"""Parsers for Qchem output files."""

from __future__ import annotations

import copy
import logging
import math
import os
import re
import warnings
from typing import Any

import networkx as nx
import numpy as np
import pandas as pd
from monty.io import zopen
from monty.json import MSONable, jsanitize

from pymatgen.analysis.graphs import MoleculeGraph
from pymatgen.analysis.local_env import OpenBabelNN
from pymatgen.core import Molecule
from pymatgen.io.qchem.utils import (
    process_parsed_coords,
    process_parsed_fock_matrix,
    read_matrix_pattern,
    read_pattern,
    read_table_pattern,
)

try:
    from openbabel import openbabel
except ImportError:
    openbabel = None


__author__ = "Samuel Blau, Brandon Wood, Shyam Dwaraknath, Evan Spotte-Smith, Ryan Kingsbury"
__copyright__ = "Copyright 2018-2022, The Materials Project"
__version__ = "0.1"
__maintainer__ = "Samuel Blau"
__email__ = "samblau1@gmail.com"
__credits__ = "Gabe Gomes"

logger = logging.getLogger(__name__)


class QCOutput(MSONable):
    """Class to parse QChem output files."""

    def __init__(self, filename: str):
        """
        Args:
            filename (str): Filename to parse.
        """
        self.filename = filename
        self.data: dict[str, Any] = {}
        self.data["errors"] = []
        self.data["warnings"] = {}
        self.text = ""
        with zopen(filename, mode="rt", encoding="ISO-8859-1") as f:
            self.text = f.read()

        # Check if output file contains multiple output files. If so, print an error message and exit
        self.data["multiple_outputs"] = read_pattern(
            self.text, {"key": r"Job\s+\d+\s+of\s+(\d+)\s+"}, terminate_on_match=True
        ).get("key")
        if self.data.get("multiple_outputs") is not None and self.data.get("multiple_outputs") != [["1"]]:
            raise ValueError(
                f"ERROR: multiple calculation outputs found in file {filename}. "
                f"Please instead call QCOutput.mulitple_outputs_from_file(QCOutput, {filename!r})"
            )

        # Parse the Q-Chem major version
        if read_pattern(
            self.text, {"key": r"A Quantum Leap Into The Future Of Chemistry\s+Q-Chem 4"}, terminate_on_match=True
        ).get("key") == [[]]:
            self.data["version"] = "4"
        elif read_pattern(
            self.text, {"key": r"A Quantum Leap Into The Future Of Chemistry\s+Q-Chem 5"}, terminate_on_match=True
        ).get("key") == [[]]:
            self.data["version"] = "5"
        elif read_pattern(
            self.text, {"key": r"A Quantum Leap Into The Future Of Chemistry\s+Q-Chem 6"}, terminate_on_match=True
        ).get("key") == [[]]:
            self.data["version"] = "6"
        else:
            self.data["version"] = "unknown"

        # Parse the molecular details: charge, multiplicity,
        # species, and initial geometry.
        self._read_charge_and_multiplicity()
        if read_pattern(self.text, {"key": r"Nuclear Repulsion Energy"}, terminate_on_match=True).get("key") == [[]]:
            self._read_species_and_inital_geometry()

        # Check if calculation finished
        self.data["completion"] = read_pattern(
            self.text,
            {"key": r"Thank you very much for using Q-Chem.\s+Have a nice day."},
            terminate_on_match=True,
        ).get("key")

        # If the calculation finished, parse the job time.
        if self.data.get("completion", []):
            temp_timings = read_pattern(
                self.text,
                {"key": r"Total job time\:\s*([\d\-\.]+)s\(wall\)\,\s*([\d\-\.]+)s\(cpu\)"},
            ).get("key")
            if temp_timings is not None:
                self.data["walltime"] = float(temp_timings[0][0])
                self.data["cputime"] = float(temp_timings[0][1])
            else:
                self.data["walltime"] = self.data["cputime"] = None

        # Check if calculation is unrestricted
        self.data["unrestricted"] = read_pattern(
            self.text,
            {"key": r"A(?:n)*\sunrestricted[\s\w\-]+SCF\scalculation\swill\sbe"},
            terminate_on_match=True,
        ).get("key")
        if not self.data["unrestricted"]:
            self.data["unrestricted"] = read_pattern(
                self.text,
                {"key": r"unrestricted = true"},
                terminate_on_match=True,
            ).get("key")

        if self.data["unrestricted"] is None and self.data["multiplicity"] != 1:
            self.data["unrestricted"] = [[]]

        # Get the value of scf_final_print in the output file
        scf_final_print = read_pattern(
            self.text,
            {"key": r"scf_final_print\s*=\s*(\d+)"},
            terminate_on_match=True,
        ).get("key")
        if scf_final_print is not None:
            self.data["scf_final_print"] = int(scf_final_print[0][0])
        else:
            self.data["scf_final_print"] = 0

        # Check if calculation uses GEN_SCFMAN, multiple potential output formats
        self.data["using_GEN_SCFMAN"] = read_pattern(
            self.text,
            {"key": r"\s+GEN_SCFMAN: A general SCF calculation manager"},
            terminate_on_match=True,
        ).get("key")
        if not self.data["using_GEN_SCFMAN"]:
            self.data["using_GEN_SCFMAN"] = read_pattern(
                self.text,
                {"key": r"\s+General SCF calculation program by"},
                terminate_on_match=True,
            ).get("key")

        # Check if the SCF failed to converge
        if read_pattern(self.text, {"key": r"SCF failed to converge"}, terminate_on_match=True).get("key") == [[]]:
            self.data["errors"] += ["SCF_failed_to_converge"]

        # Parse the SCF
        self._read_SCF()

        # Parse the Mulliken/ESP/RESP charges and dipoles
        self._read_charges_and_dipoles()

        # Check for various warnings
        self._detect_general_warnings()

        # Parse mem_total, if present
        self.data["mem_total"] = None
        if read_pattern(self.text, {"key": r"mem_total\s*="}, terminate_on_match=True).get("key") == [[]]:
            temp_mem_total = read_pattern(self.text, {"key": r"mem_total\s*=\s*(\d+)"}, terminate_on_match=True).get(
                "key"
            )
            self.data["mem_total"] = int(temp_mem_total[0][0])

        # Parse gap info, if present:
        if read_pattern(self.text, {"key": r"Generalized Kohn-Sham gap"}, terminate_on_match=True).get("key") == [[]]:
            gap_info = {}
            # If this is open-shell gap info:
            if read_pattern(self.text, {"key": r"Alpha HOMO Eigenvalue"}, terminate_on_match=True).get("key") == [[]]:
                temp_alpha_HOMO = read_pattern(
                    self.text, {"key": r"Alpha HOMO Eigenvalue\s*=\s*([\d\-\.]+)"}, terminate_on_match=True
                ).get("key")
                gap_info["alpha_HOMO"] = float(temp_alpha_HOMO[0][0])
                temp_beta_HOMO = read_pattern(
                    self.text, {"key": r"Beta  HOMO Eigenvalue\s*=\s*([\d\-\.]+)"}, terminate_on_match=True
                ).get("key")
                gap_info["beta_HOMO"] = float(temp_beta_HOMO[0][0])
                temp_alpha_LUMO = read_pattern(
                    self.text, {"key": r"Alpha LUMO Eigenvalue\s*=\s*([\d\-\.]+)"}, terminate_on_match=True
                ).get("key")
                gap_info["alpha_LUMO"] = float(temp_alpha_LUMO[0][0])
                temp_beta_LUMO = read_pattern(
                    self.text, {"key": r"Beta  LUMO Eigenvalue\s*=\s*([\d\-\.]+)"}, terminate_on_match=True
                ).get("key")
                gap_info["beta_LUMO"] = float(temp_beta_LUMO[0][0])
                temp_alpha_gap = read_pattern(
                    self.text, {"key": r"HOMO-Alpha LUMO gap\s*=\s*([\d\-\.]+)"}, terminate_on_match=True
                ).get("key")
                gap_info["alpha_gap"] = float(temp_alpha_gap[0][0])
                temp_beta_gap = read_pattern(
                    self.text, {"key": r"HOMO-Beta LUMO gap\s*=\s*([\d\-\.]+)"}, terminate_on_match=True
                ).get("key")
                gap_info["beta_gap"] = float(temp_beta_gap[0][0])

            temp_HOMO = read_pattern(
                self.text, {"key": r"    HOMO Eigenvalue\s*=\s*([\d\-\.]+)"}, terminate_on_match=True
            ).get("key")
            gap_info["HOMO"] = float(temp_HOMO[0][0])
            temp_LUMO = read_pattern(
                self.text, {"key": r"    LUMO Eigenvalue\s*=\s*([\d\-\.]+)"}, terminate_on_match=True
            ).get("key")
            gap_info["LUMO"] = float(temp_LUMO[0][0])
            temp_KSgap = read_pattern(self.text, {"key": r"KS gap\s*=\s*([\d\-\.]+)"}, terminate_on_match=True).get(
                "key"
            )
            gap_info["KSgap"] = float(temp_KSgap[0][0])
            self.data["gap_info"] = gap_info
        else:
            self.data["gap_info"] = None

        # Check to see if PCM or SMD are present
        self.data["solvent_method"] = self.data["solvent_data"] = None

        if read_pattern(self.text, {"key": r"solvent_method\s*=?\s*pcm"}, terminate_on_match=True).get("key") == [[]]:
            self.data["solvent_method"] = "PCM"
        if read_pattern(self.text, {"key": r"solvent_method\s*=?\s*smd"}, terminate_on_match=True).get("key") == [[]]:
            self.data["solvent_method"] = "SMD"
        if read_pattern(self.text, {"key": r"solvent_method\s*=?\s*isosvp"}, terminate_on_match=True).get("key") == [
            []
        ]:
            self.data["solvent_method"] = "ISOSVP"

        # if solvent_method is not None, populate solvent_data with None values for all possible keys
        # ISOSVP and CMIRS data are nested under respective keys
        # TODO - nest PCM and SMD data under respective keys in a future PR
        pcm_keys = [
            "PCM_dielectric",
            "g_electrostatic",
            "g_cavitation",
            "g_dispersion",
            "g_repulsion",
            "total_contribution_pcm",
            "solute_internal_energy",
        ]
        smd_keys = ["SMD_solvent", "smd0", "smd3", "smd4", "smd6", "smd9"]
        isosvp_keys = [
            "isosvp_dielectric",
            "final_soln_phase_e",
            "solute_internal_e",
            "total_solvation_free_e",
            "change_solute_internal_e",
            "reaction_field_free_e",
        ]
        cmirs_keys = [
            "CMIRS_enabled",
            "dispersion_e",
            "exchange_e",
            "min_neg_field_e",
            "max_pos_field_e",
        ]

        if self.data["solvent_method"] is not None:
            self.data["solvent_data"] = {}
            for key in pcm_keys + smd_keys:
                self.data["solvent_data"][key] = None
            self.data["solvent_data"]["isosvp"] = {}
            for key in isosvp_keys:
                self.data["solvent_data"]["isosvp"][key] = None
            self.data["solvent_data"]["cmirs"] = {}
            for key in cmirs_keys:
                self.data["solvent_data"]["cmirs"][key] = None

        # Parse information specific to a solvent model
        if self.data["solvent_method"] == "PCM":
            temp_dielectric = read_pattern(
                self.text, {"key": r"dielectric\s*([\d\-\.]+)"}, terminate_on_match=True
            ).get("key")
            self.data["solvent_data"]["PCM_dielectric"] = float(temp_dielectric[0][0])
            self._read_pcm_information()
        elif self.data["solvent_method"] == "SMD":
            if read_pattern(self.text, {"key": r"Unrecognized solvent"}, terminate_on_match=True).get("key") == [[]]:
                if not self.data.get("completion", []):
                    self.data["errors"] += ["unrecognized_solvent"]
                else:
                    self.data["warnings"]["unrecognized_solvent"] = True
            temp_solvent = read_pattern(self.text, {"key": r"\s[Ss]olvent:? ([a-zA-Z]+)"}).get("key")
            for val in temp_solvent:
                if val[0] != temp_solvent[0][0]:
                    if val[0] != "for":
                        self.data["warnings"]["SMD_two_solvents"] = f"{temp_solvent[0][0]} and {val[0]}"
                    elif (
                        "unrecognized_solvent" not in self.data["errors"]
                        and "unrecognized_solvent" not in self.data["warnings"]
                    ):
                        self.data["warnings"]["questionable_SMD_parsing"] = True
            self.data["solvent_data"]["SMD_solvent"] = temp_solvent[0][0]
            self._read_smd_information()
        elif self.data["solvent_method"] == "ISOSVP":
            self.data["solvent_data"]["cmirs"]["CMIRS_enabled"] = False
            self._read_isosvp_information()
            if read_pattern(
                self.text,
                {"cmirs": r"DEFESR calculation with single-center isodensity surface"},
                terminate_on_match=True,
            ).get("cmirs") == [[]]:
                # this is a CMIRS calc
                # note that all other outputs follow the same format as ISOSVP
                self._read_cmirs_information()
                # TODO - would it make more sense to set solvent_method = 'cmirs'?
                # but in the QChem input file, solvent_method is still 'isosvp'
                self.data["solvent_data"]["cmirs"]["CMIRS_enabled"] = True

                # TODO - parsing to identify the CMIRS solvent?
                # this would require parsing the $pcm_nonels section of input and comparing
                # parameters to the contents of CMIRS_SETTINGS in pymatgen.io.qchem.sets

        # Parse the final energy
        temp_final_energy = read_pattern(self.text, {"key": r"Final\senergy\sis\s+([\d\-\.]+)"}).get("key")
        if temp_final_energy is None:
            self.data["final_energy"] = None
        else:
            self.data["final_energy"] = float(temp_final_energy[0][0])

        if self.data["final_energy"] is None:
            temp_dict = read_pattern(
                self.text,
                {"final_energy": r"\s*Total\s+energy in the final basis set\s+=\s*([\d\-\.]+)"},
            )

            if temp_dict.get("final_energy") is not None:
                self.data["final_energy"] = float(temp_dict.get("final_energy")[-1][0])

        # Check if calculation is using dft_d and parse relevant info if so
        self.data["using_dft_d3"] = read_pattern(self.text, {"key": r"dft_d\s*= d3"}, terminate_on_match=True).get(
            "key"
        )
        if self.data.get("using_dft_d3", []):
            temp_d3 = read_pattern(
                self.text,
                {"key": r"\-D3 energy without 3body term =\s*([\d\.\-]+) hartrees"},
            ).get("key")
            real_d3 = np.zeros(len(temp_d3))
            if temp_d3 is None:
                self.data["dft_d3"] = None
            elif len(temp_d3) == 1:
                self.data["dft_d3"] = float(temp_d3[0][0])
            else:
                for ii, entry in enumerate(temp_d3):
                    real_d3[ii] = float(entry[0])
                self.data["dft_d3"] = real_d3

        # Parse the S2 values in the case of an unrestricted calculation
        if self.data.get("unrestricted", []):
            correct_s2 = 0.5 * (self.data["multiplicity"] - 1) * (0.5 * (self.data["multiplicity"] - 1) + 1)
            temp_S2 = read_pattern(self.text, {"key": r"<S\^2>\s=\s+([\d\-\.]+)"}).get("key")
            if temp_S2 is None:
                self.data["S2"] = None
            elif len(temp_S2) == 1:
                self.data["S2"] = float(temp_S2[0][0])
                if abs(correct_s2 - self.data["S2"]) > 0.01:
                    self.data["warnings"]["spin_contamination"] = abs(correct_s2 - self.data["S2"])
            else:
                real_S2 = np.zeros(len(temp_S2))
                have_spin_contamination = False
                for ii, entry in enumerate(temp_S2):
                    real_S2[ii] = float(entry[0])
                    if abs(correct_s2 - real_S2[ii]) > 0.01:
                        have_spin_contamination = True
                self.data["S2"] = real_S2
                if have_spin_contamination:
                    spin_contamination = np.zeros(len(self.data["S2"]))
                    for ii, entry in enumerate(self.data["S2"]):
                        spin_contamination[ii] = abs(correct_s2 - entry)
                    self.data["warnings"]["spin_contamination"] = spin_contamination

        # Parse data from CDFT calculations
        self.data["cdft"] = read_pattern(self.text, {"key": r"CDFT Becke Populations"}).get("key")
        if self.data.get("cdft", []):
            self._read_cdft()

        # Parse direct-coupling calculation output
        self.data["cdft_direct_coupling"] = read_pattern(
            self.text, {"key": r"Start with Direct-Coupling Calculation"}
        ).get("key")
        if self.data.get("cdft_direct_coupling", []):
            temp_dict = read_pattern(
                self.text,
                {
                    "Hif": r"\s*DC Matrix Element\s+Hif =\s+([\-\.0-9]+)",
                    "Sif": r"\s*DC Matrix Element\s+Sif =\s+([\-\.0-9]+)",
                    "Hii": r"\s*DC Matrix Element\s+Hii =\s+([\-\.0-9]+)",
                    "Sii": r"\s*DC Matrix Element\s+Sii =\s+([\-\.0-9]+)",
                    "Hff": r"\s*DC Matrix Element\s+Hff =\s+([\-\.0-9]+)",
                    "Sff": r"\s*DC Matrix Element\s+Sff =\s+([\-\.0-9]+)",
                    "coupling": r"\s*Effective Coupling \(in eV\) =\s+([\-\.0-9]+)",
                },
            )

            if len(temp_dict.get("Hif", [])) == 0:
                self.data["direct_coupling_Hif_Hartree"] = None
            else:
                self.data["direct_coupling_Hif_Hartree"] = float(temp_dict["Hif"][0][0])
            if len(temp_dict.get("Sif", [])) == 0:
                self.data["direct_coupling_Sif_Hartree"] = None
            else:
                self.data["direct_coupling_Sif_Hartree"] = float(temp_dict["Sif"][0][0])
            if len(temp_dict.get("Hii", [])) == 0:
                self.data["direct_coupling_Hii_Hartree"] = None
            else:
                self.data["direct_coupling_Hii_Hartree"] = float(temp_dict["Hii"][0][0])
            if len(temp_dict.get("Sii", [])) == 0:
                self.data["direct_coupling_Sii_Hartree"] = None
            else:
                self.data["direct_coupling_Sii_Hartree"] = float(temp_dict["Sii"][0][0])
            if len(temp_dict.get("Hff", [])) == 0:
                self.data["direct_coupling_Hff_Hartree"] = None
            else:
                self.data["direct_coupling_Hff_Hartree"] = float(temp_dict["Hff"][0][0])
            if len(temp_dict.get("Sff", [])) == 0:
                self.data["direct_coupling_Sff_Hartree"] = None
            else:
                self.data["direct_coupling_Sff_Hartree"] = float(temp_dict["Sff"][0][0])
            if len(temp_dict.get("coupling", [])) == 0:
                self.data["direct_coupling_eV"] = None
            else:
                self.data["direct_coupling_eV"] = float(temp_dict["coupling"][0][0])

        # Parse data from ALMO(MSDFT) calculation
        self.data["almo_msdft"] = read_pattern(
            self.text, {"key": r"ALMO\(MSDFT2?\) method for electronic coupling"}
        ).get("key")
        if self.data.get("almo_msdft", []):
            self._read_almo_msdft()

        # Parse data from Projection Operator Diabatization (POD) calculation
        self.data["pod"] = read_pattern(self.text, {"key": r"POD2? based on the RSCF Fock matrix"}).get("key")
        if self.data.get("pod", []):
            coupling = read_pattern(
                self.text,
                {"coupling": r"The D\([0-9]+\) \- A\([0-9]+\) coupling:\s+(?:[\.\-0-9]+ \()?([\-\.0-9]+) meV\)?"},
            ).get("coupling")

            if coupling is None or len(coupling) == 0:
                self.data["pod_coupling_eV"] = None
            else:
                self.data["pod_coupling_eV"] = float(coupling[0][0]) / 1000

        # Parse data from Fragment Orbital DFT (FODFT) method
        self.data["fodft"] = read_pattern(
            self.text, {"key": r"FODFT\(2n(?:[\-\+]1)?\)\@D(?:\^[\-\+])?A(?:\^\-)? for [EH]T"}
        ).get("key")
        if self.data.get("fodft", []):
            temp_dict = read_pattern(
                self.text,
                {
                    "had": r"H_ad = (?:[\-\.0-9]+) \(([\-\.0-9]+) meV\)",
                    "hda": r"H_da = (?:[\-\.0-9]+) \(([\-\.0-9]+) meV\)",
                    "coupling": r"The (?:averaged )?electronic coupling: (?:[\-\.0-9]+) \(([\-\.0-9]+) meV\)",
                },
            )

            if temp_dict.get("had") is None or len(temp_dict.get("had", [])) == 0:
                self.data["fodft_had_eV"] = None
            else:
                self.data["fodft_had_eV"] = float(temp_dict["had"][0][0]) / 1000
            if temp_dict.get("hda") is None or len(temp_dict.get("hda", [])) == 0:
                self.data["fodft_hda_eV"] = None
            else:
                self.data["fodft_hda_eV"] = float(temp_dict["hda"][0][0]) / 1000
            if temp_dict.get("coupling") is None or len(temp_dict.get("coupling", [])) == 0:
                self.data["fodft_coupling_eV"] = None
            else:
                self.data["fodft_coupling_eV"] = float(temp_dict["coupling"][0][0]) / 1000

        # Parse additional data from coupled-cluster calculations
        self.data["coupled_cluster"] = read_pattern(
            self.text, {"key": r"CCMAN2: suite of methods based on coupled cluster"}
        ).get("key")
        if self.data.get("coupled_cluster", []):
            temp_dict = read_pattern(
                self.text,
                {
                    "SCF": r"\s+SCF energy\s+=\s+([\d\-\.]+)",
                    "MP2": r"\s+MP2 energy\s+=\s+([\d\-\.]+)",
                    "CCSD_correlation": r"\s+CCSD correlation energy\s+=\s+([\d\-\.]+)",
                    "CCSD": r"\s+CCSD total energy\s+=\s+([\d\-\.]+)",
                    "CCSD(T)_correlation": r"\s+CCSD\(T\) correlation energy\s+=\s+([\d\-\.]+)",
                    "CCSD(T)": r"\s+CCSD\(T\) total energy\s+=\s+([\d\-\.]+)",
                },
            )

            if temp_dict.get("SCF") is None:
                self.data["hf_scf_energy"] = None
            else:
                self.data["hf_scf_energy"] = float(temp_dict["SCF"][0][0])

            if temp_dict.get("MP2") is None:
                self.data["mp2_energy"] = None
            else:
                self.data["mp2_energy"] = float(temp_dict["MP2"][0][0])

            if temp_dict.get("CCSD_correlation") is None:
                self.data["ccsd_correlation_energy"] = None
            else:
                self.data["ccsd_correlation_energy"] = float(temp_dict["CCSD_correlation"][0][0])

            if temp_dict.get("CCSD") is None:
                self.data["ccsd_total_energy"] = None
            else:
                self.data["ccsd_total_energy"] = float(temp_dict["CCSD"][0][0])

            if temp_dict.get("CCSD(T)_correlation") is None:
                self.data["ccsd(t)_correlation_energy"] = None
            else:
                self.data["ccsd(t)_correlation_energy"] = float(temp_dict["CCSD(T)_correlation"][0][0])

            if temp_dict.get("CCSD(T)") is None:
                self.data["ccsd(t)_total_energy"] = None
            else:
                self.data["ccsd(t)_total_energy"] = float(temp_dict["CCSD(T)"][0][0])

        # Check if the calculation is a geometry optimization. If so, parse the relevant output
        self.data["optimization"] = read_pattern(self.text, {"key": r"(?i)\s*job(?:_)*type\s*(?:=)*\s*opt"}).get("key")
        if self.data.get("optimization", []):
            # Determine if the calculation is using the new geometry optimizer
            self.data["new_optimizer"] = read_pattern(self.text, {"key": r"(?i)\s*geom_opt2\s*(?:=)*\s*3"}).get("key")
            if self.data["version"] == "6":
                temp_driver = read_pattern(self.text, {"key": r"(?i)\s*geom_opt_driver\s*(?:=)*\s*optimize"}).get("key")
                if temp_driver is None:
                    self.data["new_optimizer"] = [[]]
            # Check if we have an unexpected transition state
            tmp_transition_state = read_pattern(self.text, {"key": r"TRANSITION STATE CONVERGED"}).get("key")
            if tmp_transition_state is not None:
                self.data["warnings"]["unexpected_transition_state"] = True
            self._read_optimization_data()

        # Check if the calculation is a transition state optimization. If so, parse the relevant output
        # Note: for now, TS calculations are treated the same as optimization calculations
        self.data["transition_state"] = read_pattern(self.text, {"key": r"(?i)\s*job(?:_)*type\s*(?:=)*\s*ts"}).get(
            "key"
        )
        if self.data.get("transition_state", []):
            self._read_optimization_data()

        # Check if the calculation contains a constraint in an $opt section.
        self.data["opt_constraint"] = read_pattern(self.text, {"key": r"\$opt\s+CONSTRAINT"}).get("key")
        if self.data.get("opt_constraint"):
            temp_constraint = read_pattern(
                self.text,
                {
                    "key": r"Constraints and their Current Values\s+Value\s+Constraint\s+(\w+)\:\s+([\d\-\.]+)\s+"
                    r"([\d\-\.]+)\s+([\d\-\.]+)\s+([\d\-\.]+)\s+([\d\-\.]+)\s+([\d\-\.]+)"
                },
            ).get("key")
            if temp_constraint is not None:
                self.data["opt_constraint"] = temp_constraint[0]
                if self.data.get("opt_constraint") is not None and float(self.data["opt_constraint"][5]) != float(
                    self.data["opt_constraint"][6]
                ):
                    if abs(float(self.data["opt_constraint"][5])) != abs(float(self.data["opt_constraint"][6])):
                        raise ValueError("ERROR: Opt section value and constraint should be the same!")
                    if abs(float(self.data["opt_constraint"][5])) not in [
                        0.0,
                        180.0,
                    ]:
                        raise ValueError(
                            "ERROR: Opt section value and constraint can only differ by a sign at 0.0 and 180.0!"
                        )

        # Check if the calculation is a frequency analysis. If so, parse the relevant output
        self.data["frequency_job"] = read_pattern(
            self.text,
            {"key": r"(?i)\s*job(?:_)*type\s*(?:=)*\s*freq"},
            terminate_on_match=True,
        ).get("key")
        if self.data.get("frequency_job", []):
            self._read_frequency_data()

        # Check if the calculation is a single point. If so, parse the relevant output
        self.data["single_point_job"] = read_pattern(
            self.text,
            {"key": r"(?i)\s*job(?:_)*type\s*(?:=)*\s*sp"},
            terminate_on_match=True,
        ).get("key")

        # Check if the calculation is a force calculation. If so, parse the relevant output
        self.data["force_job"] = read_pattern(
            self.text,
            {"key": r"(?i)\s*job(?:_)*type\s*(?:=)*\s*force"},
            terminate_on_match=True,
        ).get("key")
        if self.data.get("force_job", []):
            self._read_force_data()

        # Read in the eigenvalues from the output file
        if self.data["scf_final_print"] >= 1:
            self._read_eigenvalues()

        # Read the Fock matrix from the output file
        if self.data["scf_final_print"] >= 3:
            self._read_fock_matrix()
            self._read_coefficient_matrix()

        # Check if the calculation is a PES scan. If so, parse the relevant output
        self.data["scan_job"] = read_pattern(
            self.text, {"key": r"(?i)\s*job(?:_)*type\s*(?:=)*\s*pes_scan"}, terminate_on_match=True
        ).get("key")
        if self.data.get("scan_job", []):
            self._read_scan_data()

        # Check if an NBO calculation was performed. If so, parse the relevant output
        self.data["nbo_data"] = read_pattern(
            self.text, {"key": r"N A T U R A L   A T O M I C   O R B I T A L"}, terminate_on_match=True
        ).get("key")
        if self.data.get("nbo_data", []):
            self._read_nbo_data()

        # If the calculation did not finish and no errors have been identified yet, check for other errors
        if not self.data.get("completion", []) and self.data.get("errors") == []:
            self._check_completion_errors()

    @staticmethod
    def multiple_outputs_from_file(filename, keep_sub_files=True):
        """
        Parses a QChem output file with multiple calculations
        # 1.) Separates the output into sub-files
            e.g. qcout -> qcout.0, qcout.1, qcout.2 ... qcout.N
            a.) Find delimiter for multiple calculations
            b.) Make separate output sub-files
        2.) Creates separate QCCalcs for each one from the sub-files.
        """
        to_return = []
        with zopen(filename, "rt") as f:
            text = re.split(r"\s*(?:Running\s+)*Job\s+\d+\s+of\s+\d+\s+", f.read())
        if text[0] == "":
            text = text[1:]
        for i, sub_text in enumerate(text):
            with open(f"{filename}.{i}", "w") as temp:
                temp.write(sub_text)
            tempOutput = QCOutput(f"{filename}.{i}")
            to_return.append(tempOutput)
            if not keep_sub_files:
                os.remove(f"{filename}.{i}")
        return to_return

    def _read_eigenvalues(self):
        """Parse the orbital energies from the output file. An array of the
        dimensions of the number of orbitals used in the calculation is stored.
        """
        # Find the pattern corresponding to the "Final Alpha MO Eigenvalues" section
        header_pattern = r"Final Alpha MO Eigenvalues"
        # The elements of the matrix are always floats, they are surrounded by
        # the index of the matrix, which are integers.
        elements_pattern = r"\-*\d+\.\d+"
        if not self.data.get("unrestricted", []):
            spin_unrestricted = False
            # Since this is a spin-paired calculation, there will be
            # only a single list of orbital energies (denoted as alpha)
            # in the input file. The footer will then correspond
            # to the next section, which is the MO coefficients.
            footer_pattern = r"Final Alpha MO Coefficients+\s*"
        else:
            spin_unrestricted = True
            # It is an unrestricted calculation, so there will be two sets
            # of eigenvalues. First parse the alpha eigenvalues.
            footer_pattern = r"Final Beta MO Eigenvalues"
        # Common for both spin restricted and unrestricted calculations is the alpha eigenvalues.
        alpha_eigenvalues = read_matrix_pattern(
            header_pattern, footer_pattern, elements_pattern, self.text, postprocess=float
        )
        # The beta eigenvalues are only present if this is a spin-unrestricted calculation.
        if spin_unrestricted:
            header_pattern = r"Final Beta MO Eigenvalues"
            footer_pattern = r"Final Alpha MO Coefficients+\s*"
            beta_eigenvalues = read_matrix_pattern(
                header_pattern, footer_pattern, elements_pattern, self.text, postprocess=float
            )

        self.data["alpha_eigenvalues"] = alpha_eigenvalues

        if spin_unrestricted:
            self.data["beta_eigenvalues"] = beta_eigenvalues

    def _read_fock_matrix(self):
        """Parses the Fock matrix. The matrix is read in whole
        from the output file and then transformed into the right dimensions.
        """
        # The header is the same for both spin-restricted and spin-unrestricted calculations.
        header_pattern = r"Final Alpha Fock Matrix"
        # The elements of the matrix are always floats, they are surrounded by
        # the index of the matrix, which are integers
        elements_pattern = r"\-*\d+\.\d+"
        if not self.data.get("unrestricted", []):
            spin_unrestricted = False
            footer_pattern = "SCF time:"
        else:
            spin_unrestricted = True
            footer_pattern = "Final Beta Fock Matrix"
        # Common for both spin restricted and unrestricted calculations is the alpha Fock matrix.
        alpha_fock_matrix = read_matrix_pattern(
            header_pattern, footer_pattern, elements_pattern, self.text, postprocess=float
        )
        # The beta Fock matrix is only present if this is a spin-unrestricted calculation.
        if spin_unrestricted:
            header_pattern = r"Final Beta Fock Matrix"
            footer_pattern = "SCF time:"
            beta_fock_matrix = read_matrix_pattern(
                header_pattern, footer_pattern, elements_pattern, self.text, postprocess=float
            )

        # Convert the matrices to the right dimension. Right now they are simply
        # one massive list of numbers, but we need to split them into a matrix. The
        # Fock matrix must always be a square matrix and as a result, we have to
        # modify the dimensions.
        alpha_fock_matrix = process_parsed_fock_matrix(alpha_fock_matrix)
        self.data["alpha_fock_matrix"] = alpha_fock_matrix

        if spin_unrestricted:
            # Perform the same transformation for the beta Fock matrix.
            beta_fock_matrix = process_parsed_fock_matrix(beta_fock_matrix)
            self.data["beta_fock_matrix"] = beta_fock_matrix

    def _read_coefficient_matrix(self):
        """Parses the coefficient matrix from the output file. Done is much
        the same was as the Fock matrix.
        """
        # The header is the same for both spin-restricted and spin-unrestricted calculations.
        header_pattern = r"Final Alpha MO Coefficients"
        # The elements of the matrix are always floats, they are surrounded by
        # the index of the matrix, which are integers
        elements_pattern = r"\-*\d+\.\d+"
        if not self.data.get("unrestricted", []):
            spin_unrestricted = False
            footer_pattern = "Final Alpha Density Matrix"
        else:
            spin_unrestricted = True
            footer_pattern = "Final Beta MO Coefficients"
        # Common for both spin restricted and unrestricted calculations is the alpha Fock matrix.
        alpha_coeff_matrix = read_matrix_pattern(
            header_pattern, footer_pattern, elements_pattern, self.text, postprocess=float
        )
        if spin_unrestricted:
            header_pattern = r"Final Beta MO Coefficients"
            footer_pattern = "Final Alpha Density Matrix"
            beta_coeff_matrix = read_matrix_pattern(
                header_pattern, footer_pattern, elements_pattern, self.text, postprocess=float
            )

        # Convert the matrices to the right dimension. Right now they are simply
        # one massive list of numbers, but we need to split them into a matrix. The
        # Fock matrix must always be a square matrix and as a result, we have to
        # modify the dimensions.
        alpha_coeff_matrix = process_parsed_fock_matrix(alpha_coeff_matrix)
        self.data["alpha_coeff_matrix"] = alpha_coeff_matrix

        if spin_unrestricted:
            # Perform the same transformation for the beta Fock matrix.
            beta_coeff_matrix = process_parsed_fock_matrix(beta_coeff_matrix)
            self.data["beta_coeff_matrix"] = beta_coeff_matrix

    def _read_charge_and_multiplicity(self):
        """Parses charge and multiplicity."""
        temp_charge = read_pattern(self.text, {"key": r"\$molecule\s+([\-\d]+)\s+\d"}, terminate_on_match=True).get(
            "key"
        )
        if temp_charge is not None:
            self.data["charge"] = int(temp_charge[0][0])
        else:
            temp_charge = read_pattern(
                self.text,
                {"key": r"Sum of atomic charges \=\s+([\d\-\.\+]+)"},
                terminate_on_match=True,
            ).get("key")
            if temp_charge is None:
                self.data["charge"] = None
            else:
                self.data["charge"] = int(float(temp_charge[0][0]))

        temp_multiplicity = read_pattern(
            self.text, {"key": r"\$molecule\s+[\-\d]+\s+(\d)"}, terminate_on_match=True
        ).get("key")
        if temp_multiplicity is not None:
            self.data["multiplicity"] = int(temp_multiplicity[0][0])
        else:
            temp_multiplicity = read_pattern(
                self.text,
                {"key": r"Sum of spin\s+charges \=\s+([\d\-\.\+]+)"},
                terminate_on_match=True,
            ).get("key")
            if temp_multiplicity is None:
                self.data["multiplicity"] = 1
            else:
                self.data["multiplicity"] = int(float(temp_multiplicity[0][0])) + 1

    def _read_species_and_inital_geometry(self):
        """Parses species and initial geometry."""
        header_pattern = r"Standard Nuclear Orientation \(Angstroms\)\s+I\s+Atom\s+X\s+Y\s+Z\s+-+"
        table_pattern = r"\s*\d+\s+([a-zA-Z]+)\s*([\d\-\.]+)\s*([\d\-\.]+)\s*([\d\-\.]+)\s*"
        footer_pattern = r"\s*-+"
        temp_geom = read_table_pattern(self.text, header_pattern, table_pattern, footer_pattern)
        if temp_geom is None or len(temp_geom) == 0:
            self.data["species"] = self.data["initial_geometry"] = self.data["initial_molecule"] = self.data[
                "point_group"
            ] = None
        else:
            temp_point_group = read_pattern(
                self.text,
                {"key": r"Molecular Point Group\s+([A-Za-z\d\*]+)"},
                terminate_on_match=True,
            ).get("key")
            if temp_point_group is not None:
                self.data["point_group"] = temp_point_group[0][0]
            else:
                self.data["point_group"] = None
            temp_geom = temp_geom[0]
            species = []
            geometry = np.zeros(shape=(len(temp_geom), 3), dtype=float)
            for ii, entry in enumerate(temp_geom):
                species += [entry[0]]
                for jj in range(3):
                    if "*" in entry[jj + 1]:
                        geometry[ii, jj] = 10000000000.0
                    else:
                        geometry[ii, jj] = float(entry[jj + 1])
            self.data["species"] = species
            self.data["initial_geometry"] = geometry
            if self.data["charge"] is not None and self.data["multiplicity"] is not None:
                self.data["initial_molecule"] = Molecule(
                    species=species,
                    coords=geometry,
                    charge=self.data.get("charge"),
                    spin_multiplicity=self.data.get("multiplicity"),
                )
            else:
                self.data["initial_molecule"] = None

    def _read_SCF(self):
        """Parses both old and new SCFs."""
        if self.data.get("using_GEN_SCFMAN", []):
            footer_pattern = r"(^\s*\-+\n\s+SCF time|^\s*gen_scfman_exception: SCF failed to converge)"
            header_pattern = (
                r"^\s*\-+\s+Cycle\s+Energy\s+(?:(?:DIIS)*\s+[Ee]rror)*(?:RMS Gradient)*\s+\-+"
                r"(?:\s*\-+\s+OpenMP\s+Integral\s+computing\s+Module\s+"
                r"(?:Release:\s+version\s+[\d\-\.]+\,\s+\w+\s+[\d\-\.]+\, "
                r"Q-Chem Inc\. Pittsburgh\s+)*\-+)*\n"
            )
            table_pattern = (
                r"(?:\s*Nonlocal correlation = [\d\-\.]+e[\d\-]+)*"
                r"(?:\s*Inaccurate integrated density:\n\s+Number of electrons\s+=\s+[\d\-\.]+\n\s+"
                r"Numerical integral\s+=\s+[\d\-\.]+\n\s+Relative error\s+=\s+[\d\-\.]+\s+\%\n)*\s*\d+\s+"
                r"([\d\-\.]+)\s+([\d\-\.]+)e([\d\-\.\+]+)(?:\s+Convergence criterion met)*"
                r"(?:\s+Preconditoned Steepest Descent)*(?:\s+Roothaan Step)*(?:\s+"
                r"(?:Normal\s+)*BFGS [Ss]tep)*(?:\s+LineSearch Step)*(?:\s+Line search: overstep)*"
                r"(?:\s+Dog-leg BFGS step)*(?:\s+Line search: understep)*"
                r"(?:\s+Descent step)*(?:\s+Done DIIS. Switching to GDM)*"
                r"(?:\s+Done GDM. Switching to DIIS)*"
                r"(?:(?:\s+Done GDM. Switching to GDM with quadratic line-search\s)*\s*GDM subspace size\: \d+)*"
                r"(?:\s*\-+\s+Cycle\s+Energy\s+(?:(?:DIIS)*\s+[Ee]rror)*"
                r"(?:RMS Gradient)*\s+\-+"
                r"(?:\s*\-+\s+OpenMP\s+Integral\s+computing\s+Module\s+"
                r"(?:Release:\s+version\s+[\d\-\.]+\,\s+\w+\s+[\d\-\.]+\, "
                r"Q-Chem Inc\. Pittsburgh\s+)*\-+)*\n)*"
                r"(?:\s*Line search, dEdstep = [\d\-\.]+e[\d\-\.\+]+\s+[\d\-\.]+e[\d\-\.\+]+\s+[\d\-\.]+\s*)*"
                r"(?:\s*[\d\-\.]+e[\d\-\.\+]+\s+[\d\-\.]+\s+[\d\-\.]+e[\d\-\.\+]+\s+[\d\-\.]+e[\d\-\.\+]+\s"
                r"+[\d\-\.]+\s+[\d\-\.]+e[\d\-\.\+]+\s+Optimal value differs by [\d\-\.]+e[\d\-\.\+]+ from prediction)*"
                r"(?:\s*Resetting GDM\.)*(?:\s+[\d\-\.]+e[\d\-\.\+]+\s+[\d\-\.]+\s+[\d\-\.]+e[\d\-\.\+]+)*"
                r"(?:\s+[\d\-\.]+e[\d\-\.\+]+\s+[\d\-\.]+\s+[\d\-\.]+e[\d\-\.\+]+\s+[\d\-\.]+e[\d\-\.\+]+\s+"
                r"[\d\-\.]+\s+[\d\-\.]+e[\d\-\.\+]+\s+Optimal value differs by [\d\-\.]+e[\d\-\.\+]+ from prediction)*"
                r"(?:\s*gdm_qls\: Orbitals will not converge further\.)*"
                r"(?:(\n\s*[a-z\dA-Z_\s/]+\.C|\n\s*GDM)::WARNING energy changes are now smaller than effective "
                r"accuracy\.\s*(\n\s*[a-z\dA-Z_\s/]+\.C|\n\s*GDM)::\s+calculation will continue, but THRESH "
                r"should be increased\s*"
                r"(\n\s*[a-z\dA-Z_\s/]+\.C|\n\s*GDM)::\s+or SCF_CONVERGENCE decreased"
                r"\.\s*(\n\s*[a-z\dA-Z_\s/]+\.C|\n\s*GDM)::\s+effective_thresh = [\d\-\.]+e[\d\-]+)*"
            )
        else:
            if "SCF_failed_to_converge" in self.data.get("errors"):
                footer_pattern = r"^\s*\d+\s*[\d\-\.]+\s+[\d\-\.]+E[\d\-\.]+\s+Convergence\s+failure\n"
            else:
                footer_pattern = r"^\s*\-+\n"
            header_pattern = r"^\s*\-+\s+Cycle\s+Energy\s+DIIS Error\s+\-+\n"
            table_pattern = (
                r"(?:\s*Inaccurate integrated density:\n\s+Number of electrons\s+=\s+[\d\-\.]+\n\s+"
                r"Numerical integral\s+=\s+[\d\-\.]+\n\s+Relative error\s+=\s+[\d\-\.]+\s+\%\n)*\s*\d+\s*"
                r"([\d\-\.]+)\s+([\d\-\.]+)E([\d\-\.\+]+)(?:\s*\n\s*cpu\s+[\d\-\.]+\swall\s+[\d\-\.]+)*"
                r"(?:\nin dftxc\.C, eleTot sum is:[\d\-\.]+, tauTot is\:[\d\-\.]+)*"
                r"(?:\s+Convergence criterion met)*(?:\s+Done RCA\. Switching to DIIS)*"
                r"(?:\n\s*Warning: not using a symmetric Q)*"
                r"(?:\nRecomputing EXC\s*[\d\-\.]+\s*[\d\-\.]+\s*[\d\-\.]+"
                r"(?:\s*\nRecomputing EXC\s*[\d\-\.]+\s*[\d\-\.]+\s*[\d\-\.]+)*)*"
            )

        temp_scf = read_table_pattern(self.text, header_pattern, table_pattern, footer_pattern)
        real_scf = []
        for one_scf in temp_scf:
            temp = np.zeros(shape=(len(one_scf), 2))
            for ii, entry in enumerate(one_scf):
                temp[ii, 0] = float(entry[0])
                temp[ii, 1] = float(entry[1]) * 10 ** float(entry[2])
            real_scf += [temp]

        self.data["SCF"] = real_scf

        temp_thresh_warning = read_pattern(
            self.text,
            {
                "key": r"\n[a-zA-Z_\s/]+\.C::WARNING energy changes are now smaller than effective accuracy"
                r"\.\n[a-zA-Z_\s/]+\.C::\s+calculation will continue, but THRESH should be increased\n"
                r"[a-zA-Z_\s/]+\.C::\s+or SCF_CONVERGENCE decreased\. \n"
                r"[a-zA-Z_\s/]+\.C::\s+effective_thresh = ([\d\-\.]+e[\d\-]+)"
            },
        ).get("key")
        if temp_thresh_warning is not None:
            if len(temp_thresh_warning) == 1:
                self.data["warnings"]["thresh"] = float(temp_thresh_warning[0][0])
            else:
                thresh_warning = np.zeros(len(temp_thresh_warning))
                for ii, entry in enumerate(temp_thresh_warning):
                    thresh_warning[ii] = float(entry[0])
                self.data["warnings"]["thresh"] = thresh_warning

        temp_SCF_energy = read_pattern(self.text, {"key": r"SCF   energy in the final basis set =\s*([\d\-\.]+)"}).get(
            "key"
        )
        if temp_SCF_energy is not None:
            if len(temp_SCF_energy) == 1:
                self.data["SCF_energy_in_the_final_basis_set"] = float(temp_SCF_energy[0][0])
            else:
                SCF_energy = np.zeros(len(temp_SCF_energy))
                for ii, val in enumerate(temp_SCF_energy):
                    SCF_energy[ii] = float(val[0])
                self.data["SCF_energy_in_the_final_basis_set"] = SCF_energy

        temp_Total_energy = read_pattern(
            self.text, {"key": r"Total energy in the final basis set =\s*([\d\-\.]+)"}
        ).get("key")
        if temp_Total_energy is not None:
            if len(temp_Total_energy) == 1:
                self.data["Total_energy_in_the_final_basis_set"] = float(temp_Total_energy[0][0])
            else:
                Total_energy = np.zeros(len(temp_Total_energy))
                for ii, val in enumerate(temp_Total_energy):
                    Total_energy[ii] = float(val[0])
                self.data["Total_energy_in_the_final_basis_set"] = Total_energy

    def _read_charges_and_dipoles(self):
        """
        Parses Mulliken/ESP/RESP charges.
        Parses associated dipoles.
        Also parses spins given an unrestricted SCF.
        """
        self.data["dipoles"] = {}
        temp_dipole_total = read_pattern(
            self.text, {"key": r"X\s*[\d\-\.]+\s*Y\s*[\d\-\.]+\s*Z\s*[\d\-\.]+\s*Tot\s*([\d\-\.]+)"}
        ).get("key")
        temp_dipole = read_pattern(
            self.text, {"key": r"X\s*([\d\-\.]+)\s*Y\s*([\d\-\.]+)\s*Z\s*([\d\-\.]+)\s*Tot\s*[\d\-\.]+"}
        ).get("key")
        if temp_dipole is not None:
            if len(temp_dipole_total) == 1:
                self.data["dipoles"]["total"] = float(temp_dipole_total[0][0])
                dipole = np.zeros(3)
                for ii, val in enumerate(temp_dipole[0]):
                    dipole[ii] = float(val)
                self.data["dipoles"]["dipole"] = dipole
            else:
                total = np.zeros(len(temp_dipole_total))
                for ii, val in enumerate(temp_dipole_total):
                    total[ii] = float(val[0])
                self.data["dipoles"]["total"] = total
                dipole = np.zeros(shape=(len(temp_dipole_total), 3))
                for ii, _entry in enumerate(temp_dipole):
                    for jj, _val in enumerate(temp_dipole[ii]):
                        dipole[ii][jj] = temp_dipole[ii][jj]
                self.data["dipoles"]["dipole"] = dipole

        if self.data.get("unrestricted", []):
            header_pattern = (
                r"\-+\s+Ground-State Mulliken Net Atomic Charges\s+Atom\s+Charge \(a\.u\.\)\s+"
                r"Spin\s\(a\.u\.\)\s+\-+"
            )
            table_pattern = r"\s+\d+\s\w+\s+([\d\-\.]+)\s+([\d\-\.]+)"
            footer_pattern = r"\s\s\-+\s+Sum of atomic charges"
        else:
            header_pattern = r"\-+\s+Ground-State Mulliken Net Atomic Charges\s+Atom\s+Charge \(a\.u\.\)\s+\-+"
            table_pattern = r"\s+\d+\s\w+\s+([\d\-\.]+)"
            footer_pattern = r"\s\s\-+\s+Sum of atomic charges"

        temp_mulliken = read_table_pattern(self.text, header_pattern, table_pattern, footer_pattern)
        real_mulliken = []
        for one_mulliken in temp_mulliken:
            if self.data.get("unrestricted", []):
                temp = np.zeros(shape=(len(one_mulliken), 2))
                for ii, entry in enumerate(one_mulliken):
                    temp[ii, 0] = float(entry[0])
                    temp[ii, 1] = float(entry[1])
            else:
                temp = np.zeros(len(one_mulliken))
                for ii, entry in enumerate(one_mulliken):
                    temp[ii] = float(entry[0])
            real_mulliken += [temp]

        self.data["Mulliken"] = real_mulliken

        # Check for ESP/RESP charges
        esp_or_resp = read_pattern(self.text, {"key": r"Merz-Kollman (R?ESP) Net Atomic Charges"}).get("key")
        if esp_or_resp is not None:
            header_pattern = r"Merz-Kollman (R?ESP) Net Atomic Charges\s+Atom\s+Charge \(a\.u\.\)\s+\-+"
            table_pattern = r"\s+\d+\s\w+\s+([\d\-\.]+)"
            footer_pattern = r"\s\s\-+\s+Sum of atomic charges"

            temp_esp_or_resp = read_table_pattern(self.text, header_pattern, table_pattern, footer_pattern)
            real_esp_or_resp = []
            for one_entry in temp_esp_or_resp:
                temp = np.zeros(len(one_entry))
                for ii, entry in enumerate(one_entry):
                    temp[ii] = float(entry[0])
                real_esp_or_resp += [temp]
            self.data[esp_or_resp[0][0]] = real_esp_or_resp
            temp_RESP_dipole_total = read_pattern(
                self.text,
                {"key": r"Related Dipole Moment =\s*([\d\-\.]+)\s*\(X\s*[\d\-\.]+\s*Y\s*[\d\-\.]+\s*Z\s*[\d\-\.]+\)"},
            ).get("key")
            temp_RESP_dipole = read_pattern(
                self.text,
                {
                    "key": r"Related Dipole Moment =\s*[\d\-\.]+\s*\(X\s*([\d\-\.]+)\s*Y\s*([\d\-\.]+)"
                    r"\s*Z\s*([\d\-\.]+)\)"
                },
            ).get("key")
            if temp_RESP_dipole is not None:
                if len(temp_RESP_dipole_total) == 1:
                    self.data["dipoles"]["RESP_total"] = float(temp_RESP_dipole_total[0][0])
                    RESP_dipole = np.zeros(3)
                    for ii, val in enumerate(temp_RESP_dipole[0]):
                        RESP_dipole[ii] = float(val)
                    self.data["dipoles"]["RESP_dipole"] = RESP_dipole
                else:
                    RESP_total = np.zeros(len(temp_RESP_dipole_total))
                    for ii, val in enumerate(temp_RESP_dipole_total):
                        RESP_total[ii] = float(val[0])
                    self.data["dipoles"]["RESP_total"] = RESP_total
                    RESP_dipole = np.zeros(shape=(len(temp_RESP_dipole_total), 3))
                    for ii, _entry in enumerate(temp_RESP_dipole):
                        for jj, _val in enumerate(temp_RESP_dipole[ii]):
                            RESP_dipole[ii][jj] = temp_RESP_dipole[ii][jj]
                    self.data["dipoles"]["RESP_dipole"] = RESP_dipole

    def _detect_general_warnings(self):
        # Check for inaccurate integrated density
        temp_inac_integ = read_pattern(
            self.text,
            {
                "key": r"Inaccurate integrated density:\n\s+Number of electrons\s+=\s+([\d\-\.]+)\n\s+"
                r"Numerical integral\s+=\s+([\d\-\.]+)\n\s+Relative error\s+=\s+([\d\-\.]+)\s+\%\n"
            },
        ).get("key")
        if temp_inac_integ is not None:
            inaccurate_integrated_density = np.zeros(shape=(len(temp_inac_integ), 3))
            for ii, entry in enumerate(temp_inac_integ):
                for jj, val in enumerate(entry):
                    inaccurate_integrated_density[ii][jj] = float(val)
            self.data["warnings"]["inaccurate_integrated_density"] = inaccurate_integrated_density

        # Check for an MKL error
        if read_pattern(self.text, {"key": r"Intel MKL ERROR"}, terminate_on_match=True).get("key") == [[]]:
            self.data["warnings"]["mkl"] = True

        # Check if the job is being hindered by a lack of analytical derivatives
        if read_pattern(
            self.text,
            {"key": r"Starting finite difference calculation for IDERIV"},
            terminate_on_match=True,
        ).get("key") == [[]]:
            self.data["warnings"]["missing_analytical_derivates"] = True

        # Check if the job is complaining about MO files of inconsistent size
        if read_pattern(
            self.text,
            {"key": r"Inconsistent size for SCF MO coefficient file"},
            terminate_on_match=True,
        ).get("key") == [[]]:
            self.data["warnings"]["inconsistent_size"] = True

        # Check for AO linear depend
        if read_pattern(self.text, {"key": r"Linear dependence detected in AO basis"}, terminate_on_match=True).get(
            "key"
        ) == [[]]:
            self.data["warnings"]["linear_dependence"] = True

        # Check for Hessian without desired local structure
        if read_pattern(
            self.text,
            {"key": r"\*\*WARNING\*\* Hessian does not have the Desired Local Structure"},
            terminate_on_match=True,
        ).get("key") == [[]]:
            self.data["warnings"]["hessian_local_structure"] = True

        # Check if GetCART cycle iterations ever exceeded
        if read_pattern(
            self.text,
            {"key": r"\*\*\*ERROR\*\*\* Exceeded allowed number of iterative cycles in GetCART"},
            terminate_on_match=True,
        ).get("key") == [[]]:
            self.data["warnings"]["GetCART_cycles"] = True

        # Check for problems with internal coordinates
        if read_pattern(
            self.text,
            {"key": r"\*\*WARNING\*\* Problems with Internal Coordinates"},
            terminate_on_match=True,
        ).get("key") == [[]]:
            self.data["warnings"]["internal_coordinates"] = True

        # Check for an issue with an RFO step
        if read_pattern(
            self.text,
            {"key": r"UNABLE TO DETERMINE Lambda IN RFO  \*\*\s+\*\* Taking simple Newton-Raphson step"},
            terminate_on_match=True,
        ).get("key") == [[]]:
            self.data["warnings"]["bad_lambda_take_NR_step"] = True

        # Check for a switch into Cartesian coordinates
        if read_pattern(self.text, {"key": r"SWITCHING TO CARTESIAN OPTIMIZATION"}, terminate_on_match=True).get(
            "key"
        ) == [[]]:
            self.data["warnings"]["switch_to_cartesian"] = True

        # Check for problem with eigenvalue magnitude
        if read_pattern(self.text, {"key": r"\*\*WARNING\*\* Magnitude of eigenvalue"}, terminate_on_match=True).get(
            "key"
        ) == [[]]:
            self.data["warnings"]["eigenvalue_magnitude"] = True

        # Check for problem with hereditary postivive definiteness
        if read_pattern(
            self.text,
            {"key": r"\*\*WARNING\*\* Hereditary positive definiteness endangered"},
            terminate_on_match=True,
        ).get("key") == [[]]:
            self.data["warnings"]["positive_definiteness_endangered"] = True

        # Check if there were problems with a colinear bend
        if read_pattern(
            self.text,
            {
                "key": r"\*\*\*ERROR\*\*\* Angle[\s\d]+is near\-linear\s+"
                r"But No atom available to define colinear bend"
            },
            terminate_on_match=True,
        ).get("key") == [[]]:
            self.data["warnings"]["colinear_bend"] = True

        # Check if there were problems diagonalizing B*B(t)
        if read_pattern(
            self.text,
            {"key": r"\*\*\*ERROR\*\*\* Unable to Diagonalize B\*B\(t\) in <MakeNIC>"},
            terminate_on_match=True,
        ).get("key") == [[]]:
            self.data["warnings"]["diagonalizing_BBt"] = True

    def _read_geometries(self):
        """Parses all geometries from an optimization trajectory."""
        geoms = []
        if self.data.get("new_optimizer") is None:
            header_pattern = r"\s+Optimization\sCycle:\s+\d+\s+Coordinates \(Angstroms\)\s+ATOM\s+X\s+Y\s+Z"
            table_pattern = r"\s+\d+\s+\w+\s+([\d\-\.]+)\s+([\d\-\.]+)\s+([\d\-\.]+)"
            footer_pattern = r"\s+Point Group\:\s+[\d\w\*]+\s+Number of degrees of freedom\:\s+\d+"
        elif read_pattern(
            self.text,
            {"key": r"Geometry Optimization Coordinates :\s+Cartesian"},
            terminate_on_match=True,
        ).get("key") == [[]]:
            header_pattern = (
                r"RMS\s+of Stepsize\s+[\d\-\.]+\s+-+\s+Standard Nuclear Orientation "
                r"\(Angstroms\)\s+I\s+Atom\s+X\s+Y\s+Z\s+-+"
            )
            table_pattern = r"\s*\d+\s+[a-zA-Z]+\s*([\d\-\.]+)\s*([\d\-\.]+)\s*([\d\-\.]+)\s*"
            footer_pattern = r"\s*-+"
        else:  # pylint: disable=line-too-long
            header_pattern = (
                r"Finished Iterative Coordinate Back-Transformation\s+-+\s+Standard Nuclear Orientation "
                r"\(Angstroms\)\s+I\s+Atom\s+X\s+Y\s+Z\s+-+"
            )
            table_pattern = r"\s*\d+\s+[a-zA-Z]+\s*([\d\-\.]+)\s*([\d\-\.]+)\s*([\d\-\.]+)\s*"
            footer_pattern = r"\s*-+"
        parsed_geometries = read_table_pattern(self.text, header_pattern, table_pattern, footer_pattern)
        for parsed_geometry in parsed_geometries:
            if not parsed_geometry:
                geoms.append(None)
            else:
                geoms.append(process_parsed_coords(parsed_geometry))
        if len(geoms) >= 1:
            self.data["geometries"] = geoms
            self.data["last_geometry"] = geoms[-1]
            if self.data.get("charge") is not None:
                self.data["molecule_from_last_geometry"] = Molecule(
                    species=self.data.get("species"),
                    coords=self.data.get("last_geometry"),
                    charge=self.data.get("charge"),
                    spin_multiplicity=self.data.get("multiplicity"),
                )

            # Parses optimized XYZ coordinates. If not present, parses optimized Z-matrix.
            if self.data.get("new_optimizer") is None:
                header_pattern = (
                    r"\*+\s+(OPTIMIZATION|TRANSITION STATE)\s+CONVERGED\s+\*+\s+\*+\s+Coordinates "
                    r"\(Angstroms\)\s+ATOM\s+X\s+Y\s+Z"
                )
                table_pattern = r"\s+\d+\s+\w+\s+([\d\-\.]+)\s+([\d\-\.]+)\s+([\d\-\.]+)"
                footer_pattern = r"\s+Z-matrix Print:"
            else:
                header_pattern = (
                    r"(OPTIMIZATION|TRANSITION STATE)\sCONVERGED\s+\*+\s+\*+\s+-+\s+Standard "
                    r"Nuclear Orientation \(Angstroms\)\s+I\s+Atom\s+X\s+Y\s+Z\s+-+"
                )
                table_pattern = r"\s*\d+\s+[a-zA-Z]+\s*([\d\-\.]+)\s*([\d\-\.]+)\s*([\d\-\.]+)\s*"
                footer_pattern = r"\s*-+"
            parsed_optimized_geometries = read_table_pattern(self.text, header_pattern, table_pattern, footer_pattern)

            if not parsed_optimized_geometries:
                self.data["optimized_geometry"] = None
                header_pattern = (
                    r"^\s+\*+\s+(OPTIMIZATION|TRANSITION STATE) CONVERGED\s+\*+\s+\*+\s+Z-matrix\s+"
                    r"Print:\s+\$molecule\s+[\d\-]+\s+[\d\-]+\n"
                )
                table_pattern = (
                    r"\s*(\w+)(?:\s+(\d+)\s+([\d\-\.]+)(?:\s+(\d+)\s+([\d\-\.]+)"
                    r"(?:\s+(\d+)\s+([\d\-\.]+))*)*)*(?:\s+0)*"
                )
                footer_pattern = r"^\$end\n"

                self.data["optimized_zmat"] = read_table_pattern(
                    self.text, header_pattern, table_pattern, footer_pattern
                )
            else:
                self.data["optimized_geometry"] = process_parsed_coords(parsed_optimized_geometries[0])
                self.data["optimized_geometries"] = [process_parsed_coords(i) for i in parsed_optimized_geometries]
                if self.data.get("charge") is not None:
                    self.data["molecule_from_optimized_geometry"] = Molecule(
                        species=self.data.get("species"),
                        coords=self.data.get("optimized_geometry"),
                        charge=self.data.get("charge"),
                        spin_multiplicity=self.data.get("multiplicity"),
                    )
                    self.data["molecules_from_optimized_geometries"] = []
                    for geom in self.data["optimized_geometries"]:
                        mol = Molecule(
                            species=self.data.get("species"),
                            coords=geom,
                            charge=self.data.get("charge"),
                            spin_multiplicity=self.data.get("multiplicity"),
                        )
                        self.data["molecules_from_optimized_geometries"].append(mol)

    def _get_grad_format_length(self, header):
        """
        Determines the maximum number of gradient entries printed on a line,
        which changes for different versions of Q-Chem.
        """
        found_end = False
        index = 1
        pattern = header
        while not found_end:
            if read_pattern(self.text, {"key": pattern}, terminate_on_match=True).get("key") != [[]]:
                found_end = True
            else:
                pattern = f"{pattern}\\s+{index}"
                index += 1
        return index - 2

    def _read_gradients(self):
        """Parses all gradients obtained during an optimization trajectory."""
        grad_header_pattern = r"Gradient of (?:SCF)?(?:MP2)? Energy(?: \(in au\.\))?"
        footer_pattern = r"(?:Max gradient component|Gradient time)"

        grad_format_length = self._get_grad_format_length(grad_header_pattern)
        grad_table_pattern = (
            r"(?:\s+\d+(?:\s+\d+)?(?:\s+\d+)?(?:\s+\d+)?(?:\s+\d+)?(?:\s+\d+)?)?\n\s\s\s\s[1-3]\s*(\-?[\d\.]{9,12})"
        )
        if grad_format_length > 1:
            for _ in range(1, grad_format_length):
                grad_table_pattern = grad_table_pattern + r"(?:\s*(\-?[\d\.]{9,12}))?"

        parsed_gradients = read_table_pattern(self.text, grad_header_pattern, grad_table_pattern, footer_pattern)
        if len(parsed_gradients) >= 1:
            sorted_gradients = np.zeros(shape=(len(parsed_gradients), len(self.data["initial_molecule"]), 3))
            for ii, grad in enumerate(parsed_gradients):
                for jj in range(int(len(grad) / 3)):
                    for kk in range(grad_format_length):
                        if grad[jj * 3][kk] != "None":
                            sorted_gradients[ii][jj * grad_format_length + kk][0] = grad[jj * 3][kk]
                            sorted_gradients[ii][jj * grad_format_length + kk][1] = grad[jj * 3 + 1][kk]
                            sorted_gradients[ii][jj * grad_format_length + kk][2] = grad[jj * 3 + 2][kk]

            self.data["gradients"] = sorted_gradients

            if self.data["solvent_method"] is not None:
                header_pattern = r"total gradient after adding PCM contribution --\s+-+\s+Atom\s+X\s+Y\s+Z\s+-+"
                table_pattern = r"\s+\d+\s+([\d\-\.]+)\s+([\d\-\.]+)\s+([\d\-\.]+)\s"
                footer_pattern = r"-+"

                parsed_gradients = read_table_pattern(self.text, header_pattern, table_pattern, footer_pattern)

                pcm_gradients = np.zeros(shape=(len(parsed_gradients), len(self.data["initial_molecule"]), 3))
                for ii, grad in enumerate(parsed_gradients):
                    for jj, entry in enumerate(grad):
                        for kk, val in enumerate(entry):
                            pcm_gradients[ii][jj][kk] = float(val)

                self.data["pcm_gradients"] = pcm_gradients
            else:
                self.data["pcm_gradients"] = None

            if read_pattern(self.text, {"key": r"Gradient of CDS energy"}, terminate_on_match=True).get("key") == [[]]:
                header_pattern = r"Gradient of CDS energy"

                parsed_gradients = read_table_pattern(
                    self.text, header_pattern, grad_table_pattern, grad_header_pattern
                )

                sorted_gradients = np.zeros(shape=(len(parsed_gradients), len(self.data["initial_molecule"]), 3))
                for ii, grad in enumerate(parsed_gradients):
                    for jj in range(int(len(grad) / 3)):
                        for kk in range(grad_format_length):
                            if grad[jj * 3][kk] != "None":
                                sorted_gradients[ii][jj * grad_format_length + kk][0] = grad[jj * 3][kk]
                                sorted_gradients[ii][jj * grad_format_length + kk][1] = grad[jj * 3 + 1][kk]
                                sorted_gradients[ii][jj * grad_format_length + kk][2] = grad[jj * 3 + 2][kk]

                self.data["CDS_gradients"] = sorted_gradients
            else:
                self.data["CDS_gradients"] = None

    def _read_optimization_data(self):
        if self.data.get("new_optimizer") is None or self.data["version"] == "6":
            temp_energy_trajectory = read_pattern(self.text, {"key": r"\sEnergy\sis\s+([\d\-\.]+)"}).get("key")
        else:
            temp_energy_trajectory = read_pattern(self.text, {"key": r"\sStep\s*\d+\s*:\s*Energy\s*([\d\-\.]+)"}).get(
                "key"
            )
        if self.data.get("new_optimizer") == [[]] and temp_energy_trajectory is not None:
            # Formatting of the new optimizer means we need to prepend the first energy
            temp_energy_trajectory.insert(0, [str(self.data["Total_energy_in_the_final_basis_set"][0])])
        self._read_geometries()
        self._read_gradients()
        if temp_energy_trajectory is None:
            self.data["energy_trajectory"] = []
            if read_pattern(self.text, {"key": r"Error in back_transform"}, terminate_on_match=True).get("key") == [[]]:
                self.data["errors"] += ["back_transform_error"]
            elif read_pattern(self.text, {"key": r"pinv\(\)\: svd failed"}, terminate_on_match=True).get("key") == [[]]:
                self.data["errors"] += ["svd_failed"]
        else:
            real_energy_trajectory = np.zeros(len(temp_energy_trajectory))
            for ii, entry in enumerate(temp_energy_trajectory):
                real_energy_trajectory[ii] = float(entry[0])
            self.data["energy_trajectory"] = real_energy_trajectory
            if self.data.get("new_optimizer") == [[]]:
                temp_norms = read_pattern(self.text, {"key": r"Norm of Stepsize\s*([\d\-\.]+)"}).get("key")
                if temp_norms is not None:
                    norms = np.zeros(len(temp_norms))
                    for ii, val in enumerate(temp_norms):
                        norms[ii] = float(val[0])
                    self.data["norm_of_stepsize"] = norms
            if openbabel is not None:
                self.data["structure_change"] = check_for_structure_changes(
                    self.data["initial_molecule"],
                    self.data["molecule_from_last_geometry"],
                )
            # Then, if no optimized geometry or z-matrix is found, and no errors have been previously
            # identified, check to see if the optimization failed to converge or if Lambda wasn't able
            # to be determined or if a back transform error was encountered.
            if (
                len(self.data.get("errors")) == 0
                and self.data.get("optimized_geometry") is None
                and len(self.data.get("optimized_zmat")) == 0
            ):
                if read_pattern(
                    self.text,
                    {"key": r"MAXIMUM OPTIMIZATION CYCLES REACHED"},
                    terminate_on_match=True,
                ).get("key") == [[]] or read_pattern(
                    self.text,
                    {"key": r"Maximum number of iterations reached during minimization algorithm"},
                    terminate_on_match=True,
                ).get(
                    "key"
                ) == [
                    []
                ]:
                    self.data["errors"] += ["out_of_opt_cycles"]
                elif read_pattern(
                    self.text,
                    {"key": r"UNABLE TO DETERMINE Lamda IN FormD"},
                    terminate_on_match=True,
                ).get("key") == [[]]:
                    self.data["errors"] += ["unable_to_determine_lamda"]
                elif read_pattern(self.text, {"key": r"Error in back_transform"}, terminate_on_match=True).get(
                    "key"
                ) == [[]]:
                    self.data["errors"] += ["back_transform_error"]
                elif read_pattern(self.text, {"key": r"pinv\(\)\: svd failed"}, terminate_on_match=True).get("key") == [
                    []
                ]:
                    self.data["errors"] += ["svd_failed"]

    def _read_frequency_data(self):
        """Parses cpscf_nseg, frequencies, enthalpy, entropy, and mode vectors."""
        if read_pattern(self.text, {"key": r"Calculating MO derivatives via CPSCF"}, terminate_on_match=True).get(
            "key"
        ) == [[]]:
            temp_cpscf_nseg = read_pattern(
                self.text,
                {"key": r"CPSCF will be done in([\d\s]+)segments to save memory"},
                terminate_on_match=True,
            ).get("key")
            if temp_cpscf_nseg is None:
                self.data["cpscf_nseg"] = 1
            else:
                self.data["cpscf_nseg"] = int(temp_cpscf_nseg[0][0])
        else:
            self.data["cpscf_nseg"] = 0

        raman = False
        if read_pattern(self.text, {"key": r"doraman\s*(?:=)*\s*true"}, terminate_on_match=True).get("key") == [[]]:
            raman = True

        temp_dict = read_pattern(
            self.text,
            {
                "frequencies": r"\s*Frequency:\s+(\-?[\d\.\*]+)(?:\s+(\-?[\d\.\*]+)(?:\s+(\-?[\d\.\*]+))*)*",
                "trans_dip": r"TransDip\s+(\-?[\d\.]{5,7}|\*{5,7})\s*(\-?[\d\.]{5,7}|\*{5,7})"
                r"\s*(\-?[\d\.]{5,7}|\*{5,7})\s*"
                r"(?:(\-?[\d\.]{5,7}|\*{5,7})\s*(\-?[\d\.]{5,7}|\*{5,7})\s*(\-?[\d\.]{5,7}|\*{5,7})\s*"
                r"(?:(\-?[\d\.]{5,7}|\*{5,7})\s*(\-?[\d\.]{5,7}|\*{5,7})\s*(\-?[\d\.]{5,7}|\*{5,7}))*)*",
                "IR_intens": r"\s*IR Intens:\s*(\-?[\d\.\*]+)(?:\s+(\-?[\d\.\*]+)(?:\s+(\-?[\d\.\*]+))*)*",
                "IR_active": r"\s*IR Active:\s+([YESNO]+)(?:\s+([YESNO]+)(?:\s+([YESNO]+))*)*",
                "raman_intens": r"\s*Raman Intens:\s*(\-?[\d\.\*]+)(?:\s+(\-?[\d\.\*]+)(?:\s+(\-?[\d\.\*]+))*)*",
                "depolar": r"\s*Depolar:\s*(\-?[\d\.\*]+)(?:\s+(\-?[\d\.\*]+)(?:\s+(\-?[\d\.\*]+))*)*",
                "raman_active": r"\s*Raman Active:\s+([YESNO]+)(?:\s+([YESNO]+)(?:\s+([YESNO]+))*)*",
                "ZPE": r"\s*Zero point vibrational energy:\s+([\d\-\.]+)\s+kcal/mol",
                "trans_enthalpy": r"\s*Translational Enthalpy:\s+([\d\-\.]+)\s+kcal/mol",
                "rot_enthalpy": r"\s*Rotational Enthalpy:\s+([\d\-\.]+)\s+kcal/mol",
                "vib_enthalpy": r"\s*Vibrational Enthalpy:\s+([\d\-\.]+)\s+kcal/mol",
                "gas_constant": r"\s*gas constant \(RT\):\s+([\d\-\.]+)\s+kcal/mol",
                "trans_entropy": r"\s*Translational Entropy:\s+([\d\-\.]+)\s+cal/mol\.K",
                "rot_entropy": r"\s*Rotational Entropy:\s+([\d\-\.]+)\s+cal/mol\.K",
                "vib_entropy": r"\s*Vibrational Entropy:\s+([\d\-\.]+)\s+cal/mol\.K",
                "total_enthalpy": r"\s*Total Enthalpy:\s+([\d\-\.]+)\s+kcal/mol",
                "total_entropy": r"\s*Total Entropy:\s+([\d\-\.]+)\s+cal/mol\.K",
            },
        )

        keys = [
            "ZPE",
            "trans_enthalpy",
            "rot_enthalpy",
            "vib_enthalpy",
            "gas_constant",
            "trans_entropy",
            "rot_entropy",
            "vib_entropy",
            "total_enthalpy",
            "total_entropy",
        ]
        for key in keys:
            if temp_dict.get(key) is None:
                self.data[key] = None
            else:
                self.data[key] = float(temp_dict.get(key)[0][0])

        if temp_dict.get("frequencies") is None:
            self.data["frequencies"] = self.data["IR_intens"] = self.data["IR_active"] = None
            self.data["raman_active"] = self.data["raman_intens"] = None
            self.data["depolar"] = self.data["trans_dip"] = None
        else:
            temp_freqs = [value for entry in temp_dict.get("frequencies") for value in entry]
            temp_IR_intens = [value for entry in temp_dict.get("IR_intens") for value in entry]
            IR_active = [value for entry in temp_dict.get("IR_active") for value in entry]
            temp_trans_dip = [value for entry in temp_dict.get("trans_dip") for value in entry]
            self.data["IR_active"] = IR_active

            if raman:
                raman_active = [value for entry in temp_dict.get("raman_active") for value in entry]
                temp_raman_intens = [value for entry in temp_dict.get("raman_intens") for value in entry]
                temp_depolar = [value for entry in temp_dict.get("depolar") for value in entry]
                self.data["raman_active"] = raman_active
                raman_intens = np.zeros(len(temp_raman_intens) - temp_raman_intens.count("None"))
                for ii, entry in enumerate(temp_raman_intens):
                    if entry != "None":
                        if "*" in entry:
                            raman_intens[ii] = float("inf")
                        else:
                            raman_intens[ii] = float(entry)
                self.data["raman_intens"] = raman_intens
                depolar = np.zeros(len(temp_depolar) - temp_depolar.count("None"))
                for ii, entry in enumerate(temp_depolar):
                    if entry != "None":
                        if "*" in entry:
                            depolar[ii] = float("inf")
                        else:
                            depolar[ii] = float(entry)
                self.data["depolar"] = depolar
            else:
                self.data["raman_intens"] = self.data["raman_active"] = self.data["depolar"] = None

            trans_dip = np.zeros(shape=(int((len(temp_trans_dip) - temp_trans_dip.count("None")) / 3), 3))
            for ii, entry in enumerate(temp_trans_dip):
                if entry != "None":
                    if "*" in entry:
                        trans_dip[int(ii / 3)][ii % 3] = float("inf")
                    else:
                        trans_dip[int(ii / 3)][ii % 3] = float(entry)
            self.data["trans_dip"] = trans_dip

            freqs = np.zeros(len(temp_freqs) - temp_freqs.count("None"))
            for ii, entry in enumerate(temp_freqs):
                if entry != "None":
                    if "*" in entry:
                        if ii == 0:
                            freqs[ii] = -float("inf")
                        elif ii == len(freqs) - 1:
                            freqs[ii] = float("inf")
                        elif freqs[ii - 1] == -float("inf"):
                            freqs[ii] = -float("inf")
                        elif "*" in temp_freqs[ii + 1]:
                            freqs[ii] = float("inf")
                        else:
                            raise RuntimeError(
                                "ERROR: Encountered an undefined frequency not at the beginning or end of the "
                                "frequency list, which makes no sense! Exiting..."
                            )
                        if not self.data.get("completion", []):
                            if "undefined_frequency" not in self.data["errors"]:
                                self.data["errors"] += ["undefined_frequency"]
                        elif "undefined_frequency" not in self.data["warnings"]:
                            self.data["warnings"]["undefined_frequency"] = True
                    else:
                        freqs[ii] = float(entry)
            self.data["frequencies"] = freqs

            IR_intens = np.zeros(len(temp_IR_intens) - temp_IR_intens.count("None"))
            for ii, entry in enumerate(temp_IR_intens):
                if entry != "None":
                    if "*" in entry:
                        IR_intens[ii] = float("inf")
                    else:
                        IR_intens[ii] = float(entry)
            self.data["IR_intens"] = IR_intens

            if not raman:
                header_pattern = r"\s*Raman Active:\s+[YESNO]+\s+(?:[YESNO]+\s+)*X\s+Y\s+Z\s+(?:X\s+Y\s+Z\s+)*"
            else:
                header_pattern = r"\s*Depolar:\s*\-?[\d\.\*]+\s+(?:\-?[\d\.\*]+\s+)*X\s+Y\s+Z\s+(?:X\s+Y\s+Z\s+)*"
            table_pattern = (
                r"\s*[a-zA-Z][a-zA-Z\s]\s*([\d\-\.]+)\s*([\d\-\.]+)\s*([\d\-\.]+)\s*(?:([\d\-\.]+)\s*"
                r"([\d\-\.]+)\s*([\d\-\.]+)\s*(?:([\d\-\.]+)\s*([\d\-\.]+)\s*([\d\-\.]+))*)*"
            )
            footer_pattern = (
                r"TransDip\s+\-?[\d\.\*]+\s*\-?[\d\.\*]+\s*\-?[\d\.\*]+\s*(?:\-?[\d\.\*]+\s*\-?"
                r"[\d\.\*]+\s*\-?[\d\.\*]+\s*)*"
            )
            temp_freq_mode_vecs = read_table_pattern(self.text, header_pattern, table_pattern, footer_pattern)
            freq_mode_vecs = np.zeros(shape=(len(freqs), len(temp_freq_mode_vecs[0]), 3))

            for ii, triple_FMV in enumerate(temp_freq_mode_vecs):
                for jj, line in enumerate(triple_FMV):
                    for kk, entry in enumerate(line):
                        if entry != "None":
                            freq_mode_vecs[int(ii * 3 + math.floor(kk / 3)), jj, kk % 3] = float(entry)

            self.data["frequency_mode_vectors"] = freq_mode_vecs
            freq_length = len(self.data["frequencies"])
            if (
                len(self.data["frequency_mode_vectors"]) != freq_length
                or len(self.data["IR_intens"]) != freq_length
                or len(self.data["IR_active"]) != freq_length
            ):
                self.data["warnings"]["frequency_length_inconsistency"] = True

    def _read_force_data(self):
        self._read_gradients()

    def _read_scan_data(self):
        temp_energy_trajectory = read_pattern(self.text, {"key": r"\sEnergy\sis\s+([\d\-\.]+)"}).get("key")
        if temp_energy_trajectory is None:
            self.data["energy_trajectory"] = []
        else:
            real_energy_trajectory = np.zeros(len(temp_energy_trajectory))
            for ii, entry in enumerate(temp_energy_trajectory):
                real_energy_trajectory[ii] = float(entry[0])
            self.data["energy_trajectory"] = real_energy_trajectory

        self._read_geometries()
        if openbabel is not None:
            self.data["structure_change"] = check_for_structure_changes(
                self.data["initial_molecule"],
                self.data["molecule_from_last_geometry"],
            )
        self._read_gradients()

        if len(self.data.get("errors")) == 0:
            if read_pattern(self.text, {"key": r"MAXIMUM OPTIMIZATION CYCLES REACHED"}, terminate_on_match=True).get(
                "key"
            ) == [[]]:
                self.data["errors"] += ["out_of_opt_cycles"]
            elif read_pattern(self.text, {"key": r"UNABLE TO DETERMINE Lamda IN FormD"}, terminate_on_match=True).get(
                "key"
            ) == [[]]:
                self.data["errors"] += ["unable_to_determine_lamda"]

        header_pattern = r"\s*\-+ Summary of potential scan\: \-+\s*"
        row_pattern_single = r"\s*([\-\.0-9]+)\s+([\-\.0-9]+)\s*\n"
        row_pattern_double = r"\s*([\-\.0-9]+)\s+([\-\.0-9]+)\s+([\-\.0-9]+)\s*\n"
        footer_pattern = r"\s*\-+"

        single_data = read_table_pattern(
            self.text,
            header_pattern=header_pattern,
            row_pattern=row_pattern_single,
            footer_pattern=footer_pattern,
        )

        self.data["scan_energies"] = []
        if len(single_data) == 0:
            double_data = read_table_pattern(
                self.text,
                header_pattern=header_pattern,
                row_pattern=row_pattern_double,
                footer_pattern=footer_pattern,
            )
            if len(double_data) == 0:
                self.data["scan_energies"] = None
            else:
                for line in double_data[0]:
                    params = [float(line[0]), float(line[1])]
                    energy = float(line[2])
                    self.data["scan_energies"].append({"params": params, "energy": energy})
        else:
            for line in single_data[0]:
                param = float(line[0])
                energy = float(line[1])
                self.data["scan_energies"].append({"params": param, "energy": energy})

        scan_inputs_head = r"\s*\$[Ss][Cc][Aa][Nn]"
        scan_inputs_row = r"\s*([Ss][Tt][Rr][Ee]|[Tt][Oo][Rr][Ss]|[Bb][Ee][Nn][Dd]) "
        scan_inputs_row += r"((?:[0-9]+\s+)+)([\-\.0-9]+)\s+([\-\.0-9]+)\s+([\-\.0-9]+)\s*"
        scan_inputs_foot = r"\s*\$[Ee][Nn][Dd]"

        constraints_meta = read_table_pattern(
            self.text,
            header_pattern=scan_inputs_head,
            row_pattern=scan_inputs_row,
            footer_pattern=scan_inputs_foot,
        )

        self.data["scan_variables"] = {"stre": [], "bend": [], "tors": []}
        for row in constraints_meta[0]:
            var_type = row[0].lower()
            self.data["scan_variables"][var_type].append(
                {
                    "atoms": [int(i) for i in row[1].split()],
                    "start": float(row[2]),
                    "end": float(row[3]),
                    "increment": float(row[4]),
                }
            )

        temp_constraint = read_pattern(
            self.text,
            {"key": r"\s*(Distance\(Angs\)|Angle|Dihedral)\:\s*((?:[0-9]+\s+)+)+([\.0-9]+)\s+([\.0-9]+)"},
        ).get("key")
        self.data["scan_constraint_sets"] = {"stre": [], "bend": [], "tors": []}
        if temp_constraint is not None:
            for entry in temp_constraint:
                atoms = [int(i) for i in entry[1].split()]
                current = float(entry[2])
                target = float(entry[3])
                if entry[0] == "Distance(Angs)":
                    if len(atoms) == 2:
                        self.data["scan_constraint_sets"]["stre"].append(
                            {"atoms": atoms, "current": current, "target": target}
                        )
                elif entry[0] == "Angle":
                    if len(atoms) == 3:
                        self.data["scan_constraint_sets"]["bend"].append(
                            {"atoms": atoms, "current": current, "target": target}
                        )
                elif entry[0] == "Dihedral" and len(atoms) == 4:
                    self.data["scan_constraint_sets"]["tors"].append(
                        {"atoms": atoms, "current": current, "target": target}
                    )

    def _read_pcm_information(self):
        """Parses information from PCM solvent calculations."""
        temp_dict = read_pattern(
            self.text,
            {
                "g_electrostatic": r"\s*G_electrostatic\s+=\s+([\d\-\.]+)\s+hartree\s+=\s+([\d\-\.]+)\s+kcal/mol\s*",
                "g_cavitation": r"\s*G_cavitation\s+=\s+([\d\-\.]+)\s+hartree\s+=\s+([\d\-\.]+)\s+kcal/mol\s*",
                "g_dispersion": r"\s*G_dispersion\s+=\s+([\d\-\.]+)\s+hartree\s+=\s+([\d\-\.]+)\s+kcal/mol\s*",
                "g_repulsion": r"\s*G_repulsion\s+=\s+([\d\-\.]+)\s+hartree\s+=\s+([\d\-\.]+)\s+kcal/mol\s*",
                "total_contribution_pcm": r"\s*Total\s+=\s+([\d\-\.]+)\s+hartree\s+=\s+([\d\-\.]+)\s+kcal/mol\s*",
                "solute_internal_energy": r"Solute Internal Energy \(H0\)\s*=\s*([\d\-\.]+)",
            },
        )

        for key in temp_dict:
            if temp_dict.get(key) is None:
                self.data["solvent_data"][key] = None
            elif len(temp_dict.get(key)) == 1:
                self.data["solvent_data"][key] = float(temp_dict.get(key)[0][0])
            else:
                temp_result = np.zeros(len(temp_dict.get(key)))
                for ii, entry in enumerate(temp_dict.get(key)):
                    temp_result[ii] = float(entry[0])
                self.data["solvent_data"][key] = temp_result

    def _read_smd_information(self):
        """Parses information from SMD solvent calculations."""
        temp_dict = read_pattern(
            self.text,
            {
                "smd0": r"E-EN\(g\) gas\-phase elect\-nuc energy\s*([\d\-\.]+) a\.u\.",
                "smd3": r"G\-ENP\(liq\) elect\-nuc\-pol free energy of system\s*([\d\-\.]+) a\.u\.",
                "smd4": r"G\-CDS\(liq\) cavity\-dispersion\-solvent structure\s*free energy\s*([\d\-\.]+) kcal\/mol",
                "smd6": r"G\-S\(liq\) free energy of system\s*([\d\-\.]+) a\.u\.",
                "smd9": r"DeltaG\-S\(liq\) free energy of\s*solvation\s*\(9\) = \(6\) \- \(0\)\s*([\d\-\.]+) kcal\/mol",
            },
        )

        for key in temp_dict:
            if temp_dict.get(key) is None:
                self.data["solvent_data"][key] = None
            elif len(temp_dict.get(key)) == 1:
                self.data["solvent_data"][key] = float(temp_dict.get(key)[0][0])
            else:
                temp_result = np.zeros(len(temp_dict.get(key)))
                for ii, entry in enumerate(temp_dict.get(key)):
                    temp_result[ii] = float(entry[0])
                self.data["solvent_data"][key] = temp_result

    def _read_isosvp_information(self):
        """
        Parses information from ISOSVP solvent calculations.

        There are 5 energies output, as in the example below

        --------------------------------------------------------------------------------
        The Final SS(V)PE energies and Properties
        --------------------------------------------------------------------------------

        Energies
        --------------------
        The Final Solution-Phase Energy =     -40.4850599390
        The Solute Internal Energy =          -40.4846329759
        The Change in Solute Internal Energy =  0.0000121970  (   0.00765 KCAL/MOL)
        The Reaction Field Free Energy =       -0.0004269631  (  -0.26792 KCAL/MOL)
        The Total Solvation Free Energy =      -0.0004147661  (  -0.26027 KCAL/MOL)

        In addition, we need to parse the DIELST fortran variable to get the dielectric
        constant used.
        """
        temp_dict = read_pattern(
            self.text,
            {
                "final_soln_phase_e": r"\s*The Final Solution-Phase Energy\s+=\s+([\d\-\.]+)\s*",
                "solute_internal_e": r"\s*The Solute Internal Energy\s+=\s+([\d\-\.]+)\s*",
                "total_solvation_free_e": r"\s*The Total Solvation Free Energy\s+=\s+([\d\-\.]+)\s*",
                "change_solute_internal_e": r"\s*The Change in Solute Internal Energy\s+=\s+(\s+[\d\-\.]+)"
                r"\s+\(\s+([\d\-\.]+)\s+KCAL/MOL\)\s*",
                "reaction_field_free_e": r"\s*The Reaction Field Free Energy\s+=\s+(\s+[\d\-\.]+)\s+"
                r"\(\s+([\d\-\.]+)\s+KCAL/MOL\)\s*",
                "isosvp_dielectric": r"\s*DIELST=\s+(\s+[\d\-\.]+)\s*",
            },
        )

        for key in temp_dict:
            if temp_dict.get(key) is None:
                self.data["solvent_data"]["isosvp"][key] = None
            elif len(temp_dict.get(key)) == 1:
                self.data["solvent_data"]["isosvp"][key] = float(temp_dict.get(key)[0][0])
            else:
                temp_result = np.zeros(len(temp_dict.get(key)))
                for ii, entry in enumerate(temp_dict.get(key)):
                    temp_result[ii] = float(entry[0])
                self.data["solvent_data"]["isosvp"][key] = temp_result

    def _read_cmirs_information(self):
        """
        Parses information from CMIRS solvent calculations.

        In addition to the 5 energies returned by ISOSVP (and read separately in
        _read_isosvp_information), there are 4 additional energies reported, as shown
        in the example below

        --------------------------------------------------------------------------------
        The Final SS(V)PE energies and Properties
        --------------------------------------------------------------------------------

        Energies
        --------------------
        The Final Solution-Phase Energy =     -40.4751881546
        The Solute Internal Energy =          -40.4748568841
        The Change in Solute Internal Energy =  0.0000089729  (   0.00563 KCAL/MOL)
        The Reaction Field Free Energy =       -0.0003312705  (  -0.20788 KCAL/MOL)
        The Dispersion Energy =                 0.6955550107  (  -2.27836 KCAL/MOL)
        The Exchange Energy =                   0.2652679507  (   2.15397 KCAL/MOL)
        Min. Negative Field Energy =            0.0005235850  (   0.00000 KCAL/MOL)
        Max. Positive Field Energy =            0.0179866718  (   0.00000 KCAL/MOL)
        The Total Solvation Free Energy =      -0.0005205275  (  -0.32664 KCAL/MOL)
        """
        temp_dict = read_pattern(
            self.text,
            {
                "dispersion_e": r"\s*The Dispersion Energy\s+=\s+(\s+[\d\-\.]+)\s+\(\s+([\d\-\.]+)\s+KCAL/MOL\)\s*",
                "exchange_e": r"\s*The Exchange Energy\s+=\s+(\s+[\d\-\.]+)\s+\(\s+([\d\-\.]+)\s+KCAL/MOL\)\s*",
                "min_neg_field_e": r"\s*Min. Negative Field Energy\s+=\s+(\s+[\d\-\.]+)\s+"
                r"\(\s+([\d\-\.]+)\s+KCAL/MOL\)\s*",
                "max_pos_field_e": r"\s*Max. Positive Field Energy\s+=\s+(\s+[\d\-\.]+)\s+"
                r"\(\s+([\d\-\.]+)\s+KCAL/MOL\)\s*",
            },
        )

        for key in temp_dict:
            if temp_dict.get(key) is None:
                self.data["solvent_data"]["cmirs"][key] = None
            elif len(temp_dict.get(key)) == 1:
                self.data["solvent_data"]["cmirs"][key] = float(temp_dict.get(key)[0][0])
            else:
                temp_result = np.zeros(len(temp_dict.get(key)))
                for ii, entry in enumerate(temp_dict.get(key)):
                    temp_result[ii] = float(entry[0])
                self.data["solvent_data"]["cmirs"][key] = temp_result

    def _read_nbo_data(self):
        """Parses NBO output."""
        dfs = nbo_parser(self.filename)
        nbo_data = {}
        for key, value in dfs.items():
            nbo_data[key] = [df.to_dict() for df in value]
        self.data["nbo_data"] = nbo_data

    def _read_cdft(self):
        """Parses output from charge- or spin-constrained DFT (CDFT) calculations."""
        # Parse constraint and optimization parameters
        temp_dict = read_pattern(
            self.text, {"constraint": r"Constraint\s+(\d+)\s+:\s+([\-\.0-9]+)", "multiplier": r"\s*Lam\s+([\.\-0-9]+)"}
        )

        self.data["cdft_constraints_multipliers"] = []
        for const, multip in zip(temp_dict.get("constraint", []), temp_dict.get("multiplier", [])):
            entry = {"index": int(const[0]), "constraint": float(const[1]), "multiplier": float(multip[0])}
            self.data["cdft_constraints_multipliers"].append(entry)

        # Parse Becke populations
        header_pattern = (
            r"\s*CDFT Becke Populations\s*\n\-+\s*\n\s*Atom\s+Excess Electrons\s+Population \(a\.u\.\)\s+Net Spin"
        )
        table_pattern = r"\s*(?:[0-9]+)\s+(?:[A-Za-z0-9]+)\s+([\-\.0-9]+)\s+([\.0-9]+)\s+([\-\.0-9]+)"
        footer_pattern = r"\s*\-+"

        becke_table = read_table_pattern(self.text, header_pattern, table_pattern, footer_pattern)
        if becke_table is None or len(becke_table) == 0:
            self.data["cdft_becke_excess_electrons"] = self.data["cdft_becke_population"] = self.data[
                "cdft_becke_net_spin"
            ] = None
        else:
            self.data["cdft_becke_excess_electrons"] = []
            self.data["cdft_becke_population"] = []
            self.data["cdft_becke_net_spin"] = []

            for table in becke_table:
                excess = []
                population = []
                spin = []

                for row in table:
                    excess.append(float(row[0]))
                    population.append(float(row[1]))
                    spin.append(float(row[2]))

                self.data["cdft_becke_excess_electrons"].append(excess)
                self.data["cdft_becke_population"].append(population)
                self.data["cdft_becke_net_spin"].append(spin)

        # TODO: CDFT-CI calculation outputs

    def _read_almo_msdft(self):
        """Parse output of ALMO(MSDFT) calculations for coupling between diabatic states."""
        temp_dict = read_pattern(
            self.text,
            {
                "states": r"Number of diabatic states: 2\s*\nstate 1\s*\ncharge per "
                r"fragment\s*\n((?:\s*[\-0-9]+\s*\n)+)multiplicity per fragment\s*\n((?:\s*[\-0-9]+\s*\n)+)"
                r"state 2\s*\ncharge per fragment\s*\n((?:\s*[\-0-9]+\s*\n)+)multiplicity per "
                r"fragment\s*\n((?:\s*[\-0-9]+\s*\n)+)",
                "diabat_energies": r"Energies of the diabats:\s*\n\s*state 1:\s+([\-\.0-9]+)"
                r"\s*\n\s*state 2:\s+([\-\.0-9]+)",
                "adiabat_energies": r"Energy of the adiabatic states\s*\n\s*State 1:\s+([\-\.0-9]+)\s*\n\s*"
                r"State 2:\s+([\-\.0-9]+)",
                "hamiltonian": r"Hamiltonian\s*\n\s*1\s+2\s*\n\s*1\s+([\-\.0-9]+)\s+([\-\.0-9]+)\s*\n\s*"
                r"2\s+([\-\.0-9]+)\s+([\-\.0-9]+)",
                "overlap": r"overlap\s*\n\s*1\s+2\s*\n\s*1\s+([\-\.0-9]+)\s+([\-\.0-9]+)\s*\n\s*"
                r"2\s+([\-\.0-9]+)\s+([\-\.0-9]+)",
                "s2": r"<S2>\s*\n\s*1\s+2\s*\n\s*1\s+([\-\.0-9]+)\s+([\-\.0-9]+)\s*\n\s*"
                r"2\s+([\-\.0-9]+)\s+([\-\.0-9]+)",
                "diabat_basis_coeff": r"Diabatic basis coefficients\s*\n\s*1\s+2\s*\n\s*"
                r"1\s+([\-\.0-9]+)\s+([\-\.0-9]+)\s*\n\s*2\s+([\-\.0-9]+)\s+([\-\.0-9]+)",
                "h_coupling": r"H passed to diabatic coupling calculation\s*\n\s*1\s+2\s*\n\s*"
                r"1\s+([\-\.0-9]+)\s+([\-\.0-9]+)\s*\n\s*2\s+([\-\.0-9]+)\s+([\-\.0-9]+)",
                "coupling": r"Coupling between diabats 1 and 2: (?:[\-\.0-9]+) \(([\-\.0-9]+) meV\)",
            },
        )

        # Coupling states
        if temp_dict.get("states") is None or len(temp_dict.get("states", [])) == 0:
            self.data["almo_coupling_states"] = None
        else:
            charges_1 = [int(r.strip()) for r in temp_dict["states"][0][0].strip().split("\n")]
            spins_1 = [int(r.strip()) for r in temp_dict["states"][0][1].strip().split("\n")]
            charges_2 = [int(r.strip()) for r in temp_dict["states"][0][2].strip().split("\n")]
            spins_2 = [int(r.strip()) for r in temp_dict["states"][0][3].strip().split("\n")]

            self.data["almo_coupling_states"] = [
                [[i, j] for i, j in zip(charges_1, spins_1)],
                [[i, j] for i, j in zip(charges_2, spins_2)],
            ]

        # State energies
        if temp_dict.get("diabat_energies") is None or len(temp_dict.get("diabat_energies", [])) == 0:
            self.data["almo_diabat_energies_Hartree"] = None
        else:
            self.data["almo_diabat_energies_Hartree"] = [float(x) for x in temp_dict["diabat_energies"][0]]
        if temp_dict.get("adiabat_energies") is None or len(temp_dict.get("adiabat_energies", [])) == 0:
            self.data["almo_adiabat_energies_Hartree"] = None
        else:
            self.data["almo_adiabat_energies_Hartree"] = [float(x) for x in temp_dict["adiabat_energies"][0]]

        # Matrices
        if temp_dict.get("hamiltonian") is None or len(temp_dict.get("hamiltonian", [])) == 0:
            self.data["almo_hamiltonian"] = None
        else:
            self.data["almo_hamiltonian"] = [
                [float(temp_dict["hamiltonian"][0][0]), float(temp_dict["hamiltonian"][0][1])],
                [float(temp_dict["hamiltonian"][0][2]), float(temp_dict["hamiltonian"][0][3])],
            ]
        if temp_dict.get("overlap") is None or len(temp_dict.get("overlap", [])) == 0:
            self.data["almo_overlap_matrix"] = None
        else:
            self.data["almo_overlap_matrix"] = [
                [float(temp_dict["overlap"][0][0]), float(temp_dict["overlap"][0][1])],
                [float(temp_dict["overlap"][0][2]), float(temp_dict["overlap"][0][3])],
            ]
        if temp_dict.get("s2") is None or len(temp_dict.get("s2", [])) == 0:
            self.data["almo_s2_matrix"] = None
        else:
            self.data["almo_s2_matrix"] = [
                [float(temp_dict["s2"][0][0]), float(temp_dict["s2"][0][1])],
                [float(temp_dict["s2"][0][2]), float(temp_dict["s2"][0][3])],
            ]
        if temp_dict.get("diabat_basis_coeff") is None or len(temp_dict.get("diabat_basis_coeff", [])) == 0:
            self.data["almo_diabat_basis_coeff"] = None
        else:
            self.data["almo_diabat_basis_coeff"] = [
                [float(temp_dict["diabat_basis_coeff"][0][0]), float(temp_dict["diabat_basis_coeff"][0][1])],
                [float(temp_dict["diabat_basis_coeff"][0][2]), float(temp_dict["diabat_basis_coeff"][0][3])],
            ]
        if temp_dict.get("h_coupling") is None or len(temp_dict.get("h_coupling", [])) == 0:
            self.data["almo_h_coupling_matrix"] = None
        else:
            self.data["almo_h_coupling_matrix"] = [
                [float(temp_dict["h_coupling"][0][0]), float(temp_dict["h_coupling"][0][1])],
                [float(temp_dict["h_coupling"][0][2]), float(temp_dict["h_coupling"][0][3])],
            ]

        # Electronic coupling
        if temp_dict.get("coupling") is None or len(temp_dict.get("coupling", [])) == 0:
            self.data["almo_coupling_eV"] = None
        else:
            self.data["almo_coupling_eV"] = float(temp_dict["coupling"][0][0]) / 1000

    def _check_completion_errors(self):
        """Parses potential errors that can cause jobs to crash."""
        if read_pattern(
            self.text,
            {"key": r"Coordinates do not transform within specified threshold"},
            terminate_on_match=True,
        ).get("key") == [[]]:
            self.data["errors"] += ["failed_to_transform_coords"]
        elif read_pattern(
            self.text,
            {"key": r"The Q\-Chem input file has failed to pass inspection"},
            terminate_on_match=True,
        ).get("key") == [[]]:
            self.data["errors"] += ["input_file_error"]
        elif read_pattern(self.text, {"key": r"Error opening input stream"}, terminate_on_match=True).get("key") == [
            []
        ]:
            self.data["errors"] += ["failed_to_read_input"]
        elif read_pattern(
            self.text,
            {"key": r"FileMan error: End of file reached prematurely"},
            terminate_on_match=True,
        ).get("key") == [[]]:
            self.data["errors"] += ["premature_end_FileMan_error"]
        elif read_pattern(
            self.text,
            {"key": r"need to increase the array of NLebdevPts"},
            terminate_on_match=True,
        ).get("key") == [[]]:
            self.data["errors"] += ["NLebdevPts"]
        elif read_pattern(self.text, {"key": r"method not available"}, terminate_on_match=True).get("key") == [[]]:
            self.data["errors"] += ["method_not_available"]
        elif read_pattern(
            self.text,
            {"key": r"Could not find \$molecule section in ParseQInput"},
            terminate_on_match=True,
        ).get("key") == [[]]:
            self.data["errors"] += ["read_molecule_error"]
        elif read_pattern(self.text, {"key": r"Welcome to Q-Chem"}, terminate_on_match=True).get("key") != [[]]:
            self.data["errors"] += ["never_called_qchem"]
        elif read_pattern(
            self.text,
            {"key": r"\*\*\*ERROR\*\*\* Hessian Appears to have all zero or negative eigenvalues"},
            terminate_on_match=True,
        ).get("key") == [[]]:
            self.data["errors"] += ["hessian_eigenvalue_error"]
        elif read_pattern(self.text, {"key": r"FlexNet Licensing error"}, terminate_on_match=True).get("key") == [
            []
        ] or read_pattern(self.text, {"key": r"Unable to validate license"}, terminate_on_match=True).get("key") == [
            []
        ]:
            self.data["errors"] += ["licensing_error"]
        elif read_pattern(
            self.text,
            {"key": r"Could not open driver file in ReadDriverFromDisk"},
            terminate_on_match=True,
        ).get("key") == [[]]:
            self.data["errors"] += ["driver_error"]
        elif read_pattern(self.text, {"key": r"Basis not supported for the above atom"}, terminate_on_match=True).get(
            "key"
        ) == [[]]:
            self.data["errors"] += ["basis_not_supported"]
        elif read_pattern(self.text, {"key": r"Unable to find relaxed density"}, terminate_on_match=True).get(
            "key"
        ) == [[]] or read_pattern(self.text, {"key": r"Out of Iterations- IterZ"}, terminate_on_match=True).get(
            "key"
        ) == [
            []
        ]:
            self.data["errors"] += ["failed_cpscf"]
        elif read_pattern(
            self.text,
            {"key": r"RUN_NBO6 \(rem variable\) is not correct"},
            terminate_on_match=True,
        ).get("key") == [[]]:
            self.data["errors"] += ["bad_old_nbo6_rem"]
        elif read_pattern(
            self.text,
            {"key": r"NBO_EXTERNAL \(rem variable\) is not correct"},
            terminate_on_match=True,
        ).get("key") == [[]]:
            self.data["errors"] += ["bad_new_nbo_external_rem"]
        elif read_pattern(
            self.text,
            {"key": r"gen_scfman_exception:  GDM:: Zero or negative preconditioner scaling factor"},
            terminate_on_match=True,
        ).get("key") == [[]]:
            self.data["errors"] += ["gdm_neg_precon_error"]
        elif read_pattern(self.text, {"key": r"too many atoms in ESPChgFit"}, terminate_on_match=True).get("key") == [
            []
        ]:
            self.data["errors"] += ["esp_chg_fit_error"]
        elif read_pattern(self.text, {"key": r"Please use larger MEM_STATIC"}, terminate_on_match=True).get("key") == [
            []
        ] or read_pattern(self.text, {"key": r"Please increase MEM_STATIC"}, terminate_on_match=True).get("key") == [
            []
        ]:
            self.data["errors"] += ["mem_static_too_small"]
        elif read_pattern(self.text, {"key": r"Please increase MEM_TOTAL"}, terminate_on_match=True).get("key") == [[]]:
            self.data["errors"] += ["mem_total_too_small"]
        elif self.text[-34:-2] == "Computing fast CPCM-SWIG hessian" or self.text[-16:-1] == "Roots Converged":
            self.data["errors"] += ["probably_out_of_memory"]
        else:
            tmp_failed_line_searches = read_pattern(
                self.text,
                {"key": r"\d+\s+failed line searches\.\s+Resetting"},
                terminate_on_match=False,
            ).get("key")
            if tmp_failed_line_searches is not None and len(tmp_failed_line_searches) > 10:
                self.data["errors"] += ["SCF_failed_to_converge"]
        if self.data.get("errors") == []:
            self.data["errors"] += ["unknown_error"]

    def as_dict(self):
        """
        Returns:
            MSONable dict.
        """
        dct = {}
        dct["data"] = self.data
        dct["text"] = self.text
        dct["filename"] = self.filename
        return jsanitize(dct, strict=True)


def check_for_structure_changes(mol1: Molecule, mol2: Molecule) -> str:
    """
    Compares connectivity of two molecules (using MoleculeGraph w/ OpenBabelNN).
    This function will work with two molecules with different atom orderings,
        but for proper treatment, atoms should be listed in the same order.
    Possible outputs include:
    - no_change: the bonding in the two molecules is identical
    - unconnected_fragments: the MoleculeGraph of mol1 is connected, but the
      MoleculeGraph is mol2 is not connected
    - fewer_bonds: the MoleculeGraph of mol1 has more bonds (edges) than the
      MoleculeGraph of mol2
    - more_bonds: the MoleculeGraph of mol2 has more bonds (edges) than the
      MoleculeGraph of mol1
    - bond_change: this case catches any other non-identical MoleculeGraphs
    Args:
        mol1: Pymatgen Molecule object to be compared.
        mol2: Pymatgen Molecule object to be compared.

    Returns:
        One of ["unconnected_fragments", "fewer_bonds", "more_bonds",
        "bond_change", "no_change"]
    """
    special_elements = ["Li", "Na", "Mg", "Ca", "Zn"]
    mol_list = [copy.deepcopy(mol1), copy.deepcopy(mol2)]

    if mol1.composition != mol2.composition:
        raise RuntimeError("Molecules have different compositions! Exiting...")

    for ii, site in enumerate(mol1):
        if site.specie.symbol != mol2[ii].specie.symbol:
            warnings.warn(
                "Comparing molecules with different atom ordering! "
                "Turning off special treatment for coordinating metals."
            )
            special_elements = []

    special_sites: list[list] = [[], []]
    for ii, mol in enumerate(mol_list):
        for jj, site in enumerate(mol):
            if site.specie.symbol in special_elements:
                distances = [[kk, site.distance(other_site)] for kk, other_site in enumerate(mol)]
                special_sites[ii].append([jj, site, distances])
        for jj, site in enumerate(mol):
            if site.specie.symbol in special_elements:
                del mol[jj]

    # Can add logic to check the distances in the future if desired

    initial_mol_graph = MoleculeGraph.with_local_env_strategy(mol_list[0], OpenBabelNN())
    initial_graph = initial_mol_graph.graph
    last_mol_graph = MoleculeGraph.with_local_env_strategy(mol_list[1], OpenBabelNN())
    last_graph = last_mol_graph.graph
    if initial_mol_graph.isomorphic_to(last_mol_graph):
        return "no_change"

    if nx.is_connected(initial_graph.to_undirected()) and not nx.is_connected(last_graph.to_undirected()):
        return "unconnected_fragments"
    if last_graph.number_of_edges() < initial_graph.number_of_edges():
        return "fewer_bonds"
    if last_graph.number_of_edges() > initial_graph.number_of_edges():
        return "more_bonds"
    return "bond_change"


def jump_to_header(lines: list[str], header: str) -> list[str]:
    """
    Given a list of lines, truncate the start of the list so that the first line
    of the new list contains the header.

    Args:
        lines: List of lines.
        header: Substring to match.

    Returns:
        Truncated lines.

    Raises:
        RuntimeError
    """
    # Search for the header
    for i, line in enumerate(lines):
        if header in line.strip():
            return lines[i:]

    # Search failed
    raise RuntimeError(f"{header=} could not be found in the lines.")


def get_percentage(line: str, orbital: str) -> str:
    """
    Retrieve the percent character of an orbital.

    Args:
        line: Line containing orbital and percentage.
        orbital: Type of orbital (s, p, d, f).

    Returns:
        Percentage of character.

    Raises:
        n/a
    """
    # Locate orbital in line
    index = line.find(orbital)
    line = line[index:]

    # Locate the first open bracket
    index = line.find("(")
    line = line[index:]

    # Isolate the percentage
    return line[1:7].strip()


def z_int(string: str) -> int:
    """
    Convert string to integer.
    If string empty, return -1.

    Args:
        string: Input to be cast to int.

    Returns:
        Int representation.

    Raises:
        n/a
    """
    try:
        return int(string)
    except ValueError:
        return -1


def parse_natural_populations(lines: list[str]) -> list[pd.DataFrame]:
    """
    Parse the natural populations section of NBO output.

    Args:
        lines: QChem output lines.

    Returns:
        Data frame of formatted output.

    Raises:
        RuntimeError
    """
    no_failures = True
    pop_dfs = []

    while no_failures:
        # Natural populations
        try:
            lines = jump_to_header(lines, "Summary of Natural Population Analysis:")
        except RuntimeError:
            no_failures = False

        if no_failures:
            # Jump to column names
            lines = lines[4:]
            columns = lines[0].split()

            # Jump to values
            lines = lines[2:]
            data = []
            for line in lines:
                # Termination condition
                if "=" in line:
                    break

                # Extract the values
                values = line.split()
                if len(values[0]) > 2:
                    values.insert(0, values[0][0:-3])
                    values[1] = values[1][-3:]
                data.append(
                    [
                        str(values[0]),
                        int(values[1]),
                        float(values[2]),
                        float(values[3]),
                        float(values[4]),
                        float(values[5]),
                        float(values[6]),
                    ]
                )
                if len(columns) == 8:
                    data[-1].append(float(values[7]))

            # Store values in a dataframe
            pop_dfs.append(pd.DataFrame(data=data, columns=columns))
    return pop_dfs


def parse_hyperbonds(lines: list[str]) -> list[pd.DataFrame]:
    """
    Parse the natural populations section of NBO output.

    Args:
        lines: QChem output lines.

    Returns:
        Data frame of formatted output.

    Raises:
        RuntimeError
    """
    no_failures = True
    hyperbond_dfs = []

    while no_failures:
        # hyperbonds
        try:
            lines = jump_to_header(
                lines,
                "3-Center, 4-Electron A:-B-:C Hyperbonds (A-B :C <=> A: B-C)",
            )
        except RuntimeError:
            no_failures = False

        if no_failures:
            # Jump to values
            lines = lines[2:]

            # Extract hyperbond data
            hyperbond_data = []
            for line in lines:
                # Termination condition
                if "NATURAL BOND ORBITALS" in line:
                    break
                if "SECOND ORDER PERTURBATION THEORY" in line:
                    break
                if "NHO DIRECTIONALITY AND BOND BENDING" in line:
                    break
                if "Archival summary:" in line:
                    break

                # Skip conditions
                if line.strip() == "":
                    continue
                if "NBOs" in line:
                    continue
                if "threshold" in line:
                    continue
                if "-------------" in line:
                    continue
                if "A:-B-:C" in line:
                    continue

                # Extract the values
                entry: dict[str, str | float] = {}
                entry["hyperbond index"] = int(line[0:4].strip())
                entry["bond atom 1 symbol"] = str(line[5:8].strip())
                entry["bond atom 1 index"] = int(line[8:11].strip())
                entry["bond atom 2 symbol"] = str(line[13:15].strip())
                entry["bond atom 2 index"] = int(line[15:18].strip())
                entry["bond atom 3 symbol"] = str(line[20:22].strip())
                entry["bond atom 3 index"] = int(line[22:25].strip())
                entry["pctA-B"] = float(line[27:31].strip())
                entry["pctB-C"] = float(line[32:36].strip())
                entry["occ"] = float(line[38:44].strip())
                entry["BD(A-B)"] = int(line[46:53].strip())
                entry["LP(C)"] = int(line[54:59].strip())
                entry["h(A)"] = int(line[61:65].strip())
                entry["h(B)"] = int(line[67:71].strip())
                entry["h(C)"] = int(line[73:77].strip())

                hyperbond_data.append(entry)

            # Store values in a dataframe
            hyperbond_dfs.append(pd.DataFrame(data=hyperbond_data))

    return hyperbond_dfs


def parse_hybridization_character(lines: list[str]) -> list[pd.DataFrame]:
    """
    Parse the hybridization character section of NBO output.

    Args:
        lines: QChem output lines.

    Returns:
        Data frames of formatted output.

    Raises:
        RuntimeError
    """
    # Orbitals
    orbitals = ["s", "p", "d", "f"]

    no_failures = True
    lp_and_bd_and_tc_dfs = []

    while no_failures:
        # NBO Analysis
        try:
            lines = jump_to_header(lines, "(Occupancy)   Bond orbital/ Coefficients/ Hybrids")
        except RuntimeError:
            try:
                lines = jump_to_header(lines, "(Occupancy)   Bond orbital / Coefficients / Hybrids")
            except RuntimeError:
                no_failures = False

        if no_failures:
            # Jump to values
            lines = lines[2:]

            # Save the data for different types of orbitals
            lp_data = []
            bd_data = []
            tc_data = []

            # Iterate over the lines
            i = -1
            while True:
                i += 1
                line = lines[i]

                # Termination conditions
                if "NHO DIRECTIONALITY AND BOND BENDING" in line:
                    break
                if "Archival summary:" in line:
                    break
                if "3-Center, 4-Electron A:-B-:C Hyperbonds (A-B :C <=> A: B-C)" in line:
                    break
                if "SECOND ORDER PERTURBATION THEORY ANALYSIS OF FOCK MATRIX IN NBO BASIS" in line:
                    break
                if "Thank you very much for using Q-Chem.  Have a nice day." in line:
                    break

                # Lone pair
                if "LP" in line or "LV" in line:
                    LPentry: dict[str, str | float] = {orbital: 0.0 for orbital in orbitals}
                    LPentry["bond index"] = line[0:4].strip()
                    LPentry["occupancy"] = line[7:14].strip()
                    LPentry["type"] = line[16:19].strip()
                    LPentry["orbital index"] = line[20:22].strip()
                    LPentry["atom symbol"] = line[23:25].strip()
                    LPentry["atom number"] = line[25:28].strip()

                    # Populate the orbital percentages
                    for orbital in orbitals:
                        if orbital in line:
                            LPentry[orbital] = get_percentage(line, orbital)

                    # Move one line down
                    i += 1
                    line = lines[i]

                    # Populate the orbital percentages
                    for orbital in orbitals:
                        if orbital in line:
                            LPentry[orbital] = get_percentage(line, orbital)

                    # Save the entry
                    lp_data.append(LPentry)

                # Bonding
                if "BD" in line:
                    BDentry: dict[str, str | float] = {
                        f"atom {i} {orbital}": 0.0 for orbital in orbitals for i in range(1, 3)
                    }
                    BDentry["bond index"] = line[0:4].strip()
                    BDentry["occupancy"] = line[7:14].strip()
                    BDentry["type"] = line[16:19].strip()
                    BDentry["orbital index"] = line[20:22].strip()
                    BDentry["atom 1 symbol"] = line[23:25].strip()
                    BDentry["atom 1 number"] = line[25:28].strip()
                    BDentry["atom 2 symbol"] = line[29:31].strip()
                    BDentry["atom 2 number"] = line[31:34].strip()

                    # Move one line down
                    i += 1
                    line = lines[i]

                    BDentry["atom 1 polarization"] = line[16:22].strip()
                    BDentry["atom 1 pol coeff"] = line[24:33].strip()

                    # Populate the orbital percentages
                    for orbital in orbitals:
                        if orbital in line:
                            BDentry[f"atom 1 {orbital}"] = get_percentage(line, orbital)

                    # Move one line down
                    i += 1
                    line = lines[i]

                    # Populate the orbital percentages
                    for orbital in orbitals:
                        if orbital in line:
                            BDentry[f"atom 1 {orbital}"] = get_percentage(line, orbital)

                    # Move down until you see an orbital
                    while "s" not in line:
                        i += 1
                        line = lines[i]

                    BDentry["atom 2 polarization"] = line[16:22].strip()
                    BDentry["atom 2 pol coeff"] = line[24:33].strip()

                    # Populate the orbital percentages
                    for orbital in orbitals:
                        if orbital in line:
                            BDentry[f"atom 2 {orbital}"] = get_percentage(line, orbital)

                    # Move one line down
                    i += 1
                    line = lines[i]

                    # Populate the orbital percentages
                    for orbital in orbitals:
                        if orbital in line:
                            BDentry[f"atom 2 {orbital}"] = get_percentage(line, orbital)

                    # Save the entry
                    bd_data.append(BDentry)

                # 3-center bond
                if "3C" in line:
                    TCentry: dict[str, str | float] = {
                        f"atom {i} {orbital}": 0.0 for orbital in orbitals for i in range(1, 4)
                    }
                    TCentry["bond index"] = line[0:4].strip()
                    TCentry["occupancy"] = line[7:14].strip()
                    TCentry["type"] = line[16:19].strip()
                    TCentry["orbital index"] = line[20:22].strip()
                    TCentry["atom 1 symbol"] = line[23:25].strip()
                    TCentry["atom 1 number"] = line[25:28].strip()
                    TCentry["atom 2 symbol"] = line[29:31].strip()
                    TCentry["atom 2 number"] = line[31:34].strip()
                    TCentry["atom 3 symbol"] = line[35:37].strip()
                    TCentry["atom 3 number"] = line[37:40].strip()

                    # Move one line down
                    i += 1
                    line = lines[i]

                    TCentry["atom 1 polarization"] = line[16:22].strip()
                    TCentry["atom 1 pol coeff"] = line[24:33].strip()

                    # Populate the orbital percentages
                    for orbital in orbitals:
                        if orbital in line:
                            TCentry[f"atom 1 {orbital}"] = get_percentage(line, orbital)

                    # Move one line down
                    i += 1
                    line = lines[i]

                    # Populate the orbital percentages
                    for orbital in orbitals:
                        if orbital in line:
                            TCentry[f"atom 1 {orbital}"] = get_percentage(line, orbital)

                    # Move down until you see an orbital
                    while "s" not in line:
                        i += 1
                        line = lines[i]

                    TCentry["atom 2 polarization"] = line[16:22].strip()
                    TCentry["atom 2 pol coeff"] = line[24:33].strip()

                    # Populate the orbital percentages
                    for orbital in orbitals:
                        if orbital in line:
                            TCentry[f"atom 2 {orbital}"] = get_percentage(line, orbital)

                    # Move one line down
                    i += 1
                    line = lines[i]

                    # Populate the orbital percentages
                    for orbital in orbitals:
                        if orbital in line:
                            TCentry[f"atom 2 {orbital}"] = get_percentage(line, orbital)

                    # Move down until you see an orbital
                    while "s" not in line:
                        i += 1
                        line = lines[i]

                    TCentry["atom 3 polarization"] = line[16:22].strip()
                    TCentry["atom 3 pol coeff"] = line[24:33].strip()

                    # Populate the orbital percentages
                    for orbital in orbitals:
                        if orbital in line:
                            TCentry[f"atom 3 {orbital}"] = get_percentage(line, orbital)

                    # Move one line down
                    i += 1
                    line = lines[i]

                    # Populate the orbital percentages
                    for orbital in orbitals:
                        if orbital in line:
                            TCentry[f"atom 3 {orbital}"] = get_percentage(line, orbital)

                    # Save the entry
                    tc_data.append(TCentry)

            # Store values in a dataframe
            lp_and_bd_and_tc_dfs.append(pd.DataFrame(data=lp_data))
            lp_and_bd_and_tc_dfs.append(pd.DataFrame(data=bd_data))
            lp_and_bd_and_tc_dfs.append(pd.DataFrame(data=tc_data))

    return lp_and_bd_and_tc_dfs


def parse_perturbation_energy(lines: list[str]) -> list[pd.DataFrame]:
    """
    Parse the perturbation energy section of NBO output.

    Args:
        lines: QChem output lines.

    Returns:
        Data frame of formatted output.

    Raises:
        RuntimeError
    """
    no_failures = True
    e2_dfs = []

    while no_failures:
        # 2nd order perturbation theory analysis
        try:
            header_str = "SECOND ORDER PERTURBATION THEORY ANALYSIS OF FOCK MATRIX IN NBO BASIS"
            lines = jump_to_header(lines, header_str)
        except RuntimeError:
            no_failures = False

        if no_failures:
            # Jump to values
            i = -1
            while True:
                i += 1
                line = lines[i]
                if "within" in line:
                    lines = lines[i:]
                    break

            # Extract 2nd order data
            e2_data = []
            for line in lines:
                # Termination condition
                if "NATURAL BOND ORBITALS" in line:
                    break

                # Skip conditions
                if line.strip() == "":
                    continue
                if "unit" in line:
                    continue
                if "None" in line:
                    continue

                # Extract the values
                entry: dict[str, str | float] = {}
                first_3C = False
                second_3C = False
                if line[7] == "3":
                    first_3C = True
                if line[35] == "3":
                    second_3C = True

                if line[4] == ".":
                    entry["donor bond index"] = int(line[0:4].strip())
                    entry["donor type"] = str(line[5:9].strip())
                    entry["donor orbital index"] = int(line[10:12].strip())
                    entry["donor atom 1 symbol"] = str(line[13:15].strip())
                    entry["donor atom 1 number"] = int(line[15:17].strip())
                    entry["donor atom 2 symbol"] = str(line[18:20].strip())
                    entry["donor atom 2 number"] = z_int(line[20:22].strip())
                    entry["acceptor bond index"] = int(line[25:31].strip())
                    entry["acceptor type"] = str(line[32:36].strip())
                    entry["acceptor orbital index"] = int(line[37:39].strip())
                    entry["acceptor atom 1 symbol"] = str(line[40:42].strip())
                    entry["acceptor atom 1 number"] = int(line[42:44].strip())
                    entry["acceptor atom 2 symbol"] = str(line[45:47].strip())
                    entry["acceptor atom 2 number"] = z_int(line[47:49].strip())
                    entry["perturbation energy"] = float(line[50:62].strip())
                    entry["energy difference"] = float(line[62:70].strip())
                    entry["fock matrix element"] = float(line[70:79].strip())
                elif line[5] == ".":
                    entry["donor bond index"] = int(line[0:5].strip())
                    entry["donor type"] = str(line[6:10].strip())
                    entry["donor orbital index"] = int(line[11:13].strip())

                    if first_3C:
                        tmp = str(line[14:28].strip())
                        split = tmp.split("-")
                        if len(split) != 3:
                            raise ValueError("Should have three components! Exiting...")
                        # This is a bit hacky, but making new more accurate entry
                        # keys for 3C info forces there to be a NAN for those values
                        # for all other entries, which I believe would nontrivially
                        # increase the size of the data being stored, which is already
                        # very large. So, here we have saved info that should be labeled
                        # "donor 3C 1", "donor 3C 2", and "donor 3C 3" in the
                        # "donor atom 1 symbol", "donor atom 1 number", and
                        # "donor atom 2 symbol" fields, and then put a reminder in the
                        # "donor atom 2 number" field.
                        entry["donor atom 1 symbol"] = split[0]
                        entry["donor atom 1 number"] = split[1]
                        entry["donor atom 2 symbol"] = split[2]
                        entry["donor atom 2 number"] = "info_is_from_3C"
                    else:
                        entry["donor atom 1 symbol"] = str(line[14:16].strip())
                        entry["donor atom 1 number"] = int(line[16:19].strip())
                        entry["donor atom 2 symbol"] = str(line[20:22].strip())
                        entry["donor atom 2 number"] = z_int(line[22:25].strip())

                    entry["acceptor bond index"] = int(line[28:33].strip())
                    entry["acceptor type"] = str(line[34:38].strip())
                    entry["acceptor orbital index"] = int(line[39:41].strip())

                    if second_3C:
                        tmp = str(line[42:56].strip())
                        split = tmp.split("-")
                        if len(split) != 3:
                            raise ValueError("Should have three components! Exiting...")
                        # This is a bit hacky, but making new more accurate entry
                        # keys for 3C info forces there to be a NAN for those values
                        # for all other entries, which I believe would nontrivially
                        # increase the size of the data being stored, which is already
                        # very large. So, here we have saved info that should be labeled
                        # "acceptor 3C 1", "acceptor 3C 2", and "acceptor 3C 3" in the
                        # "acceptor atom 1 symbol", "acceptor atom 1 number", and
                        # "acceptor atom 2 symbol" fields, and then put a reminder in the
                        # "acceptor atom 2 number" field.
                        entry["acceptor atom 1 symbol"] = split[0]
                        entry["acceptor atom 1 number"] = split[1]
                        entry["acceptor atom 2 symbol"] = split[2]
                        entry["acceptor atom 2 number"] = "info_is_from_3C"
                    else:
                        entry["acceptor atom 1 symbol"] = str(line[42:44].strip())
                        entry["acceptor atom 1 number"] = int(line[44:47].strip())
                        entry["acceptor atom 2 symbol"] = str(line[48:50].strip())
                        entry["acceptor atom 2 number"] = z_int(line[50:53].strip())
                    try:
                        entry["perturbation energy"] = float(line[56:63].strip())
                    except ValueError:
                        if line[56:63].strip() == "*******":
                            entry["perturbation energy"] = float("inf")
                        else:
                            raise ValueError("Unknown value error in parsing perturbation energy")
                    entry["energy difference"] = float(line[63:71].strip())
                    entry["fock matrix element"] = float(line[71:79].strip())
                e2_data.append(entry)

            # Store values in a dataframe
            e2_dfs.append(pd.DataFrame(data=e2_data))

    return e2_dfs


def nbo_parser(filename: str) -> dict[str, list[pd.DataFrame]]:
    """
    Parse all the important sections of NBO output.

    Args:
        filename: Path to QChem NBO output.

    Returns:
        Data frames of formatted output.

    Raises:
        RuntimeError
    """
    # Open the lines
    with zopen(filename, mode="rt", encoding="ISO-8859-1") as f:
        lines = f.readlines()

    # Compile the dataframes
    dfs = {}
    dfs["natural_populations"] = parse_natural_populations(lines)
    dfs["hybridization_character"] = parse_hybridization_character(lines)
    dfs["hyperbonds"] = parse_hyperbonds(lines)
    dfs["perturbation_energy"] = parse_perturbation_energy(lines)
    return dfs
