aiida.orm.nodes.data package

Module with Node sub classes for data structures.

class aiida.orm.nodes.data.Data(backend=None, user=None, computer=None, **kwargs)

Bases: aiida.orm.nodes.node.Node

This class is base class for all data objects.

Specifications of the Data class: AiiDA Data objects are subclasses of Node and should have

Multiple inheritance must be supported, i.e. Data should have methods for querying and be able to inherit other library objects such as ASE for structures.

Architecture note: The code plugin is responsible for converting a raw data object produced by code to AiiDA standard object format. The data object then validates itself according to its method. This is done independently in order to allow cross-validation of plugins.

__abstractmethods__ = frozenset([])

__copy__()

Copying a Data node is not supported, use copy.deepcopy or call Data.clone().

__deepcopy__(memo)

Create a clone of the Data node by pipiong through to the clone method and return the result.

Returns:an unstored clone of this Data node

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

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_export_format_replacements = {}

_exportcontent(fileformat, main_file_name='', **kwargs)

Converts a Data node to one (or multiple) files.

Note: Export plugins should return utf8-encoded bytes, which can be directly dumped to file.

Parameters:

• fileformat (str) – the extension, uniquely specifying the file format.
• main_file_name (str) – (empty by default) Can be used by plugin to infer sensible names for additional files, if necessary. E.g. if the main file is ‘../myplot.gnu’, the plugin may decide to store the dat file under ‘../myplot_data.dat’.
• kwargs – other parameters are passed down to the plugin

Returns:

a tuple of length 2. The first element is the content of the otuput file. The second is a dictionary (possibly empty) in the format {filename: filecontent} for any additional file that should be produced.

Return type:

(bytes, dict)

_get_converters()

Get all implemented converter formats. The convention is to find all _get_object_… methods. Returns a list of strings.

_get_exporters()

Get all implemented export formats. The convention is to find all _prepare_… methods. Returns a dictionary of method_name: method_function

_get_importers()

Get all implemented import formats. The convention is to find all _parse_… methods. Returns a list of strings.

_logger = <logging.Logger object>

_plugin_type_string = 'data.data.Data.'

_query_type_string = 'data.data.'

_source_attributes = ['db_name', 'db_uri', 'uri', 'id', 'version', 'extras', 'source_md5', 'description', 'license']

_storable = True

_unstorable_message = 'storing for this node has been disabled'

_validate()

Perform validation of the Data object.

Note

validation of data source checks license and requires attribution to be provided in field ‘description’ of source in the case of any CC-BY* license. If such requirement is too strict, one can remove/comment it out.

clone()

Create a clone of the Data node.

Returns:an unstored clone of this Data node

convert(object_format=None, *args)

Convert the AiiDA StructureData into another python object

Parameters:object_format – Specify the output format

creator

Return the creator of this node or None if it does not exist.

Returns:the creating node or None

export(path, fileformat=None, overwrite=False, **kwargs)

Save a Data object to a file.

Parameters:

• fname – string with file name. Can be an absolute or relative path.
• fileformat – kind of format to use for the export. If not present, it will try to use the extension of the file name.
• overwrite – if set to True, overwrites file found at path. Default=False
• kwargs – additional parameters to be passed to the _exportcontent method

Returns:

the list of files created

classmethod get_export_formats()

Get the list of valid export format strings

Returns:a list of valid formats

importfile(fname, fileformat=None)

Populate a Data object from a file.

Parameters:

• fname – string with file name. Can be an absolute or relative path.
• fileformat – kind of format to use for the export. If not present, it will try to use the extension of the file name.

importstring(inputstring, fileformat, **kwargs)

Converts a Data object to other text format.

Parameters:fileformat – a string (the extension) to describe the file format. Returns:a string with the structure description.

set_source(source)

Sets the dictionary describing the source of Data object.

source

Gets the dictionary describing the source of Data object. Possible fields:

• db_name: name of the source database.
• db_uri: URI of the source database.
• uri: URI of the object’s source. Should be a permanent link.
• id: object’s source identifier in the source database.
• version: version of the object’s source.
• extras: a dictionary with other fields for source description.
• source_md5: MD5 checksum of object’s source.
• description: human-readable free form description of the object’s source.
• license: a string with a type of license.

Note

some limitations for setting the data source exist, see _validate method.

Returns:dictionary describing the source of Data object.

class aiida.orm.nodes.data.BaseType(*args, **kwargs)

Bases: aiida.orm.nodes.data.data.Data

Data sub class to be used as a base for data containers that represent base python data types.

__abstractmethods__ = frozenset([])

__eq__(other)

x.__eq__(y) <==> x==y

__init__(*args, **kwargs)

Parameters:backend_entity (aiida.orm.implementation.BackendEntity) – the backend model supporting this entity

__module__ = 'aiida.orm.nodes.data.base'

__ne__(other)

x.__ne__(y) <==> x!=y

__str__() <==> str(x)

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_logger = <logging.Logger object>

_plugin_type_string = 'data.base.BaseType.'

_query_type_string = 'data.base.'

new(value=None)

value

class aiida.orm.nodes.data.ArrayData(backend=None, user=None, computer=None, **kwargs)

Bases: aiida.orm.nodes.data.data.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([])

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

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_arraynames_from_files()

Return a list of all arrays stored in the node, listing the files (and not relying on the properties).

_arraynames_from_properties()

Return a list of all arrays stored in the node, listing the attributes starting with the correct prefix.

_cached_arrays = None

_logger = <logging.Logger object>

_plugin_type_string = 'data.array.array.ArrayData.'

_query_type_string = 'data.array.array.'

_validate()

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()

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.

delete_array(name)

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)

Return an array stored in the node

Parameters:name – The name of the array to return.

get_arraynames()

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 that returns tuples (name, array) for each array stored in the node.

New in version 1.0: Renamed from iterarrays

get_shape(name)

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()

Initialize internal variables for the backend node

This needs to be called explicitly in each specific subclass implementation of the init.

iterarrays()

Iterator that returns tuples (name, array) for each array stored in the node.

set_array(name, array)

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.

class aiida.orm.nodes.data.BandsData(*args, **kwargs)

Bases: aiida.orm.nodes.data.array.kpoints.KpointsData

Class to handle bands data

__abstractmethods__ = frozenset([])

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

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_custom_export_format_replacements = {'dat': 'dat_multicolumn', 'gnu': 'gnuplot', 'pdf': 'mpl_pdf', 'png': 'mpl_png', 'py': 'mpl_singlefile'}

_get_band_segments(cartesian)

_get_bandplot_data(cartesian, prettify_format=None, join_symbol=None, get_segments=False, y_origin=0.0)

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).

_logger = <logging.Logger object>

_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)

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

_plugin_type_string = 'data.array.bands.BandsData.'

_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)

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)

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_1(*args, **kwargs)

Output data in .dat format, using multiple columns for all y values associated to the same x.

Deprecated since version 0.8.1: Use ‘dat_multicolumn’ format instead

_prepare_dat_2(*args, **kwargs)

Output data in .dat format, using blocks.

Deprecated since version 0.8.1: Use ‘dat_block’ format instead

_prepare_dat_blocks(main_file_name='', comments=True)

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)

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='', title='', comments=True, prettify_format=None, y_max_lim=None, y_min_lim=None, y_origin=0.0)

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)

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)

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)

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)

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)

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()

_query_type_string = 'data.array.bands.'

_set_pbc(value)

validate the pbc, then store them

_validate_bands_occupations(bands, occupations=None, labels=None)

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.

array_labels

Get the labels associated with the band arrays

get_bands(also_occupations=False, also_labels=False)

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)

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)

Load the kpoints from a kpoint object. :param kpointsdata: an instance of KpointsData class

show_mpl(**kwargs)

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.

units

Units in which the data in bands were stored. A string

class aiida.orm.nodes.data.KpointsData(*args, **kwargs)

Bases: aiida.orm.nodes.data.array.array.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([])

__init__(*args, **kwargs)

Parameters:backend_entity (aiida.orm.implementation.BackendEntity) – the backend model supporting this entity

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

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_change_reference(kpoints, to_cartesian=True)

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

_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

_find_bravais_info(epsilon_length=1e-05, epsilon_angle=1e-05)

Finds the Bravais lattice of the cell passed in input to the Kpoint class :note: We assume that the cell given by the cell property is the primitive unit cell.

Deprecated since version 0.11: Use the methods inside the aiida.tools.data.array.kpoints module instead.

Returns:a dictionary, with keys short_name, extended_name, index (index of the Bravais lattice), and sometimes variation (name of the variation of the Bravais lattice) and extra (a dictionary with extra parameters used by the get_special_points method)

_get_or_create_bravais_lattice(epsilon_length=1e-05, epsilon_angle=1e-05)

Try to get the bravais_lattice info if stored already, otherwise analyze the cell with the default settings and save this in the attribute.

Deprecated since version 0.11: Use the methods inside the aiida.tools.data.array.kpoints module instead.

Parameters:

• epsilon_length – threshold on lengths comparison, used to get the bravais lattice info
• epsilon_angle – threshold on angles comparison, used to get the bravais lattice info

Return bravais_lattice:  

the dictionary containing the symmetry info

_logger = <logging.Logger object>

_plugin_type_string = 'data.array.kpoints.KpointsData.'

_query_type_string = 'data.array.kpoints.'

_set_bravais_lattice(value)

Validating function to set the bravais_lattice dictionary

Deprecated since version 0.11.

_set_cell(value)

Validate if ‘value’ is a allowed crystal unit cell :param value: something compatible with a 3x3 tuple of floats

_set_labels(value)

set label names. Must pass in input a list like: [[0,'X'],[34,'L'],... ]

_set_pbc(value)

validate the pbc, then store them

_set_reciprocal_cell()

Sets the reciprocal cell in units of 1/Angstrom from the internally set cell

_validate_kpoints_weights(kpoints, weights)

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

bravais_lattice

The dictionary containing informations about the cell symmetry

Deprecated since version 0.11.

cell

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

find_bravais_lattice(epsilon_length=1e-05, epsilon_angle=1e-05)

Analyze the symmetry of the cell. Allows to relax or tighten the thresholds used to compare angles and lengths of the cell. Save the information of the cell used for later use (like getting special points). It has to be used if the user wants to be sure the right symmetries are recognized. Otherwise, this function is automatically called with the default values.

If the right symmetry is not found, be sure also you are providing cells with enough digits.

If node is already stored, just returns the symmetry found before storing (if any).

Deprecated since version 0.11: Use the methods inside the aiida.tools.data.array.kpoints module instead.

Return (str) lattice_name:  the name of the bravais lattice and its eventual variation

get_description()

Returns a string with infos retrieved from kpoints node’s properties. :param node: :return: retstr

get_kpoints(also_weights=False, cartesian=False)

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)

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()

get_special_points(cartesian=False, epsilon_length=1e-05, epsilon_angle=1e-05)

Get the special point and path of a given structure.

References:

• In 2D, coordinates are based on the paper: R. Ramirez and M. C. Bohm, Int. J. Quant. Chem., XXX, pp. 391-411 (1986)
• In 3D, coordinates are based on the paper: W. Setyawan, S. Curtarolo, Comp. Mat. Sci. 49, 299 (2010)

Deprecated since version 0.11: Use the methods inside the aiida.tools.data.array.kpoints module instead.

Parameters:

• cartesian – If true, returns points in cartesian coordinates. Crystal coordinates otherwise. Default=False
• epsilon_length – threshold on lengths comparison, used to get the bravais lattice info
• epsilon_angle – threshold on angles comparison, used to get the bravais lattice info

Returns point_coords:  

a dictionary of point_name:point_coords key,values.

Returns path:

the suggested path which goes through all high symmetry lines. A list of lists for all path segments. e.g. [(‘G’,’X’),(‘X’,’M’),…] It’s not necessarily a continuous line.

Note:

We assume that the cell given by the cell property is the primitive unit cell

labels

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

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)

set_cell(cell, pbc=None)

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)

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)

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=[0.0, 0.0, 0.0])

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=[0.0, 0.0, 0.0], force_parity=False)

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 where 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.

set_kpoints_path(value=None, kpoint_distance=None, cartesian=False, epsilon_length=1e-05, epsilon_angle=1e-05)

Set a path of kpoints in the Brillouin zone.

Deprecated since version 0.11: Use the methods inside the aiida.tools.data.array.kpoints module instead.

Parameters:

• value –

  description of the path, in various possible formats.

  None: automatically sets all irreducible high symmetry paths. Requires that a cell was set

  or

  [(‘G’,’M’), (…), …] [(‘G’,’M’,30), (…), …] [(‘G’,(0,0,0),’M’,(1,1,1)), (…), …] [(‘G’,(0,0,0),’M’,(1,1,1),30), (…), …]

• cartesian (bool) – if set to true, reads the coordinates eventually passed in value as cartesian coordinates. Default: False.
• kpoint_distance (float) – parameter controlling the distance between kpoints. Distance is given in crystal coordinates, i.e. the distance is computed in the space of b1,b2,b3. The distance set will be the closest possible to this value, compatible with the requirement of putting equispaced points between two special points (since extrema are included).
• epsilon_length (float) – threshold on lengths comparison, used to get the bravais lattice info. It has to be used if the user wants to be sure the right symmetries are recognized.
• epsilon_angle (float) – threshold on angles comparison, used to get the bravais lattice info. It has to be used if the user wants to be sure the right symmetries are recognized.

class aiida.orm.nodes.data.ProjectionData(backend=None, user=None, computer=None, **kwargs)

Bases: aiida.orm.nodes.data.orbital.OrbitalData, aiida.orm.nodes.data.array.array.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'

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_check_projections_bands(projection_array)

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)

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

_from_index_to_arrayname(index)

Used internally to determine the array names.

_logger = <logging.Logger object>

_plugin_type_string = 'data.array.projection.ProjectionData.'

_query_type_string = 'data.array.projection.'

get_pdos(**kwargs)

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)

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()

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)

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)

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)

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

class aiida.orm.nodes.data.TrajectoryData(structurelist=None, **kwargs)

Bases: aiida.orm.nodes.data.array.array.ArrayData

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

__abstractmethods__ = frozenset([])

__init__(structurelist=None, **kwargs)

Parameters:backend_entity (aiida.orm.implementation.BackendEntity) – the backend model supporting this entity

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

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_get_aiida_structure(store=False, **kwargs)

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

Parameters:

• converter – specify the converter. Default ‘ase’.
• store – If True, intermediate calculation gets stored in the AiiDA database for record. Default False.

Returns:

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

_get_cif(index=None, **kwargs)

Creates aiida.orm.nodes.data.cif.CifData

_internal_validate(stepids, cells, symbols, positions, times, velocities)

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 = <logging.Logger object>

_parse_xyz_pos(inputstring)

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)

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()

_plugin_type_string = 'data.array.trajectory.TrajectoryData.'

_prepare_cif(trajectory_index=None, main_file_name='')

Write the given trajectory to a string of format CIF.

_prepare_tcod(main_file_name='', **kwargs)

Write the given trajectory to a string of format TCOD CIF.

_prepare_xsf(index=None, main_file_name='')

Write the given trajectory to a string of format XSF (for XCrySDen).

_query_type_string = 'data.array.trajectory.'

_validate()

Verify that the required arrays are present and that their type and dimension are correct.

get_cells()

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)

Creates aiida.orm.nodes.data.cif.CifData

New in version 1.0: Renamed from _get_cif

get_index_from_stepid(stepid)

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()

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)

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 were specified, None is returned as the last 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 matrix, symbols is an array of length n, positions is a array, and velocities is either None or a 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)

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.

get_stepids()

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)

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

New in version 1.0: Renamed from _get_aiida_structure

Parameters:

• converter – specify the converter. Default ‘ase’.
• store – If True, intermediate calculation gets stored in the AiiDA database for record. Default False.

Returns:

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

get_times()

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()

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).

numsites

Return the number of stored sites, or zero if nothing has been stored yet.

numsteps

Return the number of stored steps, or zero if nothing has been stored yet.

set_structurelist(structurelist)

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)

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 , 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 , 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.

Todo

Choose suitable units for velocities

show_mpl_heatmap(**kwargs)

Show a heatmap of the trajectory with matplotlib.

show_mpl_pos(**kwargs)

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.

symbols

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

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

class aiida.orm.nodes.data.XyData(backend=None, user=None, computer=None, **kwargs)

Bases: aiida.orm.nodes.data.array.array.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([])

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

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_arrayandname_validator(array, name, units)

Validates that the array is an numpy.ndarray and that the name is of type basestring. Raises InputValidationError if this not the case.

_logger = <logging.Logger object>

_plugin_type_string = 'data.array.xy.XyData.'

_query_type_string = 'data.array.xy.'

get_x()

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()

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, x_name, x_units)

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, y_names, y_units)

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

class aiida.orm.nodes.data.Bool(*args, **kwargs)

Bases: aiida.orm.nodes.data.base.BaseType

Data sub class to represent a boolean value.

__abstractmethods__ = frozenset([])

__bool__()

__int__()

__module__ = 'aiida.orm.nodes.data.bool'

__nonzero__()

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_logger = <logging.Logger object>

_plugin_type_string = 'data.bool.Bool.'

_query_type_string = 'data.bool.'

_type

alias of __builtin__.bool

class aiida.orm.nodes.data.CifData(ase=None, filepath=None, values=None, source=None, scan_type='standard', parse_policy='eager', **kwargs)

Bases: aiida.orm.nodes.data.singlefile.SinglefileData

Wrapper for Crystallographic Interchange File (CIF)

Note

the filepath (physical) is held as the authoritative source of information, so all conversions are done through the physical file: when setting ase or values, a physical CIF file is generated first, the values are updated from the physical CIF file.

__abstractmethods__ = frozenset([])

__init__(ase=None, filepath=None, values=None, source=None, scan_type='standard', parse_policy='eager', **kwargs)

Parameters:backend_entity (aiida.orm.implementation.BackendEntity) – the backend model supporting this entity

__module__ = 'aiida.orm.nodes.data.cif'

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_ase = None

_get_aiida_structure(converter='pymatgen', store=False, **kwargs)

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

Parameters:

• converter – specify the converter. Default ‘pymatgen’.
• store – if True, intermediate calculation gets stored in the AiiDA database for record. Default False.
• primitive_cell – if True, primitive cell is returned, conventional cell if False. Default False.
• occupancy_tolerance – If total occupancy of a site is between 1 and occupancy_tolerance, the occupancies will be scaled down to 1. (pymatgen only)
• site_tolerance – This tolerance is used to determine if two sites are sitting in the same position, in which case they will be combined to a single disordered site. Defaults to 1e-4. (pymatgen only)

Returns:

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

_get_object_ase()

Converts CifData to ase.Atoms

Returns:an ase.Atoms object

_get_object_pycifrw()

Converts CifData to PyCIFRW.CifFile

Returns:a PyCIFRW.CifFile object

_logger = <logging.Logger object>

_parse_policies = ('eager', 'lazy')

_plugin_type_string = 'data.cif.CifData.'

_prepare_cif(main_file_name='')

Return CIF string of CifData object.

If parsed values are present, a CIF string is created and written to file. If no parsed values are present, the CIF string is read from file.

_prepare_tcod(main_file_name='', **kwargs)

Write the given CIF to a string of format TCOD CIF.

_query_type_string = 'data.cif.'

_scan_types = ('standard', 'flex')

_set_incompatibilities = [('ase', 'filepath'), ('ase', 'values'), ('filepath', 'values')]

_validate()

Validates MD5 hash of CIF file.

_values = None

ase

ASE object, representing the CIF.

Note

requires ASE module.

classmethod from_md5(md5)

Return a list of all CIF files that match a given MD5 hash.

Note

the hash has to be stored in a _md5 attribute, otherwise the CIF file will not be found.

generate_md5()

Computes and returns MD5 hash of the CIF file.

get_ase(**kwargs)

Returns ASE object, representing the CIF. This function differs from the property ase by the possibility to pass the keyworded arguments (kwargs) to ase.io.cif.read_cif().

Note

requires ASE module.

get_formulae(mode='sum')

Return chemical formulae specified in CIF file.

Note: This does not compute the formula, it only reads it from the appropriate tag. Use refine_inline to compute formulae.

classmethod get_or_create(filename, use_first=False, store_cif=True)

Pass the same parameter of the init; if a file with the same md5 is found, that CifData is returned.

Parameters:

• filename – an absolute filename on disk
• use_first – if False (default), raise an exception if more than one CIF file is found. If it is True, instead, use the first available CIF file.
• store_cif (bool) – If false, the CifData objects are not stored in the database. default=True.

Return (cif, created):  

where cif is the CifData object, and create is either True if the object was created, or False if the object was retrieved from the DB.

get_spacegroup_numbers()

Get the spacegroup international number.

get_structure(converter='pymatgen', store=False, **kwargs)

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

New in version 1.0: Renamed from _get_aiida_structure

Parameters:

• converter – specify the converter. Default ‘pymatgen’.
• store – if True, intermediate calculation gets stored in the AiiDA database for record. Default False.
• primitive_cell – if True, primitive cell is returned, conventional cell if False. Default False.
• occupancy_tolerance – If total occupancy of a site is between 1 and occupancy_tolerance, the occupancies will be scaled down to 1. (pymatgen only)
• site_tolerance – This tolerance is used to determine if two sites are sitting in the same position, in which case they will be combined to a single disordered site. Defaults to 1e-4. (pymatgen only)

Returns:

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

has_atomic_sites

Returns whether there are any atomic sites defined in the cif data. That is to say, it will check all the values for the _atom_site_fract_* tags and if they are all equal to ? that means there are no relevant atomic sites defined and the function will return False. In all other cases the function will return True

Returns:False when at least one atomic site fractional coordinate is not equal to ? and True otherwise

has_attached_hydrogens

Check if there are hydrogens without coordinates, specified as attached to the atoms of the structure.

Returns:True if there are attached hydrogens, False otherwise.

has_partial_occupancies

Return if the cif data contains partial occupancies

A partial occupancy is defined as site with an occupancy that differs from unity, within a precision of 1E-6

Returns:True if there are partial occupancies, False otherwise

has_undefined_atomic_sites

Return whether the cif data contains any undefined atomic sites.

An undefined atomic site is defined as a site where at least one of the fractional coordinates specified in the _atom_site_fract_* tags, cannot be successfully interpreted as a float. If the cif data contains any site that matches this description, or it does not contain any atomic site tags at all, the cif data is said to have undefined atomic sites.

Returns:boolean, True if no atomic sites are defined or if any of the defined sites contain undefined positions and False otherwise

has_unknown_species

Returns whether the cif contains atomic species that are not recognized by AiiDA.

The known species are taken from the elements dictionary in aiida.common.constants, with the exception of the “unknown” placeholder element with symbol ‘X’, as this could not be used to construct a real structure. If any of the formula of the cif data contain species that are not in that elements dictionary, the function will return True and False in all other cases. If there is no formulae to be found, it will return None

Returns:True when there are unknown species in any of the formulae, False if not, None if no formula found

parse(scan_type=None)

Parses CIF file and sets attributes.

Parameters:scan_type – See set_scan_type

put_object_from_file(path, key=None, mode='w', encoding='utf8', force=False)

Set the file.

If the source is set and the MD5 checksum of new file is different from the source, the source has to be deleted.

static read_cif(fileobj, index=-1, **kwargs)

A wrapper method that simulates the behavior of the old function ase.io.cif.read_cif by using the new generic ase.io.read function.

set_ase(aseatoms)

Set the contents of the CifData starting from an ASE atoms object

Parameters:aseatoms – the ASE atoms object

set_parse_policy(parse_policy)

Set the parse policy.

Parameters:parse_policy – Either ‘eager’ (parse CIF file on set_file) or ‘lazy’ (defer parsing until needed)

set_scan_type(scan_type)

Set the scan_type for PyCifRW.

The ‘flex’ scan_type of PyCifRW is faster for large CIF files but does not yet support the CIF2 format as of 02/2018. See the CifFile.ReadCif function

Parameters:scan_type – Either ‘standard’ or ‘flex’ (see _scan_types)

set_values(values)

Set internal representation to values.

Warning: This also writes a new CIF file.

Parameters:values – PyCifRW CifFile object

Note

requires PyCifRW module.

store(*args, **kwargs)

Store the node.

values

PyCifRW structure, representing the CIF datablocks.

Note

requires PyCifRW module.

class aiida.orm.nodes.data.Code(remote_computer_exec=None, local_executable=None, input_plugin_name=None, files=None, **kwargs)

Bases: aiida.orm.nodes.data.data.Data

A code entity. It can either be ‘local’, or ‘remote’.

• Local code: it is a collection of files/dirs (added using the add_path() method), where one file is flagged as executable (using the set_local_executable() method).
• Remote code: it is a pair (remotecomputer, remotepath_of_executable) set using the set_remote_computer_exec() method.

For both codes, one can set some code to be executed right before or right after the execution of the code, using the set_preexec_code() and set_postexec_code() methods (e.g., the set_preexec_code() can be used to load specific modules required for the code to be run).

HIDDEN_KEY = 'hidden'

__abstractmethods__ = frozenset([])

__init__(remote_computer_exec=None, local_executable=None, input_plugin_name=None, files=None, **kwargs)

Parameters:backend_entity (aiida.orm.implementation.BackendEntity) – the backend model supporting this entity

__module__ = 'aiida.orm.nodes.data.code'

__str__() <==> str(x)

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_logger = <logging.Logger object>

_plugin_type_string = 'data.code.Code.'

_query_type_string = 'data.code.'

_set_local()

Set the code as a ‘local’ code, meaning that all the files belonging to the code will be copied to the cluster, and the file set with set_exec_filename will be run.

It also deletes the flags related to the local case (if any)

_set_remote()

Set the code as a ‘remote’ code, meaning that the code itself has no files attached, but only a location on a remote computer (with an absolute path of the executable on the remote computer).

It also deletes the flags related to the local case (if any)

_validate()

Perform validation of the Data object.

Note

validation of data source checks license and requires attribution to be provided in field ‘description’ of source in the case of any CC-BY* license. If such requirement is too strict, one can remove/comment it out.

can_run_on(computer)

Return True if this code can run on the given computer, False otherwise.

Local codes can run on any machine; remote codes can run only on the machine on which they reside.

TODO: add filters to mask the remote machines on which a local code can run.

full_label

Get full label of this code.

Returns label of the form <code-label>@<computer-name>.

full_text_info(verbose=False)

Return a (multiline) string with a human-readable detailed information on this computer

classmethod get(pk=None, label=None, machinename=None)

Get a Computer object with given identifier string, that can either be the numeric ID (pk), or the label (and computername) (if unique).

Parameters:

• pk – the numeric ID (pk) for code
• label – the code label identifying the code to load
• machinename – the machine name where code is setup

Raises:

• aiida.common.NotExistent – if no code identified by the given string is found
• aiida.common.MultipleObjectsError – if the string cannot identify uniquely a code
• aiida.common.InputValidationError – if neither a pk nor a label was passed in

get_append_text()

Return the postexec_code, or an empty string if no post-exec code was defined.

get_builder()

Create and return a new ProcessBuilder for the default Calculation plugin, as obtained by the self.get_input_plugin_name() method.

Note:

it also sets the builder.code value.

Raises:

• aiida.common.MissingPluginError – if the specified plugin does not exist.
• ValueError – if no default plugin was specified.

Returns:

classmethod get_code_helper(label, machinename=None)

Parameters:

• label – the code label identifying the code to load
• machinename – the machine name where code is setup

Raises:

• aiida.common.NotExistent – if no code identified by the given string is found
• aiida.common.MultipleObjectsError – if the string cannot identify uniquely a code

get_computer_name()

Get name of this code’s computer.

get_description()

Return a string description of this Code instance.

Returns:string description of this Code instance

get_execname()

Return the executable string to be put in the script. For local codes, it is ./LOCAL_EXECUTABLE_NAME For remote codes, it is the absolute path to the executable.

classmethod get_from_string(code_string)

Get a Computer object with given identifier string in the format label@machinename. See the note below for details on the string detection algorithm.

Note

the (leftmost) ‘@’ symbol is always used to split code and computername. Therefore do not use ‘@’ in the code name if you want to use this function (‘@’ in the computer name are instead valid).

Parameters:

code_string – the code string identifying the code to load

Raises:

• aiida.common.NotExistent – if no code identified by the given string is found
• aiida.common.MultipleObjectsError – if the string cannot identify uniquely a code
• aiida.common.InputValidationError – if code_string is not of string type

get_full_text_info(verbose=False)

Return a (multiline) string with a human-readable detailed information on this computer

get_input_plugin_name()

Return the name of the default input plugin (or None if no input plugin was set.

get_local_executable()

get_prepend_text()

Return the code that will be put in the scheduler script before the execution, or an empty string if no pre-exec code was defined.

get_remote_computer()

get_remote_exec_path()

hidden

Determines whether the Code is hidden or not

hide()

Hide the code (prevents from showing it in the verdi code list)

is_local()

Return True if the code is ‘local’, False if it is ‘remote’ (see also documentation of the set_local and set_remote functions).

classmethod list_for_plugin(plugin, labels=True)

Return a list of valid code strings for a given plugin.

Parameters:

• plugin – The string of the plugin.
• labels – if True, return a list of code names, otherwise return the code PKs (integers).

Returns:

a list of string, with the code names if labels is True, otherwise a list of integers with the code PKs.

relabel(new_label, raise_error=True)

Relabel this code.

Parameters:

• new_label – new code label
• raise_error – Set to False in order to return a list of errors instead of raising them.

reveal()

Reveal the code (allows to show it in the verdi code list) By default, it is revealed

set_append_text(code)

Pass a string of code that will be put in the scheduler script after the execution of the code.

set_files(files)

Given a list of filenames (or a single filename string), add it to the path (all at level zero, i.e. without folders). Therefore, be careful for files with the same name!

Todo:decide whether to check if the Code must be a local executable to be able to call this function.

set_input_plugin_name(input_plugin)

Set the name of the default input plugin, to be used for the automatic generation of a new calculation.

set_local_executable(exec_name)

Set the filename of the local executable. Implicitly set the code as local.

set_prepend_text(code)

Pass a string of code that will be put in the scheduler script before the execution of the code.

set_remote_computer_exec(remote_computer_exec)

Set the code as remote, and pass the computer on which it resides and the absolute path on that computer.

Parameters:remote_computer_exec – a tuple (computer, remote_exec_path), where computer is a aiida.orm.Computer and remote_exec_path is the absolute path of the main executable on remote computer.

classmethod setup(**kwargs)

class aiida.orm.nodes.data.Float(*args, **kwargs)

Bases: aiida.orm.nodes.data.numeric.NumericType

Data sub class to represent a float value.

__abstractmethods__ = frozenset([])

__module__ = 'aiida.orm.nodes.data.float'

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_logger = <logging.Logger object>

_plugin_type_string = 'data.float.Float.'

_query_type_string = 'data.float.'

_type

alias of __builtin__.float

class aiida.orm.nodes.data.FolderData(backend=None, user=None, computer=None, **kwargs)

Bases: aiida.orm.nodes.data.data.Data

Data sub class to represent a folder on a file system.

__abstractmethods__ = frozenset([])

__module__ = 'aiida.orm.nodes.data.folder'

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_logger = <logging.Logger object>

_plugin_type_string = 'data.folder.FolderData.'

_query_type_string = 'data.folder.'

class aiida.orm.nodes.data.Int(*args, **kwargs)

Bases: aiida.orm.nodes.data.numeric.NumericType

Data sub class to represent an integer value.

__abstractmethods__ = frozenset([])

__module__ = 'aiida.orm.nodes.data.int'

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_logger = <logging.Logger object>

_plugin_type_string = 'data.int.Int.'

_query_type_string = 'data.int.'

_type

alias of __builtin__.int

class aiida.orm.nodes.data.List(**kwargs)

Bases: aiida.orm.nodes.data.data.Data, _abcoll.MutableSequence

Data sub class to represent a list.

_LIST_KEY = 'list'

__abstractmethods__ = frozenset([])

__delitem__(key)

__eq__(other)

x.__eq__(y) <==> x==y

__getitem__(item)

__init__(**kwargs)

Parameters:backend_entity (aiida.orm.implementation.BackendEntity) – the backend model supporting this entity

__len__()

__module__ = 'aiida.orm.nodes.data.list'

__ne__(other)

x.__ne__(y) <==> x!=y

__setitem__(key, value)

__str__() <==> str(x)

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 102

_abc_registry = <_weakrefset.WeakSet object>

_logger = <logging.Logger object>

_plugin_type_string = 'data.list.List.'

_query_type_string = 'data.list.'

_using_list_reference()

This function tells the class if we are using a list reference. This means that calls to self.get_list return a reference rather than a copy of the underlying list and therefore self.set_list need not be called. This knwoledge is essential to make sure this class is performant.

Currently the implementation assumes that if the node needs to be stored then it is using the attributes cache which is a reference.

Returns:True if using self.get_list returns a reference to the underlying sequence. False otherwise. Return type:bool

append(value)

S.append(object) – append object to the end of the sequence

count(value) → integer -- return number of occurrences of value

extend(value)

S.extend(iterable) – extend sequence by appending elements from the iterable

get_list()

Return the contents of this node.

Returns:a list

index(value) → integer -- return first index of value.

Raises ValueError if the value is not present.

insert(i, value)

S.insert(index, object) – insert object before index

pop([index]) → item -- remove and return item at index (default last).

Raise IndexError if list is empty or index is out of range.

remove(value)

S.remove(value) – remove first occurrence of value. Raise ValueError if the value is not present.

reverse()

S.reverse() – reverse IN PLACE

set_list(data)

Set the contents of this node.

Parameters:data – the list to set

sort(key=None, reverse=False)

class aiida.orm.nodes.data.OrbitalData(backend=None, user=None, computer=None, **kwargs)

Bases: aiida.orm.nodes.data.data.Data

Used for storing collections of orbitals, as well as providing methods for accessing them internally.

__abstractmethods__ = frozenset([])

__module__ = 'aiida.orm.nodes.data.orbital'

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_get_orbital_class_from_orbital_dict(orbital_dict)

Gets the orbital class from the orbital dictionary stored in DB

Parameters:orbital_dict – orbital dictionary associated with the orbital Returns:an Orbital produced using the module_name

_logger = <logging.Logger object>

_plugin_type_string = 'data.orbital.OrbitalData.'

static _prep_orbital_dict_keys_from_site(site)

Prepares the position from an input site.

Parameters:site – a site of site class Return out_dict:  a dictionary of attributes parsed from the site (currently only position)

_query_type_string = 'data.orbital.'

clear_orbitals()

Remove all orbitals that were added to the class Cannot work if OrbitalData has been already stored

get_orbitals(with_tags=False, **kwargs)

Returns all orbitals by default. If a site is provided, returns all orbitals cooresponding to the location of that site, additional arguments may be provided, which act as filters on the retrieved orbitals.

Parameters:

• site – if provided, returns all orbitals with position of site
• with_tags – if provided returns all tags stored

Kwargs:

attributes than can filter the set of returned orbitals

Return list_of_outputs:  

a list of orbitals and also tags if with_tags was set to True

set_orbitals(orbital, tag=None)

Sets the orbitals into the database. Uses the orbital’s inherent set_orbital_dict method to generate a orbital dict string at is stored along with the tags, if provided.

Parameters:

• orbital – an orbital or list of orbitals to be set
• tag – a list of strings must be of length orbital

class aiida.orm.nodes.data.Dict(**kwargs)

Bases: aiida.orm.nodes.data.data.Data

Data sub class to represent a dictionary.

__abstractmethods__ = frozenset([])

__init__(**kwargs)

Store a dictionary as a Node instance.

Usual rules for attribute names apply, in particular, keys cannot start with an underscore, or a ValueError will be raised.

Initial attributes can be changed, deleted or added as long as the node is not stored.

Parameters:dict – the dictionary to set

__module__ = 'aiida.orm.nodes.data.dict'

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_logger = <logging.Logger object>

_plugin_type_string = 'data.dict.Dict.'

_query_type_string = 'data.dict.'

dict

Return an instance of AttributeManager that transforms the dictionary into an attribute dict.

Note

this will allow one to do node.dict.key as well as node.dict[key].

Returns:an instance of the AttributeResultManager.

get_dict()

Return a dictionary with the parameters currently set.

Returns:dictionary

keys()

Iterator of valid keys stored in the Dict object.

Returns:iterator over the keys of the current dictionary

set_dict(dictionary)

Replace the current dictionary with another one.

Parameters:dictionary – dictionary to set

update_dict(dictionary)

Update the current dictionary with the keys provided in the dictionary.

Note

works exactly as dict.update() where new keys are simply added and existing keys are overwritten.

Parameters:dictionary – a dictionary with the keys to substitute

class aiida.orm.nodes.data.RemoteData(remote_path=None, **kwargs)

Bases: aiida.orm.nodes.data.data.Data

Store a link to a file or folder on a remote machine.

Remember to pass a computer!

__abstractmethods__ = frozenset([])

__init__(remote_path=None, **kwargs)

Parameters:backend_entity (aiida.orm.implementation.BackendEntity) – the backend model supporting this entity

__module__ = 'aiida.orm.nodes.data.remote'

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_clean()

Remove all content of the remote folder on the remote computer

_logger = <logging.Logger object>

_plugin_type_string = 'data.remote.RemoteData.'

_query_type_string = 'data.remote.'

_validate()

Perform validation of the Data object.

Note

validation of data source checks license and requires attribution to be provided in field ‘description’ of source in the case of any CC-BY* license. If such requirement is too strict, one can remove/comment it out.

get_authinfo()

get_computer_name()

get_remote_path()

getfile(relpath, destpath)

Connects to the remote folder and gets a string with the (full) content of the file.

Parameters:

• relpath – The relative path of the file to show.
• destpath – A path on the local computer to get the file

Returns:

a string with the file content

is_empty

Check if remote folder is empty

listdir(relpath='.')

Connects to the remote folder and lists the directory content.

Parameters:relpath – If ‘relpath’ is specified, lists the content of the given subfolder. Returns:a flat list of file/directory names (as strings).

listdir_withattributes(path='.')

Connects to the remote folder and lists the directory content.

Parameters:relpath – If ‘relpath’ is specified, lists the content of the given subfolder. Returns:a list of dictionaries, where the documentation is in :py:class:Transport.listdir_withattributes.

set_remote_path(val)

class aiida.orm.nodes.data.SinglefileData(filepath, **kwargs)

Bases: aiida.orm.nodes.data.data.Data

Data class that can be used to store a single file in its repository.

__abstractmethods__ = frozenset([])

__init__(filepath, **kwargs)

Parameters:backend_entity (aiida.orm.implementation.BackendEntity) – the backend model supporting this entity

__module__ = 'aiida.orm.nodes.data.singlefile'

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_logger = <logging.Logger object>

_plugin_type_string = 'data.singlefile.SinglefileData.'

_query_type_string = 'data.singlefile.'

_validate()

Ensure that there is one object stored in the repository, whose key matches value set for filename attr.

filename

Return the name of the file stored.

Returns:the filename under which the file is stored in the repository

get_content()

Return the content of the single file stored for this data node.

Returns:the string content of the file

open(key=None, mode='r')

Return an open file handle to the content of this data node.

Parameters:

• key – optional key within the repository, by default is the filename set in the attributes
• mode – the mode with which to open the file handle

Returns:

a file handle in read mode

put_object_from_file(path, key=None, mode='w', encoding='utf8', force=False)

Add the file located at path on file system to repository, deleting any other existing objects.

set_file(filepath)

Add the file located at path on file system to repository, deleting any other existing objects.

class aiida.orm.nodes.data.Str(*args, **kwargs)

Bases: aiida.orm.nodes.data.base.BaseType

Data sub class to represent a string value.

__abstractmethods__ = frozenset([])

__module__ = 'aiida.orm.nodes.data.str'

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_logger = <logging.Logger object>

_plugin_type_string = 'data.str.Str.'

_query_type_string = 'data.str.'

_type

alias of __builtin__.str

class aiida.orm.nodes.data.StructureData(cell=None, pbc=None, ase=None, pymatgen=None, pymatgen_structure=None, pymatgen_molecule=None, **kwargs)

Bases: aiida.orm.nodes.data.data.Data

This class contains the information about a given structure, i.e. a collection of sites together with a cell, the boundary conditions (whether they are periodic or not) and other related useful information.

__abstractmethods__ = frozenset([])

__init__(cell=None, pbc=None, ase=None, pymatgen=None, pymatgen_structure=None, pymatgen_molecule=None, **kwargs)

Parameters:backend_entity (aiida.orm.implementation.BackendEntity) – the backend model supporting this entity

__module__ = 'aiida.orm.nodes.data.structure'

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_adjust_default_cell(vacuum_factor=1.0, vacuum_addition=10.0, pbc=(False, False, False))

If the structure was imported from an xyz file, it lacks a defined cell, and the default cell is taken ([[1,0,0], [0,1,0], [0,0,1]]), leading to an unphysical definition of the structure. This method will adjust the cell

_dimensionality_label = {0: '', 1: 'length', 2: 'surface', 3: 'volume'}

_get_cif(converter='ase', store=False, **kwargs)

Creates aiida.orm.nodes.data.cif.CifData.

Parameters:

• converter – specify the converter. Default ‘ase’.
• store – If True, intermediate calculation gets stored in the AiiDA database for record. Default False.

Returns:

aiida.orm.nodes.data.cif.CifData node.

_get_object_ase()

Converts StructureData to ase.Atoms

Returns:an ase.Atoms object

_get_object_phonopyatoms()

Converts StructureData to PhonopyAtoms

Returns:a PhonopyAtoms object

_get_object_pymatgen(**kwargs)

Converts StructureData to pymatgen object

Returns:a pymatgen Structure for structures with periodic boundary conditions (in three dimensions) and Molecule otherwise

Note

Requires the pymatgen module (version >= 3.0.13, usage of earlier versions may cause errors).

_get_object_pymatgen_molecule(**kwargs)

Converts StructureData to pymatgen Molecule object

Returns:a pymatgen Molecule object corresponding to this StructureData object.

Note

Requires the pymatgen module (version >= 3.0.13, usage of earlier versions may cause errors)

_get_object_pymatgen_structure(**kwargs)

Converts StructureData to pymatgen Structure object :param add_spin: True to add the spins to the pymatgen structure. Default is False (no spin added).

Note

The spins are set according to the following rule:

• if the kind name ends with 1 -> spin=+1
• if the kind name ends with 2 -> spin=-1

Returns:a pymatgen Structure object corresponding to this StructureData object Raises:ValueError – if periodic boundary conditions does not hold in at least one dimension of real space; if there are partial occupancies together with spins (defined by kind names ending with ‘1’ or ‘2’).

Note

Requires the pymatgen module (version >= 3.0.13, usage of earlier versions may cause errors)

_logger = <logging.Logger object>

_parse_xyz(inputstring)

Read the structure from a string of format XYZ.

_plugin_type_string = 'data.structure.StructureData.'

_prepare_chemdoodle(main_file_name='')

Write the given structure to a string of format required by ChemDoodle.

_prepare_cif(main_file_name='')

Write the given structure to a string of format CIF.

_prepare_tcod(main_file_name='', **kwargs)

Write the given structure to a string of format TCOD CIF.

_prepare_xsf(main_file_name='')

Write the given structure to a string of format XSF (for XCrySDen).

_prepare_xyz(main_file_name='')

Write the given structure to a string of format XYZ.

_query_type_string = 'data.structure.'

_set_incompatibilities = [('ase', 'cell'), ('ase', 'pbc'), ('ase', 'pymatgen'), ('ase', 'pymatgen_molecule'), ('ase', 'pymatgen_structure'), ('cell', 'pymatgen'), ('cell', 'pymatgen_molecule'), ('cell', 'pymatgen_structure'), ('pbc', 'pymatgen'), ('pbc', 'pymatgen_molecule'), ('pbc', 'pymatgen_structure'), ('pymatgen', 'pymatgen_molecule'), ('pymatgen', 'pymatgen_structure'), ('pymatgen_molecule', 'pymatgen_structure')]

_validate()

Performs some standard validation tests.

append_atom(**kwargs)

Append an atom to the Structure, taking care of creating the corresponding kind.

Parameters:

• ase – the ase Atom object from which we want to create a new atom (if present, this must be the only parameter)
• position – the position of the atom (three numbers in angstrom)
• symbols – passed to the constructor of the Kind object.
• weights – passed to the constructor of the Kind object.
• name – passed to the constructor of the Kind object. See also the note below.

Note

Note on the ‘name’ parameter (that is, the name of the kind):

• if specified, no checks are done on existing species. Simply, a new kind with that name is created. If there is a name clash, a check is done: if the kinds are identical, no error is issued; otherwise, an error is issued because you are trying to store two different kinds with the same name.
• if not specified, the name is automatically generated. Before adding the kind, a check is done. If other species with the same properties already exist, no new kinds are created, but the site is added to the existing (identical) kind. (Actually, the first kind that is encountered). Otherwise, the name is made unique first, by adding to the string containing the list of chemical symbols a number starting from 1, until an unique name is found

Note

checks of equality of species are done using the compare_with() method.

append_kind(kind)

Append a kind to the StructureData. It makes a copy of the kind.

Parameters:kind – the site to append, must be a Kind object.

append_site(site)

Append a site to the StructureData. It makes a copy of the site.

Parameters:site – the site to append. It must be a Site object.

cell

Returns the cell shape.

Returns:a 3x3 list of lists.

cell_angles

Get the angles between the cell lattice vectors in degrees.

cell_lengths

Get the lengths of cell lattice vectors in angstroms.

clear_kinds()

Removes all kinds for the StructureData object.

Note

Also clear all sites!

clear_sites()

Removes all sites for the StructureData object.

get_ase()

Get the ASE object. Requires to be able to import ase.

Returns:an ASE object corresponding to this StructureData object.

Note

If any site is an alloy or has vacancies, a ValueError is raised (from the site.get_ase() routine).

get_cell_volume()

Returns the cell volume in Angstrom^3.

Returns:a float.

get_cif(converter='ase', store=False, **kwargs)

Creates aiida.orm.nodes.data.cif.CifData.

New in version 1.0: Renamed from _get_cif

Parameters:

• converter – specify the converter. Default ‘ase’.
• store – If True, intermediate calculation gets stored in the AiiDA database for record. Default False.

Returns:

aiida.orm.nodes.data.cif.CifData node.

get_composition()

Returns the chemical composition of this structure as a dictionary, where each key is the kind symbol (e.g. H, Li, Ba), and each value is the number of occurences of that element in this structure. For BaZrO3 it would return {‘Ba’:1, ‘Zr’:1, ‘O’:3}. No reduction with smallest common divisor!

Returns:a dictionary with the composition

get_description()

Returns a string with infos retrieved from StructureData node’s properties

Parameters:self – the StructureData node Returns:retsrt: the description string

get_dimensionality()

This function checks the dimensionality of the structure and calculates its length/surface/volume :return: returns the dimensionality and length/surface/volume

get_formula(mode='hill', separator='')

Return a string with the chemical formula.

Parameters:

• mode –

  a string to specify how to generate the formula, can assume one of the following values:

  • ’hill’ (default): count the number of atoms of each species, then use Hill notation, i.e. alphabetical order with C and H first if one or several C atom(s) is (are) present, e.g. ['C','H','H','H','O','C','H','H','H'] will return 'C2H6O' ['S','O','O','H','O','H','O'] will return 'H2O4S' From E. A. Hill, J. Am. Chem. Soc., 22 (8), pp 478–494 (1900)
  • ’hill_compact’: same as hill but the number of atoms for each species is divided by the greatest common divisor of all of them, e.g. ['C','H','H','H','O','C','H','H','H','O','O','O'] will return 'CH3O2'
  • ’reduce’: group repeated symbols e.g. ['Ba', 'Ti', 'O', 'O', 'O', 'Ba', 'Ti', 'O', 'O', 'O', 'Ba', 'Ti', 'Ti', 'O', 'O', 'O'] will return 'BaTiO3BaTiO3BaTi2O3'
  • ’group’: will try to group as much as possible parts of the formula e.g. ['Ba', 'Ti', 'O', 'O', 'O', 'Ba', 'Ti', 'O', 'O', 'O', 'Ba', 'Ti', 'Ti', 'O', 'O', 'O'] will return '(BaTiO3)2BaTi2O3'
  • ’count’: same as hill (i.e. one just counts the number of atoms of each species) without the re-ordering (take the order of the atomic sites), e.g. ['Ba', 'Ti', 'O', 'O', 'O','Ba', 'Ti', 'O', 'O', 'O'] will return 'Ba2Ti2O6'
  • ’count_compact’: same as count but the number of atoms for each species is divided by the greatest common divisor of all of them, e.g. ['Ba', 'Ti', 'O', 'O', 'O','Ba', 'Ti', 'O', 'O', 'O'] will return 'BaTiO3'
• separator – a string used to concatenate symbols. Default empty.

Returns:

a string with the formula

Note

in modes reduce, group, count and count_compact, the initial order in which the atoms were appended by the user is used to group and/or order the symbols in the formula

get_kind(kind_name)

Return the kind object associated with the given kind name.

Parameters:kind_name – String, the name of the kind you want to get Returns:The Kind object associated with the given kind_name, if a Kind with the given name is present in the structure. Raise:ValueError if the kind_name is not present.

get_kind_names()

Return a list of kind names (in the same order of the self.kinds property, but return the names rather than Kind objects)

Note

This is NOT necessarily a list of chemical symbols! Use get_symbols_set for chemical symbols

Returns:a list of strings.

get_pymatgen(**kwargs)

Get pymatgen object. Returns Structure for structures with periodic boundary conditions (in three dimensions) and Molecule otherwise. :param add_spin: True to add the spins to the pymatgen structure. Default is False (no spin added).

Note

The spins are set according to the following rule:

• if the kind name ends with 1 -> spin=+1
• if the kind name ends with 2 -> spin=-1

Note

Requires the pymatgen module (version >= 3.0.13, usage of earlier versions may cause errors).

get_pymatgen_molecule()

Get the pymatgen Molecule object.

Note

Requires the pymatgen module (version >= 3.0.13, usage of earlier versions may cause errors).

Returns:a pymatgen Molecule object corresponding to this StructureData object.

get_pymatgen_structure(**kwargs)

Get the pymatgen Structure object. :param add_spin: True to add the spins to the pymatgen structure. Default is False (no spin added).

Note

The spins are set according to the following rule:

• if the kind name ends with 1 -> spin=+1
• if the kind name ends with 2 -> spin=-1

Note

Requires the pymatgen module (version >= 3.0.13, usage of earlier versions may cause errors).

Returns:a pymatgen Structure object corresponding to this StructureData object. Raises:ValueError – if periodic boundary conditions do not hold in at least one dimension of real space.

get_site_kindnames()

Return a list with length equal to the number of sites of this structure, where each element of the list is the kind name of the corresponding site.

Note

This is NOT necessarily a list of chemical symbols! Use [ self.get_kind(s.kind_name).get_symbols_string() for s in self.sites] for chemical symbols

Returns:a list of strings

get_symbols_set()

Return a set containing the names of all elements involved in this structure (i.e., for it joins the list of symbols for each kind k in the structure).

Returns:a set of strings of element names.

has_vacancies

Return whether the structure has vacancies in the structure.

Returns:a boolean, True if at least one kind has a vacancy

is_alloy

Return whether the structure contains any alloy kinds.

Returns:a boolean, True if at least one kind is an alloy

kinds

Returns a list of kinds.

pbc

Get the periodic boundary conditions.

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)

reset_cell(new_cell)

Reset the cell of a structure not yet stored to a new value.

Parameters:new_cell – list specifying the cell vectors Raises:ModificationNotAllowed: if object is already stored

reset_sites_positions(new_positions, conserve_particle=True)

Replace all the Site positions attached to the Structure

Parameters:

• new_positions – list of (3D) positions for every sites.
• conserve_particle – if True, allows the possibility of removing a site. currently not implemented.

Raises:

• aiida.common.ModificationNotAllowed – if object is stored already
• ValueError – if positions are invalid

Note

it is assumed that the order of the new_positions is given in the same order of the one it’s substituting, i.e. the kind of the site will not be checked.

set_ase(aseatoms)

Load the structure from a ASE object

set_cell(value)

set_cell_angles(value)

set_cell_lengths(value)

set_pbc(value)

set_pymatgen(obj, **kwargs)

Load the structure from a pymatgen object.

Note

Requires the pymatgen module (version >= 3.0.13, usage of earlier versions may cause errors).

set_pymatgen_molecule(mol, margin=5)

Load the structure from a pymatgen Molecule object.

Parameters:margin – the margin to be added in all directions of the bounding box of the molecule.

Note

Requires the pymatgen module (version >= 3.0.13, usage of earlier versions may cause errors).

set_pymatgen_structure(struct)

Load the structure from a pymatgen Structure object.

Note

periodic boundary conditions are set to True in all three directions.

Note

Requires the pymatgen module (version >= 3.3.5, usage of earlier versions may cause errors).

Raises:ValueError – if there are partial occupancies together with spins.

sites

Returns a list of sites.

class aiida.orm.nodes.data.UpfData(filepath=None, source=None, **kwargs)

Bases: aiida.orm.nodes.data.singlefile.SinglefileData

Function not yet documented.

__abstractmethods__ = frozenset([])

__init__(filepath=None, source=None, **kwargs)

Parameters:backend_entity (aiida.orm.implementation.BackendEntity) – the backend model supporting this entity

__module__ = 'aiida.orm.nodes.data.upf'

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_logger = <logging.Logger object>

_plugin_type_string = 'data.upf.UpfData.'

_query_type_string = 'data.upf.'

_validate()

Ensure that there is one object stored in the repository, whose key matches value set for filename attr.

element

classmethod from_md5(md5)

Return a list of all UPF pseudopotentials that match a given MD5 hash.

Note that the hash has to be stored in a _md5 attribute, otherwise the pseudo will not be found.

classmethod get_or_create(filepath, use_first=False, store_upf=True)

Pass the same parameter of the init; if a file with the same md5 is found, that UpfData is returned.

Parameters:

• filepath – an absolute filepath on disk
• use_first – if False (default), raise an exception if more than one potential is found. If it is True, instead, use the first available pseudopotential.
• store_upf (bool) – If false, the UpfData objects are not stored in the database. default=True.

Return (upf, created):  

where upf is the UpfData object, and create is either True if the object was created, or False if the object was retrieved from the DB.

get_upf_family_names()

Get the list of all upf family names to which the pseudo belongs.

classmethod get_upf_group(group_label)

Return the UpfFamily group with the given name.

classmethod get_upf_groups(filter_elements=None, user=None)

Return all names of groups of type UpfFamily, possibly with some filters.

Parameters:

• filter_elements – A string or a list of strings. If present, returns only the groups that contains one Upf for every element present in the list. Default=None, meaning that all families are returned.
• user – if None (default), return the groups for all users. If defined, it should be either a DbUser instance, or a string for the username (that is, the user email).

md5sum

put_object_from_file(filename)

I pre-parse the file to store the attributes.

store(*args, **kwargs)

Store the node, reparsing the file so that the md5 and the element are correctly reset.

upffamily_type_string = 'data.upf'

class aiida.orm.nodes.data.NumericType(*args, **kwargs)

Bases: aiida.orm.nodes.data.base.BaseType

Sub class of Data to store numbers, overloading common operators (+, *, …).

__abstractmethods__ = frozenset([])

__add__(other)

Decorator wrapper.

__float__()

__ge__(other)

Decorator wrapper.

__gt__(other)

Decorator wrapper.

__int__()

__le__(other)

Decorator wrapper.

__lt__(other)

Decorator wrapper.

__mod__(other)

Decorator wrapper.

__module__ = 'aiida.orm.nodes.data.numeric'

__mul__(other)

Decorator wrapper.

__pow__(other)

Decorator wrapper.

__radd__(other)

Decorator wrapper.

__rmod__(other)

Decorator wrapper.

__rmul__(other)

Decorator wrapper.

__rsub__(other)

Decorator wrapper.

__sub__(other)

Decorator wrapper.

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_logger = <logging.Logger object>

_plugin_type_string = 'data.numeric.NumericType.'

_query_type_string = 'data.numeric.'

Subpackages

• aiida.orm.nodes.data.array package
  • Submodules

Submodules

Data sub class to be used as a base for data containers that represent base python data types.

class aiida.orm.nodes.data.base.BaseType(*args, **kwargs)

Bases: aiida.orm.nodes.data.data.Data

Data sub class to be used as a base for data containers that represent base python data types.

__abstractmethods__ = frozenset([])

__eq__(other)

x.__eq__(y) <==> x==y

__init__(*args, **kwargs)

Parameters:backend_entity (aiida.orm.implementation.BackendEntity) – the backend model supporting this entity

__module__ = 'aiida.orm.nodes.data.base'

__ne__(other)

x.__ne__(y) <==> x!=y

__str__() <==> str(x)

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_logger = <logging.Logger object>

_plugin_type_string = 'data.base.BaseType.'

_query_type_string = 'data.base.'

new(value=None)

value

aiida.orm.nodes.data.base.to_aiida_type(*args, **kw)

Turns basic Python types (str, int, float, bool) into the corresponding AiiDA types.

Data sub class to represent a boolean value.

class aiida.orm.nodes.data.bool.Bool(*args, **kwargs)

Bases: aiida.orm.nodes.data.base.BaseType

Data sub class to represent a boolean value.

__abstractmethods__ = frozenset([])

__bool__()

__int__()

__module__ = 'aiida.orm.nodes.data.bool'

__nonzero__()

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_logger = <logging.Logger object>

_plugin_type_string = 'data.bool.Bool.'

_query_type_string = 'data.bool.'

_type

alias of __builtin__.bool

Tools for handling Crystallographic Information Files (CIF)

class aiida.orm.nodes.data.cif.CifData(ase=None, filepath=None, values=None, source=None, scan_type='standard', parse_policy='eager', **kwargs)

Bases: aiida.orm.nodes.data.singlefile.SinglefileData

Wrapper for Crystallographic Interchange File (CIF)

Note

the filepath (physical) is held as the authoritative source of information, so all conversions are done through the physical file: when setting ase or values, a physical CIF file is generated first, the values are updated from the physical CIF file.

__abstractmethods__ = frozenset([])

__init__(ase=None, filepath=None, values=None, source=None, scan_type='standard', parse_policy='eager', **kwargs)

Parameters:backend_entity (aiida.orm.implementation.BackendEntity) – the backend model supporting this entity

__module__ = 'aiida.orm.nodes.data.cif'

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_ase = None

_get_aiida_structure(converter='pymatgen', store=False, **kwargs)

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

Parameters:

• converter – specify the converter. Default ‘pymatgen’.
• store – if True, intermediate calculation gets stored in the AiiDA database for record. Default False.
• primitive_cell – if True, primitive cell is returned, conventional cell if False. Default False.
• occupancy_tolerance – If total occupancy of a site is between 1 and occupancy_tolerance, the occupancies will be scaled down to 1. (pymatgen only)
• site_tolerance – This tolerance is used to determine if two sites are sitting in the same position, in which case they will be combined to a single disordered site. Defaults to 1e-4. (pymatgen only)

Returns:

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

_get_object_ase()

Converts CifData to ase.Atoms

Returns:an ase.Atoms object

_get_object_pycifrw()

Converts CifData to PyCIFRW.CifFile

Returns:a PyCIFRW.CifFile object

_logger = <logging.Logger object>

_parse_policies = ('eager', 'lazy')

_plugin_type_string = 'data.cif.CifData.'

_prepare_cif(main_file_name='')

Return CIF string of CifData object.

If parsed values are present, a CIF string is created and written to file. If no parsed values are present, the CIF string is read from file.

_prepare_tcod(main_file_name='', **kwargs)

Write the given CIF to a string of format TCOD CIF.

_query_type_string = 'data.cif.'

_scan_types = ('standard', 'flex')

_set_incompatibilities = [('ase', 'filepath'), ('ase', 'values'), ('filepath', 'values')]

_validate()

Validates MD5 hash of CIF file.

_values = None

ase

ASE object, representing the CIF.

Note

requires ASE module.

classmethod from_md5(md5)

Return a list of all CIF files that match a given MD5 hash.

Note

the hash has to be stored in a _md5 attribute, otherwise the CIF file will not be found.

generate_md5()

Computes and returns MD5 hash of the CIF file.

get_ase(**kwargs)

Returns ASE object, representing the CIF. This function differs from the property ase by the possibility to pass the keyworded arguments (kwargs) to ase.io.cif.read_cif().

Note

requires ASE module.

get_formulae(mode='sum')

Return chemical formulae specified in CIF file.

Note: This does not compute the formula, it only reads it from the appropriate tag. Use refine_inline to compute formulae.

classmethod get_or_create(filename, use_first=False, store_cif=True)

Pass the same parameter of the init; if a file with the same md5 is found, that CifData is returned.

Parameters:

• filename – an absolute filename on disk
• use_first – if False (default), raise an exception if more than one CIF file is found. If it is True, instead, use the first available CIF file.
• store_cif (bool) – If false, the CifData objects are not stored in the database. default=True.

Return (cif, created):  

where cif is the CifData object, and create is either True if the object was created, or False if the object was retrieved from the DB.

get_spacegroup_numbers()

Get the spacegroup international number.

get_structure(converter='pymatgen', store=False, **kwargs)

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

New in version 1.0: Renamed from _get_aiida_structure

Parameters:

• converter – specify the converter. Default ‘pymatgen’.
• store – if True, intermediate calculation gets stored in the AiiDA database for record. Default False.
• primitive_cell – if True, primitive cell is returned, conventional cell if False. Default False.
• occupancy_tolerance – If total occupancy of a site is between 1 and occupancy_tolerance, the occupancies will be scaled down to 1. (pymatgen only)
• site_tolerance – This tolerance is used to determine if two sites are sitting in the same position, in which case they will be combined to a single disordered site. Defaults to 1e-4. (pymatgen only)

Returns:

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

has_atomic_sites

Returns whether there are any atomic sites defined in the cif data. That is to say, it will check all the values for the _atom_site_fract_* tags and if they are all equal to ? that means there are no relevant atomic sites defined and the function will return False. In all other cases the function will return True

Returns:False when at least one atomic site fractional coordinate is not equal to ? and True otherwise

has_attached_hydrogens

Check if there are hydrogens without coordinates, specified as attached to the atoms of the structure.

Returns:True if there are attached hydrogens, False otherwise.

has_partial_occupancies

Return if the cif data contains partial occupancies

A partial occupancy is defined as site with an occupancy that differs from unity, within a precision of 1E-6

Returns:True if there are partial occupancies, False otherwise

has_undefined_atomic_sites

Return whether the cif data contains any undefined atomic sites.

An undefined atomic site is defined as a site where at least one of the fractional coordinates specified in the _atom_site_fract_* tags, cannot be successfully interpreted as a float. If the cif data contains any site that matches this description, or it does not contain any atomic site tags at all, the cif data is said to have undefined atomic sites.

Returns:boolean, True if no atomic sites are defined or if any of the defined sites contain undefined positions and False otherwise

has_unknown_species

Returns whether the cif contains atomic species that are not recognized by AiiDA.

The known species are taken from the elements dictionary in aiida.common.constants, with the exception of the “unknown” placeholder element with symbol ‘X’, as this could not be used to construct a real structure. If any of the formula of the cif data contain species that are not in that elements dictionary, the function will return True and False in all other cases. If there is no formulae to be found, it will return None

Returns:True when there are unknown species in any of the formulae, False if not, None if no formula found

parse(scan_type=None)

Parses CIF file and sets attributes.

Parameters:scan_type – See set_scan_type

put_object_from_file(path, key=None, mode='w', encoding='utf8', force=False)

Set the file.

If the source is set and the MD5 checksum of new file is different from the source, the source has to be deleted.

static read_cif(fileobj, index=-1, **kwargs)

A wrapper method that simulates the behavior of the old function ase.io.cif.read_cif by using the new generic ase.io.read function.

set_ase(aseatoms)

Set the contents of the CifData starting from an ASE atoms object

Parameters:aseatoms – the ASE atoms object

set_parse_policy(parse_policy)

Set the parse policy.

Parameters:parse_policy – Either ‘eager’ (parse CIF file on set_file) or ‘lazy’ (defer parsing until needed)

set_scan_type(scan_type)

Set the scan_type for PyCifRW.

The ‘flex’ scan_type of PyCifRW is faster for large CIF files but does not yet support the CIF2 format as of 02/2018. See the CifFile.ReadCif function

Parameters:scan_type – Either ‘standard’ or ‘flex’ (see _scan_types)

set_values(values)

Set internal representation to values.

Warning: This also writes a new CIF file.

Parameters:values – PyCifRW CifFile object

Note

requires PyCifRW module.

store(*args, **kwargs)

Store the node.

values

PyCifRW structure, representing the CIF datablocks.

Note

requires PyCifRW module.

aiida.orm.nodes.data.cif.cif_from_ase(ase, full_occupancies=False, add_fake_biso=False)

Construct a CIF datablock from the ASE structure. The code is taken from https://wiki.fysik.dtu.dk/ase/epydoc/ase.io.cif-pysrc.html#write_cif, as the original ASE code contains a bug in printing the Hermann-Mauguin symmetry space group symbol.

Parameters:ase – ASE “images” Returns:array of CIF datablocks

aiida.orm.nodes.data.cif.has_pycifrw()

Returns:True if the PyCifRW module can be imported, False otherwise.

aiida.orm.nodes.data.cif.parse_formula(formula)

Parses the Hill formulae, written with spaces for separators.

aiida.orm.nodes.data.cif.pycifrw_from_cif(datablocks, loops=None, names=None)

Constructs PyCifRW’s CifFile from an array of CIF datablocks.

Parameters:

• datablocks – an array of CIF datablocks
• loops – optional dict of lists of CIF tag loops.
• names – optional list of datablock names

Returns:

CifFile

class aiida.orm.nodes.data.code.Code(remote_computer_exec=None, local_executable=None, input_plugin_name=None, files=None, **kwargs)

Bases: aiida.orm.nodes.data.data.Data

A code entity. It can either be ‘local’, or ‘remote’.

• Local code: it is a collection of files/dirs (added using the add_path() method), where one file is flagged as executable (using the set_local_executable() method).
• Remote code: it is a pair (remotecomputer, remotepath_of_executable) set using the set_remote_computer_exec() method.

For both codes, one can set some code to be executed right before or right after the execution of the code, using the set_preexec_code() and set_postexec_code() methods (e.g., the set_preexec_code() can be used to load specific modules required for the code to be run).

HIDDEN_KEY = 'hidden'

__abstractmethods__ = frozenset([])

__init__(remote_computer_exec=None, local_executable=None, input_plugin_name=None, files=None, **kwargs)

Parameters:backend_entity (aiida.orm.implementation.BackendEntity) – the backend model supporting this entity

__module__ = 'aiida.orm.nodes.data.code'

__str__() <==> str(x)

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_logger = <logging.Logger object>

_plugin_type_string = 'data.code.Code.'

_query_type_string = 'data.code.'

_set_local()

Set the code as a ‘local’ code, meaning that all the files belonging to the code will be copied to the cluster, and the file set with set_exec_filename will be run.

It also deletes the flags related to the local case (if any)

_set_remote()

Set the code as a ‘remote’ code, meaning that the code itself has no files attached, but only a location on a remote computer (with an absolute path of the executable on the remote computer).

It also deletes the flags related to the local case (if any)

_validate()

Perform validation of the Data object.

Note

validation of data source checks license and requires attribution to be provided in field ‘description’ of source in the case of any CC-BY* license. If such requirement is too strict, one can remove/comment it out.

can_run_on(computer)

Return True if this code can run on the given computer, False otherwise.

Local codes can run on any machine; remote codes can run only on the machine on which they reside.

TODO: add filters to mask the remote machines on which a local code can run.

full_label

Get full label of this code.

Returns label of the form <code-label>@<computer-name>.

full_text_info(verbose=False)

Return a (multiline) string with a human-readable detailed information on this computer

classmethod get(pk=None, label=None, machinename=None)

Get a Computer object with given identifier string, that can either be the numeric ID (pk), or the label (and computername) (if unique).

Parameters:

• pk – the numeric ID (pk) for code
• label – the code label identifying the code to load
• machinename – the machine name where code is setup

Raises:

• aiida.common.NotExistent – if no code identified by the given string is found
• aiida.common.MultipleObjectsError – if the string cannot identify uniquely a code
• aiida.common.InputValidationError – if neither a pk nor a label was passed in

get_append_text()

Return the postexec_code, or an empty string if no post-exec code was defined.

get_builder()

Create and return a new ProcessBuilder for the default Calculation plugin, as obtained by the self.get_input_plugin_name() method.

Note:

it also sets the builder.code value.

Raises:

• aiida.common.MissingPluginError – if the specified plugin does not exist.
• ValueError – if no default plugin was specified.

Returns:

classmethod get_code_helper(label, machinename=None)

Parameters:

• label – the code label identifying the code to load
• machinename – the machine name where code is setup

Raises:

• aiida.common.NotExistent – if no code identified by the given string is found
• aiida.common.MultipleObjectsError – if the string cannot identify uniquely a code

get_computer_name()

Get name of this code’s computer.

get_description()

Return a string description of this Code instance.

Returns:string description of this Code instance

get_execname()

Return the executable string to be put in the script. For local codes, it is ./LOCAL_EXECUTABLE_NAME For remote codes, it is the absolute path to the executable.

classmethod get_from_string(code_string)

Get a Computer object with given identifier string in the format label@machinename. See the note below for details on the string detection algorithm.

Note

the (leftmost) ‘@’ symbol is always used to split code and computername. Therefore do not use ‘@’ in the code name if you want to use this function (‘@’ in the computer name are instead valid).

Parameters:

code_string – the code string identifying the code to load

Raises:

• aiida.common.NotExistent – if no code identified by the given string is found
• aiida.common.MultipleObjectsError – if the string cannot identify uniquely a code
• aiida.common.InputValidationError – if code_string is not of string type

get_full_text_info(verbose=False)

Return a (multiline) string with a human-readable detailed information on this computer

get_input_plugin_name()

Return the name of the default input plugin (or None if no input plugin was set.

get_local_executable()

get_prepend_text()

Return the code that will be put in the scheduler script before the execution, or an empty string if no pre-exec code was defined.

get_remote_computer()

get_remote_exec_path()

hidden

Determines whether the Code is hidden or not

hide()

Hide the code (prevents from showing it in the verdi code list)

is_local()

Return True if the code is ‘local’, False if it is ‘remote’ (see also documentation of the set_local and set_remote functions).

classmethod list_for_plugin(plugin, labels=True)

Return a list of valid code strings for a given plugin.

Parameters:

• plugin – The string of the plugin.
• labels – if True, return a list of code names, otherwise return the code PKs (integers).

Returns:

a list of string, with the code names if labels is True, otherwise a list of integers with the code PKs.

relabel(new_label, raise_error=True)

Relabel this code.

Parameters:

• new_label – new code label
• raise_error – Set to False in order to return a list of errors instead of raising them.

reveal()

Reveal the code (allows to show it in the verdi code list) By default, it is revealed

set_append_text(code)

Pass a string of code that will be put in the scheduler script after the execution of the code.

set_files(files)

Given a list of filenames (or a single filename string), add it to the path (all at level zero, i.e. without folders). Therefore, be careful for files with the same name!

Todo:decide whether to check if the Code must be a local executable to be able to call this function.

set_input_plugin_name(input_plugin)

Set the name of the default input plugin, to be used for the automatic generation of a new calculation.

set_local_executable(exec_name)

Set the filename of the local executable. Implicitly set the code as local.

set_prepend_text(code)

Pass a string of code that will be put in the scheduler script before the execution of the code.

set_remote_computer_exec(remote_computer_exec)

Set the code as remote, and pass the computer on which it resides and the absolute path on that computer.

Parameters:remote_computer_exec – a tuple (computer, remote_exec_path), where computer is a aiida.orm.Computer and remote_exec_path is the absolute path of the main executable on remote computer.

classmethod setup(**kwargs)

Module with Node sub class Data to be used as a base class for data structures.

class aiida.orm.nodes.data.data.Data(backend=None, user=None, computer=None, **kwargs)

Bases: aiida.orm.nodes.node.Node

This class is base class for all data objects.

Specifications of the Data class: AiiDA Data objects are subclasses of Node and should have

Multiple inheritance must be supported, i.e. Data should have methods for querying and be able to inherit other library objects such as ASE for structures.

Architecture note: The code plugin is responsible for converting a raw data object produced by code to AiiDA standard object format. The data object then validates itself according to its method. This is done independently in order to allow cross-validation of plugins.

__abstractmethods__ = frozenset([])

__copy__()

Copying a Data node is not supported, use copy.deepcopy or call Data.clone().

__deepcopy__(memo)

Create a clone of the Data node by pipiong through to the clone method and return the result.

Returns:an unstored clone of this Data node

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

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_export_format_replacements = {}

_exportcontent(fileformat, main_file_name='', **kwargs)

Converts a Data node to one (or multiple) files.

Note: Export plugins should return utf8-encoded bytes, which can be directly dumped to file.

Parameters:

• fileformat (str) – the extension, uniquely specifying the file format.
• main_file_name (str) – (empty by default) Can be used by plugin to infer sensible names for additional files, if necessary. E.g. if the main file is ‘../myplot.gnu’, the plugin may decide to store the dat file under ‘../myplot_data.dat’.
• kwargs – other parameters are passed down to the plugin

Returns:

a tuple of length 2. The first element is the content of the otuput file. The second is a dictionary (possibly empty) in the format {filename: filecontent} for any additional file that should be produced.

Return type:

(bytes, dict)

_get_converters()

Get all implemented converter formats. The convention is to find all _get_object_… methods. Returns a list of strings.

_get_exporters()

Get all implemented export formats. The convention is to find all _prepare_… methods. Returns a dictionary of method_name: method_function

_get_importers()

Get all implemented import formats. The convention is to find all _parse_… methods. Returns a list of strings.

_logger = <logging.Logger object>

_plugin_type_string = 'data.data.Data.'

_query_type_string = 'data.data.'

_source_attributes = ['db_name', 'db_uri', 'uri', 'id', 'version', 'extras', 'source_md5', 'description', 'license']

_storable = True

_unstorable_message = 'storing for this node has been disabled'

_validate()

Perform validation of the Data object.

Note

validation of data source checks license and requires attribution to be provided in field ‘description’ of source in the case of any CC-BY* license. If such requirement is too strict, one can remove/comment it out.

clone()

Create a clone of the Data node.

Returns:an unstored clone of this Data node

convert(object_format=None, *args)

Convert the AiiDA StructureData into another python object

Parameters:object_format – Specify the output format

creator

Return the creator of this node or None if it does not exist.

Returns:the creating node or None

export(path, fileformat=None, overwrite=False, **kwargs)

Save a Data object to a file.

Parameters:

• fname – string with file name. Can be an absolute or relative path.
• fileformat – kind of format to use for the export. If not present, it will try to use the extension of the file name.
• overwrite – if set to True, overwrites file found at path. Default=False
• kwargs – additional parameters to be passed to the _exportcontent method

Returns:

the list of files created

classmethod get_export_formats()

Get the list of valid export format strings

Returns:a list of valid formats

importfile(fname, fileformat=None)

Populate a Data object from a file.

Parameters:

• fname – string with file name. Can be an absolute or relative path.
• fileformat – kind of format to use for the export. If not present, it will try to use the extension of the file name.

importstring(inputstring, fileformat, **kwargs)

Converts a Data object to other text format.

Parameters:fileformat – a string (the extension) to describe the file format. Returns:a string with the structure description.

set_source(source)

Sets the dictionary describing the source of Data object.

source

Gets the dictionary describing the source of Data object. Possible fields:

• db_name: name of the source database.
• db_uri: URI of the source database.
• uri: URI of the object’s source. Should be a permanent link.
• id: object’s source identifier in the source database.
• version: version of the object’s source.
• extras: a dictionary with other fields for source description.
• source_md5: MD5 checksum of object’s source.
• description: human-readable free form description of the object’s source.
• license: a string with a type of license.

Note

some limitations for setting the data source exist, see _validate method.

Returns:dictionary describing the source of Data object.

Data sub class to represent a dictionary.

class aiida.orm.nodes.data.dict.Dict(**kwargs)

Bases: aiida.orm.nodes.data.data.Data

Data sub class to represent a dictionary.

__abstractmethods__ = frozenset([])

__init__(**kwargs)

Store a dictionary as a Node instance.

Usual rules for attribute names apply, in particular, keys cannot start with an underscore, or a ValueError will be raised.

Initial attributes can be changed, deleted or added as long as the node is not stored.

Parameters:dict – the dictionary to set

__module__ = 'aiida.orm.nodes.data.dict'

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_logger = <logging.Logger object>

_plugin_type_string = 'data.dict.Dict.'

_query_type_string = 'data.dict.'

dict

Return an instance of AttributeManager that transforms the dictionary into an attribute dict.

Note

this will allow one to do node.dict.key as well as node.dict[key].

Returns:an instance of the AttributeResultManager.

get_dict()

Return a dictionary with the parameters currently set.

Returns:dictionary

keys()

Iterator of valid keys stored in the Dict object.

Returns:iterator over the keys of the current dictionary

set_dict(dictionary)

Replace the current dictionary with another one.

Parameters:dictionary – dictionary to set

update_dict(dictionary)

Update the current dictionary with the keys provided in the dictionary.

Note

works exactly as dict.update() where new keys are simply added and existing keys are overwritten.

Parameters:dictionary – a dictionary with the keys to substitute

Data sub class to represent a float value.

class aiida.orm.nodes.data.float.Float(*args, **kwargs)

Bases: aiida.orm.nodes.data.numeric.NumericType

Data sub class to represent a float value.

__abstractmethods__ = frozenset([])

__module__ = 'aiida.orm.nodes.data.float'

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_logger = <logging.Logger object>

_plugin_type_string = 'data.float.Float.'

_query_type_string = 'data.float.'

_type

alias of __builtin__.float

Data sub class to represent a folder on a file system.

class aiida.orm.nodes.data.folder.FolderData(backend=None, user=None, computer=None, **kwargs)

Bases: aiida.orm.nodes.data.data.Data

Data sub class to represent a folder on a file system.

__abstractmethods__ = frozenset([])

__module__ = 'aiida.orm.nodes.data.folder'

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_logger = <logging.Logger object>

_plugin_type_string = 'data.folder.FolderData.'

_query_type_string = 'data.folder.'

Data sub class to represent an integer value.

class aiida.orm.nodes.data.int.Int(*args, **kwargs)

Bases: aiida.orm.nodes.data.numeric.NumericType

Data sub class to represent an integer value.

__abstractmethods__ = frozenset([])

__module__ = 'aiida.orm.nodes.data.int'

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_logger = <logging.Logger object>

_plugin_type_string = 'data.int.Int.'

_query_type_string = 'data.int.'

_type

alias of __builtin__.int

Data sub class to represent a list.

class aiida.orm.nodes.data.list.List(**kwargs)

Bases: aiida.orm.nodes.data.data.Data, _abcoll.MutableSequence

Data sub class to represent a list.

_LIST_KEY = 'list'

__abstractmethods__ = frozenset([])

__delitem__(key)

__eq__(other)

x.__eq__(y) <==> x==y

__getitem__(item)

__init__(**kwargs)

Parameters:backend_entity (aiida.orm.implementation.BackendEntity) – the backend model supporting this entity

__len__()

__module__ = 'aiida.orm.nodes.data.list'

__ne__(other)

x.__ne__(y) <==> x!=y

__setitem__(key, value)

__str__() <==> str(x)

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 102

_abc_registry = <_weakrefset.WeakSet object>

_logger = <logging.Logger object>

_plugin_type_string = 'data.list.List.'

_query_type_string = 'data.list.'

_using_list_reference()

This function tells the class if we are using a list reference. This means that calls to self.get_list return a reference rather than a copy of the underlying list and therefore self.set_list need not be called. This knwoledge is essential to make sure this class is performant.

Currently the implementation assumes that if the node needs to be stored then it is using the attributes cache which is a reference.

Returns:True if using self.get_list returns a reference to the underlying sequence. False otherwise. Return type:bool

append(value)

S.append(object) – append object to the end of the sequence

count(value) → integer -- return number of occurrences of value

extend(value)

S.extend(iterable) – extend sequence by appending elements from the iterable

get_list()

Return the contents of this node.

Returns:a list

index(value) → integer -- return first index of value.

Raises ValueError if the value is not present.

insert(i, value)

S.insert(index, object) – insert object before index

pop([index]) → item -- remove and return item at index (default last).

Raise IndexError if list is empty or index is out of range.

remove(value)

S.remove(value) – remove first occurrence of value. Raise ValueError if the value is not present.

reverse()

S.reverse() – reverse IN PLACE

set_list(data)

Set the contents of this node.

Parameters:data – the list to set

sort(key=None, reverse=False)

Module for defintion of base Data sub class for numeric based data types.

class aiida.orm.nodes.data.numeric.NumericType(*args, **kwargs)

Bases: aiida.orm.nodes.data.base.BaseType

Sub class of Data to store numbers, overloading common operators (+, *, …).

__abstractmethods__ = frozenset([])

__add__(other)

Decorator wrapper.

__float__()

__ge__(other)

Decorator wrapper.

__gt__(other)

Decorator wrapper.

__int__()

__le__(other)

Decorator wrapper.

__lt__(other)

Decorator wrapper.

__mod__(other)

Decorator wrapper.

__module__ = 'aiida.orm.nodes.data.numeric'

__mul__(other)

Decorator wrapper.

__pow__(other)

Decorator wrapper.

__radd__(other)

Decorator wrapper.

__rmod__(other)

Decorator wrapper.

__rmul__(other)

Decorator wrapper.

__rsub__(other)

Decorator wrapper.

__sub__(other)

Decorator wrapper.

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_logger = <logging.Logger object>

_plugin_type_string = 'data.numeric.NumericType.'

_query_type_string = 'data.numeric.'

class aiida.orm.nodes.data.orbital.OrbitalData(backend=None, user=None, computer=None, **kwargs)

Bases: aiida.orm.nodes.data.data.Data

Used for storing collections of orbitals, as well as providing methods for accessing them internally.

__abstractmethods__ = frozenset([])

__module__ = 'aiida.orm.nodes.data.orbital'

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_get_orbital_class_from_orbital_dict(orbital_dict)

Gets the orbital class from the orbital dictionary stored in DB

Parameters:orbital_dict – orbital dictionary associated with the orbital Returns:an Orbital produced using the module_name

_logger = <logging.Logger object>

_plugin_type_string = 'data.orbital.OrbitalData.'

static _prep_orbital_dict_keys_from_site(site)

Prepares the position from an input site.

Parameters:site – a site of site class Return out_dict:  a dictionary of attributes parsed from the site (currently only position)

_query_type_string = 'data.orbital.'

clear_orbitals()

Remove all orbitals that were added to the class Cannot work if OrbitalData has been already stored

get_orbitals(with_tags=False, **kwargs)

Returns all orbitals by default. If a site is provided, returns all orbitals cooresponding to the location of that site, additional arguments may be provided, which act as filters on the retrieved orbitals.

Parameters:

• site – if provided, returns all orbitals with position of site
• with_tags – if provided returns all tags stored

Kwargs:

attributes than can filter the set of returned orbitals

Return list_of_outputs:  

a list of orbitals and also tags if with_tags was set to True

set_orbitals(orbital, tag=None)

Sets the orbitals into the database. Uses the orbital’s inherent set_orbital_dict method to generate a orbital dict string at is stored along with the tags, if provided.

Parameters:

• orbital – an orbital or list of orbitals to be set
• tag – a list of strings must be of length orbital

class aiida.orm.nodes.data.remote.RemoteData(remote_path=None, **kwargs)

Bases: aiida.orm.nodes.data.data.Data

Store a link to a file or folder on a remote machine.

Remember to pass a computer!

__abstractmethods__ = frozenset([])

__init__(remote_path=None, **kwargs)

Parameters:backend_entity (aiida.orm.implementation.BackendEntity) – the backend model supporting this entity

__module__ = 'aiida.orm.nodes.data.remote'

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_clean()

Remove all content of the remote folder on the remote computer

_logger = <logging.Logger object>

_plugin_type_string = 'data.remote.RemoteData.'

_query_type_string = 'data.remote.'

_validate()

Perform validation of the Data object.

Note

validation of data source checks license and requires attribution to be provided in field ‘description’ of source in the case of any CC-BY* license. If such requirement is too strict, one can remove/comment it out.

get_authinfo()

get_computer_name()

get_remote_path()

getfile(relpath, destpath)

Connects to the remote folder and gets a string with the (full) content of the file.

Parameters:

• relpath – The relative path of the file to show.
• destpath – A path on the local computer to get the file

Returns:

a string with the file content

is_empty

Check if remote folder is empty

listdir(relpath='.')

Connects to the remote folder and lists the directory content.

Parameters:relpath – If ‘relpath’ is specified, lists the content of the given subfolder. Returns:a flat list of file/directory names (as strings).

listdir_withattributes(path='.')

Connects to the remote folder and lists the directory content.

Parameters:relpath – If ‘relpath’ is specified, lists the content of the given subfolder. Returns:a list of dictionaries, where the documentation is in :py:class:Transport.listdir_withattributes.

set_remote_path(val)

Data class that can be used to store a single file in its repository.

class aiida.orm.nodes.data.singlefile.SinglefileData(filepath, **kwargs)

Bases: aiida.orm.nodes.data.data.Data

Data class that can be used to store a single file in its repository.

__abstractmethods__ = frozenset([])

__init__(filepath, **kwargs)

Parameters:backend_entity (aiida.orm.implementation.BackendEntity) – the backend model supporting this entity

__module__ = 'aiida.orm.nodes.data.singlefile'

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_logger = <logging.Logger object>

_plugin_type_string = 'data.singlefile.SinglefileData.'

_query_type_string = 'data.singlefile.'

_validate()

Ensure that there is one object stored in the repository, whose key matches value set for filename attr.

filename

Return the name of the file stored.

Returns:the filename under which the file is stored in the repository

get_content()

Return the content of the single file stored for this data node.

Returns:the string content of the file

open(key=None, mode='r')

Return an open file handle to the content of this data node.

Parameters:

• key – optional key within the repository, by default is the filename set in the attributes
• mode – the mode with which to open the file handle

Returns:

a file handle in read mode

put_object_from_file(path, key=None, mode='w', encoding='utf8', force=False)

Add the file located at path on file system to repository, deleting any other existing objects.

set_file(filepath)

Add the file located at path on file system to repository, deleting any other existing objects.

Data sub class to represent a string value.

class aiida.orm.nodes.data.str.Str(*args, **kwargs)

Bases: aiida.orm.nodes.data.base.BaseType

Data sub class to represent a string value.

__abstractmethods__ = frozenset([])

__module__ = 'aiida.orm.nodes.data.str'

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_logger = <logging.Logger object>

_plugin_type_string = 'data.str.Str.'

_query_type_string = 'data.str.'

_type

alias of __builtin__.str

This module defines the classes for structures and all related functions to operate on them.

class aiida.orm.nodes.data.structure.StructureData(cell=None, pbc=None, ase=None, pymatgen=None, pymatgen_structure=None, pymatgen_molecule=None, **kwargs)

Bases: aiida.orm.nodes.data.data.Data

This class contains the information about a given structure, i.e. a collection of sites together with a cell, the boundary conditions (whether they are periodic or not) and other related useful information.

__abstractmethods__ = frozenset([])

__init__(cell=None, pbc=None, ase=None, pymatgen=None, pymatgen_structure=None, pymatgen_molecule=None, **kwargs)

Parameters:backend_entity (aiida.orm.implementation.BackendEntity) – the backend model supporting this entity

__module__ = 'aiida.orm.nodes.data.structure'

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_adjust_default_cell(vacuum_factor=1.0, vacuum_addition=10.0, pbc=(False, False, False))

If the structure was imported from an xyz file, it lacks a defined cell, and the default cell is taken ([[1,0,0], [0,1,0], [0,0,1]]), leading to an unphysical definition of the structure. This method will adjust the cell

_dimensionality_label = {0: '', 1: 'length', 2: 'surface', 3: 'volume'}

_get_cif(converter='ase', store=False, **kwargs)

Creates aiida.orm.nodes.data.cif.CifData.

Parameters:

• converter – specify the converter. Default ‘ase’.
• store – If True, intermediate calculation gets stored in the AiiDA database for record. Default False.

Returns:

aiida.orm.nodes.data.cif.CifData node.

_get_object_ase()

Converts StructureData to ase.Atoms

Returns:an ase.Atoms object

_get_object_phonopyatoms()

Converts StructureData to PhonopyAtoms

Returns:a PhonopyAtoms object

_get_object_pymatgen(**kwargs)

Converts StructureData to pymatgen object

Returns:a pymatgen Structure for structures with periodic boundary conditions (in three dimensions) and Molecule otherwise

Note

Requires the pymatgen module (version >= 3.0.13, usage of earlier versions may cause errors).

_get_object_pymatgen_molecule(**kwargs)

Converts StructureData to pymatgen Molecule object

Returns:a pymatgen Molecule object corresponding to this StructureData object.

Note

Requires the pymatgen module (version >= 3.0.13, usage of earlier versions may cause errors)

_get_object_pymatgen_structure(**kwargs)

Converts StructureData to pymatgen Structure object :param add_spin: True to add the spins to the pymatgen structure. Default is False (no spin added).

Note

The spins are set according to the following rule:

• if the kind name ends with 1 -> spin=+1
• if the kind name ends with 2 -> spin=-1

Returns:a pymatgen Structure object corresponding to this StructureData object Raises:ValueError – if periodic boundary conditions does not hold in at least one dimension of real space; if there are partial occupancies together with spins (defined by kind names ending with ‘1’ or ‘2’).

Note

Requires the pymatgen module (version >= 3.0.13, usage of earlier versions may cause errors)

_logger = <logging.Logger object>

_parse_xyz(inputstring)

Read the structure from a string of format XYZ.

_plugin_type_string = 'data.structure.StructureData.'

_prepare_chemdoodle(main_file_name='')

Write the given structure to a string of format required by ChemDoodle.

_prepare_cif(main_file_name='')

Write the given structure to a string of format CIF.

_prepare_tcod(main_file_name='', **kwargs)

Write the given structure to a string of format TCOD CIF.

_prepare_xsf(main_file_name='')

Write the given structure to a string of format XSF (for XCrySDen).

_prepare_xyz(main_file_name='')

Write the given structure to a string of format XYZ.

_query_type_string = 'data.structure.'

_set_incompatibilities = [('ase', 'cell'), ('ase', 'pbc'), ('ase', 'pymatgen'), ('ase', 'pymatgen_molecule'), ('ase', 'pymatgen_structure'), ('cell', 'pymatgen'), ('cell', 'pymatgen_molecule'), ('cell', 'pymatgen_structure'), ('pbc', 'pymatgen'), ('pbc', 'pymatgen_molecule'), ('pbc', 'pymatgen_structure'), ('pymatgen', 'pymatgen_molecule'), ('pymatgen', 'pymatgen_structure'), ('pymatgen_molecule', 'pymatgen_structure')]

_validate()

Performs some standard validation tests.

append_atom(**kwargs)

Append an atom to the Structure, taking care of creating the corresponding kind.

Parameters:

• ase – the ase Atom object from which we want to create a new atom (if present, this must be the only parameter)
• position – the position of the atom (three numbers in angstrom)
• symbols – passed to the constructor of the Kind object.
• weights – passed to the constructor of the Kind object.
• name – passed to the constructor of the Kind object. See also the note below.

Note

Note on the ‘name’ parameter (that is, the name of the kind):

• if specified, no checks are done on existing species. Simply, a new kind with that name is created. If there is a name clash, a check is done: if the kinds are identical, no error is issued; otherwise, an error is issued because you are trying to store two different kinds with the same name.
• if not specified, the name is automatically generated. Before adding the kind, a check is done. If other species with the same properties already exist, no new kinds are created, but the site is added to the existing (identical) kind. (Actually, the first kind that is encountered). Otherwise, the name is made unique first, by adding to the string containing the list of chemical symbols a number starting from 1, until an unique name is found

Note

checks of equality of species are done using the compare_with() method.

append_kind(kind)

Append a kind to the StructureData. It makes a copy of the kind.

Parameters:kind – the site to append, must be a Kind object.

append_site(site)

Append a site to the StructureData. It makes a copy of the site.

Parameters:site – the site to append. It must be a Site object.

cell

Returns the cell shape.

Returns:a 3x3 list of lists.

cell_angles

Get the angles between the cell lattice vectors in degrees.

cell_lengths

Get the lengths of cell lattice vectors in angstroms.

clear_kinds()

Removes all kinds for the StructureData object.

Note

Also clear all sites!

clear_sites()

Removes all sites for the StructureData object.

get_ase()

Get the ASE object. Requires to be able to import ase.

Returns:an ASE object corresponding to this StructureData object.

Note

If any site is an alloy or has vacancies, a ValueError is raised (from the site.get_ase() routine).

get_cell_volume()

Returns the cell volume in Angstrom^3.

Returns:a float.

get_cif(converter='ase', store=False, **kwargs)

Creates aiida.orm.nodes.data.cif.CifData.

New in version 1.0: Renamed from _get_cif

Parameters:

• converter – specify the converter. Default ‘ase’.
• store – If True, intermediate calculation gets stored in the AiiDA database for record. Default False.

Returns:

aiida.orm.nodes.data.cif.CifData node.

get_composition()

Returns the chemical composition of this structure as a dictionary, where each key is the kind symbol (e.g. H, Li, Ba), and each value is the number of occurences of that element in this structure. For BaZrO3 it would return {‘Ba’:1, ‘Zr’:1, ‘O’:3}. No reduction with smallest common divisor!

Returns:a dictionary with the composition

get_description()

Returns a string with infos retrieved from StructureData node’s properties

Parameters:self – the StructureData node Returns:retsrt: the description string

get_dimensionality()

This function checks the dimensionality of the structure and calculates its length/surface/volume :return: returns the dimensionality and length/surface/volume

get_formula(mode='hill', separator='')

Return a string with the chemical formula.

Parameters:

• mode –

  a string to specify how to generate the formula, can assume one of the following values:

  • ’hill’ (default): count the number of atoms of each species, then use Hill notation, i.e. alphabetical order with C and H first if one or several C atom(s) is (are) present, e.g. ['C','H','H','H','O','C','H','H','H'] will return 'C2H6O' ['S','O','O','H','O','H','O'] will return 'H2O4S' From E. A. Hill, J. Am. Chem. Soc., 22 (8), pp 478–494 (1900)
  • ’hill_compact’: same as hill but the number of atoms for each species is divided by the greatest common divisor of all of them, e.g. ['C','H','H','H','O','C','H','H','H','O','O','O'] will return 'CH3O2'
  • ’reduce’: group repeated symbols e.g. ['Ba', 'Ti', 'O', 'O', 'O', 'Ba', 'Ti', 'O', 'O', 'O', 'Ba', 'Ti', 'Ti', 'O', 'O', 'O'] will return 'BaTiO3BaTiO3BaTi2O3'
  • ’group’: will try to group as much as possible parts of the formula e.g. ['Ba', 'Ti', 'O', 'O', 'O', 'Ba', 'Ti', 'O', 'O', 'O', 'Ba', 'Ti', 'Ti', 'O', 'O', 'O'] will return '(BaTiO3)2BaTi2O3'
  • ’count’: same as hill (i.e. one just counts the number of atoms of each species) without the re-ordering (take the order of the atomic sites), e.g. ['Ba', 'Ti', 'O', 'O', 'O','Ba', 'Ti', 'O', 'O', 'O'] will return 'Ba2Ti2O6'
  • ’count_compact’: same as count but the number of atoms for each species is divided by the greatest common divisor of all of them, e.g. ['Ba', 'Ti', 'O', 'O', 'O','Ba', 'Ti', 'O', 'O', 'O'] will return 'BaTiO3'
• separator – a string used to concatenate symbols. Default empty.

Returns:

a string with the formula

Note

in modes reduce, group, count and count_compact, the initial order in which the atoms were appended by the user is used to group and/or order the symbols in the formula

get_kind(kind_name)

Return the kind object associated with the given kind name.

Parameters:kind_name – String, the name of the kind you want to get Returns:The Kind object associated with the given kind_name, if a Kind with the given name is present in the structure. Raise:ValueError if the kind_name is not present.

get_kind_names()

Return a list of kind names (in the same order of the self.kinds property, but return the names rather than Kind objects)

Note

This is NOT necessarily a list of chemical symbols! Use get_symbols_set for chemical symbols

Returns:a list of strings.

get_pymatgen(**kwargs)

Get pymatgen object. Returns Structure for structures with periodic boundary conditions (in three dimensions) and Molecule otherwise. :param add_spin: True to add the spins to the pymatgen structure. Default is False (no spin added).

Note

The spins are set according to the following rule:

• if the kind name ends with 1 -> spin=+1
• if the kind name ends with 2 -> spin=-1

Note

Requires the pymatgen module (version >= 3.0.13, usage of earlier versions may cause errors).

get_pymatgen_molecule()

Get the pymatgen Molecule object.

Note

Requires the pymatgen module (version >= 3.0.13, usage of earlier versions may cause errors).

Returns:a pymatgen Molecule object corresponding to this StructureData object.

get_pymatgen_structure(**kwargs)

Get the pymatgen Structure object. :param add_spin: True to add the spins to the pymatgen structure. Default is False (no spin added).

Note

The spins are set according to the following rule:

• if the kind name ends with 1 -> spin=+1
• if the kind name ends with 2 -> spin=-1

Note

Requires the pymatgen module (version >= 3.0.13, usage of earlier versions may cause errors).

Returns:a pymatgen Structure object corresponding to this StructureData object. Raises:ValueError – if periodic boundary conditions do not hold in at least one dimension of real space.

get_site_kindnames()

Return a list with length equal to the number of sites of this structure, where each element of the list is the kind name of the corresponding site.

Note

This is NOT necessarily a list of chemical symbols! Use [ self.get_kind(s.kind_name).get_symbols_string() for s in self.sites] for chemical symbols

Returns:a list of strings

get_symbols_set()

Return a set containing the names of all elements involved in this structure (i.e., for it joins the list of symbols for each kind k in the structure).

Returns:a set of strings of element names.

has_vacancies

Return whether the structure has vacancies in the structure.

Returns:a boolean, True if at least one kind has a vacancy

is_alloy

Return whether the structure contains any alloy kinds.

Returns:a boolean, True if at least one kind is an alloy

kinds

Returns a list of kinds.

pbc

Get the periodic boundary conditions.

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)

reset_cell(new_cell)

Reset the cell of a structure not yet stored to a new value.

Parameters:new_cell – list specifying the cell vectors Raises:ModificationNotAllowed: if object is already stored

reset_sites_positions(new_positions, conserve_particle=True)

Replace all the Site positions attached to the Structure

Parameters:

• new_positions – list of (3D) positions for every sites.
• conserve_particle – if True, allows the possibility of removing a site. currently not implemented.

Raises:

• aiida.common.ModificationNotAllowed – if object is stored already
• ValueError – if positions are invalid

Note

it is assumed that the order of the new_positions is given in the same order of the one it’s substituting, i.e. the kind of the site will not be checked.

set_ase(aseatoms)

Load the structure from a ASE object

set_cell(value)

set_cell_angles(value)

set_cell_lengths(value)

set_pbc(value)

set_pymatgen(obj, **kwargs)

Load the structure from a pymatgen object.

Note

Requires the pymatgen module (version >= 3.0.13, usage of earlier versions may cause errors).

set_pymatgen_molecule(mol, margin=5)

Load the structure from a pymatgen Molecule object.

Parameters:margin – the margin to be added in all directions of the bounding box of the molecule.

Note

Requires the pymatgen module (version >= 3.0.13, usage of earlier versions may cause errors).

set_pymatgen_structure(struct)

Load the structure from a pymatgen Structure object.

Note

periodic boundary conditions are set to True in all three directions.

Note

Requires the pymatgen module (version >= 3.3.5, usage of earlier versions may cause errors).

Raises:ValueError – if there are partial occupancies together with spins.

sites

Returns a list of sites.

class aiida.orm.nodes.data.structure.Kind(**kwargs)

Bases: object

This class contains the information about the species (kinds) of the system.

It can be a single atom, or an alloy, or even contain vacancies.

__dict__ = dict_proxy({'__module__': 'aiida.orm.nodes.data.structure', 'symbol': <property object>, 'symbols': <property object>, '__dict__': <attribute '__dict__' of 'Kind' objects>, 'get_symbols_string': <function get_symbols_string>, '__init__': <function __init__>, 'is_alloy': <property object>, 'compare_with': <function compare_with>, 'name': <property object>, 'has_vacancies': <property object>, '__weakref__': <attribute '__weakref__' of 'Kind' objects>, 'reset_mass': <function reset_mass>, 'set_automatic_kind_name': <function set_automatic_kind_name>, '__doc__': '\n This class contains the information about the species (kinds) of the system.\n\n It can be a single atom, or an alloy, or even contain vacancies.\n ', 'get_raw': <function get_raw>, 'set_symbols_and_weights': <function set_symbols_and_weights>, 'mass': <property object>, '__repr__': <function __repr__>, 'weights': <property object>, '__str__': <function __str__>})

__init__(**kwargs)

Create a site. One can either pass:

Parameters:

• raw – the raw python dictionary that will be converted to a Kind object.
• ase – an ase Atom object
• kind – a Kind object (to get a copy)

Or alternatively the following parameters:

Parameters:

• symbols – a single string for the symbol of this site, or a list of symbol strings
• weights – (optional) the weights for each atomic species of this site. If only a single symbol is provided, then this value is optional and the weight is set to 1.
• mass – (optional) the mass for this site in atomic mass units. If not provided, the mass is set by the self.reset_mass() function.
• name – a string that uniquely identifies the kind, and that is used to identify the sites.

__module__ = 'aiida.orm.nodes.data.structure'

__repr__() <==> repr(x)

__str__() <==> str(x)

__weakref__

list of weak references to the object (if defined)

compare_with(other_kind)

Compare with another Kind object to check if they are different.

Note

This does NOT check the ‘type’ attribute. Instead, it compares (with reasonable thresholds, where applicable): the mass, and the list of symbols and of weights. Moreover, it compares the _internal_tag, if defined (at the moment, defined automatically only when importing the Kind from ASE, if the atom has a non-zero tag). Note that the _internal_tag is only used while the class is loaded, but is not persisted on the database.

Returns:A tuple with two elements. The first one is True if the two sites are ‘equivalent’ (same mass, symbols and weights), False otherwise. The second element of the tuple is a string, which is either None (if the first element was True), or contains a ‘human-readable’ description of the first difference encountered between the two sites.

get_raw()

Return the raw version of the site, mapped to a suitable dictionary. This is the format that is actually used to store each kind of the structure in the DB.

Returns:a python dictionary with the kind.

get_symbols_string()

Return a string that tries to match as good as possible the symbols of this kind. If there is only one symbol (no alloy) with 100% occupancy, just returns the symbol name. Otherwise, groups the full string in curly brackets, and try to write also the composition (with 2 precision only).

Note

If there is a vacancy (sum of weights<1), we indicate it with the X symbol followed by 1-sum(weights) (still with 2 digits precision, so it can be 0.00)

Note

Note the difference with respect to the symbols and the symbol properties!

has_vacancies

Return whether the Kind contains vacancies, i.e. when the sum of the weights is less than one.

Note

the property uses the internal variable _sum_threshold as a threshold.

Returns:boolean, True if the sum of the weights is less than one, False otherwise

is_alloy

Return whether the Kind is an alloy, i.e. contains more than one element

Returns:boolean, True if the kind has more than one element, False otherwise.

mass

The mass of this species kind.

Returns:a float

name

Return the name of this kind. The name of a kind is used to identify the species of a site.

Returns:a string

reset_mass()

Reset the mass to the automatic calculated value.

The mass can be set manually; by default, if not provided, it is the mass of the constituent atoms, weighted with their weight (after the weight has been normalized to one to take correctly into account vacancies).

This function uses the internal _symbols and _weights values and thus assumes that the values are validated.

It sets the mass to None if the sum of weights is zero.

set_automatic_kind_name(tag=None)

Set the type to a string obtained with the symbols appended one after the other, without spaces, in alphabetical order; if the site has a vacancy, a X is appended at the end too.

set_symbols_and_weights(symbols, weights)

Set the chemical symbols and the weights for the site.

Note

Note that the kind name remains unchanged.

symbol

If the kind has only one symbol, return it; otherwise, raise a ValueError.

symbols

List of symbols for this site. If the site is a single atom, pass a list of one element only, or simply the string for that atom. For alloys, a list of elements.

Note

Note that if you change the list of symbols, the kind name remains unchanged.

weights

Weights for this species kind. Refer also to :func:validate_symbols_tuple for the validation rules on the weights.

class aiida.orm.nodes.data.structure.Site(**kwargs)

Bases: object

This class contains the information about a given site of the system.

It can be a single atom, or an alloy, or even contain vacancies.

__dict__ = dict_proxy({'__module__': 'aiida.orm.nodes.data.structure', 'kind_name': <property object>, '__str__': <function __str__>, 'get_raw': <function get_raw>, 'get_ase': <function get_ase>, '__repr__': <function __repr__>, '__dict__': <attribute '__dict__' of 'Site' objects>, 'position': <property object>, '__weakref__': <attribute '__weakref__' of 'Site' objects>, '__doc__': '\n This class contains the information about a given site of the system.\n\n It can be a single atom, or an alloy, or even contain vacancies.\n ', '__init__': <function __init__>})

__init__(**kwargs)

Create a site.

Parameters:

• kind_name – a string that identifies the kind (species) of this site. This has to be found in the list of kinds of the StructureData object. Validation will be done at the StructureData level.
• position – the absolute position (three floats) in angstrom

__module__ = 'aiida.orm.nodes.data.structure'

__repr__() <==> repr(x)

__str__() <==> str(x)

__weakref__

list of weak references to the object (if defined)

get_ase(kinds)

Return a ase.Atom object for this site.

Parameters:kinds – the list of kinds from the StructureData object.

Note

If any site is an alloy or has vacancies, a ValueError is raised (from the site.get_ase() routine).

get_raw()

Return the raw version of the site, mapped to a suitable dictionary. This is the format that is actually used to store each site of the structure in the DB.

Returns:a python dictionary with the site.

kind_name

Return the kind name of this site (a string).

The type of a site is used to decide whether two sites are identical (same mass, symbols, weights, …) or not.

position

Return the position of this site in absolute coordinates, in angstrom.

This module manages the UPF pseudopotentials in the local repository.

class aiida.orm.nodes.data.upf.UpfData(filepath=None, source=None, **kwargs)

Bases: aiida.orm.nodes.data.singlefile.SinglefileData

Function not yet documented.

__abstractmethods__ = frozenset([])

__init__(filepath=None, source=None, **kwargs)

Parameters:backend_entity (aiida.orm.implementation.BackendEntity) – the backend model supporting this entity

__module__ = 'aiida.orm.nodes.data.upf'

_abc_cache = <_weakrefset.WeakSet object>

_abc_negative_cache = <_weakrefset.WeakSet object>

_abc_negative_cache_version = 40

_abc_registry = <_weakrefset.WeakSet object>

_logger = <logging.Logger object>

_plugin_type_string = 'data.upf.UpfData.'

_query_type_string = 'data.upf.'

_validate()

Ensure that there is one object stored in the repository, whose key matches value set for filename attr.

element

classmethod from_md5(md5)

Return a list of all UPF pseudopotentials that match a given MD5 hash.

Note that the hash has to be stored in a _md5 attribute, otherwise the pseudo will not be found.

classmethod get_or_create(filepath, use_first=False, store_upf=True)

Pass the same parameter of the init; if a file with the same md5 is found, that UpfData is returned.

Parameters:

• filepath – an absolute filepath on disk
• use_first – if False (default), raise an exception if more than one potential is found. If it is True, instead, use the first available pseudopotential.
• store_upf (bool) – If false, the UpfData objects are not stored in the database. default=True.

Return (upf, created):  

where upf is the UpfData object, and create is either True if the object was created, or False if the object was retrieved from the DB.

get_upf_family_names()

Get the list of all upf family names to which the pseudo belongs.

classmethod get_upf_group(group_label)

Return the UpfFamily group with the given name.

classmethod get_upf_groups(filter_elements=None, user=None)

Return all names of groups of type UpfFamily, possibly with some filters.

Parameters:

• filter_elements – A string or a list of strings. If present, returns only the groups that contains one Upf for every element present in the list. Default=None, meaning that all families are returned.
• user – if None (default), return the groups for all users. If defined, it should be either a DbUser instance, or a string for the username (that is, the user email).

md5sum

put_object_from_file(filename)

I pre-parse the file to store the attributes.

store(*args, **kwargs)

Store the node, reparsing the file so that the md5 and the element are correctly reset.

upffamily_type_string = 'data.upf'