# PyVolGrid
**PyVolGrid** is a Python package for estimating the **total volume of overlapping spheres** using a **grid-based numerical approach**.
It is intended for applications in computational chemistry, molecular modeling, and structural bioinformatics.
(Besides its potential usefullness, this package also served as a learning project for me to get more familiar with Python packaging and distribution of precompiled wheels.)
---
## Installation
```bash
pip install pyvolgrid
```
Or clone the repository and install manually (requires a C++ compiler such as `clang` or `gcc`, and `cmake` to be installed):
```bash
git clone https://github.com/ugSUBMARINE/pyvolgrid.git
cd pyvolgrid
pip install .
```
---
## Usage
### Basic Example
```python
import numpy as np
from pyvolgrid import volume_from_spheres
# Define sphere centers and radii
coords = [[0, 0, 0], [1.5, 0, 0], [0, 1.5, 0]]
radii = [1.0, 0.8, 0.6]
# Calculate total volume
volume = volume_from_spheres(coords, radii, grid_spacing=0.1)
print(f"Total volume: {volume:.2f} cubic units")
```
### Scalar Radius
Apply the same radius to all spheres by passing a single number:
```python
# All spheres have radius 1.2
coords = [[0, 0, 0], [3, 0, 0], [0, 3, 0]]
radius = 1.2 # Single value for all spheres
volume = volume_from_spheres(coords, radius)
print(f"Volume with uniform radius: {volume:.2f}")
```
### Flexible Input Types
PyVolGrid accepts various input formats - lists, tuples, or numpy arrays:
```python
# Using tuples
coords = ((0, 0, 0), (2, 0, 0))
radii = (1.0, 0.5)
volume = volume_from_spheres(coords, radii)
# Using numpy arrays (any dtype, will be converted automatically)
coords = np.array([[0, 0, 0]], dtype=np.int32)
radius = np.float32(1.0)
volume = volume_from_spheres(coords, radius)
# Mixed types work too
coords = [[0, 0, 0]] # List
radius = 1 # Integer (converted to float)
volume = volume_from_spheres(coords, radius)
```
### Performance Tips
```python
# For optimal performance, use C-contiguous float64 arrays
coords = np.array([[0, 0, 0], [2, 0, 0]], dtype=np.float64, order='C')
radii = np.array([1.0, 0.5], dtype=np.float64)
# No copying will occur, maximizing performance
volume = volume_from_spheres(coords, radii)
```
### Grid Spacing
Adjust the grid spacing to balance accuracy vs. performance:
```python
coords = [[0, 0, 0]]
radius = 1.0
# Coarse grid (faster, less accurate)
volume_coarse = volume_from_spheres(coords, radius, grid_spacing=0.2)
# Fine grid (slower, more accurate)
volume_fine = volume_from_spheres(coords, radius, grid_spacing=0.05)
print(f"Coarse: {volume_coarse:.2f}, Fine: {volume_fine:.2f}")
```
---
## License
This project is licensed under the MIT License.
## Contributing
Contributions are welcome! Please open an issue or submit a pull request on GitHub.
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"description": "# PyVolGrid\n\n**PyVolGrid** is a Python package for estimating the **total volume of overlapping spheres** using a **grid-based numerical approach**.\nIt is intended for applications in computational chemistry, molecular modeling, and structural bioinformatics.\n\n(Besides its potential usefullness, this package also served as a learning project for me to get more familiar with Python packaging and distribution of precompiled wheels.)\n\n---\n\n## Installation\n\n```bash\npip install pyvolgrid\n```\n\nOr clone the repository and install manually (requires a C++ compiler such as `clang` or `gcc`, and `cmake` to be installed):\n\n```bash\ngit clone https://github.com/ugSUBMARINE/pyvolgrid.git\ncd pyvolgrid\npip install .\n```\n\n---\n\n## Usage\n\n### Basic Example\n\n```python\nimport numpy as np\nfrom pyvolgrid import volume_from_spheres\n\n# Define sphere centers and radii\ncoords = [[0, 0, 0], [1.5, 0, 0], [0, 1.5, 0]]\nradii = [1.0, 0.8, 0.6]\n\n# Calculate total volume\nvolume = volume_from_spheres(coords, radii, grid_spacing=0.1)\nprint(f\"Total volume: {volume:.2f} cubic units\")\n```\n\n### Scalar Radius\n\nApply the same radius to all spheres by passing a single number:\n\n```python\n# All spheres have radius 1.2\ncoords = [[0, 0, 0], [3, 0, 0], [0, 3, 0]]\nradius = 1.2 # Single value for all spheres\n\nvolume = volume_from_spheres(coords, radius)\nprint(f\"Volume with uniform radius: {volume:.2f}\")\n```\n\n### Flexible Input Types\n\nPyVolGrid accepts various input formats - lists, tuples, or numpy arrays:\n\n```python\n# Using tuples\ncoords = ((0, 0, 0), (2, 0, 0))\nradii = (1.0, 0.5)\nvolume = volume_from_spheres(coords, radii)\n\n# Using numpy arrays (any dtype, will be converted automatically)\ncoords = np.array([[0, 0, 0]], dtype=np.int32)\nradius = np.float32(1.0)\nvolume = volume_from_spheres(coords, radius)\n\n# Mixed types work too\ncoords = [[0, 0, 0]] # List\nradius = 1 # Integer (converted to float)\nvolume = volume_from_spheres(coords, radius)\n```\n\n### Performance Tips\n\n```python\n# For optimal performance, use C-contiguous float64 arrays\ncoords = np.array([[0, 0, 0], [2, 0, 0]], dtype=np.float64, order='C')\nradii = np.array([1.0, 0.5], dtype=np.float64)\n\n# No copying will occur, maximizing performance\nvolume = volume_from_spheres(coords, radii)\n```\n\n### Grid Spacing\n\nAdjust the grid spacing to balance accuracy vs. performance:\n\n```python\ncoords = [[0, 0, 0]]\nradius = 1.0\n\n# Coarse grid (faster, less accurate)\nvolume_coarse = volume_from_spheres(coords, radius, grid_spacing=0.2)\n\n# Fine grid (slower, more accurate)\nvolume_fine = volume_from_spheres(coords, radius, grid_spacing=0.05)\n\nprint(f\"Coarse: {volume_coarse:.2f}, Fine: {volume_fine:.2f}\")\n```\n\n---\n\n## License\n\nThis project is licensed under the MIT License.\n\n## Contributing\nContributions are welcome! Please open an issue or submit a pull request on GitHub.\n",
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