idx int64 0 251k | question stringlengths 53 3.53k | target stringlengths 5 1.23k | len_question int64 20 893 | len_target int64 3 238 |
|---|---|---|---|---|
237,800 | def _decodeTimestamp ( byteIter ) : dateStr = decodeSemiOctets ( byteIter , 7 ) timeZoneStr = dateStr [ - 2 : ] return datetime . strptime ( dateStr [ : - 2 ] , '%y%m%d%H%M%S' ) . replace ( tzinfo = SmsPduTzInfo ( timeZoneStr ) ) | Decodes a 7 - octet timestamp | 87 | 8 |
237,801 | def decodeUcs2 ( byteIter , numBytes ) : userData = [ ] i = 0 try : while i < numBytes : userData . append ( unichr ( ( next ( byteIter ) << 8 ) | next ( byteIter ) ) ) i += 2 except StopIteration : # Not enough bytes in iterator to reach numBytes; return what we have pass return '' . join ( userData ) | Decodes UCS2 - encoded text from the specified byte iterator up to a maximum of numBytes | 87 | 19 |
237,802 | def encode ( self ) : result = bytearray ( ) result . append ( self . id ) result . append ( self . dataLength ) result . extend ( self . data ) return result | Encodes this IE and returns the resulting bytes | 41 | 9 |
237,803 | def parseArgsPy26 ( ) : from gsmtermlib . posoptparse import PosOptionParser , Option parser = PosOptionParser ( description = 'Simple script for sending SMS messages' ) parser . add_option ( '-i' , '--port' , metavar = 'PORT' , help = 'port to which the GSM modem is connected; a number or a device name.' ) parser . add_option ( '-b' , '--baud' , metavar = 'BAUDRATE' , default = 115200 , help = 'set baud rate' ) parser . add_option ( '-p' , '--pin' , metavar = 'PIN' , default = None , help = 'SIM card PIN' ) parser . add_option ( '-d' , '--deliver' , action = 'store_true' , help = 'wait for SMS delivery report' ) parser . add_positional_argument ( Option ( '--destination' , metavar = 'DESTINATION' , help = 'destination mobile number' ) ) options , args = parser . parse_args ( ) if len ( args ) != 1 : parser . error ( 'Incorrect number of arguments - please specify a DESTINATION to send to, e.g. {0} 012789456' . format ( sys . argv [ 0 ] ) ) else : options . destination = args [ 0 ] return options | Argument parser for Python 2 . 6 | 314 | 8 |
237,804 | def lineMatching ( regexStr , lines ) : regex = re . compile ( regexStr ) for line in lines : m = regex . match ( line ) if m : return m else : return None | Searches through the specified list of strings and returns the regular expression match for the first line that matches the specified regex string or None if no match was found | 42 | 32 |
237,805 | def lineMatchingPattern ( pattern , lines ) : for line in lines : m = pattern . match ( line ) if m : return m else : return None | Searches through the specified list of strings and returns the regular expression match for the first line that matches the specified pre - compiled regex pattern or None if no match was found | 33 | 35 |
237,806 | def allLinesMatchingPattern ( pattern , lines ) : result = [ ] for line in lines : m = pattern . match ( line ) if m : result . append ( m ) return result | Like lineMatchingPattern but returns all lines that match the specified pattern | 41 | 14 |
237,807 | def clean ( decrypted : bytes ) -> str : last = decrypted [ - 1 ] if isinstance ( last , int ) : return decrypted [ : - last ] . decode ( 'utf8' ) return decrypted [ : - ord ( last ) ] . decode ( 'utf8' ) | r Strip padding from decrypted value . | 63 | 8 |
237,808 | def set_pattern_step_setpoint ( self , patternnumber , stepnumber , setpointvalue ) : _checkPatternNumber ( patternnumber ) _checkStepNumber ( stepnumber ) _checkSetpointValue ( setpointvalue , self . setpoint_max ) address = _calculateRegisterAddress ( 'setpoint' , patternnumber , stepnumber ) self . write_register ( address , setpointvalue , 1 ) | Set the setpoint value for a step . | 90 | 9 |
237,809 | def get_pattern_step_time ( self , patternnumber , stepnumber ) : _checkPatternNumber ( patternnumber ) _checkStepNumber ( stepnumber ) address = _calculateRegisterAddress ( 'time' , patternnumber , stepnumber ) return self . read_register ( address , 0 ) | Get the step time . | 64 | 5 |
237,810 | def set_pattern_step_time ( self , patternnumber , stepnumber , timevalue ) : _checkPatternNumber ( patternnumber ) _checkStepNumber ( stepnumber ) _checkTimeValue ( timevalue , self . time_max ) address = _calculateRegisterAddress ( 'time' , patternnumber , stepnumber ) self . write_register ( address , timevalue , 0 ) | Set the step time . | 83 | 5 |
237,811 | def get_pattern_actual_step ( self , patternnumber ) : _checkPatternNumber ( patternnumber ) address = _calculateRegisterAddress ( 'actualstep' , patternnumber ) return self . read_register ( address , 0 ) | Get the actual step parameter for a given pattern . | 51 | 10 |
237,812 | def set_pattern_actual_step ( self , patternnumber , value ) : _checkPatternNumber ( patternnumber ) _checkStepNumber ( value ) address = _calculateRegisterAddress ( 'actualstep' , patternnumber ) self . write_register ( address , value , 0 ) | Set the actual step parameter for a given pattern . | 61 | 10 |
237,813 | def get_pattern_additional_cycles ( self , patternnumber ) : _checkPatternNumber ( patternnumber ) address = _calculateRegisterAddress ( 'cycles' , patternnumber ) return self . read_register ( address ) | Get the number of additional cycles for a given pattern . | 49 | 11 |
237,814 | def set_pattern_additional_cycles ( self , patternnumber , value ) : _checkPatternNumber ( patternnumber ) minimalmodbus . _checkInt ( value , minvalue = 0 , maxvalue = 99 , description = 'number of additional cycles' ) address = _calculateRegisterAddress ( 'cycles' , patternnumber ) self . write_register ( address , value , 0 ) | Set the number of additional cycles for a given pattern . | 83 | 11 |
237,815 | def get_pattern_link_topattern ( self , patternnumber ) : _checkPatternNumber ( patternnumber ) address = _calculateRegisterAddress ( 'linkpattern' , patternnumber ) return self . read_register ( address ) | Get the linked pattern value for a given pattern . | 51 | 10 |
237,816 | def get_all_pattern_variables ( self , patternnumber ) : _checkPatternNumber ( patternnumber ) outputstring = '' for stepnumber in range ( 8 ) : outputstring += 'SP{0}: {1} Time{0}: {2}\n' . format ( stepnumber , self . get_pattern_step_setpoint ( patternnumber , stepnumber ) , self . get_pattern_step_time ( patternnumber , stepnumber ) ) outputstring += 'Actual step: {0}\n' . format ( self . get_pattern_actual_step ( patternnumber ) ) outputstring += 'Additional cycles: {0}\n' . format ( self . get_pattern_additional_cycles ( patternnumber ) ) outputstring += 'Linked pattern: {0}\n' . format ( self . get_pattern_link_topattern ( patternnumber ) ) return outputstring | Get all variables for a given pattern at one time . | 193 | 11 |
237,817 | def set_all_pattern_variables ( self , patternnumber , sp0 , ti0 , sp1 , ti1 , sp2 , ti2 , sp3 , ti3 , sp4 , ti4 , sp5 , ti5 , sp6 , ti6 , sp7 , ti7 , actual_step , additional_cycles , link_pattern ) : _checkPatternNumber ( patternnumber ) self . set_pattern_step_setpoint ( patternnumber , 0 , sp0 ) self . set_pattern_step_setpoint ( patternnumber , 1 , sp1 ) self . set_pattern_step_setpoint ( patternnumber , 2 , sp2 ) self . set_pattern_step_setpoint ( patternnumber , 3 , sp3 ) self . set_pattern_step_setpoint ( patternnumber , 4 , sp4 ) self . set_pattern_step_setpoint ( patternnumber , 5 , sp5 ) self . set_pattern_step_setpoint ( patternnumber , 6 , sp6 ) self . set_pattern_step_setpoint ( patternnumber , 7 , sp7 ) self . set_pattern_step_time ( patternnumber , 0 , ti0 ) self . set_pattern_step_time ( patternnumber , 1 , ti1 ) self . set_pattern_step_time ( patternnumber , 2 , ti2 ) self . set_pattern_step_time ( patternnumber , 3 , ti3 ) self . set_pattern_step_time ( patternnumber , 4 , ti4 ) self . set_pattern_step_time ( patternnumber , 5 , ti5 ) self . set_pattern_step_time ( patternnumber , 6 , ti6 ) self . set_pattern_step_time ( patternnumber , 7 , ti7 ) self . set_pattern_additional_cycles ( patternnumber , additional_cycles ) self . set_pattern_link_topattern ( patternnumber , link_pattern ) self . set_pattern_actual_step ( patternnumber , actual_step ) | Set all variables for a given pattern at one time . | 434 | 11 |
237,818 | def close ( self ) : if VERBOSE : _print_out ( '\nDummy_serial: Closing port\n' ) if not self . _isOpen : raise IOError ( 'Dummy_serial: The port is already closed' ) self . _isOpen = False self . port = None | Close a port on dummy_serial . | 67 | 8 |
237,819 | def write ( self , inputdata ) : if VERBOSE : _print_out ( '\nDummy_serial: Writing to port. Given:' + repr ( inputdata ) + '\n' ) if sys . version_info [ 0 ] > 2 : if not type ( inputdata ) == bytes : raise TypeError ( 'The input must be type bytes. Given:' + repr ( inputdata ) ) inputstring = str ( inputdata , encoding = 'latin1' ) else : inputstring = inputdata if not self . _isOpen : raise IOError ( 'Dummy_serial: Trying to write, but the port is not open. Given:' + repr ( inputdata ) ) # Look up which data that should be waiting for subsequent read commands try : response = RESPONSES [ inputstring ] except : response = DEFAULT_RESPONSE self . _waiting_data = response | Write to a port on dummy_serial . | 193 | 9 |
237,820 | def read ( self , numberOfBytes ) : if VERBOSE : _print_out ( '\nDummy_serial: Reading from port (max length {!r} bytes)' . format ( numberOfBytes ) ) if numberOfBytes < 0 : raise IOError ( 'Dummy_serial: The numberOfBytes to read must not be negative. Given: {!r}' . format ( numberOfBytes ) ) if not self . _isOpen : raise IOError ( 'Dummy_serial: Trying to read, but the port is not open.' ) # Do the actual reading from the waiting data, and simulate the influence of numberOfBytes if self . _waiting_data == DEFAULT_RESPONSE : returnstring = self . _waiting_data elif numberOfBytes == len ( self . _waiting_data ) : returnstring = self . _waiting_data self . _waiting_data = NO_DATA_PRESENT elif numberOfBytes < len ( self . _waiting_data ) : if VERBOSE : _print_out ( 'Dummy_serial: The numberOfBytes to read is smaller than the available data. ' + 'Some bytes will be kept for later. Available data: {!r} (length = {}), numberOfBytes: {}' . format ( self . _waiting_data , len ( self . _waiting_data ) , numberOfBytes ) ) returnstring = self . _waiting_data [ : numberOfBytes ] self . _waiting_data = self . _waiting_data [ numberOfBytes : ] else : # Wait for timeout, as we have asked for more data than available if VERBOSE : _print_out ( 'Dummy_serial: The numberOfBytes to read is larger than the available data. ' + 'Will sleep until timeout. Available data: {!r} (length = {}), numberOfBytes: {}' . format ( self . _waiting_data , len ( self . _waiting_data ) , numberOfBytes ) ) time . sleep ( self . timeout ) returnstring = self . _waiting_data self . _waiting_data = NO_DATA_PRESENT # TODO Adapt the behavior to better mimic the Windows behavior if VERBOSE : _print_out ( 'Dummy_serial read return data: {!r} (has length {})\n' . format ( returnstring , len ( returnstring ) ) ) if sys . version_info [ 0 ] > 2 : # Convert types to make it python3 compatible return bytes ( returnstring , encoding = 'latin1' ) else : return returnstring | Read from a port on dummy_serial . | 571 | 9 |
237,821 | def _embedPayload ( slaveaddress , mode , functioncode , payloaddata ) : _checkSlaveaddress ( slaveaddress ) _checkMode ( mode ) _checkFunctioncode ( functioncode , None ) _checkString ( payloaddata , description = 'payload' ) firstPart = _numToOneByteString ( slaveaddress ) + _numToOneByteString ( functioncode ) + payloaddata if mode == MODE_ASCII : request = _ASCII_HEADER + _hexencode ( firstPart ) + _hexencode ( _calculateLrcString ( firstPart ) ) + _ASCII_FOOTER else : request = firstPart + _calculateCrcString ( firstPart ) return request | Build a request from the slaveaddress the function code and the payload data . | 156 | 15 |
237,822 | def _predictResponseSize ( mode , functioncode , payloadToSlave ) : MIN_PAYLOAD_LENGTH = 4 # For implemented functioncodes here BYTERANGE_FOR_GIVEN_SIZE = slice ( 2 , 4 ) # Within the payload NUMBER_OF_PAYLOAD_BYTES_IN_WRITE_CONFIRMATION = 4 NUMBER_OF_PAYLOAD_BYTES_FOR_BYTECOUNTFIELD = 1 RTU_TO_ASCII_PAYLOAD_FACTOR = 2 NUMBER_OF_RTU_RESPONSE_STARTBYTES = 2 NUMBER_OF_RTU_RESPONSE_ENDBYTES = 2 NUMBER_OF_ASCII_RESPONSE_STARTBYTES = 5 NUMBER_OF_ASCII_RESPONSE_ENDBYTES = 4 # Argument validity testing _checkMode ( mode ) _checkFunctioncode ( functioncode , None ) _checkString ( payloadToSlave , description = 'payload' , minlength = MIN_PAYLOAD_LENGTH ) # Calculate payload size if functioncode in [ 5 , 6 , 15 , 16 ] : response_payload_size = NUMBER_OF_PAYLOAD_BYTES_IN_WRITE_CONFIRMATION elif functioncode in [ 1 , 2 , 3 , 4 ] : given_size = _twoByteStringToNum ( payloadToSlave [ BYTERANGE_FOR_GIVEN_SIZE ] ) if functioncode == 1 or functioncode == 2 : # Algorithm from MODBUS APPLICATION PROTOCOL SPECIFICATION V1.1b number_of_inputs = given_size response_payload_size = NUMBER_OF_PAYLOAD_BYTES_FOR_BYTECOUNTFIELD + number_of_inputs // 8 + ( 1 if number_of_inputs % 8 else 0 ) elif functioncode == 3 or functioncode == 4 : number_of_registers = given_size response_payload_size = NUMBER_OF_PAYLOAD_BYTES_FOR_BYTECOUNTFIELD + number_of_registers * _NUMBER_OF_BYTES_PER_REGISTER else : raise ValueError ( 'Wrong functioncode: {}. The payload is: {!r}' . format ( functioncode , payloadToSlave ) ) # Calculate number of bytes to read if mode == MODE_ASCII : return NUMBER_OF_ASCII_RESPONSE_STARTBYTES + response_payload_size * RTU_TO_ASCII_PAYLOAD_FACTOR + NUMBER_OF_ASCII_RESPONSE_ENDBYTES else : return NUMBER_OF_RTU_RESPONSE_STARTBYTES + response_payload_size + NUMBER_OF_RTU_RESPONSE_ENDBYTES | Calculate the number of bytes that should be received from the slave . | 660 | 15 |
237,823 | def _calculate_minimum_silent_period ( baudrate ) : _checkNumerical ( baudrate , minvalue = 1 , description = 'baudrate' ) # Avoid division by zero BITTIMES_PER_CHARACTERTIME = 11 MINIMUM_SILENT_CHARACTERTIMES = 3.5 bittime = 1 / float ( baudrate ) return bittime * BITTIMES_PER_CHARACTERTIME * MINIMUM_SILENT_CHARACTERTIMES | Calculate the silent period length to comply with the 3 . 5 character silence between messages . | 120 | 19 |
237,824 | def _numToTwoByteString ( value , numberOfDecimals = 0 , LsbFirst = False , signed = False ) : _checkNumerical ( value , description = 'inputvalue' ) _checkInt ( numberOfDecimals , minvalue = 0 , description = 'number of decimals' ) _checkBool ( LsbFirst , description = 'LsbFirst' ) _checkBool ( signed , description = 'signed parameter' ) multiplier = 10 ** numberOfDecimals integer = int ( float ( value ) * multiplier ) if LsbFirst : formatcode = '<' # Little-endian else : formatcode = '>' # Big-endian if signed : formatcode += 'h' # (Signed) short (2 bytes) else : formatcode += 'H' # Unsigned short (2 bytes) outstring = _pack ( formatcode , integer ) assert len ( outstring ) == 2 return outstring | Convert a numerical value to a two - byte string possibly scaling it . | 206 | 15 |
237,825 | def _twoByteStringToNum ( bytestring , numberOfDecimals = 0 , signed = False ) : _checkString ( bytestring , minlength = 2 , maxlength = 2 , description = 'bytestring' ) _checkInt ( numberOfDecimals , minvalue = 0 , description = 'number of decimals' ) _checkBool ( signed , description = 'signed parameter' ) formatcode = '>' # Big-endian if signed : formatcode += 'h' # (Signed) short (2 bytes) else : formatcode += 'H' # Unsigned short (2 bytes) fullregister = _unpack ( formatcode , bytestring ) if numberOfDecimals == 0 : return fullregister divisor = 10 ** numberOfDecimals return fullregister / float ( divisor ) | Convert a two - byte string to a numerical value possibly scaling it . | 183 | 15 |
237,826 | def _pack ( formatstring , value ) : _checkString ( formatstring , description = 'formatstring' , minlength = 1 ) try : result = struct . pack ( formatstring , value ) except : errortext = 'The value to send is probably out of range, as the num-to-bytestring conversion failed.' errortext += ' Value: {0!r} Struct format code is: {1}' raise ValueError ( errortext . format ( value , formatstring ) ) if sys . version_info [ 0 ] > 2 : return str ( result , encoding = 'latin1' ) # Convert types to make it Python3 compatible return result | Pack a value into a bytestring . | 144 | 9 |
237,827 | def _unpack ( formatstring , packed ) : _checkString ( formatstring , description = 'formatstring' , minlength = 1 ) _checkString ( packed , description = 'packed string' , minlength = 1 ) if sys . version_info [ 0 ] > 2 : packed = bytes ( packed , encoding = 'latin1' ) # Convert types to make it Python3 compatible try : value = struct . unpack ( formatstring , packed ) [ 0 ] except : errortext = 'The received bytestring is probably wrong, as the bytestring-to-num conversion failed.' errortext += ' Bytestring: {0!r} Struct format code is: {1}' raise ValueError ( errortext . format ( packed , formatstring ) ) return value | Unpack a bytestring into a value . | 169 | 10 |
237,828 | def _hexencode ( bytestring , insert_spaces = False ) : _checkString ( bytestring , description = 'byte string' ) separator = '' if not insert_spaces else ' ' # Use plain string formatting instead of binhex.hexlify, # in order to have it Python 2.x and 3.x compatible byte_representions = [ ] for c in bytestring : byte_representions . append ( '{0:02X}' . format ( ord ( c ) ) ) return separator . join ( byte_representions ) . strip ( ) | Convert a byte string to a hex encoded string . | 128 | 11 |
237,829 | def _hexdecode ( hexstring ) : # Note: For Python3 the appropriate would be: raise TypeError(new_error_message) from err # but the Python2 interpreter will indicate SyntaxError. # Thus we need to live with this warning in Python3: # 'During handling of the above exception, another exception occurred' _checkString ( hexstring , description = 'hexstring' ) if len ( hexstring ) % 2 != 0 : raise ValueError ( 'The input hexstring must be of even length. Given: {!r}' . format ( hexstring ) ) if sys . version_info [ 0 ] > 2 : by = bytes ( hexstring , 'latin1' ) try : return str ( binascii . unhexlify ( by ) , encoding = 'latin1' ) except binascii . Error as err : new_error_message = 'Hexdecode reported an error: {!s}. Input hexstring: {}' . format ( err . args [ 0 ] , hexstring ) raise TypeError ( new_error_message ) else : try : return hexstring . decode ( 'hex' ) except TypeError as err : raise TypeError ( 'Hexdecode reported an error: {}. Input hexstring: {}' . format ( err . message , hexstring ) ) | Convert a hex encoded string to a byte string . | 286 | 11 |
237,830 | def _bitResponseToValue ( bytestring ) : _checkString ( bytestring , description = 'bytestring' , minlength = 1 , maxlength = 1 ) RESPONSE_ON = '\x01' RESPONSE_OFF = '\x00' if bytestring == RESPONSE_ON : return 1 elif bytestring == RESPONSE_OFF : return 0 else : raise ValueError ( 'Could not convert bit response to a value. Input: {0!r}' . format ( bytestring ) ) | Convert a response string to a numerical value . | 119 | 10 |
237,831 | def _createBitpattern ( functioncode , value ) : _checkFunctioncode ( functioncode , [ 5 , 15 ] ) _checkInt ( value , minvalue = 0 , maxvalue = 1 , description = 'inputvalue' ) if functioncode == 5 : if value == 0 : return '\x00\x00' else : return '\xff\x00' elif functioncode == 15 : if value == 0 : return '\x00' else : return '\x01' | Create the bit pattern that is used for writing single bits . | 105 | 12 |
237,832 | def _twosComplement ( x , bits = 16 ) : _checkInt ( bits , minvalue = 0 , description = 'number of bits' ) _checkInt ( x , description = 'input' ) upperlimit = 2 ** ( bits - 1 ) - 1 lowerlimit = - 2 ** ( bits - 1 ) if x > upperlimit or x < lowerlimit : raise ValueError ( 'The input value is out of range. Given value is {0}, but allowed range is {1} to {2} when using {3} bits.' . format ( x , lowerlimit , upperlimit , bits ) ) # Calculate two'2 complement if x >= 0 : return x return x + 2 ** bits | Calculate the two s complement of an integer . | 150 | 11 |
237,833 | def _setBitOn ( x , bitNum ) : _checkInt ( x , minvalue = 0 , description = 'input value' ) _checkInt ( bitNum , minvalue = 0 , description = 'bitnumber' ) return x | ( 1 << bitNum ) | Set bit bitNum to True . | 58 | 7 |
237,834 | def _calculateCrcString ( inputstring ) : _checkString ( inputstring , description = 'input CRC string' ) # Preload a 16-bit register with ones register = 0xFFFF for char in inputstring : register = ( register >> 8 ) ^ _CRC16TABLE [ ( register ^ ord ( char ) ) & 0xFF ] return _numToTwoByteString ( register , LsbFirst = True ) | Calculate CRC - 16 for Modbus . | 92 | 10 |
237,835 | def _calculateLrcString ( inputstring ) : _checkString ( inputstring , description = 'input LRC string' ) register = 0 for character in inputstring : register += ord ( character ) lrc = ( ( register ^ 0xFF ) + 1 ) & 0xFF lrcString = _numToOneByteString ( lrc ) return lrcString | Calculate LRC for Modbus . | 80 | 9 |
237,836 | def _checkMode ( mode ) : if not isinstance ( mode , str ) : raise TypeError ( 'The {0} should be a string. Given: {1!r}' . format ( "mode" , mode ) ) if mode not in [ MODE_RTU , MODE_ASCII ] : raise ValueError ( "Unreconized Modbus mode given. Must be 'rtu' or 'ascii' but {0!r} was given." . format ( mode ) ) | Check that the Modbus mode is valie . | 109 | 10 |
237,837 | def _checkFunctioncode ( functioncode , listOfAllowedValues = [ ] ) : FUNCTIONCODE_MIN = 1 FUNCTIONCODE_MAX = 127 _checkInt ( functioncode , FUNCTIONCODE_MIN , FUNCTIONCODE_MAX , description = 'functioncode' ) if listOfAllowedValues is None : return if not isinstance ( listOfAllowedValues , list ) : raise TypeError ( 'The listOfAllowedValues should be a list. Given: {0!r}' . format ( listOfAllowedValues ) ) for value in listOfAllowedValues : _checkInt ( value , FUNCTIONCODE_MIN , FUNCTIONCODE_MAX , description = 'functioncode inside listOfAllowedValues' ) if functioncode not in listOfAllowedValues : raise ValueError ( 'Wrong function code: {0}, allowed values are {1!r}' . format ( functioncode , listOfAllowedValues ) ) | Check that the given functioncode is in the listOfAllowedValues . | 215 | 15 |
237,838 | def _checkResponseByteCount ( payload ) : POSITION_FOR_GIVEN_NUMBER = 0 NUMBER_OF_BYTES_TO_SKIP = 1 _checkString ( payload , minlength = 1 , description = 'payload' ) givenNumberOfDatabytes = ord ( payload [ POSITION_FOR_GIVEN_NUMBER ] ) countedNumberOfDatabytes = len ( payload ) - NUMBER_OF_BYTES_TO_SKIP if givenNumberOfDatabytes != countedNumberOfDatabytes : errortemplate = 'Wrong given number of bytes in the response: {0}, but counted is {1} as data payload length is {2}.' + ' The data payload is: {3!r}' errortext = errortemplate . format ( givenNumberOfDatabytes , countedNumberOfDatabytes , len ( payload ) , payload ) raise ValueError ( errortext ) | Check that the number of bytes as given in the response is correct . | 201 | 14 |
237,839 | def _checkResponseRegisterAddress ( payload , registeraddress ) : _checkString ( payload , minlength = 2 , description = 'payload' ) _checkRegisteraddress ( registeraddress ) BYTERANGE_FOR_STARTADDRESS = slice ( 0 , 2 ) bytesForStartAddress = payload [ BYTERANGE_FOR_STARTADDRESS ] receivedStartAddress = _twoByteStringToNum ( bytesForStartAddress ) if receivedStartAddress != registeraddress : raise ValueError ( 'Wrong given write start adress: {0}, but commanded is {1}. The data payload is: {2!r}' . format ( receivedStartAddress , registeraddress , payload ) ) | Check that the start adress as given in the response is correct . | 146 | 14 |
237,840 | def _checkResponseNumberOfRegisters ( payload , numberOfRegisters ) : _checkString ( payload , minlength = 4 , description = 'payload' ) _checkInt ( numberOfRegisters , minvalue = 1 , maxvalue = 0xFFFF , description = 'numberOfRegisters' ) BYTERANGE_FOR_NUMBER_OF_REGISTERS = slice ( 2 , 4 ) bytesForNumberOfRegisters = payload [ BYTERANGE_FOR_NUMBER_OF_REGISTERS ] receivedNumberOfWrittenReisters = _twoByteStringToNum ( bytesForNumberOfRegisters ) if receivedNumberOfWrittenReisters != numberOfRegisters : raise ValueError ( 'Wrong number of registers to write in the response: {0}, but commanded is {1}. The data payload is: {2!r}' . format ( receivedNumberOfWrittenReisters , numberOfRegisters , payload ) ) | Check that the number of written registers as given in the response is correct . | 198 | 15 |
237,841 | def _checkResponseWriteData ( payload , writedata ) : _checkString ( payload , minlength = 4 , description = 'payload' ) _checkString ( writedata , minlength = 2 , maxlength = 2 , description = 'writedata' ) BYTERANGE_FOR_WRITEDATA = slice ( 2 , 4 ) receivedWritedata = payload [ BYTERANGE_FOR_WRITEDATA ] if receivedWritedata != writedata : raise ValueError ( 'Wrong write data in the response: {0!r}, but commanded is {1!r}. The data payload is: {2!r}' . format ( receivedWritedata , writedata , payload ) ) | Check that the write data as given in the response is correct . | 153 | 13 |
237,842 | def _checkString ( inputstring , description , minlength = 0 , maxlength = None ) : # Type checking if not isinstance ( description , str ) : raise TypeError ( 'The description should be a string. Given: {0!r}' . format ( description ) ) if not isinstance ( inputstring , str ) : raise TypeError ( 'The {0} should be a string. Given: {1!r}' . format ( description , inputstring ) ) if not isinstance ( maxlength , ( int , type ( None ) ) ) : raise TypeError ( 'The maxlength must be an integer or None. Given: {0!r}' . format ( maxlength ) ) # Check values _checkInt ( minlength , minvalue = 0 , maxvalue = None , description = 'minlength' ) if len ( inputstring ) < minlength : raise ValueError ( 'The {0} is too short: {1}, but minimum value is {2}. Given: {3!r}' . format ( description , len ( inputstring ) , minlength , inputstring ) ) if not maxlength is None : if maxlength < 0 : raise ValueError ( 'The maxlength must be positive. Given: {0}' . format ( maxlength ) ) if maxlength < minlength : raise ValueError ( 'The maxlength must not be smaller than minlength. Given: {0} and {1}' . format ( maxlength , minlength ) ) if len ( inputstring ) > maxlength : raise ValueError ( 'The {0} is too long: {1}, but maximum value is {2}. Given: {3!r}' . format ( description , len ( inputstring ) , maxlength , inputstring ) ) | Check that the given string is valid . | 376 | 8 |
237,843 | def _checkInt ( inputvalue , minvalue = None , maxvalue = None , description = 'inputvalue' ) : if not isinstance ( description , str ) : raise TypeError ( 'The description should be a string. Given: {0!r}' . format ( description ) ) if not isinstance ( inputvalue , ( int , long ) ) : raise TypeError ( 'The {0} must be an integer. Given: {1!r}' . format ( description , inputvalue ) ) if not isinstance ( minvalue , ( int , long , type ( None ) ) ) : raise TypeError ( 'The minvalue must be an integer or None. Given: {0!r}' . format ( minvalue ) ) if not isinstance ( maxvalue , ( int , long , type ( None ) ) ) : raise TypeError ( 'The maxvalue must be an integer or None. Given: {0!r}' . format ( maxvalue ) ) _checkNumerical ( inputvalue , minvalue , maxvalue , description ) | Check that the given integer is valid . | 225 | 8 |
237,844 | def _checkNumerical ( inputvalue , minvalue = None , maxvalue = None , description = 'inputvalue' ) : # Type checking if not isinstance ( description , str ) : raise TypeError ( 'The description should be a string. Given: {0!r}' . format ( description ) ) if not isinstance ( inputvalue , ( int , long , float ) ) : raise TypeError ( 'The {0} must be numerical. Given: {1!r}' . format ( description , inputvalue ) ) if not isinstance ( minvalue , ( int , float , long , type ( None ) ) ) : raise TypeError ( 'The minvalue must be numeric or None. Given: {0!r}' . format ( minvalue ) ) if not isinstance ( maxvalue , ( int , float , long , type ( None ) ) ) : raise TypeError ( 'The maxvalue must be numeric or None. Given: {0!r}' . format ( maxvalue ) ) # Consistency checking if ( not minvalue is None ) and ( not maxvalue is None ) : if maxvalue < minvalue : raise ValueError ( 'The maxvalue must not be smaller than minvalue. Given: {0} and {1}, respectively.' . format ( maxvalue , minvalue ) ) # Value checking if not minvalue is None : if inputvalue < minvalue : raise ValueError ( 'The {0} is too small: {1}, but minimum value is {2}.' . format ( description , inputvalue , minvalue ) ) if not maxvalue is None : if inputvalue > maxvalue : raise ValueError ( 'The {0} is too large: {1}, but maximum value is {2}.' . format ( description , inputvalue , maxvalue ) ) | Check that the given numerical value is valid . | 385 | 9 |
237,845 | def _checkBool ( inputvalue , description = 'inputvalue' ) : _checkString ( description , minlength = 1 , description = 'description string' ) if not isinstance ( inputvalue , bool ) : raise TypeError ( 'The {0} must be boolean. Given: {1!r}' . format ( description , inputvalue ) ) | Check that the given inputvalue is a boolean . | 76 | 10 |
237,846 | def _getDiagnosticString ( ) : text = '\n## Diagnostic output from minimalmodbus ## \n\n' text += 'Minimalmodbus version: ' + __version__ + '\n' text += 'Minimalmodbus status: ' + __status__ + '\n' text += 'File name (with relative path): ' + __file__ + '\n' text += 'Full file path: ' + os . path . abspath ( __file__ ) + '\n\n' text += 'pySerial version: ' + serial . VERSION + '\n' text += 'pySerial full file path: ' + os . path . abspath ( serial . __file__ ) + '\n\n' text += 'Platform: ' + sys . platform + '\n' text += 'Filesystem encoding: ' + repr ( sys . getfilesystemencoding ( ) ) + '\n' text += 'Byteorder: ' + sys . byteorder + '\n' text += 'Python version: ' + sys . version + '\n' text += 'Python version info: ' + repr ( sys . version_info ) + '\n' text += 'Python flags: ' + repr ( sys . flags ) + '\n' text += 'Python argv: ' + repr ( sys . argv ) + '\n' text += 'Python prefix: ' + repr ( sys . prefix ) + '\n' text += 'Python exec prefix: ' + repr ( sys . exec_prefix ) + '\n' text += 'Python executable: ' + repr ( sys . executable ) + '\n' try : text += 'Long info: ' + repr ( sys . long_info ) + '\n' except : text += 'Long info: (none)\n' # For Python3 compatibility try : text += 'Float repr style: ' + repr ( sys . float_repr_style ) + '\n\n' except : text += 'Float repr style: (none) \n\n' # For Python 2.6 compatibility text += 'Variable __name__: ' + __name__ + '\n' text += 'Current directory: ' + os . getcwd ( ) + '\n\n' text += 'Python path: \n' text += '\n' . join ( sys . path ) + '\n' text += '\n## End of diagnostic output ## \n' return text | Generate a diagnostic string showing the module version the platform current directory etc . | 536 | 15 |
237,847 | def read_bit ( self , registeraddress , functioncode = 2 ) : _checkFunctioncode ( functioncode , [ 1 , 2 ] ) return self . _genericCommand ( functioncode , registeraddress ) | Read one bit from the slave . | 43 | 7 |
237,848 | def write_bit ( self , registeraddress , value , functioncode = 5 ) : _checkFunctioncode ( functioncode , [ 5 , 15 ] ) _checkInt ( value , minvalue = 0 , maxvalue = 1 , description = 'input value' ) self . _genericCommand ( functioncode , registeraddress , value ) | Write one bit to the slave . | 69 | 7 |
237,849 | def read_register ( self , registeraddress , numberOfDecimals = 0 , functioncode = 3 , signed = False ) : _checkFunctioncode ( functioncode , [ 3 , 4 ] ) _checkInt ( numberOfDecimals , minvalue = 0 , maxvalue = 10 , description = 'number of decimals' ) _checkBool ( signed , description = 'signed' ) return self . _genericCommand ( functioncode , registeraddress , numberOfDecimals = numberOfDecimals , signed = signed ) | Read an integer from one 16 - bit register in the slave possibly scaling it . | 114 | 16 |
237,850 | def write_register ( self , registeraddress , value , numberOfDecimals = 0 , functioncode = 16 , signed = False ) : _checkFunctioncode ( functioncode , [ 6 , 16 ] ) _checkInt ( numberOfDecimals , minvalue = 0 , maxvalue = 10 , description = 'number of decimals' ) _checkBool ( signed , description = 'signed' ) _checkNumerical ( value , description = 'input value' ) self . _genericCommand ( functioncode , registeraddress , value , numberOfDecimals , signed = signed ) | Write an integer to one 16 - bit register in the slave possibly scaling it . | 126 | 16 |
237,851 | def read_float ( self , registeraddress , functioncode = 3 , numberOfRegisters = 2 ) : _checkFunctioncode ( functioncode , [ 3 , 4 ] ) _checkInt ( numberOfRegisters , minvalue = 2 , maxvalue = 4 , description = 'number of registers' ) return self . _genericCommand ( functioncode , registeraddress , numberOfRegisters = numberOfRegisters , payloadformat = 'float' ) | Read a floating point number from the slave . | 94 | 9 |
237,852 | def write_float ( self , registeraddress , value , numberOfRegisters = 2 ) : _checkNumerical ( value , description = 'input value' ) _checkInt ( numberOfRegisters , minvalue = 2 , maxvalue = 4 , description = 'number of registers' ) self . _genericCommand ( 16 , registeraddress , value , numberOfRegisters = numberOfRegisters , payloadformat = 'float' ) | Write a floating point number to the slave . | 92 | 9 |
237,853 | def read_string ( self , registeraddress , numberOfRegisters = 16 , functioncode = 3 ) : _checkFunctioncode ( functioncode , [ 3 , 4 ] ) _checkInt ( numberOfRegisters , minvalue = 1 , description = 'number of registers for read string' ) return self . _genericCommand ( functioncode , registeraddress , numberOfRegisters = numberOfRegisters , payloadformat = 'string' ) | Read a string from the slave . | 92 | 7 |
237,854 | def write_string ( self , registeraddress , textstring , numberOfRegisters = 16 ) : _checkInt ( numberOfRegisters , minvalue = 1 , description = 'number of registers for write string' ) _checkString ( textstring , 'input string' , minlength = 1 , maxlength = 2 * numberOfRegisters ) self . _genericCommand ( 16 , registeraddress , textstring , numberOfRegisters = numberOfRegisters , payloadformat = 'string' ) | Write a string to the slave . | 104 | 7 |
237,855 | def write_registers ( self , registeraddress , values ) : if not isinstance ( values , list ) : raise TypeError ( 'The "values parameter" must be a list. Given: {0!r}' . format ( values ) ) _checkInt ( len ( values ) , minvalue = 1 , description = 'length of input list' ) # Note: The content of the list is checked at content conversion. self . _genericCommand ( 16 , registeraddress , values , numberOfRegisters = len ( values ) , payloadformat = 'registers' ) | Write integers to 16 - bit registers in the slave . | 121 | 11 |
237,856 | def _playsoundWin ( sound , block = True ) : from ctypes import c_buffer , windll from random import random from time import sleep from sys import getfilesystemencoding def winCommand ( * command ) : buf = c_buffer ( 255 ) command = ' ' . join ( command ) . encode ( getfilesystemencoding ( ) ) errorCode = int ( windll . winmm . mciSendStringA ( command , buf , 254 , 0 ) ) if errorCode : errorBuffer = c_buffer ( 255 ) windll . winmm . mciGetErrorStringA ( errorCode , errorBuffer , 254 ) exceptionMessage = ( '\n Error ' + str ( errorCode ) + ' for command:' '\n ' + command . decode ( ) + '\n ' + errorBuffer . value . decode ( ) ) raise PlaysoundException ( exceptionMessage ) return buf . value alias = 'playsound_' + str ( random ( ) ) winCommand ( 'open "' + sound + '" alias' , alias ) winCommand ( 'set' , alias , 'time format milliseconds' ) durationInMS = winCommand ( 'status' , alias , 'length' ) winCommand ( 'play' , alias , 'from 0 to' , durationInMS . decode ( ) ) if block : sleep ( float ( durationInMS ) / 1000.0 ) | Utilizes windll . winmm . Tested and known to work with MP3 and WAVE on Windows 7 with Python 2 . 7 . Probably works with more file formats . Probably works on Windows XP thru Windows 10 . Probably works with all versions of Python . | 293 | 54 |
237,857 | def _playsoundOSX ( sound , block = True ) : from AppKit import NSSound from Foundation import NSURL from time import sleep if '://' not in sound : if not sound . startswith ( '/' ) : from os import getcwd sound = getcwd ( ) + '/' + sound sound = 'file://' + sound url = NSURL . URLWithString_ ( sound ) nssound = NSSound . alloc ( ) . initWithContentsOfURL_byReference_ ( url , True ) if not nssound : raise IOError ( 'Unable to load sound named: ' + sound ) nssound . play ( ) if block : sleep ( nssound . duration ( ) ) | Utilizes AppKit . NSSound . Tested and known to work with MP3 and WAVE on OS X 10 . 11 with Python 2 . 7 . Probably works with anything QuickTime supports . Probably works on OS X 10 . 5 and newer . Probably works with all versions of Python . | 156 | 61 |
237,858 | def _playsoundNix ( sound , block = True ) : if not block : raise NotImplementedError ( "block=False cannot be used on this platform yet" ) # pathname2url escapes non-URL-safe characters import os try : from urllib . request import pathname2url except ImportError : # python 2 from urllib import pathname2url import gi gi . require_version ( 'Gst' , '1.0' ) from gi . repository import Gst Gst . init ( None ) playbin = Gst . ElementFactory . make ( 'playbin' , 'playbin' ) if sound . startswith ( ( 'http://' , 'https://' ) ) : playbin . props . uri = sound else : playbin . props . uri = 'file://' + pathname2url ( os . path . abspath ( sound ) ) set_result = playbin . set_state ( Gst . State . PLAYING ) if set_result != Gst . StateChangeReturn . ASYNC : raise PlaysoundException ( "playbin.set_state returned " + repr ( set_result ) ) # FIXME: use some other bus method than poll() with block=False # https://lazka.github.io/pgi-docs/#Gst-1.0/classes/Bus.html bus = playbin . get_bus ( ) bus . poll ( Gst . MessageType . EOS , Gst . CLOCK_TIME_NONE ) playbin . set_state ( Gst . State . NULL ) | Play a sound using GStreamer . | 346 | 7 |
237,859 | def remove_rows_matching ( df , column , match ) : df = df . copy ( ) mask = df [ column ] . values != match return df . iloc [ mask , : ] | Return a DataFrame with rows where column values match match are removed . | 42 | 14 |
237,860 | def remove_rows_containing ( df , column , match ) : df = df . copy ( ) mask = [ match not in str ( v ) for v in df [ column ] . values ] return df . iloc [ mask , : ] | Return a DataFrame with rows where column values containing match are removed . | 51 | 14 |
237,861 | def filter_localization_probability ( df , threshold = 0.75 ) : df = df . copy ( ) localization_probability_mask = df [ 'Localization prob' ] . values >= threshold return df . iloc [ localization_probability_mask , : ] | Remove rows with a localization probability below 0 . 75 | 62 | 10 |
237,862 | def minimum_valid_values_in_any_group ( df , levels = None , n = 1 , invalid = np . nan ) : df = df . copy ( ) if levels is None : if 'Group' in df . columns . names : levels = [ df . columns . names . index ( 'Group' ) ] # Filter by at least 7 (values in class:timepoint) at least in at least one group if invalid is np . nan : dfx = ~ np . isnan ( df ) else : dfx = df != invalid dfc = dfx . astype ( int ) . sum ( axis = 1 , level = levels ) dfm = dfc . max ( axis = 1 ) >= n mask = dfm . values return df . iloc [ mask , : ] | Filter DataFrame by at least n valid values in at least one group . | 168 | 15 |
237,863 | def search ( df , match , columns = [ 'Proteins' , 'Protein names' , 'Gene names' ] ) : df = df . copy ( ) dft = df . reset_index ( ) mask = np . zeros ( ( dft . shape [ 0 ] , ) , dtype = bool ) idx = [ 'Proteins' , 'Protein names' , 'Gene names' ] for i in idx : if i in dft . columns : mask = mask | np . array ( [ match in str ( l ) for l in dft [ i ] . values ] ) return df . iloc [ mask ] | Search for a given string in a set of columns in a processed DataFrame . | 139 | 16 |
237,864 | def filter_select_columns_intensity ( df , prefix , columns ) : # Note: I use %s.+ (not %s.*) so it forces a match with the prefix string, ONLY if it is followed by something. return df . filter ( regex = '^(%s.+|%s)$' % ( prefix , '|' . join ( columns ) ) ) | Filter dataframe to include specified columns retaining any Intensity columns . | 84 | 13 |
237,865 | def filter_intensity ( df , label = "" , with_multiplicity = False ) : label += ".*__\d" if with_multiplicity else "" dft = df . filter ( regex = "^(?!Intensity).*$" ) dfi = df . filter ( regex = '^(.*Intensity.*%s.*__\d)$' % label ) return pd . concat ( [ dft , dfi ] , axis = 1 ) | Filter to include only the Intensity values with optional specified label excluding other Intensity measurements but retaining all other columns . | 101 | 23 |
237,866 | def filter_ratio ( df , label = "" , with_multiplicity = False ) : label += ".*__\d" if with_multiplicity else "" dft = df . filter ( regex = "^(?!Ratio).*$" ) dfr = df . filter ( regex = '^(.*Ratio.*%s)$' % label ) return pd . concat ( [ dft , dfr ] , axis = 1 ) | Filter to include only the Ratio values with optional specified label excluding other Intensity measurements but retaining all other columns . | 98 | 22 |
237,867 | def read_perseus ( f ) : df = pd . read_csv ( f , delimiter = '\t' , header = [ 0 , 1 , 2 , 3 ] , low_memory = False ) df . columns = pd . MultiIndex . from_tuples ( [ ( x , ) for x in df . columns . get_level_values ( 0 ) ] ) return df | Load a Perseus processed data table | 85 | 7 |
237,868 | def write_perseus ( f , df ) : ### Generate the Perseus like type index FIELD_TYPE_MAP = { 'Amino acid' : 'C' , 'Charge' : 'C' , 'Reverse' : 'C' , 'Potential contaminant' : 'C' , 'Multiplicity' : 'C' , 'Localization prob' : 'N' , 'PEP' : 'N' , 'Score' : 'N' , 'Delta score' : 'N' , 'Score for localization' : 'N' , 'Mass error [ppm]' : 'N' , 'Intensity' : 'N' , 'Position' : 'N' , 'Proteins' : 'T' , 'Positions within proteins' : 'T' , 'Leading proteins' : 'T' , 'Protein names' : 'T' , 'Gene names' : 'T' , 'Sequence window' : 'T' , 'Unique identifier' : 'T' , } def map_field_type ( n , c ) : try : t = FIELD_TYPE_MAP [ c ] except : t = "E" # In the first element, add type indicator if n == 0 : t = "#!{Type}%s" % t return t df = df . copy ( ) df . columns = pd . MultiIndex . from_tuples ( [ ( k , map_field_type ( n , k ) ) for n , k in enumerate ( df . columns ) ] , names = [ "Label" , "Type" ] ) df = df . transpose ( ) . reset_index ( ) . transpose ( ) df . to_csv ( f , index = False , header = False ) | Export a dataframe to Perseus ; recreating the format | 381 | 12 |
237,869 | def write_phosphopath_ratio ( df , f , a , * args , * * kwargs ) : timepoint_idx = kwargs . get ( 'timepoint_idx' , None ) proteins = [ get_protein_id ( k ) for k in df . index . get_level_values ( 'Proteins' ) ] amino_acids = df . index . get_level_values ( 'Amino acid' ) positions = _get_positions ( df ) multiplicity = [ int ( k [ - 1 ] ) for k in df . index . get_level_values ( 'Multiplicity' ) ] apos = [ "%s%s" % x for x in zip ( amino_acids , positions ) ] phdfs = [ ] # Convert timepoints to 1-based ordinal. tp_map = set ( ) for c in args : tp_map . add ( c [ timepoint_idx ] ) tp_map = sorted ( tp_map ) for c in args : v = df [ a ] . mean ( axis = 1 ) . values / df [ c ] . mean ( axis = 1 ) . values tp = [ 1 + tp_map . index ( c [ timepoint_idx ] ) ] tps = tp * len ( proteins ) if timepoint_idx else [ 1 ] * len ( proteins ) prar = [ "%s-%s-%d-%d" % x for x in zip ( proteins , apos , multiplicity , tps ) ] phdf = pd . DataFrame ( np . array ( list ( zip ( prar , v ) ) ) ) phdf . columns = [ "ID" , "Ratio" ] phdfs . append ( phdf ) pd . concat ( phdfs ) . to_csv ( f , sep = '\t' , index = None ) | Write out the data frame ratio between two groups protein - Rsite - multiplicity - timepoint ID Ratio Q13619 - S10 - 1 - 1 0 . 5 Q9H3Z4 - S10 - 1 - 1 0 . 502 Q6GQQ9 - S100 - 1 - 1 0 . 504 Q86YP4 - S100 - 1 - 1 0 . 506 Q9H307 - S100 - 1 - 1 0 . 508 Q8NEY1 - S1000 - 1 - 1 0 . 51 Q13541 - S101 - 1 - 1 0 . 512 O95785 - S1012 - 2 - 1 0 . 514 O95785 - S1017 - 2 - 1 0 . 516 Q9Y4G8 - S1022 - 1 - 1 0 . 518 P35658 - S1023 - 1 - 1 0 . 52 | 419 | 187 |
237,870 | def write_r ( df , f , sep = "," , index_join = "@" , columns_join = "." ) : df = df . copy ( ) df . index = [ "@" . join ( [ str ( s ) for s in v ] ) for v in df . index . values ] df . columns = [ "." . join ( [ str ( s ) for s in v ] ) for v in df . index . values ] df . to_csv ( f , sep = sep ) | Export dataframe in a format easily importable to R | 108 | 11 |
237,871 | def gaussian ( df , width = 0.3 , downshift = - 1.8 , prefix = None ) : df = df . copy ( ) imputed = df . isnull ( ) # Keep track of what's real if prefix : mask = np . array ( [ l . startswith ( prefix ) for l in df . columns . values ] ) mycols = np . arange ( 0 , df . shape [ 1 ] ) [ mask ] else : mycols = np . arange ( 0 , df . shape [ 1 ] ) if type ( width ) is not list : width = [ width ] * len ( mycols ) elif len ( mycols ) != len ( width ) : raise ValueError ( "Length of iterable 'width' does not match # of columns" ) if type ( downshift ) is not list : downshift = [ downshift ] * len ( mycols ) elif len ( mycols ) != len ( downshift ) : raise ValueError ( "Length of iterable 'downshift' does not match # of columns" ) for i in mycols : data = df . iloc [ : , i ] mask = data . isnull ( ) . values mean = data . mean ( axis = 0 ) stddev = data . std ( axis = 0 ) m = mean + downshift [ i ] * stddev s = stddev * width [ i ] # Generate a list of random numbers for filling in values = np . random . normal ( loc = m , scale = s , size = df . shape [ 0 ] ) # Now fill them in df . iloc [ mask , i ] = values [ mask ] return df , imputed | Impute missing values by drawing from a normal distribution | 365 | 10 |
237,872 | def _pca_scores ( scores , pc1 = 0 , pc2 = 1 , fcol = None , ecol = None , marker = 'o' , markersize = 30 , label_scores = None , show_covariance_ellipse = True , optimize_label_iter = OPTIMIZE_LABEL_ITER_DEFAULT , * * kwargs ) : fig = plt . figure ( figsize = ( 8 , 8 ) ) ax = fig . add_subplot ( 1 , 1 , 1 ) levels = [ 0 , 1 ] for c in set ( scores . columns . values ) : try : data = scores [ c ] . values . reshape ( 2 , - 1 ) except : continue fc = hierarchical_match ( fcol , c , 'k' ) ec = hierarchical_match ( ecol , c ) if ec is None : ec = fc if type ( markersize ) == str : # Use as a key vs. index value in this levels idx = scores . columns . names . index ( markersize ) s = c [ idx ] elif callable ( markersize ) : s = markersize ( c ) else : s = markersize ax . scatter ( data [ pc1 , : ] , data [ pc2 , : ] , s = s , marker = marker , edgecolors = ec , c = fc ) if show_covariance_ellipse and data . shape [ 1 ] > 2 : cov = data [ [ pc1 , pc2 ] , : ] . T ellip = plot_point_cov ( cov , nstd = 2 , linestyle = 'dashed' , linewidth = 0.5 , edgecolor = ec or fc , alpha = 0.8 ) #**kwargs for ellipse styling ax . add_artist ( ellip ) if label_scores : scores_f = scores . iloc [ [ pc1 , pc2 ] ] idxs = get_index_list ( scores_f . columns . names , label_scores ) texts = [ ] for n , ( x , y ) in enumerate ( scores_f . T . values ) : t = ax . text ( x , y , build_combined_label ( scores_f . columns . values [ n ] , idxs , ', ' ) , bbox = dict ( boxstyle = 'round,pad=0.3' , fc = '#ffffff' , ec = 'none' , alpha = 0.6 ) ) texts . append ( t ) if texts and optimize_label_iter : adjust_text ( texts , lim = optimize_label_iter ) ax . set_xlabel ( scores . index [ pc1 ] , fontsize = 16 ) ax . set_ylabel ( scores . index [ pc2 ] , fontsize = 16 ) fig . tight_layout ( ) return ax | Plot a scores plot for two principal components as AxB scatter plot . | 623 | 14 |
237,873 | def modifiedaminoacids ( df , kind = 'pie' ) : colors = [ '#6baed6' , '#c6dbef' , '#bdbdbd' ] total_aas , quants = analysis . modifiedaminoacids ( df ) df = pd . DataFrame ( ) for a , n in quants . items ( ) : df [ a ] = [ n ] df . sort_index ( axis = 1 , inplace = True ) if kind == 'bar' or kind == 'both' : ax1 = df . plot ( kind = 'bar' , figsize = ( 7 , 7 ) , color = colors ) ax1 . set_ylabel ( 'Number of phosphorylated amino acids' ) ax1 . set_xlabel ( 'Amino acid' ) ax1 . set_xticks ( [ ] ) ylim = np . max ( df . values ) + 1000 ax1 . set_ylim ( 0 , ylim ) _bartoplabel ( ax1 , 100 * df . values [ 0 ] , total_aas , ylim ) ax1 . set_xlim ( ( - 0.3 , 0.3 ) ) return ax if kind == 'pie' or kind == 'both' : dfp = df . T residues = dfp . index . values dfp . index = [ "%.2f%% (%d)" % ( 100 * df [ i ] . values [ 0 ] / total_aas , df [ i ] . values [ 0 ] ) for i in dfp . index . values ] ax2 = dfp . plot ( kind = 'pie' , y = 0 , colors = colors ) ax2 . legend ( residues , loc = 'upper left' , bbox_to_anchor = ( 1.0 , 1.0 ) ) ax2 . set_ylabel ( '' ) ax2 . set_xlabel ( '' ) ax2 . figure . set_size_inches ( 6 , 6 ) for t in ax2 . texts : t . set_fontsize ( 15 ) return ax2 return ax1 , ax2 | Generate a plot of relative numbers of modified amino acids in source DataFrame . | 455 | 16 |
237,874 | def venn ( df1 , df2 , df3 = None , labels = None , ix1 = None , ix2 = None , ix3 = None , return_intersection = False , fcols = None ) : try : import matplotlib_venn as mplv except ImportError : raise ImportError ( "To plot venn diagrams, install matplotlib-venn package: pip install matplotlib-venn" ) plt . gcf ( ) . clear ( ) if labels is None : labels = [ "A" , "B" , "C" ] s1 = _process_ix ( df1 . index , ix1 ) s2 = _process_ix ( df2 . index , ix2 ) if df3 is not None : s3 = _process_ix ( df3 . index , ix3 ) kwargs = { } if fcols : kwargs [ 'set_colors' ] = [ fcols [ l ] for l in labels ] if df3 is not None : vn = mplv . venn3 ( [ s1 , s2 , s3 ] , set_labels = labels , * * kwargs ) intersection = s1 & s2 & s3 else : vn = mplv . venn2 ( [ s1 , s2 ] , set_labels = labels , * * kwargs ) intersection = s1 & s2 ax = plt . gca ( ) if return_intersection : return ax , list ( intersection ) else : return ax | Plot a 2 or 3 - part venn diagram showing the overlap between 2 or 3 pandas DataFrames . | 341 | 22 |
237,875 | def sitespeptidesproteins ( df , labels = None , colors = None , site_localization_probability = 0.75 ) : fig = plt . figure ( figsize = ( 4 , 6 ) ) ax = fig . add_subplot ( 1 , 1 , 1 ) shift = 0.5 values = analysis . sitespeptidesproteins ( df , site_localization_probability ) if labels is None : labels = [ 'Sites (Class I)' , 'Peptides' , 'Proteins' ] if colors is None : colors = [ '#756bb1' , '#bcbddc' , '#dadaeb' ] for n , ( c , l , v ) in enumerate ( zip ( colors , labels , values ) ) : ax . fill_between ( [ 0 , 1 , 2 ] , np . array ( [ shift , 0 , shift ] ) + n , np . array ( [ 1 + shift , 1 , 1 + shift ] ) + n , color = c , alpha = 0.5 ) ax . text ( 1 , 0.5 + n , "{}\n{:,}" . format ( l , v ) , ha = 'center' , color = 'k' , fontsize = 16 ) ax . set_xticks ( [ ] ) ax . set_yticks ( [ ] ) ax . set_axis_off ( ) return ax | Plot the number of sites peptides and proteins in the dataset . | 306 | 13 |
237,876 | def _areadist ( ax , v , xr , c , bins = 100 , by = None , alpha = 1 , label = None ) : y , x = np . histogram ( v [ ~ np . isnan ( v ) ] , bins ) x = x [ : - 1 ] if by is None : by = np . zeros ( ( bins , ) ) ax . fill_between ( x , y , by , facecolor = c , alpha = alpha , label = label ) return y | Plot the histogram distribution but as an area plot | 107 | 10 |
237,877 | def hierarchical_timecourse ( df , cluster_cols = True , cluster_rows = False , n_col_clusters = False , n_row_clusters = False , fcol = None , z_score = 0 , method = 'ward' , cmap = cm . PuOr_r , return_clusters = False , rdistance_fn = distance . pdist , cdistance_fn = distance . pdist , xlabel = 'Timepoint' , ylabel = 'log$_2$ Fold Change' ) : dfc , row_clusters , row_denD , col_clusters , col_denD , edges = _cluster ( df , cluster_cols = cluster_cols , cluster_rows = cluster_rows , n_col_clusters = n_col_clusters , n_row_clusters = n_row_clusters , z_score = z_score , method = 'ward' , rdistance_fn = rdistance_fn , cdistance_fn = cdistance_fn ) # FIXME: Need to apply a sort function to the DataFrame to order by the clustering # so we can slice the edges. dfh = dfc . iloc [ row_denD [ 'leaves' ] , col_denD [ 'leaves' ] ] dfh = dfh . mean ( axis = 0 , level = [ 0 , 1 ] ) vmax = np . max ( dfh . values ) color = ScalarMappable ( norm = Normalize ( vmin = 0 , vmax = vmax ) , cmap = viridis ) fig = plt . figure ( figsize = ( 12 , 6 ) ) edges = [ 0 ] + edges + [ dfh . shape [ 1 ] ] for n in range ( len ( edges ) - 1 ) : ax = fig . add_subplot ( 2 , 4 , n + 1 ) dfhf = dfh . iloc [ : , edges [ n ] : edges [ n + 1 ] ] xpos = dfhf . index . get_level_values ( 1 ) mv = dfhf . mean ( axis = 1 ) distances = [ distance . euclidean ( mv , dfhf . values [ : , n ] ) for n in range ( dfhf . shape [ 1 ] ) ] colors = [ color . to_rgba ( v ) for v in distances ] order = np . argsort ( distances ) [ : : - 1 ] for y in order : ax . plot ( xpos , dfhf . values [ : , y ] , c = colors [ y ] , alpha = 0.5 , lw = 1 ) # dfhf.index.get_level_values(1), ax . set_xticks ( xpos ) if n > 3 : ax . set_xticklabels ( xpos ) ax . set_xlabel ( xlabel ) else : ax . set_xticklabels ( [ ] ) if n % 4 != 0 : ax . set_yticklabels ( [ ] ) else : ax . set_ylabel ( ylabel ) ax . set_ylim ( ( - 3 , + 3 ) ) fig . subplots_adjust ( hspace = 0.15 , wspace = 0.15 ) if return_clusters : return fig , dfh , edges else : return fig | Hierarchical clustering of samples across timecourse experiment . | 728 | 13 |
237,878 | def subtract_column_median ( df , prefix = 'Intensity ' ) : df = df . copy ( ) df . replace ( [ np . inf , - np . inf ] , np . nan , inplace = True ) mask = [ l . startswith ( prefix ) for l in df . columns . values ] df . iloc [ : , mask ] = df . iloc [ : , mask ] - df . iloc [ : , mask ] . median ( axis = 0 ) return df | Apply column - wise normalisation to expression columns . | 107 | 10 |
237,879 | def get_protein_id_list ( df , level = 0 ) : protein_list = [ ] for s in df . index . get_level_values ( level ) : protein_list . extend ( get_protein_ids ( s ) ) return list ( set ( protein_list ) ) | Return a complete list of shortform IDs from a DataFrame | 63 | 12 |
237,880 | def hierarchical_match ( d , k , default = None ) : if d is None : return default if type ( k ) != list and type ( k ) != tuple : k = [ k ] for n , _ in enumerate ( k ) : key = tuple ( k [ 0 : len ( k ) - n ] ) if len ( key ) == 1 : key = key [ 0 ] try : d [ key ] except : pass else : return d [ key ] return default | Match a key against a dict simplifying element at a time | 100 | 12 |
237,881 | def calculate_s0_curve ( s0 , minpval , maxpval , minratio , maxratio , curve_interval = 0.1 ) : mminpval = - np . log10 ( minpval ) mmaxpval = - np . log10 ( maxpval ) maxpval_adjust = mmaxpval - mminpval ax0 = ( s0 + maxpval_adjust * minratio ) / maxpval_adjust edge_offset = ( maxratio - ax0 ) % curve_interval max_x = maxratio - edge_offset if ( max_x > ax0 ) : x = np . arange ( ax0 , max_x , curve_interval ) else : x = np . arange ( max_x , ax0 , curve_interval ) fn = lambda x : 10 ** ( - s0 / ( x - minratio ) - mminpval ) y = fn ( x ) return x , y , fn | Calculate s0 curve for volcano plot . | 221 | 10 |
237,882 | def correlation ( df , rowvar = False ) : # Create a correlation matrix for all correlations # of the columns (filled with na for all values) df = df . copy ( ) maskv = np . ma . masked_where ( np . isnan ( df . values ) , df . values ) cdf = np . ma . corrcoef ( maskv , rowvar = False ) cdf = pd . DataFrame ( np . array ( cdf ) ) cdf . columns = df . columns cdf . index = df . columns cdf = cdf . sort_index ( level = 0 , axis = 1 ) cdf = cdf . sort_index ( level = 0 ) return cdf | Calculate column - wise Pearson correlations using numpy . ma . corrcoef | 151 | 18 |
237,883 | def pca ( df , n_components = 2 , mean_center = False , * * kwargs ) : if not sklearn : assert ( 'This library depends on scikit-learn (sklearn) to perform PCA analysis' ) from sklearn . decomposition import PCA df = df . copy ( ) # We have to zero fill, nan errors in PCA df [ np . isnan ( df ) ] = 0 if mean_center : mean = np . mean ( df . values , axis = 0 ) df = df - mean pca = PCA ( n_components = n_components , * * kwargs ) pca . fit ( df . values . T ) scores = pd . DataFrame ( pca . transform ( df . values . T ) ) . T scores . index = [ 'Principal Component %d (%.2f%%)' % ( ( n + 1 ) , pca . explained_variance_ratio_ [ n ] * 100 ) for n in range ( 0 , scores . shape [ 0 ] ) ] scores . columns = df . columns weights = pd . DataFrame ( pca . components_ ) . T weights . index = df . index weights . columns = [ 'Weights on Principal Component %d' % ( n + 1 ) for n in range ( 0 , weights . shape [ 1 ] ) ] return scores , weights | Principal Component Analysis based on sklearn . decomposition . PCA | 300 | 14 |
237,884 | def plsda ( df , a , b , n_components = 2 , mean_center = False , scale = True , * * kwargs ) : if not sklearn : assert ( 'This library depends on scikit-learn (sklearn) to perform PLS-DA' ) from sklearn . cross_decomposition import PLSRegression df = df . copy ( ) # We have to zero fill, nan errors in PLSRegression df [ np . isnan ( df ) ] = 0 if mean_center : mean = np . mean ( df . values , axis = 0 ) df = df - mean sxa , _ = df . columns . get_loc_level ( a ) sxb , _ = df . columns . get_loc_level ( b ) dfa = df . iloc [ : , sxa ] dfb = df . iloc [ : , sxb ] dff = pd . concat ( [ dfa , dfb ] , axis = 1 ) y = np . ones ( dff . shape [ 1 ] ) y [ np . arange ( dfa . shape [ 1 ] ) ] = 0 plsr = PLSRegression ( n_components = n_components , scale = scale , * * kwargs ) plsr . fit ( dff . values . T , y ) # Apply the generated model to the original data x_scores = plsr . transform ( df . values . T ) scores = pd . DataFrame ( x_scores . T ) scores . index = [ 'Latent Variable %d' % ( n + 1 ) for n in range ( 0 , scores . shape [ 0 ] ) ] scores . columns = df . columns weights = pd . DataFrame ( plsr . x_weights_ ) weights . index = df . index weights . columns = [ 'Weights on Latent Variable %d' % ( n + 1 ) for n in range ( 0 , weights . shape [ 1 ] ) ] loadings = pd . DataFrame ( plsr . x_loadings_ ) loadings . index = df . index loadings . columns = [ 'Loadings on Latent Variable %d' % ( n + 1 ) for n in range ( 0 , loadings . shape [ 1 ] ) ] return scores , weights , loadings | Partial Least Squares Discriminant Analysis based on sklearn . cross_decomposition . PLSRegression | 500 | 25 |
237,885 | def enrichment_from_evidence ( dfe , modification = "Phospho (STY)" ) : dfe = dfe . reset_index ( ) . set_index ( 'Experiment' ) dfe [ 'Modifications' ] = np . array ( [ modification in m for m in dfe [ 'Modifications' ] ] ) dfe = dfe . set_index ( 'Modifications' , append = True ) dfes = dfe . sum ( axis = 0 , level = [ 0 , 1 ] ) . T columns = dfes . sum ( axis = 1 , level = 0 ) . columns total = dfes . sum ( axis = 1 , level = 0 ) . values . flatten ( ) # Total values modified = dfes . iloc [ 0 , dfes . columns . get_level_values ( 'Modifications' ) . values ] . values # Modified enrichment = modified / total return pd . DataFrame ( [ enrichment ] , columns = columns , index = [ '% Enrichment' ] ) | Calculate relative enrichment of peptide modifications from evidence . txt . | 220 | 15 |
237,886 | def enrichment_from_msp ( dfmsp , modification = "Phospho (STY)" ) : dfmsp [ 'Modifications' ] = np . array ( [ modification in m for m in dfmsp [ 'Modifications' ] ] ) dfmsp = dfmsp . set_index ( [ 'Modifications' ] ) dfmsp = dfmsp . filter ( regex = 'Intensity ' ) dfmsp [ dfmsp == 0 ] = np . nan df_r = dfmsp . sum ( axis = 0 , level = 0 ) modified = df_r . loc [ True ] . values total = df_r . sum ( axis = 0 ) . values enrichment = modified / total return pd . DataFrame ( [ enrichment ] , columns = dfmsp . columns , index = [ '% Enrichment' ] ) | Calculate relative enrichment of peptide modifications from modificationSpecificPeptides . txt . | 185 | 19 |
237,887 | def sitespeptidesproteins ( df , site_localization_probability = 0.75 ) : sites = filters . filter_localization_probability ( df , site_localization_probability ) [ 'Sequence window' ] peptides = set ( df [ 'Sequence window' ] ) proteins = set ( [ str ( p ) . split ( ';' ) [ 0 ] for p in df [ 'Proteins' ] ] ) return len ( sites ) , len ( peptides ) , len ( proteins ) | Generate summary count of modified sites peptides and proteins in a processed dataset DataFrame . | 118 | 18 |
237,888 | def modifiedaminoacids ( df ) : amino_acids = list ( df [ 'Amino acid' ] . values ) aas = set ( amino_acids ) quants = { } for aa in aas : quants [ aa ] = amino_acids . count ( aa ) total_aas = len ( amino_acids ) return total_aas , quants | Calculate the number of modified amino acids in supplied DataFrame . | 88 | 14 |
237,889 | def build_index_from_design ( df , design , remove_prefix = None , types = None , axis = 1 , auto_convert_numeric = True , unmatched_columns = 'index' ) : df = df . copy ( ) if 'Label' not in design . index . names : design = design . set_index ( 'Label' ) if remove_prefix is None : remove_prefix = [ ] if type ( remove_prefix ) is str : remove_prefix = [ remove_prefix ] unmatched_for_index = [ ] names = design . columns . values idx_levels = len ( names ) indexes = [ ] # Convert numeric only columns_to_combine; except index if auto_convert_numeric : design = design . apply ( pd . to_numeric , errors = "ignore" ) # The match columns are always strings, so the index must also be design . index = design . index . astype ( str ) # Apply type settings if types : for n , t in types . items ( ) : if n in design . columns . values : design [ n ] = design [ n ] . astype ( t ) # Build the index for lo in df . columns . values : l = copy ( lo ) for s in remove_prefix : l = l . replace ( s , '' ) # Remove trailing/forward spaces l = l . strip ( ) # Convert to numeric if possible l = numeric ( l ) # Attempt to match to the labels try : # Index idx = design . loc [ str ( l ) ] except : if unmatched_columns : unmatched_for_index . append ( lo ) else : # No match, fill with None idx = tuple ( [ None ] * idx_levels ) indexes . append ( idx ) else : # We have a matched row, store it idx = tuple ( idx . values ) indexes . append ( idx ) if axis == 0 : df . index = pd . MultiIndex . from_tuples ( indexes , names = names ) else : # If using unmatched for index, append if unmatched_columns == 'index' : df = df . set_index ( unmatched_for_index , append = True ) elif unmatched_columns == 'drop' : df = df . drop ( unmatched_for_index , axis = 1 ) df . columns = pd . MultiIndex . from_tuples ( indexes , names = names ) df = df . sort_index ( axis = 1 ) return df | Build a MultiIndex from a design table . | 535 | 9 |
237,890 | def build_index_from_labels ( df , indices , remove_prefix = None , types = None , axis = 1 ) : df = df . copy ( ) if remove_prefix is None : remove_prefix = [ ] if types is None : types = { } idx = [ df . index , df . columns ] [ axis ] indexes = [ ] for l in idx . get_level_values ( 0 ) : for s in remove_prefix : l = l . replace ( s + " " , '' ) ixr = [ ] for n , m in indices : m = re . search ( m , l ) if m : r = m . group ( 1 ) if n in types : # Map this value to a new type r = types [ n ] ( r ) else : r = None ixr . append ( r ) indexes . append ( tuple ( ixr ) ) if axis == 0 : df . index = pd . MultiIndex . from_tuples ( indexes , names = [ n for n , _ in indices ] ) else : df . columns = pd . MultiIndex . from_tuples ( indexes , names = [ n for n , _ in indices ] ) return df | Build a MultiIndex from a list of labels and matching regex | 259 | 12 |
237,891 | def combine_expression_columns ( df , columns_to_combine , remove_combined = True ) : df = df . copy ( ) for ca , cb in columns_to_combine : df [ "%s_(x+y)/2_%s" % ( ca , cb ) ] = ( df [ ca ] + df [ cb ] ) / 2 if remove_combined : for ca , cb in columns_to_combine : df . drop ( [ ca , cb ] , inplace = True , axis = 1 ) return df | Combine expression columns calculating the mean for 2 columns | 123 | 10 |
237,892 | def expand_side_table ( df ) : df = df . copy ( ) idx = df . index . names df . reset_index ( inplace = True ) def strip_multiplicity ( df ) : df . columns = [ c [ : - 4 ] for c in df . columns ] return df def strip_multiple ( s ) : for sr in [ '___1' , '___2' , '___3' ] : if s . endswith ( sr ) : s = s [ : - 4 ] return s base = df . filter ( regex = '.*(?<!___\d)$' ) # Remove columns that will match ripped multiplicity columns for c in df . columns . values : if strip_multiple ( c ) != c and strip_multiple ( c ) in list ( base . columns . values ) : base . drop ( strip_multiple ( c ) , axis = 1 , inplace = True ) multi1 = df . filter ( regex = '^.*___1$' ) multi1 = strip_multiplicity ( multi1 ) multi1 [ 'Multiplicity' ] = '___1' multi1 = pd . concat ( [ multi1 , base ] , axis = 1 ) multi2 = df . filter ( regex = '^.*___2$' ) multi2 = strip_multiplicity ( multi2 ) multi2 [ 'Multiplicity' ] = '___2' multi2 = pd . concat ( [ multi2 , base ] , axis = 1 ) multi3 = df . filter ( regex = '^.*___3$' ) multi3 = strip_multiplicity ( multi3 ) multi3 [ 'Multiplicity' ] = '___3' multi3 = pd . concat ( [ multi3 , base ] , axis = 1 ) df = pd . concat ( [ multi1 , multi2 , multi3 ] , axis = 0 ) df [ 'id' ] = [ "%s%s" % ( a , b ) for a , b in zip ( df [ 'id' ] , df [ 'Multiplicity' ] ) ] if idx [ 0 ] is not None : df . set_index ( idx , inplace = True ) return df | Perform equivalent of expand side table in Perseus by folding Multiplicity columns down onto duplicate rows | 477 | 20 |
237,893 | def apply_experimental_design ( df , f , prefix = 'Intensity ' ) : df = df . copy ( ) edt = pd . read_csv ( f , sep = '\t' , header = 0 ) edt . set_index ( 'Experiment' , inplace = True ) new_column_labels = [ ] for l in df . columns . values : try : l = edt . loc [ l . replace ( prefix , '' ) ] [ 'Name' ] except ( IndexError , KeyError ) : pass new_column_labels . append ( l ) df . columns = new_column_labels return df | Load the experimental design template from MaxQuant and use it to apply the label names to the data columns . | 141 | 21 |
237,894 | def transform_expression_columns ( df , fn = np . log2 , prefix = 'Intensity ' ) : df = df . copy ( ) mask = np . array ( [ l . startswith ( prefix ) for l in df . columns . values ] ) df . iloc [ : , mask ] = fn ( df . iloc [ : , mask ] ) df . replace ( [ np . inf , - np . inf ] , np . nan , inplace = True ) return df | Apply transformation to expression columns . | 105 | 6 |
237,895 | def fold_columns_to_rows ( df , levels_from = 2 ) : df = df . copy ( ) df . reset_index ( inplace = True , drop = True ) # Wipe out the current index df = df . T # Build all index combinations a = [ list ( set ( df . index . get_level_values ( i ) ) ) for i in range ( 0 , levels_from ) ] combinations = list ( itertools . product ( * a ) ) names = df . index . names [ : levels_from ] concats = [ ] for c in combinations : try : dfcc = df . loc [ c ] except KeyError : continue else : # Silly pandas if len ( dfcc . shape ) == 1 : continue dfcc . columns = pd . MultiIndex . from_tuples ( [ c ] * dfcc . shape [ 1 ] , names = names ) concats . append ( dfcc ) # Concatenate dfc = pd . concat ( concats , axis = 1 ) dfc . sort_index ( axis = 1 , inplace = True ) # Fix name if collapsed if dfc . index . name is None : dfc . index . name = df . index . names [ - 1 ] return dfc | Take a levels from the columns and fold down into the row index . This destroys the existing index ; existing rows will appear as columns under the new column index | 274 | 31 |
237,896 | def args ( self , args ) : self . _args = args self . _logger . log ( 'debug' , 'Args set to {}' . format ( args ) ) | Set additional arguments to be passed to the fitness function | 38 | 10 |
237,897 | def minimize ( self , minimize ) : self . _minimize = minimize self . _logger . log ( 'debug' , 'Minimize set to {}' . format ( minimize ) ) | Configures the ABC to minimize fitness function return value or derived score | 40 | 13 |
237,898 | def num_employers ( self , num_employers ) : if num_employers < 2 : self . _logger . log ( 'warn' , 'Two employers are needed: setting to two' ) num_employers = 2 self . _num_employers = num_employers self . _logger . log ( 'debug' , 'Number of employers set to {}' . format ( num_employers ) ) self . _limit = num_employers * len ( self . _value_ranges ) self . _logger . log ( 'debug' , 'Limit set to {}' . format ( self . _limit ) ) | Sets the number of employer bees ; at least two are required | 139 | 13 |
237,899 | def processes ( self , processes ) : if self . _processes > 1 : self . _pool . close ( ) self . _pool . join ( ) self . _pool = multiprocessing . Pool ( processes ) else : self . _pool = None self . _logger . log ( 'debug' , 'Number of processes set to {}' . format ( processes ) ) | Set the number of concurrent processes the ABC will utilize for fitness function evaluation ; if < = 1 single process is used | 81 | 23 |
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