net2cell 0.1.3
A multi-resolution network builder for GMNS networks
Requires Python: >=3.8
Keywords: multi-resolution, network, GMNS, transportation, GIS, OSM, meso, micro

# Net2Cell

A Python package for building multi-resolution networks from GMNS (General Modeling Network Specification) format. This tool automatically generates meso-level and micro-level networks from macro networks, supports building networks from OpenStreetMap data, and intelligently generates intersection movements.

## Features

- **Multi-Resolution Network Generation**: Automatically generate meso and micro networks from macro networks
- **OSM Network Support**: Direct network building from OpenStreetMap files (.osm/.pbf)
- **Automatic Movement Generation**: Intelligent generation of intersection turning movements
- **Flexible Configuration**: Rich parameter settings for different scenarios
- **GMNS Standard Output**: Fully compliant with GMNS format specifications

## Installation

### From PyPI

```bash
pip install net2cell
```

## Quick Start

```python
import net2cell as nc

# Load network from GMNS format CSV files
net = nc.loadNetFromCSV(
    folder='path/to/network',
    node_file='node.csv',
    link_file='link.csv'
)

# Build multi-resolution networks
nc.buildMultiResolutionNets(
    net,
    generate_micro_net=True,
    auto_movement_generation=True,
    exclusive_bike_walk_lanes=True,
    width_of_lane=3.5,
    length_of_cell=7.0
)

# Output generated networks to CSV files
nc.outputNetToCSV(
    net,
    output_folder='output',
    includes=['macro', 'meso', 'micro']
)
```

### Build Network from OpenStreetMap

```python
import net2cell as nc

# Read network from OSM file
net = nc.getNetFromFile(
    filename='map.osm',  # or 'map.osm.pbf'
    network_type='auto',  # 'auto', 'bike', 'walk', or 'all'
    strict_mode=True,
    POIs=False
)

nc.outputNetToCSV(net, output_folder='output')
```

## Input Data Requirements

### GMNS Network Files

**node.csv** (required fields):
- `node_id`: Unique node identifier
- `x_coord`: Longitude (lonlat coordinate system) or X coordinate
- `y_coord`: Latitude (lonlat coordinate system) or Y coordinate

**link.csv** (required fields):
- `link_id`: Unique link identifier
- `from_node_id`: Starting node ID
- `to_node_id`: Ending node ID
- `lanes`: Number of lanes
- `geometry`: LineString geometry in WKT format

### OpenStreetMap Files

Supports format:
- `.osm`: XML format OSM file

## Configuration Parameters

### Multi-Resolution Network Building

`buildMultiResolutionNets()` function parameters:

- `macronet`: Macro network object (required)
- `generate_micro_net` (default: True): Whether to generate micro network
- `auto_movement_generation` (default: True): Whether to automatically generate movements
- `exclusive_bike_walk_lanes` (default: True): Whether to set exclusive bike and walk lanes
- `connector_type` (default: None): Connector type
- `width_of_lane` (default: 3.5): Lane width in meters
- `length_of_cell` (default: 7.0): Cell length in meters for micro network

### OSM Network Building

`getNetFromFile()` function parameters:

- `filename`: OSM file path
- `network_type` (default: 'auto'): Network type
  - `'auto'`: Motor vehicle roads
  - `'bike'`: Bicycle paths
  - `'walk'`: Pedestrian paths
  - `'all'`: All types
- `link_types`: List of road types to include
- `POIs` (default: False): Whether to read POIs
- `POI_set`: Set of POI types
- `strict_mode` (default: True): Strict mode, keeps only the largest connected subgraph
- `bounds`: Boundary range dictionary `{'minlat', 'minlon', 'maxlat', 'maxlon'}`

## Output

The tool generates the following network output files:

### Network Output

`outputNetToCSV()` function parameters:

- `macronet`: Network object
- `output_folder` (default: ''): Output folder path
- `includes`: List of network levels to output
  - `['macro']`: Output macro network only
  - `['macro', 'meso']`: Output macro and meso networks
  - `['macro', 'meso', 'micro']`: Output all levels (default)

## Advanced Usage

### Custom Movement Specification

```python
# Disable automatic movement generation and use user-provided movement.csv
net = nc.loadNetFromCSV(
    folder='path/to/network',
    node_file='node.csv',
    link_file='link.csv',
    movement_file='movement.csv'  # Contains user-defined movements
)

# Build with automatic generation disabled
nc.buildMultiResolutionNets(
    net,
    auto_movement_generation=False
)
```

## System Requirements

- Python >= 3.8
- numpy >= 1.20.0
- pandas >= 1.3.0
- shapely >= 2.0.0
- geopandas >= 0.10.0
- networkx >= 2.6.0
- osmium >= 3.5.0

## Citation

If you use this tool in your research, please cite this tool.

## License

This project is licensed under the GNU General Public License v3 or later (GPLv3+) - see the [LICENSE](LICENSE) file for details.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.


## Support

For questions, issues, or feature requests, please contact us.