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# CuSIR
## Introduction
CuSIR is a Python code built on top of CuPy, a NumPy-like library for GPU-accelerated computing. It provides a solver for the two-dimensional diffusive SIR model, described by the following system of reaction-diffusion equations:
$$
\begin{align}
\partial_t S &= -\beta_{\mathbf{r}} S I - \gamma I + D_I \nabla^2 I \\\\\\\\
\partial_t I &= \beta_{\mathbf{r}} S I + D_S \nabla^2 S - \mathbf{v} \cdot \nabla I,
\end{align}
$$
where $S$ is the density of susceptible individuals, $I$ is the density of infected individuals, $\beta_{\mathbf{r}}$ is the transmission rate that depends on the location $\mathbf{r}$, $\gamma$ is the recovery/removal rate, $D_I$ and $D_S$ are diffusion coefficients, and $\mathbf{v}$ is the convection field.
## Requirements
To use the CuSIR package, you will need the following software and hardware:
- A CUDA-compatible GPU: A graphics processing unit (GPU) that supports CUDA. Check the list of CUDA-compatible GPUs on the NVIDIA website (https://developer.nvidia.com/cuda-gpus) to see if your GPU is supported.
- CUDA Toolkit: A parallel computing platform and programming model developed by NVIDIA for general-purpose computing on GPUs. You can download CUDA from the NVIDIA website (https://developer.nvidia.com/cuda-downloads).
*Note: Currently (January, 2023), the last version of CUDA (12) is not supported by CuPy. You will need to install any previous version of CUDA (recommended 11.2) to use CuSIR.*
- CuPy: A NumPy-like library for GPU-accelerated computing. You can install CuPy by following the instructions in the CuPy documentation (https://docs-cupy.chainer.org/en/stable/install.html).
## Installation
To install the CuSIR package, you can use pip by running the following command in your command prompt or terminal:
```bash
pip install cusir
```
This command will install the latest version of the CuSIR package. It is mandatory to meet the requirements listed above for CuSIR to work properly.
## Usage
Almost everything is implemented in the `system` class, which is located in the `system` module. The following code shows how to use the `system` class to solve the diffusive SIR model:
```python
import cusir.system as cs
# Define the spatial domain
Lx = 2**10
Ly = 2**10
# Create the system object
s = cs.system(Lx, Ly)
# Define the system parameters
s.beta = 1 # Transmission rate
s.gamma = 0.1 # Recovery/removal rate
s.D_I = 1 # Diffusion coefficient for infected individuals
s.D_S = 1 # Diffusion coefficient for susceptible individuals
# Define the initial conditions
s.set_plane_initial_conditions()
# Solve the system
for _ in range(10000):
s.update() # Update the system
s.rigid_x() # Apply rigid boundary conditions in the x-direction
#You can also use the following to do the same:
#s.solve(10000)
# Get the solution
S = s.S.get() # get() pulls the data from the GPU to the CPU as a NumPy array
I = s.I.get()
```
## License
CuSIR is licensed under the MIT license. See the `LICENSE` file for more details.
