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letuananh/chirptext
chirptext/texttaglib.py
read
def read(path, mode='tsv'): ''' Helper function to read Document in TTL-TXT format (i.e. ${docname}_*.txt) E.g. read('~/data/myfile') is the same as Document('myfile', '~/data/').read() ''' if mode == 'tsv': return TxtReader.from_path(path).read() elif mode == 'json': return read_json(path) else: raise Exception("Invalid mode - [{}] was provided".format(mode))
python
def read(path, mode='tsv'): ''' Helper function to read Document in TTL-TXT format (i.e. ${docname}_*.txt) E.g. read('~/data/myfile') is the same as Document('myfile', '~/data/').read() ''' if mode == 'tsv': return TxtReader.from_path(path).read() elif mode == 'json': return read_json(path) else: raise Exception("Invalid mode - [{}] was provided".format(mode))
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Helper function to read Document in TTL-TXT format (i.e. ${docname}_*.txt) E.g. read('~/data/myfile') is the same as Document('myfile', '~/data/').read()
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train
https://github.com/letuananh/chirptext/blob/ce60b47257b272a587c8703ea1f86cd1a45553a7/chirptext/texttaglib.py#L936-L945
letuananh/chirptext
chirptext/texttaglib.py
write
def write(path, doc, mode=MODE_TSV, **kwargs): ''' Helper function to write doc to TTL-TXT format ''' if mode == MODE_TSV: with TxtWriter.from_path(path) as writer: writer.write_doc(doc) elif mode == MODE_JSON: write_json(path, doc, **kwargs)
python
def write(path, doc, mode=MODE_TSV, **kwargs): ''' Helper function to write doc to TTL-TXT format ''' if mode == MODE_TSV: with TxtWriter.from_path(path) as writer: writer.write_doc(doc) elif mode == MODE_JSON: write_json(path, doc, **kwargs)
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Helper function to write doc to TTL-TXT format
[ "Helper", "function", "to", "write", "doc", "to", "TTL", "-", "TXT", "format" ]
train
https://github.com/letuananh/chirptext/blob/ce60b47257b272a587c8703ea1f86cd1a45553a7/chirptext/texttaglib.py#L948-L954
letuananh/chirptext
chirptext/texttaglib.py
Sentence.tcmap
def tcmap(self): ''' Create a tokens-concepts map ''' tcmap = dd(list) for concept in self.__concept_map.values(): for w in concept.tokens: tcmap[w].append(concept) return tcmap
python
def tcmap(self): ''' Create a tokens-concepts map ''' tcmap = dd(list) for concept in self.__concept_map.values(): for w in concept.tokens: tcmap[w].append(concept) return tcmap
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Create a tokens-concepts map
[ "Create", "a", "tokens", "-", "concepts", "map" ]
train
https://github.com/letuananh/chirptext/blob/ce60b47257b272a587c8703ea1f86cd1a45553a7/chirptext/texttaglib.py#L195-L201
letuananh/chirptext
chirptext/texttaglib.py
Sentence.msw
def msw(self): ''' Return a generator of tokens with more than one sense. ''' return (t for t, c in self.tcmap().items() if len(c) > 1)
python
def msw(self): ''' Return a generator of tokens with more than one sense. ''' return (t for t, c in self.tcmap().items() if len(c) > 1)
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Return a generator of tokens with more than one sense.
[ "Return", "a", "generator", "of", "tokens", "with", "more", "than", "one", "sense", "." ]
train
https://github.com/letuananh/chirptext/blob/ce60b47257b272a587c8703ea1f86cd1a45553a7/chirptext/texttaglib.py#L207-L209
letuananh/chirptext
chirptext/texttaglib.py
Sentence.surface
def surface(self, tag): ''' Get surface string that is associated with a tag object ''' if tag.cfrom is not None and tag.cto is not None and tag.cfrom >= 0 and tag.cto >= 0: return self.text[tag.cfrom:tag.cto] else: return ''
python
def surface(self, tag): ''' Get surface string that is associated with a tag object ''' if tag.cfrom is not None and tag.cto is not None and tag.cfrom >= 0 and tag.cto >= 0: return self.text[tag.cfrom:tag.cto] else: return ''
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Get surface string that is associated with a tag object
[ "Get", "surface", "string", "that", "is", "associated", "with", "a", "tag", "object" ]
train
https://github.com/letuananh/chirptext/blob/ce60b47257b272a587c8703ea1f86cd1a45553a7/chirptext/texttaglib.py#L211-L216
letuananh/chirptext
chirptext/texttaglib.py
Sentence.new_tag
def new_tag(self, label, cfrom=-1, cto=-1, tagtype='', **kwargs): ''' Create a sentence-level tag ''' tag_obj = Tag(label, cfrom, cto, tagtype=tagtype, **kwargs) return self.add_tag(tag_obj)
python
def new_tag(self, label, cfrom=-1, cto=-1, tagtype='', **kwargs): ''' Create a sentence-level tag ''' tag_obj = Tag(label, cfrom, cto, tagtype=tagtype, **kwargs) return self.add_tag(tag_obj)
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Create a sentence-level tag
[ "Create", "a", "sentence", "-", "level", "tag" ]
train
https://github.com/letuananh/chirptext/blob/ce60b47257b272a587c8703ea1f86cd1a45553a7/chirptext/texttaglib.py#L222-L225
letuananh/chirptext
chirptext/texttaglib.py
Sentence.get_tag
def get_tag(self, tagtype): ''' Get the first tag of a particular type''' for tag in self.__tags: if tag.tagtype == tagtype: return tag return None
python
def get_tag(self, tagtype): ''' Get the first tag of a particular type''' for tag in self.__tags: if tag.tagtype == tagtype: return tag return None
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Get the first tag of a particular type
[ "Get", "the", "first", "tag", "of", "a", "particular", "type" ]
train
https://github.com/letuananh/chirptext/blob/ce60b47257b272a587c8703ea1f86cd1a45553a7/chirptext/texttaglib.py#L227-L232
letuananh/chirptext
chirptext/texttaglib.py
Sentence.get_tags
def get_tags(self, tagtype): ''' Get all tags of a type ''' return [t for t in self.__tags if t.tagtype == tagtype]
python
def get_tags(self, tagtype): ''' Get all tags of a type ''' return [t for t in self.__tags if t.tagtype == tagtype]
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Get all tags of a type
[ "Get", "all", "tags", "of", "a", "type" ]
train
https://github.com/letuananh/chirptext/blob/ce60b47257b272a587c8703ea1f86cd1a45553a7/chirptext/texttaglib.py#L234-L236
letuananh/chirptext
chirptext/texttaglib.py
Sentence.add_token_object
def add_token_object(self, token): ''' Add a token object into this sentence ''' token.sent = self # take ownership of given token self.__tokens.append(token) return token
python
def add_token_object(self, token): ''' Add a token object into this sentence ''' token.sent = self # take ownership of given token self.__tokens.append(token) return token
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Add a token object into this sentence
[ "Add", "a", "token", "object", "into", "this", "sentence" ]
train
https://github.com/letuananh/chirptext/blob/ce60b47257b272a587c8703ea1f86cd1a45553a7/chirptext/texttaglib.py#L242-L246
letuananh/chirptext
chirptext/texttaglib.py
Sentence.new_concept
def new_concept(self, tag, clemma="", tokens=None, cidx=None, **kwargs): ''' Create a new concept object and add it to concept list tokens can be a list of Token objects or token indices ''' if cidx is None: cidx = self.new_concept_id() if tokens: tokens = (t if isinstance(t, Token) else self[t] for t in tokens) c = Concept(cidx=cidx, tag=tag, clemma=clemma, sent=self, tokens=tokens, **kwargs) return self.add_concept(c)
python
def new_concept(self, tag, clemma="", tokens=None, cidx=None, **kwargs): ''' Create a new concept object and add it to concept list tokens can be a list of Token objects or token indices ''' if cidx is None: cidx = self.new_concept_id() if tokens: tokens = (t if isinstance(t, Token) else self[t] for t in tokens) c = Concept(cidx=cidx, tag=tag, clemma=clemma, sent=self, tokens=tokens, **kwargs) return self.add_concept(c)
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Create a new concept object and add it to concept list tokens can be a list of Token objects or token indices
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train
https://github.com/letuananh/chirptext/blob/ce60b47257b272a587c8703ea1f86cd1a45553a7/chirptext/texttaglib.py#L254-L263
letuananh/chirptext
chirptext/texttaglib.py
Sentence.add_concept
def add_concept(self, concept_obj): ''' Add a concept to current concept list ''' if concept_obj is None: raise Exception("Concept object cannot be None") elif concept_obj in self.__concepts: raise Exception("Concept object is already inside") elif concept_obj.cidx in self.__concept_map: raise Exception("Duplicated concept ID ({})".format(concept_obj.cidx)) self.__concepts.append(concept_obj) self.__concept_map[concept_obj.cidx] = concept_obj concept_obj.sent = self return concept_obj
python
def add_concept(self, concept_obj): ''' Add a concept to current concept list ''' if concept_obj is None: raise Exception("Concept object cannot be None") elif concept_obj in self.__concepts: raise Exception("Concept object is already inside") elif concept_obj.cidx in self.__concept_map: raise Exception("Duplicated concept ID ({})".format(concept_obj.cidx)) self.__concepts.append(concept_obj) self.__concept_map[concept_obj.cidx] = concept_obj concept_obj.sent = self return concept_obj
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Add a concept to current concept list
[ "Add", "a", "concept", "to", "current", "concept", "list" ]
train
https://github.com/letuananh/chirptext/blob/ce60b47257b272a587c8703ea1f86cd1a45553a7/chirptext/texttaglib.py#L265-L276
letuananh/chirptext
chirptext/texttaglib.py
Sentence.concept
def concept(self, cid, **kwargs): ''' Get concept by concept ID ''' if cid not in self.__concept_map: if 'default' in kwargs: return kwargs['default'] else: raise KeyError("Invalid cid") else: return self.__concept_map[cid]
python
def concept(self, cid, **kwargs): ''' Get concept by concept ID ''' if cid not in self.__concept_map: if 'default' in kwargs: return kwargs['default'] else: raise KeyError("Invalid cid") else: return self.__concept_map[cid]
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Get concept by concept ID
[ "Get", "concept", "by", "concept", "ID" ]
train
https://github.com/letuananh/chirptext/blob/ce60b47257b272a587c8703ea1f86cd1a45553a7/chirptext/texttaglib.py#L289-L297
letuananh/chirptext
chirptext/texttaglib.py
Sentence.import_tokens
def import_tokens(self, tokens, import_hook=None, ignorecase=True): ''' Import a list of string as tokens ''' text = self.text.lower() if ignorecase else self.text has_hooker = import_hook and callable(import_hook) cfrom = 0 if self.__tokens: for tk in self.__tokens: if tk.cfrom and tk.cfrom > cfrom: cfrom = tk.cfrom for token in tokens: if has_hooker: import_hook(token) to_find = token.lower() if ignorecase else token start = text.find(to_find, cfrom) # stanford parser if to_find == '``' or to_find == "''": start_dq = text.find('"', cfrom) if start_dq > -1 and (start == -1 or start > start_dq): to_find = '"' start = start_dq if to_find == '`' or to_find == "'": start_dq = text.find("'", cfrom) if start_dq > -1 and (start == -1 or start > start_dq): to_find = "'" start = start_dq if start == -1: raise LookupError('Cannot find token `{t}` in sent `{s}`({l}) from {i} ({p})'.format(t=token, s=self.text, l=len(self.text), i=cfrom, p=self.text[cfrom:cfrom + 20])) cfrom = start cto = cfrom + len(to_find) self.new_token(token, cfrom, cto) cfrom = cto - 1
python
def import_tokens(self, tokens, import_hook=None, ignorecase=True): ''' Import a list of string as tokens ''' text = self.text.lower() if ignorecase else self.text has_hooker = import_hook and callable(import_hook) cfrom = 0 if self.__tokens: for tk in self.__tokens: if tk.cfrom and tk.cfrom > cfrom: cfrom = tk.cfrom for token in tokens: if has_hooker: import_hook(token) to_find = token.lower() if ignorecase else token start = text.find(to_find, cfrom) # stanford parser if to_find == '``' or to_find == "''": start_dq = text.find('"', cfrom) if start_dq > -1 and (start == -1 or start > start_dq): to_find = '"' start = start_dq if to_find == '`' or to_find == "'": start_dq = text.find("'", cfrom) if start_dq > -1 and (start == -1 or start > start_dq): to_find = "'" start = start_dq if start == -1: raise LookupError('Cannot find token `{t}` in sent `{s}`({l}) from {i} ({p})'.format(t=token, s=self.text, l=len(self.text), i=cfrom, p=self.text[cfrom:cfrom + 20])) cfrom = start cto = cfrom + len(to_find) self.new_token(token, cfrom, cto) cfrom = cto - 1
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Import a list of string as tokens
[ "Import", "a", "list", "of", "string", "as", "tokens" ]
train
https://github.com/letuananh/chirptext/blob/ce60b47257b272a587c8703ea1f86cd1a45553a7/chirptext/texttaglib.py#L315-L345
letuananh/chirptext
chirptext/texttaglib.py
Token.tag_map
def tag_map(self): ''' Build a map from tagtype to list of tags ''' tm = dd(list) for tag in self.__tags: tm[tag.tagtype].append(tag) return tm
python
def tag_map(self): ''' Build a map from tagtype to list of tags ''' tm = dd(list) for tag in self.__tags: tm[tag.tagtype].append(tag) return tm
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Build a map from tagtype to list of tags
[ "Build", "a", "map", "from", "tagtype", "to", "list", "of", "tags" ]
train
https://github.com/letuananh/chirptext/blob/ce60b47257b272a587c8703ea1f86cd1a45553a7/chirptext/texttaglib.py#L420-L425
letuananh/chirptext
chirptext/texttaglib.py
Token.find
def find(self, tagtype, **kwargs): '''Get the first tag with a type in this token ''' for t in self.__tags: if t.tagtype == tagtype: return t if 'default' in kwargs: return kwargs['default'] else: raise LookupError("Token {} is not tagged with the speficied tagtype ({})".format(self, tagtype))
python
def find(self, tagtype, **kwargs): '''Get the first tag with a type in this token ''' for t in self.__tags: if t.tagtype == tagtype: return t if 'default' in kwargs: return kwargs['default'] else: raise LookupError("Token {} is not tagged with the speficied tagtype ({})".format(self, tagtype))
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Get the first tag with a type in this token
[ "Get", "the", "first", "tag", "with", "a", "type", "in", "this", "token" ]
train
https://github.com/letuananh/chirptext/blob/ce60b47257b272a587c8703ea1f86cd1a45553a7/chirptext/texttaglib.py#L430-L438
letuananh/chirptext
chirptext/texttaglib.py
Token.find_all
def find_all(self, tagtype): ''' Find all token-level tags with the specified tagtype ''' return [t for t in self.__tags if t.tagtype == tagtype]
python
def find_all(self, tagtype): ''' Find all token-level tags with the specified tagtype ''' return [t for t in self.__tags if t.tagtype == tagtype]
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Find all token-level tags with the specified tagtype
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train
https://github.com/letuananh/chirptext/blob/ce60b47257b272a587c8703ea1f86cd1a45553a7/chirptext/texttaglib.py#L440-L442
letuananh/chirptext
chirptext/texttaglib.py
Token.new_tag
def new_tag(self, label, cfrom=None, cto=None, tagtype=None, **kwargs): ''' Create a new tag on this token ''' if cfrom is None: cfrom = self.cfrom if cto is None: cto = self.cto tag = Tag(label=label, cfrom=cfrom, cto=cto, tagtype=tagtype, **kwargs) return self.add_tag(tag)
python
def new_tag(self, label, cfrom=None, cto=None, tagtype=None, **kwargs): ''' Create a new tag on this token ''' if cfrom is None: cfrom = self.cfrom if cto is None: cto = self.cto tag = Tag(label=label, cfrom=cfrom, cto=cto, tagtype=tagtype, **kwargs) return self.add_tag(tag)
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Create a new tag on this token
[ "Create", "a", "new", "tag", "on", "this", "token" ]
train
https://github.com/letuananh/chirptext/blob/ce60b47257b272a587c8703ea1f86cd1a45553a7/chirptext/texttaglib.py#L448-L455
letuananh/chirptext
chirptext/texttaglib.py
Document.get
def get(self, sent_id, **kwargs): ''' If sent_id exists, remove and return the associated sentence object else return default. If no default is provided, KeyError will be raised.''' if sent_id is not None and not isinstance(sent_id, int): sent_id = int(sent_id) if sent_id is None or not self.has_id(sent_id): if 'default' in kwargs: return kwargs['default'] else: raise KeyError("Invalid sentence ID ({})".format(sent_id)) return self.__sent_map[sent_id]
python
def get(self, sent_id, **kwargs): ''' If sent_id exists, remove and return the associated sentence object else return default. If no default is provided, KeyError will be raised.''' if sent_id is not None and not isinstance(sent_id, int): sent_id = int(sent_id) if sent_id is None or not self.has_id(sent_id): if 'default' in kwargs: return kwargs['default'] else: raise KeyError("Invalid sentence ID ({})".format(sent_id)) return self.__sent_map[sent_id]
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If sent_id exists, remove and return the associated sentence object else return default. If no default is provided, KeyError will be raised.
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train
https://github.com/letuananh/chirptext/blob/ce60b47257b272a587c8703ea1f86cd1a45553a7/chirptext/texttaglib.py#L588-L598
letuananh/chirptext
chirptext/texttaglib.py
Document.add_sent
def add_sent(self, sent_obj): ''' Add a ttl.Sentence object to this document ''' if sent_obj is None: raise Exception("Sentence object cannot be None") elif sent_obj.ID is None: # if sentID is None, create a new ID sent_obj.ID = next(self.__idgen) elif self.has_id(sent_obj.ID): raise Exception("Sentence ID {} exists".format(sent_obj.ID)) self.__sent_map[sent_obj.ID] = sent_obj self.__sents.append(sent_obj) return sent_obj
python
def add_sent(self, sent_obj): ''' Add a ttl.Sentence object to this document ''' if sent_obj is None: raise Exception("Sentence object cannot be None") elif sent_obj.ID is None: # if sentID is None, create a new ID sent_obj.ID = next(self.__idgen) elif self.has_id(sent_obj.ID): raise Exception("Sentence ID {} exists".format(sent_obj.ID)) self.__sent_map[sent_obj.ID] = sent_obj self.__sents.append(sent_obj) return sent_obj
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Add a ttl.Sentence object to this document
[ "Add", "a", "ttl", ".", "Sentence", "object", "to", "this", "document" ]
train
https://github.com/letuananh/chirptext/blob/ce60b47257b272a587c8703ea1f86cd1a45553a7/chirptext/texttaglib.py#L603-L614
letuananh/chirptext
chirptext/texttaglib.py
Document.new_sent
def new_sent(self, text, ID=None, **kwargs): ''' Create a new sentence and add it to this Document ''' if ID is None: ID = next(self.__idgen) return self.add_sent(Sentence(text, ID=ID, **kwargs))
python
def new_sent(self, text, ID=None, **kwargs): ''' Create a new sentence and add it to this Document ''' if ID is None: ID = next(self.__idgen) return self.add_sent(Sentence(text, ID=ID, **kwargs))
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Create a new sentence and add it to this Document
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train
https://github.com/letuananh/chirptext/blob/ce60b47257b272a587c8703ea1f86cd1a45553a7/chirptext/texttaglib.py#L616-L620
letuananh/chirptext
chirptext/texttaglib.py
Document.pop
def pop(self, sent_id, **kwargs): ''' If sent_id exists, remove and return the associated sentence object else return default. If no default is provided, KeyError will be raised.''' if sent_id is not None and not isinstance(sent_id, int): sent_id = int(sent_id) if not self.has_id(sent_id): if 'default' in kwargs: return kwargs['default'] else: raise KeyError("Sentence ID {} does not exist".format(sent_id)) else: # sentence exists ... sent_obj = self.get(sent_id) self.__sent_map.pop(sent_id) self.__sents.remove(sent_obj) return sent_obj
python
def pop(self, sent_id, **kwargs): ''' If sent_id exists, remove and return the associated sentence object else return default. If no default is provided, KeyError will be raised.''' if sent_id is not None and not isinstance(sent_id, int): sent_id = int(sent_id) if not self.has_id(sent_id): if 'default' in kwargs: return kwargs['default'] else: raise KeyError("Sentence ID {} does not exist".format(sent_id)) else: # sentence exists ... sent_obj = self.get(sent_id) self.__sent_map.pop(sent_id) self.__sents.remove(sent_obj) return sent_obj
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If sent_id exists, remove and return the associated sentence object else return default. If no default is provided, KeyError will be raised.
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train
https://github.com/letuananh/chirptext/blob/ce60b47257b272a587c8703ea1f86cd1a45553a7/chirptext/texttaglib.py#L622-L637
letuananh/chirptext
chirptext/texttaglib.py
Document.read
def read(self): ''' Read tagged doc from mutliple files (sents, tokens, concepts, links, tags) ''' warnings.warn("Document.read() is deprecated and will be removed in near future.", DeprecationWarning) with TxtReader.from_doc(self) as reader: reader.read(self) return self
python
def read(self): ''' Read tagged doc from mutliple files (sents, tokens, concepts, links, tags) ''' warnings.warn("Document.read() is deprecated and will be removed in near future.", DeprecationWarning) with TxtReader.from_doc(self) as reader: reader.read(self) return self
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Read tagged doc from mutliple files (sents, tokens, concepts, links, tags)
[ "Read", "tagged", "doc", "from", "mutliple", "files", "(", "sents", "tokens", "concepts", "links", "tags", ")" ]
train
https://github.com/letuananh/chirptext/blob/ce60b47257b272a587c8703ea1f86cd1a45553a7/chirptext/texttaglib.py#L639-L644
letuananh/chirptext
chirptext/texttaglib.py
Document.read_ttl
def read_ttl(path): ''' Helper function to read Document in TTL-TXT format (i.e. ${docname}_*.txt) E.g. Document.read_ttl('~/data/myfile') is the same as Document('myfile', '~/data/').read() ''' warnings.warn("Document.read_ttl() is deprecated and will be removed in near future. Use read() instead", DeprecationWarning) doc_path = os.path.dirname(path) doc_name = os.path.basename(path) return Document(doc_name, doc_path).read()
python
def read_ttl(path): ''' Helper function to read Document in TTL-TXT format (i.e. ${docname}_*.txt) E.g. Document.read_ttl('~/data/myfile') is the same as Document('myfile', '~/data/').read() ''' warnings.warn("Document.read_ttl() is deprecated and will be removed in near future. Use read() instead", DeprecationWarning) doc_path = os.path.dirname(path) doc_name = os.path.basename(path) return Document(doc_name, doc_path).read()
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Helper function to read Document in TTL-TXT format (i.e. ${docname}_*.txt) E.g. Document.read_ttl('~/data/myfile') is the same as Document('myfile', '~/data/').read()
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train
https://github.com/letuananh/chirptext/blob/ce60b47257b272a587c8703ea1f86cd1a45553a7/chirptext/texttaglib.py#L647-L654
letuananh/chirptext
chirptext/texttaglib.py
TxtReader.read
def read(self, doc=None): ''' Read tagged doc from mutliple files (sents, tokens, concepts, links, tags) ''' if not self.sent_stream: raise Exception("There is no sentence data stream available") if doc is None: doc = Document(name=self.doc_name, path=self.doc_path) for row in self.sent_reader(): if len(row) == 2: sid, text = row doc.new_sent(text.strip(), ID=sid) elif len(row) == 4: sid, text, flag, comment = row sent = doc.new_sent(text.strip(), ID=sid) sent.flag = flag sent.comment = comment # Read tokens if available if self.token_stream: # read all tokens first sent_tokens_map = dd(list) for token_row in self.token_reader(): if len(token_row) == 6: sid, wid, token, lemma, pos, comment = token_row else: sid, wid, token, lemma, pos = token_row comment = '' sid = int(sid) sent_tokens_map[sid].append((token, lemma, pos.strip(), wid, comment)) # TODO: verify wid? # import tokens for sent in doc: sent_tokens = sent_tokens_map[sent.ID] sent.import_tokens([x[0] for x in sent_tokens]) for ((tk, lemma, pos, wid, comment), token) in zip(sent_tokens, sent.tokens): token.pos = pos token.lemma = lemma token.comment = comment # only read concepts if tokens are available if self.concept_stream: # read concepts for concept_row in self.concept_reader(): if len(concept_row) == 5: sid, cid, clemma, tag, comment = concept_row else: sid, cid, clemma, tag = concept_row comment = '' cid = int(cid) doc.get(sid).new_concept(tag.strip(), clemma=clemma, cidx=cid, comment=comment) # only read concept-token links if tokens and concepts are available for sid, cid, wid in self.link_reader(): sent = doc.get(sid) cid = int(cid) wid = int(wid.strip()) sent.concept(cid).add_token(sent[wid]) # read sentence level tags if self.tag_stream: for row in self.tag_reader(): if len(row) == 5: sid, cfrom, cto, label, tagtype = row wid = None if len(row) == 6: sid, cfrom, cto, label, tagtype, wid = row if cfrom: cfrom = int(cfrom) if cto: cto = int(cto) if wid is None or wid == '': doc.get(sid).new_tag(label, cfrom, cto, tagtype=tagtype) else: doc.get(sid)[int(wid)].new_tag(label, cfrom, cto, tagtype=tagtype) return doc
python
def read(self, doc=None): ''' Read tagged doc from mutliple files (sents, tokens, concepts, links, tags) ''' if not self.sent_stream: raise Exception("There is no sentence data stream available") if doc is None: doc = Document(name=self.doc_name, path=self.doc_path) for row in self.sent_reader(): if len(row) == 2: sid, text = row doc.new_sent(text.strip(), ID=sid) elif len(row) == 4: sid, text, flag, comment = row sent = doc.new_sent(text.strip(), ID=sid) sent.flag = flag sent.comment = comment # Read tokens if available if self.token_stream: # read all tokens first sent_tokens_map = dd(list) for token_row in self.token_reader(): if len(token_row) == 6: sid, wid, token, lemma, pos, comment = token_row else: sid, wid, token, lemma, pos = token_row comment = '' sid = int(sid) sent_tokens_map[sid].append((token, lemma, pos.strip(), wid, comment)) # TODO: verify wid? # import tokens for sent in doc: sent_tokens = sent_tokens_map[sent.ID] sent.import_tokens([x[0] for x in sent_tokens]) for ((tk, lemma, pos, wid, comment), token) in zip(sent_tokens, sent.tokens): token.pos = pos token.lemma = lemma token.comment = comment # only read concepts if tokens are available if self.concept_stream: # read concepts for concept_row in self.concept_reader(): if len(concept_row) == 5: sid, cid, clemma, tag, comment = concept_row else: sid, cid, clemma, tag = concept_row comment = '' cid = int(cid) doc.get(sid).new_concept(tag.strip(), clemma=clemma, cidx=cid, comment=comment) # only read concept-token links if tokens and concepts are available for sid, cid, wid in self.link_reader(): sent = doc.get(sid) cid = int(cid) wid = int(wid.strip()) sent.concept(cid).add_token(sent[wid]) # read sentence level tags if self.tag_stream: for row in self.tag_reader(): if len(row) == 5: sid, cfrom, cto, label, tagtype = row wid = None if len(row) == 6: sid, cfrom, cto, label, tagtype, wid = row if cfrom: cfrom = int(cfrom) if cto: cto = int(cto) if wid is None or wid == '': doc.get(sid).new_tag(label, cfrom, cto, tagtype=tagtype) else: doc.get(sid)[int(wid)].new_tag(label, cfrom, cto, tagtype=tagtype) return doc
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Read tagged doc from mutliple files (sents, tokens, concepts, links, tags)
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train
https://github.com/letuananh/chirptext/blob/ce60b47257b272a587c8703ea1f86cd1a45553a7/chirptext/texttaglib.py#L723-L792
alefnula/tea
tea/console/format.py
format_page
def format_page(text): """Format the text for output adding ASCII frame around the text. Args: text (str): Text that needs to be formatted. Returns: str: Formatted string. """ width = max(map(len, text.splitlines())) page = "+-" + "-" * width + "-+\n" for line in text.splitlines(): page += "| " + line.ljust(width) + " |\n" page += "+-" + "-" * width + "-+\n" return page
python
def format_page(text): """Format the text for output adding ASCII frame around the text. Args: text (str): Text that needs to be formatted. Returns: str: Formatted string. """ width = max(map(len, text.splitlines())) page = "+-" + "-" * width + "-+\n" for line in text.splitlines(): page += "| " + line.ljust(width) + " |\n" page += "+-" + "-" * width + "-+\n" return page
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Format the text for output adding ASCII frame around the text. Args: text (str): Text that needs to be formatted. Returns: str: Formatted string.
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train
https://github.com/alefnula/tea/blob/f5a0a724a425ec4f9dd2c7fe966ef06faf3a15a3/tea/console/format.py#L16-L30
alefnula/tea
tea/console/format.py
table
def table(text): """Format the text as a table. Text in format: first | second row 2 col 1 | 4 Will be formatted as:: +-------------+--------+ | first | second | +-------------+--------+ | row 2 col 1 | 4 | +-------------+--------+ Args: text (str): Text that needs to be formatted. Returns: str: Formatted string. """ def table_bar(col_lengths): return "+-%s-+%s" % ( "-+-".join(["-" * length for length in col_lengths]), os.linesep, ) rows = [] for line in text.splitlines(): rows.append([part.strip() for part in line.split("|")]) max_cols = max(map(len, rows)) col_lengths = [0] * max_cols for row in rows: cols = len(row) if cols < max_cols: row.extend([""] * (max_cols - cols)) for i, col in enumerate(row): col_length = len(col) if col_length > col_lengths[i]: col_lengths[i] = col_length text = table_bar(col_lengths) for i, row in enumerate(rows): cols = [] for i, col in enumerate(row): cols.append(col.ljust(col_lengths[i])) text += "| %s |%s" % (" | ".join(cols), os.linesep) text += table_bar(col_lengths) return text
python
def table(text): """Format the text as a table. Text in format: first | second row 2 col 1 | 4 Will be formatted as:: +-------------+--------+ | first | second | +-------------+--------+ | row 2 col 1 | 4 | +-------------+--------+ Args: text (str): Text that needs to be formatted. Returns: str: Formatted string. """ def table_bar(col_lengths): return "+-%s-+%s" % ( "-+-".join(["-" * length for length in col_lengths]), os.linesep, ) rows = [] for line in text.splitlines(): rows.append([part.strip() for part in line.split("|")]) max_cols = max(map(len, rows)) col_lengths = [0] * max_cols for row in rows: cols = len(row) if cols < max_cols: row.extend([""] * (max_cols - cols)) for i, col in enumerate(row): col_length = len(col) if col_length > col_lengths[i]: col_lengths[i] = col_length text = table_bar(col_lengths) for i, row in enumerate(rows): cols = [] for i, col in enumerate(row): cols.append(col.ljust(col_lengths[i])) text += "| %s |%s" % (" | ".join(cols), os.linesep) text += table_bar(col_lengths) return text
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train
https://github.com/alefnula/tea/blob/f5a0a724a425ec4f9dd2c7fe966ef06faf3a15a3/tea/console/format.py#L33-L82
alefnula/tea
tea/console/format.py
print_page
def print_page(text): """Format the text and prints it on stdout. Text is formatted by adding a ASCII frame around it and coloring the text. Colors can be added to text using color tags, for example: My [FG_BLUE]blue[NORMAL] text. My [BG_BLUE]blue background[NORMAL] text. """ color_re = re.compile(r"\[(?P<color>[FB]G_[A-Z_]+|NORMAL)\]") width = max([len(strip_colors(x)) for x in text.splitlines()]) print("\n" + hbar(width)) for line in text.splitlines(): if line == "[HBAR]": print(hbar(width)) continue tail = width - len(strip_colors(line)) sys.stdout.write("| ") previous = 0 end = len(line) for match in color_re.finditer(line): sys.stdout.write(line[previous : match.start()]) set_color(match.groupdict()["color"]) previous = match.end() sys.stdout.write(line[previous:end]) sys.stdout.write(" " * tail + " |\n") print(hbar(width))
python
def print_page(text): """Format the text and prints it on stdout. Text is formatted by adding a ASCII frame around it and coloring the text. Colors can be added to text using color tags, for example: My [FG_BLUE]blue[NORMAL] text. My [BG_BLUE]blue background[NORMAL] text. """ color_re = re.compile(r"\[(?P<color>[FB]G_[A-Z_]+|NORMAL)\]") width = max([len(strip_colors(x)) for x in text.splitlines()]) print("\n" + hbar(width)) for line in text.splitlines(): if line == "[HBAR]": print(hbar(width)) continue tail = width - len(strip_colors(line)) sys.stdout.write("| ") previous = 0 end = len(line) for match in color_re.finditer(line): sys.stdout.write(line[previous : match.start()]) set_color(match.groupdict()["color"]) previous = match.end() sys.stdout.write(line[previous:end]) sys.stdout.write(" " * tail + " |\n") print(hbar(width))
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Format the text and prints it on stdout. Text is formatted by adding a ASCII frame around it and coloring the text. Colors can be added to text using color tags, for example: My [FG_BLUE]blue[NORMAL] text. My [BG_BLUE]blue background[NORMAL] text.
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train
https://github.com/alefnula/tea/blob/f5a0a724a425ec4f9dd2c7fe966ef06faf3a15a3/tea/console/format.py#L97-L123
alefnula/tea
tea/console/format.py
wrap_text
def wrap_text(text, width=80): """Wrap text lines to maximum *width* characters. Wrapped text is aligned against the left text border. Args: text (str): Text to wrap. width (int): Maximum number of characters per line. Returns: str: Wrapped text. """ text = re.sub(r"\s+", " ", text).strip() wrapper = TextWrapper( width=width, break_long_words=False, replace_whitespace=True ) return wrapper.fill(text)
python
def wrap_text(text, width=80): """Wrap text lines to maximum *width* characters. Wrapped text is aligned against the left text border. Args: text (str): Text to wrap. width (int): Maximum number of characters per line. Returns: str: Wrapped text. """ text = re.sub(r"\s+", " ", text).strip() wrapper = TextWrapper( width=width, break_long_words=False, replace_whitespace=True ) return wrapper.fill(text)
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Wrap text lines to maximum *width* characters. Wrapped text is aligned against the left text border. Args: text (str): Text to wrap. width (int): Maximum number of characters per line. Returns: str: Wrapped text.
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train
https://github.com/alefnula/tea/blob/f5a0a724a425ec4f9dd2c7fe966ef06faf3a15a3/tea/console/format.py#L126-L142
alefnula/tea
tea/console/format.py
rjust_text
def rjust_text(text, width=80, indent=0, subsequent=None): """Wrap text and adjust it to right border. Same as L{wrap_text} with the difference that the text is aligned against the right text border. Args: text (str): Text to wrap and align. width (int): Maximum number of characters per line. indent (int): Indentation of the first line. subsequent (int or None): Indentation of all other lines, if it is ``None``, then the indentation will be same as for the first line. """ text = re.sub(r"\s+", " ", text).strip() if subsequent is None: subsequent = indent wrapper = TextWrapper( width=width, break_long_words=False, replace_whitespace=True, initial_indent=" " * (indent + subsequent), subsequent_indent=" " * subsequent, ) return wrapper.fill(text)[subsequent:]
python
def rjust_text(text, width=80, indent=0, subsequent=None): """Wrap text and adjust it to right border. Same as L{wrap_text} with the difference that the text is aligned against the right text border. Args: text (str): Text to wrap and align. width (int): Maximum number of characters per line. indent (int): Indentation of the first line. subsequent (int or None): Indentation of all other lines, if it is ``None``, then the indentation will be same as for the first line. """ text = re.sub(r"\s+", " ", text).strip() if subsequent is None: subsequent = indent wrapper = TextWrapper( width=width, break_long_words=False, replace_whitespace=True, initial_indent=" " * (indent + subsequent), subsequent_indent=" " * subsequent, ) return wrapper.fill(text)[subsequent:]
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Wrap text and adjust it to right border. Same as L{wrap_text} with the difference that the text is aligned against the right text border. Args: text (str): Text to wrap and align. width (int): Maximum number of characters per line. indent (int): Indentation of the first line. subsequent (int or None): Indentation of all other lines, if it is ``None``, then the indentation will be same as for the first line.
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train
https://github.com/alefnula/tea/blob/f5a0a724a425ec4f9dd2c7fe966ef06faf3a15a3/tea/console/format.py#L145-L168
alefnula/tea
tea/console/format.py
center_text
def center_text(text, width=80): """Center all lines of the text. It is assumed that all lines width is smaller then B{width}, because the line width will not be checked. Args: text (str): Text to wrap. width (int): Maximum number of characters per line. Returns: str: Centered text. """ centered = [] for line in text.splitlines(): centered.append(line.center(width)) return "\n".join(centered)
python
def center_text(text, width=80): """Center all lines of the text. It is assumed that all lines width is smaller then B{width}, because the line width will not be checked. Args: text (str): Text to wrap. width (int): Maximum number of characters per line. Returns: str: Centered text. """ centered = [] for line in text.splitlines(): centered.append(line.center(width)) return "\n".join(centered)
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Center all lines of the text. It is assumed that all lines width is smaller then B{width}, because the line width will not be checked. Args: text (str): Text to wrap. width (int): Maximum number of characters per line. Returns: str: Centered text.
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train
https://github.com/alefnula/tea/blob/f5a0a724a425ec4f9dd2c7fe966ef06faf3a15a3/tea/console/format.py#L171-L187
RI-imaging/qpimage
qpimage/integrity_check.py
check
def check(qpi_or_h5file, checks=["attributes", "background"]): """Checks various properties of a :class:`qpimage.core.QPImage` instance Parameters ---------- qpi_or_h5file: qpimage.core.QPImage or str A QPImage object or a path to an hdf5 file checks: list of str Which checks to perform ("attributes" and/or "background") Raises ------ IntegrityCheckError if the checks fail """ if isinstance(checks, str): checks = [checks] for ch in checks: if ch not in ["attributes", "background"]: raise ValueError("Unknown check: {}".format(check)) if isinstance(qpi_or_h5file, QPImage): qpi = qpi_or_h5file else: qpi = QPImage(h5file=qpi_or_h5file, h5mode="r") # check attributes if "attributes" in checks: check_attributes(qpi) # check background estimation if "background" in checks: check_background(qpi)
python
def check(qpi_or_h5file, checks=["attributes", "background"]): """Checks various properties of a :class:`qpimage.core.QPImage` instance Parameters ---------- qpi_or_h5file: qpimage.core.QPImage or str A QPImage object or a path to an hdf5 file checks: list of str Which checks to perform ("attributes" and/or "background") Raises ------ IntegrityCheckError if the checks fail """ if isinstance(checks, str): checks = [checks] for ch in checks: if ch not in ["attributes", "background"]: raise ValueError("Unknown check: {}".format(check)) if isinstance(qpi_or_h5file, QPImage): qpi = qpi_or_h5file else: qpi = QPImage(h5file=qpi_or_h5file, h5mode="r") # check attributes if "attributes" in checks: check_attributes(qpi) # check background estimation if "background" in checks: check_background(qpi)
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train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/integrity_check.py#L13-L45
RI-imaging/qpimage
qpimage/integrity_check.py
check_attributes
def check_attributes(qpi): """Check QPimage attributes Parameters ---------- qpi: qpimage.core.QPImage Raises ------ IntegrityCheckError if the check fails """ missing_attrs = [] for key in DATA_KEYS: if key not in qpi.meta: missing_attrs.append(key) if missing_attrs: msg = "Attributes are missing: {} ".format(missing_attrs) \ + "in {}!".format(qpi) raise IntegrityCheckError(msg)
python
def check_attributes(qpi): """Check QPimage attributes Parameters ---------- qpi: qpimage.core.QPImage Raises ------ IntegrityCheckError if the check fails """ missing_attrs = [] for key in DATA_KEYS: if key not in qpi.meta: missing_attrs.append(key) if missing_attrs: msg = "Attributes are missing: {} ".format(missing_attrs) \ + "in {}!".format(qpi) raise IntegrityCheckError(msg)
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Check QPimage attributes Parameters ---------- qpi: qpimage.core.QPImage Raises ------ IntegrityCheckError if the check fails
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train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/integrity_check.py#L48-L67
RI-imaging/qpimage
qpimage/integrity_check.py
check_background
def check_background(qpi): """Check QPimage background data Parameters ---------- qpi: qpimage.core.QPImage Raises ------ IntegrityCheckError if the check fails """ for imdat in [qpi._amp, qpi._pha]: try: fit, attrs = imdat.get_bg(key="fit", ret_attrs=True) except KeyError: # No bg correction performed pass else: kwargs = dict(attrs) # check if we have a user-defined mask image binkey = "estimate_bg_from_mask" if binkey in imdat.h5: kwargs["from_mask"] = imdat.h5[binkey][:] else: kwargs["from_mask"] = None # compute background correction with h5py.File("check.h5", driver="core", backing_store=False) as h5: # imdat.__class__ is "Amplitude" or "Phase" testimdat = imdat.__class__(h5) testimdat["raw"] = imdat.raw # Set experimental bg data if given try: bg = imdat.get_bg("data") except KeyError: pass else: testimdat.set_bg(bg, key="data") # fit bg testimdat.estimate_bg(**kwargs) # compare if not np.allclose(testimdat.get_bg(key="fit"), fit): msg = "Wrong estimated (fitted) background!" raise IntegrityCheckError(msg)
python
def check_background(qpi): """Check QPimage background data Parameters ---------- qpi: qpimage.core.QPImage Raises ------ IntegrityCheckError if the check fails """ for imdat in [qpi._amp, qpi._pha]: try: fit, attrs = imdat.get_bg(key="fit", ret_attrs=True) except KeyError: # No bg correction performed pass else: kwargs = dict(attrs) # check if we have a user-defined mask image binkey = "estimate_bg_from_mask" if binkey in imdat.h5: kwargs["from_mask"] = imdat.h5[binkey][:] else: kwargs["from_mask"] = None # compute background correction with h5py.File("check.h5", driver="core", backing_store=False) as h5: # imdat.__class__ is "Amplitude" or "Phase" testimdat = imdat.__class__(h5) testimdat["raw"] = imdat.raw # Set experimental bg data if given try: bg = imdat.get_bg("data") except KeyError: pass else: testimdat.set_bg(bg, key="data") # fit bg testimdat.estimate_bg(**kwargs) # compare if not np.allclose(testimdat.get_bg(key="fit"), fit): msg = "Wrong estimated (fitted) background!" raise IntegrityCheckError(msg)
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Check QPimage background data Parameters ---------- qpi: qpimage.core.QPImage Raises ------ IntegrityCheckError if the check fails
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train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/integrity_check.py#L70-L115
RI-imaging/qpimage
qpimage/image_data.py
write_image_dataset
def write_image_dataset(group, key, data, h5dtype=None): """Write an image to an hdf5 group as a dataset This convenience function sets all attributes such that the image can be visualized with HDFView, sets the compression and fletcher32 filters, and sets the chunk size to the image shape. Parameters ---------- group: h5py.Group HDF5 group to store data to key: str Dataset identifier data: np.ndarray of shape (M,N) Image data to store h5dtype: str The datatype in which to store the image data. The default is the datatype of `data`. Returns ------- dataset: h5py.Dataset The created HDF5 dataset object """ if h5dtype is None: h5dtype = data.dtype if key in group: del group[key] if group.file.driver == "core": kwargs = {} else: kwargs = {"fletcher32": True, "chunks": data.shape} kwargs.update(COMPRESSION) dset = group.create_dataset(key, data=data.astype(h5dtype), **kwargs) # Create and Set image attributes # HDFView recognizes this as a series of images dset.attrs.create('CLASS', b'IMAGE') dset.attrs.create('IMAGE_VERSION', b'1.2') dset.attrs.create('IMAGE_SUBCLASS', b'IMAGE_GRAYSCALE') return dset
python
def write_image_dataset(group, key, data, h5dtype=None): """Write an image to an hdf5 group as a dataset This convenience function sets all attributes such that the image can be visualized with HDFView, sets the compression and fletcher32 filters, and sets the chunk size to the image shape. Parameters ---------- group: h5py.Group HDF5 group to store data to key: str Dataset identifier data: np.ndarray of shape (M,N) Image data to store h5dtype: str The datatype in which to store the image data. The default is the datatype of `data`. Returns ------- dataset: h5py.Dataset The created HDF5 dataset object """ if h5dtype is None: h5dtype = data.dtype if key in group: del group[key] if group.file.driver == "core": kwargs = {} else: kwargs = {"fletcher32": True, "chunks": data.shape} kwargs.update(COMPRESSION) dset = group.create_dataset(key, data=data.astype(h5dtype), **kwargs) # Create and Set image attributes # HDFView recognizes this as a series of images dset.attrs.create('CLASS', b'IMAGE') dset.attrs.create('IMAGE_VERSION', b'1.2') dset.attrs.create('IMAGE_SUBCLASS', b'IMAGE_GRAYSCALE') return dset
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Write an image to an hdf5 group as a dataset This convenience function sets all attributes such that the image can be visualized with HDFView, sets the compression and fletcher32 filters, and sets the chunk size to the image shape. Parameters ---------- group: h5py.Group HDF5 group to store data to key: str Dataset identifier data: np.ndarray of shape (M,N) Image data to store h5dtype: str The datatype in which to store the image data. The default is the datatype of `data`. Returns ------- dataset: h5py.Dataset The created HDF5 dataset object
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train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/image_data.py#L326-L369
RI-imaging/qpimage
qpimage/image_data.py
ImageData.info
def info(self): """list of background correction parameters""" info = [] name = self.__class__.__name__.lower() # get bg information for key in VALID_BG_KEYS: if key in self.h5["bg_data"]: attrs = self.h5["bg_data"][key].attrs for akey in attrs: atr = attrs[akey] var = "{} background {}".format(name, akey) info.append((var, atr)) if "fit" in self.h5["bg_data"]: # mask background var_mask = "{} background from mask".format(name) if ("estimate_bg_from_mask" in self.h5 and self.h5["estimate_bg_from_mask"] is not None): # bg was computed from mask image info.append((var_mask, True)) elif ("estimate_bg_from_binary" in self.h5 and self.h5["estimate_bg_from_binary"] is not None): # bg was computed from mask image (old notation) warnings.warn("Old file format detected!", DeprecationWarning) info.append((var_mask, True)) else: info.append((var_mask, False)) return info
python
def info(self): """list of background correction parameters""" info = [] name = self.__class__.__name__.lower() # get bg information for key in VALID_BG_KEYS: if key in self.h5["bg_data"]: attrs = self.h5["bg_data"][key].attrs for akey in attrs: atr = attrs[akey] var = "{} background {}".format(name, akey) info.append((var, atr)) if "fit" in self.h5["bg_data"]: # mask background var_mask = "{} background from mask".format(name) if ("estimate_bg_from_mask" in self.h5 and self.h5["estimate_bg_from_mask"] is not None): # bg was computed from mask image info.append((var_mask, True)) elif ("estimate_bg_from_binary" in self.h5 and self.h5["estimate_bg_from_binary"] is not None): # bg was computed from mask image (old notation) warnings.warn("Old file format detected!", DeprecationWarning) info.append((var_mask, True)) else: info.append((var_mask, False)) return info
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list of background correction parameters
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train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/image_data.py#L96-L122
RI-imaging/qpimage
qpimage/image_data.py
ImageData.del_bg
def del_bg(self, key): """Remove the background image data Parameters ---------- key: str One of :const:`VALID_BG_KEYS` """ if key not in VALID_BG_KEYS: raise ValueError("Invalid bg key: {}".format(key)) if key in self.h5["bg_data"]: del self.h5["bg_data"][key] else: msg = "No bg data to clear for '{}' in {}.".format(key, self) warnings.warn(msg)
python
def del_bg(self, key): """Remove the background image data Parameters ---------- key: str One of :const:`VALID_BG_KEYS` """ if key not in VALID_BG_KEYS: raise ValueError("Invalid bg key: {}".format(key)) if key in self.h5["bg_data"]: del self.h5["bg_data"][key] else: msg = "No bg data to clear for '{}' in {}.".format(key, self) warnings.warn(msg)
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Remove the background image data Parameters ---------- key: str One of :const:`VALID_BG_KEYS`
[ "Remove", "the", "background", "image", "data" ]
train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/image_data.py#L129-L143
RI-imaging/qpimage
qpimage/image_data.py
ImageData.estimate_bg
def estimate_bg(self, fit_offset="mean", fit_profile="tilt", border_px=0, from_mask=None, ret_mask=False): """Estimate image background Parameters ---------- fit_profile: str The type of background profile to fit: - "offset": offset only - "poly2o": 2D 2nd order polynomial with mixed terms - "tilt": 2D linear tilt with offset (default) fit_offset: str The method for computing the profile offset - "fit": offset as fitting parameter - "gauss": center of a gaussian fit - "mean": simple average - "mode": mode (see `qpimage.bg_estimate.mode`) border_px: float Assume that a frame of `border_px` pixels around the image is background. from_mask: boolean np.ndarray or None Use a boolean array to define the background area. The mask image must have the same shape as the input data.`True` elements are used for background estimation. ret_mask: bool Return the mask image used to compute the background. Notes ----- If both `border_px` and `from_mask` are given, the intersection of the two resulting mask images is used. The arguments passed to this method are stored in the hdf5 file `self.h5` and are used for optional integrity checking using `qpimage.integrity_check.check`. See Also -------- qpimage.bg_estimate.estimate """ # remove existing bg before accessing imdat.image self.set_bg(bg=None, key="fit") # compute bg bgimage, mask = bg_estimate.estimate(data=self.image, fit_offset=fit_offset, fit_profile=fit_profile, border_px=border_px, from_mask=from_mask, ret_mask=True) attrs = {"fit_offset": fit_offset, "fit_profile": fit_profile, "border_px": border_px} self.set_bg(bg=bgimage, key="fit", attrs=attrs) # save `from_mask` separately (arrays vs. h5 attributes) # (if `from_mask` is `None`, this will remove the array) self["estimate_bg_from_mask"] = from_mask # return mask image if ret_mask: return mask
python
def estimate_bg(self, fit_offset="mean", fit_profile="tilt", border_px=0, from_mask=None, ret_mask=False): """Estimate image background Parameters ---------- fit_profile: str The type of background profile to fit: - "offset": offset only - "poly2o": 2D 2nd order polynomial with mixed terms - "tilt": 2D linear tilt with offset (default) fit_offset: str The method for computing the profile offset - "fit": offset as fitting parameter - "gauss": center of a gaussian fit - "mean": simple average - "mode": mode (see `qpimage.bg_estimate.mode`) border_px: float Assume that a frame of `border_px` pixels around the image is background. from_mask: boolean np.ndarray or None Use a boolean array to define the background area. The mask image must have the same shape as the input data.`True` elements are used for background estimation. ret_mask: bool Return the mask image used to compute the background. Notes ----- If both `border_px` and `from_mask` are given, the intersection of the two resulting mask images is used. The arguments passed to this method are stored in the hdf5 file `self.h5` and are used for optional integrity checking using `qpimage.integrity_check.check`. See Also -------- qpimage.bg_estimate.estimate """ # remove existing bg before accessing imdat.image self.set_bg(bg=None, key="fit") # compute bg bgimage, mask = bg_estimate.estimate(data=self.image, fit_offset=fit_offset, fit_profile=fit_profile, border_px=border_px, from_mask=from_mask, ret_mask=True) attrs = {"fit_offset": fit_offset, "fit_profile": fit_profile, "border_px": border_px} self.set_bg(bg=bgimage, key="fit", attrs=attrs) # save `from_mask` separately (arrays vs. h5 attributes) # (if `from_mask` is `None`, this will remove the array) self["estimate_bg_from_mask"] = from_mask # return mask image if ret_mask: return mask
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Estimate image background Parameters ---------- fit_profile: str The type of background profile to fit: - "offset": offset only - "poly2o": 2D 2nd order polynomial with mixed terms - "tilt": 2D linear tilt with offset (default) fit_offset: str The method for computing the profile offset - "fit": offset as fitting parameter - "gauss": center of a gaussian fit - "mean": simple average - "mode": mode (see `qpimage.bg_estimate.mode`) border_px: float Assume that a frame of `border_px` pixels around the image is background. from_mask: boolean np.ndarray or None Use a boolean array to define the background area. The mask image must have the same shape as the input data.`True` elements are used for background estimation. ret_mask: bool Return the mask image used to compute the background. Notes ----- If both `border_px` and `from_mask` are given, the intersection of the two resulting mask images is used. The arguments passed to this method are stored in the hdf5 file `self.h5` and are used for optional integrity checking using `qpimage.integrity_check.check`. See Also -------- qpimage.bg_estimate.estimate
[ "Estimate", "image", "background" ]
train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/image_data.py#L145-L206
RI-imaging/qpimage
qpimage/image_data.py
ImageData.get_bg
def get_bg(self, key=None, ret_attrs=False): """Get the background data Parameters ---------- key: None or str A user-defined key that identifies the background data. Examples are "data" for experimental data, or "fit" for an estimated background correction (see :const:`VALID_BG_KEYS`). If set to `None`, returns the combined background image (:const:`ImageData.bg`). ret_attrs: bool Also returns the attributes of the background data. """ if key is None: if ret_attrs: raise ValueError("No attributes for combined background!") return self.bg else: if key not in VALID_BG_KEYS: raise ValueError("Invalid bg key: {}".format(key)) if key in self.h5["bg_data"]: data = self.h5["bg_data"][key][:] if ret_attrs: attrs = dict(self.h5["bg_data"][key].attrs) # remove keys for image visualization in hdf5 files for h5k in ["CLASS", "IMAGE_VERSION", "IMAGE_SUBCLASS"]: if h5k in attrs: attrs.pop(h5k) ret = (data, attrs) else: ret = data else: raise KeyError("No background data for {}!".format(key)) return ret
python
def get_bg(self, key=None, ret_attrs=False): """Get the background data Parameters ---------- key: None or str A user-defined key that identifies the background data. Examples are "data" for experimental data, or "fit" for an estimated background correction (see :const:`VALID_BG_KEYS`). If set to `None`, returns the combined background image (:const:`ImageData.bg`). ret_attrs: bool Also returns the attributes of the background data. """ if key is None: if ret_attrs: raise ValueError("No attributes for combined background!") return self.bg else: if key not in VALID_BG_KEYS: raise ValueError("Invalid bg key: {}".format(key)) if key in self.h5["bg_data"]: data = self.h5["bg_data"][key][:] if ret_attrs: attrs = dict(self.h5["bg_data"][key].attrs) # remove keys for image visualization in hdf5 files for h5k in ["CLASS", "IMAGE_VERSION", "IMAGE_SUBCLASS"]: if h5k in attrs: attrs.pop(h5k) ret = (data, attrs) else: ret = data else: raise KeyError("No background data for {}!".format(key)) return ret
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Get the background data Parameters ---------- key: None or str A user-defined key that identifies the background data. Examples are "data" for experimental data, or "fit" for an estimated background correction (see :const:`VALID_BG_KEYS`). If set to `None`, returns the combined background image (:const:`ImageData.bg`). ret_attrs: bool Also returns the attributes of the background data.
[ "Get", "the", "background", "data" ]
train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/image_data.py#L208-L242
RI-imaging/qpimage
qpimage/image_data.py
ImageData.set_bg
def set_bg(self, bg, key="data", attrs={}): """Set the background data Parameters ---------- bg: numbers.Real, 2d ndarray, ImageData, or h5py.Dataset The background data. If `bg` is an `h5py.Dataset` object, it must exist in the same hdf5 file (a hard link is created). If set to `None`, the data will be removed. key: str One of :const:`VALID_BG_KEYS`) attrs: dict List of background attributes See Also -------- del_bg: removing background data """ if key not in VALID_BG_KEYS: raise ValueError("Invalid bg key: {}".format(key)) # remove previous background key if key in self.h5["bg_data"]: del self.h5["bg_data"][key] # set background if isinstance(bg, (numbers.Real, np.ndarray)): dset = write_image_dataset(group=self.h5["bg_data"], key=key, data=bg, h5dtype=self.h5dtype) for kw in attrs: dset.attrs[kw] = attrs[kw] elif isinstance(bg, h5py.Dataset): # Create a hard link # (This functionality was intended for saving memory when storing # large QPSeries with universal background data, i.e. when using # `QPSeries.add_qpimage` with the `bg_from_idx` keyword.) self.h5["bg_data"][key] = bg elif bg is not None: msg = "Unknown background data type: {}".format(bg) raise ValueError(msg)
python
def set_bg(self, bg, key="data", attrs={}): """Set the background data Parameters ---------- bg: numbers.Real, 2d ndarray, ImageData, or h5py.Dataset The background data. If `bg` is an `h5py.Dataset` object, it must exist in the same hdf5 file (a hard link is created). If set to `None`, the data will be removed. key: str One of :const:`VALID_BG_KEYS`) attrs: dict List of background attributes See Also -------- del_bg: removing background data """ if key not in VALID_BG_KEYS: raise ValueError("Invalid bg key: {}".format(key)) # remove previous background key if key in self.h5["bg_data"]: del self.h5["bg_data"][key] # set background if isinstance(bg, (numbers.Real, np.ndarray)): dset = write_image_dataset(group=self.h5["bg_data"], key=key, data=bg, h5dtype=self.h5dtype) for kw in attrs: dset.attrs[kw] = attrs[kw] elif isinstance(bg, h5py.Dataset): # Create a hard link # (This functionality was intended for saving memory when storing # large QPSeries with universal background data, i.e. when using # `QPSeries.add_qpimage` with the `bg_from_idx` keyword.) self.h5["bg_data"][key] = bg elif bg is not None: msg = "Unknown background data type: {}".format(bg) raise ValueError(msg)
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train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/image_data.py#L244-L283
RI-imaging/qpimage
qpimage/image_data.py
Amplitude._bg_combine
def _bg_combine(self, bgs): """Combine several background amplitude images""" out = np.ones(self.h5["raw"].shape, dtype=float) # bg is an h5py.DataSet for bg in bgs: out *= bg[:] return out
python
def _bg_combine(self, bgs): """Combine several background amplitude images""" out = np.ones(self.h5["raw"].shape, dtype=float) # bg is an h5py.DataSet for bg in bgs: out *= bg[:] return out
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train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/image_data.py#L293-L299
devoperate/chronos
chronos/helpers.py
git_tags
def git_tags() -> str: """ Calls ``git tag -l --sort=-v:refname`` (sorts output) and returns the output as a UTF-8 encoded string. Raises a NoGitTagsException if the repository doesn't contain any Git tags. """ try: subprocess.check_call(['git', 'fetch', '--tags']) except CalledProcessError: pass cmd = ['git', 'tag', '--list', '--sort=-v:refname'] rv = subprocess.check_output(cmd).decode('utf-8') if rv == '': raise NoGitTagsException('No Git tags are present in current repo.') return rv
python
def git_tags() -> str: """ Calls ``git tag -l --sort=-v:refname`` (sorts output) and returns the output as a UTF-8 encoded string. Raises a NoGitTagsException if the repository doesn't contain any Git tags. """ try: subprocess.check_call(['git', 'fetch', '--tags']) except CalledProcessError: pass cmd = ['git', 'tag', '--list', '--sort=-v:refname'] rv = subprocess.check_output(cmd).decode('utf-8') if rv == '': raise NoGitTagsException('No Git tags are present in current repo.') return rv
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devoperate/chronos
chronos/helpers.py
git_tag_to_semver
def git_tag_to_semver(git_tag: str) -> SemVer: """ :git_tag: A string representation of a Git tag. Searches a Git tag's string representation for a SemVer, and returns that as a SemVer object. """ pattern = re.compile(r'[0-9]+\.[0-9]+\.[0-9]+$') match = pattern.search(git_tag) if match: version = match.group(0) else: raise InvalidTagFormatException('Tag passed contains no SemVer.') return SemVer.from_str(version)
python
def git_tag_to_semver(git_tag: str) -> SemVer: """ :git_tag: A string representation of a Git tag. Searches a Git tag's string representation for a SemVer, and returns that as a SemVer object. """ pattern = re.compile(r'[0-9]+\.[0-9]+\.[0-9]+$') match = pattern.search(git_tag) if match: version = match.group(0) else: raise InvalidTagFormatException('Tag passed contains no SemVer.') return SemVer.from_str(version)
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:git_tag: A string representation of a Git tag. Searches a Git tag's string representation for a SemVer, and returns that as a SemVer object.
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devoperate/chronos
chronos/helpers.py
last_git_release_tag
def last_git_release_tag(git_tags: str) -> str: """ :git_tags: chronos.helpers.git_tags() function output. Returns the latest Git tag ending with a SemVer as a string. """ semver_re = re.compile(r'[0-9]+\.[0-9]+\.[0-9]+$') str_ver = [] for i in git_tags.split(): if semver_re.search(i): str_ver.append(i) try: return str_ver[0] except IndexError: raise NoGitTagsException
python
def last_git_release_tag(git_tags: str) -> str: """ :git_tags: chronos.helpers.git_tags() function output. Returns the latest Git tag ending with a SemVer as a string. """ semver_re = re.compile(r'[0-9]+\.[0-9]+\.[0-9]+$') str_ver = [] for i in git_tags.split(): if semver_re.search(i): str_ver.append(i) try: return str_ver[0] except IndexError: raise NoGitTagsException
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train
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devoperate/chronos
chronos/helpers.py
git_commits_since_last_tag
def git_commits_since_last_tag(last_tag: str) -> dict: """ :last_tag: The Git tag that should serve as the starting point for the commit log lookup. Calls ``git log <last_tag>.. --format='%H %s'`` and returns the output as a dict of hash-message pairs. """ try: cmd = ['git', 'log', last_tag + '..', "--format='%H %s'"] commit_log = subprocess.check_output(cmd).decode('utf-8') except CalledProcessError: raise GitTagDoesNotExistError('No such tag:', last_tag) if not commit_log: raise NoGitCommitSinceLastTagException('No commits since last tag.') pattern = re.compile(r'([a-f0-9]{40})\ (.*)') rv = {} for line in commit_log.split('\n'): match = pattern.search(line) if match: commit_hash = match.group(1) commit_msg = match.group(2) rv[commit_hash] = commit_msg return rv
python
def git_commits_since_last_tag(last_tag: str) -> dict: """ :last_tag: The Git tag that should serve as the starting point for the commit log lookup. Calls ``git log <last_tag>.. --format='%H %s'`` and returns the output as a dict of hash-message pairs. """ try: cmd = ['git', 'log', last_tag + '..', "--format='%H %s'"] commit_log = subprocess.check_output(cmd).decode('utf-8') except CalledProcessError: raise GitTagDoesNotExistError('No such tag:', last_tag) if not commit_log: raise NoGitCommitSinceLastTagException('No commits since last tag.') pattern = re.compile(r'([a-f0-9]{40})\ (.*)') rv = {} for line in commit_log.split('\n'): match = pattern.search(line) if match: commit_hash = match.group(1) commit_msg = match.group(2) rv[commit_hash] = commit_msg return rv
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devoperate/chronos
chronos/helpers.py
parse_commit_log
def parse_commit_log(commit_log: dict) -> str: """ :commit_log: chronos.helpers.git_commits_since_last_tag() output. Parse Git log and return either 'maj', 'min', or 'pat'. """ rv = 'pat' cc_patterns = patterns() for value in commit_log.values(): if re.search(cc_patterns['feat'], value): rv = 'min' if re.search(cc_patterns['BREAKING CHANGE'], value): rv = 'maj' return rv
python
def parse_commit_log(commit_log: dict) -> str: """ :commit_log: chronos.helpers.git_commits_since_last_tag() output. Parse Git log and return either 'maj', 'min', or 'pat'. """ rv = 'pat' cc_patterns = patterns() for value in commit_log.values(): if re.search(cc_patterns['feat'], value): rv = 'min' if re.search(cc_patterns['BREAKING CHANGE'], value): rv = 'maj' return rv
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train
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devoperate/chronos
chronos/semver.py
SemVer.from_str
def from_str(cls, version_str: str): """ Alternate constructor that accepts a string SemVer. """ o = cls() o.version = version_str return o
python
def from_str(cls, version_str: str): """ Alternate constructor that accepts a string SemVer. """ o = cls() o.version = version_str return o
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train
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devoperate/chronos
chronos/semver.py
SemVer.major
def major(self, major: int) -> None: """ param major Major version number property. Must be a non-negative integer. """ self.filter_negatives(major) self._major = major
python
def major(self, major: int) -> None: """ param major Major version number property. Must be a non-negative integer. """ self.filter_negatives(major) self._major = major
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train
https://github.com/devoperate/chronos/blob/5ae6047c4f13db9f5e85a0c72a3dc47f05a8d7bd/chronos/semver.py#L103-L110
devoperate/chronos
chronos/semver.py
SemVer.minor
def minor(self, minor: int) -> None: """ param minor Minor version number property. Must be a non-negative integer. """ self.filter_negatives(minor) self._minor = minor
python
def minor(self, minor: int) -> None: """ param minor Minor version number property. Must be a non-negative integer. """ self.filter_negatives(minor) self._minor = minor
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train
https://github.com/devoperate/chronos/blob/5ae6047c4f13db9f5e85a0c72a3dc47f05a8d7bd/chronos/semver.py#L120-L127
devoperate/chronos
chronos/semver.py
SemVer.patch
def patch(self, patch: int) -> None: """ param patch Patch version number property. Must be a non-negative integer. """ self.filter_negatives(patch) self._patch = patch
python
def patch(self, patch: int) -> None: """ param patch Patch version number property. Must be a non-negative integer. """ self.filter_negatives(patch) self._patch = patch
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train
https://github.com/devoperate/chronos/blob/5ae6047c4f13db9f5e85a0c72a3dc47f05a8d7bd/chronos/semver.py#L137-L144
devoperate/chronos
chronos/semver.py
SemVer.version
def version(self) -> str: """ Version version number property. Must be a string consisting of three non-negative integers delimited by periods (eg. '1.0.1'). """ version: str = ( str(self._major) + '.' + str(self._minor) + '.' + str(self._patch) ) return version
python
def version(self) -> str: """ Version version number property. Must be a string consisting of three non-negative integers delimited by periods (eg. '1.0.1'). """ version: str = ( str(self._major) + '.' + str(self._minor) + '.' + str(self._patch) ) return version
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devoperate/chronos
chronos/semver.py
SemVer.version
def version(self, version_str: str) -> None: """ param version Version version number property. Must be a string consisting of three non-negative integers delimited by periods (eg. '1.0.1'). """ ver = [] for i in version_str.split('.'): ver.append(int(i)) self.filter_negatives(int(i)) self._major, self._minor, self._patch = ver[0], ver[1], ver[2]
python
def version(self, version_str: str) -> None: """ param version Version version number property. Must be a string consisting of three non-negative integers delimited by periods (eg. '1.0.1'). """ ver = [] for i in version_str.split('.'): ver.append(int(i)) self.filter_negatives(int(i)) self._major, self._minor, self._patch = ver[0], ver[1], ver[2]
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train
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RI-imaging/qpimage
qpimage/bg_estimate.py
estimate
def estimate(data, fit_offset="mean", fit_profile="tilt", border_px=0, from_mask=None, ret_mask=False): """Estimate the background value of an image Parameters ---------- data: np.ndarray Data from which to compute the background value fit_profile: str The type of background profile to fit: - "offset": offset only - "poly2o": 2D 2nd order polynomial with mixed terms - "tilt": 2D linear tilt with offset (default) fit_offset: str The method for computing the profile offset - "fit": offset as fitting parameter - "gauss": center of a gaussian fit - "mean": simple average - "mode": mode (see `qpimage.bg_estimate.mode`) border_px: float Assume that a frame of `border_px` pixels around the image is background. from_mask: boolean np.ndarray or None Use a boolean array to define the background area. The boolean mask must have the same shape as the input data. `True` elements are used for background estimation. ret_mask: bool Return the boolean mask used to compute the background. Notes ----- If both `border_px` and `from_mask` are given, the intersection of the two is used, i.e. the positions where both, the frame mask and `from_mask`, are `True`. """ if fit_profile not in VALID_FIT_PROFILES: msg = "`fit_profile` must be one of {}, got '{}'".format( VALID_FIT_PROFILES, fit_profile) raise ValueError(msg) if fit_offset not in VALID_FIT_OFFSETS: msg = "`fit_offset` must be one of {}, got '{}'".format( VALID_FIT_OFFSETS, fit_offset) raise ValueError(msg) # initial mask image if from_mask is not None: assert isinstance(from_mask, np.ndarray) mask = from_mask.copy() else: mask = np.ones_like(data, dtype=bool) # multiply with border mask image (intersection) if border_px > 0: border_px = int(np.round(border_px)) mask_px = np.zeros_like(mask) mask_px[:border_px, :] = True mask_px[-border_px:, :] = True mask_px[:, :border_px] = True mask_px[:, -border_px:] = True # intersection np.logical_and(mask, mask_px, out=mask) # compute background image if fit_profile == "tilt": bgimg = profile_tilt(data, mask) elif fit_profile == "poly2o": bgimg = profile_poly2o(data, mask) else: bgimg = np.zeros_like(data, dtype=float) # add offsets if fit_offset == "fit": if fit_profile == "offset": msg = "`fit_offset=='fit'` only valid when `fit_profile!='offset`" raise ValueError(msg) # nothing else to do here, using offset from fit elif fit_offset == "gauss": bgimg += offset_gaussian((data - bgimg)[mask]) elif fit_offset == "mean": bgimg += np.mean((data - bgimg)[mask]) elif fit_offset == "mode": bgimg += offset_mode((data - bgimg)[mask]) if ret_mask: ret = (bgimg, mask) else: ret = bgimg return ret
python
def estimate(data, fit_offset="mean", fit_profile="tilt", border_px=0, from_mask=None, ret_mask=False): """Estimate the background value of an image Parameters ---------- data: np.ndarray Data from which to compute the background value fit_profile: str The type of background profile to fit: - "offset": offset only - "poly2o": 2D 2nd order polynomial with mixed terms - "tilt": 2D linear tilt with offset (default) fit_offset: str The method for computing the profile offset - "fit": offset as fitting parameter - "gauss": center of a gaussian fit - "mean": simple average - "mode": mode (see `qpimage.bg_estimate.mode`) border_px: float Assume that a frame of `border_px` pixels around the image is background. from_mask: boolean np.ndarray or None Use a boolean array to define the background area. The boolean mask must have the same shape as the input data. `True` elements are used for background estimation. ret_mask: bool Return the boolean mask used to compute the background. Notes ----- If both `border_px` and `from_mask` are given, the intersection of the two is used, i.e. the positions where both, the frame mask and `from_mask`, are `True`. """ if fit_profile not in VALID_FIT_PROFILES: msg = "`fit_profile` must be one of {}, got '{}'".format( VALID_FIT_PROFILES, fit_profile) raise ValueError(msg) if fit_offset not in VALID_FIT_OFFSETS: msg = "`fit_offset` must be one of {}, got '{}'".format( VALID_FIT_OFFSETS, fit_offset) raise ValueError(msg) # initial mask image if from_mask is not None: assert isinstance(from_mask, np.ndarray) mask = from_mask.copy() else: mask = np.ones_like(data, dtype=bool) # multiply with border mask image (intersection) if border_px > 0: border_px = int(np.round(border_px)) mask_px = np.zeros_like(mask) mask_px[:border_px, :] = True mask_px[-border_px:, :] = True mask_px[:, :border_px] = True mask_px[:, -border_px:] = True # intersection np.logical_and(mask, mask_px, out=mask) # compute background image if fit_profile == "tilt": bgimg = profile_tilt(data, mask) elif fit_profile == "poly2o": bgimg = profile_poly2o(data, mask) else: bgimg = np.zeros_like(data, dtype=float) # add offsets if fit_offset == "fit": if fit_profile == "offset": msg = "`fit_offset=='fit'` only valid when `fit_profile!='offset`" raise ValueError(msg) # nothing else to do here, using offset from fit elif fit_offset == "gauss": bgimg += offset_gaussian((data - bgimg)[mask]) elif fit_offset == "mean": bgimg += np.mean((data - bgimg)[mask]) elif fit_offset == "mode": bgimg += offset_mode((data - bgimg)[mask]) if ret_mask: ret = (bgimg, mask) else: ret = bgimg return ret
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Estimate the background value of an image Parameters ---------- data: np.ndarray Data from which to compute the background value fit_profile: str The type of background profile to fit: - "offset": offset only - "poly2o": 2D 2nd order polynomial with mixed terms - "tilt": 2D linear tilt with offset (default) fit_offset: str The method for computing the profile offset - "fit": offset as fitting parameter - "gauss": center of a gaussian fit - "mean": simple average - "mode": mode (see `qpimage.bg_estimate.mode`) border_px: float Assume that a frame of `border_px` pixels around the image is background. from_mask: boolean np.ndarray or None Use a boolean array to define the background area. The boolean mask must have the same shape as the input data. `True` elements are used for background estimation. ret_mask: bool Return the boolean mask used to compute the background. Notes ----- If both `border_px` and `from_mask` are given, the intersection of the two is used, i.e. the positions where both, the frame mask and `from_mask`, are `True`.
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train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/bg_estimate.py#L11-L100
RI-imaging/qpimage
qpimage/bg_estimate.py
offset_gaussian
def offset_gaussian(data): """Fit a gaussian model to `data` and return its center""" nbins = 2 * int(np.ceil(np.sqrt(data.size))) mind, maxd = data.min(), data.max() drange = (mind - (maxd - mind) / 2, maxd + (maxd - mind) / 2) histo = np.histogram(data, nbins, density=True, range=drange) dx = abs(histo[1][1] - histo[1][2]) / 2 hx = histo[1][1:] - dx hy = histo[0] # fit gaussian gauss = lmfit.models.GaussianModel() pars = gauss.guess(hy, x=hx) out = gauss.fit(hy, pars, x=hx) return out.params["center"]
python
def offset_gaussian(data): """Fit a gaussian model to `data` and return its center""" nbins = 2 * int(np.ceil(np.sqrt(data.size))) mind, maxd = data.min(), data.max() drange = (mind - (maxd - mind) / 2, maxd + (maxd - mind) / 2) histo = np.histogram(data, nbins, density=True, range=drange) dx = abs(histo[1][1] - histo[1][2]) / 2 hx = histo[1][1:] - dx hy = histo[0] # fit gaussian gauss = lmfit.models.GaussianModel() pars = gauss.guess(hy, x=hx) out = gauss.fit(hy, pars, x=hx) return out.params["center"]
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Fit a gaussian model to `data` and return its center
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train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/bg_estimate.py#L103-L116
RI-imaging/qpimage
qpimage/bg_estimate.py
offset_mode
def offset_mode(data): """Compute Mode using a histogram with `sqrt(data.size)` bins""" nbins = int(np.ceil(np.sqrt(data.size))) mind, maxd = data.min(), data.max() histo = np.histogram(data, nbins, density=True, range=(mind, maxd)) dx = abs(histo[1][1] - histo[1][2]) / 2 hx = histo[1][1:] - dx hy = histo[0] idmax = np.argmax(hy) return hx[idmax]
python
def offset_mode(data): """Compute Mode using a histogram with `sqrt(data.size)` bins""" nbins = int(np.ceil(np.sqrt(data.size))) mind, maxd = data.min(), data.max() histo = np.histogram(data, nbins, density=True, range=(mind, maxd)) dx = abs(histo[1][1] - histo[1][2]) / 2 hx = histo[1][1:] - dx hy = histo[0] idmax = np.argmax(hy) return hx[idmax]
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Compute Mode using a histogram with `sqrt(data.size)` bins
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train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/bg_estimate.py#L119-L128
RI-imaging/qpimage
qpimage/bg_estimate.py
profile_tilt
def profile_tilt(data, mask): """Fit a 2D tilt to `data[mask]`""" params = lmfit.Parameters() params.add(name="mx", value=0) params.add(name="my", value=0) params.add(name="off", value=np.average(data[mask])) fr = lmfit.minimize(tilt_residual, params, args=(data, mask)) bg = tilt_model(fr.params, data.shape) return bg
python
def profile_tilt(data, mask): """Fit a 2D tilt to `data[mask]`""" params = lmfit.Parameters() params.add(name="mx", value=0) params.add(name="my", value=0) params.add(name="off", value=np.average(data[mask])) fr = lmfit.minimize(tilt_residual, params, args=(data, mask)) bg = tilt_model(fr.params, data.shape) return bg
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Fit a 2D tilt to `data[mask]`
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train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/bg_estimate.py#L131-L139
RI-imaging/qpimage
qpimage/bg_estimate.py
profile_poly2o
def profile_poly2o(data, mask): """Fit a 2D 2nd order polynomial to `data[mask]`""" # lmfit params = lmfit.Parameters() params.add(name="mx", value=0) params.add(name="my", value=0) params.add(name="mxy", value=0) params.add(name="ax", value=0) params.add(name="ay", value=0) params.add(name="off", value=np.average(data[mask])) fr = lmfit.minimize(poly2o_residual, params, args=(data, mask)) bg = poly2o_model(fr.params, data.shape) return bg
python
def profile_poly2o(data, mask): """Fit a 2D 2nd order polynomial to `data[mask]`""" # lmfit params = lmfit.Parameters() params.add(name="mx", value=0) params.add(name="my", value=0) params.add(name="mxy", value=0) params.add(name="ax", value=0) params.add(name="ay", value=0) params.add(name="off", value=np.average(data[mask])) fr = lmfit.minimize(poly2o_residual, params, args=(data, mask)) bg = poly2o_model(fr.params, data.shape) return bg
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Fit a 2D 2nd order polynomial to `data[mask]`
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train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/bg_estimate.py#L142-L154
RI-imaging/qpimage
qpimage/bg_estimate.py
poly2o_model
def poly2o_model(params, shape): """lmfit 2nd order polynomial model""" mx = params["mx"].value my = params["my"].value mxy = params["mxy"].value ax = params["ax"].value ay = params["ay"].value off = params["off"].value bg = np.zeros(shape, dtype=float) + off x = np.arange(bg.shape[0]) - bg.shape[0] // 2 y = np.arange(bg.shape[1]) - bg.shape[1] // 2 x = x.reshape(-1, 1) y = y.reshape(1, -1) bg += ax * x**2 + ay * y**2 + mx * x + my * y + mxy * x * y return bg
python
def poly2o_model(params, shape): """lmfit 2nd order polynomial model""" mx = params["mx"].value my = params["my"].value mxy = params["mxy"].value ax = params["ax"].value ay = params["ay"].value off = params["off"].value bg = np.zeros(shape, dtype=float) + off x = np.arange(bg.shape[0]) - bg.shape[0] // 2 y = np.arange(bg.shape[1]) - bg.shape[1] // 2 x = x.reshape(-1, 1) y = y.reshape(1, -1) bg += ax * x**2 + ay * y**2 + mx * x + my * y + mxy * x * y return bg
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lmfit 2nd order polynomial model
[ "lmfit", "2nd", "order", "polynomial", "model" ]
train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/bg_estimate.py#L157-L171
RI-imaging/qpimage
qpimage/bg_estimate.py
poly2o_residual
def poly2o_residual(params, data, mask): """lmfit 2nd order polynomial residuals""" bg = poly2o_model(params, shape=data.shape) res = (data - bg)[mask] return res.flatten()
python
def poly2o_residual(params, data, mask): """lmfit 2nd order polynomial residuals""" bg = poly2o_model(params, shape=data.shape) res = (data - bg)[mask] return res.flatten()
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lmfit 2nd order polynomial residuals
[ "lmfit", "2nd", "order", "polynomial", "residuals" ]
train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/bg_estimate.py#L174-L178
RI-imaging/qpimage
qpimage/bg_estimate.py
tilt_model
def tilt_model(params, shape): """lmfit tilt model""" mx = params["mx"].value my = params["my"].value off = params["off"].value bg = np.zeros(shape, dtype=float) + off x = np.arange(bg.shape[0]) - bg.shape[0] // 2 y = np.arange(bg.shape[1]) - bg.shape[1] // 2 x = x.reshape(-1, 1) y = y.reshape(1, -1) bg += mx * x + my * y return bg
python
def tilt_model(params, shape): """lmfit tilt model""" mx = params["mx"].value my = params["my"].value off = params["off"].value bg = np.zeros(shape, dtype=float) + off x = np.arange(bg.shape[0]) - bg.shape[0] // 2 y = np.arange(bg.shape[1]) - bg.shape[1] // 2 x = x.reshape(-1, 1) y = y.reshape(1, -1) bg += mx * x + my * y return bg
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lmfit tilt model
[ "lmfit", "tilt", "model" ]
train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/bg_estimate.py#L181-L192
RI-imaging/qpimage
qpimage/bg_estimate.py
tilt_residual
def tilt_residual(params, data, mask): """lmfit tilt residuals""" bg = tilt_model(params, shape=data.shape) res = (data - bg)[mask] return res.flatten()
python
def tilt_residual(params, data, mask): """lmfit tilt residuals""" bg = tilt_model(params, shape=data.shape) res = (data - bg)[mask] return res.flatten()
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lmfit tilt residuals
[ "lmfit", "tilt", "residuals" ]
train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/bg_estimate.py#L195-L199
devoperate/chronos
chronos/cli.py
main
def main(cmd_args: list = None) -> None: """ :cmd_args: An optional list of command line arguments. Main function of chronos CLI tool. """ parser = argparse.ArgumentParser(description='Auto-versioning utility.') subparsers = parser.add_subparsers() infer_parser = subparsers.add_parser('infer', help='Infers next version.') infer_parser.set_defaults(func=infer) commit_parser = subparsers.add_parser('commit', help='Makes release commit.') commit_parser.set_defaults(func=commit) bump_parser = subparsers.add_parser('bump', help='Bumps version.') bump_parser.add_argument('type', nargs='?', default='patch', choices=['patch', 'minor', 'major'], help='The type of version to bump.') bump_parser.set_defaults(func=bump) try: if cmd_args: args = parser.parse_args(cmd_args) else: args = parser.parse_args() args.func(args) except AttributeError: parser.print_help()
python
def main(cmd_args: list = None) -> None: """ :cmd_args: An optional list of command line arguments. Main function of chronos CLI tool. """ parser = argparse.ArgumentParser(description='Auto-versioning utility.') subparsers = parser.add_subparsers() infer_parser = subparsers.add_parser('infer', help='Infers next version.') infer_parser.set_defaults(func=infer) commit_parser = subparsers.add_parser('commit', help='Makes release commit.') commit_parser.set_defaults(func=commit) bump_parser = subparsers.add_parser('bump', help='Bumps version.') bump_parser.add_argument('type', nargs='?', default='patch', choices=['patch', 'minor', 'major'], help='The type of version to bump.') bump_parser.set_defaults(func=bump) try: if cmd_args: args = parser.parse_args(cmd_args) else: args = parser.parse_args() args.func(args) except AttributeError: parser.print_help()
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train
https://github.com/devoperate/chronos/blob/5ae6047c4f13db9f5e85a0c72a3dc47f05a8d7bd/chronos/cli.py#L18-L48
devoperate/chronos
chronos/cli.py
infer
def infer(args: argparse.Namespace) -> None: """ :args: An argparse.Namespace object. This is the function called when the 'infer' sub-command is passed as an argument to the CLI. """ try: last_tag = last_git_release_tag(git_tags()) except NoGitTagsException: print(SemVer(0, 1, 0)) exit(0) commit_log = git_commits_since_last_tag(last_tag) action = parse_commit_log(commit_log) last_ver = git_tag_to_semver(last_tag) if action == 'min': new_ver = last_ver.bump_minor() elif action == 'maj': new_ver = last_ver.bump_major() else: new_ver = last_ver.bump_patch() print(new_ver)
python
def infer(args: argparse.Namespace) -> None: """ :args: An argparse.Namespace object. This is the function called when the 'infer' sub-command is passed as an argument to the CLI. """ try: last_tag = last_git_release_tag(git_tags()) except NoGitTagsException: print(SemVer(0, 1, 0)) exit(0) commit_log = git_commits_since_last_tag(last_tag) action = parse_commit_log(commit_log) last_ver = git_tag_to_semver(last_tag) if action == 'min': new_ver = last_ver.bump_minor() elif action == 'maj': new_ver = last_ver.bump_major() else: new_ver = last_ver.bump_patch() print(new_ver)
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train
https://github.com/devoperate/chronos/blob/5ae6047c4f13db9f5e85a0c72a3dc47f05a8d7bd/chronos/cli.py#L51-L76
devoperate/chronos
chronos/cli.py
bump
def bump(args: argparse.Namespace) -> None: """ :args: An argparse.Namespace object. This function is bound to the 'bump' sub-command. It increments the version integer of the user's choice ('major', 'minor', or 'patch'). """ try: last_tag = last_git_release_tag(git_tags()) except NoGitTagsException: print(SemVer(0, 1, 0)) exit(0) last_ver = git_tag_to_semver(last_tag) if args.type == 'patch': print(last_ver.bump_patch()) elif args.type == 'minor': print(last_ver.bump_minor()) elif args.type == 'major': print(last_ver.bump_major())
python
def bump(args: argparse.Namespace) -> None: """ :args: An argparse.Namespace object. This function is bound to the 'bump' sub-command. It increments the version integer of the user's choice ('major', 'minor', or 'patch'). """ try: last_tag = last_git_release_tag(git_tags()) except NoGitTagsException: print(SemVer(0, 1, 0)) exit(0) last_ver = git_tag_to_semver(last_tag) if args.type == 'patch': print(last_ver.bump_patch()) elif args.type == 'minor': print(last_ver.bump_minor()) elif args.type == 'major': print(last_ver.bump_major())
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train
https://github.com/devoperate/chronos/blob/5ae6047c4f13db9f5e85a0c72a3dc47f05a8d7bd/chronos/cli.py#L91-L111
RI-imaging/qpimage
qpimage/holo.py
find_sideband
def find_sideband(ft_data, which=+1, copy=True): """Find the side band position of a hologram The hologram is Fourier-transformed and the side band is determined by finding the maximum amplitude in Fourier space. Parameters ---------- ft_data: 2d ndarray Fourier transform of the hologram image which: +1 or -1 which sideband to search for: - +1: upper half - -1: lower half copy: bool copy `ft_data` before modification Returns ------- fsx, fsy : tuple of floats coordinates of the side band in Fourier space frequencies """ if copy: ft_data = ft_data.copy() if which not in [+1, -1]: raise ValueError("`which` must be +1 or -1!") ox, oy = ft_data.shape cx = ox // 2 cy = oy // 2 minlo = max(int(np.ceil(ox / 42)), 5) if which == +1: # remove lower part ft_data[cx - minlo:] = 0 else: ft_data[:cx + minlo] = 0 # remove values around axes ft_data[cx - 3:cx + 3, :] = 0 ft_data[:, cy - 3:cy + 3] = 0 # find maximum am = np.argmax(np.abs(ft_data)) iy = am % oy ix = int((am - iy) / oy) fx = np.fft.fftshift(np.fft.fftfreq(ft_data.shape[0]))[ix] fy = np.fft.fftshift(np.fft.fftfreq(ft_data.shape[1]))[iy] return (fx, fy)
python
def find_sideband(ft_data, which=+1, copy=True): """Find the side band position of a hologram The hologram is Fourier-transformed and the side band is determined by finding the maximum amplitude in Fourier space. Parameters ---------- ft_data: 2d ndarray Fourier transform of the hologram image which: +1 or -1 which sideband to search for: - +1: upper half - -1: lower half copy: bool copy `ft_data` before modification Returns ------- fsx, fsy : tuple of floats coordinates of the side band in Fourier space frequencies """ if copy: ft_data = ft_data.copy() if which not in [+1, -1]: raise ValueError("`which` must be +1 or -1!") ox, oy = ft_data.shape cx = ox // 2 cy = oy // 2 minlo = max(int(np.ceil(ox / 42)), 5) if which == +1: # remove lower part ft_data[cx - minlo:] = 0 else: ft_data[:cx + minlo] = 0 # remove values around axes ft_data[cx - 3:cx + 3, :] = 0 ft_data[:, cy - 3:cy + 3] = 0 # find maximum am = np.argmax(np.abs(ft_data)) iy = am % oy ix = int((am - iy) / oy) fx = np.fft.fftshift(np.fft.fftfreq(ft_data.shape[0]))[ix] fy = np.fft.fftshift(np.fft.fftfreq(ft_data.shape[1]))[iy] return (fx, fy)
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train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/holo.py#L5-L58
RI-imaging/qpimage
qpimage/holo.py
fourier2dpad
def fourier2dpad(data, zero_pad=True): """Compute the 2D Fourier transform with zero padding Parameters ---------- data: 2d fload ndarray real-valued image data zero_pad: bool perform zero-padding to next order of 2 """ if zero_pad: # zero padding size is next order of 2 (N, M) = data.shape order = np.int(max(64., 2**np.ceil(np.log(2 * max(N, M)) / np.log(2)))) # this is faster than np.pad datapad = np.zeros((order, order), dtype=float) datapad[:data.shape[0], :data.shape[1]] = data else: datapad = data # Fourier transform fft = np.fft.fftshift(np.fft.fft2(datapad)) return fft
python
def fourier2dpad(data, zero_pad=True): """Compute the 2D Fourier transform with zero padding Parameters ---------- data: 2d fload ndarray real-valued image data zero_pad: bool perform zero-padding to next order of 2 """ if zero_pad: # zero padding size is next order of 2 (N, M) = data.shape order = np.int(max(64., 2**np.ceil(np.log(2 * max(N, M)) / np.log(2)))) # this is faster than np.pad datapad = np.zeros((order, order), dtype=float) datapad[:data.shape[0], :data.shape[1]] = data else: datapad = data # Fourier transform fft = np.fft.fftshift(np.fft.fft2(datapad)) return fft
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Compute the 2D Fourier transform with zero padding Parameters ---------- data: 2d fload ndarray real-valued image data zero_pad: bool perform zero-padding to next order of 2
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train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/holo.py#L61-L84
RI-imaging/qpimage
qpimage/holo.py
get_field
def get_field(hologram, sideband=+1, filter_name="disk", filter_size=1 / 3, subtract_mean=True, zero_pad=True, copy=True): """Compute the complex field from a hologram using Fourier analysis Parameters ---------- hologram: real-valued 2d ndarray hologram data sideband: +1, -1, or tuple of (float, float) specifies the location of the sideband: - +1: sideband in the upper half in Fourier space, exact location is found automatically - -1: sideband in the lower half in Fourier space, exact location is found automatically - (float, float): sideband coordinates in frequencies in interval [1/"axes size", .5] filter_name: str specifies the filter to use, one of - "disk": binary disk with radius `filter_size` - "smooth disk": disk with radius `filter_size` convolved with a radial gaussian (`sigma=filter_size/5`) - "gauss": radial gaussian (`sigma=0.6*filter_size`) - "square": binary square with side length `filter_size` - "smooth square": square with side length `filter_size` convolved with square gaussian (`sigma=filter_size/5`) - "tukey": a square tukey window of width `2*filter_size` and `alpha=0.1` filter_size: float Size of the filter in Fourier space in fractions of the distance between central band and sideband. See `filter_shape` for interpretation of `filter_size`. subtract_mean: bool If True, remove the mean of the hologram before performing the Fourier transform. This setting is recommended as it can reduce artifacts from frequencies around the central band. zero_pad: bool Perform zero-padding before applying the FFT. Setting `zero_pad` to `False` increases speed but might introduce image distortions such as tilts in the phase and amplitude data or dark borders in the amplitude data. copy: bool If set to True, input `data` is not edited. x0 and y0 are center of the filter R is factor for "radius" of filter (sqrt(x0² + y0²)/np.pi) filter_type can be "disk" or "gauss" Notes ----- Even though the size of the "gauss" filter approximately matches the frequencies of the "disk" filter, it takes into account higher frequencies as well and thus suppresses ringing artifacts for data that contain jumps in the phase image. """ if copy: hologram = hologram.astype(dtype=np.float, copy=True) if subtract_mean: # remove contributions of the central band # (this affects more than one pixel in the FFT # because of zero-padding) if issubclass(hologram.dtype.type, np.integer): hologram = hologram.astype(np.float) hologram -= hologram.mean() # Fourier transform fft = fourier2dpad(hologram, zero_pad=zero_pad) if sideband in [+1, -1]: fsx, fsy = find_sideband(fft, which=sideband) else: fsx, fsy = sideband # shift fft to sideband location shifted = np.roll(np.roll(fft, -int(fsx * fft.shape[0]), axis=0), -int(fsy * fft.shape[1]), axis=1) # coordinates in Fourier space fx = np.fft.fftshift(np.fft.fftfreq(fft.shape[0])).reshape(-1, 1) fy = np.fft.fftshift(np.fft.fftfreq(fft.shape[1])).reshape(1, -1) # filter size based on central band - sideband - distance if filter_size >= 1: raise ValueError("`filter_size` must be < 1!") fsize = np.sqrt(fsx**2 + fsy**2) * filter_size if filter_name == "disk": afilter = (fx**2 + fy**2) < fsize**2 elif filter_name == "smooth disk": sigma = fsize / 5 tau = 2 * sigma**2 radsq = fx**2 + fy**2 disk = radsq < fsize**2 gauss = np.exp(-radsq / tau) afilter = signal.convolve(gauss, disk, mode="same") afilter /= afilter.max() elif filter_name == "gauss": sigma = fsize * .6 tau = 2 * sigma**2 afilter = np.exp(-(fx**2 + fy**2) / tau) afilter /= afilter.max() elif filter_name == "square": afilter = (np.abs(fx) < fsize) * (np.abs(fy) < fsize) elif filter_name == "smooth square": blur = fsize / 5 tau = 2 * blur**2 square = (np.abs(fx) < fsize) * (np.abs(fy) < fsize) gauss = np.exp(-(fy**2) / tau) * np.exp(-(fy**2) / tau) afilter = signal.convolve(square, gauss, mode="same") afilter /= afilter.max() elif filter_name == "tukey": alpha = 0.1 rsize = np.int(min(fx.size, fy.size)*fsize) * 2 tukey_window_x = signal.tukey(rsize, alpha=alpha).reshape(-1, 1) tukey_window_y = signal.tukey(rsize, alpha=alpha).reshape(1, -1) tukey = tukey_window_x * tukey_window_y afilter = np.zeros(shifted.shape) s1 = (np.array(shifted.shape) - rsize)//2 s2 = (np.array(shifted.shape) + rsize)//2 afilter[s1[0]:s2[0], s1[1]:s2[1]] = tukey else: raise ValueError("Unknown filter: {}".format(filter_name)) # apply filter fft_filt = afilter * shifted # inverse Fourier transform field = np.fft.ifft2(np.fft.ifftshift(fft_filt)) return field[:hologram.shape[0], :hologram.shape[1]]
python
def get_field(hologram, sideband=+1, filter_name="disk", filter_size=1 / 3, subtract_mean=True, zero_pad=True, copy=True): """Compute the complex field from a hologram using Fourier analysis Parameters ---------- hologram: real-valued 2d ndarray hologram data sideband: +1, -1, or tuple of (float, float) specifies the location of the sideband: - +1: sideband in the upper half in Fourier space, exact location is found automatically - -1: sideband in the lower half in Fourier space, exact location is found automatically - (float, float): sideband coordinates in frequencies in interval [1/"axes size", .5] filter_name: str specifies the filter to use, one of - "disk": binary disk with radius `filter_size` - "smooth disk": disk with radius `filter_size` convolved with a radial gaussian (`sigma=filter_size/5`) - "gauss": radial gaussian (`sigma=0.6*filter_size`) - "square": binary square with side length `filter_size` - "smooth square": square with side length `filter_size` convolved with square gaussian (`sigma=filter_size/5`) - "tukey": a square tukey window of width `2*filter_size` and `alpha=0.1` filter_size: float Size of the filter in Fourier space in fractions of the distance between central band and sideband. See `filter_shape` for interpretation of `filter_size`. subtract_mean: bool If True, remove the mean of the hologram before performing the Fourier transform. This setting is recommended as it can reduce artifacts from frequencies around the central band. zero_pad: bool Perform zero-padding before applying the FFT. Setting `zero_pad` to `False` increases speed but might introduce image distortions such as tilts in the phase and amplitude data or dark borders in the amplitude data. copy: bool If set to True, input `data` is not edited. x0 and y0 are center of the filter R is factor for "radius" of filter (sqrt(x0² + y0²)/np.pi) filter_type can be "disk" or "gauss" Notes ----- Even though the size of the "gauss" filter approximately matches the frequencies of the "disk" filter, it takes into account higher frequencies as well and thus suppresses ringing artifacts for data that contain jumps in the phase image. """ if copy: hologram = hologram.astype(dtype=np.float, copy=True) if subtract_mean: # remove contributions of the central band # (this affects more than one pixel in the FFT # because of zero-padding) if issubclass(hologram.dtype.type, np.integer): hologram = hologram.astype(np.float) hologram -= hologram.mean() # Fourier transform fft = fourier2dpad(hologram, zero_pad=zero_pad) if sideband in [+1, -1]: fsx, fsy = find_sideband(fft, which=sideband) else: fsx, fsy = sideband # shift fft to sideband location shifted = np.roll(np.roll(fft, -int(fsx * fft.shape[0]), axis=0), -int(fsy * fft.shape[1]), axis=1) # coordinates in Fourier space fx = np.fft.fftshift(np.fft.fftfreq(fft.shape[0])).reshape(-1, 1) fy = np.fft.fftshift(np.fft.fftfreq(fft.shape[1])).reshape(1, -1) # filter size based on central band - sideband - distance if filter_size >= 1: raise ValueError("`filter_size` must be < 1!") fsize = np.sqrt(fsx**2 + fsy**2) * filter_size if filter_name == "disk": afilter = (fx**2 + fy**2) < fsize**2 elif filter_name == "smooth disk": sigma = fsize / 5 tau = 2 * sigma**2 radsq = fx**2 + fy**2 disk = radsq < fsize**2 gauss = np.exp(-radsq / tau) afilter = signal.convolve(gauss, disk, mode="same") afilter /= afilter.max() elif filter_name == "gauss": sigma = fsize * .6 tau = 2 * sigma**2 afilter = np.exp(-(fx**2 + fy**2) / tau) afilter /= afilter.max() elif filter_name == "square": afilter = (np.abs(fx) < fsize) * (np.abs(fy) < fsize) elif filter_name == "smooth square": blur = fsize / 5 tau = 2 * blur**2 square = (np.abs(fx) < fsize) * (np.abs(fy) < fsize) gauss = np.exp(-(fy**2) / tau) * np.exp(-(fy**2) / tau) afilter = signal.convolve(square, gauss, mode="same") afilter /= afilter.max() elif filter_name == "tukey": alpha = 0.1 rsize = np.int(min(fx.size, fy.size)*fsize) * 2 tukey_window_x = signal.tukey(rsize, alpha=alpha).reshape(-1, 1) tukey_window_y = signal.tukey(rsize, alpha=alpha).reshape(1, -1) tukey = tukey_window_x * tukey_window_y afilter = np.zeros(shifted.shape) s1 = (np.array(shifted.shape) - rsize)//2 s2 = (np.array(shifted.shape) + rsize)//2 afilter[s1[0]:s2[0], s1[1]:s2[1]] = tukey else: raise ValueError("Unknown filter: {}".format(filter_name)) # apply filter fft_filt = afilter * shifted # inverse Fourier transform field = np.fft.ifft2(np.fft.ifftshift(fft_filt)) return field[:hologram.shape[0], :hologram.shape[1]]
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Compute the complex field from a hologram using Fourier analysis Parameters ---------- hologram: real-valued 2d ndarray hologram data sideband: +1, -1, or tuple of (float, float) specifies the location of the sideband: - +1: sideband in the upper half in Fourier space, exact location is found automatically - -1: sideband in the lower half in Fourier space, exact location is found automatically - (float, float): sideband coordinates in frequencies in interval [1/"axes size", .5] filter_name: str specifies the filter to use, one of - "disk": binary disk with radius `filter_size` - "smooth disk": disk with radius `filter_size` convolved with a radial gaussian (`sigma=filter_size/5`) - "gauss": radial gaussian (`sigma=0.6*filter_size`) - "square": binary square with side length `filter_size` - "smooth square": square with side length `filter_size` convolved with square gaussian (`sigma=filter_size/5`) - "tukey": a square tukey window of width `2*filter_size` and `alpha=0.1` filter_size: float Size of the filter in Fourier space in fractions of the distance between central band and sideband. See `filter_shape` for interpretation of `filter_size`. subtract_mean: bool If True, remove the mean of the hologram before performing the Fourier transform. This setting is recommended as it can reduce artifacts from frequencies around the central band. zero_pad: bool Perform zero-padding before applying the FFT. Setting `zero_pad` to `False` increases speed but might introduce image distortions such as tilts in the phase and amplitude data or dark borders in the amplitude data. copy: bool If set to True, input `data` is not edited. x0 and y0 are center of the filter R is factor for "radius" of filter (sqrt(x0² + y0²)/np.pi) filter_type can be "disk" or "gauss" Notes ----- Even though the size of the "gauss" filter approximately matches the frequencies of the "disk" filter, it takes into account higher frequencies as well and thus suppresses ringing artifacts for data that contain jumps in the phase image.
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train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/holo.py#L87-L219
RI-imaging/qpimage
qpimage/core.py
copyh5
def copyh5(inh5, outh5): """Recursively copy all hdf5 data from one group to another Data from links is copied. Parameters ---------- inh5: str, h5py.File, or h5py.Group The input hdf5 data. This can be either a file name or an hdf5 object. outh5: str, h5py.File, h5py.Group, or None The output hdf5 data. This can be either a file name or an hdf5 object. If set to `None`, a new hdf5 object is created in memory. Notes ----- All data in outh5 are overridden by the inh5 data. """ if not isinstance(inh5, h5py.Group): inh5 = h5py.File(inh5, mode="r") if outh5 is None: # create file in memory h5kwargs = {"name": "qpimage{}.h5".format(QPImage._instances), "driver": "core", "backing_store": False, "mode": "a"} outh5 = h5py.File(**h5kwargs) return_h5obj = True QPImage._instances += 1 elif not isinstance(outh5, h5py.Group): # create new file outh5 = h5py.File(outh5, mode="w") return_h5obj = False else: return_h5obj = True # begin iteration for key in inh5: if key in outh5: del outh5[key] if isinstance(inh5[key], h5py.Group): outh5.create_group(key) copyh5(inh5[key], outh5[key]) else: dset = write_image_dataset(group=outh5, key=key, data=inh5[key][:], h5dtype=inh5[key].dtype) dset.attrs.update(inh5[key].attrs) outh5.attrs.update(inh5.attrs) if return_h5obj: # in-memory or previously created instance of h5py.File return outh5 else: # properly close the file and return its name fn = outh5.filename outh5.flush() outh5.close() return fn
python
def copyh5(inh5, outh5): """Recursively copy all hdf5 data from one group to another Data from links is copied. Parameters ---------- inh5: str, h5py.File, or h5py.Group The input hdf5 data. This can be either a file name or an hdf5 object. outh5: str, h5py.File, h5py.Group, or None The output hdf5 data. This can be either a file name or an hdf5 object. If set to `None`, a new hdf5 object is created in memory. Notes ----- All data in outh5 are overridden by the inh5 data. """ if not isinstance(inh5, h5py.Group): inh5 = h5py.File(inh5, mode="r") if outh5 is None: # create file in memory h5kwargs = {"name": "qpimage{}.h5".format(QPImage._instances), "driver": "core", "backing_store": False, "mode": "a"} outh5 = h5py.File(**h5kwargs) return_h5obj = True QPImage._instances += 1 elif not isinstance(outh5, h5py.Group): # create new file outh5 = h5py.File(outh5, mode="w") return_h5obj = False else: return_h5obj = True # begin iteration for key in inh5: if key in outh5: del outh5[key] if isinstance(inh5[key], h5py.Group): outh5.create_group(key) copyh5(inh5[key], outh5[key]) else: dset = write_image_dataset(group=outh5, key=key, data=inh5[key][:], h5dtype=inh5[key].dtype) dset.attrs.update(inh5[key].attrs) outh5.attrs.update(inh5.attrs) if return_h5obj: # in-memory or previously created instance of h5py.File return outh5 else: # properly close the file and return its name fn = outh5.filename outh5.flush() outh5.close() return fn
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train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/core.py#L597-L655
RI-imaging/qpimage
qpimage/core.py
QPImage._conv_which_data
def _conv_which_data(which_data): """Convert which data to string or tuple This function improves user convenience, as `which_data` may be of several types (str, ,str with spaces and commas, list, tuple) which is internally handled by this method. """ if isinstance(which_data, str): which_data = which_data.lower().strip() if which_data.count(","): # convert comma string to list which_data = [w.strip() for w in which_data.split(",")] # remove empty strings which_data = [w for w in which_data if w] if len(which_data) == 1: return which_data[0] else: # convert to tuple return tuple(which_data) else: return which_data elif isinstance(which_data, (list, tuple)): which_data = [w.lower().strip() for w in which_data] return tuple(which_data) elif which_data is None: return None else: msg = "unknown type for `which_data`: {}".format(which_data) raise ValueError(msg)
python
def _conv_which_data(which_data): """Convert which data to string or tuple This function improves user convenience, as `which_data` may be of several types (str, ,str with spaces and commas, list, tuple) which is internally handled by this method. """ if isinstance(which_data, str): which_data = which_data.lower().strip() if which_data.count(","): # convert comma string to list which_data = [w.strip() for w in which_data.split(",")] # remove empty strings which_data = [w for w in which_data if w] if len(which_data) == 1: return which_data[0] else: # convert to tuple return tuple(which_data) else: return which_data elif isinstance(which_data, (list, tuple)): which_data = [w.lower().strip() for w in which_data] return tuple(which_data) elif which_data is None: return None else: msg = "unknown type for `which_data`: {}".format(which_data) raise ValueError(msg)
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Convert which data to string or tuple This function improves user convenience, as `which_data` may be of several types (str, ,str with spaces and commas, list, tuple) which is internally handled by this method.
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train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/core.py#L208-L237
RI-imaging/qpimage
qpimage/core.py
QPImage._get_amp_pha
def _get_amp_pha(self, data, which_data): """Convert input data to phase and amplitude Parameters ---------- data: 2d ndarray (float or complex) or list The experimental data (see `which_data`) which_data: str String or comma-separated list of strings indicating the order and type of input data. Valid values are "field", "phase", "hologram", "phase,amplitude", or "phase,intensity", where the latter two require an indexable object with the phase data as first element. Returns ------- amp, pha: tuple of (:class:`Amplitdue`, :class:`Phase`) """ which_data = QPImage._conv_which_data(which_data) if which_data not in VALID_INPUT_DATA: msg = "`which_data` must be one of {}!".format(VALID_INPUT_DATA) raise ValueError(msg) if which_data == "field": amp = np.abs(data) pha = np.angle(data) elif which_data == "phase": pha = data amp = np.ones_like(data) elif which_data == ("phase", "amplitude"): amp = data[1] pha = data[0] elif which_data == ("phase", "intensity"): amp = np.sqrt(data[1]) pha = data[0] elif which_data == "hologram": amp, pha = self._get_amp_pha(holo.get_field(data, **self.holo_kw), which_data="field") if amp.size == 0 or pha.size == 0: msg = "`data` with shape {} has zero size!".format(amp.shape) raise ValueError(msg) # phase unwrapping (take into account nans) nanmask = np.isnan(pha) if np.sum(nanmask): # create masked array # skimage.restoration.unwrap_phase cannot handle nan data # (even if masked) pham = pha.copy() pham[nanmask] = 0 pham = np.ma.masked_array(pham, mask=nanmask) pha = unwrap_phase(pham, seed=47) pha[nanmask] = np.nan else: pha = unwrap_phase(pha, seed=47) return amp, pha
python
def _get_amp_pha(self, data, which_data): """Convert input data to phase and amplitude Parameters ---------- data: 2d ndarray (float or complex) or list The experimental data (see `which_data`) which_data: str String or comma-separated list of strings indicating the order and type of input data. Valid values are "field", "phase", "hologram", "phase,amplitude", or "phase,intensity", where the latter two require an indexable object with the phase data as first element. Returns ------- amp, pha: tuple of (:class:`Amplitdue`, :class:`Phase`) """ which_data = QPImage._conv_which_data(which_data) if which_data not in VALID_INPUT_DATA: msg = "`which_data` must be one of {}!".format(VALID_INPUT_DATA) raise ValueError(msg) if which_data == "field": amp = np.abs(data) pha = np.angle(data) elif which_data == "phase": pha = data amp = np.ones_like(data) elif which_data == ("phase", "amplitude"): amp = data[1] pha = data[0] elif which_data == ("phase", "intensity"): amp = np.sqrt(data[1]) pha = data[0] elif which_data == "hologram": amp, pha = self._get_amp_pha(holo.get_field(data, **self.holo_kw), which_data="field") if amp.size == 0 or pha.size == 0: msg = "`data` with shape {} has zero size!".format(amp.shape) raise ValueError(msg) # phase unwrapping (take into account nans) nanmask = np.isnan(pha) if np.sum(nanmask): # create masked array # skimage.restoration.unwrap_phase cannot handle nan data # (even if masked) pham = pha.copy() pham[nanmask] = 0 pham = np.ma.masked_array(pham, mask=nanmask) pha = unwrap_phase(pham, seed=47) pha[nanmask] = np.nan else: pha = unwrap_phase(pha, seed=47) return amp, pha
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Convert input data to phase and amplitude Parameters ---------- data: 2d ndarray (float or complex) or list The experimental data (see `which_data`) which_data: str String or comma-separated list of strings indicating the order and type of input data. Valid values are "field", "phase", "hologram", "phase,amplitude", or "phase,intensity", where the latter two require an indexable object with the phase data as first element. Returns ------- amp, pha: tuple of (:class:`Amplitdue`, :class:`Phase`)
[ "Convert", "input", "data", "to", "phase", "and", "amplitude" ]
train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/core.py#L239-L294
RI-imaging/qpimage
qpimage/core.py
QPImage.info
def info(self): """list of tuples with QPImage meta data""" info = [] # meta data meta = self.meta for key in meta: info.append((key, self.meta[key])) # background correction for imdat in [self._amp, self._pha]: info += imdat.info return info
python
def info(self): """list of tuples with QPImage meta data""" info = [] # meta data meta = self.meta for key in meta: info.append((key, self.meta[key])) # background correction for imdat in [self._amp, self._pha]: info += imdat.info return info
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list of tuples with QPImage meta data
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train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/core.py#L322-L332
RI-imaging/qpimage
qpimage/core.py
QPImage.clear_bg
def clear_bg(self, which_data=("amplitude", "phase"), keys="fit"): """Clear background correction Parameters ---------- which_data: str or list of str From which type of data to remove the background information. The list contains either "amplitude", "phase", or both. keys: str or list of str Which type of background data to remove. One of: - "fit": the background data computed with :func:`qpimage.QPImage.compute_bg` - "data": the experimentally obtained background image """ which_data = QPImage._conv_which_data(which_data) if isinstance(keys, str): # make sure keys is a list of strings keys = [keys] # Get image data for clearing imdats = [] if "amplitude" in which_data: imdats.append(self._amp) if "phase" in which_data: imdats.append(self._pha) if not imdats: msg = "`which_data` must contain 'phase' or 'amplitude'!" raise ValueError(msg) # Perform clearing of backgrounds for imdat in imdats: for key in keys: imdat.del_bg(key)
python
def clear_bg(self, which_data=("amplitude", "phase"), keys="fit"): """Clear background correction Parameters ---------- which_data: str or list of str From which type of data to remove the background information. The list contains either "amplitude", "phase", or both. keys: str or list of str Which type of background data to remove. One of: - "fit": the background data computed with :func:`qpimage.QPImage.compute_bg` - "data": the experimentally obtained background image """ which_data = QPImage._conv_which_data(which_data) if isinstance(keys, str): # make sure keys is a list of strings keys = [keys] # Get image data for clearing imdats = [] if "amplitude" in which_data: imdats.append(self._amp) if "phase" in which_data: imdats.append(self._pha) if not imdats: msg = "`which_data` must contain 'phase' or 'amplitude'!" raise ValueError(msg) # Perform clearing of backgrounds for imdat in imdats: for key in keys: imdat.del_bg(key)
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Clear background correction Parameters ---------- which_data: str or list of str From which type of data to remove the background information. The list contains either "amplitude", "phase", or both. keys: str or list of str Which type of background data to remove. One of: - "fit": the background data computed with :func:`qpimage.QPImage.compute_bg` - "data": the experimentally obtained background image
[ "Clear", "background", "correction" ]
train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/core.py#L359-L392
RI-imaging/qpimage
qpimage/core.py
QPImage.compute_bg
def compute_bg(self, which_data="phase", fit_offset="mean", fit_profile="tilt", border_m=0, border_perc=0, border_px=0, from_mask=None, ret_mask=False): """Compute background correction Parameters ---------- which_data: str or list of str From which type of data to remove the background information. The list contains either "amplitude", "phase", or both. fit_profile: str The type of background profile to fit: - "offset": offset only - "poly2o": 2D 2nd order polynomial with mixed terms - "tilt": 2D linear tilt with offset (default) fit_offset: str The method for computing the profile offset - "fit": offset as fitting parameter - "gauss": center of a gaussian fit - "mean": simple average - "mode": mode (see `qpimage.bg_estimate.mode`) border_m: float Assume that a frame of `border_m` meters around the image is background. The value is converted to pixels and rounded. border_perc: float Assume that a frame of `border_perc` percent around the image is background. The value is converted to pixels and rounded. If the aspect ratio of the image is not one, then the average of the data's shape is used to compute the percentage in pixels. border_px: float Assume that a frame of `border_px` pixels around the image is background. from_mask: boolean np.ndarray or None Use a boolean array to define the background area. The boolean mask must have the same shape as the input data. `True` elements are used for background estimation. ret_mask: bool Return the boolean mask used to compute the background. Notes ----- The `border_*` values are translated to pixel values and the largest pixel border is used to generate a mask image for background computation. If any of the `border_*` arguments are non-zero and `from_mask` is given, the intersection of the two is used, i.e. the positions where both, the frame mask and `from_mask`, are `True`. See Also -------- qpimage.bg_estimate.estimate """ which_data = QPImage._conv_which_data(which_data) # check validity if not ("amplitude" in which_data or "phase" in which_data): msg = "`which_data` must contain 'phase' or 'amplitude'!" raise ValueError(msg) # get border in px border_list = [] if border_m: if border_m < 0: raise ValueError("`border_m` must be greater than zero!") border_list.append(border_m / self.meta["pixel size"]) if border_perc: if border_perc < 0 or border_perc > 50: raise ValueError("`border_perc` must be in interval [0, 50]!") size = np.average(self.shape) border_list.append(size * border_perc / 100) if border_px: border_list.append(border_px) # get maximum border size if border_list: border_px = np.int(np.round(np.max(border_list))) elif from_mask is None: raise ValueError("Neither `from_mask` nor `border_*` given!") elif np.all(from_mask == 0): raise ValueError("`from_mask` must not be all-zero!") # Get affected image data imdat_list = [] if "amplitude" in which_data: imdat_list.append(self._amp) if "phase" in which_data: imdat_list.append(self._pha) # Perform correction for imdat in imdat_list: mask = imdat.estimate_bg(fit_offset=fit_offset, fit_profile=fit_profile, border_px=border_px, from_mask=from_mask, ret_mask=ret_mask) return mask
python
def compute_bg(self, which_data="phase", fit_offset="mean", fit_profile="tilt", border_m=0, border_perc=0, border_px=0, from_mask=None, ret_mask=False): """Compute background correction Parameters ---------- which_data: str or list of str From which type of data to remove the background information. The list contains either "amplitude", "phase", or both. fit_profile: str The type of background profile to fit: - "offset": offset only - "poly2o": 2D 2nd order polynomial with mixed terms - "tilt": 2D linear tilt with offset (default) fit_offset: str The method for computing the profile offset - "fit": offset as fitting parameter - "gauss": center of a gaussian fit - "mean": simple average - "mode": mode (see `qpimage.bg_estimate.mode`) border_m: float Assume that a frame of `border_m` meters around the image is background. The value is converted to pixels and rounded. border_perc: float Assume that a frame of `border_perc` percent around the image is background. The value is converted to pixels and rounded. If the aspect ratio of the image is not one, then the average of the data's shape is used to compute the percentage in pixels. border_px: float Assume that a frame of `border_px` pixels around the image is background. from_mask: boolean np.ndarray or None Use a boolean array to define the background area. The boolean mask must have the same shape as the input data. `True` elements are used for background estimation. ret_mask: bool Return the boolean mask used to compute the background. Notes ----- The `border_*` values are translated to pixel values and the largest pixel border is used to generate a mask image for background computation. If any of the `border_*` arguments are non-zero and `from_mask` is given, the intersection of the two is used, i.e. the positions where both, the frame mask and `from_mask`, are `True`. See Also -------- qpimage.bg_estimate.estimate """ which_data = QPImage._conv_which_data(which_data) # check validity if not ("amplitude" in which_data or "phase" in which_data): msg = "`which_data` must contain 'phase' or 'amplitude'!" raise ValueError(msg) # get border in px border_list = [] if border_m: if border_m < 0: raise ValueError("`border_m` must be greater than zero!") border_list.append(border_m / self.meta["pixel size"]) if border_perc: if border_perc < 0 or border_perc > 50: raise ValueError("`border_perc` must be in interval [0, 50]!") size = np.average(self.shape) border_list.append(size * border_perc / 100) if border_px: border_list.append(border_px) # get maximum border size if border_list: border_px = np.int(np.round(np.max(border_list))) elif from_mask is None: raise ValueError("Neither `from_mask` nor `border_*` given!") elif np.all(from_mask == 0): raise ValueError("`from_mask` must not be all-zero!") # Get affected image data imdat_list = [] if "amplitude" in which_data: imdat_list.append(self._amp) if "phase" in which_data: imdat_list.append(self._pha) # Perform correction for imdat in imdat_list: mask = imdat.estimate_bg(fit_offset=fit_offset, fit_profile=fit_profile, border_px=border_px, from_mask=from_mask, ret_mask=ret_mask) return mask
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Compute background correction Parameters ---------- which_data: str or list of str From which type of data to remove the background information. The list contains either "amplitude", "phase", or both. fit_profile: str The type of background profile to fit: - "offset": offset only - "poly2o": 2D 2nd order polynomial with mixed terms - "tilt": 2D linear tilt with offset (default) fit_offset: str The method for computing the profile offset - "fit": offset as fitting parameter - "gauss": center of a gaussian fit - "mean": simple average - "mode": mode (see `qpimage.bg_estimate.mode`) border_m: float Assume that a frame of `border_m` meters around the image is background. The value is converted to pixels and rounded. border_perc: float Assume that a frame of `border_perc` percent around the image is background. The value is converted to pixels and rounded. If the aspect ratio of the image is not one, then the average of the data's shape is used to compute the percentage in pixels. border_px: float Assume that a frame of `border_px` pixels around the image is background. from_mask: boolean np.ndarray or None Use a boolean array to define the background area. The boolean mask must have the same shape as the input data. `True` elements are used for background estimation. ret_mask: bool Return the boolean mask used to compute the background. Notes ----- The `border_*` values are translated to pixel values and the largest pixel border is used to generate a mask image for background computation. If any of the `border_*` arguments are non-zero and `from_mask` is given, the intersection of the two is used, i.e. the positions where both, the frame mask and `from_mask`, are `True`. See Also -------- qpimage.bg_estimate.estimate
[ "Compute", "background", "correction" ]
train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/core.py#L394-L494
RI-imaging/qpimage
qpimage/core.py
QPImage.copy
def copy(self, h5file=None): """Create a copy of the current instance This is done by recursively copying the underlying hdf5 data. Parameters ---------- h5file: str, h5py.File, h5py.Group, or None see `QPImage.__init__` """ h5 = copyh5(self.h5, h5file) return QPImage(h5file=h5, h5dtype=self.h5dtype)
python
def copy(self, h5file=None): """Create a copy of the current instance This is done by recursively copying the underlying hdf5 data. Parameters ---------- h5file: str, h5py.File, h5py.Group, or None see `QPImage.__init__` """ h5 = copyh5(self.h5, h5file) return QPImage(h5file=h5, h5dtype=self.h5dtype)
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Create a copy of the current instance This is done by recursively copying the underlying hdf5 data. Parameters ---------- h5file: str, h5py.File, h5py.Group, or None see `QPImage.__init__`
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train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/core.py#L496-L507
RI-imaging/qpimage
qpimage/core.py
QPImage.refocus
def refocus(self, distance, method="helmholtz", h5file=None, h5mode="a"): """Compute a numerically refocused QPImage Parameters ---------- distance: float Focusing distance [m] method: str Refocusing method, one of ["helmholtz","fresnel"] h5file: str, h5py.Group, h5py.File, or None A path to an hdf5 data file where the QPImage is cached. If set to `None` (default), all data will be handled in memory using the "core" driver of the :mod:`h5py`'s :class:`h5py:File` class. If the file does not exist, it is created. If the file already exists, it is opened with the file mode defined by `hdf5_mode`. If this is an instance of h5py.Group or h5py.File, then this will be used to internally store all data. h5mode: str Valid file modes are (only applies if `h5file` is a path) - "r": Readonly, file must exist - "r+": Read/write, file must exist - "w": Create file, truncate if exists - "w-" or "x": Create file, fail if exists - "a": Read/write if exists, create otherwise (default) Returns ------- qpi: qpimage.QPImage Refocused phase and amplitude data See Also -------- :mod:`nrefocus`: library used for numerical focusing """ field2 = nrefocus.refocus(field=self.field, d=distance/self["pixel size"], nm=self["medium index"], res=self["wavelength"]/self["pixel size"], method=method ) if "identifier" in self: ident = self["identifier"] else: ident = "" meta_data = self.meta meta_data["identifier"] = "{}@{}{:.5e}m".format(ident, method[0], distance) qpi2 = QPImage(data=field2, which_data="field", meta_data=meta_data, h5file=h5file, h5mode=h5mode) return qpi2
python
def refocus(self, distance, method="helmholtz", h5file=None, h5mode="a"): """Compute a numerically refocused QPImage Parameters ---------- distance: float Focusing distance [m] method: str Refocusing method, one of ["helmholtz","fresnel"] h5file: str, h5py.Group, h5py.File, or None A path to an hdf5 data file where the QPImage is cached. If set to `None` (default), all data will be handled in memory using the "core" driver of the :mod:`h5py`'s :class:`h5py:File` class. If the file does not exist, it is created. If the file already exists, it is opened with the file mode defined by `hdf5_mode`. If this is an instance of h5py.Group or h5py.File, then this will be used to internally store all data. h5mode: str Valid file modes are (only applies if `h5file` is a path) - "r": Readonly, file must exist - "r+": Read/write, file must exist - "w": Create file, truncate if exists - "w-" or "x": Create file, fail if exists - "a": Read/write if exists, create otherwise (default) Returns ------- qpi: qpimage.QPImage Refocused phase and amplitude data See Also -------- :mod:`nrefocus`: library used for numerical focusing """ field2 = nrefocus.refocus(field=self.field, d=distance/self["pixel size"], nm=self["medium index"], res=self["wavelength"]/self["pixel size"], method=method ) if "identifier" in self: ident = self["identifier"] else: ident = "" meta_data = self.meta meta_data["identifier"] = "{}@{}{:.5e}m".format(ident, method[0], distance) qpi2 = QPImage(data=field2, which_data="field", meta_data=meta_data, h5file=h5file, h5mode=h5mode) return qpi2
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Compute a numerically refocused QPImage Parameters ---------- distance: float Focusing distance [m] method: str Refocusing method, one of ["helmholtz","fresnel"] h5file: str, h5py.Group, h5py.File, or None A path to an hdf5 data file where the QPImage is cached. If set to `None` (default), all data will be handled in memory using the "core" driver of the :mod:`h5py`'s :class:`h5py:File` class. If the file does not exist, it is created. If the file already exists, it is opened with the file mode defined by `hdf5_mode`. If this is an instance of h5py.Group or h5py.File, then this will be used to internally store all data. h5mode: str Valid file modes are (only applies if `h5file` is a path) - "r": Readonly, file must exist - "r+": Read/write, file must exist - "w": Create file, truncate if exists - "w-" or "x": Create file, fail if exists - "a": Read/write if exists, create otherwise (default) Returns ------- qpi: qpimage.QPImage Refocused phase and amplitude data See Also -------- :mod:`nrefocus`: library used for numerical focusing
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train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/core.py#L509-L564
RI-imaging/qpimage
qpimage/core.py
QPImage.set_bg_data
def set_bg_data(self, bg_data, which_data=None): """Set background amplitude and phase data Parameters ---------- bg_data: 2d ndarray (float or complex), list, QPImage, or `None` The background data (must be same type as `data`). If set to `None`, the background data is reset. which_data: str String or comma-separated list of strings indicating the order and type of input data. Valid values are "field", "phase", "phase,amplitude", or "phase,intensity", where the latter two require an indexable object for `bg_data` with the phase data as first element. """ if isinstance(bg_data, QPImage): if which_data is not None: msg = "`which_data` must not be set if `bg_data` is QPImage!" raise ValueError(msg) pha, amp = bg_data.pha, bg_data.amp elif bg_data is None: # Reset phase and amplitude amp, pha = None, None else: # Compute phase and amplitude from data and which_data amp, pha = self._get_amp_pha(bg_data, which_data) # Set background data self._amp.set_bg(amp, key="data") self._pha.set_bg(pha, key="data")
python
def set_bg_data(self, bg_data, which_data=None): """Set background amplitude and phase data Parameters ---------- bg_data: 2d ndarray (float or complex), list, QPImage, or `None` The background data (must be same type as `data`). If set to `None`, the background data is reset. which_data: str String or comma-separated list of strings indicating the order and type of input data. Valid values are "field", "phase", "phase,amplitude", or "phase,intensity", where the latter two require an indexable object for `bg_data` with the phase data as first element. """ if isinstance(bg_data, QPImage): if which_data is not None: msg = "`which_data` must not be set if `bg_data` is QPImage!" raise ValueError(msg) pha, amp = bg_data.pha, bg_data.amp elif bg_data is None: # Reset phase and amplitude amp, pha = None, None else: # Compute phase and amplitude from data and which_data amp, pha = self._get_amp_pha(bg_data, which_data) # Set background data self._amp.set_bg(amp, key="data") self._pha.set_bg(pha, key="data")
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Set background amplitude and phase data Parameters ---------- bg_data: 2d ndarray (float or complex), list, QPImage, or `None` The background data (must be same type as `data`). If set to `None`, the background data is reset. which_data: str String or comma-separated list of strings indicating the order and type of input data. Valid values are "field", "phase", "phase,amplitude", or "phase,intensity", where the latter two require an indexable object for `bg_data` with the phase data as first element.
[ "Set", "background", "amplitude", "and", "phase", "data" ]
train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/core.py#L566-L594
RI-imaging/qpimage
qpimage/series.py
QPSeries.add_qpimage
def add_qpimage(self, qpi, identifier=None, bg_from_idx=None): """Add a QPImage instance to the QPSeries Parameters ---------- qpi: qpimage.QPImage The QPImage that is added to the series identifier: str Identifier key for `qpi` bg_from_idx: int or None Use the background data from the data stored in this index, creating hard links within the hdf5 file. (Saves memory if e.g. all qpimages is corrected with the same data) """ if not isinstance(qpi, QPImage): raise ValueError("`fli` must be instance of QPImage!") if "identifier" in qpi and identifier is None: identifier = qpi["identifier"] if identifier and identifier in self: msg = "The identifier '{}' already ".format(identifier) \ + "exists! You can either change the identifier of " \ + " '{}' or remove it.".format(qpi) raise ValueError(msg) # determine number of qpimages num = len(self) # indices start at zero; do not add 1 name = "qpi_{}".format(num) group = self.h5.create_group(name) thisqpi = qpi.copy(h5file=group) if bg_from_idx is not None: # Create hard links refqpi = self[bg_from_idx] thisqpi._amp.set_bg(bg=refqpi._amp.h5["bg_data"]["data"]) thisqpi._pha.set_bg(bg=refqpi._pha.h5["bg_data"]["data"]) if identifier: # set identifier group.attrs["identifier"] = identifier
python
def add_qpimage(self, qpi, identifier=None, bg_from_idx=None): """Add a QPImage instance to the QPSeries Parameters ---------- qpi: qpimage.QPImage The QPImage that is added to the series identifier: str Identifier key for `qpi` bg_from_idx: int or None Use the background data from the data stored in this index, creating hard links within the hdf5 file. (Saves memory if e.g. all qpimages is corrected with the same data) """ if not isinstance(qpi, QPImage): raise ValueError("`fli` must be instance of QPImage!") if "identifier" in qpi and identifier is None: identifier = qpi["identifier"] if identifier and identifier in self: msg = "The identifier '{}' already ".format(identifier) \ + "exists! You can either change the identifier of " \ + " '{}' or remove it.".format(qpi) raise ValueError(msg) # determine number of qpimages num = len(self) # indices start at zero; do not add 1 name = "qpi_{}".format(num) group = self.h5.create_group(name) thisqpi = qpi.copy(h5file=group) if bg_from_idx is not None: # Create hard links refqpi = self[bg_from_idx] thisqpi._amp.set_bg(bg=refqpi._amp.h5["bg_data"]["data"]) thisqpi._pha.set_bg(bg=refqpi._pha.h5["bg_data"]["data"]) if identifier: # set identifier group.attrs["identifier"] = identifier
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Add a QPImage instance to the QPSeries Parameters ---------- qpi: qpimage.QPImage The QPImage that is added to the series identifier: str Identifier key for `qpi` bg_from_idx: int or None Use the background data from the data stored in this index, creating hard links within the hdf5 file. (Saves memory if e.g. all qpimages is corrected with the same data)
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train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/series.py#L130-L168
RI-imaging/qpimage
qpimage/series.py
QPSeries.get_qpimage
def get_qpimage(self, index): """Return a single QPImage of the series Parameters ---------- index: int or str Index or identifier of the QPImage Notes ----- Instead of ``qps.get_qpimage(index)``, it is possible to use the short-hand ``qps[index]``. """ if isinstance(index, str): # search for the identifier for ii in range(len(self)): qpi = self[ii] if "identifier" in qpi and qpi["identifier"] == index: group = self.h5["qpi_{}".format(ii)] break else: msg = "QPImage identifier '{}' not found!".format(index) raise KeyError(msg) else: # integer index if index < -len(self): msg = "Index {} out of bounds for QPSeries of size {}!".format( index, len(self)) raise ValueError(msg) elif index < 0: index += len(self) name = "qpi_{}".format(index) if name in self.h5: group = self.h5[name] else: msg = "Index {} not found for QPSeries of length {}".format( index, len(self)) raise KeyError(msg) return QPImage(h5file=group)
python
def get_qpimage(self, index): """Return a single QPImage of the series Parameters ---------- index: int or str Index or identifier of the QPImage Notes ----- Instead of ``qps.get_qpimage(index)``, it is possible to use the short-hand ``qps[index]``. """ if isinstance(index, str): # search for the identifier for ii in range(len(self)): qpi = self[ii] if "identifier" in qpi and qpi["identifier"] == index: group = self.h5["qpi_{}".format(ii)] break else: msg = "QPImage identifier '{}' not found!".format(index) raise KeyError(msg) else: # integer index if index < -len(self): msg = "Index {} out of bounds for QPSeries of size {}!".format( index, len(self)) raise ValueError(msg) elif index < 0: index += len(self) name = "qpi_{}".format(index) if name in self.h5: group = self.h5[name] else: msg = "Index {} not found for QPSeries of length {}".format( index, len(self)) raise KeyError(msg) return QPImage(h5file=group)
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train
https://github.com/RI-imaging/qpimage/blob/863c0fce5735b4c0ae369f75c0df9a33411b2bb2/qpimage/series.py#L170-L208
Parquery/icontract
precommit.py
main
def main() -> int: """" Main routine """ parser = argparse.ArgumentParser() parser.add_argument( "--overwrite", help="Overwrites the unformatted source files with the well-formatted code in place. " "If not set, an exception is raised if any of the files do not conform to the style guide.", action='store_true') args = parser.parse_args() overwrite = bool(args.overwrite) repo_root = pathlib.Path(__file__).parent print("YAPF'ing...") if overwrite: subprocess.check_call( [ "yapf", "--in-place", "--style=style.yapf", "--recursive", "tests", "icontract", "setup.py", "precommit.py" ], cwd=repo_root.as_posix()) else: subprocess.check_call( ["yapf", "--diff", "--style=style.yapf", "--recursive", "tests", "icontract", "setup.py", "precommit.py"], cwd=repo_root.as_posix()) print("Mypy'ing...") subprocess.check_call(["mypy", "icontract", "tests"], cwd=repo_root.as_posix()) print("Pylint'ing...") subprocess.check_call(["pylint", "--rcfile=pylint.rc", "tests", "icontract"], cwd=repo_root.as_posix()) print("Pydocstyle'ing...") subprocess.check_call(["pydocstyle", "icontract"], cwd=repo_root.as_posix()) print("Testing...") env = os.environ.copy() env['ICONTRACT_SLOW'] = 'true' # yapf: disable subprocess.check_call( ["coverage", "run", "--source", "icontract", "-m", "unittest", "discover", "tests"], cwd=repo_root.as_posix(), env=env) # yapf: enable subprocess.check_call(["coverage", "report"]) print("Doctesting...") subprocess.check_call(["python3", "-m", "doctest", "README.rst"]) for pth in (repo_root / "icontract").glob("**/*.py"): subprocess.check_call(["python3", "-m", "doctest", pth.as_posix()]) print("Checking the restructured text of the readme...") subprocess.check_call(['python3', 'setup.py', 'check', '--restructuredtext', '--strict']) return 0
python
def main() -> int: """" Main routine """ parser = argparse.ArgumentParser() parser.add_argument( "--overwrite", help="Overwrites the unformatted source files with the well-formatted code in place. " "If not set, an exception is raised if any of the files do not conform to the style guide.", action='store_true') args = parser.parse_args() overwrite = bool(args.overwrite) repo_root = pathlib.Path(__file__).parent print("YAPF'ing...") if overwrite: subprocess.check_call( [ "yapf", "--in-place", "--style=style.yapf", "--recursive", "tests", "icontract", "setup.py", "precommit.py" ], cwd=repo_root.as_posix()) else: subprocess.check_call( ["yapf", "--diff", "--style=style.yapf", "--recursive", "tests", "icontract", "setup.py", "precommit.py"], cwd=repo_root.as_posix()) print("Mypy'ing...") subprocess.check_call(["mypy", "icontract", "tests"], cwd=repo_root.as_posix()) print("Pylint'ing...") subprocess.check_call(["pylint", "--rcfile=pylint.rc", "tests", "icontract"], cwd=repo_root.as_posix()) print("Pydocstyle'ing...") subprocess.check_call(["pydocstyle", "icontract"], cwd=repo_root.as_posix()) print("Testing...") env = os.environ.copy() env['ICONTRACT_SLOW'] = 'true' # yapf: disable subprocess.check_call( ["coverage", "run", "--source", "icontract", "-m", "unittest", "discover", "tests"], cwd=repo_root.as_posix(), env=env) # yapf: enable subprocess.check_call(["coverage", "report"]) print("Doctesting...") subprocess.check_call(["python3", "-m", "doctest", "README.rst"]) for pth in (repo_root / "icontract").glob("**/*.py"): subprocess.check_call(["python3", "-m", "doctest", pth.as_posix()]) print("Checking the restructured text of the readme...") subprocess.check_call(['python3', 'setup.py', 'check', '--restructuredtext', '--strict']) return 0
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Main routine
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train
https://github.com/Parquery/icontract/blob/846e3187869a9ba790e9b893c98e5055e1cce274/precommit.py#L10-L72
Parquery/icontract
icontract/_metaclass.py
_collapse_invariants
def _collapse_invariants(bases: List[type], namespace: MutableMapping[str, Any]) -> None: """Collect invariants from the bases and merge them with the invariants in the namespace.""" invariants = [] # type: List[Contract] # Add invariants of the bases for base in bases: if hasattr(base, "__invariants__"): invariants.extend(getattr(base, "__invariants__")) # Add invariants in the current namespace if '__invariants__' in namespace: invariants.extend(namespace['__invariants__']) # Change the final invariants in the namespace if invariants: namespace["__invariants__"] = invariants
python
def _collapse_invariants(bases: List[type], namespace: MutableMapping[str, Any]) -> None: """Collect invariants from the bases and merge them with the invariants in the namespace.""" invariants = [] # type: List[Contract] # Add invariants of the bases for base in bases: if hasattr(base, "__invariants__"): invariants.extend(getattr(base, "__invariants__")) # Add invariants in the current namespace if '__invariants__' in namespace: invariants.extend(namespace['__invariants__']) # Change the final invariants in the namespace if invariants: namespace["__invariants__"] = invariants
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Collect invariants from the bases and merge them with the invariants in the namespace.
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train
https://github.com/Parquery/icontract/blob/846e3187869a9ba790e9b893c98e5055e1cce274/icontract/_metaclass.py#L16-L31
Parquery/icontract
icontract/_metaclass.py
_collapse_preconditions
def _collapse_preconditions(base_preconditions: List[List[Contract]], bases_have_func: bool, preconditions: List[List[Contract]], func: Callable[..., Any]) -> List[List[Contract]]: """ Collapse function preconditions with the preconditions collected from the base classes. :param base_preconditions: preconditions collected from the base classes (grouped by base class) :param bases_have_func: True if one of the base classes has the function :param preconditions: preconditions of the function (before the collapse) :param func: function whose preconditions we are collapsing :return: collapsed sequence of precondition groups """ if not base_preconditions and bases_have_func and preconditions: raise TypeError(("The function {} can not weaken the preconditions because the bases specify " "no preconditions at all. Hence this function must accept all possible input since " "the preconditions are OR'ed and no precondition implies a dummy precondition which is always " "fulfilled.").format(func.__qualname__)) return base_preconditions + preconditions
python
def _collapse_preconditions(base_preconditions: List[List[Contract]], bases_have_func: bool, preconditions: List[List[Contract]], func: Callable[..., Any]) -> List[List[Contract]]: """ Collapse function preconditions with the preconditions collected from the base classes. :param base_preconditions: preconditions collected from the base classes (grouped by base class) :param bases_have_func: True if one of the base classes has the function :param preconditions: preconditions of the function (before the collapse) :param func: function whose preconditions we are collapsing :return: collapsed sequence of precondition groups """ if not base_preconditions and bases_have_func and preconditions: raise TypeError(("The function {} can not weaken the preconditions because the bases specify " "no preconditions at all. Hence this function must accept all possible input since " "the preconditions are OR'ed and no precondition implies a dummy precondition which is always " "fulfilled.").format(func.__qualname__)) return base_preconditions + preconditions
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Collapse function preconditions with the preconditions collected from the base classes. :param base_preconditions: preconditions collected from the base classes (grouped by base class) :param bases_have_func: True if one of the base classes has the function :param preconditions: preconditions of the function (before the collapse) :param func: function whose preconditions we are collapsing :return: collapsed sequence of precondition groups
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train
https://github.com/Parquery/icontract/blob/846e3187869a9ba790e9b893c98e5055e1cce274/icontract/_metaclass.py#L34-L51
Parquery/icontract
icontract/_metaclass.py
_collapse_snapshots
def _collapse_snapshots(base_snapshots: List[Snapshot], snapshots: List[Snapshot]) -> List[Snapshot]: """ Collapse snapshots of pre-invocation values with the snapshots collected from the base classes. :param base_snapshots: snapshots collected from the base classes :param snapshots: snapshots of the function (before the collapse) :return: collapsed sequence of snapshots """ seen_names = set() # type: Set[str] collapsed = base_snapshots + snapshots for snap in collapsed: if snap.name in seen_names: raise ValueError("There are conflicting snapshots with the name: {!r}.\n\n" "Please mind that the snapshots are inherited from the base classes. " "Does one of the base classes defines a snapshot with the same name?".format(snap.name)) seen_names.add(snap.name) return collapsed
python
def _collapse_snapshots(base_snapshots: List[Snapshot], snapshots: List[Snapshot]) -> List[Snapshot]: """ Collapse snapshots of pre-invocation values with the snapshots collected from the base classes. :param base_snapshots: snapshots collected from the base classes :param snapshots: snapshots of the function (before the collapse) :return: collapsed sequence of snapshots """ seen_names = set() # type: Set[str] collapsed = base_snapshots + snapshots for snap in collapsed: if snap.name in seen_names: raise ValueError("There are conflicting snapshots with the name: {!r}.\n\n" "Please mind that the snapshots are inherited from the base classes. " "Does one of the base classes defines a snapshot with the same name?".format(snap.name)) seen_names.add(snap.name) return collapsed
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Collapse snapshots of pre-invocation values with the snapshots collected from the base classes. :param base_snapshots: snapshots collected from the base classes :param snapshots: snapshots of the function (before the collapse) :return: collapsed sequence of snapshots
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train
https://github.com/Parquery/icontract/blob/846e3187869a9ba790e9b893c98e5055e1cce274/icontract/_metaclass.py#L54-L73
Parquery/icontract
icontract/_metaclass.py
_collapse_postconditions
def _collapse_postconditions(base_postconditions: List[Contract], postconditions: List[Contract]) -> List[Contract]: """ Collapse function postconditions with the postconditions collected from the base classes. :param base_postconditions: postconditions collected from the base classes :param postconditions: postconditions of the function (before the collapse) :return: collapsed sequence of postconditions """ return base_postconditions + postconditions
python
def _collapse_postconditions(base_postconditions: List[Contract], postconditions: List[Contract]) -> List[Contract]: """ Collapse function postconditions with the postconditions collected from the base classes. :param base_postconditions: postconditions collected from the base classes :param postconditions: postconditions of the function (before the collapse) :return: collapsed sequence of postconditions """ return base_postconditions + postconditions
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Collapse function postconditions with the postconditions collected from the base classes. :param base_postconditions: postconditions collected from the base classes :param postconditions: postconditions of the function (before the collapse) :return: collapsed sequence of postconditions
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train
https://github.com/Parquery/icontract/blob/846e3187869a9ba790e9b893c98e5055e1cce274/icontract/_metaclass.py#L76-L84
Parquery/icontract
icontract/_metaclass.py
_decorate_namespace_function
def _decorate_namespace_function(bases: List[type], namespace: MutableMapping[str, Any], key: str) -> None: """Collect preconditions and postconditions from the bases and decorate the function at the ``key``.""" # pylint: disable=too-many-branches # pylint: disable=too-many-locals value = namespace[key] assert inspect.isfunction(value) or isinstance(value, (staticmethod, classmethod)) # Determine the function to be decorated if inspect.isfunction(value): func = value elif isinstance(value, (staticmethod, classmethod)): func = value.__func__ else: raise NotImplementedError("Unexpected value for a function: {}".format(value)) # Collect preconditions and postconditions of the function preconditions = [] # type: List[List[Contract]] snapshots = [] # type: List[Snapshot] postconditions = [] # type: List[Contract] contract_checker = icontract._checkers.find_checker(func=func) if contract_checker is not None: preconditions = contract_checker.__preconditions__ # type: ignore snapshots = contract_checker.__postcondition_snapshots__ # type: ignore postconditions = contract_checker.__postconditions__ # type: ignore # Collect the preconditions and postconditions from bases. # # Preconditions and postconditions of __init__ of base classes are deliberately ignored (and not collapsed) since # initialization is an operation specific to the concrete class and does not relate to the class hierarchy. if key not in ['__init__']: base_preconditions = [] # type: List[List[Contract]] base_snapshots = [] # type: List[Snapshot] base_postconditions = [] # type: List[Contract] bases_have_func = False for base in bases: if hasattr(base, key): bases_have_func = True # Check if there is a checker function in the base class base_func = getattr(base, key) base_contract_checker = icontract._checkers.find_checker(func=base_func) # Ignore functions which don't have preconditions or postconditions if base_contract_checker is not None: base_preconditions.extend(base_contract_checker.__preconditions__) # type: ignore base_snapshots.extend(base_contract_checker.__postcondition_snapshots__) # type: ignore base_postconditions.extend(base_contract_checker.__postconditions__) # type: ignore # Collapse preconditions and postconditions from the bases with the the function's own ones preconditions = _collapse_preconditions( base_preconditions=base_preconditions, bases_have_func=bases_have_func, preconditions=preconditions, func=func) snapshots = _collapse_snapshots(base_snapshots=base_snapshots, snapshots=snapshots) postconditions = _collapse_postconditions( base_postconditions=base_postconditions, postconditions=postconditions) if preconditions or postconditions: if contract_checker is None: contract_checker = icontract._checkers.decorate_with_checker(func=func) # Replace the function with the function decorated with contract checks if inspect.isfunction(value): namespace[key] = contract_checker elif isinstance(value, staticmethod): namespace[key] = staticmethod(contract_checker) elif isinstance(value, classmethod): namespace[key] = classmethod(contract_checker) else: raise NotImplementedError("Unexpected value for a function: {}".format(value)) # Override the preconditions and postconditions contract_checker.__preconditions__ = preconditions # type: ignore contract_checker.__postcondition_snapshots__ = snapshots # type: ignore contract_checker.__postconditions__ = postconditions
python
def _decorate_namespace_function(bases: List[type], namespace: MutableMapping[str, Any], key: str) -> None: """Collect preconditions and postconditions from the bases and decorate the function at the ``key``.""" # pylint: disable=too-many-branches # pylint: disable=too-many-locals value = namespace[key] assert inspect.isfunction(value) or isinstance(value, (staticmethod, classmethod)) # Determine the function to be decorated if inspect.isfunction(value): func = value elif isinstance(value, (staticmethod, classmethod)): func = value.__func__ else: raise NotImplementedError("Unexpected value for a function: {}".format(value)) # Collect preconditions and postconditions of the function preconditions = [] # type: List[List[Contract]] snapshots = [] # type: List[Snapshot] postconditions = [] # type: List[Contract] contract_checker = icontract._checkers.find_checker(func=func) if contract_checker is not None: preconditions = contract_checker.__preconditions__ # type: ignore snapshots = contract_checker.__postcondition_snapshots__ # type: ignore postconditions = contract_checker.__postconditions__ # type: ignore # Collect the preconditions and postconditions from bases. # # Preconditions and postconditions of __init__ of base classes are deliberately ignored (and not collapsed) since # initialization is an operation specific to the concrete class and does not relate to the class hierarchy. if key not in ['__init__']: base_preconditions = [] # type: List[List[Contract]] base_snapshots = [] # type: List[Snapshot] base_postconditions = [] # type: List[Contract] bases_have_func = False for base in bases: if hasattr(base, key): bases_have_func = True # Check if there is a checker function in the base class base_func = getattr(base, key) base_contract_checker = icontract._checkers.find_checker(func=base_func) # Ignore functions which don't have preconditions or postconditions if base_contract_checker is not None: base_preconditions.extend(base_contract_checker.__preconditions__) # type: ignore base_snapshots.extend(base_contract_checker.__postcondition_snapshots__) # type: ignore base_postconditions.extend(base_contract_checker.__postconditions__) # type: ignore # Collapse preconditions and postconditions from the bases with the the function's own ones preconditions = _collapse_preconditions( base_preconditions=base_preconditions, bases_have_func=bases_have_func, preconditions=preconditions, func=func) snapshots = _collapse_snapshots(base_snapshots=base_snapshots, snapshots=snapshots) postconditions = _collapse_postconditions( base_postconditions=base_postconditions, postconditions=postconditions) if preconditions or postconditions: if contract_checker is None: contract_checker = icontract._checkers.decorate_with_checker(func=func) # Replace the function with the function decorated with contract checks if inspect.isfunction(value): namespace[key] = contract_checker elif isinstance(value, staticmethod): namespace[key] = staticmethod(contract_checker) elif isinstance(value, classmethod): namespace[key] = classmethod(contract_checker) else: raise NotImplementedError("Unexpected value for a function: {}".format(value)) # Override the preconditions and postconditions contract_checker.__preconditions__ = preconditions # type: ignore contract_checker.__postcondition_snapshots__ = snapshots # type: ignore contract_checker.__postconditions__ = postconditions
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Collect preconditions and postconditions from the bases and decorate the function at the ``key``.
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train
https://github.com/Parquery/icontract/blob/846e3187869a9ba790e9b893c98e5055e1cce274/icontract/_metaclass.py#L87-L169
Parquery/icontract
icontract/_metaclass.py
_decorate_namespace_property
def _decorate_namespace_property(bases: List[type], namespace: MutableMapping[str, Any], key: str) -> None: """Collect contracts for all getters/setters/deleters corresponding to ``key`` and decorate them.""" # pylint: disable=too-many-locals # pylint: disable=too-many-branches # pylint: disable=too-many-statements value = namespace[key] assert isinstance(value, property) fget = value.fget # type: Optional[Callable[..., Any]] fset = value.fset # type: Optional[Callable[..., Any]] fdel = value.fdel # type: Optional[Callable[..., Any]] for func in [value.fget, value.fset, value.fdel]: func = cast(Callable[..., Any], func) if func is None: continue # Collect the preconditions and postconditions from bases base_preconditions = [] # type: List[List[Contract]] base_snapshots = [] # type: List[Snapshot] base_postconditions = [] # type: List[Contract] bases_have_func = False for base in bases: if hasattr(base, key): base_property = getattr(base, key) assert isinstance(base_property, property), \ "Expected base {} to have {} as property, but got: {}".format(base, key, base_property) if func == value.fget: base_func = getattr(base, key).fget elif func == value.fset: base_func = getattr(base, key).fset elif func == value.fdel: base_func = getattr(base, key).fdel else: raise NotImplementedError("Unhandled case: func neither value.fget, value.fset nor value.fdel") if base_func is None: continue bases_have_func = True # Check if there is a checker function in the base class base_contract_checker = icontract._checkers.find_checker(func=base_func) # Ignore functions which don't have preconditions or postconditions if base_contract_checker is not None: base_preconditions.extend(base_contract_checker.__preconditions__) # type: ignore base_snapshots.extend(base_contract_checker.__postcondition_snapshots__) # type: ignore base_postconditions.extend(base_contract_checker.__postconditions__) # type: ignore # Add preconditions and postconditions of the function preconditions = [] # type: List[List[Contract]] snapshots = [] # type: List[Snapshot] postconditions = [] # type: List[Contract] contract_checker = icontract._checkers.find_checker(func=func) if contract_checker is not None: preconditions = contract_checker.__preconditions__ # type: ignore snapshots = contract_checker.__postcondition_snapshots__ postconditions = contract_checker.__postconditions__ # type: ignore preconditions = _collapse_preconditions( base_preconditions=base_preconditions, bases_have_func=bases_have_func, preconditions=preconditions, func=func) snapshots = _collapse_snapshots(base_snapshots=base_snapshots, snapshots=snapshots) postconditions = _collapse_postconditions( base_postconditions=base_postconditions, postconditions=postconditions) if preconditions or postconditions: if contract_checker is None: contract_checker = icontract._checkers.decorate_with_checker(func=func) # Replace the function with the function decorated with contract checks if func == value.fget: fget = contract_checker elif func == value.fset: fset = contract_checker elif func == value.fdel: fdel = contract_checker else: raise NotImplementedError("Unhandled case: func neither fget, fset nor fdel") # Override the preconditions and postconditions contract_checker.__preconditions__ = preconditions # type: ignore contract_checker.__postcondition_snapshots__ = snapshots # type: ignore contract_checker.__postconditions__ = postconditions # type: ignore if fget != value.fget or fset != value.fset or fdel != value.fdel: namespace[key] = property(fget=fget, fset=fset, fdel=fdel)
python
def _decorate_namespace_property(bases: List[type], namespace: MutableMapping[str, Any], key: str) -> None: """Collect contracts for all getters/setters/deleters corresponding to ``key`` and decorate them.""" # pylint: disable=too-many-locals # pylint: disable=too-many-branches # pylint: disable=too-many-statements value = namespace[key] assert isinstance(value, property) fget = value.fget # type: Optional[Callable[..., Any]] fset = value.fset # type: Optional[Callable[..., Any]] fdel = value.fdel # type: Optional[Callable[..., Any]] for func in [value.fget, value.fset, value.fdel]: func = cast(Callable[..., Any], func) if func is None: continue # Collect the preconditions and postconditions from bases base_preconditions = [] # type: List[List[Contract]] base_snapshots = [] # type: List[Snapshot] base_postconditions = [] # type: List[Contract] bases_have_func = False for base in bases: if hasattr(base, key): base_property = getattr(base, key) assert isinstance(base_property, property), \ "Expected base {} to have {} as property, but got: {}".format(base, key, base_property) if func == value.fget: base_func = getattr(base, key).fget elif func == value.fset: base_func = getattr(base, key).fset elif func == value.fdel: base_func = getattr(base, key).fdel else: raise NotImplementedError("Unhandled case: func neither value.fget, value.fset nor value.fdel") if base_func is None: continue bases_have_func = True # Check if there is a checker function in the base class base_contract_checker = icontract._checkers.find_checker(func=base_func) # Ignore functions which don't have preconditions or postconditions if base_contract_checker is not None: base_preconditions.extend(base_contract_checker.__preconditions__) # type: ignore base_snapshots.extend(base_contract_checker.__postcondition_snapshots__) # type: ignore base_postconditions.extend(base_contract_checker.__postconditions__) # type: ignore # Add preconditions and postconditions of the function preconditions = [] # type: List[List[Contract]] snapshots = [] # type: List[Snapshot] postconditions = [] # type: List[Contract] contract_checker = icontract._checkers.find_checker(func=func) if contract_checker is not None: preconditions = contract_checker.__preconditions__ # type: ignore snapshots = contract_checker.__postcondition_snapshots__ postconditions = contract_checker.__postconditions__ # type: ignore preconditions = _collapse_preconditions( base_preconditions=base_preconditions, bases_have_func=bases_have_func, preconditions=preconditions, func=func) snapshots = _collapse_snapshots(base_snapshots=base_snapshots, snapshots=snapshots) postconditions = _collapse_postconditions( base_postconditions=base_postconditions, postconditions=postconditions) if preconditions or postconditions: if contract_checker is None: contract_checker = icontract._checkers.decorate_with_checker(func=func) # Replace the function with the function decorated with contract checks if func == value.fget: fget = contract_checker elif func == value.fset: fset = contract_checker elif func == value.fdel: fdel = contract_checker else: raise NotImplementedError("Unhandled case: func neither fget, fset nor fdel") # Override the preconditions and postconditions contract_checker.__preconditions__ = preconditions # type: ignore contract_checker.__postcondition_snapshots__ = snapshots # type: ignore contract_checker.__postconditions__ = postconditions # type: ignore if fget != value.fget or fset != value.fset or fdel != value.fdel: namespace[key] = property(fget=fget, fset=fset, fdel=fdel)
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Collect contracts for all getters/setters/deleters corresponding to ``key`` and decorate them.
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train
https://github.com/Parquery/icontract/blob/846e3187869a9ba790e9b893c98e5055e1cce274/icontract/_metaclass.py#L172-L268
Parquery/icontract
icontract/_metaclass.py
_dbc_decorate_namespace
def _dbc_decorate_namespace(bases: List[type], namespace: MutableMapping[str, Any]) -> None: """ Collect invariants, preconditions and postconditions from the bases and decorate all the methods. Instance methods are simply replaced with the decorated function/ Properties, class methods and static methods are overridden with new instances of ``property``, ``classmethod`` and ``staticmethod``, respectively. """ _collapse_invariants(bases=bases, namespace=namespace) for key, value in namespace.items(): if inspect.isfunction(value) or isinstance(value, (staticmethod, classmethod)): _decorate_namespace_function(bases=bases, namespace=namespace, key=key) elif isinstance(value, property): _decorate_namespace_property(bases=bases, namespace=namespace, key=key) else: # Ignore the value which is neither a function nor a property pass
python
def _dbc_decorate_namespace(bases: List[type], namespace: MutableMapping[str, Any]) -> None: """ Collect invariants, preconditions and postconditions from the bases and decorate all the methods. Instance methods are simply replaced with the decorated function/ Properties, class methods and static methods are overridden with new instances of ``property``, ``classmethod`` and ``staticmethod``, respectively. """ _collapse_invariants(bases=bases, namespace=namespace) for key, value in namespace.items(): if inspect.isfunction(value) or isinstance(value, (staticmethod, classmethod)): _decorate_namespace_function(bases=bases, namespace=namespace, key=key) elif isinstance(value, property): _decorate_namespace_property(bases=bases, namespace=namespace, key=key) else: # Ignore the value which is neither a function nor a property pass
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Collect invariants, preconditions and postconditions from the bases and decorate all the methods. Instance methods are simply replaced with the decorated function/ Properties, class methods and static methods are overridden with new instances of ``property``, ``classmethod`` and ``staticmethod``, respectively.
[ "Collect", "invariants", "preconditions", "and", "postconditions", "from", "the", "bases", "and", "decorate", "all", "the", "methods", "." ]
train
https://github.com/Parquery/icontract/blob/846e3187869a9ba790e9b893c98e5055e1cce274/icontract/_metaclass.py#L271-L289
Parquery/icontract
icontract/_represent.py
_representable
def _representable(value: Any) -> bool: """ Check whether we want to represent the value in the error message on contract breach. We do not want to represent classes, methods, modules and functions. :param value: value related to an AST node :return: True if we want to represent it in the violation error """ return not inspect.isclass(value) and not inspect.isfunction(value) and not inspect.ismethod(value) and not \ inspect.ismodule(value) and not inspect.isbuiltin(value)
python
def _representable(value: Any) -> bool: """ Check whether we want to represent the value in the error message on contract breach. We do not want to represent classes, methods, modules and functions. :param value: value related to an AST node :return: True if we want to represent it in the violation error """ return not inspect.isclass(value) and not inspect.isfunction(value) and not inspect.ismethod(value) and not \ inspect.ismodule(value) and not inspect.isbuiltin(value)
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Check whether we want to represent the value in the error message on contract breach. We do not want to represent classes, methods, modules and functions. :param value: value related to an AST node :return: True if we want to represent it in the violation error
[ "Check", "whether", "we", "want", "to", "represent", "the", "value", "in", "the", "error", "message", "on", "contract", "breach", "." ]
train
https://github.com/Parquery/icontract/blob/846e3187869a9ba790e9b893c98e5055e1cce274/icontract/_represent.py#L21-L31
Parquery/icontract
icontract/_represent.py
inspect_decorator
def inspect_decorator(lines: List[str], lineno: int, filename: str) -> DecoratorInspection: """ Parse the file in which the decorator is called and figure out the corresponding call AST node. :param lines: lines of the source file corresponding to the decorator call :param lineno: line index (starting with 0) of one of the lines in the decorator call :param filename: name of the file where decorator is called :return: inspected decorator call """ if lineno < 0 or lineno >= len(lines): raise ValueError(("Given line number {} of one of the decorator lines " "is not within the range [{}, {}) of lines in {}").format(lineno, 0, len(lines), filename)) # Go up till a line starts with a decorator decorator_lineno = None # type: Optional[int] for i in range(lineno, -1, -1): if _DECORATOR_RE.match(lines[i]): decorator_lineno = i break if decorator_lineno is None: raise SyntaxError("Decorator corresponding to the line {} could not be found in file {}: {!r}".format( lineno + 1, filename, lines[lineno])) # Find the decorator end -- it's either a function definition, a class definition or another decorator decorator_end_lineno = None # type: Optional[int] for i in range(lineno + 1, len(lines)): line = lines[i] if _DECORATOR_RE.match(line) or _DEF_CLASS_RE.match(line): decorator_end_lineno = i break if decorator_end_lineno is None: raise SyntaxError(("The next statement following the decorator corresponding to the line {} " "could not be found in file {}: {!r}").format(lineno + 1, filename, lines[lineno])) decorator_lines = lines[decorator_lineno:decorator_end_lineno] # We need to dedent the decorator and add a dummy decoratee so that we can parse its text as valid source code. decorator_text = textwrap.dedent("".join(decorator_lines)) + "def dummy_{}(): pass".format(uuid.uuid4().hex) atok = asttokens.ASTTokens(decorator_text, parse=True) assert isinstance(atok.tree, ast.Module), "Expected the parsed decorator text to live in an AST module." module_node = atok.tree assert len(module_node.body) == 1, "Expected the module AST of the decorator text to have a single statement." assert isinstance(module_node.body[0], ast.FunctionDef), \ "Expected the only statement in the AST module corresponding to the decorator text to be a function definition." func_def_node = module_node.body[0] assert len(func_def_node.decorator_list) == 1, \ "Expected the function AST node corresponding to the decorator text to have a single decorator." assert isinstance(func_def_node.decorator_list[0], ast.Call), \ "Expected the only decorator in the function definition AST node corresponding to the decorator text " \ "to be a call node." call_node = func_def_node.decorator_list[0] return DecoratorInspection(atok=atok, node=call_node)
python
def inspect_decorator(lines: List[str], lineno: int, filename: str) -> DecoratorInspection: """ Parse the file in which the decorator is called and figure out the corresponding call AST node. :param lines: lines of the source file corresponding to the decorator call :param lineno: line index (starting with 0) of one of the lines in the decorator call :param filename: name of the file where decorator is called :return: inspected decorator call """ if lineno < 0 or lineno >= len(lines): raise ValueError(("Given line number {} of one of the decorator lines " "is not within the range [{}, {}) of lines in {}").format(lineno, 0, len(lines), filename)) # Go up till a line starts with a decorator decorator_lineno = None # type: Optional[int] for i in range(lineno, -1, -1): if _DECORATOR_RE.match(lines[i]): decorator_lineno = i break if decorator_lineno is None: raise SyntaxError("Decorator corresponding to the line {} could not be found in file {}: {!r}".format( lineno + 1, filename, lines[lineno])) # Find the decorator end -- it's either a function definition, a class definition or another decorator decorator_end_lineno = None # type: Optional[int] for i in range(lineno + 1, len(lines)): line = lines[i] if _DECORATOR_RE.match(line) or _DEF_CLASS_RE.match(line): decorator_end_lineno = i break if decorator_end_lineno is None: raise SyntaxError(("The next statement following the decorator corresponding to the line {} " "could not be found in file {}: {!r}").format(lineno + 1, filename, lines[lineno])) decorator_lines = lines[decorator_lineno:decorator_end_lineno] # We need to dedent the decorator and add a dummy decoratee so that we can parse its text as valid source code. decorator_text = textwrap.dedent("".join(decorator_lines)) + "def dummy_{}(): pass".format(uuid.uuid4().hex) atok = asttokens.ASTTokens(decorator_text, parse=True) assert isinstance(atok.tree, ast.Module), "Expected the parsed decorator text to live in an AST module." module_node = atok.tree assert len(module_node.body) == 1, "Expected the module AST of the decorator text to have a single statement." assert isinstance(module_node.body[0], ast.FunctionDef), \ "Expected the only statement in the AST module corresponding to the decorator text to be a function definition." func_def_node = module_node.body[0] assert len(func_def_node.decorator_list) == 1, \ "Expected the function AST node corresponding to the decorator text to have a single decorator." assert isinstance(func_def_node.decorator_list[0], ast.Call), \ "Expected the only decorator in the function definition AST node corresponding to the decorator text " \ "to be a call node." call_node = func_def_node.decorator_list[0] return DecoratorInspection(atok=atok, node=call_node)
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Parse the file in which the decorator is called and figure out the corresponding call AST node. :param lines: lines of the source file corresponding to the decorator call :param lineno: line index (starting with 0) of one of the lines in the decorator call :param filename: name of the file where decorator is called :return: inspected decorator call
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train
https://github.com/Parquery/icontract/blob/846e3187869a9ba790e9b893c98e5055e1cce274/icontract/_represent.py#L183-L245
Parquery/icontract
icontract/_represent.py
find_lambda_condition
def find_lambda_condition(decorator_inspection: DecoratorInspection) -> Optional[ConditionLambdaInspection]: """ Inspect the decorator and extract the condition as lambda. If the condition is not given as a lambda function, return None. """ call_node = decorator_inspection.node lambda_node = None # type: Optional[ast.Lambda] if len(call_node.args) > 0: assert isinstance(call_node.args[0], ast.Lambda), \ ("Expected the first argument to the decorator to be a condition as lambda AST node, " "but got: {}").format(type(call_node.args[0])) lambda_node = call_node.args[0] elif len(call_node.keywords) > 0: for keyword in call_node.keywords: if keyword.arg == "condition": assert isinstance(keyword.value, ast.Lambda), \ "Expected lambda node as value of the 'condition' argument to the decorator." lambda_node = keyword.value break assert lambda_node is not None, "Expected to find a keyword AST node with 'condition' arg, but found none" else: raise AssertionError( "Expected a call AST node of a decorator to have either args or keywords, but got: {}".format( ast.dump(call_node))) return ConditionLambdaInspection(atok=decorator_inspection.atok, node=lambda_node)
python
def find_lambda_condition(decorator_inspection: DecoratorInspection) -> Optional[ConditionLambdaInspection]: """ Inspect the decorator and extract the condition as lambda. If the condition is not given as a lambda function, return None. """ call_node = decorator_inspection.node lambda_node = None # type: Optional[ast.Lambda] if len(call_node.args) > 0: assert isinstance(call_node.args[0], ast.Lambda), \ ("Expected the first argument to the decorator to be a condition as lambda AST node, " "but got: {}").format(type(call_node.args[0])) lambda_node = call_node.args[0] elif len(call_node.keywords) > 0: for keyword in call_node.keywords: if keyword.arg == "condition": assert isinstance(keyword.value, ast.Lambda), \ "Expected lambda node as value of the 'condition' argument to the decorator." lambda_node = keyword.value break assert lambda_node is not None, "Expected to find a keyword AST node with 'condition' arg, but found none" else: raise AssertionError( "Expected a call AST node of a decorator to have either args or keywords, but got: {}".format( ast.dump(call_node))) return ConditionLambdaInspection(atok=decorator_inspection.atok, node=lambda_node)
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Inspect the decorator and extract the condition as lambda. If the condition is not given as a lambda function, return None.
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train
https://github.com/Parquery/icontract/blob/846e3187869a9ba790e9b893c98e5055e1cce274/icontract/_represent.py#L248-L280
Parquery/icontract
icontract/_represent.py
repr_values
def repr_values(condition: Callable[..., bool], lambda_inspection: Optional[ConditionLambdaInspection], condition_kwargs: Mapping[str, Any], a_repr: reprlib.Repr) -> List[str]: # pylint: disable=too-many-locals """ Represent function arguments and frame values in the error message on contract breach. :param condition: condition function of the contract :param lambda_inspection: inspected lambda AST node corresponding to the condition function (None if the condition was not given as a lambda function) :param condition_kwargs: condition arguments :param a_repr: representation instance that defines how the values are represented. :return: list of value representations """ if _is_lambda(a_function=condition): assert lambda_inspection is not None, "Expected a lambda inspection when given a condition as a lambda function" else: assert lambda_inspection is None, "Expected no lambda inspection in a condition given as a non-lambda function" reprs = dict() # type: MutableMapping[str, Any] if lambda_inspection is not None: # Collect the variable lookup of the condition function: variable_lookup = [] # type: List[Mapping[str, Any]] # Add condition arguments to the lookup variable_lookup.append(condition_kwargs) # Add closure to the lookup closure_dict = dict() # type: Dict[str, Any] if condition.__closure__ is not None: # type: ignore closure_cells = condition.__closure__ # type: ignore freevars = condition.__code__.co_freevars assert len(closure_cells) == len(freevars), \ "Number of closure cells of a condition function ({}) == number of free vars ({})".format( len(closure_cells), len(freevars)) for cell, freevar in zip(closure_cells, freevars): closure_dict[freevar] = cell.cell_contents variable_lookup.append(closure_dict) # Add globals to the lookup if condition.__globals__ is not None: # type: ignore variable_lookup.append(condition.__globals__) # type: ignore # pylint: disable=protected-access recompute_visitor = icontract._recompute.Visitor(variable_lookup=variable_lookup) recompute_visitor.visit(node=lambda_inspection.node.body) recomputed_values = recompute_visitor.recomputed_values repr_visitor = Visitor( recomputed_values=recomputed_values, variable_lookup=variable_lookup, atok=lambda_inspection.atok) repr_visitor.visit(node=lambda_inspection.node.body) reprs = repr_visitor.reprs else: for key, val in condition_kwargs.items(): if _representable(value=val): reprs[key] = val parts = [] # type: List[str] for key in sorted(reprs.keys()): parts.append('{} was {}'.format(key, a_repr.repr(reprs[key]))) return parts
python
def repr_values(condition: Callable[..., bool], lambda_inspection: Optional[ConditionLambdaInspection], condition_kwargs: Mapping[str, Any], a_repr: reprlib.Repr) -> List[str]: # pylint: disable=too-many-locals """ Represent function arguments and frame values in the error message on contract breach. :param condition: condition function of the contract :param lambda_inspection: inspected lambda AST node corresponding to the condition function (None if the condition was not given as a lambda function) :param condition_kwargs: condition arguments :param a_repr: representation instance that defines how the values are represented. :return: list of value representations """ if _is_lambda(a_function=condition): assert lambda_inspection is not None, "Expected a lambda inspection when given a condition as a lambda function" else: assert lambda_inspection is None, "Expected no lambda inspection in a condition given as a non-lambda function" reprs = dict() # type: MutableMapping[str, Any] if lambda_inspection is not None: # Collect the variable lookup of the condition function: variable_lookup = [] # type: List[Mapping[str, Any]] # Add condition arguments to the lookup variable_lookup.append(condition_kwargs) # Add closure to the lookup closure_dict = dict() # type: Dict[str, Any] if condition.__closure__ is not None: # type: ignore closure_cells = condition.__closure__ # type: ignore freevars = condition.__code__.co_freevars assert len(closure_cells) == len(freevars), \ "Number of closure cells of a condition function ({}) == number of free vars ({})".format( len(closure_cells), len(freevars)) for cell, freevar in zip(closure_cells, freevars): closure_dict[freevar] = cell.cell_contents variable_lookup.append(closure_dict) # Add globals to the lookup if condition.__globals__ is not None: # type: ignore variable_lookup.append(condition.__globals__) # type: ignore # pylint: disable=protected-access recompute_visitor = icontract._recompute.Visitor(variable_lookup=variable_lookup) recompute_visitor.visit(node=lambda_inspection.node.body) recomputed_values = recompute_visitor.recomputed_values repr_visitor = Visitor( recomputed_values=recomputed_values, variable_lookup=variable_lookup, atok=lambda_inspection.atok) repr_visitor.visit(node=lambda_inspection.node.body) reprs = repr_visitor.reprs else: for key, val in condition_kwargs.items(): if _representable(value=val): reprs[key] = val parts = [] # type: List[str] for key in sorted(reprs.keys()): parts.append('{} was {}'.format(key, a_repr.repr(reprs[key]))) return parts
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Represent function arguments and frame values in the error message on contract breach. :param condition: condition function of the contract :param lambda_inspection: inspected lambda AST node corresponding to the condition function (None if the condition was not given as a lambda function) :param condition_kwargs: condition arguments :param a_repr: representation instance that defines how the values are represented. :return: list of value representations
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train
https://github.com/Parquery/icontract/blob/846e3187869a9ba790e9b893c98e5055e1cce274/icontract/_represent.py#L283-L351
Parquery/icontract
icontract/_represent.py
generate_message
def generate_message(contract: Contract, condition_kwargs: Mapping[str, Any]) -> str: """Generate the message upon contract violation.""" # pylint: disable=protected-access parts = [] # type: List[str] if contract.location is not None: parts.append("{}:\n".format(contract.location)) if contract.description is not None: parts.append("{}: ".format(contract.description)) lambda_inspection = None # type: Optional[ConditionLambdaInspection] if not _is_lambda(a_function=contract.condition): condition_text = contract.condition.__name__ else: # We need to extract the source code corresponding to the decorator since inspect.getsource() is broken with # lambdas. # Find the line corresponding to the condition lambda lines, condition_lineno = inspect.findsource(contract.condition) filename = inspect.getsourcefile(contract.condition) decorator_inspection = inspect_decorator(lines=lines, lineno=condition_lineno, filename=filename) lambda_inspection = find_lambda_condition(decorator_inspection=decorator_inspection) assert lambda_inspection is not None, \ "Expected lambda_inspection to be non-None if _is_lambda is True on: {}".format(contract.condition) condition_text = lambda_inspection.text parts.append(condition_text) repr_vals = repr_values( condition=contract.condition, lambda_inspection=lambda_inspection, condition_kwargs=condition_kwargs, a_repr=contract._a_repr) if len(repr_vals) == 0: # Do not append anything since no value could be represented as a string. # This could appear in case we have, for example, a generator expression as the return value of a lambda. pass elif len(repr_vals) == 1: parts.append(': ') parts.append(repr_vals[0]) else: parts.append(':\n') parts.append('\n'.join(repr_vals)) msg = "".join(parts) return msg
python
def generate_message(contract: Contract, condition_kwargs: Mapping[str, Any]) -> str: """Generate the message upon contract violation.""" # pylint: disable=protected-access parts = [] # type: List[str] if contract.location is not None: parts.append("{}:\n".format(contract.location)) if contract.description is not None: parts.append("{}: ".format(contract.description)) lambda_inspection = None # type: Optional[ConditionLambdaInspection] if not _is_lambda(a_function=contract.condition): condition_text = contract.condition.__name__ else: # We need to extract the source code corresponding to the decorator since inspect.getsource() is broken with # lambdas. # Find the line corresponding to the condition lambda lines, condition_lineno = inspect.findsource(contract.condition) filename = inspect.getsourcefile(contract.condition) decorator_inspection = inspect_decorator(lines=lines, lineno=condition_lineno, filename=filename) lambda_inspection = find_lambda_condition(decorator_inspection=decorator_inspection) assert lambda_inspection is not None, \ "Expected lambda_inspection to be non-None if _is_lambda is True on: {}".format(contract.condition) condition_text = lambda_inspection.text parts.append(condition_text) repr_vals = repr_values( condition=contract.condition, lambda_inspection=lambda_inspection, condition_kwargs=condition_kwargs, a_repr=contract._a_repr) if len(repr_vals) == 0: # Do not append anything since no value could be represented as a string. # This could appear in case we have, for example, a generator expression as the return value of a lambda. pass elif len(repr_vals) == 1: parts.append(': ') parts.append(repr_vals[0]) else: parts.append(':\n') parts.append('\n'.join(repr_vals)) msg = "".join(parts) return msg
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Generate the message upon contract violation.
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train
https://github.com/Parquery/icontract/blob/846e3187869a9ba790e9b893c98e5055e1cce274/icontract/_represent.py#L354-L405
Parquery/icontract
icontract/_represent.py
Visitor.visit_Name
def visit_Name(self, node: ast.Name) -> None: """ Resolve the name from the variable look-up and the built-ins. Due to possible branching (e.g., If-expressions), some nodes might lack the recomputed values. These nodes are ignored. """ if node in self._recomputed_values: value = self._recomputed_values[node] # Check if it is a non-built-in is_builtin = True for lookup in self._variable_lookup: if node.id in lookup: is_builtin = False break if not is_builtin and _representable(value=value): text = self._atok.get_text(node) self.reprs[text] = value self.generic_visit(node=node)
python
def visit_Name(self, node: ast.Name) -> None: """ Resolve the name from the variable look-up and the built-ins. Due to possible branching (e.g., If-expressions), some nodes might lack the recomputed values. These nodes are ignored. """ if node in self._recomputed_values: value = self._recomputed_values[node] # Check if it is a non-built-in is_builtin = True for lookup in self._variable_lookup: if node.id in lookup: is_builtin = False break if not is_builtin and _representable(value=value): text = self._atok.get_text(node) self.reprs[text] = value self.generic_visit(node=node)
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Resolve the name from the variable look-up and the built-ins. Due to possible branching (e.g., If-expressions), some nodes might lack the recomputed values. These nodes are ignored.
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train
https://github.com/Parquery/icontract/blob/846e3187869a9ba790e9b893c98e5055e1cce274/icontract/_represent.py#L57-L78
Parquery/icontract
icontract/_represent.py
Visitor.visit_Attribute
def visit_Attribute(self, node: ast.Attribute) -> None: """Represent the attribute by dumping its source code.""" if node in self._recomputed_values: value = self._recomputed_values[node] if _representable(value=value): text = self._atok.get_text(node) self.reprs[text] = value self.generic_visit(node=node)
python
def visit_Attribute(self, node: ast.Attribute) -> None: """Represent the attribute by dumping its source code.""" if node in self._recomputed_values: value = self._recomputed_values[node] if _representable(value=value): text = self._atok.get_text(node) self.reprs[text] = value self.generic_visit(node=node)
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Represent the attribute by dumping its source code.
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train
https://github.com/Parquery/icontract/blob/846e3187869a9ba790e9b893c98e5055e1cce274/icontract/_represent.py#L80-L89
Parquery/icontract
icontract/_represent.py
Visitor.visit_Call
def visit_Call(self, node: ast.Call) -> None: """Represent the call by dumping its source code.""" if node in self._recomputed_values: value = self._recomputed_values[node] text = self._atok.get_text(node) self.reprs[text] = value self.generic_visit(node=node)
python
def visit_Call(self, node: ast.Call) -> None: """Represent the call by dumping its source code.""" if node in self._recomputed_values: value = self._recomputed_values[node] text = self._atok.get_text(node) self.reprs[text] = value self.generic_visit(node=node)
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Represent the call by dumping its source code.
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train
https://github.com/Parquery/icontract/blob/846e3187869a9ba790e9b893c98e5055e1cce274/icontract/_represent.py#L91-L99
Parquery/icontract
icontract/_represent.py
Visitor.visit_ListComp
def visit_ListComp(self, node: ast.ListComp) -> None: """Represent the list comprehension by dumping its source code.""" if node in self._recomputed_values: value = self._recomputed_values[node] text = self._atok.get_text(node) self.reprs[text] = value self.generic_visit(node=node)
python
def visit_ListComp(self, node: ast.ListComp) -> None: """Represent the list comprehension by dumping its source code.""" if node in self._recomputed_values: value = self._recomputed_values[node] text = self._atok.get_text(node) self.reprs[text] = value self.generic_visit(node=node)
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Represent the list comprehension by dumping its source code.
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train
https://github.com/Parquery/icontract/blob/846e3187869a9ba790e9b893c98e5055e1cce274/icontract/_represent.py#L101-L109
Parquery/icontract
icontract/_represent.py
Visitor.visit_DictComp
def visit_DictComp(self, node: ast.DictComp) -> None: """Represent the dictionary comprehension by dumping its source code.""" if node in self._recomputed_values: value = self._recomputed_values[node] text = self._atok.get_text(node) self.reprs[text] = value self.generic_visit(node=node)
python
def visit_DictComp(self, node: ast.DictComp) -> None: """Represent the dictionary comprehension by dumping its source code.""" if node in self._recomputed_values: value = self._recomputed_values[node] text = self._atok.get_text(node) self.reprs[text] = value self.generic_visit(node=node)
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Represent the dictionary comprehension by dumping its source code.
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train
https://github.com/Parquery/icontract/blob/846e3187869a9ba790e9b893c98e5055e1cce274/icontract/_represent.py#L121-L129
Parquery/icontract
icontract/_checkers.py
_walk_decorator_stack
def _walk_decorator_stack(func: CallableT) -> Iterable['CallableT']: """ Iterate through the stack of decorated functions until the original function. Assume that all decorators used functools.update_wrapper. """ while hasattr(func, "__wrapped__"): yield func func = getattr(func, "__wrapped__") yield func
python
def _walk_decorator_stack(func: CallableT) -> Iterable['CallableT']: """ Iterate through the stack of decorated functions until the original function. Assume that all decorators used functools.update_wrapper. """ while hasattr(func, "__wrapped__"): yield func func = getattr(func, "__wrapped__") yield func
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Iterate through the stack of decorated functions until the original function. Assume that all decorators used functools.update_wrapper.
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train
https://github.com/Parquery/icontract/blob/846e3187869a9ba790e9b893c98e5055e1cce274/icontract/_checkers.py#L17-L28
Parquery/icontract
icontract/_checkers.py
find_checker
def find_checker(func: CallableT) -> Optional[CallableT]: """Iterate through the decorator stack till we find the contract checker.""" contract_checker = None # type: Optional[CallableT] for a_wrapper in _walk_decorator_stack(func): if hasattr(a_wrapper, "__preconditions__") or hasattr(a_wrapper, "__postconditions__"): contract_checker = a_wrapper return contract_checker
python
def find_checker(func: CallableT) -> Optional[CallableT]: """Iterate through the decorator stack till we find the contract checker.""" contract_checker = None # type: Optional[CallableT] for a_wrapper in _walk_decorator_stack(func): if hasattr(a_wrapper, "__preconditions__") or hasattr(a_wrapper, "__postconditions__"): contract_checker = a_wrapper return contract_checker
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Iterate through the decorator stack till we find the contract checker.
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train
https://github.com/Parquery/icontract/blob/846e3187869a9ba790e9b893c98e5055e1cce274/icontract/_checkers.py#L31-L38
Parquery/icontract
icontract/_checkers.py
_kwargs_from_call
def _kwargs_from_call(param_names: List[str], kwdefaults: Dict[str, Any], args: Tuple[Any, ...], kwargs: Dict[str, Any]) -> MutableMapping[str, Any]: """ Inspect the input values received at the wrapper for the actual function call. :param param_names: parameter (*i.e.* argument) names of the original (decorated) function :param kwdefaults: default argument values of the original function :param args: arguments supplied to the call :param kwargs: keyword arguments supplied to the call :return: resolved arguments as they would be passed to the function """ # pylint: disable=too-many-arguments mapping = dict() # type: MutableMapping[str, Any] # Set the default argument values as condition parameters. for param_name, param_value in kwdefaults.items(): mapping[param_name] = param_value # Override the defaults with the values actually suplied to the function. for i, func_arg in enumerate(args): mapping[param_names[i]] = func_arg for key, val in kwargs.items(): mapping[key] = val return mapping
python
def _kwargs_from_call(param_names: List[str], kwdefaults: Dict[str, Any], args: Tuple[Any, ...], kwargs: Dict[str, Any]) -> MutableMapping[str, Any]: """ Inspect the input values received at the wrapper for the actual function call. :param param_names: parameter (*i.e.* argument) names of the original (decorated) function :param kwdefaults: default argument values of the original function :param args: arguments supplied to the call :param kwargs: keyword arguments supplied to the call :return: resolved arguments as they would be passed to the function """ # pylint: disable=too-many-arguments mapping = dict() # type: MutableMapping[str, Any] # Set the default argument values as condition parameters. for param_name, param_value in kwdefaults.items(): mapping[param_name] = param_value # Override the defaults with the values actually suplied to the function. for i, func_arg in enumerate(args): mapping[param_names[i]] = func_arg for key, val in kwargs.items(): mapping[key] = val return mapping
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Inspect the input values received at the wrapper for the actual function call. :param param_names: parameter (*i.e.* argument) names of the original (decorated) function :param kwdefaults: default argument values of the original function :param args: arguments supplied to the call :param kwargs: keyword arguments supplied to the call :return: resolved arguments as they would be passed to the function
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train
https://github.com/Parquery/icontract/blob/846e3187869a9ba790e9b893c98e5055e1cce274/icontract/_checkers.py#L41-L66
Parquery/icontract
icontract/_checkers.py
_assert_precondition
def _assert_precondition(contract: Contract, resolved_kwargs: Mapping[str, Any]) -> None: """ Assert that the contract holds as a precondition. :param contract: contract to be verified :param resolved_kwargs: resolved keyword arguments (including the default values) :return: """ # Check that all arguments to the condition function have been set. missing_args = [arg_name for arg_name in contract.condition_args if arg_name not in resolved_kwargs] if missing_args: raise TypeError( ("The argument(s) of the precondition have not been set: {}. " "Does the original function define them? Did you supply them in the call?").format(missing_args)) condition_kwargs = { arg_name: value for arg_name, value in resolved_kwargs.items() if arg_name in contract.condition_arg_set } check = contract.condition(**condition_kwargs) if not check: if contract.error is not None and (inspect.ismethod(contract.error) or inspect.isfunction(contract.error)): assert contract.error_arg_set is not None, "Expected error_arg_set non-None if contract.error a function." assert contract.error_args is not None, "Expected error_args non-None if contract.error a function." error_kwargs = { arg_name: value for arg_name, value in resolved_kwargs.items() if arg_name in contract.error_arg_set } missing_args = [arg_name for arg_name in contract.error_args if arg_name not in resolved_kwargs] if missing_args: msg_parts = [] # type: List[str] if contract.location is not None: msg_parts.append("{}:\n".format(contract.location)) msg_parts.append( ("The argument(s) of the precondition error have not been set: {}. " "Does the original function define them? Did you supply them in the call?").format(missing_args)) raise TypeError(''.join(msg_parts)) raise contract.error(**error_kwargs) else: msg = icontract._represent.generate_message(contract=contract, condition_kwargs=condition_kwargs) if contract.error is None: raise ViolationError(msg) elif isinstance(contract.error, type): raise contract.error(msg)
python
def _assert_precondition(contract: Contract, resolved_kwargs: Mapping[str, Any]) -> None: """ Assert that the contract holds as a precondition. :param contract: contract to be verified :param resolved_kwargs: resolved keyword arguments (including the default values) :return: """ # Check that all arguments to the condition function have been set. missing_args = [arg_name for arg_name in contract.condition_args if arg_name not in resolved_kwargs] if missing_args: raise TypeError( ("The argument(s) of the precondition have not been set: {}. " "Does the original function define them? Did you supply them in the call?").format(missing_args)) condition_kwargs = { arg_name: value for arg_name, value in resolved_kwargs.items() if arg_name in contract.condition_arg_set } check = contract.condition(**condition_kwargs) if not check: if contract.error is not None and (inspect.ismethod(contract.error) or inspect.isfunction(contract.error)): assert contract.error_arg_set is not None, "Expected error_arg_set non-None if contract.error a function." assert contract.error_args is not None, "Expected error_args non-None if contract.error a function." error_kwargs = { arg_name: value for arg_name, value in resolved_kwargs.items() if arg_name in contract.error_arg_set } missing_args = [arg_name for arg_name in contract.error_args if arg_name not in resolved_kwargs] if missing_args: msg_parts = [] # type: List[str] if contract.location is not None: msg_parts.append("{}:\n".format(contract.location)) msg_parts.append( ("The argument(s) of the precondition error have not been set: {}. " "Does the original function define them? Did you supply them in the call?").format(missing_args)) raise TypeError(''.join(msg_parts)) raise contract.error(**error_kwargs) else: msg = icontract._represent.generate_message(contract=contract, condition_kwargs=condition_kwargs) if contract.error is None: raise ViolationError(msg) elif isinstance(contract.error, type): raise contract.error(msg)
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Assert that the contract holds as a precondition. :param contract: contract to be verified :param resolved_kwargs: resolved keyword arguments (including the default values) :return:
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train
https://github.com/Parquery/icontract/blob/846e3187869a9ba790e9b893c98e5055e1cce274/icontract/_checkers.py#L69-L120
Parquery/icontract
icontract/_checkers.py
_assert_invariant
def _assert_invariant(contract: Contract, instance: Any) -> None: """Assert that the contract holds as a class invariant given the instance of the class.""" if 'self' in contract.condition_arg_set: check = contract.condition(self=instance) else: check = contract.condition() if not check: if contract.error is not None and (inspect.ismethod(contract.error) or inspect.isfunction(contract.error)): assert contract.error_arg_set is not None, "Expected error_arg_set non-None if contract.error a function." assert contract.error_args is not None, "Expected error_args non-None if contract.error a function." if 'self' in contract.error_arg_set: raise contract.error(self=instance) else: raise contract.error() else: if 'self' in contract.condition_arg_set: msg = icontract._represent.generate_message(contract=contract, condition_kwargs={"self": instance}) else: msg = icontract._represent.generate_message(contract=contract, condition_kwargs=dict()) if contract.error is None: raise ViolationError(msg) elif isinstance(contract.error, type): raise contract.error(msg) else: raise NotImplementedError("Unhandled contract.error: {}".format(contract.error))
python
def _assert_invariant(contract: Contract, instance: Any) -> None: """Assert that the contract holds as a class invariant given the instance of the class.""" if 'self' in contract.condition_arg_set: check = contract.condition(self=instance) else: check = contract.condition() if not check: if contract.error is not None and (inspect.ismethod(contract.error) or inspect.isfunction(contract.error)): assert contract.error_arg_set is not None, "Expected error_arg_set non-None if contract.error a function." assert contract.error_args is not None, "Expected error_args non-None if contract.error a function." if 'self' in contract.error_arg_set: raise contract.error(self=instance) else: raise contract.error() else: if 'self' in contract.condition_arg_set: msg = icontract._represent.generate_message(contract=contract, condition_kwargs={"self": instance}) else: msg = icontract._represent.generate_message(contract=contract, condition_kwargs=dict()) if contract.error is None: raise ViolationError(msg) elif isinstance(contract.error, type): raise contract.error(msg) else: raise NotImplementedError("Unhandled contract.error: {}".format(contract.error))
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Assert that the contract holds as a class invariant given the instance of the class.
[ "Assert", "that", "the", "contract", "holds", "as", "a", "class", "invariant", "given", "the", "instance", "of", "the", "class", "." ]
train
https://github.com/Parquery/icontract/blob/846e3187869a9ba790e9b893c98e5055e1cce274/icontract/_checkers.py#L123-L150