Key1 : Value 1
\\n\\\n", + "Key2 : Value2
\\n\\\n", + "Key3 : Value 3
\\n\\\n", + "Key1 : Value 1
\\n\\\n", + "Key2 : Value2
\\n\\\n", + "Key3 : Value 3
\\n\\\n", + "Key1 : Value 1
\\n\\\n", + "Key2 : Value2
\\n\\\n", + "Key3 : Value 3
\\n\\\n", + "key : value
' and making + a dict out of it. All other information was simply + left out. When writing the model back to SBML, this + dict was converted in the string: + + " +key1: value1
" +key2: value2
" + ... + " + + The 'notes' attribute on an object stored this key: value + dictionary. + + The current version of 'notes' has a dedicated class + that behaves like a dict storing the key-value pairs + present inside the notes string (making it backward + compatible). In addition the the complete notes + information is stored. + + The dict and the string of 'notes' are both synchronized + with each other. + Importantly, the 'notes' attribute is not meant to to + store any machine-readable information. To enforce this + behavior the addition of new key-values inside the + 'notes' dict is not permitted. Trying to do so + will throw an ValueError. The KeyValuePairs should be used + to store key:value information for objects. + + The complete 'notes' string is + directly written to formats like "JSON", "YAML" etc when + COBRA model is written in these format. And when writing + SBML, 'notes' is initialized using the method: + + SBase.getNotesString() + + which makes the xhtml content of the notes using the string. + + Parameters + ---------- + notes_xhtml : string + The complete notes (xhtml) data in the form of a string. + """ + + # pattern checking for "key : value
" type string + PATTERN_PTAG = re.compile( + r"<(?Pkey : oldvalue
', then it will be + updated to store the new value. But if that 'key' is not + present, an ValueError will be thrown. + """ + # if notes string is empty + if self._notes_xhtml is None: + raise ValueError( + "Notes string is not a right place " + "to store key value pairs. Store them " + "at appropriate place in the document." + ) + + # if value passed is not of type 'str' + if not isinstance(value, str): + warn( + "The value must be of type string. \n" + "Converting value to 'string' type and " + "then putting in notes string...." + ) + value = str(value) + + # pattern to search for inside notes string + pattern = re.compile( + r"<(?P{}: {}
".format(k, v) for (k, v) in notes.items()] - + [""] - ) - _check( - sbase.setNotes("\n".join(tokens)), - "Setting notes on sbase: {}".format(sbase), - ) + if notes.notes_xhtml is None or len(notes.notes_xhtml) == 0: + return + _check( + sbase.setNotes(notes.notes_xhtml), "Setting notes on sbase: {}".format(sbase) + ) # ----------------------------------------------------------------------------- # Annotations # ----------------------------------------------------------------------------- -""" -cobra annotations will be dictionaries of the form: - object.annotation = { - 'provider' : [(qualifier, entity), ...] - } -A concrete example for a metabolite would look like the following - metabolite.annotation = { - 'chebi': [(isVersionOf, "CHEBI:17234), (is, "CHEBI:4167),], - 'kegg.compound': [(is, "C00031")] - } -The providers are hereby MIRIAM registry keys for collections -https://www.ebi.ac.uk/miriam/main/collections -The qualifiers are biomodel qualifiers -https://co.mbine.org/standards/qualifiers - -In the current stage the new annotation format is not completely supported yet. -""" URL_IDENTIFIERS_PATTERN = re.compile(r"^https?://identifiers.org/(.+?)[:/](.+)") -URL_IDENTIFIERS_PREFIX = "https://identifiers.org" -QUALIFIER_TYPES = { - "is": libsbml.BQB_IS, - "hasPart": libsbml.BQB_HAS_PART, - "isPartOf": libsbml.BQB_IS_PART_OF, - "isVersionOf": libsbml.BQB_IS_VERSION_OF, - "hasVersion": libsbml.BQB_HAS_VERSION, - "isHomologTo": libsbml.BQB_IS_HOMOLOG_TO, - "isDescribedBy": libsbml.BQB_IS_DESCRIBED_BY, - "isEncodedBy": libsbml.BQB_IS_ENCODED_BY, - "encodes": libsbml.BQB_ENCODES, - "occursIn": libsbml.BQB_OCCURS_IN, - "hasProperty": libsbml.BQB_HAS_PROPERTY, - "isPropertyOf": libsbml.BQB_IS_PROPERTY_OF, - "hasTaxon": libsbml.BQB_HAS_TAXON, - "unknown": libsbml.BQB_UNKNOWN, - "bqm_is": libsbml.BQM_IS, - "bqm_isDescribedBy": libsbml.BQM_IS_DESCRIBED_BY, - "bqm_isDerivedFrom": libsbml.BQM_IS_DERIVED_FROM, - "bqm_isInstanceOf": libsbml.BQM_IS_INSTANCE_OF, - "bqm_hasInstance": libsbml.BQM_HAS_INSTANCE, - "bqm_unknown": libsbml.BQM_UNKNOWN, -} +def _parse_annotation_info(uri): + """Parses provider and term from given identifiers annotation uri. + Parameters + ---------- + uri : str + uri (identifiers.org url) + Returns + ------- + (provider, identifier) if resolvable, None otherwise + """ + match = URL_IDENTIFIERS_PATTERN.match(uri) + if match: + provider, identifier = match.group(1), match.group(2) + if provider.isupper(): + identifier = "%s:%s" % (provider, identifier) + provider = provider.lower() + else: + LOGGER.warning( + f"{uri} does not conform to " + f"'http(s)://identifiers.org/collection/id' or" + f"'http(s)://identifiers.org/COLLECTION:id" + ) + return None + + return provider, identifier -def _parse_annotations(sbase): + +def _parse_annotations(sbase: libsbml.SBase) -> MetaData: """Parses cobra annotations from a given SBase object. - Annotations are dictionaries with the providers as keys. + The annotation format has been changed. We no longer have + simple dictionaries for storing annotation data. Dedicated + classes for storing CVTerm data, History data and key-value + pair data corresponding to an SBase object have been made. + The metadata classes inside cobra.core directory contains + of them. All the existing issues in old annotation format have + been solved. The new format annotation is completely backward + compatible. It can read models with old annotation format, + can convert old format annotation to new format annotation, + and writes annotation in new format only (for JSON and other + formats). The JSON schema v2 specifies the new format annotation + whereas JSON schema v1 have annotation data defined in old format. Parameters ---------- @@ -1515,17 +1444,14 @@ def _parse_annotations(sbase): Returns ------- - dict (annotation dictionary) - - FIXME: annotation format must be updated (this is a big collection of - fixes) - see: https://github.com/opencobra/cobrapy/issues/684) + MetaData + a metadata object storing COBRA annotation """ - annotation = {} + annotation = MetaData() # SBO term if sbase.isSetSBOTerm(): - # FIXME: correct handling of annotations - annotation["sbo"] = sbase.getSBOTermID() + annotation["sbo"] = [sbase.getSBOTermID()] # RDF annotation cvterms = sbase.getCVTerms() @@ -1533,60 +1459,105 @@ def _parse_annotations(sbase): return annotation for cvterm in cvterms: # type: libsbml.CVTerm + # reading the qualifier + qualifier_type = cvterm.getQualifierType() + if qualifier_type == 0: + mq_type = cvterm.getModelQualifierType() + qualifier = "bqm_" + libsbml.ModelQualifierType_toString(mq_type) + elif qualifier_type == 1: + bq_type = cvterm.getBiologicalQualifierType() + qualifier = "bqb_" + libsbml.BiolQualifierType_toString(bq_type) + else: + qualifier = "unknown_qualifier" + ext_res = {"resources": []} for k in range(cvterm.getNumResources()): - # FIXME: read and store the qualifier - uri = cvterm.getResourceURI(k) - data = _parse_annotation_info(uri) - if data is None: - continue - else: - provider, identifier = data - - if provider in annotation: - if isinstance(annotation[provider], string_types): - annotation[provider] = [annotation[provider]] - # FIXME: use a list - if identifier not in annotation[provider]: - annotation[provider].append(identifier) - else: - # FIXME: always in list - annotation[provider] = identifier + ext_res["resources"].append(uri) + ext_res["nested_data"] = _set_nested_data(cvterm) + new_cvterms = CVTerms({qualifier: CVList([ext_res])}) + annotation.add_cvterms(new_cvterms) + + # history of the component + if sbase.isSetModelHistory(): + model_history = sbase.getModelHistory() # type: libsbml.ModelHistory + + cobra_creators = [] + for index in range(model_history.getNumCreators()): + creator = model_history.getCreator(index) # type: libsbml.Creator + creator_dict = {} + if creator.isSetGivenName(): + creator_dict["first_name"] = creator.getGivenName() + if creator.isSetFamilyName(): + creator_dict["last_name"] = creator.getFamilyName() + if creator.isSetEmail(): + creator_dict["email"] = creator.getEmail() + if creator.isSetOrganisation(): + creator_dict["organization_name"] = creator.getOrganisation() + cobra_creator = Creator.from_data(creator_dict) + cobra_creators.append(cobra_creator) + annotation.history.creators = cobra_creators + + if model_history.isSetCreatedDate(): + date = model_history.getCreatedDate() # type: libsbml.Date + cobra_date = HistoryDatetime( + date.getDateAsString() + ) # type: HistoryDatetime + annotation.history.created_date = cobra_date + + cobra_modified_dates = [] + for index in range(model_history.getNumModifiedDates()): + modified_date = model_history.getModifiedDate(index) + cobra_modified_date = HistoryDatetime(modified_date.getDateAsString()) + cobra_modified_dates.append(cobra_modified_date) + annotation.history.modified_dates = cobra_modified_dates return annotation -def _parse_annotation_info(uri): - """Parses provider and term from given identifiers annotation uri. +def _set_nested_data(cvterm_obj: libsbml.CVTerm) -> CVTerms: + """ Parses the nested data corresponding to a given + libsbml.CVTerm object Parameters ---------- - uri : str - uri (identifiers.org url) + cvterm_obj : libsbml.CVTerm + The CVTerm object from which nested data is to be parsed Returns ------- - (provider, identifier) if resolvable, None otherwise + CVTerms + the parsed nested data of the given CVTerm object """ - match = URL_IDENTIFIERS_PATTERN.match(uri) - if match: - provider, identifier = match.group(1), match.group(2) - if provider.isupper(): - identifier = "%s:%s" % (provider, identifier) - provider = provider.lower() - else: - LOGGER.warning( - "%s does not conform to " - "'http(s)://identifiers.org/collection/id' or" - "'http(s)://identifiers.org/COLLECTION:id", - uri, - ) - return None + num_nested_cvterms = cvterm_obj.getNumNestedCVTerms() + cobra_nested_cvterms = CVTerms() + if num_nested_cvterms == 0: + return cobra_nested_cvterms + + for index in range(num_nested_cvterms): # type libsbml.CVTerm + # reading the qualifier + cvterm = cvterm_obj.getNestedCVTerm(index) + qualifier_type = cvterm.getQualifierType() + if qualifier_type == 0: + mq_type = cvterm.getModelQualifierType() + qualifier = "bqm_" + libsbml.ModelQualifierType_toString(mq_type) + elif qualifier_type == 1: + bq_type = cvterm.getBiologicalQualifierType() + qualifier = "bqb_" + libsbml.BiolQualifierType_toString(bq_type) + else: + qualifier = "unknown_qualifier" - return provider, identifier + ext_res = {"resources": []} + for k in range(cvterm.getNumResources()): + uri = cvterm.getResourceURI(k) + ext_res["resources"].append(uri) + ext_res["nested_data"] = _set_nested_data(cvterm) + new_cvterms = CVTerms({qualifier: CVList([ext_res])}) + cobra_nested_cvterms.add_cvterms(new_cvterms) + + return cobra_nested_cvterms -def _sbase_annotations(sbase, annotation): +def _sbase_annotations(sbase: libsbml.SBase, annotation: MetaData) -> None: """Set SBase annotations based on cobra annotations. Parameters @@ -1596,75 +1567,118 @@ def _sbase_annotations(sbase, annotation): annotation : cobra annotation structure cobra object with annotation information - FIXME: annotation format must be updated - (https://github.com/opencobra/cobrapy/issues/684) """ - if not annotation or len(annotation) == 0: - return - # standardize annotations annotation_data = deepcopy(annotation) - for key, value in annotation_data.items(): - # handling of non-string annotations (e.g. integers) - if isinstance(value, (float, int)): - value = str(value) - if isinstance(value, string_types): - annotation_data[key] = [("is", value)] + if not isinstance(annotation_data, MetaData): + raise TypeError( + f"The annotation object must be " f"of type 'Metadata': {annotation_data}" + ) - for key, value in annotation_data.items(): - for idx, item in enumerate(value): - if isinstance(item, string_types): - value[idx] = ("is", item) + if "sbo" in annotation and annotation["sbo"] != []: + sbo_term = annotation["sbo"] + _check(sbase.setSBOTerm(sbo_term[0]), "Setting SBOTerm: {}".format(sbo_term[0])) # set metaId meta_id = "meta_{}".format(sbase.getId()) sbase.setMetaId(meta_id) - # rdf_items = [] - for provider, data in iteritems(annotation_data): + # set cvterms + for key, value in annotation.cvterms.items(): + qualifier = key + if qualifier.startswith("bqb"): + qualifier_type = libsbml.BIOLOGICAL_QUALIFIER + elif qualifier.startswith("bqm"): + qualifier_type = libsbml.MODEL_QUALIFIER + else: + raise CobraSBMLError("Unsupported qualifier: " "%s" % qualifier) + + for ex_res in value: + cv = libsbml.CVTerm() # type: libsbml.CVTerm + cv.setQualifierType(qualifier_type) + if qualifier_type == libsbml.BIOLOGICAL_QUALIFIER: + cv.setBiologicalQualifierType(Qualifier[qualifier].value) + elif qualifier_type == libsbml.MODEL_QUALIFIER: + cv.setModelQualifierType(Qualifier[qualifier].value - 14) + else: + raise CobraSBMLError(f"Unsupported qualifier: {qualifier}") + for uri in ex_res.resources: + cv.addResource(uri) + + # adding the nested data + if ex_res.nested_data is not None: + _add_nested_data(cv, ex_res.nested_data) + + # finally add the cvterm + _check(sbase.addCVTerm(cv), "Setting cvterm: {}".format(cv)) + + # set history + if not annotation.history.is_empty(): + comp_history = libsbml.ModelHistory() + + for creator in annotation.history.creators: + comp_creator = libsbml.ModelCreator() + comp_creator.setGivenName(creator.first_name) + comp_creator.setFamilyName(creator.last_name) + comp_creator.setEmail(creator.email) + comp_creator.setOrganisation(creator.organization_name) + comp_history.addCreator(comp_creator) + + if annotation.history.created_date.datetime is not None: + date = libsbml.Date(annotation.history.created_date.datetime) + comp_history.setCreatedDate(date) + + for modified_date in annotation.history.modified_dates: + date = libsbml.Date(modified_date.datetime) + comp_history.addModifiedDate(date) + + # finally add the compo_history + _check( + sbase.setModelHistory(comp_history), + "Setting ModelHistory: {}".format(comp_history), + ) - # set SBOTerm - if provider in ["SBO", "sbo"]: - if provider == "SBO": - LOGGER.warning( - "'SBO' provider is deprecated, " "use 'sbo' provider instead" - ) - sbo_term = data[0][1] - _check(sbase.setSBOTerm(sbo_term), "Setting SBOTerm: {}".format(sbo_term)) - - # FIXME: sbo should also be written as CVTerm - continue - - for item in data: - qualifier_str, entity = item[0], item[1] - qualifier = QUALIFIER_TYPES.get(qualifier_str, None) - if qualifier is None: - qualifier = libsbml.BQB_IS - LOGGER.error( - "Qualifier type is not supported on " - "annotation: '{}'".format(qualifier_str) - ) +def _add_nested_data(cvterm: libsbml.CVTerm, nested_data: CVTerms): + """Sets nested data inside a libsbml.CVTerm object. + + Parameters + ---------- + cvterm : libsbml.CVTerm + the cvterm object whose nested data is to be set + nested_data : CVTerms + the nested data to be set. + + """ + for key, value in nested_data.items(): + qualifier = key + if qualifier.startswith("bqb"): qualifier_type = libsbml.BIOLOGICAL_QUALIFIER - if qualifier_str.startswith("bqm_"): - qualifier_type = libsbml.MODEL_QUALIFIER + elif qualifier.startswith("bqm"): + qualifier_type = libsbml.MODEL_QUALIFIER + else: + raise CobraSBMLError(f"Unsupported qualifier: {qualifier}") + for ex_res in value: cv = libsbml.CVTerm() # type: libsbml.CVTerm cv.setQualifierType(qualifier_type) if qualifier_type == libsbml.BIOLOGICAL_QUALIFIER: - cv.setBiologicalQualifierType(qualifier) + cv.setBiologicalQualifierType(Qualifier[qualifier].value) elif qualifier_type == libsbml.MODEL_QUALIFIER: - cv.setModelQualifierType(qualifier) + cv.setModelQualifierType(Qualifier[qualifier].value - 14) else: - raise CobraSBMLError("Unsupported qualifier: " "%s" % qualifier) - resource = "%s/%s/%s" % (URL_IDENTIFIERS_PREFIX, provider, entity) - cv.addResource(resource) - _check( - sbase.addCVTerm(cv), - "Setting cvterm: {}, resource: {}".format(cv, resource), - ) + raise CobraSBMLError(f"Unsupported qualifier: {qualifier}") + for uri in ex_res.resources: + cv.addResource(uri) + + # adding the nested data + if ex_res.nested_data is not None: + _add_nested_data(cv, ex_res.nested_data) + + # finally add the cvterm + _check(cvterm.addNestedCVTerm(cv), "Adding nested cvterm: {}".format(cv)) # ----------------------------------------------------------------------------- @@ -1676,7 +1690,7 @@ def validate_sbml_model( internal_consistency=True, check_units_consistency=False, check_modeling_practice=False, - **kwargs + **kwargs, ): """Validate SBML model and returns the model along with a list of errors. diff --git a/src/cobra/io/schema_v1.json b/src/cobra/io/schema_v1.json index edc9756fb..8fd7cb0a4 100644 --- a/src/cobra/io/schema_v1.json +++ b/src/cobra/io/schema_v1.json @@ -153,5 +153,5 @@ "metabolites", "genes" ], - "additionalProperties": false + "additionalProperties": true } diff --git a/src/cobra/io/schema_v2.json b/src/cobra/io/schema_v2.json new file mode 100644 index 000000000..137158672 --- /dev/null +++ b/src/cobra/io/schema_v2.json @@ -0,0 +1,370 @@ +{ + "$schema": "http://json-schema.org/draft-04/schema#", + "title": "COBRA", + "description": "JSON representation of COBRA model", + "definitions": { + "annotation": { + "type": "object", + "properties": { + "sbo": { + "type": "string" + }, + "cvterms": { + "type": "object", + "additionalProperties": { + "type": "array", + "items": { + "type": "object", + "properties": { + "resources": { + "type": "array", + "items": { + "type": "string" + } + }, + "nested_data": { + "$ref": "#/definitions/annotation" + } + } + } + } + }, + "history": { + "type": "object", + "properties": { + "creators": { + "type": "array", + "items": { + "type": "object", + "properties": { + "email": { + "type": "string" + }, + "first_name": { + "type": "string" + }, + "last_name": { + "type": "string" + }, + "organization_name": { + "type": "string" + } + }, + "required": [ + "first_name", + "last_name" + ], + "additionalProperties": false + } + }, + "created_date": { + "type": "string", + "format": "date-time" + }, + "modified_dates": { + "type": "array", + "items": { + "type": "string", + "format": "date-time" + } + } + }, + "required": [ + "creators", + "created_date", + "modified_dates" + ], + "additionalProperties": false + }, + "listofkeyvalue": { + "type": "array", + "items": { + "type": "object", + "properties": { + "id": { + "type": "string", + "pattern": "^[a-zA-Z|_][a-zA-Z0-9_]*$" + }, + "name": { + "type": "string" + }, + "key": { + "type": "string" + }, + "value": { + "type": "string" + }, + "uri": { + "type": "string" + } + }, + "required": [ + "key" + ], + "additionalProperties": false + } + } + } + } + }, + "type": "object", + "properties": { + "id": { + "type": "string", + "pattern": "^[a-zA-Z|_][a-zA-Z0-9_]*$" + }, + "name": { + "type": "string" + }, + "version": { + "type": "string", + "default": "1" + }, + "reactions": { + "type": "array", + "items": { + "type": "object", + "properties": { + "id": { + "type": "string", + "pattern": "^[a-zA-Z|_][a-zA-Z0-9_]*$" + }, + "name": { + "type": "string" + }, + "metabolites": { + "type": "object", + "patternProperties": { + ".*": { + "type": "number" + } + } + }, + "gene_reaction_rule": { + "type": "string" + }, + "lower_bound": { + "type": "number" + }, + "upper_bound": { + "type": "number" + }, + "objective_coefficient": { + "type": "number", + "default": 0 + }, + "subsystem": { + "type": "string" + }, + "notes": { + "type": "string" + }, + "annotation": { + "$ref": "#/definitions/annotation" + } + }, + "required": [ + "id", + "name", + "metabolites", + "lower_bound", + "upper_bound", + "gene_reaction_rule" + ], + "additionalProperties": false + } + }, + "metabolites": { + "type": "array", + "items": { + "type": "object", + "properties": { + "id": { + "type": "string", + "pattern": "^[a-zA-Z|_][a-zA-Z0-9_]*$" + }, + "name": { + "type": "string" + }, + "compartment": { + "type": "string", + "pattern": "[a-z]{1,2}" + }, + "charge": { + "type": "integer" + }, + "formula": { + "type": "string" + }, + "_bound": { + "type": "number", + "default": 0 + }, + "notes": { + "type": "string" + }, + "annotation": { + "$ref": "#/definitions/annotation" + } + }, + "required": [ + "id", + "name", + "compartment" + ], + "additionalProperties": false + } + }, + "genes": { + "type": "array", + "items": { + "type": "object", + "properties": { + "id": { + "type": "string", + "pattern": "^[a-zA-Z|_][a-zA-Z0-9_]*$" + }, + "name": { + "type": "string" + }, + "notes": { + "type": "string" + }, + "annotation": { + "$ref": "#/definitions/annotation" + } + }, + "required": [ + "id", + "name" + ], + "additionalProperties": false + } + }, + "compartments": { + "type": "object", + "patternProperties": { + "[a-z]{1,2}": { + "type": "string" + } + } + }, + "groups": { + "type": "array", + "items": { + "type": "object", + "properties": { + "id": { + "type": "string", + "pattern": "^[a-zA-Z|_][a-zA-Z0-9_]*$" + }, + "name": { + "type": "string" + }, + "notes": { + "type": "string" + }, + "annotation": { + "$ref": "#/definitions/annotation" + }, + "kind": { + "type": "string", + "enum": ["collection", "classification", "partonomy"] + }, + "members": { + "type": "array", + "items": { + "type": "object", + "properties": { + "idRef": { + "type": "string", + "pattern": "^[a-zA-Z|_][a-zA-Z0-9_]*$" + }, + "type": { + "type": "string" + } + }, + "required": ["idRef", "type"] + } + } + }, + "required": ["kind", "members"] + } + }, + "user_defined_constraints": { + "type": "array", + "items": { + "type": "object", + "properties": { + "id": { + "type": "string", + "pattern": "^[a-zA-Z|_][a-zA-Z0-9_]*$" + }, + "name": { + "type": "string" + }, + "lower_bound": { + "type": "number" + }, + "upper_bound": { + "type": "number" + }, + "constraint_comps": { + "type": "array", + "items": { + "type": "object", + "properties": { + "id": { + "type": "string", + "pattern": "^[a-zA-Z|_][a-zA-Z0-9_]*$" + }, + "name": { + "type": "string" + }, + "coefficient": { + "type": "number" + }, + "variable": { + "type": "string" + }, + "variable_type": { + "type": "string", + "enum": ["linear", "quadratic"] + }, + "notes": { + "type": "string" + }, + "annotation": { + "$ref": "#/definitions/annotation" + } + }, + "required": ["variable"], + "additionalProperties": false + } + }, + "notes": { + "type": "string" + }, + "annotation": { + "$ref": "#/definitions/annotation" + } + }, + "required": ["lower_bound", "upper_bound", "constraint_comps"], + "additionalProperties": false + } + }, + "notes": { + "type": "string" + }, + "annotation": { + "$ref": "#/definitions/annotation" + } + }, + "required": [ + "id", + "reactions", + "metabolites", + "genes" + ], + "additionalProperties": true +} diff --git a/src/cobra/manipulation/annotate.py b/src/cobra/manipulation/annotate.py index aef3f87b3..d0ead3d36 100644 --- a/src/cobra/manipulation/annotate.py +++ b/src/cobra/manipulation/annotate.py @@ -14,13 +14,13 @@ def add_SBO(model): """ for r in model.reactions: # don't annotate already annotated reactions - if r.annotation.get("sbo"): + if len(r.annotation.get("sbo")) != 0: continue # only doing exchanges if len(r.metabolites) != 1: continue met_id = list(r._metabolites)[0].id if r.id.startswith("EX_") and r.id == "EX_" + met_id: - r.annotation["sbo"] = "SBO:0000627" + r.annotation["sbo"] = ["SBO:0000627"] elif r.id.startswith("DM_") and r.id == "DM_" + met_id: - r.annotation["sbo"] = "SBO:0000628" + r.annotation["sbo"] = ["SBO:0000628"] diff --git a/src/cobra/manipulation/validate.py b/src/cobra/manipulation/validate.py index 6c5bd972e..bfec46e09 100644 --- a/src/cobra/manipulation/validate.py +++ b/src/cobra/manipulation/validate.py @@ -15,7 +15,12 @@ def check_mass_balance(model): unbalanced = {} for reaction in model.reactions: - if reaction.annotation.get("sbo") not in NOT_MASS_BALANCED_TERMS: + sbo = reaction.annotation["sbo"] + if len(sbo) == 0: + sbo = None + else: + sbo = sbo[0] + if sbo not in NOT_MASS_BALANCED_TERMS: balance = reaction.check_mass_balance() if balance: unbalanced[reaction] = balance diff --git a/src/cobra/medium/boundary_types.py b/src/cobra/medium/boundary_types.py index 2c7d0e7d9..4ce054d89 100644 --- a/src/cobra/medium/boundary_types.py +++ b/src/cobra/medium/boundary_types.py @@ -113,9 +113,9 @@ def is_boundary_type(reaction, boundary_type, external_compartment): """ # Check if the reaction has an annotation. Annotations dominate everything. sbo_term = reaction.annotation.get("sbo", "") - if isinstance(sbo_term, list): + if isinstance(sbo_term, list) and len(sbo_term) != 0: sbo_term = sbo_term[0] - sbo_term = sbo_term.upper() + sbo_term = sbo_term.upper() if sbo_term == sbo_terms[boundary_type]: return True diff --git a/src/cobra/sampling/achr.py b/src/cobra/sampling/achr.py index 616a81863..6baa7c00b 100644 --- a/src/cobra/sampling/achr.py +++ b/src/cobra/sampling/achr.py @@ -154,8 +154,7 @@ def sample(self, n, fluxes=True): names = [r.id for r in self.model.reactions] return pandas.DataFrame( - samples[:, self.fwd_idx] - samples[:, self.rev_idx], - columns=names, + samples[:, self.fwd_idx] - samples[:, self.rev_idx], columns=names, ) else: names = [v.name for v in self.model.variables] diff --git a/src/cobra/sampling/hr_sampler.py b/src/cobra/sampling/hr_sampler.py index 70d930650..c71b9c325 100644 --- a/src/cobra/sampling/hr_sampler.py +++ b/src/cobra/sampling/hr_sampler.py @@ -285,9 +285,7 @@ def generate_fva_warmup(self): primals = self.model.solver.primal_values sol = [primals[v.name] for v in self.model.variables] - self.warmup[ - self.n_warmup, - ] = sol + self.warmup[self.n_warmup,] = sol self.n_warmup += 1 # Reset objective @@ -393,33 +391,13 @@ def _bounds_dist(self, p): """Get the lower and upper bound distances. Negative is bad.""" prob = self.problem - lb_dist = ( - p - - prob.variable_bounds[ - 0, - ] - ).min() - ub_dist = ( - prob.variable_bounds[ - 1, - ] - - p - ).min() + lb_dist = (p - prob.variable_bounds[0,]).min() + ub_dist = (prob.variable_bounds[1,] - p).min() if prob.bounds.shape[0] > 0: const = prob.inequalities.dot(p) - const_lb_dist = ( - const - - prob.bounds[ - 0, - ] - ).min() - const_ub_dist = ( - prob.bounds[ - 1, - ] - - const - ).min() + const_lb_dist = (const - prob.bounds[0,]).min() + const_ub_dist = (prob.bounds[1,] - const).min() lb_dist = min(lb_dist, const_lb_dist) ub_dist = min(ub_dist, const_ub_dist) @@ -508,39 +486,13 @@ def validate(self, samples): ) feasibility = np.abs(S.dot(samples.T).T - b).max(axis=1) - lb_error = ( - samples - - bounds[ - 0, - ] - ).min(axis=1) - ub_error = ( - bounds[ - 1, - ] - - samples - ).min(axis=1) + lb_error = (samples - bounds[0,]).min(axis=1) + ub_error = (bounds[1,] - samples).min(axis=1) if samples.shape[1] == len(self.model.variables) and prob.inequalities.shape[0]: consts = prob.inequalities.dot(samples.T) - lb_error = np.minimum( - lb_error, - ( - consts - - prob.bounds[ - 0, - ] - ).min(axis=1), - ) - ub_error = np.minimum( - ub_error, - ( - prob.bounds[ - 1, - ] - - consts - ).min(axis=1), - ) + lb_error = np.minimum(lb_error, (consts - prob.bounds[0,]).min(axis=1),) + ub_error = np.minimum(ub_error, (prob.bounds[1,] - consts).min(axis=1),) valid = ( (feasibility < self.feasibility_tol) diff --git a/src/cobra/sampling/optgp.py b/src/cobra/sampling/optgp.py index b34450a93..bd5a07e11 100644 --- a/src/cobra/sampling/optgp.py +++ b/src/cobra/sampling/optgp.py @@ -234,8 +234,7 @@ def sample(self, n, fluxes=True): names = [r.id for r in self.model.reactions] return pandas.DataFrame( - chains[:, self.fwd_idx] - chains[:, self.rev_idx], - columns=names, + chains[:, self.fwd_idx] - chains[:, self.rev_idx], columns=names, ) else: names = [v.name for v in self.model.variables] diff --git a/src/cobra/summary/metabolite_summary.py b/src/cobra/summary/metabolite_summary.py index f9fdef4ac..4bc30db1c 100644 --- a/src/cobra/summary/metabolite_summary.py +++ b/src/cobra/summary/metabolite_summary.py @@ -128,11 +128,7 @@ def _generate( # Create the basic flux table. flux = pd.DataFrame( data=[ - ( - r.id, - solution[r.id], - r.get_coefficient(self._metabolite.id), - ) + (r.id, solution[r.id], r.get_coefficient(self._metabolite.id),) for r in self._reactions ], columns=["reaction", "flux", "factor"], @@ -398,13 +394,11 @@ def to_html( metabolite = self._metabolite.id production = self._html_table( - self._display_flux(self.producing_flux, names, threshold), - float_format, + self._display_flux(self.producing_flux, names, threshold), float_format, ) consumption = self._html_table( - self._display_flux(self.consuming_flux, names, threshold), - float_format, + self._display_flux(self.consuming_flux, names, threshold), float_format, ) return ( diff --git a/src/cobra/summary/model_summary.py b/src/cobra/summary/model_summary.py index 832fe6f15..c35b72320 100644 --- a/src/cobra/summary/model_summary.py +++ b/src/cobra/summary/model_summary.py @@ -118,9 +118,7 @@ def _generate( if isinstance(fva, float): logger.info("Performing flux variability analysis.") fva = flux_variability_analysis( - model=model, - reaction_list=model.boundary, - fraction_of_optimum=fva, + model=model, reaction_list=model.boundary, fraction_of_optimum=fva, ) if coefficients: self._objective: Dict["Reaction", float] = { diff --git a/src/cobra/summary/reaction_summary.py b/src/cobra/summary/reaction_summary.py index a8e7f20ba..45a01507d 100644 --- a/src/cobra/summary/reaction_summary.py +++ b/src/cobra/summary/reaction_summary.py @@ -107,24 +107,17 @@ def _generate( if isinstance(fva, float): logger.info("Performing flux variability analysis.") fva = flux_variability_analysis( - model, - reaction_list=[self._reaction], - fraction_of_optimum=fva, + model, reaction_list=[self._reaction], fraction_of_optimum=fva, ) # Create the basic flux table. self._flux = pd.DataFrame( - data={"flux": [solution[self._reaction.id]]}, - index=[self._reaction.id], + data={"flux": [solution[self._reaction.id]]}, index=[self._reaction.id], ) if fva is not None: self._flux = self._flux.join(fva) - def _string_flux( - self, - threshold: float, - float_format: str, - ) -> str: + def _string_flux(self, threshold: float, float_format: str,) -> str: """ Transform a flux data frame to a string. diff --git a/src/cobra/summary/summary.py b/src/cobra/summary/summary.py index e7403b051..f975a9873 100644 --- a/src/cobra/summary/summary.py +++ b/src/cobra/summary/summary.py @@ -27,10 +27,7 @@ class Summary(ABC): """ - def __init__( - self, - **kwargs, - ) -> None: + def __init__(self, **kwargs,) -> None: """ Initialize a summary. diff --git a/src/cobra/test/data/cvterms_alternative.json b/src/cobra/test/data/cvterms_alternative.json new file mode 100644 index 000000000..c6a154083 --- /dev/null +++ b/src/cobra/test/data/cvterms_alternative.json @@ -0,0 +1,26 @@ +{ + "bqb_hasVersion": [ + { + "resources": [ + "https://identifiers.org/chebi/CHEBI:17345", + "https://identifiers.org/chebi/CHEBI:17552", + "https://identifiers.org/chebi/CHEBI:17627" + ] + }, + { + "resources": [ + "https://identifiers.org/kegg.compound/C00035", + "https://identifiers.org/kegg.compound/C00044", + "https://identifiers.org/kegg.compound/C00144" + ] + } + ], + "bqb_isDescribedBy": [ + { + "resources": [ + "https://identifiers.org/uniprot/Q9UQM7", + "https://identifiers.org/uniprot/Q13554" + ] + } + ] +} diff --git a/src/cobra/test/data/cvterms_nested.json b/src/cobra/test/data/cvterms_nested.json new file mode 100644 index 000000000..ba62ee764 --- /dev/null +++ b/src/cobra/test/data/cvterms_nested.json @@ -0,0 +1,30 @@ +{ + "bqb_hasPart": [ + { + "resources": [ + "https://identifiers.org/uniprot/P69905", + "https://identifiers.org/uniprot/P68871", + "https://identifiers.org/kegg.compound/C00032" + ] + }, + { + "resources": [ + "https://identifiers.org/uniprot/P69905", + "https://www.uniprot.org/uniprot/P68871", + "https://identifiers.org/chebi/CHEBI:17627" + ], + "bqb_isDescribedBy": [ + { + "resources": [ + "https://identifiers.org/pubmed/1111111" + ] + }, + { + "resources": [ + "https://identifiers.org/eco/000000" + ] + } + ] + } + ] +} diff --git a/src/cobra/test/data/e_coli_core.json b/src/cobra/test/data/e_coli_core.json new file mode 100644 index 000000000..39db77321 --- /dev/null +++ b/src/cobra/test/data/e_coli_core.json @@ -0,0 +1,14786 @@ +{ +"metabolites":[ +{ +"id":"glc__D_e", +"name":"D-Glucose", +"compartment":"e", +"charge":0, +"formula":"C6H12O6", +"notes":{ +"original_bigg_ids":[ +"glc_D_e" +] +}, +"annotation":{ +"bigg.metabolite":[ +"glc__D" +], +"biocyc":[ +"META:Glucopyranose" +], +"chebi":[ +"CHEBI:12965", +"CHEBI:20999", +"CHEBI:4167", +"CHEBI:17634" +], +"hmdb":[ +"HMDB00122", +"HMDB06564" +], +"inchi_key":[ +"WQZGKKKJIJFFOK-GASJEMHNSA-N" +], +"kegg.compound":[ +"C00031" +], +"kegg.drug":[ +"D00009" +], +"metanetx.chemical":[ +"MNXM41" +], +"sabiork":[ +"1406", +"1407" +], +"sbo":"SBO:0000247", +"seed.compound":[ +"cpd26821", +"cpd00027" +] +} +}, +{ +"id":"gln__L_c", +"name":"L-Glutamine", +"compartment":"c", +"charge":0, +"formula":"C5H10N2O3", +"notes":{ +"original_bigg_ids":[ +"gln_L_c" +] +}, +"annotation":{ +"bigg.metabolite":[ +"gln__L" +], +"biocyc":[ +"META:GLN" +], +"chebi":[ +"CHEBI:42943", +"CHEBI:42899", +"CHEBI:32679", +"CHEBI:32678", +"CHEBI:58359", +"CHEBI:28300", +"CHEBI:42812", +"CHEBI:13110", +"CHEBI:18050", +"CHEBI:32666", +"CHEBI:6227", +"CHEBI:32665", +"CHEBI:21308", +"CHEBI:42814", +"CHEBI:5432", +"CHEBI:24316" +], +"hmdb":[ +"HMDB00641" +], +"inchi_key":[ +"ZDXPYRJPNDTMRX-VKHMYHEASA-N" +], +"kegg.compound":[ +"C00064", +"C00303" +], +"kegg.drug":[ +"D00015" +], +"metanetx.chemical":[ +"MNXM37" +], +"reactome.compound":[ +"113522", +"212615", +"29472" +], +"sabiork":[ +"2011", +"74" +], +"sbo":"SBO:0000247", +"seed.compound":[ +"cpd00253", +"cpd00053" +] +} +}, +{ +"id":"gln__L_e", +"name":"L-Glutamine", +"compartment":"e", +"charge":0, +"formula":"C5H10N2O3", +"notes":{ +"original_bigg_ids":[ +"gln_L_e" +] +}, +"annotation":{ +"bigg.metabolite":[ +"gln__L" +], +"biocyc":[ +"META:GLN" +], +"chebi":[ +"CHEBI:42943", +"CHEBI:42899", +"CHEBI:32679", +"CHEBI:32678", +"CHEBI:58359", +"CHEBI:28300", +"CHEBI:42812", +"CHEBI:13110", +"CHEBI:18050", +"CHEBI:32666", +"CHEBI:6227", +"CHEBI:32665", +"CHEBI:21308", +"CHEBI:42814", +"CHEBI:5432", +"CHEBI:24316" +], +"hmdb":[ +"HMDB00641" +], +"inchi_key":[ +"ZDXPYRJPNDTMRX-VKHMYHEASA-N" +], +"kegg.compound":[ +"C00064", +"C00303" +], +"kegg.drug":[ +"D00015" +], +"metanetx.chemical":[ +"MNXM37" +], +"reactome.compound":[ +"113522", +"212615", +"29472" +], +"sabiork":[ +"2011", +"74" +], +"sbo":"SBO:0000247", +"seed.compound":[ +"cpd00253", +"cpd00053" +] +} +}, +{ +"id":"glu__L_c", +"name":"L-Glutamate", +"compartment":"c", +"charge":-1, +"formula":"C5H8NO4", +"notes":{ +"original_bigg_ids":[ +"glu_L_c" +] +}, +"annotation":{ +"bigg.metabolite":[ +"glu__L" +], +"biocyc":[ +"META:Glutamates", +"META:GLT" +], +"chebi":[ +"CHEBI:76051", +"CHEBI:21301", +"CHEBI:29985", +"CHEBI:42825", +"CHEBI:29987", +"CHEBI:18237", +"CHEBI:24314", +"CHEBI:16015", +"CHEBI:13107", +"CHEBI:5431", +"CHEBI:21304", +"CHEBI:6224", +"CHEBI:14321", +"CHEBI:29988" +], +"hmdb":[ +"HMDB00148", +"HMDB60475" +], +"inchi_key":[ +"WHUUTDBJXJRKMK-VKHMYHEASA-M" +], +"kegg.compound":[ +"C00025", +"C00302" +], +"kegg.drug":[ +"D00007", +"D04341" +], +"metanetx.chemical":[ +"MNXM89557" +], +"reactome.compound":[ +"428614", +"29404", +"113552", +"210382" +], +"sabiork":[ +"73", +"2010" +], +"sbo":"SBO:0000247", +"seed.compound":[ +"cpd27177", +"cpd00023", +"cpd19002" +] +} +}, +{ +"id":"glu__L_e", +"name":"L-Glutamate", +"compartment":"e", +"charge":-1, +"formula":"C5H8NO4", +"notes":{ +"original_bigg_ids":[ +"glu_L_e" +] +}, +"annotation":{ +"bigg.metabolite":[ +"glu__L" +], +"biocyc":[ +"META:Glutamates", +"META:GLT" +], +"chebi":[ +"CHEBI:76051", +"CHEBI:21301", +"CHEBI:29985", +"CHEBI:42825", +"CHEBI:29987", +"CHEBI:18237", +"CHEBI:24314", +"CHEBI:16015", +"CHEBI:13107", +"CHEBI:5431", +"CHEBI:21304", +"CHEBI:6224", +"CHEBI:14321", +"CHEBI:29988" +], +"hmdb":[ +"HMDB00148", +"HMDB60475" +], +"inchi_key":[ +"WHUUTDBJXJRKMK-VKHMYHEASA-M" +], +"kegg.compound":[ +"C00025", +"C00302" +], +"kegg.drug":[ +"D00007", +"D04341" +], +"metanetx.chemical":[ +"MNXM89557" +], +"reactome.compound":[ +"428614", +"29404", +"113552", +"210382" +], +"sabiork":[ +"73", +"2010" +], +"sbo":"SBO:0000247", +"seed.compound":[ +"cpd27177", +"cpd00023", +"cpd19002" +] +} +}, +{ +"id":"glx_c", +"name":"Glyoxylate", +"compartment":"c", +"charge":-1, +"formula":"C2H1O3", +"notes":{ +"original_bigg_ids":[ +"glx_c" +] +}, +"annotation":{ +"bigg.metabolite":[ +"glx" +], +"biocyc":[ +"META:GLYOX" +], +"chebi":[ +"CHEBI:24420", +"CHEBI:36655", +"CHEBI:14368", +"CHEBI:24421", +"CHEBI:42767", +"CHEBI:16891", +"CHEBI:35977", +"CHEBI:5509" +], +"envipath":[ +"32de3cf4-e3e6-4168-956e-32fa5ddb0ce1/compound/cdffdb1a-3322-4cc1-9171-d857bfaa198a", +"4fd7f3e0-dd25-43ac-9453-dda3e52396e4/compound/aecbda66-6e98-4c11-aeaf-6a072f4f963c", +"5882df9c-dae1-4d80-a40e-db4724271456/compound/9dc0aa3b-447a-4b5d-8157-501b036f9626", +"650babc9-9d68-4b73-9332-11972ca26f7b/compound/43b74f4f-bc8a-4b8b-b587-c97d8e9eed48" +], +"hmdb":[ +"HMDB00119" +], +"inchi_key":[ +"HHLFWLYXYJOTON-UHFFFAOYSA-M" +], +"kegg.compound":[ +"C00048" +], +"metanetx.chemical":[ +"MNXM69" +], +"reactome.compound":[ +"904849", +"389678" +], +"sabiork":[ +"1838" +], +"sbo":"SBO:0000247", +"seed.compound":[ +"cpd00040" +] +} +}, +{ +"id":"h2o_c", +"name":"H2O H2O", +"compartment":"c", +"charge":0, +"formula":"H2O", +"notes":{ +"original_bigg_ids":[ +"h2o_c" +] +}, +"annotation":{ +"bigg.metabolite":[ +"h2o" +], +"biocyc":[ +"META:CPD-15815", +"META:OXONIUM", +"META:HYDROXYL-GROUP", +"META:WATER", +"META:OH" +], +"chebi":[ +"CHEBI:13352", +"CHEBI:30490", +"CHEBI:43228", +"CHEBI:33813", +"CHEBI:44292", +"CHEBI:44641", +"CHEBI:27313", +"CHEBI:42043", +"CHEBI:44819", +"CHEBI:29356", +"CHEBI:5594", +"CHEBI:10743", +"CHEBI:15377", +"CHEBI:42857", +"CHEBI:13365", +"CHEBI:29412", +"CHEBI:16234", +"CHEBI:13419", +"CHEBI:5585", +"CHEBI:44701" +], +"envipath":[ +"650babc9-9d68-4b73-9332-11972ca26f7b/compound/799908db-b8c9-4982-86cb-1f225e2ad08c", +"650babc9-9d68-4b73-9332-11972ca26f7b/compound/e7f34a8e-cded-4793-b6d5-792335b38636", +"5882df9c-dae1-4d80-a40e-db4724271456/compound/969d0227-3069-4e44-9525-7ae7bad84170" +], +"hmdb":[ +"HMDB02111", +"HMDB01039" +], +"inchi_key":[ +"XLYOFNOQVPJJNP-UHFFFAOYSA-N" +], +"kegg.compound":[ +"C00001", +"C01328" +], +"kegg.drug":[ +"D00001", +"D06322" +], +"metanetx.chemical":[ +"MNXM2" +], +"reactome.compound":[ +"113521", +"141343", +"2022884", +"5278291", +"29356", +"189422", 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+This is a metabolism model of Escherichia coli str. K-12 substr. MG1655 in + SBML format.
+Redistribution and use of any part of this model from BiGG Models knowledge-base, with or without modification, are permitted provided that the following conditions are met: +
For specific licensing terms about this particular model and regulations of commercial use, see + this model in BiGG Models knowledge-base.
+Key1 : Value1
+Key2 : Value2
+Key3 : Value3
+This is a metabolism model of Escherichia coli str. K-12 substr. MG1655 in\n SBML\u00a0format.
\nRedistribution and use of any part of this model from BiGG Models knowledge-base, with or without modification, are permitted provided that the following conditions are met:\n
For specific licensing terms about this particular model and regulations of commercial use, see\n this model in BiGG Models knowledge-base.
\nKey1 : Value1
\nKey2 : Value2
\nKey3 : Value3
\nKey1 : Value1
\nKey1 : Value1
\n\ +Key2 : Value2
\n\ +Key3 : Value3
\n\ +Key1 : New Value 1
\n\ +Key2 : Value2
\n\ +Key3 : New Value 3
\n\ +