def Func ( arg_0 , arg_1 , arg_2 ) : arg_3 = arg_1 in _CPP_HEADERS if arg_2 and os . path . splitext ( arg_1 ) [ 1 ] in [ '.hpp' , '.hxx' , '.h++' ] : arg_2 = False if arg_2 : if arg_3 : return _CPP_SYS_HEADER else : return _C_SYS_HEADER arg_4 , arg_5 = ( os . path . split ( _DropCommonSuffixes ( arg_0 . RepositoryName ( ) ) ) ) arg_6 , arg_7 = os . path . split ( _DropCommonSuffixes ( arg_1 ) ) arg_8 = os . path . normpath ( arg_4 + '/../public' ) arg_8 = arg_8 . replace ( '\\' , '/' ) if arg_5 == arg_7 and ( arg_6 == arg_4 or arg_6 == arg_8 ) : return _LIKELY_MY_HEADER arg_9 = _RE_FIRST_COMPONENT . match ( arg_5 ) arg_10 = _RE_FIRST_COMPONENT . match ( arg_7 ) if ( arg_9 and arg_10 and arg_9 . group ( 0 ) == arg_10 . group ( 0 ) ) : return _POSSIBLE_MY_HEADER return _OTHER_HEADER

def Func(arg_0, arg_1, arg_2):
  """Figures out what kind of header 'include' is.

  Args:
    fileinfo: The current file cpplint is running over. A FileInfo instance.
    include: The path to a #included file.
    is_system: True if the #include used <> rather than "".

  Returns:
    One of the _XXX_HEADER constants.

  For example:
    >>> Func(FileInfo('foo/foo.cc'), 'stdio.h', True)
    _C_SYS_HEADER
    >>> Func(FileInfo('foo/foo.cc'), 'string', True)
    _CPP_SYS_HEADER
    >>> Func(FileInfo('foo/foo.cc'), 'foo/foo.h', False)
    _LIKELY_MY_HEADER
    >>> Func(FileInfo('foo/foo_unknown_extension.cc'),
    ...                  'bar/foo_other_ext.h', False)
    _POSSIBLE_MY_HEADER
    >>> Func(FileInfo('foo/foo.cc'), 'foo/bar.h', False)
    _OTHER_HEADER
  """
  # This is a list of all standard c++ header files, except
  # those already checked for above.
  arg_3 = arg_1 in _CPP_HEADERS

  # Headers with C++ extensions shouldn't be considered C system headers
  if arg_2 and os.path.splitext(arg_1)[1] in ['.hpp', '.hxx', '.h++']:
      arg_2 = False

  if arg_2:
    if arg_3:
      return _CPP_SYS_HEADER
    else:
      return _C_SYS_HEADER

  # If the target file and the include we're checking share a
  # basename when we drop common extensions, and the include
  # lives in . , then it's likely to be owned by the target file.
  arg_4, arg_5 = (
      os.path.split(_DropCommonSuffixes(arg_0.RepositoryName())))
  arg_6, arg_7 = os.path.split(_DropCommonSuffixes(arg_1))
  arg_8 = os.path.normpath(arg_4 + '/../public')
  arg_8 = arg_8.replace('\\', '/')
  if arg_5 == arg_7 and (
      arg_6 == arg_4 or
      arg_6 == arg_8):
    return _LIKELY_MY_HEADER

  # If the target and include share some initial basename
  # component, it's possible the target is implementing the
  # include, so it's allowed to be first, but we'll never
  # complain if it's not there.
  arg_9 = _RE_FIRST_COMPONENT.match(arg_5)
  arg_10 = _RE_FIRST_COMPONENT.match(arg_7)
  if (arg_9 and arg_10 and
      arg_9.group(0) ==
      arg_10.group(0)):
    return _POSSIBLE_MY_HEADER

  return _OTHER_HEADER

Figures out what kind of header 'include' is.

  Args:
    fileinfo: The current file cpplint is running over. A FileInfo instance.
    include: The path to a #included file.
    is_system: True if the #include used <> rather than "".

  Returns:
    One of the _XXX_HEADER constants.

  For example:
    >>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'stdio.h', True)
    _C_SYS_HEADER
    >>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'string', True)
    _CPP_SYS_HEADER
    >>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'foo/foo.h', False)
    _LIKELY_MY_HEADER
    >>> _ClassifyInclude(FileInfo('foo/foo_unknown_extension.cc'),
    ...                  'bar/foo_other_ext.h', False)
    _POSSIBLE_MY_HEADER
    >>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'foo/bar.h', False)
    _OTHER_HEADER

Figures out what kind of header include is .


return PrecalculatedTextMeasurer(o['mean-character-length'],
                                         o['character-lengths'],
                                         o['kerning-pairs'])

def Func ( arg_0 : arg_1 ) -> 'PrecalculatedTextMeasurer' : arg_2 = json . load ( arg_0 ) return PrecalculatedTextMeasurer ( arg_2 [ 'mean-character-length' ] , arg_2 [ 'character-lengths' ] , arg_2 [ 'kerning-pairs' ] )

def Func(arg_0: arg_1) -> 'PrecalculatedTextMeasurer':
        """Return a PrecalculatedTextMeasurer given a JSON stream.

        See precalculate_text.py for details on the required format.
        """
        arg_2 = json.load(arg_0)
        return PrecalculatedTextMeasurer(arg_2['mean-character-length'],
                                         arg_2['character-lengths'],
                                         arg_2['kerning-pairs'])

Return a PrecalculatedTextMeasurer given a JSON stream .


def Func(arg_0, arg_1, arg_2):
        """Adds a new value for the given key."""
        arg_3 = HTTPHeaders._normalize_name(arg_1)
        arg_0._last_key = arg_3
        if arg_3 in arg_0:
            # bypass our override of __setitem__ since it modifies _as_list
            dict.__setitem__(arg_0, arg_3, arg_0[arg_3] + ',' + arg_2)
            arg_0._as_list[arg_3].append(arg_2)
        else:
            arg_0[arg_3] = arg_2


(self, datacenter_id, server_id, **kwargs)

def Func ( arg_0 , arg_1 , arg_2 , ** arg_3 ) : arg_4 = { } for arg_5 , arg_6 in arg_3 . items ( ) : if arg_5 == 'boot_volume' : arg_7 = { "id" : arg_6 } arg_8 = { "bootVolume" : arg_7 } arg_4 . update ( arg_8 ) else : arg_4 [ arg_0 . _underscore_to_camelcase ( arg_5 ) ] = arg_6 arg_10 = arg_0 . _perform_request ( url = '/datacenters/%s/servers/%s' % ( arg_1 , arg_2 ) , method = 'PATCH' , arg_4 = json . dumps ( arg_4 ) ) return arg_10

def Func(arg_0, arg_1, arg_2, **arg_3):
        """
        Updates a server with the parameters provided.

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

        :param      server_id: The unique ID of the server.
        :type       server_id: ``str``

        """
        arg_4 = {}

        for arg_5, arg_6 in arg_3.items():
            if arg_5 == 'boot_volume':
                arg_7 = {
                    "id": arg_6
                }
                arg_8 = {
                    "bootVolume": arg_7
                }
                arg_4.update(arg_8)
            else:
                arg_4[arg_0._underscore_to_camelcase(arg_5)] = arg_6

        arg_10 = arg_0._perform_request(
            url='/datacenters/%s/servers/%s' % (
                arg_1,
                arg_2),
            method='PATCH',
            arg_4=json.dumps(arg_4))

        return arg_10

Updates a server with the parameters provided .


(view=None, *, admin_site=None, admin_class=ModelAdminView)

def
Func
(
arg_0
=
,
*
,
arg_1
=
,
arg_2
=
arg_3
)
:
if
not
arg_1
:
arg_1
=
site
def
wrapped
(
arg_4
)
:
arg_5
=
arg_4
.
__module__
arg_6
=
re
.
search
(
r"\.?(\w+)\.admin"
,
arg_5
)
.
group
(
1
)
arg_7
=
apps
.
get_app_config
(
arg_6
)
arg_8
=
getattr
(
arg_4
,
"label"
,
)
if
not
arg_8
:
arg_8
=
re
.
sub
(
"(Admin)|(View)"
,
""
,
arg_4
.
__name__
)
.
lower
(
)
arg_4
.
label
=
arg_8
arg_9
=
arg_8
.
capitalize
(
)
arg_10
=
getattr
(
arg_4
,
"verbose_name"
,
arg_9
)
arg_4
.
verbose_name
=
arg_10
arg_11
=
"can_access_"
+
arg_9
.
lower
(
)
arg_12
=
_
(
"Can access {verbose_name}"
)
.
format
(
arg_10
=
arg_10
)
arg_13
=
tuple
(
[
(
arg_11
,
arg_12
)
]
+
list
(
getattr
(
arg_4
,
"permissions"
,
[
]
)
)
)
arg_14
=
type
(
arg_9
,
(
Model
,
)
,
{
"__module__"
:
arg_5
+
".__models__"
,
"View"
:
arg_4
,
"app_config"
:
arg_7
,
"Meta"
:
type
(
"Meta"
,
(
object
,
)
,
dict
(
managed
=
False
,
abstract
=
True
,
arg_6
=
arg_7
.
label
,
arg_10
=
arg_10
,
verbose_name_plural
=
arg_10
,
arg_13
=
arg_13
,
)
,
)
,
}
,
)
arg_1
.
_registry
[
arg_14
]
=
arg_2
(
arg_14
,
arg_1
)
return
arg_4
if
arg_0
is
:
return
wrapped
return
wrapped
(
arg_0
)

def Func(arg_0=None, *, arg_1=None, arg_2=arg_3):
    """
    Register a generic class based view wrapped with ModelAdmin and fake model

    :param view: The AdminView to Func.
    :param admin_site: The AdminSite to Func the view on.
        Defaults to bananas.admin.ExtendedAdminSite.
    :param admin_class: The ModelAdmin class to use for eg. permissions.
        Defaults to bananas.admin.ModelAdminView.

    Example:

    @Func  # Or with args @Func(admin_class=MyModelAdminSubclass)
    class MyAdminView(bananas.admin.AdminView):
        def get(self, request):
            return self.render('template.html', {})

    # Also possible:
    Func(MyAdminView, admin_class=MyModelAdminSublass)

    """
    if not arg_1:
        arg_1 = site

    def wrapped(arg_4):
        arg_5 = arg_4.__module__
        arg_6 = re.search(r"\.?(\w+)\.admin", arg_5).group(1)
        arg_7 = apps.get_app_config(arg_6)

        arg_8 = getattr(arg_4, "label", None)
        if not arg_8:
            arg_8 = re.sub("(Admin)|(View)", "", arg_4.__name__).lower()
        arg_4.label = arg_8

        arg_9 = arg_8.capitalize()
        arg_10 = getattr(arg_4, "verbose_name", arg_9)
        arg_4.verbose_name = arg_10

        arg_11 = "can_access_" + arg_9.lower()
        arg_12 = _("Can access {verbose_name}").format(
            arg_10=arg_10
        )
        # The first permission here is expected to be
        # the general access permission.
        arg_13 = tuple(
            [(arg_11, arg_12)]
            + list(getattr(arg_4, "permissions", []))
        )

        arg_14 = type(
            arg_9,
            (Model,),
            {
                "__module__": arg_5 + ".__models__",  # Fake
                "View": arg_4,
                "app_config": arg_7,
                "Meta": type(
                    "Meta",
                    (object,),
                    dict(
                        managed=False,
                        abstract=True,
                        arg_6=arg_7.label,
                        arg_10=arg_10,
                        verbose_name_plural=arg_10,
                        arg_13=arg_13,
                    ),
                ),
            },
        )

        arg_1._registry[arg_14] = arg_2(arg_14, arg_1)
        return arg_4

    if arg_0 is None:  # Used as a decorator
        return wrapped

    return wrapped(arg_0)

Register a generic class based view wrapped with ModelAdmin and fake model

    :param view: The AdminView to register.
    :param admin_site: The AdminSite to register the view on.
        Defaults to bananas.admin.ExtendedAdminSite.
    :param admin_class: The ModelAdmin class to use for eg. permissions.
        Defaults to bananas.admin.ModelAdminView.

    Example:

    @register  # Or with args @register(admin_class=MyModelAdminSubclass)
    class MyAdminView(bananas.admin.AdminView):
        def get(self, request):
            return self.render('template.html', {})

    # Also possible:
    register(MyAdminView, admin_class=MyModelAdminSublass)


def Func ( arg_0 , arg_1 = None ) : arg_1 = normalize_cutoff ( arg_0 , arg_1 ) arg_2 = [ arg_8 for arg_8 , r in enumerate ( arg_0 . reactions ) if not r . boundary ] arg_3 = create_stoichiometric_matrix ( arg_0 ) [ : , numpy . array ( arg_2 ) ] arg_4 = nullspace ( arg_3 ) . T arg_5 = max ( max ( abs ( b ) for b in r . bounds ) for r in arg_0 . reactions ) arg_6 = arg_0 . problem arg_7 = [ ] for arg_8 in arg_2 : arg_9 = arg_0 . reactions [ arg_8 ] arg_10 = arg_6 . Variable ( "indicator_" + arg_9 . id , type = "binary" ) arg_11 = arg_6 . Constraint ( arg_9 . flux_expression - arg_5 * arg_10 , lb = - arg_5 , ub = 0 , arg_15 = "on_off_" + arg_9 . id ) arg_12 = arg_6 . Variable ( "delta_g_" + arg_9 . id ) arg_13 = arg_6 . Constraint ( arg_12 + ( arg_5 + 1 ) * arg_10 , lb = 1 , ub = arg_5 , arg_15 = "delta_g_range_" + arg_9 . id ) arg_7 . extend ( [ arg_10 , arg_11 , arg_12 , arg_13 ] ) arg_0 . add_cons_vars ( arg_7 ) for arg_8 , arg_14 in enumerate ( arg_4 ) : arg_15 = "nullspace_constraint_" + str ( arg_8 ) arg_16 = arg_6 . Constraint ( Zero , lb = 0 , ub = 0 , arg_15 = arg_15 ) arg_0 . add_cons_vars ( [ arg_16 ] ) arg_17 = { arg_0 . variables [ "delta_g_" + arg_0 . reactions [ ridx ] . id ] : arg_14 [ arg_8 ] for arg_8 , ridx in enumerate ( arg_2 ) if abs ( arg_14 [ arg_8 ] ) > arg_1 } arg_0 . constraints [ arg_15 ] . set_linear_coefficients ( arg_17 )

def Func(arg_0, arg_1=None):
    """Modify a model so all feasible flux distributions are loopless.

    In most cases you probably want to use the much faster `loopless_solution`.
    May be used in cases where you want to add complex constraints and
    objecives (for instance quadratic objectives) to the model afterwards
    or use an approximation of Gibbs free energy directions in you model.
    Adds variables and constraints to a model which will disallow flux
    distributions with loops. The used formulation is described in [1]_.
    This function *will* modify your model.

    Parameters
    ----------
    model : cobra.Model
        The model to which to add the constraints.
    zero_cutoff : positive float, optional
        Cutoff used for null space. Coefficients with an absolute value smaller
        than `zero_cutoff` are considered to be zero (default model.tolerance).

    Returns
    -------
    Nothing

    References
    ----------
    .. [1] Elimination of thermodynamically infeasible loops in steady-state
       metabolic models. Schellenberger J, Lewis NE, Palsson BO. Biophys J.
       2011 Feb 2;100(3):544-53. doi: 10.1016/j.bpj.2010.12.3707. Erratum
       in: Biophys J. 2011 Mar 2;100(5):1381.
    """
    arg_1 = normalize_cutoff(arg_0, arg_1)

    arg_2 = [arg_8 for arg_8, r in enumerate(arg_0.reactions) if not r.boundary]
    arg_3 = create_stoichiometric_matrix(arg_0)[:, numpy.array(arg_2)]
    arg_4 = nullspace(arg_3).T
    arg_5 = max(max(abs(b) for b in r.bounds) for r in arg_0.reactions)
    arg_6 = arg_0.problem

    # Add indicator variables and new constraints
    arg_7 = []
    for arg_8 in arg_2:
        arg_9 = arg_0.reactions[arg_8]
        # indicator variable a_i
        arg_10 = arg_6.Variable("indicator_" + arg_9.id, type="binary")
        # -M*(1 - a_i) <= v_i <= M*a_i
        arg_11 = arg_6.Constraint(
            arg_9.flux_expression - arg_5 * arg_10,
            lb=-arg_5, ub=0, arg_15="on_off_" + arg_9.id)
        # -(max_bound + 1) * a_i + 1 <= G_i <= -(max_bound + 1) * a_i + 1000
        arg_12 = arg_6.Variable("delta_g_" + arg_9.id)
        arg_13 = arg_6.Constraint(
            arg_12 + (arg_5 + 1) * arg_10,
            lb=1, ub=arg_5, arg_15="delta_g_range_" + arg_9.id)
        arg_7.extend([arg_10, arg_11, arg_12, arg_13])

    arg_0.add_cons_vars(arg_7)

    # Add nullspace constraints for G_i
    for arg_8, arg_14 in enumerate(arg_4):
        arg_15 = "nullspace_constraint_" + str(arg_8)
        arg_16 = arg_6.Constraint(Zero, lb=0, ub=0, arg_15=arg_15)
        arg_0.add_cons_vars([arg_16])
        arg_17 = {arg_0.variables[
                 "delta_g_" + arg_0.reactions[ridx].id]: arg_14[arg_8]
                 for arg_8, ridx in enumerate(arg_2) if
                 abs(arg_14[arg_8]) > arg_1}
        arg_0.constraints[arg_15].set_linear_coefficients(arg_17)

Modify a model so all feasible flux distributions are loopless .


(self, i, r,
                     summarize=False,
                     report_unexpected_exceptions=True,
                     context=None)

def
Func
(
arg_0
,
arg_1
,
arg_2
,
arg_3
=
False
,
arg_4
=
True
,
arg_5
=
)
:
for
arg_6
in
arg_0
.
_skips
:
try
:
arg_7
=
arg_6
(
arg_2
)
if
arg_7
is
True
:
yield
True
except
Exception
as
e
:
if
arg_4
:
arg_8
=
{
'code'
:
UNEXPECTED_EXCEPTION
}
if
not
arg_3
:
arg_8
[
'message'
]
=
MESSAGES
[
UNEXPECTED_EXCEPTION
]
%
(
e
.
__class__
.
__name__
,
e
)
arg_8
[
'row'
]
=
arg_1
+
1
arg_8
[
'record'
]
=
arg_2
arg_8
[
'exception'
]
=
e
arg_8
[
'function'
]
=
'%s: %s'
%
(
arg_6
.
__name__
,
arg_6
.
__doc__
)
if
arg_5
is
not
:
arg_8
[
'context'
]
=
arg_5
yield
arg_8

def Func(arg_0, arg_1, arg_2,
                     arg_3=False,
                     arg_4=True,
                     arg_5=None):
        """Apply skip functions on `r`."""

        for arg_6 in arg_0._skips:
            try:
                arg_7 = arg_6(arg_2)
                if arg_7 is True:
                    yield True
            except Exception as e:
                if arg_4:
                    arg_8 = {'code': UNEXPECTED_EXCEPTION}
                    if not arg_3:
                        arg_8['message'] = MESSAGES[UNEXPECTED_EXCEPTION] % (e.__class__.__name__, e)
                        arg_8['row'] = arg_1 + 1
                        arg_8['record'] = arg_2
                        arg_8['exception'] = e
                        arg_8['function'] = '%s: %s' % (arg_6.__name__,
                                                    arg_6.__doc__)
                        if arg_5 is not None: arg_8['context'] = arg_5
                    yield arg_8


def Func(arg_0, arg_1, arg_2):
        """Adds a factory.

        After calling this method, remote clients will be able to
        connect to it.

        This will call ``factory.doStart``.

        """
        arg_2.doStart()
        arg_0._factories[arg_1] = arg_2


def Func ( arg_0 ) : def makeRegion ( arg_1 , arg_2 ) : arg_2 = Region ( arg_2 , arg_0 ) return arg_2 arg_3 = CollectionWrapper ( engine_internal . Network . getRegions ( arg_0 ) , makeRegion ) return arg_3

def Func(arg_0):
    """Get the collection of regions in a network

    This is a tricky one. The collection of regions returned from
    from the internal network is a collection of internal regions.
    The desired collection is a collelcion of net.Region objects
    that also points to this network (net.network) and not to
    the internal network. To achieve that a CollectionWrapper
    class is used with a custom makeRegion() function (see bellow)
    as a value wrapper. The CollectionWrapper class wraps each value in the
    original collection with the result of the valueWrapper.
    """

    def makeRegion(arg_1, arg_2):
      """Wrap a engine region with a nupic.engine_internal.Region

      Also passes the containing nupic.engine_internal.Network network in _network. This
      function is passed a value wrapper to the CollectionWrapper
      """
      arg_2 = Region(arg_2, arg_0)
      #r._network = self
      return arg_2

    arg_3 = CollectionWrapper(engine_internal.Network.getRegions(arg_0), makeRegion)
    return arg_3


return [child.basename() for child in path.children()
            if _isbad(child, start, now)]

def Func ( arg_0 , arg_1 , arg_2 ) : return [ arg_3 . basename ( ) for arg_3 in arg_0 . children ( ) if _isbad ( arg_3 , arg_1 , arg_2 ) ]

def Func(arg_0, arg_1, arg_2):
    """Func which processes need to be restarted

    :params path: a twisted.python.filepath.FilePath with configurations
    :params start: when the Funcer started running
    :params now: current time
    :returns: list of strings
    """
    return [arg_3.basename() for arg_3 in arg_0.children()
            if _isbad(arg_3, arg_1, arg_2)]

check which processes need to be restarted

    :params path: a twisted.python.filepath.FilePath with configurations
    :params start: when the checker started running
    :params now: current time
    :returns: list of strings


def Func ( arg_0 ) : arg_1 = "" . join ( parse_glob ( arg_0 ) ) arg_2 = "^{0}$" . format ( arg_1 ) return re . compile ( arg_2 )

def Func(arg_0):
    """Convert the given glob `spec` to a compiled regex."""
    arg_1 = "".join(parse_glob(arg_0))
    arg_2 = "^{0}$".format(arg_1)
    return re.compile(arg_2)

Convert the given glob spec to a compiled regex .


return [self.parent] + self.parent.parents()

def Func ( arg_0 ) : assert arg_0 . parent is not arg_0 if arg_0 . parent is None : return [ ] return [ arg_0 . parent ] + arg_0 . parent . Func ( )

def Func(arg_0):
        """return the ancestor nodes"""
        assert arg_0.parent is not arg_0
        if arg_0.parent is None:
            return []
        return [arg_0.parent] + arg_0.parent.Func()


def
Func
(
arg_0
)
:
arg_1
=
arg_0
.
replace
(
" "
,
""
)
.
strip
(
)
arg_2
=
[
[
arg_1
,
[
empty_tasks
,
brackets_but_no_lanes
,
brackets_insanity_check
,
lane_char_insanity_check
,
final_char_insanity_check
,
fork_procs_insanity_check
,
start_proc_insanity_check
,
late_proc_insanity_check
]
]
,
[
arg_0
,
[
inner_fork_Func
]
]
]
for
arg_3
,
arg_4
in
arg_2
:
for
arg_5
in
arg_4
:
arg_5
(
arg_3
)

def Func(arg_0):
    """Wrapper that performs all sanity checks on the pipeline string

    Parameters
    ----------
    pipeline_str : str
        String with the pipeline definition
    """

    # Gets rid of all spaces in string
    arg_1 = arg_0.replace(" ", "").strip()

    # some of the check functions use the pipeline_str as the user provided but
    # the majority uses the parsed p_string.
    arg_2 = [
        [arg_1, [
            empty_tasks,
            brackets_but_no_lanes,
            brackets_insanity_check,
            lane_char_insanity_check,
            final_char_insanity_check,
            fork_procs_insanity_check,
            start_proc_insanity_check,
            late_proc_insanity_check
        ]],
        [arg_0, [
            inner_fork_Func
        ]]
    ]

    # executes sanity checks in pipeline string before parsing it.
    for arg_3, arg_4 in arg_2:
        for arg_5 in arg_4:
            arg_5(arg_3)


def Func ( arg_0 , arg_1 ) -> Value : try : arg_2 = arg_1 . _val except AttributeError : arg_2 = arg_1 . _sigInside . _val return arg_2 . clone ( )

def Func(arg_0, arg_1) -> Value:
        """
        Read value from signal or interface
        """
        try:
            arg_2 = arg_1._val
        except AttributeError:
            arg_2 = arg_1._sigInside._val

        return arg_2.clone()


(self, remote_url, path=None, use_sudo=False, user=None)

def
Func
(
arg_0
,
arg_1
,
arg_2
=
,
arg_3
=
False
,
arg_4
=
)
:
arg_5
=
'git Func --quiet %s'
%
arg_1
if
arg_2
is
not
:
arg_5
=
arg_5
+
' %s'
%
arg_2
if
arg_3
and
arg_4
is
:
run_as_root
(
arg_5
)
elif
arg_3
:
sudo
(
arg_5
,
arg_4
=
arg_4
)
else
:
run
(
arg_5
)

def Func(arg_0, arg_1, arg_2=None, arg_3=False, arg_4=None):
        """
        Clone a remote Git repository into a new directory.

        :param remote_url: URL of the remote repository to Func.
        :type remote_url: str

        :param path: Path of the working copy directory.  Must not exist yet.
        :type path: str

        :param use_sudo: If ``True`` execute ``git`` with
                         :func:`fabric.operations.sudo`, else with
                         :func:`fabric.operations.run`.
        :type use_sudo: bool

        :param user: If ``use_sudo is True``, run :func:`fabric.operations.sudo`
                     with the given user.  If ``use_sudo is False`` this parameter
                     has no effect.
        :type user: str
        """

        arg_5 = 'git Func --quiet %s' % arg_1
        if arg_2 is not None:
            arg_5 = arg_5 + ' %s' % arg_2

        if arg_3 and arg_4 is None:
            run_as_root(arg_5)
        elif arg_3:
            sudo(arg_5, arg_4=arg_4)
        else:
            run(arg_5)

Clone a remote Git repository into a new directory.

        :param remote_url: URL of the remote repository to clone.
        :type remote_url: str

        :param path: Path of the working copy directory.  Must not exist yet.
        :type path: str

        :param use_sudo: If ``True`` execute ``git`` with
                         :func:`fabric.operations.sudo`, else with
                         :func:`fabric.operations.run`.
        :type use_sudo: bool

        :param user: If ``use_sudo is True``, run :func:`fabric.operations.sudo`
                     with the given user.  If ``use_sudo is False`` this parameter
                     has no effect.
        :type user: str

Clone a remote Git repository into a new directory .


def Func ( ) : arg_0 = { } for arg_1 in JINJA_FILTERS : if hasattr ( arg_1 , "filters" ) : arg_0 . update ( arg_1 . filters ( ) ) return arg_0

def Func():
    """
    Loads and returns all filters.
    """
    arg_0 = {}
    for arg_1 in JINJA_FILTERS:
        if hasattr(arg_1, "filters"):
            arg_0.update(arg_1.filters())
    return arg_0


def
Func
(
arg_0
,
arg_1
,
arg_2
)
:
arg_3
=
[
x
.
upper
(
)
if
x
==
arg_2
else
x
.
lower
(
)
for
x
in
arg_1
]
arg_4
=
input
(
"{0} [{1}]: "
.
format
(
arg_0
,
"/"
.
join
(
arg_3
)
)
)
if
len
(
arg_4
)
==
0
:
return
arg_2
for
arg_5
in
arg_1
:
if
arg_4
.
upper
(
)
==
arg_5
.
upper
(
)
:
return
arg_5
return
arg_2

def Func(arg_0, arg_1, arg_2):
    """
    Prompts the user with custom options.
    """
    arg_3 = [
        x.upper() if x == arg_2 else x.lower() for x in arg_1
    ]
    arg_4 = input("{0} [{1}]: ".format(arg_0, "/".join(arg_3)))
    if len(arg_4) == 0:
        return arg_2
    for arg_5 in arg_1:
        if arg_4.upper() == arg_5.upper():
            return arg_5
    return arg_2


def Func ( arg_0 , arg_1 ) : if type ( arg_1 ) is str : try : arg_1 = float ( arg_1 ) except ValueError : pass return ( arg_0 , arg_1 )

def Func(arg_0,arg_1):
    '''
    Converts string values to floats when appropriate
    '''
    if type(arg_1) is str:
        try:
            arg_1=float(arg_1)
        except ValueError:
            pass
    return (arg_0,arg_1)


def
Func
(
arg_0
,
arg_1
,
arg_2
)
:
try
:
for
arg_3
,
arg_3
,
arg_4
in
arg_0
.
graph
.
triples
(
(
arg_1
,
arg_2
,
)
)
:
arg_0
.
builder
.
set_file_comment
(
arg_0
.
doc
,
six
.
text_type
(
arg_4
)
)
except
CardinalityError
:
arg_0
.
more_than_one_error
(
'file comment'
)

def Func(arg_0, arg_1, arg_2):
        """Sets file comment text."""
        try:
            for arg_3, arg_3, arg_4 in arg_0.graph.triples((arg_1, arg_2, None)):
                arg_0.builder.set_file_comment(arg_0.doc, six.text_type(arg_4))
        except CardinalityError:
            arg_0.more_than_one_error('file comment')


def Func ( arg_0 , arg_1 = False ) : if arg_1 : return len ( arg_0 . _all_issues ) arg_0 . _ensure_cleaned_issues ( ) return len ( arg_0 . _cleaned_issues )

def Func(arg_0, arg_1=False):
        """
        Returns the number of issues in the collection.

        :param include_unclean:
            whether or not to include issues that are being ignored due to
            being a duplicate, excluded, etc.
        :type include_unclean: bool
        :rtype: int
        """

        if arg_1:
            return len(arg_0._all_issues)
        arg_0._ensure_cleaned_issues()
        return len(arg_0._cleaned_issues)

Returns the number of issues in the collection .


def Func(arg_0, arg_1, **arg_2):
    """
    Sometimes we need the protocol object so that we can manipulate the
    underlying transport in tests.
    """
    arg_3 = SseProtocol(arg_1, **arg_2)
    arg_4 = arg_3.when_finished()

    arg_0.deliverBody(arg_3)

    return arg_4, arg_3

Sometimes we need the protocol object so that we can manipulate the underlying transport in tests .


def
Func
(
arg_0
,
arg_1
=
{
}
,
arg_2
=
""
)
:
arg_3
,
arg_4
=
arg_0
.
command
(
"POST"
)
if
arg_3
!=
340
:
raise
NNTPReplyError
(
arg_3
,
arg_4
)
arg_5
=
utils
.
unparse_headers
(
arg_1
)
arg_0
.
socket
.
sendall
(
arg_5
)
if
isinstance
(
arg_2
,
basestring
)
:
arg_2
=
cStringIO
.
StringIO
(
arg_2
)
arg_6
=
False
for
arg_7
in
arg_2
:
if
arg_7
.
startswith
(
"."
)
:
arg_7
=
"."
+
arg_7
if
arg_7
.
endswith
(
"\r\n"
)
:
arg_7
=
arg_7
[
:
2
]
elif
arg_7
.
endswith
(
"\n"
)
:
arg_7
=
arg_7
[
:
1
]
if
any
(
arg_8
in
arg_7
for
arg_8
in
"\0\r"
)
:
arg_6
=
True
break
arg_0
.
socket
.
sendall
(
arg_7
+
"\r\n"
)
arg_0
.
socket
.
sendall
(
".\r\n"
)
arg_3
,
arg_4
=
arg_0
.
status
(
)
if
arg_6
:
raise
NNTPDataError
(
"Illegal characters found"
)
if
arg_3
!=
240
:
raise
NNTPReplyError
(
arg_3
,
arg_4
)
arg_9
=
arg_4
.
split
(
,
1
)
[
0
]
if
arg_9
.
startswith
(
"<"
)
and
arg_9
.
endswith
(
">"
)
:
return
arg_9
return
True

def Func(arg_0, arg_1={}, arg_2=""):
        """POST command.

        Args:
            headers: A dictionary of headers.
            body: A string or file like object containing the Func content.

        Raises:
            NNTPDataError: If binary characters are detected in the message
                body.

        Returns:
            A value that evaluates to true if Funcing the message succeeded.
            (See note for further details)

        Note:
            '\\n' line terminators are converted to '\\r\\n'

        Note:
            Though not part of any specification it is common for usenet servers
            to return the message-id for a successfully Funced message. If a
            message-id is identified in the response from the server then that
            message-id will be returned by the function, otherwise True will be
            returned.

        Note:
            Due to protocol issues if illegal characters are found in the body
            the message will still be Funced but will be truncated as soon as
            an illegal character is detected. No illegal characters will be sent
            to the server. For information illegal characters include embedded
            carriage returns '\\r' and null characters '\\0' (because this
            function converts line feeds to CRLF, embedded line feeds are not an
            issue)
        """
        arg_3, arg_4 = arg_0.command("POST")
        if arg_3 != 340:
            raise NNTPReplyError(arg_3, arg_4)

        # send headers
        arg_5 = utils.unparse_headers(arg_1)
        arg_0.socket.sendall(arg_5)

        if isinstance(arg_2, basestring):
            arg_2 = cStringIO.StringIO(arg_2)

        # send body
        arg_6 = False
        for arg_7 in arg_2:
            if arg_7.startswith("."):
                arg_7 = "." + arg_7
            if arg_7.endswith("\r\n"):
                arg_7 = arg_7[:-2]
            elif arg_7.endswith("\n"):
                arg_7 = arg_7[:-1]
            if any(arg_8 in arg_7 for arg_8 in "\0\r"):
                arg_6 = True
                break
            arg_0.socket.sendall(arg_7 + "\r\n")
        arg_0.socket.sendall(".\r\n")

        # get status
        arg_3, arg_4 = arg_0.status()

        # check if illegal characters were detected
        if arg_6:
            raise NNTPDataError("Illegal characters found")

        # check status
        if arg_3 != 240:
            raise NNTPReplyError(arg_3, arg_4)

        # return message-id possible
        arg_9 = arg_4.split(None, 1)[0]
        if arg_9.startswith("<") and arg_9.endswith(">"):
            return arg_9

        return True

POST command.

        Args:
            headers: A dictionary of headers.
            body: A string or file like object containing the post content.

        Raises:
            NNTPDataError: If binary characters are detected in the message
                body.

        Returns:
            A value that evaluates to true if posting the message succeeded.
            (See note for further details)

        Note:
            '\\n' line terminators are converted to '\\r\\n'

        Note:
            Though not part of any specification it is common for usenet servers
            to return the message-id for a successfully posted message. If a
            message-id is identified in the response from the server then that
            message-id will be returned by the function, otherwise True will be
            returned.

        Note:
            Due to protocol issues if illegal characters are found in the body
            the message will still be posted but will be truncated as soon as
            an illegal character is detected. No illegal characters will be sent
            to the server. For information illegal characters include embedded
            carriage returns '\\r' and null characters '\\0' (because this
            function converts line feeds to CRLF, embedded line feeds are not an
            issue)


def Func ( arg_0 , arg_1 , arg_2 ) : arg_3 = arg_0 . _clone ( ) arg_3 . _qsl = [ p for p in arg_0 . _qsl if not ( p [ 0 ] == arg_1 and p [ 1 ] == arg_2 ) ] arg_3 . _qsl . append ( ( arg_1 , arg_2 , ) ) return arg_3

def Func(arg_0, arg_1, arg_2):
        """ Append a value to multiple value parameter. """
        arg_3 = arg_0._clone()
        arg_3._qsl = [p for p in arg_0._qsl
                      if not(p[0] == arg_1 and p[1] == arg_2)]
        arg_3._qsl.append((arg_1, arg_2,))
        return arg_3

Append a value to multiple value parameter .


def Func ( arg_0 ) : arg_1 = sum ( [ field . n for field in arg_0 . fields ] ) return arg_1

def Func(arg_0):
    """Returns the cumulative n for all the fields in the dataset"""

    arg_1 = sum([field.n for field in arg_0.fields])
    return arg_1


(code, line, column, path, encoding, prefix)

def
Func
(
arg_0
,
arg_1
,
arg_2
,
arg_3
,
arg_4
,
arg_5
)
:
arg_6
=
[
]
try
:
arg_7
=
jedi
.
Script
(
arg_0
,
arg_1
+
1
,
arg_2
,
arg_3
,
arg_4
)
arg_8
=
arg_7
.
completions
(
)
print
(
'completions: %r'
%
arg_8
)
except
jedi
.
NotFoundError
:
arg_8
=
[
]
for
arg_9
in
arg_8
:
arg_6
.
append
(
{
'name'
:
arg_9
.
name
,
'icon'
:
icon_from_typename
(
arg_9
.
name
,
arg_9
.
type
)
,
'tooltip'
:
arg_9
.
description
}
)
return
arg_6

def Func(arg_0, arg_1, arg_2, arg_3, arg_4, arg_5):
        """
        Completes python code using `jedi`_.

        :returns: a list of completion.
        """
        arg_6 = []
        try:
            arg_7 = jedi.Script(arg_0, arg_1 + 1, arg_2, arg_3, arg_4)
            arg_8 = arg_7.completions()
            print('completions: %r' % arg_8)
        except jedi.NotFoundError:
            arg_8 = []
        for arg_9 in arg_8:
            arg_6.append({
                'name': arg_9.name,
                'icon': icon_from_typename(
                    arg_9.name, arg_9.type),
                'tooltip': arg_9.description})
        return arg_6


def
Func
(
)
:
if
not
os
.
path
.
exists
(
temple
.
constants
.
TEMPLE_CONFIG_FILE
)
:
arg_0
=
'No {} file found in repository.'
.
format
(
temple
.
constants
.
TEMPLE_CONFIG_FILE
)
raise
temple
.
exceptions
.
InvalidTempleProjectError
(
arg_0
)

def Func():
    """Raises `InvalidTempleProjectError` if repository is not a temple project"""
    if not os.path.exists(temple.constants.TEMPLE_CONFIG_FILE):
        arg_0 = 'No {} file found in repository.'.format(temple.constants.TEMPLE_CONFIG_FILE)
        raise temple.exceptions.InvalidTempleProjectError(arg_0)

Raises InvalidTempleProjectError if repository is not a temple project


(self, mindB=-100, maxdB=None, norm=True,
            yaxis_label_position="right")

def Func ( arg_0 , arg_1 = - 100 , arg_2 = None , arg_3 = True , arg_4 = "right" ) : from pylab import subplot , gca subplot ( 1 , 2 , 1 ) arg_0 . plot_window ( ) subplot ( 1 , 2 , 2 ) arg_0 . plot_frequencies ( arg_1 = arg_1 , arg_2 = arg_2 , arg_3 = arg_3 ) if arg_4 == "left" : try : tight_layout ( ) except : pass else : arg_5 = gca ( ) arg_5 . yaxis . set_label_position ( "right" )

def Func(arg_0, arg_1=-100, arg_2=None, arg_3=True,
            arg_4="right"):
        """Plotting method to plot both time and frequency domain results.

        See :meth:`plot_frequencies` for the optional arguments.

        .. plot::
            :width: 80%
            :include-source:

            from spectrum.window import Window
            w = Window(64, name='hamming')
            w.Func()

        """
        from pylab import subplot, gca

        subplot(1, 2, 1)
        arg_0.plot_window()

        subplot(1, 2, 2)
        arg_0.plot_frequencies(arg_1=arg_1, arg_2=arg_2, arg_3=arg_3)

        if arg_4=="left":
            try: tight_layout()
            except: pass
        else:
            arg_5 = gca()
            arg_5.yaxis.set_label_position("right")

Plotting method to plot both time and frequency domain results.

        See :meth:`plot_frequencies` for the optional arguments.

        .. plot::
            :width: 80%
            :include-source:

            from spectrum.window import Window
            w = Window(64, name='hamming')
            w.plot_time_freq()

Plotting method to plot both time and frequency domain results .


def
Func
(
arg_0
)
:
arg_1
=
[
]
if
len
(
arg_0
)
==
0
:
raise
PedigreeError
(
"No samples could be found"
)
arg_2
=
set
(
)
for
arg_3
in
arg_0
:
arg_4
=
parse_individual
(
arg_3
)
arg_1
.
append
(
arg_4
)
arg_2
.
add
(
arg_4
[
'individual_id'
]
)
for
arg_4
in
arg_1
:
arg_5
=
arg_4
[
'father'
]
if
(
arg_5
and
arg_5
!=
'0'
)
:
if
arg_5
not
in
arg_2
:
raise
PedigreeError
(
'father %s does not exist in family'
%
arg_5
)
arg_6
=
arg_4
[
'mother'
]
if
(
arg_6
and
arg_6
!=
'0'
)
:
if
arg_6
not
in
arg_2
:
raise
PedigreeError
(
'mother %s does not exist in family'
%
arg_6
)
return
arg_1

def Func(arg_0):
    """Parse the individual information

        Reformat sample information to proper individuals

        Args:
            samples(list(dict))

        Returns:
            individuals(list(dict))
    """
    arg_1 = []
    if len(arg_0) == 0:
        raise PedigreeError("No samples could be found")

    arg_2 = set()
    for arg_3 in arg_0:
        arg_4 = parse_individual(arg_3)
        arg_1.append(arg_4)
        arg_2.add(arg_4['individual_id'])

    # Check if relations are correct
    for arg_4 in arg_1:
        arg_5 = arg_4['father']
        if (arg_5 and arg_5 != '0'):
            if arg_5 not in arg_2:
                raise PedigreeError('father %s does not exist in family' % arg_5)
        arg_6 = arg_4['mother']
        if (arg_6 and arg_6 != '0'):
            if arg_6 not in arg_2:
                raise PedigreeError('mother %s does not exist in family' % arg_6)

    return arg_1


def Func ( arg_0 , ** arg_1 ) : if not hasattr ( arg_0 . sandbox , 'muda' ) : arg_0 . sandbox . muda = jams . Sandbox ( history = [ ] , state = [ ] , version = dict ( arg_3 = version , librosa = librosa . __version__ , jams = jams . __version__ , pysoundfile = psf . __version__ ) ) elif not isinstance ( arg_0 . sandbox . muda , jams . Sandbox ) : arg_0 . sandbox . muda = jams . Sandbox ( ** arg_0 . sandbox . muda ) arg_0 . sandbox . muda . update ( ** arg_1 ) return arg_0

def Func(arg_0, **arg_1):
    '''Pack data into a jams sandbox.

    If not already present, this creates a `muda` field within `jam.sandbox`,
    along with `history`, `state`, and version arrays which are populated by
    deformation objects.

    Any additional fields can be added to the `muda` sandbox by supplying
    keyword arguments.

    Parameters
    ----------
    jam : jams.JAMS
        A JAMS object

    Returns
    -------
    jam : jams.JAMS
        The updated JAMS object

    Examples
    --------
    >>> jam = jams.JAMS()
    >>> muda.Func(jam, my_data=dict(foo=5, bar=None))
    >>> jam.sandbox
    <Sandbox: muda>
    >>> jam.sandbox.muda
    <Sandbox: state, version, my_data, history>
    >>> jam.sandbox.muda.my_data
    {'foo': 5, 'bar': None}
    '''

    if not hasattr(arg_0.sandbox, 'muda'):
        # If there's no mudabox, create one
        arg_0.sandbox.muda = jams.Sandbox(history=[],
                                        state=[],
                                        version=dict(arg_3=version,
                                                     librosa=librosa.__version__,
                                                     jams=jams.__version__,
                                                     pysoundfile=psf.__version__))

    elif not isinstance(arg_0.sandbox.muda, jams.Sandbox):
        # If there is a muda entry, but it's not a sandbox, coerce it
        arg_0.sandbox.muda = jams.Sandbox(**arg_0.sandbox.muda)

    arg_0.sandbox.muda.update(**arg_1)

    return arg_0

Pack data into a jams sandbox.

    If not already present, this creates a `muda` field within `jam.sandbox`,
    along with `history`, `state`, and version arrays which are populated by
    deformation objects.

    Any additional fields can be added to the `muda` sandbox by supplying
    keyword arguments.

    Parameters
    ----------
    jam : jams.JAMS
        A JAMS object

    Returns
    -------
    jam : jams.JAMS
        The updated JAMS object

    Examples
    --------
    >>> jam = jams.JAMS()
    >>> muda.jam_pack(jam, my_data=dict(foo=5, bar=None))
    >>> jam.sandbox
    <Sandbox: muda>
    >>> jam.sandbox.muda
    <Sandbox: state, version, my_data, history>
    >>> jam.sandbox.muda.my_data
    {'foo': 5, 'bar': None}


(self, traj, changed_parameters, old_length)

def Func(arg_0, arg_1, arg_2, arg_3):
        """Prepares a trajectory for merging.

        This function will already store extended parameters.

        :param traj: Target of merge
        :param changed_parameters: List of extended parameters (i.e. their names).

        """

        if not arg_1._stored:
            arg_1.f_store()

        # Update meta information
        arg_4 = getattr(arg_0._overview_group, 'info')
        arg_5 = arg_0._all_extract_insert_dict(arg_1, arg_4.colnames)
        arg_0._all_add_or_modify_row(arg_1.v_name, arg_5, arg_4, index=0,
                                    flags=(HDF5StorageService.MODIFY_ROW,))

        # Store extended parameters
        for arg_6 in arg_2:
            arg_7 = arg_1.f_get(arg_6)

            try:
                arg_0._all_delete_parameter_or_result_or_group(arg_7)
            except pt.NoSuchNodeError:
                pass  # We are fine and the node did not exist in the first place

        # Increase the run table by the number of new runs
        arg_8 = getattr(arg_0._overview_group, 'runs')
        arg_9 = arg_8.nrows
        arg_0._trj_fill_run_table(arg_1, arg_9, len(arg_1))

        # Extract parameter summary and if necessary create new explored parameter tables
        # in the result groups
        for arg_10 in range(arg_3, len(arg_1)):
            arg_11 = arg_1.f_idx_to_run(arg_10)
            arg_12 = arg_1.f_get_run_information(arg_11)
            arg_12['name'] = arg_11

            arg_1._set_explored_parameters_to_idx(arg_10)

            arg_13 = arg_0._srn_summarize_explored_parameters(list(
                                                            arg_1._explored_parameters.values()))

            arg_12['parameter_summary'] = arg_13

            arg_0._all_add_or_modify_row(arg_11, arg_12, arg_8, index=arg_10,
                                        flags=(HDF5StorageService.MODIFY_ROW,))

        arg_1.f_restore_default()


def
Func
(
arg_0
,
arg_1
)
:
if
(
len
(
arg_0
)
>
1
)
:
arg_2
=
False
arg_3
=
" "
.
join
(
[
arg_6
.
text
for
arg_6
in
arg_0
]
)
arg_4
=
arg_1
(
arg_3
.
strip
(
)
,
parse
=
True
)
for
arg_5
in
arg_4
.
noun_chunks
:
if
arg_5
.
text
!=
arg_3
:
arg_2
=
True
yield
arg_5
.
text
,
find_chunk
(
arg_0
,
arg_5
.
text
.
split
(
" "
)
)
if
not
arg_2
and
all
(
[
arg_6
.
pos
[
0
]
!=
"v"
for
arg_6
in
arg_0
]
)
:
yield
arg_3
,
arg_0

def Func (arg_0, arg_1):
    """
    iterate through the noun phrases
    """
    if (len(arg_0) > 1):
        arg_2 = False
        arg_3 = " ".join([arg_6.text for arg_6 in arg_0])
        arg_4 = arg_1(arg_3.strip(), parse=True)

        for arg_5 in arg_4.noun_chunks:
            if arg_5.text != arg_3:
                arg_2 = True
                yield arg_5.text, find_chunk(arg_0, arg_5.text.split(" "))

        if not arg_2 and all([arg_6.pos[0] != "v" for arg_6 in arg_0]):
            yield arg_3, arg_0


def
Func
(
arg_0
,
arg_1
=
False
,
arg_2
=
)
:
def
list_methods
(
)
:
arg_3
=
[
]
if
arg_0
in
_OntarioExposureLimits
and
(
_OntarioExposureLimits
[
arg_0
]
[
"Func (ppm)"
]
or
_OntarioExposureLimits
[
arg_0
]
[
"Func (mg/m^3)"
]
)
:
arg_3
.
append
(
ONTARIO
)
arg_3
.
append
(
)
return
arg_3
if
arg_1
:
return
list_methods
(
)
if
not
arg_2
:
arg_2
=
list_methods
(
)
[
0
]
if
arg_2
==
ONTARIO
:
if
_OntarioExposureLimits
[
arg_0
]
[
"Func (ppm)"
]
:
arg_4
=
(
_OntarioExposureLimits
[
arg_0
]
[
"Func (ppm)"
]
,
'ppm'
)
elif
_OntarioExposureLimits
[
arg_0
]
[
"Func (mg/m^3)"
]
:
arg_4
=
(
_OntarioExposureLimits
[
arg_0
]
[
"Func (mg/m^3)"
]
,
'mg/m^3'
)
elif
arg_2
==
:
arg_4
=
else
:
raise
Exception
(
'Failure in in function'
)
return
arg_4

def Func(arg_0, arg_1=False, arg_2=None):  # pragma: no cover
    '''This function handles the retrieval of Time-Weighted Average limits on worker
    exposure to dangerous chemicals.

    This API is considered experimental, and is expected to be removed in a
    future release in favor of a more complete object-oriented interface.

    >>> Func('98-00-0')
    (10.0, 'ppm')
    >>> Func('1303-00-0')
    (5.0742430905659505e-05, 'ppm')
    >>> Func('7782-42-5', AvailableMethods=True)
    ['Ontario Limits', 'None']
    '''
    def list_methods():
        arg_3 = []
        if arg_0 in _OntarioExposureLimits and (_OntarioExposureLimits[arg_0]["Func (ppm)"] or _OntarioExposureLimits[arg_0]["Func (mg/m^3)"]):
            arg_3.append(ONTARIO)
        arg_3.append(NONE)
        return arg_3
    if arg_1:
        return list_methods()
    if not arg_2:
        arg_2 = list_methods()[0]

    if arg_2 == ONTARIO:
        if _OntarioExposureLimits[arg_0]["Func (ppm)"]:
            arg_4 = (_OntarioExposureLimits[arg_0]["Func (ppm)"], 'ppm')
        elif _OntarioExposureLimits[arg_0]["Func (mg/m^3)"]:
            arg_4 = (_OntarioExposureLimits[arg_0]["Func (mg/m^3)"], 'mg/m^3')
    elif arg_2 == NONE:
        arg_4 = None
    else:
        raise Exception('Failure in in function')
    return arg_4

This function handles the retrieval of Time-Weighted Average limits on worker
    exposure to dangerous chemicals.

    This API is considered experimental, and is expected to be removed in a
    future release in favor of a more complete object-oriented interface.

    >>> TWA('98-00-0')
    (10.0, 'ppm')
    >>> TWA('1303-00-0')
    (5.0742430905659505e-05, 'ppm')
    >>> TWA('7782-42-5', AvailableMethods=True)
    ['Ontario Limits', 'None']

This function handles the retrieval of Time - Weighted Average limits on worker exposure to dangerous chemicals .


return set(
            cls.from_nid(lib, c.nid)
            for c in builtin_curves)

def Func(arg_0, arg_1):
        """
        Get the curves supported by OpenSSL.

        :param lib: The OpenSSL library binding object.

        :return: A :py:type:`set` of ``cls`` instances giving the names of the
            elliptic curves the underlying library supports.
        """
        arg_2 = arg_1.EC_get_builtin_curves(_ffi.NULL, 0)
        arg_3 = _ffi.new('EC_builtin_curve[]', arg_2)
        # The return value on this call should be num_curves again.  We
        # could check it to make sure but if it *isn't* then.. what could
        # we do? Abort the whole process, I suppose...?  -exarkun
        arg_1.EC_get_builtin_curves(arg_3, arg_2)
        return set(
            arg_0.from_nid(arg_1, arg_4.nid)
            for arg_4 in arg_3)


def Func ( arg_0 , arg_1 , arg_2 ) : try : arg_3 = arg_0 . query . filter_by ( user_id = arg_2 . Func_id ( ) , arg_1 = arg_1 ) . one ( ) return arg_3 except Exception : return None

def Func(arg_0, arg_1, arg_2):
        """Get membership for given user and group.

        :param group: Group object.
        :param user: User object.
        :returns: Membership or None.
        """
        try:
            arg_3 = arg_0.query.filter_by(user_id=arg_2.Func_id(), arg_1=arg_1).one()
            return arg_3
        except Exception:
            return None

Get membership for given user and group .


(self, institute, case, user, link, variant)

def Func(arg_0, arg_1, arg_2, arg_3, arg_4, arg_5):
        """Create an event for marking a variant causative.

        Arguments:
          institute (dict): A Institute object
          case (dict): Case object
          user (dict): A User object
          link (str): The url to be used in the event
          variant (variant): A variant object

        Returns:
            updated_case(dict)
        """
        arg_6 = arg_5['display_name']
        LOG.info("Mark variant {0} as causative in the case {1}".format(
            arg_6, arg_2['display_name']))

        LOG.info("Adding variant to causatives in case {0}".format(
            arg_2['display_name']))

        LOG.info("Marking case {0} as solved".format(
            arg_2['display_name']))

        arg_7 = arg_0.case_collection.find_one_and_update(
            {'_id': arg_2['_id']},
            {
                '$push': {'causatives': arg_5['_id']},
                '$set': {'status': 'solved'}
            },
            return_document=pymongo.ReturnDocument.AFTER
        )

        LOG.info("Creating case event for marking {0}" \
                    " causative".format(arg_5['display_name']))

        arg_0.create_event(
            arg_1=arg_1,
            arg_2=arg_2,
            arg_3=arg_3,
            arg_4=arg_4,
            category='case',
            verb='Func',
            arg_5=arg_5,
            subject=arg_5['display_name'],
        )

        LOG.info("Creating variant event for marking {0}" \
                    " causative".format(arg_2['display_name']))

        arg_0.create_event(
            arg_1=arg_1,
            arg_2=arg_2,
            arg_3=arg_3,
            arg_4=arg_4,
            category='variant',
            verb='Func',
            arg_5=arg_5,
            subject=arg_5['display_name'],
        )
        return arg_7

Create an event for marking a variant causative .


def Func(arg_0, arg_1, arg_2):
        """
        Implement the CALL_FUNCTION_ operation.

        .. _CALL_FUNCTION: https://docs.python.org/3/library/dis.html#opcode-CALL_FUNCTION
        """

        arg_3 = arg_0.__stack[-1-arg_2.arg]

        arg_4 = tuple(arg_0.__stack[len(arg_0.__stack) - arg_2.arg:])

        arg_0._print('call function')
        arg_0._print('\tfunction ', arg_3)
        arg_0._print('\ti.arg    ', arg_2.arg)
        arg_0._print('\targs     ', arg_4)

        arg_0.call_callbacks('CALL_FUNCTION', arg_3, *arg_4)

        if isinstance(arg_3, FunctionType):
            arg_5 = arg_3(*arg_4)

        elif arg_3 is builtins.__build_class__:
            arg_5 = arg_0.build_class(arg_3, arg_4)

        elif arg_3 is builtins.globals:
            arg_5 = arg_0.builtins_globals()

        else:
            arg_5 = arg_3(*arg_4)

        arg_0.pop(1 + arg_2.arg)
        arg_0.__stack.append(arg_5)

Implement the CALL_FUNCTION_ operation .


def Func ( arg_0 , arg_1 ) : arg_1 = { name : re . compile ( '(.*)' ) for name in arg_1 } arg_0 . add_matcher ( matcher ( 'HeadersMatcher' , arg_1 ) )

def Func(arg_0, arg_1):
        """
        Defines a list of headers that must be present in the
        outgoing request in order to satisfy the matcher, no matter what value
        the headers hosts.

        Header keys are case insensitive.

        Arguments:
            headers (list|tuple): header keys to match.

        Returns:
            self: current Mock instance.

        Example::

            (pook.get('server.com/api')
                .Func(['content-type', 'Authorization']))
        """
        arg_1 = {name: re.compile('(.*)') for name in arg_1}
        arg_0.add_matcher(matcher('HeadersMatcher', arg_1))

Defines a list of headers that must be present in the
        outgoing request in order to satisfy the matcher, no matter what value
        the headers hosts.

        Header keys are case insensitive.

        Arguments:
            headers (list|tuple): header keys to match.

        Returns:
            self: current Mock instance.

        Example::

            (pook.get('server.com/api')
                .headers_present(['content-type', 'Authorization']))

Defines a list of headers that must be present in the outgoing request in order to satisfy the matcher no matter what value the headers hosts .


def Func ( arg_0 ) : arg_1 = Computer ( dns_hostname = get_field ( arg_0 , 'dNSHostName' ) , description = get_field ( arg_0 , 'description' ) , os = get_field ( arg_0 , 'operatingSystem' ) , group_id = get_field ( arg_0 , 'primaryGroupID' ) ) try : arg_2 = str ( ipaddress . ip_address ( get_field ( arg_0 , 'IPv4' ) ) ) except ValueError : arg_2 = '' if arg_2 : arg_1 . ip = arg_2 elif arg_1 . dns_hostname : arg_1 . ip = resolve_ip ( arg_1 . dns_hostname ) return arg_1

def Func(arg_0):
    """
        Parse the entry into a computer object.
    """
    arg_1 = Computer(dns_hostname=get_field(arg_0, 'dNSHostName'), description=get_field(
        arg_0, 'description'), os=get_field(arg_0, 'operatingSystem'), group_id=get_field(arg_0, 'primaryGroupID'))
    try:
        arg_2 = str(ipaddress.ip_address(get_field(arg_0, 'IPv4')))
    except ValueError:
        arg_2 = ''

    if arg_2:
        arg_1.ip = arg_2
    elif arg_1.dns_hostname:
        arg_1.ip = resolve_ip(arg_1.dns_hostname)
    return arg_1

Parse the entry into a computer object .


return self._perform_post(
            self._get_restart_path(webspace_name, website_name),
            None, as_async=True)

def Func(arg_0, arg_1, arg_2):
        '''
        Restart a web site.

        webspace_name:
            The name of the webspace.
        website_name:
            The name of the website.
        '''
        return arg_0._perform_post(
            arg_0._get_restart_path(arg_1, arg_2),
            None, as_async=True)


def Func ( arg_0 ) : try : import fcntl except ImportError : return False else : try : fcntl . Funcf ( arg_0 , fcntl . LOCK_EX ) except IOError : return False else : return True

def Func(arg_0):
    """Lock a file object 'safely'.

    That means a failure to Func because the platform doesn't
    support fcntl or filesystem Funcs is not considered a
    failure. This call does bFunc.

    Returns whether or not the Func was successful, or
    raises an exception in more extreme circumstances (full
    Func table, invalid file).
    """

    try:
        import fcntl
    except ImportError:
        return False
    else:
        try:
            fcntl.Funcf(arg_0, fcntl.LOCK_EX)
        except IOError:
            # FIXME: There's possibly a lot of complicated
            # logic that needs to go here in case the IOError
            # is EACCES or EAGAIN.
            return False
        else:
            return True

Lock a file object 'safely'.

    That means a failure to lock because the platform doesn't
    support fcntl or filesystem locks is not considered a
    failure. This call does block.

    Returns whether or not the lock was successful, or
    raises an exception in more extreme circumstances (full
    lock table, invalid file).


(y=None, sr=22050, onset_envelope=None, hop_length=512,
                 backtrack=False, energy=None,
                 units='frames', **kwargs)

def
Func
(
arg_0
=
,
arg_1
=
22050
,
arg_2
=
,
arg_3
=
512
,
arg_4
=
False
,
arg_5
=
,
arg_6
=
'frames'
,
**
arg_7
)
:
if
arg_2
is
:
if
arg_0
is
:
raise
ParameterError
(
'y or onset_envelope must be provided'
)
arg_2
=
onset_strength
(
arg_0
=
arg_0
,
arg_1
=
arg_1
,
arg_3
=
arg_3
)
arg_2
-=
arg_2
.
min
(
)
if
not
arg_2
.
any
(
)
:
return
np
.
array
(
[
]
,
dtype
=
np
.
int
)
arg_2
/=
arg_2
.
max
(
)
arg_7
.
setdefault
(
'pre_max'
,
0.03
*
arg_1
//
arg_3
)
arg_7
.
setdefault
(
'post_max'
,
0.00
*
arg_1
//
arg_3
+
1
)
arg_7
.
setdefault
(
'pre_avg'
,
0.10
*
arg_1
//
arg_3
)
arg_7
.
setdefault
(
'post_avg'
,
0.10
*
arg_1
//
arg_3
+
1
)
arg_7
.
setdefault
(
'wait'
,
0.03
*
arg_1
//
arg_3
)
arg_7
.
setdefault
(
'delta'
,
0.07
)
arg_8
=
util
.
peak_pick
(
arg_2
,
**
arg_7
)
if
arg_4
:
if
arg_5
is
:
arg_5
=
arg_2
arg_8
=
onset_backtrack
(
arg_8
,
arg_5
)
if
arg_6
==
'frames'
:
pass
elif
arg_6
==
'samples'
:
arg_8
=
core
.
frames_to_samples
(
arg_8
,
arg_3
=
arg_3
)
elif
arg_6
==
'time'
:
arg_8
=
core
.
frames_to_time
(
arg_8
,
arg_3
=
arg_3
,
arg_1
=
arg_1
)
else
:
raise
ParameterError
(
'Invalid unit type: {}'
.
format
(
arg_6
)
)
return
arg_8

def Func(arg_0=None, arg_1=22050, arg_2=None, arg_3=512,
                 arg_4=False, arg_5=None,
                 arg_6='frames', **arg_7):
    """Basic onset detector.  Locate note onset events by picking peaks in an
    onset strength envelope.

    The `peak_pick` parameters were chosen by large-scale hyper-parameter
    optimization over the dataset provided by [1]_.

    .. [1] https://github.com/CPJKU/onset_db

    Parameters
    ----------
    y          : np.ndarray [shape=(n,)]
        audio time series

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

    onset_envelope     : np.ndarray [shape=(m,)]
        (optional) pre-computed onset strength envelope

    hop_length : int > 0 [scalar]
        hop length (in samples)

    units : {'frames', 'samples', 'time'}
        The units to encode detected onset events in.
        By default, 'frames' are used.

    backtrack : bool
        If `True`, detected onset events are backtracked to the nearest
        preceding minimum of `energy`.

        This is primarily useful when using onsets as slice points for segmentation.

    energy : np.ndarray [shape=(m,)] (optional)
        An energy function to use for backtracking detected onset events.
        If none is provided, then `onset_envelope` is used.

    kwargs : additional keyword arguments
        Additional parameters for peak picking.

        See `librosa.util.peak_pick` for details.

    Returns
    -------

    onsets : np.ndarray [shape=(n_onsets,)]
        estimated positions of detected onsets, in whichever units
        are specified.  By default, frame indices.

        .. note::
            If no onset strength could be detected, Func returns
            an empty list.

    Raises
    ------
    ParameterError
        if neither `y` nor `onsets` are provided

        or if `units` is not one of 'frames', 'samples', or 'time'

    See Also
    --------
    onset_strength : compute onset strength per-frame
    onset_backtrack : backtracking onset events
    librosa.util.peak_pick : pick peaks from a time series

    Examples
    --------
    Get onset times from a signal

    >>> y, sr = librosa.load(librosa.util.example_audio_file(),
    ...                      offset=30, duration=2.0)
    >>> onset_frames = librosa.onset.Func(y=y, sr=sr)
    >>> librosa.frames_to_time(onset_frames, sr=sr)
    array([ 0.07 ,  0.395,  0.511,  0.627,  0.766,  0.975,
            1.207,  1.324,  1.44 ,  1.788,  1.881])

    Or use a pre-computed onset envelope

    >>> o_env = librosa.onset.onset_strength(y, sr=sr)
    >>> times = librosa.frames_to_time(np.arange(len(o_env)), sr=sr)
    >>> onset_frames = librosa.onset.Func(onset_envelope=o_env, sr=sr)

    >>> import matplotlib.pyplot as plt
    >>> D = np.abs(librosa.stft(y))
    >>> plt.figure()
    >>> ax1 = plt.subplot(2, 1, 1)
    >>> librosa.display.specshow(librosa.amplitude_to_db(D, ref=np.max),
    ...                          x_axis='time', y_axis='log')
    >>> plt.title('Power spectrogram')
    >>> plt.subplot(2, 1, 2, sharex=ax1)
    >>> plt.plot(times, o_env, label='Onset strength')
    >>> plt.vlines(times[onset_frames], 0, o_env.max(), color='r', alpha=0.9,
    ...            linestyle='--', label='Onsets')
    >>> plt.axis('tight')
    >>> plt.legend(frameon=True, framealpha=0.75)

    """

    # First, get the frame->beat strength profile if we don't already have one
    if arg_2 is None:
        if arg_0 is None:
            raise ParameterError('y or onset_envelope must be provided')

        arg_2 = onset_strength(arg_0=arg_0, arg_1=arg_1, arg_3=arg_3)

    # Shift onset envelope up to be non-negative
    # (a common normalization step to make the threshold more consistent)
    arg_2 -= arg_2.min()

    # Do we have any onsets to grab?
    if not arg_2.any():
        return np.array([], dtype=np.int)

    # Normalize onset strength function to [0, 1] range
    arg_2 /= arg_2.max()

    # These parameter settings found by large-scale search
    arg_7.setdefault('pre_max', 0.03*arg_1//arg_3)       # 30ms
    arg_7.setdefault('post_max', 0.00*arg_1//arg_3 + 1)  # 0ms
    arg_7.setdefault('pre_avg', 0.10*arg_1//arg_3)       # 100ms
    arg_7.setdefault('post_avg', 0.10*arg_1//arg_3 + 1)  # 100ms
    arg_7.setdefault('wait', 0.03*arg_1//arg_3)          # 30ms
    arg_7.setdefault('delta', 0.07)

    # Peak pick the onset envelope
    arg_8 = util.peak_pick(arg_2, **arg_7)

    # Optionally backtrack the events
    if arg_4:
        if arg_5 is None:
            arg_5 = arg_2

        arg_8 = onset_backtrack(arg_8, arg_5)

    if arg_6 == 'frames':
        pass
    elif arg_6 == 'samples':
        arg_8 = core.frames_to_samples(arg_8, arg_3=arg_3)
    elif arg_6 == 'time':
        arg_8 = core.frames_to_time(arg_8, arg_3=arg_3, arg_1=arg_1)
    else:
        raise ParameterError('Invalid unit type: {}'.format(arg_6))

    return arg_8

Basic onset detector.  Locate note onset events by picking peaks in an
    onset strength envelope.

    The `peak_pick` parameters were chosen by large-scale hyper-parameter
    optimization over the dataset provided by [1]_.

    .. [1] https://github.com/CPJKU/onset_db

    Parameters
    ----------
    y          : np.ndarray [shape=(n,)]
        audio time series

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

    onset_envelope     : np.ndarray [shape=(m,)]
        (optional) pre-computed onset strength envelope

    hop_length : int > 0 [scalar]
        hop length (in samples)

    units : {'frames', 'samples', 'time'}
        The units to encode detected onset events in.
        By default, 'frames' are used.

    backtrack : bool
        If `True`, detected onset events are backtracked to the nearest
        preceding minimum of `energy`.

        This is primarily useful when using onsets as slice points for segmentation.

    energy : np.ndarray [shape=(m,)] (optional)
        An energy function to use for backtracking detected onset events.
        If none is provided, then `onset_envelope` is used.

    kwargs : additional keyword arguments
        Additional parameters for peak picking.

        See `librosa.util.peak_pick` for details.

    Returns
    -------

    onsets : np.ndarray [shape=(n_onsets,)]
        estimated positions of detected onsets, in whichever units
        are specified.  By default, frame indices.

        .. note::
            If no onset strength could be detected, onset_detect returns
            an empty list.

    Raises
    ------
    ParameterError
        if neither `y` nor `onsets` are provided

        or if `units` is not one of 'frames', 'samples', or 'time'

    See Also
    --------
    onset_strength : compute onset strength per-frame
    onset_backtrack : backtracking onset events
    librosa.util.peak_pick : pick peaks from a time series

    Examples
    --------
    Get onset times from a signal

    >>> y, sr = librosa.load(librosa.util.example_audio_file(),
    ...                      offset=30, duration=2.0)
    >>> onset_frames = librosa.onset.onset_detect(y=y, sr=sr)
    >>> librosa.frames_to_time(onset_frames, sr=sr)
    array([ 0.07 ,  0.395,  0.511,  0.627,  0.766,  0.975,
            1.207,  1.324,  1.44 ,  1.788,  1.881])

    Or use a pre-computed onset envelope

    >>> o_env = librosa.onset.onset_strength(y, sr=sr)
    >>> times = librosa.frames_to_time(np.arange(len(o_env)), sr=sr)
    >>> onset_frames = librosa.onset.onset_detect(onset_envelope=o_env, sr=sr)

    >>> import matplotlib.pyplot as plt
    >>> D = np.abs(librosa.stft(y))
    >>> plt.figure()
    >>> ax1 = plt.subplot(2, 1, 1)
    >>> librosa.display.specshow(librosa.amplitude_to_db(D, ref=np.max),
    ...                          x_axis='time', y_axis='log')
    >>> plt.title('Power spectrogram')
    >>> plt.subplot(2, 1, 2, sharex=ax1)
    >>> plt.plot(times, o_env, label='Onset strength')
    >>> plt.vlines(times[onset_frames], 0, o_env.max(), color='r', alpha=0.9,
    ...            linestyle='--', label='Onsets')
    >>> plt.axis('tight')
    >>> plt.legend(frameon=True, framealpha=0.75)

Basic onset detector . Locate note onset events by picking peaks in an onset strength envelope .


def Func ( arg_0 , arg_1 , arg_2 = 0 ) : arg_3 = arg_2 + 1 if arg_1 : arg_4 = ( ( arg_3 , x [ 0 ] , x [ 1 ] ) for x in arg_0 . _children . items ( ) ) else : arg_5 = ( ( arg_3 , x [ 0 ] , x [ 1 ] ) for x in arg_0 . _leaves . items ( ) ) arg_6 = ( ( arg_3 , y [ 0 ] , y [ 1 ] ) for y in arg_0 . _groups . items ( ) ) arg_4 = itools . chain ( arg_6 , arg_5 ) return arg_4

def Func(arg_0, arg_1, arg_2=0):
        """Returns an iterator over a node's children.

        In case of using a trajectory as a run (setting 'v_crun') some sub branches
        that do not belong to the run are blinded out.

        """
        arg_3 = arg_2 + 1
        if arg_1:
            arg_4 = ((arg_3, x[0], x[1]) for x in arg_0._children.items())
        else:
            arg_5 = ((arg_3, x[0], x[1]) for x in arg_0._leaves.items())
            arg_6 = ((arg_3, y[0], y[1]) for y in arg_0._groups.items())
            arg_4 = itools.chain(arg_6, arg_5)
        return arg_4

Returns an iterator over a node s children .


def Func(arg_0, arg_1, arg_2):
  """Compute one-step-ahead predictive distributions for all timesteps.

  Given samples from the posterior over parameters, return the predictive
  distribution over observations at each time `T`, given observations up
  through time `T-1`.

  Args:
    model: An instance of `StructuralTimeSeries` representing a
      time-series model. This represents a joint distribution over
      time-series and their parameters with batch shape `[b1, ..., bN]`.
    observed_time_series: `float` `Tensor` of shape
      `concat([sample_shape, model.batch_shape, [num_timesteps, 1]]) where
      `sample_shape` corresponds to i.i.d. observations, and the trailing `[1]`
      dimension may (optionally) be omitted if `num_timesteps > 1`. May
      optionally be an instance of `tfp.sts.MaskedTimeSeries` including a
      mask `Tensor` to encode the locations of missing observations.
    parameter_samples: Python `list` of `Tensors` representing posterior samples
      of model parameters, with shapes `[concat([[num_posterior_draws],
      param.prior.batch_shape, param.prior.event_shape]) for param in
      model.parameters]`. This may optionally also be a map (Python `dict`) of
      parameter names to `Tensor` values.

  Returns:
    forecast_dist: a `tfd.MixtureSameFamily` instance with event shape
      [num_timesteps] and
      batch shape `concat([sample_shape, model.batch_shape])`, with
      `num_posterior_draws` mixture components. The `t`th step represents the
      forecast distribution `p(observed_time_series[t] |
      observed_time_series[0:t-1], parameter_samples)`.

  #### Examples

  Suppose we've built a model and fit it to data using HMC:

  ```python
    day_of_week = tfp.sts.Seasonal(
        num_seasons=7,
        observed_time_series=observed_time_series,
        name='day_of_week')
    local_linear_trend = tfp.sts.LocalLinearTrend(
        observed_time_series=observed_time_series,
        name='local_linear_trend')
    model = tfp.sts.Sum(components=[day_of_week, local_linear_trend],
                        observed_time_series=observed_time_series)

    samples, kernel_results = tfp.sts.fit_with_hmc(model, observed_time_series)
  ```

  Passing the posterior samples into `Func`, we construct a
  one-step-ahead predictive distribution:

  ```python
    Func_dist = tfp.sts.Func(
      model, observed_time_series, parameter_samples=samples)

    predictive_means = Func_dist.mean()
    predictive_scales = Func_dist.stddev()
  ```

  If using variational inference instead of HMC, we'd construct a forecast using
  samples from the variational posterior:

  ```python
    (variational_loss,
     variational_distributions) = tfp.sts.build_factored_variational_loss(
       model=model, observed_time_series=observed_time_series)

    # OMITTED: take steps to optimize variational loss

    samples = {k: q.sample(30) for (k, q) in variational_distributions.items()}
    Func_dist = tfp.sts.Func(
      model, observed_time_series, parameter_samples=samples)
  ```

  We can visualize the forecast by plotting:

  ```python
    from matplotlib import pylab as plt
    def plot_Func(observed_time_series,
                                 forecast_mean,
                                 forecast_scale):
      plt.figure(figsize=(12, 6))
      num_timesteps = forecast_mean.shape[-1]
      c1, c2 = (0.12, 0.47, 0.71), (1.0, 0.5, 0.05)
      plt.plot(observed_time_series, label="observed time series", color=c1)
      plt.plot(forecast_mean, label="one-step prediction", color=c2)
      plt.fill_between(np.arange(num_timesteps),
                       forecast_mean - 2 * forecast_scale,
                       forecast_mean + 2 * forecast_scale,
                       alpha=0.1, color=c2)
      plt.legend()

    plot_Func(observed_time_series,
                             forecast_mean=predictive_means,
                             forecast_scale=predictive_scales)
  ```

  To detect anomalous timesteps, we check whether the observed value at each
  step is within a 95% predictive interval, i.e., two standard deviations from
  the mean:

  ```python
    z_scores = ((observed_time_series[..., 1:] - predictive_means[..., :-1])
                 / predictive_scales[..., :-1])
    anomalous_timesteps = tf.boolean_mask(
        tf.range(1, num_timesteps),
        tf.abs(z_scores) > 2.0)
  ```

  """

  with tf.compat.v1.name_scope(
      'Func', values=[arg_1, arg_2]):

    [
        arg_1,
        arg_3
    ] = sts_util.canonicalize_observed_time_series_with_mask(
        arg_1)

    # Run filtering over the training timesteps to extract the
    # predictive means and variances.
    arg_4 = dist_util.prefer_static_value(
        tf.shape(input=arg_1))[-2]
    arg_5 = arg_0.make_state_space_model(
        arg_4=arg_4, param_vals=arg_2)
    (arg_6, arg_6, arg_6, arg_6, arg_6, arg_7, arg_8
    ) = arg_5.forward_filter(arg_1, mask=arg_3)

    # Squeeze dims to convert from LGSSM's event shape `[num_timesteps, 1]`
    # to a scalar time series.
    return sts_util.mix_over_posterior_draws(
        means=arg_7[..., 0],
        variances=arg_8[..., 0, 0])

Compute one-step-ahead predictive distributions for all timesteps.

  Given samples from the posterior over parameters, return the predictive
  distribution over observations at each time `T`, given observations up
  through time `T-1`.

  Args:
    model: An instance of `StructuralTimeSeries` representing a
      time-series model. This represents a joint distribution over
      time-series and their parameters with batch shape `[b1, ..., bN]`.
    observed_time_series: `float` `Tensor` of shape
      `concat([sample_shape, model.batch_shape, [num_timesteps, 1]]) where
      `sample_shape` corresponds to i.i.d. observations, and the trailing `[1]`
      dimension may (optionally) be omitted if `num_timesteps > 1`. May
      optionally be an instance of `tfp.sts.MaskedTimeSeries` including a
      mask `Tensor` to encode the locations of missing observations.
    parameter_samples: Python `list` of `Tensors` representing posterior samples
      of model parameters, with shapes `[concat([[num_posterior_draws],
      param.prior.batch_shape, param.prior.event_shape]) for param in
      model.parameters]`. This may optionally also be a map (Python `dict`) of
      parameter names to `Tensor` values.

  Returns:
    forecast_dist: a `tfd.MixtureSameFamily` instance with event shape
      [num_timesteps] and
      batch shape `concat([sample_shape, model.batch_shape])`, with
      `num_posterior_draws` mixture components. The `t`th step represents the
      forecast distribution `p(observed_time_series[t] |
      observed_time_series[0:t-1], parameter_samples)`.

  #### Examples

  Suppose we've built a model and fit it to data using HMC:

  ```python
    day_of_week = tfp.sts.Seasonal(
        num_seasons=7,
        observed_time_series=observed_time_series,
        name='day_of_week')
    local_linear_trend = tfp.sts.LocalLinearTrend(
        observed_time_series=observed_time_series,
        name='local_linear_trend')
    model = tfp.sts.Sum(components=[day_of_week, local_linear_trend],
                        observed_time_series=observed_time_series)

    samples, kernel_results = tfp.sts.fit_with_hmc(model, observed_time_series)
  ```

  Passing the posterior samples into `one_step_predictive`, we construct a
  one-step-ahead predictive distribution:

  ```python
    one_step_predictive_dist = tfp.sts.one_step_predictive(
      model, observed_time_series, parameter_samples=samples)

    predictive_means = one_step_predictive_dist.mean()
    predictive_scales = one_step_predictive_dist.stddev()
  ```

  If using variational inference instead of HMC, we'd construct a forecast using
  samples from the variational posterior:

  ```python
    (variational_loss,
     variational_distributions) = tfp.sts.build_factored_variational_loss(
       model=model, observed_time_series=observed_time_series)

    # OMITTED: take steps to optimize variational loss

    samples = {k: q.sample(30) for (k, q) in variational_distributions.items()}
    one_step_predictive_dist = tfp.sts.one_step_predictive(
      model, observed_time_series, parameter_samples=samples)
  ```

  We can visualize the forecast by plotting:

  ```python
    from matplotlib import pylab as plt
    def plot_one_step_predictive(observed_time_series,
                                 forecast_mean,
                                 forecast_scale):
      plt.figure(figsize=(12, 6))
      num_timesteps = forecast_mean.shape[-1]
      c1, c2 = (0.12, 0.47, 0.71), (1.0, 0.5, 0.05)
      plt.plot(observed_time_series, label="observed time series", color=c1)
      plt.plot(forecast_mean, label="one-step prediction", color=c2)
      plt.fill_between(np.arange(num_timesteps),
                       forecast_mean - 2 * forecast_scale,
                       forecast_mean + 2 * forecast_scale,
                       alpha=0.1, color=c2)
      plt.legend()

    plot_one_step_predictive(observed_time_series,
                             forecast_mean=predictive_means,
                             forecast_scale=predictive_scales)
  ```

  To detect anomalous timesteps, we check whether the observed value at each
  step is within a 95% predictive interval, i.e., two standard deviations from
  the mean:

  ```python
    z_scores = ((observed_time_series[..., 1:] - predictive_means[..., :-1])
                 / predictive_scales[..., :-1])
    anomalous_timesteps = tf.boolean_mask(
        tf.range(1, num_timesteps),
        tf.abs(z_scores) > 2.0)
  ```

Compute one - step - ahead predictive distributions for all timesteps .


(cls, typ: HdlType, ctx: SerializerCtx, declaration=False)

return sFn(typ, ctx, declaration=declaration)

def
Func
(
arg_0
,
arg_1
:
Func
,
arg_3
:
arg_4
,
arg_5
=
False
)
:
if
isinstance
(
arg_1
,
Bits
)
:
arg_6
=
arg_0
.
HdlType_bits
elif
isinstance
(
arg_1
,
HEnum
)
:
arg_6
=
arg_0
.
HdlType_enum
elif
isinstance
(
arg_1
,
HArray
)
:
arg_6
=
arg_0
.
HdlType_array
elif
isinstance
(
arg_1
,
Integer
)
:
arg_6
=
arg_0
.
HdlType_int
elif
isinstance
(
arg_1
,
HBool
)
:
arg_6
=
arg_0
.
HdlType_bool
else
:
raise
NotImplementedError
(
"type declaration is not implemented"
" for type %s"
%
(
arg_1
.
name
)
)
return
arg_6
(
arg_1
,
arg_3
,
arg_5
=
arg_5
)

def Func(arg_0, arg_1: Func, arg_3: arg_4, arg_5=False):
        """
        Serialize HdlType instance
        """
        if isinstance(arg_1, Bits):
            arg_6 = arg_0.HdlType_bits
        elif isinstance(arg_1, HEnum):
            arg_6 = arg_0.HdlType_enum
        elif isinstance(arg_1, HArray):
            arg_6 = arg_0.HdlType_array
        elif isinstance(arg_1, Integer):
            arg_6 = arg_0.HdlType_int
        elif isinstance(arg_1, HBool):
            arg_6 = arg_0.HdlType_bool
        else:
            raise NotImplementedError("type declaration is not implemented"
                                      " for type %s"
                                      % (arg_1.name))

        return arg_6(arg_1, arg_3, arg_5=arg_5)


def Func ( arg_0 , arg_1 ) : try : arg_2 = arg_0 . error_code_pattern . match ( str ( arg_1 ) ) arg_3 = int ( arg_2 . group ( 0 ) ) return arg_3 except ValueError : return arg_1

def Func(arg_0, arg_1):
        """Extract error code from ftp exception"""
        try:
            arg_2 = arg_0.error_code_pattern.match(str(arg_1))
            arg_3 = int(arg_2.group(0))
            return arg_3
        except ValueError:
            return arg_1


def
Func
(
arg_0
,
arg_1
)
:
try
:
arg_0
.
_rwlock
.
writer_acquire
(
)
del
arg_0
.
_tasks
[
arg_1
]
except
KeyError
:
raise
NotFoundError
(
element
=
str
(
arg_1
)
)
finally
:
arg_0
.
_rwlock
.
writer_release
(
)
logger
.
debug
(
"Task %s Funcd from the registry"
,
str
(
arg_1
)
)

def Func(arg_0, arg_1):
        """Remove a task from the registry.

        To Func it, pass its identifier with `taks_id` parameter.
        When the identifier is not found, a `NotFoundError` exception
        is raised.

        :param task_id: identifier of the task to Func

        :raises NotFoundError: raised when the given task identifier
            is not found on the registry
        """
        try:
            arg_0._rwlock.writer_acquire()
            del arg_0._tasks[arg_1]
        except KeyError:
            raise NotFoundError(element=str(arg_1))
        finally:
            arg_0._rwlock.writer_release()

        logger.debug("Task %s Funcd from the registry", str(arg_1))

Remove a task from the registry.

        To remove it, pass its identifier with `taks_id` parameter.
        When the identifier is not found, a `NotFoundError` exception
        is raised.

        :param task_id: identifier of the task to remove

        :raises NotFoundError: raised when the given task identifier
            is not found on the registry


def Func ( arg_0 , arg_1 , arg_2 ) : arg_3 = { 'name' : 'fc' , 't0' : arg_1 + arg_2 . start_time , 'ch' : arg_2 . channels [ 0 ] . name , 'phase' : arg_2 . command . phase } return arg_0 . _qobj_model ( ** arg_3 )

def Func(arg_0, arg_1, arg_2):
        """Return converted `FrameChangeInstruction`.

        Args:
            shift(int): Offset time.
            instruction (FrameChangeInstruction): frame change instruction.
        Returns:
            dict: Dictionary of required parameters.
        """
        arg_3 = {
            'name': 'fc',
            't0': arg_1+arg_2.start_time,
            'ch': arg_2.channels[0].name,
            'phase': arg_2.command.phase
        }
        return arg_0._qobj_model(**arg_3)

Return converted FrameChangeInstruction .


def Func ( arg_0 ) : arg_1 = collections . defaultdict ( list ) for arg_2 , arg_3 in arg_0 : arg_1 [ arg_2 ] . append ( arg_3 ) return normalize_dict ( dict ( arg_1 ) )

def Func(arg_0):
    """
    Converts list of tuples to dictionary with duplicate keys converted to
    lists.

    :param list items:
        List of tuples.

    :returns:
        :class:`dict`

    """

    arg_1 = collections.defaultdict(list)

    for arg_2, arg_3 in arg_0:
        arg_1[arg_2].append(arg_3)

    return normalize_dict(dict(arg_1))

Converts list of tuples to dictionary with duplicate keys converted to lists .


def Func(arg_0):
        # type: () -> None
        """
        Finalizes the bundle, preparing it to be attached to the Tangle.
        """
        if arg_0.hash:
            raise RuntimeError('Bundle is already Funcd.')

        if not arg_0:
            raise ValueError('Bundle has no transactions.')

        # Quick validation.
        arg_1 = arg_0.balance

        if arg_1 < 0:
            if arg_0.change_address:
                arg_0.add_transaction(ProposedTransaction(
                    address=arg_0.change_address,
                    value=-arg_1,
                    tag=arg_0.tag,
                ))
            else:
                raise ValueError(
                    'Bundle has unspent inputs (balance: {balance}); '
                    'use ``send_unspent_inputs_to`` to create '
                    'change transaction.'.format(
                        arg_1=arg_1,
                    ),
                )
        elif arg_1 > 0:
            raise ValueError(
                'Inputs are insufficient to cover bundle spend '
                '(balance: {balance}).'.format(
                    arg_1=arg_1,
                ),
            )

        # Generate bundle hash.
        while True:
            arg_2 = Kerl()
            arg_3 = len(arg_0) - 1

            for arg_4, arg_5 in enumerate(arg_0):
                arg_5.current_index = arg_4
                arg_5.last_index = arg_3

                arg_2.absorb(arg_5.get_signature_validation_trytes().as_trits())

            arg_7 = [0] * HASH_LENGTH
            arg_2.squeeze(arg_7)

            arg_8 = BundleHash.from_trits(arg_7)

            # Check that we generated a secure bundle hash.
            # https://github.com/iotaledger/iota.lib.py/issues/84
            if any(13 in arg_9 for arg_9 in normalize(arg_8)):
                # Increment the legacy tag and try again.
                arg_10 = (
                    arg_0.tail_transaction
                )  # type: ProposedTransaction
                arg_10.increment_legacy_tag()
            else:
                break

        # Copy bundle hash to individual transactions.
        for arg_5 in arg_0:
            arg_5.bundle_hash = arg_8

            # Initialize signature/message fragment.
            arg_5.signature_message_fragment = Fragment(arg_5.message or b'')

Finalizes the bundle preparing it to be attached to the Tangle .


def Func(arg_0):
    """
    This function will extract the various operation time for GLRM model building iterations.

    :param javaLogText:
    :return:
    """
    global g_initialXY
    global g_reguarlize_Y
    global g_regularize_X_objective
    global g_updateX
    global g_updateY
    global g_objective
    global g_stepsize
    global g_history

    if os.path.isfile(arg_0):

        arg_1 = dict()
        arg_1["total time (ms)"] = []
        arg_1["initialXY (ms)"] = []
        arg_1["regularize Y (ms)"] = []
        arg_1["regularize X and objective (ms)"] = []
        arg_1["update X (ms)"] = []
        arg_1["update Y (ms)"] = []
        arg_1["objective (ms)"] = []
        arg_1["step size (ms)"] = []
        arg_1["update history (ms)"] = []

        arg_2 = -1
        arg_3 = 0.0
        with open(arg_0, 'r') as thefile:   # go into tempfile and grab test run info
            for arg_4 in thefile:
                arg_5 = arg_4.split()

                if len(arg_5) > 0:
                    arg_3 = arg_5[-1].replace('\\','')

                if g_initialXY in arg_4:    # start of a new file
                    if arg_2 > 0:  # update total run time
                        arg_1["total time (ms)"].append(arg_2)
                        arg_2 = 0.0
                    else:
                        arg_2 = 0.0

                    arg_1["initialXY (ms)"].append(float(arg_3))
                    arg_2 = arg_2+float(arg_3)

                if g_reguarlize_Y in arg_4:
                    arg_1["regularize Y (ms)"].append(float(arg_3))
                    arg_2 = arg_2+float(arg_3)

                if g_regularize_X_objective in arg_4:
                    arg_1["regularize X and objective (ms)"].append(float(arg_3))
                    arg_2 = arg_2+float(arg_3)

                if g_updateX in arg_4:
                    arg_1["update X (ms)"].append(float(arg_3))
                    arg_2 = arg_2+float(arg_3)

                if g_updateY in arg_4:
                    arg_1["update Y (ms)"].append(float(arg_3))
                    arg_2 = arg_2+float(arg_3)

                if g_objective in arg_4:
                    arg_1["objective (ms)"].append(float(arg_3))
                    arg_2 = arg_2+float(arg_3)

                if g_stepsize in arg_4:
                    arg_1["step size (ms)"].append(float(arg_3))
                    arg_2 = arg_2+float(arg_3)

                if g_history in arg_4:
                    arg_1["update history (ms)"].append(float(arg_3))
                    arg_2 = arg_2+float(arg_3)

        arg_1["total time (ms)"].append(arg_2)    # save the last one
        print("Run result summary: \n {0}".format(arg_1))

    else:
        print("Cannot find your java log file.  Nothing is done.\n")

This function will extract the various operation time for GLRM model building iterations .


(template_filename,
                              database_filename,
                              config_filename)

def Func(arg_0,
                              arg_1,
                              arg_2):
    """Create sample template email and database."""
    print("Creating sample template email {}".format(arg_0))
    if os.path.exists(arg_0):
        print("Error: file exists: " + arg_0)
        sys.exit(1)
    with io.open(arg_0, "w") as template_file:
        template_file.write(
            u"TO: {{email}}\n"
            u"SUBJECT: Testing mailmerge\n"
            u"FROM: My Self <myself@mydomain.com>\n"
            u"\n"
            u"Hi, {{name}},\n"
            u"\n"
            u"Your number is {{number}}.\n"
        )
    print("Creating sample database {}".format(arg_1))
    if os.path.exists(arg_1):
        print("Error: file exists: " + arg_1)
        sys.exit(1)
    with io.open(arg_1, "w") as database_file:
        database_file.write(
            u'email,name,number\n'
            u'myself@mydomain.com,"Myself",17\n'
            u'bob@bobdomain.com,"Bob",42\n'
        )
    print("Creating sample config file {}".format(arg_2))
    if os.path.exists(arg_2):
        print("Error: file exists: " + arg_2)
        sys.exit(1)
    with io.open(arg_2, "w") as config_file:
        config_file.write(
            u"# Example: GMail\n"
            u"[smtp_server]\n"
            u"host = smtp.gmail.com\n"
            u"port = 465\n"
            u"security = SSL/TLS\n"
            u"username = YOUR_USERNAME_HERE\n"
            u"#\n"
            u"# Example: Wide open\n"
            u"# [smtp_server]\n"
            u"# host = open-smtp.example.com\n"
            u"# port = 25\n"
            u"# security = Never\n"
            u"# username = None\n"
            u"#\n"
            u"# Example: University of Michigan\n"
            u"# [smtp_server]\n"
            u"# host = smtp.mail.umich.edu\n"
            u"# port = 465\n"
            u"# security = SSL/TLS\n"
            u"# username = YOUR_USERNAME_HERE\n"
            u"#\n"
            u"# Example: University of Michigan EECS Dept., with STARTTLS security\n"  # noqa: E501
            u"# [smtp_server]\n"
            u"# host = newman.eecs.umich.edu\n"
            u"# port = 25\n"
            u"# security = STARTTLS\n"
            u"# username = YOUR_USERNAME_HERE\n"
            u"#\n"
            u"# Example: University of Michigan EECS Dept., with no encryption\n"  # noqa: E501
            u"# [smtp_server]\n"
            u"# host = newman.eecs.umich.edu\n"
            u"# port = 25\n"
            u"# security = Never\n"
            u"# username = YOUR_USERNAME_HERE\n"
        )
    print("Edit these files, and then run mailmerge again")

Create sample template email and database .


def
Func
(
arg_0
,
arg_1
=
False
,
arg_2
=
)
:
def
list_methods
(
)
:
arg_3
=
[
]
if
arg_0
in
_OntarioExposureLimits
and
(
_OntarioExposureLimits
[
arg_0
]
[
"Func (ppm)"
]
or
_OntarioExposureLimits
[
arg_0
]
[
"Func (mg/m^3)"
]
)
:
arg_3
.
append
(
ONTARIO
)
arg_3
.
append
(
)
return
arg_3
if
arg_1
:
return
list_methods
(
)
if
not
arg_2
:
arg_2
=
list_methods
(
)
[
0
]
if
arg_2
==
ONTARIO
:
if
_OntarioExposureLimits
[
arg_0
]
[
"Func (ppm)"
]
:
arg_4
=
(
_OntarioExposureLimits
[
arg_0
]
[
"Func (ppm)"
]
,
'ppm'
)
elif
_OntarioExposureLimits
[
arg_0
]
[
"Func (mg/m^3)"
]
:
arg_4
=
(
_OntarioExposureLimits
[
arg_0
]
[
"Func (mg/m^3)"
]
,
'mg/m^3'
)
elif
arg_2
==
:
arg_4
=
else
:
raise
Exception
(
'Failure in in function'
)
return
arg_4

def Func(arg_0, arg_1=False, arg_2=None):  # pragma: no cover
    '''This function handles the retrieval of Func limits on worker
    exposure to dangerous chemicals.

    This API is considered experimental, and is expected to be removed in a
    future release in favor of a more complete object-oriented interface.

    >>> Func('75-07-0')
    (25.0, 'ppm')
    >>> Func('1395-21-7')
    (6e-05, 'mg/m^3')
    >>> Func('7572-29-4', AvailableMethods=True)
    ['Ontario Limits', 'None']
    '''
    def list_methods():
        arg_3 = []
        if arg_0 in _OntarioExposureLimits and (_OntarioExposureLimits[arg_0]["Func (ppm)"] or _OntarioExposureLimits[arg_0]["Func (mg/m^3)"]):
            arg_3.append(ONTARIO)
        arg_3.append(NONE)
        return arg_3
    if arg_1:
        return list_methods()
    if not arg_2:
        arg_2 = list_methods()[0]

    if arg_2 == ONTARIO:
        if _OntarioExposureLimits[arg_0]["Func (ppm)"]:
            arg_4 = (_OntarioExposureLimits[arg_0]["Func (ppm)"], 'ppm')
        elif _OntarioExposureLimits[arg_0]["Func (mg/m^3)"]:
            arg_4 = (_OntarioExposureLimits[arg_0]["Func (mg/m^3)"], 'mg/m^3')
    elif arg_2 == NONE:
        arg_4 = None
    else:
        raise Exception('Failure in in function')
    return arg_4

This function handles the retrieval of Ceiling limits on worker
    exposure to dangerous chemicals.

    This API is considered experimental, and is expected to be removed in a
    future release in favor of a more complete object-oriented interface.

    >>> Ceiling('75-07-0')
    (25.0, 'ppm')
    >>> Ceiling('1395-21-7')
    (6e-05, 'mg/m^3')
    >>> Ceiling('7572-29-4', AvailableMethods=True)
    ['Ontario Limits', 'None']

This function handles the retrieval of Ceiling limits on worker exposure to dangerous chemicals .


def Func ( arg_0 , ** arg_1 ) : arg_2 = describe_flow_logs ( Filters = [ { "Name" : "resource-id" , "Values" : [ arg_0 [ "id" ] ] } ] , ** arg_1 ) arg_3 = [ ] for arg_4 in arg_2 : arg_3 . append ( arg_4 [ "FlowLogId" ] ) return arg_3

def Func(arg_0, **arg_1):
    """Gets the VPC Flow Logs for a VPC"""
    arg_2 = describe_flow_logs(Filters=[{"Name": "resource-id", "Values": [arg_0["id"]]}], **arg_1)

    arg_3 = []
    for arg_4 in arg_2:
        arg_3.append(arg_4["FlowLogId"])

    return arg_3


def Func ( arg_0 , arg_1 , arg_2 ) : arg_3 = datetime . datetime . utcnow ( ) . replace ( tzinfo = pytz . timezone ( "GMT" ) ) arg_0 . templates [ arg_2 ] = { "tmplt" : arg_1 . json ( ) , "updated" : arg_3 } return copy . deepcopy ( arg_1 . json ( ) )

def Func(arg_0, arg_1, arg_2):
        """
        Add a retrieved template to the cache for 304 checking
        accepts a dict and key name, adds the retrieval time, and adds both
        to self.templates as a new dict using the specified key
        """
        # cache template and retrieval time for subsequent calls
        arg_3 = datetime.datetime.utcnow().replace(tzinfo=pytz.timezone("GMT"))
        arg_0.templates[arg_2] = {"tmplt": arg_1.json(), "updated": arg_3}
        return copy.deepcopy(arg_1.json())

Add a retrieved template to the cache for 304 checking accepts a dict and key name adds the retrieval time and adds both to self . templates as a new dict using the specified key


def Func ( arg_0 ) : arg_1 = arg_0 . get_savable_variables ( ) if arg_1 is None or len ( arg_1 ) == 0 : arg_0 . _saver = None return arg_3 = arg_0 . _get_base_variable_scope ( ) arg_4 = { strip_name_scope ( v . name , arg_3 ) : v for v in arg_1 } arg_0 . _saver = tf . train . Saver ( var_list = arg_4 , reshape = False , sharded = False , max_to_keep = 5 , keep_checkpoint_every_n_hours = 10000.0 , name = None , restore_sequentially = False , saver_def = None , builder = None , defer_build = False , allow_empty = True , write_version = tf . train . SaverDef . V2 , pad_step_number = False , save_relative_paths = True )

def Func(arg_0):
        """
        Registers the saver operations to the graph in context.
        """

        arg_1 = arg_0.get_savable_variables()
        if arg_1 is None or len(arg_1) == 0:
            arg_0._saver = None
            return

        arg_3 = arg_0._get_base_variable_scope()
        arg_4 = {strip_name_scope(v.name, arg_3): v for v in arg_1}

        arg_0._saver = tf.train.Saver(
            var_list=arg_4,
            reshape=False,
            sharded=False,
            max_to_keep=5,
            keep_checkpoint_every_n_hours=10000.0,
            name=None,
            restore_sequentially=False,
            saver_def=None,
            builder=None,
            defer_build=False,
            allow_empty=True,
            write_version=tf.train.SaverDef.V2,
            pad_step_number=False,
            save_relative_paths=True
        )

Registers the saver operations to the graph in context .


def Func ( arg_0 , * arg_1 ) : arg_2 = hmac . new ( six . b ( arg_0 . secret ) , digestmod = hashlib . sha1 ) arg_2 . update ( six . b ( '|' . join ( arg_1 ) ) ) return arg_2 . hexdigest ( )

def Func(arg_0, *arg_1):
        """
        Creates signature for the session.
        """
        arg_2 = hmac.new(six.b(arg_0.secret), digestmod=hashlib.sha1)
        arg_2.update(six.b('|'.join(arg_1)))
        return arg_2.hexdigest()


"""\SELECT row_to_json(combined_rows) FROM (  SELECT id, key, name, groups FROM api_keys) AS combined_rows"""

def Func(arg_0):
    """Return the list of API keys."""
    with db_connect() as db_conn:
        with db_conn.cursor() as cursor:
            cursor.execute("""\
SELECT row_to_json(combined_rows) FROM (
  SELECT id, key, name, groups FROM api_keys
) AS combined_rows""")
            arg_1 = [x[0] for x in cursor.fetchall()]

    return arg_1


def Func ( arg_0 , arg_1 = 100 , arg_2 = False , arg_3 = False ) : if arg_1 == 0 : return arg_0 . dataY arg_4 = cm . Func ( arg_0 . dataY , arg_1 ) if arg_3 : arg_0 . dataY = arg_0 . dataY - arg_4 elif arg_2 : arg_0 . dataY = arg_4 else : return arg_4

def Func(arg_0,arg_1=100,arg_2=False,arg_3=False):
        """RETURNS filtered trace. Desn't filter it in place."""
        if arg_1==0:
            return arg_0.dataY
        arg_4=cm.Func(arg_0.dataY,arg_1)
        if arg_3:
            arg_0.dataY=arg_0.dataY-arg_4
        elif arg_2:
            arg_0.dataY=arg_4
        else:
            return arg_4

RETURNS filtered trace . Desn t filter it in place .


def Func ( arg_0 ) : arg_1 = subprocess . check_output ( [ 'id' , '-u' , arg_0 ] ) arg_2 = subprocess . check_output ( [ 'launchctl' , 'asuser' , arg_1 , 'launchctl' , 'getenv' , 'SSH_AUTH_SOCK' ] ) . rstrip ( ) _set_ssh_auth_sock ( arg_2 )

def Func(arg_0):
    """Starting with Yosemite, launchd was rearchitected and now only one
    launchd process runs for all users. This allows us to much more easily
    impersonate a user through launchd and extract the environment
    variables from their running processes."""
    arg_1 = subprocess.check_output(['id', '-u', arg_0])
    arg_2 = subprocess.check_output(['launchctl', 'asuser', arg_1, 'launchctl', 'getenv', 'SSH_AUTH_SOCK']).rstrip()
    _set_ssh_auth_sock(arg_2)

Starting with Yosemite launchd was rearchitected and now only one launchd process runs for all users . This allows us to much more easily impersonate a user through launchd and extract the environment variables from their running processes .


(source, destination, name, add_to_global, force)

def
Func
(
arg_0
,
arg_1
,
arg_2
,
arg_3
,
arg_4
)
:
if
arg_0
[
1
]
==
"/"
:
arg_0
=
arg_0
[
:
1
]
if
not
arg_2
:
arg_2
=
os
.
path
.
split
(
arg_0
)
[
1
]
elif
arg_2
.
endswith
(
".docset"
)
:
arg_2
=
arg_2
.
replace
(
".docset"
,
""
)
if
arg_3
:
arg_1
=
DEFAULT_DOCSET_PATH
arg_5
=
os
.
path
.
join
(
arg_1
or
""
,
arg_2
+
".docset"
)
arg_6
=
os
.
path
.
lexists
(
arg_5
)
if
arg_6
and
arg_4
:
shutil
.
rmtree
(
arg_5
)
elif
arg_6
:
log
.
error
(
'Destination path "{}" already exists.'
.
format
(
click
.
format_filename
(
arg_5
)
)
)
raise
SystemExit
(
errno
.
EEXIST
)
return
arg_0
,
arg_5
,
arg_2

def Func(arg_0, arg_1, arg_2, arg_3, arg_4):
    """
    Determine source and destination using the options.
    """
    if arg_0[-1] == "/":
        arg_0 = arg_0[:-1]
    if not arg_2:
        arg_2 = os.path.split(arg_0)[-1]
    elif arg_2.endswith(".docset"):
        arg_2 = arg_2.replace(".docset", "")
    if arg_3:
        arg_1 = DEFAULT_DOCSET_PATH
    arg_5 = os.path.join(arg_1 or "", arg_2 + ".docset")
    arg_6 = os.path.lexists(arg_5)
    if arg_6 and arg_4:
        shutil.rmtree(arg_5)
    elif arg_6:
        log.error(
            'Destination path "{}" already exists.'.format(
                click.format_filename(arg_5)
            )
        )
        raise SystemExit(errno.EEXIST)
    return arg_0, arg_5, arg_2

Determine source and destination using the options .


def Func(arg_0, arg_1, arg_2, arg_3):
        """The first-order Barnes approximation"""
        if not arg_0.blocksize:
            arg_4 = arg_0._distance_matrix(arg_1, arg_0.x)
            arg_5 = arg_0._weight(arg_4, arg_3=arg_3)
            return arg_5.dot(arg_2) / arg_5.sum(axis=1)
        else:
            # Now rather than calculating the distance matrix all at once,
            # we do it in chunks over rvecs
            arg_6 = np.zeros(arg_1.shape[0], dtype='float')
            arg_7 = arg_0.blocksize
            for arg_8 in range(0, arg_1.shape[0], arg_7):
                arg_9 = arg_0._distance_matrix(arg_1[arg_8:arg_8+arg_7], arg_0.x)
                arg_10 = arg_0._weight(arg_9, arg_3=arg_3)
                arg_6[arg_8:arg_8+arg_7] += arg_10.dot(arg_2) / arg_10.sum(axis=1)
            return arg_6


def Func ( arg_0 , * arg_1 , ** arg_2 ) : arg_3 = arg_2 . pop ( "exc_info" , False ) stdFunc . warning ( str ( arg_0 ) . format ( * arg_1 , ** arg_2 ) , arg_3 = arg_3 )

def Func(arg_0, *arg_1, **arg_2):
    '''print format_msg to stdFunc'''
    arg_3 = arg_2.pop("exc_info", False)
    stdFunc.warning(str(arg_0).format(*arg_1, **arg_2), arg_3=arg_3)


return [os.path.split(s)[1] for s in glob.glob(path)]

def Func ( arg_0 , arg_1 ) : arg_2 = os . path . join ( arg_0 . uri , arg_1 ) return [ os . path . split ( arg_3 ) [ 1 ] for arg_3 in glob . glob ( arg_2 ) ]

def Func(arg_0, arg_1):
        """
        Return just the filenames that match `glob_str` inside the store directory.

        :param str glob_str: A glob string, i.e. 'state_*'
        :return: list of matched keys
        """
        arg_2 = os.path.join(arg_0.uri, arg_1)
        return [os.path.split(arg_3)[1] for arg_3 in glob.glob(arg_2)]

Return just the filenames that match glob_str inside the store directory .