Interactive 3D vector visualization for Python notebooks.
Works with Marimo, Jupyter, VS Code, Colab, anywhere anywidget runs.
- Universal: One widget, every notebook environment
- 6D Visualization: X, Y, Z position + Color, Shape, Size encoding
- Backend-agnostic: NumPy, pandas, Qdrant, Chroma, Pinecone, Weaviate, LanceDB, or raw dicts
- Interactive: Orbit, pan, zoom, click, hover, box select
- Customizable: Color scales, shapes, sizes, themes
- Performant: Instanced rendering for large point clouds
uv add anywidget-vectorfrom anywidget_vector import VectorSpace
widget = VectorSpace(points=[
{"id": "a", "x": 0.5, "y": 0.3, "z": 0.8, "label": "Point A", "cluster": 0},
{"id": "b", "x": -0.2, "y": 0.7, "z": 0.1, "label": "Point B", "cluster": 1},
{"id": "c", "x": 0.1, "y": -0.4, "z": 0.6, "label": "Point C", "cluster": 0},
])
widgetwidget = VectorSpace.from_dict({
"points": [
{"id": "a", "x": 0, "y": 0, "z": 0},
{"id": "b", "x": 1, "y": 1, "z": 1},
]
})import numpy as np
positions = np.random.randn(100, 3)
widget = VectorSpace.from_numpy(positions)import pandas as pd
df = pd.DataFrame({
"x": [0.1, 0.5, 0.9],
"y": [0.2, 0.6, 0.3],
"z": [0.3, 0.1, 0.7],
"cluster": ["A", "B", "A"],
"size": [0.5, 1.0, 0.8],
})
widget = VectorSpace.from_dataframe(
df,
color_col="cluster",
size_col="size",
)import umap
embedding = umap.UMAP(n_components=3).fit_transform(high_dim_data)
widget = VectorSpace.from_umap(embedding, labels=labels)from qdrant_client import QdrantClient
client = QdrantClient("localhost", port=6333)
widget = VectorSpace.from_qdrant(client, "my_collection", limit=5000)import chromadb
client = chromadb.Client()
collection = client.get_collection("embeddings")
widget = VectorSpace.from_chroma(collection)from pinecone import Pinecone
pc = Pinecone(api_key="...")
index = pc.Index("my-index")
widget = VectorSpace.from_pinecone(index, limit=5000)import weaviate
client = weaviate.Client("http://localhost:8080")
widget = VectorSpace.from_weaviate(client, "Article", limit=5000)import lancedb
db = lancedb.connect("~/.lancedb")
table = db.open_table("vectors")
widget = VectorSpace.from_lancedb(table, limit=5000)| Dimension | Visual Channel | Example |
|---|---|---|
| X | Horizontal position | x coordinate |
| Y | Vertical position | y coordinate |
| Z | Depth position | z coordinate |
| Color | Hue/gradient | Cluster, score |
| Shape | Geometry | Category, type |
| Size | Scale | Importance, count |
widget = VectorSpace(
points=data,
color_field="score", # Field to map
color_scale="viridis", # Scale: viridis, plasma, inferno, magma, cividis, turbo
color_domain=[0, 100], # Optional: explicit range
)widget = VectorSpace(
points=data,
shape_field="category",
shape_map={
"type_a": "sphere", # Available: sphere, cube, cone,
"type_b": "cube", # tetrahedron, octahedron, cylinder
"type_c": "cone",
}
)widget = VectorSpace(
points=data,
size_field="importance",
size_range=[0.02, 0.15], # Min/max point size
)widget = VectorSpace(points=data)
@widget.on_click
def handle_click(point_id, point_data):
print(f"Clicked: {point_id}")
@widget.on_hover
def handle_hover(point_id, point_data):
if point_id:
print(f"Hovering: {point_id}")
@widget.on_selection
def handle_selection(point_ids, points_data):
print(f"Selected {len(point_ids)} points")widget.selected_points # Current selection
widget.select(["a", "b"]) # Select points
widget.clear_selection() # Clear
widget.selection_mode = "box" # Switch to box-select modewidget.camera_position # Get position [x, y, z]
widget.camera_target # Get target [x, y, z]
widget.reset_camera() # Reset to default
widget.focus_on(["a", "b"]) # Focus on specific pointsCompute distances and visualize similarity relationships between points.
| Metric | Description |
|---|---|
euclidean |
Straight-line distance (L2 norm) |
cosine |
Angle-based distance (1 - cosine similarity) |
manhattan |
Sum of absolute differences (L1 norm) |
dot_product |
Negative dot product (higher = closer) |
widget.color_by_distance("point_a")
widget.color_by_distance("point_a", metric="cosine")neighbors = widget.find_neighbors("point_a", k=5)
# Returns: [("point_b", 0.1), ("point_c", 0.2), ...]
neighbors = widget.find_neighbors("point_a", threshold=0.5)widget.show_neighbors("point_a", k=5)
widget.show_neighbors("point_a", threshold=0.3)
# Manual connection settings
widget = VectorSpace(
points=data,
show_connections=True,
k_neighbors=3,
distance_metric="cosine",
connection_color="#00ff00",
connection_opacity=0.5,
)distances = widget.compute_distances("point_a")
# Returns: {"point_b": 0.1, "point_c": 0.5, ...}
# Use high-dimensional vectors (not just x,y,z)
distances = widget.compute_distances(
"point_a",
metric="cosine",
vector_field="embedding"
)widget = VectorSpace(
points=data,
width=1000,
height=700,
background="#1a1a2e", # Dark theme default
show_axes=True,
show_grid=True,
axis_labels={"x": "PC1", "y": "PC2", "z": "PC3"},
show_tooltip=True,
tooltip_fields=["label", "x", "y", "z", "cluster"],
selection_mode="click", # "click", "multi", or "box"
use_instancing=True, # Performance: instanced rendering
)Configure a backend for interactive querying:
widget.set_backend("chroma", client=collection)
widget.set_backend("lancedb", client=table)
widget.set_backend("grafeo", client=db)widget.to_json() # Points as JSON string
widget.to_html() # Self-contained HTML string
widget.to_html(title="My Vectors") # Custom title
widget.save_html("vectors.html") # Write HTML to file| Environment | Supported |
|---|---|
| Marimo | Yes |
| JupyterLab | Yes |
| Jupyter Notebook | Yes |
| VS Code | Yes |
| Google Colab | Yes |
| Databricks | Yes |
- anywidget, custom Jupyter widgets made easy
- anywidget-graph, graph visualization widget
- Three.js, 3D JavaScript library
Apache-2.0