Add prerequisites in README.md

examples/10_HasegawaWakatani/10_hasegawa_wakatani.cpp

@@ -348,7 +348,7 @@ class HasegawaWakatani {
 
   //! The parameters used in the simulation.
   const double m_ca  = 3.0;
-  const double m_nu  = 0.01;
+  const double m_nu  = 0.001;
   const double m_eta = 3.0;
   double m_norm_coef;
   double m_dt = 0.0, m_time = 0.0;
@@ -735,7 +735,7 @@ int main(int argc, char* argv[]) {
   Kokkos::ScopeGuard guard(argc, argv);
   auto kwargs         = IO::parse_args(argc, argv);
   std::string out_dir = IO::get_arg(kwargs, "out_dir", "data_kokkos");
-  int nx = 512, nbiter = 100000;
+  int nx = 1024, nbiter = 100000;
   double lx = 10.0, dt = 0.0005;
   HasegawaWakatani model(nx, lx, nbiter, dt, out_dir);
   Kokkos::Timer timer;

examples/10_HasegawaWakatani/README.md

@@ -24,6 +24,25 @@ The nonlinear term can be described as follows, which can be efficiently compute
 
 ![Poisson bracket](https://latex.codecogs.com/svg.latex?\\{\tilde{\phi},\tilde{n}\\}_k=-\sum_{k'}\sum_{k''}\delta_{k+k'+k'',0}\left(k_{x}'k_{y}''-k_{y}'k_{x}''\right)\hat{\phi}^{\ast}_{k'}\hat{n}^{\ast}_{k''})
 
+## Prerequisites
+
+For kokkos version, we need the followings:
+
+* `CMake 3.22+`
+* `Kokkos 4.4+`
+* `gcc 8.3.0+` (CPUs)
+* `IntelLLVM 2023.0.0+` (CPUs, Intel GPUs)
+* `nvcc 11.0.0+` (NVIDIA GPUs)
+* `rocm 5.3.0+` (AMD GPUs)
+
+For python version, we need the followings:
+
+* `python 3.8+`
+* `numpy`
+* `matplotlib`
+* `xarray[io]`, `xarray[viz]`
+* `joblib`
+
 ## python and kokkos implementation
 
 As imagined, the nonlinear term is the core computational kernel of this code. In python, it is implemented by

examples/10_HasegawaWakatani/postscript.py

@@ -144,7 +144,7 @@ def Real3DtoComplex2D(A: np.ndarray) -> np.ndarray:
 
     Parameters
     ----------
-    A (xr.DataArray): A 3D array where the first dimension represents the real and imaginary parts.
+    A (np.ndarray): A 3D array where the first dimension represents the real and imaginary parts.
 
     Returns
     -------