computational-physics

Many of Freud's unit tests currently implement for loops to test various combinations of input parameters. For example, test_diffraction_DiffractionPattern.py currently includes:

for grid_size in (256, 1024):
    for output_size in (255, 256, 1023, 1024):
          for zoom in (1, 2.5, 4.123):

in the test for diffraction peaks in a simple cubic system.

With `@pytest.mark.paramet

• Split the EC Dataset into three datasets
• Implement the Normalization Condition as a new designed Boundary Condition (https://pytorch.org/docs/stable/generated/torch.trapz.html) could make things easier
• Integrate the new normalization condition into the PINN loss calculation
• Switch from x,y,t representation to a single tensor that represents all cases
• Integrate t

Currently the marker size for the particles in the mpl plots is deteremined based on the size of the simulation cell. However, with the release 1.1, it is now possible to change the forcefield and the values of A and B, therefore requiring the particle marker size to scale with both the simulation cell size and the nature of the potential model.

I reckon it is best to have the marker size be d