Hard Rod Fluid Analytical Solution in .npy (For Operator learning)

Hard rod fluid with diameter of 1.0 between 2 walls with distance L+2d=10.0,
under different chemical potential (mu) and external potential (V_ext(x)), with a sampling distance dz=0.1.

Data are stored as numpy structured ndarrays, each npy file store one type of data. 
The ndarrays has the shape of (n_sample,n_grid), with 4 fields:
the position "z", 
the density profile "rho", 
the local chemical potential "muloc"
the one-body direct correlation function "c1".
Where muloc(z)=mu-V_ext(z)

In Operator Learning, I've tried the mapping of rho to c1 as well as muloc to rho.

dataload.py contains some functions used for loading data
data_model_train.py contains the read-in function for model-training data.

data_4groups.npy saves 8000 data, with 2000 in each group, generated from the following external potentials:
group 1: V_ext=0 
group 2: V_binary[z] = -epsilon*((a/(z+a/2))^3+(a/(L+a/2-z))^3)
group 3: V_binary[z] = -mg*z
group 4: V_binary[z] = -u0*(abs(z/z0))^a0
with mu, epsilon, a, mg, u0, z0, a0 are adjustable constants of the potential 

data_mixed23.npy saves 2000 data, generated from the following external potentials:
    V_binary[z] = -epsilon*((a/(z+a/2))^3+(a/(L+a/2-z))^3) -mg*z

Folders are taped in .zip files.

Folder "~/R_data_generator" stores the generator of the data, analytical solution of 1D hard rod fluid.
Folder "~/Rdata" contains same data in .Rdata file, as R matrixs
Folder "~/R2py" contains the script to generate .npy file from .Rdata file