Filtering data#

A simpler version of PROCAR file containing only a subset of information from the original dataset can be generated with this utility. This feature is very useful when there are many bands in the PROCAR file (e.g. in heterostructures or supercells calculations) making the file size enormously large for post-processing while only bands near the Fermi-level are needed for analysis. In this case, one can filter data of selected bands near the Fermi-level. This considerably reduces the file size and makes the post-processing of data faster. In the same way, one could use the filter utility to filter the PROCAR file to extract information regarding particular spins, atoms, or orbitals in a relatively smaller PROCAR-new file.

The following example extracts information of bands ranging from index 50 to 70 from a PROCAR file (Fermi-level is near band #60) while ignoring all other bands located far from the Fermi-level, and stores resulting dataset in a new file named PROCAR-band50-70. Now the new PROCAR-band50-70 file can be used for further post-processing of data at relatively low memory requirements:

pyprocar.filter('PROCAR','PROCAR-band50-70',bands=[50,70])

To filter selected orbitals#

To make a new PROCAR file containing only three columns, one for \(s\) orbitals, one for \(p\), and one for total (real total, not total of \(s+p\)):

pyprocar.filter('PROCAR’,'PROCAR-filtered_sp’, orbitals=[[0],[1,2,3]])

If you want to select only \(p_x\) orbitals, just use orbitals=[[3]].

In the same way to plot the projection of total \(p\)-orbitals use orbitals=[[1,2,3]] (i.e. \(px+py+pz\)). Same goes for other orbitals.

To filter selected \(k\)-points#

This is helpful especially for hybrid calculations where the weighted \(k\)-points should be filtered to perform band structure plotting. To filter \(k\)-points within a minimum and maximum range:

pyprocar.filter('PROCAR’,'PROCAR-filtered_kpoints’, kpoints=[10,13])

This creates a new PROCAR file containing only the selected \(k\)-points.

To filter selected spins#

This can be used to generate a PROCAR that contains a selected spin component. For non colinear spin calculations 0 is the total spin density and 1,2,3 correspond to spins in the x,y and z directions. For example, to filter the spin in the \(S_x\) direction:

pyprocar.filter('PROCAR’,'PROCAR-filtered_spin’, spin=[1])

This creates a new PROCAR file containing only the selected spin component(s). For colinear spin calculations spin=[0] and spin=[1] selects spin up and spin down components, respectively.

To filter selected atoms#

Usage:

pyprocar.filter(‘PROCAR’,’PROCAR-filter_ATOMS’, atoms=[[0]])

PyProcar counts each row of ions in PROCAR file starting from 0. Keep in mind: atoms=0 does not define the atom_type, rather it defines only the first ion in the POSCAR. So if you have more than one ion of the same element, use atoms = [[0,1,2,3,...]].

pyprocar.scripts.scriptFilter.filter(inFile: str, outFile: str, atoms: List[int] | None = None, orbitals: List[int] | None = None, orbital_names: List[str] | None = None, bands: List[int] | None = None, spin=None, kpoints=None, human_atoms: bool = False)[source]#

This module filters the PROCAR file and re-write a new one.

Parameters:

  • inFile (str) – _description_

  • outFile (str) – _description_

  • atoms (List[int], optional) – _description_, by default None

  • orbitals (List[int], optional) – _description_, by default None

  • orbital_names (List[str], optional) – _description_, by default None

  • bands (List[int], optional) – _description_, by default None

  • spin (_type_, optional) – _description_, by default None

  • kpoints (_type_, optional) – _description_, by default None

  • human_atoms (bool, optional) – _description_, by default False

Raises:

RuntimeError – _description_