mp-pyrho 0.0.21

Tools for re-griding volumetric quantum chemistry data for machine-learning purposes.

mp-pyrho

Tools for re-griding volumetric quantum chemistry data for machine-learning purposes.

• Free software: BSD license
• Documentation: https://materialsproject.github.io/pyrho/

Features

• Restructuring of periodic grid data (mapping from one cell representation to another)
• Up-sampling of periodic grid data using fourier interpolation
• Helper functions to plot examine the data

Example Usage

Basic usage of the PGrid class

The PGrid object is defined by an N-dimensional numpy array grid_data and a N lattice vectors given as a matrix lattice. The input array is a scalar field that is defined on a regularly spaced set of grid points starting at the origin. For example, you can construct a periodic field as follows:

import numpy as np
from pyrho.core.pgrid import PGrid
from pyrho.vis.plotly import get_plotly_scatter_plot

def func(X,Y):
    return np.sin(X) * np.cos(2*Y)
a = np.linspace(0, np.pi, 27,endpoint=True)
b = np.linspace(0, np.pi, 28,endpoint=True)
arg_ = np.meshgrid(a, b, indexing='ij')
data = func(*arg_)
pg = PGrid(grid_data=data, lattice=[[np.pi,0], [0,np.pi]])
get_plotly_scatter_plot(pg.grid_data, pg.lattice, skips=1, opacity=1, marker_size=15)

The data can be examined using the helper plotting function.

The PGrid object has no concept of normalization so if you half the number of points in the domain, the range of the data will stay the same. This is different from how change density is stored in codes like VASP where the values at each point depends on the number of grid points used to store the data.

The regridding capabilties allows the user to obtain the data in any arbitrary representation. For example if we want to shift to the middle of the unitcell and create a ((1,1), (1,-1)) supercell, with a 30 by 32 grid, we can run:

pg_2x = pg.get_transformed_obj([[1,1], [1,-1]], frac_shift=[0.5, 0.5], grid_out=[30,32])
get_plotly_scatter_plot(pg_2x.grid_data, pg_2x.lattice, skips=1, opacity=1, marker_size=10)

Which looks like this:

The ChargeDensity class

The ChargeDensity object inherits from PGrid but also understands pymatgen's definition of VolumetricData. This allows us to actually read in CHGCAR objects either directly using Chgcar.from_file or read in the same data stored via hdf5 as shown below:

from pymatgen.io.vasp import Chgcar
from pyrho.core.chargeDensity import ChargeDensity
chgcar = Chgcar.from_hdf5("./test_files/Si.uc.hdf5")
chgcar = ChargeDensity.from_pmg_volumetric_data(chgcar)
get_plotly_scatter_plot(chgcar.grid_data,
                        lat_mat=chgcar.lattice,
                        factor=4,
                        mask=chgcar.grid_data > 0.3)

Here the plotting function slices the data using [::4] in each direction and filters out the data points below 0.3. This makes the final plot less busy, so we can examing the data.

This charge density can also be transformed:

chgcar_x2 = chgcar.get_transformed_obj(sc_mat = [[1,1,0],[1,-1,0],[0,0,1]], frac_shift=[0.5,0.5,0.5], grid_out=[120,120,60])
get_plotly_scatter_plot(chgcar_x2.grid_data, lat_mat=chgcar_x2.lattice, skips=4, mask=chgcar_x2.grid_data > 0.5, marker_size=10)

Note that we have shifted the origin to the center of unit cell which should be empty after the filering. In the final transformed supercell, the new origin is indicated with a star.

Credits

Jimmy-Xuan Shen: Project lead

Wennie Wang: For naming the package

History

0.0.0 (2021-02-25)

• Initial release on PyPI.