aiida.orm
#
Main module to expose all orm classes and methods
Package Contents#
Classes#
Abstract data plugin representing an executable code. |
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Some python black magic to set correctly the logger also in subclasses. |
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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). |
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An object used internally to return the attributes as a dictionary.
This is currently used in |
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ORM class that models the authorization information that allows a User to connect to a Computer. |
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Group to be used to contain selected nodes generated, whilst autogrouping is enabled. |
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Class to handle bands data |
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Data sub class to be used as a base for data containers that represent base python data types. |
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Data sub class to represent a boolean value. |
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ORM class for all nodes representing the execution of a calcfunction. |
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ORM class for all nodes representing the execution of a CalcJob. |
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Utility class to easily access the contents of the ‘default output’ node of a CalcJobNode. |
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Loader for the Calculation entity and sub classes. |
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Base class for all nodes representing the execution of a calculation process. |
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Wrapper for Crystallographic Interchange File (CIF) |
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A code entity. It can either be ‘local’, or ‘remote’. |
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Loader for the Code entity and sub classes. |
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Container class that represents the collection of objects of a particular entity type. |
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Base class to map a DbComment that represents a comment attached to a certain Node. |
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Computer entity. |
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Loader for the Computer entity and sub classes. |
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Data plugin representing an executable code in container on a remote computer. |
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The base class for all Data nodes. |
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Data sub class to represent a dictionary. |
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An AiiDA entity |
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Interface to the extras of a node or group instance. |
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Enum for referring to ORM entities in a backend-agnostic manner. |
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Data plugin that allows to easily wrap an |
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Data sub class to represent a float value. |
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Data sub class to represent a folder on a file system. |
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An AiiDA ORM implementation of group of nodes. |
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Loader for the Group entity and sub classes. |
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Group to be used to contain all nodes from an export archive that has been imported. |
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Data plugin representing an executable code on a remote computer. |
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Data sub class to represent an integer value. |
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Data plugin that allows to easily wrap objects that are JSON-able. |
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This class contains the information about the species (kinds) of the system. |
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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. |
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Class to convert a list of LinkTriple tuples into an iterator. |
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Data sub class to represent a list. |
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An AiiDA Log entity. Corresponds to a logged message against a particular AiiDA node. |
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Base class for all nodes in AiiDA. |
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Interface to the attributes of a node instance. |
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Loader for the Node entity and sub classes. |
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A manager that allows to inspect, with tab-completion, nodes linked to a given one. See an example of its use in CalculationNode.inputs. |
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Interface to the file repository of a node instance. |
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Sub class of Data to store numbers, overloading common operators ( |
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Used for storing collections of orbitals, as well as providing methods for accessing them internally. |
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Base class for entity loaders. |
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Data plugin representing an executable code stored in AiiDA’s storage. |
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Base class for all nodes representing the execution of a process |
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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) > |
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The class to query the AiiDA database. |
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Store a link to a file or folder on a remote machine. |
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Data plugin that models an archived folder on a remote computer. |
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Data plugin that models a folder with files of a completed calculation job that has been stashed through a copy. |
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Data class that can be used to store a single file in its repository. |
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This class contains the information about a given site of the system. |
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Data sub class to represent a string value. |
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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. |
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Stores a trajectory (a sequence of crystal structures with timestamps, and possibly with velocities). |
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Data sub class to represent a pseudopotential single file in UPF format. |
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Group that represents a pseudo potential family containing UpfData nodes. |
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AiiDA User |
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ORM class for all nodes representing the execution of a WorkChain. |
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ORM class for all nodes representing the execution of a workfunction. |
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Base class for all nodes representing the execution of a workflow process. |
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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. |
Functions#
Construct a CIF datablock from the ASE structure. The code is taken from https://wiki.fysik.dtu.dk/ase/ase/io/formatoptions.html#ase.io.cif.write_cif, as the original ASE code contains a bug in printing the Hermann-Mauguin symmetry space group symbol. |
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Tries to guess whether the bandsdata represent an insulator. This method is meant to be used only for electronic bands (not phonons) By default, it will try to use the occupations to guess the number of electrons and find the Fermi Energy, otherwise, it can be provided explicitely. Also, there is an implicit assumption that the kpoints grid is “sufficiently” dense, so that the bandsdata are not missing the intersection between valence and conduction band if present. Use this function with care! |
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Return the correct OrmEntityLoader for the given orm class. |
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Take the type string of a Node and create the queryable type string |
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Given the module and name of a class, determine the orm_class_type string, which codifies the orm class that is to be used. The returned string will always have a terminating period, which is required to query for the string in the database |
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Load a Code instance by one of its identifiers: pk, uuid or label |
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Load a Computer instance by one of its identifiers: pk, uuid or label |
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Load an entity instance by one of its identifiers: pk, uuid or label |
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Load a Group instance by one of its identifiers: pk, uuid or label |
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Load a node by one of its identifiers: pk or uuid. If the type of the identifier is unknown simply pass it without a keyword and the loader will attempt to infer the type |
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Return the Node sub class that corresponds to the given type string. |
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Constructs PyCifRW’s CifFile from an array of CIF datablocks. |
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Turns basic Python types (str, int, float, bool) into the corresponding AiiDA types. |
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Validate adding a link of the given type and label from a given node to ourself. |
Data#
API#
- aiida.orm.ASCENDING = 'asc'#
- class aiida.orm.AbstractCode(default_calc_job_plugin: str | None = None, append_text: str = '', prepend_text: str = '', use_double_quotes: bool = False, is_hidden: bool = False, **kwargs)#
Bases:
aiida.orm.nodes.data.data.Data
Abstract data plugin representing an executable code.
Initialization
Construct a new instance.
- Parameters:
default_calc_job_plugin – The entry point name of the default
CalcJob
plugin to use.append_text – The text that should be appended to the run line in the job script.
prepend_text – The text that should be prepended to the run line in the job script.
use_double_quotes – Whether the command line invocation of this code should be escaped with double quotes.
is_hidden – Whether the code is hidden.
- abstract can_run_on_computer(computer: aiida.orm.Computer) bool #
Return whether the code can run on a given computer.
- Parameters:
computer – The computer.
- Returns:
True
if the code can run oncomputer
,False
otherwise.
- abstract get_executable() pathlib.PurePosixPath #
Return the executable that the submission script should execute to run the code.
- Returns:
The executable to be called in the submission script.
- get_executable_cmdline_params(cmdline_params: list[str] | None = None) list #
Return the list of executable with its command line parameters.
- Parameters:
cmdline_params – List of command line parameters provided by the
CalcJob
plugin.- Returns:
List of the executable followed by its command line parameters.
- get_prepend_cmdline_params(mpi_args: list[str] | None = None, extra_mpirun_params: list[str] | None = None) list[str] #
Return List of command line parameters to be prepended to the executable in submission line. These command line parameters are typically parameters related to MPI invocations.
- Parameters:
mpi_args – List of MPI parameters provided by the
Computer.get_mpirun_command
method.extra_mpiruns_params – List of MPI parameters provided by the
metadata.options.extra_mpirun_params
input of theCalcJob
.
- Returns:
List of command line parameters to be prepended to the executable in submission line.
- validate_working_directory(folder: aiida.common.folders.Folder)#
Validate content of the working directory created by the
CalcJob
plugin.This method will be called by
presubmit()
when a new calculation job is launched, passing theFolder
that was used by the plugin used for the calculation to create the input files for the working directory. This method can be overridden by implementations of theAbstractCode
class that need to validate the contents of that folder.- Parameters:
folder – A sandbox folder that the
CalcJob
plugin wrote input files to that will be copied to the working directory for the corresponding calculation job instance.- Raises:
PluginInternalError – If the content of the sandbox folder is not valid.
- abstract property full_label: str#
Return the full label of this code.
The full label can be just the label itself but it can be something else. However, it at the very least has to include the label of the code.
- Returns:
The full label of the code.
- property default_calc_job_plugin: str | None#
Return the optional default
CalcJob
plugin.- Returns:
The entry point name of the default
CalcJob
plugin to use.
- property append_text: str#
Return the text that should be appended to the run line in the job script.
- Returns:
The text that should be appended to the run line in the job script.
- property prepend_text: str#
Return the text that should be prepended to the run line in the job script.
- Returns:
The text that should be prepended to the run line in the job script.
- property use_double_quotes: bool#
Return whether the command line invocation of this code should be escaped with double quotes.
- Returns:
True
if to escape with double quotes,False
otherwise.
Return whether the code is hidden.
- Returns:
True
if the code is hidden,False
otherwise, which is also the default.
- get_builder() aiida.engine.ProcessBuilder #
Create and return a new
ProcessBuilder
for theCalcJob
class of the plugin configured for this code.The configured calculation plugin class is defined by the
default_calc_job_plugin
property.Note
it also sets the
builder.code
value.- Returns:
a
ProcessBuilder
instance with thecode
input already populated with ourselves- Raises:
aiida.common.EntryPointError – if the specified plugin does not exist.
ValueError – if no default plugin was specified.
- static cli_validate_label_uniqueness(_, __, value)#
Validate the uniqueness of the label of the code.
- classmethod get_cli_options() collections.OrderedDict #
Return the CLI options that would allow to create an instance of this class.
- class aiida.orm.AbstractNodeMeta#
Bases:
abc.ABCMeta
Some python black magic to set correctly the logger also in subclasses.
- __new__(name, bases, namespace, **kwargs)#
- class aiida.orm.ArrayData(*args, source=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 theclear_internal_cache()
method.
Initialization
Construct a new instance, setting the
source
attribute if provided as a keyword argument.- array_prefix = 'array|'#
- _cached_arrays = None#
- initialize()#
- 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_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
- _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.
- 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.
- get_iterarrays()#
Iterator that returns tuples (name, array) for each array stored in the node.
New in version 1.0: Renamed from iterarrays
- get_array(name)#
Return an array stored in the node
- Parameters:
name – The name of the array to return.
- 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.
- 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.
- _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.
- _get_array_entries()#
Return a dictionary with the different array entries.
The idea is that this dictionary contains the array name as a key and the value is the numpy array transformed into a list. This is so that it can be transformed into a json object.
- _prepare_json(main_file_name='', comments=True)#
Dump the content of the arrays stored in this node into JSON format.
- Parameters:
comments – if True, includes comments (if it makes sense for the given format)
- class aiida.orm.AttributeManager(node)#
An object used internally to return the attributes as a dictionary. This is currently used in
Dict
, for instance.- Note:
Important! It cannot be used to change variables, just to read them. To change values (of unstored nodes), use the proper Node methods.
Initialization
- Parameters:
node – the node object.
- __dir__()#
Allow to list the keys of the dictionary
- __iter__()#
Return the keys as an iterator
- _get_dict()#
Return the internal dictionary
- __getattr__(name)#
Interface to get to dictionary values, using the key as an attribute.
- Note:
it works only for attributes that only contain letters, numbers and underscores, and do not start with a number.
- Parameters:
name – name of the key whose value is required.
- __setattr__(name, value)#
- __getitem__(name)#
Interface to get to dictionary values as a dictionary.
- Parameters:
name – name of the key whose value is required.
- class aiida.orm.AuthInfo(computer: aiida.orm.Computer, user: aiida.orm.User, backend: Optional[aiida.orm.implementation.StorageBackend] = None)#
Bases:
aiida.orm.entities.Entity
[aiida.orm.implementation.BackendAuthInfo
,aiida.orm.authinfos.AuthInfoCollection
]ORM class that models the authorization information that allows a User to connect to a Computer.
Initialization
Create an AuthInfo instance for the given computer and user.
- Parameters:
computer – a Computer instance
user – a User instance
backend – the backend to use for the instance, or use the default backend if None
- _CLS_COLLECTION = None#
- PROPERTY_WORKDIR = 'workdir'#
- property enabled: bool#
Return whether this instance is enabled.
- Returns:
True if enabled, False otherwise
- property computer: aiida.orm.Computer#
Return the computer associated with this instance.
- property user: aiida.orm.User#
Return the user associated with this instance.
- get_auth_params() Dict[str, Any] #
Return the dictionary of authentication parameters
- Returns:
a dictionary with authentication parameters
- set_auth_params(auth_params: Dict[str, Any]) None #
Set the dictionary of authentication parameters
- Parameters:
auth_params – a dictionary with authentication parameters
- get_metadata() Dict[str, Any] #
Return the dictionary of metadata
- Returns:
a dictionary with metadata
- set_metadata(metadata: Dict[str, Any]) None #
Set the dictionary of metadata
- Parameters:
metadata – a dictionary with metadata
- get_workdir() str #
Return the working directory.
If no explicit work directory is set for this instance, the working directory of the computer will be returned.
- Returns:
the working directory
- get_transport() aiida.transports.Transport #
Return a fully configured transport that can be used to connect to the computer set for this instance.
- class aiida.orm.AutoGroup(label: Optional[str] = None, user: Optional[aiida.orm.User] = None, description: str = '', type_string: Optional[str] = None, backend: Optional[aiida.orm.implementation.StorageBackend] = None)#
Bases:
aiida.orm.groups.Group
Group to be used to contain selected nodes generated, whilst autogrouping is enabled.
Initialization
Create a new group. Either pass a dbgroup parameter, to reload a group from the DB (and then, no further parameters are allowed), or pass the parameters for the Group creation.
- Parameters:
label – The group label, required on creation
description – The group description (by default, an empty string)
user – The owner of the group (by default, the automatic user)
type_string – a string identifying the type of group (by default, an empty string, indicating an user-defined group.
- class aiida.orm.BandsData#
Bases:
aiida.orm.nodes.data.array.kpoints.KpointsData
Class to handle bands data
- set_kpointsdata(kpointsdata)#
Load the kpoints from a kpoint object. :param kpointsdata: an instance of KpointsData class
- _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.
- 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
- property array_labels#
Get the labels associated with the band arrays
- property units#
Units in which the data in bands were stored. A string
- _set_pbc(value)#
validate the pbc, then store them
- 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
- _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:
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.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).
- _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_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_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)
- _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
- _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()
- _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()
- static _get_mpl_body_template(paths)#
- Parameters:
paths – paths of k-points
- 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.
- _prepare_gnuplot(main_file_name=None, 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_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.
- _get_band_segments(cartesian)#
Return the band segments.
- _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)
- class aiida.orm.BaseType(value=None, **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.
Initialization
- property value#
- __str__()#
- __eq__(other)#
- new(value=None)#
- class aiida.orm.Bool(value=None, **kwargs)#
Bases:
aiida.orm.nodes.data.base.BaseType
Data sub class to represent a boolean value.
Initialization
- _type = None#
- __int__()#
- __bool__()#
- class aiida.orm.CalcFunctionNode(backend: Optional[aiida.orm.implementation.StorageBackend] = None, user: Optional[aiida.orm.users.User] = None, computer: Optional[aiida.orm.computers.Computer] = None, **kwargs: Any)#
Bases:
aiida.orm.utils.mixins.FunctionCalculationMixin
,aiida.orm.nodes.process.calculation.calculation.CalculationNode
ORM class for all nodes representing the execution of a calcfunction.
Initialization
- Parameters:
backend_entity – the backend model supporting this entity
- _CLS_NODE_LINKS = None#
- class aiida.orm.CalcJobNode(backend: Optional[aiida.orm.implementation.StorageBackend] = None, user: Optional[aiida.orm.users.User] = None, computer: Optional[aiida.orm.computers.Computer] = None, **kwargs: Any)#
Bases:
aiida.orm.nodes.process.calculation.calculation.CalculationNode
ORM class for all nodes representing the execution of a CalcJob.
Initialization
- Parameters:
backend_entity – the backend model supporting this entity
- CALC_JOB_STATE_KEY = 'state'#
- IMMIGRATED_KEY = 'imported'#
- REMOTE_WORKDIR_KEY = 'remote_workdir'#
- RETRIEVE_LIST_KEY = 'retrieve_list'#
- RETRIEVE_TEMPORARY_LIST_KEY = 'retrieve_temporary_list'#
- SCHEDULER_JOB_ID_KEY = 'job_id'#
- SCHEDULER_STATE_KEY = 'scheduler_state'#
- SCHEDULER_LAST_CHECK_TIME_KEY = 'scheduler_lastchecktime'#
- SCHEDULER_LAST_JOB_INFO_KEY = 'last_job_info'#
- SCHEDULER_DETAILED_JOB_INFO_KEY = 'detailed_job_info'#
- _tools = None#
- property tools: aiida.tools.calculations.CalculationTools#
Return the calculation tools that are registered for the process type associated with this calculation.
If the entry point name stored in the process_type of the CalcJobNode has an accompanying entry point in the aiida.tools.calculations entry point category, it will attempt to load the entry point and instantiate it passing the node to the constructor. If the entry point does not exist, cannot be resolved or loaded, a warning will be logged and the base CalculationTools class will be instantiated and returned.
- Returns:
CalculationTools instance
- get_builder_restart() aiida.engine.processes.builder.ProcessBuilder #
Return a ProcessBuilder that is ready to relaunch the same CalcJob that created this node.
The process class will be set based on the process_type of this node and the inputs of the builder will be prepopulated with the inputs registered for this node. This functionality is very useful if a process has completed and you want to relaunch it with slightly different inputs.
In addition to prepopulating the input nodes, which is implemented by the base ProcessNode class, here we also add the options that were passed in the metadata input of the CalcJob process.
- property is_imported: bool#
Return whether the calculation job was imported instead of being an actual run.
- get_option(name: str) Optional[Any] #
Retun the value of an option that was set for this CalcJobNode
- Parameters:
name – the option name
- Returns:
the option value or None
- Raises:
ValueError for unknown option
- set_option(name: str, value: Any) None #
Set an option to the given value
- Parameters:
name – the option name
value – the value to set
- Raises:
ValueError for unknown option
- Raises:
TypeError for values with invalid type
- get_options() Dict[str, Any] #
Return the dictionary of options set for this CalcJobNode
- Returns:
dictionary of the options and their values
- set_options(options: Dict[str, Any]) None #
Set the options for this CalcJobNode
- Parameters:
options – dictionary of option and their values to set
- get_state() Optional[aiida.common.datastructures.CalcJobState] #
Return the calculation job active sub state.
The calculation job state serves to give more granular state information to CalcJobs, in addition to the generic process state, while the calculation job is active. The state can take values from the enumeration defined in aiida.common.datastructures.CalcJobState and can be used to query for calculation jobs in specific active states.
- Returns:
instance of aiida.common.datastructures.CalcJobState or None if invalid value, or not set
- set_state(state: aiida.common.datastructures.CalcJobState) None #
Set the calculation active job state.
- Raise:
ValueError if state is invalid
- set_remote_workdir(remote_workdir: str) None #
Set the absolute path to the working directory on the remote computer where the calculation is run.
- Parameters:
remote_workdir – absolute filepath to the remote working directory
- get_remote_workdir() Optional[str] #
Return the path to the remote (on cluster) scratch folder of the calculation.
- Returns:
a string with the remote path
- static _validate_retrieval_directive(directives: Sequence[Union[str, Tuple[str, str, str]]]) None #
Validate a list or tuple of file retrieval directives.
- Parameters:
directives – a list or tuple of file retrieval directives
- Raises:
ValueError – if the format of the directives is invalid
- set_retrieve_list(retrieve_list: Sequence[Union[str, Tuple[str, str, str]]]) None #
Set the retrieve list.
This list of directives will instruct the daemon what files to retrieve after the calculation has completed. list or tuple of files or paths that should be retrieved by the daemon.
- Parameters:
retrieve_list – list or tuple of with filepath directives
- get_retrieve_list() Optional[Sequence[Union[str, Tuple[str, str, str]]]] #
Return the list of files/directories to be retrieved on the cluster after the calculation has completed.
- Returns:
a list of file directives
- set_retrieve_temporary_list(retrieve_temporary_list: Sequence[Union[str, Tuple[str, str, str]]]) None #
Set the retrieve temporary list.
The retrieve temporary list stores files that are retrieved after completion and made available during parsing and are deleted as soon as the parsing has been completed.
- Parameters:
retrieve_temporary_list – list or tuple of with filepath directives
- get_retrieve_temporary_list() Optional[Sequence[Union[str, Tuple[str, str, str]]]] #
Return list of files to be retrieved from the cluster which will be available during parsing.
- Returns:
a list of file directives
- set_job_id(job_id: Union[int, str]) None #
Set the job id that was assigned to the calculation by the scheduler.
Note
the id will always be stored as a string
- Parameters:
job_id – the id assigned by the scheduler after submission
- get_job_id() Optional[str] #
Return job id that was assigned to the calculation by the scheduler.
- Returns:
the string representation of the scheduler job id
- set_scheduler_state(state: aiida.schedulers.datastructures.JobState) None #
Set the scheduler state.
- Parameters:
state – an instance of JobState
- get_scheduler_state() Optional[aiida.schedulers.datastructures.JobState] #
Return the status of the calculation according to the cluster scheduler.
- Returns:
a JobState enum instance.
- get_scheduler_lastchecktime() Optional[datetime.datetime] #
Return the time of the last update of the scheduler state by the daemon or None if it was never set.
- Returns:
a datetime object or None
- set_detailed_job_info(detailed_job_info: Optional[dict]) None #
Set the detailed job info dictionary.
- Parameters:
detailed_job_info – a dictionary with metadata with the accounting of a completed job
- get_detailed_job_info() Optional[dict] #
Return the detailed job info dictionary.
The scheduler is polled for the detailed job info after the job is completed and ready to be retrieved.
- Returns:
the dictionary with detailed job info if defined or None
- set_last_job_info(last_job_info: aiida.schedulers.datastructures.JobInfo) None #
Set the last job info.
- Parameters:
last_job_info – a JobInfo object
- get_last_job_info() Optional[aiida.schedulers.datastructures.JobInfo] #
Return the last information asked to the scheduler about the status of the job.
The last job info is updated on every poll of the scheduler, except for the final poll when the job drops from the scheduler’s job queue. For completed jobs, the last job info therefore contains the “second-to-last” job info that still shows the job as running. Please use
get_detailed_job_info()
instead.- Returns:
a JobInfo object (that closely resembles a dictionary) or None.
- get_authinfo() aiida.orm.authinfos.AuthInfo #
Return the AuthInfo that is configured for the Computer set for this node.
- Returns:
AuthInfo
- get_transport() aiida.transports.Transport #
Return the transport for this calculation.
- Returns:
Transport configured with the AuthInfo associated to the computer of this node
- get_parser_class() Optional[Type[aiida.parsers.Parser]] #
Return the output parser object for this calculation or None if no parser is set.
- Returns:
a Parser class.
- Raises:
aiida.common.exceptions.EntryPointError – if the parser entry point can not be resolved.
- get_retrieved_node() Optional[aiida.orm.FolderData] #
Return the retrieved data folder.
- Returns:
the retrieved FolderData node or None if not found
- property res: aiida.orm.utils.calcjob.CalcJobResultManager#
To be used to get direct access to the parsed parameters.
- Returns:
an instance of the CalcJobResultManager.
- Note:
a practical example on how it is meant to be used: let’s say that there is a key ‘energy’ in the dictionary of the parsed results which contains a list of floats. The command calc.res.energy will return such a list.
- get_scheduler_stdout() Optional[AnyStr] #
Return the scheduler stderr output if the calculation has finished and been retrieved, None otherwise.
- Returns:
scheduler stderr output or None
- class aiida.orm.CalcJobResultManager(node)#
Utility class to easily access the contents of the ‘default output’ node of a CalcJobNode.
A CalcJob process can mark one of its outputs as the ‘default output’. The default output node will always be returned by the CalcJob and will always be a Dict node.
If a CalcJob defines such a default output node, this utility class will simplify retrieving the result of said node through the CalcJobNode instance produced by the execution of the CalcJob.
The default results are only defined if the CalcJobNode has a process_type that can be successfully used to load the corresponding CalcJob process class and if its process spec defines a default_output_node. If both these conditions are met, the results are defined as the dictionary contained within the default output node.
Initialization
Construct an instance of the CalcJobResultManager.
- Parameters:
calc – the CalcJobNode instance.
- property node#
Return the CalcJobNode associated with this result manager instance.
- _load_results()#
Try to load the results for the CalcJobNode of this result manager.
- Raises:
ValueError – if no default output node could be loaded
- get_results()#
Return the results dictionary of the default results node of the calculation node.
This property will lazily load the dictionary.
- Returns:
the dictionary of the default result node
- __dir__()#
Add the keys of the results dictionary such that they can be autocompleted.
- __iter__()#
Return an iterator over the keys of the result dictionary.
- __getattr__(name)#
Return an attribute from the results dictionary.
- Parameters:
name – name of the result return
- Returns:
value of the attribute
- Raises:
AttributeError – if the results node cannot be retrieved or it does not contain the name attribute
- class aiida.orm.CalculationEntityLoader#
Bases:
aiida.orm.utils.loaders.OrmEntityLoader
Loader for the Calculation entity and sub classes.
- orm_base_class()#
Return the orm base class to which loaded entities should be mapped. Actual queries to load an entity may further narrow the query set by defining a more specific set of orm classes, as long as each of those is a strict sub class of the orm base class.
- Returns:
the orm base class
- classmethod _get_query_builder_label_identifier(identifier, classes, operator='==', project='*')#
Return the query builder instance that attempts to map the identifier onto an entity of the orm class, defined for this loader class, interpreting the identifier as a LABEL like identifier
- Parameters:
identifier – the LABEL identifier
classes – a tuple of orm classes to which the identifier should be mapped
operator – the operator to use in the query
project – the property or properties to project for entities matching the query
- Returns:
the query builder instance that should retrieve the entity corresponding to the identifier
- Raises:
ValueError – if the identifier is invalid
aiida.common.NotExistent – if the orm base class does not support a LABEL like identifier
- class aiida.orm.CalculationNode(backend: Optional[aiida.orm.implementation.StorageBackend] = None, user: Optional[aiida.orm.users.User] = None, computer: Optional[aiida.orm.computers.Computer] = None, **kwargs: Any)#
Bases:
aiida.orm.nodes.process.process.ProcessNode
Base class for all nodes representing the execution of a calculation process.
Initialization
- Parameters:
backend_entity – the backend model supporting this entity
- _storable = True#
- _cachable = True#
- _unstorable_message = 'storing for this node has been disabled'#
- property inputs: aiida.orm.utils.managers.NodeLinksManager#
Return an instance of NodeLinksManager to manage incoming INPUT_CALC links
The returned Manager allows you to easily explore the nodes connected to this node via an incoming INPUT_CALC link. The incoming nodes are reachable by their link labels which are attributes of the manager.
- property outputs: aiida.orm.utils.managers.NodeLinksManager#
Return an instance of NodeLinksManager to manage outgoing CREATE links
The returned Manager allows you to easily explore the nodes connected to this node via an outgoing CREATE link. The outgoing nodes are reachable by their link labels which are attributes of the manager.
- class aiida.orm.CifData(ase=None, file=None, filename=None, values=None, scan_type=None, parse_policy=None, **kwargs)#
Bases:
aiida.orm.nodes.data.singlefile.SinglefileData
Wrapper for Crystallographic Interchange File (CIF)
Note
the file (physical) is held as the authoritative source of information, so all conversions are done through the physical file: when setting
ase
orvalues
, a physical CIF file is generated first, the values are updated from the physical CIF file.Initialization
Construct a new instance and set the contents to that of the file.
- Parameters:
file – an absolute filepath or filelike object for CIF. Hint: Pass io.BytesIO(b”my string”) to construct the SinglefileData directly from a string.
filename – specify filename to use (defaults to name of provided file).
ase – ASE Atoms object to construct the CifData instance from.
values – PyCifRW CifFile object to construct the CifData instance from.
scan_type – scan type string for parsing with PyCIFRW (‘standard’ or ‘flex’). See CifFile.ReadCif
parse_policy – ‘eager’ (parse CIF file on set_file) or ‘lazy’ (defer parsing until needed)
- _SET_INCOMPATIBILITIES = [('ase', 'file'), ('ase', 'values'), ('file', 'values')]#
- _SCAN_TYPES = ('standard', 'flex')#
- _SCAN_TYPE_DEFAULT = 'standard'#
- _PARSE_POLICIES = ('eager', 'lazy')#
- _PARSE_POLICY_DEFAULT = 'eager'#
- _values = None#
- _ase = None#
- 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.
Somewhere from 3.12 to 3.17 the tag concept was bundled with each Atom object. When reading a CIF file, this is incremented and signifies the atomic species, even though the CIF file do not have specific tags embedded. On reading CIF files we thus force the ASE tag to zero for all Atom elements.
- classmethod from_md5(md5, backend=None)#
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.
- 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.
- property ase#
ASE object, representing the CIF.
Note
requires ASE module.
- 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.
- set_ase(aseatoms)#
Set the contents of the CifData starting from an ASE atoms object
- Parameters:
aseatoms – the ASE atoms object
- property values#
PyCifRW structure, representing the CIF datablocks.
Note
requires PyCifRW module.
- 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.
- parse(scan_type=None)#
Parses CIF file and sets attributes.
- Parameters:
scan_type – See set_scan_type
- store(*args, **kwargs)#
Store the node.
- set_file(file, filename=None)#
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.
- Parameters:
file – filepath or filelike object of the CIF file to store. Hint: Pass io.BytesIO(b”my string”) to construct the file directly from a string.
filename – specify filename to use (defaults to name of provided file).
- 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_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)
- get_formulae(mode='sum', custom_tags=None)#
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.
- get_spacegroup_numbers()#
Get the spacegroup international number.
- property 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
- property 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.
- property 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
- property 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
- property 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
- generate_md5()#
Computes and returns MD5 hash of the CIF file.
- 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:
- _prepare_cif(**kwargs)#
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.
- _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
- _validate()#
Validates MD5 hash of CIF file.
- class aiida.orm.Code(remote_computer_exec=None, local_executable=None, input_plugin_name=None, files=None, **kwargs)#
Bases:
aiida.orm.nodes.data.code.abstract.AbstractCode
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).
Initialization
- HIDDEN_KEY = 'hidden'#
- can_run_on_computer(computer: aiida.orm.Computer) bool #
Return whether the code can run on a given computer.
- Parameters:
computer – The computer.
- Returns:
True
if the code can run oncomputer
,False
otherwise.
- get_executable() pathlib.PurePosixPath #
Return the executable that the submission script should execute to run the code.
- Returns:
The executable to be called in the submission script.
- hide()#
Hide the code (prevents from showing it in the verdi code list)
- reveal()#
Reveal the code (allows to show it in the verdi code list) By default, it is revealed
Determines whether the Code is hidden or not
- 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.
- __str__()#
- get_computer_label()#
Get label of this code’s computer.
- property full_label#
Get full label of this code.
Returns label of the form <code-label>@<computer-name>.
- relabel(new_label)#
Relabel this code.
- Parameters:
new_label – new code label
- get_description()#
Return a string description of this Code instance.
- Returns:
string description of this Code instance
- classmethod get_code_helper(label, machinename=None, backend=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
- 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
ValueError – if neither a pk nor a label was passed in
- 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
TypeError – if code_string is not of string type
- classmethod list_for_plugin(plugin, labels=True, backend=None)#
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.
- _validate()#
- validate_remote_exec_path()#
Validate the
remote_exec_path
attribute.Checks whether the executable exists on the remote computer if a transport can be opened to it. This method is intentionally not called in
_validate
as to allow the creation ofCode
instances whose computers can not yet be connected to and as to not require the overhead of opening transports in storing a new code.- Raises:
ValidationError – if no transport could be opened or if the defined executable does not exist on the remote computer.
- set_prepend_text(code)#
Pass a string of code that will be put in the scheduler script before the execution of the code.
- 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.
- 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.
- get_input_plugin_name()#
Return the name of the default input plugin (or None if no input plugin was set.
- set_use_double_quotes(use_double_quotes: bool)#
Set whether the command line invocation of this code should be escaped with double quotes.
- Parameters:
use_double_quotes – True if to escape with double quotes, False otherwise.
- get_use_double_quotes() bool #
Return whether the command line invocation of this code should be escaped with double quotes.
- Returns:
True if to escape with double quotes, False otherwise which is also the default.
- set_append_text(code)#
Pass a string of code that will be put in the scheduler script after the execution of the code.
- get_append_text()#
Return the postexec_code, or an empty string if no post-exec code was defined.
- set_local_executable(exec_name)#
Set the filename of the local executable. Implicitly set the code as local.
- get_local_executable()#
- 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.
- get_remote_exec_path()#
- get_remote_computer()#
Return the remote computer associated with this 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)
- 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).
- 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.
- 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.
- class aiida.orm.CodeEntityLoader#
Bases:
aiida.orm.utils.loaders.OrmEntityLoader
Loader for the Code entity and sub classes.
- orm_base_class()#
Return the orm base class to which loaded entities should be mapped. Actual queries to load an entity may further narrow the query set by defining a more specific set of orm classes, as long as each of those is a strict sub class of the orm base class.
- Returns:
the orm base class
- classmethod _get_query_builder_label_identifier(identifier, classes, operator='==', project='*')#
Return the query builder instance that attempts to map the identifier onto an entity of the orm class, defined for this loader class, interpreting the identifier as a LABEL like identifier
- Parameters:
identifier – the LABEL identifier
classes – a tuple of orm classes to which the identifier should be mapped
operator – the operator to use in the query
project – the property or properties to project for entities matching the query
- Returns:
the query builder instance that should retrieve the entity corresponding to the identifier
- Raises:
ValueError – if the identifier is invalid
aiida.common.NotExistent – if the orm base class does not support a LABEL like identifier
- class aiida.orm.Collection(entity_class: Type[aiida.orm.entities.EntityType], backend: Optional[aiida.orm.implementation.StorageBackend] = None)#
Bases:
abc.ABC
,typing.Generic
[aiida.orm.entities.EntityType
]Container class that represents the collection of objects of a particular entity type.
Initialization
Construct a new entity collection.
- Parameters:
entity_class – the entity type e.g. User, Computer, etc
backend – the backend instance to get the collection for, or use the default
- abstract static _entity_base_cls() Type[aiida.orm.entities.EntityType] #
The allowed entity class or subclasses thereof.
- classmethod get_cached(entity_class: Type[aiida.orm.entities.EntityType], backend: aiida.orm.implementation.StorageBackend)#
Get the cached collection instance for the given entity class and backend.
- Parameters:
backend – the backend instance to get the collection for
- __call__(backend: aiida.orm.implementation.StorageBackend) aiida.orm.entities.CollectionType #
Get or create a cached collection using a new backend.
- property backend: aiida.orm.implementation.StorageBackend#
Return the backend.
- query(filters: Optional[aiida.orm.querybuilder.FilterType] = None, order_by: Optional[aiida.orm.querybuilder.OrderByType] = None, limit: Optional[int] = None, offset: Optional[int] = None) aiida.orm.querybuilder.QueryBuilder #
Get a query builder for the objects of this collection.
- Parameters:
filters – the keyword value pair filters to match
order_by – a list of (key, direction) pairs specifying the sort order
limit – the maximum number of results to return
offset – number of initial results to be skipped
- get(**filters: Any) aiida.orm.entities.EntityType #
Get a single collection entry that matches the filter criteria.
- Parameters:
filters – the filters identifying the object to get
- Returns:
the entry
- find(filters: Optional[aiida.orm.querybuilder.FilterType] = None, order_by: Optional[aiida.orm.querybuilder.OrderByType] = None, limit: Optional[int] = None) List[aiida.orm.entities.EntityType] #
Find collection entries matching the filter criteria.
- Parameters:
filters – the keyword value pair filters to match
order_by – a list of (key, direction) pairs specifying the sort order
limit – the maximum number of results to return
- Returns:
a list of resulting matches
- class aiida.orm.Comment(node: aiida.orm.Node, user: aiida.orm.User, content: Optional[str] = None, backend: Optional[aiida.orm.implementation.StorageBackend] = None)#
Bases:
aiida.orm.entities.Entity
[aiida.orm.implementation.BackendComment
,aiida.orm.comments.CommentCollection
]Base class to map a DbComment that represents a comment attached to a certain Node.
Initialization
Create a Comment for a given node and user
- Parameters:
node – a Node instance
user – a User instance
content – the comment content
backend – the backend to use for the instance, or use the default backend if None
- Returns:
a Comment object associated to the given node and user
- _CLS_COLLECTION = None#
- property uuid: str#
Return the UUID for this comment.
This identifier is unique across all entities types and backend instances.
- Returns:
the entity uuid
- property ctime: datetime.datetime#
- property mtime: datetime.datetime#
- set_mtime(value: datetime.datetime) None #
- property node: aiida.orm.Node#
- property user: aiida.orm.User#
- set_user(value: aiida.orm.User) None #
- class aiida.orm.Computer(label: Optional[str] = None, hostname: str = '', description: str = '', transport_type: str = '', scheduler_type: str = '', workdir: Optional[str] = None, backend: Optional[aiida.orm.implementation.StorageBackend] = None)#
Bases:
aiida.orm.entities.Entity
[aiida.orm.implementation.BackendComputer
,aiida.orm.computers.ComputerCollection
]Computer entity.
Initialization
Construct a new computer.
- _logger = 'getLogger(...)'#
- PROPERTY_MINIMUM_SCHEDULER_POLL_INTERVAL = 'minimum_scheduler_poll_interval'#
- PROPERTY_MINIMUM_SCHEDULER_POLL_INTERVAL__DEFAULT = 10.0#
- PROPERTY_WORKDIR = 'workdir'#
- PROPERTY_SHEBANG = 'shebang'#
- _CLS_COLLECTION = None#
- __repr__()#
- __str__()#
- property uuid: str#
Return the UUID for this computer.
This identifier is unique across all entities types and backend instances.
- Returns:
the entity uuid
- property logger: logging.Logger#
- _mpirun_command_validator(mpirun_cmd: Union[List[str], Tuple[str, ...]]) None #
Validates the mpirun_command variable. MUST be called after properly checking for a valid scheduler.
- validate() None #
Check if the attributes and files retrieved from the DB are valid. Raise a ValidationError if something is wrong.
Must be able to work even before storing: therefore, use the get_attr and similar methods that automatically read either from the DB or from the internal attribute cache.
For the base class, this is always valid. Subclasses will reimplement this. In the subclass, always call the super().validate() method first!
- classmethod _default_mpiprocs_per_machine_validator(def_cpus_per_machine: Optional[int]) None #
Validates the default number of CPUs per machine (node)
- classmethod default_memory_per_machine_validator(def_memory_per_machine: Optional[int]) None #
Validates the default amount of memory (kB) per machine (node)
- copy() aiida.orm.computers.Computer #
Return a copy of the current object to work with, not stored yet.
- store() aiida.orm.computers.Computer #
Store the computer in the DB.
Differently from Nodes, a computer can be re-stored if its properties are to be changed (e.g. a new mpirun command, etc.)
- delete_property(name: str, raise_exception: bool = True) None #
Delete a property from this computer
- Parameters:
name – the name of the property
raise_exception – if True raise if the property does not exist, otherwise return None
- set_property(name: str, value: Any) None #
Set a property on this computer
- Parameters:
name – the property name
value – the new value
- get_property(name: str, *args: Any) Any #
Get a property of this computer
- Parameters:
name – the property name
args – additional arguments
- Returns:
the property value
- get_use_double_quotes() bool #
Return whether the command line parameters of this computer should be escaped with double quotes.
- Returns:
True if to escape with double quotes, False otherwise which is also the default.
- set_use_double_quotes(val: bool) None #
Set whether the command line parameters of this computer should be escaped with double quotes.
- Parameters:
use_double_quotes – True if to escape with double quotes, False otherwise.
- get_mpirun_command() List[str] #
Return the mpirun command. Must be a list of strings, that will be then joined with spaces when submitting.
I also provide a sensible default that may be ok in many cases.
- set_mpirun_command(val: Union[List[str], Tuple[str, ...]]) None #
Set the mpirun command. It must be a list of strings (you can use string.split() if you have a single, space-separated string).
- get_default_mpiprocs_per_machine() Optional[int] #
Return the default number of CPUs per machine (node) for this computer, or None if it was not set.
- set_default_mpiprocs_per_machine(def_cpus_per_machine: Optional[int]) None #
Set the default number of CPUs per machine (node) for this computer. Accepts None if you do not want to set this value.
- get_default_memory_per_machine() Optional[int] #
Return the default amount of memory (kB) per machine (node) for this computer, or None if it was not set.
- set_default_memory_per_machine(def_memory_per_machine: Optional[int]) None #
Set the default amount of memory (kB) per machine (node) for this computer. Accepts None if you do not want to set this value.
- get_minimum_job_poll_interval() float #
Get the minimum interval between subsequent requests to poll the scheduler for job status.
Note
If no value was ever set for this computer it will fall back on the default provided by the associated transport class in the
DEFAULT_MINIMUM_JOB_POLL_INTERVAL
attribute. If the computer doesn’t have a transport class, or it cannot be loaded, or it doesn’t provide a job poll interval default, then this will fall back on thePROPERTY_MINIMUM_SCHEDULER_POLL_INTERVAL__DEFAULT
attribute of this class.- Returns:
The minimum interval (in seconds).
- set_minimum_job_poll_interval(interval: float) None #
Set the minimum interval between subsequent requests to update the list of jobs currently running on this computer.
- Parameters:
interval – The minimum interval in seconds
- get_workdir() str #
Get the working directory for this computer :return: The currently configured working directory
- get_authinfo(user: aiida.orm.User) aiida.orm.AuthInfo #
Return the aiida.orm.authinfo.AuthInfo instance for the given user on this computer, if the computer is configured for the given user.
- Parameters:
user – a User instance.
- Returns:
a AuthInfo instance
- Raises:
aiida.common.NotExistent – if the computer is not configured for the given user.
- property is_configured: bool#
Return whether the computer is configured for the current default user.
- Returns:
Boolean,
True
if the computer is configured for the current default user,False
otherwise.
- is_user_configured(user: aiida.orm.User) bool #
Is the user configured on this computer?
- Parameters:
user – the user to check
- Returns:
True if configured, False otherwise
- is_user_enabled(user: aiida.orm.User) bool #
Is the given user enabled to run on this computer?
- Parameters:
user – the user to check
- Returns:
True if enabled, False otherwise
- get_transport(user: Optional[aiida.orm.User] = None) aiida.transports.Transport #
Return a Transport class, configured with all correct parameters. The Transport is closed (meaning that if you want to run any operation with it, you have to open it first (i.e., e.g. for a SSH transport, you have to open a connection). To do this you can call
transports.open()
, or simply run within awith
statement:transport = Computer.get_transport() with transport: print(transports.whoami())
- Parameters:
user – if None, try to obtain a transport for the default user. Otherwise, pass a valid User.
- Returns:
a (closed) Transport, already configured with the connection parameters to the supercomputer, as configured with
verdi computer configure
for the user specified as a parameteruser
.
- get_transport_class() Type[aiida.transports.Transport] #
Get the transport class for this computer. Can be used to instantiate a transport instance.
- get_scheduler() aiida.schedulers.Scheduler #
Get a scheduler instance for this computer
- configure(user: Optional[aiida.orm.User] = None, **kwargs: Any) aiida.orm.AuthInfo #
Configure a computer for a user with valid auth params passed via kwargs
- Parameters:
user – the user to configure the computer for
- Kwargs:
the configuration keywords with corresponding values
- Returns:
the authinfo object for the configured user
- class aiida.orm.ComputerEntityLoader#
Bases:
aiida.orm.utils.loaders.OrmEntityLoader
Loader for the Computer entity and sub classes.
- orm_base_class()#
Return the orm base class to which loaded entities should be mapped. Actual queries to load an entity may further narrow the query set by defining a more specific set of orm classes, as long as each of those is a strict sub class of the orm base class.
- Returns:
the orm base class
- classmethod _get_query_builder_label_identifier(identifier, classes, operator='==', project='*')#
Return the query builder instance that attempts to map the identifier onto an entity of the orm class, defined for this loader class, interpreting the identifier as a LABEL like identifier
- Parameters:
identifier – the LABEL identifier
classes – a tuple of orm classes to which the identifier should be mapped
operator – the operator to use in the query
project – the property or properties to project for entities matching the query
- Returns:
the query builder instance that should retrieve the entity corresponding to the identifier
- Raises:
ValueError – if the identifier is invalid
aiida.common.NotExistent – if the orm base class does not support a LABEL like identifier
- class aiida.orm.ContainerizedCode(engine_command: str, image_name: str, **kwargs)#
Bases:
aiida.orm.nodes.data.code.installed.InstalledCode
Data plugin representing an executable code in container on a remote computer.
Initialization
- property filepath_executable: pathlib.PurePosixPath#
Return the filepath of the executable that this code represents.
Note
This is overridden from the base class since the path does not have to be absolute.
- Returns:
The filepath of the executable.
- property engine_command: str#
Return the engine command with image as template field of the containerized code
- Returns:
The engine command of the containerized code
- aiida.orm.DESCENDING = 'desc'#
- class aiida.orm.Data(*args, source=None, **kwargs)#
Bases:
aiida.orm.nodes.node.Node
The base class for all Data nodes.
AiiDA Data classes are subclasses of Node and must support multiple inheritance.
Architecture note: Calculation plugins are responsible for converting raw output data from simulation codes to Data nodes. Nodes are responsible for validating their content (see _validate method).
Initialization
Construct a new instance, setting the
source
attribute if provided as a keyword argument.- _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'#
- __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 piping through to the clone method and return the result.
- Returns:
an unstored clone of this Data node
- clone()#
Create a clone of the Data node.
- Returns:
an unstored clone of this Data node
- property 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.
- set_source(source)#
Sets the dictionary describing the source of Data object.
- property creator#
Return the creator of this node or None if it does not exist.
- Returns:
the creating node or None
- _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:
- 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
- _get_exporters()#
Get all implemented export formats. The convention is to find all _prepare_… methods. Returns a dictionary of method_name: method_function
- classmethod get_export_formats()#
Get the list of valid export format strings
- Returns:
a list of valid formats
- 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.
- 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.
- _get_importers()#
Get all implemented import formats. The convention is to find all _parse_… methods. Returns a list of strings.
- convert(object_format=None, *args)#
Convert the AiiDA StructureData into another python object
- Parameters:
object_format – Specify the output format
- _get_converters()#
Get all implemented converter formats. The convention is to find all _get_object_… methods. Returns a list of strings.
- class aiida.orm.Dict(value=None, **kwargs)#
Bases:
aiida.orm.nodes.data.data.Data
Data sub class to represent a dictionary.
The dictionary contents of a Dict node are stored in the database as attributes. The dictionary can be initialized through the dict argument in the constructor. After construction, values can be retrieved and updated through the item getters and setters, respectively:
node[‘key’] = ‘value’
Alternatively, the dict property returns an instance of the AttributeManager that can be used to get and set values through attribute notation:
node.dict.key = ‘value’
Note that trying to set dictionary values directly on the node, e.g. node.key = value, will not work as intended. It will merely set the key attribute on the node instance, but will not be stored in the database. As soon as the node goes out of scope, the value will be lost.
It is also relevant to note here the difference in something being an “attribute of a node” (in the sense that it is stored in the “attribute” column of the database when the node is stored) and something being an “attribute of a python object” (in the sense of being able to modify and access it as if it was a property of the variable, e.g. node.key = value). This is true of all types of nodes, but it becomes more relevant for Dict nodes where one is constantly manipulating these attributes.
Finally, all dictionary mutations will be forbidden once the node is stored.
Initialization
Initialise a
Dict
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:
value – dictionary to initialise the
Dict
node from
- __getitem__(key)#
- __setitem__(key, value)#
- __eq__(other)#
- 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
- 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
- items()#
Iterator of all items stored in the Dict node.
- property 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.
- class aiida.orm.Entity(backend_entity: aiida.orm.entities.BackendEntityType)#
Bases:
abc.ABC
,typing.Generic
[aiida.orm.entities.BackendEntityType
,aiida.orm.entities.CollectionType
]An AiiDA entity
Initialization
- Parameters:
backend_entity – the backend model supporting this entity
- objects() aiida.orm.entities.CollectionType #
Get a collection for objects of this type, with the default backend.
Deprecated since version This: will be removed in v3, use
collection
instead.- Returns:
an object that can be used to access entities of this type
- collection() aiida.orm.entities.CollectionType #
Get a collection for objects of this type, with the default backend.
- Returns:
an object that can be used to access entities of this type
- classmethod get(**kwargs)#
Get an entity of the collection matching the given filters.
- classmethod from_backend_entity(backend_entity: aiida.orm.entities.BackendEntityType) aiida.orm.entities.EntityType #
Construct an entity from a backend entity instance
- Parameters:
backend_entity – the backend entity
- Returns:
an AiiDA entity instance
- __getstate__()#
Prevent an ORM entity instance from being pickled.
- initialize() None #
Initialize instance attributes.
This will be called after the constructor is called or an entity is created from an existing backend entity.
- property id: int#
Return the id for this entity.
This identifier is guaranteed to be unique amongst entities of the same type for a single backend instance.
- Returns:
the entity’s id
- property pk: int#
Return the primary key for this entity.
This identifier is guaranteed to be unique amongst entities of the same type for a single backend instance.
- Returns:
the entity’s principal key
- store() aiida.orm.entities.EntityType #
Store the entity.
- property backend: aiida.orm.implementation.StorageBackend#
Get the backend for this entity
- property backend_entity: aiida.orm.entities.BackendEntityType#
Get the implementing class for this object
- class aiida.orm.EntityExtras(entity: Union[aiida.orm.nodes.node.Node, aiida.orm.groups.Group])#
Interface to the extras of a node or group instance.
Extras are a JSONable dictionary, stored on each entity, allowing for arbitrary data to be stored by users.
Extras are mutable, even after storing the entity, and as such are not deemed a core part of the provenance graph.
Initialization
Initialize the interface.
- property all: Dict[str, Any]#
Return the complete extras dictionary.
Warning
While the entity is unstored, this will return references of the extras on the database model, meaning that changes on the returned values (if they are mutable themselves, e.g. a list or dictionary) will automatically be reflected on the database model as well. As soon as the entity is stored, the returned extras will be a deep copy and mutations of the database extras will have to go through the appropriate set methods. Therefore, once stored, retrieving a deep copy can be a heavy operation. If you only need the keys or some values, use the iterators extras_keys and extras_items, or the getters get_extra and get_extra_many instead.
- Returns:
the extras as a dictionary
- get(key: str, default: Any = _NO_DEFAULT) Any #
Return the value of an extra.
Warning
While the entity is unstored, this will return a reference of the extra on the database model, meaning that changes on the returned value (if they are mutable themselves, e.g. a list or dictionary) will automatically be reflected on the database model as well. As soon as the entity is stored, the returned extra will be a deep copy and mutations of the database extras will have to go through the appropriate set methods.
- Parameters:
key – name of the extra
default – return this value instead of raising if the attribute does not exist
- Returns:
the value of the extra
- Raises:
AttributeError – if the extra does not exist and no default is specified
- get_many(keys: List[str]) List[Any] #
Return the values of multiple extras.
Warning
While the entity is unstored, this will return references of the extras on the database model, meaning that changes on the returned values (if they are mutable themselves, e.g. a list or dictionary) will automatically be reflected on the database model as well. As soon as the entity is stored, the returned extras will be a deep copy and mutations of the database extras will have to go through the appropriate set methods. Therefore, once stored, retrieving a deep copy can be a heavy operation. If you only need the keys or some values, use the iterators extras_keys and extras_items, or the getters get_extra and get_extra_many instead.
- Parameters:
keys – a list of extra names
- Returns:
a list of extra values
- Raises:
AttributeError – if at least one extra does not exist
- set(key: str, value: Any) None #
Set an extra to the given value.
- Parameters:
key – name of the extra
value – value of the extra
- Raises:
aiida.common.ValidationError – if the key is invalid, i.e. contains periods
- set_many(extras: Dict[str, Any]) None #
Set multiple extras.
Note
This will override any existing extras that are present in the new dictionary.
- Parameters:
extras – a dictionary with the extras to set
- Raises:
aiida.common.ValidationError – if any of the keys are invalid, i.e. contain periods
- reset(extras: Dict[str, Any]) None #
Reset the extras.
Note
This will completely clear any existing extras and replace them with the new dictionary.
- Parameters:
extras – a dictionary with the extras to set
- Raises:
aiida.common.ValidationError – if any of the keys are invalid, i.e. contain periods
- delete(key: str) None #
Delete an extra.
- Parameters:
key – name of the extra
- Raises:
AttributeError – if the extra does not exist
- delete_many(keys: List[str]) None #
Delete multiple extras.
- Parameters:
keys – names of the extras to delete
- Raises:
AttributeError – if at least one of the extra does not exist
- class aiida.orm.EntityTypes#
Bases:
enum.Enum
Enum for referring to ORM entities in a backend-agnostic manner.
- AUTHINFO = 'authinfo'#
- COMMENT = 'comment'#
- COMPUTER = 'computer'#
- GROUP = 'group'#
- LOG = 'log'#
- NODE = 'node'#
- USER = 'user'#
- LINK = 'link'#
- GROUP_NODE = 'group_node'#
- class aiida.orm.EnumData(member: enum.Enum, *args, **kwargs)#
Bases:
aiida.orm.nodes.data.data.Data
Data plugin that allows to easily wrap an
enum.Enum
member.The enum member is stored in the database by storing the value, name and the identifier (string that represents the class of the enumeration) in the
KEY_NAME
,KEY_VALUE
andKEY_IDENTIFIER
attribute, respectively. The original enum member can be reconstructured from the (loaded) node through theget_member
method. The enum itself can be retrieved from theget_enum
method. Like a normal enum member, theEnumData
plugin provides thename
andvalue
properties which return the name and value of the enum member, respectively.Initialization
Construct the node for the to enum member that is to be wrapped.
- KEY_NAME = 'name'#
- KEY_VALUE = 'value'#
- KEY_IDENTIFIER = 'identifier'#
- get_enum() Type[aiida.orm.nodes.data.enum.EnumType] #
Return the enum class reconstructed from the serialized identifier stored in the database.
- Raises:
ImportError – if the enum class represented by the stored identifier cannot be imported.
- get_member() aiida.orm.nodes.data.enum.EnumType #
Return the enum member reconstructed from the serialized data stored in the database.
For the enum member to be successfully reconstructed, the class of course has to still be importable and its implementation should not have changed since the node was stored. That is to say, the value of the member when it was stored, should still be a valid value for the enum class now.
- Raises:
ImportError – if the enum class represented by the stored identifier cannot be imported.
ValueError – if the stored enum member value is no longer valid for the imported enum class.
- class aiida.orm.Float#
Bases:
aiida.orm.nodes.data.numeric.NumericType
Data sub class to represent a float value.
- _type = None#
- class aiida.orm.FolderData(**kwargs)#
Bases:
aiida.orm.nodes.data.data.Data
Data sub class to represent a folder on a file system.
Initialization
Construct a new FolderData to which any files and folders can be added.
Use the tree keyword to simply wrap a directory:
folder = FolderData(tree=’/absolute/path/to/directory’)
Alternatively, one can construct the node first and then use the various repository methods to add objects:
folder = FolderData() folder.put_object_from_tree(‘/absolute/path/to/directory’) folder.put_object_from_filepath(‘/absolute/path/to/file.txt’) folder.put_object_from_filelike(filelike_object)
- Parameters:
tree (str) – absolute path to a folder to wrap
- class aiida.orm.Group(label: Optional[str] = None, user: Optional[aiida.orm.User] = None, description: str = '', type_string: Optional[str] = None, backend: Optional[aiida.orm.implementation.StorageBackend] = None)#
Bases:
aiida.orm.entities.Entity
[aiida.orm.implementation.BackendGroup
,aiida.orm.groups.GroupCollection
]An AiiDA ORM implementation of group of nodes.
Initialization
Create a new group. Either pass a dbgroup parameter, to reload a group from the DB (and then, no further parameters are allowed), or pass the parameters for the Group creation.
- Parameters:
label – The group label, required on creation
description – The group description (by default, an empty string)
user – The owner of the group (by default, the automatic user)
type_string – a string identifying the type of group (by default, an empty string, indicating an user-defined group.
- _CLS_COLLECTION = None#
- base() aiida.orm.groups.GroupBase #
Return the group base namespace.
- store() aiida.orm.groups.SelfType #
Verify that the group is allowed to be stored, which is the case along as type_string is set.
- entry_point() Optional[aiida.plugins.entry_point.EntryPoint] #
Return the entry point associated this group type.
- Returns:
the associated entry point or
None
if it isn’t known.
- property uuid: str#
Return the UUID for this group.
This identifier is unique across all entities types and backend instances.
- Returns:
the entity uuid
- property user: aiida.orm.User#
- Returns:
the user associated with this group
- count() int #
Return the number of entities in this group.
- Returns:
integer number of entities contained within the group
- property nodes: aiida.orm.convert.ConvertIterator#
Return a generator/iterator that iterates over all nodes and returns the respective AiiDA subclasses of Node, and also allows to ask for the number of nodes in the group using len().
- property is_empty: bool#
Return whether the group is empty, i.e. it does not contain any nodes.
- Returns:
True if it contains no nodes, False otherwise
- add_nodes(nodes: Union[aiida.orm.nodes.Node, Sequence[aiida.orm.nodes.Node]]) None #
Add a node or a set of nodes to the group.
- Note:
all the nodes and the group itself have to be stored.
- Parameters:
nodes – a single Node or a list of Nodes
- remove_nodes(nodes: Union[aiida.orm.nodes.Node, Sequence[aiida.orm.nodes.Node]]) None #
Remove a node or a set of nodes to the group.
- Note:
all the nodes and the group itself have to be stored.
- Parameters:
nodes – a single Node or a list of Nodes
- _deprecated_extra_methods = None#
- class aiida.orm.GroupEntityLoader#
Bases:
aiida.orm.utils.loaders.OrmEntityLoader
Loader for the Group entity and sub classes.
- orm_base_class()#
Return the orm base class to which loaded entities should be mapped. Actual queries to load an entity may further narrow the query set by defining a more specific set of orm classes, as long as each of those is a strict sub class of the orm base class.
- Returns:
the orm base class
- classmethod _get_query_builder_label_identifier(identifier, classes, operator='==', project='*')#
Return the query builder instance that attempts to map the identifier onto an entity of the orm class, defined for this loader class, interpreting the identifier as a LABEL like identifier
- Parameters:
identifier – the LABEL identifier
classes – a tuple of orm classes to which the identifier should be mapped
operator – the operator to use in the query
project – the property or properties to project for entities matching the query
- Returns:
the query builder instance that should retrieve the entity corresponding to the identifier
- Raises:
ValueError – if the identifier is invalid
aiida.common.NotExistent – if the orm base class does not support a LABEL like identifier
- class aiida.orm.ImportGroup(label: Optional[str] = None, user: Optional[aiida.orm.User] = None, description: str = '', type_string: Optional[str] = None, backend: Optional[aiida.orm.implementation.StorageBackend] = None)#
Bases:
aiida.orm.groups.Group
Group to be used to contain all nodes from an export archive that has been imported.
Initialization
Create a new group. Either pass a dbgroup parameter, to reload a group from the DB (and then, no further parameters are allowed), or pass the parameters for the Group creation.
- Parameters:
label – The group label, required on creation
description – The group description (by default, an empty string)
user – The owner of the group (by default, the automatic user)
type_string – a string identifying the type of group (by default, an empty string, indicating an user-defined group.
- class aiida.orm.InstalledCode(computer: aiida.orm.Computer, filepath_executable: str, **kwargs)#
Bases:
aiida.orm.nodes.data.code.legacy.Code
Data plugin representing an executable code on a remote computer.
Initialization
Construct a new instance.
- Parameters:
computer – The remote computer on which the executable is located.
filepath_executable – The absolute filepath of the executable on the remote computer.
- _validate()#
Validate the instance by checking that a computer has been defined.
:raises
aiida.common.exceptions.ValidationError
: If the state of the node is invalid.
- validate_filepath_executable()#
Validate the
filepath_executable
attribute.Checks whether the executable exists on the remote computer if a transport can be opened to it. This method is intentionally not called in
_validate
as to allow the creation ofCode
instances whose computers can not yet be connected to and as to not require the overhead of opening transports in storing a new code.- Raises:
ValidationError – if no transport could be opened or if the defined executable does not exist on the remote computer.
- can_run_on_computer(computer: aiida.orm.Computer) bool #
Return whether the code can run on a given computer.
- Parameters:
computer – The computer.
- Returns:
True
if the provided computer is the same as the one configured for this code.
- get_executable() pathlib.PurePosixPath #
Return the executable that the submission script should execute to run the code.
- Returns:
The executable to be called in the submission script.
- property computer: aiida.orm.Computer#
Return the computer of this code.
- property full_label: str#
Return the full label of this code.
The full label can be just the label itself but it can be something else. However, it at the very least has to include the label of the code.
- Returns:
The full label of the code.
- property filepath_executable: pathlib.PurePosixPath#
Return the absolute filepath of the executable that this code represents.
- Returns:
The absolute filepath of the executable.
- static cli_validate_label_uniqueness(ctx, _, value)#
Validate the uniqueness of the label of the code.
- class aiida.orm.Int#
Bases:
aiida.orm.nodes.data.numeric.NumericType
Data sub class to represent an integer value.
- _type = None#
- class aiida.orm.JsonableData(obj: aiida.orm.nodes.data.jsonable.JsonSerializableProtocol, *args, **kwargs)#
Bases:
aiida.orm.nodes.data.data.Data
Data plugin that allows to easily wrap objects that are JSON-able.
Any class that implements the
as_dict
method, returning a dictionary that is a JSON serializable representation of the object, can be wrapped and stored by this data plugin.As an example, take the
Molecule
class of thepymatgen
library, which respects the spec described above. To store an instance as aJsonableData
simply pass an instance as an argument to the constructor as follows:from pymatgen.core import Molecule molecule = Molecule(['H']. [0, 0, 0]) node = JsonableData(molecule) node.store()
Since
Molecule.as_dict
returns a dictionary that is JSON-serializable, the data plugin will call it and store the dictionary as the attributes of theJsonableData
node in the database.Note
A JSON-serializable dictionary means a dictionary that when passed to
json.dumps
does not except but produces a valid JSON string representation of the dictionary.If the wrapped class implements a class-method
from_dict
, the wrapped instance can easily be recovered from a previously stored node that was optionally loaded from the database. Thefrom_dict
method should simply accept a single argument which is the dictionary that is returned by theas_dict
method. If this criteria is satisfied, an instance wrapped and stored in aJsonableData
node can be recovered through theobj
property:loaded = load_node(node.pk) molecule = loaded.obj
Of course, this requires that the class of the originally wrapped instance can be imported in the current environment, or an
ImportError
will be raised.Initialization
Construct the node for the to be wrapped object.
- classmethod _deserialize_float_constants(data: Any)#
Deserialize the contents of a dictionary
data
deserializing infinity and NaN string constants.The
data
dictionary is recursively checked for theInfinity
,-Infinity
andNaN
strings, which are the Javascript string equivalents to the Pythonfloat('inf')
,-float('inf')
andfloat('nan')
float constants. If one of the strings is encountered, the Python float constant is returned and otherwise the original value is returned.
- _get_object() aiida.orm.nodes.data.jsonable.JsonSerializableProtocol #
Return the cached wrapped object.
Note
If the object is not yet present in memory, for example if the node was loaded from the database, the object will first be reconstructed from the state stored in the node attributes.
- property obj: aiida.orm.nodes.data.jsonable.JsonSerializableProtocol#
Return the wrapped object.
Note
This property caches the deserialized object, this means that when the node is loaded from the database, the object is deserialized only once and stored in memory as an attribute. Subsequent calls will simply return this cached object and not reload it from the database. This is fine, since nodes that are loaded from the database are by definition stored and therefore immutable, making it safe to assume that the object that is represented can not change. Note, however, that the caching also applies to unstored nodes. That means that manually changing the attributes of an unstored
JsonableData
can lead to inconsistencies with the object returned by this property.
- class aiida.orm.Kind(**kwargs)#
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.
Initialization
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.
- 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.
- 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.
- property name#
Return the name of this kind. The name of a kind is used to identify the species of a site.
- Returns:
a string
- 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.
- 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.
- property mass#
The mass of this species kind.
- Returns:
a float
- property weights#
Weights for this species kind. Refer also to :func:validate_symbols_tuple for the validation rules on the weights.
- 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!
- property symbol#
If the kind has only one symbol, return it; otherwise, raise a ValueError.
- property 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.
- set_symbols_and_weights(symbols, weights)#
Set the chemical symbols and the weights for the site.
Note
Note that the kind name remains unchanged.
- property 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.
- property 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
- __repr__()#
- __str__()#
- class aiida.orm.KpointsData(*args, source=None, **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.
Initialization
Construct a new instance, setting the
source
attribute if provided as a keyword argument.- get_description()#
Returns a string with infos retrieved from kpoints node’s properties. :param node: :return: retstr
- property cell#
The crystal unit cell. Rows are the crystal vectors in Angstroms. :return: a 3x3 numpy.array
- _set_cell(value)#
Validate if ‘value’ is a allowed crystal unit cell :param value: something compatible with a 3x3 tuple of floats
- property pbc#
The periodic boundary conditions along the vectors a1,a2,a3.
- Returns:
a tuple of three booleans, each one tells if there are periodic boundary conditions for the i-th real-space direction (i=1,2,3)
- _set_pbc(value)#
validate the pbc, then store them
- property labels#
Labels associated with the list of kpoints. List of tuples with kpoint index and kpoint name:
[(0,'G'),(13,'M'),...]
- _set_labels(value)#
set label names. Must pass in input a list like:
[[0,'X'],[34,'L'],... ]
- _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
- 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_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]
- property reciprocal_cell#
Compute reciprocal cell from the internally set cell.
- Returns:
reciprocal cell in units of 1/Angstrom with cell vectors stored as rows. Use e.g. reciprocal_cell[0] to access the first reciprocal cell vector.
- set_kpoints_mesh(mesh, offset=None)#
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.].
- 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()
- set_kpoints_mesh_from_density(distance, offset=None, 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 \(|b_i|/distance\) where \(|b_i|\) is the norm of the reciprocal cell vector.
offset – (optional) a list of three floats between 0 and 1. [0.,0.,0.] is Gamma centered mesh [0.5,0.5,0.5] is half shifted Default = [0.,0.,0.].
force_parity – (optional) if True, force each integer in the mesh to be even (except for the non-periodic directions).
- Note:
a cell should be defined first.
- Note:
the number of kpoints along non-periodic axes is always 1.
- property _dimension#
Dimensionality of the structure, found from its pbc (i.e. 1 if it’s a 1D structure, 2 if its 2D, 3 if it’s 3D …). :return dimensionality: 0, 1, 2 or 3 :note: will return 3 if pbc has not been set beforehand
- _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
- 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.
- 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.
- class aiida.orm.LinkManager(link_triples: List[aiida.orm.utils.links.LinkTriple])#
Class to convert a list of LinkTriple tuples into an iterator.
It defines convenience methods to retrieve certain subsets of LinkTriple while checking for consistency. For example:
LinkManager.one(): returns the only entry in the list or it raises an exception LinkManager.first(): returns the first entry from the list LinkManager.all(): returns all entries from list
The methods all_nodes and all_link_labels are syntactic sugar wrappers around all to get a list of only the incoming nodes or link labels, respectively.
Initialization
Initialise the collection.
- __iter__() Iterator[aiida.orm.utils.links.LinkTriple] #
Return an iterator of LinkTriple instances.
- Returns:
iterator of LinkTriple instances
- __next__() Generator[aiida.orm.utils.links.LinkTriple, None, None] #
Return the next element in the iterator.
- Returns:
LinkTriple
- __bool__()#
- next() Generator[aiida.orm.utils.links.LinkTriple, None, None] #
Return the next element in the iterator.
- Returns:
LinkTriple
- one() aiida.orm.utils.links.LinkTriple #
Return a single entry from the iterator.
If the iterator contains no or more than one entry, an exception will be raised :return: LinkTriple instance :raises ValueError: if the iterator contains anything but one entry
- first() Optional[aiida.orm.utils.links.LinkTriple] #
Return the first entry from the iterator.
- Returns:
LinkTriple instance or None if no entries were matched
- all() List[aiida.orm.utils.links.LinkTriple] #
Return all entries from the list.
- Returns:
list of LinkTriple instances
- all_nodes() List[aiida.orm.Node] #
Return a list of all nodes.
- Returns:
list of nodes
- all_link_pairs() List[aiida.orm.utils.links.LinkPair] #
Return a list of all link pairs.
- Returns:
list of LinkPair instances
- get_node_by_label(label: str) aiida.orm.Node #
Return the node from list for given label.
- Returns:
node that corresponds to the given label
- Raises:
aiida.common.NotExistent – if the label is not present among the link_triples
- nested(sort=True)#
Construct (nested) dictionary of matched nodes that mirrors the original nesting of link namespaces.
Process input and output namespaces can be nested, however the link labels that represent them in the database have a flat hierarchy, and so the link labels are flattened representations of the nested namespaces. This function reconstructs the original node nesting based on the flattened links.
- Returns:
dictionary of nested namespaces
- Raises:
KeyError – if there are duplicate link labels in a namespace
- class aiida.orm.LinkPair#
Bases:
typing.NamedTuple
- link_type: aiida.common.links.LinkType = None#
- class aiida.orm.LinkTriple#
Bases:
typing.NamedTuple
- node: aiida.orm.Node = None#
- link_type: aiida.common.links.LinkType = None#
- class aiida.orm.List(value=None, **kwargs)#
Bases:
aiida.orm.nodes.data.data.Data
,collections.abc.MutableSequence
Data sub class to represent a list.
Initialization
Initialise a
List
node instance.- Parameters:
value – list to initialise the
List
node from
- _LIST_KEY = 'list'#
- __getitem__(item)#
- __setitem__(key, value)#
- __delitem__(key)#
- __len__()#
- __str__()#
- __eq__(other)#
- append(value)#
- extend(value)#
- insert(i, value)#
- remove(value)#
- pop(**kwargs)#
Remove and return item at index (default last).
- index(value)#
Return first index of value..
- count(value)#
Return number of occurrences of value.
- sort(key=None, reverse=False)#
- reverse()#
- get_list()#
Return the contents of this node.
- Returns:
a list
- set_list(data)#
Set the contents of this node.
- Parameters:
data – the list to set
- _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:
- class aiida.orm.Log(time: datetime.datetime, loggername: str, levelname: str, dbnode_id: int, message: str = '', metadata: Optional[Dict[str, Any]] = None, backend: Optional[aiida.orm.implementation.StorageBackend] = None)#
Bases:
aiida.orm.entities.Entity
[aiida.orm.implementation.BackendLog
,aiida.orm.logs.LogCollection
]An AiiDA Log entity. Corresponds to a logged message against a particular AiiDA node.
Initialization
Construct a new log
- Parameters:
time – time
loggername – name of logger
levelname – name of log level
dbnode_id – id of database node
message – log message
metadata – metadata
backend – database backend
- _CLS_COLLECTION = None#
- property uuid: str#
Return the UUID for this log.
This identifier is unique across all entities types and backend instances.
- Returns:
the entity uuid
- property time: datetime.datetime#
Get the time corresponding to the entry
- Returns:
The entry timestamp
- property loggername: str#
The name of the logger that created this entry
- Returns:
The entry loggername
- class aiida.orm.Node(backend: Optional[aiida.orm.implementation.StorageBackend] = None, user: Optional[aiida.orm.users.User] = None, computer: Optional[aiida.orm.computers.Computer] = None, **kwargs: Any)#
Bases:
aiida.orm.entities.Entity
[aiida.orm.implementation.BackendNode
,aiida.orm.nodes.node.NodeCollection
]Base class for all nodes in AiiDA.
Stores attributes starting with an underscore.
Caches files and attributes before the first save, and saves everything only on store(). After the call to store(), attributes cannot be changed.
Only after storing (or upon loading from uuid) extras can be modified and in this case they are directly set on the db.
In the plugin, also set the _plugin_type_string, to be set in the DB in the ‘type’ field.
Initialization
- Parameters:
backend_entity – the backend model supporting this entity
- _CLS_COLLECTION = None#
- _CLS_NODE_LINKS = None#
- _CLS_NODE_CACHING = None#
- _logger: Optional[logging.Logger] = None#
- _cachable = False#
- _storable = False#
- _unstorable_message = 'only Data, WorkflowNode, CalculationNode or their subclasses can be stored'#
- base() aiida.orm.nodes.node.NodeBase #
Return the node base namespace.
- _check_mutability_attributes(keys: Optional[List[str]] = None) None #
Check if the entity is mutable and raise an exception if not.
This is called from NodeAttributes methods that modify the attributes.
- Parameters:
keys – the keys that will be mutated, or all if None
- __eq__(other: Any) bool #
Fallback equality comparison by uuid (can be overwritten by specific types)
- __copy__()#
Copying a Node is not supported in general, but only for the Data sub class.
- __deepcopy__(memo)#
Deep copying a Node is not supported in general, but only for the Data sub class.
- _validate() bool #
Validate information stored in Node object.
For the
Node
base class, this check is always valid. Subclasses can override this method to perform additional checks and should usually callsuper()._validate()
first!This method is called automatically before storing the node in the DB. Therefore, use
get()
and similar methods that automatically read either from the DB or from the internal attribute cache.
- _validate_storability() None #
Verify that the current node is allowed to be stored.
- Raises:
aiida.common.exceptions.StoringNotAllowed – if the node does not match all requirements for storing
- entry_point() Optional[aiida.plugins.entry_point.EntryPoint] #
Return the entry point associated this node class.
- Returns:
the associated entry point or
None
if it isn’t known.
- property logger: Optional[logging.Logger]#
Return the logger configured for this Node.
- Returns:
Logger object
- property computer: Optional[aiida.orm.computers.Computer]#
Return the computer of this node.
- property user: aiida.orm.users.User#
Return the user of this node.
- property ctime: datetime.datetime#
Return the node ctime.
- Returns:
the ctime
- property mtime: datetime.datetime#
Return the node mtime.
- Returns:
the mtime
- store_all(with_transaction: bool = True) aiida.orm.nodes.node.Node #
Store the node, together with all input links.
Unstored nodes from cached incoming linkswill also be stored.
- Parameters:
with_transaction – if False, do not use a transaction because the caller will already have opened one.
- store(with_transaction: bool = True) aiida.orm.nodes.node.Node #
Store the node in the database while saving its attributes and repository directory.
After being called attributes cannot be changed anymore! Instead, extras can be changed only AFTER calling this store() function.
- Note:
After successful storage, those links that are in the cache, and for which also the parent node is already stored, will be automatically stored. The others will remain unstored.
- Parameters:
with_transaction – if False, do not use a transaction because the caller will already have opened one.
- _store(with_transaction: bool = True, clean: bool = True) aiida.orm.nodes.node.Node #
Store the node in the database while saving its attributes and repository directory.
- Parameters:
with_transaction – if False, do not use a transaction because the caller will already have opened one.
clean – boolean, if True, will clean the attributes and extras before attempting to store
- _verify_are_parents_stored() None #
Verify that all parent nodes are already stored.
- Raises:
aiida.common.ModificationNotAllowed – if one of the source nodes of incoming links is not stored.
- _store_from_cache(cache_node: aiida.orm.nodes.node.Node, with_transaction: bool) None #
Store this node from an existing cache node.
Note
With the current implementation of the backend repository, which automatically deduplicates the content that it contains, we do not have to copy the contents of the source node. Since the content should be exactly equal, the repository will already contain it and there is nothing to copy. We simply replace the current
repository
instance with a clone of that of the source node, which does not actually copy any files.
- _add_outputs_from_cache(cache_node: aiida.orm.nodes.node.Node) None #
Replicate the output links and nodes from the cached node onto this node.
- get_description() str #
Return a string with a description of the node.
- Returns:
a description string
- property is_valid_cache: bool#
Hook to exclude certain
Node
classes from being considered a valid cache.The base class assumes that all node instances are valid to cache from, unless the
_VALID_CACHE_KEY
extra has been set toFalse
explicitly. Subclasses can override this property with more specific logic, but should probably also consider the value returned by this base class.
- _deprecated_repo_methods = None#
- _deprecated_attr_methods = None#
- _deprecated_extra_methods = None#
- _deprecated_comment_methods = None#
- _deprecated_caching_methods = None#
- _deprecated_links_methods = None#
- Collection()#
Return the collection type for this class.
This used to be a class argument with the value
NodeCollection
. The argument is deprecated and this property is here for backwards compatibility to print the deprecation warning.
- class aiida.orm.NodeAttributes(node: aiida.orm.nodes.node.Node)#
Interface to the attributes of a node instance.
Attributes are a JSONable dictionary, stored on each node, allowing for arbitrary data to be stored by node subclasses (and thus data plugins).
Once the node is stored, the attributes are generally deemed immutable (except for some updatable keys on process nodes, which can be mutated whilst the node is not “sealed”).
Initialization
Initialize the interface.
- property all: Dict[str, Any]#
Return the complete attributes dictionary.
Warning
While the entity is unstored, this will return references of the attributes on the database model, meaning that changes on the returned values (if they are mutable themselves, e.g. a list or dictionary) will automatically be reflected on the database model as well. As soon as the entity is stored, the returned attributes will be a deep copy and mutations of the database attributes will have to go through the appropriate set methods. Therefore, once stored, retrieving a deep copy can be a heavy operation. If you only need the keys or some values, use the iterators keys and items, or the getters get and get_many instead.
- Returns:
the attributes as a dictionary
- get(key: str, default=_NO_DEFAULT) Any #
Return the value of an attribute.
Warning
While the entity is unstored, this will return a reference of the attribute on the database model, meaning that changes on the returned value (if they are mutable themselves, e.g. a list or dictionary) will automatically be reflected on the database model as well. As soon as the entity is stored, the returned attribute will be a deep copy and mutations of the database attributes will have to go through the appropriate set methods.
- Parameters:
key – name of the attribute
default – return this value instead of raising if the attribute does not exist
- Returns:
the value of the attribute
- Raises:
AttributeError – if the attribute does not exist and no default is specified
- get_many(keys: List[str]) List[Any] #
Return the values of multiple attributes.
Warning
While the entity is unstored, this will return references of the attributes on the database model, meaning that changes on the returned values (if they are mutable themselves, e.g. a list or dictionary) will automatically be reflected on the database model as well. As soon as the entity is stored, the returned attributes will be a deep copy and mutations of the database attributes will have to go through the appropriate set methods. Therefore, once stored, retrieving a deep copy can be a heavy operation. If you only need the keys or some values, use the iterators keys and items, or the getters get and get_many instead.
- Parameters:
keys – a list of attribute names
- Returns:
a list of attribute values
- Raises:
AttributeError – if at least one attribute does not exist
- set(key: str, value: Any) None #
Set an attribute to the given value.
- Parameters:
key – name of the attribute
value – value of the attribute
- Raises:
aiida.common.ValidationError – if the key is invalid, i.e. contains periods
aiida.common.ModificationNotAllowed – if the entity is stored
- set_many(attributes: Dict[str, Any]) None #
Set multiple attributes.
Note
This will override any existing attributes that are present in the new dictionary.
- Parameters:
attributes – a dictionary with the attributes to set
- Raises:
aiida.common.ValidationError – if any of the keys are invalid, i.e. contain periods
aiida.common.ModificationNotAllowed – if the entity is stored
- reset(attributes: Dict[str, Any]) None #
Reset the attributes.
Note
This will completely clear any existing attributes and replace them with the new dictionary.
- Parameters:
attributes – a dictionary with the attributes to set
- Raises:
aiida.common.ValidationError – if any of the keys are invalid, i.e. contain periods
aiida.common.ModificationNotAllowed – if the entity is stored
- delete(key: str) None #
Delete an attribute.
- Parameters:
key – name of the attribute
- Raises:
AttributeError – if the attribute does not exist
aiida.common.ModificationNotAllowed – if the entity is stored
- delete_many(keys: List[str]) None #
Delete multiple attributes.
- Parameters:
keys – names of the attributes to delete
- Raises:
AttributeError – if at least one of the attribute does not exist
aiida.common.ModificationNotAllowed – if the entity is stored
- class aiida.orm.NodeEntityLoader#
Bases:
aiida.orm.utils.loaders.OrmEntityLoader
Loader for the Node entity and sub classes.
- orm_base_class()#
Return the orm base class to which loaded entities should be mapped. Actual queries to load an entity may further narrow the query set by defining a more specific set of orm classes, as long as each of those is a strict sub class of the orm base class.
- Returns:
the orm base class
- classmethod _get_query_builder_label_identifier(identifier, classes, operator='==', project='*')#
Return the query builder instance that attempts to map the identifier onto an entity of the orm class, defined for this loader class, interpreting the identifier as a LABEL like identifier
- Parameters:
identifier – the LABEL identifier
classes – a tuple of orm classes to which the identifier should be mapped
operator – the operator to use in the query
project – the property or properties to project for entities matching the query
- Returns:
the query builder instance that should retrieve the entity corresponding to the identifier
- Raises:
ValueError – if the identifier is invalid
aiida.common.NotExistent – if the orm base class does not support a LABEL like identifier
- class aiida.orm.NodeLinksManager(node, link_type, incoming)#
A manager that allows to inspect, with tab-completion, nodes linked to a given one. See an example of its use in CalculationNode.inputs.
Initialization
Initialise the link manager.
- Parameters:
node – the reference node object
link_type – the link_type to inspect
incoming – if True, inspect incoming links, otherwise inspect outgoing links
- _namespace_separator = '__'#
- _construct_attribute_dict(incoming)#
Construct an attribute dict from all links of the node, recreating nested namespaces from flat link labels.
- Parameters:
incoming – if True, inspect incoming links, otherwise inspect outgoing links.
- _get_keys()#
Return the valid link labels, used e.g. to make getattr() work
- _get_node_by_link_label(label)#
Return the linked node with a given link label.
Nested namespaces in link labels get represented by double underscores in the database. Up until now, the link manager didn’t automatically unroll these again into nested namespaces and so a user was forced to pass the link with double underscores to dereference the corresponding node. For example, when used with the
inputs
attribute of aProcessNode
one had to do:node.inputs.nested__sub__namespace
Now it is possible to do
node.inputs.nested.sub.namespace
which is more intuitive since the double underscore replacement is just for the database and the user shouldn’t even have to know about it. For compatibility we support the old version a bit longer and it will emit a deprecation warning.
- Parameters:
label – the link label connecting the current node to the node to get.
- __dir__()#
Allow to list all valid input links
- __iter__()#
- __getattr__(name)#
- Parameters:
name – name of the attribute to be asked to the parser results.
- __contains__(key)#
Override the operator of the base class to emit deprecation warning if double underscore is used in key.
- __getitem__(name)#
interface to get to the parser results as a dictionary.
- Parameters:
name – name of the attribute to be asked to the parser results.
- __str__()#
Return a string representation of the manager
- __repr__()#
- class aiida.orm.NodeRepository(node: aiida.orm.nodes.node.Node)#
Interface to the file repository of a node instance.
This is the compatibility layer between the Node class and the Repository class. The repository in principle has no concept of immutability, so it is implemented here. Any mutating operations will raise a ModificationNotAllowed exception if the node is stored. Otherwise the operation is just forwarded to the repository instance.
The repository instance keeps an internal mapping of the file hierarchy that it maintains, starting from an empty hierarchy if the instance was constructed normally, or from a specific hierarchy if reconstructred through the
Repository.from_serialized
classmethod. This is only the case for stored nodes, because unstored nodes do not have any files yet when they are constructed. Once the node get’s stored, the repository is asked to serialize its metadata contents which is then stored in therepository_metadata
field of the backend node. This layer explicitly does not update the metadata of the node on a mutation action. The reason is that for stored nodes these actions are anyway forbidden and for unstored nodes, the final metadata will be stored in one go, once the node is stored, so there is no need to keep updating the node metadata intermediately. Note that this does mean thatrepository_metadata
does not give accurate information, as long as the node is not yet stored.Initialization
Construct a new instance of the repository interface.
- property metadata: Dict[str, Any]#
Return the repository metadata, representing the virtual file hierarchy.
Note, this is only accurate if the node is stored.
- Returns:
the repository metadata
- _update_repository_metadata()#
Refresh the repository metadata of the node if it is stored.
- _check_mutability()#
Check if the node is mutable.
- Raises:
ModificationNotAllowed – when the node is stored and therefore immutable.
- property _repository: aiida.repository.Repository#
Return the repository instance, lazily constructing it if necessary.
Note
this property is protected because a node’s repository should not be accessed outside of its scope.
- Returns:
the file repository instance.
- _copy(repo: aiida.orm.nodes.repository.NodeRepository) None #
Copy a repository from another instance.
This is used when storing cached nodes.
- Parameters:
repo – the repository to clone.
- _clone(repo: aiida.orm.nodes.repository.NodeRepository) None #
Clone the repository from another instance.
This is used when cloning a node.
- Parameters:
repo – the repository to clone.
- serialize() Dict #
Serialize the metadata of the repository content into a JSON-serializable format.
- Returns:
dictionary with the content metadata.
- hash() str #
Generate a hash of the repository’s contents.
- Returns:
the hash representing the contents of the repository.
- list_objects(path: Optional[str] = None) List[aiida.repository.File] #
Return a list of the objects contained in this repository sorted by name, optionally in given sub directory.
- Parameters:
path – the relative path where to store the object in the repository.
- Returns:
a list of File named tuples representing the objects present in directory with the given key.
- Raises:
TypeError – if the path is not a string and relative path.
FileNotFoundError – if no object exists for the given path.
NotADirectoryError – if the object at the given path is not a directory.
- list_object_names(path: Optional[str] = None) List[str] #
Return a sorted list of the object names contained in this repository, optionally in the given sub directory.
- Parameters:
path – the relative path where to store the object in the repository.
- Returns:
a list of File named tuples representing the objects present in directory with the given key.
- Raises:
TypeError – if the path is not a string and relative path.
FileNotFoundError – if no object exists for the given path.
NotADirectoryError – if the object at the given path is not a directory.
- open(path: str, mode='r') Iterator[Union[BinaryIO, TextIO]] #
Open a file handle to an object stored under the given key.
Note
this should only be used to open a handle to read an existing file. To write a new file use the method
put_object_from_filelike
instead.- Parameters:
path – the relative path of the object within the repository.
- Returns:
yield a byte stream object.
- Raises:
TypeError – if the path is not a string and relative path.
FileNotFoundError – if the file does not exist.
IsADirectoryError – if the object is a directory and not a file.
OSError – if the file could not be opened.
- get_object(path: Optional[aiida.orm.nodes.repository.FilePath] = None) aiida.repository.File #
Return the object at the given path.
- Parameters:
path – the relative path where to store the object in the repository.
- Returns:
the File representing the object located at the given relative path.
- Raises:
TypeError – if the path is not a string or
Path
, or is an absolute path.FileNotFoundError – if no object exists for the given path.
- get_object_content(path: str, mode='r') Union[str, bytes] #
Return the content of a object identified by key.
- Parameters:
key – fully qualified identifier for the object within the repository.
- Raises:
TypeError – if the path is not a string and relative path.
FileNotFoundError – if the file does not exist.
IsADirectoryError – if the object is a directory and not a file.
OSError – if the file could not be opened.
- put_object_from_bytes(content: bytes, path: str) None #
Store the given content in the repository at the given path.
- Parameters:
path – the relative path where to store the object in the repository.
content – the content to store.
- Raises:
TypeError – if the path is not a string and relative path.
FileExistsError – if an object already exists at the given path.
- put_object_from_filelike(handle: io.BufferedReader, path: str)#
Store the byte contents of a file in the repository.
- Parameters:
handle – filelike object with the byte content to be stored.
path – the relative path where to store the object in the repository.
- Raises:
TypeError – if the path is not a string and relative path.
ModificationNotAllowed – when the node is stored and therefore immutable.
- put_object_from_file(filepath: str, path: str)#
Store a new object under path with contents of the file located at filepath on the local file system.
- Parameters:
filepath – absolute path of file whose contents to copy to the repository
path – the relative path where to store the object in the repository.
- Raises:
TypeError – if the path is not a string and relative path, or the handle is not a byte stream.
ModificationNotAllowed – when the node is stored and therefore immutable.
- put_object_from_tree(filepath: str, path: Optional[str] = None)#
Store the entire contents of filepath on the local file system in the repository with under given path.
- Parameters:
filepath – absolute path of the directory whose contents to copy to the repository.
path – the relative path where to store the objects in the repository.
- Raises:
TypeError – if the path is not a string and relative path.
ModificationNotAllowed – when the node is stored and therefore immutable.
- walk(path: Optional[aiida.orm.nodes.repository.FilePath] = None) Iterable[Tuple[pathlib.PurePosixPath, List[str], List[str]]] #
Walk over the directories and files contained within this repository.
Note
the order of the dirname and filename lists that are returned is not necessarily sorted. This is in line with the
os.walk
implementation where the order depends on the underlying file system used.- Parameters:
path – the relative path of the directory within the repository whose contents to walk.
- Returns:
tuples of root, dirnames and filenames just like
os.walk
, with the exception that the root path is always relative with respect to the repository root, instead of an absolute path and it is an instance ofpathlib.PurePosixPath
instead of a normal string
- glob() Iterable[pathlib.PurePosixPath] #
Yield a recursive list of all paths (files and directories).
- copy_tree(target: Union[str, pathlib.Path], path: Optional[aiida.orm.nodes.repository.FilePath] = None) None #
Copy the contents of the entire node repository to another location on the local file system.
- Parameters:
target – absolute path of the directory where to copy the contents to.
path – optional relative path whose contents to copy.
- delete_object(path: str)#
Delete the object from the repository.
- Parameters:
key – fully qualified identifier for the object within the repository.
- Raises:
TypeError – if the path is not a string and relative path.
FileNotFoundError – if the file does not exist.
IsADirectoryError – if the object is a directory and not a file.
OSError – if the file could not be deleted.
ModificationNotAllowed – when the node is stored and therefore immutable.
- erase()#
Delete all objects from the repository.
- Raises:
ModificationNotAllowed – when the node is stored and therefore immutable.
- class aiida.orm.NumericType#
Bases:
aiida.orm.nodes.data.base.BaseType
Sub class of Data to store numbers, overloading common operators (
+
,*
, …).- __add__(other)#
- __radd__(other)#
- __sub__(other)#
- __rsub__(other)#
- __mul__(other)#
- __rmul__(other)#
- __div__(other)#
- __rdiv__(other)#
- __truediv__(other)#
- __rtruediv__(other)#
- __floordiv__(other)#
- __rfloordiv__(other)#
- __pow__(power)#
- __lt__(other)#
- __le__(other)#
- __gt__(other)#
- __ge__(other)#
- __mod__(other)#
- __rmod__(other)#
- __float__()#
- __int__()#
- class aiida.orm.OrbitalData#
Bases:
aiida.orm.nodes.data.data.Data
Used for storing collections of orbitals, as well as providing methods for accessing them internally.
- clear_orbitals()#
Remove all orbitals that were added to the class Cannot work if OrbitalData has been already stored
- get_orbitals(**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
- Kwargs:
attributes than can filter the set of returned orbitals
- Return list_of_outputs:
a list of orbitals
- set_orbitals(orbitals)#
Sets the orbitals into the database. Uses the orbital’s inherent set_orbital_dict method to generate a orbital dict string.
- Parameters:
orbital – an orbital or list of orbitals to be set
- aiida.orm.OrderSpecifier(field, direction)#
- class aiida.orm.OrmEntityLoader#
Base class for entity loaders.
- label_ambiguity_breaker = '!'#
- abstract orm_base_class()#
Return the orm base class to which loaded entities should be mapped. Actual queries to load an entity may further narrow the query set by defining a more specific set of orm classes, as long as each of those is a strict sub class of the orm base class.
- Returns:
the orm base class
- abstract classmethod _get_query_builder_label_identifier(identifier, classes, operator='==', project='*')#
Return the query builder instance that attempts to map the identifier onto an entity of the orm class, defined for this loader class, interpreting the identifier as a LABEL like identifier
- Parameters:
identifier – the LABEL identifier
classes – a tuple of orm classes to which the identifier should be mapped
operator – the operator to use in the query
project – the property or properties to project for entities matching the query
- Returns:
the query builder instance
- Raises:
ValueError – if the identifier is invalid
aiida.common.NotExistent – if the orm base class does not support a LABEL like identifier
- classmethod _get_query_builder_id_identifier(identifier, classes)#
Return the query builder instance that attempts to map the identifier onto an entity of the orm class, defined for this loader class, interpreting the identifier as an ID like identifier
- Parameters:
identifier – the ID identifier
classes – a tuple of orm classes to which the identifier should be mapped
- Returns:
the query builder instance
- classmethod _get_query_builder_uuid_identifier(identifier, classes, query_with_dashes)#
Return the query builder instance that attempts to map the identifier onto an entity of the orm class, defined for this loader class, interpreting the identifier as a UUID like identifier
- Parameters:
identifier – the UUID identifier
classes – a tuple of orm classes to which the identifier should be mapped
- Returns:
the query builder instance
- classmethod get_query_builder(identifier, identifier_type=None, sub_classes=None, query_with_dashes=True, operator='==', project='*')#
Return the query builder instance that attempts to map the identifier onto an entity of the orm class, defined for this loader class, inferring the identifier type if it is not defined.
- Parameters:
identifier – the identifier
identifier_type – the type of the identifier
sub_classes – an optional tuple of orm classes, that should each be strict sub classes of the base orm class of the loader, that will narrow the queryset
operator – the operator to use in the query
project – the property or properties to project for entities matching the query
- Returns:
the query builder instance and a dictionary of used query parameters
- classmethod get_options(incomplete, project='*')#
Return the list of entities that match the incomplete identifier.
Note
For the time being only LABEL auto-completion is supported so the identifier type is not inferred but hard-coded to be IdentifierType.LABEL
- Parameters:
incomplete – the incomplete identifier
project – the field(s) to project for each entity that matches the incomplete identifier
- Returns:
list of entities matching the incomplete identifier
- classmethod load_entity(identifier, identifier_type=None, sub_classes=None, query_with_dashes=True)#
Load an entity that uniquely corresponds to the provided identifier of the identifier type.
- Parameters:
identifier – the identifier
identifier_type – the type of the identifier
sub_classes – an optional tuple of orm classes, that should each be strict sub classes of the base orm class of the loader, that will narrow the queryset
- Returns:
the loaded entity
- Raises:
aiida.common.MultipleObjectsError – if the identifier maps onto multiple entities
aiida.common.NotExistent – if the identifier maps onto not a single entity
- classmethod get_query_classes(sub_classes=None)#
Get the tuple of classes to be used for the entity query. If sub_classes is defined, each class will be validated by verifying that it is a sub class of the loader’s orm base class. Validate a tuple of classes if a user passes in a specific one when attempting to load an entity. Each class should be a sub class of the entity loader’s orm base class
- Parameters:
sub_classes – an optional tuple of orm classes, that should each be strict sub classes of the base orm class of the loader, that will narrow the queryset
- Returns:
the tuple of orm classes to be used for the entity query
- Raises:
ValueError – if any of the classes are not a sub class of the entity loader’s orm base class
- classmethod infer_identifier_type(value)#
This method will attempt to automatically distinguish which identifier type is implied for the given value, if the value itself has no type from which it can be inferred.
The strategy is to first attempt to convert the value to an integer. If successful, it is assumed that the value represents an ID. If that fails, we attempt to interpret the value as a base 16 encoded integer, after having removed any dashes from the string. If that succeeds, it is most likely a UUID. If it seems to be neither an ID nor a UUID, it is assumed to be a LABEL like identifier.
With this approach there is the possibility for ambiguity. Since it is allowed to pass a partial UUID, it is possible that the partial UUID is also a valid ID. Likewise, a LABEL identifier might also be a valid ID, or a valid (partial) UUID. Fortunately, these ambiguities can be solved though:
ID/UUID: can always be solved by passing a partial UUID with at least one dash
ID/LABEL: appending an exclamation point ! to the identifier, will force LABEL interpretation
UUID/LABEL: appending an exclamation point ! to the identifier, will force LABEL interpretation
As one can see, the user will always be able to include at least one dash of the UUID identifier to prevent it from being interpreted as an ID. For the potential ambiguities in LABEL identifiers, we had to introduce a special marker to provide a surefire way of breaking any ambiguity that may arise. Adding an exclamation point will break the normal strategy and the identifier will directly be interpreted as a LABEL identifier.
- Parameters:
value – the value of the identifier
- Returns:
the identifier and identifier type
- Raises:
ValueError – if the value is an invalid identifier
- class aiida.orm.PortableCode(filepath_executable: str, filepath_files: pathlib.Path, **kwargs)#
Bases:
aiida.orm.nodes.data.code.legacy.Code
Data plugin representing an executable code stored in AiiDA’s storage.
Initialization
Construct a new instance.
Note
If the files necessary for this code are not all located in a single directory or the directory contains files that should not be uploaded, and so the
filepath_files
cannot be used. One can use the methods of theaiida.orm.nodes.repository.NodeRepository
class. This can be accessed through thebase.repository
attribute of the instance after it has been constructed. For example:code = PortableCode(filepath_executable='some_name.exe') code.put_object_from_file() code.put_object_from_filelike() code.put_object_from_tree()
- Parameters:
filepath_executable – The relative filepath of the executable within the directory of uploaded files.
filepath_files – The filepath to the directory containing all the files of the code.
- _validate()#
Validate the instance by checking that an executable is defined and it is part of the repository files.
:raises
aiida.common.exceptions.ValidationError
: If the state of the node is invalid.
- can_run_on_computer(computer: aiida.orm.Computer) bool #
Return whether the code can run on a given computer.
A
PortableCode
should be able to be run on any computer in principle.- Parameters:
computer – The computer.
- Returns:
True
if the provided computer is the same as the one configured for this code.
- get_executable() pathlib.PurePosixPath #
Return the executable that the submission script should execute to run the code.
- Returns:
The executable to be called in the submission script.
- validate_working_directory(folder: aiida.common.folders.Folder)#
Validate content of the working directory created by the
CalcJob
plugin.This method will be called by
presubmit()
when a new calculation job is launched, passing theFolder
that was used by the plugin used for the calculation to create the input files for the working directory. This method can be overridden by implementations of theAbstractCode
class that need to validate the contents of that folder.- Parameters:
folder – A sandbox folder that the
CalcJob
plugin wrote input files to that will be copied to the working directory for the corresponding calculation job instance.- Raises:
PluginInternalError – The
CalcJob
plugin created a file that has the same relative filepath as the executable for this portable code.
- property full_label: str#
Return the full label of this code.
The full label can be just the label itself but it can be something else. However, it at the very least has to include the label of the code.
- Returns:
The full label of the code.
- property filepath_executable: pathlib.PurePosixPath#
Return the relative filepath of the executable that this code represents.
- Returns:
The relative filepath of the executable.
- class aiida.orm.ProcessNode(backend: Optional[aiida.orm.implementation.StorageBackend] = None, user: Optional[aiida.orm.users.User] = None, computer: Optional[aiida.orm.computers.Computer] = None, **kwargs: Any)#
Bases:
aiida.orm.utils.mixins.Sealable
,aiida.orm.nodes.node.Node
Base class for all nodes representing the execution of a process
This class and its subclasses serve as proxies in the database, for actual Process instances being run. The Process instance in memory will leverage an instance of this class (the exact sub class depends on the sub class of Process) to persist important information of its state to the database. This serves as a way for the user to inspect the state of the Process during its execution as well as a permanent record of its execution in the provenance graph, after the execution has terminated.
Initialization
- Parameters:
backend_entity – the backend model supporting this entity
- _CLS_NODE_LINKS = None#
- _CLS_NODE_CACHING = None#
- CHECKPOINT_KEY = 'checkpoints'#
- EXCEPTION_KEY = 'exception'#
- EXIT_MESSAGE_KEY = 'exit_message'#
- EXIT_STATUS_KEY = 'exit_status'#
- PROCESS_PAUSED_KEY = 'paused'#
- PROCESS_LABEL_KEY = 'process_label'#
- PROCESS_STATE_KEY = 'process_state'#
- PROCESS_STATUS_KEY = 'process_status'#
- _unstorable_message = 'only Data, WorkflowNode, CalculationNode or their subclasses can be stored'#
- property logger#
Get the logger of the Calculation object, so that it also logs to the DB.
- Returns:
LoggerAdapter object, that works like a logger, but also has the ‘extra’ embedded
- get_builder_restart() aiida.engine.processes.builder.ProcessBuilder #
Return a ProcessBuilder that is ready to relaunch the process that created this node.
The process class will be set based on the process_type of this node and the inputs of the builder will be prepopulated with the inputs registered for this node. This functionality is very useful if a process has completed and you want to relaunch it with slightly different inputs.
- Returns:
~aiida.engine.processes.builder.ProcessBuilder instance
- property process_class: Type[aiida.engine.processes.Process]#
Return the process class that was used to create this node.
- Returns:
Process class
- Raises:
ValueError – if no process type is defined, it is an invalid process type string or cannot be resolved to load the corresponding class
- set_process_type(process_type_string: str) None #
Set the process type string.
- Parameters:
process_type – the process type string identifying the class using this process node as storage.
- property process_state: Optional[plumpy.process_states.ProcessState]#
Return the process state
- Returns:
the process state instance of ProcessState enum
- set_process_state(state: Union[str, plumpy.process_states.ProcessState, None])#
Set the process state
- Parameters:
state – value or instance of ProcessState enum
- property process_status: Optional[str]#
Return the process status
The process status is a generic status message e.g. the reason it might be paused or when it is being killed
- Returns:
the process status
- set_process_status(status: Optional[str]) None #
Set the process status
The process status is a generic status message e.g. the reason it might be paused or when it is being killed. If status is None, the corresponding attribute will be deleted.
- Parameters:
status – string process status
- property is_terminated: bool#
Return whether the process has terminated
Terminated means that the process has reached any terminal state.
- Returns:
True if the process has terminated, False otherwise
- Return type:
- property is_excepted: bool#
Return whether the process has excepted
Excepted means that during execution of the process, an exception was raised that was not caught.
- Returns:
True if during execution of the process an exception occurred, False otherwise
- Return type:
- property is_killed: bool#
Return whether the process was killed
Killed means the process was killed directly by the user or by the calling process being killed.
- Returns:
True if the process was killed, False otherwise
- Return type:
- property is_finished: bool#
Return whether the process has finished
Finished means that the process reached a terminal state nominally. Note that this does not necessarily mean successfully, but there were no exceptions and it was not killed.
- Returns:
True if the process has finished, False otherwise
- Return type:
- property is_finished_ok: bool#
Return whether the process has finished successfully
Finished successfully means that it terminated nominally and had a zero exit status.
- Returns:
True if the process has finished successfully, False otherwise
- Return type:
- property is_failed: bool#
Return whether the process has failed
Failed means that the process terminated nominally but it had a non-zero exit status.
- Returns:
True if the process has failed, False otherwise
- Return type:
- property exit_status: Optional[int]#
Return the exit status of the process
- Returns:
the exit status, an integer exit code or None
- set_exit_status(status: Union[None, enum.Enum, int]) None #
Set the exit status of the process
- Parameters:
state – an integer exit code or None, which will be interpreted as zero
- property exit_message: Optional[str]#
Return the exit message of the process
- Returns:
the exit message
- set_exit_message(message: Optional[str]) None #
Set the exit message of the process, if None nothing will be done
- Parameters:
message – a string message
- property exception: Optional[str]#
Return the exception of the process or None if the process is not excepted.
If the process is marked as excepted yet there is no exception attribute, an empty string will be returned.
- Returns:
the exception message or None
- set_exception(exception: str) None #
Set the exception of the process
- Parameters:
exception – the exception message
- property checkpoint: Optional[Dict[str, Any]]#
Return the checkpoint bundle set for the process
- Returns:
checkpoint bundle if it exists, None otherwise
- set_checkpoint(checkpoint: Dict[str, Any]) None #
Set the checkpoint bundle set for the process
- Parameters:
state – string representation of the stepper state info
- property paused: bool#
Return whether the process is paused
- Returns:
True if the Calculation is marked as paused, False otherwise
- pause() None #
Mark the process as paused by setting the corresponding attribute.
This serves only to reflect that the corresponding Process is paused and so this method should not be called by anyone but the Process instance itself.
- unpause() None #
Mark the process as unpaused by removing the corresponding attribute.
This serves only to reflect that the corresponding Process is unpaused and so this method should not be called by anyone but the Process instance itself.
- property called: List[aiida.orm.nodes.process.process.ProcessNode]#
Return a list of nodes that the process called
- Returns:
list of process nodes called by this process
- property called_descendants: List[aiida.orm.nodes.process.process.ProcessNode]#
Return a list of all nodes that have been called downstream of this process
This will recursively find all the called processes for this process and its children.
- property caller: Optional[aiida.orm.nodes.process.process.ProcessNode]#
Return the process node that called this process node, or None if it does not have a caller
- Returns:
process node that called this process node instance or None
- class aiida.orm.ProjectionData(*args, source=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) >
Initialization
Construct a new instance, setting the
source
attribute if provided as a keyword argument.- _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
- 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
- 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
- _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
- 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
- static _from_index_to_arrayname(index)#
Used internally to determine the array names.
- 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
- abstract set_orbitals(**kwargs)#
This method is inherited from OrbitalData, but is blocked here. If used will raise a NotImplementedError
- class aiida.orm.QueryBuilder(backend: Optional[aiida.orm.implementation.StorageBackend] = None, *, debug: bool = False, path: Optional[Sequence[Union[str, Dict[str, Any], aiida.orm.querybuilder.EntityClsType]]] = (), filters: Optional[Dict[str, aiida.orm.querybuilder.FilterType]] = None, project: Optional[Dict[str, aiida.orm.querybuilder.ProjectType]] = None, limit: Optional[int] = None, offset: Optional[int] = None, order_by: Optional[aiida.orm.querybuilder.OrderByType] = None, distinct: bool = False)#
The class to query the AiiDA database.
Usage:
from aiida.orm.querybuilder import QueryBuilder qb = QueryBuilder() # Querying nodes: qb.append(Node) # retrieving the results: results = qb.all()
Initialization
Instantiates a QueryBuilder instance.
Which backend is used decided here based on backend-settings (taken from the user profile). This cannot be overridden so far by the user.
- Parameters:
debug – Turn on debug mode. This feature prints information on the screen about the stages of the QueryBuilder. Does not affect results.
path – A list of the vertices to traverse. Leave empty if you plan on using the method
QueryBuilder.append()
.filters – The filters to apply. You can specify the filters here, when appending to the query using
QueryBuilder.append()
or even later usingQueryBuilder.add_filter()
. Check latter gives API-details.project – The projections to apply. You can specify the projections here, when appending to the query using
QueryBuilder.append()
or even later usingQueryBuilder.add_projection()
. Latter gives you API-details.limit – Limit the number of rows to this number. Check
QueryBuilder.limit()
for more information.offset – Set an offset for the results returned. Details in
QueryBuilder.offset()
.order_by – How to order the results. As the 2 above, can be set also at later stage, check
QueryBuilder.order_by()
for more information.distinct – Whether to return de-duplicated rows
- _EDGE_TAG_DELIM = '--'#
- _VALID_PROJECTION_KEYS = ('func', 'cast')#
- property backend: aiida.orm.implementation.StorageBackend#
Return the backend used by the QueryBuilder.
- as_dict(copy: bool = True) aiida.orm.implementation.querybuilder.QueryDictType #
Convert to a JSON serialisable dictionary representation of the query.
- property queryhelp: aiida.orm.implementation.querybuilder.QueryDictType#
“Legacy name for
as_dict
method.
- classmethod from_dict(dct: Dict[str, Any]) aiida.orm.querybuilder.QueryBuilder #
Create an instance from a dictionary representation of the query.
- __deepcopy__(memo) aiida.orm.querybuilder.QueryBuilder #
Create deep copy of the instance.
- get_used_tags(vertices: bool = True, edges: bool = True) List[str] #
Returns a list of all the vertices that are being used.
- Parameters:
vertices – If True, adds the tags of vertices to the returned list
edges – If True, adds the tags of edges to the returnend list.
- Returns:
A list of tags
- _get_unique_tag(classifiers: List[aiida.orm.querybuilder.Classifier]) str #
Using the function get_tag_from_type, I get a tag. I increment an index that is appended to that tag until I have an unused tag. This function is called in
QueryBuilder.append()
when no tag is given.- Parameters:
classifiers (dict) –
Classifiers, containing the string that defines the type of the AiiDA ORM class. For subclasses of Node, this is the Node._plugin_type_string, for other they are as defined as returned by
QueryBuilder._get_ormclass()
.Can also be a list of dictionaries, when multiple classes are passed to QueryBuilder.append
- Returns:
A tag as a string (it is a single string also when passing multiple classes).
- append(cls: Optional[Union[aiida.orm.querybuilder.EntityClsType, Sequence[aiida.orm.querybuilder.EntityClsType]]] = None, entity_type: Optional[Union[str, Sequence[str]]] = None, tag: Optional[str] = None, filters: Optional[aiida.orm.querybuilder.FilterType] = None, project: Optional[aiida.orm.querybuilder.ProjectType] = None, subclassing: bool = True, edge_tag: Optional[str] = None, edge_filters: Optional[aiida.orm.querybuilder.FilterType] = None, edge_project: Optional[aiida.orm.querybuilder.ProjectType] = None, outerjoin: bool = False, joining_keyword: Optional[str] = None, joining_value: Optional[Any] = None, orm_base: Optional[str] = None, **kwargs: Any) aiida.orm.querybuilder.QueryBuilder #
Any iterative procedure to build the path for a graph query needs to invoke this method to append to the path.
- Parameters:
cls –
The Aiida-class (or backend-class) defining the appended vertice. Also supports a tuple/list of classes. This results in an all instances of this class being accepted in a query. However the classes have to have the same orm-class for the joining to work. I.e. both have to subclasses of Node. Valid is:
cls=(StructureData, Dict)
This is invalid:
cls=(Group, Node)
entity_type – The node type of the class, if cls is not given. Also here, a tuple or list is accepted.
tag – A unique tag. If none is given, I will create a unique tag myself.
filters – Filters to apply for this vertex. See
add_filter()
, the method invoked in the background, or usage examples for details.project – Projections to apply. See usage examples for details. More information also in
add_projection()
.subclassing – Whether to include subclasses of the given class (default True). E.g. Specifying a ProcessNode as cls will include CalcJobNode, WorkChainNode, CalcFunctionNode, etc..
edge_tag – The tag that the edge will get. If nothing is specified (and there is a meaningful edge) the default is tag1–tag2 with tag1 being the entity joining from and tag2 being the entity joining to (this entity).
edge_filters – The filters to apply on the edge. Also here, details in
add_filter()
.edge_project – The project from the edges. API-details in
add_projection()
.outerjoin – If True, (default is False), will do a left outerjoin instead of an inner join
Joining can be specified in two ways:
Specifying the ‘joining_keyword’ and ‘joining_value’ arguments
Specify a single keyword argument
The joining keyword wil be
with_*
ordirection
, depending on the joining entity type. The joining value is the tag name or class of the entity to join to.A small usage example how this can be invoked:
qb = QueryBuilder() # Instantiating empty querybuilder instance qb.append(cls=StructureData) # First item is StructureData node # The # next node in the path is a PwCalculation, with # the structure joined as an input qb.append( cls=PwCalculation, with_incoming=StructureData )
- Returns:
self
- order_by(order_by: aiida.orm.querybuilder.OrderByType) aiida.orm.querybuilder.QueryBuilder #
Set the entity to order by
- Parameters:
order_by – This is a list of items, where each item is a dictionary specifies what to sort for an entity
In each dictionary in that list, keys represent valid tags of entities (tables), and values are list of columns.
Usage:
#Sorting by id (ascending): qb = QueryBuilder() qb.append(Node, tag='node') qb.order_by({'node':['id']}) # or #Sorting by id (ascending): qb = QueryBuilder() qb.append(Node, tag='node') qb.order_by({'node':[{'id':{'order':'asc'}}]}) # for descending order: qb = QueryBuilder() qb.append(Node, tag='node') qb.order_by({'node':[{'id':{'order':'desc'}}]}) # or (shorter) qb = QueryBuilder() qb.append(Node, tag='node') qb.order_by({'node':[{'id':'desc'}]})
- add_filter(tagspec: Union[str, aiida.orm.querybuilder.EntityClsType], filter_spec: aiida.orm.querybuilder.FilterType) aiida.orm.querybuilder.QueryBuilder #
Adding a filter to my filters.
- Parameters:
tagspec – A tag string or an ORM class which maps to an existing tag
filter_spec – The specifications for the filter, has to be a dictionary
Usage:
qb = QueryBuilder() # Instantiating the QueryBuilder instance qb.append(Node, tag='node') # Appending a Node #let's put some filters: qb.add_filter('node',{'id':{'>':12}}) # 2 filters together: qb.add_filter('node',{'label':'foo', 'uuid':{'like':'ab%'}}) # Now I am overriding the first filter I set: qb.add_filter('node',{'id':13})
- static _process_filters(filters: aiida.orm.querybuilder.FilterType) Dict[str, Any] #
Process filters.
- _add_node_type_filter(tagspec: str, classifiers: List[aiida.orm.querybuilder.Classifier], subclassing: bool)#
Add a filter based on node type.
- Parameters:
tagspec – The tag, which has to exist already as a key in self._filters
classifiers – a dictionary with classifiers
subclassing – if True, allow for subclasses of the ormclass
- _add_process_type_filter(tagspec: str, classifiers: List[aiida.orm.querybuilder.Classifier], subclassing: bool) None #
Add a filter based on process type.
- Parameters:
tagspec – The tag, which has to exist already as a key in self._filters
classifiers – a dictionary with classifiers
subclassing – if True, allow for subclasses of the process type
Note: This function handles the case when process_type_string is None.
- _add_group_type_filter(tagspec: str, classifiers: List[aiida.orm.querybuilder.Classifier], subclassing: bool) None #
Add a filter based on group type.
- Parameters:
tagspec – The tag, which has to exist already as a key in self._filters
classifiers – a dictionary with classifiers
subclassing – if True, allow for subclasses of the ormclass
- add_projection(tag_spec: Union[str, aiida.orm.querybuilder.EntityClsType], projection_spec: aiida.orm.querybuilder.ProjectType) None #
Adds a projection
- Parameters:
tag_spec – A tag string or an ORM class which maps to an existing tag
projection_spec – The specification for the projection. A projection is a list of dictionaries, with each dictionary containing key-value pairs where the key is database entity (e.g. a column / an attribute) and the value is (optional) additional information on how to process this database entity.
If the given projection_spec is not a list, it will be expanded to a list. If the listitems are not dictionaries, but strings (No additional processing of the projected results desired), they will be expanded to dictionaries.
Usage:
qb = QueryBuilder() qb.append(StructureData, tag='struc') # Will project the uuid and the kinds qb.add_projection('struc', ['uuid', 'attributes.kinds'])
The above example will project the uuid and the kinds-attribute of all matching structures. There are 2 (so far) special keys.
The single star * will project the ORM-instance:
qb = QueryBuilder() qb.append(StructureData, tag='struc') # Will project the ORM instance qb.add_projection('struc', '*') print type(qb.first()[0]) # >>> aiida.orm.nodes.data.structure.StructureData
The double star
**
projects all possible projections of this entity:QueryBuilder().append(StructureData,tag=’s’, project=’**’).limit(1).dict()[0][‘s’].keys()
# >>> ‘user_id, description, ctime, label, extras, mtime, id, attributes, dbcomputer_id, type, uuid’
Be aware that the result of
**
depends on the backend implementation.
- set_debug(debug: bool) aiida.orm.querybuilder.QueryBuilder #
Run in debug mode. This does not affect functionality, but prints intermediate stages when creating a query on screen.
- Parameters:
debug – Turn debug on or off
- debug(msg: str, *objects: Any) None #
Log debug message.
objects will passed to the format string, e.g.
msg % objects
- limit(limit: Optional[int]) aiida.orm.querybuilder.QueryBuilder #
Set the limit (nr of rows to return)
- Parameters:
limit – integers of number of rows of rows to return
- offset(offset: Optional[int]) aiida.orm.querybuilder.QueryBuilder #
Set the offset. If offset is set, that many rows are skipped before returning. offset = 0 is the same as omitting setting the offset. If both offset and limit appear, then offset rows are skipped before starting to count the limit rows that are returned.
- Parameters:
offset – integers of nr of rows to skip
- distinct(value: bool = True) aiida.orm.querybuilder.QueryBuilder #
Asks for distinct rows, which is the same as asking the backend to remove duplicates. Does not execute the query!
If you want a distinct query:
qb = QueryBuilder() # append stuff! qb.append(...) qb.append(...) ... qb.distinct().all() #or qb.distinct().dict()
- Returns:
self
- inputs(**kwargs: Any) aiida.orm.querybuilder.QueryBuilder #
Join to inputs of previous vertice in path.
- Returns:
self
- outputs(**kwargs: Any) aiida.orm.querybuilder.QueryBuilder #
Join to outputs of previous vertice in path.
- Returns:
self
- children(**kwargs: Any) aiida.orm.querybuilder.QueryBuilder #
Join to children/descendants of previous vertice in path.
- Returns:
self
- parents(**kwargs: Any) aiida.orm.querybuilder.QueryBuilder #
Join to parents/ancestors of previous vertice in path.
- Returns:
self
- as_sql(inline: bool = False) str #
Convert the query to an SQL string representation.
Warning
This method should be used for debugging purposes only, since normally sqlalchemy will handle this process internally.
- Params inline:
Inline bound parameters (this is normally handled by the Python DB-API).
- analyze_query(execute: bool = True, verbose: bool = False) str #
Return the query plan, i.e. a list of SQL statements that will be executed.
See: https://www.postgresql.org/docs/11/sql-explain.html
- Params execute:
Carry out the command and show actual run times and other statistics.
- Params verbose:
Display additional information regarding the plan.
- static _get_aiida_entity_res(value) Any #
Convert a projected query result to front end class if it is an instance of a BackendEntity.
Values that are not an BackendEntity instance will be returned unaltered
- Parameters:
value – a projected query result to convert
- Returns:
the converted value
- first(flat: bool = False) Optional[list[Any] | Any] #
Return the first result of the query.
Calling
first
results in an execution of the underlying query.Note, this may change if several rows are valid for the query, as persistent ordering is not guaranteed unless explicitly specified.
- Parameters:
flat – if True, return just the projected quantity if there is just a single projection.
- Returns:
One row of results as a list, or None if no result returned.
- count() int #
Counts the number of rows returned by the backend.
- Returns:
the number of rows as an integer
- iterall(batch_size: Optional[int] = 100) Iterable[List[Any]] #
Same as
all()
, but returns a generator. Be aware that this is only safe if no commit will take place during this transaction. You might also want to read the SQLAlchemy documentation on https://docs.sqlalchemy.org/en/14/orm/query.html#sqlalchemy.orm.Query.yield_per- Parameters:
batch_size – The size of the batches to ask the backend to batch results in subcollections. You can optimize the speed of the query by tuning this parameter.
- Returns:
a generator of lists
- iterdict(batch_size: Optional[int] = 100) Iterable[Dict[str, Dict[str, Any]]] #
Same as
dict()
, but returns a generator. Be aware that this is only safe if no commit will take place during this transaction. You might also want to read the SQLAlchemy documentation on https://docs.sqlalchemy.org/en/14/orm/query.html#sqlalchemy.orm.Query.yield_per- Parameters:
batch_size – The size of the batches to ask the backend to batch results in subcollections. You can optimize the speed of the query by tuning this parameter.
- Returns:
a generator of dictionaries
- all(batch_size: Optional[int] = None, flat: bool = False) Union[List[List[Any]], List[Any]] #
Executes the full query with the order of the rows as returned by the backend.
The order inside each row is given by the order of the vertices in the path and the order of the projections for each vertex in the path.
- Parameters:
batch_size – the size of the batches to ask the backend to batch results in subcollections. You can optimize the speed of the query by tuning this parameter. Leave the default None if speed is not critical or if you don’t know what you’re doing.
flat – return the result as a flat list of projected entities without sub lists.
- Returns:
a list of lists of all projected entities.
- one() List[Any] #
Executes the query asking for exactly one results.
Will raise an exception if this is not the case:
- Raises:
MultipleObjectsError if more then one row can be returned
- Raises:
NotExistent if no result was found
- dict(batch_size: Optional[int] = None) List[Dict[str, Dict[str, Any]]] #
Executes the full query with the order of the rows as returned by the backend. the order inside each row is given by the order of the vertices in the path and the order of the projections for each vertice in the path.
- Parameters:
batch_size – The size of the batches to ask the backend to batch results in subcollections. You can optimize the speed of the query by tuning this parameter. Leave the default (None) if speed is not critical or if you don’t know what you’re doing!
- Returns:
A list of dictionaries of all projected entities: tag -> field -> value
Usage:
qb = QueryBuilder() qb.append( StructureData, tag='structure', filters={'uuid':{'==':myuuid}}, ) qb.append( Node, with_ancestors='structure', project=['entity_type', 'id'], # returns entity_type (string) and id (string) tag='descendant' ) # Return the dictionaries: print "qb.iterdict()" for d in qb.iterdict(): print '>>>', d
results in the following output:
qb.iterdict() >>> {'descendant': { 'entity_type': 'calculation.job.quantumespresso.pw.PwCalculation.', 'id': 7716} } >>> {'descendant': { 'entity_type': 'data.remote.RemoteData.', 'id': 8510} }
- class aiida.orm.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!
Initialization
Construct a new instance, setting the
source
attribute if provided as a keyword argument.- KEY_EXTRA_CLEANED = 'cleaned'#
- get_remote_path()#
- set_remote_path(val)#
- property is_empty#
Check if remote folder is empty
- getfile(relpath, destpath)#
Connects to the remote folder and retrieves the content of a file.
- Parameters:
relpath – The relative path of the file on the remote to retrieve.
destpath – The absolute path of where to store the file on the local machine.
- 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.
- _clean(transport=None)#
Remove all content of the remote folder on the remote computer.
When the cleaning operation is successful, the extra with the key
RemoteData.KEY_EXTRA_CLEANED
is set.- Parameters:
transport – Provide an optional transport that is already open. If not provided, a transport will be automatically opened, based on the current default user and the computer of this data node. Passing in the transport can be used for efficiency if a great number of nodes need to be cleaned for the same computer. Note that the user should take care that the correct transport is passed.
- Raises:
ValueError – If the hostname of the provided transport does not match that of the node’s computer.
- _validate()#
- get_authinfo()#
- class aiida.orm.RemoteStashData(stash_mode: aiida.common.datastructures.StashMode, **kwargs)#
Bases:
aiida.orm.nodes.data.data.Data
Data plugin that models an archived folder on a remote computer.
A stashed folder is essentially an instance of
RemoteData
that has been archived. Archiving in this context can simply mean copying the content of the folder to another location on the same or another filesystem as long as it is on the same machine. In addition, the folder may have been compressed into a single file for efficiency or even written to tape. Thestash_mode
attribute will distinguish how the folder was stashed which will allow the implementation to also unstash it and transform it back into aRemoteData
such that it can be used as an input for newCalcJobs
.This class is a non-storable base class that merely registers the
stash_mode
attribute. Only its subclasses, that actually implement a certain stash mode, can be instantiated and therefore stored. The reason for this design is that because the behavior of the class can change significantly based on the mode employed to stash the files and implementing all these variants in the same class will lead to an unintuitive interface where certain properties or methods of the class will only be available or function properly based on thestash_mode
.Initialization
Construct a new instance
- Parameters:
stash_mode – the stashing mode with which the data was stashed on the remote.
- _storable = False#
- property stash_mode: aiida.common.datastructures.StashMode#
Return the mode with which the data was stashed on the remote.
- Returns:
the stash mode.
- class aiida.orm.RemoteStashFolderData(stash_mode: aiida.common.datastructures.StashMode, target_basepath: str, source_list: List, **kwargs)#
Bases:
aiida.orm.nodes.data.remote.stash.base.RemoteStashData
Data plugin that models a folder with files of a completed calculation job that has been stashed through a copy.
This data plugin can and should be used to stash files if and only if the stash mode is StashMode.COPY.
Initialization
Construct a new instance
- Parameters:
stash_mode – the stashing mode with which the data was stashed on the remote.
target_basepath – the target basepath.
source_list – the list of source files.
- _storable = True#
- class aiida.orm.SinglefileData(file, filename=None, **kwargs)#
Bases:
aiida.orm.nodes.data.data.Data
Data class that can be used to store a single file in its repository.
Initialization
Construct a new instance and set the contents to that of the file.
- Parameters:
file – an absolute filepath or filelike object whose contents to copy. Hint: Pass io.BytesIO(b”my string”) to construct the SinglefileData directly from a string.
filename – specify filename to use (defaults to name of provided file).
- DEFAULT_FILENAME = 'file.txt'#
- property filename#
Return the name of the file stored.
- Returns:
the filename under which the file is stored in the repository
- open(path=None, mode='r')#
Return an open file handle to the content of this data node.
- Parameters:
path – the relative path of the object within the repository.
mode – the mode with which to open the file handle (default: read mode)
- Returns:
a file handle
- get_content()#
Return the content of the single file stored for this data node.
- Returns:
the content of the file as a string
- set_file(file, filename=None)#
Store the content of the file in the node’s repository, deleting any other existing objects.
- Parameters:
file – an absolute filepath or filelike object whose contents to copy Hint: Pass io.BytesIO(b”my string”) to construct the file directly from a string.
filename – specify filename to use (defaults to name of provided file).
- _validate()#
Ensure that there is one object stored in the repository, whose key matches value set for filename attr.
- class aiida.orm.Site(**kwargs)#
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.
Initialization
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
- 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.
- 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).
- property 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.
- property position#
Return the position of this site in absolute coordinates, in angstrom.
- __repr__()#
- __str__()#
- class aiida.orm.Str(value=None, **kwargs)#
Bases:
aiida.orm.nodes.data.base.BaseType
Data sub class to represent a string value.
Initialization
- _type = None#
- class aiida.orm.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.
Initialization
Construct a new instance, setting the
source
attribute if provided as a keyword argument.- _set_incompatibilities = [('ase', 'cell'), ('ase', 'pbc'), ('ase', 'pymatgen'), ('ase', 'pymatgen_molecule'), ('ase', 'pymatg...#
- _dimensionality_label = None#
- _internal_kind_tags = None#
- get_dimensionality()#
Return the dimensionality of the structure and its length/surface/volume.
Zero-dimensional structures are assigned “volume” 0.
- Returns:
returns a dictionary with keys “dim” (dimensionality integer), “label” (dimensionality label) and “value” (numerical length/surface/volume).
- set_ase(aseatoms)#
Load the structure from a ASE object
- 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.
- _validate()#
Performs some standard validation tests.
- _prepare_xsf(main_file_name='')#
Write the given structure to a string of format XSF (for XCrySDen).
- _prepare_cif(main_file_name='')#
Write the given structure to a string of format CIF.
- _prepare_chemdoodle(main_file_name='')#
Write the given structure to a string of format required by ChemDoodle.
- _prepare_xyz(main_file_name='')#
Write the given structure to a string of format XYZ.
- _parse_xyz(inputstring)#
Read the structure from a string of format XYZ.
- _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 cell. This method will adjust the cell
- get_description()#
Returns a string with infos retrieved from StructureData node’s properties
- Parameters:
self – the StructureData node
- Returns:
retsrt: the description string
- 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.
- 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_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_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_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_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_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_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.
- 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.
- 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.
- clear_kinds()#
Removes all kinds for the StructureData object.
Note
Also clear all sites!
- clear_sites()#
Removes all sites for the StructureData object.
- property sites#
Returns a list of sites.
- property kinds#
Returns a list of kinds.
- 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.
- property cell#
Returns the cell shape.
- Returns:
a 3x3 list of lists.
- set_cell(value)#
Set the cell.
- 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.
- property 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)
- set_pbc(value)#
Set the periodic boundary conditions.
- property cell_lengths#
Get the lengths of cell lattice vectors in angstroms.
- abstract set_cell_lengths(value)#
- property cell_angles#
Get the angles between the cell lattice vectors in degrees.
- abstract set_cell_angles(value)#
- property is_alloy#
Return whether the structure contains any alloy kinds.
- Returns:
a boolean, True if at least one kind is an alloy
- property has_vacancies#
Return whether the structure has vacancies in the structure.
- Returns:
a boolean, True if at least one kind has a vacancy
- get_cell_volume()#
Returns the three-dimensional cell volume in Angstrom^3.
Use the get_dimensionality method in order to get the area/length of lower-dimensional cells.
- 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:
- _get_object_phonopyatoms()#
Converts StructureData to PhonopyAtoms
- Returns:
a PhonopyAtoms object
- _get_object_ase()#
Converts
StructureData
to ase.Atoms- Returns:
an ase.Atoms 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_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)
- _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)
- class aiida.orm.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).
Initialization
- _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.
- 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
andvelocities
are optional variables. If nothing is passed forcells
orvelocities
nothing will be stored. However, if no input is given forstepids
a consecutive sequence [0,1,2,…,len(positions)-1] will be assumed.- Parameters:
symbols – string list with dimension
n
, wheren
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 theget_step_structure()
method).positions – float array with dimension \(s \times n \times 3\), where
s
is the length of thestepids
array andn
is the length of thesymbols
array. Units are angstrom. In particular,positions[i,j,k]
is thek
-th component of thej
-th atom (or site) in the structure at the time step with indexi
(identified by step numberstep[i]
and with timestamptimes[i]
).stepids – integer array with dimension
s
, wheres
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 tonumpy.arange(s)
by default) It is internally stored as an array named ‘steps’.cells – if specified float array with dimension \(s \times 3 \times 3\), where
s
is the length of thestepids
array. Units are angstrom. In particular,cells[i,j,k]
is thek
-th component of thej
-th cell vector at the time step with indexi
(identified by step numberstepid[i]
and with timestamptimes[i]
).times – if specified, float array with dimension
s
, wheres
is the length of thestepids
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.
- 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
- _validate()#
Verify that the required arrays are present and that their type and dimension are correct.
- property numsteps#
Return the number of stored steps, or zero if nothing has been stored yet.
- property numsites#
Return the number of stored sites, or zero if nothing has been stored yet.
- 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_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_cells()#
Return the array of cells, if it has already been set.
- Raises:
KeyError – if the trajectory has not been set yet.
- property symbols#
Return the array of symbols, if it has already been set.
- Raises:
KeyError – if the trajectory has not been set yet.
- get_positions()#
Return the array of positions, 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 returnNone
(both if no trajectory was not set yet, and if it the trajectory was set but no velocities were specified).
- 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 asget_step_data()
orget_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_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, cells, or times were specified, None is returned as the corresponding element.
- Returns:
A tuple in the format
(stepid, time, cell, symbols, positions, velocities)
, wherestepid
is an integer,time
is a float,cell
is a \(3 imes 3\) matrix,symbols
is an array of lengthn
, positions is a \(n imes 3\) array, and velocities is eitherNone
or a \(n imes 3\) array- Parameters:
index – The index of the step that you want to retrieve, from 0 to
self.numsteps - 1
.- Raises:
IndexError – if you require an index beyond the limits.
KeyError – if you did not store the trajectory yet.
- get_step_structure(index, custom_kinds=None)#
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 theget_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 thesymbols
array, withkind.name
set to this string. If this parameter is omitted, the automatic kind generation of AiiDAaiida.orm.nodes.data.structure.StructureData
nodes is used, meaning that the strings in thesymbols
array must be valid chemical symbols.
- Returns:
- _prepare_xsf(index=None, main_file_name='')#
Write the given trajectory to a string of format XSF (for XCrySDen).
- _prepare_cif(trajectory_index=None, main_file_name='')#
Write the given trajectory to a string of format CIF.
- get_structure(store=False, **kwargs)#
Creates
aiida.orm.nodes.data.structure.StructureData
.New in version 1.0: Renamed from _get_aiida_structure
- Parameters:
store – If True, intermediate calculation gets stored in the AiiDA database for record. Default False.
index – The index of the step that you want to retrieve, from 0 to
self.numsteps- 1
.custom_kinds – (Optional) If passed must be a list of
aiida.orm.nodes.data.structure.Kind
objects. There must be one kind object for each different string in thesymbols
array, withkind.name
set to this string. If this parameter is omitted, the automatic kind generation of AiiDAaiida.orm.nodes.data.structure.StructureData
nodes is used, meaning that the strings in thesymbols
array must be valid chemical symbols.custom_cell – (Optional) The cell matrix of the structure. If omitted, the cell will be read from the trajectory, if present, otherwise the default cell of
aiida.orm.nodes.data.structure.StructureData
will be used.
- Returns:
- get_cif(index=None, **kwargs)#
Creates
aiida.orm.nodes.data.cif.CifData
New in version 1.0: Renamed from _get_cif
- _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()
- 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.
- show_mpl_heatmap(**kwargs)#
Show a heatmap of the trajectory with matplotlib.
- class aiida.orm.UpfData#
Bases:
aiida.orm.nodes.data.singlefile.SinglefileData
Data sub class to represent a pseudopotential single file in UPF format.
- classmethod get_or_create(filepath, use_first=False, store_upf=True)#
Get the UpfData with the same md5 of the given file, or create it if it does not yet exist.
- 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 – boolean, if false, the UpfData if created will not be stored.
- Returns:
tuple of UpfData and boolean indicating whether it was created.
- store(*args, **kwargs)#
Store the node, reparsing the file so that the md5 and the element are correctly reset.
- classmethod from_md5(md5, backend=None)#
Return a list of all UpfData that match the given md5 hash.
Note
assumes hash of stored UpfData nodes is stored in the md5 attribute
- Parameters:
md5 – the file hash
- Returns:
list of existing UpfData nodes that have the same md5 hash
- set_file(file, filename=None)#
Store the file in the repository and parse it to set the element and md5 attributes.
- Parameters:
file – filepath or filelike object of the UPF potential file to store. Hint: Pass io.BytesIO(b”my string”) to construct the file directly from a string.
filename – specify filename to use (defaults to name of provided file).
- get_upf_family_names()#
Get the list of all upf family names to which the pseudo belongs.
- property element#
Return the element of the UPF pseudopotential.
- Returns:
the element
- property md5sum#
Return the md5 checksum of the UPF pseudopotential file.
- Returns:
the md5 checksum
- _validate()#
Validate the UPF potential file stored for this node.
- _prepare_upf(main_file_name='')#
Return UPF content.
- classmethod get_upf_group(group_label)#
Return the UPF family group with the given label.
- Parameters:
group_label – the family group label
- Returns:
the Group with the given label, if it exists
- classmethod get_upf_groups(filter_elements=None, user=None, backend=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. The default is None, meaning that all families are returned.
user – if None (default), return the groups for all users. If defined, it should be either a User instance or the user email.
- Returns:
list of Group entities of type UPF.
- _prepare_json(main_file_name='')#
Returns UPF PP in json format.
- class aiida.orm.UpfFamily(label: Optional[str] = None, user: Optional[aiida.orm.User] = None, description: str = '', type_string: Optional[str] = None, backend: Optional[aiida.orm.implementation.StorageBackend] = None)#
Bases:
aiida.orm.groups.Group
Group that represents a pseudo potential family containing UpfData nodes.
Initialization
Create a new group. Either pass a dbgroup parameter, to reload a group from the DB (and then, no further parameters are allowed), or pass the parameters for the Group creation.
- Parameters:
label – The group label, required on creation
description – The group description (by default, an empty string)
user – The owner of the group (by default, the automatic user)
type_string – a string identifying the type of group (by default, an empty string, indicating an user-defined group.
- class aiida.orm.User(email: str, first_name: str = '', last_name: str = '', institution: str = '', backend: Optional[aiida.orm.implementation.StorageBackend] = None)#
Bases:
aiida.orm.entities.Entity
[aiida.orm.implementation.BackendUser
,aiida.orm.users.UserCollection
]AiiDA User
Initialization
Create a new User.
- _CLS_COLLECTION = None#
- static normalize_email(email: str) str #
Normalize the address by lowercasing the domain part of the email address (taken from Django).
- class aiida.orm.WorkChainNode#
Bases:
aiida.orm.nodes.process.workflow.workflow.WorkflowNode
ORM class for all nodes representing the execution of a WorkChain.
- STEPPER_STATE_INFO_KEY = 'stepper_state_info'#
- class aiida.orm.WorkFunctionNode#
Bases:
aiida.orm.utils.mixins.FunctionCalculationMixin
,aiida.orm.nodes.process.workflow.workflow.WorkflowNode
ORM class for all nodes representing the execution of a workfunction.
- _CLS_NODE_LINKS = None#
- class aiida.orm.WorkflowNode(backend: Optional[aiida.orm.implementation.StorageBackend] = None, user: Optional[aiida.orm.users.User] = None, computer: Optional[aiida.orm.computers.Computer] = None, **kwargs: Any)#
Bases:
aiida.orm.nodes.process.process.ProcessNode
Base class for all nodes representing the execution of a workflow process.
Initialization
- Parameters:
backend_entity – the backend model supporting this entity
- _CLS_NODE_LINKS = None#
- _storable = True#
- _unstorable_message = 'storing for this node has been disabled'#
- property inputs: aiida.orm.utils.managers.NodeLinksManager#
Return an instance of NodeLinksManager to manage incoming INPUT_WORK links
The returned Manager allows you to easily explore the nodes connected to this node via an incoming INPUT_WORK link. The incoming nodes are reachable by their link labels which are attributes of the manager.
- Returns:
NodeLinksManager
- property outputs: aiida.orm.utils.managers.NodeLinksManager#
Return an instance of NodeLinksManager to manage outgoing RETURN links
The returned Manager allows you to easily explore the nodes connected to this node via an outgoing RETURN link. The outgoing nodes are reachable by their link labels which are attributes of the manager.
- Returns:
NodeLinksManager
- class aiida.orm.XyData(*args, source=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.
Initialization
Construct a new instance, setting the
source
attribute if provided as a keyword argument.- static _arrayandname_validator(array, name, units)#
Validates that the array is an numpy.ndarray and that the name is of type str. Raises TypeError or ValueError if this not the case.
- 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
- 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
- aiida.orm.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/ase/io/formatoptions.html#ase.io.cif.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.find_bandgap(bandsdata, number_electrons=None, fermi_energy=None)#
Tries to guess whether the bandsdata represent an insulator. This method is meant to be used only for electronic bands (not phonons) By default, it will try to use the occupations to guess the number of electrons and find the Fermi Energy, otherwise, it can be provided explicitely. Also, there is an implicit assumption that the kpoints grid is “sufficiently” dense, so that the bandsdata are not missing the intersection between valence and conduction band if present. Use this function with care!
- Parameters:
number_electrons – (optional, float) number of electrons in the unit cell
fermi_energy – (optional, float) value of the fermi energy.
- Note:
By default, the algorithm uses the occupations array to guess the number of electrons and the occupied bands. This is to be used with care, because the occupations could be smeared so at a non-zero temperature, with the unwanted effect that the conduction bands might be occupied in an insulator. Prefer to pass the number_of_electrons explicitly
- Note:
Only one between number_electrons and fermi_energy can be specified at the same time.
- Returns:
(is_insulator, gap), where is_insulator is a boolean, and gap a float. The gap is None in case of a metal, zero when the homo is equal to the lumo (e.g. in semi-metals).
- aiida.orm.get_loader(orm_class)#
Return the correct OrmEntityLoader for the given orm class.
- Parameters:
orm_class – the orm class
- Returns:
a subclass of OrmEntityLoader
- Raises:
ValueError – if no OrmEntityLoader subclass can be found for the given orm class
- aiida.orm.get_query_type_from_type_string(type_string)#
Take the type string of a Node and create the queryable type string
- Parameters:
type_string – the plugin_type_string attribute of a Node
- Returns:
the type string that can be used to query for
- aiida.orm.get_type_string_from_class(class_module, class_name)#
Given the module and name of a class, determine the orm_class_type string, which codifies the orm class that is to be used. The returned string will always have a terminating period, which is required to query for the string in the database
- Parameters:
class_module – module of the class
class_name – name of the class
- aiida.orm.has_pycifrw()#
- Returns:
True if the PyCifRW module can be imported, False otherwise.
- aiida.orm.load_code(identifier=None, pk=None, uuid=None, label=None, sub_classes=None, query_with_dashes=True) aiida.orm.Code #
Load a Code instance by one of its identifiers: pk, uuid or label
If the type of the identifier is unknown simply pass it without a keyword and the loader will attempt to automatically infer the type.
- Parameters:
identifier – pk (integer), uuid (string) or label (string) of a Code
pk – pk of a Code
uuid – uuid of a Code, or the beginning of the uuid
label – label of a Code
sub_classes – an optional tuple of orm classes to narrow the queryset. Each class should be a strict sub class of the ORM class of the given entity loader.
query_with_dashes (bool) – allow to query for a uuid with dashes
- Returns:
the Code instance
- Raises:
ValueError – if none or more than one of the identifiers are supplied
TypeError – if the provided identifier has the wrong type
aiida.common.NotExistent – if no matching Code is found
aiida.common.MultipleObjectsError – if more than one Code was found
- aiida.orm.load_computer(identifier=None, pk=None, uuid=None, label=None, sub_classes=None, query_with_dashes=True) aiida.orm.Computer #
Load a Computer instance by one of its identifiers: pk, uuid or label
If the type of the identifier is unknown simply pass it without a keyword and the loader will attempt to automatically infer the type.
- Parameters:
identifier – pk (integer), uuid (string) or label (string) of a Computer
pk – pk of a Computer
uuid – uuid of a Computer, or the beginning of the uuid
label – label of a Computer
sub_classes – an optional tuple of orm classes to narrow the queryset. Each class should be a strict sub class of the ORM class of the given entity loader.
query_with_dashes (bool) – allow to query for a uuid with dashes
- Returns:
the Computer instance
- Raises:
ValueError – if none or more than one of the identifiers are supplied
TypeError – if the provided identifier has the wrong type
aiida.common.NotExistent – if no matching Computer is found
aiida.common.MultipleObjectsError – if more than one Computer was found
- aiida.orm.load_entity(entity_loader=None, identifier=None, pk=None, uuid=None, label=None, sub_classes=None, query_with_dashes=True)#
Load an entity instance by one of its identifiers: pk, uuid or label
If the type of the identifier is unknown simply pass it without a keyword and the loader will attempt to automatically infer the type.
- Parameters:
identifier – pk (integer), uuid (string) or label (string) of a Code
pk – pk of a Code
uuid – uuid of a Code, or the beginning of the uuid
label – label of a Code
sub_classes – an optional tuple of orm classes to narrow the queryset. Each class should be a strict sub class of the ORM class of the given entity loader.
query_with_dashes (bool) – allow to query for a uuid with dashes
- Returns:
the Code instance
- Raises:
ValueError – if none or more than one of the identifiers are supplied
TypeError – if the provided identifier has the wrong type
aiida.common.NotExistent – if no matching Code is found
aiida.common.MultipleObjectsError – if more than one Code was found
- aiida.orm.load_group(identifier=None, pk=None, uuid=None, label=None, sub_classes=None, query_with_dashes=True) aiida.orm.Group #
Load a Group instance by one of its identifiers: pk, uuid or label
If the type of the identifier is unknown simply pass it without a keyword and the loader will attempt to automatically infer the type.
- Parameters:
identifier – pk (integer), uuid (string) or label (string) of a Group
pk – pk of a Group
uuid – uuid of a Group, or the beginning of the uuid
label – label of a Group
sub_classes – an optional tuple of orm classes to narrow the queryset. Each class should be a strict sub class of the ORM class of the given entity loader.
query_with_dashes (bool) – allow to query for a uuid with dashes
- Returns:
the Group instance
- Raises:
ValueError – if none or more than one of the identifiers are supplied
TypeError – if the provided identifier has the wrong type
aiida.common.NotExistent – if no matching Group is found
aiida.common.MultipleObjectsError – if more than one Group was found
- aiida.orm.load_node(identifier=None, pk=None, uuid=None, label=None, sub_classes=None, query_with_dashes=True) aiida.orm.Node #
Load a node by one of its identifiers: pk or uuid. If the type of the identifier is unknown simply pass it without a keyword and the loader will attempt to infer the type
- Parameters:
identifier – pk (integer) or uuid (string)
pk – pk of a node
uuid – uuid of a node, or the beginning of the uuid
label – label of a Node
sub_classes – an optional tuple of orm classes to narrow the queryset. Each class should be a strict sub class of the ORM class of the given entity loader.
query_with_dashes (bool) – allow to query for a uuid with dashes
- Returns:
the node instance
- Raises:
ValueError – if none or more than one of the identifiers are supplied
TypeError – if the provided identifier has the wrong type
aiida.common.NotExistent – if no matching Node is found
aiida.common.MultipleObjectsError – if more than one Node was found
- aiida.orm.load_node_class(type_string)#
Return the Node sub class that corresponds to the given type string.
- Parameters:
type_string – the type string of the node
- Returns:
a sub class of Node
- aiida.orm.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
- aiida.orm.to_aiida_type(value)#
Turns basic Python types (str, int, float, bool) into the corresponding AiiDA types.
- aiida.orm.validate_link(source: aiida.orm.Node, target: aiida.orm.Node, link_type: aiida.common.links.LinkType, link_label: str, backend: Optional[aiida.orm.implementation.storage_backend.StorageBackend] = None) None #
Validate adding a link of the given type and label from a given node to ourself.
This function will first validate the class types of the inputs and will subsequently validate whether a link of the specified type is allowed at all between the nodes types of the source and target.
Subsequently, the validity of the “indegree” and “outdegree” of the proposed link is validated, which means validating that the uniqueness constraints of the incoming links into the target node and the outgoing links from the source node are not violated. In AiiDA’s provenance graph each link type has one of the following three types of “degree” character:
* unique * unique pair * unique triple
Each degree character has a different unique constraint on its links, here defined for the indegree:
* unique: any target node, it can only have a single incoming link of this type, regardless of the link label. * unique pair: a node can have an infinite amount of incoming links of this type, as long as the labels within that sub set, are unique. In short, it is the link pair, i.e. the tuple of the link type and label, that has a uniquess constraint for the incoming links to a given node. * unique triple: a node can have an infinite amount of incoming links of this type, as long as the triple tuple of source node, link type and link label is unique. In other words, it is the link triple that has a uniqueness constraint for the incoming links.
The same holds for outdegree, but then it concerns outgoing links from the source node to the target node.
For illustration purposes, consider the following example provenance graphs that are considered legal, where WN, DN and CN represent a WorkflowNode, a DataNode and a CalculationNode, respectively:
1 2 3 ______ ______ ______ ______ ______ | | | | | | | | | | | WN | | DN | | DN | | WN | | WN | |______| |______| |______| |______| |______| | / | | | / a | / a a | | b a | / a _|___/ |___|_ _|___/ | | | | | | | CN | | CN | | DN | |______| |______| |______|
In example 1, the link uniqueness constraint is not violated because despite the labels having the same label a, their link types, CALL_CALC and INPUT_CALC, respectively, are different and their unique_pair indegree is not violated.
Similarly, in the second example, the constraint is not violated, because despite both links having the same link type INPUT_CALC, the have different labels, so the unique_pair indegree of the INPUT_CALC is not violated.
Finally, in the third example, we see two WorkflowNodes both returning the same DataNode and with the same label. Despite the two incoming links here having both the same type as well as the same label, the uniqueness constraint is not violated, because the indegree for RETURN links is unique_triple which means that the triple of source node and link type and label should be unique.
- Parameters:
source – the node from which the link is coming
target – the node to which the link is going
link_type – the type of link
link_label – link label
- Raises:
TypeError – if source or target is not a Node instance, or link_type is not a LinkType enum
ValueError – if the proposed link is invalid