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eventql/eventql | 7ca0dbb2e683b525620ea30dc40540a22d5eb227 | deps/3rdparty/spidermonkey/mozjs/build/pymake/pymake/data.py | python | Makefile.finishparsing | (self) | Various activities, such as "eval", are not allowed after parsing is
finished. In addition, various warnings and errors can only be issued
after the parsing data model is complete. All dependency resolution
and rule execution requires that parsing be finished. | Various activities, such as "eval", are not allowed after parsing is
finished. In addition, various warnings and errors can only be issued
after the parsing data model is complete. All dependency resolution
and rule execution requires that parsing be finished. | [
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"""
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finished. In addition, various warnings and errors can only be issued
after the parsing data model is complete. All dependency resolution
and rule execution requires that parsing be finished.
"""
self.parsingfinished = True
flavor, source, value = self.variables.get('GPATH')
if value is not None and value.resolvestr(self, self.variables, ['GPATH']).strip() != '':
raise DataError('GPATH was set: pymake does not support GPATH semantics')
flavor, source, value = self.variables.get('VPATH')
if value is None:
self._vpath = []
else:
self._vpath = filter(lambda e: e != '',
re.split('[%s\s]+' % os.pathsep,
value.resolvestr(self, self.variables, ['VPATH'])))
targets = list(self._targets.itervalues())
for t in targets:
t.explicit = True
for r in t.rules:
for p in r.prerequisites:
self.gettarget(p).explicit = True
np = self.gettarget('.NOTPARALLEL')
if len(np.rules):
self.context = process.getcontext(1)
flavor, source, value = self.variables.get('.DEFAULT_GOAL')
if value is not None:
self.defaulttarget = value.resolvestr(self, self.variables, ['.DEFAULT_GOAL']).strip()
self.error = False | [
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Tencent/CMONGO | c40380caa14e05509f46993aa8b8da966b09b0b5 | buildscripts/cpplint.py | python | _IncludeState.IsInAlphabeticalOrder | (self, clean_lines, linenum, header_path) | return True | Check if a header is in alphabetical order with the previous header.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
header_path: Canonicalized header to be checked.
Returns:
Returns true if the header is in alphabetical order. | Check if a header is in alphabetical order with the previous header. | [
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"""Check if a header is in alphabetical order with the previous header.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
header_path: Canonicalized header to be checked.
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arangodb/arangodb | 0d658689c7d1b721b314fa3ca27d38303e1570c8 | 3rdParty/V8/v7.9.317/third_party/jinja2/sandbox.py | python | SandboxedEnvironment.call_binop | (self, context, operator, left, right) | return self.binop_table[operator](left, right) | For intercepted binary operator calls (:meth:`intercepted_binops`)
this function is executed instead of the builtin operator. This can
be used to fine tune the behavior of certain operators.
.. versionadded:: 2.6 | For intercepted binary operator calls (:meth:`intercepted_binops`)
this function is executed instead of the builtin operator. This can
be used to fine tune the behavior of certain operators. | [
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"""For intercepted binary operator calls (:meth:`intercepted_binops`)
this function is executed instead of the builtin operator. This can
be used to fine tune the behavior of certain operators.
.. versionadded:: 2.6
"""
return self.binop_table[operator](left, right) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/s3transfer/futures.py | python | TransferCoordinator.status | (self) | return self._status | The status of the TransferFuture
The currently supported states are:
* not-started - Has yet to start. If in this state, a transfer
can be canceled immediately and nothing will happen.
* queued - SubmissionTask is about to submit tasks
* running - Is inprogress. In-progress as of now means that
the SubmissionTask that runs the transfer is being executed. So
there is no guarantee any transfer requests had been made to
S3 if this state is reached.
* cancelled - Was cancelled
* failed - An exception other than CancelledError was thrown
* success - No exceptions were thrown and is done. | The status of the TransferFuture | [
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"""The status of the TransferFuture
The currently supported states are:
* not-started - Has yet to start. If in this state, a transfer
can be canceled immediately and nothing will happen.
* queued - SubmissionTask is about to submit tasks
* running - Is inprogress. In-progress as of now means that
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there is no guarantee any transfer requests had been made to
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"""
return self._status | [
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CGRU/cgru | 1881a4128530e3d31ac6c25314c18314fc50c2c7 | afanasy/python/af.py | python | Block.setFiles | (self, files, TransferToServer=True) | Missing DocString
:param files:
:param TransferToServer:
:return: | Missing DocString | [
"Missing",
"DocString"
] | def setFiles(self, files, TransferToServer=True):
"""Missing DocString
:param files:
:param TransferToServer:
:return:
"""
if "files" not in self.data:
self.data["files"] = []
for afile in files:
if TransferToServer:
afile = Pathmap.toServer(afile)
self.data["files"].append(afile) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/tkinter/__init__.py | python | Text.tag_names | (self, index=None) | return self.tk.splitlist(
self.tk.call(self._w, 'tag', 'names', index)) | Return a list of all tag names. | Return a list of all tag names. | [
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] | def tag_names(self, index=None):
"""Return a list of all tag names."""
return self.tk.splitlist(
self.tk.call(self._w, 'tag', 'names', index)) | [
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htcondor/htcondor | 4829724575176d1d6c936e4693dfd78a728569b0 | src/condor_contrib/condor_pigeon/src/condor_pigeon_client/skype_linux_tools/Skype4Py/call.py | python | ICall.Answer | (self) | Answers the call. | Answers the call. | [
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'''Answers the call.
'''
self._Property('STATUS', 'INPROGRESS') | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/s3/bucketlistresultset.py | python | bucket_lister | (bucket, prefix='', delimiter='', marker='', headers=None,
encoding_type=None) | A generator function for listing keys in a bucket. | A generator function for listing keys in a bucket. | [
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encoding_type=None):
"""
A generator function for listing keys in a bucket.
"""
more_results = True
k = None
while more_results:
rs = bucket.get_all_keys(prefix=prefix, marker=marker,
delimiter=delimiter, headers=headers,
encoding_type=encoding_type)
for k in rs:
yield k
if k:
marker = rs.next_marker or k.name
if marker and encoding_type == "url":
if isinstance(marker, six.text_type):
marker = marker.encode('utf-8')
marker = urllib.parse.unquote(marker)
more_results= rs.is_truncated | [
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eclipse/sumo | 7132a9b8b6eea734bdec38479026b4d8c4336d03 | tools/contributed/sumopy/coremodules/demand/virtualpop.py | python | BikeStrategy.plan | (self, ids_person, logger=None, **kwargs) | return True | Generates a plan for these person according to this strategie.
Overriden by specific strategy. | Generates a plan for these person according to this strategie.
Overriden by specific strategy. | [
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"""
Generates a plan for these person according to this strategie.
Overriden by specific strategy.
"""
#make_plans_private(self, ids_person = None, mode = 'passenger')
# routing necessary?
virtualpop = self.get_virtualpop()
plans = virtualpop.get_plans() # self._plans
walkstages = plans.get_stagetable('walks')
ridestages = plans.get_stagetable('bikerides')
activitystages = plans.get_stagetable('activities')
activities = virtualpop.get_activities()
activitytypes = virtualpop.get_demand().activitytypes
landuse = virtualpop.get_landuse()
facilities = landuse.facilities
#parking = landuse.parking
scenario = virtualpop.get_scenario()
edges = scenario.net.edges
lanes = scenario.net.lanes
modes = scenario.net.modes
#times_est_plan = plans.times_est
# here we can determine edge weights for different modes
plans.prepare_stagetables(['walks', 'bikerides', 'activities'])
# get initial travel times for persons.
# initial travel times depend on the initial activity
# landuse.parking.clear_booking()
ids_person_act, ids_act_from, ids_act_to\
= virtualpop.get_activities_from_pattern(0, ids_person=ids_person)
if len(ids_person_act) == 0:
print 'WARNING in BikeStrategy.plan: no eligible persons found.'
return False
# ok
# temporary maps from ids_person to other parameters
nm = np.max(ids_person_act)+1
map_ids_plan = np.zeros(nm, dtype=np.int32)
map_ids_plan[ids_person_act] = virtualpop.add_plans(ids_person_act, id_strategy=self.get_id_strategy())
map_times = np.zeros(nm, dtype=np.int32)
map_times[ids_person_act] = activities.get_times_end(ids_act_from, pdf='unit')
# set start time to plans (important!)
plans.times_begin[map_ids_plan[ids_person_act]] = map_times[ids_person_act]
map_ids_fac_from = np.zeros(nm, dtype=np.int32)
map_ids_fac_from[ids_person_act] = activities.ids_facility[ids_act_from]
#map_ids_parking_from = np.zeros(nm, dtype = np.int32)
# ids_parking_from, inds_vehparking = parking.get_closest_parkings( virtualpop.ids_iauto[ids_person_act],
# facilities.centroids[activities.ids_facility[ids_act_from]])
# if len(ids_parking_from)==0:
# return False
# err
#map_ids_parking_from[ids_person_act] = ids_parking_from
n_plans = len(ids_person_act)
print 'BikeStrategy.plan n_plans=', n_plans
# print ' map_ids_parking_from[ids_person_act].shape',map_ids_parking_from[ids_person_act].shape
# set initial activity
# this is because the following steps start with travel
# and set the next activity
#names_acttype_from = activitytypes.names[activities.ids_activitytype[ids_act_from]]
# for id_plan
ind_act = 0
# make initial activity stage
ids_edge_from = facilities.ids_roadedge_closest[map_ids_fac_from[ids_person_act]]
poss_edge_from = facilities.positions_roadedge_closest[map_ids_fac_from[ids_person_act]]
poss_edge_from = self.clip_positions(poss_edge_from, ids_edge_from)
# this is the time when first activity starts
# first activity is normally not simulated
names_acttype_from = activitytypes.names[activities.ids_activitytype[ids_act_from]]
durations_act_from = activities.get_durations(ids_act_from)
times_from = map_times[ids_person_act]-durations_act_from
#times_from = activities.get_times_end(ids_act_from, pdf = 'unit')
# do initial stage
# could be common to all strategies
for id_plan,\
time,\
id_act_from,\
name_acttype_from,\
duration_act_from,\
id_edge_from,\
pos_edge_from \
in zip(map_ids_plan[ids_person_act],
times_from,
ids_act_from,
names_acttype_from,
durations_act_from,
ids_edge_from,
poss_edge_from):
id_stage_act, time = activitystages.append_stage(
id_plan, time,
ids_activity=id_act_from,
names_activitytype=name_acttype_from,
durations=duration_act_from,
ids_lane=edges.ids_lanes[id_edge_from][0],
positions=pos_edge_from,
)
# main loop while there are persons performing
# an activity at index ind_act
while len(ids_person_act) > 0:
ids_plan = map_ids_plan[ids_person_act]
ids_veh = virtualpop.ids_ibike[ids_person_act]
dists_walk_max = virtualpop.dists_walk_max[ids_person_act]
times_from = map_times[ids_person_act]
names_acttype_to = activitytypes.names[activities.ids_activitytype[ids_act_to]]
durations_act_to = activities.get_durations(ids_act_to)
ids_fac_from = map_ids_fac_from[ids_person_act]
ids_fac_to = activities.ids_facility[ids_act_to]
# origin edge and position
ids_edge_from = facilities.ids_roadedge_closest[ids_fac_from]
poss_edge_from = facilities.positions_roadedge_closest[ids_fac_from]
poss_edge_from = self.clip_positions(poss_edge_from, ids_edge_from)
centroids_from = facilities.centroids[ids_fac_from]
# this method will find and occupy parking space
#ids_parking_from = map_ids_parking_from[ids_person_act]
# print ' ids_veh.shape',ids_veh.shape
# print ' centroids_to.shape',centroids_to.shape
#ids_parking_to, inds_vehparking = parking.get_closest_parkings(ids_veh, centroids_to)
#ids_lane_parking_from = parking.ids_lane[ids_parking_from]
#ids_edge_parking_from = lanes.ids_edge[ids_lane_parking_from]
#poss_edge_parking_from = parking.positions[ids_parking_from]
# print ' ids_parking_to.shape',ids_parking_to.shape
# print ' np.max(parking.get_ids()), np.max(ids_parking_to)',np.max(parking.get_ids()), np.max(ids_parking_to)
#ids_lane_parking_to = parking.ids_lane[ids_parking_to]
#ids_edge_parking_to = lanes.ids_edge[ids_lane_parking_to]
#poss_edge_parking_to = parking.positions[ids_parking_to]
# destination edge and position
ids_edge_to = facilities.ids_roadedge_closest[ids_fac_to]
poss_edge_to1 = facilities.positions_roadedge_closest[ids_fac_to]
poss_edge_to = self.clip_positions(poss_edge_to1, ids_edge_to)
centroids_to = facilities.centroids[ids_fac_to]
# debug poscorrection..OK
# for id_edge, id_edge_sumo, length, pos_to1, pos in zip(ids_edge_to, edges.ids_sumo[ids_edge_to],edges.lengths[ids_edge_to],poss_edge_to1, poss_edge_to):
# print ' ',id_edge, 'IDe%s'%id_edge_sumo, 'L=%.2fm'%length, '%.2fm'%pos_to1, '%.2fm'%pos
dists_from_to = np.sqrt(np.sum((centroids_to - centroids_from)**2, 1))
i = 0.0
n_pers = len(ids_person_act)
for id_person, id_plan, time_from, id_act_from, id_act_to, name_acttype_to, duration_act_to, id_veh, id_edge_from, pos_edge_from, id_edge_to, pos_edge_to, dist_from_to, dist_walk_max\
in zip(ids_person_act, ids_plan, times_from, ids_act_from, ids_act_to, names_acttype_to, durations_act_to, ids_veh, ids_edge_from, poss_edge_from, ids_edge_to, poss_edge_to, dists_from_to, dists_walk_max):
if logger:
logger.progress(i/n_pers*100)
i += 1.0
print 79*'*'
print ' plan id_plan', id_plan, 'time_from', time_from, 'from', id_edge_from, pos_edge_from, 'to', id_edge_to, pos_edge_to
print ' id_edge_from', edges.ids_sumo[id_edge_from], 'id_edge_to', edges.ids_sumo[id_edge_to]
time = self.plan_bikeride(id_plan, time_from, id_veh,
id_edge_from, pos_edge_from,
id_edge_to, pos_edge_to,
dist_from_to, dist_walk_max,
walkstages, ridestages)
################
# store time estimation for this plan
# note that these are the travel times, no activity time
plans.times_est[id_plan] += time-time_from
# define current end time without last activity duration
plans.times_end[id_plan] = time
# finally add activity and respective duration
id_stage_act, time = activitystages.append_stage(
id_plan, time,
ids_activity=id_act_to,
names_activitytype=name_acttype_to,
durations=duration_act_to,
ids_lane=edges.ids_lanes[id_edge_to][0],
positions=pos_edge_to,
)
map_times[id_person] = time
# return time
##
# select persons and activities for next setp
ind_act += 1
ids_person_act, ids_act_from, ids_act_to\
= virtualpop.get_activities_from_pattern(ind_act, ids_person=ids_person_act)
# update timing with (random) activity duration!!
return True | [
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Kitware/ParaView | f760af9124ff4634b23ebbeab95a4f56e0261955 | Wrapping/Python/paraview/servermanager.py | python | ArrayInformation.GetRange | (self, component=0) | return (range[0], range[1]) | Given a component, returns its value range as a tuple of 2 values. | Given a component, returns its value range as a tuple of 2 values. | [
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"""Given a component, returns its value range as a tuple of 2 values."""
array = self.FieldData.GetFieldData().GetArrayInformation(self.Name)
range = array.GetComponentRange(component)
return (range[0], range[1]) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/idlelib/configdialog.py | python | KeysPage.var_changed_keybinding | (self, *params) | Store change to a keybinding. | Store change to a keybinding. | [
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] | def var_changed_keybinding(self, *params):
"Store change to a keybinding."
value = self.keybinding.get()
key_set = self.custom_name.get()
event = self.bindingslist.get(ANCHOR).split()[0]
if idleConf.IsCoreBinding(event):
changes.add_option('keys', key_set, event, value)
else: # Event is an extension binding.
ext_name = idleConf.GetExtnNameForEvent(event)
ext_keybind_section = ext_name + '_cfgBindings'
changes.add_option('extensions', ext_keybind_section, event, value) | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/distributions/python/ops/poisson_lognormal.py | python | PoissonLogNormalQuadratureCompound.__init__ | (self,
loc,
scale,
quadrature_grid_and_probs=None,
validate_args=False,
allow_nan_stats=True,
name="PoissonLogNormalQuadratureCompound") | Constructs the PoissonLogNormalQuadratureCompound on `R**k`.
Args:
loc: `float`-like (batch of) scalar `Tensor`; the location parameter of
the LogNormal prior.
scale: `float`-like (batch of) scalar `Tensor`; the scale parameter of
the LogNormal prior.
quadrature_grid_and_probs: Python pair of `float`-like `Tensor`s
representing the sample points and the corresponding (possibly
normalized) weight. When `None`, defaults to:
`np.polynomial.hermite.hermgauss(deg=8)`.
validate_args: Python `bool`, default `False`. When `True` distribution
parameters are checked for validity despite possibly degrading runtime
performance. When `False` invalid inputs may silently render incorrect
outputs.
allow_nan_stats: Python `bool`, default `True`. When `True`,
statistics (e.g., mean, mode, variance) use the value "`NaN`" to
indicate the result is undefined. When `False`, an exception is raised
if one or more of the statistic's batch members are undefined.
name: Python `str` name prefixed to Ops created by this class.
Raises:
TypeError: if `loc.dtype != scale[0].dtype`. | Constructs the PoissonLogNormalQuadratureCompound on `R**k`. | [
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loc,
scale,
quadrature_grid_and_probs=None,
validate_args=False,
allow_nan_stats=True,
name="PoissonLogNormalQuadratureCompound"):
"""Constructs the PoissonLogNormalQuadratureCompound on `R**k`.
Args:
loc: `float`-like (batch of) scalar `Tensor`; the location parameter of
the LogNormal prior.
scale: `float`-like (batch of) scalar `Tensor`; the scale parameter of
the LogNormal prior.
quadrature_grid_and_probs: Python pair of `float`-like `Tensor`s
representing the sample points and the corresponding (possibly
normalized) weight. When `None`, defaults to:
`np.polynomial.hermite.hermgauss(deg=8)`.
validate_args: Python `bool`, default `False`. When `True` distribution
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indicate the result is undefined. When `False`, an exception is raised
if one or more of the statistic's batch members are undefined.
name: Python `str` name prefixed to Ops created by this class.
Raises:
TypeError: if `loc.dtype != scale[0].dtype`.
"""
parameters = locals()
with ops.name_scope(name, values=[loc, scale]):
loc = ops.convert_to_tensor(loc, name="loc")
self._loc = loc
scale = ops.convert_to_tensor(scale, name="scale")
self._scale = scale
dtype = loc.dtype.base_dtype
if dtype != scale.dtype.base_dtype:
raise TypeError(
"loc.dtype(\"{}\") does not match scale.dtype(\"{}\")".format(
loc.dtype.name, scale.dtype.name))
grid, probs = distribution_util.process_quadrature_grid_and_probs(
quadrature_grid_and_probs, dtype, validate_args)
self._quadrature_grid = grid
self._quadrature_probs = probs
self._quadrature_size = distribution_util.dimension_size(probs, axis=0)
self._mixture_distribution = categorical_lib.Categorical(
logits=math_ops.log(self._quadrature_probs),
validate_args=validate_args,
allow_nan_stats=allow_nan_stats)
# The following maps the broadcast of `loc` and `scale` to each grid
# point, i.e., we are creating several log-rates that correspond to the
# different Gauss-Hermite quadrature points and (possible) batches of
# `loc` and `scale`.
self._log_rate = (loc[..., array_ops.newaxis]
+ np.sqrt(2.) * scale[..., array_ops.newaxis] * grid)
self._distribution = poisson_lib.Poisson(
log_rate=self._log_rate,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats)
super(PoissonLogNormalQuadratureCompound, self).__init__(
dtype=dtype,
reparameterization_type=distribution_lib.NOT_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[loc, scale],
name=name) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/pydoc.py | python | visiblename | (name, all=None, obj=None) | Decide whether to show documentation on a variable. | Decide whether to show documentation on a variable. | [
"Decide",
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"to",
"show",
"documentation",
"on",
"a",
"variable",
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] | def visiblename(name, all=None, obj=None):
"""Decide whether to show documentation on a variable."""
# Certain special names are redundant or internal.
# XXX Remove __initializing__?
if name in {'__author__', '__builtins__', '__cached__', '__credits__',
'__date__', '__doc__', '__file__', '__spec__',
'__loader__', '__module__', '__name__', '__package__',
'__path__', '__qualname__', '__slots__', '__version__'}:
return 0
# Private names are hidden, but special names are displayed.
if name.startswith('__') and name.endswith('__'): return 1
# Namedtuples have public fields and methods with a single leading underscore
if name.startswith('_') and hasattr(obj, '_fields'):
return True
if all is not None:
# only document that which the programmer exported in __all__
return name in all
else:
return not name.startswith('_') | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/AWSPythonSDK/1.5.8/docutils/parsers/rst/directives/__init__.py | python | positive_int | (argument) | return value | Converts the argument into an integer. Raises ValueError for negative,
zero, or non-integer values. (Directive option conversion function.) | Converts the argument into an integer. Raises ValueError for negative,
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"""
Converts the argument into an integer. Raises ValueError for negative,
zero, or non-integer values. (Directive option conversion function.)
"""
value = int(argument)
if value < 1:
raise ValueError('negative or zero value; must be positive')
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freeorion/freeorion | c266a40eccd3a99a17de8fe57c36ef6ba3771665 | default/python/common/option_tools.py | python | _get_preset_default_ai_options | () | return odict(
[
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TIMER_SECTION,
odict([(TIMERS_USE_TIMERS, False)]),
),
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]
) | returns preset default options for AI; an ordered dict of dicts.
each sub-dict corresponds to a config file section
:return: | returns preset default options for AI; an ordered dict of dicts.
each sub-dict corresponds to a config file section
:return: | [
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"""
returns preset default options for AI; an ordered dict of dicts.
each sub-dict corresponds to a config file section
:return:
"""
return odict(
[
(
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/docview.py | python | Document.DeleteAllViews | (self) | return True | Calls wxView.Close and deletes each view. Deleting the final view will
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/io/common.py | python | stringify_path | (
filepath_or_buffer: FilePathOrBuffer[AnyStr],
) | return _expand_user(filepath_or_buffer) | Attempt to convert a path-like object to a string.
Parameters
----------
filepath_or_buffer : object to be converted
Returns
-------
str_filepath_or_buffer : maybe a string version of the object
Notes
-----
Objects supporting the fspath protocol (python 3.6+) are coerced
according to its __fspath__ method.
For backwards compatibility with older pythons, pathlib.Path and
py.path objects are specially coerced.
Any other object is passed through unchanged, which includes bytes,
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filepath_or_buffer: FilePathOrBuffer[AnyStr],
) -> FilePathOrBuffer[AnyStr]:
"""Attempt to convert a path-like object to a string.
Parameters
----------
filepath_or_buffer : object to be converted
Returns
-------
str_filepath_or_buffer : maybe a string version of the object
Notes
-----
Objects supporting the fspath protocol (python 3.6+) are coerced
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For backwards compatibility with older pythons, pathlib.Path and
py.path objects are specially coerced.
Any other object is passed through unchanged, which includes bytes,
strings, buffers, or anything else that's not even path-like.
"""
if hasattr(filepath_or_buffer, "__fspath__"):
# https://github.com/python/mypy/issues/1424
return filepath_or_buffer.__fspath__() # type: ignore
elif isinstance(filepath_or_buffer, pathlib.Path):
return str(filepath_or_buffer)
return _expand_user(filepath_or_buffer) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/syntax/_java.py | python | SyntaxData.GetSyntaxSpec | (self) | return SYNTAX_ITEMS | Syntax Specifications | Syntax Specifications | [
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] | def GetSyntaxSpec(self):
"""Syntax Specifications """
return SYNTAX_ITEMS | [
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/contrib/factorization/python/ops/factorization_ops.py | python | WALSModel.col_weights | (self) | return self._col_weights | Returns a list of tensors corresponding to col weight shards. | Returns a list of tensors corresponding to col weight shards. | [
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"""Returns a list of tensors corresponding to col weight shards."""
return self._col_weights | [
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google/syzygy | 8164b24ebde9c5649c9a09e88a7fc0b0fcbd1bc5 | third_party/numpy/files/numpy/distutils/fcompiler/__init__.py | python | FCompiler._compile | (self, obj, src, ext, cc_args, extra_postargs, pp_opts) | Compile 'src' to product 'obj'. | Compile 'src' to product 'obj'. | [
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] | def _compile(self, obj, src, ext, cc_args, extra_postargs, pp_opts):
"""Compile 'src' to product 'obj'."""
src_flags = {}
if is_f_file(src) and not has_f90_header(src):
flavor = ':f77'
compiler = self.compiler_f77
src_flags = get_f77flags(src)
elif is_free_format(src):
flavor = ':f90'
compiler = self.compiler_f90
if compiler is None:
raise DistutilsExecError('f90 not supported by %s needed for %s'\
% (self.__class__.__name__,src))
else:
flavor = ':fix'
compiler = self.compiler_fix
if compiler is None:
raise DistutilsExecError('f90 (fixed) not supported by %s needed for %s'\
% (self.__class__.__name__,src))
if self.object_switch[-1]==' ':
o_args = [self.object_switch.strip(),obj]
else:
o_args = [self.object_switch.strip()+obj]
assert self.compile_switch.strip()
s_args = [self.compile_switch, src]
extra_flags = src_flags.get(self.compiler_type,[])
if extra_flags:
log.info('using compile options from source: %r' \
% ' '.join(extra_flags))
command = compiler + cc_args + extra_flags + s_args + o_args \
+ extra_postargs
display = '%s: %s' % (os.path.basename(compiler[0]) + flavor,
src)
try:
self.spawn(command,display=display)
except DistutilsExecError:
msg = str(get_exception())
raise CompileError(msg) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/boringssl/src/util/generate_build_files.py | python | WriteAsmFiles | (perlasms) | return asmfiles | Generates asm files from perlasm directives for each supported OS x
platform combination. | Generates asm files from perlasm directives for each supported OS x
platform combination. | [
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] | def WriteAsmFiles(perlasms):
"""Generates asm files from perlasm directives for each supported OS x
platform combination."""
asmfiles = {}
for osarch in OS_ARCH_COMBOS:
(osname, arch, perlasm_style, extra_args, asm_ext) = osarch
key = (osname, arch)
outDir = '%s-%s' % key
for perlasm in perlasms:
filename = os.path.basename(perlasm['input'])
output = perlasm['output']
if not output.startswith('src'):
raise ValueError('output missing src: %s' % output)
output = os.path.join(outDir, output[4:])
if output.endswith('-armx.${ASM_EXT}'):
output = output.replace('-armx',
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output = output.replace('${ASM_EXT}', asm_ext)
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asmfiles.setdefault(key, []).append(output)
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return asmfiles | [
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ducha-aiki/LSUVinit | a42ecdc0d44c217a29b65e98748d80b90d5c6279 | scripts/cpp_lint.py | python | _VerboseLevel | () | return _cpplint_state.verbose_level | Returns the module's verbosity setting. | Returns the module's verbosity setting. | [
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] | def _VerboseLevel():
"""Returns the module's verbosity setting."""
return _cpplint_state.verbose_level | [
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LeoSirius/leetcode_solutions | 9bbbaaba704862a3e0cec4ae715daf1ee4752c7b | mst10.01_Sorted_Merge_LCCI/Solution1.py | python | Solution.merge | (self, A: List[int], m: int, B: List[int], n: int) | Do not return anything, modify A in-place instead. | Do not return anything, modify A in-place instead. | [
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"""
Do not return anything, modify A in-place instead.
"""
pa, pb = m - 1, n - 1
tail = m + n - 1
while pa >= 0 and pb >= 0:
if A[pa] > B[pb]:
A[tail] = A[pa]
pa -= 1
else:
A[tail] = B[pb]
pb -= 1
tail -= 1
# 我们以A为基准，这个不用判断，若pb = -1, 则tail 必然等于pa
# while pa >= 0:
# A[tail] = A[pa]
# tail -= 1
# pa -= 1
while pb >= 0:
A[tail] = B[pb]
tail -= 1
pb -= 1 | [
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lammps/lammps | b75c3065430a75b1b5543a10e10f46d9b4c91913 | tools/polybond/lmpsdata.py | python | particlesurface.extractparticle | (self) | return self.particle | deletesatoms from particle from the list of deletedatoms
and than returns the particle. | deletesatoms from particle from the list of deletedatoms
and than returns the particle. | [
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] | def extractparticle(self):
""""deletesatoms from particle from the list of deletedatoms
and than returns the particle."""
self.particle=self.deleteatoms(self.particle,self.deletedatoms)
return self.particle | [
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lilypond/lilypond | 2a14759372979f5b796ee802b0ee3bc15d28b06b | release/binaries/lib/build.py | python | Package.install_directory | (self, c: Config) | return os.path.join(c.dependencies_install_dir, self.directory) | Return the temporary install directory for this package | Return the temporary install directory for this package | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/share/gdb/python/gdb/command/pretty_printers.py | python | register_pretty_printer_commands | () | Call from a top level script to install the pretty-printer commands. | Call from a top level script to install the pretty-printer commands. | [
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InfoPrettyPrinter()
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numba/npyufunc/parallel.py | python | threading_layer | () | Get the name of the threading layer in use for parallel CPU targets | Get the name of the threading layer in use for parallel CPU targets | [
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"""
Get the name of the threading layer in use for parallel CPU targets
"""
if _threading_layer is None:
raise ValueError("Threading layer is not initialized.")
else:
return _threading_layer | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/idlelib/config_key.py | python | GetKeysDialog.__init__ | (self, parent, title, action, current_key_sequences,
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action - string, the name of the virtual event these keys will be
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"""
parent - parent of this dialog
title - string which is the title of the popup dialog
action - string, the name of the virtual event these keys will be
mapped to
current_key_sequences - list, a list of all key sequence lists
currently mapped to virtual events, for overlap checking
_htest - bool, change box location when running htest
_utest - bool, do not wait when running unittest
"""
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self.configure(borderwidth=5)
self.resizable(height=False, width=False)
self.title(title)
self.transient(parent)
self.grab_set()
self.protocol("WM_DELETE_WINDOW", self.cancel)
self.parent = parent
self.action = action
self.current_key_sequences = current_key_sequences
self.result = ''
self.key_string = StringVar(self)
self.key_string.set('')
# Set self.modifiers, self.modifier_label.
self.set_modifiers_for_platform()
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for modifier in self.modifiers:
variable = StringVar(self)
variable.set('')
self.modifier_vars.append(variable)
self.advanced = False
self.create_widgets()
self.update_idletasks()
self.geometry(
"+%d+%d" % (
parent.winfo_rootx() +
(parent.winfo_width()/2 - self.winfo_reqwidth()/2),
parent.winfo_rooty() +
((parent.winfo_height()/2 - self.winfo_reqheight()/2)
if not _htest else 150)
) ) # Center dialog over parent (or below htest box).
if not _utest:
self.deiconify() # Geometry set, unhide.
self.wait_window() | [
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alexsax/2D-3D-Semantics | 54a532959b20203ea4c3fcc26f5c8bf678d6fdb4 | assets/utils.py | python | read_exr | ( image_fpath ) | return im | Reads an openEXR file into an RGB matrix with floats | Reads an openEXR file into an RGB matrix with floats | [
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""" Reads an openEXR file into an RGB matrix with floats """
f = OpenEXR.InputFile( image_fpath )
dw = f.header()['dataWindow']
w, h = (dw.max.x - dw.min.x + 1, dw.max.y - dw.min.y + 1)
im = np.empty( (h, w, 3) )
# Read in the EXR
FLOAT = Imath.PixelType(Imath.PixelType.FLOAT)
channels = f.channels( ["R", "G", "B"], FLOAT )
for i, channel in enumerate( channels ):
im[:,:,i] = np.reshape( array.array( 'f', channel ), (h, w) )
return im | [
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/contrib/distributions/python/ops/normal.py | python | Normal.mode | (self, name="mode") | return self.mean(name="mode") | Mode of this distribution. | Mode of this distribution. | [
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return self.mean(name="mode") | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py2/pandas/io/excel.py | python | _OpenpyxlWriter._convert_to_stop | (cls, stop_seq) | return map(cls._convert_to_color, stop_seq) | Convert ``stop_seq`` to a list of openpyxl v2 Color objects,
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Parameters
----------
stop_seq : iterable
An iterable that yields objects suitable for consumption by
``_convert_to_color``.
Returns
-------
stop : list of openpyxl.styles.Color | Convert ``stop_seq`` to a list of openpyxl v2 Color objects,
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Parameters
----------
stop_seq : iterable
An iterable that yields objects suitable for consumption by
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Returns
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stop : list of openpyxl.styles.Color | [
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Convert ``stop_seq`` to a list of openpyxl v2 Color objects,
suitable for initializing the ``GradientFill`` ``stop`` parameter.
Parameters
----------
stop_seq : iterable
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | scripts/abins/input/crystalloader.py | python | CRYSTALLoader._inside_k_block | (self, file_obj=None) | return any([key in line for key in allowed_keywords]) | Checks if end of k-points block.
:param file_obj: file object from which we read
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Checks if end of k-points block.
:param file_obj: file object from which we read
:returns: True if end of block otherwise False
"""
allowed_keywords = [b" X ", b" Y ", b" Z ", b"-", b"REAL", b"IMAGINARY", b"MODES", b"DISPERSION"]
# remove empty lines:
pos = None
while not self._parser.file_end(file_obj=file_obj):
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file_obj.seek(pos)
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line = file_obj.readline()
file_obj.seek(pos)
# if there isn't any keyword from set "allowed_keywords" it means that we reached end of k-block
# if any keyword in line we are still in k-block
return any([key in line for key in allowed_keywords]) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/mapreduce/mapreduce/model.py | python | MapreduceState.get_processed | (self) | return self.counters_map.get(context.COUNTER_MAPPER_CALLS) | Number of processed entities.
Returns:
The total number of processed entities as int. | Number of processed entities. | [
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"""Number of processed entities.
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_gdi.py | python | Locale.GetLocale | (*args, **kwargs) | return _gdi_.Locale_GetLocale(*args, **kwargs) | GetLocale(self) -> String | GetLocale(self) -> String | [
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Illumina/strelka | d7377443b62319f7c7bd70c241c4b2df3459e29a | src/python/lib/workflowUtil.py | python | getGenomeSegmentGroups | (genomeSegmentIterator, contigsExcludedFromGrouping = None) | Iterate segment groups and 'clump' small contigs together
@param genomeSegmentIterator any object which will iterate through ungrouped genome segments)
@param contigsExcludedFromGrouping defines a set of contigs which are excluded from grouping
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"""
Iterate segment groups and 'clump' small contigs together
@param genomeSegmentIterator any object which will iterate through ungrouped genome segments)
@param contigsExcludedFromGrouping defines a set of contigs which are excluded from grouping
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"""
def isGroupEligible(gseg) :
if contigsExcludedFromGrouping is None : return True
return (gseg.chromLabel not in contigsExcludedFromGrouping)
minSegmentGroupSize=200000
group = []
headSize = 0
isLastSegmentGroupEligible = True
for gseg in genomeSegmentIterator :
isSegmentGroupEligible = isGroupEligible(gseg)
if (isSegmentGroupEligible and isLastSegmentGroupEligible) and (headSize+gseg.size() <= minSegmentGroupSize) :
group.append(gseg)
headSize += gseg.size()
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if len(group) != 0 : yield(group)
group = [gseg]
headSize = gseg.size()
isLastSegmentGroupEligible = isSegmentGroupEligible
if len(group) != 0 : yield(group) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | catboost/python-package/catboost/core.py | python | CatBoostRanker.fit | (self, X, y=None, group_id=None, cat_features=None, text_features=None,
embedding_features=None, pairs=None, sample_weight=None, group_weight=None,
subgroup_id=None, pairs_weight=None, baseline=None, use_best_model=None,
eval_set=None, verbose=None, logging_level=None, plot=False, column_description=None,
verbose_eval=None, metric_period=None, silent=None, early_stopping_rounds=None,
save_snapshot=None, snapshot_file=None, snapshot_interval=None, init_model=None, callbacks=None,
log_cout=sys.stdout, log_cerr=sys.stderr) | return self | Fit the CatBoostRanker model.
Parameters
----------
X : catboost.Pool or list or numpy.ndarray or pandas.DataFrame or pandas.Series
If not catboost.Pool, 2 dimensional Feature matrix or string - file with dataset.
y : list or numpy.ndarray or pandas.DataFrame or pandas.Series, optional (default=None)
Labels, 1 dimensional array like.
Use only if X is not catboost.Pool.
group_id : numpy.ndarray or pandas.DataFrame or pandas.Series, optional (default=None)
Ranking groups, 1 dimensional array like.
Use only if X is not catboost.Pool.
cat_features : list or numpy.ndarray, optional (default=None)
If not None, giving the list of Categ columns indices.
Use only if X is not catboost.Pool.
text_features : list or numpy.ndarray, optional (default=None)
If not None, giving the list of Text columns indices.
Use only if X is not catboost.Pool.
embedding_features : list or numpy.ndarray, optional (default=None)
If not None, giving the list of Embedding columns indices.
Use only if X is not catboost.Pool.
pairs : list or numpy.ndarray or pandas.DataFrame, optional (default=None)
The pairs description in the form of a two-dimensional matrix of shape N by 2:
N is the number of pairs.
The first element of the pair is the zero-based index of the winner object from the input dataset for pairwise comparison.
The second element of the pair is the zero-based index of the loser object from the input dataset for pairwise comparison.
sample_weight : list or numpy.ndarray or pandas.DataFrame or pandas.Series, optional (default=None)
Instance weights, 1 dimensional array like.
group_weight : list or numpy.ndarray (default=None)
The weights of all objects within the defined groups from the input data in the form of one-dimensional array-like data.
Used for calculating the final values of trees. By default, it is set to 1 for all objects in all groups.
Only a weight or group_weight parameter can be used at a time
subgroup_id : list or numpy.ndarray (default=None)
Subgroup identifiers for all input objects. Supported identifier types are:
int
string types (string or unicode for Python 2 and bytes or string for Python 3).
pairs_weight : list or numpy.ndarray (default=None)
The weight of each input pair of objects in the form of one-dimensional array-like pairs.
The number of given values must match the number of specified pairs.
This information is used for calculation and optimization of Pairwise metrics .
By default, it is set to 1 for all pairs.
baseline : list or numpy.ndarray, optional (default=None)
If not None, giving 2 dimensional array like data.
Use only if X is not catboost.Pool.
use_best_model : bool, optional (default=None)
Flag to use best model
eval_set : catboost.Pool or list, optional (default=None)
A list of (X, y) tuple pairs to use as a validation set for early-stopping
verbose : bool or int
If verbose is bool, then if set to True, logging_level is set to Verbose,
if set to False, logging_level is set to Silent.
If verbose is int, it determines the frequency of writing metrics to output and
logging_level is set to Verbose.
logging_level : string, optional (default=None)
Possible values:
- 'Silent'
- 'Verbose'
- 'Info'
- 'Debug'
plot : bool, optional (default=False)
If True, draw train and eval error in Jupyter notebook
verbose_eval : bool or int
Synonym for verbose. Only one of these parameters should be set.
metric_period : int
Frequency of evaluating metrics.
silent : bool
If silent is True, logging_level is set to Silent.
If silent is False, logging_level is set to Verbose.
early_stopping_rounds : int
Activates Iter overfitting detector with od_wait set to early_stopping_rounds.
save_snapshot : bool, [default=None]
Enable progress snapshotting for restoring progress after crashes or interruptions
snapshot_file : string or pathlib.Path, [default=None]
Learn progress snapshot file path, if None will use default filename
snapshot_interval: int, [default=600]
Interval between saving snapshots (seconds)
init_model : CatBoost class or string or pathlib.Path, [default=None]
Continue training starting from the existing model.
If this parameter is a string or pathlib.Path, load initial model from the path specified by this string.
callbacks : list, optional (default=None)
List of callback objects that are applied at end of each iteration.
log_cout: output stream or callback for logging
log_cerr: error stream or callback for logging
Returns
-------
model : CatBoost | Fit the CatBoostRanker model.
Parameters
----------
X : catboost.Pool or list or numpy.ndarray or pandas.DataFrame or pandas.Series
If not catboost.Pool, 2 dimensional Feature matrix or string - file with dataset.
y : list or numpy.ndarray or pandas.DataFrame or pandas.Series, optional (default=None)
Labels, 1 dimensional array like.
Use only if X is not catboost.Pool.
group_id : numpy.ndarray or pandas.DataFrame or pandas.Series, optional (default=None)
Ranking groups, 1 dimensional array like.
Use only if X is not catboost.Pool.
cat_features : list or numpy.ndarray, optional (default=None)
If not None, giving the list of Categ columns indices.
Use only if X is not catboost.Pool.
text_features : list or numpy.ndarray, optional (default=None)
If not None, giving the list of Text columns indices.
Use only if X is not catboost.Pool.
embedding_features : list or numpy.ndarray, optional (default=None)
If not None, giving the list of Embedding columns indices.
Use only if X is not catboost.Pool.
pairs : list or numpy.ndarray or pandas.DataFrame, optional (default=None)
The pairs description in the form of a two-dimensional matrix of shape N by 2:
N is the number of pairs.
The first element of the pair is the zero-based index of the winner object from the input dataset for pairwise comparison.
The second element of the pair is the zero-based index of the loser object from the input dataset for pairwise comparison.
sample_weight : list or numpy.ndarray or pandas.DataFrame or pandas.Series, optional (default=None)
Instance weights, 1 dimensional array like.
group_weight : list or numpy.ndarray (default=None)
The weights of all objects within the defined groups from the input data in the form of one-dimensional array-like data.
Used for calculating the final values of trees. By default, it is set to 1 for all objects in all groups.
Only a weight or group_weight parameter can be used at a time
subgroup_id : list or numpy.ndarray (default=None)
Subgroup identifiers for all input objects. Supported identifier types are:
int
string types (string or unicode for Python 2 and bytes or string for Python 3).
pairs_weight : list or numpy.ndarray (default=None)
The weight of each input pair of objects in the form of one-dimensional array-like pairs.
The number of given values must match the number of specified pairs.
This information is used for calculation and optimization of Pairwise metrics .
By default, it is set to 1 for all pairs.
baseline : list or numpy.ndarray, optional (default=None)
If not None, giving 2 dimensional array like data.
Use only if X is not catboost.Pool.
use_best_model : bool, optional (default=None)
Flag to use best model
eval_set : catboost.Pool or list, optional (default=None)
A list of (X, y) tuple pairs to use as a validation set for early-stopping
verbose : bool or int
If verbose is bool, then if set to True, logging_level is set to Verbose,
if set to False, logging_level is set to Silent.
If verbose is int, it determines the frequency of writing metrics to output and
logging_level is set to Verbose.
logging_level : string, optional (default=None)
Possible values:
- 'Silent'
- 'Verbose'
- 'Info'
- 'Debug'
plot : bool, optional (default=False)
If True, draw train and eval error in Jupyter notebook
verbose_eval : bool or int
Synonym for verbose. Only one of these parameters should be set.
metric_period : int
Frequency of evaluating metrics.
silent : bool
If silent is True, logging_level is set to Silent.
If silent is False, logging_level is set to Verbose.
early_stopping_rounds : int
Activates Iter overfitting detector with od_wait set to early_stopping_rounds.
save_snapshot : bool, [default=None]
Enable progress snapshotting for restoring progress after crashes or interruptions
snapshot_file : string or pathlib.Path, [default=None]
Learn progress snapshot file path, if None will use default filename
snapshot_interval: int, [default=600]
Interval between saving snapshots (seconds)
init_model : CatBoost class or string or pathlib.Path, [default=None]
Continue training starting from the existing model.
If this parameter is a string or pathlib.Path, load initial model from the path specified by this string.
callbacks : list, optional (default=None)
List of callback objects that are applied at end of each iteration. | [
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] | def fit(self, X, y=None, group_id=None, cat_features=None, text_features=None,
embedding_features=None, pairs=None, sample_weight=None, group_weight=None,
subgroup_id=None, pairs_weight=None, baseline=None, use_best_model=None,
eval_set=None, verbose=None, logging_level=None, plot=False, column_description=None,
verbose_eval=None, metric_period=None, silent=None, early_stopping_rounds=None,
save_snapshot=None, snapshot_file=None, snapshot_interval=None, init_model=None, callbacks=None,
log_cout=sys.stdout, log_cerr=sys.stderr):
"""
Fit the CatBoostRanker model.
Parameters
----------
X : catboost.Pool or list or numpy.ndarray or pandas.DataFrame or pandas.Series
If not catboost.Pool, 2 dimensional Feature matrix or string - file with dataset.
y : list or numpy.ndarray or pandas.DataFrame or pandas.Series, optional (default=None)
Labels, 1 dimensional array like.
Use only if X is not catboost.Pool.
group_id : numpy.ndarray or pandas.DataFrame or pandas.Series, optional (default=None)
Ranking groups, 1 dimensional array like.
Use only if X is not catboost.Pool.
cat_features : list or numpy.ndarray, optional (default=None)
If not None, giving the list of Categ columns indices.
Use only if X is not catboost.Pool.
text_features : list or numpy.ndarray, optional (default=None)
If not None, giving the list of Text columns indices.
Use only if X is not catboost.Pool.
embedding_features : list or numpy.ndarray, optional (default=None)
If not None, giving the list of Embedding columns indices.
Use only if X is not catboost.Pool.
pairs : list or numpy.ndarray or pandas.DataFrame, optional (default=None)
The pairs description in the form of a two-dimensional matrix of shape N by 2:
N is the number of pairs.
The first element of the pair is the zero-based index of the winner object from the input dataset for pairwise comparison.
The second element of the pair is the zero-based index of the loser object from the input dataset for pairwise comparison.
sample_weight : list or numpy.ndarray or pandas.DataFrame or pandas.Series, optional (default=None)
Instance weights, 1 dimensional array like.
group_weight : list or numpy.ndarray (default=None)
The weights of all objects within the defined groups from the input data in the form of one-dimensional array-like data.
Used for calculating the final values of trees. By default, it is set to 1 for all objects in all groups.
Only a weight or group_weight parameter can be used at a time
subgroup_id : list or numpy.ndarray (default=None)
Subgroup identifiers for all input objects. Supported identifier types are:
int
string types (string or unicode for Python 2 and bytes or string for Python 3).
pairs_weight : list or numpy.ndarray (default=None)
The weight of each input pair of objects in the form of one-dimensional array-like pairs.
The number of given values must match the number of specified pairs.
This information is used for calculation and optimization of Pairwise metrics .
By default, it is set to 1 for all pairs.
baseline : list or numpy.ndarray, optional (default=None)
If not None, giving 2 dimensional array like data.
Use only if X is not catboost.Pool.
use_best_model : bool, optional (default=None)
Flag to use best model
eval_set : catboost.Pool or list, optional (default=None)
A list of (X, y) tuple pairs to use as a validation set for early-stopping
verbose : bool or int
If verbose is bool, then if set to True, logging_level is set to Verbose,
if set to False, logging_level is set to Silent.
If verbose is int, it determines the frequency of writing metrics to output and
logging_level is set to Verbose.
logging_level : string, optional (default=None)
Possible values:
- 'Silent'
- 'Verbose'
- 'Info'
- 'Debug'
plot : bool, optional (default=False)
If True, draw train and eval error in Jupyter notebook
verbose_eval : bool or int
Synonym for verbose. Only one of these parameters should be set.
metric_period : int
Frequency of evaluating metrics.
silent : bool
If silent is True, logging_level is set to Silent.
If silent is False, logging_level is set to Verbose.
early_stopping_rounds : int
Activates Iter overfitting detector with od_wait set to early_stopping_rounds.
save_snapshot : bool, [default=None]
Enable progress snapshotting for restoring progress after crashes or interruptions
snapshot_file : string or pathlib.Path, [default=None]
Learn progress snapshot file path, if None will use default filename
snapshot_interval: int, [default=600]
Interval between saving snapshots (seconds)
init_model : CatBoost class or string or pathlib.Path, [default=None]
Continue training starting from the existing model.
If this parameter is a string or pathlib.Path, load initial model from the path specified by this string.
callbacks : list, optional (default=None)
List of callback objects that are applied at end of each iteration.
log_cout: output stream or callback for logging
log_cerr: error stream or callback for logging
Returns
-------
model : CatBoost
"""
params = deepcopy(self._init_params)
_process_synonyms(params)
if 'loss_function' in params:
CatBoostRanker._check_is_compatible_loss(params['loss_function'])
self._fit(X, y, cat_features, text_features, embedding_features, pairs,
sample_weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, use_best_model,
eval_set, verbose, logging_level, plot, column_description, verbose_eval, metric_period,
silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model, callbacks, log_cout, log_cerr)
return self | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/eager/function.py | python | _TapeGradientFunctions.record | (self, flat_outputs, inference_args, input_tangents) | Record the function call operation.
For backprop, indicates the backward function to use and which new Tensors
must be watched. For forwardprop from eager, the function call itself will
have produced tangents which need to be recorded.
Args:
flat_outputs: The result of running `forward`.
inference_args: A flat list of Tensors with inference inputs to the
operation.
input_tangents: A flat list of Tensors with input tangents consumed by the
operation. | Record the function call operation. | [
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"call",
"operation",
"."
] | def record(self, flat_outputs, inference_args, input_tangents):
"""Record the function call operation.
For backprop, indicates the backward function to use and which new Tensors
must be watched. For forwardprop from eager, the function call itself will
have produced tangents which need to be recorded.
Args:
flat_outputs: The result of running `forward`.
inference_args: A flat list of Tensors with inference inputs to the
operation.
input_tangents: A flat list of Tensors with input tangents consumed by the
operation.
"""
backward_function, to_record = self._wrap_backward_function(
self._forward_graph, self._backward, flat_outputs)
if self._forwardprop_output_indices:
tape.record_operation_backprop_only(
self._forward.signature.name,
to_record, inference_args,
backward_function)
tape.record_operation_forwardprop_only(
self._forward.signature.name,
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backward_function,
self._forwardprop_output_indices)
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tape.record_operation(self._forward.signature.name,
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/distribution/kl.py | python | kl_divergence | (p, q) | return _dispatch(type(p), type(q))(p, q) | r"""
Kullback-Leibler divergence between distribution p and q.
.. math::
KL(p||q) = \int p(x)log\frac{p(x)}{q(x)} \mathrm{d}x
Args:
p (Distribution): ``Distribution`` object.
q (Distribution): ``Distribution`` object.
Returns:
Tensor: Batchwise KL-divergence between distribution p and q.
Examples:
.. code-block:: python
import paddle
p = paddle.distribution.Beta(alpha=0.5, beta=0.5)
q = paddle.distribution.Beta(alpha=0.3, beta=0.7)
print(paddle.distribution.kl_divergence(p, q))
# Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# [0.21193528]) | r"""
Kullback-Leibler divergence between distribution p and q. | [
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"-",
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] | def kl_divergence(p, q):
r"""
Kullback-Leibler divergence between distribution p and q.
.. math::
KL(p||q) = \int p(x)log\frac{p(x)}{q(x)} \mathrm{d}x
Args:
p (Distribution): ``Distribution`` object.
q (Distribution): ``Distribution`` object.
Returns:
Tensor: Batchwise KL-divergence between distribution p and q.
Examples:
.. code-block:: python
import paddle
p = paddle.distribution.Beta(alpha=0.5, beta=0.5)
q = paddle.distribution.Beta(alpha=0.3, beta=0.7)
print(paddle.distribution.kl_divergence(p, q))
# Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# [0.21193528])
"""
return _dispatch(type(p), type(q))(p, q) | [
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trilinos/Trilinos | 6168be6dd51e35e1cd681e9c4b24433e709df140 | packages/seacas/scripts/exomerge3.py | python | ExodusModel._get_local_index | (self, this_id, id_list, entity='entity') | return id_list.index(this_id) | Return the local index corresponding to the given id.
If an entity is not present, throw an error.
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0
>>> model._get_local_index('first', [10, 20, 30])
10 | Return the local index corresponding to the given id. | [
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>>> model._get_local_index(10, [10, 20, 30])
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"""
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return len(id_list) - 1
if this_id not in id_list:
entity_list = ', '.join([str(x) for x in id_list])
self._error(
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'A reference to %s "%s" was encountered but is not '
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(entity, str(this_id), len(id_list), entity, entity_list))
return id_list.index(this_id) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/compiler_machinery.py | python | PassManager.dependency_analysis | (self) | return dep_chain | Computes dependency analysis | Computes dependency analysis | [
"Computes",
"dependency",
"analysis"
] | def dependency_analysis(self):
"""
Computes dependency analysis
"""
deps = dict()
for (pss, _) in self.passes:
x = _pass_registry.get(pss).pass_inst
au = AnalysisUsage()
x.get_analysis_usage(au)
deps[type(x)] = au
requires_map = dict()
for k, v in deps.items():
requires_map[k] = v.get_required_set()
def resolve_requires(key, rmap):
def walk(lkey, rmap):
dep_set = set(rmap[lkey])
if dep_set:
for x in dep_set:
dep_set |= (walk(x, rmap))
return dep_set
else:
return set()
ret = set()
for k in key:
ret |= walk(k, rmap)
return ret
dep_chain = dict()
for k, v in requires_map.items():
dep_chain[k] = set(v) | (resolve_requires(v, requires_map))
return dep_chain | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/aui.py | python | AuiPaneInfo.FloatingSize | (*args, **kwargs) | return _aui.AuiPaneInfo_FloatingSize(*args, **kwargs) | FloatingSize(self, Size size) -> AuiPaneInfo | FloatingSize(self, Size size) -> AuiPaneInfo | [
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"""FloatingSize(self, Size size) -> AuiPaneInfo"""
return _aui.AuiPaneInfo_FloatingSize(*args, **kwargs) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_vendor/appdirs.py | python | user_state_dir | (appname=None, appauthor=None, version=None, roaming=False) | return path | r"""Return full path to the user-specific state dir for this application.
"appname" is the name of application.
If None, just the system directory is returned.
"appauthor" (only used on Windows) is the name of the
appauthor or distributing body for this application. Typically
it is the owning company name. This falls back to appname. You may
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"version" is an optional version path element to append to the
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Only applied when appname is present.
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network setup for roaming profiles, this user data will be
sync'd on login. See
<http://technet.microsoft.com/en-us/library/cc766489(WS.10).aspx>
for a discussion of issues.
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Unix: ~/.local/state/<AppName> # or in $XDG_STATE_HOME, if defined
Win *: same as user_data_dir
For Unix, we follow this Debian proposal <https://wiki.debian.org/XDGBaseDirectorySpecification#state>
to extend the XDG spec and support $XDG_STATE_HOME.
That means, by default "~/.local/state/<AppName>". | r"""Return full path to the user-specific state dir for this application. | [
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r"""Return full path to the user-specific state dir for this application.
"appname" is the name of application.
If None, just the system directory is returned.
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"""
if system in ["win32", "darwin"]:
path = user_data_dir(appname, appauthor, None, roaming)
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/sched.py | python | scheduler.empty | (self) | Check whether the queue is empty. | Check whether the queue is empty. | [
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"""Check whether the queue is empty."""
with self._lock:
return not self._queue | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Tools/CryVersionSelector/release_project.py | python | get_available_configurations | (engine_path) | return available_configurations | Returns the configurations that are available for this engine. | Returns the configurations that are available for this engine. | [
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"""
Returns the configurations that are available for this engine.
"""
available_configurations = []
for key, value in DEFAULT_CONFIGURATIONS:
win_path = os.path.join(engine_path, value, "GameLauncher.exe")
linux_path = os.path.join(engine_path, value, "GameLauncher")
if os.path.isfile(win_path) or os.path.isfile(linux_path):
available_configurations.append((key, value))
return available_configurations | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/subprocess.py | python | check_call | (*popenargs, **kwargs) | return 0 | Run command with arguments. Wait for command to complete. If
the exit code was zero then return, otherwise raise
CalledProcessError. The CalledProcessError object will have the
return code in the returncode attribute.
The arguments are the same as for the call function. Example:
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"""Run command with arguments. Wait for command to complete. If
the exit code was zero then return, otherwise raise
CalledProcessError. The CalledProcessError object will have the
return code in the returncode attribute.
The arguments are the same as for the call function. Example:
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"""
retcode = call(*popenargs, **kwargs)
if retcode:
cmd = kwargs.get("args")
if cmd is None:
cmd = popenargs[0]
raise CalledProcessError(retcode, cmd)
return 0 | [
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cms-sw/cmssw | fd9de012d503d3405420bcbeec0ec879baa57cf2 | EventFilter/L1TXRawToDigi/python/util/rrapi.py | python | RRApi.tables | (self, workspace) | return self.get([workspace, "tables"]) | Get tables for workspace (all apps) | Get tables for workspace (all apps) | [
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"""
Get tables for workspace (all apps)
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return self.get([workspace, "tables"]) | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/plan/cspaceutils.py | python | EmbeddedCSpace.project | (self,xamb) | return [xamb[i] for i in self.mapping] | Ambient space -> embedded space | Ambient space -> embedded space | [
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"""Ambient space -> embedded space"""
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return [xamb[i] for i in self.mapping] | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py2/sklearn/neighbors/base.py | python | UnsupervisedMixin.fit | (self, X, y=None) | return self._fit(X) | Fit the model using X as training data
Parameters
----------
X : {array-like, sparse matrix, BallTree, KDTree}
Training data. If array or matrix, shape [n_samples, n_features],
or [n_samples, n_samples] if metric='precomputed'. | Fit the model using X as training data | [
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"""Fit the model using X as training data
Parameters
----------
X : {array-like, sparse matrix, BallTree, KDTree}
Training data. If array or matrix, shape [n_samples, n_features],
or [n_samples, n_samples] if metric='precomputed'.
"""
return self._fit(X) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_internal/utils/glibc.py | python | glibc_version_string_confstr | () | return version | Primary implementation of glibc_version_string using os.confstr. | Primary implementation of glibc_version_string using os.confstr. | [
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# type: () -> Optional[str]
"Primary implementation of glibc_version_string using os.confstr."
# os.confstr is quite a bit faster than ctypes.DLL. It's also less likely
# to be broken or missing. This strategy is used in the standard library
# platform module:
# https://github.com/python/cpython/blob/fcf1d003bf4f0100c9d0921ff3d70e1127ca1b71/Lib/platform.py#L175-L183
if sys.platform == "win32":
return None
try:
# os.confstr("CS_GNU_LIBC_VERSION") returns a string like "glibc 2.17":
_, version = os.confstr("CS_GNU_LIBC_VERSION").split()
except (AttributeError, OSError, ValueError):
# os.confstr() or CS_GNU_LIBC_VERSION not available (or a bad value)...
return None
return version | [
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openvinotoolkit/openvino | dedcbeafa8b84cccdc55ca64b8da516682b381c7 | tools/pot/openvino/tools/pot/configs/config.py | python | Config._configure_engine_params | (self) | Converts engine config section into engine params dict | Converts engine config section into engine params dict | [
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""" Converts engine config section into engine params dict
"""
engine = self.engine
if 'type' not in engine or engine.type == 'accuracy_checker':
self._configure_ac_params()
self.engine.type = 'accuracy_checker'
elif engine.type == 'simplified' or engine.type == 'data_free':
if engine.data_source is None:
raise KeyError(f'Missed data dir for {engine.type} engine')
self.engine.device = engine.device if engine.device else 'CPU'
engine.data_source = Path(engine.data_source)
else:
raise KeyError('Unsupported engine type') | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/prompt-toolkit/py3/prompt_toolkit/key_binding/key_bindings.py | python | KeyBindings.get_bindings_for_keys | (self, keys: KeysTuple) | return self._get_bindings_for_keys_cache.get(keys, get) | Return a list of key bindings that can handle this key.
(This return also inactive bindings, so the `filter` still has to be
called, for checking it.)
:param keys: tuple of keys. | Return a list of key bindings that can handle this key.
(This return also inactive bindings, so the `filter` still has to be
called, for checking it.) | [
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Return a list of key bindings that can handle this key.
(This return also inactive bindings, so the `filter` still has to be
called, for checking it.)
:param keys: tuple of keys.
"""
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result: List[Tuple[int, Binding]] = []
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any_count = 0
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result.append((any_count, b))
# Place bindings that have more 'Any' occurrences in them at the end.
result = sorted(result, key=lambda item: -item[0])
return [item[1] for item in result]
return self._get_bindings_for_keys_cache.get(keys, get) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/autograph/operators/slices.py | python | _tf_tensor_get_item | (target, i) | return target[i] | Overload of get_item that stages a Tensor (not Tensor list) read. | Overload of get_item that stages a Tensor (not Tensor list) read. | [
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"""Overload of get_item that stages a Tensor (not Tensor list) read."""
return target[i] | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/python2_version/klampt/robotsim.py | python | RobotModel.getCoriolisForces | (self) | return _robotsim.RobotModel_getCoriolisForces(self) | getCoriolisForces(RobotModel self)
Returns the Coriolis forces C(q,dq)*dq for current config and velocity. Takes
O(n) time, which is faster than computing matrix and doing product. ("Forces"
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"""
getCoriolisForces(RobotModel self)
Returns the Coriolis forces C(q,dq)*dq for current config and velocity. Takes
O(n) time, which is faster than computing matrix and doing product. ("Forces"
is somewhat of a misnomer; the result is a joint torque vector)
"""
return _robotsim.RobotModel_getCoriolisForces(self) | [
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google/shaka-packager | e1b0c7c45431327fd3ce193514a5407d07b39b22 | packager/third_party/protobuf/python/google/protobuf/internal/containers.py | python | RepeatedScalarFieldContainer.__setitem__ | (self, key, value) | Sets the item on the specified position. | Sets the item on the specified position. | [
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raise ValueError('Extended slices not supported')
self.__setslice__(key.start, key.stop, value)
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self._message_listener.Modified() | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/io/matlab/mio.py | python | mat_reader_factory | (file_name, appendmat=True, **kwargs) | Create reader for matlab .mat format files.
Parameters
----------
%(file_arg)s
%(append_arg)s
%(load_args)s
%(struct_arg)s
Returns
-------
matreader : MatFileReader object
Initialized instance of MatFileReader class matching the mat file
type detected in `filename`.
file_opened : bool
Whether the file was opened by this routine. | Create reader for matlab .mat format files. | [
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"""
Create reader for matlab .mat format files.
Parameters
----------
%(file_arg)s
%(append_arg)s
%(load_args)s
%(struct_arg)s
Returns
-------
matreader : MatFileReader object
Initialized instance of MatFileReader class matching the mat file
type detected in `filename`.
file_opened : bool
Whether the file was opened by this routine.
"""
byte_stream, file_opened = _open_file(file_name, appendmat)
mjv, mnv = get_matfile_version(byte_stream)
if mjv == 0:
return MatFile4Reader(byte_stream, **kwargs), file_opened
elif mjv == 1:
return MatFile5Reader(byte_stream, **kwargs), file_opened
elif mjv == 2:
raise NotImplementedError('Please use HDF reader for matlab v7.3 files')
else:
raise TypeError('Did not recognize version %s' % mjv) | [
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microsoft/checkedc-clang | a173fefde5d7877b7750e7ce96dd08cf18baebf2 | clang/utils/check_cfc/check_cfc.py | python | set_output_file | (args, output_file) | return args | Set the output file within the arguments. Appends or replaces as
appropriate. | Set the output file within the arguments. Appends or replaces as
appropriate. | [
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"""Set the output file within the arguments. Appends or replaces as
appropriate."""
if is_output_specified(args):
args = replace_output_file(args, output_file)
else:
args = add_output_file(args, output_file)
return args | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/random.py | python | Random.gauss | (self, mu, sigma) | return mu + z*sigma | Gaussian distribution.
mu is the mean, and sigma is the standard deviation. This is
slightly faster than the normalvariate() function.
Not thread-safe without a lock around calls. | Gaussian distribution. | [
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] | def gauss(self, mu, sigma):
"""Gaussian distribution.
mu is the mean, and sigma is the standard deviation. This is
slightly faster than the normalvariate() function.
Not thread-safe without a lock around calls.
"""
# When x and y are two variables from [0, 1), uniformly
# distributed, then
#
# cos(2*pi*x)*sqrt(-2*log(1-y))
# sin(2*pi*x)*sqrt(-2*log(1-y))
#
# are two *independent* variables with normal distribution
# (mu = 0, sigma = 1).
# (Lambert Meertens)
# (corrected version; bug discovered by Mike Miller, fixed by LM)
# Multithreading note: When two threads call this function
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# same return value. The window is very small though. To
# avoid this, you have to use a lock around all calls. (I
# didn't want to slow this down in the serial case by using a
# lock here.)
random = self.random
z = self.gauss_next
self.gauss_next = None
if z is None:
x2pi = random() * TWOPI
g2rad = _sqrt(-2.0 * _log(1.0 - random()))
z = _cos(x2pi) * g2rad
self.gauss_next = _sin(x2pi) * g2rad
return mu + z*sigma | [
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rdiankov/openrave | d1a23023fd4b58f077d2ca949ceaf1b91f3f13d7 | python/ikfast_generator_cpp.py | python | CodeGenerator.generateSolution | (self, node,declarearray=True,acceptfreevars=True) | return code.getvalue() | writes the solution of one variable
:param declarearray: if False, will return the equations to be written without evaluating them. Used for conditioned solutions. | writes the solution of one variable
:param declarearray: if False, will return the equations to be written without evaluating them. Used for conditioned solutions. | [
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"""writes the solution of one variable
:param declarearray: if False, will return the equations to be written without evaluating them. Used for conditioned solutions.
"""
code = StringIO()
numsolutions = 0
eqcode = StringIO()
name = node.jointname
self._solutioncounter += 1
log.info('c=%d var=%s', self._solutioncounter, name)
node.HasFreeVar = False
allnumsolutions = 0
#log.info('generateSolution %s (%d)', name, len(node.dictequations))
self.WriteDictEquations(node.dictequations, code=eqcode)
# for var,value in node.dictequations:
# eqcode.write('IkReal %s;\n'%var)
# self.WriteEquations2(lambda k: var,value,code=eqcode)
if node.jointeval is not None:
numsolutions = len(node.jointeval)
equations = []
names = []
for i,expr in enumerate(node.jointeval):
if acceptfreevars and self.freevars is not None:
m = None
for freevar in self.freevars:
if expr.has(Symbol(freevar)):
# has free variables, so have to look for a*freevar+b form
a = Wild('a',exclude=[Symbol(freevar)])
b = Wild('b',exclude=[Symbol(freevar)])
m = expr.match(a*Symbol(freevar)+b)
if m is not None:
self.freevardependencies.append((freevar,name))
assert(len(node.jointeval)==1)
self.WriteEquations2(lambda i: '%smul'%name, m[a], code=code)
self.WriteEquations2(lambda i: name, m[b],code=code)
node.HasFreeVar = True
return code.getvalue()
else:
log.error('failed to extract free variable %s for %s from: eq=%s', freevar,node.jointname, expr)
# m = dict()
# m[a] = Float(-1,30)
# m[b] = Float(0,30)
equations.append(expr)
names.append('%sarray[%d]'%(name,allnumsolutions+i))
equations.append(sin(Symbol('%sarray[%d]'%(name,allnumsolutions+i))))
names.append('s%sarray[%d]'%(name,allnumsolutions+i))
equations.append(cos(Symbol('%sarray[%d]'%(name,allnumsolutions+i))))
names.append('c%sarray[%d]'%(name,allnumsolutions+i))
self.WriteEquations2(lambda i: names[i], equations,code=eqcode)
if node.AddPiIfNegativeEq:
for i in range(numsolutions):
eqcode.write('%sarray[%d] = %sarray[%d] > 0 ? %sarray[%d]-IKPI : %sarray[%d]+IKPI;\n'%(name,allnumsolutions+numsolutions+i,name,allnumsolutions+i,name,allnumsolutions+i,name,allnumsolutions+i))
eqcode.write('s%sarray[%d] = -s%sarray[%d];\n'%(name,allnumsolutions+numsolutions+i,name,allnumsolutions+i))
eqcode.write('c%sarray[%d] = -c%sarray[%d];\n'%(name,allnumsolutions+numsolutions+i,name,allnumsolutions+i))
numsolutions *= 2
for i in range(numsolutions):
if node.isHinge:
eqcode.write('if( %sarray[%d] > IKPI )\n{\n %sarray[%d]-=IK2PI;\n}\nelse if( %sarray[%d] < -IKPI )\n{ %sarray[%d]+=IK2PI;\n}\n'%(name,allnumsolutions+i,name,allnumsolutions+i,name,allnumsolutions+i,name,allnumsolutions+i))
eqcode.write('%svalid[%d] = true;\n'%(name,allnumsolutions+i))
allnumsolutions += numsolutions
# might also have cos solutions ...
if node.jointevalcos is not None:
numsolutions = 2*len(node.jointevalcos)
self.WriteEquations2(lambda i: 'c%sarray[%d]'%(name,allnumsolutions+2*i),node.jointevalcos,code=eqcode)
for i in range(len(node.jointevalcos)):
eqcode.write('if( c%sarray[%d] >= -1-IKFAST_SINCOS_THRESH && c%sarray[%d] <= 1+IKFAST_SINCOS_THRESH )\n{\n'%(name,allnumsolutions+2*i,name,allnumsolutions+2*i))
eqcode.write(' %svalid[%d] = %svalid[%d] = true;\n'%(name,allnumsolutions+2*i,name,allnumsolutions+2*i+1))
eqcode.write(' %sarray[%d] = IKacos(c%sarray[%d]);\n'%(name,allnumsolutions+2*i,name,allnumsolutions+2*i))
eqcode.write(' s%sarray[%d] = IKsin(%sarray[%d]);\n'%(name,allnumsolutions+2*i,name,allnumsolutions+2*i))
# second solution
eqcode.write(' c%sarray[%d] = c%sarray[%d];\n'%(name,allnumsolutions+2*i+1,name,allnumsolutions+2*i))
eqcode.write(' %sarray[%d] = -%sarray[%d];\n'%(name,allnumsolutions+2*i+1,name,allnumsolutions+2*i))
eqcode.write(' s%sarray[%d] = -s%sarray[%d];\n'%(name,allnumsolutions+2*i+1,name,allnumsolutions+2*i))
eqcode.write('}\n')
eqcode.write('else if( isnan(c%sarray[%d]) )\n{\n'%(name,allnumsolutions+2*i))
eqcode.write(' // probably any value will work\n')
eqcode.write(' %svalid[%d] = true;\n'%(name,allnumsolutions+2*i))
eqcode.write(' c%sarray[%d] = 1; s%sarray[%d] = 0; %sarray[%d] = 0;\n'%(name,allnumsolutions+2*i,name,allnumsolutions+2*i,name,allnumsolutions+2*i))
eqcode.write('}\n')
allnumsolutions += numsolutions
if node.jointevalsin is not None:
numsolutions = 2*len(node.jointevalsin)
self.WriteEquations2(lambda i: 's%sarray[%d]'%(name,allnumsolutions+2*i),node.jointevalsin,code=eqcode)
for i in range(len(node.jointevalsin)):
eqcode.write('if( s%sarray[%d] >= -1-IKFAST_SINCOS_THRESH && s%sarray[%d] <= 1+IKFAST_SINCOS_THRESH )\n{\n'%(name,allnumsolutions+2*i,name,allnumsolutions+2*i))
eqcode.write(' %svalid[%d] = %svalid[%d] = true;\n'%(name,allnumsolutions+2*i,name,allnumsolutions+2*i+1))
eqcode.write(' %sarray[%d] = IKasin(s%sarray[%d]);\n'%(name,allnumsolutions+2*i,name,allnumsolutions+2*i))
eqcode.write(' c%sarray[%d] = IKcos(%sarray[%d]);\n'%(name,allnumsolutions+2*i,name,allnumsolutions+2*i))
# second solution
eqcode.write(' s%sarray[%d] = s%sarray[%d];\n'%(name,allnumsolutions+2*i+1,name,allnumsolutions+2*i))
eqcode.write(' %sarray[%d] = %sarray[%d] > 0 ? (IKPI-%sarray[%d]) : (-IKPI-%sarray[%d]);\n'%(name,allnumsolutions+2*i+1,name,allnumsolutions+2*i,name,allnumsolutions+2*i,name,allnumsolutions+2*i))
eqcode.write(' c%sarray[%d] = -c%sarray[%d];\n'%(name,allnumsolutions+2*i+1,name,allnumsolutions+2*i))
eqcode.write('}\n')
eqcode.write('else if( isnan(s%sarray[%d]) )\n{\n'%(name,allnumsolutions+2*i))
eqcode.write(' // probably any value will work\n')
eqcode.write(' %svalid[%d] = true;\n'%(name,allnumsolutions+2*i))
eqcode.write(' c%sarray[%d] = 1; s%sarray[%d] = 0; %sarray[%d] = 0;\n'%(name,allnumsolutions+2*i,name,allnumsolutions+2*i,name,allnumsolutions+2*i))
eqcode.write('}\n')
allnumsolutions += numsolutions
if not declarearray:
return eqcode.getvalue(),allnumsolutions
code.write('{\nIkReal %sarray[%d], c%sarray[%d], s%sarray[%d];\n'%(name,allnumsolutions,name,allnumsolutions,name,allnumsolutions))
code.write('bool %svalid[%d]={false};\n'%(name,allnumsolutions))
code.write('_n%s = %d;\n'%(name,allnumsolutions))
code.write(eqcode.getvalue())
if allnumsolutions > 1:
if allnumsolutions >= 256:
log.error('num solutions is %d>=256, which exceeds unsigned char',allnumsolutions)
code.write('for(int i%s = 0; i%s < %d; ++i%s)\n{\n'%(name,name,allnumsolutions,name))
code.write('if( !%svalid[i%s] )\n{\n continue;\n}\n'%(name,name))
code.write('_i%s[0] = i%s; _i%s[1] = -1;\n'%(name,name,name))
# check for a similar solution
code.write('for(int ii%s = i%s+1; ii%s < %d; ++ii%s)\n{\n'%(name,name,name,allnumsolutions,name))
code.write('if( %svalid[ii%s] && IKabs(c%sarray[i%s]-c%sarray[ii%s]) < IKFAST_SOLUTION_THRESH && IKabs(s%sarray[i%s]-s%sarray[ii%s]) < IKFAST_SOLUTION_THRESH )\n{\n %svalid[ii%s]=false; _i%s[1] = ii%s; break; \n}\n'%(name,name,name,name,name,name,name,name,name,name,name,name,name,name))
code.write('}\n')
code.write('%s = %sarray[i%s]; c%s = c%sarray[i%s]; s%s = s%sarray[i%s];\n'%(name,name,name,name,name,name,name,name,name))
if node.AddHalfTanValue:
code.write('ht%s = IKtan(%s/2);\n'%(name,name))
if node.getEquationsUsed() is not None and len(node.getEquationsUsed()) > 0:
code.write('{\nIkReal evalcond[%d];\n'%len(node.getEquationsUsed()))
self.WriteEquations2(lambda i: 'evalcond[%d]'%(i),node.getEquationsUsed(),code=code)
code.write('if( ')
for i in range(len(node.getEquationsUsed())):
if i != 0:
code.write(' || ')
# using smaller node.thresh increases the missing solution rate, really not sure whether the solutions themselves
# are bad due to double precision arithmetic, or due to singularities
code.write('IKabs(evalcond[%d]) > IKFAST_EVALCOND_THRESH '%(i))
code.write(' )\n{\n')
#code += 'cout <<'
#code += '<<'.join(['evalcond[%d]'%i for i in range(len(node.getEquationsUsed()))])
#code += '<< endl;'
code.write('continue;\n}\n')
code.write('}\n')
code.write('\n')
return code.getvalue() | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/ops/template.py | python | Template.local_variables | (self) | Returns the list of global variables created by the Template. | Returns the list of global variables created by the Template. | [
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"""Returns the list of global variables created by the Template."""
if self._variables_created:
return ops.get_collection(ops.GraphKeys.LOCAL_VARIABLES,
self.variable_scope_name)
else:
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arangodb/arangodb | 0d658689c7d1b721b314fa3ca27d38303e1570c8 | 3rdParty/boost/1.78.0/libs/metaparse/tools/benchmark/benchmark.py | python | plot_temp_diagrams | (config, results, temp_dir) | return img_files | Plot temporary diagrams | Plot temporary diagrams | [
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}
files = config['files']
img_files = []
if any('slt' in result for result in results) and 'bmp' in files.values()[0]:
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f['slt'] = f['bmp'].replace('bmp', 'slt')
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img_files.append(os.path.join(temp_dir, '_{0}.png'.format(measured)))
plot(
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/__init__.py | python | connect_cognito_identity | (aws_access_key_id=None,
aws_secret_access_key=None,
**kwargs) | return CognitoIdentityConnection(
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
**kwargs
) | Connect to Amazon Cognito Identity
:type aws_access_key_id: string
:param aws_access_key_id: Your AWS Access Key ID
:type aws_secret_access_key: string
:param aws_secret_access_key: Your AWS Secret Access Key
rtype: :class:`boto.cognito.identity.layer1.CognitoIdentityConnection`
:return: A connection to the Amazon Cognito Identity service | Connect to Amazon Cognito Identity | [
"Connect",
"to",
"Amazon",
"Cognito",
"Identity"
] | def connect_cognito_identity(aws_access_key_id=None,
aws_secret_access_key=None,
**kwargs):
"""
Connect to Amazon Cognito Identity
:type aws_access_key_id: string
:param aws_access_key_id: Your AWS Access Key ID
:type aws_secret_access_key: string
:param aws_secret_access_key: Your AWS Secret Access Key
rtype: :class:`boto.cognito.identity.layer1.CognitoIdentityConnection`
:return: A connection to the Amazon Cognito Identity service
"""
from boto.cognito.identity.layer1 import CognitoIdentityConnection
return CognitoIdentityConnection(
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
**kwargs
) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/android/loading/request_track.py | python | Request.SetHTTPResponseHeader | (self, header, header_value) | Sets the value of a HTTP response header. | Sets the value of a HTTP response header. | [
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assert header.islower()
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if name.lower() == header:
del self.response_headers[name]
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/setuptools/py2/setuptools/command/easy_install.py | python | easy_install.select_scheme | (self, name) | Sets the install directories by applying the install schemes. | Sets the install directories by applying the install schemes. | [
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] | def select_scheme(self, name):
"""Sets the install directories by applying the install schemes."""
# it's the caller's problem if they supply a bad name!
scheme = INSTALL_SCHEMES[name]
for key in SCHEME_KEYS:
attrname = 'install_' + key
if getattr(self, attrname) is None:
setattr(self, attrname, scheme[key]) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/richtext.py | python | RichTextCtrl.SetCaretPositionForDefaultStyle | (*args, **kwargs) | return _richtext.RichTextCtrl_SetCaretPositionForDefaultStyle(*args, **kwargs) | SetCaretPositionForDefaultStyle(self, long pos) | SetCaretPositionForDefaultStyle(self, long pos) | [
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"""SetCaretPositionForDefaultStyle(self, long pos)"""
return _richtext.RichTextCtrl_SetCaretPositionForDefaultStyle(*args, **kwargs) | [
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PixarAnimationStudios/USD | faed18ce62c8736b02413635b584a2f637156bad | pxr/usdImaging/usdviewq/appController.py | python | AppController.setFrameField | (self, frame) | Set the frame field to the given `frame`.
Args:
frame (str|int|float): The new frame value. | Set the frame field to the given `frame`. | [
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"""Set the frame field to the given `frame`.
Args:
frame (str|int|float): The new frame value.
"""
frame = round(float(frame), ndigits=2)
self._ui.frameField.setText(str(frame)) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/Editra.py | python | Editra.DestroySplash | (self) | Destroy the splash screen | Destroy the splash screen | [
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"""Destroy the splash screen"""
# If is created and not dead already
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self.splash.Destroy()
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/aui.py | python | AuiToolBar.SetToolPacking | (*args, **kwargs) | return _aui.AuiToolBar_SetToolPacking(*args, **kwargs) | SetToolPacking(self, int packing) | SetToolPacking(self, int packing) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/groupby/groupby.py | python | GroupBy.expanding | (self, *args, **kwargs) | return ExpandingGroupby(self, *args, **kwargs) | Return an expanding grouper, providing expanding
functionality per group. | Return an expanding grouper, providing expanding
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] | def expanding(self, *args, **kwargs):
"""
Return an expanding grouper, providing expanding
functionality per group.
"""
from pandas.core.window import ExpandingGroupby
return ExpandingGroupby(self, *args, **kwargs) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/firefighter/bin/packaging.py | python | TempAppDir | (root_dir, symlinks) | Sets up and tears down a directory for deploying or running an app.
Args:
root_dir: The root directory of the app.
symlinks: If true, use symbolic links instead of copying files. This allows
the dev server to detect file changes in the repo, and is faster.
Yields:
The path to the temporary directory. | Sets up and tears down a directory for deploying or running an app. | [
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] | def TempAppDir(root_dir, symlinks):
"""Sets up and tears down a directory for deploying or running an app.
Args:
root_dir: The root directory of the app.
symlinks: If true, use symbolic links instead of copying files. This allows
the dev server to detect file changes in the repo, and is faster.
Yields:
The path to the temporary directory.
"""
if symlinks:
link = os.symlink
else:
def Link(src, dest):
if os.path.isdir(src):
return shutil.copytree(src, dest)
else:
return shutil.copy2(src, dest)
link = Link
gcloud_lib_dir = _GcloudLibDir()
temp_app_dir = tempfile.mkdtemp(prefix='app-')
try:
for module in Modules(root_dir):
module_source_dir = os.path.join(root_dir, module)
module_dest_dir = os.path.join(temp_app_dir, module)
os.mkdir(module_dest_dir)
# Copy/symlink module into app directory.
for node in os.listdir(module_source_dir):
link(os.path.join(module_source_dir, node),
os.path.join(module_dest_dir, node))
# Copy/symlink base/ into module directory.
link(os.path.join(root_dir, 'base'),
os.path.join(module_dest_dir, 'base'))
# Copy/symlink Gcloud library dependencies into module directory.
third_party_dest_dir = os.path.join(module_dest_dir, 'third_party')
os.mkdir(third_party_dest_dir)
open(os.path.join(third_party_dest_dir, '__init__.py'), 'w').close()
for library in constants.GCLOUD_THIRD_PARTY_LIBRARIES:
link(os.path.join(gcloud_lib_dir, library),
os.path.join(third_party_dest_dir, library))
yield temp_app_dir
finally:
shutil.rmtree(temp_app_dir) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/lib-tk/tkSimpleDialog.py | python | Dialog.buttonbox | (self) | add standard button box.
override if you do not want the standard buttons | add standard button box. | [
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] | def buttonbox(self):
'''add standard button box.
override if you do not want the standard buttons
'''
box = Frame(self)
w = Button(box, text="OK", width=10, command=self.ok, default=ACTIVE)
w.pack(side=LEFT, padx=5, pady=5)
w = Button(box, text="Cancel", width=10, command=self.cancel)
w.pack(side=LEFT, padx=5, pady=5)
self.bind("<Return>", self.ok)
self.bind("<Escape>", self.cancel)
box.pack() | [
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] | https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/tools/python/src/Lib/lib-tk/tkSimpleDialog.py#L105-L121 | ||
baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/ndlstm/python/misc.py | python | pool_as_vector | (images, scope=None) | Reduce images to vectors by averaging all pixels. | Reduce images to vectors by averaging all pixels. | [
"Reduce",
"images",
"to",
"vectors",
"by",
"averaging",
"all",
"pixels",
"."
] | def pool_as_vector(images, scope=None):
"""Reduce images to vectors by averaging all pixels."""
with ops.name_scope(scope, "PoolAsVector", [images]):
return math_ops.reduce_mean(images, [1, 2]) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/signal/windows/windows.py | python | cosine | (M, sym=True) | return _truncate(w, needs_trunc) | Return a window with a simple cosine shape.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an empty
array is returned.
sym : bool, optional
When True (default), generates a symmetric window, for use in filter
design.
When False, generates a periodic window, for use in spectral analysis.
Returns
-------
w : ndarray
The window, with the maximum value normalized to 1 (though the value 1
does not appear if `M` is even and `sym` is True).
Notes
-----
.. versionadded:: 0.13.0
Examples
--------
Plot the window and its frequency response:
>>> from scipy import signal
>>> from scipy.fftpack import fft, fftshift
>>> import matplotlib.pyplot as plt
>>> window = signal.cosine(51)
>>> plt.plot(window)
>>> plt.title("Cosine window")
>>> plt.ylabel("Amplitude")
>>> plt.xlabel("Sample")
>>> plt.figure()
>>> A = fft(window, 2048) / (len(window)/2.0)
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(np.abs(fftshift(A / abs(A).max())))
>>> plt.plot(freq, response)
>>> plt.axis([-0.5, 0.5, -120, 0])
>>> plt.title("Frequency response of the cosine window")
>>> plt.ylabel("Normalized magnitude [dB]")
>>> plt.xlabel("Normalized frequency [cycles per sample]")
>>> plt.show() | Return a window with a simple cosine shape. | [
"Return",
"a",
"window",
"with",
"a",
"simple",
"cosine",
"shape",
"."
] | def cosine(M, sym=True):
"""Return a window with a simple cosine shape.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an empty
array is returned.
sym : bool, optional
When True (default), generates a symmetric window, for use in filter
design.
When False, generates a periodic window, for use in spectral analysis.
Returns
-------
w : ndarray
The window, with the maximum value normalized to 1 (though the value 1
does not appear if `M` is even and `sym` is True).
Notes
-----
.. versionadded:: 0.13.0
Examples
--------
Plot the window and its frequency response:
>>> from scipy import signal
>>> from scipy.fftpack import fft, fftshift
>>> import matplotlib.pyplot as plt
>>> window = signal.cosine(51)
>>> plt.plot(window)
>>> plt.title("Cosine window")
>>> plt.ylabel("Amplitude")
>>> plt.xlabel("Sample")
>>> plt.figure()
>>> A = fft(window, 2048) / (len(window)/2.0)
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(np.abs(fftshift(A / abs(A).max())))
>>> plt.plot(freq, response)
>>> plt.axis([-0.5, 0.5, -120, 0])
>>> plt.title("Frequency response of the cosine window")
>>> plt.ylabel("Normalized magnitude [dB]")
>>> plt.xlabel("Normalized frequency [cycles per sample]")
>>> plt.show()
"""
if _len_guards(M):
return np.ones(M)
M, needs_trunc = _extend(M, sym)
w = np.sin(np.pi / M * (np.arange(0, M) + .5))
return _truncate(w, needs_trunc) | [
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OSGeo/gdal | 3748fc4ba4fba727492774b2b908a2130c864a83 | swig/python/osgeo/ogr.py | python | Layer.DeleteField | (self, *args) | return _ogr.Layer_DeleteField(self, *args) | r"""
DeleteField(Layer self, int iField) -> OGRErr
OGRErr
OGR_L_DeleteField(OGRLayerH hLayer, int iField)
Delete an existing field on a layer.
You must use this to delete existing fields on a real layer.
Internally the OGRFeatureDefn for the layer will be updated to reflect
the deleted field. Applications should never modify the OGRFeatureDefn
used by a layer directly.
This function should not be called while there are feature objects in
existence that were obtained or created with the previous layer
definition.
Not all drivers support this function. You can query a layer to check
if it supports it with the OLCDeleteField capability. Some drivers may
only support this method while there are still no features in the
layer. When it is supported, the existing features of the backing
file/database should be updated accordingly.
This function is the same as the C++ method OGRLayer::DeleteField().
Parameters:
-----------
hLayer: handle to the layer.
iField: index of the field to delete.
OGRERR_NONE on success.
OGR 1.9.0 | r"""
DeleteField(Layer self, int iField) -> OGRErr
OGRErr
OGR_L_DeleteField(OGRLayerH hLayer, int iField) | [
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"(",
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")"
] | def DeleteField(self, *args):
r"""
DeleteField(Layer self, int iField) -> OGRErr
OGRErr
OGR_L_DeleteField(OGRLayerH hLayer, int iField)
Delete an existing field on a layer.
You must use this to delete existing fields on a real layer.
Internally the OGRFeatureDefn for the layer will be updated to reflect
the deleted field. Applications should never modify the OGRFeatureDefn
used by a layer directly.
This function should not be called while there are feature objects in
existence that were obtained or created with the previous layer
definition.
Not all drivers support this function. You can query a layer to check
if it supports it with the OLCDeleteField capability. Some drivers may
only support this method while there are still no features in the
layer. When it is supported, the existing features of the backing
file/database should be updated accordingly.
This function is the same as the C++ method OGRLayer::DeleteField().
Parameters:
-----------
hLayer: handle to the layer.
iField: index of the field to delete.
OGRERR_NONE on success.
OGR 1.9.0
"""
return _ogr.Layer_DeleteField(self, *args) | [
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hakuna-m/wubiuefi | caec1af0a09c78fd5a345180ada1fe45e0c63493 | src/openpgp/sap/pkt/Packet.py | python | OldLength.fill | (self, d) | oldlength.fill(d)
Set the OldLength object's data, filling its attributes.
Tag data (d) must be either 1, 2, or 4 octets containing
the length (integer count) of the packet body
(see rfc2004 4.2.1).
:Parameters:
- `d`: character (StringType, length == 1)
:Returns: Nothing
Example:
>>> oldlength_octets = [0x88, 0x88]
>>> oldlength_data_string = ''.join(map(chr, oldlength_octets))
>>> oldlength = OldLength()
>>> oldlength.fill(oldlength_data_string)
>>> oldlength.size
34952 | oldlength.fill(d) | [
"oldlength",
".",
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"""oldlength.fill(d)
Set the OldLength object's data, filling its attributes.
Tag data (d) must be either 1, 2, or 4 octets containing
the length (integer count) of the packet body
(see rfc2004 4.2.1).
:Parameters:
- `d`: character (StringType, length == 1)
:Returns: Nothing
Example:
>>> oldlength_octets = [0x88, 0x88]
>>> oldlength_data_string = ''.join(map(chr, oldlength_octets))
>>> oldlength = OldLength()
>>> oldlength.fill(oldlength_data_string)
>>> oldlength.size
34952
"""
if len(d) in [1, 2, 4]:
self._d, self.size = d, STN.str2int(d)
elif 0 == len(d):
self._d, self.size = '', "UNDEFINED"
else:
raise PGPFormatError, "Old packet length data must come in 0, 1, 2, or 4 octets. Received->(%s octets)." % (str(len(d))) | [
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deepmind/open_spiel | 4ca53bea32bb2875c7385d215424048ae92f78c8 | open_spiel/python/jax/dqn.py | python | DQN._epsilon_greedy | (self, info_state, legal_actions, epsilon) | return action, probs | Returns a valid epsilon-greedy action and valid action probs.
Action probabilities are given by a softmax over legal q-values.
Args:
info_state: hashable representation of the information state.
legal_actions: list of legal actions at `info_state`.
epsilon: float, probability of taking an exploratory action.
Returns:
A valid epsilon-greedy action and valid action probabilities. | Returns a valid epsilon-greedy action and valid action probs. | [
"Returns",
"a",
"valid",
"epsilon",
"-",
"greedy",
"action",
"and",
"valid",
"action",
"probs",
"."
] | def _epsilon_greedy(self, info_state, legal_actions, epsilon):
"""Returns a valid epsilon-greedy action and valid action probs.
Action probabilities are given by a softmax over legal q-values.
Args:
info_state: hashable representation of the information state.
legal_actions: list of legal actions at `info_state`.
epsilon: float, probability of taking an exploratory action.
Returns:
A valid epsilon-greedy action and valid action probabilities.
"""
probs = np.zeros(self._num_actions)
legal_one_hot = np.zeros(self._num_actions)
legal_one_hot[legal_actions] = 1
if np.random.rand() < epsilon:
action = np.random.choice(legal_actions)
probs[legal_actions] = 1.0 / len(legal_actions)
else:
info_state = np.reshape(info_state, [1, -1])
q_values = self.hk_network_apply(self.params_q_network, info_state)
legal_q_values = q_values[0] + (
1 - legal_one_hot) * ILLEGAL_ACTION_LOGITS_PENALTY
action = int(np.argmax(legal_q_values))
probs[action] = 1.0
return action, probs | [
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meritlabs/merit | 49dc96043f882b982175fb66934e2bf1e6b1920c | contrib/devtools/github-merge.py | python | retrieve_pr_info | (repo,pull) | Retrieve pull request information from github.
Return None if no title can be found, or an error happens. | Retrieve pull request information from github.
Return None if no title can be found, or an error happens. | [
"Retrieve",
"pull",
"request",
"information",
"from",
"github",
".",
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"None",
"if",
"no",
"title",
"can",
"be",
"found",
"or",
"an",
"error",
"happens",
"."
] | def retrieve_pr_info(repo,pull):
'''
Retrieve pull request information from github.
Return None if no title can be found, or an error happens.
'''
try:
req = Request("https://api.github.com/repos/"+repo+"/pulls/"+pull)
result = urlopen(req)
reader = codecs.getreader('utf-8')
obj = json.load(reader(result))
return obj
except Exception as e:
print('Warning: unable to retrieve pull information from github: %s' % e)
return None | [
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eclipse/sumo | 7132a9b8b6eea734bdec38479026b4d8c4336d03 | tools/contributed/sumopy/plugins/hcprt/hcprt.py | python | PrtPlatoonStops.get_closest_for_trip | (self, coords_from, coords_to, walkspeeds=None, ids_person=None) | return ids_prtstop_closest_from, ids_edge_closest_from, ids_prtstop_closest_to, ids_edge_closest_to | Returns the prt stops and stop edges that minimize
door to door trip time.
Door from and to positions of each trip are given as coordinates. | Returns the prt stops and stop edges that minimize
door to door trip time.
Door from and to positions of each trip are given as coordinates. | [
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] | def get_closest_for_trip(self, coords_from, coords_to, walkspeeds=None, ids_person=None):
"""
Returns the prt stops and stop edges that minimize
door to door trip time.
Door from and to positions of each trip are given as coordinates.
"""
net = self.get_scenario().net
ptstops = net.ptstops
lanes = net.lanes
n = len(coords_from)
print 'get_closest_for_trip', n
times_stop_to_stop = self.parent.times_stop_to_stop[1:, 1:]
ids_prtstop = self.get_ids()
n_stops = len(self)
ids_ptstop = self.ids_ptstop[ids_prtstop]
coords_stop = ptstops.centroids[ids_ptstop]
ids_edge_stop = net.lanes.ids_edge[ptstops.ids_lane[ids_ptstop]]
inds_closest_from = np.zeros(n, dtype=np.int32)
inds_closest_to = np.zeros(n, dtype=np.int32)
if walkspeeds is None:
# default walk speed in m/s
walkspeeds = 0.8*np.ones(n, dtype=np.float32)
i = 0
for coord_from, coord_to, walkspeed in zip(coords_from, coords_to, walkspeeds):
walktimes_from = 1.0/walkspeed*np.sqrt(np.sum((coord_from-coords_stop)**2, 1))
walktimes_to = 1.0/walkspeed*np.sqrt(np.sum((coord_to-coords_stop)**2, 1))
times_door_to_door = times_stop_to_stop+np.array([walktimes_from]).transpose()
times_door_to_door += walktimes_to
ind_from, ind_to = np.divmod(np.argmin(times_door_to_door), n_stops)
inds_closest_from[i] = ind_from
inds_closest_to[i] = ind_to
if 0: # ids_person is not None:
id_person = ids_person[i]
if id_person == 17214:
# print ' walktimes_from',(np.array([walktimes_from]).transpose()).shape
# print ' walktimes_to',walktimes_to.shape
# print ' times_stop_to_stop',times_stop_to_stop.shape
print ' id_person', id_person
print ' coord_from, coord_to', coord_from, coord_to
print ' times_door_to_door\n', times_door_to_door
print ' ind_from, ind_to', ind_from, ind_to
print ' id_stop_from, id_stop_to', ids_prtstop[ind_from], ids_prtstop[ind_to]
i += 1
ids_prtstop_closest_from = ids_prtstop[inds_closest_from]
ids_edge_closest_from = ids_edge_stop[inds_closest_from]
ids_prtstop_closest_to = ids_prtstop[inds_closest_to]
ids_edge_closest_to = ids_edge_stop[inds_closest_to]
return ids_prtstop_closest_from, ids_edge_closest_from, ids_prtstop_closest_to, ids_edge_closest_to | [
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tiny-dnn/tiny-dnn | c0f576f5cb7b35893f62127cb7aec18f77a3bcc5 | third_party/cpplint.py | python | _FunctionState.Check | (self, error, filename, linenum) | Report if too many lines in function body.
Args:
error: The function to call with any errors found.
filename: The name of the current file.
linenum: The number of the line to check. | Report if too many lines in function body. | [
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"""Report if too many lines in function body.
Args:
error: The function to call with any errors found.
filename: The name of the current file.
linenum: The number of the line to check.
"""
if not self.in_a_function:
return
if Match(r'T(EST|est)', self.current_function):
base_trigger = self._TEST_TRIGGER
else:
base_trigger = self._NORMAL_TRIGGER
trigger = base_trigger * 2**_VerboseLevel()
if self.lines_in_function > trigger:
error_level = int(math.log(self.lines_in_function / base_trigger, 2))
# 50 => 0, 100 => 1, 200 => 2, 400 => 3, 800 => 4, 1600 => 5, ...
if error_level > 5:
error_level = 5
error(filename, linenum, 'readability/fn_size', error_level,
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self.current_function, self.lines_in_function, trigger)) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/data_flow_ops.py | python | Barrier.incomplete_size | (self, name=None) | return gen_data_flow_ops.barrier_incomplete_size(
self._barrier_ref, name=name) | Compute the number of incomplete elements in the given barrier.
Args:
name: A name for the operation (optional).
Returns:
A single-element tensor containing the number of incomplete elements in
the given barrier. | Compute the number of incomplete elements in the given barrier. | [
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"""Compute the number of incomplete elements in the given barrier.
Args:
name: A name for the operation (optional).
Returns:
A single-element tensor containing the number of incomplete elements in
the given barrier.
"""
if name is None:
name = "%s_BarrierIncompleteSize" % self._name
return gen_data_flow_ops.barrier_incomplete_size(
self._barrier_ref, name=name) | [
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mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | src/third_party/scons-3.1.2/scons-local-3.1.2/SCons/Tool/GettextCommon.py | python | _init_po_files | (target, source, env) | return 0 | Action function for `POInit` builder. | Action function for `POInit` builder. | [
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""" Action function for `POInit` builder. """
nop = lambda target, source, env: 0
if 'POAUTOINIT' in env:
autoinit = env['POAUTOINIT']
else:
autoinit = False
# Well, if everything outside works well, this loop should do single
# iteration. Otherwise we are rebuilding all the targets even, if just
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for tgt in target:
if not tgt.exists():
if autoinit:
action = SCons.Action.Action('$MSGINITCOM', '$MSGINITCOMSTR')
else:
msg = 'File ' + repr(str(tgt)) + ' does not exist. ' \
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action = SCons.Action.Action(nop, msg)
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fdivitto/ESPWebFramework | 5c7b644b2d15795d4b2b8d7dda3897222aada7b3 | script/esptool.py | python | div_roundup | (a, b) | return (int(a) + int(b) - 1) / int(b) | Return a/b rounded up to nearest integer,
equivalent result to int(math.ceil(float(int(a)) / float(int(b))), only
without possible floating point accuracy errors. | Return a/b rounded up to nearest integer,
equivalent result to int(math.ceil(float(int(a)) / float(int(b))), only
without possible floating point accuracy errors. | [
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""" Return a/b rounded up to nearest integer,
equivalent result to int(math.ceil(float(int(a)) / float(int(b))), only
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"""
return (int(a) + int(b) - 1) / int(b) | [
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psmoveservice/PSMoveService | 22bbe20e9de53f3f3581137bce7b88e2587a27e7 | misc/python/examples/filter_datafiles.py | python | quat_from_axang | (axang, dt=1) | return result / np.linalg.norm(result) | Convert an axis-angle (3D) representation of orientation into a quaternion (4D)
:param axang:
:param dt:
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"""
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"""
s = np.linalg.norm(axang)
rot_angle = s * dt
if rot_angle > 0:
rot_axis = axang / s
result = np.hstack((np.cos(rot_angle/2), rot_axis*np.sin(rot_angle/2)))
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result = np.asarray([1., 0., 0., 0.])
return result / np.linalg.norm(result) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/richtext.py | python | TextAttrDimension.SetValue | (*args) | return _richtext.TextAttrDimension_SetValue(*args) | SetValue(self, int value)
SetValue(self, int value, TextAttrDimensionFlags flags)
SetValue(self, TextAttrDimension dim) | SetValue(self, int value)
SetValue(self, int value, TextAttrDimensionFlags flags)
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"""
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return _richtext.TextAttrDimension_SetValue(*args) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/macpath.py | python | expandvars | (path) | return path | Dummy to retain interface-compatibility with other operating systems. | Dummy to retain interface-compatibility with other operating systems. | [
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google/or-tools | 2cb85b4eead4c38e1c54b48044f92087cf165bce | ortools/sat/python/cp_model.py | python | CpModel.AddBoolOr | (self, literals) | return ct | Adds `Or(literals) == true`. | Adds `Or(literals) == true`. | [
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"""Adds `Or(literals) == true`."""
ct = Constraint(self.__model.constraints)
model_ct = self.__model.constraints[ct.Index()]
model_ct.bool_or.literals.extend(
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return ct | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Draft/draftguitools/gui_subelements.py | python | SubelementHighlight.get_editable_objects_from_selection | (self) | Get editable Draft objects for the selection. | Get editable Draft objects for the selection. | [
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"""Get editable Draft objects for the selection."""
for obj in Gui.Selection.getSelection():
if obj.isDerivedFrom("Part::Part2DObject"):
self.editable_objects.append(obj)
elif (hasattr(obj, "Base")
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/numpy/fallback.py | python | get_func | (obj, doc) | return wrapper | Get new numpy function with object and doc | Get new numpy function with object and doc | [
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@wraps(obj)
def wrapper(*args, **kwargs):
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wrapper.__doc__ = doc
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_aarch64/python2.7/dist-packages/rospkg/os_detect.py | python | OsDetect.get_detector | (self, name=None) | Get detector used for specified OS name, or the detector for this OS if name is ``None``.
:raises: :exc:`KeyError` | Get detector used for specified OS name, or the detector for this OS if name is ``None``.
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/python/google/platform_utils_mac.py | python | PlatformUtility.GetStopHttpdCommand | (self) | return [self._bash, "-c", self._httpd_cmd_string + ' -k stop && sleep 5'] | Returns a list of strings that contains the command line+args needed to
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apple/swift-lldb | d74be846ef3e62de946df343e8c234bde93a8912 | third_party/Python/module/pexpect-4.6/pexpect/pty_spawn.py | python | spawn.sendeof | (self) | This sends an EOF to the child. This sends a character which causes
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Slicer/Slicer | ba9fadf332cb0303515b68d8d06a344c82e3e3e5 | Modules/Scripted/DataProbe/DataProbe.py | python | DataProbeTest.runTest | (self) | Run as few or as many tests as needed here. | Run as few or as many tests as needed here. | [
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yuxng/PoseCNN | 9f3dd7b7bce21dcafc05e8f18ccc90da3caabd04 | lib/datasets/rgbd_scene.py | python | rgbd_scene._load_image_set_index | (self) | return image_index | Load the indexes listed in this dataset's image set file. | Load the indexes listed in this dataset's image set file. | [
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with open(image_set_file) as f:
image_index = [x.rstrip('\n') for x in f.readlines()]
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/AWSPythonSDK/1.5.8/docutils/utils/math/math2html.py | python | LimitsProcessor.modifylimits | (self, contents, index) | Modify a limits commands so that the limits appear above and below. | Modify a limits commands so that the limits appear above and below. | [
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alibaba/weex_js_engine | 2bdf4b6f020c1fc99c63f649718f6faf7e27fdde | jni/v8core/v8/build/gyp/pylib/gyp/generator/make.py | python | MakefileWriter.WriteDoCmd | (self, outputs, inputs, command, part_of_all, comment=None,
postbuilds=False) | Write a Makefile rule that uses do_cmd.
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suffix = ''
if postbuilds:
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self.WriteMakeRule(outputs, inputs,
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# Add our outputs to the list of targets we read depfiles from.
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/asyncio/locks.py | python | Semaphore.locked | (self) | return self._value == 0 | Returns True if semaphore can not be acquired immediately. | Returns True if semaphore can not be acquired immediately. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/common-code/Utils/cgf_utils/data_utils.py | python | Data.DATA | (self) | return self.__data | Get the current data. | Get the current data. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_core.py | python | Menu.GetEventHandler | (*args, **kwargs) | return _core_.Menu_GetEventHandler(*args, **kwargs) | GetEventHandler(self) -> EvtHandler | GetEventHandler(self) -> EvtHandler | [
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quantOS-org/DataCore | e2ef9bd2c22ee9e2845675b6435a14fa607f3551 | mdlink/deps/windows/protobuf-2.5.0/python/google/protobuf/internal/python_message.py | python | _AddEqualsMethod | (message_descriptor, cls) | Helper for _AddMessageMethods(). | Helper for _AddMessageMethods(). | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/closure_linter/closure_linter/common/tokenizer.py | python | Tokenizer.TokenizeFile | (self, file) | return self.__first_token | Tokenizes the given file.
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# The current mode.
self.mode = self.__starting_mode
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self.__last_token = None
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for line in file:
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self.__TokenizeLine(line)
return self.__first_token | [
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rdkit/rdkit | ede860ae316d12d8568daf5ee800921c3389c84e | rdkit/Chem/MolStandardize/charge.py | python | Uncharger.__call__ | (self, mol) | return self.uncharge(mol) | Calling an Uncharger instance like a function is the same as calling its uncharge(mol) method. | Calling an Uncharger instance like a function is the same as calling its uncharge(mol) method. | [
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"""Calling an Uncharger instance like a function is the same as calling its uncharge(mol) method."""
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