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.
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- 1$\begingroup$ 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". $\endgroup$leopold.talirz– leopold.talirz2021-06-27 11:17:02 +00:00Commented Jun 27, 2021 at 11:17
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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'] 