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

from __future__ import annotations

import copy
import logging
import math
import os
import re
import struct
import warnings
from typing import TYPE_CHECKING, 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

if TYPE_CHECKING:
    from numpy.typing import NDArray

__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 file:
            self.text = file.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\-\.]+)"},
            ) or read_pattern(  # support Q-Chem 6.1.1+ (gh-3580)
                self.text,
                {"final_energy": r"\s+Total energy\s+=\s+([\d\-\.]+)"},
            )

            if e_final_match := temp_dict.get("final_energy"):
                self.data["final_energy"] = float(e_final_match[-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, mode="rt") as file:
            text = re.split(r"\s*(?:Running\s+)*Job\s+\d+\s+of\s+\d+\s+", file.read())
        if text[0] == "":
            text = text[1:]
        for i, sub_text in enumerate(text):
            with open(f"{filename}.{i}", mode="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 dipole/multipole moments.
        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 in range(len(temp_dipole)):
                    for jj, _val in enumerate(temp_dipole[ii]):
                        dipole[ii][jj] = temp_dipole[ii][jj]
                self.data["dipoles"]["dipole"] = dipole

        self.data["multipoles"] = dict()

        quad_mom_pat = (
            r"\s*Quadrupole Moments \(Debye\-Ang\)\s+XX\s+([\-\.0-9]+)\s+XY\s+([\-\.0-9]+)\s+YY"
            r"\s+([\-\.0-9]+)\s+XZ\s+([\-\.0-9]+)\s+YZ\s+([\-\.0-9]+)\s+ZZ\s+([\-\.0-9]+)"
        )
        temp_quadrupole_moment = read_pattern(self.text, {"key": quad_mom_pat}).get("key")
        if temp_quadrupole_moment is not None:
            keys = ("XX", "XY", "YY", "XZ", "YZ", "ZZ")
            if len(temp_quadrupole_moment) == 1:
                self.data["multipoles"]["quadrupole"] = {
                    key: float(temp_quadrupole_moment[0][idx]) for idx, key in enumerate(keys)
                }
            else:
                self.data["multipoles"]["quadrupole"] = list()
                for qpole in temp_quadrupole_moment:
                    self.data["multipoles"]["quadrupole"].append(
                        {key: float(qpole[idx]) for idx, key in enumerate(keys)}
                    )

        octo_mom_pat = (
            r"\s*Octopole Moments \(Debye\-Ang\^2\)\s+XXX\s+([\-\.0-9]+)\s+XXY\s+([\-\.0-9]+)"
            r"\s+XYY\s+([\-\.0-9]+)\s+YYY\s+([\-\.0-9]+)\s+XXZ\s+([\-\.0-9]+)\s+XYZ\s+([\-\.0-9]+)"
            r"\s+YYZ\s+([\-\.0-9]+)\s+XZZ\s+([\-\.0-9]+)\s+YZZ\s+([\-\.0-9]+)\s+ZZZ\s+([\-\.0-9]+)"
        )
        temp_octopole_moment = read_pattern(self.text, {"key": octo_mom_pat}).get("key")
        if temp_octopole_moment is not None:
            keys = ("XXX", "XXY", "XYY", "YYY", "XXZ", "XYZ", "YYZ", "XZZ", "YZZ", "ZZZ")
            if len(temp_octopole_moment) == 1:
                self.data["multipoles"]["octopole"] = {
                    key: float(temp_octopole_moment[0][idx]) for idx, key in enumerate(keys)
                }
            else:
                self.data["multipoles"]["octopole"] = list()
                for opole in temp_octopole_moment:
                    self.data["multipoles"]["octopole"].append({key: float(opole[idx]) for idx, key in enumerate(keys)})

        hexadeca_mom_pat = (
            r"\s*Hexadecapole Moments \(Debye\-Ang\^3\)\s+XXXX\s+([\-\.0-9]+)\s+XXXY\s+([\-\.0-9]+)"
            r"\s+XXYY\s+([\-\.0-9]+)\s+XYYY\s+([\-\.0-9]+)\s+YYYY\s+([\-\.0-9]+)\s+XXXZ\s+([\-\.0-9]+)"
            r"\s+XXYZ\s+([\-\.0-9]+)\s+XYYZ\s+([\-\.0-9]+)\s+YYYZ\s+([\-\.0-9]+)\s+XXZZ\s+([\-\.0-9]+)"
            r"\s+XYZZ\s+([\-\.0-9]+)\s+YYZZ\s+([\-\.0-9]+)\s+XZZZ\s+([\-\.0-9]+)\s+YZZZ\s+([\-\.0-9]+)"
            r"\s+ZZZZ\s+([\-\.0-9]+)"
        )
        temp_hexadecapole_moment = read_pattern(self.text, {"key": hexadeca_mom_pat}).get("key")
        if temp_hexadecapole_moment is not None:
            keys = (
                "XXXX",
                "XXXY",
                "XXYY",
                "XYYY",
                "YYYY",
                "XXXZ",
                "XXYZ",
                "XYYZ",
                "YYYZ",
                "XXZZ",
                "XYZZ",
                "YYZZ",
                "XZZZ",
                "YZZZ",
                "ZZZZ",
            )

            if len(temp_hexadecapole_moment) == 1:
                self.data["multipoles"]["hexadecapole"] = {
                    key: float(temp_hexadecapole_moment[0][idx]) for idx, key in enumerate(keys)
                }
            else:
                self.data["multipoles"]["hexadecapole"] = list()
                for hpole in temp_hexadecapole_moment:
                    self.data["multipoles"]["hexadecapole"].append(
                        {key: float(hpole[idx]) for idx, key in enumerate(keys)}
                    )

        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 in range(len(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:
            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: list[pd.DataFrame] = []

    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] = dict.fromkeys(orbitals, 0.0)
                    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 += (pd.DataFrame(lp_data), pd.DataFrame(bd_data), pd.DataFrame(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 file:
        lines = file.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


def gradient_parser(filename: str = "131.0") -> NDArray:
    """
    Parse the gradient data from a gradient scratch file.

    Args:
        filename: Path to the gradient scratch file. Defaults to "131.0".

    Returns:
        NDArray: The gradient, in units of Hartree/Bohr.
    """
    # Read the gradient scratch file in 8 byte chunks
    tmp_grad_data: list[float] = []
    with zopen(filename, mode="rb") as file:
        binary = file.read()
    tmp_grad_data.extend(struct.unpack("d", binary[ii * 8 : (ii + 1) * 8])[0] for ii in range(len(binary) // 8))
    grad = [
        [
            float(tmp_grad_data[ii * 3]),
            float(tmp_grad_data[ii * 3 + 1]),
            float(tmp_grad_data[ii * 3 + 2]),
        ]
        for ii in range(len(tmp_grad_data) // 3)
    ]
    return np.array(grad)


def hessian_parser(filename: str = "132.0", n_atoms: int | None = None) -> NDArray:
    """
    Parse the Hessian data from a Hessian scratch file.

    Args:
        filename: Path to the Hessian scratch file. Defaults to "132.0".
        n_atoms: Number of atoms in the molecule. If None, no reshaping will be done.

    Returns:
        NDArray: Hessian, formatted as 3n_atoms x 3n_atoms. Units are Hartree/Bohr^2/amu.
    """
    hessian: list[float] = []
    with zopen(filename, mode="rb") as file:
        binary = file.read()
    hessian.extend(struct.unpack("d", binary[ii * 8 : (ii + 1) * 8])[0] for ii in range(len(binary) // 8))
    if n_atoms:
        return np.reshape(hessian, (n_atoms * 3, n_atoms * 3))
    return np.array(hessian)


def orbital_coeffs_parser(filename: str = "53.0") -> NDArray:
    """
    Parse the orbital coefficients from a scratch file.

    Args:
        filename: Path to the orbital coefficients file. Defaults to "53.0".

    Returns:
        NDArray: The orbital coefficients
    """
    orbital_coeffs: list[float] = []
    with zopen(filename, mode="rb") as file:
        binary = file.read()
    orbital_coeffs.extend(struct.unpack("d", binary[ii * 8 : (ii + 1) * 8])[0] for ii in range(len(binary) // 8))
    return np.array(orbital_coeffs)
