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curious-containers/cc-core
cc_core/commons/cwl.py
parse_cwl_type
def parse_cwl_type(cwl_type_string): """ Parses cwl type information from a cwl type string. Examples: - "File[]" -> {'type': 'File', 'isArray': True, 'isOptional': False} - "int?" -> {'type': 'int', 'isArray': False, 'isOptional': True} :param cwl_type_string: The cwl type string to extract information from :return: A dictionary containing information about the parsed cwl type string """ is_optional = cwl_type_string.endswith('?') if is_optional: cwl_type_string = cwl_type_string[:-1] is_array = cwl_type_string.endswith('[]') if is_array: cwl_type_string = cwl_type_string[:-2] return {'type': cwl_type_string, 'isArray': is_array, 'isOptional': is_optional}
python
def parse_cwl_type(cwl_type_string): """ Parses cwl type information from a cwl type string. Examples: - "File[]" -> {'type': 'File', 'isArray': True, 'isOptional': False} - "int?" -> {'type': 'int', 'isArray': False, 'isOptional': True} :param cwl_type_string: The cwl type string to extract information from :return: A dictionary containing information about the parsed cwl type string """ is_optional = cwl_type_string.endswith('?') if is_optional: cwl_type_string = cwl_type_string[:-1] is_array = cwl_type_string.endswith('[]') if is_array: cwl_type_string = cwl_type_string[:-2] return {'type': cwl_type_string, 'isArray': is_array, 'isOptional': is_optional}
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Parses cwl type information from a cwl type string. Examples: - "File[]" -> {'type': 'File', 'isArray': True, 'isOptional': False} - "int?" -> {'type': 'int', 'isArray': False, 'isOptional': True} :param cwl_type_string: The cwl type string to extract information from :return: A dictionary containing information about the parsed cwl type string
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train
https://github.com/curious-containers/cc-core/blob/eaeb03a4366016aff54fcc6953d052ae12ed599b/cc_core/commons/cwl.py#L223-L244
curious-containers/cc-core
cc_core/commons/cwl.py
cwl_input_directories
def cwl_input_directories(cwl_data, job_data, input_dir=None): """ Searches for Directories and in the cwl data and produces a dictionary containing input file information. :param cwl_data: The cwl data as dictionary :param job_data: The job data as dictionary :param input_dir: TODO :return: Returns the a dictionary containing information about input files. The keys of this dictionary are the input/output identifiers of the files specified in the cwl description. The corresponding value is a dictionary again with the following keys and values: - 'isOptional': A bool indicating whether this input directory is optional - 'isArray': A bool indicating whether this could be a list of directories - 'files': A list of input file descriptions A input file description is a dictionary containing the following information - 'path': The path to the specified directory - 'debugInfo': A field to possibly provide debug information """ results = {} for input_identifier, input_data in cwl_data['inputs'].items(): cwl_type = parse_cwl_type(input_data['type']) (is_optional, is_array, cwl_type) = itemgetter('isOptional', 'isArray', 'type')(cwl_type) if cwl_type == 'Directory': result = { 'isOptional': is_optional, 'isArray': is_array, 'directories': None } if input_identifier in job_data: arg = job_data[input_identifier] if is_array: result['directories'] = [_input_directory_description(input_identifier, i, input_dir) for i in arg] else: result['directories'] = [_input_directory_description(input_identifier, arg, input_dir)] results[input_identifier] = result return results
python
def cwl_input_directories(cwl_data, job_data, input_dir=None): """ Searches for Directories and in the cwl data and produces a dictionary containing input file information. :param cwl_data: The cwl data as dictionary :param job_data: The job data as dictionary :param input_dir: TODO :return: Returns the a dictionary containing information about input files. The keys of this dictionary are the input/output identifiers of the files specified in the cwl description. The corresponding value is a dictionary again with the following keys and values: - 'isOptional': A bool indicating whether this input directory is optional - 'isArray': A bool indicating whether this could be a list of directories - 'files': A list of input file descriptions A input file description is a dictionary containing the following information - 'path': The path to the specified directory - 'debugInfo': A field to possibly provide debug information """ results = {} for input_identifier, input_data in cwl_data['inputs'].items(): cwl_type = parse_cwl_type(input_data['type']) (is_optional, is_array, cwl_type) = itemgetter('isOptional', 'isArray', 'type')(cwl_type) if cwl_type == 'Directory': result = { 'isOptional': is_optional, 'isArray': is_array, 'directories': None } if input_identifier in job_data: arg = job_data[input_identifier] if is_array: result['directories'] = [_input_directory_description(input_identifier, i, input_dir) for i in arg] else: result['directories'] = [_input_directory_description(input_identifier, arg, input_dir)] results[input_identifier] = result return results
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https://github.com/curious-containers/cc-core/blob/eaeb03a4366016aff54fcc6953d052ae12ed599b/cc_core/commons/cwl.py#L275-L318
curious-containers/cc-core
cc_core/commons/cwl.py
cwl_output_files
def cwl_output_files(cwl_data, inputs_to_reference, output_dir=None): """ Returns a dictionary containing information about the output files given in cwl_data. :param cwl_data: The cwl data from where to extract the output file information. :param inputs_to_reference: Inputs which are used to resolve input references. :param output_dir: Path to the directory where output files are expected. :return: A dictionary containing information about every output file. """ results = {} for key, val in cwl_data['outputs'].items(): cwl_type = parse_cwl_type(val['type']) (is_optional, is_array, cwl_type) = itemgetter('isOptional', 'isArray', 'type')(cwl_type) if not cwl_type == 'File': continue result = { 'isOptional': is_optional, 'path': None, 'size': None, 'debugInfo': None } glob_path = os.path.expanduser(val['outputBinding']['glob']) if output_dir and not os.path.isabs(glob_path): glob_path = os.path.join(os.path.expanduser(output_dir), glob_path) glob_path = resolve_input_references(glob_path, inputs_to_reference) matches = glob(glob_path) try: if len(matches) != 1: raise FileError('glob path "{}" does not match exactly one file'.format(glob_path)) file_path = matches[0] result['path'] = file_path if not os.path.isfile(file_path): raise FileError('path is not a file') result['size'] = os.path.getsize(file_path) / (1024 * 1024) except: result['debugInfo'] = exception_format() results[key] = result return results
python
def cwl_output_files(cwl_data, inputs_to_reference, output_dir=None): """ Returns a dictionary containing information about the output files given in cwl_data. :param cwl_data: The cwl data from where to extract the output file information. :param inputs_to_reference: Inputs which are used to resolve input references. :param output_dir: Path to the directory where output files are expected. :return: A dictionary containing information about every output file. """ results = {} for key, val in cwl_data['outputs'].items(): cwl_type = parse_cwl_type(val['type']) (is_optional, is_array, cwl_type) = itemgetter('isOptional', 'isArray', 'type')(cwl_type) if not cwl_type == 'File': continue result = { 'isOptional': is_optional, 'path': None, 'size': None, 'debugInfo': None } glob_path = os.path.expanduser(val['outputBinding']['glob']) if output_dir and not os.path.isabs(glob_path): glob_path = os.path.join(os.path.expanduser(output_dir), glob_path) glob_path = resolve_input_references(glob_path, inputs_to_reference) matches = glob(glob_path) try: if len(matches) != 1: raise FileError('glob path "{}" does not match exactly one file'.format(glob_path)) file_path = matches[0] result['path'] = file_path if not os.path.isfile(file_path): raise FileError('path is not a file') result['size'] = os.path.getsize(file_path) / (1024 * 1024) except: result['debugInfo'] = exception_format() results[key] = result return results
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https://github.com/curious-containers/cc-core/blob/eaeb03a4366016aff54fcc6953d052ae12ed599b/cc_core/commons/cwl.py#L321-L368
greenbender/python-fifo
fifo/__init__.py
Fifo.read
def read(self, length=-1): """ Reads from the FIFO. Reads as much data as possible from the FIFO up to the specified length. If the length argument is negative or ommited all data currently available in the FIFO will be read. If there is no data available in the FIFO an empty string is returned. Args: length: The amount of data to read from the FIFO. Defaults to -1. """ if 0 <= length < len(self): newpos = self.pos + length data = self.buf[self.pos:newpos] self.pos = newpos self.__discard() return data data = self.buf[self.pos:] self.clear() return data
python
def read(self, length=-1): """ Reads from the FIFO. Reads as much data as possible from the FIFO up to the specified length. If the length argument is negative or ommited all data currently available in the FIFO will be read. If there is no data available in the FIFO an empty string is returned. Args: length: The amount of data to read from the FIFO. Defaults to -1. """ if 0 <= length < len(self): newpos = self.pos + length data = self.buf[self.pos:newpos] self.pos = newpos self.__discard() return data data = self.buf[self.pos:] self.clear() return data
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train
https://github.com/greenbender/python-fifo/blob/ffabb6c8b844086dd3a490d0b42bbb5aa8fbb932/fifo/__init__.py#L76-L97
greenbender/python-fifo
fifo/__init__.py
Fifo.readuntil
def readuntil(self, token, size=0): """ Reads data from the FIFO until a token is encountered. If no token is encountered as much data is read from the FIFO as possible keeping in mind that the FIFO must retain enough data to perform matches for the token across writes. Args: token: The token to read until. size: The minimum amount of data that should be left in the FIFO. This is only used if it is greater than the length of the token. When ommited this value will default to the length of the token. Returns: A tuple of (found, data) where found is a boolean indicating whether the token was found, and data is all the data that could be read from the FIFO. Note: When a token is found the token is also read from the buffer and returned in the data. """ self.__append() i = self.buf.find(token, self.pos) if i < 0: index = max(len(token) - 1, size) newpos = max(len(self.buf) - index, self.pos) data = self.buf[self.pos:newpos] self.pos = newpos self.__discard() return False, data newpos = i + len(token) data = self.buf[self.pos:newpos] self.pos = newpos self.__discard() return True, data
python
def readuntil(self, token, size=0): """ Reads data from the FIFO until a token is encountered. If no token is encountered as much data is read from the FIFO as possible keeping in mind that the FIFO must retain enough data to perform matches for the token across writes. Args: token: The token to read until. size: The minimum amount of data that should be left in the FIFO. This is only used if it is greater than the length of the token. When ommited this value will default to the length of the token. Returns: A tuple of (found, data) where found is a boolean indicating whether the token was found, and data is all the data that could be read from the FIFO. Note: When a token is found the token is also read from the buffer and returned in the data. """ self.__append() i = self.buf.find(token, self.pos) if i < 0: index = max(len(token) - 1, size) newpos = max(len(self.buf) - index, self.pos) data = self.buf[self.pos:newpos] self.pos = newpos self.__discard() return False, data newpos = i + len(token) data = self.buf[self.pos:newpos] self.pos = newpos self.__discard() return True, data
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train
https://github.com/greenbender/python-fifo/blob/ffabb6c8b844086dd3a490d0b42bbb5aa8fbb932/fifo/__init__.py#L136-L173
greenbender/python-fifo
fifo/__init__.py
Fifo.peekline
def peekline(self): """ Peeks a line into the FIFO. Perfroms the same function as readline() without removing data from the FIFO. See readline() for further information. """ self.__append() i = self.buf.find(self.eol, self.pos) if i < 0: return '' newpos = i + len(self.eol) return self.buf[self.pos:newpos]
python
def peekline(self): """ Peeks a line into the FIFO. Perfroms the same function as readline() without removing data from the FIFO. See readline() for further information. """ self.__append() i = self.buf.find(self.eol, self.pos) if i < 0: return '' newpos = i + len(self.eol) return self.buf[self.pos:newpos]
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train
https://github.com/greenbender/python-fifo/blob/ffabb6c8b844086dd3a490d0b42bbb5aa8fbb932/fifo/__init__.py#L200-L214
greenbender/python-fifo
fifo/__init__.py
Fifo.peekuntil
def peekuntil(self, token, size=0): """ Peeks for token into the FIFO. Performs the same function as readuntil() without removing data from the FIFO. See readuntil() for further information. """ self.__append() i = self.buf.find(token, self.pos) if i < 0: index = max(len(token) - 1, size) newpos = max(len(self.buf) - index, self.pos) return False, self.buf[self.pos:newpos] newpos = i + len(token) return True, self.buf[self.pos:newpos]
python
def peekuntil(self, token, size=0): """ Peeks for token into the FIFO. Performs the same function as readuntil() without removing data from the FIFO. See readuntil() for further information. """ self.__append() i = self.buf.find(token, self.pos) if i < 0: index = max(len(token) - 1, size) newpos = max(len(self.buf) - index, self.pos) return False, self.buf[self.pos:newpos] newpos = i + len(token) return True, self.buf[self.pos:newpos]
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Peeks for token into the FIFO. Performs the same function as readuntil() without removing data from the FIFO. See readuntil() for further information.
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train
https://github.com/greenbender/python-fifo/blob/ffabb6c8b844086dd3a490d0b42bbb5aa8fbb932/fifo/__init__.py#L216-L232
linkhub-sdk/popbill.py
popbill/kakaoService.py
KakaoService.getURL
def getURL(self, CorpNum, UserID, ToGo): """ :param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param UserID: νŒλΉŒνšŒμ› 아이디 :param ToGo: [PLUSFRIEND-ν”ŒλŸ¬μŠ€μΉœκ΅¬κ³„μ •κ΄€λ¦¬, SENDER-λ°œμ‹ λ²ˆν˜Έκ΄€λ¦¬, TEMPLATE-μ•Œλ¦Όν†‘ν…œν”Œλ¦Ώκ΄€λ¦¬, BOX-μΉ΄μΉ΄μ˜€ν†‘μ „μ†‘λ‚΄μš©] :return: 팝빌 URL """ if ToGo == None or ToGo == '': raise PopbillException(-99999999, "TOGO값이 μž…λ ₯λ˜μ§€ μ•Šμ•˜μŠ΅λ‹ˆλ‹€.") if ToGo == 'SENDER': result = self._httpget('/Message/?TG=' + ToGo, CorpNum, UserID) else: result = self._httpget('/KakaoTalk/?TG=' + ToGo, CorpNum, UserID) return result.url
python
def getURL(self, CorpNum, UserID, ToGo): """ :param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param UserID: νŒλΉŒνšŒμ› 아이디 :param ToGo: [PLUSFRIEND-ν”ŒλŸ¬μŠ€μΉœκ΅¬κ³„μ •κ΄€λ¦¬, SENDER-λ°œμ‹ λ²ˆν˜Έκ΄€λ¦¬, TEMPLATE-μ•Œλ¦Όν†‘ν…œν”Œλ¦Ώκ΄€λ¦¬, BOX-μΉ΄μΉ΄μ˜€ν†‘μ „μ†‘λ‚΄μš©] :return: 팝빌 URL """ if ToGo == None or ToGo == '': raise PopbillException(-99999999, "TOGO값이 μž…λ ₯λ˜μ§€ μ•Šμ•˜μŠ΅λ‹ˆλ‹€.") if ToGo == 'SENDER': result = self._httpget('/Message/?TG=' + ToGo, CorpNum, UserID) else: result = self._httpget('/KakaoTalk/?TG=' + ToGo, CorpNum, UserID) return result.url
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:param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param UserID: νŒλΉŒνšŒμ› 아이디 :param ToGo: [PLUSFRIEND-ν”ŒλŸ¬μŠ€μΉœκ΅¬κ³„μ •κ΄€λ¦¬, SENDER-λ°œμ‹ λ²ˆν˜Έκ΄€λ¦¬, TEMPLATE-μ•Œλ¦Όν†‘ν…œν”Œλ¦Ώκ΄€λ¦¬, BOX-μΉ΄μΉ΄μ˜€ν†‘μ „μ†‘λ‚΄μš©] :return: 팝빌 URL
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train
https://github.com/linkhub-sdk/popbill.py/blob/68a0dd7f7a937603318e93be321fde73c50b96cc/popbill/kakaoService.py#L28-L42
linkhub-sdk/popbill.py
popbill/kakaoService.py
KakaoService.getPlusFriendMgtURL
def getPlusFriendMgtURL(self, CorpNum, UserID): """ ν”ŒλŸ¬μŠ€μΉœκ΅¬ 계정관리 νŒμ—… URL :param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param UserID: νŒλΉŒνšŒμ› 아이디 :return: 팝빌 URL """ result = self._httpget('/KakaoTalk/?TG=PLUSFRIEND', CorpNum, UserID) return result.url
python
def getPlusFriendMgtURL(self, CorpNum, UserID): """ ν”ŒλŸ¬μŠ€μΉœκ΅¬ 계정관리 νŒμ—… URL :param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param UserID: νŒλΉŒνšŒμ› 아이디 :return: 팝빌 URL """ result = self._httpget('/KakaoTalk/?TG=PLUSFRIEND', CorpNum, UserID) return result.url
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ν”ŒλŸ¬μŠ€μΉœκ΅¬ 계정관리 νŒμ—… URL :param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param UserID: νŒλΉŒνšŒμ› 아이디 :return: 팝빌 URL
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train
https://github.com/linkhub-sdk/popbill.py/blob/68a0dd7f7a937603318e93be321fde73c50b96cc/popbill/kakaoService.py#L44-L52
linkhub-sdk/popbill.py
popbill/kakaoService.py
KakaoService.getATSTemplateMgtURL
def getATSTemplateMgtURL(self, CorpNum, UserID): """ μ•Œλ¦Όν†‘ ν…œν”Œλ¦Ώκ΄€λ¦¬ νŒμ—… URL :param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param UserID: νŒλΉŒνšŒμ› 아이디 :return: 팝빌 URL """ result = self._httpget('/KakaoTalk/?TG=TEMPLATE', CorpNum, UserID) return result.url
python
def getATSTemplateMgtURL(self, CorpNum, UserID): """ μ•Œλ¦Όν†‘ ν…œν”Œλ¦Ώκ΄€λ¦¬ νŒμ—… URL :param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param UserID: νŒλΉŒνšŒμ› 아이디 :return: 팝빌 URL """ result = self._httpget('/KakaoTalk/?TG=TEMPLATE', CorpNum, UserID) return result.url
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μ•Œλ¦Όν†‘ ν…œν”Œλ¦Ώκ΄€λ¦¬ νŒμ—… URL :param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param UserID: νŒλΉŒνšŒμ› 아이디 :return: 팝빌 URL
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train
https://github.com/linkhub-sdk/popbill.py/blob/68a0dd7f7a937603318e93be321fde73c50b96cc/popbill/kakaoService.py#L64-L72
linkhub-sdk/popbill.py
popbill/kakaoService.py
KakaoService.getSentListURL
def getSentListURL(self, CorpNum, UserID): """ μΉ΄μΉ΄μ˜€ν†‘ 전솑내역 νŒμ—… URL :param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param UserID: νŒλΉŒνšŒμ› 아이디 :return: 팝빌 URL """ result = self._httpget('/KakaoTalk/?TG=BOX', CorpNum, UserID) return result.url
python
def getSentListURL(self, CorpNum, UserID): """ μΉ΄μΉ΄μ˜€ν†‘ 전솑내역 νŒμ—… URL :param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param UserID: νŒλΉŒνšŒμ› 아이디 :return: 팝빌 URL """ result = self._httpget('/KakaoTalk/?TG=BOX', CorpNum, UserID) return result.url
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μΉ΄μΉ΄μ˜€ν†‘ 전솑내역 νŒμ—… URL :param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param UserID: νŒλΉŒνšŒμ› 아이디 :return: 팝빌 URL
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train
https://github.com/linkhub-sdk/popbill.py/blob/68a0dd7f7a937603318e93be321fde73c50b96cc/popbill/kakaoService.py#L74-L82
linkhub-sdk/popbill.py
popbill/kakaoService.py
KakaoService.sendATS_same
def sendATS_same(self, CorpNum, TemplateCode, Sender, Content, AltContent, AltSendType, SndDT, KakaoMessages, UserID=None, RequestNum=None, ButtonList=None): """ μ•Œλ¦Όν†‘ λŒ€λŸ‰ 전솑 :param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param TemplateCode: ν…œν”Œλ¦Ώμ½”λ“œ :param Sender: λ°œμ‹ λ²ˆν˜Έ :param Content: [동보] μ•Œλ¦Όν†‘ λ‚΄μš© :param AltContent: [동보] λŒ€μ²΄λ¬Έμž λ‚΄μš© :param AltSendType: λŒ€μ²΄λ¬Έμž μœ ν˜• [곡백-미전솑, C-μ•Œλ¦Όν†‘λ‚΄μš©, A-λŒ€μ²΄λ¬Έμžλ‚΄μš©] :param SndDT: μ˜ˆμ•½μΌμ‹œ [μž‘μ„±ν˜•μ‹ : yyyyMMddHHmmss] :param KakaoMessages: μ•Œλ¦Όν†‘ λ‚΄μš© (λ°°μ—΄) :param UserID: νŒλΉŒνšŒμ› 아이디 :param RequestNum : μš”μ²­λ²ˆν˜Έ :return: receiptNum (μ ‘μˆ˜λ²ˆν˜Έ) """ if TemplateCode is None or TemplateCode == '': raise PopbillException(-99999999, "μ•Œλ¦Όν†‘ ν…œν”Œλ¦Ώμ½”λ“œκ°€ μž…λ ₯λ˜μ§€ μ•Šμ•˜μŠ΅λ‹ˆλ‹€.") if Sender is None or Sender == '': raise PopbillException(-99999999, "λ°œμ‹ λ²ˆν˜Έκ°€ μž…λ ₯λ˜μ§€ μ•Šμ•˜μŠ΅λ‹ˆλ‹€.") req = {} if TemplateCode is not None or TemplateCode != '': req['templateCode'] = TemplateCode if Sender is not None or Sender != '': req['snd'] = Sender if Content is not None or Content != '': req['content'] = Content if AltContent is not None or AltContent != '': req['altContent'] = AltContent if AltSendType is not None or AltSendType != '': req['altSendType'] = AltSendType if SndDT is not None or SndDT != '': req['sndDT'] = SndDT if KakaoMessages is not None or KakaoMessages != '': req['msgs'] = KakaoMessages if ButtonList is not None: req['btns'] = ButtonList if RequestNum is not None or RequestNum != '': req['requestnum'] = RequestNum postData = self._stringtify(req) result = self._httppost('/ATS', postData, CorpNum, UserID) return result.receiptNum
python
def sendATS_same(self, CorpNum, TemplateCode, Sender, Content, AltContent, AltSendType, SndDT, KakaoMessages, UserID=None, RequestNum=None, ButtonList=None): """ μ•Œλ¦Όν†‘ λŒ€λŸ‰ 전솑 :param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param TemplateCode: ν…œν”Œλ¦Ώμ½”λ“œ :param Sender: λ°œμ‹ λ²ˆν˜Έ :param Content: [동보] μ•Œλ¦Όν†‘ λ‚΄μš© :param AltContent: [동보] λŒ€μ²΄λ¬Έμž λ‚΄μš© :param AltSendType: λŒ€μ²΄λ¬Έμž μœ ν˜• [곡백-미전솑, C-μ•Œλ¦Όν†‘λ‚΄μš©, A-λŒ€μ²΄λ¬Έμžλ‚΄μš©] :param SndDT: μ˜ˆμ•½μΌμ‹œ [μž‘μ„±ν˜•μ‹ : yyyyMMddHHmmss] :param KakaoMessages: μ•Œλ¦Όν†‘ λ‚΄μš© (λ°°μ—΄) :param UserID: νŒλΉŒνšŒμ› 아이디 :param RequestNum : μš”μ²­λ²ˆν˜Έ :return: receiptNum (μ ‘μˆ˜λ²ˆν˜Έ) """ if TemplateCode is None or TemplateCode == '': raise PopbillException(-99999999, "μ•Œλ¦Όν†‘ ν…œν”Œλ¦Ώμ½”λ“œκ°€ μž…λ ₯λ˜μ§€ μ•Šμ•˜μŠ΅λ‹ˆλ‹€.") if Sender is None or Sender == '': raise PopbillException(-99999999, "λ°œμ‹ λ²ˆν˜Έκ°€ μž…λ ₯λ˜μ§€ μ•Šμ•˜μŠ΅λ‹ˆλ‹€.") req = {} if TemplateCode is not None or TemplateCode != '': req['templateCode'] = TemplateCode if Sender is not None or Sender != '': req['snd'] = Sender if Content is not None or Content != '': req['content'] = Content if AltContent is not None or AltContent != '': req['altContent'] = AltContent if AltSendType is not None or AltSendType != '': req['altSendType'] = AltSendType if SndDT is not None or SndDT != '': req['sndDT'] = SndDT if KakaoMessages is not None or KakaoMessages != '': req['msgs'] = KakaoMessages if ButtonList is not None: req['btns'] = ButtonList if RequestNum is not None or RequestNum != '': req['requestnum'] = RequestNum postData = self._stringtify(req) result = self._httppost('/ATS', postData, CorpNum, UserID) return result.receiptNum
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μ•Œλ¦Όν†‘ λŒ€λŸ‰ 전솑 :param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param TemplateCode: ν…œν”Œλ¦Ώμ½”λ“œ :param Sender: λ°œμ‹ λ²ˆν˜Έ :param Content: [동보] μ•Œλ¦Όν†‘ λ‚΄μš© :param AltContent: [동보] λŒ€μ²΄λ¬Έμž λ‚΄μš© :param AltSendType: λŒ€μ²΄λ¬Έμž μœ ν˜• [곡백-미전솑, C-μ•Œλ¦Όν†‘λ‚΄μš©, A-λŒ€μ²΄λ¬Έμžλ‚΄μš©] :param SndDT: μ˜ˆμ•½μΌμ‹œ [μž‘μ„±ν˜•μ‹ : yyyyMMddHHmmss] :param KakaoMessages: μ•Œλ¦Όν†‘ λ‚΄μš© (λ°°μ—΄) :param UserID: νŒλΉŒνšŒμ› 아이디 :param RequestNum : μš”μ²­λ²ˆν˜Έ :return: receiptNum (μ ‘μˆ˜λ²ˆν˜Έ)
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train
https://github.com/linkhub-sdk/popbill.py/blob/68a0dd7f7a937603318e93be321fde73c50b96cc/popbill/kakaoService.py#L153-L199
linkhub-sdk/popbill.py
popbill/kakaoService.py
KakaoService.sendFTS_same
def sendFTS_same(self, CorpNum, PlusFriendID, Sender, Content, AltContent, AltSendType, SndDT, KakaoMessages, KakaoButtons, AdsYN=False, UserID=None, RequestNum=None): """ μΉœκ΅¬ν†‘ ν…μŠ€νŠΈ λŒ€λŸ‰ 전솑 :param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param PlusFriendID: ν”ŒλŸ¬μŠ€μΉœκ΅¬ 아이디 :param Sender: λ°œμ‹ λ²ˆν˜Έ :param Content: [동보] μΉœκ΅¬ν†‘ λ‚΄μš© :param AltContent: [동보] λŒ€μ²΄λ¬Έμž λ‚΄μš© :param AltSendType: λŒ€μ²΄λ¬Έμž μœ ν˜• [곡백-미전솑, C-μ•Œλ¦Όν†‘λ‚΄μš©, A-λŒ€μ²΄λ¬Έμžλ‚΄μš©] :param SndDT: μ˜ˆμ•½μΌμ‹œ [μž‘μ„±ν˜•μ‹ : yyyyMMddHHmmss] :param KakaoMessages: μΉœκ΅¬ν†‘ λ‚΄μš© (λ°°μ—΄) :param KakaoButtons: λ²„νŠΌ λͺ©λ‘ (μ΅œλŒ€ 5개) :param AdsYN: κ΄‘κ³  전솑여뢀 :param UserID: νŒλΉŒνšŒμ› 아이디 :param RequestNum : μš”μ²­λ²ˆν˜Έ :return: receiptNum (μ ‘μˆ˜λ²ˆν˜Έ) """ if PlusFriendID is None or PlusFriendID == '': raise PopbillException(-99999999, "ν”ŒλŸ¬μŠ€μΉœκ΅¬ 아이디가 μž…λ ₯λ˜μ§€ μ•Šμ•˜μŠ΅λ‹ˆλ‹€.") if Sender is None or Sender == '': raise PopbillException(-99999999, "λ°œμ‹ λ²ˆν˜Έκ°€ μž…λ ₯λ˜μ§€ μ•Šμ•˜μŠ΅λ‹ˆλ‹€.") req = {} if PlusFriendID is not None or PlusFriendID != '': req['plusFriendID'] = PlusFriendID if Sender is not None or Sender != '': req['snd'] = Sender if AltSendType is not None or AltSendType != '': req['altSendType'] = AltSendType if Content is not None or Content != '': req['content'] = Content if AltContent is not None or AltContent != '': req['altContent'] = AltContent if SndDT is not None or SndDT != '': req['sndDT'] = SndDT if KakaoMessages: req['msgs'] = KakaoMessages if KakaoButtons: req['btns'] = KakaoButtons if AdsYN: req['adsYN'] = True if RequestNum is not None or RequestNum != '': req['requestNum'] = RequestNum postData = self._stringtify(req) result = self._httppost('/FTS', postData, CorpNum, UserID) return result.receiptNum
python
def sendFTS_same(self, CorpNum, PlusFriendID, Sender, Content, AltContent, AltSendType, SndDT, KakaoMessages, KakaoButtons, AdsYN=False, UserID=None, RequestNum=None): """ μΉœκ΅¬ν†‘ ν…μŠ€νŠΈ λŒ€λŸ‰ 전솑 :param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param PlusFriendID: ν”ŒλŸ¬μŠ€μΉœκ΅¬ 아이디 :param Sender: λ°œμ‹ λ²ˆν˜Έ :param Content: [동보] μΉœκ΅¬ν†‘ λ‚΄μš© :param AltContent: [동보] λŒ€μ²΄λ¬Έμž λ‚΄μš© :param AltSendType: λŒ€μ²΄λ¬Έμž μœ ν˜• [곡백-미전솑, C-μ•Œλ¦Όν†‘λ‚΄μš©, A-λŒ€μ²΄λ¬Έμžλ‚΄μš©] :param SndDT: μ˜ˆμ•½μΌμ‹œ [μž‘μ„±ν˜•μ‹ : yyyyMMddHHmmss] :param KakaoMessages: μΉœκ΅¬ν†‘ λ‚΄μš© (λ°°μ—΄) :param KakaoButtons: λ²„νŠΌ λͺ©λ‘ (μ΅œλŒ€ 5개) :param AdsYN: κ΄‘κ³  전솑여뢀 :param UserID: νŒλΉŒνšŒμ› 아이디 :param RequestNum : μš”μ²­λ²ˆν˜Έ :return: receiptNum (μ ‘μˆ˜λ²ˆν˜Έ) """ if PlusFriendID is None or PlusFriendID == '': raise PopbillException(-99999999, "ν”ŒλŸ¬μŠ€μΉœκ΅¬ 아이디가 μž…λ ₯λ˜μ§€ μ•Šμ•˜μŠ΅λ‹ˆλ‹€.") if Sender is None or Sender == '': raise PopbillException(-99999999, "λ°œμ‹ λ²ˆν˜Έκ°€ μž…λ ₯λ˜μ§€ μ•Šμ•˜μŠ΅λ‹ˆλ‹€.") req = {} if PlusFriendID is not None or PlusFriendID != '': req['plusFriendID'] = PlusFriendID if Sender is not None or Sender != '': req['snd'] = Sender if AltSendType is not None or AltSendType != '': req['altSendType'] = AltSendType if Content is not None or Content != '': req['content'] = Content if AltContent is not None or AltContent != '': req['altContent'] = AltContent if SndDT is not None or SndDT != '': req['sndDT'] = SndDT if KakaoMessages: req['msgs'] = KakaoMessages if KakaoButtons: req['btns'] = KakaoButtons if AdsYN: req['adsYN'] = True if RequestNum is not None or RequestNum != '': req['requestNum'] = RequestNum postData = self._stringtify(req) result = self._httppost('/FTS', postData, CorpNum, UserID) return result.receiptNum
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μΉœκ΅¬ν†‘ ν…μŠ€νŠΈ λŒ€λŸ‰ 전솑 :param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param PlusFriendID: ν”ŒλŸ¬μŠ€μΉœκ΅¬ 아이디 :param Sender: λ°œμ‹ λ²ˆν˜Έ :param Content: [동보] μΉœκ΅¬ν†‘ λ‚΄μš© :param AltContent: [동보] λŒ€μ²΄λ¬Έμž λ‚΄μš© :param AltSendType: λŒ€μ²΄λ¬Έμž μœ ν˜• [곡백-미전솑, C-μ•Œλ¦Όν†‘λ‚΄μš©, A-λŒ€μ²΄λ¬Έμžλ‚΄μš©] :param SndDT: μ˜ˆμ•½μΌμ‹œ [μž‘μ„±ν˜•μ‹ : yyyyMMddHHmmss] :param KakaoMessages: μΉœκ΅¬ν†‘ λ‚΄μš© (λ°°μ—΄) :param KakaoButtons: λ²„νŠΌ λͺ©λ‘ (μ΅œλŒ€ 5개) :param AdsYN: κ΄‘κ³  전솑여뢀 :param UserID: νŒλΉŒνšŒμ› 아이디 :param RequestNum : μš”μ²­λ²ˆν˜Έ :return: receiptNum (μ ‘μˆ˜λ²ˆν˜Έ)
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train
https://github.com/linkhub-sdk/popbill.py/blob/68a0dd7f7a937603318e93be321fde73c50b96cc/popbill/kakaoService.py#L219-L268
linkhub-sdk/popbill.py
popbill/kakaoService.py
KakaoService.sendFMS_same
def sendFMS_same(self, CorpNum, PlusFriendID, Sender, Content, AltContent, AltSendType, SndDT, FilePath, ImageURL, KakaoMessages, KakaoButtons, AdsYN=False, UserID=None, RequestNum=None): """ μΉœκ΅¬ν†‘ 이미지 λŒ€λŸ‰ 전솑 :param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param PlusFriendID: ν”ŒλŸ¬μŠ€μΉœκ΅¬ 아이디 :param Sender: λ°œμ‹ λ²ˆν˜Έ :param Content: [동보] μΉœκ΅¬ν†‘ λ‚΄μš© :param AltContent: [동보] λŒ€μ²΄λ¬Έμž λ‚΄μš© :param AltSendType: λŒ€μ²΄λ¬Έμž μœ ν˜• [곡백-미전솑, C-μ•Œλ¦Όν†‘λ‚΄μš©, A-λŒ€μ²΄λ¬Έμžλ‚΄μš©] :param SndDT: μ˜ˆμ•½μΌμ‹œ [μž‘μ„±ν˜•μ‹ : yyyyMMddHHmmss] :param FilePath: 파일경둜 :param ImageURL: 이미지URL :param KakaoMessages: μΉœκ΅¬ν†‘ λ‚΄μš© (λ°°μ—΄) :param KakaoButtons: λ²„νŠΌ λͺ©λ‘ (μ΅œλŒ€ 5개) :param AdsYN: κ΄‘κ³  전솑여뢀 :param UserID: νŒλΉŒνšŒμ› 아이디 :param RequestNum : μš”μ²­λ²ˆν˜Έ :return: receiptNum (μ ‘μˆ˜λ²ˆν˜Έ) """ if PlusFriendID is None or PlusFriendID == '': raise PopbillException(-99999999, "ν”ŒλŸ¬μŠ€μΉœκ΅¬ 아이디가 μž…λ ₯λ˜μ§€ μ•Šμ•˜μŠ΅λ‹ˆλ‹€.") if Sender is None or Sender == '': raise PopbillException(-99999999, "λ°œμ‹ λ²ˆν˜Έκ°€ μž…λ ₯λ˜μ§€ μ•Šμ•˜μŠ΅λ‹ˆλ‹€.") req = {} if PlusFriendID is not None or PlusFriendID != '': req['plusFriendID'] = PlusFriendID if Sender is not None or Sender != '': req['snd'] = Sender if Content is not None or Content != '': req['content'] = Content if AltContent is not None or AltContent != '': req['altContent'] = AltContent if AltSendType is not None or AltSendType != '': req['altSendType'] = AltSendType if SndDT is not None or SndDT != '': req['sndDT'] = SndDT if KakaoMessages is not None or KakaoMessages != '': req['msgs'] = KakaoMessages if ImageURL is not None or ImageURL != '': req['imageURL'] = ImageURL if KakaoButtons: req['btns'] = KakaoButtons if AdsYN: req['adsYN'] = True if RequestNum is not None or RequestNum != '': req['requestNum'] = RequestNum postData = self._stringtify(req) files = [] try: with open(FilePath, "rb") as F: files = [File(fieldName='file', fileName=F.name, fileData=F.read())] except IOError: raise PopbillException(-99999999, "ν•΄λ‹Ήκ²½λ‘œμ— 파일이 μ—†κ±°λ‚˜ 읽을 수 μ—†μŠ΅λ‹ˆλ‹€.") result = self._httppost_files('/FMS', postData, files, CorpNum, UserID) return result.receiptNum
python
def sendFMS_same(self, CorpNum, PlusFriendID, Sender, Content, AltContent, AltSendType, SndDT, FilePath, ImageURL, KakaoMessages, KakaoButtons, AdsYN=False, UserID=None, RequestNum=None): """ μΉœκ΅¬ν†‘ 이미지 λŒ€λŸ‰ 전솑 :param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param PlusFriendID: ν”ŒλŸ¬μŠ€μΉœκ΅¬ 아이디 :param Sender: λ°œμ‹ λ²ˆν˜Έ :param Content: [동보] μΉœκ΅¬ν†‘ λ‚΄μš© :param AltContent: [동보] λŒ€μ²΄λ¬Έμž λ‚΄μš© :param AltSendType: λŒ€μ²΄λ¬Έμž μœ ν˜• [곡백-미전솑, C-μ•Œλ¦Όν†‘λ‚΄μš©, A-λŒ€μ²΄λ¬Έμžλ‚΄μš©] :param SndDT: μ˜ˆμ•½μΌμ‹œ [μž‘μ„±ν˜•μ‹ : yyyyMMddHHmmss] :param FilePath: 파일경둜 :param ImageURL: 이미지URL :param KakaoMessages: μΉœκ΅¬ν†‘ λ‚΄μš© (λ°°μ—΄) :param KakaoButtons: λ²„νŠΌ λͺ©λ‘ (μ΅œλŒ€ 5개) :param AdsYN: κ΄‘κ³  전솑여뢀 :param UserID: νŒλΉŒνšŒμ› 아이디 :param RequestNum : μš”μ²­λ²ˆν˜Έ :return: receiptNum (μ ‘μˆ˜λ²ˆν˜Έ) """ if PlusFriendID is None or PlusFriendID == '': raise PopbillException(-99999999, "ν”ŒλŸ¬μŠ€μΉœκ΅¬ 아이디가 μž…λ ₯λ˜μ§€ μ•Šμ•˜μŠ΅λ‹ˆλ‹€.") if Sender is None or Sender == '': raise PopbillException(-99999999, "λ°œμ‹ λ²ˆν˜Έκ°€ μž…λ ₯λ˜μ§€ μ•Šμ•˜μŠ΅λ‹ˆλ‹€.") req = {} if PlusFriendID is not None or PlusFriendID != '': req['plusFriendID'] = PlusFriendID if Sender is not None or Sender != '': req['snd'] = Sender if Content is not None or Content != '': req['content'] = Content if AltContent is not None or AltContent != '': req['altContent'] = AltContent if AltSendType is not None or AltSendType != '': req['altSendType'] = AltSendType if SndDT is not None or SndDT != '': req['sndDT'] = SndDT if KakaoMessages is not None or KakaoMessages != '': req['msgs'] = KakaoMessages if ImageURL is not None or ImageURL != '': req['imageURL'] = ImageURL if KakaoButtons: req['btns'] = KakaoButtons if AdsYN: req['adsYN'] = True if RequestNum is not None or RequestNum != '': req['requestNum'] = RequestNum postData = self._stringtify(req) files = [] try: with open(FilePath, "rb") as F: files = [File(fieldName='file', fileName=F.name, fileData=F.read())] except IOError: raise PopbillException(-99999999, "ν•΄λ‹Ήκ²½λ‘œμ— 파일이 μ—†κ±°λ‚˜ 읽을 수 μ—†μŠ΅λ‹ˆλ‹€.") result = self._httppost_files('/FMS', postData, files, CorpNum, UserID) return result.receiptNum
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μΉœκ΅¬ν†‘ 이미지 λŒ€λŸ‰ 전솑 :param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param PlusFriendID: ν”ŒλŸ¬μŠ€μΉœκ΅¬ 아이디 :param Sender: λ°œμ‹ λ²ˆν˜Έ :param Content: [동보] μΉœκ΅¬ν†‘ λ‚΄μš© :param AltContent: [동보] λŒ€μ²΄λ¬Έμž λ‚΄μš© :param AltSendType: λŒ€μ²΄λ¬Έμž μœ ν˜• [곡백-미전솑, C-μ•Œλ¦Όν†‘λ‚΄μš©, A-λŒ€μ²΄λ¬Έμžλ‚΄μš©] :param SndDT: μ˜ˆμ•½μΌμ‹œ [μž‘μ„±ν˜•μ‹ : yyyyMMddHHmmss] :param FilePath: 파일경둜 :param ImageURL: 이미지URL :param KakaoMessages: μΉœκ΅¬ν†‘ λ‚΄μš© (λ°°μ—΄) :param KakaoButtons: λ²„νŠΌ λͺ©λ‘ (μ΅œλŒ€ 5개) :param AdsYN: κ΄‘κ³  전솑여뢀 :param UserID: νŒλΉŒνšŒμ› 아이디 :param RequestNum : μš”μ²­λ²ˆν˜Έ :return: receiptNum (μ ‘μˆ˜λ²ˆν˜Έ)
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train
https://github.com/linkhub-sdk/popbill.py/blob/68a0dd7f7a937603318e93be321fde73c50b96cc/popbill/kakaoService.py#L289-L351
linkhub-sdk/popbill.py
popbill/kakaoService.py
KakaoService.cancelReserve
def cancelReserve(self, CorpNum, ReceiptNum, UserID=None): """ μ˜ˆμ•½μ „μ†‘ μ·¨μ†Œ :param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param ReceiptNum: μ ‘μˆ˜λ²ˆν˜Έ :param UserID: νŒλΉŒνšŒμ› 아이디 :return: code (μš”μ²­μ— λŒ€ν•œ μƒνƒœ μ‘λ‹΅μ½”λ“œ), message (μš”μ²­μ— λŒ€ν•œ 응닡 λ©”μ‹œμ§€) """ if ReceiptNum == None or len(ReceiptNum) != 18: raise PopbillException(-99999999, "μ ‘μˆ˜λ²ˆν˜Έκ°€ μ˜¬λ°”λ₯΄μ§€ μ•ŠμŠ΅λ‹ˆλ‹€.") return self._httpget('/KakaoTalk/' + ReceiptNum + '/Cancel', CorpNum, UserID)
python
def cancelReserve(self, CorpNum, ReceiptNum, UserID=None): """ μ˜ˆμ•½μ „μ†‘ μ·¨μ†Œ :param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param ReceiptNum: μ ‘μˆ˜λ²ˆν˜Έ :param UserID: νŒλΉŒνšŒμ› 아이디 :return: code (μš”μ²­μ— λŒ€ν•œ μƒνƒœ μ‘λ‹΅μ½”λ“œ), message (μš”μ²­μ— λŒ€ν•œ 응닡 λ©”μ‹œμ§€) """ if ReceiptNum == None or len(ReceiptNum) != 18: raise PopbillException(-99999999, "μ ‘μˆ˜λ²ˆν˜Έκ°€ μ˜¬λ°”λ₯΄μ§€ μ•ŠμŠ΅λ‹ˆλ‹€.") return self._httpget('/KakaoTalk/' + ReceiptNum + '/Cancel', CorpNum, UserID)
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μ˜ˆμ•½μ „μ†‘ μ·¨μ†Œ :param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param ReceiptNum: μ ‘μˆ˜λ²ˆν˜Έ :param UserID: νŒλΉŒνšŒμ› 아이디 :return: code (μš”μ²­μ— λŒ€ν•œ μƒνƒœ μ‘λ‹΅μ½”λ“œ), message (μš”μ²­μ— λŒ€ν•œ 응닡 λ©”μ‹œμ§€)
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train
https://github.com/linkhub-sdk/popbill.py/blob/68a0dd7f7a937603318e93be321fde73c50b96cc/popbill/kakaoService.py#L353-L364
linkhub-sdk/popbill.py
popbill/kakaoService.py
KakaoService.getUnitCost
def getUnitCost(self, CorpNum, MsgType, UserID=None): """ 전솑단가 확인 :param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param MsgType: μΉ΄μΉ΄μ˜€ν†‘ μœ ν˜• :param UserID: 팝빌 νšŒμ›μ•„μ΄λ”” :return: unitCost """ if MsgType is None or MsgType == "": raise PopbillException(-99999999, "μ „μ†‘μœ ν˜•μ΄ μž…λ ₯λ˜μ§€ μ•Šμ•˜μŠ΅λ‹ˆλ‹€.") result = self._httpget("/KakaoTalk/UnitCost?Type=" + MsgType, CorpNum) return float(result.unitCost)
python
def getUnitCost(self, CorpNum, MsgType, UserID=None): """ 전솑단가 확인 :param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param MsgType: μΉ΄μΉ΄μ˜€ν†‘ μœ ν˜• :param UserID: 팝빌 νšŒμ›μ•„μ΄λ”” :return: unitCost """ if MsgType is None or MsgType == "": raise PopbillException(-99999999, "μ „μ†‘μœ ν˜•μ΄ μž…λ ₯λ˜μ§€ μ•Šμ•˜μŠ΅λ‹ˆλ‹€.") result = self._httpget("/KakaoTalk/UnitCost?Type=" + MsgType, CorpNum) return float(result.unitCost)
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전솑단가 확인 :param CorpNum: νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ :param MsgType: μΉ΄μΉ΄μ˜€ν†‘ μœ ν˜• :param UserID: 팝빌 νšŒμ›μ•„μ΄λ”” :return: unitCost
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train
https://github.com/linkhub-sdk/popbill.py/blob/68a0dd7f7a937603318e93be321fde73c50b96cc/popbill/kakaoService.py#L448-L460
mediawiki-utilities/python-mwreverts
mwreverts/utilities/revdocs2reverts.py
revdocs2reverts
def revdocs2reverts(rev_docs, radius=defaults.RADIUS, use_sha1=False, resort=False, verbose=False): """ Converts a sequence of page-partitioned revision documents into a sequence of reverts. :Params: rev_docs : `iterable` ( `dict` ) a page-partitioned sequence of revision documents radius : `int` The maximum number of revisions that a revert can reference. use_sha1 : `bool` Use the sha1 field as the checksum for comparison. resort : `bool` If True, re-sort the revisions of each page. verbose : `bool` Print dots and stuff """ page_rev_docs = groupby(rev_docs, lambda rd: rd.get('page')) for page_doc, rev_docs in page_rev_docs: if verbose: sys.stderr.write(page_doc.get('title') + ": ") sys.stderr.flush() if resort: if verbose: sys.stderr.write("(sorting) ") sys.stderr.flush() rev_docs = sorted( rev_docs, key=lambda r: (r.get('timestamp'), r.get('id'))) detector = Detector(radius=radius) for rev_doc in rev_docs: if not use_sha1 and 'text' not in rev_doc: logger.warn("Skipping {0}: 'text' field not found in {0}" .format(rev_doc['id'], rev_doc)) continue if use_sha1: checksum = rev_doc.get('sha1') or DummyChecksum() elif 'text' in rev_doc: text_bytes = bytes(rev_doc['text'], 'utf8', 'replace') checksum = hashlib.sha1(text_bytes).digest() revert = detector.process(checksum, rev_doc) if revert: yield revert.to_json() if verbose: sys.stderr.write("r") sys.stderr.flush() else: if verbose: sys.stderr.write(".") sys.stderr.flush() if verbose: sys.stderr.write("\n") sys.stderr.flush()
python
def revdocs2reverts(rev_docs, radius=defaults.RADIUS, use_sha1=False, resort=False, verbose=False): """ Converts a sequence of page-partitioned revision documents into a sequence of reverts. :Params: rev_docs : `iterable` ( `dict` ) a page-partitioned sequence of revision documents radius : `int` The maximum number of revisions that a revert can reference. use_sha1 : `bool` Use the sha1 field as the checksum for comparison. resort : `bool` If True, re-sort the revisions of each page. verbose : `bool` Print dots and stuff """ page_rev_docs = groupby(rev_docs, lambda rd: rd.get('page')) for page_doc, rev_docs in page_rev_docs: if verbose: sys.stderr.write(page_doc.get('title') + ": ") sys.stderr.flush() if resort: if verbose: sys.stderr.write("(sorting) ") sys.stderr.flush() rev_docs = sorted( rev_docs, key=lambda r: (r.get('timestamp'), r.get('id'))) detector = Detector(radius=radius) for rev_doc in rev_docs: if not use_sha1 and 'text' not in rev_doc: logger.warn("Skipping {0}: 'text' field not found in {0}" .format(rev_doc['id'], rev_doc)) continue if use_sha1: checksum = rev_doc.get('sha1') or DummyChecksum() elif 'text' in rev_doc: text_bytes = bytes(rev_doc['text'], 'utf8', 'replace') checksum = hashlib.sha1(text_bytes).digest() revert = detector.process(checksum, rev_doc) if revert: yield revert.to_json() if verbose: sys.stderr.write("r") sys.stderr.flush() else: if verbose: sys.stderr.write(".") sys.stderr.flush() if verbose: sys.stderr.write("\n") sys.stderr.flush()
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Converts a sequence of page-partitioned revision documents into a sequence of reverts. :Params: rev_docs : `iterable` ( `dict` ) a page-partitioned sequence of revision documents radius : `int` The maximum number of revisions that a revert can reference. use_sha1 : `bool` Use the sha1 field as the checksum for comparison. resort : `bool` If True, re-sort the revisions of each page. verbose : `bool` Print dots and stuff
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train
https://github.com/mediawiki-utilities/python-mwreverts/blob/d379ac941e14e235ad82a48bd445a3dfa6cc022e/mwreverts/utilities/revdocs2reverts.py#L54-L114
MSchnei/pyprf_feature
pyprf_feature/analysis/old/pRF_hrfutils.py
spm_hrf_compat
def spm_hrf_compat(t, peak_delay=6, under_delay=16, peak_disp=1, under_disp=1, p_u_ratio=6, normalize=True, ): """ SPM HRF function from sum of two gamma PDFs This function is designed to be partially compatible with SPMs `spm_hrf.m` function. The SPN HRF is a *peak* gamma PDF (with location `peak_delay` and dispersion `peak_disp`), minus an *undershoot* gamma PDF (with location `under_delay` and dispersion `under_disp`, and divided by the `p_u_ratio`). Parameters ---------- t : array-like vector of times at which to sample HRF peak_delay : float, optional delay of peak peak_disp : float, optional width (dispersion) of peak under_delay : float, optional delay of undershoot under_disp : float, optional width (dispersion) of undershoot p_u_ratio : float, optional peak to undershoot ratio. Undershoot divided by this value before subtracting from peak. normalize : {True, False}, optional If True, divide HRF values by their sum before returning. SPM does this by default. Returns ------- hrf : array vector length ``len(t)`` of samples from HRF at times `t` Notes ----- See ``spm_hrf.m`` in the SPM distribution. """ if len([v for v in [peak_delay, peak_disp, under_delay, under_disp] if v <= 0]): raise ValueError("delays and dispersions must be > 0") # gamma.pdf only defined for t > 0 hrf = np.zeros(t.shape, dtype=np.float) pos_t = t[t > 0] peak = sps.gamma.pdf(pos_t, peak_delay / peak_disp, loc=0, scale=peak_disp) undershoot = sps.gamma.pdf(pos_t, under_delay / under_disp, loc=0, scale=under_disp) hrf[t > 0] = peak - undershoot / p_u_ratio if not normalize: return hrf return hrf / np.max(hrf)
python
def spm_hrf_compat(t, peak_delay=6, under_delay=16, peak_disp=1, under_disp=1, p_u_ratio=6, normalize=True, ): """ SPM HRF function from sum of two gamma PDFs This function is designed to be partially compatible with SPMs `spm_hrf.m` function. The SPN HRF is a *peak* gamma PDF (with location `peak_delay` and dispersion `peak_disp`), minus an *undershoot* gamma PDF (with location `under_delay` and dispersion `under_disp`, and divided by the `p_u_ratio`). Parameters ---------- t : array-like vector of times at which to sample HRF peak_delay : float, optional delay of peak peak_disp : float, optional width (dispersion) of peak under_delay : float, optional delay of undershoot under_disp : float, optional width (dispersion) of undershoot p_u_ratio : float, optional peak to undershoot ratio. Undershoot divided by this value before subtracting from peak. normalize : {True, False}, optional If True, divide HRF values by their sum before returning. SPM does this by default. Returns ------- hrf : array vector length ``len(t)`` of samples from HRF at times `t` Notes ----- See ``spm_hrf.m`` in the SPM distribution. """ if len([v for v in [peak_delay, peak_disp, under_delay, under_disp] if v <= 0]): raise ValueError("delays and dispersions must be > 0") # gamma.pdf only defined for t > 0 hrf = np.zeros(t.shape, dtype=np.float) pos_t = t[t > 0] peak = sps.gamma.pdf(pos_t, peak_delay / peak_disp, loc=0, scale=peak_disp) undershoot = sps.gamma.pdf(pos_t, under_delay / under_disp, loc=0, scale=under_disp) hrf[t > 0] = peak - undershoot / p_u_ratio if not normalize: return hrf return hrf / np.max(hrf)
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[ "SPM", "HRF", "function", "from", "sum", "of", "two", "gamma", "PDFs" ]
train
https://github.com/MSchnei/pyprf_feature/blob/49004ede7ae1ddee07a30afe9ce3e2776750805c/pyprf_feature/analysis/old/pRF_hrfutils.py#L32-L94
MSchnei/pyprf_feature
pyprf_feature/analysis/old/pRF_hrfutils.py
dspmt
def dspmt(t): """ SPM canonical HRF derivative, HRF derivative values for time values `t` This is the canonical HRF derivative function as used in SPM. It is the numerical difference of the HRF sampled at time `t` minus the values sampled at time `t` -1 """ t = np.asarray(t) return spmt(t) - spmt(t - 1)
python
def dspmt(t): """ SPM canonical HRF derivative, HRF derivative values for time values `t` This is the canonical HRF derivative function as used in SPM. It is the numerical difference of the HRF sampled at time `t` minus the values sampled at time `t` -1 """ t = np.asarray(t) return spmt(t) - spmt(t - 1)
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SPM canonical HRF derivative, HRF derivative values for time values `t` This is the canonical HRF derivative function as used in SPM. It is the numerical difference of the HRF sampled at time `t` minus the values sampled at time `t` -1
[ "SPM", "canonical", "HRF", "derivative", "HRF", "derivative", "values", "for", "time", "values", "t" ]
train
https://github.com/MSchnei/pyprf_feature/blob/49004ede7ae1ddee07a30afe9ce3e2776750805c/pyprf_feature/analysis/old/pRF_hrfutils.py#L115-L124
MSchnei/pyprf_feature
pyprf_feature/analysis/old/pRF_hrfutils.py
cnvlTc
def cnvlTc(idxPrc, aryPrfTcChunk, lstHrf, varTr, varNumVol, queOut, varOvsmpl=10, varHrfLen=32, ): """ Convolution of time courses with HRF model. """ # *** prepare hrf time courses for convolution print("---------Process " + str(idxPrc) + ": Prepare hrf time courses for convolution") # get frame times, i.e. start point of every volume in seconds vecFrms = np.arange(0, varTr * varNumVol, varTr) # get supersampled frames times, i.e. start point of every volume in # seconds, since convolution takes place in temp. upsampled space vecFrmTms = np.arange(0, varTr * varNumVol, varTr / varOvsmpl) # get resolution of supersampled frame times varRes = varTr / float(varOvsmpl) # prepare empty list that will contain the arrays with hrf time courses lstBse = [] for hrfFn in lstHrf: # needs to be a multiple of oversample vecTmpBse = hrfFn(np.linspace(0, varHrfLen, (varHrfLen // varTr) * varOvsmpl)) lstBse.append(vecTmpBse) # *** prepare pixel time courses for convolution print("---------Process " + str(idxPrc) + ": Prepare pixel time courses for convolution") # adjust the input, if necessary, such that input is 2D, with last dim time tplInpShp = aryPrfTcChunk.shape aryPrfTcChunk = aryPrfTcChunk.reshape((-1, aryPrfTcChunk.shape[-1])) # Prepare an empty array for ouput aryConv = np.zeros((aryPrfTcChunk.shape[0], len(lstHrf), aryPrfTcChunk.shape[1])) print("---------Process " + str(idxPrc) + ": Convolve") # Each time course is convolved with the HRF separately, because the # numpy convolution function can only be used on one-dimensional data. # Thus, we have to loop through time courses: for idxTc in range(0, aryConv.shape[0]): # Extract the current time course: vecTc = aryPrfTcChunk[idxTc, :] # upsample the pixel time course, so that it matches the hrf time crs vecTcUps = np.zeros(int(varNumVol * varTr/varRes)) vecOns = vecFrms[vecTc.astype(bool)] vecInd = np.round(vecOns / varRes).astype(np.int) vecTcUps[vecInd] = 1. # *** convolve for indBase, base in enumerate(lstBse): # perform the convolution col = np.convolve(base, vecTcUps, mode='full')[:vecTcUps.size] # get function for downsampling f = interp1d(vecFrmTms, col) # downsample to original space and assign to ary aryConv[idxTc, indBase, :] = f(vecFrms) # determine output shape tplOutShp = tplInpShp[:-1] + (len(lstHrf), ) + (tplInpShp[-1], ) # Create list containing the convolved timecourses, and the process ID: lstOut = [idxPrc, aryConv.reshape(tplOutShp)] # Put output to queue: queOut.put(lstOut)
python
def cnvlTc(idxPrc, aryPrfTcChunk, lstHrf, varTr, varNumVol, queOut, varOvsmpl=10, varHrfLen=32, ): """ Convolution of time courses with HRF model. """ # *** prepare hrf time courses for convolution print("---------Process " + str(idxPrc) + ": Prepare hrf time courses for convolution") # get frame times, i.e. start point of every volume in seconds vecFrms = np.arange(0, varTr * varNumVol, varTr) # get supersampled frames times, i.e. start point of every volume in # seconds, since convolution takes place in temp. upsampled space vecFrmTms = np.arange(0, varTr * varNumVol, varTr / varOvsmpl) # get resolution of supersampled frame times varRes = varTr / float(varOvsmpl) # prepare empty list that will contain the arrays with hrf time courses lstBse = [] for hrfFn in lstHrf: # needs to be a multiple of oversample vecTmpBse = hrfFn(np.linspace(0, varHrfLen, (varHrfLen // varTr) * varOvsmpl)) lstBse.append(vecTmpBse) # *** prepare pixel time courses for convolution print("---------Process " + str(idxPrc) + ": Prepare pixel time courses for convolution") # adjust the input, if necessary, such that input is 2D, with last dim time tplInpShp = aryPrfTcChunk.shape aryPrfTcChunk = aryPrfTcChunk.reshape((-1, aryPrfTcChunk.shape[-1])) # Prepare an empty array for ouput aryConv = np.zeros((aryPrfTcChunk.shape[0], len(lstHrf), aryPrfTcChunk.shape[1])) print("---------Process " + str(idxPrc) + ": Convolve") # Each time course is convolved with the HRF separately, because the # numpy convolution function can only be used on one-dimensional data. # Thus, we have to loop through time courses: for idxTc in range(0, aryConv.shape[0]): # Extract the current time course: vecTc = aryPrfTcChunk[idxTc, :] # upsample the pixel time course, so that it matches the hrf time crs vecTcUps = np.zeros(int(varNumVol * varTr/varRes)) vecOns = vecFrms[vecTc.astype(bool)] vecInd = np.round(vecOns / varRes).astype(np.int) vecTcUps[vecInd] = 1. # *** convolve for indBase, base in enumerate(lstBse): # perform the convolution col = np.convolve(base, vecTcUps, mode='full')[:vecTcUps.size] # get function for downsampling f = interp1d(vecFrmTms, col) # downsample to original space and assign to ary aryConv[idxTc, indBase, :] = f(vecFrms) # determine output shape tplOutShp = tplInpShp[:-1] + (len(lstHrf), ) + (tplInpShp[-1], ) # Create list containing the convolved timecourses, and the process ID: lstOut = [idxPrc, aryConv.reshape(tplOutShp)] # Put output to queue: queOut.put(lstOut)
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Convolution of time courses with HRF model.
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train
https://github.com/MSchnei/pyprf_feature/blob/49004ede7ae1ddee07a30afe9ce3e2776750805c/pyprf_feature/analysis/old/pRF_hrfutils.py#L143-L219
MSchnei/pyprf_feature
pyprf_feature/analysis/old/pRF_hrfutils.py
funcHrf
def funcHrf(varNumVol, varTr): """Create double gamma function. Source: http://www.jarrodmillman.com/rcsds/lectures/convolution_background.html """ vecX = np.arange(0, varNumVol, 1) # Expected time of peak of HRF [s]: varHrfPeak = 6.0 / varTr # Expected time of undershoot of HRF [s]: varHrfUndr = 12.0 / varTr # Scaling factor undershoot (relative to peak): varSclUndr = 0.35 # Gamma pdf for the peak vecHrfPeak = gamma.pdf(vecX, varHrfPeak) # Gamma pdf for the undershoot vecHrfUndr = gamma.pdf(vecX, varHrfUndr) # Combine them vecHrf = vecHrfPeak - varSclUndr * vecHrfUndr # Scale maximum of HRF to 1.0: vecHrf = np.divide(vecHrf, np.max(vecHrf)) return vecHrf
python
def funcHrf(varNumVol, varTr): """Create double gamma function. Source: http://www.jarrodmillman.com/rcsds/lectures/convolution_background.html """ vecX = np.arange(0, varNumVol, 1) # Expected time of peak of HRF [s]: varHrfPeak = 6.0 / varTr # Expected time of undershoot of HRF [s]: varHrfUndr = 12.0 / varTr # Scaling factor undershoot (relative to peak): varSclUndr = 0.35 # Gamma pdf for the peak vecHrfPeak = gamma.pdf(vecX, varHrfPeak) # Gamma pdf for the undershoot vecHrfUndr = gamma.pdf(vecX, varHrfUndr) # Combine them vecHrf = vecHrfPeak - varSclUndr * vecHrfUndr # Scale maximum of HRF to 1.0: vecHrf = np.divide(vecHrf, np.max(vecHrf)) return vecHrf
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Create double gamma function. Source: http://www.jarrodmillman.com/rcsds/lectures/convolution_background.html
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train
https://github.com/MSchnei/pyprf_feature/blob/49004ede7ae1ddee07a30afe9ce3e2776750805c/pyprf_feature/analysis/old/pRF_hrfutils.py#L226-L251
MSchnei/pyprf_feature
pyprf_feature/analysis/old/pRF_hrfutils.py
cnvlTcOld
def cnvlTcOld(idxPrc, aryPrfTcChunk, varTr, varNumVol, queOut): """ Old version: Convolution of time courses with one canonical HRF model. """ # Create 'canonical' HRF time course model: vecHrf = funcHrf(varNumVol, varTr) # adjust the input, if necessary, such that input is 2D, with last dim time tplInpShp = aryPrfTcChunk.shape aryPrfTcChunk = aryPrfTcChunk.reshape((-1, aryPrfTcChunk.shape[-1])) # Prepare an empty array for ouput aryConv = np.zeros(np.shape(aryPrfTcChunk)) # Each time course is convolved with the HRF separately, because the # numpy convolution function can only be used on one-dimensional data. # Thus, we have to loop through time courses: for idxTc, vecTc in enumerate(aryPrfTcChunk): # In order to avoid an artefact at the end of the time series, we have # to concatenate an empty array to both the design matrix and the HRF # model before convolution. vecTc = np.append(vecTc, np.zeros(100)) vecHrf = np.append(vecHrf, np.zeros(100)) # Convolve design matrix with HRF model: aryConv[idxTc, :] = np.convolve(vecTc, vecHrf, mode='full')[:varNumVol] # determine output shape tplOutShp = tplInpShp[:-1] + (1, ) + (tplInpShp[-1], ) # Create list containing the convolved timecourses, and the process ID: lstOut = [idxPrc, aryConv.reshape(tplOutShp)] # Put output to queue: queOut.put(lstOut)
python
def cnvlTcOld(idxPrc, aryPrfTcChunk, varTr, varNumVol, queOut): """ Old version: Convolution of time courses with one canonical HRF model. """ # Create 'canonical' HRF time course model: vecHrf = funcHrf(varNumVol, varTr) # adjust the input, if necessary, such that input is 2D, with last dim time tplInpShp = aryPrfTcChunk.shape aryPrfTcChunk = aryPrfTcChunk.reshape((-1, aryPrfTcChunk.shape[-1])) # Prepare an empty array for ouput aryConv = np.zeros(np.shape(aryPrfTcChunk)) # Each time course is convolved with the HRF separately, because the # numpy convolution function can only be used on one-dimensional data. # Thus, we have to loop through time courses: for idxTc, vecTc in enumerate(aryPrfTcChunk): # In order to avoid an artefact at the end of the time series, we have # to concatenate an empty array to both the design matrix and the HRF # model before convolution. vecTc = np.append(vecTc, np.zeros(100)) vecHrf = np.append(vecHrf, np.zeros(100)) # Convolve design matrix with HRF model: aryConv[idxTc, :] = np.convolve(vecTc, vecHrf, mode='full')[:varNumVol] # determine output shape tplOutShp = tplInpShp[:-1] + (1, ) + (tplInpShp[-1], ) # Create list containing the convolved timecourses, and the process ID: lstOut = [idxPrc, aryConv.reshape(tplOutShp)] # Put output to queue: queOut.put(lstOut)
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Old version: Convolution of time courses with one canonical HRF model.
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train
https://github.com/MSchnei/pyprf_feature/blob/49004ede7ae1ddee07a30afe9ce3e2776750805c/pyprf_feature/analysis/old/pRF_hrfutils.py#L254-L296
pmacosta/pcsv
pcsv/dsort.py
dsort
def dsort(fname, order, has_header=True, frow=0, ofname=None): r""" Sort file data. :param fname: Name of the comma-separated values file to sort :type fname: FileNameExists_ :param order: Sort order :type order: :ref:`CsvColFilter` :param has_header: Flag that indicates whether the comma-separated values file to sort has column headers in its first line (True) or not (False) :type has_header: boolean :param frow: First data row (starting from 1). If 0 the row where data starts is auto-detected as the first row that has a number (integer of float) in at least one of its columns :type frow: NonNegativeInteger_ :param ofname: Name of the output comma-separated values file, the file that will contain the sorted data. If None the sorting is done "in place" :type ofname: FileName_ or None .. [[[cog cog.out(exobj.get_sphinx_autodoc(raised=True)) ]]] .. Auto-generated exceptions documentation for pcsv.dsort.dsort :raises: * OSError (File *[fname]* could not be found) * RuntimeError (Argument \`fname\` is not valid) * RuntimeError (Argument \`frow\` is not valid) * RuntimeError (Argument \`has_header\` is not valid) * RuntimeError (Argument \`ofname\` is not valid) * RuntimeError (Argument \`order\` is not valid) * RuntimeError (Column headers are not unique in file *[fname]*) * RuntimeError (File *[fname]* has no valid data) * RuntimeError (File *[fname]* is empty) * RuntimeError (Invalid column specification) * ValueError (Column *[column_identifier]* not found) .. [[[end]]] """ ofname = fname if ofname is None else ofname obj = CsvFile(fname=fname, has_header=has_header, frow=frow) obj.dsort(order) obj.write(fname=ofname, header=has_header, append=False)
python
def dsort(fname, order, has_header=True, frow=0, ofname=None): r""" Sort file data. :param fname: Name of the comma-separated values file to sort :type fname: FileNameExists_ :param order: Sort order :type order: :ref:`CsvColFilter` :param has_header: Flag that indicates whether the comma-separated values file to sort has column headers in its first line (True) or not (False) :type has_header: boolean :param frow: First data row (starting from 1). If 0 the row where data starts is auto-detected as the first row that has a number (integer of float) in at least one of its columns :type frow: NonNegativeInteger_ :param ofname: Name of the output comma-separated values file, the file that will contain the sorted data. If None the sorting is done "in place" :type ofname: FileName_ or None .. [[[cog cog.out(exobj.get_sphinx_autodoc(raised=True)) ]]] .. Auto-generated exceptions documentation for pcsv.dsort.dsort :raises: * OSError (File *[fname]* could not be found) * RuntimeError (Argument \`fname\` is not valid) * RuntimeError (Argument \`frow\` is not valid) * RuntimeError (Argument \`has_header\` is not valid) * RuntimeError (Argument \`ofname\` is not valid) * RuntimeError (Argument \`order\` is not valid) * RuntimeError (Column headers are not unique in file *[fname]*) * RuntimeError (File *[fname]* has no valid data) * RuntimeError (File *[fname]* is empty) * RuntimeError (Invalid column specification) * ValueError (Column *[column_identifier]* not found) .. [[[end]]] """ ofname = fname if ofname is None else ofname obj = CsvFile(fname=fname, has_header=has_header, frow=frow) obj.dsort(order) obj.write(fname=ofname, header=has_header, append=False)
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r""" Sort file data. :param fname: Name of the comma-separated values file to sort :type fname: FileNameExists_ :param order: Sort order :type order: :ref:`CsvColFilter` :param has_header: Flag that indicates whether the comma-separated values file to sort has column headers in its first line (True) or not (False) :type has_header: boolean :param frow: First data row (starting from 1). If 0 the row where data starts is auto-detected as the first row that has a number (integer of float) in at least one of its columns :type frow: NonNegativeInteger_ :param ofname: Name of the output comma-separated values file, the file that will contain the sorted data. If None the sorting is done "in place" :type ofname: FileName_ or None .. [[[cog cog.out(exobj.get_sphinx_autodoc(raised=True)) ]]] .. Auto-generated exceptions documentation for pcsv.dsort.dsort :raises: * OSError (File *[fname]* could not be found) * RuntimeError (Argument \`fname\` is not valid) * RuntimeError (Argument \`frow\` is not valid) * RuntimeError (Argument \`has_header\` is not valid) * RuntimeError (Argument \`ofname\` is not valid) * RuntimeError (Argument \`order\` is not valid) * RuntimeError (Column headers are not unique in file *[fname]*) * RuntimeError (File *[fname]* has no valid data) * RuntimeError (File *[fname]* is empty) * RuntimeError (Invalid column specification) * ValueError (Column *[column_identifier]* not found) .. [[[end]]]
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train
https://github.com/pmacosta/pcsv/blob/cd1588c19b0cd58c38bc672e396db940f88ffbd7/pcsv/dsort.py#L37-L93
bachya/pyflunearyou
example.py
main
async def main() -> None: """Create the aiohttp session and run the example.""" logging.basicConfig(level=logging.INFO) async with ClientSession() as websession: try: # Create a client: client = Client(websession) # Get user data for the client's latitude/longitude: user_coord_resp = await client.user_reports.status_by_coordinates( LATITUDE, LONGITUDE) _LOGGER.info( 'User data by latitude/longitude (%s, %s): %s', LATITUDE, LONGITUDE, user_coord_resp) # Get user data for the a specific ZIP code: user_zip_resp = await client.user_reports.status_by_zip(ZIP_CODE) _LOGGER.info( 'User data by ZIP code (%s): %s', ZIP_CODE, user_zip_resp) # Get CDC data for the client's latitude/longitude: cdc_coord_resp = await client.cdc_reports.status_by_coordinates( LATITUDE, LONGITUDE) _LOGGER.info( 'CDC data by latitude/longitude (%s, %s): %s', LATITUDE, LONGITUDE, cdc_coord_resp) # Get CDC data for North Dakota cdc_state_resp = await client.cdc_reports.status_by_state(STATE) _LOGGER.info( 'CDC data by state name (%s): %s', STATE, cdc_state_resp) except FluNearYouError as err: print(err)
python
async def main() -> None: """Create the aiohttp session and run the example.""" logging.basicConfig(level=logging.INFO) async with ClientSession() as websession: try: # Create a client: client = Client(websession) # Get user data for the client's latitude/longitude: user_coord_resp = await client.user_reports.status_by_coordinates( LATITUDE, LONGITUDE) _LOGGER.info( 'User data by latitude/longitude (%s, %s): %s', LATITUDE, LONGITUDE, user_coord_resp) # Get user data for the a specific ZIP code: user_zip_resp = await client.user_reports.status_by_zip(ZIP_CODE) _LOGGER.info( 'User data by ZIP code (%s): %s', ZIP_CODE, user_zip_resp) # Get CDC data for the client's latitude/longitude: cdc_coord_resp = await client.cdc_reports.status_by_coordinates( LATITUDE, LONGITUDE) _LOGGER.info( 'CDC data by latitude/longitude (%s, %s): %s', LATITUDE, LONGITUDE, cdc_coord_resp) # Get CDC data for North Dakota cdc_state_resp = await client.cdc_reports.status_by_state(STATE) _LOGGER.info( 'CDC data by state name (%s): %s', STATE, cdc_state_resp) except FluNearYouError as err: print(err)
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train
https://github.com/bachya/pyflunearyou/blob/16a2f839c8df851e925e010a6b5c5708386febac/example.py#L18-L50
osilkin98/PyBRY
pybry/lbryd_api.py
LbryApi.call
def call(cls, method, params=None, timeout=600): """ Makes a Call to the LBRY API :param str method: Method to call from the LBRY API. See the full list of methods at https://lbryio.github.io/lbry/cli/ :param dict params: Parameters to give the method selected :param float timeout: The number of seconds to wait for a connection until we time out; 600 By Default. :raises LBRYException: If the request returns an error when calling the API :return: A Python `dict` object containing the data requested from the API :rtype: dict """ params = [] if params is None else params return cls.make_request(SERVER_ADDRESS, method, params, timeout=timeout)
python
def call(cls, method, params=None, timeout=600): """ Makes a Call to the LBRY API :param str method: Method to call from the LBRY API. See the full list of methods at https://lbryio.github.io/lbry/cli/ :param dict params: Parameters to give the method selected :param float timeout: The number of seconds to wait for a connection until we time out; 600 By Default. :raises LBRYException: If the request returns an error when calling the API :return: A Python `dict` object containing the data requested from the API :rtype: dict """ params = [] if params is None else params return cls.make_request(SERVER_ADDRESS, method, params, timeout=timeout)
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train
https://github.com/osilkin98/PyBRY/blob/af86805a8077916f72f3fe980943d4cd741e61f0/pybry/lbryd_api.py#L14-L28
linkhub-sdk/popbill.py
popbill/htTaxinvoiceService.py
HTTaxinvoiceService.getJobState
def getJobState(self, CorpNum, JobID, UserID=None): """ μˆ˜μ§‘ μƒνƒœ 확인 args CorpNum : νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ JobID : μž‘μ—…μ•„μ΄λ”” UserID : νŒλΉŒνšŒμ› 아이디 return μˆ˜μ§‘ μƒνƒœ 정보 raise PopbillException """ if JobID == None or len(JobID) != 18: raise PopbillException(-99999999, "μž‘μ—…μ•„μ΄λ””(jobID)κ°€ μ˜¬λ°”λ₯΄μ§€ μ•ŠμŠ΅λ‹ˆλ‹€.") return self._httpget('/HomeTax/Taxinvoice/' + JobID + '/State', CorpNum, UserID)
python
def getJobState(self, CorpNum, JobID, UserID=None): """ μˆ˜μ§‘ μƒνƒœ 확인 args CorpNum : νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ JobID : μž‘μ—…μ•„μ΄λ”” UserID : νŒλΉŒνšŒμ› 아이디 return μˆ˜μ§‘ μƒνƒœ 정보 raise PopbillException """ if JobID == None or len(JobID) != 18: raise PopbillException(-99999999, "μž‘μ—…μ•„μ΄λ””(jobID)κ°€ μ˜¬λ°”λ₯΄μ§€ μ•ŠμŠ΅λ‹ˆλ‹€.") return self._httpget('/HomeTax/Taxinvoice/' + JobID + '/State', CorpNum, UserID)
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train
https://github.com/linkhub-sdk/popbill.py/blob/68a0dd7f7a937603318e93be321fde73c50b96cc/popbill/htTaxinvoiceService.py#L71-L85
linkhub-sdk/popbill.py
popbill/htTaxinvoiceService.py
HTTaxinvoiceService.search
def search(self, CorpNum, JobID, Type, TaxType, PurposeType, TaxRegIDType, TaxRegIDYN, TaxRegID, Page, PerPage, Order, UserID=None): """ μˆ˜μ§‘ κ²°κ³Ό 쑰회 args CorpNum : νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ JobID : μž‘μ—…μ•„μ΄λ”” Type : λ¬Έμ„œν˜•νƒœ λ°°μ—΄, N-μΌλ°˜μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ, M-μˆ˜μ •μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ TaxType : κ³Όμ„Έν˜•νƒœ λ°°μ—΄, T-κ³Όμ„Έ, N-λ©΄μ„Έ, Z-μ˜μ„Έ PurposeType : 영수/청ꡬ, R-영수, C-청ꡬ, N-μ—†μŒ TaxRegIDType : 쒅사업μž₯번호 μ‚¬μ—…μžμœ ν˜•, S-κ³΅κΈ‰μž, B-κ³΅κΈ‰λ°›λŠ”μž, T-μˆ˜νƒμž TaxRegIDYN : 쒅사업μž₯번호 유무, 곡백-μ „μ²΄μ‘°νšŒ, 0-쒅사업μž₯번호 μ—†μŒ, 1-쒅사업μž₯번호 있음 TaxRegID : 쒅사업μž₯번호, 콀마(",")둜 ꡬ뢄 ν•˜μ—¬ ꡬ성 ex) '0001,0002' Page : νŽ˜μ΄μ§€ 번호 PerPage : νŽ˜μ΄μ§€λ‹Ή λͺ©λ‘ 개수, μ΅œλŒ€ 1000개 Order : μ •λ ¬ λ°©ν–₯, D-λ‚΄λ¦Όμ°¨μˆœ, A-μ˜€λ¦„μ°¨μˆœ UserID : νŒλΉŒνšŒμ› 아이디 return μˆ˜μ§‘ κ²°κ³Ό 정보 raise PopbillException """ if JobID == None or len(JobID) != 18: raise PopbillException(-99999999, "μž‘μ—…μ•„μ΄λ””(jobID)κ°€ μ˜¬λ°”λ₯΄μ§€ μ•ŠμŠ΅λ‹ˆλ‹€.") uri = '/HomeTax/Taxinvoice/' + JobID uri += '?Type=' + ','.join(Type) uri += '&TaxType=' + ','.join(TaxType) uri += '&PurposeType=' + ','.join(PurposeType) uri += '&TaxRegIDType=' + TaxRegIDType uri += '&TaxRegID=' + TaxRegID uri += '&Page=' + str(Page) uri += '&PerPage=' + str(PerPage) uri += '&Order=' + Order if TaxRegIDYN != '': uri += '&TaxRegIDYN=' + TaxRegIDYN return self._httpget(uri, CorpNum, UserID)
python
def search(self, CorpNum, JobID, Type, TaxType, PurposeType, TaxRegIDType, TaxRegIDYN, TaxRegID, Page, PerPage, Order, UserID=None): """ μˆ˜μ§‘ κ²°κ³Ό 쑰회 args CorpNum : νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ JobID : μž‘μ—…μ•„μ΄λ”” Type : λ¬Έμ„œν˜•νƒœ λ°°μ—΄, N-μΌλ°˜μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ, M-μˆ˜μ •μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ TaxType : κ³Όμ„Έν˜•νƒœ λ°°μ—΄, T-κ³Όμ„Έ, N-λ©΄μ„Έ, Z-μ˜μ„Έ PurposeType : 영수/청ꡬ, R-영수, C-청ꡬ, N-μ—†μŒ TaxRegIDType : 쒅사업μž₯번호 μ‚¬μ—…μžμœ ν˜•, S-κ³΅κΈ‰μž, B-κ³΅κΈ‰λ°›λŠ”μž, T-μˆ˜νƒμž TaxRegIDYN : 쒅사업μž₯번호 유무, 곡백-μ „μ²΄μ‘°νšŒ, 0-쒅사업μž₯번호 μ—†μŒ, 1-쒅사업μž₯번호 있음 TaxRegID : 쒅사업μž₯번호, 콀마(",")둜 ꡬ뢄 ν•˜μ—¬ ꡬ성 ex) '0001,0002' Page : νŽ˜μ΄μ§€ 번호 PerPage : νŽ˜μ΄μ§€λ‹Ή λͺ©λ‘ 개수, μ΅œλŒ€ 1000개 Order : μ •λ ¬ λ°©ν–₯, D-λ‚΄λ¦Όμ°¨μˆœ, A-μ˜€λ¦„μ°¨μˆœ UserID : νŒλΉŒνšŒμ› 아이디 return μˆ˜μ§‘ κ²°κ³Ό 정보 raise PopbillException """ if JobID == None or len(JobID) != 18: raise PopbillException(-99999999, "μž‘μ—…μ•„μ΄λ””(jobID)κ°€ μ˜¬λ°”λ₯΄μ§€ μ•ŠμŠ΅λ‹ˆλ‹€.") uri = '/HomeTax/Taxinvoice/' + JobID uri += '?Type=' + ','.join(Type) uri += '&TaxType=' + ','.join(TaxType) uri += '&PurposeType=' + ','.join(PurposeType) uri += '&TaxRegIDType=' + TaxRegIDType uri += '&TaxRegID=' + TaxRegID uri += '&Page=' + str(Page) uri += '&PerPage=' + str(PerPage) uri += '&Order=' + Order if TaxRegIDYN != '': uri += '&TaxRegIDYN=' + TaxRegIDYN return self._httpget(uri, CorpNum, UserID)
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μˆ˜μ§‘ κ²°κ³Ό 쑰회 args CorpNum : νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ JobID : μž‘μ—…μ•„μ΄λ”” Type : λ¬Έμ„œν˜•νƒœ λ°°μ—΄, N-μΌλ°˜μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ, M-μˆ˜μ •μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ TaxType : κ³Όμ„Έν˜•νƒœ λ°°μ—΄, T-κ³Όμ„Έ, N-λ©΄μ„Έ, Z-μ˜μ„Έ PurposeType : 영수/청ꡬ, R-영수, C-청ꡬ, N-μ—†μŒ TaxRegIDType : 쒅사업μž₯번호 μ‚¬μ—…μžμœ ν˜•, S-κ³΅κΈ‰μž, B-κ³΅κΈ‰λ°›λŠ”μž, T-μˆ˜νƒμž TaxRegIDYN : 쒅사업μž₯번호 유무, 곡백-μ „μ²΄μ‘°νšŒ, 0-쒅사업μž₯번호 μ—†μŒ, 1-쒅사업μž₯번호 있음 TaxRegID : 쒅사업μž₯번호, 콀마(",")둜 ꡬ뢄 ν•˜μ—¬ ꡬ성 ex) '0001,0002' Page : νŽ˜μ΄μ§€ 번호 PerPage : νŽ˜μ΄μ§€λ‹Ή λͺ©λ‘ 개수, μ΅œλŒ€ 1000개 Order : μ •λ ¬ λ°©ν–₯, D-λ‚΄λ¦Όμ°¨μˆœ, A-μ˜€λ¦„μ°¨μˆœ UserID : νŒλΉŒνšŒμ› 아이디 return μˆ˜μ§‘ κ²°κ³Ό 정보 raise PopbillException
[ "μˆ˜μ§‘", "κ²°κ³Ό", "쑰회", "args", "CorpNum", ":", "νŒλΉŒνšŒμ›", "μ‚¬μ—…μžλ²ˆν˜Έ", "JobID", ":", "μž‘μ—…μ•„μ΄λ””", "Type", ":", "λ¬Έμ„œν˜•νƒœ", "λ°°μ—΄", "N", "-", "μΌλ°˜μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ", "M", "-", "μˆ˜μ •μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ", "TaxType", ":", "κ³Όμ„Έν˜•νƒœ", "λ°°μ—΄", "T", "-", "κ³Όμ„Έ", "N", "-", "λ©΄μ„Έ", "Z", "-", "μ˜μ„Έ", "PurposeType", ":", "영수", "/", "청ꡬ", "R", "-", "영수", "C", "-", "청ꡬ", "N", "-", "μ—†μŒ", "TaxRegIDType", ":", "쒅사업μž₯번호", "μ‚¬μ—…μžμœ ν˜•", "S", "-", "κ³΅κΈ‰μž", "B", "-", "κ³΅κΈ‰λ°›λŠ”μž", "T", "-", "μˆ˜νƒμž", "TaxRegIDYN", ":", "쒅사업μž₯번호", "유무", "곡백", "-", "μ „μ²΄μ‘°νšŒ", "0", "-", "쒅사업μž₯번호", "μ—†μŒ", "1", "-", "쒅사업μž₯번호", "있음", "TaxRegID", ":", "쒅사업μž₯번호", "콀마", "(", ")", "둜", "ꡬ뢄", "ν•˜μ—¬", "ꡬ성", "ex", ")", "0001", "0002", "Page", ":", "νŽ˜μ΄μ§€", "번호", "PerPage", ":", "νŽ˜μ΄μ§€λ‹Ή", "λͺ©λ‘", "개수", "μ΅œλŒ€", "1000개", "Order", ":", "μ •λ ¬", "λ°©ν–₯", "D", "-", "λ‚΄λ¦Όμ°¨μˆœ", "A", "-", "μ˜€λ¦„μ°¨μˆœ", "UserID", ":", "νŒλΉŒνšŒμ›", "아이디", "return", "μˆ˜μ§‘", "κ²°κ³Ό", "정보", "raise", "PopbillException" ]
train
https://github.com/linkhub-sdk/popbill.py/blob/68a0dd7f7a937603318e93be321fde73c50b96cc/popbill/htTaxinvoiceService.py#L100-L137
linkhub-sdk/popbill.py
popbill/htTaxinvoiceService.py
HTTaxinvoiceService.summary
def summary(self, CorpNum, JobID, Type, TaxType, PurposeType, TaxRegIDType, TaxRegIDYN, TaxRegID, UserID=None): """ μˆ˜μ§‘ κ²°κ³Ό μš”μ•½μ •λ³΄ 쑰회 args CorpNum : νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ JobID : μž‘μ—…μ•„μ΄λ”” Type : λ¬Έμ„œν˜•νƒœ λ°°μ—΄, N-μΌλ°˜μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ, M-μˆ˜μ •μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ TaxType : κ³Όμ„Έν˜•νƒœ λ°°μ—΄, T-κ³Όμ„Έ, N-λ©΄μ„Έ, Z-μ˜μ„Έ PurposeType : 영수/청ꡬ, R-영수, C-청ꡬ, N-μ—†μŒ TaxRegIDType : 쒅사업μž₯번호 μ‚¬μ—…μžμœ ν˜•, S-κ³΅κΈ‰μž, B-κ³΅κΈ‰λ°›λŠ”μž, T-μˆ˜νƒμž TaxRegIDYN : 쒅사업μž₯번호 유무, 곡백-μ „μ²΄μ‘°νšŒ, 0-쒅사업μž₯번호 μ—†μŒ, 1-쒅사업μž₯번호 있음 TaxRegID : 쒅사업μž₯번호, 콀마(",")둜 ꡬ뢄 ν•˜μ—¬ ꡬ성 ex) '0001,0002' UserID : νŒλΉŒνšŒμ› 아이디 return μˆ˜μ§‘ κ²°κ³Ό μš”μ•½μ •λ³΄ raise PopbillException """ if JobID == None or len(JobID) != 18: raise PopbillException(-99999999, "μž‘μ—…μ•„μ΄λ””(jobID)κ°€ μ˜¬λ°”λ₯΄μ§€ μ•ŠμŠ΅λ‹ˆλ‹€.") uri = '/HomeTax/Taxinvoice/' + JobID + '/Summary' uri += '?Type=' + ','.join(Type) uri += '&TaxType=' + ','.join(TaxType) uri += '&PurposeType=' + ','.join(PurposeType) uri += '&TaxRegIDType=' + TaxRegIDType uri += '&TaxRegID=' + TaxRegID if TaxRegIDYN != '': uri += '&TaxRegIDYN=' + TaxRegIDYN return self._httpget(uri, CorpNum, UserID)
python
def summary(self, CorpNum, JobID, Type, TaxType, PurposeType, TaxRegIDType, TaxRegIDYN, TaxRegID, UserID=None): """ μˆ˜μ§‘ κ²°κ³Ό μš”μ•½μ •λ³΄ 쑰회 args CorpNum : νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ JobID : μž‘μ—…μ•„μ΄λ”” Type : λ¬Έμ„œν˜•νƒœ λ°°μ—΄, N-μΌλ°˜μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ, M-μˆ˜μ •μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ TaxType : κ³Όμ„Έν˜•νƒœ λ°°μ—΄, T-κ³Όμ„Έ, N-λ©΄μ„Έ, Z-μ˜μ„Έ PurposeType : 영수/청ꡬ, R-영수, C-청ꡬ, N-μ—†μŒ TaxRegIDType : 쒅사업μž₯번호 μ‚¬μ—…μžμœ ν˜•, S-κ³΅κΈ‰μž, B-κ³΅κΈ‰λ°›λŠ”μž, T-μˆ˜νƒμž TaxRegIDYN : 쒅사업μž₯번호 유무, 곡백-μ „μ²΄μ‘°νšŒ, 0-쒅사업μž₯번호 μ—†μŒ, 1-쒅사업μž₯번호 있음 TaxRegID : 쒅사업μž₯번호, 콀마(",")둜 ꡬ뢄 ν•˜μ—¬ ꡬ성 ex) '0001,0002' UserID : νŒλΉŒνšŒμ› 아이디 return μˆ˜μ§‘ κ²°κ³Ό μš”μ•½μ •λ³΄ raise PopbillException """ if JobID == None or len(JobID) != 18: raise PopbillException(-99999999, "μž‘μ—…μ•„μ΄λ””(jobID)κ°€ μ˜¬λ°”λ₯΄μ§€ μ•ŠμŠ΅λ‹ˆλ‹€.") uri = '/HomeTax/Taxinvoice/' + JobID + '/Summary' uri += '?Type=' + ','.join(Type) uri += '&TaxType=' + ','.join(TaxType) uri += '&PurposeType=' + ','.join(PurposeType) uri += '&TaxRegIDType=' + TaxRegIDType uri += '&TaxRegID=' + TaxRegID if TaxRegIDYN != '': uri += '&TaxRegIDYN=' + TaxRegIDYN return self._httpget(uri, CorpNum, UserID)
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μˆ˜μ§‘ κ²°κ³Ό μš”μ•½μ •λ³΄ 쑰회 args CorpNum : νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ JobID : μž‘μ—…μ•„μ΄λ”” Type : λ¬Έμ„œν˜•νƒœ λ°°μ—΄, N-μΌλ°˜μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ, M-μˆ˜μ •μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ TaxType : κ³Όμ„Έν˜•νƒœ λ°°μ—΄, T-κ³Όμ„Έ, N-λ©΄μ„Έ, Z-μ˜μ„Έ PurposeType : 영수/청ꡬ, R-영수, C-청ꡬ, N-μ—†μŒ TaxRegIDType : 쒅사업μž₯번호 μ‚¬μ—…μžμœ ν˜•, S-κ³΅κΈ‰μž, B-κ³΅κΈ‰λ°›λŠ”μž, T-μˆ˜νƒμž TaxRegIDYN : 쒅사업μž₯번호 유무, 곡백-μ „μ²΄μ‘°νšŒ, 0-쒅사업μž₯번호 μ—†μŒ, 1-쒅사업μž₯번호 있음 TaxRegID : 쒅사업μž₯번호, 콀마(",")둜 ꡬ뢄 ν•˜μ—¬ ꡬ성 ex) '0001,0002' UserID : νŒλΉŒνšŒμ› 아이디 return μˆ˜μ§‘ κ²°κ³Ό μš”μ•½μ •λ³΄ raise PopbillException
[ "μˆ˜μ§‘", "κ²°κ³Ό", "μš”μ•½μ •λ³΄", "쑰회", "args", "CorpNum", ":", "νŒλΉŒνšŒμ›", "μ‚¬μ—…μžλ²ˆν˜Έ", "JobID", ":", "μž‘μ—…μ•„μ΄λ””", "Type", ":", "λ¬Έμ„œν˜•νƒœ", "λ°°μ—΄", "N", "-", "μΌλ°˜μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ", "M", "-", "μˆ˜μ •μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ", "TaxType", ":", "κ³Όμ„Έν˜•νƒœ", "λ°°μ—΄", "T", "-", "κ³Όμ„Έ", "N", "-", "λ©΄μ„Έ", "Z", "-", "μ˜μ„Έ", "PurposeType", ":", "영수", "/", "청ꡬ", "R", "-", "영수", "C", "-", "청ꡬ", "N", "-", "μ—†μŒ", "TaxRegIDType", ":", "쒅사업μž₯번호", "μ‚¬μ—…μžμœ ν˜•", "S", "-", "κ³΅κΈ‰μž", "B", "-", "κ³΅κΈ‰λ°›λŠ”μž", "T", "-", "μˆ˜νƒμž", "TaxRegIDYN", ":", "쒅사업μž₯번호", "유무", "곡백", "-", "μ „μ²΄μ‘°νšŒ", "0", "-", "쒅사업μž₯번호", "μ—†μŒ", "1", "-", "쒅사업μž₯번호", "있음", "TaxRegID", ":", "쒅사업μž₯번호", "콀마", "(", ")", "둜", "ꡬ뢄", "ν•˜μ—¬", "ꡬ성", "ex", ")", "0001", "0002", "UserID", ":", "νŒλΉŒνšŒμ›", "아이디", "return", "μˆ˜μ§‘", "κ²°κ³Ό", "μš”μ•½μ •λ³΄", "raise", "PopbillException" ]
train
https://github.com/linkhub-sdk/popbill.py/blob/68a0dd7f7a937603318e93be321fde73c50b96cc/popbill/htTaxinvoiceService.py#L139-L169
linkhub-sdk/popbill.py
popbill/htTaxinvoiceService.py
HTTaxinvoiceService.getTaxinvoice
def getTaxinvoice(self, CorpNum, NTSConfirmNum, UserID=None): """ μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ 상세정보 확인 args CorpNum : νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ NTSConfirmNum : κ΅­μ„Έμ²­ 승인번호 UserID : νŒλΉŒνšŒμ› 아이디 return μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ 정보객체 raise PopbillException """ if NTSConfirmNum == None or len(NTSConfirmNum) != 24: raise PopbillException(-99999999, "κ΅­μ„Έμ²­μŠΉμΈλ²ˆν˜Έ(NTSConfirmNum)κ°€ μ˜¬λ°”λ₯΄μ§€ μ•ŠμŠ΅λ‹ˆλ‹€.") return self._httpget('/HomeTax/Taxinvoice/' + NTSConfirmNum, CorpNum, UserID)
python
def getTaxinvoice(self, CorpNum, NTSConfirmNum, UserID=None): """ μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ 상세정보 확인 args CorpNum : νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ NTSConfirmNum : κ΅­μ„Έμ²­ 승인번호 UserID : νŒλΉŒνšŒμ› 아이디 return μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ 정보객체 raise PopbillException """ if NTSConfirmNum == None or len(NTSConfirmNum) != 24: raise PopbillException(-99999999, "κ΅­μ„Έμ²­μŠΉμΈλ²ˆν˜Έ(NTSConfirmNum)κ°€ μ˜¬λ°”λ₯΄μ§€ μ•ŠμŠ΅λ‹ˆλ‹€.") return self._httpget('/HomeTax/Taxinvoice/' + NTSConfirmNum, CorpNum, UserID)
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μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ 상세정보 확인 args CorpNum : νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ NTSConfirmNum : κ΅­μ„Έμ²­ 승인번호 UserID : νŒλΉŒνšŒμ› 아이디 return μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ 정보객체 raise PopbillException
[ "μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ", "상세정보", "확인", "args", "CorpNum", ":", "νŒλΉŒνšŒμ›", "μ‚¬μ—…μžλ²ˆν˜Έ", "NTSConfirmNum", ":", "κ΅­μ„Έμ²­", "승인번호", "UserID", ":", "νŒλΉŒνšŒμ›", "아이디", "return", "μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ", "정보객체", "raise", "PopbillException" ]
train
https://github.com/linkhub-sdk/popbill.py/blob/68a0dd7f7a937603318e93be321fde73c50b96cc/popbill/htTaxinvoiceService.py#L171-L185
linkhub-sdk/popbill.py
popbill/htTaxinvoiceService.py
HTTaxinvoiceService.getXML
def getXML(self, CorpNum, NTSConfirmNum, UserID=None): """ μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ 상세정보 확인 - XML args CorpNum : νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ NTSConfirmNum : κ΅­μ„Έμ²­ 승인번호 UserID : νŒλΉŒνšŒμ› 아이디 return μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ 정보객체 raise PopbillException """ if NTSConfirmNum == None or len(NTSConfirmNum) != 24: raise PopbillException(-99999999, "κ΅­μ„Έμ²­μŠΉμΈλ²ˆν˜Έ(NTSConfirmNum)κ°€ μ˜¬λ°”λ₯΄μ§€ μ•ŠμŠ΅λ‹ˆλ‹€.") return self._httpget('/HomeTax/Taxinvoice/' + NTSConfirmNum + '?T=xml', CorpNum, UserID)
python
def getXML(self, CorpNum, NTSConfirmNum, UserID=None): """ μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ 상세정보 확인 - XML args CorpNum : νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ NTSConfirmNum : κ΅­μ„Έμ²­ 승인번호 UserID : νŒλΉŒνšŒμ› 아이디 return μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ 정보객체 raise PopbillException """ if NTSConfirmNum == None or len(NTSConfirmNum) != 24: raise PopbillException(-99999999, "κ΅­μ„Έμ²­μŠΉμΈλ²ˆν˜Έ(NTSConfirmNum)κ°€ μ˜¬λ°”λ₯΄μ§€ μ•ŠμŠ΅λ‹ˆλ‹€.") return self._httpget('/HomeTax/Taxinvoice/' + NTSConfirmNum + '?T=xml', CorpNum, UserID)
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μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ 상세정보 확인 - XML args CorpNum : νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ NTSConfirmNum : κ΅­μ„Έμ²­ 승인번호 UserID : νŒλΉŒνšŒμ› 아이디 return μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ 정보객체 raise PopbillException
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train
https://github.com/linkhub-sdk/popbill.py/blob/68a0dd7f7a937603318e93be321fde73c50b96cc/popbill/htTaxinvoiceService.py#L187-L201
linkhub-sdk/popbill.py
popbill/htTaxinvoiceService.py
HTTaxinvoiceService.getPopUpURL
def getPopUpURL(self, CorpNum, NTSConfirmNum, UserID=None): """ ν™ˆνƒμŠ€ μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ 보기 νŒμ—… URL args CorpNum : νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ NTSConfirmNum : κ΅­μ„Έμ²­ 승인 번호 UserID : νŒλΉŒνšŒμ› 아이디 return μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ 보기 νŒμ—… URL λ°˜ν™˜ raise PopbillException """ if NTSConfirmNum == None or len(NTSConfirmNum) != 24: raise PopbillException(-99999999, "κ΅­μ„Έμ²­μŠΉμΈλ²ˆν˜Έ(NTSConfirmNum)κ°€ μ˜¬λ°”λ₯΄μ§€ μ•ŠμŠ΅λ‹ˆλ‹€.") return self._httpget('/HomeTax/Taxinvoice/' + NTSConfirmNum + '/PopUp', CorpNum, UserID).url
python
def getPopUpURL(self, CorpNum, NTSConfirmNum, UserID=None): """ ν™ˆνƒμŠ€ μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ 보기 νŒμ—… URL args CorpNum : νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ NTSConfirmNum : κ΅­μ„Έμ²­ 승인 번호 UserID : νŒλΉŒνšŒμ› 아이디 return μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ 보기 νŒμ—… URL λ°˜ν™˜ raise PopbillException """ if NTSConfirmNum == None or len(NTSConfirmNum) != 24: raise PopbillException(-99999999, "κ΅­μ„Έμ²­μŠΉμΈλ²ˆν˜Έ(NTSConfirmNum)κ°€ μ˜¬λ°”λ₯΄μ§€ μ•ŠμŠ΅λ‹ˆλ‹€.") return self._httpget('/HomeTax/Taxinvoice/' + NTSConfirmNum + '/PopUp', CorpNum, UserID).url
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ν™ˆνƒμŠ€ μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ 보기 νŒμ—… URL args CorpNum : νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ NTSConfirmNum : κ΅­μ„Έμ²­ 승인 번호 UserID : νŒλΉŒνšŒμ› 아이디 return μ „μžμ„ΈκΈˆκ³„μ‚°μ„œ 보기 νŒμ—… URL λ°˜ν™˜ raise PopbillException
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train
https://github.com/linkhub-sdk/popbill.py/blob/68a0dd7f7a937603318e93be321fde73c50b96cc/popbill/htTaxinvoiceService.py#L252-L267
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/publisher/__init__.py
Publisher.make_document
def make_document(self, titlestring): """ This method may be used to create a new document for writing as xml to the OPS subdirectory of the ePub structure. """ #root = etree.XML('''<?xml version="1.0"?>\ #<!DOCTYPE html PUBLIC '-//W3C//DTD XHTML 1.1//EN' 'http://www.w3.org/TR/xhtml11/DTD/xhtml11.dtd'>\ #<html xml:lang="en-US" xmlns="http://www.w3.org/1999/xhtml" xmlns:ops="http://www.idpf.org/2007/ops">\ #</html>''') root = etree.XML('''<?xml version="1.0"?>\ <!DOCTYPE html>\ <html xmlns="http://www.w3.org/1999/xhtml">\ </html>''') document = etree.ElementTree(root) html = document.getroot() head = etree.SubElement(html, 'head') etree.SubElement(html, 'body') title = etree.SubElement(head, 'title') title.text = titlestring #The href for the css stylesheet is a standin, can be overwritten etree.SubElement(head, 'link', {'href': 'css/default.css', 'rel': 'stylesheet', 'type': 'text/css'}) return document
python
def make_document(self, titlestring): """ This method may be used to create a new document for writing as xml to the OPS subdirectory of the ePub structure. """ #root = etree.XML('''<?xml version="1.0"?>\ #<!DOCTYPE html PUBLIC '-//W3C//DTD XHTML 1.1//EN' 'http://www.w3.org/TR/xhtml11/DTD/xhtml11.dtd'>\ #<html xml:lang="en-US" xmlns="http://www.w3.org/1999/xhtml" xmlns:ops="http://www.idpf.org/2007/ops">\ #</html>''') root = etree.XML('''<?xml version="1.0"?>\ <!DOCTYPE html>\ <html xmlns="http://www.w3.org/1999/xhtml">\ </html>''') document = etree.ElementTree(root) html = document.getroot() head = etree.SubElement(html, 'head') etree.SubElement(html, 'body') title = etree.SubElement(head, 'title') title.text = titlestring #The href for the css stylesheet is a standin, can be overwritten etree.SubElement(head, 'link', {'href': 'css/default.css', 'rel': 'stylesheet', 'type': 'text/css'}) return document
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This method may be used to create a new document for writing as xml to the OPS subdirectory of the ePub structure.
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/publisher/__init__.py#L196-L224
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/publisher/__init__.py
Publisher.write_document
def write_document(self, name, document): """ This function will write a document to an XML file. """ with open(name, 'wb') as out: out.write(etree.tostring(document, encoding='utf-8', pretty_print=True))
python
def write_document(self, name, document): """ This function will write a document to an XML file. """ with open(name, 'wb') as out: out.write(etree.tostring(document, encoding='utf-8', pretty_print=True))
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This function will write a document to an XML file.
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/publisher/__init__.py#L281-L288
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/publisher/__init__.py
Publisher.format_date_string
def format_date_string(self, date_tuple): """ Receives a date_tuple object, and outputs a string for placement in the article content. """ months = ['', 'January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'] date_string = '' if date_tuple.season: return '{0}, {1}'.format(date_tuple.season, date_tuple.year) else: if not date_tuple.month and not date_tuple.day: return '{0}'.format(date_tuple.year) if date_tuple.month: date_string += months[int(date_tuple.month)] if date_tuple.day: date_string += ' ' + date_tuple.day return ', '.join([date_string, date_tuple.year])
python
def format_date_string(self, date_tuple): """ Receives a date_tuple object, and outputs a string for placement in the article content. """ months = ['', 'January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'] date_string = '' if date_tuple.season: return '{0}, {1}'.format(date_tuple.season, date_tuple.year) else: if not date_tuple.month and not date_tuple.day: return '{0}'.format(date_tuple.year) if date_tuple.month: date_string += months[int(date_tuple.month)] if date_tuple.day: date_string += ' ' + date_tuple.day return ', '.join([date_string, date_tuple.year])
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/publisher/__init__.py#L539-L556
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/publisher/__init__.py
Publisher.has_out_of_flow_tables
def has_out_of_flow_tables(self): """ Returns True if the article has out-of-flow tables, indicates separate tables document. This method is used to indicate whether rendering this article's content will result in the creation of out-of-flow HTML tables. This method has a base class implementation representing a common logic; if an article has a graphic(image) representation of a table then the HTML representation will be placed out-of-flow if it exists, if there is no graphic(image) represenation then the HTML representation will be placed in-flow. Returns ------- bool True if there are out-of-flow HTML tables, False otherwise """ if self.article.body is None: return False for table_wrap in self.article.body.findall('.//table-wrap'): graphic = table_wrap.xpath('./graphic | ./alternatives/graphic') table = table_wrap.xpath('./table | ./alternatives/table') if graphic and table: return True return False
python
def has_out_of_flow_tables(self): """ Returns True if the article has out-of-flow tables, indicates separate tables document. This method is used to indicate whether rendering this article's content will result in the creation of out-of-flow HTML tables. This method has a base class implementation representing a common logic; if an article has a graphic(image) representation of a table then the HTML representation will be placed out-of-flow if it exists, if there is no graphic(image) represenation then the HTML representation will be placed in-flow. Returns ------- bool True if there are out-of-flow HTML tables, False otherwise """ if self.article.body is None: return False for table_wrap in self.article.body.findall('.//table-wrap'): graphic = table_wrap.xpath('./graphic | ./alternatives/graphic') table = table_wrap.xpath('./table | ./alternatives/table') if graphic and table: return True return False
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Returns True if the article has out-of-flow tables, indicates separate tables document. This method is used to indicate whether rendering this article's content will result in the creation of out-of-flow HTML tables. This method has a base class implementation representing a common logic; if an article has a graphic(image) representation of a table then the HTML representation will be placed out-of-flow if it exists, if there is no graphic(image) represenation then the HTML representation will be placed in-flow. Returns ------- bool True if there are out-of-flow HTML tables, False otherwise
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/publisher/__init__.py#L686-L711
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/navigation/__init__.py
Navigation.process
def process(self, article): """ Ingests an Article to create navigation structures and parse global metadata. """ if self.article is not None and not self.collection: log.warning('Could not process additional article. Navigation only \ handles one article unless collection mode is set.') return False if article.publisher is None: log.error('''Navigation cannot be generated for an Article \ without a publisher!''') return self.article = article self.article_doi = self.article.doi.split('/')[1] self.all_dois.append(self.article.doi) if self.collection: pass else: self.title = self.article.publisher.nav_title() for author in self.article.publisher.nav_contributors(): self.contributors.add(author) #Analyze the structure of the article to create internal mapping self.map_navigation()
python
def process(self, article): """ Ingests an Article to create navigation structures and parse global metadata. """ if self.article is not None and not self.collection: log.warning('Could not process additional article. Navigation only \ handles one article unless collection mode is set.') return False if article.publisher is None: log.error('''Navigation cannot be generated for an Article \ without a publisher!''') return self.article = article self.article_doi = self.article.doi.split('/')[1] self.all_dois.append(self.article.doi) if self.collection: pass else: self.title = self.article.publisher.nav_title() for author in self.article.publisher.nav_contributors(): self.contributors.add(author) #Analyze the structure of the article to create internal mapping self.map_navigation()
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Ingests an Article to create navigation structures and parse global metadata.
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/navigation/__init__.py#L63-L88
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/navigation/__init__.py
Navigation.map_navigation
def map_navigation(self): """ This is a wrapper for depth-first recursive analysis of the article """ #All articles should have titles title_id = 'titlepage-{0}'.format(self.article_doi) title_label = self.article.publisher.nav_title() title_source = 'main.{0}.xhtml#title'.format(self.article_doi) title_navpoint = navpoint(title_id, title_label, self.play_order, title_source, []) self.nav.append(title_navpoint) #When processing a collection of articles, we will want all subsequent #navpoints for this article to be located under the title if self.collection: nav_insertion = title_navpoint.children else: nav_insertion = self.nav #If the article has a body, we'll need to parse it for navigation if self.article.body is not None: #Here is where we invoke the recursive parsing! for nav_pt in self.recursive_article_navmap(self.article.body): nav_insertion.append(nav_pt) #Add a navpoint to the references if appropriate if self.article.root.xpath('./back/ref'): ref_id = 'references-{0}'.format(self.article_doi) ref_label = 'References' ref_source = 'biblio.{0}.xhtml#references'.format(self.article_doi) ref_navpoint = navpoint(ref_id, ref_label, self.play_order, ref_source, []) nav_insertion.append(ref_navpoint)
python
def map_navigation(self): """ This is a wrapper for depth-first recursive analysis of the article """ #All articles should have titles title_id = 'titlepage-{0}'.format(self.article_doi) title_label = self.article.publisher.nav_title() title_source = 'main.{0}.xhtml#title'.format(self.article_doi) title_navpoint = navpoint(title_id, title_label, self.play_order, title_source, []) self.nav.append(title_navpoint) #When processing a collection of articles, we will want all subsequent #navpoints for this article to be located under the title if self.collection: nav_insertion = title_navpoint.children else: nav_insertion = self.nav #If the article has a body, we'll need to parse it for navigation if self.article.body is not None: #Here is where we invoke the recursive parsing! for nav_pt in self.recursive_article_navmap(self.article.body): nav_insertion.append(nav_pt) #Add a navpoint to the references if appropriate if self.article.root.xpath('./back/ref'): ref_id = 'references-{0}'.format(self.article_doi) ref_label = 'References' ref_source = 'biblio.{0}.xhtml#references'.format(self.article_doi) ref_navpoint = navpoint(ref_id, ref_label, self.play_order, ref_source, []) nav_insertion.append(ref_navpoint)
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This is a wrapper for depth-first recursive analysis of the article
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/navigation/__init__.py#L90-L121
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/navigation/__init__.py
Navigation.recursive_article_navmap
def recursive_article_navmap(self, src_element, depth=0, first=True): """ This function recursively traverses the content of an input article to add the correct elements to the NCX file's navMap and Lists. """ if depth > self.nav_depth: self.nav_depth = depth navpoints = [] tagnames = ['sec', 'fig', 'table-wrap'] for child in src_element: try: tagname = child.tag except AttributeError: continue else: if tagname not in tagnames: continue #Safely handle missing id attributes if 'id' not in child.attrib: child.attrib['id'] = self.auto_id #If in collection mode, we'll prepend the article DOI to avoid #collisions if self.collection: child_id = '-'.join([self.article_doi, child.attrib['id']]) else: child_id = child.attrib['id'] #Attempt to infer the correct text as a label #Skip the element if we cannot child_title = child.find('title') if child_title is None: continue # If there is no immediate title, skip this element label = element_methods.all_text(child_title) if not label: continue # If no text in the title, skip this element source = 'main.{0}.xhtml#{1}'.format(self.article_doi, child.attrib['id']) if tagname == 'sec': children = self.recursive_article_navmap(child, depth=depth + 1) navpoints.append(navpoint(child_id, label, self.play_order, source, children)) #figs and table-wraps do not have children elif tagname == 'fig': # Add navpoints to list_of_figures self.figures_list.append(navpoint(child.attrib['id'], label, None, source, [])) elif tagname == 'table-wrap': # Add navpoints to list_of_tables self.tables_list.append(navpoint(child.attrib['id'], label, None, source, [])) return navpoints
python
def recursive_article_navmap(self, src_element, depth=0, first=True): """ This function recursively traverses the content of an input article to add the correct elements to the NCX file's navMap and Lists. """ if depth > self.nav_depth: self.nav_depth = depth navpoints = [] tagnames = ['sec', 'fig', 'table-wrap'] for child in src_element: try: tagname = child.tag except AttributeError: continue else: if tagname not in tagnames: continue #Safely handle missing id attributes if 'id' not in child.attrib: child.attrib['id'] = self.auto_id #If in collection mode, we'll prepend the article DOI to avoid #collisions if self.collection: child_id = '-'.join([self.article_doi, child.attrib['id']]) else: child_id = child.attrib['id'] #Attempt to infer the correct text as a label #Skip the element if we cannot child_title = child.find('title') if child_title is None: continue # If there is no immediate title, skip this element label = element_methods.all_text(child_title) if not label: continue # If no text in the title, skip this element source = 'main.{0}.xhtml#{1}'.format(self.article_doi, child.attrib['id']) if tagname == 'sec': children = self.recursive_article_navmap(child, depth=depth + 1) navpoints.append(navpoint(child_id, label, self.play_order, source, children)) #figs and table-wraps do not have children elif tagname == 'fig': # Add navpoints to list_of_figures self.figures_list.append(navpoint(child.attrib['id'], label, None, source, [])) elif tagname == 'table-wrap': # Add navpoints to list_of_tables self.tables_list.append(navpoint(child.attrib['id'], label, None, source, [])) return navpoints
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This function recursively traverses the content of an input article to add the correct elements to the NCX file's navMap and Lists.
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/navigation/__init__.py#L123-L183
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/navigation/__init__.py
Navigation.render_EPUB2
def render_EPUB2(self, location): """ Creates the NCX specified file for EPUB2 """ def make_navlabel(text): """ Creates and returns a navLabel element with the supplied text. """ navlabel = etree.Element('navLabel') navlabel_text = etree.SubElement(navlabel, 'text') navlabel_text.text = text return navlabel def make_navMap(nav=None): if nav is None: nav_element = etree.Element('navMap') for nav_point in self.nav: nav_element.append(make_navMap(nav=nav_point)) else: nav_element = etree.Element('navPoint') nav_element.attrib['id'] = nav.id nav_element.attrib['playOrder'] = nav.playOrder nav_element.append(make_navlabel(nav.label)) content_element = etree.SubElement(nav_element, 'content') content_element.attrib['src'] = nav.source for child in nav.children: nav_element.append(make_navMap(nav=child)) return nav_element root = etree.XML('''\ <?xml version="1.0"?>\ <ncx version="2005-1" xmlns="http://www.daisy.org/z3986/2005/ncx/">\ <head>\ <meta name="dtb:uid" content="{uid}"/>\ <meta name="dtb:depth" content="{depth}"/>\ <meta name="dtb:totalPageCount" content="0"/>\ <meta name="dtb:maxPageNumber" content="0"/>\ <meta name="dtb:generator" content="OpenAccess_EPUB {version}"/>\ </head>\ </ncx>'''.format(**{'uid': ','.join(self.all_dois), 'depth': self.nav_depth, 'version': __version__})) document = etree.ElementTree(root) ncx = document.getroot() #Create the docTitle element doctitle = etree.SubElement(ncx, 'docTitle') doctitle_text = etree.SubElement(doctitle, 'text') doctitle_text.text = self.title #Create the docAuthor elements for contributor in self.contributors: if contributor.role == 'author': docauthor = etree.SubElement(ncx, 'docAuthor') docauthor_text = etree.SubElement(docauthor, 'text') docauthor_text.text = contributor.name #Create the navMap element ncx.append(make_navMap()) if self.figures_list: navlist = etree.SubElement(ncx, 'navList') navlist.append(make_navlabel('List of Figures')) for nav_pt in self.figures_list: navtarget = etree.SubElement(navlist, 'navTarget') navtarget.attrib['id'] = nav_pt.id navtarget.append(self.make_navlabel(nav_pt.label)) content = etree.SubElement(navtarget, 'content') content.attrib['src'] = nav_pt.source if self.tables_list: navlist = etree.SubElement(ncx, 'navList') navlist.append(make_navlabel('List of Tables')) for nav_pt in self.tables_list: navtarget = etree.SubElement(navlist, 'navTarget') navtarget.attrib['id'] = nav_pt.id navtarget.append(self.make_navlabel(nav_pt.label)) content = etree.SubElement(navtarget, 'content') content.attrib['src'] = nav_pt.source with open(os.path.join(location, 'EPUB', 'toc.ncx'), 'wb') as output: output.write(etree.tostring(document, encoding='utf-8', pretty_print=True))
python
def render_EPUB2(self, location): """ Creates the NCX specified file for EPUB2 """ def make_navlabel(text): """ Creates and returns a navLabel element with the supplied text. """ navlabel = etree.Element('navLabel') navlabel_text = etree.SubElement(navlabel, 'text') navlabel_text.text = text return navlabel def make_navMap(nav=None): if nav is None: nav_element = etree.Element('navMap') for nav_point in self.nav: nav_element.append(make_navMap(nav=nav_point)) else: nav_element = etree.Element('navPoint') nav_element.attrib['id'] = nav.id nav_element.attrib['playOrder'] = nav.playOrder nav_element.append(make_navlabel(nav.label)) content_element = etree.SubElement(nav_element, 'content') content_element.attrib['src'] = nav.source for child in nav.children: nav_element.append(make_navMap(nav=child)) return nav_element root = etree.XML('''\ <?xml version="1.0"?>\ <ncx version="2005-1" xmlns="http://www.daisy.org/z3986/2005/ncx/">\ <head>\ <meta name="dtb:uid" content="{uid}"/>\ <meta name="dtb:depth" content="{depth}"/>\ <meta name="dtb:totalPageCount" content="0"/>\ <meta name="dtb:maxPageNumber" content="0"/>\ <meta name="dtb:generator" content="OpenAccess_EPUB {version}"/>\ </head>\ </ncx>'''.format(**{'uid': ','.join(self.all_dois), 'depth': self.nav_depth, 'version': __version__})) document = etree.ElementTree(root) ncx = document.getroot() #Create the docTitle element doctitle = etree.SubElement(ncx, 'docTitle') doctitle_text = etree.SubElement(doctitle, 'text') doctitle_text.text = self.title #Create the docAuthor elements for contributor in self.contributors: if contributor.role == 'author': docauthor = etree.SubElement(ncx, 'docAuthor') docauthor_text = etree.SubElement(docauthor, 'text') docauthor_text.text = contributor.name #Create the navMap element ncx.append(make_navMap()) if self.figures_list: navlist = etree.SubElement(ncx, 'navList') navlist.append(make_navlabel('List of Figures')) for nav_pt in self.figures_list: navtarget = etree.SubElement(navlist, 'navTarget') navtarget.attrib['id'] = nav_pt.id navtarget.append(self.make_navlabel(nav_pt.label)) content = etree.SubElement(navtarget, 'content') content.attrib['src'] = nav_pt.source if self.tables_list: navlist = etree.SubElement(ncx, 'navList') navlist.append(make_navlabel('List of Tables')) for nav_pt in self.tables_list: navtarget = etree.SubElement(navlist, 'navTarget') navtarget.attrib['id'] = nav_pt.id navtarget.append(self.make_navlabel(nav_pt.label)) content = etree.SubElement(navtarget, 'content') content.attrib['src'] = nav_pt.source with open(os.path.join(location, 'EPUB', 'toc.ncx'), 'wb') as output: output.write(etree.tostring(document, encoding='utf-8', pretty_print=True))
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Creates the NCX specified file for EPUB2
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/navigation/__init__.py#L185-L266
MSchnei/pyprf_feature
pyprf_feature/simulation/pRF_functions.py
funcGauss1D
def funcGauss1D(x, mu, sig): """ Create 1D Gaussian. Source: http://mathworld.wolfram.com/GaussianFunction.html """ arrOut = np.exp(-np.power((x - mu)/sig, 2.)/2) # normalize # arrOut = arrOut/(np.sqrt(2.*np.pi)*sig) # normalize (laternative) arrOut = arrOut/np.sum(arrOut) return arrOut
python
def funcGauss1D(x, mu, sig): """ Create 1D Gaussian. Source: http://mathworld.wolfram.com/GaussianFunction.html """ arrOut = np.exp(-np.power((x - mu)/sig, 2.)/2) # normalize # arrOut = arrOut/(np.sqrt(2.*np.pi)*sig) # normalize (laternative) arrOut = arrOut/np.sum(arrOut) return arrOut
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Create 1D Gaussian. Source: http://mathworld.wolfram.com/GaussianFunction.html
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train
https://github.com/MSchnei/pyprf_feature/blob/49004ede7ae1ddee07a30afe9ce3e2776750805c/pyprf_feature/simulation/pRF_functions.py#L20-L29
MSchnei/pyprf_feature
pyprf_feature/simulation/pRF_functions.py
funcGauss2D
def funcGauss2D(varSizeX, varSizeY, varPosX, varPosY, varSd): """ Create 2D Gaussian kernel. Source: http://mathworld.wolfram.com/GaussianFunction.html """ varSizeX = int(varSizeX) varSizeY = int(varSizeY) # aryX and aryY are in reversed order, this seems to be necessary: aryY, aryX = sp.mgrid[0:varSizeX, 0:varSizeY] # The actual creation of the Gaussian array: aryGauss = ( ( np.power((aryX - varPosX), 2.0) + np.power((aryY - varPosY), 2.0) ) / (2.0 * np.power(varSd, 2.0)) ) aryGauss = np.exp(-aryGauss) # normalize # aryGauss = aryGauss/(2*np.pi*np.power(varSd, 2)) return aryGauss
python
def funcGauss2D(varSizeX, varSizeY, varPosX, varPosY, varSd): """ Create 2D Gaussian kernel. Source: http://mathworld.wolfram.com/GaussianFunction.html """ varSizeX = int(varSizeX) varSizeY = int(varSizeY) # aryX and aryY are in reversed order, this seems to be necessary: aryY, aryX = sp.mgrid[0:varSizeX, 0:varSizeY] # The actual creation of the Gaussian array: aryGauss = ( ( np.power((aryX - varPosX), 2.0) + np.power((aryY - varPosY), 2.0) ) / (2.0 * np.power(varSd, 2.0)) ) aryGauss = np.exp(-aryGauss) # normalize # aryGauss = aryGauss/(2*np.pi*np.power(varSd, 2)) return aryGauss
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Create 2D Gaussian kernel. Source: http://mathworld.wolfram.com/GaussianFunction.html
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train
https://github.com/MSchnei/pyprf_feature/blob/49004ede7ae1ddee07a30afe9ce3e2776750805c/pyprf_feature/simulation/pRF_functions.py#L33-L58
MSchnei/pyprf_feature
pyprf_feature/simulation/pRF_functions.py
funcConvPar
def funcConvPar(aryDm, vecHrf, varNumVol): """ Function for convolution of pixel-wise 'design matrix' with HRF model. """ # In order to avoid an artefact at the end of the time series, we have to # concatenate an empty array to both the design matrix and the HRF model # before convolution. aryDm = np.concatenate((aryDm, np.zeros((aryDm.shape[0], 100))), axis=1) vecHrf = np.concatenate((vecHrf, np.zeros((100,)))) aryDmConv = np.empty((aryDm.shape[0], varNumVol)) for idx in range(0, aryDm.shape[0]): vecDm = aryDm[idx, :] # Convolve design matrix with HRF model: aryDmConv[idx, :] = np.convolve(vecDm, vecHrf, mode='full')[:varNumVol] return aryDmConv
python
def funcConvPar(aryDm, vecHrf, varNumVol): """ Function for convolution of pixel-wise 'design matrix' with HRF model. """ # In order to avoid an artefact at the end of the time series, we have to # concatenate an empty array to both the design matrix and the HRF model # before convolution. aryDm = np.concatenate((aryDm, np.zeros((aryDm.shape[0], 100))), axis=1) vecHrf = np.concatenate((vecHrf, np.zeros((100,)))) aryDmConv = np.empty((aryDm.shape[0], varNumVol)) for idx in range(0, aryDm.shape[0]): vecDm = aryDm[idx, :] # Convolve design matrix with HRF model: aryDmConv[idx, :] = np.convolve(vecDm, vecHrf, mode='full')[:varNumVol] return aryDmConv
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Function for convolution of pixel-wise 'design matrix' with HRF model.
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train
https://github.com/MSchnei/pyprf_feature/blob/49004ede7ae1ddee07a30afe9ce3e2776750805c/pyprf_feature/simulation/pRF_functions.py#L112-L131
MSchnei/pyprf_feature
pyprf_feature/simulation/pRF_functions.py
simulateAR1
def simulateAR1(n, beta, sigma, c, burnin, varNumCNR, varNumTP, ): """ Simulates an AR(1) model using the parameters beta, c, and sigma. Returns an array with length n n := number of time points beta := correlation weight sigma := standard deviation of the noise, can be a vector c := constant added to the noise, default 0 based on: source1: https://github.com/ndronen/misc/blob/master/python/ar1.py source2: http://stats.stackexchange.com/questions/22742/ problem-simulating-ar2-process source3: https://kurtverstegen.wordpress.com/2013/12/07/simulation/ """ # Output array with noise time courses noise = np.empty((varNumCNR, varNumTP)) if burnin == 1: burnin = 100 n = n + burnin noiseTemp = c + sp.random.normal(0, 1, n) sims = np.zeros(n) sims[0] = noiseTemp[0] for i in range(1, n): sims[i] = beta*sims[i-1] + noiseTemp[i] sims = sims[burnin:] noise = sigma[:, np.newaxis]*sp.stats.mstats.zscore(sims) return noise
python
def simulateAR1(n, beta, sigma, c, burnin, varNumCNR, varNumTP, ): """ Simulates an AR(1) model using the parameters beta, c, and sigma. Returns an array with length n n := number of time points beta := correlation weight sigma := standard deviation of the noise, can be a vector c := constant added to the noise, default 0 based on: source1: https://github.com/ndronen/misc/blob/master/python/ar1.py source2: http://stats.stackexchange.com/questions/22742/ problem-simulating-ar2-process source3: https://kurtverstegen.wordpress.com/2013/12/07/simulation/ """ # Output array with noise time courses noise = np.empty((varNumCNR, varNumTP)) if burnin == 1: burnin = 100 n = n + burnin noiseTemp = c + sp.random.normal(0, 1, n) sims = np.zeros(n) sims[0] = noiseTemp[0] for i in range(1, n): sims[i] = beta*sims[i-1] + noiseTemp[i] sims = sims[burnin:] noise = sigma[:, np.newaxis]*sp.stats.mstats.zscore(sims) return noise
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Simulates an AR(1) model using the parameters beta, c, and sigma. Returns an array with length n n := number of time points beta := correlation weight sigma := standard deviation of the noise, can be a vector c := constant added to the noise, default 0 based on: source1: https://github.com/ndronen/misc/blob/master/python/ar1.py source2: http://stats.stackexchange.com/questions/22742/ problem-simulating-ar2-process source3: https://kurtverstegen.wordpress.com/2013/12/07/simulation/
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train
https://github.com/MSchnei/pyprf_feature/blob/49004ede7ae1ddee07a30afe9ce3e2776750805c/pyprf_feature/simulation/pRF_functions.py#L149-L185
MSchnei/pyprf_feature
pyprf_feature/simulation/pRF_functions.py
funcNrlTcMotPred
def funcNrlTcMotPred(idxPrc, varPixX, varPixY, NrlMdlChunk, varNumTP, aryBoxCar, # aryCond path, varNumNrlMdls, varNumMtDrctn, varPar, queOut): """ Function for creating neural time course models. This function should be used to create neural models if different predictors for every motion direction are included. """ # # if hd5 method is used: open file for reading # filename = 'aryBoxCar' + str(idxPrc) + '.hdf5' # hdf5_path = os.path.join(path, filename) # fileH = tables.openFile(hdf5_path, mode='r') # Output array with pRF model time courses at all modelled standard # deviations for current pixel position: aryOut = np.empty((len(NrlMdlChunk), varNumTP, varNumMtDrctn), dtype='float32') # Prepare status indicator if this is the first of the parallel processes: if idxPrc == 1: # We create a status indicator for the time consuming pRF model finding # algorithm. Number of steps of the status indicator: varStsStpSze = 20 # Number of pRF models to fit: varNumLoops = varNumNrlMdls/varPar # Vector with pRF values at which to give status feedback: vecStatus = np.linspace(0, varNumLoops, num=(varStsStpSze+1), endpoint=True) vecStatus = np.ceil(vecStatus) vecStatus = vecStatus.astype(int) # Vector with corresponding percentage values at which to give status # feedback: vecStatusPrc = np.linspace(0, 100, num=(varStsStpSze+1), endpoint=True) vecStatusPrc = np.ceil(vecStatusPrc) vecStatusPrc = vecStatusPrc.astype(int) # Counter for status indicator: varCntSts01 = 0 varCntSts02 = 0 # Loop through all Gauss parameters that are in this chunk for idx, NrlMdlTrpl in enumerate(NrlMdlChunk): # Status indicator (only used in the first of the parallel # processes): if idxPrc == 1: # Status indicator: if varCntSts02 == vecStatus[varCntSts01]: # Prepare status message: strStsMsg = ('---------Progress: ' + str(vecStatusPrc[varCntSts01]) + ' % --- ' + str(vecStatus[varCntSts01]) + ' loops out of ' + str(varNumLoops)) print(strStsMsg) # Only increment counter if the last value has not been # reached yet: if varCntSts01 < varStsStpSze: varCntSts01 = varCntSts01 + int(1) # x pos of Gauss model: NrlMdlTrpl[0] # y pos of Gauss model: NrlMdlTrpl[1] # std of Gauss model: NrlMdlTrpl[2] # index of tng crv model: NrlMdlTrpl[3] varTmpX = int(np.around(NrlMdlTrpl[0], 0)) varTmpY = int(np.around(NrlMdlTrpl[1], 0)) # Create pRF model (2D): aryGauss = funcGauss2D(varPixX, varPixY, varTmpX, varTmpY, NrlMdlTrpl[2]) # Multiply pixel-wise box car model with Gaussian pRF models: aryNrlTcTmp = np.multiply(aryBoxCar, aryGauss[:, :, None, None]) # Calculate sum across x- and y-dimensions - the 'area under the # Gaussian surface'. This is essentially an unscaled version of the # neural time course model (i.e. not yet scaled for the size of # the pRF). aryNrlTcTmp = np.sum(aryNrlTcTmp, axis=(0, 1)) # Normalise the nrl time course model to the size of the pRF. This # gives us the ratio of 'activation' of the pRF at each time point, # or, in other words, the neural time course model. aryNrlTcTmp = np.divide(aryNrlTcTmp, np.sum(aryGauss, axis=(0, 1))) # Put model time courses into the function's output array: aryOut[idx, :, :] = aryNrlTcTmp # Status indicator (only used in the first of the parallel # processes): if idxPrc == 1: # Increment status indicator counter: varCntSts02 = varCntSts02 + 1 # Output list: lstOut = [idxPrc, aryOut, ] queOut.put(lstOut)
python
def funcNrlTcMotPred(idxPrc, varPixX, varPixY, NrlMdlChunk, varNumTP, aryBoxCar, # aryCond path, varNumNrlMdls, varNumMtDrctn, varPar, queOut): """ Function for creating neural time course models. This function should be used to create neural models if different predictors for every motion direction are included. """ # # if hd5 method is used: open file for reading # filename = 'aryBoxCar' + str(idxPrc) + '.hdf5' # hdf5_path = os.path.join(path, filename) # fileH = tables.openFile(hdf5_path, mode='r') # Output array with pRF model time courses at all modelled standard # deviations for current pixel position: aryOut = np.empty((len(NrlMdlChunk), varNumTP, varNumMtDrctn), dtype='float32') # Prepare status indicator if this is the first of the parallel processes: if idxPrc == 1: # We create a status indicator for the time consuming pRF model finding # algorithm. Number of steps of the status indicator: varStsStpSze = 20 # Number of pRF models to fit: varNumLoops = varNumNrlMdls/varPar # Vector with pRF values at which to give status feedback: vecStatus = np.linspace(0, varNumLoops, num=(varStsStpSze+1), endpoint=True) vecStatus = np.ceil(vecStatus) vecStatus = vecStatus.astype(int) # Vector with corresponding percentage values at which to give status # feedback: vecStatusPrc = np.linspace(0, 100, num=(varStsStpSze+1), endpoint=True) vecStatusPrc = np.ceil(vecStatusPrc) vecStatusPrc = vecStatusPrc.astype(int) # Counter for status indicator: varCntSts01 = 0 varCntSts02 = 0 # Loop through all Gauss parameters that are in this chunk for idx, NrlMdlTrpl in enumerate(NrlMdlChunk): # Status indicator (only used in the first of the parallel # processes): if idxPrc == 1: # Status indicator: if varCntSts02 == vecStatus[varCntSts01]: # Prepare status message: strStsMsg = ('---------Progress: ' + str(vecStatusPrc[varCntSts01]) + ' % --- ' + str(vecStatus[varCntSts01]) + ' loops out of ' + str(varNumLoops)) print(strStsMsg) # Only increment counter if the last value has not been # reached yet: if varCntSts01 < varStsStpSze: varCntSts01 = varCntSts01 + int(1) # x pos of Gauss model: NrlMdlTrpl[0] # y pos of Gauss model: NrlMdlTrpl[1] # std of Gauss model: NrlMdlTrpl[2] # index of tng crv model: NrlMdlTrpl[3] varTmpX = int(np.around(NrlMdlTrpl[0], 0)) varTmpY = int(np.around(NrlMdlTrpl[1], 0)) # Create pRF model (2D): aryGauss = funcGauss2D(varPixX, varPixY, varTmpX, varTmpY, NrlMdlTrpl[2]) # Multiply pixel-wise box car model with Gaussian pRF models: aryNrlTcTmp = np.multiply(aryBoxCar, aryGauss[:, :, None, None]) # Calculate sum across x- and y-dimensions - the 'area under the # Gaussian surface'. This is essentially an unscaled version of the # neural time course model (i.e. not yet scaled for the size of # the pRF). aryNrlTcTmp = np.sum(aryNrlTcTmp, axis=(0, 1)) # Normalise the nrl time course model to the size of the pRF. This # gives us the ratio of 'activation' of the pRF at each time point, # or, in other words, the neural time course model. aryNrlTcTmp = np.divide(aryNrlTcTmp, np.sum(aryGauss, axis=(0, 1))) # Put model time courses into the function's output array: aryOut[idx, :, :] = aryNrlTcTmp # Status indicator (only used in the first of the parallel # processes): if idxPrc == 1: # Increment status indicator counter: varCntSts02 = varCntSts02 + 1 # Output list: lstOut = [idxPrc, aryOut, ] queOut.put(lstOut)
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Function for creating neural time course models. This function should be used to create neural models if different predictors for every motion direction are included.
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train
https://github.com/MSchnei/pyprf_feature/blob/49004ede7ae1ddee07a30afe9ce3e2776750805c/pyprf_feature/simulation/pRF_functions.py#L359-L485
MSchnei/pyprf_feature
pyprf_feature/simulation/pRF_functions.py
funcFindPrf
def funcFindPrf(idxPrc, aryFuncChnk, aryPrfTc, aryMdls, queOut): """ Function for finding best pRF model for voxel time course. This function should be used if there is only one predictor. """ # Number of voxels to be fitted in this chunk: varNumVoxChnk = aryFuncChnk.shape[0] # Number of volumes: varNumVol = aryFuncChnk.shape[1] # Vectors for pRF finding results [number-of-voxels times one]: vecBstXpos = np.zeros(varNumVoxChnk) vecBstYpos = np.zeros(varNumVoxChnk) vecBstSd = np.zeros(varNumVoxChnk) # vecBstR2 = np.zeros(varNumVoxChnk) # Vector for best R-square value. For each model fit, the R-square value is # compared to this, and updated if it is lower than the best-fitting # solution so far. We initialise with an arbitrary, high value vecBstRes = np.add(np.zeros(varNumVoxChnk), 100000.0) # We reshape the voxel time courses, so that time goes down the column, # i.e. from top to bottom. aryFuncChnk = aryFuncChnk.T # Constant term for the model: vecConst = np.ones((varNumVol), dtype=np.float32) # Change type to float 32: aryFuncChnk = aryFuncChnk.astype(np.float32) aryPrfTc = aryPrfTc.astype(np.float32) # Number of pRF models to fit: varNumMdls = len(aryMdls) # Prepare status indicator if this is the first of the parallel processes: if idxPrc == 0: # We create a status indicator for the time consuming pRF model finding # algorithm. Number of steps of the status indicator: varStsStpSze = 20 # Vector with pRF values at which to give status feedback: vecStatPrf = np.linspace(0, varNumMdls, num=(varStsStpSze+1), endpoint=True) vecStatPrf = np.ceil(vecStatPrf) vecStatPrf = vecStatPrf.astype(int) # Vector with corresponding percentage values at which to give status # feedback: vecStatPrc = np.linspace(0, 100, num=(varStsStpSze+1), endpoint=True) vecStatPrc = np.ceil(vecStatPrc) vecStatPrc = vecStatPrc.astype(int) # Counter for status indicator: varCntSts01 = 0 varCntSts02 = 0 # Loop through pRF models: for idxMdls in range(0, varNumMdls): # Status indicator (only used in the first of the parallel # processes): if idxPrc == 0: # Status indicator: if varCntSts02 == vecStatPrf[varCntSts01]: # Prepare status message: strStsMsg = ('---------Progress: ' + str(vecStatPrc[varCntSts01]) + ' % --- ' + str(vecStatPrf[varCntSts01]) + ' pRF models out of ' + str(varNumMdls)) print(strStsMsg) # Only increment counter if the last value has not been # reached yet: if varCntSts01 < varStsStpSze: varCntSts01 = varCntSts01 + int(1) # Current pRF time course model: vecMdlTc = aryPrfTc[idxMdls, :].flatten() # We create a design matrix including the current pRF time # course model, and a constant term: aryDsgn = np.vstack([vecMdlTc, vecConst]).T # Calculation of the ratio of the explained variance (R square) # for the current model for all voxel time courses. # print('------------np.linalg.lstsq on pRF: ' + # str(idxX) + # 'x ' + # str(idxY) + # 'y ' + # str(idxSd) + # 'z --- START') # varTmeTmp01 = time.time() # Change type to float32: # aryDsgn = aryDsgn.astype(np.float32) # Calculate the least-squares solution for all voxels: vecTmpRes = np.linalg.lstsq(aryDsgn, aryFuncChnk)[1] # varTmeTmp02 = time.time() # varTmeTmp03 = np.around((varTmeTmp02 - varTmeTmp01), # decimals=2) # print('------------np.linalg.lstsq on pRF: ' + # str(idxX) + # 'x ' + # str(idxY) + # 'y ' + # str(idxSd) + # 'z --- DONE elapsed time: ' + # str(varTmeTmp03) + # 's') # Check whether current residuals are lower than previously # calculated ones: vecLgcTmpRes = np.less(vecTmpRes, vecBstRes) # Replace best x and y position values, and SD values. vecBstXpos[vecLgcTmpRes] = aryMdls[idxMdls][0] vecBstYpos[vecLgcTmpRes] = aryMdls[idxMdls][1] vecBstSd[vecLgcTmpRes] = aryMdls[idxMdls][2] # Replace best residual values: vecBstRes[vecLgcTmpRes] = vecTmpRes[vecLgcTmpRes] # varTmeTmp04 = time.time() # varTmeTmp05 = np.around((varTmeTmp04 - varTmeTmp02), # decimals=2) # print('------------selection of best-fitting pRF model: ' + # str(idxX) + # 'x ' + # str(idxY) + # 'y ' + # str(idxSd) + # 'z --- elapsed time: ' + # str(varTmeTmp05) + # 's') # Status indicator (only used in the first of the parallel # processes): if idxPrc == 0: # Increment status indicator counter: varCntSts02 = varCntSts02 + 1 # After finding the best fitting model for each voxel, we still have to # calculate the coefficient of determination (R-squared) for each voxel. We # start by calculating the total sum of squares (i.e. the deviation of the # data from the mean). The mean of each time course: vecFuncMean = np.mean(aryFuncChnk, axis=0) # Deviation from the mean for each datapoint: vecFuncDev = np.subtract(aryFuncChnk, vecFuncMean[None, :]) # Sum of squares: vecSsTot = np.sum(np.power(vecFuncDev, 2.0), axis=0) # Coefficient of determination: vecBstR2 = np.subtract(1.0, np.divide(vecBstRes, vecSsTot)) # Output list: lstOut = [idxPrc, vecBstXpos, vecBstYpos, vecBstSd, vecBstR2] queOut.put(lstOut)
python
def funcFindPrf(idxPrc, aryFuncChnk, aryPrfTc, aryMdls, queOut): """ Function for finding best pRF model for voxel time course. This function should be used if there is only one predictor. """ # Number of voxels to be fitted in this chunk: varNumVoxChnk = aryFuncChnk.shape[0] # Number of volumes: varNumVol = aryFuncChnk.shape[1] # Vectors for pRF finding results [number-of-voxels times one]: vecBstXpos = np.zeros(varNumVoxChnk) vecBstYpos = np.zeros(varNumVoxChnk) vecBstSd = np.zeros(varNumVoxChnk) # vecBstR2 = np.zeros(varNumVoxChnk) # Vector for best R-square value. For each model fit, the R-square value is # compared to this, and updated if it is lower than the best-fitting # solution so far. We initialise with an arbitrary, high value vecBstRes = np.add(np.zeros(varNumVoxChnk), 100000.0) # We reshape the voxel time courses, so that time goes down the column, # i.e. from top to bottom. aryFuncChnk = aryFuncChnk.T # Constant term for the model: vecConst = np.ones((varNumVol), dtype=np.float32) # Change type to float 32: aryFuncChnk = aryFuncChnk.astype(np.float32) aryPrfTc = aryPrfTc.astype(np.float32) # Number of pRF models to fit: varNumMdls = len(aryMdls) # Prepare status indicator if this is the first of the parallel processes: if idxPrc == 0: # We create a status indicator for the time consuming pRF model finding # algorithm. Number of steps of the status indicator: varStsStpSze = 20 # Vector with pRF values at which to give status feedback: vecStatPrf = np.linspace(0, varNumMdls, num=(varStsStpSze+1), endpoint=True) vecStatPrf = np.ceil(vecStatPrf) vecStatPrf = vecStatPrf.astype(int) # Vector with corresponding percentage values at which to give status # feedback: vecStatPrc = np.linspace(0, 100, num=(varStsStpSze+1), endpoint=True) vecStatPrc = np.ceil(vecStatPrc) vecStatPrc = vecStatPrc.astype(int) # Counter for status indicator: varCntSts01 = 0 varCntSts02 = 0 # Loop through pRF models: for idxMdls in range(0, varNumMdls): # Status indicator (only used in the first of the parallel # processes): if idxPrc == 0: # Status indicator: if varCntSts02 == vecStatPrf[varCntSts01]: # Prepare status message: strStsMsg = ('---------Progress: ' + str(vecStatPrc[varCntSts01]) + ' % --- ' + str(vecStatPrf[varCntSts01]) + ' pRF models out of ' + str(varNumMdls)) print(strStsMsg) # Only increment counter if the last value has not been # reached yet: if varCntSts01 < varStsStpSze: varCntSts01 = varCntSts01 + int(1) # Current pRF time course model: vecMdlTc = aryPrfTc[idxMdls, :].flatten() # We create a design matrix including the current pRF time # course model, and a constant term: aryDsgn = np.vstack([vecMdlTc, vecConst]).T # Calculation of the ratio of the explained variance (R square) # for the current model for all voxel time courses. # print('------------np.linalg.lstsq on pRF: ' + # str(idxX) + # 'x ' + # str(idxY) + # 'y ' + # str(idxSd) + # 'z --- START') # varTmeTmp01 = time.time() # Change type to float32: # aryDsgn = aryDsgn.astype(np.float32) # Calculate the least-squares solution for all voxels: vecTmpRes = np.linalg.lstsq(aryDsgn, aryFuncChnk)[1] # varTmeTmp02 = time.time() # varTmeTmp03 = np.around((varTmeTmp02 - varTmeTmp01), # decimals=2) # print('------------np.linalg.lstsq on pRF: ' + # str(idxX) + # 'x ' + # str(idxY) + # 'y ' + # str(idxSd) + # 'z --- DONE elapsed time: ' + # str(varTmeTmp03) + # 's') # Check whether current residuals are lower than previously # calculated ones: vecLgcTmpRes = np.less(vecTmpRes, vecBstRes) # Replace best x and y position values, and SD values. vecBstXpos[vecLgcTmpRes] = aryMdls[idxMdls][0] vecBstYpos[vecLgcTmpRes] = aryMdls[idxMdls][1] vecBstSd[vecLgcTmpRes] = aryMdls[idxMdls][2] # Replace best residual values: vecBstRes[vecLgcTmpRes] = vecTmpRes[vecLgcTmpRes] # varTmeTmp04 = time.time() # varTmeTmp05 = np.around((varTmeTmp04 - varTmeTmp02), # decimals=2) # print('------------selection of best-fitting pRF model: ' + # str(idxX) + # 'x ' + # str(idxY) + # 'y ' + # str(idxSd) + # 'z --- elapsed time: ' + # str(varTmeTmp05) + # 's') # Status indicator (only used in the first of the parallel # processes): if idxPrc == 0: # Increment status indicator counter: varCntSts02 = varCntSts02 + 1 # After finding the best fitting model for each voxel, we still have to # calculate the coefficient of determination (R-squared) for each voxel. We # start by calculating the total sum of squares (i.e. the deviation of the # data from the mean). The mean of each time course: vecFuncMean = np.mean(aryFuncChnk, axis=0) # Deviation from the mean for each datapoint: vecFuncDev = np.subtract(aryFuncChnk, vecFuncMean[None, :]) # Sum of squares: vecSsTot = np.sum(np.power(vecFuncDev, 2.0), axis=0) # Coefficient of determination: vecBstR2 = np.subtract(1.0, np.divide(vecBstRes, vecSsTot)) # Output list: lstOut = [idxPrc, vecBstXpos, vecBstYpos, vecBstSd, vecBstR2] queOut.put(lstOut)
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We", "# start by calculating the total sum of squares (i.e. the deviation of the", "# data from the mean). The mean of each time course:", "vecFuncMean", "=", "np", ".", "mean", "(", "aryFuncChnk", ",", "axis", "=", "0", ")", "# Deviation from the mean for each datapoint:", "vecFuncDev", "=", "np", ".", "subtract", "(", "aryFuncChnk", ",", "vecFuncMean", "[", "None", ",", ":", "]", ")", "# Sum of squares:", "vecSsTot", "=", "np", ".", "sum", "(", "np", ".", "power", "(", "vecFuncDev", ",", "2.0", ")", ",", "axis", "=", "0", ")", "# Coefficient of determination:", "vecBstR2", "=", "np", ".", "subtract", "(", "1.0", ",", "np", ".", "divide", "(", "vecBstRes", ",", "vecSsTot", ")", ")", "# Output list:", "lstOut", "=", "[", "idxPrc", ",", "vecBstXpos", ",", "vecBstYpos", ",", "vecBstSd", ",", "vecBstR2", "]", "queOut", ".", "put", "(", "lstOut", ")" ]
Function for finding best pRF model for voxel time course. This function should be used if there is only one predictor.
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train
https://github.com/MSchnei/pyprf_feature/blob/49004ede7ae1ddee07a30afe9ce3e2776750805c/pyprf_feature/simulation/pRF_functions.py#L627-L817
MSchnei/pyprf_feature
pyprf_feature/simulation/pRF_functions.py
funcFindPrfMltpPrdXVal
def funcFindPrfMltpPrdXVal(idxPrc, aryFuncChnkTrn, aryFuncChnkTst, aryPrfMdlsTrnConv, aryPrfMdlsTstConv, aryMdls, queOut): """ Function for finding best pRF model for voxel time course. This function should be used if there are several predictors. """ # Number of voxels to be fitted in this chunk: varNumVoxChnk = aryFuncChnkTrn.shape[0] # Number of volumes: varNumVolTrn = aryFuncChnkTrn.shape[2] varNumVolTst = aryFuncChnkTst.shape[2] # get number of cross validations varNumXval = aryPrfMdlsTrnConv.shape[2] # Vectors for pRF finding results [number-of-voxels times one]: vecBstXpos = np.zeros(varNumVoxChnk) vecBstYpos = np.zeros(varNumVoxChnk) vecBstSd = np.zeros(varNumVoxChnk) # vecBstR2 = np.zeros(varNumVoxChnk) # Vector for temporary residuals values that are obtained during # the different loops of cross validation vecTmpResXVal = np.empty((varNumVoxChnk, varNumXval), dtype='float32') # Vector for best residual values. vecBstRes = np.add(np.zeros(varNumVoxChnk), 100000.0) # Constant term for the model: vecConstTrn = np.ones((varNumVolTrn), dtype=np.float32) vecConstTst = np.ones((varNumVolTst), dtype=np.float32) # Change type to float 32: aryPrfMdlsTrnConv = aryPrfMdlsTrnConv.astype(np.float32) aryPrfMdlsTstConv = aryPrfMdlsTstConv.astype(np.float32) # Number of pRF models to fit: varNumMdls = len(aryMdls) # Prepare status indicator if this is the first of the parallel processes: if idxPrc == 0: # We create a status indicator for the time consuming pRF model finding # algorithm. Number of steps of the status indicator: varStsStpSze = 20 # Vector with pRF values at which to give status feedback: vecStatPrf = np.linspace(0, varNumMdls, num=(varStsStpSze+1), endpoint=True) vecStatPrf = np.ceil(vecStatPrf) vecStatPrf = vecStatPrf.astype(int) # Vector with corresponding percentage values at which to give status # feedback: vecStatPrc = np.linspace(0, 100, num=(varStsStpSze+1), endpoint=True) vecStatPrc = np.ceil(vecStatPrc) vecStatPrc = vecStatPrc.astype(int) # Counter for status indicator: varCntSts01 = 0 varCntSts02 = 0 # Loop through pRF models: for idxMdls in range(0, varNumMdls): # Status indicator (only used in the first of the parallel # processes): if idxPrc == 0: # Status indicator: if varCntSts02 == vecStatPrf[varCntSts01]: # Prepare status message: strStsMsg = ('---------Progress: ' + str(vecStatPrc[varCntSts01]) + ' % --- ' + str(vecStatPrf[varCntSts01]) + ' pRF models out of ' + str(varNumMdls)) print(strStsMsg) # Only increment counter if the last value has not been # reached yet: if varCntSts01 < varStsStpSze: varCntSts01 = varCntSts01 + int(1) # Loop through different cross validations for idxXval in range(0, varNumXval): # Current pRF time course model: vecMdlTrn = aryPrfMdlsTrnConv[idxMdls, :, idxXval, :] vecMdlTst = aryPrfMdlsTstConv[idxMdls, :, idxXval, :] # We create a design matrix including the current pRF time # course model, and a constant term: aryDsgnTrn = np.vstack([vecMdlTrn, vecConstTrn]).T aryDsgnTst = np.vstack([vecMdlTst, vecConstTst]).T # Calculate the least-squares solution for all voxels # and get parameter estimates from the training fit aryTmpPrmEst = np.linalg.lstsq(aryDsgnTrn, aryFuncChnkTrn[:, idxXval, :].T)[0] # calculate predicted model fit based on training data aryTmpMdlTc = np.dot(aryDsgnTst, aryTmpPrmEst) # calculate residual sum of squares between test data and # predicted model fit based on training data vecTmpResXVal[:, idxXval] = np.sum( (np.subtract(aryFuncChnkTst[:, idxXval, :].T, aryTmpMdlTc))**2, axis=0) vecTmpRes = np.mean(vecTmpResXVal, axis=1) # Check whether current residuals are lower than previously # calculated ones: vecLgcTmpRes = np.less(vecTmpRes, vecBstRes) # Replace best x and y position values, and SD values. vecBstXpos[vecLgcTmpRes] = aryMdls[idxMdls][0] vecBstYpos[vecLgcTmpRes] = aryMdls[idxMdls][1] vecBstSd[vecLgcTmpRes] = aryMdls[idxMdls][2] # Replace best residual values: vecBstRes[vecLgcTmpRes] = vecTmpRes[vecLgcTmpRes] # Status indicator (only used in the first of the parallel # processes): if idxPrc == 0: # Increment status indicator counter: varCntSts02 = varCntSts02 + 1 # Output list: lstOut = [idxPrc, vecBstXpos, vecBstYpos, vecBstSd, ] queOut.put(lstOut)
python
def funcFindPrfMltpPrdXVal(idxPrc, aryFuncChnkTrn, aryFuncChnkTst, aryPrfMdlsTrnConv, aryPrfMdlsTstConv, aryMdls, queOut): """ Function for finding best pRF model for voxel time course. This function should be used if there are several predictors. """ # Number of voxels to be fitted in this chunk: varNumVoxChnk = aryFuncChnkTrn.shape[0] # Number of volumes: varNumVolTrn = aryFuncChnkTrn.shape[2] varNumVolTst = aryFuncChnkTst.shape[2] # get number of cross validations varNumXval = aryPrfMdlsTrnConv.shape[2] # Vectors for pRF finding results [number-of-voxels times one]: vecBstXpos = np.zeros(varNumVoxChnk) vecBstYpos = np.zeros(varNumVoxChnk) vecBstSd = np.zeros(varNumVoxChnk) # vecBstR2 = np.zeros(varNumVoxChnk) # Vector for temporary residuals values that are obtained during # the different loops of cross validation vecTmpResXVal = np.empty((varNumVoxChnk, varNumXval), dtype='float32') # Vector for best residual values. vecBstRes = np.add(np.zeros(varNumVoxChnk), 100000.0) # Constant term for the model: vecConstTrn = np.ones((varNumVolTrn), dtype=np.float32) vecConstTst = np.ones((varNumVolTst), dtype=np.float32) # Change type to float 32: aryPrfMdlsTrnConv = aryPrfMdlsTrnConv.astype(np.float32) aryPrfMdlsTstConv = aryPrfMdlsTstConv.astype(np.float32) # Number of pRF models to fit: varNumMdls = len(aryMdls) # Prepare status indicator if this is the first of the parallel processes: if idxPrc == 0: # We create a status indicator for the time consuming pRF model finding # algorithm. Number of steps of the status indicator: varStsStpSze = 20 # Vector with pRF values at which to give status feedback: vecStatPrf = np.linspace(0, varNumMdls, num=(varStsStpSze+1), endpoint=True) vecStatPrf = np.ceil(vecStatPrf) vecStatPrf = vecStatPrf.astype(int) # Vector with corresponding percentage values at which to give status # feedback: vecStatPrc = np.linspace(0, 100, num=(varStsStpSze+1), endpoint=True) vecStatPrc = np.ceil(vecStatPrc) vecStatPrc = vecStatPrc.astype(int) # Counter for status indicator: varCntSts01 = 0 varCntSts02 = 0 # Loop through pRF models: for idxMdls in range(0, varNumMdls): # Status indicator (only used in the first of the parallel # processes): if idxPrc == 0: # Status indicator: if varCntSts02 == vecStatPrf[varCntSts01]: # Prepare status message: strStsMsg = ('---------Progress: ' + str(vecStatPrc[varCntSts01]) + ' % --- ' + str(vecStatPrf[varCntSts01]) + ' pRF models out of ' + str(varNumMdls)) print(strStsMsg) # Only increment counter if the last value has not been # reached yet: if varCntSts01 < varStsStpSze: varCntSts01 = varCntSts01 + int(1) # Loop through different cross validations for idxXval in range(0, varNumXval): # Current pRF time course model: vecMdlTrn = aryPrfMdlsTrnConv[idxMdls, :, idxXval, :] vecMdlTst = aryPrfMdlsTstConv[idxMdls, :, idxXval, :] # We create a design matrix including the current pRF time # course model, and a constant term: aryDsgnTrn = np.vstack([vecMdlTrn, vecConstTrn]).T aryDsgnTst = np.vstack([vecMdlTst, vecConstTst]).T # Calculate the least-squares solution for all voxels # and get parameter estimates from the training fit aryTmpPrmEst = np.linalg.lstsq(aryDsgnTrn, aryFuncChnkTrn[:, idxXval, :].T)[0] # calculate predicted model fit based on training data aryTmpMdlTc = np.dot(aryDsgnTst, aryTmpPrmEst) # calculate residual sum of squares between test data and # predicted model fit based on training data vecTmpResXVal[:, idxXval] = np.sum( (np.subtract(aryFuncChnkTst[:, idxXval, :].T, aryTmpMdlTc))**2, axis=0) vecTmpRes = np.mean(vecTmpResXVal, axis=1) # Check whether current residuals are lower than previously # calculated ones: vecLgcTmpRes = np.less(vecTmpRes, vecBstRes) # Replace best x and y position values, and SD values. vecBstXpos[vecLgcTmpRes] = aryMdls[idxMdls][0] vecBstYpos[vecLgcTmpRes] = aryMdls[idxMdls][1] vecBstSd[vecLgcTmpRes] = aryMdls[idxMdls][2] # Replace best residual values: vecBstRes[vecLgcTmpRes] = vecTmpRes[vecLgcTmpRes] # Status indicator (only used in the first of the parallel # processes): if idxPrc == 0: # Increment status indicator counter: varCntSts02 = varCntSts02 + 1 # Output list: lstOut = [idxPrc, vecBstXpos, vecBstYpos, vecBstSd, ] queOut.put(lstOut)
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Function for finding best pRF model for voxel time course. This function should be used if there are several predictors.
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train
https://github.com/MSchnei/pyprf_feature/blob/49004ede7ae1ddee07a30afe9ce3e2776750805c/pyprf_feature/simulation/pRF_functions.py#L1015-L1168
pmacosta/pcsv
pcsv/concatenate.py
concatenate
def concatenate( fname1, fname2, dfilter1=None, dfilter2=None, has_header1=True, has_header2=True, frow1=0, frow2=0, ofname=None, ocols=None, ): r""" Concatenate two comma-separated values file. Data rows from the second file are appended at the end of the data rows from the first file :param fname1: Name of the first comma-separated values file, the file whose data appears first in the output file :type fname1: FileNameExists_ :param fname2: Name of the second comma-separated values file, the file whose data appears last in the output file :type fname2: FileNameExists_ :param dfilter1: Row and/or column filter for the first file. If None no data filtering is done on the file :type dfilter1: :ref:`CsvDataFilter` or None :param dfilter2: Row and/or column filter for the second file. If None no data filtering is done on the file :type dfilter2: :ref:`CsvDataFilter` or None :param has_header1: Flag that indicates whether the first comma-separated values file has column headers in its first line (True) or not (False) :type has_header1: boolean :param has_header2: Flag that indicates whether the second comma-separated values file has column headers in its first line (True) or not (False) :type has_header2: boolean :param frow1: First comma-separated values file first data row (starting from 1). If 0 the row where data starts is auto-detected as the first row that has a number (integer of float) in at least one of its columns :type frow1: NonNegativeInteger_ :param frow2: Second comma-separated values file first data row (starting from 1). If 0 the row where data starts is auto-detected as the first row that has a number (integer of float) in at least one of its columns :type frow2: NonNegativeInteger_ :param ofname: Name of the output comma-separated values file, the file that will contain the data from the first and second files. If None the first file is replaced "in place" :type ofname: FileName_ or None :param ocols: Column names of the output comma-separated values file. If None the column names in the first file are used if **has_header1** is True or the column names in the second files are used if **has_header1** is False and **has_header2** is True, otherwise no header is used :type ocols: list or None .. [[[cog cog.out(exobj.get_sphinx_autodoc(raised=True)) ]]] .. Auto-generated exceptions documentation for .. pcsv.concatenate.concatenate :raises: * OSError (File *[fname]* could not be found) * RuntimeError (Argument \`dfilter1\` is not valid) * RuntimeError (Argument \`dfilter2\` is not valid) * RuntimeError (Argument \`fname1\` is not valid) * RuntimeError (Argument \`fname2\` is not valid) * RuntimeError (Argument \`frow1\` is not valid) * RuntimeError (Argument \`frow2\` is not valid) * RuntimeError (Argument \`ocols\` is not valid) * RuntimeError (Argument \`ofname\` is not valid) * RuntimeError (Column headers are not unique in file *[fname]*) * RuntimeError (File *[fname]* has no valid data) * RuntimeError (File *[fname]* is empty) * RuntimeError (Files have different number of columns) * RuntimeError (Invalid column specification) * RuntimeError (Number of columns in data files and output columns are different) * ValueError (Column *[column_identifier]* not found) .. [[[end]]] """ # pylint: disable=R0913,R0914 iro = pexdoc.exh.addex(RuntimeError, "Files have different number of columns") iom = pexdoc.exh.addex( RuntimeError, "Number of columns in data files and output columns are different" ) # Read and validate file 1 obj1 = CsvFile(fname=fname1, dfilter=dfilter1, has_header=has_header1, frow=frow1) # Read and validate file 2 obj2 = CsvFile(fname=fname2, dfilter=dfilter2, has_header=has_header2, frow=frow2) # Assign output data structure ofname = fname1 if ofname is None else ofname # Create new header if (ocols is None) and has_header1: ocols = [obj1.header()] if obj1.cfilter is None else [obj1.cfilter] elif (ocols is None) and has_header2: ocols = [obj2.header()] if obj2.cfilter is None else [obj2.cfilter] elif ocols is None: ocols = [] else: iom((obj1.cfilter is not None) and (len(obj1.cfilter) != len(ocols))) ocols = [ocols] # Miscellaneous data validation iro(_C(obj1.cfilter, obj2.cfilter) and (len(obj1.cfilter) != len(obj2.cfilter))) # Write final output data = ocols + obj1.data(filtered=True) + obj2.data(filtered=True) write(fname=ofname, data=data, append=False)
python
def concatenate( fname1, fname2, dfilter1=None, dfilter2=None, has_header1=True, has_header2=True, frow1=0, frow2=0, ofname=None, ocols=None, ): r""" Concatenate two comma-separated values file. Data rows from the second file are appended at the end of the data rows from the first file :param fname1: Name of the first comma-separated values file, the file whose data appears first in the output file :type fname1: FileNameExists_ :param fname2: Name of the second comma-separated values file, the file whose data appears last in the output file :type fname2: FileNameExists_ :param dfilter1: Row and/or column filter for the first file. If None no data filtering is done on the file :type dfilter1: :ref:`CsvDataFilter` or None :param dfilter2: Row and/or column filter for the second file. If None no data filtering is done on the file :type dfilter2: :ref:`CsvDataFilter` or None :param has_header1: Flag that indicates whether the first comma-separated values file has column headers in its first line (True) or not (False) :type has_header1: boolean :param has_header2: Flag that indicates whether the second comma-separated values file has column headers in its first line (True) or not (False) :type has_header2: boolean :param frow1: First comma-separated values file first data row (starting from 1). If 0 the row where data starts is auto-detected as the first row that has a number (integer of float) in at least one of its columns :type frow1: NonNegativeInteger_ :param frow2: Second comma-separated values file first data row (starting from 1). If 0 the row where data starts is auto-detected as the first row that has a number (integer of float) in at least one of its columns :type frow2: NonNegativeInteger_ :param ofname: Name of the output comma-separated values file, the file that will contain the data from the first and second files. If None the first file is replaced "in place" :type ofname: FileName_ or None :param ocols: Column names of the output comma-separated values file. If None the column names in the first file are used if **has_header1** is True or the column names in the second files are used if **has_header1** is False and **has_header2** is True, otherwise no header is used :type ocols: list or None .. [[[cog cog.out(exobj.get_sphinx_autodoc(raised=True)) ]]] .. Auto-generated exceptions documentation for .. pcsv.concatenate.concatenate :raises: * OSError (File *[fname]* could not be found) * RuntimeError (Argument \`dfilter1\` is not valid) * RuntimeError (Argument \`dfilter2\` is not valid) * RuntimeError (Argument \`fname1\` is not valid) * RuntimeError (Argument \`fname2\` is not valid) * RuntimeError (Argument \`frow1\` is not valid) * RuntimeError (Argument \`frow2\` is not valid) * RuntimeError (Argument \`ocols\` is not valid) * RuntimeError (Argument \`ofname\` is not valid) * RuntimeError (Column headers are not unique in file *[fname]*) * RuntimeError (File *[fname]* has no valid data) * RuntimeError (File *[fname]* is empty) * RuntimeError (Files have different number of columns) * RuntimeError (Invalid column specification) * RuntimeError (Number of columns in data files and output columns are different) * ValueError (Column *[column_identifier]* not found) .. [[[end]]] """ # pylint: disable=R0913,R0914 iro = pexdoc.exh.addex(RuntimeError, "Files have different number of columns") iom = pexdoc.exh.addex( RuntimeError, "Number of columns in data files and output columns are different" ) # Read and validate file 1 obj1 = CsvFile(fname=fname1, dfilter=dfilter1, has_header=has_header1, frow=frow1) # Read and validate file 2 obj2 = CsvFile(fname=fname2, dfilter=dfilter2, has_header=has_header2, frow=frow2) # Assign output data structure ofname = fname1 if ofname is None else ofname # Create new header if (ocols is None) and has_header1: ocols = [obj1.header()] if obj1.cfilter is None else [obj1.cfilter] elif (ocols is None) and has_header2: ocols = [obj2.header()] if obj2.cfilter is None else [obj2.cfilter] elif ocols is None: ocols = [] else: iom((obj1.cfilter is not None) and (len(obj1.cfilter) != len(ocols))) ocols = [ocols] # Miscellaneous data validation iro(_C(obj1.cfilter, obj2.cfilter) and (len(obj1.cfilter) != len(obj2.cfilter))) # Write final output data = ocols + obj1.data(filtered=True) + obj2.data(filtered=True) write(fname=ofname, data=data, append=False)
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r""" Concatenate two comma-separated values file. Data rows from the second file are appended at the end of the data rows from the first file :param fname1: Name of the first comma-separated values file, the file whose data appears first in the output file :type fname1: FileNameExists_ :param fname2: Name of the second comma-separated values file, the file whose data appears last in the output file :type fname2: FileNameExists_ :param dfilter1: Row and/or column filter for the first file. If None no data filtering is done on the file :type dfilter1: :ref:`CsvDataFilter` or None :param dfilter2: Row and/or column filter for the second file. If None no data filtering is done on the file :type dfilter2: :ref:`CsvDataFilter` or None :param has_header1: Flag that indicates whether the first comma-separated values file has column headers in its first line (True) or not (False) :type has_header1: boolean :param has_header2: Flag that indicates whether the second comma-separated values file has column headers in its first line (True) or not (False) :type has_header2: boolean :param frow1: First comma-separated values file first data row (starting from 1). If 0 the row where data starts is auto-detected as the first row that has a number (integer of float) in at least one of its columns :type frow1: NonNegativeInteger_ :param frow2: Second comma-separated values file first data row (starting from 1). If 0 the row where data starts is auto-detected as the first row that has a number (integer of float) in at least one of its columns :type frow2: NonNegativeInteger_ :param ofname: Name of the output comma-separated values file, the file that will contain the data from the first and second files. If None the first file is replaced "in place" :type ofname: FileName_ or None :param ocols: Column names of the output comma-separated values file. If None the column names in the first file are used if **has_header1** is True or the column names in the second files are used if **has_header1** is False and **has_header2** is True, otherwise no header is used :type ocols: list or None .. [[[cog cog.out(exobj.get_sphinx_autodoc(raised=True)) ]]] .. Auto-generated exceptions documentation for .. pcsv.concatenate.concatenate :raises: * OSError (File *[fname]* could not be found) * RuntimeError (Argument \`dfilter1\` is not valid) * RuntimeError (Argument \`dfilter2\` is not valid) * RuntimeError (Argument \`fname1\` is not valid) * RuntimeError (Argument \`fname2\` is not valid) * RuntimeError (Argument \`frow1\` is not valid) * RuntimeError (Argument \`frow2\` is not valid) * RuntimeError (Argument \`ocols\` is not valid) * RuntimeError (Argument \`ofname\` is not valid) * RuntimeError (Column headers are not unique in file *[fname]*) * RuntimeError (File *[fname]* has no valid data) * RuntimeError (File *[fname]* is empty) * RuntimeError (Files have different number of columns) * RuntimeError (Invalid column specification) * RuntimeError (Number of columns in data files and output columns are different) * ValueError (Column *[column_identifier]* not found) .. [[[end]]]
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train
https://github.com/pmacosta/pcsv/blob/cd1588c19b0cd58c38bc672e396db940f88ffbd7/pcsv/concatenate.py#L46-L179
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/utils/images.py
move_images_to_cache
def move_images_to_cache(source, destination): """ Handles the movement of images to the cache. Must be helpful if it finds that the folder for this article already exists. """ if os.path.isdir(destination): log.debug('Cached images for this article already exist') return else: log.debug('Cache location: {0}'.format(destination)) try: shutil.copytree(source, destination) except: log.exception('Images could not be moved to cache') else: log.info('Moved images to cache'.format(destination))
python
def move_images_to_cache(source, destination): """ Handles the movement of images to the cache. Must be helpful if it finds that the folder for this article already exists. """ if os.path.isdir(destination): log.debug('Cached images for this article already exist') return else: log.debug('Cache location: {0}'.format(destination)) try: shutil.copytree(source, destination) except: log.exception('Images could not be moved to cache') else: log.info('Moved images to cache'.format(destination))
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Handles the movement of images to the cache. Must be helpful if it finds that the folder for this article already exists.
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/utils/images.py#L19-L34
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/utils/images.py
explicit_images
def explicit_images(images, image_destination, rootname, config): """ The method used to handle an explicitly defined image directory by the user as a parsed argument. """ log.info('Explicit image directory specified: {0}'.format(images)) if '*' in images: images = images.replace('*', rootname) log.debug('Wildcard expansion for image directory: {0}'.format(images)) try: shutil.copytree(images, image_destination) except: #The following is basically a recipe for log.exception() but with a #CRITICAL level if the execution should be killed immediately #log.critical('Unable to copy from indicated directory', exc_info=True) log.exception('Unable to copy from indicated directory') return False else: return True
python
def explicit_images(images, image_destination, rootname, config): """ The method used to handle an explicitly defined image directory by the user as a parsed argument. """ log.info('Explicit image directory specified: {0}'.format(images)) if '*' in images: images = images.replace('*', rootname) log.debug('Wildcard expansion for image directory: {0}'.format(images)) try: shutil.copytree(images, image_destination) except: #The following is basically a recipe for log.exception() but with a #CRITICAL level if the execution should be killed immediately #log.critical('Unable to copy from indicated directory', exc_info=True) log.exception('Unable to copy from indicated directory') return False else: return True
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/utils/images.py#L37-L55
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/utils/images.py
input_relative_images
def input_relative_images(input_path, image_destination, rootname, config): """ The method used to handle Input-Relative image inclusion. """ log.debug('Looking for input relative images') input_dirname = os.path.dirname(input_path) for path in config.input_relative_images: if '*' in path: path = path.replace('*', rootname) log.debug('Wildcard expansion for image directory: {0}'.format(path)) images = os.path.normpath(os.path.join(input_dirname, path)) if os.path.isdir(images): log.info('Input-Relative image directory found: {0}'.format(images)) shutil.copytree(images, image_destination) return True return False
python
def input_relative_images(input_path, image_destination, rootname, config): """ The method used to handle Input-Relative image inclusion. """ log.debug('Looking for input relative images') input_dirname = os.path.dirname(input_path) for path in config.input_relative_images: if '*' in path: path = path.replace('*', rootname) log.debug('Wildcard expansion for image directory: {0}'.format(path)) images = os.path.normpath(os.path.join(input_dirname, path)) if os.path.isdir(images): log.info('Input-Relative image directory found: {0}'.format(images)) shutil.copytree(images, image_destination) return True return False
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/utils/images.py#L58-L73
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/utils/images.py
image_cache
def image_cache(article_cache, img_dir): """ The method to be used by get_images() for copying images out of the cache. """ log.debug('Looking for image directory in the cache') if os.path.isdir(article_cache): log.info('Cached image directory found: {0}'.format(article_cache)) shutil.copytree(article_cache, img_dir) return True return False
python
def image_cache(article_cache, img_dir): """ The method to be used by get_images() for copying images out of the cache. """ log.debug('Looking for image directory in the cache') if os.path.isdir(article_cache): log.info('Cached image directory found: {0}'.format(article_cache)) shutil.copytree(article_cache, img_dir) return True return False
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The method to be used by get_images() for copying images out of the cache.
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/utils/images.py#L76-L85
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/utils/images.py
get_images
def get_images(output_directory, explicit, input_path, config, parsed_article): """ Main logic controller for the placement of images into the output directory Controlling logic for placement of the appropriate imager files into the EPUB directory. This function interacts with interface arguments as well as the local installation config.py file. These may change behavior of this function in terms of how it looks for images relative to the input, where it finds explicit images, whether it will attempt to download images, and whether successfully downloaded images will be stored in the cache. Parameters ---------- output_directory : str The directory path where the EPUB is being constructed/output explicit : str A directory path to a user specified directory of images. Allows * wildcard expansion. input_path : str The absolute path to the input XML file. config : config module The imported configuration module parsed_article : openaccess_epub.article.Article object The Article instance for the article being converted to EPUB """ #Split the DOI journal_doi, article_doi = parsed_article.doi.split('/') log.debug('journal-doi : {0}'.format(journal_doi)) log.debug('article-doi : {0}'.format(article_doi)) #Get the rootname for wildcard expansion rootname = utils.file_root_name(input_path) #Specify where to place the images in the output img_dir = os.path.join(output_directory, 'EPUB', 'images-{0}'.format(article_doi)) log.info('Using {0} as image directory target'.format(img_dir)) #Construct path to cache for article article_cache = os.path.join(config.image_cache, journal_doi, article_doi) #Use manual image directory, explicit images if explicit: success = explicit_images(explicit, img_dir, rootname, config) if success and config.use_image_cache: move_images_to_cache(img_dir, article_cache) #Explicit images prevents all other image methods return success #Input-Relative import, looks for any one of the listed options if config.use_input_relative_images: #Prevents other image methods only if successful if input_relative_images(input_path, img_dir, rootname, config): if config.use_image_cache: move_images_to_cache(img_dir, article_cache) return True #Use cache for article if it exists if config.use_image_cache: #Prevents other image methods only if successful if image_cache(article_cache, img_dir): return True #Download images from Internet if config.use_image_fetching: os.mkdir(img_dir) if journal_doi == '10.3389': fetch_frontiers_images(article_doi, img_dir) if config.use_image_cache: move_images_to_cache(img_dir, article_cache) return True elif journal_doi == '10.1371': success = fetch_plos_images(article_doi, img_dir, parsed_article) if success and config.use_image_cache: move_images_to_cache(img_dir, article_cache) return success else: log.error('Fetching images for this publisher is not supported!') return False return False
python
def get_images(output_directory, explicit, input_path, config, parsed_article): """ Main logic controller for the placement of images into the output directory Controlling logic for placement of the appropriate imager files into the EPUB directory. This function interacts with interface arguments as well as the local installation config.py file. These may change behavior of this function in terms of how it looks for images relative to the input, where it finds explicit images, whether it will attempt to download images, and whether successfully downloaded images will be stored in the cache. Parameters ---------- output_directory : str The directory path where the EPUB is being constructed/output explicit : str A directory path to a user specified directory of images. Allows * wildcard expansion. input_path : str The absolute path to the input XML file. config : config module The imported configuration module parsed_article : openaccess_epub.article.Article object The Article instance for the article being converted to EPUB """ #Split the DOI journal_doi, article_doi = parsed_article.doi.split('/') log.debug('journal-doi : {0}'.format(journal_doi)) log.debug('article-doi : {0}'.format(article_doi)) #Get the rootname for wildcard expansion rootname = utils.file_root_name(input_path) #Specify where to place the images in the output img_dir = os.path.join(output_directory, 'EPUB', 'images-{0}'.format(article_doi)) log.info('Using {0} as image directory target'.format(img_dir)) #Construct path to cache for article article_cache = os.path.join(config.image_cache, journal_doi, article_doi) #Use manual image directory, explicit images if explicit: success = explicit_images(explicit, img_dir, rootname, config) if success and config.use_image_cache: move_images_to_cache(img_dir, article_cache) #Explicit images prevents all other image methods return success #Input-Relative import, looks for any one of the listed options if config.use_input_relative_images: #Prevents other image methods only if successful if input_relative_images(input_path, img_dir, rootname, config): if config.use_image_cache: move_images_to_cache(img_dir, article_cache) return True #Use cache for article if it exists if config.use_image_cache: #Prevents other image methods only if successful if image_cache(article_cache, img_dir): return True #Download images from Internet if config.use_image_fetching: os.mkdir(img_dir) if journal_doi == '10.3389': fetch_frontiers_images(article_doi, img_dir) if config.use_image_cache: move_images_to_cache(img_dir, article_cache) return True elif journal_doi == '10.1371': success = fetch_plos_images(article_doi, img_dir, parsed_article) if success and config.use_image_cache: move_images_to_cache(img_dir, article_cache) return success else: log.error('Fetching images for this publisher is not supported!') return False return False
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Main logic controller for the placement of images into the output directory Controlling logic for placement of the appropriate imager files into the EPUB directory. This function interacts with interface arguments as well as the local installation config.py file. These may change behavior of this function in terms of how it looks for images relative to the input, where it finds explicit images, whether it will attempt to download images, and whether successfully downloaded images will be stored in the cache. Parameters ---------- output_directory : str The directory path where the EPUB is being constructed/output explicit : str A directory path to a user specified directory of images. Allows * wildcard expansion. input_path : str The absolute path to the input XML file. config : config module The imported configuration module parsed_article : openaccess_epub.article.Article object The Article instance for the article being converted to EPUB
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/utils/images.py#L88-L168
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/utils/images.py
make_image_cache
def make_image_cache(img_cache): """ Initiates the image cache if it does not exist """ log.info('Initiating the image cache at {0}'.format(img_cache)) if not os.path.isdir(img_cache): utils.mkdir_p(img_cache) utils.mkdir_p(os.path.join(img_cache, '10.1371')) utils.mkdir_p(os.path.join(img_cache, '10.3389'))
python
def make_image_cache(img_cache): """ Initiates the image cache if it does not exist """ log.info('Initiating the image cache at {0}'.format(img_cache)) if not os.path.isdir(img_cache): utils.mkdir_p(img_cache) utils.mkdir_p(os.path.join(img_cache, '10.1371')) utils.mkdir_p(os.path.join(img_cache, '10.3389'))
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Initiates the image cache if it does not exist
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/utils/images.py#L171-L179
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/utils/images.py
fetch_frontiers_images
def fetch_frontiers_images(doi, output_dir): """ Fetch the images from Frontiers' website. This method may fail to properly locate all the images and should be avoided if the files can be accessed locally. Downloading the images to an appropriate directory in the cache, or to a directory specified by passed argument are the preferred means to access images. """ log.info('Fetching Frontiers images') log.warning('This method may fail to locate all images.') def download_image(fetch, img_file): try: image = urllib.request.urlopen(fetch) except urllib.error.HTTPError as e: if e.code == 503: # Server overloaded time.sleep(1) # Wait one second try: image = urllib.request.urlopen(fetch) except: return None elif e.code == 500: print('urllib.error.HTTPError {0}'.format(e.code)) return None else: with open(img_file, 'wb') as outimage: outimage.write(image.read()) return True def check_equation_completion(images): """ In some cases, equations images are not exposed in the fulltext (hidden behind a rasterized table). This attempts to look for gaps and fix them """ log.info('Checking for complete equations') files = os.listdir(output_dir) inline_equations = [] for e in files: if e[0] == 'i': inline_equations.append(e) missing = [] highest = 0 if inline_equations: inline_equations.sort() highest = int(inline_equations[-1][1:4]) i = 1 while i < highest: name = 'i{0}.gif'.format(str(i).zfill(3)) if name not in inline_equations: missing.append(name) i += 1 get = images[0][:-8] for m in missing: loc = os.path.join(output_dir, m) download_image(get + m, loc) print('Downloaded image {0}'.format(loc)) #It is possible that we need to go further than the highest highest += 1 name = 'i{0}.gif'.format(str(highest).zfill(3)) loc = os.path.join(output_dir, name) while download_image(get + name, loc): print('Downloaded image {0}'.format(loc)) highest += 1 name = 'i{0}.gif'.format(str(highest).zfill(3)) print('Processing images for {0}...'.format(doi)) #We use the DOI of the article to locate the page. doistr = 'http://dx.doi.org/{0}'.format(doi) logging.debug('Accessing DOI address-{0}'.format(doistr)) page = urllib.request.urlopen(doistr) if page.geturl()[-8:] == 'abstract': full = page.geturl()[:-8] + 'full' elif page.geturl()[-4:] == 'full': full = page.geturl() print(full) page = urllib.request.urlopen(full) with open('temp', 'w') as temp: temp.write(page.read()) images = [] with open('temp', 'r') as temp: for l in temp.readlines(): images += re.findall('<a href="(?P<href>http://\w{7}.\w{3}.\w{3}.rackcdn.com/\d{5}/f\w{4}-\d{2}-\d{5}-HTML/image_m/f\w{4}-\d{2}-\d{5}-\D{1,2}\d{3}.\D{3})', l) images += re.findall('<a href="(?P<href>http://\w{7}.\w{3}.\w{3}.rackcdn.com/\d{5}/f\w{4}-\d{2}-\d{5}-r2/image_m/f\w{4}-\d{2}-\d{5}-\D{1,2}\d{3}.\D{3})', l) images += re.findall('<img src="(?P<src>http://\w{7}.\w{3}.\w{3}.rackcdn.com/\d{5}/f\w{4}-\d{2}-\d{5}-HTML/image_n/f\w{4}-\d{2}-\d{5}-\D{1,2}\d{3}.\D{3})', l) os.remove('temp') for i in images: loc = os.path.join(output_dir, i.split('-')[-1]) download_image(i, loc) print('Downloaded image {0}'.format(loc)) if images: check_equation_completion(images) print("Done downloading images")
python
def fetch_frontiers_images(doi, output_dir): """ Fetch the images from Frontiers' website. This method may fail to properly locate all the images and should be avoided if the files can be accessed locally. Downloading the images to an appropriate directory in the cache, or to a directory specified by passed argument are the preferred means to access images. """ log.info('Fetching Frontiers images') log.warning('This method may fail to locate all images.') def download_image(fetch, img_file): try: image = urllib.request.urlopen(fetch) except urllib.error.HTTPError as e: if e.code == 503: # Server overloaded time.sleep(1) # Wait one second try: image = urllib.request.urlopen(fetch) except: return None elif e.code == 500: print('urllib.error.HTTPError {0}'.format(e.code)) return None else: with open(img_file, 'wb') as outimage: outimage.write(image.read()) return True def check_equation_completion(images): """ In some cases, equations images are not exposed in the fulltext (hidden behind a rasterized table). This attempts to look for gaps and fix them """ log.info('Checking for complete equations') files = os.listdir(output_dir) inline_equations = [] for e in files: if e[0] == 'i': inline_equations.append(e) missing = [] highest = 0 if inline_equations: inline_equations.sort() highest = int(inline_equations[-1][1:4]) i = 1 while i < highest: name = 'i{0}.gif'.format(str(i).zfill(3)) if name not in inline_equations: missing.append(name) i += 1 get = images[0][:-8] for m in missing: loc = os.path.join(output_dir, m) download_image(get + m, loc) print('Downloaded image {0}'.format(loc)) #It is possible that we need to go further than the highest highest += 1 name = 'i{0}.gif'.format(str(highest).zfill(3)) loc = os.path.join(output_dir, name) while download_image(get + name, loc): print('Downloaded image {0}'.format(loc)) highest += 1 name = 'i{0}.gif'.format(str(highest).zfill(3)) print('Processing images for {0}...'.format(doi)) #We use the DOI of the article to locate the page. doistr = 'http://dx.doi.org/{0}'.format(doi) logging.debug('Accessing DOI address-{0}'.format(doistr)) page = urllib.request.urlopen(doistr) if page.geturl()[-8:] == 'abstract': full = page.geturl()[:-8] + 'full' elif page.geturl()[-4:] == 'full': full = page.geturl() print(full) page = urllib.request.urlopen(full) with open('temp', 'w') as temp: temp.write(page.read()) images = [] with open('temp', 'r') as temp: for l in temp.readlines(): images += re.findall('<a href="(?P<href>http://\w{7}.\w{3}.\w{3}.rackcdn.com/\d{5}/f\w{4}-\d{2}-\d{5}-HTML/image_m/f\w{4}-\d{2}-\d{5}-\D{1,2}\d{3}.\D{3})', l) images += re.findall('<a href="(?P<href>http://\w{7}.\w{3}.\w{3}.rackcdn.com/\d{5}/f\w{4}-\d{2}-\d{5}-r2/image_m/f\w{4}-\d{2}-\d{5}-\D{1,2}\d{3}.\D{3})', l) images += re.findall('<img src="(?P<src>http://\w{7}.\w{3}.\w{3}.rackcdn.com/\d{5}/f\w{4}-\d{2}-\d{5}-HTML/image_n/f\w{4}-\d{2}-\d{5}-\D{1,2}\d{3}.\D{3})', l) os.remove('temp') for i in images: loc = os.path.join(output_dir, i.split('-')[-1]) download_image(i, loc) print('Downloaded image {0}'.format(loc)) if images: check_equation_completion(images) print("Done downloading images")
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/utils/images.py#L182-L273
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/utils/images.py
fetch_plos_images
def fetch_plos_images(article_doi, output_dir, document): """ Fetch the images for a PLoS article from the internet. PLoS images are known through the inspection of <graphic> and <inline-graphic> elements. The information in these tags are then parsed into appropriate URLs for downloading. """ log.info('Processing images for {0}...'.format(article_doi)) #A dict of URLs for PLoS subjournals journal_urls = {'pgen': 'http://www.plosgenetics.org/article/{0}', 'pcbi': 'http://www.ploscompbiol.org/article/{0}', 'ppat': 'http://www.plospathogens.org/article/{0}', 'pntd': 'http://www.plosntds.org/article/{0}', 'pmed': 'http://www.plosmedicine.org/article/{0}', 'pbio': 'http://www.plosbiology.org/article/{0}', 'pone': 'http://www.plosone.org/article/{0}', 'pctr': 'http://clinicaltrials.ploshubs.org/article/{0}'} #Identify subjournal name for base URL subjournal_name = article_doi.split('.')[1] base_url = journal_urls[subjournal_name] #Acquire <graphic> and <inline-graphic> xml elements graphics = document.document.getroot().findall('.//graphic') graphics += document.document.getroot().findall('.//inline-graphic') #Begin to download log.info('Downloading images, this may take some time...') for graphic in graphics: nsmap = document.document.getroot().nsmap xlink_href = graphic.attrib['{' + nsmap['xlink'] + '}' + 'href'] #Equations are handled a bit differently than the others #Here we decide that an image name starting with "e" is an equation if xlink_href.split('.')[-1].startswith('e'): resource = 'fetchObject.action?uri=' + xlink_href + '&representation=PNG' else: resource = xlink_href + '/largerimage' full_url = base_url.format(resource) try: image = urllib.request.urlopen(full_url) except urllib.error.HTTPError as e: if e.code == 503: # Server overload error time.sleep(1) # Wait a second try: image = urllib.request.urlopen(full_url) except: return False # Happened twice, give up else: log.error('urllib.error.HTTPError {0}'.format(e.code)) return False else: img_name = xlink_href.split('.')[-1] + '.png' img_path = os.path.join(output_dir, img_name) with open(img_path, 'wb') as output: output.write(image.read()) log.info('Downloaded image {0}'.format(img_name)) log.info('Done downloading images') return True
python
def fetch_plos_images(article_doi, output_dir, document): """ Fetch the images for a PLoS article from the internet. PLoS images are known through the inspection of <graphic> and <inline-graphic> elements. The information in these tags are then parsed into appropriate URLs for downloading. """ log.info('Processing images for {0}...'.format(article_doi)) #A dict of URLs for PLoS subjournals journal_urls = {'pgen': 'http://www.plosgenetics.org/article/{0}', 'pcbi': 'http://www.ploscompbiol.org/article/{0}', 'ppat': 'http://www.plospathogens.org/article/{0}', 'pntd': 'http://www.plosntds.org/article/{0}', 'pmed': 'http://www.plosmedicine.org/article/{0}', 'pbio': 'http://www.plosbiology.org/article/{0}', 'pone': 'http://www.plosone.org/article/{0}', 'pctr': 'http://clinicaltrials.ploshubs.org/article/{0}'} #Identify subjournal name for base URL subjournal_name = article_doi.split('.')[1] base_url = journal_urls[subjournal_name] #Acquire <graphic> and <inline-graphic> xml elements graphics = document.document.getroot().findall('.//graphic') graphics += document.document.getroot().findall('.//inline-graphic') #Begin to download log.info('Downloading images, this may take some time...') for graphic in graphics: nsmap = document.document.getroot().nsmap xlink_href = graphic.attrib['{' + nsmap['xlink'] + '}' + 'href'] #Equations are handled a bit differently than the others #Here we decide that an image name starting with "e" is an equation if xlink_href.split('.')[-1].startswith('e'): resource = 'fetchObject.action?uri=' + xlink_href + '&representation=PNG' else: resource = xlink_href + '/largerimage' full_url = base_url.format(resource) try: image = urllib.request.urlopen(full_url) except urllib.error.HTTPError as e: if e.code == 503: # Server overload error time.sleep(1) # Wait a second try: image = urllib.request.urlopen(full_url) except: return False # Happened twice, give up else: log.error('urllib.error.HTTPError {0}'.format(e.code)) return False else: img_name = xlink_href.split('.')[-1] + '.png' img_path = os.path.join(output_dir, img_name) with open(img_path, 'wb') as output: output.write(image.read()) log.info('Downloaded image {0}'.format(img_name)) log.info('Done downloading images') return True
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Fetch the images for a PLoS article from the internet. PLoS images are known through the inspection of <graphic> and <inline-graphic> elements. The information in these tags are then parsed into appropriate URLs for downloading.
[ "Fetch", "the", "images", "for", "a", "PLoS", "article", "from", "the", "internet", "." ]
train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/utils/images.py#L276-L336
MSchnei/pyprf_feature
pyprf_feature/analysis/save_fit_tc_nii.py
save_tc_to_nii
def save_tc_to_nii(strCsvCnfg, lgcTest=False, lstRat=None, lgcMdlRsp=False, strPathHrf=None, lgcSaveRam=False): """ Save empirical and fitted time courses to nii file format. Parameters ---------- strCsvCnfg : str Absolute file path of config file used for pRF fitting. lgcTest : boolean Whether this is a test (pytest). If yes, absolute path of pyprf libary will be prepended to config file paths. lstRat : None or list Ratio of size of center to size of suppressive surround. lgcMdlRsp : boolean Should the aperture responses for the winner model also be saved? strPathHrf : str or None: Path to npy file with custom hrf parameters. If None, defaults parameters were used. lgcSaveRam : boolean Whether to also save a nii file that uses little RAM. Notes ----- This function does not return any arguments but, instead, saves nii files to disk. """ # %% Load configuration settings that were used for fitting # Load config parameters from csv file into dictionary: dicCnfg = load_config(strCsvCnfg, lgcTest=lgcTest) # Load config parameters from dictionary into namespace: cfg = cls_set_config(dicCnfg) # if fitting was done with custom hrf, make sure to retrieve results with # '_hrf' appendix if strPathHrf is not None: cfg.strPathOut = cfg.strPathOut + '_hrf' # If suppressive surround flag is on, make sure to retrieve results with # '_supsur' appendix if lstRat is not None: cfg.strPathOut = cfg.strPathOut + '_supsur' cfg.strPathMdl = cfg.strPathMdl + '_supsur' # Append 1.0 as the first entry, which is the key for fitting without # surround (only centre) lstRat.insert(0, 1.0) # %% Load previous pRF fitting results # Derive paths to the x, y, sigma winner parameters from pyprf_feature lstWnrPrm = [cfg.strPathOut + '_x_pos.nii.gz', cfg.strPathOut + '_y_pos.nii.gz', cfg.strPathOut + '_SD.nii.gz'] # Check if fitting has been performed, i.e. whether parameter files exist # Throw error message if they do not exist. errorMsg = 'Files that should have resulted from fitting do not exist. \ \nPlease perform pRF fitting first, calling e.g.: \ \npyprf_feature -config /path/to/my_config_file.csv' assert os.path.isfile(lstWnrPrm[0]), errorMsg assert os.path.isfile(lstWnrPrm[1]), errorMsg assert os.path.isfile(lstWnrPrm[2]), errorMsg # Load the x, y, sigma winner parameters from pyprf_feature aryIntGssPrm = load_res_prm(lstWnrPrm, lstFlsMsk=[cfg.strPathNiiMask])[0][0] # Load beta parameters estimates, aka weights for time courses lstPathBeta = [cfg.strPathOut + '_Betas.nii.gz'] aryBetas = load_res_prm(lstPathBeta, lstFlsMsk=[cfg.strPathNiiMask])[0][0] assert os.path.isfile(lstPathBeta[0]), errorMsg # Load ratio image, if fitting was obtained with suppressive surround if lstRat is not None: lstPathRatio = [cfg.strPathOut + '_Ratios.nii.gz'] aryRatio = load_res_prm(lstPathRatio, lstFlsMsk=[cfg.strPathNiiMask])[0][0] assert os.path.isfile(lstPathRatio[0]), errorMsg # Some voxels were excluded because they did not have sufficient mean # and/or variance - exclude their initial parameters, too # Get inclusion mask and nii header aryLgcMsk, aryLgcVar, hdrMsk, aryAff, aryFunc, tplNiiShp = prep_func( cfg.strPathNiiMask, cfg.lstPathNiiFunc, varAvgThr=-100) # Apply inclusion mask aryIntGssPrm = aryIntGssPrm[aryLgcVar, :] aryBetas = aryBetas[aryLgcVar, :] if lstRat is not None: aryRatio = aryRatio[aryLgcVar, :] # Get array with model parameters that were fitted on a grid # [x positions, y positions, sigmas] aryMdlParams = crt_mdl_prms((int(cfg.varVslSpcSzeX), int(cfg.varVslSpcSzeY)), cfg.varNum1, cfg.varExtXmin, cfg.varExtXmax, cfg.varNum2, cfg.varExtYmin, cfg.varExtYmax, cfg.varNumPrfSizes, cfg.varPrfStdMin, cfg.varPrfStdMax, kwUnt='deg', kwCrd=cfg.strKwCrd) # Load logical for parameter exclusion in unstimulated area lgcMdlInc = np.load(cfg.strPathMdl + '_lgcMdlInc.npy') # Apply logical aryMdlParams = aryMdlParams[lgcMdlInc, :] # Get corresponding pRF model time courses aryPrfTc = np.load(cfg.strPathMdl + '.npy') # The model time courses will be preprocessed such that they are smoothed # (temporally) with same factor as the data and that they will be z-scored: aryPrfTc = prep_models(aryPrfTc, varSdSmthTmp=cfg.varSdSmthTmp) if lgcMdlRsp: aryMdlRsp = np.load(cfg.strPathMdl + '_mdlRsp.npy') # %% Derive fitted time course models for all voxels # Initialize array that will collect the fitted time courses aryFitTc = np.zeros((aryFunc.shape), dtype=np.float32) # If desired, initiliaze array that will collect model responses underlying # the fitted time course if lgcMdlRsp: if lstRat is not None: aryFitMdlRsp = np.zeros((aryIntGssPrm.shape[0], aryMdlRsp.shape[1], aryMdlRsp.shape[3]), dtype=np.float32) else: aryFitMdlRsp = np.zeros((aryIntGssPrm.shape[0], aryMdlRsp.shape[1]), dtype=np.float32) # create vector that allows to check whether every voxel is visited # exactly once vecVxlTst = np.zeros(aryIntGssPrm.shape[0]) # Find unique rows of fitted model parameters aryUnqRows, aryUnqInd = fnd_unq_rws(aryIntGssPrm, return_index=False, return_inverse=True) # Loop over all best-fitting model parameter combinations found print('---Assign models to voxels') for indRow, vecPrm in enumerate(aryUnqRows): # Get logical for voxels for which this prm combi was the best lgcVxl = [aryUnqInd == indRow][0] if np.all(np.invert(lgcVxl)): print('---No voxel found') # Mark those voxels that were visited vecVxlTst[lgcVxl] += 1 # Get logical index for the model number # This can only be 1 index, so we directly get 1st entry of array lgcMdl = np.where(np.isclose(aryMdlParams, vecPrm, atol=0.01).all(axis=1))[0][0] # Tell user if no model was found if lgcMdl is None: print('---No model found') # Get model time courses aryMdlTc = aryPrfTc[lgcMdl, ...] # Get beta parameter estimates aryWeights = aryBetas[lgcVxl, :] # If fitting was done with surround suppression, find ratios for voxels # and the indices of these ratios in lstRat if lstRat is not None: aryVxlRatio = aryRatio[lgcVxl, :] indRat = [ind for ind, rat1 in enumerate(lstRat) for rat2 in aryVxlRatio[:, 0] if np.isclose(rat1, rat2)] indVxl = range(len(indRat)) # Combine model time courses and weights to yield fitted time course if lstRat is not None: aryFitTcTmp = np.tensordot(aryWeights, aryMdlTc, axes=([1], [0])) aryFitTc[lgcVxl, :] = aryFitTcTmp[indVxl, indRat, :] else: aryFitTc[lgcVxl, :] = np.dot(aryWeights, aryMdlTc) # If desired by user, also save the model responses per voxels if lgcMdlRsp: # If desired also save the model responses that won if lstRat is not None: aryFitMdlRsp[lgcVxl, :] = aryMdlRsp[lgcMdl, :, indRat, :] else: aryFitMdlRsp[lgcVxl, :] = aryMdlRsp[lgcMdl, :] # check that every voxel was visited exactly once errMsg = 'At least one voxel visited more than once for tc recreation' assert len(vecVxlTst) == np.sum(vecVxlTst), errMsg # %% Export preprocessed voxel time courses as nii # List with name suffices of output images: lstNiiNames = ['_EmpTc'] # Create full path names from nii file names and output path lstNiiNames = [cfg.strPathOut + strNii + '.nii.gz' for strNii in lstNiiNames] # export aryFunc as a single 4D nii file print('---Save empirical time courses') export_nii(aryFunc, lstNiiNames, aryLgcMsk, aryLgcVar, tplNiiShp, aryAff, hdrMsk, outFormat='4D') print('------Done.') # If desired by user, also save RAM-saving version of nii if lgcSaveRam: strPthRamOut = cfg.strPathOut + '_EmpTc_saveRAM' + '.nii.gz' imgNii = nb.Nifti1Image(np.expand_dims(np.expand_dims(aryFunc, axis=1), axis=1), affine=np.eye(4)) nb.save(imgNii, strPthRamOut) # %% Export fitted time courses and, if desired, model responses as nii # List with name suffices of output images: lstNiiNames = ['_FitTc'] # Create full path names from nii file names and output path lstNiiNames = [cfg.strPathOut + strNii + '.nii.gz' for strNii in lstNiiNames] # export aryFitTc as a single 4D nii file print('---Save fitted time courses') export_nii(aryFitTc, lstNiiNames, aryLgcMsk, aryLgcVar, tplNiiShp, aryAff, hdrMsk, outFormat='4D') print('------Done.') if lgcMdlRsp: # Create full path name strNpyName = cfg.strPathOut + '_FitMdlRsp' + '.npy' # Save aryFitMdlRsp as npy file print('---Save fitted model responses') np.save(strNpyName, aryFitMdlRsp) print('------Done.') # Save the mask so we know which voxels these parameters belonged to strNpyMskName = cfg.strPathOut + '_FitMdlRsp_Mask' + '.npy' aryLgcMsk[aryLgcMsk] = aryLgcVar print('---Save mask for fitted model responses') np.save(strNpyMskName, aryLgcMsk) print('------Done.') # If desired by user, also save RAM-saving version of nii if lgcSaveRam: strPthRamOut = cfg.strPathOut + '_FitTc_saveRAM' + '.nii.gz' imgNii = nb.Nifti1Image(np.expand_dims(np.expand_dims(aryFitTc, axis=1), axis=1), affine=np.eye(4)) nb.save(imgNii, strPthRamOut)
python
def save_tc_to_nii(strCsvCnfg, lgcTest=False, lstRat=None, lgcMdlRsp=False, strPathHrf=None, lgcSaveRam=False): """ Save empirical and fitted time courses to nii file format. Parameters ---------- strCsvCnfg : str Absolute file path of config file used for pRF fitting. lgcTest : boolean Whether this is a test (pytest). If yes, absolute path of pyprf libary will be prepended to config file paths. lstRat : None or list Ratio of size of center to size of suppressive surround. lgcMdlRsp : boolean Should the aperture responses for the winner model also be saved? strPathHrf : str or None: Path to npy file with custom hrf parameters. If None, defaults parameters were used. lgcSaveRam : boolean Whether to also save a nii file that uses little RAM. Notes ----- This function does not return any arguments but, instead, saves nii files to disk. """ # %% Load configuration settings that were used for fitting # Load config parameters from csv file into dictionary: dicCnfg = load_config(strCsvCnfg, lgcTest=lgcTest) # Load config parameters from dictionary into namespace: cfg = cls_set_config(dicCnfg) # if fitting was done with custom hrf, make sure to retrieve results with # '_hrf' appendix if strPathHrf is not None: cfg.strPathOut = cfg.strPathOut + '_hrf' # If suppressive surround flag is on, make sure to retrieve results with # '_supsur' appendix if lstRat is not None: cfg.strPathOut = cfg.strPathOut + '_supsur' cfg.strPathMdl = cfg.strPathMdl + '_supsur' # Append 1.0 as the first entry, which is the key for fitting without # surround (only centre) lstRat.insert(0, 1.0) # %% Load previous pRF fitting results # Derive paths to the x, y, sigma winner parameters from pyprf_feature lstWnrPrm = [cfg.strPathOut + '_x_pos.nii.gz', cfg.strPathOut + '_y_pos.nii.gz', cfg.strPathOut + '_SD.nii.gz'] # Check if fitting has been performed, i.e. whether parameter files exist # Throw error message if they do not exist. errorMsg = 'Files that should have resulted from fitting do not exist. \ \nPlease perform pRF fitting first, calling e.g.: \ \npyprf_feature -config /path/to/my_config_file.csv' assert os.path.isfile(lstWnrPrm[0]), errorMsg assert os.path.isfile(lstWnrPrm[1]), errorMsg assert os.path.isfile(lstWnrPrm[2]), errorMsg # Load the x, y, sigma winner parameters from pyprf_feature aryIntGssPrm = load_res_prm(lstWnrPrm, lstFlsMsk=[cfg.strPathNiiMask])[0][0] # Load beta parameters estimates, aka weights for time courses lstPathBeta = [cfg.strPathOut + '_Betas.nii.gz'] aryBetas = load_res_prm(lstPathBeta, lstFlsMsk=[cfg.strPathNiiMask])[0][0] assert os.path.isfile(lstPathBeta[0]), errorMsg # Load ratio image, if fitting was obtained with suppressive surround if lstRat is not None: lstPathRatio = [cfg.strPathOut + '_Ratios.nii.gz'] aryRatio = load_res_prm(lstPathRatio, lstFlsMsk=[cfg.strPathNiiMask])[0][0] assert os.path.isfile(lstPathRatio[0]), errorMsg # Some voxels were excluded because they did not have sufficient mean # and/or variance - exclude their initial parameters, too # Get inclusion mask and nii header aryLgcMsk, aryLgcVar, hdrMsk, aryAff, aryFunc, tplNiiShp = prep_func( cfg.strPathNiiMask, cfg.lstPathNiiFunc, varAvgThr=-100) # Apply inclusion mask aryIntGssPrm = aryIntGssPrm[aryLgcVar, :] aryBetas = aryBetas[aryLgcVar, :] if lstRat is not None: aryRatio = aryRatio[aryLgcVar, :] # Get array with model parameters that were fitted on a grid # [x positions, y positions, sigmas] aryMdlParams = crt_mdl_prms((int(cfg.varVslSpcSzeX), int(cfg.varVslSpcSzeY)), cfg.varNum1, cfg.varExtXmin, cfg.varExtXmax, cfg.varNum2, cfg.varExtYmin, cfg.varExtYmax, cfg.varNumPrfSizes, cfg.varPrfStdMin, cfg.varPrfStdMax, kwUnt='deg', kwCrd=cfg.strKwCrd) # Load logical for parameter exclusion in unstimulated area lgcMdlInc = np.load(cfg.strPathMdl + '_lgcMdlInc.npy') # Apply logical aryMdlParams = aryMdlParams[lgcMdlInc, :] # Get corresponding pRF model time courses aryPrfTc = np.load(cfg.strPathMdl + '.npy') # The model time courses will be preprocessed such that they are smoothed # (temporally) with same factor as the data and that they will be z-scored: aryPrfTc = prep_models(aryPrfTc, varSdSmthTmp=cfg.varSdSmthTmp) if lgcMdlRsp: aryMdlRsp = np.load(cfg.strPathMdl + '_mdlRsp.npy') # %% Derive fitted time course models for all voxels # Initialize array that will collect the fitted time courses aryFitTc = np.zeros((aryFunc.shape), dtype=np.float32) # If desired, initiliaze array that will collect model responses underlying # the fitted time course if lgcMdlRsp: if lstRat is not None: aryFitMdlRsp = np.zeros((aryIntGssPrm.shape[0], aryMdlRsp.shape[1], aryMdlRsp.shape[3]), dtype=np.float32) else: aryFitMdlRsp = np.zeros((aryIntGssPrm.shape[0], aryMdlRsp.shape[1]), dtype=np.float32) # create vector that allows to check whether every voxel is visited # exactly once vecVxlTst = np.zeros(aryIntGssPrm.shape[0]) # Find unique rows of fitted model parameters aryUnqRows, aryUnqInd = fnd_unq_rws(aryIntGssPrm, return_index=False, return_inverse=True) # Loop over all best-fitting model parameter combinations found print('---Assign models to voxels') for indRow, vecPrm in enumerate(aryUnqRows): # Get logical for voxels for which this prm combi was the best lgcVxl = [aryUnqInd == indRow][0] if np.all(np.invert(lgcVxl)): print('---No voxel found') # Mark those voxels that were visited vecVxlTst[lgcVxl] += 1 # Get logical index for the model number # This can only be 1 index, so we directly get 1st entry of array lgcMdl = np.where(np.isclose(aryMdlParams, vecPrm, atol=0.01).all(axis=1))[0][0] # Tell user if no model was found if lgcMdl is None: print('---No model found') # Get model time courses aryMdlTc = aryPrfTc[lgcMdl, ...] # Get beta parameter estimates aryWeights = aryBetas[lgcVxl, :] # If fitting was done with surround suppression, find ratios for voxels # and the indices of these ratios in lstRat if lstRat is not None: aryVxlRatio = aryRatio[lgcVxl, :] indRat = [ind for ind, rat1 in enumerate(lstRat) for rat2 in aryVxlRatio[:, 0] if np.isclose(rat1, rat2)] indVxl = range(len(indRat)) # Combine model time courses and weights to yield fitted time course if lstRat is not None: aryFitTcTmp = np.tensordot(aryWeights, aryMdlTc, axes=([1], [0])) aryFitTc[lgcVxl, :] = aryFitTcTmp[indVxl, indRat, :] else: aryFitTc[lgcVxl, :] = np.dot(aryWeights, aryMdlTc) # If desired by user, also save the model responses per voxels if lgcMdlRsp: # If desired also save the model responses that won if lstRat is not None: aryFitMdlRsp[lgcVxl, :] = aryMdlRsp[lgcMdl, :, indRat, :] else: aryFitMdlRsp[lgcVxl, :] = aryMdlRsp[lgcMdl, :] # check that every voxel was visited exactly once errMsg = 'At least one voxel visited more than once for tc recreation' assert len(vecVxlTst) == np.sum(vecVxlTst), errMsg # %% Export preprocessed voxel time courses as nii # List with name suffices of output images: lstNiiNames = ['_EmpTc'] # Create full path names from nii file names and output path lstNiiNames = [cfg.strPathOut + strNii + '.nii.gz' for strNii in lstNiiNames] # export aryFunc as a single 4D nii file print('---Save empirical time courses') export_nii(aryFunc, lstNiiNames, aryLgcMsk, aryLgcVar, tplNiiShp, aryAff, hdrMsk, outFormat='4D') print('------Done.') # If desired by user, also save RAM-saving version of nii if lgcSaveRam: strPthRamOut = cfg.strPathOut + '_EmpTc_saveRAM' + '.nii.gz' imgNii = nb.Nifti1Image(np.expand_dims(np.expand_dims(aryFunc, axis=1), axis=1), affine=np.eye(4)) nb.save(imgNii, strPthRamOut) # %% Export fitted time courses and, if desired, model responses as nii # List with name suffices of output images: lstNiiNames = ['_FitTc'] # Create full path names from nii file names and output path lstNiiNames = [cfg.strPathOut + strNii + '.nii.gz' for strNii in lstNiiNames] # export aryFitTc as a single 4D nii file print('---Save fitted time courses') export_nii(aryFitTc, lstNiiNames, aryLgcMsk, aryLgcVar, tplNiiShp, aryAff, hdrMsk, outFormat='4D') print('------Done.') if lgcMdlRsp: # Create full path name strNpyName = cfg.strPathOut + '_FitMdlRsp' + '.npy' # Save aryFitMdlRsp as npy file print('---Save fitted model responses') np.save(strNpyName, aryFitMdlRsp) print('------Done.') # Save the mask so we know which voxels these parameters belonged to strNpyMskName = cfg.strPathOut + '_FitMdlRsp_Mask' + '.npy' aryLgcMsk[aryLgcMsk] = aryLgcVar print('---Save mask for fitted model responses') np.save(strNpyMskName, aryLgcMsk) print('------Done.') # If desired by user, also save RAM-saving version of nii if lgcSaveRam: strPthRamOut = cfg.strPathOut + '_FitTc_saveRAM' + '.nii.gz' imgNii = nb.Nifti1Image(np.expand_dims(np.expand_dims(aryFitTc, axis=1), axis=1), affine=np.eye(4)) nb.save(imgNii, strPthRamOut)
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"cfg", ".", "strPathMdl", "+", "'_supsur'", "# Append 1.0 as the first entry, which is the key for fitting without", "# surround (only centre)", "lstRat", ".", "insert", "(", "0", ",", "1.0", ")", "# %% Load previous pRF fitting results", "# Derive paths to the x, y, sigma winner parameters from pyprf_feature", "lstWnrPrm", "=", "[", "cfg", ".", "strPathOut", "+", "'_x_pos.nii.gz'", ",", "cfg", ".", "strPathOut", "+", "'_y_pos.nii.gz'", ",", "cfg", ".", "strPathOut", "+", "'_SD.nii.gz'", "]", "# Check if fitting has been performed, i.e. whether parameter files exist", "# Throw error message if they do not exist.", "errorMsg", "=", "'Files that should have resulted from fitting do not exist. \\\n \\nPlease perform pRF fitting first, calling e.g.: \\\n \\npyprf_feature -config /path/to/my_config_file.csv'", "assert", "os", ".", "path", ".", "isfile", "(", "lstWnrPrm", "[", "0", "]", ")", ",", "errorMsg", "assert", "os", ".", "path", ".", "isfile", "(", "lstWnrPrm", "[", "1", "]", ")", ",", "errorMsg", "assert", "os", ".", "path", ".", "isfile", "(", "lstWnrPrm", "[", "2", "]", ")", ",", "errorMsg", "# Load the x, y, sigma winner parameters from pyprf_feature", "aryIntGssPrm", "=", "load_res_prm", "(", "lstWnrPrm", ",", "lstFlsMsk", "=", "[", "cfg", ".", "strPathNiiMask", "]", ")", "[", "0", "]", "[", "0", "]", "# Load beta parameters estimates, aka weights for time courses", "lstPathBeta", "=", "[", "cfg", ".", "strPathOut", "+", "'_Betas.nii.gz'", "]", "aryBetas", "=", "load_res_prm", "(", "lstPathBeta", ",", "lstFlsMsk", "=", "[", "cfg", ".", "strPathNiiMask", "]", ")", "[", "0", "]", "[", "0", "]", "assert", "os", ".", "path", ".", "isfile", "(", "lstPathBeta", "[", "0", "]", ")", ",", "errorMsg", "# Load ratio image, if fitting was obtained with suppressive surround", "if", "lstRat", "is", "not", "None", ":", "lstPathRatio", "=", "[", "cfg", ".", "strPathOut", "+", "'_Ratios.nii.gz'", "]", "aryRatio", "=", "load_res_prm", "(", "lstPathRatio", ",", "lstFlsMsk", "=", "[", "cfg", ".", "strPathNiiMask", "]", ")", "[", "0", "]", "[", "0", "]", "assert", "os", ".", "path", ".", "isfile", "(", "lstPathRatio", "[", "0", "]", ")", ",", "errorMsg", "# Some voxels were excluded because they did not have sufficient mean", "# and/or variance - exclude their initial parameters, too", "# Get inclusion mask and nii header", "aryLgcMsk", ",", "aryLgcVar", ",", "hdrMsk", ",", "aryAff", ",", "aryFunc", ",", "tplNiiShp", "=", "prep_func", "(", "cfg", ".", "strPathNiiMask", ",", "cfg", ".", "lstPathNiiFunc", ",", "varAvgThr", "=", "-", "100", ")", "# Apply inclusion mask", "aryIntGssPrm", "=", "aryIntGssPrm", "[", "aryLgcVar", ",", ":", "]", "aryBetas", "=", "aryBetas", "[", "aryLgcVar", ",", ":", "]", "if", "lstRat", "is", "not", "None", ":", "aryRatio", "=", "aryRatio", "[", "aryLgcVar", ",", ":", "]", "# Get array with model parameters that were fitted on a grid", "# [x positions, y positions, sigmas]", "aryMdlParams", "=", "crt_mdl_prms", "(", "(", "int", "(", "cfg", ".", "varVslSpcSzeX", ")", ",", "int", "(", "cfg", ".", "varVslSpcSzeY", ")", ")", ",", "cfg", ".", "varNum1", ",", "cfg", ".", "varExtXmin", ",", "cfg", ".", "varExtXmax", ",", "cfg", ".", "varNum2", ",", "cfg", ".", "varExtYmin", ",", "cfg", ".", "varExtYmax", ",", "cfg", ".", "varNumPrfSizes", ",", "cfg", ".", "varPrfStdMin", ",", "cfg", ".", "varPrfStdMax", ",", "kwUnt", "=", "'deg'", ",", "kwCrd", "=", "cfg", ".", "strKwCrd", ")", "# Load logical for parameter exclusion in unstimulated area", "lgcMdlInc", "=", "np", ".", "load", "(", "cfg", ".", "strPathMdl", "+", "'_lgcMdlInc.npy'", ")", "# Apply logical", "aryMdlParams", "=", "aryMdlParams", "[", "lgcMdlInc", ",", ":", "]", "# Get corresponding pRF model time courses", "aryPrfTc", "=", "np", ".", "load", "(", "cfg", ".", "strPathMdl", "+", "'.npy'", ")", "# The model time courses will be preprocessed such that they are smoothed", "# (temporally) with same factor as the data and that they will be z-scored:", "aryPrfTc", "=", "prep_models", "(", "aryPrfTc", ",", "varSdSmthTmp", "=", "cfg", ".", "varSdSmthTmp", ")", "if", "lgcMdlRsp", ":", "aryMdlRsp", "=", "np", ".", "load", "(", "cfg", ".", "strPathMdl", "+", "'_mdlRsp.npy'", ")", "# %% Derive fitted time course models for all voxels", "# Initialize array that will collect the fitted time courses", "aryFitTc", "=", "np", ".", "zeros", "(", "(", "aryFunc", ".", "shape", ")", ",", "dtype", "=", "np", ".", "float32", ")", "# If desired, initiliaze array that will collect model responses underlying", "# the fitted time course", "if", "lgcMdlRsp", ":", "if", "lstRat", "is", "not", "None", ":", "aryFitMdlRsp", "=", "np", ".", "zeros", "(", "(", "aryIntGssPrm", ".", "shape", "[", "0", "]", ",", "aryMdlRsp", ".", "shape", "[", "1", "]", ",", "aryMdlRsp", ".", "shape", "[", "3", "]", ")", ",", "dtype", "=", "np", ".", "float32", ")", "else", ":", "aryFitMdlRsp", "=", "np", ".", "zeros", "(", "(", "aryIntGssPrm", ".", "shape", "[", "0", "]", ",", "aryMdlRsp", ".", "shape", "[", "1", "]", ")", ",", "dtype", "=", "np", ".", "float32", ")", "# create vector that allows to check whether every voxel is visited", "# exactly once", "vecVxlTst", "=", "np", ".", "zeros", "(", "aryIntGssPrm", ".", "shape", "[", "0", "]", ")", "# Find unique rows of fitted model parameters", "aryUnqRows", ",", "aryUnqInd", "=", "fnd_unq_rws", "(", "aryIntGssPrm", ",", "return_index", "=", "False", ",", "return_inverse", "=", "True", ")", "# Loop over all best-fitting model parameter combinations found", "print", "(", "'---Assign models to voxels'", ")", "for", "indRow", ",", "vecPrm", "in", "enumerate", "(", "aryUnqRows", ")", ":", "# Get logical for voxels for which this prm combi was the best", "lgcVxl", "=", "[", "aryUnqInd", "==", "indRow", "]", "[", "0", "]", "if", "np", ".", "all", "(", "np", ".", "invert", "(", "lgcVxl", ")", ")", ":", "print", "(", "'---No voxel found'", ")", "# Mark those voxels that were visited", "vecVxlTst", "[", "lgcVxl", "]", "+=", "1", "# Get logical index for the model number", "# This can only be 1 index, so we directly get 1st entry of array", "lgcMdl", "=", "np", ".", "where", "(", "np", ".", "isclose", "(", "aryMdlParams", ",", "vecPrm", ",", "atol", "=", "0.01", ")", ".", "all", "(", "axis", "=", "1", ")", ")", "[", "0", "]", "[", "0", "]", "# Tell user if no model was found", "if", "lgcMdl", "is", "None", ":", "print", "(", "'---No model found'", ")", "# Get model time courses", "aryMdlTc", "=", "aryPrfTc", "[", "lgcMdl", ",", "...", "]", "# Get beta parameter estimates", "aryWeights", "=", "aryBetas", "[", "lgcVxl", ",", ":", "]", "# If fitting was done with surround suppression, find ratios for voxels", "# and the indices of these ratios in lstRat", "if", "lstRat", "is", "not", "None", ":", "aryVxlRatio", "=", "aryRatio", "[", "lgcVxl", ",", ":", "]", "indRat", "=", "[", "ind", "for", "ind", ",", "rat1", "in", "enumerate", "(", "lstRat", ")", "for", "rat2", "in", "aryVxlRatio", "[", ":", ",", "0", "]", "if", "np", ".", "isclose", "(", "rat1", ",", "rat2", ")", "]", "indVxl", "=", "range", "(", "len", "(", "indRat", ")", ")", "# Combine model time courses and weights to yield fitted time course", "if", "lstRat", "is", "not", "None", ":", "aryFitTcTmp", "=", "np", ".", "tensordot", "(", "aryWeights", ",", "aryMdlTc", ",", "axes", "=", "(", "[", "1", "]", ",", "[", "0", "]", ")", ")", "aryFitTc", "[", "lgcVxl", ",", ":", "]", "=", "aryFitTcTmp", "[", "indVxl", ",", "indRat", ",", ":", "]", "else", ":", "aryFitTc", "[", "lgcVxl", ",", ":", "]", "=", "np", ".", "dot", "(", "aryWeights", ",", "aryMdlTc", ")", "# If desired by user, also save the model responses per voxels", "if", "lgcMdlRsp", ":", "# If desired also save the model responses that won", "if", "lstRat", "is", "not", "None", ":", "aryFitMdlRsp", "[", "lgcVxl", ",", ":", "]", "=", "aryMdlRsp", "[", "lgcMdl", ",", ":", ",", "indRat", ",", ":", "]", "else", ":", "aryFitMdlRsp", "[", "lgcVxl", ",", ":", "]", "=", "aryMdlRsp", "[", "lgcMdl", ",", ":", "]", "# check that every voxel was visited exactly once", "errMsg", "=", "'At least one voxel visited more than once for tc recreation'", "assert", "len", "(", "vecVxlTst", ")", "==", "np", ".", "sum", "(", "vecVxlTst", ")", ",", "errMsg", "# %% Export preprocessed voxel time courses as nii", "# List with name suffices of output images:", "lstNiiNames", "=", "[", "'_EmpTc'", "]", "# Create full path names from nii file names and output path", "lstNiiNames", "=", "[", "cfg", ".", "strPathOut", "+", "strNii", "+", "'.nii.gz'", "for", "strNii", "in", "lstNiiNames", "]", "# export aryFunc as a single 4D nii file", "print", "(", "'---Save empirical time courses'", ")", "export_nii", "(", "aryFunc", ",", "lstNiiNames", ",", "aryLgcMsk", ",", "aryLgcVar", ",", "tplNiiShp", ",", "aryAff", ",", "hdrMsk", ",", "outFormat", "=", "'4D'", ")", "print", "(", "'------Done.'", ")", "# If desired by user, also save RAM-saving version of nii", "if", "lgcSaveRam", ":", "strPthRamOut", "=", "cfg", ".", "strPathOut", "+", "'_EmpTc_saveRAM'", "+", "'.nii.gz'", "imgNii", "=", "nb", ".", "Nifti1Image", "(", "np", ".", "expand_dims", "(", "np", ".", "expand_dims", "(", "aryFunc", ",", "axis", "=", "1", ")", ",", "axis", "=", "1", ")", ",", "affine", "=", "np", ".", "eye", "(", "4", ")", ")", "nb", ".", "save", "(", "imgNii", ",", "strPthRamOut", ")", "# %% Export fitted time courses and, if desired, model responses as nii", "# List with name suffices of output images:", "lstNiiNames", "=", "[", "'_FitTc'", "]", "# Create full path names from nii file names and output path", "lstNiiNames", "=", "[", "cfg", ".", "strPathOut", "+", "strNii", "+", "'.nii.gz'", "for", "strNii", "in", "lstNiiNames", "]", "# export aryFitTc as a single 4D nii file", "print", "(", "'---Save fitted time courses'", ")", "export_nii", "(", "aryFitTc", ",", "lstNiiNames", ",", "aryLgcMsk", ",", "aryLgcVar", ",", "tplNiiShp", ",", "aryAff", ",", "hdrMsk", ",", "outFormat", "=", "'4D'", ")", "print", "(", "'------Done.'", ")", "if", "lgcMdlRsp", ":", "# Create full path name", "strNpyName", "=", "cfg", ".", "strPathOut", "+", "'_FitMdlRsp'", "+", "'.npy'", "# Save aryFitMdlRsp as npy file", "print", "(", "'---Save fitted model responses'", ")", "np", ".", "save", "(", "strNpyName", ",", "aryFitMdlRsp", ")", "print", "(", "'------Done.'", ")", "# Save the mask so we know which voxels these parameters belonged to", "strNpyMskName", "=", "cfg", ".", "strPathOut", "+", "'_FitMdlRsp_Mask'", "+", "'.npy'", "aryLgcMsk", "[", "aryLgcMsk", "]", "=", "aryLgcVar", "print", "(", "'---Save mask for fitted model responses'", ")", "np", ".", "save", "(", "strNpyMskName", ",", "aryLgcMsk", ")", "print", "(", "'------Done.'", ")", "# If desired by user, also save RAM-saving version of nii", "if", "lgcSaveRam", ":", "strPthRamOut", "=", "cfg", ".", "strPathOut", "+", "'_FitTc_saveRAM'", "+", "'.nii.gz'", "imgNii", "=", "nb", ".", "Nifti1Image", "(", "np", ".", "expand_dims", "(", "np", ".", "expand_dims", "(", "aryFitTc", ",", "axis", "=", "1", ")", ",", "axis", "=", "1", ")", ",", "affine", "=", "np", ".", "eye", "(", "4", ")", ")", "nb", ".", "save", "(", "imgNii", ",", "strPthRamOut", ")" ]
Save empirical and fitted time courses to nii file format. Parameters ---------- strCsvCnfg : str Absolute file path of config file used for pRF fitting. lgcTest : boolean Whether this is a test (pytest). If yes, absolute path of pyprf libary will be prepended to config file paths. lstRat : None or list Ratio of size of center to size of suppressive surround. lgcMdlRsp : boolean Should the aperture responses for the winner model also be saved? strPathHrf : str or None: Path to npy file with custom hrf parameters. If None, defaults parameters were used. lgcSaveRam : boolean Whether to also save a nii file that uses little RAM. Notes ----- This function does not return any arguments but, instead, saves nii files to disk.
[ "Save", "empirical", "and", "fitted", "time", "courses", "to", "nii", "file", "format", "." ]
train
https://github.com/MSchnei/pyprf_feature/blob/49004ede7ae1ddee07a30afe9ce3e2776750805c/pyprf_feature/analysis/save_fit_tc_nii.py#L39-L292
MSchnei/pyprf_feature
pyprf_feature/analysis/pyprf_sim.py
pyprf_sim
def pyprf_sim(strPrior, strStmApr, lgcNoise=False, lgcRtnNrl=True, lstRat=None, lgcTest=False): """ Simulate pRF response given pRF parameters and stimulus apertures. Parameters ---------- strPrior : str Absolute file path of config file used for pRF fitting. strStmApr : str Absolute file path to stimulus aperture used in in-silico experiment. lgcNoise : boolean Should noise be added to the simulated pRF time course. By default, no noise is added. lgcRtnNrl : boolean Should neural time course, unconvolved with hrf, be returned as well? lstRat : None or list Ratio of size of center to size of suppressive surround. lgcTest : boolean Whether this is a test (pytest). If yes, absolute path of pyprf libary will be prepended to config file paths. Notes ----- [1] This function does not return any arguments but, instead, saves nii filex to disk. [2] strStmApr should be a path to a npy file that contains a 3D numpy array. This arrays consists of binary images in boolean array from that represent the stimulus aperture. Images are stacked along last axis. """ # %% Load configuration settings that were used for fitting # Load config parameters from csv file into dictionary: dicCnfg = load_config(strPrior, lgcTest=lgcTest) # Load config parameters from dictionary into namespace: cfg = cls_set_config(dicCnfg) # If suppressive surround flag is on, make sure to retrieve results from # that fitting if lstRat is not None: cfg.strPathOut = cfg.strPathOut + '_supsur' # %% Load previous pRF fitting results # Derive paths to the x, y, sigma winner parameters from pyprf_feature lstWnrPrm = [cfg.strPathOut + '_x_pos.nii.gz', cfg.strPathOut + '_y_pos.nii.gz', cfg.strPathOut + '_SD.nii.gz'] # Check if fitting has been performed, i.e. whether parameter files exist # Throw error message if they do not exist. errorMsg = 'Files that should have resulted from fitting do not exist. \ \nPlease perform pRF fitting first, calling e.g.: \ \npyprf_feature -config /path/to/my_config_file.csv' assert os.path.isfile(lstWnrPrm[0]), errorMsg assert os.path.isfile(lstWnrPrm[1]), errorMsg assert os.path.isfile(lstWnrPrm[2]), errorMsg # Load the x, y, sigma winner parameters from pyprf_feature aryIntGssPrm = load_res_prm(lstWnrPrm, lstFlsMsk=[cfg.strPathNiiMask])[0][0] # Also load suppresive surround params if suppressive surround flag was on if lstRat is not None: # Load beta parameters estimates, aka weights, this is later needed to # scale responses of the center wrt to the surround lstPathBeta = [cfg.strPathOut + '_Betas.nii.gz'] aryBetas = load_res_prm(lstPathBeta, lstFlsMsk=[cfg.strPathNiiMask])[0][0] # Load ratio of prf sizes lstPathRat = [cfg.strPathOut + '_Ratios.nii.gz'] aryRat = load_res_prm(lstPathRat, lstFlsMsk=[cfg.strPathNiiMask])[0][0] # Some voxels were excluded because they did not have sufficient mean # and/or variance - exclude their initial parameters, too # Get inclusion mask and nii header aryLgcMsk, aryLgcVar, hdrMsk, aryAff, _, tplNiiShp = prep_func( cfg.strPathNiiMask, cfg.lstPathNiiFunc, varAvgThr=100.) # Apply inclusion mask aryIntGssPrm = aryIntGssPrm[aryLgcVar, :] if lstRat is not None: aryBetas = aryBetas[aryLgcVar, :] aryRat = aryRat[aryLgcVar] # %% Load stimulus aperture and create model responses to stimuli # Load stimulus aperture aryStmApr = np.load(strStmApr) # Which dimensions does the representation have in pixel space? tplStmApr = aryStmApr.shape[:2] # Convert winner parameters from degrees of visual angle to pixel vecIntX, vecIntY, vecIntSd = rmp_deg_pixel_xys(aryIntGssPrm[:, 0], aryIntGssPrm[:, 1], aryIntGssPrm[:, 2], tplStmApr, cfg.varExtXmin, cfg.varExtXmax, cfg.varExtYmin, cfg.varExtYmax) aryIntGssPrmPxl = np.column_stack((vecIntX, vecIntY, vecIntSd)) # Create 2D Gauss model responses to spatial conditions. print('---Create 2D Gauss model responses to spatial conditions') aryMdlRsp = crt_mdl_rsp(aryStmApr, tplStmApr, aryIntGssPrmPxl, cfg.varPar) # If supsur flag was provided, also create responses with supsur params # and combine positive center response with negative surround response if lstRat is not None: aryIntGssPrmPxlSur = np.copy(aryIntGssPrmPxl) # Adjust pRF sizes using the ratio of pRF sizes aryIntGssPrmPxlSur[:, 2] = np.multiply(aryIntGssPrmPxlSur[:, 2], aryRat) aryMdlRspSur = crt_mdl_rsp(aryStmApr, tplStmApr, aryIntGssPrmPxlSur, cfg.varPar) # Now the responses of the center and the surround need to be combined # in a meaningful way. One way this could be done is to take the ratio # of gain parameters that were found when fitting (i.e. betas) varGainRat = np.divide(aryBetas[:, 0], aryBetas[:, 1]) aryMdlRsp = np.subtract(aryMdlRsp, np.multiply(varGainRat, aryMdlRspSur)) # %% Convolve time courses with hrf function # First temporally upsamle the model response aryMdlRspUps = np.repeat(aryMdlRsp, cfg.varTmpOvsmpl, axis=-1) # Convolve with hrf function arySimRsp = crt_prf_tc(aryMdlRspUps, aryMdlRsp.shape[-1], cfg.varTr, cfg.varTmpOvsmpl, 1, tplStmApr, cfg.varPar) # Squeeze simulated reponse. This step is necessary because crt_prf_tc is, # in principle, capable of convolving with deriavtes of canonical function if arySimRsp.shape[1] > 1: print('***WARNING: pyprf_sim expects 1 hrf function, currently***') arySimRsp = np.squeeze(arySimRsp) # Save memory by deleting upsampled time course del(aryMdlRspUps) # %% Add auto-correlated noise if lgcNoise: print('***Adding noise feature not yet implemented***') # %% Export simulated prf, and if desired neural, time courses as nii # List with name suffices of output images: lstNiiNames = ['_SimPrfTc'] # Create full path names from nii file names and output path lstNiiNames = [cfg.strPathOut + strNii + '.nii.gz' for strNii in lstNiiNames] # export beta parameter as a single 4D nii file print('---Save simulated pRF time courses') export_nii(arySimRsp, lstNiiNames, aryLgcMsk, aryLgcVar, tplNiiShp, aryAff, hdrMsk, outFormat='4D') print('------Done.') if lgcRtnNrl: # List with name suffices of output images: lstNiiNames = ['_SimNrlTc'] # Create full path names from nii file names and output path lstNiiNames = [cfg.strPathOut + strNii + '.nii.gz' for strNii in lstNiiNames] # export beta parameter as a single 4D nii file print('---Save simulated neural time courses') export_nii(aryMdlRsp, lstNiiNames, aryLgcMsk, aryLgcVar, tplNiiShp, aryAff, hdrMsk, outFormat='4D') print('------Done.')
python
def pyprf_sim(strPrior, strStmApr, lgcNoise=False, lgcRtnNrl=True, lstRat=None, lgcTest=False): """ Simulate pRF response given pRF parameters and stimulus apertures. Parameters ---------- strPrior : str Absolute file path of config file used for pRF fitting. strStmApr : str Absolute file path to stimulus aperture used in in-silico experiment. lgcNoise : boolean Should noise be added to the simulated pRF time course. By default, no noise is added. lgcRtnNrl : boolean Should neural time course, unconvolved with hrf, be returned as well? lstRat : None or list Ratio of size of center to size of suppressive surround. lgcTest : boolean Whether this is a test (pytest). If yes, absolute path of pyprf libary will be prepended to config file paths. Notes ----- [1] This function does not return any arguments but, instead, saves nii filex to disk. [2] strStmApr should be a path to a npy file that contains a 3D numpy array. This arrays consists of binary images in boolean array from that represent the stimulus aperture. Images are stacked along last axis. """ # %% Load configuration settings that were used for fitting # Load config parameters from csv file into dictionary: dicCnfg = load_config(strPrior, lgcTest=lgcTest) # Load config parameters from dictionary into namespace: cfg = cls_set_config(dicCnfg) # If suppressive surround flag is on, make sure to retrieve results from # that fitting if lstRat is not None: cfg.strPathOut = cfg.strPathOut + '_supsur' # %% Load previous pRF fitting results # Derive paths to the x, y, sigma winner parameters from pyprf_feature lstWnrPrm = [cfg.strPathOut + '_x_pos.nii.gz', cfg.strPathOut + '_y_pos.nii.gz', cfg.strPathOut + '_SD.nii.gz'] # Check if fitting has been performed, i.e. whether parameter files exist # Throw error message if they do not exist. errorMsg = 'Files that should have resulted from fitting do not exist. \ \nPlease perform pRF fitting first, calling e.g.: \ \npyprf_feature -config /path/to/my_config_file.csv' assert os.path.isfile(lstWnrPrm[0]), errorMsg assert os.path.isfile(lstWnrPrm[1]), errorMsg assert os.path.isfile(lstWnrPrm[2]), errorMsg # Load the x, y, sigma winner parameters from pyprf_feature aryIntGssPrm = load_res_prm(lstWnrPrm, lstFlsMsk=[cfg.strPathNiiMask])[0][0] # Also load suppresive surround params if suppressive surround flag was on if lstRat is not None: # Load beta parameters estimates, aka weights, this is later needed to # scale responses of the center wrt to the surround lstPathBeta = [cfg.strPathOut + '_Betas.nii.gz'] aryBetas = load_res_prm(lstPathBeta, lstFlsMsk=[cfg.strPathNiiMask])[0][0] # Load ratio of prf sizes lstPathRat = [cfg.strPathOut + '_Ratios.nii.gz'] aryRat = load_res_prm(lstPathRat, lstFlsMsk=[cfg.strPathNiiMask])[0][0] # Some voxels were excluded because they did not have sufficient mean # and/or variance - exclude their initial parameters, too # Get inclusion mask and nii header aryLgcMsk, aryLgcVar, hdrMsk, aryAff, _, tplNiiShp = prep_func( cfg.strPathNiiMask, cfg.lstPathNiiFunc, varAvgThr=100.) # Apply inclusion mask aryIntGssPrm = aryIntGssPrm[aryLgcVar, :] if lstRat is not None: aryBetas = aryBetas[aryLgcVar, :] aryRat = aryRat[aryLgcVar] # %% Load stimulus aperture and create model responses to stimuli # Load stimulus aperture aryStmApr = np.load(strStmApr) # Which dimensions does the representation have in pixel space? tplStmApr = aryStmApr.shape[:2] # Convert winner parameters from degrees of visual angle to pixel vecIntX, vecIntY, vecIntSd = rmp_deg_pixel_xys(aryIntGssPrm[:, 0], aryIntGssPrm[:, 1], aryIntGssPrm[:, 2], tplStmApr, cfg.varExtXmin, cfg.varExtXmax, cfg.varExtYmin, cfg.varExtYmax) aryIntGssPrmPxl = np.column_stack((vecIntX, vecIntY, vecIntSd)) # Create 2D Gauss model responses to spatial conditions. print('---Create 2D Gauss model responses to spatial conditions') aryMdlRsp = crt_mdl_rsp(aryStmApr, tplStmApr, aryIntGssPrmPxl, cfg.varPar) # If supsur flag was provided, also create responses with supsur params # and combine positive center response with negative surround response if lstRat is not None: aryIntGssPrmPxlSur = np.copy(aryIntGssPrmPxl) # Adjust pRF sizes using the ratio of pRF sizes aryIntGssPrmPxlSur[:, 2] = np.multiply(aryIntGssPrmPxlSur[:, 2], aryRat) aryMdlRspSur = crt_mdl_rsp(aryStmApr, tplStmApr, aryIntGssPrmPxlSur, cfg.varPar) # Now the responses of the center and the surround need to be combined # in a meaningful way. One way this could be done is to take the ratio # of gain parameters that were found when fitting (i.e. betas) varGainRat = np.divide(aryBetas[:, 0], aryBetas[:, 1]) aryMdlRsp = np.subtract(aryMdlRsp, np.multiply(varGainRat, aryMdlRspSur)) # %% Convolve time courses with hrf function # First temporally upsamle the model response aryMdlRspUps = np.repeat(aryMdlRsp, cfg.varTmpOvsmpl, axis=-1) # Convolve with hrf function arySimRsp = crt_prf_tc(aryMdlRspUps, aryMdlRsp.shape[-1], cfg.varTr, cfg.varTmpOvsmpl, 1, tplStmApr, cfg.varPar) # Squeeze simulated reponse. This step is necessary because crt_prf_tc is, # in principle, capable of convolving with deriavtes of canonical function if arySimRsp.shape[1] > 1: print('***WARNING: pyprf_sim expects 1 hrf function, currently***') arySimRsp = np.squeeze(arySimRsp) # Save memory by deleting upsampled time course del(aryMdlRspUps) # %% Add auto-correlated noise if lgcNoise: print('***Adding noise feature not yet implemented***') # %% Export simulated prf, and if desired neural, time courses as nii # List with name suffices of output images: lstNiiNames = ['_SimPrfTc'] # Create full path names from nii file names and output path lstNiiNames = [cfg.strPathOut + strNii + '.nii.gz' for strNii in lstNiiNames] # export beta parameter as a single 4D nii file print('---Save simulated pRF time courses') export_nii(arySimRsp, lstNiiNames, aryLgcMsk, aryLgcVar, tplNiiShp, aryAff, hdrMsk, outFormat='4D') print('------Done.') if lgcRtnNrl: # List with name suffices of output images: lstNiiNames = ['_SimNrlTc'] # Create full path names from nii file names and output path lstNiiNames = [cfg.strPathOut + strNii + '.nii.gz' for strNii in lstNiiNames] # export beta parameter as a single 4D nii file print('---Save simulated neural time courses') export_nii(aryMdlRsp, lstNiiNames, aryLgcMsk, aryLgcVar, tplNiiShp, aryAff, hdrMsk, outFormat='4D') print('------Done.')
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Simulate pRF response given pRF parameters and stimulus apertures. Parameters ---------- strPrior : str Absolute file path of config file used for pRF fitting. strStmApr : str Absolute file path to stimulus aperture used in in-silico experiment. lgcNoise : boolean Should noise be added to the simulated pRF time course. By default, no noise is added. lgcRtnNrl : boolean Should neural time course, unconvolved with hrf, be returned as well? lstRat : None or list Ratio of size of center to size of suppressive surround. lgcTest : boolean Whether this is a test (pytest). If yes, absolute path of pyprf libary will be prepended to config file paths. Notes ----- [1] This function does not return any arguments but, instead, saves nii filex to disk. [2] strStmApr should be a path to a npy file that contains a 3D numpy array. This arrays consists of binary images in boolean array from that represent the stimulus aperture. Images are stacked along last axis.
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train
https://github.com/MSchnei/pyprf_feature/blob/49004ede7ae1ddee07a30afe9ce3e2776750805c/pyprf_feature/analysis/pyprf_sim.py#L35-L209
bachya/pyflunearyou
pyflunearyou/helpers/__init__.py
get_nearest_by_numeric_key
def get_nearest_by_numeric_key(data: dict, key: int) -> Any: """Return the dict element whose numeric key is closest to a target.""" return data.get(key, data[min(data.keys(), key=lambda k: abs(k - key))])
python
def get_nearest_by_numeric_key(data: dict, key: int) -> Any: """Return the dict element whose numeric key is closest to a target.""" return data.get(key, data[min(data.keys(), key=lambda k: abs(k - key))])
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Return the dict element whose numeric key is closest to a target.
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train
https://github.com/bachya/pyflunearyou/blob/16a2f839c8df851e925e010a6b5c5708386febac/pyflunearyou/helpers/__init__.py#L5-L7
olivier-m/rafter
rafter/blueprints.py
Blueprint.resource
def resource(self, uri, methods=frozenset({'GET'}), host=None, strict_slashes=None, stream=False, version=None, name=None, **kwargs): """ Create a blueprint resource route from a decorated function. :param uri: endpoint at which the route will be accessible. :param methods: list of acceptable HTTP methods. :param host: :param strict_slashes: :param version: :param name: user defined route name for url_for :return: function or class instance Accepts any keyword argument that will be passed to the app resource. """ if strict_slashes is None: strict_slashes = self.strict_slashes def decorator(handler): self.resources.append(( FutureRoute(handler, uri, methods, host, strict_slashes, stream, version, name), kwargs)) return handler return decorator
python
def resource(self, uri, methods=frozenset({'GET'}), host=None, strict_slashes=None, stream=False, version=None, name=None, **kwargs): """ Create a blueprint resource route from a decorated function. :param uri: endpoint at which the route will be accessible. :param methods: list of acceptable HTTP methods. :param host: :param strict_slashes: :param version: :param name: user defined route name for url_for :return: function or class instance Accepts any keyword argument that will be passed to the app resource. """ if strict_slashes is None: strict_slashes = self.strict_slashes def decorator(handler): self.resources.append(( FutureRoute(handler, uri, methods, host, strict_slashes, stream, version, name), kwargs)) return handler return decorator
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train
https://github.com/olivier-m/rafter/blob/aafcf8fd019f24abcf519307c4484cc6b4697c04/rafter/blueprints.py#L46-L72
olivier-m/rafter
rafter/blueprints.py
Blueprint.add_resource
def add_resource(self, handler, uri, methods=frozenset({'GET'}), host=None, strict_slashes=None, version=None, name=None, **kwargs): """ Create a blueprint resource route from a function. :param uri: endpoint at which the route will be accessible. :param methods: list of acceptable HTTP methods. :param host: :param strict_slashes: :param version: :param name: user defined route name for url_for :return: function or class instance Accepts any keyword argument that will be passed to the app resource. """ self.resource(uri=uri, methods=methods, host=host, strict_slashes=strict_slashes, version=version, name=name, **kwargs)(handler)
python
def add_resource(self, handler, uri, methods=frozenset({'GET'}), host=None, strict_slashes=None, version=None, name=None, **kwargs): """ Create a blueprint resource route from a function. :param uri: endpoint at which the route will be accessible. :param methods: list of acceptable HTTP methods. :param host: :param strict_slashes: :param version: :param name: user defined route name for url_for :return: function or class instance Accepts any keyword argument that will be passed to the app resource. """ self.resource(uri=uri, methods=methods, host=host, strict_slashes=strict_slashes, version=version, name=name, **kwargs)(handler)
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train
https://github.com/olivier-m/rafter/blob/aafcf8fd019f24abcf519307c4484cc6b4697c04/rafter/blueprints.py#L74-L92
lvieirajr/mongorest
mongorest/database.py
_get_db
def _get_db(): """ Returns the connection to the database using the settings. This function should not be called outside of this file. Use db instead. """ from .settings import settings mongo = settings.MONGODB if 'URI' in mongo and mongo['URI']: uri = mongo['URI'] else: uri = 'mongodb://' if all(mongo.get(key) for key in ('USERNAME', 'PASSWORD')): uri += '{0}:{1}@'.format(mongo['USERNAME'], mongo['PASSWORD']) if 'HOSTS' in mongo and mongo['HOSTS']: uri += ','.join( '{0}:{1}'.format(host, port) for (host, port) in zip(mongo['HOSTS'], mongo['PORTS']), ) else: uri += '{0}:{1}'.format(mongo['HOST'], mongo.get('PORT', 27017)) uri += '/' + mongo['DATABASE'] if 'OPTIONS' in mongo and mongo['OPTIONS']: uri += '?{0}'.format('&'.join(mongo['OPTIONS'])) client = ConnectionFailureProxy(MongoClient(uri, connect=False)) database = client[parse_uri(uri)['database']] return database
python
def _get_db(): """ Returns the connection to the database using the settings. This function should not be called outside of this file. Use db instead. """ from .settings import settings mongo = settings.MONGODB if 'URI' in mongo and mongo['URI']: uri = mongo['URI'] else: uri = 'mongodb://' if all(mongo.get(key) for key in ('USERNAME', 'PASSWORD')): uri += '{0}:{1}@'.format(mongo['USERNAME'], mongo['PASSWORD']) if 'HOSTS' in mongo and mongo['HOSTS']: uri += ','.join( '{0}:{1}'.format(host, port) for (host, port) in zip(mongo['HOSTS'], mongo['PORTS']), ) else: uri += '{0}:{1}'.format(mongo['HOST'], mongo.get('PORT', 27017)) uri += '/' + mongo['DATABASE'] if 'OPTIONS' in mongo and mongo['OPTIONS']: uri += '?{0}'.format('&'.join(mongo['OPTIONS'])) client = ConnectionFailureProxy(MongoClient(uri, connect=False)) database = client[parse_uri(uri)['database']] return database
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Returns the connection to the database using the settings. This function should not be called outside of this file. Use db instead.
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train
https://github.com/lvieirajr/mongorest/blob/00f4487ded33254434bc51ff09d48c7a936bd465/mongorest/database.py#L91-L124
MSchnei/pyprf_feature
pyprf_feature/analysis/old/pRF_calcR2_getBetas.py
getBetas
def getBetas(idxPrc, aryPrfTc, lstAllMdlInd, aryFuncChnk, aryBstIndChnk, betaSw, queOut): """Calculate voxel betas and R^2 for the best model. Parameters ---------- idxPrc : TODO (?) aryPrfTc : np.array, shape (?) Population receptive field time courses. lstAllMdlInd : list List of the indices of all models. aryFuncChnk : TODO Chunk of something(?) aryBstIndChnk : np.array, shape (?) Points for every voxel to the index of the best model betaSw : str, iterator, or np.array, shape (?) Best beta correlation coefficients found in training. queOut : TODO Queue output (?) Notes ----- This is done after fitting with cross validation, since during the fitting process, we never fit the model to the entire data. """ # get number of motion directions varNumMtnDrctns = aryPrfTc.shape[3] varNumVoxChnk = aryBstIndChnk.shape[0] # prepare array for best beta weights if type(betaSw) is sklearn.model_selection._split.KFold: aryEstimMtnCrvTrn = np.zeros((varNumVoxChnk, varNumMtnDrctns, betaSw.get_n_splits()), dtype='float32') aryEstimMtnCrvTst = np.zeros((varNumVoxChnk, varNumMtnDrctns, betaSw.get_n_splits()), dtype='float32') resTrn = np.zeros((varNumVoxChnk, betaSw.get_n_splits()), dtype='float32') resTst = np.zeros((varNumVoxChnk, betaSw.get_n_splits()), dtype='float32') aryErrorTrn = np.zeros((varNumVoxChnk), dtype='float32') aryErrorTst = np.zeros((varNumVoxChnk), dtype='float32') contrast = np.array([ [1, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 1, 0], ]) denomTrn = np.zeros((varNumVoxChnk, betaSw.get_n_splits(), len(contrast)), dtype='float32') denomTst = np.zeros((varNumVoxChnk, betaSw.get_n_splits(), len(contrast)), dtype='float32') elif type(betaSw) is np.ndarray and betaSw.dtype == 'bool': aryEstimMtnCrvTrn = np.zeros((varNumVoxChnk, varNumMtnDrctns, ), dtype='float32') aryEstimMtnCrvTst = np.zeros((varNumVoxChnk, varNumMtnDrctns, ), dtype='float32') resTrn = np.zeros((varNumVoxChnk), dtype='float32') resTst = np.zeros((varNumVoxChnk), dtype='float32') aryErrorTrn = np.zeros((varNumVoxChnk), dtype='float32') aryErrorTst = np.zeros((varNumVoxChnk), dtype='float32') contrast = np.array([ [1, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 1, 0], ]) denomTrn = np.zeros((varNumVoxChnk, len(contrast)), dtype='float32') denomTst = np.zeros((varNumVoxChnk, len(contrast)), dtype='float32') else: aryEstimMtnCrv = np.zeros((varNumVoxChnk, varNumMtnDrctns), dtype='float32') # prepare array for best residuals vecBstRes = np.zeros(varNumVoxChnk, dtype='float32') vecBstRes[:] = np.inf # prepare counter to check that every voxel is matched to one winner mdl vecLgcCounter = np.zeros(varNumVoxChnk, dtype='float32') # We reshape the voxel time courses, so that time goes down the column aryFuncChnk = aryFuncChnk.T # Change type to float 32: aryFuncChnk = aryFuncChnk.astype(np.float32) aryPrfTc = aryPrfTc.astype(np.float32) # Prepare status indicator if this is the first of the parallel processes: if idxPrc == 0: # We create a status indicator for the time consuming pRF model finding # algorithm. Number of steps of the status indicator: varStsStpSze = 20 # Number of pRF models to fit: varNumMdls = len(lstAllMdlInd) # Vector with pRF values at which to give status feedback: vecStatPrf = np.linspace(0, varNumMdls, num=(varStsStpSze+1), endpoint=True) vecStatPrf = np.ceil(vecStatPrf) vecStatPrf = vecStatPrf.astype(int) # Vector with corresponding percentage values at which to give status # feedback: vecStatPrc = np.linspace(0, 100, num=(varStsStpSze+1), endpoint=True) vecStatPrc = np.ceil(vecStatPrc) vecStatPrc = vecStatPrc.astype(int) # Counter for status indicator: varCntSts01 = 0 varCntSts02 = 0 # Loop through pRF models: for idx, mdlInd in enumerate(lstAllMdlInd): # Status indicator (only used in the first of the parallel # processes): if idxPrc == 0: # Status indicator: if varCntSts02 == vecStatPrf[varCntSts01]: # Prepare status message: strStsMsg = ('---------Progress: ' + str(vecStatPrc[varCntSts01]) + ' % --- ' + str(vecStatPrf[varCntSts01]) + ' pRF models out of ' + str(varNumMdls)) print(strStsMsg) # Only increment counter if the last value has not been # reached yet: if varCntSts01 < varStsStpSze: varCntSts01 = varCntSts01 + int(1) # check whether any model had this particular x, y, sigma combination # as its best model lgcTemp = [aryBstIndChnk == idx][0] if np.greater(np.sum(lgcTemp), 0): # get current design matrix aryDsgnTmp = aryPrfTc[mdlInd].T if betaSw is 'train': # training aryTmpPrmEst, aryTmpRes = np.linalg.lstsq( aryDsgnTmp, aryFuncChnk[:, lgcTemp])[0:2] aryEstimMtnCrv[lgcTemp, :] = aryTmpPrmEst.T vecBstRes[lgcTemp] = aryTmpRes elif type(betaSw) is np.ndarray and betaSw.dtype == 'float': # get beta weights for axis of motion tuning curves aryEstimMtnCrv[lgcTemp, :] = np.linalg.lstsq( aryDsgnTmp, aryFuncChnk[:, lgcTemp])[0].T # calculate prediction aryPredTc = np.dot(aryDsgnTmp, betaSw[lgcTemp, :].T) # Sum of squares: vecBstRes[lgcTemp] = np.sum((aryFuncChnk[:, lgcTemp] - aryPredTc) ** 2, axis=0) elif type(betaSw) is np.ndarray and betaSw.dtype == 'bool': # get beta weights for training betas, resTrn[lgcTemp] = np.linalg.lstsq( aryDsgnTmp[betaSw, :], aryFuncChnk[betaSw][:, lgcTemp])[0:2] aryEstimMtnCrvTrn[lgcTemp, :] = betas.T # get beta weights for validation betas, resTst[lgcTemp] = np.linalg.lstsq( aryDsgnTmp[~betaSw, :], aryFuncChnk[~betaSw][:, lgcTemp])[0:2] aryEstimMtnCrvTrn[lgcTemp, :] = betas.T # calculate CC for training aryCcTrn = np.linalg.pinv( np.dot(aryDsgnTmp[betaSw, :].T, aryDsgnTmp[betaSw, :])) aryCcTst = np.linalg.pinv( np.dot(aryDsgnTmp[~betaSw, :].T, aryDsgnTmp[~betaSw, :])) # calculate Error for training aryErrorTrn[lgcTemp] = np.var( np.subtract(aryFuncChnk[betaSw][:, lgcTemp], np.dot(aryDsgnTmp[betaSw, :], aryEstimMtnCrvTrn[lgcTemp, :].T)), axis=0) # calculate Error for test aryErrorTst[lgcTemp] = np.var( np.subtract(aryFuncChnk[~betaSw][:, lgcTemp], np.dot(aryDsgnTmp[~betaSw, :], aryEstimMtnCrvTst[lgcTemp, :].T)), axis=0) # calculate denominator for training for indContr, contr in enumerate(contrast): denomTrn[lgcTemp, indContr] = np.sqrt( aryErrorTrn[lgcTemp] * np.dot( np.dot(contr, aryCcTrn), contr.T)) denomTst[lgcTemp, indContr] = np.sqrt( aryErrorTst[lgcTemp] * np.dot( np.dot(contr, aryCcTst), contr.T)) elif type(betaSw) is sklearn.model_selection._split.KFold: for idxCV, (idxTrn, idxVal) in enumerate(betaSw.split(aryDsgnTmp)): # get beta weights for training betas, resTrn[lgcTemp, idxCV] = np.linalg.lstsq( aryDsgnTmp[idxTrn], aryFuncChnk[idxTrn][:, lgcTemp])[0:2] aryEstimMtnCrvTrn[lgcTemp, :, idxCV] = betas.T # get beta weights for validation betas, resTst[lgcTemp, idxCV] = np.linalg.lstsq( aryDsgnTmp[idxVal], aryFuncChnk[idxVal][:, lgcTemp])[0:2] aryEstimMtnCrvTst[lgcTemp, :, idxCV] = betas.T # calculate CC for training aryCcTrn = np.linalg.pinv( np.dot(aryDsgnTmp[idxTrn].T, aryDsgnTmp[idxTrn])) aryCcTst = np.linalg.pinv( np.dot(aryDsgnTmp[idxVal].T, aryDsgnTmp[idxVal])) # calculate Error for training aryErrorTrn[lgcTemp] = np.var( np.subtract(aryFuncChnk[idxTrn][:, lgcTemp], np.dot(aryDsgnTmp[idxTrn], aryEstimMtnCrvTrn[lgcTemp, :, idxCV].T)), axis=0) # calculate Error for test aryErrorTst[lgcTemp] = np.var( np.subtract(aryFuncChnk[idxVal][:, lgcTemp], np.dot(aryDsgnTmp[idxVal], aryEstimMtnCrvTst[lgcTemp, :, idxCV].T)), axis=0) # calculate denominator for training for indContr, contr in enumerate(contrast): denomTrn[lgcTemp, idxCV, indContr] = np.sqrt( aryErrorTrn[lgcTemp] * np.dot( np.dot(contr, aryCcTrn), contr.T)) denomTst[lgcTemp, idxCV, indContr] = np.sqrt( aryErrorTst[lgcTemp] * np.dot( np.dot(contr, aryCcTst), contr.T)) # increase logical counter to verify later that every voxel # was visited only once vecLgcCounter[lgcTemp] += 1 # Status indicator (only used in the first of the parallel # processes): if idxPrc == 0: # Increment status indicator counter: varCntSts02 = varCntSts02 + 1 # check that every voxel was visited only once strErrMsg = ('It looks like at least voxel was revisted more than once. ' + 'Check whether the R2 was calculated correctly') assert np.sum(vecLgcCounter) == len(vecLgcCounter), strErrMsg if type(betaSw) is sklearn.model_selection._split.KFold: # calculate t-values aryTvalsTrn = np.empty((varNumVoxChnk, contrast.shape[0], betaSw.get_n_splits())) aryTvalsTst = np.empty((varNumVoxChnk, contrast.shape[0], betaSw.get_n_splits())) for ind1, contr in enumerate(contrast): for ind2 in range(betaSw.get_n_splits()): aryTvalsTrn[:, ind1, ind2] = np.divide( np.dot(contr, aryEstimMtnCrvTrn[:, :, ind2].T), denomTrn[:, ind2, ind1]) aryTvalsTst[:, ind1, ind2] = np.divide( np.dot(contr, aryEstimMtnCrvTst[:, :, ind2].T), denomTst[:, ind2, ind1]) # Output list: lstOut = [idxPrc, aryEstimMtnCrvTrn, aryEstimMtnCrvTst, aryTvalsTrn, aryTvalsTst, ] queOut.put(lstOut) elif type(betaSw) is np.ndarray and betaSw.dtype == 'bool': # calculate t-values aryTvalsTrn = np.empty((varNumVoxChnk, contrast.shape[0], )) aryTvalsTst = np.empty((varNumVoxChnk, contrast.shape[0], )) for ind1, contr in enumerate(contrast): aryTvalsTrn[:, ind1] = np.divide( np.dot(contr, aryEstimMtnCrvTrn.T), denomTrn[:, ind1]) aryTvalsTst[:, ind1] = np.divide( np.dot(contr, aryEstimMtnCrvTst.T), denomTst[:, ind1]) # Output list: lstOut = [idxPrc, aryEstimMtnCrvTrn, aryEstimMtnCrvTst, aryTvalsTrn, aryTvalsTst, ] queOut.put(lstOut) else: # After finding the best fitting model for each voxel, we still have to # calculate the coefficient of determination (R-squared) for each voxel. We # start by calculating the total sum of squares (i.e. the deviation of the # data from the mean). The mean of each time course: vecFuncMean = np.mean(aryFuncChnk, axis=0) # Deviation from the mean for each datapoint: vecFuncDev = np.subtract(aryFuncChnk, vecFuncMean[None, :]) # Sum of squares: vecSsTot = np.sum(np.power(vecFuncDev, 2.0), axis=0) # Coefficient of determination: vecBstR2 = np.subtract(1.0, np.divide(vecBstRes, vecSsTot)) # Output list: lstOut = [idxPrc, vecBstR2, aryEstimMtnCrv] queOut.put(lstOut)
python
def getBetas(idxPrc, aryPrfTc, lstAllMdlInd, aryFuncChnk, aryBstIndChnk, betaSw, queOut): """Calculate voxel betas and R^2 for the best model. Parameters ---------- idxPrc : TODO (?) aryPrfTc : np.array, shape (?) Population receptive field time courses. lstAllMdlInd : list List of the indices of all models. aryFuncChnk : TODO Chunk of something(?) aryBstIndChnk : np.array, shape (?) Points for every voxel to the index of the best model betaSw : str, iterator, or np.array, shape (?) Best beta correlation coefficients found in training. queOut : TODO Queue output (?) Notes ----- This is done after fitting with cross validation, since during the fitting process, we never fit the model to the entire data. """ # get number of motion directions varNumMtnDrctns = aryPrfTc.shape[3] varNumVoxChnk = aryBstIndChnk.shape[0] # prepare array for best beta weights if type(betaSw) is sklearn.model_selection._split.KFold: aryEstimMtnCrvTrn = np.zeros((varNumVoxChnk, varNumMtnDrctns, betaSw.get_n_splits()), dtype='float32') aryEstimMtnCrvTst = np.zeros((varNumVoxChnk, varNumMtnDrctns, betaSw.get_n_splits()), dtype='float32') resTrn = np.zeros((varNumVoxChnk, betaSw.get_n_splits()), dtype='float32') resTst = np.zeros((varNumVoxChnk, betaSw.get_n_splits()), dtype='float32') aryErrorTrn = np.zeros((varNumVoxChnk), dtype='float32') aryErrorTst = np.zeros((varNumVoxChnk), dtype='float32') contrast = np.array([ [1, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 1, 0], ]) denomTrn = np.zeros((varNumVoxChnk, betaSw.get_n_splits(), len(contrast)), dtype='float32') denomTst = np.zeros((varNumVoxChnk, betaSw.get_n_splits(), len(contrast)), dtype='float32') elif type(betaSw) is np.ndarray and betaSw.dtype == 'bool': aryEstimMtnCrvTrn = np.zeros((varNumVoxChnk, varNumMtnDrctns, ), dtype='float32') aryEstimMtnCrvTst = np.zeros((varNumVoxChnk, varNumMtnDrctns, ), dtype='float32') resTrn = np.zeros((varNumVoxChnk), dtype='float32') resTst = np.zeros((varNumVoxChnk), dtype='float32') aryErrorTrn = np.zeros((varNumVoxChnk), dtype='float32') aryErrorTst = np.zeros((varNumVoxChnk), dtype='float32') contrast = np.array([ [1, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 1, 0], ]) denomTrn = np.zeros((varNumVoxChnk, len(contrast)), dtype='float32') denomTst = np.zeros((varNumVoxChnk, len(contrast)), dtype='float32') else: aryEstimMtnCrv = np.zeros((varNumVoxChnk, varNumMtnDrctns), dtype='float32') # prepare array for best residuals vecBstRes = np.zeros(varNumVoxChnk, dtype='float32') vecBstRes[:] = np.inf # prepare counter to check that every voxel is matched to one winner mdl vecLgcCounter = np.zeros(varNumVoxChnk, dtype='float32') # We reshape the voxel time courses, so that time goes down the column aryFuncChnk = aryFuncChnk.T # Change type to float 32: aryFuncChnk = aryFuncChnk.astype(np.float32) aryPrfTc = aryPrfTc.astype(np.float32) # Prepare status indicator if this is the first of the parallel processes: if idxPrc == 0: # We create a status indicator for the time consuming pRF model finding # algorithm. Number of steps of the status indicator: varStsStpSze = 20 # Number of pRF models to fit: varNumMdls = len(lstAllMdlInd) # Vector with pRF values at which to give status feedback: vecStatPrf = np.linspace(0, varNumMdls, num=(varStsStpSze+1), endpoint=True) vecStatPrf = np.ceil(vecStatPrf) vecStatPrf = vecStatPrf.astype(int) # Vector with corresponding percentage values at which to give status # feedback: vecStatPrc = np.linspace(0, 100, num=(varStsStpSze+1), endpoint=True) vecStatPrc = np.ceil(vecStatPrc) vecStatPrc = vecStatPrc.astype(int) # Counter for status indicator: varCntSts01 = 0 varCntSts02 = 0 # Loop through pRF models: for idx, mdlInd in enumerate(lstAllMdlInd): # Status indicator (only used in the first of the parallel # processes): if idxPrc == 0: # Status indicator: if varCntSts02 == vecStatPrf[varCntSts01]: # Prepare status message: strStsMsg = ('---------Progress: ' + str(vecStatPrc[varCntSts01]) + ' % --- ' + str(vecStatPrf[varCntSts01]) + ' pRF models out of ' + str(varNumMdls)) print(strStsMsg) # Only increment counter if the last value has not been # reached yet: if varCntSts01 < varStsStpSze: varCntSts01 = varCntSts01 + int(1) # check whether any model had this particular x, y, sigma combination # as its best model lgcTemp = [aryBstIndChnk == idx][0] if np.greater(np.sum(lgcTemp), 0): # get current design matrix aryDsgnTmp = aryPrfTc[mdlInd].T if betaSw is 'train': # training aryTmpPrmEst, aryTmpRes = np.linalg.lstsq( aryDsgnTmp, aryFuncChnk[:, lgcTemp])[0:2] aryEstimMtnCrv[lgcTemp, :] = aryTmpPrmEst.T vecBstRes[lgcTemp] = aryTmpRes elif type(betaSw) is np.ndarray and betaSw.dtype == 'float': # get beta weights for axis of motion tuning curves aryEstimMtnCrv[lgcTemp, :] = np.linalg.lstsq( aryDsgnTmp, aryFuncChnk[:, lgcTemp])[0].T # calculate prediction aryPredTc = np.dot(aryDsgnTmp, betaSw[lgcTemp, :].T) # Sum of squares: vecBstRes[lgcTemp] = np.sum((aryFuncChnk[:, lgcTemp] - aryPredTc) ** 2, axis=0) elif type(betaSw) is np.ndarray and betaSw.dtype == 'bool': # get beta weights for training betas, resTrn[lgcTemp] = np.linalg.lstsq( aryDsgnTmp[betaSw, :], aryFuncChnk[betaSw][:, lgcTemp])[0:2] aryEstimMtnCrvTrn[lgcTemp, :] = betas.T # get beta weights for validation betas, resTst[lgcTemp] = np.linalg.lstsq( aryDsgnTmp[~betaSw, :], aryFuncChnk[~betaSw][:, lgcTemp])[0:2] aryEstimMtnCrvTrn[lgcTemp, :] = betas.T # calculate CC for training aryCcTrn = np.linalg.pinv( np.dot(aryDsgnTmp[betaSw, :].T, aryDsgnTmp[betaSw, :])) aryCcTst = np.linalg.pinv( np.dot(aryDsgnTmp[~betaSw, :].T, aryDsgnTmp[~betaSw, :])) # calculate Error for training aryErrorTrn[lgcTemp] = np.var( np.subtract(aryFuncChnk[betaSw][:, lgcTemp], np.dot(aryDsgnTmp[betaSw, :], aryEstimMtnCrvTrn[lgcTemp, :].T)), axis=0) # calculate Error for test aryErrorTst[lgcTemp] = np.var( np.subtract(aryFuncChnk[~betaSw][:, lgcTemp], np.dot(aryDsgnTmp[~betaSw, :], aryEstimMtnCrvTst[lgcTemp, :].T)), axis=0) # calculate denominator for training for indContr, contr in enumerate(contrast): denomTrn[lgcTemp, indContr] = np.sqrt( aryErrorTrn[lgcTemp] * np.dot( np.dot(contr, aryCcTrn), contr.T)) denomTst[lgcTemp, indContr] = np.sqrt( aryErrorTst[lgcTemp] * np.dot( np.dot(contr, aryCcTst), contr.T)) elif type(betaSw) is sklearn.model_selection._split.KFold: for idxCV, (idxTrn, idxVal) in enumerate(betaSw.split(aryDsgnTmp)): # get beta weights for training betas, resTrn[lgcTemp, idxCV] = np.linalg.lstsq( aryDsgnTmp[idxTrn], aryFuncChnk[idxTrn][:, lgcTemp])[0:2] aryEstimMtnCrvTrn[lgcTemp, :, idxCV] = betas.T # get beta weights for validation betas, resTst[lgcTemp, idxCV] = np.linalg.lstsq( aryDsgnTmp[idxVal], aryFuncChnk[idxVal][:, lgcTemp])[0:2] aryEstimMtnCrvTst[lgcTemp, :, idxCV] = betas.T # calculate CC for training aryCcTrn = np.linalg.pinv( np.dot(aryDsgnTmp[idxTrn].T, aryDsgnTmp[idxTrn])) aryCcTst = np.linalg.pinv( np.dot(aryDsgnTmp[idxVal].T, aryDsgnTmp[idxVal])) # calculate Error for training aryErrorTrn[lgcTemp] = np.var( np.subtract(aryFuncChnk[idxTrn][:, lgcTemp], np.dot(aryDsgnTmp[idxTrn], aryEstimMtnCrvTrn[lgcTemp, :, idxCV].T)), axis=0) # calculate Error for test aryErrorTst[lgcTemp] = np.var( np.subtract(aryFuncChnk[idxVal][:, lgcTemp], np.dot(aryDsgnTmp[idxVal], aryEstimMtnCrvTst[lgcTemp, :, idxCV].T)), axis=0) # calculate denominator for training for indContr, contr in enumerate(contrast): denomTrn[lgcTemp, idxCV, indContr] = np.sqrt( aryErrorTrn[lgcTemp] * np.dot( np.dot(contr, aryCcTrn), contr.T)) denomTst[lgcTemp, idxCV, indContr] = np.sqrt( aryErrorTst[lgcTemp] * np.dot( np.dot(contr, aryCcTst), contr.T)) # increase logical counter to verify later that every voxel # was visited only once vecLgcCounter[lgcTemp] += 1 # Status indicator (only used in the first of the parallel # processes): if idxPrc == 0: # Increment status indicator counter: varCntSts02 = varCntSts02 + 1 # check that every voxel was visited only once strErrMsg = ('It looks like at least voxel was revisted more than once. ' + 'Check whether the R2 was calculated correctly') assert np.sum(vecLgcCounter) == len(vecLgcCounter), strErrMsg if type(betaSw) is sklearn.model_selection._split.KFold: # calculate t-values aryTvalsTrn = np.empty((varNumVoxChnk, contrast.shape[0], betaSw.get_n_splits())) aryTvalsTst = np.empty((varNumVoxChnk, contrast.shape[0], betaSw.get_n_splits())) for ind1, contr in enumerate(contrast): for ind2 in range(betaSw.get_n_splits()): aryTvalsTrn[:, ind1, ind2] = np.divide( np.dot(contr, aryEstimMtnCrvTrn[:, :, ind2].T), denomTrn[:, ind2, ind1]) aryTvalsTst[:, ind1, ind2] = np.divide( np.dot(contr, aryEstimMtnCrvTst[:, :, ind2].T), denomTst[:, ind2, ind1]) # Output list: lstOut = [idxPrc, aryEstimMtnCrvTrn, aryEstimMtnCrvTst, aryTvalsTrn, aryTvalsTst, ] queOut.put(lstOut) elif type(betaSw) is np.ndarray and betaSw.dtype == 'bool': # calculate t-values aryTvalsTrn = np.empty((varNumVoxChnk, contrast.shape[0], )) aryTvalsTst = np.empty((varNumVoxChnk, contrast.shape[0], )) for ind1, contr in enumerate(contrast): aryTvalsTrn[:, ind1] = np.divide( np.dot(contr, aryEstimMtnCrvTrn.T), denomTrn[:, ind1]) aryTvalsTst[:, ind1] = np.divide( np.dot(contr, aryEstimMtnCrvTst.T), denomTst[:, ind1]) # Output list: lstOut = [idxPrc, aryEstimMtnCrvTrn, aryEstimMtnCrvTst, aryTvalsTrn, aryTvalsTst, ] queOut.put(lstOut) else: # After finding the best fitting model for each voxel, we still have to # calculate the coefficient of determination (R-squared) for each voxel. We # start by calculating the total sum of squares (i.e. the deviation of the # data from the mean). The mean of each time course: vecFuncMean = np.mean(aryFuncChnk, axis=0) # Deviation from the mean for each datapoint: vecFuncDev = np.subtract(aryFuncChnk, vecFuncMean[None, :]) # Sum of squares: vecSsTot = np.sum(np.power(vecFuncDev, 2.0), axis=0) # Coefficient of determination: vecBstR2 = np.subtract(1.0, np.divide(vecBstRes, vecSsTot)) # Output list: lstOut = [idxPrc, vecBstR2, aryEstimMtnCrv] queOut.put(lstOut)
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"T", "# calculate CC for training", "aryCcTrn", "=", "np", ".", "linalg", ".", "pinv", "(", "np", ".", "dot", "(", "aryDsgnTmp", "[", "betaSw", ",", ":", "]", ".", "T", ",", "aryDsgnTmp", "[", "betaSw", ",", ":", "]", ")", ")", "aryCcTst", "=", "np", ".", "linalg", ".", "pinv", "(", "np", ".", "dot", "(", "aryDsgnTmp", "[", "~", "betaSw", ",", ":", "]", ".", "T", ",", "aryDsgnTmp", "[", "~", "betaSw", ",", ":", "]", ")", ")", "# calculate Error for training", "aryErrorTrn", "[", "lgcTemp", "]", "=", "np", ".", "var", "(", "np", ".", "subtract", "(", "aryFuncChnk", "[", "betaSw", "]", "[", ":", ",", "lgcTemp", "]", ",", "np", ".", "dot", "(", "aryDsgnTmp", "[", "betaSw", ",", ":", "]", ",", "aryEstimMtnCrvTrn", "[", "lgcTemp", ",", ":", "]", ".", "T", ")", ")", ",", "axis", "=", "0", ")", "# calculate Error for test", "aryErrorTst", "[", "lgcTemp", "]", "=", "np", ".", "var", "(", "np", ".", "subtract", "(", "aryFuncChnk", "[", "~", "betaSw", "]", "[", ":", ",", "lgcTemp", "]", ",", "np", ".", "dot", "(", "aryDsgnTmp", "[", "~", "betaSw", ",", ":", "]", ",", "aryEstimMtnCrvTst", "[", "lgcTemp", ",", ":", "]", ".", "T", ")", ")", ",", "axis", "=", "0", ")", "# calculate denominator for training", "for", "indContr", ",", "contr", "in", "enumerate", "(", "contrast", ")", ":", "denomTrn", "[", "lgcTemp", ",", "indContr", "]", "=", "np", ".", "sqrt", "(", "aryErrorTrn", "[", "lgcTemp", "]", "*", "np", ".", "dot", "(", "np", ".", "dot", "(", "contr", ",", "aryCcTrn", ")", ",", "contr", ".", "T", ")", ")", "denomTst", "[", "lgcTemp", ",", "indContr", "]", "=", "np", ".", "sqrt", "(", "aryErrorTst", "[", "lgcTemp", "]", "*", "np", ".", "dot", "(", "np", ".", "dot", "(", "contr", ",", "aryCcTst", ")", ",", "contr", ".", "T", ")", ")", "elif", "type", "(", "betaSw", ")", "is", "sklearn", ".", "model_selection", ".", "_split", ".", "KFold", ":", "for", "idxCV", ",", "(", "idxTrn", ",", "idxVal", ")", "in", "enumerate", "(", "betaSw", ".", "split", "(", "aryDsgnTmp", ")", ")", ":", "# get beta weights for training", "betas", ",", "resTrn", "[", "lgcTemp", ",", "idxCV", "]", "=", "np", ".", "linalg", ".", "lstsq", "(", "aryDsgnTmp", "[", "idxTrn", "]", ",", "aryFuncChnk", "[", "idxTrn", "]", "[", ":", ",", "lgcTemp", "]", ")", "[", "0", ":", "2", "]", "aryEstimMtnCrvTrn", "[", "lgcTemp", ",", ":", ",", "idxCV", "]", "=", "betas", ".", "T", "# get beta weights for validation", "betas", ",", "resTst", "[", "lgcTemp", ",", "idxCV", "]", "=", "np", ".", "linalg", ".", "lstsq", "(", "aryDsgnTmp", "[", "idxVal", "]", ",", "aryFuncChnk", "[", "idxVal", "]", "[", ":", ",", "lgcTemp", "]", ")", "[", "0", ":", "2", "]", "aryEstimMtnCrvTst", "[", "lgcTemp", ",", ":", ",", "idxCV", "]", "=", "betas", ".", "T", "# calculate CC for training", "aryCcTrn", "=", "np", ".", "linalg", ".", "pinv", "(", "np", ".", "dot", "(", "aryDsgnTmp", "[", "idxTrn", "]", ".", "T", ",", "aryDsgnTmp", "[", "idxTrn", "]", ")", ")", "aryCcTst", "=", "np", ".", "linalg", ".", "pinv", "(", "np", ".", "dot", "(", "aryDsgnTmp", "[", "idxVal", "]", ".", "T", ",", "aryDsgnTmp", "[", "idxVal", "]", ")", ")", "# calculate Error for training", "aryErrorTrn", "[", "lgcTemp", "]", "=", "np", ".", "var", "(", "np", ".", "subtract", "(", "aryFuncChnk", "[", "idxTrn", "]", "[", ":", ",", "lgcTemp", "]", ",", "np", ".", "dot", "(", "aryDsgnTmp", "[", "idxTrn", "]", ",", "aryEstimMtnCrvTrn", "[", "lgcTemp", ",", ":", ",", "idxCV", "]", ".", "T", ")", ")", ",", "axis", "=", "0", ")", "# calculate Error for test", "aryErrorTst", "[", "lgcTemp", "]", "=", "np", ".", "var", "(", "np", ".", "subtract", "(", "aryFuncChnk", "[", "idxVal", "]", "[", ":", ",", "lgcTemp", "]", ",", "np", ".", "dot", "(", "aryDsgnTmp", "[", "idxVal", "]", ",", "aryEstimMtnCrvTst", "[", "lgcTemp", ",", ":", ",", "idxCV", "]", ".", "T", ")", ")", ",", "axis", "=", "0", ")", "# calculate denominator for training", "for", "indContr", ",", "contr", "in", "enumerate", "(", "contrast", ")", ":", "denomTrn", "[", "lgcTemp", ",", "idxCV", ",", "indContr", "]", "=", "np", ".", "sqrt", "(", "aryErrorTrn", "[", "lgcTemp", "]", "*", "np", ".", "dot", "(", "np", ".", "dot", "(", "contr", ",", "aryCcTrn", ")", ",", "contr", ".", "T", ")", ")", "denomTst", "[", "lgcTemp", ",", "idxCV", ",", "indContr", "]", "=", "np", ".", "sqrt", "(", "aryErrorTst", "[", "lgcTemp", "]", "*", "np", ".", "dot", "(", "np", ".", "dot", "(", "contr", ",", "aryCcTst", ")", ",", "contr", ".", "T", ")", ")", "# increase logical counter to verify later that every voxel", "# was visited only once", "vecLgcCounter", "[", "lgcTemp", "]", "+=", "1", "# Status indicator (only used in the first of the parallel", "# processes):", "if", "idxPrc", "==", "0", ":", "# Increment status indicator counter:", "varCntSts02", "=", "varCntSts02", "+", "1", "# check that every voxel was visited only once", "strErrMsg", "=", "(", "'It looks like at least voxel was revisted more than once. '", "+", "'Check whether the R2 was calculated correctly'", ")", "assert", "np", ".", "sum", "(", "vecLgcCounter", ")", "==", "len", "(", "vecLgcCounter", ")", ",", "strErrMsg", "if", "type", "(", "betaSw", ")", "is", "sklearn", ".", "model_selection", ".", "_split", ".", "KFold", ":", "# calculate t-values", "aryTvalsTrn", "=", "np", ".", "empty", "(", "(", "varNumVoxChnk", ",", "contrast", ".", "shape", "[", "0", "]", ",", "betaSw", ".", "get_n_splits", "(", ")", ")", ")", "aryTvalsTst", "=", "np", ".", "empty", "(", "(", "varNumVoxChnk", ",", "contrast", ".", "shape", "[", "0", "]", ",", "betaSw", ".", "get_n_splits", "(", ")", ")", ")", "for", "ind1", ",", "contr", "in", "enumerate", "(", "contrast", ")", ":", "for", "ind2", "in", "range", "(", "betaSw", ".", "get_n_splits", "(", ")", ")", ":", "aryTvalsTrn", "[", ":", ",", "ind1", ",", "ind2", "]", "=", "np", ".", "divide", "(", "np", ".", "dot", "(", "contr", ",", "aryEstimMtnCrvTrn", "[", ":", ",", ":", ",", "ind2", "]", ".", "T", ")", ",", "denomTrn", "[", ":", ",", "ind2", ",", "ind1", "]", ")", "aryTvalsTst", "[", ":", ",", "ind1", ",", "ind2", "]", "=", "np", ".", "divide", "(", "np", ".", "dot", "(", "contr", ",", "aryEstimMtnCrvTst", "[", ":", ",", ":", ",", "ind2", "]", ".", "T", ")", ",", "denomTst", "[", ":", ",", "ind2", ",", "ind1", "]", ")", "# Output list:", "lstOut", "=", "[", "idxPrc", ",", "aryEstimMtnCrvTrn", ",", "aryEstimMtnCrvTst", ",", "aryTvalsTrn", ",", "aryTvalsTst", ",", "]", "queOut", ".", "put", "(", "lstOut", ")", "elif", "type", "(", "betaSw", ")", "is", "np", ".", "ndarray", "and", "betaSw", ".", "dtype", "==", "'bool'", ":", "# calculate t-values", "aryTvalsTrn", "=", "np", ".", "empty", "(", "(", "varNumVoxChnk", ",", "contrast", ".", "shape", "[", "0", "]", ",", ")", ")", "aryTvalsTst", "=", "np", ".", "empty", "(", "(", "varNumVoxChnk", ",", "contrast", ".", "shape", "[", "0", "]", ",", ")", ")", "for", "ind1", ",", "contr", "in", "enumerate", "(", "contrast", ")", ":", "aryTvalsTrn", "[", ":", ",", "ind1", "]", "=", "np", ".", "divide", "(", "np", ".", "dot", "(", "contr", ",", "aryEstimMtnCrvTrn", ".", "T", ")", ",", "denomTrn", "[", ":", ",", "ind1", "]", ")", "aryTvalsTst", "[", ":", ",", "ind1", "]", "=", "np", ".", "divide", "(", "np", ".", "dot", "(", "contr", ",", "aryEstimMtnCrvTst", ".", "T", ")", ",", "denomTst", "[", ":", ",", "ind1", "]", ")", "# Output list:", "lstOut", "=", "[", "idxPrc", ",", "aryEstimMtnCrvTrn", ",", "aryEstimMtnCrvTst", ",", "aryTvalsTrn", ",", "aryTvalsTst", ",", "]", "queOut", ".", "put", "(", "lstOut", ")", "else", ":", "# After finding the best fitting model for each voxel, we still have to", "# calculate the coefficient of determination (R-squared) for each voxel. We", "# start by calculating the total sum of squares (i.e. the deviation of the", "# data from the mean). The mean of each time course:", "vecFuncMean", "=", "np", ".", "mean", "(", "aryFuncChnk", ",", "axis", "=", "0", ")", "# Deviation from the mean for each datapoint:", "vecFuncDev", "=", "np", ".", "subtract", "(", "aryFuncChnk", ",", "vecFuncMean", "[", "None", ",", ":", "]", ")", "# Sum of squares:", "vecSsTot", "=", "np", ".", "sum", "(", "np", ".", "power", "(", "vecFuncDev", ",", "2.0", ")", ",", "axis", "=", "0", ")", "# Coefficient of determination:", "vecBstR2", "=", "np", ".", "subtract", "(", "1.0", ",", "np", ".", "divide", "(", "vecBstRes", ",", "vecSsTot", ")", ")", "# Output list:", "lstOut", "=", "[", "idxPrc", ",", "vecBstR2", ",", "aryEstimMtnCrv", "]", "queOut", ".", "put", "(", "lstOut", ")" ]
Calculate voxel betas and R^2 for the best model. Parameters ---------- idxPrc : TODO (?) aryPrfTc : np.array, shape (?) Population receptive field time courses. lstAllMdlInd : list List of the indices of all models. aryFuncChnk : TODO Chunk of something(?) aryBstIndChnk : np.array, shape (?) Points for every voxel to the index of the best model betaSw : str, iterator, or np.array, shape (?) Best beta correlation coefficients found in training. queOut : TODO Queue output (?) Notes ----- This is done after fitting with cross validation, since during the fitting process, we never fit the model to the entire data.
[ "Calculate", "voxel", "betas", "and", "R^2", "for", "the", "best", "model", "." ]
train
https://github.com/MSchnei/pyprf_feature/blob/49004ede7ae1ddee07a30afe9ce3e2776750805c/pyprf_feature/analysis/old/pRF_calcR2_getBetas.py#L25-L359
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/publisher/frontiers.py
Frontiers.nav_creators
def nav_creators(self, article): """ Frontiers method. Given an Article class instance, this is responsible for returning the names for creators of the article. For our purposes, it is sufficient to list only the authors, returning their name, role=aut, and file-as name. This returns a list of Creator(name, role, file_as) """ creator_list = [] for contrib_group in article.metadata.front.article_meta.contrib_group: for contrib in contrib_group.contrib: if not contrib.attrs['contrib-type'] == 'author': continue if contrib.collab: auth = etree.tostring(contrib.collab[0].node, method='text', encoding='utf-8') file_as = auth elif contrib.anonymous: auth = 'Anonymous' file_as = auth else: name = contrib.name[0] # Work with only first name listed surname = name.surname.text given = name.given_names if given: # Given is optional if given.text: # Odd instances of empty tags auth = ' '.join([surname, given.text]) given_initial = given.text[0] file_as = ', '.join([surname, given_initial]) else: auth = surname file_as = auth else: auth = surname file_as = auth new_creator = creator(auth, 'aut', file_as) creator_list.append(new_creator) return creator_list
python
def nav_creators(self, article): """ Frontiers method. Given an Article class instance, this is responsible for returning the names for creators of the article. For our purposes, it is sufficient to list only the authors, returning their name, role=aut, and file-as name. This returns a list of Creator(name, role, file_as) """ creator_list = [] for contrib_group in article.metadata.front.article_meta.contrib_group: for contrib in contrib_group.contrib: if not contrib.attrs['contrib-type'] == 'author': continue if contrib.collab: auth = etree.tostring(contrib.collab[0].node, method='text', encoding='utf-8') file_as = auth elif contrib.anonymous: auth = 'Anonymous' file_as = auth else: name = contrib.name[0] # Work with only first name listed surname = name.surname.text given = name.given_names if given: # Given is optional if given.text: # Odd instances of empty tags auth = ' '.join([surname, given.text]) given_initial = given.text[0] file_as = ', '.join([surname, given_initial]) else: auth = surname file_as = auth else: auth = surname file_as = auth new_creator = creator(auth, 'aut', file_as) creator_list.append(new_creator) return creator_list
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Frontiers method. Given an Article class instance, this is responsible for returning the names for creators of the article. For our purposes, it is sufficient to list only the authors, returning their name, role=aut, and file-as name. This returns a list of Creator(name, role, file_as)
[ "Frontiers", "method", "." ]
train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/publisher/frontiers.py#L27-L65
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/publisher/frontiers.py
Frontiers.nav_title
def nav_title(self, article): """ Frontiers method. Given an Article class instance, this will return a string representing the title of the article. This is done for PloS by serializing the text in the Article's """ article_title = article.metadata.front.article_meta.title_group.article_title.node return str(etree.tostring(article_title, method='text', encoding='utf-8'), encoding='utf-8')
python
def nav_title(self, article): """ Frontiers method. Given an Article class instance, this will return a string representing the title of the article. This is done for PloS by serializing the text in the Article's """ article_title = article.metadata.front.article_meta.title_group.article_title.node return str(etree.tostring(article_title, method='text', encoding='utf-8'), encoding='utf-8')
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Frontiers method. Given an Article class instance, this will return a string representing the title of the article. This is done for PloS by serializing the text in the Article's
[ "Frontiers", "method", "." ]
train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/publisher/frontiers.py#L67-L76
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/nlm_transform/citation.py
format_citation
def format_citation(citation, citation_type=None): """ This method may be built to support elements from different Tag Suite versions with the following tag names: citation, element-citation, mixed-citation, and nlm-citation The citation-type attribute is optional, and may also be empty; if it has a value then it should appear in the following prescribed list, or it will be treated as 'other'. book Book or book series commun Informal or personal communication, such as a phone call or an email message confproc Conference proceedings discussion Discussion among a group in some forum β€” public, private, or electronic β€” which may or may not be moderated, for example, a single discussion thread in a listserv gov Government publication or government standard journal Journal article list Listserv or discussion group (as an entity, as opposed to a single discussion thread which uses the value β€œdiscussion”) other None of the listed types. patent Patent or patent application thesis Work written as part of the completion of an advanced degree web Website This method will accept a passed citation_type argument which will override checking of the element's citation-type attribute and force the formatting according to the passed string value. Note that this may not be appropriate in many cases. """ cite_types = {'book': self.format_book_citation, 'commun': self.format_commun_citation, 'confproc': self.format_confproc_citation, 'discussion': self.format_discussion_citation, 'gov': self.format_gov_citation, 'journal': self.format_journal_citation, 'list': self.format_list_citation, 'other': self.format_other_citation, 'patent': self.format_patent_citation, 'thesis': self.format_thesis_citation, 'web': self.format_web_citation, '': self.format_other_citation, # Empty becomes 'other' None: self.format_other_citation} # None becomes 'other' #Only check if no citation_type value is passed if citation_type is None: #Get the citation-type attribute value if 'citation-type' in nlm_citation.attrib: citation_type = nlm_citation.attrib['citation-type'] #Pass the citation to the appropriate function and return result return cite_types[citation_type](citation) @staticmethod def format_book_citation(self, citation): """ citation-type=\"book\" """ #Get the count of authors author_group_count = int(citation.xpath('count(person-group) + count(collab)')) #Detect if there are non-authors if citation.xpath('person-group[@person-group-type!=\'author\']'): non_authors = True else: non_authors= False #Detect article-title if citation.xpath('article-title'): article_title = True else: article_title = False #Find out if there is at least one author or compiler auth_or_comp = False for person_group in citation.findall('person-group'): if 'person-group-type' in person_group.attrib: if person_group.attrib['person-group-type'] in ['author', 'compiler']: auth_or_comp = True break #These pieces of information allow us to provide two special use cases #and one general use case. #First special case: if author_group_count > 0 and non_authors and article_title: pass #Second special case elif auth_or_comp: pass #General case else: pass @staticmethod def format_commun_citation(self, citation): """ citation-type=\"commun\" """ @staticmethod def format_confproc_citation(self, citation): """ citation-type=\"confproc\" """ @staticmethod def format_discussion_citation(self, citation): """ citation-type=\"discussion\" """ @staticmethod def format_gov_citation(self, citation): """ citation-type=\"gov\" """ @staticmethod def format_journal_citation(self, citation): """ citation-type=\"journal\" """ @staticmethod def format_list_citation(self, citation): """ citation-type=\"list\" """ @staticmethod def format_other_citation(self, citation): """ citation-type=\"other\" """ @staticmethod def format_patent_citation(self, citation): """ citation-type=\"patent\" """ @staticmethod def format_thesis_citation(self, citation): """ citation-type=\"thesis\" """ #Treat the same as "book" return format_book_citation(citation) @staticmethod def format_web_citation(self, citation): """ citation-type=\"web\" """
python
def format_citation(citation, citation_type=None): """ This method may be built to support elements from different Tag Suite versions with the following tag names: citation, element-citation, mixed-citation, and nlm-citation The citation-type attribute is optional, and may also be empty; if it has a value then it should appear in the following prescribed list, or it will be treated as 'other'. book Book or book series commun Informal or personal communication, such as a phone call or an email message confproc Conference proceedings discussion Discussion among a group in some forum β€” public, private, or electronic β€” which may or may not be moderated, for example, a single discussion thread in a listserv gov Government publication or government standard journal Journal article list Listserv or discussion group (as an entity, as opposed to a single discussion thread which uses the value β€œdiscussion”) other None of the listed types. patent Patent or patent application thesis Work written as part of the completion of an advanced degree web Website This method will accept a passed citation_type argument which will override checking of the element's citation-type attribute and force the formatting according to the passed string value. Note that this may not be appropriate in many cases. """ cite_types = {'book': self.format_book_citation, 'commun': self.format_commun_citation, 'confproc': self.format_confproc_citation, 'discussion': self.format_discussion_citation, 'gov': self.format_gov_citation, 'journal': self.format_journal_citation, 'list': self.format_list_citation, 'other': self.format_other_citation, 'patent': self.format_patent_citation, 'thesis': self.format_thesis_citation, 'web': self.format_web_citation, '': self.format_other_citation, # Empty becomes 'other' None: self.format_other_citation} # None becomes 'other' #Only check if no citation_type value is passed if citation_type is None: #Get the citation-type attribute value if 'citation-type' in nlm_citation.attrib: citation_type = nlm_citation.attrib['citation-type'] #Pass the citation to the appropriate function and return result return cite_types[citation_type](citation) @staticmethod def format_book_citation(self, citation): """ citation-type=\"book\" """ #Get the count of authors author_group_count = int(citation.xpath('count(person-group) + count(collab)')) #Detect if there are non-authors if citation.xpath('person-group[@person-group-type!=\'author\']'): non_authors = True else: non_authors= False #Detect article-title if citation.xpath('article-title'): article_title = True else: article_title = False #Find out if there is at least one author or compiler auth_or_comp = False for person_group in citation.findall('person-group'): if 'person-group-type' in person_group.attrib: if person_group.attrib['person-group-type'] in ['author', 'compiler']: auth_or_comp = True break #These pieces of information allow us to provide two special use cases #and one general use case. #First special case: if author_group_count > 0 and non_authors and article_title: pass #Second special case elif auth_or_comp: pass #General case else: pass @staticmethod def format_commun_citation(self, citation): """ citation-type=\"commun\" """ @staticmethod def format_confproc_citation(self, citation): """ citation-type=\"confproc\" """ @staticmethod def format_discussion_citation(self, citation): """ citation-type=\"discussion\" """ @staticmethod def format_gov_citation(self, citation): """ citation-type=\"gov\" """ @staticmethod def format_journal_citation(self, citation): """ citation-type=\"journal\" """ @staticmethod def format_list_citation(self, citation): """ citation-type=\"list\" """ @staticmethod def format_other_citation(self, citation): """ citation-type=\"other\" """ @staticmethod def format_patent_citation(self, citation): """ citation-type=\"patent\" """ @staticmethod def format_thesis_citation(self, citation): """ citation-type=\"thesis\" """ #Treat the same as "book" return format_book_citation(citation) @staticmethod def format_web_citation(self, citation): """ citation-type=\"web\" """
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This method may be built to support elements from different Tag Suite versions with the following tag names: citation, element-citation, mixed-citation, and nlm-citation The citation-type attribute is optional, and may also be empty; if it has a value then it should appear in the following prescribed list, or it will be treated as 'other'. book Book or book series commun Informal or personal communication, such as a phone call or an email message confproc Conference proceedings discussion Discussion among a group in some forum β€” public, private, or electronic β€” which may or may not be moderated, for example, a single discussion thread in a listserv gov Government publication or government standard journal Journal article list Listserv or discussion group (as an entity, as opposed to a single discussion thread which uses the value β€œdiscussion”) other None of the listed types. patent Patent or patent application thesis Work written as part of the completion of an advanced degree web Website This method will accept a passed citation_type argument which will override checking of the element's citation-type attribute and force the formatting according to the passed string value. Note that this may not be appropriate in many cases.
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/nlm_transform/citation.py#L21-L174
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/utils/inputs.py
plos_doi_to_xmlurl
def plos_doi_to_xmlurl(doi_string): """ Attempts to resolve a PLoS DOI into a URL path to the XML file. """ #Create URL to request DOI resolution from http://dx.doi.org doi_url = 'http://dx.doi.org/{0}'.format(doi_string) log.debug('DOI URL: {0}'.format(doi_url)) #Open the page, follow the redirect try: resolved_page = urllib.request.urlopen(doi_url) except urllib.error.URLError as err: print('Unable to resolve DOI URL, or could not connect') raise err else: #Given the redirection, attempt to shape new request for PLoS servers resolved_address = resolved_page.geturl() log.debug('DOI resolved to {0}'.format(resolved_address)) parsed = urllib.parse.urlparse(resolved_address) xml_url = '{0}://{1}'.format(parsed.scheme, parsed.netloc) xml_url += '/article/fetchObjectAttachment.action?uri=' xml_path = parsed.path.replace(':', '%3A').replace('/', '%2F') xml_path = xml_path.split('article%2F')[1] xml_url += '{0}{1}'.format(xml_path, '&representation=XML') log.debug('Shaped PLoS request for XML {0}'.format(xml_url)) #Return this url to the calling function return xml_url
python
def plos_doi_to_xmlurl(doi_string): """ Attempts to resolve a PLoS DOI into a URL path to the XML file. """ #Create URL to request DOI resolution from http://dx.doi.org doi_url = 'http://dx.doi.org/{0}'.format(doi_string) log.debug('DOI URL: {0}'.format(doi_url)) #Open the page, follow the redirect try: resolved_page = urllib.request.urlopen(doi_url) except urllib.error.URLError as err: print('Unable to resolve DOI URL, or could not connect') raise err else: #Given the redirection, attempt to shape new request for PLoS servers resolved_address = resolved_page.geturl() log.debug('DOI resolved to {0}'.format(resolved_address)) parsed = urllib.parse.urlparse(resolved_address) xml_url = '{0}://{1}'.format(parsed.scheme, parsed.netloc) xml_url += '/article/fetchObjectAttachment.action?uri=' xml_path = parsed.path.replace(':', '%3A').replace('/', '%2F') xml_path = xml_path.split('article%2F')[1] xml_url += '{0}{1}'.format(xml_path, '&representation=XML') log.debug('Shaped PLoS request for XML {0}'.format(xml_url)) #Return this url to the calling function return xml_url
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Attempts to resolve a PLoS DOI into a URL path to the XML file.
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/utils/inputs.py#L23-L48
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/utils/inputs.py
doi_input
def doi_input(doi_string, download=True): """ This method accepts a DOI string and attempts to download the appropriate xml file. If successful, it returns a path to that file. As with all URL input types, the success of this method depends on supporting per-publisher conventions and will fail on unsupported publishers """ log.debug('DOI Input - {0}'.format(doi_string)) doi_string = doi_string[4:] if '10.1371' in doi_string: # Corresponds to PLoS log.debug('DOI string shows PLoS') xml_url = plos_doi_to_xmlurl(doi_string) else: log.critical('DOI input for this publisher is not supported') sys.exit('This publisher is not yet supported by OpenAccess_EPUB') return url_input(xml_url, download)
python
def doi_input(doi_string, download=True): """ This method accepts a DOI string and attempts to download the appropriate xml file. If successful, it returns a path to that file. As with all URL input types, the success of this method depends on supporting per-publisher conventions and will fail on unsupported publishers """ log.debug('DOI Input - {0}'.format(doi_string)) doi_string = doi_string[4:] if '10.1371' in doi_string: # Corresponds to PLoS log.debug('DOI string shows PLoS') xml_url = plos_doi_to_xmlurl(doi_string) else: log.critical('DOI input for this publisher is not supported') sys.exit('This publisher is not yet supported by OpenAccess_EPUB') return url_input(xml_url, download)
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This method accepts a DOI string and attempts to download the appropriate xml file. If successful, it returns a path to that file. As with all URL input types, the success of this method depends on supporting per-publisher conventions and will fail on unsupported publishers
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/utils/inputs.py#L51-L66
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/utils/inputs.py
url_input
def url_input(url_string, download=True): """ This method expects a direct URL link to an xml file. It will apply no modifications to the received URL string, so ensure good input. """ log.debug('URL Input - {0}'.format(url_string)) try: open_xml = urllib.request.urlopen(url_string) except urllib.error.URLError as err: print('utils.input.url_input received a bad URL, or could not connect') raise err else: #Employ a quick check on the mimetype of the link if not open_xml.headers['Content-Type'] == 'text/xml': sys.exit('URL request does not appear to be XML') filename = open_xml.headers['Content-Disposition'].split('\"')[1] if download: with open(filename, 'wb') as xml_file: xml_file.write(open_xml.read()) return openaccess_epub.utils.file_root_name(filename)
python
def url_input(url_string, download=True): """ This method expects a direct URL link to an xml file. It will apply no modifications to the received URL string, so ensure good input. """ log.debug('URL Input - {0}'.format(url_string)) try: open_xml = urllib.request.urlopen(url_string) except urllib.error.URLError as err: print('utils.input.url_input received a bad URL, or could not connect') raise err else: #Employ a quick check on the mimetype of the link if not open_xml.headers['Content-Type'] == 'text/xml': sys.exit('URL request does not appear to be XML') filename = open_xml.headers['Content-Disposition'].split('\"')[1] if download: with open(filename, 'wb') as xml_file: xml_file.write(open_xml.read()) return openaccess_epub.utils.file_root_name(filename)
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This method expects a direct URL link to an xml file. It will apply no modifications to the received URL string, so ensure good input.
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/utils/inputs.py#L69-L88
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/utils/inputs.py
frontiersZipInput
def frontiersZipInput(zip_path, output_prefix, download=None): """ This method provides support for Frontiers production using base zipfiles as the input for ePub creation. It expects a valid pathname for one of the two zipfiles, and that both zipfiles are present in the same directory. """ log.debug('frontiersZipInput called') #If there is a problem with the input, it should clearly describe the issue pathname, pathext = os.path.splitext(zip_path) path, name = os.path.split(pathname) if not pathext == '.zip': # Checks for a path to zipfile log.error('Pathname provided does not end with .zip') print('Invalid file path: Does not have a zip extension.') sys.exit(1) #Construct the pair of zipfile pathnames file_root = name.split('-r')[0] zipname1 = "{0}-r{1}.zip".format(file_root, '1') zipname2 = "{0}-r{1}.zip".format(file_root, '2') #Construct the pathnames for output output = os.path.join(output_prefix, file_root) if os.path.isdir(output): shutil.rmtree(output) # Delete previous output output_meta = os.path.join(output, 'META-INF') images_output = os.path.join(output, 'EPUB', 'images') with zipfile.ZipFile(os.path.join(path, zipname1), 'r') as xml_zip: zip_dir = '{0}-r1'.format(file_root) xml = '/'.join([zip_dir, '{0}.xml'.format(file_root)]) try: xml_zip.extract(xml) except KeyError: log.critical('There is no item {0} in the zipfile'.format(xml)) sys.exit('There is no item {0} in the zipfile'.format(xml)) else: if not os.path.isdir(output_meta): os.makedirs(output_meta) shutil.copy(xml, os.path.join(output_meta)) os.remove(xml) os.rmdir(zip_dir) with zipfile.ZipFile(os.path.join(path, zipname2), 'r') as image_zip: zip_dir = '{0}-r2'.format(file_root) for i in image_zip.namelist(): if 'image_m' in i: image_zip.extract(i) if not os.path.isdir(images_output): os.makedirs(images_output) unzipped_images = os.path.join(zip_dir, 'images', 'image_m') for i in os.listdir(unzipped_images): shutil.copy(os.path.join(unzipped_images, i), images_output) shutil.rmtree(zip_dir) return file_root
python
def frontiersZipInput(zip_path, output_prefix, download=None): """ This method provides support for Frontiers production using base zipfiles as the input for ePub creation. It expects a valid pathname for one of the two zipfiles, and that both zipfiles are present in the same directory. """ log.debug('frontiersZipInput called') #If there is a problem with the input, it should clearly describe the issue pathname, pathext = os.path.splitext(zip_path) path, name = os.path.split(pathname) if not pathext == '.zip': # Checks for a path to zipfile log.error('Pathname provided does not end with .zip') print('Invalid file path: Does not have a zip extension.') sys.exit(1) #Construct the pair of zipfile pathnames file_root = name.split('-r')[0] zipname1 = "{0}-r{1}.zip".format(file_root, '1') zipname2 = "{0}-r{1}.zip".format(file_root, '2') #Construct the pathnames for output output = os.path.join(output_prefix, file_root) if os.path.isdir(output): shutil.rmtree(output) # Delete previous output output_meta = os.path.join(output, 'META-INF') images_output = os.path.join(output, 'EPUB', 'images') with zipfile.ZipFile(os.path.join(path, zipname1), 'r') as xml_zip: zip_dir = '{0}-r1'.format(file_root) xml = '/'.join([zip_dir, '{0}.xml'.format(file_root)]) try: xml_zip.extract(xml) except KeyError: log.critical('There is no item {0} in the zipfile'.format(xml)) sys.exit('There is no item {0} in the zipfile'.format(xml)) else: if not os.path.isdir(output_meta): os.makedirs(output_meta) shutil.copy(xml, os.path.join(output_meta)) os.remove(xml) os.rmdir(zip_dir) with zipfile.ZipFile(os.path.join(path, zipname2), 'r') as image_zip: zip_dir = '{0}-r2'.format(file_root) for i in image_zip.namelist(): if 'image_m' in i: image_zip.extract(i) if not os.path.isdir(images_output): os.makedirs(images_output) unzipped_images = os.path.join(zip_dir, 'images', 'image_m') for i in os.listdir(unzipped_images): shutil.copy(os.path.join(unzipped_images, i), images_output) shutil.rmtree(zip_dir) return file_root
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This method provides support for Frontiers production using base zipfiles as the input for ePub creation. It expects a valid pathname for one of the two zipfiles, and that both zipfiles are present in the same directory.
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/utils/inputs.py#L91-L140
curious-containers/cc-core
cc_core/commons/red.py
_red_listing_validation
def _red_listing_validation(key, listing): """ Raises an RedValidationError, if the given listing does not comply with cwl_job_listing_schema. If listing is None or an empty list, no exception is thrown. :param key: The input key to build an error message if needed. :param listing: The listing to validate :raise RedValidationError: If the given listing does not comply with cwl_job_listing_schema """ if listing: try: jsonschema.validate(listing, cwl_job_listing_schema) except ValidationError as e: raise RedValidationError('REDFILE listing of input "{}" does not comply with jsonschema: {}' .format(key, e.context))
python
def _red_listing_validation(key, listing): """ Raises an RedValidationError, if the given listing does not comply with cwl_job_listing_schema. If listing is None or an empty list, no exception is thrown. :param key: The input key to build an error message if needed. :param listing: The listing to validate :raise RedValidationError: If the given listing does not comply with cwl_job_listing_schema """ if listing: try: jsonschema.validate(listing, cwl_job_listing_schema) except ValidationError as e: raise RedValidationError('REDFILE listing of input "{}" does not comply with jsonschema: {}' .format(key, e.context))
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Raises an RedValidationError, if the given listing does not comply with cwl_job_listing_schema. If listing is None or an empty list, no exception is thrown. :param key: The input key to build an error message if needed. :param listing: The listing to validate :raise RedValidationError: If the given listing does not comply with cwl_job_listing_schema
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train
https://github.com/curious-containers/cc-core/blob/eaeb03a4366016aff54fcc6953d052ae12ed599b/cc_core/commons/red.py#L211-L226
curious-containers/cc-core
cc_core/commons/red.py
red_get_mount_connectors
def red_get_mount_connectors(red_data, ignore_outputs): """ Returns a list of mounting connectors :param red_data: The red data to be searched :param ignore_outputs: If outputs should be ignored :return: A list of connectors with active mount option. """ keys = [] batches = red_data.get('batches') inputs = red_data.get('inputs') if batches: for batch in batches: keys.extend(red_get_mount_connectors_from_inputs(batch['inputs'])) elif inputs: keys.extend(red_get_mount_connectors_from_inputs(inputs)) if not ignore_outputs: outputs = red_data.get('outputs') if batches: for batch in batches: batch_outputs = batch.get('outputs') if batch_outputs: keys.extend(red_get_mount_connectors_from_outputs(batch_outputs)) elif outputs: keys.extend(red_get_mount_connectors_from_outputs(outputs)) return keys
python
def red_get_mount_connectors(red_data, ignore_outputs): """ Returns a list of mounting connectors :param red_data: The red data to be searched :param ignore_outputs: If outputs should be ignored :return: A list of connectors with active mount option. """ keys = [] batches = red_data.get('batches') inputs = red_data.get('inputs') if batches: for batch in batches: keys.extend(red_get_mount_connectors_from_inputs(batch['inputs'])) elif inputs: keys.extend(red_get_mount_connectors_from_inputs(inputs)) if not ignore_outputs: outputs = red_data.get('outputs') if batches: for batch in batches: batch_outputs = batch.get('outputs') if batch_outputs: keys.extend(red_get_mount_connectors_from_outputs(batch_outputs)) elif outputs: keys.extend(red_get_mount_connectors_from_outputs(outputs)) return keys
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Returns a list of mounting connectors :param red_data: The red data to be searched :param ignore_outputs: If outputs should be ignored :return: A list of connectors with active mount option.
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train
https://github.com/curious-containers/cc-core/blob/eaeb03a4366016aff54fcc6953d052ae12ed599b/cc_core/commons/red.py#L261-L291
curious-containers/cc-core
cc_core/commons/red.py
cleanup
def cleanup(connector_manager, red_data, tmp_dir): """ Invokes the cleanup functions for all inputs. """ for key, arg in red_data['inputs'].items(): val = arg if isinstance(arg, list): for index, i in enumerate(arg): if not isinstance(i, dict): continue # connector_class should be one of 'File' or 'Directory' connector_class = i['class'] input_key = '{}_{}'.format(key, index) path = os.path.join(tmp_dir, input_key) connector_data = i['connector'] internal = {URL_SCHEME_IDENTIFIER: path} if connector_class == 'File': connector_manager.receive_cleanup(connector_data, input_key, internal) elif connector_class == 'Directory': connector_manager.receive_directory_cleanup(connector_data, input_key, internal) elif isinstance(arg, dict): # connector_class should be one of 'File' or 'Directory' connector_class = arg['class'] input_key = key path = os.path.join(tmp_dir, input_key) connector_data = val['connector'] internal = {URL_SCHEME_IDENTIFIER: path} if connector_class == 'File': connector_manager.receive_cleanup(connector_data, input_key, internal) elif connector_class == 'Directory': connector_manager.receive_directory_cleanup(connector_data, input_key, internal) try: os.rmdir(tmp_dir) except (OSError, FileNotFoundError): # Maybe, raise a warning here, because not all connectors have cleaned up their contents correctly. pass
python
def cleanup(connector_manager, red_data, tmp_dir): """ Invokes the cleanup functions for all inputs. """ for key, arg in red_data['inputs'].items(): val = arg if isinstance(arg, list): for index, i in enumerate(arg): if not isinstance(i, dict): continue # connector_class should be one of 'File' or 'Directory' connector_class = i['class'] input_key = '{}_{}'.format(key, index) path = os.path.join(tmp_dir, input_key) connector_data = i['connector'] internal = {URL_SCHEME_IDENTIFIER: path} if connector_class == 'File': connector_manager.receive_cleanup(connector_data, input_key, internal) elif connector_class == 'Directory': connector_manager.receive_directory_cleanup(connector_data, input_key, internal) elif isinstance(arg, dict): # connector_class should be one of 'File' or 'Directory' connector_class = arg['class'] input_key = key path = os.path.join(tmp_dir, input_key) connector_data = val['connector'] internal = {URL_SCHEME_IDENTIFIER: path} if connector_class == 'File': connector_manager.receive_cleanup(connector_data, input_key, internal) elif connector_class == 'Directory': connector_manager.receive_directory_cleanup(connector_data, input_key, internal) try: os.rmdir(tmp_dir) except (OSError, FileNotFoundError): # Maybe, raise a warning here, because not all connectors have cleaned up their contents correctly. pass
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train
https://github.com/curious-containers/cc-core/blob/eaeb03a4366016aff54fcc6953d052ae12ed599b/cc_core/commons/red.py#L454-L495
curious-containers/cc-core
cc_core/commons/red.py
ConnectorManager._execute_connector
def _execute_connector(connector_command, top_level_argument, *file_contents, listing=None): """ Executes a connector by executing the given connector_command. The content of args will be the content of the files handed to the connector cli. :param connector_command: The connector command to execute. :param top_level_argument: The top level command line argument for the connector cli. (Like 'receive' or 'send_validate') :param file_contents: A dict of information handed over to the connector cli. :param listing: A listing to provide to the connector cli. Will be ignored if None. :return: A tuple containing the return code of the connector and the stderr of the command as str. """ # create temp_files for every file_content temp_files = [] for file_content in file_contents: if file_content is None: continue tmp_file = tempfile.NamedTemporaryFile('w') json.dump(file_content, tmp_file) tmp_file.flush() temp_files.append(tmp_file) tmp_listing_file = None if listing: tmp_listing_file = tempfile.NamedTemporaryFile('w') json.dump(listing, tmp_listing_file) tmp_listing_file.flush() command = [connector_command, top_level_argument] command.extend([t.name for t in temp_files]) if tmp_listing_file: command.append('--listing {}'.format(tmp_listing_file.name)) result = execute(' '.join(command)) # close temp_files for temp_file in temp_files: temp_file.close() if tmp_listing_file: tmp_listing_file.close() return result['returnCode'], result['stdErr']
python
def _execute_connector(connector_command, top_level_argument, *file_contents, listing=None): """ Executes a connector by executing the given connector_command. The content of args will be the content of the files handed to the connector cli. :param connector_command: The connector command to execute. :param top_level_argument: The top level command line argument for the connector cli. (Like 'receive' or 'send_validate') :param file_contents: A dict of information handed over to the connector cli. :param listing: A listing to provide to the connector cli. Will be ignored if None. :return: A tuple containing the return code of the connector and the stderr of the command as str. """ # create temp_files for every file_content temp_files = [] for file_content in file_contents: if file_content is None: continue tmp_file = tempfile.NamedTemporaryFile('w') json.dump(file_content, tmp_file) tmp_file.flush() temp_files.append(tmp_file) tmp_listing_file = None if listing: tmp_listing_file = tempfile.NamedTemporaryFile('w') json.dump(listing, tmp_listing_file) tmp_listing_file.flush() command = [connector_command, top_level_argument] command.extend([t.name for t in temp_files]) if tmp_listing_file: command.append('--listing {}'.format(tmp_listing_file.name)) result = execute(' '.join(command)) # close temp_files for temp_file in temp_files: temp_file.close() if tmp_listing_file: tmp_listing_file.close() return result['returnCode'], result['stdErr']
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train
https://github.com/curious-containers/cc-core/blob/eaeb03a4366016aff54fcc6953d052ae12ed599b/cc_core/commons/red.py#L36-L79
curious-containers/cc-core
cc_core/commons/red.py
ConnectorManager.directory_listing_content_check
def directory_listing_content_check(directory_path, listing): """ Checks if a given listing is present under the given directory path. :param directory_path: The path to the base directory :param listing: The listing to check :return: None if no errors could be found, otherwise a string describing the error """ if listing: for sub in listing: path = os.path.join(directory_path, sub['basename']) if sub['class'] == 'File': if not os.path.isfile(path): return 'listing contains "{}" but this file could not be found on disk.'.format(path) elif sub['class'] == 'Directory': if not os.path.isdir(path): return 'listing contains "{}" but this directory could not be found on disk'.format(path) listing = sub.get('listing') if listing: return ConnectorManager.directory_listing_content_check(path, listing) return None
python
def directory_listing_content_check(directory_path, listing): """ Checks if a given listing is present under the given directory path. :param directory_path: The path to the base directory :param listing: The listing to check :return: None if no errors could be found, otherwise a string describing the error """ if listing: for sub in listing: path = os.path.join(directory_path, sub['basename']) if sub['class'] == 'File': if not os.path.isfile(path): return 'listing contains "{}" but this file could not be found on disk.'.format(path) elif sub['class'] == 'Directory': if not os.path.isdir(path): return 'listing contains "{}" but this directory could not be found on disk'.format(path) listing = sub.get('listing') if listing: return ConnectorManager.directory_listing_content_check(path, listing) return None
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Checks if a given listing is present under the given directory path. :param directory_path: The path to the base directory :param listing: The listing to check :return: None if no errors could be found, otherwise a string describing the error
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train
https://github.com/curious-containers/cc-core/blob/eaeb03a4366016aff54fcc6953d052ae12ed599b/cc_core/commons/red.py#L133-L153
curious-containers/cc-core
cc_core/commons/gpu_info.py
get_cuda_devices
def get_cuda_devices(): """ Imports pycuda at runtime and reads GPU information. :return: A list of available cuda GPUs. """ devices = [] try: import pycuda.autoinit import pycuda.driver as cuda for device_id in range(cuda.Device.count()): vram = cuda.Device(device_id).total_memory() devices.append(GPUDevice(device_id, vram)) except ImportError: raise InsufficientGPUError('No Nvidia-GPUs could be found, because "pycuda" could not be imported.') return devices
python
def get_cuda_devices(): """ Imports pycuda at runtime and reads GPU information. :return: A list of available cuda GPUs. """ devices = [] try: import pycuda.autoinit import pycuda.driver as cuda for device_id in range(cuda.Device.count()): vram = cuda.Device(device_id).total_memory() devices.append(GPUDevice(device_id, vram)) except ImportError: raise InsufficientGPUError('No Nvidia-GPUs could be found, because "pycuda" could not be imported.') return devices
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train
https://github.com/curious-containers/cc-core/blob/eaeb03a4366016aff54fcc6953d052ae12ed599b/cc_core/commons/gpu_info.py#L44-L63
curious-containers/cc-core
cc_core/commons/gpu_info.py
match_gpus
def match_gpus(available_devices, requirements): """ Determines sufficient GPUs for the given requirements and returns a list of GPUDevices. If there aren't sufficient GPUs a InsufficientGPUException is thrown. :param available_devices: A list of GPUDevices :param requirements: A list of GPURequirements :return: A list of sufficient devices """ if not requirements: return [] if not available_devices: raise InsufficientGPUError("No GPU devices available, but {} devices required.".format(len(requirements))) available_devices = available_devices.copy() used_devices = [] for req in requirements: dev = search_device(req, available_devices) if dev: used_devices.append(dev) available_devices.remove(dev) else: raise InsufficientGPUError("Not all GPU requirements could be fulfilled.") return used_devices
python
def match_gpus(available_devices, requirements): """ Determines sufficient GPUs for the given requirements and returns a list of GPUDevices. If there aren't sufficient GPUs a InsufficientGPUException is thrown. :param available_devices: A list of GPUDevices :param requirements: A list of GPURequirements :return: A list of sufficient devices """ if not requirements: return [] if not available_devices: raise InsufficientGPUError("No GPU devices available, but {} devices required.".format(len(requirements))) available_devices = available_devices.copy() used_devices = [] for req in requirements: dev = search_device(req, available_devices) if dev: used_devices.append(dev) available_devices.remove(dev) else: raise InsufficientGPUError("Not all GPU requirements could be fulfilled.") return used_devices
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train
https://github.com/curious-containers/cc-core/blob/eaeb03a4366016aff54fcc6953d052ae12ed599b/cc_core/commons/gpu_info.py#L110-L139
curious-containers/cc-core
cc_core/commons/gpu_info.py
get_gpu_requirements
def get_gpu_requirements(gpus_reqs): """ Extracts the GPU from a dictionary requirements as list of GPURequirements. :param gpus_reqs: A dictionary {'count': <count>} or a list [{min_vram: <min_vram>}, {min_vram: <min_vram>}, ...] :return: A list of GPURequirements """ requirements = [] if gpus_reqs: if type(gpus_reqs) is dict: count = gpus_reqs.get('count') if count: for i in range(count): requirements.append(GPURequirement()) elif type(gpus_reqs) is list: for gpu_req in gpus_reqs: requirements.append(GPURequirement(min_vram=gpu_req['minVram'])) return requirements else: # If no requirements are supplied return []
python
def get_gpu_requirements(gpus_reqs): """ Extracts the GPU from a dictionary requirements as list of GPURequirements. :param gpus_reqs: A dictionary {'count': <count>} or a list [{min_vram: <min_vram>}, {min_vram: <min_vram>}, ...] :return: A list of GPURequirements """ requirements = [] if gpus_reqs: if type(gpus_reqs) is dict: count = gpus_reqs.get('count') if count: for i in range(count): requirements.append(GPURequirement()) elif type(gpus_reqs) is list: for gpu_req in gpus_reqs: requirements.append(GPURequirement(min_vram=gpu_req['minVram'])) return requirements else: # If no requirements are supplied return []
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Extracts the GPU from a dictionary requirements as list of GPURequirements. :param gpus_reqs: A dictionary {'count': <count>} or a list [{min_vram: <min_vram>}, {min_vram: <min_vram>}, ...] :return: A list of GPURequirements
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train
https://github.com/curious-containers/cc-core/blob/eaeb03a4366016aff54fcc6953d052ae12ed599b/cc_core/commons/gpu_info.py#L142-L163
curious-containers/cc-core
cc_core/commons/gpu_info.py
set_nvidia_environment_variables
def set_nvidia_environment_variables(environment, gpu_ids): """ Updates a dictionary containing environment variables to setup Nvidia-GPUs. :param environment: The environment variables to update :param gpu_ids: A list of GPU ids """ if gpu_ids: nvidia_visible_devices = "" for gpu_id in gpu_ids: nvidia_visible_devices += "{},".format(gpu_id) environment["NVIDIA_VISIBLE_DEVICES"] = nvidia_visible_devices
python
def set_nvidia_environment_variables(environment, gpu_ids): """ Updates a dictionary containing environment variables to setup Nvidia-GPUs. :param environment: The environment variables to update :param gpu_ids: A list of GPU ids """ if gpu_ids: nvidia_visible_devices = "" for gpu_id in gpu_ids: nvidia_visible_devices += "{},".format(gpu_id) environment["NVIDIA_VISIBLE_DEVICES"] = nvidia_visible_devices
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Updates a dictionary containing environment variables to setup Nvidia-GPUs. :param environment: The environment variables to update :param gpu_ids: A list of GPU ids
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train
https://github.com/curious-containers/cc-core/blob/eaeb03a4366016aff54fcc6953d052ae12ed599b/cc_core/commons/gpu_info.py#L166-L178
curious-containers/cc-core
cc_core/commons/gpu_info.py
GPURequirement.is_sufficient
def is_sufficient(self, device): """ Returns whether the device is sufficient for this requirement. :param device: A GPUDevice instance. :type device: GPUDevice :return: True if the requirement is fulfilled otherwise False """ sufficient = True if (self.min_vram is not None) and (device.vram < self.min_vram): sufficient = False return sufficient
python
def is_sufficient(self, device): """ Returns whether the device is sufficient for this requirement. :param device: A GPUDevice instance. :type device: GPUDevice :return: True if the requirement is fulfilled otherwise False """ sufficient = True if (self.min_vram is not None) and (device.vram < self.min_vram): sufficient = False return sufficient
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Returns whether the device is sufficient for this requirement. :param device: A GPUDevice instance. :type device: GPUDevice :return: True if the requirement is fulfilled otherwise False
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train
https://github.com/curious-containers/cc-core/blob/eaeb03a4366016aff54fcc6953d052ae12ed599b/cc_core/commons/gpu_info.py#L28-L41
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/utils/__init__.py
cache_location
def cache_location(): '''Cross-platform placement of cached files''' plat = platform.platform() log.debug('Platform read as: {0}'.format(plat)) if plat.startswith('Windows'): log.debug('Windows platform detected') return os.path.join(os.environ['APPDATA'], 'OpenAccess_EPUB') elif plat.startswith('Darwin'): log.debug('Mac platform detected') elif plat.startswith('Linux'): log.debug('Linux platform detected') else: log.warning('Unhandled platform for cache_location') #This code is written for Linux and Mac, don't expect success for others path = os.path.expanduser('~') if path == '~': path = os.path.expanduser('~user') if path == '~user': log.critical('Could not resolve the correct cache location') sys.exit('Could not resolve the correct cache location') cache_loc = os.path.join(path, '.OpenAccess_EPUB') log.debug('Cache located: {0}'.format(cache_loc)) return cache_loc
python
def cache_location(): '''Cross-platform placement of cached files''' plat = platform.platform() log.debug('Platform read as: {0}'.format(plat)) if plat.startswith('Windows'): log.debug('Windows platform detected') return os.path.join(os.environ['APPDATA'], 'OpenAccess_EPUB') elif plat.startswith('Darwin'): log.debug('Mac platform detected') elif plat.startswith('Linux'): log.debug('Linux platform detected') else: log.warning('Unhandled platform for cache_location') #This code is written for Linux and Mac, don't expect success for others path = os.path.expanduser('~') if path == '~': path = os.path.expanduser('~user') if path == '~user': log.critical('Could not resolve the correct cache location') sys.exit('Could not resolve the correct cache location') cache_loc = os.path.join(path, '.OpenAccess_EPUB') log.debug('Cache located: {0}'.format(cache_loc)) return cache_loc
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Cross-platform placement of cached files
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/utils/__init__.py#L86-L109
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/utils/__init__.py
load_config_module
def load_config_module(): """ If the config.py file exists, import it as a module. If it does not exist, call sys.exit() with a request to run oaepub configure. """ import imp config_path = config_location() try: config = imp.load_source('config', config_path) except IOError: log.critical('Config file not found. oaepub exiting...') sys.exit('Config file not found. Please run \'oaepub configure\'') else: log.debug('Config file loaded from {0}'.format(config_path)) return config
python
def load_config_module(): """ If the config.py file exists, import it as a module. If it does not exist, call sys.exit() with a request to run oaepub configure. """ import imp config_path = config_location() try: config = imp.load_source('config', config_path) except IOError: log.critical('Config file not found. oaepub exiting...') sys.exit('Config file not found. Please run \'oaepub configure\'') else: log.debug('Config file loaded from {0}'.format(config_path)) return config
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If the config.py file exists, import it as a module. If it does not exist, call sys.exit() with a request to run oaepub configure.
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/utils/__init__.py#L133-L147
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/utils/__init__.py
evaluate_relative_path
def evaluate_relative_path(working=os.getcwd(), relative=''): """ This function receives two strings representing system paths. The first is the working directory and it should be an absolute path. The second is the relative path and it should not be absolute. This function will render an OS-appropriate absolute path, which is the normalized path from working to relative. """ return os.path.normpath(os.path.join(working, relative))
python
def evaluate_relative_path(working=os.getcwd(), relative=''): """ This function receives two strings representing system paths. The first is the working directory and it should be an absolute path. The second is the relative path and it should not be absolute. This function will render an OS-appropriate absolute path, which is the normalized path from working to relative. """ return os.path.normpath(os.path.join(working, relative))
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This function receives two strings representing system paths. The first is the working directory and it should be an absolute path. The second is the relative path and it should not be absolute. This function will render an OS-appropriate absolute path, which is the normalized path from working to relative.
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/utils/__init__.py#L156-L164
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/utils/__init__.py
get_absolute_path
def get_absolute_path(some_path): """ This function will return an appropriate absolute path for the path it is given. If the input is absolute, it will return unmodified; if the input is relative, it will be rendered as relative to the current working directory. """ if os.path.isabs(some_path): return some_path else: return evaluate_relative_path(os.getcwd(), some_path)
python
def get_absolute_path(some_path): """ This function will return an appropriate absolute path for the path it is given. If the input is absolute, it will return unmodified; if the input is relative, it will be rendered as relative to the current working directory. """ if os.path.isabs(some_path): return some_path else: return evaluate_relative_path(os.getcwd(), some_path)
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This function will return an appropriate absolute path for the path it is given. If the input is absolute, it will return unmodified; if the input is relative, it will be rendered as relative to the current working directory.
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/utils/__init__.py#L167-L176
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/utils/__init__.py
get_output_directory
def get_output_directory(args): """ Determination of the directory for output placement involves possibilities for explicit user instruction (absolute path or relative to execution) and implicit default configuration (absolute path or relative to input) from the system global configuration file. This function is responsible for reliably returning the appropriate output directory which will contain any log(s), ePub(s), and unzipped output of OpenAccess_EPUB. It utilizes the parsed args, passed as an object, and is self-sufficient in accessing the config file. All paths returned by this function are absolute. """ #Import the global config file as a module import imp config_path = os.path.join(cache_location(), 'config.py') try: config = imp.load_source('config', config_path) except IOError: print('Could not find {0}, please run oae-quickstart'.format(config_path)) sys.exit() #args.output is the explicit user instruction, None if unspecified if args.output: #args.output may be an absolute path if os.path.isabs(args.output): return args.output # return as is #or args.output may be a relative path, relative to cwd else: return evaluate_relative_path(relative=args.output) #config.default_output for default behavior without explicit instruction else: #config.default_output may be an absolute_path if os.path.isabs(config.default_output): return config.default_output #or config.default_output may be a relative path, relative to input else: if args.input: # The case of single input if 'http://www' in args.input: #Fetched from internet by URL raw_name = url_input(args.input, download=False) abs_input_path = os.path.join(os.getcwd(), raw_name+'.xml') elif args.input[:4] == 'doi:': #Fetched from internet by DOI raw_name = doi_input(args.input, download=False) abs_input_path = os.path.join(os.getcwd(), raw_name+'.xml') else: #Local option, could be anywhere abs_input_path = get_absolute_path(args.input) abs_input_parent = os.path.split(abs_input_path)[0] return evaluate_relative_path(abs_input_parent, config.default_output) elif args.batch: # The case of Batch Mode #Batch should only work on a supplied directory abs_batch_path = get_absolute_path(args.batch) return abs_batch_path elif args.zip: #Zip is a local-only option, behaves just like local xml abs_input_path = get_absolute_path(args.zip) abs_input_parent = os.path.split(abs_input_path)[0] return evaluate_relative_path(abs_input_parent, config.default_output) elif args.collection: return os.getcwd() else: # Un-handled or currently unsupported options print('The output location could not be determined...') sys.exit()
python
def get_output_directory(args): """ Determination of the directory for output placement involves possibilities for explicit user instruction (absolute path or relative to execution) and implicit default configuration (absolute path or relative to input) from the system global configuration file. This function is responsible for reliably returning the appropriate output directory which will contain any log(s), ePub(s), and unzipped output of OpenAccess_EPUB. It utilizes the parsed args, passed as an object, and is self-sufficient in accessing the config file. All paths returned by this function are absolute. """ #Import the global config file as a module import imp config_path = os.path.join(cache_location(), 'config.py') try: config = imp.load_source('config', config_path) except IOError: print('Could not find {0}, please run oae-quickstart'.format(config_path)) sys.exit() #args.output is the explicit user instruction, None if unspecified if args.output: #args.output may be an absolute path if os.path.isabs(args.output): return args.output # return as is #or args.output may be a relative path, relative to cwd else: return evaluate_relative_path(relative=args.output) #config.default_output for default behavior without explicit instruction else: #config.default_output may be an absolute_path if os.path.isabs(config.default_output): return config.default_output #or config.default_output may be a relative path, relative to input else: if args.input: # The case of single input if 'http://www' in args.input: #Fetched from internet by URL raw_name = url_input(args.input, download=False) abs_input_path = os.path.join(os.getcwd(), raw_name+'.xml') elif args.input[:4] == 'doi:': #Fetched from internet by DOI raw_name = doi_input(args.input, download=False) abs_input_path = os.path.join(os.getcwd(), raw_name+'.xml') else: #Local option, could be anywhere abs_input_path = get_absolute_path(args.input) abs_input_parent = os.path.split(abs_input_path)[0] return evaluate_relative_path(abs_input_parent, config.default_output) elif args.batch: # The case of Batch Mode #Batch should only work on a supplied directory abs_batch_path = get_absolute_path(args.batch) return abs_batch_path elif args.zip: #Zip is a local-only option, behaves just like local xml abs_input_path = get_absolute_path(args.zip) abs_input_parent = os.path.split(abs_input_path)[0] return evaluate_relative_path(abs_input_parent, config.default_output) elif args.collection: return os.getcwd() else: # Un-handled or currently unsupported options print('The output location could not be determined...') sys.exit()
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/utils/__init__.py#L179-L243
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/utils/__init__.py
file_root_name
def file_root_name(name): """ Returns the root name of a file from a full file path. It will not raise an error if the result is empty, but an warning will be issued. """ base = os.path.basename(name) root = os.path.splitext(base)[0] if not root: warning = 'file_root_name returned an empty root name from \"{0}\"' log.warning(warning.format(name)) return root
python
def file_root_name(name): """ Returns the root name of a file from a full file path. It will not raise an error if the result is empty, but an warning will be issued. """ base = os.path.basename(name) root = os.path.splitext(base)[0] if not root: warning = 'file_root_name returned an empty root name from \"{0}\"' log.warning(warning.format(name)) return root
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Returns the root name of a file from a full file path. It will not raise an error if the result is empty, but an warning will be issued.
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/utils/__init__.py#L246-L258
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/utils/__init__.py
files_with_ext
def files_with_ext(extension, directory='.', recursive=False): """ Generator function that will iterate over all files in the specified directory and return a path to the files which possess a matching extension. You should include the period in your extension, and matching is not case sensitive: '.xml' will also match '.XML' and vice versa. An empty string passed to extension will match extensionless files. """ if recursive: log.info('Recursively searching {0} for files with extension "{1}"'.format(directory, extension)) for dirname, subdirnames, filenames in os.walk(directory): for filename in filenames: filepath = os.path.join(dirname, filename) _root, ext = os.path.splitext(filepath) if extension.lower() == ext.lower(): yield filepath else: log.info('Looking in {0} for files with extension: "{1}"'.format(directory, extension)) for name in os.listdir(directory): filepath = os.path.join(directory, name) if not os.path.isfile(filepath): # Skip non-files continue _root, ext = os.path.splitext(filepath) if extension.lower() == ext.lower(): yield filepath
python
def files_with_ext(extension, directory='.', recursive=False): """ Generator function that will iterate over all files in the specified directory and return a path to the files which possess a matching extension. You should include the period in your extension, and matching is not case sensitive: '.xml' will also match '.XML' and vice versa. An empty string passed to extension will match extensionless files. """ if recursive: log.info('Recursively searching {0} for files with extension "{1}"'.format(directory, extension)) for dirname, subdirnames, filenames in os.walk(directory): for filename in filenames: filepath = os.path.join(dirname, filename) _root, ext = os.path.splitext(filepath) if extension.lower() == ext.lower(): yield filepath else: log.info('Looking in {0} for files with extension: "{1}"'.format(directory, extension)) for name in os.listdir(directory): filepath = os.path.join(directory, name) if not os.path.isfile(filepath): # Skip non-files continue _root, ext = os.path.splitext(filepath) if extension.lower() == ext.lower(): yield filepath
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Generator function that will iterate over all files in the specified directory and return a path to the files which possess a matching extension. You should include the period in your extension, and matching is not case sensitive: '.xml' will also match '.XML' and vice versa. An empty string passed to extension will match extensionless files.
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/utils/__init__.py#L261-L288
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/utils/__init__.py
epubcheck
def epubcheck(epubname, config=None): """ This method takes the name of an epub file as an argument. This name is the input for the java execution of a locally installed epubcheck-.jar. The location of this .jar file is configured in config.py. """ if config is None: config = load_config_module() r, e = os.path.splitext(epubname) if not e: log.warning('Missing file extension, appending ".epub"') e = '.epub' epubname = r + e elif not e == '.epub': log.warning('File does not have ".epub" extension, appending it') epubname += '.epub' subprocess.call(['java', '-jar', config.epubcheck_jarfile, epubname])
python
def epubcheck(epubname, config=None): """ This method takes the name of an epub file as an argument. This name is the input for the java execution of a locally installed epubcheck-.jar. The location of this .jar file is configured in config.py. """ if config is None: config = load_config_module() r, e = os.path.splitext(epubname) if not e: log.warning('Missing file extension, appending ".epub"') e = '.epub' epubname = r + e elif not e == '.epub': log.warning('File does not have ".epub" extension, appending it') epubname += '.epub' subprocess.call(['java', '-jar', config.epubcheck_jarfile, epubname])
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This method takes the name of an epub file as an argument. This name is the input for the java execution of a locally installed epubcheck-.jar. The location of this .jar file is configured in config.py.
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/utils/__init__.py#L291-L307
SavinaRoja/OpenAccess_EPUB
src/openaccess_epub/utils/__init__.py
dir_exists
def dir_exists(directory): """ If a directory already exists that will be overwritten by some action, this will ask the user whether or not to continue with the deletion. If the user responds affirmatively, then the directory will be removed. If the user responds negatively, then the process will abort. """ log.info('Directory exists! Asking the user') reply = input('''The directory {0} already exists. It will be overwritten if the operation continues. Replace? [Y/n]'''.format(directory)) if reply.lower() in ['y', 'yes', '']: shutil.rmtree(directory) os.makedirs(directory) else: log.critical('Aborting process, user declined overwriting {0}'.format(directory)) sys.exit('Aborting process!')
python
def dir_exists(directory): """ If a directory already exists that will be overwritten by some action, this will ask the user whether or not to continue with the deletion. If the user responds affirmatively, then the directory will be removed. If the user responds negatively, then the process will abort. """ log.info('Directory exists! Asking the user') reply = input('''The directory {0} already exists. It will be overwritten if the operation continues. Replace? [Y/n]'''.format(directory)) if reply.lower() in ['y', 'yes', '']: shutil.rmtree(directory) os.makedirs(directory) else: log.critical('Aborting process, user declined overwriting {0}'.format(directory)) sys.exit('Aborting process!')
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If a directory already exists that will be overwritten by some action, this will ask the user whether or not to continue with the deletion. If the user responds affirmatively, then the directory will be removed. If the user responds negatively, then the process will abort.
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train
https://github.com/SavinaRoja/OpenAccess_EPUB/blob/6b77ba30b7394fd003920e7a7957bca963a90656/src/openaccess_epub/utils/__init__.py#L310-L327
bachya/pyflunearyou
pyflunearyou/helpers/geo.py
get_nearest_by_coordinates
def get_nearest_by_coordinates( data: list, latitude_key: str, longitude_key: str, target_latitude: float, target_longitude: float) -> Any: """Get the closest dict entry based on latitude/longitude.""" return min( data, key=lambda p: haversine( target_latitude, target_longitude, float(p[latitude_key]), float(p[longitude_key]) ))
python
def get_nearest_by_coordinates( data: list, latitude_key: str, longitude_key: str, target_latitude: float, target_longitude: float) -> Any: """Get the closest dict entry based on latitude/longitude.""" return min( data, key=lambda p: haversine( target_latitude, target_longitude, float(p[latitude_key]), float(p[longitude_key]) ))
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Get the closest dict entry based on latitude/longitude.
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train
https://github.com/bachya/pyflunearyou/blob/16a2f839c8df851e925e010a6b5c5708386febac/pyflunearyou/helpers/geo.py#L7-L18
bachya/pyflunearyou
pyflunearyou/helpers/geo.py
haversine
def haversine(lat1: float, lon1: float, lat2: float, lon2: float) -> float: """Determine the distance between two latitude/longitude pairs.""" lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2]) # haversine formula dlon = lon2 - lon1 dlat = lat2 - lat1 calc_a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2 calc_c = 2 * asin(sqrt(calc_a)) return 6371 * calc_c
python
def haversine(lat1: float, lon1: float, lat2: float, lon2: float) -> float: """Determine the distance between two latitude/longitude pairs.""" lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2]) # haversine formula dlon = lon2 - lon1 dlat = lat2 - lat1 calc_a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2 calc_c = 2 * asin(sqrt(calc_a)) return 6371 * calc_c
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train
https://github.com/bachya/pyflunearyou/blob/16a2f839c8df851e925e010a6b5c5708386febac/pyflunearyou/helpers/geo.py#L21-L31
ChrisTimperley/Kaskara
python/kaskara/insertions.py
InsertionPointDB.in_file
def in_file(self, fn: str) -> Iterator[InsertionPoint]: """ Returns an iterator over all of the insertion points in a given file. """ logger.debug("finding insertion points in file: %s", fn) yield from self.__file_insertions.get(fn, [])
python
def in_file(self, fn: str) -> Iterator[InsertionPoint]: """ Returns an iterator over all of the insertion points in a given file. """ logger.debug("finding insertion points in file: %s", fn) yield from self.__file_insertions.get(fn, [])
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Returns an iterator over all of the insertion points in a given file.
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train
https://github.com/ChrisTimperley/Kaskara/blob/3d182d95b2938508e5990eddd30321be15e2f2ef/python/kaskara/insertions.py#L91-L96
ChrisTimperley/Kaskara
python/kaskara/insertions.py
InsertionPointDB.at_line
def at_line(self, line: FileLine) -> Iterator[InsertionPoint]: """ Returns an iterator over all of the insertion points located at a given line. """ logger.debug("finding insertion points at line: %s", str(line)) filename = line.filename # type: str line_num = line.num # type: int for ins in self.in_file(filename): if line_num == ins.location.line: logger.debug("found insertion point at line [%s]: %s", str(line), ins) yield ins
python
def at_line(self, line: FileLine) -> Iterator[InsertionPoint]: """ Returns an iterator over all of the insertion points located at a given line. """ logger.debug("finding insertion points at line: %s", str(line)) filename = line.filename # type: str line_num = line.num # type: int for ins in self.in_file(filename): if line_num == ins.location.line: logger.debug("found insertion point at line [%s]: %s", str(line), ins) yield ins
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Returns an iterator over all of the insertion points located at a given line.
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train
https://github.com/ChrisTimperley/Kaskara/blob/3d182d95b2938508e5990eddd30321be15e2f2ef/python/kaskara/insertions.py#L98-L110
linkhub-sdk/popbill.py
popbill/closedownService.py
ClosedownService.getUnitCost
def getUnitCost(self, CorpNum): """ νœ΄νμ—…μ‘°νšŒ 단가 확인. args CorpNum : νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ return λ°œν–‰λ‹¨κ°€ by float raise PopbillException """ result = self._httpget('/CloseDown/UnitCost', CorpNum) return float(result.unitCost)
python
def getUnitCost(self, CorpNum): """ νœ΄νμ—…μ‘°νšŒ 단가 확인. args CorpNum : νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ return λ°œν–‰λ‹¨κ°€ by float raise PopbillException """ result = self._httpget('/CloseDown/UnitCost', CorpNum) return float(result.unitCost)
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νœ΄νμ—…μ‘°νšŒ 단가 확인. args CorpNum : νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ return λ°œν–‰λ‹¨κ°€ by float raise PopbillException
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train
https://github.com/linkhub-sdk/popbill.py/blob/68a0dd7f7a937603318e93be321fde73c50b96cc/popbill/closedownService.py#L39-L51
linkhub-sdk/popbill.py
popbill/closedownService.py
ClosedownService.checkCorpNum
def checkCorpNum(self, MemberCorpNum, CheckCorpNum): """ νœ΄νμ—…μ‘°νšŒ - 단건 args MemberCorpNum : νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ CorpNum : μ‘°νšŒν•  μ‚¬μ—…μžλ²ˆν˜Έ MgtKey : λ¬Έμ„œκ΄€λ¦¬λ²ˆν˜Έ return νœ΄νμ—…μ •λ³΄ object raise PopbillException """ if MemberCorpNum == None or MemberCorpNum == "" : raise PopbillException(-99999999,"νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έκ°€ μž…λ ₯λ˜μ§€ μ•Šμ•˜μŠ΅λ‹ˆλ‹€.") if CheckCorpNum == None or CheckCorpNum == "" : raise PopbillException(-99999999,"μ‘°νšŒν•  μ‚¬μ—…μžλ²ˆν˜Έκ°€ μž…λ ₯λ˜μ§€ μ•Šμ•˜μŠ΅λ‹ˆλ‹€.") return self._httpget('/CloseDown?CN=' +CheckCorpNum, MemberCorpNum)
python
def checkCorpNum(self, MemberCorpNum, CheckCorpNum): """ νœ΄νμ—…μ‘°νšŒ - 단건 args MemberCorpNum : νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ CorpNum : μ‘°νšŒν•  μ‚¬μ—…μžλ²ˆν˜Έ MgtKey : λ¬Έμ„œκ΄€λ¦¬λ²ˆν˜Έ return νœ΄νμ—…μ •λ³΄ object raise PopbillException """ if MemberCorpNum == None or MemberCorpNum == "" : raise PopbillException(-99999999,"νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έκ°€ μž…λ ₯λ˜μ§€ μ•Šμ•˜μŠ΅λ‹ˆλ‹€.") if CheckCorpNum == None or CheckCorpNum == "" : raise PopbillException(-99999999,"μ‘°νšŒν•  μ‚¬μ—…μžλ²ˆν˜Έκ°€ μž…λ ₯λ˜μ§€ μ•Šμ•˜μŠ΅λ‹ˆλ‹€.") return self._httpget('/CloseDown?CN=' +CheckCorpNum, MemberCorpNum)
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νœ΄νμ—…μ‘°νšŒ - 단건 args MemberCorpNum : νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ CorpNum : μ‘°νšŒν•  μ‚¬μ—…μžλ²ˆν˜Έ MgtKey : λ¬Έμ„œκ΄€λ¦¬λ²ˆν˜Έ return νœ΄νμ—…μ •λ³΄ object raise PopbillException
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train
https://github.com/linkhub-sdk/popbill.py/blob/68a0dd7f7a937603318e93be321fde73c50b96cc/popbill/closedownService.py#L53-L71
linkhub-sdk/popbill.py
popbill/closedownService.py
ClosedownService.checkCorpNums
def checkCorpNums(self, MemberCorpNum, CorpNumList): """ νœ΄νμ—…μ‘°νšŒ λŒ€λŸ‰ 확인, μ΅œλŒ€ 1000건 args MemberCorpNum : νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ CorpNumList : μ‘°νšŒν•  μ‚¬μ—…μžλ²ˆν˜Έ λ°°μ—΄ return νœ΄νμ—…μ •λ³΄ Object as List raise PopbillException """ if CorpNumList == None or len(CorpNumList) < 1: raise PopbillException(-99999999,"쑰죄할 μ‚¬μ—…μžλ²ˆν˜Έ λͺ©λ‘μ΄ μž…λ ₯λ˜μ§€ μ•Šμ•˜μŠ΅λ‹ˆλ‹€.") postData = self._stringtify(CorpNumList) return self._httppost('/CloseDown',postData,MemberCorpNum)
python
def checkCorpNums(self, MemberCorpNum, CorpNumList): """ νœ΄νμ—…μ‘°νšŒ λŒ€λŸ‰ 확인, μ΅œλŒ€ 1000건 args MemberCorpNum : νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ CorpNumList : μ‘°νšŒν•  μ‚¬μ—…μžλ²ˆν˜Έ λ°°μ—΄ return νœ΄νμ—…μ •λ³΄ Object as List raise PopbillException """ if CorpNumList == None or len(CorpNumList) < 1: raise PopbillException(-99999999,"쑰죄할 μ‚¬μ—…μžλ²ˆν˜Έ λͺ©λ‘μ΄ μž…λ ₯λ˜μ§€ μ•Šμ•˜μŠ΅λ‹ˆλ‹€.") postData = self._stringtify(CorpNumList) return self._httppost('/CloseDown',postData,MemberCorpNum)
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νœ΄νμ—…μ‘°νšŒ λŒ€λŸ‰ 확인, μ΅œλŒ€ 1000건 args MemberCorpNum : νŒλΉŒνšŒμ› μ‚¬μ—…μžλ²ˆν˜Έ CorpNumList : μ‘°νšŒν•  μ‚¬μ—…μžλ²ˆν˜Έ λ°°μ—΄ return νœ΄νμ—…μ •λ³΄ Object as List raise PopbillException
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train
https://github.com/linkhub-sdk/popbill.py/blob/68a0dd7f7a937603318e93be321fde73c50b96cc/popbill/closedownService.py#L73-L88
MSchnei/pyprf_feature
pyprf_feature/analysis/old/pRF_mdlCrt.py
loadPng
def loadPng(varNumVol, tplPngSize, strPathPng): """Load PNG files. Parameters ---------- varNumVol : float Number of volumes, i.e. number of time points in all runs. tplPngSize : tuple Shape of the stimulus image (i.e. png). strPathPng: str Path to the folder cointaining the png files. Returns ------- aryPngData : 2d numpy array, shape [png_x, png_y, n_vols] Stack of stimulus data. """ print('------Load PNGs') # Create list of png files to load: lstPngPaths = [None] * varNumVol for idx01 in range(0, varNumVol): lstPngPaths[idx01] = (strPathPng + str(idx01) + '.png') # Load png files. The png data will be saved in a numpy array of the # following order: aryPngData[x-pixel, y-pixel, PngNumber]. The # sp.misc.imread function actually contains three values per pixel (RGB), # but since the stimuli are black-and-white, any one of these is sufficient # and we discard the others. aryPngData = np.zeros((tplPngSize[0], tplPngSize[1], varNumVol)) for idx01 in range(0, varNumVol): aryPngData[:, :, idx01] = np.array(Image.open(lstPngPaths[idx01])) # Convert RGB values (0 to 255) to integer ones and zeros: aryPngData = (aryPngData > 0).astype(int) return aryPngData
python
def loadPng(varNumVol, tplPngSize, strPathPng): """Load PNG files. Parameters ---------- varNumVol : float Number of volumes, i.e. number of time points in all runs. tplPngSize : tuple Shape of the stimulus image (i.e. png). strPathPng: str Path to the folder cointaining the png files. Returns ------- aryPngData : 2d numpy array, shape [png_x, png_y, n_vols] Stack of stimulus data. """ print('------Load PNGs') # Create list of png files to load: lstPngPaths = [None] * varNumVol for idx01 in range(0, varNumVol): lstPngPaths[idx01] = (strPathPng + str(idx01) + '.png') # Load png files. The png data will be saved in a numpy array of the # following order: aryPngData[x-pixel, y-pixel, PngNumber]. The # sp.misc.imread function actually contains three values per pixel (RGB), # but since the stimuli are black-and-white, any one of these is sufficient # and we discard the others. aryPngData = np.zeros((tplPngSize[0], tplPngSize[1], varNumVol)) for idx01 in range(0, varNumVol): aryPngData[:, :, idx01] = np.array(Image.open(lstPngPaths[idx01])) # Convert RGB values (0 to 255) to integer ones and zeros: aryPngData = (aryPngData > 0).astype(int) return aryPngData
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Load PNG files. Parameters ---------- varNumVol : float Number of volumes, i.e. number of time points in all runs. tplPngSize : tuple Shape of the stimulus image (i.e. png). strPathPng: str Path to the folder cointaining the png files. Returns ------- aryPngData : 2d numpy array, shape [png_x, png_y, n_vols] Stack of stimulus data.
[ "Load", "PNG", "files", "." ]
train
https://github.com/MSchnei/pyprf_feature/blob/49004ede7ae1ddee07a30afe9ce3e2776750805c/pyprf_feature/analysis/old/pRF_mdlCrt.py#L29-L66
MSchnei/pyprf_feature
pyprf_feature/analysis/old/pRF_mdlCrt.py
loadPrsOrd
def loadPrsOrd(vecRunLngth, strPathPresOrd, vecVslStim): """Load presentation order of motion directions. Parameters ---------- vecRunLngth : list Number of volumes in every run strPathPresOrd : str Path to the npy vector containing order of presented motion directions. vecVslStim: list Key of (stimulus) condition presented in every run Returns ------- aryPresOrdAprt : 1d numpy array, shape [n_vols] Presentation order of aperture position. aryPresOrdMtn : 1d numpy array, shape [n_vols] Presentation order of motion direction. """ print('------Load presentation order of motion directions') aryPresOrd = np.empty((0, 2)) for idx01 in range(0, len(vecRunLngth)): # reconstruct file name # ---> consider: some runs were shorter than others(replace next row) filename1 = (strPathPresOrd + str(vecVslStim[idx01]) + '.pickle') # filename1 = (strPathPresOrd + str(idx01+1) + '.pickle') # load array with open(filename1, 'rb') as handle: array1 = pickle.load(handle) tempCond = array1["Conditions"] tempCond = tempCond[:vecRunLngth[idx01], :] # add temp array to aryPresOrd aryPresOrd = np.concatenate((aryPresOrd, tempCond), axis=0) aryPresOrdAprt = aryPresOrd[:, 0].astype(int) aryPresOrdMtn = aryPresOrd[:, 1].astype(int) return aryPresOrdAprt, aryPresOrdMtn
python
def loadPrsOrd(vecRunLngth, strPathPresOrd, vecVslStim): """Load presentation order of motion directions. Parameters ---------- vecRunLngth : list Number of volumes in every run strPathPresOrd : str Path to the npy vector containing order of presented motion directions. vecVslStim: list Key of (stimulus) condition presented in every run Returns ------- aryPresOrdAprt : 1d numpy array, shape [n_vols] Presentation order of aperture position. aryPresOrdMtn : 1d numpy array, shape [n_vols] Presentation order of motion direction. """ print('------Load presentation order of motion directions') aryPresOrd = np.empty((0, 2)) for idx01 in range(0, len(vecRunLngth)): # reconstruct file name # ---> consider: some runs were shorter than others(replace next row) filename1 = (strPathPresOrd + str(vecVslStim[idx01]) + '.pickle') # filename1 = (strPathPresOrd + str(idx01+1) + '.pickle') # load array with open(filename1, 'rb') as handle: array1 = pickle.load(handle) tempCond = array1["Conditions"] tempCond = tempCond[:vecRunLngth[idx01], :] # add temp array to aryPresOrd aryPresOrd = np.concatenate((aryPresOrd, tempCond), axis=0) aryPresOrdAprt = aryPresOrd[:, 0].astype(int) aryPresOrdMtn = aryPresOrd[:, 1].astype(int) return aryPresOrdAprt, aryPresOrdMtn
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train
https://github.com/MSchnei/pyprf_feature/blob/49004ede7ae1ddee07a30afe9ce3e2776750805c/pyprf_feature/analysis/old/pRF_mdlCrt.py#L69-L105
MSchnei/pyprf_feature
pyprf_feature/analysis/old/pRF_mdlCrt.py
crtPwBoxCarFn
def crtPwBoxCarFn(varNumVol, aryPngData, aryPresOrd, vecMtDrctn): """Create pixel-wise boxcar functions. Parameters ---------- input1 : 2d numpy array, shape [n_samples, n_measurements] Description of input 1. input2 : float, positive Description of input 2. Returns ------- data : 2d numpy array, shape [n_samples, n_measurements] Closed data. Reference --------- [1] """ print('------Create pixel-wise boxcar functions') aryBoxCar = np.empty(aryPngData.shape[0:2] + (len(vecMtDrctn),) + (varNumVol,), dtype='int64') for ind, num in enumerate(vecMtDrctn): aryCondTemp = np.zeros((aryPngData.shape), dtype='int64') lgcTempMtDrctn = [aryPresOrd == num][0] aryCondTemp[:, :, lgcTempMtDrctn] = np.copy( aryPngData[:, :, lgcTempMtDrctn]) aryBoxCar[:, :, ind, :] = aryCondTemp return aryBoxCar
python
def crtPwBoxCarFn(varNumVol, aryPngData, aryPresOrd, vecMtDrctn): """Create pixel-wise boxcar functions. Parameters ---------- input1 : 2d numpy array, shape [n_samples, n_measurements] Description of input 1. input2 : float, positive Description of input 2. Returns ------- data : 2d numpy array, shape [n_samples, n_measurements] Closed data. Reference --------- [1] """ print('------Create pixel-wise boxcar functions') aryBoxCar = np.empty(aryPngData.shape[0:2] + (len(vecMtDrctn),) + (varNumVol,), dtype='int64') for ind, num in enumerate(vecMtDrctn): aryCondTemp = np.zeros((aryPngData.shape), dtype='int64') lgcTempMtDrctn = [aryPresOrd == num][0] aryCondTemp[:, :, lgcTempMtDrctn] = np.copy( aryPngData[:, :, lgcTempMtDrctn]) aryBoxCar[:, :, ind, :] = aryCondTemp return aryBoxCar
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[ "Create", "pixel", "-", "wise", "boxcar", "functions", "." ]
train
https://github.com/MSchnei/pyprf_feature/blob/49004ede7ae1ddee07a30afe9ce3e2776750805c/pyprf_feature/analysis/old/pRF_mdlCrt.py#L108-L135
MSchnei/pyprf_feature
pyprf_feature/analysis/old/pRF_mdlCrt.py
crtGauss2D
def crtGauss2D(varSizeX, varSizeY, varPosX, varPosY, varSd): """Create 2D Gaussian kernel. Parameters ---------- varSizeX : int, positive Width of the visual field. varSizeY : int, positive Height of the visual field.. varPosX : int, positive X position of centre of 2D Gauss. varPosY : int, positive Y position of centre of 2D Gauss. varSd : float, positive Standard deviation of 2D Gauss. Returns ------- aryGauss : 2d numpy array, shape [varSizeX, varSizeY] 2d Gaussian. Reference --------- [1] """ varSizeX = int(varSizeX) varSizeY = int(varSizeY) # aryX and aryY are in reversed order, this seems to be necessary: aryY, aryX = sp.mgrid[0:varSizeX, 0:varSizeY] # The actual creation of the Gaussian array: aryGauss = ( (np.square((aryX - varPosX)) + np.square((aryY - varPosY))) / (2.0 * np.square(varSd)) ) aryGauss = np.exp(-aryGauss) / (2 * np.pi * np.square(varSd)) return aryGauss
python
def crtGauss2D(varSizeX, varSizeY, varPosX, varPosY, varSd): """Create 2D Gaussian kernel. Parameters ---------- varSizeX : int, positive Width of the visual field. varSizeY : int, positive Height of the visual field.. varPosX : int, positive X position of centre of 2D Gauss. varPosY : int, positive Y position of centre of 2D Gauss. varSd : float, positive Standard deviation of 2D Gauss. Returns ------- aryGauss : 2d numpy array, shape [varSizeX, varSizeY] 2d Gaussian. Reference --------- [1] """ varSizeX = int(varSizeX) varSizeY = int(varSizeY) # aryX and aryY are in reversed order, this seems to be necessary: aryY, aryX = sp.mgrid[0:varSizeX, 0:varSizeY] # The actual creation of the Gaussian array: aryGauss = ( (np.square((aryX - varPosX)) + np.square((aryY - varPosY))) / (2.0 * np.square(varSd)) ) aryGauss = np.exp(-aryGauss) / (2 * np.pi * np.square(varSd)) return aryGauss
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Create 2D Gaussian kernel. Parameters ---------- varSizeX : int, positive Width of the visual field. varSizeY : int, positive Height of the visual field.. varPosX : int, positive X position of centre of 2D Gauss. varPosY : int, positive Y position of centre of 2D Gauss. varSd : float, positive Standard deviation of 2D Gauss. Returns ------- aryGauss : 2d numpy array, shape [varSizeX, varSizeY] 2d Gaussian. Reference --------- [1]
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train
https://github.com/MSchnei/pyprf_feature/blob/49004ede7ae1ddee07a30afe9ce3e2776750805c/pyprf_feature/analysis/old/pRF_mdlCrt.py#L138-L175
MSchnei/pyprf_feature
pyprf_feature/analysis/old/pRF_mdlCrt.py
cnvlGauss2D
def cnvlGauss2D(idxPrc, aryBoxCar, aryMdlParamsChnk, tplPngSize, varNumVol, queOut): """Spatially convolve boxcar functions with 2D Gaussian. Parameters ---------- idxPrc : 2d numpy array, shape [n_samples, n_measurements] Description of input 1. aryBoxCar : float, positive Description of input 2. aryMdlParamsChnk : 2d numpy array, shape [n_samples, n_measurements] Description of input 1. tplPngSize : float, positive Description of input 2. varNumVol : 2d numpy array, shape [n_samples, n_measurements] Description of input 1. queOut : float, positive Description of input 2. Returns ------- data : 2d numpy array, shape [n_samples, n_measurements] Closed data. Reference --------- [1] """ # Number of combinations of model parameters in the current chunk: varChnkSze = np.size(aryMdlParamsChnk, axis=0) # Determine number of motion directions varNumMtnDrtn = aryBoxCar.shape[2] # Output array with pRF model time courses: aryOut = np.zeros([varChnkSze, varNumMtnDrtn, varNumVol]) # Loop through different motion directions: for idxMtn in range(0, varNumMtnDrtn): # Loop through combinations of model parameters: for idxMdl in range(0, varChnkSze): # Spatial parameters of current model: varTmpX = aryMdlParamsChnk[idxMdl, 1] varTmpY = aryMdlParamsChnk[idxMdl, 2] varTmpSd = aryMdlParamsChnk[idxMdl, 3] # Create pRF model (2D): aryGauss = crtGauss2D(tplPngSize[0], tplPngSize[1], varTmpX, varTmpY, varTmpSd) # Multiply pixel-time courses with Gaussian pRF models: aryPrfTcTmp = np.multiply(aryBoxCar[:, :, idxMtn, :], aryGauss[:, :, None]) # Calculate sum across x- and y-dimensions - the 'area under the # Gaussian surface'. This is essentially an unscaled version of the # pRF time course model (i.e. not yet scaled for size of the pRF). aryPrfTcTmp = np.sum(aryPrfTcTmp, axis=(0, 1)) # Put model time courses into function's output with 2d Gaussian # arrray: aryOut[idxMdl, idxMtn, :] = aryPrfTcTmp # Put column with the indicies of model-parameter-combinations into the # output array (in order to be able to put the pRF model time courses into # the correct order after the parallelised function): lstOut = [idxPrc, aryOut] # Put output to queue: queOut.put(lstOut)
python
def cnvlGauss2D(idxPrc, aryBoxCar, aryMdlParamsChnk, tplPngSize, varNumVol, queOut): """Spatially convolve boxcar functions with 2D Gaussian. Parameters ---------- idxPrc : 2d numpy array, shape [n_samples, n_measurements] Description of input 1. aryBoxCar : float, positive Description of input 2. aryMdlParamsChnk : 2d numpy array, shape [n_samples, n_measurements] Description of input 1. tplPngSize : float, positive Description of input 2. varNumVol : 2d numpy array, shape [n_samples, n_measurements] Description of input 1. queOut : float, positive Description of input 2. Returns ------- data : 2d numpy array, shape [n_samples, n_measurements] Closed data. Reference --------- [1] """ # Number of combinations of model parameters in the current chunk: varChnkSze = np.size(aryMdlParamsChnk, axis=0) # Determine number of motion directions varNumMtnDrtn = aryBoxCar.shape[2] # Output array with pRF model time courses: aryOut = np.zeros([varChnkSze, varNumMtnDrtn, varNumVol]) # Loop through different motion directions: for idxMtn in range(0, varNumMtnDrtn): # Loop through combinations of model parameters: for idxMdl in range(0, varChnkSze): # Spatial parameters of current model: varTmpX = aryMdlParamsChnk[idxMdl, 1] varTmpY = aryMdlParamsChnk[idxMdl, 2] varTmpSd = aryMdlParamsChnk[idxMdl, 3] # Create pRF model (2D): aryGauss = crtGauss2D(tplPngSize[0], tplPngSize[1], varTmpX, varTmpY, varTmpSd) # Multiply pixel-time courses with Gaussian pRF models: aryPrfTcTmp = np.multiply(aryBoxCar[:, :, idxMtn, :], aryGauss[:, :, None]) # Calculate sum across x- and y-dimensions - the 'area under the # Gaussian surface'. This is essentially an unscaled version of the # pRF time course model (i.e. not yet scaled for size of the pRF). aryPrfTcTmp = np.sum(aryPrfTcTmp, axis=(0, 1)) # Put model time courses into function's output with 2d Gaussian # arrray: aryOut[idxMdl, idxMtn, :] = aryPrfTcTmp # Put column with the indicies of model-parameter-combinations into the # output array (in order to be able to put the pRF model time courses into # the correct order after the parallelised function): lstOut = [idxPrc, aryOut] # Put output to queue: queOut.put(lstOut)
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Spatially convolve boxcar functions with 2D Gaussian. Parameters ---------- idxPrc : 2d numpy array, shape [n_samples, n_measurements] Description of input 1. aryBoxCar : float, positive Description of input 2. aryMdlParamsChnk : 2d numpy array, shape [n_samples, n_measurements] Description of input 1. tplPngSize : float, positive Description of input 2. varNumVol : 2d numpy array, shape [n_samples, n_measurements] Description of input 1. queOut : float, positive Description of input 2. Returns ------- data : 2d numpy array, shape [n_samples, n_measurements] Closed data. Reference --------- [1]
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train
https://github.com/MSchnei/pyprf_feature/blob/49004ede7ae1ddee07a30afe9ce3e2776750805c/pyprf_feature/analysis/old/pRF_mdlCrt.py#L178-L250