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proycon/pynlpl
pynlpl/formats/folia.py
Document.save
def save(self, filename=None): """Save the document to file. Arguments: * filename (str): The filename to save to. If not set (``None``, default), saves to the same file as loaded from. """ if not filename: filename = self.filename if not filename: raise Exception("No filename specified") if filename[-4:].lower() == '.bz2': f = bz2.BZ2File(filename,'wb') f.write(self.xmlstring().encode('utf-8')) f.close() elif filename[-3:].lower() == '.gz': f = gzip.GzipFile(filename,'wb') #pylint: disable=redefined-variable-type f.write(self.xmlstring().encode('utf-8')) f.close() else: f = io.open(filename,'w',encoding='utf-8') f.write(self.xmlstring()) f.close()
python
def save(self, filename=None): """Save the document to file. Arguments: * filename (str): The filename to save to. If not set (``None``, default), saves to the same file as loaded from. """ if not filename: filename = self.filename if not filename: raise Exception("No filename specified") if filename[-4:].lower() == '.bz2': f = bz2.BZ2File(filename,'wb') f.write(self.xmlstring().encode('utf-8')) f.close() elif filename[-3:].lower() == '.gz': f = gzip.GzipFile(filename,'wb') #pylint: disable=redefined-variable-type f.write(self.xmlstring().encode('utf-8')) f.close() else: f = io.open(filename,'w',encoding='utf-8') f.write(self.xmlstring()) f.close()
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Save the document to file. Arguments: * filename (str): The filename to save to. If not set (``None``, default), saves to the same file as loaded from.
[ "Save", "the", "document", "to", "file", "." ]
train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/folia.py#L6547-L6568
[ "filename" ]
What does this function do?
[ "Save", "the", "document", "to", "file", "." ]
proycon/pynlpl
pynlpl/formats/folia.py
Document.append
def append(self,text): """Add a text (or speech) to the document: Example 1:: doc.append(folia.Text) Example 2:: doc.append( folia.Text(doc, id='example.text') ) Example 3:: doc.append(folia.Speech) """ if text is Text: text = Text(self, id=self.id + '.text.' + str(len(self.data)+1) ) elif text is Speech: text = Speech(self, id=self.id + '.speech.' + str(len(self.data)+1) ) #pylint: disable=redefined-variable-type else: assert isinstance(text, Text) or isinstance(text, Speech) self.data.append(text) return text
python
def append(self,text): """Add a text (or speech) to the document: Example 1:: doc.append(folia.Text) Example 2:: doc.append( folia.Text(doc, id='example.text') ) Example 3:: doc.append(folia.Speech) """ if text is Text: text = Text(self, id=self.id + '.text.' + str(len(self.data)+1) ) elif text is Speech: text = Speech(self, id=self.id + '.speech.' + str(len(self.data)+1) ) #pylint: disable=redefined-variable-type else: assert isinstance(text, Text) or isinstance(text, Speech) self.data.append(text) return text
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Add a text (or speech) to the document: Example 1:: doc.append(folia.Text) Example 2:: doc.append( folia.Text(doc, id='example.text') ) Example 3:: doc.append(folia.Speech)
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/folia.py#L6621-L6643
[ "text" ]
What does this function do?
[ "Add", "a", "text", "(", "or", "speech", ")", "to", "the", "document", ":" ]
proycon/pynlpl
pynlpl/formats/folia.py
Document.xmldeclarations
def xmldeclarations(self): """Internal method to generate XML nodes for all declarations""" l = [] E = ElementMaker(namespace="http://ilk.uvt.nl/folia",nsmap={None: "http://ilk.uvt.nl/folia", 'xml' : "http://www.w3.org/XML/1998/namespace"}) for annotationtype, set in self.annotations: label = None #Find the 'label' for the declarations dynamically (aka: AnnotationType --> String) for key, value in vars(AnnotationType).items(): if value == annotationtype: label = key break #gather attribs if (annotationtype == AnnotationType.TEXT or annotationtype == AnnotationType.PHON) and set == 'undefined' and len(self.annotationdefaults[annotationtype][set]) == 0: #this is the implicit TextContent declaration, no need to output it explicitly continue attribs = {} if set and set != 'undefined': attribs['{' + NSFOLIA + '}set'] = set for key, value in self.annotationdefaults[annotationtype][set].items(): if key == 'annotatortype': if value == AnnotatorType.MANUAL: attribs['{' + NSFOLIA + '}' + key] = 'manual' elif value == AnnotatorType.AUTO: attribs['{' + NSFOLIA + '}' + key] = 'auto' elif key == 'datetime': attribs['{' + NSFOLIA + '}' + key] = value.strftime("%Y-%m-%dT%H:%M:%S") #proper iso-formatting elif value: attribs['{' + NSFOLIA + '}' + key] = value if label: l.append( makeelement(E,'{' + NSFOLIA + '}' + label.lower() + '-annotation', **attribs) ) else: raise Exception("Invalid annotation type") return l
python
def xmldeclarations(self): """Internal method to generate XML nodes for all declarations""" l = [] E = ElementMaker(namespace="http://ilk.uvt.nl/folia",nsmap={None: "http://ilk.uvt.nl/folia", 'xml' : "http://www.w3.org/XML/1998/namespace"}) for annotationtype, set in self.annotations: label = None #Find the 'label' for the declarations dynamically (aka: AnnotationType --> String) for key, value in vars(AnnotationType).items(): if value == annotationtype: label = key break #gather attribs if (annotationtype == AnnotationType.TEXT or annotationtype == AnnotationType.PHON) and set == 'undefined' and len(self.annotationdefaults[annotationtype][set]) == 0: #this is the implicit TextContent declaration, no need to output it explicitly continue attribs = {} if set and set != 'undefined': attribs['{' + NSFOLIA + '}set'] = set for key, value in self.annotationdefaults[annotationtype][set].items(): if key == 'annotatortype': if value == AnnotatorType.MANUAL: attribs['{' + NSFOLIA + '}' + key] = 'manual' elif value == AnnotatorType.AUTO: attribs['{' + NSFOLIA + '}' + key] = 'auto' elif key == 'datetime': attribs['{' + NSFOLIA + '}' + key] = value.strftime("%Y-%m-%dT%H:%M:%S") #proper iso-formatting elif value: attribs['{' + NSFOLIA + '}' + key] = value if label: l.append( makeelement(E,'{' + NSFOLIA + '}' + label.lower() + '-annotation', **attribs) ) else: raise Exception("Invalid annotation type") return l
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Internal method to generate XML nodes for all declarations
[ "Internal", "method", "to", "generate", "XML", "nodes", "for", "all", "declarations" ]
train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/folia.py#L6653-L6690
[]
What does this function do?
[ "Internal", "method", "to", "generate", "XML", "nodes", "for", "all", "declarations" ]
proycon/pynlpl
pynlpl/formats/folia.py
Document.jsondeclarations
def jsondeclarations(self): """Return all declarations in a form ready to be serialised to JSON. Returns: list of dict """ l = [] for annotationtype, set in self.annotations: label = None #Find the 'label' for the declarations dynamically (aka: AnnotationType --> String) for key, value in vars(AnnotationType).items(): if value == annotationtype: label = key break #gather attribs if (annotationtype == AnnotationType.TEXT or annotationtype == AnnotationType.PHON) and set == 'undefined' and len(self.annotationdefaults[annotationtype][set]) == 0: #this is the implicit TextContent declaration, no need to output it explicitly continue jsonnode = {'annotationtype': label.lower()} if set and set != 'undefined': jsonnode['set'] = set for key, value in self.annotationdefaults[annotationtype][set].items(): if key == 'annotatortype': if value == AnnotatorType.MANUAL: jsonnode[key] = 'manual' elif value == AnnotatorType.AUTO: jsonnode[key] = 'auto' elif key == 'datetime': jsonnode[key] = value.strftime("%Y-%m-%dT%H:%M:%S") #proper iso-formatting elif value: jsonnode[key] = value if label: l.append( jsonnode ) else: raise Exception("Invalid annotation type") return l
python
def jsondeclarations(self): """Return all declarations in a form ready to be serialised to JSON. Returns: list of dict """ l = [] for annotationtype, set in self.annotations: label = None #Find the 'label' for the declarations dynamically (aka: AnnotationType --> String) for key, value in vars(AnnotationType).items(): if value == annotationtype: label = key break #gather attribs if (annotationtype == AnnotationType.TEXT or annotationtype == AnnotationType.PHON) and set == 'undefined' and len(self.annotationdefaults[annotationtype][set]) == 0: #this is the implicit TextContent declaration, no need to output it explicitly continue jsonnode = {'annotationtype': label.lower()} if set and set != 'undefined': jsonnode['set'] = set for key, value in self.annotationdefaults[annotationtype][set].items(): if key == 'annotatortype': if value == AnnotatorType.MANUAL: jsonnode[key] = 'manual' elif value == AnnotatorType.AUTO: jsonnode[key] = 'auto' elif key == 'datetime': jsonnode[key] = value.strftime("%Y-%m-%dT%H:%M:%S") #proper iso-formatting elif value: jsonnode[key] = value if label: l.append( jsonnode ) else: raise Exception("Invalid annotation type") return l
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Return all declarations in a form ready to be serialised to JSON. Returns: list of dict
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/folia.py#L6692-L6731
[]
What does this function do?
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proycon/pynlpl
pynlpl/formats/folia.py
Document.xml
def xml(self): """Serialise the document to XML. Returns: lxml.etree.Element See also: :meth:`Document.xmlstring` """ self.pendingvalidation() E = ElementMaker(namespace="http://ilk.uvt.nl/folia",nsmap={'xml' : "http://www.w3.org/XML/1998/namespace", 'xlink':"http://www.w3.org/1999/xlink"}) attribs = {} attribs['{http://www.w3.org/XML/1998/namespace}id'] = self.id #if self.version: # attribs['version'] = self.version #else: attribs['version'] = FOLIAVERSION attribs['generator'] = 'pynlpl.formats.folia-v' + LIBVERSION metadataattribs = {} metadataattribs['{' + NSFOLIA + '}type'] = self.metadatatype if isinstance(self.metadata, ExternalMetaData): metadataattribs['{' + NSFOLIA + '}src'] = self.metadata.url e = E.FoLiA( E.metadata( E.annotations( *self.xmldeclarations() ), *self.xmlmetadata(), **metadataattribs ) , **attribs) for text in self.data: e.append(text.xml()) return e
python
def xml(self): """Serialise the document to XML. Returns: lxml.etree.Element See also: :meth:`Document.xmlstring` """ self.pendingvalidation() E = ElementMaker(namespace="http://ilk.uvt.nl/folia",nsmap={'xml' : "http://www.w3.org/XML/1998/namespace", 'xlink':"http://www.w3.org/1999/xlink"}) attribs = {} attribs['{http://www.w3.org/XML/1998/namespace}id'] = self.id #if self.version: # attribs['version'] = self.version #else: attribs['version'] = FOLIAVERSION attribs['generator'] = 'pynlpl.formats.folia-v' + LIBVERSION metadataattribs = {} metadataattribs['{' + NSFOLIA + '}type'] = self.metadatatype if isinstance(self.metadata, ExternalMetaData): metadataattribs['{' + NSFOLIA + '}src'] = self.metadata.url e = E.FoLiA( E.metadata( E.annotations( *self.xmldeclarations() ), *self.xmlmetadata(), **metadataattribs ) , **attribs) for text in self.data: e.append(text.xml()) return e
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Serialise the document to XML. Returns: lxml.etree.Element See also: :meth:`Document.xmlstring`
[ "Serialise", "the", "document", "to", "XML", "." ]
train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/folia.py#L6733-L6773
[]
What does this function do?
[ "Serialise", "the", "document", "to", "XML", "." ]
proycon/pynlpl
pynlpl/formats/folia.py
Document.json
def json(self): """Serialise the document to a ``dict`` ready for serialisation to JSON. Example:: import json jsondoc = json.dumps(doc.json()) """ self.pendingvalidation() jsondoc = {'id': self.id, 'children': [], 'declarations': self.jsondeclarations() } if self.version: jsondoc['version'] = self.version else: jsondoc['version'] = FOLIAVERSION jsondoc['generator'] = 'pynlpl.formats.folia-v' + LIBVERSION for text in self.data: jsondoc['children'].append(text.json()) return jsondoc
python
def json(self): """Serialise the document to a ``dict`` ready for serialisation to JSON. Example:: import json jsondoc = json.dumps(doc.json()) """ self.pendingvalidation() jsondoc = {'id': self.id, 'children': [], 'declarations': self.jsondeclarations() } if self.version: jsondoc['version'] = self.version else: jsondoc['version'] = FOLIAVERSION jsondoc['generator'] = 'pynlpl.formats.folia-v' + LIBVERSION for text in self.data: jsondoc['children'].append(text.json()) return jsondoc
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Serialise the document to a ``dict`` ready for serialisation to JSON. Example:: import json jsondoc = json.dumps(doc.json())
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/folia.py#L6775-L6794
[]
What does this function do?
[ "Serialise", "the", "document", "to", "a", "dict", "ready", "for", "serialisation", "to", "JSON", "." ]
proycon/pynlpl
pynlpl/formats/folia.py
Document.xmlmetadata
def xmlmetadata(self): """Internal method to serialize metadata to XML""" E = ElementMaker(namespace="http://ilk.uvt.nl/folia",nsmap={None: "http://ilk.uvt.nl/folia", 'xml' : "http://www.w3.org/XML/1998/namespace"}) elements = [] if self.metadatatype == "native": if isinstance(self.metadata, NativeMetaData): for key, value in self.metadata.items(): elements.append(E.meta(value,id=key) ) else: if isinstance(self.metadata, ForeignData): #in-document m = self.metadata while m is not None: elements.append(m.xml()) m = m.next for metadata_id, submetadata in self.submetadata.items(): subelements = [] attribs = { "{http://www.w3.org/XML/1998/namespace}id": metadata_id, "type": self.submetadatatype[metadata_id] } if isinstance(submetadata, NativeMetaData): for key, value in submetadata.items(): subelements.append(E.meta(value,id=key) ) elif isinstance(submetadata, ExternalMetaData): attribs['src'] = submetadata.url elif isinstance(submetadata, ForeignData): #in-document m = submetadata while m is not None: subelements.append(m.xml()) m = m.next elements.append( E.submetadata(*subelements, **attribs)) return elements
python
def xmlmetadata(self): """Internal method to serialize metadata to XML""" E = ElementMaker(namespace="http://ilk.uvt.nl/folia",nsmap={None: "http://ilk.uvt.nl/folia", 'xml' : "http://www.w3.org/XML/1998/namespace"}) elements = [] if self.metadatatype == "native": if isinstance(self.metadata, NativeMetaData): for key, value in self.metadata.items(): elements.append(E.meta(value,id=key) ) else: if isinstance(self.metadata, ForeignData): #in-document m = self.metadata while m is not None: elements.append(m.xml()) m = m.next for metadata_id, submetadata in self.submetadata.items(): subelements = [] attribs = { "{http://www.w3.org/XML/1998/namespace}id": metadata_id, "type": self.submetadatatype[metadata_id] } if isinstance(submetadata, NativeMetaData): for key, value in submetadata.items(): subelements.append(E.meta(value,id=key) ) elif isinstance(submetadata, ExternalMetaData): attribs['src'] = submetadata.url elif isinstance(submetadata, ForeignData): #in-document m = submetadata while m is not None: subelements.append(m.xml()) m = m.next elements.append( E.submetadata(*subelements, **attribs)) return elements
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Internal method to serialize metadata to XML
[ "Internal", "method", "to", "serialize", "metadata", "to", "XML" ]
train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/folia.py#L6796-L6828
[]
What does this function do?
[ "Internal", "method", "to", "serialize", "metadata", "to", "XML" ]
proycon/pynlpl
pynlpl/formats/folia.py
Document.parsexmldeclarations
def parsexmldeclarations(self, node): """Internal method to parse XML declarations""" if self.debug >= 1: print("[PyNLPl FoLiA DEBUG] Processing Annotation Declarations",file=stderr) self.declareprocessed = True for subnode in node: #pylint: disable=too-many-nested-blocks if not isinstance(subnode.tag, str): continue if subnode.tag[:25] == '{' + NSFOLIA + '}' and subnode.tag[-11:] == '-annotation': prefix = subnode.tag[25:][:-11] type = None if prefix.upper() in vars(AnnotationType): type = vars(AnnotationType)[prefix.upper()] else: raise Exception("Unknown declaration: " + subnode.tag) if 'set' in subnode.attrib and subnode.attrib['set']: set = subnode.attrib['set'] else: set = 'undefined' if (type,set) in self.annotations: if type == AnnotationType.TEXT: #explicit Text declaration, remove the implicit declaration: a = [] for t,s in self.annotations: if not (t == AnnotationType.TEXT and s == 'undefined'): a.append( (t,s) ) self.annotations = a #raise ValueError("Double declaration of " + subnode.tag + ", set '" + set + "' + is already declared") //doubles are okay says Ko else: self.annotations.append( (type, set) ) #Load set definition if set and self.loadsetdefinitions and set not in self.setdefinitions: if set[:7] == "http://" or set[:8] == "https://" or set[:6] == "ftp://": try: self.setdefinitions[set] = SetDefinition(set,verbose=self.verbose) #will raise exception on error except DeepValidationError: print("WARNING: Set " + set + " could not be downloaded, ignoring!",file=sys.stderr) #warning and ignore #Set defaults if type in self.annotationdefaults and set in self.annotationdefaults[type]: #handle duplicate. If ambiguous: remove defaults if 'annotator' in subnode.attrib: if not ('annotator' in self.annotationdefaults[type][set]): self.annotationdefaults[type][set]['annotator'] = subnode.attrib['annotator'] elif self.annotationdefaults[type][set]['annotator'] != subnode.attrib['annotator']: del self.annotationdefaults[type][set]['annotator'] if 'annotatortype' in subnode.attrib: if not ('annotatortype' in self.annotationdefaults[type][set]): self.annotationdefaults[type][set]['annotatortype'] = subnode.attrib['annotatortype'] elif self.annotationdefaults[type][set]['annotatortype'] != subnode.attrib['annotatortype']: del self.annotationdefaults[type][set]['annotatortype'] else: defaults = {} if 'annotator' in subnode.attrib: defaults['annotator'] = subnode.attrib['annotator'] if 'annotatortype' in subnode.attrib: if subnode.attrib['annotatortype'] == 'auto': defaults['annotatortype'] = AnnotatorType.AUTO else: defaults['annotatortype'] = AnnotatorType.MANUAL if 'datetime' in subnode.attrib: if isinstance(subnode.attrib['datetime'], datetime): defaults['datetime'] = subnode.attrib['datetime'] else: defaults['datetime'] = parse_datetime(subnode.attrib['datetime']) if not type in self.annotationdefaults: self.annotationdefaults[type] = {} self.annotationdefaults[type][set] = defaults if 'external' in subnode.attrib and subnode.attrib['external']: if self.debug >= 1: print("[PyNLPl FoLiA DEBUG] Loading external document: " + subnode.attrib['external'],file=stderr) if not type in self.standoffdocs: self.standoffdocs[type] = {} self.standoffdocs[type][set] = {} #check if it is already loaded, if multiple references are made to the same doc we reuse the instance standoffdoc = None for t in self.standoffdocs: for s in self.standoffdocs[t]: for source in self.standoffdocs[t][s]: if source == subnode.attrib['external']: standoffdoc = self.standoffdocs[t][s] break if standoffdoc: break if standoffdoc: break if not standoffdoc: if subnode.attrib['external'][:7] == 'http://' or subnode.attrib['external'][:8] == 'https://': #document is remote, download (in memory) try: f = urlopen(subnode.attrib['external']) except: raise DeepValidationError("Unable to download standoff document: " + subnode.attrib['external']) try: content = u(f.read()) except IOError: raise DeepValidationError("Unable to download standoff document: " + subnode.attrib['external']) f.close() standoffdoc = Document(string=content, parentdoc=self, setdefinitions=self.setdefinitions) elif os.path.exists(subnode.attrib['external']): #document is on disk: standoffdoc = Document(file=subnode.attrib['external'], parentdoc=self, setdefinitions=self.setdefinitions) else: #document not found raise DeepValidationError("Unable to find standoff document: " + subnode.attrib['external']) self.standoffdocs[type][set][subnode.attrib['external']] = standoffdoc standoffdoc.parentdoc = self if self.debug >= 1: print("[PyNLPl FoLiA DEBUG] Found declared annotation " + subnode.tag + ". Defaults: " + repr(defaults),file=stderr)
python
def parsexmldeclarations(self, node): """Internal method to parse XML declarations""" if self.debug >= 1: print("[PyNLPl FoLiA DEBUG] Processing Annotation Declarations",file=stderr) self.declareprocessed = True for subnode in node: #pylint: disable=too-many-nested-blocks if not isinstance(subnode.tag, str): continue if subnode.tag[:25] == '{' + NSFOLIA + '}' and subnode.tag[-11:] == '-annotation': prefix = subnode.tag[25:][:-11] type = None if prefix.upper() in vars(AnnotationType): type = vars(AnnotationType)[prefix.upper()] else: raise Exception("Unknown declaration: " + subnode.tag) if 'set' in subnode.attrib and subnode.attrib['set']: set = subnode.attrib['set'] else: set = 'undefined' if (type,set) in self.annotations: if type == AnnotationType.TEXT: #explicit Text declaration, remove the implicit declaration: a = [] for t,s in self.annotations: if not (t == AnnotationType.TEXT and s == 'undefined'): a.append( (t,s) ) self.annotations = a #raise ValueError("Double declaration of " + subnode.tag + ", set '" + set + "' + is already declared") //doubles are okay says Ko else: self.annotations.append( (type, set) ) #Load set definition if set and self.loadsetdefinitions and set not in self.setdefinitions: if set[:7] == "http://" or set[:8] == "https://" or set[:6] == "ftp://": try: self.setdefinitions[set] = SetDefinition(set,verbose=self.verbose) #will raise exception on error except DeepValidationError: print("WARNING: Set " + set + " could not be downloaded, ignoring!",file=sys.stderr) #warning and ignore #Set defaults if type in self.annotationdefaults and set in self.annotationdefaults[type]: #handle duplicate. If ambiguous: remove defaults if 'annotator' in subnode.attrib: if not ('annotator' in self.annotationdefaults[type][set]): self.annotationdefaults[type][set]['annotator'] = subnode.attrib['annotator'] elif self.annotationdefaults[type][set]['annotator'] != subnode.attrib['annotator']: del self.annotationdefaults[type][set]['annotator'] if 'annotatortype' in subnode.attrib: if not ('annotatortype' in self.annotationdefaults[type][set]): self.annotationdefaults[type][set]['annotatortype'] = subnode.attrib['annotatortype'] elif self.annotationdefaults[type][set]['annotatortype'] != subnode.attrib['annotatortype']: del self.annotationdefaults[type][set]['annotatortype'] else: defaults = {} if 'annotator' in subnode.attrib: defaults['annotator'] = subnode.attrib['annotator'] if 'annotatortype' in subnode.attrib: if subnode.attrib['annotatortype'] == 'auto': defaults['annotatortype'] = AnnotatorType.AUTO else: defaults['annotatortype'] = AnnotatorType.MANUAL if 'datetime' in subnode.attrib: if isinstance(subnode.attrib['datetime'], datetime): defaults['datetime'] = subnode.attrib['datetime'] else: defaults['datetime'] = parse_datetime(subnode.attrib['datetime']) if not type in self.annotationdefaults: self.annotationdefaults[type] = {} self.annotationdefaults[type][set] = defaults if 'external' in subnode.attrib and subnode.attrib['external']: if self.debug >= 1: print("[PyNLPl FoLiA DEBUG] Loading external document: " + subnode.attrib['external'],file=stderr) if not type in self.standoffdocs: self.standoffdocs[type] = {} self.standoffdocs[type][set] = {} #check if it is already loaded, if multiple references are made to the same doc we reuse the instance standoffdoc = None for t in self.standoffdocs: for s in self.standoffdocs[t]: for source in self.standoffdocs[t][s]: if source == subnode.attrib['external']: standoffdoc = self.standoffdocs[t][s] break if standoffdoc: break if standoffdoc: break if not standoffdoc: if subnode.attrib['external'][:7] == 'http://' or subnode.attrib['external'][:8] == 'https://': #document is remote, download (in memory) try: f = urlopen(subnode.attrib['external']) except: raise DeepValidationError("Unable to download standoff document: " + subnode.attrib['external']) try: content = u(f.read()) except IOError: raise DeepValidationError("Unable to download standoff document: " + subnode.attrib['external']) f.close() standoffdoc = Document(string=content, parentdoc=self, setdefinitions=self.setdefinitions) elif os.path.exists(subnode.attrib['external']): #document is on disk: standoffdoc = Document(file=subnode.attrib['external'], parentdoc=self, setdefinitions=self.setdefinitions) else: #document not found raise DeepValidationError("Unable to find standoff document: " + subnode.attrib['external']) self.standoffdocs[type][set][subnode.attrib['external']] = standoffdoc standoffdoc.parentdoc = self if self.debug >= 1: print("[PyNLPl FoLiA DEBUG] Found declared annotation " + subnode.tag + ". Defaults: " + repr(defaults),file=stderr)
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Internal method to parse XML declarations
[ "Internal", "method", "to", "parse", "XML", "declarations" ]
train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/folia.py#L6833-L6948
[ "node" ]
What does this function do?
[ "Internal", "method", "to", "parse", "XML", "declarations" ]
proycon/pynlpl
pynlpl/formats/folia.py
Document.declare
def declare(self, annotationtype, set, **kwargs): """Declare a new annotation type to be used in the document. Keyword arguments can be used to set defaults for any annotation of this type and set. Arguments: annotationtype: The type of annotation, this is conveyed by passing the corresponding annototion class (such as :class:`PosAnnotation` for example), or a member of :class:`AnnotationType`, such as ``AnnotationType.POS``. set (str): the set, should formally be a URL pointing to the set definition Keyword Arguments: annotator (str): Sets a default annotator annotatortype: Should be either ``AnnotatorType.MANUAL`` or ``AnnotatorType.AUTO``, indicating whether the annotation was performed manually or by an automated process. datetime (datetime.datetime): Sets the default datetime alias (str): Defines alias that may be used in set attribute of elements instead of the full set name Example:: doc.declare(folia.PosAnnotation, 'http://some/path/brown-tag-set', annotator="mytagger", annotatortype=folia.AnnotatorType.AUTO) """ if (sys.version > '3' and not isinstance(set,str)) or (sys.version < '3' and not isinstance(set,(str,unicode))): raise ValueError("Set parameter for declare() must be a string") if inspect.isclass(annotationtype): annotationtype = annotationtype.ANNOTATIONTYPE if annotationtype in self.alias_set and set in self.alias_set[annotationtype]: raise ValueError("Set " + set + " conflicts with alias, may not be equal!") if not (annotationtype, set) in self.annotations: self.annotations.append( (annotationtype,set) ) if set and self.loadsetdefinitions and not set in self.setdefinitions: if set[:7] == "http://" or set[:8] == "https://" or set[:6] == "ftp://": self.setdefinitions[set] = SetDefinition(set,verbose=self.verbose) #will raise exception on error if not annotationtype in self.annotationdefaults: self.annotationdefaults[annotationtype] = {} self.annotationdefaults[annotationtype][set] = kwargs if 'alias' in kwargs: if annotationtype in self.set_alias and set in self.set_alias[annotationtype] and self.set_alias[annotationtype][set] != kwargs['alias']: raise ValueError("Redeclaring set " + set + " with another alias ('"+kwargs['alias']+"') is not allowed!") if annotationtype in self.alias_set and kwargs['alias'] in self.alias_set[annotationtype] and self.alias_set[annotationtype][kwargs['alias']] != set: raise ValueError("Redeclaring alias " + kwargs['alias'] + " with another set ('"+set+"') is not allowed!") if annotationtype in self.set_alias and kwargs['alias'] in self.set_alias[annotationtype]: raise ValueError("Alias " + kwargs['alias'] + " conflicts with set name, may not be equal!") if annotationtype not in self.alias_set: self.alias_set[annotationtype] = {} if annotationtype not in self.set_alias: self.set_alias[annotationtype] = {} self.alias_set[annotationtype][kwargs['alias']] = set self.set_alias[annotationtype][set] = kwargs['alias']
python
def declare(self, annotationtype, set, **kwargs): """Declare a new annotation type to be used in the document. Keyword arguments can be used to set defaults for any annotation of this type and set. Arguments: annotationtype: The type of annotation, this is conveyed by passing the corresponding annototion class (such as :class:`PosAnnotation` for example), or a member of :class:`AnnotationType`, such as ``AnnotationType.POS``. set (str): the set, should formally be a URL pointing to the set definition Keyword Arguments: annotator (str): Sets a default annotator annotatortype: Should be either ``AnnotatorType.MANUAL`` or ``AnnotatorType.AUTO``, indicating whether the annotation was performed manually or by an automated process. datetime (datetime.datetime): Sets the default datetime alias (str): Defines alias that may be used in set attribute of elements instead of the full set name Example:: doc.declare(folia.PosAnnotation, 'http://some/path/brown-tag-set', annotator="mytagger", annotatortype=folia.AnnotatorType.AUTO) """ if (sys.version > '3' and not isinstance(set,str)) or (sys.version < '3' and not isinstance(set,(str,unicode))): raise ValueError("Set parameter for declare() must be a string") if inspect.isclass(annotationtype): annotationtype = annotationtype.ANNOTATIONTYPE if annotationtype in self.alias_set and set in self.alias_set[annotationtype]: raise ValueError("Set " + set + " conflicts with alias, may not be equal!") if not (annotationtype, set) in self.annotations: self.annotations.append( (annotationtype,set) ) if set and self.loadsetdefinitions and not set in self.setdefinitions: if set[:7] == "http://" or set[:8] == "https://" or set[:6] == "ftp://": self.setdefinitions[set] = SetDefinition(set,verbose=self.verbose) #will raise exception on error if not annotationtype in self.annotationdefaults: self.annotationdefaults[annotationtype] = {} self.annotationdefaults[annotationtype][set] = kwargs if 'alias' in kwargs: if annotationtype in self.set_alias and set in self.set_alias[annotationtype] and self.set_alias[annotationtype][set] != kwargs['alias']: raise ValueError("Redeclaring set " + set + " with another alias ('"+kwargs['alias']+"') is not allowed!") if annotationtype in self.alias_set and kwargs['alias'] in self.alias_set[annotationtype] and self.alias_set[annotationtype][kwargs['alias']] != set: raise ValueError("Redeclaring alias " + kwargs['alias'] + " with another set ('"+set+"') is not allowed!") if annotationtype in self.set_alias and kwargs['alias'] in self.set_alias[annotationtype]: raise ValueError("Alias " + kwargs['alias'] + " conflicts with set name, may not be equal!") if annotationtype not in self.alias_set: self.alias_set[annotationtype] = {} if annotationtype not in self.set_alias: self.set_alias[annotationtype] = {} self.alias_set[annotationtype][kwargs['alias']] = set self.set_alias[annotationtype][set] = kwargs['alias']
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Declare a new annotation type to be used in the document. Keyword arguments can be used to set defaults for any annotation of this type and set. Arguments: annotationtype: The type of annotation, this is conveyed by passing the corresponding annototion class (such as :class:`PosAnnotation` for example), or a member of :class:`AnnotationType`, such as ``AnnotationType.POS``. set (str): the set, should formally be a URL pointing to the set definition Keyword Arguments: annotator (str): Sets a default annotator annotatortype: Should be either ``AnnotatorType.MANUAL`` or ``AnnotatorType.AUTO``, indicating whether the annotation was performed manually or by an automated process. datetime (datetime.datetime): Sets the default datetime alias (str): Defines alias that may be used in set attribute of elements instead of the full set name Example:: doc.declare(folia.PosAnnotation, 'http://some/path/brown-tag-set', annotator="mytagger", annotatortype=folia.AnnotatorType.AUTO)
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/folia.py#L6972-L7018
[ "annotationtype", "set", "kwargs" ]
What does this function do?
[ "Declare", "a", "new", "annotation", "type", "to", "be", "used", "in", "the", "document", "." ]
proycon/pynlpl
pynlpl/formats/folia.py
Document.declared
def declared(self, annotationtype, set): """Checks if the annotation type is present (i.e. declared) in the document. Arguments: annotationtype: The type of annotation, this is conveyed by passing the corresponding annototion class (such as :class:`PosAnnotation` for example), or a member of :class:`AnnotationType`, such as ``AnnotationType.POS``. set (str): the set, should formally be a URL pointing to the set definition (aliases are also supported) Example:: if doc.declared(folia.PosAnnotation, 'http://some/path/brown-tag-set'): .. Returns: bool """ if inspect.isclass(annotationtype): annotationtype = annotationtype.ANNOTATIONTYPE return ( (annotationtype,set) in self.annotations) or (set in self.alias_set and self.alias_set[set] and (annotationtype, self.alias_set[set]) in self.annotations )
python
def declared(self, annotationtype, set): """Checks if the annotation type is present (i.e. declared) in the document. Arguments: annotationtype: The type of annotation, this is conveyed by passing the corresponding annototion class (such as :class:`PosAnnotation` for example), or a member of :class:`AnnotationType`, such as ``AnnotationType.POS``. set (str): the set, should formally be a URL pointing to the set definition (aliases are also supported) Example:: if doc.declared(folia.PosAnnotation, 'http://some/path/brown-tag-set'): .. Returns: bool """ if inspect.isclass(annotationtype): annotationtype = annotationtype.ANNOTATIONTYPE return ( (annotationtype,set) in self.annotations) or (set in self.alias_set and self.alias_set[set] and (annotationtype, self.alias_set[set]) in self.annotations )
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Checks if the annotation type is present (i.e. declared) in the document. Arguments: annotationtype: The type of annotation, this is conveyed by passing the corresponding annototion class (such as :class:`PosAnnotation` for example), or a member of :class:`AnnotationType`, such as ``AnnotationType.POS``. set (str): the set, should formally be a URL pointing to the set definition (aliases are also supported) Example:: if doc.declared(folia.PosAnnotation, 'http://some/path/brown-tag-set'): .. Returns: bool
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/folia.py#L7020-L7036
[ "annotationtype", "set" ]
What does this function do?
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proycon/pynlpl
pynlpl/formats/folia.py
Document.defaultset
def defaultset(self, annotationtype): """Obtain the default set for the specified annotation type. Arguments: annotationtype: The type of annotation, this is conveyed by passing the corresponding annototion class (such as :class:`PosAnnotation` for example), or a member of :class:`AnnotationType`, such as ``AnnotationType.POS``. Returns: the set (str) Raises: :class:`NoDefaultError` if the annotation type does not exist or if there is ambiguity (multiple sets for the same type) """ if inspect.isclass(annotationtype) or isinstance(annotationtype,AbstractElement): annotationtype = annotationtype.ANNOTATIONTYPE try: return list(self.annotationdefaults[annotationtype].keys())[0] except KeyError: raise NoDefaultError except IndexError: raise NoDefaultError
python
def defaultset(self, annotationtype): """Obtain the default set for the specified annotation type. Arguments: annotationtype: The type of annotation, this is conveyed by passing the corresponding annototion class (such as :class:`PosAnnotation` for example), or a member of :class:`AnnotationType`, such as ``AnnotationType.POS``. Returns: the set (str) Raises: :class:`NoDefaultError` if the annotation type does not exist or if there is ambiguity (multiple sets for the same type) """ if inspect.isclass(annotationtype) or isinstance(annotationtype,AbstractElement): annotationtype = annotationtype.ANNOTATIONTYPE try: return list(self.annotationdefaults[annotationtype].keys())[0] except KeyError: raise NoDefaultError except IndexError: raise NoDefaultError
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Obtain the default set for the specified annotation type. Arguments: annotationtype: The type of annotation, this is conveyed by passing the corresponding annototion class (such as :class:`PosAnnotation` for example), or a member of :class:`AnnotationType`, such as ``AnnotationType.POS``. Returns: the set (str) Raises: :class:`NoDefaultError` if the annotation type does not exist or if there is ambiguity (multiple sets for the same type)
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/folia.py#L7039-L7058
[ "annotationtype" ]
What does this function do?
[ "Obtain", "the", "default", "set", "for", "the", "specified", "annotation", "type", "." ]
proycon/pynlpl
pynlpl/formats/folia.py
Document.defaultannotator
def defaultannotator(self, annotationtype, set=None): """Obtain the default annotator for the specified annotation type and set. Arguments: annotationtype: The type of annotation, this is conveyed by passing the corresponding annototion class (such as :class:`PosAnnotation` for example), or a member of :class:`AnnotationType`, such as ``AnnotationType.POS``. set (str): the set, should formally be a URL pointing to the set definition Returns: the set (str) Raises: :class:`NoDefaultError` if the annotation type does not exist or if there is ambiguity (multiple sets for the same type) """ if inspect.isclass(annotationtype) or isinstance(annotationtype,AbstractElement): annotationtype = annotationtype.ANNOTATIONTYPE if not set: set = self.defaultset(annotationtype) try: return self.annotationdefaults[annotationtype][set]['annotator'] except KeyError: raise NoDefaultError
python
def defaultannotator(self, annotationtype, set=None): """Obtain the default annotator for the specified annotation type and set. Arguments: annotationtype: The type of annotation, this is conveyed by passing the corresponding annototion class (such as :class:`PosAnnotation` for example), or a member of :class:`AnnotationType`, such as ``AnnotationType.POS``. set (str): the set, should formally be a URL pointing to the set definition Returns: the set (str) Raises: :class:`NoDefaultError` if the annotation type does not exist or if there is ambiguity (multiple sets for the same type) """ if inspect.isclass(annotationtype) or isinstance(annotationtype,AbstractElement): annotationtype = annotationtype.ANNOTATIONTYPE if not set: set = self.defaultset(annotationtype) try: return self.annotationdefaults[annotationtype][set]['annotator'] except KeyError: raise NoDefaultError
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Obtain the default annotator for the specified annotation type and set. Arguments: annotationtype: The type of annotation, this is conveyed by passing the corresponding annototion class (such as :class:`PosAnnotation` for example), or a member of :class:`AnnotationType`, such as ``AnnotationType.POS``. set (str): the set, should formally be a URL pointing to the set definition Returns: the set (str) Raises: :class:`NoDefaultError` if the annotation type does not exist or if there is ambiguity (multiple sets for the same type)
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/folia.py#L7061-L7080
[ "annotationtype", "set" ]
What does this function do?
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proycon/pynlpl
pynlpl/formats/folia.py
Document.title
def title(self, value=None): """Get or set the document's title from/in the metadata No arguments: Get the document's title from metadata Argument: Set the document's title in metadata """ if not (value is None): if (self.metadatatype == "native"): self.metadata['title'] = value else: self._title = value if (self.metadatatype == "native"): if 'title' in self.metadata: return self.metadata['title'] else: return None else: return self._title
python
def title(self, value=None): """Get or set the document's title from/in the metadata No arguments: Get the document's title from metadata Argument: Set the document's title in metadata """ if not (value is None): if (self.metadatatype == "native"): self.metadata['title'] = value else: self._title = value if (self.metadatatype == "native"): if 'title' in self.metadata: return self.metadata['title'] else: return None else: return self._title
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Get or set the document's title from/in the metadata No arguments: Get the document's title from metadata Argument: Set the document's title in metadata
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/folia.py#L7127-L7144
[ "value" ]
What does this function do?
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proycon/pynlpl
pynlpl/formats/folia.py
Document.date
def date(self, value=None): """Get or set the document's date from/in the metadata. No arguments: Get the document's date from metadata Argument: Set the document's date in metadata """ if not (value is None): if (self.metadatatype == "native"): self.metadata['date'] = value else: self._date = value if (self.metadatatype == "native"): if 'date' in self.metadata: return self.metadata['date'] else: return None else: return self._date
python
def date(self, value=None): """Get or set the document's date from/in the metadata. No arguments: Get the document's date from metadata Argument: Set the document's date in metadata """ if not (value is None): if (self.metadatatype == "native"): self.metadata['date'] = value else: self._date = value if (self.metadatatype == "native"): if 'date' in self.metadata: return self.metadata['date'] else: return None else: return self._date
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Get or set the document's date from/in the metadata. No arguments: Get the document's date from metadata Argument: Set the document's date in metadata
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/folia.py#L7146-L7163
[ "value" ]
What does this function do?
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proycon/pynlpl
pynlpl/formats/folia.py
Document.publisher
def publisher(self, value=None): """No arguments: Get the document's publisher from metadata Argument: Set the document's publisher in metadata """ if not (value is None): if (self.metadatatype == "native"): self.metadata['publisher'] = value else: self._publisher = value if (self.metadatatype == "native"): if 'publisher' in self.metadata: return self.metadata['publisher'] else: return None else: return self._publisher
python
def publisher(self, value=None): """No arguments: Get the document's publisher from metadata Argument: Set the document's publisher in metadata """ if not (value is None): if (self.metadatatype == "native"): self.metadata['publisher'] = value else: self._publisher = value if (self.metadatatype == "native"): if 'publisher' in self.metadata: return self.metadata['publisher'] else: return None else: return self._publisher
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No arguments: Get the document's publisher from metadata Argument: Set the document's publisher in metadata
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/folia.py#L7165-L7180
[ "value" ]
What does this function do?
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proycon/pynlpl
pynlpl/formats/folia.py
Document.license
def license(self, value=None): """No arguments: Get the document's license from metadata Argument: Set the document's license in metadata """ if not (value is None): if (self.metadatatype == "native"): self.metadata['license'] = value else: self._license = value if (self.metadatatype == "native"): if 'license' in self.metadata: return self.metadata['license'] else: return None else: return self._license
python
def license(self, value=None): """No arguments: Get the document's license from metadata Argument: Set the document's license in metadata """ if not (value is None): if (self.metadatatype == "native"): self.metadata['license'] = value else: self._license = value if (self.metadatatype == "native"): if 'license' in self.metadata: return self.metadata['license'] else: return None else: return self._license
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No arguments: Get the document's license from metadata Argument: Set the document's license in metadata
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/folia.py#L7182-L7197
[ "value" ]
What does this function do?
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proycon/pynlpl
pynlpl/formats/folia.py
Document.language
def language(self, value=None): """No arguments: Get the document's language (ISO-639-3) from metadata Argument: Set the document's language (ISO-639-3) in metadata """ if not (value is None): if (self.metadatatype == "native"): self.metadata['language'] = value else: self._language = value if self.metadatatype == "native": if 'language' in self.metadata: return self.metadata['language'] else: return None else: return self._language
python
def language(self, value=None): """No arguments: Get the document's language (ISO-639-3) from metadata Argument: Set the document's language (ISO-639-3) in metadata """ if not (value is None): if (self.metadatatype == "native"): self.metadata['language'] = value else: self._language = value if self.metadatatype == "native": if 'language' in self.metadata: return self.metadata['language'] else: return None else: return self._language
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No arguments: Get the document's language (ISO-639-3) from metadata Argument: Set the document's language (ISO-639-3) in metadata
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/folia.py#L7199-L7214
[ "value" ]
What does this function do?
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proycon/pynlpl
pynlpl/formats/folia.py
Document.parsemetadata
def parsemetadata(self, node): """Internal method to parse metadata""" if 'type' in node.attrib: self.metadatatype = node.attrib['type'] else: #no type specified, default to native self.metadatatype = "native" if 'src' in node.attrib: self.metadata = ExternalMetaData(node.attrib['src']) elif self.metadatatype == "native": self.metadata = NativeMetaData() else: self.metadata = None #may be set below to ForeignData for subnode in node: if subnode.tag == '{' + NSFOLIA + '}annotations': self.parsexmldeclarations(subnode) elif subnode.tag == '{' + NSFOLIA + '}meta': if self.metadatatype == "native": if subnode.text: self.metadata[subnode.attrib['id']] = subnode.text else: raise MetaDataError("Encountered a meta element but metadata type is not native!") elif subnode.tag == '{' + NSFOLIA + '}provenance': #forward compatibility with FoLiA 2.0; ignore provenance print("WARNING: Ignoring provenance data. Use foliapy instead of pynlpl.formats.folia for FoLiA v2.0 compatibility!",file=sys.stderr) pass elif subnode.tag == '{' + NSFOLIA + '}foreign-data': if self.metadatatype == "native": raise MetaDataError("Encountered a foreign-data element but metadata type is native!") elif self.metadata is not None: #multiple foreign-data elements, chain: e = self.metadata while e.next is not None: e = e.next e.next = ForeignData(self, node=subnode) else: self.metadata = ForeignData(self, node=subnode) elif subnode.tag == '{' + NSFOLIA + '}submetadata': self.parsesubmetadata(subnode) elif subnode.tag == '{http://www.mpi.nl/IMDI/Schema/IMDI}METATRANSCRIPT': #backward-compatibility for old IMDI without foreign-key E = ElementMaker(namespace=NSFOLIA,nsmap={None: NSFOLIA, 'xml' : "http://www.w3.org/XML/1998/namespace"}) self.metadatatype = "imdi" self.metadata = ForeignData(self, node=subnode)
python
def parsemetadata(self, node): """Internal method to parse metadata""" if 'type' in node.attrib: self.metadatatype = node.attrib['type'] else: #no type specified, default to native self.metadatatype = "native" if 'src' in node.attrib: self.metadata = ExternalMetaData(node.attrib['src']) elif self.metadatatype == "native": self.metadata = NativeMetaData() else: self.metadata = None #may be set below to ForeignData for subnode in node: if subnode.tag == '{' + NSFOLIA + '}annotations': self.parsexmldeclarations(subnode) elif subnode.tag == '{' + NSFOLIA + '}meta': if self.metadatatype == "native": if subnode.text: self.metadata[subnode.attrib['id']] = subnode.text else: raise MetaDataError("Encountered a meta element but metadata type is not native!") elif subnode.tag == '{' + NSFOLIA + '}provenance': #forward compatibility with FoLiA 2.0; ignore provenance print("WARNING: Ignoring provenance data. Use foliapy instead of pynlpl.formats.folia for FoLiA v2.0 compatibility!",file=sys.stderr) pass elif subnode.tag == '{' + NSFOLIA + '}foreign-data': if self.metadatatype == "native": raise MetaDataError("Encountered a foreign-data element but metadata type is native!") elif self.metadata is not None: #multiple foreign-data elements, chain: e = self.metadata while e.next is not None: e = e.next e.next = ForeignData(self, node=subnode) else: self.metadata = ForeignData(self, node=subnode) elif subnode.tag == '{' + NSFOLIA + '}submetadata': self.parsesubmetadata(subnode) elif subnode.tag == '{http://www.mpi.nl/IMDI/Schema/IMDI}METATRANSCRIPT': #backward-compatibility for old IMDI without foreign-key E = ElementMaker(namespace=NSFOLIA,nsmap={None: NSFOLIA, 'xml' : "http://www.w3.org/XML/1998/namespace"}) self.metadatatype = "imdi" self.metadata = ForeignData(self, node=subnode)
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Internal method to parse metadata
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/folia.py#L7216-L7261
[ "node" ]
What does this function do?
[ "Internal", "method", "to", "parse", "metadata" ]
proycon/pynlpl
pynlpl/formats/folia.py
Document.select
def select(self, Class, set=None, recursive=True, ignore=True): """See :meth:`AbstractElement.select`""" if self.mode == Mode.MEMORY: for t in self.data: if Class.__name__ == 'Text': yield t else: for e in t.select(Class,set,recursive,ignore): yield e
python
def select(self, Class, set=None, recursive=True, ignore=True): """See :meth:`AbstractElement.select`""" if self.mode == Mode.MEMORY: for t in self.data: if Class.__name__ == 'Text': yield t else: for e in t.select(Class,set,recursive,ignore): yield e
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See :meth:`AbstractElement.select`
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/folia.py#L7427-L7435
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What does this function do?
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proycon/pynlpl
pynlpl/formats/folia.py
Document.count
def count(self, Class, set=None, recursive=True,ignore=True): """See :meth:`AbstractElement.count`""" if self.mode == Mode.MEMORY: s = 0 for t in self.data: s += sum( 1 for e in t.select(Class,recursive,True ) ) return s
python
def count(self, Class, set=None, recursive=True,ignore=True): """See :meth:`AbstractElement.count`""" if self.mode == Mode.MEMORY: s = 0 for t in self.data: s += sum( 1 for e in t.select(Class,recursive,True ) ) return s
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See :meth:`AbstractElement.count`
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/folia.py#L7437-L7443
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What does this function do?
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proycon/pynlpl
pynlpl/formats/folia.py
Document.paragraphs
def paragraphs(self, index = None): """Return a generator of all paragraphs found in the document. If an index is specified, return the n'th paragraph only (starting at 0)""" if index is None: return self.select(Paragraph) else: if index < 0: index = sum(t.count(Paragraph) for t in self.data) + index for t in self.data: for i,e in enumerate(t.select(Paragraph)) : if i == index: return e raise IndexError
python
def paragraphs(self, index = None): """Return a generator of all paragraphs found in the document. If an index is specified, return the n'th paragraph only (starting at 0)""" if index is None: return self.select(Paragraph) else: if index < 0: index = sum(t.count(Paragraph) for t in self.data) + index for t in self.data: for i,e in enumerate(t.select(Paragraph)) : if i == index: return e raise IndexError
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Return a generator of all paragraphs found in the document. If an index is specified, return the n'th paragraph only (starting at 0)
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/folia.py#L7445-L7458
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What does this function do?
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proycon/pynlpl
pynlpl/formats/folia.py
Document.sentences
def sentences(self, index = None): """Return a generator of all sentence found in the document. Except for sentences in quotes. If an index is specified, return the n'th sentence only (starting at 0)""" if index is None: return self.select(Sentence,None,True,[Quote]) else: if index < 0: index = sum(t.count(Sentence,None,True,[Quote]) for t in self.data) + index for t in self.data: for i,e in enumerate(t.select(Sentence,None,True,[Quote])) : if i == index: return e raise IndexError
python
def sentences(self, index = None): """Return a generator of all sentence found in the document. Except for sentences in quotes. If an index is specified, return the n'th sentence only (starting at 0)""" if index is None: return self.select(Sentence,None,True,[Quote]) else: if index < 0: index = sum(t.count(Sentence,None,True,[Quote]) for t in self.data) + index for t in self.data: for i,e in enumerate(t.select(Sentence,None,True,[Quote])) : if i == index: return e raise IndexError
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Return a generator of all sentence found in the document. Except for sentences in quotes. If an index is specified, return the n'th sentence only (starting at 0)
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/folia.py#L7460-L7473
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What does this function do?
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proycon/pynlpl
pynlpl/formats/folia.py
Document.text
def text(self, cls='current', retaintokenisation=False): """Returns the text of the entire document (returns a unicode instance) See also: :meth:`AbstractElement.text` """ #backward compatibility, old versions didn't have cls as first argument, so if a boolean is passed first we interpret it as the 2nd: if cls is True or cls is False: retaintokenisation = cls cls = 'current' s = "" for c in self.data: if s: s += "\n\n\n" try: s += c.text(cls, retaintokenisation) except NoSuchText: continue return s
python
def text(self, cls='current', retaintokenisation=False): """Returns the text of the entire document (returns a unicode instance) See also: :meth:`AbstractElement.text` """ #backward compatibility, old versions didn't have cls as first argument, so if a boolean is passed first we interpret it as the 2nd: if cls is True or cls is False: retaintokenisation = cls cls = 'current' s = "" for c in self.data: if s: s += "\n\n\n" try: s += c.text(cls, retaintokenisation) except NoSuchText: continue return s
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Returns the text of the entire document (returns a unicode instance) See also: :meth:`AbstractElement.text`
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/folia.py#L7493-L7512
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What does this function do?
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proycon/pynlpl
pynlpl/fsa.py
NFA._states
def _states(self, state, processedstates=[]): #pylint: disable=dangerous-default-value """Iterate over all states in no particular order""" processedstates.append(state) for nextstate in state.epsilon: if not nextstate in processedstates: self._states(nextstate, processedstates) for _, nextstate in state.transitions: if not nextstate in processedstates: self._states(nextstate, processedstates) return processedstates
python
def _states(self, state, processedstates=[]): #pylint: disable=dangerous-default-value """Iterate over all states in no particular order""" processedstates.append(state) for nextstate in state.epsilon: if not nextstate in processedstates: self._states(nextstate, processedstates) for _, nextstate in state.transitions: if not nextstate in processedstates: self._states(nextstate, processedstates) return processedstates
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Iterate over all states in no particular order
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/fsa.py#L97-L109
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What does this function do?
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proycon/pynlpl
pynlpl/common.py
log
def log(msg, **kwargs): """Generic log method. Will prepend timestamp. Keyword arguments: system - Name of the system/module indent - Integer denoting the desired level of indentation streams - List of streams to output to stream - Stream to output to (singleton version of streams) """ if 'debug' in kwargs: if 'currentdebug' in kwargs: if kwargs['currentdebug'] < kwargs['debug']: return False else: return False #no currentdebug passed, assuming no debug mode and thus skipping message s = "[" + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + "] " if 'system' in kwargs: s += "[" + system + "] " if 'indent' in kwargs: s += ("\t" * int(kwargs['indent'])) s += u(msg) if s[-1] != '\n': s += '\n' if 'streams' in kwargs: streams = kwargs['streams'] elif 'stream' in kwargs: streams = [kwargs['stream']] else: streams = [stderr] for stream in streams: stream.write(s) return s
python
def log(msg, **kwargs): """Generic log method. Will prepend timestamp. Keyword arguments: system - Name of the system/module indent - Integer denoting the desired level of indentation streams - List of streams to output to stream - Stream to output to (singleton version of streams) """ if 'debug' in kwargs: if 'currentdebug' in kwargs: if kwargs['currentdebug'] < kwargs['debug']: return False else: return False #no currentdebug passed, assuming no debug mode and thus skipping message s = "[" + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + "] " if 'system' in kwargs: s += "[" + system + "] " if 'indent' in kwargs: s += ("\t" * int(kwargs['indent'])) s += u(msg) if s[-1] != '\n': s += '\n' if 'streams' in kwargs: streams = kwargs['streams'] elif 'stream' in kwargs: streams = [kwargs['stream']] else: streams = [stderr] for stream in streams: stream.write(s) return s
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/common.py#L98-L136
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What does this function do?
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proycon/pynlpl
pynlpl/search.py
AbstractSearch.searchbest
def searchbest(self): """Returns the single best result (if multiple have the same score, the first match is returned)""" finalsolution = None bestscore = None for solution in self: if bestscore == None: bestscore = solution.score() finalsolution = solution elif self.minimize: score = solution.score() if score < bestscore: bestscore = score finalsolution = solution elif not self.minimize: score = solution.score() if score > bestscore: bestscore = score finalsolution = solution return finalsolution
python
def searchbest(self): """Returns the single best result (if multiple have the same score, the first match is returned)""" finalsolution = None bestscore = None for solution in self: if bestscore == None: bestscore = solution.score() finalsolution = solution elif self.minimize: score = solution.score() if score < bestscore: bestscore = score finalsolution = solution elif not self.minimize: score = solution.score() if score > bestscore: bestscore = score finalsolution = solution return finalsolution
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Returns the single best result (if multiple have the same score, the first match is returned)
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train
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What does this function do?
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proycon/pynlpl
pynlpl/search.py
AbstractSearch.searchtop
def searchtop(self,n=10): """Return the top n best resulta (or possibly less if not enough is found)""" solutions = PriorityQueue([], lambda x: x.score, self.minimize, length=n, blockworse=False, blockequal=False,duplicates=False) for solution in self: solutions.append(solution) return solutions
python
def searchtop(self,n=10): """Return the top n best resulta (or possibly less if not enough is found)""" solutions = PriorityQueue([], lambda x: x.score, self.minimize, length=n, blockworse=False, blockequal=False,duplicates=False) for solution in self: solutions.append(solution) return solutions
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train
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What does this function do?
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proycon/pynlpl
pynlpl/search.py
AbstractSearch.searchlast
def searchlast(self,n=10): """Return the last n results (or possibly less if not found). Note that the last results are not necessarily the best ones! Depending on the search type.""" solutions = deque([], n) for solution in self: solutions.append(solution) return solutions
python
def searchlast(self,n=10): """Return the last n results (or possibly less if not found). Note that the last results are not necessarily the best ones! Depending on the search type.""" solutions = deque([], n) for solution in self: solutions.append(solution) return solutions
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Return the last n results (or possibly less if not found). Note that the last results are not necessarily the best ones! Depending on the search type.
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/search.py#L270-L275
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What does this function do?
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proycon/pynlpl
pynlpl/clients/cornetto.py
CornettoClient.get_syn_ids_by_lemma
def get_syn_ids_by_lemma(self, lemma): """Returns a list of synset IDs based on a lemma""" if not isinstance(lemma,unicode): lemma = unicode(lemma,'utf-8') http, resp, content = self.connect() params = "" fragment = "" path = "cdb_syn" if self.debug: printf( "cornettodb/views/query_remote_syn_lemma: db_opt: %s" % path ) query_opt = "dict_search" if self.debug: printf( "cornettodb/views/query_remote_syn_lemma: query_opt: %s" % query_opt ) qdict = {} qdict[ "action" ] = "queryList" qdict[ "word" ] = lemma.encode('utf-8') query = urllib.urlencode( qdict ) db_url_tuple = ( self.scheme, self.host + ':' + str(self.port), path, params, query, fragment ) db_url = urlparse.urlunparse( db_url_tuple ) if self.debug: printf( "db_url: %s" % db_url ) resp, content = http.request( db_url, "GET" ) if self.debug: printf( "resp:\n%s" % resp ) printf( "content:\n%s" % content ) # printf( "content is of type: %s" % type( content ) ) dict_list = [] dict_list = eval( content ) # string to list synsets = [] items = len( dict_list ) if self.debug: printf( "items: %d" % items ) # syn dict: like lu dict, but without pos: part-of-speech for dict in dict_list: if self.debug: printf( dict ) seq_nr = dict[ "seq_nr" ] # sense number value = dict[ "value" ] # lexical unit identifier form = dict[ "form" ] # lemma label = dict[ "label" ] # label to be shown if self.debug: printf( "seq_nr: %s" % seq_nr ) printf( "value: %s" % value ) printf( "form: %s" % form ) printf( "label: %s" % label ) if value != "": synsets.append( value ) return synsets
python
def get_syn_ids_by_lemma(self, lemma): """Returns a list of synset IDs based on a lemma""" if not isinstance(lemma,unicode): lemma = unicode(lemma,'utf-8') http, resp, content = self.connect() params = "" fragment = "" path = "cdb_syn" if self.debug: printf( "cornettodb/views/query_remote_syn_lemma: db_opt: %s" % path ) query_opt = "dict_search" if self.debug: printf( "cornettodb/views/query_remote_syn_lemma: query_opt: %s" % query_opt ) qdict = {} qdict[ "action" ] = "queryList" qdict[ "word" ] = lemma.encode('utf-8') query = urllib.urlencode( qdict ) db_url_tuple = ( self.scheme, self.host + ':' + str(self.port), path, params, query, fragment ) db_url = urlparse.urlunparse( db_url_tuple ) if self.debug: printf( "db_url: %s" % db_url ) resp, content = http.request( db_url, "GET" ) if self.debug: printf( "resp:\n%s" % resp ) printf( "content:\n%s" % content ) # printf( "content is of type: %s" % type( content ) ) dict_list = [] dict_list = eval( content ) # string to list synsets = [] items = len( dict_list ) if self.debug: printf( "items: %d" % items ) # syn dict: like lu dict, but without pos: part-of-speech for dict in dict_list: if self.debug: printf( dict ) seq_nr = dict[ "seq_nr" ] # sense number value = dict[ "value" ] # lexical unit identifier form = dict[ "form" ] # lemma label = dict[ "label" ] # label to be shown if self.debug: printf( "seq_nr: %s" % seq_nr ) printf( "value: %s" % value ) printf( "form: %s" % form ) printf( "label: %s" % label ) if value != "": synsets.append( value ) return synsets
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Returns a list of synset IDs based on a lemma
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/clients/cornetto.py#L96-L160
[ "lemma" ]
What does this function do?
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proycon/pynlpl
pynlpl/clients/cornetto.py
CornettoClient.get_synset_xml
def get_synset_xml(self,syn_id): """ call cdb_syn with synset identifier -> returns the synset xml; """ http, resp, content = self.connect() params = "" fragment = "" path = "cdb_syn" if self.debug: printf( "cornettodb/views/query_remote_syn_id: db_opt: %s" % path ) # output_opt: plain, html, xml # 'xml' is actually xhtml (with markup), but it is not valid xml! # 'plain' is actually valid xml (without markup) output_opt = "plain" if self.debug: printf( "cornettodb/views/query_remote_syn_id: output_opt: %s" % output_opt ) action = "runQuery" if self.debug: printf( "cornettodb/views/query_remote_syn_id: action: %s" % action ) printf( "cornettodb/views/query_remote_syn_id: query: %s" % syn_id ) qdict = {} qdict[ "action" ] = action qdict[ "query" ] = syn_id qdict[ "outtype" ] = output_opt query = urllib.urlencode( qdict ) db_url_tuple = ( self.scheme, self.host + ':' + str(self.port), path, params, query, fragment ) db_url = urlparse.urlunparse( db_url_tuple ) if self.debug: printf( "db_url: %s" % db_url ) resp, content = http.request( db_url, "GET" ) if self.debug: printf( "resp:\n%s" % resp ) # printf( "content:\n%s" % content ) # printf( "content is of type: %s" % type( content ) ) #<type 'str'> xml_data = eval( content ) return etree.fromstring( xml_data )
python
def get_synset_xml(self,syn_id): """ call cdb_syn with synset identifier -> returns the synset xml; """ http, resp, content = self.connect() params = "" fragment = "" path = "cdb_syn" if self.debug: printf( "cornettodb/views/query_remote_syn_id: db_opt: %s" % path ) # output_opt: plain, html, xml # 'xml' is actually xhtml (with markup), but it is not valid xml! # 'plain' is actually valid xml (without markup) output_opt = "plain" if self.debug: printf( "cornettodb/views/query_remote_syn_id: output_opt: %s" % output_opt ) action = "runQuery" if self.debug: printf( "cornettodb/views/query_remote_syn_id: action: %s" % action ) printf( "cornettodb/views/query_remote_syn_id: query: %s" % syn_id ) qdict = {} qdict[ "action" ] = action qdict[ "query" ] = syn_id qdict[ "outtype" ] = output_opt query = urllib.urlencode( qdict ) db_url_tuple = ( self.scheme, self.host + ':' + str(self.port), path, params, query, fragment ) db_url = urlparse.urlunparse( db_url_tuple ) if self.debug: printf( "db_url: %s" % db_url ) resp, content = http.request( db_url, "GET" ) if self.debug: printf( "resp:\n%s" % resp ) # printf( "content:\n%s" % content ) # printf( "content is of type: %s" % type( content ) ) #<type 'str'> xml_data = eval( content ) return etree.fromstring( xml_data )
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call cdb_syn with synset identifier -> returns the synset xml;
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/clients/cornetto.py#L227-L272
[ "syn_id" ]
What does this function do?
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proycon/pynlpl
pynlpl/clients/cornetto.py
CornettoClient.get_lus_from_synset
def get_lus_from_synset(self, syn_id): """Returns a list of (word, lu_id) tuples given a synset ID""" root = self.get_synset_xml(syn_id) elem_synonyms = root.find( ".//synonyms" ) lus = [] for elem_synonym in elem_synonyms: synonym_str = elem_synonym.get( "c_lu_id-previewtext" ) # get "c_lu_id-previewtext" attribute # synonym_str ends with ":<num>" synonym = synonym_str.split( ':' )[ 0 ].strip() lus.append( (synonym, elem_synonym.get( "c_lu_id") ) ) return lus
python
def get_lus_from_synset(self, syn_id): """Returns a list of (word, lu_id) tuples given a synset ID""" root = self.get_synset_xml(syn_id) elem_synonyms = root.find( ".//synonyms" ) lus = [] for elem_synonym in elem_synonyms: synonym_str = elem_synonym.get( "c_lu_id-previewtext" ) # get "c_lu_id-previewtext" attribute # synonym_str ends with ":<num>" synonym = synonym_str.split( ':' )[ 0 ].strip() lus.append( (synonym, elem_synonym.get( "c_lu_id") ) ) return lus
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Returns a list of (word, lu_id) tuples given a synset ID
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/clients/cornetto.py#L275-L288
[ "syn_id" ]
What does this function do?
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proycon/pynlpl
pynlpl/clients/cornetto.py
CornettoClient.get_lu_from_synset
def get_lu_from_synset(self, syn_id, lemma = None): """Returns (lu_id, synonyms=[(word, lu_id)] ) tuple given a synset ID and a lemma""" if not lemma: return self.get_lus_from_synset(syn_id) #alias if not isinstance(lemma,unicode): lemma = unicode(lemma,'utf-8') root = self.get_synset_xml(syn_id) elem_synonyms = root.find( ".//synonyms" ) lu_id = None synonyms = [] for elem_synonym in elem_synonyms: synonym_str = elem_synonym.get( "c_lu_id-previewtext" ) # get "c_lu_id-previewtext" attribute # synonym_str ends with ":<num>" synonym = synonym_str.split( ':' )[ 0 ].strip() if synonym != lemma: synonyms.append( (synonym, elem_synonym.get("c_lu_id")) ) if self.debug: printf( "synonym add: %s" % synonym ) else: lu_id = elem_synonym.get( "c_lu_id" ) # get "c_lu_id" attribute if self.debug: printf( "lu_id: %s" % lu_id ) printf( "synonym skip lemma: %s" % synonym ) return lu_id, synonyms
python
def get_lu_from_synset(self, syn_id, lemma = None): """Returns (lu_id, synonyms=[(word, lu_id)] ) tuple given a synset ID and a lemma""" if not lemma: return self.get_lus_from_synset(syn_id) #alias if not isinstance(lemma,unicode): lemma = unicode(lemma,'utf-8') root = self.get_synset_xml(syn_id) elem_synonyms = root.find( ".//synonyms" ) lu_id = None synonyms = [] for elem_synonym in elem_synonyms: synonym_str = elem_synonym.get( "c_lu_id-previewtext" ) # get "c_lu_id-previewtext" attribute # synonym_str ends with ":<num>" synonym = synonym_str.split( ':' )[ 0 ].strip() if synonym != lemma: synonyms.append( (synonym, elem_synonym.get("c_lu_id")) ) if self.debug: printf( "synonym add: %s" % synonym ) else: lu_id = elem_synonym.get( "c_lu_id" ) # get "c_lu_id" attribute if self.debug: printf( "lu_id: %s" % lu_id ) printf( "synonym skip lemma: %s" % synonym ) return lu_id, synonyms
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/clients/cornetto.py#L291-L317
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What does this function do?
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proycon/pynlpl
pynlpl/formats/dutchsemcor.py
WSDSystemOutput.senses
def senses(self, bestonly=False): """Returns a list of all predicted senses""" l = [] for word_id, senses,distance in self: for sense, confidence in senses: if not sense in l: l.append(sense) if bestonly: break return l
python
def senses(self, bestonly=False): """Returns a list of all predicted senses""" l = [] for word_id, senses,distance in self: for sense, confidence in senses: if not sense in l: l.append(sense) if bestonly: break return l
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Returns a list of all predicted senses
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/dutchsemcor.py#L139-L147
[ "bestonly" ]
What does this function do?
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proycon/pynlpl
pynlpl/clients/frogclient.py
FrogClient.process
def process(self,input_data, source_encoding="utf-8", return_unicode = True, oldfrog=False): """Receives input_data in the form of a str or unicode object, passes this to the server, with proper consideration for the encodings, and returns the Frog output as a list of tuples: (word,pos,lemma,morphology), each of these is a proper unicode object unless return_unicode is set to False, in which case raw strings will be returned. Return_unicode is no longer optional, it is fixed to True, parameter is still there only for backwards-compatibility.""" if isinstance(input_data, list) or isinstance(input_data, tuple): input_data = " ".join(input_data) input_data = u(input_data, source_encoding) #decode (or preferably do this in an earlier stage) input_data = input_data.strip(' \t\n') s = input_data.encode(self.server_encoding) +b'\r\n' if not oldfrog: s += b'EOT\r\n' self.socket.sendall(s) #send to socket in desired encoding output = [] done = False while not done: data = b"" while not data.endswith(b'\n'): moredata = self.socket.recv(self.BUFSIZE) if not moredata: break data += moredata data = u(data,self.server_encoding) for line in data.strip(' \t\r\n').split('\n'): if line == "READY": done = True break elif line: line = line.split('\t') #split on tab if len(line) > 4 and line[0].isdigit(): #first column is token number if line[0] == '1' and output: if self.returnall: output.append( (None,None,None,None, None,None,None, None) ) else: output.append( (None,None,None,None) ) fields = line[1:] parse1=parse2=ner=chunk="" word,lemma,morph,pos = fields[0:4] if len(fields) > 5: ner = fields[5] if len(fields) > 6: chunk = fields[6] if len(fields) >= 8: parse1 = fields[7] parse2 = fields[8] if len(fields) < 5: raise Exception("Can't process response line from Frog: ", repr(line), " got unexpected number of fields ", str(len(fields) + 1)) if self.returnall: output.append( (word,lemma,morph,pos,ner,chunk,parse1,parse2) ) else: output.append( (word,lemma,morph,pos) ) return output
python
def process(self,input_data, source_encoding="utf-8", return_unicode = True, oldfrog=False): """Receives input_data in the form of a str or unicode object, passes this to the server, with proper consideration for the encodings, and returns the Frog output as a list of tuples: (word,pos,lemma,morphology), each of these is a proper unicode object unless return_unicode is set to False, in which case raw strings will be returned. Return_unicode is no longer optional, it is fixed to True, parameter is still there only for backwards-compatibility.""" if isinstance(input_data, list) or isinstance(input_data, tuple): input_data = " ".join(input_data) input_data = u(input_data, source_encoding) #decode (or preferably do this in an earlier stage) input_data = input_data.strip(' \t\n') s = input_data.encode(self.server_encoding) +b'\r\n' if not oldfrog: s += b'EOT\r\n' self.socket.sendall(s) #send to socket in desired encoding output = [] done = False while not done: data = b"" while not data.endswith(b'\n'): moredata = self.socket.recv(self.BUFSIZE) if not moredata: break data += moredata data = u(data,self.server_encoding) for line in data.strip(' \t\r\n').split('\n'): if line == "READY": done = True break elif line: line = line.split('\t') #split on tab if len(line) > 4 and line[0].isdigit(): #first column is token number if line[0] == '1' and output: if self.returnall: output.append( (None,None,None,None, None,None,None, None) ) else: output.append( (None,None,None,None) ) fields = line[1:] parse1=parse2=ner=chunk="" word,lemma,morph,pos = fields[0:4] if len(fields) > 5: ner = fields[5] if len(fields) > 6: chunk = fields[6] if len(fields) >= 8: parse1 = fields[7] parse2 = fields[8] if len(fields) < 5: raise Exception("Can't process response line from Frog: ", repr(line), " got unexpected number of fields ", str(len(fields) + 1)) if self.returnall: output.append( (word,lemma,morph,pos,ner,chunk,parse1,parse2) ) else: output.append( (word,lemma,morph,pos) ) return output
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Receives input_data in the form of a str or unicode object, passes this to the server, with proper consideration for the encodings, and returns the Frog output as a list of tuples: (word,pos,lemma,morphology), each of these is a proper unicode object unless return_unicode is set to False, in which case raw strings will be returned. Return_unicode is no longer optional, it is fixed to True, parameter is still there only for backwards-compatibility.
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/clients/frogclient.py#L40-L98
[ "input_data", "source_encoding", "return_unicode", "oldfrog" ]
What does this function do?
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proycon/pynlpl
pynlpl/clients/frogclient.py
FrogClient.align
def align(self,inputwords, outputwords): """For each inputword, provides the index of the outputword""" alignment = [] cursor = 0 for inputword in inputwords: if len(outputwords) > cursor and outputwords[cursor] == inputword: alignment.append(cursor) cursor += 1 elif len(outputwords) > cursor+1 and outputwords[cursor+1] == inputword: alignment.append(cursor+1) cursor += 2 else: alignment.append(None) cursor += 1 return alignment
python
def align(self,inputwords, outputwords): """For each inputword, provides the index of the outputword""" alignment = [] cursor = 0 for inputword in inputwords: if len(outputwords) > cursor and outputwords[cursor] == inputword: alignment.append(cursor) cursor += 1 elif len(outputwords) > cursor+1 and outputwords[cursor+1] == inputword: alignment.append(cursor+1) cursor += 2 else: alignment.append(None) cursor += 1 return alignment
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For each inputword, provides the index of the outputword
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/clients/frogclient.py#L115-L129
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What does this function do?
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proycon/pynlpl
pynlpl/textprocessors.py
calculate_overlap
def calculate_overlap(haystack, needle, allowpartial=True): """Calculate the overlap between two sequences. Yields (overlap, placement) tuples (multiple because there may be multiple overlaps!). The former is the part of the sequence that overlaps, and the latter is -1 if the overlap is on the left side, 0 if it is a subset, 1 if it overlaps on the right side, 2 if its an identical match""" needle = tuple(needle) haystack = tuple(haystack) solutions = [] #equality check if needle == haystack: return [(needle, 2)] if allowpartial: minl =1 else: minl = len(needle) for l in range(minl,min(len(needle), len(haystack))+1): #print "LEFT-DEBUG", l,":", needle[-l:], " vs ", haystack[:l] #print "RIGHT-DEBUG", l,":", needle[:l], " vs ", haystack[-l:] #Search for overlap left (including partial overlap!) if needle[-l:] == haystack[:l]: #print "LEFT MATCH" solutions.append( (needle[-l:], -1) ) #Search for overlap right (including partial overlap!) if needle[:l] == haystack[-l:]: #print "RIGHT MATCH" solutions.append( (needle[:l], 1) ) if len(needle) <= len(haystack): options = list(iter(Windower(haystack,len(needle),beginmarker=None,endmarker=None))) for option in options[1:-1]: if option == needle: #print "SUBSET MATCH" solutions.append( (needle, 0) ) return solutions
python
def calculate_overlap(haystack, needle, allowpartial=True): """Calculate the overlap between two sequences. Yields (overlap, placement) tuples (multiple because there may be multiple overlaps!). The former is the part of the sequence that overlaps, and the latter is -1 if the overlap is on the left side, 0 if it is a subset, 1 if it overlaps on the right side, 2 if its an identical match""" needle = tuple(needle) haystack = tuple(haystack) solutions = [] #equality check if needle == haystack: return [(needle, 2)] if allowpartial: minl =1 else: minl = len(needle) for l in range(minl,min(len(needle), len(haystack))+1): #print "LEFT-DEBUG", l,":", needle[-l:], " vs ", haystack[:l] #print "RIGHT-DEBUG", l,":", needle[:l], " vs ", haystack[-l:] #Search for overlap left (including partial overlap!) if needle[-l:] == haystack[:l]: #print "LEFT MATCH" solutions.append( (needle[-l:], -1) ) #Search for overlap right (including partial overlap!) if needle[:l] == haystack[-l:]: #print "RIGHT MATCH" solutions.append( (needle[:l], 1) ) if len(needle) <= len(haystack): options = list(iter(Windower(haystack,len(needle),beginmarker=None,endmarker=None))) for option in options[1:-1]: if option == needle: #print "SUBSET MATCH" solutions.append( (needle, 0) ) return solutions
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Calculate the overlap between two sequences. Yields (overlap, placement) tuples (multiple because there may be multiple overlaps!). The former is the part of the sequence that overlaps, and the latter is -1 if the overlap is on the left side, 0 if it is a subset, 1 if it overlaps on the right side, 2 if its an identical match
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/textprocessors.py#L228-L262
[ "haystack", "needle", "allowpartial" ]
What does this function do?
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proycon/pynlpl
pynlpl/textprocessors.py
tokenize
def tokenize(text, regexps=TOKENIZERRULES): """Tokenizes a string and returns a list of tokens :param text: The text to tokenise :type text: string :param regexps: Regular expressions to use as tokeniser rules in tokenisation (default=_pynlpl.textprocessors.TOKENIZERRULES_) :type regexps: Tuple/list of regular expressions to use in tokenisation :rtype: Returns a list of tokens Examples: >>> for token in tokenize("This is a test."): ... print(token) This is a test . """ for i,regexp in list(enumerate(regexps)): if isstring(regexp): regexps[i] = re.compile(regexp) tokens = [] begin = 0 for i, c in enumerate(text): if begin > i: continue elif i == begin: m = False for regexp in regexps: m = regexp.findall(text[i:i+300]) if m: tokens.append(m[0]) begin = i + len(m[0]) break if m: continue if c in string.punctuation or c in WHITESPACE: prev = text[i-1] if i > 0 else "" next = text[i+1] if i < len(text)-1 else "" if (c == '.' or c == ',') and prev.isdigit() and next.isdigit(): #punctuation in between numbers, keep as one token pass elif (c == "'" or c == "`") and prev.isalpha() and next.isalpha(): #quote in between chars, keep... pass elif c not in WHITESPACE and next == c: #group clusters of identical punctuation together continue elif c == '\r' and prev == '\n': #ignore begin = i+1 continue else: token = text[begin:i] if token: tokens.append(token) if c not in WHITESPACE: tokens.append(c) #anything but spaces and newlines (i.e. punctuation) counts as a token too begin = i + 1 #set the begin cursor if begin <= len(text) - 1: token = text[begin:] tokens.append(token) return tokens
python
def tokenize(text, regexps=TOKENIZERRULES): """Tokenizes a string and returns a list of tokens :param text: The text to tokenise :type text: string :param regexps: Regular expressions to use as tokeniser rules in tokenisation (default=_pynlpl.textprocessors.TOKENIZERRULES_) :type regexps: Tuple/list of regular expressions to use in tokenisation :rtype: Returns a list of tokens Examples: >>> for token in tokenize("This is a test."): ... print(token) This is a test . """ for i,regexp in list(enumerate(regexps)): if isstring(regexp): regexps[i] = re.compile(regexp) tokens = [] begin = 0 for i, c in enumerate(text): if begin > i: continue elif i == begin: m = False for regexp in regexps: m = regexp.findall(text[i:i+300]) if m: tokens.append(m[0]) begin = i + len(m[0]) break if m: continue if c in string.punctuation or c in WHITESPACE: prev = text[i-1] if i > 0 else "" next = text[i+1] if i < len(text)-1 else "" if (c == '.' or c == ',') and prev.isdigit() and next.isdigit(): #punctuation in between numbers, keep as one token pass elif (c == "'" or c == "`") and prev.isalpha() and next.isalpha(): #quote in between chars, keep... pass elif c not in WHITESPACE and next == c: #group clusters of identical punctuation together continue elif c == '\r' and prev == '\n': #ignore begin = i+1 continue else: token = text[begin:i] if token: tokens.append(token) if c not in WHITESPACE: tokens.append(c) #anything but spaces and newlines (i.e. punctuation) counts as a token too begin = i + 1 #set the begin cursor if begin <= len(text) - 1: token = text[begin:] tokens.append(token) return tokens
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Tokenizes a string and returns a list of tokens :param text: The text to tokenise :type text: string :param regexps: Regular expressions to use as tokeniser rules in tokenisation (default=_pynlpl.textprocessors.TOKENIZERRULES_) :type regexps: Tuple/list of regular expressions to use in tokenisation :rtype: Returns a list of tokens Examples: >>> for token in tokenize("This is a test."): ... print(token) This is a test .
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/textprocessors.py#L317-L386
[ "text", "regexps" ]
What does this function do?
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proycon/pynlpl
pynlpl/textprocessors.py
split_sentences
def split_sentences(tokens): """Split sentences (based on tokenised data), returns sentences as a list of lists of tokens, each sentence is a list of tokens""" begin = 0 for i, token in enumerate(tokens): if is_end_of_sentence(tokens, i): yield tokens[begin:i+1] begin = i+1 if begin <= len(tokens)-1: yield tokens[begin:]
python
def split_sentences(tokens): """Split sentences (based on tokenised data), returns sentences as a list of lists of tokens, each sentence is a list of tokens""" begin = 0 for i, token in enumerate(tokens): if is_end_of_sentence(tokens, i): yield tokens[begin:i+1] begin = i+1 if begin <= len(tokens)-1: yield tokens[begin:]
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Split sentences (based on tokenised data), returns sentences as a list of lists of tokens, each sentence is a list of tokens
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/textprocessors.py#L403-L411
[ "tokens" ]
What does this function do?
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proycon/pynlpl
pynlpl/textprocessors.py
strip_accents
def strip_accents(s, encoding= 'utf-8'): """Strip characters with diacritics and return a flat ascii representation""" if sys.version < '3': if isinstance(s,unicode): return unicodedata.normalize('NFKD', s).encode('ASCII', 'ignore') else: return unicodedata.normalize('NFKD', unicode(s,encoding)).encode('ASCII', 'ignore') else: if isinstance(s,bytes): s = str(s,encoding) return str(unicodedata.normalize('NFKD', s).encode('ASCII', 'ignore'),'ascii')
python
def strip_accents(s, encoding= 'utf-8'): """Strip characters with diacritics and return a flat ascii representation""" if sys.version < '3': if isinstance(s,unicode): return unicodedata.normalize('NFKD', s).encode('ASCII', 'ignore') else: return unicodedata.normalize('NFKD', unicode(s,encoding)).encode('ASCII', 'ignore') else: if isinstance(s,bytes): s = str(s,encoding) return str(unicodedata.normalize('NFKD', s).encode('ASCII', 'ignore'),'ascii')
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Strip characters with diacritics and return a flat ascii representation
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/textprocessors.py#L415-L424
[ "s", "encoding" ]
What does this function do?
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proycon/pynlpl
pynlpl/textprocessors.py
swap
def swap(tokens, maxdist=2): """Perform a swap operation on a sequence of tokens, exhaustively swapping all tokens up to the maximum specified distance. This is a subset of all permutations.""" assert maxdist >= 2 tokens = list(tokens) if maxdist > len(tokens): maxdist = len(tokens) l = len(tokens) for i in range(0,l - 1): for permutation in permutations(tokens[i:i+maxdist]): if permutation != tuple(tokens[i:i+maxdist]): newtokens = tokens[:i] newtokens += permutation newtokens += tokens[i+maxdist:] yield newtokens if maxdist == len(tokens): break
python
def swap(tokens, maxdist=2): """Perform a swap operation on a sequence of tokens, exhaustively swapping all tokens up to the maximum specified distance. This is a subset of all permutations.""" assert maxdist >= 2 tokens = list(tokens) if maxdist > len(tokens): maxdist = len(tokens) l = len(tokens) for i in range(0,l - 1): for permutation in permutations(tokens[i:i+maxdist]): if permutation != tuple(tokens[i:i+maxdist]): newtokens = tokens[:i] newtokens += permutation newtokens += tokens[i+maxdist:] yield newtokens if maxdist == len(tokens): break
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Perform a swap operation on a sequence of tokens, exhaustively swapping all tokens up to the maximum specified distance. This is a subset of all permutations.
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/textprocessors.py#L426-L441
[ "tokens", "maxdist" ]
What does this function do?
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proycon/pynlpl
pynlpl/textprocessors.py
find_keyword_in_context
def find_keyword_in_context(tokens, keyword, contextsize=1): """Find a keyword in a particular sequence of tokens, and return the local context. Contextsize is the number of words to the left and right. The keyword may have multiple word, in which case it should to passed as a tuple or list""" if isinstance(keyword,tuple) and isinstance(keyword,list): l = len(keyword) else: keyword = (keyword,) l = 1 n = l + contextsize*2 focuspos = contextsize + 1 for ngram in Windower(tokens,n,None,None): if ngram[focuspos:focuspos+l] == keyword: yield ngram[:focuspos], ngram[focuspos:focuspos+l],ngram[focuspos+l+1:]
python
def find_keyword_in_context(tokens, keyword, contextsize=1): """Find a keyword in a particular sequence of tokens, and return the local context. Contextsize is the number of words to the left and right. The keyword may have multiple word, in which case it should to passed as a tuple or list""" if isinstance(keyword,tuple) and isinstance(keyword,list): l = len(keyword) else: keyword = (keyword,) l = 1 n = l + contextsize*2 focuspos = contextsize + 1 for ngram in Windower(tokens,n,None,None): if ngram[focuspos:focuspos+l] == keyword: yield ngram[:focuspos], ngram[focuspos:focuspos+l],ngram[focuspos+l+1:]
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Find a keyword in a particular sequence of tokens, and return the local context. Contextsize is the number of words to the left and right. The keyword may have multiple word, in which case it should to passed as a tuple or list
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/textprocessors.py#L444-L455
[ "tokens", "keyword", "contextsize" ]
What does this function do?
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proycon/pynlpl
pynlpl/datatypes.py
FIFOQueue.pop
def pop(self): """Retrieve the next element in line, this will remove it from the queue""" e = self.data[self.start] self.start += 1 if self.start > 5 and self.start > len(self.data)//2: self.data = self.data[self.start:] self.start = 0 return e
python
def pop(self): """Retrieve the next element in line, this will remove it from the queue""" e = self.data[self.start] self.start += 1 if self.start > 5 and self.start > len(self.data)//2: self.data = self.data[self.start:] self.start = 0 return e
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Retrieve the next element in line, this will remove it from the queue
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train
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[]
What does this function do?
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proycon/pynlpl
pynlpl/datatypes.py
PriorityQueue.append
def append(self, item): """Adds an item to the priority queue (in the right place), returns True if successfull, False if the item was blocked (because of a bad score)""" f = self.f(item) if callable(f): score = f() else: score = f if not self.duplicates: for s, i in self.data: if s == score and item == i: #item is a duplicate, don't add it return False if self.length and len(self.data) == self.length: #Fixed-length priority queue, abort when queue is full and new item scores worst than worst scoring item. if self.minimize: worstscore = self.data[-1][0] if score >= worstscore: return False else: worstscore = self.data[0][0] if score <= worstscore: return False if self.blockworse and self.bestscore != None: if self.minimize: if score > self.bestscore: return False else: if score < self.bestscore: return False if self.blockequal and self.bestscore != None: if self.bestscore == score: return False if (self.bestscore == None) or (self.minimize and score < self.bestscore) or (not self.minimize and score > self.bestscore): self.bestscore = score bisect.insort(self.data, (score, item)) if self.length: #fixed length queue: queue is now too long, delete worst items while len(self.data) > self.length: if self.minimize: del self.data[-1] else: del self.data[0] return True
python
def append(self, item): """Adds an item to the priority queue (in the right place), returns True if successfull, False if the item was blocked (because of a bad score)""" f = self.f(item) if callable(f): score = f() else: score = f if not self.duplicates: for s, i in self.data: if s == score and item == i: #item is a duplicate, don't add it return False if self.length and len(self.data) == self.length: #Fixed-length priority queue, abort when queue is full and new item scores worst than worst scoring item. if self.minimize: worstscore = self.data[-1][0] if score >= worstscore: return False else: worstscore = self.data[0][0] if score <= worstscore: return False if self.blockworse and self.bestscore != None: if self.minimize: if score > self.bestscore: return False else: if score < self.bestscore: return False if self.blockequal and self.bestscore != None: if self.bestscore == score: return False if (self.bestscore == None) or (self.minimize and score < self.bestscore) or (not self.minimize and score > self.bestscore): self.bestscore = score bisect.insort(self.data, (score, item)) if self.length: #fixed length queue: queue is now too long, delete worst items while len(self.data) > self.length: if self.minimize: del self.data[-1] else: del self.data[0] return True
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Adds an item to the priority queue (in the right place), returns True if successfull, False if the item was blocked (because of a bad score)
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/datatypes.py#L97-L142
[ "item" ]
What does this function do?
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proycon/pynlpl
pynlpl/datatypes.py
PriorityQueue.pop
def pop(self): """Retrieve the next element in line, this will remove it from the queue""" if self.minimize: return self.data.pop(0)[1] else: return self.data.pop()[1]
python
def pop(self): """Retrieve the next element in line, this will remove it from the queue""" if self.minimize: return self.data.pop(0)[1] else: return self.data.pop()[1]
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/datatypes.py#L173-L178
[]
What does this function do?
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proycon/pynlpl
pynlpl/datatypes.py
PriorityQueue.score
def score(self, i): """Return the score for item x (cheap lookup), Item 0 is always the best item""" if self.minimize: return self.data[i][0] else: return self.data[(-1 * i) - 1][0]
python
def score(self, i): """Return the score for item x (cheap lookup), Item 0 is always the best item""" if self.minimize: return self.data[i][0] else: return self.data[(-1 * i) - 1][0]
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Return the score for item x (cheap lookup), Item 0 is always the best item
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/datatypes.py#L181-L186
[ "i" ]
What does this function do?
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proycon/pynlpl
pynlpl/datatypes.py
PriorityQueue.prune
def prune(self, n): """prune all but the first (=best) n items""" if self.minimize: self.data = self.data[:n] else: self.data = self.data[-1 * n:]
python
def prune(self, n): """prune all but the first (=best) n items""" if self.minimize: self.data = self.data[:n] else: self.data = self.data[-1 * n:]
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prune all but the first (=best) n items
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/datatypes.py#L188-L193
[ "n" ]
What does this function do?
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proycon/pynlpl
pynlpl/datatypes.py
PriorityQueue.randomprune
def randomprune(self,n): """prune down to n items at random, disregarding their score""" self.data = random.sample(self.data, n)
python
def randomprune(self,n): """prune down to n items at random, disregarding their score""" self.data = random.sample(self.data, n)
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prune down to n items at random, disregarding their score
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/datatypes.py#L196-L198
[ "n" ]
What does this function do?
[ "prune", "down", "to", "n", "items", "at", "random", "disregarding", "their", "score" ]
proycon/pynlpl
pynlpl/datatypes.py
PriorityQueue.prunebyscore
def prunebyscore(self, score, retainequalscore=False): """Deletes all items below/above a certain score from the queue, depending on whether minimize is True or False. Note: It is recommended (more efficient) to use blockworse=True / blockequal=True instead! Preventing the addition of 'worse' items.""" if retainequalscore: if self.minimize: f = lambda x: x[0] <= score else: f = lambda x: x[0] >= score else: if self.minimize: f = lambda x: x[0] < score else: f = lambda x: x[0] > score self.data = filter(f, self.data)
python
def prunebyscore(self, score, retainequalscore=False): """Deletes all items below/above a certain score from the queue, depending on whether minimize is True or False. Note: It is recommended (more efficient) to use blockworse=True / blockequal=True instead! Preventing the addition of 'worse' items.""" if retainequalscore: if self.minimize: f = lambda x: x[0] <= score else: f = lambda x: x[0] >= score else: if self.minimize: f = lambda x: x[0] < score else: f = lambda x: x[0] > score self.data = filter(f, self.data)
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Deletes all items below/above a certain score from the queue, depending on whether minimize is True or False. Note: It is recommended (more efficient) to use blockworse=True / blockequal=True instead! Preventing the addition of 'worse' items.
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/datatypes.py#L205-L217
[ "score", "retainequalscore" ]
What does this function do?
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proycon/pynlpl
pynlpl/datatypes.py
Tree.append
def append(self, item): """Add an item to the Tree""" if not isinstance(item, Tree): return ValueError("Can only append items of type Tree") if not self.children: self.children = [] item.parent = self self.children.append(item)
python
def append(self, item): """Add an item to the Tree""" if not isinstance(item, Tree): return ValueError("Can only append items of type Tree") if not self.children: self.children = [] item.parent = self self.children.append(item)
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Add an item to the Tree
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/datatypes.py#L261-L267
[ "item" ]
What does this function do?
[ "Add", "an", "item", "to", "the", "Tree" ]
proycon/pynlpl
pynlpl/datatypes.py
Trie.size
def size(self): """Size is number of nodes under the trie, including the current node""" if self.children: return sum( ( c.size() for c in self.children.values() ) ) + 1 else: return 1
python
def size(self): """Size is number of nodes under the trie, including the current node""" if self.children: return sum( ( c.size() for c in self.children.values() ) ) + 1 else: return 1
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Size is number of nodes under the trie, including the current node
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/datatypes.py#L361-L366
[]
What does this function do?
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proycon/pynlpl
pynlpl/datatypes.py
Trie.walk
def walk(self, leavesonly=True, maxdepth=None, _depth = 0): """Depth-first search, walking through trie, returning all encounterd nodes (by default only leaves)""" if self.children: if not maxdepth or (maxdepth and _depth < maxdepth): for key, child in self.children.items(): if child.leaf(): yield child else: for results in child.walk(leavesonly, maxdepth, _depth + 1): yield results
python
def walk(self, leavesonly=True, maxdepth=None, _depth = 0): """Depth-first search, walking through trie, returning all encounterd nodes (by default only leaves)""" if self.children: if not maxdepth or (maxdepth and _depth < maxdepth): for key, child in self.children.items(): if child.leaf(): yield child else: for results in child.walk(leavesonly, maxdepth, _depth + 1): yield results
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Depth-first search, walking through trie, returning all encounterd nodes (by default only leaves)
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/datatypes.py#L392-L401
[ "leavesonly", "maxdepth", "_depth" ]
What does this function do?
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proycon/pynlpl
pynlpl/formats/sonar.py
CorpusDocument.sentences
def sentences(self): """Iterate over all sentences (sentence_id, sentence) in the document, sentence is a list of 4-tuples (word,id,pos,lemma)""" prevp = 0 prevs = 0 sentence = []; sentence_id = "" for word, id, pos, lemma in iter(self): try: doc_id, ptype, p, s, w = re.findall('([\w\d-]+)\.(p|head)\.(\d+)\.s\.(\d+)\.w\.(\d+)',id)[0] if ((p != prevp) or (s != prevs)) and sentence: yield sentence_id, sentence sentence = [] sentence_id = doc_id + '.' + ptype + '.' + str(p) + '.s.' + str(s) prevp = p except IndexError: doc_id, s, w = re.findall('([\w\d-]+)\.s\.(\d+)\.w\.(\d+)',id)[0] if s != prevs and sentence: yield sentence_id, sentence sentence = [] sentence_id = doc_id + '.s.' + str(s) sentence.append( (word,id,pos,lemma) ) prevs = s if sentence: yield sentence_id, sentence
python
def sentences(self): """Iterate over all sentences (sentence_id, sentence) in the document, sentence is a list of 4-tuples (word,id,pos,lemma)""" prevp = 0 prevs = 0 sentence = []; sentence_id = "" for word, id, pos, lemma in iter(self): try: doc_id, ptype, p, s, w = re.findall('([\w\d-]+)\.(p|head)\.(\d+)\.s\.(\d+)\.w\.(\d+)',id)[0] if ((p != prevp) or (s != prevs)) and sentence: yield sentence_id, sentence sentence = [] sentence_id = doc_id + '.' + ptype + '.' + str(p) + '.s.' + str(s) prevp = p except IndexError: doc_id, s, w = re.findall('([\w\d-]+)\.s\.(\d+)\.w\.(\d+)',id)[0] if s != prevs and sentence: yield sentence_id, sentence sentence = [] sentence_id = doc_id + '.s.' + str(s) sentence.append( (word,id,pos,lemma) ) prevs = s if sentence: yield sentence_id, sentence
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Iterate over all sentences (sentence_id, sentence) in the document, sentence is a list of 4-tuples (word,id,pos,lemma)
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/sonar.py#L85-L108
[]
What does this function do?
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proycon/pynlpl
pynlpl/formats/sonar.py
CorpusDocument.paragraphs
def paragraphs(self, with_id = False): """Extracts paragraphs, returns list of plain-text(!) paragraphs""" prevp = 0 partext = [] for word, id, pos, lemma in iter(self): doc_id, ptype, p, s, w = re.findall('([\w\d-]+)\.(p|head)\.(\d+)\.s\.(\d+)\.w\.(\d+)',id)[0] if prevp != p and partext: yield ( doc_id + "." + ptype + "." + prevp , " ".join(partext) ) partext = [] partext.append(word) prevp = p if partext: yield (doc_id + "." + ptype + "." + prevp, " ".join(partext) )
python
def paragraphs(self, with_id = False): """Extracts paragraphs, returns list of plain-text(!) paragraphs""" prevp = 0 partext = [] for word, id, pos, lemma in iter(self): doc_id, ptype, p, s, w = re.findall('([\w\d-]+)\.(p|head)\.(\d+)\.s\.(\d+)\.w\.(\d+)',id)[0] if prevp != p and partext: yield ( doc_id + "." + ptype + "." + prevp , " ".join(partext) ) partext = [] partext.append(word) prevp = p if partext: yield (doc_id + "." + ptype + "." + prevp, " ".join(partext) )
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Extracts paragraphs, returns list of plain-text(!) paragraphs
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/sonar.py#L110-L122
[ "with_id" ]
What does this function do?
[ "Extracts", "paragraphs", "returns", "list", "of", "plain", "-", "text", "(", "!", ")", "paragraphs" ]
proycon/pynlpl
pynlpl/formats/sonar.py
CorpusDocumentX.validate
def validate(self, formats_dir="../formats/"): """checks if the document is valid""" #TODO: download XSD from web if self.inline: xmlschema = ElementTree.XMLSchema(ElementTree.parse(StringIO("\n".join(open(formats_dir+"dcoi-dsc.xsd").readlines())))) xmlschema.assertValid(self.tree) #return xmlschema.validate(self) else: xmlschema = ElementTree.XMLSchema(ElementTree.parse(StringIO("\n".join(open(formats_dir+"dutchsemcor-standalone.xsd").readlines())))) xmlschema.assertValid(self.tree)
python
def validate(self, formats_dir="../formats/"): """checks if the document is valid""" #TODO: download XSD from web if self.inline: xmlschema = ElementTree.XMLSchema(ElementTree.parse(StringIO("\n".join(open(formats_dir+"dcoi-dsc.xsd").readlines())))) xmlschema.assertValid(self.tree) #return xmlschema.validate(self) else: xmlschema = ElementTree.XMLSchema(ElementTree.parse(StringIO("\n".join(open(formats_dir+"dutchsemcor-standalone.xsd").readlines())))) xmlschema.assertValid(self.tree)
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checks if the document is valid
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/sonar.py#L235-L244
[ "formats_dir" ]
What does this function do?
[ "checks", "if", "the", "document", "is", "valid" ]
proycon/pynlpl
pynlpl/formats/sonar.py
CorpusDocumentX.xpath
def xpath(self, expression): """Executes an xpath expression using the correct namespaces""" global namespaces return self.tree.xpath(expression, namespaces=namespaces)
python
def xpath(self, expression): """Executes an xpath expression using the correct namespaces""" global namespaces return self.tree.xpath(expression, namespaces=namespaces)
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Executes an xpath expression using the correct namespaces
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/sonar.py#L247-L250
[ "expression" ]
What does this function do?
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proycon/pynlpl
pynlpl/formats/taggerdata.py
Taggerdata.align
def align(self, referencewords, datatuple): """align the reference sentence with the tagged data""" targetwords = [] for i, (word,lemma,postag) in enumerate(zip(datatuple[0],datatuple[1],datatuple[2])): if word: subwords = word.split("_") for w in subwords: #split multiword expressions targetwords.append( (w, lemma, postag, i, len(subwords) > 1 ) ) #word, lemma, pos, index, multiword? referencewords = [ w.lower() for w in referencewords ] alignment = [] for i, referenceword in enumerate(referencewords): found = False best = 0 distance = 999999 for j, (targetword, lemma, pos, index, multiword) in enumerate(targetwords): if referenceword == targetword and abs(i-j) < distance: found = True best = j distance = abs(i-j) if found: alignment.append(targetwords[best]) else: alignment.append((None,None,None,None,False)) #no alignment found return alignment
python
def align(self, referencewords, datatuple): """align the reference sentence with the tagged data""" targetwords = [] for i, (word,lemma,postag) in enumerate(zip(datatuple[0],datatuple[1],datatuple[2])): if word: subwords = word.split("_") for w in subwords: #split multiword expressions targetwords.append( (w, lemma, postag, i, len(subwords) > 1 ) ) #word, lemma, pos, index, multiword? referencewords = [ w.lower() for w in referencewords ] alignment = [] for i, referenceword in enumerate(referencewords): found = False best = 0 distance = 999999 for j, (targetword, lemma, pos, index, multiword) in enumerate(targetwords): if referenceword == targetword and abs(i-j) < distance: found = True best = j distance = abs(i-j) if found: alignment.append(targetwords[best]) else: alignment.append((None,None,None,None,False)) #no alignment found return alignment
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align the reference sentence with the tagged data
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/taggerdata.py#L99-L125
[ "referencewords", "datatuple" ]
What does this function do?
[ "align", "the", "reference", "sentence", "with", "the", "tagged", "data" ]
proycon/pynlpl
pynlpl/formats/foliaset.py
SetDefinition.mainset
def mainset(self): """Returns information regarding the set""" if self.mainsetcache: return self.mainsetcache set_uri = self.get_set_uri() for row in self.graph.query("SELECT ?seturi ?setid ?setlabel ?setopen ?setempty WHERE { ?seturi rdf:type skos:Collection . OPTIONAL { ?seturi skos:notation ?setid } OPTIONAL { ?seturi skos:prefLabel ?setlabel } OPTIONAL { ?seturi fsd:open ?setopen } OPTIONAL { ?seturi fsd:empty ?setempty } FILTER NOT EXISTS { ?y skos:member ?seturi . ?y rdf:type skos:Collection } }"): self.mainsetcache = {'uri': str(row.seturi), 'id': str(row.setid), 'label': str(row.setlabel) if row.setlabel else "", 'open': bool(row.setopen), 'empty': bool(row.setempty) } return self.mainsetcache raise DeepValidationError("Unable to find main set (set_uri=" + str(set_uri)+"), this should not happen")
python
def mainset(self): """Returns information regarding the set""" if self.mainsetcache: return self.mainsetcache set_uri = self.get_set_uri() for row in self.graph.query("SELECT ?seturi ?setid ?setlabel ?setopen ?setempty WHERE { ?seturi rdf:type skos:Collection . OPTIONAL { ?seturi skos:notation ?setid } OPTIONAL { ?seturi skos:prefLabel ?setlabel } OPTIONAL { ?seturi fsd:open ?setopen } OPTIONAL { ?seturi fsd:empty ?setempty } FILTER NOT EXISTS { ?y skos:member ?seturi . ?y rdf:type skos:Collection } }"): self.mainsetcache = {'uri': str(row.seturi), 'id': str(row.setid), 'label': str(row.setlabel) if row.setlabel else "", 'open': bool(row.setopen), 'empty': bool(row.setempty) } return self.mainsetcache raise DeepValidationError("Unable to find main set (set_uri=" + str(set_uri)+"), this should not happen")
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Returns information regarding the set
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/foliaset.py#L351-L359
[]
What does this function do?
[ "Returns", "information", "regarding", "the", "set" ]
proycon/pynlpl
pynlpl/formats/foliaset.py
SetDefinition.subset
def subset(self, subset_id): """Returns information regarding the set""" if subset_id in self.subsetcache: return self.subsetcache[subset_id] set_uri = self.get_set_uri(subset_id) for row in self.graph.query("SELECT ?seturi ?setid ?setlabel ?setopen WHERE { ?seturi rdf:type skos:Collection . OPTIONAL { ?seturi skos:notation ?setid } OPTIONAL { ?seturi skos:prefLabel ?setlabel } OPTIONAL { ?seturi fsd:open ?setopen } FILTER (?seturi = <" + str(set_uri)+">) }"): self.subsetcache[str(row.setid)] = {'uri': str(row.seturi), 'id': str(row.setid), 'label': str(row.setlabel) if row.setlabel else "", 'open': bool(row.setopen) } return self.subsetcache[str(row.setid)] raise DeepValidationError("Unable to find subset (set_uri=" + str(set_uri)+")")
python
def subset(self, subset_id): """Returns information regarding the set""" if subset_id in self.subsetcache: return self.subsetcache[subset_id] set_uri = self.get_set_uri(subset_id) for row in self.graph.query("SELECT ?seturi ?setid ?setlabel ?setopen WHERE { ?seturi rdf:type skos:Collection . OPTIONAL { ?seturi skos:notation ?setid } OPTIONAL { ?seturi skos:prefLabel ?setlabel } OPTIONAL { ?seturi fsd:open ?setopen } FILTER (?seturi = <" + str(set_uri)+">) }"): self.subsetcache[str(row.setid)] = {'uri': str(row.seturi), 'id': str(row.setid), 'label': str(row.setlabel) if row.setlabel else "", 'open': bool(row.setopen) } return self.subsetcache[str(row.setid)] raise DeepValidationError("Unable to find subset (set_uri=" + str(set_uri)+")")
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Returns information regarding the set
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/foliaset.py#L361-L369
[ "subset_id" ]
What does this function do?
[ "Returns", "information", "regarding", "the", "set" ]
proycon/pynlpl
pynlpl/formats/foliaset.py
SetDefinition.orderedclasses
def orderedclasses(self, set_uri_or_id=None, nestedhierarchy=False): """Higher-order generator function that yields class information in the right order, combines calls to :meth:`SetDefinition.classes` and :meth:`SetDefinition.classorder`""" classes = self.classes(set_uri_or_id, nestedhierarchy) for classid in self.classorder(classes): yield classes[classid]
python
def orderedclasses(self, set_uri_or_id=None, nestedhierarchy=False): """Higher-order generator function that yields class information in the right order, combines calls to :meth:`SetDefinition.classes` and :meth:`SetDefinition.classorder`""" classes = self.classes(set_uri_or_id, nestedhierarchy) for classid in self.classorder(classes): yield classes[classid]
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Higher-order generator function that yields class information in the right order, combines calls to :meth:`SetDefinition.classes` and :meth:`SetDefinition.classorder`
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/foliaset.py#L371-L375
[ "set_uri_or_id", "nestedhierarchy" ]
What does this function do?
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proycon/pynlpl
pynlpl/formats/foliaset.py
SetDefinition.classes
def classes(self, set_uri_or_id=None, nestedhierarchy=False): """Returns a dictionary of classes for the specified (sub)set (if None, default, the main set is selected)""" if set_uri_or_id and set_uri_or_id.startswith(('http://','https://')): set_uri = set_uri_or_id else: set_uri = self.get_set_uri(set_uri_or_id) assert set_uri is not None classes= {} uri2idmap = {} for row in self.graph.query("SELECT ?classuri ?classid ?classlabel ?parentclass ?seqnr WHERE { ?classuri rdf:type skos:Concept ; skos:notation ?classid. <" + str(set_uri) + "> skos:member ?classuri . OPTIONAL { ?classuri skos:prefLabel ?classlabel } OPTIONAL { ?classuri skos:narrower ?parentclass } OPTIONAL { ?classuri fsd:sequenceNumber ?seqnr } }"): classinfo = {'uri': str(row.classuri), 'id': str(row.classid),'label': str(row.classlabel) if row.classlabel else "" } if nestedhierarchy: uri2idmap[str(row.classuri)] = str(row.classid) if row.parentclass: classinfo['parentclass'] = str(row.parentclass) #uri if row.seqnr: classinfo['seqnr'] = int(row.seqnr) classes[str(row.classid)] = classinfo if nestedhierarchy: #build hierarchy removekeys = [] for classid, classinfo in classes.items(): if 'parentclass' in classinfo: removekeys.append(classid) parentclassid = uri2idmap[classinfo['parentclass']] if 'subclasses' not in classes[parentclassid]: classes[parentclassid]['subclasses'] = {} classes[parentclassid]['subclasses'][classid] = classinfo for key in removekeys: del classes[key] return classes
python
def classes(self, set_uri_or_id=None, nestedhierarchy=False): """Returns a dictionary of classes for the specified (sub)set (if None, default, the main set is selected)""" if set_uri_or_id and set_uri_or_id.startswith(('http://','https://')): set_uri = set_uri_or_id else: set_uri = self.get_set_uri(set_uri_or_id) assert set_uri is not None classes= {} uri2idmap = {} for row in self.graph.query("SELECT ?classuri ?classid ?classlabel ?parentclass ?seqnr WHERE { ?classuri rdf:type skos:Concept ; skos:notation ?classid. <" + str(set_uri) + "> skos:member ?classuri . OPTIONAL { ?classuri skos:prefLabel ?classlabel } OPTIONAL { ?classuri skos:narrower ?parentclass } OPTIONAL { ?classuri fsd:sequenceNumber ?seqnr } }"): classinfo = {'uri': str(row.classuri), 'id': str(row.classid),'label': str(row.classlabel) if row.classlabel else "" } if nestedhierarchy: uri2idmap[str(row.classuri)] = str(row.classid) if row.parentclass: classinfo['parentclass'] = str(row.parentclass) #uri if row.seqnr: classinfo['seqnr'] = int(row.seqnr) classes[str(row.classid)] = classinfo if nestedhierarchy: #build hierarchy removekeys = [] for classid, classinfo in classes.items(): if 'parentclass' in classinfo: removekeys.append(classid) parentclassid = uri2idmap[classinfo['parentclass']] if 'subclasses' not in classes[parentclassid]: classes[parentclassid]['subclasses'] = {} classes[parentclassid]['subclasses'][classid] = classinfo for key in removekeys: del classes[key] return classes
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Returns a dictionary of classes for the specified (sub)set (if None, default, the main set is selected)
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/foliaset.py#L381-L414
[ "set_uri_or_id", "nestedhierarchy" ]
What does this function do?
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proycon/pynlpl
pynlpl/formats/foliaset.py
SetDefinition.classorder
def classorder(self,classes): """Return a list of class IDs in order for presentational purposes: order is determined first and foremost by explicit ordering, else alphabetically by label or as a last resort by class ID""" return [ classid for classid, classitem in sorted( ((classid, classitem) for classid, classitem in classes.items() if 'seqnr' in classitem) , key=lambda pair: pair[1]['seqnr'] )] + \ [ classid for classid, classitem in sorted( ((classid, classitem) for classid, classitem in classes.items() if 'seqnr' not in classitem) , key=lambda pair: pair[1]['label'] if 'label' in pair[1] else pair[1]['id']) ]
python
def classorder(self,classes): """Return a list of class IDs in order for presentational purposes: order is determined first and foremost by explicit ordering, else alphabetically by label or as a last resort by class ID""" return [ classid for classid, classitem in sorted( ((classid, classitem) for classid, classitem in classes.items() if 'seqnr' in classitem) , key=lambda pair: pair[1]['seqnr'] )] + \ [ classid for classid, classitem in sorted( ((classid, classitem) for classid, classitem in classes.items() if 'seqnr' not in classitem) , key=lambda pair: pair[1]['label'] if 'label' in pair[1] else pair[1]['id']) ]
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Return a list of class IDs in order for presentational purposes: order is determined first and foremost by explicit ordering, else alphabetically by label or as a last resort by class ID
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train
https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/formats/foliaset.py#L416-L419
[ "classes" ]
What does this function do?
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scrapinghub/js2xml
js2xml/lexer.py
CustomLexer.build
def build(self, **kwargs): """Build the lexer.""" self.lexer = ply.lex.lex(object=self, **kwargs)
python
def build(self, **kwargs): """Build the lexer.""" self.lexer = ply.lex.lex(object=self, **kwargs)
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Build the lexer.
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train
https://github.com/scrapinghub/js2xml/blob/d01b79e1a82de157deffcc1a22f4e0b6bfa07715/js2xml/lexer.py#L74-L76
[ "kwargs" ]
What does this function do?
[ "Build", "the", "lexer", "." ]
scrapinghub/js2xml
js2xml/utils/vars.py
make_varname
def make_varname(tree): """ <left> tree </left> """ if tree.tag == 'identifier': return tree.attrib['name'] if tree.tag in ('string', 'boolean'): return tree.text if tree.tag == 'number': return tree.attrib['value'] if tree.tag in ('property', 'object'): return make_varname(_xpath_one(tree, '*')) if tree.tag.endswith('accessor'): kind = tree.tag[:-len('accessor')] obj = make_varname(_xpath_one(tree, 'object')) prop = make_varname(_xpath_one(tree, 'property')) if kind == 'dot': fmt = '%s.%s' elif kind == 'bracket': fmt = '%s[%s]' else: raise ValueError("Unknown accessor: %s" % tree.tag) return fmt % (obj, prop) raise ValueError("Unknown tag: %s" % tree.tag)
python
def make_varname(tree): """ <left> tree </left> """ if tree.tag == 'identifier': return tree.attrib['name'] if tree.tag in ('string', 'boolean'): return tree.text if tree.tag == 'number': return tree.attrib['value'] if tree.tag in ('property', 'object'): return make_varname(_xpath_one(tree, '*')) if tree.tag.endswith('accessor'): kind = tree.tag[:-len('accessor')] obj = make_varname(_xpath_one(tree, 'object')) prop = make_varname(_xpath_one(tree, 'property')) if kind == 'dot': fmt = '%s.%s' elif kind == 'bracket': fmt = '%s[%s]' else: raise ValueError("Unknown accessor: %s" % tree.tag) return fmt % (obj, prop) raise ValueError("Unknown tag: %s" % tree.tag)
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<left> tree </left>
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train
https://github.com/scrapinghub/js2xml/blob/d01b79e1a82de157deffcc1a22f4e0b6bfa07715/js2xml/utils/vars.py#L55-L83
[ "tree" ]
What does this function do?
[ "<left", ">", "tree", "<", "/", "left", ">" ]
mvantellingen/wsgi-basic-auth
src/wsgi_basic_auth.py
_users_from_environ
def _users_from_environ(env_prefix=''): """Environment value via `user:password|user2:password2`""" auth_string = os.environ.get(env_prefix + 'WSGI_AUTH_CREDENTIALS') if not auth_string: return {} result = {} for credentials in auth_string.split('|'): username, password = credentials.split(':', 1) result[username] = password return result
python
def _users_from_environ(env_prefix=''): """Environment value via `user:password|user2:password2`""" auth_string = os.environ.get(env_prefix + 'WSGI_AUTH_CREDENTIALS') if not auth_string: return {} result = {} for credentials in auth_string.split('|'): username, password = credentials.split(':', 1) result[username] = password return result
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Environment value via `user:password|user2:password2`
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train
https://github.com/mvantellingen/wsgi-basic-auth/blob/4e829bff21526f587f8d1a8592b63e1abd862a74/src/wsgi_basic_auth.py#L102-L112
[ "env_prefix" ]
What does this function do?
[ "Environment", "value", "via", "user", ":", "password|user2", ":", "password2" ]
mvantellingen/wsgi-basic-auth
src/wsgi_basic_auth.py
_exclude_paths_from_environ
def _exclude_paths_from_environ(env_prefix=''): """Environment value via `/login;/register`""" paths = os.environ.get(env_prefix + 'WSGI_AUTH_EXCLUDE_PATHS') if not paths: return [] return paths.split(';')
python
def _exclude_paths_from_environ(env_prefix=''): """Environment value via `/login;/register`""" paths = os.environ.get(env_prefix + 'WSGI_AUTH_EXCLUDE_PATHS') if not paths: return [] return paths.split(';')
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Environment value via `/login;/register`
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train
https://github.com/mvantellingen/wsgi-basic-auth/blob/4e829bff21526f587f8d1a8592b63e1abd862a74/src/wsgi_basic_auth.py#L115-L120
[ "env_prefix" ]
What does this function do?
[ "Environment", "value", "via", "/", "login", ";", "/", "register" ]
mvantellingen/wsgi-basic-auth
src/wsgi_basic_auth.py
_include_paths_from_environ
def _include_paths_from_environ(env_prefix=''): """Environment value via `/login;/register`""" paths = os.environ.get(env_prefix + 'WSGI_AUTH_PATHS') if not paths: return [] return paths.split(';')
python
def _include_paths_from_environ(env_prefix=''): """Environment value via `/login;/register`""" paths = os.environ.get(env_prefix + 'WSGI_AUTH_PATHS') if not paths: return [] return paths.split(';')
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Environment value via `/login;/register`
[ "Environment", "value", "via", "/", "login", ";", "/", "register" ]
train
https://github.com/mvantellingen/wsgi-basic-auth/blob/4e829bff21526f587f8d1a8592b63e1abd862a74/src/wsgi_basic_auth.py#L123-L128
[ "env_prefix" ]
What does this function do?
[ "Environment", "value", "via", "/", "login", ";", "/", "register" ]
mvantellingen/wsgi-basic-auth
src/wsgi_basic_auth.py
BasicAuth.is_authorized
def is_authorized(self, request): """Check if the user is authenticated for the given request. The include_paths and exclude_paths are first checked. If authentication is required then the Authorization HTTP header is checked against the credentials. """ if self._is_request_in_include_path(request): if self._is_request_in_exclude_path(request): return True else: auth = request.authorization if auth and auth[0] == 'Basic': credentials = b64decode(auth[1]).decode('UTF-8') username, password = credentials.split(':', 1) return self._users.get(username) == password else: return False else: return True
python
def is_authorized(self, request): """Check if the user is authenticated for the given request. The include_paths and exclude_paths are first checked. If authentication is required then the Authorization HTTP header is checked against the credentials. """ if self._is_request_in_include_path(request): if self._is_request_in_exclude_path(request): return True else: auth = request.authorization if auth and auth[0] == 'Basic': credentials = b64decode(auth[1]).decode('UTF-8') username, password = credentials.split(':', 1) return self._users.get(username) == password else: return False else: return True
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Check if the user is authenticated for the given request. The include_paths and exclude_paths are first checked. If authentication is required then the Authorization HTTP header is checked against the credentials.
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train
https://github.com/mvantellingen/wsgi-basic-auth/blob/4e829bff21526f587f8d1a8592b63e1abd862a74/src/wsgi_basic_auth.py#L48-L68
[ "request" ]
What does this function do?
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mvantellingen/wsgi-basic-auth
src/wsgi_basic_auth.py
BasicAuth._login
def _login(self, environ, start_response): """Send a login response back to the client.""" response = HTTPUnauthorized() response.www_authenticate = ('Basic', {'realm': self._realm}) return response(environ, start_response)
python
def _login(self, environ, start_response): """Send a login response back to the client.""" response = HTTPUnauthorized() response.www_authenticate = ('Basic', {'realm': self._realm}) return response(environ, start_response)
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Send a login response back to the client.
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train
https://github.com/mvantellingen/wsgi-basic-auth/blob/4e829bff21526f587f8d1a8592b63e1abd862a74/src/wsgi_basic_auth.py#L70-L74
[ "environ", "start_response" ]
What does this function do?
[ "Send", "a", "login", "response", "back", "to", "the", "client", "." ]
mvantellingen/wsgi-basic-auth
src/wsgi_basic_auth.py
BasicAuth._is_request_in_include_path
def _is_request_in_include_path(self, request): """Check if the request path is in the `_include_paths` list. If no specific include paths are given then we assume that authentication is required for all paths. """ if self._include_paths: for path in self._include_paths: if request.path.startswith(path): return True return False else: return True
python
def _is_request_in_include_path(self, request): """Check if the request path is in the `_include_paths` list. If no specific include paths are given then we assume that authentication is required for all paths. """ if self._include_paths: for path in self._include_paths: if request.path.startswith(path): return True return False else: return True
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Check if the request path is in the `_include_paths` list. If no specific include paths are given then we assume that authentication is required for all paths.
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train
https://github.com/mvantellingen/wsgi-basic-auth/blob/4e829bff21526f587f8d1a8592b63e1abd862a74/src/wsgi_basic_auth.py#L76-L89
[ "request" ]
What does this function do?
[ "Check", "if", "the", "request", "path", "is", "in", "the", "_include_paths", "list", "." ]
mvantellingen/wsgi-basic-auth
src/wsgi_basic_auth.py
BasicAuth._is_request_in_exclude_path
def _is_request_in_exclude_path(self, request): """Check if the request path is in the `_exclude_paths` list""" if self._exclude_paths: for path in self._exclude_paths: if request.path.startswith(path): return True return False else: return False
python
def _is_request_in_exclude_path(self, request): """Check if the request path is in the `_exclude_paths` list""" if self._exclude_paths: for path in self._exclude_paths: if request.path.startswith(path): return True return False else: return False
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Check if the request path is in the `_exclude_paths` list
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train
https://github.com/mvantellingen/wsgi-basic-auth/blob/4e829bff21526f587f8d1a8592b63e1abd862a74/src/wsgi_basic_auth.py#L91-L99
[ "request" ]
What does this function do?
[ "Check", "if", "the", "request", "path", "is", "in", "the", "_exclude_paths", "list" ]
click-contrib/click-repl
click_repl/__init__.py
bootstrap_prompt
def bootstrap_prompt(prompt_kwargs, group): """ Bootstrap prompt_toolkit kwargs or use user defined values. :param prompt_kwargs: The user specified prompt kwargs. """ prompt_kwargs = prompt_kwargs or {} defaults = { "history": InMemoryHistory(), "completer": ClickCompleter(group), "message": u"> ", } for key in defaults: default_value = defaults[key] if key not in prompt_kwargs: prompt_kwargs[key] = default_value return prompt_kwargs
python
def bootstrap_prompt(prompt_kwargs, group): """ Bootstrap prompt_toolkit kwargs or use user defined values. :param prompt_kwargs: The user specified prompt kwargs. """ prompt_kwargs = prompt_kwargs or {} defaults = { "history": InMemoryHistory(), "completer": ClickCompleter(group), "message": u"> ", } for key in defaults: default_value = defaults[key] if key not in prompt_kwargs: prompt_kwargs[key] = default_value return prompt_kwargs
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Bootstrap prompt_toolkit kwargs or use user defined values. :param prompt_kwargs: The user specified prompt kwargs.
[ "Bootstrap", "prompt_toolkit", "kwargs", "or", "use", "user", "defined", "values", "." ]
train
https://github.com/click-contrib/click-repl/blob/2d78dc520eb0bb5b813bad3b72344edbd22a7f4e/click_repl/__init__.py#L146-L165
[ "prompt_kwargs", "group" ]
What does this function do?
[ "Bootstrap", "prompt_toolkit", "kwargs", "or", "use", "user", "defined", "values", "." ]
click-contrib/click-repl
click_repl/__init__.py
repl
def repl( # noqa: C901 old_ctx, prompt_kwargs=None, allow_system_commands=True, allow_internal_commands=True, ): """ Start an interactive shell. All subcommands are available in it. :param old_ctx: The current Click context. :param prompt_kwargs: Parameters passed to :py:func:`prompt_toolkit.shortcuts.prompt`. If stdin is not a TTY, no prompt will be printed, but only commands read from stdin. """ # parent should be available, but we're not going to bother if not group_ctx = old_ctx.parent or old_ctx group = group_ctx.command isatty = sys.stdin.isatty() # Delete the REPL command from those available, as we don't want to allow # nesting REPLs (note: pass `None` to `pop` as we don't want to error if # REPL command already not present for some reason). repl_command_name = old_ctx.command.name if isinstance(group_ctx.command, click.CommandCollection): available_commands = { cmd_name: cmd_obj for source in group_ctx.command.sources for cmd_name, cmd_obj in source.commands.items() } else: available_commands = group_ctx.command.commands available_commands.pop(repl_command_name, None) prompt_kwargs = bootstrap_prompt(prompt_kwargs, group) if isatty: def get_command(): return prompt(**prompt_kwargs) else: get_command = sys.stdin.readline while True: try: command = get_command() except KeyboardInterrupt: continue except EOFError: break if not command: if isatty: continue else: break if allow_system_commands and dispatch_repl_commands(command): continue if allow_internal_commands: try: result = handle_internal_commands(command) if isinstance(result, six.string_types): click.echo(result) continue except ExitReplException: break try: args = shlex.split(command) except ValueError as e: click.echo("{}: {}".format(type(e).__name__, e)) continue try: with group.make_context(None, args, parent=group_ctx) as ctx: group.invoke(ctx) ctx.exit() except click.ClickException as e: e.show() except ClickExit: pass except SystemExit: pass except ExitReplException: break
python
def repl( # noqa: C901 old_ctx, prompt_kwargs=None, allow_system_commands=True, allow_internal_commands=True, ): """ Start an interactive shell. All subcommands are available in it. :param old_ctx: The current Click context. :param prompt_kwargs: Parameters passed to :py:func:`prompt_toolkit.shortcuts.prompt`. If stdin is not a TTY, no prompt will be printed, but only commands read from stdin. """ # parent should be available, but we're not going to bother if not group_ctx = old_ctx.parent or old_ctx group = group_ctx.command isatty = sys.stdin.isatty() # Delete the REPL command from those available, as we don't want to allow # nesting REPLs (note: pass `None` to `pop` as we don't want to error if # REPL command already not present for some reason). repl_command_name = old_ctx.command.name if isinstance(group_ctx.command, click.CommandCollection): available_commands = { cmd_name: cmd_obj for source in group_ctx.command.sources for cmd_name, cmd_obj in source.commands.items() } else: available_commands = group_ctx.command.commands available_commands.pop(repl_command_name, None) prompt_kwargs = bootstrap_prompt(prompt_kwargs, group) if isatty: def get_command(): return prompt(**prompt_kwargs) else: get_command = sys.stdin.readline while True: try: command = get_command() except KeyboardInterrupt: continue except EOFError: break if not command: if isatty: continue else: break if allow_system_commands and dispatch_repl_commands(command): continue if allow_internal_commands: try: result = handle_internal_commands(command) if isinstance(result, six.string_types): click.echo(result) continue except ExitReplException: break try: args = shlex.split(command) except ValueError as e: click.echo("{}: {}".format(type(e).__name__, e)) continue try: with group.make_context(None, args, parent=group_ctx) as ctx: group.invoke(ctx) ctx.exit() except click.ClickException as e: e.show() except ClickExit: pass except SystemExit: pass except ExitReplException: break
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Start an interactive shell. All subcommands are available in it. :param old_ctx: The current Click context. :param prompt_kwargs: Parameters passed to :py:func:`prompt_toolkit.shortcuts.prompt`. If stdin is not a TTY, no prompt will be printed, but only commands read from stdin.
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train
https://github.com/click-contrib/click-repl/blob/2d78dc520eb0bb5b813bad3b72344edbd22a7f4e/click_repl/__init__.py#L168-L257
[ "", "prompt_kwargs", "allow_system_commands", "allow_internal_commands", "" ]
What does this function do?
[ "Start", "an", "interactive", "shell", ".", "All", "subcommands", "are", "available", "in", "it", "." ]
click-contrib/click-repl
click_repl/__init__.py
register_repl
def register_repl(group, name="repl"): """Register :func:`repl()` as sub-command *name* of *group*.""" group.command(name=name)(click.pass_context(repl))
python
def register_repl(group, name="repl"): """Register :func:`repl()` as sub-command *name* of *group*.""" group.command(name=name)(click.pass_context(repl))
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Register :func:`repl()` as sub-command *name* of *group*.
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train
https://github.com/click-contrib/click-repl/blob/2d78dc520eb0bb5b813bad3b72344edbd22a7f4e/click_repl/__init__.py#L260-L262
[ "group", "name" ]
What does this function do?
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click-contrib/click-repl
click_repl/__init__.py
handle_internal_commands
def handle_internal_commands(command): """Run repl-internal commands. Repl-internal commands are all commands starting with ":". """ if command.startswith(":"): target = _get_registered_target(command[1:], default=None) if target: return target()
python
def handle_internal_commands(command): """Run repl-internal commands. Repl-internal commands are all commands starting with ":". """ if command.startswith(":"): target = _get_registered_target(command[1:], default=None) if target: return target()
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Run repl-internal commands. Repl-internal commands are all commands starting with ":".
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train
https://github.com/click-contrib/click-repl/blob/2d78dc520eb0bb5b813bad3b72344edbd22a7f4e/click_repl/__init__.py#L283-L292
[ "command" ]
What does this function do?
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nel215/ksvd
ksvd/__init__.py
ApproximateKSVD.fit
def fit(self, X): """ Parameters ---------- X: shape = [n_samples, n_features] """ D = self._initialize(X) for i in range(self.max_iter): gamma = self._transform(D, X) e = np.linalg.norm(X - gamma.dot(D)) if e < self.tol: break D, gamma = self._update_dict(X, D, gamma) self.components_ = D return self
python
def fit(self, X): """ Parameters ---------- X: shape = [n_samples, n_features] """ D = self._initialize(X) for i in range(self.max_iter): gamma = self._transform(D, X) e = np.linalg.norm(X - gamma.dot(D)) if e < self.tol: break D, gamma = self._update_dict(X, D, gamma) self.components_ = D return self
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train
https://github.com/nel215/ksvd/blob/917f65a9f15ee7689253b27f0107a9773cf837f6/ksvd/__init__.py#L67-L82
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What does this function do?
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graphql-python/graphql-relay-py
graphql_relay/node/node.py
node_definitions
def node_definitions(id_fetcher, type_resolver=None, id_resolver=None): ''' Given a function to map from an ID to an underlying object, and a function to map from an underlying object to the concrete GraphQLObjectType it corresponds to, constructs a `Node` interface that objects can implement, and a field config for a `node` root field. If the type_resolver is omitted, object resolution on the interface will be handled with the `isTypeOf` method on object types, as with any GraphQL interface without a provided `resolveType` method. ''' node_interface = GraphQLInterfaceType( 'Node', description='An object with an ID', fields=lambda: OrderedDict(( ('id', GraphQLField( GraphQLNonNull(GraphQLID), description='The id of the object.', resolver=id_resolver, )), )), resolve_type=type_resolver ) node_field = GraphQLField( node_interface, description='Fetches an object given its ID', args=OrderedDict(( ('id', GraphQLArgument( GraphQLNonNull(GraphQLID), description='The ID of an object' )), )), resolver=lambda obj, args, *_: id_fetcher(args.get('id'), *_) ) return node_interface, node_field
python
def node_definitions(id_fetcher, type_resolver=None, id_resolver=None): ''' Given a function to map from an ID to an underlying object, and a function to map from an underlying object to the concrete GraphQLObjectType it corresponds to, constructs a `Node` interface that objects can implement, and a field config for a `node` root field. If the type_resolver is omitted, object resolution on the interface will be handled with the `isTypeOf` method on object types, as with any GraphQL interface without a provided `resolveType` method. ''' node_interface = GraphQLInterfaceType( 'Node', description='An object with an ID', fields=lambda: OrderedDict(( ('id', GraphQLField( GraphQLNonNull(GraphQLID), description='The id of the object.', resolver=id_resolver, )), )), resolve_type=type_resolver ) node_field = GraphQLField( node_interface, description='Fetches an object given its ID', args=OrderedDict(( ('id', GraphQLArgument( GraphQLNonNull(GraphQLID), description='The ID of an object' )), )), resolver=lambda obj, args, *_: id_fetcher(args.get('id'), *_) ) return node_interface, node_field
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train
https://github.com/graphql-python/graphql-relay-py/blob/17ce2efa3c396df42791ae00667120b5fae64610/graphql_relay/node/node.py#L15-L49
[ "id_fetcher", "type_resolver", "id_resolver" ]
What does this function do?
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graphql-python/graphql-relay-py
graphql_relay/node/node.py
from_global_id
def from_global_id(global_id): ''' Takes the "global ID" created by toGlobalID, and retuns the type name and ID used to create it. ''' unbased_global_id = unbase64(global_id) _type, _id = unbased_global_id.split(':', 1) return _type, _id
python
def from_global_id(global_id): ''' Takes the "global ID" created by toGlobalID, and retuns the type name and ID used to create it. ''' unbased_global_id = unbase64(global_id) _type, _id = unbased_global_id.split(':', 1) return _type, _id
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Takes the "global ID" created by toGlobalID, and retuns the type name and ID used to create it.
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train
https://github.com/graphql-python/graphql-relay-py/blob/17ce2efa3c396df42791ae00667120b5fae64610/graphql_relay/node/node.py#L60-L67
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What does this function do?
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graphql-python/graphql-relay-py
graphql_relay/node/node.py
global_id_field
def global_id_field(type_name, id_fetcher=None): ''' Creates the configuration for an id field on a node, using `to_global_id` to construct the ID from the provided typename. The type-specific ID is fetcher by calling id_fetcher on the object, or if not provided, by accessing the `id` property on the object. ''' return GraphQLField( GraphQLNonNull(GraphQLID), description='The ID of an object', resolver=lambda obj, args, context, info: to_global_id( type_name or info.parent_type.name, id_fetcher(obj, context, info) if id_fetcher else obj.id ) )
python
def global_id_field(type_name, id_fetcher=None): ''' Creates the configuration for an id field on a node, using `to_global_id` to construct the ID from the provided typename. The type-specific ID is fetcher by calling id_fetcher on the object, or if not provided, by accessing the `id` property on the object. ''' return GraphQLField( GraphQLNonNull(GraphQLID), description='The ID of an object', resolver=lambda obj, args, context, info: to_global_id( type_name or info.parent_type.name, id_fetcher(obj, context, info) if id_fetcher else obj.id ) )
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Creates the configuration for an id field on a node, using `to_global_id` to construct the ID from the provided typename. The type-specific ID is fetcher by calling id_fetcher on the object, or if not provided, by accessing the `id` property on the object.
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train
https://github.com/graphql-python/graphql-relay-py/blob/17ce2efa3c396df42791ae00667120b5fae64610/graphql_relay/node/node.py#L70-L84
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graphql-python/graphql-relay-py
graphql_relay/connection/arrayconnection.py
connection_from_list
def connection_from_list(data, args=None, **kwargs): ''' A simple function that accepts an array and connection arguments, and returns a connection object for use in GraphQL. It uses array offsets as pagination, so pagination will only work if the array is static. ''' _len = len(data) return connection_from_list_slice( data, args, slice_start=0, list_length=_len, list_slice_length=_len, **kwargs )
python
def connection_from_list(data, args=None, **kwargs): ''' A simple function that accepts an array and connection arguments, and returns a connection object for use in GraphQL. It uses array offsets as pagination, so pagination will only work if the array is static. ''' _len = len(data) return connection_from_list_slice( data, args, slice_start=0, list_length=_len, list_slice_length=_len, **kwargs )
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A simple function that accepts an array and connection arguments, and returns a connection object for use in GraphQL. It uses array offsets as pagination, so pagination will only work if the array is static.
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train
https://github.com/graphql-python/graphql-relay-py/blob/17ce2efa3c396df42791ae00667120b5fae64610/graphql_relay/connection/arrayconnection.py#L7-L21
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What does this function do?
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graphql-python/graphql-relay-py
graphql_relay/connection/arrayconnection.py
connection_from_promised_list
def connection_from_promised_list(data_promise, args=None, **kwargs): ''' A version of `connectionFromArray` that takes a promised array, and returns a promised connection. ''' return data_promise.then(lambda data: connection_from_list(data, args, **kwargs))
python
def connection_from_promised_list(data_promise, args=None, **kwargs): ''' A version of `connectionFromArray` that takes a promised array, and returns a promised connection. ''' return data_promise.then(lambda data: connection_from_list(data, args, **kwargs))
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A version of `connectionFromArray` that takes a promised array, and returns a promised connection.
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train
https://github.com/graphql-python/graphql-relay-py/blob/17ce2efa3c396df42791ae00667120b5fae64610/graphql_relay/connection/arrayconnection.py#L24-L29
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What does this function do?
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graphql-python/graphql-relay-py
graphql_relay/connection/arrayconnection.py
connection_from_list_slice
def connection_from_list_slice(list_slice, args=None, connection_type=None, edge_type=None, pageinfo_type=None, slice_start=0, list_length=0, list_slice_length=None): ''' Given a slice (subset) of an array, returns a connection object for use in GraphQL. This function is similar to `connectionFromArray`, but is intended for use cases where you know the cardinality of the connection, consider it too large to materialize the entire array, and instead wish pass in a slice of the total result large enough to cover the range specified in `args`. ''' connection_type = connection_type or Connection edge_type = edge_type or Edge pageinfo_type = pageinfo_type or PageInfo args = args or {} before = args.get('before') after = args.get('after') first = args.get('first') last = args.get('last') if list_slice_length is None: list_slice_length = len(list_slice) slice_end = slice_start + list_slice_length before_offset = get_offset_with_default(before, list_length) after_offset = get_offset_with_default(after, -1) start_offset = max( slice_start - 1, after_offset, -1 ) + 1 end_offset = min( slice_end, before_offset, list_length ) if isinstance(first, int): end_offset = min( end_offset, start_offset + first ) if isinstance(last, int): start_offset = max( start_offset, end_offset - last ) # If supplied slice is too large, trim it down before mapping over it. _slice = list_slice[ max(start_offset - slice_start, 0): list_slice_length - (slice_end - end_offset) ] edges = [ edge_type( node=node, cursor=offset_to_cursor(start_offset + i) ) for i, node in enumerate(_slice) ] first_edge_cursor = edges[0].cursor if edges else None last_edge_cursor = edges[-1].cursor if edges else None lower_bound = after_offset + 1 if after else 0 upper_bound = before_offset if before else list_length return connection_type( edges=edges, page_info=pageinfo_type( start_cursor=first_edge_cursor, end_cursor=last_edge_cursor, has_previous_page=isinstance(last, int) and start_offset > lower_bound, has_next_page=isinstance(first, int) and end_offset < upper_bound ) )
python
def connection_from_list_slice(list_slice, args=None, connection_type=None, edge_type=None, pageinfo_type=None, slice_start=0, list_length=0, list_slice_length=None): ''' Given a slice (subset) of an array, returns a connection object for use in GraphQL. This function is similar to `connectionFromArray`, but is intended for use cases where you know the cardinality of the connection, consider it too large to materialize the entire array, and instead wish pass in a slice of the total result large enough to cover the range specified in `args`. ''' connection_type = connection_type or Connection edge_type = edge_type or Edge pageinfo_type = pageinfo_type or PageInfo args = args or {} before = args.get('before') after = args.get('after') first = args.get('first') last = args.get('last') if list_slice_length is None: list_slice_length = len(list_slice) slice_end = slice_start + list_slice_length before_offset = get_offset_with_default(before, list_length) after_offset = get_offset_with_default(after, -1) start_offset = max( slice_start - 1, after_offset, -1 ) + 1 end_offset = min( slice_end, before_offset, list_length ) if isinstance(first, int): end_offset = min( end_offset, start_offset + first ) if isinstance(last, int): start_offset = max( start_offset, end_offset - last ) # If supplied slice is too large, trim it down before mapping over it. _slice = list_slice[ max(start_offset - slice_start, 0): list_slice_length - (slice_end - end_offset) ] edges = [ edge_type( node=node, cursor=offset_to_cursor(start_offset + i) ) for i, node in enumerate(_slice) ] first_edge_cursor = edges[0].cursor if edges else None last_edge_cursor = edges[-1].cursor if edges else None lower_bound = after_offset + 1 if after else 0 upper_bound = before_offset if before else list_length return connection_type( edges=edges, page_info=pageinfo_type( start_cursor=first_edge_cursor, end_cursor=last_edge_cursor, has_previous_page=isinstance(last, int) and start_offset > lower_bound, has_next_page=isinstance(first, int) and end_offset < upper_bound ) )
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Given a slice (subset) of an array, returns a connection object for use in GraphQL. This function is similar to `connectionFromArray`, but is intended for use cases where you know the cardinality of the connection, consider it too large to materialize the entire array, and instead wish pass in a slice of the total result large enough to cover the range specified in `args`.
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train
https://github.com/graphql-python/graphql-relay-py/blob/17ce2efa3c396df42791ae00667120b5fae64610/graphql_relay/connection/arrayconnection.py#L32-L107
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What does this function do?
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graphql-python/graphql-relay-py
graphql_relay/connection/arrayconnection.py
cursor_for_object_in_connection
def cursor_for_object_in_connection(data, _object): ''' Return the cursor associated with an object in an array. ''' if _object not in data: return None offset = data.index(_object) return offset_to_cursor(offset)
python
def cursor_for_object_in_connection(data, _object): ''' Return the cursor associated with an object in an array. ''' if _object not in data: return None offset = data.index(_object) return offset_to_cursor(offset)
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Return the cursor associated with an object in an array.
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train
https://github.com/graphql-python/graphql-relay-py/blob/17ce2efa3c396df42791ae00667120b5fae64610/graphql_relay/connection/arrayconnection.py#L134-L142
[ "data", "_object" ]
What does this function do?
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graphql-python/graphql-relay-py
graphql_relay/connection/arrayconnection.py
get_offset_with_default
def get_offset_with_default(cursor=None, default_offset=0): ''' Given an optional cursor and a default offset, returns the offset to use; if the cursor contains a valid offset, that will be used, otherwise it will be the default. ''' if not is_str(cursor): return default_offset offset = cursor_to_offset(cursor) try: return int(offset) except: return default_offset
python
def get_offset_with_default(cursor=None, default_offset=0): ''' Given an optional cursor and a default offset, returns the offset to use; if the cursor contains a valid offset, that will be used, otherwise it will be the default. ''' if not is_str(cursor): return default_offset offset = cursor_to_offset(cursor) try: return int(offset) except: return default_offset
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Given an optional cursor and a default offset, returns the offset to use; if the cursor contains a valid offset, that will be used, otherwise it will be the default.
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train
https://github.com/graphql-python/graphql-relay-py/blob/17ce2efa3c396df42791ae00667120b5fae64610/graphql_relay/connection/arrayconnection.py#L145-L158
[ "cursor", "default_offset" ]
What does this function do?
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patrickfuller/jgraph
python/notebook.py
draw
def draw(data, size=(600, 400), node_size=2.0, edge_size=0.25, default_node_color=0x5bc0de, default_edge_color=0xaaaaaa, z=100, shader='basic', optimize=True, directed=True, display_html=True, show_save=False): """Draws an interactive 3D visualization of the inputted graph. Args: data: Either an adjacency list of tuples (ie. [(1,2),...]) or object size: (Optional) Dimensions of visualization, in pixels node_size: (Optional) Defaults to 2.0 edge_size: (Optional) Defaults to 0.25 default_node_color: (Optional) If loading data without specified 'color' properties, this will be used. Default is 0x5bc0de default_edge_color: (Optional) If loading data without specified 'color' properties, this will be used. Default is 0xaaaaaa z: (Optional) Starting z position of the camera. Default is 100. shader: (Optional) Specifies shading algorithm to use. Can be 'toon', 'basic', 'phong', or 'lambert'. Default is 'basic'. optimize: (Optional) Runs a force-directed layout algorithm on the graph. Default True. directed: (Optional) Includes arrows on edges to indicate direction. Default True. display_html: If True (default), embed the html in a IPython display. If False, return the html as a string. show_save: If True, displays a save icon for rendering graph as an image. Inputting an adjacency list into `data` results in a 'default' graph type. For more customization, use the more expressive object format. """ # Catch errors on string-based input before getting js involved shader_options = ['toon', 'basic', 'phong', 'lambert'] if shader not in shader_options: raise Exception('Invalid shader! Please use one of: ' + ', '.join(shader_options)) if isinstance(default_edge_color, int): default_edge_color = hex(default_edge_color) if isinstance(default_node_color, int): default_node_color = hex(default_node_color) # Guess the input format and handle accordingly if isinstance(data, list): graph = json_formatter.dumps(generate(data, iterations=1)) elif isinstance(data, dict): # Convert color hex to string for json handling for node_key in data['nodes']: node = data['nodes'][node_key] if 'color' in node and isinstance(node['color'], int): node['color'] = hex(node['color']) for edge in data['edges']: if 'color' in edge and isinstance(edge['color'], int): edge['color'] = hex(edge['color']) graph = json_formatter.dumps(data) else: # Support both files and strings try: with open(data) as in_file: graph = in_file.read() except: graph = data div_id = uuid.uuid4() html = '''<div id="graph-%(id)s"></div> <script type="text/javascript"> require.config({baseUrl: '/', paths: {jgraph: ['%(local)s', '%(remote)s']}}); require(['jgraph'], function () { var $d = $('#graph-%(id)s'); $d.width(%(w)d); $d.height(%(h)d); $d.jgraph = jQuery.extend({}, jgraph); $d.jgraph.create($d, {nodeSize: %(node_size)f, edgeSize: %(edge_size)f, defaultNodeColor: '%(node_color)s', defaultEdgeColor: '%(edge_color)s', shader: '%(shader)s', z: %(z)d, runOptimization: %(optimize)s, directed: %(directed)s, showSave: %(show_save)s}); $d.jgraph.draw(%(graph)s); $d.resizable({ aspectRatio: %(w)d / %(h)d, resize: function (evt, ui) { $d.jgraph.renderer.setSize(ui.size.width, ui.size.height); } }); }); </script>''' % dict(id=div_id, local=local_path[:-3], remote=remote_path[:-3], w=size[0], h=size[1], node_size=node_size, edge_size=edge_size, node_color=default_node_color, edge_color=default_edge_color, shader=shader, z=z, graph=graph, optimize='true' if optimize else 'false', directed='true' if directed else 'false', show_save='true' if show_save else 'false') # Execute js and display the results in a div (see script for more) if display_html: display(HTML(html)) else: return html
python
def draw(data, size=(600, 400), node_size=2.0, edge_size=0.25, default_node_color=0x5bc0de, default_edge_color=0xaaaaaa, z=100, shader='basic', optimize=True, directed=True, display_html=True, show_save=False): """Draws an interactive 3D visualization of the inputted graph. Args: data: Either an adjacency list of tuples (ie. [(1,2),...]) or object size: (Optional) Dimensions of visualization, in pixels node_size: (Optional) Defaults to 2.0 edge_size: (Optional) Defaults to 0.25 default_node_color: (Optional) If loading data without specified 'color' properties, this will be used. Default is 0x5bc0de default_edge_color: (Optional) If loading data without specified 'color' properties, this will be used. Default is 0xaaaaaa z: (Optional) Starting z position of the camera. Default is 100. shader: (Optional) Specifies shading algorithm to use. Can be 'toon', 'basic', 'phong', or 'lambert'. Default is 'basic'. optimize: (Optional) Runs a force-directed layout algorithm on the graph. Default True. directed: (Optional) Includes arrows on edges to indicate direction. Default True. display_html: If True (default), embed the html in a IPython display. If False, return the html as a string. show_save: If True, displays a save icon for rendering graph as an image. Inputting an adjacency list into `data` results in a 'default' graph type. For more customization, use the more expressive object format. """ # Catch errors on string-based input before getting js involved shader_options = ['toon', 'basic', 'phong', 'lambert'] if shader not in shader_options: raise Exception('Invalid shader! Please use one of: ' + ', '.join(shader_options)) if isinstance(default_edge_color, int): default_edge_color = hex(default_edge_color) if isinstance(default_node_color, int): default_node_color = hex(default_node_color) # Guess the input format and handle accordingly if isinstance(data, list): graph = json_formatter.dumps(generate(data, iterations=1)) elif isinstance(data, dict): # Convert color hex to string for json handling for node_key in data['nodes']: node = data['nodes'][node_key] if 'color' in node and isinstance(node['color'], int): node['color'] = hex(node['color']) for edge in data['edges']: if 'color' in edge and isinstance(edge['color'], int): edge['color'] = hex(edge['color']) graph = json_formatter.dumps(data) else: # Support both files and strings try: with open(data) as in_file: graph = in_file.read() except: graph = data div_id = uuid.uuid4() html = '''<div id="graph-%(id)s"></div> <script type="text/javascript"> require.config({baseUrl: '/', paths: {jgraph: ['%(local)s', '%(remote)s']}}); require(['jgraph'], function () { var $d = $('#graph-%(id)s'); $d.width(%(w)d); $d.height(%(h)d); $d.jgraph = jQuery.extend({}, jgraph); $d.jgraph.create($d, {nodeSize: %(node_size)f, edgeSize: %(edge_size)f, defaultNodeColor: '%(node_color)s', defaultEdgeColor: '%(edge_color)s', shader: '%(shader)s', z: %(z)d, runOptimization: %(optimize)s, directed: %(directed)s, showSave: %(show_save)s}); $d.jgraph.draw(%(graph)s); $d.resizable({ aspectRatio: %(w)d / %(h)d, resize: function (evt, ui) { $d.jgraph.renderer.setSize(ui.size.width, ui.size.height); } }); }); </script>''' % dict(id=div_id, local=local_path[:-3], remote=remote_path[:-3], w=size[0], h=size[1], node_size=node_size, edge_size=edge_size, node_color=default_node_color, edge_color=default_edge_color, shader=shader, z=z, graph=graph, optimize='true' if optimize else 'false', directed='true' if directed else 'false', show_save='true' if show_save else 'false') # Execute js and display the results in a div (see script for more) if display_html: display(HTML(html)) else: return html
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Draws an interactive 3D visualization of the inputted graph. Args: data: Either an adjacency list of tuples (ie. [(1,2),...]) or object size: (Optional) Dimensions of visualization, in pixels node_size: (Optional) Defaults to 2.0 edge_size: (Optional) Defaults to 0.25 default_node_color: (Optional) If loading data without specified 'color' properties, this will be used. Default is 0x5bc0de default_edge_color: (Optional) If loading data without specified 'color' properties, this will be used. Default is 0xaaaaaa z: (Optional) Starting z position of the camera. Default is 100. shader: (Optional) Specifies shading algorithm to use. Can be 'toon', 'basic', 'phong', or 'lambert'. Default is 'basic'. optimize: (Optional) Runs a force-directed layout algorithm on the graph. Default True. directed: (Optional) Includes arrows on edges to indicate direction. Default True. display_html: If True (default), embed the html in a IPython display. If False, return the html as a string. show_save: If True, displays a save icon for rendering graph as an image. Inputting an adjacency list into `data` results in a 'default' graph type. For more customization, use the more expressive object format.
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train
https://github.com/patrickfuller/jgraph/blob/7297450f26ae8cba21914668a5aaa755de8aa14d/python/notebook.py#L29-L133
[ "data", "size" ]
What does this function do?
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patrickfuller/jgraph
python/notebook.py
generate
def generate(data, iterations=1000, force_strength=5.0, dampening=0.01, max_velocity=2.0, max_distance=50, is_3d=True): """Runs a force-directed algorithm on a graph, returning a data structure. Args: data: An adjacency list of tuples (ie. [(1,2),...]) iterations: (Optional) Number of FDL iterations to run in coordinate generation force_strength: (Optional) Strength of Coulomb and Hooke forces (edit this to scale the distance between nodes) dampening: (Optional) Multiplier to reduce force applied to nodes max_velocity: (Optional) Maximum distance a node can move in one step max_distance: (Optional) The maximum inter-node distance considered is_3d: (Optional) Generates three-dimensional coordinates Outputs a json-serializable Python object. To visualize, pass the output to `jgraph.draw(...)`. """ edges = [{'source': s, 'target': t} for s, t in data] nodes = force_directed_layout.run(edges, iterations, force_strength, dampening, max_velocity, max_distance, is_3d) return {'edges': edges, 'nodes': nodes}
python
def generate(data, iterations=1000, force_strength=5.0, dampening=0.01, max_velocity=2.0, max_distance=50, is_3d=True): """Runs a force-directed algorithm on a graph, returning a data structure. Args: data: An adjacency list of tuples (ie. [(1,2),...]) iterations: (Optional) Number of FDL iterations to run in coordinate generation force_strength: (Optional) Strength of Coulomb and Hooke forces (edit this to scale the distance between nodes) dampening: (Optional) Multiplier to reduce force applied to nodes max_velocity: (Optional) Maximum distance a node can move in one step max_distance: (Optional) The maximum inter-node distance considered is_3d: (Optional) Generates three-dimensional coordinates Outputs a json-serializable Python object. To visualize, pass the output to `jgraph.draw(...)`. """ edges = [{'source': s, 'target': t} for s, t in data] nodes = force_directed_layout.run(edges, iterations, force_strength, dampening, max_velocity, max_distance, is_3d) return {'edges': edges, 'nodes': nodes}
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Runs a force-directed algorithm on a graph, returning a data structure. Args: data: An adjacency list of tuples (ie. [(1,2),...]) iterations: (Optional) Number of FDL iterations to run in coordinate generation force_strength: (Optional) Strength of Coulomb and Hooke forces (edit this to scale the distance between nodes) dampening: (Optional) Multiplier to reduce force applied to nodes max_velocity: (Optional) Maximum distance a node can move in one step max_distance: (Optional) The maximum inter-node distance considered is_3d: (Optional) Generates three-dimensional coordinates Outputs a json-serializable Python object. To visualize, pass the output to `jgraph.draw(...)`.
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train
https://github.com/patrickfuller/jgraph/blob/7297450f26ae8cba21914668a5aaa755de8aa14d/python/notebook.py#L136-L159
[ "data", "iterations", "force_strength", "dampening", "max_velocity", "max_distance", "is_3d" ]
What does this function do?
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patrickfuller/jgraph
python/json_formatter.py
compress
def compress(obj): """Outputs json without whitespace.""" return json.dumps(obj, sort_keys=True, separators=(',', ':'), cls=CustomEncoder)
python
def compress(obj): """Outputs json without whitespace.""" return json.dumps(obj, sort_keys=True, separators=(',', ':'), cls=CustomEncoder)
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Outputs json without whitespace.
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train
https://github.com/patrickfuller/jgraph/blob/7297450f26ae8cba21914668a5aaa755de8aa14d/python/json_formatter.py#L18-L21
[ "obj" ]
What does this function do?
[ "Outputs", "json", "without", "whitespace", "." ]
patrickfuller/jgraph
python/json_formatter.py
dumps
def dumps(obj): """Outputs json with formatting edits + object handling.""" return json.dumps(obj, indent=4, sort_keys=True, cls=CustomEncoder)
python
def dumps(obj): """Outputs json with formatting edits + object handling.""" return json.dumps(obj, indent=4, sort_keys=True, cls=CustomEncoder)
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Outputs json with formatting edits + object handling.
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train
https://github.com/patrickfuller/jgraph/blob/7297450f26ae8cba21914668a5aaa755de8aa14d/python/json_formatter.py#L24-L26
[ "obj" ]
What does this function do?
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patrickfuller/jgraph
python/json_formatter.py
CustomEncoder.encode
def encode(self, obj): """Fired for every object.""" s = super(CustomEncoder, self).encode(obj) # If uncompressed, postprocess for formatting if len(s.splitlines()) > 1: s = self.postprocess(s) return s
python
def encode(self, obj): """Fired for every object.""" s = super(CustomEncoder, self).encode(obj) # If uncompressed, postprocess for formatting if len(s.splitlines()) > 1: s = self.postprocess(s) return s
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Fired for every object.
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train
https://github.com/patrickfuller/jgraph/blob/7297450f26ae8cba21914668a5aaa755de8aa14d/python/json_formatter.py#L31-L37
[ "obj" ]
What does this function do?
[ "Fired", "for", "every", "object", "." ]
patrickfuller/jgraph
python/json_formatter.py
CustomEncoder.postprocess
def postprocess(self, json_string): """Displays each entry on its own line.""" is_compressing, is_hash, compressed, spaces = False, False, [], 0 for row in json_string.split('\n'): if is_compressing: if (row[:spaces + 5] == ' ' * (spaces + 4) + ('"' if is_hash else '{')): compressed.append(row.rstrip()) elif (len(row) > spaces and row[:spaces] == ' ' * spaces and re.match('[\]\}],?', row[spaces:].rstrip())): compressed.append(row.rstrip()) is_compressing = False else: compressed[-1] += ' ' + row.strip() else: compressed.append(row.rstrip()) if any(a in row for a in ['edges', 'nodes']): # Fix to handle issues that arise with empty lists if '[]' in row: continue spaces = sum(1 for _ in takewhile(str.isspace, row)) is_compressing, is_hash = True, '{' in row return '\n'.join(compressed)
python
def postprocess(self, json_string): """Displays each entry on its own line.""" is_compressing, is_hash, compressed, spaces = False, False, [], 0 for row in json_string.split('\n'): if is_compressing: if (row[:spaces + 5] == ' ' * (spaces + 4) + ('"' if is_hash else '{')): compressed.append(row.rstrip()) elif (len(row) > spaces and row[:spaces] == ' ' * spaces and re.match('[\]\}],?', row[spaces:].rstrip())): compressed.append(row.rstrip()) is_compressing = False else: compressed[-1] += ' ' + row.strip() else: compressed.append(row.rstrip()) if any(a in row for a in ['edges', 'nodes']): # Fix to handle issues that arise with empty lists if '[]' in row: continue spaces = sum(1 for _ in takewhile(str.isspace, row)) is_compressing, is_hash = True, '{' in row return '\n'.join(compressed)
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Displays each entry on its own line.
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train
https://github.com/patrickfuller/jgraph/blob/7297450f26ae8cba21914668a5aaa755de8aa14d/python/json_formatter.py#L39-L61
[ "json_string" ]
What does this function do?
[ "Displays", "each", "entry", "on", "its", "own", "line", "." ]
patrickfuller/jgraph
python/force_directed_layout.py
run
def run(edges, iterations=1000, force_strength=5.0, dampening=0.01, max_velocity=2.0, max_distance=50, is_3d=True): """Runs a force-directed-layout algorithm on the input graph. iterations - Number of FDL iterations to run in coordinate generation force_strength - Strength of Coulomb and Hooke forces (edit this to scale the distance between nodes) dampening - Multiplier to reduce force applied to nodes max_velocity - Maximum distance a node can move in one step max_distance - The maximum distance considered for interactions """ # Get a list of node ids from the edge data nodes = set(e['source'] for e in edges) | set(e['target'] for e in edges) # Convert to a data-storing object and initialize some values d = 3 if is_3d else 2 nodes = {n: {'velocity': [0.0] * d, 'force': [0.0] * d} for n in nodes} # Repeat n times (is there a more Pythonic way to do this?) for _ in repeat(None, iterations): # Add in Coulomb-esque node-node repulsive forces for node1, node2 in combinations(nodes.values(), 2): _coulomb(node1, node2, force_strength, max_distance) # And Hooke-esque edge spring forces for edge in edges: _hooke(nodes[edge['source']], nodes[edge['target']], force_strength * edge.get('size', 1), max_distance) # Move by resultant force for node in nodes.values(): # Constrain the force to the bounds specified by input parameter force = [_constrain(dampening * f, -max_velocity, max_velocity) for f in node['force']] # Update velocities and reset force node['velocity'] = [v + dv for v, dv in zip(node['velocity'], force)] node['force'] = [0] * d # Clean and return for node in nodes.values(): del node['force'] node['location'] = node['velocity'] del node['velocity'] # Even if it's 2D, let's specify three dimensions if not is_3d: node['location'] += [0.0] return nodes
python
def run(edges, iterations=1000, force_strength=5.0, dampening=0.01, max_velocity=2.0, max_distance=50, is_3d=True): """Runs a force-directed-layout algorithm on the input graph. iterations - Number of FDL iterations to run in coordinate generation force_strength - Strength of Coulomb and Hooke forces (edit this to scale the distance between nodes) dampening - Multiplier to reduce force applied to nodes max_velocity - Maximum distance a node can move in one step max_distance - The maximum distance considered for interactions """ # Get a list of node ids from the edge data nodes = set(e['source'] for e in edges) | set(e['target'] for e in edges) # Convert to a data-storing object and initialize some values d = 3 if is_3d else 2 nodes = {n: {'velocity': [0.0] * d, 'force': [0.0] * d} for n in nodes} # Repeat n times (is there a more Pythonic way to do this?) for _ in repeat(None, iterations): # Add in Coulomb-esque node-node repulsive forces for node1, node2 in combinations(nodes.values(), 2): _coulomb(node1, node2, force_strength, max_distance) # And Hooke-esque edge spring forces for edge in edges: _hooke(nodes[edge['source']], nodes[edge['target']], force_strength * edge.get('size', 1), max_distance) # Move by resultant force for node in nodes.values(): # Constrain the force to the bounds specified by input parameter force = [_constrain(dampening * f, -max_velocity, max_velocity) for f in node['force']] # Update velocities and reset force node['velocity'] = [v + dv for v, dv in zip(node['velocity'], force)] node['force'] = [0] * d # Clean and return for node in nodes.values(): del node['force'] node['location'] = node['velocity'] del node['velocity'] # Even if it's 2D, let's specify three dimensions if not is_3d: node['location'] += [0.0] return nodes
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Runs a force-directed-layout algorithm on the input graph. iterations - Number of FDL iterations to run in coordinate generation force_strength - Strength of Coulomb and Hooke forces (edit this to scale the distance between nodes) dampening - Multiplier to reduce force applied to nodes max_velocity - Maximum distance a node can move in one step max_distance - The maximum distance considered for interactions
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train
https://github.com/patrickfuller/jgraph/blob/7297450f26ae8cba21914668a5aaa755de8aa14d/python/force_directed_layout.py#L10-L59
[ "edges", "iterations", "force_strength", "dampening", "max_velocity", "max_distance", "is_3d" ]
What does this function do?
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patrickfuller/jgraph
python/force_directed_layout.py
_coulomb
def _coulomb(n1, n2, k, r): """Calculates Coulomb forces and updates node data.""" # Get relevant positional data delta = [x2 - x1 for x1, x2 in zip(n1['velocity'], n2['velocity'])] distance = sqrt(sum(d ** 2 for d in delta)) # If the deltas are too small, use random values to keep things moving if distance < 0.1: delta = [uniform(0.1, 0.2) for _ in repeat(None, 3)] distance = sqrt(sum(d ** 2 for d in delta)) # If the distance isn't huge (ie. Coulomb is negligible), calculate if distance < r: force = (k / distance) ** 2 n1['force'] = [f - force * d for f, d in zip(n1['force'], delta)] n2['force'] = [f + force * d for f, d in zip(n2['force'], delta)]
python
def _coulomb(n1, n2, k, r): """Calculates Coulomb forces and updates node data.""" # Get relevant positional data delta = [x2 - x1 for x1, x2 in zip(n1['velocity'], n2['velocity'])] distance = sqrt(sum(d ** 2 for d in delta)) # If the deltas are too small, use random values to keep things moving if distance < 0.1: delta = [uniform(0.1, 0.2) for _ in repeat(None, 3)] distance = sqrt(sum(d ** 2 for d in delta)) # If the distance isn't huge (ie. Coulomb is negligible), calculate if distance < r: force = (k / distance) ** 2 n1['force'] = [f - force * d for f, d in zip(n1['force'], delta)] n2['force'] = [f + force * d for f, d in zip(n2['force'], delta)]
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Calculates Coulomb forces and updates node data.
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train
https://github.com/patrickfuller/jgraph/blob/7297450f26ae8cba21914668a5aaa755de8aa14d/python/force_directed_layout.py#L62-L77
[ "n1", "n2", "k", "r" ]
What does this function do?
[ "Calculates", "Coulomb", "forces", "and", "updates", "node", "data", "." ]
pypyr/pypyr-cli
pypyr/steps/contextclearall.py
run_step
def run_step(context): """Wipe the entire context. Args: Context is a dictionary or dictionary-like. Does not require any specific keys in context. """ logger.debug("started") context.clear() logger.info(f"Context wiped. New context size: {len(context)}") logger.debug("done")
python
def run_step(context): """Wipe the entire context. Args: Context is a dictionary or dictionary-like. Does not require any specific keys in context. """ logger.debug("started") context.clear() logger.info(f"Context wiped. New context size: {len(context)}") logger.debug("done")
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Wipe the entire context. Args: Context is a dictionary or dictionary-like. Does not require any specific keys in context.
[ "Wipe", "the", "entire", "context", "." ]
train
https://github.com/pypyr/pypyr-cli/blob/4003f999cd5eb030b4c7407317de728f5115a80f/pypyr/steps/contextclearall.py#L8-L20
[ "context" ]
What does this function do?
[ "Wipe", "the", "entire", "context", "." ]
pypyr/pypyr-cli
pypyr/parser/jsonfile.py
get_parsed_context
def get_parsed_context(context_arg): """Parse input context string and returns context as dictionary.""" assert context_arg, ("pipeline must be invoked with context arg set. For " "this json parser you're looking for something " "like: " "pypyr pipelinename './myjsonfile.json'") logger.debug("starting") # open the json file on disk so that you can initialize the dictionary logger.debug(f"attempting to open file: {context_arg}") with open(context_arg) as json_file: payload = json.load(json_file) logger.debug(f"json file loaded into context. Count: {len(payload)}") logger.debug("done") return payload
python
def get_parsed_context(context_arg): """Parse input context string and returns context as dictionary.""" assert context_arg, ("pipeline must be invoked with context arg set. For " "this json parser you're looking for something " "like: " "pypyr pipelinename './myjsonfile.json'") logger.debug("starting") # open the json file on disk so that you can initialize the dictionary logger.debug(f"attempting to open file: {context_arg}") with open(context_arg) as json_file: payload = json.load(json_file) logger.debug(f"json file loaded into context. Count: {len(payload)}") logger.debug("done") return payload
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Parse input context string and returns context as dictionary.
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train
https://github.com/pypyr/pypyr-cli/blob/4003f999cd5eb030b4c7407317de728f5115a80f/pypyr/parser/jsonfile.py#L10-L24
[ "context_arg" ]
What does this function do?
[ "Parse", "input", "context", "string", "and", "returns", "context", "as", "dictionary", "." ]
pypyr/pypyr-cli
pypyr/steps/pathcheck.py
run_step
def run_step(context): """pypyr step that checks if a file or directory path exists. Args: context: pypyr.context.Context. Mandatory. The following context key must exist - pathsToCheck. str/path-like or list of str/paths. Path to file on disk to check. All inputs support formatting expressions. Supports globs. This step creates pathCheckOut in context, containing the results of the path check operation. pathCheckOut: 'inpath': exists: true # bool. True if path exists. count: 0 # int. Number of files found for in path. found: ['path1', 'path2'] # list of strings. Paths of files found. [count] is 0 if no files found. If you specified a single input path to check and it exists, it's going to be 1. If you specified multiple in paths or a glob expression that found more than 1 result, well, take a guess. [found] is a list of all the paths found for the [inpath]. If you passed in a glob or globs, will contain the globs found for [inpath]. This means you can do an existence evaluation like this in a formatting expression: '{pathCheckOut[inpathhere][exists]}' Returns: None. updates context arg. Raises: pypyr.errors.KeyNotInContextError: pathExists missing in context. pypyr.errors.KeyInContextHasNoValueError: pathCheck exists but is None. """ logger.debug("started") context.assert_key_has_value(key='pathCheck', caller=__name__) paths_to_check = context['pathCheck'] if not paths_to_check: raise KeyInContextHasNoValueError("context['pathCheck'] must have a " f"value for {__name__}.") # pathsToCheck can be a string or a list in case there are multiple paths if isinstance(paths_to_check, list): check_me = paths_to_check else: # assuming it's a str/path at this point check_me = [paths_to_check] out = {} total_found = 0 for path in check_me: logger.debug(f"checking path: {path}") formatted_path = context.get_formatted_string(path) found_paths = pypyr.utils.filesystem.get_glob(formatted_path) no_of_paths = len(found_paths) out[path] = { 'exists': no_of_paths > 0, 'count': no_of_paths, 'found': found_paths } total_found = total_found + no_of_paths context['pathCheckOut'] = out logger.info(f'checked {len(out)} path(s) and found {total_found}') logger.debug("done")
python
def run_step(context): """pypyr step that checks if a file or directory path exists. Args: context: pypyr.context.Context. Mandatory. The following context key must exist - pathsToCheck. str/path-like or list of str/paths. Path to file on disk to check. All inputs support formatting expressions. Supports globs. This step creates pathCheckOut in context, containing the results of the path check operation. pathCheckOut: 'inpath': exists: true # bool. True if path exists. count: 0 # int. Number of files found for in path. found: ['path1', 'path2'] # list of strings. Paths of files found. [count] is 0 if no files found. If you specified a single input path to check and it exists, it's going to be 1. If you specified multiple in paths or a glob expression that found more than 1 result, well, take a guess. [found] is a list of all the paths found for the [inpath]. If you passed in a glob or globs, will contain the globs found for [inpath]. This means you can do an existence evaluation like this in a formatting expression: '{pathCheckOut[inpathhere][exists]}' Returns: None. updates context arg. Raises: pypyr.errors.KeyNotInContextError: pathExists missing in context. pypyr.errors.KeyInContextHasNoValueError: pathCheck exists but is None. """ logger.debug("started") context.assert_key_has_value(key='pathCheck', caller=__name__) paths_to_check = context['pathCheck'] if not paths_to_check: raise KeyInContextHasNoValueError("context['pathCheck'] must have a " f"value for {__name__}.") # pathsToCheck can be a string or a list in case there are multiple paths if isinstance(paths_to_check, list): check_me = paths_to_check else: # assuming it's a str/path at this point check_me = [paths_to_check] out = {} total_found = 0 for path in check_me: logger.debug(f"checking path: {path}") formatted_path = context.get_formatted_string(path) found_paths = pypyr.utils.filesystem.get_glob(formatted_path) no_of_paths = len(found_paths) out[path] = { 'exists': no_of_paths > 0, 'count': no_of_paths, 'found': found_paths } total_found = total_found + no_of_paths context['pathCheckOut'] = out logger.info(f'checked {len(out)} path(s) and found {total_found}') logger.debug("done")
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pypyr step that checks if a file or directory path exists. Args: context: pypyr.context.Context. Mandatory. The following context key must exist - pathsToCheck. str/path-like or list of str/paths. Path to file on disk to check. All inputs support formatting expressions. Supports globs. This step creates pathCheckOut in context, containing the results of the path check operation. pathCheckOut: 'inpath': exists: true # bool. True if path exists. count: 0 # int. Number of files found for in path. found: ['path1', 'path2'] # list of strings. Paths of files found. [count] is 0 if no files found. If you specified a single input path to check and it exists, it's going to be 1. If you specified multiple in paths or a glob expression that found more than 1 result, well, take a guess. [found] is a list of all the paths found for the [inpath]. If you passed in a glob or globs, will contain the globs found for [inpath]. This means you can do an existence evaluation like this in a formatting expression: '{pathCheckOut[inpathhere][exists]}' Returns: None. updates context arg. Raises: pypyr.errors.KeyNotInContextError: pathExists missing in context. pypyr.errors.KeyInContextHasNoValueError: pathCheck exists but is None.
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train
https://github.com/pypyr/pypyr-cli/blob/4003f999cd5eb030b4c7407317de728f5115a80f/pypyr/steps/pathcheck.py#L10-L83
[ "context" ]
What does this function do?
[ "pypyr", "step", "that", "checks", "if", "a", "file", "or", "directory", "path", "exists", "." ]
pypyr/pypyr-cli
pypyr/steps/filewritejson.py
run_step
def run_step(context): """Write payload out to json file. Args: context: pypyr.context.Context. Mandatory. The following context keys expected: - fileWriteJson - path. mandatory. path-like. Write output file to here. Will create directories in path for you. - payload. optional. Write this key to output file. If not specified, output entire context. Returns: None. Raises: pypyr.errors.KeyNotInContextError: fileWriteJson or fileWriteJson['path'] missing in context. pypyr.errors.KeyInContextHasNoValueError: fileWriteJson or fileWriteJson['path'] exists but is None. """ logger.debug("started") context.assert_child_key_has_value('fileWriteJson', 'path', __name__) out_path = context.get_formatted_string(context['fileWriteJson']['path']) # doing it like this to safeguard against accidentally dumping all context # with potentially sensitive values in it to disk if payload exists but is # None. is_payload_specified = 'payload' in context['fileWriteJson'] logger.debug(f"opening destination file for writing: {out_path}") os.makedirs(os.path.abspath(os.path.dirname(out_path)), exist_ok=True) with open(out_path, 'w') as outfile: if is_payload_specified: payload = context['fileWriteJson']['payload'] formatted_iterable = context.get_formatted_iterable(payload) else: formatted_iterable = context.get_formatted_iterable(context) json.dump(formatted_iterable, outfile, indent=2, ensure_ascii=False) logger.info(f"formatted context content and wrote to {out_path}") logger.debug("done")
python
def run_step(context): """Write payload out to json file. Args: context: pypyr.context.Context. Mandatory. The following context keys expected: - fileWriteJson - path. mandatory. path-like. Write output file to here. Will create directories in path for you. - payload. optional. Write this key to output file. If not specified, output entire context. Returns: None. Raises: pypyr.errors.KeyNotInContextError: fileWriteJson or fileWriteJson['path'] missing in context. pypyr.errors.KeyInContextHasNoValueError: fileWriteJson or fileWriteJson['path'] exists but is None. """ logger.debug("started") context.assert_child_key_has_value('fileWriteJson', 'path', __name__) out_path = context.get_formatted_string(context['fileWriteJson']['path']) # doing it like this to safeguard against accidentally dumping all context # with potentially sensitive values in it to disk if payload exists but is # None. is_payload_specified = 'payload' in context['fileWriteJson'] logger.debug(f"opening destination file for writing: {out_path}") os.makedirs(os.path.abspath(os.path.dirname(out_path)), exist_ok=True) with open(out_path, 'w') as outfile: if is_payload_specified: payload = context['fileWriteJson']['payload'] formatted_iterable = context.get_formatted_iterable(payload) else: formatted_iterable = context.get_formatted_iterable(context) json.dump(formatted_iterable, outfile, indent=2, ensure_ascii=False) logger.info(f"formatted context content and wrote to {out_path}") logger.debug("done")
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Write payload out to json file. Args: context: pypyr.context.Context. Mandatory. The following context keys expected: - fileWriteJson - path. mandatory. path-like. Write output file to here. Will create directories in path for you. - payload. optional. Write this key to output file. If not specified, output entire context. Returns: None. Raises: pypyr.errors.KeyNotInContextError: fileWriteJson or fileWriteJson['path'] missing in context. pypyr.errors.KeyInContextHasNoValueError: fileWriteJson or fileWriteJson['path'] exists but is None.
[ "Write", "payload", "out", "to", "json", "file", "." ]
train
https://github.com/pypyr/pypyr-cli/blob/4003f999cd5eb030b4c7407317de728f5115a80f/pypyr/steps/filewritejson.py#L10-L53
[ "context" ]
What does this function do?
[ "Write", "payload", "out", "to", "json", "file", "." ]
pypyr/pypyr-cli
pypyr/steps/pype.py
run_step
def run_step(context): """Run another pipeline from this step. The parent pipeline is the current, executing pipeline. The invoked, or child pipeline is the pipeline you are calling from this step. Args: context: dictionary-like pypyr.context.Context. context is mandatory. Uses the following context keys in context: - pype - name. mandatory. str. Name of pipeline to execute. This {name}.yaml must exist in the working directory/pipelines dir. - pipeArg. string. optional. String to pass to the context_parser - the equivalent to context arg on the pypyr cli. Only used if skipParse==False. - raiseError. bool. optional. Defaults to True. If False, log, but swallow any errors that happen during the invoked pipeline execution. Swallowing means that the current/parent pipeline will carry on with the next step even if an error occurs in the invoked pipeline. - skipParse. bool. optional. Defaults to True. skip the context_parser on the invoked pipeline. - useParentContext. optional. bool. Defaults to True. Pass the current (i.e parent) pipeline context to the invoked (child) pipeline. - loader: str. optional. Absolute name of pipeline loader module. If not specified will use pypyr.pypeloaders.fileloader. Returns: None Raises: pypyr.errors.KeyNotInContextError: if ['pype'] or ['pype']['name'] is missing. pypyr.errors.KeyInContextHasNoValueError: ['pype']['name'] exists but is empty. """ logger.debug("started") (pipeline_name, use_parent_context, pipe_arg, skip_parse, raise_error, loader, ) = get_arguments(context) try: if use_parent_context: logger.info(f"pyping {pipeline_name}, using parent context.") pipelinerunner.load_and_run_pipeline( pipeline_name=pipeline_name, pipeline_context_input=pipe_arg, context=context, parse_input=not skip_parse, loader=loader ) else: logger.info(f"pyping {pipeline_name}, without parent context.") pipelinerunner.load_and_run_pipeline( pipeline_name=pipeline_name, pipeline_context_input=pipe_arg, working_dir=context.working_dir, parse_input=not skip_parse, loader=loader ) logger.info(f"pyped {pipeline_name}.") except Exception as ex_info: # yes, yes, don't catch Exception. Have to, though, in order to swallow # errs if !raise_error logger.error(f"Something went wrong pyping {pipeline_name}. " f"{type(ex_info).__name__}: {ex_info}") if raise_error: logger.debug("Raising original exception to caller.") raise else: logger.debug( f"raiseError is False. Swallowing error in {pipeline_name}.") logger.debug("done")
python
def run_step(context): """Run another pipeline from this step. The parent pipeline is the current, executing pipeline. The invoked, or child pipeline is the pipeline you are calling from this step. Args: context: dictionary-like pypyr.context.Context. context is mandatory. Uses the following context keys in context: - pype - name. mandatory. str. Name of pipeline to execute. This {name}.yaml must exist in the working directory/pipelines dir. - pipeArg. string. optional. String to pass to the context_parser - the equivalent to context arg on the pypyr cli. Only used if skipParse==False. - raiseError. bool. optional. Defaults to True. If False, log, but swallow any errors that happen during the invoked pipeline execution. Swallowing means that the current/parent pipeline will carry on with the next step even if an error occurs in the invoked pipeline. - skipParse. bool. optional. Defaults to True. skip the context_parser on the invoked pipeline. - useParentContext. optional. bool. Defaults to True. Pass the current (i.e parent) pipeline context to the invoked (child) pipeline. - loader: str. optional. Absolute name of pipeline loader module. If not specified will use pypyr.pypeloaders.fileloader. Returns: None Raises: pypyr.errors.KeyNotInContextError: if ['pype'] or ['pype']['name'] is missing. pypyr.errors.KeyInContextHasNoValueError: ['pype']['name'] exists but is empty. """ logger.debug("started") (pipeline_name, use_parent_context, pipe_arg, skip_parse, raise_error, loader, ) = get_arguments(context) try: if use_parent_context: logger.info(f"pyping {pipeline_name}, using parent context.") pipelinerunner.load_and_run_pipeline( pipeline_name=pipeline_name, pipeline_context_input=pipe_arg, context=context, parse_input=not skip_parse, loader=loader ) else: logger.info(f"pyping {pipeline_name}, without parent context.") pipelinerunner.load_and_run_pipeline( pipeline_name=pipeline_name, pipeline_context_input=pipe_arg, working_dir=context.working_dir, parse_input=not skip_parse, loader=loader ) logger.info(f"pyped {pipeline_name}.") except Exception as ex_info: # yes, yes, don't catch Exception. Have to, though, in order to swallow # errs if !raise_error logger.error(f"Something went wrong pyping {pipeline_name}. " f"{type(ex_info).__name__}: {ex_info}") if raise_error: logger.debug("Raising original exception to caller.") raise else: logger.debug( f"raiseError is False. Swallowing error in {pipeline_name}.") logger.debug("done")
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Run another pipeline from this step. The parent pipeline is the current, executing pipeline. The invoked, or child pipeline is the pipeline you are calling from this step. Args: context: dictionary-like pypyr.context.Context. context is mandatory. Uses the following context keys in context: - pype - name. mandatory. str. Name of pipeline to execute. This {name}.yaml must exist in the working directory/pipelines dir. - pipeArg. string. optional. String to pass to the context_parser - the equivalent to context arg on the pypyr cli. Only used if skipParse==False. - raiseError. bool. optional. Defaults to True. If False, log, but swallow any errors that happen during the invoked pipeline execution. Swallowing means that the current/parent pipeline will carry on with the next step even if an error occurs in the invoked pipeline. - skipParse. bool. optional. Defaults to True. skip the context_parser on the invoked pipeline. - useParentContext. optional. bool. Defaults to True. Pass the current (i.e parent) pipeline context to the invoked (child) pipeline. - loader: str. optional. Absolute name of pipeline loader module. If not specified will use pypyr.pypeloaders.fileloader. Returns: None Raises: pypyr.errors.KeyNotInContextError: if ['pype'] or ['pype']['name'] is missing. pypyr.errors.KeyInContextHasNoValueError: ['pype']['name'] exists but is empty.
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train
https://github.com/pypyr/pypyr-cli/blob/4003f999cd5eb030b4c7407317de728f5115a80f/pypyr/steps/pype.py#L10-L93
[ "context" ]
What does this function do?
[ "Run", "another", "pipeline", "from", "this", "step", "." ]
pypyr/pypyr-cli
pypyr/steps/pype.py
get_arguments
def get_arguments(context): """Parse arguments for pype from context and assign default values. Args: context: pypyr.context.Context. context is mandatory. Returns: tuple (pipeline_name, #str use_parent_context, #bool pipe_arg, #str skip_parse, #bool raise_error #bool ) Raises: pypyr.errors.KeyNotInContextError: if ['pype']['name'] is missing. pypyr.errors.KeyInContextHasNoValueError: if ['pype']['name'] exists but is None. """ context.assert_key_has_value(key='pype', caller=__name__) pype = context.get_formatted('pype') try: pipeline_name = pype['name'] if pipeline_name is None: raise KeyInContextHasNoValueError( "pypyr.steps.pype ['pype']['name'] exists but is empty.") except KeyError as err: raise KeyNotInContextError( "pypyr.steps.pype missing 'name' in the 'pype' context item. " "You need to specify the pipeline name to run another " "pipeline.") from err use_parent_context = pype.get('useParentContext', True) pipe_arg = pype.get('pipeArg', None) skip_parse = pype.get('skipParse', True) raise_error = pype.get('raiseError', True) loader = pype.get('loader', None) return ( pipeline_name, use_parent_context, pipe_arg, skip_parse, raise_error, loader, )
python
def get_arguments(context): """Parse arguments for pype from context and assign default values. Args: context: pypyr.context.Context. context is mandatory. Returns: tuple (pipeline_name, #str use_parent_context, #bool pipe_arg, #str skip_parse, #bool raise_error #bool ) Raises: pypyr.errors.KeyNotInContextError: if ['pype']['name'] is missing. pypyr.errors.KeyInContextHasNoValueError: if ['pype']['name'] exists but is None. """ context.assert_key_has_value(key='pype', caller=__name__) pype = context.get_formatted('pype') try: pipeline_name = pype['name'] if pipeline_name is None: raise KeyInContextHasNoValueError( "pypyr.steps.pype ['pype']['name'] exists but is empty.") except KeyError as err: raise KeyNotInContextError( "pypyr.steps.pype missing 'name' in the 'pype' context item. " "You need to specify the pipeline name to run another " "pipeline.") from err use_parent_context = pype.get('useParentContext', True) pipe_arg = pype.get('pipeArg', None) skip_parse = pype.get('skipParse', True) raise_error = pype.get('raiseError', True) loader = pype.get('loader', None) return ( pipeline_name, use_parent_context, pipe_arg, skip_parse, raise_error, loader, )
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Parse arguments for pype from context and assign default values. Args: context: pypyr.context.Context. context is mandatory. Returns: tuple (pipeline_name, #str use_parent_context, #bool pipe_arg, #str skip_parse, #bool raise_error #bool ) Raises: pypyr.errors.KeyNotInContextError: if ['pype']['name'] is missing. pypyr.errors.KeyInContextHasNoValueError: if ['pype']['name'] exists but is None.
[ "Parse", "arguments", "for", "pype", "from", "context", "and", "assign", "default", "values", "." ]
train
https://github.com/pypyr/pypyr-cli/blob/4003f999cd5eb030b4c7407317de728f5115a80f/pypyr/steps/pype.py#L96-L143
[ "context" ]
What does this function do?
[ "Parse", "arguments", "for", "pype", "from", "context", "and", "assign", "default", "values", "." ]
pypyr/pypyr-cli
pypyr/pypeloaders/fileloader.py
get_pipeline_path
def get_pipeline_path(pipeline_name, working_directory): """Look for the pipeline in the various places it could be. First checks the cwd. Then checks pypyr/pipelines dir. Args: pipeline_name: string. Name of pipeline to find working_directory: string. Path in which to look for pipeline_name.yaml Returns: Absolute path to the pipeline_name.yaml file Raises: PipelineNotFoundError: if pipeline_name.yaml not found in working_dir or in {pypyr install dir}/pipelines. """ logger.debug("starting") # look for name.yaml in the pipelines/ sub-directory logger.debug(f"current directory is {working_directory}") # looking for {cwd}/pipelines/[pipeline_name].yaml pipeline_path = os.path.abspath(os.path.join( working_directory, 'pipelines', pipeline_name + '.yaml')) if os.path.isfile(pipeline_path): logger.debug(f"Found {pipeline_path}") else: logger.debug(f"{pipeline_name} not found in current " "directory/pipelines folder. Looking in pypyr install " "directory instead.") pypyr_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) logger.debug(f"pypyr installation directory is: {pypyr_dir}") pipeline_path = os.path.abspath(os.path.join( pypyr_dir, 'pipelines', pipeline_name + '.yaml')) if os.path.isfile(pipeline_path): logger.debug(f"Found {pipeline_path}") else: raise PipelineNotFoundError(f"{pipeline_name}.yaml not found in " f"either " f"{working_directory}/pipelines " f"or {pypyr_dir}/pipelines") logger.debug("done") return pipeline_path
python
def get_pipeline_path(pipeline_name, working_directory): """Look for the pipeline in the various places it could be. First checks the cwd. Then checks pypyr/pipelines dir. Args: pipeline_name: string. Name of pipeline to find working_directory: string. Path in which to look for pipeline_name.yaml Returns: Absolute path to the pipeline_name.yaml file Raises: PipelineNotFoundError: if pipeline_name.yaml not found in working_dir or in {pypyr install dir}/pipelines. """ logger.debug("starting") # look for name.yaml in the pipelines/ sub-directory logger.debug(f"current directory is {working_directory}") # looking for {cwd}/pipelines/[pipeline_name].yaml pipeline_path = os.path.abspath(os.path.join( working_directory, 'pipelines', pipeline_name + '.yaml')) if os.path.isfile(pipeline_path): logger.debug(f"Found {pipeline_path}") else: logger.debug(f"{pipeline_name} not found in current " "directory/pipelines folder. Looking in pypyr install " "directory instead.") pypyr_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) logger.debug(f"pypyr installation directory is: {pypyr_dir}") pipeline_path = os.path.abspath(os.path.join( pypyr_dir, 'pipelines', pipeline_name + '.yaml')) if os.path.isfile(pipeline_path): logger.debug(f"Found {pipeline_path}") else: raise PipelineNotFoundError(f"{pipeline_name}.yaml not found in " f"either " f"{working_directory}/pipelines " f"or {pypyr_dir}/pipelines") logger.debug("done") return pipeline_path
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Look for the pipeline in the various places it could be. First checks the cwd. Then checks pypyr/pipelines dir. Args: pipeline_name: string. Name of pipeline to find working_directory: string. Path in which to look for pipeline_name.yaml Returns: Absolute path to the pipeline_name.yaml file Raises: PipelineNotFoundError: if pipeline_name.yaml not found in working_dir or in {pypyr install dir}/pipelines.
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train
https://github.com/pypyr/pypyr-cli/blob/4003f999cd5eb030b4c7407317de728f5115a80f/pypyr/pypeloaders/fileloader.py#L11-L61
[ "pipeline_name", "working_directory" ]
What does this function do?
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pypyr/pypyr-cli
pypyr/pypeloaders/fileloader.py
get_pipeline_definition
def get_pipeline_definition(pipeline_name, working_dir): """Open and parse the pipeline definition yaml. Parses pipeline yaml and returns dictionary representing the pipeline. pipeline_name.yaml should be in the working_dir/pipelines/ directory. Args: pipeline_name: string. Name of pipeline. This will be the file-name of the pipeline - i.e {pipeline_name}.yaml working_dir: path. Start looking in ./working_dir/pipelines/pipeline_name.yaml Returns: dict describing the pipeline, parsed from the pipeline yaml. Raises: FileNotFoundError: pipeline_name.yaml not found in the various pipeline dirs. """ logger.debug("starting") pipeline_path = get_pipeline_path( pipeline_name=pipeline_name, working_directory=working_dir) logger.debug(f"Trying to open pipeline at path {pipeline_path}") try: with open(pipeline_path) as yaml_file: pipeline_definition = pypyr.yaml.get_pipeline_yaml( yaml_file) logger.debug( f"found {len(pipeline_definition)} stages in pipeline.") except FileNotFoundError: logger.error( "The pipeline doesn't exist. Looking for a file here: " f"{pipeline_name}.yaml in the /pipelines sub directory.") raise logger.debug("pipeline definition loaded") logger.debug("done") return pipeline_definition
python
def get_pipeline_definition(pipeline_name, working_dir): """Open and parse the pipeline definition yaml. Parses pipeline yaml and returns dictionary representing the pipeline. pipeline_name.yaml should be in the working_dir/pipelines/ directory. Args: pipeline_name: string. Name of pipeline. This will be the file-name of the pipeline - i.e {pipeline_name}.yaml working_dir: path. Start looking in ./working_dir/pipelines/pipeline_name.yaml Returns: dict describing the pipeline, parsed from the pipeline yaml. Raises: FileNotFoundError: pipeline_name.yaml not found in the various pipeline dirs. """ logger.debug("starting") pipeline_path = get_pipeline_path( pipeline_name=pipeline_name, working_directory=working_dir) logger.debug(f"Trying to open pipeline at path {pipeline_path}") try: with open(pipeline_path) as yaml_file: pipeline_definition = pypyr.yaml.get_pipeline_yaml( yaml_file) logger.debug( f"found {len(pipeline_definition)} stages in pipeline.") except FileNotFoundError: logger.error( "The pipeline doesn't exist. Looking for a file here: " f"{pipeline_name}.yaml in the /pipelines sub directory.") raise logger.debug("pipeline definition loaded") logger.debug("done") return pipeline_definition
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Open and parse the pipeline definition yaml. Parses pipeline yaml and returns dictionary representing the pipeline. pipeline_name.yaml should be in the working_dir/pipelines/ directory. Args: pipeline_name: string. Name of pipeline. This will be the file-name of the pipeline - i.e {pipeline_name}.yaml working_dir: path. Start looking in ./working_dir/pipelines/pipeline_name.yaml Returns: dict describing the pipeline, parsed from the pipeline yaml. Raises: FileNotFoundError: pipeline_name.yaml not found in the various pipeline dirs.
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train
https://github.com/pypyr/pypyr-cli/blob/4003f999cd5eb030b4c7407317de728f5115a80f/pypyr/pypeloaders/fileloader.py#L64-L107
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What does this function do?
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pypyr/pypyr-cli
pypyr/dsl.py
SpecialTagDirective.to_yaml
def to_yaml(cls, representer, node): """How to serialize this class back to yaml.""" return representer.represent_scalar(cls.yaml_tag, node.value)
python
def to_yaml(cls, representer, node): """How to serialize this class back to yaml.""" return representer.represent_scalar(cls.yaml_tag, node.value)
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How to serialize this class back to yaml.
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train
https://github.com/pypyr/pypyr-cli/blob/4003f999cd5eb030b4c7407317de728f5115a80f/pypyr/dsl.py#L55-L57
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What does this function do?
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