Source code for atomrdf.graph

"""
Graph module contains the basic RDFGraph object in atomrdf. This object gets a structure
as an input and annotates it with the CMSO ontology (PLDO and PODO too as needed). The annotated
object is stored in triplets.

NOTES
-----
- To ensure domain and range checking works as expected, always add type before adding further properties!

Classes
-------
- KnowledgeGraph: Represents a knowledge graph that stores and annotates structure objects.

Attributes
----------
- defstyledict: A dictionary containing default styles for visualizing the graph.

"""


from rdflib import Graph, XSD, RDF, RDFS, BNode, URIRef

import os
import numpy as np
import inspect
from ase.io import write
import copy
import pandas as pd
import yaml
import uuid
import json
import shutil
import tarfile
import logging
import warnings
import re
import pickle

# from pyscal3.core import System
from pyscal3.atoms import Atoms

from atomrdf.visualize import visualize_graph, visualize_provenance
from atomrdf.network.network import OntologyNetwork
from atomrdf.network.ontology import read_ontology
from atomrdf.structure import System
import atomrdf.properties as prp
from atomrdf.stores import create_store
import atomrdf.json_io as json_io
from atomrdf.workflow.workflow import Workflow
from atomrdf.sample import Sample
import atomrdf.mp as amp 

from atomrdf.namespace import Namespace, CMSO, PLDO, PODO, ASMO, PROV, MATH, UNSAFECMSO, UNSAFEASMO, Literal

# read element data file
file_location = os.path.dirname(__file__).split("/")
file_location = "/".join(file_location[:-1])
file_location = os.path.join(os.path.dirname(__file__), "data/element.yml")
with open(file_location, "r") as fin:
    element_indetifiers = yaml.safe_load(fin)


defstyledict = {
    "edgecolor": "#263238",
    "BNode": {
        "color": "#D9D9D9",
        "shape": "box",
        "style": "filled",
        "fontsize": "8",
        "fontname": "Helvetica",
    },
    "URIRef": {
        "color": "#C9DAF8",
        "shape": "box",
        "style": "filled",
        "fontsize": "8",
        "fontname": "Helvetica",
    },
    "Literal": {
        "color": "#E6B8AF",
        "shape": "parallelogram",
        "style": "filled",
        "fontsize": "8",
        "fontname": "Helvetica",
    },
}

def _clean_string(input_string):
    input_string = re.sub(r'\W', '_', input_string)
    if input_string[0].isdigit():
        input_string = "s" + input_string
    return input_string

def _replace_keys(refdict, indict):
    for key, val in indict.items():
        if key in refdict.keys():
            if isinstance(val, dict):
                _replace_keys(refdict[key], indict[key])
            else:
                refdict[key] = val
    return refdict


def _dummy_log(str):
    pass

def _name(term):
    try:
        return str(term.toPython())
    except:
        return str(term)
    
def _prepare_log(file):
    logger = logging.getLogger(__name__)
    handler = logging.FileHandler(file)
    formatter = logging.Formatter("%(asctime)s %(name)-12s %(levelname)-8s %(message)s")
    handler.setFormatter(formatter)
    logger.addHandler(handler)
    logger.setLevel(logging.DEBUG)
    logger.propagate = False
    return logger

[docs] class KnowledgeGraph: """ Represents a knowledge graph. Parameters ---------- graph_file : str, optional The path to the graph file to be parsed. Default is None. store : str, optional The type of store to use. Default is "Memory". store_file : str, optional The path to the store file. Default is None. identifier : str, optional The identifier for the graph. Default is "http://default_graph". ontology : Ontology, optional The ontology object to be used. Default is None. structure_store : StructureStore, optional The structure store object to be used. Default is None. enable_log : bool, optional Whether to enable logging. Default is False. If true, a log file named atomrdf.log will be created in the current working directory. Attributes ---------- graph : rdflib.Graph The RDF graph. sgraph : rdflib.Graph The structure graph for a single chosen sample ontology : Ontology The ontology object. terms : dict The dictionary of ontology terms. store : str The type of store used. Methods ------- add_structure(structure) Add a structure to the knowledge graph. add(triple, validate=True) Add a triple to the knowledge graph. triples(triple) Return the triples in the knowledge graph that match the given triple pattern. """ def __init__( self, graph_file=None, store="Memory", store_file=None, identifier="http://default_graph", ontology=None, structure_store=None, enable_log=False, ): """ Initialize the KnowledgeGraph object. Parameters ---------- graph_file : str, optional The path to the graph file to be parsed. Default is None. store : str, optional The type of store to use. Default is "Memory". store_file : str, optional The path to the store file. Default is None. identifier : str, optional The identifier for the graph. Default is "http://default_graph". ontology : Ontology, optional The ontology object to be used. Default is None. structure_store : StructureStore, optional The structure store object to be used. Default is None. enable_log : bool, optional Whether to enable logging. Default is False. """ create_store( self, store, identifier, store_file=store_file, structure_store=structure_store, ) # enable logging if enable_log: logger = _prepare_log(os.path.join(os.getcwd(), "atomrdf.log")) self.log = logger.info else: self.log = _dummy_log # start binding self.graph.bind("cmso", CMSO) self.graph.bind("pldo", PLDO) if graph_file is not None: if os.path.exists(graph_file): self.graph.parse(graph_file) self.sgraph = None if ontology is None: ontology = read_ontology() self.ontology = ontology self.terms = self.ontology.terms self.store = store self._n_triples = 0 self._initialize_graph() self.workflow = Workflow(self)
[docs] def add_structure(self, structure): """ Add a structure to the knowledge graph. Parameters ---------- structure : Structure The structure object to be added. Returns ------- None Notes ----- This method adds a structure object to the knowledge graph. The structure object should be an instance of the Structure class. The structure object is assigned to the graph and converted to RDF format. """ structure.graph = self structure.to_graph()
def _is_valid(self, input_list): valid = False flat_list = [] for x in input_list: if isinstance(x, list): flat_list.extend(x) else: flat_list.append(x) for x in flat_list: if x is not None: valid = True break return valid def _is_ontoterm(self, term): return type(term).__name__ == "OntoTerm" def _modify_triple(self, triple): modified_triple = [] for term in triple: if self._is_ontoterm(term): modified_triple.append(term.namespace_object) else: modified_triple.append(term) return tuple(modified_triple) def _check_domain_if_uriref(self, triple): found = True dm = self.value(triple[0], RDF.type) if dm is not None: # we need to check domain = triple[1].domain if len(domain) > 0: if "owl:Thing" not in domain: if triple[1].namespace_with_prefix not in dm: # cross ontology term self.log( f"ignoring possible cross ontology connection between {triple[1].namespace} and {dm}" ) return True, None found = False for d in domain: if d.split(":")[-1] in dm: found = True break return found, dm # def _check_domain_if_ontoterm(self, triple): # found = True # domain = triple[0].domain # if len(domain) > 0: # if 'owl:Thing' not in domain: # if triple[1].namespace != triple[0].namespace: # #cross ontology term # self.log(f'ignoring possible cross ontology connection between {triple[1].namespace} and {triple[0].namespace}') # return True, None # found = False # if triple[1].name in domain: # found = True # return found, triple[0].name def _check_domain(self, triple): if self._is_ontoterm(triple[1]): # check if type was provided found = True dm = None if type(triple[0]).__name__ == "URIRef": found, dm = self._check_domain_if_uriref(triple) # elif self._is_ontoterm(triple[0]): # found, dm = self._check_domain_if_ontoterm(triple) if not found: raise ValueError(f"{dm} not in domain of {triple[1].name}") self.log(f"checked {triple[1].name} against domain {dm}") def _check_range_if_uriref(self, triple): found = True rn = self.value(triple[2], RDF.type) if rn is not None: # we need to check rang = triple[1].range if len(rang) > 0: if "owl:Thing" not in rang: if triple[1].namespace_with_prefix not in rn: # cross ontology term self.log( f"ignoring possible cross ontology connection between {triple[1].namespace} and {rn}" ) return True, None found = False for r in rang: if r.split(":")[-1] in rn: found = True break return found, rn # def _check_range_if_ontoterm(self, triple): # found = True # rang = triple[1].range # if len(rang) > 0: # if 'owl:Thing' not in rang: # if triple[1].namespace != triple[2].namespace: # #cross ontology term # self.log(f'ignoring possible cross ontology connection between {triple[1].namespace} and {triple[2].namespace}') # return True, None # # found = False # if triple[2].name in rang: # found = True # return found, triple[2].name def _check_range_if_literal(self, triple): found = True if triple[2].datatype is None: self.log( f"WARNING: {triple[1].name} has a range with unspecified datatype!" ) warnings.warn(f"{triple[1].name} has a range with unspecified datatype!") return True, None destination_range = triple[2].datatype.toPython().split("#")[-1] if destination_range == "string": destination_range = "str" elif destination_range == "integer": destination_range = "int" rang = triple[1].range if len(rang) > 0: found = False if destination_range in rang: found = True return found, destination_range def _check_range(self, triple): if self._is_ontoterm(triple[1]): # check if type was provided found = True dm = None if type(triple[2]).__name__ == "URIRef": found, dm = self._check_range_if_uriref(triple) # elif self._is_ontoterm(triple[2]): # found, dm = self._check_range_if_ontoterm(triple) elif type(triple[2]).__name__ == "Literal": found, dm = self._check_range_if_literal(triple) if not found: raise ValueError(f"{dm} not in range of {triple[1].name}") self.log(f"checked {triple[1].name} against range {dm}")
[docs] def add(self, triple, validate=True): """ Add a triple to the knowledge graph. Parameters ---------- triple : tuple The triple to be added in the form (subject, predicate, object). validate : bool, optional Whether to validate the triple against the domain and range. Default is True. Returns ------- None Notes ----- This method adds a triple to the knowledge graph. The triple should be provided as a tuple in the form (subject, predicate, object). By default, the triple is validated against the domain and range. If the `validate` parameter is set to False, the validation is skipped. Examples -------- >>> graph = Graph() >>> graph.add(("Alice", "likes", "Bob")) >>> graph.add(("Bob", "age", 25), validate=False) """ modified_triple = self._modify_triple(triple) self.log(f"attempting to add triple: {self._n_triples}") self.log(f"- {modified_triple[0].toPython()}") self.log(f"- {modified_triple[1].toPython()}") self.log(f"- {modified_triple[2].toPython()}") if validate: self._check_domain(triple) self._check_range(triple) if str(modified_triple[2].toPython()) == "None": self.log(f"rejecting None valued triple") return self.graph.add(modified_triple) self._n_triples += 1 self.log("added")
[docs] def triples(self, triple): """ Return the triples in the knowledge graph that match the given triple pattern. Parameters ---------- triple : tuple The triple pattern to match in the form (subject, predicate, object). Returns ------- generator A generator that yields the matching triples. Examples -------- >>> graph = KnowledgeGraph() >>> graph.add(("Alice", "likes", "Bob")) >>> graph.add(("Alice", "dislikes", "Charlie")) >>> graph.add(("Bob", "likes", "Alice")) >>> for triple in graph.triples(("Alice", None, None)): ... print(triple) ('Alice', 'likes', 'Bob') ('Alice', 'dislikes', 'Charlie') """ modified_triple = self._modify_triple(triple) return self.graph.triples(modified_triple)
[docs] def value(self, arg1, arg2): """ Get the value of a triple in the knowledge graph. Parameters ---------- arg1 : object The subject of the triple. arg2 : object The predicate of the triple. Returns ------- object or None The value of the triple if it exists, otherwise None. Notes ----- This method retrieves the value of a triple in the knowledge graph. The triple is specified by providing the subject and predicate as arguments. If the triple exists in the graph, the corresponding value is returned. If the triple does not exist, None is returned. Examples -------- >>> graph = KnowledgeGraph() >>> graph.add(("Alice", "likes", "Bob")) >>> value = graph.value("Alice", "likes") >>> print(value) Bob """ modified_double = self._modify_triple((arg1, arg2)) return self.graph.value(modified_double[0], modified_double[1])
[docs] def remove(self, triple): """ Remove a triple from the knowledge graph. Parameters ---------- triple : tuple The triple to be removed in the form (subject, predicate, object). Returns ------- None Notes ----- This method removes a triple from the knowledge graph. The triple should be provided as a tuple in the form (subject, predicate, object). Examples -------- >>> graph = KnowledgeGraph() >>> graph.add(("Alice", "likes", "Bob")) >>> graph.remove(("Alice", "likes", "Bob")) """ modified_triple = self._modify_triple(triple) return self.graph.remove(modified_triple)
[docs] def create_node(self, namestring, classtype, label=None): """ Create a new node in the graph. Parameters ---------- namestring : str The name of the node. classtype : Object from a given ontology The class type of the node. Returns ------- URIRef The newly created node. """ item = URIRef(namestring) self.add((item, RDF.type, classtype)) if label is not None: self.add((item, RDFS.label, Literal(_clean_string(label)))) return item
def _initialize_graph(self): """ Create the RDF Graph from the data stored Parameters ---------- names: bool if True, alphanumeric names will be used instead of random BNodes name_index: string Prefix to be added to identifiers, default 01 Returns ------- None """ # extra triples self.add((CMSO.SimulationCellLength, RDFS.subClassOf, CMSO.Length)) self.add((CMSO.LatticeParameter, RDFS.subClassOf, CMSO.Length)) self.add( (CMSO.Length, CMSO.hasUnit, URIRef("http://qudt.org/vocab/unit/ANGSTROM")) ) self.add((CMSO.SimulationCellAngle, RDFS.subClassOf, CMSO.Angle)) self.add((CMSO.LatticeAngle, RDFS.subClassOf, CMSO.Angle)) self.add((CMSO.Angle, CMSO.hasUnit, URIRef("http://qudt.org/vocab/unit/DEG"))) self.add((CMSO.LatticeVector, RDFS.subClassOf, CMSO.Vector)) self.add((CMSO.SimulationCellVector, RDFS.subClassOf, CMSO.Vector)) # self.add((CMSO.PositionVector, RDFS.subClassOf, CMSO.Vector)) self.add( (CMSO.Vector, CMSO.hasUnit, URIRef("http://qudt.org/vocab/unit/ANGSTROM")) )
[docs] def add_calculated_quantity(self, sample, propertyname, value, unit=None): """ Add a calculated quantity to a sample. Parameters ---------- sample : URIRef The URIRef of the sample to which the calculated quantity is being added. propertyname : str The name of the calculated property. value : str The value of the calculated property. unit : str, optional The unit of the calculated property. Default is None. The unit should be from QUDT. See http://qudt.org/vocab/unit/ Returns ------- None Notes ----- This method adds a calculated quantity to a sample in the knowledge graph. The calculated quantity is represented as a triple with the sample as the subject, the calculated property as the predicate, and the value as the object. The calculated property is created as a node in the graph with the given name and value. If a unit is provided, it is also added as a property of the calculated property node. Examples -------- >>> graph = KnowledgeGraph() >>> sample = graph.create_node("Sample1", CMSO.Sample) >>> graph.add_calculated_quantity(sample, "energy", "10.5", "eV") """ prop = self.create_node(propertyname, CMSO.CalculatedProperty) self.add((sample, CMSO.hasCalculatedProperty, prop)) self.add((prop, RDFS.label, Literal(propertyname))) self.add((prop, ASMO.hasValue, Literal(value))) if unit is not None: self.add((prop, ASMO.hasUnit, URIRef(f"http://qudt.org/vocab/unit/{unit}")))
[docs] def inspect_sample(self, sample): """ Inspects a sample and retrieves information about its atoms, material, defects, composition, crystal structure, space group, calculated properties, and units. Parameters ---------- sample: The sample to inspect. Returns ------- string: A string containing the information about the sample. """ natoms = self.value(sample, CMSO.hasNumberOfAtoms).toPython() material = list([k[2] for k in self.triples((sample, CMSO.hasMaterial, None))])[ 0 ] defects = list([k[2] for k in self.triples((material, CMSO.hasDefect, None))]) composition = list( [ k[2].toPython() for k in self.triples((material, CMSO.hasElementRatio, None)) ] ) crystalstructure = self.value(material, CMSO.hasStructure) spacegroupsymbol = self.value(crystalstructure, CMSO.hasSpaceGroupSymbol).toPython() lattice = self.value(sample, CMSO.hasNumberOfAtoms).toPython() defect_types = list([self.value(d, RDF.type).toPython() for d in defects]) prop_nodes = list( [k[2] for k in self.triples((sample, CMSO.hasCalculatedProperty, None))] ) props = list([self.value(prop_node, RDFS.label) for prop_node in prop_nodes]) propvals = list([self.value(d, ASMO.hasValue).toPython() for d in prop_nodes]) units = list([self.value(d, ASMO.hasUnit).toPython() for d in prop_nodes]) st = [] st.append(f"Sample with {natoms} atoms.\n") st.append("Material:\n") st.append(" ".join(composition)) st.append("\n") st.append(f"Space Group symbol: {spacegroupsymbol}\n") if len(defect_types) > 0: st.append("With defects:\n") for d in defect_types: st.append(f"{d}\n") if len(props) > 0: st.append("With calculated properties:\n") for x in range(len(props)): st.append( f"{props[x]} with value: {propvals[x]} and unit: {units[x]}\n" ) return " ".join(st)
[docs] def visualize(self, *args, **kwargs): """ Visualizes the graph using the specified arguments. This method is a wrapper around the `visualise` method and passes the same arguments to it. Parameters ---------- *args: Variable length argument list. **kwargs: Arbitrary keyword arguments. Returns ------- dot: The visualization of the RDF tree. """ self.visualise(*args, **kwargs)
[docs] def visualise( self, styledict=None, rankdir="BT", hide_types=False, workflow_view=False, sample_view=False, size=None, layout="neato", ): """ Visualize the RDF tree of the Graph. Parameters ---------- styledict : dict, optional If provided, allows customization of color and other properties. rankdir : str, optional The direction of the graph layout. Default is "BT" (bottom to top). hide_types : bool, optional Whether to hide the types in the visualization. Default is False. workflow_view : bool, optional Whether to enable the workflow view. Default is False. sample_view : bool, optional Whether to enable the sample view. Default is False. size : tuple, optional The size of the visualization. Default is None. layout : str, optional The name of the layout algorithm for the graph. Default is "neato". Returns ------- graphviz.dot.Digraph The visualization of the RDF tree. Notes ----- The `styledict` parameter allows customization of the visualization style. It has the following options: BNode: color : str The color of the BNode boxes. shape : str The shape of the BNode boxes. style : str The style of the BNode boxes. URIRef: color : str The color of the URIRef boxes. shape : str The shape of the URIRef boxes. style : str The style of the URIRef boxes. Literal: color : str The color of the Literal boxes. shape : str The shape of the Literal boxes. style : str The style of the Literal boxes. """ if size is not None: size = f"{size[0]},{size[1]}" sdict = defstyledict.copy() if styledict is not None: sdict = _replace_keys(sdict, styledict) return visualize_graph( self.graph, styledict=sdict, rankdir=rankdir, hide_types=hide_types, workflow_view=workflow_view, sample_view=sample_view, size=size, layout=layout, )
[docs] def write(self, filename, format="json-ld"): """ Write the serialised version of the graph to a file Parameters ---------- filename: string name of output file format: string, {'turtle', 'xml', 'json-ld', 'ntriples', 'n3'} output format to be written to Returns ------- None """ with open(filename, "w") as fout: fout.write(self.graph.serialize(format=format))
[docs] def close(self, filename, format="json-ld"): """ Close the graph and write to a file Parameters ---------- filename: string name of output file Returns ------- None """ self.write(filename, format=format)
[docs] def archive(self, package_name, format="turtle", compress=True, add_simulations=False): """ Publish a dataset from graph including per atom quantities. Parameters: ----------- package_name : str The name of the package to be created. format : str, optional The format in which the dataset should be written. Default is "turtle". compress : bool, optional Whether to compress the package into a tarball. Default is True. Raises: ------- ValueError If the package_name already exists or if the tarball already exists. Notes: ------ This method creates a package containing a dataset from the graph, including per atom quantities. The package consists of a folder named package_name, which contains the dataset and related files. If compress is True, the package is compressed into a tarball. The method performs the following steps: 1. Checks if the package_name already exists. If it does, raises a ValueError. 2. If compress is True, checks if the tarball already exists. If it does, raises a ValueError. 3. Creates a folder named package_name. 4. Creates a subfolder named rdf_structure_store within the package folder. 5. Copies the files associated with each sample to the rdf_structure_store folder, while fixing the paths. 6. Updates the paths in the graph to point to the copied files. 7. Writes the dataset to a file named "triples" within the package folder. 8. If compress is True, compresses the package folder into a tarball. 9. Removes the package folder. """ # first step make a folder if os.path.exists(package_name): raise ValueError(f"{package_name} already exists") if compress: if os.path.exists(f"{package_name}.tar.gz"): raise ValueError(f"{package_name} tarball already exists") os.mkdir(package_name) structure_store = f"{package_name}/rdf_structure_store" os.mkdir(structure_store) # now go through each sample, and copy the file, at the same time fix the paths for sample in self.sample_ids: filepath = self.value(URIRef(f"{sample}_Position"), CMSO.hasPath).toPython() shutil.copy(filepath, structure_store) # now we have to remove the old path, and fix new for val in ["Position", "Species"]: self.remove((URIRef(f"{sample}_{val}"), CMSO.hasPath, None)) # assign corrected path new_relpath = "/".join(["rdf_structure_store", filepath.split("/")[-1]]) self.add( ( URIRef(f"{sample}_{val}"), CMSO.hasPath, Literal(new_relpath, datatype=XSD.string), ) ) #copy simulation files if needed if add_simulations: sim_store = f"{package_name}/simulation_store" os.mkdir(sim_store) activities = self.activity_ids for activity in activities: path = self.value(activity, CMSO.hasPath) if path is not None: newpath = "/".join([sim_store, activity.toPython()]) shutil.copytree(path, newpath) #remove old path self.remove((activity, CMSO.hasPath, None)) self.add((activity, CMSO.hasPath, Literal(newpath, datatype=XSD.string))) triple_file = os.path.join(package_name, "triples") self.write(triple_file, format=format) if compress: with tarfile.open(f"{package_name}.tar.gz", "w:gz") as tar: tar.add(package_name, arcname=os.path.basename(package_name)) shutil.rmtree(package_name)
[docs] @classmethod def unarchive( cls, package_name, compress=True, store="Memory", store_file=None, identifier="http://default_graph", ontology=None, ): """ Unarchives a package and returns an instance of the Graph class. Parameters ---------- package_name : str The name of the package to unarchive. compress : bool, optional Whether to compress the package. Defaults to True. store : str, optional The type of store to use. Defaults to "Memory". store_file : str, optional The file to use for the store. Defaults to None. identifier : str, optional The identifier for the graph. Defaults to "http://default_graph". ontology : str, optional The ontology to use. Defaults to None. Returns ------- Graph An instance of the Graph class. Raises ------ FileNotFoundError If the package file is not found. tarfile.TarError If there is an error while extracting the package. """ if compress: package_base_name = ".".join(package_name.split(".")[:-2]) with tarfile.open(package_name) as fin: fin.extractall(".") # os.remove(package_name) # copy things out else: package_base_name = package_name return cls( store=store, store_file=store_file, identifier=identifier, graph_file=f"{package_base_name}/triples", structure_store=f"{package_base_name}/rdf_structure_store", ontology=ontology, )
[docs] def query(self, inquery, return_df=True): """ Query the graph using SPARQL Parameters ---------- inquery: string SPARQL query to be executed return_df: bool, optional if True, returns the results as a pandas DataFrame. Default is True. Returns ------- res: pandas DataFrame pandas dataframe results """ res = self.graph.query(inquery) if res is not None: if return_df: for line in inquery.split("\n"): if "SELECT DISTINCT" in line: break labels = [x[1:] for x in line.split()[2:]] return pd.DataFrame(res, columns=labels) else: return res raise ValueError("SPARQL query returned None")
[docs] def auto_query( self, source, destination, return_query=False, enforce_types=True, return_df=True, ): """ Automatically generates and executes a query based on the provided parameters. Parameters ---------- source : OntoTerm The source of the query. destination : OntoTerm The destination of the query. return_query : bool, optional If True, returns the generated query instead of executing it. Defaults to False. enforce_types : bool, optional If provided, enforces the specified type for the query. Defaults to None. return_df: bool, optional if True, returns the results as a pandas DataFrame. Default is True. Returns ------- pandas DataFrame or str The result of the query execution. If `return_query` is True, returns the generated query as a string. Otherwise, returns the result of the query execution as a pandas DataFrame. """ query = self.ontology.create_query( source, destination, enforce_types=enforce_types ) if return_query: return query res = self.query(query, return_df=return_df) return res
################################# # Methods to interact with sample #################################
[docs] def query_sample( self, destination, return_query=False, enforce_types=None ): """ Query the knowledge graph for atomic scale samples. Parameters ---------- destination : OntoTerm The destination of the query. return_query : bool, optional If True, returns the generated query instead of executing it. Defaults to False. enforce_types : bool, optional If provided, enforces the specified type for the query. Defaults to None. Returns ------- pandas DataFrame or str The result of the query execution. If `return_query` is True, returns the generated query as a string. Otherwise, returns the result of the query execution as a pandas DataFrame. """ return self.auto_query( self.ontology.terms.cmso.AtomicScaleSample, destination, return_query=return_query, enforce_types=enforce_types, )
@property def n_samples(self): """ Number of samples in the Graph """ return len([x for x in self.triples((None, RDF.type, CMSO.AtomicScaleSample))]) @property def sample_ids(self): """ Returns a list of all Samples in the graph """ return [x[0] for x in self.triples((None, RDF.type, CMSO.AtomicScaleSample))] @property def sample_names(self): """ Returns a list of all Sample names in the graph """ samples = [x[0] for x in self.triples((None, RDF.type, CMSO.AtomicScaleSample))] samples_names = [] for sample in samples: sample_name = self.value(sample, RDFS.label) if sample_name is not None: samples_names.append(sample_name.toPython()) else: samples_names.append(sample.toPython()) return samples_names @property def samples(self): sample_ids = self.sample_ids sample_names = self.sample_names sample_objects = [] for ids, name in zip(sample_ids, sample_names): sample_objects.append(Sample(name, ids, self)) return sample_objects @property def activity_ids(self): """ Returns a list of all Samples in the graph """ return [x[0] for x in self.triples((None, RDF.type, PROV.Activity))]
[docs] def iterate_graph(self, item, create_new_graph=False): """ Iterate through the graph starting from the given item. Parameters ---------- item : object The item to start the iteration from. create_new_graph : bool, optional If True, create a new KnowledgeGraph object to store the iteration results. Default is False. The results are stored in `self.sgraph`. Returns ------- None """ if isinstance(item, str): item = URIRef(item) if create_new_graph: self.sgraph = KnowledgeGraph() triples = list(self.triples((item, None, None))) for triple in triples: self.sgraph.graph.add(triple) self.iterate_graph(triple[2])
[docs] def get_sample(self, sample, no_atoms=False): """ Get the Sample as a KnowledgeGraph Parameters ---------- sample: string sample id no_atoms: bool, optional if True, returns the number of atoms in the sample Returns ------- sgraph: :py:class:`RDFGraph` the RDFGraph of the queried sample na: int, only retured if no_atoms is True """ if isinstance(sample, str): sample = URIRef(sample) self.iterate_graph(sample, create_new_graph=True) if no_atoms: na = self.sgraph.value(sample, CMSO.hasNumberOfAtoms).toPython() return self.sgraph, na return self.sgraph
def get_label(self, item): label = self.graph.value(item, RDFS.label) if label is not None: return label.toPython() def get_sample_label(self, sample): label = self.get_label(sample) return label def change_label(self, sample, label): self.graph.remove((sample, RDFS.label, None)) self.graph.add((sample, RDFS.label, Literal(label, datatype=XSD.string)))
[docs] def get_system_from_sample(self, sample): """ Get a pyscal :py:class:`atomrdf.structure.System` from the selected sample Parameters ---------- sample: string sample id Returns ------- system: :py:class:`atomrdf.structure.System` corresponding system """ simcell = self.value(sample, CMSO.hasSimulationCell) cell_vectors = [[], [], []] for s in self.triples((simcell, CMSO.hasVector, None)): cell_vectors[0].append(self.value(s[2], CMSO.hasComponent_x).toPython()) cell_vectors[1].append(self.value(s[2], CMSO.hasComponent_y).toPython()) cell_vectors[2].append(self.value(s[2], CMSO.hasComponent_z).toPython()) # cell_vectors filepath = self.value(URIRef(f"{sample}_Position"), CMSO.hasPath).toPython() position_identifier = self.value( URIRef(f"{sample}_Position"), CMSO.hasIdentifier ).toPython() species_identifier = self.value( URIRef(f"{sample}_Species"), CMSO.hasIdentifier ).toPython() # open the file for reading with open(filepath, "r") as fin: data = json.load(fin) positions = data[position_identifier]["value"] species = data[species_identifier]["value"] atoms = {"positions": positions, "species": species} at = Atoms() at.from_dict(atoms) sys = System() sys.box = cell_vectors sys.atoms = at sys.sample = sample sys.graph = self sys._name = sample.toPython().split('sample:')[-1] return sys
[docs] def to_file(self, sample, filename=None, format="poscar", add_sample_id=True, input_data=None, pseudopotentials=None, kspacing=None, kpts=None, koffset=(0, 0, 0), crystal_coordinates=False): """ Save a given sample to a file Parameters ---------- sample ID of the sample filename: string name of output file format: string, {"lammps-dump","lammps-data", "poscar", 'cif', 'quantum-espresso'} or any format supported by ase input_data : str, optional Additional input data to include in the output file. Defaults to None. Only valid for quantum-espresso format. See ASE write docs for more information. pseudopotentials : str, optional The path to the pseudopotentials file. Defaults to None. Only valid for quantum-espresso format. See ASE write docs for more information. kspacing : float, optional The k-spacing value to include in the output file. Defaults to None. Only valid for quantum-espresso format. See ASE write docs for more information. kpts : list, optional A list of k-points to include in the output file. Defaults to None. Only valid for quantum-espresso format. See ASE write docs for more information. koffset : tuple, optional The k-offset values to include in the output file. Defaults to (0, 0, 0). Only valid for quantum-espresso format. See ASE write docs for more information. crystal_coordinates : bool, optional Whether to include crystal coordinates in the output file. Defaults to False. Only valid for quantum-espresso format. See ASE write docs for more information. Returns ------- None """ if filename is None: filename = os.path.join(os.getcwd(), "out") sys = self.get_system_from_sample(sample) sys.to_file(filename=filename, format=format, add_sample_id=add_sample_id, input_data=input_data, pseudopotentials=pseudopotentials, kspacing=kspacing, kpts=kpts, koffset=koffset, crystal_coordinates=crystal_coordinates)
def enable_workflow(self, workflow_object, workflow_environment=None, workflow_module=None): self.workflow.inform_graph(workflow_object, workflow_environment=workflow_environment, workflow_module=workflow_module) def add_workflow(self, job, workflow_environment=None, workflow_module=None, job_dicts=None, add_intermediate_jobs=False): self.workflow.to_graph(job, workflow_environment=workflow_environment, workflow_module=workflow_module, job_dicts=job_dicts, add_intermediate_jobs=add_intermediate_jobs) def find_property(self, label): prop_list = list(self.graph.triples((None, RDFS.label, label))) if len(prop_list) == 0: raise RuntimeError(f'Property {label} not found in the graph') prop = prop_list[0][0] return prop def get_string_label(self, item): label = self.get_label(item) if label is None: try: label = str(item.toPython()) except: label = str(item) if "activity" in label: method = self.value(item, ASMO.hasComputationalMethod) if method is not None: method_name = self.value(method, RDF.type) if method_name is not None: label = method_name.toPython().split("/")[-1] return label def _add_to_dict(self, prop, indict): name = _name(prop) if name not in indict.keys(): indict[name] = {} indict[name]['found'] = False indict[name]['label'] = self.get_string_label(prop) def _get_ancestor(self, prop, prov): #note that only one operation and parent are present! if isinstance(prop, str): prop = URIRef(prop) propname = _name(prop) operation = [x[1] for x in self.triples((prop, ASMO.wasCalculatedBy, None))] if len(operation) > 0: parent = [x[2] for x in self.triples((prop, ASMO.wasCalculatedBy, None))] operation = operation[0] parent = parent[0] prov[propname]['operation'] = 'output_parameter' prov[propname]['inputs'] = {} prov[propname]['inputs']['0'] = _name(parent) self._add_to_dict(parent, prov) prov[_name(parent)]['inputs'] = {} associated_samples = [x[0] for x in self.triples((None, PROV.wasGeneratedBy, parent))] for count, sample in enumerate(associated_samples): prov[_name(parent)]['inputs'][str(count)] = _name(sample) self._add_to_dict(sample, prov) prov[_name(parent)]['found'] = True prov[_name(parent)]['operation'] = 'sample_for_activity' else: operation = [x[1] for x in self.triples((None, None, prop))] parent = [list(self.triples((None, op, prop)))[0][0] for op in operation] if len(operation) == 0: prov[propname]['found'] = True return prov operation = operation[0] parent = parent[0] if operation.toPython() == "http://purls.helmholtz-metadaten.de/asmo/hasInputParameter": #print(f'we ran 1 for {operation.toPython()}') prov[propname]['operation'] = 'input_parameter' prov[propname]['inputs'] = {} prov[propname]['inputs']['0'] = _name(parent) self._add_to_dict(parent, prov) prov[_name(parent)]['inputs'] = {} associated_samples = [x[0] for x in self.triples((None, PROV.wasGeneratedBy, parent))] for count, sample in enumerate(associated_samples): prov[_name(parent)]['inputs'][str(count)] = _name(sample) self._add_to_dict(sample, prov) prov[_name(parent)]['found'] = True prov[_name(parent)]['operation'] = 'sample_for_activity' elif operation.toPython() == "http://purls.helmholtz-metadaten.de/cmso/hasCalculatedProperty": #print(f'we ran 2 for {operation.toPython()}') prov[propname]['operation'] = 'output_parameter' prov[propname]['inputs'] = {} prov[propname]['inputs']['0'] = _name(parent) self._add_to_dict(parent, prov) prov[_name(parent)]['found'] = True prov[_name(parent)]['operation'] = 'sample_output' elif operation == MATH.hasSum: addends = list(x[2] for x in self.triples((parent, MATH.hasAddend, None))) prov[propname]['operation'] = 'addition' prov[propname]['inputs'] = {} for count, term in enumerate(addends): prov[propname]['inputs'][f'{count}'] = _name(term) self._add_to_dict(term, prov) elif operation == MATH.hasDifference: minuend = self.value(parent, MATH.hasMinuend) subtrahend = self.value(parent, MATH.hasSubtrahend) prov[propname]['operation'] = 'subtraction' prov[propname]['inputs'] = {} prov[propname]['inputs']['0'] = _name(minuend) prov[propname]['inputs']['1'] = _name(subtrahend) self._add_to_dict(minuend, prov) self._add_to_dict(subtrahend, prov) elif operation == MATH.hasProduct: factors = list(x[2] for x in self.triples((parent, MATH.hasFactor, None))) prov[propname]['operation'] = 'multiplication' prov[propname]['inputs'] = {} for count, term in enumerate(factors): prov[propname]['inputs'][f'{count}'] = _name(term) self._add_to_dict(term, prov) elif operation == MATH.hasQuotient: divisor = self.value(parent, MATH.hasDivisor) dividend = self.value(parent, MATH.hasDividend) prov[propname]['operation'] = 'division' prov[propname]['inputs'] = {} prov[propname]['inputs']['0'] = _name(divisor) prov[propname]['inputs']['1'] = _name(dividend) self._add_to_dict(divisor, prov) self._add_to_dict(dividend, prov) prov[propname]['found'] = True return prov def generate_provenance(self, prop=None, label=None, visualize=False): if (prop is None) and (label is None): raise ValueError('Either prop or label must be provided') if prop is None: prop = self.find_property(label) name = _name(prop) prov = {} self._add_to_dict(prop, prov) done = False while not done: done = True keys = list(prov.keys()) for prop in keys: if not prov[prop]['found']: prov = self._get_ancestor(prop, prov) done = False if visualize: return visualize_provenance(prov) return prov def query_structure_from_mp(self, api_key, chemical_system=None, material_ids=None, is_stable=True, conventional=True, add_to_graph=True): docs = amp.query_mp(api_key, chemical_system = chemical_system, material_ids = material_ids, is_stable = is_stable) structures = [] for doc in docs: struct = doc['structure'] if conventional: aseatoms = struct.to_conventional().to_ase_atoms() else: aseatoms = struct.to_primitive().to_ase_atoms() sys = System.read.ase(aseatoms) symmetry = doc['symmetry'] if add_to_graph: sys.graph = self sys.to_graph() targets = [None, symmetry['symbol'], symmetry['number'], None, None, None] sys._add_crystal_structure(targets=targets) #add energy self.add_calculated_quantity(sys.sample, 'EnergyPerAtom', doc['energy_per_atom'], unit='EV') structures.append(sys) if len(structures) == 1: return structures[0] else: return structures