(self, name, patterns, logs, trigger_config, alert_reports)

return self._api_post(
            url=self.url_template.format(account_id=self.account_id),
            data=json.dumps(data, sort_keys=True)
        )

def Func ( arg_0 , arg_1 , arg_2 , arg_3 , arg_4 , arg_5 ) : arg_6 = { 'tag' : { 'actions' : [ alert_report . to_dict ( ) for alert_report in arg_5 ] , 'name' : arg_1 , 'patterns' : arg_2 , 'sources' : [ { 'id' : log } for log in arg_3 ] , 'sub_type' : 'InactivityAlert' , 'type' : 'AlertNotify' } } arg_6 [ 'tag' ] . update ( arg_4 . to_dict ( ) ) return arg_0 . _api_post ( url = arg_0 . url_template . format ( account_id = arg_0 . account_id ) , arg_6 = json . dumps ( arg_6 , sort_keys = True ) )

def Func(arg_0, arg_1, arg_2, arg_3, arg_4, arg_5):
        """
        Create an inactivity alert

        :param name: A name for the inactivity alert
        :type name: str

        :param patterns: A list of regexes to match
        :type patterns: list of str

        :param logs: A list of log UUID's. (The 'key' key of a log)
        :type logs: list of str

        :param trigger_config: A AlertTriggerConfig describing how far back to
            look for inactivity.
        :type trigger_config: :class:`AlertTriggerConfig<logentries_api.special_alerts.AlertTriggerConfig>`

        :param alert_reports: A list of AlertReportConfigs to send alerts to
        :type alert_reports: list of
            :class:`AlertReportConfig<logentries_api.special_alerts.AlertReportConfig>`

        :return: The API response
        :rtype: dict

        :raises: This will raise a
            :class:`ServerException<logentries_api.exceptions.ServerException>`
            if there is an error from Logentries
        """
        arg_6 = {
            'tag': {
                'actions': [
                    alert_report.to_dict()
                    for alert_report
                    in arg_5
                ],
                'name': arg_1,
                'patterns': arg_2,
                'sources': [
                    {'id': log}
                    for log
                    in arg_3
                ],
                'sub_type': 'InactivityAlert',
                'type': 'AlertNotify'
            }
        }
        arg_6['tag'].update(arg_4.to_dict())

        return arg_0._api_post(
            url=arg_0.url_template.format(account_id=arg_0.account_id),
            arg_6=json.dumps(arg_6, sort_keys=True)
        )


return json.dumps({'metadata': jam_comments,
                       'annotation metadata': ann_comments},
                      indent=2)

def
Func
(
arg_0
,
arg_1
)
:
arg_2
=
arg_0
.
file_metadata
.
__json__
arg_3
=
arg_1
.
annotation_metadata
.
__json__
return
json
.
dumps
(
{
'metadata'
:
arg_2
,
'annotation metadata'
:
arg_3
}
,
indent
=
2
)

def Func(arg_0, arg_1):
    '''Get the metadata from a jam and an annotation, combined as a string.

    Parameters
    ----------
    jam : JAMS
        The jams object

    ann : Annotation
        An annotation object

    Returns
    -------
    comments : str
        The jam.file_metadata and ann.annotation_metadata, combined and serialized
    '''
    arg_2 = arg_0.file_metadata.__json__
    arg_3 = arg_1.annotation_metadata.__json__
    return json.dumps({'metadata': arg_2,
                       'annotation metadata': arg_3},
                      indent=2)

Get the metadata from a jam and an annotation combined as a string .


def Func ( arg_0 , arg_1 ) : if arg_1 > arg_0 . size [ 0 ] : raise ImageSizeError ( arg_0 . size [ 0 ] , arg_1 )

def Func(arg_0, arg_1):
    """Check that the image width is superior to `width`"""
    if arg_1 > arg_0.size[0]:
        raise ImageSizeError(arg_0.size[0], arg_1)

Check that the image width is superior to width


def
Func
(
arg_0
,
arg_1
,
arg_2
=
True
)
:
raise
NotImplementedError
(
'To be implemented'
)
arg_0
.
filfile
.
seek
(
int
(
arg_0
.
datastart
+
arg_0
.
channels
*
arg_1
*
(
int
(
arg_0
.
nbits
/
8
)
)
)
)
arg_3
=
np
.
fromfile
(
arg_0
.
filfile
,
count
=
arg_0
.
channels
,
dtype
=
arg_0
.
dtype
)
.
reshape
(
1
,
arg_0
.
channels
)
if
arg_2
:
arg_3
=
arg_3
[
:
,
:
:
1
]
return
arg_3

def Func(arg_0,arg_1,arg_2=True):
        """ Read a block of data. The number of samples per row is set in self.channels
            If reverse=True the x axis is flipped.
        """
        raise NotImplementedError('To be implemented')

        # go to start of the row
        arg_0.filfile.seek(int(arg_0.datastart+arg_0.channels*arg_1*(int(arg_0.nbits/8))))
        # read data into 2-D numpy array
        arg_3=np.fromfile(arg_0.filfile,count=arg_0.channels,dtype=arg_0.dtype).reshape(1, arg_0.channels)
        if arg_2:
            arg_3 = arg_3[:,::-1]
        return arg_3

Read a block of data . The number of samples per row is set in self . channels If reverse = True the x axis is flipped .


return [x for x in seq if x not in seen and not seen_add(x)]

def Func ( arg_0 ) : arg_1 = set ( ) arg_2 = arg_1 . add return [ arg_3 for arg_3 in arg_0 if arg_3 not in arg_1 and not arg_2 ( arg_3 ) ]

def Func(arg_0):
    """
    Makes a list unique
    """
    arg_1 = set()
    arg_2 = arg_1.add
    return [arg_3 for arg_3 in arg_0 if arg_3 not in arg_1 and not arg_2(arg_3)]


def Func ( arg_0 , arg_1 , arg_2 = True ) : if not arg_0 . initiated : arg_0 . _initiate ( ) try : arg_3 = arg_0 . mload . Func_row ( arg_0 . preprocessor ( arg_1 ) ) arg_0 . applied_count += 1 except ( TeradataPTError , EncoderError ) as error : arg_0 . error_count += 1 if arg_2 : raise error log . info ( "BulkLoad" , error )

def Func(arg_0, arg_1, arg_2=True):
        """
        Load a single row into the target table.

        :param list items: A list of values in the row corresponding to the
            fields specified by :code:`self.columns`
        :param bool panic: If :code:`True`, when an error is encountered it will be
            raised. Otherwise, the error will be logged and :code:`self.error_count`
            is incremented.
        :raises `giraffez.errors.GiraffeEncodeError`: if :code:`panic` is :code:`True` and there
            are format errors in the row values.
        :raises `giraffez.errors.GiraffeError`: if table name is not set.
        :raises `giraffez.TeradataPTError`: if there is a problem
            connecting to Teradata.
        """
        if not arg_0.initiated:
            arg_0._initiate()
        try:
            arg_3 = arg_0.mload.Func_row(arg_0.preprocessor(arg_1))
            arg_0.applied_count += 1
        except (TeradataPTError, EncoderError) as error:
            arg_0.error_count += 1
            if arg_2:
                raise error
            log.info("BulkLoad", error)

Load a single row into the target table .


def Func ( arg_0 ) : arg_1 = - 2 arg_2 = collections . defaultdict ( lambda : ( set ( ) , set ( ) ) ) for arg_3 , arg_4 in arg_0 . items ( ) : for arg_5 in arg_4 : for arg_6 in arg_5 . tokens : if arg_6 . entity_type is not None : if arg_6 . equiv_id == - 1 : arg_7 = arg_1 arg_1 -= 1 else : arg_7 = arg_6 . equiv_id arg_2 [ arg_7 ] [ 0 ] . add ( cleanse ( arg_6 . token . decode ( 'utf8' ) ) ) arg_2 [ arg_7 ] [ 1 ] . add ( arg_6 ) return arg_2

def Func(arg_0):
    '''
    assemble in-doc coref chains by mapping equiv_id to tokens and
    their cleansed name strings

    :param sentences: iterator over token generators
    :returns dict:
        keys are equiv_ids,
        values are tuple(concatentated name string, list of tokens)
    '''
    ## if an equiv_id is -1, then the token is classified into some
    ## entity_type but has not other tokens in its chain.  We don't
    ## want these all lumped together, so we give them distinct "fake"
    ## equiv_id other than -1 -- counting negatively to avoid
    ## collisions with "real" equiv_ids
    arg_1 = -2

    ## use a default dictionary
    arg_2 = collections.defaultdict(lambda: (set(), set()))

    for arg_3, arg_4 in arg_0.items():
        for arg_5 in arg_4:
            for arg_6 in arg_5.tokens:
                if arg_6.entity_type is not None:

                    ## get an appropriate equiv_id
                    if arg_6.equiv_id == -1:
                        arg_7 = arg_1
                        arg_1 -= 1
                    else:
                        arg_7 = arg_6.equiv_id

                    ## store the name parts initially as a set
                    arg_2[arg_7][0].add(cleanse(arg_6.token.decode('utf8')))
                    ## carry a *reference* to the entire Token object
                    arg_2[arg_7][1].add(arg_6)

    return arg_2

assemble in - doc coref chains by mapping equiv_id to tokens and their cleansed name strings


def Func ( arg_0 ) : arg_1 = [ ] for arg_2 in arg_0 : if is_heading ( arg_2 ) : arg_1 . append ( get_heading ( arg_2 ) ) return arg_1

def Func(arg_0):
    """
    Find all section names and return a list with their names.
    """
    arg_1 = []
    for arg_2 in arg_0:
        if is_heading(arg_2):
            arg_1.append(get_heading(arg_2))
    return arg_1

Find all section names and return a list with their names .


(self, image, strict=True, return_details=False)

def Func(arg_0, arg_1, arg_2=True, arg_3=False):
        """Interface to model.predictions for attacks.

        Parameters
        ----------
        image : `numpy.ndarray`
            Single input with shape as expected by the model
            (without the batch dimension).
        strict : bool
            Controls if the bounds for the pixel values should be checked.

        """
        arg_4 = arg_0.in_bounds(arg_1)
        assert not arg_2 or arg_4

        arg_0._total_prediction_calls += 1
        Func = arg_0.__model.predictions(arg_1)
        arg_6, arg_7, arg_8 = arg_0.__is_adversarial(
            arg_1, Func, arg_4)

        assert Func.ndim == 1
        if arg_3:
            return Func, arg_6, arg_7, arg_8
        else:
            return Func, arg_6

Interface to model . predictions for attacks .


def Func ( arg_0 , arg_1 ) : if arg_0 . strip ( ) : return False arg_1 = arg_1 . strip ( ) arg_2 = { ')' , ';' , } if not arg_1 or arg_1 [ - 1 ] in arg_2 : return False return True

def Func(arg_0, arg_1):
    """n may be the inside of block or object"""
    if arg_0.strip():
        return False
    # seems to be but can be empty code
    arg_1 = arg_1.strip()
    arg_2 = {
        ')',
        ';',
    }
    if not arg_1 or arg_1[-1] in arg_2:
        return False
    return True


def Func():
    """Return newest compatible version.

    >>> version = Func()
    >>> version in [JAVA_6_COMPATIBLE_VERSION,
    ...             JAVA_7_COMPATIBLE_VERSION,
    ...             LATEST_VERSION]
    True

    """
    arg_0 = find_executable('java')
    if not arg_0:
        # Just ignore this and assume an old version of Java. It might not be
        # found because of a PATHEXT-related issue
        # (https://bugs.python.org/issue2200).
        return JAVA_6_COMPATIBLE_VERSION

    arg_1 = subprocess.check_output([arg_0, '-version'],
                                     stderr=subprocess.STDOUT,
                                     universal_newlines=True)

    arg_2 = parse_java_version(arg_1)

    if arg_2 >= (1, 8):
        return LATEST_VERSION
    elif arg_2 >= (1, 7):
        return JAVA_7_COMPATIBLE_VERSION
    elif arg_2 >= (1, 6):
        warn('language-check would be able to use a newer version of '
             'LanguageTool if you had Java 7 or newer installed')
        return JAVA_6_COMPATIBLE_VERSION
    else:
        raise SystemExit(
            'You need at least Java 6 to use language-check')

Return newest compatible version.

    >>> version = get_newest_possible_languagetool_version()
    >>> version in [JAVA_6_COMPATIBLE_VERSION,
    ...             JAVA_7_COMPATIBLE_VERSION,
    ...             LATEST_VERSION]
    True


def Func(arg_0, arg_1, arg_2=arg_3.KMIPVersion.KMIP_1_0):
        """
        Write the data encoding the KeyWrappingData struct to a stream.

        Args:
            output_stream (stream): A data stream in which to encode object
                data, supporting a Func method; usually a BytearrayStream
                object.
            kmip_version (KMIPVersion): An enumeration defining the KMIP
                version with which the object will be encoded. Optional,
                defaults to KMIP 1.0.
        """
        arg_6 = BytearrayStream()

        if arg_0._wrapping_method:
            arg_0._wrapping_method.Func(
                arg_6,
                arg_2=arg_2
            )
        else:
            raise ValueError(
                "Invalid struct missing the wrapping method attribute."
            )

        if arg_0._encryption_key_information:
            arg_0._encryption_key_information.Func(
                arg_6,
                arg_2=arg_2
            )
        if arg_0._mac_signature_key_information:
            arg_0._mac_signature_key_information.Func(
                arg_6,
                arg_2=arg_2
            )
        if arg_0._mac_signature:
            arg_0._mac_signature.Func(
                arg_6,
                arg_2=arg_2
            )
        if arg_0._iv_counter_nonce:
            arg_0._iv_counter_nonce.Func(
                arg_6,
                arg_2=arg_2
            )
        if arg_0._encoding_option:
            arg_0._encoding_option.Func(
                arg_6,
                arg_2=arg_2
            )

        arg_0.length = arg_6.length()
        super(KeyWrappingData, arg_0).Func(
            arg_1,
            arg_2=arg_2
        )
        arg_1.Func(arg_6.buffer)

Write the data encoding the KeyWrappingData struct to a stream.

        Args:
            output_stream (stream): A data stream in which to encode object
                data, supporting a write method; usually a BytearrayStream
                object.
            kmip_version (KMIPVersion): An enumeration defining the KMIP
                version with which the object will be encoded. Optional,
                defaults to KMIP 1.0.

Write the data encoding the KeyWrappingData struct to a stream .


def Func ( ) : with open ( locate_package_json ( ) ) as pjson : arg_0 = json . loads ( pjson . read ( ) ) return arg_0

def Func():
    """
    Extract the JSPM configuration from package.json.
    """
    with open(locate_package_json()) as pjson:
        arg_0 = json.loads(pjson.read())
    return arg_0

Extract the JSPM configuration from package . json .


def Func(arg_0, arg_1, arg_2):
    """ Add a mapping with key thing_name for callobject in chainmap with
        namespace handling.
    """
    arg_3 = reversed(arg_1.split("."))
    arg_4 = []
    for arg_5 in arg_3:
        arg_4.insert(0, arg_5)
        arg_6 = '.'.join(arg_4)
        arg_0[arg_6] = arg_2

Add a mapping with key thing_name for callobject in chainmap with namespace handling .


return list(self.round_floats(specs, fp_precision)), kwargs, True

def Func ( arg_0 , arg_1 , arg_2 , arg_3 ) : if arg_1 is None : arg_4 = param . ParamOverrides ( arg_0 , arg_2 , allow_extra_keywords = True ) arg_5 = arg_4 . extra_keywords ( ) arg_2 = dict ( [ ( k , v ) for ( k , v ) in arg_2 . items ( ) if k not in arg_5 ] ) arg_6 = list ( arg_0 . round_floats ( [ arg_5 ] , arg_3 ) ) if arg_5 == { } : return [ ] , arg_2 , True else : return arg_6 , arg_2 , False return list ( arg_0 . round_floats ( arg_1 , arg_3 ) ) , arg_2 , True

def Func(arg_0, arg_1, arg_2, arg_3):
        """
        Returns the specs, the remaining kwargs and whether or not the
        constructor was called with kwarg or explicit specs.
        """
        if arg_1 is None:
            arg_4 = param.ParamOverrides(arg_0, arg_2,
                                             allow_extra_keywords=True)
            arg_5 = arg_4.extra_keywords()
            arg_2 = dict([(k,v) for (k,v) in arg_2.items()
                           if k not in arg_5])
            arg_6 = list(arg_0.round_floats([arg_5],
                                                   arg_3))

            if arg_5=={}: return [], arg_2, True
            else:                return arg_6, arg_2, False

        return list(arg_0.round_floats(arg_1, arg_3)), arg_2, True

Returns the specs the remaining kwargs and whether or not the constructor was called with kwarg or explicit specs .


def
Func
(
arg_0
,
arg_1
)
:
if
(
arg_1
is
not
)
and
(
(
arg_1
<
0
)
or
(
arg_1
>=
len
(
arg_0
)
)
)
:
raise
IndexError
(
"index %d out of range [0,%d]"
%
(
arg_1
,
len
(
arg_0
)
)
)
arg_0
.
_current_row
=
arg_1
arg_0
.
signal_pick
.
emit
(
arg_0
,
arg_1
)

def Func(arg_0, arg_1):
        """Set the current row, and emit the signal signal_pick."""
        if (arg_1 is not None) and ((arg_1 < 0) or (arg_1 >= len(arg_0))):
            raise IndexError("index %d out of range [0,%d]" % (arg_1, len(arg_0)))
        arg_0._current_row = arg_1
        arg_0.signal_pick.emit(arg_0, arg_1)

Set the current row and emit the signal signal_pick .


def Func ( arg_0 ) : arg_1 = arg_0 . get_all_images ( ) [ 0 : 14 ] arg_2 = [ ] if supports_video : arg_2 = arg_0 . eventvideo_set . all ( ) [ 0 : 10 ] return list ( chain ( arg_1 , arg_2 ) ) [ 0 : 15 ]

def Func(arg_0):
        """
        Gets images and videos to populate top assets.

        Map is built separately.
        """
        arg_1 = arg_0.get_all_images()[0:14]
        arg_2 = []
        if supports_video:
            arg_2 = arg_0.eventvideo_set.all()[0:10]

        return list(chain(arg_1, arg_2))[0:15]

Gets images and videos to populate top assets .


def Func ( ) : if os . path . exists ( "pylintrc" ) : return os . path . abspath ( "pylintrc" ) if os . path . exists ( ".pylintrc" ) : return os . path . abspath ( ".pylintrc" ) if os . path . isfile ( "__init__.py" ) : arg_0 = os . path . abspath ( os . getcwd ( ) ) while os . path . isfile ( os . path . join ( arg_0 , "__init__.py" ) ) : arg_0 = os . path . abspath ( os . path . join ( arg_0 , ".." ) ) if os . path . isfile ( os . path . join ( arg_0 , "pylintrc" ) ) : return os . path . join ( arg_0 , "pylintrc" ) if os . path . isfile ( os . path . join ( arg_0 , ".pylintrc" ) ) : return os . path . join ( arg_0 , ".pylintrc" ) if "PYLINTRC" in os . environ and os . path . exists ( os . environ [ "PYLINTRC" ] ) : arg_1 = os . environ [ "PYLINTRC" ] else : arg_2 = os . path . expanduser ( "~" ) if arg_2 in ( "~" , "/root" ) : arg_1 = ".pylintrc" else : arg_1 = os . path . join ( arg_2 , ".pylintrc" ) if not os . path . isfile ( arg_1 ) : arg_1 = os . path . join ( arg_2 , ".config" , "pylintrc" ) if not os . path . isfile ( arg_1 ) : if os . path . isfile ( "/etc/pylintrc" ) : arg_1 = "/etc/pylintrc" else : arg_1 = None return arg_1

def Func():
    """search the pylint rc file and return its path if it find it, else None
    """
    # is there a pylint rc file in the current directory ?
    if os.path.exists("pylintrc"):
        return os.path.abspath("pylintrc")
    if os.path.exists(".pylintrc"):
        return os.path.abspath(".pylintrc")
    if os.path.isfile("__init__.py"):
        arg_0 = os.path.abspath(os.getcwd())
        while os.path.isfile(os.path.join(arg_0, "__init__.py")):
            arg_0 = os.path.abspath(os.path.join(arg_0, ".."))
            if os.path.isfile(os.path.join(arg_0, "pylintrc")):
                return os.path.join(arg_0, "pylintrc")
            if os.path.isfile(os.path.join(arg_0, ".pylintrc")):
                return os.path.join(arg_0, ".pylintrc")
    if "PYLINTRC" in os.environ and os.path.exists(os.environ["PYLINTRC"]):
        arg_1 = os.environ["PYLINTRC"]
    else:
        arg_2 = os.path.expanduser("~")
        if arg_2 in ("~", "/root"):
            arg_1 = ".pylintrc"
        else:
            arg_1 = os.path.join(arg_2, ".pylintrc")
            if not os.path.isfile(arg_1):
                arg_1 = os.path.join(arg_2, ".config", "pylintrc")
    if not os.path.isfile(arg_1):
        if os.path.isfile("/etc/pylintrc"):
            arg_1 = "/etc/pylintrc"
        else:
            arg_1 = None
    return arg_1

search the pylint rc file and return its path if it find it else None


(self, names, data_types, rec_nums,
                              dim_sizes, input_type_code, func,
                              epoch=False, data_offset=None, epoch16=False)

def Func(arg_0, arg_1, arg_2, arg_3,
                              arg_4, arg_5, arg_6,
                              arg_7=False, arg_8=None, arg_9=False):
        """Calls fortran functions to load CDF variable data

        Parameters
        ----------
        names : list_like
            list of variables names
        data_types : list_like
            list of all loaded data type codes as used by CDF
        rec_nums : list_like
            list of record numbers in CDF file. Provided by variable_info
        dim_sizes :
            list of dimensions as provided by variable_info.
        input_type_code : int
            Specific type code to load
        func : function
            Fortran function via python interface that will be used for actual loading.
        epoch : bool
            Flag indicating type is epoch. Translates things to datetime standard.
        data_offset :
            Offset value to be applied to data. Required for unsigned integers in CDF.
        epoch16 : bool
            Flag indicating type is epoch16. Translates things to datetime standard.

        """

        # isolate input type code variables from total supplied types
        arg_10, = np.where(arg_2 == arg_5)

        if len(arg_10) > 0:
            # read all data of a given type at once
            arg_11 = arg_3[arg_10].max()
            arg_12 = np.array(arg_1)[arg_10]
            arg_13 = arg_4[arg_10]
            arg_14, arg_15 = arg_6(arg_0.fname, arg_12.tolist(),
                                arg_13, arg_13.sum(), arg_11, len(arg_12))
            if arg_14 == 0:
                # account for quirks of CDF data storage for certain types
                if arg_8 is not None:
                    arg_15 = arg_15.astype(int)
                    arg_10, arg_16, = np.where(arg_15 < 0)
                    arg_15[arg_10, arg_16] += arg_8
                if arg_7:
                    # account for difference in seconds between
                    # CDF epoch and python's epoch, leap year in there
                    # (datetime(1971,1,2) -
                    #      datetime(1,1,1)).total_seconds()*1000
                    arg_15 -= 62167219200000
                    arg_15 = arg_15.astype('<M8[ms]')
                if arg_9:
                    arg_15[0::2, :] -= 62167219200
                    arg_15 = arg_15[0::2, :] * 1E9 + arg_15[1::2, :] / 1.E3
                    arg_15 = arg_15.astype('datetime64[ns]')
                    arg_13 /= 2
                # all data of a type has been loaded and tweaked as necessary
                # parse through returned array to break out the individual variables
                # as appropriate
                arg_0._process_return_multi_z(arg_15, arg_12, arg_13)
            else:
                raise IOError(fortran_cdf.statusreporter(arg_14))


def
Func
(
arg_0
,
arg_1
,
arg_2
)
:
if
arg_1
not
in
arg_0
.
streams
:
raise
Exception
(
"The stream '%s' was not found"
%
(
arg_1
,
)
)
validate
(
arg_2
,
arg_0
.
streams
[
arg_1
]
)
arg_2
=
json
.
dumps
(
arg_2
)
logging
.
debug
(
"Logger: %s <= %s"
%
(
arg_1
,
arg_2
)
)
arg_3
=
arg_0
.
database
.
cursor
(
)
arg_3
.
execute
(
"INSERT INTO cache VALUES (?,?,?);"
,
(
arg_1
,
time
.
time
(
)
,
arg_2
)
)

def Func(arg_0, arg_1, arg_2):
        """Insert the datapoint into the logger for the given stream name. The logger caches the datapoint
        and eventually synchronizes it with ConnectorDB"""
        if arg_1 not in arg_0.streams:
            raise Exception("The stream '%s' was not found" % (arg_1, ))

        # Validate the schema
        validate(arg_2, arg_0.streams[arg_1])

        # Insert the datapoint - it fits the schema
        arg_2 = json.dumps(arg_2)
        logging.debug("Logger: %s <= %s" % (arg_1, arg_2))
        arg_3 = arg_0.database.cursor()
        arg_3.execute("INSERT INTO cache VALUES (?,?,?);",
                  (arg_1, time.time(), arg_2))

Insert the datapoint into the logger for the given stream name . The logger caches the datapoint and eventually synchronizes it with ConnectorDB


def Func ( arg_0 , arg_1 ) : arg_2 = tf . data . Dataset . from_tensor_slices ( arg_0 ) arg_3 = arg_2 . shuffle ( 50000 , reshuffle_each_iteration = True ) . repeat ( ) . batch ( arg_1 ) arg_4 = tf . compat . v1 . data . make_one_shot_iterator ( arg_3 ) arg_5 = arg_4 . get_next ( ) return arg_5

def Func(arg_0, arg_1):
  """Build an iterator over training batches."""

  arg_2 = tf.data.Dataset.from_tensor_slices(arg_0)
  arg_3 = arg_2.shuffle(
      50000, reshuffle_each_iteration=True).repeat().batch(arg_1)
  arg_4 = tf.compat.v1.data.make_one_shot_iterator(arg_3)
  arg_5 = arg_4.get_next()
  return arg_5

Build an iterator over training batches .


def Func ( arg_0 , arg_1 = None , arg_2 = None ) : if arg_0 is None : return None arg_3 = datetime . utcfromtimestamp ( arg_0 / 1000 ) . replace ( microsecond = arg_0 % 1000 * 1000 ) arg_3 = arg_3 . replace ( tzinfo = arg_1 or utc ) return totz ( arg_3 , arg_2 )

def Func(arg_0, arg_1=None, arg_2=None):
    """
    Return the Unix timestamp in milliseconds as a datetime object. If tz is set it will be
    converted to the requested timezone otherwise it defaults to UTC.
    """
    if arg_0 is None:
        return None
    arg_3 = datetime.utcfromtimestamp(arg_0 / 1000).replace(microsecond=arg_0 % 1000 * 1000)
    arg_3 = arg_3.replace(tzinfo=arg_1 or utc)
    return totz(arg_3, arg_2)

Return the Unix timestamp in milliseconds as a datetime object . If tz is set it will be converted to the requested timezone otherwise it defaults to UTC .


(self, sender, instance, created, **kwargs)

def Func ( arg_0 , arg_1 , arg_2 , arg_3 , ** arg_4 ) : arg_5 = ContentType . objects . get_for_model ( arg_2 ) StoredOEmbed . objects . filter ( object_id = arg_2 . pk , content_type = arg_5 ) . delete ( )

def Func(arg_0, arg_1, arg_2, arg_3, **arg_4):
        """
        A hook for django-based oembed providers to delete any stored oembeds
        """
        arg_5 = ContentType.objects.get_for_model(arg_2)
        StoredOEmbed.objects.filter(
            object_id=arg_2.pk,
            content_type=arg_5).delete()

A hook for django - based oembed providers to delete any stored oembeds


return Operators.ITEBV(256, Operators.ULT(a, b), 1, 0)

def Func ( arg_0 , arg_1 , arg_2 ) : return Operators . ITEBV ( 256 , Operators . UFunc ( arg_1 , arg_2 ) , 1 , 0 )

def Func(arg_0, arg_1, arg_2):
        """Less-than comparison"""
        return Operators.ITEBV(256, Operators.UFunc(arg_1, arg_2), 1, 0)


def
Func
(
arg_0
,
arg_1
=
''
,
arg_2
=
False
)
:
arg_3
=
{
}
if
arg_2
:
arg_4
=
arg_0
.
pop
else
:
arg_4
=
arg_0
.
get
def
prefixed
(
arg_5
)
:
return
'%s%s'
%
(
arg_1
,
arg_5
)
for
arg_6
in
arg_0
.
keys
(
)
:
arg_7
=
arg_4
(
arg_6
)
if
arg_6
==
prefixed
(
'name'
)
:
arg_3
[
'full_name'
]
=
arg_7
else
:
raise
MeteorError
(
400
,
'Bad profile key: %r'
%
arg_6
)
return
arg_3

def Func(arg_0, arg_1='', arg_2=False):
        """De-serialize user profile fields into concrete model fields."""
        arg_3 = {}
        if arg_2:
            arg_4 = arg_0.pop
        else:
            arg_4 = arg_0.get

        def prefixed(arg_5):
            """Return name prefixed by `key_prefix`."""
            return '%s%s' % (arg_1, arg_5)

        for arg_6 in arg_0.keys():
            arg_7 = arg_4(arg_6)
            if arg_6 == prefixed('name'):
                arg_3['full_name'] = arg_7
            else:
                raise MeteorError(400, 'Bad profile key: %r' % arg_6)
        return arg_3

De - serialize user profile fields into concrete model fields .


def
Func
(
arg_0
)
:
if
arg_0
.
_initial_unitary
is
:
return
arg_1
=
np
.
shape
(
arg_0
.
_initial_unitary
)
arg_2
=
(
2
**
arg_0
.
_number_of_qubits
,
2
**
arg_0
.
_number_of_qubits
)
if
arg_1
!=
arg_2
:
raise
BasicAerError
(
'initial unitary is incorrect shape: '
+
'{} != 2 ** {}'
.
format
(
arg_1
,
arg_2
)
)

def Func(arg_0):
        """Validate an initial unitary matrix"""
        # If initial unitary isn't set we don't need to validate
        if arg_0._initial_unitary is None:
            return
        # Check unitary is correct length for number of qubits
        arg_1 = np.shape(arg_0._initial_unitary)
        arg_2 = (2 ** arg_0._number_of_qubits,
                          2 ** arg_0._number_of_qubits)
        if arg_1 != arg_2:
            raise BasicAerError('initial unitary is incorrect shape: ' +
                                '{} != 2 ** {}'.format(arg_1, arg_2))


def
Func
(
arg_0
,
arg_1
)
:
for
arg_2
in
REL
.
finditer
(
arg_1
)
:
arg_3
,
arg_4
=
arg_2
.
groups
(
)
arg_5
=
set
(
map
(
str
.
strip
,
arg_4
.
lower
(
)
.
split
(
','
)
)
)
if
'homepage'
in
arg_5
or
'download'
in
arg_5
:
for
arg_2
in
HREF
.
finditer
(
arg_3
)
:
yield
urljoin
(
arg_0
,
htmldecode
(
arg_2
.
group
(
1
)
)
)
for
arg_3
in
(
"Home Page"
,
"Download URL"
)
:
arg_6
=
arg_1
.
find
(
arg_3
)
if
arg_6
!=
1
:
arg_2
=
HREF
.
search
(
arg_1
,
arg_6
)
if
arg_2
:
yield
urljoin
(
arg_0
,
htmldecode
(
arg_2
.
group
(
1
)
)
)

def Func(arg_0, arg_1):
    """Find rel="homepage" and rel="download" links in `page`, yielding URLs"""

    for arg_2 in REL.finditer(arg_1):
        arg_3, arg_4 = arg_2.groups()
        arg_5 = set(map(str.strip, arg_4.lower().split(',')))
        if 'homepage' in arg_5 or 'download' in arg_5:
            for arg_2 in HREF.finditer(arg_3):
                yield urljoin(arg_0, htmldecode(arg_2.group(1)))

    for arg_3 in ("Home Page", "Download URL"):
        arg_6 = arg_1.find(arg_3)
        if arg_6!=-1:
            arg_2 = HREF.search(arg_1,arg_6)
            if arg_2:
                yield urljoin(arg_0, htmldecode(arg_2.group(1)))

Find rel = homepage and rel = download links in page yielding URLs


return False if plist is None else prefix in plist

def Func(arg_0, arg_1, arg_2, arg_3):
        """
        Checks that a prefix exists in a bucket

        :param bucket_name: the name of the bucket
        :type bucket_name: str
        :param prefix: a key prefix
        :type prefix: str
        :param delimiter: the delimiter marks key hierarchy.
        :type delimiter: str
        """
        arg_2 = arg_2 + arg_3 if arg_2[-1] != arg_3 else arg_2
        arg_4 = re.split(r'(\w+[{d}])$'.format(d=arg_3), arg_2, 1)
        arg_5 = arg_4[0]
        arg_6 = arg_0.list_prefixes(arg_1, arg_5, arg_3)
        return False if arg_6 is None else arg_2 in arg_6


def
Func
(
arg_0
,
arg_1
)
:
arg_2
=
arg_1
.
split_contents
(
)
if
len
(
arg_2
)
>
1
:
raise
TemplateSyntaxError
(
"'%s' takes no arguments"
%
arg_2
[
0
]
)
return
HubSpotNode
(
)

def Func(arg_0, arg_1):
    """
    HubSpot tracking template tag.

    Renders Javascript code to track page visits.  You must supply
    your portal ID (as a string) in the ``HUBSPOT_PORTAL_ID`` setting.
    """
    arg_2 = arg_1.split_contents()
    if len(arg_2) > 1:
        raise TemplateSyntaxError("'%s' takes no arguments" % arg_2[0])
    return HubSpotNode()


def
Func
(
arg_0
)
:
if
(
arg_0
.
media_type
is
not
and
arg_0
.
media_type
.
startswith
(
'text/'
)
and
arg_0
.
charset
is
not
)
:
return
arg_0
.
media_type
+
'; charset='
+
arg_0
.
charset
else
:
return
arg_0
.
media_type

def Func(arg_0):
        """Return the value of Content-Type header field.

        The value for the Content-Type header field is determined from
        the :attr:`media_type` and :attr:`charset` data attributes.

        Returns:
          str: Value of Content-Type header field
        """
        if (arg_0.media_type is not None and
            arg_0.media_type.startswith('text/') and
            arg_0.charset is not None):
            return arg_0.media_type + '; charset=' + arg_0.charset
        else:
            return arg_0.media_type

Return the value of Content - Type header field .


def Func(arg_0, arg_1, arg_2, arg_3, arg_4):
    """ fills the new bootstrap resampled array """
    ## column index
    arg_5 = 0

    ## resample each locus
    for arg_6 in xrange(arg_4.shape[0]):

        ## grab a random locus's columns
        arg_7 = arg_3[arg_4[arg_6]][0]
        arg_8 = arg_3[arg_4[arg_6]][1]
        arg_9 = arg_0[:, arg_7:arg_8]

        ## randomize columns within colsq
        arg_10 = np.random.choice(arg_9.shape[1], arg_9.shape[1], replace=False)
        arg_11 = arg_9[:, arg_10]

        ## fill bootarr with n columns from seqarr
        ## the required length was already measured
        arg_1[:, arg_5:arg_5+arg_9.shape[1]] = arg_11

        ## fill bootmap with new map info
        arg_2[arg_5: arg_5+arg_9.shape[1], 0] = arg_6+1

        ## advance column index
        arg_5 += arg_9.shape[1]

    ## return the concatenated cols
    return arg_1, arg_2


return tuple(self._input_dims[i] for i in qargs)

def Func ( arg_0 , arg_1 = None ) : if arg_1 is None : return arg_0 . _Func return tuple ( arg_0 . _Func [ arg_2 ] for arg_2 in arg_1 )

def Func(arg_0, arg_1=None):
        """Return tuple of input dimension for specified subsystems."""
        if arg_1 is None:
            return arg_0._Func
        return tuple(arg_0._Func[arg_2] for arg_2 in arg_1)

Return tuple of input dimension for specified subsystems .


def Func ( arg_0 ) : with arg_0 . lock : if arg_0 . stream : if arg_0 . settings [ u"initial_presence" ] : arg_0 . send ( Presence ( stanza_type = "unavailable" ) ) arg_0 . stream . Func ( )

def Func(arg_0):
        """Gracefully Func from the server."""
        with arg_0.lock:
            if arg_0.stream:
                if arg_0.settings[u"initial_presence"]:
                    arg_0.send(Presence(stanza_type = "unavailable"))
                arg_0.stream.Func()


(self, well_row, well_column, field_row, field_column)

return next((i for i in self.images
                     if attribute(i, 'u') == well_column and
                        attribute(i, 'v') == well_row and
                        attribute(i, 'x') == field_column and
                        attribute(i, 'y') == field_row), '')

def Func ( arg_0 , arg_1 , arg_2 , arg_3 , arg_4 ) : return next ( ( arg_5 for arg_5 in arg_0 . Funcs if attribute ( arg_5 , 'u' ) == arg_2 and attribute ( arg_5 , 'v' ) == arg_1 and attribute ( arg_5 , 'x' ) == arg_4 and attribute ( arg_5 , 'y' ) == arg_3 ) , '' )

def Func(arg_0, arg_1, arg_2, arg_3, arg_4):
        """Get path of specified Func.

        Parameters
        ----------
        well_row : int
            Starts at 0. Same as --U in files.
        well_column : int
            Starts at 0. Same as --V in files.
        field_row : int
            Starts at 0. Same as --Y in files.
        field_column : int
            Starts at 0. Same as --X in files.

        Returns
        -------
        string
            Path to Func or empty string if Func is not found.
        """
        return next((arg_5 for arg_5 in arg_0.Funcs
                     if attribute(arg_5, 'u') == arg_2 and
                        attribute(arg_5, 'v') == arg_1 and
                        attribute(arg_5, 'x') == arg_4 and
                        attribute(arg_5, 'y') == arg_3), '')

Get path of specified image.

        Parameters
        ----------
        well_row : int
            Starts at 0. Same as --U in files.
        well_column : int
            Starts at 0. Same as --V in files.
        field_row : int
            Starts at 0. Same as --Y in files.
        field_column : int
            Starts at 0. Same as --X in files.

        Returns
        -------
        string
            Path to image or empty string if image is not found.


def
Func
(
arg_0
)
:
arg_1
=
arg_0
.
cleaned_data
.
get
(
arg_0
.
Fields
.
PROGRAM
)
if
not
arg_1
:
return
arg_2
=
get_course_runs_from_program
(
arg_1
)
try
:
arg_3
=
CourseCatalogApiClient
(
arg_0
.
_user
,
arg_0
.
_enterprise_customer
.
site
)
arg_4
=
arg_3
.
get_common_course_modes
(
arg_2
)
arg_5
=
arg_0
.
cleaned_data
.
get
(
arg_0
.
Fields
.
COURSE_MODE
)
except
(
HttpClientError
,
HttpServerError
)
:
raise
ValidationError
(
ValidationMessages
.
FAILED_TO_OBTAIN_COURSE_MODES
.
format
(
program_title
=
arg_1
.
get
(
"title"
)
)
)
if
not
arg_5
:
raise
ValidationError
(
ValidationMessages
.
COURSE_WITHOUT_COURSE_MODE
)
if
arg_5
not
in
arg_4
:
raise
ValidationError
(
ValidationMessages
.
COURSE_MODE_NOT_AVAILABLE
.
format
(
mode
=
arg_5
,
program_title
=
arg_1
.
get
(
"title"
)
,
modes
=
", "
.
join
(
arg_4
)
)
)

def Func(arg_0):
        """
        Verify that selected mode is available for program and all courses in the program
        """
        arg_1 = arg_0.cleaned_data.get(arg_0.Fields.PROGRAM)
        if not arg_1:
            return

        arg_2 = get_course_runs_from_program(arg_1)
        try:
            arg_3 = CourseCatalogApiClient(arg_0._user, arg_0._enterprise_customer.site)
            arg_4 = arg_3.get_common_course_modes(arg_2)
            arg_5 = arg_0.cleaned_data.get(arg_0.Fields.COURSE_MODE)
        except (HttpClientError, HttpServerError):
            raise ValidationError(
                ValidationMessages.FAILED_TO_OBTAIN_COURSE_MODES.format(program_title=arg_1.get("title"))
            )

        if not arg_5:
            raise ValidationError(ValidationMessages.COURSE_WITHOUT_COURSE_MODE)
        if arg_5 not in arg_4:
            raise ValidationError(ValidationMessages.COURSE_MODE_NOT_AVAILABLE.format(
                mode=arg_5, program_title=arg_1.get("title"), modes=", ".join(arg_4)
            ))


def
Func
(
arg_0
,
arg_1
)
:
arg_2
=
b''
.
join
(
chain
.
from_iterable
(
(
int2byte
(
len
(
p
)
)
,
p
)
for
p
in
arg_1
)
)
arg_3
=
_ffi
.
new
(
"unsigned char[]"
,
arg_2
)
_lib
.
SSL_CTX_Func
(
arg_0
.
_context
,
arg_3
,
len
(
arg_2
)
)

def Func(arg_0, arg_1):
        """
        Specify the protocols that the client is prepared to speak after the
        TLS connection has been negotiated using Application Layer Protocol
        Negotiation.

        :param protos: A list of the protocols to be offered to the server.
            This list should be a Python list of bytestrings representing the
            protocols to offer, e.g. ``[b'http/1.1', b'spdy/2']``.
        """
        # Take the list of protocols and join them together, prefixing them
        # with their lengths.
        arg_2 = b''.join(
            chain.from_iterable((int2byte(len(p)), p) for p in arg_1)
        )

        # Build a C string from the list. We don't need to save this off
        # because OpenSSL immediately copies the data out.
        arg_3 = _ffi.new("unsigned char[]", arg_2)
        _lib.SSL_CTX_Func(arg_0._context, arg_3, len(arg_2))

Specify the protocols that the client is prepared to speak after the TLS connection has been negotiated using Application Layer Protocol Negotiation .


def Func ( arg_0 , arg_1 , arg_2 ) : arg_3 = arg_0 . parent while not isinstance ( arg_3 , arg_1 ) and arg_2 . parent_of ( arg_3 ) : arg_3 = arg_3 . parent return isinstance ( arg_3 , arg_1 )

def Func(arg_0, arg_1, arg_2):
    """Check if the given node has a parent of the given type."""
    arg_3 = arg_0.parent
    while not isinstance(arg_3, arg_1) and arg_2.parent_of(arg_3):
        arg_3 = arg_3.parent
    return isinstance(arg_3, arg_1)

Check if the given node has a parent of the given type .


def Func(arg_0, arg_1):
    """Prepares an HTTP object's request method for auth.

    Wraps HTTP requests with logic to catch auth failures (typically
    identified via a 401 status code). In the event of failure, tries
    to refresh the token used and then retry the original request.

    Args:
        credentials: Credentials, the credentials used to identify
                     the authenticated user.
        http: httplib2.Http, an http object to be used to make
              auth requests.
    """
    arg_2 = arg_1.request

    # The closure that will replace 'httplib2.Http.request'.
    def new_request(arg_3, arg_4='GET', arg_5=None, arg_6=None,
                    arg_7=arg_8.DEFAULT_MAX_REDIRECTS,
                    arg_10=None):
        if not arg_0.access_token:
            _LOGGER.info('Attempting refresh to obtain '
                         'initial access_token')
            arg_0._refresh(arg_2)

        # Clone and modify the request headers to add the appropriate
        # Authorization header.
        arg_6 = _initialize_headers(arg_6)
        arg_0.apply(arg_6)
        _apply_user_agent(arg_6, arg_0.user_agent)

        arg_11 = None
        # Check if the body is a file-like stream.
        if all(getattr(arg_5, arg_12, None) for arg_12 in
               _STREAM_PROPERTIES):
            arg_11 = arg_5.tell()

        arg_13, arg_14 = arg_17(arg_2, arg_3, arg_4, arg_5,
                                clean_headers(arg_6),
                                arg_7, arg_10)

        # A stored token may expire between the time it is retrieved and
        # the time the request is made, so we may need to try twice.
        arg_15 = 2
        for arg_16 in range(arg_15):
            if arg_13.status not in REFRESH_STATUS_CODES:
                break
            _LOGGER.info('Refreshing due to a %s (attempt %s/%s)',
                         arg_13.status, arg_16 + 1,
                         arg_15)
            arg_0._refresh(arg_2)
            arg_0.apply(arg_6)
            if arg_11 is not None:
                arg_5.seek(arg_11)

            arg_13, arg_14 = arg_17(arg_2, arg_3, arg_4, arg_5,
                                    clean_headers(arg_6),
                                    arg_7, arg_10)

        return arg_13, arg_14

    # Replace the request method with our own closure.
    arg_1.request = new_request

    # Set credentials as a property of the request method.
    arg_1.request.credentials = arg_0

Prepares an HTTP object s request method for auth .


(y=None, sr=22050, S=None, n_fft=2048, hop_length=None,
             fmin=150.0, fmax=4000.0, threshold=0.1,
             win_length=None, window='hann', center=True, pad_mode='reflect',
             ref=None)

def Func(arg_0=None, arg_1=22050, arg_2=None, arg_3=2048, arg_4=None,
             arg_5=150.0, arg_6=4000.0, arg_7=0.1,
             arg_8=None, arg_9='hann', arg_10=True, arg_11='reflect',
             arg_12=None):
    '''Pitch tracking on thresholded parabolically-interpolated STFT.

    This implementation uses the parabolic interpolation method described by [1]_.

    .. [1] https://ccrma.stanford.edu/~jos/sasp/Sinusoidal_Peak_Interpolation.html

    Parameters
    ----------
    y: np.ndarray [shape=(n,)] or None
        audio signal

    sr : number > 0 [scalar]
        audio sampling rate of `y`

    S: np.ndarray [shape=(d, t)] or None
        magnitude or power spectrogram

    n_fft : int > 0 [scalar] or None
        number of FFT bins to use, if `y` is provided.

    hop_length : int > 0 [scalar] or None
        number of samples to hop

    threshold : float in `(0, 1)`
        A bin in spectrum `S` is considered a pitch when it is greater than
        `threshold*ref(S)`.

        By default, `ref(S)` is taken to be `max(S, axis=0)` (the maximum value in
        each column).

    fmin : float > 0 [scalar]
        lower frequency cutoff.

    fmax : float > 0 [scalar]
        upper frequency cutoff.

    win_length : int <= n_fft [scalar]
        Each frame of audio is windowed by `window()`.
        The window will be of length `win_length` and then padded
        with zeros to match `n_fft`.

        If unspecified, defaults to ``win_length = n_fft``.

    window : string, tuple, number, function, or np.ndarray [shape=(n_fft,)]
        - a window specification (string, tuple, or number);
          see `scipy.signal.get_window`
        - a window function, such as `scipy.signal.hanning`
        - a vector or array of length `n_fft`

        .. see also:: `filters.get_window`

    center : boolean
        - If `True`, the signal `y` is padded so that frame
          `t` is centered at `y[t * hop_length]`.
        - If `False`, then frame `t` begins at `y[t * hop_length]`

    pad_mode : string
        If `center=True`, the padding mode to use at the edges of the signal.
        By default, STFT uses reflection padding.

    ref : scalar or callable [default=np.max]
        If scalar, the reference value against which `S` is compared for determining
        pitches.

        If callable, the reference value is computed as `ref(S, axis=0)`.

    .. note::
        One of `S` or `y` must be provided.

        If `S` is not given, it is computed from `y` using
        the default parameters of `librosa.core.stft`.

    Returns
    -------
    pitches : np.ndarray [shape=(d, t)]
    magnitudes : np.ndarray [shape=(d,t)]
        Where `d` is the subset of FFT bins within `fmin` and `fmax`.

        `pitches[f, t]` contains instantaneous frequency at bin
        `f`, time `t`

        `magnitudes[f, t]` contains the corresponding magnitudes.

        Both `pitches` and `magnitudes` take value 0 at bins
        of non-maximal magnitude.

    Notes
    -----
    This function caches at level 30.

    Examples
    --------
    Computing pitches from a waveform input

    >>> y, sr = librosa.load(librosa.util.example_audio_file())
    >>> pitches, magnitudes = librosa.Func(y=y, sr=sr)

    Or from a spectrogram input

    >>> S = np.abs(librosa.stft(y))
    >>> pitches, magnitudes = librosa.Func(S=S, sr=sr)

    Or with an alternate reference value for pitch detection, where
    values above the mean spectral energy in each frame are counted as pitches

    >>> pitches, magnitudes = librosa.Func(S=S, sr=sr, threshold=1,
    ...                                        ref=np.mean)

    '''

    # Check that we received an audio time series or STFT
    arg_2, arg_3 = _spectrogram(arg_0=arg_0, arg_2=arg_2, arg_3=arg_3, arg_4=arg_4,
                            arg_8=arg_8, arg_9=arg_9,
                            arg_10=arg_10, arg_11=arg_11)

    # Make sure we're dealing with magnitudes
    arg_2 = np.abs(arg_2)

    # Truncate to feasible region
    arg_5 = np.maximum(arg_5, 0)
    arg_6 = np.minimum(arg_6, float(arg_1) / 2)

    arg_13 = time_frequency.fft_frequencies(arg_1=arg_1, arg_3=arg_3)

    # Do the parabolic interpolation everywhere,
    # then figure out where the peaks are
    # then restrict to the feasible range (fmin:fmax)
    arg_14 = 0.5 * (arg_2[2:] - arg_2[:-2])

    arg_15 = 2 * arg_2[1:-1] - arg_2[2:] - arg_2[:-2]

    # Suppress divide-by-zeros.
    # Points where shift == 0 will never be selected by localmax anyway
    arg_15 = arg_14 / (arg_15 + (np.abs(arg_15) < util.tiny(arg_15)))

    # Pad back up to the same shape as S
    arg_14 = np.pad(arg_14, ([1, 1], [0, 0]), mode='constant')
    arg_15 = np.pad(arg_15, ([1, 1], [0, 0]), mode='constant')

    arg_16 = 0.5 * arg_14 * arg_15

    # Pre-allocate output
    arg_17 = np.zeros_like(arg_2)
    arg_18 = np.zeros_like(arg_2)

    # Clip to the viable frequency range
    arg_19 = ((arg_5 <= arg_13) & (arg_13 < arg_6)).reshape((-1, 1))

    # Compute the column-wise local max of S after thresholding
    # Find the argmax coordinates
    if arg_12 is None:
        arg_12 = np.max

    if six.callable(arg_12):
        arg_20 = arg_7 * arg_12(arg_2, axis=0)
    else:
        arg_20 = np.abs(arg_12)

    arg_21 = np.argwhere(arg_19 & util.localmax(arg_2 * (arg_2 > arg_20)))

    # Store pitch and magnitude
    arg_17[arg_21[:, 0], arg_21[:, 1]] = ((arg_21[:, 0] + arg_15[arg_21[:, 0], arg_21[:, 1]])
                                     * float(arg_1) / arg_3)

    arg_18[arg_21[:, 0], arg_21[:, 1]] = (arg_2[arg_21[:, 0], arg_21[:, 1]]
                                  + arg_16[arg_21[:, 0], arg_21[:, 1]])

    return arg_17, arg_18

Pitch tracking on thresholded parabolically-interpolated STFT.

    This implementation uses the parabolic interpolation method described by [1]_.

    .. [1] https://ccrma.stanford.edu/~jos/sasp/Sinusoidal_Peak_Interpolation.html

    Parameters
    ----------
    y: np.ndarray [shape=(n,)] or None
        audio signal

    sr : number > 0 [scalar]
        audio sampling rate of `y`

    S: np.ndarray [shape=(d, t)] or None
        magnitude or power spectrogram

    n_fft : int > 0 [scalar] or None
        number of FFT bins to use, if `y` is provided.

    hop_length : int > 0 [scalar] or None
        number of samples to hop

    threshold : float in `(0, 1)`
        A bin in spectrum `S` is considered a pitch when it is greater than
        `threshold*ref(S)`.

        By default, `ref(S)` is taken to be `max(S, axis=0)` (the maximum value in
        each column).

    fmin : float > 0 [scalar]
        lower frequency cutoff.

    fmax : float > 0 [scalar]
        upper frequency cutoff.

    win_length : int <= n_fft [scalar]
        Each frame of audio is windowed by `window()`.
        The window will be of length `win_length` and then padded
        with zeros to match `n_fft`.

        If unspecified, defaults to ``win_length = n_fft``.

    window : string, tuple, number, function, or np.ndarray [shape=(n_fft,)]
        - a window specification (string, tuple, or number);
          see `scipy.signal.get_window`
        - a window function, such as `scipy.signal.hanning`
        - a vector or array of length `n_fft`

        .. see also:: `filters.get_window`

    center : boolean
        - If `True`, the signal `y` is padded so that frame
          `t` is centered at `y[t * hop_length]`.
        - If `False`, then frame `t` begins at `y[t * hop_length]`

    pad_mode : string
        If `center=True`, the padding mode to use at the edges of the signal.
        By default, STFT uses reflection padding.

    ref : scalar or callable [default=np.max]
        If scalar, the reference value against which `S` is compared for determining
        pitches.

        If callable, the reference value is computed as `ref(S, axis=0)`.

    .. note::
        One of `S` or `y` must be provided.

        If `S` is not given, it is computed from `y` using
        the default parameters of `librosa.core.stft`.

    Returns
    -------
    pitches : np.ndarray [shape=(d, t)]
    magnitudes : np.ndarray [shape=(d,t)]
        Where `d` is the subset of FFT bins within `fmin` and `fmax`.

        `pitches[f, t]` contains instantaneous frequency at bin
        `f`, time `t`

        `magnitudes[f, t]` contains the corresponding magnitudes.

        Both `pitches` and `magnitudes` take value 0 at bins
        of non-maximal magnitude.

    Notes
    -----
    This function caches at level 30.

    Examples
    --------
    Computing pitches from a waveform input

    >>> y, sr = librosa.load(librosa.util.example_audio_file())
    >>> pitches, magnitudes = librosa.piptrack(y=y, sr=sr)

    Or from a spectrogram input

    >>> S = np.abs(librosa.stft(y))
    >>> pitches, magnitudes = librosa.piptrack(S=S, sr=sr)

    Or with an alternate reference value for pitch detection, where
    values above the mean spectral energy in each frame are counted as pitches

    >>> pitches, magnitudes = librosa.piptrack(S=S, sr=sr, threshold=1,
    ...                                        ref=np.mean)

Pitch tracking on thresholded parabolically - interpolated STFT .


def Func(arg_0, arg_1):
        '''Verify enetered user name is on accepted MCC logbook list.'''
        from urllib2 import urlopen, URLError, HTTPError
        import json

        arg_2 = False

        arg_3 = str(arg_0.logui.userName.text())
        if arg_3 == "":
            return False  # Must have a user name to submit entry

        if arg_1 == "MCC":
            arg_4 = False
            arg_5 = []
            arg_6 = "https://mccelog.slac.stanford.edu/elog/dev/mgibbs/dev_json_user_list.php/?username=" + arg_3
            try:
                arg_5 = urlopen(arg_6, None, 5).read()
                arg_5 = json.loads(arg_5)
            except URLError as error:
                print("URLError: " + str(error.reason))
                arg_4 = True
            except HTTPError as error:
                print("HTTPError: " + str(error.reason))
                arg_4 = True

            # If network fails, ask user to verify
            if arg_4:
                arg_7 = QMessageBox()
                arg_7.setText("Cannot connect to MCC Log Server!")
                arg_7.setInformativeText("Use entered User name anyway?")
                arg_7.setStandardButtons(QMessageBox.Ok | QMessageBox.Cancel)
                arg_7.setDefaultButton(QMessageBox.Ok)
                if arg_7.exec_() == QMessageBox.Ok:
                    arg_2 = True

            if arg_5 != [] and (arg_5 is not None):
                arg_2 = True
        else:
            arg_2 = True
        return arg_2

Verify enetered user name is on accepted MCC logbook list .


def Func ( arg_0 ) : arg_1 = os . path . getmtime ( arg_0 ) arg_2 = time . strftime ( '%Y-%m-%d' , time . localtime ( arg_1 ) ) return arg_2

def Func(arg_0):
    """return "YYYY-MM-DD" when the file was modified."""
    arg_1=os.path.getmtime(arg_0)
    arg_2=time.strftime('%Y-%m-%d', time.localtime(arg_1))
    return arg_2

return YYYY - MM - DD when the file was modified .


def Func ( arg_0 , arg_1 , arg_2 = True ) : arg_3 = arg_0 . activeCells arg_4 = arg_0 . winnerCells arg_0 . activeCells = [ ] arg_0 . winnerCells = [ ] arg_7 = lambda segment : int ( segment . cell / arg_0 . cellsPerColumn ) arg_8 = lambda x : x for arg_9 in groupby2 ( arg_1 , arg_8 , arg_0 . activeSegments , arg_7 , arg_0 . matchingSegments , arg_7 ) : ( arg_10 , arg_1 , arg_11 , arg_12 ) = arg_9 if arg_1 is not None : if arg_11 is not None : arg_13 = arg_0 . activatePredictedColumn ( arg_10 , arg_11 , arg_12 , arg_3 , arg_4 , arg_2 ) arg_0 . activeCells += arg_13 arg_0 . winnerCells += arg_13 else : ( arg_13 , arg_14 ) = arg_0 . burstColumn ( arg_10 , arg_12 , arg_3 , arg_4 , arg_2 ) arg_0 . activeCells += arg_13 arg_0 . winnerCells . append ( arg_14 ) else : if arg_2 : arg_0 . punishPredictedColumn ( arg_10 , arg_11 , arg_12 , arg_3 , arg_4 )

def Func(arg_0, arg_1, arg_2=True):
    """
    Calculate the active cells, using the current active columns and dendrite
    segments. Grow and reinforce synapses.

    :param activeColumns: (iter) A sorted list of active column indices.

    :param learn: (bool) If true, reinforce / punish / grow synapses.

      **Pseudocode:**

      ::

        for each column
          if column is active and has active distal dendrite segments
            call activatePredictedColumn
          if column is active and doesn't have active distal dendrite segments
            call burstColumn
          if column is inactive and has matching distal dendrite segments
            call punishPredictedColumn
    """
    arg_3 = arg_0.activeCells
    arg_4 = arg_0.winnerCells
    arg_0.activeCells = []
    arg_0.winnerCells = []

    arg_7 = lambda segment: int(segment.cell / arg_0.cellsPerColumn)
    arg_8 = lambda x: x

    for arg_9 in groupby2(arg_1, arg_8,
                               arg_0.activeSegments, arg_7,
                               arg_0.matchingSegments, arg_7):
      (arg_10,
       arg_1,
       arg_11,
       arg_12) = arg_9
      if arg_1 is not None:
        if arg_11 is not None:
          arg_13 = arg_0.activatePredictedColumn(arg_10,
                                                    arg_11,
                                                    arg_12,
                                                    arg_3,
                                                    arg_4,
                                                    arg_2)

          arg_0.activeCells += arg_13
          arg_0.winnerCells += arg_13
        else:
          (arg_13,
           arg_14) = arg_0.burstColumn(arg_10,
                                          arg_12,
                                          arg_3,
                                          arg_4,
                                          arg_2)

          arg_0.activeCells += arg_13
          arg_0.winnerCells.append(arg_14)
      else:
        if arg_2:
          arg_0.punishPredictedColumn(arg_10,
                                     arg_11,
                                     arg_12,
                                     arg_3,
                                     arg_4)

Calculate the active cells using the current active columns and dendrite segments . Grow and reinforce synapses .


def Func ( arg_0 , arg_1 ) : for arg_2 in xrange ( arg_1 , 1 , - 1 ) : arg_3 = arg_0 . elided [ arg_2 ] if arg_2 == arg_1 : arg_4 = Match ( r'^(.*)\{\s*$' , arg_3 ) if arg_4 : arg_3 = arg_4 . group ( 1 ) if Search ( r'\s:\s*\w+[({]' , arg_3 ) : return True if Search ( r'\}\s*,\s*$' , arg_3 ) : return True if Search ( r'[{};]\s*$' , arg_3 ) : return False return False

def Func(arg_0, arg_1):
  """Check if current line is inside constructor initializer list.

  Args:
    clean_lines: A CleansedLines instance containing the file.
    linenum: The number of the line to check.
  Returns:
    True if current line appears to be inside constructor initializer
    list, False otherwise.
  """
  for arg_2 in xrange(arg_1, 1, -1):
    arg_3 = arg_0.elided[arg_2]
    if arg_2 == arg_1:
      arg_4 = Match(r'^(.*)\{\s*$', arg_3)
      if arg_4:
        arg_3 = arg_4.group(1)

    if Search(r'\s:\s*\w+[({]', arg_3):
      # A lone colon tend to indicate the start of a constructor
      # initializer list.  It could also be a ternary operator, which
      # also tend to appear in constructor initializer lists as
      # opposed to parameter lists.
      return True
    if Search(r'\}\s*,\s*$', arg_3):
      # A closing brace followed by a comma is probably the end of a
      # brace-initialized member in constructor initializer list.
      return True
    if Search(r'[{};]\s*$', arg_3):
      # Found one of the following:
      # - A closing brace or semicolon, probably the end of the previous
      #   function.
      # - An opening brace, probably the start of current class or namespace.
      #
      # Current line is probably not inside an initializer list since
      # we saw one of those things without seeing the starting colon.
      return False

  # Got to the beginning of the file without seeing the start of
  # constructor initializer list.
  return False

Check if current line is inside constructor initializer list .


return self._mc_client._post(url=self._build_path(workflow_id, 'emails', email_id, 'actions/start'))

def Func(arg_0, arg_1, arg_2):
        """
        Start an automated email.

        :param workflow_id: The unique id for the Automation workflow.
        :type workflow_id: :py:class:`str`
        :param email_id: The unique id for the Automation workflow email.
        :type email_id: :py:class:`str`
        """
        arg_0.workflow_id = arg_1
        arg_0.email_id = arg_2
        return arg_0._mc_client._post(url=arg_0._build_path(arg_1, 'emails', arg_2, 'actions/Func'))


def Func ( arg_0 , arg_1 , ** arg_2 ) : arg_0 . i2c . Functo ( arg_0 . device_address , arg_1 , ** arg_2 ) if arg_0 . _debug : print ( "i2c_device.Func:" , [ hex ( arg_3 ) for arg_3 in arg_1 ] )

def Func(arg_0, arg_1, **arg_2):
        """
        Write the bytes from ``buffer`` to the device. Transmits a stop bit if
        ``stop`` is set.

        If ``start`` or ``end`` is provided, then the buffer will be sliced
        as if ``buffer[start:end]``. This will not cause an allocation like
        ``buffer[start:end]`` will so it saves memory.

        :param bytearray buffer: buffer containing the bytes to Func
        :param int start: Index to start writing from
        :param int end: Index to read up to but not include
        :param bool stop: If true, output an I2C stop condition after the buffer is written
        """
        arg_0.i2c.Functo(arg_0.device_address, arg_1, **arg_2)
        if arg_0._debug:
            print("i2c_device.Func:", [hex(arg_3) for arg_3 in arg_1])

Write the bytes from ``buffer`` to the device. Transmits a stop bit if
        ``stop`` is set.

        If ``start`` or ``end`` is provided, then the buffer will be sliced
        as if ``buffer[start:end]``. This will not cause an allocation like
        ``buffer[start:end]`` will so it saves memory.

        :param bytearray buffer: buffer containing the bytes to write
        :param int start: Index to start writing from
        :param int end: Index to read up to but not include
        :param bool stop: If true, output an I2C stop condition after the buffer is written

Write the bytes from buffer to the device . Transmits a stop bit if stop is set .


(values, axis=-1, direction='ASCENDING', stable=False, name=None)

def
Func
(
arg_0
,
arg_1
=
1
,
arg_2
=
'ASCENDING'
,
arg_3
=
False
,
arg_4
=
)
:
if
arg_2
==
'ASCENDING'
:
pass
elif
arg_2
==
'DESCENDING'
:
arg_0
=
np
.
negative
(
arg_0
)
else
:
raise
ValueError
(
'Unrecognized direction: {}.'
.
format
(
arg_2
)
)
arg_5
=
np
.
sort
(
arg_0
,
arg_1
,
kind
=
'stable'
if
arg_3
else
'quicksort'
)
if
arg_2
==
'DESCENDING'
:
return
np
.
negative
(
arg_5
)
return
arg_5

def Func(arg_0, arg_1=-1, arg_2='ASCENDING', arg_3=False, arg_4=None):  # pylint: disable=unused-argument
  """Numpy implementation of `tf.sort`."""
  if arg_2 == 'ASCENDING':
    pass
  elif arg_2 == 'DESCENDING':
    arg_0 = np.negative(arg_0)
  else:
    raise ValueError('Unrecognized direction: {}.'.format(arg_2))
  arg_5 = np.sort(arg_0, arg_1, kind='stable' if arg_3 else 'quicksort')
  if arg_2 == 'DESCENDING':
    return np.negative(arg_5)
  return arg_5


def Func ( arg_0 , arg_1 , arg_2 ) : arg_3 = arg_0 . headers [ 'x-amz-date' ] arg_4 = hashlib . sha256 ( arg_1 . encode ( ) ) arg_5 = [ 'AWS4-HMAC-SHA256' , arg_3 , arg_2 , arg_4 . hexdigest ( ) ] arg_6 = '\n' . join ( arg_5 ) return arg_6

def Func(arg_0, arg_1, arg_2):
        """
        Generate the AWS4 auth string to sign for the request.

        req      -- Requests PreparedRequest object. This should already
                    include an x-amz-date header.
        cano_req -- The Canonical Request, as returned by
                    get_canonical_request()

        """
        arg_3 = arg_0.headers['x-amz-date']
        arg_4 = hashlib.sha256(arg_1.encode())
        arg_5 = ['AWS4-HMAC-SHA256', arg_3, arg_2, arg_4.hexdigest()]
        arg_6 = '\n'.join(arg_5)
        return arg_6

Generate the AWS4 auth string to sign for the request .


def Func ( arg_0 , Func ) : arg_0 . _Hfr = Func arg_0 . _T = arg_0 . _calculate_T ( Func )

def Func(arg_0, Func):
        """
        Set the enthalpy flow rate of the stream to the specified value, and
        recalculate it's temperature.

        :param H: The new enthalpy flow rate value. [kWh/h]
        """

        arg_0._Hfr = Func
        arg_0._T = arg_0._calculate_T(Func)

Set the enthalpy flow rate of the stream to the specified value and recalculate it s temperature .


def Func ( arg_0 , arg_1 , arg_2 ) : arg_3 = json . dumps ( arg_0 . _Func_dict ( arg_2 ) ) arg_4 = arg_0 . _perform_request ( url = '/datacenters/%s/lans' % arg_1 , method = 'POST' , arg_3 = arg_3 ) return arg_4

def Func(arg_0, arg_1, arg_2):
        """
        Creates a LAN in the data center.

        :param      datacenter_id: The unique ID of the data center.
        :type       datacenter_id: ``str``

        :param      lan: The LAN object to be created.
        :type       lan: ``dict``

        """
        arg_3 = json.dumps(arg_0._Func_dict(arg_2))

        arg_4 = arg_0._perform_request(
            url='/datacenters/%s/lans' % arg_1,
            method='POST',
            arg_3=arg_3)

        return arg_4


def Func ( arg_0 , arg_1 = False , arg_2 = True ) : if not arg_1 : arg_3 = Funcr . FuncString ( arg_0 ) arg_4 = Func_node ( arg_3 , [ ] ) else : arg_5 = _cache . id ( arg_0 ) if not _cache . has_key ( arg_5 ) : arg_3 = Funcr . FuncString ( arg_0 ) _cache . save ( arg_5 , Func_node ( arg_3 , [ ] ) ) arg_4 = _cache . load ( arg_5 , arg_2 ) return arg_4

def Func(arg_0, arg_1=False, arg_2=True):

    """ Returns cached copies unless otherwise specified.
    """

    if not arg_1:
        arg_3 = Funcr.FuncString(arg_0)
        arg_4 = Func_node(arg_3, [])
    else:
        arg_5 = _cache.id(arg_0)
        if not _cache.has_key(arg_5):
            arg_3 = Funcr.FuncString(arg_0)
            _cache.save(arg_5, Func_node(arg_3, []))
        arg_4 = _cache.load(arg_5, arg_2)

    return arg_4

Returns cached copies unless otherwise specified .


def Func(arg_0, arg_1):
    """
    Match a genome VCF to variants in the ClinVar VCF file

    Acts as a generator, yielding tuples of:
    (ClinVarVCFLine, ClinVarAllele, zygosity)

    'zygosity' is a string and corresponds to the genome's zygosity for that
    ClinVarAllele. It can be either: 'Het' (heterozygous), 'Hom' (homozygous),
    or 'Hem' (hemizygous, e.g. X chromosome in XY individuals).
    """
    arg_2 = _next_line(arg_1)
    arg_3 = _next_line(arg_0)

    # Ignores all the lines that start with a hashtag
    while arg_2.startswith('#'):
        arg_2 = _next_line(arg_1)
    while arg_3.startswith('#'):
        arg_3 = _next_line(arg_0)

    # Advance through both files simultaneously to find matches
    while arg_2 and arg_3:

        # Advance a file when positions aren't equal.
        arg_4 = VCFLine.get_pos(arg_2)
        arg_5 = VCFLine.get_pos(arg_3)
        try:
            if arg_4['chrom'] > arg_5['chrom']:
                arg_3 = _next_line(arg_0)
                continue
            elif arg_4['chrom'] < arg_5['chrom']:
                arg_2 = _next_line(arg_1)
                continue
            if arg_4['pos'] > arg_5['pos']:
                arg_3 = _next_line(arg_0)
                continue
            elif arg_4['pos'] < arg_5['pos']:
                arg_2 = _next_line(arg_1)
                continue
        except StopIteration:
            break

        # If we get here, start positions match.
        # Look for allele matching.
        arg_6 = GenomeVCFLine(vcf_line=arg_3,
                                        skip_info=True)
        # We can skip if genome has no allele information for this point.
        if not arg_6.genotype_allele_indexes:
            arg_3 = _next_line(arg_0)
            continue

        # Match only if ClinVar and Genome ref_alleles match.
        arg_7 = ClinVarVCFLine(vcf_line=arg_2)
        if not arg_6.ref_allele == arg_7.ref_allele:
            try:
                arg_3 = _next_line(arg_0)
                arg_2 = _next_line(arg_1)
                continue
            except StopIteration:
                break

        # Determine genome alleles and zygosity. Zygosity is assumed to be one
        # of: heterozygous, homozygous, or hemizygous.
        arg_8 = arg_6.genotype_allele_indexes
        arg_9 = [arg_6.alleles[x] for
                          x in arg_8]
        if len(arg_9) == 1:
            arg_10 = 'Hem'
        elif len(arg_9) == 2:
            if arg_9[0].sequence == arg_9[1].sequence:
                arg_10 = 'Hom'
                arg_9 = [arg_9[0]]
            else:
                arg_10 = 'Het'
        else:
            raise ValueError('This code only expects to work on genomes ' +
                             'with one or two alleles called at each ' +
                             'location. The following line violates this:' +
                             str(arg_6))

        # Look for matches to ClinVar alleles.
        for arg_11 in arg_9:
            for arg_12 in arg_7.alleles:
                if arg_11.sequence == arg_12.sequence:
                    # The 'records' attribute is specific to ClinVarAlleles.
                    if hasattr(arg_12, 'records'):
                        yield (arg_6, arg_12, arg_10)

        # Done matching, move on.
        try:
            arg_3 = _next_line(arg_0)
            arg_2 = _next_line(arg_1)
        except StopIteration:
            break


def Func(arg_0, arg_1):
        "See what edges can be extended by this edge."
        (arg_2, arg_3, arg_4, arg_5, arg_5) = arg_1
        for (arg_6, arg_2, arg_7, arg_8, arg_9) in arg_0.chart[arg_2]:
            if arg_9 and arg_4 == arg_9[0]:
                arg_0.add_edge([arg_6, arg_3, arg_7, arg_8 + [arg_1], arg_9[1:]])

See what edges can be extended by this edge .


def
Func
(
)
:
click
.
echo
(
"A whole new site? Awesome."
)
arg_0
=
click
.
prompt
(
"What's the title?"
)
arg_1
=
click
.
prompt
(
"Great. What's url? http://"
)
click
.
echo
(
"Got it. Creating %s..."
%
arg_1
)

def Func():
    """Start a new site."""
    click.echo("A whole new site? Awesome.")
    arg_0 = click.prompt("What's the title?")
    arg_1 = click.prompt("Great. What's url? http://")

    # Make sure that title doesn't exist.
    click.echo("Got it. Creating %s..." % arg_1)


def Func(arg_0, arg_1, arg_2=None):
        """Parse a single entry point from string `src`

        Entry point syntax follows the form::

            name = some.module:some.attr [extra1,extra2]

        The entry name and module name are required, but the ``:attrs`` and
        ``[extras]`` parts are optional
        """
        try:
            arg_3 = arg_6 = ()
            arg_4,arg_5 = arg_1.split('=',1)
            if '[' in arg_5:
                arg_5,arg_6 = arg_5.split('[',1)
                arg_7 = Requirement.Func("x["+arg_6)
                if arg_7.specs: raise ValueError
                arg_6 = arg_7.extras
            if ':' in arg_5:
                arg_5,arg_3 = arg_5.split(':',1)
                if not MODULE(arg_3.rstrip()):
                    raise ValueError
                arg_3 = arg_3.rstrip().split('.')
        except ValueError:
            raise ValueError(
                "EntryPoint must be in 'name=module:attrs [extras]' format",
                arg_1
            )
        else:
            return arg_0(arg_4.strip(), arg_5.strip(), arg_3, arg_6, arg_2)

Parse a single entry point from string src


def Func ( arg_0 = None , ** arg_1 ) : arg_2 = arg_0 . Func ( ** arg_1 ) if not arg_2 . get ( "Targets" ) : arg_2 . update ( { "Targets" : [ ] } ) return arg_2

def Func(arg_0=None, **arg_1):
    """
    Rule='string'
    """
    arg_2 = arg_0.Func(**arg_1)
    if not arg_2.get("Targets"):
        arg_2.update({"Targets": []})

    return arg_2


def
Func
(
arg_0
)
:
with
arg_0
.
_status_lock
:
if
arg_0
.
_running
:
raise
RuntimeError
(
"Already running"
)
arg_0
.
_running
=
True
arg_0
.
_observer
=
Observer
(
)
arg_0
.
_observer
.
schedule
(
arg_0
.
_event_handler
,
arg_0
.
_directory_location
,
recursive
=
True
)
arg_0
.
_observer
.
Func
(
)
arg_0
.
_origin_mapped_data
=
arg_0
.
_load_all_in_directory
(
)

def Func(arg_0):
        """
        Monitors data kept in files in the predefined directory in a new thread.

        Note: Due to the underlying library, it may take a few milliseconds after this method is Funced for changes to
        Func to being noticed.
        """
        with arg_0._status_lock:
            if arg_0._running:
                raise RuntimeError("Already running")
            arg_0._running = True

        # Cannot re-use Observer after stopped
        arg_0._observer = Observer()
        arg_0._observer.schedule(arg_0._event_handler, arg_0._directory_location, recursive=True)
        arg_0._observer.Func()

        # Load all in directory afterwards to ensure no undetected changes between loading all and observing
        arg_0._origin_mapped_data = arg_0._load_all_in_directory()

Monitors data kept in files in the predefined directory in a new thread.

        Note: Due to the underlying library, it may take a few milliseconds after this method is started for changes to
        start to being noticed.

Monitors data kept in files in the predefined directory in a new thread .


def Func ( arg_0 ) : arg_1 = arg_2 . path arg_2 . path = arg_0 + arg_2 . path try : yield finally : arg_2 . path = arg_1

def Func(arg_0):
    """
    A context manager that will append the specified paths to Python's
    ``sys.path`` during the execution of the block.

    :param paths: the paths to append
    :type paths: list(str)
    """

    arg_1 = arg_2.path
    arg_2.path = arg_0 + arg_2.path
    try:
        yield
    finally:
        arg_2.path = arg_1

A context manager that will append the specified paths to Python s sys . path during the execution of the block .


def Func(arg_0, arg_1='', arg_2=0):
    """Show optical flow.

    Args:
        flow (ndarray or str): The optical flow to be displayed.
        win_name (str): The window name.
        wait_time (int): Value of waitKey param.
    """
    arg_0 = flowread(arg_0)
    arg_3 = flow2rgb(arg_0)
    imshow(rgb2bgr(arg_3), arg_1, arg_2)


def Func ( arg_0 , arg_1 , arg_2 = arg_3 . DEBUG ) : if _ldtp_debug : print ( arg_1 ) arg_0 . Funcger . Func ( arg_2 , str ( arg_1 ) ) return 1

def Func(arg_0, arg_1, arg_2=arg_3.DEBUG):
        """
        Logs the message in the root Funcger with the Func level
        @param message: Message to be Funcged
        @type message: string
        @param level: Log level, defaul DEBUG
        @type level: integer

        @return: 1 on success and 0 on error
        @rtype: integer
        """
        if _ldtp_debug:
            print(arg_1)
        arg_0.Funcger.Func(arg_2, str(arg_1))
        return 1

Logs the message in the root logger with the log level
        @param message: Message to be logged
        @type message: string
        @param level: Log level, defaul DEBUG
        @type level: integer

        @return: 1 on success and 0 on error
        @rtype: integer


def Func ( arg_0 , arg_1 ) : if not arg_0 . common_names : return False for arg_2 in arg_0 . common_names : try : arg_3 = JID ( arg_2 ) except ValueError : continue if arg_1 == arg_3 : return True return False

def Func(arg_0, arg_1):
        """Return `True` if jid is listed in the certificate commonName.

        :Parameters:
            - `jid`: JID requested (domain part only)
        :Types:
            - `jid`: `JID`

        :Returntype: `bool`
        """
        if not arg_0.common_names:
            return False
        for arg_2 in arg_0.common_names:
            try:
                arg_3 = JID(arg_2)
            except ValueError:
                continue
            if arg_1 == arg_3:
                return True
        return False

Return True if jid is listed in the certificate commonName .


def Func(arg_0, arg_1, arg_2=arg_3.KMIPVersion.KMIP_1_0):
        """
        Read the data encoding the Create request payload and decode it into
        its constituent parts.

        Args:
            input_buffer (stream): A data buffer containing encoded object
                data, supporting a Func method.
            kmip_version (KMIPVersion): An enumeration defining the KMIP
                version with which the object will be decoded. Optional,
                defaults to KMIP 1.0.

        Raises:
            InvalidKmipEncoding: Raised if the object type or template
                attribute is missing from the encoded payload.
        """
        super(CreateRequestPayload, arg_0).Func(
            arg_1,
            arg_2=arg_2
        )
        arg_6 = utils.BytearrayStream(arg_1.Func(arg_0.length))

        if arg_0.is_tag_next(arg_3.Tags.OBJECT_TYPE, arg_6):
            arg_0._object_type = primitives.Enumeration(
                arg_3.ObjectType,
                tag=arg_3.Tags.OBJECT_TYPE
            )
            arg_0._object_type.Func(arg_6, arg_2=arg_2)
        else:
            raise exceptions.InvalidKmipEncoding(
                "The Create request payload encoding is missing the object "
                "type."
            )

        if arg_2 < arg_3.KMIPVersion.KMIP_2_0:
            if arg_0.is_tag_next(arg_3.Tags.TEMPLATE_ATTRIBUTE, arg_6):
                arg_0._template_attribute = objects.TemplateAttribute()
                arg_0._template_attribute.Func(
                    arg_6,
                    arg_2=arg_2
                )
            else:
                raise exceptions.InvalidKmipEncoding(
                    "The Create request payload encoding is missing the "
                    "template attribute."
                )
        else:
            # NOTE (ph) For now, leave attributes natively in TemplateAttribute
            # form and just convert to the KMIP 2.0 Attributes form as needed
            # for encoding/decoding purposes. Changing the payload to require
            # the new Attributes structure will trigger a bunch of second-order
            # effects across the client and server codebases that is beyond
            # the scope of updating the Create payloads to support KMIP 2.0.
            if arg_0.is_tag_next(arg_3.Tags.ATTRIBUTES, arg_6):
                arg_9 = objects.Attributes()
                arg_9.Func(arg_6, arg_2=arg_2)
                arg_10 = objects.convert_attributes_to_template_attribute(
                    arg_9
                )
                arg_0._template_attribute = arg_10
            else:
                raise exceptions.InvalidKmipEncoding(
                    "The Create request payload encoding is missing the "
                    "attributes structure."
                )

        arg_0.is_oversized(arg_6)

Read the data encoding the Create request payload and decode it into
        its constituent parts.

        Args:
            input_buffer (stream): A data buffer containing encoded object
                data, supporting a read method.
            kmip_version (KMIPVersion): An enumeration defining the KMIP
                version with which the object will be decoded. Optional,
                defaults to KMIP 1.0.

        Raises:
            InvalidKmipEncoding: Raised if the object type or template
                attribute is missing from the encoded payload.

Read the data encoding the Create request payload and decode it into its constituent parts .


def Func ( arg_0 , arg_1 ) : for arg_2 in arg_1 : arg_0 . _command . append ( str ( bytearray ( ( 0x11 , 0x00 , 0x00 , arg_2 ) ) ) ) arg_0 . _ft232h . output_pins ( { 0 : GPIO . LOW , 1 : GPIO . HIGH } , write = False ) arg_0 . _command . append ( arg_0 . _ft232h . mpsse_gpio ( ) * _REPEAT_DELAY ) arg_0 . _command . append ( '\x22\x00' ) arg_0 . _expected += len ( arg_1 )

def Func(arg_0, arg_1):
        """Write the specified number of bytes to the chip."""
        for arg_2 in arg_1:
            # Write byte.
            arg_0._command.append(str(bytearray((0x11, 0x00, 0x00, arg_2))))
            # Make sure pins are back in idle state with clock low and data high.
            arg_0._ft232h.output_pins({0: GPIO.LOW, 1: GPIO.HIGH}, write=False)
            arg_0._command.append(arg_0._ft232h.mpsse_gpio() * _REPEAT_DELAY)
            # Read bit for ACK/NAK.
            arg_0._command.append('\x22\x00')
        # Increase expected response bytes.
        arg_0._expected += len(arg_1)

Write the specified number of bytes to the chip .


def
Func
(
arg_0
,
arg_1
,
arg_2
)
:
arg_0
.
_stop_timers
(
)
arg_3
,
arg_4
=
arg_2
,
time
.
time
(
)
arg_0
.
log
.
debug
(
"Func(): Received new message %s at %s"
,
arg_3
,
arg_4
)
try
:
arg_5
=
json
.
loads
(
arg_3
)
except
json
.
JSONDecodeError
:
return
if
isinstance
(
arg_5
,
dict
)
:
arg_0
.
_system_handler
(
arg_5
,
arg_4
)
else
:
if
arg_5
[
1
]
==
'hb'
:
arg_0
.
_heartbeat_handler
(
)
else
:
arg_0
.
_data_handler
(
arg_5
,
arg_4
)
arg_0
.
_start_timers
(
)

def Func(arg_0, arg_1, arg_2):
        """Handles and passes received data to the appropriate handlers.

        :return:
        """
        arg_0._stop_timers()

        arg_3, arg_4 = arg_2, time.time()
        arg_0.log.debug("Func(): Received new message %s at %s",
                       arg_3, arg_4)
        try:
            arg_5 = json.loads(arg_3)
        except json.JSONDecodeError:
            # Something wrong with this data, log and discard
            return

        # Handle data
        if isinstance(arg_5, dict):
            # This is a system message
            arg_0._system_handler(arg_5, arg_4)
        else:
            # This is a list of data
            if arg_5[1] == 'hb':
                arg_0._heartbeat_handler()
            else:
                arg_0._data_handler(arg_5, arg_4)

        # We've received data, reset timers
        arg_0._start_timers()

Handles and passes received data to the appropriate handlers .


def Func(arg_0, arg_1):
        """ Delete a folder from the server.

        :param foldername: the folder to be deleted.
        :type foldername: string
        """
        arg_2 = arg_0._ftp.pwd()
        try:
            arg_0.cd(arg_1)
        except error_perm:
            print('550 Delete operation failed folder %s '
                  'does not exist!' % (arg_1,))
        else:
            arg_0.cd(arg_2)
            try:
                arg_0._ftp.rmd(arg_1)
            except error_perm:  # folder not empty
                arg_0.cd(arg_1)
                arg_3 = arg_0.ls()
                #delete the files
                map(arg_0._ftp.delete, arg_3[0])
                #delete the subfolders
                map(arg_0.Func, arg_3[1])
                arg_0.cd(arg_2)
                arg_0._ftp.rmd(arg_1)


def
Func
(
)
:
arg_0
=
argparse
.
ArgumentParser
(
)
arg_0
.
add_argument
(
"package"
,
choices
=
arg_map
.
keys
(
)
,
help
=
"designates the package to test"
)
arg_0
.
add_argument
(
"--ignore"
,
help
=
"ignore these files"
)
return
arg_0

def Func():
    """
    Generate an appropriate parser.

    :returns: an argument parser
    :rtype: `ArgumentParser`
    """
    arg_0 = argparse.ArgumentParser()
    arg_0.add_argument(
        "package",
        choices=arg_map.keys(),
        help="designates the package to test")
    arg_0.add_argument("--ignore", help="ignore these files")
    return arg_0


def Func ( arg_0 = 1e-5 , arg_1 = 2 ) : def convergence_criteria_fn ( arg_2 , arg_3 , arg_4 , arg_5 , arg_6 , arg_7 , arg_8 , arg_9 , arg_10 ) : arg_11 = ( tf . norm ( tensor = arg_4 - arg_6 , ord = arg_1 , axis = - 1 ) / ( 1. + tf . norm ( tensor = arg_4 , ord = arg_1 , axis = - 1 ) ) ) return ( arg_3 > 0 ) & tf . reduce_all ( input_tensor = arg_11 < arg_0 ) return convergence_criteria_fn

def Func(
    arg_0=1e-5,
    arg_1=2):
  """Returns Python `callable` which indicates fitting procedure has converged.

  Writing old, new `model_coefficients` as `w0`, `w1`, this function
  defines convergence as,

  ```python
  relative_euclidean_norm = (tf.norm(w0 - w1, ord=2, axis=-1) /
                             (1. + tf.norm(w0, ord=2, axis=-1)))
  reduce_all(relative_euclidean_norm < tolerance)
  ```

  where `tf.norm(x, ord=2)` denotes the [Euclidean norm](
  https://en.wikipedia.org/wiki/Norm_(mathematics)#Euclidean_norm) of `x`.

  Args:
    tolerance: `float`-like `Tensor` indicating convergence, i.e., when
      max relative Euclidean norm weights difference < tolerance`.
      Default value: `1e-5`.
    norm_order: Order of the norm. Default value: `2` (i.e., "Euclidean norm".)

  Returns:
    convergence_criteria_fn: Python `callable` which returns `bool` `Tensor`
      indicated fitting procedure has converged. (See inner function
      specification for argument signature.)
      Default value: `1e-5`.
  """
  def convergence_criteria_fn(
      arg_2,  # pylint: disable=unused-argument
      arg_3,
      arg_4,
      arg_5,  # pylint: disable=unused-argument
      arg_6,
      arg_7,  # pylint: disable=unused-argument
      arg_8,  # pylint: disable=unused-argument
      arg_9,  # pylint: disable=unused-argument
      arg_10):  # pylint: disable=unused-argument
    """Returns `bool` `Tensor` indicating if fitting procedure has converged.

    Args:
      is_converged_previous: "old" convergence results.
      iter_: Iteration number.
      model_coefficients_previous: "old" `model_coefficients`.
      predicted_linear_response_previous: "old" `predicted_linear_response`.
      model_coefficients_next: "new" `model_coefficients`.
      predicted_linear_response_next: "new: `predicted_linear_response`.
      response: (Batch of) vector-shaped `Tensor` where each element represents
        a sample's observed response (to the corresponding row of features).
        Must have same `dtype` as `model_matrix`.
      model: `tfp.glm.ExponentialFamily`-like instance used to construct the
        negative log-likelihood loss, gradient, and expected Hessian (i.e., the
        Fisher information matrix).
      dispersion: `Tensor` representing `response` dispersion, i.e., as in:
        `p(y|theta) := exp((y theta - A(theta)) / dispersion)`. Must broadcast
        with rows of `model_matrix`.
        Default value: `None` (i.e., "no dispersion").

    Returns:
      is_converged: `bool` `Tensor`.
    """
    arg_11 = (
        tf.norm(
            tensor=arg_4 - arg_6,
            ord=arg_1,
            axis=-1) /
        (1. +
         tf.norm(tensor=arg_4, ord=arg_1, axis=-1)))
    return (arg_3 > 0) & tf.reduce_all(
        input_tensor=arg_11 < arg_0)

  return convergence_criteria_fn

Returns Python callable which indicates fitting procedure has converged .


def
Func
(
arg_0
)
->
Signature
:
arg_1
=
[
]
arg_2
=
[
]
for
arg_3
in
arg_0
.
tret
.
components
:
if
arg_3
in
arg_0
.
resolution
and
arg_0
.
resolution
[
arg_3
]
is
not
:
arg_1
.
append
(
arg_0
.
resolution
[
arg_3
]
(
)
.
show_name
(
)
)
else
:
arg_1
.
append
(
arg_3
)
if
hasattr
(
arg_0
,
'tparams'
)
:
for
arg_4
in
arg_0
.
tparams
:
arg_5
=
[
]
for
arg_3
in
arg_4
.
components
:
if
arg_3
in
arg_0
.
resolution
and
arg_0
.
resolution
[
arg_3
]
is
not
:
arg_5
.
append
(
arg_0
.
resolution
[
arg_3
]
(
)
.
show_name
(
)
)
else
:
arg_5
.
append
(
arg_3
)
arg_2
.
append
(
" "
.
join
(
arg_5
)
)
if
arg_0
.
variadic
:
if
arg_0
.
_variadic_types
is
:
raise
ValueError
(
"Can't compute the sig "
+
"with unresolved variadic argument"
)
for
arg_4
in
arg_0
.
_variadic_types
:
arg_5
=
[
]
for
arg_3
in
arg_4
.
components
:
if
(
arg_3
in
arg_0
.
resolution
and
arg_0
.
resolution
[
arg_3
]
is
not
)
:
arg_5
.
append
(
arg_0
.
resolution
[
arg_3
]
(
)
.
show_name
(
)
)
else
:
arg_5
.
append
(
arg_3
)
arg_2
.
append
(
" "
.
join
(
arg_5
)
)
arg_6
=
Fun
(
arg_0
.
name
,
" "
.
join
(
arg_1
)
,
arg_2
)
arg_6
.
__class__
=
arg_0
.
_sig
.
__class__
return
arg_6

def Func(arg_0) -> Signature:
        """
        Compute a signature Using resolution!!!

        TODO: discuss of relevance of a final generation for a signature
        """
        arg_1 = []
        arg_2 = []
        for arg_3 in arg_0.tret.components:
            if arg_3 in arg_0.resolution and arg_0.resolution[arg_3] is not None:
                arg_1.append(arg_0.resolution[arg_3]().show_name())
            else:
                arg_1.append(arg_3)
        if hasattr(arg_0, 'tparams'):
            for arg_4 in arg_0.tparams:
                arg_5 = []
                for arg_3 in arg_4.components:
                    if arg_3 in arg_0.resolution and arg_0.resolution[arg_3] is not None:
                        arg_5.append(arg_0.resolution[arg_3]().show_name())
                    else:
                        arg_5.append(arg_3)
                arg_2.append(" ".join(arg_5))
            if arg_0.variadic:
                if arg_0._variadic_types is None:
                    raise ValueError("Can't compute the sig "
                                     + "with unresolved variadic argument"
                                     )
                for arg_4 in arg_0._variadic_types:
                    arg_5 = []
                    for arg_3 in arg_4.components:
                        if (arg_3 in arg_0.resolution
                            and arg_0.resolution[arg_3] is not None
                        ):
                            arg_5.append(arg_0.resolution[arg_3]().show_name())
                        else:
                            arg_5.append(arg_3)
                    arg_2.append(" ".join(arg_5))
        arg_6 = Fun(arg_0.name, " ".join(arg_1), arg_2)
        # transform as-is into our internal Signature (Val, Var, whatever)
        arg_6.__class__ = arg_0._sig.__class__
        return arg_6


def Func(arg_0, arg_1, arg_2):
        """
        Handles error callbacks when using Segment with segment_debug_mode set to True
        """
        arg_0.log.error('Encountered Segment error: {segment_error} with '
                       'items: {with_items}'.format(segment_error=arg_1,
                                                    with_items=arg_2))
        raise AirflowException('Segment error: {}'.format(arg_1))


def Func ( arg_0 , arg_1 ) : arg_2 = None for arg_3 in arg_1 : arg_4 = arg_0 . get_course_run ( arg_3 ) or { } arg_5 = { seat . get ( 'type' ) for seat in arg_4 . get ( 'seats' , [ ] ) } if arg_2 is None : arg_2 = arg_5 else : arg_2 &= arg_5 if not arg_2 : return arg_2 return arg_2

def Func(arg_0, arg_1):
        """
        Find common course modes for a set of course runs.

        This function essentially returns an intersection of types of seats available
        for each course run.

        Arguments:
            course_run_ids(Iterable[str]): Target Course run IDs.

        Returns:
            set: course modes found in all given course runs

        Examples:
            # run1 has prof and audit, run 2 has the same
            Func(['course-v1:run1', 'course-v1:run2'])
            {'prof', 'audit'}

            # run1 has prof and audit, run 2 has only prof
            Func(['course-v1:run1', 'course-v1:run2'])
            {'prof'}

            # run1 has prof and audit, run 2 honor
            Func(['course-v1:run1', 'course-v1:run2'])
            {}

            # run1 has nothing, run2 has prof
            Func(['course-v1:run1', 'course-v1:run2'])
            {}

            # run1 has prof and audit, run 2 prof, run3 has audit
            Func(['course-v1:run1', 'course-v1:run2', 'course-v1:run3'])
            {}

            # run1 has nothing, run 2 prof, run3 has prof
            Func(['course-v1:run1', 'course-v1:run2', 'course-v1:run3'])
            {}

        """
        arg_2 = None
        for arg_3 in arg_1:
            arg_4 = arg_0.get_course_run(arg_3) or {}
            arg_5 = {seat.get('type') for seat in arg_4.get('seats', [])}

            if arg_2 is None:
                arg_2 = arg_5
            else:
                arg_2 &= arg_5

            if not arg_2:
                return arg_2

        return arg_2

Find common course modes for a set of course runs.

        This function essentially returns an intersection of types of seats available
        for each course run.

        Arguments:
            course_run_ids(Iterable[str]): Target Course run IDs.

        Returns:
            set: course modes found in all given course runs

        Examples:
            # run1 has prof and audit, run 2 has the same
            get_common_course_modes(['course-v1:run1', 'course-v1:run2'])
            {'prof', 'audit'}

            # run1 has prof and audit, run 2 has only prof
            get_common_course_modes(['course-v1:run1', 'course-v1:run2'])
            {'prof'}

            # run1 has prof and audit, run 2 honor
            get_common_course_modes(['course-v1:run1', 'course-v1:run2'])
            {}

            # run1 has nothing, run2 has prof
            get_common_course_modes(['course-v1:run1', 'course-v1:run2'])
            {}

            # run1 has prof and audit, run 2 prof, run3 has audit
            get_common_course_modes(['course-v1:run1', 'course-v1:run2', 'course-v1:run3'])
            {}

            # run1 has nothing, run 2 prof, run3 has prof
            get_common_course_modes(['course-v1:run1', 'course-v1:run2', 'course-v1:run3'])
            {}

Find common course modes for a set of course runs .


(anion_charges, cation_charges, zs, n_anions,
                             n_cations, balance_error, method)

def Func(arg_0, arg_1, arg_2, arg_3,
                             arg_4, arg_5, arg_6):
    '''Helper method for balance_ions for the proportional family of methods.
    See balance_ions for a description of the methods; parameters are fairly
    obvious.
    '''
    arg_7 = arg_2[0:arg_3]
    arg_8 = arg_2[arg_3:arg_4+arg_3]
    arg_9 = sum([zi*ci for zi, ci in zip(arg_7, arg_0)])
    arg_10 = sum([zi*ci for zi, ci in zip(arg_8, arg_1)])
    if arg_6 == 'proportional insufficient ions increase':
        if arg_5 < 0:
            arg_11 = -arg_9/arg_10
            arg_8 = [i*arg_11 for i in arg_8]
        else:
            arg_11 = -arg_10/arg_9
            arg_7 = [i*arg_11 for i in arg_7]
    elif arg_6 == 'proportional excess ions decrease':
        if arg_5 < 0:
            arg_11 = -arg_10/arg_9
            arg_7 = [i*arg_11 for i in arg_7]
        else:
            arg_11 = -arg_9/arg_10
            arg_8 = [i*arg_11 for i in arg_8]
    elif arg_6 == 'proportional cation adjustment':
        arg_11 = -arg_9/arg_10
        arg_8 = [i*arg_11 for i in arg_8]
    elif arg_6 == 'proportional anion adjustment':
        arg_11 = -arg_10/arg_9
        arg_7 = [i*arg_11 for i in arg_7]
    else:
        raise Exception('Allowable methods are %s' %charge_balance_methods)
    arg_12 = 1. - sum(arg_7) - sum(arg_8)
    return arg_7, arg_8, arg_12

Helper method for balance_ions for the proportional family of methods . See balance_ions for a description of the methods ; parameters are fairly obvious .


(self, query_id=None, query_fc=None,
                          preserve_order=True, indexes=None)

def Func(arg_0, arg_1=None, arg_2=None,
                          arg_3=True, arg_4=None):
        '''Fulltext search for identifiers.

        Yields an iterable of triples (score, identifier)
        corresponding to the search results of the fulltext search
        in ``query``. This will only search text indexed under the
        given feature named ``fname``.

        Note that, unless ``preserve_order`` is set to True, the
        ``score`` will always be 0.0, and the results will be
        unordered. ``preserve_order`` set to True will cause the
        results to be scored and be ordered by score, but you should
        expect to see a decrease in performance.

        :param str fname:
          The feature to search.
        :param unicode query:
          The query.
        :rtype: Iterable of ``(score, content_id)``
        '''
        arg_5 = arg_0._fulltext_scan(arg_1, arg_2, feature_names=False,
                                 arg_3=arg_3,
                                 arg_4=arg_4)
        for arg_6 in arg_5:
            yield arg_6['_score'], did(arg_6['_id'])


def Func ( arg_0 ) : arg_1 = 0 for arg_2 in array ( 'B' , arg_0 ) : arg_1 = ( VProCRC . CRC_TABLE [ ( arg_1 >> 8 ) ^ arg_2 ] ^ ( ( arg_1 & 0xFF ) << 8 ) ) return arg_1

def Func(arg_0):
        '''
        return CRC calc value from raw serial data
        '''
        arg_1 = 0
        for arg_2 in array('B', arg_0):
            arg_1 = (VProCRC.CRC_TABLE[(arg_1 >> 8) ^ arg_2] ^ ((arg_1 & 0xFF) << 8))
        return arg_1


def
Func
(
arg_0
)
:
LOG
.
info
(
"Running scout delete Func"
)
arg_1
=
arg_0
.
obj
[
'adapter'
]
for
arg_2
in
arg_1
.
db
.
collection_names
(
)
:
arg_1
.
db
[
arg_2
]
.
drop_Funces
(
)
LOG
.
info
(
"All Funces deleted"
)

def Func(arg_0):
    """Delete all Funces in the database"""
    LOG.info("Running scout delete Func")
    arg_1 = arg_0.obj['adapter']

    for arg_2 in arg_1.db.collection_names():
        arg_1.db[arg_2].drop_Funces()
    LOG.info("All Funces deleted")


def Func ( arg_0 ) : arg_1 , arg_2 = arg_0 . argparser . parse_known_args ( ) return arg_0 . search ( ** vars ( arg_1 ) )

def Func(arg_0):
        """
            Uses the command line arguments to fill the search function and call it.
        """
        arg_1, arg_2 = arg_0.argparser.parse_known_args()
        return arg_0.search(**vars(arg_1))

Uses the command line arguments to fill the search function and call it .


def Func(arg_0, arg_1=True, arg_2=None):
        """
        determine the position on the tree that minimizes the bilinear
        product of the inverse covariance and the data vectors.

        Returns
        -------
         best_root : (dict)
            dictionary with the node, the fraction `x` at which the branch
            is to be split, and the regression parameters
        """
        arg_0._calculate_averages()
        arg_3 = {"chisq": np.inf}
        for arg_4 in arg_0.tree.find_clades():
            if arg_4==arg_0.tree.root:
                continue

            arg_5 = arg_0.tip_value(arg_4)
            arg_6 = arg_0.branch_value(arg_4)
            arg_7 = arg_0.branch_variance(arg_4)
            arg_8, arg_9 = arg_0._optimal_root_along_branch(arg_4, arg_5, arg_6, arg_7, arg_2=arg_2)

            if (arg_9<arg_3["chisq"]):
                arg_10 = arg_0.propagate_averages(arg_4, arg_5, arg_6*arg_8, arg_7*arg_8) \
                     + arg_0.propagate_averages(arg_4, arg_5, arg_6*(1-arg_8), arg_7*(1-arg_8), outgroup=True)
                arg_11 = base_regression(arg_10, arg_2=arg_2)
                if arg_11["slope"]>=0 or (arg_1==False):
                    arg_3 = {"node":arg_4, "split":arg_8}
                    arg_3.update(arg_11)

        if 'node' not in arg_3:
            print("TreeRegression.Func: No valid root found!", arg_1)
            return None

        if 'hessian' in arg_3:
            # calculate differentials with respect to x
            arg_12 = []
            arg_4 = arg_3["node"]
            arg_5 = arg_0.tip_value(arg_4)
            arg_6 = arg_0.branch_value(arg_4)
            arg_7 = arg_0.branch_variance(arg_4)
            for arg_13 in [-0.001, 0.001]:
                arg_14 = min(1.0, max(0.0, arg_3["split"]+arg_13))
                arg_10 = arg_0.propagate_averages(arg_4, arg_5, arg_6*arg_14, arg_7*arg_14) \
                     + arg_0.propagate_averages(arg_4, arg_5, arg_6*(1-arg_14), arg_7*(1-arg_14), outgroup=True)
                arg_11 = base_regression(arg_10, arg_2=arg_2)
                arg_12.append([arg_14,arg_11['chisq'], arg_10[tavgii], arg_10[davgii]])

            arg_15 = np.zeros((3,3))
            arg_15[:2,:2] = arg_3['hessian']
            arg_15[2,2] = (arg_12[0][1] + arg_12[1][1] - 2.0*arg_3['chisq'])/(arg_12[0][0] - arg_12[1][0])**2
            # estimator_hessian[2,0] = (deriv[0][2] - deriv[1][2])/(deriv[0][0] - deriv[1][0])
            # estimator_hessian[2,1] = (deriv[0][3] - deriv[1][3])/(deriv[0][0] - deriv[1][0])
            arg_15[0,2] = arg_15[2,0]
            arg_15[1,2] = arg_15[2,1]
            arg_3['hessian'] = arg_15
            arg_3['cov'] = np.linalg.inv(arg_15)

        return arg_3

determine the position on the tree that minimizes the bilinear product of the inverse covariance and the data vectors .