revert master drones

Author: dbroberg

atomate/vasp/drones.py

@@ -20,7 +20,6 @@
 from monty.io import zopen
 from monty.json import jsanitize
 from monty.os.path import which
-from monty.shutil import decompress_file
 
 import numpy as np
 
@@ -29,8 +28,7 @@
 from pymatgen.core.operations import SymmOp
 from pymatgen.electronic_structure.bandstructure import BandStructureSymmLine
 from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
-from pymatgen.io.vasp import BSVasprun, Vasprun, Outcar, Locpot, Chgcar, \
-    parse_defect_states, Procar, Wavecar
+from pymatgen.io.vasp import BSVasprun, Vasprun, Outcar, Locpot, Chgcar
 from pymatgen.io.vasp.inputs import Poscar, Potcar, Incar, Kpoints
 from pymatgen.apps.borg.hive import AbstractDrone
 from pymatgen.command_line.bader_caller import bader_analysis_from_path
@@ -85,8 +83,7 @@ class VaspDrone(AbstractDrone):
 
     def __init__(self, runs=None, parse_dos="auto", bandstructure_mode="auto",
                  parse_locpot=True, additional_fields=None, use_full_uri=True,
-                 parse_bader=bader_exe_exists, parse_chgcar=False, parse_aeccar=False,
-                 defect_wf_parsing=None):
+                 parse_bader=bader_exe_exists, parse_chgcar=False, parse_aeccar=False):
         """
         Initialize a Vasp drone to parse vasp outputs
         Args:
@@ -109,10 +106,6 @@ def __init__(self, runs=None, parse_dos="auto", bandstructure_mode="auto",
             parse_bader (bool): Run and parse Bader charge data. Defaults to True if Bader is present
             parse_chgcar (bool): Run and parse CHGCAR file
             parse_aeccar (bool): Run and parse AECCAR0 and AECCAR2 files
-            defect_wf_parsing (Site): If Site is provided, drone considers Procar and
-                Wavecar parsing relative to the position of Site.
-                 Useful for consideration of defect localization
-                Defaults to None (no extra procar or wavecar parsing occurs)
         """
         self.parse_dos = parse_dos
         self.additional_fields = additional_fields or {}
@@ -124,7 +117,6 @@ def __init__(self, runs=None, parse_dos="auto", bandstructure_mode="auto",
         self.parse_bader = parse_bader
         self.parse_chgcar = parse_chgcar
         self.parse_aeccar = parse_aeccar
-        self.defect_wf_parsing = defect_wf_parsing
 
     def assimilate(self, path):
         """
@@ -218,35 +210,6 @@ def generate_doc(self, dir_name, vasprun_files, outcar_files):
                 logger.error("Bad run stats for {}.".format(fullpath))
             d["run_stats"] = run_stats
 
-            # store defect localization/band filling information
-            if self.defect_wf_parsing:
-                # need to make sure all procars and wavecars are zipped
-                for propat in self.filter_files(fullpath, file_pattern="PROCAR").values():
-                    decompress_file(os.path.join(fullpath, propat))
-                for wavepat in self.filter_files(fullpath, file_pattern="WAVECAR").values():
-                    decompress_file(os.path.join(fullpath, wavepat))
-                procar_paths = [os.path.join(fullpath, ppath) for ppath in
-                                self.filter_files(fullpath, file_pattern="PROCAR").values()]
-                wavecar_paths = [os.path.join(fullpath, wpath) for wpath in
-                                 self.filter_files(fullpath, file_pattern="WAVECAR").values()]
-
-                for i, d_calc in enumerate(d["calcs_reversed"]):
-                    if d_calc.get("output"):
-                        if len(procar_paths) and len(wavecar_paths):
-                            procar = Procar(procar_paths[i])
-                            wavecar = Wavecar(wavecar_paths[i])
-                            structure = Structure.from_dict(d_calc["output"]["structure"])
-                            defect_data = parse_defect_states(structure, self.defect_wf_parsing, wavecar, procar)
-                            d_calc["output"].update({"defect": defect_data})
-
-                        filename = list(vasprun_files.values())[i]
-                        vasprun_file = os.path.join(dir_name, filename)
-                        vrun = Vasprun(vasprun_file)
-                        eigenvalues = vrun.eigenvalues.copy()
-                        kpoint_weights = vrun.actual_kpoints_weights
-                        vr_eigenvalue_dict = {'eigenvalues': eigenvalues, 'kpoint_weights': kpoint_weights}
-                        d_calc["output"].update({"vr_eigenvalue_dict": vr_eigenvalue_dict})
-
             # reverse the calculations data order so newest calc is first
             d["calcs_reversed"].reverse()