### Basic Tracking Example in Python

Source: https://github.com/royerlab/tracksdata/blob/main/docs/examples.md

Demonstrates the core workflow of TracksData. This example requires the TracksData library to be installed.

```python
from pathlib import Path

from iohub.reader import build_reader
from tracksdata import TracksData


def main():
    # Load data from a CSV file
    data_path = Path("../data/tracks.csv")
    reader = build_reader(data_path)
    tracks = TracksData(reader)

    # Print basic information about the tracks
    print(f"Loaded {len(tracks)} tracks.")
    print(f"Number of frames: {tracks.max_frame + 1}")
    print(f"Number of objects: {tracks.max_object_id + 1}")

    # Access a specific track
    track_id = 0
    track = tracks[track_id]
    print(f"Track {track_id}: {len(track)} points")

    # Iterate over frames and objects
    for frame in range(tracks.max_frame + 1):
        if frame in tracks:
            objects_in_frame = tracks[frame]
            print(f"Frame {frame}: {len(objects_in_frame)} objects")

if __name__ == "__main__":
    main()

```

--------------------------------

### Verify TracksData Installation

Source: https://github.com/royerlab/tracksdata/blob/main/docs/installation.md

Import the library and print its version to confirm a successful installation. This is a quick check after installation.

```python
import tracksdata as td
print(td.__version__)
```

--------------------------------

### Install TracksData from Source (Development)

Source: https://github.com/royerlab/tracksdata/blob/main/docs/installation.md

Clone the repository and install the latest development version locally. This is useful for contributing to the project.

```bash
git clone https://github.com/royerlab/tracksdata.git
cd tracksdata
pip install .
```

--------------------------------

### Install TracksData from PyPI

Source: https://github.com/royerlab/tracksdata/blob/main/docs/installation.md

Use this command to install the latest stable release of TracksData from the Python Package Index.

```bash
pip install tracksdata
```

--------------------------------

### Install TracksData with Documentation Dependencies

Source: https://github.com/royerlab/tracksdata/blob/main/docs/installation.md

Install TracksData with dependencies required for building the documentation. This is typically used by developers working on the documentation.

```bash
pip install .[docs]
```

--------------------------------

### Install TracksData with Testing Dependencies

Source: https://github.com/royerlab/tracksdata/blob/main/docs/installation.md

Install TracksData along with the necessary dependencies for running tests. Use this if you plan to contribute or verify the library's functionality.

```bash
pip install .[test]
```

--------------------------------

### Manage Documentation Versions with Mike

Source: https://github.com/royerlab/tracksdata/blob/main/docs/contributing.md

Install dependencies, deploy new versions, set the default version, list all versions, or delete a specific version using the 'mike' tool.

```bash
uv sync --extra docs
```

```bash
uv run mike deploy --push --update-aliases v1.0.0 latest
```

```bash
uv run mike set-default --push latest
```

```bash
uv run mike list
```

```bash
uv run mike delete --push v0.9.0
```

--------------------------------

### Serve Documentation Locally

Source: https://github.com/royerlab/tracksdata/blob/main/docs/contributing.md

Build and serve the documentation locally with live reload enabled for development.

```bash
uv run mkdocs serve
```

--------------------------------

### Build Static Documentation Site

Source: https://github.com/royerlab/tracksdata/blob/main/docs/contributing.md

Generate a static build of the documentation site for deployment.

```bash
uv run mkdocs build
```

--------------------------------

### Create and Solve a Simple Tracking Graph

Source: https://github.com/royerlab/tracksdata/blob/main/docs/getting_started.md

This snippet demonstrates the basic workflow of creating an in-memory graph, generating random nodes, adding edges based on distance, and solving the tracking problem using the NearestNeighborsSolver. It's useful for initial testing and understanding the core API.

```python
import numpy as np
import tracksdata as td

# Create a graph
graph = td.graph.InMemoryGraph()

# Generate random nodes for testing
node_generator = td.nodes.RandomNodes(
    n_time_points=5,
    n_nodes_per_tp=(10, 15),
    n_dim=2
)
node_generator.add_nodes(graph)

# Connect nearby nodes across time
edge_generator = td.edges.DistanceEdges(
    distance_threshold=0.3,
    n_neighbors=3
)
edge_generator.add_edges(graph)

# Solve the tracking problem
solver = td.solvers.NearestNeighborsSolver(edge_weight="distance")
solution = solver.solve(graph)

print(f"Original graph has {graph.num_nodes()} nodes and {graph.num_edges()} edges")
print(f"Solution has {solution.num_nodes()} nodes and {solution.num_edges()} edges")
```

--------------------------------

### Add Custom Node and Edge Attributes

Source: https://github.com/royerlab/tracksdata/blob/main/docs/faq.md

Demonstrates how to add new attribute keys to the graph and use them when adding nodes or edges.

```python
# Add new attribute keys to the graph
graph.add_node_attr_key("my_feature", 0.0)
graph.add_edge_attr_key("confidence", 1.0)

# Use them when adding nodes/edges
graph.add_node({"t": 0, "x": 10, "my_feature": 42.0})
graph.add_edge(source_id, target_id, {"confidence": 0.95})
```

--------------------------------

### Convert TracksData to Napari Format

Source: https://github.com/royerlab/tracksdata/blob/main/docs/faq.md

Illustrates how to convert TracksData results into a format compatible with napari for visualization, including labels, tracks data, and graph information.

```python
import tracksdata as td

labels = ...

tracks_df, track_graph, track_labels = td.functional.to_napari_format(
    solution_graph, shape=labels.shape, mask_key="mask",
)

viewer = napari.Viewer()
viewer.add_labels(track_labels)
viewer.add_tracks(tracks_df, graph=track_graph)
napari.run()
```

--------------------------------

### Create Custom Nodes Operator

Source: https://github.com/royerlab/tracksdata/blob/main/docs/faq.md

Shows how to create a custom operator for adding nodes by inheriting from BaseNodesOperator and implementing the add_nodes method.

```python
import tracksdata as td

class CustomNodes(td.nodes.BaseNodesOperator):
    def add_nodes(
        self,
        graph: td.graph.BaseGraph,
        *,
        t: int | None = None,
        **kwargs: Any,
    ) -> None:
        # Your custom logic here to add nodes to the graph
        pass
```

--------------------------------

### Create SQLGraph Attribute Indexes

Source: https://github.com/royerlab/tracksdata/blob/main/docs/concepts.md

Create composite or unique indexes on node or edge attributes in SQLGraph for faster filtering. This is particularly useful for large datasets.

```python
graph.create_node_attr_index(["t", "label"])
graph.create_edge_attr_index("score", unique=True)
```

--------------------------------

### Access and Manipulate Node and Edge Attributes

Source: https://github.com/royerlab/tracksdata/blob/main/docs/concepts.md

Demonstrates how to access node and edge attributes using TracksData's attribute system. Supports direct access, mathematical expressions, and comparison operations for filtering.

```python
import tracksdata as td

x_coords = td.NodeAttr("x")
distance_cost = td.EdgeAttr("distance") + 0.1 * td.EdgeAttr("angle_change")
recent_nodes = td.NodeAttr("t") >= 10
large_objects = td.NodeAttr("area") > 100
```

--------------------------------

### Extract Nodes from Labeled Images and Filter by Time

Source: https://github.com/royerlab/tracksdata/blob/main/docs/getting_started.md

This snippet shows how to extract nodes from segmented images using RegionPropsNodes, including custom properties, and then filter the graph to retrieve node data at a specific time point. This is useful for processing real-world detection data.

```python
import tracksdata as td

# Extract nodes from labeled images
node_op = td.nodes.RegionPropsNodes(extra_properties=["area", "eccentricity"])
node_op.add_nodes(graph, labels=labels)

# Filter nodes by time
graph_filter = graph.filter(td.NodeAttr("t") == 0)
node_data = graph_filter.node_attrs()
print(node_data)
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.