aiida.orm.nodes.data.array package#

Module with Node sub classes for array based data structures.

Submodules#

AiiDA ORM data class storing (numpy) arrays

class aiida.orm.nodes.data.array.array.ArrayData(arrays: ndarray | dict[str, ndarray] | None = None, **kwargs)[source]#

Bases: Data

Store a set of arrays on disk (rather than on the database) in an efficient way using numpy.save() (therefore, this class requires numpy to be installed).

Each array is stored within the Node folder as a different .npy file.

Note:: Before storing, no caching is done: if you perform a get_array() call, the array will be re-read from disk. If instead the ArrayData node has already been stored, the array is cached in memory after the first read, and the cached array is used thereafter. If too much RAM memory is used, you can clear the cache with the clear_internal_cache() method.

__abstractmethods__ = frozenset({})#

__init__(arrays: ndarray | dict[str, ndarray] | None = None, **kwargs)[source]#

Construct a new instance and set one or multiple numpy arrays.

Parameters:: arrays – An optional single numpy array, or dictionary of numpy arrays to store.

__module__ = 'aiida.orm.nodes.data.array.array'#

__parameters__ = ()#

__plugin_type_string: ClassVar[str]#

__query_type_string: ClassVar[str]#

_abc_impl = <_abc._abc_data object>#

_arraynames_from_files() → list[str][source]#: Return a list of all arrays stored in the node, listing the files (and not relying on the properties).

_arraynames_from_properties() → list[str][source]#: Return a list of all arrays stored in the node, listing the attributes starting with the correct prefix.

_get_array_entries() → dict[str, Any][source]#

Return a dictionary with the different array entries.

The idea is that this dictionary contains the array name as a key and the value is the numpy array transformed into a list. This is so that it can be transformed into a json object.

_logger: Logger | None = <Logger aiida.orm.nodes.data.array.array.ArrayData (REPORT)>#

_prepare_json(main_file_name='', comments=True) → tuple[bytes, dict][source]#

Dump the content of the arrays stored in this node into JSON format.

Parameters:: comments – if True, includes comments (if it makes sense for the given format)

_validate() → bool[source]#: Check if the list of .npy files stored inside the node and the list of properties match. Just a name check, no check on the size since this would require to reload all arrays and this may take time and memory.

array_prefix = 'array|'#

clear_internal_cache() → None[source]#: Clear the internal memory cache where the arrays are stored after being read from disk (used in order to reduce at minimum the readings from disk). This function is useful if you want to keep the node in memory, but you do not want to waste memory to cache the arrays in RAM.

default_array_name = 'default'#

delete_array(name: str) → None[source]#

Delete an array from the node. Can only be called before storing.

Parameters:: name – The name of the array to delete from the node.

get_array(name: str | None = None) → ndarray[source]#

Return an array stored in the node

Parameters:: name – The name of the array to return. The name can be omitted in case the node contains only a single array, which will be returned in that case. If name is None and the node contains multiple arrays or no arrays at all a ValueError is raised.
Raises:: ValueError – If name is None and the node contains more than one arrays or no arrays at all.

get_arraynames() → list[str][source]#: Return a list of all arrays stored in the node, listing the files (and not relying on the properties).

New in version 0.7: Renamed from arraynames

get_iterarrays() → Iterator[tuple[str, ndarray]][source]#: Iterator that returns tuples (name, array) for each array stored in the node.

New in version 1.0: Renamed from iterarrays

get_shape(name: str) → tuple[int, ...][source]#

Return the shape of an array (read from the value cached in the properties for efficiency reasons).

Parameters:: name – The name of the array.

initialize()[source]#

set_array(name: str, array: ndarray) → None[source]#

Store a new numpy array inside the node. Possibly overwrite the array if it already existed.

Internally, it stores a name.npy file in numpy format.

Parameters:

name – The name of the array.
array – The numpy array to store.

aiida.orm.nodes.data.array.array._(value)[source]#

aiida.orm.nodes.data.array.array.clean_array(array: ndarray) → list[source]#

Replacing np.nan and np.inf/-np.inf for Nones.

The function will also sanitize the array removing np.nan and np.inf for None of this way the resulting JSON is always valid. Both np.nan and np.inf/-np.inf are set to None to be in accordance with the ECMA-262 standard.

Parameters:: array – input array to be cleaned
Returns:: cleaned list to be serialized
Return type:: list

This module defines the classes related to band structures or dispersions in a Brillouin zone, and how to operate on them.

class aiida.orm.nodes.data.array.bands.BandsData(arrays: ndarray | dict[str, ndarray] | None = None, **kwargs)[source]#

Bases: KpointsData

Class to handle bands data

__abstractmethods__ = frozenset({})#

__module__ = 'aiida.orm.nodes.data.array.bands'#

__parameters__ = ()#

__plugin_type_string: ClassVar[str]#

__query_type_string: ClassVar[str]#

_abc_impl = <_abc._abc_data object>#

_cached_arrays: dict[str, ndarray]#

_get_band_segments(cartesian)[source]#: Return the band segments.

_get_bandplot_data(cartesian, prettify_format=None, join_symbol=None, get_segments=False, y_origin=0.0)[source]#

Get data to plot a band structure

Parameters:

cartesian – if True, distances (for the x-axis) are computed in cartesian coordinates, otherwise they are computed in reciprocal coordinates. cartesian=True will fail if no cell has been set.
prettify_format – by default, strings are not prettified. If you want to prettify them, pass a valid prettify_format string (see valid options in the docstring of :py:func:prettify_labels).
join_symbols – by default, strings are not joined. If you pass a string, this is used to join strings that are much closer than a given threshold. The most typical string is the pipe symbol: |.
get_segments – if True, also computes the band split into segments
y_origin – if present, shift bands so to set the value specified at y=0

Returns:

a plot_info dictiorary, whose keys are x (array of distances for the x axis of the plot); y (array of bands), labels (list of tuples in the format (float x value of the label, label string), band_type_idx (array containing an index for each band: if there is only one spin, then it’s an array of zeros, of length equal to the number of bands at each point; if there are two spins, then it’s an array of zeros or ones depending on the type of spin; the length is always equalt to the total number of bands per kpoint).

static _get_mpl_body_template(paths)[source]#

Parameters:: paths – paths of k-points

_logger: Logger | None = <Logger aiida.orm.nodes.data.array.bands.BandsData (REPORT)>#

_matplotlib_get_dict(main_file_name='', comments=True, title='', legend=None, legend2=None, y_max_lim=None, y_min_lim=None, y_origin=0.0, prettify_format=None, **kwargs)[source]#

Prepare the data to send to the python-matplotlib plotting script.

Parameters:

comments – if True, print comments (if it makes sense for the given format)
plot_info – a dictionary
setnumber_offset – an offset to be applied to all set numbers (i.e. s0 is replaced by s[offset], s1 by s[offset+1], etc.)
color_number – the color number for lines, symbols, error bars and filling (should be less than the parameter MAX_NUM_AGR_COLORS defined below)
title – the title
legend – the legend (applied only to the first of the set)
legend2 – the legend for second-type spins (applied only to the first of the set)
y_max_lim – the maximum on the y axis (if None, put the maximum of the bands)
y_min_lim – the minimum on the y axis (if None, put the minimum of the bands)
y_origin – the new origin of the y axis -> all bands are replaced by bands-y_origin
prettify_format – if None, use the default prettify format. Otherwise specify a string with the prettifier to use.
kwargs – additional customization variables; only a subset is accepted, see internal variable ‘valid_additional_keywords

_prepare_agr(main_file_name='', comments=True, setnumber_offset=0, color_number=1, color_number2=2, legend='', title='', y_max_lim=None, y_min_lim=None, y_origin=0.0, prettify_format=None)[source]#

Prepare an xmgrace agr file.

Parameters:

comments – if True, print comments (if it makes sense for the given format)
plot_info – a dictionary
setnumber_offset – an offset to be applied to all set numbers (i.e. s0 is replaced by s[offset], s1 by s[offset+1], etc.)
color_number – the color number for lines, symbols, error bars and filling (should be less than the parameter MAX_NUM_AGR_COLORS defined below)
color_number2 – the color number for lines, symbols, error bars and filling for the second-type spins (should be less than the parameter MAX_NUM_AGR_COLORS defined below)
legend – the legend (applied only to the first set)
title – the title
y_max_lim – the maximum on the y axis (if None, put the maximum of the bands); applied after shifting the origin by y_origin
y_min_lim – the minimum on the y axis (if None, put the minimum of the bands); applied after shifting the origin by y_origin
y_origin – the new origin of the y axis -> all bands are replaced by bands-y_origin
prettify_format – if None, use the default prettify format. Otherwise specify a string with the prettifier to use.

_prepare_agr_batch(main_file_name='', comments=True, prettify_format=None)[source]#

Prepare two files, data and batch, to be plot with xmgrace as: xmgrace -batch file.dat

Parameters:

main_file_name – if the user asks to write the main content on a file, this contains the filename. This should be used to infer a good filename for the additional files. In this case, we remove the extension, and add ‘_data.dat’
comments – if True, print comments (if it makes sense for the given format)
prettify_format – if None, use the default prettify format. Otherwise specify a string with the prettifier to use.

_prepare_dat_blocks(main_file_name='', comments=True)[source]#

Format suitable for gnuplot using blocks. Columns with x and y (path and band energy). Several blocks, separated by two empty lines, one per energy band.

Parameters:: comments – if True, print comments (if it makes sense for the given format)

_prepare_dat_multicolumn(main_file_name='', comments=True)[source]#

Write an N x M matrix. First column is the distance between kpoints, The other columns are the bands. Header contains number of kpoints and the number of bands (commented).

Parameters:: comments – if True, print comments (if it makes sense for the given format)

_prepare_gnuplot(main_file_name=None, title='', comments=True, prettify_format=None, y_max_lim=None, y_min_lim=None, y_origin=0.0)[source]#

Prepare an gnuplot script to plot the bands, with the .dat file returned as an independent file.

Parameters:

main_file_name – if the user asks to write the main content on a file, this contains the filename. This should be used to infer a good filename for the additional files. In this case, we remove the extension, and add ‘_data.dat’
title – if specified, add a title to the plot
comments – if True, print comments (if it makes sense for the given format)
prettify_format – if None, use the default prettify format. Otherwise specify a string with the prettifier to use.

_prepare_json(main_file_name='', comments=True)[source]#

Prepare a json file in a format compatible with the AiiDA band visualizer

Parameters:: comments – if True, print comments (if it makes sense for the given format)

_prepare_mpl_pdf(main_file_name='', *args, **kwargs)[source]#

Prepare a python script using matplotlib to plot the bands, with the JSON returned as an independent file.

For the possible parameters, see documentation of _matplotlib_get_dict()

_prepare_mpl_png(main_file_name='', *args, **kwargs)[source]#

Prepare a python script using matplotlib to plot the bands, with the JSON returned as an independent file.

For the possible parameters, see documentation of _matplotlib_get_dict()

_prepare_mpl_singlefile(*args, **kwargs)[source]#

Prepare a python script using matplotlib to plot the bands

For the possible parameters, see documentation of _matplotlib_get_dict()

_prepare_mpl_withjson(main_file_name='', *args, **kwargs)[source]#

Prepare a python script using matplotlib to plot the bands, with the JSON returned as an independent file.

For the possible parameters, see documentation of _matplotlib_get_dict()

_set_pbc(value)[source]#: validate the pbc, then store them

_validate_bands_occupations(bands, occupations=None, labels=None)[source]#: Validate the list of bands and of occupations before storage. Kpoints must be set in advance. Bands and occupations must be convertible into arrays of Nkpoints x Nbands floats or Nspins x Nkpoints x Nbands; Nkpoints must correspond to the number of kpoints.

property array_labels#: Get the labels associated with the band arrays

get_bands(also_occupations=False, also_labels=False)[source]#: Returns an array (nkpoints x num_bands or nspins x nkpoints x num_bands) of energies. :param also_occupations: if True, returns also the occupations array. Default = False

set_bands(bands, units=None, occupations=None, labels=None)[source]#: Set an array of band energies of dimension (nkpoints x nbands). Kpoints must be set in advance. Can contain floats or None. :param bands: a list of nkpoints lists of nbands bands, or a 2D array of shape (nkpoints x nbands), with band energies for each kpoint :param units: optional, energy units :param occupations: optional, a 2D list or array of floats of same shape as bands, with the occupation associated to each band

set_kpointsdata(kpointsdata)[source]#: Load the kpoints from a kpoint object. :param kpointsdata: an instance of KpointsData class

show_mpl(**kwargs)[source]#

Call a show() command for the band structure using matplotlib. This uses internally the ‘mpl_singlefile’ format, with empty main_file_name.

Other kwargs are passed to self._exportcontent.

property units#: Units in which the data in bands were stored. A string

aiida.orm.nodes.data.array.bands._extract_formula(akinds, asites, args)[source]#

Extract formula from the structure object.

Parameters:

akinds – list of kinds, e.g. [{‘mass’: 55.845, ‘name’: ‘Fe’, ‘symbols’: [‘Fe’], ‘weights’: [1.0]}, {‘mass’: 15.9994, ‘name’: ‘O’, ‘symbols’: [‘O’], ‘weights’: [1.0]}]
asites – list of structure sites e.g. [{‘position’: [0.0, 0.0, 0.0], ‘kind_name’: ‘Fe’}, {‘position’: [2.0, 2.0, 2.0], ‘kind_name’: ‘O’}]
args (dict) – a namespace with parsed command line parameters, here only ‘element’ and ‘element_only’ are used

Returns:

a string with formula if the formula is found

aiida.orm.nodes.data.array.bands.find_bandgap(bandsdata, number_electrons=None, fermi_energy=None)[source]#

Tries to guess whether the bandsdata represent an insulator. This method is meant to be used only for electronic bands (not phonons) By default, it will try to use the occupations to guess the number of electrons and find the Fermi Energy, otherwise, it can be provided explicitely. Also, there is an implicit assumption that the kpoints grid is “sufficiently” dense, so that the bandsdata are not missing the intersection between valence and conduction band if present. Use this function with care!

Parameters:

number_electrons – (optional, float) number of electrons in the unit cell
fermi_energy – (optional, float) value of the fermi energy.

Note:

By default, the algorithm uses the occupations array to guess the number of electrons and the occupied bands. This is to be used with care, because the occupations could be smeared so at a non-zero temperature, with the unwanted effect that the conduction bands might be occupied in an insulator. Prefer to pass the number_of_electrons explicitly

Note:

Only one between number_electrons and fermi_energy can be specified at the same time.

Returns:

(is_insulator, gap), where is_insulator is a boolean, and gap a float. The gap is None in case of a metal, zero when the homo is equal to the lumo (e.g. in semi-metals).

aiida.orm.nodes.data.array.bands.get_bands_and_parents_structure(args, backend=None)[source]#

Search for bands and return bands and the closest structure that is a parent of the instance.

Returns:

A list of sublists, each latter containing (in order):: pk as string, formula as string, creation date, bandsdata-label

aiida.orm.nodes.data.array.bands.prepare_header_comment(uuid, plot_info, comment_char='#')[source]#: Prepare the header.

Module of the KpointsData class, defining the AiiDA data type for storing lists and meshes of k-points (i.e., points in the reciprocal space of a periodic crystal structure).

class aiida.orm.nodes.data.array.kpoints.KpointsData(arrays: ndarray | dict[str, ndarray] | None = None, **kwargs)[source]#

Bases: ArrayData

Class to handle array of kpoints in the Brillouin zone. Provide methods to generate either user-defined k-points or path of k-points along symmetry lines. Internally, all k-points are defined in terms of crystal (fractional) coordinates. Cell and lattice vector coordinates are in Angstroms, reciprocal lattice vectors in Angstrom^-1 . :note: The methods setting and using the Bravais lattice info assume the PRIMITIVE unit cell is provided in input to the set_cell or set_cell_from_structure methods.

__abstractmethods__ = frozenset({})#

__module__ = 'aiida.orm.nodes.data.array.kpoints'#

__parameters__ = ()#

__plugin_type_string: ClassVar[str]#

__query_type_string: ClassVar[str]#

_abc_impl = <_abc._abc_data object>#

_cached_arrays: dict[str, ndarray]#

_change_reference(kpoints, to_cartesian=True)[source]#: Change reference system, from cartesian to crystal coordinates (units of b1,b2,b3) or viceversa. :param kpoints: a list of (3) point coordinates :return kpoints: a list of (3) point coordinates in the new reference

property _dimension#: Dimensionality of the structure, found from its pbc (i.e. 1 if it’s a 1D structure, 2 if its 2D, 3 if it’s 3D …). :return dimensionality: 0, 1, 2 or 3 :note: will return 3 if pbc has not been set beforehand

_logger: Logger | None = <Logger aiida.orm.nodes.data.array.kpoints.KpointsData (REPORT)>#

_set_cell(value)[source]#: Validate if ‘value’ is a allowed crystal unit cell :param value: something compatible with a 3x3 tuple of floats

_set_labels(value)[source]#: set label names. Must pass in input a list like: [[0,'X'],[34,'L'],... ]

_set_pbc(value)[source]#: validate the pbc, then store them

_validate_kpoints_weights(kpoints, weights)[source]#: Validate the list of kpoints and of weights before storage. Kpoints and weights must be convertible respectively to an array of N x dimension and N floats

property cell#: The crystal unit cell. Rows are the crystal vectors in Angstroms. :return: a 3x3 numpy.array

get_description()[source]#: Returns a string with infos retrieved from kpoints node’s properties. :param node: :return: retstr

get_kpoints(also_weights=False, cartesian=False)[source]#

Return the list of kpoints

Parameters:

also_weights – if True, returns also the list of weights. Default = False
cartesian – if True, returns points in cartesian coordinates, otherwise, returns in crystal coordinates. Default = False.

get_kpoints_mesh(print_list=False)[source]#

Get the mesh of kpoints.

Parameters:: print_list – default=False. If True, prints the mesh of kpoints as a list
Raises:: AttributeError – if no mesh has been set
Return mesh,offset:: (if print_list=False) a list of 3 integers and a list of three floats 0<x<1, representing the mesh and the offset of kpoints
Return kpoints:: (if print_list = True) an explicit list of kpoints coordinates, similar to what returned by get_kpoints()

property labels#: Labels associated with the list of kpoints. List of tuples with kpoint index and kpoint name: [(0,'G'),(13,'M'),...]

property pbc#

The periodic boundary conditions along the vectors a1,a2,a3.

Returns:: a tuple of three booleans, each one tells if there are periodic boundary conditions for the i-th real-space direction (i=1,2,3)

property reciprocal_cell#

Compute reciprocal cell from the internally set cell.

Returns:: reciprocal cell in units of 1/Angstrom with cell vectors stored as rows. Use e.g. reciprocal_cell[0] to access the first reciprocal cell vector.

set_cell(cell, pbc=None)[source]#

Set a cell to be used for symmetry analysis. To set a cell from an AiiDA structure, use “set_cell_from_structure”.

Parameters:

cell – 3x3 matrix of cell vectors. Orientation: each row represent a lattice vector. Units are Angstroms.
pbc – list of 3 booleans, True if in the nth crystal direction the structure is periodic. Default = [True,True,True]

set_cell_from_structure(structuredata)[source]#

Set a cell to be used for symmetry analysis from an AiiDA structure. Inherits both the cell and the pbc’s. To set manually a cell, use “set_cell”

Parameters:: structuredata – an instance of StructureData

set_kpoints(kpoints, cartesian=False, labels=None, weights=None, fill_values=0)[source]#

Set the list of kpoints. If a mesh has already been stored, raise a ModificationNotAllowed

Parameters:

kpoints –
a list of kpoints, each kpoint being a list of one, two or three coordinates, depending on self.pbc: if structure is 1D (only one True in self.pbc) one allows singletons or scalars for each k-point, if it’s 2D it can be a length-2 list, and in all cases it can be a length-3 list. Examples:
- [[0.,0.,0.],[0.1,0.1,0.1],…] for 1D, 2D or 3D
- [[0.,0.],[0.1,0.1,],…] for 1D or 2D
- [[0.],[0.1],…] for 1D
- [0., 0.1, …] for 1D (list of scalars)
For 0D (all pbc are False), the list can be any of the above or empty - then only Gamma point is set. The value of k for the non-periodic dimension(s) is set by fill_values
cartesian – if True, the coordinates given in input are treated as in cartesian units. If False, the coordinates are crystal, i.e. in units of b1,b2,b3. Default = False
labels – optional, the list of labels to be set for some of the kpoints. See labels for more info
weights – optional, a list of floats with the weight associated to the kpoint list
fill_values – scalar to be set to all non-periodic dimensions (indicated by False in self.pbc), or list of values for each of the non-periodic dimensions.

set_kpoints_mesh(mesh, offset=None)[source]#

Set KpointsData to represent a uniformily spaced mesh of kpoints in the Brillouin zone. This excludes the possibility of set/get kpoints

Parameters:

mesh – a list of three integers, representing the size of the kpoint mesh along b1,b2,b3.
offset – (optional) a list of three floats between 0 and 1. [0.,0.,0.] is Gamma centered mesh [0.5,0.5,0.5] is half shifted [1.,1.,1.] by periodicity should be equivalent to [0.,0.,0.] Default = [0.,0.,0.].

set_kpoints_mesh_from_density(distance, offset=None, force_parity=False)[source]#

Set a kpoints mesh using a kpoints density, expressed as the maximum distance between adjacent points along a reciprocal axis

Parameters:

distance – distance (in 1/Angstrom) between adjacent kpoints, i.e. the number of kpoints along each reciprocal axis i is \(|b_i|/distance\) where \(|b_i|\) is the norm of the reciprocal cell vector.
offset – (optional) a list of three floats between 0 and 1. [0.,0.,0.] is Gamma centered mesh [0.5,0.5,0.5] is half shifted Default = [0.,0.,0.].
force_parity – (optional) if True, force each integer in the mesh to be even (except for the non-periodic directions).

Note:

a cell should be defined first.

Note:

the number of kpoints along non-periodic axes is always 1.

Data plugin to represet arrays of projected wavefunction components.

class aiida.orm.nodes.data.array.projection.ProjectionData(arrays: ndarray | dict[str, ndarray] | None = None, **kwargs)[source]#

Bases: OrbitalData, ArrayData

A class to handle arrays of projected wavefunction data. That is projections of a orbitals, usually an atomic-hydrogen orbital, onto a given bloch wavefunction, the bloch wavefunction being indexed by s, n, and k. E.g. the elements are the projections described as < orbital | Bloch wavefunction (s,n,k) >

__abstractmethods__ = frozenset({})#

__module__ = 'aiida.orm.nodes.data.array.projection'#

__parameters__ = ()#

__plugin_type_string: ClassVar[str]#

__query_type_string: ClassVar[str]#

_abc_impl = <_abc._abc_data object>#

_check_projections_bands(projection_array)[source]#

Checks to make sure that a reference bandsdata is already set, and that projection_array is of the same shape of the bands data

Parameters:: projwfc_arrays – nk x nb x nwfc array, to be checked against bands
Raise:: AttributeError if energy is not already set
Raise:: AttributeError if input_array is not of same shape as dos_energy

_find_orbitals_and_indices(**kwargs)[source]#

Finds all the orbitals and their indicies associated with kwargs essential for retrieving the other indexed array parameters

Parameters:: kwargs – kwargs that can call orbitals as in get_orbitals()
Returns:: retrieve_indexes, list of indicicies of orbitals corresponding to the kwargs
Returns:: all_orbitals, list of orbitals to which the indexes correspond

static _from_index_to_arrayname(index)[source]#: Used internally to determine the array names.

_logger: Logger | None = <Logger aiida.orm.nodes.data.array.projection.ProjectionData (REPORT)>#

get_pdos(**kwargs)[source]#

Retrieves all the pdos arrays corresponding to the input kwargs

Parameters:: kwargs – inputs describing the orbitals associated with the pdos arrays
Returns:: a list of tuples containing the orbital, energy array and pdos array associated with all orbitals that correspond to kwargs

get_projections(**kwargs)[source]#

Retrieves all the pdos arrays corresponding to the input kwargs

Parameters:: kwargs – inputs describing the orbitals associated with the pdos arrays
Returns:: a list of tuples containing the orbital, and projection arrays associated with all orbitals that correspond to kwargs

get_reference_bandsdata()[source]#

Returns the reference BandsData, using the set uuid via set_reference_bandsdata

Returns:

a BandsData instance

Raises:

AttributeError – if the bandsdata has not been set yet
exceptions.NotExistent – if the bandsdata uuid did not retrieve bandsdata

set_orbitals(**kwargs)[source]#: This method is inherited from OrbitalData, but is blocked here. If used will raise a NotImplementedError

set_projectiondata(list_of_orbitals, list_of_projections=None, list_of_energy=None, list_of_pdos=None, tags=None, bands_check=True)[source]#

Stores the projwfc_array using the projwfc_label, after validating both.

Parameters:

list_of_orbitals – list of orbitals, of class orbital data. They should be the ones up on which the projection array corresponds with.
list_of_projections – list of arrays of projections of a atomic wavefunctions onto bloch wavefunctions. Since the projection is for every bloch wavefunction which can be specified by its spin (if used), band, and kpoint the dimensions must be nspin x nbands x nkpoints for the projwfc array. Or nbands x nkpoints if spin is not used.
energy_axis – list of energy axis for the list_of_pdos
list_of_pdos – a list of projected density of states for the atomic wavefunctions, units in states/eV
tags – A list of tags, not supported currently.
bands_check – if false, skips checks of whether the bands has been already set, and whether the sizes match. For use in parsers, where the BandsData has not yet been stored and therefore get_reference_bandsdata cannot be called

set_reference_bandsdata(value)[source]#

Sets a reference bandsdata, creates a uuid link between this data object and a bandsdata object, must be set before any projection arrays

Parameters:: value – a BandsData instance, a uuid or a pk
Raise:: exceptions.NotExistent if there was no BandsData associated with uuid or pk

AiiDA class to deal with crystal structure trajectories.

class aiida.orm.nodes.data.array.trajectory.TrajectoryData(structurelist=None, **kwargs)[source]#

Bases: ArrayData

Stores a trajectory (a sequence of crystal structures with timestamps, and possibly with velocities).

__abstractmethods__ = frozenset({})#

__init__(structurelist=None, **kwargs)[source]#

Construct a new instance and set one or multiple numpy arrays.

Parameters:: arrays – An optional single numpy array, or dictionary of numpy arrays to store.

__module__ = 'aiida.orm.nodes.data.array.trajectory'#

__parameters__ = ()#

__plugin_type_string: ClassVar[str]#

__query_type_string: ClassVar[str]#

_abc_impl = <_abc._abc_data object>#

_cached_arrays: dict[str, ndarray]#

_internal_validate(stepids, cells, symbols, positions, times, velocities)[source]#: Internal function to validate the type and shape of the arrays. See the documentation of py:meth:.set_trajectory for a description of the valid shape and type of the parameters.

_logger: Logger | None = <Logger aiida.orm.nodes.data.array.trajectory.TrajectoryData (REPORT)>#

_parse_xyz_pos(inputstring)[source]#

Load positions from a XYZ file.

Note

The steps and symbols must be set manually before calling this import function as a consistency measure. Even though the symbols and steps could be extracted from the XYZ file, the data present in the XYZ file may or may not be correct and the same logic would have to be present in the XYZ-velocities function. It was therefore decided not to implement it at all but require it to be set explicitly.

Usage:

from aiida.orm.nodes.data.array.trajectory import TrajectoryData

t = TrajectoryData()
# get sites and number of timesteps
t.set_array('steps', arange(ntimesteps))
t.set_array('symbols', array([site.kind for site in s.sites]))
t.importfile('some-calc/AIIDA-PROJECT-pos-1.xyz', 'xyz_pos')

_parse_xyz_vel(inputstring)[source]#: Load velocities from a XYZ file.

Note

The steps and symbols must be set manually before calling this import function as a consistency measure. See also comment for _parse_xyz_pos()

_prepare_cif(trajectory_index=None, main_file_name='')[source]#: Write the given trajectory to a string of format CIF.

_prepare_xsf(index=None, main_file_name='')[source]#: Write the given trajectory to a string of format XSF (for XCrySDen).

_validate()[source]#: Verify that the required arrays are present and that their type and dimension are correct.

get_cells()[source]#

Return the array of cells, if it has already been set.

Raises:: KeyError – if the trajectory has not been set yet.

get_cif(index=None, **kwargs)[source]#: Creates aiida.orm.nodes.data.cif.CifData

New in version 1.0: Renamed from _get_cif

get_index_from_stepid(stepid)[source]#

Given a value for the stepid (i.e., a value among those of the steps array), return the array index of that stepid, that can be used in other methods such as get_step_data() or get_step_structure().

New in version 0.7: Renamed from get_step_index

Note

Note that this function returns the first index found (i.e. if multiple steps are present with the same value, only the index of the first one is returned).

Raises:: ValueError – if no step with the given value is found.

get_positions()[source]#

Return the array of positions, if it has already been set.

Raises:: KeyError – if the trajectory has not been set yet.

get_step_data(index)[source]#

Return a tuple with all information concerning the stepid with given index (0 is the first step, 1 the second step and so on). If you know only the step value, use the get_index_from_stepid() method to get the corresponding index.

If no velocities, cells, or times were specified, None is returned as the corresponding element.

Returns:

A tuple in the format (stepid, time, cell, symbols, positions, velocities), where stepid is an integer, time is a float, cell is a \(3 imes 3\) matrix, symbols is an array of length n, positions is a \(n imes 3\) array, and velocities is either None or a \(n imes 3\) array

Parameters:

index – The index of the step that you want to retrieve, from 0 to self.numsteps - 1.

Raises:

IndexError – if you require an index beyond the limits.
KeyError – if you did not store the trajectory yet.

get_step_structure(index, custom_kinds=None)[source]#

Return an AiiDA aiida.orm.nodes.data.structure.StructureData node (not stored yet!) with the coordinates of the given step, identified by its index. If you know only the step value, use the get_index_from_stepid() method to get the corresponding index.

Note

The periodic boundary conditions are always set to True.

New in version 0.7: Renamed from step_to_structure

Parameters:

index – The index of the step that you want to retrieve, from 0 to self.numsteps- 1.
custom_kinds – (Optional) If passed must be a list of aiida.orm.nodes.data.structure.Kind objects. There must be one kind object for each different string in the symbols array, with kind.name set to this string. If this parameter is omitted, the automatic kind generation of AiiDA aiida.orm.nodes.data.structure.StructureData nodes is used, meaning that the strings in the symbols array must be valid chemical symbols.

Returns:

aiida.orm.nodes.data.structure.StructureData node.

get_stepids()[source]#

Return the array of steps, if it has already been set.

New in version 0.7: Renamed from get_steps

Raises:: KeyError – if the trajectory has not been set yet.

get_structure(store=False, **kwargs)[source]#

Creates aiida.orm.nodes.data.structure.StructureData.

New in version 1.0: Renamed from _get_aiida_structure

Parameters:

store – If True, intermediate calculation gets stored in the AiiDA database for record. Default False.
index – The index of the step that you want to retrieve, from 0 to self.numsteps- 1.
custom_kinds – (Optional) If passed must be a list of aiida.orm.nodes.data.structure.Kind objects. There must be one kind object for each different string in the symbols array, with kind.name set to this string. If this parameter is omitted, the automatic kind generation of AiiDA aiida.orm.nodes.data.structure.StructureData nodes is used, meaning that the strings in the symbols array must be valid chemical symbols.
custom_cell – (Optional) The cell matrix of the structure. If omitted, the cell will be read from the trajectory, if present, otherwise the default cell of aiida.orm.nodes.data.structure.StructureData will be used.

Returns:

aiida.orm.nodes.data.structure.StructureData node.

get_times()[source]#

Return the array of times (in ps), if it has already been set.

Raises:: KeyError – if the trajectory has not been set yet.

get_velocities()[source]#: Return the array of velocities, if it has already been set.

Note

This function (differently from all other get_* functions, will not raise an exception if the velocities are not set, but rather return None (both if no trajectory was not set yet, and if it the trajectory was set but no velocities were specified).

property numsites#: Return the number of stored sites, or zero if nothing has been stored yet.

property numsteps#: Return the number of stored steps, or zero if nothing has been stored yet.

set_structurelist(structurelist)[source]#

Create trajectory from the list of aiida.orm.nodes.data.structure.StructureData instances.

Parameters:: structurelist – a list of aiida.orm.nodes.data.structure.StructureData instances.
Raises:: ValueError – if symbol lists of supplied structures are different

set_trajectory(symbols, positions, stepids=None, cells=None, times=None, velocities=None)[source]#

Store the whole trajectory, after checking that types and dimensions are correct.

Parameters stepids, cells and velocities are optional variables. If nothing is passed for cells or velocities nothing will be stored. However, if no input is given for stepids a consecutive sequence [0,1,2,…,len(positions)-1] will be assumed.

Parameters:

symbols – string list with dimension n, where n is the number of atoms (i.e., sites) in the structure. The same list is used for each step. Normally, the string should be a valid chemical symbol, but actually any unique string works and can be used as the name of the atomic kind (see also the get_step_structure() method).
positions – float array with dimension \(s \times n \times 3\), where s is the length of the stepids array and n is the length of the symbols array. Units are angstrom. In particular, positions[i,j,k] is the k-th component of the j-th atom (or site) in the structure at the time step with index i (identified by step number step[i] and with timestamp times[i]).
stepids – integer array with dimension s, where s is the number of steps. Typically represents an internal counter within the code. For instance, if you want to store a trajectory with one step every 10, starting from step 65, the array will be [65,75,85,...]. No checks are done on duplicate elements or on the ordering, but anyway this array should be sorted in ascending order, without duplicate elements. (If not specified, stepids will be set to numpy.arange(s) by default) It is internally stored as an array named ‘steps’.
cells – if specified float array with dimension \(s \times 3 \times 3\), where s is the length of the stepids array. Units are angstrom. In particular, cells[i,j,k] is the k-th component of the j-th cell vector at the time step with index i (identified by step number stepid[i] and with timestamp times[i]).
times – if specified, float array with dimension s, where s is the length of the stepids array. Contains the timestamp of each step in picoseconds (ps).
velocities – if specified, must be a float array with the same dimensions of the positions array. The array contains the velocities in the atoms.

show_mpl_heatmap(**kwargs)[source]#: Show a heatmap of the trajectory with matplotlib.

show_mpl_pos(**kwargs)[source]#

Shows the positions as a function of time, separate for XYZ coordinates

Parameters:

stepsize (int) – The stepsize for the trajectory, set higher than 1 to reduce number of points
mintime (int) – Time to start from
maxtime (int) – Maximum time
elements (list) – A list of atomic symbols that should be displayed. If not specified, all atoms are displayed.
indices (list) – A list of indices of that atoms that can be displayed. If not specified, all atoms of the correct species are displayed.
dont_block (bool) – If True, interpreter is not blocked when figure is displayed.

property symbols#

Return the array of symbols, if it has already been set.

Raises:: KeyError – if the trajectory has not been set yet.

aiida.orm.nodes.data.array.trajectory.plot_positions_XYZ(times, positions, indices_to_show, color_list, label, positions_unit='A', times_unit='ps', dont_block=False, mintime=None, maxtime=None, n_labels=10)[source]#

Plot with matplotlib the positions of the coordinates of the atoms over time for a trajectory

Parameters:

times – array of times
positions – array of positions
indices_to_show – list of indices of to show (0, 1, 2 for X, Y, Z)
color_list – list of valid color specifications for matplotlib
label – label for this plot to put in the title
positions_unit – label for the units of positions (for the x label)
times_unit – label for the units of times (for the y label)
dont_block – passed to plt.show() as block=not dont_block
mintime – if specified, cut the time axis at the specified min value
maxtime – if specified, cut the time axis at the specified max value
n_labels – how many labels (t, coord) to put

This module defines the classes related to Xy data. That is data that contains collections of y-arrays bound to a single x-array, and the methods to operate on them.

Bases: ArrayData

A subclass designed to handle arrays that have an “XY” relationship to each other. That is there is one array, the X array, and there are several Y arrays, which can be considered functions of X.

__abstractmethods__ = frozenset({})#

Construct a new instance, optionally setting the x and y arrays.

Note

If the x_array is specified, all other keywords need to be specified as well.

Parameters:

x_array – The x array.
y_arrays – The y arrays.
x_name – The name of the x array.
x_units – The unit of the x array.
y_names – The names of the y arrays.
y_units – The units of the y arrays.

__module__ = 'aiida.orm.nodes.data.array.xy'#

__parameters__ = ()#

__plugin_type_string: ClassVar[str]#

__query_type_string: ClassVar[str]#

_abc_impl = <_abc._abc_data object>#

static _arrayandname_validator(array: ndarray, name: str, units: str) → None[source]#: Validates that the array is an numpy.ndarray and that the name is of type str. Raises TypeError or ValueError if this not the case.

_cached_arrays: dict[str, ndarray]#

_logger: Logger | None = <Logger aiida.orm.nodes.data.array.xy.XyData (REPORT)>#

get_x() → tuple[str, ndarray, str][source]#: Tries to retrieve the x array and x name raises a NotExistent exception if no x array has been set yet. :return x_name: the name set for the x_array :return x_array: the x array set earlier :return x_units: the x units set earlier

get_y() → list[tuple[str, ndarray, str]][source]#: Tries to retrieve the y arrays and the y names, raises a NotExistent exception if they have not been set yet, or cannot be retrieved :return y_names: list of strings naming the y_arrays :return y_arrays: list of y_arrays :return y_units: list of strings giving the units for the y_arrays

set_x(x_array: ndarray, x_name: str, x_units: str) → None[source]#

Sets the array and the name for the x values.

Parameters:

x_array – A numpy.ndarray, containing only floats
x_name – a string for the x array name
x_units – the units of x

set_y(y_arrays: 'ndarray' | Sequence['ndarray'], y_names: str | Sequence[str], y_units: str | Sequence[str]) → None[source]#: Set array(s) for the y part of the dataset. Also checks if the x_array has already been set, and that, the shape of the y_arrays agree with the x_array. :param y_arrays: A list of y_arrays, numpy.ndarray :param y_names: A list of strings giving the names of the y_arrays :param y_units: A list of strings giving the units of the y_arrays

aiida.orm.nodes.data.array.xy.check_convert_single_to_tuple(item: Any | Sequence[Any]) → Sequence[Any][source]#

Checks if the item is a list or tuple, and converts it to a list if it is not already a list or tuple

Parameters:: item – an object which may or may not be a list or tuple
Returns:: item_list: the input item unchanged if list or tuple and [item] otherwise