Communicates with S-EWSNet (https://doi.org/10.1098/rsos.231767), a deep learning framework for modelling and anticipating regime shifts in dynamical spatial systems, and returns the model's prediction for the inputted spatial time series.

## Usage

```
sewsnet_predict(
x,
id = NULL,
envname,
delta = 0.1,
inp_size = 25,
model_path = default_sewsnet_path()
)
```

## Source

Deb, S., Ekansh, M., Paras, G. et al. (2024) Optimal sampling of spatial patterns improves deep learning-based early warning signals of critical transitions. Royal Society Open Science. 11, 231767.

## Arguments

- x
A list of square integer matrices representing presence/absence pixels. Pixels could be vegetation presence. Ensure entires are integers not numeric.

- id
Vector identifying each entry in x. Could be year, plot identity etc.

- envname
A string naming the Python environment prepared by

`ewsnet_init()`

.- delta
Numeric. Difference in densities.

- inp_size
Numeric. Size of clipped Fourier transformed square matrix.

- model_path
A string naming the path to the S-EWSnet model installed by

`sewsnet_reset()`

.

## Value

A dataframe of S-EWSNet predictions. Values represent the estimated probability that the quoted event will occur.

## Examples

```
#A dummy dataset of a patchy savanna
#monitored over 10 sites/years.
vegetation_data <- vector("list", length = 50)
vegetation_data <- lapply(vegetation_data,function(x){
matrix(rbinom(128^2,1,0.6),nrow = 128,ncol=128)
})
#Activate python environment (only necessary
#on first opening of R session).
if (FALSE) { # \dontrun{
ewsnet_init(envname = "EWSNET_env")
} # }
#Generate EWSNet predictions.
if (FALSE) { # \dontrun{
pred <- sewsnet_predict(
vegetation_data,
delta = 0.1,
inp_size = 25,
envname = "EWSNET_env")
} # }
```