# Path: cclib/method/calculationmethod.py
# class Method:
#     """Abstract base class for all cclib method classes.
# 
#     Subclasses defined by cclib:
#         CDA - charde decomposition analysis
#         CSPA - C-squared population analysis
#         Density - density matrix calculation
#         FragmentAnalysis - fragment analysis for ADF output
#         LPA - Löwdin population analysis
#         MBO - Mayer's bond orders
#         Moments - multipole moments calculations
#         MPA - Mulliken population analysis
#         Nuclear - properties of atomic nuclei
#         OPA - overlap population analysis
#         Population - base class for population analyses
#         Volume - volume/grid calculations
# 
#     All the modules containing methods should be importable.
#     """
#     required_attrs = ()
#     def __init__(self, data, progress=None, loglevel=logging.INFO, logname="Log"):
#         """Initialise the Logfile object.
# 
#         This constructor is typically called by the constructor of a subclass.
#         """
# 
#         self.data = data
#         self.progress = progress
#         self.loglevel = loglevel
#         self.logname = logname
#         self._check_required_attributes()
#         self.logger = logging.getLogger('%s %s' % (self.logname, self.data))
#         self.logger.setLevel(self.loglevel)
#         self.logformat = "[%(name)s %(levelname)s] %(message)s"
#         handler = logging.StreamHandler(sys.stdout)
#         handler.setFormatter(logging.Formatter(self.logformat))
#         self.logger.addHandler(handler)
# 
#     def _check_required_attributes(self):
#         """Check if required attributes are present in data."""
#         missing = [x for x in self.required_attrs
#                     if not hasattr(self.data, x)]
#         if missing:
#             missing = ' '.join(missing)
#             raise MissingAttributeError(
#                 'Could not parse required attributes to use method: ' + missing)
# 
# Path: cclib/method/volume.py
# def electrondensity_spin(ccdata, volume, mocoeffslist):
#     """Calculate the magnitude of the electron density at every point in a volume for either up or down spin
# 
#     Inputs:
#         ccdata -- ccData object
#         volume -- Volume object (will not be altered)
#         mocoeffslist -- list of molecular orbital to calculate electron density from;
#                         i.e. [ccdata.mocoeffs[0][1:2]]
# 
#     Output:
#         Volume object with wavefunction at each grid point stored in data attribute
# 
#     Attributes:
#         coords -- the coordinates of the atoms
#         mocoeffs -- mocoeffs for all of the occupied eigenvalues
#         gbasis -- gbasis from a parser object
#         volume -- a template Volume object (will not be altered)
# 
#     Note: mocoeffs is a list of NumPy arrays. The list will be of length 1.
#     """
#     assert (
#         len(mocoeffslist) == 1
#     ), "mocoeffslist input to the function should have length of 1."
#     _check_pyquante()
#     bfs = getbfs(ccdata)
# 
#     density = copy.copy(volume)
#     density.data = numpy.zeros(density.data.shape, "d")
# 
#     x, y, z = getGrid(density)
#     gridpoints = numpy.asanyarray(
#         tuple((xp, yp, zp) for xp in x for yp in y for zp in z)
#     )
# 
#     # For occupied orbitals
#     # `mocoeff` and `gbasis` in ccdata object is ordered in a way `homos` can specify which orbital
#     # is the highest lying occupied orbital in mocoeff and gbasis.
#     for mocoeffs in mocoeffslist:
#         for mocoeff in mocoeffs:
#             wavefn = numpy.zeros(density.data.shape, "d")
#             for bs in range(len(bfs)):
#                 if abs(mocoeff[bs]) > 0.0:
#                     wavefn += numpy.resize(
#                         pyamp(bfs, bs, gridpoints) * mocoeff[bs], density.data.shape
#                     )
#             density.data += wavefn ** 2
#     return density


import copy
import random
import numpy
import logging
import math
import os
import sys
            import h5py
from cclib.method.calculationmethod import Method
from cclib.method.volume import electrondensity_spin
from cclib.parser.utils import convertor
from cclib.parser.utils import find_package
from typing import List
                    density_ceil = numpy.asanyarray(list(proatomdb[keystring_ceil]["rho"]))

                density = (charge_ceil - charge) * density_floor + (
                    charge - charge_floor
                ) * density_ceil

                del h5py

        else:
            raise MissingInputError("Pro-atom densities were not found in the specified path.")

        if charge == charge_floor:
            density = density_floor

        return density, radiusgrid

    def calculate(self, indices=None, fupdate=0.05):
        """ Charge density on a Cartesian grid is a common routine required for Stockholder-type
            and related methods. This abstract class prepares the grid if input Volume object
            is empty.
        """
        # Obtain charge densities on the grid if it does not contain one.
        if not numpy.any(self.volume.data):
            self.logger.info("Calculating charge densities on the provided empty grid.")
            if len(self.data.mocoeffs) == 1:
                self.charge_density = electrondensity_spin(
                    self.data, self.volume, [self.data.mocoeffs[0][: self.data.homos[0] + 1]]
                )
                self.charge_density.data *= 2
            else:

# Path: cclib/method/calculationmethod.py
# class Method:
#     """Abstract base class for all cclib method classes.
# 
#     Subclasses defined by cclib:
#         CDA - charde decomposition analysis
#         CSPA - C-squared population analysis
#         Density - density matrix calculation
#         FragmentAnalysis - fragment analysis for ADF output
#         LPA - Löwdin population analysis
#         MBO - Mayer's bond orders
#         Moments - multipole moments calculations
#         MPA - Mulliken population analysis
#         Nuclear - properties of atomic nuclei
#         OPA - overlap population analysis
#         Population - base class for population analyses
#         Volume - volume/grid calculations
# 
#     All the modules containing methods should be importable.
#     """
#     required_attrs = ()
#     def __init__(self, data, progress=None, loglevel=logging.INFO, logname="Log"):
#         """Initialise the Logfile object.
# 
#         This constructor is typically called by the constructor of a subclass.
#         """
# 
#         self.data = data
#         self.progress = progress
#         self.loglevel = loglevel
#         self.logname = logname
#         self._check_required_attributes()
#         self.logger = logging.getLogger('%s %s' % (self.logname, self.data))
#         self.logger.setLevel(self.loglevel)
#         self.logformat = "[%(name)s %(levelname)s] %(message)s"
#         handler = logging.StreamHandler(sys.stdout)
#         handler.setFormatter(logging.Formatter(self.logformat))
#         self.logger.addHandler(handler)
# 
#     def _check_required_attributes(self):
#         """Check if required attributes are present in data."""
#         missing = [x for x in self.required_attrs
#                     if not hasattr(self.data, x)]
#         if missing:
#             missing = ' '.join(missing)
#             raise MissingAttributeError(
#                 'Could not parse required attributes to use method: ' + missing)
# 
# Path: cclib/method/volume.py
# def electrondensity_spin(ccdata, volume, mocoeffslist):
#     """Calculate the magnitude of the electron density at every point in a volume for either up or down spin
# 
#     Inputs:
#         ccdata -- ccData object
#         volume -- Volume object (will not be altered)
#         mocoeffslist -- list of molecular orbital to calculate electron density from;
#                         i.e. [ccdata.mocoeffs[0][1:2]]
# 
#     Output:
#         Volume object with wavefunction at each grid point stored in data attribute
# 
#     Attributes:
#         coords -- the coordinates of the atoms
#         mocoeffs -- mocoeffs for all of the occupied eigenvalues
#         gbasis -- gbasis from a parser object
#         volume -- a template Volume object (will not be altered)
# 
#     Note: mocoeffs is a list of NumPy arrays. The list will be of length 1.
#     """
#     assert (
#         len(mocoeffslist) == 1
#     ), "mocoeffslist input to the function should have length of 1."
#     _check_pyquante()
#     bfs = getbfs(ccdata)
# 
#     density = copy.copy(volume)
#     density.data = numpy.zeros(density.data.shape, "d")
# 
#     x, y, z = getGrid(density)
#     gridpoints = numpy.asanyarray(
#         tuple((xp, yp, zp) for xp in x for yp in y for zp in z)
#     )
# 
#     # For occupied orbitals
#     # `mocoeff` and `gbasis` in ccdata object is ordered in a way `homos` can specify which orbital
#     # is the highest lying occupied orbital in mocoeff and gbasis.
#     for mocoeffs in mocoeffslist:
#         for mocoeff in mocoeffs:
#             wavefn = numpy.zeros(density.data.shape, "d")
#             for bs in range(len(bfs)):
#                 if abs(mocoeff[bs]) > 0.0:
#                     wavefn += numpy.resize(
#                         pyamp(bfs, bs, gridpoints) * mocoeff[bs], density.data.shape
#                     )
#             density.data += wavefn ** 2
#     return density


# Path: cclib/method/stockholder.py
import copy
import random
import numpy
import logging
import math
import os
import sys
            import h5py
from cclib.method.calculationmethod import Method
from cclib.method.volume import electrondensity_spin
from cclib.parser.utils import convertor
from cclib.parser.utils import find_package
from typing import List

                    density_ceil = numpy.asanyarray(list(proatomdb[keystring_ceil]["rho"]))

                density = (charge_ceil - charge) * density_floor + (
                    charge - charge_floor
                ) * density_ceil

                del h5py

        else:
            raise MissingInputError("Pro-atom densities were not found in the specified path.")

        if charge == charge_floor:
            density = density_floor

        return density, radiusgrid

    def calculate(self, indices=None, fupdate=0.05):
        """ Charge density on a Cartesian grid is a common routine required for Stockholder-type
            and related methods. This abstract class prepares the grid if input Volume object
            is empty.
        """
        # Obtain charge densities on the grid if it does not contain one.
        if not numpy.any(self.volume.data):
            self.logger.info("Calculating charge densities on the provided empty grid.")
            if len(self.data.mocoeffs) == 1:
                self.charge_density = electrondensity_spin(
                    self.data, self.volume, [self.data.mocoeffs[0][: self.data.homos[0] + 1]]
                )
                self.charge_density.data *= 2
            else:

                self.charge_density = electrondensity_spin(