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JAXScape is a minimal JAX library for connectivity analysis at scales. It provide key utilities to build your own connectivity analysis workflow, including

• differentiable and GPU-accelerated graph distance metrics
• differentiable raster to graph and graph to raster mappings
• moving window utilities for implementing large-scale connectivity analysis pipelines

JAXScape implements custom methods and leverages JAX's capabilities to accelerate distance computations on CPUs/GPUs/TPUs, while ensuring differentiability across the codebase for awesome sensitivity analysis and optimization.

Why JAXScape?

We report below some key runtimes from a comprehensive benchmark testing JAXScape on a RTX 3090 GPU against other related CPU-based software in Python, R, and Julia backends. See the benchmark documentation for the full setup, raw scorecards, convergence status, and fit-quality metrics.

Resistance distance calculation

On a 1000 x 1000 spatial grid, JAXScape is 74x faster than Circuitscape.jl with cg+amg solver and 17x faster than Circuitscape.jl with cholmod solver.

Sensitivity analysis

On a 175 x 175 spatial grid, JAXScape is 12x faster than gdistance shortestPath for shortest-path sensitivity and 5.0x faster than gdistance passage for resistance sensitivity.

Inverse landscape genetics

On a 100 x 100 spatial grid, JAXScape is 32x faster than ResistanceGA and reaches a final relative RMSE about 16x lower.

Installation

uv add jaxscape

For GPU compatibility, install JAX following the official JAX installation guide. JAXScape will automatically use the JAX backend you have configured.

You may be required to install optional linear solvers for large-scale resistance distance computations. CholmodSolver, PyAMGSolver, and AMJaxCGSolver are documented on the linear solver page.

Quick start

1. Download sample data

curl -sL https://github.com/vboussange/jaxscape/releases/download/v0.0.6/data.zip -o data.zip && unzip -q data.zip && rm data.zip

2. Run the analysis

import numpy as np
import matplotlib.pyplot as plt
import jax.numpy as jnp
from jaxscape import GridGraph
from jaxscape import LCPDistance, ResistanceDistance, RSPDistance

permeability = jnp.array(np.loadtxt("data/permeability.csv", delimiter=",")) + 0.001
barriers = np.asarray(permeability <= 0.1)

# Create a grid graph where edge weights are the average permeability of the two nodes
grid = GridGraph(grid=permeability, fun=lambda x, y: (x + y) / 2)

# We set the source to the top left pixel, and compute distances to all other pixels with three different distance metrics
source = grid.coord_to_index(jnp.array([0]), jnp.array([0]))

distances = {
    "LCP distance": LCPDistance(),
    "Resistance distance": ResistanceDistance(),
    "RSP distance": RSPDistance(theta=0.01, cost=lambda x: -jnp.log(x)),
}

distance_cmap = plt.cm.magma.copy()
distance_cmap.set_bad(color="lightgray")

fig, axs = plt.subplots(1, 4, figsize=(12, 4))

permeability_im = axs[0].imshow(permeability, cmap="gray", vmin=0, vmax=1)
axs[0].set_xticks([])
axs[0].set_yticks([])
for spine in axs[0].spines.values():
    spine.set_visible(True)
axs[0].set_title("Permeability")
fig.colorbar(permeability_im, ax=axs[0], shrink=0.2)

for ax, (title, distance) in zip(axs[1:], distances.items()):
    # Compute distances from all nodes to the source
    dist_to_node = distance(grid, source)

    # Convert from node values to a 2D array and hide barrier cells
    dist_array = np.asarray(grid.node_values_to_array(dist_to_node.ravel()))
    dist_array = np.ma.masked_where(barriers, dist_array)
    
    # Plotting
    im = ax.imshow(dist_array, cmap=distance_cmap)
    ax.axis("off")
    ax.set_title(title)
    fig.colorbar(im, ax=ax, shrink=0.2)

fig.suptitle("Permeability and distance to the top-left pixel")
plt.tight_layout()
plt.show()

But what's really cool about JAXscape is that you can autodiff through thoses distances! Check out the documentation to learn about applications and more!

Documentation

Comprehensive documentation is available at https://vboussange.github.io/jaxscape

Benchmarking

JAXScape includes a benchmark workspace that profiles internal JAXScape implementations and compares them with external landscape-connectivity tools. Published results and task definitions are available in the benchmark documentation. To reproduce the suite or run it on a local CPU/GPU device, follow the instructions in benchmark/README.md.

License

jaxscape is distributed under the terms of the MIT license.

Citation

If you use JAXScape in your research, please cite:

@software{jaxscape2024,
  author = {Boussange, Victor},
  title = {JAXScape: A minimal JAX library for connectivity modelling at scale},
  year = {2025},
  doi = {10.5281/zenodo.15267703},
  url = {https://github.com/vboussange/jaxscape}
}