The MultiMolecule Class

The API of the FOX.MultiMolecule class.

API FOX.MultiMolecule

class FOX.MultiMolecule(coords, atoms=None, bonds=None, properties=None, atoms_alias=None, lattice=None)[source]

A class designed for handling a and manipulating large numbers of molecules.

More specifically, different conformations of a single molecule as derived from, for example, an intrinsic reaction coordinate calculation (IRC) or a molecular dymanics trajectory (MD). The class has access to four attributes (further details are provided under parameters):

Parameters:
  • coords (np.ndarray[np.float64], shape \((m, n, 3)\)) – A 3D array with the cartesian coordinates of \(m\) molecules with \(n\) atoms.

  • atoms (dict[str, list[str]]) – A dictionary with atomic symbols as keys and matching atomic indices as values. Stored in the MultiMolecule.atoms attribute.

  • bonds (np.ndarray[np.int64], shape \((k, 3)\)) – A 2D array with indices of the atoms defining all \(k\) bonds (columns 1 & 2) and their respective bond orders multiplied by 10 (column 3). Stored in the MultiMolecule.bonds attribute.

  • properties (plams.Settings) – A Settings instance for storing miscellaneous user-defined (meta-)data. Is devoid of keys by default. Stored in the MultiMolecule.properties attribute.

  • lattice (np.ndarray[np.float64], shape \((m, 3, 3)\) or \((3, 3)\), optional) – Lattice vectors for periodic systems. For non-periodic systems this value should be None.

atoms

A dictionary with atomic symbols as keys and matching atomic indices as values.

Type:

dict[str, list[str]]

bonds

A 2D array with indices of the atoms defining all \(k\) bonds (columns 1 & 2) and their respective bond orders multiplied by 10 (column 3).

Type:

np.ndarray[np.int64], shape \((k, 3)\)

properties

A Settings instance for storing miscellaneous user-defined (meta-)data. Is devoid of keys by default.

Type:

plams.Settings

lattice

Lattice vectors for periodic systems. For non-periodic systems this value should be None.

Type:

np.ndarray[np.float64], shape \((m, 3, 3)\) or \((3, 3)\), optional

round(decimals=0, *, inplace=False)[source]

Round the Cartesian coordinates of this instance to a given number of decimals.

Parameters:
  • decimals (int) – The number of decimals per element.

  • inplace (bool) – Instead of returning the new coordinates, perform an inplace update of this instance.

delete_atoms(atom_subset)[source]

Create a copy of this instance with all atoms in atom_subset removed.

Parameters:

atom_subset (Sequence[str]) – Perform the calculation on a subset of atoms in this instance, as determined by their atomic index or atomic symbol. Include all \(n\) atoms per molecule in this instance if None.

Returns:

A new molecule with all atoms in atom_subset removed.

Return type:

FOX.MultiMolecule

Raises:

TypeError – Raised if atom_subset is None.

get_supercell(supercell_size)[source]

Construct a new supercell by duplicating the molecule.

Parameters:

supercell_size (tuple[int, int, int]) – The number of new unit cells along each of the three Cartesian axes.

Returns:

The new supercell constructed from self.

Return type:

FOX.MultiMolecule

concatenate(other, lattice=None, axis=1)[source]

Concatenate one or more molecules along the user-specified axis.

Parameters:
Returns:

The newly concatenated molecule.

Return type:

FOX.MultiMolecule

add_atoms(coords, symbols='Xx')[source]

Create a copy of this instance with all atoms in atom_subset appended.

Examples

>>> import numpy as np
>>> from FOX import MultiMolecule, example_xyz

>>> mol = MultiMolecule.from_xyz(example_xyz)
>>> coords: np.ndarray = np.random.rand(73, 3)  # Add 73 new atoms with random coords
>>> symbols = 'Br'

>>> mol_new: MultiMolecule = mol.add_atoms(coords, symbols)

>>> print(repr(mol))
MultiMolecule(..., shape=(4905, 227, 3), dtype='float64')
>>> print(repr(mol_new))
MultiMolecule(..., shape=(4905, 300, 3), dtype='float64')
Parameters:
  • coords (array-like) – A \((3,)\), \((n, 3)\), \((m, 3)\) or \((m, n, 3)\) array-like object with m == len(self). Represents the Cartesian coordinates of the to-be added atoms.

  • symbols (str or Iterable[str]) – One or more atomic symbols of the to-be added atoms.

Returns:

A new molecule with all atoms in atom_subset appended.

Return type:

FOX.MultiMolecule

guess_bonds(atom_subset=None)[source]

Guess bonds within the molecules based on atom type and inter-atomic distances.

Bonds are guessed based on the first molecule in this instance Performs an inplace modification of self.bonds

Parameters:

atom_subset (Sequence[str], optional) – A tuple of atomic symbols. Bonds are guessed between all atoms whose atomic symbol is in atom_subset. If None, guess bonds for all atoms in this instance.

random_slice(start=0, stop=None, p=0.5, inplace=False)[source]

Construct a new MultiMolecule instance by randomly slicing this instance.

The probability of including a particular element is equivalent to p.

Parameters:
  • start (int) – Start of the interval.

  • stop (int, optional) – End of the interval.

  • p (float) – The probability of including each particular molecule in this instance. Values must be between 0 (0%) and 1 (100%).

  • inplace (bool) – Instead of returning the new coordinates, perform an inplace update of this instance.

Returns:

If inplace is True, return a new molecule.

Return type:

FOX.MultiMolecule or None

Raises:

ValueError – Raised if p is smaller than 0.0 or larger than 1.0.

reset_origin(mol_subset=None, atom_subset=None, inplace=True, rot_ref=None)[source]

Reallign all molecules in this instance.

All molecules in this instance are rotating and translating, by performing a partial partial Procrustes superimposition with respect to the first molecule in this instance.

The superimposition is carried out with respect to the first molecule in this instance.

Parameters:
  • mol_subset (slice, optional) – Perform the calculation on a subset of molecules in this instance, as determined by their moleculair index. Include all \(m\) molecules in this instance if None.

  • atom_subset (Sequence[str], optional) – Perform the calculation on a subset of atoms in this instance, as determined by their atomic index or atomic symbol. Include all \(n\) atoms per molecule in this instance if None.

  • inplace (bool) – Instead of returning the new coordinates, perform an inplace update of this instance.

Returns:

If inplace is True, return a new MultiMolecule instance.

Return type:

FOX.MultiMolecule or None

sort(sort_by='symbol', reverse=False, inplace=True)[source]

Sort the atoms in this instance and self.atoms, performing in inplace update.

Parameters:
  • sort_by (str or Sequence[int]) – The property which is to be used for sorting. Accepted values: "symbol" (i.e. alphabetical), "atnum", "mass", "radius" or "connectors". See the plams.PeriodicTable module for more details. Alternatively, a user-specified sequence of indices can be provided for sorting.

  • reverse (bool) – Sort in reversed order.

  • inplace (bool) – Instead of returning the new coordinates, perform an inplace update of this instance.

Returns:

If inplace is True, return a new MultiMolecule instance.

Return type:

FOX.MultiMolecule or None

residue_argsort(concatenate=True)[source]

Return the indices that would sort this instance by residue number.

Residues are defined based on moleculair fragments based on self.bonds.

Parameters:

concatenate (bool) – If False, returned a nested list with atomic indices. Each sublist contains the indices of a single residue.

Returns:

A 1D array of indices that would sort \(n\) atoms this instance.

Return type:

np.ndarray[np.int64], shape \((n,)\)

get_center_of_mass(mol_subset=None, atom_subset=None)[source]

Get the center of mass.

Parameters:
  • mol_subset (slice, optional) – Perform the calculation on a subset of molecules in this instance, as determined by their moleculair index. Include all \(m\) molecules in this instance if None.

  • atom_subset (Sequence[str], optional) – Perform the calculation on a subset of atoms in this instance, as determined by their atomic index or atomic symbol. Include all \(n\) atoms per molecule in this instance if None.

Returns:

A 2D array with the centres of mass of \(m\) molecules with \(n\) atoms.

Return type:

np.ndarray[np.float64], shape \((m, 3)\)

get_bonds_per_atom(atom_subset=None)[source]

Get the number of bonds per atom in this instance.

Parameters:

atom_subset (Sequence[str], optional) – Perform the calculation on a subset of atoms in this instance, as determined by their atomic index or atomic symbol. Include all \(n\) atoms per molecule in this instance if None.

Returns:

A 1D array with the number of bonds per atom, for all \(n\) atoms in this instance.

Return type:

\(n\) np.ndarray [np.int64]

init_average_velocity(timestep=1.0, rms=False, mol_subset=None, atom_subset=None)[source]

Calculate the average atomic velocty.

The average velocity (in fs/A) is calculated for all atoms in atom_subset over the course of a trajectory.

The velocity is averaged over all atoms in a particular atom subset.

Parameters:
  • timestep (float) – The stepsize, in femtoseconds, between subsequent frames.

  • rms (bool) – Calculate the root-mean squared average velocity instead.

  • mol_subset (slice, optional) – Perform the calculation on a subset of molecules in this instance, as determined by their moleculair index. Include all \(m\) molecules in this instance if None.

  • atom_subset (Sequence[str], optional) – Perform the calculation on a subset of atoms in this instance, as determined by their atomic index or atomic symbol. Include all \(n\) atoms per molecule in this instance if None.

Returns:

A dataframe holding \(m-1\) velocities averaged over one or more atom subsets.

Return type:

pd.DataFrame

init_time_averaged_velocity(timestep=1.0, rms=False, mol_subset=None, atom_subset=None)[source]

Calculate the time-averaged velocty.

The time-averaged velocity (in fs/A) is calculated for all atoms in atom_subset over the course of a trajectory.

Parameters:
  • timestep (float) – The stepsize, in femtoseconds, between subsequent frames.

  • rms (bool) – Calculate the root-mean squared time-averaged velocity instead.

  • mol_subset (slice, optional) – Perform the calculation on a subset of molecules in this instance, as determined by their moleculair index. Include all \(m\) molecules in this instance if None.

  • atom_subset (Sequence[str], optional) – Perform the calculation on a subset of atoms in this instance, as determined by their atomic index or atomic symbol. Include all \(n\) atoms per molecule in this instance if None.

Returns:

A dataframe holding \(m-1\) time-averaged velocities.

Return type:

pd.DataFrame

init_rmsd(mol_subset=None, atom_subset=None, reset_origin=True)[source]

Initialize the RMSD calculation, returning a dataframe.

Parameters:
  • mol_subset (slice, optional) – Perform the calculation on a subset of molecules in this instance, as determined by their moleculair index. Include all \(m\) molecules in this instance if None.

  • atom_subset (Sequence[str], optional) – Perform the calculation on a subset of atoms in this instance, as determined by their atomic index or atomic symbol. Include all \(n\) atoms per molecule in this instance if None.

  • reset_origin (bool) – Reset the origin of each molecule in this instance by means of a partial Procrustes superimposition, translating and rotating the molecules.

Returns:

A dataframe of RMSDs with one column for every string or list of ints in atom_subset. Keys consist of atomic symbols (e.g. "Cd") if atom_subset contains strings, otherwise a more generic ‘series ‘ + str(int) scheme is adopted (e.g. "series 2"). Molecular indices are used as index.

Return type:

pd.DataFrame

init_rmsf(mol_subset=None, atom_subset=None, reset_origin=True)[source]

Initialize the RMSF calculation, returning a dataframe.

Parameters:
  • mol_subset (slice, optional) – Perform the calculation on a subset of molecules in this instance, as determined by their moleculair index. Include all \(m\) molecules in this instance if None.

  • atom_subset (Sequence[str], optional) – Perform the calculation on a subset of atoms in this instance, as determined by their atomic index or atomic symbol. Include all \(n\) atoms per molecule in this instance if None.

  • reset_origin (bool) – Reset the origin of each molecule in this instance by means of a partial Procrustes superimposition, translating and rotating the molecules.

Returns:

A dataframe of RMSFs with one column for every string or list of ints in atom_subset. Keys consist of atomic symbols (e.g. "Cd") if atom_subset contains strings, otherwise a more generic ‘series ‘ + str(int) scheme is adopted (e.g. "series 2"). Molecular indices are used as indices.

Return type:

pd.DataFrame

get_average_velocity(timestep=1.0, rms=False, mol_subset=None, atom_subset=None)[source]

Return the mean or root-mean squared velocity.

Parameters:
  • timestep (float) – The stepsize, in femtoseconds, between subsequent frames.

  • rms (bool) – Calculate the root-mean squared average velocity instead.

  • mol_subset (slice, optional) – Perform the calculation on a subset of molecules in this instance, as determined by their moleculair index. Include all \(m\) molecules in this instance if None.

  • atom_subset (Sequence[str], optional) – Perform the calculation on a subset of atoms in this instance, as determined by their atomic index or atomic symbol. Include all \(n\) atoms per molecule in this instance if None.

Returns:

A 1D array holding \(m-1\) velocities averaged over one or more atom subsets.

Return type:

np.ndarray[np.float64], shape \((m-1,)\)

get_time_averaged_velocity(timestep=1.0, rms=False, mol_subset=None, atom_subset=None)[source]

Return the mean or root-mean squared velocity (mean = time-averaged).

Parameters:
  • timestep (float) – The stepsize, in femtoseconds, between subsequent frames.

  • rms (bool) – Calculate the root-mean squared average velocity instead.

  • mol_subset (slice, optional) – Perform the calculation on a subset of molecules in this instance, as determined by their moleculair index. Include all \(m\) molecules in this instance if None.

  • atom_subset (Sequence[str], optional) – Perform the calculation on a subset of atoms in this instance, as determined by their atomic index or atomic symbol. Include all \(n\) atoms per molecule in this instance if None.

Returns:

A 1D array holding \(n\) time-averaged velocities.

Return type:

np.ndarray[np.float64], shape \((n,)\)

get_velocity(timestep=1.0, norm=True, mol_subset=None, atom_subset=None)[source]

Return the atomic velocties.

The velocity (in fs/A) is calculated for all atoms in atom_subset over the course of a trajectory.

Parameters:
  • timestep (float) – The stepsize, in femtoseconds, between subsequent frames.

  • norm (bool) – If True return the norm of the \(x\), \(y\) and \(z\) velocity components.

  • mol_subset (slice, optional) – Perform the calculation on a subset of molecules in this instance, as determined by their moleculair index. Include all \(m\) molecules in this instance if None.

  • atom_subset (Sequence[str], optional) – Perform the calculation on a subset of atoms in this instance, as determined by their atomic index or atomic symbol. Include all \(n\) atoms per molecule in this instance if None.

Returns:

A 2D or 3D array of atomic velocities, the number of dimensions depending on the value of norm (True = 2D; False = 3D).

Return type:

np.ndarray[np.float64], shape \((m, n)\) or \((m, n, 3)\)

get_rmsd(mol_subset=None, atom_subset=None)[source]

Calculate the root mean square displacement (RMSD).

The RMSD is calculated with respect to the first molecule in this instance.

Parameters:
  • mol_subset (slice, optional) – Perform the calculation on a subset of molecules in this instance, as determined by their moleculair index. Include all \(m\) molecules in this instance if None.

  • atom_subset (Sequence[str], optional) – Perform the calculation on a subset of atoms in this instance, as determined by their atomic index or atomic symbol. Include all \(n\) atoms per molecule in this instance if None.

Returns:

A dataframe with the RMSD as a function of the XYZ frame numbers.

Return type:

pd.DataFrame

get_rmsf(mol_subset=None, atom_subset=None)[source]

Calculate the root mean square fluctuation (RMSF).

Parameters:
  • mol_subset (slice, optional) – Perform the calculation on a subset of molecules in this instance, as determined by their moleculair index. Include all \(m\) molecules in this instance if None.

  • atom_subset (Sequence[str], optional) – Perform the calculation on a subset of atoms in this instance, as determined by their atomic index or atomic symbol. Include all \(n\) atoms per molecule in this instance if None.

Returns:

A dataframe with the RMSF as a function of atomic indices.

Return type:

pd.DataFrame

Calculate and return properties which can help determining shell structures.

Warning

Depercated.

static get_at_idx(rmsf, idx_series, dist_dict)[source]

Create subsets of atomic indices.

Warning

Depercated.

init_rdf(mol_subset=None, atom_subset=None, *, dr=0.05, r_max=12.0, periodic=None, atom_pairs=None)[source]

Initialize the calculation of radial distribution functions (RDFs).

RDFs are calculated for all possible atom-pairs in atom_subset and returned as a dataframe.

Parameters:
  • mol_subset (slice, optional) – Perform the calculation on a subset of molecules in this instance, as determined by their moleculair index. Include all \(m\) molecules in this instance if None.

  • atom_subset (Sequence[str], optional) – Perform the calculation on a subset of atoms in this instance, as determined by their atomic index or atomic symbol. Include all \(n\) atoms per molecule in this instance if None.

  • dr (float) – The integration step-size in Ångström, i.e. the distance between concentric spheres.

  • r_max (float) – The maximum to be evaluated interatomic distance in Ångström.

  • periodic (str, optional) – If specified, correct for the systems periodicity if self.lattice is not None. Accepts "x", "y" and/or "z".

  • atom_pairs (Iterable[tuple[str, str]]) – An explicit list of atom-pairs for the to-be calculated distances. Note that atom_pairs and atom_subset are mutually exclusive.

Returns:

A dataframe of radial distribution functions, averaged over all conformations in xyz_array. Keys are of the form: at_symbol1 + ‘ ‘ + at_symbol2 (e.g. "Cd Cd"). Radii are used as index.

Return type:

pd.DataFrame

init_debye_scattering(half_angle, wavelength, mol_subset=None, atom_subset=None, *, periodic=None, atom_pairs=None)[source]

Initialize the calculation of Debye scattering factors.

Scatering factors are calculated for all possible atom-pairs in atom_subset and returned as a dataframe.

Parameters:
  • half_angle (float or np.ndarray) – One or more half angles. Units should be in radian.

  • wavelength (float) – One or wavelengths. Units should be in nanometer.

  • mol_subset (slice, optional) – Perform the calculation on a subset of molecules in this instance, as determined by their moleculair index. Include all \(m\) molecules in this instance if None.

  • atom_subset (Sequence[str], optional) – Perform the calculation on a subset of atoms in this instance, as determined by their atomic index or atomic symbol. Include all \(n\) atoms per molecule in this instance if None.

  • periodic (str, optional) – If specified, correct for the systems periodicity if self.lattice is not None. Accepts "x", "y" and/or "z".

  • atom_pairs (Iterable[tuple[str, str]]) – An explicit list of atom-pairs for the to-be calculated distances. Note that atom_pairs and atom_subset are mutually exclusive.

Returns:

A dataframe of with the Debye scattering, averaged over all conformations. Keys are of the form: at_symbol1 + ‘ ‘ + at_symbol2 (e.g. "Cd Cd").

Return type:

pd.DataFrame

get_dist_mat(mol_subset=None, atom_subset=(None, None), lattice=None, periodicity=range(0, 3))[source]

Create and return a distance matrix for all molecules and atoms in this instance.

Parameters:
  • mol_subset (slice, optional) – Perform the calculation on a subset of molecules in this instance, as determined by their moleculair index. Include all \(m\) molecules in this instance if None.

  • atom_subset (Sequence[str], optional) – Perform the calculation on a subset of atoms in this instance, as determined by their atomic index or atomic symbol. Include all \(n\) atoms per molecule in this instance if None.

  • lattice (np.ndarray[np.float64], shape \((3, 3)\) or \((m, 3, 3)\), optional) – If not None, use the specified lattice vectors for correcting periodic effects.

  • periodicty (str) – The axes along which the system’s periodicity extends; accepts "x", "y" and/or "z". Only relevant if lattice is not None.

Returns:

A 3D distance matrix of \(m\) molecules, created out of two sets of \(n\) and \(k\) atoms.

Return type:

np.ndarray[np.float64], shape \((m, n, k)\)

get_pair_dict(atom_subset, r=2)[source]

Take a subset of atoms and return a dictionary.

Parameters:
  • atom_subset (Sequence[str], optional) – Perform the calculation on a subset of atoms in this instance, as determined by their atomic index or atomic symbol. Include all \(n\) atoms per molecule in this instance if None.

  • r (int) – The length of the to-be returned subsets.

init_power_spectrum(mol_subset=None, atom_subset=None, freq_max=4000, timestep=1)[source]

Calculate and return the power spectrum associated with this instance.

Parameters:
  • mol_subset (slice, optional) – Perform the calculation on a subset of molecules in this instance, as determined by their moleculair index. Include all \(m\) molecules in this instance if None.

  • atom_subset (Sequence[str], optional) – Perform the calculation on a subset of atoms in this instance, as determined by their atomic index or atomic symbol. Include all \(n\) atoms per molecule in this instance if None.

  • freq_max (int) – The maximum to be returned wavenumber (cm**-1).

  • timestep (float) – The stepsize, in femtoseconds, between subsequent frames.

Returns:

A DataFrame containing the power spectrum for each set of atoms in atom_subset.

Return type:

pd.DataFrame

get_vacf(mol_subset=None, atom_subset=None, timestep=1)[source]

Calculate and return the velocity autocorrelation function (VACF).

Parameters:
  • mol_subset (slice, optional) – Perform the calculation on a subset of molecules in this instance, as determined by their moleculair index. Include all \(m\) molecules in this instance if None.

  • atom_subset (Sequence[str], optional) – Perform the calculation on a subset of atoms in this instance, as determined by their atomic index or atomic symbol. Include all \(n\) atoms per molecule in this instance if None.

  • timestep (float) – The stepsize, in femtoseconds, between subsequent frames.

Returns:

A DataFrame containing the power spectrum for each set of atoms in atom_subset.

Return type:

pd.DataFrame

init_adf(mol_subset=None, atom_subset=None, *, r_max=8.0, weight=<function neg_exp>, periodic=None, atom_pairs=None)[source]

Initialize the calculation of distance-weighted angular distribution functions (ADFs).

ADFs are calculated for all possible atom-pairs in atom_subset and returned as a dataframe.

Parameters:
  • mol_subset (slice, optional) – Perform the calculation on a subset of molecules in this instance, as determined by their moleculair index. Include all \(m\) molecules in this instance if None.

  • atom_subset (Sequence[str], optional) – Perform the calculation on a subset of atoms in this instance, as determined by their atomic index or atomic symbol. Include all \(n\) atoms per molecule in this instance if None.

  • r_max (float) – The maximum inter-atomic distance (in Angstrom) for which angles are constructed. The distance cuttoff can be disabled by settings this value to np.inf, "np.inf" or "inf".

  • weight (Callable[[np.ndarray], np.ndarray], optional) – A callable for creating a weighting factor from inter-atomic distances. The callable should take an array as input and return an array. Given an angle \(\phi_{ijk}\), to the distance \(r_{ijk}\) is defined as \(max[r_{ij}, r_{jk}]\). Set to None to disable distance weighting.

  • periodic (str, optional) – If specified, correct for the systems periodicity if self.lattice is not None. Accepts "x", "y" and/or "z".

  • atom_pairs (Iterable[tuple[str, str, str]]) – An explicit list of atom-triples for the to-be calculated angles. Note that atom_pairs and atom_subset are mutually exclusive.

Returns:

A dataframe of angular distribution functions, averaged over all conformations in this instance.

Return type:

pd.DataFrame

Note

Disabling the distance cuttoff is strongly recommended (i.e. it is faster) for large values of r_max. As a rough guideline, r_max="inf" is roughly as fast as r_max=15.0 (though this is, of course, system dependant).

Note

The ADF construction will be conducted in parralel if the DASK package is installed. DASK can be installed, via anaconda, with the following command: conda install -n FOX -y -c conda-forge dask.

T

View of the transposed array.

Same as self.transpose().

Examples

>>> a = np.array([[1, 2], [3, 4]])
>>> a
array([[1, 2],
       [3, 4]])
>>> a.T
array([[1, 3],
       [2, 4]])
>>> a = np.array([1, 2, 3, 4])
>>> a
array([1, 2, 3, 4])
>>> a.T
array([1, 2, 3, 4])

See also

transpose

all(axis=None, out=None, keepdims=False, *, where=True)

Returns True if all elements evaluate to True.

Refer to numpy.all for full documentation.

See also

numpy.all

equivalent function

any(axis=None, out=None, keepdims=False, *, where=True)

Returns True if any of the elements of a evaluate to True.

Refer to numpy.any for full documentation.

See also

numpy.any

equivalent function

argmax(axis=None, out=None, *, keepdims=False)

Return indices of the maximum values along the given axis.

Refer to numpy.argmax for full documentation.

See also

numpy.argmax

equivalent function

argmin(axis=None, out=None, *, keepdims=False)

Return indices of the minimum values along the given axis.

Refer to numpy.argmin for detailed documentation.

See also

numpy.argmin

equivalent function

argpartition(kth, axis=-1, kind='introselect', order=None)

Returns the indices that would partition this array.

Refer to numpy.argpartition for full documentation.

New in version 1.8.0.

See also

numpy.argpartition

equivalent function

argsort(axis=-1, kind=None, order=None)

Returns the indices that would sort this array.

Refer to numpy.argsort for full documentation.

See also

numpy.argsort

equivalent function

astype(dtype, order='K', casting='unsafe', subok=True, copy=True)

Copy of the array, cast to a specified type.

Parameters:
  • dtype (str or dtype) – Typecode or data-type to which the array is cast.

  • order ({'C', 'F', 'A', 'K'}, optional) – Controls the memory layout order of the result. ‘C’ means C order, ‘F’ means Fortran order, ‘A’ means ‘F’ order if all the arrays are Fortran contiguous, ‘C’ order otherwise, and ‘K’ means as close to the order the array elements appear in memory as possible. Default is ‘K’.

  • casting ({'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional) –

    Controls what kind of data casting may occur. Defaults to ‘unsafe’ for backwards compatibility.

    • ’no’ means the data types should not be cast at all.

    • ’equiv’ means only byte-order changes are allowed.

    • ’safe’ means only casts which can preserve values are allowed.

    • ’same_kind’ means only safe casts or casts within a kind, like float64 to float32, are allowed.

    • ’unsafe’ means any data conversions may be done.

  • subok (bool, optional) – If True, then sub-classes will be passed-through (default), otherwise the returned array will be forced to be a base-class array.

  • copy (bool, optional) – By default, astype always returns a newly allocated array. If this is set to false, and the dtype, order, and subok requirements are satisfied, the input array is returned instead of a copy.

Returns:

arr_t – Unless copy is False and the other conditions for returning the input array are satisfied (see description for copy input parameter), arr_t is a new array of the same shape as the input array, with dtype, order given by dtype, order.

Return type:

ndarray

Notes

Changed in version 1.17.0: Casting between a simple data type and a structured one is possible only for “unsafe” casting. Casting to multiple fields is allowed, but casting from multiple fields is not.

Changed in version 1.9.0: Casting from numeric to string types in ‘safe’ casting mode requires that the string dtype length is long enough to store the max integer/float value converted.

Raises:

ComplexWarning – When casting from complex to float or int. To avoid this, one should use a.real.astype(t).

Examples

>>> x = np.array([1, 2, 2.5])
>>> x
array([1. ,  2. ,  2.5])
>>> x.astype(int)
array([1, 2, 2])
property atnum

Get the atomic numbers of all atoms in MultiMolecule.atoms as 1D array.

property atom1

Get or set the indices of the first atoms in all bonds of MultiMolecule.bonds as 1D array.

property atom12

Get or set the indices of the atoms for all bonds in MultiMolecule.bonds as 2D array.

property atom2

Get or set the indices of the second atoms in all bonds of MultiMolecule.bonds as 1D array.

base

Base object if memory is from some other object.

Examples

The base of an array that owns its memory is None:

>>> x = np.array([1,2,3,4])
>>> x.base is None
True

Slicing creates a view, whose memory is shared with x:

>>> y = x[2:]
>>> y.base is x
True
byteswap(inplace=False)

Swap the bytes of the array elements

Toggle between low-endian and big-endian data representation by returning a byteswapped array, optionally swapped in-place. Arrays of byte-strings are not swapped. The real and imaginary parts of a complex number are swapped individually.

Parameters:

inplace (bool, optional) – If True, swap bytes in-place, default is False.

Returns:

out – The byteswapped array. If inplace is True, this is a view to self.

Return type:

ndarray

Examples

>>> A = np.array([1, 256, 8755], dtype=np.int16)
>>> list(map(hex, A))
['0x1', '0x100', '0x2233']
>>> A.byteswap(inplace=True)
array([  256,     1, 13090], dtype=int16)
>>> list(map(hex, A))
['0x100', '0x1', '0x3322']

Arrays of byte-strings are not swapped

>>> A = np.array([b'ceg', b'fac'])
>>> A.byteswap()
array([b'ceg', b'fac'], dtype='|S3')
A.newbyteorder().byteswap() produces an array with the same values

but different representation in memory

>>> A = np.array([1, 2, 3])
>>> A.view(np.uint8)
array([1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0,
       0, 0], dtype=uint8)
>>> A.newbyteorder().byteswap(inplace=True)
array([1, 2, 3])
>>> A.view(np.uint8)
array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0,
       0, 3], dtype=uint8)
choose(choices, out=None, mode='raise')

Use an index array to construct a new array from a set of choices.

Refer to numpy.choose for full documentation.

See also

numpy.choose

equivalent function

clip(min=None, max=None, out=None, **kwargs)

Return an array whose values are limited to [min, max]. One of max or min must be given.

Refer to numpy.clip for full documentation.

See also

numpy.clip

equivalent function

compress(condition, axis=None, out=None)

Return selected slices of this array along given axis.

Refer to numpy.compress for full documentation.

See also

numpy.compress

equivalent function

conj()

Complex-conjugate all elements.

Refer to numpy.conjugate for full documentation.

See also

numpy.conjugate

equivalent function

conjugate()

Return the complex conjugate, element-wise.

Refer to numpy.conjugate for full documentation.

See also

numpy.conjugate

equivalent function

property connectors

Get the atomic connectors of all atoms in MultiMolecule.atoms as 1D array.

copy(order='C', *, deep=True)

Create a copy of this instance.

Parameters:
  • order (str) – Controls the memory layout of the copy. See ndarray.copy for details.

  • copy_attr (bool) – Whether or not the attributes of this instance should be returned as copies or views.

Returns:

A copy of this instance.

Return type:

FOX.MultiMolecule

ctypes

An object to simplify the interaction of the array with the ctypes module.

This attribute creates an object that makes it easier to use arrays when calling shared libraries with the ctypes module. The returned object has, among others, data, shape, and strides attributes (see Notes below) which themselves return ctypes objects that can be used as arguments to a shared library.

Parameters:

None

Returns:

c – Possessing attributes data, shape, strides, etc.

Return type:

Python object

See also

numpy.ctypeslib

Notes

Below are the public attributes of this object which were documented in “Guide to NumPy” (we have omitted undocumented public attributes, as well as documented private attributes):

_ctypes.data

A pointer to the memory area of the array as a Python integer. This memory area may contain data that is not aligned, or not in correct byte-order. The memory area may not even be writeable. The array flags and data-type of this array should be respected when passing this attribute to arbitrary C-code to avoid trouble that can include Python crashing. User Beware! The value of this attribute is exactly the same as self._array_interface_['data'][0].

Note that unlike data_as, a reference will not be kept to the array: code like ctypes.c_void_p((a + b).ctypes.data) will result in a pointer to a deallocated array, and should be spelt (a + b).ctypes.data_as(ctypes.c_void_p)

_ctypes.shape

A ctypes array of length self.ndim where the basetype is the C-integer corresponding to dtype('p') on this platform (see ~numpy.ctypeslib.c_intp). This base-type could be ctypes.c_int, ctypes.c_long, or ctypes.c_longlong depending on the platform. The ctypes array contains the shape of the underlying array.

Type:

(c_intp*self.ndim)

_ctypes.strides

A ctypes array of length self.ndim where the basetype is the same as for the shape attribute. This ctypes array contains the strides information from the underlying array. This strides information is important for showing how many bytes must be jumped to get to the next element in the array.

Type:

(c_intp*self.ndim)

_ctypes.data_as(obj)

Return the data pointer cast to a particular c-types object. For example, calling self._as_parameter_ is equivalent to self.data_as(ctypes.c_void_p). Perhaps you want to use the data as a pointer to a ctypes array of floating-point data: self.data_as(ctypes.POINTER(ctypes.c_double)).

The returned pointer will keep a reference to the array.

_ctypes.shape_as(obj)

Return the shape tuple as an array of some other c-types type. For example: self.shape_as(ctypes.c_short).

_ctypes.strides_as(obj)

Return the strides tuple as an array of some other c-types type. For example: self.strides_as(ctypes.c_longlong).

If the ctypes module is not available, then the ctypes attribute of array objects still returns something useful, but ctypes objects are not returned and errors may be raised instead. In particular, the object will still have the as_parameter attribute which will return an integer equal to the data attribute.

Examples

>>> import ctypes
>>> x = np.array([[0, 1], [2, 3]], dtype=np.int32)
>>> x
array([[0, 1],
       [2, 3]], dtype=int32)
>>> x.ctypes.data
31962608 # may vary
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32))
<__main__.LP_c_uint object at 0x7ff2fc1fc200> # may vary
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)).contents
c_uint(0)
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint64)).contents
c_ulong(4294967296)
>>> x.ctypes.shape
<numpy.core._internal.c_long_Array_2 object at 0x7ff2fc1fce60> # may vary
>>> x.ctypes.strides
<numpy.core._internal.c_long_Array_2 object at 0x7ff2fc1ff320> # may vary
cumprod(axis=None, dtype=None, out=None)

Return the cumulative product of the elements along the given axis.

Refer to numpy.cumprod for full documentation.

See also

numpy.cumprod

equivalent function

cumsum(axis=None, dtype=None, out=None)

Return the cumulative sum of the elements along the given axis.

Refer to numpy.cumsum for full documentation.

See also

numpy.cumsum

equivalent function

data

Python buffer object pointing to the start of the array’s data.

diagonal(offset=0, axis1=0, axis2=1)

Return specified diagonals. In NumPy 1.9 the returned array is a read-only view instead of a copy as in previous NumPy versions. In a future version the read-only restriction will be removed.

Refer to numpy.diagonal() for full documentation.

See also

numpy.diagonal

equivalent function

dtype

Data-type of the array’s elements.

Warning

Setting arr.dtype is discouraged and may be deprecated in the future. Setting will replace the dtype without modifying the memory (see also ndarray.view and ndarray.astype).

Parameters:

None

Returns:

d

Return type:

numpy dtype object

See also

ndarray.astype

Cast the values contained in the array to a new data-type.

ndarray.view

Create a view of the same data but a different data-type.

numpy.dtype

Examples

>>> x
array([[0, 1],
       [2, 3]])
>>> x.dtype
dtype('int32')
>>> type(x.dtype)
<type 'numpy.dtype'>
dump(file)

Dump a pickle of the array to the specified file. The array can be read back with pickle.load or numpy.load.

Parameters:

file (str or Path) –

A string naming the dump file.

Changed in version 1.17.0: pathlib.Path objects are now accepted.

dumps()

Returns the pickle of the array as a string. pickle.loads will convert the string back to an array.

Parameters:

None

fill(value)

Fill the array with a scalar value.

Parameters:

value (scalar) – All elements of a will be assigned this value.

Examples

>>> a = np.array([1, 2])
>>> a.fill(0)
>>> a
array([0, 0])
>>> a = np.empty(2)
>>> a.fill(1)
>>> a
array([1.,  1.])

Fill expects a scalar value and always behaves the same as assigning to a single array element. The following is a rare example where this distinction is important:

>>> a = np.array([None, None], dtype=object)
>>> a[0] = np.array(3)
>>> a
array([array(3), None], dtype=object)
>>> a.fill(np.array(3))
>>> a
array([array(3), array(3)], dtype=object)

Where other forms of assignments will unpack the array being assigned:

>>> a[...] = np.array(3)
>>> a
array([3, 3], dtype=object)
flags

Information about the memory layout of the array.

C_CONTIGUOUS(C)

The data is in a single, C-style contiguous segment.

F_CONTIGUOUS(F)

The data is in a single, Fortran-style contiguous segment.

OWNDATA(O)

The array owns the memory it uses or borrows it from another object.

WRITEABLE(W)

The data area can be written to. Setting this to False locks the data, making it read-only. A view (slice, etc.) inherits WRITEABLE from its base array at creation time, but a view of a writeable array may be subsequently locked while the base array remains writeable. (The opposite is not true, in that a view of a locked array may not be made writeable. However, currently, locking a base object does not lock any views that already reference it, so under that circumstance it is possible to alter the contents of a locked array via a previously created writeable view onto it.) Attempting to change a non-writeable array raises a RuntimeError exception.

ALIGNED(A)

The data and all elements are aligned appropriately for the hardware.

WRITEBACKIFCOPY(X)

This array is a copy of some other array. The C-API function PyArray_ResolveWritebackIfCopy must be called before deallocating to the base array will be updated with the contents of this array.

FNC

F_CONTIGUOUS and not C_CONTIGUOUS.

FORC

F_CONTIGUOUS or C_CONTIGUOUS (one-segment test).

BEHAVED(B)

ALIGNED and WRITEABLE.

CARRAY(CA)

BEHAVED and C_CONTIGUOUS.

FARRAY(FA)

BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.

Notes

The flags object can be accessed dictionary-like (as in a.flags['WRITEABLE']), or by using lowercased attribute names (as in a.flags.writeable). Short flag names are only supported in dictionary access.

Only the WRITEBACKIFCOPY, WRITEABLE, and ALIGNED flags can be changed by the user, via direct assignment to the attribute or dictionary entry, or by calling ndarray.setflags.

The array flags cannot be set arbitrarily:

  • WRITEBACKIFCOPY can only be set False.

  • ALIGNED can only be set True if the data is truly aligned.

  • WRITEABLE can only be set True if the array owns its own memory or the ultimate owner of the memory exposes a writeable buffer interface or is a string.

Arrays can be both C-style and Fortran-style contiguous simultaneously. This is clear for 1-dimensional arrays, but can also be true for higher dimensional arrays.

Even for contiguous arrays a stride for a given dimension arr.strides[dim] may be arbitrary if arr.shape[dim] == 1 or the array has no elements. It does not generally hold that self.strides[-1] == self.itemsize for C-style contiguous arrays or self.strides[0] == self.itemsize for Fortran-style contiguous arrays is true.

flat

A 1-D iterator over the array.

This is a numpy.flatiter instance, which acts similarly to, but is not a subclass of, Python’s built-in iterator object.

See also

flatten

Return a copy of the array collapsed into one dimension.

flatiter

Examples

>>> x = np.arange(1, 7).reshape(2, 3)
>>> x
array([[1, 2, 3],
       [4, 5, 6]])
>>> x.flat[3]
4
>>> x.T
array([[1, 4],
       [2, 5],
       [3, 6]])
>>> x.T.flat[3]
5
>>> type(x.flat)
<class 'numpy.flatiter'>

An assignment example:

>>> x.flat = 3; x
array([[3, 3, 3],
       [3, 3, 3]])
>>> x.flat[[1,4]] = 1; x
array([[3, 1, 3],
       [3, 1, 3]])
flatten(order='C')

Return a copy of the array collapsed into one dimension.

Parameters:

order ({'C', 'F', 'A', 'K'}, optional) – ‘C’ means to flatten in row-major (C-style) order. ‘F’ means to flatten in column-major (Fortran- style) order. ‘A’ means to flatten in column-major order if a is Fortran contiguous in memory, row-major order otherwise. ‘K’ means to flatten a in the order the elements occur in memory. The default is ‘C’.

Returns:

y – A copy of the input array, flattened to one dimension.

Return type:

ndarray

See also

ravel

Return a flattened array.

flat

A 1-D flat iterator over the array.

Examples

>>> a = np.array([[1,2], [3,4]])
>>> a.flatten()
array([1, 2, 3, 4])
>>> a.flatten('F')
array([1, 3, 2, 4])
getfield(dtype, offset=0)

Returns a field of the given array as a certain type.

A field is a view of the array data with a given data-type. The values in the view are determined by the given type and the offset into the current array in bytes. The offset needs to be such that the view dtype fits in the array dtype; for example an array of dtype complex128 has 16-byte elements. If taking a view with a 32-bit integer (4 bytes), the offset needs to be between 0 and 12 bytes.

Parameters:
  • dtype (str or dtype) – The data type of the view. The dtype size of the view can not be larger than that of the array itself.

  • offset (int) – Number of bytes to skip before beginning the element view.

Examples

>>> x = np.diag([1.+1.j]*2)
>>> x[1, 1] = 2 + 4.j
>>> x
array([[1.+1.j,  0.+0.j],
       [0.+0.j,  2.+4.j]])
>>> x.getfield(np.float64)
array([[1.,  0.],
       [0.,  2.]])

By choosing an offset of 8 bytes we can select the complex part of the array for our view:

>>> x.getfield(np.float64, offset=8)
array([[1.,  0.],
       [0.,  4.]])
imag

The imaginary part of the array.

Examples

>>> x = np.sqrt([1+0j, 0+1j])
>>> x.imag
array([ 0.        ,  0.70710678])
>>> x.imag.dtype
dtype('float64')
item(*args)

Copy an element of an array to a standard Python scalar and return it.

Parameters:

*args (Arguments (variable number and type)) –

  • none: in this case, the method only works for arrays with one element (a.size == 1), which element is copied into a standard Python scalar object and returned.

  • int_type: this argument is interpreted as a flat index into the array, specifying which element to copy and return.

  • tuple of int_types: functions as does a single int_type argument, except that the argument is interpreted as an nd-index into the array.

Returns:

z – A copy of the specified element of the array as a suitable Python scalar

Return type:

Standard Python scalar object

Notes

When the data type of a is longdouble or clongdouble, item() returns a scalar array object because there is no available Python scalar that would not lose information. Void arrays return a buffer object for item(), unless fields are defined, in which case a tuple is returned.

item is very similar to a[args], except, instead of an array scalar, a standard Python scalar is returned. This can be useful for speeding up access to elements of the array and doing arithmetic on elements of the array using Python’s optimized math.

Examples

>>> np.random.seed(123)
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[2, 2, 6],
       [1, 3, 6],
       [1, 0, 1]])
>>> x.item(3)
1
>>> x.item(7)
0
>>> x.item((0, 1))
2
>>> x.item((2, 2))
1
itemset(*args)

Insert scalar into an array (scalar is cast to array’s dtype, if possible)

There must be at least 1 argument, and define the last argument as item. Then, a.itemset(*args) is equivalent to but faster than a[args] = item. The item should be a scalar value and args must select a single item in the array a.

Parameters:

*args (Arguments) – If one argument: a scalar, only used in case a is of size 1. If two arguments: the last argument is the value to be set and must be a scalar, the first argument specifies a single array element location. It is either an int or a tuple.

Notes

Compared to indexing syntax, itemset provides some speed increase for placing a scalar into a particular location in an ndarray, if you must do this. However, generally this is discouraged: among other problems, it complicates the appearance of the code. Also, when using itemset (and item) inside a loop, be sure to assign the methods to a local variable to avoid the attribute look-up at each loop iteration.

Examples

>>> np.random.seed(123)
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[2, 2, 6],
       [1, 3, 6],
       [1, 0, 1]])
>>> x.itemset(4, 0)
>>> x.itemset((2, 2), 9)
>>> x
array([[2, 2, 6],
       [1, 0, 6],
       [1, 0, 9]])
itemsize

Length of one array element in bytes.

Examples

>>> x = np.array([1,2,3], dtype=np.float64)
>>> x.itemsize
8
>>> x = np.array([1,2,3], dtype=np.complex128)
>>> x.itemsize
16
property loc

A getter and setter for atom-type-based slicing.

Get, set and del operations are performed using the list(s) of atomic indices associated with the provided atomic symbol(s). Accepts either one or more atomic symbols.

Examples

>>> mol = MultiMolecule(...)
>>> mol.atoms = {
...     'Cd': [0, 1, 2, 3, 4, 5],
...     'Se': [6, 7, 8, 9, 10, 11],
...     'O': [12, 13, 14],
... }

>>> (mol.loc['Cd'] == mol[mol.atoms['Cd']]).all()
True

>>> idx = []
>>> for atom in ["Cd", "Se", "O"]:
...     idx += mol.atoms[atom].tolist()
>>> (mol.loc['Cd', 'Se', 'O'] == mol[idx]).all()
True

>>> mol.loc['Cd'] = 1
>>> print((mol.loc['Cd'] == 1).all())
True

>>> del mol.loc['Cd']
ValueError: cannot delete array elements
Parameters:

mol (FOX.MultiMolecule) – A MultiMolecule instance; see _MolLoc.mol.

mol

A MultiMolecule instance.

Type:

FOX.MultiMolecule

atoms_view

A read-only view of _MolLoc.mol.atoms.

Type:

Mapping

property mass

Get the atomic masses of all atoms in MultiMolecule.atoms as 1D array.

max(axis=None, out=None, keepdims=False, initial=<no value>, where=True)

Return the maximum along a given axis.

Refer to numpy.amax for full documentation.

See also

numpy.amax

equivalent function

mean(axis=None, dtype=None, out=None, keepdims=False, *, where=True)

Returns the average of the array elements along given axis.

Refer to numpy.mean for full documentation.

See also

numpy.mean

equivalent function

min(axis=None, out=None, keepdims=False, initial=<no value>, where=True)

Return the minimum along a given axis.

Refer to numpy.amin for full documentation.

See also

numpy.amin

equivalent function

nbytes

Total bytes consumed by the elements of the array.

Notes

Does not include memory consumed by non-element attributes of the array object.

See also

sys.getsizeof

Memory consumed by the object itself without parents in case view. This does include memory consumed by non-element attributes.

Examples

>>> x = np.zeros((3,5,2), dtype=np.complex128)
>>> x.nbytes
480
>>> np.prod(x.shape) * x.itemsize
480
ndim

Number of array dimensions.

Examples

>>> x = np.array([1, 2, 3])
>>> x.ndim
1
>>> y = np.zeros((2, 3, 4))
>>> y.ndim
3
newbyteorder(new_order='S', /)

Return the array with the same data viewed with a different byte order.

Equivalent to:

arr.view(arr.dtype.newbytorder(new_order))

Changes are also made in all fields and sub-arrays of the array data type.

Parameters:

new_order (string, optional) –

Byte order to force; a value from the byte order specifications below. new_order codes can be any of:

  • ’S’ - swap dtype from current to opposite endian

  • {‘<’, ‘little’} - little endian

  • {‘>’, ‘big’} - big endian

  • {‘=’, ‘native’} - native order, equivalent to sys.byteorder

  • {‘|’, ‘I’} - ignore (no change to byte order)

The default value (‘S’) results in swapping the current byte order.

Returns:

new_arr – New array object with the dtype reflecting given change to the byte order.

Return type:

array

nonzero()

Return the indices of the elements that are non-zero.

Refer to numpy.nonzero for full documentation.

See also

numpy.nonzero

equivalent function

property order

Get or set the bond orders for all bonds in MultiMolecule.bonds as 1D array.

partition(kth, axis=-1, kind='introselect', order=None)

Rearranges the elements in the array in such a way that the value of the element in kth position is in the position it would be in a sorted array. All elements smaller than the kth element are moved before this element and all equal or greater are moved behind it. The ordering of the elements in the two partitions is undefined.

New in version 1.8.0.

Parameters:
  • kth (int or sequence of ints) –

    Element index to partition by. The kth element value will be in its final sorted position and all smaller elements will be moved before it and all equal or greater elements behind it. The order of all elements in the partitions is undefined. If provided with a sequence of kth it will partition all elements indexed by kth of them into their sorted position at once.

    Deprecated since version 1.22.0: Passing booleans as index is deprecated.

  • axis (int, optional) – Axis along which to sort. Default is -1, which means sort along the last axis.

  • kind ({'introselect'}, optional) – Selection algorithm. Default is ‘introselect’.

  • order (str or list of str, optional) – When a is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string, and not all fields need to be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties.

See also

numpy.partition

Return a partitioned copy of an array.

argpartition

Indirect partition.

sort

Full sort.

Notes

See np.partition for notes on the different algorithms.

Examples

>>> a = np.array([3, 4, 2, 1])
>>> a.partition(3)
>>> a
array([2, 1, 3, 4])
>>> a.partition((1, 3))
>>> a
array([1, 2, 3, 4])
prod(axis=None, dtype=None, out=None, keepdims=False, initial=1, where=True)

Return the product of the array elements over the given axis

Refer to numpy.prod for full documentation.

See also

numpy.prod

equivalent function

ptp(axis=None, out=None, keepdims=False)

Peak to peak (maximum - minimum) value along a given axis.

Refer to numpy.ptp for full documentation.

See also

numpy.ptp

equivalent function

put(indices, values, mode='raise')

Set a.flat[n] = values[n] for all n in indices.

Refer to numpy.put for full documentation.

See also

numpy.put

equivalent function

property radius

Get the atomic radii of all atoms in MultiMolecule.atoms as 1d array.

ravel([order])

Return a flattened array.

Refer to numpy.ravel for full documentation.

See also

numpy.ravel

equivalent function

ndarray.flat

a flat iterator on the array.

real

The real part of the array.

Examples

>>> x = np.sqrt([1+0j, 0+1j])
>>> x.real
array([ 1.        ,  0.70710678])
>>> x.real.dtype
dtype('float64')

See also

numpy.real

equivalent function

repeat(repeats, axis=None)

Repeat elements of an array.

Refer to numpy.repeat for full documentation.

See also

numpy.repeat

equivalent function

reshape(shape, order='C')

Returns an array containing the same data with a new shape.

Refer to numpy.reshape for full documentation.

See also

numpy.reshape

equivalent function

Notes

Unlike the free function numpy.reshape, this method on ndarray allows the elements of the shape parameter to be passed in as separate arguments. For example, a.reshape(10, 11) is equivalent to a.reshape((10, 11)).

resize(new_shape, refcheck=True)

Change shape and size of array in-place.

Parameters:
  • new_shape (tuple of ints, or n ints) – Shape of resized array.

  • refcheck (bool, optional) – If False, reference count will not be checked. Default is True.

Return type:

None

Raises:
  • ValueError – If a does not own its own data or references or views to it exist, and the data memory must be changed. PyPy only: will always raise if the data memory must be changed, since there is no reliable way to determine if references or views to it exist.

  • SystemError – If the order keyword argument is specified. This behaviour is a bug in NumPy.

See also

resize

Return a new array with the specified shape.

Notes

This reallocates space for the data area if necessary.

Only contiguous arrays (data elements consecutive in memory) can be resized.

The purpose of the reference count check is to make sure you do not use this array as a buffer for another Python object and then reallocate the memory. However, reference counts can increase in other ways so if you are sure that you have not shared the memory for this array with another Python object, then you may safely set refcheck to False.

Examples

Shrinking an array: array is flattened (in the order that the data are stored in memory), resized, and reshaped:

>>> a = np.array([[0, 1], [2, 3]], order='C')
>>> a.resize((2, 1))
>>> a
array([[0],
       [1]])
>>> a = np.array([[0, 1], [2, 3]], order='F')
>>> a.resize((2, 1))
>>> a
array([[0],
       [2]])

Enlarging an array: as above, but missing entries are filled with zeros:

>>> b = np.array([[0, 1], [2, 3]])
>>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple
>>> b
array([[0, 1, 2],
       [3, 0, 0]])

Referencing an array prevents resizing…

>>> c = a
>>> a.resize((1, 1))
Traceback (most recent call last):
...
ValueError: cannot resize an array that references or is referenced ...

Unless refcheck is False:

>>> a.resize((1, 1), refcheck=False)
>>> a
array([[0]])
>>> c
array([[0]])
searchsorted(v, side='left', sorter=None)

Find indices where elements of v should be inserted in a to maintain order.

For full documentation, see numpy.searchsorted

See also

numpy.searchsorted

equivalent function

setfield(val, dtype, offset=0)

Put a value into a specified place in a field defined by a data-type.

Place val into a’s field defined by dtype and beginning offset bytes into the field.

Parameters:
  • val (object) – Value to be placed in field.

  • dtype (dtype object) – Data-type of the field in which to place val.

  • offset (int, optional) – The number of bytes into the field at which to place val.

Return type:

None

See also

getfield

Examples

>>> x = np.eye(3)
>>> x.getfield(np.float64)
array([[1.,  0.,  0.],
       [0.,  1.,  0.],
       [0.,  0.,  1.]])
>>> x.setfield(3, np.int32)
>>> x.getfield(np.int32)
array([[3, 3, 3],
       [3, 3, 3],
       [3, 3, 3]], dtype=int32)
>>> x
array([[1.0e+000, 1.5e-323, 1.5e-323],
       [1.5e-323, 1.0e+000, 1.5e-323],
       [1.5e-323, 1.5e-323, 1.0e+000]])
>>> x.setfield(np.eye(3), np.int32)
>>> x
array([[1.,  0.,  0.],
       [0.,  1.,  0.],
       [0.,  0.,  1.]])
setflags(write=None, align=None, uic=None)

Set array flags WRITEABLE, ALIGNED, WRITEBACKIFCOPY, respectively.

These Boolean-valued flags affect how numpy interprets the memory area used by a (see Notes below). The ALIGNED flag can only be set to True if the data is actually aligned according to the type. The WRITEBACKIFCOPY and flag can never be set to True. The flag WRITEABLE can only be set to True if the array owns its own memory, or the ultimate owner of the memory exposes a writeable buffer interface, or is a string. (The exception for string is made so that unpickling can be done without copying memory.)

Parameters:
  • write (bool, optional) – Describes whether or not a can be written to.

  • align (bool, optional) – Describes whether or not a is aligned properly for its type.

  • uic (bool, optional) – Describes whether or not a is a copy of another “base” array.

Notes

Array flags provide information about how the memory area used for the array is to be interpreted. There are 7 Boolean flags in use, only four of which can be changed by the user: WRITEBACKIFCOPY, WRITEABLE, and ALIGNED.

WRITEABLE (W) the data area can be written to;

ALIGNED (A) the data and strides are aligned appropriately for the hardware (as determined by the compiler);

WRITEBACKIFCOPY (X) this array is a copy of some other array (referenced by .base). When the C-API function PyArray_ResolveWritebackIfCopy is called, the base array will be updated with the contents of this array.

All flags can be accessed using the single (upper case) letter as well as the full name.

Examples

>>> y = np.array([[3, 1, 7],
...               [2, 0, 0],
...               [8, 5, 9]])
>>> y
array([[3, 1, 7],
       [2, 0, 0],
       [8, 5, 9]])
>>> y.flags
  C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False
>>> y.setflags(write=0, align=0)
>>> y.flags
  C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : False
  ALIGNED : False
  WRITEBACKIFCOPY : False
>>> y.setflags(uic=1)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: cannot set WRITEBACKIFCOPY flag to True
shape

Tuple of array dimensions.

The shape property is usually used to get the current shape of an array, but may also be used to reshape the array in-place by assigning a tuple of array dimensions to it. As with numpy.reshape, one of the new shape dimensions can be -1, in which case its value is inferred from the size of the array and the remaining dimensions. Reshaping an array in-place will fail if a copy is required.

Warning

Setting arr.shape is discouraged and may be deprecated in the future. Using ndarray.reshape is the preferred approach.

Examples

>>> x = np.array([1, 2, 3, 4])
>>> x.shape
(4,)
>>> y = np.zeros((2, 3, 4))
>>> y.shape
(2, 3, 4)
>>> y.shape = (3, 8)
>>> y
array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])
>>> y.shape = (3, 6)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: total size of new array must be unchanged
>>> np.zeros((4,2))[::2].shape = (-1,)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: Incompatible shape for in-place modification. Use
`.reshape()` to make a copy with the desired shape.

See also

numpy.shape

Equivalent getter function.

numpy.reshape

Function similar to setting shape.

ndarray.reshape

Method similar to setting shape.

size

Number of elements in the array.

Equal to np.prod(a.shape), i.e., the product of the array’s dimensions.

Notes

a.size returns a standard arbitrary precision Python integer. This may not be the case with other methods of obtaining the same value (like the suggested np.prod(a.shape), which returns an instance of np.int_), and may be relevant if the value is used further in calculations that may overflow a fixed size integer type.

Examples

>>> x = np.zeros((3, 5, 2), dtype=np.complex128)
>>> x.size
30
>>> np.prod(x.shape)
30
squeeze(axis=None)

Remove axes of length one from a.

Refer to numpy.squeeze for full documentation.

See also

numpy.squeeze

equivalent function

std(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)

Returns the standard deviation of the array elements along given axis.

Refer to numpy.std for full documentation.

See also

numpy.std

equivalent function

strides

Tuple of bytes to step in each dimension when traversing an array.

The byte offset of element (i[0], i[1], ..., i[n]) in an array a is:

offset = sum(np.array(i) * a.strides)

A more detailed explanation of strides can be found in the “ndarray.rst” file in the NumPy reference guide.

Warning

Setting arr.strides is discouraged and may be deprecated in the future. numpy.lib.stride_tricks.as_strided should be preferred to create a new view of the same data in a safer way.

Notes

Imagine an array of 32-bit integers (each 4 bytes):

x = np.array([[0, 1, 2, 3, 4],
              [5, 6, 7, 8, 9]], dtype=np.int32)

This array is stored in memory as 40 bytes, one after the other (known as a contiguous block of memory). The strides of an array tell us how many bytes we have to skip in memory to move to the next position along a certain axis. For example, we have to skip 4 bytes (1 value) to move to the next column, but 20 bytes (5 values) to get to the same position in the next row. As such, the strides for the array x will be (20, 4).

Examples

>>> y = np.reshape(np.arange(2*3*4), (2,3,4))
>>> y
array([[[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]],
       [[12, 13, 14, 15],
        [16, 17, 18, 19],
        [20, 21, 22, 23]]])
>>> y.strides
(48, 16, 4)
>>> y[1,1,1]
17
>>> offset=sum(y.strides * np.array((1,1,1)))
>>> offset/y.itemsize
17
>>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)
>>> x.strides
(32, 4, 224, 1344)
>>> i = np.array([3,5,2,2])
>>> offset = sum(i * x.strides)
>>> x[3,5,2,2]
813
>>> offset / x.itemsize
813
sum(axis=None, dtype=None, out=None, keepdims=False, initial=0, where=True)

Return the sum of the array elements over the given axis.

Refer to numpy.sum for full documentation.

See also

numpy.sum

equivalent function

swapaxes(axis1, axis2)

Return a view of the array with axis1 and axis2 interchanged.

Refer to numpy.swapaxes for full documentation.

See also

numpy.swapaxes

equivalent function

property symbol

Get the atomic symbols of all atoms in MultiMolecule.atoms as 1D array.

take(indices, axis=None, out=None, mode='raise')

Return an array formed from the elements of a at the given indices.

Refer to numpy.take for full documentation.

See also

numpy.take

equivalent function

tobytes(order='C')

Construct Python bytes containing the raw data bytes in the array.

Constructs Python bytes showing a copy of the raw contents of data memory. The bytes object is produced in C-order by default. This behavior is controlled by the order parameter.

New in version 1.9.0.

Parameters:

order ({'C', 'F', 'A'}, optional) – Controls the memory layout of the bytes object. ‘C’ means C-order, ‘F’ means F-order, ‘A’ (short for Any) means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. Default is ‘C’.

Returns:

s – Python bytes exhibiting a copy of a’s raw data.

Return type:

bytes

See also

frombuffer

Inverse of this operation, construct a 1-dimensional array from Python bytes.

Examples

>>> x = np.array([[0, 1], [2, 3]], dtype='<u2')
>>> x.tobytes()
b'\x00\x00\x01\x00\x02\x00\x03\x00'
>>> x.tobytes('C') == x.tobytes()
True
>>> x.tobytes('F')
b'\x00\x00\x02\x00\x01\x00\x03\x00'
tofile(fid, sep='', format='%s')

Write array to a file as text or binary (default).

Data is always written in ‘C’ order, independent of the order of a. The data produced by this method can be recovered using the function fromfile().

Parameters:
  • fid (file or str or Path) –

    An open file object, or a string containing a filename.

    Changed in version 1.17.0: pathlib.Path objects are now accepted.

  • sep (str) – Separator between array items for text output. If “” (empty), a binary file is written, equivalent to file.write(a.tobytes()).

  • format (str) – Format string for text file output. Each entry in the array is formatted to text by first converting it to the closest Python type, and then using “format” % item.

Notes

This is a convenience function for quick storage of array data. Information on endianness and precision is lost, so this method is not a good choice for files intended to archive data or transport data between machines with different endianness. Some of these problems can be overcome by outputting the data as text files, at the expense of speed and file size.

When fid is a file object, array contents are directly written to the file, bypassing the file object’s write method. As a result, tofile cannot be used with files objects supporting compression (e.g., GzipFile) or file-like objects that do not support fileno() (e.g., BytesIO).

tolist()

Return the array as an a.ndim-levels deep nested list of Python scalars.

Return a copy of the array data as a (nested) Python list. Data items are converted to the nearest compatible builtin Python type, via the ~numpy.ndarray.item function.

If a.ndim is 0, then since the depth of the nested list is 0, it will not be a list at all, but a simple Python scalar.

Parameters:

none

Returns:

y – The possibly nested list of array elements.

Return type:

object, or list of object, or list of list of object, or …

Notes

The array may be recreated via a = np.array(a.tolist()), although this may sometimes lose precision.

Examples

For a 1D array, a.tolist() is almost the same as list(a), except that tolist changes numpy scalars to Python scalars:

>>> a = np.uint32([1, 2])
>>> a_list = list(a)
>>> a_list
[1, 2]
>>> type(a_list[0])
<class 'numpy.uint32'>
>>> a_tolist = a.tolist()
>>> a_tolist
[1, 2]
>>> type(a_tolist[0])
<class 'int'>

Additionally, for a 2D array, tolist applies recursively:

>>> a = np.array([[1, 2], [3, 4]])
>>> list(a)
[array([1, 2]), array([3, 4])]
>>> a.tolist()
[[1, 2], [3, 4]]

The base case for this recursion is a 0D array:

>>> a = np.array(1)
>>> list(a)
Traceback (most recent call last):
  ...
TypeError: iteration over a 0-d array
>>> a.tolist()
1
tostring(order='C')

A compatibility alias for tobytes, with exactly the same behavior.

Despite its name, it returns bytes not strs.

Deprecated since version 1.19.0.

trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)

Return the sum along diagonals of the array.

Refer to numpy.trace for full documentation.

See also

numpy.trace

equivalent function

transpose(*axes)

Returns a view of the array with axes transposed.

Refer to numpy.transpose for full documentation.

Parameters:

axes (None, tuple of ints, or n ints) –

  • None or no argument: reverses the order of the axes.

  • tuple of ints: i in the j-th place in the tuple means that the array’s i-th axis becomes the transposed array’s j-th axis.

  • n ints: same as an n-tuple of the same ints (this form is intended simply as a “convenience” alternative to the tuple form).

Returns:

p – View of the array with its axes suitably permuted.

Return type:

ndarray

See also

transpose

Equivalent function.

ndarray.T

Array property returning the array transposed.

ndarray.reshape

Give a new shape to an array without changing its data.

Examples

>>> a = np.array([[1, 2], [3, 4]])
>>> a
array([[1, 2],
       [3, 4]])
>>> a.transpose()
array([[1, 3],
       [2, 4]])
>>> a.transpose((1, 0))
array([[1, 3],
       [2, 4]])
>>> a.transpose(1, 0)
array([[1, 3],
       [2, 4]])
>>> a = np.array([1, 2, 3, 4])
>>> a
array([1, 2, 3, 4])
>>> a.transpose()
array([1, 2, 3, 4])
var(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)

Returns the variance of the array elements, along given axis.

Refer to numpy.var for full documentation.

See also

numpy.var

equivalent function

view([dtype][, type])

New view of array with the same data.

Note

Passing None for dtype is different from omitting the parameter, since the former invokes dtype(None) which is an alias for dtype('float_').

Parameters:
  • dtype (data-type or ndarray sub-class, optional) – Data-type descriptor of the returned view, e.g., float32 or int16. Omitting it results in the view having the same data-type as a. This argument can also be specified as an ndarray sub-class, which then specifies the type of the returned object (this is equivalent to setting the type parameter).

  • type (Python type, optional) – Type of the returned view, e.g., ndarray or matrix. Again, omission of the parameter results in type preservation.

Notes

a.view() is used two different ways:

a.view(some_dtype) or a.view(dtype=some_dtype) constructs a view of the array’s memory with a different data-type. This can cause a reinterpretation of the bytes of memory.

a.view(ndarray_subclass) or a.view(type=ndarray_subclass) just returns an instance of ndarray_subclass that looks at the same array (same shape, dtype, etc.) This does not cause a reinterpretation of the memory.

For a.view(some_dtype), if some_dtype has a different number of bytes per entry than the previous dtype (for example, converting a regular array to a structured array), then the last axis of a must be contiguous. This axis will be resized in the result.

Changed in version 1.23.0: Only the last axis needs to be contiguous. Previously, the entire array had to be C-contiguous.

Examples

>>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])

Viewing array data using a different type and dtype:

>>> y = x.view(dtype=np.int16, type=np.matrix)
>>> y
matrix([[513]], dtype=int16)
>>> print(type(y))
<class 'numpy.matrix'>

Creating a view on a structured array so it can be used in calculations

>>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])
>>> xv = x.view(dtype=np.int8).reshape(-1,2)
>>> xv
array([[1, 2],
       [3, 4]], dtype=int8)
>>> xv.mean(0)
array([2.,  3.])

Making changes to the view changes the underlying array

>>> xv[0,1] = 20
>>> x
array([(1, 20), (3,  4)], dtype=[('a', 'i1'), ('b', 'i1')])

Using a view to convert an array to a recarray:

>>> z = x.view(np.recarray)
>>> z.a
array([1, 3], dtype=int8)

Views share data:

>>> x[0] = (9, 10)
>>> z[0]
(9, 10)

Views that change the dtype size (bytes per entry) should normally be avoided on arrays defined by slices, transposes, fortran-ordering, etc.:

>>> x = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int16)
>>> y = x[:, ::2]
>>> y
array([[1, 3],
       [4, 6]], dtype=int16)
>>> y.view(dtype=[('width', np.int16), ('length', np.int16)])
Traceback (most recent call last):
    ...
ValueError: To change to a dtype of a different size, the last axis must be contiguous
>>> z = y.copy()
>>> z.view(dtype=[('width', np.int16), ('length', np.int16)])
array([[(1, 3)],
       [(4, 6)]], dtype=[('width', '<i2'), ('length', '<i2')])

However, views that change dtype are totally fine for arrays with a contiguous last axis, even if the rest of the axes are not C-contiguous:

>>> x = np.arange(2 * 3 * 4, dtype=np.int8).reshape(2, 3, 4)
>>> x.transpose(1, 0, 2).view(np.int16)
array([[[ 256,  770],
        [3340, 3854]],

       [[1284, 1798],
        [4368, 4882]],

       [[2312, 2826],
        [5396, 5910]]], dtype=int16)
property x

Get or set the x coordinates for all atoms in instance as 2D array.

property y

Get or set the y coordinates for all atoms in this instance as 2D array.

property z

Get or set the z coordinates for all atoms in this instance as 2D array.

as_pdb(filename, mol_subset=0)[source]

Convert a MultiMolecule object into one or more Protein DataBank files (.pdb).

Utilizes the plams.Molecule.write method.

Parameters:
  • filename (path-like object) – The path+filename (including extension) of the to be created file.

  • mol_subset (slice, optional) – Perform the calculation on a subset of molecules in this instance, as determined by their moleculair index. Include all \(m\) molecules in this instance if None.

as_mol2(filename, mol_subset=0)[source]

Convert a FOX.MultiMolecule object into one or more .mol2 files.

Utilizes the plams.Molecule.write method.

Parameters:
  • filename (path-like object) – The path+filename (including extension) of the to be created file.

  • mol_subset (slice, optional) – Perform the calculation on a subset of molecules in this instance, as determined by their moleculair index. Include all \(m\) molecules in this instance if None.

as_mol(filename, mol_subset=0)[source]

Convert a MultiMolecule object into one or more .mol files.

Utilizes the plams.Molecule.write method.

Parameters:
  • filename (path-like object) – The path+filename (including extension) of the to be created file.

  • mol_subset (slice, optional) – Perform the calculation on a subset of molecules in this instance, as determined by their moleculair index. Include all \(m\) molecules in this instance if None.

as_xyz(filename, mol_subset=None)[source]

Create an .xyz file out of this instance.

Comments will be constructed by iteration through MultiMolecule.properties["comments"] if the following two conditions are fulfilled:

  • The "comments" key is actually present in MultiMolecule.properties.

  • MultiMolecule.properties["comments"] is an iterable.

Parameters:
  • filename (path-like object) – The path+filename (including extension) of the to be created file.

  • mol_subset (slice, optional) – Perform the calculation on a subset of molecules in this instance, as determined by their moleculair index. Include all \(m\) molecules in this instance if None.

as_gro(filename, mol_subset=0)[source]

Create an GROMACS .gro file out of this instance.

Parameters:
  • filename (path-like object) – The path+filename (including extension) of the to be created file.

  • mol_subset (int, optional) – The index of the molecule in this instance that will be converted into the .gro file.

as_mass_weighted(mol_subset=None, atom_subset=None, inplace=False)[source]

Transform the Cartesian of this instance into mass-weighted Cartesian coordinates.

Parameters:
  • mol_subset (slice, optional) – Perform the calculation on a subset of molecules in this instance, as determined by their moleculair index. Include all \(m\) molecules in this instance if None.

  • atom_subset (Sequence[str], optional) – Perform the calculation on a subset of atoms in this instance, as determined by their atomic index or atomic symbol. Include all \(n\) atoms per molecule in this instance if None.

  • inplace (bool) – Instead of returning the new coordinates, perform an inplace update of this instance.

Returns:

if inplace = False return a new MultiMolecule instance with the mass-weighted Cartesian coordinates of \(m\) molecules with \(n\) atoms.

Return type:

np.ndarray[np.float64], shape \((m, n, 3)\), optional

from_mass_weighted(mol_subset=None, atom_subset=None)[source]

Transform this instance from mass-weighted Cartesian into Cartesian coordinates.

Performs an inplace update of this instance.

Parameters:
  • mol_subset (slice, optional) – Perform the calculation on a subset of molecules in this instance, as determined by their moleculair index. Include all \(m\) molecules in this instance if None.

  • atom_subset (Sequence[str], optional) – Perform the calculation on a subset of atoms in this instance, as determined by their atomic index or atomic symbol. Include all \(n\) atoms per molecule in this instance if None.

as_Molecule(mol_subset=None, atom_subset=None)[source]

Convert this instance into a list of plams.Molecule.

Parameters:
  • mol_subset (slice, optional) – Perform the calculation on a subset of molecules in this instance, as determined by their moleculair index. Include all \(m\) molecules in this instance if None.

  • atom_subset (Sequence[str], optional) – Perform the calculation on a subset of atoms in this instance, as determined by their atomic index or atomic symbol. Include all \(n\) atoms per molecule in this instance if None.

Returns:

A list of \(m\) PLAMS molecules constructed from this instance.

Return type:

list[plams.Molecule]

classmethod from_Molecule(mol_list, subset=frozenset({'atoms'}))[source]

Construct a MultiMolecule instance from one or more PLAMS molecules.

Parameters:
  • mol_list (plams.Molecule or Sequence[plams.Molecule]) – A PLAMS molecule or list of PLAMS molecules.

  • subset (Container[str], optional) – Transfer a subset of plams.Molecule attributes to this instance. If None, transfer all attributes. Accepts one or more of the following values as strings: "properties", "atoms", "lattice" and/or "bonds".

Returns:

A molecule constructed from mol_list.

Return type:

FOX.MultiMolecule

as_ase(mol_subset=None, atom_subset=None, **kwargs)[source]

Convert this instance into a list of ASE Atoms.

Parameters:
  • mol_subset (slice, optional) – Perform the calculation on a subset of molecules in this instance, as determined by their moleculair index. Include all \(m\) molecules in this instance if None.

  • atom_subset (Sequence[str], optional) – Perform the calculation on a subset of atoms in this instance, as determined by their atomic index or atomic symbol. Include all \(n\) atoms per molecule in this instance if None.

  • **kwargs (Any) – Further keyword arguments for ase.Atoms.

Returns:

A list of ASE Atoms constructed from this instance.

Return type:

list[ase.Atoms]

classmethod from_ase(mol_list)[source]

Construct a MultiMolecule instance from one or more ASE Atoms.

Parameters:

mol_list (ase.Atoms or Sequence[ase.Atoms]) – An ASE Atoms instance or a list thereof.

Returns:

A molecule constructed from mol_list.

Return type:

FOX.MultiMolecule

classmethod from_xyz(filename, bonds=None, properties=None, read_comment=False)[source]

Construct a MultiMolecule instance from a (multi) .xyz file.

Comment lines extracted from the .xyz file are stored, as array, under MultiMolecule.properties["comments"].

Parameters:
  • filename (path-like object) – The path+filename of an .xyz file.

  • bonds (np.ndarray[np.int64], shape \((k, 3)\)) – An optional 2D array with indices of the atoms defining all \(k\) bonds (columns 1 & 2) and their respective bond orders multiplied by 10 (column 3). Stored in the MultieMolecule.bonds attribute.

  • properties (dict, optional) – A Settings object (subclass of dictionary) intended for storing miscellaneous user-defined (meta-)data. Is devoid of keys by default. Stored in the MultiMolecule.properties attribute.

  • read_comments (bool) – If True, extract all comment lines from the passed .xyz file and store them under properties.comments.

Returns:

A molecule constructed from filename.

Return type:

FOX.MultiMolecule

classmethod from_kf(filename, bonds=None, properties=None)[source]

Construct a MultiMolecule instance from a KF binary file.

Parameters:
  • filename (path-like object) – The path+filename of an KF binary file.

  • bonds (np.ndarray[np.int64], shape \((k, 3)\)) – An optional 2D array with indices of the atoms defining all \(k\) bonds (columns 1 & 2) and their respective bond orders multiplied by 10 (column 3). Stored in the MultieMolecule.bonds attribute.

  • properties (dict) – A Settings object (subclass of dictionary) intended for storing miscellaneous user-defined (meta-)data. Is devoid of keys by default. Stored in the MultiMolecule.properties attribute.

Returns:

A molecule constructed from filename.

Return type:

FOX.MultiMolecule