,t).dispose(),a instanceof ih){p.splice(h,1,...a.children.map(e=>e.size));for(let e=0;e0;)a.removeChild(0)}else{let e=new id(a.view,ig(a.orientation),a.size),t=c?a.orthogonalSize:T.Invisible(a.orthogonalSize);l.addChild(e,t,h)}a.dispose();for(let e=0;e=t.children.length)throw Error("Invalid location");let r=t.children[n];return i.push(t),this.getNode(o,r,i)}}let ib=["disableAutoResizing","proportionalLayout","orientation","hideBorders","className"];class iy extends tK{get element(){return this._element}get disableResizing(){return this._disableResizing}set disableResizing(e){this._disableResizing=e}constructor(e,t=!1){super(),this._disableResizing=t,this._element=e,this.addDisposables(tX(this._element,e=>{if(this.isDisposed||this.disableResizing||!this._element.offsetParent||!function(e){let t=e;for(;null==t?void 0:t.parentNode;)if(t.parentNode===document)return!0;else t=t.parentNode instanceof DocumentFragment?t.parentNode.host:t.parentNode;return!1}(this._element))return;let{width:t,height:i}=e.contentRect;this.layout(t,i)}))}}let ix=ii();function i_(e){switch(e){case"left":return"left";case"right":return"right";case"above":return"top";case"below":return"bottom";default:return"center"}}class iD extends iy{get id(){return this._id}get size(){return this._groups.size}get groups(){return Array.from(this._groups.values()).map(e=>e.value)}get width(){return this.gridview.width}get height(){return this.gridview.height}get minimumHeight(){return this.gridview.minimumHeight}get maximumHeight(){return this.gridview.maximumHeight}get minimumWidth(){return this.gridview.minimumWidth}get maximumWidth(){return this.gridview.maximumWidth}get activeGroup(){return this._activeGroup}get locked(){return this.gridview.locked}set locked(e){this.gridview.locked=e}constructor(e,t){var i;super(document.createElement("div"),t.disableAutoResizing),this._id=ix.next(),this._groups=new Map,this._onDidRemove=new tB,this.onDidRemove=this._onDidRemove.event,this._onDidAdd=new tB,this.onDidAdd=this._onDidAdd.event,this._onDidMaximizedChange=new tB,this.onDidMaximizedChange=this._onDidMaximizedChange.event,this._onDidActiveChange=new tB,this.onDidActiveChange=this._onDidActiveChange.event,this._bufferOnDidLayoutChange=new tU,this.onDidLayoutChange=this._bufferOnDidLayoutChange.onEvent,this._onDidViewVisibilityChangeMicroTaskQueue=new tU,this.onDidViewVisibilityChangeMicroTaskQueue=this._onDidViewVisibilityChangeMicroTaskQueue.onEvent,this.element.style.height="100%",this.element.style.width="100%",this._classNames=new t4(this.element),this._classNames.setClassNames(null!=(i=t.className)?i:""),e.appendChild(this.element),this.gridview=new iw(!!t.proportionalLayout,t.styles,t.orientation,t.locked,t.margin),this.gridview.locked=!!t.locked,this.element.appendChild(this.gridview.element),this.layout(0,0,!0),this.addDisposables(this.gridview.onDidMaximizedNodeChange(e=>{this._onDidMaximizedChange.fire({panel:e.view,isMaximized:e.isMaximized})}),this.gridview.onDidViewVisibilityChange(()=>this._onDidViewVisibilityChangeMicroTaskQueue.fire()),this.onDidViewVisibilityChangeMicroTaskQueue(()=>{this.layout(this.width,this.height,!0)}),O.from(()=>{var e;null==(e=this.element.parentElement)||e.removeChild(this.element)}),this.gridview.onDidChange(()=>{this._bufferOnDidLayoutChange.fire()}),N.any(this.onDidAdd,this.onDidRemove,this.onDidActiveChange)(()=>{this._bufferOnDidLayoutChange.fire()}),this._onDidMaximizedChange,this._onDidViewVisibilityChangeMicroTaskQueue,this._bufferOnDidLayoutChange)}setVisible(e,t){this.gridview.setViewVisible(ip(e.element),t),this._bufferOnDidLayoutChange.fire()}isVisible(e){return this.gridview.isViewVisible(ip(e.element))}updateOptions(e){var t,i,n,o;e.proportionalLayout,e.orientation&&(this.gridview.orientation=e.orientation),"disableResizing"in e&&(this.disableResizing=null!=(t=e.disableAutoResizing)&&t),"locked"in e&&(this.locked=null!=(i=e.locked)&&i),"margin"in e&&(this.gridview.margin=null!=(n=e.margin)?n:0),"className"in e&&this._classNames.setClassNames(null!=(o=e.className)?o:"")}maximizeGroup(e){this.gridview.maximizeView(e),this.doSetGroupActive(e)}isMaximizedGroup(e){return this.gridview.maximizedView()===e}exitMaximizedGroup(){this.gridview.exitMaximizedView()}hasMaximizedGroup(){return this.gridview.hasMaximizedView()}doAddGroup(e,t=[0],i){this.gridview.addView(e,null!=i?i:T.Distribute,t),this._onDidAdd.fire(e)}doRemoveGroup(e,t){if(!this._groups.has(e.id))throw Error("invalid operation");let i=this._groups.get(e.id),n=this.gridview.remove(e,T.Distribute);if(!i||(null==t?void 0:t.skipDispose)||(i.disposable.dispose(),i.value.dispose(),this._groups.delete(e.id),this._onDidRemove.fire(e)),!(null==t?void 0:t.skipActive)&&this._activeGroup===e){let e=Array.from(this._groups.values());this.doSetGroupActive(e.length>0?e[0].value:void 0)}return n}getPanel(e){var t;return null==(t=this._groups.get(e))?void 0:t.value}doSetGroupActive(e){this._activeGroup!==e&&(this._activeGroup&&this._activeGroup.setActive(!1),e&&e.setActive(!0),this._activeGroup=e,this._onDidActiveChange.fire(e))}removeGroup(e){this.doRemoveGroup(e)}moveToNext(e){var t;if(e||(e={}),!e.group){if(!this.activeGroup)return;e.group=this.activeGroup}let i=ip(e.group.element),n=null==(t=this.gridview.next(i))?void 0:t.view;this.doSetGroupActive(n)}moveToPrevious(e){var t;if(e||(e={}),!e.group){if(!this.activeGroup)return;e.group=this.activeGroup}let i=ip(e.group.element),n=null==(t=this.gridview.previous(i))?void 0:t.view;this.doSetGroupActive(n)}layout(e,t,i){(i||e!==this.width||t!==this.height)&&(this.gridview.element.style.height=`${t}px`,this.gridview.element.style.width=`${e}px`,this.gridview.layout(e,t))}dispose(){for(let e of(this._onDidActiveChange.dispose(),this._onDidAdd.dispose(),this._onDidRemove.dispose(),this.groups))e.dispose();this.gridview.dispose(),super.dispose()}}class iC{get minimumSize(){return this.component.minimumSize}get maximumSize(){return this.component.maximumSize}get width(){return this.component.width}get height(){return this.component.height}get length(){return this.component.length}get orientation(){return this.component.orientation}get panels(){return this.component.panels}get onDidLayoutFromJSON(){return this.component.onDidLayoutFromJSON}get onDidLayoutChange(){return this.component.onDidLayoutChange}get onDidAddView(){return this.component.onDidAddView}get onDidRemoveView(){return this.component.onDidRemoveView}constructor(e){this.component=e}removePanel(e,t){this.component.removePanel(e,t)}focus(){this.component.focus()}getPanel(e){return this.component.getPanel(e)}layout(e,t){return this.component.layout(e,t)}addPanel(e){return this.component.addPanel(e)}movePanel(e,t){this.component.movePanel(e,t)}fromJSON(e){this.component.fromJSON(e)}toJSON(){return this.component.toJSON()}clear(){this.component.clear()}updateOptions(e){this.component.updateOptions(e)}dispose(){this.component.dispose()}}class iS{get minimumSize(){return this.component.minimumSize}get maximumSize(){return this.component.maximumSize}get width(){return this.component.width}get height(){return this.component.height}get panels(){return this.component.panels}get onDidLayoutChange(){return this.component.onDidLayoutChange}get onDidLayoutFromJSON(){return this.component.onDidLayoutFromJSON}get onDidAddView(){return this.component.onDidAddView}get onDidRemoveView(){return this.component.onDidRemoveView}get onDidDrop(){return this.component.onDidDrop}get onUnhandledDragOverEvent(){return this.component.onUnhandledDragOverEvent}constructor(e){this.component=e}removePanel(e){this.component.removePanel(e)}getPanel(e){return this.component.getPanel(e)}movePanel(e,t){this.component.movePanel(e,t)}focus(){this.component.focus()}layout(e,t){this.component.layout(e,t)}addPanel(e){return this.component.addPanel(e)}fromJSON(e){this.component.fromJSON(e)}toJSON(){return this.component.toJSON()}clear(){this.component.clear()}updateOptions(e){this.component.updateOptions(e)}dispose(){this.component.dispose()}}class iE{get width(){return this.component.width}get height(){return this.component.height}get minimumHeight(){return this.component.minimumHeight}get maximumHeight(){return this.component.maximumHeight}get minimumWidth(){return this.component.minimumWidth}get maximumWidth(){return this.component.maximumWidth}get onDidLayoutChange(){return this.component.onDidLayoutChange}get onDidAddPanel(){return this.component.onDidAddGroup}get onDidRemovePanel(){return this.component.onDidRemoveGroup}get onDidActivePanelChange(){return this.component.onDidActiveGroupChange}get onDidLayoutFromJSON(){return this.component.onDidLayoutFromJSON}get panels(){return this.component.groups}get orientation(){return this.component.orientation}set orientation(e){this.component.updateOptions({orientation:e})}constructor(e){this.component=e}focus(){this.component.focus()}layout(e,t,i=!1){this.component.layout(e,t,i)}addPanel(e){return this.component.addPanel(e)}removePanel(e,t){this.component.removePanel(e,t)}movePanel(e,t){this.component.movePanel(e,t)}getPanel(e){return this.component.getPanel(e)}fromJSON(e){return this.component.fromJSON(e)}toJSON(){return this.component.toJSON()}clear(){this.component.clear()}updateOptions(e){this.component.updateOptions(e)}dispose(){this.component.dispose()}}class iA{get id(){return this.component.id}get width(){return this.component.width}get height(){return this.component.height}get minimumHeight(){return this.component.minimumHeight}get maximumHeight(){return this.component.maximumHeight}get minimumWidth(){return this.component.minimumWidth}get maximumWidth(){return this.component.maximumWidth}get size(){return this.component.size}get totalPanels(){return this.component.totalPanels}get onDidActiveGroupChange(){return this.component.onDidActiveGroupChange}get onDidAddGroup(){return this.component.onDidAddGroup}get onDidRemoveGroup(){return this.component.onDidRemoveGroup}get onDidActivePanelChange(){return this.component.onDidActivePanelChange}get onDidAddPanel(){return this.component.onDidAddPanel}get onDidRemovePanel(){return this.component.onDidRemovePanel}get onDidMovePanel(){return this.component.onDidMovePanel}get onDidLayoutFromJSON(){return this.component.onDidLayoutFromJSON}get onDidLayoutChange(){return this.component.onDidLayoutChange}get onDidDrop(){return this.component.onDidDrop}get onWillDrop(){return this.component.onWillDrop}get onWillShowOverlay(){return this.component.onWillShowOverlay}get onWillDragGroup(){return this.component.onWillDragGroup}get onWillDragPanel(){return this.component.onWillDragPanel}get onUnhandledDragOverEvent(){return this.component.onUnhandledDragOverEvent}get onDidPopoutGroupSizeChange(){return this.component.onDidPopoutGroupSizeChange}get onDidPopoutGroupPositionChange(){return this.component.onDidPopoutGroupPositionChange}get onDidOpenPopoutWindowFail(){return this.component.onDidOpenPopoutWindowFail}get panels(){return this.component.panels}get groups(){return this.component.groups}get activePanel(){return this.component.activePanel}get activeGroup(){return this.component.activeGroup}constructor(e){this.component=e}focus(){this.component.focus()}getPanel(e){return this.component.getGroupPanel(e)}layout(e,t,i=!1){this.component.layout(e,t,i)}addPanel(e){return this.component.addPanel(e)}removePanel(e){this.component.removePanel(e)}addGroup(e){return this.component.addGroup(e)}closeAllGroups(){return this.component.closeAllGroups()}removeGroup(e){this.component.removeGroup(e)}getGroup(e){return this.component.getPanel(e)}addFloatingGroup(e,t){return this.component.addFloatingGroup(e,t)}fromJSON(e,t){this.component.fromJSON(e,t)}toJSON(){return this.component.toJSON()}clear(){this.component.clear()}moveToNext(e){this.component.moveToNext(e)}moveToPrevious(e){this.component.moveToPrevious(e)}maximizeGroup(e){this.component.maximizeGroup(e.group)}hasMaximizedGroup(){return this.component.hasMaximizedGroup()}exitMaximizedGroup(){this.component.exitMaximizedGroup()}get onDidMaximizedGroupChange(){return this.component.onDidMaximizedGroupChange}addPopoutGroup(e,t){return this.component.addPopoutGroup(e,t)}updateOptions(e){this.component.updateOptions(e)}dispose(){this.component.dispose()}}class ik extends tK{constructor(e,t){super(),this.el=e,this.disabled=t,this.dataDisposable=new tJ,this.pointerEventsDisposable=new tJ,this._onDragStart=new tB,this.onDragStart=this._onDragStart.event,this.addDisposables(this._onDragStart,this.dataDisposable,this.pointerEventsDisposable),this.configure()}setDisabled(e){this.disabled=e}isCancelled(e){return!1}configure(){this.addDisposables(this._onDragStart,t$(this.el,"dragstart",e=>{if(e.defaultPrevented||this.isCancelled(e)||this.disabled)return void e.preventDefault();let t=t3();this.pointerEventsDisposable.value={dispose:()=>{t.release()}},this.el.classList.add("dv-dragged"),setTimeout(()=>this.el.classList.remove("dv-dragged"),0),this.dataDisposable.value=this.getData(e),this._onDragStart.fire(e),e.dataTransfer&&(e.dataTransfer.effectAllowed="move",e.dataTransfer.items.length>0||e.dataTransfer.setData("text/plain",""))}),t$(this.el,"dragend",()=>{this.pointerEventsDisposable.dispose(),setTimeout(()=>{this.dataDisposable.dispose()},0)}))}}class iz extends tK{constructor(e,t){super(),this.element=e,this.callbacks=t,this.target=null,this.registerListeners()}onDragEnter(e){this.target=e.target,this.callbacks.onDragEnter(e)}onDragOver(e){e.preventDefault(),this.callbacks.onDragOver&&this.callbacks.onDragOver(e)}onDragLeave(e){this.target===e.target&&(this.target=null,this.callbacks.onDragLeave(e))}onDragEnd(e){this.target=null,this.callbacks.onDragEnd(e)}onDrop(e){this.callbacks.onDrop(e)}registerListeners(){this.addDisposables(t$(this.element,"dragenter",e=>{this.onDragEnter(e)},!0)),this.addDisposables(t$(this.element,"dragover",e=>{this.onDragOver(e)},!0)),this.addDisposables(t$(this.element,"dragleave",e=>{this.onDragLeave(e)})),this.addDisposables(t$(this.element,"dragend",e=>{this.onDragEnd(e)})),this.addDisposables(t$(this.element,"drop",e=>{this.onDrop(e)}))}}class iP extends tV{get nativeEvent(){return this.options.nativeEvent}get position(){return this.options.position}constructor(e){super(),this.options=e}}function iR(e){switch(e){case"above":return"top";case"below":return"bottom";case"left":return"left";case"right":return"right";case"within":return"center";default:throw Error(`invalid direction '${e}'`)}}let iN={value:20,type:"percentage"},iO={value:50,type:"percentage"};class iI extends tK{get disabled(){return this._disabled}set disabled(e){this._disabled=e}get state(){return this._state}constructor(e,t){super(),this.element=e,this.options=t,this._onDrop=new tB,this.onDrop=this._onDrop.event,this._onWillShowOverlay=new tB,this.onWillShowOverlay=this._onWillShowOverlay.event,this._disabled=!1,this._acceptedTargetZonesSet=new Set(this.options.acceptedTargetZones),this.dnd=new iz(this.element,{onDragEnter:()=>{var e,t,i;null==(i=null==(t=(e=this.options).getOverrideTarget)?void 0:t.call(e))||i.getElements()},onDragOver:e=>{var t,i,n,o,r,s,a;iI.ACTUAL_TARGET=this;let l=null==(i=(t=this.options).getOverrideTarget)?void 0:i.call(t);if(0===this._acceptedTargetZonesSet.size){if(l)return;this.removeDropTarget();return}let d=null!=(r=null==(o=(n=this.options).getOverlayOutline)?void 0:o.call(n))?r:this.element,h=d.offsetWidth,u=d.offsetHeight;if(0===h||0===u)return;let c=e.currentTarget.getBoundingClientRect(),p=(null!=(s=e.clientX)?s:0)-c.left,m=(null!=(a=e.clientY)?a:0)-c.top,f=this.calculateQuadrant(this._acceptedTargetZonesSet,p,m,h,u);if(this.isAlreadyUsed(e)||null===f)return void this.removeDropTarget();if(!this.options.canDisplayOverlay(e,f)){if(l)return;this.removeDropTarget();return}let g=new iP({nativeEvent:e,position:f});(this._onWillShowOverlay.fire(g),g.defaultPrevented)?this.removeDropTarget():(this.markAsUsed(e),l||this.targetElement||(this.targetElement=document.createElement("div"),this.targetElement.className="dv-drop-target-dropzone",this.overlayElement=document.createElement("div"),this.overlayElement.className="dv-drop-target-selection",this._state="center",this.targetElement.appendChild(this.overlayElement),d.classList.add("dv-drop-target"),d.append(this.targetElement)),this.toggleClasses(f,h,u),this._state=f)},onDragLeave:()=>{var e,t;(null==(t=(e=this.options).getOverrideTarget)?void 0:t.call(e))||this.removeDropTarget()},onDragEnd:e=>{var t,i;let n=null==(i=(t=this.options).getOverrideTarget)?void 0:i.call(t);n&&iI.ACTUAL_TARGET===this&&this._state&&(e.stopPropagation(),this._onDrop.fire({position:this._state,nativeEvent:e})),this.removeDropTarget(),null==n||n.clear()},onDrop:e=>{var t,i,n;e.preventDefault();let o=this._state;this.removeDropTarget(),null==(n=null==(i=(t=this.options).getOverrideTarget)?void 0:i.call(t))||n.clear(),o&&(e.stopPropagation(),this._onDrop.fire({position:o,nativeEvent:e}))}}),this.addDisposables(this._onDrop,this._onWillShowOverlay,this.dnd)}setTargetZones(e){this._acceptedTargetZonesSet=new Set(e)}setOverlayModel(e){this.options.overlayModel=e}dispose(){this.removeDropTarget(),super.dispose()}markAsUsed(e){e[iI.USED_EVENT_ID]=!0}isAlreadyUsed(e){let t=e[iI.USED_EVENT_ID];return"boolean"==typeof t&&t}toggleClasses(e,t,i){var n,o,r,s,a,l,d;let h=null==(o=(n=this.options).getOverrideTarget)?void 0:o.call(n);if(!h&&!this.overlayElement)return;let u=t<100,c=i<100,p="left"===e,m="right"===e,f="top"===e,g="bottom"===e,v=!u&&m,w=!u&&p,b=!c&&f,y=!c&&g,x=1,_=null!=(s=null==(r=this.options.overlayModel)?void 0:r.size)?s:iO;if("percentage"===_.type?x=it(_.value,0,100)/100:((v||w)&&(x=it(0,_.value,t)/t),(b||y)&&(x=it(0,_.value,i)/i)),h){let n=null!=(d=null==(l=(a=this.options).getOverlayOutline)?void 0:l.call(a))?d:this.element,o=n.getBoundingClientRect(),r=h.getElements(void 0,n),s=r.root,c=r.overlay,_=s.getBoundingClientRect(),D=o.top-_.top,C=o.left-_.left,S={top:D,left:C,width:t,height:i};if(v?(S.left=C+t*(1-x),S.width=t*x):w?S.width=t*x:b?S.height=i*x:y&&(S.top=D+i*(1-x),S.height=i*x),u&&p&&(S.width=4),u&&m&&(S.left=C+t-4,S.width=4),!function(e,t){let{top:i,left:n,width:o,height:r}=t,s=`${Math.round(i)}px`,a=`${Math.round(n)}px`,l=`${Math.round(o)}px`,d=`${Math.round(r)}px`;return e.style.top!==s||e.style.left!==a||e.style.width!==l||e.style.height!==d}(c,S))return;!function(e,t){let{top:i,left:n,width:o,height:r}=t,s=`${Math.round(i)}px`,a=`${Math.round(n)}px`,l=`${Math.round(o)}px`,d=`${Math.round(r)}px`;e.style.top=s,e.style.left=a,e.style.width=l,e.style.height=d,e.style.visibility="visible",e.style.transform&&""!==e.style.transform||(e.style.transform="translate3d(0, 0, 0)")}(c,S),c.className=`dv-drop-target-anchor${this.options.className?` ${this.options.className}`:""}`,tQ(c,"dv-drop-target-left",p),tQ(c,"dv-drop-target-right",m),tQ(c,"dv-drop-target-top",f),tQ(c,"dv-drop-target-bottom",g),tQ(c,"dv-drop-target-center","center"===e),r.changed&&(tQ(c,"dv-drop-target-anchor-container-changed",!0),setTimeout(()=>{tQ(c,"dv-drop-target-anchor-container-changed",!1)},10));return}if(!this.overlayElement)return;let D={top:"0px",left:"0px",width:"100%",height:"100%"};v?(D.left=`${100*(1-x)}%`,D.width=`${100*x}%`):w?D.width=`${100*x}%`:b?D.height=`${100*x}%`:y&&(D.top=`${100*(1-x)}%`,D.height=`${100*x}%`),function(e,t){let{top:i,left:n,width:o,height:r}=t;e.style.top=i,e.style.left=n,e.style.width=o,e.style.height=r,e.style.visibility="visible",e.style.transform&&""!==e.style.transform||(e.style.transform="translate3d(0, 0, 0)")}(this.overlayElement,D),tQ(this.overlayElement,"dv-drop-target-small-vertical",c),tQ(this.overlayElement,"dv-drop-target-small-horizontal",u),tQ(this.overlayElement,"dv-drop-target-left",p),tQ(this.overlayElement,"dv-drop-target-right",m),tQ(this.overlayElement,"dv-drop-target-top",f),tQ(this.overlayElement,"dv-drop-target-bottom",g),tQ(this.overlayElement,"dv-drop-target-center","center"===e)}calculateQuadrant(e,t,i,n,o){var r,s,a,l,d,h,u,c,p,m,f,g,v,w;let b,y,x=null!=(s=null==(r=this.options.overlayModel)?void 0:r.activationSize)?s:iN;return"percentage"===x.type?(a=e,l=t,d=i,h=n,u=o,c=x.value,b=100*l/h,y=100*d/u,a.has("left")&&b100-c?"right":a.has("top")&&y100-c?"bottom":a.has("center")?"center":null):(p=e,m=t,f=i,g=n,v=o,w=x.value,p.has("left")&&mg-w?"right":p.has("top")&&fv-w?"bottom":p.has("center")?"center":null)}removeDropTarget(){var e;this.targetElement&&(this._state=void 0,null==(e=this.targetElement.parentElement)||e.classList.remove("dv-drop-target"),this.targetElement.remove(),this.targetElement=void 0,this.overlayElement=void 0)}}iI.USED_EVENT_ID="__dockview_droptarget_event_is_used__";let iM=["disableAutoResizing","disableDnd","className"];class iL extends tW{constructor(e,t,i,n){super(),this.nativeEvent=e,this.position=t,this.getData=i,this.panel=n}}class iT extends tV{constructor(){super()}}class ij extends tK{get isFocused(){return this._isFocused}get isActive(){return this._isActive}get isVisible(){return this._isVisible}get width(){return this._width}get height(){return this._height}constructor(e,t){super(),this.id=e,this.component=t,this._isFocused=!1,this._isActive=!1,this._isVisible=!0,this._width=0,this._height=0,this._parameters={},this.panelUpdatesDisposable=new tJ,this._onDidDimensionChange=new tB,this.onDidDimensionsChange=this._onDidDimensionChange.event,this._onDidChangeFocus=new tB,this.onDidFocusChange=this._onDidChangeFocus.event,this._onWillFocus=new tB,this.onWillFocus=this._onWillFocus.event,this._onDidVisibilityChange=new tB,this.onDidVisibilityChange=this._onDidVisibilityChange.event,this._onWillVisibilityChange=new tB,this.onWillVisibilityChange=this._onWillVisibilityChange.event,this._onDidActiveChange=new tB,this.onDidActiveChange=this._onDidActiveChange.event,this._onActiveChange=new tB,this.onActiveChange=this._onActiveChange.event,this._onDidParametersChange=new tB,this.onDidParametersChange=this._onDidParametersChange.event,this.addDisposables(this.onDidFocusChange(e=>{this._isFocused=e.isFocused}),this.onDidActiveChange(e=>{this._isActive=e.isActive}),this.onDidVisibilityChange(e=>{this._isVisible=e.isVisible}),this.onDidDimensionsChange(e=>{this._width=e.width,this._height=e.height}),this.panelUpdatesDisposable,this._onDidDimensionChange,this._onDidChangeFocus,this._onDidVisibilityChange,this._onDidActiveChange,this._onWillFocus,this._onActiveChange,this._onWillFocus,this._onWillVisibilityChange,this._onDidParametersChange)}getParameters(){return this._parameters}initialize(e){this.panelUpdatesDisposable.value=this._onDidParametersChange.event(t=>{this._parameters=t,e.update({params:t})})}setVisible(e){this._onWillVisibilityChange.fire({isVisible:e})}setActive(){this._onActiveChange.fire()}updateParameters(e){this._onDidParametersChange.fire(e)}}class iG extends ij{constructor(e,t){super(e,t),this._onDidConstraintsChangeInternal=new tB,this.onDidConstraintsChangeInternal=this._onDidConstraintsChangeInternal.event,this._onDidConstraintsChange=new tB({replay:!0}),this.onDidConstraintsChange=this._onDidConstraintsChange.event,this._onDidSizeChange=new tB,this.onDidSizeChange=this._onDidSizeChange.event,this.addDisposables(this._onDidConstraintsChangeInternal,this._onDidConstraintsChange,this._onDidSizeChange)}setConstraints(e){this._onDidConstraintsChangeInternal.fire(e)}setSize(e){this._onDidSizeChange.fire(e)}}class iV extends iG{set pane(e){this._pane=e}constructor(e,t){super(e,t),this._onDidExpansionChange=new tB({replay:!0}),this.onDidExpansionChange=this._onDidExpansionChange.event,this._onMouseEnter=new tB({}),this.onMouseEnter=this._onMouseEnter.event,this._onMouseLeave=new tB({}),this.onMouseLeave=this._onMouseLeave.event,this.addDisposables(this._onDidExpansionChange,this._onMouseEnter,this._onMouseLeave)}setExpanded(e){var t;null==(t=this._pane)||t.setExpanded(e)}get isExpanded(){var e;return!!(null==(e=this._pane)?void 0:e.isExpanded())}}class iW extends tK{get element(){return this._element}get width(){return this._width}get height(){return this._height}get params(){var e;return null==(e=this._params)?void 0:e.params}constructor(e,t,i){super(),this.id=e,this.component=t,this.api=i,this._height=0,this._width=0,this._element=document.createElement("div"),this._element.tabIndex=-1,this._element.style.outline="none",this._element.style.height="100%",this._element.style.width="100%",this._element.style.overflow="hidden";const n=new t1(this._element);this.addDisposables(this.api,n.onDidFocus(()=>{this.api._onDidChangeFocus.fire({isFocused:!0})}),n.onDidBlur(()=>{this.api._onDidChangeFocus.fire({isFocused:!1})}),n)}focus(){let e=new iT;this.api._onWillFocus.fire(e),e.defaultPrevented||this._element.focus()}layout(e,t){this._width=e,this._height=t,this.api._onDidDimensionChange.fire({width:e,height:t}),this.part&&this._params&&this.part.update(this._params.params)}init(e){this._params=e,this.part=this.getComponent()}update(e){var t,i;for(let i of(this._params=Object.assign(Object.assign({},this._params),{params:Object.assign(Object.assign({},null==(t=this._params)?void 0:t.params),e.params)}),Object.keys(e.params)))void 0===e.params[i]&&delete this._params.params[i];null==(i=this.part)||i.update({params:this._params.params})}toJSON(){var e,t;let i=null!=(t=null==(e=this._params)?void 0:e.params)?t:{};return{id:this.id,component:this.component,params:Object.keys(i).length>0?i:void 0}}dispose(){var e;this.api.dispose(),null==(e=this.part)||e.dispose(),super.dispose()}}class iH extends iW{set orientation(e){this._orientation=e}get orientation(){return this._orientation}get minimumSize(){return this.headerSize+(this.isExpanded()?this._minimumBodySize:0)}get maximumSize(){return this.headerSize+(this.isExpanded()?this._maximumBodySize:0)}get size(){return this._size}get orthogonalSize(){return this._orthogonalSize}set orthogonalSize(e){this._orthogonalSize=e}get minimumBodySize(){return this._minimumBodySize}set minimumBodySize(e){this._minimumBodySize="number"==typeof e?e:0}get maximumBodySize(){return this._maximumBodySize}set maximumBodySize(e){this._maximumBodySize="number"==typeof e?e:1/0}get headerVisible(){return this._headerVisible}set headerVisible(e){this._headerVisible=e,this.header.style.display=e?"":"none"}constructor(e){super(e.id,e.component,new iV(e.id,e.component)),this._onDidChangeExpansionState=new tB({replay:!0}),this.onDidChangeExpansionState=this._onDidChangeExpansionState.event,this._onDidChange=new tB,this.onDidChange=this._onDidChange.event,this._orthogonalSize=0,this._size=0,this._isExpanded=!1,this.api.pane=this,this.api.initialize(this),this.headerSize=e.headerSize,this.headerComponent=e.headerComponent,this._minimumBodySize=e.minimumBodySize,this._maximumBodySize=e.maximumBodySize,this._isExpanded=e.isExpanded,this._headerVisible=e.isHeaderVisible,this._onDidChangeExpansionState.fire(this.isExpanded()),this._orientation=e.orientation,this.element.classList.add("dv-pane"),this.addDisposables(this.api.onWillVisibilityChange(e=>{let{isVisible:t}=e,{accessor:i}=this._params;i.setVisible(this,t)}),this.api.onDidSizeChange(e=>{this._onDidChange.fire({size:e.size})}),t$(this.element,"mouseenter",e=>{this.api._onMouseEnter.fire(e)}),t$(this.element,"mouseleave",e=>{this.api._onMouseLeave.fire(e)})),this.addDisposables(this._onDidChangeExpansionState,this.onDidChangeExpansionState(e=>{this.api._onDidExpansionChange.fire({isExpanded:e})}),this.api.onDidFocusChange(e=>{this.header&&(e.isFocused?tY(this.header,"focused"):tq(this.header,"focused"))})),this.renderOnce()}setVisible(e){this.api._onDidVisibilityChange.fire({isVisible:e})}setActive(e){this.api._onDidActiveChange.fire({isActive:e})}isExpanded(){return this._isExpanded}setExpanded(e){this._isExpanded!==e&&(this._isExpanded=e,e?(this.animationTimer&&clearTimeout(this.animationTimer),this.body&&this.element.appendChild(this.body)):this.animationTimer=setTimeout(()=>{var e;null==(e=this.body)||e.remove()},200),this._onDidChange.fire(e?{size:this.width}:{}),this._onDidChangeExpansionState.fire(e))}layout(e,t){this._size=e,this._orthogonalSize=t;let[i,n]=this.orientation===I.HORIZONTAL?[e,t]:[t,e];super.layout(i,n)}init(e){var t,i;super.init(e),"number"==typeof e.minimumBodySize&&(this.minimumBodySize=e.minimumBodySize),"number"==typeof e.maximumBodySize&&(this.maximumBodySize=e.maximumBodySize),this.bodyPart=this.getBodyComponent(),this.headerPart=this.getHeaderComponent(),this.bodyPart.init(Object.assign(Object.assign({},e),{api:this.api})),this.headerPart.init(Object.assign(Object.assign({},e),{api:this.api})),null==(t=this.body)||t.append(this.bodyPart.element),null==(i=this.header)||i.append(this.headerPart.element),"boolean"==typeof e.isExpanded&&this.setExpanded(e.isExpanded)}toJSON(){let e=this._params;return Object.assign(Object.assign({},super.toJSON()),{headerComponent:this.headerComponent,title:e.title})}renderOnce(){this.header=document.createElement("div"),this.header.tabIndex=0,this.header.className="dv-pane-header",this.header.style.height=`${this.headerSize}px`,this.header.style.lineHeight=`${this.headerSize}px`,this.header.style.minHeight=`${this.headerSize}px`,this.header.style.maxHeight=`${this.headerSize}px`,this.element.appendChild(this.header),this.body=document.createElement("div"),this.body.className="dv-pane-body",this.element.appendChild(this.body)}getComponent(){return{update:e=>{var t,i;null==(t=this.bodyPart)||t.update({params:e}),null==(i=this.headerPart)||i.update({params:e})},dispose:()=>{var e,t;null==(e=this.bodyPart)||e.dispose(),null==(t=this.headerPart)||t.dispose()}}}}class iF extends iH{constructor(e){super({id:e.id,component:e.component,headerComponent:e.headerComponent,orientation:e.orientation,isExpanded:e.isExpanded,isHeaderVisible:!0,headerSize:e.headerSize,minimumBodySize:e.minimumBodySize,maximumBodySize:e.maximumBodySize}),this._onDidDrop=new tB,this.onDidDrop=this._onDidDrop.event,this._onUnhandledDragOverEvent=new tB,this.onUnhandledDragOverEvent=this._onUnhandledDragOverEvent.event,this.accessor=e.accessor,this.addDisposables(this._onDidDrop,this._onUnhandledDragOverEvent),e.disableDnd||this.initDragFeatures()}initDragFeatures(){if(!this.header)return;let e=this.id,t=this.accessor.id;this.header.draggable=!0,this.handler=new class extends ik{getData(){return tT.getInstance().setData([new tL(t,e)],tL.prototype),{dispose:()=>{tT.getInstance().clearData(tL.prototype)}}}}(this.header),this.target=new iI(this.element,{acceptedTargetZones:["top","bottom"],overlayModel:{activationSize:{type:"percentage",value:50}},canDisplayOverlay:(e,t)=>{let i=tG();if(i&&i.paneId!==this.id&&i.viewId===this.accessor.id)return!0;let n=new iL(e,t,tG,this);return this._onUnhandledDragOverEvent.fire(n),n.isAccepted}}),this.addDisposables(this._onDidDrop,this.handler,this.target,this.target.onDrop(e=>{this.onDrop(e)}))}onDrop(e){let t=tG();if(!t||t.viewId!==this.accessor.id)return void this._onDidDrop.fire(Object.assign(Object.assign({},e),{panel:this,api:new iS(this.accessor),getData:tG}));let i=this._params.containerApi,n=t.paneId,o=i.getPanel(n);if(!o)return void this._onDidDrop.fire(Object.assign(Object.assign({},e),{panel:this,getData:tG,api:new iS(this.accessor)}));let r=i.panels,s=r.indexOf(o),a=i.panels.indexOf(this);("left"===e.position||"top"===e.position)&&(a=Math.max(0,a-1)),("right"===e.position||"bottom"===e.position)&&(s>a&&a++,a=Math.min(r.length-1,a)),i.movePanel(s,a)}}class iB extends tK{get element(){return this._element}constructor(e,t){super(),this.accessor=e,this.group=t,this.disposable=new tJ,this._onDidFocus=new tB,this.onDidFocus=this._onDidFocus.event,this._onDidBlur=new tB,this.onDidBlur=this._onDidBlur.event,this._element=document.createElement("div"),this._element.className="dv-content-container",this._element.tabIndex=-1,this.addDisposables(this._onDidFocus,this._onDidBlur);const i=t.dropTargetContainer;this.dropTarget=new iI(this.element,{getOverlayOutline:()=>{var t;return(null==(t=e.options.theme)?void 0:t.dndPanelOverlay)==="group"?this.element.parentElement:null},className:"dv-drop-target-content",acceptedTargetZones:["top","bottom","left","right","center"],canDisplayOverlay:(e,t)=>{if("no-drop-target"===this.group.locked||this.group.locked&&"center"===t)return!1;let i=tj();return(!!i||!e.shiftKey||"floating"===this.group.location.type)&&(!!i&&i.viewId===this.accessor.id||this.group.canDisplayOverlay(e,t,"content"))},getOverrideTarget:i?()=>i.model:void 0}),this.addDisposables(this.dropTarget)}show(){this.element.style.display=""}hide(){this.element.style.display="none"}renderPanel(e,t={asActive:!0}){let i,n=t.asActive||this.panel&&this.group.isPanelActive(this.panel);switch(this.panel&&this.panel.view.content.element.parentElement===this._element&&this._element.removeChild(this.panel.view.content.element),this.panel=e,e.api.renderer){case"onlyWhenVisible":this.group.renderContainer.detatch(e),this.panel&&n&&this._element.appendChild(this.panel.view.content.element),i=this._element;break;case"always":e.view.content.element.parentElement===this._element&&this._element.removeChild(e.view.content.element),i=this.group.renderContainer.attach({panel:e,referenceContainer:this});break;default:throw Error(`dockview: invalid renderer type '${e.api.renderer}'`)}if(n){let e=new t1(i);this.focusTracker=e;let t=new tK;t.addDisposables(e,e.onDidFocus(()=>this._onDidFocus.fire()),e.onDidBlur(()=>this._onDidBlur.fire())),this.disposable.value=t}}openPanel(e){this.panel!==e&&this.renderPanel(e)}layout(e,t){}closePanel(){var e;this.panel&&"onlyWhenVisible"===this.panel.api.renderer&&(null==(e=this.panel.view.content.element.parentElement)||e.removeChild(this.panel.view.content.element)),this.panel=void 0}dispose(){this.disposable.dispose(),super.dispose()}refreshFocusState(){var e;(null==(e=this.focusTracker)?void 0:e.refreshState)&&this.focusTracker.refreshState()}}function i$(e,t,i){var n,o;tY(t,"dv-dragged"),t.style.top="-9999px",document.body.appendChild(t),e.setDragImage(t,null!=(n=null==i?void 0:i.x)?n:0,null!=(o=null==i?void 0:i.y)?o:0),setTimeout(()=>{tq(t,"dv-dragged"),t.remove()},0)}class iU extends ik{constructor(e,t,i,n,o){super(e,o),this.accessor=t,this.group=i,this.panel=n,this.panelTransfer=tT.getInstance()}getData(e){return this.panelTransfer.setData([new tM(this.accessor.id,this.group.id,this.panel.id)],tM.prototype),{dispose:()=>{this.panelTransfer.clearData(tM.prototype)}}}}class iK extends tK{get element(){return this._element}constructor(e,t,i){super(),this.panel=e,this.accessor=t,this.group=i,this.content=void 0,this._onPointDown=new tB,this.onPointerDown=this._onPointDown.event,this._onDropped=new tB,this.onDrop=this._onDropped.event,this._onDragStart=new tB,this.onDragStart=this._onDragStart.event,this._element=document.createElement("div"),this._element.className="dv-tab",this._element.tabIndex=0,this._element.draggable=!this.accessor.options.disableDnd,tQ(this.element,"dv-inactive-tab",!0),this.dragHandler=new iU(this._element,this.accessor,this.group,this.panel,!!this.accessor.options.disableDnd),this.dropTarget=new iI(this._element,{acceptedTargetZones:["left","right"],overlayModel:{activationSize:{value:50,type:"percentage"}},canDisplayOverlay:(e,t)=>{if(this.group.locked)return!1;let i=tj();return!!i&&this.accessor.id===i.viewId||this.group.model.canDisplayOverlay(e,t,"tab")},getOverrideTarget:()=>{var e;return null==(e=i.model.dropTargetContainer)?void 0:e.model}}),this.onWillShowOverlay=this.dropTarget.onWillShowOverlay,this.addDisposables(this._onPointDown,this._onDropped,this._onDragStart,this.dragHandler.onDragStart(e=>{if(e.dataTransfer){let t=getComputedStyle(this.element),i=this.element.cloneNode(!0);Array.from(t).forEach(e=>i.style.setProperty(e,t.getPropertyValue(e),t.getPropertyPriority(e))),i.style.position="absolute",i$(e.dataTransfer,i,{y:-10,x:30})}this._onDragStart.fire(e)}),this.dragHandler,t$(this._element,"pointerdown",e=>{this._onPointDown.fire(e)}),this.dropTarget.onDrop(e=>{this._onDropped.fire(e)}),this.dropTarget)}setActive(e){tQ(this.element,"dv-active-tab",e),tQ(this.element,"dv-inactive-tab",!e)}setContent(e){this.content&&this._element.removeChild(this.content.element),this.content=e,this._element.appendChild(this.content.element)}updateDragAndDropState(){this._element.draggable=!this.accessor.options.disableDnd,this.dragHandler.setDisabled(!!this.accessor.options.disableDnd)}dispose(){super.dispose()}}class iJ{get kind(){return this.options.kind}get nativeEvent(){return this.event.nativeEvent}get position(){return this.event.position}get defaultPrevented(){return this.event.defaultPrevented}get panel(){return this.options.panel}get api(){return this.options.api}get group(){return this.options.group}preventDefault(){this.event.preventDefault()}getData(){return this.options.getData()}constructor(e,t){this.event=e,this.options=t}}class iZ extends ik{constructor(e,t,i,n){super(e,n),this.accessor=t,this.group=i,this.panelTransfer=tT.getInstance(),this.addDisposables(t$(e,"pointerdown",e=>{e.shiftKey&&(e[t2]=!0)},!0))}isCancelled(e){return"floating"===this.group.api.location.type&&!e.shiftKey}getData(e){let t=e.dataTransfer;this.panelTransfer.setData([new tM(this.accessor.id,this.group.id,null)],tM.prototype);let i=window.getComputedStyle(this.el),n=i.getPropertyValue("--dv-activegroup-visiblepanel-tab-background-color"),o=i.getPropertyValue("--dv-activegroup-visiblepanel-tab-color");if(t){let e=document.createElement("div");e.style.backgroundColor=n,e.style.color=o,e.style.padding="2px 8px",e.style.height="24px",e.style.fontSize="11px",e.style.lineHeight="20px",e.style.borderRadius="12px",e.style.position="absolute",e.style.pointerEvents="none",e.style.top="-9999px",e.textContent=`Multiple Panels (${this.group.size})`,i$(t,e,{y:-10,x:30})}return{dispose:()=>{this.panelTransfer.clearData(tM.prototype)}}}}class iX extends tK{get element(){return this._element}constructor(e,t){super(),this.accessor=e,this.group=t,this._onDrop=new tB,this.onDrop=this._onDrop.event,this._onDragStart=new tB,this.onDragStart=this._onDragStart.event,this._element=document.createElement("div"),this._element.className="dv-void-container",this._element.draggable=!this.accessor.options.disableDnd,tQ(this._element,"dv-draggable",!this.accessor.options.disableDnd),this.addDisposables(this._onDrop,this._onDragStart,t$(this._element,"pointerdown",()=>{this.accessor.doSetGroupActive(this.group)})),this.handler=new iZ(this._element,e,t,!!this.accessor.options.disableDnd),this.dropTarget=new iI(this._element,{acceptedTargetZones:["center"],canDisplayOverlay:(e,i)=>{let n=tj();return!!n&&this.accessor.id===n.viewId||t.model.canDisplayOverlay(e,i,"header_space")},getOverrideTarget:()=>{var e;return null==(e=t.model.dropTargetContainer)?void 0:e.model}}),this.onWillShowOverlay=this.dropTarget.onWillShowOverlay,this.addDisposables(this.handler,this.handler.onDragStart(e=>{this._onDragStart.fire(e)}),this.dropTarget.onDrop(e=>{this._onDrop.fire(e)}),this.dropTarget)}updateDragAndDropState(){this._element.draggable=!this.accessor.options.disableDnd,tQ(this._element,"dv-draggable",!this.accessor.options.disableDnd),this.handler.setDisabled(!!this.accessor.options.disableDnd)}}class iq extends tK{get element(){return this._element}constructor(e){super(),this.scrollableElement=e,this._scrollLeft=0,this._element=document.createElement("div"),this._element.className="dv-scrollable",this._horizontalScrollbar=document.createElement("div"),this._horizontalScrollbar.className="dv-scrollbar-horizontal",this.element.appendChild(e),this.element.appendChild(this._horizontalScrollbar),this.addDisposables(t$(this.element,"wheel",e=>{this._scrollLeft+=e.deltaY*iq.MouseWheelSpeed,this.calculateScrollbarStyles()}),t$(this._horizontalScrollbar,"pointerdown",e=>{e.preventDefault(),tQ(this.element,"dv-scrollable-scrolling",!0);let t=e.clientX,i=this._scrollLeft,n=e=>{let n=e.clientX-t,{clientWidth:o}=this.element,{scrollWidth:r}=this.scrollableElement;this._scrollLeft=i+n/(o/r),this.calculateScrollbarStyles()},o=()=>{tQ(this.element,"dv-scrollable-scrolling",!1),document.removeEventListener("pointermove",n),document.removeEventListener("pointerup",o),document.removeEventListener("pointercancel",o)};document.addEventListener("pointermove",n),document.addEventListener("pointerup",o),document.addEventListener("pointercancel",o)}),t$(this.element,"scroll",()=>{this.calculateScrollbarStyles()}),t$(this.scrollableElement,"scroll",()=>{this._scrollLeft=this.scrollableElement.scrollLeft,this.calculateScrollbarStyles()}),tX(this.element,()=>{tQ(this.element,"dv-scrollable-resizing",!0),this._animationTimer&&clearTimeout(this._animationTimer),this._animationTimer=setTimeout(()=>{clearTimeout(this._animationTimer),tQ(this.element,"dv-scrollable-resizing",!1)},500),this.calculateScrollbarStyles()}))}calculateScrollbarStyles(){let{clientWidth:e}=this.element,{scrollWidth:t}=this.scrollableElement;if(t>e){let i=e/t*e;this._horizontalScrollbar.style.width=`${i}px`,this._scrollLeft=it(this._scrollLeft,0,this.scrollableElement.scrollWidth-e),this.scrollableElement.scrollLeft=this._scrollLeft;let n=this._scrollLeft/(t-e);this._horizontalScrollbar.style.left=`${(e-i)*n}px`}else this._horizontalScrollbar.style.width="0px",this._horizontalScrollbar.style.left="0px",this._scrollLeft=0}}iq.MouseWheelSpeed=1;class iY extends tK{get showTabsOverflowControl(){return this._showTabsOverflowControl}set showTabsOverflowControl(e){if(this._showTabsOverflowControl!=e&&(this._showTabsOverflowControl=e,e)){let e=new tZ(this._tabsList);this._observerDisposable.value=new tK(e,e.onDidChange(e=>{let t=e.hasScrollX||e.hasScrollY;this.toggleDropdown({reset:!t})}),t$(this._tabsList,"scroll",()=>{this.toggleDropdown({reset:!1})}))}}get element(){return this._element}get panels(){return this._tabs.map(e=>e.value.panel.id)}get size(){return this._tabs.length}get tabs(){return this._tabs.map(e=>e.value)}constructor(e,t,i){if(super(),this.group=e,this.accessor=t,this._observerDisposable=new tJ,this._tabs=[],this.selectedIndex=-1,this._showTabsOverflowControl=!1,this._onTabDragStart=new tB,this.onTabDragStart=this._onTabDragStart.event,this._onDrop=new tB,this.onDrop=this._onDrop.event,this._onWillShowOverlay=new tB,this.onWillShowOverlay=this._onWillShowOverlay.event,this._onOverflowTabsChange=new tB,this.onOverflowTabsChange=this._onOverflowTabsChange.event,this._tabsList=document.createElement("div"),this._tabsList.className="dv-tabs-container dv-horizontal",this.showTabsOverflowControl=i.showTabsOverflowControl,"native"===t.options.scrollbars)this._element=this._tabsList;else{const e=new iq(this._tabsList);this._element=e.element,this.addDisposables(e)}this.addDisposables(this._onOverflowTabsChange,this._observerDisposable,this._onWillShowOverlay,this._onDrop,this._onTabDragStart,t$(this.element,"pointerdown",e=>{e.defaultPrevented||0===e.button&&this.accessor.doSetGroupActive(this.group)}),O.from(()=>{for(let{value:e,disposable:t}of this._tabs)t.dispose(),e.dispose();this._tabs=[]}))}indexOf(e){return this._tabs.findIndex(t=>t.value.panel.id===e)}isActive(e){return this.selectedIndex>-1&&this._tabs[this.selectedIndex].value===e}setActivePanel(e){let t=0;for(let i of this._tabs){let n=e.id===i.value.panel.id;if(i.value.setActive(n),n){let e=i.value.element,n=e.parentElement;(tn.scrollLeft+n.clientWidth)&&(n.scrollLeft=t)}t+=i.value.element.clientWidth}}openPanel(e,t=this._tabs.length){if(this._tabs.find(t=>t.value.panel.id===e.id))return;let i=new iK(e,this.accessor,this.group);i.setContent(e.view.tab);let n=new tK(i.onDragStart(t=>{this._onTabDragStart.fire({nativeEvent:t,panel:e})}),i.onPointerDown(t=>{if(t.defaultPrevented)return;let n=!this.accessor.options.disableFloatingGroups,o="floating"===this.group.api.location.type&&1===this.size;if(n&&!o&&t.shiftKey){t.preventDefault();let e=this.accessor.getGroupPanel(i.panel.id),{top:n,left:o}=i.element.getBoundingClientRect(),{top:r,left:s}=this.accessor.element.getBoundingClientRect();this.accessor.addFloatingGroup(e,{x:o-s,y:n-r,inDragMode:!0});return}0===t.button&&this.group.activePanel!==e&&this.group.model.openPanel(e)}),i.onDrop(e=>{this._onDrop.fire({event:e.nativeEvent,index:this._tabs.findIndex(e=>e.value===i)})}),i.onWillShowOverlay(e=>{this._onWillShowOverlay.fire(new iJ(e,{kind:"tab",panel:this.group.activePanel,api:this.accessor.api,group:this.group,getData:tj}))}));this.addTab({value:i,disposable:n},t)}delete(e){let t=this.indexOf(e),{value:i,disposable:n}=this._tabs.splice(t,1)[0];n.dispose(),i.dispose(),i.element.remove()}addTab(e,t=this._tabs.length){if(t<0||t>this._tabs.length)throw Error("invalid location");this._tabsList.insertBefore(e.value.element,this._tabsList.children[t]),this._tabs=[...this._tabs.slice(0,t),e,...this._tabs.slice(t)],this.selectedIndex<0&&(this.selectedIndex=t)}toggleDropdown(e){let t=e.reset?[]:this._tabs.filter(e=>{var t,i;let n,o;return t=e.value.element,i=this._tabsList,n=t5(t),o=t5(i),!!(n.lefto.left+o.width)}).map(e=>e.value.panel.id);this._onOverflowTabsChange.fire({tabs:t,reset:e.reset})}updateDragAndDropState(){for(let e of this._tabs)e.value.updateDragAndDropState()}}let iQ=e=>{let t=document.createElementNS("http://www.w3.org/2000/svg","svg");t.setAttributeNS(null,"height",e.height),t.setAttributeNS(null,"width",e.width),t.setAttributeNS(null,"viewBox",e.viewbox),t.setAttributeNS(null,"aria-hidden","false"),t.setAttributeNS(null,"focusable","false"),t.classList.add("dv-svg");let i=document.createElementNS("http://www.w3.org/2000/svg","path");return i.setAttributeNS(null,"d",e.path),t.appendChild(i),t},i0=()=>iQ({width:"11",height:"11",viewbox:"0 0 15 25",path:"M2.15 24.1L0 21.95L9.9 12.05L0 2.15L2.15 0L14.2 12.05L2.15 24.1Z"});class i1 extends tK{get onTabDragStart(){return this.tabs.onTabDragStart}get panels(){return this.tabs.panels}get size(){return this.tabs.size}get hidden(){return this._hidden}set hidden(e){this._hidden=e,this.element.style.display=e?"none":""}get element(){return this._element}constructor(e,t){super(),this.accessor=e,this.group=t,this._hidden=!1,this.dropdownPart=null,this._overflowTabs=[],this._dropdownDisposable=new tJ,this._onDrop=new tB,this.onDrop=this._onDrop.event,this._onGroupDragStart=new tB,this.onGroupDragStart=this._onGroupDragStart.event,this._onWillShowOverlay=new tB,this.onWillShowOverlay=this._onWillShowOverlay.event,this._element=document.createElement("div"),this._element.className="dv-tabs-and-actions-container",tQ(this._element,"dv-full-width-single-tab","fullwidth"===this.accessor.options.singleTabMode),this.rightActionsContainer=document.createElement("div"),this.rightActionsContainer.className="dv-right-actions-container",this.leftActionsContainer=document.createElement("div"),this.leftActionsContainer.className="dv-left-actions-container",this.preActionsContainer=document.createElement("div"),this.preActionsContainer.className="dv-pre-actions-container",this.tabs=new iY(t,e,{showTabsOverflowControl:!e.options.disableTabsOverflowList}),this.voidContainer=new iX(this.accessor,this.group),this._element.appendChild(this.preActionsContainer),this._element.appendChild(this.tabs.element),this._element.appendChild(this.leftActionsContainer),this._element.appendChild(this.voidContainer.element),this._element.appendChild(this.rightActionsContainer),this.addDisposables(this.tabs.onDrop(e=>this._onDrop.fire(e)),this.tabs.onWillShowOverlay(e=>this._onWillShowOverlay.fire(e)),e.onDidOptionsChange(()=>{this.tabs.showTabsOverflowControl=!e.options.disableTabsOverflowList}),this.tabs.onOverflowTabsChange(e=>{this.toggleDropdown(e)}),this.tabs,this._onWillShowOverlay,this._onDrop,this._onGroupDragStart,this.voidContainer,this.voidContainer.onDragStart(e=>{this._onGroupDragStart.fire({nativeEvent:e,group:this.group})}),this.voidContainer.onDrop(e=>{this._onDrop.fire({event:e.nativeEvent,index:this.tabs.size})}),this.voidContainer.onWillShowOverlay(e=>{this._onWillShowOverlay.fire(new iJ(e,{kind:"header_space",panel:this.group.activePanel,api:this.accessor.api,group:this.group,getData:tj}))}),t$(this.voidContainer.element,"pointerdown",e=>{if(!e.defaultPrevented&&!this.accessor.options.disableFloatingGroups&&e.shiftKey&&"floating"!==this.group.api.location.type){e.preventDefault();let{top:t,left:i}=this.element.getBoundingClientRect(),{top:n,left:o}=this.accessor.element.getBoundingClientRect();this.accessor.addFloatingGroup(this.group,{x:i-o+20,y:t-n+20,inDragMode:!0})}}))}show(){this.hidden||(this.element.style.display="")}hide(){this._element.style.display="none"}setRightActionsElement(e){this.rightActions!==e&&(this.rightActions&&(this.rightActions.remove(),this.rightActions=void 0),e&&(this.rightActionsContainer.appendChild(e),this.rightActions=e))}setLeftActionsElement(e){this.leftActions!==e&&(this.leftActions&&(this.leftActions.remove(),this.leftActions=void 0),e&&(this.leftActionsContainer.appendChild(e),this.leftActions=e))}setPrefixActionsElement(e){this.preActions!==e&&(this.preActions&&(this.preActions.remove(),this.preActions=void 0),e&&(this.preActionsContainer.appendChild(e),this.preActions=e))}isActive(e){return this.tabs.isActive(e)}indexOf(e){return this.tabs.indexOf(e)}setActive(e){}delete(e){this.tabs.delete(e),this.updateClassnames()}setActivePanel(e){this.tabs.setActivePanel(e)}openPanel(e,t=this.tabs.size){this.tabs.openPanel(e,t),this.updateClassnames()}closePanel(e){this.delete(e.id)}updateClassnames(){tQ(this._element,"dv-single-tab",1===this.size)}toggleDropdown(e){let t,i,n,o=e.reset?[]:e.tabs;if(this._overflowTabs=o,this._overflowTabs.length>0&&this.dropdownPart)return void this.dropdownPart.update({tabs:o.length});if(0===this._overflowTabs.length)return void this._dropdownDisposable.dispose();let r=document.createElement("div");r.className="dv-tabs-overflow-dropdown-root";let s=((t=document.createElement("div")).className="dv-tabs-overflow-dropdown-default",(i=document.createElement("span")).textContent="",n=i0(),t.appendChild(n),t.appendChild(i),{element:t,update:e=>{i.textContent=`${e.tabs}`}});s.update({tabs:o.length}),this.dropdownPart=s,r.appendChild(s.element),this.rightActionsContainer.prepend(r),this._dropdownDisposable.value=new tK(O.from(()=>{var e,t;r.remove(),null==(t=null==(e=this.dropdownPart)?void 0:e.dispose)||t.call(e),this.dropdownPart=null}),t$(r,"pointerdown",e=>{e.preventDefault()},{capture:!0}),t$(r,"click",e=>{let t=document.createElement("div");for(let e of(t.style.overflow="auto",t.className="dv-tabs-overflow-container",this.tabs.tabs.filter(e=>this._overflowTabs.includes(e.panel.id)))){let i=this.group.panels.find(t=>t===e.panel),n=i.view.createTabRenderer("headerOverflow").element,o=document.createElement("div");tQ(o,"dv-tab",!0),tQ(o,"dv-active-tab",i.api.isActive),tQ(o,"dv-inactive-tab",!i.api.isActive),o.addEventListener("click",t=>{this.accessor.popupService.close(),t.defaultPrevented||(e.element.scrollIntoView(),e.panel.api.setActive())}),o.appendChild(n),t.appendChild(o)}let i=function(e){let t=e;for(;t&&("auto"===t.style.zIndex||""===t.style.zIndex);)t=t.parentElement;return t}(r);this.accessor.popupService.openPopover(t,{x:e.clientX,y:e.clientY,zIndex:(null==i?void 0:i.style.zIndex)?`calc(${i.style.zIndex} * 2)`:void 0})}))}updateDragAndDropState(){this.tabs.updateDragAndDropState(),this.voidContainer.updateDragAndDropState()}}class i2 extends tW{constructor(e,t,i,n,o){super(),this.nativeEvent=e,this.target=t,this.position=i,this.getData=n,this.group=o}}let i5=["disableAutoResizing","hideBorders","singleTabMode","disableFloatingGroups","floatingGroupBounds","popoutUrl","defaultRenderer","debug","rootOverlayModel","locked","disableDnd","className","noPanelsOverlay","dndEdges","theme","disableTabsOverflowList","scrollbars"];class i3 extends tV{get nativeEvent(){return this.options.nativeEvent}get position(){return this.options.position}get panel(){return this.options.panel}get group(){return this.options.group}get api(){return this.options.api}constructor(e){super(),this.options=e}getData(){return this.options.getData()}}class i4 extends i3{get kind(){return this._kind}constructor(e){super(e),this._kind=e.kind}}class i9 extends tK{get element(){throw Error("dockview: not supported")}get activePanel(){return this._activePanel}get locked(){return this._locked}set locked(e){this._locked=e,tQ(this.container,"dv-locked-groupview","no-drop-target"===e||e)}get isActive(){return this._isGroupActive}get panels(){return this._panels}get size(){return this._panels.length}get isEmpty(){return 0===this._panels.length}get hasWatermark(){return!!(this.watermark&&this.container.contains(this.watermark.element))}get header(){return this.tabsContainer}get isContentFocused(){return!!document.activeElement&&t0(document.activeElement,this.contentContainer.element)}get location(){return this._location}set location(e){switch(this._location=e,tQ(this.container,"dv-groupview-floating",!1),tQ(this.container,"dv-groupview-popout",!1),e.type){case"grid":this.contentContainer.dropTarget.setTargetZones(["top","bottom","left","right","center"]);break;case"floating":this.contentContainer.dropTarget.setTargetZones(["center"]),this.contentContainer.dropTarget.setTargetZones(e?["center"]:["top","bottom","left","right","center"]),tQ(this.container,"dv-groupview-floating",!0);break;case"popout":this.contentContainer.dropTarget.setTargetZones(["center"]),tQ(this.container,"dv-groupview-popout",!0)}this.groupPanel.api._onDidLocationChange.fire({location:this.location})}constructor(e,t,i,n,o){var r;super(),this.container=e,this.accessor=t,this.id=i,this.options=n,this.groupPanel=o,this._isGroupActive=!1,this._locked=!1,this._location={type:"grid"},this.mostRecentlyUsed=[],this._overwriteRenderContainer=null,this._overwriteDropTargetContainer=null,this._onDidChange=new tB,this.onDidChange=this._onDidChange.event,this._width=0,this._height=0,this._panels=[],this._panelDisposables=new Map,this._onMove=new tB,this.onMove=this._onMove.event,this._onDidDrop=new tB,this.onDidDrop=this._onDidDrop.event,this._onWillDrop=new tB,this.onWillDrop=this._onWillDrop.event,this._onWillShowOverlay=new tB,this.onWillShowOverlay=this._onWillShowOverlay.event,this._onTabDragStart=new tB,this.onTabDragStart=this._onTabDragStart.event,this._onGroupDragStart=new tB,this.onGroupDragStart=this._onGroupDragStart.event,this._onDidAddPanel=new tB,this.onDidAddPanel=this._onDidAddPanel.event,this._onDidPanelTitleChange=new tB,this.onDidPanelTitleChange=this._onDidPanelTitleChange.event,this._onDidPanelParametersChange=new tB,this.onDidPanelParametersChange=this._onDidPanelParametersChange.event,this._onDidRemovePanel=new tB,this.onDidRemovePanel=this._onDidRemovePanel.event,this._onDidActivePanelChange=new tB,this.onDidActivePanelChange=this._onDidActivePanelChange.event,this._onUnhandledDragOverEvent=new tB,this.onUnhandledDragOverEvent=this._onUnhandledDragOverEvent.event,tQ(this.container,"dv-groupview",!0),this._api=new iA(this.accessor),this.tabsContainer=new i1(this.accessor,this.groupPanel),this.contentContainer=new iB(this.accessor,this),e.append(this.tabsContainer.element,this.contentContainer.element),this.header.hidden=!!n.hideHeader,this.locked=null!=(r=n.locked)&&r,this.addDisposables(this._onTabDragStart,this._onGroupDragStart,this._onWillShowOverlay,this.tabsContainer.onTabDragStart(e=>{this._onTabDragStart.fire(e)}),this.tabsContainer.onGroupDragStart(e=>{this._onGroupDragStart.fire(e)}),this.tabsContainer.onDrop(e=>{this.handleDropEvent("header",e.event,"center",e.index)}),this.contentContainer.onDidFocus(()=>{this.accessor.doSetGroupActive(this.groupPanel)}),this.contentContainer.onDidBlur(()=>{}),this.contentContainer.dropTarget.onDrop(e=>{this.handleDropEvent("content",e.nativeEvent,e.position)}),this.tabsContainer.onWillShowOverlay(e=>{this._onWillShowOverlay.fire(e)}),this.contentContainer.dropTarget.onWillShowOverlay(e=>{this._onWillShowOverlay.fire(new iJ(e,{kind:"content",panel:this.activePanel,api:this._api,group:this.groupPanel,getData:tj}))}),this._onMove,this._onDidChange,this._onDidDrop,this._onWillDrop,this._onDidAddPanel,this._onDidRemovePanel,this._onDidActivePanelChange,this._onUnhandledDragOverEvent,this._onDidPanelTitleChange,this._onDidPanelParametersChange)}focusContent(){this.contentContainer.element.focus()}set renderContainer(e){this.panels.forEach(e=>{this.renderContainer.detatch(e)}),this._overwriteRenderContainer=e,this.panels.forEach(e=>{this.rerender(e)})}get renderContainer(){var e;return null!=(e=this._overwriteRenderContainer)?e:this.accessor.overlayRenderContainer}set dropTargetContainer(e){this._overwriteDropTargetContainer=e}get dropTargetContainer(){var e;return null!=(e=this._overwriteDropTargetContainer)?e:this.accessor.rootDropTargetContainer}initialize(){this.options.panels&&this.options.panels.forEach(e=>{this.doAddPanel(e)}),this.options.activePanel&&this.openPanel(this.options.activePanel),this.setActive(this.isActive,!0),this.updateContainer(),this.accessor.options.createRightHeaderActionComponent&&(this._rightHeaderActions=this.accessor.options.createRightHeaderActionComponent(this.groupPanel),this.addDisposables(this._rightHeaderActions),this._rightHeaderActions.init({containerApi:this._api,api:this.groupPanel.api,group:this.groupPanel}),this.tabsContainer.setRightActionsElement(this._rightHeaderActions.element)),this.accessor.options.createLeftHeaderActionComponent&&(this._leftHeaderActions=this.accessor.options.createLeftHeaderActionComponent(this.groupPanel),this.addDisposables(this._leftHeaderActions),this._leftHeaderActions.init({containerApi:this._api,api:this.groupPanel.api,group:this.groupPanel}),this.tabsContainer.setLeftActionsElement(this._leftHeaderActions.element)),this.accessor.options.createPrefixHeaderActionComponent&&(this._prefixHeaderActions=this.accessor.options.createPrefixHeaderActionComponent(this.groupPanel),this.addDisposables(this._prefixHeaderActions),this._prefixHeaderActions.init({containerApi:this._api,api:this.groupPanel.api,group:this.groupPanel}),this.tabsContainer.setPrefixActionsElement(this._prefixHeaderActions.element))}rerender(e){this.contentContainer.renderPanel(e,{asActive:!1})}indexOf(e){return this.tabsContainer.indexOf(e.id)}toJSON(){var e;let t={views:this.tabsContainer.panels,activeView:null==(e=this._activePanel)?void 0:e.id,id:this.id};return!1!==this.locked&&(t.locked=this.locked),this.header.hidden&&(t.hideHeader=!0),t}moveToNext(e){let t;e||(e={}),e.panel||(e.panel=this.activePanel);let i=e.panel?this.panels.indexOf(e.panel):-1;if(i0)t=i-1;else{if(e.suppressRoll)return;t=this.panels.length-1}this.openPanel(this.panels[t])}containsPanel(e){return this.panels.includes(e)}init(e){}update(e){}focus(){var e;null==(e=this._activePanel)||e.focus()}openPanel(e,t={}){("number"!=typeof t.index||t.index>this.panels.length)&&(t.index=this.panels.length);let i=!!t.skipSetActive;(e.updateParentGroup(this.groupPanel,{skipSetActive:t.skipSetActive}),this.doAddPanel(e,t.index,{skipSetActive:i}),this._activePanel===e)?this.contentContainer.renderPanel(e,{asActive:!0}):(i||this.doSetActivePanel(e),t.skipSetGroupActive||this.accessor.doSetGroupActive(this.groupPanel),t.skipSetActive||this.updateContainer())}removePanel(e,t={skipSetActive:!1}){let i="string"==typeof e?e:e.id,n=this._panels.find(e=>e.id===i);if(!n)throw Error("invalid operation");return this._removePanel(n,t)}closeAllPanels(){if(this.panels.length>0)for(let e of[...this.panels])this.doClose(e);else this.accessor.removeGroup(this.groupPanel)}closePanel(e){this.doClose(e)}doClose(e){let t=1===this.panels.length&&1===this.accessor.groups.length;this.accessor.removePanel(e,t&&"emptyGroup"===this.accessor.options.noPanelsOverlay?{removeEmptyGroup:!1}:void 0)}isPanelActive(e){return this._activePanel===e}updateActions(e){this.tabsContainer.setRightActionsElement(e)}setActive(e,t=!1){(t||this.isActive!==e)&&(this._isGroupActive=e,tQ(this.container,"dv-active-group",e),tQ(this.container,"dv-inactive-group",!e),this.tabsContainer.setActive(this.isActive),!this._activePanel&&this.panels.length>0&&this.doSetActivePanel(this.panels[0]),this.updateContainer())}layout(e,t){var i;this._width=e,this._height=t,this.contentContainer.layout(this._width,this._height),(null==(i=this._activePanel)?void 0:i.layout)&&this._activePanel.layout(this._width,this._height)}_removePanel(e,t){let i=this._activePanel===e;if(this.doRemovePanel(e),i&&this.panels.length>0){let e=this.mostRecentlyUsed[0];this.openPanel(e,{skipSetActive:t.skipSetActive,skipSetGroupActive:t.skipSetActiveGroup})}return this._activePanel&&0===this.panels.length&&this.doSetActivePanel(void 0),t.skipSetActive||this.updateContainer(),e}doRemovePanel(e){let t=this.panels.indexOf(e);if(this._activePanel===e&&this.contentContainer.closePanel(),this.tabsContainer.delete(e.id),this._panels.splice(t,1),this.mostRecentlyUsed.includes(e)){let t=this.mostRecentlyUsed.indexOf(e);this.mostRecentlyUsed.splice(t,1)}let i=this._panelDisposables.get(e.id);i&&(i.dispose(),this._panelDisposables.delete(e.id)),this._onDidRemovePanel.fire({panel:e})}doAddPanel(e,t=this.panels.length,i={skipSetActive:!1}){let n=this._panels.indexOf(e);this.tabsContainer.show(),this.contentContainer.show(),this.tabsContainer.openPanel(e,t),i.skipSetActive||this.contentContainer.openPanel(e),n>-1||(this.updateMru(e),this.panels.splice(t,0,e),this._panelDisposables.set(e.id,new tK(e.api.onDidTitleChange(e=>this._onDidPanelTitleChange.fire(e)),e.api.onDidParametersChange(e=>this._onDidPanelParametersChange.fire(e)))),this._onDidAddPanel.fire({panel:e}))}doSetActivePanel(e){this._activePanel!==e&&(this._activePanel=e,e&&(this.tabsContainer.setActivePanel(e),this.contentContainer.openPanel(e),e.layout(this._width,this._height),this.updateMru(e),this.contentContainer.refreshFocusState(),this._onDidActivePanelChange.fire({panel:e})))}updateMru(e){this.mostRecentlyUsed.includes(e)&&this.mostRecentlyUsed.splice(this.mostRecentlyUsed.indexOf(e),1),this.mostRecentlyUsed=[e,...this.mostRecentlyUsed]}updateContainer(){var e,t;if(this.panels.forEach(e=>e.runEvents()),this.isEmpty&&!this.watermark){let e=this.accessor.createWatermarkComponent();e.init({containerApi:this._api,group:this.groupPanel}),this.watermark=e,t$(this.watermark.element,"pointerdown",()=>{this.isActive||this.accessor.doSetGroupActive(this.groupPanel)}),this.contentContainer.element.appendChild(this.watermark.element)}!this.isEmpty&&this.watermark&&(this.watermark.element.remove(),null==(t=(e=this.watermark).dispose)||t.call(e),this.watermark=void 0)}canDisplayOverlay(e,t,i){let n=new i2(e,i,t,tj,this.accessor.getPanel(this.id));return this._onUnhandledDragOverEvent.fire(n),n.isAccepted}handleDropEvent(e,t,i,n){if("no-drop-target"===this.locked)return;let o="number"==typeof n?this.panels[n]:void 0,r=new i4({nativeEvent:t,position:i,panel:o,getData:()=>tj(),kind:function(){switch(e){case"header":return"number"==typeof n?"tab":"header_space";case"content":return"content"}}(),group:this.groupPanel,api:this._api});if(this._onWillDrop.fire(r),r.defaultPrevented)return;let s=tj();if(s&&s.viewId===this.accessor.id){if("content"===e&&s.groupId===this.id&&("center"===i||null===s.panelId)||"header"===e&&s.groupId===this.id&&null===s.panelId)return;if(null===s.panelId){let{groupId:e}=s;this._onMove.fire({target:i,groupId:e,index:n});return}if(-1!==this.tabsContainer.indexOf(s.panelId)&&1===this.tabsContainer.size)return;let{groupId:t,panelId:o}=s;if(this.id===t&&!i&&this.tabsContainer.indexOf(o)===n)return;this._onMove.fire({target:i,groupId:s.groupId,itemId:s.panelId,index:n})}else this._onDidDrop.fire(new i3({nativeEvent:t,position:i,panel:o,getData:()=>tj(),group:this.groupPanel,api:this._api}))}updateDragAndDropState(){this.tabsContainer.updateDragAndDropState()}dispose(){var e,t,i;for(let n of(super.dispose(),null==(e=this.watermark)||e.element.remove(),null==(i=null==(t=this.watermark)?void 0:t.dispose)||i.call(t),this.watermark=void 0,this.panels))n.dispose();this.tabsContainer.dispose(),this.contentContainer.dispose()}}class i6 extends ij{constructor(e,t,i){super(e,t),this._onDidConstraintsChangeInternal=new tB,this.onDidConstraintsChangeInternal=this._onDidConstraintsChangeInternal.event,this._onDidConstraintsChange=new tB,this.onDidConstraintsChange=this._onDidConstraintsChange.event,this._onDidSizeChange=new tB,this.onDidSizeChange=this._onDidSizeChange.event,this.addDisposables(this._onDidConstraintsChangeInternal,this._onDidConstraintsChange,this._onDidSizeChange),i&&this.initialize(i)}setConstraints(e){this._onDidConstraintsChangeInternal.fire(e)}setSize(e){this._onDidSizeChange.fire(e)}}class i8 extends iW{get priority(){return this._priority}get snap(){return this._snap}get minimumWidth(){return this.__minimumWidth()}get minimumHeight(){return this.__minimumHeight()}get maximumHeight(){return this.__maximumHeight()}get maximumWidth(){return this.__maximumWidth()}__minimumWidth(){let e="function"==typeof this._minimumWidth?this._minimumWidth():this._minimumWidth;return e!==this._evaluatedMinimumWidth&&(this._evaluatedMinimumWidth=e,this.updateConstraints()),e}__maximumWidth(){let e="function"==typeof this._maximumWidth?this._maximumWidth():this._maximumWidth;return e!==this._evaluatedMaximumWidth&&(this._evaluatedMaximumWidth=e,this.updateConstraints()),e}__minimumHeight(){let e="function"==typeof this._minimumHeight?this._minimumHeight():this._minimumHeight;return e!==this._evaluatedMinimumHeight&&(this._evaluatedMinimumHeight=e,this.updateConstraints()),e}__maximumHeight(){let e="function"==typeof this._maximumHeight?this._maximumHeight():this._maximumHeight;return e!==this._evaluatedMaximumHeight&&(this._evaluatedMaximumHeight=e,this.updateConstraints()),e}get isActive(){return this.api.isActive}get isVisible(){return this.api.isVisible}constructor(e,t,i,n){super(e,t,null!=n?n:new i6(e,t)),this._evaluatedMinimumWidth=0,this._evaluatedMaximumWidth=Number.MAX_SAFE_INTEGER,this._evaluatedMinimumHeight=0,this._evaluatedMaximumHeight=Number.MAX_SAFE_INTEGER,this._minimumWidth=0,this._minimumHeight=0,this._maximumWidth=Number.MAX_SAFE_INTEGER,this._maximumHeight=Number.MAX_SAFE_INTEGER,this._snap=!1,this._onDidChange=new tB,this.onDidChange=this._onDidChange.event,"number"==typeof(null==i?void 0:i.minimumWidth)&&(this._minimumWidth=i.minimumWidth),"number"==typeof(null==i?void 0:i.maximumWidth)&&(this._maximumWidth=i.maximumWidth),"number"==typeof(null==i?void 0:i.minimumHeight)&&(this._minimumHeight=i.minimumHeight),"number"==typeof(null==i?void 0:i.maximumHeight)&&(this._maximumHeight=i.maximumHeight),this.api.initialize(this),this.addDisposables(this.api.onWillVisibilityChange(e=>{let{isVisible:t}=e,{accessor:i}=this._params;i.setVisible(this,t)}),this.api.onActiveChange(()=>{let{accessor:e}=this._params;e.doSetGroupActive(this)}),this.api.onDidConstraintsChangeInternal(e=>{("number"==typeof e.minimumWidth||"function"==typeof e.minimumWidth)&&(this._minimumWidth=e.minimumWidth),("number"==typeof e.minimumHeight||"function"==typeof e.minimumHeight)&&(this._minimumHeight=e.minimumHeight),("number"==typeof e.maximumWidth||"function"==typeof e.maximumWidth)&&(this._maximumWidth=e.maximumWidth),("number"==typeof e.maximumHeight||"function"==typeof e.maximumHeight)&&(this._maximumHeight=e.maximumHeight)}),this.api.onDidSizeChange(e=>{this._onDidChange.fire({height:e.height,width:e.width})}),this._onDidChange)}setVisible(e){this.api._onDidVisibilityChange.fire({isVisible:e})}setActive(e){this.api._onDidActiveChange.fire({isActive:e})}init(e){e.maximumHeight&&(this._maximumHeight=e.maximumHeight),e.minimumHeight&&(this._minimumHeight=e.minimumHeight),e.maximumWidth&&(this._maximumWidth=e.maximumWidth),e.minimumWidth&&(this._minimumWidth=e.minimumWidth),this._priority=e.priority,this._snap=!!e.snap,super.init(e),"boolean"==typeof e.isVisible&&this.setVisible(e.isVisible)}updateConstraints(){this.api._onDidConstraintsChange.fire({minimumWidth:this._evaluatedMinimumWidth,maximumWidth:this._evaluatedMaximumWidth,minimumHeight:this._evaluatedMinimumHeight,maximumHeight:this._evaluatedMaximumHeight})}toJSON(){let e=super.toJSON(),t=e=>e===Number.MAX_SAFE_INTEGER?void 0:e,i=e=>e<=0?void 0:e;return Object.assign(Object.assign({},e),{minimumHeight:i(this.minimumHeight),maximumHeight:t(this.maximumHeight),minimumWidth:i(this.minimumWidth),maximumWidth:t(this.maximumWidth),snap:this.snap,priority:this.priority})}}let i7="dockview: DockviewGroupPanelApiImpl not initialized";class ne extends i6{get location(){if(!this._group)throw Error(i7);return this._group.model.location}constructor(e,t){super(e,"__dockviewgroup__"),this.accessor=t,this._onDidLocationChange=new tB,this.onDidLocationChange=this._onDidLocationChange.event,this._onDidActivePanelChange=new tB,this.onDidActivePanelChange=this._onDidActivePanelChange.event,this.addDisposables(this._onDidLocationChange,this._onDidActivePanelChange,this._onDidVisibilityChange.event(e=>{e.isVisible&&this._pendingSize&&(super.setSize(this._pendingSize),this._pendingSize=void 0)}))}setSize(e){this._pendingSize=Object.assign({},e),super.setSize(e)}close(){if(this._group)return this.accessor.removeGroup(this._group)}getWindow(){return"popout"===this.location.type?this.location.getWindow():window}moveTo(e){var t,i,n,o;if(!this._group)throw Error(i7);let r=null!=(t=e.group)?t:this.accessor.addGroup({direction:function(e){switch(e){case"top":return"above";case"bottom":return"below";case"left":return"left";case"right":return"right";case"center":return"within";default:throw Error(`invalid position '${e}'`)}}(null!=(i=e.position)?i:"right"),skipSetActive:null!=(n=e.skipSetActive)&&n});this.accessor.moveGroupOrPanel({from:{groupId:this._group.id},to:{group:r,position:e.group&&null!=(o=e.position)?o:"center",index:e.index},skipSetActive:e.skipSetActive})}maximize(){if(!this._group)throw Error(i7);"grid"===this.location.type&&this.accessor.maximizeGroup(this._group)}isMaximized(){if(!this._group)throw Error(i7);return this.accessor.isMaximizedGroup(this._group)}exitMaximized(){if(!this._group)throw Error(i7);this.isMaximized()&&this.accessor.exitMaximizedGroup()}initialize(e){this._group=e}}class nt extends i8{get minimumWidth(){var e;if("number"==typeof this._explicitConstraints.minimumWidth)return this._explicitConstraints.minimumWidth;let t=null==(e=this.activePanel)?void 0:e.minimumWidth;return"number"==typeof t?t:super.__minimumWidth()}get minimumHeight(){var e;if("number"==typeof this._explicitConstraints.minimumHeight)return this._explicitConstraints.minimumHeight;let t=null==(e=this.activePanel)?void 0:e.minimumHeight;return"number"==typeof t?t:super.__minimumHeight()}get maximumWidth(){var e;if("number"==typeof this._explicitConstraints.maximumWidth)return this._explicitConstraints.maximumWidth;let t=null==(e=this.activePanel)?void 0:e.maximumWidth;return"number"==typeof t?t:super.__maximumWidth()}get maximumHeight(){var e;if("number"==typeof this._explicitConstraints.maximumHeight)return this._explicitConstraints.maximumHeight;let t=null==(e=this.activePanel)?void 0:e.maximumHeight;return"number"==typeof t?t:super.__maximumHeight()}get panels(){return this._model.panels}get activePanel(){return this._model.activePanel}get size(){return this._model.size}get model(){return this._model}get locked(){return this._model.locked}set locked(e){this._model.locked=e}get header(){return this._model.header}constructor(e,t,i){var n,o,r,s,a,l;super(t,"groupview_default",{minimumHeight:null!=(o=null==(n=i.constraints)?void 0:n.minimumHeight)?o:100,minimumWidth:null!=(s=null==(r=i.constraints)?void 0:r.minimumWidth)?s:100,maximumHeight:null==(a=i.constraints)?void 0:a.maximumHeight,maximumWidth:null==(l=i.constraints)?void 0:l.maximumWidth},new ne(t,e)),this._explicitConstraints={},this.api.initialize(this),this._model=new i9(this.element,e,t,i,this),this.addDisposables(this.model.onDidActivePanelChange(e=>{this.api._onDidActivePanelChange.fire(e)}),this.api.onDidConstraintsChangeInternal(e=>{void 0!==e.minimumWidth&&(this._explicitConstraints.minimumWidth="function"==typeof e.minimumWidth?e.minimumWidth():e.minimumWidth),void 0!==e.minimumHeight&&(this._explicitConstraints.minimumHeight="function"==typeof e.minimumHeight?e.minimumHeight():e.minimumHeight),void 0!==e.maximumWidth&&(this._explicitConstraints.maximumWidth="function"==typeof e.maximumWidth?e.maximumWidth():e.maximumWidth),void 0!==e.maximumHeight&&(this._explicitConstraints.maximumHeight="function"==typeof e.maximumHeight?e.maximumHeight():e.maximumHeight)}))}focus(){this.api.isActive||this.api.setActive(),super.focus()}initialize(){this._model.initialize()}setActive(e){super.setActive(e),this.model.setActive(e)}layout(e,t){super.layout(e,t),this.model.layout(e,t)}getComponent(){return this._model}toJSON(){return this.model.toJSON()}}let ni={name:"abyss",className:"dockview-theme-abyss"};class nn extends i6{get location(){return this.group.api.location}get title(){return this.panel.title}get isGroupActive(){return this.group.isActive}get renderer(){return this.panel.renderer}set group(e){let t=this._group;this._group!==e&&(this._group=e,this._onDidGroupChange.fire({}),this.setupGroupEventListeners(t),this._onDidLocationChange.fire({location:this.group.api.location}))}get group(){return this._group}get tabComponent(){return this._tabComponent}constructor(e,t,i,n,o){super(e.id,n),this.panel=e,this.accessor=i,this._onDidTitleChange=new tB,this.onDidTitleChange=this._onDidTitleChange.event,this._onDidActiveGroupChange=new tB,this.onDidActiveGroupChange=this._onDidActiveGroupChange.event,this._onDidGroupChange=new tB,this.onDidGroupChange=this._onDidGroupChange.event,this._onDidRendererChange=new tB,this.onDidRendererChange=this._onDidRendererChange.event,this._onDidLocationChange=new tB,this.onDidLocationChange=this._onDidLocationChange.event,this.groupEventsDisposable=new tJ,this._tabComponent=o,this.initialize(e),this._group=t,this.setupGroupEventListeners(),this.addDisposables(this.groupEventsDisposable,this._onDidRendererChange,this._onDidTitleChange,this._onDidGroupChange,this._onDidActiveGroupChange,this._onDidLocationChange)}getWindow(){return this.group.api.getWindow()}moveTo(e){var t,i;this.accessor.moveGroupOrPanel({from:{groupId:this._group.id,panelId:this.panel.id},to:{group:null!=(t=e.group)?t:this._group,position:e.group&&null!=(i=e.position)?i:"center",index:e.index},skipSetActive:e.skipSetActive})}setTitle(e){this.panel.setTitle(e)}setRenderer(e){this.panel.setRenderer(e)}close(){this.group.model.closePanel(this.panel)}maximize(){this.group.api.maximize()}isMaximized(){return this.group.api.isMaximized()}exitMaximized(){this.group.api.exitMaximized()}setupGroupEventListeners(e){var t;let i=null!=(t=null==e?void 0:e.isActive)&&t;this.groupEventsDisposable.value=new tK(this.group.api.onDidVisibilityChange(e=>{let t=!e.isVisible&&this.isVisible,i=e.isVisible&&!this.isVisible,n=this.group.model.isPanelActive(this.panel);(t||i&&n)&&this._onDidVisibilityChange.fire(e)}),this.group.api.onDidLocationChange(e=>{this.group===this.panel.group&&this._onDidLocationChange.fire(e)}),this.group.api.onDidActiveChange(()=>{this.group===this.panel.group&&i!==this.isGroupActive&&(i=this.isGroupActive,this._onDidActiveGroupChange.fire({isActive:this.isGroupActive}))}))}}class no extends tK{get params(){return this._params}get title(){return this._title}get group(){return this._group}get renderer(){var e;return null!=(e=this._renderer)?e:this.accessor.renderer}get minimumWidth(){return this._minimumWidth}get minimumHeight(){return this._minimumHeight}get maximumWidth(){return this._maximumWidth}get maximumHeight(){return this._maximumHeight}constructor(e,t,i,n,o,r,s,a){super(),this.id=e,this.accessor=n,this.containerApi=o,this.view=s,this._renderer=a.renderer,this._group=r,this._minimumWidth=a.minimumWidth,this._minimumHeight=a.minimumHeight,this._maximumWidth=a.maximumWidth,this._maximumHeight=a.maximumHeight,this.api=new nn(this,this._group,n,t,i),this.addDisposables(this.api.onActiveChange(()=>{n.setActivePanel(this)}),this.api.onDidSizeChange(e=>{this.group.api.setSize(e)}),this.api.onDidRendererChange(()=>{this.group.model.rerender(this)}))}init(e){this._params=e.params,this.view.init(Object.assign(Object.assign({},e),{api:this.api,containerApi:this.containerApi})),this.setTitle(e.title)}focus(){let e=new iT;this.api._onWillFocus.fire(e),!e.defaultPrevented&&(this.api.isActive||this.api.setActive())}toJSON(){return{id:this.id,contentComponent:this.view.contentComponent,tabComponent:this.view.tabComponent,params:Object.keys(this._params||{}).length>0?this._params:void 0,title:this.title,renderer:this._renderer,minimumHeight:this._minimumHeight,maximumHeight:this._maximumHeight,minimumWidth:this._minimumWidth,maximumWidth:this._maximumWidth}}setTitle(e){e!==this.title&&(this._title=e,this.api._onDidTitleChange.fire({title:e}))}setRenderer(e){e!==this.renderer&&(this._renderer=e,this.api._onDidRendererChange.fire({renderer:e}))}update(e){var t;for(let i of(this._params=Object.assign(Object.assign({},null!=(t=this._params)?t:{}),e.params),Object.keys(e.params)))void 0===e.params[i]&&delete this._params[i];this.view.update({params:this._params})}updateFromStateModel(e){var t,i,n;this._maximumHeight=e.maximumHeight,this._minimumHeight=e.minimumHeight,this._maximumWidth=e.maximumWidth,this._minimumWidth=e.minimumWidth,this.update({params:null!=(t=e.params)?t:{}}),this.setTitle(null!=(i=e.title)?i:this.id),this.setRenderer(null!=(n=e.renderer)?n:this.accessor.renderer)}updateParentGroup(e,t){this._group=e,this.api.group=this._group;let i=this._group.model.isPanelActive(this),n=this.group.api.isActive&&i;(null==t?void 0:t.skipSetActive)||this.api.isActive===n||this.api._onDidActiveChange.fire({isActive:this.group.api.isActive&&i}),this.api.isVisible!==i&&this.api._onDidVisibilityChange.fire({isVisible:i})}runEvents(){let e=this._group.model.isPanelActive(this),t=this.group.api.isActive&&e;this.api.isActive!==t&&this.api._onDidActiveChange.fire({isActive:this.group.api.isActive&&e}),this.api.isVisible!==e&&this.api._onDidVisibilityChange.fire({isVisible:e})}layout(e,t){this.api._onDidDimensionChange.fire({width:e,height:t}),this.view.layout(e,t)}dispose(){this.api.dispose(),this.view.dispose()}}class nr extends tK{get element(){return this._element}constructor(){super(),this._element=document.createElement("div"),this._element.className="dv-default-tab",this._content=document.createElement("div"),this._content.className="dv-default-tab-content",this.action=document.createElement("div"),this.action.className="dv-default-tab-action",this.action.appendChild(iQ({width:"11",height:"11",viewbox:"0 0 28 28",path:"M2.1 27.3L0 25.2L11.55 13.65L0 2.1L2.1 0L13.65 11.55L25.2 0L27.3 2.1L15.75 13.65L27.3 25.2L25.2 27.3L13.65 15.75L2.1 27.3Z"})),this._element.appendChild(this._content),this._element.appendChild(this.action),this.render()}init(e){this._title=e.title,this.addDisposables(e.api.onDidTitleChange(e=>{this._title=e.title,this.render()}),t$(this.action,"pointerdown",e=>{e.preventDefault()}),t$(this.action,"click",t=>{t.defaultPrevented||(t.preventDefault(),e.api.close())})),this.render()}render(){var e;this._content.textContent!==this._title&&(this._content.textContent=null!=(e=this._title)?e:"")}}class ns{get content(){return this._content}get tab(){return this._tab}constructor(e,t,i,n){this.accessor=e,this.id=t,this.contentComponent=i,this.tabComponent=n,this._content=this.createContentComponent(this.id,i),this._tab=this.createTabComponent(this.id,n)}createTabRenderer(e){var t;let i=this.createTabComponent(this.id,this.tabComponent);return this._params&&i.init(Object.assign(Object.assign({},this._params),{tabLocation:e})),this._updateEvent&&(null==(t=i.update)||t.call(i,this._updateEvent)),i}init(e){this._params=e,this.content.init(e),this.tab.init(Object.assign(Object.assign({},e),{tabLocation:"header"}))}layout(e,t){var i,n;null==(n=(i=this.content).layout)||n.call(i,e,t)}update(e){var t,i,n,o;this._updateEvent=e,null==(i=(t=this.content).update)||i.call(t,e),null==(o=(n=this.tab).update)||o.call(n,e)}dispose(){var e,t,i,n;null==(t=(e=this.content).dispose)||t.call(e),null==(n=(i=this.tab).dispose)||n.call(i)}createContentComponent(e,t){return this.accessor.options.createComponent({id:e,name:t})}createTabComponent(e,t){let i=null!=t?t:this.accessor.options.defaultTabComponent;if(i){if(this.accessor.options.createTabComponent){let t=this.accessor.options.createTabComponent({id:e,name:i});return t||new nr}console.warn(`dockview: tabComponent '${t}' was not found. falling back to the default tab.`)}return new nr}}class na{constructor(e){this.accessor=e}fromJSON(e,t){var i,n;let o=e.id,r=e.params,s=e.title,a=e.view,l=a?a.content.id:null!=(i=e.contentComponent)?i:"unknown",d=a?null==(n=a.tab)?void 0:n.id:e.tabComponent,h=new ns(this.accessor,o,l,d),u=new no(o,l,d,this.accessor,new iA(this.accessor),t,h,{renderer:e.renderer,minimumWidth:e.minimumWidth,minimumHeight:e.minimumHeight,maximumWidth:e.maximumWidth,maximumHeight:e.maximumHeight});return u.init({title:null!=s?s:o,params:null!=r?r:{}}),u}}class nl extends tK{get element(){return this._element}constructor(){super(),this._element=document.createElement("div"),this._element.className="dv-watermark"}init(e){}}let nd=new class{constructor(){this._orderedList=[]}push(e){this._orderedList=[...this._orderedList.filter(t=>t!==e),e],this.update()}destroy(e){this._orderedList=this._orderedList.filter(t=>t!==e),this.update()}update(){for(let e=0;e{let e=null,t=t3();i.value=new tK({dispose:()=>{t.release()}},t$(window,"pointermove",t=>{let i=this.options.container.getBoundingClientRect(),n=t.clientX-i.left,o=t.clientY-i.top;tQ(this._element,"dv-resize-container-dragging",!0);let r=this._element.getBoundingClientRect();null===e&&(e={x:t.clientX-r.left,y:t.clientY-r.top});let s=Math.max(0,this.getMinimumWidth(r.width)),a=Math.max(0,this.getMinimumHeight(r.height)),l=it(o-e.y,-a,Math.max(0,i.height-r.height+a)),d=it(e.y-o+i.height-r.height,-a,Math.max(0,i.height-r.height+a)),h=it(n-e.x,-s,Math.max(0,i.width-r.width+s)),u=it(e.x-n+i.width-r.width,-s,Math.max(0,i.width-r.width+s)),c={};l<=d?c.top=l:c.bottom=d,h<=u?c.left=h:c.right=u,this.setBounds(c)}),t$(window,"pointerup",()=>{tQ(this._element,"dv-resize-container-dragging",!1),i.dispose(),this._onDidChangeEnd.fire()}))};this.addDisposables(i,t$(e,"pointerdown",e=>{e.defaultPrevented?e.preventDefault():e[t2]||n()}),t$(this.options.content,"pointerdown",e=>{e.defaultPrevented||!e[t2]&&e.shiftKey&&n()}),t$(this.options.content,"pointerdown",()=>{nd.push(this._element)},!0)),t.inDragMode&&n()}setupResize(e){let t=document.createElement("div");t.className=`dv-resize-handle-${e}`,this._element.appendChild(t);let i=new tJ;this.addDisposables(i,t$(t,"pointerdown",t=>{t.preventDefault();let n=null,o=t3();i.value=new tK(t$(window,"pointermove",t=>{let i,o,r,s,a,l,d=this.options.container.getBoundingClientRect(),h=this._element.getBoundingClientRect(),u=t.clientY-d.top,c=t.clientX-d.left;null===n&&(n={originalY:u,originalHeight:h.height,originalX:c,originalWidth:h.width});let p=()=>{i=it(u,0,n.originalY+n.originalHeight>d.height?Math.max(0,d.height-nh.MINIMUM_HEIGHT):Math.max(0,n.originalY+n.originalHeight-nh.MINIMUM_HEIGHT)),r=n.originalY+n.originalHeight-i,o=d.height-i-r},m=()=>{let e=(i=n.originalY-n.originalHeight)<0&&"number"==typeof this.options.minimumInViewportHeight?-i+this.options.minimumInViewportHeight:nh.MINIMUM_HEIGHT,t=d.height-Math.max(0,i);r=it(u-i,e,t),o=d.height-i-r},f=()=>{s=it(c,0,n.originalX+n.originalWidth>d.width?Math.max(0,d.width-nh.MINIMUM_WIDTH):Math.max(0,n.originalX+n.originalWidth-nh.MINIMUM_WIDTH)),l=n.originalX+n.originalWidth-s,a=d.width-s-l},g=()=>{let e=(s=n.originalX-n.originalWidth)<0&&"number"==typeof this.options.minimumInViewportWidth?-s+this.options.minimumInViewportWidth:nh.MINIMUM_WIDTH,t=d.width-Math.max(0,s);l=it(c-s,e,t),a=d.width-s-l};switch(e){case"top":p();break;case"bottom":m();break;case"left":f();break;case"right":g();break;case"topleft":p(),f();break;case"topright":p(),g();break;case"bottomleft":m(),f();break;case"bottomright":m(),g()}let v={};i<=o?v.top=i:v.bottom=o,s<=a?v.left=s:v.right=a,v.height=r,v.width=l,this.setBounds(v)}),{dispose:()=>{o.release()}},t$(window,"pointerup",()=>{i.dispose(),this._onDidChangeEnd.fire()}))}))}getMinimumWidth(e){return"number"==typeof this.options.minimumInViewportWidth?e-this.options.minimumInViewportWidth:0}getMinimumHeight(e){return"number"==typeof this.options.minimumInViewportHeight?e-this.options.minimumInViewportHeight:0}dispose(){nd.destroy(this._element),this._element.remove(),super.dispose()}}nh.MINIMUM_HEIGHT=20,nh.MINIMUM_WIDTH=20;class nu extends tK{constructor(e,t){super(),this.group=e,this.overlay=t,this.addDisposables(t)}position(e){this.overlay.setBounds(e)}}class nc{constructor(){this.cache=new Map,this.currentFrameId=0,this.rafId=null}getPosition(e){let t=this.cache.get(e);if(t&&t.frameId===this.currentFrameId)return t.rect;this.scheduleFrameUpdate();let i=t5(e);return this.cache.set(e,{rect:i,frameId:this.currentFrameId}),i}invalidate(){this.currentFrameId++}scheduleFrameUpdate(){this.rafId||(this.rafId=requestAnimationFrame(()=>{this.currentFrameId++,this.rafId=null}))}}class np extends tK{constructor(e,t){super(),this.element=e,this.accessor=t,this.map={},this._disposed=!1,this.positionCache=new nc,this.pendingUpdates=new Set,this.addDisposables(O.from(()=>{for(let e of Object.values(this.map))e.disposable.dispose(),e.destroy.dispose();this._disposed=!0}))}updateAllPositions(){if(!this._disposed)for(let e of(this.positionCache.invalidate(),Object.values(this.map)))e.panel.api.isVisible&&e.resize&&e.resize()}detatch(e){if(this.map[e.api.id]){let{disposable:t,destroy:i}=this.map[e.api.id];return t.dispose(),i.dispose(),delete this.map[e.api.id],!0}return!1}attach(e){let{panel:t,referenceContainer:i}=e;if(!this.map[t.api.id]){let e,i=((e=document.createElement("div")).tabIndex=-1,e);i.className="dv-render-overlay",this.map[t.api.id]={panel:t,disposable:O.NONE,destroy:O.NONE,element:i}}let n=this.map[t.api.id].element;t.view.content.element.parentElement!==n&&n.appendChild(t.view.content.element),n.parentElement!==this.element&&this.element.appendChild(n);let o=()=>{let e=t.api.id;this.pendingUpdates.has(e)||(this.pendingUpdates.add(e),requestAnimationFrame(()=>{if(this.pendingUpdates.delete(e),this.isDisposed||!this.map[e])return;let o=this.positionCache.getPosition(i.element),r=this.positionCache.getPosition(this.element),s=o.left-r.left,a=o.top-r.top,l=o.width,d=o.height;n.style.left=`${s}px`,n.style.top=`${a}px`,n.style.width=`${l}px`,n.style.height=`${d}px`,tQ(n,"dv-render-overlay-float","floating"===t.group.api.location.type)}))},r=()=>{t.api.isVisible&&(this.positionCache.invalidate(),o()),n.style.display=t.api.isVisible?"":"none"},s=new tJ,a=()=>{"floating"===t.api.location.type?queueMicrotask(()=>{let e=this.accessor.floatingGroups.find(e=>e.group===t.api.group);if(!e)return;let i=e.overlay.element,o=()=>{let e=Number(i.getAttribute("aria-level"));n.style.zIndex=`calc(var(--dv-overlay-z-index, 999) + ${2*e+1})`},r=new MutationObserver(()=>{o()});s.value=O.from(()=>r.disconnect()),r.observe(i,{attributeFilter:["aria-level"],attributes:!0}),o()}):n.style.zIndex=""},l=new tK(s,new iz(n,{onDragEnd:e=>{i.dropTarget.dnd.onDragEnd(e)},onDragEnter:e=>{i.dropTarget.dnd.onDragEnter(e)},onDragLeave:e=>{i.dropTarget.dnd.onDragLeave(e)},onDrop:e=>{i.dropTarget.dnd.onDrop(e)},onDragOver:e=>{i.dropTarget.dnd.onDragOver(e)}}),t.api.onDidVisibilityChange(()=>{r()}),t.api.onDidDimensionsChange(()=>{t.api.isVisible&&o()}),t.api.onDidLocationChange(()=>{a()}));return this.map[t.api.id].destroy=O.from(()=>{var e;t.view.content.element.parentElement===n&&n.removeChild(t.view.content.element),null==(e=n.parentElement)||e.removeChild(n)}),a(),queueMicrotask(()=>{this.isDisposed||r()}),this.map[t.api.id].disposable.dispose(),this.map[t.api.id].disposable=l,this.map[t.api.id].resize=o,n}}class nm extends tK{get window(){var e,t;return null!=(t=null==(e=this._window)?void 0:e.value)?t:null}constructor(e,t,i){super(),this.target=e,this.className=t,this.options=i,this._onWillClose=new tB,this.onWillClose=this._onWillClose.event,this._onDidClose=new tB,this.onDidClose=this._onDidClose.event,this._window=null,this.addDisposables(this._onWillClose,this._onDidClose,{dispose:()=>{this.close()}})}dimensions(){if(!this._window)return null;let e=this._window.value.screenX,t=this._window.value.screenY;return{top:t,left:e,width:this._window.value.innerWidth,height:this._window.value.innerHeight}}close(){var e,t;this._window&&(this._onWillClose.fire(),null==(t=(e=this.options).onWillClose)||t.call(e,{id:this.target,window:this._window.value}),this._window.disposable.dispose(),this._window=null,this._onDidClose.fire())}open(){var e,t,i,n,o,r;return i=this,n=void 0,o=void 0,r=function*(){if(this._window)throw Error("instance of popout window is already open");let i=`${this.options.url}`,n=Object.entries({top:this.options.top,left:this.options.left,width:this.options.width,height:this.options.height}).map(([e,t])=>`${e}=${t}`).join(","),o=window.open(i,this.target,n);if(!o)return null;let r=new tK;this._window={value:o,disposable:r},r.addDisposables(O.from(()=>{o.close()}),t$(window,"beforeunload",()=>{this.close()}));let s=this.createPopoutWindowContainer();return this.className&&s.classList.add(this.className),null==(t=(e=this.options).onDidOpen)||t.call(e,{id:this.target,window:o}),new Promise((e,t)=>{o.addEventListener("unload",e=>{}),o.addEventListener("load",()=>{try{let t=o.document;t.title=document.title,t.body.appendChild(s),function(e,t){for(let i of Array.from(t)){if(i.href){let t=e.createElement("link");t.href=i.href,t.type=i.type,t.rel="stylesheet",e.head.appendChild(t)}let t=[];try{i.cssRules&&(t=Array.from(i.cssRules).map(e=>e.cssText))}catch(e){}for(let i of t){let t=e.createElement("style");t.appendChild(e.createTextNode(i)),e.head.appendChild(t)}}}(t,window.document.styleSheets),t$(o,"beforeunload",()=>{this.close()}),e(s)}catch(e){t(e)}})})},new(o||(o=Promise))(function(e,t){function s(e){try{l(r.next(e))}catch(e){t(e)}}function a(e){try{l(r.throw(e))}catch(e){t(e)}}function l(t){var i;t.done?e(t.value):((i=t.value)instanceof o?i:new o(function(e){e(i)})).then(s,a)}l((r=r.apply(i,n||[])).next())})}createPopoutWindowContainer(){let e=document.createElement("div");return e.classList.add("dv-popout-window"),e.id="dv-popout-window",e.style.position="absolute",e.style.width="100%",e.style.height="100%",e.style.top="0px",e.style.left="0px",e}}class nf extends tK{constructor(e){super(),this.accessor=e,this.init()}init(){let e=new Set,t=new Set;this.addDisposables(this.accessor.onDidAddPanel(t=>{if(e.has(t.api.id))throw Error(`dockview: Invalid event sequence. [onDidAddPanel] called for panel ${t.api.id} but panel already exists`);e.add(t.api.id)}),this.accessor.onDidRemovePanel(t=>{if(e.has(t.api.id))e.delete(t.api.id);else throw Error(`dockview: Invalid event sequence. [onDidRemovePanel] called for panel ${t.api.id} but panel does not exists`)}),this.accessor.onDidAddGroup(e=>{if(t.has(e.api.id))throw Error(`dockview: Invalid event sequence. [onDidAddGroup] called for group ${e.api.id} but group already exists`);t.add(e.api.id)}),this.accessor.onDidRemoveGroup(e=>{if(t.has(e.api.id))t.delete(e.api.id);else throw Error(`dockview: Invalid event sequence. [onDidRemoveGroup] called for group ${e.api.id} but group does not exists`)}))}}class ng extends tK{constructor(e){super(),this.root=e,this._active=null,this._activeDisposable=new tJ,this._element=document.createElement("div"),this._element.className="dv-popover-anchor",this._element.style.position="relative",this.root.prepend(this._element),this.addDisposables(O.from(()=>{this.close()}),this._activeDisposable)}openPopover(e,t){var i;this.close();let n=document.createElement("div");n.style.position="absolute",n.style.zIndex=null!=(i=t.zIndex)?i:"var(--dv-overlay-z-index)",n.appendChild(e);let o=this._element.getBoundingClientRect(),r=o.left,s=o.top;n.style.top=`${t.y-s}px`,n.style.left=`${t.x-r}px`,this._element.appendChild(n),this._active=n,this._activeDisposable.value=new tK(t$(window,"pointerdown",e=>{var t;let i=e.target;if(!(i instanceof HTMLElement))return;let o=i;for(;o&&o!==n;)o=null!=(t=null==o?void 0:o.parentElement)?t:null;o||this.close()})),requestAnimationFrame(()=>{!function(e,t,i={buffer:10}){let n=i.buffer,o=e.getBoundingClientRect(),r=t.getBoundingClientRect(),s=0,a=0,l=o.left-r.left,d=o.top-r.top,h=o.bottom-r.bottom,u=o.right-r.right;ln&&(s=-n-u),dn&&(a=-h-n),(0!==s||0!==a)&&(e.style.transform=`translate(${s}px, ${a}px)`)}(n,this.root)})}close(){this._active&&(this._active.remove(),this._activeDisposable.dispose(),this._active=null)}}class nv extends tK{get disabled(){return this._disabled}set disabled(e){var t;this.disabled!==e&&(this._disabled=e,e&&(null==(t=this.model)||t.clear()))}get model(){if(!this.disabled)return{clear:()=>{var e;this._model&&(null==(e=this._model.root.parentElement)||e.removeChild(this._model.root)),this._model=void 0},exists:()=>!!this._model,getElements:(e,t)=>{let i=this._outline!==t;if(this._outline=t,this._model)return this._model.changed=i,this._model;let n=this.createContainer(),o=this.createAnchor();if(this._model={root:n,overlay:o,changed:i},n.appendChild(o),this.element.appendChild(n),(null==e?void 0:e.target)instanceof HTMLElement){let t=e.target.getBoundingClientRect(),i=this.element.getBoundingClientRect();o.style.left=`${t.left-i.left}px`,o.style.top=`${t.top-i.top}px`}return this._model}}}constructor(e,t){super(),this.element=e,this._disabled=!1,this._disabled=t.disabled,this.addDisposables(O.from(()=>{var e;null==(e=this.model)||e.clear()}))}createContainer(){let e=document.createElement("div");return e.className="dv-drop-target-container",e}createAnchor(){let e=document.createElement("div");return e.className="dv-drop-target-anchor",e.style.visibility="hidden",e}}let nw={activationSize:{type:"pixels",value:10},size:{type:"pixels",value:20}};function nb(e){let t=e.from.activePanel;[...e.from.panels].map(t=>{let i=e.from.model.removePanel(t);return e.from.model.renderContainer.detatch(t),i}).forEach(i=>{e.to.model.openPanel(i,{skipSetActive:t!==i,skipSetGroupActive:!0})})}class ny extends iD{get orientation(){return this.gridview.orientation}get totalPanels(){return this.panels.length}get panels(){return this.groups.flatMap(e=>e.panels)}get options(){return this._options}get activePanel(){let e=this.activeGroup;if(e)return e.activePanel}get renderer(){var e;return null!=(e=this.options.defaultRenderer)?e:"onlyWhenVisible"}get api(){return this._api}get floatingGroups(){return this._floatingGroups}get popoutRestorationPromise(){return this._popoutRestorationPromise}constructor(e,t){var i,n,o;super(e,{proportionalLayout:!0,orientation:I.HORIZONTAL,styles:t.hideBorders?{separatorBorder:"transparent"}:void 0,disableAutoResizing:t.disableAutoResizing,locked:t.locked,margin:null!=(n=null==(i=t.theme)?void 0:i.gap)?n:0,className:t.className}),this.nextGroupId=ii(),this._deserializer=new na(this),this._watermark=null,this._onWillDragPanel=new tB,this.onWillDragPanel=this._onWillDragPanel.event,this._onWillDragGroup=new tB,this.onWillDragGroup=this._onWillDragGroup.event,this._onDidDrop=new tB,this.onDidDrop=this._onDidDrop.event,this._onWillDrop=new tB,this.onWillDrop=this._onWillDrop.event,this._onWillShowOverlay=new tB,this.onWillShowOverlay=this._onWillShowOverlay.event,this._onUnhandledDragOverEvent=new tB,this.onUnhandledDragOverEvent=this._onUnhandledDragOverEvent.event,this._onDidRemovePanel=new tB,this.onDidRemovePanel=this._onDidRemovePanel.event,this._onDidAddPanel=new tB,this.onDidAddPanel=this._onDidAddPanel.event,this._onDidPopoutGroupSizeChange=new tB,this.onDidPopoutGroupSizeChange=this._onDidPopoutGroupSizeChange.event,this._onDidPopoutGroupPositionChange=new tB,this.onDidPopoutGroupPositionChange=this._onDidPopoutGroupPositionChange.event,this._onDidOpenPopoutWindowFail=new tB,this.onDidOpenPopoutWindowFail=this._onDidOpenPopoutWindowFail.event,this._onDidLayoutFromJSON=new tB,this.onDidLayoutFromJSON=this._onDidLayoutFromJSON.event,this._onDidActivePanelChange=new tB({replay:!0}),this.onDidActivePanelChange=this._onDidActivePanelChange.event,this._onDidMovePanel=new tB,this.onDidMovePanel=this._onDidMovePanel.event,this._onDidMaximizedGroupChange=new tB,this.onDidMaximizedGroupChange=this._onDidMaximizedGroupChange.event,this._floatingGroups=[],this._popoutGroups=[],this._popoutRestorationPromise=Promise.resolve(),this._onDidRemoveGroup=new tB,this.onDidRemoveGroup=this._onDidRemoveGroup.event,this._onDidAddGroup=new tB,this.onDidAddGroup=this._onDidAddGroup.event,this._onDidOptionsChange=new tB,this.onDidOptionsChange=this._onDidOptionsChange.event,this._onDidActiveGroupChange=new tB,this.onDidActiveGroupChange=this._onDidActiveGroupChange.event,this._moving=!1,this._options=t,this.popupService=new ng(this.element),this._themeClassnames=new t4(this.element),this._api=new iA(this),this.rootDropTargetContainer=new nv(this.element,{disabled:!0}),this.overlayRenderContainer=new np(this.gridview.element,this),this._rootDropTarget=new iI(this.element,{className:"dv-drop-target-edge",canDisplayOverlay:(e,t)=>{let i=tj();if(i)return i.viewId===this.id&&("center"!==t||0===this.gridview.length);if("center"===t&&0!==this.gridview.length)return!1;let n=new i2(e,"edge",t,tj);return this._onUnhandledDragOverEvent.fire(n),n.isAccepted},acceptedTargetZones:["top","bottom","left","right","center"],overlayModel:null!=(o=t.rootOverlayModel)?o:nw,getOverrideTarget:()=>{var e;return null==(e=this.rootDropTargetContainer)?void 0:e.model}}),this.updateDropTargetModel(t),tQ(this.gridview.element,"dv-dockview",!0),tQ(this.element,"dv-debug",!!t.debug),this.updateTheme(),this.updateWatermark(),t.debug&&this.addDisposables(new nf(this)),this.addDisposables(this.rootDropTargetContainer,this.overlayRenderContainer,this._onWillDragPanel,this._onWillDragGroup,this._onWillShowOverlay,this._onDidActivePanelChange,this._onDidAddPanel,this._onDidRemovePanel,this._onDidLayoutFromJSON,this._onDidDrop,this._onWillDrop,this._onDidMovePanel,this._onDidMovePanel.event(()=>{this.debouncedUpdateAllPositions()}),this._onDidAddGroup,this._onDidRemoveGroup,this._onDidActiveGroupChange,this._onUnhandledDragOverEvent,this._onDidMaximizedGroupChange,this._onDidOptionsChange,this._onDidPopoutGroupSizeChange,this._onDidPopoutGroupPositionChange,this._onDidOpenPopoutWindowFail,this.onDidViewVisibilityChangeMicroTaskQueue(()=>{this.updateWatermark()}),this.onDidAdd(e=>{this._moving||this._onDidAddGroup.fire(e)}),this.onDidRemove(e=>{this._moving||this._onDidRemoveGroup.fire(e)}),this.onDidActiveChange(e=>{this._moving||this._onDidActiveGroupChange.fire(e)}),this.onDidMaximizedChange(e=>{this._onDidMaximizedGroupChange.fire({group:e.panel,isMaximized:e.isMaximized})}),N.any(this.onDidAdd,this.onDidRemove)(()=>{this.updateWatermark()}),N.any(this.onDidAddPanel,this.onDidRemovePanel,this.onDidAddGroup,this.onDidRemove,this.onDidMovePanel,this.onDidActivePanelChange,this.onDidPopoutGroupPositionChange,this.onDidPopoutGroupSizeChange)(()=>{this._bufferOnDidLayoutChange.fire()}),O.from(()=>{for(let e of[...this._floatingGroups])e.dispose();for(let e of[...this._popoutGroups])e.disposable.dispose()}),this._rootDropTarget,this._rootDropTarget.onWillShowOverlay(e=>{this.gridview.length>0&&"center"===e.position||this._onWillShowOverlay.fire(new iJ(e,{kind:"edge",panel:void 0,api:this._api,group:void 0,getData:tj}))}),this._rootDropTarget.onDrop(e=>{var t;let i=new i4({nativeEvent:e.nativeEvent,position:e.position,panel:void 0,api:this._api,group:void 0,getData:tj,kind:"edge"});if(this._onWillDrop.fire(i),i.defaultPrevented)return;let n=tj();n?this.moveGroupOrPanel({from:{groupId:n.groupId,panelId:null!=(t=n.panelId)?t:void 0},to:{group:this.orthogonalize(e.position),position:"center"}}):this._onDidDrop.fire(new i3({nativeEvent:e.nativeEvent,position:e.position,panel:void 0,api:this._api,group:void 0,getData:tj}))}),this._rootDropTarget)}setVisible(e,t){switch(e.api.location.type){case"grid":super.setVisible(e,t);break;case"floating":{let i=this.floatingGroups.find(t=>t.group===e);i&&(i.overlay.setVisible(t),e.api._onDidVisibilityChange.fire({isVisible:t}));break}case"popout":console.warn("dockview: You cannot hide a group that is in a popout window")}}addPopoutGroup(e,t){var i,n,o,r,s;if(e instanceof no&&1===e.group.size)return this.addPopoutGroup(e.group,t);let a=function(e){let t,i=e;for(;null!==i&&"string"!=typeof(t=(function(e){let t=[];for(let i=0;ie.startsWith("dockview-theme-")));)i=i.parentElement;return t}(this.gridview.element),l=this.element,d=(null==t?void 0:t.position)?t.position:e instanceof nt?e.element.getBoundingClientRect():e.group?e.group.element.getBoundingClientRect():l.getBoundingClientRect(),h=null!=(n=null==(i=null==t?void 0:t.overridePopoutGroup)?void 0:i.id)?n:this.getNextGroupId(),u=new nm(`${this.id}-${h}`,null!=a?a:"",{url:null!=(s=null!=(o=null==t?void 0:t.popoutUrl)?o:null==(r=this.options)?void 0:r.popoutUrl)?s:"/popout.html",left:window.screenX+d.left,top:window.screenY+d.top,width:d.width,height:d.height,onDidOpen:null==t?void 0:t.onDidOpen,onWillClose:null==t?void 0:t.onWillClose}),c=new tK(u,u.onDidClose(()=>{c.dispose()}));return u.open().then(i=>{var n,o,r,s;let a,l,d,p,m,f,g,v,w;if(u.isDisposed)return!1;let b=(null==t?void 0:t.referenceGroup)?t.referenceGroup:e instanceof no?e.group:e,y=e.api.location.type,x=null!==b.element.parentElement;if(x?(null==t?void 0:t.overridePopoutGroup)?a=t.overridePopoutGroup:(a=this.createGroup({id:h}),i&&this._onDidAddGroup.fire(a)):a=b,null===i)return console.error("dockview: failed to create popout. perhaps you need to allow pop-ups for this website"),c.dispose(),this._onDidOpenPopoutWindowFail.fire(),this.movingLock(()=>nb({from:a,to:b})),b.api.isVisible||b.api.setVisible(!0),!1;let _=document.createElement("div");_.className="dv-overlay-render-container";let D=new np(_,this);if(a.model.renderContainer=D,a.layout(u.window.innerWidth,u.window.innerHeight),!(null==t?void 0:t.overridePopoutGroup)&&x)if(e instanceof no)this.movingLock(()=>{let t=b.model.removePanel(e);a.model.openPanel(t)});else switch(this.movingLock(()=>nb({from:b,to:a})),y){case"grid":b.api.setVisible(!1);break;case"floating":case"popout":l=null==(n=this._floatingGroups.find(t=>t.group.api.id===e.api.id))?void 0:n.overlay.toJSON(),this.removeGroup(b)}i.classList.add("dv-dockview"),i.style.overflow="hidden",i.appendChild(_),i.appendChild(a.element);let C=document.createElement("div"),S=new nv(C,{disabled:this.rootDropTargetContainer.disabled});i.appendChild(C),a.model.dropTargetContainer=S,a.model.location={type:"popout",getWindow:()=>u.window,popoutUrl:null==t?void 0:t.popoutUrl},x&&"grid"===e.api.location.type&&e.api.setVisible(!1),this.doSetGroupAndPanelActive(a),c.addDisposables(a.api.onDidActiveChange(e=>{var t;e.isActive&&(null==(t=u.window)||t.focus())}),a.api.onWillFocus(()=>{var e;null==(e=u.window)||e.focus()}));let E=x&&b&&this.getPanel(b.id),A={window:u,popoutGroup:a,referenceGroup:E?b.id:void 0,disposable:{dispose:()=>(c.dispose(),d)}},k=(o=u.window,m=new tB,f=o.screenX,g=o.screenY,(v=()=>{if(o.closed)return;let e=o.screenX,t=o.screenY;(e!==f||t!==g)&&(clearTimeout(p),p=setTimeout(()=>{m.fire()},100),f=e,g=t),requestAnimationFrame(v)})(),m);return c.addDisposables(k,(r=u.window,s=()=>{this._onDidPopoutGroupSizeChange.fire({width:u.window.innerWidth,height:u.window.innerHeight,group:a})},new tK(t$(r,"resize",()=>{clearTimeout(w),w=setTimeout(()=>{s()},100)}))),k.event(()=>{this._onDidPopoutGroupPositionChange.fire({screenX:u.window.screenX,screenY:u.window.screenX,group:a})}),t$(u.window,"resize",()=>{a.layout(u.window.innerWidth,u.window.innerHeight)}),D,O.from(()=>{if(!this.isDisposed){if(x&&this.getPanel(b.id))this.movingLock(()=>nb({from:a,to:b})),b.api.isVisible||b.api.setVisible(!0),this.getPanel(a.id)&&this.doRemoveGroup(a,{skipPopoutAssociated:!0});else if(this.getPanel(a.id)){if(a.model.renderContainer=this.overlayRenderContainer,a.model.dropTargetContainer=this.rootDropTargetContainer,d=a,!this._popoutGroups.find(e=>e.popoutGroup===a))return;l?this.addFloatingGroup(a,{height:l.height,width:l.width,position:l}):(this.doRemoveGroup(a,{skipDispose:!0,skipActive:!0,skipPopoutReturn:!0}),a.model.location={type:"grid"},this.movingLock(()=>{this.doAddGroup(a,[0])})),this.doSetGroupAndPanelActive(a)}}})),this._popoutGroups.push(A),this.updateWatermark(),!0}).catch(e=>(console.error("dockview: failed to create popout.",e),!1))}addFloatingGroup(e,t){var i,n,o,r,s;let a;if(e instanceof no)a=this.createGroup(),this._onDidAddGroup.fire(a),this.movingLock(()=>this.removePanel(e,{removeEmptyGroup:!0,skipDispose:!0,skipSetActiveGroup:!0})),this.movingLock(()=>a.model.openPanel(e,{skipSetGroupActive:!0}));else{a=e;let n=null==(i=this._popoutGroups.find(e=>e.popoutGroup===a))?void 0:i.referenceGroup,o=n?this.getPanel(n):void 0;"boolean"==typeof(null==t?void 0:t.skipRemoveGroup)&&t.skipRemoveGroup||(o?(this.movingLock(()=>nb({from:e,to:o})),this.doRemoveGroup(e,{skipPopoutReturn:!0,skipPopoutAssociated:!0}),this.doRemoveGroup(o,{skipDispose:!0}),a=o):this.doRemoveGroup(e,{skipDispose:!0,skipPopoutReturn:!0,skipPopoutAssociated:!1}))}let l=function(){if(null==t?void 0:t.position){let e={};return"left"in t.position?e.left=Math.max(t.position.left,0):"right"in t.position?e.right=Math.max(t.position.right,0):e.left=100,"top"in t.position?e.top=Math.max(t.position.top,0):"bottom"in t.position?e.bottom=Math.max(t.position.bottom,0):e.top=100,"number"==typeof t.width?e.width=Math.max(t.width,0):e.width=300,"number"==typeof t.height?e.height=Math.max(t.height,0):e.height=300,e}return{left:"number"==typeof(null==t?void 0:t.x)?Math.max(t.x,0):100,top:"number"==typeof(null==t?void 0:t.y)?Math.max(t.y,0):100,width:"number"==typeof(null==t?void 0:t.width)?Math.max(t.width,0):300,height:"number"==typeof(null==t?void 0:t.height)?Math.max(t.height,0):300}}(),d=new nh(Object.assign(Object.assign({container:this.gridview.element,content:a.element},l),{minimumInViewportWidth:"boundedWithinViewport"===this.options.floatingGroupBounds?void 0:null!=(o=null==(n=this.options.floatingGroupBounds)?void 0:n.minimumWidthWithinViewport)?o:100,minimumInViewportHeight:"boundedWithinViewport"===this.options.floatingGroupBounds?void 0:null!=(s=null==(r=this.options.floatingGroupBounds)?void 0:r.minimumHeightWithinViewport)?s:100})),h=a.element.querySelector(".dv-void-container");if(!h)throw Error("dockview: failed to find drag handle");d.setupDrag(h,{inDragMode:"boolean"==typeof(null==t?void 0:t.inDragMode)&&t.inDragMode});let u=new nu(a,d),c=new tK(a.api.onDidActiveChange(e=>{e.isActive&&d.bringToFront()}),tX(a.element,e=>{let{width:t,height:i}=e.contentRect;a.layout(t,i)}));u.addDisposables(d.onDidChange(()=>{a.layout(a.width,a.height)}),d.onDidChangeEnd(()=>{this._bufferOnDidLayoutChange.fire()}),a.onDidChange(e=>{d.setBounds({height:null==e?void 0:e.height,width:null==e?void 0:e.width})}),{dispose:()=>{c.dispose(),ie(this._floatingGroups,u),a.model.location={type:"grid"},this.updateWatermark()}}),this._floatingGroups.push(u),a.model.location={type:"floating"},(null==t?void 0:t.skipActiveGroup)||this.doSetGroupAndPanelActive(a),this.updateWatermark()}orthogonalize(e,t){switch(this.gridview.normalize(),e){case"top":case"bottom":this.gridview.orientation===I.HORIZONTAL&&this.gridview.insertOrthogonalSplitviewAtRoot();break;case"left":case"right":this.gridview.orientation===I.VERTICAL&&this.gridview.insertOrthogonalSplitviewAtRoot()}switch(e){case"top":case"left":case"center":return this.createGroupAtLocation([0],void 0,t);case"bottom":case"right":return this.createGroupAtLocation([this.gridview.length],void 0,t);default:throw Error(`dockview: unsupported position ${e}`)}}updateOptions(e){var t,i;if(super.updateOptions(e),"floatingGroupBounds"in e)for(let n of this._floatingGroups){switch(e.floatingGroupBounds){case"boundedWithinViewport":n.overlay.minimumInViewportHeight=void 0,n.overlay.minimumInViewportWidth=void 0;break;case void 0:n.overlay.minimumInViewportHeight=100,n.overlay.minimumInViewportWidth=100;break;default:n.overlay.minimumInViewportHeight=null==(t=e.floatingGroupBounds)?void 0:t.minimumHeightWithinViewport,n.overlay.minimumInViewportWidth=null==(i=e.floatingGroupBounds)?void 0:i.minimumWidthWithinViewport}n.overlay.setBounds()}this.updateDropTargetModel(e);let n=this.options.disableDnd;this._options=Object.assign(Object.assign({},this.options),e),n!==this.options.disableDnd&&this.updateDragAndDropState(),"theme"in e&&this.updateTheme(),this.layout(this.gridview.width,this.gridview.height,!0)}layout(e,t,i){if(super.layout(e,t,i),this._floatingGroups)for(let e of this._floatingGroups)e.overlay.setBounds()}updateDragAndDropState(){for(let e of this.groups)e.model.updateDragAndDropState()}focus(){var e;null==(e=this.activeGroup)||e.focus()}getGroupPanel(e){return this.panels.find(t=>t.id===e)}setActivePanel(e){e.group.model.openPanel(e),this.doSetGroupAndPanelActive(e.group)}moveToNext(e={}){var t;if(!e.group){if(!this.activeGroup)return;e.group=this.activeGroup}if(e.includePanel&&e.group&&e.group.activePanel!==e.group.panels[e.group.panels.length-1])return void e.group.model.moveToNext({suppressRoll:!0});let i=ip(e.group.element),n=null==(t=this.gridview.next(i))?void 0:t.view;this.doSetGroupAndPanelActive(n)}moveToPrevious(e={}){var t;if(!e.group){if(!this.activeGroup)return;e.group=this.activeGroup}if(e.includePanel&&e.group&&e.group.activePanel!==e.group.panels[0])return void e.group.model.moveToPrevious({suppressRoll:!0});let i=ip(e.group.element),n=null==(t=this.gridview.previous(i))?void 0:t.view;n&&this.doSetGroupAndPanelActive(n)}toJSON(){var e;let t=this.gridview.serialize(),i=this.panels.reduce((e,t)=>(e[t.id]=t.toJSON(),e),{}),n=this._floatingGroups.map(e=>({data:e.group.toJSON(),position:e.overlay.toJSON()})),o=this._popoutGroups.map(e=>({data:e.popoutGroup.toJSON(),gridReferenceGroup:e.referenceGroup,position:e.window.dimensions(),url:"popout"===e.popoutGroup.api.location.type?e.popoutGroup.api.location.popoutUrl:void 0})),r={grid:t,panels:i,activeGroup:null==(e=this.activeGroup)?void 0:e.id};return n.length>0&&(r.floatingGroups=n),o.length>0&&(r.popoutGroups=o),r}fromJSON(e,t){var i,n;let o,r=new Map;if(null==t?void 0:t.reuseExistingPanels){o=this.createGroup(),this._groups.delete(o.api.id);let t=Object.keys(e.panels);for(let e of this.panels)t.includes(e.api.id)&&r.set(e.api.id,e);this.movingLock(()=>{Array.from(r.values()).forEach(e=>{this.moveGroupOrPanel({from:{groupId:e.api.group.api.id,panelId:e.api.id},to:{group:o,position:"center"},keepEmptyGroups:!0})})})}if(this.clear(),"object"!=typeof e||null===e)throw Error("dockview: serialized layout must be a non-null object");let{grid:s,panels:a,activeGroup:l}=e;if("branch"!==s.root.type||!Array.isArray(s.root.data))throw Error("dockview: root must be of type branch");try{let t=this.width,d=this.height,h=e=>{let{id:t,locked:i,hideHeader:n,views:s,activeView:l}=e;if("string"!=typeof t)throw Error("dockview: group id must be of type string");let d=this.createGroup({id:t,locked:!!i,hideHeader:!!n});this._onDidAddGroup.fire(d);let h=[];for(let e of s){let t=r.get(e);if(o&&t)this.movingLock(()=>{o.model.removePanel(t)}),h.push(t),t.updateFromStateModel(a[e]);else{let t=this._deserializer.fromJSON(a[e],d);h.push(t)}}for(let e=0;e{d.model.openPanel(t,{skipSetActive:!i,skipSetGroupActive:!0})}):d.model.openPanel(t,{skipSetActive:!i,skipSetGroupActive:!0})}return!d.activePanel&&d.panels.length>0&&d.model.openPanel(d.panels[d.panels.length-1],{skipSetGroupActive:!0}),d};for(let n of(this.gridview.deserialize(s,{fromJSON:e=>h(e.data)}),this.layout(t,d,!0),null!=(i=e.floatingGroups)?i:[])){let{data:e,position:t}=n,i=h(e);this.addFloatingGroup(i,{position:t,width:t.width,height:t.height,skipRemoveGroup:!0,inDragMode:!1})}let u=null!=(n=e.popoutGroups)?n:[],c=[];for(let e of(u.forEach((e,t)=>{let{data:i,position:n,gridReferenceGroup:o,url:r}=e,s=h(i),a=new Promise(e=>{setTimeout(()=>{this.addPopoutGroup(s,{position:null!=n?n:void 0,overridePopoutGroup:o?s:void 0,referenceGroup:o?this.getPanel(o):void 0,popoutUrl:r}),e()},100*t)});c.push(a)}),this._popoutRestorationPromise=Promise.all(c).then(()=>void 0),this._floatingGroups))e.overlay.setBounds();if("string"==typeof l){let e=this.getPanel(l);e&&this.doSetGroupAndPanelActive(e)}}catch(e){for(let t of(console.error("dockview: failed to deserialize layout. Reverting changes",e),this.groups))for(let e of t.panels)this.removePanel(e,{removeEmptyGroup:!1,skipDispose:!1});for(let e of this.groups)e.dispose(),this._groups.delete(e.id),this._onDidRemoveGroup.fire(e);for(let e of[...this._floatingGroups])e.dispose();throw this.clear(),e}this.updateWatermark(),this.debouncedUpdateAllPositions(),this._onDidLayoutFromJSON.fire()}clear(){let e=Array.from(this._groups.values()).map(e=>e.value),t=!!this.activeGroup;for(let t of e)this.removeGroup(t,{skipActive:!0});t&&this.doSetGroupAndPanelActive(void 0),this.gridview.clear()}closeAllGroups(){for(let e of this._groups.entries()){let[t,i]=e;i.value.model.closeAllPanels()}}addPanel(e){var t,i;let n,o,r;if(this.panels.find(t=>t.id===e.id))throw Error(`dockview: panel with id ${e.id} already exists`);if(e.position&&e.floating)throw Error("dockview: you can only provide one of: position, floating as arguments to .addPanel(...)");let s={width:e.initialWidth,height:e.initialHeight};if(e.position)if(+!e.position.referencePanel){if(+!e.position.referenceGroup){let t=this.orthogonalize(iR(e.position.direction)),i=this.createPanel(e,t);return t.model.openPanel(i,{skipSetActive:e.inactive,skipSetGroupActive:e.inactive,index:o}),e.inactive||this.doSetGroupAndPanelActive(t),t.api.setSize({height:null==s?void 0:s.height,width:null==s?void 0:s.width}),i}else if(n="string"==typeof e.position.referenceGroup?null==(t=this._groups.get(e.position.referenceGroup))?void 0:t.value:e.position.referenceGroup,o=e.position.index,!n)throw Error(`dockview: referenceGroup '${e.position.referenceGroup}' does not exist`)}else{let t="string"==typeof e.position.referencePanel?this.getGroupPanel(e.position.referencePanel):e.position.referencePanel;if(o=e.position.index,!t)throw Error(`dockview: referencePanel '${e.position.referencePanel}' does not exist`);n=this.findGroup(t)}else n=this.activeGroup;if(n){let t=i_((null==(i=e.position)?void 0:i.direction)||"within");if(e.floating){let t=this.createGroup();this._onDidAddGroup.fire(t);let i="object"==typeof e.floating&&null!==e.floating?e.floating:{};this.addFloatingGroup(t,Object.assign(Object.assign({},i),{inDragMode:!1,skipRemoveGroup:!0,skipActiveGroup:!0})),r=this.createPanel(e,t),t.model.openPanel(r,{skipSetActive:e.inactive,skipSetGroupActive:e.inactive,index:o})}else if("floating"===n.api.location.type||"center"===t)r=this.createPanel(e,n),n.model.openPanel(r,{skipSetActive:e.inactive,skipSetGroupActive:e.inactive,index:o}),n.api.setSize({width:null==s?void 0:s.width,height:null==s?void 0:s.height}),e.inactive||this.doSetGroupAndPanelActive(n);else{let i=ip(n.element),a=im(this.gridview.orientation,i,t),l=this.createGroupAtLocation(a,this.orientationAtLocation(a)===I.VERTICAL?null==s?void 0:s.height:null==s?void 0:s.width);r=this.createPanel(e,l),l.model.openPanel(r,{skipSetActive:e.inactive,skipSetGroupActive:e.inactive,index:o}),e.inactive||this.doSetGroupAndPanelActive(l)}}else if(e.floating){let t=this.createGroup();this._onDidAddGroup.fire(t);let i="object"==typeof e.floating&&null!==e.floating?e.floating:{};this.addFloatingGroup(t,Object.assign(Object.assign({},i),{inDragMode:!1,skipRemoveGroup:!0,skipActiveGroup:!0})),r=this.createPanel(e,t),t.model.openPanel(r,{skipSetActive:e.inactive,skipSetGroupActive:e.inactive,index:o})}else{let t=this.createGroupAtLocation([0],this.gridview.orientation===I.VERTICAL?null==s?void 0:s.height:null==s?void 0:s.width);r=this.createPanel(e,t),t.model.openPanel(r,{skipSetActive:e.inactive,skipSetGroupActive:e.inactive,index:o}),e.inactive||this.doSetGroupAndPanelActive(t)}return r}removePanel(e,t={removeEmptyGroup:!0}){let i=e.group;if(!i)throw Error(`dockview: cannot remove panel ${e.id}. it's missing a group.`);i.model.removePanel(e,{skipSetActiveGroup:t.skipSetActiveGroup}),t.skipDispose||(e.group.model.renderContainer.detatch(e),e.dispose()),0===i.size&&t.removeEmptyGroup&&this.removeGroup(i,{skipActive:t.skipSetActiveGroup})}createWatermarkComponent(){return this.options.createWatermarkComponent?this.options.createWatermarkComponent():new nl}updateWatermark(){var e,t;if(0===this.groups.filter(e=>"grid"===e.api.location.type&&e.api.isVisible).length){if(!this._watermark){this._watermark=this.createWatermarkComponent(),this._watermark.init({containerApi:new iA(this)});let e=document.createElement("div");e.className="dv-watermark-container",e.setAttribute("data-testid","watermark-component"),e.appendChild(this._watermark.element),this.gridview.element.appendChild(e)}}else this._watermark&&(this._watermark.element.parentElement.remove(),null==(t=(e=this._watermark).dispose)||t.call(e),this._watermark=null)}addGroup(e){var t;if(e){let i;if(+!e.referencePanel){if(+!e.referenceGroup){let t=this.orthogonalize(iR(e.direction),e);return e.skipSetActive||this.doSetGroupAndPanelActive(t),t}else if(!(i="string"==typeof e.referenceGroup?null==(t=this._groups.get(e.referenceGroup))?void 0:t.value:e.referenceGroup))throw Error(`dockview: reference group ${e.referenceGroup} does not exist`)}else{let t="string"==typeof e.referencePanel?this.panels.find(t=>t.id===e.referencePanel):e.referencePanel;if(!t)throw Error(`dockview: reference panel ${e.referencePanel} does not exist`);if(!(i=this.findGroup(t)))throw Error(`dockview: reference group for reference panel ${e.referencePanel} does not exist`)}let n=i_(e.direction||"within"),o=ip(i.element),r=im(this.gridview.orientation,o,n),s=this.createGroup(e),a=this.getLocationOrientation(r)===I.VERTICAL?e.initialHeight:e.initialWidth;return this.doAddGroup(s,r,a),e.skipSetActive||this.doSetGroupAndPanelActive(s),s}{let t=this.createGroup(e);return this.doAddGroup(t),this.doSetGroupAndPanelActive(t),t}}getLocationOrientation(e){return e.length%2==0&&this.gridview.orientation===I.HORIZONTAL?I.HORIZONTAL:I.VERTICAL}removeGroup(e,t){this.doRemoveGroup(e,t)}doRemoveGroup(e,t){var i;let n=[...e.panels];if(!(null==t?void 0:t.skipDispose))for(let e of n)this.removePanel(e,{removeEmptyGroup:!1,skipDispose:null!=(i=null==t?void 0:t.skipDispose)&&i});let o=this.activePanel;if("floating"===e.api.location.type){let i=this._floatingGroups.find(t=>t.group===e);if(i){if((null==t?void 0:t.skipDispose)||(i.group.dispose(),this._groups.delete(e.id),this._onDidRemoveGroup.fire(e)),ie(this._floatingGroups,i),i.dispose(),!(null==t?void 0:t.skipActive)&&this._activeGroup===e){let e=Array.from(this._groups.values());this.doSetGroupAndPanelActive(e.length>0?e[0].value:void 0)}return i.group}throw Error("dockview: failed to find floating group")}if("popout"===e.api.location.type){let i=this._popoutGroups.find(t=>t.popoutGroup===e);if(i){if(!(null==t?void 0:t.skipDispose)){if(!(null==t?void 0:t.skipPopoutAssociated)){let e=i.referenceGroup?this.getPanel(i.referenceGroup):void 0;e&&0===e.panels.length&&this.removeGroup(e)}i.popoutGroup.dispose(),this._groups.delete(e.id),this._onDidRemoveGroup.fire(e)}ie(this._popoutGroups,i);let n=i.disposable.dispose();if((null==t?void 0:t.skipPopoutReturn)||!n||(this.doAddGroup(n,[0]),this.doSetGroupAndPanelActive(n)),!(null==t?void 0:t.skipActive)&&this._activeGroup===e){let e=Array.from(this._groups.values());this.doSetGroupAndPanelActive(e.length>0?e[0].value:void 0)}return this.updateWatermark(),i.popoutGroup}throw Error("dockview: failed to find popout group")}let r=super.doRemoveGroup(e,t);return(null==t?void 0:t.skipActive)||this.activePanel===o||this._onDidActivePanelChange.fire(this.activePanel),r}debouncedUpdateAllPositions(){void 0!==this._updatePositionsFrameId&&cancelAnimationFrame(this._updatePositionsFrameId),this._updatePositionsFrameId=requestAnimationFrame(()=>{this._updatePositionsFrameId=void 0,this.overlayRenderContainer.updateAllPositions()})}movingLock(e){let t=this._moving;try{return this._moving=!0,e()}finally{this._moving=t}}moveGroupOrPanel(e){var t;let i=e.to.group,n=e.from.groupId,o=e.from.panelId,r=e.to.position,s=e.to.index,a=n?null==(t=this._groups.get(n))?void 0:t.value:void 0;if(!a)throw Error(`dockview: Failed to find group id ${n}`);if(void 0===o)return void this.moveGroup({from:{group:a},to:{group:i,position:r},skipSetActive:e.skipSetActive});if(r&&"center"!==r){let e=ip(i.element),t=im(this.gridview.orientation,e,r);if(a.size<2){let[e,n]=t9(t);if("grid"===a.api.location.type){let[t,i]=t9(ip(a.element));if(t6(t,e)){this.gridview.moveView(t,i,n),this._onDidMovePanel.fire({panel:this.getGroupPanel(o),from:a});return}}if("popout"===a.api.location.type){let e=this._popoutGroups.find(e=>e.popoutGroup===a),i=this.movingLock(()=>e.popoutGroup.model.removePanel(e.popoutGroup.panels[0],{skipSetActive:!0,skipSetActiveGroup:!0}));this.doRemoveGroup(a,{skipActive:!0});let n=this.createGroupAtLocation(t);this.movingLock(()=>n.model.openPanel(i,{skipSetActive:!0})),this.doSetGroupAndPanelActive(n),this._onDidMovePanel.fire({panel:this.getGroupPanel(o),from:a});return}let s=this.movingLock(()=>this.doRemoveGroup(a,{skipActive:!0,skipDispose:!0})),l=ip(i.element),d=im(this.gridview.orientation,l,r);this.movingLock(()=>this.doAddGroup(s,d)),this.doSetGroupAndPanelActive(s),this._onDidMovePanel.fire({panel:this.getGroupPanel(o),from:a})}else{let t=this.movingLock(()=>a.model.removePanel(o,{skipSetActive:!1,skipSetActiveGroup:!0}));if(!t)throw Error(`dockview: No panel with id ${o}`);let i=im(this.gridview.orientation,e,r),n=this.createGroupAtLocation(i);this.movingLock(()=>n.model.openPanel(t,{skipSetGroupActive:!0})),this.doSetGroupAndPanelActive(n),this._onDidMovePanel.fire({panel:t,from:a})}}else{let t=this.movingLock(()=>a.model.removePanel(o,{skipSetActive:!1,skipSetActiveGroup:!0}));if(!t)throw Error(`dockview: No panel with id ${o}`);e.keepEmptyGroups||0!==a.model.size||this.doRemoveGroup(a,{skipActive:!0});let n=0===i.model.size;this.movingLock(()=>{var o;return i.model.openPanel(t,{index:s,skipSetActive:null!=(o=e.skipSetActive)&&o&&!n,skipSetGroupActive:!0})}),e.skipSetActive||this.doSetGroupAndPanelActive(i),this._onDidMovePanel.fire({panel:t,from:a})}}moveGroup(e){let t=e.from.group,i=e.to.group,n=e.to.position;if("center"===n){let n=t.activePanel,o=this.movingLock(()=>[...t.panels].map(e=>t.model.removePanel(e.id,{skipSetActive:!0})));(null==t?void 0:t.model.size)===0&&this.doRemoveGroup(t,{skipActive:!0}),this.movingLock(()=>{for(let e of o)i.model.openPanel(e,{skipSetActive:e!==n,skipSetGroupActive:!0})}),!0!==e.skipSetActive?this.doSetGroupAndPanelActive(i):this.activePanel||this.doSetGroupAndPanelActive(i)}else{switch(t.api.location.type){case"grid":this.gridview.removeView(ip(t.element));break;case"floating":{let e=this._floatingGroups.find(e=>e.group===t);if(!e)throw Error("dockview: failed to find floating group");e.dispose();break}case"popout":{let e=this._popoutGroups.find(e=>e.popoutGroup===t);if(!e)throw Error("dockview: failed to find popout group");let n=this._popoutGroups.indexOf(e);if(n>=0&&this._popoutGroups.splice(n,1),e.referenceGroup){let t=this.getPanel(e.referenceGroup);t&&!t.api.isVisible&&this.doRemoveGroup(t,{skipActive:!0})}e.window.dispose(),"grid"===i.api.location.type?(t.model.renderContainer=this.overlayRenderContainer,t.model.dropTargetContainer=this.rootDropTargetContainer,t.model.location={type:"grid"}):"floating"===i.api.location.type&&(t.model.renderContainer=this.overlayRenderContainer,t.model.dropTargetContainer=this.rootDropTargetContainer,t.model.location={type:"floating"})}}if("grid"===i.api.location.type){let e,o=ip(i.element),r=im(this.gridview.orientation,o,n);switch(this.gridview.orientation){case I.VERTICAL:e=o.length%2==0?t.api.width:t.api.height;break;case I.HORIZONTAL:e=o.length%2==0?t.api.height:t.api.width}this.gridview.addView(t,e,r)}else if("floating"===i.api.location.type){let e=this._floatingGroups.find(e=>e.group===i);if(e){let i,n,o=e.overlay.toJSON();i="left"in o?o.left+50:"right"in o?Math.max(0,o.right-o.width-50):50,n="top"in o?o.top+50:"bottom"in o?Math.max(0,o.bottom-o.height-50):50,this.addFloatingGroup(t,{height:o.height,width:o.width,position:{left:i,top:n}})}}}if(t.panels.forEach(e=>{this._onDidMovePanel.fire({panel:e,from:t})}),this.debouncedUpdateAllPositions(),!1===e.skipSetActive){let e=null!=i?i:t;this.doSetGroupAndPanelActive(e)}}doSetGroupActive(e){super.doSetGroupActive(e);let t=this.activePanel;this._moving||t===this._onDidActivePanelChange.value||this._onDidActivePanelChange.fire(t)}doSetGroupAndPanelActive(e){super.doSetGroupActive(e);let t=this.activePanel;e&&this.hasMaximizedGroup()&&!this.isMaximizedGroup(e)&&this.exitMaximizedGroup(),this._moving||t===this._onDidActivePanelChange.value||this._onDidActivePanelChange.fire(t)}getNextGroupId(){let e=this.nextGroupId.next();for(;this._groups.has(e);)e=this.nextGroupId.next();return e}createGroup(e){e||(e={});let t=null==e?void 0:e.id;if(t&&this._groups.has(e.id)&&(console.warn(`dockview: Duplicate group id ${null==e?void 0:e.id}. reassigning group id to avoid errors`),t=void 0),!t)for(t=this.nextGroupId.next();this._groups.has(t);)t=this.nextGroupId.next();let i=new nt(this,t,e);if(i.init({params:{},accessor:this}),!this._groups.has(i.id)){let e=new tK(i.model.onTabDragStart(e=>{this._onWillDragPanel.fire(e)}),i.model.onGroupDragStart(e=>{this._onWillDragGroup.fire(e)}),i.model.onMove(e=>{let{groupId:t,itemId:n,target:o,index:r}=e;this.moveGroupOrPanel({from:{groupId:t,panelId:n},to:{group:i,position:o,index:r}})}),i.model.onDidDrop(e=>{this._onDidDrop.fire(e)}),i.model.onWillDrop(e=>{this._onWillDrop.fire(e)}),i.model.onWillShowOverlay(e=>{this.options.disableDnd?e.preventDefault():this._onWillShowOverlay.fire(e)}),i.model.onUnhandledDragOverEvent(e=>{this._onUnhandledDragOverEvent.fire(e)}),i.model.onDidAddPanel(e=>{this._moving||this._onDidAddPanel.fire(e.panel)}),i.model.onDidRemovePanel(e=>{this._moving||this._onDidRemovePanel.fire(e.panel)}),i.model.onDidActivePanelChange(e=>{this._moving||e.panel===this.activePanel&&this._onDidActivePanelChange.value!==e.panel&&this._onDidActivePanelChange.fire(e.panel)}),N.any(i.model.onDidPanelTitleChange,i.model.onDidPanelParametersChange)(()=>{this._bufferOnDidLayoutChange.fire()}));this._groups.set(i.id,{value:i,disposable:e})}return i.initialize(),i}createPanel(e,t){var i,n,o;let r=e.component,s=null!=(i=e.tabComponent)?i:this.options.defaultTabComponent,a=new ns(this,e.id,r,s),l=new no(e.id,r,s,this,this._api,t,a,{renderer:e.renderer,minimumWidth:e.minimumWidth,minimumHeight:e.minimumHeight,maximumWidth:e.maximumWidth,maximumHeight:e.maximumHeight});return l.init({title:null!=(n=e.title)?n:e.id,params:null!=(o=null==e?void 0:e.params)?o:{}}),l}createGroupAtLocation(e,t,i){let n=this.createGroup(i);return this.doAddGroup(n,e,t),n}findGroup(e){var t;return null==(t=Array.from(this._groups.values()).find(t=>t.value.model.containsPanel(e)))?void 0:t.value}orientationAtLocation(e){let t=this.gridview.orientation;return e.length%2==1?t:ig(t)}updateDropTargetModel(e){"dndEdges"in e&&(this._rootDropTarget.disabled="boolean"==typeof e.dndEdges&&!1===e.dndEdges,"object"==typeof e.dndEdges&&null!==e.dndEdges?this._rootDropTarget.setOverlayModel(e.dndEdges):this._rootDropTarget.setOverlayModel(nw)),"rootOverlayModel"in e&&this.updateDropTargetModel({dndEdges:e.dndEdges})}updateTheme(){var e,t;let i=null!=(e=this._options.theme)?e:ni;this._themeClassnames.setClassNames(i.className),this.gridview.margin=null!=(t=i.gap)?t:0,"absolute"===i.dndOverlayMounting?this.rootDropTargetContainer.disabled=!1:this.rootDropTargetContainer.disabled=!0}}class nx extends iD{get orientation(){return this.gridview.orientation}set orientation(e){this.gridview.orientation=e}get options(){return this._options}get deserializer(){return this._deserializer}set deserializer(e){this._deserializer=e}constructor(e,t){var i;super(e,{proportionalLayout:null==(i=t.proportionalLayout)||i,orientation:t.orientation,styles:t.hideBorders?{separatorBorder:"transparent"}:void 0,disableAutoResizing:t.disableAutoResizing,className:t.className}),this._onDidLayoutfromJSON=new tB,this.onDidLayoutFromJSON=this._onDidLayoutfromJSON.event,this._onDidRemoveGroup=new tB,this.onDidRemoveGroup=this._onDidRemoveGroup.event,this._onDidAddGroup=new tB,this.onDidAddGroup=this._onDidAddGroup.event,this._onDidActiveGroupChange=new tB,this.onDidActiveGroupChange=this._onDidActiveGroupChange.event,this._options=t,this.addDisposables(this._onDidAddGroup,this._onDidRemoveGroup,this._onDidActiveGroupChange,this.onDidAdd(e=>{this._onDidAddGroup.fire(e)}),this.onDidRemove(e=>{this._onDidRemoveGroup.fire(e)}),this.onDidActiveChange(e=>{this._onDidActiveGroupChange.fire(e)}))}updateOptions(e){super.updateOptions(e);let t="string"==typeof e.orientation&&this.gridview.orientation!==e.orientation;this._options=Object.assign(Object.assign({},this.options),e),t&&(this.gridview.orientation=e.orientation),this.layout(this.gridview.width,this.gridview.height,!0)}removePanel(e){this.removeGroup(e)}toJSON(){var e;return{grid:this.gridview.serialize(),activePanel:null==(e=this.activeGroup)?void 0:e.id}}setVisible(e,t){this.gridview.setViewVisible(ip(e.element),t)}setActive(e){this._groups.forEach((t,i)=>{t.value.setActive(e===t.value)})}focus(){var e;null==(e=this.activeGroup)||e.focus()}fromJSON(e){this.clear();let{grid:t,activePanel:i}=e;try{let e=[],n=this.width,o=this.height;if(this.gridview.deserialize(t,{fromJSON:t=>{let{data:i}=t,n=this.options.createComponent({id:i.id,name:i.component});return e.push(()=>n.init({params:i.params,minimumWidth:i.minimumWidth,maximumWidth:i.maximumWidth,minimumHeight:i.minimumHeight,maximumHeight:i.maximumHeight,priority:i.priority,snap:!!i.snap,accessor:this,isVisible:t.visible})),this._onDidAddGroup.fire(n),this.registerPanel(n),n}}),this.layout(n,o,!0),e.forEach(e=>e()),"string"==typeof i){let e=this.getPanel(i);e&&this.doSetGroupActive(e)}}catch(e){for(let e of this.groups)e.dispose(),this._groups.delete(e.id),this._onDidRemoveGroup.fire(e);throw this.clear(),e}this._onDidLayoutfromJSON.fire()}clear(){let e=this.activeGroup;for(let e of Array.from(this._groups.values()))e.disposable.dispose(),this.doRemoveGroup(e.value,{skipActive:!0});e&&this.doSetGroupActive(void 0),this.gridview.clear()}movePanel(e,t){var i;let n,o=this.gridview.remove(e),r=null==(i=this._groups.get(t.reference))?void 0:i.value;if(!r)throw Error(`reference group ${t.reference} does not exist`);let s=i_(t.direction);if("center"===s)throw Error(`${s} not supported as an option`);{let e=ip(r.element);n=im(this.gridview.orientation,e,s)}this.doAddGroup(o,n,t.size)}addPanel(e){var t,i,n,o;let r=null!=(t=e.location)?t:[0];if(null==(i=e.position)?void 0:i.referencePanel){let t=null==(n=this._groups.get(e.position.referencePanel))?void 0:n.value;if(!t)throw Error(`reference group ${e.position.referencePanel} does not exist`);let i=i_(e.position.direction);if("center"===i)throw Error(`${i} not supported as an option`);{let e=ip(t.element);r=im(this.gridview.orientation,e,i)}}let s=this.options.createComponent({id:e.id,name:e.component});return s.init({params:null!=(o=e.params)?o:{},minimumWidth:e.minimumWidth,maximumWidth:e.maximumWidth,minimumHeight:e.minimumHeight,maximumHeight:e.maximumHeight,priority:e.priority,snap:!!e.snap,accessor:this,isVisible:!0}),this.doAddGroup(s,r,e.size),this.registerPanel(s),this.doSetGroupActive(s),s}registerPanel(e){let t=new tK(e.api.onDidFocusChange(t=>{t.isFocused&&this._groups.forEach(t=>{let i=t.value;i!==e?i.setActive(!1):i.setActive(!0)})}));this._groups.set(e.id,{value:e,disposable:t})}moveGroup(e,t,i){let n=this.getPanel(t);if(!n)throw Error("invalid operation");let o=ip(e.element),[r,s]=t9(im(this.gridview.orientation,o,i)),[a,l]=t9(ip(n.element));if(t6(a,r))return void this.gridview.moveView(a,l,s);let d=this.doRemoveGroup(n,{skipActive:!0,skipDispose:!0}),h=ip(e.element),u=im(this.gridview.orientation,h,i);this.doAddGroup(d,u)}removeGroup(e){super.removeGroup(e)}dispose(){super.dispose(),this._onDidLayoutfromJSON.dispose()}}class n_ extends iy{get panels(){return this.splitview.getViews()}get options(){return this._options}get length(){return this._panels.size}get orientation(){return this.splitview.orientation}get splitview(){return this._splitview}set splitview(e){this._splitview&&this._splitview.dispose(),this._splitview=e,this._splitviewChangeDisposable.value=new tK(this._splitview.onDidSashEnd(()=>{this._onDidLayoutChange.fire(void 0)}),this._splitview.onDidAddView(e=>this._onDidAddView.fire(e)),this._splitview.onDidRemoveView(e=>this._onDidRemoveView.fire(e)))}get minimumSize(){return this.splitview.minimumSize}get maximumSize(){return this.splitview.maximumSize}get height(){return this.splitview.orientation===I.HORIZONTAL?this.splitview.orthogonalSize:this.splitview.size}get width(){return this.splitview.orientation===I.HORIZONTAL?this.splitview.size:this.splitview.orthogonalSize}constructor(e,t){var i;super(document.createElement("div"),t.disableAutoResizing),this._splitviewChangeDisposable=new tJ,this._panels=new Map,this._onDidLayoutfromJSON=new tB,this.onDidLayoutFromJSON=this._onDidLayoutfromJSON.event,this._onDidAddView=new tB,this.onDidAddView=this._onDidAddView.event,this._onDidRemoveView=new tB,this.onDidRemoveView=this._onDidRemoveView.event,this._onDidLayoutChange=new tB,this.onDidLayoutChange=this._onDidLayoutChange.event,this.element.style.height="100%",this.element.style.width="100%",this._classNames=new t4(this.element),this._classNames.setClassNames(null!=(i=t.className)?i:""),e.appendChild(this.element),this._options=t,this.splitview=new is(this.element,t),this.addDisposables(this._onDidAddView,this._onDidLayoutfromJSON,this._onDidRemoveView,this._onDidLayoutChange)}updateOptions(e){var t,i;"className"in e&&this._classNames.setClassNames(null!=(t=e.className)?t:""),"disableResizing"in e&&(this.disableResizing=null!=(i=e.disableAutoResizing)&&i),"string"==typeof e.orientation&&(this.splitview.orientation=e.orientation),this._options=Object.assign(Object.assign({},this.options),e),this.splitview.layout(this.splitview.size,this.splitview.orthogonalSize)}focus(){var e;null==(e=this._activePanel)||e.focus()}movePanel(e,t){this.splitview.moveView(e,t)}setVisible(e,t){let i=this.panels.indexOf(e);this.splitview.setViewVisible(i,t)}setActive(e,t){this._activePanel=e,this.panels.filter(t=>t!==e).forEach(e=>{e.api._onDidActiveChange.fire({isActive:!1}),t||e.focus()}),e.api._onDidActiveChange.fire({isActive:!0}),t||e.focus()}removePanel(e,t){let i=this._panels.get(e.id);if(!i)throw Error(`unknown splitview panel ${e.id}`);i.dispose(),this._panels.delete(e.id);let n=this.panels.findIndex(t=>t===e);this.splitview.removeView(n,t).dispose();let o=this.panels;o.length>0&&this.setActive(o[o.length-1])}getPanel(e){return this.panels.find(t=>t.id===e)}addPanel(e){var t;if(this._panels.has(e.id))throw Error(`panel ${e.id} already exists`);let i=this.options.createComponent({id:e.id,name:e.component});i.orientation=this.splitview.orientation,i.init({params:null!=(t=e.params)?t:{},minimumSize:e.minimumSize,maximumSize:e.maximumSize,snap:e.snap,priority:e.priority,accessor:this});let n="number"==typeof e.size?e.size:T.Distribute,o="number"==typeof e.index?e.index:void 0;return this.splitview.addView(i,n,o),this.doAddView(i),this.setActive(i),i}layout(e,t){let[i,n]=this.splitview.orientation===I.HORIZONTAL?[e,t]:[t,e];this.splitview.layout(i,n)}doAddView(e){let t=e.api.onDidFocusChange(t=>{t.isFocused&&this.setActive(e,!0)});this._panels.set(e.id,t)}toJSON(){var e;return{views:this.splitview.getViews().map((e,t)=>({size:this.splitview.getViewSize(t),data:e.toJSON(),snap:!!e.snap,priority:e.priority})),activeView:null==(e=this._activePanel)?void 0:e.id,size:this.splitview.size,orientation:this.splitview.orientation}}fromJSON(e){this.clear();let{views:t,orientation:i,size:n,activeView:o}=e,r=[],s=this.width,a=this.height;if(this.splitview=new is(this.element,{orientation:i,proportionalLayout:this.options.proportionalLayout,descriptor:{size:n,views:t.map(e=>{let t=e.data;if(this._panels.has(t.id))throw Error(`panel ${t.id} already exists`);let n=this.options.createComponent({id:t.id,name:t.component});return r.push(()=>{var i;n.init({params:null!=(i=t.params)?i:{},minimumSize:t.minimumSize,maximumSize:t.maximumSize,snap:e.snap,priority:e.priority,accessor:this})}),n.orientation=i,this.doAddView(n),setTimeout(()=>{this._onDidAddView.fire(n)},0),{size:e.size,view:n}})}}),this.layout(s,a),r.forEach(e=>e()),"string"==typeof o){let e=this.getPanel(o);e&&this.setActive(e)}this._onDidLayoutfromJSON.fire()}clear(){for(let e of this._panels.values())e.dispose();for(this._panels.clear();this.splitview.length>0;)this.splitview.removeView(0,T.Distribute,!0).dispose()}dispose(){for(let e of this._panels.values())e.dispose();this._panels.clear();let e=this.splitview.getViews();for(let t of(this._splitviewChangeDisposable.dispose(),this.splitview.dispose(),e))t.dispose();this.element.remove(),super.dispose()}}class nD extends tK{get element(){return this._element}constructor(){super(),this._expandedIcon=iQ({width:"11",height:"11",viewbox:"0 0 24 15",path:"M12 14.15L0 2.15L2.15 0L12 9.9L21.85 0.0499992L24 2.2L12 14.15Z"}),this._collapsedIcon=i0(),this.disposable=new tJ,this.apiRef={api:null},this._element=document.createElement("div"),this.element.className="dv-default-header",this._content=document.createElement("span"),this._expander=document.createElement("div"),this._expander.className="dv-pane-header-icon",this.element.appendChild(this._expander),this.element.appendChild(this._content),this.addDisposables(t$(this._element,"click",()=>{var e;null==(e=this.apiRef.api)||e.setExpanded(!this.apiRef.api.isExpanded)}))}init(e){this.apiRef.api=e.api,this._content.textContent=e.title,this.updateIcon(),this.disposable.value=e.api.onDidExpansionChange(()=>{this.updateIcon()})}updateIcon(){var e;let t=!!(null==(e=this.apiRef.api)?void 0:e.isExpanded);tQ(this._expander,"collapsed",!t),t?(this._expander.contains(this._collapsedIcon)&&this._collapsedIcon.remove(),this._expander.contains(this._expandedIcon)||this._expander.appendChild(this._expandedIcon)):(this._expander.contains(this._expandedIcon)&&this._expandedIcon.remove(),this._expander.contains(this._collapsedIcon)||this._expander.appendChild(this._collapsedIcon))}update(e){}dispose(){this.disposable.dispose(),super.dispose()}}let nC=ii(),nS=Number.MAX_SAFE_INTEGER;class nE extends iF{constructor(e){super({accessor:e.accessor,id:e.id,component:e.component,headerComponent:e.headerComponent,orientation:e.orientation,isExpanded:e.isExpanded,disableDnd:e.disableDnd,headerSize:e.headerSize,minimumBodySize:e.minimumBodySize,maximumBodySize:e.maximumBodySize}),this.options=e}getBodyComponent(){return this.options.body}getHeaderComponent(){return this.options.header}}class nA extends iy{get id(){return this._id}get panels(){return this.paneview.getPanes()}set paneview(e){this._paneview=e,this._disposable.value=new tK(this._paneview.onDidChange(()=>{this._onDidLayoutChange.fire(void 0)}),this._paneview.onDidAddView(e=>this._onDidAddView.fire(e)),this._paneview.onDidRemoveView(e=>this._onDidRemoveView.fire(e)))}get paneview(){return this._paneview}get minimumSize(){return this.paneview.minimumSize}get maximumSize(){return this.paneview.maximumSize}get height(){return this.paneview.orientation===I.HORIZONTAL?this.paneview.orthogonalSize:this.paneview.size}get width(){return this.paneview.orientation===I.HORIZONTAL?this.paneview.size:this.paneview.orthogonalSize}get options(){return this._options}constructor(e,t){var i;super(document.createElement("div"),t.disableAutoResizing),this._id=nC.next(),this._disposable=new tJ,this._viewDisposables=new Map,this._onDidLayoutfromJSON=new tB,this.onDidLayoutFromJSON=this._onDidLayoutfromJSON.event,this._onDidLayoutChange=new tB,this.onDidLayoutChange=this._onDidLayoutChange.event,this._onDidDrop=new tB,this.onDidDrop=this._onDidDrop.event,this._onDidAddView=new tB,this.onDidAddView=this._onDidAddView.event,this._onDidRemoveView=new tB,this.onDidRemoveView=this._onDidRemoveView.event,this._onUnhandledDragOverEvent=new tB,this.onUnhandledDragOverEvent=this._onUnhandledDragOverEvent.event,this.element.style.height="100%",this.element.style.width="100%",this.addDisposables(this._onDidLayoutChange,this._onDidLayoutfromJSON,this._onDidDrop,this._onDidAddView,this._onDidRemoveView,this._onUnhandledDragOverEvent),this._classNames=new t4(this.element),this._classNames.setClassNames(null!=(i=t.className)?i:""),e.appendChild(this.element),this._options=t,this.paneview=new il(this.element,{orientation:I.VERTICAL}),this.addDisposables(this._disposable)}setVisible(e,t){let i=this.panels.indexOf(e);this.paneview.setViewVisible(i,t)}focus(){}updateOptions(e){var t,i;"className"in e&&this._classNames.setClassNames(null!=(t=e.className)?t:""),"disableResizing"in e&&(this.disableResizing=null!=(i=e.disableAutoResizing)&&i),this._options=Object.assign(Object.assign({},this.options),e)}addPanel(e){var t,i;let n,o=this.options.createComponent({id:e.id,name:e.component});e.headerComponent&&this.options.createHeaderComponent&&(n=this.options.createHeaderComponent({id:e.id,name:e.headerComponent})),n||(n=new nD);let r=new nE({id:e.id,component:e.component,headerComponent:e.headerComponent,header:n,body:o,orientation:I.VERTICAL,isExpanded:!!e.isExpanded,disableDnd:!!this.options.disableDnd,accessor:this,headerSize:null!=(t=e.headerSize)?t:22,minimumBodySize:0,maximumBodySize:nS});this.doAddPanel(r);let s="number"==typeof e.size?e.size:T.Distribute,a="number"==typeof e.index?e.index:void 0;return r.init({params:null!=(i=e.params)?i:{},minimumBodySize:e.minimumBodySize,maximumBodySize:e.maximumBodySize,isExpanded:e.isExpanded,title:e.title,containerApi:new iS(this),accessor:this}),this.paneview.addPane(r,s,a),r.orientation=this.paneview.orientation,r}removePanel(e){let t=this.panels.findIndex(t=>t===e);this.paneview.removePane(t),this.doRemovePanel(e)}movePanel(e,t){this.paneview.moveView(e,t)}getPanel(e){return this.panels.find(t=>t.id===e)}layout(e,t){let[i,n]=this.paneview.orientation===I.HORIZONTAL?[e,t]:[t,e];this.paneview.layout(i,n)}toJSON(){return{views:this.paneview.getPanes().map((e,t)=>{let i,n;return{size:this.paneview.getViewSize(t),data:e.toJSON(),minimumSize:(i=e.minimumBodySize)<=0?void 0:i,maximumSize:(n=e.maximumBodySize)===Number.MAX_SAFE_INTEGER||n===1/0?void 0:n,headerSize:e.headerSize,expanded:e.isExpanded()}}),size:this.paneview.size}}fromJSON(e){this.clear();let{views:t,size:i}=e,n=[],o=this.width,r=this.height;this.paneview=new il(this.element,{orientation:I.VERTICAL,descriptor:{size:i,views:t.map(e=>{var t,i,o;let r,s=e.data,a=this.options.createComponent({id:s.id,name:s.component});s.headerComponent&&this.options.createHeaderComponent&&(r=this.options.createHeaderComponent({id:s.id,name:s.headerComponent})),r||(r=new nD);let l=new nE({id:s.id,component:s.component,headerComponent:s.headerComponent,header:r,body:a,orientation:I.VERTICAL,isExpanded:!!e.expanded,disableDnd:!!this.options.disableDnd,accessor:this,headerSize:null!=(t=e.headerSize)?t:22,minimumBodySize:null!=(i=e.minimumSize)?i:0,maximumBodySize:null!=(o=e.maximumSize)?o:nS});return this.doAddPanel(l),n.push(()=>{var t;l.init({params:null!=(t=s.params)?t:{},minimumBodySize:e.minimumSize,maximumBodySize:e.maximumSize,title:s.title,isExpanded:!!e.expanded,containerApi:new iS(this),accessor:this}),l.orientation=this.paneview.orientation}),setTimeout(()=>{this._onDidAddView.fire(l)},0),{size:e.size,view:l}})}}),this.layout(o,r),n.forEach(e=>e()),this._onDidLayoutfromJSON.fire()}clear(){for(let[e,t]of 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e!==this._evaluatedMaximumSize&&(this._evaluatedMaximumSize=e,this.updateConstraints()),e}get snap(){return this._snap}constructor(e,t){super(e,t,new iG(e,t)),this._evaluatedMinimumSize=0,this._evaluatedMaximumSize=1/0,this._minimumSize=0,this._maximumSize=1/0,this._snap=!1,this._onDidChange=new tB,this.onDidChange=this._onDidChange.event,this.api.initialize(this),this.addDisposables(this._onDidChange,this.api.onWillVisibilityChange(e=>{let{isVisible:t}=e,{accessor:i}=this._params;i.setVisible(this,t)}),this.api.onActiveChange(()=>{let{accessor:e}=this._params;e.setActive(this)}),this.api.onDidConstraintsChangeInternal(e=>{("number"==typeof e.minimumSize||"function"==typeof e.minimumSize)&&(this._minimumSize=e.minimumSize),("number"==typeof e.maximumSize||"function"==typeof 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t;null==(t=this._part)||t.update(e.params)}updatePanels(){this.update({params:{panels:this._group.model.panels}})}updateActivePanel(){this.update({params:{activePanel:this._group.model.activePanel}})}updateGroupActive(){this.update({params:{isGroupActive:this._group.api.isActive}})}}function nj(e,t){return e?i=>new nT(e,t,i):void 0}let nG="props.defaultTabComponent",nV=W.default.forwardRef((e,t)=>{let i=W.default.useRef(null),n=W.default.useRef(),[o,r]=nO();W.default.useImperativeHandle(t,()=>i.current,[]);let s=W.default.useRef({});return W.default.useEffect(()=>{let t={};i5.forEach(i=>{let n=e[i];i in e&&n!==s.current[i]&&(t[i]=n)}),n.current&&n.current.updateOptions(t),s.current=e},i5.map(t=>e[t])),W.default.useEffect(()=>{var t;if(!i.current)return;let o=null!=(t=e.tabComponents)?t:{};e.defaultTabComponent&&(o[nG]=e.defaultTabComponent);let 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EmbeddingsLoading...
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c:[["$","meta","0",{"charSet":"utf-8"}],["$","meta","1",{"name":"viewport","content":"width=device-width, initial-scale=1"}]] From be64ffcc6c20eda7f5753ca1f788019ad4e2d57d Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Sat, 31 Jan 2026 15:33:10 +0100 Subject: [PATCH 48/75] ci: auto-export frontend on push --- .github/workflows/require_frontend_export.yml | 53 ++++++++++++++++++- 1 file changed, 52 insertions(+), 1 deletion(-) diff --git a/.github/workflows/require_frontend_export.yml b/.github/workflows/require_frontend_export.yml index aed9466..97675fe 100644 --- a/.github/workflows/require_frontend_export.yml +++ b/.github/workflows/require_frontend_export.yml @@ -16,6 +16,9 @@ on: jobs: require-frontend-export: runs-on: ubuntu-latest + if: github.actor != 'github-actions[bot]' + permissions: + contents: write steps: - name: Checkout repository @@ -23,7 +26,8 @@ jobs: with: fetch-depth: 0 - - name: Verify static export updated when frontend changes + - name: Detect frontend and static changes + id: changes run: | set -euo pipefail @@ -58,6 +62,53 @@ jobs: static_changed="true" fi + needs_export="false" + if [[ "$frontend_changed" == "true" && "$static_changed" != "true" ]]; then + needs_export="true" + fi + + { + echo "frontend_changed=$frontend_changed" + echo "static_changed=$static_changed" + echo "needs_export=$needs_export" + } >> "$GITHUB_OUTPUT" + + - name: Set up Node + if: ${{ github.event_name == 'push' && steps.changes.outputs.needs_export == 'true' }} + uses: actions/setup-node@v4 + with: + node-version: 20 + + - name: Export frontend + if: ${{ github.event_name == 'push' && steps.changes.outputs.needs_export == 'true' }} + run: | + bash scripts/export_frontend.sh + + - name: Commit exported static assets + if: ${{ github.event_name == 'push' && steps.changes.outputs.needs_export == 'true' }} + run: | + set -euo pipefail + + if [[ -z "$(git status --porcelain)" ]]; then + echo "No export changes to commit." + exit 0 + fi + + git status --short + git config user.name "github-actions[bot]" + git config user.email "github-actions[bot]@users.noreply.github.com" + git add src/hyperview/server/static + git commit -m "chore: export frontend static assets" + git push + + - name: Verify static export updated when frontend changes + if: ${{ !(github.event_name == 'push' && steps.changes.outputs.needs_export == 'true') }} + run: | + set -euo pipefail + + frontend_changed="${{ steps.changes.outputs.frontend_changed }}" + static_changed="${{ steps.changes.outputs.static_changed }}" + if [[ "$frontend_changed" == "true" && "$static_changed" != "true" ]]; then echo "" echo "ERROR: frontend/ changed but src/hyperview/server/static/ was not updated." From ba5ddf93949e680ee66e74439e8992bc977b9a36 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Sat, 31 Jan 2026 15:38:21 +0100 Subject: [PATCH 49/75] ci: use git-auto-commit-action, upgrade Next.js to 16.1.6 - Use stefanzweifel/git-auto-commit-action@v7 for robust auto-commit - Add [skip ci] to auto-commit message to prevent loops - Upgrade Next.js from 16.0.7 to 16.1.6 - Simplify workflow conditions --- .github/workflows/require_frontend_export.yml | 58 +++++-------- frontend/next-env.d.ts | 2 +- frontend/package-lock.json | 86 +++++++++---------- frontend/package.json | 3 +- pyproject.toml | 1 + 5 files changed, 68 insertions(+), 82 deletions(-) diff --git a/.github/workflows/require_frontend_export.yml b/.github/workflows/require_frontend_export.yml index 97675fe..f97688d 100644 --- a/.github/workflows/require_frontend_export.yml +++ b/.github/workflows/require_frontend_export.yml @@ -16,6 +16,7 @@ on: jobs: require-frontend-export: runs-on: ubuntu-latest + # Skip if the push was made by github-actions to avoid loops if: github.actor != 'github-actions[bot]' permissions: contents: write @@ -42,6 +43,7 @@ jobs: # Handle initial push (no previous commit) if [[ "$base_sha" == "0000000000000000000000000000000000000000" ]]; then echo "Initial push - skipping check" + echo "needs_export=false" >> "$GITHUB_OUTPUT" exit 0 fi fi @@ -74,47 +76,31 @@ jobs: } >> "$GITHUB_OUTPUT" - name: Set up Node - if: ${{ github.event_name == 'push' && steps.changes.outputs.needs_export == 'true' }} + if: github.event_name == 'push' && steps.changes.outputs.needs_export == 'true' uses: actions/setup-node@v4 with: node-version: 20 - name: Export frontend - if: ${{ github.event_name == 'push' && steps.changes.outputs.needs_export == 'true' }} - run: | - bash scripts/export_frontend.sh - - - name: Commit exported static assets - if: ${{ github.event_name == 'push' && steps.changes.outputs.needs_export == 'true' }} - run: | - set -euo pipefail + if: github.event_name == 'push' && steps.changes.outputs.needs_export == 'true' + run: bash scripts/export_frontend.sh - if [[ -z "$(git status --porcelain)" ]]; then - echo "No export changes to commit." - exit 0 - fi - - git status --short - git config user.name "github-actions[bot]" - git config user.email "github-actions[bot]@users.noreply.github.com" - git add src/hyperview/server/static - git commit -m "chore: export frontend static assets" - git push + - name: Commit and push exported static assets + if: github.event_name == 'push' && steps.changes.outputs.needs_export == 'true' + uses: stefanzweifel/git-auto-commit-action@v7 + with: + commit_message: "chore: export frontend static assets [skip ci]" + file_pattern: "src/hyperview/server/static/**" - - name: Verify static export updated when frontend changes - if: ${{ !(github.event_name == 'push' && steps.changes.outputs.needs_export == 'true') }} + - name: Fail PR if frontend changed without static export + if: github.event_name == 'pull_request' && steps.changes.outputs.needs_export == 'true' run: | - set -euo pipefail - - frontend_changed="${{ steps.changes.outputs.frontend_changed }}" - static_changed="${{ steps.changes.outputs.static_changed }}" - - if [[ "$frontend_changed" == "true" && "$static_changed" != "true" ]]; then - echo "" - echo "ERROR: frontend/ changed but src/hyperview/server/static/ was not updated." - echo "Run: bash scripts/export_frontend.sh" - echo "Then commit the updated src/hyperview/server/static/ output." - exit 1 - fi - - echo "OK: export requirements satisfied." + echo "" + echo "ERROR: frontend/ changed but src/hyperview/server/static/ was not updated." + echo "" + echo "Run locally:" + echo " bash scripts/export_frontend.sh" + echo " git add src/hyperview/server/static" + echo " git commit -m 'chore: export frontend static assets'" + echo "" + exit 1 diff --git a/frontend/next-env.d.ts b/frontend/next-env.d.ts index c4b7818..9edff1c 100644 --- a/frontend/next-env.d.ts +++ b/frontend/next-env.d.ts @@ -1,6 +1,6 @@ /// /// -import "./.next/dev/types/routes.d.ts"; +import "./.next/types/routes.d.ts"; // NOTE: This file should not be edited // see https://nextjs.org/docs/app/api-reference/config/typescript for more information. diff --git a/frontend/package-lock.json b/frontend/package-lock.json index c193d55..451ac55 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -1,12 +1,12 @@ { "name": "hyperview-frontend", - "version": "0.1.0", + "version": "0.1.1", "lockfileVersion": 3, "requires": true, "packages": { "": { "name": "hyperview-frontend", - "version": "0.1.0", + "version": "0.1.1", "dependencies": { "@radix-ui/react-collapsible": "^1.1.12", "@radix-ui/react-dialog": "^1.1.15", @@ -27,7 +27,7 @@ "hyper-scatter": "^0.1.0", "justified-layout": "^4.1.0", "lucide-react": "^0.562.0", - "next": "^16.0.7", + "next": "^16.1.6", "react": "18.3.1", "react-dom": "18.3.1", "tailwind-merge": "^3.4.0", @@ -864,9 +864,9 @@ } }, "node_modules/@next/env": { - "version": "16.0.7", - "resolved": "https://registry.npmjs.org/@next/env/-/env-16.0.7.tgz", - "integrity": "sha512-gpaNgUh5nftFKRkRQGnVi5dpcYSKGcZZkQffZ172OrG/XkrnS7UBTQ648YY+8ME92cC4IojpI2LqTC8sTDhAaw==", + "version": "16.1.6", + "resolved": "https://registry.npmjs.org/@next/env/-/env-16.1.6.tgz", + "integrity": "sha512-N1ySLuZjnAtN3kFnwhAwPvZah8RJxKasD7x1f8shFqhncnWZn4JMfg37diLNuoHsLAlrDfM3g4mawVdtAG8XLQ==", "license": "MIT" }, "node_modules/@next/eslint-plugin-next": { @@ -880,9 +880,9 @@ } }, "node_modules/@next/swc-darwin-arm64": { - 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"version": "16.0.7", - "resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-gnu/-/swc-linux-arm64-gnu-16.0.7.tgz", - "integrity": "sha512-mloD5WcPIeIeeZqAIP5c2kdaTa6StwP4/2EGy1mUw8HiexSHGK/jcM7lFuS3u3i2zn+xH9+wXJs6njO7VrAqww==", + "version": "16.1.6", + "resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-gnu/-/swc-linux-arm64-gnu-16.1.6.tgz", + "integrity": "sha512-OJYkCd5pj/QloBvoEcJ2XiMnlJkRv9idWA/j0ugSuA34gMT6f5b7vOiCQHVRpvStoZUknhl6/UxOXL4OwtdaBw==", "cpu": [ "arm64" ], @@ -928,9 +928,9 @@ } }, "node_modules/@next/swc-linux-arm64-musl": { - "version": "16.0.7", - "resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-musl/-/swc-linux-arm64-musl-16.0.7.tgz", - "integrity": "sha512-+ksWNrZrthisXuo9gd1XnjHRowCbMtl/YgMpbRvFeDEqEBd523YHPWpBuDjomod88U8Xliw5DHhekBC3EOOd9g==", + "version": "16.1.6", + "resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-musl/-/swc-linux-arm64-musl-16.1.6.tgz", + "integrity": "sha512-S4J2v+8tT3NIO9u2q+S0G5KdvNDjXfAv06OhfOzNDaBn5rw84DGXWndOEB7d5/x852A20sW1M56vhC/tRVbccQ==", "cpu": [ "arm64" ], @@ -944,9 +944,9 @@ } }, "node_modules/@next/swc-linux-x64-gnu": { - "version": "16.0.7", - "resolved": "https://registry.npmjs.org/@next/swc-linux-x64-gnu/-/swc-linux-x64-gnu-16.0.7.tgz", - "integrity": "sha512-4WtJU5cRDxpEE44Ana2Xro1284hnyVpBb62lIpU5k85D8xXxatT+rXxBgPkc7C1XwkZMWpK5rXLXTh9PFipWsA==", + "version": "16.1.6", + "resolved": "https://registry.npmjs.org/@next/swc-linux-x64-gnu/-/swc-linux-x64-gnu-16.1.6.tgz", + "integrity": "sha512-2eEBDkFlMMNQnkTyPBhQOAyn2qMxyG2eE7GPH2WIDGEpEILcBPI/jdSv4t6xupSP+ot/jkfrCShLAa7+ZUPcJQ==", "cpu": [ "x64" ], @@ -960,9 +960,9 @@ } }, "node_modules/@next/swc-linux-x64-musl": { - "version": "16.0.7", - "resolved": "https://registry.npmjs.org/@next/swc-linux-x64-musl/-/swc-linux-x64-musl-16.0.7.tgz", - "integrity": "sha512-HYlhqIP6kBPXalW2dbMTSuB4+8fe+j9juyxwfMwCe9kQPPeiyFn7NMjNfoFOfJ2eXkeQsoUGXg+O2SE3m4Qg2w==", + "version": "16.1.6", + "resolved": "https://registry.npmjs.org/@next/swc-linux-x64-musl/-/swc-linux-x64-musl-16.1.6.tgz", + "integrity": "sha512-oicJwRlyOoZXVlxmIMaTq7f8pN9QNbdes0q2FXfRsPhfCi8n8JmOZJm5oo1pwDaFbnnD421rVU409M3evFbIqg==", "cpu": [ "x64" ], @@ -976,9 +976,9 @@ } }, "node_modules/@next/swc-win32-arm64-msvc": { - "version": "16.0.7", - "resolved": "https://registry.npmjs.org/@next/swc-win32-arm64-msvc/-/swc-win32-arm64-msvc-16.0.7.tgz", - "integrity": "sha512-EviG+43iOoBRZg9deGauXExjRphhuYmIOJ12b9sAPy0eQ6iwcPxfED2asb/s2/yiLYOdm37kPaiZu8uXSYPs0Q==", + "version": "16.1.6", + "resolved": "https://registry.npmjs.org/@next/swc-win32-arm64-msvc/-/swc-win32-arm64-msvc-16.1.6.tgz", + "integrity": "sha512-gQmm8izDTPgs+DCWH22kcDmuUp7NyiJgEl18bcr8irXA5N2m2O+JQIr6f3ct42GOs9c0h8QF3L5SzIxcYAAXXw==", "cpu": [ "arm64" ], @@ -992,9 +992,9 @@ } }, "node_modules/@next/swc-win32-x64-msvc": { - "version": "16.0.7", - "resolved": "https://registry.npmjs.org/@next/swc-win32-x64-msvc/-/swc-win32-x64-msvc-16.0.7.tgz", - "integrity": "sha512-gniPjy55zp5Eg0896qSrf3yB1dw4F/3s8VK1ephdsZZ129j2n6e1WqCbE2YgcKhW9hPB9TVZENugquWJD5x0ug==", + "version": "16.1.6", + "resolved": "https://registry.npmjs.org/@next/swc-win32-x64-msvc/-/swc-win32-x64-msvc-16.1.6.tgz", + "integrity": "sha512-NRfO39AIrzBnixKbjuo2YiYhB6o9d8v/ymU9m/Xk8cyVk+k7XylniXkHwjs4s70wedVffc6bQNbufk5v0xEm0A==", "cpu": [ "x64" ], @@ -3062,7 +3062,6 @@ "version": "2.9.2", "resolved": "https://registry.npmjs.org/baseline-browser-mapping/-/baseline-browser-mapping-2.9.2.tgz", "integrity": "sha512-PxSsosKQjI38iXkmb3d0Y32efqyA0uW4s41u4IVBsLlWLhCiYNpH/AfNOVWRqCQBlD8TFJTz6OUWNd4DFJCnmw==", - "dev": true, "license": "Apache-2.0", "bin": { "baseline-browser-mapping": "dist/cli.js" @@ -5481,13 +5480,14 @@ "license": "MIT" }, "node_modules/next": { - "version": "16.0.7", - "resolved": "https://registry.npmjs.org/next/-/next-16.0.7.tgz", - "integrity": "sha512-3mBRJyPxT4LOxAJI6IsXeFtKfiJUbjCLgvXO02fV8Wy/lIhPvP94Fe7dGhUgHXcQy4sSuYwQNcOLhIfOm0rL0A==", + "version": "16.1.6", + "resolved": "https://registry.npmjs.org/next/-/next-16.1.6.tgz", + "integrity": "sha512-hkyRkcu5x/41KoqnROkfTm2pZVbKxvbZRuNvKXLRXxs3VfyO0WhY50TQS40EuKO9SW3rBj/sF3WbVwDACeMZyw==", "license": "MIT", "dependencies": { - "@next/env": "16.0.7", + "@next/env": "16.1.6", "@swc/helpers": "0.5.15", + "baseline-browser-mapping": "^2.8.3", "caniuse-lite": "^1.0.30001579", "postcss": "8.4.31", "styled-jsx": "5.1.6" @@ -5499,14 +5499,14 @@ "node": ">=20.9.0" }, "optionalDependencies": { - "@next/swc-darwin-arm64": "16.0.7", - "@next/swc-darwin-x64": "16.0.7", - "@next/swc-linux-arm64-gnu": "16.0.7", - "@next/swc-linux-arm64-musl": "16.0.7", - "@next/swc-linux-x64-gnu": "16.0.7", - "@next/swc-linux-x64-musl": "16.0.7", - "@next/swc-win32-arm64-msvc": "16.0.7", - "@next/swc-win32-x64-msvc": "16.0.7", + "@next/swc-darwin-arm64": "16.1.6", + "@next/swc-darwin-x64": "16.1.6", + "@next/swc-linux-arm64-gnu": "16.1.6", + "@next/swc-linux-arm64-musl": "16.1.6", + "@next/swc-linux-x64-gnu": "16.1.6", + "@next/swc-linux-x64-musl": "16.1.6", + "@next/swc-win32-arm64-msvc": "16.1.6", + "@next/swc-win32-x64-msvc": "16.1.6", "sharp": "^0.34.4" }, "peerDependencies": { diff --git a/frontend/package.json b/frontend/package.json index 2418e05..d257e0f 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -29,7 +29,7 @@ "hyper-scatter": "^0.1.0", "justified-layout": "^4.1.0", "lucide-react": "^0.562.0", - "next": "^16.0.7", + "next": "^16.1.6", "react": "18.3.1", "react-dom": "18.3.1", "tailwind-merge": "^3.4.0", @@ -49,4 +49,3 @@ "typescript": "^5.6.3" } } - diff --git a/pyproject.toml b/pyproject.toml index c672ba7..f1b22f4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -57,6 +57,7 @@ hyperview = "hyperview.cli:main" Homepage = "https://github.com/Hyper3Labs/HyperView" Documentation = "https://github.com/Hyper3Labs/HyperView#readme" Repository = "https://github.com/Hyper3Labs/HyperView" +Issues = "https://github.com/Hyper3Labs/HyperView/issues" [build-system] requires = ["hatchling"] From 7d90321e5a17d15a701d82000fcfb0694614e836 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Sat, 31 Jan 2026 17:38:07 +0100 Subject: [PATCH 50/75] Add PyPI release workflow with trusted publishing - Add .github/workflows/release.yml for automated releases - Build frontend in CI (remove committed static assets) - Use uv publish with OIDC trusted publishing - Smoke test wheel and sdist before publishing - Trigger on v* tags and workflow_dispatch --- .github/workflows/release.yml | 94 ++++++ .gitignore | 10 +- pyproject.toml | 12 +- src/hyperview/__init__.py | 16 +- src/hyperview/core/dataset.py | 4 +- src/hyperview/server/static/404.html | 1 - src/hyperview/server/static/404/index.html | 1 - .../server/static/__next.__PAGE__.txt | 9 - src/hyperview/server/static/__next._full.txt | 18 -- src/hyperview/server/static/__next._head.txt | 7 - src/hyperview/server/static/__next._index.txt | 5 - src/hyperview/server/static/__next._tree.txt | 3 - .../U-zHksGY4392lcZLjE49t/_buildManifest.js | 15 - .../_clientMiddlewareManifest.json | 1 - .../U-zHksGY4392lcZLjE49t/_ssgManifest.js | 1 - .../_next/static/chunks/10c3a2ade180023c.js | 1 - .../_next/static/chunks/2eaa7c8c8b6f5b3b.js | 13 - .../_next/static/chunks/3686d57edf1023fb.js | 301 ------------------ .../_next/static/chunks/a6dad97d9634a72d.js | 1 - .../_next/static/chunks/b3cfdd883c96563c.js | 1 - .../_next/static/chunks/b96ffc94b0d8fbba.js | 2 - .../_next/static/chunks/cb115cad1deddecd.js | 1 - .../_next/static/chunks/e7ae0fd4a43b4ae1.css | 3 - .../_next/static/chunks/f89d541331847b49.js | 4 - .../chunks/turbopack-904be3b11442f4b8.js | 3 - .../media/1bffadaabf893a1e-s.7cd81963.woff2 | Bin 85272 -> 0 bytes .../media/2bbe8d2671613f1f-s.76dcb0b2.woff2 | Bin 10280 -> 0 bytes .../media/2c55a0e60120577a-s.2a48534a.woff2 | Bin 25844 -> 0 bytes .../media/5476f68d60460930-s.c995e352.woff2 | Bin 19044 -> 0 bytes .../media/83afe278b6a6bb3c-s.p.3a6ba036.woff2 | Bin 48432 -> 0 bytes .../media/9c72aa0f40e4eef8-s.18a48cbc.woff2 | Bin 18744 -> 0 bytes .../media/ad66f9afd8947f86-s.7a40eb73.woff2 | Bin 11272 -> 0 bytes .../server/static/_not-found/__next._full.txt | 13 - .../server/static/_not-found/__next._head.txt | 7 - .../static/_not-found/__next._index.txt | 5 - .../_not-found/__next._not-found.__PAGE__.txt | 5 - .../static/_not-found/__next._not-found.txt | 4 - .../server/static/_not-found/__next._tree.txt | 2 - .../server/static/_not-found/index.html | 1 - .../server/static/_not-found/index.txt | 13 - src/hyperview/server/static/index.html | 1 - src/hyperview/server/static/index.txt | 18 -- 42 files changed, 117 insertions(+), 479 deletions(-) create mode 100644 .github/workflows/release.yml delete mode 100644 src/hyperview/server/static/404.html delete mode 100644 src/hyperview/server/static/404/index.html delete mode 100644 src/hyperview/server/static/__next.__PAGE__.txt delete mode 100644 src/hyperview/server/static/__next._full.txt delete mode 100644 src/hyperview/server/static/__next._head.txt delete mode 100644 src/hyperview/server/static/__next._index.txt delete mode 100644 src/hyperview/server/static/__next._tree.txt delete mode 100644 src/hyperview/server/static/_next/static/U-zHksGY4392lcZLjE49t/_buildManifest.js delete mode 100644 src/hyperview/server/static/_next/static/U-zHksGY4392lcZLjE49t/_clientMiddlewareManifest.json delete mode 100644 src/hyperview/server/static/_next/static/U-zHksGY4392lcZLjE49t/_ssgManifest.js delete mode 100644 src/hyperview/server/static/_next/static/chunks/10c3a2ade180023c.js delete mode 100644 src/hyperview/server/static/_next/static/chunks/2eaa7c8c8b6f5b3b.js delete mode 100644 src/hyperview/server/static/_next/static/chunks/3686d57edf1023fb.js delete mode 100644 src/hyperview/server/static/_next/static/chunks/a6dad97d9634a72d.js delete mode 100644 src/hyperview/server/static/_next/static/chunks/b3cfdd883c96563c.js delete mode 100644 src/hyperview/server/static/_next/static/chunks/b96ffc94b0d8fbba.js delete mode 100644 src/hyperview/server/static/_next/static/chunks/cb115cad1deddecd.js delete mode 100644 src/hyperview/server/static/_next/static/chunks/e7ae0fd4a43b4ae1.css delete mode 100644 src/hyperview/server/static/_next/static/chunks/f89d541331847b49.js delete mode 100644 src/hyperview/server/static/_next/static/chunks/turbopack-904be3b11442f4b8.js delete mode 100644 src/hyperview/server/static/_next/static/media/1bffadaabf893a1e-s.7cd81963.woff2 delete mode 100644 src/hyperview/server/static/_next/static/media/2bbe8d2671613f1f-s.76dcb0b2.woff2 delete mode 100644 src/hyperview/server/static/_next/static/media/2c55a0e60120577a-s.2a48534a.woff2 delete mode 100644 src/hyperview/server/static/_next/static/media/5476f68d60460930-s.c995e352.woff2 delete mode 100644 src/hyperview/server/static/_next/static/media/83afe278b6a6bb3c-s.p.3a6ba036.woff2 delete mode 100644 src/hyperview/server/static/_next/static/media/9c72aa0f40e4eef8-s.18a48cbc.woff2 delete mode 100644 src/hyperview/server/static/_next/static/media/ad66f9afd8947f86-s.7a40eb73.woff2 delete mode 100644 src/hyperview/server/static/_not-found/__next._full.txt delete mode 100644 src/hyperview/server/static/_not-found/__next._head.txt delete mode 100644 src/hyperview/server/static/_not-found/__next._index.txt delete mode 100644 src/hyperview/server/static/_not-found/__next._not-found.__PAGE__.txt delete mode 100644 src/hyperview/server/static/_not-found/__next._not-found.txt delete mode 100644 src/hyperview/server/static/_not-found/__next._tree.txt delete mode 100644 src/hyperview/server/static/_not-found/index.html delete mode 100644 src/hyperview/server/static/_not-found/index.txt delete mode 100644 src/hyperview/server/static/index.html delete mode 100644 src/hyperview/server/static/index.txt diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml new file mode 100644 index 0000000..a63397c --- /dev/null +++ b/.github/workflows/release.yml @@ -0,0 +1,94 @@ +name: Release + +on: + push: + tags: + - "v*" # Publish on any tag starting with v, e.g., v0.1.0 + workflow_dispatch: + +jobs: + build: + name: Build distributions + runs-on: ubuntu-latest + permissions: + contents: read + steps: + - name: Checkout + uses: actions/checkout@v6 + with: + fetch-depth: 0 # Required for hatch-vcs + + - name: Set up Python + uses: actions/setup-python@v6 + with: + python-version: "3.12" + + - name: Install uv + uses: astral-sh/setup-uv@v7 + with: + enable-cache: true + + # Build frontend + - name: Set up Node.js + uses: actions/setup-node@v4 + with: + node-version: 20 + cache: npm + cache-dependency-path: frontend/package-lock.json + + - name: Build frontend + run: | + cd frontend + npm ci + npm run build + rm -rf ../src/hyperview/server/static + mkdir -p ../src/hyperview/server/static + cp -r out/* ../src/hyperview/server/static/ + + # Build Python package (includes frontend assets) + - name: Build + run: uv build --no-sources + + # Smoke test: verify the built package can be imported + - name: Smoke test (wheel) + run: | + uv run --isolated --no-project --with dist/*.whl \ + python -c "import hyperview; print(f'hyperview {hyperview.__version__}')" + + - name: Smoke test (sdist) + run: | + uv run --isolated --no-project --with dist/*.tar.gz \ + python -c "import hyperview; print(f'hyperview {hyperview.__version__}')" + + - name: Upload dist artifacts + uses: actions/upload-artifact@v5 + with: + name: python-package-distributions + path: dist/ + if-no-files-found: error + + pypi: + name: Publish to PyPI + needs: build + if: startsWith(github.ref, 'refs/tags/v') + runs-on: ubuntu-latest + environment: + name: pypi + url: https://pypi.org/p/hyperview + permissions: + id-token: write + contents: read + steps: + - name: Download dist artifacts + uses: actions/download-artifact@v6 + with: + name: python-package-distributions + path: dist/ + + - name: Install uv + uses: astral-sh/setup-uv@v7 + with: + enable-cache: true + + - name: Publish to PyPI + run: uv publish diff --git a/.gitignore b/.gitignore index 5b94b3c..20670e4 100644 --- a/.gitignore +++ b/.gitignore @@ -31,9 +31,8 @@ frontend/node_modules/ frontend/.next/ frontend/out/ -# Bundled frontend in Python package (built with scripts/export_frontend.sh) -# Not ignored - needed for pip install from git / sdist -# src/hyperview/server/static/ +# Bundled frontend in Python package (built in CI during release) +src/hyperview/server/static/ # Python package build dist/ @@ -71,4 +70,7 @@ TESTS.md AGENTS.md **/AGENTS.md .github/agents/ -.specstory/ \ No newline at end of file +.specstory/ + +# Generated version file (hatch-vcs) +src/hyperview/_version.py diff --git a/pyproject.toml b/pyproject.toml index f1b22f4..58f80e7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,12 +1,12 @@ [project] name = "hyperview" -version = "0.1.0" +dynamic = ["version"] description = "Open-source dataset curation with hyperbolic embeddings visualization" readme = "README.md" license = { text = "MIT" } requires-python = ">=3.10" authors = [ - { name = "HyperView Team" } + { name = "hyper3labs" } ] keywords = ["embeddings", "visualization", "hyperbolic", "dataset", "curation", "machine-learning"] classifiers = [ @@ -60,12 +60,18 @@ Repository = "https://github.com/Hyper3Labs/HyperView" Issues = "https://github.com/Hyper3Labs/HyperView/issues" [build-system] -requires = ["hatchling"] +requires = ["hatchling", "hatch-vcs"] build-backend = "hatchling.build" [tool.hatch.metadata] allow-direct-references = true +[tool.hatch.version] +source = "vcs" + +[tool.hatch.build.hooks.vcs] +version-file = "src/hyperview/_version.py" + [tool.hatch.build.targets.wheel] packages = ["src/hyperview"] # Include frontend static assets (pre-built before packaging) diff --git a/src/hyperview/__init__.py b/src/hyperview/__init__.py index f2b7743..d055599 100644 --- a/src/hyperview/__init__.py +++ b/src/hyperview/__init__.py @@ -1,18 +1,14 @@ """HyperView - Open-source dataset curation with hyperbolic embeddings visualization.""" -from hyperview.api import Dataset, launch -from hyperview.embeddings.engine import ( - EmbeddingSpec, - get_provider_info, - list_embedding_providers, -) +from . import _version as _version +from . import api as _api + +Dataset = _api.Dataset +launch = _api.launch +__version__ = _version.__version__ -__version__ = "0.1.0" __all__ = [ "Dataset", - "EmbeddingSpec", - "get_provider_info", "launch", - "list_embedding_providers", "__version__", ] diff --git a/src/hyperview/core/dataset.py b/src/hyperview/core/dataset.py index 7704234..74429c7 100644 --- a/src/hyperview/core/dataset.py +++ b/src/hyperview/core/dataset.py @@ -369,7 +369,7 @@ def compute_embeddings( hyper-models name (e.g. 'hycoclip-vit-s') for hyperbolic embeddings. provider: Explicit provider identifier. If not specified, auto-detected: 'hyper-models' if model matches a hyper-models name, else 'embed-anything'. - Available providers: `hyperview.list_embedding_providers()`. + Available providers: `hyperview.embeddings.list_embedding_providers()`. checkpoint: Checkpoint path/URL (hf://... or local path) for weight-only models. batch_size: Batch size for processing. show_progress: Whether to show progress bar. @@ -384,7 +384,7 @@ def compute_embeddings( if not model: raise ValueError( "model is required. Examples: 'openai/clip-vit-base-patch32' (CLIP), " - "'hycoclip-vit-s' (hyperbolic). See hyperview.list_embedding_providers()." + "'hycoclip-vit-s' (hyperbolic). 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u=d.map(e=>this.viewItems[e]),c=d.map(e=>i[e]),p=h.map(e=>this.viewItems[e]),m=h.map(e=>i[e]),f=d.reduce((e,t)=>e+this.viewItems[t].minimumSize-i[t],0),g=d.reduce((e,t)=>e+this.viewItems[t].maximumSize-i[t],0),v=0===h.length?1/0:h.reduce((e,t)=>e+i[t]-this.viewItems[t].minimumSize,0),w=Math.max(f,0===h.length?-1/0:h.reduce((e,t)=>e+i[t]-this.viewItems[t].maximumSize,0)),b=Math.min(v,g),y=!1;if(a){let e=this.viewItems[a.index],i=t>=a.limitDelta;y=i!==e.visible,e.setVisible(i,a.size)}if(!y&&l){let e=this.viewItems[l.index],i=t{let i=void 0===e.visible||e.visible?e.size:{type:"invisible",cachedVisibleSize:e.size},n=e.view;this.addView(n,i,t,!0)}),this._contentSize=this.viewItems.reduce((e,t)=>e+t.size,0),this.saveProportions())}style(e){(null==e?void 0:e.separatorBorder)==="transparent"?(tq(this.element,"dv-separator-border"),this.element.style.removeProperty("--dv-separator-border")):(tY(this.element,"dv-separator-border"),(null==e?void 0:e.separatorBorder)&&this.element.style.setProperty("--dv-separator-border",e.separatorBorder))}isViewVisible(e){if(e<0||e>=this.viewItems.length)throw Error("Index out of bounds");return this.viewItems[e].visible}setViewVisible(e,t){if(e<0||e>=this.viewItems.length)throw Error("Index out of bounds");let i=this.viewItems[e];i.setVisible(t,i.size),this.distributeEmptySpace(e),this.layoutViews(),this.saveProportions()}getViewSize(e){return e<0||e>=this.viewItems.length?-1:this.viewItems[e].size}resizeView(e,t){if(e<0||e>=this.viewItems.length)return;let i=io(this.viewItems.length).filter(t=>t!==e),n=[...i.filter(e=>this.viewItems[e].priority===L.Low),e],o=i.filter(e=>this.viewItems[e].priority===L.High),r=this.viewItems[e];t=it(t=Math.round(t),r.minimumSize,Math.min(r.maximumSize,this._size)),r.size=t,this.relayout(n,o)}getViews(){return this.viewItems.map(e=>e.view)}onDidChange(e,t){let i=this.viewItems.indexOf(e);if(i<0||i>=this.viewItems.length)return;t=it(t="number"==typeof t?t:e.size,e.minimumSize,e.maximumSize),e.size=t;let n=io(this.viewItems.length).filter(e=>e!==i),o=[...n.filter(e=>this.viewItems[e].priority===L.Low),i],r=n.filter(e=>this.viewItems[e].priority===L.High);this.relayout([...o,i],r)}addView(e,t={type:"distribute"},i=this.viewItems.length,n){let o,r=document.createElement("div");r.className="dv-view",r.appendChild(e.element),o="number"==typeof t?t:"split"===t.type?this.getViewSize(t.index)/2:"invisible"===t.type?{cachedVisibleSize:t.cachedVisibleSize}:e.minimumSize;let s=e.onDidChange(e=>this.onDidChange(a,e.size)),a=new ir(r,e,o,{dispose:()=>{s.dispose(),this.viewContainer.removeChild(r)}});if(i===this.viewItems.length?this.viewContainer.appendChild(r):this.viewContainer.insertBefore(r,this.viewContainer.children.item(i)),this.viewItems.splice(i,0,a),this.viewItems.length>1){let e=document.createElement("div");e.className="dv-sash";let t=t=>{let i,n;for(let e of this.viewItems)e.enabled=!1;let o=t3(),r=this._orientation===I.HORIZONTAL?t.clientX:t.clientY,s=function(e,t){for(let i=0;it.container===e),a=this.viewItems.map(e=>e.size),l=io(s,-1),d=io(s+1,this.viewItems.length),h=l.reduce((e,t)=>e+(this.viewItems[t].minimumSize-a[t]),0),u=l.reduce((e,t)=>e+(this.viewItems[t].viewMaximumSize-a[t]),0),c=0===d.length?1/0:d.reduce((e,t)=>e+(a[t]-this.viewItems[t].minimumSize),0),p=Math.max(h,0===d.length?-1/0:d.reduce((e,t)=>e+(a[t]-this.viewItems[t].viewMaximumSize),0)),m=Math.min(c,u),f=this.findFirstSnapIndex(l),g=this.findFirstSnapIndex(d);if("number"==typeof f){let e=this.viewItems[f],t=Math.floor(e.viewMinimumSize/2);i={index:f,limitDelta:e.visible?p-t:p+t,size:e.size}}if("number"==typeof g){let e=this.viewItems[g],t=Math.floor(e.viewMinimumSize/2);n={index:g,limitDelta:e.visible?m+t:m-t,size:e.size}}let v=e=>{let t=this._orientation===I.HORIZONTAL?e.clientX:e.clientY;this.resize(s,t-r,a,void 0,void 0,p,m,i,n),this.distributeEmptySpace(),this.layoutViews()},w=()=>{for(let e of this.viewItems)e.enabled=!0;o.release(),this.saveProportions(),document.removeEventListener("pointermove",v),document.removeEventListener("pointerup",w),document.removeEventListener("pointercancel",w),document.removeEventListener("contextmenu",w),this._onDidSashEnd.fire(void 0)};document.addEventListener("pointermove",v),document.addEventListener("pointerup",w),document.addEventListener("pointercancel",w),document.addEventListener("contextmenu",w)};e.addEventListener("pointerdown",t),this.sashContainer.appendChild(e),this.sashes.push({container:e,disposable:()=>{e.removeEventListener("pointerdown",t),this.sashContainer.removeChild(e)}})}n||this.relayout([i]),n||"number"==typeof t||"distribute"!==t.type||this.distributeViewSizes(),this._onDidAddView.fire(e)}distributeViewSizes(){let e=[],t=0;for(let i of this.viewItems)i.maximumSize-i.minimumSize>0&&(e.push(i),t+=i.size);let i=Math.floor(t/e.length);for(let t of e)t.size=it(i,t.minimumSize,t.maximumSize);let n=io(this.viewItems.length),o=n.filter(e=>this.viewItems[e].priority===L.Low),r=n.filter(e=>this.viewItems[e].priority===L.High);this.relayout(o,r)}removeView(e,t,i=!1){let n=this.viewItems.splice(e,1)[0];if(n.dispose(),this.viewItems.length>=1){let t=Math.max(e-1,0);this.sashes.splice(t,1)[0].disposable()}return i||this.relayout(),t&&"distribute"===t.type&&this.distributeViewSizes(),this._onDidRemoveView.fire(n.view),n.view}getViewCachedVisibleSize(e){if(e<0||e>=this.viewItems.length)throw Error("Index out of bounds");return this.viewItems[e].cachedVisibleSize}moveView(e,t){let i=this.getViewCachedVisibleSize(e),n=void 0===i?this.getViewSize(e):T.Invisible(i),o=this.removeView(e,void 0,!0);this.addView(o,n,t)}layout(e,t){let i=Math.max(this.size,this._contentSize);if(this.size=e,this.orthogonalSize=t,this.proportions){let t=0;for(let i=0;i0&&(n.size=it(Math.round(o*e/t),n.minimumSize,n.maximumSize))}}else{let t=io(this.viewItems.length),n=t.filter(e=>this.viewItems[e].priority===L.Low),o=t.filter(e=>this.viewItems[e].priority===L.High);this.resize(this.viewItems.length-1,e-i,void 0,n,o)}this.distributeEmptySpace(),this.layoutViews()}relayout(e,t){let i=this.viewItems.reduce((e,t)=>e+t.size,0);this.resize(this.viewItems.length-1,this._size-i,void 0,e,t),this.distributeEmptySpace(),this.layoutViews(),this.saveProportions()}distributeEmptySpace(e){let t=this.viewItems.reduce((e,t)=>e+t.size,0),i=this.size-t,n=io(this.viewItems.length-1,-1),o=n.filter(e=>this.viewItems[e].priority===L.Low);for(let e of n.filter(e=>this.viewItems[e].priority===L.High))t8(n,e);for(let e of o)t7(n,e);"number"==typeof e&&t7(n,e);for(let e=0;0!==i&&e0&&(this._proportions=this.viewItems.map(e=>e.visible?e.size/this._contentSize:void 0))}layoutViews(){if(this._contentSize=this.viewItems.reduce((e,t)=>e+t.size,0),this.updateSashEnablement(),0===this.viewItems.length)return;let e=this.viewItems.filter(e=>e.visible),t=Math.max(0,e.length-1),i=this.margin*t/Math.max(1,e.length),n=0,o=[],r=this.viewItems.reduce((e,t,i)=>{let n=+!!t.visible;return 0===i?e.push(n):e.push(e[i-1]+n),e},[]);this.viewItems.forEach((e,s)=>{n+=this.viewItems[s].size,o.push(n);let a=e.visible?e.size-i:0,l=Math.max(0,r[s]-1),d=0===s||0===l?0:o[s-1]+l/t*i;if(s0)break;if(!e.visible&&e.snap)return t}}updateSashEnablement(){let e=!1,t=this.viewItems.map(t=>e=t.size-t.minimumSize>0||e);e=!1;let i=this.viewItems.map(t=>e=t.maximumSize-t.size>0||e),n=[...this.viewItems].reverse();e=!1;let o=n.map(t=>e=t.size-t.minimumSize>0||e).reverse();e=!1;let r=n.map(t=>e=t.maximumSize-t.size>0||e).reverse(),s=0;for(let e=0;e0||this.startSnappingEnabled)?this.updateSash(n,M.MINIMUM):h&&t[e]&&(s{let t=new tK(e.onDidChangeExpansionState(()=>{this.setupAnimation(),this._onDidChange.fire(void 0)}));this.paneItems.push({pane:e,disposable:{dispose:()=>{t.dispose()}}}),e.orthogonalSize=this.splitview.orthogonalSize}),this.addDisposables(this._onDidChange,this.splitview.onDidSashEnd(()=>{this._onDidChange.fire(void 0)}),this.splitview.onDidAddView(()=>{this._onDidChange.fire()}),this.splitview.onDidRemoveView(()=>{this._onDidChange.fire()}))}setViewVisible(e,t){this.splitview.setViewVisible(e,t)}addPane(e,t,i=this.splitview.length,n=!1){let o=e.onDidChangeExpansionState(()=>{this.setupAnimation(),this._onDidChange.fire(void 0)});this.paneItems.splice(i,0,{pane:e,disposable:{dispose:()=>{o.dispose()}}}),e.orthogonalSize=this.splitview.orthogonalSize,this.splitview.addView(e,t,i,n)}getViewSize(e){return this.splitview.getViewSize(e)}getPanes(){return this.splitview.getViews()}removePane(e,t={skipDispose:!1}){let i=this.paneItems.splice(e,1)[0];return this.splitview.removeView(e),t.skipDispose||(i.disposable.dispose(),i.pane.dispose()),i}moveView(e,t){if(e===t)return;let i=this.removePane(e,{skipDispose:!0});this.skipAnimation=!0;try{this.addPane(i.pane,i.pane.size,t,!1)}finally{this.skipAnimation=!1}}layout(e,t){this.splitview.layout(e,t)}setupAnimation(){this.skipAnimation||(this.animationTimer&&(clearTimeout(this.animationTimer),this.animationTimer=void 0),tY(this.element,"dv-animated"),this.animationTimer=setTimeout(()=>{this.animationTimer=void 0,tq(this.element,"dv-animated")},200))}dispose(){super.dispose(),this.animationTimer&&(clearTimeout(this.animationTimer),this.animationTimer=void 0),this.paneItems.forEach(e=>{e.disposable.dispose(),e.pane.dispose()}),this.paneItems=[],this.splitview.dispose(),this.element.remove()}}class id{get minimumWidth(){return this.view.minimumWidth}get maximumWidth(){return this.view.maximumWidth}get minimumHeight(){return this.view.minimumHeight}get maximumHeight(){return this.view.maximumHeight}get priority(){return this.view.priority}get snap(){return this.view.snap}get minimumSize(){return this.orientation===I.HORIZONTAL?this.minimumHeight:this.minimumWidth}get maximumSize(){return this.orientation===I.HORIZONTAL?this.maximumHeight:this.maximumWidth}get minimumOrthogonalSize(){return this.orientation===I.HORIZONTAL?this.minimumWidth:this.minimumHeight}get maximumOrthogonalSize(){return this.orientation===I.HORIZONTAL?this.maximumWidth:this.maximumHeight}get orthogonalSize(){return this._orthogonalSize}get size(){return this._size}get element(){return this.view.element}get width(){return this.orientation===I.HORIZONTAL?this.orthogonalSize:this.size}get height(){return this.orientation===I.HORIZONTAL?this.size:this.orthogonalSize}constructor(e,t,i,n=0){this.view=e,this.orientation=t,this._onDidChange=new tB,this.onDidChange=this._onDidChange.event,this._orthogonalSize=i,this._size=n,this._disposable=this.view.onDidChange(e=>{e?this._onDidChange.fire({size:this.orientation===I.VERTICAL?e.width:e.height,orthogonalSize:this.orientation===I.VERTICAL?e.height:e.width}):this._onDidChange.fire({})})}setVisible(e){this.view.setVisible&&this.view.setVisible(e)}layout(e,t){this._size=e,this._orthogonalSize=t,this.view.layout(this.width,this.height)}dispose(){this._onDidChange.dispose(),this._disposable.dispose()}}class ih extends tK{get width(){return this.orientation===I.HORIZONTAL?this.size:this.orthogonalSize}get height(){return this.orientation===I.HORIZONTAL?this.orthogonalSize:this.size}get minimumSize(){return 0===this.children.length?0:Math.max(...this.children.map((e,t)=>this.splitview.isViewVisible(t)?e.minimumOrthogonalSize:0))}get maximumSize(){return Math.min(...this.children.map((e,t)=>this.splitview.isViewVisible(t)?e.maximumOrthogonalSize:1/0))}get minimumOrthogonalSize(){return this.splitview.minimumSize}get maximumOrthogonalSize(){return this.splitview.maximumSize}get orthogonalSize(){return this._orthogonalSize}get size(){return this._size}get minimumWidth(){return this.orientation===I.HORIZONTAL?this.minimumOrthogonalSize:this.minimumSize}get minimumHeight(){return this.orientation===I.HORIZONTAL?this.minimumSize:this.minimumOrthogonalSize}get maximumWidth(){return this.orientation===I.HORIZONTAL?this.maximumOrthogonalSize:this.maximumSize}get maximumHeight(){return this.orientation===I.HORIZONTAL?this.maximumSize:this.maximumOrthogonalSize}get priority(){if(0===this.children.length)return L.Normal;let e=this.children.map(e=>void 0===e.priority?L.Normal:e.priority);return e.some(e=>e===L.High)?L.High:e.some(e=>e===L.Low)?L.Low:L.Normal}get disabled(){return this.splitview.disabled}set disabled(e){this.splitview.disabled=e}get margin(){return this.splitview.margin}set margin(e){this.splitview.margin=e,this.children.forEach(t=>{t instanceof ih&&(t.margin=e)})}constructor(e,t,i,n,o,r,s,a){if(super(),this.orientation=e,this.proportionalLayout=t,this.styles=i,this._childrenDisposable=O.NONE,this.children=[],this._onDidChange=new tB,this.onDidChange=this._onDidChange.event,this._onDidVisibilityChange=new tB,this.onDidVisibilityChange=this._onDidVisibilityChange.event,this._orthogonalSize=o,this._size=n,this.element=document.createElement("div"),this.element.className="dv-branch-node",a){const e={views:a.map(e=>({view:e.node,size:e.node.size,visible:!(e.node instanceof id)||void 0===e.visible||e.visible})),size:this.orthogonalSize};this.children=a.map(e=>e.node),this.splitview=new is(this.element,{orientation:this.orientation,descriptor:e,proportionalLayout:t,styles:i,margin:s})}else this.splitview=new is(this.element,{orientation:this.orientation,proportionalLayout:t,styles:i,margin:s}),this.splitview.layout(this.size,this.orthogonalSize);this.disabled=r,this.addDisposables(this._onDidChange,this._onDidVisibilityChange,this.splitview.onDidSashEnd(()=>{this._onDidChange.fire({})})),this.setupChildrenEvents()}setVisible(e){}isChildVisible(e){if(e<0||e>=this.children.length)throw Error("Invalid index");return this.splitview.isViewVisible(e)}setChildVisible(e,t){if(e<0||e>=this.children.length)throw Error("Invalid index");if(this.splitview.isViewVisible(e)===t)return;let i=0===this.splitview.contentSize;this.splitview.setViewVisible(e,t);let n=0===this.splitview.contentSize;(t&&i||!t&&n)&&this._onDidVisibilityChange.fire({visible:t})}moveChild(e,t){if(e===t)return;if(e<0||e>=this.children.length)throw Error("Invalid from index");e=this.children.length)throw Error("Invalid index");return this.splitview.getViewSize(e)}resizeChild(e,t){if(e<0||e>=this.children.length)throw Error("Invalid index");this.splitview.resizeView(e,t)}layout(e,t){this._size=t,this._orthogonalSize=e,this.splitview.layout(t,e)}addChild(e,t,i,n){if(i<0||i>this.children.length)throw Error("Invalid index");this.splitview.addView(e,t,i,n),this._addChild(e,i)}getChildCachedVisibleSize(e){if(e<0||e>=this.children.length)throw Error("Invalid index");return this.splitview.getViewCachedVisibleSize(e)}removeChild(e,t){if(e<0||e>=this.children.length)throw Error("Invalid index");return this.splitview.removeView(e,t),this._removeChild(e)}_addChild(e,t){this.children.splice(t,0,e),this.setupChildrenEvents()}_removeChild(e){let[t]=this.children.splice(e,1);return this.setupChildrenEvents(),t}setupChildrenEvents(){this._childrenDisposable.dispose(),this._childrenDisposable=new tK(N.any(...this.children.map(e=>e.onDidChange))(e=>{this._onDidChange.fire({size:e.orthogonalSize})}),...this.children.map((e,t)=>e instanceof ih?e.onDidVisibilityChange(({visible:e})=>{this.setChildVisible(t,e)}):O.NONE))}dispose(){this._childrenDisposable.dispose(),this.splitview.dispose(),this.children.forEach(e=>e.dispose()),super.dispose()}}function iu(e,t){if(e instanceof id)return e;if(e instanceof ih)return iu(e.children[t?e.children.length-1:0],t);throw Error("invalid node")}function ic(e,t,i){if(!(e instanceof ih))return new id(e.view,ig(e.orientation),i);{let n=new ih(ig(e.orientation),e.proportionalLayout,e.styles,t,i,e.disabled,e.margin),o=0;for(let r=e.children.length-1;r>=0;r--){let s=e.children[r],a=s instanceof ih?s.orthogonalSize:s.size,l=0===e.size?0:Math.round(t*a/e.size);o+=l,0===r&&(l+=t-o),n.addChild(ic(s,i,l),l,0,!0)}return n}}function ip(e){let t=e.parentElement;if(!t)throw Error("Invalid grid element");if(/\bdv-grid-view\b/.test(t.className))return[];let i=function(e){let t=e.parentElement;if(!t)throw Error("Invalid grid element");let i=t.firstElementChild,n=0;for(;i!==e&&i!==t.lastElementChild&&i;)i=i.nextElementSibling,n++;return n}(t);return[...ip(t.parentElement.parentElement.parentElement),i]}function im(e,t,i){var n,o;if(n=e,(t.length%2==0?ig(n):n)!==("top"===(o=i)||"bottom"===o?I.VERTICAL:I.HORIZONTAL))return[...t,+("right"===i||"bottom"===i)];{let[e,n]=t9(t),o=n;return("right"===i||"bottom"===i)&&(o+=1),[...e,o]}}let ig=e=>e===I.HORIZONTAL?I.VERTICAL:I.HORIZONTAL,iv=(e,t)=>{let i=t===I.VERTICAL?e.box.width:e.box.height;return e.children?{type:"branch",data:e.children.map(e=>iv(e,ig(t))),size:i}:"number"==typeof e.cachedVisibleSize?{type:"leaf",data:e.view.toJSON(),size:e.cachedVisibleSize,visible:!1}:{type:"leaf",data:e.view.toJSON(),size:i}};class iw{get length(){return this._root?this._root.children.length:0}get orientation(){return this.root.orientation}set orientation(e){if(this.root.orientation===e)return;let{size:t,orthogonalSize:i}=this.root;this.root=ic(this.root,i,t),this.root.layout(t,i)}get width(){return this.root.width}get height(){return this.root.height}get minimumWidth(){return this.root.minimumWidth}get minimumHeight(){return this.root.minimumHeight}get maximumWidth(){return this.root.maximumHeight}get maximumHeight(){return this.root.maximumHeight}get locked(){return this._locked}set locked(e){this._locked=e;let t=[this.root];for(;t.length>0;){let i=t.pop();i instanceof ih&&(i.disabled=e,t.push(...i.children))}}get margin(){return this._margin}set margin(e){this._margin=e,this.root.margin=e}maximizedView(){var e;return null==(e=this._maximizedNode)?void 0:e.leaf.view}hasMaximizedView(){return void 0!==this._maximizedNode}maximizeView(e){var t;let i=ip(e.element),[n,o]=this.getNode(i);if(!(o instanceof id)||(null==(t=this._maximizedNode)?void 0:t.leaf)===o)return;this.hasMaximizedView()&&this.exitMaximizedView(),iv(this.getView(),this.orientation);let r=[];!function e(t,i){for(let n=0;n=0;n--){let o=i.children[n];o instanceof id?e.includes(o)||i.setChildVisible(n,!0):t(o)}}(this.root);let t=this._maximizedNode.leaf;this._maximizedNode=void 0,this._onDidMaximizedNodeChange.fire({view:t.view,isMaximized:!1})}serialize(){let e,t=this.maximizedView();t&&(e=ip(t.element)),this.hasMaximizedView()&&this.exitMaximizedView();let i={root:iv(this.getView(),this.orientation),width:this.width,height:this.height,orientation:this.orientation};return e&&(i.maximizedNode={location:e}),t&&this.maximizeView(t),i}dispose(){this.disposable.dispose(),this._onDidChange.dispose(),this._onDidMaximizedNodeChange.dispose(),this._onDidViewVisibilityChange.dispose(),this.root.dispose(),this._maximizedNode=void 0,this.element.remove()}clear(){let e=this.root.orientation;this.root=new ih(e,this.proportionalLayout,this.styles,this.root.size,this.root.orthogonalSize,this.locked,this.margin)}deserialize(e,t){let i=e.orientation,n=i===I.VERTICAL?e.height:e.width;if(this._deserialize(e.root,i,t,n),this.layout(e.width,e.height),e.maximizedNode){let t=e.maximizedNode.location,[i,n]=this.getNode(t);if(!(n instanceof id))return;this.maximizeView(n.view)}}_deserialize(e,t,i,n){this.root=this._deserializeNode(e,t,i,n)}_deserializeNode(e,t,i,n){var o;let r;if("branch"===e.type){let o=e.data.map(n=>({node:this._deserializeNode(n,ig(t),i,e.size),visible:n.visible}));r=new ih(t,this.proportionalLayout,this.styles,e.size,n,this.locked,this.margin,o)}else{let s=i.fromJSON(e);"boolean"==typeof e.visible&&(null==(o=s.setVisible)||o.call(s,e.visible)),r=new id(s,t,n,e.size)}return r}get root(){return this._root}set root(e){let t=this._root;t&&(t.dispose(),this._maximizedNode=void 0,this.element.removeChild(t.element)),this._root=e,this.element.appendChild(this._root.element),this.disposable.value=this._root.onDidChange(e=>{this._onDidChange.fire(e)})}normalize(){if(!this._root||1!==this._root.children.length)return;let e=this.root,t=e.children[0];if(t instanceof id)return;e.element.remove();let i=e.removeChild(0);e.dispose(),i.dispose(),this._root=function e(t,i,n){if(!(t instanceof ih))return new id(t.view,t.orientation,n);{let o=new ih(t.orientation,t.proportionalLayout,t.styles,i,n,t.disabled,t.margin);for(let i=t.children.length-1;i>=0;i--){let n=t.children[i];o.addChild(e(n,n.size,n.orthogonalSize),n.size,0,!0)}return o}}(t,t.size,t.orthogonalSize),this.element.appendChild(this._root.element),this.disposable.value=this._root.onDidChange(e=>{this._onDidChange.fire(e)})}insertOrthogonalSplitviewAtRoot(){if(!this._root)return;let e=this.root;if(e.element.remove(),this._root=new ih(ig(e.orientation),this.proportionalLayout,this.styles,this.root.orthogonalSize,this.root.size,this.locked,this.margin),0===e.children.length);else if(1===e.children.length){let t=e.children[0];e.removeChild(0).dispose(),e.dispose(),this._root.addChild(ic(t,t.orthogonalSize,t.size),T.Distribute,0)}else this._root.addChild(e,T.Distribute,0);this.element.appendChild(this._root.element),this.disposable.value=this._root.onDidChange(e=>{this._onDidChange.fire(e)})}next(e){return this.progmaticSelect(e)}previous(e){return this.progmaticSelect(e,!0)}getView(e){let t=e?this.getNode(e)[1]:this.root;return this._getViews(t,this.orientation)}_getViews(e,t,i){let n={height:e.height,width:e.width};if(e instanceof id)return{box:n,view:e.view,cachedVisibleSize:i};let o=[];for(let i=0;i-1;n--){let o=i[n],r=e[n]||0;if(t?r-1>-1:r+1l.getChildSize(t));if(l.removeChild(h,t).dispose(),a instanceof ih){p.splice(h,1,...a.children.map(e=>e.size));for(let e=0;e0;)a.removeChild(0)}else{let e=new id(a.view,ig(a.orientation),a.size),t=c?a.orthogonalSize:T.Invisible(a.orthogonalSize);l.addChild(e,t,h)}a.dispose();for(let e=0;e=t.children.length)throw Error("Invalid location");let r=t.children[n];return i.push(t),this.getNode(o,r,i)}}let ib=["disableAutoResizing","proportionalLayout","orientation","hideBorders","className"];class iy extends tK{get element(){return this._element}get disableResizing(){return this._disableResizing}set disableResizing(e){this._disableResizing=e}constructor(e,t=!1){super(),this._disableResizing=t,this._element=e,this.addDisposables(tX(this._element,e=>{if(this.isDisposed||this.disableResizing||!this._element.offsetParent||!function(e){let t=e;for(;null==t?void 0:t.parentNode;)if(t.parentNode===document)return!0;else t=t.parentNode instanceof DocumentFragment?t.parentNode.host:t.parentNode;return!1}(this._element))return;let{width:t,height:i}=e.contentRect;this.layout(t,i)}))}}let ix=ii();function i_(e){switch(e){case"left":return"left";case"right":return"right";case"above":return"top";case"below":return"bottom";default:return"center"}}class iD extends iy{get id(){return this._id}get size(){return this._groups.size}get groups(){return Array.from(this._groups.values()).map(e=>e.value)}get width(){return this.gridview.width}get height(){return this.gridview.height}get minimumHeight(){return this.gridview.minimumHeight}get maximumHeight(){return this.gridview.maximumHeight}get minimumWidth(){return this.gridview.minimumWidth}get maximumWidth(){return this.gridview.maximumWidth}get activeGroup(){return this._activeGroup}get locked(){return this.gridview.locked}set locked(e){this.gridview.locked=e}constructor(e,t){var i;super(document.createElement("div"),t.disableAutoResizing),this._id=ix.next(),this._groups=new Map,this._onDidRemove=new tB,this.onDidRemove=this._onDidRemove.event,this._onDidAdd=new tB,this.onDidAdd=this._onDidAdd.event,this._onDidMaximizedChange=new tB,this.onDidMaximizedChange=this._onDidMaximizedChange.event,this._onDidActiveChange=new tB,this.onDidActiveChange=this._onDidActiveChange.event,this._bufferOnDidLayoutChange=new tU,this.onDidLayoutChange=this._bufferOnDidLayoutChange.onEvent,this._onDidViewVisibilityChangeMicroTaskQueue=new tU,this.onDidViewVisibilityChangeMicroTaskQueue=this._onDidViewVisibilityChangeMicroTaskQueue.onEvent,this.element.style.height="100%",this.element.style.width="100%",this._classNames=new t4(this.element),this._classNames.setClassNames(null!=(i=t.className)?i:""),e.appendChild(this.element),this.gridview=new iw(!!t.proportionalLayout,t.styles,t.orientation,t.locked,t.margin),this.gridview.locked=!!t.locked,this.element.appendChild(this.gridview.element),this.layout(0,0,!0),this.addDisposables(this.gridview.onDidMaximizedNodeChange(e=>{this._onDidMaximizedChange.fire({panel:e.view,isMaximized:e.isMaximized})}),this.gridview.onDidViewVisibilityChange(()=>this._onDidViewVisibilityChangeMicroTaskQueue.fire()),this.onDidViewVisibilityChangeMicroTaskQueue(()=>{this.layout(this.width,this.height,!0)}),O.from(()=>{var e;null==(e=this.element.parentElement)||e.removeChild(this.element)}),this.gridview.onDidChange(()=>{this._bufferOnDidLayoutChange.fire()}),N.any(this.onDidAdd,this.onDidRemove,this.onDidActiveChange)(()=>{this._bufferOnDidLayoutChange.fire()}),this._onDidMaximizedChange,this._onDidViewVisibilityChangeMicroTaskQueue,this._bufferOnDidLayoutChange)}setVisible(e,t){this.gridview.setViewVisible(ip(e.element),t),this._bufferOnDidLayoutChange.fire()}isVisible(e){return this.gridview.isViewVisible(ip(e.element))}updateOptions(e){var t,i,n,o;e.proportionalLayout,e.orientation&&(this.gridview.orientation=e.orientation),"disableResizing"in e&&(this.disableResizing=null!=(t=e.disableAutoResizing)&&t),"locked"in e&&(this.locked=null!=(i=e.locked)&&i),"margin"in e&&(this.gridview.margin=null!=(n=e.margin)?n:0),"className"in e&&this._classNames.setClassNames(null!=(o=e.className)?o:"")}maximizeGroup(e){this.gridview.maximizeView(e),this.doSetGroupActive(e)}isMaximizedGroup(e){return this.gridview.maximizedView()===e}exitMaximizedGroup(){this.gridview.exitMaximizedView()}hasMaximizedGroup(){return this.gridview.hasMaximizedView()}doAddGroup(e,t=[0],i){this.gridview.addView(e,null!=i?i:T.Distribute,t),this._onDidAdd.fire(e)}doRemoveGroup(e,t){if(!this._groups.has(e.id))throw Error("invalid operation");let i=this._groups.get(e.id),n=this.gridview.remove(e,T.Distribute);if(!i||(null==t?void 0:t.skipDispose)||(i.disposable.dispose(),i.value.dispose(),this._groups.delete(e.id),this._onDidRemove.fire(e)),!(null==t?void 0:t.skipActive)&&this._activeGroup===e){let e=Array.from(this._groups.values());this.doSetGroupActive(e.length>0?e[0].value:void 0)}return n}getPanel(e){var t;return null==(t=this._groups.get(e))?void 0:t.value}doSetGroupActive(e){this._activeGroup!==e&&(this._activeGroup&&this._activeGroup.setActive(!1),e&&e.setActive(!0),this._activeGroup=e,this._onDidActiveChange.fire(e))}removeGroup(e){this.doRemoveGroup(e)}moveToNext(e){var t;if(e||(e={}),!e.group){if(!this.activeGroup)return;e.group=this.activeGroup}let i=ip(e.group.element),n=null==(t=this.gridview.next(i))?void 0:t.view;this.doSetGroupActive(n)}moveToPrevious(e){var t;if(e||(e={}),!e.group){if(!this.activeGroup)return;e.group=this.activeGroup}let i=ip(e.group.element),n=null==(t=this.gridview.previous(i))?void 0:t.view;this.doSetGroupActive(n)}layout(e,t,i){(i||e!==this.width||t!==this.height)&&(this.gridview.element.style.height=`${t}px`,this.gridview.element.style.width=`${e}px`,this.gridview.layout(e,t))}dispose(){for(let e of(this._onDidActiveChange.dispose(),this._onDidAdd.dispose(),this._onDidRemove.dispose(),this.groups))e.dispose();this.gridview.dispose(),super.dispose()}}class iC{get minimumSize(){return this.component.minimumSize}get maximumSize(){return this.component.maximumSize}get width(){return this.component.width}get height(){return this.component.height}get length(){return this.component.length}get orientation(){return this.component.orientation}get panels(){return this.component.panels}get onDidLayoutFromJSON(){return this.component.onDidLayoutFromJSON}get onDidLayoutChange(){return this.component.onDidLayoutChange}get onDidAddView(){return this.component.onDidAddView}get onDidRemoveView(){return this.component.onDidRemoveView}constructor(e){this.component=e}removePanel(e,t){this.component.removePanel(e,t)}focus(){this.component.focus()}getPanel(e){return this.component.getPanel(e)}layout(e,t){return this.component.layout(e,t)}addPanel(e){return this.component.addPanel(e)}movePanel(e,t){this.component.movePanel(e,t)}fromJSON(e){this.component.fromJSON(e)}toJSON(){return this.component.toJSON()}clear(){this.component.clear()}updateOptions(e){this.component.updateOptions(e)}dispose(){this.component.dispose()}}class iS{get minimumSize(){return this.component.minimumSize}get maximumSize(){return this.component.maximumSize}get width(){return this.component.width}get height(){return this.component.height}get panels(){return this.component.panels}get onDidLayoutChange(){return this.component.onDidLayoutChange}get onDidLayoutFromJSON(){return this.component.onDidLayoutFromJSON}get onDidAddView(){return this.component.onDidAddView}get onDidRemoveView(){return this.component.onDidRemoveView}get onDidDrop(){return this.component.onDidDrop}get onUnhandledDragOverEvent(){return this.component.onUnhandledDragOverEvent}constructor(e){this.component=e}removePanel(e){this.component.removePanel(e)}getPanel(e){return this.component.getPanel(e)}movePanel(e,t){this.component.movePanel(e,t)}focus(){this.component.focus()}layout(e,t){this.component.layout(e,t)}addPanel(e){return this.component.addPanel(e)}fromJSON(e){this.component.fromJSON(e)}toJSON(){return this.component.toJSON()}clear(){this.component.clear()}updateOptions(e){this.component.updateOptions(e)}dispose(){this.component.dispose()}}class iE{get width(){return this.component.width}get height(){return this.component.height}get minimumHeight(){return this.component.minimumHeight}get maximumHeight(){return this.component.maximumHeight}get minimumWidth(){return this.component.minimumWidth}get maximumWidth(){return this.component.maximumWidth}get onDidLayoutChange(){return this.component.onDidLayoutChange}get onDidAddPanel(){return this.component.onDidAddGroup}get onDidRemovePanel(){return this.component.onDidRemoveGroup}get onDidActivePanelChange(){return this.component.onDidActiveGroupChange}get onDidLayoutFromJSON(){return this.component.onDidLayoutFromJSON}get panels(){return this.component.groups}get orientation(){return this.component.orientation}set orientation(e){this.component.updateOptions({orientation:e})}constructor(e){this.component=e}focus(){this.component.focus()}layout(e,t,i=!1){this.component.layout(e,t,i)}addPanel(e){return this.component.addPanel(e)}removePanel(e,t){this.component.removePanel(e,t)}movePanel(e,t){this.component.movePanel(e,t)}getPanel(e){return this.component.getPanel(e)}fromJSON(e){return this.component.fromJSON(e)}toJSON(){return this.component.toJSON()}clear(){this.component.clear()}updateOptions(e){this.component.updateOptions(e)}dispose(){this.component.dispose()}}class iA{get id(){return this.component.id}get width(){return this.component.width}get height(){return this.component.height}get minimumHeight(){return this.component.minimumHeight}get maximumHeight(){return this.component.maximumHeight}get minimumWidth(){return this.component.minimumWidth}get maximumWidth(){return this.component.maximumWidth}get size(){return this.component.size}get totalPanels(){return this.component.totalPanels}get onDidActiveGroupChange(){return this.component.onDidActiveGroupChange}get onDidAddGroup(){return this.component.onDidAddGroup}get onDidRemoveGroup(){return this.component.onDidRemoveGroup}get onDidActivePanelChange(){return this.component.onDidActivePanelChange}get onDidAddPanel(){return this.component.onDidAddPanel}get onDidRemovePanel(){return this.component.onDidRemovePanel}get onDidMovePanel(){return this.component.onDidMovePanel}get onDidLayoutFromJSON(){return this.component.onDidLayoutFromJSON}get onDidLayoutChange(){return this.component.onDidLayoutChange}get onDidDrop(){return this.component.onDidDrop}get onWillDrop(){return this.component.onWillDrop}get onWillShowOverlay(){return this.component.onWillShowOverlay}get onWillDragGroup(){return this.component.onWillDragGroup}get onWillDragPanel(){return this.component.onWillDragPanel}get onUnhandledDragOverEvent(){return this.component.onUnhandledDragOverEvent}get onDidPopoutGroupSizeChange(){return this.component.onDidPopoutGroupSizeChange}get onDidPopoutGroupPositionChange(){return this.component.onDidPopoutGroupPositionChange}get onDidOpenPopoutWindowFail(){return this.component.onDidOpenPopoutWindowFail}get panels(){return this.component.panels}get groups(){return this.component.groups}get activePanel(){return this.component.activePanel}get activeGroup(){return this.component.activeGroup}constructor(e){this.component=e}focus(){this.component.focus()}getPanel(e){return this.component.getGroupPanel(e)}layout(e,t,i=!1){this.component.layout(e,t,i)}addPanel(e){return this.component.addPanel(e)}removePanel(e){this.component.removePanel(e)}addGroup(e){return this.component.addGroup(e)}closeAllGroups(){return this.component.closeAllGroups()}removeGroup(e){this.component.removeGroup(e)}getGroup(e){return this.component.getPanel(e)}addFloatingGroup(e,t){return this.component.addFloatingGroup(e,t)}fromJSON(e,t){this.component.fromJSON(e,t)}toJSON(){return this.component.toJSON()}clear(){this.component.clear()}moveToNext(e){this.component.moveToNext(e)}moveToPrevious(e){this.component.moveToPrevious(e)}maximizeGroup(e){this.component.maximizeGroup(e.group)}hasMaximizedGroup(){return this.component.hasMaximizedGroup()}exitMaximizedGroup(){this.component.exitMaximizedGroup()}get onDidMaximizedGroupChange(){return this.component.onDidMaximizedGroupChange}addPopoutGroup(e,t){return this.component.addPopoutGroup(e,t)}updateOptions(e){this.component.updateOptions(e)}dispose(){this.component.dispose()}}class ik extends tK{constructor(e,t){super(),this.el=e,this.disabled=t,this.dataDisposable=new tJ,this.pointerEventsDisposable=new tJ,this._onDragStart=new tB,this.onDragStart=this._onDragStart.event,this.addDisposables(this._onDragStart,this.dataDisposable,this.pointerEventsDisposable),this.configure()}setDisabled(e){this.disabled=e}isCancelled(e){return!1}configure(){this.addDisposables(this._onDragStart,t$(this.el,"dragstart",e=>{if(e.defaultPrevented||this.isCancelled(e)||this.disabled)return void e.preventDefault();let t=t3();this.pointerEventsDisposable.value={dispose:()=>{t.release()}},this.el.classList.add("dv-dragged"),setTimeout(()=>this.el.classList.remove("dv-dragged"),0),this.dataDisposable.value=this.getData(e),this._onDragStart.fire(e),e.dataTransfer&&(e.dataTransfer.effectAllowed="move",e.dataTransfer.items.length>0||e.dataTransfer.setData("text/plain",""))}),t$(this.el,"dragend",()=>{this.pointerEventsDisposable.dispose(),setTimeout(()=>{this.dataDisposable.dispose()},0)}))}}class iz extends tK{constructor(e,t){super(),this.element=e,this.callbacks=t,this.target=null,this.registerListeners()}onDragEnter(e){this.target=e.target,this.callbacks.onDragEnter(e)}onDragOver(e){e.preventDefault(),this.callbacks.onDragOver&&this.callbacks.onDragOver(e)}onDragLeave(e){this.target===e.target&&(this.target=null,this.callbacks.onDragLeave(e))}onDragEnd(e){this.target=null,this.callbacks.onDragEnd(e)}onDrop(e){this.callbacks.onDrop(e)}registerListeners(){this.addDisposables(t$(this.element,"dragenter",e=>{this.onDragEnter(e)},!0)),this.addDisposables(t$(this.element,"dragover",e=>{this.onDragOver(e)},!0)),this.addDisposables(t$(this.element,"dragleave",e=>{this.onDragLeave(e)})),this.addDisposables(t$(this.element,"dragend",e=>{this.onDragEnd(e)})),this.addDisposables(t$(this.element,"drop",e=>{this.onDrop(e)}))}}class iP extends tV{get nativeEvent(){return this.options.nativeEvent}get position(){return this.options.position}constructor(e){super(),this.options=e}}function iR(e){switch(e){case"above":return"top";case"below":return"bottom";case"left":return"left";case"right":return"right";case"within":return"center";default:throw Error(`invalid direction '${e}'`)}}let iN={value:20,type:"percentage"},iO={value:50,type:"percentage"};class iI extends tK{get disabled(){return this._disabled}set disabled(e){this._disabled=e}get state(){return this._state}constructor(e,t){super(),this.element=e,this.options=t,this._onDrop=new tB,this.onDrop=this._onDrop.event,this._onWillShowOverlay=new tB,this.onWillShowOverlay=this._onWillShowOverlay.event,this._disabled=!1,this._acceptedTargetZonesSet=new Set(this.options.acceptedTargetZones),this.dnd=new iz(this.element,{onDragEnter:()=>{var e,t,i;null==(i=null==(t=(e=this.options).getOverrideTarget)?void 0:t.call(e))||i.getElements()},onDragOver:e=>{var t,i,n,o,r,s,a;iI.ACTUAL_TARGET=this;let l=null==(i=(t=this.options).getOverrideTarget)?void 0:i.call(t);if(0===this._acceptedTargetZonesSet.size){if(l)return;this.removeDropTarget();return}let d=null!=(r=null==(o=(n=this.options).getOverlayOutline)?void 0:o.call(n))?r:this.element,h=d.offsetWidth,u=d.offsetHeight;if(0===h||0===u)return;let c=e.currentTarget.getBoundingClientRect(),p=(null!=(s=e.clientX)?s:0)-c.left,m=(null!=(a=e.clientY)?a:0)-c.top,f=this.calculateQuadrant(this._acceptedTargetZonesSet,p,m,h,u);if(this.isAlreadyUsed(e)||null===f)return void this.removeDropTarget();if(!this.options.canDisplayOverlay(e,f)){if(l)return;this.removeDropTarget();return}let g=new iP({nativeEvent:e,position:f});(this._onWillShowOverlay.fire(g),g.defaultPrevented)?this.removeDropTarget():(this.markAsUsed(e),l||this.targetElement||(this.targetElement=document.createElement("div"),this.targetElement.className="dv-drop-target-dropzone",this.overlayElement=document.createElement("div"),this.overlayElement.className="dv-drop-target-selection",this._state="center",this.targetElement.appendChild(this.overlayElement),d.classList.add("dv-drop-target"),d.append(this.targetElement)),this.toggleClasses(f,h,u),this._state=f)},onDragLeave:()=>{var e,t;(null==(t=(e=this.options).getOverrideTarget)?void 0:t.call(e))||this.removeDropTarget()},onDragEnd:e=>{var t,i;let n=null==(i=(t=this.options).getOverrideTarget)?void 0:i.call(t);n&&iI.ACTUAL_TARGET===this&&this._state&&(e.stopPropagation(),this._onDrop.fire({position:this._state,nativeEvent:e})),this.removeDropTarget(),null==n||n.clear()},onDrop:e=>{var t,i,n;e.preventDefault();let o=this._state;this.removeDropTarget(),null==(n=null==(i=(t=this.options).getOverrideTarget)?void 0:i.call(t))||n.clear(),o&&(e.stopPropagation(),this._onDrop.fire({position:o,nativeEvent:e}))}}),this.addDisposables(this._onDrop,this._onWillShowOverlay,this.dnd)}setTargetZones(e){this._acceptedTargetZonesSet=new Set(e)}setOverlayModel(e){this.options.overlayModel=e}dispose(){this.removeDropTarget(),super.dispose()}markAsUsed(e){e[iI.USED_EVENT_ID]=!0}isAlreadyUsed(e){let t=e[iI.USED_EVENT_ID];return"boolean"==typeof t&&t}toggleClasses(e,t,i){var n,o,r,s,a,l,d;let h=null==(o=(n=this.options).getOverrideTarget)?void 0:o.call(n);if(!h&&!this.overlayElement)return;let u=t<100,c=i<100,p="left"===e,m="right"===e,f="top"===e,g="bottom"===e,v=!u&&m,w=!u&&p,b=!c&&f,y=!c&&g,x=1,_=null!=(s=null==(r=this.options.overlayModel)?void 0:r.size)?s:iO;if("percentage"===_.type?x=it(_.value,0,100)/100:((v||w)&&(x=it(0,_.value,t)/t),(b||y)&&(x=it(0,_.value,i)/i)),h){let n=null!=(d=null==(l=(a=this.options).getOverlayOutline)?void 0:l.call(a))?d:this.element,o=n.getBoundingClientRect(),r=h.getElements(void 0,n),s=r.root,c=r.overlay,_=s.getBoundingClientRect(),D=o.top-_.top,C=o.left-_.left,S={top:D,left:C,width:t,height:i};if(v?(S.left=C+t*(1-x),S.width=t*x):w?S.width=t*x:b?S.height=i*x:y&&(S.top=D+i*(1-x),S.height=i*x),u&&p&&(S.width=4),u&&m&&(S.left=C+t-4,S.width=4),!function(e,t){let{top:i,left:n,width:o,height:r}=t,s=`${Math.round(i)}px`,a=`${Math.round(n)}px`,l=`${Math.round(o)}px`,d=`${Math.round(r)}px`;return e.style.top!==s||e.style.left!==a||e.style.width!==l||e.style.height!==d}(c,S))return;!function(e,t){let{top:i,left:n,width:o,height:r}=t,s=`${Math.round(i)}px`,a=`${Math.round(n)}px`,l=`${Math.round(o)}px`,d=`${Math.round(r)}px`;e.style.top=s,e.style.left=a,e.style.width=l,e.style.height=d,e.style.visibility="visible",e.style.transform&&""!==e.style.transform||(e.style.transform="translate3d(0, 0, 0)")}(c,S),c.className=`dv-drop-target-anchor${this.options.className?` ${this.options.className}`:""}`,tQ(c,"dv-drop-target-left",p),tQ(c,"dv-drop-target-right",m),tQ(c,"dv-drop-target-top",f),tQ(c,"dv-drop-target-bottom",g),tQ(c,"dv-drop-target-center","center"===e),r.changed&&(tQ(c,"dv-drop-target-anchor-container-changed",!0),setTimeout(()=>{tQ(c,"dv-drop-target-anchor-container-changed",!1)},10));return}if(!this.overlayElement)return;let D={top:"0px",left:"0px",width:"100%",height:"100%"};v?(D.left=`${100*(1-x)}%`,D.width=`${100*x}%`):w?D.width=`${100*x}%`:b?D.height=`${100*x}%`:y&&(D.top=`${100*(1-x)}%`,D.height=`${100*x}%`),function(e,t){let{top:i,left:n,width:o,height:r}=t;e.style.top=i,e.style.left=n,e.style.width=o,e.style.height=r,e.style.visibility="visible",e.style.transform&&""!==e.style.transform||(e.style.transform="translate3d(0, 0, 0)")}(this.overlayElement,D),tQ(this.overlayElement,"dv-drop-target-small-vertical",c),tQ(this.overlayElement,"dv-drop-target-small-horizontal",u),tQ(this.overlayElement,"dv-drop-target-left",p),tQ(this.overlayElement,"dv-drop-target-right",m),tQ(this.overlayElement,"dv-drop-target-top",f),tQ(this.overlayElement,"dv-drop-target-bottom",g),tQ(this.overlayElement,"dv-drop-target-center","center"===e)}calculateQuadrant(e,t,i,n,o){var r,s,a,l,d,h,u,c,p,m,f,g,v,w;let b,y,x=null!=(s=null==(r=this.options.overlayModel)?void 0:r.activationSize)?s:iN;return"percentage"===x.type?(a=e,l=t,d=i,h=n,u=o,c=x.value,b=100*l/h,y=100*d/u,a.has("left")&&b100-c?"right":a.has("top")&&y100-c?"bottom":a.has("center")?"center":null):(p=e,m=t,f=i,g=n,v=o,w=x.value,p.has("left")&&mg-w?"right":p.has("top")&&fv-w?"bottom":p.has("center")?"center":null)}removeDropTarget(){var e;this.targetElement&&(this._state=void 0,null==(e=this.targetElement.parentElement)||e.classList.remove("dv-drop-target"),this.targetElement.remove(),this.targetElement=void 0,this.overlayElement=void 0)}}iI.USED_EVENT_ID="__dockview_droptarget_event_is_used__";let iM=["disableAutoResizing","disableDnd","className"];class iL extends tW{constructor(e,t,i,n){super(),this.nativeEvent=e,this.position=t,this.getData=i,this.panel=n}}class iT extends tV{constructor(){super()}}class ij extends tK{get isFocused(){return this._isFocused}get isActive(){return this._isActive}get isVisible(){return this._isVisible}get width(){return this._width}get height(){return this._height}constructor(e,t){super(),this.id=e,this.component=t,this._isFocused=!1,this._isActive=!1,this._isVisible=!0,this._width=0,this._height=0,this._parameters={},this.panelUpdatesDisposable=new tJ,this._onDidDimensionChange=new tB,this.onDidDimensionsChange=this._onDidDimensionChange.event,this._onDidChangeFocus=new tB,this.onDidFocusChange=this._onDidChangeFocus.event,this._onWillFocus=new tB,this.onWillFocus=this._onWillFocus.event,this._onDidVisibilityChange=new tB,this.onDidVisibilityChange=this._onDidVisibilityChange.event,this._onWillVisibilityChange=new tB,this.onWillVisibilityChange=this._onWillVisibilityChange.event,this._onDidActiveChange=new tB,this.onDidActiveChange=this._onDidActiveChange.event,this._onActiveChange=new tB,this.onActiveChange=this._onActiveChange.event,this._onDidParametersChange=new tB,this.onDidParametersChange=this._onDidParametersChange.event,this.addDisposables(this.onDidFocusChange(e=>{this._isFocused=e.isFocused}),this.onDidActiveChange(e=>{this._isActive=e.isActive}),this.onDidVisibilityChange(e=>{this._isVisible=e.isVisible}),this.onDidDimensionsChange(e=>{this._width=e.width,this._height=e.height}),this.panelUpdatesDisposable,this._onDidDimensionChange,this._onDidChangeFocus,this._onDidVisibilityChange,this._onDidActiveChange,this._onWillFocus,this._onActiveChange,this._onWillFocus,this._onWillVisibilityChange,this._onDidParametersChange)}getParameters(){return this._parameters}initialize(e){this.panelUpdatesDisposable.value=this._onDidParametersChange.event(t=>{this._parameters=t,e.update({params:t})})}setVisible(e){this._onWillVisibilityChange.fire({isVisible:e})}setActive(){this._onActiveChange.fire()}updateParameters(e){this._onDidParametersChange.fire(e)}}class iG extends ij{constructor(e,t){super(e,t),this._onDidConstraintsChangeInternal=new tB,this.onDidConstraintsChangeInternal=this._onDidConstraintsChangeInternal.event,this._onDidConstraintsChange=new tB({replay:!0}),this.onDidConstraintsChange=this._onDidConstraintsChange.event,this._onDidSizeChange=new tB,this.onDidSizeChange=this._onDidSizeChange.event,this.addDisposables(this._onDidConstraintsChangeInternal,this._onDidConstraintsChange,this._onDidSizeChange)}setConstraints(e){this._onDidConstraintsChangeInternal.fire(e)}setSize(e){this._onDidSizeChange.fire(e)}}class iV extends iG{set pane(e){this._pane=e}constructor(e,t){super(e,t),this._onDidExpansionChange=new tB({replay:!0}),this.onDidExpansionChange=this._onDidExpansionChange.event,this._onMouseEnter=new tB({}),this.onMouseEnter=this._onMouseEnter.event,this._onMouseLeave=new tB({}),this.onMouseLeave=this._onMouseLeave.event,this.addDisposables(this._onDidExpansionChange,this._onMouseEnter,this._onMouseLeave)}setExpanded(e){var t;null==(t=this._pane)||t.setExpanded(e)}get isExpanded(){var e;return!!(null==(e=this._pane)?void 0:e.isExpanded())}}class iW extends tK{get element(){return this._element}get width(){return this._width}get height(){return this._height}get params(){var e;return null==(e=this._params)?void 0:e.params}constructor(e,t,i){super(),this.id=e,this.component=t,this.api=i,this._height=0,this._width=0,this._element=document.createElement("div"),this._element.tabIndex=-1,this._element.style.outline="none",this._element.style.height="100%",this._element.style.width="100%",this._element.style.overflow="hidden";const n=new t1(this._element);this.addDisposables(this.api,n.onDidFocus(()=>{this.api._onDidChangeFocus.fire({isFocused:!0})}),n.onDidBlur(()=>{this.api._onDidChangeFocus.fire({isFocused:!1})}),n)}focus(){let e=new iT;this.api._onWillFocus.fire(e),e.defaultPrevented||this._element.focus()}layout(e,t){this._width=e,this._height=t,this.api._onDidDimensionChange.fire({width:e,height:t}),this.part&&this._params&&this.part.update(this._params.params)}init(e){this._params=e,this.part=this.getComponent()}update(e){var t,i;for(let i of(this._params=Object.assign(Object.assign({},this._params),{params:Object.assign(Object.assign({},null==(t=this._params)?void 0:t.params),e.params)}),Object.keys(e.params)))void 0===e.params[i]&&delete this._params.params[i];null==(i=this.part)||i.update({params:this._params.params})}toJSON(){var e,t;let i=null!=(t=null==(e=this._params)?void 0:e.params)?t:{};return{id:this.id,component:this.component,params:Object.keys(i).length>0?i:void 0}}dispose(){var e;this.api.dispose(),null==(e=this.part)||e.dispose(),super.dispose()}}class iH extends iW{set orientation(e){this._orientation=e}get orientation(){return this._orientation}get minimumSize(){return this.headerSize+(this.isExpanded()?this._minimumBodySize:0)}get maximumSize(){return this.headerSize+(this.isExpanded()?this._maximumBodySize:0)}get size(){return this._size}get orthogonalSize(){return this._orthogonalSize}set orthogonalSize(e){this._orthogonalSize=e}get minimumBodySize(){return this._minimumBodySize}set minimumBodySize(e){this._minimumBodySize="number"==typeof e?e:0}get maximumBodySize(){return this._maximumBodySize}set maximumBodySize(e){this._maximumBodySize="number"==typeof e?e:1/0}get headerVisible(){return this._headerVisible}set headerVisible(e){this._headerVisible=e,this.header.style.display=e?"":"none"}constructor(e){super(e.id,e.component,new iV(e.id,e.component)),this._onDidChangeExpansionState=new tB({replay:!0}),this.onDidChangeExpansionState=this._onDidChangeExpansionState.event,this._onDidChange=new tB,this.onDidChange=this._onDidChange.event,this._orthogonalSize=0,this._size=0,this._isExpanded=!1,this.api.pane=this,this.api.initialize(this),this.headerSize=e.headerSize,this.headerComponent=e.headerComponent,this._minimumBodySize=e.minimumBodySize,this._maximumBodySize=e.maximumBodySize,this._isExpanded=e.isExpanded,this._headerVisible=e.isHeaderVisible,this._onDidChangeExpansionState.fire(this.isExpanded()),this._orientation=e.orientation,this.element.classList.add("dv-pane"),this.addDisposables(this.api.onWillVisibilityChange(e=>{let{isVisible:t}=e,{accessor:i}=this._params;i.setVisible(this,t)}),this.api.onDidSizeChange(e=>{this._onDidChange.fire({size:e.size})}),t$(this.element,"mouseenter",e=>{this.api._onMouseEnter.fire(e)}),t$(this.element,"mouseleave",e=>{this.api._onMouseLeave.fire(e)})),this.addDisposables(this._onDidChangeExpansionState,this.onDidChangeExpansionState(e=>{this.api._onDidExpansionChange.fire({isExpanded:e})}),this.api.onDidFocusChange(e=>{this.header&&(e.isFocused?tY(this.header,"focused"):tq(this.header,"focused"))})),this.renderOnce()}setVisible(e){this.api._onDidVisibilityChange.fire({isVisible:e})}setActive(e){this.api._onDidActiveChange.fire({isActive:e})}isExpanded(){return this._isExpanded}setExpanded(e){this._isExpanded!==e&&(this._isExpanded=e,e?(this.animationTimer&&clearTimeout(this.animationTimer),this.body&&this.element.appendChild(this.body)):this.animationTimer=setTimeout(()=>{var e;null==(e=this.body)||e.remove()},200),this._onDidChange.fire(e?{size:this.width}:{}),this._onDidChangeExpansionState.fire(e))}layout(e,t){this._size=e,this._orthogonalSize=t;let[i,n]=this.orientation===I.HORIZONTAL?[e,t]:[t,e];super.layout(i,n)}init(e){var t,i;super.init(e),"number"==typeof e.minimumBodySize&&(this.minimumBodySize=e.minimumBodySize),"number"==typeof e.maximumBodySize&&(this.maximumBodySize=e.maximumBodySize),this.bodyPart=this.getBodyComponent(),this.headerPart=this.getHeaderComponent(),this.bodyPart.init(Object.assign(Object.assign({},e),{api:this.api})),this.headerPart.init(Object.assign(Object.assign({},e),{api:this.api})),null==(t=this.body)||t.append(this.bodyPart.element),null==(i=this.header)||i.append(this.headerPart.element),"boolean"==typeof e.isExpanded&&this.setExpanded(e.isExpanded)}toJSON(){let e=this._params;return Object.assign(Object.assign({},super.toJSON()),{headerComponent:this.headerComponent,title:e.title})}renderOnce(){this.header=document.createElement("div"),this.header.tabIndex=0,this.header.className="dv-pane-header",this.header.style.height=`${this.headerSize}px`,this.header.style.lineHeight=`${this.headerSize}px`,this.header.style.minHeight=`${this.headerSize}px`,this.header.style.maxHeight=`${this.headerSize}px`,this.element.appendChild(this.header),this.body=document.createElement("div"),this.body.className="dv-pane-body",this.element.appendChild(this.body)}getComponent(){return{update:e=>{var t,i;null==(t=this.bodyPart)||t.update({params:e}),null==(i=this.headerPart)||i.update({params:e})},dispose:()=>{var e,t;null==(e=this.bodyPart)||e.dispose(),null==(t=this.headerPart)||t.dispose()}}}}class iF extends iH{constructor(e){super({id:e.id,component:e.component,headerComponent:e.headerComponent,orientation:e.orientation,isExpanded:e.isExpanded,isHeaderVisible:!0,headerSize:e.headerSize,minimumBodySize:e.minimumBodySize,maximumBodySize:e.maximumBodySize}),this._onDidDrop=new tB,this.onDidDrop=this._onDidDrop.event,this._onUnhandledDragOverEvent=new tB,this.onUnhandledDragOverEvent=this._onUnhandledDragOverEvent.event,this.accessor=e.accessor,this.addDisposables(this._onDidDrop,this._onUnhandledDragOverEvent),e.disableDnd||this.initDragFeatures()}initDragFeatures(){if(!this.header)return;let e=this.id,t=this.accessor.id;this.header.draggable=!0,this.handler=new class extends ik{getData(){return tT.getInstance().setData([new tL(t,e)],tL.prototype),{dispose:()=>{tT.getInstance().clearData(tL.prototype)}}}}(this.header),this.target=new iI(this.element,{acceptedTargetZones:["top","bottom"],overlayModel:{activationSize:{type:"percentage",value:50}},canDisplayOverlay:(e,t)=>{let i=tG();if(i&&i.paneId!==this.id&&i.viewId===this.accessor.id)return!0;let n=new iL(e,t,tG,this);return this._onUnhandledDragOverEvent.fire(n),n.isAccepted}}),this.addDisposables(this._onDidDrop,this.handler,this.target,this.target.onDrop(e=>{this.onDrop(e)}))}onDrop(e){let t=tG();if(!t||t.viewId!==this.accessor.id)return void this._onDidDrop.fire(Object.assign(Object.assign({},e),{panel:this,api:new iS(this.accessor),getData:tG}));let i=this._params.containerApi,n=t.paneId,o=i.getPanel(n);if(!o)return void this._onDidDrop.fire(Object.assign(Object.assign({},e),{panel:this,getData:tG,api:new iS(this.accessor)}));let r=i.panels,s=r.indexOf(o),a=i.panels.indexOf(this);("left"===e.position||"top"===e.position)&&(a=Math.max(0,a-1)),("right"===e.position||"bottom"===e.position)&&(s>a&&a++,a=Math.min(r.length-1,a)),i.movePanel(s,a)}}class iB extends tK{get element(){return this._element}constructor(e,t){super(),this.accessor=e,this.group=t,this.disposable=new tJ,this._onDidFocus=new tB,this.onDidFocus=this._onDidFocus.event,this._onDidBlur=new tB,this.onDidBlur=this._onDidBlur.event,this._element=document.createElement("div"),this._element.className="dv-content-container",this._element.tabIndex=-1,this.addDisposables(this._onDidFocus,this._onDidBlur);const i=t.dropTargetContainer;this.dropTarget=new iI(this.element,{getOverlayOutline:()=>{var t;return(null==(t=e.options.theme)?void 0:t.dndPanelOverlay)==="group"?this.element.parentElement:null},className:"dv-drop-target-content",acceptedTargetZones:["top","bottom","left","right","center"],canDisplayOverlay:(e,t)=>{if("no-drop-target"===this.group.locked||this.group.locked&&"center"===t)return!1;let i=tj();return(!!i||!e.shiftKey||"floating"===this.group.location.type)&&(!!i&&i.viewId===this.accessor.id||this.group.canDisplayOverlay(e,t,"content"))},getOverrideTarget:i?()=>i.model:void 0}),this.addDisposables(this.dropTarget)}show(){this.element.style.display=""}hide(){this.element.style.display="none"}renderPanel(e,t={asActive:!0}){let i,n=t.asActive||this.panel&&this.group.isPanelActive(this.panel);switch(this.panel&&this.panel.view.content.element.parentElement===this._element&&this._element.removeChild(this.panel.view.content.element),this.panel=e,e.api.renderer){case"onlyWhenVisible":this.group.renderContainer.detatch(e),this.panel&&n&&this._element.appendChild(this.panel.view.content.element),i=this._element;break;case"always":e.view.content.element.parentElement===this._element&&this._element.removeChild(e.view.content.element),i=this.group.renderContainer.attach({panel:e,referenceContainer:this});break;default:throw Error(`dockview: invalid renderer type '${e.api.renderer}'`)}if(n){let e=new t1(i);this.focusTracker=e;let t=new tK;t.addDisposables(e,e.onDidFocus(()=>this._onDidFocus.fire()),e.onDidBlur(()=>this._onDidBlur.fire())),this.disposable.value=t}}openPanel(e){this.panel!==e&&this.renderPanel(e)}layout(e,t){}closePanel(){var e;this.panel&&"onlyWhenVisible"===this.panel.api.renderer&&(null==(e=this.panel.view.content.element.parentElement)||e.removeChild(this.panel.view.content.element)),this.panel=void 0}dispose(){this.disposable.dispose(),super.dispose()}refreshFocusState(){var e;(null==(e=this.focusTracker)?void 0:e.refreshState)&&this.focusTracker.refreshState()}}function i$(e,t,i){var n,o;tY(t,"dv-dragged"),t.style.top="-9999px",document.body.appendChild(t),e.setDragImage(t,null!=(n=null==i?void 0:i.x)?n:0,null!=(o=null==i?void 0:i.y)?o:0),setTimeout(()=>{tq(t,"dv-dragged"),t.remove()},0)}class iU extends ik{constructor(e,t,i,n,o){super(e,o),this.accessor=t,this.group=i,this.panel=n,this.panelTransfer=tT.getInstance()}getData(e){return this.panelTransfer.setData([new tM(this.accessor.id,this.group.id,this.panel.id)],tM.prototype),{dispose:()=>{this.panelTransfer.clearData(tM.prototype)}}}}class iK extends tK{get element(){return this._element}constructor(e,t,i){super(),this.panel=e,this.accessor=t,this.group=i,this.content=void 0,this._onPointDown=new tB,this.onPointerDown=this._onPointDown.event,this._onDropped=new tB,this.onDrop=this._onDropped.event,this._onDragStart=new tB,this.onDragStart=this._onDragStart.event,this._element=document.createElement("div"),this._element.className="dv-tab",this._element.tabIndex=0,this._element.draggable=!this.accessor.options.disableDnd,tQ(this.element,"dv-inactive-tab",!0),this.dragHandler=new iU(this._element,this.accessor,this.group,this.panel,!!this.accessor.options.disableDnd),this.dropTarget=new iI(this._element,{acceptedTargetZones:["left","right"],overlayModel:{activationSize:{value:50,type:"percentage"}},canDisplayOverlay:(e,t)=>{if(this.group.locked)return!1;let i=tj();return!!i&&this.accessor.id===i.viewId||this.group.model.canDisplayOverlay(e,t,"tab")},getOverrideTarget:()=>{var e;return null==(e=i.model.dropTargetContainer)?void 0:e.model}}),this.onWillShowOverlay=this.dropTarget.onWillShowOverlay,this.addDisposables(this._onPointDown,this._onDropped,this._onDragStart,this.dragHandler.onDragStart(e=>{if(e.dataTransfer){let t=getComputedStyle(this.element),i=this.element.cloneNode(!0);Array.from(t).forEach(e=>i.style.setProperty(e,t.getPropertyValue(e),t.getPropertyPriority(e))),i.style.position="absolute",i$(e.dataTransfer,i,{y:-10,x:30})}this._onDragStart.fire(e)}),this.dragHandler,t$(this._element,"pointerdown",e=>{this._onPointDown.fire(e)}),this.dropTarget.onDrop(e=>{this._onDropped.fire(e)}),this.dropTarget)}setActive(e){tQ(this.element,"dv-active-tab",e),tQ(this.element,"dv-inactive-tab",!e)}setContent(e){this.content&&this._element.removeChild(this.content.element),this.content=e,this._element.appendChild(this.content.element)}updateDragAndDropState(){this._element.draggable=!this.accessor.options.disableDnd,this.dragHandler.setDisabled(!!this.accessor.options.disableDnd)}dispose(){super.dispose()}}class iJ{get kind(){return this.options.kind}get nativeEvent(){return this.event.nativeEvent}get position(){return this.event.position}get defaultPrevented(){return this.event.defaultPrevented}get panel(){return this.options.panel}get api(){return this.options.api}get group(){return this.options.group}preventDefault(){this.event.preventDefault()}getData(){return this.options.getData()}constructor(e,t){this.event=e,this.options=t}}class iZ extends ik{constructor(e,t,i,n){super(e,n),this.accessor=t,this.group=i,this.panelTransfer=tT.getInstance(),this.addDisposables(t$(e,"pointerdown",e=>{e.shiftKey&&(e[t2]=!0)},!0))}isCancelled(e){return"floating"===this.group.api.location.type&&!e.shiftKey}getData(e){let t=e.dataTransfer;this.panelTransfer.setData([new tM(this.accessor.id,this.group.id,null)],tM.prototype);let i=window.getComputedStyle(this.el),n=i.getPropertyValue("--dv-activegroup-visiblepanel-tab-background-color"),o=i.getPropertyValue("--dv-activegroup-visiblepanel-tab-color");if(t){let e=document.createElement("div");e.style.backgroundColor=n,e.style.color=o,e.style.padding="2px 8px",e.style.height="24px",e.style.fontSize="11px",e.style.lineHeight="20px",e.style.borderRadius="12px",e.style.position="absolute",e.style.pointerEvents="none",e.style.top="-9999px",e.textContent=`Multiple Panels (${this.group.size})`,i$(t,e,{y:-10,x:30})}return{dispose:()=>{this.panelTransfer.clearData(tM.prototype)}}}}class iX extends tK{get element(){return this._element}constructor(e,t){super(),this.accessor=e,this.group=t,this._onDrop=new tB,this.onDrop=this._onDrop.event,this._onDragStart=new tB,this.onDragStart=this._onDragStart.event,this._element=document.createElement("div"),this._element.className="dv-void-container",this._element.draggable=!this.accessor.options.disableDnd,tQ(this._element,"dv-draggable",!this.accessor.options.disableDnd),this.addDisposables(this._onDrop,this._onDragStart,t$(this._element,"pointerdown",()=>{this.accessor.doSetGroupActive(this.group)})),this.handler=new iZ(this._element,e,t,!!this.accessor.options.disableDnd),this.dropTarget=new iI(this._element,{acceptedTargetZones:["center"],canDisplayOverlay:(e,i)=>{let n=tj();return!!n&&this.accessor.id===n.viewId||t.model.canDisplayOverlay(e,i,"header_space")},getOverrideTarget:()=>{var e;return null==(e=t.model.dropTargetContainer)?void 0:e.model}}),this.onWillShowOverlay=this.dropTarget.onWillShowOverlay,this.addDisposables(this.handler,this.handler.onDragStart(e=>{this._onDragStart.fire(e)}),this.dropTarget.onDrop(e=>{this._onDrop.fire(e)}),this.dropTarget)}updateDragAndDropState(){this._element.draggable=!this.accessor.options.disableDnd,tQ(this._element,"dv-draggable",!this.accessor.options.disableDnd),this.handler.setDisabled(!!this.accessor.options.disableDnd)}}class iq extends tK{get element(){return this._element}constructor(e){super(),this.scrollableElement=e,this._scrollLeft=0,this._element=document.createElement("div"),this._element.className="dv-scrollable",this._horizontalScrollbar=document.createElement("div"),this._horizontalScrollbar.className="dv-scrollbar-horizontal",this.element.appendChild(e),this.element.appendChild(this._horizontalScrollbar),this.addDisposables(t$(this.element,"wheel",e=>{this._scrollLeft+=e.deltaY*iq.MouseWheelSpeed,this.calculateScrollbarStyles()}),t$(this._horizontalScrollbar,"pointerdown",e=>{e.preventDefault(),tQ(this.element,"dv-scrollable-scrolling",!0);let t=e.clientX,i=this._scrollLeft,n=e=>{let n=e.clientX-t,{clientWidth:o}=this.element,{scrollWidth:r}=this.scrollableElement;this._scrollLeft=i+n/(o/r),this.calculateScrollbarStyles()},o=()=>{tQ(this.element,"dv-scrollable-scrolling",!1),document.removeEventListener("pointermove",n),document.removeEventListener("pointerup",o),document.removeEventListener("pointercancel",o)};document.addEventListener("pointermove",n),document.addEventListener("pointerup",o),document.addEventListener("pointercancel",o)}),t$(this.element,"scroll",()=>{this.calculateScrollbarStyles()}),t$(this.scrollableElement,"scroll",()=>{this._scrollLeft=this.scrollableElement.scrollLeft,this.calculateScrollbarStyles()}),tX(this.element,()=>{tQ(this.element,"dv-scrollable-resizing",!0),this._animationTimer&&clearTimeout(this._animationTimer),this._animationTimer=setTimeout(()=>{clearTimeout(this._animationTimer),tQ(this.element,"dv-scrollable-resizing",!1)},500),this.calculateScrollbarStyles()}))}calculateScrollbarStyles(){let{clientWidth:e}=this.element,{scrollWidth:t}=this.scrollableElement;if(t>e){let i=e/t*e;this._horizontalScrollbar.style.width=`${i}px`,this._scrollLeft=it(this._scrollLeft,0,this.scrollableElement.scrollWidth-e),this.scrollableElement.scrollLeft=this._scrollLeft;let n=this._scrollLeft/(t-e);this._horizontalScrollbar.style.left=`${(e-i)*n}px`}else this._horizontalScrollbar.style.width="0px",this._horizontalScrollbar.style.left="0px",this._scrollLeft=0}}iq.MouseWheelSpeed=1;class iY extends tK{get showTabsOverflowControl(){return this._showTabsOverflowControl}set showTabsOverflowControl(e){if(this._showTabsOverflowControl!=e&&(this._showTabsOverflowControl=e,e)){let e=new tZ(this._tabsList);this._observerDisposable.value=new tK(e,e.onDidChange(e=>{let t=e.hasScrollX||e.hasScrollY;this.toggleDropdown({reset:!t})}),t$(this._tabsList,"scroll",()=>{this.toggleDropdown({reset:!1})}))}}get element(){return this._element}get panels(){return this._tabs.map(e=>e.value.panel.id)}get size(){return this._tabs.length}get tabs(){return this._tabs.map(e=>e.value)}constructor(e,t,i){if(super(),this.group=e,this.accessor=t,this._observerDisposable=new tJ,this._tabs=[],this.selectedIndex=-1,this._showTabsOverflowControl=!1,this._onTabDragStart=new tB,this.onTabDragStart=this._onTabDragStart.event,this._onDrop=new tB,this.onDrop=this._onDrop.event,this._onWillShowOverlay=new tB,this.onWillShowOverlay=this._onWillShowOverlay.event,this._onOverflowTabsChange=new tB,this.onOverflowTabsChange=this._onOverflowTabsChange.event,this._tabsList=document.createElement("div"),this._tabsList.className="dv-tabs-container dv-horizontal",this.showTabsOverflowControl=i.showTabsOverflowControl,"native"===t.options.scrollbars)this._element=this._tabsList;else{const e=new iq(this._tabsList);this._element=e.element,this.addDisposables(e)}this.addDisposables(this._onOverflowTabsChange,this._observerDisposable,this._onWillShowOverlay,this._onDrop,this._onTabDragStart,t$(this.element,"pointerdown",e=>{e.defaultPrevented||0===e.button&&this.accessor.doSetGroupActive(this.group)}),O.from(()=>{for(let{value:e,disposable:t}of this._tabs)t.dispose(),e.dispose();this._tabs=[]}))}indexOf(e){return this._tabs.findIndex(t=>t.value.panel.id===e)}isActive(e){return this.selectedIndex>-1&&this._tabs[this.selectedIndex].value===e}setActivePanel(e){let t=0;for(let i of this._tabs){let n=e.id===i.value.panel.id;if(i.value.setActive(n),n){let e=i.value.element,n=e.parentElement;(tn.scrollLeft+n.clientWidth)&&(n.scrollLeft=t)}t+=i.value.element.clientWidth}}openPanel(e,t=this._tabs.length){if(this._tabs.find(t=>t.value.panel.id===e.id))return;let i=new iK(e,this.accessor,this.group);i.setContent(e.view.tab);let n=new tK(i.onDragStart(t=>{this._onTabDragStart.fire({nativeEvent:t,panel:e})}),i.onPointerDown(t=>{if(t.defaultPrevented)return;let n=!this.accessor.options.disableFloatingGroups,o="floating"===this.group.api.location.type&&1===this.size;if(n&&!o&&t.shiftKey){t.preventDefault();let e=this.accessor.getGroupPanel(i.panel.id),{top:n,left:o}=i.element.getBoundingClientRect(),{top:r,left:s}=this.accessor.element.getBoundingClientRect();this.accessor.addFloatingGroup(e,{x:o-s,y:n-r,inDragMode:!0});return}0===t.button&&this.group.activePanel!==e&&this.group.model.openPanel(e)}),i.onDrop(e=>{this._onDrop.fire({event:e.nativeEvent,index:this._tabs.findIndex(e=>e.value===i)})}),i.onWillShowOverlay(e=>{this._onWillShowOverlay.fire(new iJ(e,{kind:"tab",panel:this.group.activePanel,api:this.accessor.api,group:this.group,getData:tj}))}));this.addTab({value:i,disposable:n},t)}delete(e){let t=this.indexOf(e),{value:i,disposable:n}=this._tabs.splice(t,1)[0];n.dispose(),i.dispose(),i.element.remove()}addTab(e,t=this._tabs.length){if(t<0||t>this._tabs.length)throw Error("invalid location");this._tabsList.insertBefore(e.value.element,this._tabsList.children[t]),this._tabs=[...this._tabs.slice(0,t),e,...this._tabs.slice(t)],this.selectedIndex<0&&(this.selectedIndex=t)}toggleDropdown(e){let t=e.reset?[]:this._tabs.filter(e=>{var t,i;let n,o;return t=e.value.element,i=this._tabsList,n=t5(t),o=t5(i),!!(n.lefto.left+o.width)}).map(e=>e.value.panel.id);this._onOverflowTabsChange.fire({tabs:t,reset:e.reset})}updateDragAndDropState(){for(let e of this._tabs)e.value.updateDragAndDropState()}}let iQ=e=>{let t=document.createElementNS("http://www.w3.org/2000/svg","svg");t.setAttributeNS(null,"height",e.height),t.setAttributeNS(null,"width",e.width),t.setAttributeNS(null,"viewBox",e.viewbox),t.setAttributeNS(null,"aria-hidden","false"),t.setAttributeNS(null,"focusable","false"),t.classList.add("dv-svg");let i=document.createElementNS("http://www.w3.org/2000/svg","path");return i.setAttributeNS(null,"d",e.path),t.appendChild(i),t},i0=()=>iQ({width:"11",height:"11",viewbox:"0 0 15 25",path:"M2.15 24.1L0 21.95L9.9 12.05L0 2.15L2.15 0L14.2 12.05L2.15 24.1Z"});class i1 extends tK{get onTabDragStart(){return this.tabs.onTabDragStart}get panels(){return this.tabs.panels}get size(){return this.tabs.size}get hidden(){return this._hidden}set hidden(e){this._hidden=e,this.element.style.display=e?"none":""}get element(){return this._element}constructor(e,t){super(),this.accessor=e,this.group=t,this._hidden=!1,this.dropdownPart=null,this._overflowTabs=[],this._dropdownDisposable=new tJ,this._onDrop=new tB,this.onDrop=this._onDrop.event,this._onGroupDragStart=new tB,this.onGroupDragStart=this._onGroupDragStart.event,this._onWillShowOverlay=new tB,this.onWillShowOverlay=this._onWillShowOverlay.event,this._element=document.createElement("div"),this._element.className="dv-tabs-and-actions-container",tQ(this._element,"dv-full-width-single-tab","fullwidth"===this.accessor.options.singleTabMode),this.rightActionsContainer=document.createElement("div"),this.rightActionsContainer.className="dv-right-actions-container",this.leftActionsContainer=document.createElement("div"),this.leftActionsContainer.className="dv-left-actions-container",this.preActionsContainer=document.createElement("div"),this.preActionsContainer.className="dv-pre-actions-container",this.tabs=new iY(t,e,{showTabsOverflowControl:!e.options.disableTabsOverflowList}),this.voidContainer=new iX(this.accessor,this.group),this._element.appendChild(this.preActionsContainer),this._element.appendChild(this.tabs.element),this._element.appendChild(this.leftActionsContainer),this._element.appendChild(this.voidContainer.element),this._element.appendChild(this.rightActionsContainer),this.addDisposables(this.tabs.onDrop(e=>this._onDrop.fire(e)),this.tabs.onWillShowOverlay(e=>this._onWillShowOverlay.fire(e)),e.onDidOptionsChange(()=>{this.tabs.showTabsOverflowControl=!e.options.disableTabsOverflowList}),this.tabs.onOverflowTabsChange(e=>{this.toggleDropdown(e)}),this.tabs,this._onWillShowOverlay,this._onDrop,this._onGroupDragStart,this.voidContainer,this.voidContainer.onDragStart(e=>{this._onGroupDragStart.fire({nativeEvent:e,group:this.group})}),this.voidContainer.onDrop(e=>{this._onDrop.fire({event:e.nativeEvent,index:this.tabs.size})}),this.voidContainer.onWillShowOverlay(e=>{this._onWillShowOverlay.fire(new iJ(e,{kind:"header_space",panel:this.group.activePanel,api:this.accessor.api,group:this.group,getData:tj}))}),t$(this.voidContainer.element,"pointerdown",e=>{if(!e.defaultPrevented&&!this.accessor.options.disableFloatingGroups&&e.shiftKey&&"floating"!==this.group.api.location.type){e.preventDefault();let{top:t,left:i}=this.element.getBoundingClientRect(),{top:n,left:o}=this.accessor.element.getBoundingClientRect();this.accessor.addFloatingGroup(this.group,{x:i-o+20,y:t-n+20,inDragMode:!0})}}))}show(){this.hidden||(this.element.style.display="")}hide(){this._element.style.display="none"}setRightActionsElement(e){this.rightActions!==e&&(this.rightActions&&(this.rightActions.remove(),this.rightActions=void 0),e&&(this.rightActionsContainer.appendChild(e),this.rightActions=e))}setLeftActionsElement(e){this.leftActions!==e&&(this.leftActions&&(this.leftActions.remove(),this.leftActions=void 0),e&&(this.leftActionsContainer.appendChild(e),this.leftActions=e))}setPrefixActionsElement(e){this.preActions!==e&&(this.preActions&&(this.preActions.remove(),this.preActions=void 0),e&&(this.preActionsContainer.appendChild(e),this.preActions=e))}isActive(e){return this.tabs.isActive(e)}indexOf(e){return this.tabs.indexOf(e)}setActive(e){}delete(e){this.tabs.delete(e),this.updateClassnames()}setActivePanel(e){this.tabs.setActivePanel(e)}openPanel(e,t=this.tabs.size){this.tabs.openPanel(e,t),this.updateClassnames()}closePanel(e){this.delete(e.id)}updateClassnames(){tQ(this._element,"dv-single-tab",1===this.size)}toggleDropdown(e){let t,i,n,o=e.reset?[]:e.tabs;if(this._overflowTabs=o,this._overflowTabs.length>0&&this.dropdownPart)return void this.dropdownPart.update({tabs:o.length});if(0===this._overflowTabs.length)return void this._dropdownDisposable.dispose();let r=document.createElement("div");r.className="dv-tabs-overflow-dropdown-root";let s=((t=document.createElement("div")).className="dv-tabs-overflow-dropdown-default",(i=document.createElement("span")).textContent="",n=i0(),t.appendChild(n),t.appendChild(i),{element:t,update:e=>{i.textContent=`${e.tabs}`}});s.update({tabs:o.length}),this.dropdownPart=s,r.appendChild(s.element),this.rightActionsContainer.prepend(r),this._dropdownDisposable.value=new tK(O.from(()=>{var e,t;r.remove(),null==(t=null==(e=this.dropdownPart)?void 0:e.dispose)||t.call(e),this.dropdownPart=null}),t$(r,"pointerdown",e=>{e.preventDefault()},{capture:!0}),t$(r,"click",e=>{let t=document.createElement("div");for(let e of(t.style.overflow="auto",t.className="dv-tabs-overflow-container",this.tabs.tabs.filter(e=>this._overflowTabs.includes(e.panel.id)))){let i=this.group.panels.find(t=>t===e.panel),n=i.view.createTabRenderer("headerOverflow").element,o=document.createElement("div");tQ(o,"dv-tab",!0),tQ(o,"dv-active-tab",i.api.isActive),tQ(o,"dv-inactive-tab",!i.api.isActive),o.addEventListener("click",t=>{this.accessor.popupService.close(),t.defaultPrevented||(e.element.scrollIntoView(),e.panel.api.setActive())}),o.appendChild(n),t.appendChild(o)}let i=function(e){let t=e;for(;t&&("auto"===t.style.zIndex||""===t.style.zIndex);)t=t.parentElement;return t}(r);this.accessor.popupService.openPopover(t,{x:e.clientX,y:e.clientY,zIndex:(null==i?void 0:i.style.zIndex)?`calc(${i.style.zIndex} * 2)`:void 0})}))}updateDragAndDropState(){this.tabs.updateDragAndDropState(),this.voidContainer.updateDragAndDropState()}}class i2 extends tW{constructor(e,t,i,n,o){super(),this.nativeEvent=e,this.target=t,this.position=i,this.getData=n,this.group=o}}let i5=["disableAutoResizing","hideBorders","singleTabMode","disableFloatingGroups","floatingGroupBounds","popoutUrl","defaultRenderer","debug","rootOverlayModel","locked","disableDnd","className","noPanelsOverlay","dndEdges","theme","disableTabsOverflowList","scrollbars"];class i3 extends tV{get nativeEvent(){return this.options.nativeEvent}get position(){return this.options.position}get panel(){return this.options.panel}get group(){return this.options.group}get api(){return this.options.api}constructor(e){super(),this.options=e}getData(){return this.options.getData()}}class i4 extends i3{get kind(){return this._kind}constructor(e){super(e),this._kind=e.kind}}class i9 extends tK{get element(){throw Error("dockview: not supported")}get activePanel(){return this._activePanel}get locked(){return this._locked}set locked(e){this._locked=e,tQ(this.container,"dv-locked-groupview","no-drop-target"===e||e)}get isActive(){return this._isGroupActive}get panels(){return this._panels}get size(){return this._panels.length}get isEmpty(){return 0===this._panels.length}get hasWatermark(){return!!(this.watermark&&this.container.contains(this.watermark.element))}get header(){return this.tabsContainer}get isContentFocused(){return!!document.activeElement&&t0(document.activeElement,this.contentContainer.element)}get location(){return this._location}set location(e){switch(this._location=e,tQ(this.container,"dv-groupview-floating",!1),tQ(this.container,"dv-groupview-popout",!1),e.type){case"grid":this.contentContainer.dropTarget.setTargetZones(["top","bottom","left","right","center"]);break;case"floating":this.contentContainer.dropTarget.setTargetZones(["center"]),this.contentContainer.dropTarget.setTargetZones(e?["center"]:["top","bottom","left","right","center"]),tQ(this.container,"dv-groupview-floating",!0);break;case"popout":this.contentContainer.dropTarget.setTargetZones(["center"]),tQ(this.container,"dv-groupview-popout",!0)}this.groupPanel.api._onDidLocationChange.fire({location:this.location})}constructor(e,t,i,n,o){var r;super(),this.container=e,this.accessor=t,this.id=i,this.options=n,this.groupPanel=o,this._isGroupActive=!1,this._locked=!1,this._location={type:"grid"},this.mostRecentlyUsed=[],this._overwriteRenderContainer=null,this._overwriteDropTargetContainer=null,this._onDidChange=new tB,this.onDidChange=this._onDidChange.event,this._width=0,this._height=0,this._panels=[],this._panelDisposables=new Map,this._onMove=new tB,this.onMove=this._onMove.event,this._onDidDrop=new tB,this.onDidDrop=this._onDidDrop.event,this._onWillDrop=new tB,this.onWillDrop=this._onWillDrop.event,this._onWillShowOverlay=new tB,this.onWillShowOverlay=this._onWillShowOverlay.event,this._onTabDragStart=new tB,this.onTabDragStart=this._onTabDragStart.event,this._onGroupDragStart=new tB,this.onGroupDragStart=this._onGroupDragStart.event,this._onDidAddPanel=new tB,this.onDidAddPanel=this._onDidAddPanel.event,this._onDidPanelTitleChange=new tB,this.onDidPanelTitleChange=this._onDidPanelTitleChange.event,this._onDidPanelParametersChange=new tB,this.onDidPanelParametersChange=this._onDidPanelParametersChange.event,this._onDidRemovePanel=new tB,this.onDidRemovePanel=this._onDidRemovePanel.event,this._onDidActivePanelChange=new tB,this.onDidActivePanelChange=this._onDidActivePanelChange.event,this._onUnhandledDragOverEvent=new tB,this.onUnhandledDragOverEvent=this._onUnhandledDragOverEvent.event,tQ(this.container,"dv-groupview",!0),this._api=new iA(this.accessor),this.tabsContainer=new i1(this.accessor,this.groupPanel),this.contentContainer=new iB(this.accessor,this),e.append(this.tabsContainer.element,this.contentContainer.element),this.header.hidden=!!n.hideHeader,this.locked=null!=(r=n.locked)&&r,this.addDisposables(this._onTabDragStart,this._onGroupDragStart,this._onWillShowOverlay,this.tabsContainer.onTabDragStart(e=>{this._onTabDragStart.fire(e)}),this.tabsContainer.onGroupDragStart(e=>{this._onGroupDragStart.fire(e)}),this.tabsContainer.onDrop(e=>{this.handleDropEvent("header",e.event,"center",e.index)}),this.contentContainer.onDidFocus(()=>{this.accessor.doSetGroupActive(this.groupPanel)}),this.contentContainer.onDidBlur(()=>{}),this.contentContainer.dropTarget.onDrop(e=>{this.handleDropEvent("content",e.nativeEvent,e.position)}),this.tabsContainer.onWillShowOverlay(e=>{this._onWillShowOverlay.fire(e)}),this.contentContainer.dropTarget.onWillShowOverlay(e=>{this._onWillShowOverlay.fire(new iJ(e,{kind:"content",panel:this.activePanel,api:this._api,group:this.groupPanel,getData:tj}))}),this._onMove,this._onDidChange,this._onDidDrop,this._onWillDrop,this._onDidAddPanel,this._onDidRemovePanel,this._onDidActivePanelChange,this._onUnhandledDragOverEvent,this._onDidPanelTitleChange,this._onDidPanelParametersChange)}focusContent(){this.contentContainer.element.focus()}set renderContainer(e){this.panels.forEach(e=>{this.renderContainer.detatch(e)}),this._overwriteRenderContainer=e,this.panels.forEach(e=>{this.rerender(e)})}get renderContainer(){var e;return null!=(e=this._overwriteRenderContainer)?e:this.accessor.overlayRenderContainer}set dropTargetContainer(e){this._overwriteDropTargetContainer=e}get dropTargetContainer(){var e;return null!=(e=this._overwriteDropTargetContainer)?e:this.accessor.rootDropTargetContainer}initialize(){this.options.panels&&this.options.panels.forEach(e=>{this.doAddPanel(e)}),this.options.activePanel&&this.openPanel(this.options.activePanel),this.setActive(this.isActive,!0),this.updateContainer(),this.accessor.options.createRightHeaderActionComponent&&(this._rightHeaderActions=this.accessor.options.createRightHeaderActionComponent(this.groupPanel),this.addDisposables(this._rightHeaderActions),this._rightHeaderActions.init({containerApi:this._api,api:this.groupPanel.api,group:this.groupPanel}),this.tabsContainer.setRightActionsElement(this._rightHeaderActions.element)),this.accessor.options.createLeftHeaderActionComponent&&(this._leftHeaderActions=this.accessor.options.createLeftHeaderActionComponent(this.groupPanel),this.addDisposables(this._leftHeaderActions),this._leftHeaderActions.init({containerApi:this._api,api:this.groupPanel.api,group:this.groupPanel}),this.tabsContainer.setLeftActionsElement(this._leftHeaderActions.element)),this.accessor.options.createPrefixHeaderActionComponent&&(this._prefixHeaderActions=this.accessor.options.createPrefixHeaderActionComponent(this.groupPanel),this.addDisposables(this._prefixHeaderActions),this._prefixHeaderActions.init({containerApi:this._api,api:this.groupPanel.api,group:this.groupPanel}),this.tabsContainer.setPrefixActionsElement(this._prefixHeaderActions.element))}rerender(e){this.contentContainer.renderPanel(e,{asActive:!1})}indexOf(e){return this.tabsContainer.indexOf(e.id)}toJSON(){var e;let t={views:this.tabsContainer.panels,activeView:null==(e=this._activePanel)?void 0:e.id,id:this.id};return!1!==this.locked&&(t.locked=this.locked),this.header.hidden&&(t.hideHeader=!0),t}moveToNext(e){let t;e||(e={}),e.panel||(e.panel=this.activePanel);let i=e.panel?this.panels.indexOf(e.panel):-1;if(i0)t=i-1;else{if(e.suppressRoll)return;t=this.panels.length-1}this.openPanel(this.panels[t])}containsPanel(e){return this.panels.includes(e)}init(e){}update(e){}focus(){var e;null==(e=this._activePanel)||e.focus()}openPanel(e,t={}){("number"!=typeof t.index||t.index>this.panels.length)&&(t.index=this.panels.length);let i=!!t.skipSetActive;(e.updateParentGroup(this.groupPanel,{skipSetActive:t.skipSetActive}),this.doAddPanel(e,t.index,{skipSetActive:i}),this._activePanel===e)?this.contentContainer.renderPanel(e,{asActive:!0}):(i||this.doSetActivePanel(e),t.skipSetGroupActive||this.accessor.doSetGroupActive(this.groupPanel),t.skipSetActive||this.updateContainer())}removePanel(e,t={skipSetActive:!1}){let i="string"==typeof e?e:e.id,n=this._panels.find(e=>e.id===i);if(!n)throw Error("invalid operation");return this._removePanel(n,t)}closeAllPanels(){if(this.panels.length>0)for(let e of[...this.panels])this.doClose(e);else this.accessor.removeGroup(this.groupPanel)}closePanel(e){this.doClose(e)}doClose(e){let t=1===this.panels.length&&1===this.accessor.groups.length;this.accessor.removePanel(e,t&&"emptyGroup"===this.accessor.options.noPanelsOverlay?{removeEmptyGroup:!1}:void 0)}isPanelActive(e){return this._activePanel===e}updateActions(e){this.tabsContainer.setRightActionsElement(e)}setActive(e,t=!1){(t||this.isActive!==e)&&(this._isGroupActive=e,tQ(this.container,"dv-active-group",e),tQ(this.container,"dv-inactive-group",!e),this.tabsContainer.setActive(this.isActive),!this._activePanel&&this.panels.length>0&&this.doSetActivePanel(this.panels[0]),this.updateContainer())}layout(e,t){var i;this._width=e,this._height=t,this.contentContainer.layout(this._width,this._height),(null==(i=this._activePanel)?void 0:i.layout)&&this._activePanel.layout(this._width,this._height)}_removePanel(e,t){let i=this._activePanel===e;if(this.doRemovePanel(e),i&&this.panels.length>0){let e=this.mostRecentlyUsed[0];this.openPanel(e,{skipSetActive:t.skipSetActive,skipSetGroupActive:t.skipSetActiveGroup})}return this._activePanel&&0===this.panels.length&&this.doSetActivePanel(void 0),t.skipSetActive||this.updateContainer(),e}doRemovePanel(e){let t=this.panels.indexOf(e);if(this._activePanel===e&&this.contentContainer.closePanel(),this.tabsContainer.delete(e.id),this._panels.splice(t,1),this.mostRecentlyUsed.includes(e)){let t=this.mostRecentlyUsed.indexOf(e);this.mostRecentlyUsed.splice(t,1)}let i=this._panelDisposables.get(e.id);i&&(i.dispose(),this._panelDisposables.delete(e.id)),this._onDidRemovePanel.fire({panel:e})}doAddPanel(e,t=this.panels.length,i={skipSetActive:!1}){let n=this._panels.indexOf(e);this.tabsContainer.show(),this.contentContainer.show(),this.tabsContainer.openPanel(e,t),i.skipSetActive||this.contentContainer.openPanel(e),n>-1||(this.updateMru(e),this.panels.splice(t,0,e),this._panelDisposables.set(e.id,new tK(e.api.onDidTitleChange(e=>this._onDidPanelTitleChange.fire(e)),e.api.onDidParametersChange(e=>this._onDidPanelParametersChange.fire(e)))),this._onDidAddPanel.fire({panel:e}))}doSetActivePanel(e){this._activePanel!==e&&(this._activePanel=e,e&&(this.tabsContainer.setActivePanel(e),this.contentContainer.openPanel(e),e.layout(this._width,this._height),this.updateMru(e),this.contentContainer.refreshFocusState(),this._onDidActivePanelChange.fire({panel:e})))}updateMru(e){this.mostRecentlyUsed.includes(e)&&this.mostRecentlyUsed.splice(this.mostRecentlyUsed.indexOf(e),1),this.mostRecentlyUsed=[e,...this.mostRecentlyUsed]}updateContainer(){var e,t;if(this.panels.forEach(e=>e.runEvents()),this.isEmpty&&!this.watermark){let e=this.accessor.createWatermarkComponent();e.init({containerApi:this._api,group:this.groupPanel}),this.watermark=e,t$(this.watermark.element,"pointerdown",()=>{this.isActive||this.accessor.doSetGroupActive(this.groupPanel)}),this.contentContainer.element.appendChild(this.watermark.element)}!this.isEmpty&&this.watermark&&(this.watermark.element.remove(),null==(t=(e=this.watermark).dispose)||t.call(e),this.watermark=void 0)}canDisplayOverlay(e,t,i){let n=new i2(e,i,t,tj,this.accessor.getPanel(this.id));return this._onUnhandledDragOverEvent.fire(n),n.isAccepted}handleDropEvent(e,t,i,n){if("no-drop-target"===this.locked)return;let o="number"==typeof n?this.panels[n]:void 0,r=new i4({nativeEvent:t,position:i,panel:o,getData:()=>tj(),kind:function(){switch(e){case"header":return"number"==typeof n?"tab":"header_space";case"content":return"content"}}(),group:this.groupPanel,api:this._api});if(this._onWillDrop.fire(r),r.defaultPrevented)return;let s=tj();if(s&&s.viewId===this.accessor.id){if("content"===e&&s.groupId===this.id&&("center"===i||null===s.panelId)||"header"===e&&s.groupId===this.id&&null===s.panelId)return;if(null===s.panelId){let{groupId:e}=s;this._onMove.fire({target:i,groupId:e,index:n});return}if(-1!==this.tabsContainer.indexOf(s.panelId)&&1===this.tabsContainer.size)return;let{groupId:t,panelId:o}=s;if(this.id===t&&!i&&this.tabsContainer.indexOf(o)===n)return;this._onMove.fire({target:i,groupId:s.groupId,itemId:s.panelId,index:n})}else this._onDidDrop.fire(new i3({nativeEvent:t,position:i,panel:o,getData:()=>tj(),group:this.groupPanel,api:this._api}))}updateDragAndDropState(){this.tabsContainer.updateDragAndDropState()}dispose(){var e,t,i;for(let n of(super.dispose(),null==(e=this.watermark)||e.element.remove(),null==(i=null==(t=this.watermark)?void 0:t.dispose)||i.call(t),this.watermark=void 0,this.panels))n.dispose();this.tabsContainer.dispose(),this.contentContainer.dispose()}}class i6 extends ij{constructor(e,t,i){super(e,t),this._onDidConstraintsChangeInternal=new tB,this.onDidConstraintsChangeInternal=this._onDidConstraintsChangeInternal.event,this._onDidConstraintsChange=new tB,this.onDidConstraintsChange=this._onDidConstraintsChange.event,this._onDidSizeChange=new tB,this.onDidSizeChange=this._onDidSizeChange.event,this.addDisposables(this._onDidConstraintsChangeInternal,this._onDidConstraintsChange,this._onDidSizeChange),i&&this.initialize(i)}setConstraints(e){this._onDidConstraintsChangeInternal.fire(e)}setSize(e){this._onDidSizeChange.fire(e)}}class i8 extends iW{get priority(){return this._priority}get snap(){return this._snap}get minimumWidth(){return this.__minimumWidth()}get minimumHeight(){return this.__minimumHeight()}get maximumHeight(){return this.__maximumHeight()}get maximumWidth(){return this.__maximumWidth()}__minimumWidth(){let e="function"==typeof this._minimumWidth?this._minimumWidth():this._minimumWidth;return e!==this._evaluatedMinimumWidth&&(this._evaluatedMinimumWidth=e,this.updateConstraints()),e}__maximumWidth(){let e="function"==typeof this._maximumWidth?this._maximumWidth():this._maximumWidth;return e!==this._evaluatedMaximumWidth&&(this._evaluatedMaximumWidth=e,this.updateConstraints()),e}__minimumHeight(){let e="function"==typeof this._minimumHeight?this._minimumHeight():this._minimumHeight;return e!==this._evaluatedMinimumHeight&&(this._evaluatedMinimumHeight=e,this.updateConstraints()),e}__maximumHeight(){let e="function"==typeof this._maximumHeight?this._maximumHeight():this._maximumHeight;return e!==this._evaluatedMaximumHeight&&(this._evaluatedMaximumHeight=e,this.updateConstraints()),e}get isActive(){return this.api.isActive}get isVisible(){return this.api.isVisible}constructor(e,t,i,n){super(e,t,null!=n?n:new i6(e,t)),this._evaluatedMinimumWidth=0,this._evaluatedMaximumWidth=Number.MAX_SAFE_INTEGER,this._evaluatedMinimumHeight=0,this._evaluatedMaximumHeight=Number.MAX_SAFE_INTEGER,this._minimumWidth=0,this._minimumHeight=0,this._maximumWidth=Number.MAX_SAFE_INTEGER,this._maximumHeight=Number.MAX_SAFE_INTEGER,this._snap=!1,this._onDidChange=new tB,this.onDidChange=this._onDidChange.event,"number"==typeof(null==i?void 0:i.minimumWidth)&&(this._minimumWidth=i.minimumWidth),"number"==typeof(null==i?void 0:i.maximumWidth)&&(this._maximumWidth=i.maximumWidth),"number"==typeof(null==i?void 0:i.minimumHeight)&&(this._minimumHeight=i.minimumHeight),"number"==typeof(null==i?void 0:i.maximumHeight)&&(this._maximumHeight=i.maximumHeight),this.api.initialize(this),this.addDisposables(this.api.onWillVisibilityChange(e=>{let{isVisible:t}=e,{accessor:i}=this._params;i.setVisible(this,t)}),this.api.onActiveChange(()=>{let{accessor:e}=this._params;e.doSetGroupActive(this)}),this.api.onDidConstraintsChangeInternal(e=>{("number"==typeof e.minimumWidth||"function"==typeof e.minimumWidth)&&(this._minimumWidth=e.minimumWidth),("number"==typeof e.minimumHeight||"function"==typeof e.minimumHeight)&&(this._minimumHeight=e.minimumHeight),("number"==typeof e.maximumWidth||"function"==typeof e.maximumWidth)&&(this._maximumWidth=e.maximumWidth),("number"==typeof e.maximumHeight||"function"==typeof e.maximumHeight)&&(this._maximumHeight=e.maximumHeight)}),this.api.onDidSizeChange(e=>{this._onDidChange.fire({height:e.height,width:e.width})}),this._onDidChange)}setVisible(e){this.api._onDidVisibilityChange.fire({isVisible:e})}setActive(e){this.api._onDidActiveChange.fire({isActive:e})}init(e){e.maximumHeight&&(this._maximumHeight=e.maximumHeight),e.minimumHeight&&(this._minimumHeight=e.minimumHeight),e.maximumWidth&&(this._maximumWidth=e.maximumWidth),e.minimumWidth&&(this._minimumWidth=e.minimumWidth),this._priority=e.priority,this._snap=!!e.snap,super.init(e),"boolean"==typeof e.isVisible&&this.setVisible(e.isVisible)}updateConstraints(){this.api._onDidConstraintsChange.fire({minimumWidth:this._evaluatedMinimumWidth,maximumWidth:this._evaluatedMaximumWidth,minimumHeight:this._evaluatedMinimumHeight,maximumHeight:this._evaluatedMaximumHeight})}toJSON(){let e=super.toJSON(),t=e=>e===Number.MAX_SAFE_INTEGER?void 0:e,i=e=>e<=0?void 0:e;return Object.assign(Object.assign({},e),{minimumHeight:i(this.minimumHeight),maximumHeight:t(this.maximumHeight),minimumWidth:i(this.minimumWidth),maximumWidth:t(this.maximumWidth),snap:this.snap,priority:this.priority})}}let i7="dockview: DockviewGroupPanelApiImpl not initialized";class ne extends i6{get location(){if(!this._group)throw Error(i7);return this._group.model.location}constructor(e,t){super(e,"__dockviewgroup__"),this.accessor=t,this._onDidLocationChange=new tB,this.onDidLocationChange=this._onDidLocationChange.event,this._onDidActivePanelChange=new tB,this.onDidActivePanelChange=this._onDidActivePanelChange.event,this.addDisposables(this._onDidLocationChange,this._onDidActivePanelChange,this._onDidVisibilityChange.event(e=>{e.isVisible&&this._pendingSize&&(super.setSize(this._pendingSize),this._pendingSize=void 0)}))}setSize(e){this._pendingSize=Object.assign({},e),super.setSize(e)}close(){if(this._group)return this.accessor.removeGroup(this._group)}getWindow(){return"popout"===this.location.type?this.location.getWindow():window}moveTo(e){var t,i,n,o;if(!this._group)throw Error(i7);let r=null!=(t=e.group)?t:this.accessor.addGroup({direction:function(e){switch(e){case"top":return"above";case"bottom":return"below";case"left":return"left";case"right":return"right";case"center":return"within";default:throw Error(`invalid position '${e}'`)}}(null!=(i=e.position)?i:"right"),skipSetActive:null!=(n=e.skipSetActive)&&n});this.accessor.moveGroupOrPanel({from:{groupId:this._group.id},to:{group:r,position:e.group&&null!=(o=e.position)?o:"center",index:e.index},skipSetActive:e.skipSetActive})}maximize(){if(!this._group)throw Error(i7);"grid"===this.location.type&&this.accessor.maximizeGroup(this._group)}isMaximized(){if(!this._group)throw Error(i7);return this.accessor.isMaximizedGroup(this._group)}exitMaximized(){if(!this._group)throw Error(i7);this.isMaximized()&&this.accessor.exitMaximizedGroup()}initialize(e){this._group=e}}class nt extends i8{get minimumWidth(){var e;if("number"==typeof this._explicitConstraints.minimumWidth)return this._explicitConstraints.minimumWidth;let t=null==(e=this.activePanel)?void 0:e.minimumWidth;return"number"==typeof t?t:super.__minimumWidth()}get minimumHeight(){var e;if("number"==typeof this._explicitConstraints.minimumHeight)return this._explicitConstraints.minimumHeight;let t=null==(e=this.activePanel)?void 0:e.minimumHeight;return"number"==typeof t?t:super.__minimumHeight()}get maximumWidth(){var e;if("number"==typeof this._explicitConstraints.maximumWidth)return this._explicitConstraints.maximumWidth;let t=null==(e=this.activePanel)?void 0:e.maximumWidth;return"number"==typeof t?t:super.__maximumWidth()}get maximumHeight(){var e;if("number"==typeof this._explicitConstraints.maximumHeight)return this._explicitConstraints.maximumHeight;let t=null==(e=this.activePanel)?void 0:e.maximumHeight;return"number"==typeof t?t:super.__maximumHeight()}get panels(){return this._model.panels}get activePanel(){return this._model.activePanel}get size(){return this._model.size}get model(){return this._model}get locked(){return this._model.locked}set locked(e){this._model.locked=e}get header(){return this._model.header}constructor(e,t,i){var n,o,r,s,a,l;super(t,"groupview_default",{minimumHeight:null!=(o=null==(n=i.constraints)?void 0:n.minimumHeight)?o:100,minimumWidth:null!=(s=null==(r=i.constraints)?void 0:r.minimumWidth)?s:100,maximumHeight:null==(a=i.constraints)?void 0:a.maximumHeight,maximumWidth:null==(l=i.constraints)?void 0:l.maximumWidth},new ne(t,e)),this._explicitConstraints={},this.api.initialize(this),this._model=new i9(this.element,e,t,i,this),this.addDisposables(this.model.onDidActivePanelChange(e=>{this.api._onDidActivePanelChange.fire(e)}),this.api.onDidConstraintsChangeInternal(e=>{void 0!==e.minimumWidth&&(this._explicitConstraints.minimumWidth="function"==typeof e.minimumWidth?e.minimumWidth():e.minimumWidth),void 0!==e.minimumHeight&&(this._explicitConstraints.minimumHeight="function"==typeof e.minimumHeight?e.minimumHeight():e.minimumHeight),void 0!==e.maximumWidth&&(this._explicitConstraints.maximumWidth="function"==typeof e.maximumWidth?e.maximumWidth():e.maximumWidth),void 0!==e.maximumHeight&&(this._explicitConstraints.maximumHeight="function"==typeof e.maximumHeight?e.maximumHeight():e.maximumHeight)}))}focus(){this.api.isActive||this.api.setActive(),super.focus()}initialize(){this._model.initialize()}setActive(e){super.setActive(e),this.model.setActive(e)}layout(e,t){super.layout(e,t),this.model.layout(e,t)}getComponent(){return this._model}toJSON(){return this.model.toJSON()}}let ni={name:"abyss",className:"dockview-theme-abyss"};class nn extends i6{get location(){return this.group.api.location}get title(){return this.panel.title}get isGroupActive(){return this.group.isActive}get renderer(){return this.panel.renderer}set group(e){let t=this._group;this._group!==e&&(this._group=e,this._onDidGroupChange.fire({}),this.setupGroupEventListeners(t),this._onDidLocationChange.fire({location:this.group.api.location}))}get group(){return this._group}get tabComponent(){return this._tabComponent}constructor(e,t,i,n,o){super(e.id,n),this.panel=e,this.accessor=i,this._onDidTitleChange=new tB,this.onDidTitleChange=this._onDidTitleChange.event,this._onDidActiveGroupChange=new tB,this.onDidActiveGroupChange=this._onDidActiveGroupChange.event,this._onDidGroupChange=new tB,this.onDidGroupChange=this._onDidGroupChange.event,this._onDidRendererChange=new tB,this.onDidRendererChange=this._onDidRendererChange.event,this._onDidLocationChange=new tB,this.onDidLocationChange=this._onDidLocationChange.event,this.groupEventsDisposable=new tJ,this._tabComponent=o,this.initialize(e),this._group=t,this.setupGroupEventListeners(),this.addDisposables(this.groupEventsDisposable,this._onDidRendererChange,this._onDidTitleChange,this._onDidGroupChange,this._onDidActiveGroupChange,this._onDidLocationChange)}getWindow(){return this.group.api.getWindow()}moveTo(e){var t,i;this.accessor.moveGroupOrPanel({from:{groupId:this._group.id,panelId:this.panel.id},to:{group:null!=(t=e.group)?t:this._group,position:e.group&&null!=(i=e.position)?i:"center",index:e.index},skipSetActive:e.skipSetActive})}setTitle(e){this.panel.setTitle(e)}setRenderer(e){this.panel.setRenderer(e)}close(){this.group.model.closePanel(this.panel)}maximize(){this.group.api.maximize()}isMaximized(){return this.group.api.isMaximized()}exitMaximized(){this.group.api.exitMaximized()}setupGroupEventListeners(e){var t;let i=null!=(t=null==e?void 0:e.isActive)&&t;this.groupEventsDisposable.value=new tK(this.group.api.onDidVisibilityChange(e=>{let t=!e.isVisible&&this.isVisible,i=e.isVisible&&!this.isVisible,n=this.group.model.isPanelActive(this.panel);(t||i&&n)&&this._onDidVisibilityChange.fire(e)}),this.group.api.onDidLocationChange(e=>{this.group===this.panel.group&&this._onDidLocationChange.fire(e)}),this.group.api.onDidActiveChange(()=>{this.group===this.panel.group&&i!==this.isGroupActive&&(i=this.isGroupActive,this._onDidActiveGroupChange.fire({isActive:this.isGroupActive}))}))}}class no extends tK{get params(){return this._params}get title(){return this._title}get group(){return this._group}get renderer(){var e;return null!=(e=this._renderer)?e:this.accessor.renderer}get minimumWidth(){return this._minimumWidth}get minimumHeight(){return this._minimumHeight}get maximumWidth(){return this._maximumWidth}get maximumHeight(){return this._maximumHeight}constructor(e,t,i,n,o,r,s,a){super(),this.id=e,this.accessor=n,this.containerApi=o,this.view=s,this._renderer=a.renderer,this._group=r,this._minimumWidth=a.minimumWidth,this._minimumHeight=a.minimumHeight,this._maximumWidth=a.maximumWidth,this._maximumHeight=a.maximumHeight,this.api=new nn(this,this._group,n,t,i),this.addDisposables(this.api.onActiveChange(()=>{n.setActivePanel(this)}),this.api.onDidSizeChange(e=>{this.group.api.setSize(e)}),this.api.onDidRendererChange(()=>{this.group.model.rerender(this)}))}init(e){this._params=e.params,this.view.init(Object.assign(Object.assign({},e),{api:this.api,containerApi:this.containerApi})),this.setTitle(e.title)}focus(){let e=new iT;this.api._onWillFocus.fire(e),!e.defaultPrevented&&(this.api.isActive||this.api.setActive())}toJSON(){return{id:this.id,contentComponent:this.view.contentComponent,tabComponent:this.view.tabComponent,params:Object.keys(this._params||{}).length>0?this._params:void 0,title:this.title,renderer:this._renderer,minimumHeight:this._minimumHeight,maximumHeight:this._maximumHeight,minimumWidth:this._minimumWidth,maximumWidth:this._maximumWidth}}setTitle(e){e!==this.title&&(this._title=e,this.api._onDidTitleChange.fire({title:e}))}setRenderer(e){e!==this.renderer&&(this._renderer=e,this.api._onDidRendererChange.fire({renderer:e}))}update(e){var t;for(let i of(this._params=Object.assign(Object.assign({},null!=(t=this._params)?t:{}),e.params),Object.keys(e.params)))void 0===e.params[i]&&delete this._params[i];this.view.update({params:this._params})}updateFromStateModel(e){var t,i,n;this._maximumHeight=e.maximumHeight,this._minimumHeight=e.minimumHeight,this._maximumWidth=e.maximumWidth,this._minimumWidth=e.minimumWidth,this.update({params:null!=(t=e.params)?t:{}}),this.setTitle(null!=(i=e.title)?i:this.id),this.setRenderer(null!=(n=e.renderer)?n:this.accessor.renderer)}updateParentGroup(e,t){this._group=e,this.api.group=this._group;let i=this._group.model.isPanelActive(this),n=this.group.api.isActive&&i;(null==t?void 0:t.skipSetActive)||this.api.isActive===n||this.api._onDidActiveChange.fire({isActive:this.group.api.isActive&&i}),this.api.isVisible!==i&&this.api._onDidVisibilityChange.fire({isVisible:i})}runEvents(){let e=this._group.model.isPanelActive(this),t=this.group.api.isActive&&e;this.api.isActive!==t&&this.api._onDidActiveChange.fire({isActive:this.group.api.isActive&&e}),this.api.isVisible!==e&&this.api._onDidVisibilityChange.fire({isVisible:e})}layout(e,t){this.api._onDidDimensionChange.fire({width:e,height:t}),this.view.layout(e,t)}dispose(){this.api.dispose(),this.view.dispose()}}class nr extends tK{get element(){return this._element}constructor(){super(),this._element=document.createElement("div"),this._element.className="dv-default-tab",this._content=document.createElement("div"),this._content.className="dv-default-tab-content",this.action=document.createElement("div"),this.action.className="dv-default-tab-action",this.action.appendChild(iQ({width:"11",height:"11",viewbox:"0 0 28 28",path:"M2.1 27.3L0 25.2L11.55 13.65L0 2.1L2.1 0L13.65 11.55L25.2 0L27.3 2.1L15.75 13.65L27.3 25.2L25.2 27.3L13.65 15.75L2.1 27.3Z"})),this._element.appendChild(this._content),this._element.appendChild(this.action),this.render()}init(e){this._title=e.title,this.addDisposables(e.api.onDidTitleChange(e=>{this._title=e.title,this.render()}),t$(this.action,"pointerdown",e=>{e.preventDefault()}),t$(this.action,"click",t=>{t.defaultPrevented||(t.preventDefault(),e.api.close())})),this.render()}render(){var e;this._content.textContent!==this._title&&(this._content.textContent=null!=(e=this._title)?e:"")}}class ns{get content(){return this._content}get tab(){return this._tab}constructor(e,t,i,n){this.accessor=e,this.id=t,this.contentComponent=i,this.tabComponent=n,this._content=this.createContentComponent(this.id,i),this._tab=this.createTabComponent(this.id,n)}createTabRenderer(e){var t;let i=this.createTabComponent(this.id,this.tabComponent);return this._params&&i.init(Object.assign(Object.assign({},this._params),{tabLocation:e})),this._updateEvent&&(null==(t=i.update)||t.call(i,this._updateEvent)),i}init(e){this._params=e,this.content.init(e),this.tab.init(Object.assign(Object.assign({},e),{tabLocation:"header"}))}layout(e,t){var i,n;null==(n=(i=this.content).layout)||n.call(i,e,t)}update(e){var t,i,n,o;this._updateEvent=e,null==(i=(t=this.content).update)||i.call(t,e),null==(o=(n=this.tab).update)||o.call(n,e)}dispose(){var e,t,i,n;null==(t=(e=this.content).dispose)||t.call(e),null==(n=(i=this.tab).dispose)||n.call(i)}createContentComponent(e,t){return this.accessor.options.createComponent({id:e,name:t})}createTabComponent(e,t){let i=null!=t?t:this.accessor.options.defaultTabComponent;if(i){if(this.accessor.options.createTabComponent){let t=this.accessor.options.createTabComponent({id:e,name:i});return t||new nr}console.warn(`dockview: tabComponent '${t}' was not found. falling back to the default tab.`)}return new nr}}class na{constructor(e){this.accessor=e}fromJSON(e,t){var i,n;let o=e.id,r=e.params,s=e.title,a=e.view,l=a?a.content.id:null!=(i=e.contentComponent)?i:"unknown",d=a?null==(n=a.tab)?void 0:n.id:e.tabComponent,h=new ns(this.accessor,o,l,d),u=new no(o,l,d,this.accessor,new iA(this.accessor),t,h,{renderer:e.renderer,minimumWidth:e.minimumWidth,minimumHeight:e.minimumHeight,maximumWidth:e.maximumWidth,maximumHeight:e.maximumHeight});return u.init({title:null!=s?s:o,params:null!=r?r:{}}),u}}class nl extends tK{get element(){return this._element}constructor(){super(),this._element=document.createElement("div"),this._element.className="dv-watermark"}init(e){}}let nd=new class{constructor(){this._orderedList=[]}push(e){this._orderedList=[...this._orderedList.filter(t=>t!==e),e],this.update()}destroy(e){this._orderedList=this._orderedList.filter(t=>t!==e),this.update()}update(){for(let e=0;e{let e=null,t=t3();i.value=new tK({dispose:()=>{t.release()}},t$(window,"pointermove",t=>{let i=this.options.container.getBoundingClientRect(),n=t.clientX-i.left,o=t.clientY-i.top;tQ(this._element,"dv-resize-container-dragging",!0);let r=this._element.getBoundingClientRect();null===e&&(e={x:t.clientX-r.left,y:t.clientY-r.top});let s=Math.max(0,this.getMinimumWidth(r.width)),a=Math.max(0,this.getMinimumHeight(r.height)),l=it(o-e.y,-a,Math.max(0,i.height-r.height+a)),d=it(e.y-o+i.height-r.height,-a,Math.max(0,i.height-r.height+a)),h=it(n-e.x,-s,Math.max(0,i.width-r.width+s)),u=it(e.x-n+i.width-r.width,-s,Math.max(0,i.width-r.width+s)),c={};l<=d?c.top=l:c.bottom=d,h<=u?c.left=h:c.right=u,this.setBounds(c)}),t$(window,"pointerup",()=>{tQ(this._element,"dv-resize-container-dragging",!1),i.dispose(),this._onDidChangeEnd.fire()}))};this.addDisposables(i,t$(e,"pointerdown",e=>{e.defaultPrevented?e.preventDefault():e[t2]||n()}),t$(this.options.content,"pointerdown",e=>{e.defaultPrevented||!e[t2]&&e.shiftKey&&n()}),t$(this.options.content,"pointerdown",()=>{nd.push(this._element)},!0)),t.inDragMode&&n()}setupResize(e){let t=document.createElement("div");t.className=`dv-resize-handle-${e}`,this._element.appendChild(t);let i=new tJ;this.addDisposables(i,t$(t,"pointerdown",t=>{t.preventDefault();let n=null,o=t3();i.value=new tK(t$(window,"pointermove",t=>{let i,o,r,s,a,l,d=this.options.container.getBoundingClientRect(),h=this._element.getBoundingClientRect(),u=t.clientY-d.top,c=t.clientX-d.left;null===n&&(n={originalY:u,originalHeight:h.height,originalX:c,originalWidth:h.width});let p=()=>{i=it(u,0,n.originalY+n.originalHeight>d.height?Math.max(0,d.height-nh.MINIMUM_HEIGHT):Math.max(0,n.originalY+n.originalHeight-nh.MINIMUM_HEIGHT)),r=n.originalY+n.originalHeight-i,o=d.height-i-r},m=()=>{let e=(i=n.originalY-n.originalHeight)<0&&"number"==typeof this.options.minimumInViewportHeight?-i+this.options.minimumInViewportHeight:nh.MINIMUM_HEIGHT,t=d.height-Math.max(0,i);r=it(u-i,e,t),o=d.height-i-r},f=()=>{s=it(c,0,n.originalX+n.originalWidth>d.width?Math.max(0,d.width-nh.MINIMUM_WIDTH):Math.max(0,n.originalX+n.originalWidth-nh.MINIMUM_WIDTH)),l=n.originalX+n.originalWidth-s,a=d.width-s-l},g=()=>{let e=(s=n.originalX-n.originalWidth)<0&&"number"==typeof this.options.minimumInViewportWidth?-s+this.options.minimumInViewportWidth:nh.MINIMUM_WIDTH,t=d.width-Math.max(0,s);l=it(c-s,e,t),a=d.width-s-l};switch(e){case"top":p();break;case"bottom":m();break;case"left":f();break;case"right":g();break;case"topleft":p(),f();break;case"topright":p(),g();break;case"bottomleft":m(),f();break;case"bottomright":m(),g()}let v={};i<=o?v.top=i:v.bottom=o,s<=a?v.left=s:v.right=a,v.height=r,v.width=l,this.setBounds(v)}),{dispose:()=>{o.release()}},t$(window,"pointerup",()=>{i.dispose(),this._onDidChangeEnd.fire()}))}))}getMinimumWidth(e){return"number"==typeof this.options.minimumInViewportWidth?e-this.options.minimumInViewportWidth:0}getMinimumHeight(e){return"number"==typeof this.options.minimumInViewportHeight?e-this.options.minimumInViewportHeight:0}dispose(){nd.destroy(this._element),this._element.remove(),super.dispose()}}nh.MINIMUM_HEIGHT=20,nh.MINIMUM_WIDTH=20;class nu extends tK{constructor(e,t){super(),this.group=e,this.overlay=t,this.addDisposables(t)}position(e){this.overlay.setBounds(e)}}class nc{constructor(){this.cache=new Map,this.currentFrameId=0,this.rafId=null}getPosition(e){let t=this.cache.get(e);if(t&&t.frameId===this.currentFrameId)return t.rect;this.scheduleFrameUpdate();let i=t5(e);return this.cache.set(e,{rect:i,frameId:this.currentFrameId}),i}invalidate(){this.currentFrameId++}scheduleFrameUpdate(){this.rafId||(this.rafId=requestAnimationFrame(()=>{this.currentFrameId++,this.rafId=null}))}}class np extends tK{constructor(e,t){super(),this.element=e,this.accessor=t,this.map={},this._disposed=!1,this.positionCache=new nc,this.pendingUpdates=new Set,this.addDisposables(O.from(()=>{for(let e of Object.values(this.map))e.disposable.dispose(),e.destroy.dispose();this._disposed=!0}))}updateAllPositions(){if(!this._disposed)for(let e of(this.positionCache.invalidate(),Object.values(this.map)))e.panel.api.isVisible&&e.resize&&e.resize()}detatch(e){if(this.map[e.api.id]){let{disposable:t,destroy:i}=this.map[e.api.id];return t.dispose(),i.dispose(),delete this.map[e.api.id],!0}return!1}attach(e){let{panel:t,referenceContainer:i}=e;if(!this.map[t.api.id]){let e,i=((e=document.createElement("div")).tabIndex=-1,e);i.className="dv-render-overlay",this.map[t.api.id]={panel:t,disposable:O.NONE,destroy:O.NONE,element:i}}let n=this.map[t.api.id].element;t.view.content.element.parentElement!==n&&n.appendChild(t.view.content.element),n.parentElement!==this.element&&this.element.appendChild(n);let o=()=>{let e=t.api.id;this.pendingUpdates.has(e)||(this.pendingUpdates.add(e),requestAnimationFrame(()=>{if(this.pendingUpdates.delete(e),this.isDisposed||!this.map[e])return;let o=this.positionCache.getPosition(i.element),r=this.positionCache.getPosition(this.element),s=o.left-r.left,a=o.top-r.top,l=o.width,d=o.height;n.style.left=`${s}px`,n.style.top=`${a}px`,n.style.width=`${l}px`,n.style.height=`${d}px`,tQ(n,"dv-render-overlay-float","floating"===t.group.api.location.type)}))},r=()=>{t.api.isVisible&&(this.positionCache.invalidate(),o()),n.style.display=t.api.isVisible?"":"none"},s=new tJ,a=()=>{"floating"===t.api.location.type?queueMicrotask(()=>{let e=this.accessor.floatingGroups.find(e=>e.group===t.api.group);if(!e)return;let i=e.overlay.element,o=()=>{let e=Number(i.getAttribute("aria-level"));n.style.zIndex=`calc(var(--dv-overlay-z-index, 999) + ${2*e+1})`},r=new MutationObserver(()=>{o()});s.value=O.from(()=>r.disconnect()),r.observe(i,{attributeFilter:["aria-level"],attributes:!0}),o()}):n.style.zIndex=""},l=new tK(s,new iz(n,{onDragEnd:e=>{i.dropTarget.dnd.onDragEnd(e)},onDragEnter:e=>{i.dropTarget.dnd.onDragEnter(e)},onDragLeave:e=>{i.dropTarget.dnd.onDragLeave(e)},onDrop:e=>{i.dropTarget.dnd.onDrop(e)},onDragOver:e=>{i.dropTarget.dnd.onDragOver(e)}}),t.api.onDidVisibilityChange(()=>{r()}),t.api.onDidDimensionsChange(()=>{t.api.isVisible&&o()}),t.api.onDidLocationChange(()=>{a()}));return this.map[t.api.id].destroy=O.from(()=>{var e;t.view.content.element.parentElement===n&&n.removeChild(t.view.content.element),null==(e=n.parentElement)||e.removeChild(n)}),a(),queueMicrotask(()=>{this.isDisposed||r()}),this.map[t.api.id].disposable.dispose(),this.map[t.api.id].disposable=l,this.map[t.api.id].resize=o,n}}class nm extends tK{get window(){var e,t;return null!=(t=null==(e=this._window)?void 0:e.value)?t:null}constructor(e,t,i){super(),this.target=e,this.className=t,this.options=i,this._onWillClose=new tB,this.onWillClose=this._onWillClose.event,this._onDidClose=new tB,this.onDidClose=this._onDidClose.event,this._window=null,this.addDisposables(this._onWillClose,this._onDidClose,{dispose:()=>{this.close()}})}dimensions(){if(!this._window)return null;let e=this._window.value.screenX,t=this._window.value.screenY;return{top:t,left:e,width:this._window.value.innerWidth,height:this._window.value.innerHeight}}close(){var e,t;this._window&&(this._onWillClose.fire(),null==(t=(e=this.options).onWillClose)||t.call(e,{id:this.target,window:this._window.value}),this._window.disposable.dispose(),this._window=null,this._onDidClose.fire())}open(){var e,t,i,n,o,r;return i=this,n=void 0,o=void 0,r=function*(){if(this._window)throw Error("instance of popout window is already open");let i=`${this.options.url}`,n=Object.entries({top:this.options.top,left:this.options.left,width:this.options.width,height:this.options.height}).map(([e,t])=>`${e}=${t}`).join(","),o=window.open(i,this.target,n);if(!o)return null;let r=new tK;this._window={value:o,disposable:r},r.addDisposables(O.from(()=>{o.close()}),t$(window,"beforeunload",()=>{this.close()}));let s=this.createPopoutWindowContainer();return this.className&&s.classList.add(this.className),null==(t=(e=this.options).onDidOpen)||t.call(e,{id:this.target,window:o}),new Promise((e,t)=>{o.addEventListener("unload",e=>{}),o.addEventListener("load",()=>{try{let t=o.document;t.title=document.title,t.body.appendChild(s),function(e,t){for(let i of Array.from(t)){if(i.href){let t=e.createElement("link");t.href=i.href,t.type=i.type,t.rel="stylesheet",e.head.appendChild(t)}let t=[];try{i.cssRules&&(t=Array.from(i.cssRules).map(e=>e.cssText))}catch(e){}for(let i of t){let t=e.createElement("style");t.appendChild(e.createTextNode(i)),e.head.appendChild(t)}}}(t,window.document.styleSheets),t$(o,"beforeunload",()=>{this.close()}),e(s)}catch(e){t(e)}})})},new(o||(o=Promise))(function(e,t){function s(e){try{l(r.next(e))}catch(e){t(e)}}function a(e){try{l(r.throw(e))}catch(e){t(e)}}function l(t){var i;t.done?e(t.value):((i=t.value)instanceof o?i:new o(function(e){e(i)})).then(s,a)}l((r=r.apply(i,n||[])).next())})}createPopoutWindowContainer(){let e=document.createElement("div");return e.classList.add("dv-popout-window"),e.id="dv-popout-window",e.style.position="absolute",e.style.width="100%",e.style.height="100%",e.style.top="0px",e.style.left="0px",e}}class nf extends tK{constructor(e){super(),this.accessor=e,this.init()}init(){let e=new Set,t=new Set;this.addDisposables(this.accessor.onDidAddPanel(t=>{if(e.has(t.api.id))throw Error(`dockview: Invalid event sequence. [onDidAddPanel] called for panel ${t.api.id} but panel already exists`);e.add(t.api.id)}),this.accessor.onDidRemovePanel(t=>{if(e.has(t.api.id))e.delete(t.api.id);else throw Error(`dockview: Invalid event sequence. [onDidRemovePanel] called for panel ${t.api.id} but panel does not exists`)}),this.accessor.onDidAddGroup(e=>{if(t.has(e.api.id))throw Error(`dockview: Invalid event sequence. [onDidAddGroup] called for group ${e.api.id} but group already exists`);t.add(e.api.id)}),this.accessor.onDidRemoveGroup(e=>{if(t.has(e.api.id))t.delete(e.api.id);else throw Error(`dockview: Invalid event sequence. [onDidRemoveGroup] called for group ${e.api.id} but group does not exists`)}))}}class ng extends tK{constructor(e){super(),this.root=e,this._active=null,this._activeDisposable=new tJ,this._element=document.createElement("div"),this._element.className="dv-popover-anchor",this._element.style.position="relative",this.root.prepend(this._element),this.addDisposables(O.from(()=>{this.close()}),this._activeDisposable)}openPopover(e,t){var i;this.close();let n=document.createElement("div");n.style.position="absolute",n.style.zIndex=null!=(i=t.zIndex)?i:"var(--dv-overlay-z-index)",n.appendChild(e);let o=this._element.getBoundingClientRect(),r=o.left,s=o.top;n.style.top=`${t.y-s}px`,n.style.left=`${t.x-r}px`,this._element.appendChild(n),this._active=n,this._activeDisposable.value=new tK(t$(window,"pointerdown",e=>{var t;let i=e.target;if(!(i instanceof HTMLElement))return;let o=i;for(;o&&o!==n;)o=null!=(t=null==o?void 0:o.parentElement)?t:null;o||this.close()})),requestAnimationFrame(()=>{!function(e,t,i={buffer:10}){let n=i.buffer,o=e.getBoundingClientRect(),r=t.getBoundingClientRect(),s=0,a=0,l=o.left-r.left,d=o.top-r.top,h=o.bottom-r.bottom,u=o.right-r.right;ln&&(s=-n-u),dn&&(a=-h-n),(0!==s||0!==a)&&(e.style.transform=`translate(${s}px, ${a}px)`)}(n,this.root)})}close(){this._active&&(this._active.remove(),this._activeDisposable.dispose(),this._active=null)}}class nv extends tK{get disabled(){return this._disabled}set disabled(e){var t;this.disabled!==e&&(this._disabled=e,e&&(null==(t=this.model)||t.clear()))}get model(){if(!this.disabled)return{clear:()=>{var e;this._model&&(null==(e=this._model.root.parentElement)||e.removeChild(this._model.root)),this._model=void 0},exists:()=>!!this._model,getElements:(e,t)=>{let i=this._outline!==t;if(this._outline=t,this._model)return this._model.changed=i,this._model;let n=this.createContainer(),o=this.createAnchor();if(this._model={root:n,overlay:o,changed:i},n.appendChild(o),this.element.appendChild(n),(null==e?void 0:e.target)instanceof HTMLElement){let t=e.target.getBoundingClientRect(),i=this.element.getBoundingClientRect();o.style.left=`${t.left-i.left}px`,o.style.top=`${t.top-i.top}px`}return this._model}}}constructor(e,t){super(),this.element=e,this._disabled=!1,this._disabled=t.disabled,this.addDisposables(O.from(()=>{var e;null==(e=this.model)||e.clear()}))}createContainer(){let e=document.createElement("div");return e.className="dv-drop-target-container",e}createAnchor(){let e=document.createElement("div");return e.className="dv-drop-target-anchor",e.style.visibility="hidden",e}}let nw={activationSize:{type:"pixels",value:10},size:{type:"pixels",value:20}};function nb(e){let t=e.from.activePanel;[...e.from.panels].map(t=>{let i=e.from.model.removePanel(t);return e.from.model.renderContainer.detatch(t),i}).forEach(i=>{e.to.model.openPanel(i,{skipSetActive:t!==i,skipSetGroupActive:!0})})}class ny extends iD{get orientation(){return this.gridview.orientation}get totalPanels(){return this.panels.length}get panels(){return this.groups.flatMap(e=>e.panels)}get options(){return this._options}get activePanel(){let e=this.activeGroup;if(e)return e.activePanel}get renderer(){var e;return null!=(e=this.options.defaultRenderer)?e:"onlyWhenVisible"}get api(){return this._api}get floatingGroups(){return this._floatingGroups}get popoutRestorationPromise(){return this._popoutRestorationPromise}constructor(e,t){var i,n,o;super(e,{proportionalLayout:!0,orientation:I.HORIZONTAL,styles:t.hideBorders?{separatorBorder:"transparent"}:void 0,disableAutoResizing:t.disableAutoResizing,locked:t.locked,margin:null!=(n=null==(i=t.theme)?void 0:i.gap)?n:0,className:t.className}),this.nextGroupId=ii(),this._deserializer=new na(this),this._watermark=null,this._onWillDragPanel=new tB,this.onWillDragPanel=this._onWillDragPanel.event,this._onWillDragGroup=new tB,this.onWillDragGroup=this._onWillDragGroup.event,this._onDidDrop=new tB,this.onDidDrop=this._onDidDrop.event,this._onWillDrop=new tB,this.onWillDrop=this._onWillDrop.event,this._onWillShowOverlay=new tB,this.onWillShowOverlay=this._onWillShowOverlay.event,this._onUnhandledDragOverEvent=new tB,this.onUnhandledDragOverEvent=this._onUnhandledDragOverEvent.event,this._onDidRemovePanel=new tB,this.onDidRemovePanel=this._onDidRemovePanel.event,this._onDidAddPanel=new tB,this.onDidAddPanel=this._onDidAddPanel.event,this._onDidPopoutGroupSizeChange=new tB,this.onDidPopoutGroupSizeChange=this._onDidPopoutGroupSizeChange.event,this._onDidPopoutGroupPositionChange=new tB,this.onDidPopoutGroupPositionChange=this._onDidPopoutGroupPositionChange.event,this._onDidOpenPopoutWindowFail=new tB,this.onDidOpenPopoutWindowFail=this._onDidOpenPopoutWindowFail.event,this._onDidLayoutFromJSON=new tB,this.onDidLayoutFromJSON=this._onDidLayoutFromJSON.event,this._onDidActivePanelChange=new tB({replay:!0}),this.onDidActivePanelChange=this._onDidActivePanelChange.event,this._onDidMovePanel=new tB,this.onDidMovePanel=this._onDidMovePanel.event,this._onDidMaximizedGroupChange=new tB,this.onDidMaximizedGroupChange=this._onDidMaximizedGroupChange.event,this._floatingGroups=[],this._popoutGroups=[],this._popoutRestorationPromise=Promise.resolve(),this._onDidRemoveGroup=new tB,this.onDidRemoveGroup=this._onDidRemoveGroup.event,this._onDidAddGroup=new tB,this.onDidAddGroup=this._onDidAddGroup.event,this._onDidOptionsChange=new tB,this.onDidOptionsChange=this._onDidOptionsChange.event,this._onDidActiveGroupChange=new tB,this.onDidActiveGroupChange=this._onDidActiveGroupChange.event,this._moving=!1,this._options=t,this.popupService=new ng(this.element),this._themeClassnames=new t4(this.element),this._api=new iA(this),this.rootDropTargetContainer=new nv(this.element,{disabled:!0}),this.overlayRenderContainer=new np(this.gridview.element,this),this._rootDropTarget=new iI(this.element,{className:"dv-drop-target-edge",canDisplayOverlay:(e,t)=>{let i=tj();if(i)return i.viewId===this.id&&("center"!==t||0===this.gridview.length);if("center"===t&&0!==this.gridview.length)return!1;let n=new i2(e,"edge",t,tj);return this._onUnhandledDragOverEvent.fire(n),n.isAccepted},acceptedTargetZones:["top","bottom","left","right","center"],overlayModel:null!=(o=t.rootOverlayModel)?o:nw,getOverrideTarget:()=>{var e;return null==(e=this.rootDropTargetContainer)?void 0:e.model}}),this.updateDropTargetModel(t),tQ(this.gridview.element,"dv-dockview",!0),tQ(this.element,"dv-debug",!!t.debug),this.updateTheme(),this.updateWatermark(),t.debug&&this.addDisposables(new nf(this)),this.addDisposables(this.rootDropTargetContainer,this.overlayRenderContainer,this._onWillDragPanel,this._onWillDragGroup,this._onWillShowOverlay,this._onDidActivePanelChange,this._onDidAddPanel,this._onDidRemovePanel,this._onDidLayoutFromJSON,this._onDidDrop,this._onWillDrop,this._onDidMovePanel,this._onDidMovePanel.event(()=>{this.debouncedUpdateAllPositions()}),this._onDidAddGroup,this._onDidRemoveGroup,this._onDidActiveGroupChange,this._onUnhandledDragOverEvent,this._onDidMaximizedGroupChange,this._onDidOptionsChange,this._onDidPopoutGroupSizeChange,this._onDidPopoutGroupPositionChange,this._onDidOpenPopoutWindowFail,this.onDidViewVisibilityChangeMicroTaskQueue(()=>{this.updateWatermark()}),this.onDidAdd(e=>{this._moving||this._onDidAddGroup.fire(e)}),this.onDidRemove(e=>{this._moving||this._onDidRemoveGroup.fire(e)}),this.onDidActiveChange(e=>{this._moving||this._onDidActiveGroupChange.fire(e)}),this.onDidMaximizedChange(e=>{this._onDidMaximizedGroupChange.fire({group:e.panel,isMaximized:e.isMaximized})}),N.any(this.onDidAdd,this.onDidRemove)(()=>{this.updateWatermark()}),N.any(this.onDidAddPanel,this.onDidRemovePanel,this.onDidAddGroup,this.onDidRemove,this.onDidMovePanel,this.onDidActivePanelChange,this.onDidPopoutGroupPositionChange,this.onDidPopoutGroupSizeChange)(()=>{this._bufferOnDidLayoutChange.fire()}),O.from(()=>{for(let e of[...this._floatingGroups])e.dispose();for(let e of[...this._popoutGroups])e.disposable.dispose()}),this._rootDropTarget,this._rootDropTarget.onWillShowOverlay(e=>{this.gridview.length>0&&"center"===e.position||this._onWillShowOverlay.fire(new iJ(e,{kind:"edge",panel:void 0,api:this._api,group:void 0,getData:tj}))}),this._rootDropTarget.onDrop(e=>{var t;let i=new i4({nativeEvent:e.nativeEvent,position:e.position,panel:void 0,api:this._api,group:void 0,getData:tj,kind:"edge"});if(this._onWillDrop.fire(i),i.defaultPrevented)return;let n=tj();n?this.moveGroupOrPanel({from:{groupId:n.groupId,panelId:null!=(t=n.panelId)?t:void 0},to:{group:this.orthogonalize(e.position),position:"center"}}):this._onDidDrop.fire(new i3({nativeEvent:e.nativeEvent,position:e.position,panel:void 0,api:this._api,group:void 0,getData:tj}))}),this._rootDropTarget)}setVisible(e,t){switch(e.api.location.type){case"grid":super.setVisible(e,t);break;case"floating":{let i=this.floatingGroups.find(t=>t.group===e);i&&(i.overlay.setVisible(t),e.api._onDidVisibilityChange.fire({isVisible:t}));break}case"popout":console.warn("dockview: You cannot hide a group that is in a popout window")}}addPopoutGroup(e,t){var i,n,o,r,s;if(e instanceof no&&1===e.group.size)return this.addPopoutGroup(e.group,t);let a=function(e){let t,i=e;for(;null!==i&&"string"!=typeof(t=(function(e){let t=[];for(let i=0;ie.startsWith("dockview-theme-")));)i=i.parentElement;return t}(this.gridview.element),l=this.element,d=(null==t?void 0:t.position)?t.position:e instanceof nt?e.element.getBoundingClientRect():e.group?e.group.element.getBoundingClientRect():l.getBoundingClientRect(),h=null!=(n=null==(i=null==t?void 0:t.overridePopoutGroup)?void 0:i.id)?n:this.getNextGroupId(),u=new nm(`${this.id}-${h}`,null!=a?a:"",{url:null!=(s=null!=(o=null==t?void 0:t.popoutUrl)?o:null==(r=this.options)?void 0:r.popoutUrl)?s:"/popout.html",left:window.screenX+d.left,top:window.screenY+d.top,width:d.width,height:d.height,onDidOpen:null==t?void 0:t.onDidOpen,onWillClose:null==t?void 0:t.onWillClose}),c=new tK(u,u.onDidClose(()=>{c.dispose()}));return u.open().then(i=>{var n,o,r,s;let a,l,d,p,m,f,g,v,w;if(u.isDisposed)return!1;let b=(null==t?void 0:t.referenceGroup)?t.referenceGroup:e instanceof no?e.group:e,y=e.api.location.type,x=null!==b.element.parentElement;if(x?(null==t?void 0:t.overridePopoutGroup)?a=t.overridePopoutGroup:(a=this.createGroup({id:h}),i&&this._onDidAddGroup.fire(a)):a=b,null===i)return console.error("dockview: failed to create popout. perhaps you need to allow pop-ups for this website"),c.dispose(),this._onDidOpenPopoutWindowFail.fire(),this.movingLock(()=>nb({from:a,to:b})),b.api.isVisible||b.api.setVisible(!0),!1;let _=document.createElement("div");_.className="dv-overlay-render-container";let D=new np(_,this);if(a.model.renderContainer=D,a.layout(u.window.innerWidth,u.window.innerHeight),!(null==t?void 0:t.overridePopoutGroup)&&x)if(e instanceof no)this.movingLock(()=>{let t=b.model.removePanel(e);a.model.openPanel(t)});else switch(this.movingLock(()=>nb({from:b,to:a})),y){case"grid":b.api.setVisible(!1);break;case"floating":case"popout":l=null==(n=this._floatingGroups.find(t=>t.group.api.id===e.api.id))?void 0:n.overlay.toJSON(),this.removeGroup(b)}i.classList.add("dv-dockview"),i.style.overflow="hidden",i.appendChild(_),i.appendChild(a.element);let C=document.createElement("div"),S=new nv(C,{disabled:this.rootDropTargetContainer.disabled});i.appendChild(C),a.model.dropTargetContainer=S,a.model.location={type:"popout",getWindow:()=>u.window,popoutUrl:null==t?void 0:t.popoutUrl},x&&"grid"===e.api.location.type&&e.api.setVisible(!1),this.doSetGroupAndPanelActive(a),c.addDisposables(a.api.onDidActiveChange(e=>{var t;e.isActive&&(null==(t=u.window)||t.focus())}),a.api.onWillFocus(()=>{var e;null==(e=u.window)||e.focus()}));let E=x&&b&&this.getPanel(b.id),A={window:u,popoutGroup:a,referenceGroup:E?b.id:void 0,disposable:{dispose:()=>(c.dispose(),d)}},k=(o=u.window,m=new tB,f=o.screenX,g=o.screenY,(v=()=>{if(o.closed)return;let e=o.screenX,t=o.screenY;(e!==f||t!==g)&&(clearTimeout(p),p=setTimeout(()=>{m.fire()},100),f=e,g=t),requestAnimationFrame(v)})(),m);return c.addDisposables(k,(r=u.window,s=()=>{this._onDidPopoutGroupSizeChange.fire({width:u.window.innerWidth,height:u.window.innerHeight,group:a})},new tK(t$(r,"resize",()=>{clearTimeout(w),w=setTimeout(()=>{s()},100)}))),k.event(()=>{this._onDidPopoutGroupPositionChange.fire({screenX:u.window.screenX,screenY:u.window.screenX,group:a})}),t$(u.window,"resize",()=>{a.layout(u.window.innerWidth,u.window.innerHeight)}),D,O.from(()=>{if(!this.isDisposed){if(x&&this.getPanel(b.id))this.movingLock(()=>nb({from:a,to:b})),b.api.isVisible||b.api.setVisible(!0),this.getPanel(a.id)&&this.doRemoveGroup(a,{skipPopoutAssociated:!0});else if(this.getPanel(a.id)){if(a.model.renderContainer=this.overlayRenderContainer,a.model.dropTargetContainer=this.rootDropTargetContainer,d=a,!this._popoutGroups.find(e=>e.popoutGroup===a))return;l?this.addFloatingGroup(a,{height:l.height,width:l.width,position:l}):(this.doRemoveGroup(a,{skipDispose:!0,skipActive:!0,skipPopoutReturn:!0}),a.model.location={type:"grid"},this.movingLock(()=>{this.doAddGroup(a,[0])})),this.doSetGroupAndPanelActive(a)}}})),this._popoutGroups.push(A),this.updateWatermark(),!0}).catch(e=>(console.error("dockview: failed to create popout.",e),!1))}addFloatingGroup(e,t){var i,n,o,r,s;let a;if(e instanceof no)a=this.createGroup(),this._onDidAddGroup.fire(a),this.movingLock(()=>this.removePanel(e,{removeEmptyGroup:!0,skipDispose:!0,skipSetActiveGroup:!0})),this.movingLock(()=>a.model.openPanel(e,{skipSetGroupActive:!0}));else{a=e;let n=null==(i=this._popoutGroups.find(e=>e.popoutGroup===a))?void 0:i.referenceGroup,o=n?this.getPanel(n):void 0;"boolean"==typeof(null==t?void 0:t.skipRemoveGroup)&&t.skipRemoveGroup||(o?(this.movingLock(()=>nb({from:e,to:o})),this.doRemoveGroup(e,{skipPopoutReturn:!0,skipPopoutAssociated:!0}),this.doRemoveGroup(o,{skipDispose:!0}),a=o):this.doRemoveGroup(e,{skipDispose:!0,skipPopoutReturn:!0,skipPopoutAssociated:!1}))}let l=function(){if(null==t?void 0:t.position){let e={};return"left"in t.position?e.left=Math.max(t.position.left,0):"right"in t.position?e.right=Math.max(t.position.right,0):e.left=100,"top"in t.position?e.top=Math.max(t.position.top,0):"bottom"in t.position?e.bottom=Math.max(t.position.bottom,0):e.top=100,"number"==typeof t.width?e.width=Math.max(t.width,0):e.width=300,"number"==typeof t.height?e.height=Math.max(t.height,0):e.height=300,e}return{left:"number"==typeof(null==t?void 0:t.x)?Math.max(t.x,0):100,top:"number"==typeof(null==t?void 0:t.y)?Math.max(t.y,0):100,width:"number"==typeof(null==t?void 0:t.width)?Math.max(t.width,0):300,height:"number"==typeof(null==t?void 0:t.height)?Math.max(t.height,0):300}}(),d=new nh(Object.assign(Object.assign({container:this.gridview.element,content:a.element},l),{minimumInViewportWidth:"boundedWithinViewport"===this.options.floatingGroupBounds?void 0:null!=(o=null==(n=this.options.floatingGroupBounds)?void 0:n.minimumWidthWithinViewport)?o:100,minimumInViewportHeight:"boundedWithinViewport"===this.options.floatingGroupBounds?void 0:null!=(s=null==(r=this.options.floatingGroupBounds)?void 0:r.minimumHeightWithinViewport)?s:100})),h=a.element.querySelector(".dv-void-container");if(!h)throw Error("dockview: failed to find drag handle");d.setupDrag(h,{inDragMode:"boolean"==typeof(null==t?void 0:t.inDragMode)&&t.inDragMode});let u=new nu(a,d),c=new tK(a.api.onDidActiveChange(e=>{e.isActive&&d.bringToFront()}),tX(a.element,e=>{let{width:t,height:i}=e.contentRect;a.layout(t,i)}));u.addDisposables(d.onDidChange(()=>{a.layout(a.width,a.height)}),d.onDidChangeEnd(()=>{this._bufferOnDidLayoutChange.fire()}),a.onDidChange(e=>{d.setBounds({height:null==e?void 0:e.height,width:null==e?void 0:e.width})}),{dispose:()=>{c.dispose(),ie(this._floatingGroups,u),a.model.location={type:"grid"},this.updateWatermark()}}),this._floatingGroups.push(u),a.model.location={type:"floating"},(null==t?void 0:t.skipActiveGroup)||this.doSetGroupAndPanelActive(a),this.updateWatermark()}orthogonalize(e,t){switch(this.gridview.normalize(),e){case"top":case"bottom":this.gridview.orientation===I.HORIZONTAL&&this.gridview.insertOrthogonalSplitviewAtRoot();break;case"left":case"right":this.gridview.orientation===I.VERTICAL&&this.gridview.insertOrthogonalSplitviewAtRoot()}switch(e){case"top":case"left":case"center":return this.createGroupAtLocation([0],void 0,t);case"bottom":case"right":return this.createGroupAtLocation([this.gridview.length],void 0,t);default:throw Error(`dockview: unsupported position ${e}`)}}updateOptions(e){var t,i;if(super.updateOptions(e),"floatingGroupBounds"in e)for(let n of this._floatingGroups){switch(e.floatingGroupBounds){case"boundedWithinViewport":n.overlay.minimumInViewportHeight=void 0,n.overlay.minimumInViewportWidth=void 0;break;case void 0:n.overlay.minimumInViewportHeight=100,n.overlay.minimumInViewportWidth=100;break;default:n.overlay.minimumInViewportHeight=null==(t=e.floatingGroupBounds)?void 0:t.minimumHeightWithinViewport,n.overlay.minimumInViewportWidth=null==(i=e.floatingGroupBounds)?void 0:i.minimumWidthWithinViewport}n.overlay.setBounds()}this.updateDropTargetModel(e);let n=this.options.disableDnd;this._options=Object.assign(Object.assign({},this.options),e),n!==this.options.disableDnd&&this.updateDragAndDropState(),"theme"in e&&this.updateTheme(),this.layout(this.gridview.width,this.gridview.height,!0)}layout(e,t,i){if(super.layout(e,t,i),this._floatingGroups)for(let e of this._floatingGroups)e.overlay.setBounds()}updateDragAndDropState(){for(let e of this.groups)e.model.updateDragAndDropState()}focus(){var e;null==(e=this.activeGroup)||e.focus()}getGroupPanel(e){return this.panels.find(t=>t.id===e)}setActivePanel(e){e.group.model.openPanel(e),this.doSetGroupAndPanelActive(e.group)}moveToNext(e={}){var t;if(!e.group){if(!this.activeGroup)return;e.group=this.activeGroup}if(e.includePanel&&e.group&&e.group.activePanel!==e.group.panels[e.group.panels.length-1])return void e.group.model.moveToNext({suppressRoll:!0});let i=ip(e.group.element),n=null==(t=this.gridview.next(i))?void 0:t.view;this.doSetGroupAndPanelActive(n)}moveToPrevious(e={}){var t;if(!e.group){if(!this.activeGroup)return;e.group=this.activeGroup}if(e.includePanel&&e.group&&e.group.activePanel!==e.group.panels[0])return void e.group.model.moveToPrevious({suppressRoll:!0});let i=ip(e.group.element),n=null==(t=this.gridview.previous(i))?void 0:t.view;n&&this.doSetGroupAndPanelActive(n)}toJSON(){var e;let t=this.gridview.serialize(),i=this.panels.reduce((e,t)=>(e[t.id]=t.toJSON(),e),{}),n=this._floatingGroups.map(e=>({data:e.group.toJSON(),position:e.overlay.toJSON()})),o=this._popoutGroups.map(e=>({data:e.popoutGroup.toJSON(),gridReferenceGroup:e.referenceGroup,position:e.window.dimensions(),url:"popout"===e.popoutGroup.api.location.type?e.popoutGroup.api.location.popoutUrl:void 0})),r={grid:t,panels:i,activeGroup:null==(e=this.activeGroup)?void 0:e.id};return n.length>0&&(r.floatingGroups=n),o.length>0&&(r.popoutGroups=o),r}fromJSON(e,t){var i,n;let o,r=new Map;if(null==t?void 0:t.reuseExistingPanels){o=this.createGroup(),this._groups.delete(o.api.id);let t=Object.keys(e.panels);for(let e of this.panels)t.includes(e.api.id)&&r.set(e.api.id,e);this.movingLock(()=>{Array.from(r.values()).forEach(e=>{this.moveGroupOrPanel({from:{groupId:e.api.group.api.id,panelId:e.api.id},to:{group:o,position:"center"},keepEmptyGroups:!0})})})}if(this.clear(),"object"!=typeof e||null===e)throw Error("dockview: serialized layout must be a non-null object");let{grid:s,panels:a,activeGroup:l}=e;if("branch"!==s.root.type||!Array.isArray(s.root.data))throw Error("dockview: root must be of type branch");try{let t=this.width,d=this.height,h=e=>{let{id:t,locked:i,hideHeader:n,views:s,activeView:l}=e;if("string"!=typeof t)throw Error("dockview: group id must be of type string");let d=this.createGroup({id:t,locked:!!i,hideHeader:!!n});this._onDidAddGroup.fire(d);let h=[];for(let e of s){let t=r.get(e);if(o&&t)this.movingLock(()=>{o.model.removePanel(t)}),h.push(t),t.updateFromStateModel(a[e]);else{let t=this._deserializer.fromJSON(a[e],d);h.push(t)}}for(let e=0;e{d.model.openPanel(t,{skipSetActive:!i,skipSetGroupActive:!0})}):d.model.openPanel(t,{skipSetActive:!i,skipSetGroupActive:!0})}return!d.activePanel&&d.panels.length>0&&d.model.openPanel(d.panels[d.panels.length-1],{skipSetGroupActive:!0}),d};for(let n of(this.gridview.deserialize(s,{fromJSON:e=>h(e.data)}),this.layout(t,d,!0),null!=(i=e.floatingGroups)?i:[])){let{data:e,position:t}=n,i=h(e);this.addFloatingGroup(i,{position:t,width:t.width,height:t.height,skipRemoveGroup:!0,inDragMode:!1})}let u=null!=(n=e.popoutGroups)?n:[],c=[];for(let e of(u.forEach((e,t)=>{let{data:i,position:n,gridReferenceGroup:o,url:r}=e,s=h(i),a=new Promise(e=>{setTimeout(()=>{this.addPopoutGroup(s,{position:null!=n?n:void 0,overridePopoutGroup:o?s:void 0,referenceGroup:o?this.getPanel(o):void 0,popoutUrl:r}),e()},100*t)});c.push(a)}),this._popoutRestorationPromise=Promise.all(c).then(()=>void 0),this._floatingGroups))e.overlay.setBounds();if("string"==typeof l){let e=this.getPanel(l);e&&this.doSetGroupAndPanelActive(e)}}catch(e){for(let t of(console.error("dockview: failed to deserialize layout. Reverting changes",e),this.groups))for(let e of t.panels)this.removePanel(e,{removeEmptyGroup:!1,skipDispose:!1});for(let e of this.groups)e.dispose(),this._groups.delete(e.id),this._onDidRemoveGroup.fire(e);for(let e of[...this._floatingGroups])e.dispose();throw this.clear(),e}this.updateWatermark(),this.debouncedUpdateAllPositions(),this._onDidLayoutFromJSON.fire()}clear(){let e=Array.from(this._groups.values()).map(e=>e.value),t=!!this.activeGroup;for(let t of e)this.removeGroup(t,{skipActive:!0});t&&this.doSetGroupAndPanelActive(void 0),this.gridview.clear()}closeAllGroups(){for(let e of this._groups.entries()){let[t,i]=e;i.value.model.closeAllPanels()}}addPanel(e){var t,i;let n,o,r;if(this.panels.find(t=>t.id===e.id))throw Error(`dockview: panel with id ${e.id} already exists`);if(e.position&&e.floating)throw Error("dockview: you can only provide one of: position, floating as arguments to .addPanel(...)");let s={width:e.initialWidth,height:e.initialHeight};if(e.position)if(+!e.position.referencePanel){if(+!e.position.referenceGroup){let t=this.orthogonalize(iR(e.position.direction)),i=this.createPanel(e,t);return t.model.openPanel(i,{skipSetActive:e.inactive,skipSetGroupActive:e.inactive,index:o}),e.inactive||this.doSetGroupAndPanelActive(t),t.api.setSize({height:null==s?void 0:s.height,width:null==s?void 0:s.width}),i}else if(n="string"==typeof e.position.referenceGroup?null==(t=this._groups.get(e.position.referenceGroup))?void 0:t.value:e.position.referenceGroup,o=e.position.index,!n)throw Error(`dockview: referenceGroup '${e.position.referenceGroup}' does not exist`)}else{let t="string"==typeof e.position.referencePanel?this.getGroupPanel(e.position.referencePanel):e.position.referencePanel;if(o=e.position.index,!t)throw Error(`dockview: referencePanel '${e.position.referencePanel}' does not exist`);n=this.findGroup(t)}else n=this.activeGroup;if(n){let t=i_((null==(i=e.position)?void 0:i.direction)||"within");if(e.floating){let t=this.createGroup();this._onDidAddGroup.fire(t);let i="object"==typeof e.floating&&null!==e.floating?e.floating:{};this.addFloatingGroup(t,Object.assign(Object.assign({},i),{inDragMode:!1,skipRemoveGroup:!0,skipActiveGroup:!0})),r=this.createPanel(e,t),t.model.openPanel(r,{skipSetActive:e.inactive,skipSetGroupActive:e.inactive,index:o})}else if("floating"===n.api.location.type||"center"===t)r=this.createPanel(e,n),n.model.openPanel(r,{skipSetActive:e.inactive,skipSetGroupActive:e.inactive,index:o}),n.api.setSize({width:null==s?void 0:s.width,height:null==s?void 0:s.height}),e.inactive||this.doSetGroupAndPanelActive(n);else{let i=ip(n.element),a=im(this.gridview.orientation,i,t),l=this.createGroupAtLocation(a,this.orientationAtLocation(a)===I.VERTICAL?null==s?void 0:s.height:null==s?void 0:s.width);r=this.createPanel(e,l),l.model.openPanel(r,{skipSetActive:e.inactive,skipSetGroupActive:e.inactive,index:o}),e.inactive||this.doSetGroupAndPanelActive(l)}}else if(e.floating){let t=this.createGroup();this._onDidAddGroup.fire(t);let i="object"==typeof e.floating&&null!==e.floating?e.floating:{};this.addFloatingGroup(t,Object.assign(Object.assign({},i),{inDragMode:!1,skipRemoveGroup:!0,skipActiveGroup:!0})),r=this.createPanel(e,t),t.model.openPanel(r,{skipSetActive:e.inactive,skipSetGroupActive:e.inactive,index:o})}else{let t=this.createGroupAtLocation([0],this.gridview.orientation===I.VERTICAL?null==s?void 0:s.height:null==s?void 0:s.width);r=this.createPanel(e,t),t.model.openPanel(r,{skipSetActive:e.inactive,skipSetGroupActive:e.inactive,index:o}),e.inactive||this.doSetGroupAndPanelActive(t)}return r}removePanel(e,t={removeEmptyGroup:!0}){let i=e.group;if(!i)throw Error(`dockview: cannot remove panel ${e.id}. it's missing a group.`);i.model.removePanel(e,{skipSetActiveGroup:t.skipSetActiveGroup}),t.skipDispose||(e.group.model.renderContainer.detatch(e),e.dispose()),0===i.size&&t.removeEmptyGroup&&this.removeGroup(i,{skipActive:t.skipSetActiveGroup})}createWatermarkComponent(){return this.options.createWatermarkComponent?this.options.createWatermarkComponent():new nl}updateWatermark(){var e,t;if(0===this.groups.filter(e=>"grid"===e.api.location.type&&e.api.isVisible).length){if(!this._watermark){this._watermark=this.createWatermarkComponent(),this._watermark.init({containerApi:new iA(this)});let e=document.createElement("div");e.className="dv-watermark-container",e.setAttribute("data-testid","watermark-component"),e.appendChild(this._watermark.element),this.gridview.element.appendChild(e)}}else this._watermark&&(this._watermark.element.parentElement.remove(),null==(t=(e=this._watermark).dispose)||t.call(e),this._watermark=null)}addGroup(e){var t;if(e){let i;if(+!e.referencePanel){if(+!e.referenceGroup){let t=this.orthogonalize(iR(e.direction),e);return e.skipSetActive||this.doSetGroupAndPanelActive(t),t}else if(!(i="string"==typeof e.referenceGroup?null==(t=this._groups.get(e.referenceGroup))?void 0:t.value:e.referenceGroup))throw Error(`dockview: reference group ${e.referenceGroup} does not exist`)}else{let t="string"==typeof e.referencePanel?this.panels.find(t=>t.id===e.referencePanel):e.referencePanel;if(!t)throw Error(`dockview: reference panel ${e.referencePanel} does not exist`);if(!(i=this.findGroup(t)))throw Error(`dockview: reference group for reference panel ${e.referencePanel} does not exist`)}let n=i_(e.direction||"within"),o=ip(i.element),r=im(this.gridview.orientation,o,n),s=this.createGroup(e),a=this.getLocationOrientation(r)===I.VERTICAL?e.initialHeight:e.initialWidth;return this.doAddGroup(s,r,a),e.skipSetActive||this.doSetGroupAndPanelActive(s),s}{let t=this.createGroup(e);return this.doAddGroup(t),this.doSetGroupAndPanelActive(t),t}}getLocationOrientation(e){return e.length%2==0&&this.gridview.orientation===I.HORIZONTAL?I.HORIZONTAL:I.VERTICAL}removeGroup(e,t){this.doRemoveGroup(e,t)}doRemoveGroup(e,t){var i;let n=[...e.panels];if(!(null==t?void 0:t.skipDispose))for(let e of n)this.removePanel(e,{removeEmptyGroup:!1,skipDispose:null!=(i=null==t?void 0:t.skipDispose)&&i});let o=this.activePanel;if("floating"===e.api.location.type){let i=this._floatingGroups.find(t=>t.group===e);if(i){if((null==t?void 0:t.skipDispose)||(i.group.dispose(),this._groups.delete(e.id),this._onDidRemoveGroup.fire(e)),ie(this._floatingGroups,i),i.dispose(),!(null==t?void 0:t.skipActive)&&this._activeGroup===e){let e=Array.from(this._groups.values());this.doSetGroupAndPanelActive(e.length>0?e[0].value:void 0)}return i.group}throw Error("dockview: failed to find floating group")}if("popout"===e.api.location.type){let i=this._popoutGroups.find(t=>t.popoutGroup===e);if(i){if(!(null==t?void 0:t.skipDispose)){if(!(null==t?void 0:t.skipPopoutAssociated)){let e=i.referenceGroup?this.getPanel(i.referenceGroup):void 0;e&&0===e.panels.length&&this.removeGroup(e)}i.popoutGroup.dispose(),this._groups.delete(e.id),this._onDidRemoveGroup.fire(e)}ie(this._popoutGroups,i);let n=i.disposable.dispose();if((null==t?void 0:t.skipPopoutReturn)||!n||(this.doAddGroup(n,[0]),this.doSetGroupAndPanelActive(n)),!(null==t?void 0:t.skipActive)&&this._activeGroup===e){let e=Array.from(this._groups.values());this.doSetGroupAndPanelActive(e.length>0?e[0].value:void 0)}return this.updateWatermark(),i.popoutGroup}throw Error("dockview: failed to find popout group")}let r=super.doRemoveGroup(e,t);return(null==t?void 0:t.skipActive)||this.activePanel===o||this._onDidActivePanelChange.fire(this.activePanel),r}debouncedUpdateAllPositions(){void 0!==this._updatePositionsFrameId&&cancelAnimationFrame(this._updatePositionsFrameId),this._updatePositionsFrameId=requestAnimationFrame(()=>{this._updatePositionsFrameId=void 0,this.overlayRenderContainer.updateAllPositions()})}movingLock(e){let t=this._moving;try{return this._moving=!0,e()}finally{this._moving=t}}moveGroupOrPanel(e){var t;let i=e.to.group,n=e.from.groupId,o=e.from.panelId,r=e.to.position,s=e.to.index,a=n?null==(t=this._groups.get(n))?void 0:t.value:void 0;if(!a)throw Error(`dockview: Failed to find group id ${n}`);if(void 0===o)return void this.moveGroup({from:{group:a},to:{group:i,position:r},skipSetActive:e.skipSetActive});if(r&&"center"!==r){let e=ip(i.element),t=im(this.gridview.orientation,e,r);if(a.size<2){let[e,n]=t9(t);if("grid"===a.api.location.type){let[t,i]=t9(ip(a.element));if(t6(t,e)){this.gridview.moveView(t,i,n),this._onDidMovePanel.fire({panel:this.getGroupPanel(o),from:a});return}}if("popout"===a.api.location.type){let e=this._popoutGroups.find(e=>e.popoutGroup===a),i=this.movingLock(()=>e.popoutGroup.model.removePanel(e.popoutGroup.panels[0],{skipSetActive:!0,skipSetActiveGroup:!0}));this.doRemoveGroup(a,{skipActive:!0});let n=this.createGroupAtLocation(t);this.movingLock(()=>n.model.openPanel(i,{skipSetActive:!0})),this.doSetGroupAndPanelActive(n),this._onDidMovePanel.fire({panel:this.getGroupPanel(o),from:a});return}let s=this.movingLock(()=>this.doRemoveGroup(a,{skipActive:!0,skipDispose:!0})),l=ip(i.element),d=im(this.gridview.orientation,l,r);this.movingLock(()=>this.doAddGroup(s,d)),this.doSetGroupAndPanelActive(s),this._onDidMovePanel.fire({panel:this.getGroupPanel(o),from:a})}else{let t=this.movingLock(()=>a.model.removePanel(o,{skipSetActive:!1,skipSetActiveGroup:!0}));if(!t)throw Error(`dockview: No panel with id ${o}`);let i=im(this.gridview.orientation,e,r),n=this.createGroupAtLocation(i);this.movingLock(()=>n.model.openPanel(t,{skipSetGroupActive:!0})),this.doSetGroupAndPanelActive(n),this._onDidMovePanel.fire({panel:t,from:a})}}else{let t=this.movingLock(()=>a.model.removePanel(o,{skipSetActive:!1,skipSetActiveGroup:!0}));if(!t)throw Error(`dockview: No panel with id ${o}`);e.keepEmptyGroups||0!==a.model.size||this.doRemoveGroup(a,{skipActive:!0});let n=0===i.model.size;this.movingLock(()=>{var o;return i.model.openPanel(t,{index:s,skipSetActive:null!=(o=e.skipSetActive)&&o&&!n,skipSetGroupActive:!0})}),e.skipSetActive||this.doSetGroupAndPanelActive(i),this._onDidMovePanel.fire({panel:t,from:a})}}moveGroup(e){let t=e.from.group,i=e.to.group,n=e.to.position;if("center"===n){let n=t.activePanel,o=this.movingLock(()=>[...t.panels].map(e=>t.model.removePanel(e.id,{skipSetActive:!0})));(null==t?void 0:t.model.size)===0&&this.doRemoveGroup(t,{skipActive:!0}),this.movingLock(()=>{for(let e of o)i.model.openPanel(e,{skipSetActive:e!==n,skipSetGroupActive:!0})}),!0!==e.skipSetActive?this.doSetGroupAndPanelActive(i):this.activePanel||this.doSetGroupAndPanelActive(i)}else{switch(t.api.location.type){case"grid":this.gridview.removeView(ip(t.element));break;case"floating":{let e=this._floatingGroups.find(e=>e.group===t);if(!e)throw Error("dockview: failed to find floating group");e.dispose();break}case"popout":{let e=this._popoutGroups.find(e=>e.popoutGroup===t);if(!e)throw Error("dockview: failed to find popout group");let n=this._popoutGroups.indexOf(e);if(n>=0&&this._popoutGroups.splice(n,1),e.referenceGroup){let t=this.getPanel(e.referenceGroup);t&&!t.api.isVisible&&this.doRemoveGroup(t,{skipActive:!0})}e.window.dispose(),"grid"===i.api.location.type?(t.model.renderContainer=this.overlayRenderContainer,t.model.dropTargetContainer=this.rootDropTargetContainer,t.model.location={type:"grid"}):"floating"===i.api.location.type&&(t.model.renderContainer=this.overlayRenderContainer,t.model.dropTargetContainer=this.rootDropTargetContainer,t.model.location={type:"floating"})}}if("grid"===i.api.location.type){let e,o=ip(i.element),r=im(this.gridview.orientation,o,n);switch(this.gridview.orientation){case I.VERTICAL:e=o.length%2==0?t.api.width:t.api.height;break;case I.HORIZONTAL:e=o.length%2==0?t.api.height:t.api.width}this.gridview.addView(t,e,r)}else if("floating"===i.api.location.type){let e=this._floatingGroups.find(e=>e.group===i);if(e){let i,n,o=e.overlay.toJSON();i="left"in o?o.left+50:"right"in o?Math.max(0,o.right-o.width-50):50,n="top"in o?o.top+50:"bottom"in o?Math.max(0,o.bottom-o.height-50):50,this.addFloatingGroup(t,{height:o.height,width:o.width,position:{left:i,top:n}})}}}if(t.panels.forEach(e=>{this._onDidMovePanel.fire({panel:e,from:t})}),this.debouncedUpdateAllPositions(),!1===e.skipSetActive){let e=null!=i?i:t;this.doSetGroupAndPanelActive(e)}}doSetGroupActive(e){super.doSetGroupActive(e);let t=this.activePanel;this._moving||t===this._onDidActivePanelChange.value||this._onDidActivePanelChange.fire(t)}doSetGroupAndPanelActive(e){super.doSetGroupActive(e);let t=this.activePanel;e&&this.hasMaximizedGroup()&&!this.isMaximizedGroup(e)&&this.exitMaximizedGroup(),this._moving||t===this._onDidActivePanelChange.value||this._onDidActivePanelChange.fire(t)}getNextGroupId(){let e=this.nextGroupId.next();for(;this._groups.has(e);)e=this.nextGroupId.next();return e}createGroup(e){e||(e={});let t=null==e?void 0:e.id;if(t&&this._groups.has(e.id)&&(console.warn(`dockview: Duplicate group id ${null==e?void 0:e.id}. reassigning group id to avoid errors`),t=void 0),!t)for(t=this.nextGroupId.next();this._groups.has(t);)t=this.nextGroupId.next();let i=new nt(this,t,e);if(i.init({params:{},accessor:this}),!this._groups.has(i.id)){let e=new tK(i.model.onTabDragStart(e=>{this._onWillDragPanel.fire(e)}),i.model.onGroupDragStart(e=>{this._onWillDragGroup.fire(e)}),i.model.onMove(e=>{let{groupId:t,itemId:n,target:o,index:r}=e;this.moveGroupOrPanel({from:{groupId:t,panelId:n},to:{group:i,position:o,index:r}})}),i.model.onDidDrop(e=>{this._onDidDrop.fire(e)}),i.model.onWillDrop(e=>{this._onWillDrop.fire(e)}),i.model.onWillShowOverlay(e=>{this.options.disableDnd?e.preventDefault():this._onWillShowOverlay.fire(e)}),i.model.onUnhandledDragOverEvent(e=>{this._onUnhandledDragOverEvent.fire(e)}),i.model.onDidAddPanel(e=>{this._moving||this._onDidAddPanel.fire(e.panel)}),i.model.onDidRemovePanel(e=>{this._moving||this._onDidRemovePanel.fire(e.panel)}),i.model.onDidActivePanelChange(e=>{this._moving||e.panel===this.activePanel&&this._onDidActivePanelChange.value!==e.panel&&this._onDidActivePanelChange.fire(e.panel)}),N.any(i.model.onDidPanelTitleChange,i.model.onDidPanelParametersChange)(()=>{this._bufferOnDidLayoutChange.fire()}));this._groups.set(i.id,{value:i,disposable:e})}return i.initialize(),i}createPanel(e,t){var i,n,o;let r=e.component,s=null!=(i=e.tabComponent)?i:this.options.defaultTabComponent,a=new ns(this,e.id,r,s),l=new no(e.id,r,s,this,this._api,t,a,{renderer:e.renderer,minimumWidth:e.minimumWidth,minimumHeight:e.minimumHeight,maximumWidth:e.maximumWidth,maximumHeight:e.maximumHeight});return l.init({title:null!=(n=e.title)?n:e.id,params:null!=(o=null==e?void 0:e.params)?o:{}}),l}createGroupAtLocation(e,t,i){let n=this.createGroup(i);return this.doAddGroup(n,e,t),n}findGroup(e){var t;return null==(t=Array.from(this._groups.values()).find(t=>t.value.model.containsPanel(e)))?void 0:t.value}orientationAtLocation(e){let t=this.gridview.orientation;return e.length%2==1?t:ig(t)}updateDropTargetModel(e){"dndEdges"in e&&(this._rootDropTarget.disabled="boolean"==typeof e.dndEdges&&!1===e.dndEdges,"object"==typeof e.dndEdges&&null!==e.dndEdges?this._rootDropTarget.setOverlayModel(e.dndEdges):this._rootDropTarget.setOverlayModel(nw)),"rootOverlayModel"in e&&this.updateDropTargetModel({dndEdges:e.dndEdges})}updateTheme(){var e,t;let i=null!=(e=this._options.theme)?e:ni;this._themeClassnames.setClassNames(i.className),this.gridview.margin=null!=(t=i.gap)?t:0,"absolute"===i.dndOverlayMounting?this.rootDropTargetContainer.disabled=!1:this.rootDropTargetContainer.disabled=!0}}class nx extends iD{get orientation(){return this.gridview.orientation}set orientation(e){this.gridview.orientation=e}get options(){return this._options}get deserializer(){return this._deserializer}set deserializer(e){this._deserializer=e}constructor(e,t){var i;super(e,{proportionalLayout:null==(i=t.proportionalLayout)||i,orientation:t.orientation,styles:t.hideBorders?{separatorBorder:"transparent"}:void 0,disableAutoResizing:t.disableAutoResizing,className:t.className}),this._onDidLayoutfromJSON=new tB,this.onDidLayoutFromJSON=this._onDidLayoutfromJSON.event,this._onDidRemoveGroup=new tB,this.onDidRemoveGroup=this._onDidRemoveGroup.event,this._onDidAddGroup=new tB,this.onDidAddGroup=this._onDidAddGroup.event,this._onDidActiveGroupChange=new tB,this.onDidActiveGroupChange=this._onDidActiveGroupChange.event,this._options=t,this.addDisposables(this._onDidAddGroup,this._onDidRemoveGroup,this._onDidActiveGroupChange,this.onDidAdd(e=>{this._onDidAddGroup.fire(e)}),this.onDidRemove(e=>{this._onDidRemoveGroup.fire(e)}),this.onDidActiveChange(e=>{this._onDidActiveGroupChange.fire(e)}))}updateOptions(e){super.updateOptions(e);let t="string"==typeof e.orientation&&this.gridview.orientation!==e.orientation;this._options=Object.assign(Object.assign({},this.options),e),t&&(this.gridview.orientation=e.orientation),this.layout(this.gridview.width,this.gridview.height,!0)}removePanel(e){this.removeGroup(e)}toJSON(){var e;return{grid:this.gridview.serialize(),activePanel:null==(e=this.activeGroup)?void 0:e.id}}setVisible(e,t){this.gridview.setViewVisible(ip(e.element),t)}setActive(e){this._groups.forEach((t,i)=>{t.value.setActive(e===t.value)})}focus(){var e;null==(e=this.activeGroup)||e.focus()}fromJSON(e){this.clear();let{grid:t,activePanel:i}=e;try{let e=[],n=this.width,o=this.height;if(this.gridview.deserialize(t,{fromJSON:t=>{let{data:i}=t,n=this.options.createComponent({id:i.id,name:i.component});return e.push(()=>n.init({params:i.params,minimumWidth:i.minimumWidth,maximumWidth:i.maximumWidth,minimumHeight:i.minimumHeight,maximumHeight:i.maximumHeight,priority:i.priority,snap:!!i.snap,accessor:this,isVisible:t.visible})),this._onDidAddGroup.fire(n),this.registerPanel(n),n}}),this.layout(n,o,!0),e.forEach(e=>e()),"string"==typeof i){let e=this.getPanel(i);e&&this.doSetGroupActive(e)}}catch(e){for(let e of this.groups)e.dispose(),this._groups.delete(e.id),this._onDidRemoveGroup.fire(e);throw this.clear(),e}this._onDidLayoutfromJSON.fire()}clear(){let e=this.activeGroup;for(let e of Array.from(this._groups.values()))e.disposable.dispose(),this.doRemoveGroup(e.value,{skipActive:!0});e&&this.doSetGroupActive(void 0),this.gridview.clear()}movePanel(e,t){var i;let n,o=this.gridview.remove(e),r=null==(i=this._groups.get(t.reference))?void 0:i.value;if(!r)throw Error(`reference group ${t.reference} does not exist`);let s=i_(t.direction);if("center"===s)throw Error(`${s} not supported as an option`);{let e=ip(r.element);n=im(this.gridview.orientation,e,s)}this.doAddGroup(o,n,t.size)}addPanel(e){var t,i,n,o;let r=null!=(t=e.location)?t:[0];if(null==(i=e.position)?void 0:i.referencePanel){let t=null==(n=this._groups.get(e.position.referencePanel))?void 0:n.value;if(!t)throw Error(`reference group ${e.position.referencePanel} does not exist`);let i=i_(e.position.direction);if("center"===i)throw Error(`${i} not supported as an option`);{let e=ip(t.element);r=im(this.gridview.orientation,e,i)}}let s=this.options.createComponent({id:e.id,name:e.component});return s.init({params:null!=(o=e.params)?o:{},minimumWidth:e.minimumWidth,maximumWidth:e.maximumWidth,minimumHeight:e.minimumHeight,maximumHeight:e.maximumHeight,priority:e.priority,snap:!!e.snap,accessor:this,isVisible:!0}),this.doAddGroup(s,r,e.size),this.registerPanel(s),this.doSetGroupActive(s),s}registerPanel(e){let t=new tK(e.api.onDidFocusChange(t=>{t.isFocused&&this._groups.forEach(t=>{let i=t.value;i!==e?i.setActive(!1):i.setActive(!0)})}));this._groups.set(e.id,{value:e,disposable:t})}moveGroup(e,t,i){let n=this.getPanel(t);if(!n)throw Error("invalid operation");let o=ip(e.element),[r,s]=t9(im(this.gridview.orientation,o,i)),[a,l]=t9(ip(n.element));if(t6(a,r))return void this.gridview.moveView(a,l,s);let d=this.doRemoveGroup(n,{skipActive:!0,skipDispose:!0}),h=ip(e.element),u=im(this.gridview.orientation,h,i);this.doAddGroup(d,u)}removeGroup(e){super.removeGroup(e)}dispose(){super.dispose(),this._onDidLayoutfromJSON.dispose()}}class n_ extends iy{get panels(){return this.splitview.getViews()}get options(){return this._options}get length(){return this._panels.size}get orientation(){return this.splitview.orientation}get splitview(){return this._splitview}set splitview(e){this._splitview&&this._splitview.dispose(),this._splitview=e,this._splitviewChangeDisposable.value=new tK(this._splitview.onDidSashEnd(()=>{this._onDidLayoutChange.fire(void 0)}),this._splitview.onDidAddView(e=>this._onDidAddView.fire(e)),this._splitview.onDidRemoveView(e=>this._onDidRemoveView.fire(e)))}get minimumSize(){return this.splitview.minimumSize}get maximumSize(){return this.splitview.maximumSize}get height(){return this.splitview.orientation===I.HORIZONTAL?this.splitview.orthogonalSize:this.splitview.size}get width(){return this.splitview.orientation===I.HORIZONTAL?this.splitview.size:this.splitview.orthogonalSize}constructor(e,t){var i;super(document.createElement("div"),t.disableAutoResizing),this._splitviewChangeDisposable=new tJ,this._panels=new Map,this._onDidLayoutfromJSON=new tB,this.onDidLayoutFromJSON=this._onDidLayoutfromJSON.event,this._onDidAddView=new tB,this.onDidAddView=this._onDidAddView.event,this._onDidRemoveView=new tB,this.onDidRemoveView=this._onDidRemoveView.event,this._onDidLayoutChange=new tB,this.onDidLayoutChange=this._onDidLayoutChange.event,this.element.style.height="100%",this.element.style.width="100%",this._classNames=new t4(this.element),this._classNames.setClassNames(null!=(i=t.className)?i:""),e.appendChild(this.element),this._options=t,this.splitview=new is(this.element,t),this.addDisposables(this._onDidAddView,this._onDidLayoutfromJSON,this._onDidRemoveView,this._onDidLayoutChange)}updateOptions(e){var t,i;"className"in e&&this._classNames.setClassNames(null!=(t=e.className)?t:""),"disableResizing"in e&&(this.disableResizing=null!=(i=e.disableAutoResizing)&&i),"string"==typeof e.orientation&&(this.splitview.orientation=e.orientation),this._options=Object.assign(Object.assign({},this.options),e),this.splitview.layout(this.splitview.size,this.splitview.orthogonalSize)}focus(){var e;null==(e=this._activePanel)||e.focus()}movePanel(e,t){this.splitview.moveView(e,t)}setVisible(e,t){let i=this.panels.indexOf(e);this.splitview.setViewVisible(i,t)}setActive(e,t){this._activePanel=e,this.panels.filter(t=>t!==e).forEach(e=>{e.api._onDidActiveChange.fire({isActive:!1}),t||e.focus()}),e.api._onDidActiveChange.fire({isActive:!0}),t||e.focus()}removePanel(e,t){let i=this._panels.get(e.id);if(!i)throw Error(`unknown splitview panel ${e.id}`);i.dispose(),this._panels.delete(e.id);let n=this.panels.findIndex(t=>t===e);this.splitview.removeView(n,t).dispose();let o=this.panels;o.length>0&&this.setActive(o[o.length-1])}getPanel(e){return this.panels.find(t=>t.id===e)}addPanel(e){var t;if(this._panels.has(e.id))throw Error(`panel ${e.id} already exists`);let i=this.options.createComponent({id:e.id,name:e.component});i.orientation=this.splitview.orientation,i.init({params:null!=(t=e.params)?t:{},minimumSize:e.minimumSize,maximumSize:e.maximumSize,snap:e.snap,priority:e.priority,accessor:this});let n="number"==typeof e.size?e.size:T.Distribute,o="number"==typeof e.index?e.index:void 0;return this.splitview.addView(i,n,o),this.doAddView(i),this.setActive(i),i}layout(e,t){let[i,n]=this.splitview.orientation===I.HORIZONTAL?[e,t]:[t,e];this.splitview.layout(i,n)}doAddView(e){let t=e.api.onDidFocusChange(t=>{t.isFocused&&this.setActive(e,!0)});this._panels.set(e.id,t)}toJSON(){var e;return{views:this.splitview.getViews().map((e,t)=>({size:this.splitview.getViewSize(t),data:e.toJSON(),snap:!!e.snap,priority:e.priority})),activeView:null==(e=this._activePanel)?void 0:e.id,size:this.splitview.size,orientation:this.splitview.orientation}}fromJSON(e){this.clear();let{views:t,orientation:i,size:n,activeView:o}=e,r=[],s=this.width,a=this.height;if(this.splitview=new is(this.element,{orientation:i,proportionalLayout:this.options.proportionalLayout,descriptor:{size:n,views:t.map(e=>{let t=e.data;if(this._panels.has(t.id))throw Error(`panel ${t.id} already exists`);let n=this.options.createComponent({id:t.id,name:t.component});return r.push(()=>{var i;n.init({params:null!=(i=t.params)?i:{},minimumSize:t.minimumSize,maximumSize:t.maximumSize,snap:e.snap,priority:e.priority,accessor:this})}),n.orientation=i,this.doAddView(n),setTimeout(()=>{this._onDidAddView.fire(n)},0),{size:e.size,view:n}})}}),this.layout(s,a),r.forEach(e=>e()),"string"==typeof o){let e=this.getPanel(o);e&&this.setActive(e)}this._onDidLayoutfromJSON.fire()}clear(){for(let e of this._panels.values())e.dispose();for(this._panels.clear();this.splitview.length>0;)this.splitview.removeView(0,T.Distribute,!0).dispose()}dispose(){for(let e of this._panels.values())e.dispose();this._panels.clear();let e=this.splitview.getViews();for(let t of(this._splitviewChangeDisposable.dispose(),this.splitview.dispose(),e))t.dispose();this.element.remove(),super.dispose()}}class nD extends tK{get element(){return this._element}constructor(){super(),this._expandedIcon=iQ({width:"11",height:"11",viewbox:"0 0 24 15",path:"M12 14.15L0 2.15L2.15 0L12 9.9L21.85 0.0499992L24 2.2L12 14.15Z"}),this._collapsedIcon=i0(),this.disposable=new tJ,this.apiRef={api:null},this._element=document.createElement("div"),this.element.className="dv-default-header",this._content=document.createElement("span"),this._expander=document.createElement("div"),this._expander.className="dv-pane-header-icon",this.element.appendChild(this._expander),this.element.appendChild(this._content),this.addDisposables(t$(this._element,"click",()=>{var e;null==(e=this.apiRef.api)||e.setExpanded(!this.apiRef.api.isExpanded)}))}init(e){this.apiRef.api=e.api,this._content.textContent=e.title,this.updateIcon(),this.disposable.value=e.api.onDidExpansionChange(()=>{this.updateIcon()})}updateIcon(){var e;let t=!!(null==(e=this.apiRef.api)?void 0:e.isExpanded);tQ(this._expander,"collapsed",!t),t?(this._expander.contains(this._collapsedIcon)&&this._collapsedIcon.remove(),this._expander.contains(this._expandedIcon)||this._expander.appendChild(this._expandedIcon)):(this._expander.contains(this._expandedIcon)&&this._expandedIcon.remove(),this._expander.contains(this._collapsedIcon)||this._expander.appendChild(this._collapsedIcon))}update(e){}dispose(){this.disposable.dispose(),super.dispose()}}let nC=ii(),nS=Number.MAX_SAFE_INTEGER;class nE extends iF{constructor(e){super({accessor:e.accessor,id:e.id,component:e.component,headerComponent:e.headerComponent,orientation:e.orientation,isExpanded:e.isExpanded,disableDnd:e.disableDnd,headerSize:e.headerSize,minimumBodySize:e.minimumBodySize,maximumBodySize:e.maximumBodySize}),this.options=e}getBodyComponent(){return this.options.body}getHeaderComponent(){return this.options.header}}class nA extends iy{get id(){return this._id}get panels(){return this.paneview.getPanes()}set paneview(e){this._paneview=e,this._disposable.value=new tK(this._paneview.onDidChange(()=>{this._onDidLayoutChange.fire(void 0)}),this._paneview.onDidAddView(e=>this._onDidAddView.fire(e)),this._paneview.onDidRemoveView(e=>this._onDidRemoveView.fire(e)))}get paneview(){return this._paneview}get minimumSize(){return this.paneview.minimumSize}get maximumSize(){return this.paneview.maximumSize}get height(){return this.paneview.orientation===I.HORIZONTAL?this.paneview.orthogonalSize:this.paneview.size}get width(){return this.paneview.orientation===I.HORIZONTAL?this.paneview.size:this.paneview.orthogonalSize}get options(){return this._options}constructor(e,t){var i;super(document.createElement("div"),t.disableAutoResizing),this._id=nC.next(),this._disposable=new tJ,this._viewDisposables=new Map,this._onDidLayoutfromJSON=new tB,this.onDidLayoutFromJSON=this._onDidLayoutfromJSON.event,this._onDidLayoutChange=new tB,this.onDidLayoutChange=this._onDidLayoutChange.event,this._onDidDrop=new tB,this.onDidDrop=this._onDidDrop.event,this._onDidAddView=new tB,this.onDidAddView=this._onDidAddView.event,this._onDidRemoveView=new tB,this.onDidRemoveView=this._onDidRemoveView.event,this._onUnhandledDragOverEvent=new tB,this.onUnhandledDragOverEvent=this._onUnhandledDragOverEvent.event,this.element.style.height="100%",this.element.style.width="100%",this.addDisposables(this._onDidLayoutChange,this._onDidLayoutfromJSON,this._onDidDrop,this._onDidAddView,this._onDidRemoveView,this._onUnhandledDragOverEvent),this._classNames=new t4(this.element),this._classNames.setClassNames(null!=(i=t.className)?i:""),e.appendChild(this.element),this._options=t,this.paneview=new il(this.element,{orientation:I.VERTICAL}),this.addDisposables(this._disposable)}setVisible(e,t){let i=this.panels.indexOf(e);this.paneview.setViewVisible(i,t)}focus(){}updateOptions(e){var t,i;"className"in e&&this._classNames.setClassNames(null!=(t=e.className)?t:""),"disableResizing"in e&&(this.disableResizing=null!=(i=e.disableAutoResizing)&&i),this._options=Object.assign(Object.assign({},this.options),e)}addPanel(e){var t,i;let n,o=this.options.createComponent({id:e.id,name:e.component});e.headerComponent&&this.options.createHeaderComponent&&(n=this.options.createHeaderComponent({id:e.id,name:e.headerComponent})),n||(n=new nD);let r=new nE({id:e.id,component:e.component,headerComponent:e.headerComponent,header:n,body:o,orientation:I.VERTICAL,isExpanded:!!e.isExpanded,disableDnd:!!this.options.disableDnd,accessor:this,headerSize:null!=(t=e.headerSize)?t:22,minimumBodySize:0,maximumBodySize:nS});this.doAddPanel(r);let s="number"==typeof e.size?e.size:T.Distribute,a="number"==typeof e.index?e.index:void 0;return r.init({params:null!=(i=e.params)?i:{},minimumBodySize:e.minimumBodySize,maximumBodySize:e.maximumBodySize,isExpanded:e.isExpanded,title:e.title,containerApi:new iS(this),accessor:this}),this.paneview.addPane(r,s,a),r.orientation=this.paneview.orientation,r}removePanel(e){let t=this.panels.findIndex(t=>t===e);this.paneview.removePane(t),this.doRemovePanel(e)}movePanel(e,t){this.paneview.moveView(e,t)}getPanel(e){return this.panels.find(t=>t.id===e)}layout(e,t){let[i,n]=this.paneview.orientation===I.HORIZONTAL?[e,t]:[t,e];this.paneview.layout(i,n)}toJSON(){return{views:this.paneview.getPanes().map((e,t)=>{let i,n;return{size:this.paneview.getViewSize(t),data:e.toJSON(),minimumSize:(i=e.minimumBodySize)<=0?void 0:i,maximumSize:(n=e.maximumBodySize)===Number.MAX_SAFE_INTEGER||n===1/0?void 0:n,headerSize:e.headerSize,expanded:e.isExpanded()}}),size:this.paneview.size}}fromJSON(e){this.clear();let{views:t,size:i}=e,n=[],o=this.width,r=this.height;this.paneview=new il(this.element,{orientation:I.VERTICAL,descriptor:{size:i,views:t.map(e=>{var t,i,o;let r,s=e.data,a=this.options.createComponent({id:s.id,name:s.component});s.headerComponent&&this.options.createHeaderComponent&&(r=this.options.createHeaderComponent({id:s.id,name:s.headerComponent})),r||(r=new nD);let l=new nE({id:s.id,component:s.component,headerComponent:s.headerComponent,header:r,body:a,orientation:I.VERTICAL,isExpanded:!!e.expanded,disableDnd:!!this.options.disableDnd,accessor:this,headerSize:null!=(t=e.headerSize)?t:22,minimumBodySize:null!=(i=e.minimumSize)?i:0,maximumBodySize:null!=(o=e.maximumSize)?o:nS});return this.doAddPanel(l),n.push(()=>{var t;l.init({params:null!=(t=s.params)?t:{},minimumBodySize:e.minimumSize,maximumBodySize:e.maximumSize,title:s.title,isExpanded:!!e.expanded,containerApi:new iS(this),accessor:this}),l.orientation=this.paneview.orientation}),setTimeout(()=>{this._onDidAddView.fire(l)},0),{size:e.size,view:l}})}}),this.layout(o,r),n.forEach(e=>e()),this._onDidLayoutfromJSON.fire()}clear(){for(let[e,t]of 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e!==this._evaluatedMaximumSize&&(this._evaluatedMaximumSize=e,this.updateConstraints()),e}get snap(){return this._snap}constructor(e,t){super(e,t,new iG(e,t)),this._evaluatedMinimumSize=0,this._evaluatedMaximumSize=1/0,this._minimumSize=0,this._maximumSize=1/0,this._snap=!1,this._onDidChange=new tB,this.onDidChange=this._onDidChange.event,this.api.initialize(this),this.addDisposables(this._onDidChange,this.api.onWillVisibilityChange(e=>{let{isVisible:t}=e,{accessor:i}=this._params;i.setVisible(this,t)}),this.api.onActiveChange(()=>{let{accessor:e}=this._params;e.setActive(this)}),this.api.onDidConstraintsChangeInternal(e=>{("number"==typeof e.minimumSize||"function"==typeof e.minimumSize)&&(this._minimumSize=e.minimumSize),("number"==typeof e.maximumSize||"function"==typeof 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t;null==(t=this._part)||t.update(e.params)}updatePanels(){this.update({params:{panels:this._group.model.panels}})}updateActivePanel(){this.update({params:{activePanel:this._group.model.activePanel}})}updateGroupActive(){this.update({params:{isGroupActive:this._group.api.isActive}})}}function nj(e,t){return e?i=>new nT(e,t,i):void 0}let nG="props.defaultTabComponent",nV=W.default.forwardRef((e,t)=>{let i=W.default.useRef(null),n=W.default.useRef(),[o,r]=nO();W.default.useImperativeHandle(t,()=>i.current,[]);let s=W.default.useRef({});return W.default.useEffect(()=>{let t={};i5.forEach(i=>{let n=e[i];i in e&&n!==s.current[i]&&(t[i]=n)}),n.current&&n.current.updateOptions(t),s.current=e},i5.map(t=>e[t])),W.default.useEffect(()=>{var t;if(!i.current)return;let o=null!=(t=e.tabComponents)?t:{};e.defaultTabComponent&&(o[nG]=e.defaultTabComponent);let 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3===i.length?[parseInt(i[0]+i[0],16)/255,parseInt(i[1]+i[1],16)/255,parseInt(i[2]+i[2],16)/255,1]:6===i.length||8===i.length?[parseInt(i.slice(0,2),16)/255,parseInt(i.slice(2,4),16)/255,parseInt(i.slice(4,6),16)/255,8===i.length?parseInt(i.slice(6,8),16)/255:1]:[1,1,1,1]}function y(e,t,i){let r=e.createShader(t);if(!r)throw Error("Failed to create shader");if(e.shaderSource(r,i),e.compileShader(r),!e.getShaderParameter(r,e.COMPILE_STATUS)){let t=e.getShaderInfoLog(r)??"unknown";throw e.deleteShader(r),Error(`Shader compile failed: ${t}`)}return r}function _(e,t,i){let r=y(e,e.VERTEX_SHADER,t),o=y(e,e.FRAGMENT_SHADER,i),s=e.createProgram();if(!s)throw Error("Failed to create program");if(e.attachShader(s,r),e.attachShader(s,o),e.linkProgram(s),e.deleteShader(r),e.deleteShader(o),!e.getProgramParameter(s,e.LINK_STATUS)){let t=e.getProgramInfoLog(s)??"unknown";throw e.deleteProgram(s),Error(`Program link failed: ${t}`)}return s}function v(e,t,i,r){e.width=Math.max(1,Math.floor(t*r)),e.height=Math.max(1,Math.floor(i*r)),e.style.width=`${t}px`,e.style.height=`${i}px`}let A=`#version 300 es -precision highp float; -precision highp int; - -flat in uint v_label; - -// Present in the vertex stage too; redeclare here so we can compute AA width. -uniform float u_dpr; -uniform float u_pointRadiusCss; - -uniform sampler2D u_paletteTex; -uniform int u_paletteSize; -uniform int u_paletteWidth; - -out vec4 outColor; - -void main() { - vec2 p = gl_PointCoord * 2.0 - 1.0; - // Anti-aliased circle: avoid harsh discard edges that can look like - // "weird polygons" at small sizes or without MSAA. - float r = length(p); - // Ensure at least ~1px transition (in point-local coordinates) so small - // points remain visually circular. - float radiusPx = max(u_pointRadiusCss * u_dpr, 1.0); - // Slightly wider than 1px helps circles stay round-looking when zoomed out - // (where points are perceptually tiny and aliasing is more obvious). - float aa = max(fwidth(r), 1.5 / radiusPx); - float alpha = 1.0 - smoothstep(1.0 - aa, 1.0 + aa, r); - if (alpha <= 0.0) discard; - - int size = max(u_paletteSize, 1); - int w = max(u_paletteWidth, 1); - int idx = int(v_label) % size; - int x = idx % w; - int y = idx / w; - vec4 c = texelFetch(u_paletteTex, ivec2(x, y), 0); - outColor = vec4(c.rgb, c.a * alpha); -} -`,S=`#version 300 es -precision highp float; -precision highp int; - -flat in uint v_label; - -uniform sampler2D u_paletteTex; -uniform int u_paletteSize; -uniform int u_paletteWidth; - -out vec4 outColor; - -void main() { - int size = max(u_paletteSize, 1); - int w = max(u_paletteWidth, 1); - int idx = int(v_label) % size; - int x = idx % w; - int y = idx / w; - outColor = texelFetch(u_paletteTex, ivec2(x, y), 0); -} -`,P=`#version 300 es -precision highp float; -precision highp int; - -uniform float u_dpr; -uniform float u_pointRadiusCss; - -uniform vec4 u_color; -uniform float u_pointSizePx; -uniform float u_ringThicknessPx; -uniform int u_ringMode; // 0 = solid, 1 = ring - -out vec4 outColor; - -void main() { - vec2 p = gl_PointCoord * 2.0 - 1.0; - float r = length(p); - float radiusPx = max(u_pointRadiusCss * u_dpr, 1.0); - float aa = max(fwidth(r), 1.5 / radiusPx); - float outer = 1.0 - smoothstep(1.0 - aa, 1.0 + aa, r); - if (outer <= 0.0) discard; - - float alpha = outer; - - if (u_ringMode == 1) { - float radiusPx = u_pointSizePx * 0.5; - float t = clamp(u_ringThicknessPx / max(radiusPx, 1e-6), 0.0, 1.0); - float inner = 1.0 - t; - // Keep only the outer ring with an anti-aliased inner boundary. - float innerMask = smoothstep(inner - aa, inner + aa, r); - alpha *= innerMask; - if (alpha <= 0.0) discard; - } - - outColor = vec4(u_color.rgb, u_color.a * alpha); -} -`,M=`#version 300 es -precision highp float; - -out vec2 v_uv; - -void main() { - // Fullscreen triangle - // (-1,-1), (3,-1), (-1,3) - if (gl_VertexID == 0) { - gl_Position = vec4(-1.0, -1.0, 0.0, 1.0); - v_uv = vec2(0.0, 0.0); - } else if (gl_VertexID == 1) { - gl_Position = vec4(3.0, -1.0, 0.0, 1.0); - v_uv = vec2(2.0, 0.0); - } else { - gl_Position = vec4(-1.0, 3.0, 0.0, 1.0); - v_uv = vec2(0.0, 2.0); - } -} -`,F=`#version 300 es -precision highp float; - -in vec2 v_uv; - -uniform sampler2D u_tex; - -out vec4 outColor; - -void main() { - vec2 uv = clamp(v_uv, 0.0, 1.0); - outColor = texture(u_tex, uv); -} -`,B=`#version 300 es -precision highp float; -precision highp int; - -uniform vec2 u_cssSize; -uniform float u_dpr; -uniform float u_displayZoom; - -uniform vec4 u_diskFillColor; -uniform vec4 u_diskBorderColor; -uniform vec4 u_gridColor; -uniform float u_diskBorderWidthPx; -uniform float u_gridWidthPx; - -out vec4 outColor; - -void main() { - // Convert framebuffer pixels to CSS pixels - vec2 fragCss = gl_FragCoord.xy / max(u_dpr, 1.0); - vec2 center = u_cssSize * 0.5; - - float diskRadius = min(u_cssSize.x, u_cssSize.y) * 0.45 * u_displayZoom; - vec2 p = fragCss - center; - float dist = length(p); - - // Reference-like styling - vec3 diskFill = u_diskFillColor.rgb; - vec3 diskBorder = u_diskBorderColor.rgb; - - float borderWidth = max(u_diskBorderWidthPx, 0.0); - float halfW = 0.5 * borderWidth; - - // Anti-aliasing width (CSS px). Keep at least 1px for crisp edges. - float aa = max(1.0, fwidth(dist)); - - // Discard outside disk+border region so the clear color remains intact. - if (dist > diskRadius + halfW + aa) discard; - - // Outer fade for anti-aliased boundary - float outerAlpha = 1.0 - smoothstep(diskRadius + halfW - aa, diskRadius + halfW + aa, dist); - - // Border mask - float borderInner = smoothstep(diskRadius - halfW - aa, diskRadius - halfW + aa, dist); - float borderOuter = 1.0 - smoothstep(diskRadius + halfW - aa, diskRadius + halfW + aa, dist); - float borderMask = clamp(borderInner * borderOuter, 0.0, 1.0); - - vec3 col = mix(diskFill, diskBorder, borderMask); - - // ------------------------------------------------------------------------ - // Reference-like hyperbolic grid overlay - // ------------------------------------------------------------------------ - // Matches HyperbolicReference.drawHyperbolicGrid(): - // - 8 radial lines (geodesics through origin) - // - 5 concentric circles - vec3 gridCol = u_gridColor.rgb; - float gridWidth = max(u_gridWidthPx, 0.0); - float halfGrid = 0.5 * gridWidth; - - // AA width for thin lines in CSS pixel space. - float aaLine = max(1.0, fwidth(dist)); - - float gridMask = 0.0; - - // Concentric circles (5) - for (int i = 1; i <= 5; i++) { - float r = (float(i) / 6.0) * diskRadius; - float d = abs(dist - r); - float m = 1.0 - smoothstep(halfGrid - aaLine, halfGrid + aaLine, d); - gridMask = max(gridMask, m); - } - - // Radial lines (8), angle = (i/8)*pi - // Distance to line through origin with direction (cos a, sin a): |cross(p, dir)| - for (int i = 0; i < 8; i++) { - float a = (float(i) / 8.0) * 3.141592653589793; - vec2 dir = vec2(cos(a), sin(a)); - float d = abs(p.x * dir.y - p.y * dir.x); - float m = 1.0 - smoothstep(halfGrid - aaLine, halfGrid + aaLine, d); - gridMask = max(gridMask, m); - } - - // Apply grid on top of disk fill/border. Use u_gridColor alpha as intensity. - col = mix(col, gridCol, clamp(gridMask, 0.0, 1.0) * clamp(u_gridColor.a, 0.0, 1.0)); - outColor = vec4(col, outerAlpha); -} -`,D=`#version 300 es -precision highp float; -precision highp int; - -layout(location = 0) in vec2 a_pos; -layout(location = 1) in uint a_label; - -uniform vec2 u_center; -uniform vec2 u_cssSize; -uniform float u_zoom; -uniform float u_dpr; -uniform float u_pointRadiusCss; - -flat out uint v_label; - -void main() { - float baseScale = min(u_cssSize.x, u_cssSize.y) * 0.4 * u_zoom; - float sx = u_cssSize.x * 0.5 + (a_pos.x - u_center.x) * baseScale; - float sy = u_cssSize.y * 0.5 - (a_pos.y - u_center.y) * baseScale; - - vec2 dbufSize = u_cssSize * u_dpr; - vec2 dbuf = vec2(sx, sy) * u_dpr; - - float cx = (dbuf.x / dbufSize.x) * 2.0 - 1.0; - float cy = 1.0 - (dbuf.y / dbufSize.y) * 2.0; - - gl_Position = vec4(cx, cy, 0.0, 1.0); - gl_PointSize = (u_pointRadiusCss * 2.0) * u_dpr; - v_label = a_label; -} -`,C=`#version 300 es -precision highp float; -precision highp int; - -layout(location = 0) in vec2 a_pos; -layout(location = 1) in uint a_label; - -uniform vec2 u_cssSize; -uniform float u_dpr; -uniform float u_pointRadiusCss; - -uniform vec2 u_a; // camera translation (ax, ay) -uniform float u_displayZoom; // visual zoom - -flat out uint v_label; - -vec2 mobiusTransform(vec2 z, vec2 a) { - // (z - a) / (1 - conj(a) * z) - vec2 num = z - a; - - // denom = 1 - (ax*zx + ay*zy) + i * (-(ax*zy - ay*zx)) - float denomX = 1.0 - (a.x * z.x + a.y * z.y); - float denomY = -(a.x * z.y - a.y * z.x); - float denomNormSq = denomX * denomX + denomY * denomY; - if (denomNormSq < 1e-12) { - // Push outside clip - return vec2(2.0, 2.0); - } - - // complex division - float rx = (num.x * denomX + num.y * denomY) / denomNormSq; - float ry = (num.y * denomX - num.x * denomY) / denomNormSq; - return vec2(rx, ry); -} - -void main() { - vec2 w = mobiusTransform(a_pos, u_a); - float r2 = dot(w, w); - if (r2 >= 1.0) { - gl_Position = vec4(2.0, 2.0, 0.0, 1.0); - gl_PointSize = 0.0; - v_label = a_label; - return; - } - - float diskRadius = min(u_cssSize.x, u_cssSize.y) * 0.45 * u_displayZoom; - float sx = u_cssSize.x * 0.5 + w.x * diskRadius; - float sy = u_cssSize.y * 0.5 - w.y * diskRadius; - - vec2 dbufSize = u_cssSize * u_dpr; - vec2 dbuf = vec2(sx, sy) * u_dpr; - - float cx = (dbuf.x / dbufSize.x) * 2.0 - 1.0; - float cy = 1.0 - (dbuf.y / dbufSize.y) * 2.0; - - gl_Position = vec4(cx, cy, 0.0, 1.0); - gl_PointSize = (u_pointRadiusCss * 2.0) * u_dpr; - v_label = a_label; -} -`;class U{canvas=null;width=0;height=0;deviceDpr=1;canvasDpr=1;dpr=1;dataset=null;selection=new Set;hoveredIndex=-1;pointRadiusCss=3;colors=s;backgroundColor="#0a0a0a";poincareDiskFillColor="#141414";poincareDiskBorderColor="#666666";poincareGridColor="#66666633";poincareDiskBorderWidthPx=2;poincareGridWidthPx=.5;paletteSize=0;paletteDirty=!0;paletteTex=null;paletteTexW=0;paletteTexH=0;paletteBytes=new Uint8Array(0);paletteTexUnit=1;scratchIds=[];hoverPosScratch=new Float32Array(2);hoverLabScratch=new Uint16Array(1);hoverIndexScratch=new Uint32Array(1);lastViewChangeTs=0;markViewChanged(){this.lastViewChangeTs=performance.now()}endInteraction(){this.lastViewChangeTs=0}markBackdropDirty(){this.backdropDirty=!0}uploadPoincareDiskStyleUniforms(){let e=this.gl,t=this.poincareDisk;if(!e||!t)return;let i=T(this.poincareDiskFillColor),r=T(this.poincareDiskBorderColor),o=T(this.poincareGridColor);t.uDiskFillColor&&e.uniform4f(t.uDiskFillColor,i[0],i[1],i[2],i[3]),t.uDiskBorderColor&&e.uniform4f(t.uDiskBorderColor,r[0],r[1],r[2],r[3]),t.uGridColor&&e.uniform4f(t.uGridColor,o[0],o[1],o[2],o[3]),t.uDiskBorderWidthPx&&e.uniform1f(t.uDiskBorderWidthPx,this.poincareDiskBorderWidthPx),t.uGridWidthPx&&e.uniform1f(t.uGridWidthPx,this.poincareGridWidthPx)}getBackdropZoom(){return 1}dataIndex=null;gl=null;vao=null;posBuffer=null;labelBuffer=null;hoverVao=null;hoverPosBuffer=null;hoverLabelBuffer=null;selectionVao=null;selectionPosBuffer=null;selectionLabelBuffer=null;selectionOverlayCount=0;selectionEbo=null;hoverEbo=null;interactionEbo=null;interactionCount=0;maxBaseDrawPoints=4e6;maxGpuUploadPoints=1e7;gpuUsesFullDataset=!0;gpuPointCount=0;policy={fragmentBudget:1e8,circleBudget:6e7,squareOnRatio:1,squareOffRatio:.75,minPointsDpr:.35};renderAsSquares=!1;__debugPolicy=null;backdropTex=null;backdropFbo=null;backdropW=0;backdropH=0;backdropDpr=1;backdropZoom=NaN;backdropDirty=!0;pointsTex=null;pointsFbo=null;pointsW=0;pointsH=0;programComposite=null;uCompositeTex=null;poincareDisk=null;pointsCircle=null;pointsSquare=null;programSolid=null;uSolidColor=null;uSolidPointSizePx=null;uSolidRingThicknessPx=null;uSolidRingMode=null;uCssSizeSolid=null;uDprSolid=null;uPointRadiusSolid=null;selectionDirty=!0;hoverDirty=!0;init(e,t){this.canvas=e,this.width=t.width,this.height=t.height,this.deviceDpr=t.devicePixelRatio??window.devicePixelRatio??1,this.canvasDpr=this.deviceDpr,this.dpr=this.deviceDpr;let i="string"==typeof t.poincareDiskFillColor;t.backgroundColor&&(this.backgroundColor=t.backgroundColor),t.pointRadius&&(this.pointRadiusCss=t.pointRadius),t.colors&&(this.colors=t.colors),this.poincareDiskFillColor=i?t.poincareDiskFillColor:this.poincareDiskFillColor,t.poincareDiskBorderColor&&(this.poincareDiskBorderColor=t.poincareDiskBorderColor),t.poincareGridColor&&(this.poincareGridColor=t.poincareGridColor),"number"==typeof t.poincareDiskBorderWidthPx&&Number.isFinite(t.poincareDiskBorderWidthPx)&&(this.poincareDiskBorderWidthPx=Math.max(0,t.poincareDiskBorderWidthPx)),"number"==typeof t.poincareGridWidthPx&&Number.isFinite(t.poincareGridWidthPx)&&(this.poincareGridWidthPx=Math.max(0,t.poincareGridWidthPx)),this.paletteDirty=!0}chooseRenderDpr(e){let t=this.deviceDpr,i=Math.max(1,this.width)*Math.max(1,this.height),r=e>this.maxBaseDrawPoints?this.estimateSubsampleCount(e):e,o=Math.sqrt((e>=1e6?i>1e6?2e5:5e5:e>=5e5?14e5:e>=25e4?21e5:8e6)/i),s=Math.max(.5,this.pointRadiusCss),a=Math.max(1,r)*Math.PI*s*s,n=Math.sqrt(this.policy.fragmentBudget/a),l=e>=1e6?1:e>=5e5?1.25:1.5;return Math.max(e>=1e6?this.policy.minPointsDpr:e>=5e5?.75:1,Math.min(t,l,o,n))}estimateSubsampleCount(e){if(e<5e5)return e;let t=Math.min(e,Math.max(25e4,Math.min(this.maxBaseDrawPoints,Math.floor(.25*e)))),i=Math.max(1,Math.floor(e/t));return Math.min(t,Math.ceil(e/i))}estimatePointFragments(e,t){let i=Math.max(.5,this.pointRadiusCss),r=Math.max(0,t);return Math.max(0,e)*Math.PI*i*i*r*r}updateSquarePointPolicy(e){let t=this.policy.circleBudget*this.policy.squareOnRatio,i=this.policy.circleBudget*this.policy.squareOffRatio;if(this.dpr<=.75){this.renderAsSquares=!0;return}this.renderAsSquares?e<=i&&(this.renderAsSquares=!1):e>=t&&(this.renderAsSquares=!0)}setDataset(e){this.dataset=e,this.selection=new Set,this.hoveredIndex=-1,this.selectionDirty=!0,this.hoverDirty=!0;let t=this.chooseRenderDpr(e.n);t!==this.dpr&&(this.dpr=t),this.dataIndex=new R(e.positions,void 0,64),this.gl&&this.uploadDatasetToGPU(),this.markBackdropDirty()}resize(e,t){this.width=e,this.height=t,this.gl&&this.canvas&&(v(this.canvas,e,t,this.canvasDpr),this.gl.viewport(0,0,this.canvas.width,this.canvas.height),this.markBackdropDirty())}setSelection(e){let t=e.size;this.selection=t<=2e5?new Set(e):e,this.selectionDirty=!0,this.gl&&this.uploadSelectionToGPU()}getSelection(){return this.selection.size<=2e5?new Set(this.selection):this.selection}setHovered(e){this.hoveredIndex=e,this.hoverDirty=!0,this.gl&&this.uploadHoverToGPU()}destroy(){let e=this.gl;e&&(this.pointsCircle&&e.deleteProgram(this.pointsCircle.program),this.pointsSquare&&e.deleteProgram(this.pointsSquare.program),this.programSolid&&e.deleteProgram(this.programSolid),this.poincareDisk&&e.deleteProgram(this.poincareDisk.program),this.vao&&e.deleteVertexArray(this.vao),this.hoverVao&&e.deleteVertexArray(this.hoverVao),this.selectionVao&&e.deleteVertexArray(this.selectionVao),this.posBuffer&&e.deleteBuffer(this.posBuffer),this.labelBuffer&&e.deleteBuffer(this.labelBuffer),this.hoverPosBuffer&&e.deleteBuffer(this.hoverPosBuffer),this.hoverLabelBuffer&&e.deleteBuffer(this.hoverLabelBuffer),this.selectionPosBuffer&&e.deleteBuffer(this.selectionPosBuffer),this.selectionLabelBuffer&&e.deleteBuffer(this.selectionLabelBuffer),this.selectionEbo&&e.deleteBuffer(this.selectionEbo),this.hoverEbo&&e.deleteBuffer(this.hoverEbo),this.interactionEbo&&e.deleteBuffer(this.interactionEbo),this.backdropFbo&&e.deleteFramebuffer(this.backdropFbo),this.backdropTex&&e.deleteTexture(this.backdropTex),this.pointsFbo&&e.deleteFramebuffer(this.pointsFbo),this.pointsTex&&e.deleteTexture(this.pointsTex),this.paletteTex&&e.deleteTexture(this.paletteTex),this.programComposite&&e.deleteProgram(this.programComposite)),this.gl=null,this.vao=null,this.hoverVao=null,this.selectionVao=null,this.posBuffer=null,this.labelBuffer=null,this.hoverPosBuffer=null,this.hoverLabelBuffer=null,this.selectionPosBuffer=null,this.selectionLabelBuffer=null,this.selectionOverlayCount=0,this.selectionEbo=null,this.hoverEbo=null,this.interactionEbo=null,this.interactionCount=0,this.gpuUsesFullDataset=!0,this.gpuPointCount=0,this.backdropFbo=null,this.backdropTex=null,this.backdropW=0,this.backdropH=0,this.backdropDpr=1,this.backdropZoom=NaN,this.backdropDirty=!0,this.pointsFbo=null,this.pointsTex=null,this.pointsW=0,this.pointsH=0,this.programComposite=null,this.uCompositeTex=null,this.pointsCircle=null,this.pointsSquare=null,this.programSolid=null,this.poincareDisk=null,this.paletteTex=null,this.paletteTexW=0,this.paletteTexH=0,this.paletteSize=0,this.paletteDirty=!0}uploadPaletteUniforms(){let e=this.gl;if(!e)return;let t=this.colors.length,i=Math.max(1,Math.min(t,65536)),r=e.getParameter(e.MAX_TEXTURE_SIZE),o=Math.min(r,i),s=Math.ceil(i/o);if(s>r)throw Error(`Palette too large for WebGL texture: size=${i}, maxTex=${r}`);let a=o*s;if(this.paletteBytes.length!==4*a?this.paletteBytes=new Uint8Array(4*a):this.paletteBytes.fill(0),0===t)this.paletteBytes[0]=255,this.paletteBytes[1]=255,this.paletteBytes[2]=255,this.paletteBytes[3]=255;else for(let e=0;e{t&&(e.useProgram(t.program),t.uPaletteTex&&e.uniform1i(t.uPaletteTex,this.paletteTexUnit),t.uPaletteSize&&e.uniform1i(t.uPaletteSize,this.paletteSize),t.uPaletteWidth&&e.uniform1i(t.uPaletteWidth,this.paletteTexW))};n(this.pointsCircle),n(this.pointsSquare),this.paletteDirty=!1}bindPaletteTexture(){let e=this.gl;e&&this.paletteTex&&(e.activeTexture(e.TEXTURE0+this.paletteTexUnit),e.bindTexture(e.TEXTURE_2D,this.paletteTex),e.activeTexture(e.TEXTURE0))}async countSelection(e,t={}){let i=this.dataset,r=this.dataIndex;if(!i||!r)return 0;if(e.indices)return e.indices.size;if("geometry"!==e.kind||!e.geometry)return 0;let o=e.geometry.coords;if(o.length/2<3)return 0;let s=e.geometry.bounds;if(!s){let e=1/0,t=1/0,i=-1/0,r=-1/0;for(let s=0;si&&(i=a),nr&&(r=n)}s={xMin:e,yMin:t,xMax:i,yMax:r}}let a=t.shouldCancel,n=t.onProgress,l="number"==typeof t.yieldEveryMs&&Number.isFinite(t.yieldEveryMs)?Math.max(0,t.yieldEveryMs):8,h=s.xMin-1e-12,u=s.yMin-1e-12,d=s.xMax+1e-12,f=s.yMax+1e-12,c=(e,t,i)=>ei?i:0|e,p=c(Math.floor((h-r.bounds.minX)/r.cellSizeX),0,r.cellsX-1),m=c(Math.floor((u-r.bounds.minY)/r.cellSizeY),0,r.cellsY-1),x=c(Math.floor((d-r.bounds.minX)/r.cellSizeX),0,r.cellsX-1),b=c(Math.floor((f-r.bounds.minY)/r.cellSizeY),0,r.cellsY-1),E=i.positions,R=r.ids,T=r.offsets,y=0,_=0,v=16384,A=l>0?performance.now():0;for(let e=m;e<=b;e++){let t=e*r.cellsX;for(let e=p;e<=x;e++){let i=t+e,r=T[i],h=T[i+1];for(let e=r;es.xMax)&&!(rs.yMax)&&(g(i,r,o)&&y++,_++,l>0&&_>=v)){if(v=_+16384,a?.())return y;performance.now()-A>=l&&(n?.(y,_),await new Promise(e=>requestAnimationFrame(()=>e())),A=performance.now())}}}}return n?.(y,_),y}ensureGL(){if(this.gl)return;if(!this.canvas)throw Error("Renderer not initialized");v(this.canvas,this.width,this.height,this.canvasDpr);let e=this.canvas.getContext("webgl2",{antialias:!1,alpha:!1,depth:!1,stencil:!1,preserveDrawingBuffer:!1,premultipliedAlpha:!1,desynchronized:!0});if(!e)throw Error("Failed to get WebGL2 context (is the canvas already using 2D context?)");this.gl=e,e.disable(e.DEPTH_TEST),e.disable(e.CULL_FACE),e.enable(e.BLEND),e.blendFunc(e.SRC_ALPHA,e.ONE_MINUS_SRC_ALPHA);let[t,i,r,o]=T(this.backgroundColor);e.clearColor(t,i,r,o),e.viewport(0,0,this.canvas.width,this.canvas.height),this.createProgramsAndBuffers(),this.uploadPaletteUniforms(),this.dataset&&this.uploadDatasetToGPU(),this.uploadSelectionToGPU(),this.uploadHoverToGPU(),this.markBackdropDirty()}ensurePointsResources(){if(!this.gl||!this.canvas)return;let e=this.gl,t=Math.max(1,Math.floor(this.width*this.dpr)),i=Math.max(1,Math.floor(this.height*this.dpr)),r=e.getParameter(e.MAX_TEXTURE_SIZE);if(t>r||i>r){let e=Math.min(1,r/t,r/i);t=Math.max(1,Math.floor(t*e)),i=Math.max(1,Math.floor(i*e))}if(!this.pointsTex){if(this.pointsTex=e.createTexture(),!this.pointsTex)throw Error("Failed to create points texture");e.bindTexture(e.TEXTURE_2D,this.pointsTex),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE),e.bindTexture(e.TEXTURE_2D,null)}if(!this.pointsFbo&&(this.pointsFbo=e.createFramebuffer(),!this.pointsFbo))throw Error("Failed to create points framebuffer");if(t!==this.pointsW||i!==this.pointsH){this.pointsW=t,this.pointsH=i,e.bindTexture(e.TEXTURE_2D,this.pointsTex),e.texImage2D(e.TEXTURE_2D,0,e.RGBA,t,i,0,e.RGBA,e.UNSIGNED_BYTE,null),e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,this.pointsFbo),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,this.pointsTex,0);let r=e.checkFramebufferStatus(e.FRAMEBUFFER);if(e.bindFramebuffer(e.FRAMEBUFFER,null),r!==e.FRAMEBUFFER_COMPLETE)throw Error(`Points framebuffer incomplete: ${r}`)}}ensureBackdropResources(){if(!this.gl||!this.canvas||"poincare"!==this.geometryKind()||!this.poincareDisk||!this.vao)return;let e=this.gl,t=Math.max(1,this.canvasDpr),i=Math.max(1,Math.floor(this.width*t)),r=Math.max(1,Math.floor(this.height*t)),o=e.getParameter(e.MAX_TEXTURE_SIZE);if(i>o||r>o){let e=Math.min(1,o/i,o/r);i=Math.max(1,Math.floor(i*e)),r=Math.max(1,Math.floor(r*e)),this.backdropDpr=t*e}else this.backdropDpr=t;if(!this.backdropTex){if(this.backdropTex=e.createTexture(),!this.backdropTex)throw Error("Failed to create backdrop texture");e.bindTexture(e.TEXTURE_2D,this.backdropTex),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.NEAREST),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.NEAREST),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE),e.bindTexture(e.TEXTURE_2D,null)}if(!this.backdropFbo&&(this.backdropFbo=e.createFramebuffer(),!this.backdropFbo))throw Error("Failed to create backdrop framebuffer");if(i!==this.backdropW||r!==this.backdropH){this.backdropW=i,this.backdropH=r,e.bindTexture(e.TEXTURE_2D,this.backdropTex),e.texImage2D(e.TEXTURE_2D,0,e.RGBA,i,r,0,e.RGBA,e.UNSIGNED_BYTE,null),e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,this.backdropFbo),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,this.backdropTex,0);let t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(e.bindFramebuffer(e.FRAMEBUFFER,null),t!==e.FRAMEBUFFER_COMPLETE)throw Error(`Backdrop framebuffer incomplete: ${t}`);this.markBackdropDirty()}}renderBackdropIfNeeded(){if(!this.gl||!this.canvas||"poincare"!==this.geometryKind()||!this.poincareDisk||!this.vao||(this.ensureBackdropResources(),!this.backdropFbo))return;let e=this.getBackdropZoom(),t=Number.isFinite(this.backdropZoom)&&1e-12>=Math.abs(this.backdropZoom-e);if(!this.backdropDirty&&t)return;let i=this.gl;i.bindFramebuffer(i.FRAMEBUFFER,this.backdropFbo),i.viewport(0,0,this.backdropW,this.backdropH);let[r,o,s,a]=T(this.backgroundColor);i.clearColor(r,o,s,a),i.clear(i.COLOR_BUFFER_BIT),i.useProgram(this.poincareDisk.program),this.bindViewUniformsForProgram(this.poincareDisk.program),this.uploadPoincareDiskStyleUniforms(),this.poincareDisk.uCssSize&&i.uniform2f(this.poincareDisk.uCssSize,this.width,this.height),this.poincareDisk.uDpr&&i.uniform1f(this.poincareDisk.uDpr,this.backdropDpr),i.bindVertexArray(this.vao),i.drawArrays(i.TRIANGLES,0,3),i.bindFramebuffer(i.FRAMEBUFFER,null),i.viewport(0,0,this.canvas.width,this.canvas.height),this.backdropZoom=e,this.backdropDirty=!1}createProgramsAndBuffers(){let e=this.gl,t="euclidean"===this.geometryKind()?D:C,i=_(e,t,A),r=_(e,t,S);if(this.programSolid=_(e,t,P),this.programComposite=_(e,M,F),e.useProgram(this.programComposite),this.uCompositeTex=e.getUniformLocation(this.programComposite,"u_tex"),"poincare"===this.geometryKind()){let t=_(e,M,B);this.poincareDisk={program:t,uCssSize:e.getUniformLocation(t,"u_cssSize"),uDpr:e.getUniformLocation(t,"u_dpr"),uDiskFillColor:e.getUniformLocation(t,"u_diskFillColor"),uDiskBorderColor:e.getUniformLocation(t,"u_diskBorderColor"),uGridColor:e.getUniformLocation(t,"u_gridColor"),uDiskBorderWidthPx:e.getUniformLocation(t,"u_diskBorderWidthPx"),uGridWidthPx:e.getUniformLocation(t,"u_gridWidthPx")},e.useProgram(t),this.uploadPoincareDiskStyleUniforms()}if(e.useProgram(i),this.pointsCircle={program:i,uPaletteTex:e.getUniformLocation(i,"u_paletteTex"),uPaletteSize:e.getUniformLocation(i,"u_paletteSize"),uPaletteWidth:e.getUniformLocation(i,"u_paletteWidth"),uCssSize:e.getUniformLocation(i,"u_cssSize"),uDpr:e.getUniformLocation(i,"u_dpr"),uPointRadius:e.getUniformLocation(i,"u_pointRadiusCss")},e.useProgram(r),this.pointsSquare={program:r,uPaletteTex:e.getUniformLocation(r,"u_paletteTex"),uPaletteSize:e.getUniformLocation(r,"u_paletteSize"),uPaletteWidth:e.getUniformLocation(r,"u_paletteWidth"),uCssSize:e.getUniformLocation(r,"u_cssSize"),uDpr:e.getUniformLocation(r,"u_dpr"),uPointRadius:e.getUniformLocation(r,"u_pointRadiusCss")},e.useProgram(this.programSolid),this.uSolidColor=e.getUniformLocation(this.programSolid,"u_color"),this.uSolidPointSizePx=e.getUniformLocation(this.programSolid,"u_pointSizePx"),this.uSolidRingThicknessPx=e.getUniformLocation(this.programSolid,"u_ringThicknessPx"),this.uSolidRingMode=e.getUniformLocation(this.programSolid,"u_ringMode"),this.uCssSizeSolid=e.getUniformLocation(this.programSolid,"u_cssSize"),this.uDprSolid=e.getUniformLocation(this.programSolid,"u_dpr"),this.uPointRadiusSolid=e.getUniformLocation(this.programSolid,"u_pointRadiusCss"),this.vao=e.createVertexArray(),this.posBuffer=e.createBuffer(),this.labelBuffer=e.createBuffer(),this.hoverVao=e.createVertexArray(),this.hoverPosBuffer=e.createBuffer(),this.hoverLabelBuffer=e.createBuffer(),this.selectionVao=e.createVertexArray(),this.selectionPosBuffer=e.createBuffer(),this.selectionLabelBuffer=e.createBuffer(),this.selectionEbo=e.createBuffer(),this.hoverEbo=e.createBuffer(),this.interactionEbo=e.createBuffer(),!this.vao||!this.posBuffer||!this.labelBuffer||!this.hoverVao||!this.hoverPosBuffer||!this.hoverLabelBuffer||!this.selectionVao||!this.selectionPosBuffer||!this.selectionLabelBuffer||!this.selectionEbo||!this.hoverEbo||!this.interactionEbo)throw Error("Failed to allocate WebGL resources");e.bindVertexArray(this.vao),e.bindBuffer(e.ARRAY_BUFFER,this.posBuffer),e.enableVertexAttribArray(0),e.vertexAttribPointer(0,2,e.FLOAT,!1,0,0),e.bindBuffer(e.ARRAY_BUFFER,this.labelBuffer),e.enableVertexAttribArray(1),e.vertexAttribIPointer(1,1,e.UNSIGNED_SHORT,0,0),e.bindVertexArray(null),e.bindBuffer(e.ARRAY_BUFFER,null),e.bindVertexArray(this.hoverVao),e.bindBuffer(e.ARRAY_BUFFER,this.hoverPosBuffer),e.enableVertexAttribArray(0),e.vertexAttribPointer(0,2,e.FLOAT,!1,0,0),e.bindBuffer(e.ARRAY_BUFFER,this.hoverLabelBuffer),e.enableVertexAttribArray(1),e.vertexAttribIPointer(1,1,e.UNSIGNED_SHORT,0,0),e.bindVertexArray(null),e.bindBuffer(e.ARRAY_BUFFER,null),e.bindVertexArray(this.selectionVao),e.bindBuffer(e.ARRAY_BUFFER,this.selectionPosBuffer),e.enableVertexAttribArray(0),e.vertexAttribPointer(0,2,e.FLOAT,!1,0,0),e.bindBuffer(e.ARRAY_BUFFER,this.selectionLabelBuffer),e.enableVertexAttribArray(1),e.vertexAttribIPointer(1,1,e.UNSIGNED_SHORT,0,0),e.bindVertexArray(null),e.bindBuffer(e.ARRAY_BUFFER,null)}uploadDatasetToGPU(){let e=this.gl,t=this.dataset;if(!t)return;e.bindVertexArray(this.vao);let i=t.n<=this.maxGpuUploadPoints;if(this.gpuUsesFullDataset=i,i)e.bindBuffer(e.ARRAY_BUFFER,this.posBuffer),e.bufferData(e.ARRAY_BUFFER,t.positions,e.STATIC_DRAW),e.bindBuffer(e.ARRAY_BUFFER,this.labelBuffer),e.bufferData(e.ARRAY_BUFFER,t.labels,e.STATIC_DRAW),this.gpuPointCount=t.n;else{let i=t.n,r=Math.min(i,Math.max(25e4,Math.min(this.maxBaseDrawPoints,Math.floor(.25*i)))),o=Math.max(1,Math.floor(i/r)),s=Math.min(r,Math.ceil(i/o)),a=new Float32Array(2*s),n=new Uint16Array(s),l=0;for(let e=0;e=5e5){let t=Math.min(i,Math.max(25e4,Math.min(this.maxBaseDrawPoints,Math.floor(.25*i)))),r=Math.max(1,Math.floor(i/t)),o=Math.min(t,Math.ceil(i/r)),s=new Uint32Array(o),a=0;for(let e=0;e=r)break;this.selectionOverlayCount=a,e.bindVertexArray(this.selectionVao),e.bindBuffer(e.ARRAY_BUFFER,this.selectionPosBuffer),e.bufferData(e.ARRAY_BUFFER,o,e.DYNAMIC_DRAW),e.bindBuffer(e.ARRAY_BUFFER,this.selectionLabelBuffer),e.bufferData(e.ARRAY_BUFFER,s,e.DYNAMIC_DRAW),e.bindVertexArray(null),e.bindBuffer(e.ARRAY_BUFFER,null),this.selectionDirty=!1;return}let t=Math.min(this.selection.size,25e4);if(this.selectionOverlayCount=t,0===t){this.selectionDirty=!1;return}let i=new Uint32Array(t),r=0;for(let e of this.selection)if(i[r++]=e,r>=t)break;this.selectionOverlayCount=r,e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,this.selectionEbo),e.bufferData(e.ELEMENT_ARRAY_BUFFER,i,e.DYNAMIC_DRAW),e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null),this.selectionDirty=!1}uploadHoverToGPU(){if(!this.gl||!this.hoverEbo)return;let e=this.gl;if(!this.gpuUsesFullDataset){let t=this.dataset;if(!t||!this.hoverVao||!this.hoverPosBuffer||!this.hoverLabelBuffer)return;let i=this.hoveredIndex>=0&&this.hoveredIndex=0?(r[0]=t.positions[2*i],r[1]=t.positions[2*i+1],o[0]=t.labels[i]):(r[0]=2,r[1]=2,o[0]=0),e.bindVertexArray(this.hoverVao),e.bindBuffer(e.ARRAY_BUFFER,this.hoverPosBuffer),e.bufferData(e.ARRAY_BUFFER,r,e.DYNAMIC_DRAW),e.bindBuffer(e.ARRAY_BUFFER,this.hoverLabelBuffer),e.bufferData(e.ARRAY_BUFFER,o,e.DYNAMIC_DRAW),e.bindVertexArray(null),e.bindBuffer(e.ARRAY_BUFFER,null),this.hoverDirty=!1;return}let t=this.hoveredIndex>=0?this.hoveredIndex:0,i=this.hoverIndexScratch;i[0]=t,e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,this.hoverEbo),e.bufferData(e.ELEMENT_ARRAY_BUFFER,i,e.DYNAMIC_DRAW),e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null),this.hoverDirty=!1}render(){this.ensureGL();let e=this.gl,t=this.dataset;if(!t)return;let i=performance.now()-this.lastViewChangeTs<80,r=!!this.interactionEbo&&this.interactionCount>0,o=i&&"poincare"===this.geometryKind()&&t.n>=2e6&&r,s=t.n>this.maxBaseDrawPoints&&r,l=this.gpuUsesFullDataset&&(o||s),h=this.gpuUsesFullDataset?l?this.interactionCount:t.n:this.gpuPointCount,u=this.estimatePointFragments(h,this.dpr);if(this.updateSquarePointPolicy(u),this.selectionDirty&&this.uploadSelectionToGPU(),this.hoverDirty&&this.uploadHoverToGPU(),e.bindFramebuffer(e.FRAMEBUFFER,null),e.viewport(0,0,this.canvas.width,this.canvas.height),e.disable(e.BLEND),"poincare"===this.geometryKind())if(this.renderBackdropIfNeeded(),this.backdropTex&&this.programComposite)e.useProgram(this.programComposite),e.activeTexture(e.TEXTURE0),e.bindTexture(e.TEXTURE_2D,this.backdropTex),this.uCompositeTex&&e.uniform1i(this.uCompositeTex,0),e.bindVertexArray(this.vao),e.drawArrays(e.TRIANGLES,0,3),e.bindTexture(e.TEXTURE_2D,null);else{let[t,i,r,o]=T(this.backgroundColor);e.clearColor(t,i,r,o),e.clear(e.COLOR_BUFFER_BIT)}else{let[t,i,r,o]=T(this.backgroundColor);e.clearColor(t,i,r,o),e.clear(e.COLOR_BUFFER_BIT)}if(this.ensurePointsResources(),!this.pointsFbo||!this.pointsTex||!this.programComposite)return;e.bindFramebuffer(e.FRAMEBUFFER,this.pointsFbo),e.viewport(0,0,this.pointsW,this.pointsH),e.clearColor(0,0,0,0),e.clear(e.COLOR_BUFFER_BIT);let d=this.renderAsSquares?this.pointsSquare:this.pointsCircle;if(d){if(e.useProgram(d.program),this.bindViewUniformsForProgram(d.program),this.paletteDirty&&this.uploadPaletteUniforms(),this.bindPaletteTexture(),d.uCssSize&&e.uniform2f(d.uCssSize,this.width,this.height),d.uDpr&&e.uniform1f(d.uDpr,this.dpr),d.uPointRadius&&e.uniform1f(d.uPointRadius,this.pointRadiusCss),this.renderAsSquares?e.disable(e.BLEND):(e.enable(e.BLEND),e.blendFunc(e.SRC_ALPHA,e.ONE_MINUS_SRC_ALPHA)),e.bindVertexArray(this.vao),l)e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,this.interactionEbo),e.drawElements(e.POINTS,this.interactionCount,e.UNSIGNED_INT,0),e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null);else{let i=this.gpuUsesFullDataset?t.n:this.gpuPointCount;e.drawArrays(e.POINTS,0,i)}if(this.__debugPolicy={pointsDpr:this.dpr,deviceDpr:this.deviceDpr,canvasDpr:this.canvasDpr,renderAsSquares:this.renderAsSquares,useLod:l,baseDrawCount:h,interactionCount:this.interactionCount,gpuUsesFullDataset:this.gpuUsesFullDataset,gpuPointCount:this.gpuPointCount,estimatedPointFragments:u,fragmentBudget:this.policy.fragmentBudget,isInteracting:i},!i&&this.selection.size>0){if(e.useProgram(this.programSolid),this.bindViewUniformsForProgram(this.programSolid),this.uCssSizeSolid&&e.uniform2f(this.uCssSizeSolid,this.width,this.height),this.uDprSolid&&e.uniform1f(this.uDprSolid,this.dpr),this.uPointRadiusSolid&&e.uniform1f(this.uPointRadiusSolid,this.pointRadiusCss+1),this.uSolidColor){let[t,i,r,o]=T(a);e.uniform4f(this.uSolidColor,t,i,r,o)}this.uSolidRingMode&&e.uniform1i(this.uSolidRingMode,0),this.uSolidRingThicknessPx&&e.uniform1f(this.uSolidRingThicknessPx,0),this.uSolidPointSizePx&&e.uniform1f(this.uSolidPointSizePx,(this.pointRadiusCss+1)*2*this.dpr),this.gpuUsesFullDataset?(e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,this.selectionEbo),e.drawElements(e.POINTS,this.selectionOverlayCount,e.UNSIGNED_INT,0),e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)):this.selectionVao&&this.selectionOverlayCount>0&&(e.bindVertexArray(this.selectionVao),e.drawArrays(e.POINTS,0,this.selectionOverlayCount),e.bindVertexArray(this.vao))}if(!i&&this.hoveredIndex>=0&&this.hoveredIndex0))return null;let n=o/a*(1+1e-12),l=n*n,h=d(e,t,this.view,this.width,this.height),f=.5*this.width,c=.5*this.height,p=this.view.centerX,m=this.view.centerY,x=-1,g=1/0;if(r.forEachInAABB(h.x-n,h.y-n,h.x+n,h.y+n,r=>{let o=i.positions[2*r],n=i.positions[2*r+1],u=o-h.x,d=n-h.y;if(u*u+d*d>l)return;let b=f+(o-p)*a-e,E=c-(n-m)*a-t,R=b*b+E*E;R<=s&&(Rn&&(n=t),iu&&(u=i)}let f={xMin:s,yMin:a,xMax:n,yMax:u},c=performance.now()-r;return h({type:"polygon",coords:o,bounds:f},t.positions,c,(e,t,i)=>!(ef.xMax)&&!(tf.yMax)&&g(e,t,i))}projectToScreen(e,t){return u(e,t,this.view,this.width,this.height)}unprojectFromScreen(e,t){return d(e,t,this.view,this.width,this.height)}}class k extends U{view=f();uniformCache=new Map;lastPanScreenX=0;lastPanScreenY=0;hasPanAnchor=!1;geometryKind(){return"poincare"}getBackdropZoom(){return this.view.displayZoom}setDataset(e){if("poincare"!==e.geometry)throw Error("HyperbolicWebGLCandidate only supports poincare 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+ "integrity": "sha512-LYfpUkmqwl0h9A2HL09Mms427Q1RZWuOHsukfVcKRq9q95iQxdw0ix1JQrqbcDR9PH1QDwf5Qo8OZb5lksZ8Xg==", "dev": true, "license": "MIT", "dependencies": { @@ -7408,9 +7368,9 @@ } }, "node_modules/zustand": { - "version": "5.0.9", - "resolved": "https://registry.npmjs.org/zustand/-/zustand-5.0.9.tgz", - "integrity": "sha512-ALBtUj0AfjJt3uNRQoL1tL2tMvj6Gp/6e39dnfT6uzpelGru8v1tPOGBzayOWbPJvujM8JojDk3E1LxeFisBNg==", + "version": "5.0.10", + "resolved": "https://registry.npmjs.org/zustand/-/zustand-5.0.10.tgz", + "integrity": "sha512-U1AiltS1O9hSy3rul+Ub82ut2fqIAefiSuwECWt6jlMVUGejvf+5omLcRBSzqbRagSM3hQZbtzdeRc6QVScXTg==", "license": "MIT", "engines": { "node": ">=12.20.0" From 9fb0dbe3569b4a81f446f5295f11123c1c321f7a Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Sat, 31 Jan 2026 17:51:48 +0100 Subject: [PATCH 52/75] Fix: include frontend static assets in sdist for wheel builds --- pyproject.toml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 58f80e7..d687f7d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -75,6 +75,7 @@ version-file = "src/hyperview/_version.py" [tool.hatch.build.targets.wheel] packages = ["src/hyperview"] # Include frontend static assets (pre-built before packaging) +# artifacts is for untracked files that exist; force-include ensures they're packaged artifacts = ["src/hyperview/server/static/**"] [tool.hatch.build.targets.sdist] @@ -83,6 +84,8 @@ include = [ "/README.md", "/LICENSE", ] +# Include built frontend for reproducible source builds +artifacts = ["src/hyperview/server/static/**"] [tool.ruff] line-length = 100 From c73eeeb4a45508862af0d1bbf66c26caf9106e91 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Sat, 31 Jan 2026 17:57:44 +0100 Subject: [PATCH 53/75] Remove require_frontend_export workflow (frontend now built in CI) --- .github/workflows/require_frontend_export.yml | 106 ------------------ notebooks/hyperview_clip_umap.py | 75 +++++++++++++ 2 files changed, 75 insertions(+), 106 deletions(-) delete mode 100644 .github/workflows/require_frontend_export.yml create mode 100644 notebooks/hyperview_clip_umap.py diff --git a/.github/workflows/require_frontend_export.yml b/.github/workflows/require_frontend_export.yml deleted file mode 100644 index f97688d..0000000 --- a/.github/workflows/require_frontend_export.yml +++ /dev/null @@ -1,106 +0,0 @@ -name: Require Frontend Export - -on: - pull_request: - types: [opened, synchronize, reopened] - paths: - - "frontend/**" - - "scripts/export_frontend.sh" - - "src/hyperview/server/static/**" - push: - paths: - - "frontend/**" - - "scripts/export_frontend.sh" - - "src/hyperview/server/static/**" - -jobs: - require-frontend-export: - runs-on: ubuntu-latest - # Skip if the push was made by github-actions to avoid loops - if: github.actor != 'github-actions[bot]' - permissions: - contents: write - - steps: - - name: Checkout repository - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - - name: Detect frontend and static changes - id: changes - run: | - set -euo pipefail - - # Determine base and head SHAs based on event type - if [[ "${{ github.event_name }}" == "pull_request" ]]; then - base_sha="${{ github.event.pull_request.base.sha }}" - head_sha="${{ github.event.pull_request.head.sha }}" - else - # For push events, compare with the previous commit - head_sha="${{ github.sha }}" - base_sha="${{ github.event.before }}" - # Handle initial push (no previous commit) - if [[ "$base_sha" == "0000000000000000000000000000000000000000" ]]; then - echo "Initial push - skipping check" - echo "needs_export=false" >> "$GITHUB_OUTPUT" - exit 0 - fi - fi - - changed_files="$(git diff --name-only "$base_sha" "$head_sha")" - - echo "Changed files:" - echo "$changed_files" - - frontend_changed="false" - static_changed="false" - - if echo "$changed_files" | grep -qE '^(frontend/|scripts/export_frontend\.sh$)'; then - frontend_changed="true" - fi - - if echo "$changed_files" | grep -q '^src/hyperview/server/static/'; then - static_changed="true" - fi - - needs_export="false" - if [[ "$frontend_changed" == "true" && "$static_changed" != "true" ]]; then - needs_export="true" - fi - - { - echo "frontend_changed=$frontend_changed" - echo "static_changed=$static_changed" - echo "needs_export=$needs_export" - } >> "$GITHUB_OUTPUT" - - - name: Set up Node - if: github.event_name == 'push' && steps.changes.outputs.needs_export == 'true' - uses: actions/setup-node@v4 - with: - node-version: 20 - - - name: Export frontend - if: github.event_name == 'push' && steps.changes.outputs.needs_export == 'true' - run: bash scripts/export_frontend.sh - - - name: Commit and push exported static assets - if: github.event_name == 'push' && steps.changes.outputs.needs_export == 'true' - uses: stefanzweifel/git-auto-commit-action@v7 - with: - commit_message: "chore: export frontend static assets [skip ci]" - file_pattern: "src/hyperview/server/static/**" - - - name: Fail PR if frontend changed without static export - if: github.event_name == 'pull_request' && steps.changes.outputs.needs_export == 'true' - run: | - echo "" - echo "ERROR: frontend/ changed but src/hyperview/server/static/ was not updated." - echo "" - echo "Run locally:" - echo " bash scripts/export_frontend.sh" - echo " git add src/hyperview/server/static" - echo " git commit -m 'chore: export frontend static assets'" - echo "" - exit 1 diff --git a/notebooks/hyperview_clip_umap.py b/notebooks/hyperview_clip_umap.py new file mode 100644 index 0000000..e6b5c8d --- /dev/null +++ b/notebooks/hyperview_clip_umap.py @@ -0,0 +1,75 @@ +# %% [markdown] +# # HyperView Demo: Image Embeddings + UMAP Visualization +# +# Demonstrates computing image embeddings and visualizing them in 2D using UMAP +# with the HyperView Dataset API. Supports CLIP and SigLip models. + +# %% +import json +import random +import urllib.parse +import urllib.request +from pathlib import Path + +import hyperview as hv + +# %% [markdown] +# ## Download Random Space Images (NASA, Public Domain) +# +# This demo downloads a random set of space images from NASA's Images and Video Library +# (https://images.nasa.gov). NASA imagery is generally public domain (US Government work). + +# %% +NUM_IMAGES = 48 +CACHE_DIR = Path(__file__).parent / "_nasa_space_images" +RANDOM_SEED = None # Set to an int (e.g. 0) for reproducible sampling +NASA_QUERIES = ["black hole", "nebula", "galaxy", "jwst", "hubble"] + + +CACHE_DIR.mkdir(parents=True, exist_ok=True) +image_files = [ + p + for p in CACHE_DIR.iterdir() + if p.is_file() and p.suffix.lower() in {".jpg", ".jpeg", ".png", ".webp", ".tif", ".tiff"} +] +if len(image_files) < NUM_IMAGES: + rng = random.Random(RANDOM_SEED) + query = rng.choice(NASA_QUERIES) + params = {"q": query, "media_type": "image", "page": "1"} + api_url = f"https://images-api.nasa.gov/search?{urllib.parse.urlencode(params)}" + items = json.load(urllib.request.urlopen(api_url))["collection"]["items"] + rng.shuffle(items) + + for i, item in enumerate(items[:NUM_IMAGES]): + href = item["links"][0]["href"] + ext = Path(urllib.parse.urlparse(href).path).suffix or ".jpg" + urllib.request.urlretrieve(href, CACHE_DIR / f"nasa_{i:03d}{ext}") + +dataset = hv.Dataset("nasa_space_demo", persist=False) +added, skipped = dataset.add_images_dir(str(CACHE_DIR), label_from_folder=False) +print(f"✓ Loaded {added} NASA space images" + (f" ({skipped} already present)" if skipped else "")) + +# %% [markdown] +# ## Compute Embeddings & UMAP Projection +# +# EmbedAnything supports many HuggingFace vision models, but not all. +# For example, `microsoft/resnet-18` is currently rejected by EmbedAnything +# ("Model not supported"). Common choices that work: +# - CLIP: openai/clip-vit-base-patch32, openai/clip-vit-base-patch16, etc. +# - SigLip: google/siglip-base-patch16-224, google/siglip-large-patch16-384 +# - Jina CLIP: jinaai/jina-clip-v2 + +# %% +# Use any HuggingFace model supported by EmbedAnything +MODEL = "google/siglip-base-patch16-224" + +dataset.compute_embeddings(model=MODEL, show_progress=True) +dataset.compute_visualization() + +# %% [markdown] +# ## Launch HyperView + +# %% +hv.launch(dataset, port=6262, open_browser=True) + + From d8fde182c588f0d1e532ca5f6ec2e8273ff3e9eb Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Sat, 31 Jan 2026 19:23:30 +0100 Subject: [PATCH 54/75] Fix: update Colab smoke test to download NASA images and compute real embeddings --- notebooks/colab_smoke_test.ipynb | 94 ++++++++++++++++---------------- 1 file changed, 48 insertions(+), 46 deletions(-) diff --git a/notebooks/colab_smoke_test.ipynb b/notebooks/colab_smoke_test.ipynb index b60a470..0cea259 100644 --- a/notebooks/colab_smoke_test.ipynb +++ b/notebooks/colab_smoke_test.ipynb @@ -9,7 +9,7 @@ "\n", "This notebook verifies that HyperView can launch inside Google Colab and open the UI in a new browser tab.\n", "\n", - "The smoke test uses a synthetic dataset (random images + precomputed 2D coordinates), so it does not download models or run embedding computation." + "The smoke test downloads a small set of public-domain NASA images and computes real embeddings using a lightweight model." ] }, { @@ -17,27 +17,19 @@ "execution_count": null, "id": "945a1017", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "zsh:1: command not found: pip\n", - "Cloning into 'HyperView'...\n", - "remote: Enumerating objects: 577, done.\u001b[K\n", - "remote: Counting objects: 100% (577/577), done.\u001b[K\n", - "remote: Compressing objects: 100% (328/328), done.\u001b[K\n", - "remote: Total 577 (delta 240), reused 518 (delta 193), pack-reused 0 (from 0)\u001b[K\n", - "Receiving objects: 100% (577/577), 4.29 MiB | 8.76 MiB/s, done.\n", - "Resolving deltas: 100% (240/240), done.\n", - "/Users/matin/hyperview_org/HyperView/notebooks/HyperView\n", - "\u001b[2mUsing Python 3.12.8 environment at: /Users/matin/hyperview_org/HyperView/.venv\u001b[0m\n", - "\u001b[2K\u001b[1m\u001b[31merror\u001b[39m\u001b[0m: /Users/matin/hyperview_org/HyperView/notebooks/HyperView does not appear to be a Python project, as neither `pyproject.toml` nor `setup.py` are present in the directory\n" - ] - } - ], + "outputs": [], + "source": [ + "%pip install hyperview" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "856febf3", + "metadata": {}, + "outputs": [], "source": [ - "%pip install -q git+https://github.com/HackerRoomAI/HyperView.git@hyperview-base" + "import hyperview as hv" ] }, { @@ -47,36 +39,46 @@ "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", + "import json\n", + "import random\n", + "import urllib.parse\n", + "import urllib.request\n", + "from pathlib import Path\n", "\n", - "import hyperview as hv\n", - "from hyperview.core.sample import SampleFromArray\n", - "\n", - "dataset = hv.Dataset(\"colab_smoke\", persist=False)\n", + "# Download a small set of NASA space images (public domain)\n", + "NUM_IMAGES = 24\n", + "CACHE_DIR = Path(\"/tmp/_nasa_smoke_images\")\n", + "NASA_QUERIES = [\"black hole\", \"nebula\", \"galaxy\", \"jwst\", \"hubble\"]\n", "\n", - "rng = np.random.default_rng(0)\n", - "num_samples = 200\n", + "CACHE_DIR.mkdir(parents=True, exist_ok=True)\n", + "image_files = [\n", + " p for p in CACHE_DIR.iterdir()\n", + " if p.is_file() and p.suffix.lower() in {\".jpg\", \".jpeg\", \".png\", \".webp\"}\n", + "]\n", + "if len(image_files) < NUM_IMAGES:\n", + " rng = random.Random(42)\n", + " query = rng.choice(NASA_QUERIES)\n", + " params = {\"q\": query, \"media_type\": \"image\", \"page\": \"1\"}\n", + " api_url = f\"https://images-api.nasa.gov/search?{urllib.parse.urlencode(params)}\"\n", + " items = json.load(urllib.request.urlopen(api_url))[\"collection\"][\"items\"]\n", + " rng.shuffle(items)\n", "\n", - "for i in range(num_samples):\n", - " img = (rng.random((64, 64, 3)) * 255).astype(np.uint8)\n", + " for i, item in enumerate(items[:NUM_IMAGES]):\n", + " href = item[\"links\"][0][\"href\"]\n", + " ext = Path(urllib.parse.urlparse(href).path).suffix or \".jpg\"\n", + " urllib.request.urlretrieve(href, CACHE_DIR / f\"nasa_{i:03d}{ext}\")\n", "\n", - " sample = SampleFromArray.from_array(\n", - " id=f\"s{i}\",\n", - " image_array=img,\n", - " label=f\"class_{i % 10}\",\n", - " metadata={\"i\": i},\n", - " )\n", - "\n", - " # Precomputed 2D Euclidean projection\n", - " sample.embedding_2d = [float(rng.normal()), float(rng.normal())]\n", - "\n", - " # Precomputed 2D hyperbolic projection (inside unit disk)\n", - " r = float(rng.random() * 0.95)\n", - " theta = float(rng.random() * 2 * np.pi)\n", - " sample.embedding_2d_hyperbolic = [float(r * np.cos(theta)), float(r * np.sin(theta))]\n", + "# Create dataset and add images\n", + "dataset = hv.Dataset(\"colab_smoke\", persist=False)\n", + "added, skipped = dataset.add_images_dir(str(CACHE_DIR), label_from_folder=False)\n", + "print(f\"✓ Loaded {added} NASA images\" + (f\" ({skipped} already present)\" if skipped else \"\"))\n", "\n", - " dataset.add_sample(sample)\n", + "# Compute embeddings with a lightweight model\n", + "MODEL = \"google/siglip-base-patch16-224\"\n", + "dataset.compute_embeddings(model=MODEL, show_progress=True)\n", + "dataset.compute_visualization()\n", "\n", + "# Launch HyperView\n", "hv.launch(dataset, port=6262)" ] }, @@ -85,7 +87,7 @@ "id": "b1cb3148", "metadata": {}, "source": [ - "Click the link printed above to open HyperView in a new tab. If needed, copy/paste the URL." + "Click the link printed above to open HyperView in a new tab. In Colab, you may need to copy/paste the URL." ] } ], From 2f4a81e39c9b1def3d5d3f5aa1af42699d65abea Mon Sep 17 00:00:00 2001 From: Antonio Rueda-Toicen Date: Sun, 1 Feb 2026 12:06:22 +0100 Subject: [PATCH 55/75] cifar-100 colab notebook --- ...yperView_CLIP_HyCoCLIP_UMAP_CIFAR100.ipynb | 3030 +++++++++++++++++ 1 file changed, 3030 insertions(+) create mode 100644 notebooks/HyperView_CLIP_HyCoCLIP_UMAP_CIFAR100.ipynb diff --git a/notebooks/HyperView_CLIP_HyCoCLIP_UMAP_CIFAR100.ipynb b/notebooks/HyperView_CLIP_HyCoCLIP_UMAP_CIFAR100.ipynb new file mode 100644 index 0000000..3fa96fe --- /dev/null +++ b/notebooks/HyperView_CLIP_HyCoCLIP_UMAP_CIFAR100.ipynb @@ -0,0 +1,3030 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "47ee06e6", + "metadata": { + "id": "47ee06e6" + }, + "source": [ + "# HyperView: CLIP vs HyCoCLIP with 2D UMAP Projection on CIFAR-100\n", + "\n", + "This notebook compares two embedding spaces on the same image set.\n", + "\n", + "- [**CLIP**](https://github.com/openai/CLIP) maps images and text into one shared vector space.\n", + " \n", + "- [**HyCoCLIP**](https://github.com/PalAvik/hycoclip) maps images into a space designed for hierarchy and tree like structure.\n", + " \n", + "- We then compute a **2D projection** of each embedding space with [UMAP](https://umap-learn.readthedocs.io/en/latest/) so we can inspect clusters and label structure.\n", + " \n", + "\n", + "HyperView allows us to compare the embedding spaces of these different geometries.\n", + "\n", + "In this demo, we use the [CIFAR-100](https://huggingface.co/datasets/uoft-cs/cifar100) dataset of tiny images, available through HuggingFace." + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Install\n", + "\n", + "HyperView is a library for dataset curation and model analysis. It provides tools for interactive visualization of the embedding space." + ], + "metadata": { + "id": "hyaagNXkyUBN" + }, + "id": "hyaagNXkyUBN" + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "945a1017", + "metadata": { + "id": "945a1017" + }, + "outputs": [], + "source": [ + "%%capture\n", + "# If you run this in Google Colab, install HyperView with this command.\n", + "!uv pip install hyperview" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Import HyperView" + ], + "metadata": { + "id": "hYtO76VBy7C9" + }, + "id": "hYtO76VBy7C9" + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "We import HyperView and use it as the main interface for:\n", + "\n", + "- reading data\n", + " \n", + "- computing embeddings with pretrained models\n", + " \n", + "- computing a 2D projection for visualization\n", + " \n", + "- launching the interactive viewer\n", + " " + ], + "metadata": { + "id": "v-TvwC2Qyw42" + }, + "id": "v-TvwC2Qyw42" + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "58b82256", + "metadata": { + "id": "58b82256" + }, + "outputs": [], + "source": [ + "import hyperview as hv" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Configuration" + ], + "metadata": { + "id": "nH4CjViEy-U4" + }, + "id": "nH4CjViEy-U4" + }, + { + "cell_type": "markdown", + "source": [ + "We specify the dataset and model settings in a single dictionary.\n", + "\n", + "### Data source\n", + "\n", + "- We load **CIFAR-100** from Hugging Face.\n", + " \n", + "- We use the **test** split.\n", + " \n", + "- `img` is the image field.\n", + " \n", + "- We use `coarse_label` for labels." + ], + "metadata": { + "id": "36tl8PxjzBT6" + }, + "id": "36tl8PxjzBT6" + }, + { + "cell_type": "markdown", + "source": [ + "Why coarse labels:\n", + "\n", + "- HyperView disables distinct label coloring when there are more than 20 labels.\n", + " \n", + "- CIFAR-100 has 100 fine labels, but 20 coarse labels.\n", + " \n", + "- Using coarse labels keeps label coloring useful in the viewer.\n", + " " + ], + "metadata": { + "id": "SIR8CsvfzW3I" + }, + "id": "SIR8CsvfzW3I" + }, + { + "cell_type": "markdown", + "source": [ + "### Sampling\n", + "\n", + "- `NUM_SAMPLES = 200` keeps embedding and layout computation fast enough for a demo.\n", + " \n", + "- Increase this if you want denser clusters, but expect more compute time.\n", + " " + ], + "metadata": { + "id": "tXpwwCXozezd" + }, + "id": "tXpwwCXozezd" + }, + { + "cell_type": "markdown", + "source": [ + "### Models\n", + "\n", + "- `openai/clip-vit-base-patch32` is a common CLIP baseline.\n", + " \n", + "- `hycoclip-vit-s` is a HyCoCLIP model that targets hierarchical structure." + ], + "metadata": { + "id": "NCtE2BN0zk3G" + }, + "id": "NCtE2BN0zk3G" + }, + { + "cell_type": "code", + "source": [ + "DATASET_NAME = \"cifar100_coarse_clip_hyper_models\"\n", + "HF_DATASET = \"uoft-cs/cifar100\"\n", + "HF_SPLIT = \"test\"\n", + "HF_IMAGE_KEY = \"img\"\n", + "# NOTE: HyperView disables distinct label coloring when there are >20 labels.\n", + "# CIFAR-100 has 100 fine labels, but only 20 coarse labels.\n", + "HF_LABEL_KEY = \"coarse_label\"\n", + "NUM_SAMPLES = 200\n", + "CLIP_MODEL_ID = \"openai/clip-vit-base-patch32\"\n", + "HYPER_MODELS_MODEL_ID = \"hycoclip-vit-s\"" + ], + "metadata": { + "id": "Wto-RfOFzCdJ" + }, + "id": "Wto-RfOFzCdJ", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Load CIFAR-100 into a HyperView dataset\n", + "\n", + "We create a HyperView `Dataset` object and then import samples from Hugging Face.\n", + "\n", + "Key parameters:\n", + "\n", + "- `persist=False` keeps this dataset in memory for this run.\n", + " \n", + "- `max_samples=NUM_SAMPLES` subsamples the split.\n", + " \n", + "\n", + "At the end, `len(dataset)` is the number of loaded examples." + ], + "metadata": { + "id": "nEdV-nZTzs3G" + }, + "id": "nEdV-nZTzs3G" + }, + { + "cell_type": "code", + "source": [ + "print(\"Loading CIFAR-100 from Hugging Face...\")\n", + "dataset = hv.Dataset(DATASET_NAME, persist=False)\n", + "dataset.add_from_huggingface(\n", + " HF_DATASET,\n", + " split=HF_SPLIT,\n", + " image_key=HF_IMAGE_KEY,\n", + " label_key=HF_LABEL_KEY,\n", + " max_samples=NUM_SAMPLES,\n", + ")\n", + "print(f\"Loaded {len(dataset)} samples\")\n", + "\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 506, + "referenced_widgets": [ + "162a6522d4a246b78865ab61ca713730", + "e40b36bfd1f64d129f17ee0bb4aef334", + "fc5fe01c2fc64bbb8f9b7adba05cf789", + "187ce225eb4041299d9233cee10f198f", + "c863c8acaef84e488a5016b6869888fe", + "f6137d250fa044d393e65d3443a63f23", + "698107a8b57d4746bc299795c75a5234", + "b4b55da4f86a4c229e5d2b2129353c0a", + "ef34f8327ba943629f00788360462e2a", + "89a229a5967d4b80ac9c031e980300a9", + "5cdbe6ac7f514818be2ae019cb430d94", + "b18ef99b82b244099c5c2843f1109136", + "534bbfee2fbb4adfa5e986e57a7ae613", + 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}, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "cifar100/train-00000-of-00001.parquet: 0%| | 0.00/119M [00:00" + ], + "text/html": [ + "

HyperView is running in Colab. Open HyperView in a new tab.

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https://6262-gpu-t4-hm-kg95f2888y5u-c.europe-west4-1.prod.colab.dev/

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We compute a 2D layout so we can see neighborhood structure.\n", + "\n", + "- `compute_visualization(..., geometry=\"euclidean\")` treats the embedding space as Euclidean.\n", + " \n", + "- This matches how CLIP vectors are often used with cosine similarity or dot product in a flat vector space.\n", + " \n", + "\n", + "Note on “UMAP” \n", + "This notebook focuses on a 2D projection step. HyperView computes a 2D layout for the viewer. The title calls this UMAP. In practice, you should treat this as a nonlinear projection that preserves local neighborhoods better than PCA." + ], + "metadata": { + "id": "xvAMFY_Dz71Z" + }, + "id": "xvAMFY_Dz71Z" + }, + { + "cell_type": "markdown", + "source": [ + "## Compute HyCoCLIP embeddings\n", + "\n", + "### Why a different geometry\n", + "\n", + "Some datasets have hierarchical label structure. A tree structure is hard to represent in Euclidean space without distortion.\n", + "\n", + "Hyperbolic spaces can represent tree growth patterns with less distortion than Euclidean spaces. A common model is the **Poincaré ball**, which is a way to work with hyperbolic geometry in a bounded region.\n", + "\n", + "### What we do in code\n", + "\n", + "- `compute_embeddings(model=HYPER_MODELS_MODEL_ID)` computes embeddings from the HyCoCLIP model.\n", + " \n", + "- `compute_visualization(..., geometry=\"poincare\")` tells HyperView to treat distances and neighborhoods using Poincaré geometry.\n", + " \n", + "\n", + "How to read the plot\n", + "\n", + "- In Poincaré style layouts, points near the center and points near the boundary can have different distance behavior than Euclidean layouts.\n", + " \n", + "- Focus on neighborhood membership and cluster separation rather than raw coordinate scale." + ], + "metadata": { + "id": "zEkSKKPN0GZz" + }, + "id": "zEkSKKPN0GZz" + }, + { + "cell_type": "code", + "source": [ + "clip_space = dataset.compute_embeddings(CLIP_MODEL_ID)\n", + "dataset.compute_visualization(space_key=clip_space, geometry=\"euclidean\")\n", + "hyper_space = dataset.compute_embeddings(model=HYPER_MODELS_MODEL_ID)\n", + "dataset.compute_visualization(space_key=hyper_space, geometry=\"poincare\")" + ], + "metadata": { + "id": "rXl9yJKyx3hQ" + }, + "id": "rXl9yJKyx3hQ", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Launch the interactive viewer\n", + "\n", + "`hv.launch(dataset, open_browser=True)` starts a local server and opens the viewer.\n", + "\n", + "In the UI, you should be able to:\n", + "\n", + "- switch between embedding spaces (CLIP vs HyCoCLIP)\n", + " \n", + "- inspect image thumbnails for selected points\n", + " \n", + "- compare how coarse labels cluster under each geometry\n", + " \n" + ], + "metadata": { + "id": "rGEUqA0W0O4g" + }, + "id": "rGEUqA0W0O4g" + }, + { + "cell_type": "code", + "source": [ + "hv.launch(dataset, open_browser=True)" + ], + "metadata": { + "id": "o3TdCwD2wIf2" + }, + "id": "o3TdCwD2wIf2", + "execution_count": null, + "outputs": [] + }, + { + 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a/notebooks/HyperView_CLIP_HyCoCLIP_UMAP_CIFAR100.ipynb b/notebooks/HyperView_CLIP_HyCoCLIP_UMAP_CIFAR100.ipynb deleted file mode 100644 index 3fa96fe..0000000 --- a/notebooks/HyperView_CLIP_HyCoCLIP_UMAP_CIFAR100.ipynb +++ /dev/null @@ -1,3030 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "47ee06e6", - "metadata": { - "id": "47ee06e6" - }, - "source": [ - "# HyperView: CLIP vs HyCoCLIP with 2D UMAP Projection on CIFAR-100\n", - "\n", - "This notebook compares two embedding spaces on the same image set.\n", - "\n", - "- [**CLIP**](https://github.com/openai/CLIP) maps images and text into one shared vector space.\n", - " \n", - "- [**HyCoCLIP**](https://github.com/PalAvik/hycoclip) maps images into a space designed for hierarchy and tree like structure.\n", - " \n", - "- We then compute a **2D projection** of each embedding space with [UMAP](https://umap-learn.readthedocs.io/en/latest/) so we can inspect clusters and label structure.\n", - " \n", - "\n", - "HyperView allows us to compare the embedding spaces of these different geometries.\n", - "\n", - "In this demo, we use the [CIFAR-100](https://huggingface.co/datasets/uoft-cs/cifar100) dataset of tiny images, available through HuggingFace." - ] - }, - { - "cell_type": "markdown", - "source": [ - "## Install\n", - "\n", - "HyperView is a library for dataset curation and model analysis. It provides tools for interactive visualization of the embedding space." - ], - "metadata": { - "id": "hyaagNXkyUBN" - }, - "id": "hyaagNXkyUBN" - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "945a1017", - "metadata": { - "id": "945a1017" - }, - "outputs": [], - "source": [ - "%%capture\n", - "# If you run this in Google Colab, install HyperView with this command.\n", - "!uv pip install hyperview" - ] - }, - { - "cell_type": "markdown", - "source": [ - "## Import HyperView" - ], - "metadata": { - "id": "hYtO76VBy7C9" - }, - "id": "hYtO76VBy7C9" - }, - { - "cell_type": "markdown", - "source": [ - "\n", - "We import HyperView and use it as the main interface for:\n", - "\n", - "- reading data\n", - " \n", - "- computing embeddings with pretrained models\n", - " \n", - "- computing a 2D projection for visualization\n", - " \n", - "- launching the interactive viewer\n", - " " - ], - "metadata": { - "id": "v-TvwC2Qyw42" - }, - "id": "v-TvwC2Qyw42" - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "58b82256", - "metadata": { - "id": "58b82256" - }, - "outputs": [], - "source": [ - "import hyperview as hv" - ] - }, - { - "cell_type": "markdown", - "source": [ - "## Configuration" - ], - "metadata": { - "id": "nH4CjViEy-U4" - }, - "id": "nH4CjViEy-U4" - }, - { - "cell_type": "markdown", - "source": [ - "We specify the dataset and model settings in a single dictionary.\n", - "\n", - "### Data source\n", - "\n", - "- We load **CIFAR-100** from Hugging Face.\n", - " \n", - "- We use the **test** split.\n", - " \n", - "- `img` is the image field.\n", - " \n", - "- We use `coarse_label` for labels." - ], - "metadata": { - "id": "36tl8PxjzBT6" - }, - "id": "36tl8PxjzBT6" - }, - { - "cell_type": "markdown", - "source": [ - "Why coarse labels:\n", - "\n", - "- HyperView disables distinct label coloring when there are more than 20 labels.\n", - " \n", - "- CIFAR-100 has 100 fine labels, but 20 coarse labels.\n", - " \n", - "- Using coarse labels keeps label coloring useful in the viewer.\n", - " " - ], - "metadata": { - "id": "SIR8CsvfzW3I" - }, - "id": "SIR8CsvfzW3I" - }, - { - "cell_type": "markdown", - "source": [ - "### Sampling\n", - "\n", - "- `NUM_SAMPLES = 200` keeps embedding and layout computation fast enough for a demo.\n", - " \n", - "- Increase this if you want denser clusters, but expect more compute time.\n", - " " - ], - "metadata": { - "id": "tXpwwCXozezd" - }, - "id": "tXpwwCXozezd" - }, - { - "cell_type": "markdown", - "source": [ - "### Models\n", - "\n", - "- `openai/clip-vit-base-patch32` is a common CLIP baseline.\n", - " \n", - "- `hycoclip-vit-s` is a HyCoCLIP model that targets hierarchical structure." - ], - "metadata": { - "id": "NCtE2BN0zk3G" - }, - "id": "NCtE2BN0zk3G" - }, - { - "cell_type": "code", - "source": [ - "DATASET_NAME = \"cifar100_coarse_clip_hyper_models\"\n", - "HF_DATASET = \"uoft-cs/cifar100\"\n", - "HF_SPLIT = \"test\"\n", - "HF_IMAGE_KEY = \"img\"\n", - "# NOTE: HyperView disables distinct label coloring when there are >20 labels.\n", - "# CIFAR-100 has 100 fine labels, but only 20 coarse labels.\n", - "HF_LABEL_KEY = \"coarse_label\"\n", - "NUM_SAMPLES = 200\n", - "CLIP_MODEL_ID = \"openai/clip-vit-base-patch32\"\n", - "HYPER_MODELS_MODEL_ID = \"hycoclip-vit-s\"" - ], - "metadata": { - "id": "Wto-RfOFzCdJ" - }, - "id": "Wto-RfOFzCdJ", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## Load CIFAR-100 into a HyperView dataset\n", - "\n", - "We create a HyperView `Dataset` object and then import samples from Hugging Face.\n", - "\n", - "Key parameters:\n", - "\n", - "- `persist=False` keeps this dataset in memory for this run.\n", - " \n", - "- `max_samples=NUM_SAMPLES` subsamples the split.\n", - " \n", - "\n", - "At the end, `len(dataset)` is the number of loaded examples." - ], - "metadata": { - "id": "nEdV-nZTzs3G" - }, - "id": "nEdV-nZTzs3G" - }, - { - "cell_type": "code", - "source": [ - "print(\"Loading CIFAR-100 from Hugging Face...\")\n", - "dataset = hv.Dataset(DATASET_NAME, persist=False)\n", - "dataset.add_from_huggingface(\n", - " HF_DATASET,\n", - " split=HF_SPLIT,\n", - " image_key=HF_IMAGE_KEY,\n", - " label_key=HF_LABEL_KEY,\n", - " max_samples=NUM_SAMPLES,\n", - ")\n", - "print(f\"Loaded {len(dataset)} samples\")\n", - "\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 506, - "referenced_widgets": [ - "162a6522d4a246b78865ab61ca713730", - "e40b36bfd1f64d129f17ee0bb4aef334", - "fc5fe01c2fc64bbb8f9b7adba05cf789", - "187ce225eb4041299d9233cee10f198f", - "c863c8acaef84e488a5016b6869888fe", - "f6137d250fa044d393e65d3443a63f23", - "698107a8b57d4746bc299795c75a5234", - "b4b55da4f86a4c229e5d2b2129353c0a", - "ef34f8327ba943629f00788360462e2a", - "89a229a5967d4b80ac9c031e980300a9", - "5cdbe6ac7f514818be2ae019cb430d94", - "b18ef99b82b244099c5c2843f1109136", - "534bbfee2fbb4adfa5e986e57a7ae613", - "842ae4c3bb17440f837bf284babdc261", - "4a4d809d0c8440a38cc9d3a978a30316", - "5ba55ab80c3d44779c0466b085d567e5", - "2f2d2700b29f4541b090e193e367559d", - 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HyperView is running in Colab. Open HyperView in a new tab.

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https://6262-gpu-t4-hm-kg95f2888y5u-c.europe-west4-1.prod.colab.dev/

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We compute a 2D layout so we can see neighborhood structure.\n", - "\n", - "- `compute_visualization(..., geometry=\"euclidean\")` treats the embedding space as Euclidean.\n", - " \n", - "- This matches how CLIP vectors are often used with cosine similarity or dot product in a flat vector space.\n", - " \n", - "\n", - "Note on “UMAP” \n", - "This notebook focuses on a 2D projection step. HyperView computes a 2D layout for the viewer. The title calls this UMAP. In practice, you should treat this as a nonlinear projection that preserves local neighborhoods better than PCA." - ], - "metadata": { - "id": "xvAMFY_Dz71Z" - }, - "id": "xvAMFY_Dz71Z" - }, - { - "cell_type": "markdown", - "source": [ - "## Compute HyCoCLIP embeddings\n", - "\n", - "### Why a different geometry\n", - "\n", - "Some datasets have hierarchical label structure. A tree structure is hard to represent in Euclidean space without distortion.\n", - "\n", - "Hyperbolic spaces can represent tree growth patterns with less distortion than Euclidean spaces. A common model is the **Poincaré ball**, which is a way to work with hyperbolic geometry in a bounded region.\n", - "\n", - "### What we do in code\n", - "\n", - "- `compute_embeddings(model=HYPER_MODELS_MODEL_ID)` computes embeddings from the HyCoCLIP model.\n", - " \n", - "- `compute_visualization(..., geometry=\"poincare\")` tells HyperView to treat distances and neighborhoods using Poincaré geometry.\n", - " \n", - "\n", - "How to read the plot\n", - "\n", - "- In Poincaré style layouts, points near the center and points near the boundary can have different distance behavior than Euclidean layouts.\n", - " \n", - "- Focus on neighborhood membership and cluster separation rather than raw coordinate scale." - ], - "metadata": { - "id": "zEkSKKPN0GZz" - }, - "id": "zEkSKKPN0GZz" - }, - { - "cell_type": "code", - "source": [ - "clip_space = dataset.compute_embeddings(CLIP_MODEL_ID)\n", - "dataset.compute_visualization(space_key=clip_space, geometry=\"euclidean\")\n", - "hyper_space = dataset.compute_embeddings(model=HYPER_MODELS_MODEL_ID)\n", - "dataset.compute_visualization(space_key=hyper_space, geometry=\"poincare\")" - ], - "metadata": { - "id": "rXl9yJKyx3hQ" - }, - "id": "rXl9yJKyx3hQ", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## Launch the interactive viewer\n", - "\n", - "`hv.launch(dataset, open_browser=True)` starts a local server and opens the viewer.\n", - "\n", - "In the UI, you should be able to:\n", - "\n", - "- switch between embedding spaces (CLIP vs HyCoCLIP)\n", - " \n", - "- inspect image thumbnails for selected points\n", - " \n", - "- compare how coarse labels cluster under each geometry\n", - " \n" - ], - "metadata": { - "id": "rGEUqA0W0O4g" - }, - "id": "rGEUqA0W0O4g" - }, - { - "cell_type": "code", - "source": [ - "hv.launch(dataset, open_browser=True)" - ], - "metadata": { - "id": "o3TdCwD2wIf2" - }, - "id": "o3TdCwD2wIf2", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## Suggested checks\n", - "\n", - "- Are nearest neighbors under Euclidean CLIP similar to nearest neighbors under Poincaré HyCoCLIP?\n", - "\n", - "\n", - " " - ], - "metadata": { - "id": "FRjieuy10Yed" - }, - "id": "FRjieuy10Yed" - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.8" - }, - "colab": { - "provenance": [], - "machine_shape": "hm", - "gpuType": "T4" - }, - "accelerator": "GPU", - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "162a6522d4a246b78865ab61ca713730": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - 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It provides tools for interactive visualization of the embedding space." + ], + "metadata": { + "id": "hyaagNXkyUBN" + }, + "id": "hyaagNXkyUBN" + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "945a1017", + "metadata": { + "id": "945a1017" + }, + "outputs": [], + "source": [ + "%%capture\n", + "# If you run this in Google Colab, install HyperView with this command.\n", + "!uv pip install hyperview" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Import HyperView" + ], + "metadata": { + "id": "hYtO76VBy7C9" + }, + "id": "hYtO76VBy7C9" + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "We import HyperView and use it as the main interface for:\n", + "\n", + "- reading data\n", + " \n", + "- computing embeddings with pretrained models\n", + " \n", + "- computing a 2D projection for visualization\n", + " \n", + "- launching the interactive viewer\n", + " " + ], + "metadata": { + "id": "v-TvwC2Qyw42" + }, + "id": "v-TvwC2Qyw42" + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "58b82256", + "metadata": { + "id": "58b82256" + }, + "outputs": [], + "source": [ + "import hyperview as hv" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Configuration" + ], + "metadata": { + "id": "nH4CjViEy-U4" + }, + "id": "nH4CjViEy-U4" + }, + { + "cell_type": "markdown", + "source": [ + "We specify the dataset and model settings in a single dictionary.\n", + "\n", + "### Data source\n", + "\n", + "- We load **CIFAR-100** from Hugging Face.\n", + " \n", + "- We use the **test** split.\n", + " \n", + "- `img` is the image field.\n", + " \n", + "- We use `coarse_label` for labels." + ], + "metadata": { + "id": "36tl8PxjzBT6" + }, + "id": "36tl8PxjzBT6" + }, + { + "cell_type": "markdown", + "source": [ + "Why coarse labels:\n", + "\n", + "- HyperView disables distinct label coloring when there are more than 20 labels.\n", + " \n", + "- CIFAR-100 has 100 fine labels, but 20 coarse labels.\n", + " \n", + "- Using coarse labels keeps label coloring useful in the viewer.\n", + " " + ], + "metadata": { + "id": "SIR8CsvfzW3I" + }, + "id": "SIR8CsvfzW3I" + }, + { + "cell_type": "markdown", + "source": [ + "### Sampling\n", + "\n", + "- `NUM_SAMPLES = 200` keeps embedding and layout computation fast enough for a demo.\n", + " \n", + "- Increase this if you want denser clusters, but expect more compute time.\n", + " " + ], + "metadata": { + "id": "tXpwwCXozezd" + }, + "id": "tXpwwCXozezd" + }, + { + "cell_type": "markdown", + "source": [ + "### Models\n", + "\n", + "- `openai/clip-vit-base-patch32` is a common CLIP baseline.\n", + " \n", + "- `hycoclip-vit-s` is a HyCoCLIP model that targets hierarchical structure." + ], + "metadata": { + "id": "NCtE2BN0zk3G" + }, + "id": "NCtE2BN0zk3G" + }, + { + "cell_type": "code", + "source": [ + "DATASET_NAME = \"cifar100_coarse_clip_hyper_models\"\n", + "HF_DATASET = \"uoft-cs/cifar100\"\n", + "HF_SPLIT = \"test\"\n", + "HF_IMAGE_KEY = \"img\"\n", + "# NOTE: HyperView disables distinct label coloring when there are >20 labels.\n", + "# CIFAR-100 has 100 fine labels, but only 20 coarse labels.\n", + "HF_LABEL_KEY = \"coarse_label\"\n", + "NUM_SAMPLES = 200\n", + "CLIP_MODEL_ID = \"openai/clip-vit-base-patch32\"\n", + "HYPER_MODELS_MODEL_ID = \"hycoclip-vit-s\"" + ], + "metadata": { + "id": "Wto-RfOFzCdJ" + }, + "id": "Wto-RfOFzCdJ", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Load CIFAR-100 into a HyperView dataset\n", + "\n", + "We create a HyperView `Dataset` object and then import samples from Hugging Face.\n", + "\n", + "Key parameters:\n", + "\n", + "- `persist=False` keeps this dataset in memory for this run.\n", + " \n", + "- `max_samples=NUM_SAMPLES` subsamples the split.\n", + " \n", + "\n", + "At the end, `len(dataset)` is the number of loaded examples." + ], + "metadata": { + "id": "nEdV-nZTzs3G" + }, + "id": "nEdV-nZTzs3G" + }, + { + "cell_type": "code", + "source": [ + "print(\"Loading CIFAR-100 from Hugging Face...\")\n", + "dataset = hv.Dataset(DATASET_NAME, persist=False)\n", + "dataset.add_from_huggingface(\n", + " HF_DATASET,\n", + " split=HF_SPLIT,\n", + " image_key=HF_IMAGE_KEY,\n", + " label_key=HF_LABEL_KEY,\n", + " max_samples=NUM_SAMPLES,\n", + ")\n", + "print(f\"Loaded {len(dataset)} samples\")\n", + "\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 506, + "referenced_widgets": [ + "162a6522d4a246b78865ab61ca713730", + "e40b36bfd1f64d129f17ee0bb4aef334", + "fc5fe01c2fc64bbb8f9b7adba05cf789", + "187ce225eb4041299d9233cee10f198f", + "c863c8acaef84e488a5016b6869888fe", + "f6137d250fa044d393e65d3443a63f23", + "698107a8b57d4746bc299795c75a5234", + "b4b55da4f86a4c229e5d2b2129353c0a", + "ef34f8327ba943629f00788360462e2a", + "89a229a5967d4b80ac9c031e980300a9", + "5cdbe6ac7f514818be2ae019cb430d94", + "b18ef99b82b244099c5c2843f1109136", + "534bbfee2fbb4adfa5e986e57a7ae613", + 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}, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "cifar100/train-00000-of-00001.parquet: 0%| | 0.00/119M [00:00" + ], + "text/html": [ + "

HyperView is running in Colab. Open HyperView in a new tab.

" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "

https://6262-gpu-t4-hm-kg95f2888y5u-c.europe-west4-1.prod.colab.dev/

" + ] + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 3 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Compute CLIP embeddings\n", + "\n", + "### What CLIP embeddings represent\n", + "\n", + "CLIP trains an image encoder and a text encoder so that matching image text pairs have nearby vectors. When we compute image embeddings here, each image becomes a single vector in a shared space that also supports text vectors.\n", + "\n", + "### What we do in code\n", + "\n", + "- `compute_embeddings(CLIP_MODEL_ID)` runs the CLIP image encoder and stores one vector per image.\n", + " \n", + "- The returned `space_key` is a handle to that embedding space inside HyperView.\n", + " \n", + "\n", + "### Why we compute a 2D visualization\n", + "\n", + "High dimensional vectors are hard to inspect. We compute a 2D layout so we can see neighborhood structure.\n", + "\n", + "- `compute_visualization(..., geometry=\"euclidean\")` treats the embedding space as Euclidean.\n", + " \n", + "- This matches how CLIP vectors are often used with cosine similarity or dot product in a flat vector space.\n", + " \n", + "\n", + "Note on “UMAP” \n", + "This notebook focuses on a 2D projection step. HyperView computes a 2D layout for the viewer. The title calls this UMAP. In practice, you should treat this as a nonlinear projection that preserves local neighborhoods better than PCA." + ], + "metadata": { + "id": "xvAMFY_Dz71Z" + }, + "id": "xvAMFY_Dz71Z" + }, + { + "cell_type": "markdown", + "source": [ + "## Compute HyCoCLIP embeddings\n", + "\n", + "### Why a different geometry\n", + "\n", + "Some datasets have hierarchical label structure. A tree structure is hard to represent in Euclidean space without distortion.\n", + "\n", + "Hyperbolic spaces can represent tree growth patterns with less distortion than Euclidean spaces. A common model is the **Poincaré ball**, which is a way to work with hyperbolic geometry in a bounded region.\n", + "\n", + "### What we do in code\n", + "\n", + "- `compute_embeddings(model=HYPER_MODELS_MODEL_ID)` computes embeddings from the HyCoCLIP model.\n", + " \n", + "- `compute_visualization(..., geometry=\"poincare\")` tells HyperView to treat distances and neighborhoods using Poincaré geometry.\n", + " \n", + "\n", + "How to read the plot\n", + "\n", + "- In Poincaré style layouts, points near the center and points near the boundary can have different distance behavior than Euclidean layouts.\n", + " \n", + "- Focus on neighborhood membership and cluster separation rather than raw coordinate scale." + ], + "metadata": { + "id": "zEkSKKPN0GZz" + }, + "id": "zEkSKKPN0GZz" + }, + { + "cell_type": "code", + "source": [ + "clip_space = dataset.compute_embeddings(CLIP_MODEL_ID)\n", + "dataset.compute_visualization(space_key=clip_space, geometry=\"euclidean\")\n", + "hyper_space = dataset.compute_embeddings(model=HYPER_MODELS_MODEL_ID)\n", + "dataset.compute_visualization(space_key=hyper_space, geometry=\"poincare\")" + ], + "metadata": { + "id": "rXl9yJKyx3hQ" + }, + "id": "rXl9yJKyx3hQ", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Launch the interactive viewer\n", + "\n", + "`hv.launch(dataset, open_browser=True)` starts a local server and opens the viewer.\n", + "\n", + "In the UI, you should be able to:\n", + "\n", + "- switch between embedding spaces (CLIP vs HyCoCLIP)\n", + " \n", + "- inspect image thumbnails for selected points\n", + " \n", + "- compare how coarse labels cluster under each geometry\n", + " \n" + ], + "metadata": { + "id": "rGEUqA0W0O4g" + }, + "id": "rGEUqA0W0O4g" + }, + { + "cell_type": "code", + "source": [ + "hv.launch(dataset, open_browser=True)" + ], + "metadata": { + "id": "o3TdCwD2wIf2" + }, + "id": "o3TdCwD2wIf2", + "execution_count": null, + "outputs": [] + }, + { + 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b/notebooks/quickstart_cifar_100.ipynb deleted file mode 100644 index 3fa96fe..0000000 --- a/notebooks/quickstart_cifar_100.ipynb +++ /dev/null @@ -1,3030 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "47ee06e6", - "metadata": { - "id": "47ee06e6" - }, - "source": [ - "# HyperView: CLIP vs HyCoCLIP with 2D UMAP Projection on CIFAR-100\n", - "\n", - "This notebook compares two embedding spaces on the same image set.\n", - "\n", - "- [**CLIP**](https://github.com/openai/CLIP) maps images and text into one shared vector space.\n", - " \n", - "- [**HyCoCLIP**](https://github.com/PalAvik/hycoclip) maps images into a space designed for hierarchy and tree like structure.\n", - " \n", - "- We then compute a **2D projection** of each embedding space with [UMAP](https://umap-learn.readthedocs.io/en/latest/) so we can inspect clusters and label structure.\n", - " \n", - "\n", - "HyperView allows us to compare the embedding spaces of these different geometries.\n", - "\n", - "In this demo, we use the [CIFAR-100](https://huggingface.co/datasets/uoft-cs/cifar100) dataset of tiny images, available through HuggingFace." - ] - }, - { - "cell_type": "markdown", - "source": [ - "## Install\n", - "\n", - "HyperView is a library for dataset curation and model analysis. It provides tools for interactive visualization of the embedding space." - ], - "metadata": { - "id": "hyaagNXkyUBN" - }, - "id": "hyaagNXkyUBN" - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "945a1017", - "metadata": { - "id": "945a1017" - }, - "outputs": [], - "source": [ - "%%capture\n", - "# If you run this in Google Colab, install HyperView with this command.\n", - "!uv pip install hyperview" - ] - }, - { - "cell_type": "markdown", - "source": [ - "## Import HyperView" - ], - "metadata": { - "id": "hYtO76VBy7C9" - }, - "id": "hYtO76VBy7C9" - }, - { - "cell_type": "markdown", - "source": [ - "\n", - "We import HyperView and use it as the main interface for:\n", - "\n", - "- reading data\n", - " \n", - "- computing embeddings with pretrained models\n", - " \n", - "- computing a 2D projection for visualization\n", - " \n", - "- launching the interactive viewer\n", - " " - ], - "metadata": { - "id": "v-TvwC2Qyw42" - }, - "id": "v-TvwC2Qyw42" - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "58b82256", - "metadata": { - "id": "58b82256" - }, - "outputs": [], - "source": [ - "import hyperview as hv" - ] - }, - { - "cell_type": "markdown", - "source": [ - "## Configuration" - ], - "metadata": { - "id": "nH4CjViEy-U4" - }, - "id": "nH4CjViEy-U4" - }, - { - "cell_type": "markdown", - "source": [ - "We specify the dataset and model settings in a single dictionary.\n", - "\n", - "### Data source\n", - "\n", - "- We load **CIFAR-100** from Hugging Face.\n", - " \n", - "- We use the **test** split.\n", - " \n", - "- `img` is the image field.\n", - " \n", - "- We use `coarse_label` for labels." - ], - "metadata": { - "id": "36tl8PxjzBT6" - }, - "id": "36tl8PxjzBT6" - }, - { - "cell_type": "markdown", - "source": [ - "Why coarse labels:\n", - "\n", - "- HyperView disables distinct label coloring when there are more than 20 labels.\n", - " \n", - "- CIFAR-100 has 100 fine labels, but 20 coarse labels.\n", - " \n", - "- Using coarse labels keeps label coloring useful in the viewer.\n", - " " - ], - "metadata": { - "id": "SIR8CsvfzW3I" - }, - "id": "SIR8CsvfzW3I" - }, - { - "cell_type": "markdown", - "source": [ - "### Sampling\n", - "\n", - "- `NUM_SAMPLES = 200` keeps embedding and layout computation fast enough for a demo.\n", - " \n", - "- Increase this if you want denser clusters, but expect more compute time.\n", - " " - ], - "metadata": { - "id": "tXpwwCXozezd" - }, - "id": "tXpwwCXozezd" - }, - { - "cell_type": "markdown", - "source": [ - "### Models\n", - "\n", - "- `openai/clip-vit-base-patch32` is a common CLIP baseline.\n", - " \n", - "- `hycoclip-vit-s` is a HyCoCLIP model that targets hierarchical structure." - ], - "metadata": { - "id": "NCtE2BN0zk3G" - }, - "id": "NCtE2BN0zk3G" - }, - { - "cell_type": "code", - "source": [ - "DATASET_NAME = \"cifar100_coarse_clip_hyper_models\"\n", - "HF_DATASET = \"uoft-cs/cifar100\"\n", - "HF_SPLIT = \"test\"\n", - "HF_IMAGE_KEY = \"img\"\n", - "# NOTE: HyperView disables distinct label coloring when there are >20 labels.\n", - "# CIFAR-100 has 100 fine labels, but only 20 coarse labels.\n", - "HF_LABEL_KEY = \"coarse_label\"\n", - "NUM_SAMPLES = 200\n", - "CLIP_MODEL_ID = \"openai/clip-vit-base-patch32\"\n", - "HYPER_MODELS_MODEL_ID = \"hycoclip-vit-s\"" - ], - "metadata": { - "id": "Wto-RfOFzCdJ" - }, - "id": "Wto-RfOFzCdJ", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## Load CIFAR-100 into a HyperView dataset\n", - "\n", - "We create a HyperView `Dataset` object and then import samples from Hugging Face.\n", - "\n", - "Key parameters:\n", - "\n", - "- `persist=False` keeps this dataset in memory for this run.\n", - " \n", - "- `max_samples=NUM_SAMPLES` subsamples the split.\n", - " \n", - "\n", - "At the end, `len(dataset)` is the number of loaded examples." - ], - "metadata": { - "id": "nEdV-nZTzs3G" - }, - "id": "nEdV-nZTzs3G" - }, - { - "cell_type": "code", - "source": [ - "print(\"Loading CIFAR-100 from Hugging Face...\")\n", - "dataset = hv.Dataset(DATASET_NAME, persist=False)\n", - 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HyperView is running in Colab. Open HyperView in a new tab.

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https://6262-gpu-t4-hm-kg95f2888y5u-c.europe-west4-1.prod.colab.dev/

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We compute a 2D layout so we can see neighborhood structure.\n", - "\n", - "- `compute_visualization(..., geometry=\"euclidean\")` treats the embedding space as Euclidean.\n", - " \n", - "- This matches how CLIP vectors are often used with cosine similarity or dot product in a flat vector space.\n", - " \n", - "\n", - "Note on “UMAP” \n", - "This notebook focuses on a 2D projection step. HyperView computes a 2D layout for the viewer. The title calls this UMAP. In practice, you should treat this as a nonlinear projection that preserves local neighborhoods better than PCA." - ], - "metadata": { - "id": "xvAMFY_Dz71Z" - }, - "id": "xvAMFY_Dz71Z" - }, - { - "cell_type": "markdown", - "source": [ - "## Compute HyCoCLIP embeddings\n", - "\n", - "### Why a different geometry\n", - "\n", - "Some datasets have hierarchical label structure. A tree structure is hard to represent in Euclidean space without distortion.\n", - "\n", - "Hyperbolic spaces can represent tree growth patterns with less distortion than Euclidean spaces. A common model is the **Poincaré ball**, which is a way to work with hyperbolic geometry in a bounded region.\n", - "\n", - "### What we do in code\n", - "\n", - "- `compute_embeddings(model=HYPER_MODELS_MODEL_ID)` computes embeddings from the HyCoCLIP model.\n", - " \n", - "- `compute_visualization(..., geometry=\"poincare\")` tells HyperView to treat distances and neighborhoods using Poincaré geometry.\n", - " \n", - "\n", - "How to read the plot\n", - "\n", - "- In Poincaré style layouts, points near the center and points near the boundary can have different distance behavior than Euclidean layouts.\n", - " \n", - "- Focus on neighborhood membership and cluster separation rather than raw coordinate scale." - ], - "metadata": { - "id": "zEkSKKPN0GZz" - }, - "id": "zEkSKKPN0GZz" - }, - { - "cell_type": "code", - "source": [ - "clip_space = dataset.compute_embeddings(CLIP_MODEL_ID)\n", - "dataset.compute_visualization(space_key=clip_space, geometry=\"euclidean\")\n", - "hyper_space = dataset.compute_embeddings(model=HYPER_MODELS_MODEL_ID)\n", - "dataset.compute_visualization(space_key=hyper_space, geometry=\"poincare\")" - ], - "metadata": { - "id": "rXl9yJKyx3hQ" - }, - "id": "rXl9yJKyx3hQ", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## Launch the interactive viewer\n", - "\n", - "`hv.launch(dataset, open_browser=True)` starts a local server and opens the viewer.\n", - "\n", - "In the UI, you should be able to:\n", - "\n", - "- switch between embedding spaces (CLIP vs HyCoCLIP)\n", - " \n", - "- inspect image thumbnails for selected points\n", - " \n", - "- compare how coarse labels cluster under each geometry\n", - " \n" - ], - "metadata": { - "id": "rGEUqA0W0O4g" - }, - "id": "rGEUqA0W0O4g" - }, - { - "cell_type": "code", - "source": [ - "hv.launch(dataset, open_browser=True)" - ], - "metadata": { - "id": "o3TdCwD2wIf2" - }, - "id": "o3TdCwD2wIf2", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## Suggested checks\n", - "\n", - 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It provides tools for interactive visualization of the embedding space." + ], + "metadata": { + "id": "hyaagNXkyUBN" + }, + "id": "hyaagNXkyUBN" + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "945a1017", + "metadata": { + "id": "945a1017" + }, + "outputs": [], + "source": [ + "%%capture\n", + "# If you run this in Google Colab, install HyperView with this command.\n", + "!uv pip install hyperview" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Import HyperView" + ], + "metadata": { + "id": "hYtO76VBy7C9" + }, + "id": "hYtO76VBy7C9" + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "We import HyperView and use it as the main interface for:\n", + "\n", + "- reading data\n", + " \n", + "- computing embeddings with pretrained models\n", + " \n", + "- computing a 2D projection for visualization\n", + " \n", + "- launching the interactive viewer\n", + " " + ], + "metadata": { + "id": "v-TvwC2Qyw42" + }, + "id": "v-TvwC2Qyw42" + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "58b82256", + "metadata": { + "id": "58b82256" + }, + "outputs": [], + "source": [ + "import hyperview as hv" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Configuration" + ], + "metadata": { + "id": "nH4CjViEy-U4" + }, + "id": "nH4CjViEy-U4" + }, + { + "cell_type": "markdown", + "source": [ + "We specify the dataset and model settings in a single dictionary.\n", + "\n", + "### Data source\n", + "\n", + "- We load **CIFAR-100** from Hugging Face.\n", + " \n", + "- We use the **test** split.\n", + " \n", + "- `img` is the image field.\n", + " \n", + "- We use `coarse_label` for labels." + ], + "metadata": { + "id": "36tl8PxjzBT6" + }, + "id": "36tl8PxjzBT6" + }, + { + "cell_type": "markdown", + "source": [ + "Why coarse labels:\n", + "\n", + "- HyperView disables distinct label coloring when there are more than 20 labels.\n", + " \n", + "- CIFAR-100 has 100 fine labels, but 20 coarse labels.\n", + " \n", + "- Using coarse labels keeps label coloring useful in the viewer.\n", + " " + ], + "metadata": { + "id": "SIR8CsvfzW3I" + }, + "id": "SIR8CsvfzW3I" + }, + { + "cell_type": "markdown", + "source": [ + "### Sampling\n", + "\n", + "- `NUM_SAMPLES = 200` keeps embedding and layout computation fast enough for a demo.\n", + " \n", + "- Increase this if you want denser clusters, but expect more compute time.\n", + " " + ], + "metadata": { + "id": "tXpwwCXozezd" + }, + "id": "tXpwwCXozezd" + }, + { + "cell_type": "markdown", + "source": [ + "### Models\n", + "\n", + "- `openai/clip-vit-base-patch32` is a common CLIP baseline.\n", + " \n", + "- `hycoclip-vit-s` is a HyCoCLIP model that targets hierarchical structure." + ], + "metadata": { + "id": "NCtE2BN0zk3G" + }, + "id": "NCtE2BN0zk3G" + }, + { + "cell_type": "code", + "source": [ + "DATASET_NAME = \"cifar100_coarse_clip_hyper_models\"\n", + "HF_DATASET = \"uoft-cs/cifar100\"\n", + "HF_SPLIT = \"test\"\n", + "HF_IMAGE_KEY = \"img\"\n", + "# NOTE: HyperView disables distinct label coloring when there are >20 labels.\n", + "# CIFAR-100 has 100 fine labels, but only 20 coarse labels.\n", + "HF_LABEL_KEY = \"coarse_label\"\n", + "NUM_SAMPLES = 200\n", + "CLIP_MODEL_ID = \"openai/clip-vit-base-patch32\"\n", + "HYPER_MODELS_MODEL_ID = \"hycoclip-vit-s\"" + ], + "metadata": { + "id": "Wto-RfOFzCdJ" + }, + "id": "Wto-RfOFzCdJ", + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Load CIFAR-100 into a HyperView dataset\n", + "\n", + "We create a HyperView `Dataset` object and then import samples from Hugging Face.\n", + "\n", + "Key parameters:\n", + "\n", + "- `persist=False` keeps this dataset in memory for this run.\n", + " \n", + "- `max_samples=NUM_SAMPLES` subsamples the split.\n", + " \n", + "\n", + "At the end, `len(dataset)` is the number of loaded examples." + ], + "metadata": { + "id": "nEdV-nZTzs3G" + }, + "id": "nEdV-nZTzs3G" + }, + { + "cell_type": "code", + "source": [ + "print(\"Loading CIFAR-100 from Hugging Face...\")\n", + "dataset = hv.Dataset(DATASET_NAME, persist=False)\n", + "dataset.add_from_huggingface(\n", + " HF_DATASET,\n", + " split=HF_SPLIT,\n", + " image_key=HF_IMAGE_KEY,\n", + " label_key=HF_LABEL_KEY,\n", + " max_samples=NUM_SAMPLES,\n", + ")\n", + "print(f\"Loaded {len(dataset)} samples\")\n", + "\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Vb0PAH7gvsP7", + "outputId": "953a9176-fa36-4874-9263-838df3bf0180" + }, + "id": "Vb0PAH7gvsP7", + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loading CIFAR-100 from Hugging Face...\n", + "Loading 200 samples from uoft-cs/cifar100...\n", + "Images saved to: /root/.hyperview/media/huggingface/uoft-cs_cifar100/test\n", + "Loaded 200 samples\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Compute CLIP embeddings\n", + "\n", + "### What CLIP embeddings represent\n", + "\n", + "CLIP trains an image encoder and a text encoder so that matching image text pairs have nearby vectors. When we compute image embeddings here, each image becomes a single vector in a shared space that also supports text vectors.\n", + "\n", + "### What we do in code\n", + "\n", + "- `compute_embeddings(CLIP_MODEL_ID)` runs the CLIP image encoder and stores one vector per image.\n", + " \n", + "- The returned `space_key` is a handle to that embedding space inside HyperView.\n", + " \n", + "\n", + "### Why we compute a 2D visualization\n", + "\n", + "High dimensional vectors are hard to inspect. We compute a 2D layout so we can see neighborhood structure.\n", + "\n", + "- `compute_visualization(..., geometry=\"euclidean\")` treats the embedding space as Euclidean.\n", + " \n", + "- This matches how CLIP vectors are often used with cosine similarity or dot product in a flat vector space.\n", + " \n", + "\n", + "Note on “UMAP” \n", + "This notebook focuses on a 2D projection step. HyperView computes a 2D layout for the viewer. The title calls this UMAP. In practice, you should treat this as a nonlinear projection that preserves local neighborhoods better than PCA." + ], + "metadata": { + "id": "xvAMFY_Dz71Z" + }, + "id": "xvAMFY_Dz71Z" + }, + { + "cell_type": "markdown", + "source": [ + "## Compute HyCoCLIP embeddings\n", + "\n", + "### Why a different geometry\n", + "\n", + "Some datasets have hierarchical label structure. A tree structure is hard to represent in Euclidean space without distortion.\n", + "\n", + "Hyperbolic spaces can represent tree growth patterns with less distortion than Euclidean spaces. A common model is the **Poincaré ball**, which is a way to work with hyperbolic geometry in a bounded region.\n", + "\n", + "### What we do in code\n", + "\n", + "- `compute_embeddings(model=HYPER_MODELS_MODEL_ID)` computes embeddings from the HyCoCLIP model.\n", + " \n", + "- `compute_visualization(..., geometry=\"poincare\")` tells HyperView to treat distances and neighborhoods using Poincaré geometry.\n", + " \n", + "\n", + "How to read the plot\n", + "\n", + "- In Poincaré style layouts, points near the center and points near the boundary can have different distance behavior than Euclidean layouts.\n", + " \n", + "- Focus on neighborhood membership and cluster separation rather than raw coordinate scale." + ], + "metadata": { + "id": "zEkSKKPN0GZz" + }, + "id": "zEkSKKPN0GZz" + }, + { + "cell_type": "code", + "source": [ + "clip_space = dataset.compute_embeddings(CLIP_MODEL_ID)\n", + "clip_space" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + }, + "id": "rXl9yJKyx3hQ", + "outputId": "472d1e1e-bde6-4b4c-cfe5-5485c7ca18bd" + }, + "id": "rXl9yJKyx3hQ", + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "All 200 samples already have embeddings in space 'embed-anything__openai_clip-vit-base-patch32__4771034973d8'\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'embed-anything__openai_clip-vit-base-patch32__4771034973d8'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 14 + } + ] + }, + { + "cell_type": "code", + "source": [ + "hyperbolic_clip_space = dataset.compute_embeddings(model=HYPER_MODELS_MODEL_ID)\n", + "hyperbolic_clip_space" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + }, + "id": "bxivI8P79IiK", + "outputId": "16c01abb-848e-4915-c88b-4972a532c7eb" + }, + "id": "bxivI8P79IiK", + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Computing embeddings for 200 samples...\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'hyper-models__hycoclip-vit-s__b63e9ee38a30'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 10 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## UMAP Parameters: `n_neighbors` and `min_dist`\n", + "\n", + "UMAP creates 2D visualizations through a two-phase process:\n", + "\n", + "1. **Local structure learning** (controlled by `n_neighbors`)\n", + "2. **Layout optimization** (controlled by `min_dist`)\n", + "\n", + "These parameters have different effects depending on whether you're working with Euclidean or hyperbolic embeddings.\n", + "\n", + "\n", + "## `n_neighbors`: Balancing Local vs Global Structure\n", + "\n", + "The `n_neighbors` parameter determines how many nearby points UMAP considers when learning the manifold structure.\n", + "\n", + "**Small values (5-15):**\n", + "- UMAP focuses on immediate neighbors\n", + "- Preserves fine-grained local structure\n", + "- Creates tight, distinct clusters\n", + "- May fragment large-scale patterns\n", + "- Better for finding small subgroups\n", + "\n", + "**Large values (30-100):**\n", + "- UMAP considers broader neighborhoods\n", + "- Captures global dataset organization\n", + "- Creates more connected layouts\n", + "- May blur boundaries between clusters\n", + "- Better for understanding overall structure\n", + "\n", + "**Technical detail:** UMAP constructs a k-nearest neighbor graph using this parameter. The graph encodes which points should remain close in the 2D projection.\n", + "\n", + "**Default: 15**, which is a middle ground that works for most datasets\n", + "\n", + "## `min_dist`: Controlling Point Spacing\n", + "\n", + "The `min_dist` parameter sets the minimum allowed distance between points in the final 2D layout. This is the distance that UMAP forces between **all points** in the 2D layout. When `min-dist` is large points that are in the samle cluster are pushed apart from each other. The visual effect is that we see more empty space everywhere in the plot.\n", + "\n", + "**Small values (0.0-0.1):**\n", + "- Points can be placed very close together\n", + "- Clusters appear dense and compact\n", + "- Easier to see cluster boundaries\n", + "- Can create overlapping point clouds, making them difficult to select with HyperView's lasso tool\n", + "- Good for large datasets where you want to see density\n", + "\n", + "**Large values (0.3-0.99):**\n", + "- Forces points to spread out\n", + "- Individual points are more visible and the layout appears less crowded\n", + "- May exaggerate distances between similar items\n", + "- Good for small datasets or when you need to see each point\n", + "\n", + "**Technical detail:** This parameter affects the \"attractive force\" in UMAP's layout.\n", + "\n", + "**Default: 0.1** (high density some clumping of embedding projections)" + ], + "metadata": { + "id": "PG3rQ6lNAoyV" + }, + "id": "PG3rQ6lNAoyV" + }, + { + "cell_type": "markdown", + "source": [ + "## Parameter Interaction in UMAP\n", + "\n", + "The two parameters work together to shape your visualization:\n", + "\n", + "**n_neighbors + min_dist together:**\n", + "\n", + "| n_neighbors | min_dist | Result |\n", + "|------------|----------|--------|\n", + "| Small (5) | Small (0.01) | Many tight, separated micro-clusters |\n", + "| Small (5) | Large (0.5) | Fragmented layout with forced spacing |\n", + "| Large (50) | Small (0.01) | Dense, continuous manifold structure |\n", + "| Large (50) | Large (0.5) | Smooth, evenly distributed layout |\n", + "\n", + "**Practical example:**\n", + "```python\n", + "# For finding fine subgroups in cell types\n", + "compute_visualization(n_neighbors=10, min_dist=0.05)\n", + "\n", + "# For understanding broad relationships in a corpus\n", + "compute_visualization(n_neighbors=50, min_dist=0.3)" + ], + "metadata": { + "id": "zV_EJELHBkKt" + }, + "id": "zV_EJELHBkKt" + }, + { + "cell_type": "markdown", + "source": [ + "## Parameter Interaction in UMAP\n", + "\n", + "The two parameters work together to shape your visualization:\n", + "\n", + "**n_neighbors + min_dist together:**\n", + "\n", + "| n_neighbors | min_dist | Result |\n", + "|------------|----------|--------|\n", + "| Small (5) | Small (0.01) | Many tight, separated and small clusters |\n", + "| Small (5) | Large (0.5) | Fragmented layout with forced spacing |\n", + "| Large (50) | Small (0.01) | Dense, continuous structure |\n", + "| Large (50) | Large (0.5) | Smooth, evenly distributed layout |\n", + "\n", + "**Snippets:**\n", + "```python\n", + "# For finding fine subgroups in cell types\n", + "compute_visualization(n_neighbors=10, min_dist=0.05)\n", + "\n", + "# For understanding broad relationships in a corpus\n", + "compute_visualization(n_neighbors=50, min_dist=0.3)\n", + "\n", + "\n", + "\n" + ], + "metadata": { + "id": "p4-fczUnDfds" + }, + "id": "p4-fczUnDfds" + }, + { + "cell_type": "markdown", + "source": [ + "### Euclidean UMAP (Standard Embeddings)\n", + "\n", + "```markdown\n", + "## UMAP on Euclidean Embeddings\n", + "\n", + "For standard models (CLIP, ResNet, etc.), embeddings live in flat Euclidean space.\n", + "\n", + "**What happens:**\n", + "1. UMAP measures distances using the specified metric (default: cosine)\n", + "2. Constructs a neighbor graph in the high-dimensional space\n", + "3. Optimizes a 2D layout in flat Euclidean space\n", + "4. Output coordinates are (x, y) pairs with no geometric constraints (can be as far away from each other as originally computed)\n", + "\n", + "**Metric choice matters:**\n", + "- `metric=\"cosine\"`: Best for normalized embeddings (CLIP, sentence transformers)\n", + "- `metric=\"euclidean\"`: Best for embeddings where magnitude matters\n", + "- `metric=\"manhattan\"`: Sometimes better for sparse or categorical data" + ], + "metadata": { + "id": "zXoChx71EfDP" + }, + "id": "zXoChx71EfDP" + }, + { + "cell_type": "code", + "source": [ + "dataset.compute_visualization(space_key=clip_space, # Which embedding space to project\n", + " method=\"umap\", # Projection method (only 'umap' supported)\n", + " geometry=\"euclidean\", # Output geometry: 'euclidean' or 'poincare'\n", + " n_neighbors=15, # UMAP: Number of neighbors (default: 15)\n", + " min_dist=0.1, # UMAP: Minimum distance (default: 0.1)\n", + " metric=\"cosine\", # UMAP: Distance metric (default: 'cosine')\n", + " force=True) # Force recomputation if layout exists\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + }, + "id": "Fd50hTlc9K31", + "outputId": "7272fbef-0a2e-4d2c-8178-9b33764a20c2" + }, + "id": "Fd50hTlc9K31", + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Computing euclidean umap layout for 200 samples...\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'embed-anything__openai_clip-vit-base-patch32__4771034973d8__euclidean_umap_92b543de'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 9 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Cell 6: Hyperbolic UMAP (HyCoCLIP and Hyperboloid Embeddings)\n", + "\n", + "```markdown\n", + "## UMAP on Hyperbolic Embeddings\n", + "\n", + "For hyperbolic models (HyCoCLIP), embeddings live in curved hyperbolic space (hyperboloid model).\n", + "\n", + "**What happens:**\n", + "1. HyperView converts hyperboloid coordinates to the Poincaré ball (unit disk)\n", + "2. UMAP measures distances using Poincaré distance (hyperbolic geometry)\n", + "3. Optimizes a 2D layout using hyperbolic distance in the output space\n", + "4. Output coordinates are (x, y) pairs **constrained to the unit disk**\n", + "\n", + "**Key differences from Euclidean:**\n", + "- Distance metric is **automatically** \"poincare\" (you cannot override this)\n", + "- Points near the disk center have more \"room\" (hyperbolic space expands near edges)\n", + "- Same Euclidean distance means different hyperbolic distances at different radii\n", + "- Natural hierarchy: broader or more ambiguous concepts toward center, specific items toward edges. Mislabeled examples also appear near the center as they are hard to separate.\n", + "\n", + "**Parameter effects:**\n", + "- `n_neighbors`: Works the same as Euclidean (controls local vs global)\n", + "- `min_dist`: Interprets distances **in hyperbolic geometry**\n", + " - Same numeric value creates different visual spacing than in Euclidean\n", + " - Points near the boundary can appear closer in visual distance but farther in hyperbolic distance\n", + "\n", + "**Example:**\n", + "```python\n", + "# Hyperbolic CLIP embeddings\n", + "dataset.compute_embeddings(model=\"hycoclip-vit-s\") # Creates hyperboloid space\n", + "dataset.compute_visualization(\n", + " geometry=\"poincare\", # Output to Poincaré disk\n", + " # metric is automatically \"poincare\" (ignores what you pass)\n", + " n_neighbors=15,\n", + " min_dist=0.1\n", + ")" + ], + "metadata": { + "id": "etnAtM19EyUH" + }, + "id": "etnAtM19EyUH" + }, + { + "cell_type": "code", + "source": [ + "dataset.compute_visualization(space_key=hyperbolic_clip_space,\n", + " method=\"umap\",\n", + " geometry=\"poincare\", # Use Poincare ball method to project hyperbolic embedding to 2D plane\n", + " # metric is overriden to \"poincare\", see https://github.com/Hyper3Labs/HyperView/blob/main/src/hyperview/embeddings/projection.py#L102\n", + " n_neighbors=15,\n", + " min_dist=0.1,\n", + " )" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + }, + "id": "tXWs_FXQ9OX2", + "outputId": "156e937b-8d5c-4321-872b-bce25de69648" + }, + "id": "tXWs_FXQ9OX2", + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Computing poincare umap layout for 200 samples...\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'hyper-models__hycoclip-vit-s__b63e9ee38a30__poincare_umap_92b543de'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 15 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Launch the interactive app in HyperView\n", + "\n", + "`hv.launch(dataset, open_browser=True)` starts a local server and opens the viewer.\n", + "\n", + "In the UI, you should be able to:\n", + "\n", + "- switch between embedding spaces (CLIP vs HyCoCLIP)\n", + " \n", + "- inspect image thumbnails for selected points\n", + " \n", + "- compare how coarse labels cluster under each geometry\n", + " \n" + ], + "metadata": { + "id": "rGEUqA0W0O4g" + }, + "id": "rGEUqA0W0O4g" + }, + { + "cell_type": "code", + "source": [ + "hv.launch(dataset, open_browser=True)" + ], + "metadata": { + "id": "o3TdCwD2wIf2" + }, + "id": "o3TdCwD2wIf2", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Euclidean vs Poincaré: Visual Differences\n", + "\n", + "**Euclidean geometry (`geometry=\"euclidean\"`):**\n", + "- Points spread across an unbounded 2D plane\n", + "- Distance is uniform everywhere (1 unit = 1 unit, regardless of location)\n", + "- Typical output: points in a square/rectangular region\n", + "- Best for: Datasets without strong hierarchical structure\n", + "\n", + "**Poincaré geometry (`geometry=\"poincare\"`):**\n", + "- Points constrained to a unit disk (circle with radius 1)\n", + "- Distance is **non-uniform**: more space near edges, compressed near center\n", + "- Typical output: hierarchical organization\n", + " - General/abstract concepts cluster near the center\n", + " - Specific/detailed items pushed toward the boundary\n", + "- Best for: Datasets with natural hierarchies (taxonomies, concept trees)\n", + "\n", + "**When to use Poincaré output:**\n", + "1. You have hyperbolic embeddings (HyCoCLIP)\n", + "2. Your data has hierarchical structure (animal taxonomy, document topics)\n", + "3. You want to visualize relationships at multiple scales simultaneously\n", + "\n", + "**When to use Euclidean output:**\n", + "1. You have standard embeddings (CLIP, ResNet)\n", + "2. Your data has flat or network structure\n", + "3. You want familiar, intuitive spatial relationships\n", + "\n" + ], + "metadata": { + "id": "FqwDwyE9Fdpw" + }, + "id": "FqwDwyE9Fdpw" + }, + { + "cell_type": "markdown", + "source": [ + "## Suggested checks\n", + "\n", + "- Are nearest neighbors under Euclidean CLIP similar to nearest neighbors under Poincaré HyCoCLIP?\n", + "\n", + "\n", + " " + ], + "metadata": { + "id": "FRjieuy10Yed" + }, + "id": "FRjieuy10Yed" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" + }, + "colab": { + "provenance": [], + "machine_shape": "hm", + "gpuType": "T4" + }, + "accelerator": "GPU" + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From 086b06309bc220cf67afcb33dea92f61871d0c20 Mon Sep 17 00:00:00 2001 From: Antonio Rueda-Toicen Date: Sun, 1 Feb 2026 13:33:12 +0100 Subject: [PATCH 60/75] Notebook installation guide for VSCode --- notebooks/INSTALLATION.md | 82 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 82 insertions(+) create mode 100644 notebooks/INSTALLATION.md diff --git a/notebooks/INSTALLATION.md b/notebooks/INSTALLATION.md new file mode 100644 index 0000000..7101f11 --- /dev/null +++ b/notebooks/INSTALLATION.md @@ -0,0 +1,82 @@ +# Installation Instructions + +This guide shows how to set up and run the demo notebook in VSCode. + +## Prerequisites + +- [uv](https://docs.astral.sh/uv/) package manager installed +- VSCode with Python extension + +## Setup Steps + +### 1. Initialize the Project + +```bash +uv init +``` + +This creates a new project named `hyperview-demo-notebook`. + +### 2. Create Virtual Environment + +```bash +uv venv .venv +``` + +This creates a virtual environment using Python 3.13.2 in the `.venv` directory. + +### 3. Activate Virtual Environment + +```bash +source .venv/bin/activate +``` + +Your terminal prompt should now show `(.venv)` at the beginning. + +### 4. Install Required Packages + +Install the packages in the following order: + +```bash +uv pip install ipykernel +``` + +```bash +uv pip install jupyter +``` + +```bash +uv pip install hyperview +``` + +**Note:** Do not use commas between package names when installing multiple packages at once. Use spaces instead or install packages separately. + +## Verify Installation + +After installation, you should have: + +- Jupyter notebook support (68+ packages) +- IPython kernel for running notebooks (30+ packages) +- Hyperview and dependencies (59+ packages) + +## Running the Notebook + +1. Open the notebook file in VSCode +2. Select the Python interpreter from `.venv` when prompted +3. Run the notebook cells + +## Troubleshooting + +### Virtual Environment Not Activating + +If you see "no such file or directory" when activating, check the path: + +```bash +source .venv/bin/activate +``` + +Make sure you typed `activate` correctly (not `acticate`). + +### Package Installation Errors + +If you get parsing errors during installation, avoid using commas in the package list. Install packages separately or use spaces to separate package names. From 93f480922a41d320d91455b734f7b62b1d898173 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Sun, 1 Feb 2026 15:41:58 +0100 Subject: [PATCH 61/75] Update README and demo script for CIFAR-100: fix image link, adjust dataset name and label key --- README.md | 29 +++++------------------------ assets/screenshot.png | Bin 1476413 -> 1479425 bytes scripts/demo_hyperbolic_clip.py | 6 ++++-- 3 files changed, 9 insertions(+), 26 deletions(-) diff --git a/README.md b/README.md index 68f17d0..f228ad0 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@

- HyperView Screenshot + HyperView Screenshot
Watch the Demo Video @@ -28,13 +28,7 @@ ### Installation ```bash -git clone https://github.com/Hyper3Labs/HyperView.git -cd HyperView - -# Install with uv -uv venv .venv -source .venv/bin/activate -uv pip install -e ".[dev]" +pip install hyperview ``` ### Run the Demo @@ -79,26 +73,13 @@ hv.launch(dataset) # Opens http://127.0.0.1:6262 See [docs/colab.md](docs/colab.md) for a fast Colab smoke test and notebook-friendly launch behavior. -### Save and Load Datasets - -```python -# Save dataset with embeddings -dataset.save("my_dataset.json") - -# Load later -dataset = hv.Dataset.load("my_dataset.json") -hv.launch(dataset) -``` - ## Why Hyperbolic? -Traditional Euclidean embeddings struggle with hierarchical data. In Euclidean space, volume grows polynomially ($r^d$), causing **Representation Collapse** where minority classes get crushed together. +Traditional Euclidean embeddings struggle with hierarchical data. In Euclidean space, volume grows polynomially ($r^d$), causing **[Representation Collapse](https://hyper3labs.github.io/collapse)** where minority classes get crushed together. -**Hyperbolic space** (Poincaré disk) has exponential volume growth ($e^r$), naturally preserving hierarchical structure and keeping rare classes distinct. +**[Hyperbolic space](https://hyper3labs.github.io/warp)** (Poincaré disk) has exponential volume growth ($e^r$), naturally preserving hierarchical structure and keeping rare classes distinct. -

- Euclidean vs Hyperbolic -

+**[Try the live demo →](https://hyper3labs.github.io/HyperView/)** ## Contributing diff --git a/assets/screenshot.png b/assets/screenshot.png index 6e9b00b491d2758712e5d37724a597158b7df62e..92a7285ba1ea9b143e22c4156bdda0ae542b19c1 100644 GIT binary patch literal 1479425 zcmb4pb9`Ri(sq(2jT+mwoyN9p-!U3nZEV}N%_fa)qm6CzyL--aPM_y}zklBS+jsWL z!mPDtX3v_r)`Tj^iNnEQ!+?N*z)4DoD1m@L#ejf73_*Pa)|?v@eFp*g#BU)itRN{Y zOsL>sXKG;$00EH*O;m@}P##9l(vFJ}5Q5f~I*>(-K^BmLqPvMQARz@63S~l6IRPm4 zG={@0O{pUF!-qhKqQZt7orgo90d#4X5;>@Hj=Qcpue}}5ecwM{XS3{2(0%ZGg09lY z2(|=?VW3Xv3;xI~R$Rm+F$oIx2?Vai|Cf%Qe+n874s;G&*1PR_m9U+;Ixax#{p7v6 zXczq0i;q3ZBqz!HR;Eun^vjPG%1UbK)6kACLrT6itpj z2vQu|rC|xD>DVqHs*Q=-QVaHi+2gw&y=z9j+dR6@^k6R3lmeuCw*Zbo&h4q(OhQ5P zglA{~kwJk9h)YN!{WAn6so|s`GO8Cv5zKqguUcFWBnw2P zgjvbS%>-ct)0ZULyJH?=fyj-vLoPz;Erw=RpAYFxI^(AKmV`J6E0XIE<6S|ijEr1; z!!XW(nZeLRr9Bj?w)Gmg9BLT$WKhXd#pBC~aS|ucCxtzSgc=crn*pnjh+2`teJ4XG zC8coWg^2NSfM#F|)n2b+hJXWOCve2gF=QPk3^~F0a!06z5bepWsD+VTF_uCv;|E-{ 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z%lSK4HnrJ(hrV5OvGcBvEnbYb)o_Mz@?15*E8W`JxgOlDa2lv1_RbGAoSw{qQsDO- zO49)9kW~JztMzB=3@eC-ooW!AcZp+`{g}z?)Y(W0_HsV|>LqFv{qg@%50NNKRe!B$C4X*9_*RTGs!f+2vPJ+b)o5p?-{lM({)373Z-tWIwT90L$8nbfd#t?o)$Ub2`v1Ou zY`Z`r`E@L|*~G5?uUsRRhfWFgr@#K&(tF>ArRq;hFG*(vd&r$JK|kB+|2(2qn!shK63Hn>>aVcU1?qe zozm6co8#|W`^8Y@2Y+uF4^(>(6k-0;1^pbzOjz1^z*YclF(5VOE6*CSF4i-ytvUdTj f5dMBs;Nc{+vDA=wOveGA;3z*=dsh9_Ead+H7J`J# diff --git a/scripts/demo_hyperbolic_clip.py b/scripts/demo_hyperbolic_clip.py index 282e7a8..6da1a79 100644 --- a/scripts/demo_hyperbolic_clip.py +++ b/scripts/demo_hyperbolic_clip.py @@ -3,11 +3,13 @@ import hyperview as hv -DATASET_NAME = "cifar100_clip_hyper_models" +DATASET_NAME = "cifar100_coarse_clip_hyper_models" HF_DATASET = "uoft-cs/cifar100" HF_SPLIT = "test" HF_IMAGE_KEY = "img" -HF_LABEL_KEY = "fine_label" +# NOTE: HyperView disables distinct label coloring when there are >20 labels. +# CIFAR-100 has 100 fine labels, but only 20 coarse labels. +HF_LABEL_KEY = "coarse_label" NUM_SAMPLES = 200 CLIP_MODEL_ID = "openai/clip-vit-base-patch32" HYPER_MODELS_MODEL_ID = "hycoclip-vit-s" From a6dbf82a191d697e227ddbc88b2e63e53d44b7a0 Mon Sep 17 00:00:00 2001 From: Antonio Rueda-Toicen Date: Sun, 1 Feb 2026 21:38:52 +0100 Subject: [PATCH 62/75] Remove unnecessary note on activation command --- notebooks/INSTALLATION.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/notebooks/INSTALLATION.md b/notebooks/INSTALLATION.md index 7101f11..2e332d8 100644 --- a/notebooks/INSTALLATION.md +++ b/notebooks/INSTALLATION.md @@ -75,8 +75,6 @@ If you see "no such file or directory" when activating, check the path: source .venv/bin/activate ``` -Make sure you typed `activate` correctly (not `acticate`). - ### Package Installation Errors If you get parsing errors during installation, avoid using commas in the package list. Install packages separately or use spaces to separate package names. From e1ecd007a5435e9a8c7d74153f310c40a0377596 Mon Sep 17 00:00:00 2001 From: Antonio Rueda-Toicen Date: Tue, 3 Feb 2026 17:36:22 +0100 Subject: [PATCH 63/75] Place hyper-models outside of hyperview (same level) --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index d687f7d..a502d46 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -100,7 +100,7 @@ asyncio_mode = "auto" testpaths = ["tests"] [tool.uv.workspace] -members = ["hyper_models"] +members = ["../hyper-models"] [tool.uv.sources] hyper-models = { workspace = true } From f2ebafa9ff38fc3a67640c1be44cb82d59a77120 Mon Sep 17 00:00:00 2001 From: Antonio Rueda-Toicen Date: Tue, 3 Feb 2026 17:38:00 +0100 Subject: [PATCH 64/75] Update CONTRIBUTING.md with hyper-models clone Added instructions to clone hyper-models repository. --- CONTRIBUTING.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index dc95ba9..a35dbe9 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -14,6 +14,7 @@ Clone the repo and install dependencies: ```bash git clone https://github.com/Hyper3Labs/HyperView.git +git clone https://github.com/Hyper3Labs/hyper-models cd HyperView # Create virtual environment and install dev dependencies From 04212aae3c9bc82b3ad7064327c1c32aa71a15dc Mon Sep 17 00:00:00 2001 From: Antonio Rueda-Toicen Date: Wed, 4 Feb 2026 13:54:26 +0100 Subject: [PATCH 65/75] Update demo link to HuggingFace Spaces --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index f228ad0..d4b8f57 100644 --- a/README.md +++ b/README.md @@ -79,7 +79,7 @@ Traditional Euclidean embeddings struggle with hierarchical data. In Euclidean s **[Hyperbolic space](https://hyper3labs.github.io/warp)** (Poincaré disk) has exponential volume growth ($e^r$), naturally preserving hierarchical structure and keeping rare classes distinct. -**[Try the live demo →](https://hyper3labs.github.io/HyperView/)** +**[Try the live demo on HuggingFace Spaces→](https://huggingface.co/spaces/hyper3labs/HyperView)** ## Contributing From c5aa6689aec18bd933cb1bbd6c2e769311a4caba Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Sat, 7 Feb 2026 15:24:01 +0100 Subject: [PATCH 66/75] Update CONTRIBUTING.md to include instructions for local development of hyper-models and modify README.md for demo link. Remove hyper-models workspace configuration from pyproject.toml. --- CONTRIBUTING.md | 27 ++++++++++++++++++++++++++- README.md | 13 +++++++++---- pyproject.toml | 6 ------ 3 files changed, 35 insertions(+), 11 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index a35dbe9..c50efae 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -14,7 +14,6 @@ Clone the repo and install dependencies: ```bash git clone https://github.com/Hyper3Labs/HyperView.git -git clone https://github.com/Hyper3Labs/hyper-models cd HyperView # Create virtual environment and install dev dependencies @@ -28,6 +27,32 @@ npm install cd .. ``` +### Optional: Co-develop `hyper-models` locally (uv workspace) + +By default, HyperView installs `hyper-models` from PyPI. + +If you want to develop HyperView and `hyper-models` side-by-side (without publishing `hyper-models`), you can tell **uv** to use a local checkout instead: + +1) Clone `hyper-models` next to the HyperView repo (as a sibling directory): + +```bash +cd .. +git clone https://github.com/Hyper3Labs/hyper-models +cd HyperView +``` + +2) Add the following to HyperView's `pyproject.toml` (keep this change local unless the maintainers decide otherwise): + +```toml +[tool.uv.workspace] +members = ["../hyper-models"] + +[tool.uv.sources] +hyper-models = { workspace = true } +``` + +3) Use uv's project commands (`uv run ...`, `uv sync`, etc.) so `tool.uv.sources` is respected. + ## Running Locally For the best development experience, run the backend and frontend in separate terminals. diff --git a/README.md b/README.md index d4b8f57..03957dd 100644 --- a/README.md +++ b/README.md @@ -2,14 +2,14 @@ > **Open-source dataset curation + embedding visualization (Euclidean + Poincaré disk)** -[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/Hyper3Labs/HyperView) +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/Hyper3Labs/HyperView) [![Open in HF Spaces](https://huggingface.co/datasets/huggingface/badges/resolve/main/open-in-hf-spaces-sm.svg)](https://huggingface.co/spaces/hyper3labs/HyperView) --- @@ -21,6 +21,11 @@ - **HuggingFace Integration**: Load datasets directly from HuggingFace Hub - **Fast Embeddings**: Uses EmbedAnything for CLIP-based image embeddings +## Updates + +- **01-02-26** — [The Geometry of Image Embeddings, Hands-on Coding Workshop](https://www.meetup.com/berlin-computer-vision-group/events/312927919/) (Berlin Computer Vision Group) +- **17-01-26** — [The Geometry of Image Embeddings, Hands-on Coding Workshop, Part I](https://www.meetup.com/berlin-computer-vision-group/events/312636174/) (Berlin Computer Vision Group) + ## Quick Start **Docs:** [docs/datasets.md](docs/datasets.md) · [docs/colab.md](docs/colab.md) · [CONTRIBUTING.md](CONTRIBUTING.md) · [TESTS.md](TESTS.md) @@ -28,7 +33,7 @@ ### Installation ```bash -pip install hyperview +uv pip install hyperview ``` ### Run the Demo diff --git a/pyproject.toml b/pyproject.toml index a502d46..fc26f6c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -98,9 +98,3 @@ ignore = ["E501"] [tool.pytest.ini_options] asyncio_mode = "auto" testpaths = ["tests"] - -[tool.uv.workspace] -members = ["../hyper-models"] - -[tool.uv.sources] -hyper-models = { workspace = true } From b6d754c5ab268c8b67186da53da19bdd94acd38d Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Sun, 8 Feb 2026 00:47:50 +0100 Subject: [PATCH 67/75] Update README.md to add Discord community section and recent demo event, and refine related projects list --- README.md | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 03957dd..2c6d5f5 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ > **Open-source dataset curation + embedding visualization (Euclidean + Poincaré disk)** -[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/Hyper3Labs/HyperView) [![Open in HF Spaces](https://huggingface.co/datasets/huggingface/badges/resolve/main/open-in-hf-spaces-sm.svg)](https://huggingface.co/spaces/hyper3labs/HyperView) +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/Hyper3Labs/HyperView) [![Open in HF Spaces](https://huggingface.co/datasets/huggingface/badges/resolve/main/open-in-hf-spaces-sm.svg)](https://huggingface.co/spaces/hyper3labs/HyperView) [![Discord](https://img.shields.io/badge/Discord-hyper%C2%B3labs-5865F2?logo=discord&logoColor=white)](https://discord.gg/Az7k4Ure)

    @@ -25,6 +25,7 @@ - **01-02-26** — [The Geometry of Image Embeddings, Hands-on Coding Workshop](https://www.meetup.com/berlin-computer-vision-group/events/312927919/) (Berlin Computer Vision Group) - **17-01-26** — [The Geometry of Image Embeddings, Hands-on Coding Workshop, Part I](https://www.meetup.com/berlin-computer-vision-group/events/312636174/) (Berlin Computer Vision Group) +- **11-12-25** — [Hacker Room Demo Day #2](https://youtu.be/KnOiaNXN3Q0?t=2483) (Merantix AI Campus Berlin) — First version of HyperView presented ## Quick Start @@ -86,6 +87,12 @@ Traditional Euclidean embeddings struggle with hierarchical data. In Euclidean s **[Try the live demo on HuggingFace Spaces→](https://huggingface.co/spaces/hyper3labs/HyperView)** +## Community + +**Weekly Open Discussion** — Every Tuesday at 15:00 UTC on [Discord](https://discord.gg/Az7k4Ure?event=1469730571440885944) + +Join us to see the latest features demoed live, walk through new code, and get help with local setup. Whether you're a core maintainer or looking for your first contribution, everyone is welcome. + ## Contributing Development setup, frontend hot-reload, and backend API notes live in [CONTRIBUTING.md](CONTRIBUTING.md). @@ -93,12 +100,7 @@ Development setup, frontend hot-reload, and backend API notes live in [CONTRIBUT ## Related projects - **hyper-scatter**: High-performance WebGL scatterplot engine (Euclidean + Poincaré) used by the frontend: https://github.com/Hyper3Labs/hyper-scatter -- **hyper-models**: Non-Euclidean model zoo + ONNX exports (e.g. for hyperbolic VLM experiments): https://github.com/Hyper3Labs/hyper-models - -## References - -- [Poincaré Embeddings for Learning Hierarchical Representations](https://arxiv.org/abs/1705.08039) (Nickel & Kiela, 2017) -- [Hyperbolic Neural Networks](https://arxiv.org/abs/1805.09112) (Ganea et al., 2018) +- **hyper-models**: Non-Euclidean model zoo + ONNX exports : https://github.com/Hyper3Labs/hyper-models ## License From 65f96bc9d781631fcb90bf5d6ca0dff6082ea917 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Mon, 16 Feb 2026 21:00:10 +0000 Subject: [PATCH 68/75] Initial plan From 1b3e041c508efec4596f0dd58996399dd1f2eadf Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Mon, 16 Feb 2026 21:01:13 +0000 Subject: [PATCH 69/75] Fix devin-review workflow TTY error by emulating terminal Co-authored-by: mnm-matin <45293386+mnm-matin@users.noreply.github.com> --- .github/workflows/devin-review.yml | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/.github/workflows/devin-review.yml b/.github/workflows/devin-review.yml index 5754ba5..8c5c644 100644 --- a/.github/workflows/devin-review.yml +++ b/.github/workflows/devin-review.yml @@ -20,4 +20,9 @@ jobs: node-version: '20' - name: Run Devin Review - run: npx devin-review ${{ github.event.pull_request.html_url }} + # Use script to emulate a TTY as devin-review requires terminal features + # The -q flag suppresses script output, -e exits with command exit code, + # and -c runs the command. /dev/null discards script's own output. + # CI=true ensures the tool runs in non-interactive CI mode. + run: | + script -q -e -c "CI=true npx devin-review ${{ github.event.pull_request.html_url }}" /dev/null From 4f93ea9b9be4b9521a09668116e67f77389b134f Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Mon, 16 Feb 2026 21:02:25 +0000 Subject: [PATCH 70/75] Improve workflow: use env block for CI variable Co-authored-by: mnm-matin <45293386+mnm-matin@users.noreply.github.com> --- .github/workflows/devin-review.yml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/.github/workflows/devin-review.yml b/.github/workflows/devin-review.yml index 8c5c644..ea1410b 100644 --- a/.github/workflows/devin-review.yml +++ b/.github/workflows/devin-review.yml @@ -23,6 +23,7 @@ jobs: # Use script to emulate a TTY as devin-review requires terminal features # The -q flag suppresses script output, -e exits with command exit code, # and -c runs the command. /dev/null discards script's own output. - # CI=true ensures the tool runs in non-interactive CI mode. + env: + CI: true # Ensures the tool runs in non-interactive CI mode run: | - script -q -e -c "CI=true npx devin-review ${{ github.event.pull_request.html_url }}" /dev/null + script -q -e -c "npx devin-review ${{ github.event.pull_request.html_url }}" /dev/null From d0a5bd16a598ee50d2c6bab7dfd9a4f06eee26af Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Mon, 16 Feb 2026 23:57:37 +0100 Subject: [PATCH 71/75] Refine frontend panels and label color settings --- .gitignore | 2 + frontend/package-lock.json | 58 +--- frontend/package.json | 2 +- frontend/src/app/globals.css | 7 + frontend/src/components/DockviewWorkspace.tsx | 19 +- frontend/src/components/ExplorerPanel.tsx | 185 +++++------ frontend/src/components/Header.tsx | 89 ++++-- frontend/src/components/PanelContextBar.tsx | 179 +++++++++++ frontend/src/components/PanelHeader.tsx | 14 +- frontend/src/components/PanelTitle.tsx | 40 +++ frontend/src/components/ScatterPanel.tsx | 197 +++++++++++- frontend/src/components/icons.tsx | 7 +- frontend/src/components/index.ts | 2 + frontend/src/components/ui/dropdown-menu.tsx | 28 +- frontend/src/components/ui/radio-group.tsx | 44 --- frontend/src/components/useLabelLegend.ts | 25 +- frontend/src/lib/colorTransfer.ts | 26 ++ frontend/src/lib/labelColors.ts | 288 ++++++++++++------ frontend/src/lib/labelLegend.ts | 46 +-- frontend/src/store/useColorSettings.ts | 36 +++ frontend/src/types/index.ts | 2 - scripts/demo_hyperbolic_clip.py | 18 +- src/hyperview/core/dataset.py | 20 -- src/hyperview/server/app.py | 4 - 24 files changed, 890 insertions(+), 448 deletions(-) create mode 100644 frontend/src/components/PanelContextBar.tsx create mode 100644 frontend/src/components/PanelTitle.tsx delete mode 100644 frontend/src/components/ui/radio-group.tsx create mode 100644 frontend/src/lib/colorTransfer.ts create mode 100644 frontend/src/store/useColorSettings.ts diff --git a/.gitignore b/.gitignore index 20670e4..f1a301e 100644 --- a/.gitignore +++ b/.gitignore @@ -70,6 +70,8 @@ TESTS.md AGENTS.md **/AGENTS.md .github/agents/ +.github/instructions/ +.github/hooks/ .specstory/ # Generated version file (hatch-vcs) diff --git a/frontend/package-lock.json b/frontend/package-lock.json index 399c8d5..c8f7be1 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -12,7 +12,6 @@ "@radix-ui/react-dialog": "^1.1.15", "@radix-ui/react-dropdown-menu": "^2.1.16", "@radix-ui/react-popover": "^1.1.15", - "@radix-ui/react-radio-group": "^1.3.8", "@radix-ui/react-scroll-area": "^1.2.10", "@radix-ui/react-separator": "^1.1.8", "@radix-ui/react-slot": "^1.2.4", @@ -30,6 +29,7 @@ "next": "^16.1.6", "react": "18.3.1", "react-dom": "18.3.1", + "react-icons": "^5.5.0", "tailwind-merge": "^3.4.0", "tailwindcss-animate": "^1.0.7", "zustand": "^5.0.1" @@ -1585,38 +1585,6 @@ } } }, - "node_modules/@radix-ui/react-radio-group": { - "version": "1.3.8", - "resolved": "https://registry.npmjs.org/@radix-ui/react-radio-group/-/react-radio-group-1.3.8.tgz", - "integrity": "sha512-VBKYIYImA5zsxACdisNQ3BjCBfmbGH3kQlnFVqlWU4tXwjy7cGX8ta80BcrO+WJXIn5iBylEH3K6ZTlee//lgQ==", - "license": "MIT", - "dependencies": { - "@radix-ui/primitive": "1.1.3", - "@radix-ui/react-compose-refs": "1.1.2", - "@radix-ui/react-context": "1.1.2", - "@radix-ui/react-direction": "1.1.1", - "@radix-ui/react-presence": "1.1.5", - "@radix-ui/react-primitive": "2.1.3", - "@radix-ui/react-roving-focus": "1.1.11", - "@radix-ui/react-use-controllable-state": "1.2.2", - "@radix-ui/react-use-previous": "1.1.1", - "@radix-ui/react-use-size": "1.1.1" - }, - "peerDependencies": { - "@types/react": "*", - "@types/react-dom": "*", - "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", - "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" - }, - "peerDependenciesMeta": { - "@types/react": { - "optional": true - }, - "@types/react-dom": { - "optional": true - } - } - }, "node_modules/@radix-ui/react-roving-focus": { "version": "1.1.11", "resolved": "https://registry.npmjs.org/@radix-ui/react-roving-focus/-/react-roving-focus-1.1.11.tgz", @@ -1934,21 +1902,6 @@ } } }, - "node_modules/@radix-ui/react-use-previous": { - "version": "1.1.1", - "resolved": "https://registry.npmjs.org/@radix-ui/react-use-previous/-/react-use-previous-1.1.1.tgz", - "integrity": "sha512-2dHfToCj/pzca2Ck724OZ5L0EVrr3eHRNsG/b3xQJLA2hZpVCS99bLAX+hm1IHXDEnzU6by5z/5MIY794/a8NQ==", - "license": "MIT", - "peerDependencies": { - "@types/react": "*", - "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" - }, - "peerDependenciesMeta": { - "@types/react": { - "optional": true - } - } - }, "node_modules/@radix-ui/react-use-rect": { "version": "1.1.1", "resolved": "https://registry.npmjs.org/@radix-ui/react-use-rect/-/react-use-rect-1.1.1.tgz", @@ -6063,6 +6016,15 @@ "react": "^18.3.1" } }, + "node_modules/react-icons": { + "version": "5.5.0", + "resolved": "https://registry.npmjs.org/react-icons/-/react-icons-5.5.0.tgz", + "integrity": "sha512-MEFcXdkP3dLo8uumGI5xN3lDFNsRtrjbOEKDLD7yv76v4wpnEq2Lt2qeHaQOr34I/wPN3s3+N08WkQ+CW37Xiw==", + "license": "MIT", + "peerDependencies": { + "react": "*" + } + }, "node_modules/react-is": { "version": "16.13.1", "resolved": "https://registry.npmjs.org/react-is/-/react-is-16.13.1.tgz", diff --git a/frontend/package.json b/frontend/package.json index d257e0f..a3c667a 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -14,7 +14,6 @@ "@radix-ui/react-dialog": "^1.1.15", "@radix-ui/react-dropdown-menu": "^2.1.16", "@radix-ui/react-popover": "^1.1.15", - "@radix-ui/react-radio-group": "^1.3.8", "@radix-ui/react-scroll-area": "^1.2.10", "@radix-ui/react-separator": "^1.1.8", "@radix-ui/react-slot": "^1.2.4", @@ -32,6 +31,7 @@ "next": "^16.1.6", "react": "18.3.1", "react-dom": "18.3.1", + "react-icons": "^5.5.0", "tailwind-merge": "^3.4.0", "tailwindcss-animate": "^1.0.7", "zustand": "^5.0.1" diff --git a/frontend/src/app/globals.css b/frontend/src/app/globals.css index 4272f94..417f195 100644 --- a/frontend/src/app/globals.css +++ b/frontend/src/app/globals.css @@ -165,6 +165,13 @@ line-height: 16px; } +.hyperview-dockview .dv-tab .dv-react-part { + height: 100%; + min-width: 0; + display: flex; + align-items: center; +} + .hyperview-dockview .dv-scrollable .dv-scrollbar-horizontal { height: 6px; border-radius: 3px; diff --git a/frontend/src/components/DockviewWorkspace.tsx b/frontend/src/components/DockviewWorkspace.tsx index 42d3f41..a54200a 100644 --- a/frontend/src/components/DockviewWorkspace.tsx +++ b/frontend/src/components/DockviewWorkspace.tsx @@ -28,6 +28,7 @@ import { ScatterPanel } from "./ScatterPanel"; import { ExplorerPanel } from "./ExplorerPanel"; import { PlaceholderPanel } from "./PlaceholderPanel"; import { HyperViewLogo } from "./icons"; +import { PanelTitle } from "./PanelTitle"; const LAYOUT_STORAGE_KEY = "hyperview:dockview-layout:v4"; @@ -254,7 +255,7 @@ export function useDockviewApi() { newPanel = api.addPanel({ id: panelId, component: "explorer", - title: "Explorer", + title: "Labels", position, initialWidth: targetWidth, minimumWidth: MIN_SIDE_PANEL_WIDTH, @@ -344,10 +345,13 @@ type TabWithIconProps = IDockviewPanelHeaderProps & { const TabWithIcon = React.memo(function TabWithIcon({ api, icon }: TabWithIconProps) { return ( -

    + ); }); @@ -459,6 +463,7 @@ function applyZonePolicies(api: DockviewApi) { if (explorer) { explorer.group.locked = true; explorer.group.header.hidden = true; + explorer.api.setActive(); } // Hide tab headers for placeholder panels @@ -607,17 +612,17 @@ export function DockviewWorkspace() { api.addPanel({ id: PANEL.EXPLORER, component: "explorer", - title: "Explorer", + title: "Labels", position: getZonePosition("left"), initialWidth: getDefaultLeftPanelWidth(containerWidth), minimumWidth: MIN_SIDE_PANEL_WIDTH, maximumWidth: getDefaultLeftPanelWidth(containerWidth), - inactive: true, }); if (explorerPanel) { explorerPanel.group.locked = true; explorerPanel.group.header.hidden = true; + explorerPanel.api.setActive(); } setLeftPanelOpen(!!explorerPanel); diff --git a/frontend/src/components/ExplorerPanel.tsx b/frontend/src/components/ExplorerPanel.tsx index 4942f72..b18c36a 100644 --- a/frontend/src/components/ExplorerPanel.tsx +++ b/frontend/src/components/ExplorerPanel.tsx @@ -4,7 +4,7 @@ import React from "react"; import { useStore } from "@/store/useStore"; import { Panel } from "./Panel"; import { PanelHeader } from "./PanelHeader"; -import { Tag, Search, ChevronDown, ChevronRight } from "lucide-react"; +import { Search, Tag } from "lucide-react"; import { cn } from "@/lib/utils"; import { FALLBACK_LABEL_COLOR, MISSING_LABEL_COLOR, normalizeLabel } from "@/lib/labelColors"; import { useLabelLegend } from "./useLabelLegend"; @@ -23,7 +23,6 @@ export function ExplorerPanel({ className }: ExplorerPanelProps) { } = useStore(); const [labelSearch, setLabelSearch] = React.useState(""); const [isSearchOpen, setIsSearchOpen] = React.useState(false); - const [isLabelsExpanded, setIsLabelsExpanded] = React.useState(true); const searchInputRef = React.useRef(null); const resolvedLayoutKey = @@ -34,20 +33,11 @@ export function ExplorerPanel({ className }: ExplorerPanelProps) { const { labelCounts, - labelUniverse, - distinctLabelCount, - distinctColoringDisabled, labelColorMap, legendLabels, } = useLabelLegend({ datasetInfo, embeddings, labelSearch, labelFilter }); const hasCounts = labelCounts.size > 0; - const baseLabelCount = labelUniverse.length > 0 - ? labelUniverse.filter((label) => label !== "undefined").length - : distinctLabelCount; - const displayCount = labelSearch.trim().length > 0 - ? legendLabels.length - : baseLabelCount; const activeLabel = labelFilter ? normalizeLabel(labelFilter) : null; @@ -67,114 +57,79 @@ export function ExplorerPanel({ className }: ExplorerPanelProps) { return ( - - - {/* Scrollable content area */} -
    - {/* Labels section */} -
    - {/* Section header - collapsible with search icon */} -
    - - - {/* Search toggle button */} - -
    - - {/* Search input - shown when search is toggled */} - {isSearchOpen && ( -
    - setLabelSearch(e.target.value)} - placeholder="Filter labels..." - className="w-full h-6 px-2 rounded bg-background border border-border text-[12px] leading-[16px] text-foreground placeholder:text-muted-foreground/50 outline-none focus:ring-1 focus:ring-ring focus:border-ring" - /> -
    + }> + {/* Search toggle button in header toolbar */} + + - {/* Labels list - collapsible */} - {isLabelsExpanded && ( -
    - {distinctColoringDisabled && ( -
    - Too many labels ({distinctLabelCount}) to color distinctly; using one color. -
    - )} + {/* Search input - shown when search is toggled */} + {isSearchOpen && ( +
    + setLabelSearch(e.target.value)} + placeholder="Filter labels..." + className="w-full h-6 px-2 rounded bg-background border border-border text-[12px] leading-[16px] text-foreground placeholder:text-muted-foreground/50 outline-none focus:ring-1 focus:ring-ring focus:border-ring" + /> +
    + )} - {legendLabels.length === 0 ? ( -
    - No labels available -
    - ) : ( -
    - {legendLabels.map((label) => { - const color = - label === "undefined" - ? MISSING_LABEL_COLOR - : labelColorMap[label] ?? FALLBACK_LABEL_COLOR; - const normalized = normalizeLabel(label); - const isActive = activeLabel === normalized; - const isDimmed = activeLabel && !isActive; + {/* Scrollable labels list */} +
    + {legendLabels.length === 0 ? ( +
    + No labels available +
    + ) : ( +
    + {legendLabels.map((label) => { + const color = + label === "undefined" + ? MISSING_LABEL_COLOR + : labelColorMap[label] ?? FALLBACK_LABEL_COLOR; + const normalized = normalizeLabel(label); + const isActive = activeLabel === normalized; + const isDimmed = activeLabel && !isActive; - return ( - - ); - })} -
    - )} -
    - )} -
    + return ( + + ); + })} +
    + )}
    ); diff --git a/frontend/src/components/Header.tsx b/frontend/src/components/Header.tsx index 5970969..b50f9cc 100644 --- a/frontend/src/components/Header.tsx +++ b/frontend/src/components/Header.tsx @@ -2,12 +2,17 @@ import { useStore } from "@/store/useStore"; import { Button } from "@/components/ui/button"; -import { HyperViewLogo, DiscordIcon } from "./icons"; +import { HyperViewLogo } from "./icons"; +import { FaDiscord } from "react-icons/fa"; import { CENTER_PANEL_DEFS, useDockviewApi } from "./DockviewWorkspace"; import { DropdownMenu, DropdownMenuContent, DropdownMenuItem, + DropdownMenuLabel, + DropdownMenuRadioGroup, + DropdownMenuRadioItem, + DropdownMenuSeparator, DropdownMenuTrigger, } from "@/components/ui/dropdown-menu"; import { @@ -32,17 +37,31 @@ import { PanelRight, Settings, Search, + Github, } from "lucide-react"; import { useState } from "react"; import { cn } from "@/lib/utils"; +import { isLabelColorMapId } from "@/lib/labelColors"; +import { + LABEL_COLOR_MAP_OPTIONS, + useColorSettings, +} from "@/store/useColorSettings"; const PANEL_CONFIG = CENTER_PANEL_DEFS; +const GITHUB_URL = "https://github.com/Hyper3Labs/HyperView"; const DISCORD_URL = process.env.NEXT_PUBLIC_DISCORD_URL ?? "https://discord.gg/Qf2pXtY4Vf"; export function Header() { const { datasetInfo, leftPanelOpen, rightPanelOpen, bottomPanelOpen } = useStore(); const dockview = useDockviewApi(); const [datasetPickerOpen, setDatasetPickerOpen] = useState(false); + const labelColorMapId = useColorSettings((state) => state.labelColorMapId); + const setLabelColorMapId = useColorSettings((state) => state.setLabelColorMapId); + + const handleLabelColorMapChange = (nextValue: string) => { + if (!isLabelColorMapId(nextValue)) return; + setLabelColorMapId(nextValue); + }; const handlePanelToggle = (panelId: string) => { if (!dockview?.api) return; @@ -60,7 +79,7 @@ export function Header() { ); return ( -
    +
    {/* Left side: Logo + View menu */}
    {/* Logo */} @@ -75,10 +94,10 @@ export function Header() { @@ -90,7 +109,7 @@ export function Header() { handlePanelToggle(panel.id)} - className="flex items-center justify-between h-7 text-[12px] leading-[16px]" + className="justify-between" > @@ -107,7 +126,7 @@ export function Header() { {/* Reset layout */} dockview.resetLayout()} - className="flex items-center gap-2 h-7 text-[12px] leading-[16px]" + className="gap-1.5" > Reset Layout @@ -126,10 +145,10 @@ export function Header() { size="sm" role="combobox" aria-expanded={datasetPickerOpen} - className="h-6 min-w-[200px] max-w-[400px] px-3 text-[12px] leading-[16px] tracking-[-0.15px] text-muted-foreground hover:text-foreground bg-muted/40 hover:bg-muted/60 border border-border/50 rounded-md justify-start gap-2" + className="h-6 w-[600px] max-w-[60vw] px-3 text-[12px] leading-[16px] tracking-[-0.15px] text-muted-foreground hover:text-foreground bg-muted/40 hover:bg-muted/60 border border-border/50 rounded-md justify-start gap-2" > - + {datasetInfo?.name ?? "No dataset loaded"} @@ -166,8 +185,19 @@ export function Header() {
    - {/* Right side: Discord + Panel toggles + Settings */} + {/* Right side: GitHub + Discord + Panel toggles + Settings */}
    + {/* GitHub link */} + + + + {/* Discord link */} - + {/* Separator */} @@ -230,14 +260,37 @@ export function Header() { {/* Separator */}
    - {/* Settings button */} - + {/* Settings menu */} + + + + + + Color Settings + + Label Palette + + {LABEL_COLOR_MAP_OPTIONS.map((option) => ( + + {option.label} + + ))} + + + setLabelColorMapId("auto")}>Reset to Auto + +
    ); diff --git a/frontend/src/components/PanelContextBar.tsx b/frontend/src/components/PanelContextBar.tsx new file mode 100644 index 0000000..5eb6c14 --- /dev/null +++ b/frontend/src/components/PanelContextBar.tsx @@ -0,0 +1,179 @@ +"use client"; + +import { Check, ChevronDown } from "lucide-react"; +import { type ReactNode } from "react"; + +import { cn } from "@/lib/utils"; +import { Button } from "@/components/ui/button"; +import { + DropdownMenu, + DropdownMenuContent, + DropdownMenuItem, + DropdownMenuLabel, + DropdownMenuRadioGroup, + DropdownMenuRadioItem, + DropdownMenuSeparator, + DropdownMenuTrigger, +} from "@/components/ui/dropdown-menu"; + +export interface PanelContextOption { + value: string; + label: string; + group?: string; + disabled?: boolean; +} + +interface PanelContextBaseItem { + id: string; + label: string; + showLabel?: boolean; + value: string; + placeholder?: string; + valueTitle?: string; + valueClassName?: string; +} + +export interface PanelContextStaticItem extends PanelContextBaseItem { + kind?: "static"; +} + +export interface PanelContextSelectItem extends PanelContextBaseItem { + kind: "select"; + options: PanelContextOption[]; + onValueChange: (value: string) => void; + disabled?: boolean; +} + +export type PanelContextItem = PanelContextStaticItem | PanelContextSelectItem; + +interface PanelContextBarProps { + items: PanelContextItem[]; + rightContent?: ReactNode; + className?: string; +} + +export function PanelContextBar({ items, rightContent, className }: PanelContextBarProps) { + const visibleItems = items.filter((item) => item.value.trim().length > 0 || item.kind === "select"); + + if (visibleItems.length === 0 && !rightContent) { + return null; + } + + return ( +
    +
    + {visibleItems.map((item, index) => { + const valueTitle = item.valueTitle ?? item.value; + const showLabel = item.showLabel ?? item.label.trim().length > 0; + const selectedLabel = + item.kind === "select" + ? item.options.find((option) => option.value === item.value)?.label + : undefined; + const displayValue = selectedLabel ?? item.value ?? item.placeholder ?? "Select"; + + return ( +
    + {index > 0 &&
    + ); + })} +
    + + {rightContent &&
    {rightContent}
    } +
    + ); +} diff --git a/frontend/src/components/PanelHeader.tsx b/frontend/src/components/PanelHeader.tsx index 78201c4..399fc25 100644 --- a/frontend/src/components/PanelHeader.tsx +++ b/frontend/src/components/PanelHeader.tsx @@ -2,6 +2,7 @@ import { ReactNode } from "react"; import { cn } from "@/lib/utils"; +import { PanelTitle } from "./PanelTitle"; interface PanelHeaderProps { icon?: ReactNode; @@ -25,18 +26,13 @@ export function PanelHeader({ icon, title, subtitle, children, className }: Pane return (
    -
    - {icon && ( - // 14px icon (3.5 tailwind units) with 4px gap to text - {icon} - )} - {/* 12px font, medium weight, tight tracking */} - {title} +
    + {subtitle && ( - {subtitle} + {subtitle} )}
    {children && ( diff --git a/frontend/src/components/PanelTitle.tsx b/frontend/src/components/PanelTitle.tsx new file mode 100644 index 0000000..095fb39 --- /dev/null +++ b/frontend/src/components/PanelTitle.tsx @@ -0,0 +1,40 @@ +"use client"; + +import { ReactNode } from "react"; + +import { cn } from "@/lib/utils"; + +interface PanelTitleProps { + title?: string; + icon?: ReactNode; + className?: string; + titleClassName?: string; + iconClassName?: string; + fullHeight?: boolean; +} + +export function PanelTitle({ + title, + icon, + className, + titleClassName, + iconClassName, + fullHeight = false, +}: PanelTitleProps) { + return ( +
    + {icon && ( + + {icon} + + )} + {title ?? ""} +
    + ); +} diff --git a/frontend/src/components/ScatterPanel.tsx b/frontend/src/components/ScatterPanel.tsx index fc68cd6..1091e23 100644 --- a/frontend/src/components/ScatterPanel.tsx +++ b/frontend/src/components/ScatterPanel.tsx @@ -1,8 +1,24 @@ "use client"; import { useCallback, useEffect, useMemo, useState } from "react"; +import { Settings2 } from "lucide-react"; import { useStore } from "@/store/useStore"; import { Panel } from "./Panel"; +import { Button } from "@/components/ui/button"; +import { + DropdownMenu, + DropdownMenuContent, + DropdownMenuLabel, + DropdownMenuRadioGroup, + DropdownMenuRadioItem, + DropdownMenuSeparator, + DropdownMenuTrigger, +} from "@/components/ui/dropdown-menu"; +import { + PanelContextBar, + type PanelContextItem, + type PanelContextOption, +} from "./PanelContextBar"; import { useHyperScatter } from "./useHyperScatter"; import { useLabelLegend } from "./useLabelLegend"; import type { Geometry } from "@/types"; @@ -34,6 +50,7 @@ export function ScatterPanel({ } = useStore(); const [localGeometry, setLocalGeometry] = useState("euclidean"); + const [localLayoutKey, setLocalLayoutKey] = useState(null); // Check which geometries are available const availableGeometries = useMemo(() => { @@ -51,7 +68,12 @@ export function ScatterPanel({ const resolvedGeometry = geometry ?? localGeometry; const resolvedLayoutKey = useMemo(() => { - if (!datasetInfo) return layoutKey ?? null; + if (!datasetInfo) return localLayoutKey ?? layoutKey ?? null; + + if (localLayoutKey) { + const exists = datasetInfo.layouts.some((layout) => layout.layout_key === localLayoutKey); + if (exists) return localLayoutKey; + } if (layoutKey) { const exists = datasetInfo.layouts.some((layout) => layout.layout_key === layoutKey); @@ -60,7 +82,176 @@ export function ScatterPanel({ const layout = findLayoutByGeometry(datasetInfo.layouts, resolvedGeometry); return layout?.layout_key ?? datasetInfo.layouts[0]?.layout_key ?? null; - }, [datasetInfo, layoutKey, resolvedGeometry]); + }, [datasetInfo, layoutKey, localLayoutKey, resolvedGeometry]); + + useEffect(() => { + if (!datasetInfo || !localLayoutKey) return; + const exists = datasetInfo.layouts.some((layout) => layout.layout_key === localLayoutKey); + if (!exists) { + setLocalLayoutKey(null); + } + }, [datasetInfo, localLayoutKey]); + + const resolvedLayout = useMemo(() => { + if (!datasetInfo || !resolvedLayoutKey) return null; + return datasetInfo.layouts.find((layout) => layout.layout_key === resolvedLayoutKey) ?? null; + }, [datasetInfo, resolvedLayoutKey]); + + const resolvedSpace = useMemo(() => { + if (!datasetInfo || !resolvedLayout) return null; + return datasetInfo.spaces.find((space) => space.space_key === resolvedLayout.space_key) ?? null; + }, [datasetInfo, resolvedLayout]); + + const geometryLayouts = useMemo(() => { + if (!datasetInfo) return []; + return datasetInfo.layouts.filter((layout) => layout.geometry === resolvedGeometry); + }, [datasetInfo, resolvedGeometry]); + + const modelOptions = useMemo(() => { + if (!datasetInfo || geometryLayouts.length === 0) return []; + + const seenSpaceKeys = new Set(); + + return geometryLayouts.flatMap((layout) => { + if (seenSpaceKeys.has(layout.space_key)) return []; + seenSpaceKeys.add(layout.space_key); + + const space = datasetInfo.spaces.find((candidate) => candidate.space_key === layout.space_key); + + return [ + { + value: layout.space_key, + label: space?.model_id ?? layout.space_key, + group: space?.provider, + }, + ]; + }); + }, [datasetInfo, geometryLayouts]); + + const selectedSpaceKey = resolvedLayout?.space_key ?? modelOptions[0]?.value ?? ""; + + const selectedProjectionMethod = resolvedLayout?.method ?? ""; + + const selectedModelLabel = + modelOptions.find((option) => option.value === selectedSpaceKey)?.label ?? + resolvedSpace?.model_id ?? + ""; + + const projectionMethodOptions = useMemo(() => { + const methodsForSelectedModel = geometryLayouts + .filter((layout) => layout.space_key === selectedSpaceKey) + .map((layout) => layout.method); + + const sourceMethods = + methodsForSelectedModel.length > 0 + ? methodsForSelectedModel + : geometryLayouts.map((layout) => layout.method); + + return Array.from(new Set(sourceMethods)).sort(); + }, [geometryLayouts, selectedSpaceKey]); + + const handleModelChange = useCallback( + (nextSpaceKey: string) => { + if (!nextSpaceKey || geometryLayouts.length === 0) return; + + const targetLayout = + geometryLayouts.find( + (layout) => + layout.space_key === nextSpaceKey && layout.method === selectedProjectionMethod + ) ?? geometryLayouts.find((layout) => layout.space_key === nextSpaceKey); + + if (targetLayout) { + setLocalLayoutKey(targetLayout.layout_key); + } + }, + [geometryLayouts, selectedProjectionMethod] + ); + + const handleProjectionMethodChange = useCallback( + (nextMethod: string) => { + if (!nextMethod || geometryLayouts.length === 0) return; + + const targetLayout = + geometryLayouts.find( + (layout) => + layout.method === nextMethod && + layout.space_key === selectedSpaceKey + ) ?? geometryLayouts.find((layout) => layout.method === nextMethod); + + if (targetLayout) { + setLocalLayoutKey(targetLayout.layout_key); + } + }, + [geometryLayouts, selectedSpaceKey] + ); + + const contextItems = useMemo(() => { + return [ + { + id: "model", + kind: "select", + label: "Model", + showLabel: false, + value: selectedSpaceKey, + placeholder: "Select model", + valueTitle: selectedModelLabel, + valueClassName: "max-w-[340px]", + options: modelOptions, + onValueChange: handleModelChange, + disabled: modelOptions.length === 0, + }, + ]; + }, [handleModelChange, modelOptions, selectedModelLabel, selectedSpaceKey]); + + const projectionSettings = useMemo( + () => ( + + + + + + + Projection method + + + {projectionMethodOptions.length > 0 ? ( + + {projectionMethodOptions.map((method) => ( + + {method} + + ))} + + ) : ( +
    + No projection methods available +
    + )} +     +     + ), + [ + handleProjectionMethodChange, + projectionMethodOptions, + selectedProjectionMethod, + ] + ); const embeddings = resolvedLayoutKey ? embeddingsByLayoutKey[resolvedLayoutKey] ?? null : null; @@ -124,6 +315,8 @@ export function ScatterPanel({ return ( + + {/* Main content area - min-h-0 prevents flex overflow */}
    {/* Canvas container */} diff --git a/frontend/src/components/icons.tsx b/frontend/src/components/icons.tsx index 2d38561..46494f7 100644 --- a/frontend/src/components/icons.tsx +++ b/frontend/src/components/icons.tsx @@ -65,9 +65,4 @@ export const SphericalIcon = () => ( ); -/** Discord icon - official simplified logo */ -export const DiscordIcon = ({ className = "w-4 h-4" }: { className?: string }) => ( - - - -); + diff --git a/frontend/src/components/index.ts b/frontend/src/components/index.ts index f178483..0cc632b 100644 --- a/frontend/src/components/index.ts +++ b/frontend/src/components/index.ts @@ -4,6 +4,8 @@ export { DockviewWorkspace } from "./DockviewWorkspace"; export { Header } from "./Header"; export { Panel, PanelFooter } from "./Panel"; export { PanelHeader } from "./PanelHeader"; +export { PanelTitle } from "./PanelTitle"; +export { PanelContextBar } from "./PanelContextBar"; export { ExplorerPanel } from "./ExplorerPanel"; export { PlaceholderPanel } from "./PlaceholderPanel"; export * from "./icons"; diff --git a/frontend/src/components/ui/dropdown-menu.tsx b/frontend/src/components/ui/dropdown-menu.tsx index 5a20503..cd9a158 100644 --- a/frontend/src/components/ui/dropdown-menu.tsx +++ b/frontend/src/components/ui/dropdown-menu.tsx @@ -27,7 +27,7 @@ const DropdownMenuSubTrigger = React.forwardRef< , React.ComponentPropsWithoutRef ->(({ className, sideOffset = 4, ...props }, ref) => ( +>(({ className, sideOffset = 3, ...props }, ref) => ( svg]:size-4 [&>svg]:shrink-0", + "relative flex h-6 cursor-default select-none items-center gap-1.5 rounded px-1.5 text-[12px] leading-[16px] outline-none focus:bg-accent/70 focus:text-accent-foreground data-[disabled]:pointer-events-none data-[disabled]:opacity-50 [&>svg]:size-3.5 [&>svg]:shrink-0", inset && "pl-8", className )} @@ -100,15 +100,15 @@ const DropdownMenuCheckboxItem = React.forwardRef< - + - + {children} @@ -124,14 +124,14 @@ const DropdownMenuRadioItem = React.forwardRef< - + - + {children} @@ -148,7 +148,7 @@ const DropdownMenuLabel = React.forwardRef< (({ className, ...props }, ref) => ( )) @@ -175,7 +175,7 @@ const DropdownMenuShortcut = ({ }: React.HTMLAttributes) => { return ( ) diff --git a/frontend/src/components/ui/radio-group.tsx b/frontend/src/components/ui/radio-group.tsx deleted file mode 100644 index 0bdf685..0000000 --- a/frontend/src/components/ui/radio-group.tsx +++ /dev/null @@ -1,44 +0,0 @@ -"use client" - -import * as React from "react" -import * as RadioGroupPrimitive from "@radix-ui/react-radio-group" -import { Circle } from "lucide-react" - -import { cn } from "@/lib/utils" - -const RadioGroup = React.forwardRef< - React.ElementRef, - React.ComponentPropsWithoutRef ->(({ className, ...props }, ref) => { - return ( - - ) -}) -RadioGroup.displayName = RadioGroupPrimitive.Root.displayName - -const RadioGroupItem = React.forwardRef< - React.ElementRef, - React.ComponentPropsWithoutRef ->(({ className, ...props }, ref) => { - return ( - - - - - - ) -}) -RadioGroupItem.displayName = RadioGroupPrimitive.Item.displayName - -export { RadioGroup, RadioGroupItem } diff --git a/frontend/src/components/useLabelLegend.ts b/frontend/src/components/useLabelLegend.ts index 9c58da0..f14ee8c 100644 --- a/frontend/src/components/useLabelLegend.ts +++ b/frontend/src/components/useLabelLegend.ts @@ -2,14 +2,13 @@ import { useMemo } from "react"; import type { DatasetInfo, EmbeddingsData } from "@/types"; import { - MAX_DISTINCT_LABEL_COLORS, buildLabelColorMap, buildLabelCounts, buildLabelUniverse, buildLabelsInfo, buildLegendLabels, - getDistinctLabelCount, } from "@/lib/labelLegend"; +import { useColorSettings } from "@/store/useColorSettings"; interface UseLabelLegendArgs { datasetInfo: DatasetInfo | null; @@ -24,6 +23,8 @@ export function useLabelLegend({ labelSearch = "", labelFilter = null, }: UseLabelLegendArgs) { + const labelColorMapId = useColorSettings((state) => state.labelColorMapId); + const labelCounts = useMemo(() => buildLabelCounts(embeddings), [embeddings]); const labelUniverse = useMemo( @@ -31,25 +32,15 @@ export function useLabelLegend({ [datasetInfo?.labels, embeddings?.labels] ); - const distinctLabelCount = useMemo(() => { - const fromCounts = getDistinctLabelCount(labelCounts); - if (fromCounts > 0) return fromCounts; - let n = labelUniverse.length; - if (labelUniverse.includes("undefined")) n -= 1; - return n; - }, [labelCounts, labelUniverse]); - - const distinctColoringDisabled = distinctLabelCount > MAX_DISTINCT_LABEL_COLORS; - const labelsInfo = useMemo( () => buildLabelsInfo({ datasetLabels: datasetInfo?.labels ?? [], embeddings, - distinctColoringDisabled, + labelColorMapId, labelFilter, }), - [datasetInfo?.labels, embeddings, distinctColoringDisabled, labelFilter] + [datasetInfo?.labels, embeddings, labelColorMapId, labelFilter] ); const labelColorMap = useMemo( @@ -57,10 +48,10 @@ export function useLabelLegend({ buildLabelColorMap({ labelsInfo, labelUniverse, - distinctColoringDisabled, + labelColorMapId, labelFilter, }), - [labelsInfo, labelUniverse, distinctColoringDisabled, labelFilter] + [labelsInfo, labelUniverse, labelColorMapId, labelFilter] ); const legendLabels = useMemo( @@ -76,8 +67,6 @@ export function useLabelLegend({ return { labelCounts, labelUniverse, - distinctLabelCount, - distinctColoringDisabled, labelsInfo, labelColorMap, legendLabels, diff --git a/frontend/src/lib/colorTransfer.ts b/frontend/src/lib/colorTransfer.ts new file mode 100644 index 0000000..fab7532 --- /dev/null +++ b/frontend/src/lib/colorTransfer.ts @@ -0,0 +1,26 @@ +import { + FALLBACK_LABEL_COLOR, + createLabelColorMap, + normalizeLabel, + type LabelColorMapId, +} from "@/lib/labelColors"; + +export interface CategoricalLabelTransferFunction { + kind: "categorical"; + paletteId: LabelColorMapId; + colorFor: (label: string | null | undefined) => string; +} + +export function createCategoricalLabelTransferFunction(params: { + labels: string[]; + paletteId: LabelColorMapId; +}): CategoricalLabelTransferFunction { + const { labels, paletteId } = params; + const colorMap = createLabelColorMap(labels, { paletteId }); + + return { + kind: "categorical", + paletteId, + colorFor: (label) => colorMap[normalizeLabel(label)] ?? FALLBACK_LABEL_COLOR, + }; +} diff --git a/frontend/src/lib/labelColors.ts b/frontend/src/lib/labelColors.ts index 273b519..d38e64b 100644 --- a/frontend/src/lib/labelColors.ts +++ b/frontend/src/lib/labelColors.ts @@ -3,75 +3,117 @@ const MISSING_LABEL_SENTINEL = "undefined"; export const MISSING_LABEL_COLOR = "#39d3cc"; // matches --accent-cyan export const FALLBACK_LABEL_COLOR = "#8b949e"; // matches --muted-foreground -// Rerun-style auto colors: distribute hues by golden ratio conjugate. -// See: context/repos/rerun/crates/viewer/re_viewer_context/src/utils/color.rs -const GOLDEN_RATIO_CONJUGATE = (Math.sqrt(5) - 1) / 2; // ~0.61803398875 - -function fnv1a32(input: string): number { - // 32-bit FNV-1a hash - let hash = 0x811c9dc5; - for (let i = 0; i < input.length; i++) { - hash ^= input.charCodeAt(i); - hash = Math.imul(hash, 0x01000193); - } - return hash >>> 0; +export const LABEL_COLOR_MAP_IDS = ["auto", "classic20", "tab10", "tab20", "wong"] as const; +export type LabelColorMapId = (typeof LABEL_COLOR_MAP_IDS)[number]; + +const AUTO_DEFAULT_PALETTE_ID: LabelColorMapId = "tab20"; +const OVERFLOW_HUE_STEP_DEGREES = 137.508; + +export function isLabelColorMapId(value: string): value is LabelColorMapId { + return LABEL_COLOR_MAP_IDS.includes(value as LabelColorMapId); } +const CLASSIC_20_LABEL_PALETTE = [ + "#e6194b", + "#3cb44b", + "#ffe119", + "#4363d8", + "#f58231", + "#911eb4", + "#46f0f0", + "#f032e6", + "#bcf60c", + "#fabebe", + "#008080", + "#e6beff", + "#9a6324", + "#fffac8", + "#800000", + "#aaffc3", + "#808000", + "#ffd8b1", + "#000075", + "#808080", +]; + +const TAB_10_LABEL_PALETTE = [ + "#4e79a7", + "#f28e2b", + "#e15759", + "#76b7b2", + "#59a14f", + "#edc948", + "#b07aa1", + "#ff9da7", + "#9c755f", + "#bab0ab", +]; + +const TAB_20_LABEL_PALETTE = [ + "#1f77b4", + "#aec7e8", + "#ff7f0e", + "#ffbb78", + "#2ca02c", + "#98df8a", + "#d62728", + "#ff9896", + "#9467bd", + "#c5b0d5", + "#8c564b", + "#c49c94", + "#e377c2", + "#f7b6d2", + "#7f7f7f", + "#c7c7c7", + "#bcbd22", + "#dbdb8d", + "#17becf", + "#9edae5", +]; + +const WONG_LABEL_PALETTE = [ + "#e69f00", + "#56b4e9", + "#009e73", + "#f0e442", + "#0072b2", + "#d55e00", + "#cc79a7", +]; + function clamp01(v: number): number { if (v < 0) return 0; if (v > 1) return 1; return v; } -function hsvToHex(h: number, s: number, v: number): string { - // h/s/v in [0, 1] - const hh = ((h % 1) + 1) % 1; - const ss = clamp01(s); - const vv = clamp01(v); - - const x = hh * 6; - const i = Math.floor(x); - const f = x - i; - - const p = vv * (1 - ss); - const q = vv * (1 - ss * f); - const t = vv * (1 - ss * (1 - f)); - - let r = 0; - let g = 0; - let b = 0; - - switch (i % 6) { - case 0: - r = vv; - g = t; - b = p; - break; - case 1: - r = q; - g = vv; - b = p; - break; - case 2: - r = p; - g = vv; - b = t; - break; - case 3: - r = p; - g = q; - b = vv; - break; - case 4: - r = t; - g = p; - b = vv; - break; - case 5: - r = vv; - g = p; - b = q; - break; +function hslToHex(hDegrees: number, saturation: number, lightness: number): string { + const h = ((((hDegrees % 360) + 360) % 360) / 360) % 1; + const s = clamp01(saturation); + const l = clamp01(lightness); + + let r = l; + let g = l; + let b = l; + + if (s > 0) { + const q = l < 0.5 ? l * (1 + s) : l + s - l * s; + const p = 2 * l - q; + + const hueToChannel = (tIn: number): number => { + let t = tIn; + if (t < 0) t += 1; + if (t > 1) t -= 1; + if (t < 1 / 6) return p + (q - p) * 6 * t; + if (t < 1 / 2) return q; + if (t < 2 / 3) return p + (q - p) * (2 / 3 - t) * 6; + return p; + }; + + r = hueToChannel(h + 1 / 3); + g = hueToChannel(h); + b = hueToChannel(h - 1 / 3); } const toHex = (x: number) => { @@ -82,20 +124,27 @@ function hsvToHex(h: number, s: number, v: number): string { return `#${toHex(r)}${toHex(g)}${toHex(b)}`; } -export function labelToColor(label: string): string { - if (!label) return FALLBACK_LABEL_COLOR; - if (label === MISSING_LABEL_SENTINEL) return MISSING_LABEL_COLOR; - - return colorForKey(label); -} - -function colorForKey(key: string): string { - const seed = fnv1a32(key); - const val = seed & 0xffff; // map to u16 - const h = (val * GOLDEN_RATIO_CONJUGATE) % 1; - - // Match Rerun's defaults: saturation 0.85, value 0.5 - return hsvToHex(h, 0.85, 0.5); +function createOverflowColor(index: number, seedPrefix: LabelColorMapId): string { + const paletteSeedShift = + seedPrefix === "tab10" + ? 17 + : seedPrefix === "tab20" + ? 43 + : seedPrefix === "wong" + ? 71 + : seedPrefix === "classic20" + ? 101 + : 131; + + const hue = (paletteSeedShift + index * OVERFLOW_HUE_STEP_DEGREES) % 360; + const saturationBands = [0.72, 0.64, 0.78]; + const lightnessBands = [0.46, 0.54, 0.38, 0.62]; + + const saturation = saturationBands[index % saturationBands.length]; + const lightness = + lightnessBands[Math.floor(index / saturationBands.length) % lightnessBands.length]; + + return hslToHex(hue, saturation, lightness); } function stableLabelSort(a: string, b: string): number { @@ -104,20 +153,19 @@ function stableLabelSort(a: string, b: string): number { return a.localeCompare(b); } -/** - * Builds a deterministic, collision-free label → color mapping for the given - * label universe. - * - * Notes: - * - No modulo/cycling: if there are N labels, we produce N colors. - * - Deterministic: same input set yields same mapping. - * - Collision-free within the provided set via deterministic rehashing. - */ -export function createLabelColorMap(labels: string[]): Record { - const unique = Array.from(new Set(labels.map((l) => normalizeLabel(l)))).sort(stableLabelSort); +function createAutoLabelColorMap(unique: string[]): Record { + return createPaletteLabelColorMap(unique, TAB_20_LABEL_PALETTE, AUTO_DEFAULT_PALETTE_ID); +} +function createPaletteLabelColorMap( + unique: string[], + palette: string[], + seedPrefix: LabelColorMapId +): Record { const colors: Record = {}; const used = new Set(); + let nonMissingIndex = 0; + let overflowIndex = 0; for (const label of unique) { if (label === MISSING_LABEL_SENTINEL) { @@ -126,28 +174,68 @@ export function createLabelColorMap(labels: string[]): Record { continue; } - let attempt = 0; - // Deterministic collision resolution (should be extremely rare). - while (attempt < 32) { - const candidate = colorForKey(attempt === 0 ? label : `${label}#${attempt}`); - const normalized = candidate.toLowerCase(); - if (!used.has(normalized)) { - colors[label] = candidate; - used.add(normalized); - break; - } - attempt++; + let candidate = + nonMissingIndex < palette.length + ? (palette[nonMissingIndex] ?? FALLBACK_LABEL_COLOR) + : createOverflowColor(overflowIndex, seedPrefix); + + let safety = 0; + while (used.has(candidate.toLowerCase()) && safety < 2048) { + overflowIndex += 1; + candidate = createOverflowColor(overflowIndex, seedPrefix); + safety += 1; + } + + if (nonMissingIndex >= palette.length) { + overflowIndex += 1; } - if (!colors[label]) { - // Should never happen, but keep UI resilient. - colors[label] = FALLBACK_LABEL_COLOR; + if (used.has(candidate.toLowerCase())) { + candidate = FALLBACK_LABEL_COLOR; } + + colors[label] = candidate; + used.add(candidate.toLowerCase()); + nonMissingIndex += 1; } return colors; } +/** + * Builds a deterministic label → color mapping for the given label universe. + * + * Notes: + * - `auto` mode uses `tab20` as the default categorical palette. + * - `classic20`, `tab10`, `tab20`, and `wong` use fixed categorical palettes + * first, then deterministic overflow colors for additional labels. + */ +export function createLabelColorMap( + labels: string[], + options?: { paletteId?: LabelColorMapId } +): Record { + const unique = Array.from(new Set(labels.map((l) => normalizeLabel(l)))).sort(stableLabelSort); + const paletteId = options?.paletteId ?? "auto"; + + if (paletteId === "classic20") { + return createPaletteLabelColorMap(unique, CLASSIC_20_LABEL_PALETTE, "classic20"); + } + + if (paletteId === "tab10") { + return createPaletteLabelColorMap(unique, TAB_10_LABEL_PALETTE, "tab10"); + } + + if (paletteId === "tab20") { + return createPaletteLabelColorMap(unique, TAB_20_LABEL_PALETTE, "tab20"); + } + + if (paletteId === "wong") { + return createPaletteLabelColorMap(unique, WONG_LABEL_PALETTE, "wong"); + } + + return createAutoLabelColorMap(unique); +} + export function normalizeLabel(label: string | null | undefined): string { return label && label.length > 0 ? label : MISSING_LABEL_SENTINEL; } diff --git a/frontend/src/lib/labelLegend.ts b/frontend/src/lib/labelLegend.ts index 2d43e93..22546e3 100644 --- a/frontend/src/lib/labelLegend.ts +++ b/frontend/src/lib/labelLegend.ts @@ -2,13 +2,11 @@ import type { EmbeddingsData } from "@/types"; import { FALLBACK_LABEL_COLOR, MISSING_LABEL_COLOR, - createLabelColorMap, normalizeLabel, + type LabelColorMapId, } from "@/lib/labelColors"; +import { createCategoricalLabelTransferFunction } from "@/lib/colorTransfer"; -// Past ~20-30 categories, color-as-encoding becomes unreliable for most users. -// We choose a conservative upper bound and fall back to a single color. -export const MAX_DISTINCT_LABEL_COLORS = 20; export const UNSELECTED_LABEL_ALPHA = 0.12; export interface ScatterLabelsInfo { @@ -65,12 +63,6 @@ export function buildLabelCounts(embeddings: EmbeddingsData | null): Map): number { - let n = labelCounts.size; - if (labelCounts.has("undefined")) n -= 1; - return n; -} - export function buildLabelUniverse( datasetLabels: string[], embeddingsLabels: (string | null)[] | null @@ -117,14 +109,14 @@ export function buildLabelUniverse( export function buildLabelsInfo(params: { datasetLabels: string[]; embeddings: EmbeddingsData | null; - distinctColoringDisabled: boolean; + labelColorMapId: LabelColorMapId; labelFilter?: string | null; unselectedAlpha?: number; }): ScatterLabelsInfo | null { const { datasetLabels, embeddings, - distinctColoringDisabled, + labelColorMapId, labelFilter = null, unselectedAlpha = UNSELECTED_LABEL_ALPHA, } = params; @@ -156,13 +148,11 @@ export function buildLabelsInfo(params: { categories[i] = labelToCategory[key] ?? undefinedIndex; } - let palette: string[]; - if (distinctColoringDisabled) { - palette = universe.map((l) => (l === "undefined" ? MISSING_LABEL_COLOR : FALLBACK_LABEL_COLOR)); - } else { - const colors = createLabelColorMap(universe); - palette = universe.map((l) => colors[l] ?? FALLBACK_LABEL_COLOR); - } + const transfer = createCategoricalLabelTransferFunction({ + labels: universe, + paletteId: labelColorMapId, + }); + let palette = universe.map((l) => transfer.colorFor(l)); const filteredPalette = applyLabelFilterToPalette({ palette, @@ -177,14 +167,14 @@ export function buildLabelsInfo(params: { export function buildLabelColorMap(params: { labelsInfo: ScatterLabelsInfo | null; labelUniverse: string[]; - distinctColoringDisabled: boolean; + labelColorMapId: LabelColorMapId; labelFilter?: string | null; unselectedAlpha?: number; }): Record { const { labelsInfo, labelUniverse, - distinctColoringDisabled, + labelColorMapId, labelFilter = null, unselectedAlpha = UNSELECTED_LABEL_ALPHA, } = params; @@ -199,16 +189,12 @@ export function buildLabelColorMap(params: { if (labelUniverse.length === 0) return map; - if (distinctColoringDisabled) { - for (const label of labelUniverse) { - map[label] = label === "undefined" ? MISSING_LABEL_COLOR : FALLBACK_LABEL_COLOR; - } - return map; - } - - const colors = createLabelColorMap(labelUniverse); + const transfer = createCategoricalLabelTransferFunction({ + labels: labelUniverse, + paletteId: labelColorMapId, + }); for (const label of labelUniverse) { - map[label] = colors[label] ?? FALLBACK_LABEL_COLOR; + map[label] = transfer.colorFor(label); } if (!labelFilter || !labelUniverse.includes(labelFilter)) return map; diff --git a/frontend/src/store/useColorSettings.ts b/frontend/src/store/useColorSettings.ts new file mode 100644 index 0000000..56d0971 --- /dev/null +++ b/frontend/src/store/useColorSettings.ts @@ -0,0 +1,36 @@ +import { create } from "zustand"; +import { createJSONStorage, persist } from "zustand/middleware"; + +import type { LabelColorMapId } from "@/lib/labelColors"; + +export interface LabelColorMapOption { + value: LabelColorMapId; + label: string; +} + +export const LABEL_COLOR_MAP_OPTIONS: LabelColorMapOption[] = [ + { value: "auto", label: "Auto" }, + { value: "tab20", label: "Tab 20" }, + { value: "tab10", label: "Tab 10" }, + { value: "wong", label: "Wong" }, + { value: "classic20", label: "Classic 20" }, +]; + +interface ColorSettingsState { + labelColorMapId: LabelColorMapId; + setLabelColorMapId: (value: LabelColorMapId) => void; +} + +export const useColorSettings = create()( + persist( + (set) => ({ + labelColorMapId: "auto", + setLabelColorMapId: (value) => set({ labelColorMapId: value }), + }), + { + name: "hyperview-color-settings", + version: 1, + storage: createJSONStorage(() => localStorage), + } + ) +); diff --git a/frontend/src/types/index.ts b/frontend/src/types/index.ts index 2cd5560..8efc617 100644 --- a/frontend/src/types/index.ts +++ b/frontend/src/types/index.ts @@ -34,7 +34,6 @@ export interface DatasetInfo { name: string; num_samples: number; labels: string[]; - label_colors?: Record; spaces: SpaceInfo[]; layouts: LayoutInfo[]; } @@ -45,7 +44,6 @@ export interface EmbeddingsData { ids: string[]; labels: (string | null)[]; coords: [number, number][]; - label_colors?: Record; } export interface SamplesResponse { diff --git a/scripts/demo_hyperbolic_clip.py b/scripts/demo_hyperbolic_clip.py index 6da1a79..74c4bfb 100644 --- a/scripts/demo_hyperbolic_clip.py +++ b/scripts/demo_hyperbolic_clip.py @@ -1,22 +1,20 @@ #!/usr/bin/env python -"""Demo: CLIP (Euclidean) + hyper-models (Poincaré) on CIFAR-100.""" +"""Demo: CLIP (Euclidean) + HyCoCLIP (Poincaré) on Imagenette.""" import hyperview as hv -DATASET_NAME = "cifar100_coarse_clip_hyper_models" -HF_DATASET = "uoft-cs/cifar100" -HF_SPLIT = "test" -HF_IMAGE_KEY = "img" -# NOTE: HyperView disables distinct label coloring when there are >20 labels. -# CIFAR-100 has 100 fine labels, but only 20 coarse labels. -HF_LABEL_KEY = "coarse_label" -NUM_SAMPLES = 200 +DATASET_NAME = "imagenette_val_clip_hycoclip_demo" +HF_DATASET = "Multimodal-Fatima/Imagenette_validation" +HF_SPLIT = "validation" +HF_IMAGE_KEY = "image" +HF_LABEL_KEY = "label" +NUM_SAMPLES = 300 CLIP_MODEL_ID = "openai/clip-vit-base-patch32" HYPER_MODELS_MODEL_ID = "hycoclip-vit-s" def main() -> None: - print("Loading CIFAR-100 from Hugging Face...") + print("Loading Imagenette validation from Hugging Face...") dataset = hv.Dataset(DATASET_NAME, persist=False) dataset.add_from_huggingface( HF_DATASET, diff --git a/src/hyperview/core/dataset.py b/src/hyperview/core/dataset.py index 74429c7..ce62480 100644 --- a/src/hyperview/core/dataset.py +++ b/src/hyperview/core/dataset.py @@ -66,14 +66,6 @@ def __init__( from hyperview.storage import MemoryBackend self._storage = MemoryBackend(self.name) - # Color palette for deterministic label color assignment - _COLOR_PALETTE = [ - "#e6194b", "#3cb44b", "#ffe119", "#4363d8", "#f58231", - "#911eb4", "#46f0f0", "#f032e6", "#bcf60c", "#fabebe", - "#008080", "#e6beff", "#9a6324", "#fffac8", "#800000", - "#aaffc3", "#808000", "#ffd8b1", "#000075", "#808080", - ] - def __len__(self) -> int: return len(self._storage) @@ -495,18 +487,6 @@ def find_similar_by_vector( """ return self._storage.find_similar_by_vector(vector, k, space_key) - @staticmethod - def _compute_label_color(label: str, palette: list[str]) -> str: - """Compute a deterministic color for a label.""" - digest = hashlib.md5(label.encode("utf-8")).digest() - idx = int.from_bytes(digest[:4], "big") % len(palette) - return palette[idx] - - def get_label_colors(self) -> dict[str, str]: - """Get the color mapping for labels (computed deterministically).""" - labels = self._storage.get_unique_labels() - return {label: self._compute_label_color(label, self._COLOR_PALETTE) for label in labels} - def set_coords( self, geometry: str, diff --git a/src/hyperview/server/app.py b/src/hyperview/server/app.py index 26e897a..9de8c00 100644 --- a/src/hyperview/server/app.py +++ b/src/hyperview/server/app.py @@ -79,7 +79,6 @@ class DatasetResponse(BaseModel): name: str num_samples: int labels: list[str] - label_colors: dict[str, str] spaces: list[SpaceInfoResponse] layouts: list[LayoutInfoResponse] @@ -92,7 +91,6 @@ class EmbeddingsResponse(BaseModel): ids: list[str] labels: list[str | None] coords: list[list[float]] - label_colors: dict[str, str] class SimilarSampleResponse(BaseModel): @@ -174,7 +172,6 @@ async 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dataset loading: add shuffle and seed parameters for diverse sampling --- .gitignore | 2 ++ scripts/demo_hyperbolic_clip.py | 1 + src/hyperview/core/dataset.py | 30 +++++++++++++++++++++++++----- 3 files changed, 28 insertions(+), 5 deletions(-) diff --git a/.gitignore b/.gitignore index f1a301e..031517d 100644 --- a/.gitignore +++ b/.gitignore @@ -72,6 +72,8 @@ AGENTS.md .github/agents/ .github/instructions/ .github/hooks/ +.github/skills/ +.agents/ .specstory/ # Generated version file (hatch-vcs) diff --git a/scripts/demo_hyperbolic_clip.py b/scripts/demo_hyperbolic_clip.py index 74c4bfb..1da0aaa 100644 --- a/scripts/demo_hyperbolic_clip.py +++ b/scripts/demo_hyperbolic_clip.py @@ -22,6 +22,7 @@ def main() -> None: image_key=HF_IMAGE_KEY, label_key=HF_LABEL_KEY, max_samples=NUM_SAMPLES, + shuffle=True, ) print(f"Loaded {len(dataset)} samples") diff --git a/src/hyperview/core/dataset.py b/src/hyperview/core/dataset.py index ce62480..09cba36 100644 --- a/src/hyperview/core/dataset.py +++ b/src/hyperview/core/dataset.py @@ -197,6 +197,8 @@ def add_from_huggingface( label_key: str | None = "fine_label", label_names_key: str | None = None, max_samples: int | None = None, + shuffle: bool = False, + seed: int = 42, show_progress: bool = True, skip_existing: bool = True, image_format: str = "auto", @@ -214,6 +216,8 @@ def add_from_huggingface( label_key: Key for the label column (can be None). label_names_key: Key for label names in dataset info. max_samples: Maximum number of samples to load. + shuffle: If True, shuffle the dataset before sampling (ensures diverse classes). + seed: Random seed for shuffling (default: 42). show_progress: Whether to print progress. skip_existing: If True (default), skip samples that already exist in storage. image_format: Image format to save: "auto" (detect from source, fallback PNG), @@ -240,6 +244,22 @@ def add_from_huggingface( except Exception: ds = cast(Any, load_dataset(dataset_name, split=split)) + source_fingerprint = ds._fingerprint if hasattr(ds, "_fingerprint") else None + + dataset_size = len(ds) + total = dataset_size if max_samples is None else min(dataset_size, max_samples) + + # Select source row indices explicitly so sampled subsets are clear and + # sample IDs remain stable for the same underlying row. + selected_indices: list[int] | None = None + if shuffle: + rng = np.random.default_rng(seed) + selected_indices = rng.permutation(dataset_size)[:total].tolist() + ds = ds.select(selected_indices) + elif max_samples is not None: + selected_indices = list(range(total)) + ds = ds.select(selected_indices) + # Get label names if available label_names = None if label_key and label_names_key: @@ -251,7 +271,7 @@ def add_from_huggingface( # Extract dataset metadata for robust sample IDs config_name = getattr(ds.info, "config_name", None) or "default" - fingerprint = ds._fingerprint[:8] if hasattr(ds, "_fingerprint") and ds._fingerprint else "unknown" + fingerprint = source_fingerprint[:8] if source_fingerprint else "unknown" version = str(ds.info.version) if ds.info.version else None # Get media directory for this dataset @@ -259,7 +279,6 @@ def add_from_huggingface( media_dir = config.get_huggingface_media_dir(dataset_name, split) samples = [] - total = len(ds) if max_samples is None else min(len(ds), max_samples) if show_progress: print(f"Loading {total} samples from {dataset_name}...") @@ -268,6 +287,7 @@ def add_from_huggingface( for i in iterator: item = ds[i] + source_index = selected_indices[i] if selected_indices is not None else i image = item[image_key] # Handle PIL Image or numpy array @@ -287,7 +307,7 @@ def add_from_huggingface( # Generate robust sample ID with config and fingerprint safe_name = dataset_name.replace("/", "_") - sample_id = f"{safe_name}_{config_name}_{fingerprint}_{split}_{i}" + sample_id = f"{safe_name}_{config_name}_{fingerprint}_{split}_{source_index}" # Determine image format and extension if image_format == "auto": @@ -311,8 +331,8 @@ def add_from_huggingface( "source": dataset_name, "config": config_name, "split": split, - "index": i, - "fingerprint": ds._fingerprint if hasattr(ds, "_fingerprint") else None, + "index": source_index, + "fingerprint": source_fingerprint, "version": version, } From b035d7739c5788e89b54903f674eeaccc6496582 Mon Sep 17 00:00:00 2001 From: Matin Mahmood <45293386+mnm-matin@users.noreply.github.com> Date: Sat, 21 Feb 2026 18:32:56 +0100 Subject: [PATCH 74/75] feat: cosmos cookoff fork baseline --- .gitignore | 6 + README.md | 4 +- frontend/next-env.d.ts | 2 +- frontend/next.config.ts | 2 +- frontend/src/app/page.tsx | 83 +- frontend/src/components/CurationPanel.tsx | 243 ++++++ frontend/src/components/DockviewWorkspace.tsx | 90 ++- frontend/src/components/GlobalSeekBar.tsx | 92 +++ frontend/src/components/Header.tsx | 4 +- frontend/src/components/ImageGrid.tsx | 2 +- frontend/src/components/VideoGrid.tsx | 383 +++++++++ frontend/src/components/VideoPanel.tsx | 194 +++++ frontend/src/components/index.ts | 4 + frontend/src/lib/api.ts | 50 +- frontend/src/store/useStore.ts | 151 +++- frontend/src/types/index.ts | 62 ++ scripts/create_cookoff_fixture.py | 171 +++++ scripts/curate_submission_metrics.py | 262 +++++++ scripts/demo.py | 2 +- scripts/demo_hyperbolic_clip.py | 4 +- scripts/load_cosmos_curate.py | 725 ++++++++++++++++++ scripts/load_nuscenes.py | 330 ++++++++ src/hyperview/server/app.py | 287 ++++++- 23 files changed, 3092 insertions(+), 61 deletions(-) create mode 100644 frontend/src/components/CurationPanel.tsx create mode 100644 frontend/src/components/GlobalSeekBar.tsx create mode 100644 frontend/src/components/VideoGrid.tsx create mode 100644 frontend/src/components/VideoPanel.tsx create mode 100644 scripts/create_cookoff_fixture.py create mode 100644 scripts/curate_submission_metrics.py create mode 100644 scripts/load_cosmos_curate.py create mode 100644 scripts/load_nuscenes.py diff --git a/.gitignore b/.gitignore index 031517d..195707d 100644 --- a/.gitignore +++ b/.gitignore @@ -78,3 +78,9 @@ AGENTS.md # Generated version file (hatch-vcs) src/hyperview/_version.py + +# Local workspace overlays (not part of upstream HyperView fork) +cosmos-curate/ +PLAN.md +TASK.md +submission/ diff --git a/README.md b/README.md index 2c6d5f5..aa253c3 100644 --- a/README.md +++ b/README.md @@ -47,7 +47,7 @@ This will: 1. Load 500 samples from CIFAR-100 2. Compute CLIP embeddings 3. Generate Euclidean and Poincaré visualizations -4. Start the server at **http://127.0.0.1:6262** +4. Start the server at **http://127.0.0.1:6263** ### Python API @@ -72,7 +72,7 @@ dataset.compute_embeddings(model="openai/clip-vit-base-patch32") dataset.compute_visualization() # Launch the UI -hv.launch(dataset) # Opens http://127.0.0.1:6262 +hv.launch(dataset, port=6263) # Opens http://127.0.0.1:6263 ``` ### Google Colab diff --git a/frontend/next-env.d.ts b/frontend/next-env.d.ts index 9edff1c..c4b7818 100644 --- a/frontend/next-env.d.ts +++ b/frontend/next-env.d.ts @@ -1,6 +1,6 @@ /// /// -import "./.next/types/routes.d.ts"; +import "./.next/dev/types/routes.d.ts"; // NOTE: This file should not be edited // see https://nextjs.org/docs/app/api-reference/config/typescript for more information. diff --git a/frontend/next.config.ts b/frontend/next.config.ts index 7dbfdbd..ae8d588 100644 --- a/frontend/next.config.ts +++ b/frontend/next.config.ts @@ -15,7 +15,7 @@ const nextConfig: NextConfig = { return [ { source: "/api/:path*", - destination: "http://127.0.0.1:6262/api/:path*", + destination: "http://127.0.0.1:6263/api/:path*", }, ]; }, diff --git a/frontend/src/app/page.tsx b/frontend/src/app/page.tsx index a04d188..8632790 100644 --- a/frontend/src/app/page.tsx +++ b/frontend/src/app/page.tsx @@ -2,11 +2,13 @@ import { useEffect, useCallback, useMemo, useRef, useState } from "react"; import { Header } from "@/components"; +import { GlobalSeekBar } from "@/components/GlobalSeekBar"; import { DockviewWorkspace, DockviewProvider } from "@/components/DockviewWorkspace"; import { useStore } from "@/store/useStore"; import type { Sample } from "@/types"; import { fetchDataset, + postCurationFilter, fetchSamples, fetchSamplesBatch, fetchLassoSelection, @@ -36,10 +38,12 @@ export default function Home() { lassoIsLoading, setLassoResults, labelFilter, + curationQuery, } = useStore(); const [loadingMore, setLoadingMore] = useState(false); const labelFilterRef = useRef(labelFilter ?? null); + const curationQueryRef = useRef(curationQuery); // Initial data load - runs once on mount // Store setters are stable and don't need to be in deps @@ -97,6 +101,7 @@ export default function Home() { useEffect(() => { if (labelFilterRef.current === labelFilter) return; if (isLassoSelection) return; + if (curationQuery) return; labelFilterRef.current = labelFilter ?? null; @@ -116,7 +121,60 @@ export default function Home() { return () => { cancelled = true; }; - }, [isLassoSelection, labelFilter, setSamples]); + }, [curationQuery, isLassoSelection, labelFilter, setSamples]); + + // Refetch samples when curation filters are applied (non-lasso mode) + useEffect(() => { + if (!curationQuery) return; + if (isLassoSelection) return; + + let cancelled = false; + const run = async () => { + try { + const res = await postCurationFilter({ + ...curationQuery, + offset: 0, + limit: SAMPLES_PER_PAGE, + }); + if (cancelled) return; + setSamples(res.samples, res.total); + } catch (err) { + if (cancelled) return; + console.error("Failed to load curation-filtered samples:", err); + } + }; + + run(); + return () => { + cancelled = true; + }; + }, [curationQuery, isLassoSelection, setSamples]); + + // When curation filters are cleared, restore standard paginated samples. + useEffect(() => { + const previous = curationQueryRef.current; + curationQueryRef.current = curationQuery; + + if (!previous || curationQuery !== null) return; + if (isLassoSelection) return; + + let cancelled = false; + const run = async () => { + try { + const res = await fetchSamples(0, SAMPLES_PER_PAGE, labelFilter ?? undefined); + if (cancelled) return; + setSamples(res.samples, res.total); + } catch (err) { + if (cancelled) return; + console.error("Failed to restore samples after clearing curation filters:", err); + } + }; + + run(); + return () => { + cancelled = true; + }; + }, [curationQuery, isLassoSelection, labelFilter, setSamples]); // Fetch initial lasso selection page when a new lasso query begins. useEffect(() => { @@ -175,6 +233,25 @@ export default function Home() { return; } + if (curationQuery) { + if (samplesLoaded >= totalSamples) return; + + setLoadingMore(true); + try { + const res = await postCurationFilter({ + ...curationQuery, + offset: samplesLoaded, + limit: SAMPLES_PER_PAGE, + }); + appendSamples(res.samples); + } catch (err) { + console.error("Failed to load more curation-filtered samples:", err); + } finally { + setLoadingMore(false); + } + return; + } + if (samplesLoaded >= totalSamples) return; setLoadingMore(true); @@ -198,6 +275,7 @@ export default function Home() { totalSamples, setLassoResults, labelFilter, + curationQuery, ]); const displayedSamples = useMemo(() => { @@ -236,7 +314,7 @@ export default function Home() {

      {error}

      - Make sure the HyperView backend is running on port 6262. + Make sure the HyperView backend is running on port 6263.

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z@z#6zj8schp5WzADk)z305>F6fs+U&ZrrS(O03~JB8djN zC^p2JqkevijtGGrD4oUHAXm$s^ie_($30tXZ}jk$LCNtarD!Pqo3R@pmd#B{VdnJk zpj&l$u+r}%pi5*e@q;!`ygS?EgHW{B1I??s2pEx`$OwhUWz}RY^PY|Z1+6jbf@DM` ztkOL@E?V?ie%q_up=@euULeMJeB-|P)oQr$qkUTv68^wcGh7>VM1A=6z# zo5~vk$9V%vdpSYct0=A4P=7$ zK~?r&7F<+D?HrBTxp96Ef8VN6_~=Og$MYAlFVFmMUu^o{z8KLH_VJ%Au2K5xb|qcU zqhiOcURXgknm-GufPPBs;;?FDx%gnL(c5E(HY&$4XpRFpS?%oJa zn~h$pdhV-e=P8|n*7mNBxKe0L*ER3|@VPGVd~Rv+tZQ$3eRA|YE?+KB+>!q(HD8gn z!!=K&>38B4Zp9#x7!w`Dk+DW&yk4s1BR%P3nX@z<*I-wkE4s2I{v4w$k>mS(ul9U$MAFrF_HtCoDr|)P)*2NT9*m#@jdsgL^GiG_H!~ z-azjs3N@fGGPjQyu7mZLkR8d+5pbv|t*H*fXB)

    @@ -266,6 +344,7 @@ export default function Home() { >
    + {/* Main content - dockable panels */}
    diff --git a/frontend/src/components/CurationPanel.tsx b/frontend/src/components/CurationPanel.tsx new file mode 100644 index 0000000..8c7467d --- /dev/null +++ b/frontend/src/components/CurationPanel.tsx @@ -0,0 +1,243 @@ +"use client"; + +import { useEffect, useMemo, useState } from "react"; +import { BarChart3, Filter } from "lucide-react"; +import { fetchCurationStats } from "@/lib/api"; +import type { CurationFilterRequest, CurationStats, HistogramBin } from "@/types"; +import { useStore } from "@/store/useStore"; +import { Panel } from "./Panel"; +import { PanelHeader } from "./PanelHeader"; + +interface CurationPanelProps { + className?: string; +} + +function Histogram({ bins }: { bins: HistogramBin[] }) { + const maxCount = useMemo(() => { + let value = 0; + for (const bin of bins) value = Math.max(value, bin.count); + return value; + }, [bins]); + + if (bins.length === 0 || maxCount <= 0) { + return
    No score data available.
    ; + } + + return ( +
    + {bins.map((bin) => ( +
    + ))} +
    + ); +} + +function scoreLabel(value: number): string { + return value.toFixed(1); +} + +export function CurationPanel({ className = "" }: CurationPanelProps) { + const { + datasetInfo, + curationFilters, + setCurationFilters, + setCurationQuery, + resetCurationFilters, + } = useStore(); + + const [stats, setStats] = useState(null); + const [isLoading, setIsLoading] = useState(false); + const [error, setError] = useState(null); + + useEffect(() => { + let cancelled = false; + setIsLoading(true); + setError(null); + + fetchCurationStats() + .then((data) => { + if (cancelled) return; + setStats(data); + }) + .catch((err) => { + if (cancelled) return; + setStats(null); + setError(err instanceof Error ? err.message : "Failed to load curation stats"); + }) + .finally(() => { + if (cancelled) return; + setIsLoading(false); + }); + + return () => { + cancelled = true; + }; + }, [datasetInfo?.name]); + + const applyFilters = () => { + const request: CurationFilterRequest = { + min_aesthetic_score: + curationFilters.minAestheticScore > 0 ? curationFilters.minAestheticScore : undefined, + min_motion_score: + curationFilters.minMotionScore > 0 ? curationFilters.minMotionScore : undefined, + max_cosine_similarity: + curationFilters.maxCosineSimilarity < 1 + ? curationFilters.maxCosineSimilarity + : undefined, + caption_query: curationFilters.captionQuery.trim() || undefined, + dedup_status: + curationFilters.dedupStatus === "all" ? undefined : curationFilters.dedupStatus, + }; + setCurationQuery(request); + }; + + const clearFilters = () => { + resetCurationFilters(); + setCurationQuery(null); + }; + + return ( + + } /> + +
    +
    +
    Dataset
    + {isLoading ? ( +
    Loading curation stats…
    + ) : error ? ( +
    {error}
    + ) : ( +
    +
    samples: {stats?.total_samples ?? 0}
    +
    with video: {stats?.with_video ?? 0}
    +
    with caption: {stats?.with_caption ?? 0}
    +
    + dedup kept: {stats?.dedup_counts?.kept ?? 0} +
    +
    + )} +
    + +
    +
    Aesthetic score
    + +
    + min score + + setCurationFilters({ minAestheticScore: Number(event.target.value) }) + } + className="flex-1 accent-primary" + /> + + {scoreLabel(curationFilters.minAestheticScore)} + +
    +
    + +
    +
    Motion score
    + +
    + min score + + setCurationFilters({ minMotionScore: Number(event.target.value) }) + } + className="flex-1 accent-primary" + /> + + {scoreLabel(curationFilters.minMotionScore)} + +
    +
    + +
    +
    Dedup + Caption
    + +
    + max sim + + setCurationFilters({ maxCosineSimilarity: Number(event.target.value) }) + } + className="flex-1 accent-primary" + /> + + {curationFilters.maxCosineSimilarity.toFixed(2)} + +
    + +
    + dedup + +
    + +
    + caption + + setCurationFilters({ captionQuery: event.target.value }) + } + placeholder="e.g. pedestrian" + className="flex-1 h-7 rounded bg-background border border-border px-2 text-foreground placeholder:text-muted-foreground" + /> +
    +
    +
    + +
    + + +
    +     + ); +} diff --git a/frontend/src/components/DockviewWorkspace.tsx b/frontend/src/components/DockviewWorkspace.tsx index a54200a..fe48bca 100644 --- a/frontend/src/components/DockviewWorkspace.tsx +++ b/frontend/src/components/DockviewWorkspace.tsx @@ -18,15 +18,17 @@ import { type IWatermarkPanelProps, themeAbyss, } from "dockview"; -import { Circle, Disc, Grid3X3 } from "lucide-react"; +import { Circle, Disc, Film, Grid3X3, SlidersHorizontal } from "lucide-react"; import type { Geometry, Sample } from "@/types"; import { useStore } from "@/store/useStore"; import { findLayoutByGeometry } from "@/lib/layouts"; -import { ImageGrid } from "./ImageGrid"; +import { VideoGrid } from "./VideoGrid"; import { ScatterPanel } from "./ScatterPanel"; import { ExplorerPanel } from "./ExplorerPanel"; import { PlaceholderPanel } from "./PlaceholderPanel"; +import { VideoPanel } from "./VideoPanel"; +import { CurationPanel } from "./CurationPanel"; import { HyperViewLogo } from "./icons"; import { PanelTitle } from "./PanelTitle"; @@ -36,6 +38,8 @@ const LAYOUT_STORAGE_KEY = "hyperview:dockview-layout:v4"; const PANEL = { EXPLORER: "explorer", GRID: "grid", + VIDEO: "video", + CURATION: "curation", SCATTER_EUCLIDEAN: "scatter-euclidean", SCATTER_POINCARE: "scatter-poincare", SCATTER_DEFAULT: "scatter-default", @@ -45,13 +49,17 @@ const PANEL = { const CENTER_PANEL_IDS = [ PANEL.GRID, + PANEL.VIDEO, + PANEL.CURATION, PANEL.SCATTER_EUCLIDEAN, PANEL.SCATTER_POINCARE, PANEL.SCATTER_DEFAULT, ] as const; export const CENTER_PANEL_DEFS = [ - { id: PANEL.GRID, label: "Samples", icon: Grid3X3 }, + { id: PANEL.GRID, label: "Clip Previews", icon: Grid3X3 }, + { id: PANEL.VIDEO, label: "Video", icon: Film }, + { id: PANEL.CURATION, label: "Curation", icon: SlidersHorizontal }, { id: PANEL.SCATTER_EUCLIDEAN, label: "Euclidean", icon: Circle }, { id: PANEL.SCATTER_POINCARE, label: "Hyperbolic", icon: Disc }, ] as const; @@ -164,13 +172,35 @@ export function useDockviewApi() { api.addPanel({ id: PANEL.GRID, component: "grid", - title: "Samples", + title: "Clip Previews", tabComponent: "samplesTab", renderer: "always", ...baseOptions, }); break; + case PANEL.VIDEO: + api.addPanel({ + id: PANEL.VIDEO, + component: "video", + title: "Video", + tabComponent: "videoTab", + renderer: "always", + ...baseOptions, + }); + break; + + case PANEL.CURATION: + api.addPanel({ + id: PANEL.CURATION, + component: "curation", + title: "Curation", + tabComponent: "curationTab", + renderer: "always", + ...baseOptions, + }); + break; + case PANEL.SCATTER_EUCLIDEAN: api.addPanel({ id: PANEL.SCATTER_EUCLIDEAN, @@ -368,11 +398,19 @@ const SamplesTab = React.memo(function SamplesTab(props: IDockviewPanelHeaderPro return } />; }); +const VideoTab = React.memo(function VideoTab(props: IDockviewPanelHeaderProps) { + return } />; +}); + +const CurationTab = React.memo(function CurationTab(props: IDockviewPanelHeaderProps) { + return } />; +}); + // Grid panel uses context to get samples const GridDockPanel = React.memo(function GridDockPanel() { const ctx = useDockviewContext(); return ( - ; }); +const VideoDockPanel = React.memo(function VideoDockPanel( + props: IDockviewPanelProps<{ sampleId?: string }> +) { + return ; +}); + +const CurationDockPanel = React.memo(function CurationDockPanel() { + return ; +}); + // Watermark shown when dock is empty - just the logo, no text const Watermark = React.memo(function Watermark(_props: IWatermarkPanelProps) { return ( @@ -410,6 +458,8 @@ const Watermark = React.memo(function Watermark(_props: IWatermarkPanelProps) { // Stable components object - never changes const COMPONENTS = { grid: GridDockPanel, + video: VideoDockPanel, + curation: CurationDockPanel, scatter: ScatterDockPanel, explorer: ExplorerDockPanel, placeholder: PlaceholderDockPanel, @@ -420,6 +470,8 @@ const TAB_COMPONENTS = { euclideanTab: EuclideanTab, hyperbolicTab: HyperbolicTab, samplesTab: SamplesTab, + videoTab: VideoTab, + curationTab: CurationTab, }; // ----------------------------------------------------------------------------- @@ -500,11 +552,37 @@ export function DockviewWorkspace() { api.addPanel({ id: PANEL.GRID, component: "grid", - title: "Samples", + title: "Clip Previews", tabComponent: "samplesTab", renderer: "always", }); + api.getPanel(PANEL.VIDEO) ?? + api.addPanel({ + id: PANEL.VIDEO, + component: "video", + title: "Video", + tabComponent: "videoTab", + position: { + referencePanel: gridPanel.id, + direction: "within", + }, + renderer: "always", + }); + + api.getPanel(PANEL.CURATION) ?? + api.addPanel({ + id: PANEL.CURATION, + component: "curation", + title: "Curation", + tabComponent: "curationTab", + position: { + referencePanel: gridPanel.id, + direction: "within", + }, + renderer: "always", + }); + let scatterPanel: typeof gridPanel | null = null; if (hasLayouts && euclideanLayout) { diff --git a/frontend/src/components/GlobalSeekBar.tsx b/frontend/src/components/GlobalSeekBar.tsx new file mode 100644 index 0000000..8e296e3 --- /dev/null +++ b/frontend/src/components/GlobalSeekBar.tsx @@ -0,0 +1,92 @@ +"use client"; + +import { Pause, Play, SkipBack, SkipForward } from "lucide-react"; +import { useMemo } from "react"; +import { useStore } from "@/store/useStore"; + +function formatTime(seconds: number): string { + if (!Number.isFinite(seconds) || seconds < 0) return "00:00"; + const total = Math.floor(seconds); + const mins = Math.floor(total / 60); + const secs = total % 60; + return `${String(mins).padStart(2, "0")}:${String(secs).padStart(2, "0")}`; +} + +export function GlobalSeekBar() { + const { + activeVideoId, + globalSeekTime, + setGlobalSeekTime, + isTimelinePlaying, + setTimelinePlaying, + videoDurations, + } = useStore(); + + const duration = useMemo(() => { + if (!activeVideoId) return 0; + return videoDurations[activeVideoId] ?? 0; + }, [activeVideoId, videoDurations]); + + const canSeek = Boolean(activeVideoId) && duration > 0; + + const jump = (delta: number) => { + if (!canSeek) return; + const next = Math.max(0, Math.min(duration, globalSeekTime + delta)); + setGlobalSeekTime(next); + }; + + return ( +
    + + + + + + + + {formatTime(globalSeekTime)} + + + setGlobalSeekTime(Number(event.target.value))} + disabled={!canSeek} + className="flex-1 accent-primary disabled:opacity-50" + /> + + {formatTime(duration)} + +
    + {activeVideoId ? `active: ${activeVideoId}` : "Select a video sample to enable global timeline"} +
    +
    + ); +} diff --git a/frontend/src/components/Header.tsx b/frontend/src/components/Header.tsx index b50f9cc..ebc3521 100644 --- a/frontend/src/components/Header.tsx +++ b/frontend/src/components/Header.tsx @@ -47,9 +47,9 @@ import { useColorSettings, } from "@/store/useColorSettings"; -const PANEL_CONFIG = CENTER_PANEL_DEFS; const GITHUB_URL = "https://github.com/Hyper3Labs/HyperView"; const DISCORD_URL = process.env.NEXT_PUBLIC_DISCORD_URL ?? "https://discord.gg/Qf2pXtY4Vf"; +const PANEL_CONFIG = CENTER_PANEL_DEFS; export function Header() { const { datasetInfo, leftPanelOpen, rightPanelOpen, bottomPanelOpen } = useStore(); @@ -173,7 +173,7 @@ export function Header() { > {datasetInfo.name} - {datasetInfo.num_samples.toLocaleString()} samples + {datasetInfo.num_samples.toLocaleString()} clips diff --git a/frontend/src/components/ImageGrid.tsx b/frontend/src/components/ImageGrid.tsx index 7e1e457..d1fd07f 100644 --- a/frontend/src/components/ImageGrid.tsx +++ b/frontend/src/components/ImageGrid.tsx @@ -299,7 +299,7 @@ export function ImageGrid({ samples, onLoadMore, hasMore }: ImageGridProps) { /> ) : (
    - No image + No preview
    )} diff --git a/frontend/src/components/VideoGrid.tsx b/frontend/src/components/VideoGrid.tsx new file mode 100644 index 0000000..261e1b8 --- /dev/null +++ b/frontend/src/components/VideoGrid.tsx @@ -0,0 +1,383 @@ +"use client"; + +import { useCallback, useEffect, useMemo, useRef, useState } from "react"; +import { useVirtualizer } from "@tanstack/react-virtual"; +import justifiedLayout from "justified-layout"; +import { Play } from "lucide-react"; +import { useStore } from "@/store/useStore"; +import { getVideoUrl } from "@/lib/api"; +import { Panel } from "./Panel"; +import { CheckIcon } from "./icons"; +import type { Sample } from "@/types"; + +interface VideoGridProps { + samples: Sample[]; + onLoadMore?: () => void; + hasMore?: boolean; +} + +const BOX_SPACING = 2; +const TARGET_ROW_HEIGHT = 180; +const DEFAULT_ASPECT_RATIO = 1; + +function getAspectRatio(sample: Sample): number { + if (sample.width && sample.height && sample.height > 0) { + return sample.width / sample.height; + } + return DEFAULT_ASPECT_RATIO; +} + +function computeLayout( + samples: Sample[], + containerWidth: number +): { + boxes: Array<{ width: number; height: number; top: number; left: number }>; + containerHeight: number; +} { + if (samples.length === 0 || containerWidth <= 0) { + return { boxes: [], containerHeight: 0 }; + } + + const aspectRatios = samples.map(getAspectRatio); + + const geometry = justifiedLayout(aspectRatios, { + containerWidth, + containerPadding: 0, + boxSpacing: BOX_SPACING, + targetRowHeight: TARGET_ROW_HEIGHT, + targetRowHeightTolerance: 0.25, + showWidows: true, + }); + + return { + boxes: geometry.boxes, + containerHeight: geometry.containerHeight, + }; +} + +interface RowData { + startIndex: number; + endIndex: number; + top: number; + height: number; +} + +function groupIntoRows( + boxes: Array<{ width: number; height: number; top: number; left: number }> +): RowData[] { + if (boxes.length === 0) return []; + + const rows: RowData[] = []; + let currentRowTop = boxes[0].top; + let currentRowStart = 0; + let currentRowHeight = boxes[0].height; + + for (let i = 1; i < boxes.length; i++) { + const box = boxes[i]; + if (Math.abs(box.top - currentRowTop) > 1) { + rows.push({ + startIndex: currentRowStart, + endIndex: i, + top: currentRowTop, + height: currentRowHeight, + }); + currentRowStart = i; + currentRowTop = box.top; + currentRowHeight = box.height; + } else { + currentRowHeight = Math.max(currentRowHeight, box.height); + } + } + + rows.push({ + startIndex: currentRowStart, + endIndex: boxes.length, + top: currentRowTop, + height: currentRowHeight, + }); + + return rows; +} + +function hasVideo(sample: Sample): boolean { + const metadata = sample.metadata as Record; + const videoPath = metadata.video_path; + const clipLocation = metadata.clip_location; + + if (typeof videoPath === "string" && videoPath.trim()) return true; + if (typeof clipLocation === "string" && clipLocation.trim()) return true; + return sample.filepath.toLowerCase().endsWith(".mp4"); +} + +function metadataScore(sample: Sample, key: string): string { + const metadata = sample.metadata as Record; + const value = metadata[key]; + if (typeof value !== "number" || Number.isNaN(value)) return "—"; + return value.toFixed(1); +} + +function captionSnippet(sample: Sample): string | null { + const metadata = sample.metadata as Record; + for (const key of ["caption_answer", "first_caption", "caption_raw"]) { + const value = metadata[key]; + if (typeof value === "string" && value.trim()) { + return value.trim(); + } + } + return null; +} + +export function VideoGrid({ samples, onLoadMore, hasMore }: VideoGridProps) { + const containerRef = useRef(null); + const [containerWidth, setContainerWidth] = useState(0); + + const { + selectedIds, + isLassoSelection, + selectionSource, + toggleSelection, + addToSelection, + setHoveredId, + hoveredId, + labelFilter, + activeVideoId, + setActiveVideoId, + setVideoDuration, + previewVideoId, + setPreviewVideoId, + } = useStore(); + + useEffect(() => { + const container = containerRef.current; + if (!container) return; + + const updateWidth = () => { + const width = container.clientWidth; + if (width > 0 && width !== containerWidth) { + setContainerWidth(width); + } + }; + + updateWidth(); + + const resizeObserver = new ResizeObserver(() => { + requestAnimationFrame(updateWidth); + }); + resizeObserver.observe(container); + + return () => resizeObserver.disconnect(); + }, [containerWidth]); + + const { boxes, containerHeight } = useMemo( + () => computeLayout(samples, containerWidth), + [samples, containerWidth] + ); + + const rows = useMemo(() => groupIntoRows(boxes), [boxes]); + + const virtualizer = useVirtualizer({ + count: rows.length, + getScrollElement: () => containerRef.current, + estimateSize: (index) => rows[index]?.height ?? TARGET_ROW_HEIGHT, + overscan: 3, + getItemKey: (index) => { + const row = rows[index]; + if (!row) return `row-${index}`; + const rowSamples = samples.slice(row.startIndex, row.endIndex); + return rowSamples.map((s) => s.id).join("-") || `row-${index}`; + }, + }); + + useEffect(() => { + const container = containerRef.current; + if (!container || !onLoadMore || !hasMore) return; + + const handleScroll = () => { + const { scrollTop, scrollHeight, clientHeight } = container; + if (scrollHeight - scrollTop - clientHeight < 500) { + onLoadMore(); + } + }; + + container.addEventListener("scroll", handleScroll); + return () => container.removeEventListener("scroll", handleScroll); + }, [onLoadMore, hasMore]); + + useEffect(() => { + containerRef.current?.scrollTo({ top: 0 }); + }, [labelFilter]); + + useEffect(() => { + if (isLassoSelection) return; + if (selectionSource !== "scatter") return; + if (selectedIds.size === 0) return; + + try { + virtualizer.scrollToIndex(0, { align: "start" }); + } catch { + containerRef.current?.scrollTo({ top: 0 }); + } + }, [isLassoSelection, selectedIds, selectionSource, virtualizer]); + + const handleClick = useCallback( + (sample: Sample, event: React.MouseEvent) => { + setActiveVideoId(sample.id); + + if (event.metaKey || event.ctrlKey) { + toggleSelection(sample.id); + } else if (event.shiftKey && selectedIds.size > 0) { + const selectedArray = Array.from(selectedIds); + const lastSelected = selectedArray[selectedArray.length - 1]; + const lastIndex = samples.findIndex((s) => s.id === lastSelected); + const currentIndex = samples.findIndex((s) => s.id === sample.id); + + if (lastIndex !== -1 && currentIndex !== -1) { + const start = Math.min(lastIndex, currentIndex); + const end = Math.max(lastIndex, currentIndex); + const rangeIds = samples.slice(start, end + 1).map((s) => s.id); + addToSelection(rangeIds); + } + } else { + const newSet = new Set(); + newSet.add(sample.id); + useStore.getState().setSelectedIds(newSet, "grid"); + } + }, + [samples, selectedIds, toggleSelection, addToSelection, setActiveVideoId] + ); + + const virtualRows = virtualizer.getVirtualItems(); + + return ( + +
    +
    +
    + {virtualRows.map((virtualRow) => { + const row = rows[virtualRow.index]; + if (!row) return null; + + const rowSamples = samples.slice(row.startIndex, row.endIndex); + const rowBoxes = boxes.slice(row.startIndex, row.endIndex); + + return ( +
    + {rowSamples.map((sample, i) => { + const box = rowBoxes[i]; + if (!box) return null; + + const isSelected = isLassoSelection ? true : selectedIds.has(sample.id); + const isHovered = hoveredId === sample.id; + const isVideo = hasVideo(sample); + const showPreview = isVideo && previewVideoId === sample.id; + const isActiveVideo = activeVideoId === sample.id; + const caption = captionSnippet(sample); + + return ( +
    handleClick(sample, e)} + onMouseEnter={() => { + setHoveredId(sample.id); + setPreviewVideoId(isVideo ? sample.id : null); + }} + onMouseLeave={() => { + setHoveredId(null); + if (previewVideoId === sample.id) { + setPreviewVideoId(null); + } + }} + > + {showPreview ? ( +
    + ); + })} +
    + ); + })} +
    +
    +
    +     + ); +} diff --git a/frontend/src/components/VideoPanel.tsx b/frontend/src/components/VideoPanel.tsx new file mode 100644 index 0000000..bae5717 --- /dev/null +++ b/frontend/src/components/VideoPanel.tsx @@ -0,0 +1,194 @@ +"use client"; + +import { useEffect, useMemo, useRef, useState } from "react"; +import { Panel } from "./Panel"; +import { fetchAnnotations, getVideoUrl } from "@/lib/api"; +import { useStore } from "@/store/useStore"; +import type { VideoAnnotation } from "@/types"; + +interface VideoPanelProps { + className?: string; + sampleId?: string; +} + +function formatScore(value: number | null): string { + if (typeof value !== "number" || Number.isNaN(value)) return "—"; + return value.toFixed(2); +} + +export function VideoPanel({ className = "", sampleId }: VideoPanelProps) { + const { + selectedIds, + activeVideoId, + setActiveVideoId, + globalSeekTime, + setGlobalSeekTime, + isTimelinePlaying, + setTimelinePlaying, + setVideoDuration, + annotationCache, + cacheAnnotation, + } = useStore(); + + const videoRef = useRef(null); + + const activeSampleId = useMemo(() => { + if (sampleId) return sampleId; + if (activeVideoId) return activeVideoId; + if (selectedIds.size === 0) return null; + return Array.from(selectedIds)[0] ?? null; + }, [activeVideoId, sampleId, selectedIds]); + + const [annotation, setAnnotation] = useState(null); + const [isLoading, setIsLoading] = useState(false); + const [error, setError] = useState(null); + const [videoFailed, setVideoFailed] = useState(false); + + useEffect(() => { + setVideoFailed(false); + }, [activeSampleId]); + + useEffect(() => { + if (!activeSampleId) return; + if (activeVideoId === activeSampleId) return; + setActiveVideoId(activeSampleId); + }, [activeSampleId, activeVideoId, setActiveVideoId]); + + useEffect(() => { + if (!activeSampleId) { + setAnnotation(null); + setError(null); + setTimelinePlaying(false); + return; + } + + const cached = annotationCache[activeSampleId]; + if (cached) { + setAnnotation(cached); + setError(null); + setIsLoading(false); + return; + } + + let cancelled = false; + setIsLoading(true); + setError(null); + + fetchAnnotations(activeSampleId) + .then((data) => { + if (cancelled) return; + setAnnotation(data); + cacheAnnotation(activeSampleId, data); + }) + .catch((err) => { + if (cancelled) return; + setAnnotation(null); + setError(err instanceof Error ? err.message : "Failed to load annotations"); + }) + .finally(() => { + if (cancelled) return; + setIsLoading(false); + }); + + return () => { + cancelled = true; + }; + }, [activeSampleId, annotationCache, cacheAnnotation, setTimelinePlaying]); + + useEffect(() => { + const video = videoRef.current; + if (!video) return; + + const delta = Math.abs(video.currentTime - globalSeekTime); + if (!isTimelinePlaying && delta > 0.08) { + video.currentTime = globalSeekTime; + } + + if (isTimelinePlaying) { + void video.play(); + } else { + video.pause(); + } + }, [activeSampleId, globalSeekTime, isTimelinePlaying]); + + if (!activeSampleId) { + return ( + +
    + Select a clip preview in the grid or scatter plot to open its video. +
    +     + ); + } + + return ( + +
    + {!videoFailed ? ( +
    + +
    +
    + aesthetic: {formatScore(annotation?.aesthetic_score ?? null)} + motion: {formatScore(annotation?.motion_score ?? null)} + + dedup: {annotation?.dedup_status ?? "unknown"} + + + sim: {formatScore(annotation?.cosine_sim_score ?? null)} + +
    + + {isLoading ? ( +
    Loading annotations…
    + ) : error ? ( +
    {error}
    + ) : ( + <> +
    + {annotation?.caption ?? annotation?.raw_caption ?? "No caption available."} +
    + {annotation?.reasoning ? ( +
    + Reasoning +
    {annotation.reasoning}
    +
    + ) : null} + + )} +
    +     + ); +} diff --git a/frontend/src/components/index.ts b/frontend/src/components/index.ts index 0cc632b..370b265 100644 --- a/frontend/src/components/index.ts +++ b/frontend/src/components/index.ts @@ -1,11 +1,15 @@ export { ImageGrid } from "./ImageGrid"; +export { VideoGrid } from "./VideoGrid"; export { ScatterPanel } from "./ScatterPanel"; export { DockviewWorkspace } from "./DockviewWorkspace"; export { Header } from "./Header"; +export { GlobalSeekBar } from "./GlobalSeekBar"; export { Panel, PanelFooter } from "./Panel"; export { PanelHeader } from "./PanelHeader"; export { PanelTitle } from "./PanelTitle"; export { PanelContextBar } from "./PanelContextBar"; export { ExplorerPanel } from "./ExplorerPanel"; export { PlaceholderPanel } from "./PlaceholderPanel"; +export { VideoPanel } from "./VideoPanel"; +export { CurationPanel } from "./CurationPanel"; export * from "./icons"; diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts index e340b00..1d420dc 100644 --- a/frontend/src/lib/api.ts +++ b/frontend/src/lib/api.ts @@ -1,6 +1,15 @@ -import type { DatasetInfo, EmbeddingsData, Sample, SamplesResponse } from "@/types"; +import type { + CurationFilterRequest, + CurationFilterResponse, + CurationStats, + DatasetInfo, + EmbeddingsData, + Sample, + SamplesResponse, + VideoAnnotation, +} from "@/types"; -const API_BASE = process.env.NODE_ENV === "development" ? "http://127.0.0.1:6262" : ""; +const API_BASE = process.env.NODE_ENV === "development" ? "http://127.0.0.1:6263" : ""; export async function fetchDataset(): Promise { const res = await fetch(`${API_BASE}/api/dataset`); @@ -101,3 +110,40 @@ export async function fetchLassoSelection(args: { } return res.json(); } + +export function getVideoUrl(sampleId: string): string { + return `${API_BASE}/api/video/${sampleId}`; +} + +export async function fetchAnnotations(sampleId: string): Promise { + const res = await fetch(`${API_BASE}/api/annotations/${sampleId}`); + if (!res.ok) { + throw new Error(`Failed to fetch annotations: ${res.statusText}`); + } + return res.json(); +} + +export async function fetchCurationStats(): Promise { + const res = await fetch(`${API_BASE}/api/curation/stats`); + if (!res.ok) { + throw new Error(`Failed to fetch curation stats: ${res.statusText}`); + } + return res.json(); +} + +export async function postCurationFilter( + request: CurationFilterRequest +): Promise { + const res = await fetch(`${API_BASE}/api/curation/filter`, { + method: "POST", + headers: { + "Content-Type": "application/json", + }, + body: JSON.stringify(request), + }); + + if (!res.ok) { + throw new Error(`Failed to filter curation samples: ${res.statusText}`); + } + return res.json(); +} diff --git a/frontend/src/store/useStore.ts b/frontend/src/store/useStore.ts index c440b10..6285652 100644 --- a/frontend/src/store/useStore.ts +++ b/frontend/src/store/useStore.ts @@ -1,7 +1,31 @@ import { create } from "zustand"; -import type { DatasetInfo, EmbeddingsData, Sample } from "@/types"; +import type { + CurationFilterRequest, + DatasetInfo, + EmbeddingsData, + Sample, + VideoAnnotation, +} from "@/types"; import { normalizeLabel } from "@/lib/labelColors"; +type SelectionSource = "scatter" | "grid" | "lasso" | "label" | "curation" | null; + +export interface CurationFilterState { + minAestheticScore: number; + minMotionScore: number; + maxCosineSimilarity: number; + captionQuery: string; + dedupStatus: "all" | "kept" | "removed" | "unknown"; +} + +const DEFAULT_CURATION_FILTERS: CurationFilterState = { + minAestheticScore: 0, + minMotionScore: 0, + maxCosineSimilarity: 1, + captionQuery: "", + dedupStatus: "all", +}; + function computeLabelSelection(embeddings: EmbeddingsData, label: string): Set { const target = normalizeLabel(label); const ids = new Set(); @@ -14,7 +38,6 @@ function computeLabelSelection(embeddings: EmbeddingsData, label: string): Set void; setBottomPanelOpen: (open: boolean) => void; - // Dataset info datasetInfo: DatasetInfo | null; setDatasetInfo: (info: DatasetInfo) => void; - // Samples samples: Sample[]; totalSamples: number; - // Number of samples loaded via offset/limit pagination (excludes ad-hoc fetched samples) samplesLoaded: number; setSamples: (samples: Sample[], total: number) => void; appendSamples: (samples: Sample[]) => void; addSamplesIfMissing: (samples: Sample[]) => void; - // Embeddings (cached per layout key) embeddingsByLayoutKey: Record; setEmbeddingsForLayout: (layoutKey: string, data: EmbeddingsData) => void; - // Active layout (for sidebar context) activeLayoutKey: string | null; setActiveLayoutKey: (layoutKey: string | null) => void; - // Label filter (sidebar-driven) labelFilter: string | null; setLabelFilter: (label: string | null) => void; - // Selection selectedIds: Set; isLassoSelection: boolean; - selectionSource: "scatter" | "grid" | "lasso" | "label" | null; - setSelectedIds: (ids: Set, source?: "scatter" | "grid" | "label") => void; + selectionSource: SelectionSource; + setSelectedIds: (ids: Set, source?: "scatter" | "grid" | "label" | "curation") => void; toggleSelection: (id: string) => void; addToSelection: (ids: string[]) => void; clearSelection: () => void; - // Lasso selection (server-driven) lassoQuery: { layoutKey: string; polygon: number[] } | null; lassoSamples: Sample[]; lassoTotal: number; @@ -65,21 +80,37 @@ interface AppState { setLassoResults: (samples: Sample[], total: number, append?: boolean) => void; clearLassoSelection: () => void; - // Hover state + activeVideoId: string | null; + setActiveVideoId: (id: string | null) => void; + globalSeekTime: number; + setGlobalSeekTime: (seconds: number) => void; + isTimelinePlaying: boolean; + setTimelinePlaying: (playing: boolean) => void; + videoDurations: Record; + setVideoDuration: (sampleId: string, duration: number) => void; + previewVideoId: string | null; + setPreviewVideoId: (sampleId: string | null) => void; + + curationFilters: CurationFilterState; + setCurationFilters: (patch: Partial) => void; + resetCurationFilters: () => void; + curationQuery: CurationFilterRequest | null; + setCurationQuery: (query: CurationFilterRequest | null) => void; + + annotationCache: Record; + cacheAnnotation: (sampleId: string, annotation: VideoAnnotation) => void; + hoveredId: string | null; setHoveredId: (id: string | null) => void; - // Loading states isLoading: boolean; setIsLoading: (loading: boolean) => void; - // Error state error: string | null; setError: (error: string | null) => void; } -export const useStore = create((set, get) => ({ - // Panel visibility (for header toggles) +export const useStore = create((set) => ({ leftPanelOpen: false, rightPanelOpen: false, bottomPanelOpen: false, @@ -87,11 +118,9 @@ export const useStore = create((set, get) => ({ setRightPanelOpen: (open) => set({ rightPanelOpen: open }), setBottomPanelOpen: (open) => set({ bottomPanelOpen: open }), - // Dataset info datasetInfo: null, setDatasetInfo: (info) => set({ datasetInfo: info }), - // Samples samples: [], totalSamples: 0, samplesLoaded: 0, @@ -100,10 +129,7 @@ export const useStore = create((set, get) => ({ set((state) => { const existingIds = new Set(state.samples.map((s) => s.id)); const toAdd = newSamples.filter((s) => !existingIds.has(s.id)); - - // Advance pagination cursor by what the API returned (even if some IDs were prefetched). const samplesLoaded = state.samplesLoaded + newSamples.length; - if (toAdd.length === 0) return { samplesLoaded }; return { samples: [...state.samples, ...toAdd], samplesLoaded }; }), @@ -115,7 +141,6 @@ export const useStore = create((set, get) => ({ return { samples: [...state.samples, ...toAdd] }; }), - // Embeddings embeddingsByLayoutKey: {}, setEmbeddingsForLayout: (layoutKey, data) => set((state) => { @@ -135,7 +160,6 @@ export const useStore = create((set, get) => ({ }; }), - // Active layout activeLayoutKey: null, setActiveLayoutKey: (layoutKey) => set((state) => { @@ -160,7 +184,6 @@ export const useStore = create((set, get) => ({ }; }), - // Label filter labelFilter: null, setLabelFilter: (label) => set((state) => { @@ -185,19 +208,26 @@ export const useStore = create((set, get) => ({ return nextState; }), - // Selection selectedIds: new Set(), isLassoSelection: false, selectionSource: null, setSelectedIds: (ids, source = "grid") => - set({ - selectedIds: ids, - selectionSource: ids.size > 0 ? source : null, - isLassoSelection: false, - lassoQuery: null, - lassoSamples: [], - lassoTotal: 0, - lassoIsLoading: false, + set((state) => { + const nextActive = + ids.size > 0 && source !== "label" + ? (Array.from(ids)[0] ?? state.activeVideoId) + : state.activeVideoId; + + return { + selectedIds: ids, + selectionSource: ids.size > 0 ? source : null, + isLassoSelection: false, + lassoQuery: null, + lassoSamples: [], + lassoTotal: 0, + lassoIsLoading: false, + activeVideoId: nextActive, + }; }), toggleSelection: (id) => set((state) => { @@ -207,7 +237,6 @@ export const useStore = create((set, get) => ({ } else { newSet.add(id); } - // Manual selection from image grid, not lasso return { selectedIds: newSet, selectionSource: newSet.size > 0 ? "grid" : null, @@ -216,13 +245,16 @@ export const useStore = create((set, get) => ({ lassoSamples: [], lassoTotal: 0, lassoIsLoading: false, + activeVideoId: + newSet.size > 0 + ? (newSet.has(id) ? id : (Array.from(newSet)[0] ?? state.activeVideoId)) + : state.activeVideoId, }; }), addToSelection: (ids) => set((state) => { const newSet = new Set(state.selectedIds); ids.forEach((id) => newSet.add(id)); - // Manual selection from image grid, not lasso return { selectedIds: newSet, selectionSource: newSet.size > 0 ? "grid" : null, @@ -231,6 +263,7 @@ export const useStore = create((set, get) => ({ lassoSamples: [], lassoTotal: 0, lassoIsLoading: false, + activeVideoId: ids[ids.length - 1] ?? state.activeVideoId, }; }), clearSelection: () => @@ -244,7 +277,6 @@ export const useStore = create((set, get) => ({ lassoIsLoading: false, }), - // Lasso selection (server-driven) lassoQuery: null, lassoSamples: [], lassoTotal: 0, @@ -275,15 +307,54 @@ export const useStore = create((set, get) => ({ lassoIsLoading: false, }), - // Hover + activeVideoId: null, + setActiveVideoId: (id) => set({ activeVideoId: id }), + globalSeekTime: 0, + setGlobalSeekTime: (seconds) => set({ globalSeekTime: Math.max(0, seconds) }), + isTimelinePlaying: false, + setTimelinePlaying: (playing) => set({ isTimelinePlaying: playing }), + videoDurations: {}, + setVideoDuration: (sampleId, duration) => + set((state) => ({ + videoDurations: { + ...state.videoDurations, + [sampleId]: Math.max(0, duration), + }, + })), + previewVideoId: null, + setPreviewVideoId: (sampleId) => set({ previewVideoId: sampleId }), + + curationFilters: DEFAULT_CURATION_FILTERS, + setCurationFilters: (patch) => + set((state) => ({ curationFilters: { ...state.curationFilters, ...patch } })), + resetCurationFilters: () => set({ curationFilters: { ...DEFAULT_CURATION_FILTERS }, curationQuery: null }), + curationQuery: null, + setCurationQuery: (query) => + set({ + curationQuery: query, + isLassoSelection: false, + lassoQuery: null, + lassoSamples: [], + lassoTotal: 0, + lassoIsLoading: false, + selectionSource: query ? "curation" : null, + }), + + annotationCache: {}, + cacheAnnotation: (sampleId, annotation) => + set((state) => ({ + annotationCache: { + ...state.annotationCache, + [sampleId]: annotation, + }, + })), + hoveredId: null, setHoveredId: (id) => set({ hoveredId: id }), - // Loading isLoading: false, setIsLoading: (loading) => set({ isLoading: loading }), - // Error error: null, setError: (error) => set({ error }), })); diff --git a/frontend/src/types/index.ts b/frontend/src/types/index.ts index 8efc617..8aeb275 100644 --- a/frontend/src/types/index.ts +++ b/frontend/src/types/index.ts @@ -52,3 +52,65 @@ export interface SamplesResponse { limit: number; samples: Sample[]; } + +export interface VideoAnnotation { + id: string; + caption: string | null; + reasoning: string | null; + raw_caption: string | null; + aesthetic_score: number | null; + motion_score: number | null; + dedup_status: string | null; + dedup_keep: boolean | null; + cosine_sim_score: number | null; + source_video: string | null; + video_path: string | null; + span: [number, number] | number[] | null; +} + +export interface HistogramBin { + start: number; + end: number; + count: number; +} + +export interface ScoreSummary { + count: number; + min: number | null; + max: number | null; + avg: number | null; +} + +export interface CurationStats { + total_samples: number; + with_video: number; + with_caption: number; + dedup_counts: Record; + score_summary: { + aesthetic: ScoreSummary; + motion: ScoreSummary; + }; + aesthetic_histogram: HistogramBin[]; + motion_histogram: HistogramBin[]; +} + +export type DedupStatus = "kept" | "removed" | "unknown"; + +export interface CurationFilterRequest { + min_aesthetic_score?: number; + min_motion_score?: number; + max_cosine_similarity?: number; + caption_query?: string; + dedup_status?: DedupStatus; + offset?: number; + limit?: number; + include_thumbnails?: boolean; +} + +export interface CurationFilterResponse { + total: number; + offset: number; + limit: number; + sample_ids: string[]; + samples: Sample[]; +} diff --git a/scripts/create_cookoff_fixture.py b/scripts/create_cookoff_fixture.py new file mode 100644 index 0000000..815f9d2 --- /dev/null +++ b/scripts/create_cookoff_fixture.py @@ -0,0 +1,171 @@ +"""Create a lightweight Cosmos-Curate-like fixture for local UI development. + +This script builds a synthetic split output using existing MP4 clips so the +frontend/backend can be developed without running full cosmos-curate pipelines. + +Output structure: + / + split_output/ + clips/*.mp4 + metas/v0/*.json + previews/*_0_20.webp + ce1_embd/*.pickle + summary.json + dedup_output/ + kmeans_centroids.npy + +Usage: + uv run python scripts/create_cookoff_fixture.py \ + --source-clips-path /path/to/mp4s \ + --output-root /tmp/cookoff_fixture \ + --num-clips 40 +""" + +from __future__ import annotations + +import argparse +import json +import pickle +import random +import shutil +import uuid +from pathlib import Path + +import numpy as np +from PIL import Image, ImageDraw + + +CAPTION_TEMPLATES = [ + ( + "The ego vehicle approaches an intersection with moderate traffic. " + "A lead vehicle slows down near a traffic signal while pedestrians remain on the sidewalk." + ), + ( + "The car continues through an urban road segment with lane markers clearly visible. " + "Oncoming traffic is sparse and no sudden hazards are observed." + ), + ( + "The vehicle moves past parked cars and approaches a crosswalk. " + "A cyclist appears on the right edge of the scene and the ego car reduces speed." + ), + ( + "The camera captures a highway merge with multiple vehicles changing lanes. " + "The ego vehicle keeps steady distance and follows lane guidance." + ), +] + + +def _normalize_dir(path_str: str, *, create: bool = False) -> Path: + path = Path(path_str).expanduser().resolve() + if create: + path.mkdir(parents=True, exist_ok=True) + elif not path.exists(): + raise FileNotFoundError(f"Path does not exist: {path}") + return path + + +def _make_preview(path: Path, *, index: int, clip_id: str) -> None: + width, height = 640, 360 + img = Image.new("RGB", (width, height), color=(20 + (index * 17) % 120, 40, 70 + (index * 11) % 120)) + draw = ImageDraw.Draw(img) + draw.rectangle((16, 16, width - 16, height - 16), outline=(255, 255, 255), width=2) + draw.text((28, 28), f"Cookoff Fixture Clip {index + 1}", fill=(255, 255, 255)) + draw.text((28, 60), clip_id[:18], fill=(220, 220, 220)) + path.parent.mkdir(parents=True, exist_ok=True) + img.save(path, "WEBP", quality=92) + + +def _make_caption(index: int) -> str: + base = CAPTION_TEMPLATES[index % len(CAPTION_TEMPLATES)] + return ( + f"{base} The model reasons over traffic flow, relative motion, and road context.\n" + f"{base}" + ) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Create synthetic Cosmos-Curate fixture") + parser.add_argument("--source-clips-path", required=True, help="Directory containing source MP4 clips") + parser.add_argument("--output-root", default="/tmp/cookoff_fixture", help="Fixture output root") + parser.add_argument("--num-clips", type=int, default=40, help="Number of clips to include") + parser.add_argument( + "--embedding-dim", + type=int, + default=256, + help="Embedding vector dimension (default matches cosmos-embed1-224p)", + ) + parser.add_argument("--seed", type=int, default=42, help="Random seed") + args = parser.parse_args() + + rng = np.random.default_rng(args.seed) + random.seed(args.seed) + + source_dir = _normalize_dir(args.source_clips_path) + output_root = _normalize_dir(args.output_root, create=True) + + split_output = _normalize_dir(str(output_root / "split_output"), create=True) + dedup_output = _normalize_dir(str(output_root / "dedup_output"), create=True) + + clips_dir = _normalize_dir(str(split_output / "clips"), create=True) + metas_dir = _normalize_dir(str(split_output / "metas" / "v0"), create=True) + previews_dir = _normalize_dir(str(split_output / "previews"), create=True) + embd_dir = _normalize_dir(str(split_output / "ce1_embd"), create=True) + + source_clips = sorted(source_dir.glob("*.mp4")) + if not source_clips: + raise RuntimeError(f"No .mp4 files found under {source_dir}") + + selected = source_clips[: max(1, min(args.num_clips, len(source_clips)))] + + for index, src_clip in enumerate(selected): + clip_id = str(uuid.uuid4()) + dst_clip = clips_dir / f"{clip_id}.mp4" + shutil.copy2(src_clip, dst_clip) + + preview_path = previews_dir / f"{clip_id}_0_20.webp" + _make_preview(preview_path, index=index, clip_id=clip_id) + + caption = _make_caption(index) + meta = { + "clip_uuid": clip_id, + "source_video": str(src_clip), + "clip_location": str(dst_clip), + "span": [0.0, 2.0], + "dimensions": [1600, 900], + "framerate": 10.0, + "num_frames": 20, + "aesthetic_score": round(random.uniform(4.8, 9.6), 2), + "motion_score": round(random.uniform(3.1, 9.8), 2), + "captions": {"cosmos_r2": caption}, + } + (metas_dir / f"{clip_id}.json").write_text(json.dumps(meta, indent=2), encoding="utf-8") + + vec = rng.normal(loc=0.0, scale=1.0, size=(args.embedding_dim,)).astype(np.float32) + with (embd_dir / f"{clip_id}.pickle").open("wb") as f: + pickle.dump(vec, f) + + summary = { + "total_videos_processed": len(selected), + "total_clips_generated": len(selected), + "pipeline_duration_seconds": round(2.5 + len(selected) * 0.05, 2), + "models_used": ["transnetv2", "cosmos_r2", "cosmos_embed1_224p"], + "embedding_algorithm": "cosmos-embed1-224p", + "total_num_clips_passed": len(selected), + "total_num_clips_with_caption": len(selected), + "total_num_clips_with_embeddings": len(selected), + "num_input_videos": len(selected), + "num_processed_videos": len(selected), + "pipeline_run_time": round(0.2 + len(selected) * 0.02, 2), + "total_video_duration": float(len(selected) * 2), + "total_clip_duration": float(len(selected) * 2), + } + (split_output / "summary.json").write_text(json.dumps(summary, indent=2), encoding="utf-8") + + np.save(dedup_output / "kmeans_centroids.npy", rng.normal(size=(4, args.embedding_dim)).astype(np.float32)) + + print(f"Created fixture at: {output_root}") + print(f"Clips: {len(selected)}") + + +if __name__ == "__main__": + main() diff --git a/scripts/curate_submission_metrics.py b/scripts/curate_submission_metrics.py new file mode 100644 index 0000000..c87a6a3 --- /dev/null +++ b/scripts/curate_submission_metrics.py @@ -0,0 +1,262 @@ +"""Compute submission KPIs from Cosmos-Curate outputs. + +This script aggregates split + dedup artifacts into metrics used in the hackathon +submission and demo narrative. +""" + +from __future__ import annotations + +import argparse +import csv +import json +from pathlib import Path +from typing import Any + +import pyarrow.parquet as pq + + +def _normalize_path(path_str: str) -> Path: + path = Path(path_str).expanduser().resolve() + if not path.exists(): + raise FileNotFoundError(f"Path does not exist: {path}") + return path + + +def _read_json(path: Path) -> dict[str, Any]: + with path.open("r", encoding="utf-8") as f: + return json.load(f) + + +def _dedup_summary_path(dedup_output_path: Path, dedup_eps: float) -> Path: + tag = f"{dedup_eps:.6g}".rstrip("0").rstrip(".") + return dedup_output_path / "extraction" / f"dedup_summary_{tag}.csv" + + +def _safe_div(numerator: float, denominator: float) -> float: + if denominator <= 0: + return 0.0 + return numerator / denominator + + +def _load_split_metrics(split_output_path: Path) -> dict[str, Any]: + summary_path = split_output_path / "summary.json" + if not summary_path.exists(): + raise FileNotFoundError(f"Missing split summary file: {summary_path}") + + summary = _read_json(summary_path) + clips_passed = int(summary.get("total_num_clips_passed", 0)) + clips_with_caption = int(summary.get("total_num_clips_with_caption", 0)) + clips_with_embeddings = int(summary.get("total_num_clips_with_embeddings", 0)) + clips_transcoded = int(summary.get("total_num_clips_transcoded", 0)) + + return { + "embedding_algorithm": summary.get("embedding_algorithm"), + "num_input_videos": int(summary.get("num_input_videos", 0)), + "num_processed_videos": int(summary.get("num_processed_videos", 0)), + "pipeline_run_time_min": float(summary.get("pipeline_run_time", 0.0)), + "total_video_duration_sec": float(summary.get("total_video_duration", 0.0)), + "total_clip_duration_sec": float(summary.get("total_clip_duration", 0.0)), + "clips_passed": clips_passed, + "clips_transcoded": clips_transcoded, + "clips_with_caption": clips_with_caption, + "clips_with_embeddings": clips_with_embeddings, + "caption_coverage_pct": _safe_div(clips_with_caption, clips_passed) * 100.0, + "embedding_coverage_pct": _safe_div(clips_with_embeddings, clips_passed) * 100.0, + } + + +def _load_dedup_metrics(dedup_output_path: Path, dedup_eps: float) -> dict[str, Any]: + summary_path = _dedup_summary_path(dedup_output_path, dedup_eps) + + if summary_path.exists(): + with summary_path.open("r", encoding="utf-8") as f: + reader = csv.DictReader(f) + rows = list(reader) + if rows: + row = rows[0] + kept = int(row.get("kept", 0)) + removed = int(row.get("removed", 0)) + total = int(row.get("total", 0)) + reduction_pct = _safe_div(removed, total) * 100.0 + return { + "eps": float(row.get("eps", dedup_eps)), + "kept": kept, + "removed": removed, + "total": total, + "reduction_pct": reduction_pct, + } + + threshold = 1.0 - dedup_eps + pruning_dir = dedup_output_path / "extraction" / "semdedup_pruning_tables" + if not pruning_dir.exists(): + return { + "eps": dedup_eps, + "kept": 0, + "removed": 0, + "total": 0, + "reduction_pct": 0.0, + } + + kept = 0 + total = 0 + for parquet_path in sorted(pruning_dir.glob("cluster_*.parquet")): + table = pq.read_table(parquet_path, columns=["cosine_sim_score"]) + scores = table.column("cosine_sim_score").to_pylist() + for score in scores: + if score is None: + continue + total += 1 + if float(score) <= threshold: + kept += 1 + + removed = max(total - kept, 0) + return { + "eps": dedup_eps, + "kept": kept, + "removed": removed, + "total": total, + "reduction_pct": _safe_div(removed, total) * 100.0, + } + + +def _top_duplicate_pairs( + dedup_output_path: Path, + *, + dedup_eps: float, + top_k: int, +) -> list[dict[str, Any]]: + pruning_dir = dedup_output_path / "extraction" / "semdedup_pruning_tables" + if not pruning_dir.exists() or top_k <= 0: + return [] + + threshold = 1.0 - dedup_eps + rows: list[dict[str, Any]] = [] + + for parquet_path in sorted(pruning_dir.glob("cluster_*.parquet")): + table = pq.read_table(parquet_path, columns=["id", "max_id", "cosine_sim_score"]) + ids = table.column("id").to_pylist() + max_ids = table.column("max_id").to_pylist() + scores = table.column("cosine_sim_score").to_pylist() + + for clip_id, max_id, score in zip(ids, max_ids, scores): + if clip_id is None or max_id is None or score is None: + continue + score_f = float(score) + if score_f <= threshold: + continue + rows.append( + { + "id": str(clip_id), + "max_id": str(max_id), + "cosine_sim_score": score_f, + "cluster_file": parquet_path.name, + } + ) + + rows.sort(key=lambda x: x["cosine_sim_score"], reverse=True) + return rows[:top_k] + + +def _to_markdown(metrics: dict[str, Any]) -> str: + split_metrics = metrics["split"] + dedup_metrics = metrics.get("dedup") + + lines = [ + "# Cosmos-Curate Submission Metrics", + "", + "## Split Pipeline", + "", + f"- Input videos: **{split_metrics['num_input_videos']}**", + f"- Processed videos: **{split_metrics['num_processed_videos']}**", + f"- Clips passed: **{split_metrics['clips_passed']}**", + f"- Caption coverage: **{split_metrics['caption_coverage_pct']:.2f}%**", + f"- Embedding coverage: **{split_metrics['embedding_coverage_pct']:.2f}%**", + f"- Embedding algorithm: **{split_metrics['embedding_algorithm']}**", + f"- Pipeline runtime: **{split_metrics['pipeline_run_time_min']:.2f} min**", + ] + + if dedup_metrics is not None: + lines += [ + "", + "## Semantic Dedup", + "", + f"- Epsilon: **{dedup_metrics['eps']}**", + f"- Total clips considered: **{dedup_metrics['total']}**", + f"- Kept clips: **{dedup_metrics['kept']}**", + f"- Removed duplicates: **{dedup_metrics['removed']}**", + f"- Reduction: **{dedup_metrics['reduction_pct']:.2f}%**", + ] + + top_pairs = metrics.get("top_duplicate_pairs", []) + if top_pairs: + lines += [ + "", + "## Top Duplicate Pairs", + "", + "| id | max_id | cosine_sim_score | cluster_file |", + "|---|---|---:|---|", + ] + for row in top_pairs: + lines.append( + f"| {row['id']} | {row['max_id']} | {row['cosine_sim_score']:.6f} | {row['cluster_file']} |" + ) + + return "\n".join(lines) + "\n" + + +def main() -> None: + parser = argparse.ArgumentParser(description="Compute hackathon KPIs from Cosmos-Curate outputs") + parser.add_argument("--split-output-path", required=True, help="Path to Cosmos-Curate split output") + parser.add_argument("--dedup-output-path", default=None, help="Path to Cosmos-Curate dedup output") + parser.add_argument("--dedup-eps", type=float, default=0.01, help="Epsilon used in dedup") + parser.add_argument("--top-duplicates", type=int, default=20, help="Top duplicate pairs to include") + parser.add_argument( + "--output-json", + default=None, + help="Output JSON path (default: /submission_metrics.json)", + ) + parser.add_argument( + "--output-markdown", + default=None, + help="Output markdown path (default: /submission_metrics.md)", + ) + args = parser.parse_args() + + split_output_path = _normalize_path(args.split_output_path) + dedup_output_path = _normalize_path(args.dedup_output_path) if args.dedup_output_path else None + + output_json = ( + Path(args.output_json).expanduser().resolve() + if args.output_json + else split_output_path / "submission_metrics.json" + ) + output_markdown = ( + Path(args.output_markdown).expanduser().resolve() + if args.output_markdown + else split_output_path / "submission_metrics.md" + ) + + metrics: dict[str, Any] = { + "split": _load_split_metrics(split_output_path), + } + + if dedup_output_path is not None: + metrics["dedup"] = _load_dedup_metrics(dedup_output_path, args.dedup_eps) + metrics["top_duplicate_pairs"] = _top_duplicate_pairs( + dedup_output_path, + dedup_eps=args.dedup_eps, + top_k=args.top_duplicates, + ) + + output_json.parent.mkdir(parents=True, exist_ok=True) + output_json.write_text(json.dumps(metrics, indent=2, sort_keys=True) + "\n", encoding="utf-8") + + output_markdown.parent.mkdir(parents=True, exist_ok=True) + output_markdown.write_text(_to_markdown(metrics), encoding="utf-8") + + print(f"Wrote metrics JSON: {output_json}") + print(f"Wrote metrics markdown: {output_markdown}") + + +if __name__ == "__main__": + main() diff --git a/scripts/demo.py b/scripts/demo.py index c703922..dbb821c 100644 --- a/scripts/demo.py +++ b/scripts/demo.py @@ -22,7 +22,7 @@ def main(): "--samples", type=int, default=50000, help="Number of samples to load (default: 50000)" ) parser.add_argument( - "--port", type=int, default=6262, help="Port to run server on (default: 6262)" + "--port", type=int, default=6263, help="Port to run server on (default: 6263)" ) parser.add_argument( "--no-browser", action="store_true", help="Don't open browser automatically" diff --git a/scripts/demo_hyperbolic_clip.py b/scripts/demo_hyperbolic_clip.py index 1da0aaa..7e781eb 100644 --- a/scripts/demo_hyperbolic_clip.py +++ b/scripts/demo_hyperbolic_clip.py @@ -31,9 +31,9 @@ def main() -> None: hyper_space = dataset.compute_embeddings(model=HYPER_MODELS_MODEL_ID) dataset.compute_visualization(space_key=hyper_space, geometry="poincare") - print("Launching at http://127.0.0.1:6262") + print("Launching at http://127.0.0.1:6263") - hv.launch(dataset, open_browser=True) + hv.launch(dataset, open_browser=True, port=6263) if __name__ == "__main__": diff --git a/scripts/load_cosmos_curate.py b/scripts/load_cosmos_curate.py new file mode 100644 index 0000000..e69d7cd --- /dev/null +++ b/scripts/load_cosmos_curate.py @@ -0,0 +1,725 @@ +"""Load Cosmos-Curate outputs into HyperView for hackathon demos. + +This script builds a HyperView dataset directly from Cosmos-Curate artifacts: +- split output metadata (`metas/v0/*.json` or `metas_jsonl/v0/*.jsonl`) +- split output embeddings parquet (`*_embd_parquet/*.parquet`) +- optional dedup output (`extraction/semdedup_pruning_tables/*.parquet`) + +It then: +1) adds clip samples with preview thumbnails, +2) imports precomputed embeddings as a HyperView space, +3) computes Euclidean + Poincaré 2D layouts, +4) launches HyperView. +""" + +from __future__ import annotations + +import argparse +import base64 +import csv +import io +import json +import pickle +import sys +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +import numpy as np +import pyarrow.parquet as pq +from PIL import Image + +sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "src")) + +import hyperview as hv +from hyperview.core.sample import Sample +from hyperview.storage.schema import make_layout_key + + +@dataclass(frozen=True) +class ClipRecord: + clip_id: str + filepath: str + label: str | None + metadata: dict[str, Any] + thumbnail_base64: str | None = None + + +def _normalize_embedding_algorithm(raw_name: str | None) -> str: + name = (raw_name or "").strip().lower().replace("_", "-") + aliases = { + "internvideo2-mm": "internvideo2", + "iv2": "internvideo2", + "cosmos-embed1": "cosmos-embed1-224p", + } + return aliases.get(name, name) + + +def _infer_embedding_algorithm(split_output_path: Path, split_summary: dict[str, Any]) -> str: + summary_value = _normalize_embedding_algorithm(split_summary.get("embedding_algorithm")) + if summary_value: + return summary_value + + for model_name in split_summary.get("models_used", []) or []: + model_norm = _normalize_embedding_algorithm(str(model_name)) + if "cosmos-embed1" in model_norm: + if model_norm == "cosmos-embed1": + return "cosmos-embed1-224p" + return model_norm + if "internvideo2" in model_norm: + return "internvideo2" + + if (split_output_path / "ce1_embd_parquet").exists() or (split_output_path / "ce1_embd").exists(): + return "cosmos-embed1-224p" + if (split_output_path / "iv2_embd_parquet").exists() or (split_output_path / "iv2_embd").exists(): + return "internvideo2" + + raise ValueError( + "Could not infer embedding algorithm from split summary or directory structure. " + "Expected summary.embedding_algorithm/models_used or one of ce1_embd[_parquet]/iv2_embd[_parquet]." + ) + + +def _normalize_path(path_str: str) -> Path: + path = Path(path_str).expanduser().resolve() + if not path.exists(): + raise FileNotFoundError(f"Path does not exist: {path}") + return path + + +def _read_json(path: Path) -> dict[str, Any]: + with path.open("r", encoding="utf-8") as f: + return json.load(f) + + +def _embedding_parquet_dir(split_output_path: Path, embedding_algorithm: str) -> Path: + algo = _normalize_embedding_algorithm(embedding_algorithm) + if algo == "internvideo2": + return split_output_path / "iv2_embd_parquet" + if algo.startswith("cosmos-embed1"): + return split_output_path / "ce1_embd_parquet" + return split_output_path / f"{algo}_embd_parquet" + + +def _embedding_pickle_dir(split_output_path: Path, embedding_algorithm: str) -> Path: + algo = _normalize_embedding_algorithm(embedding_algorithm) + if algo == "internvideo2": + return split_output_path / "iv2_embd" + if algo.startswith("cosmos-embed1"): + return split_output_path / "ce1_embd" + return split_output_path / f"{algo}_embd" + + +def _embedding_model_id(embedding_algorithm: str) -> str: + algo = _normalize_embedding_algorithm(embedding_algorithm) + mapping = { + "cosmos-embed1-224p": "nvidia/Cosmos-Embed1-224p", + "cosmos-embed1-336p": "nvidia/Cosmos-Embed1-336p", + "cosmos-embed1-448p": "nvidia/Cosmos-Embed1-448p", + "internvideo2": "OpenGVLab/InternVideo2-Stage2_1B-224p-f4", + } + return mapping.get(algo, embedding_algorithm) + + +def _expected_embedding_dim(embedding_algorithm: str) -> int | None: + algo = _normalize_embedding_algorithm(embedding_algorithm) + expected = { + "cosmos-embed1-224p": 256, + "cosmos-embed1-336p": 768, + "cosmos-embed1-448p": 768, + } + return expected.get(algo) + + +def _dedup_summary_path(dedup_output_path: Path, dedup_eps: float) -> Path: + tag = f"{dedup_eps:.6g}".rstrip("0").rstrip(".") + return dedup_output_path / "extraction" / f"dedup_summary_{tag}.csv" + + +def _load_dedup_maps(dedup_output_path: Path, dedup_eps: float) -> tuple[dict[str, bool], dict[str, float]]: + keep_map: dict[str, bool] = {} + score_map: dict[str, float] = {} + pruning_dir = dedup_output_path / "extraction" / "semdedup_pruning_tables" + if not pruning_dir.exists(): + return keep_map, score_map + + threshold = 1.0 - dedup_eps + for parquet_path in sorted(pruning_dir.glob("cluster_*.parquet")): + table = pq.read_table(parquet_path, columns=["id", "cosine_sim_score"]) + ids = table.column("id").to_pylist() + scores = table.column("cosine_sim_score").to_pylist() + for clip_id, score in zip(ids, scores): + if clip_id is None or score is None: + continue + clip_id_str = str(clip_id) + score_f = float(score) + keep_map[clip_id_str] = score_f <= threshold + score_map[clip_id_str] = score_f + return keep_map, score_map + + +def _select_caption(window: dict[str, Any], caption_field: str | None) -> str | None: + if caption_field and caption_field in window: + value = window.get(caption_field) + if isinstance(value, str) and value.strip(): + return value.strip() + + for key, value in window.items(): + if key.endswith("_caption") and isinstance(value, str) and value.strip(): + return value.strip() + return None + + +def _select_caption_from_row(row: dict[str, Any], caption_field: str | None) -> str | None: + windows = row.get("windows") or [] + first_window = windows[0] if isinstance(windows, list) and windows else {} + if isinstance(first_window, dict): + caption = _select_caption(first_window, caption_field) + if caption: + return caption + + captions_obj = row.get("captions") + if isinstance(captions_obj, dict): + preferred_keys = [caption_field] if caption_field else [] + preferred_keys += ["cosmos_r2", "cosmos_r2_caption", "caption", "default"] + + for key in preferred_keys: + if not key: + continue + value = captions_obj.get(key) + if isinstance(value, str) and value.strip(): + return value.strip() + + for value in captions_obj.values(): + if isinstance(value, str) and value.strip(): + return value.strip() + + for key in ("caption", "first_caption", "cosmos_r2_caption"): + value = row.get(key) + if isinstance(value, str) and value.strip(): + return value.strip() + + return None + + +def _extract_reasoning_and_answer(caption_text: str | None) -> tuple[str | None, str | None]: + if not caption_text: + return None, None + + reasoning: str | None = None + answer: str | None = None + + think_start = caption_text.find("") + think_end = caption_text.find("") + if think_start != -1 and think_end != -1 and think_end > think_start: + reasoning = caption_text[think_start + len("") : think_end].strip() or None + + answer_start = caption_text.find("") + answer_end = caption_text.find("") + if answer_start != -1 and answer_end != -1 and answer_end > answer_start: + answer = caption_text[answer_start + len("") : answer_end].strip() or None + + if answer is None: + answer = caption_text.strip() or None + + return reasoning, answer + + +def _default_label( + *, + label_mode: str, + source_video: str, + keep_flag: bool | None, +) -> str: + if label_mode == "dedup-status": + if keep_flag is True: + return "kept" + if keep_flag is False: + return "removed" + return "unknown" + return Path(source_video).stem or "unknown" + + +def _iter_clip_metadata_rows(split_output_path: Path) -> list[dict[str, Any]]: + metas_dir = split_output_path / "metas" / "v0" + metas_jsonl_dir = split_output_path / "metas_jsonl" / "v0" + + rows: list[dict[str, Any]] = [] + if metas_dir.exists(): + for json_path in sorted(metas_dir.glob("*.json")): + rows.append(_read_json(json_path)) + return rows + + if metas_jsonl_dir.exists(): + for jsonl_path in sorted(metas_jsonl_dir.glob("*.jsonl")): + with jsonl_path.open("r", encoding="utf-8") as f: + for line in f: + line = line.strip() + if line: + rows.append(json.loads(line)) + return rows + + raise FileNotFoundError( + "No clip metadata found. Expected one of: " + f"{metas_dir} or {metas_jsonl_dir}." + ) + + +def _placeholder_image(split_output_path: Path) -> Path: + placeholder_path = split_output_path / "_hyperview_placeholder.jpg" + if not placeholder_path.exists(): + placeholder_path.parent.mkdir(parents=True, exist_ok=True) + Image.new("RGB", (96, 96), color=(35, 35, 35)).save(placeholder_path, "JPEG", quality=92) + return placeholder_path + + +def _thumbnail_base64_from_image(path: Path, fallback: Path) -> str | None: + for candidate in (path, fallback): + try: + with Image.open(candidate) as img: + if img.mode in ("RGBA", "P"): + img = img.convert("RGB") + buffer = io.BytesIO() + img.save(buffer, format="JPEG", quality=85) + return base64.b64encode(buffer.getvalue()).decode("utf-8") + except Exception: + continue + return None + + +def _build_clip_records( + *, + split_output_path: Path, + metadata_rows: list[dict[str, Any]], + dedup_keep_map: dict[str, bool], + dedup_score_map: dict[str, float], + caption_field: str | None, + label_mode: str, + max_samples: int, + allow_placeholder: bool, +) -> list[ClipRecord]: + records: list[ClipRecord] = [] + placeholder_path = _placeholder_image(split_output_path) + previews_dir = split_output_path / "previews" + + for row in metadata_rows: + clip_id = str(row.get("span_uuid") or row.get("clip_uuid") or row.get("id") or "").strip() + if not clip_id: + continue + + windows = row.get("windows") or [] + first_window = windows[0] if windows else {} + start_frame = first_window.get("start_frame") if isinstance(first_window, dict) else None + end_frame = first_window.get("end_frame") if isinstance(first_window, dict) else None + if start_frame is None or end_frame is None: + num_frames = row.get("num_frames") + if isinstance(num_frames, int) and num_frames > 0: + start_frame = 0 + end_frame = num_frames + + preview_path: Path | None = None + if start_frame is not None and end_frame is not None: + nested_candidate = previews_dir / clip_id / f"{start_frame}_{end_frame}.webp" + flat_candidate = previews_dir / f"{clip_id}_{start_frame}_{end_frame}.webp" + if nested_candidate.exists(): + preview_path = nested_candidate + elif flat_candidate.exists(): + preview_path = flat_candidate + + if preview_path is None: + flat_matches = sorted(previews_dir.glob(f"{clip_id}_*.webp")) + if flat_matches: + preview_path = flat_matches[0] + + if preview_path is None: + nested_matches = sorted((previews_dir / clip_id).glob("*.webp")) if (previews_dir / clip_id).exists() else [] + if nested_matches: + preview_path = nested_matches[0] + + if preview_path is None and allow_placeholder: + preview_path = placeholder_path + if preview_path is None: + continue + + source_video = str(row.get("source_video", "")) + clip_location = row.get("clip_location") + video_path = str(clip_location).strip() if clip_location else "" + if not video_path: + default_clip = split_output_path / "clips" / f"{clip_id}.mp4" + if default_clip.exists(): + video_path = str(default_clip) + + keep_flag = dedup_keep_map.get(clip_id) + cosine_sim_score = dedup_score_map.get(clip_id) + caption = _select_caption_from_row(row, caption_field) + caption_reasoning, caption_answer = _extract_reasoning_and_answer(caption) + + span = row.get("duration_span") + if span is None: + span = row.get("span") + + metadata = { + "clip_id": clip_id, + "source_video": source_video, + "clip_location": clip_location, + "video_path": video_path or None, + "duration_span": span, + "span": row.get("span"), + "window_count": len(windows), + "first_caption": caption, + "caption_raw": caption, + "caption_answer": caption_answer, + "caption_reasoning": caption_reasoning, + "has_caption": bool(row.get("has_caption", False) or caption), + "aesthetic_score": row.get("aesthetic_score"), + "motion_score": row.get("motion_score"), + "dimensions": row.get("dimensions"), + "framerate": row.get("framerate"), + "num_frames": row.get("num_frames"), + "dedup_keep": keep_flag, + "dedup_status": "kept" if keep_flag is True else ("removed" if keep_flag is False else "unknown"), + "cosine_sim_score": cosine_sim_score, + "dedup_cosine_similarity": cosine_sim_score, + "preview_path": str(preview_path), + "preview_is_placeholder": preview_path == placeholder_path, + } + + label = _default_label(label_mode=label_mode, source_video=source_video, keep_flag=keep_flag) + sample_filepath = video_path or str(preview_path) + + records.append( + ClipRecord( + clip_id=clip_id, + filepath=sample_filepath, + label=label, + metadata=metadata, + thumbnail_base64=_thumbnail_base64_from_image(preview_path, placeholder_path), + ) + ) + + if max_samples > 0 and len(records) >= max_samples: + break + + return records + + +def _extract_dimensions(metadata: dict[str, Any]) -> tuple[int | None, int | None]: + dims = metadata.get("dimensions") + if not isinstance(dims, (list, tuple)) or len(dims) < 2: + return None, None + + try: + width = int(dims[0]) + height = int(dims[1]) + except (TypeError, ValueError): + return None, None + + if width <= 0 or height <= 0: + return None, None + + return width, height + + +def _load_embeddings( + split_output_path: Path, + embedding_algorithm: str, + *, + clip_ids: set[str], +) -> tuple[list[str], np.ndarray]: + algo = _normalize_embedding_algorithm(embedding_algorithm) + parquet_dir = _embedding_parquet_dir(split_output_path, algo) + pickle_dir = _embedding_pickle_dir(split_output_path, algo) + + vectors_by_id: dict[str, np.ndarray] = {} + + def _store_vector(clip_id: str, payload: Any) -> None: + if clip_id in vectors_by_id: + return + if clip_id not in clip_ids: + return + + vec: np.ndarray | None = None + if isinstance(payload, np.ndarray): + vec = payload.astype(np.float32) + elif isinstance(payload, (list, tuple)): + vec = np.asarray(payload, dtype=np.float32) + elif isinstance(payload, dict): + for key in ("embedding", "vector", "emb", "embd"): + if key in payload: + vec = np.asarray(payload[key], dtype=np.float32) + break + + if vec is None or vec.ndim != 1 or vec.size == 0 or np.any(~np.isfinite(vec)): + return + vectors_by_id[clip_id] = vec + + if parquet_dir.exists(): + for parquet_path in sorted(parquet_dir.glob("*.parquet")): + table = pq.read_table(parquet_path) + cols = {name.lower(): name for name in table.column_names} + + id_col_name = next((cols[c] for c in ("id", "clip_uuid", "span_uuid", "uuid") if c in cols), None) + vec_col_name = next( + (cols[c] for c in ("embedding", "vector", "embeddings", "embd") if c in cols), + None, + ) + + if id_col_name is None or vec_col_name is None: + continue + + id_values = table.column(id_col_name).to_pylist() + emb_values = table.column(vec_col_name).to_pylist() + + for clip_id_raw, emb in zip(id_values, emb_values): + if clip_id_raw is None or emb is None: + continue + _store_vector(str(clip_id_raw), emb) + + if (not vectors_by_id) and pickle_dir.exists(): + for pickle_path in sorted(pickle_dir.glob("*.pickle")): + clip_id = pickle_path.stem + if clip_id not in clip_ids: + continue + try: + with pickle_path.open("rb") as f: + payload = pickle.load(f) + except Exception: + continue + _store_vector(clip_id, payload) + + if not vectors_by_id: + return [], np.empty((0, 0), dtype=np.float32) + + ids = list(vectors_by_id.keys()) + vectors = list(vectors_by_id.values()) + + if not vectors: + return [], np.empty((0, 0), dtype=np.float32) + + dim = vectors[0].shape[0] + filtered_ids: list[str] = [] + filtered_vectors: list[np.ndarray] = [] + for clip_id, vec in zip(ids, vectors): + if vec.shape[0] == dim: + filtered_ids.append(clip_id) + filtered_vectors.append(vec) + + if not filtered_vectors: + return [], np.empty((0, 0), dtype=np.float32) + + return filtered_ids, np.vstack(filtered_vectors).astype(np.float32) + + +def _dedup_summary_metrics(dedup_output_path: Path, dedup_eps: float) -> dict[str, Any] | None: + summary_path = _dedup_summary_path(dedup_output_path, dedup_eps) + if not summary_path.exists(): + return None + + with summary_path.open("r", encoding="utf-8") as f: + reader = csv.DictReader(f) + rows = list(reader) + if not rows: + return None + + row = rows[0] + total = int(row.get("total", 0)) + kept = int(row.get("kept", 0)) + removed = int(row.get("removed", 0)) + reduction_pct = (removed / total * 100.0) if total > 0 else 0.0 + + return { + "eps": float(row.get("eps", dedup_eps)), + "kept": kept, + "removed": removed, + "total": total, + "reduction_pct": reduction_pct, + } + + +def _manual_small_layout_coords(num_points: int, *, poincare: bool) -> np.ndarray: + if num_points <= 0: + return np.empty((0, 2), dtype=np.float32) + if num_points == 1: + coords = np.array([[0.0, 0.0]], dtype=np.float32) + elif num_points == 2: + coords = np.array([[-0.35, 0.0], [0.35, 0.0]], dtype=np.float32) + else: + angles = np.linspace(0, 2 * np.pi, num_points, endpoint=False, dtype=np.float32) + radius = 0.6 if poincare else 1.0 + coords = np.stack([radius * np.cos(angles), radius * np.sin(angles)], axis=1).astype(np.float32) + + if poincare: + norms = np.linalg.norm(coords, axis=1, keepdims=True) + mask = norms >= 0.98 + if np.any(mask): + scale = (0.98 / np.maximum(norms[mask], 1e-9)).reshape(-1, 1) + coords[mask[:, 0]] *= scale + return coords + + +def _create_small_layouts(dataset: hv.Dataset, *, space_key: str, ids: list[str]) -> None: + euclidean_key = make_layout_key(space_key, method="manual", geometry="euclidean") + poincare_key = make_layout_key(space_key, method="manual", geometry="poincare") + + dataset._storage.ensure_layout( + layout_key=euclidean_key, + space_key=space_key, + method="manual", + geometry="euclidean", + params={"reason": "too_few_samples_for_umap"}, + ) + dataset._storage.ensure_layout( + layout_key=poincare_key, + space_key=space_key, + method="manual", + geometry="poincare", + params={"reason": "too_few_samples_for_umap"}, + ) + + dataset._storage.add_layout_coords(euclidean_key, ids, _manual_small_layout_coords(len(ids), poincare=False)) + dataset._storage.add_layout_coords(poincare_key, ids, _manual_small_layout_coords(len(ids), poincare=True)) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Load Cosmos-Curate outputs into HyperView") + parser.add_argument("--split-output-path", required=True, help="Path to Cosmos-Curate split output directory") + parser.add_argument("--dedup-output-path", default=None, help="Path to Cosmos-Curate dedup output directory") + parser.add_argument("--dedup-eps", type=float, default=0.01, help="Epsilon used for semantic dedup") + parser.add_argument("--caption-field", default="cosmos_r2_caption", help="Preferred caption field in windows") + parser.add_argument( + "--label-mode", + choices=["source-video", "dedup-status"], + default="dedup-status", + help="How labels are assigned in HyperView", + ) + parser.add_argument("--dataset-name", default="cosmos_curate_submission", help="HyperView dataset name") + parser.add_argument( + "--persist", + action=argparse.BooleanOptionalAction, + default=True, + help="Persist dataset in LanceDB (--persist) or keep in-memory (--no-persist)", + ) + parser.add_argument("--max-samples", type=int, default=0, help="Max clips to load (0 = all)") + parser.add_argument( + "--allow-placeholder", + action=argparse.BooleanOptionalAction, + default=True, + help="Use a generated placeholder image when preview webp is missing", + ) + parser.add_argument("--port", type=int, default=6263, help="Port for HyperView") + parser.add_argument("--no-browser", action="store_true", help="Do not auto-open browser") + parser.add_argument("--reuse-server", action="store_true", help="Reuse compatible running HyperView server") + args = parser.parse_args() + + split_output_path = _normalize_path(args.split_output_path) + dedup_output_path = _normalize_path(args.dedup_output_path) if args.dedup_output_path else None + + summary_path = split_output_path / "summary.json" + if not summary_path.exists(): + raise FileNotFoundError(f"Missing split summary file: {summary_path}") + split_summary = _read_json(summary_path) + embedding_algorithm = _infer_embedding_algorithm(split_output_path, split_summary) + + print(f"Loading split output from: {split_output_path}") + print(f"Embedding algorithm: {embedding_algorithm}") + + dedup_keep_map: dict[str, bool] = {} + dedup_score_map: dict[str, float] = {} + dedup_metrics: dict[str, Any] | None = None + if dedup_output_path is not None: + dedup_keep_map, dedup_score_map = _load_dedup_maps(dedup_output_path, args.dedup_eps) + dedup_metrics = _dedup_summary_metrics(dedup_output_path, args.dedup_eps) + print(f"Loaded dedup flags for {len(dedup_keep_map)} clips") + + metadata_rows = _iter_clip_metadata_rows(split_output_path) + records = _build_clip_records( + split_output_path=split_output_path, + metadata_rows=metadata_rows, + dedup_keep_map=dedup_keep_map, + dedup_score_map=dedup_score_map, + caption_field=args.caption_field, + label_mode=args.label_mode, + max_samples=args.max_samples, + allow_placeholder=args.allow_placeholder, + ) + if not records: + raise RuntimeError("No clip samples loaded from split output") + + dataset = hv.Dataset(args.dataset_name, persist=args.persist) + samples: list[Sample] = [] + for record in records: + width, height = _extract_dimensions(record.metadata) + samples.append( + Sample( + id=record.clip_id, + filepath=record.filepath, + label=record.label, + metadata=record.metadata, + thumbnail_base64=record.thumbnail_base64, + width=width, + height=height, + ) + ) + dataset._storage.add_samples_batch(samples) + print(f"Added/updated {len(samples)} samples in dataset '{args.dataset_name}'") + + ids, vectors = _load_embeddings( + split_output_path, + embedding_algorithm, + clip_ids={record.clip_id for record in records}, + ) + if len(ids) == 0: + raise RuntimeError( + "No embeddings found for loaded clips. Checked both parquet and pickle embedding directories." + ) + + expected_dim = _expected_embedding_dim(embedding_algorithm) + if expected_dim is not None and int(vectors.shape[1]) != expected_dim: + print( + "WARNING: Loaded embedding dimension does not match expected model output: " + f"algorithm={embedding_algorithm} expected_dim={expected_dim} loaded_dim={vectors.shape[1]}" + ) + + model_id = _embedding_model_id(embedding_algorithm) + space_key = f"cosmos_curate__{embedding_algorithm.replace('/', '_')}" + dataset._storage.ensure_space( + model_id=model_id, + dim=int(vectors.shape[1]), + config={ + "provider": "cosmos-curate", + "geometry": "euclidean", + "algorithm": embedding_algorithm, + "source": "cosmos-curate", + }, + space_key=space_key, + ) + dataset._storage.add_embeddings(space_key=space_key, ids=ids, vectors=vectors) + print(f"Imported {len(ids)} embeddings (dim={vectors.shape[1]}) into space '{space_key}'") + + if len(ids) >= 3: + print("Computing Euclidean layout...") + dataset.compute_visualization(space_key=space_key, geometry="euclidean") + print("Computing Poincaré layout...") + dataset.compute_visualization(space_key=space_key, geometry="poincare") + else: + print("Too few samples for UMAP; creating manual Euclidean/Poincaré layouts...") + _create_small_layouts(dataset, space_key=space_key, ids=ids) + + if dedup_metrics is not None: + print( + "Dedup summary: " + f"kept={dedup_metrics['kept']} removed={dedup_metrics['removed']} " + f"total={dedup_metrics['total']} reduction={dedup_metrics['reduction_pct']:.2f}%" + ) + + print(f"Launching HyperView at http://127.0.0.1:{args.port}") + hv.launch( + dataset, + port=args.port, + open_browser=not args.no_browser, + reuse_server=args.reuse_server, + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/load_nuscenes.py b/scripts/load_nuscenes.py new file mode 100644 index 0000000..ea8e73a --- /dev/null +++ b/scripts/load_nuscenes.py @@ -0,0 +1,330 @@ +"""Load nuScenes mini dataset into HyperView for embedding visualization. + +This script: +1. Loads nuScenes mini (10 scenes, ~404 samples) +2. Extracts front-camera images and their object annotations (hierarchical labels) +3. Crops annotated objects from camera images +4. Loads them into HyperView as samples with hierarchical category labels +5. Computes CLIP embeddings + both Euclidean and Poincaré projections +6. Launches HyperView to visualize + +Usage: + uv run python scripts/load_nuscenes.py [--dataroot ~/nuscenes] [--max-crops 2000] +""" + +import argparse +import hashlib +import os +import sys +from pathlib import Path + +import numpy as np +from PIL import Image + +# Add src to path for local dev +sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "src")) + +import hyperview as hv +from hyperview.core.sample import Sample + + +def get_2d_box(nusc, ann, sample_data): + """Project a 3D annotation to 2D bounding box on the camera image. + + Returns (xmin, ymin, xmax, ymax) or None if not visible. + """ + from nuscenes.utils.data_classes import Box + from nuscenes.utils.geometry_utils import view_points + from pyquaternion import Quaternion + + # Get the annotation box in global frame + box = Box( + ann["translation"], + ann["size"], + Quaternion(ann["rotation"]), + ) + + # Move box to ego vehicle frame + ego_pose = nusc.get("ego_pose", sample_data["ego_pose_token"]) + box.translate(-np.array(ego_pose["translation"])) + box.rotate(Quaternion(ego_pose["rotation"]).inverse) + + # Move box to sensor frame + cal_sensor = nusc.get("calibrated_sensor", sample_data["calibrated_sensor_token"]) + box.translate(-np.array(cal_sensor["translation"])) + box.rotate(Quaternion(cal_sensor["rotation"]).inverse) + + # Check if box is in front of camera + if box.center[2] <= 0: + return None + + # Project corners to 2D + camera_intrinsic = np.array(cal_sensor["camera_intrinsic"]) + corners_3d = box.corners() # (3, 8) + corners_2d = view_points(corners_3d, camera_intrinsic, normalize=True)[:2] # (2, 8) + + # Get bounding rectangle + xmin = max(0, int(np.min(corners_2d[0]))) + ymin = max(0, int(np.min(corners_2d[1]))) + xmax = min(1600, int(np.max(corners_2d[0]))) + ymax = min(900, int(np.max(corners_2d[1]))) + + # Filter out tiny or off-screen boxes + w = xmax - xmin + h = ymax - ymin + if w < 20 or h < 20 or xmin >= 1600 or ymin >= 900: + return None + + return (xmin, ymin, xmax, ymax) + + +def load_nuscenes_crops(dataroot: str, max_crops: int = 2000, camera: str = "CAM_FRONT"): + """Extract object crops from nuScenes mini with hierarchical labels. + + Returns list of (image_path, category_name, metadata) tuples. + """ + from nuscenes.nuscenes import NuScenes + + print(f"Loading nuScenes mini from {dataroot}...") + nusc = NuScenes(version="v1.0-mini", dataroot=dataroot, verbose=True) + + # Create output directory for crops + crops_dir = Path(dataroot) / "crops" + crops_dir.mkdir(exist_ok=True) + + crops = [] + total_anns = 0 + + for sample in nusc.sample: + # Get camera data + cam_token = sample["data"].get(camera) + if cam_token is None: + continue + cam_data = nusc.get("sample_data", cam_token) + img_path = os.path.join(dataroot, cam_data["filename"]) + + if not os.path.exists(img_path): + continue + + img = Image.open(img_path) + + for ann_token in sample["anns"]: + ann = nusc.get("sample_annotation", ann_token) + category = ann["category_name"] # e.g. "vehicle.car", "human.pedestrian.adult" + + # Project to 2D + bbox = get_2d_box(nusc, ann, cam_data) + if bbox is None: + continue + + xmin, ymin, xmax, ymax = bbox + crop = img.crop((xmin, ymin, xmax, ymax)) + + # Skip very small crops + if crop.size[0] < 24 or crop.size[1] < 24: + continue + + # Save crop + crop_id = hashlib.md5(f"{ann_token}_{camera}".encode()).hexdigest()[:12] + crop_path = crops_dir / f"{crop_id}.jpg" + if not crop_path.exists(): + crop.convert("RGB").save(crop_path, "JPEG", quality=90) + + # Parse hierarchy + parts = category.split(".") + top_level = parts[0] if len(parts) >= 1 else category + mid_level = ".".join(parts[:2]) if len(parts) >= 2 else category + + visibility_token = ann.get("visibility_token", "") + vis = nusc.get("visibility", visibility_token)["description"] if visibility_token else "" + + metadata = { + "category": category, + "top_level": top_level, + "mid_level": mid_level, + "visibility": vis, + "num_lidar_pts": ann.get("num_lidar_pts", 0), + "scene_token": sample["scene_token"], + "sample_token": sample["token"], + "camera": camera, + "bbox": list(bbox), + } + + crops.append((str(crop_path), category, metadata, crop_id)) + total_anns += 1 + + if len(crops) >= max_crops: + break + + if len(crops) >= max_crops: + break + + print(f"Extracted {len(crops)} object crops from {total_anns} annotations") + + # Print category distribution + from collections import Counter + cat_counts = Counter(c[1] for c in crops) + print("\nCategory distribution:") + for cat, count in cat_counts.most_common(): + print(f" {cat}: {count}") + + return crops, nusc + + +def load_nuscenes_scenes(dataroot: str, camera: str = "CAM_FRONT"): + """Load full camera images (scene-level) with scene metadata. + + Good for a broader overview. Each sample is one camera keyframe. + """ + from nuscenes.nuscenes import NuScenes + + print(f"Loading nuScenes mini scenes from {dataroot}...") + nusc = NuScenes(version="v1.0-mini", dataroot=dataroot, verbose=True) + + images = [] + for sample in nusc.sample: + cam_token = sample["data"].get(camera) + if cam_token is None: + continue + cam_data = nusc.get("sample_data", cam_token) + img_path = os.path.join(dataroot, cam_data["filename"]) + + if not os.path.exists(img_path): + continue + + # Get scene info + scene = nusc.get("scene", sample["scene_token"]) + log = nusc.get("log", scene["log_token"]) + + # Count annotations by top-level category + ann_summary = {} + for ann_token in sample["anns"]: + ann = nusc.get("sample_annotation", ann_token) + top = ann["category_name"].split(".")[0] + ann_summary[top] = ann_summary.get(top, 0) + 1 + + sample_id = hashlib.md5(f"{sample['token']}_{camera}".encode()).hexdigest()[:12] + + metadata = { + "scene_name": scene["name"], + "scene_description": scene["description"], + "location": log["location"], + "num_annotations": len(sample["anns"]), + "annotation_summary": ann_summary, + "timestamp": sample["timestamp"], + } + + # Label by location + description for scene-level clustering + label = log["location"] + + images.append((img_path, label, metadata, sample_id)) + + print(f"Loaded {len(images)} scene images") + return images, nusc + + +def main(): + parser = argparse.ArgumentParser(description="Load nuScenes into HyperView") + parser.add_argument("--dataroot", default=os.path.expanduser("~/nuscenes"), + help="Path to nuScenes data") + parser.add_argument("--max-crops", type=int, default=2000, + help="Max number of object crops to extract") + parser.add_argument("--mode", choices=["crops", "scenes", "both"], default="crops", + help="What to load: 'crops' (object-level), 'scenes' (image-level), or 'both'") + parser.add_argument("--model", default="openai/clip-vit-base-patch32", + help="Embedding model to use") + parser.add_argument("--port", type=int, default=6263) + parser.add_argument("--no-browser", action="store_true") + parser.add_argument( + "--dataset-name", + default="nuscenes_mini", + help="HyperView dataset name. Use a unique name for isolated runs.", + ) + parser.add_argument( + "--persist", + action=argparse.BooleanOptionalAction, + default=True, + help=( + "Persist dataset in LanceDB (--persist, default) or keep in-memory only (--no-persist). " + "Use --no-persist for a guaranteed fresh small run." + ), + ) + parser.add_argument( + "--reuse-server", + action="store_true", + help="Attach to an existing HyperView server on --port if compatible.", + ) + args = parser.parse_args() + + # Check dataroot exists + if not os.path.isdir(os.path.join(args.dataroot, "v1.0-mini")): + print(f"ERROR: nuScenes mini not found at {args.dataroot}") + print("Download: wget https://www.nuscenes.org/data/v1.0-mini.tgz") + print(f"Extract: tar -xf v1.0-mini.tgz -C {args.dataroot}") + sys.exit(1) + + dataset = hv.Dataset(args.dataset_name, persist=args.persist) + + if args.persist and len(dataset) > 0: + print( + f"NOTE: dataset '{args.dataset_name}' already has {len(dataset)} samples. " + "Use --no-persist or a different --dataset-name for a fresh small run." + ) + + if args.mode in ("crops", "both"): + crops, nusc = load_nuscenes_crops(args.dataroot, max_crops=args.max_crops) + + print(f"\nAdding {len(crops)} crops to HyperView...") + samples = [] + for filepath, category, metadata, crop_id in crops: + sample = Sample( + id=f"crop_{crop_id}", + filepath=filepath, + label=category, # Full hierarchical label like "vehicle.car" + metadata=metadata, + ) + samples.append(sample) + + dataset._storage.add_samples_batch(samples) + print(f"Added {len(samples)} crop samples") + + if args.mode in ("scenes", "both"): + images, nusc = load_nuscenes_scenes(args.dataroot) + + print(f"\nAdding {len(images)} scene images to HyperView...") + samples = [] + for filepath, label, metadata, sample_id in images: + sample = Sample( + id=f"scene_{sample_id}", + filepath=filepath, + label=label, + metadata=metadata, + ) + samples.append(sample) + + dataset._storage.add_samples_batch(samples) + print(f"Added {len(samples)} scene samples") + + # Compute embeddings + print(f"\nComputing embeddings with {args.model}...") + space_key = dataset.compute_embeddings(model=args.model, show_progress=True) + print(f"Embeddings computed (space_key={space_key})") + + # Compute both visualizations + print("\nComputing Euclidean projection...") + dataset.compute_visualization(space_key=space_key, geometry="euclidean") + + print("Computing Poincaré projection...") + dataset.compute_visualization(space_key=space_key, geometry="poincare") + + print("\nDone! Launching HyperView...") + hv.launch( + dataset, + port=args.port, + open_browser=not args.no_browser, + reuse_server=args.reuse_server, + ) + + +if __name__ == "__main__": + main() diff --git a/src/hyperview/server/app.py b/src/hyperview/server/app.py index 9de8c00..86d4f9b 100644 --- a/src/hyperview/server/app.py +++ b/src/hyperview/server/app.py @@ -6,7 +6,7 @@ from fastapi import Depends, FastAPI, HTTPException, Query from fastapi.middleware.cors import CORSMiddleware -from fastapi.responses import JSONResponse +from fastapi.responses import FileResponse, JSONResponse from fastapi.staticfiles import StaticFiles from pydantic import BaseModel @@ -37,6 +37,19 @@ class LassoSelectionRequest(BaseModel): include_thumbnails: bool = True +class CurationFilterRequest(BaseModel): + """Request model for curation filtering.""" + + min_aesthetic_score: float | None = None + min_motion_score: float | None = None + max_cosine_similarity: float | None = None + caption_query: str | None = None + dedup_status: str | None = None + offset: int = 0 + limit: int = 100 + include_thumbnails: bool = True + + class SampleResponse(BaseModel): """Response model for a sample.""" @@ -113,6 +126,150 @@ class SimilaritySearchResponse(BaseModel): results: list[SimilarSampleResponse] +def _extract_caption_sections(caption_text: str | None) -> tuple[str | None, str | None]: + if not caption_text: + return None, None + + reasoning: str | None = None + answer: str | None = None + + think_start = caption_text.find("") + think_end = caption_text.find("") + if think_start != -1 and think_end != -1 and think_end > think_start: + reasoning = caption_text[think_start + len("") : think_end].strip() or None + + answer_start = caption_text.find("") + answer_end = caption_text.find("") + if answer_start != -1 and answer_end != -1 and answer_end > answer_start: + answer = caption_text[answer_start + len("") : answer_end].strip() or None + + if answer is None: + answer = caption_text.strip() or None + + return reasoning, answer + + +def _resolve_video_file(sample: Any) -> Path | None: + metadata = sample.metadata if isinstance(sample.metadata, dict) else {} + candidate_paths: list[str] = [] + + for key in ("video_path", "clip_location"): + value = metadata.get(key) + if isinstance(value, str) and value.strip(): + candidate_paths.append(value.strip()) + + if isinstance(sample.filepath, str) and sample.filepath.lower().endswith(".mp4"): + candidate_paths.append(sample.filepath) + + for candidate in candidate_paths: + path = Path(candidate).expanduser() + if path.exists() and path.is_file(): + return path.resolve() + + return None + + +def _metadata_number(metadata: dict[str, Any], *keys: str) -> float | None: + for key in keys: + value = metadata.get(key) + if isinstance(value, (int, float)): + numeric = float(value) + if np.isfinite(numeric): + return numeric + return None + + +def _metadata_text(metadata: dict[str, Any], *keys: str) -> str | None: + for key in keys: + value = metadata.get(key) + if isinstance(value, str) and value.strip(): + return value.strip() + return None + + +def _sample_has_video(sample: Any) -> bool: + metadata = sample.metadata if isinstance(sample.metadata, dict) else {} + if _metadata_text(metadata, "video_path", "clip_location"): + return True + return isinstance(sample.filepath, str) and sample.filepath.lower().endswith(".mp4") + + +def _sample_caption_text(metadata: dict[str, Any]) -> str | None: + return _metadata_text( + metadata, + "caption_answer", + "first_caption", + "caption_raw", + "caption", + ) + + +def _histogram(values: list[float], *, bins: int, lower: float, upper: float) -> list[dict[str, float | int]]: + if not values: + return [] + + if upper <= lower: + upper = lower + 1.0 + + counts, edges = np.histogram(np.asarray(values, dtype=np.float32), bins=bins, range=(lower, upper)) + result: list[dict[str, float | int]] = [] + for idx, count in enumerate(counts.tolist()): + result.append( + { + "start": float(edges[idx]), + "end": float(edges[idx + 1]), + "count": int(count), + } + ) + return result + + +def _score_summary(values: list[float]) -> dict[str, float | int | None]: + if not values: + return {"count": 0, "min": None, "max": None, "avg": None} + + arr = np.asarray(values, dtype=np.float32) + return { + "count": int(arr.size), + "min": float(np.min(arr)), + "max": float(np.max(arr)), + "avg": float(np.mean(arr)), + } + + +def _matches_curation_filter(sample: Any, request: CurationFilterRequest) -> bool: + metadata = sample.metadata if isinstance(sample.metadata, dict) else {} + + aesthetic = _metadata_number(metadata, "aesthetic_score") + if request.min_aesthetic_score is not None: + if aesthetic is None or aesthetic < request.min_aesthetic_score: + return False + + motion = _metadata_number(metadata, "motion_score") + if request.min_motion_score is not None: + if motion is None or motion < request.min_motion_score: + return False + + similarity = _metadata_number(metadata, "cosine_sim_score", "dedup_cosine_similarity") + if request.max_cosine_similarity is not None: + if similarity is None or similarity > request.max_cosine_similarity: + return False + + if request.dedup_status: + dedup_status = _metadata_text(metadata, "dedup_status") or "unknown" + if dedup_status != request.dedup_status: + return False + + if request.caption_query: + caption_text = _sample_caption_text(metadata) + if caption_text is None: + return False + if request.caption_query.lower() not in caption_text.lower(): + return False + + return True + + def create_app(dataset: Dataset | None = None, session_id: str | None = None) -> FastAPI: """Create the FastAPI application. @@ -376,6 +533,134 @@ async def get_thumbnail(sample_id: str, ds: Dataset = Depends(get_dataset)): except KeyError: raise HTTPException(status_code=404, detail=f"Sample not found: {sample_id}") + @app.get("/api/video/{sample_id}") + async def get_video(sample_id: str, ds: Dataset = Depends(get_dataset)): + """Stream an MP4 clip for a sample.""" + try: + sample = ds[sample_id] + except KeyError: + raise HTTPException(status_code=404, detail=f"Sample not found: {sample_id}") + + video_path = _resolve_video_file(sample) + if video_path is None: + raise HTTPException(status_code=404, detail=f"No video file found for sample: {sample_id}") + + return FileResponse(str(video_path), media_type="video/mp4", filename=video_path.name) + + @app.get("/api/annotations/{sample_id}") + async def get_annotations(sample_id: str, ds: Dataset = Depends(get_dataset)): + """Return caption/reasoning and curation scores for a sample.""" + try: + sample = ds[sample_id] + except KeyError: + raise HTTPException(status_code=404, detail=f"Sample not found: {sample_id}") + + metadata = sample.metadata if isinstance(sample.metadata, dict) else {} + + raw_caption = metadata.get("caption_raw") or metadata.get("first_caption") + raw_caption_str = raw_caption if isinstance(raw_caption, str) else None + + reasoning = metadata.get("caption_reasoning") if isinstance(metadata.get("caption_reasoning"), str) else None + answer = metadata.get("caption_answer") if isinstance(metadata.get("caption_answer"), str) else None + + if answer is None and raw_caption_str: + parsed_reasoning, parsed_answer = _extract_caption_sections(raw_caption_str) + reasoning = reasoning or parsed_reasoning + answer = parsed_answer + + return { + "id": sample.id, + "caption": answer, + "reasoning": reasoning, + "raw_caption": raw_caption_str, + "aesthetic_score": metadata.get("aesthetic_score"), + "motion_score": metadata.get("motion_score"), + "dedup_status": metadata.get("dedup_status"), + "dedup_keep": metadata.get("dedup_keep"), + "cosine_sim_score": metadata.get("cosine_sim_score") or metadata.get("dedup_cosine_similarity"), + "source_video": metadata.get("source_video"), + "video_path": metadata.get("video_path") or metadata.get("clip_location"), + "span": metadata.get("span") or metadata.get("duration_span"), + } + + @app.get("/api/curation/stats") + async def get_curation_stats(ds: Dataset = Depends(get_dataset)): + """Return dataset-level curation statistics for dashboarding.""" + samples = ds.samples + + total = len(samples) + with_video = 0 + with_caption = 0 + dedup_counts: dict[str, int] = {"kept": 0, "removed": 0, "unknown": 0} + aesthetic_values: list[float] = [] + motion_values: list[float] = [] + + for sample in samples: + metadata = sample.metadata if isinstance(sample.metadata, dict) else {} + + if _sample_has_video(sample): + with_video += 1 + + if _sample_caption_text(metadata): + with_caption += 1 + + dedup_status = _metadata_text(metadata, "dedup_status") or "unknown" + dedup_counts[dedup_status] = dedup_counts.get(dedup_status, 0) + 1 + + aesthetic = _metadata_number(metadata, "aesthetic_score") + if aesthetic is not None: + aesthetic_values.append(aesthetic) + + motion = _metadata_number(metadata, "motion_score") + if motion is not None: + motion_values.append(motion) + + return { + "total_samples": total, + "with_video": with_video, + "with_caption": with_caption, + "dedup_counts": dedup_counts, + "score_summary": { + "aesthetic": _score_summary(aesthetic_values), + "motion": _score_summary(motion_values), + }, + "aesthetic_histogram": _histogram(aesthetic_values, bins=12, lower=0.0, upper=10.0), + "motion_histogram": _histogram(motion_values, bins=12, lower=0.0, upper=10.0), + } + + @app.post("/api/curation/filter") + async def post_curation_filter(request: CurationFilterRequest, ds: Dataset = Depends(get_dataset)): + """Filter samples by curation metadata and return paginated results.""" + if request.offset < 0: + raise HTTPException(status_code=400, detail="offset must be >= 0") + if request.limit < 1 or request.limit > 2000: + raise HTTPException(status_code=400, detail="limit must be between 1 and 2000") + + if request.dedup_status and request.dedup_status not in {"kept", "removed", "unknown"}: + raise HTTPException( + status_code=400, + detail="dedup_status must be one of: kept, removed, unknown", + ) + + filtered_ids: list[str] = [] + for sample in ds.samples: + if _matches_curation_filter(sample, request): + filtered_ids.append(sample.id) + + total = len(filtered_ids) + start = int(request.offset) + end = int(request.offset + request.limit) + page_ids = filtered_ids[start:end] + page_samples = ds.get_samples_by_ids(page_ids) + + return { + "total": total, + "offset": request.offset, + "limit": request.limit, + "sample_ids": page_ids, + "samples": [s.to_api_dict(include_thumbnail=request.include_thumbnails) for s in page_samples], + } + # Serve static frontend files static_dir = Path(__file__).parent / "static" if static_dir.exists(): From 906985787a64df96780e4558768f063fad9a9787 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sun, 1 Mar 2026 22:30:00 +0000 Subject: [PATCH 75/75] chore(deps): bump minimatch in /frontend Bumps and [minimatch](https://github.com/isaacs/minimatch). These dependencies needed to be updated together. Updates `minimatch` from 3.1.2 to 3.1.5 - [Changelog](https://github.com/isaacs/minimatch/blob/main/changelog.md) - [Commits](https://github.com/isaacs/minimatch/compare/v3.1.2...v3.1.5) Updates `minimatch` from 9.0.5 to 9.0.9 - [Changelog](https://github.com/isaacs/minimatch/blob/main/changelog.md) - [Commits](https://github.com/isaacs/minimatch/compare/v3.1.2...v3.1.5) --- updated-dependencies: - dependency-name: minimatch dependency-version: 3.1.5 dependency-type: indirect - dependency-name: minimatch dependency-version: 9.0.9 dependency-type: indirect ... Signed-off-by: dependabot[bot] --- frontend/package-lock.json | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/frontend/package-lock.json b/frontend/package-lock.json index c8f7be1..40500a2 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -2293,13 +2293,13 @@ } }, "node_modules/@typescript-eslint/typescript-estree/node_modules/minimatch": { - "version": "9.0.5", - "resolved": "https://registry.npmjs.org/minimatch/-/minimatch-9.0.5.tgz", - "integrity": "sha512-G6T0ZX48xgozx7587koeX9Ys2NYy6Gmv//P89sEte9V9whIapMNF4idKxnW2QtCcLiTWlb/wfCabAtAFWhhBow==", + "version": "9.0.9", + "resolved": "https://registry.npmjs.org/minimatch/-/minimatch-9.0.9.tgz", + "integrity": "sha512-OBwBN9AL4dqmETlpS2zasx+vTeWclWzkblfZk7KTA5j3jeOONz/tRCnZomUyvNg83wL5Zv9Ss6HMJXAgL8R2Yg==", "dev": true, "license": "ISC", "dependencies": { - "brace-expansion": "^2.0.1" + "brace-expansion": "^2.0.2" }, "engines": { "node": ">=16 || 14 >=14.17" @@ -5321,9 +5321,9 @@ } }, "node_modules/minimatch": { - "version": "3.1.2", - "resolved": "https://registry.npmjs.org/minimatch/-/minimatch-3.1.2.tgz", - "integrity": "sha512-J7p63hRiAjw1NDEww1W7i37+ByIrOWO5XQQAzZ3VOcL0PNybwpfmV/N05zFAzwQ9USyEcX6t3UO+K5aqBQOIHw==", + "version": "3.1.5", + "resolved": "https://registry.npmjs.org/minimatch/-/minimatch-3.1.5.tgz", + "integrity": "sha512-VgjWUsnnT6n+NUk6eZq77zeFdpW2LWDzP6zFGrCbHXiYNul5Dzqk2HHQ5uFH2DNW5Xbp8+jVzaeNt94ssEEl4w==", "dev": true, "license": "ISC", "dependencies": {

    - + HyperView Screenshot
    - Watch the Demo Video + Try the live demo on HuggingFace Spaces

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