# Can a file in the Gaussian CUBE format be voxelized?

My aim is to voxelize a CHGCAR file. I am trying to build up a workflow to do the same. With the aid of the Matter Modeling community members, I have figured out a way to convert a CHGCAR file into the CUBE format. Now I want to further process the CUBE file, in an attempt to voxelize it.

• I read here that "Voxelization is the process of converting data structures that store geometric information in a continuous domain (such as a 3D triangular mesh) into a rasterized image (a discrete grid)." A cube file stores information on a discrete 3d grid, i.e. it already is "voxelized". Jun 27 at 11:17

I'm not sure precisely what you mean, but if you want to convert a Cube file to an ($$N\times4$$) matrix where $$N$$ is the number of points and the 4 columns are (x,y,z,value), you can try the following code snippet taken from my GitHub repository here, which I reproduce below:


cube_filepath = '/path/to/mycube.cube' # Path to Cube File

def cube_to_xyzval(cube_file):
"""
Converts cube to pandas DataFrame

Args:
cube_file (string): path to cube file

Returns:
pd_data (Pandas dataframe): dataframe of (x,y,z,val) grid
"""
at_coord=[]
spacing_vec=[]
nline = 0
values=[]
data = []
with open(cube_file,'r') as f:
for line in f:
nline += 1
if nline == 3:
nat=int(line.split()[0])
elif nline >3 and nline <= 6:
spacing_vec.append(line.split())
elif nline > 6 and nline <= 6+nat:
at_coord.append(line.split())
elif nline > 5 and nline > 6+nat:
for i in line.split():
values.append(float(i))
idx = -1
for i in range(0,int(spacing_vec[0][0])):
for j in range(0,int(spacing_vec[1][0])):
for k in range(0,int(spacing_vec[2][0])):
idx += 1
x,y,z = i*float(spacing_vec[0][1]),j*float(spacing_vec[1][2]),k*float(spacing_vec[2][3])
data.append([x,y,z,values[idx]])
pd_data = pd.DataFrame(data)
return pd_data

df = cube_to_xyzval(cube_filepath)
df.columns = ['x','y','z','val']